path: root/megapixels/notebooks/msc/plotter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot Data Previews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import os\n",
    "from os.path import join\n",
    "from pathlib import Path\n",
    "from glob import glob\n",
    "import json\n",
    "from pprint import pprint\n",
    "\n",
    "#import plotly.plotly as py\n",
    "import plotly.offline as py\n",
    "import plotly.graph_objs as go\n",
    "from plotly import tools\n",
    "\n",
    "import matplotlib.ticker as ticker\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib as mpl\n",
    "from matplotlib.gridspec import GridSpec\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from PIL import Image, ImageDraw\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm_notebook as tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load files\n",
    "fp_origins = '/data_store/datasets/msc/summaries/megapixels_origins.csv'\n",
    "fp_origins_top = '/data_store/datasets/msc/summaries/megapixels_origins_top.csv'\n",
    "fp_overview = '/data_store/datasets/msc/summaries/megapixels_overview.csv'\n",
    "fp_sector = '/data_store/datasets/msc/summaries/summary_sector.csv'\n",
    "fp_country = '/data_store/datasets/msc/summaries/summary_countries.csv'\n",
    "fp_country_top = '/data_store/datasets/msc/summaries/summary_countries_top.csv'\n",
    "fp_dir_out = '/data_store/datasets/msc/viz/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_origins = pd.read_csv(fp_origins).fillna(0)\n",
    "df_overview = pd.read_csv(fp_overview).fillna('')\n",
    "df_country = pd.read_csv(fp_country).fillna('').set_index('country')\n",
    "df_sector = pd.read_csv(fp_sector).fillna('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_origins_plt = df_origins.drop(['images', 'videos', 'key', 'name_full'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create custom color maps\n",
    "import matplotlib as mpl\n",
    "import matplotlib.cm as mplcm\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "\n",
    "def get_color_map(cmap='prism', ncolors=20, as_hex=False, reverse=False, bgr=True):\n",
    "  norm  = mpl.colors.Normalize(vmin=0, vmax=ncolors-1)\n",
    "  scalars = mplcm.ScalarMappable(norm=norm, cmap=cmap)\n",
    "  colors = [scalars.to_rgba(i) for i in range(ncolors)]\n",
    "  colors = [(int(255*c[0]),int(255*c[1]),int(255*c[2])) for c in colors]  \n",
    "  if reverse:\n",
    "    colors = colors[::-1]\n",
    "  if bgr:\n",
    "    colors = [c[::-1] for c in colors]\n",
    "  if as_hex:\n",
    "    colors = ['#{:02x}{:02x}{:02x}'.format(c[0],c[1],c[2]) for c in colors]\n",
    "  return colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "sums = {}\n",
    "for k in df_origins_plt.keys():\n",
    "  if not ('cooperative' in k.lower() or 'studio' in k.lower()):\n",
    "    sums[k] = int(df_origins_plt[k].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>images</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Search Engines</th>\n",
       "      <td>30127200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flickr.com</th>\n",
       "      <td>11783888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IMDb.com</th>\n",
       "      <td>5251410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CCTV</th>\n",
       "      <td>959312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wikimedia.org</th>\n",
       "      <td>183500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mugshots</th>\n",
       "      <td>113268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YouTube.com</th>\n",
       "      <td>31888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Livecams</th>\n",
       "      <td>23834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other Sources Combined</th>\n",
       "      <td>13210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          images\n",
       "source                          \n",
       "Search Engines          30127200\n",
       "Flickr.com              11783888\n",
       "IMDb.com                 5251410\n",
       "CCTV                      959312\n",
       "Wikimedia.org             183500\n",
       "Mugshots                  113268\n",
       "YouTube.com                31888\n",
       "Livecams                   23834\n",
       "Other Sources Combined     13210"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sums = pd.DataFrame({'images':list(sums.values())}, index=sums.keys())\n",
    "\n",
    "# get top N\n",
    "ntop = 8\n",
    "k = 'images'\n",
    "df_top = df_sums.nlargest(ntop, k)\n",
    "df_bot = df_sums.nsmallest(len(df_sums) - ntop, k)\n",
    "df_tmp = pd.DataFrame.from_dict({'tmp': ['Other Sources Combined'], k: df_bot[k].sum()}).set_index('tmp')\n",
    "df_sums = df_top.append(df_tmp)\n",
    "df_sums.index.name = 'source'\n",
    "df_sums.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['#ff0000', '#0056c3', '#ffd700', '#a200ce', '#54fe00']\n",
      "['Accent', 'Accent_r', 'Blues', 'Blues_r', 'BrBG', 'BrBG_r', 'BuGn', 'BuGn_r', 'BuPu', 'BuPu_r']\n"
     ]
    }
   ],
   "source": [
    "colors = get_color_map(ncolors=5, bgr=False, as_hex=True)\n",
    "print(colors)\n",
    "color_list = list(dir(mplcm))\n",
    "print(color_list[:10])\n",
    "colors_msc = ['#6d9438', '#d2dcbe', '#a7bb7e', '#aaaaaa','#999999', '#bbbbbb']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['IMDb.com', 'WikiMedia.com', 'Flickr.com', 'Search Engines', 'Livecams', 'CCTV', 'YouTube.com']\n",
    "colors = get_color_map(cmap='Accent_r',ncolors=ntop+4, as_hex=True, bgr=False, reverse=False)\n",
    "plot = df_sums.plot.pie(y='images',figsize=(15,15),\n",
    "  title='Sources of Non-Cooperative Facial Recognition Training Images', colors=colors_msc,\n",
    "                        autopct='%0.1f%%', fontsize=12, labeldistance=1.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save PDF\n",
    "fig = plot.get_figure()\n",
    "fig.savefig(join(fp_dir_out,'summary_sources.pdf'))\n",
    "fig.savefig(join(fp_dir_out,'summary_sources.png'))\n",
    "\n",
    "# Save CSV\n",
    "df_sums.to_csv(fp_origins_top, index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA08AAANRCAYAAAA74ofxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzs3Xd8VGXePv7rnkknISEF0gsJ6SFAlGUoIiy2Z4F1RR+ai7ryABoc94uK/NRV12XdRcV1I6OsnaKLiAKCClhGagRBCUMJ5UgvoadQAknO749zomNIGUKSe8r1fr3mxWROu+bMJMxn7nKEqqogIiIiIiKixhlkByAiIiIiInIFLJ6IiIiIiIgcwOKJiIiIiIjIASyeiIiIiIiIHMDiiYiIiIiIyAEsnoiIiIiIiBzA4omIWoUQIlEIoQohvBpY/qwQYq5+P14IUSGEMLZtSs8jhFghhBjdyPK3hBBPtGUmVyCESBFCtNi1PYQQNwohtrXU/vR97hRC9GvJfTZxvLFCiG/b6nhERM6AxRMRNUoIsU8IcUEvbkqEEO8KIQJb8hiqqh5QVTVQVdXqq8x2rxCiWs9We5vRktmaOP5Yu+OXCiF+FELc1lbHbw5VVW9WVfV9oP4Pv6qqjlVV9fmWPq4QYqoQ4rJ+rs4KIdYKIXq29HGciRCilxBimf7eOC2EWC+EGAMAqqp+q6pqlt26h4QQN17FvucKIZ61f0xV1TRVVVe3VP62JoS4TQixWghRLoQ4LoT4VgjxuzY47lWdeyLybCyeiMgRQ1RVDQTQA8D1AJ6SnMdeoV541d4mtvHxV+vnpgOAtwDMF0IEtXEGV/G+fq4iAKwG8JHkPL/Ski2fQoi+AL4C8DWAzgDCAEwE8D8tdQx3IoQYAeBDAO8AiAEQBeCvAIbKzAUADbWeE5FnYvFERA5TVfUwgC8AZAM/t0oNql1u3xXPzp+EEEeEEEeFEI/Ut9+6XfyEEKF6C9cRIcQZIcSiq80qhBgqhNisf4t9QAjxlzrLbxBCfKe3ChwUQvxRf9xPCPGy/liJEOI1IYRfU8dTVbUGwBwAgQBS7I7TRz/OWT3PDXbLwoQQ7+nn5owQ4mO7ZROEEHuEEKeEEIuEEFF2y24TQuzSs7+qt+Lcqy8bK4RYKYT4l37Mn4QQN9ttu0ZvscsBMANAP7016KS+/FctGg3lEEJ46a/ZeH35GSFEgSOvjaqqlwF8ACBeCNHB7lhDhRBFeu41Qohsu2VP6O+HMiFEcW1LgRDCoC9ThBAnhRDzavepL1sghDim7/NbIUSG3T7nCiEseuvQOf1cBOjn7oB+flcJIXztthmjt1ScEEJMaeRpvgTgbVVVX1RV9ZSq+V5V1RH6fgYJIfbp9/8LIBrAF/prMamx7EKIBwEMB/CEvv5C/fGfW1D093GB/t46rL+nfeyPLYSYrD+PI0JvEdOXDxZC7NB/dw4JIf5fI8/ToP+OlOrbDND3MVIIsd5+RSHE40KIBXV3IIQwAJgO4BlVVd9VVbVMVdVqVVWtqqqOt3stnxZC7Bdaq9R7Qoj2dc+l3T7tz8VUIcR/9de7XAixVQjRo5Fzn6K/t+8TQhwAsEIIsVwI8UCdY2wXQgxu5NwQkRti8UREDhNCxEH75vzHq9hsAIAuAG4GMEXYFVuNmAMgAEAWgI4A/nWVUQGgAsDdAIIBDAHwcO0HHSFEEoDPALwMrUWgOwCbvt1LAJIAdNVzJwJ4sqmDCa3wuw/AJQAH9cfiAHwK4BkAoQCmAPhECBGmb/YBAB8AmQA6Afi3vt3NAJ4DcCe0b+GPAKjtatcRwHwAjwEIB7AXQN3ub7315xMG7dy9XTevqqo2aC0hq/UWu/B6nlODOez8D4A8aOfwbkdeX70YGQPgBIAy/bHrAbwJYKye+x0Ai4UQPkKILADjAfRQVbU9gNsAHNB3NwnA7wDcACAWwDkA9kXcUmivYySArdDeW/ZGQWvhCAJQCO18dQXwG2iv2RMAauzW7w2tOL4FwF+FEF3qeX5B0F6TKwqF+qiqOhLaub1Nfy1ebiy7qqqvQWuleV5f/w/17PZpANfpz6U7gD4A/j+75bEA/KEVDhMAvF5bjAB4F8D9qqoG6duvbCR+bwDF0N6LfwOwUAgRAmARgLQ65+duXHn+Ae39H43Gz9dYffsbASRDa+n9dyPr13W7fuwQaF8AFQCNnntAe0+lQ3t/zdKPDwAQQuRBe87LriIDEbkDVVV544033hq8AdgHrRA5C2A/gNcA+NstG2S37rMA5ur3EwGoANLtlr8A7dv4htb1gtZdpwZABwey3QugSs9We+vVwLozALyo3/8LgI/qWccA4CKABLvH+gHY3cA+x9od/zKA8wCG2S1/EsC7dbb5GsBoAHH6tsH17HcWtA/GtT+3B1AN7QPvn6AVPLXLBICjAO61y1RcZ1sVQLj+85o6635b59hzATzrQA4vfb+97JZ/AuDRBs7VVGiF5Vl9HycA3GC3/E1oLQ/22yjQPvSnASgB8FsAXnXW2Q2gv93PcQAqARjqyRCuZ25n91zfsVtu1LfNqmfbFH3bSLvHfgBwZz3rJujrpjTy3h0EYJ/dz4cA3NjI+vVlf7bOOj/vA9rv6s12y34HYI/dsSsAGO2WnwZwnX7/iP7eCGri928stC8KRJ1zMtLuNf2rfr8bgJMAvOvZT3/9uXk1cqyVAMbZ/ZxV+zrXPZf1nIupAJbZLesKoKKhc2/3WsfbPeavv3c76z+/AqCgsfPDG2+8ueeNLU9E5IjbVVUNUVU1QVXVB1VVvXAV2x60u78f2jfMjYkDcFpV1TMO7v87PVvt7TsAEEKY9K5OJ4QQpdA+6NW2rsRB+2BeVyQAXwC1XcfOQvv2v2Mjx1+jqmoItFaKzwH0tVuWAGBk7b70/fWCdg7iAJxUVbW0nn1GQztXAABVVcsAnIHW+hMNu3OqqqoK7cOfvWN298/r/zZnko/GcjR0rMaO84F+riIB7ITWIlIrAcDjdc5VFIAYVVV3AngEWivYcb0LVqS+XTyAJXbb2KB98O0ohDAKIV4QWtfFMgB79G3sW9ns35+doLUE1vfeqD0Hjjzf03qGqHqWOcTB7I2Jgt1rp9+3f91Oqr+eoMX+ufwB2lijA/rv0G8aOc4h/T1of5za3/FZ0L4oALRWmw9VrctmXafsMjfkV+9F/b4PtPFzjqj7urVzYBv737ML0FrGRgttbNwI1N+KRkRujsUTEV2Lc9C619WKrGedOLv78dC+1W7MQQChetefazEPwMcA4lRVDYY2mYOwO0ZyPduUQGsdSbMrxoL17Rulqmo5gAcA3C+E6Gp3nHfrFHftVFV9UV8WbtdVyt4RaMUEgJ+7gXUAcBhaK1Os3TKBX38ovhpNTb3dWI5mU1X1BLRueFOFEJ30hw9Ca6WwP1cBqqrO17eZq6pqH2hdKo0A/qFvdwjATXW289OLnDHQuhUOhNZ9s3YsWu37APj1Oah9/et7b1zN8ysHsAHAsKvZrM7PTWVv6rU7CrvXDtrvnkOvm6qq61VVHQrtS4Ol0H6XGhJb5+eff8dVVV0DaOP+AIxEw8XGdn2bxs7Xr96L+nEuQWvB/NXfIb0LbRgcV++5rFMUAr8UgzcDOKOq6vdXcQwichMsnojoWmwGMEII4S2EuA7a2Ji6/iK0QfhZ0MYEfdjYDlVVPQptTMJrQogO+r5vaGybBgRBa8G6KIToBe2b4lpzAdwqhBgmtIkPwoUQufo38W8BeEUIESE0scJuwoUmsp+ANlandnKKOQD+IIS4SW9J8BNCDBBCRKuqehDabGwWIURInef5X+hFmD4+6B/QuuodgvZhtocQYoj+IfFhOP7te10lAGKFEN4NLG8sxzVRVXUbtC6Mj+oPvQEgXwhxvX7eA/Xn2E4IkaGfN18AF/RbbavJTADPCyHiAW1MmBCidoa2IGhdu05B+3D99yYyVQN4D9rrH6m/Zn0aOT+NeQzAWH0CglA9W3chxAcNrF8CbVa+Wk1lr7t+Xf8F8LT+3o6A9p6sO5nLFYQQ/kKIUUKI9norUTl+Odf1iRJCTNR/j0ZAKzztxwHNAfA6gHO1rcJ1qdpkK48AeFYIcY8Qor3QJojoJ4SYafd8JgltcpkgaOfjv/q2xQCChBC36K/VMwCu5jVr6lzWWqPvdxrY6kTksVg8EdG1+Au0D0tnoA26r++D4UpoXY6+BvCSqqorHNjvH6GNISoGcBzAn5uR7QEA/xBClEMb9D+/doGqqnuhTSLxOLQuVj8AyNEXPwKtS9AGAKUAVkAbtO+ofwEYKoTIUlV1H7QuUH+B9g35AX3/tX97aweg74L2Ae4hPd8yaF3UFkJrQYiH3v1JVdUSaDOtvQztg3UytAk8Kq8iY60voY0ZKhFCHKu7sLEcLeRFAA8IIcJVVV0P7TV7Hdr7aRd+OT++0MbLnYTW/aoDfpku/2VoH9a/1l/rddCm0we0iQ+O6Ldt+rKm/D8AOwBsgvbeeB6/bqlyiKpdb2kQtIkl9gkhTuvP7fMGNnke2gQUZ4UQf3Yg+1sAcoU2y2F9Ey38FUARtG6MWwCsxy+tdU25B8B+vbvg/dB+HxuyDtr4o9PQxjEOq9Pldja02TkbLTZUVZ0HbfKO/4P2nI9Be+8t1ld5E9oXL6sB/AStqHtY3/YMtN+dWdBa107j1930mlL33DeUUdWfRzaunDiFiDyEuLJVmoiIXIU+/uIItIkLXPYCqeSehBDtoH0Bkq1/aeHShBB/AjBGVdUbZWchIjnY8kRE5GKEELcKIYL1bmx/gTZr3wbJsYjqkw9grZsUTgEAHoTWxZSIPBSvmk1E5Hr6Qus25AOtS9ftqqo2p9seUasRQhyC1v3297KzXCshxO+gdf1djibGbRKRe2O3PSIiIiIiIgew2x4REREREZEDWDwRERERERE5gMUTERERERGRA1g8EREREREROYDFExERERERkQNYPBERERERETmAxRMREREREZEDeJFcIiIiIqIGbNq0qaOXl9dbALLBhgdXVwNga1VV1di8vLzjzdkBiyciIiIiogZ4eXm9FRkZmREREXHGYDCosvNQ89XU1IgTJ05kHjt27C0AQ5uzD1bPREREREQNy46IiChj4eT6DAaDGhERUQqtFbF5+2jBPERERERE7sbAwsl96K9ls2sgFk9ERERERE7uwIEDXoMHD+4cFxeXnZycnNW/f/+Ul156KXzAgAEp9a0/fPjwhE2bNvm1dU53xzFPREREREQOMltMeS25v4L8wk1NrVNTU4OhQ4emjBo16tTSpUt/AoB169b5L1y4MKShbT788MP9LZmTNGx5IiIiIiJyYkuXLg3y8vJSJ0+efKL2sd69e1/o379/xblz54y33npr56SkpKyhQ4cm1dTUAAB69uyZtmrVqgAACAgI6P7QQw/FpKWlZebm5qYfPHjQCwA++OCD4K5du6ZnZGRk9u7dO7X2cWoYiyciIiIiIie2ZcsW/9zc3PP1LduxY4e/xWI5uGfPnm0HDhzw/fLLLwPrrnPhwgWDyWSq2Llz53aTyVTx6quvRgDATTfdVLF58+biHTt2bL/zzjtPP/fcc5Gt/VxcHatLIiIiIiIXlZOTcy45OfkyAGRlZZ1XFMWn7jre3t7qiBEjSgEgLy/v3FdffdUeAPbu3etz++23x544ccL70qVLhri4uMq2Te962PJEREREROTEcnJyLhQVFQXUt8zX1/fnmQCNRiOqqqpE3XW8vLxUg8FQe//ndSZOnBj/4IMPHt+1a9f2GTNm7K+srGRt0ASeICIiIiIiJzZkyJDyS5cuienTp4fXPrZy5coAq9V6RRe9q1FeXm6Mj4+/DADvvfde2LXm9AQsnoiIiIiInJjBYMCnn36qfP311+3j4uKyU1JSsp555pno6Ojoy9ey3yeffPLIyJEjk/Py8tLCwsKqWiqvOxOqymt+ERERERHVp6ioaF9ubu5J2Tmo5RQVFYXn5uYmNmdbtjwRERERERE5gMUTERERERGRA1g8EREREREROYDFExERERERkQNYPBERERERETmAxRMREREREZEDWDwRERERETmpnTt3+nTp0iXL/rFJkyZFP/30050a227VqlUB9957bxwALF26NOjLL79sd7XHjomJyTl69KhX3cdfeeWVsNTU1MzU1NTMLl26ZM2dOzcEAAoKCsL27dvn3dR+HV3PGV1xMoiIiIiIqH42xZrXkvvLSR6wqSX3V+uGG244f8MNN5wHgG+++SYoMDCw+qabbjp3rftVFMV7+vTpUZs3b94RFhZWXVpaaqgtsObOnRverVu3C4mJiY1evNfR9ZwRW56IiIiIiFxUz5490x544IGYnJycjMTExOxly5YFAlpr04ABA1J27tzpM3v27IiZM2d2Sk9Pz1y2bFngkSNHvG655Zbk7OzsjOzs7IwVK1a0A4Bjx44Z+/Tp0yUjIyNz1KhRCaqqXnG8o0ePerdr164mODi4GgCCg4Nr0tPTL7377rsdtm7dGjBmzJjO6enpmRUVFeLRRx+Nys7OzujSpUvWyJEjE2pqalDfeqtXrw64/vrr07KysjL69u3bZf/+/d4AMHXq1I7JyclZqampmYMHD+7chqe1QSyeiIiIiIhcWFVVlbDZbDumTZt28Lnnnou2X5aWlnZpzJgxJyZMmFBSXFy8/dZbb60YP3583KRJk0q2bt26Y+HChcqECRMSAWDKlCnRJpOpYseOHduHDh169ujRoz51j9WrV6/z4eHhl+Pi4nLuvPPOxA8++CAYAO67774z2dnZ52fPnv1TcXHx9sDAQPWxxx47vnXr1h27d+/eduHCBcO8efOC667n7e0Ns9kcv3jxYmXbtm077rnnnpOPPvpoDAAUFBREbt26dfuuXbu2v/fee/vb4FQ2id32iIiIiIiclBCiycfvuuuuMwDQu3fvc4899tgVBU9da9eubb97927/2p8rKiqMZ86cMXz33XdBn3zyyR4AGDFiROn48eOr627r5eWFVatW7V65cmXAihUr2k+ZMiVu48aN7V5++eUjddf94osvgl5++eXIixcvGs6ePeuVmZl5AUCp/Tpbtmzx3b17t//AgQNTAaCmpgYRERGXASAtLe3CH/7wh6ShQ4eeHT169NmmnldbYPFEREREROSkOnXqVFVaWmq0f+z06dPGpKSkytqf/fz8VEArbKqrq+uvtuyoqoqNGzfuCAwMvKJfnsHQdMc0g8GAAQMGnB8wYMD52267rWzs2LGJdYun8+fPi0ceeSRh/fr121NSUi5PmjQp+uLFi1fsXFVVkZKScmHz5s3FdZdZrdbdX3zxRdCiRYtCXnjhhejdu3dv9faWO88Eu+0RERERETmp4ODgmo4dO15evHhxEACUlJQYv/322+CBAwdWOLqPoKCg6vLy8p8LsL59+5ZNmzatY+3P69at8weAXr16lb/zzjthADB//vz2ZWVlxrr72rdvn/eaNWsCan/euHFjQExMzCUACAwMrK4t9M6fP28AgMjIyKrS0lLDkiVLOtRuY79e165dL54+fdrrq6++agcAlZWVYuPGjX7V1dVQFMVnyJAh5a+99tqh8vJyY90iUgYWT0RERERETmzWrFl7n3/++aj09PTM/v37pz3++ONHsrKyKpveUjNs2LCzn332WUjthBFvvPHGwR9++KFdampqZnJyctaMGTMiAOCf//znkbVr1wZmZmZmLF++PDgqKupS3X1dunRJPProo7FJSUlZ6enpmQsWLOgwY8aMgwAwZsyYkw899FBCenp6pp+fX83o0aNPZGZmZt12220pubm5P8/0Z79eVVUV5s2bp0yZMiU2LS0tMysrK3PlypWBVVVVYtSoUUmpqamZ2dnZmePHjy8JDw+/ohthWxP1zaJBRERERERAUVHRvtzc3JOyc1DLKSoqCs/NzU1szrZseSIiIiIiInIAiyciIiIiIiIHsHgiIiIiIiJyAIsnIiIiIiIiB7B4IiIiIiIicgCLJyIiIiIiIgeweCIiIiIiclI7d+706dKlS5b9Y5MmTYp++umnOzW23apVqwLuvffeOABYunRp0Jdfftnuao8dExOTc/ToUa/GHl+9enVATExMztq1a/3ff//94CeeeCLyao9Tn6VLlwYNGDAgpSX21ZKuOBlERERERFS/eV89k9eS+xsx6K+bWnJ/tW644YbzN9xww3kA+Oabb4ICAwOrb7rppnNNbXc11q9f7z9ixIjkuXPnKn369LnQp0+fCwBKW/IYzoYtT0RERERELqpnz55pDzzwQExOTk5GYmJi9rJlywKBX1pudu7c6TN79uyImTNndkpPT89ctmxZ4JEjR7xuueWW5Ozs7Izs7OyMFStWtAOAY8eOGfv06dMlIyMjc9SoUQmqqjZ43KKiIr9hw4alvPPOO3sHDBhwHgAKCgrCxowZEw8Aw4YNS7z33nvjunfvnh4bG5vz7rvvdgCA6upq3H333fEpKSlZAwYMSOnfv39K7bIFCxa0T0pKysrLy0tbsGBBSO2xSkpKjIMGDUpOTU3NzM3NTV+/fr0/oLXA3XHHHYl9+vTpEhMTkzNr1qyQCRMmxKampmb269evS2VlpWjp883iiYiIiIjIhVVVVQmbzbZj2rRpB5977rlo+2VpaWmXxowZc2LChAklxcXF22+99daK8ePHx02aNKlk69atOxYuXKhMmDAhEQCmTJkSbTKZKnbs2LF96NChZ48ePerT0DGHDx+eMn369AO33HJLRUPrlJSUeG/cuLF48eLFu5955pkYAJg9e3aHgwcP+uzcuXPbrFmz9v3444+BAHD+/HkxceLExE8//XTP999/v/P48ePetfuZPHlydG5u7vldu3Zt/9vf/nb4nnvuSapdtn//ft9vvvlmz4IFC/ZMmDAhaeDAgWW7du3a7ufnVzN//vzgZp/UBrB4IiIiIiJyUkLU33hi//hdd911BgB69+597tChQw0WPLXWrl3b/uGHH45PT0/PHDJkSEpFRYXxzJkzhu+++y7oT3/60ykAGDFiRGn79u2rG9pHnz59yt5+++3wqqqqBo8zdOjQs0ajEXl5eRdPnTrlDQCrV68OvOOOO84YjUbEx8dX9erVqxwANm/e7BcbG1uZk5NTaTAYMHr06FO1+9mwYUPQ/ffff0rfZ/nZs2e9Tp06ZQSAQYMGlfr6+qo9e/a8UF1dLe68884yAMjKyrqwd+/eJs/F1WLxRERERETkpDp16lRVWlpqtH/s9OnTxvDw8J+rFj8/PxUAvLy8UF1d3WRXNVVVsXHjxh3FxcXbi4uLtx8/fnxLhw4dagDAYHCsPHjzzTcPAMCYMWMSGlqnNlftMe3/rU9DhWJ92wghVADw9fVVAcBoNMLLy0utzW8wGFBVVcVue0REREREniI4OLimY8eOlxcvXhwEaON/vv322+CBAwc22F2urqCgoOry8vKfC7C+ffuWTZs2rWPtz+vWrfMHgF69epW/8847YQAwf/789mVlZcYr96YxGAxYvHjxT3v27PH785//HN3QenX169evYtGiRR2qq6tx8OBBr/Xr1wcBQLdu3S4eOnTIZ9u2bb4AMG/evNDabXr16lX+7rvvhgHaWK4OHTpUhYaG1jh6zJbE4omIiIiIyInNmjVr7/PPPx+Vnp6e2b9//7THH3/8SFZWVqWj2w8bNuzsZ599FlI7YcQbb7xx8IcffmiXmpqamZycnDVjxowIAPjnP/95ZO3atYGZmZkZy5cvD46KirrU2H79/f3VL774Ys+yZctC/vGPf0Q4kuWee+45ExUVdSk1NTXrvvvuS8jNzT0XEhJSHRAQoL766qv7Bw8enJKXl5cWFxf387GnTZt25IcffghITU3NfPLJJ2Pee++9vY4+95YmGms6IyIiIiLyZEVFRftyc3NPys7hTkpLSw3BwcE1x44dM15//fUZa9euLY6Pj2948FQLKyoqCs/NzU1szra8zhMREREREbWZm266qUtZWZnx8uXL4rHHHjvaloXTtWLxREREREREbWbDhg07ZWdoLo55IiIiIiIicgCLJyIiIiIiIgeweCIiIiIiInIAiyciIiIiIiIHsHgiIiIiInJys2fPDhFC5P34449+zdl+zpw5IZs2bbrqbQsKCsLGjBkTDwAvvPBCxIwZM8Kac3x3wdn2iIiIiIgc9NFHH+W15P7uuuuuTY6sN2/evNAePXpUzJkzJ7R79+5HrvY4ixYtCqmqqirNy8u7WHfZ5cuX4e3t3eQ+Jk+efOJqj+tu2PJEREREROTESktLDRs3bgx899139y1cuLADACxdujRowIABKbXrjBkzJr6goCAMAB588MGY5OTkrNTU1Mxx48bFfvnll+2++uqrkKeeeio2PT09c9u2bb49e/ZMmzhxYsz111+fNnXq1E4ffPBBcNeuXdMzMjIye/funXrw4MErGlkmTZoU/fTTT3cCgOnTp4dnZ2dnpKWlZd5yyy3J5eXlHlFXsOWJiIiIiMiJvf/++yE33nhjadeuXStDQkKq16xZE9DQuiUlJcbPP/+8w08//bTVYDDg5MmTxvDw8OpBgwadHTx4cOl99913pnbds2fPGr///vudAHDixAnjiBEjig0GA15++eXw5557LvLNN9881NBxRo8efeaRRx45CQBmszm6oKAg/Mknnzzeks/bGXlEhUhERERE5Krmz58fOnLkyDMAMGzYsNNz5swJbWjd0NDQal9f35oRI0YkzJo1KyQwMLCmoXVHjhx5uvb+3r17ffr169clNTU1s6CgILK4uNi/sUybNm3yz8vLS0tNTc38+OOPw7Zt29assViuhsUTEREREZGTOnbsmPG7775rn5+fnxATE5MzY8aMyE8//bSDl5eXWlPzS11UWVkpAMDb2xubN2/eMWzYsLOLFi0KufHGG7s0tO+goKCfdzBx4sT4Bx988PiuXbu2z5gxY39lZWWjdcK4ceOSZsyYcWDXrl3bH3/88SNNre8uPOJJEhERERG5ojlz5nS44447Th05csR2+PBh27Fjx7bExsZeAoA9e/b4X7hwQZw6dcq4Zs2a9oA2Pur06dPG4cOHl86cOfPgjh07AgDrjabQAAAgAElEQVQgMDCwuqysrMHP/uXl5cb4+PjLAPDee+81OaPe+fPnDfHx8ZcrKyvFvHnzGmwJczcc80RERERE5KQ++uijsMmTJx+1f+z3v//9mTlz5oQOGTLkTEZGRlZSUtLFrKys84A2jmnw4MEptS1RU6dOPQgAo0ePPv3AAw8kzpw5s9OCBQuUusd58sknj4wcOTK5U6dOl6677rpzBw4c8G0s15QpU4707NkzIyYm5lJGRsb5iooKY8s9a+clVFWVnYGIiIiIyCkVFRXty83NPSk7B7WcoqKi8Nzc3MTmbMtue0RERERERA5g8UREREREROQAFk9EREREREQOYPFERERERETkABZPREREREREDmDxRERERERE5AAWT0RERERETu7gwYNeQ4YMSYqNjc3JysrK6NatW/rs2bNDZOfyNLxILhERERGRg9544428ltzfuHHjNjW1Tk1NDYYMGZIyatSoU0uWLNkLALt27fL56KOPHCqeqqqq4OXFj/0tgS1PRERERERObMmSJUHe3t7q5MmTT9Q+lpqaeunJJ588XlVVhfHjx8dmZ2dnpKamZr744ovhALB06dKg3/zmN6lDhgxJSktLy9q5c6dPUlJS1vDhwxO6dOmSNXTo0KRFixYF9ejRIz0hISHbarUGAIDVag3o3r17ekZGRmb37t3Ti4qKfAGgoKAg7Oabb07u169fl4SEhOwJEybEAsC//vWv8Pvvvz+uNtf06dPDx44dG9u2Z6jtsAQlIiIiInJiNpvNv2vXrufrW/bKK6+EBwcHV2/dunXHhQsXxPXXX58+ZMiQMgDYsmVLux9//HFbenr6pZ07d/ocPHjQ78MPP/wpLy9vf9euXTPef//9sI0bNxZ/8MEHIX//+9+jBgwYoOTm5l7csGFDsbe3NxYtWhQ0efLk2OXLlysAsH379oCioqLt/v7+NSkpKdmPPvpoyf333386Kysrs7Ky8pCvr686d+7c8P/85z/72/L8tCUWT0RERERELuSPf/xj/IYNGwK9vb3V2NjYyuLi4oBPP/20AwCUl5cbt2/f7ufj46N27dr1XHp6+qXa7WJiYip79ux5AQBSU1MvDBw4sMxgMKBHjx7np06dGg0Ap0+fNg4fPjxp3759fkII9fLly6J2+759+5aFhYVVA0BKSspFRVF8U1JSKvr06VP+4YcfBufk5Fy8fPmyqD2GO2K3PSIiIiIiJ5aTk3Nhy5YtAbU/z5