examples/us_house_of_rep.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Data-Preparation\" data-toc-modified-id=\"Data-Preparation-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Data Preparation</a></span><ul class=\"toc-item\"><li><span><a href=\"#Loading\" data-toc-modified-id=\"Loading-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Loading</a></span></li><li><span><a href=\"#Preprocessing\" data-toc-modified-id=\"Preprocessing-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Preprocessing</a></span></li><li><span><a href=\"#Exploration\" data-toc-modified-id=\"Exploration-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Exploration</a></span></li></ul></li><li><span><a href=\"#Seat-Allocations\" data-toc-modified-id=\"Seat-Allocations-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Seat Allocations</a></span></li><li><span><a href=\"#Results\" data-toc-modified-id=\"Results-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Results</a></span><ul class=\"toc-item\"><li><span><a href=\"#Shifts-in-Representation\" data-toc-modified-id=\"Shifts-in-Representation-3.1\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>Shifts in Representation</a></span></li><li><span><a href=\"#Effects-on-Presidential-Representation\" data-toc-modified-id=\"Effects-on-Presidential-Representation-3.2\"><span class=\"toc-item-num\">3.2&nbsp;&nbsp;</span>Effects on Presidential Representation</a></span></li><li><span><a href=\"#Disproportionality\" data-toc-modified-id=\"Disproportionality-3.3\"><span class=\"toc-item-num\">3.3&nbsp;&nbsp;</span>Disproportionality</a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**US HoR Seat Allocation**\n",
    "\n",
    "This example uses Wikidata statistics sourced by [wikirepo](/~https://github.com/andrewtavis/wikirepo) as a basis for the allocation and analysis of seats to states in the US House of Representatives given populations at different times. The Huntington-Hill method of apportionment will be used - the method used by the US HoR for seat allocation. Analysis will look at seat allocations over time, how seats are likely to change given recent population shifts, and further show how HoR allocations affect representation in the US presidential election via Electoral College vote assignment.\n",
    "\n",
    "To assure that all US citizens (nationals in the case of American Samoa) are represented:\n",
    "- Puerto Rico and the US Virgin Islands will be considered as a single state\n",
    "    - Abbreviated as \"Puerto Rico and USVI\" for plotting purposes\n",
    "\n",
    "- American Samoa, Guam, the Northern Mariana Islands, and the United States Minor Outlying Islands will be combined to form Pacific Island States\n",
    "    - This new state is the least populous in the hypothetical union\n",
    "    \n",
    "- Washington DC will be considered as a state\n",
    "\n",
    "This notebook can also be opened in [Google Colab](https://colab.research.google.com/github/andrewtavis/poli-sci-kit/blob/main/examples/us_house_of_rep.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install poli-sci-kit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install pltviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install wikirepo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-12T15:50:24.677599Z",
     "start_time": "2020-12-12T15:50:23.305929Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:99% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To avoid exceeding your IOPub rate, you made need to load Jupyter via:\n",
    "# jupyter notebook --NotebookApp.iopub_data_rate_limit=1.0e10\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import date\n",
    "\n",
    "import wikirepo\n",
    "from wikirepo.data import data_utils, lctn_utils, wd_utils\n",
    "\n",
    "from poli_sci_kit import appointment\n",
    "from poli_sci_kit import utils\n",
    "\n",
    "import pltviz\n",
    "from pltviz.utils import add_num_commas\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style=\"darkgrid\")\n",
    "sns.set(rc={'figure.figsize':(15,5)})\n",
    "\n",
    "import logging\n",
    "logging.getLogger().setLevel(logging.CRITICAL) # to disable alerts about empty legend handles \n",
    "\n",
    "pd.set_option(\"display.max_rows\", 16)\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:99% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll be querying population statistics for states between 1960 and the current date, as 1959 was the year that Alaska and Hawaii became the 49th and 50th states.\n",
    "\n",
    "**Note:** the `sub_lctns` argument can be used to either select locations that we do want (deselecting all others), or indicate those that we don't want. Passing strings selects them, and strings with `~` as the first character negates their selection. We're negating the District of Columbia, as we'll be using the data from Washington DC instead.\n",
    "\n",
    "For this example we're querying US state information, so we'll be using `depth = 1`. For counties in the US we'd use `depth = 2`. \n",
    "\n",
    "The following are the needed variables for our poli-sci-kit import:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-12T15:50:26.528093Z",
     "start_time": "2020-12-12T15:50:26.525121Z"
    }
   },
   "outputs": [],
   "source": [
    "ents_dict = wd_utils.EntitiesDict()\n",
    "depth = 1\n",
    "country = 'United States of America'\n",
    "# country = 'Q30' # we could also pass its QID\n",
    "sub_lctns = '~District of Columbia'\n",
    "interval = 'yearly'\n",
    "timespan = (date(1960,1,1), date.today())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's load in a locations dictionary of the states and territories, checking what we've queried afterwards:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-12T15:51:19.677043Z",
     "start_time": "2020-12-12T15:50:28.453296Z"
    }
   },
   "outputs": [],
   "source": [
    "lctns_dict = lctn_utils.gen_lctns_dict(ents_dict=ents_dict,\n",
    "                                       depth=depth,\n",
    "                                       locations=country, \n",
    "                                       sub_lctns=sub_lctns,\n",
    "                                       timespan=timespan,\n",
    "                                       interval=interval, \n",
    "                                       verbose=False) # False for web display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-12T15:51:40.832285Z",
     "start_time": "2020-12-12T15:51:40.827692Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alabama, Alaska, American Samoa, Arizona, Arkansas, California, Colorado, Connecticut, Delaware, Florida, Georgia, Guam, Hawaii, Idaho, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York, North Carolina, North Dakota, Northern Mariana Islands, Ohio, Oklahoma, Oregon, Pennsylvania, Puerto Rico, Rhode Island, South Carolina, South Dakota, Tennessee, Texas, United States Minor Outlying Islands, United States Virgin Islands, Utah, Vermont, Virginia, Washington, Washington, D.C., West Virginia, Wisconsin, Wyoming\n"
     ]
    }
   ],
   "source": [
    "print(*sorted(lctns_dict.key_lbls_at_depth(ents_dict=ents_dict, depth=1)), sep=', ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From here we use `lctns_dict` as the value for `locations` in `wikirepo.data.query` for state population data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-12T15:51:54.409645Z",
     "start_time": "2020-12-12T15:51:42.181962Z"
    }
   },
   "outputs": [],
   "source": [
    "df = wikirepo.data.query(ents_dict=ents_dict,\n",
    "                         depth=depth, locations=lctns_dict,\n",
    "                         timespan=timespan, interval=interval,\n",
    "                         climate_props=None,\n",
    "                         demographic_props='population', \n",
    "                         economic_props=None, \n",
    "                         electoral_poll_props=None, \n",
    "                         electoral_result_props=None,\n",
    "                         geographic_props=None, \n",
    "                         institutional_props=None,\n",
    "                         political_props=None,\n",
    "                         misc_props=None,\n",
    "                         verbose=False) # False for web display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-12T15:52:27.219047Z",
     "start_time": "2020-12-12T15:52:27.216192Z"
    }
   },
   "outputs": [],
   "source": [
    "df_states = df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we'll subset by decennial census years and include the 2015 estimate. Afterwards we'll interpolate any values that are missing using `data_utils.interp_by_subset`, which takes arguments from [pandas.DataFrame.interpolate](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.interpolate.html) and [scipy.interpolate](https://docs.scipy.org/doc/scipy/reference/interpolate.html) and applies them to a dataframe subsetted by a given depth column (`sub_lctn` in this case). The values of the resulting columns will then be converted to integers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-12T15:52:28.626324Z",
     "start_time": "2020-12-12T15:52:28.532391Z"
    }
   },
   "outputs": [],
   "source": [
    "decades_and_2015 = ['1960', '1970', '1980', '1990', '2000', '2010', '2015']\n",
    "df_states = df_states[df_states['year'].isin(decades_and_2015)]\n",
    "\n",
    "df_states = data_utils.interp_by_subset(df=df_states, \n",
    "                                        depth=1, \n",
    "                                        col_name='population', \n",
    "                                        method='slinear', \n",
    "                                        fill_value='extrapolate', \n",
    "                                        limit_direction='both')\n",
    "\n",
    "df_states['population'] = df_states['population'].astype(int)\n",
    "df_states.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-12T15:52:29.126654Z",
     "start_time": "2020-12-12T15:52:29.115566Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>sub_lctn</th>\n",
       "      <th>qid</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>California</td>\n",
       "      <td>Q99</td>\n",
       "      <td>2015</td>\n",
       "      <td>39144818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>California</td>\n",
       "      <td>Q99</td>\n",
       "      <td>2010</td>\n",
       "      <td>37253956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>California</td>\n",
       "      <td>Q99</td>\n",
       "      <td>2000</td>\n",
       "      <td>33871648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>California</td>\n",
       "      <td>Q99</td>\n",
       "      <td>1990</td>\n",
       "      <td>29760021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>California</td>\n",
       "      <td>Q99</td>\n",
       "      <td>1980</td>\n",
       "      <td>23667902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>California</td>\n",
       "      <td>Q99</td>\n",
       "      <td>1970</td>\n",
       "      <td>19953134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>California</td>\n",
       "      <td>Q99</td>\n",
       "      <td>1960</td>\n",
       "      <td>15717204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    location    sub_lctn  qid  year  population\n",
       "14  United States of America  California  Q99  2015    39144818\n",
       "15  United States of America  California  Q99  2010    37253956\n",
       "16  United States of America  California  Q99  2000    33871648\n",
       "17  United States of America  California  Q99  1990    29760021\n",
       "18  United States of America  California  Q99  1980    23667902\n",
       "19  United States of America  California  Q99  1970    19953134\n",
       "20  United States of America  California  Q99  1960    15717204"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_states[df_states['sub_lctn'] == 'California']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's combine populations and rename the regions as we discussed in the introduction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-12T15:52:30.920949Z",
     "start_time": "2020-12-12T15:52:30.836040Z"
    }
   },
   "outputs": [],
   "source": [
    "df_new_states = data_utils.sum_df_prop_vals(df=df_states,\n",
    "                                            target_lctn='Puerto Rico', \n",
    "                                            vals_lctn='United States Virgin Islands', \n",
    "                                            lctn_col='sub_lctn', \n",
    "                                            time_col='year',\n",
    "                                            prop_col='population',\n",
    "                                            subtract=False,\n",
    "                                            drop_vals_lctn=True)\n",
    "\n",
    "df_new_states = data_utils.sum_df_prop_vals(df=df_new_states,\n",
    "                                            target_lctn='American Samoa', \n",
    "                                            vals_lctn='Guam', \n",
    "                                            lctn_col='sub_lctn', \n",
    "                                            time_col='year',\n",
    "                                            prop_col='population',\n",
    "                                            subtract=False,\n",
    "                                            drop_vals_lctn=True)\n",
    "\n",
    "df_new_states = data_utils.sum_df_prop_vals(df=df_new_states,\n",
    "                                            target_lctn='American Samoa', \n",
    "                                            vals_lctn='Northern Mariana Islands', \n",
    "                                            lctn_col='sub_lctn', \n",
    "                                            time_col='year',\n",
    "                                            prop_col='population',\n",
    "                                            subtract=False,\n",
    "                                            drop_vals_lctn=True)\n",
    "\n",
    "df_new_states = data_utils.sum_df_prop_vals(df=df_new_states,\n",
    "                                            target_lctn='American Samoa', \n",
    "                                            vals_lctn='United States Minor Outlying Islands', \n",
    "                                            lctn_col='sub_lctn', \n",
    "                                            time_col='year',\n",
    "                                            prop_col='population',\n",
    "                                            subtract=False,\n",
    "                                            drop_vals_lctn=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-12T15:52:31.177497Z",
     "start_time": "2020-12-12T15:52:31.168301Z"
    }
   },
   "outputs": [],
   "source": [
