{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Election data example\n",
    "\n",
    "By Ben Welsh\n",
    "\n",
    "How you can use Python and pandas to work with California elections data [published by](https://www.californiacivicdata.org/2017/10/31/processed-files/) the California Civic Data Coalition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read in data from the Coalition's API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "candidates = pd.read_csv(\"https://calaccess.download/latest/Candidates.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>party_name</th>\n",
       "      <th>election_name</th>\n",
       "      <th>election_date</th>\n",
       "      <th>office</th>\n",
       "      <th>is_incumbent</th>\n",
       "      <th>special_election</th>\n",
       "      <th>created_at</th>\n",
       "      <th>updated_at</th>\n",
       "      <th>ocd_person_id</th>\n",
       "      <th>ocd_candidacy_id</th>\n",
       "      <th>ocd_election_id</th>\n",
       "      <th>ocd_post_id</th>\n",
       "      <th>ocd_contest_id</th>\n",
       "      <th>ocd_party_id</th>\n",
       "      <th>latest_calaccess_filer_id</th>\n",
       "      <th>calaccess_filer_id_count</th>\n",
       "      <th>latest_form501_filing_id</th>\n",
       "      <th>form501_filing_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KAMALA D. HARRIS</td>\n",
       "      <td>DEMOCRATIC</td>\n",
       "      <td>2026 PRIMARY</td>\n",
       "      <td>2026-06-02</td>\n",
       "      <td>Governor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>2017-10-30 23:54:23.795175+00</td>\n",
       "      <td>2017-10-30 23:54:23.824374+00</td>\n",
       "      <td>ocd-person/717b9339-bcaa-437d-8e36-b8417ce02325</td>\n",
       "      <td>ocd-candidacy/320f9fb9-1f0a-44b1-84c0-a07fa54f...</td>\n",
       "      <td>ocd-election/b0d7be16-6f9e-4c5e-a537-c32167bbdbf8</td>\n",
       "      <td>ocd-post/b63d25d2-aff0-4afb-9dd1-e99474c8eb13</td>\n",
       "      <td>ocd-contest/d33b6822-b3b9-4ed5-97db-19c3f16718aa</td>\n",
       "      <td>ocd-organization/169e9416-915b-4462-94cf-82e11...</td>\n",
       "      <td>1313503</td>\n",
       "      <td>1</td>\n",
       "      <td>2149075.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RAJIV DALAL</td>\n",
       "      <td>DEMOCRATIC</td>\n",
       "      <td>2026 PRIMARY</td>\n",
       "      <td>2026-06-02</td>\n",
       "      <td>Lieutenant Governor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>2017-10-30 23:53:58.104137+00</td>\n",
       "      <td>2017-10-30 23:53:58.136437+00</td>\n",
       "      <td>ocd-person/b3a7a574-0b7c-4f1c-9c08-c68aabeef412</td>\n",
       "      <td>ocd-candidacy/7fac375c-011f-4bcf-b3f1-749eee7b...</td>\n",
       "      <td>ocd-election/b0d7be16-6f9e-4c5e-a537-c32167bbdbf8</td>\n",
       "      <td>ocd-post/3497cad1-e09e-4731-97c7-338b76746800</td>\n",
       "      <td>ocd-contest/5ca2421a-6acd-4d5a-924e-0fbb8e7387ab</td>\n",
       "      <td>ocd-organization/169e9416-915b-4462-94cf-82e11...</td>\n",
       "      <td>1376272</td>\n",
       "      <td>1</td>\n",
       "      <td>2053509.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>JIMMY GOMEZ</td>\n",
       "      <td>DEMOCRATIC</td>\n",
       "      <td>2026 PRIMARY</td>\n",
       "      <td>2026-06-02</td>\n",
       "      <td>Lieutenant Governor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>2017-10-30 23:54:25.942073+00</td>\n",
       "      <td>2017-10-30 23:54:25.973413+00</td>\n",
       "      <td>ocd-person/f446bd3c-d19b-435c-9fa1-fe2165dfcd52</td>\n",
       "      <td>ocd-candidacy/14e643a7-aaa5-4243-917c-10238cf2...</td>\n",
       "      <td>ocd-election/b0d7be16-6f9e-4c5e-a537-c32167bbdbf8</td>\n",
       "      <td>ocd-post/3497cad1-e09e-4731-97c7-338b76746800</td>\n",
       "      <td>ocd-contest/5ca2421a-6acd-4d5a-924e-0fbb8e7387ab</td>\n",
       "      <td>ocd-organization/169e9416-915b-4462-94cf-82e11...</td>\n",
       "      <td>1334135</td>\n",
       "      <td>1</td>\n",
       "      <td>2181321.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JOHN A. PEREZ</td>\n",
       "      <td>DEMOCRATIC</td>\n",
       "      <td>2022 PRIMARY</td>\n",
       "      <td>2022-06-07</td>\n",
       "      <td>Lieutenant Governor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>2017-10-30 23:53:47.749057+00</td>\n",
       "      <td>2017-10-30 23:53:47.780872+00</td>\n",
       "      <td>ocd-person/5da11996-26f5-4b60-a6ff-95d760badbb4</td>\n",
       "      <td>ocd-candidacy/7d57e357-db8d-4bb0-8c2d-0dd1265e...</td>\n",
       "      <td>ocd-election/f69bfdc3-72b0-4125-8dcf-40a56f55cc1d</td>\n",
       "      <td>ocd-post/3497cad1-e09e-4731-97c7-338b76746800</td>\n",
       "      <td>ocd-contest/fc547f9a-7b67-46d7-8083-7f9070da27a6</td>\n",
       "      <td>ocd-organization/169e9416-915b-4462-94cf-82e11...</td>\n",
       "      <td>1304250</td>\n",
       "      <td>1</td>\n",
       "      <td>2181318.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JAMES W. SILVA</td>\n",
       "      <td>REPUBLICAN</td>\n",
       "      <td>2022 PRIMARY</td>\n",
       "      <td>2022-06-07</td>\n",
       "      <td>State Senate 34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>f</td>\n",
       "      <td>2017-10-25 02:26:07.003425+00</td>\n",
       "      <td>2017-10-25 02:26:07.032436+00</td>\n",
       "      <td>ocd-person/d3d6c7c8-9dd1-4844-ac46-5d1d61038b07</td>\n",
       "      <td>ocd-candidacy/330fae87-429c-44f0-a97d-12641e74...</td>\n",
       "      <td>ocd-election/f69bfdc3-72b0-4125-8dcf-40a56f55cc1d</td>\n",
       "      <td>ocd-post/2a28ec6a-80f2-4c0f-b4c5-9b99ab61b98a</td>\n",
       "      <td>ocd-contest/5d72c16f-f46f-470f-b2c4-65f428b3b1a4</td>\n",
       "      <td>ocd-organization/731c91f2-7707-4ac5-9943-4fefa...</td>\n",
       "      <td>1241308</td>\n",
       "      <td>1</td>\n",
       "      <td>2164579.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               name  party_name election_name election_date  \\\n",
       "0  KAMALA D. HARRIS  DEMOCRATIC  2026 PRIMARY    2026-06-02   \n",
       "1       RAJIV DALAL  DEMOCRATIC  2026 PRIMARY    2026-06-02   \n",
       "2       JIMMY GOMEZ  DEMOCRATIC  2026 PRIMARY    2026-06-02   \n",
       "3     JOHN A. PEREZ  DEMOCRATIC  2022 PRIMARY    2022-06-07   \n",
       "4    JAMES W. SILVA  REPUBLICAN  2022 PRIMARY    2022-06-07   \n",
       "\n",
       "                office is_incumbent special_election  \\\n",
       "0             Governor          NaN                f   \n",
       "1  Lieutenant Governor          NaN                f   \n",
       "2  Lieutenant Governor          NaN                f   \n",
       "3  Lieutenant Governor          NaN                f   \n",
       "4      State Senate 34          NaN                f   \n",
       "\n",
       "                      created_at                     updated_at  \\\n",
       "0  2017-10-30 23:54:23.795175+00  2017-10-30 23:54:23.824374+00   \n",
       "1  2017-10-30 23:53:58.104137+00  2017-10-30 23:53:58.136437+00   \n",
       "2  2017-10-30 23:54:25.942073+00  2017-10-30 23:54:25.973413+00   \n",
       "3  2017-10-30 23:53:47.749057+00  2017-10-30 23:53:47.780872+00   \n",
       "4  2017-10-25 02:26:07.003425+00  2017-10-25 02:26:07.032436+00   \n",
       "\n",
       "                                     ocd_person_id  \\\n",
       "0  ocd-person/717b9339-bcaa-437d-8e36-b8417ce02325   \n",
       "1  ocd-person/b3a7a574-0b7c-4f1c-9c08-c68aabeef412   \n",
       "2  ocd-person/f446bd3c-d19b-435c-9fa1-fe2165dfcd52   \n",
       "3  ocd-person/5da11996-26f5-4b60-a6ff-95d760badbb4   \n",
       "4  ocd-person/d3d6c7c8-9dd1-4844-ac46-5d1d61038b07   \n",
       "\n",
       "                                    ocd_candidacy_id  \\\n",
       "0  ocd-candidacy/320f9fb9-1f0a-44b1-84c0-a07fa54f...   \n",
       "1  ocd-candidacy/7fac375c-011f-4bcf-b3f1-749eee7b...   \n",
       "2  ocd-candidacy/14e643a7-aaa5-4243-917c-10238cf2...   \n",
       "3  ocd-candidacy/7d57e357-db8d-4bb0-8c2d-0dd1265e...   \n",
       "4  ocd-candidacy/330fae87-429c-44f0-a97d-12641e74...   \n",
       "\n",
       "                                     ocd_election_id  \\\n",
       "0  ocd-election/b0d7be16-6f9e-4c5e-a537-c32167bbdbf8   \n",
       "1  ocd-election/b0d7be16-6f9e-4c5e-a537-c32167bbdbf8   \n",
       "2  ocd-election/b0d7be16-6f9e-4c5e-a537-c32167bbdbf8   \n",
       "3  ocd-election/f69bfdc3-72b0-4125-8dcf-40a56f55cc1d   \n",
       "4  ocd-election/f69bfdc3-72b0-4125-8dcf-40a56f55cc1d   \n",
       "\n",
       "                                     ocd_post_id  \\\n",
       "0  ocd-post/b63d25d2-aff0-4afb-9dd1-e99474c8eb13   \n",
       "1  ocd-post/3497cad1-e09e-4731-97c7-338b76746800   \n",
       "2  ocd-post/3497cad1-e09e-4731-97c7-338b76746800   \n",
       "3  ocd-post/3497cad1-e09e-4731-97c7-338b76746800   \n",
       "4  ocd-post/2a28ec6a-80f2-4c0f-b4c5-9b99ab61b98a   \n",
       "\n",
       "                                     ocd_contest_id  \\\n",
       "0  ocd-contest/d33b6822-b3b9-4ed5-97db-19c3f16718aa   \n",
       "1  ocd-contest/5ca2421a-6acd-4d5a-924e-0fbb8e7387ab   \n",
       "2  ocd-contest/5ca2421a-6acd-4d5a-924e-0fbb8e7387ab   \n",
       "3  ocd-contest/fc547f9a-7b67-46d7-8083-7f9070da27a6   \n",
       "4  ocd-contest/5d72c16f-f46f-470f-b2c4-65f428b3b1a4   \n",
       "\n",
       "                                        ocd_party_id  \\\n",
       "0  ocd-organization/169e9416-915b-4462-94cf-82e11...   \n",
       "1  ocd-organization/169e9416-915b-4462-94cf-82e11...   \n",
       "2  ocd-organization/169e9416-915b-4462-94cf-82e11...   \n",
       "3  ocd-organization/169e9416-915b-4462-94cf-82e11...   \n",
       "4  ocd-organization/731c91f2-7707-4ac5-9943-4fefa...   \n",
       "\n",
       "  latest_calaccess_filer_id  calaccess_filer_id_count  \\\n",
       "0                   1313503                         1   \n",
       "1                   1376272                         1   \n",
       "2                   1334135                         1   \n",
       "3                   1304250                         1   \n",
       "4                   1241308                         1   \n",
       "\n",
       "   latest_form501_filing_id  form501_filing_count  \n",
       "0                 2149075.0                   1.0  \n",
       "1                 2053509.0                   1.0  \n",
       "2                 2181321.0                   1.0  \n",
       "3                 2181318.0                   1.0  \n",
       "4                 2164579.0                   1.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidates.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How many candidates?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7162"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(candidates)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How has the total changed over time?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cast the election date column into the datetime data type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "candidates.election_date = pd.to_datetime(candidates.election_date)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Group the elections into two-year cycles to simplify the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_cycle(dt):\n",
    "    if dt.year % 2 == 0:\n",
    "        return dt.year\n",
    "    else:\n",