kz58C3336768yZM16qqorp06cfKC4u3l5cXLz98OHDtjvuuKMMAAICAmrs9+Pj46PW3jcYDPDz81MBwGg0orq6WgDA448/HtO/f//y3bt3b1uyZMmeS5cuGerb3mg0/lxYjRs37uSsWbPC3njjjbC77777ZGudB2fA4omIiIiIyIkNGTKkvLKyUkybNi2i9rGKigoDANx0002lr7/+ekRlZaUAgC1btviWlZU1+zN+WVmZMTY29hIA/Oc//wl3ZJuBAweeO3r0qM/ChQvD7r///tPNPbYrYLc9IiIiIiInZjAYsGTJEiU/Pz+uoKAgMjQ0tCogIKD62WefPfSnP/3pzL59+3xzcnIyVFUVoaGhlz///HOlucd6/PHHj40dOzapoKAgsl+/fmWObnf77bef2bJlS0BERER1c4/tCoSqqk2vRURERETkgYqKivbl5ua6dVe0ljBgwICUP//5zyW///3vy2VnaUpRUVF4bm5uYnO2Zbc9IiIiIiJqlpMnTxoTExOz/fz8alyhcLpW7LZHRERERETNEh4eXr1v376tsnO0FbY8EREREREROYAtT0RE1KrMFpMBQAiAUABh+n1faP8HXe3N2+6+EUAVgPMAzun/2t9v8LGC/MLK1n3WRETkjlg8ERGRw8wWU3v8UgTV/hvWxGMhcLKeDmaLqRq/LqzKAJQAONrQrSC/0G0v+khERI5h8URERD8zW0xRAJLq3Drr/0ZDa/lxB0YAQfrNIWaLqRSNFFf67UhBfqHDU/sSEZFrYfFERORB9Jaj2mIoqc79RAD+0sI5v2D9lt7YSmaL6SSAnQCK9Vvt/Z8K8gvd+vonRNR6FEXxHjduXPyePXv8a2pqMGjQoNLXX3/90A8//OB38OBBn+HDh5cCwKRJk6IDAwOrn3vuuRLZmd0RiyciIjdjtpgEgC4AugHIBZCKXwqkUInRPEW4futT5/FLZotpD+oprAryC0vbNiIRNddXX32V15L7GzRo0Kam1qmpqcHtt9+eMnbs2OMPP/ywUlVVhVGjRiU8/PDDMVlZWRc2btzYrrZ4ulZVVVXw8mKJ0BCeGSIiF2a2mPwA5ADoDq1Y6qb/HCgzF9XLB0CmfvsVs8V0DFcWVUUF+YVH2jQhETmlJUuWBPn6+tY8/PDDpwDAy8sLM2fOPJiYmNjVy8tLVVUV6enpgY888shRANixY4d/z549044cOeIzYcKEkqeeeuo4ALz22muhr7/+eqfLly+LHj16nJs9e/Z+Ly8vBAQEdB83blzJN9980/7FF188tHjx4uDly5eHGI1G9cYbbyx74403Dsl8/s6ExRMRkYswW0zh+HWR1A1AGrTxO+TaIvVbf/sHzRbTUQAb7W7fF+QXnmj7eEQkk81m88/NzT1v/1hoaGhNTEzMpdGjR5/ctWuX3+zZsw8AwKRJk/z37Nnjt27dup1nz541ZmRkZD/22GMntm3b5rtgwYLQjRs3Fvv6+qp33313/MyZM8MmTpx46sKFC4bs7OwLr7zyypGSkhLj+PHjE3/66aetBoMBJ0+e5P8xdlg8ERE5IbPFlAQgD78ulqKlhiIZogAM0W8AALPFdAC/FFPrAWwoyC+skBOPiNqCqqoQQqgNPH7F+jfffPNZf39/1d/fvyo0NPTyoUOHvJYtWxa0devWgNzc3AwAuHjxoqFjx45VAGA0GnHvvfeeAYDQ0NBqX1/fmhEjRiT87ne/K22p7oDugsUTEZFk+nWQugLop9/6QvvQTFSfeP12h/5ztdli2gpgHYBCAIUF+YV7ZIUjopaXk5NzYfHixR3sHzt9+rTh2LFjPkaj8YqiytfX9+fHjEYjqqqqhKqq4q677jplsVgO113fx8enpnack7e3NzZv3rzj008/bT9v3rwOr7/+esfvvvtuVys8LZfE4omIqI2ZLSZfAD2hFUn9APSGNosbUXMYoU0MkgvgAQAwW0wnoBdSAFYBWM+Z/ohc19ChQ8ufeuopw4wZM8ImTpx4qqqqCg8++GDcXXfddTIyMvLyhg0b2jW1j1tvvbXsjjvuSHniiSdKYmJiqkpKSoylpaXG1NTUS/brlZaWGioqKgzDhw8vvfHGGytSU1NzWu+ZuR4WT0RErcymWL2hFUsDAAy8s+/DhgVr/t2/ic2IrkUEgKH6DQBKzRbTNwBWAFhekF+4V1oyIrpqBoMBixYt2jNu3LiEF198MaqmpgYDBw4sLSgoOFxWVmZ46aWXotLT0zNrJ4yoT15e3sWnnnrq8G9/+9vUmpoaeHt7qwUFBQfqFk9nz541Dh48OKWyslIAwNSpUw+29vNzJUJVr2jpIyKia2BTrAYAPQAMhFYw9YXd7HeqWnPireVPRUiKRwQACrRCagWAb3hhX6KGFRUV7cvNzT0pOwe1nKKiovDc3NzE5mzLliciohZgU6whAP4H2sD+WwB0aGhdIQwRoUGRP50uP9a5rfIR1ZEMrYvfAwCqzBbTd/ilmPq+IL+wRmY4IiJnxeKJiKiZbIq1M37pGtUPV/E3NTkq9xCLJ3ISXtBaR/sCeA7A6Tpd/A7IDEdE5ExYPBEROUjvjvcbaK1LQwFkNXdfiR0zvb7ftbylohG1pFAAd+o3mC2mndAKqU8BWDnxBBF5MhZPRESNsCnWAAA3QSuWfgegU0vst327sOSW2A9RG0jTbw8BOGG2mD4BMB/AShZS5CFqampqhMFg4EQBbqCmpkYAaHbXZBZPRER12BSr/YVJfwvAv6WPYRCGTh0CO+47U3E8saX3TdSKIgCM128ldoXUKo6TIje29cSJE5kRERGlLKBcW01NjThx4kQwgK3N3Qdn2yMiAmBTrB0A/C+AuwH0AXDlJdtb2I+KdfXG3V/2a+3jELWBYwA+hlZIrWEhRe5k06ZNHb28vN4CkA3AIDsPXZMaAFurqqrG5uXlHW/ODlg8EZHHsilWH2hd8f4IbaY837Y8/pmKkrUL1vy7T1sek6gNHMEvhdTagvxCftAgIrfB4omIPI5NsfaBVjDdBW1wvBQ1as3Rt5c/FSXr+ERt4DCABdAKqUIWUkTk6lg8EZFHsCnWLtC65N0NwGmmCJ+/+uX9pedOJsjOQdQGDgL4EMDbBfmFxbLDEBE1B4snInJbNsUaDmA4tFam30iOU69Ne75e/cOerznuiTzNKgD/AfBxQX5hpewwRESOYvFERG7Fplh9oU0r/kcAtwLwlpuocafLj639eG0Bxz2RpzoFYBaANwryC3fKDkNE1BQWT0TkFmyKNR7AgwDuBxAuOY7DamqqD7+94i8xsnMQOQG2RhGR02PxREQuzaZYfwtgIrRrMhklx2mWD1e9dKjs/OlY2TmInARbo4jIabF4IiKXY1OsgQDuAZAPIENynGu2cfeXa3/UZgAkol9biV9aoy7JDkNExOKJiFyGTbGmQmtlugdAe8lxWsypsqNrPln3al/ZOYic2En80hq1S3YYIvJcLJ6IyKnZFKsBwGBoRdMgAEJuopZXU1N98O0Vf4mTnYPIRVgBTC/IL/xMdhAi8jwsnojIKdkUayi0yR8eBJAoN03rm7fyxcPlF85w4ggix20B8AKAeQX5hdWywxCRZ2DxREROxaZY0wE8CmAUAH/JcdrMhl3L1xb9tJLjnoiu3j4A06FdfPeC5CxE5OZYPBGRU7Ap1q4AngIwDIBBcpw2d7L08JqFhRaOeyJqvhMACgBYCvILz8gOQ0TuicUTEUllU6zXA/gLtHFNbjeeyVHVNdX731nxlwTZOYjcQAWANwC8XJBfeFh2GCJyLyyeiEgKm2LtC61oull2FmfxwbfTjp67WBolOweRm7gEYC6AF3i9KCJqKSyeiKhN2RTrIGhF0w2yszib9Tu/WLdl7+resnMQuZkaAIsB/LMgv3CD7DBE5NpYPBFRm7Ap1sEAngTQS3YWZ3Wi9NDqRYWv9ZOdg8iNWQFMK8gvXC47CBG5JhZPRNRqbIpVALgD2kQQ3STHcXrVNVV731nxdJLsHEQeYCOAxwvyC7+RHYSIXAuLJyJqcTbFagQwAsATADIlx3Ep71v/WXK+sqyT7BxEHmIFtCJqs+wgROQaWDwRUYvSu+dNA4umZiks/mzd1n1rOe6JqO2oAP4L4KmC/MK9ssMQkXPzuGupEFHrsCnWHjbF+g2AJWDh1GydI3OqZGcg8jAC2kW5i80WU4HZYoqQHcjVCCGeFULMbWT5NiHEjW0YiajVsHgiomtiU6wJNsU6F9oYggGy87i68KDoWNkZiDyUD4CHAChmi+lps8XUTnYgZyOEGCWE2CiEqBBCHBVCfCGEaPLi3qqqZqmq+m0bRCRqdey2R0TNYlOswdDGNJkB+EmO41bmWv9x4kJlOb/9JpKrBMBzAN4oyC/0+BZhIcQkAFMATACwHNp1tG6FdtmJcwBSVFW9W15CorbBliciuio2xeptU6wPA1AATAYLpxbXuVP2HtkZiAidAFgAbDdbTP9rtpiE7ECyCCGCoRWS+aqqfqKq6jlVVS+rqrpEVdXH9NV8hBCzhRDleje96+y23yeEGKTff1YIMb+RdacIIRR92XYhxB/a9MkSNYHFExE5zKZY7wKwA8ArAMIkx3FbnaO6XpadgYh+1gXAhwA2mC2mgbLDSGKC9kXZwkbWGQpgHoAQAJ8CmNHMdRUA/QAEA/grgLlCiKhmJydqYSyeiKhJNsXa26ZY1wGYDyBZdh53F94+Olp2BiK6wnUAvjZbTMvNFpOnXbcuDMBJVVUb6764RlXVz1VVrQYwB0Buc9ZVVfUjVVWPqKpao6rqhwB2A+jZAs+BqEWweCKiBtkUaxebYv0YwFpo3zxSGzAavJL9fNqdlJ2DiOp1M4BNZovpVbPFFCw7TBs5BSBcCOHVyDrH7O6fB+DXyPoNriuEGCOE2CyEOCuEOAsgG0D4NWQnalEsnojoCjbF2s6mWF8AsA3AHbLzeBohhOC4JyKnZgAwEdr05qNkh2kDhQAuAri9NQ8ihEgA8Ca0cxumqmoIgK3QppMncgosnojoV/SL3G4D8BgAb8lxPFbnqK6VsjMQUZMiAbxvtpi+MltMqbLDtBZVVUsBPA3AIoS4XQgRIITwFkLcJoR4oQUP1Q7aRYtPAIAQ4j5oLU9EToPFExEBAGyKNUbvorcEQILsPJ4uIjiG456IXMdvAWwxW0zPmS0mt5yBVFXVlwFMAvAUtOLmILQWokUteIztAKZDa+kqAZADrds4kdPgdZ6IPJxNsdZ2P5kKIEhyHNKpqqrO/ebvZy5ePh8qOwsRXRUFwMSC/MJlsoMQUctj8UTkwWyKNQ/AfwDkyc5CV1q9deH64kPf/0Z2DiJqlgUA/lyQX3hYdhAiajksnog8kE2xBkFracoHYJQchxpw5JSy6rPv375Bdg4iarZyaGOFXi3IL6yWHYaIrh3HPBF5GJtivQPahW7NYOHk1CKC4zrJzkBE1yQIwL8AbDRbTL1khyGia8eWJyIPYVOs8dCu4j5EdhZyjKqqNbO//lv5paqLnnItGSJ3pkKbhntKQX7hGdlhiKh52PJE5OZsitXLplgfBbAdLJxcihDCkNQpa6fsHETUIgSAcQB2esi1oYjcElueiNyYTbFmA5gNoLvsLNQ8h0/uWfn5xnf6y85BRC3uIwATCvILT8sOQkSOY8sTkRuyKVZhU6yTAGwECyeX1jGE456I3NRdAGxmi+kW2UGIyHFseSJyMzbFGgfgPQADJUehFsBxT0Qe4TUAjxXkF56XHYSIGseWJyI3YlOsowBsAQsntyGEMCR0zNglOwcRtaoHAfxgtpiulx2EiBrHliciN2BTrB2gfXM5QnYWankHT+xauWzTexz3ROT+qqBdg+/vBfmFVbLDENGV2PJE5OJsinUQtNYmFk5uqlOH+AjZGYioTXgBeBbAGrPF1EVyFiKqB4snIhdlU6x+NsX6bwArAMTKzkOtx9vom+Zt9C2XnYOI2sxvAGw2W0wPyA5CRL/GbntELsimWLsDmAsgU3YWahvWog837jladJ3sHETU5r4A8KeC/MJjsoMQEVueiFyKTbEabIr1/wOwHiycPEpydLcK2RmISIrbAGw1W0zDZAchIhZPRC7DpliTAKwE8DwAb8lxqI1FhiSEy85ARNKEAVhgtphmmy2m9rLDEHkyFk9ELsCmWO8AsBlAX9lZSA5vL990L6PPOdk5iEiqPwIoMltMebKDEHkqFk9ETsymWL1tivVfAD4GwG8bPZgQwis+Iq1Ydg4iki4RwFpOJkEkByeMIHJSNsUaC2A+AJPsLOQc9pfs+HbFj3NulJ2DiJzGXADjC/ILz8sOQuQp2PJE5IRsivVmAD+ChRPZiQxNDJOdgYicyt0ANpgtpjTZQYg8BYsnIieiz6b3V2hT03KCAPoVHy+/dC+DN79hJiJ7WQA2mi2m4bKDEHkCdtsjchI2xRoK4AMAt8jOQs7rqx8/+HFvydbusnMQkVN6FcAjBfmFl2UHIXJXbHkicgI2xdoNwEawcKImpETnlsrOQERO6yEA35gtpk6ygxC5KxZPRJLZFOtoAOsAJMnOQs4vskNSB9kZiMip9QWwyWwx/UZ2ECJ3xOKJSBKbYvWyKdZ/Q5styV92HnINvt7+6UaD1wXZOYjIqcUAWGW2mMbKDkLkbjjmiUgCm2LtBG0a8htkZyHXs+KHuT/uP76d456IyBH/AWAuyC+8JDsIkTtgyxNRG7Mp1usAbAILJ2omjnsioqswHoDVbDFFyg5C5A5YPBG1IZtiHQpgJbQuFUTNEh3ameOeiOhq9IY2Dqqn7CBEro7FE1EbsSlWM4CFAAJkZyHX5usdkGYwGCtl5yAilxINrQVqqOwgRK6MxRNRK9MvfPsKgH+Dv3PUAoQQfrFhKcWycxCRywkA8InZYnpQdhAiV8UPckStyKZYAwB8DOBh2VnIvaREdzsrOwMRuSQjAIvZYppmtpiE7DBErobFE1Er0WfU+xbA7ZKjkBuKDk1uLzsDEbm0yQDeN1tMPrKDELkSFk9ErcCmWDMAfAfgetlZyD35+bRLNwgjpx4momsxEsAKs8UUIjsIkatg8UTUwmyKdQCAdQASJUchNyaE8I8OS94pOwcRubz+ANaaLaZ42UGIXAGLJ6IWZFOsYwAsB8Bv8ajVpUR3OyU7AxG5hUwA35ktJl58m6gJLJ6IWohNsT4LYBYAb8lRyEPEhHHcExG1mCgAq8wW062ygxA5MxZPRNfIpli9bYp1FoBnZGchz+LvE5huEIbLsnMQkdsIBLDEbDHdLzsIkbNi8UR0DWyKtT20bnpjZGchzyOECIgK7czrPRFRS/IC8JbZYvqr7CBEzkioqio7A5FLsinWUGiF03Wys5Dn2nX4h5UrbQv6y85BRG5pFoD/K8gvZAs3kY4tT0TNYFOsHaFdw4mFE0kVG5bSTnYGInJb9wD4zGwxBckOQuQsWDwRXSWbYo0BsApAjuwsRP6+QelCGKpk5yAit3UTgOVmi4kT1BCBxRPRVbEp1gRohVOa7CxEACCECIzqkMjrPRFRazIB+NJsMQXLDkIkG4snIgfZFGsKgNUAOsvOQmQvJbrbCdkZiMjt9QTwldli6iA7CJFMLJ6IHGBTrJnQWpziZGchqis2vAvHPRFRW7gOwNdmiylUdhAiWVg8ETXBpli7QZscIkpyFKJ6Bfi2TxMQ1bJzEJFH6A7gG7PFFC47CJEMLJ6IGmFTrD0BfAMgQnYWooYIIdp36pDAcU9E1FZyAVjNFlNH2UGI2hqLJ6IG2BRrPwBfAWD/bnJ6HPdERG0sG1oB1Ul2EKK2xOKJqB42xToIwDIAvLYFuYS48NQA2RmIyONkAvjWbDGxWzt5DBZPRHXYFOtgAEsB8MMouYx2fsGpgKiRnYOIPE46tAIqRnYQorbA4onIjk2x3g7gEwC+srMQXQ0hRHCnkPhdsnMQkUdKBbDSbDFxRlpyeyyeiHQ2xXoTgA8BeMvOQtQcKdG5JbIzEJHHSoZWQCXIDkLUmlg8EQGwKVYTgIUAfGRnIWquuIg0P9kZiMijJUEroJJkByFqLSyeyOPZFGtXAJ8D4IVGyaUF+oWkAUKVnYOIPFoCtAKqs+wgRK2BxRN5NJti7QJgBYAQ2VmIrpUQIiQiOJbjnohItjgAKziNObkjFk/ksWyKNRbAlwD4x53cRkp07jHZGYiIoI2B+sJsMfGSH+RWWDyRR7Ip1ghohRMHtpJbiY9I57gnInIW3QEsMltMHE9MboPFE3kcm2JtD+0CuOmysxC1tCD/Dl0AcNwTETmLgQDmmi0mfuYkt8A3MnkUm2L1h3YB3B6ysxC1BiFEaHj76D2ycxAR2bkLwL9lhyBqCSyeyGPYFKs3gI8B9JOdhag1pUR3Oyo7AxFRHRPNFtNTskMQXSsWT+QRbIrVAGAOgNtkZyFqbfERGbzQMxE5o7+ZLaaxskMQXQsWT+QpZgIYLjsEUVtoH9Chi+wMREQNmGm2mH4vOwRRc7F4IrdnU6wvAPg/2TmI2ooQhvCwoChFdg4ionoYAcwzW0zsQk8uicUTuTWbYn0IwGOycxC1teTo3MOyMxARNcAPwKdmiylHdhCiq8XiidyWTbHeCuBfsnMQyZDQMZPjnojImYUAWGa2mHi9RXIpLJ7ILdkUaxaAD6F1DyDyOMEBoSmyMxARNSEawHKzxRQuOwiRo1g8kduxKdYIaNdyai87C5EsQhgiOgR22is7BxFRE9IAfGa2mNrJDkLkCBZP5FZsitUXwEIAiZKjEEmXEp17SHYGIiIH9AQw32wx8XMpOT2+ScndvAWgj+wQRM4goWMmu60Skav4HwD/kB2CqCksnsht2BTrkwDulp2DyFkEtwvvLDsDEdFVmGy2mEbKDkHUGBZP5BZsivVOAH+TnYPImRiEITKkXcf9snMQEV2Ft80WU3fZIYgawuKJXJ5NsV4HYDYAITsLkbNJju56QHYGIqKr4A9gkdliipAdhKg+LJ7IpdkUayyAT6H9sSWiOhI7ZvHvPBG5mngAH5ktJi/ZQYjq4n+q5LJsirUdtMIpSnYWImcV0i4iSXYGIqJm6A/gFdkhiOpi8UQuyaZYBYC5ANgvmqgRBoMhOjggnF33iMgV5ZstpvtlhyCyx+KJXNU/AdwuOwSRK0iOzmXxRESu6jWzxWSSHYKoFosncjk2xfq/ACbLzkHkKpI6ZcmOQETUXD4APjZbTNGygxABLJ7IxdgUaxdoF8IlIgeFtItIlJ2BiOgaRAFYaLaYfGUHIWLxRC7Dplj9ACwAECQ7C5ErMRiMse0DQg/LzkFEdA16ApgpOwQRiydyJTMAdJUdgsgVJUfm7pWdgYjoGt1rtpjMskOQZ2PxRC7Bplj/CIAz7hA1U2Ikxz0RkVuYbraYBsoOQZ6LxRM5PZtizQTwuuwcRK6sQ2CnBNkZiIhagBeA+WaLKVF2EPJMLJ7IqekXwl0AoJ3sLESuzGgwxgX6hxyRnYOIqAWEAfjAbDEZZQchz8PiiZzdTAAZskMQuYPkyK4c90RE7sIE4BnZIcjzsHgip2VTrP8H4G7ZOYjcRVJkjio7AxFRC3rCbDH1kx2CPAuLJ3JKNsWaC6BAdg4idxIa1ClOdgYiohZkBDDXbDGFyA5CnoPFEzkdm2INAvARAD/ZWYjcidHgldDOL/iY7BxERC0oHsAbskOQ52DxRM7oLQBdZIcgckedI7v+JDsDEVELu8tsMXULgbIAACAASURBVPFyJtQmWDyRU7Ep1okA/ld2DiJ31Tkyu1p2BiKiVvBvs8WUKjsEuT8WT+Q0bIq1B4DpsnMQubOw9lGxsjMQEbWCdgD+a7aYfGQHIffG4omcgk2x+gKYA4B/9IhakdHglRTgG3Rcdg4iolbQA8DfZYcg98biiZzFVACZskMQeYLOkTl7ZGcgImolj5gtpkGyQ5D7YvFE0tkUax8Ak2TnIPIUnaO6ctwTEbkrAWC22WIKlx2E3BOLJ5LKplgDALwHvheJ2kxYUFS07AxERK0oCsA7skOQe+IHVpJtGoAU2SGIPImX0TvZ3yfwhOwcREStaIjZYsqXHYLcD4snksamWAcC4B82Igk6R+YosjMQEbWyl8wWU7bsEOReWDyRFDbFGgStSV3IzkLkiTpH5VySnYGIqJX5QZu+3Fd2EHIfLJ5IlpcBJMgOQeSpwtvHRMnOQETUBrIBPCU7BLkPFk/U5myK9TYAY2XnIPJkRoNXip9Pu1OycxARtYHHzRZTluwQ5B5YPFGbsinWDgDekp2DyNMJIURSp+zdsnMQEbUBbwBvmi0mfu6la8Y3EbW1VwFwmmQiJ5Ac1ZXjnojIU5gAPCA7BLk+Fk/UZmyK9Q8ARsvOQUSaiOCYSNkZiIja0D/MFlOs7BDk2lg8UZuwKdZwADNl5yCiXxgN3l18vQPOyM5BRNRGggBYZIcg18biidrK68D/z959h8d5FWj/v496s1xkW5KrbMklseWSEBKFkGAYwgKbwCYshLpZevspsLAv2aXkDS2wEJZk0b6wu/SWXTqBXcICCjUQII6jdHssyXJVG7UpmnZ+f0hJ3K0yM+d5Zr6f6/IVecrz3JIjae45zzlHy12HAPCUqXlPW5j3BKCQXN3e0fYS1yHgX74vT8aYzxpj3p/B4/UYYwKZOh6krmDnCyXxgwrwoObGbVHXGQAgx/6lvaNtkesQ8Cfn5ckYY40xLSfd9n+NMV+byfOttW+21n5o+nnPMsYczEbO6eOvMsZ8xxgzaIwZNcZ0GWOun76vafpzKZnF8fK+qHUFOysk3e46B4DTW7Zwdb3rDACQYw2S/sl1CPiT8/LkM1+V1KepzV3rJL1G0jGnibzvRknrXYcAcHolxaUby0oqRl3nAIAce317R9vlrkPAfzxfnp4YTTLGvMsY02+MOWKM+dvj7v+SMebDxphqSf8jaYUxZmL6zwpjTJEx5kZjTNAYM2SM+S9jzJLjnv9qY0zv9H3vPUeciyR9yVobttYmrbW7rbX/M33fr6b/OzJ97jZjTLMx5hfTxx40xnzdGLNo+rxflbRG0p3Tj/8/07dfYoz5nTFmxBizxxjzrOOyXm+M2W+MGTfGdBtjPL1yXVews1nSe1znAHBmxpiipvotj7nOAQA5ZiT9W3tHW7nrIPAXz5enaQ2SFkpaKel1kjqMMYuPf4C1Nizp+ZIOW2trpv8cltQu6cWSrtDU/kIhTa+0Yow5X1MLGbx6+r46SWdbwvL30+e+zhiz5qT7nnj3YtH0ue/R1DfmLdPHPk/Sakn/dzrvqyUdkHTV9OP/yRizUtKPJX1Y0hJJ75b0HWPMsulyeLuk51trF0i6VNL9M/rquXO7pArXIQCcHfOeABSoTZLe5zoE/MUv5Skh6YPW2oS19r8lTWjqf/iZeJOk91prD1prJzVVXl4yPTfpJZJ+ZK391fR975eUPsux/lrSr6cf122Mud8Yc9GZHmyt3Wet/V9r7aS1dkDSpzRV4s7kVZL+21r739batLX2fyX9SdILpu9PS9pqjKm01h6x1j40ky+AC13BzhfrqdwAPKx+0RpWwgRQqN7T3tG21XUI+IcXylNKUulJt5VqqjA9Ychamzzu7xFJNTM8/lpJ35u+DG5E0iPT56zX1IhQ3xMPnB69GjrTgay1IWvtjdbaLdPPv1/S940x5nSPN8YsN8bcYYw5ZIwZk/Q1SUvPkfWvn8g6nfcySY3T2V4m6c2SjhhjfmyM2TzDr0FOdQU7qyR92nUOADNTUly2sbSkfMx1DgBwoFTSv7d3tHnhNTF8wAv/oxyQ1HTSbesk9c7hWPY0t/Vp6lK3Rcf9qbDWHpJ0RFOX0kmSjDFVmrp079wnsnZQ0ic1VcCWnOHct0zfvs1aW6upkaXji9bJz+mT9NWTslZbaz82fc67rLXPldQo6VFJ/z6TrA68V1NFEIAPGGOK1y4/j3lPAArVJZLe6joE/MEL5ek/Jb1vehnwoumlu6+S9O05HOuYpDpjzMLjbvuspI8YY9ZK0vT8oRdN3/dtSX9pjLnMGFMm6YM6y9fEGPNxY8xWY0yJMWaBpLdI2metHZI0oKnL6o5fWW6Bpi4xHJmez/T3p8l7/OO/JukqY8zzjDHFxpiK6QUzVhlj6o0xV0/PfZqcPm5qFl+bnOgKdm7U1FwtAD7S0rgj4joDADj00faOtgbXIeB9XihPH5T0O0m/0dRiDv8k6ZXW2gdneyBr7aOSvilp//Rlbysk3Sbph5J+aowZ19SiDxdPP/4hSW+T9A1NjUKFJJ1tn6gqSd+TNCJpv6ZGV66ePlZE0kck/Xb63JdIulnSBZJGNbUQxHdPOt4tmiqOI8aYd1tr+yS9SNI/aqqM9WmqcBVN/3mXpMOShjU1d8qL75J8RlKZ6xAAZqd+8ZqzXVIMAPlugaZekwJnZaw93dVmwOx1BTv/WtJ/uc4BYPastckv/+yD0URqcoHrLADgSErSjtvfds+s38BH4fDCyBPyQFews0ZTqwkC8CFjTMma5ZuZ9wSgkBVLutV1CHgb5QmZ8gGdfY8sAB7X0rh9wnUGAHDsyvaOtr9wHQLeRXnCvHUFO8+X9A7XOQDMT/3itcx7AgDpk+0dbcWuQ8CbKE/IhNt16l5dAHymrKRic0lxWdh1DgBwbIuk17kOAW+iPGFeuoKdz5f0HNc5AMyfMaZkzbJNj7rOAQAe8MH2jjYW0MEpSlwHgH91BTuLJH3cdQ4AmdPcuH1i/9Eu1zHgM6lEWnf/V7f6Hh1VLJLUwmUVartqtZq2LJYk7b1vSH/4cZ8mRuKqWVyutqtWq3n7ktMea2Ikrrv/s1uHg2MqKSvWRc9bqdZn1kuSJqNJ/eTze3W0d0JNWxbpua9pUVHR1N7zP//GfjVtWXTG4wKzVC/pRknvdR0E3sLIE+bjNZJaXYcAkDmNS9Ytdp0B/pNOW9UsKtM17zhfb/rERbrkhav1ky/s1dhQTBMjcf30y/t02TVr9aZPXqRnvHiNfvqlfYqMJ057rJ9+eZ9q68r1ulsu1FVv3qR77jygg4+PSpIe/E2/lq6u1us/eqHGhye1f8+wJOnI/nFFRuMUJ2TaO9s72la7DgFvoTxhTrqCnRWSPuQ6B4DMKiup2FxcVBJ1nQP+UlperItfuFq1dRUyRUbrWhertq5C/QfCmhiZVHllsZq2LJYxRuu2LlZJeZFGB2KnHCc+mdKhvWO66HkrVVxcpGWrqtWyo04P3zMgSRobimnVhloVlxapsblWo4OTSqetfv3dXl3+kqYcf9YoAJWSPuo6BLyF8oS5eodYmhzIO8aYstXMe8I8RcbiGumPakljlZavqdHihkrtf2BY6bRVcM+wikuKtHRl1alPtCf858mPh45EJEl1jVXqe2xUyXhaR4JjWtJYqT13H9Xa8xdp4bKKrH9eKEivbO9oe5rrEPAOyhNmrSvYWaep64AB5KGWxu2jrjPAv1KptO768j5tvniZljRUqqjIaPPTl+mnX9qnf33HH/TTL+3TruvWqbT81JWgyyqK1bh+gf74PweVTKTV3xdW8P4hJeNpSdL5ly5XPJrSf33yQTU212rZyio9du+AduxqUOcd+/Wdf35I99zZl+tPGfnNSPqU6xDwDmOtPfejgON0BTs/JemdrnMAyI5YPLLnq7/48HbXOeA/Nm1115f2KR5L6YVv2qji4iIdeHRUd31hr65+22YtX12t/r6wfvS5x3T1Wzdr2arqU44xNjypX/5nt472TmhhXbnq19Zo+GhUf9V+/imP/Z/PP67z25ZrdDCmwYMR7Xr5Ov3gM49q53Matfb8Rbn4lFE4rrn9bfd8z3UIuMfIE2alK9i5RtJbXecAkD3lpZWbi4tKTp2QApyFtVY///p+RcYTesHrp4qTJA0eDGtFywLVr62RKTKqX1uj+qYa9T16+gHO2iXluuotm/WGjz1NL/37VsXCSdWvrTnlcb0Pj8haae35izR0OKLla6pljNHyNdUaPBTJ6ueKgvTx9o429rQE5QmzdpOkctchAGSPMaZ81dINzHvCrNx9R7eGj0X1l2/epJKyp15e1K+t0eHguAYOTu2/PNAX1pF946ef8yRp+GhU8VhKqWRaj947oAOPjmrnsxtPeEwykdbvfnBAz7x2rSSptq5Ch/aOKZVM68j+cS1cyq8pZNwGSW9zHQLucdkeZqwr2LlR0sOSTr1QHUBe2X+065c/v/+bV7jOAX8YG57Ulz+wW8Ul5sl9lyRp18vXa9NFS7Xnl0e1p/OIIuMJVdaUqvXyel3wnBWSpMf+OKg/3XVIr3zf1JWi93ce0R/vOqRkPK1lq6r1zGvXnjLy9Psf9amsolgXBKaOMRlN6n8+v1fHeqb3f/qblhNyABkyLGn97W+7h3mhBYzyhBnrCnb+p6SXus4BIPti8fD9X/3FR3a4zgEAHnPT7W+754OuQ8AdLtvDjHQFO3dI+mvXOQDkRnlp1eaiouJJ1zkAwGPe0d7RttB1CLhDecJMfURTy3UCKADGmIqVdS2Puc4BAB6zWNINrkPAHcoTzqkr2PkMSS9wnQNAbrU07hh2nQEAPIjRpwJGecJMfMh1AAC5t7KuudZ1BgDwoMWS2l2HgBuUJ5xVV7DzEkm7XOcAkHsVZdWbi0xx3HUOAPCgd7Z3tPEGUwGiPOFcbnQdAIAbxpiqFXXr2e8JAE7F3KcCRXnCGXUFO8+TdLXrHADcYd4TAJwRo08FiPKEs3mPWGEPKGgrl7bwwgAATm+xpLe6DoHcojzhtLqCnaslvcJ1DgBuVZbVbDKmKOk6BwB41DvbO9oqXYdA7lCecCbvklTqOgQAt4wx1SuWMO8JAM5guaTXuQ6B3KE84RRdwc46Sa93nQOAN7Ss2D7oOgMAeNi72zvaSlyHQG5QnnA6/5+katchAHjDyroNNa4zAICHrZX0StchkBuUJ5ygK9hZLentrnMA8I6q8gXMewKAs7uxvaON19UFgH9knOwNkupchwDgHcaYBQ2Lmx5znQMAPGyzpL9yHQLZR3nCk7qCnaWS/s51DgDe09K4fcB1BgDwuH9wHQDZR3nC8V4pabXrEAC8Z/WyjcyDBICzu7C9o+1ZrkMguyhPkCR1BTuNpP/jOgcAb6oqr91kZFKucwCAx73NdQBkF+UJT3iRpPNchwDgTcaY2vrFax53nQMAPO7F7R1tK12HQPZQnvCE97gOAMDbWhp39LvOAAAeVyLpTa5DIHsoT1BXsPNiSZe4zgHA21Yv21TpOgMA+MAb2jvaSl2HQHZQniBJb3YdAID3VVcs3CSZtOscAOBxDZKudR0C2UF5KnBdwc5Fkl7mOgcA7zPGLFy+aPVe1zkAwAdYOCJPUZ7wN5K4FAfAjLQ0bj/qOgMA+MBl7R1t212HQOZRnsCkRgAztmb5Zt5sAYCZYfQpD1GeClhXsPMKsTw5gFmoqVi0QZJ1nQMAfOCV7R1ti1yHQGZRngobC0UAmBVjzOJlC1cx7wkAzq1K0vWuQyCzKE8FqivYuUzSNa5zAPCflsYdzHsCgJl5a3tHm3EdAplDeSpcr5VU5joEAP9Zs3wzPzsAYGY2SHqu6xDIHMpTAeoKdhpJb3SdA4A/LahczLwnAJi5t7sOgMyhPBWmKyWtdx0CgD8ZY+qW1q4Ius4BAD7xwvaOtibXIZAZlKfCxEIRAOaluXHHYdcZAMAnisRrr7xBeSowXcHOlZL+0nUOAP62dvnmUtcZAMBHXtfe0VbuOgTmj/JUeF4vqcR1CAD+Vlu1pMV1BgDwkaVileO8QHkqIF3BzmJNlScAmBdjipYtWdDAvCcAmLlXug6A+aM8FZYXSlrlOgSA/NDSuP2Q6wwA4CNXtne01bkOgfmhPBWWV7sOACB/rK0/n3lPADBzpZJe6joE5ofyVCC6gp0LxEIRADKotqqu2XUGAPCZV7gOgPmhPBWOF0uqcB0CQP4oMkXLF9fUd7vOAQA+8oz2jra1rkNg7ihPhePlrgMAyD8tjdsPus4AAD5ixGsyX6M8FYCuYGedpIDrHADyz9r68/k9AgCzw6p7PsYvvcLwEk1NUgSAjFpYvXS96wwA4DNb2zvaWl2HwNxQngoDw8MAsqLIFDUuql7W6zoHAPgMo08+RXnKc13BzhWSnuk6B4D81dy4/YDrDADgMy9v72gzrkNg9ihP+e9l4t8ZQBY1Me8JAGZrjaTLXIfA7PELL/9xyR6ArFpUvXyd6wwA4EPs+eRDlKc81hXsbJZ0kescAPJbUVHRitqquj7XOQDAZ/66vaONBb18hvKU365zHQBAYWhp3M6iEQAwO3WSnuc6BGaH8pTfuGQPQE401W9xHQEA/IhV93yG8pSnuoKdrZJ4NQMgJxbXMO8JAObg6vaOthrXITBzlKf8xagTgJwpKipeuaByyUHXOQDAZ6okvch1CMwc5Sl/vcx1AACFpblxW4/rDADgQ1e5DoCZozzloa5g5zZJ613nAFBY1jVsdR0BAPzoee0dbcWuQ2BmKE/56QWuAyC74pNx3XTjJ/S8y6/TJdtfqJde9Ub9+pd/ePL+aDSmD3/g07r8ohfr0h1X6fqX33DGY1287QUn/NmxMaBbbr5dknT0cL9eee3bdNmFL9InP/r/Tnjem//2PXqo67HsfILwpcU19WtcZwAAH1ok6VLXITAzJa4DICsoT3kumUqpvnGZvvCNT6txxXL9+u4/6O/bP6jv/PjzWrmqQR9876eUSqX0/Z98SQsXLdBjjwTPeKw/PPDfT34ciUS165Jr9dznXyFJ+o/PfkNXX3OlXnD1c/Syq9+k51/1bG1p3aSf/LhTq1Y3akvrpqx/rvCP4qLiNTUVi45MxEYaXWcBAJ95oaRfuw6Bc2PkKc90BTsXSWpznQPZVVVVqbfecL1WrmpQUVGRrnh2m1auatTDDz6u7v0HdPfPf6cPfPjvtKRukYqLi3X+1o0zOu7//uRXWlK3WBdetE2SdOjgUT29bacWLKjR1m2bdfDAEU2Mh/WFz35T7e9+fTY/RfhUc+O2btcZAMCHeOPbJyhP+edKMaJYcIYGh9Xb3aeWDU3q2vOoGlfW619v+5Iuv+jFuuYFr9P//uRXMzrOD797l6568XNljJEktWxs0u9/82eNjU3ooa7HtH7DWn3m01/UK//2WtXWsrIqTrWuYWvKdQYA8KHW9o621a5D4NwoT/mHdy4KTCKR1I1/91Fdfc3ztK55jY4dGdC+x7tVs6BaP//tt/SPN7Xrff/nY9q/r/esxzly+Jj+fO8DuvqapzY7f/2bX6H7/tSl177iHbruVS9SMpHU3kf361nPbtN73vlhXf/yG/TNr3wv258ifGTJggZ++QPA3PAazgcYocgjXcFOI+kvXOdA7qTTab333beotLRE/3BTuySpoqJMJaUleuPbXq2SkmI97eLtuujiHfrdb/6k9S1rz3isO7/3U+28cKtWrX5qusrCRbX6xO0fePJcf/vyd+h9H3qnPv+5b6plwzp9+OPv0Utf9CY9vW2nmjc0ZfVzhT8UF5U0VVcsPBqOjTa4zgIAPvNCSZ9zHQJnx8hTfrlQUr3rEMgNa61u+odPaGgwpE913KzS0qn3QjZsbp7T8e783v+eMOp0sm/f8SO17jhPGzau097HurWldaNKy0q1YdM67Xu8Z07nRH5a39C633UGAPChZ7d3tJW7DoGzozzlF4Z7C8iHP/Bp7d93QP/ybx9RRcVTP2svvGibGhuX6/Of/YaSyZR2//lB/enePXrGMy8647Huv+9BHTs2qCunV9k72dBQSHd87Qd6a/v1kqSVqxr0x9/fr0g4qoe7HteqNSyuhqesb2hNus4AAD5ULelZrkPg7ChP+YXyVCAOHzqqb33zTj32yD7tarv2yT2afvyDn6m0tES3ffZD+vXdf9ClO6/Szf94qz7yiRu1rnlqC55//9ev6y2vvfGE4/3wuz9V4MrLVF1Tddrz3XrLZ/Xmt79aVdWVkqbmQv3h97v13Ge+TM96zqUsWY4T1C1oZN4TAMwNr+U8zlhrXWdABnQFO5dKOiYKMQAP+HrnLf2RyfHlrnMAgM8Eb3/bPS2uQ+DMeKGdP/5C/HsC8Ij1Da1n3pkZAHAmze0dbTPbnBFO8GI7fzDMC8Az1je0JlxnAACfeqHrADgzylMe6Ap2Fkk68zJpAJBjdbUrVrrOAAA+xRviHkZ5yg+XSFriOgQAPKGkuLS5oqx60HUOAPChy9s72mpch8DpUZ7yA+9QAPCc9Q2te11nAAAfKpMUcB0Cp0d5yg/PdR0AAE62vmFb3HUGAPAppmN4FOXJ57qCnVWSLnCdAwBOtmzhyhWuMwCAT13mOgBOj/Lkf5dIKnEdAgBOVlxU0lJRWjXsOgcA+NCW9o62Ra5D4FSUJ/97husAAHA6xhjT1LD1cdc5AMCHjKQ21yFwKsqT/zGsC8CzmhuZ9wQAc8Qb5B5EefKxrmBnsXhXAoCHLVu4qt51BgDwKcqTB1Ge/K1V