    "df_new_states['sub_lctn'] = df_new_states['sub_lctn'].replace(\"Puerto Rico\", \"Puerto Rico and USVI\")\n",
    "df_new_states['sub_lctn'] = df_new_states['sub_lctn'].replace(\"American Samoa\", \"Pacific Island States\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-12T15:52:31.512656Z",
     "start_time": "2020-12-12T15:52:31.508583Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_new_states['sub_lctn'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a bar plot of the 2015 populations in `df_new_states`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-12T15:52:32.451689Z",
     "start_time": "2020-12-12T15:52:32.447589Z"
    }
   },
   "outputs": [],
   "source": [
    "df_2015_pops = df_new_states[df_new_states['year'] == '2015']\n",
    "df_2015_pops = df_2015_pops.sort_values(by=['population'], ascending=False)\n",
    "df_2015_pops.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-12T15:52:34.230363Z",
     "start_time": "2020-12-12T15:52:32.747823Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = pltviz.bar(counts=df_2015_pops['population'], \n",
    "                labels=df_2015_pops['sub_lctn'], \n",
    "                faction_labels=None, \n",
    "                colors=None, \n",
    "                horizontal=False, \n",
    "                stacked=False, \n",
    "                label_bars=False, \n",
    "                axis=None)\n",
    "\n",
    "# Initialize empty handles and labels\n",
    "handles, labels = pltviz.legend.gen_elements()\n",
    "\n",
    "ax.legend(handles=handles, labels=labels,\n",
    "          title='Total Population: {}'.format(add_num_commas(sum(df_2015_pops['population']))), \n",
    "          bbox_to_anchor=(1, 1),\n",
    "          title_fontsize=20, fontsize=5, \n",
    "          ncol=2, loc='upper right', \n",
    "          frameon=True, facecolor='#ffffff', framealpha=1)\n",
    "\n",
    "ax.axes.set_title('2015 US State Populations', fontsize=25)\n",
    "ax.set_ylabel('Population', fontsize=15)\n",
    "ax.set_xticklabels(labels=df_2015_pops['sub_lctn'], rotation=90)\n",
    "ax.set_xlabel('State', fontsize=15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here's how the populations of a few select states have changed over time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-12T15:52:34.235516Z",
     "start_time": "2020-12-12T15:52:34.232331Z"
    }
   },
   "outputs": [],
   "source": [
    "selected_states = ['California', 'Georgia', 'Hawaii', 'Michigan', 'New York', 'Oregon', 'Texas', 'Wyoming']\n",
    "df_selected = df_new_states[df_new_states['sub_lctn'].isin(selected_states)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-12T15:52:34.541370Z",
     "start_time": "2020-12-12T15:52:34.238097Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = pltviz.comp_line(df=df_selected, \n",
    "                      dependent_cols='population', \n",
    "                      indep_stats='year',\n",
    "                      group_col='sub_lctn')\n",
    "\n",
    "colors = [utils.rgb_to_hex(c) for c in sns.color_palette(n_colors=len(selected_states), desat=1)]\n",
    "handles = pltviz.legend.gen_handles(colors=colors, size=10, marker='o')\n",
    "labels = list(df_selected['sub_lctn'].unique())\n",
    "\n",
    "ax.legend(handles=handles, labels=labels, \n",
    "          title='States',\n",
    "          bbox_to_anchor=(0, 1),\n",
    "          ncol=2, loc='upper left', fontsize=13, \n",
    "          title_fontsize=17, \n",
    "          frameon=True, facecolor='#ffffff', framealpha=1)\n",
    "\n",
    "ax.axes.set_title('Selected State Populations Over Time', fontsize=25)\n",
    "ax.set_xlabel('Year', fontsize=15)\n",
    "ax.set_ylabel('Population', fontsize=15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Seat Allocations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example will maintain 435 as the total number of seats in the House of Representatives. As stated before, we'll also be using [Huntington-Hill](https://en.wikipedia.org/wiki/Huntington%E2%80%93Hill_method) method for apportionment in this example, which is the same method used as the US House of Representatives for determining the number of seats per state given populations. It is a version of highest averages apportionment, where in this case the populations of each state are iteratively divided by a uniquely determined denominator that is derived from the number of seats each state has already received. In comparison to other highest averages methods, the Huntington-Hill method generally favors smaller groups more. \n",
    "\n",
    "Each state starts with one seat, and the next seat is given to the state that has the highest quotient after division. The divisor $D_{si}$ for a state $s$ at any given iteration $i$ given its population $P_s$ and current seat allocation $a_{si}$ is:\n",
    "\n",
    "$$D_{si} = \\frac{P_s}{\\sqrt{a_{si}(a_{si} + 1)}}$$\n",
    "\n",
    "Whichever state $s$ that has the highest value $D_{si}$ gets the next seat to be allocated, and then the divisors are calculated again for all states given the newest allocation.\n",
    "\n",
    "We'll now define the seat total and add a column to `df_new_states` for apportionments for the given years:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-12T15:52:35.289337Z",
     "start_time": "2020-12-12T15:52:35.286329Z"
    }
   },
   "outputs": [],
   "source": [
    "total_seats = 435\n",
    "df_new_states['hor_allocation'] = [np.nan] * len(df_new_states)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Populations in `df_new_states` will now be subsetted by year, and the HoR seats will be allocated:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-12T15:52:36.395838Z",
     "start_time": "2020-12-12T15:52:36.231246Z"
    }
   },
   "outputs": [],
   "source": [
    "for y in decades_and_2015:\n",
    "    df_new_states.loc[df_new_states.loc[df_new_states['year'] == y].index, 'hor_allocation'] = \\\n",
    "        appointment.methods.highest_averages(averaging_style='Huntington-Hill', \n",
    "                                             shares=df_new_states.loc[df_new_states[df_new_states['year'] == y].index, 'population'], \n",
    "                                             total_alloc=total_seats, \n",
    "                                             alloc_threshold=None, \n",
    "                                             min_alloc=1, # must be 1 for Huntington-Hill to avoid division by 0\n",
    "                                             tie_break = 'majority', \n",
    "                                             majority_bonus=False, \n",
    "                                             modifier=None)\n",
    "    \n",
    "df_new_states['hor_allocation'] = df_new_states['hor_allocation'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-12T15:52:36.615999Z",
     "start_time": "2020-12-12T15:52:36.605543Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>sub_lctn</th>\n",
       "      <th>qid</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>hor_allocation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>California</td>\n",
       "      <td>Q99</td>\n",
       "      <td>2015</td>\n",
       "      <td>39144818</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>California</td>\n",
       "      <td>Q99</td>\n",
       "      <td>2010</td>\n",
       "      <td>37253956</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>California</td>\n",
       "      <td>Q99</td>\n",
       "      <td>2000</td>\n",
       "      <td>33871648</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>California</td>\n",
       "      <td>Q99</td>\n",
       "      <td>1990</td>\n",
       "      <td>29760021</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>California</td>\n",
       "      <td>Q99</td>\n",
       "      <td>1980</td>\n",
       "      <td>23667902</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>California</td>\n",
       "      <td>Q99</td>\n",
       "      <td>1970</td>\n",
       "      <td>19953134</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>California</td>\n",
       "      <td>Q99</td>\n",
       "      <td>1960</td>\n",
       "      <td>15717204</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    location    sub_lctn  qid  year  population  \\\n",
       "14  United States of America  California  Q99  2015    39144818   \n",
       "15  United States of America  California  Q99  2010    37253956   \n",
       "16  United States of America  California  Q99  2000    33871648   \n",
       "17  United States of America  California  Q99  1990    29760021   \n",
       "18  United States of America  California  Q99  1980    23667902   \n",
       "19  United States of America  California  Q99  1970    19953134   \n",
       "20  United States of America  California  Q99  1960    15717204   \n",
       "\n",
       "    hor_allocation  \n",
       "14              52  \n",
       "15              52  \n",
       "16              51  \n",
       "17              52  \n",
       "18              45  \n",
       "19              42  \n",
       "20              37  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new_states[df_new_states['sub_lctn'] == 'California']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shifts in Representation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at how the seat allocations of the US HoR would shift given the 2015 estimate values vs. the 2010 census:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-12T15:52:38.143373Z",
     "start_time": "2020-12-12T15:52:38.067106Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Texas', 2),\n",
       " ('Washington', 1),\n",
       " ('South Carolina', 1),\n",
       " ('Georgia', 1),\n",
       " ('Florida', 1),\n",
       " ('Rhode Island', -1),\n",
       " ('Pennsylvania', -1),\n",
       " ('Ohio', -1),\n",
       " ('New York', -1),\n",
       " ('Michigan', -1),\n",
       " ('Illinois', -1)]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff_2015_2010 = [(state, df_new_states[(df_new_states['sub_lctn'] == state) & (df_new_states['year'] == '2015')]['hor_allocation'].values[0] - \\\n",
    "                      df_new_states[(df_new_states['sub_lctn'] == state) & (df_new_states['year'] == '2010')]['hor_allocation'].values[0]) \\\n",
    "                          for state in df_new_states['sub_lctn'].unique()]\n",
    "\n",
    "diff_2015_2010 = [d for d in diff_2015_2010 if d[1] != 0]\n",
    "diff_2015_2010.sort(key=lambda x: x[1])\n",
    "diff_2015_2010[::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's how the seat allocations would have shifted over time for the selected states from before given the example's setting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-12T15:52:38.783036Z",
     "start_time": "2020-12-12T15:52:38.779441Z"
    }
   },
   "outputs": [],
   "source": [
    "df_selected = df_new_states[df_new_states['sub_lctn'].isin(selected_states)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-12T15:52:39.412874Z",
     "start_time": "2020-12-12T15:52:39.131429Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = pltviz.comp_line(df=df_selected, \n",
    "                      dependent_cols='hor_allocation', \n",
    "                      indep_stats='year',\n",
    "                      group_col='sub_lctn')\n",
    "\n",
    "colors = [utils.rgb_to_hex(c) for c in sns.color_palette(n_colors=len(selected_states), desat=1)]\n",
    "handles = pltviz.legend.gen_handles(colors=colors, size=7, marker='o')\n",
    "labels = list(df_selected['sub_lctn'].unique())\n",
    "\n",
    "ax.legend(handles=handles, labels=labels, \n",
    "          title='States',\n",
    "          bbox_to_anchor=(0, 1),\n",
    "          ncol=2, loc='upper left', fontsize=10, \n",
    "          title_fontsize=12, \n",
    "          frameon=True, facecolor='#ffffff', framealpha=1)\n",
    "\n",
    "ax.axes.set_title('Selected State HoR Allocations Over Time', fontsize=25)\n",
    "ax.set_xlabel('Year', fontsize=15)\n",
    "ax.set_ylabel('Seats in HoR', fontsize=15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Effects on Presidential Representation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the seats in the HoR determine the votes that a state has for President in the Electoral College, let's further analyze this. Electoral College votes are calculated by adding the state's representation in the HoR and in the senate (two senators per state). We'll calculate the presidential votes for each state for all years, and then graph the population per Electoral College vote for the selected states over time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-12T15:52:40.116957Z",
     "start_time": "2020-12-12T15:52:40.107524Z"
    }
   },
   "outputs": [],
   "source": [
    "df_new_states['ec_votes'] = df_new_states['hor_allocation'] + 2\n",
    "df_new_states['pop_per_ecv'] = df_new_states['population'] / df_new_states['ec_votes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-12T15:52:40.421793Z",
     "start_time": "2020-12-12T15:52:40.410100Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>sub_lctn</th>\n",
       "      <th>qid</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>hor_allocation</th>\n",
       "      <th>ec_votes</th>\n",
       "      <th>pop_per_ecv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>California</td>\n",
       "      <td>Q99</td>\n",
       "      <td>2015</td>\n",
       "      <td>39144818</td>\n",
       "      <td>52</td>\n",
       "      <td>54</td>\n",
       "      <td>724904.037037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>California</td>\n",
       "      <td>Q99</td>\n",
       "      <td>2010</td>\n",
       "      <td>37253956</td>\n",
       "      <td>52</td>\n",
       "      <td>54</td>\n",
       "      <td>689888.074074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>California</td>\n",
       "      <td>Q99</td>\n",
       "      <td>2000</td>\n",
       "      <td>33871648</td>\n",
       "      <td>51</td>\n",
       "      <td>53</td>\n",
       "      <td>639087.698113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>California</td>\n",
       "      <td>Q99</td>\n",
       "      <td>1990</td>\n",
       "      <td>29760021</td>\n",
       "      <td>52</td>\n",
       "      <td>54</td>\n",
       "      <td>551111.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>California</td>\n",
       "      <td>Q99</td>\n",
       "      <td>1980</td>\n",
       "      <td>23667902</td>\n",
       "      <td>45</td>\n",
       "      <td>47</td>\n",
       "      <td>503572.382979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>California</td>\n",
       "      <td>Q99</td>\n",
       "      <td>1970</td>\n",
       "      <td>19953134</td>\n",
       "      <td>42</td>\n",
       "      <td>44</td>\n",
       "      <td>453480.318182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>California</td>\n",
       "      <td>Q99</td>\n",
       "      <td>1960</td>\n",
       "      <td>15717204</td>\n",
       "      <td>37</td>\n",
       "      <td>39</td>\n",
       "      <td>403005.230769</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    location    sub_lctn  qid  year  population  \\\n",
       "14  United States of America  California  Q99  2015    39144818   \n",
       "15  United States of America  California  Q99  2010    37253956   \n",
       "16  United States of America  California  Q99  2000    33871648   \n",
       "17  United States of America  California  Q99  1990    29760021   \n",
       "18  United States of America  California  Q99  1980    23667902   \n",
       "19  United States of America  California  Q99  1970    19953134   \n",
       "20  United States of America  California  Q99  1960    15717204   \n",
       "\n",
       "    hor_allocation  ec_votes    pop_per_ecv  \n",
       "14              52        54  724904.037037  \n",
       "15              52        54  689888.074074  \n",
       "16              51        53  639087.698113  \n",
       "17              52        54  551111.500000  \n",
       "18              45        47  503572.382979  \n",
       "19              42        44  453480.318182  \n",
       "20              37        39  403005.230769  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new_states[df_new_states['sub_lctn'] == 'California']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-12T15:52:40.825555Z",
     "start_time": "2020-12-12T15:52:40.821666Z"
    }
   },
   "outputs": [],
   "source": [
    "df_selected = df_new_states[df_new_states['sub_lctn'].isin(selected_states)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-12T15:52:41.480276Z",
     "start_time": "2020-12-12T15:52:41.195621Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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77qn1eNbEPffcE3R+0tPT5aFDh8qPPvqovHz5crmwsLDO5e+///4at2u32+WePXvKVqtVfvTRR+WKigplXnl5uTxt2jRlmzt37gxatqbzF1juvPPOk61Wq3zbbbfJhw4dUubl5eXJ48aNk61Wq3z++efLDoejXvs/ceJE2Wq1yoMHD65X+9qo7ZxnZ2cr06uPHVmu/djVxmuvvSZbrVa5S5cu8rZt25Tp27Ztkzt27Cinp6fLL730klxZWanM27p1qzJG77333hq3b7Va5SuvvFLeu3evMu/333+Xu3btKlutVvnJJ59UphcXF8tnnHGGbLVa5eXLlwetb+3atXKnTp1qnFcb1Y9benq6/N577ynXBLvdLt92222y1WqVe/XqJZeUlCjLHTp0SD7zzDNlq9UqP/TQQ3JxcbEyLzMzUx45cqRstVrla6+9Nmh7r7zyirK9CRMmyE6nU5Zl+ajjPEBgbN5///31an84n332mWy1WuW+ffvWuN5T8Zr1zjvvyFarVW7fvr08f/78oGv2ihUrlGvWtGnTgpYbMGCAss4vv/xSmR441oFj0alTJ3n16tVBy+7evVu5rl1++eVB8471d7N69WqlHxs2bKjXMrXxxBNPKL/Br776Spnu8Xjk+fPny+3bt5etVqv83nvvBS0X2P7PP/+sTKttLBzvPeLuu++WrVarPGjQoKDrQ/VjefjYPd4xdjQkSZIHDhwoW61Wec6cOTW2eeyxx2Sr1SqPHz8+aPr//vc/2Wq1yr1795Z//fVXZbrT6ZRnzpyp7MfXX38dtFxt46Kx9lEgqAsRMykQNBDt27dn0aJFQW/7ysrKWLNmDS+88AJXXnklffr04aWXXsLhcByx/Isvvqi8vb7++uuDLB29evXi6aefBnyJYQLlHGrju+++Y/PmzcTFxfH222/TqlUrZV5ERATTp0+nc+fO2Gw23n//fWXet99+y44dOwgPD2fOnDlB7napqalKH1atWkVpaWmdfXC5XMyaNQuAp556KujNt0ql4qKLLlLeNs+aNUupV5iXl8dnn30GwPPPPx/kMtyyZUtee+21Gi0C9eHZZ5/lnnvuUSxWsiyza9cuFixYwN13303v3r257rrr2LRp0zGtd+PGjbjdbmJjY3nooYcICwtT5hkMBqZMmaL0ub5xmIsWLSIzM5MOHTowa9asoIRBsbGxvPzyyyQlJZGRkcHixYvrtc5AgohTPYvw8uXLefnll1Gr1bzwwgu0bdtWmTdr1ixcLhejR4/mrrvuCrIotW/fnldeeQWNRsOyZcvYvXv3EevW6XS8+uqrtGzZUpnWtWtXxVrw+++/K9P37duH0+kkKiqKiy66KGg9ffr0Ydy4cVxwwQXHNR5vvPFGbrjhBtRq3204KiqKl156iebNm1NYWMiCBQuUtu+88w52u52BAwcybdq0INfmlJQUXnvtNYxGIxs3bmTNmjU1bu/+++9X3HGP9fx//vnn9aoxGfi9Hw+nwjXL6XTy+uuvA3DnnXcyatQo5fyAL/Pxk08+Cfiyaebk5Byxjh49egSNlcCx/vnnn9FqtVx77bVHWB7T0tK45ZZbgPpfH2qjehKYE/mdHzp0iIULFwI+S231TKUajYZRo0YpIQevvvoq5eXlx7yN471H7Nmzh+XLl6NSqZg9e3bQ9SEtLY05c+YQGhp6xPaOd4wdDZVKxfDhwwGf22lN+xnIfl098c4ff/zBDz/8APjKip111lnKvJCQECZOnMhVV10FUO/yH421jwJBXQgxKRA0IK1bt+ajjz5iyZIl3H777XTt2jXoQbOwsJA33niDSy65hEOHDinTc3JylGLfNbkbAfTv3x+z2UxlZSXr1q2rsx+BQtSDBw+use6bSqVSthO4mQF8//33ynIxMTFHLNetWzcWL17ML7/8ctS05ps3b1ZiPgcNGlRjm0suuQS1Wk1ubi7//PMPAD/++COSJJGUlMSZZ555xDIpKSn1dr08HI1Gw7hx41i7di0zZsxg2LBhQbEjkiSxfv16Ro0apWTnrQ+DBg1i8+bNfPvttzXWrHM6nUoSjJpeJNRE4BxedNFFNbpQhoaGKjG01c9hXQRErtvtrlf7k8HGjRuZOnUqsixz3333MXDgQGWey+Xixx9/BGr/naSnp9O2bVtkWa7xuHTs2LFGV9DAQ1d1wdG8eXO0Wi3FxcVMmTKF7du3By0zYcIEXnnllRpj6I7GmDFjjpim1+uVh9LvvvtOmR4YC7Xtc0xMjOLSWNM+x8bGkpycfMx9DBBwyT7aX03uffXlVLhmbdy4kZKSErRaLaNGjaqxzUUXXUR8fDxer5fVq1cfMb8218EXXniBLVu21FpLNvDbdLlcSJJUZz/rovqxC4iv4+HHH3/E4/EQGxt7xIuUAKNHj0an01FaWhoUL11fjvceEXC77d69O+np6Ucsk5SUpCS6q87xjrH6cMUVV6BWq9m5c+cR8d2rV6/GbrcTHx9P3759lemBbXTq1KlWd/+bbroJ8MXj1+dFQ2Puo0BQGyJmUiBoBNq1a0e7du244447cDgc/P777/z0008sXbqUwsJCsrKymDhxIh9//DEAu3btUpadMGFCret1Op0A7N27t87tB246P/zwwxEPwAFKSkoAyMjIQJZlVCqVEmtT/U3v4VRPBlAXgX1yu921PpiBT+BJksTevXvp1KmTUnOxegzI4bRr165WC0x9CA8PZ9iwYUrR6MzMTNatW8c333zDTz/9hCzLvPTSS7Rv377W2JuaCA0NZfv27Wzfvl2JXdq9eze7du1SBJwsy/VaV+AcLlq0KEhYVKegoAA4+ngIEBBRdru9Xu2bmoyMDCZMmIDL5WLEiBFKnFD1+S6XC/DFptWW+ObAgQNAzcfl8JIwAQKWjOoP4NHR0dxyyy288cYbLFmyhCVLlhAbG8vZZ59Nnz596Nev33FZf+Li4khISKhxXuC3l5GRAUB5ebmSMOa1116rtaRQoE1N+3yiyTaaos7kqXDNChy71NTUWustqlQq2rdvT25ubo31YWuLhwfftc7lcrFu3Tr27t1LdnY2GRkZbN++nYMHDyrtJEkKsogeC9W3fzQPlroIHIt27drV2heDwUDLli3ZuXMn+/btY8CAAce0jeO9RwSWq+ucd+zYUYlPDXC8Y6w+JCYmcs455/DTTz+xbNmyIJEbSDI2fPjwoBeDgWNc1/hs0aIFRqORsrIy9u3bV+d9ERp3HwWC2hBiUiBoZMLCwujduze9e/dm4sSJPPDAA3z55Zf88ccfbN26lQ4dOgRZQ6q72dXG0dy1AundDx48GPSQUhNer5fy8nKMRqMiMmp6o3msBProcrnqtU+BG1zgs64+HEsG0/qQmppKamoqV199NevXr+e2226jrKyMjz76qN5ics2aNcycOVN5ex4gLi6OCy+8kB9//PGodciqEziHGRkZirCojaONhwAB185Dhw5RWlpar6LZRUVFVFRU1JjltCEpKipi3Lhx2O12zjzzTB577LEj2lTfz+p1L2ujpuNyrC6pkyZNomPHjsyfP5+NGzeSn5+vZFDUarVcdNFFPPLII8dUgLyuUg2BcR/oe/VSDfWxTNS0z4dneT0VORWuWYE+HO1cBoRmTa6dNblXgk8wzZ49mwULFgS9zNFoNFitVjp16nTU0hL1ISUlBY1Gg9frZdeuXUGuk3Wxbds22rRpo3hWNMSxOBrHe48IiOS6znlNLwOOd4zVlyuuuIKffvqJ5cuXc88996BSqSgqKmLt2rWoVCqlJuXh/TnaMQ4PD6esrKxex7ix91EgqAkhJgWCBuCRRx7h119/Zfjw4dx22221tgsNDeWJJ55g1apVuN1u9u3bR4cOHZSboslk4rfffjvh/gRcph5++GFGjx59zMsdz4NBbevq0KFDvWP6oOpBu656Z5WVlcfUl61btzJ16lSKi4tZtWpVnQ/XZ555JqNGjWLOnDk1Wh5q4tdff+XWW29FkiS6dOnCsGHDsFqtpKWlKSVgqrs31YewsDBKS0t54403jvmNf20MGjSIp59+Gq/Xy6+//sp555131GUWLVrEiy++SIsWLVi2bFm9y2AcC06nk/Hjx5OZmUmLFi2YNWtWjaKv+sPj77//XmO21sbgvPPO47zzzqOsrEzJ4rpmzRr27t3LF198oZyn+lJRUVHrvMADdsDiWT3+dtmyZUe1TJyunArXrMB4OtrLmYCoOZbx98gjj7B48WI0Gg1XXXUVPXv2pE2bNrRo0YLQ0FB+/vnnBhGTZrOZbt26sWHDBn7++ed6Hcvc3FyGDx9OWFgYL7zwAgMHDmzUYxHgeO8RgeXqukfUNB6Od4zVl8GDB2MymTh48CAbN26kZ8+eLF++HLfbTa9evY5wNa/vMQ7Mr88xbux9FAhqQsRMCgQNgNPpJDMzU4lXqAuj0ajcFAIPjAGLkd1uJz8/v9ZlN27cyJ49e44qpgLrq+4+ezgHDx7kjz/+IC8vT5kWKEtR13K33nor48ePZ8uWLfXqQ0ZGRq2xO7Is8+uvvwa5LwaW2759e60uoTUlVqmLyMhIduzYwaFDh44abwpVrmL1dWF86623kCSJs88+m48++ojRo0dz5plnKkLS5XIds8tZfc5hRkYGf/31F0VFRfVaZ3JyMp07dwZ8SV2O5nLrcrn45JNPAF9MYWMISVmWuf/++9m8eTORkZG8/vrrtVrukpOTFTexusbAli1b2LFjxwkLjMrKSsVtGXy/3YEDBzJlyhS++uor7rnnHsDnUlZf6zD4fnu1PQgHYqcD9QEjIyOVWMC69nnHjh1s27btmKzfpxKnwjUrEDebmZlZ6/mRJEnxPqitHujh5ObmKq6O06ZN44knnmDYsGG0bdtWsWRWj6E/UQL1Q9esWVMva/ZHH32ELMu43W46deoEVB2Lbdu21RrDWVZWpnhN1PdYVOd47xGBFyqHxyZWpyYXz+MdY/UlJCREiUf88ssvgz6rJ94JEDjGW7durXWde/bsUV4+1ecYN/Y+CgQ1IcSkQNAABG4gf//991HfsP7000/Y7XZMJpPyYJ+WlqbcKAL1zQ5n06ZNjBo1iosuuog//vijzm0ELFkrVqxQCmMfzgMPPMBVV13F3XffrUwLZBn87rvvahQ/27dv54cffuD777/HbDYDBMXTVBcnPXv2JCIigvLy8lqPybJlyxgzZgxDhgxRHqYGDhyITqcjNze3xljB/Pz8GhNf1EVycjJdu3YFfIkw6nqjLUkSX331FQDnnntu0LxAbMnhIiyQ1bFt27Y1JstZsmSJEjN5+ENTbfEqgXP46aef1vjywOPxMH78eEaMGMGzzz5b6/4czgMPPIBKpWLz5s1K5sraeOGFF8jJyUGtVjN+/Pijrru2sXC0bXz11VdotVpeeeWVoOyDh2M0GpWkTLXFDmZnZ3PttddyySWXsHLlynr1oTY+/vhjLr30UiZPnlzj/lRPBHUsyU68Xq8iLqrjcDiUwubVs2cGxuH8+fNrfLAvLS1lzJgxXHbZZcydO7fe/TiVOBWuWd27dycqKgqPx8OHH35YYx++/PJL8vPzUalU9fY2OHDggLKdmuLjJEkKukZ6vd56rbc2Ro4cidVqxev18sADD9QZI71lyxbeffddAK6++mrlxUW/fv3QarXk5+crmUgPZ/78+Xg8HsLCwmpMlnY0jvceEUh4tXnzZvbs2XPEMkVFRUqSnuoc7xg7FgKurN9++y379+/nzz//xGQy1egFEujPli1banXzDWRcTUhICIrDrO1e1BT7KBAcjhCTAkED0Lt3byWz5kMPPcT06dOPSBvvdDr57LPPuOuuuwCYOHFikNtKIM36m2++yVtvvaW8hQWfRTIwv0uXLkctOH/RRRdhtVopKSnh5ptvDnpLWVZWxmOPPcYvv/yCSqVi3LhxyryLL76YFi1aUFJSwu233x70tnzv3r3ce++9AFxwwQWKy05118NA4pPA9MC6p0+fzmeffRb0IPztt9/y6KOPAr6U+ykpKYDPzTWQwe7BBx8MsiQeOHCA8ePH1+kmWBtTp04lLCyMnTt3MnLkSL799tugYwy+t8Djx49n06ZNtGjR4oikEIHzVX0/oeoN85dffhn0cON0Opk/f75STgCOdNENHL9AApUAo0aNIjY2lszMTG677bagbRYVFXHXXXexZ88edDqdcrzqQ5cuXfjf//4HwMsvv8w999xzxFvsnJwc7r33XuVBZsKECZxxxhlHXXdtY6E2PvnkE6VY+fTp0+nVq9dRl7njjjvQaDQsX76cp59+Osj6uHPnTsaNG4fb7SYpKUlJsHS8DBkyBJ1Ox86dO3nqqaeCxl1RUREvvfQSAJ07d1aESn154YUXgh54i4qKuPPOOzlw4AAtW7YMKmw+btw4DAYDmzZtYvLkyUGW6P379zNu3DhsNhsRERF1JjI5lTkVrllhYWHKul955RU+/PDDoGvW119/zSOPPALAlVdeGVRepi5SU1OVl0xvvfVWUEbnAwcOMHHiRDZu3KhMq2/G59rQarVMnz6diIgI/vrrL6666ipWrlwZJFKdTicfffQRN9xwAy6XC6vVGiQuEhMTufLKKwGfy2T1FzOSJPHRRx8pZT3Gjx9/TDHDAY73HtGuXTsGDx6MJEncfvvtQRb7AwcOKDHvh3O8Y+xYSE9Pp2PHjuTn5/Pcc88hyzLDhg2r0auja9euysuQO++8MyjExeVy8corryieIffdd1/Qi8fa7kVNsY8CweGImEmBoIGYMWMGBoOBJUuW8MEHH/DBBx/QrFkzoqOjcTqdipuOTqfjnnvu4dprrw1afujQoWRkZDBr1ixmzJjBnDlzaNGiBUVFRYrQCNRZPBo6nY7XXnuNW265hW3btnHxxRfTsmVLwsLCyMjIUB6Kp06dGpRgJiQkhNmzZ3PLLbewceNGBg4cSJs2bXA6nWRlZeH1emnfvj2PP/64skyLFi0wGAxUVFRw5ZVX0rx5c6ZPn07btm0ZO3Ys2dnZfPLJJzzwwAM8//zzNG/enNzcXMXFplu3bkFiC+D2229n3759rFq1ihtuuEHZxs6dO1Gr1fTv3/+Ys7l27tyZ119/nSlTprB3714mTJiAwWAgKSmJ0NBQ8vLyyM3NBXwPK7NmzToiMUG7du0AX7zehRdeSOvWrXn11VeZMGECv/zyC/n5+QwbNowWLVoQEhJCZmYmFRUVWCwWWrZsyfbt249wZ2vfvj0//PADy5YtY8eOHfTo0YNHH32UqKgoXn/9dW677TZ++eUXBg0aROvWrVGpVOzbtw+Xy4VWq+XFF1+sMT1+XUyaNAmTycTzzz/P8uXLWb58ObGxsSQkJFBSUkJmZibgG0cTJ05k7Nix9VqvyWQiKSmJ/fv3M2HCBFq1asXEiRNrTGJUUFCgjCOz2cyqVatYunQpTqezRivgrbfeSv/+/enevTvTpk3j0Ucf5f3332fhwoWkpaVRXl5OZmYmsiwTExPDO++8c8JuuXFxcTz11FNMnjyZDz74gE8//ZSUlBS8Xi9ZWVk4nU7MZjPTp08/5nUnJydz++23k5SUhMlkYteuXbhcLpo1a8Yrr7wS1PfU1FRmzpzJpEmTWL58OV9//TWtW7fG7XYrLoIGg4E333xTca1uSH788UeuueaaerXt378/t9566zFv41S5Zt18883k5OSwYMECnnjiCWbNmkVycjKHDh1SrlkXXHABDz74YL33zWKxcOONN/L222+zfPly1qxZQ0pKStCYPeuss9i0aRMej4dDhw7VmaSpPnTq1IkPP/yQW2+9lYyMDCZOnIjBYCA5ORm1Ws3evXuV7ODnnHMOL7744hEJbaZOnap4iEycOFHJQpydna1YgUePHl3v60NNHO89Ytq0aWRmZrJr1y4uvvhiWrdujUajYdeuXWi1WtLT09mxY0eQp8jxjrFjZcSIEfz999+KAK/JxTXAc889x6233srmzZu5/vrrSUpKwmKxsG/fPsrKytBoNNx1112K63KA2u5FTbWPAkF1hJgUCBqIkJAQnnnmGUaNGsWKFSv47bffyM3NZfv27YSFhdGyZUv69OnDiBEjanXlmzBhAn369GHevHls3LiR7du3o9PpaN++Peeddx5jxoypd6KD5ORkPv/8cxYsWMDXX3+txFqazWb69u3LddddR8+ePY9YrnXr1ixdupT333+fb7/9Vkkf3qZNGy6++GLGjBkT9KAbHh7Oyy+/zAsvvMDevXvJyckhJyeHtm3bolKpmDZtGhdccAELFy7kjz/+YNu2bej1erp06cLFF1/MVVdddcRDf0hICC+//DJLlizhk08+YdeuXahUKnr16sVdd93Fjz/+eFylQXr16sXKlStZunQpa9euVVLyO51OoqOjGTBgABdccAGXXHJJje6ql112GXv37mXp0qXk5OTgdruRJImOHTuydOlSZs+ezebNm8nKyiIkJISUlBTOPfdcrr/+elavXs0DDzzA6tWrmTJlivKWeezYseTl5fHNN9+QkZER9Ib/jDPOYNmyZcybN4/vv/+ezMxM3G43sbGxnHnmmdx44411psevixtvvJEBAwbwySefsH79ejIzM/nnn38IDQ2lXbt29OrVi2uuuUaxBtSXl19+menTp7Nt2zYyMjKU0g2HU1lZqbiG2my2WsufBKjusnXFFVfQpUsX5s6dyy+//KKMj7S0NM4991xuuummBhNVl1xyCQkJCcyfP58///xTsQanpqYyYMAAbrjhhuMqDzJv3jzeeOMNvvrqK3bv3k1SUhIXXnghY8aMqdHK2b9/f7788kvef/991q5dy759+/B6vSQlJdG7d29uuummE6olWReFhYW1uswdzvHEzgU4Va5Zjz32GIMHD2bBggXKNctsNjNgwABGjBhRYw3DozF58mTOOOMMPvjgA/bt28eOHTuIioqiV69eDB8+nGHDhnHdddexYcMGfvjhh+P+XVcnPT2dr776is8//5zvv/+e7du3s3fvXlQqFbGxsXTq1InLLrvsCHf+AAGh/tVXX/HZZ5/x999/s23bNmJjYznnnHO46qqr6p0ttjaO9x5hsVj45JNPePvtt1m5ciVZWVno9XoGDBjAHXfcwUcffcSOHTuCEljB8Y+xY+Hiiy/mmWeeobKykk6dOtX5ss9kMjFv3jw+//xzvvjiC3bs2EF+fj7x8fFceOGFjBo1ivbt2x+xXG33IrVa3ST7KBBURyXXN7BFIBAIBALBcfPbb79x/fXXA76kG4EyDAKBoGG58847+frrr5k0adJxWcoFAkH9ETGTAoFAIBAIBILTgp9//plBgwYpeQQOx+FwsH79eoAarXoCgaBhEWJSIBAIBAKBQHBa0K5dO3Jzc1m5ciXvvPNOUCblwsJC7rnnHmw2G61atapXUi+BQHBiCDdXgUAgEAiaAOHmKhA0DO+//z5PP/00UJX4y+FwkJ2djdvtJiEhgTfffPOYk5MJBIJjR9zJBAKBQCAQCASnDTfccAPdu3dn7ty5/PXXX+zduxedTkfr1q0ZPHgwo0aNOuZyPQKB4PgQlkmBQCAQCAQCgUAgEBwzImZSIBAIBAKBQCAQCATHjHBzPQo2WzmSJIy3gsYhOtpIYWHZye6G4F+KGF+CxkSML0FjIsaXoDER46v+qNUqzObaa5wLMXkUJEkWYlLQqIjxJWhMxPgSNCZifAkaEzG+BI2JGF8Ng3BzFQgEAoFAIBAIBALBMSPEpEAgEAgEAoFAIBAIjhkhJgUCgUAgEAgEAoFAcMyImMnjQJYliouLKC8vwev1nOzuCE5jsrJOdg8aB70+lOjoRLRa3cnuikAgEAgEAoGgkRBi8jgoKDiITqelXbu2hISEoFKpTnaXBIJTBkmSyM3NpbDwIPHxKSe7OwKBQCAQCASCRkK4uR4HDkc5aWmt0Ov1QkgKBIehVquJj4/H6aw82V0RCAQCgUAgEDQiQkweJ2q1OHQCQW2I34dAIBAIBALBvx/xxCc4pZHl46sBdLzLCQQCgUAgEAgEgvohxKSgydmzZw/33Xcf/fr1o2PHjpx11lnccsstrFmzRmljs9mYPHkyGzduPKZ1y7LMq6++yttvv93Q3RYIBAKBQCAQCP7VeLwSWbkl/PRnDh99/Q+vL95cZ3uRgEfQpOzatYurrrqKdu3acd999xEXF4fNZmPJkiWMGzeOxx57jGuuuYa//vqLL774ghEjRhzT+l0uF7NmzeL2229vpD0QCAQCgUAgEAhOf0orXGTnlpB1qISs3FKyDpWwP78Ur+Tz8NNp1XRqE1fnOoSYFDQp7733HgaDgffee4+QkBBl+vnnn88NN9zASy+9xFVXXXUSeygQCAQCgUAgEPx7kGSZ3KJysg+VkpVbQuahErJzSygqqUqWaDLqSUmI5IzWsaQmRJIcH0FCdDg6rabOdQsxKWhSCgoKAF/5iOqoVCruvPNO/vjjDz7++GMee+wxAK6//nrOPPNM5s2bhyRJvP/++yxZsoTMzExkWaZVq1aMGzeOiy66iJycHAYNGgTAq6++yquvvsqOHTsA2Lt3Ly+++CK//vorHo+Hzp07c++993LGGWcoffj11195+eWXlWU6derEnXfeSbdu3Rr7sAgEAoFAIBAIBCdMpctDdm4p2X7RmJVbQk5uKU63FwC1SkWzWCNtUy0kx0eSmhBJSkIkkeH649qeEJOCJmXAgAGsWbOGkSNHcsUVV3D22WdjtVpRq9V0796d7t27U1RUhNPp5Omnn+aRRx7hrLPOAuCll17i/fffZ9KkSXTo0AG73c7bb7/NvffeS8eOHUlISGD+/PmMHj2aESNGMHLkSACysrK46qqrSExM5IknniAkJIS5c+cyevRoFixYQPv27cnOzubWW2/l3HPP5Y477sDlcvH6669z880388MPP2AymU7iURMIBAKBQCAQCKqQZZmikkqyFDfVErIOlZJXVE4gDaUhVEtKfCT9uyaT4heNzWKNhBzF2ngsCDEpaFKuueYaioqKePPNN3n66acBMBqNnHnmmYwYMYJBgwZhsVho1aoVAK1bt6Z169YAHDx4kDvuuIObbrpJWV/z5s25/PLL2bBhA1dccQWdOnUCICEhgS5dugA+K6VKpWLu3LmYzWYA+vfvz8UXX8wLL7zAO++8w19//YXD4eD6669XLJGtWrVi4cKFlJeXCzEpEAgEAoFAIDgpeLwS+/N9MY1ZflfVrNwSyh1upU2c2UBKQiS9OyX5hGN8BNFRYahUqkbtmxCTgiZnwoQJjBkzhrVr17J+/Xo2btzI999/z/fff8/FF1/MjBkzalwuMN1ms5GRkUFmZia//fYbAG63u8ZlAH755Rd69OhBREQEHo8H8LnVDhgwgPnz5+NyuejSpQthYWHceuutXHjhhfTp04fevXtz3333NfDeCwQCgUAgEAgENVNS7iQrt5RsxdpYwoGCMiUpTohWTfP4SHq2S/CLRl98Y5hed1L6K8Sk4KRgNBoZMmQIQ4YMASAnJ4cnnniC5cuXc8kll9T4FmXr1q1MmzaNzZs3ExISQlpaGlarFai7rqTNZuO7776jQ4cOtc5v1qwZ8+fPZ86cOSxbtoyPP/6Y0NBQhg0bxkMPPURoaGgD7LVAIBAIBAKBQACS5EuKE0iGE/i0lTqVNuYIPcnxkXSxxpEc73NTTbCEo1Y3rrVRlmXKSpzk7i+h1F7J+ZfW/AwNTSgmFy1axPz585X/c3JyuPTSSxk8eDBPP/00TqeTIUOGMGnSJAC2bdvGgw8+SHl5OT169ODxxx9Hq9Vy4MABJk+eTGFhIS1btmTGjBmEh4dTUlLCvffeS3Z2NhaLhZkzZxIbG4vL5eLBBx/k77//JjQ0lBkzZpCWltZUuy2oRm5uLiNGjGDcuHFcd911QfOaN2/Ok08+Sd++fdm1a5ciEgOUlZVxyy230KJFC5YuXUqbNm3QaDTs3r2bpUuX1rndiIgIevbsybhx42qcH3B97dixI7NmzcLtdvPnn3/yxRdf8PHHH5OYmMiECRNOYM8FAoFAIBAIBP9VHE6PrwRHtTIcObkluDy+hJQatYpmMUbat4zxWRr9bqrHmxTnWCkvc5K7v5TcnGIKc0ooyy1D4/RiUKuIiw2HU0FMjhw5UkmIsmvXLiZMmMDYsWO55pprmDdvHomJifzvf/9jzZo19O/fn8mTJ/Pkk0/SpUsXHnjgAT755BOuvfZaHn/8ca699lqGDh3K7Nmzee2115g8eTIzZ86kR48evPnmmyxZsoTp06czc+ZM5s2bR1hYGF999RUbNmxg6tSpfPLJJ02124JqxMTEoNPpWLBgAZdffjnh4eFB8zMyMgBIT09HrVYHzdu7dy9FRUU8/PDDtG3bVpm+Zs0aoCo77OHLAZx55pns3LmT9PT0oHIkzzzzDDabjWeeeYYlS5bwzDPPsGLFCiwWCz169KBHjx589dVXHDhwoEH2XyAQCAQCgUDw70WWZQqLHUrNxoB4zLNVKG3CQ3WkJEQyoHsqKQkRvqQ4McajluBoCCSXl8qCcooyiyk9WEploQOpzEWIV8agVtFSo6aNSgWhevA75ekMdQvak+Lm+thjjzFp0iSys7NJTU0lOTkZgGHDhrFy5Upat25NZWWlkkDl8ssv55VXXmHkyJFs2LCB2bNnK9NHjx7N5MmTWb16NR9++CEAF198MU888QRut5vVq1czceJEAHr27ElRUREHDhygWbNmTb/j/3E0Gg2PP/44t912G8OHD2fUqFGkp6cjyzK///47c+fOZeDAgfTt25e//voLgNWrVxMVFUWrVq2IiIjgzTffRK/XExYWxpo1a5Rz7nA4ANDpdBgMBjZv3syGDRvo0aMHd9xxB1deeSU333wzo0ePJjIykhUrVvDJJ58wceJEVCoVPXv2xOVyMX78eG655RbCw8P56quvKC0tVVxxBQKBQCAQCAQCAJfHy4H8Mp97akA45pZQUenPzwHEWcJJTYykb5fmSnyjJTK0UZLiyB4JqcyFt9SFVOb7c9srqSyswFviQu30oPVHhYX6/2RZxq3TIEfq0EbpCYsxoIsKRR0RgtoYgiYiBI0xpK7NNr2Y/OWXX6isrGTIkCEsX76c2NhYZV5cXBy5ubnk5eUFTY+NjSU3NxebzYbRaESr1QZNB4KW0Wq1GI1GioqKalzXoUOH6i0mo6ONR0zLyjr2/Rb46Nu3L5999hnvvPMOc+fOpaCgAI1GQ8uWLZkwYQKjRo0CoH379gwZMoQPP/yQtWvXsnz5cl5//XWef/557rnnHkJDQ2nTpg1vvPEGTz/9NBs2bOCWW24BYPz48bz55puMHTuWFStW0KZNGxYsWMDMmTN58MEHcbvdpKam8vjjj3P11VcDkJSUxDvvvMMrr7zCgw8+iMPhoHXr1sycOZM+ffqctON1uhMbG3Gyu/CfR5wDQWMixpegMRHjS9CYHMv4spdWsjfHzt79Nvbut7M3x052bgmSPymOPkRDy2Ymzu2eSsvmJtKSzLRIimqwpDiyV8JT6sJdXIm7uBJPidP/3YnHP81bfmQyykpJotwrUyFJeHRqQsxhGOONWFJMJLaJxhhvRKU50qvvWGhyMblw4UJuvPFGwOeaWF2Zy7KMSqWqdXrgszq1KXtZllGr1UcsE5heXwoLy5SBImgY0tPTee655+pso9FomDlzZtC0nj171uii/OWXXwb9P3bsWMaOHRs0rW3btrzxxht1brNr16689957dbYRHBv5+aUnuwv/aWJjI8Q5EDQaYnwJGhMxvgSNSW3jyytJHCosV+IaA4lxisuqkuJYIkNJjo+kU1orUhOiSE6IIN58ZFKcspJKyqg8al9kWUaucOMtcyEFrIqlruD/y11wmByRtWo8OjUVskyJ00NhhYtyj0SFJCGFaolINBLXPIr4pEiSm0ViOMzCWAlUFpUftX9qtapG41qAJhWTLpeLDRs28MwzzwC+WoD5+fnK/Pz8fOLi4o6YXlBQQFxcHBaLhdLSUrxeLxqNRmkPPqtmQUEBCQkJeDwepTZgfHw8eXl5pKSkBK1LIBAIBAKBQCAQ/DepqHSTnVtaLSlOCTl5pbirJcVJio2gY1oMqf5MqsnxkUQY6nb7rI4sy8hOr18UOqtcUA8TixxuuNKoUEeEoDHq0TaPxKWGEpeHglIXh4oqyMkrw+nvZ2iYjvikCOLbRdOiWSQJzSMJj9A3en3JAE0qJnfs2EGLFi0wGAwAdO7cmX379pGZmUnz5s1Zvnw5V1xxBUlJSej1ejZt2kT37t1ZunQp/fr1Q6fT0aNHD1asWMGwYcNYsmQJ/fr1A3xF6JcsWcKtt97KihUr6NGjBzqdjv79+7N06VJ69OjBxo0b0ev1Il5SIBAIBAKBQCD4l+Nwesi3VZBnqyDfVkGurZx8m4N8ewWHCquscsYwX1KcgT1SSfXHNjaLNaI9iguo7PIGiUJvqVMRi1KZTzDiloIXUqtQh+tQR4SgSwhHHWFBbQzxxSmG6yh1e8krqODQgRJyD5SS93cJHv86QvQa4ppF0vHsZOKbRRKfFEGUOazJhGNNNKmYzM7OJiEhQflfr9fzzDPPcMcdd+B0Ounfvz8XXngh4CtQ/9BDD1FWVkaHDh24/vrrAXj00UeZMmUKr7/+OomJibz44osATJw4kSlTpjB06FAiIiKUAvfXXXcdjzzyCEOHDiUkJOSo7pUCgUAgEAgEAoHg1EeSZeylleTZKsgr8otGe9X30gpXUHtDqJY4czjWVAt9OjcnJd6XTdUccWRSHNkj4bVXBrubHiYYZaf3iD6pw3W+5DWWMHSpUWgi9FVi0RiC2qBDpfaF75XYKzm4v4Tc/SXk/l5C3oFSnP4EPlqdmtjECDp2TyIhKZL4pEjM0QZUjVxj8lhRyXVVexfUGDOZlbWT7t27n6QeCQSnB5s2bSIlxXr0hoJGQ8QcCRoTMb4EjYkYX4IATrdXsS76RGO5TzDaKiiwOXB7qyx/KhVER4URZzYQZzYQazYQbw4n1v9/eJgvIU5MtJHcjCKkMueR8Yn+/+WKIxPaqEK1SpbT6gJRE6FXLIu1JbQpK3H6ROMBv3jcX4LDvw21RkVsQgTxzSKI9wvH6Nhw1CeYHKchOKViJgUCgUAgEAgEAoEggCzLFJc5q8Si3yU18L168huA0BAtcRYDSbERdGkTT7zFoIjF6KiwINfUQHIbT34Fnq35lBZU4ClwUGCvPCJOUaVTo/ZbEUNiDWiCxKLvU6WrXy1IR7lLEY2H9peQu7+U8lLffqjUKqJjw2nVNlYRjjHxRrTaky8cjwchJgUCgUAgEAgEAkGj4fL4rIv5NQjGfFsFLk816yJgjgwlzmygc+tYxcIYZwknzmzAGKarMUZQ9kp4bZVU5lfgLajA4/+THR6ljToiBG2MAVPHOCo1qmALo/74ZJGz0hNkbczdX0KJvVLZGUu0geRWZuKTIklIiiQ2IQJdSP1E6emAEJNNiNPp5FBuPjabHbVKRpJVmM0mEuJj0ev1J7t7gsNwOp3k5uZjt9l9VzYZTGYT8eJ8CQQCgUAgECjIskxphauaK2qF4oqaV1SBrTS4REaITkO82UC8JZyOabHEm6usizGmMHTausWW5HDjKajAmx8QjQ68RY4qa6NGhTY6jJCWJrQxBrQxBjQxBtShgVr1x+dG7XZ5yTtYGiQcbYUVyvxIcxgJzaPofFZz4pMiiUuMRB/675Zb/+69O4UoLS1l954MftqSx89/5WErdWKO0NP7jDj6dLLROq0FERGiOO+pQmlpKXv3ZPLP5kK2bS6krMSNMVJHu67RtO9qp1VaqjhfAoFAIBAI/jN4vBIFdgd5tvIjXFHzbRVUuoKT0Zgj9MSaDXRoFeO3LoYRZ/ZZFyPDQ+qVgVSWZF8SnIIKn6tqgc/qKJVXxTOqw3VoYgyEpEYpolFjDj3hRDUej0TBoVIlzvFQTglF+eUEss0YI/XEJ0XSrksiCc0jiWsWQdgxlA35tyDEZBPgdDrZvSeD2Z9vJ/NQVRriwhInX/yczZ97ipgwHNq3szaIxcvr9fLBBx+wbNkyvF4vbrebAQMGMHHiREJCah/k6enprFu3js2bN7Nu3Toeeughtm3bxh133EFkZCSvvPIKzZs3P6G+5ebmMnHiRBYuXHhC62lMnE4ne/dk8uXCveQfdCjTS4vdrF99iH07ihl6NbRt1+aEz1dOTg6DBg3iySefZOTIkcr0d955h127dik1WRuCt99+mwULFrB06VKMxqpA6rvuugutVqtkQD4aU6ZMoU2bNtx8880N1jeBQCAQCAQnF1mWKXO4a3RFzbNVUFTioHraTp1WrbigtkuNJq5a7GKsyUBIPeMLA0hOT5V7ar7D972wArz+japVaCyh6JIj0fitjdoYA2qD7oT33euVKMor98U3+l1WC3LLkPzbDgvXkZAUSev2cUqcozFCeKmBEJNNwqHcfH7akhckJKuTeaicn/7KI9piJiXlxMQawGOPPUZxcTFz584lIiKCiooK7r33Xh588EGef/75oy4/aNAgBg0aBMB3333HWWedxfTp00+4XwDx8fGntJAEyM3N55/NhUFCsjr5Bx38s7kQi8VMckrSCW9PrVbz7LPP0r17d1q1anXC66uNm2++mZ9//pmnn35aOZ9Lly5l27ZtLF68uNG2KxAIBAKB4NTA45UoKqkkr6hm62KF0xPUPipcT6w5jPRUC3GmQOyiTzBGGfWoj6O+oSzLSMVOJaYx4KoqlVaV8VCFatHGGgjtFF/lpmoJrTVT6rHg9UrkHSxh29+HFFfVvIOleP1xm/pQLXHNIul+TirxSRHEJ0UREaU/qbUcT2WEmGwCbDY7P/+VV2ebn7fk0bdT/AmLyZycHJYtW8ZPP/2kWJ8MBgOPP/44v//+O/v27eOJJ56gvLyc/Px82rZty8yZM4MsbIsXL+brr79m6NChLFiwAK/XS2VlJS+88AKzZ8/myy+/RKPR0LJlSx5++GFiY2O57rrriIqKYu/evVxzzTWsWrWKLl268Pvvv3Pw4EF69erFtGnTOHDgAMOGDWPz5s0UFBTwyCOPUFhYSH5+PklJScycOZPo6OgTOgYnit1mZ9vmwjrbbNtcSIdu0Q0iJkNDQ7nxxhu59957Wbhw4RHWY5fLxYwZM9iwYQNer5f27dvz0EMP8emnn7J161aef/553G43Z511Fg8++CBXXHEFGzdu5Nlnn2XRokXKelQqFc8++yyXXnopq1evpl27djz//PO8/fbbhIeH8/HHHzNv3jzUajUxMTE8/PDDtGzZkilTpmC328nOzubcc88N6ttTTz3Fjh07eO211wgPDz/hYyEQCAQCgeDEqKh0B1sWiyrIs/tKahQWVyJVMy9qNWpiTWHEmg20STZXJbvx/+lDTkwqyC4vnkJ/TKPfVdVbWIHs9ifcUYHGFIo20Yj2jGqxjeE1J9mp93ZlmfJSJ7aCCmyFFcqnvaCCYptDKfunC9EQlxhB5zObKwlyoixhQjgeA0JMNgFqlYyt1FlnG1uZC7XqxEt+bt26ldatWwe5MQLExsZywQUX8Oyzz3LZZZdx6aWX4na7ufzyy1m9ejUXXHDBEeu65JJLyMzMxGaz8cgjj/DZZ5+xdu1aPv30UwwGA7NmzWLKlCm88847AERGRrJixQoAVq1aRVZWFvPmzaOiooIhQ4awfv36IDfZL7/8ki5dujBu3DhkWWbcuHEsXbqUm2666YSPwwmhgrKSI2sLVaes1O1LytNA3Hbbbaxbt46XXnqJ+++/P2jem2++iUajYfHixahUKl588UVmzJjBuHHjmDNnDpIksWnTJgwGA7/88gtXXHEF33//Peeff/4R24mLi2P69Ok89thjtGzZkttvv522bduybt063n77bT7++GMsFguLFy9mwoQJfPnllwBUVlYq36dMmYIsyzzxxBPk5eXx1ltv1ek+LRAIBAKBoOGQJJmiEkeNrqh5tgrKHcHPMBGGEGLNBtKam+l1RqD2ou/THBGK+gRjC8FvbSx1HZYUpwKpuOr5VxWiQRNjQN8+1i8aw9BGG1CdQEmMygq3XyyWK6LRXugTjh53VYZYrU6NyWIgJsFImw5xpLSKJsyowxIb3iD7/19GiMkmQJJVmCP0FJbULijNxhAk+cQHs1qtRpKkWudPnjyZn3/+mbfeeouMjAzy8vKoqKiotX11fvzxRy6//HIMBgMA119/PW+88QYul88toUePHkHtBwwYgFqtxmg0kpqaSnFxcZCYHDNmDBs3buS9994jIyODXbt20blz52Pd5YZHBmOkjtLi2gWlMUIHJ679FdRqNc8//zyXXXYZffr0CZq3evVqSktL+eWXXwBwu91ER0fTrFkzEhMT+fvvv1m7di3jxo3jzTffRJZlvv/+e958880atzVw4EBWrlxJUVERV199NQBr167loosuwmKxAHD55Zczffp0cnJyAOjevXvQOt5//30KCwtZsmSJEJICgUAgEDQwDqcnqHRGrq2cfJtPQBbYK/BWq5GoUauI8VsXz0pMDIpdjDMbCNOfeExhdWSPhKfwsEyqhRXIzqoEPOoovc89tV2MEt+ojqhf0p3Dcbu8ikBULIz+/ysrqp7VVGoVUeYwzNEGkltZMEUbMMcYMEcbMEbogxLyHG821/8aktuN80AORHeqtY0Qk02A2Wyi9xlxfPFzdq1teneKw2I2nfC2OnXqxN69eykrKwuyTubm5vLwww9jMBjwer0MGTKEc889l4MHDyLL9VNFkiQFXQQkScLjqfKtD4jMAKGhocp3lUp1xHaef/55tmzZwhVXXMFZZ52Fx+Opd18aE5PZRLuu0axffajWNu26RmM2mxt0u4mJiTz++OPcf//9XHbZZcp0SZJ44IEH6N+/PwDl5eU4nb4XE4MHD+bHH3/k559/Zs6cOSxfvpwVK1YQGhpKSkpKrdtKTk4OGh81vYCQZVk5v4ef2549e9KtWzemTp3Kxx9/jE7XsDcqgUAgEAj+zUiyjL20UimdkWfzl9Lwfy+tcAW1Dw/VEWs2kJoQSc92CT7rol80WiJD0agbvuC9LMtI5e4jMql67ZVVL9R1arTRBvTWaL9o9Fsbj7GOotcrUWJzYCt0YC8sD3JPLTvMGGOM1GOOMdCmQxzmaoIx0hyGpgFiKv/LeEpLcezcgWPndip27qBy7x5CLBaaz59b6zJCTDYBCfGx9Olk4889RTUm4UlNCKfPGXHEx8ee8Lbi4+MZNmwYDzzwAE899RRGo5GysjIee+wxTCYT33//PfPnz6dt27bs2rWLP//8kyFDhtRr3X379uWzzz5j6NChGAwG5s2bR8+ePY/bMvXTTz9xxx13MHjwYHJzc/nll1+49NJLj2tdDUl8fCztu9rZt6O4xiQ8sYlhtO8aTVx8TINv+8ILL+THH39k7ty5DB06FIA+ffrw4Ycf0qtXL7RarfJS4Mknn+T8889nwoQJ6PV64uPj6d27N88//zxXXnnlMW23b9++PPbYY4wZMwaLxcJnn32GyWQiNTW1xvYdO3Zk9OjRfPvtt7z66qtMmjTphPddIBAIBIJ/E063N9gFtahcqb1YYHPg9la9yFWpIDoqjDizge5t4/2uqOGKhTE8rHFf2speCW+Rw59JtQJvge+7XFllNFBHhKCNMRDSxlKVSfUYEtPIkkxZqTPYHdUvGottDuRq1tbQMB3mGJ+FsbpgNEUb0B2jUBXUjCzLuA8domLndhw7d1Cxczuu/ft9MzUaQlu0xDz4fCK6dKlzPUJMNgF6vZ7WaS2YMBx++iuPn7fkYStzYTaG0LtTHH3OiKN1WosGKQsC8Oijj/Laa69x9dVXo9FocLlcDB48mDvuuINFixYxYcIEDAYDRqORnj17kpWVVa/1jhgxgoMHDzJy5EgkSSI1NbXe5SRqYsKECTz33HO8/PLL6HQ6unXrVu++NCZ6vZ5WaakMvZqqOpOlbowRgTqT0bRKS22w83U4Dz30EJs2bVL+Hz9+PM8++yzDhw/H6/XSrl07pkyZAkDr1q1RqVT06tUL8AnP1157rcYY2Lro3bs3N9xwA2PGjEGSJCwWC3PmzEFdx5tOlUrFU089xWWXXUb//v3p1q3bceytQCAQCASnJ7IsU1zmrOaKGhy/WFwWbFELDdESZzGQFBtBV2t8ULKb6KgwtE1kVZMq3EdkUvXaKiEg5jQqtNEGQlqZ0caGVSXF0ddPNjgqXEEJbxTxWHRkHKM52kBsghFrx4CVMRxTdNh/sl5jYyN73FTu20dFwPK4YwfekmIA1IZwwqxWonr3Iyw9nbBWrVH7n3OPFlOqkk8Fv8JTmMLCMiXjU4CsrJ1HxJDVB6fTSW5uPkU2O2qVjCSrsJhNxMfHNpowERw/TqeTvNwCbDabL9mODGazmbj4GHG+6sGmTZtISbGe7G78pxExIYLGRIwvQWNyqowvl8dLgd1RrZSGgzxbVVkNl6eadREwR4b63E9N/phFS5V10Rh2YhlKjxVZkvHaHH73VIfiqipXizNUh+vQxBrQRhvQxvpEo8YUGhRfWBNul/eIhDcBS2NltQRAan8co6maZbG2OMam5FQZX42Jt6yMil07cOzwWR0r9+xGdvvOjS4uHoM1nbD0toRZ09EnNUdViwFBrVYRHW2scR4Iy2STotfrSUlp3iC1JAWNj16vJzklqUHKfwgEAoFAIDj1kGWZ0gpXNVfUCsUVNa+oAltpZVB7vU5DnNlAvCWcM9Jiq6yLFgMxUWHotCfHBVOq9CgxjYqrapEDvH6DiFqFxhJGSGqUYmnUxoShrsN9tiqOseIIS2NtcYzWjnGYYwyYosMxxxiINIWKOMYmQJZl3Hm5VOzYgWPHNip27sC135dEEY2G0NQWmAefT5g1nTBrW3QNmPdDiEmBQCAQCAQCwb8Wj1fyWRdtFeTZyoNcUfNtFVS6vEHtzRF64szhdGgVo2REDVgXI8OPLyNpQyFLMlJxJZ6C6vGNFUhlVQl7VGFaX2xj53glk6rGHIqqBlGnxDEq7qjlioWx2F4ZHMdo0GGONpCSZgmyMposp1cco+x147bnIVW4UOn0oA1BpTq9BK/scVOZkUHFjqp4R29xwGXVQFgbK1Hn9PFZHluloa6WFLOhEWJSIBAIBAKBQHDaIssy5ZXuqqyoh4nFwhIH1YO6dFq1IhDbtYgOEouxJgMhulNDGEkur2JpVDKqFjog4FqrAo05DF1SRFUm1RgD6vDgeENZlqvVYwx2Sa0tjjGuWSTpZyRgig47beMYZbcTr20/3qIcpKIcvP4/qfgQdin4BQKaEFS6ENDqUWn1VSKzxu+BNnW116PShgS1R6M97hcR3rIyHLt2KslyHHt2I/tL8+liYwk/oxMGq99ltXlyrS6rjYEQkwKBQCAQCASCUxqvJFFYXFmVFdVWQZ6/lEa+rYIKpyeofVS4njiLAWuqhTiTIaj2YpRRj/okWhcPR5ZlpBLXEW6qUjVXUpVegzbGQGjHWCWTqsYShkpbJRpcTg8FRQ5se23B5TUKK3A6qmVlrRbHmJJmUWIYzTEGwiPqn531VEGqLAsSi96iHCTbfqSSvKpGKjXqqAQ0liRC0s4iMimZ0uJy8DiR3S5kTyW4ncgeF7LH6f/u/3OUKN9x++d7a69FXisqdS0CNdT3XecTnbImBG+lF1deKZW5dpwHC3EX2H3rUKvQJyYQeWZXwtLSMLRpgzYmzrcebYhvPU0oJEGISYFAIBAIBALBKYLD6WZXtp2iP3LYl20jz+4TiwV2B1I186JWoybWFEas2UCbZLNiXYy3hBNrCkMfcmo+4spuL55CR5Vo9CfHkau52mpMoWjjwtF2iEUbE+bLpGr0udd6PRLF/jhG+66CoHqM5aXBcYwRUXpM0eGkn5EQVF4j4jSMY5RlGbnC7heL2UhF+xXhKFfYqxpqdGjMzdAktCGk/QA0luZoLM1RRyWi0lbFh5piI3CfQAIeWfKCx1Wj+AwIzqrpLmS30ydc/eK16rsT2eXEdTAfZ0E5rkIHLrsbyeUb6yqNjM4Ixua+T50R1Jr94N4P29dRsb2Gzml0Pkuozi9WtXrQ+S2lyne/+FS+195eYzBCdLtaj8Wp+UsTCAQCgUAgEPzrKSx2sDOriF3ZNnZm2cjOK1FcUiMMIcSaDbRKMnF2x2ZB8YvmyNBTyrp4OLIsI5W5lHqNAauj114J/v1T6dRoYgzo06OVTKra6DDQqCktcZJfWI49rwzbtrxa4xjDDDpMMQZSW5/ecYwBZFlCKskPtjTa9iMVZiO7KqoahoShsTRHl9rVJxYDojEyFpW68fdbpdZASBiqkLBjXtZbUe5zWd0RcFndh+z0vQjQxcRi7FEty2qzRJA8VWLV7fSLWGedAvXINj5BK1eWHdEej6vO/mpN8cTdO6/2+cd8BATHjdPppCA3F7vdhoQaNRImk5mY+HhRauIUxFcaJBebze6rJizLmM0m4sT5EggEAoHgmJEkmZy8UnZmFbEz28bOrCKKSnzZUkNDNKQ1N3NpvzZYUyyc2SmJisPqNJ6qyB4Jb5EjuHZjYQVyZZW1UR2p9yXFaWPxCcfoMJxaNcVFDp918WAJtr8OKWU2vNVKjuhCNJgsYVVxjAG31GgDoYbas7GeysheD1LxIbyF2T6xWE04Vhc3qrAon2hM76NYGTWW5qjCLaeFO64sy7gL8nHs2K6IR2dONsgyqNWEpqRiOncgBr941Fmia1iLHvThjdhHCTzuwyysLuX70epMCjHZRJSWlpK1dzdsW416x2rUZYVgjMaefi72dueS0qo1ERERJ7ubAj+lpaXs3bOHfX9vYN8/m3CU2gmLMNGyfXdaduxJq7Q0cb4EAoFAIKgDp8vDnv12dmbZ2JldxJ4cOw5/bKM5Qk+bFAvWZDPWFAvJ8RFoqsV6hYeFnJJiUip3+UWjQ0mK47U5FGsjWjXa6DBC0ixoYwzIUXrKVDKFJf6MqTl27H8ewFZQgbPysDhGSxjmaJ+VsaoeYzjhESc3g+yJUFcSHKolwVFHxKC2NEef1AFNdHM0Zp+1UR12ej1ryV4vlZkZPvG4cweOndvx2GwAqMPCCGttJeKss33Jclq3Rh167JbNhkalUvvcWnV6qKE7QkyeAjidTrL27ka18gXU+XurZpTmo9m4CClzE1kX3kPrtu0bzOL16aef8vHHH1NeXo7L5SI5OZm77rqLzp07N8j662Ls2LHcf//9tG7dutG31Rg4nU727tnDz8s+wJa7X5leUWJj66/fcmDfNhh2PW3btWuw87VgwQIWLFiAx+NBpVLRvn17Jk2aRLNmzRpk/QKBQCAQNDb20kp2ZtvY5RePmQdLkGQZFdA8LoJeHZthTbHQJsVMTFTYKS2QZK+E11YZnEm1oAK5eiIbYwiamDB0LaJwhmkpkWUKHW5sRQ7smUXYNuXUEMcYijnGQNtOwRbGSFMo6tMsjrE6krPcJxb9lsaAeJRK8qny6w1OgqO2JPksjeak43IXPRXwuazuwrHTLx5371JcVrUxMRjatSfM2haDNR19SkqTuOA2NUJMNgEFubk+i2R1IVkNdf5evNtXU2CJJik55YS39+KLL7JhwwZmzpxJUlISAOvWreN///sfixcvbnSB8tZbbzXq+hubvNxc9v29IUhIVseWu599WzdisUSTnJJ8wtt79tln2b59O3PmzCExMRFJkvjiiy+46qqrWLRoEQkJCSe8DYFAIBAIGhJJljlYUKZYHXdl2ciz+WLaQrRqWiWZuLhPGm2SzbRONhMeeuq6Y0oOd5WlMWBtLHJAIDZRrUITHYaqWQSVIWpKZJkCp5sCWyW2PQWUbAwuPXJ4HGPAwhhlCTst4xgDBCfBCbY0HjUJjrk5alNwEpzTDVmW8RQUKOU5KnZsx5md5XNZVanQp7bA1H+A32W1LbromlxW/30IMdkE2O021DtW19lGvX019nYDT1hMFhQUMHfuXL755hvi4uKU6b169WLKlCk4HA5yc3N54oknOHjwIG63m6FDh3LrrbcC8O233/Lqq68iSRLh4eFMnTqVTp06MWvWLP744w/y8vJIT09n2rRpPProo/z5559EREQoVshnnnmGgQMH8vLLL9OhQweeeuop/vzzT8rLy5FlmSeffJLu3buf0D42NjabnX3/bKqzzb6tG2nV8cwTFpOHDh1i4cKFrF69mqioKADUajWXXXYZf//9N3PmzGHNmjV06tSJHTt2cPfdd9OpU6daz9/ixYt58803CQ0N5eyzz+aDDz7gn3/+we1288wzz7Bu3To0Gg2dOnVi6tSpGI1GBg4cyPDhw1m3bh0HDx7k0ksv5a677jqh/RIIBALBvwuXx8u+A8VKspxdWTbKK33lESLDQ2iTbGZQz1SsyWZSE6PQnoJWNlmS8dorgzOp5lcglVcr8xCmRYoIwZEYTrEkk1fp4qCtEtvOvCPiGM3RBuKbRdK2U1W2VFO0gdCw01cwwWFJcGz7g+IaZWd5VcOQMDTm6klwkvxJcOL+FRY42eulMivTLxy34di5A09REQDq0FDCWrch4vKRGKzphLZugybs9LSunihCTDYBEmpfjGRdlBUhceIX3j/++IO0tLQgIRngsssuA+D666/nhhtuYODAgTidTsaOHUtKSgrp6ek8+uijLFy4kOTkZNatW8f48eNZuXIlAPv372f58uVotVpeeOEFvF4vX331FRUVFVx77bW0b98+aHt//vkneXl5fPzxx6jVat58803eeuutU15MolLhKLXX2cRRVuxLynOC/Pnnn7Rq1UoRktU555xzmDlzJgBt2rRRvtd2/qxWKzNmzGDx4sUkJCTw6quv4vX64hFef/118vLyWLp0KRqNhgcffJDnnnuOJ554AoCKigo++ugjcnNzOe+887jiiitITj5xq6tAIBAITk9KK1zVsqwWkXGwBI/XJ6YSY8Lp0S4Ba4qZNskW4i2GU85lVXJ6jsik6il0gF8QyirwGnRU6NQUR+jIq3STY6ugJL+aG6tGhclfjzHVGoMpOuxfEccYQEmCc7il8YgkOJFoLMnorL0VK6Mm+vRJglNfvA4Hjt07/clydlC5ZxdSpS9BlDY6GkN6O8Ks6RjS26JPTkGlOf0Fc0MgxGQToEYCYzSU5tfeyGjxtTtB5Op+FkBZWRmjRo0CfIJhwIABbNiwgeLiYl5++WVl+vbt27HZbJx99tmKiOjVqxcWi4W///4bgC5duqDV+obMmjVrmDp1Kmq1GqPRyPDhw9mxY0fQtrt27UpUVBQLFy4kOzub3377jfDwxstG1WDIMmERJipKbLU2CTNGwWHH+njxeDw1Tne5XMpFukePHoDvXNV2/vLy8ujdu7fiFjt69GhmzZoFwI8//sikSZPQ6XxvS6+77jomTJigbGvQoEEAxMfHEx0dTXFxsRCTAoFA8B9BlmVyiyqCxOPBQp8FSqtR07JZFOef1cIX75hsJsIQcpJ7XIUsy0jFzmDRWFCBVFIlhrwaFRU6NXYkcl0uDpW5KPZKvqcuFURGhWKKNtCyebU4xhgDkVGndxxjgEASHF9txuyqchv2g0FJcFTGGDTR/iQ4liQ0luTTMglOfXEX5PviHP3JcpxZmVUuqympRPXtT1i6L95RFxN7srt7yiLEZBNgMpmxp5+LZuOiWttIbc/FZDaf8LY6derEvn37sNlsmM1mjEYjS5cuBWDWrFkcOHAAWZZZuHAhYX5zfFFREXq9nsWLFx/xhkmWZUXsGAwGZbpWqw0Srmr1kRfb1atXM336dG688UYGDRpEq1at+OKLL054Hxsbs9mXtXXrr9/W2qZlhx6YG+B8denShczMTPLz84mNDb5Q/fbbb3Tt2pUff/xROfaSJNV5/qqfE021N2aSJAWdW0mScLur3HqqJxJSqVRHvJQQCAQCwb8Hj1ci82BxVbxjto2Scp/4Cg/T0SbZTN8uybRJMdOyWRQh2lPDAiO7vXj81sbqSXEUayPgUIPNI5HncFHkkbB5vDgkmbBwnU8kpplpHx1e5ZZqCUOrOzX270RRkuBUz5paVxKcVj2V+oyncxKc6siyjOSowFtcgqfYjqekBG/gs6RYmbanMB9XfgEAKr2esNZWYoZf4avvmNYGTbVnXkHdCDHZBMTEx2Nvdy5S5qYak/BIsa2g7bnExMWf8Lbi4+O5/vrrmThxIs8884ySbGf//v38/vvvpKWl0aVLF9577z3Gjx9PSUkJ11xzDRMmTKBXr17Mnj2b7Oxsxc314MGDdO7cmc2bNwdtp3///nz22WecffbZOJ1Oli9ffkT21p9//pkBAwZw7bXXUllZyVtvvaW4XZ7KxMXH07JjTw7s21ZjEh5zfBItO/QgLv5IV+JjJT4+nuuuu467776bGTNmEB/vGwOfffYZq1at4pNPPuHHH39U2huNxlrPX58+fXjjjTfIzc0lPj6eRYuqXl707duXBQsW0LNnTzQaDR9++CG9e/c+4f4LBAKB4NSnvNLNbr/FcVe2jT377bj9AizObOCM1rFYky1YU8wkxhhRn2TXRVmWcdsrce614SmowHWoDHd+BapyN4GeuZGxuSWKPF5s/r9ytYrIGAPmuHBM0bE091sZ/w1xjAFqToKzH68tB7m8mkfVvygJjuz1VhODxUd8ekqK8QY+S0qQq70sr47GaEQTGYU2KoqoM85AndySMGs6oSmpwmX1BBBisgnQ6/WktGpN1oX34N2+GvX21VBWBEYLUttzoa2vzmRDlZmYNGkSX3zxBffccw8Oh4PS0lKioqK46KKLGDVqFIWFhUybNo1hw4bhcrm4+OKLueSSSwB49NFHuf322/F6vYSGhvLGG2/UWE/xf//7H0888QTDhg0jIiKC6OhoQkNDg9pcffXV3HPPPQwbNgyPx0Pv3r1ZtWoVkiTVaMk8VdDr9bRKS4Nh17Nv60b2bd2Io6yYMGMULTv0oGWHHrRKS2uw83XPPfewaNEibrvtNlwuFy6XizPOOIOFCxcq2XirM2PGjFrP39SpU7n55psJCQmhXbt2ivXytttu49lnn+Wyyy7D4/HQqVMnHn744Qbpv0AgEAhOHWRZpqDY4ROOWTZ2ZtvYn1eKDKhVKlokRjKwe6ov3jHFjMkYetR1NiaSw42n0GdtrDxYhiu/HFWJi0KpykOmxCspgrFYkvEadeijwzDHGDHHGGgRbcAUYyDcePrHMQaQZQmptKBGS2ONSXBSOvvdUk/9JDiyLCM7K/H4rYfekmqffuuhMq24BG9Zac0r0mjQRpnQRkaiiYpCn5zsF4v+aX7hqImKQhsRESSiY2MjyM+vZb2CY0IlC3+2OiksLEOSgg9RVtbO40oi43Q6KcjLxW6z+ZLyIGEym4mJi28wYdJUfPnllxiNRvr3748kSdxxxx307t2ba6+99mR3rcFwOp3k5eZhs9l8yXZkGbPZTFx83Cl5vrKzs1m6dCnjx49HrVazatUq3nrrrSALZVOyadMmUlKsJ2XbAh/iZiloTMT4OjXwShLZuaXszCry13gswuavbRim19K6uZk2yWasKWbSkkzoQ06OHUFyefEWOfAWOnDmllF5qAyKnWjcVfkinJKM3ePF7pVw6jV4jSHo4gxExYYrmVL/LXGMAY5MguOzMnqL9oOnqkalLwlO82puqb5PlfHUSIIjS168pWXBVsLimiyHvmmyy1XjetQGg08ARkahjfQLwcBnVPA0teH4Ez+J61f9UatVREcba50vLJNNiF6vJyk5pUFqSZ5s2rRpwyOPPMKLL76I2+3mrLPOYuTIkSe7Ww2KXq8nOSW5QWpJNgUJCQnk5eUxbNgwNBoNERERPPXUUye7WwKBQCBoQBxOD3v226tcVnNsVLp8ISTRUWGkp0ZjTTFjTbbQPC4CtbpphYbskfDaK/EU+mIaHYfKkIocaCqrwlw8sozdI2H3eClTA5F6QuLDMTWLICYhgtZxRponm/9VD/uyx4nXdgCpMMcvFv3i0X4IpKpEfCpjDBpLEvqO56GxJCniUR0W2eR9llzOumMPi0vwlvg/S0tqTkyoVvsFYCTaSBMhiYl+y2EUmshItFEm/6dPQKp1p5cLrqCJxeT333/Pq6++isPhoHfv3jz00EP88ssvPP300zidToYMGcKkSZMA2LZtGw8++CDl5eX06NGDxx9/HK1Wy4EDB5g8eTKFhYW0bNmSGTNmEB4eTklJCffeey/Z2dlYLBZmzpxJbGwsLpeLBx98kL///pvQ0FBmzJhBWlpaU+72vxKr1crChQtPdjcE1dDpdEqpD4FAIBD8OygqqWRXdhE7s2zsyi4i81BJIOEkyfGR9OncXMmyGh3VdAlUZElGKnH6RGOBA+ehMtwFFairxTVKskyxV8LukSiWJLzhOnSxBiL8orFjgpHwCP0pYVlrKA5PghPIoHpkEpx4NJbmTZoER5YkvOXlR8YcHh6H6LckBspiHI46NFSxDuriEwhrk664lCpWRf+nJjwc1Skc2iQ4cZpMTGZnZ/Poo4+yaNEioqOjGTNmDGvWrOHRRx9l3rx5JCYm8r///Y81a9bQv39/Jk+ezJNPPkmXLl144IEH+OSTT7j22mt5/PHHufbaaxk6dCizZ8/mtddeY/LkycycOZMePXrw5ptvsmTJEqZPn87MmTOZN28eYWFhfPXVV2zYsIGpU6fyySefNNVuCwQCgUAgENQLSZbZn1eqCMedWTYKih0AhOg0tG5u4pK+rbGmWGjd3ESYvvGtOLIsI5W58RZW4Cly4Morx5VbDqUu1P4wIFmWKZVkij1e7B6fi6raEoqhWQSxCRG0jPfFNmr+Je6pviQ4xf4yG/uRbDl4/RbHWpPgtBtQVW6jAZPgSG53NSEYHIPoE4UleEp8sYee0hKoKRGiSoUmoso6GJbW2m8xrCYOq1kR1adgqI/g5NFkYvKbb77hoosuUmrgvfTSS2RmZpKamqrUsxs2bBgrV66kdevWVFZW0qVLFwAuv/xyXnnlFUaOHMmGDRuYPXu2Mn306NFMnjyZ1atX8+GHHwJw8cUX88QTT+B2u1m9ejUTJ04EoGfPnhQVFXHgwAEly6lAIBAIBALBycDp9rLP77K6M9vG7mwbFU6fy2OUUY81xcz5Z7fEmmImJT4SbSOLMcnhxlvowFPowF1QgSu3DMlWidpb5b5Y4ZWwe/0uqirAFEpYQgSWxAji4o20jzeiD/13RFEdmQTHb2Us2o/sLKtqGJQEpyqu8XiS4MiyjFRRUe/YQ6miosb1qEJCFCGoi44htGVacMxh9bjECOMpm6xHcOrTZL/2zMxMdDodt956KwcPHuTcc8+lTZs2QbX14uLiyM3NJS8vL2h6bGwsubm52Gw2jEYjWq02aDoQtIxWq8VoNFJUVFTjug4dOlRvMVlTwGlW1rHvv0DwXyQ29t9Z6Ph0QpwDQWMixtexYS+tZOveAv7Zk8/WvQXszirC67fupSZG0b9HKh3SYmjfKpaE6PBGc/+UnB6c+eU4c8upzC2jfH8JrrxyqKyK3XNJMjZ/Mpxir4TKFIoh0UhsiomkpCi6J0USZQ5rVBfVphpfsteDu+gAzvwsXHnZuPIzceVn4crPRnZXJcHRhEcREptCSKf+hMSl+L7HpqCNjKnzOEgeD257MW67HZfdjttmw20vxmW34bbZcdmLcdttvnn24ppLW6hU6CIj0ZmiCDGZCEm0ojNFoTOZCDGbfZ8mEzqz71MTdvrXjGxsxPWrYWgyMen1etm4cSPz5s3DYDBw2223ERoaGvTjk2UZlUp1RIH1wPTAZ3Vq+/HKsoxarT5imcD0+lJTNtfjxel0cigvF5vdjlpWIalkzCYTCadhNtf/Ak6nk4LcAux2OxIyalSYTCZi4mPE+aon/6bkCacjIludoDER46tuZFnmYGE5u/xWx51ZNnKLfCUddBo1rZJMDOnVCmuKmdbJZoxhIdUXpqCgrJY1H0MfvBJeW6Xf2liBK68CT0EFqooqsaIkw/H6XFRdejXaGAORCb64xvSAi6o2+NnJ7fU2SB9rozHGl5IE5/ByG7UkwQnpUHMSHFmWcVZWUlFSjOfvTLzFW/wlLWqOPfSW1XycVDpdUBKasGbJRCglLUz+pDV+K2JEZJ21EGXA6f+jzAO1ldMQAOL6dSycMtlcY2Ji6NWrFxaLBYDBgwezcuVKNNV+GPn5+cTFxZGQkEB+fr4yvaCggLi4OCwWC6WlpXi9XjQajdIefFbNgoICEhIS8Hg8lJeXYzKZiI+PJy8vj5SUlKB1NTWlpaXs3reXXw/8zq+HNmOvLMYUGsXZCV0529aN1i1b1VjPUXByKC0tJXNPJvI/pcjbSqHMg9eopahdCbb2NlLTUsX5EggEAoGC2+Ml42AxO/21HXdlFVHm8Ik2Y5gOa4qFc7slY02x0CIxEp224dwKq5LhOPAWVuAuqMCdV4Fc6kTlfx8uyTIl/mQ4dq/PRVVtDiXcLxqbxxvpEm8kNOz0z6YpO8v9Lqk5iluq15aDVJxHbUlwVKZmEGJGVocjOZx4iu24S0rwHMzHU7JbyWoaqIVYa2mL8HBFAOqTktG271hV3iIyuLyFOqxxLbsCQVPQZGJywIAB3H///ZSUlBAeHs7atWu58MILefPNN8nMzKR58+YsX76cK664gqSkJPR6PZs2baJ79+4sXbqUfv36odPp6NGjBytWrGDYsGEsWbKEfv36AdC/f3+WLFnCrbfeyooVK+jRowc6nY7+/fuzdOlSevTowcaNG9Hr9U0eL+l0Otm9by9v/bWA7NIDyvSiSjsrMn7gr8IdjOUa2qe3bRCLV3p6OuvWrVOEO8DixYv5+uuvmTNnzgmvvzYefPBBhg4dSlpaGhMnTjxts706nU4y92QirTgEedVuFqUe5PU25IxyMi+CNu3anPD5evLJJ9mwYQMAe/bsISkpidBQXwHpjz/+WPkuEAgEglOLMoeLXX6L467sIvbtL8bt9dVMTIgOp2t6PNYUC9Zkc4O5rMqyjFTuT4ZT6KvZ6MorR7JVoqrmRVXqj2kMZFGVI0IITQgnOiGCmAQj1ngjEVGhp7WQUZLg+JPfSLaquEa5vMjXxgteSYsqNBo5xIIcnoKMHsmrwev04M0pxftPHp7iXXhLS2subaHRBCWh0Sc191kTI6PQRJnQRlUrbxEZ2WCJdQSC04UmE5OdO3fmlltu4dprr8XtdtO7d2+uueYaWrVqxR133IHT6aR///5ceOGFAMyYMYOHHnqIsrIyOnTowPXXXw/Ao48+ypQpU3j99ddJTEzkxRdfBGDixIlMmTKFoUOHEhERwYwZMwC47rrreOSRRxg6dCghISE899xzTbXLCofycvn1wO9BQrI62aUH+PXg70RbLKQ0Pz1qGtbE9OnTle+nq5AEKMgtQP6nNFhIVifPhbytlILoApKSk05oWw899JDyfeDAgcyYMYMzzjjjhNYpEAgEgoZFlmXybBV+8eir77g/3+e6qFGraNEsisFnpiolOiLDT/zFsFTpCRKN7vxyPIUOVG5JaeOQJGz+eo12r4RLr0EXa8CSEEFMvJGO8UbMseFotadvFtWqJDj78RRm4T6YgftQNp6CQ3grHEhukNwqJK8GWRWK5NUguSLxOpzILjcgAfn+Px/qsDDFShiSkEiYtW1QSYvqVkR1eOPFrgoE/waaNN3WiBEjGDFiRNC0Xr168cUXXxzRtm3btnz66adHTE9KSmLevHlHTDeZTLzxxhtHTNfr9Tz77LMn0OsTx2a38+uhzXW2+fXgZno1694kYnLfvn088cQTlJeXk5+fT9u2bZk5c6ZSs/Ouu+4iLy+Pvn37MnfuXM4++2yWLl3KDz/8wFNPPcVjjz1GZmYmdrud8PBwZsyYQatWrbjuuusYNWoUHTt2ZNiwYWzeXPc+n6rY7Xafa2sdyP+UYm9vP2ExWRuLFi1iwYIFSJKEyWTi4YcfpmXLltx444106NCB++67j19++YUpU6awePFiSktLazyner2eV155hW+++QadTofZbObpp58+Ka7eAoFAcLrg8UpkHSpRhOPObBvFZb5ELAa9ljYpZnqdkYQ1xUyrZiZCdMfvsiq7vXiKfILRU+ATj578iqBkOG5ZpshvaQxkUdVGhxGVEEVMvJFWCUZi4oyEGk5vq5inrBjHXxtwbPuDg7kHcBYV4S2vQHJJftEIUF3Y+UWyWq1YC3XVrIiaKFNVzGGUf1pkFOqQkBq2LhAIjod/R+7mUxy1rMJeWVxnG7uzBLXccG++xowZE5RoqLi4mPT0dAA++eQTLrvsMi699FLcbjeXX345q1ev5vzzz+fpp5/mrrvuYu3atcTGxvLLL79w9tln8/3333PBBRfw448/EhkZyccffwzAI488wocffsjDDz/cYH0/2UjIvuD1uijzIKkaJjHT4axfv54lS5bw4YcfEhYWxk8//cTtt9/OV199xfPPP8/w4cPp1q0bTzzxBC+88AIxMTG88847NZ7TTp06MXfuXNatW0dISAjvvvsuW7ZsYfDgwY3Sd4FAIDgdqah0szvHrojHPfvtuNy+enwxpjA6tIrBmmzGmmKhWawR9XFYqmSvhNdeqZTe8BT4kuFIpS5FHnmRFcFo90jYJQki9Rjjw4lJiCAh3kjHBCORUaGo1KevtUyWvLgOHaJy9w4c2/+gMmMfrrxCvNUSA6nUoA4L8WdQjUJriUEbk4AuJl4RhQFrosZoRHUMyRUFAkHDIcRkEyCpZEyhURRV2mttY9JHNqg4mTt3bo0xkwCTJ0/m559/5q233iIjI4O8vDwqKio477zzyM3NpaCggLVr13LbbbexePFibr/9djZs2MBTTz1FeHg4ycnJzJs3j8zMTNavX0/Xrl0brN+nAmpUeI1aKK1DUBq1DSr+q7N69WoyMzO5+uqrlWklJSXY7Xbi4uKYNm0a48eP54477qBnz55A7ec0Pj6etm3bMnz4cPr160e/fv3o1atXo/RbIBAIThcKih2+LKtZNnZl28jOLUEGVCpITYji3K6+RDltUsyYI44tdl2WZaRifzKcoirR6LVXViXDAUolCZvLq2RRdYZqCIv1icaY+HBaJkRgiTGgPQGr56mAp7QUZ3YWlZkZVO7ehjNjL668QmRvwF1XRhumIsRiRN+xJaFt2mPodDZJXTs3arZYgUDQMAgx2QSYTSbOTujKiowfam1zdmJXLGZzk/Tn7rvvxuv1MmTIEM4991wOHjyolEw599xzWbNmDVu2bOG5555jzpw5rFy5kq5duxIeHs5HH33EJ598wqhRoxg2bBgmk4mcnJwm6XdTYTKZKGpXgrzeVmsbVfsITBZTo2xfkiQuvfRSJk+erPyfl5dHVFQUALt37yYmJoYtW7Yoy9R1TufPn89ff/3FunXreOqpp+jbty/33Xdfo/RdIBAITjUkSSY7r4Rd/iyrO7OKKCqpBCA0REPr5mYu698Ga4qFtOYmQkPq92hUlQzHocQ2egor8BY6wFv1crhclilyebB7q1xUddEGohMiiUkwkh5vJCbeSJjh9Ha9lD0enAcP4MzKxJmV6ROPmXvxFleFjai1MloDGBJ16JOSCW3dltAOPQhp3g6VNjjOVMQpCgSnB0JMNgEJcfGcbevGX4U7akzCkxzRjLMTuxEf2zRxbD/99BPz58+nbdu27Nq1iz///JMhQ4YAcP755/PMM89gtVoJCQnh7LPP5sUXX2TSpEnKssOHD2fkyJGUlJTw+OOPk5aW1iT9bipi4mOwtfdlba0xCU9cCKp2EcTExTTK9vv06cNDDz3EmDFjiIuLY8GCBXzwwQesXLmSLVu28MEHH/DZZ58xadIk5s6dy5gxY2o9p9u3b+eee+7hk08+oVOnTsTExLBkyZJG6bdAIBCcCjhdHvbst/tLdBSxO9tOpcvnaWKOCMWaYqaN32U1OT4CTT3cI33JcKrEYsBNFZe3arvIFLm92N2+ZDh2rxeiQjH7xWJyvJGuCUaiTGGntYsqgKfYTmVWJs6sLJ9wzMrEtT8H2eP36FGBNgx0BhlDiowuLoaw1m3Rp3VC26wtalMzIRYFglMQr9dDmb2AkqJcSoryKCnKw+upZPhNd9W6jBCTTYBer6d1y1aM5Rp+Pfg7vx7cjN1ZgkkfydmJXTk70VdnsiHKgtSHSZMmMWHCBAwGA0ajkZ49e5KVlQX4EiLl5eVxzTXXAD5hs2LFCgYOHAjATTfdxCOPPKIkR+rSpQs7d+5skn43FXq9ntS0VDIvAnlbqS+za5kHjFpU7SNQtYsgNS210c5Xnz59GDt2LDfddBMqlQqj0cirr75KeXk5d999Nw899BDx8fE888wzjBw5kp49e9Z6TkeOHMmQIUO44oorMBgMhIaGBmWQFQgEgtMde2mlYnHclW0j82AJkiyjAprHRXBOp2ZYky1YU8xER9Vd1y8oGU7A4ljgQK4Wy+cB7F4vRS4vdo8Xmz+LamS8kZh4s1J6Izo2/LR3UZXcblz7c/zC0W9xzMrCW1KVB0ITFoLWIGOIdaM1yGgjdIS2aIMuqS3aZm3RJFhRh4m6zALBqYIsS1SUFvsFo1802vIoKcqlzF6ILFdljA41GElokV7n+lSyXFNRHUGAwsIyJCn4EGVl7aR79+7HvC6n00lufh5FNhtqWYWkkrGYzcTHxjWZkBTUH6fTSUFeAfYiO5JKRi2rMFlMxMTFiPNVDzZt2kRKivVkd+M/TWxsBPn5dWcmFgiOl5MxviRZ5kB+WVCJjjxbBQAhWjVpzU208QvHtOZmwkNrzm56eDKcgJuqVOys2hZQIssUOt2+ZDheiVJkDDHhxCYaifZbHGPijRjCT3MXVVnGU1TkE4vZAeGYhfPAfpB8D5YqrRadxYjWABpVKVq9G50BNCYL2mbpaBPbok1siya2BSrNidsqxPVL0Jj8F8aXs7JcsS5WF46ltjw87irPO60uhAhzHJGWeCItvs8oSzwRllj0oeGo1Sqio421bkdYJpsQvV5PSvPk07qW5H8JvV5PUnJSo5X/EAgEAkHduDxe9u0vZme2L1nO7mwb5ZU+K2FkeAjWFAuDevrqO6YmRKLVBLusyrKMVOJUajUG3FS9tkrwvyiWgXKgyOX2uan6haPWFOoXjLGkJfhEY5T59HdRlZxOnDnZintq4FMqL1faaC0WQqIjiTwjBY26BI23EE2oC5XajiYmFW3imT6rY2I66ohY4bIqEJwkvB43pbZ8ihW31FyflbEwF6ejKoGVSqXGaIom0hJPQmq6TzT6BaQhwnRCv2EhJgUCgUAgEJwSlJQ7/VZHG7uyi8g4WILHn/UzMSacHu0SsKb44h3jzAblAUiWZeQKN65CB96CiiBXVTxVLlsOFdg9XgqdHmz+8hueUC3meCOxCSYS44109Luo6kJObxdVWZZx5+fhzMqqclPNzsR16BD4ndJUej2hySkYz2iPLtxncVRV7kflLgAKIMSANtGKNnEw2sR0tAltUIWEndwdEwj+Y0iSREWJjRKbTzAWF+ZSYsultCiPsuIifK/EfIQZo4g0x5Fi7aJYGSMtcRhNMWgawGOgJoSYFAgEAoFA0OTIssyhonJfllW/y+rBQp91TKtR07JZFBec1YI2KRbaJJuJ8Gc7lSo9eIscVP6VF+SmKjurkuG4VFAsSRRU+l1UPT4XVVO8kZjmkcTEG2npd1ENN57+YQveioojrI3O7Cwkh8PXQKVCFxdPaEoqET17og1XoVGXQlkWUv4e8HqgDNRRCWhbd/UJx8S2qKObo1KJ+o0CQWMjyzJOR3lwHGPguy0fyVtVrk4XEkqkJY7YpFaknXG2IhgjzHGE6Jv+ZY8QkwKBQCAQCBodj1ci42Cxv0SHz221tMIXtxMepsOabKZvl2SsKWZaNItCJ4PXVumr0bjpEMV+0SiVVyXD8aqgFCiodFeV3/BKhJlCffGMCbG09YvGKIsB9WnuoipLXty5eVRmZSjJcJxZmbjz85Q2aoMBfUoqUX36oU9JQRcVhkZVglS4F8+B7Ui56wCQNFo0cWnoO1/kj3dMRx1uOkl7JhD8N3C7nJTa8hUrY4nfylhSlIerskJpp1ZriDDHEmGOo1mrDkocY6QljtDwyFPKtVyISYFAIBAIBA1OucPN7hyf1XFnto29++24/S6ncWYDndvE+Up0NDcRp9UiBayMWwop+yE7OBmOCspVUOTy+qyN/pqNcqiWmAQjMWlRpMQb6RZvJDrOeNq7qAJ4y0qpzM4KKr/hzMlGdvqPi0pFSLNmhKW1xjRgEPqUVPSJceC24T24A8/BHXh3rsHtqsANqMIi0TZri/6M83yJcuJaodKe3omDBIJTEUnyUlZcWM26WPVZURpcw9wQYSbSEkeLdj2C3VKjolGrT4/rWK1i0uPxoNUKrSkQCAQCgaBuZFmmwO5QLI67sm3szytFBjRqFamJUQzsnkL72EhSw0IILff6MqhuL8a7LpfiaslwKjUq7B4veQ43Nn/NRocKLHHhxLTwuagGrI0GY8gp9Yb+eJC9XlwHDyhWRp/VMQtPUaHSRmOMQJ+ainngYPTJqehTUtA3bw6VJXgO7sBzcDueHeso/ykDZAlQoY5ORmftjbaZ3+oYlXDaHyuB4FRBlmUqy0v8ZTVyfXGM/kyppbZ8JKnK7T5EH0ZkdDwJKVafYIyOJ9IcT4Q5Fl3I6e9mX6ta7NOnD5deeilXXHEFVqtI798QOJ1O8g/lYrfZkNVqVJKEyWwmNiFelJo4BXE6neTl5WGz2ZVpZrOJuDhRykUgEPy38XgldmUVsX5LjpIsx1bqs5iF6bV0SjJzfkoMLUL1mL0g2yrx7HPAznIkoAJwalSUyDL5FS6fi6pHotgrEWEOU1xUk/2i0WQJQ605/WP3PMXFVVbGbJ+bqmt/DrLb77qr0aBvloShXXtCU1J91saUVLQmE0hevAUZeA5sx7N9CSU/7EAu8wtOXSja+DaE9rzcZ3VMtKLWh5+0/RQI/i24nZVVLqmHWRndrkqlnVqjJdIcS1R0AsltOgdZGfVhxn/1i5xaxeTo0aNZtmwZH3zwAe3bt2fEiBFcfPHFRESIwrPHQ2lpKRm791C+5kfK1v6Et6gIjcVCWd8+2Pr3o0XrNHFsTyFKS0vZu3cfu3fvZs+e3VRUVGAwGEhLa03r1q1p1aqlOF8CgeBfi1eSKCqppMDuoMBeQb7dQb69wv+/g6ISB7IMoSoV6ZEGLooxkZKkxyKBttSNbPOArQwow6FRUQoUOt3kO9x+0ehFE6rzuai2jCXNLxqj48IJ0Z/+XlGS243rwP5qCXGyqMzKwFtcrLTRmszoU1II7zikSjg2a4ZK66uNKVWW4j24E8+2FVQe3IHn0G7w+AS7OiLGZ3Fs5q/tGJOK6jRxiRMITjW8Xg9l9oIjBaMtD0dZcbWWKoxRFiIscbTqeDaRljii/FZGQ6QZtfr0f+F1PKhkWZbrarB582aWLl3KypUrcTgcDB48mBEjRtCrV6+m6uNJpbCwDEkKPkRZWTvp3r17vdfhdDrZ+c828l9+Bfe+jCPm61q2IHbinVjbt2sQi1dOTg6DBg2iZ8+ezJ8/P2jelClT+Pzzz/n000956623eOWVV2pdz5QpU2jTpg0333zzEfMuvfRS5s2bR2Rk5An391TD6XSyffsOVq/+gaKioiPmWywWzj13AG3bpp/w+br55pvp168fY8aMAWDfvn1ceOGF/O9//+Puu+8GoLCwkP79+7Nu3bpGEbDfffcd69at46GHHmrQ9W7atImUFOHVcDL5LxRlFhwfXknC5heL+X7BWFDsIN9W4ReLlUiB8hGASaWmVUQYKQY9iTodFtREeUBTWZVhUFKrKNeosLk95Ja7sLm9FHsk3GqwxIb7rY0Rvs94I+ER/wIXVVnGYys6ovyG88AB8Prc3FQ6Hfqk5oqV0SccU9BGRgWtR7IfwHNgh+K2KhXl+Gaq1GhiWyruqtrEtqgjok/G7jYp4volaEgCbqk+d9Rc3I4iDu3PoaQolzJ7IbJcVUJIH2asZlmM93+PI8IUi1b334szVqtVREcba51/1Nd/Xbt2pWvXrjz00EOsXr2aL774gv/973/ExMQwfPhwhg8fTvPmzRu00/828g/lUr7mxxqFJIB7XwblP/5IQbSFpJSUBtmmXq9n37597N+/n6SkJAAqKir4/fffAUhKSqpTSB6NpUuXNkg/T0Xy8vLYvXt3jUISoKioiD17dmOxmElOTj6hbfXr14/ffvtNEZM//PADAwYM4LvvvlPE5K+//kq3bt0azRI6aNAgBg0a1CjrFggEJwdJkikqrfSJRL81MWBZzLdXUFRcJRbBJxjjI0JJiwijS3QU8fHRmGQVBqeEtsKNyutv6wTJLeHQQIEsc6jSRYHTg93jpUKSiTSHERMfTkz7GFrFG4mJj8Ac/e9wUZVcTpw5OcHlN7Ky8JZVCR5tdDShKakYu/VQrI0hCYmoNMFWQ9njxL3/H1+inAPb8RzcgVzpW49Kb0SbmE5Iej+0zdLRxrdGpQtt0n0VCE5XvB43pbZ8iotyKSk85P/MpbjoEG5nlVuqVhdChDkOS3wyLdp1J9Ic749ljEMfJlzEj4V6+5JotVoGDx7M4MGDKS0tZdWqVXz44Ye88cYbbN26tTH7eNpjt9koW/tTnW3KfvwJW//+DSYmNRoNQ4YMYdmyZdx6660ArFq1ikGDBvHuu++yfv16Xn31VZYvX055eTlPPvkkv//+OxqNhsGDBzNp0iTAZ5m++uqrKSgooE2bNrzwwgsYDAbS09NZt24dUVFRPPfcc3z//fdERETQqVMn9uzZw7x58/jjjz94/vnncblc5Ofnc8455/DUU0+Rk5PDDTfcQP/+/fnzzz8pKSlh8uTJnHfeeQ2y7yeKzWZnz57ddbbZvXs3rVu3aRAxOXv2bCRJQq1W88MPPzBp0iTuvvtusrKySElJYd26dfTq1Yurr76ahQsXAnDgwAGuvPJKvv/+e7Zs2cJzzz2Hw+FAp9Nx11130a9fPxYvXsyqVauQJIkDBw4QHx/PlVdeyfz588nIyODGG2/kpptuYvHixXz99dfMmTOH6667ji5duvD7779z8OBBevXqxbRp01Cr1SxevJg333yT0NBQzj77bD744AP++eefE9p/gUBwfEiSjK002A01YF0ssDsoLHbgPcyrxhyhJy7KQNcEE0kpOmI1WiK9MvpyD+oyN2qXFxyAw4uMlwpk8j0SdpeHEq+k/DlkmXCjntgEI1EpEbTzWxpj4o3/ChdVWZbxFBQogjHw6Tp0EALWWr0efXIKET3PrLI2JqegMdb89l4qt/lFo084evP2geSz6qrNzdC16umzOjZri9rcTNR2FAjqQJZlKitKKSnKVSyNxYWHKCnMpay4gOpOl4YIE5GWeFp1OJNIS4LPLdWSQItWyRT469oKToxjvuofPHiQL7/8kpUrV7Jz50769OnTGP36VyGr1XhrsXIF8BYVITewr/Vll13G5MmTFTG5ZMkSHnjgAd59992gdq+88gpOp5MVK1bg9Xq56aabWL9+PQC5ubl88MEHhISEMHLkSFatWsVll12mLLto0SK2bt3K8uXLUalU3Hbbbcq8Dz74gDvvvJOzzjqL8vJyBg0axN9//43JZCI7O5s+ffrw8MMP8/XXX/PUU0+dMmISfFbcE5lfX1q2bElkZCQ7duygWbNm7Nu3jy5dutCvXz++//57brjhBtatW8fs2bOZN28eu3btok2bNixatIjhw4dTXl7OnXfeyeuvv07nzp3ZtWsXo0eP5tNPPwVg48aNLFu2jPj4eIYNG8aXX37J3Llz2blzJ1deeSU33HDDEX3Kyspi3rx5VFRUMGTIENavX09MTAwzZsxg8eLFJCQk8Oqrr+L1eo9YViAQNAySLGMvdfqtiUdaF2sSiyajnhhTGK2aRdHXGk+CTofJIxPqkNCWu1CXudGWe1GVewCfkKmUJAq9EiWeKrHo1KpRRYRgNIUSERWKMSqUuKhQjFF6Ik2hhEfo0WjU/wo3RK/DgTM7y/dXTThKDofSRhcXjz4lhche56BPaUFoSgq6uHhUtdyzZcmLtzAbz4Ht/hId25FK/HUgNSFo49PQd724Kstq2L8vXEQgaAgCsYzFhYeCRWNRblBNRo1WR6Q5DktCMi3b9yQyOl6JZdTpa7bq1/b7FRw79RKTRUVFrFy5ki+//JLNmzfTokULhg8fzuuvv05sbGxj9/G0RyVJaCwWvAUFtbbRWCyoJKnW+cdDx44d0Wg0/P3330RHR1NeXl5jZt5ffvmFqVOnotFo0Gg0Spzl559/zuDBgwkLCwOgTZs2R7h+rlmzhksvvVSJHbzqqquYN28eAM888ww//vgjb7zxBnv37sXpdFJRUYHJZEKn09G/f38A2rdvj91ub9B9P1EMBgPl5bW/sTIYDA22rYCra3R0NOeccw5qtZoBAwbw4YcfMnjwYFQqFW3btmXkyJEsWrSI+++/n88//5x58+axZcsWUlJS6Ny5M+A7R926dWP9+vWoVCrOOOMMEhMTAWjevDl9+vRBrVaTnJyM0+nEUe2BKcCAAQNQq9UYjUZSU1MpLi5m+/bt9O7dm4SEBMCXoGvWrFkNdgwEgv8akixTXOb0WRVt1ayLxQ5FPB4uFqMCYjHJxJntE7GE6jB7weDwoiv3oC5zoanwoD/gQUuZspxHlin1C8UyWcal1yCHh6Ax6zGYw4iICqVZVCjpUT7x+G+o0Xg4siThzstVym84szOpzMzEnZertFGHhaFPSSWqTz9ffGNyis/a6L8H1rpuZwWeQzv9sY478BzaCS7ftVVlMPlqO3a+CG2zdDSxLVFpdI26rwLB6Ualo8zniuoXioHPUltBUCxjmDGKSEs8Ldp1J8qS4BONloT/t3fncXLVVd74P3evfa/qvTudhYABEqADokMiOsOWhCXAqICMogKiyPAbooEgDGGJQoTICCozyDwyPKM4QKIMZHx0DD7CjEQeBXHCDEKWTnqr6q32ulX3fn9/3KpbdbuqO91J733er1e/aulb1beSbzp16pzvOXB6/ZTNn0GjBpPJZBI///nP8eKLL+I///M/YbPZcNFFF2HTpk1YtWrVNJ7i3Ofz+5E8588w/MLOUY9xrfkz+AP+Sf/ZF198MX7yk58gEAjgkksuqXmMKIqWJgjd3d2w2Wzm90o4jsPIfk0jZ5FWdrK65pprsHz5cpxzzjm48MIL8eabb5qPlyTJPHa2NWDw+31YsmQp3nrrzVGPWbp0Kfx+36T8vDVr1uBf/uVfoCiKuXfx7LPPxp133on/+I//wEc+8hEAwCc+8QlcccUVOPPMM7FsmVFi++6771b9+THGUCgUIEkSZNm6UXw8s2NLf/dA+e9cEATL370gzL83m4RMJp0xxJM5s/zULEMtNrzpH86goFk/QPQ6jWBxUYMXZyyvh1cRYed4OFQGOZUH4ir4VB7yERX2zhycIz5ZT2o6UhwwIPHQHRI4rwIpaIcj7ITXZ0OzzwabXZp1v3Mnm5ZKWbKM2WLmkeWMTqjgOMj1DbC1L4Zv7bnGzMbWNkih8FH/bBhj0ON9KHS/A63LyDpqsUMAmNEoJ9gKZfkaCKWsoycy7/+8CRkPXdOQHC5mGc19jMa+xlym/OF9acSGL9yEthNPL5em+usg28b+YIfMjFHfWX7oQx9CoVDAmWeeiQceeADnn38+zdY7RuH6OgyuXYP0738/ajdX55o1CNXVTfrPvuSSS3DllVfC5/PhBz/4Qc1jzj77bLzwwgs466yzUCgU8OUvfxmf//znx/X8a9euxa5du3DJJZeA53m88MILAIB4PI4//OEP+Pu//3t4vV785je/waFDh6BPcvZ1KkQiESxduhSHD3eO2s11yZKliEQik/LzzjrrLNx7771QVRV/+7d/C8AI6FasWIF/+qd/wm233QYAaGhowKpVq/DAAw+YnVdXrVqF999/H2+99RZOPfVUvPvuu9i7dy+++tWv4ne/+92knB9gzJ397ne/i97eXtTV1eHHP/7xpD03IXMRYwzDqZy1/HQwg+hwsQx1KIP8iGDR45QR8jnQWu/Gae0huCQRNp6HyAA+o4ENZcEl8xAPZmHXM/AIPDwCD6EiGMmDISvyUG0iVLcMMWCDLeKCs9GNVr8dPL9wAhemaVB7uq3jNzoPolBRBSS4XFBa2+A792Pl8RtNzeDH+X6GaXloffuNvY6lRjnpIeObsh1i/QmwnfVBY79j/TJwyuRVrRAyF+WyqWKW0Wh6Ey+Wp8YH+8Aq3gPanB54A3VoXX4avIE6eIL18Abq4PQGF+yIjblq1GDy+uuvx2WXXWZ2AiXHTlEULFq6BLjly0j96ldI/qo8Z9K15s/gXGPMmZyKYL2urg5LlhgzLH0+X81jvvSlL+H+++/HJZdcAk3TcNFFF+G8887Dv//7vx/1+Tdu3Ij9+/fj0ksvhcPhQHNzM+x2Ozwej7mGHA4H6urqcPrpp+PgwYPH3bRmqimKgsWL2wEA7733J/zpT+U5k0uXLsWSJcacycn6+7Lb7Vi0aBHy+bylY+vatWvx0EMP4ayzzjLv27hxI+69916zRDgQCOBb3/oW7r33XmSzWXAch23btqG9vX1Sg8n29nbcfvvt+OxnPwtZlnHSSSeZ5c+EzEeMMcRTakVWsXLOotHoJl+wBotuh4yw14aWoAunNPnhFEUo4MBrDFpOQzqeAxvOQexJwsWn4BV4eEQjYLRVvHlikoi8LIC5JOg+G8SwA856F+SwE5xdXJCZrkI8bpam5kqdVI8cBsvnjQMEAUpjIxwnnAjlzyvGb/gDE/rz0tPDRslqab9j758AzfgZvCcCqfVUiA3LITScCCHYQrMdyYKk6xqSw/1m0FguTe1BNl0used5AW5/GN5gPVqWrTQyjMWgUbbRBy/zxVHnTL711ltYvtw6T+9nP/sZQqEQTj/99Ck/wZk2GXMmS3K5HGK9vRgcGATjeXC6Dn/Aj1Bd3ZzN+v76179Gf3+/WUJ73333QVEUbNq0aYbP7Pjlcjn09fVhcHDIvM/v9yESiczI35eu69i6dSsaGxtx/fXXT+vP7uzsxK5du3DTTTeB53n87Gc/w9///d+PmaGkOZMzbz40SJkqjDEk0uqI8tPyZf9QBmqNYDHksSHoUOCRJTgEARIDuAJDPltAOp5DfDgLSWNmVtEj8PCKArySAAfHofLzdl0WwHsViEE75KAdgt8O0W8D71HAzYFRGlOxvlghj1xXV9X4jcLQoHmM4PWWs4wtbbC1tUFubAIvTWwvImM69IEjxj7HYtZRH+oyvsmLECKLi3Mdi11WnZO/FYWMjn5/zTw1mzEDxVKWcXigF4nBKHStPGNWsbuKXVLr4A3WwxushydQB5cvCH6