    "        return dt.year +1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "candidates['election_year'] = candidates.election_date.apply(get_cycle)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Group by year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "by_year = candidates.groupby(\"election_year\").size().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chart the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f333a95e950>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4vteTqxy2n8vJyHX5ZDoNuU5DrtOQ6zTkOg25TmM+ucp0GnKdxnxyhSlUVbq7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\nCC8DN3N7sAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f333a95eed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "by_year.set_index(\"election_year\").plot.bar(figsize=(20, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How about the different parties? How have their totals changed over time?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Group by party"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "by_party = candidates.groupby([\"party_name\", \"election_year\"]).size().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transpose it into a crosstab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "party_crosstab = by_party.set_index([\n",
    "    \"election_year\", \"party_name\"\n",
    "]).unstack(1).reset_index().fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clean up the crosstab so it's closer to a simple DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "party_crosstab = party_crosstab.reset_index()\n",
    "party_crosstab.columns = party_crosstab.columns.droplevel(0)\n",
    "party_crosstab = party_crosstab.rename_axis(None, axis=1)\n",
    "party_crosstab = party_crosstab.set_index(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chart each party"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f333a962f50>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f333ae9eb10>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f333ae22ad0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f333ad87b10>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f333ad0aa10>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f333acb3a50>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f333abf8490>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f333ab7d310>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f333ab5ea50>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f333aa2da50>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f33386376d0>], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cAAAAAJIsLy9neXl5rrrVvWI+s/oBVb+W5KtJfiXJEd29q6pOSHJOd59cVf9jWL+iqu6e\n5KbuPmyF8/TeXhsAAACA1VVVunt2gk6S+b4O9oCqOnhYv1eSxyX5aJL3JTl1qHZGkncN6xcN2xn2\nX3bnmw4AAADAelhzJlBVPSKTFz3fbVje2t2/UVX/JskFSQ5J8qEkz+zur1fVPZO8McmjknwxyWnd\nfe0K5zUTCAAAAGAd7Wkm0F4/DrZehEAAAAAA62ufHgcDAAAAYPMTAgEAAACMgBAIAAAAYASEQAAA\nAAAjIAQCAAAAGAEhEAAAAMAICIEAAAAARkAIBAAAADACQiAAAACAERACAQAAAIyAEAgAAABgBIRA\nAAAAACMgBAIAAAAYASEQAAAAwAgIgQAAAABGQAgEAAAAMAJrhkBVdWRVXVZVH62qq6rql4byQ6rq\nkqq6uqourqqDp455VVVdU1UfrqrjFnkDAAAAAKxtnplA30jy/O7+7iSPTvILVfWwJGcnubS7j01y\nWZIXJ0lVnZzkod19TJLnJHnNQloOAAAAwNzWDIG6++bu/vCw/s9JPpbkyCSnJDl/qHb+sJ3h5xuG\n+lckObiqDl/ndgMAAACwF/bqnUBVdXSS45J8IMnh3b09mQRFSQ4bqm1Nct3UYTcMZQAAAABskLlD\noKq6b5K3J/nlYUZQr1Z1hbLV6gIAAABwF9gyT6Wq2pJJAPTG7n7XULy9qg7v7u1VdUSSzw3l1yd5\n0NThRya5caXzbtu27bb1paWlLC0t7VXjAQAAAMZseXk5y8vLc9Wt7rUn6VTVG5J8obufP1V2bpId\n3X1uVZ2d5Du6++yqelKSX+juJ1fVCUle2d0nrHDOnufaAAAAAMynqtLdKz2ltXYIVFU/nOQvk1yV\nyWNdneRXk3wwydsymfXz2SSndvfO4ZhXJ3likq8keXZ3X7nCeYVAAAAAAOton0KgRRECAQAAAKyv\nPYVAe/V1MAAAAAA2JyEQAAAAwAgIgQAAAABGQAgEAAAAMAJCIAAAAIAREAIBAAAAjIAQCAAAAGAE\nhEAAAAAAIyAEAgAAABgBIRAAAADACAiBAAAAAEZACAQAAAAwAkIgAAAAgBEQAgEAAACMgBAIAAAA\nYASEQAAAAAAjIAQCAAAAGIE1Q6Cqel1Vba+qf5gqO6SqLqmqq6vq4qo6eGrfq6rqmqr6cFUdt6iG\nAwAAADC/eWYCnZfkCTNlZye5tLuPTXJZkhcnSVWdnOSh3X1Mkuckec06thUAAACAO2nNEKi735/k\nlpniU5KcP6yfP2zvLn/DcNwVSQ6uqsPXp6kAAAAA3Fl39p1Ah3X39iTp7puTHDaUb01y3VS9G4Yy\nAAAAADbQer8YulYo63W+BgAAAAB7acudPG57VR3e3dur6ogknxvKr0/yoKl6Rya5cbWTbNu27bb1\npaWlLC0t3cnmAAAAAIzP8vJylpeX56pb3WtP1Kmqo5P8SXc/Ytg+N8mO7j63qs5O8h3dfXZVPSnJ\nL3T3k6vqhCSv7O4TVjlnz3NtAAAAAOZTVenulZ7UWjsEqqo3J1lKcv8k25Ock+TCJH+UyayfzyY5\ntbt3DvVfneSJSb6S5NndfeUq5xUCAQAAAKyjfQqBFkUIBAAAALC+9hQCrfeLoQEAAADYDwmBAAAA\nAEZACAQAAAAwAkIgAAAAgBEQAgEAAACMgBAIAAAAYASEQAAAAAAjIAQCAAAAGAEhEAAAAMAICIEA\nAAAARkAIBAAAADACQiAAAACAERACAQAAAIyAEAgAAABgBIRAALBBjjji6FTVpliOOOLoje4uAAD2\nUXX3xly4qjfq2gCwP6iqJJvl34UV/94GANj/VVW6u1baZyYQAAAAwAgsJASqqidW1cer6hNV9aJF\nXOOutLy8vNFNOCDp18XQr4uhX9efPl2U5Y1uwAHJeF0M/boY+nUx9Ov606eLoV8X40Dp13UPgarq\nbkleneQJSb4nyU9X1cPW+zp3pQPll31nLeqdFY997GO9s2IBxj5eF0W/rj99uijLG92AA5Lxuhj6\ndTH062Lo1/WnTxdDvy7GgdKvi5gJdHySa7r7M9399SQXJDllAde5y1x77bUb3YQNtX37ZzJ5Z8V6\nL+es+zknbR23sY/XRYWWv/7rvy60XGdjH6tsLsYrm4nxymZhrLKZHCjjdREh0NYk101tXz+UbVoH\nyi+bcRj7eBVabh5jH6tsLsYrm4nxymZhrLKZHCjjdd2/DlZVT0/y+O7+34ftZyb5ge7+5Zl6PjEC\nAAAAsM5W+zrYlgVc6/okR01tH5nkxnkbBAAAAMD6W8TjYH+b5N9W1YOr6h5JTkty0QKuAwAAAMCc\n1n0mUHd/s6p+McklmYRMr+vuj633dQAAAACY37q/EwgAAACA/c8iHgcDAAAAYD8jBAIAAAAYASEQ\nAAAAwAgIgQAAAABGQAgEAAAAMAJCIAAAAIAREAIBAAAAjIAQCAAAAGAEhEAAwLqqquWq2lFV3zZT\n/gdVtauqnjJT/sqh/PRh+4yq+kZVfXlYbh1+HjHsv7aqvjqU3VhV51XVvafOd15VvWxq+9uqaltV\nfWI416er6rVVddQK7fv67utMlW8b2vcTU2V3H8qOGra3VtXbq+rzVXVLVX1k9/2s0D8nVtU3V7i/\nHxz2v6+qfnaF4x48XHP2uFOn6hxfVX86tOELVfWBqjqzqp4xVf+rU9e/taq+vPpvEwA4kAiBAIB1\nU1UPTvKYJLuSPHVmdye5OskZU/XvnuTpST45U/dvuvugYbnf8PPmqfM8ubsPSnJckkclefEemvWO\nJE9JclqSg5M8MsnfJzlpqh33TvLjSXYm+ZkV2v3FJC+rqpop3+2NST6T5EFJ7p/k9CTb99CmG1a4\nvyv2UH/6mgfPHPdHwz08OsmfJ3lfkod29wOS/HySJ3b3m3fXT3Ly1PV3lwEAIyAEAgDW0+lJLk/y\nB0nOXGH/u5P8cFUdPGw/MclHkty8Qt09qSTp7s8luTiTMOiOlaoel0nY89TuvrK7d3X3rd39+919\n3lTVpye5JcnLVmn3xUn+NcmzZtsw+IEk53f3/xyu8ZHuvngv72letUr5y5Oc193/rbt3JEl3f6i7\nT1tQOwCATUYIBACsp9OT/GGSNyd5QlU9cGb/vyS5KJNZObvrvyGrBxt7VFVHZjKz5ZpVqpyU5IPd\nfeMc7X5zkrcmeVhVzYZKu5K8NMk5w+ylWZcn+b2q+qmqetDcN3Dn3KGvqupeSR6dyawnAIAVCYEA\ngHVRVY9JclSSt3X3lZk84vWMFaq+MckZVXVQkh9JcuEKdR49vFdox/B+m9mQ58LhXTafzeSxq22r\nNOv+SW5ao91HJXlskjcPM4suzdQja7t197uTfD7Jz61wmlOT/GWSlyT5dFVdWVX/yx4uu3Xm/nYM\nQc5aKsnnZ447Nskhmfxdt8d7BQDGTQgEAKyX05Nc0t23DNtvycphyl8neWAmgcm7u/trK5zr8u4+\ndFgO6e5jZvafMrzL5sQkD0vygFXa9MUk37lGu5+V5KPdfdVUu5+xyoyflyT5L0m+feaevtTdv9rd\nj0hyeCaPuL1zD9e8Yeb+Du3uf1mjncnknUD3nznu6kweZduVte8VABgxIRAAsM+q6tuT/GSSE6vq\npqq6Kcnzkjyyqh6xwiF/mOT5Sc6/s5dMku7+q+Ecr1il3qVJjq+q79rDuZ6V5CFT7X5FJqHSybMV\nu/vSTGY4PTe3fzH0dJ0dSf5bku+qqkPmu529cofHwYYA6fIkP3HH6gAAE0IgAGA9/FiSbyR5eCZf\n33rksP7+TGYIzXpVkv/Y3e9f5Xx7846gVyb5j1X1fbM7uvvPk7w3yTur6t8Pn3a/b1U9Z/h0+qOT\nPCSTFzvvbvf3ZJVZTIOXJPmV2zW26req6nuG898vk5Dok1Ozovb2/r6tqu45tWyZOm61Y38lyZlV\n9YKqOnRo1yOr6i1rXAsAGAkhEACwHk5P8vruvqG7P7d7SfLqJD9TVbf7m6O7b+nu900XzZzvhKr6\n8rDcOvz8/pXqdvcXMpkN9NJV2vb0JO/J5KXPO5NcleT7M5kldHqSC7v7ozPt/t0kT6mq75g9WXf/\nTZIPzrTj3pk8/nVLJjOFHpTkqau0J0m+c4X7+7Gp/b+X5KtTy+un7v2WmeOeN7Tr8iQ/msnLsD9V\nVV9I8pokf7qHdgAAI1LdK85kvn2lqmuTfCmTZ82/3t3HD9Ob35rkwUmuTfKT3f2lof6rMplC/ZUk\nZ3b3hxfSegAAAADmMu9MoF1Jlrr7Ud19/FB2dpJLu/vYJJcleXGSVNXJSR46vMDxOZn8HygAAAAA\nNtC8IVCtUPeUfOtljucP27vL35Ak3X1FkoOr6vB9bCcAAAAA+2DeEKiTXFxVf1tVPzeUHd7d25Ok\nu29OcthQvjXJdVPH3jCUAQAAALBBtqxdJUnyQ919c1U9MMklVXV1Vvksalb+YsUd6lbV2i8jAgAA\nAGCvdPeKXxOdaybQMNMn3f35JBcmOT7J9t2PeVXVEUk+N1S/PpMvYux2ZJIbVznvplhOPPHEDW/D\ngbicc845G96GA3ExXhezGK/rvxiri1mM1cUsxutiFuN1MYvxupjFeF3/xVhdzGKsLmbZTON1T9YM\ngarq3lV132H9Pkken8mnVS9KcuZQ7cwk7xrWL8rkc6upqhOS7OzhsbHN6uijj97oJsDcjFc2C2OV\nzcR4ZTMxXtksjFU2kwNlvM7zONjhSd45PL61JcmbuvuSqvq7JG+rqp9N8tkkpyZJd7+nqp5UVZ/M\n5BPxz15Q2+8yB8ovm3EwXtksjFU2E+OVzcR4ZbMwVtlMDpTxumYI1N3/lOS4Fcp3JHncKsf84r43\nbf+xtLS00U04IOnXxdCvi6Ff158+XQz9uhj6dTH062Lo18XQr+tPny6Gfl2MA6Vfa63nxRZ24are\nqGsDAAAAHIiqKr0vL4YGAAAAYHMTAgEAAACMgBAIAAAAYASEQAAAAAAjIAQCAAAAGAEhEAAAAMAI\nCIEAAAAARkAIBAAAADACQiAAAACAERACAQAAAIyAEAgAAABgBIRAAAAAACMgBAIAAAAYASEQAAAA\nwAgIgQAAAABGYO4QqKruVlVXVtVFw/bRVfWBqrq6qt5SVVuG8ntU1QVVdU1VXV5VRy2q8QAAAADM\nZ29mAv1yko9ObZ+b5BXdfWySnUnOGsrPSrKju49J8sokL1+PhgIAAABw580VAlXVkUmelOS1U8U/\nmuQdw/r5SZ42rJ8ybCfJ25OctO/NBAAAAGBfzDsT6HeSvDBJJ0lV3T/JLd29a9h/fZKtw/rWJNcl\nSXd/M8nOqjp03VoMAAAAwF5bMwSqqicn2d7dH05Su4un1nfrqX23O8XUPgAAAAA2wJY56vxwkqdW\n1ZOS3CvJ/TJ518/BVXW3YTbQkUluHOpfn+RBSW6sqrsnOai7b1npxNu2bbttfWlpKUtLS3fyNgAA\nAADGZ3l5OcvLy3PVre75J+lU1YlJXtDdT62qtyb54+5+a1X9fpKPdPdrquq5Sb63u59bVacleVp3\nn7bCuXpvrg0AAADAnlVVunv2Ka0ke/d1sFlnJ3l+VX0iyaFJXjeUvy7JA6rqmiTPG+oBAAAAsIH2\naibQul7YTCAAAACAdbWomUAAAAAAbBJCIAAAAIAREAIBAAAAjIAQCAAAAGAEhEAAAAAAIyAEAgAA\nABgBIRAAAADACAiBAAAAAEZACAQAAAAwAkIgAAAAgBEQAgEAAACMgBAIAAAAYASEQAAAAAAjIAQC\nAAAAGAEhEAAAAMAICIEAAAAARkAIBAAAADACa4ZAVXXPqrqiqj5UVVdV1TlD+dFV9YGqurqq3lJV\nW4bye1TVBVV1TVVdXlVHLfomAAAAANizNUOg7v5aksd296OSHJfk5Kr6wSTnJnlFdx+bZGeSs4ZD\nzkqyo7uPSfLKJC9fSMsBAAAAmNtcj4N191eH1Xsm2ZKkkzw2yTuG8vOTPG1YP2XYTpK3JzlpXVoK\nAAAAwJ02VwhUVXerqg8luTnJe5N8KsnO7t41VLk+ydZhfWuS65Kku7+ZZGdVHbqurQYAAABgr8w7\nE2jX8DjYkUmOT/LwlaoNP2umvKb2AQAAALABtuxN5e7+clX9RZITknxHVd1tmA10ZJIbh2rXJ3lQ\nkhur6u4tidKsAAAgAElEQVRJDuruW1Y637Zt225bX1paytLS0l7fAAAAAMBYLS8vZ3l5ea661b3n\nSTpV9YAkX+/uL1XVvZJcnOS3kpyR5I+7+61V9ftJPtLdr6mq5yb53u5+blWdluRp3X3aCuftta4N\nAAAAwPyqKt09+5TWZN8cIdAjMnnR892G5a3d/RtV9W+SXJDkkCQfSvLM7v56Vd0zyRuTPCrJF5Oc\n1t3XrnBeIRAAAADAOtqnEGhRhEAAAAAA62tPIdBcL4YGAAAAYHMTAgEAAACMgBAIAAAAYASEQAAA\nAAAjIAQCAAAAGAEhEAAAAMAICIEAAAAARkAIBAAAADACQiAAAACAERACAQAAAIyAEAgAAABgBIRA\nAAAAACMgBAIAAAAYASEQAAAAwAgIgQAAAABGQAgEAAAAMAJCIAAAAIARWDMEqqojq+qyqvpoVV1V\nVb80lB9SVZdU1dVVdXFVHTx1zKuq6pqq+nBVHbfIGwAAAABgbfPMBPpGkud393cneXSSX6iqhyU5\nO8ml3X1sksuSvDhJqurkJA/t7mOSPCfJaxbScgAAAADmtmYI1N03d/eHh/V/TvKxJEcmOSXJ+UO1\n84ftDD/fMNS/IsnBVXX4OrcbAAAAgL2wV+8EqqqjkxyX5ANJDu/u7ckkKEpy2FBta5Lrpg67YSgD\nAAAAYINsmbdiVd03yduT/HJ3/3NV9WpVVyhbse62bdtuW19aWsrS0tK8zQEAAAAYveXl5SwvL89V\nt7pXy3KmKlVtSfLuJH/W3b87lH0syVJ3b6+qI5K8r7sfXlWvGdbfOtT7eJITd88amjpnz3NtAAAA\nAOZTVenulSbozP042OuTfHR3ADS4KMmZw/qZSd41VX76cOETkuycDYAAAAAAuGutOROoqn44yV8m\nuSqTx7o6ya8m+WCStyV5UJLPJjm1u3cOx7w6yROTfCXJs7v7yhXOayYQAAAAwDra00yguR4HWwQh\nEAAAAMD6Wo/HwQAAAADYxIRAAAAAACMgBAIAAAAYASEQAAAAwAgIgQAAAABGQAgEAAAAMAJCIAAA\nAIAREAIBAAAAjIAQCAAAAGAEhEAAAAAAIyAEAgAAABgBIRAAAADACAiBAAAAAEZACAQAAAAwAkIg\nAAAAgBEQAgEAAACMgBAIAAAAYATWDIGq6nVVtb2q/mGq7JCquqSqrq6qi6vq4Kl9r6qqa6rqw1V1\n3KIaDgAAAMD85pkJdF6SJ8yUnZ3k0u4+NsllSV6cJFV1cpKHdvcxSZ6T5DXr2FYAAAAA7qQ1Q6Du\nfn+SW2aKT0ly/rB+/rC9u/wNw3FXJDm4qg5fn6YCAAAAcGfd2XcCHdbd25Oku29OcthQvjXJdVP1\nbhjKAAAAANhAW9b5fLVCWa9Wedu2bbetLy0tZWlpaZ2bAwAAAHDgWl5ezvLy8lx1q3vVjOZblaoe\nnORPuvv7hu2PJVnq7u1VdUSS93X3w6vqNcP6W4d6H09y4u5ZQzPn7HmuDQAAAMB8qirdvdIknbkf\nB6vcfpbPRUnOHNbPTPKuqfLTh4uekGTnSgEQAAAAAHetNWcCVdWbkywluX+S7UnOSXJhkj9K8qAk\nn01yanfvHOq/OskTk3wlybO7+8pVzmsmEAAAAMA62tNMoLkeB1sEIRAAAADA+lqPx8EAAAAA2MSE\nQAAAAAAjIAQCAAAAGAEhEAAAAMAICIEAAAAARkAIBAAAADACQiAAAACAERACAQAAAIyAEAgAAABg\nBIRAAAAAACMgBAIAAAAYASEQAAAAwAgIgQAAAABGQAgEAAAAMAJCIAAAAIAREAIBAAAAjMBCQqCq\nemJVfbyqPlFVL1rENQAAAACY37qHQFV1tySvTvKEJN+T5Ker6mHrfZ270vLy8kY34YCkXxdDvy6G\nfl1/+nQx9Oti6NfF0K+LoV8XQ7+uP326GPp1MQ6Ufl3ETKDjk1zT3Z/p7q8nuSDJKQu4zl3mQPll\n72/062Lo18XQr+tPny6Gfl0M/boY+nUx9Oti6Nf1p08XQ78uxoHSr4sIgbYmuW5q+/qhbNO69tpr\nN7oJMDfjlc3CWGUzMV7ZTIxXNgtjlc3kQBmviwiBaoWyXsB17jIHyi+bcTBe2SyMVTYT45XNxHhl\nszBW2UwOlPFa3eubz1TVCUm2dfcTh+2zk3R3nztTb1MHQwAAAAD7o+5eaYLOQkKguye5OslJSW5K\n8sEkP93dH1vXCwEAAAAwty3rfcLu/mZV/WKSSzJ53Ox1AiAAAACAjbXuM4EAAAAA2P8s4sXQAAAA\nAOxnhEAAAAAAIyAEAgAAABgBIRAAAADACAiBAAAAAEZACAQAAAAwAkIgAAAAgBEQAgEAAACMgBAI\nANgvVNW1VfXVqvpSVe2oqvdX1XOqqob951XV16rqy8Nya1V9aNj34KraVVV/N3PO+1fVv1bVp2fK\nz6yqf6iqr1TVjVX1e1V18Eydf1dVb6uqz1fVLVX14ar6zzWx+3q72/LpqnrRKve1PNzPt02VvWdo\n/5eH9n1tavv3qurEqrpu5jxPqKq/GOpsr6r3VdX/um+9DgCMiRAIANhfdJInd/fBSR6c5LeSvCjJ\na6fqnNvdBw3L/br7UTPnuE9VfffU9jOSfGq6QlW9IMn/leQFSQ5KcsJwvfdW1ZahzkOTfCDJZ5J8\nb3cfkuTUJN+f5H5T7T24uw8a9r20qk6audaDkzwmya4kT73tRrufNLT/oCRvGu7rfsN9PXfq/LvP\n8/Qkb0vyB0m2dvfhSX4tyVNW6UsAgDsQAgEA+5NKku6+tbvfneSnkpwxE+zsyRuTnDm1fXqSN9x2\n8qr7JdmW5Be7+73d/c3u/mySn8wkCHrmUPXXk/x1d7+wu7cPbbqmu5/Z3V9eob1/n+Qfkxw3057T\nk1yeSXhzZu68VyT59e4+r7tvHa75V939nH04JwAwMkIgAGC/1d1/m+SGJP9hnupJ/jDJacMjWw9P\nct8kH5yq88NJ7pnknTPX+UqSP0vyH4eik5K8fY5r7n5U7YQk35PkkzP7Tx/a9OYkT6iqB85xzttf\noOphSY5M8o69PRYAYJoQCADY392Y5NBh/YXD+3VuGX6eN1P3+iQfzyTMud0soMH9k3yhu3etcJ2b\nkjxgqt5Na7Srkny+qr6a5