0gLXIQDgTEqKSjeUl1aOuM4BAD709PaONua1ewzlyd+4ZA+ApxljiprqtzDvCQBmr0rSTtchcCLKk79RngB4XnPjtqjrDADgU1y65zGUJ3+jPAHwvOULVzPvCQDmhvLkMZQnn+oKdq6VtNJ1DgA4l5Liso1lJRWjrnMAgA9RnjyG8uRfjDoB8IWpeU/nP+Y6BwD4UGN7R9s61yHwFMqTf1GeAPhGc+N25j0BwNww+uQhlCf/4hsJgG8sX7RmmesMAOBTvObzEMqTD3UFOxdJ2uI6BwDMVGlx2abSkvIx1zkAwIcoTx5CefKnS8W/HQAfMcYUr11+Hvs9AcDsbWnvaFvoOgSm8ALcn3gHAoDvtDRun3CdAQB8qEhSm+sQmEJ58qcLXQcAgNmqX7SWeU8AMDcsFOYRlCd/anUdAABmq7SkfFNJcRmjTwAwexe7DoAplCef6Qp21kla4ToHAMyWMaZk7fLNj7rOAQA+xBvnHkF58p9trgMAwFw1M+8JAOaivr2jbanrEKA8+RHlCYBvNSxu4pc/AMzNVtcBQHnyI8oTAN8qK6nYVFJUGnGdAwB8iPLkAZQn/+GaVwC+ZYwpXb18E/OeAGD2KE8eQHnyka5gZ5GkLa5zAMB8tDTuGHOdAQB8iPLkAZQnf2mRVOU6BADMR+OSpiWuMwCAD/EGugdQnvyF+U4AfK+spHJzcVFJ1HUOAPCZRe0dbatchyh0lCd/oTwB8D1jTNnqZRuZ9wQAs8ele45RnvyFxSIA5IWWxh2jrjMAgA9RnhyjPPkLI08A8kLjkvWLXWcAAB+iPDlGefKJrmBnjaR1rnMAQCaUl1ZuLi4qibnOAQA+Q3lyjPLkH62SjOsQAJAJxpjylXUtzHsCgNk5r72jjdfvDvHF9w/mOwHIKy0rmPcEALNUJWm96xCFjPLkHwzTAsgrK+qaa11nAAAf4jWhQ5Qn/2hxHQAAMqmitGpzUVHxpOscAOAzlCeHKE/+0eQ6AABkkjGmknlPADBrlCeHKE/+sdZ1AADItJbG7SHXGQDAZ853HaCQUZ58oCvYuVxTEwQBIK+srGtZ6DoDAPgMb6g7RHnyhybXAQAgGyrKqjcVmaKE6xwA4CO17R1tLLjjCOXJH5pcBwCAbDDGVDUuWc+8JwCYndWuAxQqypM/NLkOAADZsmHFzmHXGQDAZyhPjlCe/KHJdQAAyJaVS1tqXGcAAJ+hPDlCefKHda4DAEC2VJbVbDKmKOk6BwD4yBrXAQoV5ckfmlwHAIBsMcbUNC5e95jrHADgI4w8OUJ58geWpASQ11pW7BhwnQEAfITy5AjlyeO6gp31kipd5wCAbFq1dAPzngBg5ihPjlCevK/JdQAAyLaq8gWbjEzKdQ4A8IlVrgMUKsqT9zW5DgAA2WaMWdCwuIl5TwAwM5XtHW3LXIcoRJQn72tyHQAAcoF5TwAwK1y65wDlyfuaXAcAgFxYtWxjlesMAOAjlCcHKE/exzr+AApCdXntRsmkXecAAJ+gPDlAefK+etcBACAXjDEL6xevedx1DgDwCcqTA5Qn72MyIICC0dK445jrDADgE1yd5ADlyfsoTwAKxuplG9nXDgBmhpEnByhPHtYV7KwWG+QCKCA1FYuY9wQAM0N5coDy5G1LXQcAgFwyxixavnDVXtc5AMAHeJ3oAOXJ27hkD0DBaVmx46jrDADgA1XtHW0lrkMUGsqTt1GeABScNcs2VbjOAAA+Ues6QKGhPHkb5QlAwampXLxBknWdAwB8gPKUY5Qnb1viOgAA5JoxZsnS2pX7XOcAAB9Y6DpAoaE8edti1wEAwIWWFTuOuM4AAD5AecoxypO3LXIdAABcWLt8c5nrDADgA1y2l2OUJ2+jPAEoSAsqF7eIeU8AcC6MPOXYrMuTMWaxMWZbNsLgFJQnAAXJmKKldbUrgq5zAIDHMfKUYzMqT8aYu40xtcaYJZL2SPqiMeZT2Y0GMecJQAFradx+2HUGAPA4Rp5ybKYjTwuttWOSrpH0RWvthZIC2YuFaYw8AShYa5efV+o6AwB4HCNPOTbT8lRijGmU9FJJP8piHpyI8gSgYNVWLWlxnQEAPI6RpxybaXn6oKS7JO2z1v7RGLNe0t7sxcI0viEAFCxjipYtWdCw33UOAPAwRp5ybEblyVr7LWvtNmvtW6f/vt9ae212o0FSuesAAOBSaGlWpAAAIABJREFUS+P2Q64zAICH8UZ7jpXM5EHGmGWS3iCp6fjnWGtfm51YmMb1/gAK2trl5xff+/hdrmMAgFdRnnJsRuVJ0g8k/VrSzySlshcHT+gKdhaJfbgAFLja6rpm1xkAwMO4bC/HZlqeqqy178lqEpyMUScABa/IFNUvrlneE5rob3KdBQA8iJGnHJvpyMaPjDEvyGoSnIzyBACSmhu397nOAAAexchTjs20PN2gqQIVM8aMT/8Zy2YwUJ4AQJKa6rcUu84AAB5V4zpAoZnRZXvW2gXZDoJTUJ4AQNLC6qXrXGcAAI/izaUcm+mcJxljrpZ0+fRf77bWslludpW5DgAAXlBkihoXVi/tHQ0PrnWdBQA8hsXFcmxGX3BjzMc0denew9N/bpi+DdnDyBMATGth3hMAnA7lKcdm+gV/gaTnWmu/YK39gqS/mL4N2UN5AoBpTfVbjOsMAOBF7R1tFKgcms0Xe9FxH7MsYvZRngBg2qLqZU2uMwCARzHvKYdmOufpFkm7jTGdkoym5j79Q9ZSQaI8AcCTioqKV9ZW1R0ciwytcp0FADyGkaccmtEX21r7TUmXSPru9J82a+0d2QwGyhMAHK+5cVuP6wwA4EGUpxw66xfbGLN5+r8XSGqUdFBSn6QV07cheyhPAHCcdfVbXUcAAC/isr0cOtdle38n6Y2Sbj3NfVbSszOeCE+gPAHANGttWmmT2Lw08GvXWQDASyKJYdcRCspZy5O19o3THz7fWhs7/j5jTEXWUkGiPAGAopPje/tD3YcnosMb07HqBRuWXL7GGLPcdS4A8BBWI82hmS4Y8TtJJ1+md7rbkDkz3sAYAPJJIjl5bGCk59GRiWON1qY3StogSSa54OFkcbK7tLSU8gQAT0m7DlBIzvoC3RjTIGmlpEpjzE491WxrJVVlOVuhS7oOAAC5kkonJ4bHDu0ZGjtYkUoldkqqP/kxRanqsmg0Gi0tZWAeAI5Decqhc41uPE/S9ZJWSfrUcbePS/rHLGXClNi5HwIA/mWtTY2G+3cPjvTGJxORHZKecdYnpMoawpPhSG1tbW4CAoA/WNcBCsm55jx9WdKXjTHXWmu/k6NMmBJ1HQAAsiEcG3mkP9QzEImNnCfpaTN6kjURY4qaY7FYxFqbNMZwaTMATGHkKYdm9MvHWvsdY8wLJW2RVHHc7R/MVjAw8gQgf8QT0YP9oZ59Y5H+1dba8ySdN6sDJKv2G5mt1toSa+1eY8zsng8A+YvylEMzKk/GmM9qao7TLkn/Ieklku7NYi4w8gTA51KpxOjgaN8DofHDtal0cpumLgGfE5OoCU1/GJ+cnByorKykPAHAFMpTDs30sodLrbXbjDEPWGtvNsbcKum72QwGRp4A+I+16cTIxNH7BkYO2EQytlPSMzNxXJOsfuLDRCQSKa6srMzEYQHA7yYDgQDlKYdmWp6eGAWJGGNWSBqStC47kTCN8gTANyYiww/2j/QMRyfHtkq6OOMnSJXXTX+UiEQiq+vq6s76cAAoEGHXAQrNTMvTj4wxiyR9QtJ9mlrV4z+ylgoSl+0B8LhYPNwzEOruGYsMrZfs1qydyColFT3xhl0ykUissdYOGGOWZe2cAOAPlKccm2l5+idr7aSk7xhjfqSpRSMYGckuvr4APCeZig8Njhx4KDR+pC5tU1skNWX9pKmKHiPTPP23hCQlk8nu0tJSyhOAQkd5yrGZlqd7JF0gSdMlatIYc98TtyHzWpt32a5g56SkctdZABS2dDoVC40f2T042leSTE3ukHR5Ls9vkjVHJT1RnpKSFIvFImyWCwCUp1w7a3kyxjRIWimp0hizU5KZvqtWU6vvIbtiojwBcMBaa8cjg3sGRnrHY/GJ7ZLaXGUxierkcX9NSVI4HF68YMECR4kAwDMoTzl2rpGn50m6XlPLy37quNvHJf1jljLhKTFJC12HAFA4opPj+/pD3YcmosMbJO1wnUeSlKp8siUZY5LWWkWj0RZrbcoYU+wyGgA4RnnKsbOWJ2vtlyV92RhzrbX2OznKhKewaASArEskJ48NjPQ8OjJxrMHa9CZJLa4zncAWNx33t5QkWWurrbWPGWM2uQkFAJ5Aecqxc1229ypr7dckNRlj/u7k+621nzrN05A5LBoBICvS6VR4aOzg/cNjByuTqcR2SVe4znRaqdIjRqbx+Fue+CAejx+rqKigPAEoZJSnHDvXZXtP7EpYc5r7bIaz4FSUJwAZY61NjYb77x8c6Y1NJiI7JD3DdaZzMcnqPkmnLU+RSKS4oqIi96EAwDsoTzl2rsv2Pjf94XpJN1hrRyTJGLNY0q1ZzgYu2wOQAeHY6CMDoe7+cGzkPEkXus4zK4maE34OGmPS1k69dxcOh1ctWbLESSwA8AjKU47NdKnybU8UJ0my1oamV99DdlGeAMxJPBE91D/Ss28sPLDK2vR5ks5znWkuTKrq5BVH0098kEgk1lprh4wxdTmOBQBeQXnKsZmWpyJjzGJrbUiSjDFLZvFczF3IdQAA/pFKJUcHx/oeCI0dqk2lk9s0tdWEv6VLV518y/F/SaVSwZKSEsoTgEJFecqxmRagWyX9zhjzbU3NdXqppI9kLRWe0O86AABvszadGJk4untw5EAqnoxdIOmZrjNlTLpo1MicUJ6MMSfMt41Go1H2ewJQwCZcByg0MypP1tqvGGP+JOnZmtoo9xpr7cNZTQaJ8gTgDCaiw139oZ5QdHJsq6Snu86TFcnqHknbT7r1hJGncDhcS3kCUMAGXAcoNDO+9G66LFGYcotvCABPmoyHe/tDPd1jkcF1km11nSfbTLJm5JTbjDnh79FodIO1Nm2MKcpZMADwDt5ozzHmLXkb3xBAgUum4kODIwceCo0fqUvb1BZJa11nyhWTrD7d76gTLtuz1tZYax83xmzMUSwA8BJeK+YY5cnb+IYAClA6nYqFxo/sHhrtK06kJndKutx1JidSZctOvunkkSdJisfjRysqKihPAAoRrxVzjPLkbXxDAAXCWmvHI0N7BkZ6JmLxiVZJba4zOWXNpGTWzeShbJYLoEAlAoEAKzPnGOXJ2yhPQJ6LTo4H+0PdfRPR4Y2SdrjO4xmpyv1G5pS9qU438hQOh1ewWS6AAjToOkAhojx527CklKRi10EAZE4iOdk/MNL7yMjE0Xpr05slNbvO5DUmUTM008cmEomm6c3bF2czEwB4DG+yO0B58rDW5l22K9g5KKnedRYA85NOp8JDYwfvHx47VJFMxXdIusJ1Ji8zyerUaW8/3dCTZKY3y31almMBgJdQnhygPHlfvyhPgC9Za1Nj4f7dAyO98clEZLukZ7jO5BupitNeh3eG8qRYLDZRU1OT3UwA4C2UJwcoT97HNwbgM5HY6KP9oe5j4djIeZIYDZktKytb1HSGe0+7n1M4HK6lPAEoMLxGdIDy5H1slAv4QDwRPdQ/0rNvLDywanoe02bXmXwrXX7AyJx2P6szjTxFIpEWNssFUGAoTw5QnryPbwzAo1Kp5OjgWF9XaOzQglQ6uU3SSteZ8oFJVB/WmTcDPm05stbWWmv3GmM2ZC8ZAHgKb7A7QHnyPsoT4CHWphMjE8d2D44cSMeT0R2SLnOdKd+YZE38jPedZWQpkUgcLS8vpzwBKBS8RnSA8uR9fGMAHjARHX6wP9QzHJ0c2yrp6a7z5LVkZfWZ7jLGnHHrhkgkovLy8uxkAgDvOew6QCGiPHnfMdcBgEI1GQ/39o/09IyFB5sku9V1noJhS9ac5d4zlqdwOLxi8WK2egJQMHpcByhElCfvO+A6AFBIkqn48ODIgQdD40eWpG1qq8489wbZkC4ZMDLLz3T32Uae4vH4emvtqDFmYXbCAYBnjAcCgRlvJo7MoTx53z7XAYB8l06nYqHxI7uHRvuKE6nJnZIud52pUJlk9QFJy87ykDOWJ01tlruXzXIBFIBu1wEKFeXJ41qbd010BTv7JZ3xnVgAs2etteORoT0DIz3jsfjENkltrjNBUqJm/Gx3G2PO+nsrFouF2e8JQAGgPDlCefKHoChPQEZEJ8eD/aGegxPR4RbJ7nCdBycyyapzrfhwtpEnhcPhasoTgAJAeXKE8uQP+8S74sCcJZKT/QMjvY+MTBytn97Attl1JpxBurThbHcbY0rPdn80Gt1grbVn2kwXAPJEj+sAhYry5A9B1wEAv0mnU+GhsUN7hscOliVT8Z2SrnCdCedgiyYkc64FOs76eyudTi+01gaNMRRkAPmMkSdHKE/+wKIRwAxYa1Nj4f77B0Z6Y5OJyA5Jl7rOhFlIVvUYmbMuCX+ukSdJSiQSh8vLyylPAPIZ5ckRypM/MPIEnEUkNvpof6j7WDg2slnSha7zYG5MomZ4Bg87Z3lis1wABYDy5AjlyR8oT8BJ4ono4YGRnsdHwwOrpucxbXadCfNjktXnnKc0k5GncDjcwGa5APLYYCAQmHAdolBRnnygtXnXQFewc0xSressgEupVHJ0aKyva3jscE0qndguaYXrTMigVFndDB51ziGleDzebK0dM8bwMxNAPupxHaCQUZ78Iyhpp+sQQK5Zm06MTBzbPThyIBVPRndKusx1JmSBVVIqWn+uh03v82QlnW2UqiiVSu0rKSm5IGP5AMA7uGTPIcqTf1CeUFAmoqEH+0Pdw9HJsS2Snu46D7IsVdFtZDbM8NEJSWVne8Dk5ORYSQm/4gDkJcqTQ/xm8Q9W3EPem4xHevtHurvHwoPrJHvWVdeQX0yipl/STMtTXOcoT+FwuKa6unreuQDAgx5zHaCQUZ78g0UjkJeSqfjw4Ejfg6GJw4vT6VSrpHPt84M8ZJI1yVk8PHGuB0QikWY2ywWQpx50HaCQUZ78g5En5I20TU+Gxg/fNzTSV5xITe6UdLnrTHAsVbFwFo8+Z3lKp9OLNXVpy7o5ZwIA77GSHnIdopBRnvyDkSf4mrXWjkeG9gyM9EzE4hOtktpcZ4KH2OKmWTz6nOVJkuLx+OHy8nLKE4B80hMIBMKuQxQyypN/HJQUk1ThOggwG7HJieCxUHffRHR4g2R3uM4DD0qVHjIyK2fxjBld4heNRlNslgsgz3DJnmOUJ59obd5lu4KdD0p6musswLkkkpMDAyO9D49OHKtP29RmSc2uM8G7TLLmoKSMl6dwONywaNGiuYUCAG+iPDlGefKX3aI8waPS6VRkeOzQ7qGxgxXJVHyHpCtcZ4I/mERNbJZPmVF5mpycbLHWThhjauYQCwC8iPLkGOXJX+5zHQA4nrU2NRYe2DMw0hudTIS3S3qG60zwoVTlbC9HnunKfEXpdHpvcXExe+QByBcsFuEY5clfdrsOAEhSJDb6WH+o52g4Ftos6QLXeeBz6dJVs3m4MSZlrZ3RY2Ox2Cj7PQHIE0lJj7oOUegoT/7ygKSUpGLXQVB44ono4YGR3r2j4f6V1qY3SdrkOhPyQLo4NMvFIqSpn4MzEg6HqylPAPLEvkAgMOk6RKGjPPlIa/OuaFew8zFJ57vOgsKQSifHhkb79gyPHV6QSie2SVrhOhPyTLK6V9LiWT5rxuUpEomsn+WxAcCrmO/kAZQn/9ktyhOyyNp0YmTi2P2DIweS8WR0p6Rnus6E/GUSNaOzfo4x6ZletpdOp+ustb3GmLWzDgcA3kJ58gDKk//slvRK1yGQfyaioYcGQj1DkcnRLZIucp0HhcEkq+byeyg9mwcnEomDZWVllCcAfkd58gDKk/+waAQyZjIROdAf6t4/Hh5ssrJbXOdBAUqXLZ/Ls2bz4EgkkiorK5vDaQDAUx5wHQCUJz+iPGFekqlEaHD0wIOh8SOL0ulkq6Q1rjOhQFkTk8y62T7NGDOr8hQOh+vZLBeAz4Uk7XMdApQn32lt3hXqCnb2SuISFMxY2qYnQ+NHdg+NHjCJ5CTzmOANycr9RmYuczhnNuFp2vRmuWFjDMvuAfCrewOBwKx+9iE7KE/+dJ8oTzgHa62diA490B/qHYvFx7dJusR1JuB4JlkzNKfnGTPbFxDF05vl7pjL+QDAA/7gOgCmUJ78abekv3IdAt4Um5wI9oe6D45Hh5slu911HuBMTKJ6ru+izvp5bJYLwOcoTx5BefIn5j3hBInk5MDASO/DoxPH6tM2tVlSs+tMwDmlK5bM5WnGmFk/JxKJVFKeAPjYva4DYArlyZ8oT1A6nYoMjx26f2jsYFkyFd8p6QrXmYAZs0rLFjXl6nSRSGTWC1MAgEcEA4HAoOsQmEJ58qHW5l2HuoKdA5KWuc6C3LLWpsfCA7sHRnpjk4nwdkmXus4EzEm6vNfMYaU9aW4jT6lUapm19oAxhtUlAfgNl+x5COXJv/4k6fmuQyA3IrHRx/tDPUfCsdAmSRe6zgPMl0nUHJWU09GgRCLRV1ZWRnkC4DeUJw+hPPnXr0R5ymvxROzIwEjPY6Ph/pXWpjdK2ug6E5ApJlk9OefnzmXoSWyWC8C3fu86AJ5CefKvX7oOgMxLpZNjQ6MHHxgeO1SdSie2S2p0nQnIimTlgnk8e67laRmb5QLwmUlJ97sOgadQnvzrT5IikqpcB8H8WJtOjkwcu29w5EAqnozukHSZ60xA1tmSOV8+N8eBJ8VisQ3W2qgxpnKu5waAHLs/EAjEXYfAUyhPPtXavCvRFez8naSA6yyYm4lo6KGBUM9gZHJ0i6Snu84D5Ey6pN/ILJ/r040xxXN8akk6nX6ouLiY/c8A+AXznTyG8uRvvxTlyVcmE5ED/aGe7vHwwBoru8V1HsAFk6g+IGnO5UlzvGxPkiYnJ0NVVQzYA/ANypPHUJ78jXlPPpBMJUKDowe6QuNHFqfTyVZJrPaFwpasCc/n6fMYeVI4HK6kPAHwkV+7DoATUZ787Q+SopK4ft9j0jY9OTJ+ZPfgaJ9JJGM7JV3uOhPgFSZZNd8l74rm+kQ2ywXgI3sDgUCf6xA4EeXJx1qbd8W7gp2/l7TLdRZI1lo7ER3u6g/1jMbi49skXeI6E+BJ6dIV83n6fEaeUqnUcmvtQWPMqvlkAIAc+IXrADgV5cn/finKk1Ox+MT+/lB333hkuFmy21znATzNFo1LZr6Xrs65PElSIpE4UFZWRnkC4HU/dx0Ap6I8+R/znhxIJOMDgyO9D49MHF2etqnzJK13nQnwhWRVj5Fpnc8h5jPyJEnRaDTBZrkAPM5K6nQdAqeiPPnf7zW1gVq56yD5Lp1ORYbHDt0/NHawLJmK75B0hetMgN+YRM3wvI8xz/IUDoeXLVy4cL4xACCbHggEAoOuQ+BUlCefa23eFesKdt4r6Zmus+Qja216LDxw/8BIb3QyEd4m6VLXmQA/M8nqeRWfafP63TW9WW7MGFORgSwAkA3Md/IoylN++KUoTxkViY091j/SfTQcDW2SdIHrPEDeSJXVzfcQxpj5/u4qTafTjxQXFzNHEYBXMd/JoyhP+eFuSe9zHcLv4snYkYFQz2Oj4f6V1qY3SdrkOhOQV6wSUlEm5geWzvcAbJYLwMOSkn7lOgROj/KUH+6RlFAGXlAUmlQ6OT40enDP8Nih6lQ6sV1So+tMQN5KVXYbmY3zPUwGRp4UiUTKKU8APOqPgUBg3HUInB7lKQ+0Nu+KdAU7/yjm48yItenk6ET/7oGR3kQ8Gd0p6TLXmYBCYBI1A5LmXZ6UgTeKwuFw09KlSzMQBQAyjvlOHkZ5yh93ifJ0VuFo6KH+UM9QZHL0fEkXuc4DFBqTrE5m5DjGzLs8pVKpBmvtYWPMvDbsBYAsYL6Th1Ge8sedkm52HcJrJhORvv5QT3A8PLDWym5xnQcoaKmKRRk6UkY2aUokEr1lZWWUJwBeEpP0O9chcGaUpzzR2rxrd1ews0/SatdZXEumEqHB0QMPhsaPLEynk9vE1wTwBlvclKEjZWR+ZzQajbNZLgCP+VUgEJh0HQJnRnnKLz+S9BbXIVxI23R8ZPzIfYOjfSaRjO0US7cD3pIqO2hkVmXiUMaYIkkpSfPaMyoSidSxWS4Aj/mh6wA4O8pTfvmhCqg8WWvtRHS4ayDUMxqNj7dKusR1JgCnZ5LVByVlpDxNi0uqnM8BotHoRmvtpDGmPEOZAGC+KE8eR3nKL52SJiTVuA6STbH4RHd/qLt3PDLcIlk2uQR8wCRqMn0ZSkLzLE+SytLp9IPFxcVbMxEIAObp/kAg0Oc6BM6O8pRHWpt3TXYFO38q6RrXWTItkYwPDI72PjIyfnRp2qbOl7TOdSYAs5CqnG/ROVk8IweJx4cqMx4NAOaEUScfoDzlnzuVJ+UpnU5Fh8cP7x4a7StLpuI7JF3uOhOAOUqXZvKSPUnKyLLn4XC4jPIEwCMoTz5Aeco/P5aUllTkOshcWGvTY5GBPQMjveHJeHi72LsK8L908bBRxvdTSmTiIJFIpCkTxwGAeToYCAT+7DoEzo3ylGdam3cNdAU7fy+flY7I5Njj/aHuI+FoaKOkna7zAMigZHWPpCWZPmpGDpJMNlprjxhjGjNxPACYoztdB8DMUJ7y0w/lg/IUT8aODIR6Hh8N96+wNr1R0kbXmQBknknUjGfhsBkpT5KUTCZ7S0tLKU8AXOKSPZ+gPOWnOyV9zHWI00mlk+NDowf3DI8dqk6lE9sl8YIFyHMmWZWN3zUZK0/RaHSytDQj++4CwFyMS/qF6xCYGcpTHmpt3vVwV7AzKKnZdRZJsjadHJ3o3z0w2puIJ6I7JV3mOhOAHEqXNWT6kMaYlLU2I8cKh8NLamtrM3IsAJiDnwYCgYysIIrsozzlrzslvcNlgHB05OH+ke7BSGz0PEkXucwCwBFropJpysKRU5k6UCwW22CtjRtjyjJ1TACYBS7Z8xHKU/76oRyUp8lE5OBAqGffWHhgrZU9P9fnB+AxyapuI5ONnwUZK0/W2gpr7UPGmC2ZOiYAzFBKUyslwycoT/nr15JGJC3K9omSqcTI0OiBB4bHjyxOp5NbJWV6PxcAPmWSNUNZOnTG5jxJ0uTk5CD7PQFw4NeBQCBbPyeRBZSnPNXavCvZFez8kaRXZeP4aZuOj4wf2T042mcTydgFYgNbAKdhEtXZOa4x6UzNeZKkSCTCZrkAXPim6wCYHcpTfvumMlierLV2IjrcNTDSMxqdHG+VdHGmjg0gT6XLF2fryJk8WDgcXl1XV5fJQwLAucQlfdt1CMwO5Sm//VTSoKSl8zlILD7R3R/q6R2PDDVLdltmogHIe1Zp2aL12Ti0MSZzw06SksnkKmvtMWNMfSaPCwBncVcgEBh2HQKzQ3nKY9OX7n1L0ltm+9xEMj44ONr78Mj40aVpmzpf0rrMJwSQ11LlPUYmK+VJGR55kqRkMtldWlpKeQKQK99wHQCzR3nKf9/QDMtTOp2KDo8f3j002leaTMV3inlMAObBJGuOSspWecroyJMkRaPRGJvlAsiRCbFEuS9RnvLfbyX1Slp7ujuttenxyOD9/SM90cl4eJukS3OaDkDeMomarG36mOnL9iQ2ywWQUz8IBAIR1yEwe5SnPNfavMt2BTvvkPSe42+PTo7t7Q91H5qIhjZJusBNOgB5LVXhqyYyvVluwhjD8BOAbOOSPZ+iPBWGb0h6TzwZOzIQ6n18NHys0dr0RkkbXAcDkMdsyZpsHdoYk/FjWmsrrbUPG5OVTX0B4AmDmlrUCz5EeSoArc27Hvju3R/7XjwZfZGkRtd5ABSAdOlRI9PgOsZssVkugBz4ViAQyOhG38idItcBkBvxZPR34t8bQI6YRHVfVo+fhZEnSYpEIsVZOTAAPIVL9nyMF9OF42uSUq5DACgQyZqsToTOYnnK2qWGAKCpRbx+6zoE5o7yVCCuC9x8VNJdrnMAKAwmWVme7VNk46CJRGK1tXYgG8cGAEl3BAKBjK8WityhPBWWL7kOAKBApMtWZPPwJltDT5KSyeT+bB0bQMH7qusAmB/KU2H5oaSQ6xAA8pwtGpO0Ostnydrvr1gsFsvWsQEUtN8GAoGHXIfA/FCeCsh1gZsnJd3hOgeAPJeo7jHK3siQlN2Rp3A4vDBbxwZQ0P7NdQDMH0uVF54vSXqL6xAA8pdJ1ozk4DRZe/MvGo1utNamjDGsvOcT8Xhc//Iv/6L77rtP4+PjWrFihV772tfq6U9/uhKJhG655RY9/vjjOnbsmD75yU9q+/btZzzWu971Lj3yyCMqLp7651+6dKm++MUvSpKCwaBuueUWhUIhveIVr9C1114rSUomk3rnO9+p97///Vq+fHn2P2H4UUjSf7kOgflj5KnAXBe4+V5Jj7jOASB/mWRV1n+3ZLPYWGurrLV7s3V8ZF4qldKyZct066236vvf/76uv/56ffjDH9bRo0clSVu3btWNN96oJUuWzOh4b3/723XnnXfqzjvvfLI4SdLnP/95vfGNb9TnPvc5ff3rX9fw8LAk6dvf/rYuu+wyihPO5quBQIBLgvMA5akwfdl1AAB5LFW+LNunMMZk9fdXPB7vz+bxkVmVlZV6zWteo4aGBhUVFemSSy5RQ0OD9u7dq9LSUl1zzTXaunWriorm97/N0aNHtWPHDi1dulQrV65Uf3+/+vv79Zvf/ObJUSjgDLhkL09QngrTVyQlXIcAkIesEpJZl4MzZfWSOjbL9bdQKKSDBw9q7dq1c3r+F77wBV177bW64YYbtGfPnidvb2pq0p///GcNDAzo2LFjWrFihTo6OvSGN7xBJSXMhMAZsVBEHuE7vQBdF7j5yB0/u+nbkl7uOguAPJOq7DYyG7N9mmzPRwqHw6tmeokXvCWZTOqWW27RlVdeqTVrZr/n8etf/3qtXbtWJSUluvvuu/X+979fn/3sZ7VixQq96U1v0m233aZQKKQ3v/nNevDBB1VZWanGxkZ94AMf0MTEhF70ohfpiiuuyMJnBh9j1CmPUJ4K1z+L8gQgw0yipl9S1suTsjzylEgk1lprB41lX8nBAAAgAElEQVQxS7N5HmRWOp3Wxz/+cZWUlOjtb3/7nI5x3nnnPfnxlVdeqc7OTt1777168YtfrPr6en30ox+VJMViMd1www362Mc+ps985jN61rOepYsvvlhveMMbtHPnTtXW1mbkc4LvsVBEnuGyvQJ1XeDmP0q6x3UOAPnFJGvSOTmPMVl/8y+VSnVn+xzIHGutbr31VoVCId10000Zu4zOGCNr7Sm3f+1rX9Pzn/98LV68WN3d3dq4caOqq6u1dOlSHT58OCPnRl5goYg8Q3kqbJ92HQBAnkmV52qPpKyXp2g0Gs72OZA5t912mw4cOKAPfehDKi8vP+G+eDyueDwuSUokEorH46ctRBMTE/rjH/+oeDyuVCqln//85+rq6tLTnva0Ex7X29urPXv26KqrrpIkNTQ0aPfu3QqFQjp06BCr7uF4XLKXZ8zpfnigMNzxs5tKJO2XtNp1FgB5wMoWjWwZNzJZv14plUodGBsbm/2Ellmoqqq6v6GhYUc2z4HMOHbsmF71qleptLT0yf2ZJOkd73iHnvOc5+hVr3qVjh07dsJzvvrVr6qhoUHf+MY39OCDD+qjH/2oRkZG9N73vld9fX0qKirS6tWrdf311+vCCy884bnvfve79brXve7JS/yCweCTz3/5y1+ul7zkJdn/pOEHvw0EApe5DoHMojwVuDt+dtN7JH3MdQ4AeSBVdqB4bGNWC82Tp0qljoyNjTVm8xzGmImmpqZKNssFMEd/EwgEvuI6BDKLy/bwb5IirkMA8D+TqMnZRA9jTGm2z2GtrbHW7sv2eQDkpX6xUEReojwVuOsCN4c0te8TAMyLSdbkclJ01suTxGa5AOasg4Ui8hPlCZJ0mySu3wQwP8nK6lydKhcjT5IUiURMLs4DIK9EJf2r6xDIDsoTdF3g5kcl3eU6BwCfsyWrcni28nM/ZP7C4fDKXJwHQF75SiAQGHQdAtlBecITbnMdAICPpYsHjUxWF3A43vQiDlnfUyqRSKyz1oayfR4AecNK+mfXIZA9lCc84S5Jj7gOAcCfTLK618FpE7k4SSqVYtEIADP1o0Ag8JjrEMgeyhMkSdcFbraSbnedA4BPJWomHJw1nouTxGIxViQFMFO3ug6A7KI84XhfkTTsOgQA/zHJ6pws4HCSnIw8hcPhBbk4DwDf+3MgEPil6xDILsoTnnRd4OaIpH93nQOAD6VLGxycNSflKRKJbLDWZn1+FQDfY9SpAFCecLIOSUnXIQD4iDURyTQ5OHNOflZZaxdYa4O5OBcA3+qT9C3XIZB9lCec4LrAzX2Svus6BwAfSVZ1GxkXv09yMvIkSfF4/GiuzgXAl24LBAK8+VwAKE84nU+7DgDAP0yiZsjRqXP2QoXNcgGcxZiY9lAwKE84xXWBm++R9AfXOQD4g0lWuyoWuSxPK3J1LgC+82+BQGDMdQjkBuUJZ/JB1wEA+ESqfImrM+fqRPF4fJ21diRX5wPgG1FJn3QdArlDecJpXRe4+b8l/c51DgAeZ5WSita7OLUxJmflSZJJpVIsGgHgZP8vEAgccx0CuUN5wtm833UAAB6XqugxMpWuzp7Lk8VisfFcng+A50Uk/ZPrEMitEtcB4F3XBW7+xR0/u6lT0i7XWZAf7vr+vfr/27vz+Lirev/jr0/Splu6ULqXkgYKFMplBxVRUA4iiiguMFBBUCwq3t/vhygq6g2Dggp6uaJcQKiKIgSKXtR7FSEXqOybArbs3SldadK0M1lmOb8/vtOSrpm0mTmzvJ+PRx5pZ74z33eCduY953zPmXvf8yxbvJpjTziYL1760c33dXWmuO3n9/PE3Plk0lka9h1P07+ft93nueKSW3n9pTeoqY0+/xk9ZgT//suLAFiyYCU//f5/sb51I6ef/R4+9Il3ApBOZ7j84l9y8Xc+xZ7jRhb2B60ilq5fCewb6PRFLU+JRGJ4fX19MU8pIqXtRo06VR+VJ+nNt4FHQ4eQyrDHnsM5feZ7eOGZBXR3bXmt/83/8d9kM1l+PPtL1A8fwuIFO18Z+rwvn8L7P3TENrc3z36AT89y7L3PeL4+6yaOfd/BjBpdz5/vfoJjjjtQxamfWWpYsKV5zSzrvS/a+To6OqZ5772ZaeU9EdGoU5XStD3ZqZiLPwb8JXQOqQzHvOdAjn73dOpHbDnL681la/n7469wwf87lRGjhlFTW8M+++/a4marV7Yx47BGRo8ZwYTJo1m7ej1rV6/nqUde2jwKJf0oM2R4yLMX82TZbHak935hMc8pIiVL1zpVKY08ST6+DZwSOoRUrtdfXs6YcaO4+9cP8XDLC+wxejifOPd43vGeA3f4mOZfPMAds/+XSXvtyZmffT8HHToVgClTx/LCswuYOm0ia1a1MX7SHvz8x3/i7M87BgyoLdJPVEV8bUPIsxf7hKlU6s1BgwaFmqYoIqVBo05VTOVJehVz8b83tzT9F3B66CxSmdataWfZ4tUc857p3ND8FV59cRlXf/sO9tp7DJMbxm5z/NkXnMjkhrEMGFDLYw/N55rvNPODG2cxftJoZl54Er/4yZ9pa93IOV84mVfmLWPwkDrGTdyDH/1bM4mNnZz80WN45/EHBfhJK0xm4ArDJoY6vZlli33OZDJpgwYNKvZpRaS03OCcWx06hIShaXuSr38Div5GRapD3aCB1A6o4fSZ72XAwFoOOnQqMw6bygvPbn+G1LQD92LI0EEMrBvA8R84lP1nTOEfT70OwNjxo/j6VWfz/RtmcdSxB3D3rQ8x88KTuO2m+3nXCTP42ndj/Oam+9jY3lHMH7EiWXrYG4EjFH3kKZFITCj2OUWkpGjUqcqpPEleYi4+D2gOnUMq096N43br8WbAdhYO+P1tc3nfKYczao96li1azT77T2LosMHsOWY4K99ct1vnFCBVnwh5ejMrennq7u7e13u/vtjnFZGS8Z8adapuKk/SF5cDwVbWkvKXyWTp7k6TzXqy2ejPmUyW6Yc0MGbcSP5wxyNkMllembeUF59fwiFHbXtpSWJjJ88//frmxz7yv//k5X8u3ebYN5as4cXnl3DSR44CYNyEUcz7xyLaWjeyYvk6xmjVvd1mmaGh9nfapOjliWiz3NcDnFdEwtOok2DFXOZVyl9zS9Ns4LOhc0h5uvvXD/G73/xti9s+cc57+eS5J7Bs8Wpu/vc/sXTRasaMG8mZ57+fo4+bDsA9tz/My/OW8o2rZtLeluCH37qDN5etpabGmDRlDJ867wQOOXLL8vTdr97KWZ87kWkH7gXk9n+66vesb0vwsbOO48OffFdxfugKVtM6Y5lhU0Kdf+PGjXNTqdTxxT7vuHHjHqqvrz+h2OcVkeB+5Jz7WugQEpbKk/RJc0tTA/AqUBc6i4gElK1ZX7v+oKDDd6HKU319/TPjxo07qtjnFZGg2oB9nXOa813lNG1P+iTm4kuAW0LnEJHA0sMWhY4Qaq/aZDI5zeuTR5Fqc6WKk4DKk+ya7wFaqkykilm6vj10hlCy2ewoIHh5FJGiWQT8NHQIKQ0qT9JnMRdfAdwQOoeIhGPpocFfPyzU0BPQ3d39Zqhzi0jRXeac6wodQkpD8Bc/KVs/ADaGDiEigWQG7d768v0jWHlKJpOatidSHZ50zmmrFtlM5Ul2SczF1wDXhc4hIgF46wZrDB0j5MhTMpnUZrki1eGS0AGktKg8ye64BtDFkyLVJjNkoWEDQ8cws2CvYV1dXft67zeEOr+IFMXvnXOPhg4hpUXlSXZZzMXbgMtC5xCR4rJU/ZrQGXJCvobVZLPZ1wKeX0QKKwV8PXQIKT0qT7K7bgaeCR1CRIrH0sNK4nqfkCNPAJ2dnRp5EqlcNzjnXg8dQkqPypPslpiLZ4EvAdnQWUSkSDKDRoWOkBP0NSyRSAwNeX4RKZg24IrQIaQ0qTzJbou5+NPA7NA5RKQIPB5fG3yxCAAzqw15/mQyuW/I84tIwVzlnHsrdAgpTSpP0l++CegfGpFKlx201LDhoWNA+PKUzWZHe+8Xh8wgIv1uMVpNWHZC5Un6RczF30KLR4hUPEsNWxE6Qw9ByxNAd3f3G6EziEi/ulgb4srOqDxJf7oFeCp0CBEpHEvXd4bOsImZDQidoaOjoyQWzxCRfvHfzrl7QoeQ0qbyJP0mt3jERWjxCJHKlR5SHzpCD8HLUyKRGBc6g4j0iyTwr6FDSOlTeZJ+FXPxZ4iWLxeRSuQHTAkdYZNSGHnq6uqa5r3fGDqHiOy2K51zi0OHkNKn8iSFcBmwNnQIEeln2QFrDBsfOkYPwcsTUJvNZrUXjEh5exn4UegQUh5UnqTfxVx8HdHqeyJSQSw9bGnoDD2Z2cDQGQA6OzvbQmcQkd3yJedcd+gQUh5UnqRQZgNPhg4hIv0oVb8hdIStlER5SiaT2ixXpHz91jn3YOgQUj5UnqQgYi7u0eIRIhXF0kMHhc7QU6mMPCWTyX1CZxCRXdIGXBI6hJQXlScpmJiLPwv8PHQOEekn2YETQkfYSl3oAACZTGaM935J6Bwi0mffds6tCh1CyovKkxSaFo8QqQTeEmANoWNspSRGngBSqdTy0BlEpE+eAW4IHULKj8qTFFTMxVuBb4TOISK7KT10oWEl9ZphZgakQ+cASCaTJZFDRPKSBb7onNOlBdJnJfVCKBXrF8AToUOIyK6z1PDW0Bl2oCRWyEokEmNDZxCRvN3gnHsmdAgpTypPUnC5xSO+RIl8QiwifWfpoRY6ww6kQgcA6Orq2s97nwydQ0R6tRjNiJHdoPIkRRFz8X8AV4bOISK7KDNoz9ARdqAkyhMwIJvNvhY6hIjslAc+65zbGDqIlC+VJymm7wFPhw4hIn3kSUNNqS7HXSrlia6uLm2WK1La/lN7OsnuUnmSoom5eBo4B+gInUVE+iAzeLFhg0PH2IGSKU+JRKJUf0ciAguAr4cOIeVP5UmKKubirwCXhs4hIvmzdH0p74NSMtdSarNckZLlgfOdc4nQQaT8qTxJCNcD94UOISL5sVR9yRSU7SiZbJlMZqz3/o3QOURkG9c55x4OHUIqg8qTFF1u9b3zgVJd+lhEesoMHhE6wk6UTHkCSKVSS0NnEJEtvAZ8M3QIqRwqTxJEzMXfBL4YOoeI5MHXTg0dYUfMLBM6Q08dHR0lcw2WiJAFznPO6Vpr6TcqTxJMzMXvBO4InUNEdiIzcLlhe4SOsRMlVZ60Wa5ISbnWOfdY6BBSWVSeJLSLgOWhQ4jI9lm6vtT//1lS0/Y6Ozv38953hs4hIrwMfDt0CKk8Kk8SVMzFW4HziFbCEZFSk6pPho