WfuBC62v8jmvO5N13341nn30WTz31FD74wQ+a9z/33HP41a9+hWuuuQZbtmyZvLOd5xRFQVNr66SN/5gNli1bhieffBL/8A//AF3XceKJJ5qlmnOdoihoaWmZFZnUZDKJc889F6effjo2b9487T+/vr4efX192LBhAwRBgNvtxgMPPDDt50HIeJWCxZFdUGPD5YY3I4NFl01C2G1Hk8OO5V4XbLwAQWeAqkNNF5BM5JA9nIOGHBIAIPLwCjwCdgl+RYJbkmAPiBAqn1bgIPhtEHx247L05bOBnwdjNI4VYwyFoSEzy5jtPIjcwYPIdR0Bip2iOVGE3NQM56mnGkFjKdvo9R3bz8xnUej9kxk4at3/A5Yzsiic3QOxYTmUFR+F2HAihLrF4MS5+SEvIROh6zpS8QHrTMZiA5xMKm4ex/E83D4jy9i85BQzePQE62Czjx5okPlv1P/JfvjDH+LFF1/Eww8/bCmzA4Dvfve7ePHFF3HXXXfhpJNOwsaNG6f8RMnsVFdXh6eeemqmT2Pec7lc2Lt374z9fEmSsHXr1hn7+YSMxBhDMpO3lp4Wm9uUSlPVfMX4GgZ4bBLCLgUNioyljXbI4MBrgJbTkEvlkU7mwJJGsDgMIA7Ab5cQcSsI2iR4nAqcDJBVHYJqHY3Du2QIPhsEv714aXzxLnlBlqVW0tUccocPI3fokFGqWgwgtUQ5KyAGglBaW+E67XRzbqNc3wBuHE3DRv25if7iXMd3UOj6b2jR/UCxMyQfaIa09IMQG5dDbDgRvK9hwf89kfktn8uaDW8sDXAGei1ZRtnmhDdYh8bFK8wMozdYB7cvDJ6a75EaRv0t/aMf/QibN2/GhRdeWPU9juOwYcMGRKNRPPPMMwsymCwNmSeEVDtK9TwhR1UKFmMjy1CHM+Z92YqAjmeARxERcthQJ4tYFPJBZhxQ0JHPFJBN5aGldCCVh448EgAEkYfba4PfIyMUcMAnCXBxHGwFBjFTAJIqoDOAAcho4BQYgWLziKDRZwMn0v8HjDEU+mPl8RvFjKPa1QUUfydwsgyluQWuM1Ybmca2NthaWiG43Ed59qP8bF2DFj1QHM9hBI8sWWzEI8oQ65fB1nFZcb/jCeBtx/fzCJmNGNORig8W9zFaZzNmksPmcRzHw+ULwRuoQ2P7ScUso1GeanNQlpFMzKjB5IEDByylrbV85CMfwWOPPTbpJzXbSZKEdDoNl4v+wRFSi6qqEISFW8JHjo4xhnS2gOhQuiK7aM0wZlXj03KOARIAlyQiYFcQkUS0ejwQNIDlNaiZAvI5DUgDSOfBkEeGA3i3ArfXhkjIBY9bgc8mwMPzsOuAlNPAJXLQhrJgSQ1IZo0T4zkIXgVCyA5hqd8IGksB4wJtfjMS0zVoyRTisSMYfGsfcgcPGOM3Dh2Eni4PEpfCESitbfCc+UFjf2NrG+S6uklpWqPnUkaDnNJ+x553gYIx+oNzBSA2nAix8WIjeAwtAke/j8g8kleziA/0WWYylgJHrZA3j5MUu5FlXHSSOZPRE6yD2x+m/6PJpBl1Jdnt9jGHtgMwZ9ktNB5PEO+99x6WLFkCh8NBGUpCKui6jkOHOuF0emb6VMgMS2XzRsnpYEWQOFy+nskVAGb8RySDg1MQ4LPLCIkiGu1O8DKDntWgZoslWBqAbAEMBTCHBIfXBlfQAY/XBpfXBrdHgVsRi6WoGvThHLTBLLTBLPS+ISPDWMScEnifDcrSQLk8tdT8ZoGN19CSSWjJBLSE8VVIJqAl4uZt83uly1TKzDQCAG+zGUHj2R+uaIzTAsHhnJxzZAz6UHc569j939D7O41vcjyE8CIoKz5WbpTjDk3KzyVkJjHGkE4MVuxjLGcb04lyQyqO4+DyhuAJ1qGhbbklaLQ53PQBGJlyowaTp556Knbv3o0TTzxx1Af/67/+K5YtWzYlJzabld4kv//++8jn80c5mpCFx253IhQKz/RpkCmWzubN8tPKrqilDGM6W4AAQAIHmXFwCDy8ioSQIKBBsAOSjkJOAyt1zNYAqBpEicHltcEVtMHjs8HlKV56Fbg9NrjsEvh0HoXBjBksaoeT0IZiQF5HAUABAEQegs8Gsc4J4cSgmWEUfHbwyvzb+8MKBSPgSyZRSMShJZJGUJgsXRaDxUQ5cNTTo39ozEkSBLcHgtsNweWGrS1kXHe7Ibg9CLY3I+eLQAqFwU3ih6qsoELre7/YKKc42zFjNALhZAeEhuWQT/gzI3isWwpOpjFFZO4q5FXEB/oquqWWZjP2oZDPmcdJig2eQB3qW08oBoxG8xuPPwJBXHiJHTJ7jBpM/tVf/RU+//nPo76+Hh//+MerPtn43//7f+PJJ5/EI488MuUnORs5nR7KvJDjRq2pyWyWyeXNGYtmhnG4HDhmMgXI4CAxI7PoEHi4JAkBjkdEV8AgQtcq0oE6wGsMLo8At88Gt7f0pcDltcFTvK3YRYABeryUWcxAG8pC2z8MbSiLeMr6IR7vUSD4bZCa3BX7GO3gXdKc/VSeFfIojAwGK7ODpaCwImtYWWI6EqcoENxuiC4jGJQjEQguIygUXK5i0OiCWBE88kcZgRSapN9femrIknXU+t4Dig1BeG89pEWnl7OOgWZwHFUDkbmFMYZMcrg4YqPXEjSm4gMVR3JweQPwBOtQ17LUnMnoCdbD7vTM2d9nZH4bNZg8++yzceutt+Lee+/Ft7/9bZx88snweDwYHh7GH/7wB8TjcXzxi1/EX/zFX0zn+RJCCJkkmVyh5ozF2FAGscEM1Gy+GCxykMHBxvFwiQICHIdQQQLTRmT3dMChiEaA6FFGBIzGl8Mlgy+WkTLGwDIFI1AczEJ7fwjqYAaZwSy04ZzR/KaIswkQfHZIrd5yhtFvg+Cd/c1vdFWtLhUdGQxaMohJ6JnMqM/H22xmwCe43ZDr6s1soeB2Q6z4nnHpAi/PjjEXTNegDxw2x3MUut+BPtxrfFOQIESWQFm1ztjz2LAcvMM7sydMyARohfyILGNvMcvYi7yaNY8TJQXeYB0izUvgDX7YzDS6/RGIkjyDr4CQiePYUdou/td//Rd+/OMf47/+678wPDwMv9+PVatWYePGjRMucf3Upz6FgYEBiMVW31u3bkUqlcK2bduQy+Vw4YUX4tZbbwUA7Nu3D1u2bEEqlUJHRwfuueceiKKIrq4ubNq0Cf39/Whvb8f27dvhdDoRj8dx2223obOzE4FAADt27EA4HIaqqtiyZQvefvtt2Gw2bN++HUuWLBn3Off3J6Hr1JmSTA3KTJKp5PLY8M6fotXjMwZT6B/MopAtFANFQGYcbDwPOy9AYgBXqP69JytCVXDo9hpNbtw+G1xuBaJUXT7KCno5YBwqZhqL11muYsQGz1k6pFpmMtpnRxmXrubKewlrZAe1EeWlhUQcLJcb9fl4u712drAUDFYGhqWM4SzpVTCe319MzaDQ8z/WRjmqkUHl7F6IjScaXw3LIYQXg6NyPVI0W/9/ZIwhm4pbOqWWLpPDA6jcnO30BCpKUo0sozdYD7vLS1nGGTZb19dsxPMcgsHRm44eNZicLIwxrFmzBr/85S/NYDKbzeKCCy7A008/jYaGBtxwww249tprsXbtWqxfvx733XcfVq1ahTvuuAMnn3wyrrrqKtxwww24+OKLsW7dOjz22GNIp9PYtGkTtm7divr6elx//fXYuXMn9uzZgx07duDJJ5/EwYMHsXXrVuzduxcPPfQQnn322XGfNwWTZCrRLzNyPDRdx0A8i+hgGn2DaUQHjRLUvv4UhgYyyGfy5n5FGRwUzsguCrrRIbUSL3C1g8SKL8U2evc/xhj0pFrewzhULk/V46r1ZzklS5dUM2CcxuY3jDGwXK6i4Yw1a1hZVloZMDJVHfU5eYcTgtsICquzg9aA0Pi+a04HTyN/fzHGoCei0LoqSlZjB4uzHTkIwRYIxcBRbDgRvLeO3lCTUc30/49aIY/EYLTmbMZ8rlw5IEoyPIE6cx6jpxgwuv0RSLOkIoBUm+n1NZccLZgc9Z3Bz372M5x77rljdmtNpVJ45JFHcOeddx71RN5//30AwHXXXYehoSH85V/+JU444QS0tbWhpaUFALBhwwbs3r0bS5cuRTabxapVqwAAGzduxKOPPoorr7wSe/fuNceRbNy4Eddccw02bdqEPXv24JlnngEArF+/Hlu3bkU+n8eePXtwyy23AABWr16NgYEBdHV1obGx8ajnTAghMy2VyZuBYt9gGn0DKcQG0hjsTyOdzEHUOcjMaHKjgIMCHl4GeMEBKJdLOVwyPJVlpz4b3J5i0OizweGQxxXI6bmKstTKwHEoCxR08zhO4sH7bJDqXRBOqggcfTZw8uQ2v2GMQc9mR+wvLDedKYwMFEuB4RgN1ASXywwCpUAQtrZFluYzgquUQXSZt7kFNtCbaQUUet4tZx27/xssVdz/JdmM2Y6rL4fYuBxi/QnglMnp7krIZGGMIZtOWEZrlDKNyaGYZWayw+2HJxDB4hVnmgGjJ1AHp8dH+3jJgjZqMHnLLbfg17/+NYLBoHnfxz72MfzgBz9AU1MTACCTyeCZZ54ZVzAZj8dx9tln42tf+xry+TyuvfZafO5zn0M4XO74GIlE0Nvbi76+Psv94XAYvb29GBwchMvlMjObpfsBWB4jiiJcLhcGBgZqPldPT8+4g8mxInFCJkM4TMOzF7J8QUN0MI3uWBI9sRS6Ywl0dcfRH00hPpiBrurFzKJRiqqAhwQgAsCYvggIIg+P345g2Al/0AF/wAFvwA5fwAGf3w6P3w5BGP+bHabpUAezUGMpqLE01P401FgauVgaWrIiM8cBkt8OW8gB5YQg5KADcsgBOeSE6JaPKevEGIOWTiMfjyM/HEchHkd+eNi4HY+jMBwvXy8ekx8rMOQ4iG43JK8HkscDe1MDRO9ySB4PJI8XoscNyeuF5HFD8ngheT0QF2BgWAvTNBQS/SgMR5Ef7kNhOFq8blz+KXoIrNhtUvTVwbVkJeytK2Br/QCUunb6MyTHbbL+f9S0AoZifRjo68JgtBsDfV0Y6OvGYF83splyR2NRlOAPN6ChdTECHR9GINKIQKQB/nADZMU2KedCZg96/zU5Rg0ma1W/DgwMQNf1Gkcf3WmnnYbTTjvNvH3FFVfg0UcfxRlnnGH5mRzHQdd1y5uQ0v2ly0qjvVlhjIHn+arHlO4fLypzJVOJyizmP8YYEmm1nFnsT6G3N4nBgTSSwzmombzZ4EZmHCQAPDg4ATjBA+AhygJcXqUcHPqMURlun3Hd7qjdtbS0vgYGqsc/mM1vKvYvmpnG+MjmNyIEvw1iqwdKxUxGwauAqwhSSyM50jkVyKlgug49nS6PpLCUkVY3nykUL6FpVecLAOD5YiawWDoaDMPethhuM1tYXU4qOB3g+LGDGgZALX5BBTAwelfU+YIxHSw9DD3RDz0Zg56IQU/2Wy5ZarBYolpBtoN3BcG7Q/B2XIS8f7HRKMdlfPCcL34lFsCfIZlax/L/YzaTLM5k7LFkGRODMbCKtWx3eeEN1KH1xDMs+xmdXn/NLONwvLSyyXxB77/G75jLXCfbb3/7W+TzeZx99tkAjDcyTU1NiEaj5jHRaBSRSAT19fWW+2OxGCKRCAKBABKJBDRNgyAI5vGAkdWMxWKor69HoVBAKpWCz+dDXV0d+vr60NraankuQgiZLGpBKza2SaM7mkRvTwID/Wkkh7PIpvIQNGYGjBIADkamMQAAECHZRDg9CnwBO4IhJ7zFgNHtNYJGWTn2X9Usr0EbypXHa1QEjkytCNqEYvOboB3yUr85XkPw28DbxGJgmCrOKOyDun9E05mKYNAsM00mgNE+gBQES6mo3NAI+7ITynMNi19ixb5D3uGY1HmG85WxHzRpBIqJGFgyVhE0Fi+T/eb4DZMgmYGi1HKKeZ13h8C7QuDdQUupKr0ZIzNB1zUkh2IjxmwY+xpzFVlGXhDhCUTgDzej7cTT4Q3UG3MZA3WQFZpNSshkmbZgMpFI4NFHH8UPf/hD5PN5vPDCC7jnnnvw13/91zh48CCam5vx4osv4vLLL0dTUxMURcEbb7yBM844A7t27cKaNWsgSRI6Ojrw0ksvYcOGDdi5cyfWrFkDAFi7di127tyJG2+8ES+99BI6OjogSRLWrl2LXbt2oaOjA7/97W+hKArtlySETAhjDMPJHHoHUujuSaCnN4H+/jQSQ1nkUnnoqmY2uRFRzhC6ALg4HrJdhMNjZBbDYSf8AUcxs2jsWxSOY7SFnitAT6rQE6rRAKd4qSdUDCVUFIazluN5lwzBp0Ba7AanMEDMg7E0tHwCerIL+XgChSPx6gxiMgmM1q9NEIrzCY39g0pTS41mM9asIW93UPOVY8TUTDmLWCOjqCf7gbz17x0cD94VAOcKQahbCmnpByuCxBB4VxCcnebYkdkjm04heuR9DFfMZDSyjFHoevmDMJvTY2QZl59mBozeYB2cnuCEKtEIIcdm2oLJc889F2+++SYuvfRS6LqOq666Cqeddhq+/vWv4+abb0Yul8PatWtxwQUXAAC2b9+OO++8E8lkEitWrMC1114LALj77ruxefNmfOc730FDQwMefvhhAMYez82bN2PdunVwu93Yvn07AGMcyV133YV169ZBlmU8+OCD0/WSCSFzSE4toHcgjcNdw+jpSaA/lkJ8KItsUoWW0yDpgAwOfEWwqABQeA6S0wanW4HXb0c47EQ44oTHZ4fHZ4fTNb7GNrWwvGYJDsvBYg56Mg89kQPLj8j8cTC2UnJ58EIWBWEYem4IhWQU+XgPtPgQtFRq1MCQkyRLB1Kltc0aFFYEjaXmM7zNRkHIJGGFvBEUlkpPi5lEVpFZZLlk1eM4hw+8OwQh0AypbVV1RtHhO2q5LyEzRSvkMdDbieiR/Yh170f0yH6k4gPm93legDsQgTdYj5YTVpaDxkAdZJtjBs+cEDLqaJCTTjoJr776KgKBgHnf6aefjl27dpndV2OxGM455xzs27dves52BtCeSTKVqExs+ug6Q99ACocPG8FirBgsZpIqtGwBvGb0PuUwIigSOIh20QwWQ2EH6uvd8Aed8PiMcRnH1GimoBvBYUWgaASLOTN4tMxgLOIcEgSXDM7GQWdZ6Nk48vE+qNHDyB15H4XhPpTmnPGyDL4YFFZnB62dSMVieSmnKBQYThGma2CpwRFZxJhZjqon+8HSQ1WP42wuMyjk3WEji+gu3naFjIyjMP0jRuj3FzkWjDEkh/sR6zKCxljXfgz0dprZRqcngHBTO1qXLINoM+Y0urxB8PRhCJlE9Ptr/I55z2RpLmSlQqGACy+8cPLOjhBCJgljDENDGRw6PIzuHqMb6vBQFpmEikK2AK7AII0IFBkASeRgd8hwuGR4ix1RG+rdiNS54PHaIR3DGAum6dBTeTMoNALGnKUMlWUKVY/jbCJ4lwzepUBqcIN3yYAM6JkhqMM9UHsOIXf4EHJvHII2PGw+jnc4obS0wNVxKpSWVigtLVCaWtCwpIn+s5wmjDGjoY1lf2JF0JjoN8ZmjGxoI9nMMlMp3F4OEN3BcvmpRF0kydyVz2XR33MQ0YrgMZs2fi+JkoxgfRtOOvNjCDe2I9TYDofLC4De7BMyV4waTG7btm06z4MQQsbEdIZ4PIsjR+Lo7o4jGjNGZ6SLwSLyOoQRwaIOBibyUGwi7G4FHp8NoZATDQ1uNDV54PXawU9gZEbpPPR0vpxNTOSqsot6qrrrHycLRqDoliFGnBDcsnlbKAaN+WgPcp2HkDvciew7xmUhFis/h6JAaWqGa9XpUJpbzMBR9PkpmziFjIY2qWIzmxEZRfOyH9BG/L2bDW2CkFpOLje0KV5y7hA4mfaOkvmDMR3D/b1m0Bjt2o/hWJc5IcAbrEfTkpMRalyEcGM7fOFGyjgSMseNGkxedtll03kehJAFTivoSAxn0duXRHdXHLFYCkODGaQTORQyBSDPRhagogAGJnIQbCLsQRs8PjuCIaMMtbXZh0BgYm/US+MyLHsTEyNKUVN5y9gMAIDIm8Gh1OY1mty4ysEi71LAK8YbJlYoQO3pRu7wQeT+aASMuc5OqL095j5GThQhNzbBccKJUD7WWgwcWyCFwtTNdAqwfNayP7FWZrFWQxvOGQDvDkKoWwJp6VnljGKxqQ01tCHzXTaTRKzrAGJHjMAx1r0f+Zzxb0VW7Ag1taNt+SqEGhcj1NgGxeY8yjMSQuaaaWvAQwhZ2NRcAfGhLAYH0ujujiMWLQWLKvLpPFihOljMg0ETAEERYfNLcPtsCAWdqK93o7nZi7qQC/w4m9sY2SWtGBTmLI1tKoPFqkCR54oBoQyp0W1eF0pBolsGpwhVQQPTdeSjfcjsf9coTe3sNC67uspzFDkOcn0DlNZWeD70Z0Z5anML5Lp6cCL9ep4MTCs2tKlsYDMis8iyozS0cQUh+Jsgta60ZBR5VwickxrakIVF1zQMRg8jWhE8Jgb7ABgzv33hJrR/YLVZruoJRGrObCSEzC/0boUQctwYY8ik84gPZZEYyqCvL4VoXxJDA2mkEirUdB7QrEGaDmYMOOcZBEWAzafA5bUhUMwsNjd5URd0wiaP79eUntPMTGLtxjYqUKjufMoXM4hivROCy29mEkuZRs4+doMdxhgKAwPWgPHwYeSOHAbL5czjpHAYSnMrXKedAaW5FUpzM+TGJvCyPO4/Z2JlNLQZqrE/sXy9ZkMbxWXuSRQbllub2biD4J1BcOL0N7QhZDZJJ4YQPfK+kXHs2o/+nkPQCkYpt93pQaixHctWfgihxnYE61shybS3l5CFiIJJQshR6TpDKpFDfCiL+FAGg/1pRKMpDA6kkYrnoKYLVRk9DQwqGFSOgZME2LySESwGHKivd6Gx3oO6oBMelwL+KKWALK9VBYgjR2YwtbrzKe+UjCxi0A6pzWuUnrqVcvmpQ5rQ2I5CfLgYMHaaextzhzuhp9PmMaLPD6WlBf6P/rmxp7G5BXJTMwQ7DcmeCKPkOF5jf2Ixu5iMQU8epaFNqG1E6WlxryI1tCHEopBXy6M5insd04lBAAAviAjWt+KE09aYWUenh/ZpE0IM4womf/jDH+Iv/uIvEAwGp/p8CCEzoJDXkBjOFoNFI2CMRVMYGsggGc9BzeRL0yZMeTDki8GixnNQPBJcXgX+oBN1dS40RFyIBJwIee2QpdHLAVlBh5aq3ptoBovJHFi2xogMu9H5VPApkJrd4N1KMVgs7lV0SuAm2FynREunjOxiRcCY6+yEFq/ooOp0wtbSCu+HzzH2NDa3QG5ugeh2H9PPXEgYY2Bq2ig3tYzJsGYWqxvaiGZQKDStsO5PLDW1UZz0JpeQMTDGkByKWbqrDvR1gunGBzMubxCRliUIN7Yj3LgY/kgTBMrUE0JGMa5g8pvf/CbOOussCiYJmaOymTwSxUCxFDQODqQxOJBGcjiHfNY6poIVS1BLmcU8xyA5JLg8MnwBB+rqXKgLuRDxOxD2O+B2yDXfwDOdQU+pyEfTIwLE8l5Flq7R+VQRyvsUG1xmKWqp8ynvksGJx78XR8/lkDty2BIw5g4fQqG/v+JcFCgtrXCdfgZsLa2Qm5uhNLdC9PkoaBkFy+csWcTqzOI4GtosOdPcn1gKGjmHh/ZgETJBajaDWE9Fk5yuA8hljH3Coqwg1LAIJ591HkKNixBqbIfd6ZnhMyaEzCXjCiZPOukkvPbaa2hvb5/q8yGETBDTGdIp1cwqJoYzxvXBbDFYzKKQt5YC6mYJajFg5Bk4kYfLq8AXsCMcdqEu6ETY70DE70DQa4c4IsvHGDNmKSZUqN2pEZnF4t7FdHVGk5N4s9RUDjtGdD01GttwY2Qyj+nPqJBHrru7XKJa3N+Y7+std1CVJKOD6kkfKGYajbEbUjBEHVQrGA1tBkY0s6nMLEbH0dDmVDOjaI7JcPqpoQ0hx0nXdQz3dxulqsWs41CsB6VfxN5QA1qWnYpQYzvCje3whhrA0+83QshxGFcwGQwGcd999+G73/0uWlpaYLNZ95t8//vfn5KTI4QAmqYjGc8hPpQpNrgpl6MOD2aQjGehj2xuwwE56EagCEDlGQo8g91VDBaDTkQCTjOzGPE74LSXy5gsIzIGVeQ7k8iNnKlYa0SGwBWzhwqkVm9F19OK8lNl6rZqM11DvrevItNYLFPtruigyvOQGxpgW9QO75+tsXZQFRZ2MMN0DSw9VLU/0cwsJqJg6WGM/ITAbGjjCkGoX2bZn8i7jICRGtoQMvkyqbgxmqPbCB77uw8irxZHc9icCDe1Y9FJHQg1tiPU0AbZ5pjhMyaEzDfjeldns9lw6aWXTvGpELIw5VXNDBRLwaJRimrcl0rkSskzk84DOTBkmW5mFlUAkl2EN2BHOOBAa0WgGPY7EPDYIPB8eURGqdS0Pwf9QByJpHWv4sjuq+A5s9xUanRXZRN5lwzONnbn08nCGEOhP2bd19h5yOigmi+XzUqROigtLXCd3mEEjS2tkBsawUsLL7AxG9qYWcRSgBgDK2YU9dQgoI/YnyrZzOyhFDzdOiKjeJ2TqbkQIVNN0woY7DtsaZKTHIoBADiOR6CuGYtPPhOhxsUIN7bD7Q9TKT4hZMpxjI18m0oq9fcnoY/MvhAyQblsAYP9aQzF0hjsT2EwlsZgv7FfMZ1SrQdzgC5wUMGQ1jTkSvsWwaAJHLx+G8IBJ8I+hyWzGPbbYVck6KoG60iMnKXrqZZUgXyNERnOiuCwMpNY3KfIOaQZeWNSGB4uj92o7KCayZjHiIEAlKaWYpbRKE9VmprA2xZWkMMYA0v2oxDdDy26H3JuAOlojxk4Qhux1gTRCAxHdDzlSiWo1NCGjCEcdiMaTcz0acxLjDGkE4PGHsfiXsf+nkPQNWN/u93lRbhpsdldNVjfClGaX2OGaH2RqUTra/x4nkMw6Br1++OuNxseHsaPfvQj7N+/H7fddhv27t2LZcuWYcmSJZNyooTMdYWCjuGBDAZjKSNw7E8bQWMsXR0wSjxUniGlacjwutnoRgWDw6UgErAj7HfgBJ/dEjB67TKQyhezh8V9id0ZaP8zjGxSRTpRe0QG55AguGUIAWNEhjVgVIzOpxMYkTEVtFSqujy18xC0RPmXveByQ2lpKZantppdVAXX6L/k5ivGdOhD3dD69pvBoxbdD5aJF4/gUPAEwZxBCOF2SItXj8goUkMbQmaLvJrDQM8hRLvLwWMmaXSPFkQJgfpWnHjGRyyjOQghZDYYVzC5f/9+XH311XC73Thy5Ahuuukm/OxnP8Ptt9+OJ598EqeffvpUnychswLTGRLxrJlZLAWLA7EUEsNZy1YyJgA5DkhpGrI8Qw4MOY5BFzmEA0aAuLLRB5ciGgGjx46gJEDMadYZip0ZaPuGoSdUDI7ougoAnE00soceBVKTG9aupwp417GPyJgKejaLXNcRa3nq4cMoDJQ7qPI2G5TmFrg7zjQCxhYj2yh4vAsyS8a0PLT+TjNgLPTthxY7UO6IyosQgi2QFq+GEG6HGG6HEGpDpClCn7wSMsswxpAY7EPU7K66H4N9R8CKM1Pd/jAa2pYb+xwb243RHAKNBSeEzE7jKnO9/vrr0dTUhLvvvhunnXYafvKTn6CpqQlbtmzBoUOH8Mwzz0zHuc4IKnNdeBhjyKbz5WCxP42BaAqxviQSQ9ZmN4wz9i5moCNbDBZzHIPdJSMYLGYUK8pRw04FTlWH3p9BYSADSdWR6U9DS4wxIsMl12hko5T3KU7CiIypwAp55Lq6rJnGw53I9/VZO6g2NcNWChibjVJVMRhakEEjADA1Ay12EIXo+9D6ihnH/k5AL36QINkghBZBjCyGEG6HEGmHEGgGJ1TvA6UyHjKVaH2Nj5pNI9Z9oGKv4wGo2RQAQJJt5kgOI+u4CDYHzaoFaH2RqUXra/wmpcz1zTffxFe/+tURT8zj+uuvx8aNG4/vDAmZIXlVq8guphDtTaI/mkJ8KANNLe8pZDA6o+Y4ZgSMvLF30e23IRR0oGVEV9SQzwGZ46ANZFDozxiXB1PQ/l8MWkJFqQgRIg/dZwNnFyEvKnU+VaydT+XZ312U6RrU3l5zRmNp/Iba013uoCoIkOsbYGtfDO85H4HS0gJbcyukusiCHgehZ+JmwFiIvg8tuh/6YDdKKW7O7oEQbody2nozeOR99VSaSsgspesahqLdZoOcWNd+DPf3FL/LwRduQOvyVQgXg0dPsJ5GcxBC5rRx103kcrmq+/r7+yHL82vDN5lfNE1HfMgoS+3vS6KnO47+aBrJoSzyI0pG1WJmsRQwSg4Rbr8NwbAT7X5rwOh1KeA4DkzToQ1lofUXA8d3e5DqTyMxnCuXvPIcBL8NUoMLwskOCEE7xIAdvFdBJOKZM5+MMcZQiMWQLQaM6uFOZA93Qq3soMpxkCIRKM2tcK8+s9wMp6FhQY+GMBvj9L1vKVVlyZh5DO8OQQi3Qz7hHAiRdojhxeBcgQWboSVkLsgkhxHtOmAGj/3dB1HIG++XFLsL4aZ2tK84E+HGdgQb2iArC6spGCFk/htXMPnRj34UO3bswCOPPGLe19nZiQceeAAf+chHpurcCBkXxhhSCRUD0SR6uhPo7o5jIJpGajiLfKZg2cdYgLF3McsxFETA5pLhDdgRijhRF3KVO6P6HJClcsaM6Qx6PIdCfxravn4k+jPQ+jPQhrLlWYscIPhsEIMOCCcEjcugHYJXmVV7Fo+GMQZteMjMMFaO3bB2UA1CaWmBc8XJ5RLVpmbwijKDZz/zyo1x3jca4xQzjyxb+tCAA+9vhNh4IsRIO4RwMeNop9I2QmYzrZDHQN9hs0FOrGs/ksPGXm+O5xGoa8HSU882Slab2uHyLtxyfULIwjGuPZPxeByf//zn8cc//hGFQgE+nw/Dw8NYuXIlHn/8cQQCgek41xlBeyZnj2wmj1hfEp2dQ+jpTmAwlkZqOIdCJg9UTLrQKwJGThFgd8vwBOyI1LnQEHEbAWPAAa9TqfqPnjFmzF4slaj2p81S1cq5i7xHgRCwQwzajUxj0AHBb5vw/sWZrtnXkskaHVQ7oSUrOqi63cUGOOXuqUpzCwSnc8bOe7awNMYpBY+xg+XGOIIIIdhq7G0MtxulqqE2cJJtWs5vptcXmd/m8/pijCE1PIBo1/tm1nGg97A5msPh8ZulqqHGdgTqWubdaI6ZNp/XF5l5tL7Gb1L2THo8HvzoRz/Ca6+9hn379kGSJCxbtgxnn332pJ0oIQCQVwvo6orjUOcQersTGOpPIxXPQUsXwFUEjAwMKgCVZxBsIhweGd6gA5F6FxobPKgrzmGszC5WYoyBpfPIFzOMRsCYhtafAauYwcg7JQhBO6RT64zAMWCUqHJzYC9jJT2bRe7I4RH7Gg+hMDhoHsPb7VBaWuE+8yyzEY7S3ArR653BM589xtMYRwy3Q/nAR4uNcRZDCDTVbIxDCJld8moW/d2HzIxjtGs/siljh7sgSgjWt+GkjnPNRjkOt29mT5gQQmaJCfWabmxsRDKZBM/zaG1tnapzIvNcTi3gUOcQOjuH0debwFB/BplEDlqmAF5j4FDOFubBoIkcBKcAh0eBP+hAXb0bzS1e1IdcNbOLI+nZQjnDWBE4smx5HiNnEyEG7VBOCkEIOszAkbfNrXbsej4PtbvLyDJWBI75aJ95DCfLUJqa4TzlVChN5bEbYiBIJVlFVY1x+vZDH6LGOITMB4zpiA/0VXRX3Y+h6BGUCrU8gQga208qZh0Xwx9uBC/MrQ8QCSFkuozrnXIikcCtt96KV1991fxly3Eczj//fHzjG9+AssD3SBErxhjiqRwOd8dxuHMYfb1JDA+kkUmo0LMaRI2BrwgYNTDoEg/JKcHhVRAIOVDf4EFLixeNdZ5Rs4sj6aoGbaAUMBpZRm0gAz1VHrnByQKEgB3ykkCxRNUIHDm7OKcCKaZpUHt7zLLUUomq2tMN6MXMqiBAaWyEfeky+D7yUTPbKEUWdgfVSowxsETMKE+tKFVlyYqZl+4whEg75BPPMUtVOSc1xiFkrshlUhXdVY1mOWrO2P8tKXaEGhfhlGUXGk1yGhfBZh+9nIsQQojVuILJrVu34siRI3jqqaewatUq6LqO3/3ud9i6dSseeugh3HnnnVN9nmSWyRc0xIYy6OpLoOtIHH29ScQHMsgmVbCsBokBYkXAyADwEgfFJcPpVRAIO9HQ6EZrqx/1EdeEWqOzgm4du1EMHPWEWj5I5CEGbJBavWb3VCFoN+YyzqEggOk68v2xqvJUtavL0kFVrquH0tIC95kfhK3FyDbK9fULuoPqSEzXjMY40f2jN8YJNEFq+oC5x5Ea4xAyt+i6hsG+LsS63jdLVuMDRmUGx3HwhZuw6KQOc7ajN1hHFQWEEHIcxtWAp6OjA0888QROP/10y/179+7FzTffjP/8z/+cshOcaQu1AQ9jDIm0ir7BNHpiKXR1xdEfTSI+mEUuqQKqDoVxkDEiMJM4SA4JLp8xUqOh0YO2Nj+CQQf4CXY0tYzdqMg46jXGbpSyjKWmOLxHAcfP/qCxtAGcMYbC0JAlYMx1GmM39GzWPF4MhSqa4BTHbjQ2LfgOqiOxQh7aQCe04iiO2o1x2ioa47RPa2Oc6UINBshUmg3rK50YQqzrgBk49vccRCFvfLBoc7gRblps7nMMNrRCkufXv/H5bDasLzJ/0foav0lpwGOz2SCK1Ye63fSJ/VymFjT0D2XQN5hG70AKPb1J9PclkRzKIpfMQ9QZFMZBAWfuY5QByCIHxWOH229DKOxEQ5MHTU1e+AIOSMfQmKY8dqPYPbUYOGqDI8ZueEeM3QjYIfjm1tgNPZ9HPhZFvq8Xam8vhvp7Mfzue8gd7oSWTJrHCV4vlOYWeNeeW9FFtRmCgzqojsTUDLTogXKp6miNcVYUG+OEF0MINIMT5tZ+WEIWOq2QR3/PIbNkNdq1H+m40USM5wUE6luwbOWHi8HjYji9VI5OCCFTbVzvpr7whS/grrvuwvbt27F06VIAQG9vLx544AHcdNNNU3qC5NiVsou9