K+T/F53v+u2nVWPSXJUkrd195WZBETPWOOcK9l972u1BwBgj4RAAMD+\nbmuSHcP6b3f3od19yPDz2SvU3/1I2GmZzMKZ9oUkD6iqlf4G+s5hf5J8cdjek84kLLpPkv8zydLu\ndwoNTk9ySXffMmy/JckZa5xzJV+cah8AwJ0mBAIA9ltV9QNJvivJ+/fisHckeXKST3X3dTP7Lk/y\ntSQ/PnOd+yQ5OcmlQ9GlSX5inib2xO8M533ucL5vz+Q9QydW1U1VdVOS5yV5ZFU9Yi/uJd19dZLr\n5mwPAMCqhEAAwH6nqu5XVU/JZPbMG7v7H+c5LEm6+6tJHpvkf5utMLzU+WVJ/vvwyfUtVXV0Jl/e\n+my+NXPonCQ/VFXnVtXhQ5v+bVW9saoOmr7elN9K8qKqukeSH0vyjSQPT/LIYXl4kr/KnZsN9IJM\nvj52xtA3VVWPqar/906cCwAYqS1rVwEAuMv8SVV9I5NPqn80yX9LMh10/EpVPW9YryT/0t2HDdu3\nfVJ9eAfPirr7t6vqC8O5H5Lky5m8KPoZ3f31oc6nq+rRSX4jyT9W1d2TXJvkvCS3Jjlk+nrDMX9a\nVTsyCZ+enOT13X3DdJ2q+n+S/G5V/crUe4lud55V2vyOqro1yUuS/Pck/5LJ18h+e61jAQB2q+41\n/+5IVV2b5EuZ/EH29e4+vqoOSfLWTD6nem2Sn+zuLw31X5XJlOqvJDmzuz+8kNYDAAAAMJd5Hwfb\nlWSpux/V3ccPZWcnubS7j01yWZIXJ0lVnZzkod19TJLnJHnNOrcZAAAAgL00bwhUK9Q9Jcn5w/r5\nw/bu8jckSXdfkeTg3c/SAwAAALAx5g2BOsnFVfW3VfVzQ9nh3b09Sbr75iS7n8ffmskXLHa7YSgD\nAAAAYIPM+2LoH+rum6vqgUkuqaqrs/pLDGe/lJE91AUAAADgLjBXCDTM9El3f76qLkxyfJLtVXV4\nd2+vqiOSfG6ofn2SB00dfmSSG2fPWVWCIQAAAIB11t0rTdBZ+3Gwqrp3Vd13WL9PkscnuSrJRUnO\nHKqdmeRdw/pFSU4f6p+QZOfux8ZWaNSmWE488cQNb8OBuJxzzjkb3oYDcTFeF7MYr+u/GKuLWYzV\nxSzG62IW43Uxi/G6mMV4Xf/FWF3MYqwuZtlM43VP5pkJdHiSdw4zd7YkeVN3X1JVf5fkbVX1s0k+\nm+TUIdh5T1U9qao+mckn4p89xzX2a0cfffRGNwHmZryyWRirbCbGK5uJ8cpmYayymRwo43XNEKi7\n/ynJcSuU70jyuFWO+cV9b9r+40D5ZTMOxiubhbHKZmK8spkYr2wWxiqbyYEyXuf9OtioLS0tbXQT\nDkj6dTH062Lo1/WnTxdDvy6Gfl0M/boY+nUx9Ov606eLoV8X40Dp11rrebGFXbiqN+raAAAAAAei\nqkrf2RdDAwAAALD5CYEAAAAARkAIBAAAADACQiAAAACAERACAQAAAIyAEAgAAABgBIRAAAAAACMg\nBAIAAAAYASEQAAAAwAgIgQAAAABGQAgEAAAAMAJCIAAAAIAREAIBAAAAjIAQCAAAAGAEhEAAAAAA\nIzB3CFRVd6uqK6vqomH76Kr6QFVdXVVvqaotQ/k9quqCqrqmqi6vqqMW1XgAABbv6COOSFVtiuXo\nI47Y6O4CgP3W3swE+uUkH53aPjfJK7r72CQ7k5w1lJ+VZEd3H5PklUlevh4NBQBgY3xm+/Z0simW\nz2zfvqhuAIBNb64QqKqOTPKkJK+dKv7RJO8Y1s9P8rRh/ZRhO0nenuSkfW8mAAAAAPti3plAv5Pk\nhZn8D5ZU1f2T3NLdu4b91yfZOqxvTXJdknT3N5PsrKpD163FAAAAAOy1LWtVqKonJ9ne3R+uqqXd\nxcMyraf23e4UU/tuZ9u2bbetLy0tZWlpaaVqAAAAAKxgeXk5y8vLc9Wt7hXzmW9VqPrNJM9M8o0k\n90pyvyQXJnl8kiO6e1dVnZDknO4+uar+x7B+RVXdPclN3X3YCuftta4NAMDGq6qV/4/efqiS+BsT\ngDGrqnT37ASdJHM8Dtbdv9rdR3X3Q5KcluSy7n5mkvclOXWodkaSdw3rFw3bGfZfti+NB4ADlS8u\nAQBwV1pzJtDtKledmOQF3f3Uqvo3SS5IckiSDyV5Znd/varumeSNSR6V5ItJTuvua1c4l5lAAIya\n2RVsFsYqAGwee5oJtFch0HoSAgEwdv7Dms3CWAWAzWOfHgcDAAAAYPMTAgEHFO9YAQAAWJnHwYAD\nikcW2EyMVzYLYxUANg+PgwEAAACMnBAIAAAAYASEQAAAAAAjIAQCAAAAGAEhEAAAAMAICIEAAAAA\nRkAIBBvk6COOSFVtiuXoI47Y6O4CAABgH1V3b8yFq3qjrg37g6rKZvknoJJsln9e9SubifG6GEcf\ncUQ+s337RjdjLg8+/PBce/PNG92MNRmrALB5VFW6u1bcJwSCjeEP6sXQr2wmxuti6Nf1p08BYPPY\nUwjkcTDW5LElAAAA2PzMBGJN/u/fYujXxdCvbCbG62Lo1/WnTwFg8zATCAAAAGDkhEAAAAAAIyAE\nAgAAABiBNUOgqrpnVV1RVR+qqquq6pyh/Oiq+kBVXV1Vb6mqLUP5Parqgqq6pqour6qjFn0TAAAA\nAOzZmiFQd38tyWO7+1FJjktyclX9YJJzk7yiu49NsjPJWcMhZyXZ0d3HJHllkpcvpOUAALCJ+QIr\nAHe1uR4H6+6vDqv3TLIlSSd5bJJ3DOXnJ3nasH7KsJ0kb09y0rq0FAAADiCf2b49nWyK5TPbty+q\nGwC4C80VAlXV3arqQ0luTvLeJJ9KsrO7dw1Vrk+ydVjfmuS6JOnub+b/Z+/+ozW76/rQvz8wkoX8\nCAmQmdUkQxTTiBUM7WoM2poTdSGBtmHdEottDEFaqMoCL60SvLdm6K1doHWJKdXc1aIOXIEgFAhW\nJeUmR6uFFC9EsYQYrUMmgRksIYQfaoF87h9nT3hycmbOc2aenWfO2a/XWnud/Xz3d+/9fT7Za3Lm\nPd+9d3JPVZ2+0FEDAAAAsCW75uk0hD1Pr6rHJnlnkqds1G34uf5d9DWz7QH27dt3//rKykpWVlbm\nGQ4AAAAASVZXV7O6ujpX3+reMJ85+g5VP5Hki0l+LMme7r6vqi5McnV3X1JVvzms31xVD0/yye4+\nY4Pj9FbPzXJU1cYp3kmokmyX60pdx6GubCeu13Go6+Kp6TjUFYAxVFW6e/0EnSTzvR3sCVV16rD+\nyCTfneSjSW5KctnQ7QVJ3j2sXz98zrD9xuMfOgAAAACLsOlMoKp6atYe9PywYbmuu3+yqr4uyVuT\nnJbkw0ku7+4vVdUpSd6U5OlJPp3k+d19YIPjmgm0TfhXqnGo6zjUle3E9ToOdV08NR2HugIwhmPN\nBNry7WCLIgTaPvyCMg51HYe6sp24XsehrounpuNQVwDGcEK3gwEAAACw/QmBAAAAACZACAQAAAAw\nAUIgAAAAgAkQAgEAAABMgBAIAAAAYAKEQAAAAAATsONCoHP27ElVnfTLOXv2LLtUAAAAwIRUdy/n\nxFU9xrmrKsv5RltTSZZV+63aLjVN1HUs6jqO7VTXc/bsyccPH172MObypN27c+DQoWUPYy6u13Go\n6+Kp6TjUFYAxVFW6uzbcJgRaju30P9LtUtNEXceiruNQ13Go6zjUdRzbpa5qOg51BWAMxwqBdtzt\nYAAAAAA8mBAIAAAAYAKEQAAAAAATIAQCAAAAmAAhEAAAAMAECIEAAAAAJkAIBAAAADABm4ZAVXVW\nVd1YVR+tqo9U1cuG9tOq6oaquq2q3ltVp87sc01V3V5Vt1TV+WN+AQAAAAA2N89MoC8neUV3f1OS\nZyT54ar6xiRXJXlfd5+X5MYkr0qSqrokyZO7+9wkL0ly7SgjBwAAAGBum4ZA3X2ou28Z1j+f5NYk\nZyW5NMn+odv+4XOGn28c+t+c5NSq2r3gcQMAAACwBVt6JlBVnZPk/CQfSLK7uw8na0FRkjOGbmcm\nOTiz211DGwAAAABLsmvejlX16CRvT/Ly7v58VfXRum7QtmHfffv23b++srKSlZWVeYcDAAAAMHmr\nq6tZXV2dq291Hy3LmelUtSvJryX5je7+uaHt1iQr3X24qvYkuam7n1JV1w7r1w39PpbkoiOzhmaO\n2fOce6uqauPE6SRTScb4/mPYLjVN1HUs6joOdR2Huo5DXcexXeqqpuNQVwDGUFXp7o0m6Mx9O9gv\nJvnokQBocH2SK4f1K5O8e6b9iuHEFya5Z30ABAAAAMBDa9OZQFX17Ul+O8lHsnZbVyf58ST/Lcnb\nkpyd5I4kl3X3PcM+r0/yrCRfSPLC7v7QBsc1E2ib/GvKdqlpoq5jUddxqOs41HUc6jqO7VJXNR2H\nugIwhmPNBJrrdrAxCIG2z/9It0tNE3Udi7qOQ13Hoa7jUNdxbJe6quk41BWAMSzidjAAAICT3jl7\n9qSqtsVyzp49yy4XMDFmAi3JdvrXlO1S00Rdx6Ku41DXcajrONR1HNulrmo6DnUdh7oCU2cmEAAA\nAMDECYEAAAAAJkAIBAAAADABQiAAAACACRACAQAAAEyAEAgAAABgAoRAAAAAABMgBAIAAACYACEQ\nAAAAwAQIgQAAAAAmQAgEAAAAMAFCIAAAAIAJEAIBAAAATIAQCAAAAGACNg2BquoNVXW4qv5gpu20\nqrqhqm6rqvdW1akz266pqtur6paqOn+sgQMAAAAwv3lmAv1Sku9Z13ZVkvd193lJbkzyqiSpqkuS\nPLm7z03ykiTXLnCsAAAAABynTUOg7v6dJJ9Z13xpkv3D+v7h85H2Nw773Zzk1KravZihAgAAAHC8\njveZQGd09+Ek6e5DSc4Y2s9McnCm311DGwAAAABLtOgHQ9cGbb3gcwAAAACwRbuOc7/DVbW7uw9X\n1Z4knxra70xy9ky/s5J84mgH2bdv3/3rKysrWVlZOc7hAAAAAEzP6upqVldX5+pb3ZtP1Kmqc5K8\np7ufOnx+bZK7u/u1VXVVksd191VV9ewkP9zdz6mqC5O8rrsvPMoxe55zb1VVbYupR5VkjO8/hu1S\n00Rdx6Ku41DXcajrONR1HNulrmo6DnUdh7oCU1dV6e6N7tTafCZQVb05yUqSx1fVHUmuTvKaJL9a\nVT+Q5I4klyVJd/96VT27qv44yReSvHAxXwEAAACAEzHXTKBRTmwm0LZJ/bdLTRN1HYu6jkNdx6Gu\n41DXcWyXuqrpONR1HOoKTN2xZgIt+sHQAAAAAJyEhEAAAAAAEyAEAgAAAJgAIRAAAADABAiBAAAA\nACZACAQAAAAwAUIgAAAAgAkQAgEAAABMgBAIAAAAYAKEQAAAAAATIAQCAAAAmAAhEAAAAMAECIEA\nAAAAJkAIBAAAADABQiAAAACACRACAQAAAEyAEAgAAABgAkYJgarqWVX1sar6o6p65RjneCitLnsA\nO9TqsgewQ60uewA71OqyB7ADrS57ADvU6rIHsEOtLnsAO9TqsgewQ60uewA71OqyB7Bk5+zZk6ra\nFss5e/Ysu1xLtbq6uuwh7Eg7pa4LD4Gq6mFJXp/ke5L8tSTfV1XfuOjzPJRWlz2AHWp12QPYoVaX\nPYAdanXZA9iBVpc9gB1qddkD2KFWlz2AHWp12QPYoVaXPYAdanXZA1iyjx8+nE4Wuly94OMdWT5+\n+PBYZdgWdkpYcbLZKXUdYybQBUlu7+6Pd/eXkrw1yaUjnOchc2DZA4AtOLDsAcCcDix7ALAFB5Y9\nANiCA8seAMzpwLIHAFtw4MCBZQ9hIcYIgc5McnDm851D27Z1YNkDgC04sOwBwJwOLHsAsAUHlj0A\n2IIDyx4AzOnAsgcAW7BTQqDq7sUesOp5SZ7Z3S8ePl+e5G9298vX9VvsiQEAAABId9dG7btGONed\nSfbOfD4rySfmHRAAAAAAizfG7WAfTPINVfWkqnpEkucnuX6E8wAAAAAwp4XPBOrur1TVS5PckLWQ\n6Q3dfeuizwMAAADA/Bb+TCAAAAAATj5j3A4GAAAAwElGCAQAAAAwAUIgAAAAgAkQAgEAAABMgBAI\nAAAAYAKEQAAAAAATIAQCAAAAmAAhEAAAAMAECIEAgKWrqudX1Qeq6vNVdaiq3l9VPziz/Zeq6i+r\n6t6q+p9V9d6qOm9m+wuq6svD9nur6nPDzz3D9gNV9cV1264Ztl1ZVfdV1T9bN6aDVfUdRxnv+vHc\nMDuedcd93rr2i6rqK8O+n62qW6vqymHb2TPj+9yw/+dn2v7PqvpoVX3NzPEeX1WHq+qZx/0fAACY\nBCEQALBUQ/jys0lem2R3d+9J8k+TfNts2JHktd392CRnJvlEkjesO9R/7e7HDstjhp+Hhm2d5Dnr\ntr1sZtvdSV5ZVY/ewtCPjOesJJ9K8kvrtl+R5NNJXrDBvncNYzg1ySuS/PuqOre7D86M7zHD2J46\n0/avkhxMcvXMsV6X5Ne6+4YtjB0AmCAhEACwNFX12CSvTvKD3f3O7v5CknT373f393f3l9bv091/\nmeRtSb5lq6c7xrZbk7w/a4HMlnT3XyR5c5Jvvv9EVU9K8h1JXpzke6rqicfY/zeyFkI97ShjXj/u\nFyf5wap62jD75+LjGTcAMD1CIABgmZ6R5BFJrp93h6p6VJJ/mOT2BY6jk/yLJP97VT1uKzsOs4f+\nUZIPzzRfkeT3uvudST42bN9o36qqv5fk8Un+eK6Bdn88yU9kbebRL2QtQPvsVsYMAEyTEAgAWKYn\nJPmf3X3fkYaq+t2q+szwDJ+/NdP3R6vq7iT3Jvm2rAUts55RVXcPy2eqan1I9K6ZbXdX1YtmN3b3\nHyS5Ickr5xz7kfH8UZJHJblyZtv3J/mVYf3NefAtYWcO+/55knckeUV3//6c5013/7skX0pyS3e/\nZ979AIBpEwIBAMv06SRPqKr7fyfp7m/v7tOGbbO/q/x0d5+e5ElZC08e8CDmJO/v7tOH5bTuPnfd\n9ktntp3e3eufKZSszbD5waraPcfYf3o4zl/p7ud2958mSVV9e5KvS3Ld0O8tSZ5WVbO3e901fJfH\nJLkmyXfOcb71bk3y349jPwBgooRAAMAyvT/JXya5dN4duvvOJD+S5JqqOmUL5zrWM4GOHPu2JP8x\nyY9n7Rax43Fk1s8tVfXJJB8YjrV+5lKGZx5dlbWQ6O8d5/kAAOYiBAIAlmZ4ls2/TPLzVfX3q+pR\nw3Nyzk/ytcfY731J7krykpnmTUOeOf3LJC9MsqVnAyXJEEpdluSfJDk/aw+v/pYkL0ty+eyMpyOG\nIOhn8sA3fgEALJwQCABYqu7+6ay93erHkhxOcihrDzz+sST/9Ri7/pusPZfnyGvkL6yqe4flc8PP\nvzHT/z0z2++tqnccZTwHkrwpa8/5Oeqwj9L+3CRfTPKm7v7UkSVrr7N/WJJnHWW/X0xydlU9Z87z\nbLYNAOBBqnvz3x+q6kCSzya5L8mXuvuCqjota/e6PynJgSTfe+TNFFV1TZJLknwhyZXdfcsoowcA\nAABgLvPOBLovyUp3P727Lxjarkryvu4+L8mNSV6VJFV1SZInDw9jfEmSaxc8ZgAAAAC2aN4QqDbo\ne2mS/cP6/nz1gY6XJnljknT3zUlOnfMNGwAAAACMZN4QqJO8t6o+WFX/eGjb3d2Hk6S7DyU5Y2g/\nM8nBmX3vGtoAAAAAWJJdc/b7tu4+VFVPTHJDVd2Woz+McKM3czyob1V5mCEAAADAgnX3hm9NnWsm\n0DDTJ939Z0neleSCJIeP3OZVVXuSfGrofmeSs2d2PyvJJ45y3G2xXHTRRUsfw05crr766qWPYScu\nrm9hFL8AACAASURBVNdxFtfr4hfX6jiLa3WcxfU6zuJ6HWdxvY6zuF4Xv7hWx1lcq+Ms2+l6PZZN\nQ6Cq+tqqevSw/qgkz0zykSTXJ7ly6HZlkncP69cnuWLof2GSe3q4bWy7Ouecc5Y9BJib65XtwrXK\nduJ6ZTtxvbJduFbZTnbK9TrP7WC7k7xzuH1rV5Jf6e4bqur3krytqn4gyR1JLkuS7v71qnp2Vf1x\n1l4R/8KRxv6Q2Sn/sZkG1yvbhWuV7cT1ynbiemW7cK2yneyU63XTEKi7/zTJ+Ru0353ku4+yz0tP\nfGgnj5WVlWUPYUdS13Go6zjUdfHUdBzqOg51HYe6jkNdx6Gui6em41DXceyUutZm94uNduKqXta5\nAQAAAHaiqkqfyIOhAQAAANjehEAAAAAAEyAEAgAAAJgAIRAAAADABAiBAAAAACZACAQAAAAwAUIg\nAAAAgAkQAgEAAABMgBAIAAAAYAKEQAAAAAATIAQCAAAAmAAhEAAAAMAECIEAAAAAJkAIBAAAADAB\nQiAAAACACRACAQAAAEzA3CFQVT2sqj5UVdcPn8+pqg9U1W1V9Zaq2jW0P6Kq3lpVt1fV+6tq71iD\nBwAAAGA+W5kJ9PIkH535/NokP9Pd5yW5J8mLhvYXJbm7u89N8rokP7WIgQIAAABw/OYKgarqrCTP\nTvIfZpq/M8k7hvX9SZ47rF86fE6Styf5rhMfJgAAAAAnYt6ZQD+b5EeTdJJU1eOTfKa77xu235nk\nzGH9zCQHk6S7v5Lknqo6fWEjBgAAAGDLdm3Woaqek+Rwd99SVStHmodlVs9se8AhZrY9wL59++5f\nX1lZycrKykbdAAAAANjA6upqVldX5+pb3RvmM1/tUPWvk1ye5MtJHpnkMUneleSZSfZ0931VdWGS\nq7v7kqr6zWH95qp6eJJPdvcZGxy3Nzs3AAAAAPOrqnT3+gk6Sea4Hay7f7y793b31yd5fpIbu/vy\nJDcluWzo9oIk7x7Wrx8+Z9h+44kMHgAAAIATt5W3g613VZJXVNUfJTk9yRuG9jckeUJV3Z7kR4Z+\nAAAAACzRpreDjXZit4MBAAAALNQJ3Q4GAAAAwPYnBAIAAACYACEQAAAAwAQIgQAAAAAmQAgEAAAA\nMAFCIAAAAIAJEAIBAAAATIAQCAAAAGAChEAAAAAAEyAEAgAAAJgAIRAAAADABAiBAAAAACZACAQA\nAAAwAUIgAAAAgAkQAgEAAABMgBAIAAAAYAI2DYGq6pSqurmqPlxVH6mqq4f2c6rqA1V1W1W9pap2\nDe2PqKq3VtXtVfX+qto79pcAAAAA4Ng2DYG6+y+TXNzdT09yfpJLqupbk7w2yc9093lJ7knyomGX\nFyW5u7vPTfK6JD81ysgBAAAAmNtct4N19xeH1VOS7ErSSS5O8o6hfX+S5w7rlw6fk+TtSb5rISMF\nAAAA4LjNFQJV1cOq6sNJDiX5z0n+JMk93X3f0OXOJGcO62cmOZgk3f2VJPdU1ekLHTUAAAAAWzLv\nTKD7htvBzkpyQZKnbNRt+Fnr2mtmGwAAAABLsGsrnbv73qr6rSQXJnlcVT1smA10VpJPDN3uTHJ2\nkk9U1cOTPLa7P7PR8fbt23f/+srKSlZWVrb8BQAAAACmanV1Naurq3P1re5jT9Kpqick+VJ3f7aq\nHpnkvUlek+QFSf5jd19XVb+Q5Pe7+9qq+qEk39zdP1RVz0/y3O5+/gbH7c3ODQAAAMD8qirdvf4u\nrbVtc4RAT83ag54fNizXdfdPVtXXJXlrktOSfDjJ5d39pao6Jcmbkjw9yaeTPL+7D2xwXCEQAAAA\nwAKdUAg0FiEQAAAAwGIdKwSa68HQAAAAAGxvQiAAAACACRACAQAAAEyAEAgAAABgAoRAAAAAABMg\nBAIAAACYACEQAAAAwAQIgQAAAAAmQAgEAAAAMAFCIAAAAIAJEAIBAAAATIAQCAAAAGAChEAAAAAA\nEyAEAgAAAJgAIRAAAADABAiBAAAAACZACAQAAAAwAZuGQFV1VlXdWFUfraqPVNXLhvbTquqGqrqt\nqt5bVafO7HNNVd1eVbdU1fljfgEAAAAANjfPTKAvJ3lFd39Tkmck+eGq+sYkVyV5X3efl+TGJK9K\nkqq6JMmTu/vcJC9Jcu0oIwcAAABgbpuGQN19qLtvGdY/n+TWJGcluTTJ/qHb/uFzhp9vHPrfnOTU\nqtq94HEDAAAAsAVbeiZQVZ2T5PwkH0iyu7sPJ2tBUZIzhm5nJjk4s9tdQxsAAAAAS7Jr3o5V9egk\nb0/y8u7+fFX10bpu0LZh33379t2/vrKykpWVlXmHAwAAADB5q6urWV1dnatvdR8ty5npVLUrya8l\n+Y3u/rmh7dYkK919uKr2JLmpu59SVdcO69cN/T6W5KIjs4ZmjtnznBsAAACA+VRVunujCTpz3w72\ni0k+eiQAGlyf5Mph/cok755pv2I48YVJ7lkfAAEAAADw0Np0JlBVfXuS307ykazd1tVJfjzJf0vy\ntiRnJ7kjyWXdfc+wz+uTPCvJF5K8sLs/tMFxzQQCAAAAWKBjzQSa63awMQiBAAAAABZrEbeDAQAA\nALCNCYEAAAAAJkAIBAAAADABQiAAAACACRACAQAAAEyAEAgAAABgAoRAAAAAABMgBAIAAACYACEQ\nAAAAwAQIgQAAAAAmQAgEAAAAMAFCIAAAAIAJEAIBAAAATIAQCAAAAGAChEAAAAAAEyAEAgAAAJgA\nIRAAAADABGwaAlXVG6rqcFX9wUzbaVV1Q1XdVlXvrapTZ7ZdU1W3V9UtVXX+WAMHAAAAYH7zzAT6\npSTfs67tqiTv6+7zktyY5FVJUlWXJHlyd5+b5CVJrl3gWAEAAAA4TpuGQN39O0k+s6750iT7h/X9\nw+cj7W8c9rs5yalVtXsxQwUAAADgeB3vM4HO6O7DSdLdh5KcMbSfmeTgTL+7hjYAAAAAlmjXgo9X\nG7T10Trv27fv/vWVlZWsrKwseDgAAAAAO9fq6mpWV1fn6lvdR81ovtqp6klJ3tPdTxs+35pkpbsP\nV9WeJDd191Oq6tph/bqh38eSXHRk1tC6Y/Y85wYAAABgPlWV7t5oks7ct4NVHjjL5/okVw7rVyZ5\n90z7FcNJL0xyz0YBEAAAAAAPrU1nAlXVm5OsJHl8ksNJrk7yriS/muTsJHckuay77xn6vz7Js5J8\nIckLu/tDRzmumUAAAAAAC3SsmUBz3Q42BiEQAAAAwGIt4nYwAAAAALYxIRAAAADABAiBAAAAACZA\nCAQAAAAwAUIgAAAAgAkQAgEAAABMgBAIAAAAYAKEQAAAAAATIAQCAAAAmAAhEAAAAMAECIEAAAAA\nJkAIBAAAADABQiAAAACACRACATvKnr17U1XbYtmzd++yywUAAExIdfdyTlzVyzo3sHNVVXLTTcse\nxnwuvjj+HJy2PXv35vDBg8sexlx2n312Dt1xx7KHAQDAJqoq3V0bbdv1UA8GAFhz+ODBbRNaHr74\n4mUPAQCAE+R2MDbl9hoAAADY/kaZCVRVz0ryuqyFTG/o7teOcZ6HyurqalZWVpY9jKUZ7V+qb7kl\nOf/8hR5yO/1LtdtAtpkRrtepm/qfraNxrY7C9ToOdR2Huo5DXRdPTcehruPYKXVdeAhUVQ9L8vok\n35XkE0k+WFXv7u6PLfpcD5Wd8h/7pDPxv6iMFq798i8nV1650ENup3BtNBO/Xsfgz9aRuFZH4Xod\nh7qOQ13Hoa6Lp6bjUNdx7JS6jnE72AVJbu/uj3f3l5K8NcmlI5xnQ2PcuvTqV7/abUtsH4cOLXsE\nMJcDBw4sewgwt6lfr2PdGu53rHFM/Xpl+3Ctsp3slOt1jNvBzkwye4/LnVkLhh4So8yu+JEfSV73\nuoUe0swKRiMEYgRj3b64f//+hR/T7Yu4XhfP7NXtZaf8ReV4jXnL/atf/eqFHm+7/Bkwlqlfq2wv\nO+V6Xfgr4qvqeUme2d0vHj5fnuRvdvfL1/XzXmQAAACABXsoXxF/Z5LZebhnZe3ZQHMNCAAAAIDF\nG+OZQB9M8g1V9aSqekSS5ye5foTzAAAAADCnhc8E6u6vVNVLk9yQr74i/tZFnwcAAACA+S38mUAA\nAAAAnHzGuB0MAAAAgJOMEAgAAABgAoRAAAAAABMgBAIAAACYACEQAAAAwAQIgQAAAAAmQAgEAAAA\nMAFCIADgpFFVf1pV37mu7aKqOjjzebWq/ryq7q2qzwyfv3lm+9VV9b+G7fdW1eeq6u6Z7fcNbfdW\n1cGq+pmqqmHbH87s9+XhPEf6XjVzjJXhOP983VifNLQfOcb/qKpXzvEdNzvee9a1v6mqfmIrtQUA\nEAIBANtBr1v/oe5+bJLHJ/mtJG9a1/+t3f3YYXlMd5++bv+nDftflOQfJPmBJOnubz6yX5L/Mpzn\nMUPba2aOcUWSTyd5wVHGeupwjMuS/Iuq+q5Nvt+xjpckF1bVMzY5BgDAMQmBAIDtqJKku+9L8tYk\nT9nivkf2/x9JfjfJ+cc6zwMaqh6Z5HlJfjjJuVX1148xvv8vyX8/xvHnPd5PJfnJox0DAGAeQiAA\nYNuqqkckuTzJB45z/29M8reT3L6F3Z6X5HNJfjXJDVmbxfOgQw/HvzDJX0vyxydwvE7y75L81fW3\nkQEAbIUQCADYjq4ZnvPzuSQ/lOTV67b/g6q6e2b5f9dt/1BVfT7JR5PclOQXtnDuK7J2u1kneXOS\n76uqh89sryR/VlVfzNoso5/v7nefwPGS5C+yNhPoX21hnAAADyAEAgC2o5d19+ndfUqSv5vkHbMP\nh05y3bD9yLL+mTxP7+5HJ/neJN+a5FHznLSqzkpycdbCmiS5Pskjkzxnpltn7VlFj0ryz5OsVNWu\noxzv7DmOd8S/T7K7qv7OPGMFAFhPCAQAbGvd/TtZu93qmVvY7cgze96etVvJrp5zvyuGfd9TVZ9M\n8idJTsmDb+GqXvOzSf4ya7OVNvL9cx4v3f3lrM14+r/mHCsAwAMIgQCAk80jquqUI0uSrzlW5+Gt\nWU9J8ofHeb7XJHlxVZ0xR9/vT7Ivaw96/pZheV6Sv1NVpx0Z0gbHf+Xw/KITPd7/k7WQ6JI5xgoA\n8ABCIADgZPOfknxxZvmJPPAV8Uny+qq6t6ruTbI/yf/R3TfMbP8HR7ZX1eeGn08Ytj3gWN39h0lW\nk/zounM8oF9VfWuSJ2XtGT+fmlnek7UHS3/fUY7/n5LcneSfzG4/nuMNb0O7OslpG9QEAOCYau0Z\nhJt0qjqQ5LNJ7kvype6+YPjXqeuy9svLgSTf292fHfpfk7V/ofpCkiu7+5ZRRg8AAADAXOadCXRf\nkpXufnp3XzC0XZXkfd19XpIbk7wqSarqkiRP7u5zk7wkybULHjMAAAAAWzRvCFQb9L00a9OvM/y8\ndKb9jUnS3TcnObWqdp/gOAEAAAA4AfOGQJ3kvVX1war6x0Pb7u4+nCTdfSjJkYcpnpnk4My+dw1t\nAAAAACzJrjn7fVt3H6qqJya5oapuy9EfRrj+jRg5Rl8AAAAAHgJzhUDDTJ90959V1buSXJDkcFXt\n7u7DVbUnyaeG7ncmOXtm97OSfGL9MatKMAQAAACwYN290QSdzW8Hq6qvrapHD+uPSvLMJB9Jcn2S\nK4duVyZ597B+fZIrhv4XJrnnyG1jGwxqWywXXXTR0sewE5err7566WPYiYvrdZzF9br4xbU6zuJa\nHWdxvY6zuF7HWVyv4yyu18UvrtVxFtfqOMt2ul6PZZ6ZQLuTvHOYubMrya909w1V9XtJ3lZVP5Dk\njiSXDcHOr1fVs6vqj7P2ivgXznGOk9o555yz7CHA3FyvbBeuVbYT1yvbieuV7cK1ynayU67XTUOg\n7v7TJOdv0H53ku8+yj4vPfGhnTx2yn9spsH1ynbhWmU7cb2ynbhe2S5cq2wnO+V6nfftYJO2srKy\n7CHsSOo6DnUdh7ounpqOQ13Hoa7jUNdxqOs41HXx1HQc6jqOnVLX2ux+sdFOXNXLOjcAAADATlRV\n6eN9MDQAAAAA258QCAAAAGAChEAAAAAAEyAEAgAAAJgAIRAAAADABAiBAAAAACZACAQAAAAwAUIg\nAAAAgAkQAgEAAABMgBAIAAAAYAKEQAAAAAATIAQCAAAAmAAhEAAAAMAECIEAAAAAJkAIBAAAADAB\nc4dAVfWwqvpQVV0/fD6nqj5QVbdV1VuqatfQ/oiqemtV3V5V76+qvWMNHgAAAID5bGUm0MuTfHTm\n82uT/Ex3n5fkniQvGtpflOTu7j43yeuS/NQiBgoAAADA8ZsrBKqqs5I8O8l/mGn+ziTvGNb3J3nu\nsH7p8DlJ3p7ku058mAAAAACciHlnAv1skh9N0klSVY9P8pnuvm/YfmeSM4f1M5McTJLu/kqSe6rq\n9IWNGAAAAIAt27VZh6p6TpLD3X1LVa0caR6WWT2z7QGHmNn2APv27bt/fWVlJSsrKxt1AwAAAGAD\nq6urWV1dnatvdW+Yz3y1Q9W/TnJ5ki8neWSSxyR5V5JnJtnT3fdV1YVJru7uS6rqN4f1m6vq4Uk+\n2d1nbHDc3uzcAAAAAMyvqtLd6yfoJJnjdrDu/vHu3tvdX5/k+Ulu7O7Lk9yU5LKh2wuSvHtYv374\nnGH7jScyeAAAAABO3FbeDrbeVUleUVV/lOT0JG8Y2t+Q5AlVdXuSHxn6AQAAALBEm94ONtqJ3Q4G\nAAAAsFAndDsYAAAAANufEAgAAABgAoRAAAAAABMgBAIAAACYACEQAAAAwAQIgQAAAAAmQAgEAAAA\nMAFCIAAAAIAJEAIBAAAATIAQCAAAAGAChEAAAAAAEyAEAgAAAJgAIRAAAADABAiBAAAAACZACAQA\nAAAwAUIgAAAAgAkQAgEAAABMwKYhUFWdUlU3V9WHq+ojVXX10H5OVX2gqm6rqrdU1a6h/RFV9daq\nur2q3l9Ve8f+EgAAAAAc26YhUHf/ZZKLu/vpSc5PcklVfWuS1yb5me4+L8k9SV407PKiJHd397lJ\nXpfkp0YZOQAAAABzm+t2sO7+4rB6SpJdSTrJxUneMbTvT/LcYf3S4XOSvD3Jdy1kpAAAAAAct7lC\noKp6WFV9OMmhJP85yZ8kuae77xu63JnkzGH9zCQHk6S7v5Lknqo6faGjBgAAAGBLds3TaQh7nl5V\nj03yziRP2ajb8LPWtdfMtgfYt2/f/esrKytZWVmZZzgAAAAAJFldXc3q6upcfat7w3zm6DtU/USS\nLyb5sSR7uvu+qrowydXdfUlV/eawfnNVPTzJJ7v7jA2O01s9NwAAAABHV1Xp7vUTdJLM93awJ1TV\nqcP6I5N8d5KPJrkpyWVDtxckefewfv3wOcP2G49/6AAAAAAswqYzgarqqVl70PPDhuW67v7Jqvq6\nJG9NclqSDye5vLu/VFWnJHlTkqcn+XSS53f3gQ2OayYQAAAAwAIdaybQlm8HWxQhEAAAAMBindDt\nYAAAAABsf0IgAAAAgAkQAgEAAABMgBAIAAAAYAKEQAAAAAATIAQCAAAAmAAhEAAAAMAECIEAAAAA\nJkAIBAAAADABQiAAAACACRACAQAAAEyAEAgAAABgAoRAAAAAABMgBAIAAACYACEQAAAAwAQIgQAA\nAAAmQAgEAAAAMAGbhkBVdVZV3VhVH62qj1TVy4b206rqhqq6rareW1WnzuxzTVXdXlW3VNX5Y34B\nAAAAADY3z0ygLyd5RXd/U5JnJPnhqvrGJFcleV93n5fkxiSvSpKquiTJk7v73CQvSXLtKCMHAAAA\nYG6bhkDdfai7bxnWP5/k1iRnJbk0yf6h2/7hc4afbxz635zk1KraveBxAwAAALAFW3omUFWdk+T8\nJB9Isru7DydrQVGSM4ZuZyY5OLPbXUMbAAAAAEuya96OVfXoJG9P8vLu/nxV9dG6btC2Yd99+/bd\nv76yspKVlZV5hwMAAAAweaurq1ldXZ2rb3UfLcuZ6VS1K8mvJfmN7v65oe3WJCvdfbiq9iS5qbuf\nUlXXDuvXDf0+luSiI7OGZo7Z85wbAAAAgPlUVbp7owk6c98O9otJPnokABpcn+TKYf3KJO+eab9i\nOPGFSe5ZHwABAAAA8NDadCZQVX17kt9O8pGs3dbVSX48yX9L8rYkZye5I8ll3X3PsM/rkzwryReS\nvLC7P7TBcc0EAgAAAFigY80Emut2sDEIgQAAAAAWaxG3gwEAAACwjQmBAAAAACZACAQAAAAwAUIg\nAAAAgAkQAgEAAABMgBAIAAAAYAKEQAAAAAATIAQCAAAAmAAhEAAAAMAECIEAAAAAJkAIBAAAADAB\nQiAAAACACdhxIdDePXtTVSf9snfP3mWXCgAAAJiQ6u7lnLiqxzh3VeWm3LTw4y7axbk4y6o9AAAA\nsDNVVbq7Ntq242YCAQAAAPBgQiAAAACACdg0BKqqN1TV4ar6g5m206rqhqq6rareW1Wnzmy7pqpu\nr6pbqur8sQbOQ2e7PGfJs5YAAADg6HbN0eeXkvzbJG+cabsqyfu6+6eq6pVJXpXkqqq6JMmTu/vc\nqvrWJNcmuXDRg+ahdfDwwW3xnKUkufjwxcseAgAAAJyUNp0J1N2/k+Qz65ovTbJ/WN8/fD7S/sZh\nv5uTnFpVuxczVAAAAACO1/E+E+iM7j6cJN19KMkZQ/uZSQ7O9LtraAMAAABgiRb9YOiNXkHmPeiw\nAc9aAgAA4KE0zzOBNnK4qnZ39+Gq2pPkU0P7nUnOnul3VpJPHO0g+/btu399ZWUlKysrxzkc2H48\nawkAAIATtbq6mtXV1bn6VvfmE3Wq6pwk7+nupw6fX5vk7u5+bVVdleRx3X1VVT07yQ9393Oq6sIk\nr+vuDR8MXVU9z7m3qqq2xV+sL87FGeP7j2G71DRR17Fsp7oCAABMWVWluze6U2vzmUBV9eYkK0ke\nX1V3JLk6yWuS/GpV/UCSO5JcliTd/etV9eyq+uMkX0jywsV8BQAAAABOxKYhUHf/w6Ns+u6j9H/p\nCY0IAAAAgIVb9IOhAQAAADgJCYGAHcVb1wAAADZ2vG8HAzgpeesa28nePXtz8PDBZQ9jLmfvPjt3\nHLpj2cMAAOAECIEAYEmElgAAPJTcDgYAAAAwAUIgAAAAgAkQAgEAAABMgBAIAAAAYAKEQAAAAAAT\nIAQCAAAAmAAhEAAAAMAECIEA2NTePXtTVdti2btn77LLBQAAJ6Vdyx4AACe/g4cP5qbctOxhzOXi\nwxcvewgAAHBSMhMIAAAAYAKEQAAAAAATIAQCAAAAmAAhEAAAAMAEjBICVdWzqupjVfVHVfXKMc7x\nULoltyx7CDuSuo5DXcehrounpuNQ13Gsrq4uewg7krqOQ13Hoa6Lp6bjUNdx7JS6LjwEqqqHJXl9\nku9J8teSfF9VfeOiz/NQ8gv1ONR1HOo6DnVdPDUdh7qOY6f84neymXpd9+7Zm6pa+HLxxRcv/Jh7\n9+xddrmWburX6xjUdBzqOo6dUtcxXhF/QZLbu/vjSVJVb01yaZKPjXCuh8ShHFr2EGBurle2C9cq\n28mBAweWPYSl2rtnbw4ePjjKsV/96lcv9Hhn7z47dxy6Y6HHHMvBwwdzU25a+HFfk9fkqly10GNe\nfPjihR4PEn+2sr3slOt1jBDozCSzvyXcmbVgaNvyFxW2E9cr24VrlbGMFVjs379/4cfcLoHFWGHF\nL+eXc2WuXOgxhRX+fBVabh875S/VTMNOuV6ruxd7wKrnJXlmd794+Hx5kr/Z3S9f12+xJwYAAAAg\n3V0btY8xE+jOJLM3DZ+V5BPzDggAAACAxRvj7WAfTPINVfWkqnpEkucnuX6E8wAAAAAwp4XPBOru\nr1TVS5PckLWQ6Q3dfeuizwMAAADA/Bb+TCAAAAAATj5j3A4GAAAAwElGCAQAAAAwAUIgAAAAgAkQ\nAgEAAABMgBAIAAAAYAKEQAAAAAATIAQCAAAAmAAhEAAAAMAECIEAgKWpqgNVdaiqHjnT9qKqummD\nvv+jqv5wXdsfVtW9w/Llqvrzqvrc8Pmqqrq6qt60wbHuq6qvH9ZXh/3urapPVdU7qmr3BvtcOez3\nvHXtF1XVwTm/701V9QPH2P61VfX5qvq1de1XVdV/Wtd2+wb9/qiqvneesQAA0yMEAgCWqZM8PMmP\nbNB+v6r6jiRPTPL1VfU37u/U/c3d/djufmyS/5Lkh7r7MUPbazY61gZtPez32CTfkOTRSf7NBvtc\nkeTTSV6wyfFOxPOS/EWSZ64Lon47ybdVVSXJsG1Xkr++ru3JQ18AgAcRAgEAy/bTSf5ZVT32GH1e\nkORdSX49G4cwR9Sc51zfr5Kku+8dznP+AzZWPSnJdyR5cZLvqaonznmerXpBkl9I8gdJ/tFM+weT\nPGJmXN+R5KYkt61r+5PuPjTS2ACAbU4IBAAs2+8lWU3yoxttHG4Ve16SX0ny5iTfV1W7xhhIVT0+\nyf+W5PZ1m65I8nvd/c4kH8sDA5pFnXtvkpV89XveH3Z195eS3Jy1oCfDz99O8jsbtAEAbEgIBACc\nDK5O8tIhhFnv72ftFqn3Jvm1rN0+9pwFn/+aqvpMkj9L8vgkL1u3/fuzFs4k6wKaBboiye9398eS\nvCXJN1XVt8xs/618NfD521m7/e131rX91gjjAgB2CCEQALB03f3fsxbwvGqDzVckeVuv+V9J3pn5\nQ5gvJ/ma2YaZWURfmml+WXefluSpSU5LctZM/29P8nVJrhua3pLkaVX1tDnHMK/7g6bu/mTWZvXM\nfs/fTvK3qupxSZ7Q3X+S5L9m7VlBj0vyzTETCAA4BiEQAHCy2JfknyQ580hDVZ2Z5DuTXF5Vn6yq\nT2ZtZtCzq+r0OY55R5Jz1rV9fdbCobvWdx7CqJ9M8vMzzUeCmFuG838gaw+CvmKO88+lqp6R5Nwk\nr5r5nhdk7da3I7+vvT/J47L2XKLfHcb7uSSfGNru6u6PL2pMAMDOIwQCAE4Kw8yW6/LAW7GuVccC\nvAAAIABJREFUyNrDj/9qkm8Zlr+a5M4k3zfHYX8zyXlV9Y+qatcQHP1kkl/t7vuOss/+JGdU1d+t\nqlOSXJa1cOr8mTG8LGvB1JHfpaqqTpldjjGmr1nXd1eSK5PckOQpM+d4apKvTXLJUJ+/yNrzk16R\ntVvBjvjdoc0sIADgmIRAAMAyrX+1+r/MWvBxpP2KJP+uu/+suz91ZEnyf+fBt4Q96DXt3f1nSZ6d\n5J8m+VTW3rr1mSQ/dLT9hocwX5PkXyR5bpIvJnnTuvO/IWu/Rz1r2O2vDP2+mOTPk3yxqr7+KN/5\n52f7Ze32sucluWbd9zyQ5E3rvudvJXli1p4FdMR/Gdo8DwgAOKbqftDvSw/uVHUgyWeT3JfkS919\nQVWdlrV/rXtSkgNJvre7Pzv0vyZr/2r1hSRXdvcto4weAAAAgLnMOxPoviQr3f307r5gaLsqyfu6\n+7wkN2Z4kGNVXZLkyd19bpKXJLl2wWMGAAAAYIvmDYFqg76XZu2e+Qw/L51pf2OSdPfNSU6tqt0n\nOE4AAAAATsC8IVAneW9VfbCq/vHQtru7DydJdx9KcsbQfmaSgzP73pWZt3wAAAAA8NDbNWe/b+vu\nQ1X1xCQ3VNVt2eDhi4PaoO1Bfatq84cRAQAAALAl3b1RNjPfTKBhps+RN2y8K8kFSQ4fuc2rqvZk\n7Y0bydorW8+e2f2sJJ84ynG3xXLRRRctfQw7cbn66quXPoaduLhex1lcr4tfXKvjLK7VcRbX6zjL\n/8/e3YdbVtV3gv/+sFpaoxLQUGUKkMQQNEYDPRNDXrq5qFHR9GB3SyQ9NGDItNNqNGPGBJ1OKJPJ\niyaOhjATZjpoStsIBhPATIzEwE3GGSVJAwkzIpI8IbxIlUYoXxNF+c0fdxcervfWPVV1Drfu3Z/P\n8+zn7r322nuts+6iKL6svY/5Op/NfJ3PZr7OfjNX57OZq/PZNtJ83Zc1Q6CqenRVPWbY/4Ykz01y\nc5Krk5w3VDsvyVXD/tVZ+jrXVNUpSfb08NjYRnX88cevdxdgauYrG4W5ykZivrKRmK9sFOYqG8lm\nma/TPA62NcnvDY9vbUnyru6+pqr+Isl7qupHk9yR5Mwk6e4/qKoXVNVfZ+kr4l86p74/bDbLL5tx\nMF/ZKMxVNhLzlY3EfGWjMFfZSDbLfF0zBOruv01y0grl9yZ5zirXvPLgu3boWFhYWO8ubErGdT6M\n63wY19kzpvNhXOfDuM6HcZ0P4zofxnX2jOl8GNf52CzjWms9Lza3hqt6vdoGAAAA2IyqKn0wL4YG\nAAAAYGMTAgEAAACMgBAIAAAAYASEQAAAAAAjIAQCAAAAGAEhEAAAAMAICIEAAAAARkAIBAAAADAC\nQiAAAACAERACAQAAAIyAEAgAAABgBIRAAAAAACMgBAIAAAAYASEQAAAAwAgIgQAAAABGQAgEAAAA\nMAJTh0BVdVhV3VBVVw/Hx1fVR6rq1qp6d1VtGcofWVWXVdVtVfXhqjpuXp0HAAAAYDr7sxLo1Uk+\nOnH8xiRv7u4Tk+xJcv5Qfn6Se7v7hCRvTfKmWXQUAAAAgAM3VQhUVcckeUGS35woflaS9w77O5O8\naNg/YzhOkiuSPPvguwkAAADAwZh2JdBbkrw2SSdJVT0+yX3d/cBw/q4k24f97UnuTJLu/mqSPVV1\n1Mx6DAAAAMB+27JWhap6YZLd3X1TVS3sLR62ST1x7iG3mDj3EDt27Hhwf2FhIQsLCytVAwAAAGAF\ni4uLWVxcnKpuda+Yz3ytQtUvJjk7yVeSPCrJY5NcmeS5SbZ19wNVdUqSC7v79Kr6w2H/+qp6RJJ7\nuvvoFe7ba7UNAAAAwPSqKt29fIFOkikeB+vu13f3cd39rUnOSnJtd5+d5LokZw7Vzk1y1bB/9XCc\n4fy1B9N5AAAAAA7e/nw72HIXJHlNVX08yVFJLh3KL03yhKq6LclPDPUAAAAAWEdrPg42t4Y9DgYA\nAAAwUwf1OBgAAAAAG58QCAAAAGAEhEAAAAAAIyAEAgAAABgBIRAAAADACAiBAAAAAEZACAQAAAAw\nAkIgAAAAgBEQAgEAAACMgBAIAAAAYASEQAAAAAAjIAQCAAAAGAEhEAAAAMAICIEAAAAARkAIBAAA\nADACQiAAAACAEVgzBKqqw6vq+qq6sapurqoLh/Ljq+ojVXVrVb27qrYM5Y+sqsuq6raq+nBVHTfv\nDwEAAADAvq0ZAnX3l5Kc1t0nJzkpyelV9T1J3pjkzd19YpI9Sc4fLjk/yb3dfUKStyZ501x6DgAA\nAMDUpnocrLu/OOwenmRLkk5yWpL3DuU7k7xo2D9jOE6SK5I8eyY9BQAAAOCATRUCVdVhVXVjkl1J\n/ijJ3yTZ090PDFXuSrJ92N+e5M4k6e6vJtlTVUfNtNcAAAAA7JdpVwI9MDwOdkySZyZ56krVhp+1\nrLwmzgEAAACwDrbsT+Xu/mxV/UmSU5J8Y1UdNqwGOibJJ4ZqdyU5NsknquoRSR7X3fetdL8dO3Y8\nuL+wsJCFhYX9/gAAAAAAY7W4uJjFxcWp6lb3vhfpVNUTktzf3Z+pqkcl+UCSX05ybpLf7e7Lq+o3\nkvxld19SVS9P8p3d/fKqOivJi7r7rBXu22u1DQAAAMD0qirdvfwpraVzU4RAT8/Si54PG7bLu/sX\nqupbklyW5MgkNyY5u7vvr6rDk7wzyclJPp3krO6+fYX7CoEAAAAAZuigQqB5EQIBAAAAzNa+QqCp\nXgwNAAAAwMYmBAIAAAAYASEQAAAAwAgIgQAAAABGQAgEAAAAMAJCIAAAAIAREAIBAAAAjIAQCAAA\nAGAEhEAAAAAAIyAEAgAAABgBIRAAAADACAiBAAAAAEZACAQAAAAwAkIgAAAAgBEQAgEAAACMgBAI\nAAAAYASEQAAAAAAjsGYIVFXHVNW1VfXRqrq5ql41lB9ZVddU1a1V9YGqOmLimouq6raquqmqTprn\nBwAAAABgbdOsBPpKktd093ck+d4kr6iqpyS5IMkHu/vEJNcmeV2SVNXpSZ7c3SckeVmSS+bScwAA\nAACmtmYI1N27uvumYf/zSW5JckySM5LsHKrtHI4z/HzHUP/6JEdU1dYZ9xsAAACA/bBf7wSqquOT\nnJTkI0m2dvfuZCkoSnL0UG17kjsnLrt7KAMAAABgnWyZtmJVPSbJFUle3d2fr6pereoKZSvW3bFj\nx4P7CwsLWVhYmLY7AAAAAKO3uLiYxcXFqepW92pZzkSlqi1Jfj/J+7v714ayW5IsdPfuqtqW5Lru\nfmpVXTLsXz7U+1iSU/euGpq4Z0/TNgAAAADTqap090oLdKZ+HOxtST66NwAaXJ3kvGH/vCRXTZSf\nMzR8SpI9ywMgAAAAAB5ea64EqqrvT/KnSW7O0mNdneT1Sf4syXuSHJvkjiRndvee4ZqLkzw/yReS\nvLS7b1jhvlYCAQAAAMzQvlYCTfU42DwIgQAAAABmaxaPgwEAAACwgQmBAAAAAEZACAQAAAAwAkIg\nAAAAgBEQAgEAAACMgBAIAAAAYASEQAAAAAAjIAQCAAAAGAEhEAAAAMAICIEAAAAARkAIBAAAADAC\nQiAAAACAERACAQAAAIyAEAgAAABgBIRAAAAAACMgBAIAAAAYASEQAAAAwAisGQJV1aVVtbuq/mqi\n7Miquqaqbq2qD1TVERPnLqqq26rqpqo6aV4dBwAAAGB606wEenuS5y0ruyDJB7v7xCTXJnldklTV\n6Ume3N0nJHlZkktm2FcAAAAADtCaIVB3fyjJfcuKz0iyc9jfORzvLX/HcN31SY6oqq2z6SoAAAAA\nB+pA3wl0dHfvTpLu3pXk6KF8e5I7J+rdPZQBAAAAsI62zPh+tUJZr1Z5x44dD+4vLCxkYWFhxt0B\nAAAA2LwWFxezuLg4Vd3qXjWj+VqlqicleV93P2M4viXJQnfvrqptSa7r7qdW1SXD/uVDvY8lOXXv\nqqFl9+xp2gYAAABgOlWV7l5pkc7Uj4NVHrrK5+ok5w375yW5aqL8nKHRU5LsWSkAAgAAAODhteZK\noKr67SQLSR6fZHeSC5NcmeR3khyb5I4kZ3b3nqH+xUmen+QLSV7a3Tescl8rgQAAAABmaF8rgaZ6\nHGwehEAAAAAAszWLx8EAAAAA2MCEQAAAAAAjIAQCAAAAGAEhEAAAAMAICIEAAAAARkAIBAAAADAC\nQiAAAACAERACAQAAAIyAEAgAAABgBDZdCHTccdtSVYf8dtxx29Z7qAAAAIARqe5en4areh5tV1Wu\nu27mt525005L1mvsAQAAgM2pqtLdtdK5TbcSCAAAAICvJwQCAAAAGAEhEAAAAMAICIEAAAAARkAI\nBAAAADACQiAAAACAEZhLCFRVz6+qj1XVx6vqp+fRxsPpppvWuweb0+Li4np3YVMyrvNhXGfPmM6H\ncZ0P4zofxnU+jOt8GNfZM6bzYVznY7OM68xDoKo6LMnFSZ6X5GlJfqSqnjLrdh5OYw+BjjtuW6pq\n5ttpp50283sed9y29R6udbdZ/nA61BjX2TOm82Fc58O4zodxnQ/jOh/GdfaM6XwY1/nYLOO6ZQ73\nfGaS27r775Kkqi5LckaSj82hrYfFrl3r3YP1deedu3PddbO/72/9VnLeebO952mn7Z7tDTeg22+/\nfb27AFMxV9lIzFc2EvOVjcJcZSPZLPN1Ho+DbU9y58TxXUPZhjX2EIj5mNcKq507d1phxYawWf5F\nyjiYr2wk5isbhbnKRrJZ5mt192xvWPXiJM/t7n8/HJ+d5Lu7+9XL6s22YQAAAADS3bVS+TweB7sr\nyXETx8ck+cS0HQIAAABg9ubxONifJ/m2qnpSVT0yyVlJrp5DOwAAAABMaeYrgbr7q1X1yiTXZClk\nurS7b5l1OwAAAABMb+bvBAIAAADg0DOPx8EAAAAAOMQIgQAAAABGQAgEAAAAMAJCIAAAAIAREAIB\nAAAAjIAQCAAAAGAEhEAAAAAAIyAEAgAeVFW3V9WuqnrURNn5VXXdsnqvraqPV9UXhmt+qaoeuY/7\nLlbVP1TVZ6vqk1X13qrauqzOjqp6oKr+62Xl51bVV4Zr91TVjVX1wuHcD1TV54Zznx+u/+xE2duq\n6oPL7vftVfWZqnraCv28sKq+PFx7b1V9qKpOWasfw/knTbQ/2Yczh/O/VVVfWuXc7VX1xWXnLpph\nuw8Z16p6clU9sOyzP6+q/mS4dndVXVdVP7RCHybb2Lba7xwAOPQIgQCASZ3kEUl+YoXyJElV/XqS\nH0tydpLHJjk9ybOSvGeN+768ux+X5NuTfGOStyyrc3aSTyc5d4Xr/5/uflx3f2OS30hyWVU9rrs/\n1N2PHe77tKGdIybK/vskW6vqxybu9X8k+dXu/v9W6etlw7XflOT/TvLetfqx7HMeMdR57PDzdybO\nvXEf51647NyrZtjup5P8z8s+5+Tv9MVZ+v39VpLt3b01yc8m+Zcr9GGyjV2rjCEAcAgSAgEAy/1K\nkp9cFjIkSarqhCT/Icm/7e4/6+4HuvuWJP8myfOramEf960k6e49WQpWvnPivv8iyROTvDrJj1TV\nln3c551JviHJCftqZ2jry0nOT/JLVfXEqnpZlgKoX9zH/fde+9UkO5Nsq6qj9qMftULdaUx73YG0\nuzPJM6rqn69y/s1J3tDdb+/uzyVJd/9f3f2yKfsEAGwAQiAAYLm/SLKY5LUrnHtWkju7+79MFnb3\nXUk+kuQH17p5VT0hS6HRDRPF5yR5X762muiHVrn2EUl+NMmXk/zdWm0NffuzLK1weWeSn0/y0iHg\nWaufhyd5aZK7uvve/ejHgYZAazqIdr+YpeDr68KvqnpKkmPy0BVPAMAmJAQCAFZyYZJXVtXjl5U/\nIck9q1xzz3B+Nb9eVfcmuTHJ3Ul+MkmG9w+dmeRd3f2VJFfk6x8J+97h2n9I8qYkZ3f33+/H5/mZ\nJN+W5B3dfeMadV8ytPV3SU5OcsZ+9KOSfGp4n9B9w88TJ86/duLcJ5e1e+Wy686fYbvJ0mNwx1XV\n85aV713ltNrv9SF9mGjjtjXqAwCHGCEQAPB1hvfl/H6S1y079fdZemxrJU8czq/mx7v7qO4+trvP\n6e5PD+X/Osn9Sd4/HP92khcsC6A+3N1HZelRrquT/IvpP03S3f+Y5G+TfHSK6pcP/dzW3c/p7pv2\nox+d5PHD9UcOP2+dOP8rE+eOXnbtGcuuu3SG7e59NO7nh21y1dDe38Nqv9eH9GGijdUexwMADlFC\nIABgNTuS/HdJtk+UXZvk2Pr6b/A6NskpSR7yTVxTOifJY5LcUVX3ZOmRsC1JfmR5xe7+YpJXJPl3\nVfVdB9DWTKzRj7m9E2gG7b49yRFJ/tXEPW9NcmeWHtEDADYxIRAAsKLu/psklyd51UTZbUn+9yTv\nqqrvqarDhq9avyLJNd193cp3W1lVbU/y7CQvTHJSku9K8owsPfJ03ir9ujfJf8rSI2tfd8v9af9g\nrNKPmncfDqbd4V1Ib0jy08tO/WSSnxm+Cv6xteQHquqSZW0AABuYEAgAmNTLjn8uyaMny7v7FUl+\nM8l/TvK5JH+QpRVCL96P++51dpIbuvuPu/uTe7ckFyV5elV9xyrX/VqS06vqO5eVr9bOWucO1PJ+\ndJL7quqzVfW54edPTHmv9w319277elHzwbT77iy9/2fyd/reJC/J0jep3Z1kV5Z+91dNXHfKRN/2\ntvFfTfnZAIBDQHWv/fehqro9yWeSPJDk/u5+ZlUdmaX/O/ikJLcn+eHu/sxQ/6Ikpyf5QpLzlj1L\nDwAAAMDDbNqVQA8kWejuk7v7mUPZBUk+2N0nZun//r0uSarq9CRPHl4W+LIkl6x0QwAAAAAePtOG\nQLVC3TOS7Bz2d+ZrX596RpJ3JEl3X5/kiKraepD9BAAAAOAgTBsCdZIPVNWfV9WPDWVbu3t3knT3\nriR7v+Z0e5a+YWKvu/PQbxUBAAAA4GG2Zcp639fdu6rqm5JcU1W3ZvWXK670zRHzeBEjAAAAAFOa\nKgQaVvqkuz9VVVcmeWaS3VW1tbt3V9W2JJ8cqt+V5NiJy49J8onl96wqwRAAAADAjHX3Sgt01n4c\nrKoeXVWPGfa/Iclzk9yc5Ook5w3VzsvXvkL06iTnDPVPSbJn72NjK3RqQ2ynnnrquvdhM24XXnjh\nuvdhM27m63w283X2m7k6n81cnc9mvs5nM1/ns5mv89nM19lv5up8NnN1PttGmq/7Ms1KoK1Jfm9Y\nubMlybu6+5qq+osk76mqH01yR5Izh2DnD6rqBVX111n6iviXTtHGIe34449f7y7A1MxXNgpzlY3E\nfGUjMV/ZKMxVNpLNMl/XDIG6+2+TnLRC+b1JnrPKNa88+K4dOjbLL5txMF/ZKMxVNhLzlY3EfGWj\nMFfZSDbLfJ3228FGbWFhYb27sCkZ1/kwrvNhXGfPmM6HcZ0P4zofxnU+jOt8GNfZM6bzYVznY7OM\na631vNjcGq7q9WobAAAAYDOqqvSBvhgaAAAAgI1PCAQAAAAwAkIgAAAAgBEQAgEAAACMgBAIAAAA\nYASEQAAAAAAjIAQCAAAAGAEhEAAAAMAICIEAAAAARkAIBAAAADACQiAAAACAERACAQAAAIyAEAgA\nAABgBIRAAAAAACMgBAIAAAAYgalDoKo6rKpuqKqrh+Pjq+ojVXVrVb27qrYM5Y+sqsuq6raq+nBV\nHTevzgMAAAAwnf1ZCfTqJB+dOH5jkjd394lJ9iQ5fyg/P8m93X1CkrcmedMsOgoAAADAgZsqBKqq\nY5K8IMlvThQ/K8l7h/2dSV407J8xHCfJFUmeffDdBAAAAOBgTLsS6C1JXpukk6SqHp/kvu5+YDh/\nV5Ltw/72JHcmSXd/NcmeqjpqZj0GAAAAYL9tWatCVb0wye7uvqmqFvYWD9uknjj3kFtMnHuIHTt2\nPLi/sLCQhYWFlaoBAAAAsILFxcUsLi5OVbe6V8xnvlah6heTnJ3kK0keleSxSa5M8twk27r7gao6\nJcmF3X16Vf3hsH99VT0iyT3dffQK9+212gYAAABgelWV7l6+QCfJFI+Ddffru/u47v7WJGcluba7\nz05yXZIzh2rnJrlq2L96OM5w/tqD6TwAAAAAB29/vh1suQuSvKaqPp7kqCSXDuWXJnlCVd2W5CeG\negAAAACsozUfB5tbwx4HAwAAAJipg3ocDAAAAICNTwgEAAAAMAJCIAAAAIAREAIBAAAAjIAQCAAA\nAGAEhEAAAAAAIyAEAgAAABgBIRAAAADACAiBAAAAAEZACAQAAAAwAkIgAAAAgBEQAgEAAACMgBAI\nAAAAYASEQAAAAAAjIAQCAAAAGAEhEAAAAMAICIEAAAAARmDNEKiqDq+q66vqxqq6uaouHMqPr6qP\nVNWtVfXuqtoylD+yqi6rqtuq6sNVddy8PwQAAAAA+7ZmCNTdX0pyWnefnOSkJKdX1fckeWOSN3f3\niUn2JDl/uOT8JPd29wlJ3prkTXPpOQAAAABTm+pxsO7+4rB7eJItSTrJaUneO5TvTPKiYf+M4ThJ\nrkjy7Jn0FAAAAIADNlUIVFWHVdWNSXYl+aMkf5NkT3c/MFS5K8n2YX97kjuTpLu/mmRPVR01014D\nAAAAsF+2TFNpCHtOrqrHJfm9JE9dqdrws5aV18S5h9ixY8eD+wsLC1lYWJimOwAAAAAkWVxczOLi\n4lR1q3vFfGb1C6p+NskXk/xUkm3d/UBVnZLkwu4+var+cNi/vqoekeSe7j56hfv0/rYNAAAAwOqq\nKt29fIFOkum+HewJVXXEsP+oJM9J8tEk1yU5c6h2bpKrhv2rh+MM56898K4DAAAAMAtrrgSqqqdn\n6UXPhw3b5d39C1X1LUkuS3JkkhuTnN3d91fV4UnemeTkJJ9OclZ3377Cfa0EAgAAAJihfa0E2u/H\nwWZFCAQAAAAwWwf1OBgAAAAAG58QCAAAAGAEhEAAAAAAIyAEAgAAABgBIRAAAADACAiBAAAAAEZA\nCAQAAAAwAkIgAAAAgBEQAgEAAACMgBAIAAAAYASEQAAAAAAjIAQCAAAAGAEhEAAAAMAICIEAAAAA\nRkAIBAAAADACQiAAAACAERACAQAAAIzAmiFQVR1TVddW1Uer6uaqetVQfmRVXVNVt1bVB6rqiIlr\nLqqq26rqpqo6aZ4fAAAAAIC1TbMS6CtJXtPd35Hke5O8oqqekuSCJB/s7hOTXJvkdUlSVacneXJ3\nn5DkZUkumUvPAQAAAJjamiFQd+/q7puG/c8nuSXJMUnOSLJzqLZzOM7w8x1D/euTHFFVW2fcbwAA\nAAD2w369E6iqjk9yUpKPJNna3buTpaAoydFDte1J7py47O6hDAAAAIB1smXailX1mCRXJHl1d3++\nqnq1qiuUrVh3x44dD+4vLCxkYWFh2u4AAAAAjN7i4mIWFxenqlvdq2U5E5WqtiT5/STv7+5fG8pu\nSbLQ3buraluS67r7qVV1ybB/+VDvY0lO3btqaOKePU3bAAAAAEynqtLdKy3QmfpxsLcl+ejeAGhw\ndZLzhv3zklw1UX7O0PApSfYsD4AAAAAAeHituRKoqr4/yZ8muTlLj3V1ktcn+bMk70lybJI7kpzZ\n3XuGay5O8vwkX0jy0u6+YYX7WgkEAAAAMEP7Wgk01eNg8yAEAgAAAJitWTwOBgAAAMAGJgQCAAAA\nGAEhEAAAAMAICIEAAAAARkAIBAAAADACQiAAAACAERACAQAAAIyAEAgAAABgBIRAAAAAACMgBAIA\nAAAYASEQAAAAwAgIgQAAAABGQAgEAAAAMAJCIAAAAIAREAIBAAAAjIAQCAAAAGAE1gyBqurSqtpd\nVX81UXZkVV1TVbdW1Qeq6oiJcxdV1W1VdVNVnTSvjgMAAAAwvWlWAr09yfOWlV2Q5IPdfWKSa5O8\nLkmq6vQkT+7uE5K8LMklM+wrAAAAAAdozRCouz+U5L5lxWck2Tns7xyO95a/Y7ju+iRHVNXW2XQV\nAAAAgAN1oO8EOrq7dydJd+9KcvRQvj3JnRP17h7KAAAAAFhHs34xdK1Q1jNuAwAAAID9tOUAr9td\nVVu7e3dVbUvyyaH8riTHTtQ7JsknVrvJjh07HtxfWFjIwsLCAXYHAAAAYHwWFxezuLg4Vd3qXnuh\nTlUdn+R93f304fiNSe7t7jdW1QVJvrG7L6iqFyR5RXe/sKpOSfLW7j5llXv2NG0DAAAAMJ2qSnev\n9KTW2iFQVf12koUkj0+yO8mFSa5M8jtZWvVzR5Izu3vPUP/iJM9P8oUkL+3uG1a5rxCIUdu2bVt2\n79693t2YytatW7Nr16717gYAAABrOKgQaF6EQIxd1Yr/TB6y/PMKAABw6NtXCDTrF0MDAAAAcAgS\nAgEAAACMgBAIAAAAYASEQAAAAAAjIAQCAAAAGAEhEAAAAMAICIEAAAAARkAIBAAAADACQiAAAACA\nERACAQAAAIyAEAgAAABgBIRAAAAAACMgBAIAAAAYASEQAAD7tG3btlTVhti2bdu23sMFAIes6u71\nabiq16ttOBRU1Xp3Yb/45xXYKLZt25bdu3evdzemsnXr1uzatWu9u7Em/85iI/FnADB2VZXuXvFf\n3kIgWCf+Qg34D5X58Ofr7BlTNhLzFRg7IRAcgvwFBfDnwHwY19kzpmwk5iswdvsKgebyTqCqen5V\nfayqPl5VPz2PNh5Oi4uL692FTcm4spGYr7NnTAHmw5+vbBTm6nwY1/nYLOM68xCoqg5LcnGS5yV5\nWpIfqaqnzLqdh9Nm+WUfaowrG4n5OnvGFBi7eb1w+7TTTvPCbTYEfxeYD+M6H5tlXOexEuiZSW7r\n7r/r7vuTXJbkjDm087C5/fbb17sLAJuOP1uBsdso7wRLNlZf2Tj8XYCNZLPM13mEQNuT3DlxfNdQ\ntmFtll/2gZrX/6V6wxve4P9SMXPm68Yx9j9bAWDs/F2AjWSzzNeZvxi6ql6c5Lnd/e/tz1hlAAAg\nAElEQVSH47OTfHd3v3pZPW9AAwAAAJix1V4MvWUObd2V5LiJ42OSfGLaDgEAAAAwe/N4HOzPk3xb\nVT2pqh6Z5KwkV8+hHQAAAACmNPOVQN391ap6ZZJrshQyXdrdt8y6HQAAAACmN/N3AgEAAABw6JnH\n42AAAAAAHGKEQAAAAAAjIAQCAAAAGAEhEAAAAMAICIEAAAAARkAIBAAAADACQiAAAACAERACAQAA\nAIyAEAgA2Kequr2qvlhVn62qe6rqbVX16OHcYlX9w3Bu73bVsuuPr6qvVtXFq9z/VVV1c1V9vqru\nqKrLq+ppw7nfqqovTdz7c1V14xr9XbW9qnqgqv5yWdnPV9Xbhv0nDXX2tndPVV1dVc9Zo80Hhr7t\n7eO9Q/mpQ18+W1Wfqapbquq8Na79bFX9j8O5C6vqy8s+/72rXPupqvqjqvrhFfr3Q1V1/TDGn6qq\nd1bV9onz5w73+tVl171oKH/bvj4/ALAxCIEAgLV0khd29+OS/LMk353kP06ce3l3P25iO2PZ9eck\nuTfJWVX1TyZPVNVFSX48ySuTHJnk25NcmeSFE/d/48S9H9vdJ6/R31XbG3xzVZ21xuc9Yvi835Xk\ng0l+r6rOWeOaZ0z08aiJc3cP5UckeU2S/1RVJ+zj2sd192QYc9myz3/UStcmOTHJziQXV9XP7K1Q\nVS9O8q4kb0ny+CRPS/LlJB+qqiMm7vU3SV5SVZN/P/x3SW7dx+cGADYQIRAAMI1Kku6+J8n7k3zn\n8nP7cE6WQqP7k/zLBy+q+rYkL09yVnf/SXff393/2N3v7u43HURfV2xvwpuS/NyysGO5vZ/3k919\nUZIdSd64Rv21xiHd/f4sBVTP2N9r12q3u+/t7v+c5D8keX1VHTnU+dUkP9fdl3X3l7r7k0l+LMnn\nk/wPE/faleTmJM9LkuH670ty9QH2DQA4xAiBAICpVdWxSV6Q5IYp6//zJNuTXJbkd7IU0Oz17CR3\ndvd/mWH/9tVesrRy5neTfCbJeftx699NcnRVnXgQfauq+m+ytBrnrw/0PlO4KsmWJM8c+ntskism\nK3R3J3lvkh+cLE7yjiTnDsdnZWlV1pfn2FcA4GEkBAIApnHl8C6aP01yXZJfmjj361V1b1XdN/x8\nw8S5c5L8QXd/JslvJzm9qp4wnHt8knumaPu1y+7/9n3U3Vd7ydKqmU7ys0l+dpXHxVbyieHao/ZR\n54aJPr51onz7MHb/kKXg5TXd/ZcrXDv5GSfDmZcMZXu3P95XR7v7K0n+fujr3s++0jjfM3F+ryuT\nnFpVj8vSWL5jX20BABvLlvXuAACwIZzR3detcu7Hu/vrXhxcVf80yZlJzk+S7v5IVd2Z5N8muSjJ\np5M8cYq2f6W7f3atSlO096Dufn9V3ZHkZVO0nyytLkqWHuVazcnd/bcrlN/d3ccNgdMvJ3nW8v7s\n49okuby79/U+ooeoqi1Jvmno699nKbx6YpK/W1b1icP5B3X3P1bV/5mlx+ke390frqoXTNs2AHBo\nsxIIAJjGgbyz5l8leVyS/234lq17knxzvvaI1h8nOaaq/tmM+rhWe8v9TJL/Kcmjp7j3v06yu7v3\n9ZLkfY5Rd9+f5IIkzxgeC5v62v30oiy9D+n6ob93Zikc+1pjVZXk32TppdfLvTNLL7C2CggANhkh\nEAAwL+cmuTTJ07P0LVvfleQHkpxcVU/r7r9O8htJ3j18lfo/qarDq+olVfVTs25veeXu/pMsvQj5\n3GWnHnzZclUdXVWvzFJgdMEB9Gl5m/cneXOSCw/2XstV1ZFV9d8muTjJL3f3nuHUa5P8x6o6q6r+\naVVty9I4PTbJW5ffZxiXHxzuAwBsIkIgAGAtvcb5i6vqs8P2uar686r65iw99vSW4Ru29m43ZOnb\nxc5Nku5+VZbChv81yX1ZemHyi5K8b+L+P7Xs/p9c3oFp21vhs/zHLH01/WR5J7mvqj6X5K+SPD/J\ni7t750GM0aS3JTm2ql44ce1fTny+z1bV/zJR/yXLPv9nJ95z9OC1SW5L8qNJXt3dD76Xqbvfk6Wv\nen9Nkk8l+X+THJ7k+7v7vhU/TPd1EyESALBJ1NKXQ6xRqer2LH2LxgNJ7u/uZw5fG3p5kicluT3J\nDw8vYUxVXZTk9CRfSHJed980l94DAAAAMJVpVwI9kGShu0/u7mcOZRck+WB3n5jk2iSvS5KqOj3J\nk7v7hCy9bPGSGfcZAAAAgP00bQhUK9Q9I8neZdE7h+O95e9Iku6+PskRVbX1IPsJAAAAwEGYNgTq\nJB8YnvH/saFsa3fvTpLu3pXk6KF8e5a+hWKvu/O1r1UFAAAAYB1smbLe93X3rqr6piTXVNWtWf0F\niCt9xenX1a2q/XmBIgAAAABT6O6VspnpVgINK33S3Z9KcmWSZybZvfcxr+GrRvd+U8ddSY6duPyY\nJJ9Y5b4bYjv11FPXvQ+bcbvwwgvXvQ+bcTNf57OZr7PfzNX5bObqfDbzdT6b+TqfzXydz2a+zn4z\nV+ezmavz2TbSfN2XNUOgqnp0VT1m2P+GJM9NcnOSq5OcN1Q7L8lVw/7VSc4Z6p+SZE8Pj41tVMcf\nf/x6dwGmZr6yUZirbCTmKxuJ+cpGYa6ykWyW+TrN42Bbk/ze8PjWliTv6u5rquovkrynqn40yR1J\nzkyS7v6DqnpBVf11lr4i/qVz6vvDZrP8shkH85WNwlxlIzFf2UjMVzYKc5WNZLPM1zVDoO7+2yQn\nrVB+b5LnrHLNKw++a4eOhYWF9e7CpmRc58O4zodxnT1jOh/GdT6M63wY1/kwrvNhXGfPmM6HcZ2P\nzTKutdbzYnNruKrXq20AAACAzaiq0gfzYmgAAAAANjYhEAAAAMAICIEAAAAARkAIBAAAADACQiAA\nAACAERACAQAAAIyAEAgAAABgBIRAAAAAACMgBAIAAAAYASEQAAAAwAgIgQAAAABGQAgEAAAAMAJC\nIAAAAIAREAIBAAAAjIAQCAAAAGAEhEAAAAAAIzB1CFRVh1XVDVV19XB8fFV9pKpurap3V9WWofyR\nVXVZVd1WVR+uquPm1XkAAAAAprM/K4FeneSjE8dvTPLm7j4xyZ4k5w/l5ye5t7tPSPLWJG+aRUcB\nAAAAOHBThUBVdUySFyT5zYniZyV577C/M8mLhv0zhuMkuSLJsw++mwAAAAAcjGlXAr0lyWuTdJJU\n1eOT3NfdDwzn70qyfdjfnuTOJOnurybZU1VHzazHAAAAAOy3LWtVqKoXJtnd3TdV1cLe4mGb1BPn\nHnKLiXMPsWPHjgf3FxYWsrCwsFI1AAAAAFawuLiYxcXFqepW94r5zNcqVP1ikrOTfCXJo5I8NsmV\nSZ6bZFt3P1BVpyS5sLtPr6o/HPavr6pHJLmnu49e4b69VtsAAAAATK+q0t3LF+gkmeJxsO5+fXcf\n193fmuSsJNd299lJrkty5lDt3CRXDftXD8cZzl97MJ0HAAAA4ODtz7eDLXdBktdU1ceTHJXk0qH8\n0iRPqKrbkvzEUA8AAACAdbTm42Bza9jjYAAAAAAzdVCPgwEAAACw8QmBAAAAAEZACAQAAAAwAkIg\nAAAAgBEQAgEAAACMgBAIAAAAYASEQAAAAAAjIAQCAAAAGAEhEAAAAMAICIEAAAAARkAIBAAAADAC\nQiAAAACAERACAQAAAIyAEAgAAABgBIRAAAAAACMgBAIAAAAYgTVDoKo6vKqur6obq+rmqrpwKD++\nqj5SVbdW1burastQ/siquqyqbquqD1fVcfP+EAAAAADs25ohUHd/Kclp3X1ykpOSnF5V35PkjUne\n3N0nJtmT5PzhkvOT3NvdJyR5a5I3zaXnAAAAAExtqsfBuvuLw+7hSbYk6SSnJXnvUL4zyYuG/TOG\n4yS5IsmzZ9JTAAAAAA7YVCFQVR1WVTcm2ZXkj5L8TZI93f3AUOWuJNuH/e1J7kyS7v5qkj1VddRM\new0AAADAfpl2JdADw+NgxyR5ZpKnrlRt+FnLymviHAAAAADrYMv+VO7uz1bVnyQ5Jck3VtVhw2qg\nY5J8Yqh2V5Jjk3yiqh6R5HHdfd9K99uxY8eD+wsLC1lYWNjvDwAAAAAwVouLi1lcXJyqbnXve5FO\nVT0hyf3d/ZmqelSSDyT55STnJvnd7r68qn4jyV929yVV9fIk39ndL6+qs5K8qLvPWuG+vVbbAAAA\nAEyvqtLdy5/SWjo3RQj09Cy96PmwYbu8u3+hqr4lyWVJjkxyY5Kzu/v+qjo8yTuTnJzk00nO6u7b\nV7ivEAgAAABghg4qBJoXIRAAAADAbO0rBJrqxdAAAAAAbGxCIAAAAIAREAIBAAAAjIAQCAAAAGAE\nhEAAAAAAIyAEAgAAABgBIRAAAADACAiBAAAAAEZACAQAAAAwAkIgAAAAgBEQAgEAAACMgBAIAAAA\nYASEQAAAAAAjIAQCAAAAGAEhEAAAAMAICIEAAAAARkAIBAAAADACa4ZAVXVMVV1bVR+tqpur6lVD\n+ZFVdU1V3VpVH6iqIyauuaiqbquqm6rqpHl+AAAAAADWNs1KoK8keU13f0eS703yiqp6SpILknyw\nu09Mcm2S1yVJVZ2e5MndfUKSlyW5ZC49BwAAAGBqa4ZA3b2ru28a9j+f5JYkxyQ5I8nOodrO4TjD\nz3cM9a9PckRVbZ1xvwEAAADYD/v1TqCqOj7JSUk+kmRrd+9OloKiJEcP1bYnuXPisruHMgAAAADW\nyZZpK1bVY5JckeTV3f35qurVqq5QtmLdHTt2PLi/sLCQhYWFabsDAAAAMHqLi4tZXFycqm51r5bl\nTFSq2pLk95O8v7t/bSi7JclCd++uqm1Jruvup1bVJcP+5UO9jyU5de+qoYl79jRtAwAAADCdqkp3\nr7RAZ+rHwd6W5KN7A