6wM2aWDZ1hKwO1Wa5IcBmi6Xr6IEP6ncqTBBdz8RbgZ6FziMi2LDNkaOgMvSi18kRXV9e60BlEqtz3nXNPhg4hlUnlSUrF14mG2EWklGQHTgodoRclV54SicSg0BlEqtgjwOWhQ0jlMu81W0pKQ3NL01HA48CA0FlEBMjWttWuP3DUju6+7rrrmDdvHl1dXYwaNYrTTjuNE088cadPGY/HmT9/PnfccQe1tbVkMhl++tOf8txzz7H//vtz8cUXM2TIEAB+//vfU1dXx6mnnrrD52tvb380k8m8e+vbZ8+ezcsvv0x3dzcjRozg5JNP5rjjjtvm8cuXL+fuu+9myZIlJBIJbrrppi3uv/POO3niiScYP348F154IXvsEa2d8eSTT7J48WLOPPPMbZ6ztrZ2VUNDw/id/iJEpBDWAYc555aFDiKVSyNPUjJiLv4M8N3QOUQkJz100c7uPv3007n++uu59dZbufTSS2lubmbhwoU7PP7hhx8mm91yoOjJJ6OZNbNnz2bIkCHcf//9AKxevZpnn32WU045pbeU2x15OuWUU7jqqqv4yU9+wkUXXcQf/vAHlixZss1xtbW1HHnkkZx77rnb3Ldo0SKWLl3K1VdfzbRp07j33nsB6Ojo4P777+e0007bbqBMJjPee1/qC22IVKLPqjhJoak8Sam5CtA8ZZESYKnh7Tu7f8qUKQwcODA61gwzY+XKlds9NplMcvfddzNz5swtbl+9ejUzZsygtraWGTNmsHr1agB+8YtfcM4551BbW7vzjGbbnT4xadKkzdk2WbNmzTbHTZgwgeOOO45Jk7adnbh27VqmTZvGwIEDmT59OmvXrgXgnnvu4QMf+MDmEbLtSaVS2zY1ESmknzrn/hA6hFQ+TY+SkhJz8XRzS9M5wHNAqV+oLlLRLD2019eIW265hYceeoju7m4aGxs54ogjtnvc7bffzkknncSoUVvOAtx7772ZO3cu73vf+5g/fz4HHnggTz31FCNGjGD69On5xNzh3PPbb7+dxx57jFQqxZQpUzj44IPzeb7NJk2axIMPPkh3dzcvv/wyEydOZPHixaxatYqzzjprp4/t6OhI19XV9el8IrLL/gF8LXQIqQ4aeZKSE3Px14Cvhs4hUvWydeN6O+SCCy7g17/+NVdccQXHHHMMAwZs27cWLFjAK6+8st0peIcffjjjxo3jG9/4BkOHDuXd7343c+bMYebMmdxxxx00NTVxyy23kE73fTuns88+m+uuu46vfe1rHH744duMRPVm8uTJHHHEEfzwhz9k3bp1fPCDH+Suu+7izDPP5IEHHuCaa65h9uzZJJPbruaeSCT27HNgEdkVG4GYc64rdBCpDipPUpJiLn4DcFfoHCJVy1sXWGM+h9bU1DB9+nTeeust7rvvvi3uy2az3HLLLZx//vnbnYJnZsycOZMf/ehHXHjhhdxzzz2cdNJJLFiwgIULF3L55ZeTTqd54IEHtntuM+s127Rp02hra2Pu3Ln5/DhbcM7xne98h1mzZvH0008zbdo0vPc8/PDDfOUrX2HChAmbr4XqKbdZrt7MiRTeRc65V0OHkOqh8iSl7LPAvNAhRKpSeshCw/o0tTubzbJq1aotbuvo6GDhwoVce+21fP7zn+eb3/wmAF/4whd46aWXtjh26dKlvPrqqzjnWLp0KY2NjZgZ++67L0uXLt3uOXsrT5tkMpntXvOUr/b2dh5++GFOPfVUli9fzuTJk6mtrWXq1KksX77dtSHqstms3tCJFNZvnHO/Dh1CqovKk5SsmIsngNOBttBZRKqNpevf2tn969ev59FHH6Wzs5NsNstzzz3Ho48+us11RUOHDuWmm27immuu4Zprrtlcnn74wx+y3377bT7Oe8/s2bM577zzqKmpYdy4cbzyyiuk02lefPFFxo3rdQbhZu3t7Tz99NObs82fP5+nn36aAw44YJtjvfekUqnN0wJTqRSpVGqb4+bMmcNHPvIR6urqGDNmDEuWLKGzs5NXX32VMWPGbDeHNssVKahXgS+FDiHVRwtGSEmLufjrzS1Nnwb+BOT3EbOI7DZLDdvp5rNmxn333cfNN9+M954xY8bwmc98hqOPPpq1a9dy8cUXc+211zJmzJgtFono7u4GYOTIkVtM43vwwQeZMmUK++67LwDveMc7eOqpp/jc5z7H/vvvz0knnbSjHNv8u2BmzJ07l9/+9rd47xk9ejRnnHEGhx12GOvWrePyyy/n8ssvZ/To0bz11lt861vf2vzYL3/5y+y5555cddVVm2975ZVXSCaTHH744QA0NjZy8MEH881vfnPz/k/bk0wmBw0dqnVvRAqgCzjTObcxdBCpPtokV8pCc0vTvwHx0DlEqkVN24H/NF/7L6Fz9CaZTP6tq6vrvaFzbM+AAQNW7L333hND5xCpQBc55/4zdAipTpq2J+Xiu8AfQ4cQqQqeLL4mr8UiQjOzkn0dS6fTE733K0LnEKkwt6o4SUgl+6Ij0lPMxT1wDtEcZxEppOygpYbVh46Rp5KezptOp7VZrkj/+TvwhdAhpLqpPEnZiLl4O9ECEprjLFJAlqovm9GSUh55Aujo6NBy5SL9Yy3wcedcZ+ggUt1K+kVHZGsxF38ROC90DpFKZulh3aEz5KvUy5M2yxXpFxngLOecRnIluJJ+0RHZnpiL/w74QegcIhUrPWRY6Ah9UNKvYx0dHft578umjIqUqMuccy2hQ4hAib/oiOzEt4D7QocQqUh+QEPoCPkys9rejwpqkPf+tdAhRMrYHc65q0OHENlE5UnKUszFs8BZwKLQWUQqSnbAasPGho7RB6Venujq6trphsMiskN/Bz4XOoRITypPUrZiLr4O+DjQETqLSKWw1LCloTP0RRmMPJFIJLQhvUjfrQI+5pzTa7yUFJUnKWsxF38O+HzoHCIVI11fbqtZlnx5SiaTZTMNUqREdAOfcM4tCx1EZGsqT1L2Yi7+W+AnoXOIVAJLDx0UOkNfmFnJj+qk0+nJ3vuVoXOIlJGLnHOPhg4hsj0qT1IpvgrMDR1CpOxlB04MHaGPSr48AaTT6cWhM4iUif9wzt0SOoTIjqg8SUWIuXgaOANYHDiKSPnyNRvBymqKWTmMPIE2yxXJ0++BS0KHENkZlSepGDEXXw2cTLQLuYj0VXroQsMsdIw+Ghg6QD4SicQeoTOIlLjHgU8757Khg4jsjMqTVJSYi78KfBhIhM4iUm4sVd8WOkNflcvIU2dn5/7e+1ToHCIl6jXgNK2sJ+VA5UkqTszFnwI+AeiNikgfWHpYuY06QZmMPHnvB2uzXJHtWgOc4pzTrBEpCypPUpFiLv5X4LOAD51FpGxk6sppc1wAzKxsVgfs6upaEzqDSInpIBpxWhA6iEi+VJ6kYsVc/Dbg0tA5RMqCJw01jaFj7IK60AHylUwmy2KUTKRIssBM59wToYOI9IXKk1S0mIv/CPhx6BwiJS8zZJFRPqM4m5hZDdGbsJKXSCSmhM4gUkIuds79V+gQIn2l8iTV4GvAbaFDiJQyS9WvDp1hN3SHDpCPdDo9xXuvqXsi0V5O14UOIbIrVJ6k4sVc3BNd//TX0FlESpWlh6VDZ9gNZVGeANLp9KLQGUQC+x0VtpeTmT1kZheEziHFofIkVSHm4imiFfieCp1FpCRlBo8MHWE3lM3Kmp2dncnQGUQCeowy2MvJzM4zs3+aWdLMVprZDWY2Knff5Wam2SxVTOVJqkbMxRNEe0C9GjqLSMnxtVNDR9gNZVOetFmuVLF/AB9yznWGDrIzZnYJ8EOiKf8jgXcCDcD9ZlbQBWosovfmJU7/gaSqxFx8LXAysCJ0FpGSkalbbkSfqpapsply2NHRsZ/3vmzyivST+cAHnHPrQwfZGTMbAcSBf/Xe3+u9T3nvFwNnEBWoC4DLgDPNbKOZPd/j4Q1m9qiZbTCz+8xsTI/nfaeZPWZmbWb2vJmd0OO+h8zsSjN7FEgC++RGvhbmnmuRmc0s/E8v+VJ5kqoTc/HFwAeBkv5HXKRYLD3sjdAZdlPZjDx574dqs1ypMq8DJ5XJJrjHAoOB3/e80Xu/EfgL8B7gKuBO73299/7QHoedDZwPjCPaQuGrAGY2Gfgf4HvA6NztvzOznvvqnQPMAoYTbRp8HXCK9354LtNz/ftjyu5QeZKqFHPxF4CPAl2hs4iEZqn6kp5Gk4eyGsnp7u7WintSLZYCJzrnymW2xxhg7Q5Gh1fk7t+RX3rvX/XedwB3AYflbv808Gfv/Z+991nv/f3AM8CHejz2V977+bnzpom2XzjYzIZ471d47+fv7g8m/UflSapWzMXnAjMpkz1iRAomM2RI6Ai7KRM6QF8kEona0BlEimAl4JxzS0MH6YO1wBgzG7Cd+ybm7t+RlT3+nATqc39uAD6Vm7LXZmZtwHG559tk2aY/eO8TwJnAF4AVZvY/Zja97z+KFIrKk1S1mIv/DrgodA6RoLID9wodYXeYWVmNPCWTybL+fYvk4S2iqXrlNkX1caIZKR/veaOZDQNOAf4X8H18zmXAb7z3o3p8DfPe/6DHMVs8p/f+r977k4gK1svAzX08pxSQypNUvZiL3whcGjqHSBDZ2lbDJoWOsZvKauQplUo1eO/L4foPkV3RDpzsnJsXOkhfee/XEy0Y8VMz+6CZDTSzqcAc4A3gN8AqYGofVsW7DfiImZ1sZrVmNtjMTjCz7X6IYmbjzey0XGHrAjZSZv/GVTqVJxEg5uLXAP83dA6RoksPWxw6Qj8ouzcWmUxmYegMIgWQJFqO/NnQQXaV9/5qohX1fkRUBJ8kGj060XvfRVSkAN4ys7/n8XzLiK6xvoxoMYhlRMug7+g9eA3RJsJvAuuA44Ev7erPI/3PvO/r6KNI5WpuaboQuAGw0FlEisESk+bWdI8+PnSO3dHW1va09/7o0Dn6YuzYsQ8NHz78hNA5RPpRF3Cqc64ldBCRQtLIk0gPMRe/CfgcWkRCqoSlhw4MnWF3mVnZ/f81kUiU875aIlvrBj6l4iTVQOVJZCsxF/8l0Z4LZTcVSKTPsnXjQkfoB2VXnnKb5erfGKkEncDpzrk/hQ4iUgwqTyLbEXPx24EYZbT5pkifeesEawwdox+UXXnKrba1IHQOkd2UJJqq9+fQQUSKReVJZAdiLn438Emi6QgilSc9ZKFhZb/nkJmV5cW73d3dK3s/SqRkbSBaVe9/QwcRKSaVJ5GdiLn4H4lWyekMnUWkv1l6+FuhM/STsixPyWRSr8FSrlqJNsB9JHQQkWLTP9wivYi5+L3Ah4mmJ4hUDEsNK8vSsbVyHXlKJBLaLFfK0Vrg/c65p0IHEQlB5UkkDzEXfwD4INE0BZHKkB00OnSEflKW5SmVSk313q8LnUOkD1YCxzvnngsdRCQUlSeRPMVc/GHgA8D60FlEdpsni6+phMUiMCvfbdkymYwWjZBysQx4r3PuxdBBREJSeRLpg5iLPwGcSLTrt0j5ygxaYtiw0DGqXUdHh6YDSzlYSFScXgsdRCQ0lSeRPoq5+LPA+4E1obOI7CpL168InaG/lPPIUzKZHB46g0gvXiEqTotDBxEpBSpPIrsg5uLPAycQzf8WKTuWqtceZiUgmUzu570vu3mIP9wAAB+CSURBVH2qpGo8S3SN0/LQQURKhcqTyC6KufiLwHuBN0JnEemzzOD60BH6i5Xx0JP3frg2y5USdS9wgnNuVeggIqVE5UlkN8Rc/DXgWOCfobOI9IkfsHfoCP2lnMsTaLNcKUm/AD7inNsYOohIqVF5EtlNMRdfBhwHtITOIpKX7IBVho0NHaMflfVrWTKZLOvyJxUn7pz7nHMuHTqISCkq6xcckVIRc/F24EPArwJHEemVpeqXhs7Qn8p95CmZTE4KnUEESAMXOOcuD5xDpKSpPIn0k5iLp2Iufj7QFDqLyE6l6ytteeyyfi3r7u5u9N63hc4hVS0BnOacmx06iEipK+sXHJFSFHPxK4DPAFrNTEqSpYfUhc7Qn8ys3F/LLJPJvB46hFStVUQr6v0ldBCRclDuLzgiJSnm4r8GPgisD51FZBvZusmhI/QnM6sNnWF3dXZ26sJ8CeFV4F3OuWdDBxEpFypPIgUSc/EHgHcDFXV9iZQ5X9MOTAkdo5+VfXlKJBIjQmeQqvM4cKxzblHoICLlROVJpIBiLj4fOAZ4InQWEQDSQxcb5b3AwtYqYeSpo6NjX++9D51DqsYc4ETn3Fuhg4iUG5UnkQKLufgq4ATgtsBRRLBUfWvoDAVQ9uUpm82O1Ga5UgQe+LZz7gznXEfoMCLlSOVJpAhiLt4Vc/FzgMuIXrxEgrD0sLIvGlszswGhM/SHVCqlzXKlkNqBjzrnrgwdRKScqTyJFFHMxb8PfJxoWViR4ssMGhM6QgFURCFMJpP6YEUK5TXgnc65P4UOIlLuVJ5Eiizm4vcAxwHLQmeRKuNJge0TOkZ/M7OBoTP0h0Qioc1ypRDuBY5xzr0UOohIJVB5Egkg5uLPAUejhSSkmDJDFhpWUXs85VTEtL3u7u59vPfa3kD60zXAh51z2oRZpJ+oPIkE0mMhiV8HjiJVwlL1a0NnKIRKGXlCm+VK/+kEPu2cu9Q5lw0dRqSSqDyJBJRbSOIzwAWAVj6SgrL0sHToDAVSKeVJm+VKf3gDOM4599vQQUQqkcqTSAmIufhsov2gNCddCiczeFToCIVQQSNPJJPJYaEzSFl7FDjKOfds6CAilUrlSaRExFx8HnAUcGvoLFKBPB5fOzV0jAKpmOu4ksnkftosV3bRtcD7nHOrQgcRqWSmf6NFSk9zS9NngOsBfQot/SNTt6y2ff8poWMUSmtr5ez9O3Xq1IU1NTUVtyqiFMw64DwtQy5SHBp5EilBMRe/lWg1vnmhs0hlsHT98tAZCiwVOkB/SaVSb4bOIGXjMeBwFSeR4lF5EilRMRd/ieg6qFtCZ5HyZ6n6rtAZCqw7dID+os1yJQ8e+CFwvHNuaegwItVE0/ZEykBzS9PZwE1AfegsUp5q2g54yvzAY0LnKJTW1tY2oCIWxBg0aNBrkydP3i90DilZa4FznXN/CR1EpBpp5EmkDMRc/HbgSOD50FmkTPkBe4eOUGAVM22vq6trX+99e+gcUpL+Bhym4iQSjsqTSJmIufirwDuBG0NnkTKTrX3LsAmhYxRYxZQnoCabzS4IHUJKShb4HvB+51ylX78oUtI0bU+kDDW3NJ0B3AyMCJ1FSp91j/h7TWLvI0LnKKTW1talQMWMro0fP/6hYcOGnRA6h5SEVcCnnXMtoYOIiEaeRMpSzMXvAo4AtBGi9C5VXw1TwCpp5IlEIqFtCgTgf4im6ak4iZQIlacKZGY3mtl3+vH5FpuZ28XHzjezE/ory1bP7c1sWiGeuxzEXHwBcCzw09BZpLRZemjFbCK7E5nQAfpTMpms2n/bBIANwAXOuVOdcytDhxGRt6k8laDtlQIzu9zMbsvn8d77L3jvv5t73Alm9kYhcuae/1dm9r0ef59hZivM7JJclhne+4cKdf5qF3Px7piL/x/go8CK0HmkRGXrxoeOUATp0AH6Uzab3cN7vyh0DgniQeBfnHOzQwcRkW2pPEm/MbPDiP7Rv9J7/+PQeapJzMX/CBwE/DJ0Fikx3pJgjaFjFJqZVdTIE0B3d7c2y60uHcD/BU50zi0JHUZEtk/lqQxtGk0ys0vMbHVupOf8Hvf/ysy+Z2bDgL8Ak8xsY+5rkpnVmNk3zGyBmb1lZneZ2egejz/HzJbk7vtWnpmOAVqAy7z3P+tx++Ypf7nRs7vM7NdmtiE3pe+oHsceYWb/yN03x8zu3GpU62u5n/VNM/vsVucfmXveNbns3zazmtx955nZo2Z2rZm1mdlCMzs2d/uy3O/wM33971BqYi7eFnPxzwIfAPSJtUTSQxcZVg3/1lfUyBNAR0dHxRVC2aEniK5tus45p5W8REpYNbygVqoJwEhgMvA54Hoz26PnAd77BHAK8Kb3vj739Sbwf4CPAccDk4BW4HoAMzsIuAE4J3ffnsBevWQ5BrgXuNh7f0svx54GNBNtZvlH4Ge589YB/wX8ChgN3AGcvulBZvZB4KvAScB+wNbXYP009/vYJ/dznQuc3+P+dwAv5H6e23MZjgamAZ8GfmZmFbEBbczF7wf+BfgJ0fK2UsUsVf9W6AxFUnFFI5FIVMN0y2rXDVwGHOecezV0GBHpncpT+UoBV3jvU977PwMbgQPyfOyFwLe8929477uAy4FPmtkA4JPAf3vv/5a77zv0/gb8ncB6olGu3jzivf+z9z4D/AY4tMdzDACuy/1Mvwee6vG4M4Bfeu/n5Urh5ZvuMLNa4Ezgm977Dd77xcCPiQrgJou897/MnfdOYArR76/Le38f0QtYxVygHXPxRMzF/x9wHPBS6DwSjqWHWegMxWBmFfdBQVdX1zTv/cbQOaRgngOOcs593zlXceVfpFKpPJWmDDBwq9sGsuVSvG9573tOU0kC+Y6cNAD/lZvC1kb05joDjCcabVq26cBcUentk+vrgaeB+7ce/dqOnqsGJYHBudI2CVjut9x4bFmPP0/a6u8954OPAeq2um0J0ajcJqt6/LkDwHu/9W0VMfLUU8zFHwcOI9pcsaKWcpY8ZQaN7v2gilBx5QmozWazr4UOIf0uA1wJHOOc+2foMCLSNypPpWkpMHWr2xrZshzka3tzp5cBp3jvR/X4Guy9X060YtuUTQea2VCiqW47kwFm5nL/1cx2ZePWFcBkM+v5KfmUre7v+feem2GuJSoGDVvdr13Y2bwi33eAo4BnQueRIvJkoGaf0DGKpBLLE52dnetDZ5B+9RTRaNO3nXP6QEukDKk8laY7gW+b2V65xR0c8BHg7l14rlXAnmY2ssdtNwJXmlkDgJmNNbOP5u67GzjVzI7LXYd0BXn878R7nwI+RVRk/pxbrKIvHicqYV82swG5PMf0uP8u4DwzOyhX6Jp6nDuTu/9KMxue+7m+AuS1tHu1iLn4C0TTIy8lN/omFS4zeLFhQ0LHKIZKnLYH2iy3grQDXwbe5Zx7LnQYEdl1Kk+l6QrgMeARosUcrgZmeu/n9fWJvPcvEy2+sDA3TW8S0UICfwTuM7MNRKv8vCN3/HzgIqJFFVbkzp/XPlHe+27g40An8Cez/N+09Xjs54A2okUc/hvoyt3/F+A/gAeA13Pfe/pXIAEsJPq93Q78It/zV4uYi2diLn4NcAgwN3QeKSxL16/q/aiKUZErlHV0dFT8MvNV4C5gunPueudcRZZ8kWpiW15iIlI6zOxJ4EbvvfYuKoDmliYDZhGV812ZaiklrmZDw0OWHn5C6BzFsGHDhr+l0+n3hs5RCI2NjUvNbO/ej5QSswj4knPu3tBBRKT/aORJSoaZHW9mE3LT9j5DNDqiF50Cibm4j7n4TcAM4H9C55ECyAypplJcsZ8EplKpZb0fJSUkBXwfmKHiJFJ5BoQOINLDAUTTG+qBBcAnvfcrwkaqfDEXfwM4tbml6WyiqZFjA0eS/uJrG3o/qDJsudZMZUkmk5m6urrQMSQ/jwIXOufmhw4iIoWhkScpGd77n3vvx3vvh3nvD/HeazSkiGIufjvRBsRXk7vWTMpYZuBKw3pbKVPKQCKRGBc6g/RqHfB54D0qTiKVTdc8icg2mluapgI/INp8WMqQdY16qia51zG9H1kZEonE3O7u7uND5yiQdGNjY3dupVEpLRngFuA7zrk1ocOISOGpPInIDjW3NL0L+HeiJc6ljNjGvebWpEZVapnYRoWXJxoaGp6rra09LHQO2cJ9wCXOuT6vhCsi5UvXPInIDsVc/HHgXc0tTWcSjURNDZtI8mWZoYNCZygmq+SLnoCurq71Q4dq4KlEvAR81Tn359BBRKT4dM2TiPQq5uJ3AtOBrwPrA8eRfGQHTg4docgqujwlEonBoTMIa4k2uj1ExUmkemnanoj0SXNL01jgcqI9ojR6XYqyNetr1h84wqjs0ZieOjo6Huns7DwudI5Cqa2tXdPQ0KCVMMPoBn4KfM851xY6jIiEpfIkIrukuaXpQOAa4MOhs8hWuoc/X5toODR0jGLq6Oh4tLOz892hcxRSY2PjMjObEjpHlfk9cKlzbkHoICJSGvSpsYjskpiLv0S0P5QDfky0qbGUAEvXV92n42ZW8dPQU6nU0rq6OpWn4ngW+Ipz7m+hg4hIaan4FxsRKayYi7cAhwMXANrUuARYemjVfTBWDeWpo6MjEzpDFXgR+BRwtIqTiGyPpu2JSL9pbmkaRrSoxCWAlgYLpKb1oFeMmgNC5yimrq6up5PJ5NGhcxTS4MGDX5w0adJBoXNUqFeBONDsnMuGDiMipUvlSUT6XXNL0yTga0SLSqhEFZO37pq2g8ywgaGjFFN3d/ffE4nEEaFzFFiqsbExbWZDQgepIAuBK4DbnHMa2RORXqk8iUjBNLc0jQH+H9HyviMDx6kO6aEv127YZ3roGMXW3d39fCKRqPhFMhoaGl6ora3V9YW7bynwXeBXzrl06DAiUj5UnkSk4JpbmkYAFxEVqXGB41Q06xj3SE3nuIpdsntHUqnUvI0bNx4cOkehTZgw4aGhQ4eeEDpHGVsOXAnMds51hw4jIuVH5UlEiqa5pWkI8Hngq4BWDSuAmg2Ncy097PjQOYotlUq9vHHjxoofcRs+fPiTY8eOfUfoHGVoJfAD4CbnXGfoMCJSvlSeRKTomluaBgLnEi0usV/gOBWlpm36C+YHVN20rnQ6/dqGDRsq/n9LtbW1qxoaGsaHzlFGFgPXArc455KBs4hIBVB5EpFgmluaaomWBb4M+JfAccqfx9e0zdhg2IjQUYotk8ksam9vbwydoxgaGxvfMLO9QucocX8n2sR7jhaCEJH+pPIkIsE1tzQZcCrwLUBTknZVpm5Jbfv+DaFjhJDJZN5ob2+vikKx1157PV5XV/eu0DlK1F+Bq51zD4QOIiKVSeVJREpKc0vT+4lK1PtDZyk31jn6iZqOSe8MnSOEbDa7cv369RNC5yiGPffcc+7IkSOr7rq2nUgBzcCPnHMvhA4jIpVN5UlESlJzS9M7iErUqYAFjlMWajbu/ZClRpwQOkcI2Wz2rfXr1+8ZOkcxDB48eP6kSZNmhM5RAjYANwPXOufeCB1GRKqDypOIlLTmlqaDgS8Cnwaq7lqevqhpO+Bp8wOPDp0jBO/9hra2tuGhcxRJqrGxMWNmg0MHCWQF8BPgRufc+tBhRKS6qDyJSFlobmkaBsSAC4GqLAi9qWmdsdKwqpi6tjXvfVdbW9ug0DmKpaGh4Z+1tbXVtsjKg8ANwD3OuVToMCJSnVSeRKTsNLc0HU5Uos4GqmW0YeeyA9bUrp8+NnSMULz32ba2tprQOYplwoQJc4cOHVoN1z21ArcSjTK9EjqMiIjKk4iUreaWpnrgLKIidWTgOEFZ98hnaxJTqvp30NramgFqQ+cohhEjRjw+ZsyYSl5x7ymiUaY7nXMdocOIiGyi8iQiFaG5pelIohJ1FlAfOE7RWWLy3JruPaphJGKHWltbO4AhoXMUQ21t7YqGhoaJoXP0swRwO3CDc+4focOIiGyPypOIVJTmlqbhRNP5LgQODxynaGrW7/eYZQcdGzpHSK2tre1U0aIijY2NK8ysEgrUPOBG4DfOufbQYUREdkblSUQqVnNL09HALKLRqGGB4xRUTetBi4yaxtA5QmptbV0LjAmdo1imTJny+MCBA8t16t5a4C6iwvRE6DAiIvlSeRKRitfc0jQCmEk0GnVo4Dj9z1uipu2gIYZVzYIJ29Pa2roSqJrVBseMGTN3xIgR5TRVsxP4I3AbcK9WzBORcqTyJCJVJbdS36eATwL7BY7TP1LD5tVubDw4dIzQWltblwFTQucoliFDhvxz4sSJpb5ceRZ4iKgw/U7T8kSk3Kk8iUjVam5pOoSoRH0SODBwnF1myfF/q+ka+97QOUJrbW1dBFTT1MWuxsZGM7O60EG24wWiwnS7c2556DAiIv1F5UlEBGhuaTqIt4tUqX+av4Wa9n0etszQ94TOEVpra+trVMpoYp6mTp06v6amZkboHDnLgGbgNufcC6HDiIgUgsqTiMhWmlua9uftIlXyK/bVtB74olF7UOgcobW2tr5EGY8g7oqJEyfOHTJkSMjrnuYB9wD3OOeeDZhDRKQoVJ5ERHaiuaVpH94uUkcHjrMtT6ambUbKsMGho4TW1tY2z3tfVdd+BdgsNws8xtuFaUERzy0iEpzKk4hInppbmvbm7SL1TsDCJgLSg1+v3TBtWugYpaCtre15733lraa4EwMGDHhz7733nlTg03QC9xMVpj8559YU+HwiIiVL5UlEZBc0tzRNBj4BfBQ4Fggy8mOdYx6r6ZhQ1ZvjbtLW1vZ37/0RoXMUW2Nj40oz6+8l2tcB/0NUmP7qnEv08/OLiJQllScRkd3U3NI0mKhAvT/3dTQwoBjnrtkwda6l68tpr5+CaWtre9p7X3pTKwtsypQpTwwcOPCdu/k0XUTT8VpyX8865zK7HU5EpMIU5cVdRKSSxVy8E3gg90VzS1M98B7eLlOHAYXZwDYzeERBnrcMmVm2Gj8Q7Ojo6Bo4cGBfH+aB53m7LD3snEv2dzYRkUqjkScRkQJrbmnaAziBt8tUv62MV9M6o9WwPfrr+crZ+vXrH89ms8VcPKEkDBky5IWJEyceksehS3i7LP2vrl0SEek7lScRkSJrbmmaALyPt8vUPrv0RJmBb9a2H1DoxQLKxvr16x/NZrPvDp2j2Mysc+rUqbVmtvXw01LgUeBvQItz7vXipxMRqSwqTyIigTW3NDXwdpF6HzA5n8dZ1x5P1SQnH1PIbOWkvb39kUwmc1zoHCFMnTr1hZqamm6isvQY8KhzbnngWCIiFUflSUSkxDS3NO0FHAUc2eP72K2Ps41T5takRmqxiJz29va/ZTKZ94bOUSRLgKeAJ4Gn6urqnjnvvPM6AmcSEal4Kk8iImWguaVpCm8XqSOBI2vW77/QsnXvCJusdGzYsOFv6XS6EsvTm8A/gWfIlaVZs2atChtJRKQ6qTyJiJSpu+5unmy+9jDgEODQ3Pf9gdqgwQLZsGHD3HQ6Xc4jcWuB+cC8Hl/zZ82a1Ro0lYiIbKbyJCJSQebMmTOYaDW/Q3Jf+wH7Ao0E2si3WDZu3Dg3lUqVQ3lq5+2StLksaTRJRKT0qTyJiFSBOXPmGDCJaGW/fXNf+/T4vs01VeWmxMpTAliU+1qY+/4qUUlaFjKYiIjsOpUnERFhzpw5w9m2UG36PgkYEi5dfhKJxNzu7u5ilCcPrAGW5b7eyH1fQq4wzZo1S3soiYhUIJUnERHpVa5cjQcm5L6P7/H3ccCewOjc9z2BrfccKrjdKE8poBV4C1i3k++bCtMbs2bN6uqX0CIiUlZUnkREpN/lylbPQjWM6JqrQdv5vr3btvfdgAyQ3s5XpqOjY1FnZ+dYoHMHX0m2U5JmzZrVXrjfhIiIVBKVJxERERERkTzUhA4gIiIiIiJSDlSeRERERERE8qDyJCIiIiIikgeVJxERERERkTyoPImIiIiIiORB5UlERERERCQPKk8iIiIiIiJ5UHkSERERERHJg8qTiIiIiIhIHlSeRERERERE8qDyJCIiIiIikgeVJxERERERkTyoPImIiIiIiORB5UlERERERCQPKk8iIiIiIiJ5UHkSERERERHJg8qTiIiIiIhIHlSeRERERERE8qDyJCIiIiIikgeVJxERERERkTyoPImIiIiIiORB5UlERERERCQPKk8iIiIiIiJ5UHkSERERERHJg8qTiEgVM7PzzOyRHn/faGb7hMxUicwsZmZPmlnCzFbn/vwlM7PQ2UREJH8qTyIiRWRmD5lZq5kN6ofn+pWZfa8/cm3iva/33i/sz+esdmZ2CfAT4BpgAjAe+ALwbqCuj881oN8DiohI3lSeRESKxMymAu8BPHBaEc6nN9qBmdlI4ArgS977u733G3zkH977md77LjMbZGY/MrOlZrbKzG40syG5x59gZm+Y2dfNbCXwyx63XZobxVphZh8zsw+Z2atmts7MLuuR4Rgze9zM2nLH/szM6nrc783sC2b2Wq7YX2+RQbnn+pcex44zsw4zG1vEX6OISMlQeRIRKZ5zgSeAXwGf2XRjbjTqgh5/3zyVLvcm9trcm+T1ZvaCmR1sZrOAmcClual2f8odvzj3RvsFIGFmA8zsG2a2wMw2mNmLZnb6jgLm3khPy/35w2b2DzNrN7NlZnZ5//9KKt67gEHAH3ZyzA+B/YHDgGnAZODfetw/ARgNNACzetw2uMexNwOfBo4kKuj/1mP6ZQa4GBiTy3Mi8KWtMpwKHA0cCpwBnOy97wKac8+7yVlAi/d+Te8/uohI5VF5EhEpnnOB3+a+Tjaz8Xk85gPAe4neXI8CzgTe8t7/PPc8V+em2n2kx2POAj4MjPLep4EFRG+oRwJx4DYzm5jHuRO5zKNyz/dFM/tYHo+Tt40B1ub+OwBgZo/lRoE6zOx44PPAxd77dd77DcBVQKzHc2SBJu99l/e+I3dbCrjSe58iKjhjgJ/kRrbmA/OBQwC8989675/w3qe994uBm4Djt8r5A+99m/d+KfAgUZEDuBU428w2vV84B/jN7v9aRETKk8qTiEgRmNlxRCMHd3nvnyUqNGfn8dAUMByYDpj3/iXv/YpeHnOd937Zpjfa3vs53vs3vfdZ7/2dwGvAMb2d2Hv/kPf+n7nHvQDcwbZvumXn3gLG9JxC6b0/1ns/KnffeGAo8GyuULUB9wI9p8Wt8d53bv283vtM7s+bCtWqHvd3APUAZra/mf23ma00s3aicjZmq+db2ePPyU2P9d4/SVSijzez6UQjY3/M/8cXEaksKk8iIsXxGeA+7/3a3N9vp8fUvR3x3j8A/Ay4HlhlZj83sxG9PGxZz7+Y2blm9lyPN+cHs+2b522Y2TvM7EEzW2Nm64kWOej1cbKFx4Eu4KM7uH8tUdGZ4b0flfsa6b2v73GM380MNwAvA/t570cAlwF9WeXvVqKpe+cAd2+nyImIVA2VJxGRAstd/H8G0af3K3MX/l8MHGpmhxJ9sj+0x0Mm9Hy89/467/2RwAyi6Xtf23TXDk65+XYzayC6HubLwJ65EY955Pfm+XaiUYYp3vuRwI15Pk5yvPdtRFMl/9PMPmlm9WZWY2aHAcOIpuTdDFxrZuMAzGyymZ3cjzGGA+3Axtzo0Rf7+PjfAKcTFahf92MuEZGyo/IkIlJ4HyO6aP8gomtJDgMOBB4muqboOeDjZjY0t1jD5zY90MyOzo0ADSQqWZ2554JomlZvezINIypTa3LPdz7RyFM+hgPrvPedZnYM+U0zlK14768GvgJcCqwm+u92E/B14LHc99eBJ3LT6lqAA/oxwleJ/tttICpqd/blwd77N4C/E/3v6OF+zCUiUnbM+92dDSAiIjtjZvcC8733l2x1+xnAdURl5naildBeAO4HnPf+ODM7EbiWqCR1An8FLvTebzSz/YA5wFTgIe/9x8xsMXCB976lx3muJBptyBKNHBwJ/MZ7f4uZnZc7/rjcsZ5oetfrZvZJ4MdEK73NBRYTLULRc/U1qQJm9gvgTe/9t0NnEREJSeVJREREdsii/cmeAw733i8Km0ZEJCxN2xMREZHtMrPvEl0jd42Kk4iIRp5ERERERETyopEnERERERGRPKg8iYiIiIiI5EHlSUREREREJA8qTyIiIiIiInlQeRIREREREcmDypOIiIiIiEgeVJ5ERERERETyoPIkIiIiIiKSB5UnERERERGRPKg8iYiIiIiI5EHlSUREREREJA8qTyIiIiIiInlQeRIREREREcmDypOIiIiIiEgeVJ5ERERERETyoPIkIiIiIiKSB5UnERERERGRPKg8iYiIiIiI5EHlSUREREREJA8qTyIiIiIiInlQeRIREREREcmDypOIiIiIiEgeVJ5ERERERETyoPIkIiIiIiKSB5UnERERERGRPKg8iYiIiIiI5EHlSUREREREJA8qTyIiIiIiInlQeRIREREREcmDypOIiIiIiEgeVJ5ERERERETyoPIkIiIiIiKSB5UnERERERGRPKg8iYiIiIiI5EHlSUREREREJA8qTyIiIiIiInlQeRIREREREcmDypOIiIiIiEgeVJ5ERERERETyoPIkIiIiIiKSB5UnERERERGRPKg8iYiIiIiI5EHlSUREREREJA8qTyIiIiIiInlQeRIREREREcmDypOIiIiIiEgeVJ5ERERERETyoPIkIiIiIiKSB5UnERERERGRPKg8iYiIiIiI5EHlSUREREREJA8qTyIiIiIiInlQeRIREREREcmDypOIiIiIiEge/j+qlZ2scYguNwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ntop = 5\n",
    "df_top = df_country.nlargest(ntop, 'citations')\n",
    "df_bot = df_country.nsmallest(len(df_country) - ntop, 'citations')\n",
    "df_tmp = pd.DataFrame.from_dict({'country': ['Others'], 'citations': df_bot['citations'].sum()}).set_index('country')\n",
    "df_top = df_top.append(df_tmp)\n",
    "plot = df_top.plot.pie(y='citations', figsize=(15,15), autopct='%0.1f%%', colors=colors_msc,\n",
    "                       fontsize=12, title='Public Face Recognition Research Citations by Country')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save images\n",
    "fig = plot.get_figure()\n",
    "fig.savefig(join(fp_dir_out,'summary_countries.pdf'))\n",
    "fig.savefig(join(fp_dir_out,'summary_countries.png'))\n",
    "\n",
    "# save CSV\n",
    "df_top.to_csv(fp_country_top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IGNORE BELOW"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matplotlib Pandas Bar Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "\n",
    "# fivethirtyeight, bmh, grayscale, dark_background, ggplot\n",
    "matplotlib.style.use('dark_background')\n",
    "\n",
    "\n",
    "data = [[2000, 2000, 2000, 2001, 2001, 2001, 2002, 2002, 2002],\n",
    "        ['Jan', 'Feb', 'Mar', 'Jan', 'Feb', 'Mar', 'Jan', 'Feb', 'Mar'],\n",
    "        [1, 2, 3, 4, 5, 6, 7, 8, 9]]\n",
    "\n",
    "rows = zip(data[0], data[1], data[2])\n",
    "headers = ['Year', 'Month', 'Value']\n",
    "df = pd.DataFrame(rows, columns=headers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.rcParams['font.family'] = 'Ubuntu'\n",
    "\n",
    "# fivethirtyeight, bmh, grayscale, dark_background, ggplot\n",
    "matplotlib.style.use('dark_background')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/adam/anaconda3/envs/megapixels/lib/python3.6/site-packages/matplotlib/__init__.py:886: MatplotlibDeprecationWarning:\n",
      "\n",
      "\n",