A2lEB9PoG0yjL5ZEfzSF5FAWhXQBCjgzYBSKAaMLgIsXYHNLcPuNGYyNjV6E61zwBx2wOY6tdJIxBj2plpvgjDp2Q4YQcEBe5CuO3bBD8NsnPHZjJjDGoMWHofb2It/XB7WvF/nil9rXh8LgAFBRDCA4nZCbmuE+6+zinsZWKM3NED3UQbUWPT1sBoylUtWqxjiRxbC1rSqXqVJjHELmHMYYksMxS5Ocwd7D0HWjcZrLG0SkaTFCq42sY6CuBQKV9RNCyLQbV5nreeedh66uLmiaBo/HA0mSMDAwAF3XqzKWb7/99pSd7EyY7WWuanHvohksDqbR15/CYCyF5HAOQqGYXSwGjFJlWSoHKE4JXr8d4ToX6hrc8Iec8AcdcLmPvUy0NHajXJpqBI41x24EKuY0ljqozvKxG3ouh3y0D2pfXzFILAWMfVCjfWC5nOV4MRCAFKmDHIkYl2HjUorUoWFZC2Kx5Cg/aeEqN8Z5v6Kraq3GOIstparUGMeKynjIVJrM9aXmMujvPljRKGc/smnjd6MoKQg2tJkzHcONi2B30Qdu8x39/iJTidbX+E1KmesXvvCFSTshMjFGZ1Rj72IpYIwOpdE3kMJgfxrZhAoFvBks2or7GP0A/DCCMtkuwhuwI1znRijihC/ogD/ogNdvh3Cc2T5j7Ea5CU4pgGTZgnkMZxMhVI7dKDXDmaVjN5iuozA4WAwYeysCRuO2NjRkOZ632YzgsL4ezlNXWgPGcBi8PPogawp8RjTGKe5xNBrjFINsjgfvbzQa45jB4yLwNiqzJ2QuYkzHcKzHLFWNde3HULRcYeAN1qNpySlm8OgLN4CnDtSEEDIrjevd/GWXXTbV57Gg1couRgfTiA6k0D+YAZfXYavILjo4AXYdcAAwdjECosTDF3IgFHbBHzKCRX/IAV/QAVk5/qBtXGM3JB5C0AF5ib/YEMfIOM7GsRtaJlPOJlaUoeb7epGPRcudUgGA4yAFQ5AiEbhWnQ65mFU0Mo0RCG7PrHt9s1XNxjjRA0ChmM0tNsaRlnzQaIoTXlxsjEMNhgiZq7LppCXjGOs+gHzOaIYl2xwIN7ajbflpCDUuRqixDYqN9oYTQshcMTtTQ/PM6NlF4/pwPGtmFRVwsHM8nLyAkMYQ0st/RTzPwRd0IBBywGcGjEZZqt0pTUpAwwo6tMFSaWpp/EYaetw6dkPw2yC1eMw5jbNt7AbTNOQH+kcEjOXAUUtYSxt4hwNypA5KSyvcHauLWcWIETCGQjRi4xhYGuOUMo4DnUBxzxNkO8TQIignf8wIGsPt1BiHkDlO1zQMRg9b9jomBqMAAI7j4Y80of0DZyLc2I5wUzvc/sis+X+DEELIxNG7tklSc+9iRfCYVzXIFQGjRxTh4Hm0FzgwzZp18fhsZnbRCBqd8IcccHtt4Cdp3IUxdiNn7Z7an4E2nK0eu1Hvgrhi9o3d0JJJM0isKkmNxQBNKx8sCJBCIciROtjO/KCRXQwbmUU5UgfBRUOqj4elMU4xcNSHelBujOOFEGmHbdFpZqkq76X5boTMdan4oKW76kDPIWgFo7LD7vQg3LQYy1b+mdEkp74VkkxVBoQQMp9MezD5jW98A4ODg/j617+O1157Ddu2bUMul8OFF16IW2+9FQCwb98+bNmyBalUCh0dHbjnnnsgiiK6urqwadMm9Pf3o729Hdu3b4fT6UQ8Hsdtt92Gzs5OBAIB7NixA+FwGKqqYsuWLXj77bdhs9mwfft2LFmy5JjOu1Z2sfL6YMIIwiQANsbBIQjwyRLCHI96pkCrDGwA2CXJLEUtBYu+oAO+gB2iNHl7QyxjNwYqSlRrjN0QgnbIJwSKexodMz52gxXyyMdi5fLTaF+xS6pRkqqnU5bjBbcbUqQO9sVL4fngh4rlqMW9i4EgOIH23Bwvxhj0RNQIHM3GOO+DJQfMY3hPBEK4HfKJayEWA0fO6afsAyFzhK5pyGWSyGaSyKXLl7lMCtl0EtlMArl0ConBHiSHi6M5BBHB+laccNoac6+j00P/7gkhZL6b1mDyP/7jP/DCCy/gIx/5CLLZLO644w48/fTTaGhowA033IBXXnkFa9euxaZNm3Dfffdh1apVuOOOO/Dss8/iqquuwj333IOrrroK69atw2OPPYbHH38cmzZtwo4dO9DR0YEnnngCO3fuxP33348dO3bg6aefht1ux8svv4y9e/fi9ttvx7PPPjuhc37yp3/Au4cG0DeYhpo3AkKBAQo4BBQZPlnCEkjgbSIK6QJYKUDTAYnj4A/ajYY3I/Yx2uyTWzZZNXZjoDSzMQsUKjqoumUjaCyN3QgYHVRnYuwGYwxaIl5732JfH/L9McsYDU4Ui9nEOniXnVDct1gMGMMRCA7HtL+G+cxsjNNnBIyjN8Y5GUKkvTyKw0ZZXkJmC8Z0qNmMERymk8XLFHKZRPF2yhIwZjNJ5HOZUZ9PVuxQHC4odheaF58Id7AF4aZ2+CPNEKhEnRBCFpxx/eZnjOEnP/kJzjrrLNTX1+PJJ5/ECy+8gJUrV2LLli1wjONN/NDQEB555BHceOONeOedd/DWW2+hra0NLS0tAIANGzZg9+7dWLp0KbLZLFatWgUA2LhxIx599FFceeWV2Lt3Lx577DHz/muuuQabNm3Cnj178MwzzwAA1q9fj61btyKfz2PPnj245ZZbAACrV6/GwMAAurq60NjYOO4/oERvCuECjxaXG1xeRz5dQEEtZhnTAJ8twOO3w1dXDhZL2Uane/L3EDLGwDKFcvfUUnnqQAZMLWc/S2M3bCeHZ3Tshq6qyEejlhEaarTPCBb7eqFns5bjBZ8PcqQO9hNPhNdsdGOUpIp+PzieyiKnAivkofUfss5wtDTGkSAEWyEt/SDEcLtRqhqkxjiETCfGGAp5FblMshwclgLBTBK5dArZdMLIIFZkExnTaz6fIEpQHC7Y7C4oDhfcvhAUuws2h6t8v90FxeE0r/MVFR7UWp8QQsi4gslvf/vb+P73v4+nnnoKhw8fxje/+U18/OMfx29+8xs89NBDuPvuu4/6HHfddRduvfVWdHd3AwD6+voQDofN70ciEfT29lbdHw6H0dvbi8HBQbhcLnOuZen+kc8liiJcLhcGBgZqPldPT8+Egkl3XMNQfxaK345gnQehOhdCEZd56Q85IExRKaiWySPXl0KuN2m51NLlTqOCXYRS54Kyqt64jDihRFwQHNPTMIYxBnVgANnuHmS7u5Ht6UGmuwfZnh5ku3ugxmKW43lFga2hHs7GetjPOA22hnrYGhpgq6+Dra4egt02Lec9m4TD0zviQs+lke1+D7nuPyHXVbzsO2g2xuEVB5SGJVBWXwSlYQlsjUshh1upMc4cNd3ri4yfphWQSSWQSSWLl8WvZMJ6O5VAOplANpVAoZCv+Vwcz8PudBe/XPD4m2F3uuEw76v4chmXk7F/kdYXmUq0vshUovU1Ocb17vCFF17AQw89hFWrVuHee+/FqlWrcPfdd+N3v/sdbr755qMGkz/+8Y/R0NCAs88+G88//zwAQNd1S9aOMQaO40a9v3RZabSsH2MMPM9XPaZ0/0Rces0qON1yzfEaDAwDA6kaj5oYpmpmE5xCsTxV6681dsMOsd0HW6A0dsMOzlHu4loofqVSWSCVrf3DjoGezRaziTVGaUT7wNSKTq8cB9EfgByJwP6Bk+GpKEWVIxEIXl/V35sOIA0gncwDydpvlOarqf5k32iM836xVHV/sTFOt/l9ozHOYthOv8QsVa1sjKMWvzAwetkbmb0oczR9RpaTlkpKy+WlpaxhMYM4gXJSm8MDd6DRyBjaXWb20LztcEJW7ONuaJUrALlh81/3MaP1RaYSrS8ylWh9jR/PcwgGR9/CNK5gMhqN4uSTTwYA/PrXv8all14KwMj0JZPJoz7+pZdeQjQaxSWXXILh4WGk02kcOXIEQkW5TDQaRSQSQX19PaLRqHl/LBZDJBJBIBBAIpGApmkQBME8HjCymrFYDPX19SgUCkilUvD5fKirq0NfXx9aW1stzzURwYgTus6OfuA41B67kYEez5UPEjgIAfu0jt1guobC4KARJPaWg8VSSao2PGw5nrfbIUUiUBob4Vp1WsXMxTpIoTB4icZoTDezMU7ffiN4LJaq1myMc9JHjFJVaoxDSE1mOamlhHSUhjSZJHLF0lLGav9fIYgSbA43FLuzWE4aHr2c1OGGYnNaykkJIYSQ2WpcwWRLSwvefvttDAwM4ODBg1izZg0A4Je//KW553EsTz31lHn9+eefx+uvv4577rkH5513Hg4ePIjm5ma8+OKLuPzyy9HU1ARFUfDGG2/gjDPOwK5du7BmzRpIkoSOjg689NJL2LBhA3bu3Gmex9q1a7Fz507ceOONeOmll9DR0QFJkrB27Vrs2rULHR0d+O1vfwtFUSZU4nqsmKZDG84VA8a0uadRGxoxdsNng1jnhPiBkBk4TtXYDS2dKmYV+yz7F/N9fcjHomCFQvlgjoMUCkGK1MF9eoc1WAxHILjdFIDMoFJjnNIIjlJXVZaraIwTaILUfLLZFIca45CFTNMKZqMZM3NoBoQVQWKpIU0maY63GInj+GIA6IRid8EbrIetZakRHBb3Ho7MIIqSPM2vmBBCCJke4womP/e5z+HWW28Fz/NYvXo1VqxYgccffxyPPfYYHnjggWP6wYqi4Otf/zpuvvlm5HI5rF27FhdccAEAYPv27bjzzjuRTCaxYsUKXHvttQCAu+++G5s3b8Z3vvMdNDQ04OGHHwYA3HLLLdi8eTPWrVsHt9uN7du3AwA+9alP4a677sK6desgyzIefPDBYzrX0TDGoA/nrN1TR47dAMD7FIhBB+RlpbEbdgg+26SO3WCahnx/bEQZajFY7OuDlrSm8nmnE3KkDkrbIrhXn1nRGbUOUjAITqT9cbNBuTFORalq7KC1MU6oDdKys4uNcdqpMQ6Z10rlpGajmZHlpCMb0mQSyOdGL/svlZPaHG44PX4E6lrMYHDk5UTLSQkhhJD5jmOj1eWM8M477+Dw4cM455xzoCgKXnvtNUiShNWrV0/1Oc6o/v4kNE0vj90o7m0ca+yGGHRACBh7Gidr7AZjDFoyiXy0IrPYWzFKoz8G6BUd+wQBUihcsV+xDlJduTOq4HQe9zmR41dZs89yaRRiB6D1lcdwaAOHzcY4kB1meaoQaYcYbgfvb6LGOGRUs31PiFFOmjP3EFYGhNUZw9LX+MtJbZZAsFxOWiovpXLS4zPb1xeZ22h9kalE62v8jrZnctzB5EK1/+nfIXdg2DJ2g3NI5l7GUuAoBOzgleN7U8IKeeSjsarMYum2nrE2axA8HksJqmyO0ohADATA8fQmaTZhhTxYLgWWS4Ll0tAzcdizvRg+sA9a337owz3msZzDByHcDjHSDiG8uNgYJ0IZETIh0/2fZbmctDzDsFZAWNmQRtcKNZ9rZDmpzeGG4nBSOeksQm/GyFSi9UWmEq2v8ZuUBjzvvfce7r//fvzud79DPl+9j+Ttt98+9jOc7TgOyvKg2T1VCNjB24+twQxjDFp8GGpvbzHDaC1JLQwMABWxPSdJkMIRSJEIHMtPrAgYjc6ovM0+Wa+SjAPTCmBquhgQjvKVTUEvXVfTYNlk8XtpQKvunJhCRWOcD5xrznDknf7pf4GEVGBMRy6bLjeaSSeLg+5TtRvSZJLjKCd1w+ZwWctJR2YMi5eSYqe92YQQQsgsN65gcuvWrejq6sJf//Vfw+PxTPU5zSqeC5dOqJurrubKjW6i1mY3al8fWC5nOV70+SFFInCetMLYr1jKNNbVQfT6wE1wlAkZHWM6mJopBn3lIK+cLay8bXzpFdeRP8q4FV4ApzjByQ7j0uYC7woY1xUnOMUFXnFYbkeWLcdAkooDyNSylpMmigGgtSFN5eXRyklFSS5mBIvdSf3h6nLSiksqJyWEEELmp3EFk3/4wx/w1FNPYeXKlVN9PrMe03UUhgYtnVErS1ILQ4OW4zlFMctQnSefWgwYi3sYw2HwkzA0eqFgjAH5bM1Ab2R2kKmpcnBYChzVDMrtdGvhwFmCPScEX4PltvHlKAaGFffZnICoTDiTIthdQJLKLMj46boONZtGLpMsXhYzhZk0ctlSN1Lj+1o+g1Qijmw6Ob5yUocbvlAjlZMSQgghZFzGFUz6fD7I8sJ88zD0y39Hev/75c6o0T6wylJfjoMYDEIOR+Bcucra8CZSB8HjoVKtIsYYoKnWAK8Y8Ok1s4PJqkwhmD72D5Ht4GSnkQG0ucC7Q+BCbUYAKBvZQm5EdpBTHOAVp/FY2pNIpgljDPlcthgAloPCUqCYy6SMMtPidbUYIKpjDLrnOA6yzWlkDO1OuH1BeIJN5n7DkQ1obHYqJyWEEELIsRtXMHnjjTfigQcewNatW9HW1gZ+AZVexnY+h3w8DqmuHkpTM1ynnV7R6KYOYigEXjq2PZRzEdPyIwK86oBPt2QGrdlCjJIdMYmyGeBxihOcwwvB31gjO1jry0FNh8iMKOTVcnYwk0QuWw7+cpXZw2waakUmkY3x4Yik2KHYHGY5qdsfhmIrlpbay/crFcGjpNgsH4hQgwFCCCGETKVxBZNPPfUUOjs7cdFFF4HjuKpgcj434Fm87UGwefTJPdO1chOZ7BiNZIoBoj4iW2jONxwNL1oDPJsTvCdiZAWLGUAjO+isyBC6ynsNxYUTmJPZR9c05LIpa+loJoVcNlkMDlNVAaKaTY864B4w9hdWZgt9oUbzeuX9lV+yzQGePhghhBBCyCw3rmDy+uuvn+rzmLV4h3NCDXimGmM6oGaKZaFjN4+plS2EOnqJHACA4809gaWAj3f6a2YC+Rr3QZTnTeBN5q7SYPuawV+NvYWlvYd5dfQmSxzPW7KBbl8IoYY2a1BYkUksfQn0AQkhhBBC5qlxBZOXXXbZVJ/HgsEYAwo5S2ZQz1WUhR4tW6imj7pv0OwmWgr6vHUQwtZsoaU0VHaCL2YLIdkoGCSzBmMMBTVnZgdzFaWjqqXxjDWTqGbTGL3ZEgfZ5jDLRe0ubzFbaASC8sigsBgoivLEGywRQgghhMxn4womAWDv3r343ve+h/fffx9PP/00nn/+ebS0tODSSy+dwtObnVhBrSoHLWUH9XFkC6FrY/8AyWbpGMq7AuCCLda9hKN9yXbaN0hmJa2QL2YDU6OWi468X82koI/x70WSbWZZqGJ3weUNGUFhcW+hXFk+aiuVkFKjJUIIIYSQyTCuYPKVV17Bl7/8ZVx88cV4/fXXoes6OI7Dli1boGkaLr/88qk+zxmT+tm3ke8/bAkeaw2ftxBka9Bnc4P31leNkjCCv5G3HeCEccf4hEw7XdfM7N/IvYVqtiJYrLiuZlMo5Ef/dyOIkqVc1BusLwd/FcFhZeMZ2eaAQP9WCCGEEEImVbaQQzwXRzyXgKrnsDZ45qjHjuud2Le//W185StfwdVXX40XX3wRAPClL30JHo8H3//+9+d1MMlySXCyHbw7aGkWM3L4vCV4FBfmGBUytzCmG6MpqoK/sRvP5MccTcFbykWdHj8CdS1V+wnlEXsLaW4hIYQQQsjU0XQNcTWBeC6B4WwccTWB4WLAGM/FMZxLmNdzFYmzsDOItScdZzD5pz/9CWvWrKm6/9xzz8X27duP4eXMHa4Nm2dVAx5CRmKMFUdTpEZkB2s1nrFmCxkbfW3Lir0c8Dmc8ATritnCyqDQWkZqjKagfYWEEEIIIVONMYZ0Po3hXK3A0Lg9nEsgrsaRVFM1n8MpOeBR3PAoHrT7WuFRPPAobngVNzyyB2Gnf8xzGFcw6ff70dnZiZaWFsv9b7/9NkKh0DhfLiHkaDStYO4VrFUuaswvrL5fH2N+pygplpET/khzMSB0FstHqzOGxmgK2ldICCGEEDLdVE01A0EjSCwFhqWAsXxdY9W9JSReKgaEHkScISwLLC4GjMZ9pUu37IZ0lC1DPD92kmBcweRf/uVf4p577sEdd9wBADh06BB+85vf4OGHH8YnP/nJ8TwFIQsaYwzZdAKJwSgSg32ID0aRGIpCz2eQjMfN4LCgjj7HkxdES7moxx+B3FBZLlrZeKZ4v81BoykIIYQQQmaYznQk1KRRYloZGKrWEtPhXALZQvWoMg4c3LLLzCI2uOqN7GHxtrfi0iZOX6XYuILJG264AYlEAjfffDNUVcVnP/tZiKKIz3zmM7jpppum+hwJmRMYY8im4kagONiHxGDUcr1yhiHH8XB6/HD7AnC4fPCFGytGUZT2HFobz4gSjaYghBBCCJktGGPIFrIYrigtrSoxLV5PqEmwGmPLbKLNDASbPU1YYWYPPZZMoktyQpiFExs4NtamqRHS6TTee+89SJKERYsWwWazTeW5zQr9/UnaM0lMjDFkUnEkBvpGBI19SAxFLZlFjuPh8gXh8Ufg9ofh9oeL1yNwegMQBBHhsBvRaGIGXxGZz2h9kalE64tMJVpfZCodbX3ltQISxQY1w2OUmMZzCeT1fNXjBU6wBIIjy0vLmUQ3ZGF2NyHkeQ7BoGvU748rM3nHHXfgM5/5DJYtW4ZTTjnFvH9gYABXXnklfvGLXxz/mRIySzCmI5OMGwFiqSR1MIrEQDFgrBhxwfE83L4Q3P4I6lqWwROImEGj0xMAL8y+T5AIIYQQQhYaxhhS+RSGs3EcUQs41Ndr7WKqlrOJ6Xy65nO4ZCc8shEIRvwhS3lpKVD0Km44JMeCqSYbVzD5/PPPY/fu3XjooYfwsY99zLxf13V0dXVN2ckRMlUY05FODFuDxcFytlErlD9l4nkBLl8IHn8E9W3LjWCxGDQ6PQHws7DkgBBCCCFkIdCZjkSunEUcLo6+KN0eyg6P2axGFmQza9jgqsPy4LJyBlG2ZhVnY5npTBv3xO9Pf/rTuOWWW/ClL30JN95441SeEyGTgjEdqfiQtRS1VJo6FLMGjIJYzDCG0bDoRHj8Ybj9lQEjdTYlhBBCCJkumq5VBIhxS4BYeT2eS9Tci+iUHMVMoQf1rgg8igc+xQOvzYPWSD1YVjSb1ZBjN+5g8uqrr8YZZ5yBW2+9Fe+++y4eeOCBBZO+JbOXrutIJwYrAsUo4sVy1MRg1DIygxdEswS1cfGKij2MYTjcfgoYCSGEEEKmmKqp1cGhmU0cNu+rNRex1NHUazOCxBZPU7HU1AgSS8GiR/aMOfKC9uROnnEHkwDw4Q9/GP/8z/+Mm266CVdffTW+8Y1vTNV5EWLSdR2p+EB1dnEwisRQzBIwCqJkBolNi1cY5ag+oyzV4faB4yhgJIQQQgiZbNlC1gwQh6qCRWNW4nA2jnQhU/VYnuPNctKgPYAlvkXwFANGb0Ww6JGp1HS2mVAwCQBLlizBj370I9x88834q7/6q6k4J7IA6bqGVHwA8YFoVVlqcigGXS/XuBsBYwTeYD2al546IsPopYCREEIIIWQSMMaQzqdrlpuWAsZ48f6cplY9XuTFcqmp09iPWBkclq67ZCd4ev82J40rmFy9ejUkqTz43Ofz4amnnsLf/u3f4l/+5V+m7OTI/KJrGpLx/nIp6mAUiSHjenI4Bqbr5rGipMDtD8MXbkTLCSvNYNHjj8Du8lKJNSGEEELIMdKZjqSaqlFuWn27oBeqHq+YTWs8aPW2wFvqZDoim7iQupouVBOaM7kQ0ZzJidE1DcnhfnOshiXDONxvDRhlxSxBdfvDcPsi8ASMxjd2p2dB/PKhmn0ylWh9kalE64tMJVpfx0bTNcTVxKjNaiqb1uhMr3q8Q7Qb5aSKNSj02azlpnO9aQ2tr/E75jmT1113Hb71rW/B7XbjuuuuG/UJOI7Dk08+eXxnSeYUTSsgORQbsYfRuJ4aHgCr+OUkyTa4/WEE6lqx6MQzzA6pHn8YtgUSMBJCCCGEHI+8VkBcjWMoaw0QSyWmpfEXSTVVs7OpS3bCp3jhUTxodDUUM4juqpJTWZBn4NWRuWzUYLKurs58ox+JROhN/wKjFfJGhtEsR+0z9zOm4gOoTGhLig0efwShhkVo/8Bqy1gNm8NNa4cQQgghpIZcIWfJGA5lrc1qSven8umqx3LgzKY1fpsPi3ytNfcjehQ3RH7CbVIIGZdxlbl2dXWhvr6+anSCpmnYt28fTj755Ck7wZk2n8tctUIeiaEYEoPFQHGoz9zPmIoPAhWfbMmKHe5iOarHF4E7ECkGjWEodhcFjMeIyizIVKL1RaYSrS8yleby+mKMIVPIjrEfsTz+IlvIVT1e4ISa+w/L2UQvvDYP3LKLmtYco7m8vqbbMZe5VvrYxz6GV199FYFAwHJ/d3c3rr76arz55pvHd5ZkyhTyKpJDsapy1MRgtDpgtDnh8YcRaVlq7GX0h80AUrE5KWAkhBBCyILFGEMqnxpz/EUpYMzr+arHS7xkBojN7kasCJ9oDRaLAaNTovdcZO4YNZh87rnnsGvXLgDGP54vfvGLlo6uANDb24twODy1Z0iOqpBXqwLFxGAf4oNRpBODlmMVuxNufwR1LUuNpjf+iNkpVbE7Z+gVEEIIIYTMDJ3pSOQSFQFhoiJgHK7Yn5iAxrSqx9tEmxkMtnvb4K0rNq0ZkV20iTYKEsm8M2ow+ed//uf4/e9/D8YYXn/9dTQ1NcFmK3du4jgOH/jAB7Bx48ZpOdGFLq/mkCyO0UgMRREfjCJRvJ5ODFmOtTlccPsjqG87wQwUjT2MISg2ChgJIYQQMv9pujZ6R9OK2/FcombTGqfkMLOF9a5Izf2IXsUDRVRm4NURMjuMGkx6vV7ce++9AID6+np89rOfhd1uP64f9q1vfQv/9m//Bo7jcMUVV+Azn/kMXnvtNWzbtg25XA4XXnghbr31VgDAvn37sGXLFqRSKXR0dOCee+6BKIro6urCpk2b0N/fj/b2dmzfvh1OpxPxeBy33XYbOjs7EQgEsGPHDoTDYaiqii1btuDtt9+GzWbD9u3bsWTJkuN6HVMlr2ZrZBiN65nksOVYm8MNdyCChrYTjWCxtIfRF4FsO76/J0IIIYSQ2UrV1BoBYgLD2WFLwJhUU1WP5cDBLbvMgLDF0wRPjdEXHtkDSaCmNYQczaj/Sv7f//t/WLlyJQRBwIc+9CHs27dv1Cc5/fTTj/qDXn/9dfznf/4nfvKTn6BQKOCiiy7C2WefjTvuuANPP/00GhoacMMNN+CVV17B2rVrsWnTJtx3331YtWoV7rjjDjz77LO46qqrcM899+Cqq67CunXr8Nhjj+Hxxx/Hpk2bsGPHDnR0dOCJJ57Azp07cf/992PHjh14+umnYbfb8fLLL2Pv3r24/fbb8eyzzx7bn9YkyOeyRlaxOIexfD2KTCpuOdbu9MDtD6Ox/QMVHVKNDKOsUMBICCGEkLlPZzqyhRzS+TRS+TTS+TTeSWjojPaNmI1ojMDIFLJVz8FzvBkMhuxBLPEtgqciQPTZvPAqRtMagRdm4FUSMj+NGkxeddVVePXVVxEMBnHVVVeB4zjUavzKcdyYgWbJmWeeiR/84AcQRRG9vb3QNA3xeBxtbW1oaWkBAGzYsAG7d+/G0qVLkc1msWrVKgDAxo0b8eijj+LKK6/E3r178dhjj5n3X3PNNdi0aRP27NmDZ555BgCwfv16bN26Ffl8Hnv27MEtt9wCAFi9ejUGBgbQ1dWFxsbGif1JTYCazRijNIqZxcRAH+JDxmU2be0cZXd54fFH0LTk5OL8xVJZahiSPLcHwhJCCCFkYWCMIa/njWBQTSNVyBiXxeAwlc8gXfN6Gul8pmaZKQCIvGgGhPXOOiwPLqtZbuqSndTZlJAZMGow+Ytf/MLs3vqLX/xiUn6YJEl49NFH8f3vfx8XXHAB+vr6LA18IpEIent7q+4Ph8Po7e3F4OAgXC4XRFG03A/A8hhRFOFyuTAwMFDzuXp6eo47mFSzaUtWsbLxTTadtBzrcPvg9oXRvOzUYilqGJ5ABC5fGJJMdfaEEEIImR00XUM6n6kIAtPF26ma91deL+iFUZ+XAweH5IBTssMhOeCQHAg7QnBIdjhlBxyio3xdcqC1LgItLcAh2qlpDSGz2KjBZFNTU83rABCLxRAKhY7pB375y1/G5z//edx44404cOCA5RcEYwwcx0HX9Zr3ly4rjfYLhjEGnuerHlO6f7y0bD/6e49gKNaLwViPcRntqQoYXd4A/OF61Leshi9YB3+4Dr5QPXzBCAWMZEzhsHumT4HMY7S+yFSi9TU76UxHJp9FMpdCUk0jmUshpaaRyKWQUlOW+8uXxjGZfHUJaSW7ZINTdsCtOOFUnAh7AnDJDrgUZ8WlE84R99kl28Qzh75j/zMg5Gjo99fkGHNn8euvv457770X3/zmN3HCCSeY93/ta1/D/v37sW3bNpx22mnj+kHvvfceVFXFSSedBLvdjvPOOw+7d++GIJTr1qPRKCKRCOrr6xGNRs37Y7EYIpEIAoEAEokENE2DIAjm8YCR1YzFYqivr0ehUEAqlYLP50NdXR36+vrQ2tpqea7x2vXUI4gPxgBwcHr8cPvDaD3hNLMU1eOPwOULQZTkmo8fGlYBqOP+eWRhoaG5ZCrR+iJTidbX1LKUjVZmBFXr7YmWjQJG6ahDssNZzBB6JA/qHXXmbeul3XJ9QvsNdQBZIJ3VkEZ1M5yx0PoiU4nW1/jxPIdg0DXq90cNJt9++218/vOfx1lnnQWn0zpO4jOf+Qy+973v4TOf+Qx+9KMfYfny5Uc9kcOHD+PRRx/FP//zPwMwSmc/8YlP4MEHH8TBgwfR3NyMF198EZdffjmampqgKAreeOMNnHHGGdi1axfWrFkDSZLQ0dGBl156CRs2bMDOnTuxZs0aAMDatWuxc+dO3HjjjXjppZfQ0dEBSZKwdu1a7Nq1Cx0dHfjtb38LRVEmVOJ69oVXQ3F44faFIIjS0R9ACCGEEFI0WtloZbOZqoDxmMtGg+b1UvBXKhs1b0sOyELtD8AJIWSiOFarqw6AL3zhC/D5fNi2bduoD7755psBAH/3d383rh/2d3/3d3j55ZchCALOO+883HzzzfiP//gPczTI2rVrcfvtt4PjOLzzzju48847kUwmsWLFCmzbtg2yLOPIkSPYvHkz+vv70dDQgIcffhherxdDQ0PYvHkzOjs74Xa7sX37djQ3NyOXy+Guu+7C22+/DVmWcd9992HFihXj/gPq709C10f/dI+Q40GfjJGpROuLTKWFtL4YY8gWsmMEf9WZwZSaRrqQRraQG/O5baICh+goBn3lbGHldWeN24qozOuGMwtpfZHpR+tr/I6WmRw1mPzwhz+MJ598EieeeOKoD/7d736HL3/5y/i///f/Hv+ZzlIUTJKpRL/MyFSi9UWm0lxcX6qmHkPZqHF97LJRoWZ5qDUwdNYMEGlMRW1zcX2RuYPW1/gdc5lrOp2uKm8dKRQKIZlMjnkMIYQQQshkKZWNjl4eWn1/6frRy0YrA8CjlI2Kdjhk436Jl6jjKCFkQRo1mFy0aBHeeustcwZkLW+99RYaGhqm5MQIIYQQMj+Vy0YzSOdTZvZvMspGFUGxBH71zrqqEtLqzKEDtnleNkoIIVNh1GBy/fr1+Na3voWzzjqr5hiQaDSKHTt2YP369VN6goQQQgiZnWqVjVYGfmOVkE6kbNRn86LJ3TAiEKSyUUIImWmjBpPXXnstdu/ejXXr1uGKK67AypUr4Xa7MTw8jDfffBPPPfccmpub8fnPf346z5cQQgghU0jVVMRzCQznEhjOxRHPxYu348XbCcRzCSTVJPLHVTZaMXJCprJRQgiZi0ZtwAMA2WwWjzzyCF544QXE43Hz/mAwiMsuuww33XQTHA7HtJzoTKEGPGQq0QZwMpVofZESnelIqqmKYDBeESxab2cL1UPrOXBwyy54FDe8Ng88sgd1Pj+QF0cpIaWyUXJ86PcXmUq0vsbvmLu5Vsrn8+js7EQ8Hoff70dra+uC+cSQgkkyleiXGZlKtL7mv1whZwaEY10m1CR0plc9XhEUeBWPESQqbngUT8Vtjxk8uiRnVfkorS8ylWh9kalE62v8jrmbayVJkrB48eJJOylCCCGE1KbpGpJqEsPFclJreWm8IkhMIKdVN6PhOR4e2Q2PYny1eJqKQaLbDBK9igduxQ2bqMzAKySEEDJfjCuYJIQQQsixM7qX5sxgcLgYGJaDxfLthJqs2ZzGLtrMoLDN21IMFo3bRhbRyCS6ZCeVlxJCCJkWFEwSQgghx0jTNcTVBIazxcxh8bqZSVTjxdsJ5PV81eN5jjczhQG7H4t8rebtymDRo7ghC/IMvEJCCCFkdBRMEkIIIRUYY0gXMtbGNNk44mpFJjFrBIpJNVXzOZySwwwGF/sXWTKHlZlEh2SnLCIhhJA5i4JJQgghC0JeKyChWvcgjrwsBY+FGiMvRF40m9SEnUEsDbRb9iCWrrtlNySB/nslhBAy/9H/doQQQuYsxhhS+bQ5D7GyaU28Yi/icC6BdD5d8zlcshMe2QOvzY065xIjoyh74LV5zODRo7jhEO0LppM5IYQQMh4UTBJCCJl1VE01O5ZaG9WMzCQmoDGt6vESLxnlpDYP6p11OCGwtBgcesxOp8a8RHfVyAtCCCGEjA8Fk4QQQqaFznQk1ZRlxMVoXU0zhWzV4zlwcMsucy9io6u+qlFNaS+iTVQoi0gIIYRMMQomCSGEHJdcIVcjc2idiVjqdKozverxiqCYHUub3Q3whJbX2Ivohkt2URaREEIImUUomCSEEFJFZzoSatIceWEtL7XuRcxpuarHc+DMgNCjuNHiabLcrswo2kRlBl4hIYQQQo4XBZOEELJAMMaQLeSMIFAtBoPZikyiWr6dUJNgYFXPYRNtxYyhG63elooMorti9IUHLtlJIy8IIYSQeY6CSUIImQcYY0ioScTS/YhlBhBL90N9L4OeoX4jSCyWmqqaWvVYnuPNTKHf7sMiX+uIvYgeswxVFuQZeHWEEEIImY0omCSEkDkik88ilulHf3oAsUw/YuZlP/ozg1WBokt2wF3sXLrY12bJHHormtY4JAdlEQkhhBAyYRRMEkLILJHXChjIDJiZxVhmAP0VmcbUiDmJNlFByB5ExBnGB0LLEXIEEbIHEXIEELQH0NwQQjSamKFXQwghhJD5joJJQgiZJjrTMZQdLgeJFRnG/swAhrLDln2KIi8gYAsg5Aigrb4ZQYcRKBoBYxBOyUHjLwghhBAyYyiYJISQScIYQyqfLmYVi0GimVkcwEB2AAVdM4/nwMFn8yJoD2B5cGk5q+gIImwPwmvzUPkpIYQQQmYtCiYJIWQCcoUc+jMDiKb70W8pRzWyjNmCdUyGU3Ii5AigxdOIVfWnIGQPGOWojiACNj8kgX4NE0IIIWRuoncxhBBSQdM1DGQHjRLUGvsWE2rScrzESwg7ggg6AlgWWGKWoQaLl3bJNkOvhBBCCCFkalEwSQhZUBhjiOfiZumppSQ104+BzJBl3yLP8QjYfAg5glhZdzKCpcxi8dItu2jfIiGEEEIWJAomCSHzTjqfsWQVK0tS+zODyOt5y/EexY2QPYgl/sU4qylQ0RE1CL/NC4EXZuiVEEIIIYTMXhRMEkLmnLyWLwaHA5asYjRtzGBMFzKW4+2iDSFHEPWuOpwS+QCCxWAx5AgiaPdDFuQZeiWEEEIIIXMXBZOEkFlHZzoGs0NmGWplo5tYuh/DubjleJEXi+WnASz2LaooQzU6ozolxwy9EkIIIYSQ+YuCSULItGOMIaEmy0FiMcNodEQ1Zi7qTDeP58DBb/Mh5AjgA6ETzaxiad+iR3HTCA1CCCGEkGlGwSQhZEpkC9mqMlQj02hcz2mq5Xi37ELQHkCbtwVn1K9E0BFA2BFEyB6E3+6DyNOvK0IIIYSQ2YTenRFCjklBL2AgM2gpP62ct5hUU5bjFUFBqBggnhhaZpm3GLQHYBOVGXolhBBCCCHkWExrMPntb38bL7/8MgBg7dq1+MpXvoLXXnsN27ZtQy6Xw4UXXohbb70VALBv3z5s2bIFqVQKHR0duOeeeyCKIrq6urBp0yb09/ejvb0d27dvh9PpRDwex2233YbOzk4EAgHs2LED4XAYqqpiy5YtePvtt2Gz2bB9+3YsWbJkOl82IXOSznQM5+LoTw8gWhyfUSpDjaX7MZQdtozQEDgBQbsfQUcAp3lONbuhluYuumQnjdAghBBCCJlHpi2YfO211/DrX/8aL7zwAjiOw+c+9zm8+OKL2L59O55++mk0NDTghhtuwCuvvIK1a9di06ZNuO+++7Bq1SrccccdePbZZ3HVVVfhnnvuwVVXXYV169bhsccew+OPP45NmzZhx44d6OjowBNPPIGdO3fi/vvvx44dO/D000/Dbrfj5Zdfxt69e3H77bfj2Wefna6XTcisxRhDOp8uZxXNDKNRhtqfGURBL1ge47N5EbIHsDy4tGreos/mpX2LhBBCCCELyLQFk+FwGJs3b4YsGy34lyxZggMHDqCtrQ0tLS0AgA0bNmD37t1YunQpstksVq1aBQDYuHEjHn30UVx55ZXYu3cvHnvsMfP+a665Bps2bcKePXvwzDPPAADWr1+PrVu3Ip/PY8+ePbjlllsAAKtXr8bAwAC6urrQ2Ng4XS+dkBmjaqplv2J0RGfUbCFrOd4hORCyB9DkbsTKupMRsgcRLGYWg3Y/JEGaoVdCCCGEEEJmm2kLJpctW2ZeP3DgAF5++WVcc801CIfD5v2RSAS9vb3o6+uz3B8Oh9Hb24vBwUG4XC6Iomi5H4DlMaIowuVyYWBgoOZz9fT0UDBJ5gVN18ojNMwGN+WS1LiasBwv8VKx/DSApYHFCFXMWwzZA7BL9hl6JYQQQgghZK6Z9gY87777Lm644QZ85StfgSAIOHDggPk9xhg4joOu65a9VaX7S5eVRtuDxRgDz/NVjyndP17BoGvcxxJyLMJh96jfY4xhKBtHXyKG3mQMfcmocVm8HUtZR2jwHI+QM4CIK4TFoZWoc4cQcYVR5woh4g7BZ/PQvsUFZqz1RcjxovVFphKtLzKVaH2NjTEGTdNQKBTGPG5ag8k33ngDX/7yl3HHHXdg3bp1eP311xGNRs3vR6NRRCIR1NfXW+6PxWKIRCIIBAJIJBLQNA2CIJjHA0ZWMxaLob6+HoVCAalUCj6fD3V1dejr60Nra6vlucarvz8JXWdHP5CQYxAOu3Goq69i32LlvEXjdl7PWx7jkd0IOgJoc7fijLpVxayikWH023wQeKHmzyokgVgyOR0vi8wS4bAb0Wji6AcScgxofZGpROuLTKW5vr6MQK+AQqH0lUc+b1yW7jO+b9zO5/MVxxr3Vz6+8vuaVr7OGIPX68UXv/jFUc9l2oLJ7u5ufPGLX8QjjzyCs88+GwCwcuVK7N+/HwcPHkRzczNefPFFXH755WhqaoKiKHjjjTdwxhlnYNeuXVizZg0kSUJHRwdeeuklbNiwATt37sSaNWsAGN1hd+7ciRtvvBEvvfQSOjo6IEkS1q5di127dqGjowO//e1voSgKlbiSKaczHSk1jbiaQDyXQCKXMK/Hc0kk1ASGc3EM5oaQzFlHaNhEG0L2AOqcEXwgdKI5TqPUGVUW5Bl6VYQQQgghZCy6rluCtnJgVzvoqwzgxhv0adrY2cLRSJIEURQhCMZl6ctms0GSJAiCCFG0fs/lGrtKk2OMTUva7b777sNzzz1nZggB4BOf+AQWLVpkjgZZu3Ytbr/9dnAch3feeQd33nknkskkVqxYgW3btkGWZRw5cgSbN29Gf38/Ghoa8PDDD8Pr9WJoaAibN29GZ2cn3G43tm/fjubmZuRyOdx11114++23Icsy7rvvPqxYsWLc502ZSVKi6RqSahJxNVkMCo0AMZFLIDHivqSaspSflgicALfigkd2w6O40eSPwMm5zXmLIXsADslBpahkUsz1T17J7Ebri0wlWl9kKjDGUCgU4PHI6OkZrAr6KjN6Ewn6KrN5mqZN+Lw4jjODuFLAVyvoG/17pceWr48MDAVBOKb3lzzPjbntb9qCybmKgsn5raAXkMglK7KGCSQqMoilYDGuJpBS05a5iiUSL8GjuOAuBogexV28br3PI7uqAkX6z5JMJVpfZCrR+iJTidYXqYUxhnxeRS6Xg6qqUNUcVDVX47aKfL72/azGh/1jEQShKoCrDOpGBnbW75WyfeXblQFf6YsfZYvSbHC0YHLaG/AQMtVUTTVLSSuzheVy06R5PV3I1HwORZDhVtzwyG5EnGEs9bcbt4v3eRS3+X2bqFAmkRBCCCHkKHRdLwZ2pYAwZ7ltBIBq1f2l66qaO+rPEEURsqxAlhUoigK73Q6v11e8T4YsKwgE3Mjl9KrMXinwK5eDihNq3LkQUTBJZj3GGHJariJzmKwoMU1ayk3jagLZQu1fNHbRZmYNm9wN8IROgEd2G2WnxcCwVIKqiMo0v0pCCCGEkNmtUCiMmREsXR8ZAJaygoVC/qg/oxTwlS7dbjckKQhFUSz3174tjyvLR5nvyUPBJJkRjDFkCllrIGjJIFpLT0d2NC1xSg4zQGz1tlhLSy1lpy5IgjTNr5IQQgghZHYw9gvma2YFRysVHXnM0fYDchxnBnilYM/r9R81ACxlEiVJokzgHEPBJJk0OtORzqdr7DdMVgeLagIFvfoXEgcOLtlpBoNhf8jcb1hZWloKEEcbg0EIIYQQMp8wxqqygCMzgLUzguXbR2uVIgiCJdgrZQZL1yuDxMpjSkGhKEq09WeBoWCSjElnOpJqqqp76cgxF6Xy01odTHmON0tI3bIbDa66irJSawbRJTvBc/SJFCGEEELmF13XxhUA1iodNfYTqkf9GaIoQVHKQZ7d7oDX6x8zAKwMFEWRQgMyMbRiFiBN1ypKS2t3Mi3tS0yqqZodTEVeMMtJfYoXrZ7mYmDoGtHR1A2HZKcAkRBCCCFzljEkXhu1BLRWRnBkOWmhcPTZgCNLQN1uT0XwZw38qoPC8e0XJGQyUTA5T+S1fNWsw3JwmLQEiql8uuZzyIIMj+yCWzHmHi72LbLMRKzMItpFG5UxEEIIIWROMEZK5KsygOUxE7UDwMpjdX3skRIcx1cFfH6/vyr4s5aKlm9LkkzvrcicQ8HkLJYr5IpBYXJEaWn1LMRsIVvzOWyiYpaT1jvrcEJgqSVArCw3tVEHU0IIIYTMQrqum4GfNQAcf/OY8e4XLGX6FMUGt9s7ZgBYGSiKokjBIFlwKJicRowxZIsdTCvHW4ycf1gqQc1ptWvjHZLDLCdt8TZVdS0tl5i6IAvyNL9KQgghhCwkRpfQAjStgEKhUHFdK17mK64XRlxqNe4rgOMYUqm0GTTm80cfKSFJkiXYczic8PsDI0pCq0tFS0GhIFCJKCETRcHkcWKMGR1MK0pMExUZxMpZiAk1WXPEBQcOTtlhZgjbvW3wKC7LvsPKOYgiT39thBBCCKlN1/URQd34AjhNq31/+blqf/9o5Z9jEQTBHBQvCEJxeLwAu93ICgaD1RnBWiMmjP2C1J+BkOlGUclR/GnwfUSTA5b9h2awWAwUNVY94oLneGPERTEYrHdFKgJDV0UnUxdcNOKCEEIImZcYY2ZwV525qw7Qjj/w08BqdFYfLyOoEysuBfO6LMsjAr/ysdXXhRrPVf2co5WF0lB5QuYGCiaP4vu/fwbRVD8AQOAEy37DJnejmUE0g8PidafsoA6mhJBpwxhDJpNGIpFAMhlHMpmE3S4inVbB8zw4jgfHccXr1Zccx4PnrZfVx431vfJjaz0n7SMis0WpK+fYAVw5WKsV3NUK4MYq5zxWHMeNCMRKmTvjuqIoVcGaKAqjBnCjB37GZenfLiGEjBcFk0fx+dOuhcIrcCtuOEQ7/ZIlhMwITdOQSiWRTCaQTCaQSMTN66Wv4yk1m2pGcFk7aB1PgGpcjh74Hv05jh48jxVET0UgToG2Qdf1MYK00QO4ctZu7P14tYLEY8Xz/CjZNgGSJMFut48rgDt64Gd8j8Y8EEJmOwomj6Ld1wZdH7v7FyGEHK98Xi1mFUtf8WLQmEAqlUAqlap6jMPhhMvlRigUwaJFS+ByueFyueF2G5f19X709cXBmA5dZ5bLUuld+bLWfeXL0mMrb5e+P/IY6/fKt2s9R/m8at1X/ZyapoGxwojja5/raM9xtI6OM2GsrO54A+Dje46xgu7aQXR3t4LBweQ4M3ejl3Me73670bJsimKD0zmRAG7szJ0gCLQnjxBCRqBgkhBCphhjDNls1hIgjswq5nLW8T48z8PpdMHl8qCxsaUYIHrMgNHlch+182DpjTaxKgWUYwWkYwWmtQLx0b838SC6ViA+9nmVvqdB08Z7zqMF4sceaHMcVxWUVV7KsjxGACfVfNzYe++EBZ/VJYSQmUbvMggh5Djpuo50OlWz9LT0NXLflCRJZlAYidQVr3vMrKLd7qAsyBQpldzSn2+1UkA5nsx1MOhCPJ4zAzzab0cIIQsPBZOEEHIUhUKhKjisDBxTqWRVRsdms8Hl8sDnC6C5uc0MEkvZRUVR6I03mXVKgTaAo2a+/X43CgXqtkkIIQsZBZOEkAUvl8uNUoJq3JfJZCzHcxxn7lesr2+0lJ6WgkZRlGbo1RBCCCGETA8KJgkh81r1yIxEVdCYz6uWxwiCYAaHra0hS/mpy+WG0+mkLouEEEIIWfAomCSEzGnHMjJDlhW4XG54PB40NjZZMosulwd2O40BIoQQQgg5GgomCSGzWu2RGUnz+tgjM8I1RmZ4IMvyDLwSQgghhJD5hYJJQsiMGTkyo1YJ6siRGRzHw+VyweVyH/PIDEIIIYQQcvwomCSETJnSyIyxSlBHjswQRcnMItLIDEIIIYSQ2YuCSULIMZv8kRkuKIqN9isSQgghhMwBFEwSQkY1mSMzSl+SRCMzCCGEEELmAwomCVmgxhqZkUoZl0cfmVFqbOOhkRmEEEIIIQsMBZOEzFO1R2YkLYFj7ZEZLrhcHtTXN1lmK9LIDEIIIYQQUomCSULmqOqRGdYy1FojM+x2B9xuGplBCCGEEEKOHwWThMxC5ZEZ1SWoxzoyw+l0QRTpnzwhhBBCCJkc9M6SzDoju39ab7PifWM/pvo4VvWYWsfUuj3eY2qf++jHGAHjEA4f7j2ukRmlslSHg0ZmEEIIIYSQ6UPB5FHs3v0TJBLJ4q2pDS5Ge+7ax82foIoYRo7MsJagumlkBiGEEEIImVUomDwKp9MFoPINvHHdeE9feR0jjqu+v9ZxowUHI++vPo7DyLtqHTP2eYznmFqvafJf9+jPNZde99FfU+UxHMehvj4ITRNpZAYhhBBCCJlzpjWYTCaT+MQnPoHvfve7aG5uxmuvvYZt27Yhl8vhwgsvxK233goA2LdvH7Zs2YJUKoWOjg7cc889EEURXV1d2LRpE/r7+9He3o7t27fD6XQiHo/jtttuQ2dnJwKBAHbs2IFwOAxVVbFlyxa8/fbbsNls2L59O5YsWTKhcz7nnI9C1ymDRqZGOOxGNJqY6dMghBBCCCFkwqZtg9Wbb76JT37ykzhw4AAAIJvN4o477sDjjz+Ol156CW+//TZeeeUVAMCmTZtw11134d/+7d/AGMOzzz4LALjnnntw1VVXYffu3Tj55JPx+OOPAwB27NiBjo4OvPzyy7jyyitx//33AwCefvpp2O12vPzyy7jjjjtw++23T9fLJYQQQgghhJB5bdqCyWeffRZ33303IpEIAOCtt95CW1sbWlpaIIoiNmzYgN27d+PIkSPIZrNYtWoVAGDjxo3YvXs38vk89u7di/PPP99yPwDs2bMHGzZsAACsX78ev/rVr5DP57Fnzx5cfPHFAIDVq1djYGAAXV1d0/WSCSGEEEIIIWTemrYy11K2sKSvrw/hcNi8HYlE0NvbW3V/OBxGb28vBgcH4XKVRxuU7h/5XKIowuVyYWBgoOZz9fT0oLGxcdznHQy6Jv5iCZmAcNg906dA5jFaX2Qq0foiU4nWF5lKtL4mx4w14NF13dKMhDEGjuNGvb90WWm05jWMMfA8X/WY0v0T0d+fpD2TZMrQnkkylWh9kalE64tMJVpfZCrR+ho/nufGTK7N2FC6+vp6RKNR83Y0GkUkEqm6PxaLIRKJIBAIIJFIQNM0y/GAkdWMxWIAgEKhgFQqBZ/Ph7q6OvT19VU9FyGEEEIIIYSQ4zNjweTKlSuxf/9+HDx4EJqm4cUXX8SaNWvQ1NQERVHwxhtvAAB27dqFNWvWQJIkdHR04KWXXgIA7Ny5E2vWrAEArF27Fjt37gQAvPTSS+jo6IAkSVi7di127doFAPjtb38LRVEmVOJKCCGEEEIIIaS2GStzVRQFX//613HzzTcjl8th7dq1uOCCCwAA27dvx5133olkMokVK1bg2muvBQDcfffd2Lx5M77zne+goaEBDz/8MADglltuwebNm7Fu3Tq43W5s374dAPCpT30Kd911F9atWwdZlvHggw/OzIslhBBCCCGEkHmGY4zRhsAx0J5JMpWoZp9MJVpfZCrR+iJTidYXmUq0vsZv1u6ZJIQQQgghhBAyd81YmetcwfO1O8YSMllojZGpROuLTCVaX2Qq0foiU4nW1/gc7c+JylwJIYQQQgghhEwYlbkSQgghhBBCCJkwCiYJIYQQQgghhEwYBZOEEEIIIYQQQiaMgklCCCGEEEIIIRNGwSQhhBBCCCGEkAmjYJIQQgghhBBCyIRRMEkIIYQQQgghZMIomCSEEEIIIYQQMmEUTBJCCCGEEEIImbAFGUwmk0msX78ehw8fBgA8//zzuOiii7Bhwwbcd999KBQKAID3338fn/rUp3DxxRfjs5/9LIaHhwEAXV1duPrqq3HBBRfgC1/4AlKp1Iy9FjI7jWeN7du3D5dccon5dc4552D9+vUAaI2RsY33d9gf//hHXH755bj44otxww03IB6PA6D1RcY23vX1yiuvYMOGDdiwYQP+5m/+xlxHtL7IaL797W9j3bp1WLduHR588EEAwGuvvYYNGzbgvPPOwyOPPGIeu2/fPmzcuBHnn38+tmzZYq47Wl9kNBNZXyVf+cpX8Pzzz5u3aX0dA7bA/P73v2fr169nK1asYJ2dney9995j55xzDuvt7WWMMXb33Xez73//+0zXdXbeeeexV155hTHG2EMPPcQefPBBxhhj119/PXvxxRcZY4x9+9vfNu8nhLHxr7FK6XSarVu3ju3du5cxRmuMjG4i6+uTn/wk27NnD2OMsW3btrGHH36YMUbri4xuvOtreHiYffCDH2TvvvsuY4yxJ554gt17772MMVpfpLZXX32VffzjH2e5XI6pqsquvfZa9tOf/pStXbuWHTp0iOXzeXbdddeZv7PWrVvHfve73zHGGLv99tvZM888wxij9UVqm+j66unpYTfccAM79dRT2XPPPWc+D62viVtwmclnn30Wd999NyKRCADgv//7v7Fq1Srz9rnnnouf//zn+OMf/wiHw4E1a9YAAG688UZcffXVyOfz2Lt3L84//3wAwMaNG7F79+6ZeTFkVhrvGqv0ve99D6tXr0ZHRwetMTKmiawvXdfNT1UzmQxsNhutLzKm8a6vAwcOoLGxEUuXLrXcT+uLjCYcDmPz5s2QZRmSJGHJkiU4cOAA2tra0NLSAlEUsWHDBuzevRtHjhxBNpvFqlWrAJTXEa0vMpqJrC8A+OlPf4qPfexjuPDCC83noPV1bBZcMHn//fejo6PDvH3iiSfizTffRHd3NzRNw+7duxGLxXDo0CGEQiHccccduOyyy3D33XfD4XBgcHAQLpcLoigCMBZvb2/vTL0cMguNd42VJBIJPPvss/jSl74EALTGyJgmsr42b96MO++8E3/2Z3+G1157DZ/4xCdofZExjXd9LVq0CD09PXjnnXcAAC+//DJisRitLzKqZcuWmcHhgQMH8PLLL4PjOITDYfOYSCSC3t5e9PX1We4vrSNaX2Q0E1lfAPC5z30OV155peU5aH0dmwUXTI7U3t6Ov/mbv8EXvvAFXH311Vi+fDkkSUKhUMDrr7+OT37yk3jhhRfQ0tKCr3/962CMgeM4y3OMvE1IpdHWWMlPfvIT/PmftK/z6gAABxdJREFU/zmCwSAA0BojEzLa+spms9iyZQv+8R//Eb/+9a9x1VVX4atf/SqtLzIho60vj8eDb3zjG/ja176Gyy+/HJFIBJIk0foiR/Xuu+/iuuuuw1e+8hW0tLRY1kdp/ei6XvN+Wl/kaMazvkZD6+vYLPhgMpfL4dRTT8XOnTvxwx/+EHV1dWhpaUE4HEZbWxtOOeUUAMD69evx1ltvIRAIIJFIQNM0AEA0GjXLfwipZbQ1VvLzn/8cF110kXmb1hiZiNHW1//8z/9AURSceuqpAICPf/zjeP3112l9kQkZbX1pmob6+nr8+Mc/xnPPPYeTTjoJLS0ttL7ImN544w18+tOfxt/8zd/gsssuQ319PaLRqPn90noZeX8sFkMkEqH1RcY03vU1Glpfx2bBB5PpdBqf/vSnkUwmoaoq/umf/gkXXXQRTjvtNAwMDJglPP/+7/+OFStWQJIkdHR04KWXXgIA7Ny509xXSUgto60xwPgU7I9//CNOO+0083haY2QiRltfbW1t6Onpwfvvvw8A+MUvfoFTTjmF1heZkNHWF8dxuO6669Db2wvGGP7xH/8RF110Ea0vMqru7m588YtfxPbt27Fu3ToAwMqVK7F//34cPHgQmqbhxRdfxJo1a9DU1ARFUfDGG28AAHbt2oU1a9bQ+iKjmsj6Gg2tr2PDMcbYTJ/ETPjoRz+KH/zgB2hubsaPf/xj/OM//iMKhQLWr1+Pm2++GQDw5ptv4t5770Umk0F9fT0efPBBBINBHDlyBJs3b0Z/fz8aGhrw8MMPw+v1zvArIrPNeNZYf38/Lr74Yrz66quWx9IaI0cznvX1yiuv4Jvf/CYYYwgGg7j33nvR0tJC64sc1XjW1549e/DNb34Tqqri7LPPxpYtWyBJEq0vUtN9992H5557Dq2treZ9n/jEJ7Bo0SJs27YNuVwOa9euxe233w6O4/DOO+/gzjvvRDKZxIoVK7Bt2zbIskzri9Q00fVVsnnzZpx55pnYuHEjAHr/dSwWbDBJCCGEEEIIIeTYLfgyV0IIIYQQQgghE0fBJCGEEEIIIYSQCaNgkhBCCCGEEELIhFEwSQghhBBCCCFkwiiYJIQQQgghhBAyYRRMEkIIIYQQQgiZMAomCSGEkGlw0003YfXq1YhGo1Xf+/3vf4+TTjoJTz/99AycGSGEEHJsaM4kIYQQMg36+vqwbt06fOhDH8K3vvUt8/58Po+NGzfC5/PhBz/4gWWgNiGEEDKbUWaSEEIImQaRSARf/epXsXv3bvzyl7807//7v/97HD58GNu2baNAkhBCyJxCwSQhhBAyTa644gp8+MMfxtatW5HNZnHo0CF85zvfwebNm9Hc3Iyf//znuPjii3HKKafgggsuwJNPPgld183H/+Y3v8E111yD0047DSeffDIuueQS/OpXvzK//9GPfhTf+MY3cP755+ODH/wg/vjHP87EyySEELJAUJkrIYQQMo0OHz6MDRs24LrrrsMf/vAHaJqGJ598Eq+88gpuueUW3HnnnTjzzDPx7rvvYuvWrbjyyivxpS99Cd3d3Tj//PPxmc98BldccQVSqRR27NiBN998E6+88gpkWcZHP/pRDAwM4IknnoCiKDjllFPA8/S5MSGEkKkhzvQJEEIIIQtJc3Mz/r//7//D17/+ddjtdvz0pz8FAHz3u9/FJz/5SVxxxRUAgNbWVqRSKXzta1/DTTfdhHw+j1tuuQXXXXedWQ776U9/Gn/1V3+F/v5+NDQ0ADCyk2eeeebMvDhCCCELCgWThBBCyDS75ppr8L3vfQ8bN240g8B9+/bhD3/4A374wx+ax+m6jmw2iyNHjqC1tRWXXnop/tf/+l/47//+bxw8eBD79u0DAGiaZj6mpaVlel8MIYSQBYuCSUIIIWSacRwHWZZhs9nM+yRJwuc+9zls2LCh6vi6ujq8++67uOqqq7By5UqcffbZuOiii1AoFHDjjTdajlUUZcrPnxBCCAEomCSEEEJmhaVLl+LAgQNoa2sz7/s//+f/4F//9V/x4IMP4vnnn0dDQwP+4R/+wfx+KYtJ7Q8IIYTMBNqVTwghhMwCX/jCF/Cv//qveOKJJ3DgwAHs2bMHd911F2w2G2RZRiAQwJEjR/Dqq6/iyJEj2LVrFx555BEAgKqqM3z2hBBCFiLKTBJCCCGzwJo1a/Dggw/iiSeewKOPPopAIIBLL70Ut956KwDg2muvxXvvvYdbb70VmqZhyZIluOeee3D77bfjD3/4A5YsWTLDr4AQQshCQ6NBCCGEEEIIIYRMGJW5EkIIIYQQQgiZMAomCSGEEEIIIYRMGAWThBBCCCGEEEImjIJJQgghhBBCCCETRsEkIYQQQgghhJAJo2CSEEIIIYQQQsiEUTBJCCGEEEIIIWTCKJgkhBBCCCGEEDJh/z/f+a4CIv75dgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = pltviz.comp_line(df=df_selected, \n",
    "                      dependent_cols='pop_per_ecv', \n",
    "                      indep_stats='year',\n",
    "                      group_col='sub_lctn')\n",
    "\n",
    "colors = [utils.rgb_to_hex(c) for c in sns.color_palette(n_colors=len(selected_states), desat=1)]\n",
    "handles = pltviz.legend.gen_handles(colors=colors, size=10, marker='o')\n",
    "labels = list(df_selected['sub_lctn'].unique())\n",
    "\n",
    "ax.legend(handles=handles, labels=labels, \n",
    "          title='States',\n",
    "          bbox_to_anchor=(0, 1),\n",
    "          ncol=2, loc='upper left', fontsize=12, \n",
    "          title_fontsize=17, \n",
    "          frameon=True, facecolor='#ffffff', framealpha=1)\n",
    "\n",
    "ax.axes.set_title('Selected State Citizens per Ellectoral College Vote', fontsize=25)\n",
    "ax.set_xlabel('Year', fontsize=15)\n",
    "ax.set_ylabel('Citizens per ECV', fontsize=15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Disproportionality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's close by calculating the disproportionality of the 2015 Electoral College votes as opposed to that of the 2015 allocations for seats in the House of Representatives. The measure of disproportionality we'll use is the [Gallagher index](https://en.wikipedia.org/wiki/Gallagher_index), which is similar to sum of squared residuals between shares and allocations. The typical settings for its use are deriving the fairness of allocations given either votes or populations in comparison to their resulting representations. The equation is as follows:\n",
    "\n",
    "$$LSq = \\sqrt{\\frac{1}{2}\\sum_{i=1}^{n}(S_i - A_i)^2}$$\n",
    "\n",
    "with $S_i$ and $A_i$ being the shares and allocations of group $i$ respectively. \n",
    "\n",
    "The indexes for the House of Representatives and Electoral College using 2015 population estimates are the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-12T15:52:43.149286Z",
     "start_time": "2020-12-12T15:52:43.143046Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.003337488951836875"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hor_dispr_2015 = appointment.metrics.dispr_index(shares=df_new_states[df_new_states['year'] == '2015']['population'].values, \n",
    "                                                 allocations=df_new_states[df_new_states['year'] == '2015']['hor_allocation'].values, \n",
    "                                                 metric_type='Gallagher')\n",
    "hor_dispr_2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-12T15:52:43.620348Z",
     "start_time": "2020-12-12T15:52:43.614072Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.022726461450575"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ec_dispr_2015 = appointment.metrics.dispr_index(shares=df_new_states[df_new_states['year'] == '2015']['population'].values, \n",
    "                                                allocations=df_new_states[df_new_states['year'] == '2015']['ec_votes'].values, \n",
    "                                                metric_type='Gallagher')\n",
    "ec_dispr_2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-12T15:52:44.079480Z",
     "start_time": "2020-12-12T15:52:44.076058Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.809449193252578"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ec_dispr_2015 / hor_dispr_2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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