6DB1UnOG/bPS3LVRPk5Q8OnJNmzPAACAAAA4OG15kqgqvr+JH+a5OYsPdbV\nSV6f5M+SvCfJsUnuSHJmd+8Zrrk4yfOTfCHJS7v7hhXuayUQAAAAwAztayXQVI+DzYMQCAAAAGC2\nZvE4GAAAAAAbmBAIAAAAYASEQAAAAAAjIAQCAAAAGAEhEAAAAMAICIEAAAAARkAIBAAAADACQiAA\nAACAERACAQAAAIyAEAgAAABgBIRAAAAAACMgBAIAAAAYASEQAAAAwAgIgQAAAABGQAgEAAAAMAJC\nIAAAAIAREAIBAAAAjMCaIVBVXVpVu6vqrybKjqyqa6rq1qr6QFUdMXHuoqq6rapuqqqT5tVxAAAA\nAKY3zUqgtyd53rKyC5J8sLtPTHJtktclSVWdnuTJ3X1CkpcluWSGfQUAAADgAK0ZAnX3h5Lct6z4\njCQ7h/2dw/He8ncM112f5Iiq2jqbrgIAAABwoA70nUBHd/fuJOnuXUmOHsq3J7lzot7dQxkAAAAA\n62jLjO9XK5T1apV37Njx4P7CwkIWFhZm3B0AAACAzWtxcTGLi4tT1a3uVTOar1WqelKS93X3M4bj\nW5IsdPfuqtqW5LrufmpVXTLsXz7U+1iSU/euGlp2z56mbQAAAACmU1Xp7pUW6Uz9OFjloat8rk5y\n3rB/XpKrJsrPGRo9JcmelQIgAAAAAB5ea64EqqrfTrKQ5PFJdie5MMmVSX4nybFJ7khyZnfvGepf\nnOT5Sb6Q5KXdfcMq97USCAAAAGCG9rUSaKrHweZBCAQAAAAwW7N4HAwAAACADUwIBAAAADACQiAA\nAACAERACAQAAAIyAEAgAAABgBIRAAAAAACMgBAIA+P/Zu/9wzc6yPvTfO4ykICQmQTJ2AgylaUCE\nJudqaVoq2QgFAj0Gq1FoMQTxSEUqlhYJHmsGW3oZejhyKFZOj5QGjhIQBGIvlYhhV1ES9UAqlhiC\nOvlBkuFHCD8rDcl9/thr4pvNntnvnnlX9rx7fT7Xta693mc9a61n3/PADF+etV4AgAkQAgEAAABM\ngBAIAAAAYAKEQAAAAAATIAQCAAAAmAAhELCj7N69N1W1FNvu3Xu3u1xsM/MVAID7UnX39ty4qrfr\n3sDOVVVJluW/Wyr+e3DazFcAABatqtLdtdExK4EAAAAAJkAIBAAAADABQiAAAACACRACAQAAAEzA\nKCFQVT2jqv6kqj5eVa8Y4x73pdXV1e0ewo6kruNQ17GsbvcAdhxzdSyr2z2AHcl8HYe6jkNdx6Gu\ni6em41DXceyUui48BKqq45K8IcnTkzw2yXOr6tGLvs99aaf8YR9r1HUc6jqW1e0ewI5jro5ldbsH\nsCOZr+NQ13Go6zjUdfHUdBzqOo6dUtcxVgI9Icn13X1Dd9+Z5LIk541wn/vM/v37t3sI7EC7d+9N\nVS18e9WrXrXwa+7evXe7y8U2G2O+jjFXzVcS83UM/s5aLv7tyrIwV1kmO2W+jhEC7Uly08znm4e2\npbVT/rA5thw4cEOSHmE7Z+HXXBsrUzbOfF38XDVfSczXMYz3d9bFC7/mstR0TP7tyrIwV1kmO2W+\nVncv9oJV35Pkad39Q8Pn5yX529390nX9FntjAAAAANLdtVH7rhHudXOSh898Pi3JLfMOCAAAAIDF\nG+NxsD9I8ter6hFVdf8kz0ly+Qj3AQAAAGBOC18J1N13VdVLklyRtZDpTd197aLvAwAAAMD8Fv5O\nIAAAAACOPWM8DgYAAADAMUYIBAAAADABQiAAAACACRACAQAAAEyAEAgAAABgAoRAAAAAABMgBAIA\nAACYACEQAHCfqqr9VfWVqvpCVd1SVW+uqgfOHH9zVX11OP6FqvpiVX1kOPaIqrp7o2PD8T1V9f9W\n1WeGY1dV1bPW3f/u4dgXquqmqnptVdXM8dWhz+PWnfeeof1Jh/i9Zsf9maq6oqrOWNfnwuEa37Ou\n/Zyqums49/NVdW1VXTgce9jMeL84nP+lmbafrKqPVdU3zFzvlKo6UFVP28IfDQCwwwmBAID7Wid5\nVnefkOTMJGcleeW6Ppd09wnD9uDuPmvd+SeuP1ZVJyX5YJK/SPKYJA9J8rokv1RV/2jd+Y8f7n9O\nku9L8gPrjl+X5IKDDVV1cpK/k+RTm/xulwzXPW3o++Z1xy9I8tkkz9/g3E8Ov9OJSV6W5P+pqtO7\n+6bh9zyhux88jO9xM23/JslNSS6eudbrkvyX7r5ik/ECABMiBAIAtkMlSXd/Ksn7shYGbfn8dV6W\n5Ivd/YPd/enu/mp3X5bk1Un+z3XnHrz/nyX53Q3u/4tJvm9mhdBzk/xKkv85z+C6+y+S/FKSb7vn\nplWPSPKkJD+U5OlV9c2HOf/Xk9ye5PEbHL5n/DN+KMkPV9Xjh9U/T85aPQAA7iEEAgC2TVWdluTc\nJNdv9dQN2p6a5F0btL8jycOr6vQN7v/oJN+e5BPrDt2S5GNJDj5OdUGStxzivl8/uKoHJfknST4y\n03xBkj/s7ncn+ZPh+EbnVlV9Z5JTNhjXhrr7hiQ/lbWVRz+f5Ie7+/PznAsATIcQCADYDu+pqi8k\nuTHJgST71h1/eVXdXlWfG37OPlZVST49c+zgipeHJLl1g3vdOnP8oA9X1ZeyFvR8IMl/2OC8tyR5\nflX9jaw9fnb1HL/Xy6vq9iQfT/KNSS6cOfb9WVthlKytElr/SNie4dz/kbUw62Xd/d/muGeSpLt/\nLsmdSa7p7l+d9zwAYDqEQADAdjhv5p08j869A5ok+XfdfXJ3nzT8fMHMsU5yysyxg496fSbJt2xw\nr4Ntn55pO6u7H5Tke7P2rp9v3OC8dyf5jiT/LMlb5/y9Do77r3b3s7v7z5Okqp6Y5JFJ3j70e1uS\nx1fV7ONen+zuk5M8OMnrh3tv1bVJ/vsRnAcATIAQCADYDgffyfM7SS5N8tojOX+d9yf57g3avy/J\njd09+2jVwfu/M8lVufdLlTMc+x9Jfj3JP83aqqCjcXDVzzVVdetwz87My6dn7ntnkouyFhJ951He\nFwDgHkIgAGC7vS7JP1i3KuZwDvVenp9NckJVvamqTq2q46vquVn75rF/eZjr/UySH6qqh25w7JVJ\nzunum+Yc29cPtur4JOcn+d+y9gLqvzlsP5rkeVX1df8eG4Kg12aDcAoA4EgJgQCA+1rf60P3Z7K2\nGuhfzTT/eFV9Ydi+WFWfOtT5M9e5PcnfT/KArL3r5zNJfizJ84YVP4e6/x8nWU3y8vXHu/u27v69\nze69ybFnJ/lKkrd296cObknelLV/iz3jEOf9pyQPq6pnzXmfzY4BABNX3Zv/W6Gq9if5fJK7k9zZ\n3U+oqpOy9lz7I5LsT/K9B7+Foqpen7Vv+vhykgu7+5pRRg8AAADAXOZdCXR3kpXuPqu7nzC0XZTk\n/d19RpIrs7ZcOlV1bpJHdffpSV6U5I0LHjMAAAAAWzRvCFQb9D0va0u3M/w8b6b9LUkyfJXqiVV1\n6lGOEwAAAICjMG8I1EneV1V/UFU/OLSd2t0HkrXn5ZMcfJniniSzL0/85NAGAAAAwDbZNWe/v9fd\nt1XVNye5oqquy6FfPLjRN3Z4SSEAAADANporBBpW+qS7P11V70nyhCQHqurU7j5QVbuTHPzWjpuT\nPGzm9NOS3LL+mlUlGAIAAABYsO7eaIHO5o+DVdUDq+pBw/43Jnlako8muTzJhUO3C5O8d9i/PMkF\nQ/+zk9xx8LGxDQa1FNs555yz7WPYidvFF1+87WPYiZv5Os5mvi5+M1fH2czVcTbzdZzNfB1nM1/H\n2czXxW/m6jibuTrOtkzz9XDmWQl0apJ3Dyt3diX5xe6+oqr+MMk7quoHktyY5Pwh2Pm1qnpmVX0i\na18R/4I57nFM27t373YPAeZmvrIszFWWifnKMjFfWRbmKstkp8zXTUOg7v7zJGdu0H57kqce4pyX\nHP3Qjh075Q+baTBfWRbmKsvEfGWZmK8sC3OVZbJT5uu83w42aSsrK9s9hB1JXcehruNQ18VT03Go\n6zjUdRzqOg51HYe6Lp6ajkNdx7FT6lqbPS822o2rervuDQAAALATVVX6SF8MDQAAAMDyEwIBAAAA\nTIAQCAAAAGAChEAAAAAAEyAEAgAAAJgAIRAAAADABAiBAAAAACZACAQAAAAwAUIgAAAAgAkQAgEA\nAABMgBAIAAAAYAKEQAAAAAATIAQCAAAAmAAhEAAAAMAECIEAAAAAJmDuEKiqjquqD1fV5cPnvVV1\nVVVdV1Vvq6pdQ/v9q+qyqrq+qj5UVQ8fa/AAAAAAzGcrK4FemuRjM58vSfLa7j4jyR1JXji0vzDJ\n7d19epLXJXnNIgYKAAAAwJGbKwSqqtOSPDPJL8w0f0eSdw37lyZ59rB/3vA5Sd6Z5ClHP0wAAAAA\njsa8K4F+NsnLk3SSVNUpST7X3XcPx29OsmfY35PkpiTp7ruS3FFVJy9sxAAAAABs2a7NOlTVs5Ic\n6O5rqmrlYPOwzeqZY/e6xMyxe9m3b989+ysrK1lZWdmoGwAAAAAbWF1dzerq6lx9q3vDfOYvO1T9\n2yTPS/K1JA9I8uAk70nytCS7u/vuqjo7ycXdfW5V/cawf3VV3S/Jrd390A2u25vdGwAAAID5VVW6\ne/0CnSRzPA7W3T/R3Q/v7r+W5DlJruzu5yX5QJLzh27PT/LeYf/y4XOG41cezeABAAAAOHpb+Xaw\n9S5K8rKq+niSk5O8aWh/U5KHVNX1SX5s6AcAAADANtr0cbDRbuxxMAAAAICFOqrHwQAAAABYfkIg\nAAAAgAkQAgEAAABMgBAIAAAAYAKEQAAAAAATIAQCAAAAmAAhEAAAAMAECIEAAAAAJkAIBAAAADAB\nQiAAAACACRACAQAAAEyAEAgAAABgAoRAAAAAABMgBAIAAACYACEQAAAAwAQIgQAAAAAmQAgEAAAA\nMAGbhkBVdXxVXV1VH6mqj1bVxUP73qq6qqquq6q3VdWuof3+VXVZVV1fVR+qqoeP/UsAAAAAcHib\nhkDd/dUkT+7us5KcmeTcqvo7SS5J8truPiPJHUleOJzywiS3d/fpSV6X5DWjjBwAAACAuc31OFh3\nf2XYPT7JriSd5MlJ3jW0X5rk2cP+ecPnJHlnkqcsZKQAAAAAHLG5QqCqOq6qPpLktiS/meRPk9zR\n3XcPXW5OsmfY35PkpiTp7ruS3FFVJy901AAAAABsya55Og1hz1lVdUKSdyd5zEbdhp+1rr1mjt3L\nvn377tlfWVnJysrKPMMBAAAAIMnq6mpWV1fn6lvdG+Yzhz6h6qeSfCXJjyfZ3d13V9XZSS7u7nOr\n6jeG/aur6n5Jbu3uh25wnd7qvQEAAAA4tKpKd69foJNkvm8He0hVnTjsPyDJU5N8LMkHkpw/dHt+\nkvcO+5cPnzMcv/LIhw4AAADAImy6EqiqHpe1Fz0fN2xv7+5XV9Ujk1yW5KQkH0nyvO6+s6qOT/LW\nJGcl+WyS53T3/g2uayUQAAAAwAIdbiXQlh8HWxQhEAAAAMBiHdXjYAAAAAAsPyEQAAAAwAQIgQAA\nAAAmQAgEAAAAMAFCIAAAAIAJEAIBAAAATIAQCAAAAGAChEAAAAAAEyAEAgAAAJgAIRAAAADABAiB\nAAAAACZACAQAAAAwAUIgAAAAgAkQAgEAAABMgBAIAAAAYAKEQAAAAAATIAQCAAAAmIBNQ6CqOq2q\nrqyqj1XVR6vqR4f2k6rqiqq6rqreV1Unzpzz+qq6vqquqaozx/wFAAAAANjcPCuBvpbkZd39rUn+\nbpIfqapHJ7koyfu7+4wkVyZ5ZZJU1blJHtXdpyd5UZI3jjJyAAAAAOa2aQjU3bd19zXD/peSXJvk\ntCTnJbl06Hbp8DnDz7cM/a9OcmJVnbrgcQMAAACwBVt6J1BV7U1yZpKrkpza3QeStaAoyUOHbnuS\n3DRz2ieHNgAAAAC2ya55O1bVg5K8M8lLu/tLVdWH6rpB24Z99+3bd8/+yspKVlZW5h0OAAAAwOSt\nrq5mdXV1rr7VfagsZ6ZT1a4k/yXJr3f3/zW0XZtkpbsPVNXuJB/o7sdU1RuH/RHrN9EAACAASURB\nVLcP/f4kyTkHVw3NXLPnuTcAAAAA86mqdPdGC3TmfhzsPyX52MEAaHB5kguH/QuTvHem/YLhxmcn\nuWN9AAQAAADAfWvTlUBV9cQkv53ko1l7rKuT/ESS30/yjiQPS3JjkvO7+47hnDckeUaSLyd5QXd/\neIPrWgkEAAAAsECHWwk01+NgYxACAQAAACzWIh4HAwAAAGCJCYEAAAAAJkAIBAAAADABQiAAAACA\nCRACAQAAAEyAEAgAAABgAoRAAAAAABMgBAIAAACYACEQAAAAwAQIgQAAAAAmQAgEAAAAMAFCIAAA\nAIAJEAIBAAAATIAQCAAAAGAChEAAAAAAEyAEAgAAAJiATUOgqnpTVR2oqj+aaTupqq6oquuq6n1V\ndeLMsddX1fVVdU1VnTnWwAEAAACY3zwrgd6c5Onr2i5K8v7uPiPJlUlemSRVdW6SR3X36UlelOSN\nCxwrAAAAAEdo0xCouz+Y5HPrms9Lcumwf+nw+WD7W4bzrk5yYlWdupihAgAAAHCkjvSdQA/t7gNJ\n0t23JXno0L4nyU0z/T45tAEAAACwjRb9YujaoK0XfA8AAAAAtmjXEZ53oKpO7e4DVbU7yaeG9puT\nPGym32lJbjnURfbt23fP/srKSlZWVo5wOAAAAADTs7q6mtXV1bn6VvfmC3Wqam+SX+3uxw2fL0ly\ne3dfUlUXJfmm7r6oqp6Z5Ee6+1lVdXaS13X32Ye4Zs9zbwAAAADmU1Xp7o2e1No8BKqqX0qykuSU\nJAeSXJzkPUl+OWurfm5Mcn533zH0f0OSZyT5cpIXdPeHD3FdIRAAAADAAh1VCDQWIRAAAADAYh0u\nBFr0i6EBAAAAOAYJgQAAAAAmQAgEAAAAMAFCIAAAAIAJEAIBAAAATIAQCAAAAGAChEAAAAAAEyAE\nAgAAAJgAIRAAAADABAiBAAAAACZACAQAAAAwAUIgAAAAgAkQAgEAAABMgBAIAAAAYAKEQAAAAAAT\nIAQCAAAAmAAhEAAAAMAEjBICVdUzqupPqurjVfWKMe4BAAAAwPwWHgJV1XFJ3pDk6Ukem+S5VfXo\nRd/nvrS6urrdQ9iR1HUc6joOdV08NR2Huo5DXcehruNQ13Go6+Kp6TjUdRw7pa5jrAR6QpLru/uG\n7r4zyWVJzhvhPhvafdruVNVCtyc/+ckLv+bu03bfVyU5Zu2U/xAda9R1HOq6eGo6DnUdh7qOQ13H\noa7jUNfFU9NxqOs4dkpdd41wzT1Jbpr5fHPWgqH7xIFPHkj2Lfii707yXYu95IF9BxZ7wRHtPm33\nWl1H8KpXvWqh1zt1z6m57ebbFnrNZbN///7tHgLMxVxlmZivLBPzlWVhrrJMdsp8HSMEqg3aeoT7\n3Hfu2O4BbK9RgrUk+UCSJy/2ksK1NZdeeulCrydcYww75S9SpsF8ZZmYrywLc5VlslPma3UvNp+p\nqrOT7OvuZwyfL0rS3X3Jun7LHQwBAAAAHIO6e6MFOqOEQPdLcl2SpyS5NcnvJ3lud1+70BsBAAAA\nMLeFPw7W3XdV1UuSXJG1F0+/SQAEAAAAsL0WvhIIAAAAgGPPGF8RDwAAAMAxRggEAAAAMAFCIAAA\nAIAJEAIBAAAATIAQCAAAAGAChEAAAAAAEyAEAgAAAJgAIRAAAADABAiBAIBjTlXtr6qvVNUXquqW\nqnpzVT1wOPbmqvrqcOwLVfXFqvrIcOwRVXX3zLE/q6pXrDt23Lp7vbmqfnrYf35VfW3m/E9U1T+d\n6bvhNYZjF1fVW9e1/WhVfbSqvlRVN1bV26vqsev67Buu+bfWtT9/aP8X69pvqqonHUldAYBpEwIB\nAMeiTvKs7j4hyZlJzkryypnjl3T3CcP24O4+a925Jw7n/uMkP1VVT5s5tpnfO3jtJOcneU1V/c11\n1z/cuJMkVfX6JP8syUuSnJTkbyR5T5JnrTvneUk+m+T5G1zv9iSvqKoHzTFuAIDDEgIBAMeqSpLu\n/lSS92UtDNrquVcl+e9Jvu1IBtDdH0lybZLHbOW8qjo9yYuTPKe7/2t339ndf9Hdb+vu18z0e1KS\nb0ny0iTPrapd6y51bZIPJXnZkYwfAGCWEAgAOKZV1WlJzk1y/VZOG859YpJvTfLhI7z3305yepI/\n3OKp35Hkpu7+/zbpd0GSX03yjuHzP1x3vJP8qyT/vKq+aYtjAAC4FyEQAHCsek9VfSHJjUkOJNk3\nc+zlVXV7VX1u+PnmmWOV5NNV9dkk/zHJK7p7dQv3/bvDNb+Y5Kokb+3uT2xx7KckufVwHarqAVl7\n3OwXu/trSd6ZDR4J6+4/SnJFkldscQwAAPciBAIAjlXnDe/lOSfJo5M8ZObYv+vuk7v7pOHnC2aO\ndZJTuvuU7n5sd//c0P614ec3rLvPNyS5c+bzh4ZrPjjJ7iTfVlWv3uLYP5u1x7wO5x8N9/314fMv\nJXlmVZ2yQd+fSvLDVXXqFscBAHAPIRAAcKw6+F6f30lyaZLXbvXcdW7NWuiyd137I5PcsNFFuvvT\nSd6V5H/dwr2T5LeSnFZV/8th+lyQ5EFJbqyqW7P2SNiuJM/dYBzXJfmVJD+R+V5uDQDwdYRAAMAy\neF2Sp858S9dGIU8Od6y7785aoPPqqjq5qnZV1XOz9tLnX9/o/GFVzncl+eN1x/9KVR0/s93rnsPj\nY/8hyduq6pyq+oah3/dV1Y9X1V9N8pSsfVPYmUn+ZpLHJ3lNkgsP8Xv9dJIXJPFuIADgiAiBAIBj\n0b1Wu3T3Z5K8JclPDsdeXlVfGLYvVtWnDnXuOi/O2teu/1HW3jP04iTPHFb8HHT2wWtn7ZvFDiT5\n0XXX/2KSryT5H8PPJ3/dL9D90iRvSPJzST6X5BNJnp21F0F/f5IPd/dvdfenDm5JXp/kcVX1rRtc\nb3+Styb5xsP8fgAAh1Tdm68orqr9ST6f5O4kd3b3E6rqpCRvT/KIJPuTfG93f37o//qsfYvHl5Nc\n2N3XjDJ6AAAAAOYy70qgu5OsdPdZ3f2Eoe2iJO/v7jOSXJnklUlSVecmeVR3n57kRUneuOAxAwAA\nALBF84ZAtUHf87L2ksYMP8+baX9LknT31UlO9E0WAAAAANtr3hCok7yvqv6gqn5waDu1uw8kSXff\nluShQ/ueJDfNnPvJoQ0AAACAbbJrzn5/r7tvq6pvTnJFVV2XQ790caNv5Pi6vlXl600BAAAAFqy7\nN/y21LlWAg0rfTJ8c8Z7kjwhyYGDj3lV1e4kB7+V4+YkD5s5/bQktxziukuxnXPOOds+hp24XXzx\nxds+hp24ma/jbObr4jdzdZzNXB1nM1/H2czXcTbzdZzNfF38Zq6Os5mr42zLNF8PZ9MQqKoeWFUP\nGva/McnTknw0yeVJLhy6XZjkvcP+5UkuGPqfneSOHh4bW1Z79+7d7iHA3MxXloW5yjIxX1km5ivL\nwlxlmeyU+TrP42CnJnn38PjWriS/2N1XVNUfJnlHVf1AkhuTnJ8k3f1rVfXMqvpE1r4i/gUjjf0+\ns1P+sJkG85VlYa6yTMxXlon5yrIwV1kmO2W+bhoCdfefJzlzg/bbkzz1EOe85OiHduxYWVnZ7iHs\nSOo6DnUdh7ounpqOQ13Hoa7jUNdxqOs41HXx1HQc6jqOnVLX2ux5sdFuXNXbdW8AAACAnaiq0kfz\nYmgAAAAAlpsQCAAAAGAChEAAAAAAEyAEAgAAAJgAIRAAAADABAiBAAAAACZACAQAAAAwAUIgAAAA\ngAkQAgEAAABMgBAIAAAAYAKEQAAAAAATIAQCAAAAmAAhEAAAAMAECIEAAAAAJkAIBAAAADABQiAA\nAACACZg7BKqq46rqw1V1+fB5b1VdVVXXVdXbqmrX0H7/qrqsqq6vqg9V1cPHGjwAAAAA89nKSqCX\nJvnYzOdLkry2u89IckeSFw7tL0xye3efnuR1SV6ziIECAAAAcOTmCoGq6rQkz0zyCzPN35HkXcP+\npUmePeyfN3xOkncmecrRDxMAAACAozHvSqCfTfLyJJ0kVXVKks91993D8ZuT7Bn29yS5KUm6+64k\nd1TVyQsbMQAAAABbtmuzDlX1rCQHuvuaqlo52Dxss3rm2L0uMXPsXvbt23fP/srKSlZWVjbqBgAA\nAMAGVldXs7q6Olff6t4wn/nLDlX/NsnzknwtyQOSPDjJe5I8Lcnu7r67qs5OcnF3n1tVvzHsX11V\n90tya3c/dIPr9mb3BgAAAGB+VZXuXr9AJ8kcj4N1909098O7+68leU6SK7v7eUk+kOT8odvzk7x3\n2L98+Jzh+JVHM3gAAAAAjt5Wvh1svYuSvKyqPp7k5CRvGtrflOQhVXV9kh8b+gEAAACwjTZ9HGy0\nG3scDAAAAGChjupxMAAAAACWnxAIAAAAYAKEQAAAAAATIAQCgG2yd/fuVNVSbHt3797ucgEAcJS8\nGBoAtklVZVn+Jqwk/t4GADj2eTE0AAAAwMQJgQAAAAAmQAgEAAAAMAFCIAAAAIAJEAIBAAAATIAQ\nCAAAAGAChEAAAAAAEyAEAgAAAJgAIRAAAADABAiBAAAAACZACAQAAAAwAZuGQFV1fFVdXVUfqaqP\nVtXFQ/veqrqqqq6rqrdV1a6h/f5VdVlVXV9VH6qqh4/9SzCuvbt3p6qWYtu7e/d2l2tu6joOdQUA\nANhYdffmnaoe2N1fqar7JfndJC9N8rIk7+zuX66qn09yTXf/31X1w0ke190vrqrvS/Jd3f2cDa7Z\n89yb7VdVWZY/qUqyLPNKXcehriwT8xUAgEWrqnR3bXRsrsfBuvsrw+7xSXYl6SRPTvKuof3SJM8e\n9s8bPifJO5M85QjGDAAAAMACzRUCVdVxVfWRJLcl+c0kf5rkju6+e+hyc5I9w/6eJDclSXffleSO\nqjp5oaMGAOA+41FbANgZds3TaQh7zqqqE5K8O8ljNuo2/Fy/5KhmjgEAsGRuOHBgaf4xVwcObPcQ\nAOCYNVcIdFB3f6Gq/muSs5N8U1UdNwREpyW5Zeh2c5KHJblleIfQCd39uY2ut2/fvnv2V1ZWsrKy\nsuVfAAAAAGCqVldXs7q6OlffTV8MXVUPSXJnd3++qh6Q5H1JfibJ85P8Sne/fXgx9H/r7jdW1YuT\nfNvwYujnJHm2F0MvNy8uHYe6jkNdWSbmK8vCXAWA5XG4F0PPEwI9Lmsvej5u2N7e3a+uqkcmuSzJ\nSUk+kuR53X1nVR2f5K1Jzkry2STP6e79G1xXCLQk/MNvHOo6DnVlmZivLAtzFQCWx1GFQGMRAi0P\n//Abh7qOQ11ZJuYry8JcBYDlcdRfEQ8AAADAchMCAQAAAEyAEAgAAABgAoRAAMCOsnf37lTVUmx7\nd+/e7nIBABPixdBsyssgx6Gu41BXlon5Og51XTw1BYDl4cXQAAAAABO340KgZVkCbvk3AAAAcF/a\ncY+DLcty5WVaqrwsNU3UdSzqOo5lqivjMF/Hoa6Lp6YAsDw8DgYAAAAwcUIgAADYBsvyGgOvMgDY\nOTwOtk2WaanystQ0UdexqOs4lqmujMN8HYe6Lp6ajkNdARiDx8EAAAAAJk4IBAAAADABQiAAAACA\nCRACAQAAAEyAEAgAAABgAoRAAGzK1xgDAMDy2zQEqqrTqurKqvpYVX20qn50aD+pqq6oquuq6n1V\ndeLMOa+vquur6pqqOnPMXwCA8d1w4EA6WYrthgMHxioDAAAstXlWAn0tycu6+1uT/N0kP1JVj05y\nUZL3d/cZSa5M8sokqapzkzyqu09P8qIkbxxl5AAAAADMbdMQqLtv6+5rhv0vJbk2yWlJzkty6dDt\n0uFzhp9vGfpfneTEqjp1weMGAAAAYAu29E6gqtqb5MwkVyU5tbsPJGtBUZKHDt32JLlp5rRPDm0A\nAAAAbJO5Q6CqelCSdyZ56bAiqA/VdYO2Q/UFAAAA4D6wa55OVbUrawHQW7v7vUPzgao6tbsPVNXu\nJJ8a2m9O8rCZ009LcstG1923b989+ysrK1lZWdnS4AEAAACmbHV1Naurq3P1re7NF+lU1VuSfKa7\nXzbTdkmS27v7kqq6KMk3dfdFVfXMJD/S3c+qqrOTvK67z97gmj3PvbeqqpZi2VElGeP3H8Oy1DRR\n17Go6zjUdRzqOg51Hcey1FVNx6GuAIyhqtLdGz2ltXkIVFVPTPLbST6av/wG3p9I8vtJ3pG1VT83\nJjm/u+8YznlDkmck+XKSF3T3hze4rhBoSf4iXZaaJuo6FnUdh7qOQ13Hoa7jWJa6quk41BWAMRxV\nCDQWIdDy/EW6LDVN1HUs6joOdR2Huo5DXcexLHVV03GoKwBjOFwItKVvBwMAAABgOQmBAAAAACZA\nCAQAAAAwAUIgAAAAgAkQAgEAAABMgBAIAAAAYAKEQAAAAAATIAQCAAAAmAAhEAAAAMAECIEAAAAA\nJkAIBAAAADABQiAAAACACRACAQAAAEyAEAgAAABgAoRAAAAAABMgBAIAAACYACEQAAAAwARsGgJV\n1Zuq6kBV/dFM20lVdUVVXVdV76uqE2eOvb6qrq+qa6rqzLEGDgAAAMD85lkJ9OYkT1/XdlGS93f3\nGUmuTPLKJKmqc5M8qrtPT/KiJG9c4FgBAAAAOEKbhkDd/cEkn1vXfF6SS4f9S4fPB9vfMpx3dZIT\nq+rUxQwVAAAAgCN1pO8Eemh3H0iS7r4tyUOH9j1Jbprp98mhDQAAAIBttGvB16sN2vpQnfft23fP\n/srKSlZWVhY8HAAAAICda3V1Naurq3P1re5DZjR/2anqEUl+tbsfP3y+NslKdx+oqt1JPtDdj6mq\nNw77bx/6/UmScw6uGlp3zZ7n3ltVVYdOnY4hlWSM338My1LTRF3Hoq7jUNdxqOs41HUcy1JXNR2H\nugIwhqpKd2+0SGfux8Eq917lc3mSC4f9C5O8d6b9guGmZye5Y6MACAAAAID71qaPg1XVLyVZSXJK\nVd2Y5OIkP5Pkl6vqB5LcmOT8JOnuX6uqZ1bVJ5J8OckLxho4AAAAAPOb63GwUW7scbClWVK7LDVN\n1HUs6joOdR2Huo5DXcexLHVV03Go6zj27t6dGw4sx8MIjzj11Oy/7bbtHgawwxzucTAh0DZZpr9I\nl6WmibqORV3Hoa7jUNdxqOs4lqWuajoOdR2HugJTt4h3AgEAAACwxIRAAAAAABMgBAIAAACYACEQ\nAAAAwAQIgQAAAAAmQAgEAAAAMAFCIAAAAIAJEAIBAAAATIAQCAAAAGAChEAAAAAAEyAEAgAAAJgA\nIRAAAADABAiBAAAAACZACAQAAAAwAUIgAAAAgAkQAgEAAABMwCghUFU9o6r+pKo+XlWvGOMe96XV\n7R7ADrW63QPYoVa3ewA71Op2D2AHWt3uAexQq9s9gB1qdbsHsEOtbvcAdqjV7R7ADrW63QPYgVZX\nV7d7CDuSuo5jp9R14SFQVR2X5A1Jnp7ksUmeW1WPXvR97kur2z2AHWp1uwewQ61u9wB2qNXtHsAO\ntLrdA9ihVrd7ADvU6nYPYIda3e4B7FCr2z2AHWp1uwewA+2U/1F9rFHXceyUuo6xEugJSa7v7hu6\n+84klyU5b4T73Gf2b/cAYAv2b/cAYE77t3sAsAX7t3sAsAX7t3sA7Eh7d+9OVS10e9WrXrXwa1ZV\n9u7evd3lYgfav3//dg9hIcYIgfYkuWnm881D29Lav90DgC3Yv90DgDnt3+4BwBbs3+4BwBbs3+4B\nsCPdcOBAOlnods6Cr3dwu+HAgbHKwITtlBCounuxF6z6niRP6+4fGj4/L8nf7u6Xruu32BsDAAAA\nkO6ujdp3jXCvm5M8fObzaUlumXdAAAAAACzeGI+D/UGSv15Vj6iq+yd5TpLLR7gPAAAAAHNa+Eqg\n7r6rql6S5IqshUxv6u5rF30fAAAAAOa38HcCAQAAAHDsGeNxMAAAAACOMUIgAAAAgAkQAgEAAABM\ngBAIAAAAYAKEQAAAAAATIAQCAAAAmAAhEAAAAMAECIEAgKVQVXdX1V9b13ZxVb1l2D9n6PPv1/X5\nnaq6YNh/flX9zsyxE6rqd6vql6vqflX1n4dr/K2ZPo+qqrvXXfMfVtXVVfWlqvp0Vb21qvYMx+5X\nVV9cd41/ssF1/0lVXTvs7xuOf/fM8fsNbQ8/usoBAKwRAgEAy6Ln6PPlJBdsEpx0klTVSUl+M8mf\nJfm+7r5rOPbZJP/mUPeuqu9J8otJfjbJKUkem+R/JvlgVZ04XOf3kpwzc/63J7l2g7b/OnP9zyb5\n6aqqje4LAHC0hEAAwLKozbvkjiT/Ocm+w16o6pQkv5Xko939/d09u9Ln0iSPr6pvP8Tp/0eSn+7u\ny7r7q939qSQ/mORLSf750Od3kjxp5pxvT3JJvj4E+u2Zz+/LWpj0/bNDPdzvAQCwFUIgAGAn6SSv\nTvLdVXX6IfqckrUVOB/q7h/c4PhXkvzbYbuXqnp0kocleee9btrdSd6V5B8MTb+d5InDOQ9J8sAk\n70jyhJm2R+feIdDdSf5Vkour6n6b/aIAAFslBAIAdpRhZc4bk/z0Ibo8LMnpWVsxdCj/McnDq+rp\n69pPGX7eusE5tyZ5yLB/dZIHVtXjkvz9JB/s7r9I8mczbfu7++Z1Y/8vST6dtZVFAAALJQQCAJbF\nXUm+YV3bNyS5c4O+lyR5elU9foNj1yT5l0l+o6rO3OhG3f0/k/zrYZt9JOszw89v2eC0bzl4vLu/\nmuT3s/b415Oy9nhYkvzuTNtvb3CNJPnJJP97kr9yiOMAAEdECAQALIsbk+xd1/bIJDes79jdtyd5\nXdZCnK97uXJ3//skP5Pkiqp67CHu9+YkJyb5rpnzrktyc5LzZzsOL3P+7iTvn2k++F6gv5+/DIEO\ntq1/H9Ds2N6f5BNJXrzR2AEAjtSu7R4AAMCc3p7kJ6vqj5PckuQpSf5h1t4BtJGfzdo3f22ou/9d\nVR2f5P1V9aTuvn7d8buq6lVJXr/u1Jcn+Y9VdXOS9yT5pqy9P+jBWQueDvrtJP80yde6+9qh7YNJ\nfmE451ArgZK11UDvPcxxAIAtsxIIAFgWP521r17/YJLbs7aS5x9398c26tzdX0zymiQnH+qC3f1v\nshbK/FZVPXKDLm/L2rt+euacd2TtG7xelrX39/xxkuOTPLG7Pzdz7u8lOSHJVTPn3j6cc6C7//Qw\n4/q9rD1OZiUQALAwtfZlFpt0qtqf5PNZ+9aKO7v7CVV1Utb+H7lHJNmf5Hu7+/ND/9cnOTfJl5Nc\n2N3XjDJ6AAAAAOYy70qgu5OsdPdZ3f2Eoe2iJO/v7jOSXJnklUlSVecmeVR3n57kRVn7dg4AAAAA\nttG8IVBt0Pe8JJcO+5cOnw+2vyVJuvvqJCdW1alHOU4AAAAAjsK8IVAneV9V/UFV/eDQdmp3H0iS\n7r4tyUOH9j1Jbpo595NDGwAAAADbZN5vB/t73X1bVX1z1r5K9boc+kWFtUGblxoCAAAAbKO5QqBh\npU+6+9NV9Z4kT0hyoKpO7e4DVbU7yaeG7jcnedjM6adl7Wtc76WqBEMAAAAAC9bdGy3Q2fxxsKp6\nYFU9aNj/xiRPS/LRJJcnuXDodmGS9w77lye5YOh/dpI7Dj42tsGglmI755xztn0MO3G7+OKLt30M\nO3EzX8fZzNfFb+bqOJu5Os5mvo6zma/jbObrOJv5uvjNXB1nM1fH2ZZpvh7OPCuBTk3y7mHlzq4k\nv9jdV1TVHyZ5R1X9QJIbk5w/BDu/VlXPrKpPZO0r4l8wxz2OaXv37t3uIcDczFeWhbnKMjFfWSbm\nK8vCXGWZ7JT5umkI1N1/nuTMDdpvT/LUQ5zzkqMf2rFjp/xhMw3mK8vCXGWZmK8sE/OVZWGuskx2\nynyd99vBJm1lZWW7h7Ajqes41HUc6rp4ajoOdR2Huo5DXcehruNQ18VT03Go6zh2Sl1rs+fFRrtx\nVW/XvQEAAAB2oqpKH+mLoQEAAABYfkIgAAAAgAkQAgEAAABMgBAIAAAAYAKEQAAAAAATIAQCAAAA\nmAAhEAAAAMAECIEAAAAAJkAIBAAAADABQiAAAACACRACAQAAAEyAEAgAAABgAoRAAAAAABMgBAIA\nAACYACEQAAAAwATMHQJV1XFV9eGqunz4vLeqrqqq66rqbVW1a2i/f1VdVlXXV9WHqurhYw0eAAAA\ngPlsZSXQS5N8bObzJUle291nJLkjyQuH9hcmub27T0/yuiSvWcRAAQAAADhyc4VAVXVakmcm+YWZ\n5u9I8q5h/9Ikzx72zxs+J8k7kzzl6IcJAAAAwNGYdyXQzyZ5eZJOkqo6Jcnnuvvu4fjNSfYM+3uS\n3JQk3X1Xkjuq6uSFjRgAAACALds0BKqqZyU50N3XJKmDzTP7B/XMsXtdYuYYAAAAANtg1xx9npjk\nO6vqmUkekOTBWXvXz4lVddywGui0JLcM/W9O8rAkt1TV/ZKc0N2f2+jC+/btu2d/ZWUlKysrR/hr\nAAAAAEzP6upqVldX5+pb3fMv0qmqc5L8i+7+zqp6e5Jf6e63V9XPJ/lv3f3Gqnpxkm/r7hdX1XOS\nPLu7n7PBtXor9wYAAADg8Koq3b3+Ka0kW/t2sPUuSvKyqvp4kpOTvGlof1OSh1TV9Ul+bOgHAAAA\nwDba0kqghd7YSiAAAACAhRprJRAAAAAAS0IIBAAAADABQiAAAACACRACAQAAAEyAEAgAAABgAoRA\nAAAAABMgBAIAAACYACEQAAAAwAQIgQAAAAAmQAgEAAAAMAFCIAAAAIAJEAIBAAAATIAQCAAAAGAC\nhEAAAAAAEyAEAgAAAJgAIRAAAADABAiBAAAAACZg0xCoqo6vqqur6iNV9dGqunho31tVV1XVdVX1\ntqraNbTfv6ouq6rrq+pDVfXwsX8JAAAAAA5v0xCou7+a5MndfVaSM5OcW1V/J8klSV7b3WckuSPJ\nC4dTXpjk9u4+PcnrkrxmlJEDAAAAMLe5Hgfr7q8Mu8cn2ZWkkzw5ybuGd4wYbwAAHP1JREFU9kuT\nPHvYP2/4nCTvTPKUhYwUAAAAgCM2VwhUVcdV1UeS3JbkN5P8aZI7uvvuocvNSfYM+3uS3JQk3X1X\nkjuq6uSFjhoAAACALdk1T6ch7Dmrqk5I8u4kj9mo2/Cz1rXXzLF72bdv3z37KysrWVlZmWc4AAAA\nACRZXV3N6urqXH2re8N85tAnVP1Ukq8k+fEku7v77qo6O8nF3X1uVf3GsH91Vd0vya3d/dANrtNb\nvTcAAAAAh1ZV6e71C3SSzPftYA+pqhOH/QckeWqSjyX5QJLzh27PT/LeYf/y4XOG41ce+dABAAAA\nWIRNVwJV1eOy9qLn44bt7d396qp6ZJLLkpyU5CNJntfdd1bV8UnemuSsJJ9N8pzu3r/Bda0EAgAA\nAFigw60E2vLjYIsiBAIAAABYrKN6HAwAAACA5ScEAgAAAJgAIRAAAADABAiBAAAAACZACAQAAMD/\n3969R0tWlnce//64aFS0vQQb5aKjESEGba/gMitpggY0CeQyaHQSAo4uk3hLMjOKxkwgxkRyG8kY\nNS41QZ1IAmYEnQio0BqTcBmxAZXb0gCN2J2ooKIxw+WZP/ZuOVSf7j7Uqbc3u+r7WasXdfbep87D\nr3e/deqpd+9X0gKwCSRJkiRJkrQAbAJJkiRJkiQtAJtAkiRJkiRJC8AmkCRJkiRJ0gKwCSRJkiRJ\nkrQAbAJJkiRJkiQtAJtAkiRJkiRJC8AmkCRJkiRJ0gKwCSRJkiRJkrQAbAJJkiRJkiQtAJtAkiRJ\nkiRJC8AmkCRJkiRJ0gLYaRMoyX5Jzk/yhSRXJHlVv/0hSc5LcnWSc5OsWfI9f5rk2iQbk6xr+T8g\nSZIkSZKknVvJTKDbgd+oqh8Engm8PMlBwInAx6vq8cD5wOsAkjwXeGxVPQ54GfCOJpVLkiRJkiRp\nxXbaBKqqzVW1sX98K3AlsB9wDHBaf9hp/df0/31vf/xFwJoka2dctyRJkiRJku6Be3RPoCSPBtYB\nFwJrq2oLdI0i4OH9YfsCm5Z825f7bZIkSZIkSRrIHis9MMlewJnAq6vq1iS1vUOX2bbssSeddNL3\nHq9fv57169evtBxJkiRJkqSFt2HDBjZs2LCiY1O1vV7OkoOSPYCPAB+tqlP7bVcC66tqS5J9gAuq\n6uAk7+gf/3V/3FXAj26dNbTkOWslP1uSJEmSJEkrk4SqWm6CzoovB3sP8IWtDaDe2cDx/ePjgbOW\nbD+u/8GHAbdMNoAkSZIkSZK0a+10JlCSZwGfAq6gu6yrgNcDFwN/A+wP3AAcW1W39N/zVuAo4NvA\nCVV16TLP60wgSZIkSZKkGdrRTKAVXQ7Wgk0gSZIkSZKk2ZrF5WCSJEmSJEkaMZtAkiRJkiRJC8Am\nkCRJkiRJ0gKwCSRJkiRJkrQAbAJJkiRJkiQtAJtAkiRJkiRJC8AmkCRJkiRJ0gKwCSRJkiRJkrQA\nbAJJkiRJkiQtAJtAkiRJkiRJC8AmkCRJkiRJ0gKwCSRJkiRJkrQAbAJJkiRJkiQtAJtAkiRJkiRJ\nC8AmkCRJkiRJ0gKwCSRJkiRJkrQAdtoESvLuJFuSXL5k20OSnJfk6iTnJlmzZN+fJrk2ycYk61oV\nLkmSJEmSpJVbyUygvwCOnNh2IvDxqno8cD7wOoAkzwUeW1WPA14GvGOGtUqSJEmSJGlKO20CVdWn\ngZsnNh8DnNY/Pq3/euv29/bfdxGwJsna2ZQqSZIkSZKkaU17T6CHV9UWgKraDDy8374vsGnJcV/u\nt0mSJEmSJGlAs74xdJbZVjP+GZIkSZIkSbqH9pjy+7YkWVtVW5LsA/xLv/1GYP8lx+0H3LS9Jznp\npJO+93j9+vWsX79+ynIkSZIkSZIWz4YNG9iwYcOKjk3VzifqJHk08OGqOqT/+hTg61V1SpITgQdX\n1YlJnge8vKp+IslhwFuq6rDtPGet5GdLkiRJkiRpZZJQVctdqbXzJlCSvwLWAw8DtgC/DXwIOINu\n1s8NwLFVdUt//FuBo4BvAydU1aXbeV6bQJIkSZIkSTO0qiZQKzaBJEmSJEmSZmtHTaBZ3xhakiRJ\nkiRJ90I2gSRJkiRJkhaATSBJkiRJkqQFYBNIkiRJkiRpAdgEkiRJkiRJWgA2gSRJkiRJkhaATSBJ\nkiRJkqQFYBNIkiRJkiRpAdgEkiRJkiRJWgA2gSRJkiRJkhaATSBJkiRJkqQFYBNIkiRJkiRpAdgE\nkiRJkiRJWgA2gSRJkiRJkhaATSBJkiRJkqQFYBNIkiRJkiRpAdgEkiRJkiRJWgBNmkBJjkpyVZJr\nkry2xc+QJEmSJEnSys28CZRkN+CtwJHAE4AXJjlo1j9nV9qwYcPQJcwlc23DXNsw19kz0zbMtQ1z\nbcNc2zDXNsx19sy0DXNtY15ybTET6BnAtVV1fVXdBpwOHNPg5+wy8/KXfW9jrm2YaxvmOntm2oa5\ntmGubZhrG+bahrnOnpm2Ya5tzEuuLZpA+wKblnx9Y79ttK677rqhS5BWzPNVY+G5qjHxfNWYLPr5\nus8BB5Bk5n9OPvnkmT/nPgccMHRcg1r0c1XjMi/n6x4NnjPLbKsGP2eXmZe/bN277HPAAWzZtGnn\nB07htNNOm+nzrd1/fzbfcMNMn1NybG03Dpx88skzf85FHwcW/Xxt+Zo16/N1TOeqvwu0sWXTJrjg\ngtk/8V/+JRx//Eyfcsvhh8/0+cZm0cdWjcu8nK+pmm1/JslhwElVdVT/9YlAVdUpE8eNujEkSZIk\nSZJ0b1RVy03QadIE2h24GjgC+ApwMfDCqrpypj9IkiRJkiRJKzbzy8Gq6o4krwDOo7vn0LttAEmS\nJEmSJA1r5jOBJEmSJEmSdO/TYnUwSZIkSZIk3cu0WB1MkiRptJI8dAWH3VlVtzQvRpLmRJKnrOCw\n26rqiubFzJEkv7GCw75dVX/evJg5Ms+5ejnYhCRnr+Cwr1fV8a1rmSdJvrmzQ4CvVNWBu6KeeZHk\n8hUc9q9VdUTzYuaEmbbh2NqGY2sbSb4L3ESX3/bsXlUH7KKS5oLnaxvm2oa5zl6SbwGXsOOx9T9U\n1aN3TUXzIclXgLez41z/k+fqPTPPuToTaFsHAy/Zwf4Af7aLapknX6yqJ+/ogCSf3VXFzJHdgeft\nYH+Albz51l3MtA3H1jYcW9u40lyb8Hxtw1zbMNfZu6SqfmxHByQ5f1cVM0feV1W/s6MDkjxgVxUz\nR+Y2V2cCTUjy/Kr6m9Ueo7tL8piq+tJqj9HdJfnhqvr0ao/RXcy0DcfWNhxb20jyfVX13dUeo7vz\nfG3DXNswV0nzyiaQNGeSPKWqLh26jnmS5GFV9bWh65C2J8laYF+ggJuqasvAJUkrkuToqnJ25Qwl\neRDwOOBLVXXz0PWMWZI9qur2/vFewEF0uX592MrGK8ka4CiWvGYB53qPtdVJchBdphdV1a1Lth9V\nVecMV9l4JTmUbmbwN5PcD3gd8GTgC8DvVdU3Bi1wFVwdbEKSNUnenOSqJF/r/1zZb3vw0PWNVZL9\nk5ye5O+TvD7Jnkv2fWjI2sYsyVMm/jwVODvJk1d48z1N6P+tf3//+GlJvgRclOT6JD86cHmj1Wd5\nQZL39+PBx5J8I8klSXY43V7bl2RdkguBDcAfAH8IfDLJhY4B00vyxD7DTUnemeQhS/ZdPGRtY5bk\nZyf+/Bzwzq1fD13fWPXj6tbXrSOBzwOnABuTHDtocSOW5HhgS5JrkjwXuJwu18uSvHDQ4kYqyXHA\npcB64P7AA4DDgc/0+zSFJK8CzgJeCXwuyTFLdv/eMFXNhfcA3+kfnwo8iG4M+A7wF0MVNQvOBJqQ\n5FzgfOC0qtrcb9sH+CXg2VX1nCHrG6skHwM+CFwI/GfgqcBPVdXXknx2Z9dca3lJ7qTL9N+XbD6s\n31Y7u+5a20pyRVUd0j++AHhNVV2S5EDgr6rqacNWOE79G+ffBh5M16z49ao6M8kRwO9W1TMHLXCk\nkmwEXlZVF01sPwz486p60jCVjVuSTwO/SzeWvgQ4ATi6qr7oa9b0ktwOnAP8C3fdaPM/AmfSvWa9\neKjaxmzidesfgRdV1XV9Y+gTjgPTSXIFXYPigcBlwJP7MWAt8LGqeuKgBY5QkquBQydn/fSN9ovG\neIPde4P+XH1mVd2a5NF0Y+r7qupUX7Oml+TKqjq4f3xpVT1lyb6NVbVuuOpWx5lA23p0VZ2ytQEE\nUFWbq+oUwFVAprd3Vb2jqjZW1SuBtwGfSvJYuqmgms7zgduAP6yqw6vqcGBz/9gG0HT2TLL1pvn3\nq6pLAKrqGuC+w5U1entW1Uer6gN0b/bOpHvwCeD7hi1t1B4w2QACqKoL6T5h1XT2qqpzquqWqvoj\n4BXAOX1zzdes6T0TuB/d6kAvrqoTgK9W1Qk2gFZlt/4SMIA7gRsAquqruAjMatxRVV+tqn8Gbq2q\nLwJ4ue2qhOXH0DvZ8QpM2rHdt14CVlXX0c20em6SP8FcV+NzSU7oH1+W5GkA/QfDtw1X1ur5wrCt\n65O8hm4m0Bb43r0Wjgc2DVnYyO259CaaVfX+JJuBc/GNytT6mRTnAG/sB6n/gm9QVuvPgL9L8ma6\nN31vAf4WOALYOGhl4/bdJD8OrAEqyU9X1Yf6S+zuGLi2Mftokv8DvJe7XqP2B46jm3Gh6STJmq3X\n+1fVBf2lSx8EHjpsaePVz6p8Dt0lC+cneS2+Zs3CycAFSf4M+AfgjCRnAT+G48Bq3JDk9+lmAl2V\n5I/pfh94NvCVQSsbrzcBlyY5j7tesw4AngO8cbCqxm9zknVVtRGgnxH0k3SXMx0ybGmj9hLg1CRv\nAL4K/FOSTXTn7o5WvL3X83KwCf10xBOBY4C1/ebNdEtCn+KN4KaT5NeBS6vqkxPbnwz8gZfZrV6f\n5Z8AP1RVew9dz5glORz4ZeBAumb5jcCHgPdU1ag7/0NJ8iS6y8DuBH4d+BW6y2y/DLy0qv5xwPJG\nrb9XxTF0N4QM3fl6dlX93aCFjViSF9Hd/PXCie0HAL9VVS8dprL5keSRwFuAp1XVY4auZ+yS/ADw\nUiZet6rq3EELG7F+dtXL6RqVbwWOpLs09Hq6y5htBE2hf691JHd/zTrXm5hPL8l+wO1Lr2RZsu9Z\nVfUPA5Q1N5I8EHgM/dg6D7MBbQJJcyRJgAdW1TeHrkWSJEmSdO/iPYGWkeTIJG9PcnaSs/rHRw1d\n15gl2SPJy5Kck+TyJJcl+WiSX86SlcJ0z0zmSne50l+b6+o4BrSxnVyPHLqueZXknUPXMI/MtQ1z\nbSPJfx+6hnnk+TqduFpwE0kOiStazlzmeKVQZwJN6O//cSDd/RVu7DfvR3d/hWur6tVD1TZmST4A\n3AKcxt1z/SXgoVX1gqFqGzNznT3HgDbMtY0k27s/TYDLqmq/XVnPvDDXNsx110tyQ1W5sMkUPF9n\nL64W3ERc0bKJec7VJtCEJNcstzxhf5nNNVX1uAHKGr0kV1fV47ezb9nMtXPmOnuOAW2YaxtJ7qC7\nP8XS1T+q/3rfqrrPIIWNnLm2Ya5tJNneJeChW+XShWCm4Pk6e5lYVjvJLwCvA44Gzli6BLdWbplc\nDwfeCfwi8DZznc485+qLwra+m+QZVTU5xevpwHeHKGhO3JzkWOCDVXUnQJLdgGMBbwQ3PXOdPceA\nNsy1jS8BR1TVDZM7+hUsNB1zbcNc27gFePpyNys111XxfJ09VwtuI3FFyxbmNlebQNs6Hnh7fxfw\nrZcs7A98s9+n6fw8cArwtiQ3032K8mDg/H6fpmOus3c8jgEtHI+5tvAW4CHANm9S6FZj03TMtQ1z\nbeO9wKOA5Vas+atdXMs88XydvXcBhwLfWy24qj7ef6BpptM7BTiY7rIlAKrq8iRHAL81WFXjN7e5\nejnYdiTZhyVLFy635J6mk+RhdOfeV4euZZ6Y62w5BrRhrpIkSdJwnAm0jCRrgB+le6NSwE1Jzq2q\nW4atbNySHAQcQ59rkpuAs6rqqmErGzdznT3HgDbMtY3JMQC4CTi7qq4ctLCRM9c2zLWNfnw9irvn\n6vi6Sp6vs5VkD7obQv8M8EjuyvQs4N1VdduA5Y1W/+//dcBPA3v3m/+FLtc3Ow5MZ55zdYn4CUmO\nAy4F1gP3p7s+9XDgM/0+TSHJa4HT6T79vxi4pH98epITh6xtzMx19hwD2jDXNnYwBnzAMWB65tqG\nubbh+NqG52sT7wPWAScBzwN+AjgZeBLw/uHKGr2/obsX6PqqelhVPYxuDLgZOGPQysZtbnP1crAJ\nSa4GDp3s7CV5CHCRqy1NJ8k1wBMmO/xJ7gN83pWBpmOus+cY0Ia5tuEY0Ia5tmGubTi+tuH5Onuu\natvGTnLd7j7t2Dzn6kygbYVuauKkO7n7EpG6Z+6km/Y56RH9Pk3HXGfPMaANc23DMaANc23DXNtw\nfG3D83X2bk5ybL+SLdCtapvkBbiq7Wpcn+Q1SdZu3ZBkbT+bzZXspje3uXpPoG29Cbg0yXnc9Zd7\nAPAc4I2DVTV+vwZ8Ism13D3XHwBeMVhV42eus+cY0Ia5tuEY0Ia5tmGubTi+tuH5OnuTq9pCt6rt\nBbiq7Wq8ADgR+GSSh/fbtgBnA88frKrxm9tcvRxsGf302SNZsoIN3c317FCvQt/1fwZ3z/WSqrpj\n0MJGzlxnzzGgDXNtwzGgDXNtw1zbcHxtw/O1HVe1lYZjE0iSJEmS1FySBwF7V9UXJ7Y/saouH6is\n0etXstsXuLCqvr1k+1FVdc5wlY3bvObqPYEmJNk/yelJ/j7J65PsuWTfh4asbcySPDHJhUk2JXln\n/4nV1n0XD1nbmJnr7DkGtGGubTgGtGGubZhrG46vbXi+zl6S5wNXAR9M8vkkT1+y+y+HqWr8kryK\nbtnyVwKfT3LMkt2/N0xV4zfPudoE2tZ7gA10f9mPoLsG8GH9vkcNVdQceBvdcpCHANcAn07y2H7f\nntv7Ju2Uuc6eY0Ab5tqGY0Ab5tqGubbh+NqG5+vsvR54alWtA04A3pfkZ/t93sR8ei+ly/WngfXA\nbyV5db/PXKc3t7l6Y+ht7V1V7+gfvzLJLwCfSnI0y6+8oJXZa8mUuT9K8hngnCS/iLmuhrnOnmNA\nG+bahmNAG+bahrm24fjahufr7O1eVV8BqKqLkxwOfCTJfpjpauxeVbcCVNV1SdYDZyZ5FCNvVgxs\nbnO1CbStPZN8X1V9F6Cq3p9kM3Au8IBhSxu1JFlTVd8AqKoLkvwc8EHgocOWNmrmOnuOAW2YaxuO\nAW2Yaxvm2objaxuer7P3rSSP3Xo/oKr6Sv/G+kPAEwatbNw2J1lXVRsBqurWJD9JN0vwkGFLG7W5\nzdXLwbb1LuDQpRuq6uPAscDnBqloPpwCHLx0Q3/ztyOAvx2kovlgrrPnGNCGubbhGNCGubZhrm04\nvrbh+Tp7v8LEDIqq+hZwFPDiQSqaD8cBm5duqKrbq+o44EeGKWkuzG2urg4mSZIkSZK0AJwJJEmS\nJEmStABsAkmSJEmSJC0Am0CSJEmSJEkLwCbQCiU5JsmhOz9S90SSX03ygiSuVDdD5jp7jgFtmGsb\njgFtmGsb5tqG42sbnq+zl+S0JG9P8kND1zJPknw8yUf7Fa00I/OQq02glTsUeEOSjw5dyJwJ8MO4\nysKsmevsOQa0Ya5tOAa0Ya5tmGsbjq9teL7O3luBjwO/OHQhc+Y44A3Ao4YuZM6MPldXB5MkSZIk\nSVoAzgRaRpKDkrw2yZ8mObV/fPDQdc2rJCcMXcOY9efrEUn2mth+1FA1jV2SZyR5ev/4B5P8RpLn\nDV3XvEny3qFrmDdJfrg/X3986FrGLMmhSR7UP75fkpOTfDjJKUnWDF3fWCV5VZL9h65j3iS5T5Lj\nkjy7//pFSd6a5OVJ9hy6vjFL8tgk/7V/P/DHSX7ZMWB6SdYkeXOSq5J8rf9zZb/twUPXN4+cCTi9\nJA9K8vtJ3pfkRRP73jZUXbPgTKAJSV4LvBA4Hbix37wf8PPA6VX15qFqm1dJbqiqA4auY4ySvAp4\nOXAlsA54dVWd1e+7tKqeMmR9Y5Tkt4HnAnsAH6ObTr8BeDZwblW9abjqxivJ2ZObgMOB8wGq6uhd\nXtQcSHJxVT2jf/xSuvHgfwM/DnzY16zpJPk88KSquj3JO4HvAGcCR/Tbf3bQAkcqyTeAbwNfBD4A\nnFFV/zpsVeOX5H/RvWbdH7gF2IvuUqUj6H7X/6UByxut/nesnwI+CTwP2AjcDPwM8KtVtWG46sYp\nybl0r/unVdXmfts+wPHAEVX1nAHLG60k2/t9P8BHquoRu7KeeZHkg8C1wIXAi4HbgBdV1b+P/X2W\nTaAJSa4BnlBVt01svw/w+ap63DCVjVuSy7e3Cziwqu67K+uZF0muAJ5ZVbcmeTTdm5T3VdWpST5b\nVU8etMAR6jNdB9wX2AzsV1XfTHI/4KKqeuKgBY5UkkuBLwDvAoru3/4H6BrsVNUnh6tuvJb+O09y\nCfC8qvrXJA8ALqyqQ4atcJySXFlVB/eP7/aLXpKNVbVuuOrGK8lngafSNdVfABwNfIZuLPjbqvrW\ngOWNVpLLq+qJ/Y2Kvww8sqruSBLgMl+3prP194E+y/sDf1dV65McAJzl71j3XJKrq+rx93SfdizJ\nHXTNyiyz+7Cqut8uLmkuTL7eJ/lNuobw0cDHxtwE8q7227oTeCRw/cT2R/T7NJ21wJF0n6AsFeAf\nd305c2P3qroVoKquS7IeODPJo1j+hUA7d3tV3QF8J8kXq+qbAFX1b0kcA6b3NODVwG8C/62qNib5\nN5s/q7ZbkofQXd6drbMqqurbSW4ftrRR+1ySE6rqL4DLkjytqv5vkgPpPgnUdKqq7gTOA87rL1V6\nLt0M7D8C9h6yuBHbrf+w8gF0s4HWAF+n+zDDy8FWZw/gDrosHwhQVTd4md3Urk/yGrqZQFsAkqyl\nmwm0acjCRu5K4GVVde3kjiTmOr37Jtmtf92iqt6U5EbgU3QzLkfLJtC2fg34RJJruWswOgD4AeAV\ng1U1fh8B9qqqjZM7kmzY9eXMjc1J1m3NtZ8R9JPAewBnAEzn/yW5f1V9h+4Ta6C7jh0bwVPrX0D/\nR5Iz+v9uwdegWVhDN5MiQCXZp6o29/cIsxE8vZcApyZ5A/BV4J/6X6Q39fs0nbudk/2s67OBs/vZ\nlprOu4GrgN3pGu1nJPkScBjd7Q00nXcBlyS5EPgR4BSAJHvTNdl0z70AOBH4ZJKH99u20I0Dzx+s\nqvE7ie3f6/eVu7COefNh4MfoVq4DoKpO63+H/Z+DVTUDXg62jCS7Ac8A9qX7heVG4JJ+doB0r5Fk\nP7qZK5uX2fesqvqHAcoatST3rap/X2b79wOPqKorBihr7iT5CeBZVfX6oWuZR/2lC2ur6p+HrmXM\nkjwQeAxdw/LGrZ9cazpJDqyqa4auYx4leSRAVd3U32D32cANVXXxsJWNW5InAAcDn6uqq4auR5Jm\nwSaQJEmSJGkwSy7B1QyZaxtjz9Ul4if0Ny9d9TG6O3Ntw1xnz0zbMNc2zLUNc23DXNsw1zbMdZc7\neegC5pS5tjHqXJ0JNCHJv9EtBbfdQ4A1Lml+z5hrG+Y6e2bahrm2Ya5tmGsb5tqGubZhrrPnasFt\nmGsb85yrN+Xc1kErOMZ7A91z5tqGuc6embZhrm2Yaxvm2oa5tmGubZjr7LlacBvm2sbc5moTaEJV\nTS4Nrxkw1zbMdfbMtA1zbcNc2zDXNsy1DXNtw1ybcLXgNsy1jbnN1cvBJEmSJEmSFoA3hpYkSZIk\nSVoANoEkSZIkSU254lob5trGPOfq5WCSJEmSpKZcca0Nc21jnnP1xtCSJEmSpNZcca0Nc21jbnN1\nJpAkSZIkSdIC8J5AkiRJkiRJC8AmkCRJkiRJ0gKwCSRJkiRJkrQAbAJJkiRJkiQtAJtAkiRJkiRJ\nC+D/Az7u+8PpBtZsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f333af1a650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "party_crosstab.plot.bar(\n",
    "    subplots=True,\n",
    "    figsize=(20, 30),\n",
    "    legend=False,\n",
    "    sharey=True,\n",
    "    ylim=(0, 500)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How do the two major parties compare to the third parties?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a new column that groups the third parties together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "candidates['party_type'] = candidates.party_name.apply(\n",
    "    lambda x: x if x in ['DEMOCRATIC', 'REPUBLICAN'] else 'THIRD'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regroup the data with that column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "by_party_type = candidates.groupby([\"party_type\", \"election_year\"]).size().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, transpose it to a crosstab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "party_type_crosstab = by_party_type.set_index(\n",
    "    [\"election_year\", \"party_type\"]\n",
    ").unstack(1).reset_index().fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clean up the crosstab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "party_type_crosstab = party_type_crosstab.reset_index()\n",
    "party_type_crosstab.columns = ['INDEX', 'YEAR', 'DEMOCRATIC', 'REPUBLICAN', 'THIRD']\n",
    "party_type_crosstab = party_type_crosstab.rename_axis(None, axis=1)\n",
    "party_type_crosstab.drop(\"INDEX\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chart the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f33373afad0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f3337fded10>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f333b05c150>], dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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33f2xk0uS1yT5qqr68l2OdyLJG5M84gG+HwDAKVX33jOKq+pEkj9Lcm+Sz3b3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\nAGAA/x9OnFd99KAhjgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3338383450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "party_type_crosstab.set_index(\"YEAR\").plot.bar(\n",
    "    subplots=True,\n",
    "    figsize=(20, 15),\n",
    "    legend=False,\n",
    "    sharey=True,\n",
    "    ylim=(0, 500)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print out the data so we can eyeball it as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YEAR</th>\n",
       "      <th>DEMOCRATIC</th>\n",
       "      <th>REPUBLICAN</th>\n",
       "      <th>THIRD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>253.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>201.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2002</td>\n",
       "      <td>337.0</td>\n",
       "      <td>319.0</td>\n",
       "      <td>178.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004</td>\n",
       "      <td>347.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>224.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2006</td>\n",
       "      <td>378.0</td>\n",
       "      <td>319.0</td>\n",
       "      <td>169.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008</td>\n",
       "      <td>249.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2010</td>\n",
       "      <td>394.0</td>\n",
       "      <td>368.0</td>\n",
       "      <td>197.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2012</td>\n",
       "      <td>342.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014</td>\n",
       "      <td>417.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016</td>\n",
       "      <td>312.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018</td>\n",
       "      <td>181.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2022</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2026</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    YEAR  DEMOCRATIC  REPUBLICAN  THIRD\n",
       "0   2000       253.0       259.0  201.0\n",
       "1   2002       337.0       319.0  178.0\n",
       "2   2004       347.0       320.0  224.0\n",
       "3   2006       378.0       319.0  169.0\n",
       "4   2008       249.0       223.0   57.0\n",
       "5   2010       394.0       368.0  197.0\n",
       "6   2012       342.0       271.0   45.0\n",
       "7   2014       417.0       306.0   61.0\n",
       "8   2016       312.0       249.0   27.0\n",
       "9   2018       181.0        85.0   46.0\n",
       "10  2020        17.0         6.0    0.0\n",
       "11  2022         1.0         1.0    0.0\n",
       "12  2026         3.0         0.0    0.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "party_type_crosstab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How has the gap between the GOP and the Dems changed?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate a new column with the gap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "party_type_crosstab['DEM_VS_GOP'] = party_type_crosstab['DEMOCRATIC'] - party_type_crosstab['REPUBLICAN']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chart it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f33375942d0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f333e1b40d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "party_type_crosstab.plot.bar(x=\"YEAR\", y=\"DEM_VS_GOP\", legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspect it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YEAR</th>\n",
       "      <th>DEMOCRATIC</th>\n",
       "      <th>REPUBLICAN</th>\n",
       "      <th>THIRD</th>\n",
       "      <th>DEM_VS_GOP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>253.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>-6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2002</td>\n",
       "      <td>337.0</td>\n",
       "      <td>319.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004</td>\n",
       "      <td>347.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2006</td>\n",
       "      <td>378.0</td>\n",
       "      <td>319.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008</td>\n",
       "      <td>249.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2010</td>\n",
       "      <td>394.0</td>\n",
       "      <td>368.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2012</td>\n",
       "      <td>342.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014</td>\n",
       "      <td>417.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016</td>\n",
       "      <td>312.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018</td>\n",
       "      <td>181.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2022</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2026</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    YEAR  DEMOCRATIC  REPUBLICAN  THIRD  DEM_VS_GOP\n",
       "0   2000       253.0       259.0  201.0        -6.0\n",
       "1   2002       337.0       319.0  178.0        18.0\n",
       "2   2004       347.0       320.0  224.0        27.0\n",
       "3   2006       378.0       319.0  169.0        59.0\n",
       "4   2008       249.0       223.0   57.0        26.0\n",
       "5   2010       394.0       368.0  197.0        26.0\n",
       "6   2012       342.0       271.0   45.0        71.0\n",
       "7   2014       417.0       306.0   61.0       111.0\n",
       "8   2016       312.0       249.0   27.0        63.0\n",
       "9   2018       181.0        85.0   46.0        96.0\n",
       "10  2020        17.0         6.0    0.0        11.0\n",
       "11  2022         1.0         1.0    0.0         0.0\n",
       "12  2026         3.0         0.0    0.0         3.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "party_type_crosstab"
   ]
  }
 ],
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