      "examples.directory is deprecated; in the future, examples will be found relative to the 'datapath' directory.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "KeysView(RcParams({'_internal.classic_mode': False,\n",
       "          'agg.path.chunksize': 0,\n",
       "          'animation.avconv_args': [],\n",
       "          'animation.avconv_path': 'avconv',\n",
       "          'animation.bitrate': -1,\n",
       "          'animation.codec': 'h264',\n",
       "          'animation.convert_args': [],\n",
       "          'animation.convert_path': 'convert',\n",
       "          'animation.embed_limit': 20.0,\n",
       "          'animation.ffmpeg_args': [],\n",
       "          'animation.ffmpeg_path': 'ffmpeg',\n",
       "          'animation.frame_format': 'png',\n",
       "          'animation.html': 'none',\n",
       "          'animation.html_args': [],\n",
       "          'animation.writer': 'ffmpeg',\n",
       "          'axes.autolimit_mode': 'data',\n",
       "          'axes.axisbelow': True,\n",
       "          'axes.edgecolor': 'white',\n",
       "          'axes.facecolor': 'black',\n",
       "          'axes.formatter.limits': [-7, 7],\n",
       "          'axes.formatter.min_exponent': 0,\n",
       "          'axes.formatter.offset_threshold': 4,\n",
       "          'axes.formatter.use_locale': False,\n",
       "          'axes.formatter.use_mathtext': False,\n",
       "          'axes.formatter.useoffset': True,\n",
       "          'axes.grid': True,\n",
       "          'axes.grid.axis': 'both',\n",
       "          'axes.grid.which': 'major',\n",
       "          'axes.labelcolor': 'white',\n",
       "          'axes.labelpad': 4.0,\n",
       "          'axes.labelsize': 'large',\n",
       "          'axes.labelweight': 'normal',\n",
       "          'axes.linewidth': 1.0,\n",
       "          'axes.prop_cycle': cycler('color', ['#8dd3c7', '#feffb3', '#bfbbd9', '#fa8174', '#81b1d2', '#fdb462', '#b3de69', '#bc82bd', '#ccebc4', '#ffed6f']),\n",
       "          'axes.spines.bottom': True,\n",
       "          'axes.spines.left': True,\n",
       "          'axes.spines.right': True,\n",
       "          'axes.spines.top': True,\n",
       "          'axes.titlepad': 6.0,\n",
       "          'axes.titlesize': 'x-large',\n",
       "          'axes.titleweight': 'normal',\n",
       "          'axes.unicode_minus': True,\n",
       "          'axes.xmargin': 0.05,\n",
       "          'axes.ymargin': 0.05,\n",
       "          'axes3d.grid': True,\n",
       "          'backend': 'module://ipykernel.pylab.backend_inline',\n",
       "          'backend.qt4': None,\n",
       "          'backend.qt5': None,\n",
       "          'backend_fallback': True,\n",
       "          'boxplot.bootstrap': None,\n",
       "          'boxplot.boxprops.color': 'black',\n",
       "          'boxplot.boxprops.linestyle': '-',\n",
       "          'boxplot.boxprops.linewidth': 1.0,\n",
       "          'boxplot.capprops.color': 'black',\n",
       "          'boxplot.capprops.linestyle': '-',\n",
       "          'boxplot.capprops.linewidth': 1.0,\n",
       "          'boxplot.flierprops.color': 'black',\n",
       "          'boxplot.flierprops.linestyle': 'none',\n",
       "          'boxplot.flierprops.linewidth': 1.0,\n",
       "          'boxplot.flierprops.marker': 'o',\n",
       "          'boxplot.flierprops.markeredgecolor': 'black',\n",
       "          'boxplot.flierprops.markerfacecolor': 'none',\n",
       "          'boxplot.flierprops.markersize': 6.0,\n",
       "          'boxplot.meanline': False,\n",
       "          'boxplot.meanprops.color': 'C2',\n",
       "          'boxplot.meanprops.linestyle': '--',\n",
       "          'boxplot.meanprops.linewidth': 1.0,\n",
       "          'boxplot.meanprops.marker': '^',\n",
       "          'boxplot.meanprops.markeredgecolor': 'C2',\n",
       "          'boxplot.meanprops.markerfacecolor': 'C2',\n",
       "          'boxplot.meanprops.markersize': 6.0,\n",
       "          'boxplot.medianprops.color': 'C1',\n",
       "          'boxplot.medianprops.linestyle': '-',\n",
       "          'boxplot.medianprops.linewidth': 1.0,\n",
       "          'boxplot.notch': False,\n",
       "          'boxplot.patchartist': False,\n",
       "          'boxplot.showbox': True,\n",
       "          'boxplot.showcaps': True,\n",
       "          'boxplot.showfliers': True,\n",
       "          'boxplot.showmeans': False,\n",
       "          'boxplot.vertical': True,\n",
       "          'boxplot.whiskerprops.color': 'black',\n",
       "          'boxplot.whiskerprops.linestyle': '-',\n",
       "          'boxplot.whiskerprops.linewidth': 1.0,\n",
       "          'boxplot.whiskers': 1.5,\n",
       "          'contour.corner_mask': True,\n",
       "          'contour.negative_linestyle': 'dashed',\n",
       "          'datapath': '/home/adam/anaconda3/envs/megapixels/lib/python3.6/site-packages/matplotlib/mpl-data',\n",
       "          'date.autoformatter.day': '%Y-%m-%d',\n",
       "          'date.autoformatter.hour': '%m-%d %H',\n",
       "          'date.autoformatter.microsecond': '%M:%S.%f',\n",
       "          'date.autoformatter.minute': '%d %H:%M',\n",
       "          'date.autoformatter.month': '%Y-%m',\n",
       "          'date.autoformatter.second': '%H:%M:%S',\n",
       "          'date.autoformatter.year': '%Y',\n",
       "          'docstring.hardcopy': False,\n",
       "          'errorbar.capsize': 0.0,\n",
       "          'examples.directory': '',\n",
       "          'figure.autolayout': False,\n",
       "          'figure.constrained_layout.h_pad': 0.04167,\n",
       "          'figure.constrained_layout.hspace': 0.02,\n",
       "          'figure.constrained_layout.use': False,\n",
       "          'figure.constrained_layout.w_pad': 0.04167,\n",
       "          'figure.constrained_layout.wspace': 0.02,\n",
       "          'figure.dpi': 72.0,\n",
       "          'figure.edgecolor': 'black',\n",
       "          'figure.facecolor': 'black',\n",
       "          'figure.figsize': [6.0, 4.0],\n",
       "          'figure.frameon': True,\n",
       "          'figure.max_open_warning': 20,\n",
       "          'figure.subplot.bottom': 0.125,\n",
       "          'figure.subplot.hspace': 0.2,\n",
       "          'figure.subplot.left': 0.125,\n",
       "          'figure.subplot.right': 0.9,\n",
       "          'figure.subplot.top': 0.88,\n",
       "          'figure.subplot.wspace': 0.2,\n",
       "          'figure.titlesize': 'large',\n",
       "          'figure.titleweight': 'normal',\n",
       "          'font.cursive': ['Apple Chancery',\n",
       "                           'Textile',\n",
       "                           'Zapf Chancery',\n",
       "                           'Sand',\n",
       "                           'Script MT',\n",
       "                           'Felipa',\n",
       "                           'cursive'],\n",
       "          'font.family': ['Ubuntu'],\n",
       "          'font.fantasy': ['Comic Sans MS',\n",
       "                           'Chicago',\n",
       "                           'Charcoal',\n",
       "                           'Impact',\n",
       "                           'Western',\n",
       "                           'Humor Sans',\n",
       "                           'xkcd',\n",
       "                           'fantasy'],\n",
       "          'font.monospace': ['DejaVu Sans Mono',\n",
       "                             'Bitstream Vera Sans Mono',\n",
       "                             'Computer Modern Typewriter',\n",
       "                             'Andale Mono',\n",
       "                             'Nimbus Mono L',\n",
       "                             'Courier New',\n",
       "                             'Courier',\n",
       "                             'Fixed',\n",
       "                             'Terminal',\n",
       "                             'monospace'],\n",
       "          'font.sans-serif': ['DejaVu Sans',\n",
       "                              'Bitstream Vera Sans',\n",
       "                              'Computer Modern Sans Serif',\n",
       "                              'Lucida Grande',\n",
       "                              'Verdana',\n",
       "                              'Geneva',\n",
       "                              'Lucid',\n",
       "                              'Arial',\n",
       "                              'Helvetica',\n",
       "                              'Avant Garde',\n",
       "                              'sans-serif'],\n",
       "          'font.serif': ['DejaVu Serif',\n",
       "                         'Bitstream Vera Serif',\n",
       "                         'Computer Modern Roman',\n",
       "                         'New Century Schoolbook',\n",
       "                         'Century Schoolbook L',\n",
       "                         'Utopia',\n",
       "                         'ITC Bookman',\n",
       "                         'Bookman',\n",
       "                         'Nimbus Roman No9 L',\n",
       "                         'Times New Roman',\n",
       "                         'Times',\n",
       "                         'Palatino',\n",
       "                         'Charter',\n",
       "                         'serif'],\n",
       "          'font.size': 10.0,\n",
       "          'font.stretch': 'normal',\n",
       "          'font.style': 'normal',\n",
       "          'font.variant': 'normal',\n",
       "          'font.weight': 'normal',\n",
       "          'grid.alpha': 1.0,\n",
       "          'grid.color': 'white',\n",
       "          'grid.linestyle': '-',\n",
       "          'grid.linewidth': 0.8,\n",
       "          'hatch.color': 'black',\n",
       "          'hatch.linewidth': 1.0,\n",
       "          'hist.bins': 10,\n",
       "          'image.aspect': 'equal',\n",
       "          'image.cmap': 'viridis',\n",
       "          'image.composite_image': True,\n",
       "          'image.interpolation': 'nearest',\n",
       "          'image.lut': 256,\n",
       "          'image.origin': 'upper',\n",
       "          'image.resample': True,\n",
       "          'interactive': True,\n",
       "          'keymap.all_axes': ['a'],\n",
       "          'keymap.back': ['left', 'c', 'backspace'],\n",
       "          'keymap.copy': ['ctrl+c', 'cmd+c'],\n",
       "          'keymap.forward': ['right', 'v'],\n",
       "          'keymap.fullscreen': ['f', 'ctrl+f'],\n",
       "          'keymap.grid': ['g'],\n",
       "          'keymap.grid_minor': ['G'],\n",
       "          'keymap.help': ['f1'],\n",
       "          'keymap.home': ['h', 'r', 'home'],\n",
       "          'keymap.pan': ['p'],\n",
       "          'keymap.quit': ['ctrl+w', 'cmd+w', 'q'],\n",
       "          'keymap.quit_all': ['W', 'cmd+W', 'Q'],\n",
       "          'keymap.save': ['s', 'ctrl+s'],\n",
       "          'keymap.xscale': ['k', 'L'],\n",
       "          'keymap.yscale': ['l'],\n",
       "          'keymap.zoom': ['o'],\n",
       "          'legend.borderaxespad': 0.5,\n",
       "          'legend.borderpad': 0.4,\n",
       "          'legend.columnspacing': 2.0,\n",
       "          'legend.edgecolor': '0.8',\n",
       "          'legend.facecolor': 'inherit',\n",
       "          'legend.fancybox': True,\n",
       "          'legend.fontsize': 'medium',\n",
       "          'legend.framealpha': 0.8,\n",
       "          'legend.frameon': True,\n",
       "          'legend.handleheight': 0.7,\n",
       "          'legend.handlelength': 2.0,\n",
       "          'legend.handletextpad': 0.8,\n",
       "          'legend.labelspacing': 0.5,\n",
       "          'legend.loc': 'best',\n",
       "          'legend.markerscale': 1.0,\n",
       "          'legend.numpoints': 1,\n",
       "          'legend.scatterpoints': 1,\n",
       "          'legend.shadow': False,\n",
       "          'legend.title_fontsize': None,\n",
       "          'lines.antialiased': True,\n",
       "          'lines.color': 'white',\n",
       "          'lines.dash_capstyle': 'butt',\n",
       "          'lines.dash_joinstyle': 'round',\n",
       "          'lines.dashdot_pattern': [6.4, 1.6, 1.0, 1.6],\n",
       "          'lines.dashed_pattern': [3.7, 1.6],\n",
       "          'lines.dotted_pattern': [1.0, 1.65],\n",
       "          'lines.linestyle': '-',\n",
       "          'lines.linewidth': 1.5,\n",
       "          'lines.marker': 'None',\n",
       "          'lines.markeredgecolor': 'auto',\n",
       "          'lines.markeredgewidth': 1.0,\n",
       "          'lines.markerfacecolor': 'auto',\n",
       "          'lines.markersize': 6.0,\n",
       "          'lines.scale_dashes': True,\n",
       "          'lines.solid_capstyle': 'projecting',\n",
       "          'lines.solid_joinstyle': 'round',\n",
       "          'markers.fillstyle': 'full',\n",
       "          'mathtext.bf': 'sans:bold',\n",
       "          'mathtext.cal': 'cursive',\n",
       "          'mathtext.default': 'it',\n",
       "          'mathtext.fallback_to_cm': True,\n",
       "          'mathtext.fontset': 'dejavusans',\n",
       "          'mathtext.it': 'sans:italic',\n",
       "          'mathtext.rm': 'sans',\n",
       "          'mathtext.sf': 'sans',\n",
       "          'mathtext.tt': 'monospace',\n",
       "          'patch.antialiased': True,\n",
       "          'patch.edgecolor': 'white',\n",
       "          'patch.facecolor': '#348ABD',\n",
       "          'patch.force_edgecolor': False,\n",
       "          'patch.linewidth': 0.5,\n",
       "          'path.effects': [],\n",
       "          'path.simplify': True,\n",
       "          'path.simplify_threshold': 0.1111111111111111,\n",
       "          'path.sketch': None,\n",
       "          'path.snap': True,\n",
       "          'pdf.compression': 6,\n",
       "          'pdf.fonttype': 3,\n",
       "          'pdf.inheritcolor': False,\n",
       "          'pdf.use14corefonts': False,\n",
       "          'pgf.preamble': [],\n",
       "          'pgf.rcfonts': True,\n",
       "          'pgf.texsystem': 'xelatex',\n",
       "          'polaraxes.grid': True,\n",
       "          'ps.distiller.res': 6000,\n",
       "          'ps.fonttype': 3,\n",
       "          'ps.papersize': 'letter',\n",
       "          'ps.useafm': False,\n",
       "          'ps.usedistiller': False,\n",
       "          'savefig.bbox': None,\n",
       "          'savefig.directory': '~',\n",
       "          'savefig.dpi': 'figure',\n",
       "          'savefig.edgecolor': 'black',\n",
       "          'savefig.facecolor': 'black',\n",
       "          'savefig.format': 'png',\n",
       "          'savefig.frameon': True,\n",
       "          'savefig.jpeg_quality': 95,\n",
       "          'savefig.orientation': 'portrait',\n",
       "          'savefig.pad_inches': 0.1,\n",
       "          'savefig.transparent': False,\n",
       "          'scatter.marker': 'o',\n",
       "          'svg.fonttype': 'path',\n",
       "          'svg.hashsalt': None,\n",
       "          'svg.image_inline': True,\n",
       "          'text.antialiased': True,\n",
       "          'text.color': 'white',\n",
       "          'text.hinting': 'auto',\n",
       "          'text.hinting_factor': 8,\n",
       "          'text.latex.preamble': [],\n",
       "          'text.latex.preview': False,\n",
       "          'text.latex.unicode': True,\n",
       "          'text.usetex': False,\n",
       "          'timezone': 'UTC',\n",
       "          'tk.window_focus': False,\n",
       "          'toolbar': 'toolbar2',\n",
       "          'verbose.fileo': 'sys.stdout',\n",
       "          'verbose.level': 'silent',\n",
       "          'webagg.address': '127.0.0.1',\n",
       "          'webagg.open_in_browser': True,\n",
       "          'webagg.port': 8988,\n",
       "          'webagg.port_retries': 50,\n",
       "          'xtick.alignment': 'center',\n",
       "          'xtick.bottom': True,\n",
       "          'xtick.color': 'white',\n",
       "          'xtick.direction': 'out',\n",
       "          'xtick.labelbottom': True,\n",
       "          'xtick.labelsize': 'medium',\n",
       "          'xtick.labeltop': False,\n",
       "          'xtick.major.bottom': True,\n",
       "          'xtick.major.pad': 3.5,\n",
       "          'xtick.major.size': 3.5,\n",
       "          'xtick.major.top': True,\n",
       "          'xtick.major.width': 0.8,\n",
       "          'xtick.minor.bottom': True,\n",
       "          'xtick.minor.pad': 3.4,\n",
       "          'xtick.minor.size': 2.0,\n",
       "          'xtick.minor.top': True,\n",
       "          'xtick.minor.visible': False,\n",
       "          'xtick.minor.width': 0.6,\n",
       "          'xtick.top': False,\n",
       "          'ytick.alignment': 'center_baseline',\n",
       "          'ytick.color': 'white',\n",
       "          'ytick.direction': 'out',\n",
       "          'ytick.labelleft': True,\n",
       "          'ytick.labelright': False,\n",
       "          'ytick.labelsize': 'medium',\n",
       "          'ytick.left': True,\n",
       "          'ytick.major.left': True,\n",
       "          'ytick.major.pad': 3.5,\n",
       "          'ytick.major.right': True,\n",
       "          'ytick.major.size': 3.5,\n",
       "          'ytick.major.width': 0.8,\n",
       "          'ytick.minor.left': True,\n",
       "          'ytick.minor.pad': 3.4,\n",
       "          'ytick.minor.right': True,\n",
       "          'ytick.minor.size': 2.0,\n",
       "          'ytick.minor.visible': False,\n",
       "          'ytick.minor.width': 0.6,\n",
       "          'ytick.right': False}))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpl.rcParams.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "rcp = mpl.rcParams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "rcp['axes.facecolor'] = 'black'\n",
    "rcp['axes.edgecolor'] = 'white'\n",
    "rcp['axes.grid'] = True\n",
    "rcp['axes.grid.axis'] = 'x'\n",
    "rcp['legend.frameon'] = True\n",
    "rcp['ps.papersize'] = 'A4'\n",
    "rcp['figure.frameon'] = False\n",
    "rcp['axes.spines.bottom'] = True\n",
    "rcp['axes.spines.left'] = False\n",
    "rcp['axes.spines.right'] = False\n",
    "rcp['axes.spines.top'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(figsize=(10,7))  \n",
    "\n",
    "months = df['Month'].drop_duplicates()\n",
    "margin_bottom = np.zeros(len(df['Year'].drop_duplicates()))\n",
    "colors = [\"#006D2C\", \"#31A354\",\"#74C476\"]\n",
    "\n",
    "for num, month in enumerate(months):\n",
    "    values = list(df[df['Month'] == month].loc[:, 'Value'])\n",
    "\n",
    "    df[df['Month'] == month].plot.bar(x='Year',y='Value', ax=ax, stacked=True, \n",
    "                                    bottom = margin_bottom, color=colors[num], label=month)\n",
    "    margin_bottom += values\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matplotlib Bar Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "countries = ['Norway', 'Germany', 'Canada', 'United States', 'Netherlands']\n",
    "bronzes = np.array([10,7,10,6,6])\n",
    "silvers = np.array([14,10,8,8,6])\n",
    "golds = np.array([14,14,11,9,8])\n",
    "ind = [country for country in countries]\n",
    " \n",
    "plt.bar(ind, golds, width=0.6, label='golds', color='gold', bottom=silvers+bronzes)\n",
    "plt.bar(ind, silvers, width=0.6, label='silvers', color='silver', bottom=bronzes)\n",
    "plt.bar(ind, bronzes, width=0.6, label='bronzes', color='#CD7F32')\n",
    " \n",
    "plt.xticks(ind, countries)\n",
    "plt.ylabel(\"Medals\")\n",
    "plt.xlabel(\"Countries\")\n",
    "plt.legend(loc=\"upper right\")\n",
    "plt.title(\"2018 Winter Olympics Top Scorers\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ParticipantLine</th>\n",
       "      <th>Source</th>\n",
       "      <th>EatingOccasion</th>\n",
       "      <th>AtHome</th>\n",
       "      <th>Calories</th>\n",
       "      <th>FoodCode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>73570.015.0</td>\n",
       "      <td>Store</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>True</td>\n",
       "      <td>428</td>\n",
       "      <td>lemon (not cream or meringue)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>73598.015.0</td>\n",
       "      <td>Restaurant with Waiter-Waitress</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>False</td>\n",
       "      <td>726</td>\n",
       "      <td>pumpkin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>73633.08.0</td>\n",
       "      <td>Store</td>\n",
       "      <td>Lunch</td>\n",
       "      <td>False</td>\n",
       "      <td>309</td>\n",
       "      <td>pumpkin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>73653.018.0</td>\n",
       "      <td>From Somewhere Else-Gift</td>\n",
       "      <td>Snack</td>\n",
       "      <td>True</td>\n",
       "      <td>123</td>\n",
       "      <td>lemon (not cream or meringue)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>73726.017.0</td>\n",
       "      <td>Bake Sale</td>\n",
       "      <td>Snack</td>\n",
       "      <td>True</td>\n",
       "      <td>17</td>\n",
       "      <td>blueberry</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ParticipantLine                           Source EatingOccasion  AtHome  \\\n",
       "0     73570.015.0                            Store         Dinner    True   \n",
       "1     73598.015.0  Restaurant with Waiter-Waitress         Dinner   False   \n",
       "2      73633.08.0                            Store          Lunch   False   \n",
       "3     73653.018.0         From Somewhere Else-Gift          Snack    True   \n",
       "4     73726.017.0                        Bake Sale          Snack    True   \n",
       "\n",
       "   Calories                       FoodCode  \n",
       "0       428  lemon (not cream or meringue)  \n",
       "1       726                        pumpkin  \n",
       "2       309                        pumpkin  \n",
       "3       123  lemon (not cream or meringue)  \n",
       "4        17                      blueberry  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pies = pd.read_csv('toy_data_pies.csv')\n",
    "df_pies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pie_sources = df_pies.groupby('Source').agg('count')\n",
    "df_pie_flavors = df_pies.groupby('FoodCode').agg('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ParticipantLine</th>\n",
       "      <th>Source</th>\n",
       "      <th>EatingOccasion</th>\n",
       "      <th>AtHome</th>\n",
       "      <th>Calories</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FoodCode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>blueberry</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lemon (not cream or meringue)</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pumpkin</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               ParticipantLine  Source  EatingOccasion  \\\n",
       "FoodCode                                                                 \n",
       "blueberry                                    1       1               1   \n",
       "lemon (not cream or meringue)                2       2               2   \n",
       "pumpkin                                      2       2               2   \n",
       "\n",
       "                               AtHome  Calories  \n",
       "FoodCode                                         \n",
       "blueberry                           1         1  \n",
       "lemon (not cream or meringue)       2         2  \n",
       "pumpkin                             2         2  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pie_flavors.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "source_labels = df_pie_sources.FoodCode.sort_values().index\n",
    "source_counts = df_pie_sources.FoodCode.sort_values()\n",
    "\n",
    "flavor_labels = df_pie_flavors.Source.sort_values().index\n",
    "flavor_counts = df_pie_flavors.Source.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+0AAAFQCAYAAADKjJ+RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzs3XeYXWW5/vHvvScz6aRnmPRAQugtVOkMQ4sIRDgCohJBRATsh6Pyo3iO7ejRgwXliA6gYENsoAKG3pEekaJAmJAeSCAhdeb5/fGuITvD9Lb3zNyf69rXzKz2PmvttfesZ71lKSIwMzMzMzMzs+KTK3QAZmZmZmZmZtY4J+1mZmZmZmZmRcpJu5mZmZmZmVmRctJuZmZmZmZmVqSctJuZmZmZmZkVKSftZmZmZmZmZkXKSbuZWQFJOkNS5L3elPSkpPMk9ctb7mVJV3dy2TtKqpY0X9J6Sask3SPpAkkDOrOsnkzSpZIOb2T61ZJeLkA8L+edL3WSaiTdIGn7dmzrBEmfbmT67tl+j+ycqLuPpDsl3dmJ22vu/V/QWeU0U3bkfxfkzZuSzTurvdvN+3t4Nm3PjsbcRIxntGLZEyTdLWmppLXZ99LvJB2dt8yhWZztun7tyee1mfVtTtrNzIrDycD+wHuBh4HvAhfnzT8R+M/OKkzSycBjwC7Zdo8ETgXuBy4DPtpZZfUClwDvSNpIx+3Ebo6l3i2k8+VA0nmyD3CPpLFt3M4JwDuSdmB30n73xOTm3OzVWZp6/3uyq0jnT73hpP3s1KS9tSRdAPwWeAE4E5gF/Fc2O//YH0qKs73Xrz35vDazPuwdd27NzKwgnoiIf2a/3yppGvBJssQ9Ih7vrIIkTQeuBf4MnBwRm/Jm/0nSN4HtOqu83ioi/lXA4pdHxIPZ7/dLehG4Ezgd+FbBomqGpFJgU0REiwt3QEQ805Xb7w0iYgHQpa0E2uizwO8i4sy8abcDP2pvrbqZWW/iL0Izs+L0CDC0vua0sebxkqZKuk7Ssqx5+xOSWlPz+ynSTdtzGyTsAETEsoi4L6+cGZJ+K2ll1mz1wfwmq9ky9c14p0u6WdLqrHnrxfkX3ZKGSPqupFeymJdI+mt90+6mmtNmzWJD0qF50+6UdK+ko7N9XyvpcUn7Suon6SuSFkl6LWvKPDhv3fpyzpX0raxJ7luSbpI0JW+5+gTzi3lN0i/N5r2jebykCknXSlqe7d9Tkk5vsEx9l4j9svfvDUkLJX1H7e+W8Ej2c1pWxhhJV0p6PtuvGknXSxqfF8fVwIeA8Xn79nJ27KuzxV7ImzclW6+fpM9Lejbbx4WS/ic/9gbH978lLQTWA8Nbu/9ZOf8p6V+S1mXH9F5JBzZ3INSgeXzeufMeSd/LtrNM0s8kDW9hW02+/3nL7KHUreQtSS9IOqeR7UxV+z6rbZL3mThC0mNZTPMkndBgubebx2fv60vZrB/l7ecZecvPVvrcv6X0PfBrSZMabHOQpCskrVD6/P8BmNDK0EcCixubERF19TGTaskBNtbHmVf+Zdk+r8re49sl7Zc3/ww6fl6365w0M+so17SbmRWnqUAtsLqxmZImAg8BS0lJ+DLgfcBvJJ0QEX9oZttHAI9ExKKWgpA0DrgXeBM4D1gFfBy4WdK7I+LPDVb5LenC+NvAcaSm9jVsvlj+NvAe4AukprCjgANIzXPbYxrwDeDLpGP138Afslc/4Axgh2yZpcC/N1j/88ATwBxgLPAVUkuHnSJiI6kJ8QPA1cCV2TqN1lAq3RS4CxiR7V8Nqeb7p5IGRcT/NVjlp8DPgdlZOZcCr7M5MWmLqdnPldnPkcC6bP+WAeOAzwD3Sdo+ItaRmvePAfYmvSeQEusFpKbJF5G6bdTvb/358jPSe/t1UneKHbJtTSF178j3RdINhbOBkiymei3t/4Wkc/uLpPdoK2Av2t+0+XLgJuA0YAbpXKkl3bhoSkvv/1bA9cD/Al8inUc/kPRcRNwBHf6stse2pH39KrCc9L7fkL3v/2xk+UWk9+DGbJ36eP6VxX8O8APSZ/hLwFDSe3WXpF0j4s1s+Suz/bqM9J5XkY5NazwMfEipxcjvI+L5Rpa5inQT4ExSt5DaBvPHk75fFgCDSZ+9uyXtFRFPATfT8fO6s89JM7PWiQi//PLLL78K9CIllUFKIvqREr6Pki5If5e33MvA1Xl//5h08T+qwfZuIzW1b67MtcDPWxnfN4FNwLS8aSXAc8BjedMuzfZjToP1nwZuzft7HvCtZsqbkm3njAbTD82mH5o37U5gI7BN3rT3ZMv9tcH6NwIvNVLOM0Aub/oB2fQz86YF8F+NxHo18HLe3+c1jDGb/ldSwlbS4D2/rMFyNwHPt+I9eRm4LjtfyoCdgfuyc2bPJtYpASZm5Z7YYB8WNHNeTmsw/aBs+gcbTH9/Nn33Bsf3MUBNbLvZ/c/+vrEdn6k7gTsbOXeuabDc90g3EdTC9pp7/wM4LG9af1Ki/H950zryWb00K6NfM5+Vsxr5TEzPmzY2Oze+0HC7zW0rmz6EdKPuJ42UvQH4ZPb3jKyM/2iw3A9o5PPcyL5sBzyVLRvZMfw5cGRrj0cj53s/0vfU5Z14XrfrnPTLL7/86ujLzePNzIrDs6SL7deAK0hJ2YebWf5o4E/AqqzJZj+lEaZvAXaTtFUnxXUw8GDk1dBFRC3pgnr3Rsq5ucHf84D8ZrSPAGdI+oKkvSSVdDC+5yPixby/n81+3tJguWeBCZLUYPoNkTW/BYjULWABWw7S1VoHA69GxJ0Npv+MVKO9Y4PpDY/V02x5rJpzGul8WZ+tN440PsFj9QtI+pjSkwhWk268vJLNmtHKMhpzNClZ+02D8+7WbP7BDZb/XUQ01Ye9pf1/BDhW0pclHSiprANxN1Vef6C8A9t8K7IadYCIWE9qQZK/H931Wa33QkS8kBfTUtJNo9aeW/n2J9UmX9cg9gWkz1T9+70vqcvlrxqs/4vWFBKpZn0P4BBSq5knSIM83iLpotZsI+sScIekFaTzfSPpZkBrzvfWntedfU6ambWKm8ebmRWHE0kXwm8C8yM1X27OWOCD2asxo4A3mphXA0xuZVwjgcYGwVsMiNQyIL+c1xostx7I76d9frbuh0kX569Juhb4YkS81cqY8r3e4O8NzUzvR6qBy+/Hv6SRbS4hNbVtq5Fsbmqbb3He/HyNHav+rSzrz6RBCmuBhRGxxX5IOh/4DmlQus+RjkcOeJAt34+2Gkuq3W+02wbpvMvXXBeMlvb/K6Sa8NNJ3Q1WS7oB+FxELG91xM2XBx07Hg3Ps/rt5m+zI5/V+nO14XlbP41Gpjfcz8Ziaq36pxH8tYn59ftfkf1s+Hlq7PPVqOxm4N3Zq75rzl+ASyR9PyIaO9Zky+5JujFyC6n5/CLSZ+MqWrffrT2vO/ucNDNrFSftZmbFYV403t+0KSuAe0j9LxuzsJl1/wqcJWnriGh08Kc8rwFbNzJ9a1Kz0cYShCZFxGpSP+vPS5oMnAR8jZRUX8jmPs8Na7AaJoOdpbFa1nJSTV9bvUbjtXr1x29FO7bZZFkR8bdm5p8CzI2Iz9RPkDS1meVbawXpPTqoifkNz7t2jxQfaUyBrwNfl7Q18G7STYhBpL7TPUVHPqtLs5/j2DxYHHnToA2JcTvUn7NnAH9vZH59f/b6mzPlQH7Ll3a3YoiIhZKuIvXPn07q996U95JuXszOzhsAJI1g8zgPzWnVed2Lzkkz62GctJuZ9Ux/ITVd/XtErG3jut8mDZh1haSTsxqut0kaDczImorfBXxS0pSIeDmbX0K6QH08Ng9C1WYRMR/4H0nvJ/XLhpSArM/7u96s9pbTgpMkXRqbR6g+gDTY1QN5y2wABrZiW3cBJ0s6IPJG3yc1ZV8K/KOTYm6NQbyz9nZOI8utp/F9q6+FbjjvL6SbK8MiYm6HImyD7ObSVZKO5Z3nRldr7fvflI58Vu/Mfr6XNL5EvveSEs0H6bim3u/7SYn5tIi4ppn1HwLqgH8j3YSrd0prCpc0MSJqGpm1ffaz/uZifpz53z2DSDXr+aPJH07qEpB/s6PTzusCn5Nm1sc4aTcz65kuJtU83S3pe6TByUaQLh63iYgm+8NHxAuSPkjqa/2gpB+S+uEOJtU0fZQ0SvR9pAT/DOA2SZeQEsFzSX1F25xIS3qANDr106SmqIcAuwHXZLGFpF8CZ0p6njSQ1CzSYGJdYSjwO0lXkvqdf5V0LK7NW+YZYJakv5CaAy+MiMZqR68GPgHcKOmLpO4O7yeNov3RhjdHuthfgAslfYF0nhxOatXQ0DPASEkfA/4GrIuIp7PpAB+XdA2pf/BTEXGnpJ+TRiP/VrbtOtLAZMcCF0bjI3+3maTfA0+SBrN7ndTn+Wg2j+LeXVr7/jelI5/V57Pa5q8qPf7xDtK127Gkz+llzTUbb4MlpNrmUyQ9BawhDdy4QtLngO9LGkPqlrGK1H3kENKAf9dHxHOSrge+pPSIx/rR449tZfnzJN1BevrES6R+9McC5wC/ioj68Rjqz8vPSPozUJu1OPkL8EngaknVpO+n/we82qCcDp3XRXROmlkf46TdzKwHiohXJO1FGk35K6SEcwVp4LfmasTq1/+1pGdI/Z0vITXhXksawfn/kUa8rm+ieiCpSegPSH2OnwBmRcRf2hH63aTauP8g/Q96EfhURHwnb5lPkPpfX8rmwa3OJ43c3Nm+Snps3NWkmxZ3AOflN7EljQr/HeCPpP2/LIttCxGxRtIhpEeJfY10Q+A54AMR8bMuiL05XyI9Ru9TpD69dwFHsWXTZUh9fvcjnUPDgfnAlIh4Uum52GcDHyG9D1NJCefppPfjw6RHX63Ppt9C5zbVvpv0aK6Pk2pSXyEd2y93Yhmt0ar3vykd/aySkvNnSTfPLiAlk88AH4mIH7c2jhZirJN0VhbfX0mfzTmkJ1ZcKamG9F1xGlBKSobvZstuJB8l3Yj7LKl7y+3Z8ve2IoQLScnxl0hN6muB50nfE/+bt9xNpIE6zyXdDBFp9P9bJF0AfJrUAmEeaQyBLQax64TzuljOSTPrY9T0oK5mZma9k6QppBq9j0TEVYWNxszMzKxpfuSbmZmZmZmZWZFy0m5mZmZmZmZWpNw83szMzMzMzKxIuabdzMzMzMzMrEg5aTczMzMzMzMrUk7azczMzMzMzIqUk3YzMzMzMzOzIuWk3czMzMzMzKxIOWk3MzMzMzMzK1JO2s3MzMzMzMyKlJN2MzMzMzMzsyLlpN3MzMzMzMysSDlpNzMzMzMzMytSTtrNzMzMzMzMipSTdjMzMzMzM7Mi5aTdzMzMzMzMrEg5aTczMzMzMzMrUk7azczMzMzMzIqUk3YzMzMzMzOzIuWk3czMzMzMzKxIOWk3MzMzMzMzK1JO2s3MzMzMzMyKlJN2MzMzMzMzsyLlpN3MzMzMzMysSDlpNzMzMzMzMytSTtrNzMzMzMzMipSTdjMzMzMzM7Mi5aTdzMzMzMzMrEg5aTczMzMzMzMrUk7azczMzMzMzIqUk3YzMzMzMzOzIuWk3czMzMzMzKxIOWk3MzMzMzMzK1JO2s3MzMzMzMyKlJN2MzMzMzMzsyLlpN3MzMzMzMysSDlpNzMzMzMzMytSTtrNzMzMzMzMipSTdjMzMzMzM7Mi5aTdzMzMzMzMrEg5aTczMzMzMzMrUk7azczMzMzMzIqUk3YzMzMzMzOzIuWk3awBSSFpWoFjOEPSvd21XrGQNFDSHyWtkvTrNq77fkm3dlVsZmZmZt1BSbWk1yU93MZ1D5L0XFfFZoXhpN16JUkHSro/S/5ek3SfpL27sfw7JZ3VRduekt1YWJ33erIryiqAk4ByYFREnNxwpqRLJW3M9nll9h7vDxAR10XEkd0dsJmZmRVWoa/7usCBQBUwISL2aTgzq6Spza6H3pD0hKR3A0TEPRExo7sDtq7lpN16HUlbATcB3wVGAuOBy4D1hYyrCwyPiCHZa7dCBSGpXydubjLwfERsamaZX0bEEGAMcC9woyR1YgxmZmbWQ3TndV8nX/M0ZzLwckSsaWaZB7LroeHAj4FfSRrZLdFZt3PSbr3RdgAR8fOIqI2ItRFxa0Q8Vb+ApA9L+kfW7OgWSZMb25Ck/pK+KekVSUsk/VDSwLz5x2d3N9+Q9C9JR0v6MnAQ8L3sDuj3smW3l3Rbdgf4OUn/lredUZL+kG3nYWDbzjgQki6XVJNt91FJB2XTx0lam//lLmkPScsllUrKSbpI0nxJSyVdK2lYtlx9Tf+Zkl4Bbpc0QNLPJK3IasAfkVTeREw7ZC0RVkr6u6T3ZNMvAy4G3pcdtzOb27eI2AhcA2wNjGrYNaCF432spGckvSnpVUmfbe8xNjMzs4Jq9rqvhWuaQyUtyN+YpJclHZH9fqmkG7JrnDeAMySVSPpCdt33ZnZ9NTFbvslrj4aya7E/ZMv+U9JHsulnAlcB+2fXQ5c1t/MRUQf8BBgIbNNwn7JyfiNpmaSXJF2QN28fSX/LrhOXSPpW6w+7dScn7dYbPQ/USrpG0jGSRuTPlHQC8AVgNqm29h7g501s6+ukfwa7A9NId28vzrazD3At8DnSXc6DSXdFv5ht87ysFvw8SYOB24DrgbHAqcAVknbKyvk+sA6oAD6cvTrDI1nsI7Oyfy1pQEQsBB4A3pu37GnADVkyfEb2OgzYBhgCfK/Btg8BdgCOAj4EDAMmAqOAc4C1DYORVAr8EbiVdBzOB66TNCMiLgG+QlaTHhE/bm7HJPXPYlwQEcsbzGvpeP8Y+GhEDAV2Bm5vriwzMzMrWs1e99G6a5rmHA/cQLrWuw74NOm64lhgK9I121utuPZo6OfAAmAcqXvgVyRVZtc/55DVpGfXR01Sqv0/C1gNvNBgXo503fUk6Rq2EvikpKOyRS4HLo+IrUgVRr9q1RGxbuek3XqdiHiD1BcogB8By7I7mfU1vx8FvhoR/8iaYX8F2F0NatslCfgI8KmIeC0i3syWPSVb5EzgJxFxW0TURcSrEfFsE2G9m5TQV0fEpoh4DPgNcJKkElLyfHFErImIeaQa5JYsz2qrVzZVUxwRP4uIFVmZ/wP0B+r7OV1P+odSv6+nZNMA3g98KyJejIjVwOeBU7Rls7BLs3jXAhtJyfq07C73o9n70NB+pH+WX4uIDRFxO6lJ26mt2N96/yZpJVADzAROaGSZJo93Nn8jsKOkrSLi9Wy+mZmZ9TCtuO5rzTVNcx6IiN9l13prSQnyRRHxXCRPRsQKWr72eFtWM38gcGFErIuIJ0i16x9ow67vl10PLSZdR50YEasaLLM3MCYivpRdd71IOkb117IbgWmSRkfE6oh4sA3lWzdy0m69UpaQnxERE0g1qeOA/81mTwYur094gdcAke5A5hsDDAIezVv2L9l0SLXK/2plSJOBffOS7JWkfyJbZ9vrR0pC681vxTZHR8Tw7PXNxhaQ9BmlbgCrsjKHAaOz2TeQml6NI7USCFILAUjHKz+G+VmM+U3e8+P9KXAL8AtJCyX9d1ar3tA4oCZrypW/7YbHvjm/yvZ5bEQcHhGPNrJMc8cb0k2SY4H5ku5SNpidmZmZ9TwtXPe15pqmOTUN/m7q+q+la49844D6CqH8uNpyPfRgdj00OiL2i4i/NhHTuAYxfYHN+34mqUXps0pdG9/dhvKtG3XXYApmBRMRz0q6mlTDDunL98sRcV0Lqy4nNfHeKSJebWR+DU33PY9Glr0rIqoaLpjVtG8i/ROor6mf1EJsLVLqv34hqSnU3yOiTtLrpBsURMRKpUek/RupmfvPI6I+7oWkL/p6k7IYlwATsmlv72PWpP4y4DJJU4A/Ac+RmqHnWwhMlJTLS9wnkZq2daYmj3cW7yPA8dmNhfNIzcEmdnIMZmZm1s0aue5r7ppmHKmCBnj7mmwMW2rsmm5bYF4j05u89mhgITBS0tC8xH0S0Nj1ZkfUAC9FxPTGZkbEC8CpWTP62cANkka1MACeFYBr2q3XyQYB+YykCdnfE0nNhuqb/PwQ+Hx9HyNJwyS94/FiWVL5I+DbksZmy47P6wf0Y2COpEqlQU7GS9o+m7eE1G+q3k3AdpI+oDTQW6mkvSXtEBG1wI3ApZIGSdqR1Ee8o4aS/iktA/pJupjU9yrf9cAHSTXP1+dN/znwKUlTJQ1hc1/zRkd1l3SYpF2yf3ZvkJpb1Tay6EPAGuDfs2NwKHAc8It27mNTmjzeksqUnuk+LLvZ8EYTsZqZmVmRa8V1X3PXNM8DAyTNym7kX0TqSticq4D/lDRdya6SRtHMtUfDDUREDXA/8FWlwXx3JdV6t1Sh1FYPA29IulDSQKVB9HZW9jg8SadLGpNd867M1vE1URFy0m690ZvAvsBDktaQvrTnAZ8BiIjfkgaY+4XSSKDzgGOa2NaFwD+BB7Nl/0rWJzwiHgbmAN8GVgF3sflO7uWk/uqvS/pOdhf1SFIfooWk/kdfZ/M/hvNIfb0XA1cD1R0+Cqm5+p9J/5Dmkwa6a9jE6w/AdGBJROQ/6/0npCbvdwMvZeue30xZW5Oa278B/IN0LH7WcKGI2AC8h3S8lwNXAB9sZiyAdmnF8f4A8HL2np4DnN6Z5ZuZmVm3afa6j2auabI+4OeSEvFXSRULW4wm34hvkVro3Uq67vkxMLAV1x4NnQpMyZb9LXBJRNzW+t1uWVYxdBxpUOKXSNdeV5G6SwIcDfxd0mrStespEbGuM2OwzqHNrWHNzMzMzMzMrJi4pt3MzMzMzMysSDlpNzMzMzMzMytSTtrNzMzMzMzMipSTdjMzMzMzM7Mi5aTdzMzMzMzMrEg5aTczMzMzMzMrUk7azczMzMzMzIqUk3YzMzMzMzOzIuWk3czMzMzMzKxIOWk3MzMzMzMzK1JO2s3MzMzMzMyKlJN2MzMzMzMzsyLlpN3MzMzMzMysSDlpNzMzMzMzMytSTtrNzMzMzMzMipSTdjMzMzMzM7Mi5aTdzMzMzMzMrEg5aTczMzMzMzMrUk7azczMzMzMzIqUk3YzMzMzMzOzIuWk3czMzMx6FUm1kp7Ie03p4vLKJd0k6UlJz0j6U1eW14p4zpD0vW4qZ1mDY72jpCmS5nVSGftIulPSC5Iek3SzpF2yeedI+mD2+/ZZ+Y9Lminp3M4o36wY9Ct0AGZmZmZmnWxtROze1ExJ/SJiUyeW9yXgtoi4PNv+rp247W4nqSQialu5+C8j4rwG60/ppDjKgV8Bp0XE/dm0A4Ftgacj4od5i58A/D4iLsnKPxe4ojPiMCs017SbmZmZWa+X1Qr/WtIfgVuVfEPSPElPS3pfttyhku6S9CtJz0v6mqT3S3o4W27bRjZfASyo/yMinsq21aEyJI2R9BtJj2SvA7LpT0sanm1/RV5t808lHZGFMU7SX7Ia6v/OOw5HSnogq7X+taQh2fSXJV0s6V7gZEnbZus/KukeSdu387jvlO3XE5KekjQ9m3563vQrJZU0svp5wDX1CXt2bO+NiN9l27hU0mclHQt8EjhL0h3A14Bts21/oz1xmxUT17SbmZmZWW8zUNIT2e8vRcSJ2e/7A7tGxGuS3gvsDuwGjAYekXR3ttxuwA7Aa8CLwFURsY+kTwDnkxLEfN8HfinpPOCvQHVELARmd7CMy4FvR8S9kiYBt2Tr3AccAMzP1j0IuBbYD/gYcFJW7h7AeuA5Sd8F1gIXAUdExBpJFwKfJrUUAFgXEQcCSJoLnBMRL0jal1RrfXgjx/p9We13vf0bzD8HuDwirpNUBpRI2gF4H3BARGyUdAXw/mwf8u0EXNNImVuIiD9J+iGwOiK+mdW079xcawuznsRJu5mZmZn1Nk01j78tIl7Lfj8Q+HnWDHyJpLuAvYE3gEciYhGApH8Bt2brPA0c1nCjEXGLpG2Ao4FjgMcl7dwJZRwB7CipvqitJA0F7gEOJiXtPwDOljQeeC0iVmfLz42IVdn2nwEmA8OBHYH7smXKgAfyduWX2fJDgHcBv84ru38jxxMabx6f/+cDwBclTQBuzG4CVAIzSTcxAAYCS5vYfv52HwK2Am6NiE+0tLxZb+Gk3czMzMz6ijV5v6vJpVLtdL26vL/raOL6ObsZcD1wvaSbSEl1R8vIAftHxNr8FbPa+o8Dk4AvAieSatfvaWL7tdk2RbpxcWoTMdUfnxywsjNqqiPi+izZngXcIumsLI5rIuLz+ctKOhG4JPvzLODvwJ7A77Nt7SvpJODdHY3LrCdxn3YzMzMz64vuJjXtLpE0hpRkP9yeDUk6XNKg7PehpIHSXumEMm4l9euuL2d3gIioITW3nx4RLwL3Ap9ly6S9MQ8CB0ialm1vkKTtGi4UEW8AL0k6OVtOknZrQ9xvy1ogvBgR3wH+AOwKzAVOkjQ2W2akpMkR8duI2D17/Y3U7eAMSe/K2+SgVhT7JjC0PfGaFSMn7WZmZmbWF/0WeAp4Ergd+PeIWNzObc0E/ibpKVJz8Ksi4pFOKOMCYK9sALdnSP3D6z0EPJ/9fg8wnpS8NykilgFnAD/PYn0QaGqAufcDZ0p6klTjfXwTy71PWz7y7V0N5wPzsjEGtgeujYhnSH3rb83iuI00mF/DeBdn639V0j8l3U9qUdDs4+wiYgWpC8A8D0RnvYEiotAxmFkXqZk9q4zmb85tmHjjzXXdFY+ZmZlZX1Aze1Z/mu4eERNvvHl9E/PM3sFJu1kPVDN71mjS6LEzSHemx5CayTV8DWxhU3XASmB5I69lwEvAs8ALE2+8eUOn74iZmZlZD1Eze5ZIA/ptD0wDymn8+msUUNrC5jaSnhzQ8NprOWlQvn8C/wDmT7zxZidsfZyTdrMiVjN7Vjmpyd0OpH8Q9T9HdXMotaRHyjybvf5Bair3+MQbb97YzbGYmZmZdama2bNmkB7Ltz2br8G2o3V96jvTWuA5trwGe3LijTc/181xWAE5aTcrIjWzZ00lDVJzUPZzemEjatFaUn+4e7LXAxNvvHlN86uYmZmZFY+a2bNKSAl6/TXYgcDYggbVsmWkMQzuJl2DPTHxxptrCxuSdRUn7WYFVDN71njSI1AOjYiDsmeY9mSbgMdI/0BuA+4sfJXPAAAgAElEQVRwTbyZmZkVm5rZs3YHjgEOjoh3Sdqq0DF10JvA/aRrsD9PvPHmxwscj3UiJ+1m3axm9qwdgdkRcTwwU1Jzz3Dt6d4A/rx+Xdxwz1/W3zKnbu6bhQ7IzMzM+p6sNv1g4ISIOF7S5ELH1MXmA39Yvrj2hice3HjfnLq5roXvwZy0m3WDmtmzpgPvi4hTJO1U6Hi628oVdXf/7Z4N+wB/An4J3DSnbu5bBQ7LzMzMerFs4LgDSddgJ9c/F74veebxjQ8vnF87BbgB+BVwz5y6uX5yUA/jpN2si2SPWzupLuLjuXc+s7RPefSB9fNeXxI75016E/gpcMWcurl/L1BYZmZm1gtlT9k5sy7iY7neX6PepNq6WHvnH9cpQgPyJs8HrgSumlM3d1mBQrM2ctJu1slqZs+aVBdxTsDZJVJ3j/JedN7aWLv+3ps2lOWa7gZwN3AFcOOcurnu/25mZmbtUjN71n61EecLTspJZYWOp9BeWPLW0vkP5JpqXbCeVPt+xZy6ufd3Y1jWDk7azTpB1vzqyE11dReUSEdLyhU6pmLxwsK18+c/3Kq73IuBHwFXzqmb+2oXh2VmZma9QM3sWYOA0zbV1Z3fL5fbtdDxFJP7/7Z6wVsL+rVmkOPHgR8A17n7YnFy0m7WQfNPPPbE2oivlOZy2xc6lmJ0z4OrF65f3G9cG1bZCFwN/NecurmvdE1UZmZm1pPVzJ41qLYuzkdcWCKNKHQ8xWZjXV3tXTetq6MuV9qG1VYA3wC+6+S9uDhpN2unF0845j21EV8fUFLiZL0J6zfVbrj7pg39RLtaHmwArgK+PKdu7sJODs3MzMx6oJrZswasra29oJ/0H6W5nJP1JryyfN2C5++lvY8SXgp8DfjBnLq56zoxLGsnJ+1mbfTSCcccvSniGwNKSnZueem+7V+L177y0oOa1MHNrCMNmPLVOXVzl3RCWGZmZtbD1MyeVbautvbcnPTFslxudKHjKXYPPr66ZvX8fhM7uJlFwFeAH82pm7u+E8KydnLSbtZKL55wzH6b6uIHA/uV7F7oWHqK+x5e/erahf3Gd9Lm3gK+B/znnLq5qztpm2ZmZlbEambP0rra2jNz0pfLck0OqmZ5auui7o6b126kNte/kzZZA1wCXD2nbq6TxwJw0m7WguePP3rEutraK4eXlp7c9ADo1tCG2rqNd9+0XoT6dfKmXwU+Oadu7g2dvF0zMzMrIk/MOmKP/rmSa4aU9tul0LH0JAteW7fw2btpy3hCrXUf8LE5dXOf7oJtWzOctJs145FjKj85oqz0v/qXlAwudCw9zUtL19b86351tFlWc/4CnDenbu6/urAMMzMz62bPHHfk4LW1dd8f3b/sAzk/kafNHnpyzStvvlTS0e6JTdkEXA5c6paP3cdJu1kjHj32iD3653I/G1ZWumOhY+mp2vCYkY5YB3wV+Lr7WpmZmfV8jxxT+aFhpaXfHtivxIPMtUNtRNxx89p1bMoN7OKiFpBaPv6mi8sxnLSbbeFvx1aWCa4Y1b//nBLf2W23dj5mpCOeBz4yp27u3d1UnpmZmXWiR46pnFSWy/1qZP+yfQsdS0+2cOX6Rc/cGRXdWOSfgLPn1M19tRvL7HOclJhlfn/Iu/bpnyv559gBA850wt4xi17bsKgbE3aA7YA7qnOVX6rOVZZ0Y7lmZmbWQbdUHnTOiLLSZ52wd9yrCzZt6OYijwWerM5VHt/N5fYprmm3Pq+qolyf2n76f20/bOjnSnPdmmj2Wg8+vqZm9fySruzP3pz7gNPm1M19pUDlm5mZWSt8e+ZuW+05asQN2wwZXFXoWHqDuoi4/c9r17IhN6hAIVwBfMbPdu98TtqtT7ty3z233mX4sN9PHDxon0LH0lt0wWNG2uN1UnN597MyMzMrQr84cN9Ddhy21S9H9C8rL3QsvcXiN9YvmXd7FPp4Pg2cMqdu7jMFjqNXcRNg67NuOHj/2QeNHf2sE/bOtWjl+kUFTtgBRgA3VOcqr6zOVXb1QCxmZmbWSlUV5frz4Qf+9z6jR/7VCXvnenXBpmKo4d4FeKQ6V3l2oQPpTVzTbn1OVUV5yXkztv3WbiOGn+e+652vix8z0h5PAsfNqZtbU+hAzMzM+rJP7TB9xIkTx988dcjg/QsdS28TEdx+y9o3Y11uaKFjyXMtqeVjd/ez73WcsFifctyEihGf2n763D1HjrjACXvnq42IN2s0ptBxNLAb8FB1rnJmoQMxMzPrqz6/0/YzT5k88Skn7F1j+ZqNy4osYQf4IHBbda5yZKED6emctFifce52207/7A4zHt5lxLBDCh1Lb7Vk1YbF3fBc0PaoAO6uzlWeUOhAzMzM+pKqinJ9dfedTz5t6sTbxw0aOKHQ8fRWC17duKbQMTThYODB6lzl9EIH0pM5abc+4ZJddzz0w9Om3LfN0MHTCh1Lb/bqgk0bCx1DMwYBv6nOVX620IGYmZn1BVUV5bnjJlR8/r2TJ/x0eFnZVoWOpzd7bT4jCh1DM6aTEveDCx1IT+U+7darVVWU68iK8jNnjd/6f4eWlg4udDy9WRE8ZqQt/g/4+Jy6uZsKHYiZmVlvVFVR3v/0qZOuOKR8zBnukti1VqzZsOLx2+pGFTqOVthA6uN+baED6Wn8AbJeq6qivN+RFeX/b/bE8Vc4Ye96S9/csLSHJOwAZwO/rc5VlhU6EDMzs96mqqJ8+Jxtp9x4+NZjP+yEvestWLjxzULH0EplwDVu9dh2/hBZr1RVUT7w4LGjvzl74viLykpypYWOpy8okseMtMW7Sc3lnbibmZl1kqqK8nEfmDrptweNHX1soWPpK16bz7BCx9BG33Di3jZO2q3XqaooH3jAmFH/c9qUSec6Ye8eEcHKV9QTRwZ14m5mZtZJqirKx79/6qRfHrb12EMLHUtf8fraja/Xri4p5v7sTXHi3gZO2q1XqU/YPzB18llO2LtPkT5mpLWcuJuZmXVQlrD/onLrsQcWOpa+ZMGiDasKHUMHOHFvJSft1mtUVZQPOmDMqG85Ye9+RfyYkdZy4m5mZtZOVRXlE5ywF8by+fTUSpN6TtxbocWkXdLq7gikJUpul9Sux0VIOkPSuM6OqzeS9CdJw7uxvF9I6tCzG6sqygftP3rkNz4wdfKZTti73+vz6YlN4xuqT9z7dWQjkqZImtfI9Dsl7dXObV4qyf/QzMys6FRVlE84dcrE65ywd7831m1cVbuqpCeMGt+Sb1TnKj/d0Y1IqpX0hKQnJT0m6V0tLN/oNVsbyvuipL9Leiord98Wlr9a0kntKasn1bQfCzwZEW+0c/0zgHYl7ZJK2llmp8tuXnTJ+1a/7Yg4NiJWdkUZTfgB8O/tXbmqonzQ9KFDLn3/1MkfdsLe/Vas2bCi7q2S3vLs1XcD3y90EJ2pse+MYvpOMzOznquqonzCURXlVx6x9Vg/f7sAahZvfL3QMXSib1bnKk/u4DbWRsTuEbEb8Hngq50QV6Mk7U+6btwzInYFjgBquqq8NiV/kj4n6ZHsbsJl2bQpkp6VdJWkeZKuk3SEpPskvSBpn2y5kZJ+l637oKRds+mXSvpJVgv1oqQLmij+/cDv88r8h6QfZXc3bpU0MJu3e7b9pyT9VtKI7I7GXsB12V2QgQ32a5qkv+bdldlW0qGS7pB0PfB0ttzpkh7OtnFl/YWvpB9I+lsWy2V5231Z0lckPZDN31PSLZL+JemcJo7xp7PjOE/SJxvs7xXAY8DEBuu0qpxm3r8ttp1tb3QLx3nvbDsPSPpG/V0qpRYN38sr8yZJh2a/H5kt/5ikX0saki12D3CEpDbXcFZVlJeNLCv77DnTt/nIoH4lA9q6vnVcD3rMSGud3QnNtPpJuib7jNwgaYtH4SmvBZOkkyRdnf0+RtJvss/pI5IOyFttN6XWRi9I+kje+q39XK+W9CVJDwEXSfpt3jaqJN3YwX02M7M+pKqivHyPEcO/+d5J44+UVOhw+qRl86M3PdJYwLXVucpma6vbYCvgdQBJQyTNzXKQpyUd/47CpW0kPZ7lOCVZflN/ffXRRrZfASyPiPUAEbE8IhZm27o4W3eepP9TIx8QSTMl3SXp0Sxvq2huZ1qdtEs6EpgO7APsDsyUVH9XbRpwObArsD1wGnAg8FngC9kylwGPZ3civgBcm7f57YGjsm1fIqmx2tIDgEfz/p4OfD8idgJWAu/Npl8LXJiV8zRwSUTcAPwNeH9292Vtg21fl21rN+BdwKJs+j7AFyNiR0k7AO8DDoiI3YFa0o0EsmX2yvb/EGU3JDI1EbE/KTG9GjgJ2A/4UsMdlDQTmAPsmy3zEUl7ZLNnANdGxB4RMb+R49NsOS28f81tu6njXA2ck5VZ20g8DfdtNHARcERE7El6Pz4NEBF1wD+B3VraTr6qivJcWS73oQu2n3bOiP5l3dac37bUAx8z0hpfr85VntiB9WcA/5d9D70BnNvK9S4Hvh0Re5M+a1flzdsVmAXsD1wsaVwbP9eDgXkRsS/pe2EHSWOyZeeQPtNmZmYtqqooHzJ58KCLz5w25bh+uVyHupVZ+6xev2n1ptdLxrS8ZI8yAPhDda5ySjvXH6hUufos6RrqP7Pp64ATsxzkMOB/8hNpSTOA3wBzIuIR4ExgVXY9tjcpJ5vaoKxbSZUiz0u6QtIhefO+FxF7R8TOwEBSjfzbslz3u8BJETET+Anw5eZ2rC017Udmr8dJNTfbky4WAV6KiKez5OvvwNyICFLSPCVb5kDgpwARcTswSlL9xf7NEbE+IpYDS4HyRsofGRH5NXovRcQT2e+PAlOy7Q2PiLuy6dcAzTbXkTQUGB8Rv81iWxcRb2WzH46Il7LfK4GZwCOSnsj+3iab92+SHsuOzU7AjnlF/CH7+TTwUES8GRHLgHV6Z7/xA4HfRsSaiFgN3AgclM2bHxEPNrMrLZXT3PvX3LYbO87DgaERcX82/fpm4qq3H+m43Jcdvw8Bk/PmL6Xt3ReO/dh223xu0uBBzd6Zsq7Tgx8z0pIc8LPqXGW7+qCTbqLdl/3+M9JnuzWOAL6XfUb+AGyVfUcB/D4i1mbfk3eQEvW2fK5rSf+QyL6ffwqcnn2e9wf+3PbdNDOzvqaqorx0RFnpJ86bMe3UQf36DWp5DesKNUs2rCh0DF1kLHBzda6yPZVC9c3jtweOBq7NknMBX5H0FPBXYDyb880xpNbcp+flPEcCH8yuxx4CRrH5+gqALFebCZwNLAN+KemMbPZhkh6S9DRwOCk/zDcD2Bm4LSvjImBCczvWljtjAr4aEVduMVGaAqzPm1SX93ddXhmNtZuJ7Gf++rVNxLVJqb91XRPrDGxkndZorj1P/ojYAq6JiM9vsXK66/JZYO+IeD1r5prfTDv/WDQ8Tg33s7WxNKalcpp7/5rbdmPHubk4N7HlzaD6YyHgtog4tYn1BgANW0A0qaqifJ/Tpky8bLcRwzs0gJ11zIKFG1ZBr0zaAQYBf6zOVe47p27uK21cN9rwd/73RQ7Yv2FroOxmcGPbaMvnel1E5LeKqQb+SLr7/OuI2NTYjpiZmdWrqihXWS73gfNnTDt3VP+y3vr/v0dYNr9uQM8anqxNdgRuqM5VHjOnbm67rk8i4oGspe8Y0thoY4CZEbFR0stsvv5aReqLfgCp8hnS9dX5EXFLC2XUAncCd2YJ+ock/QK4AtgrImokXcqW13r12/971mK5VdryTt8CfLi+H7Kk8ZLGtmH9u8mak2d9nJe3cVC559hcs92oiFgFvC6pvnb6A0B9rfub8M5HImQxLJB0QhZb/4b9TzNzgZPq91mpj/5kUn+JNcAqSeXAMW3Yp4buBk6QNEjSYOBEUnP3ztDR9+9tEfE68Kak/bJJp+TNfhnYXVJO0kRSbSDAg8ABkqZl5Q+StF3eetux+YPSrKqK8mmVW4/9SuXWY/dsT/zWeXrBY0ZasjVwU3Wusq19xiYpDVACcCpwb4P5SyTtoDRAXH4z/FuB8+r/kLR73rzjJQ2QNAo4FHiEDnyus35XC0l3d69u7Y6ZmVmfdszZ06b++5Qhg/1EpgJas6H2rfXLSxprmdybHEFqQt4ukrYHSoAVwDBgaZawH8aWrX03ACeQatZPy6bdAnysvsu2pO2y3Cx/+zO05dOvdgfmszlBX55dnzU2WvxzwJj6a0VJpZIa1sZvodU17RFxa9av+4Gs1mc1cDqt6M+cuRSozpolvEVqHt0WN5MuVP/ZwnIfAn6YJd4vkvpqQroo/aGktbyzJusDwJWSvgRsBN4xcmFEPCPpIuDW7EJ7I/DxiHhQ0uOkhPNF4L6G67ZWRDyW1dQ/nE26KiIez2rNOqQT3r+GzgR+JGkN6Q7Tqmz6fcBLpGb680hNdomIZVmTkZ9L6p8texHwfHazY21ELKIFVRXl5VMGD7r45EkTDmxkTAfrRm+s27iq9o1e8ZiRluxCGlH+jDas8w/S3dYrgRdIT0g4Lm/+fwA3ke7szgPqB2W8APh+9j3Zj3Qjr34wyYdJ34OTgP+sT7o7+Lm+DhgTEc+0Yd/MzKwPqqoo3+uoivIL9xw1YkahY2mrhW+t5VOPPsmydeuRxGlTJnLmtKms3LCBcx9+nAVvrWXCoIFcsc+eDC9759Bav56/gO8+l1KQ82dM4+TJE1hfW8tZDz7KorXr+OA2k/ngNikPvPDxp/nA1EnsPLzrhvxZsHT9Mik3ueUle7xzqnOV98ypm9uarriQ9WnPfhfwoYiolXQd8EdJfwOeAJ7NXyki1kh6N6m5+hpSf/gpwGNZ8/plpMQ+3xDgu1k3w02kHPXsiFgp6UekXOhlUiXLFiJig9JA6d/Junf3A/6XZiowlbo2Fj+lEfWujYiqQsdiaRTGrC8Hkv4DqIiIT7RzW58C3oiIHze3XFVF+cCyXO7iL+2244fHDhjQrlYC1nn+/vJbLy96Ijel0HF0ozPm1M29ptBBdCalJz083tJnz8zM+raqivKJU4cM/p//2GnG8aW5XFmh42mrJevWsXTdenYZPozVGzcx6457+dF+M/n1K68yvLSUj8/Ylu8/9y9WbdzIF3befot1V27YwKw77uPmww4AxKw77uXmww7k4RWv8cRrK/nsjttx7B338pfDD+KZVW9w9b9e5r/33LXxQDrJPfevWbR+aUlfGdNpNTBzTt3c5wsdSCH1mI4QWS3sjyT1ludB93SzstEZ55EGy/uvDmxrJWnQwCZVVZQLOPXMaVOOc8JeHHrZY0Za44rqXOUOhQ6is0h6lDQi/c8KHYuZmRWvqoryQf3LSi44a6ep+/XEhB2gfMAAdslqvoeU9mPa0CEsXreO2xYt4aTJ4wE4afJ4bl205B3r3rVkOQeNHc3wsjKGl5Vy0NjR3LVkGaUS62pr2ZRXAfrNZ57n0zts945tdKa1G2vXrVuS27pLCykuQ4BfVecq+7e4ZC/WY5J2gIj4VRv7wVsXiYhfZqMz7hwRs7KR6tu7repWDIK1/wFjRp2218gRzfb3sO7RSx8z0pJBwPXVucoeecHSUETMjIiDI3u+qJmZWUNZpcn7P/O+nY/e6fjyUWtGpedQ92Q1a97i76veYI8Rw1m+fj3lA1IX5PIBA1i+/p3/EhevW8e4gZvHu64YOIDF69Zx0NjRLFu/nuPvvJ9zpm/DrYuWsMvwYWw9sOGYY51rwfINS9X3+ojuBnyt0EEUUo9K2q1vqqoorxhVVnbOaVMm7df3vqOKU82SDcsLHUOB7E7HWpWYmZn1GKUjSt594MzyTxy0W/nOA/r3GzTt0FHjNuzSb36tor1jIhXUmk2b+OjDj3HJLjsytPSdfdcb01hPYgH9cjm+u/ce/PnwA5k1voKf/PNlzp4+lS899QwffeixRmvtO8OS+XVtefpXb/KJ6lxln+0m7aTdilpVRXkp8NGPbbfNgQP7lfS15thFa9n8uvY+YrE3+Ex1rvKwQgdhZmbWlY6bOXHr0aMHnHz+CTtOq680kcSUGcMmD6kc+vq6gXWvFzjENtlYV8dHH3qMEyeM45jxqXX56P79WbJuHZD6vY/u/84W2BUDB7Bw7ebxqxetXfd27Xy9a1+cz3snjefR116nNJfjin32eHvgus60flPthrWL+1TT+HwCrqnOVfaFQZDfwUm7Fbv3VFWUH7LN0CFTCx2IJX3kMSPNyQE/qs5Vdm37NzMzswI5bubEUuDsC47eca9hg8rekcmOGN5/9MSjRg15awKvFCC8NosIPvfY00wbOoSPTN/8BOmqrcdyw/xXAbhh/qtUVbzz8uaQ8tHcs3Q5KzdsZOWGjdyzdDmHlI9+e/7KDRuZu3gpJ00az7pNdeQkBKyvrev0/Xh1xYbFQn05f6sAvlnoIAqhL7/pVuSqKsqnD+nX78TjJ4ybWehYbLP0mJE+301hW+DzhQ7CzMysixy977aj99t98ogmB2At7ZcrnbbfqEl1+5TVbMoV9/goj6x4nRtrXuX+ZSs4+vZ7OPr2e7h98VLO3W5b7lm2nINvvZN7li3n49ulhP7J11fy7489BcDwsjIumDGN4+68j+PuvI9PbD+N4WWbh7e5/NkXuGD7aUji4PLRPLVyFVVz7+HUKRM7fT8WvVJb0ukb7Xk+VJ2rPLDQQXS3HvPIN+tbqirKy4DLzpux7VF7jhyxR6Hjsc3uuX/N4vVLS/pq06x864Fd5tTNfaHQgZiZmXWW42ZOrCjJ6cv/d9b+x44a2r9VLetWr9n4xuL7Vq0f9Ib62iC13WZDbd3Gu29aL0J9tU97vnnAHnPq5rY0kHWv4Zp2K1aH7zBs6I67jxi+e6EDsc2yx4z05abx+foD3y90EGZmZp3luJkTc8Dpcw6ZNqO1CTvAkMGlW21zxKhRa6fnXg7XCHaJha+tX+yE/W07A58qdBDdyUm7FZ2qivIxgvd+aJspM3Nuh11UFixb3xcfM9Kcqupc5fsKHYSZmVkn2Wv8yEH7HLXruL3aumIup9y2u42Y0u+QQYs3lMaargiuL1v0Sq2vv7Z0SXWusvP7IBQpJ+1WVLLngZ564sTx240d0H98oeOxLS15JXyH952+XZ2r3KrQQZiZmXXEcTMnDgE++Imjt9+ltF+urMUVmlA+dlDF1kePyK0Z3fOf6V4sNtbV1a551S0dGxgMXF7oILqLk3YrNrsMLy3dv6pi7N6FDsS21McfM9KcCuCSQgdhZmbWQccdumP5NttVDJvR0Q0N6F8ycPqho8dt2LXnPtO9mCx6fcNi6nKte7B833Jida7yqEIH0R2ctFvRqKooHwicccqUiVP7l5T05eeAFyU/ZqRZ51bnKt0yxMzMeqTjZk6cAhx56rum7tqZ252y3bDJQyqHrlw3sG5lZ263r1n4yibf+GjalwsdQHfwBbgVk8qRZaXle4wc7tHii5AfM9KsAcAXCh2EmZlZWx03c6KA04/Zffzo8mEDO/0G9Ijh/UdNPGrU4Lcmqkc8073Y1NZF3eoFbhrfjJnVucrjCx1EV3PSbkWhqqJ8KHDcaVMmTS3Ntb8flXWNDbV1G9cuLPE/jOad1ZcGRDEzs15jR2Dae/eZNLOrCijtlyudtu/ISbFP/5qNJcX9TPdis2jl+sXU5voXOo4id2l1rrJXD9TnpN2KReXo/mVDdx0xrM2jlVrX82NGWqUMuKjQQZiZmbVW9oi3k98zc8Lo0UMHVHR1eeMnDZk46qhh69/aKpZ2dVm9xcIFm/rMs8g7YHdgdqGD6EpO2q3gqirKhwHHnjZ10jb9ch5koxj5MSOtNqc6Vzm10EGYmZm10s6CKSfsNanbKk2GDCrdapsjRo32M91bVhsRb9TkxhQ6jh7isupcZa/NbXvtjlmPcsTYAf2H7Dxsqy5rlmXt58eMtEkp8P8KHYSZmVlL6mvZT9x70piRQ/p36//5+me6lx4yeMmG0ljdnWX3JEtXbVjCxpwHZ26dnYB/K3QQXcVJuxVUVUX5cODoUyZP3KZfLufm10XIjxlpsw9U5yqnFDoIMzOzFuwKTHr3nhMK1jVx7NiBW299zIiSNaPj1ULFUMxeXbDJ/f/bptdWnDhpt0KrGlhSUrrj8K12K3Qg1riFr2yqK3QMPUw/4JxCB2FmZtaU42ZOLAH+7dAdyweOHNJ/bCFjGVBWMnD6oaPHb9it1M90z1MXEStfkZvGt82O1bnKwwsdRFdw0m4FU1VRvhVw5HETKkaV5XIDCh2PvVP2mJGC/jPvoT5cnav0SK9mZlasdgLGzdpjwo6FDqTelOlbTR56xNCV6wbG64WOpRgse3PDUjbkBhU6jh7o3EIH0BXcHNkKaW+gZJ9RI4umL3ttBO++4z7KB/Tn6nftzStr3uK8Rx5n5YaN7Dx8GP+7126U5XJU/+tlrnvpFcYPGsiP9ptJWS7Hw8tf4y8LF3PxrkXz/6/DsseMjOvqcl6LdVzFM6xiA0IcwjiqNJHVsZEfMo/lrGM0A/gYOzNYpfwtlvI7XmIw/TifXRmiUpbGW9zIi5yjnbs63NYYA5wEXFfoQMzMzBpx1LgRA+u2GTt0h0IHkm/4sP6jhhxVuvHlR1e+MqgmJhU6nkJa8OqmdVDSqdts6/VWQ/fFIv7IywAcxxQOUAUbo47v8hSvs57DGM/hmgDA1fEshzGeyRraqfvQCsdX5yrHzambu7C7C+5Krmm3gqiqKC8Bjt1r5IjSkf3LuvwRI631k3++xLShg9/++6t/f5azpk3l7iMPZVhpP375cg0Av3i5hlsrD2KnYVtx15JlRATfee6fXLD99EKF3iW66zEjOcT7mM6XtR9fZCa3s4BXYw1/Yj47MIKvaX92YAR/Yj4At/AKX2Qm72JrHmQxADfyIieyTXeE21q98k6vmZn1bMfNnFgB7HjK/lOnlORUdLlAv/pnuu/bf8GmklhX6HgKISJYOV8jO3u7bb3eyrc6NvJ7XuIi9ughrjoAACAASURBVOL/sRe/5yXWxEbmsYLJDOUy9uEuUp78SrxJEIVI2CFVSp9diIK7UtF9UK3PmAGMrKoo36XQgdRbtHYtc5cs45QpE4H0hXn/shUcO25rAE6aNIFbFi15e/mNdXWsra2lNJfjNzWvclj5GIaX9Z7x2rrzMSPD1f/tL/aB6kcFg1nJeh5nOQeQ7ukcQAWPsRwAITZRxwbqKCHH87GS4fSnXEXViuxd1blKj9VgZmbF5oCSnOpmTi2elo6NGT9xyISRRw3b8NZWsaTlpXuX5Ws2Lot1uU7PeNt6vZVvHivYiZEMUSmDVcpOjORpVlBCjo3UUcfmp/f9jpc4obAVKR+pzlX2qhblTtqtUKpGlpXWbTNk8E6FDqTepU/9gy/stD050iPJX9+wka1KS+mXSx+TioEDWLw23fA9+/+zd9/hcVXX3se/e2Y06r1ZkmW59y5jTLHBGNs0AQlpkBtSL+l5c0nuTUgCJCG5hCSXEEJIQkicGAgklBiGJowxYFwwFrbcuy2PqiWN+kiacvb7xxkbAXLXzJmR1ud59Fhlym9UfPY6e5+9xo3m+jfW4/H5mJOVyVNHarh5dIll2cPBqjYjTbqbI3QwmjTa8ZGhzEvDM1Q8HfgAuI5R3EslO/FwPvm4OEwZIyMd9XTIbLsQQoioUVZanABcfm1pcXpyQlya1XlOJdTTPa97vH1I9XSvrvF7w/0cpzPe6qsVH1m8t11PJvG04mMKmbTh42ds4kpGsFk3UkIqmcrSrX0KgeutDDDQBtUZCBEbFhfk5wIzrx1eWGi3qaj4HXy1roGceCfTM9NZ39gMgObDxwZl1vPcMKKIG0YUAfCbXfv4wuiRrG5o5OkjNRQmJnD7tEnYjt04RlVXB3oH+lqqU+nRAX7Pdm5kHInKQT8/AgCmqCymYK4aW6vrmE429Xh5WR8hGQc3Mp54FdnsJ/DpZbZF3/m8sUp60AohhIgGM4D4hZOHxcxKMJtNqTHTM0Y2FnTXd67rTIn321KszhRunioywvn4pzve6qu/cTGAXdn4MuYcXEAb3MsWvsV0ntD7aKaHCxnGLGs2wf8K8JQVTxwOMtMurHABYEzOSIuaWfZNnhZW1h3lwvLVfOOdzaxrauYnW3fR7vcTMMyOZ3XdPeQnvH+T+/ruHipbWllSmM/v9uznwbmzcNpsvNX44WVFscTQWrdFuM1IQBv8nu3MI59SZW5Yn4aTVm22KG3VvaTifN99enWQtdSxkCKe5gBfYBIlpB2/zj0KJANXWx1CCCGEKCstVsDVOanx3cOzkqJqE5jTkZubOKzgyix7Vy6Duqd7c5evWXvt6eF6/LMZb4E5s+7hvbbxLfSS8YHbraaGiyjgAO3YUXyVqTwf2rjOApcusy0aNC3zpGgXEbW4IN8BLClOSuzOdjqLrM5zzPenTGTjlZexbulCHjhvFhfmZHP/eTO5ICebF2vNAvCpI9UsKch/3/3+b9devjt5PAA9wSAKsClFT4y3No90mxGtNcvYTQFJLFXvbRY7ixzWUgfAWuqYRc777vcSVSymGIey4cP8nis4/n6UuMHqAEIIIQQwAii+ZvbwQlsUbkB3OhKc9sRxl2QX+WfEVQXREdksN9Kqa/0d4Xrssx1vAUwlmx146NJ+urSfHXiYSvbxr3dpP5U0cSHD8BE8frmp37oxmZ1BtEQ+Jv9gRUwbDSQtHJY3RsXA8vHbpk7kz/sPMf+V12nx+flkyfDjX9ve2gbA1AzzZOgnS4pZvGoN21vbuCT/w//ZxRKzzUjk7KON9dSzmxbu1Bu5U29kq27iKkrYSQvf1+vZSQtX8d6+AS26l8N0HF9ytZQR/JwK1lHP+eSf6KmscNUy26KI7w0ghBBCfMAMwJhVkhXzvWlLxqWVpC5ObetJ1B6rsww0TxVhm2U/0/HWId3OMr0LgBQVRxkjuYtN3MUmyhhJSp+2cM+F9hdSSjGVLA7Tzh1sZAFh7xx8MoNm4kQNoT0dRBRYXJD/KWDRL2ZNXZKXkDCk+29GK601r5V3d4Rj19Ih7KOfN1b92+oQQgghhqbQ0vhfZSY7HQ/fcuE37Lbo2PjlXAWCRuBwRWtN0hE9KHYDbu32t24qD4b1evYhxg/kfd5Y1Wp1kHMlM+0iYkK92S8clpDgzY2PL7Y6j+hfuNqMDHGD5kyvEEKImFQEZF09a3jxYCnYARx2m2Ps3KwSPS++OmCL/Z7u7jpfzBeXUSYOuNbqEANBinYRSSOBlEXm0vjoXxs/RFXX+LuszjAIXbPMtujDu7oIIYQQkTEdYPao2F8a35+i4SnDs69I93vTjZju6d5UhUyaDLxBMXEiRbuIpJlAcHJG6iSrg4gT81SRaXWGQSgduNzqEEIIIYae0NL4BakJjvYR2cljrc4TLslJcamjF+XkdU+wHzaIvet/23sC7cE2e/apbynO0JJltkUxfzJEinYREYsL8m3A/AS7rS0vIWFQXHc0GIW7zcgQt9TqAEIIIYakYUDepZOH5TjsNofVYcLJZlNqzLSMkfGXJB/1OY2w7cIeDu5636DbVC9KJAALrA5xrqRoF5FSDKTNzc7KtqvBcy3VYBPONiOC+VYHEEIIMSRNAZgyPGPIbACcm5uYX3BFVlxXHtVWZzldjVU6Yq12h6CYH4NJ0S4iZSygJqWnySx7FAtnmxHBjGW2RWlWhxBCCDHkzAHaR+amDKkxWLzTnjBuQfZw/8y4qqAyorqne2dvoDPQYs+zOscgJjPtQpymGUBHcVLikDpgxJKWbn9LsNMu17OHjw24yOoQQgghho6y0uI4YGyi096VmxZfZHUeK5SMTStJW5zW3pMUvT3d3Q2+ZqszDHJzltkWJVod4lxI0S7CbnFBvgOY4FCqIzchfrjVeUT/qut8bVZnGAJi/kyvEEKImDIcUBeNzyu022xD9vLE9LT4rBFLs9K8JarK6iz9OVplJFidYZCLA+ZZHeJcSNEuIqEQcJRmZebF2WzS9ipKSZuRiJCiXQghRCSNAtT0kswhv9LRYbc5xp6XVcIF8dV+u9FtdZ5jvP6g19ckS+MjIKbHYFK0i0gYAajJGWlDZgOUWCNtRiJmzjLbIjmbLoQQIlJmAJ2jclNkDBZSWJQyPOeKjIA3Q9dbnQXAfbS3USmlrM4xBMT0ZnRStItImAF4RyQlFlsdRPRP2oxEjBNzQyAhhBAirMpKi+3ARAXtw9JlDNZXcmJc6ujLsvN7oqCn+9EqLatQI2PeMtuimK19Yza4iA2h/uyTgbbMeKcs/YlSjVU62eoMQ8gUqwMIIYQYEgoAx5hhqclxDrk88YNsNqVGT8sYGX9p8tFep9FuRYZuf7Cnp8E2zIrnHoKSgZi9TESKdhFuOUCC06aCKQ6H7EwehUJtRnKtzjGETLQ6gBBCiCGhGLBNKEjLsTpINMvNScwvvCLL2ZWvI97TvbrJd1SWxkdUzI7BpGgX4ZYL6HGpqVk2peT3LQpJm5GIm2R1ACGEEEPCCMBfkpMiRfspxDvtCePm5wz3z4o7Esme7g1VhiNSzyWAGB6DSRElwi0XsI1KSZaZ3CglbUYiLmYPGEIIIWJKCeAtyEiUMdhpKhmTNiJtcXpHd7IR9r1+egOGr7velh/u5xHvE7NjMCnaRbiNBHoLExPkLG8UkjYjliheZlskewgIIYQIt+GANzs1XsZgZyA9zZlZsiQ77D3da5p7GxTKHs7nEB8iRbsQJ1ACeHMS5IARjaqlzYgVFDDB6hBCCCEGr7LS4mQgFfBlJjtlDHaG3uvpnlATrp7u9UeCUodFnlzTLsQHLS7IV0Ah4M1yOmVpVhRqkDYjVonZM71CCCFiQg5g5KYlJCQ6HbK66ywVFiUX5V6RGRzonu6+oOH31tplaXzkZS+zLYrJmkSKdhFOGYADCCY77BlWhxHvJ21GLDXK6gBCCCEGtRyAMXkpMv46R0mJjpTRi7LzeyYOXE/3Wo+vAa1kEzprxOQYTIp2EU45gLYrpZw2m2x2FmWkzYilZKmiEEKIcCoEyE6Nl1n2AWBTSo2emjEyYWFyY2/8ufd0rzsSGJDiX5yVmByDSdEuwikdUDnxzkQpDqOPtBmxVEweMIQQQsSMYUBPRpIzyeogg0lOdmJe4RVZ8V35nHVP94BhBLtqZNd4C8XkGEyKdhFOSYAtJz5eDhhRpjcQlDYj1orJA4YQQoiYkQH406VoH3Dxcfb4cfOzhwdmxx0JKsN/pvevbfHVY9hkTyHrxOQYTIp2EU7pQDAr3ilLs6JMTbOvXtqMWComN0ERQggRM9IBf2pCnBTtYTJidNqItCXpnT3JRvOZ3K/2SCAYrkzitMTkGEyKdhFOmYA/PU4OGNGm7khQCnZrxeRZXiGEEDEjFfCnJMjO8eGUnurMLFmSndE18vR6ugcNbXRWy0pHi8XkGEyKdhFOGYA/Lc4hRXsU8QUNf7e0GbFaTB4whBBCRL+y0mIFpAD+pHgZg4Wb3W6zj5uTVaIuPHVP9/q23nqCtvhIZRP9iskxmBTtIpzSAX+yw5FodRDxHmkzEhWSltkWyd+FEEKIcEgAFKATnXY51kRIQWFyUe6VmUFv5ol7ute4A4FIZhL9kqJdiA9IB/xxNluc1UHEe6TNSNSQgZQQMUopFVRKbVFKbVdKuZRSZ9ULWyn1baWUpTOh55JBKfVTpdTl/T2OUqrzFPdVSqkmpVRm6OMCpZRWSl3c5zaNSqnskzzGtUqp74fev14pNflsXkefx7tOKbWiz8e3KaX29/m4TCn13Cke4+FjOZRSPziXPOcgGdAADnv4x2Du5i4W/ewVpv73s0z/n+e4/+VdAHg6e1l690om3rqCpXevpKWrt9/7L3/zABNvXcHEW1ew/M0DAPT6g1x1zypmfO85/rByz/HbfuXh9Ww+7An3SzprSQmOlNGXZef3TLIfNtBG368ZWut2ty0mC8ZBJibHX1K0i3BKBfx2Jb9n0cIvbUaiiax2ECJ2dWutZ2qtpwIe4Otn+Tjfxuy0cs6UOusVVGedQWt9h9b61bN5HK21Bt4GLgh96kJgc+hflFITgCat9Qk3+dJaP6e1/kXow+uBMyra+/mereuTh9D77UqpvD4Z157sMbXWX9Ja7wx92G/RHjphEc6xURKhot0WgTGYw6b41adL2f6r61j7kyv5w8o97Kxu5Z7ntnPZlAJ233s9l00p4J7ndnzovp7OXu56Zivrfnol6++6krue2UpLVy+vbK1l9qgsNt9dxsOv7QOgssqDoWHWyKxwv6RzYlNKjZ6SMTJhYXJTb7w+3tO9od3XgN8mlytYLybHX1JMiXCKAwxFWA9M4gzUSZuRaCKbAQoxOKwHio59oJT6b6XUO0qprUqpn4Q+l6yUekEpVRmanf+kUupbQCGwWim1OnS7PyilNimldhy7b+jzh5VSOaH35yilXg+9/2Ol1ENKqVeA5UqpkUqpNUqpd0NvxwrgS5VSryulnlJK7VZKPRYqHD+Uoc9zzlVKPRN6/zqlVLdSyqmUSlBKHQx9/m9KqY+d6HGUUj8PveYNSqn+ThivJVSkh/69l/cX8etCj1OmlHpbKbVZKfXqscdSSn1OKfVA6HVeC/wqtAJiTOjtZaVUReh7MrFP5ntDOe/pG0Zr3Qi0KaXGhj5VBDz9gYzHMp3oZ/V66Gf0CyAxlOex0M9ml1LqQeBdoFgptUQptT70s3pSKZUSeoxfKKV2hn6Hfh363MdDvzuVSqk3+/le9nW8KAnzyQEACjKTmD3KXBCRmhjHxMJ0alq8uN6t5ub5owG4ef5onqtwf+i+r2yt5fJpBWSlxJOZHM/l0woor6zFYbfR7QsSMN6brL7zqUp+/LEZ4X45AyYnOzGv6IrMeE92sAWgxu3vf6mBiLSYHH/F5JkGEf0WF+QrzJNC2qZQVucRpnY73tQpthNeayXCy9A6zmm3dSY74lr9PYaczBIiximl7MAi4C+hj5cA44C5mNcUP6eUWoDZYqhWa3116HbpWus2pdStwEKtdVPoIX+otfaEHneVUmq61nrrKWKUAhdrrbuVuTx9sda6Ryk1DngcmBO63SxgClCLWSxfpLW+v58Mx7wbug/AfGA7cB7m2PHtvjc8weMkAxu01j9USv0S+E/gZx94jnXAHaH35wJ3Ys7Yw/tntd8C5mmttVLqS8D/AN/p8/zrQsvWn9daPwWglFoFfEVrvU8pdT7wIHBZ6C7jgcu11v213loHXBj6GewDNgBLlVLPA9OBd0K3O+nPSmv9faXUN7TWM0N5RgITgM9rrb8WOgnzo1COLqXU94BblVIPAB8BJoZe77FLL+4Almqta9SpL8ew9XknomOww42dbKnycP6YHBrauinINCeWCzKTONrW86Hb17R4GZ713uRzUVYSNS1ePnZ+CY+9dZAL73iJ714zBVeFm9kjsyjMjK2JamecPX7qwrz4Xbva6unMVWmFSsZgFgka2pnidDbG2WyntdN/tJGiXYSLjdDSLLmA2no9KhhUsxObl4zKHmd1FkE2UGJ1CCHEOUlUSm0BRgIVwMrQ55eE3jaHPk7BLOLXAL9WSt2DWViuOcHjfkIpdQvm+KwAc7n3qYr257TWx3asjgMeUErNBIKYxekxG7XW1QB9sr91ogfVWgeUUvuVUpMwC+p7gQWYs1Qnyt+XD3g+9H4FsLif22wEZimlkoE4rXWnUupgaKb7QuD/QrcbDvxTKVUAOIFDJ3vi0Iz1hcCTSh2vWfvu2P3kCQp2eG/23465imIjZsE8C9ijtT5WeZ7Nz6pKa70h9P680H3WhjI6Q8/XDvQADyulXuC97+Fa4G9KqX8Bz5zieY4X7ZEcg3X2+PnEfW9w72fOIy3p9Bb16X4CKhQOu41HvzEfAH/A4Mp7XmXFdxbynUc34W7q4jPzR1NWWjyQ8cOmrkf5im68JH7KN1OGWZ1FkIX5f2PMkZkeES7Hj5KGxjjZDUV4taUZrRmL09WoUal5p761iCDZQVaI2NUdmkEtwSy2jl3TroC7Q9e7z9Raj9Va/0VrvRdzRnwbcLdS6o4PPqBSahTwXWCR1no68ALmLuBg/n9xbMyW8IG7dvV5/7+ABmAG5gx738qp79LcIKc3cbMGuBLwA68CF4feTrU8G8Afum79hM+ntfYC+4EvYM7sgzmzfRWQBxzbgex3wANa62nAl/nw9+CDbEBrn5/DTK31pD5fP/49U0qVh5awPxz61DrMov1CYL3WuiP0fJcSmvk/xc/qZPr+rBSwsk++yVrrL2qtA5gnSZ7GvE7/ZQCt9VcwZ+aLgS3qJBv08b4xmI7IGMwfMPj4fW9w40Wj+Mh5IwDIT0+krsULQF2Ll7z0D3+LhmclUe3xHv+4xuOlMPP9+4T94dU93Dx/DOv3NeJ02Hj8W/P5+YptYXw1A2e3Pbk3d8n5zoyclEyrs4jjYnL8JUW7CJfj5051hA4Y4v0MtPaM0a3jL8/JyEqLl7/16BOTZ3qFEO/RWrcB3wK+q5SKA8qBL/S5NrlIKZWnlCoEvFrrR4FfA7NDD9GBuWkrQBpmUdcWumb7yj5PdRiz6Ae44SSR0oG60HH3M5zetZt9M3zQm5jL1deHrvfOBiYCH95R7OSPczJrjz1H6OP1wP/DXFp/bCyRDtSE3v/sCR7n+PNrrduBQ0qpj8Pxjd/6vRhaa700VDR/KfSpnZjX58/nvRUTW4CvELqenZP/rPryh34v+rMBuOjY9fNKqSSl1PjQ70661vpFzO/LseX1Y7TWb2ut7wCaMIv3E+kzBgv/xInWmv/883omFaXzX1e9txfgNbOHs3zNQQCWrzlI2ezhH7rvkumFrNxWS0tXLy1dvazcVsuS6YXHv97S1csLm6v5zPzRdPcGsSmFQtHrj+5DaI9WPUdKRnVNXjgr3umUhc1RJrp/eU5ABvIiXI4fMIJI0R5pPU6j07gooWfqrJwMu022FIhSMXmmVwjxflrrzUAl8Cmt9SvAP4D1SqltwFOYheQ0YGNoWfoPee/a7oeAl5RSq7XWlZhF4g7gr7x/l/KfAL9VSq3h5APOB4HPKqU2YC6N7zrJbY85nqGfr70N5PPezPpWYGufYvp0H+dk1gKjea9ofxdzOfy6Prf5MeZS9zWYBWt/ngD+O7RZ3Rjg08AXlVKVmN/T604nTJ9d7Zu01v7Qp9eHMq4L3eZkP6u+HgK2KqUe6+d5GoHPAY8rpbZiFvETMX9fng997g3M1RNgbrK3TSm1HfPnUXmyl3HsnUjMtK/d28ijbx1k9Y56Sm97ntLbnufFLTV8r2wqr26vY+KtK3h1ex3fu3YqAJsONnPLn80fd1ZKPD+8fjrzbn+Jebe/xI8+Mp2slPeuZLjrma384PrpKKVYMr2QioPNzPy+iy8ujN6r/ert8S3+ubMcI8cVJVudRfQrJsdfqv//d4U4N6GN6JYBhz83umTegvzcpVZnGipac4zmkfMyM5MSHHJSLnoZQJz9puVyQksIIcSAKistHgPcBlTf/9m5nx6Rkzz2VPcR587Q2jiUmdc+Zva4DJtNhmBR7BWVd0vM1SWyXkOExcq6Br24ID8AqI5AwHvKO4hzFlRGoHOK3Tt5Yu7JrnMT0cEjBbsQQogwOX588fpkDBYJncre2TFhrGNcce6pdvYX1jvRap2oJkW7CCcv4Gj3++WAEWZdicH2tAtSEyZlJaZZnUWclpg8YAghhIgJx7oJ0NUrRXu4HXEmN+efNyWzKNEp0+uxISbHYFK0i3BqB+JbfVK0h5NnuPaMn5OT6XTIxesxJCYPGEIIIWKCl9AO8h3d/tPZ10CcBT/43flF3WOnjZIVjrElJsdgUrSLcGoDijy9Pinaw8BvN3r8s5yBqSPTs6zOIs5YTB4whBBCxAQvoc2mO3pk4iQcmlVcG9MmJo7NS5cVjrEnJsdgsoxDhFMbEHe0p1fO8g6w9rRgS/riNMfYkekpVmcRZ6XR6gBCCCEGJ1eFOwD0AvbWLpk4GWiHkjI8GQvmpOXmpTutziLOSkyOwWSmXYRTKxDXEQj4A4YRcNhs8vt2jgy0bhurWidNz820yWr4WBaTZ3mFEELEjA4grkWK9gHTrW3djSNHGmPGFcoKx9gWk2MwKaJEOLUAcQC9htHpsNlkR81z0O00Op1zEx1ThqVkWp1FnLN6qwMIIYQY1NqAzIa2nk6rgwwG9Y6ElpTZk9NGpiXZrc4izllMjsGkaBfh1AlogDafvznZ4ZCi/Sy15BqeUfMyM5Lipff6ILHH6gBCCCEGtTYgf09tmydo6KDdpqTYPAuG1sbhzPz20bPHZkrv9UEhABywOsTZkN8+EU7Hi/bmXl9MXj9itaAy/O1Tbe1TLsnNkoJ9UNlldQAhhBCDWjPgDBhat3f7PVaHiUUd2DqOTpzkGztnfIYU7IPGAZV3i9/qEGdDfgNFODUTKtobenpi8voRK3UlBdviL0vREydmys6kg0sX4LY6hBBCiEHNDTgBPJ29MgY7Q0ecyR7nRXOSC4tzEqzOIgbUbqsDnC1ZHi/CqQnzxJBye7vlgHEGPMWGZ0JpTmac9F4fjPbYb1qurQ4hhBBiUGsEDICj7T1NY/JTLY4TG/zgrx42vGfM1JGy2dzgFLMrHWWmXYTNyroGH+Zse/ze9g5ZHn8afHajp/s8R9fU83OzpGAftGL2gCGEECJmHJ8sqfF4ZQx2GjwqrrVjxnTGTB0pZzgGr5gdg8lMuwg3NzC2oafX0xsMdsfb7YlWB4pW7enBlmEXZKRmpDhlKdbgFrNLs4QQQsSMFsxLFG0HGjpkteNJaK05lJLlGXnexEyHwy4TJoNbzI7BZKZdhNthIBmg3R+Qg0Y/DLTRMo7W8YtyMzNSnHIibfCL2bO8QgghYoOrwh0E6oDEHdWtTVrLVVn96UZ5q0eN8Y69YEqWFOxDghTtQpxA7bF3Gnp66qwMEo16nEYnFyf4pszIzrDJavih4l2rAwghhBgSjgBJ7d1+f3u3v9nqMNGmzp7QEpw7K75kbGGS1VlEROxXebe0Wx3ibEnRLsKtidAO8vs7OqsszhJVWvKCnoKlmUnDh6XIcviho9p+0/JDVocQQggxJBwGEgHczV4Zg4UENcaBzLzWgktKM9PSkqR//dCxxuoA50KKdhFuDYAC1KbmFjlgYPZe75hma5+yIC8rUXqvDzUxfcAQQggRU46vdtxT1yZjMKBd2TsbJ0/yjSsdLysch543rQ5wLqRgEGG1sq6hG3MzupTa7p6uDr/fY3UmK3UmG23xl6XoCROk9/oQJUW7EEKISDleqG/c33TEyiDR4IgzxZNwUWlSYVG2rHAcmmJ6DCZFu4iESiAdoLa7Z8ie6fWM0J5Ri7PT8jITnVZnEZaJ6bO8QgghYoerwt2JuRld8p669tauHn/MXs97Lvwa/6GC4o6RC2ZmJSQ4pfYZgrTWtSrvlgNW5zgX8osrImEf5hJ5DnZ0Dbmi3ecwurvPi+uaOjdHeq8PYVrrZmCn1TmEEEIMKZVABkB1y9C7rr1JxbV2zJrOmCkl0nt9CFNKxfQsO0jRLiKjitDv2uaWoXVde1uG0ZK5OD1uTElastVZhLWUUm/Zb1ouPXeEEEJE0l7ADrCvrmPIjMHM3uuZnqxL5qTn5KTFWZ1HWC7mVzpK0S7CbmVdQwdQDyTv7+hq9QYCg355loE2WsbTOuGynMz0ZOm9LgB4w+oAQgghhpzjhfo7B5uGRNHejfJWjx7rHTNPeq+L42J+DCZFu4iU49e1H+rs2m1xlrDqiTc6mJ/omzJdeq+L93nW6gBCCCGGnFbAAyRWVrU0tXl9g7pfe509wWOcPzu+ZEyB9F4XAGitD6i8W3ZYneNcSdEuImU34AB4p7ll0F7X25JnNBcsyUwenp8sO5OK47TWm+03LT9odQ4hhBBDi6vCrelzXfuumrZBOQYLaoyDWfltBZeUZqWmJkrvdXGcXkjndwAAIABJREFUUuppqzMMBCnaRaTsAzRge+to05GeYLDL6kADKaAMf8d0W/uUBbnZ0ntdfNBgOWAIIYSISZuBOIA3djXssjjLgGvH3tE0eZJv7Oxx6bLCUfTjKasDDAQpLkRErKxr6AK2A1kG6MODaIl8Z7LRmrgolQnjpfe6OCEp2oUQQlhlHxAAHOv3NdZ1dPtbrQ40UKriU5oTLp6TXCC910U/DEO7Vd4t71idYyBI0S4iaS2QBFDhaY35M71aazwjtGf0kuz03IwE2ZlU9Etrvct+0/JBc5JKCCFEbHFVuHuBTUA2wJ66tpgfg/m08h0qKO4YNX9mdkJCnNQzol822+BZ6Si/5CKSjhUu6o2GxkO9wWC3pWnOgc9hdPee7+yaOjcny2GXtVjixJRST1qdQQghxJC3EYgHWLP7aExf195kc7Z2zZ6upPe6OA1StAtxplbWNbRjFu6ZAa2NI13ePVZnOhttGUZL1uJ05+gR0ntdnJZBc8AQQggRs/YAQcD+5q6G6q7e2Gu/q7XmYEpWS9aC0vTs7FRZ4ShOytC6AVhndY6BIkW7iLQ1QArAusbmSouznBEDbbSOp3XCopzMtGSn7EwqTsnQeov9puVbrc4hhBBiaHNVuLuBLUCWBra5W2Lq2OTF1l0zZqx37LzJmdJ7XZwOm1LLVd4thtU5BooU7SLSjl1Hpd442nS4zedvtDTNaeqONzrUgkTf5OnZGTYlxwpxemxKPWh1BiGEECJkA5AI8NTbVZsMrbXFeU5LrSOxRZ8/yzlitPReF6dHa20Af7Q6x0CSol1E1Mq6hlZgB5AD8K6nZZO1iU6tJd/wFC7JTC7Kk97r4vQFDd0BPGZ1DiGEECJkJ+AH4vbXd7QdaeraZ3Wgkwlqggey8tsKF8zOlN7r4kwEg8YqlXfLQatzDCQp2oUVXiG0i7yruq7Sbxg+i/P0K2DT/o4Zto4p83OzpPe6OAt/s9+03Gt1CCGEEAKOL5FfDeQBrNpeF7WtsNqUo6NpyqTAOOm9Ls6Cw2G/3+oMA00KEWGFXUArkNTq9/fu7+jcZnWgD+pMNlqTLkthwrhM2ZlUnBW7Tf3e6gxCCCHEB6wBHIB6/t3q/e3d/harA31QVUKqJ/Gi0uSCwux4q7OI2BMIBKuBF63OMdCkaBcRt7KuIQC8RGiJ/Mq6hqg506u1xlNi9l7Pkd7r4iz5g8ab9puWx2R3BCGEEIOXq8JdA+wFMjXwzoGmqLlM0Qe+w4UjOkZdPCNLeq+Ls2W3234/mDagO0b+IIRVNgIasG9paWto7Ol1Wx3I5zC8vnlO79TzpPe6ODdxdtvvrM4ghBBCnMDLQCrAkxsObw4EjYDFeWiyOVu9s2ao0ZNHyApHcdYMQ/uUUn+xOkc4SNEuLBHakG4DkAvw5tFGS/sotmUEPVlL0uNHFafJzqTinPiDxhFghdU5hBBCiBPYDnQCCfVtPd07q9u2WBUk1Hvdk7WgND1Leq+LcxQ0jEdV3i0x0ZnqTEnRLqy0GogHeKGmfren11cX6QAG2midQOuERblZaUnSe12cO5tSd9hvWm75rIUQQgjRH1eF2w+UE9qQ7m9v7l8TNIxgpHN4sXlrxozzjp03OUt6r4tzZRhGIM5h/4nVOcJFinZhpQPAYSALYFX90dcj+eTdCaHe69Ok97oYGD3+4BG7TT1qdQ4hhBDiFNYCBuA4eLSzfeuR1opIPnmtI9HDvNnxI0YPkxWOYkB09/gfVXm3HLE6R7hI0S4ss7KuQQNPAWkAL9XW723u7a2NxHO3DDOai6T3uhhgSvEj+03LIz5bIYQQQpwJV4W7BXO2vQDgb2/sXxOJa9uDmuDB7GFthQtmZ6WkJMgKRzEggoYRSE6K/5HVOcJJinZhtZ2YM+7ZAK/UNawO55MFbNrXMcPeMeXi3OwEp/ReFwOnxx88GO+wP2Z1DiGEEOI0rcScbY+raurqrDzSEtad5NuwtzdPmRwYO2us9F4XA6qn279M5d1SY3WOcJKiRViqz2x7qvnx0f3h2km+IznYlrQoRU0YlyE7k4oBZ1PqB/ablg+6FiNCCCEGJ1eFuxVzJ/lhAH99ff9b/qDhD8dzVSWkepLmz0kdVpglvdfFgAoGDV9ycvwdVucINynaRTTYjdkzNAegvLb+9YF8cK01npG6ZcySnLScdOm9LgZejy+4z+mw/cvqHEIIIcQZehUIAnE1Hm/X5sOejQP54L3Qe7jI7L0eHx8n0+tiwPX0+h9WebfUW50j3KRoF5YLzbY/TWi2/bWGxoPVXu++gXhsX5w2e6/PycmU3usibBTfsN+0XFsdQwghhDgTrgp3G/AioWvbH1q1961uX6BrIB670eZs6Z49wzZ6kvReF+Hh8wc6kpPib7c6RyRI0S6ixV5gB6H2I8sPVr0YMM5tQ5TWTMOTvVh6r4vwaunsLU/+7GOvWJ1DCCGEOEurAD/gbOro7XlxS805HdMMrfXB1OyWnEvmZGZlSe91ET4dnT0/UHm3eKzOEQlStIuoEJptfxJIBuz7O7paNzW3vHk2jxVUOtg6QbVOvCwnKzUpTnYmFWHjCxi9Gr5odQ4hhBDibLkq3B3ACqAQ4JE1B7fWtngPn81jdWHz1o0d3zP2/EmZdruUGSJ8Wtq8u7IzU35vdY5Ikb8mETVW1jUcwtzJtADg7wer1rX7/U1n8hjdCUaHfUFiYPK0LOm9LsKusb3n7txb/jmodysVQggxJKwC6oBMgIdW7X0haOgz2ly1xpHoUfNmxxePyk8MR0AhjjEMw+ju9n1G5d0yZC5NlKJdRJsVQA+Q3GsYwX+7a1843Tt6hhmeoiVZyYW5ybIz6TkKGgZzfvA81/7qNQAOHe3ggjteZOKtK7jx/jfxBcxW5A+U72bG957jml+uOv65t/Yc5TuPhrVrTFRo8/oOFWUl/czqHEIIIcS5clW4fcDfgAzAtqWqpWnTwaZ1p3PfoNbBgzkFbcWXlkrv9TMUDBrMXvQzyj79AACHqpqYd8XdjJ93O5/6z4fw+fq/UvTu377EuPN/xMQL76B89Q4AGps6mF/2S6Yt+AkrXtxy/LbX3/wgtfWt4X8xEXS0qeORoun/U2F1jkiSol1ElZV1DZ3AI4SubX+jofHwgY7ObSe7T8CmfZ0z7R1TL87NSnDKWqyBcP/Lu5lYmH7849ue2My3r5zE7nuvJzPZyV9f3w/AX1/fz+a7y5g5MovyrbVorfn5v7fyo49Msyp6RBha686ewOfsNy0PWp1FCCGEGAiuCvceYA2hFY8PlO9+s6PHf9Jqr1U52punTg2MnTkm/WS3E/377Z9XMWncsOMff/9nz/DtL1/O3g13kZGRzF/+sfZD99m5p5Z/rtjE9jfv5KXHv8XXv/cPgkGDx//9Djd/4gLWvfg9fv2guS2Bq7ySWdOLKRyWEbHXFG7ebl9LYoLz61bniDQpcEQ0egfYSahwX3bgcLkvaPT0d8POFKM1+fIUNX6s9F4fKNXNXby4pYYvLBwLmC3zVu+o54a5JQB8ZsEYnt3kPn57f9DA2xsgzm7j0bcOcuXMIjIH+WKHo209T4/4xlNnteeCEEIIEcWeAgJAQkdPwP/021UvneiGVYlpnuT5c1KHFWQO7oN+mFTXtvDiym188dMXA+Z467W3dvOxstkAfPYT83j2pS0fut+zL1fyyevnEB8fx6iSHMaOymPju4eIi7PT3eOntzeAzaYIBIL89qFV/PfXlkb0dYWbp7Xr2xlj/9+AdDiIJVK0i6izsq7BAB4FEgBHbXdP10u19c/3vU2o97pn9OLs9Ow06b0+kG59ZBO/uHE2x/YEaO7sJSPZiSO0iGF4VhK1LV7ztldN5qI7X6Kpo5eLxuexfM1Bvnr5BMuyR0J7t78paOjPW51DCCGEGGiuCncr8ASh2fYVm9x7d1S3bu57m15N7+Giks5RF03Pinc6ZAOhs/Rft/+Le+64AVuoI3Gzp4uMtCQcDvMKg+GFmdTUfXihQ019K8VFmcc/LirIpKa+lZs+OpdXXt/BlTf+lju/ew0PLnuDz3ziApKSnJF5QRFQW9/6avGM7y23OocVpGgXUWllXUMN4AKKAJ6trt2xv6NzK0Cvw+j2XeD0Tp2TkyW91wfW8+9Wk5eeQOmo7OOf0/1s8aEwv+3/MX80m/73GpZ/7WJ+8+JOvrlkIi9X1vCJ+97g1kfewTAG1/4gQUPrAw0dnx3xjac6rc4ihBBChMlbwAEgF+CXru0vt3f7PBDqvV460zZ6UnGKlQFj3fOvbCU3J5XSGSXHP6f7GXD1t6dy/7dTpKcl8vxj3+SdV37I7OkjeGHlVm64Zhb/eesjfPyLf2L9OwcG9DVEWntHd6u7tuWTVuewihTtIpq9CNQAOQAP7jnwYm2qry5naYZz1HDpvR4O6/YexVVRzZj/9wyffmANq3fWc+sj79Da5SMQNDeRrfZ4Kch8/8awtS1eNh1s5to5xfx8xTYe/9Z84uPsrNpRZ8XLCJu9de3L5/zg+RetziGEEEKEi6vCHQT+AsQD8W1ev+/Pr+1bsT8502P2Xk+RFY7naO3GA7jKKxk15wfc+OWHeW3tbv7r9n/R2u4lENrYt7q2pd9r0YcXZOKuaTn+cU1dC4X5799S4Kf/9wI/+PZVPP7MO5TOGMFf7ruZH/7vivC+qDAyDEPv2lf/pXlX3D0kerL3R4p2EbVW1jX0An8CElGkdMQHL3imsbYzKV6WYoXL/35qNlUP3MCB336Ux74xn4WTh/HI1+dz6eR8nt5YBcAjbx7g2tLi993vzie38JOPzwSgxxdEobApRXfv4Nmnra7Fu3/1zvovWZ1DCCGECDdXhbsGeAwowmbLXuPunLX1qLddeq8PjLt/9BHcW+7h0Kb/5fE/fYnLLprIo3/4IgsvmsBTrncB+Pu/NnDtFTM+dN9rl87gnys20dvr51BVE/sOHmXu7FHHv77vYAN19a1ccuF4vN0+bEqhlKKn1x+x1zfQdu6te2TeFXc/bXUOK8lfnohqK+sa3MA/HGn2xfZEm7Gjvu3J1TvqX7U611Bz942z+c2Lu5hw6wqaO3v5wqVjj39t82HzpOeskVkAfP7Sscz8vovNhz0snVFoSd6B1tUb6N582HPtN5e93X/vFSGEEGLweQO7fS/JSYtwxh165Km3/15V3bzf6lCD2S9+9FF+88dXGXf+j/C0dPLFmy4C4LmXK7njnucAmDKxkI9fW8qU+T/myhvv54Ff3Ejfkyk/uvtZ7rrtOgBu/Mh5/P2f67ngql/wna8tifwLGgDVtS37/vLY2i9ancNqqr/rIoSIJktHFdji0u23KbsqAWoB7rv5vBtH5qaMtziaGAIMrfXaPUe/eulPy/9kdRYhhBAiksounZSLM+4ulPIC7QX56Un3/vTjX0lOipeuPSLsOrt6veWrd8z52Bf+uMvqLFaTmXYR9coP1RnKrn4HeIF0gLue2fpMS5ev0dpkYiioONj8+P+9sPMhq3MIIYQQkeZ6fVcjSt0HZAGOuoY274N/ff2JQCAYu2utRUzw+4PBt97ef4sU7CYp2kVMcFW424EHgUwgrrmzt/cXz277R7cvMOT6NIrI2XakpeLnK7Z9wVXhliVJQgghhiRXeeVu4GmgGGDN2/trn1jxzr9lta4IF601r76563dXfuq3j1mdJVpI0S5ihqvCvRdzU5RiwLanrr31j6/ufSIQNOQ6YzHgDjd2uv/46t4yV4W71+osQgghhMVeBN4FhgP869mKXavf2iN7DImweOvt/S//4W9vfM/qHNFEinYRa14FVgIjAN7Y1VD91NtVK+RsrxhITe09rY+tPXjtH1/dM7h61gkhhBBnwVVeGQT+DNQB+QD3PbRq7fZdNe9aGkwMOtt21Wz71e9f+YSrvNJndZZoIkW7iCmhZcqPA5WEzvY+sf7wjjd3N7xmaTAxaHT1BnqefLvqc3f8a8sWq7MIIYQQ0cJVXukF7gN8mJcrcuevXC9U17YctDSYGDSq3M01Dy1/8ypXeWWH1VmijRTtIua4KtwB4CGggdDZ3t+8uGvNzupWKbLEOfEHjeC/3znyw28se/tZq7MIIYQQ0cZVXtkM/AZIBpL8/qBx+z3P/qulzSubA4tz0uzpbHv0qbeve+Avq6utzhKNpGgXMclV4e7CPNsbADIA7nyq0rWvvn2HpcFEzAoEjeC/Nhz+7VNvV91ndRYhhBAiWrnKKw8Dv8OcOIlr9nT13vnL5x5pa+9utjaZiFWe1q6Ovz6+7jM/untFhdVZopUU7SJmuSrcjZhne1OBZH/QMH7wxOZnpHAXZyoQNIJ/f/PAsic3VN3mqnAbVucRQgghopmrvLKS9zYHth8+0txx+z3P/l0Kd3GmPK1dHff9adW3/+cnT7mszhLNpGgXMc1V4T6IOeOegxTu4iwEgkbwr6/vf9z1bvWtrgq3bHoihBBCnJ6VgAsoQQp3cRY8rV0dv3rgldu3bHf/zeos0U6KdhHzXBXubZgz7lK4izNyrGB/cUvN11wVbtn0RAghhDhNrvJKDTyFFO7iLBwr2Hfsqf2dq7xSVjmeghTtYlBwVbi3Avcihbs4TaGC/R9SsAshhBBnRwp3cTZCBfuPpGA/fUr6W4vBpKy0eBpwK9AEdMXZbbaffnzmtZOK0mdYHE1EkV5/0Pe3N/Y/8VJl7TekYBdCCCHOTdnSGQr4GFAGVAHBkuLslB//9zX/kZ2Zkm9tOhFNjja1t/7mj6vu3LGn9gEp2E+fFO1i0Plg4Q5w69WTF8yfkLdQKWVpNmG9Nq+v47cv73rk3UOe70vBLoQQQgyM/gr39LRE589uu+5jJcOzx1mbTkSDfQeP1tzzu5d/crSp4y9SsJ8ZKdrFoBQq3L8NtANtADddOGrqR+eOuM5htzksDScsU+PxHr372W1/r/Z475KCXQghhBhYocL9o8B1QDXgc9ht6vbvXH3FrGkj5lqbTlhpQ8XBPb/6/Ss/9/uDj0nBfuakaBeDVllp8WjgvwA7cBTg0kn5w79y+YQbE5z2JEvDiYjb7m6t+sVz2+7r7An8yVXh7rY6jxBCCDEYhQr3S4HPAY1AJ8DXPn/J3CWXTrnCZpNlj0OJYWj97MtbNi17fN2dwMuhfRDEGZKiXQxqZaXFuZgz7vmYZ3yZWJiW8f1rp306I9mZY2k4ETGrd9Rv+135rp8ZmqddFe6g1XmEEEKIwa5s6YypwDeBXsADcN2VM8Z95uPzPuaMczgtDSciwucP+B9+9K3XX35tx22u8soKq/PEMinaxaBXVlqcDHwFmAYcAYzslPj42z86/aMjc1PGW5tOhJM/YPifWH94w9Mbq24H3nRVuOU/PCGEECJCypbOKMZc9ZgE1APMnTUy/5tfuuwT6WmJWZaGE2HV0uZtu/+hVS9WbD3yQ1d55SGr88Q6KdrFkFBWWhwHfApYDLgBP8BXLx8/9/JpBUvsNpvdynxi4B1t62n8vxd2vLanrv0uV4VbWv8JIYQQFihbOiMTc8Z9JOYYTKenJTpv+39XXjV5fIF09xmEKne49//6wZXPtLV3/8pVXtlkdZ7BQIp2MWSUlRYrzKL9RszN6VoBzhuTnf/1xRM/JsvlBwetNev3Ne747Uu7XukNGPe6KtzVVmcSQgghhrKypTMSgM8DFwA1mEvm+Y+PnT/t+qtmXu2Mc8RbmU8MDJ8/4Hv8mXc2Pv38u08Df3aVV3ZZnWmwkKJdDDllpcVjga8BaZgHDp2a4Ij73rXTrphanDHb2nTiXHT7At6/rN6/4dXtdU8Cj7gq3HKwEEIIIaJA2dIZNmAB8BmgG7M1L5PGF2R+56uLb8jLSS2yMp84N/VH2xp++UD5W/sPNf4ZeNVVXil7CA0gKdrFkFRWWpyCedC4AKgFegA+ecHIyR+dO6Is3mFPsDKfOHOHGzur73lu+xt1rd1/ANbJ9etCCCFE9ClbOmM48FWgEHO5vOF0Omzf/drihXNnj7rIpmR3+ViitWbNhv3b7v/zqld8/uDv5fr18JCiXQxZoeXy84GbMZdpNQKU5CSnfH3JxKXjC9KmWplPnJ4eX9D7701HNv9r/eFVGv7oqnDXWZ1JCCGEECcWWi7/cczLFhuALoAFF4wr+vynLrwmOytlmJX5xOlp8nQe/dPf36x4+91D/wYec5VXeq3ONFhJ0S6GvLLS4iLM3eWHYy6XDwBcObNo9KcuGHl1epJTdjeNQlprth5p2X5/+e4dzR29K4BnXBVun9W5hBBCCHFqoX7uM4FbABvm7vLaYbepL392wdyFF0+4TFrDRSe/P+h7efWOTX/9x9qdwaDxV2CD9F8PLynahQDKSovjgWuAqzGXyh8FSHTa7V9fPOHieeNyL3bYbQ4rM4r3eDp7jz702t53Nuxr2gMsc1W4t1udSQghhBBnrmzpjBzg00Ap5qrHDoCRI7JTv/nFy5aOG503xcp84v32HmjYe9+fXt1aXde6EXjEVV5Zb3WmoUCKdiH6KCstHg78BzAJc7mWF2DK8Iysr1w+/qri7OQxVuYb6vxBw7dya23FX1/fvydg6GeBl10V7h6rcwkhhBDi7IVm3WcAnwXSMfcbCgCULZk+5pPXz7kqLVX6ulupvaO75e//XL9x5Ru79gHLgQpXeaVhda6hQop2IT6grLTYBszF3KguAXPJvAFwzazhY66dU7wwLy1BdjiNoEDQCGw+7Kl8ePW+Aw1tPRXAo64Kd63VuYQQQggxcMqWzkjEXPV4NeYO80cBEuId9s996sLZl144YX5SkjPVyoxDjdfr61j5xs7Njz79dlVvb+Bl4DlXeWWn1bmGGinahTiBstLiVOAjwGWYG6Q0Hvva9XOKx18ze/ilOakJBVblGwqChg5uO9Ky5eHV+/ZVe7zNmGd2N7kq3HJmVwghhBikQjvM3wxMwBx/dQIkJTodn7/xwtIF88bNT0x0JluZcbDr7vZ1vb5u78Zlj6+r6en1HwD+LjvDW0eKdiFOoay0eAzwCWAi0A40AyjghvNLJl41s2hhVkp8noURB52goY2d1a2VD6/et6eqqasTKMdcCi9ndoUQQoghINTX/TzMMVg2ZvHeBZCakhD3hZsuOu+iuWMuSoiPS7Iw5qDT0+vvXrNh39t/+cdat9frawGeAtZJ33VrSdEuxGkItYebgNmeZAxm8e4Bs3j/5AUjp1w2teACWTZ/bvxBw7+rpm3b397Yv+/g0c4uYCVQ7qpwt1idTQghhBCRV7Z0RhxwPvAxzOvdGwntOZSeluj8wk0XzT1v5sjzUpLj0yyMGfO6vL3tGyoObV72j7Xu9s6eVuAZzGK91+psQop2Ic5IqHifjHngGAW0AccLygvH5xZcPWv4eRMK0qbJbvOnr7XL17Rhf2PFE+sO17d6fQawCnNmvdnqbEIIIYSwXtnSGfHAPOAGIBXzevduAIfdpq6/auaEhRdPPG94QcZopZSFSWOH1pqautaDq9bs3rLipS3twaDhBf4NvOUqr5SNfqOIFO1CnIXQZnVTMJdsDQd6MQ8eBkBuWkLCJ+aVzJw7Juc86fPeP8PQxqHGzj3lW2srX9la2x369BvAi64Kd+PJ7iuEEEKIoals6YwE4ELMfYdSMFc/Hp9AmTqpKOuGq2edN3VS0cx4pyPBophRrdcX6Nmxu7byyecqdu/YU6sxT348C7zpKq/sPsXdhQWkaBfiHISK97HAIszrrjTQROjMrwKunFk0+tLJw2aNykuZEGe3xVkWNkq0dvmatrlbtv9rQ9Uhd3OXwtxc5mVgvavC7bE4nhBCCCFiQGjmfRpwFebqxwBmu94AQEpyvOPj15ZOnTtr1IyCYekltiE+/W5oresb2o5sfPfwtn8++87RLq/PBriBF4BKmVmPblK0CzFAykqLMzGXbV0BpGEWox7MQp7keIfj6lnDx88dmzOlJCd53FAq4Nu8Ps+umrYdr26v27PpYDOAA9iDWazvcFW4/ZYGFEIIIURMCvV4LwYuARZgjjGaCe04DzCiKCvl6sXTJs+cWjwlPy+teKgU8IbWuuFou7tyh3uH65Wth901LQmYq0LXAauBw67ySikGY4AU7UIMsLLSYgfm0vmlmDvOg3nte9ux2yQ57Y6lM4pGzxmdPXF0Xsr4RKdjULUtMbTWTR29tTurW3e/vrNh35YqD0A84AdeA9ZIn3UhhBBCDKSypTNSgFLMPu85mAVqM6EVkADDCzKSlyycMn7mlOEThxdmjnY47INqD6JAIBiorms9WLndvful13ZU1da3JgJ2zEsIXgI2usor261NKc6UFO1ChFFZaXEO5tKtS4ASzFn3dswC/vgf34ySzJzzRueUjB2WOmJ4VlJJSkJcuiWBz1LQ0MGmjp7aqsauI9urW6vW7T1a19TRmwI4gSDwDrAB2OOqcMsupEIIIYQIm9Ds+whgNuYYLA2zgG8h1DYOICHeYZ83Z3TBzCnFJaNH5pYU5KcXx9p18L2+QE9dQ5v7YFVjVeWO6iPrNh5o6fUFMgAb5mqDN4F3MWfVDUvDirMmRbsQEVJWWpyHufP8RZht48CceW6lzxlggDH5qWkXjMstmVCQVlKQmViYnuTMjrPbnJFNfGJdPf725k7f0aqmTvc2d+uRtXuO1nb1BhIxD4pgvq6NQAVmoS7XSQkhhBAi4kL93kswJ1EuBnJDX/JijsGOX6Jnsyk1Z0ZJ3uwZJSPGjcotyc1OzU9NTciy22y2iAfvh2EYRntHj6exubNh/6GjR97deqTqnS2HPYah04FkzAmhFmAtsBU4JP3VBwcp2oWwQFlpcRowHrOInwZkh74UxJyF76LPTDzA6LyUtImF6TklOck5wzISc3JSE3Izk53ZCU57SjiuzQoEDX9Hj7/V0+lrOtre01Tj8TYePNrRtKO6tbnN6zcwC/SUUE4NHMQ8QOwFDroq3IGBziSEEEIIcbZCM/AJqljzAAAGwklEQVT5wDhgOuY4LAFzVroHcwz2vomGuDi7bcqEgqyxo/JyiouycoblpuVkZ6XkpKclZodrVr7XF+hpb+/2NHk6Gxsa25uO1Hia9h862rRzb73H5wvEYfarT8Acf/mAXUAlsB+olevUBx8p2oWIAmWlxRmYZ4HHAzOAQsz/iG2Yu6B6Q28fWlpuU0rlpcUn5KUnJmWnxidlJTuT0pOcSakJcUnJ8Y5EpThhQe8PGoGOnoC33evztnp9Xk+nz9vY0eNtaO32dvQE/KHnTwSSQv8SyhXA3EiuEjgEVLsq3L6B+W4IIYQQQoRfaBY+D3MMNjX0lo65lN6GWRAfG4N9aDLC6XTYhuWmJeXlpCZlZyUnZaQnJaWnJSalpSQmJcQ7nJxoDKbRPb0BX0dnj7eto9vb2ub1Nns6vQ2NHd76xnavzxcwMDfUS8ScQY/HnNg5tuR9K7ADqALqZdn74CdFuxBRqKy0OBkYhrmEqwgYidkPPp33ZuBtmAcVfz9vp5rlVkDcCd506HGPHWjqMFuCHMLsRd8I1Lsq3DG13Eop9Tfgea31Ux/4/MPAvVrrnZYEE0IIIURUCM3EZ2LOxudgFvMjMcdi8bxXzCvMItqPWdj3HYOdqoC20f/4y8H7x2B+oAY4AhzGHH8dBZrPdSZdKfVD4KbQazCALwMXAA9prb3n8tgiPKRoFyKGlJUWJ2IeRHIwl6anARmht/TQWxrmkqmTMTDP1HZgLgVr7fPWGfq3CfDEWnF+Iicq2oUQQgghTiZUzKdijr+yMVcgZmAW+MfGYGmh2zj5wCWOfRwrxtt5b2PiY+OwFswZfQ9mgd4ejmXuSqkLgHuBS7XWvUqpnFDmdcAcrXXTGTyWXWs9KMaJ0W5QtTgQYrBzVbi7MWe93Se7XVlp8ak2TNGuCrelZ+yUUiMx+7S/DczCvBb+ZmAnoYOGUmoO8Gut9aVKqR8Do4ACzMsIbgXmAVdinoku01r7lVKHgX8CC0NPdZPWev8HnvsuzJ6uX8BsQfddrfUmpVQn8FvgGszNAa/TWjeE5RsghBBCiJgQKp6PFdoHT3S7UHF/qn2GtMXXnBcATVrrXoDQeOtbmJdmrlZKNWmtFyqlbgR+gPl6XtBafw8gNFa6F7O18XeUUt2hj1MwJ3w+p7Wui/irGuSkaBdiEHJVuGPl2qYJwBe11muVUn8FvnaK24/BLMYnA+uBG7TW/6OU+jdmT9YVodu1a63nKqVuBu7DLMIBUEr9EvOM+Oe11voDe/glAxu01j8M3e4/gZ+d86sUQgghxKAXKsajfRnzK8AdSqm9wKvAP7XW9yulbgUWhor4QuAezJ73Lf+/vTt2saOK4gD8O4RgoxhEW7GRFFoIwSKN9laCQtgqVvo3KGonWIoWYmGniIigEo1o4QYUURGEKCJaCBYWARstJDEeizsPhsey4LKyd833NTNvZrhM9d783rlzbpKPqurh7n4n41np2+5+tqpOJrmUUeS4UlXnkjyXURThEE2xfAFww/qluz9b9l/LWIplPxe7+1qSy0lOZFTqs3y+a3XdG6vt2dXxZ5Kc6u4neu93g64mubDsf701JgDAsdbdf2SE8cczpuG/WVWPbV12f5Ld7r7S3X8leT3JA8u560neXvZPZzTv+7iqvknydEYPJg6ZSjtwlLaD86Yz/eYPxe138zdTuf6uqmur4L3psrrXuOv9r5Kcqarbuvu3Pe5nPeb1+I4EAP5nlvfQd5PsVtXlJOe3Ltlviv+fq/fYK8l33X12n+s5BCrtwFG6c2mIkiQ7ST7N6JB6Zjn2yAHHPbfafr46/mGS55O8X1W3HHBsAIBjqapOV9Xdq0P3ZSwd93tGI71k9Bt6sKpur6oTGc9ol/YY7ockd2ye5arqZFXd89/d/Y1LFQk4St8nOV9VryT5McnLSb5M8mpVPZXxo3EQN1XVFxl/TO6sT3T3W0tgf6+qHjr4rQMAHDs3J3mpqk5lzG78KWOq/E6Si1X169KI7skkn2RU0z/o7ne3B+ruq1X1aJIXq+rWjGz5QsYa8hwiS74BR2LpHn+hu+895HF/zr9csgQAAGZlejwAAABMSqUdAAAAJqXSDgAAAJMS2gEAAGBSQjsAAABMSmgHAACASQntAAAAMCmhHQAAACYltAMAAMCkhHYAAACYlNAOAAAAkxLaAQAAYFJCOwAAAExKaAcAAIBJCe0AAAAwKaEdAAAAJiW0AwAAwKSEdgAAAJiU0A4AAACTEtoBAABgUkI7AAAATEpoBwAAgEkJ7QAAADApoR0AAAAmJbQDAADApIR2AAAAmNQ/lHHxjXKNszIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make square figures and axes\n",
    "plt.figure(1, figsize=(20,10))\n",
    "the_grid = GridSpec(2, 2)\n",
    "\n",
    "\n",
    "cmap = plt.get_cmap('Spectral')\n",
    "colors = [cmap(i) for i in np.linspace(0, 1, 8)]\n",
    "\n",
    "\n",
    "plt.subplot(the_grid[0, 1], aspect=1, title='Source of Pies')\n",
    "\n",
    "source_pie = plt.pie(source_counts, labels=source_labels, autopct='%1.1f%%', shadow=True, colors=colors)\n",
    "\n",
    "\n",
    "plt.subplot(the_grid[0, 0], aspect=1, title='Selected Flavors of Pies')\n",
    "\n",
    "flavor_pie = plt.pie(flavor_counts,labels=flavor_labels, autopct='%.0f%%', shadow=True, colors=colors)\n",
    "\n",
    "plt.suptitle('Pie Consumption Patterns in the United States', fontsize=16)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing critical items\n",
    "from IPython.core.display import HTML, SVG\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "#import xport \n",
    "import IPython \n",
    "from ipywidgets import Layout\n",
    "from ipywidgets import widgets\n",
    "from IPython.display import display\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "domain": {
          "x": [
           0,
           0.4
          ],
          "y": [
           0,
           1
          ]
         },
         "labels": [
          "Bake Sale",
          "From Somewhere Else-Gift",
          "Restaurant with Waiter-Waitress",
          "Store"
         ],
         "marker": {
          "colors": [
           [
            0.6196078431372549,
            0.00392156862745098,
            0.25882352941176473,
            1
           ],
           [
            0.8853517877739331,
            0.3190311418685121,
            0.29042675893886966,
            1
           ],
           [
            0.9873125720876587,
            0.6473663975394078,
            0.3642445213379469,
            1
           ],
           [
            0.9971549404075356,
            0.9118031526336025,
            0.6010765090349866,
            1
           ],
           [
            0.9288735101883892,
            0.9715494040753557,
            0.6380622837370243,
            1
           ],
           [
            0.6334486735870821,
            0.8521337946943485,
            0.6436755094194541,
            1
           ],
           [
            0.2800461361014994,
            0.6269896193771626,
            0.7024221453287197,
            1
           ],
           [
            0.3686274509803922,
            0.30980392156862746,
            0.6352941176470588,
            1
           ]
          ],
          "line": {
           "color": "#FFF",
           "width": 2
          }
         },
         "name": "Sources of Pie",
         "showlegend": false,
         "textinfo": "label+percent",
         "type": "pie",
         "uid": "3e9e11c0-ca96-4e0f-800b-6b1bb12d4d86",
         "values": [
          1,
          1,
          1,
          2
         ]
        },
        {
         "domain": {
          "x": [
           0.6,
           1
          ],
          "y": [
           0,
           1
          ]
         },
         "labels": [
          "blueberry",
          "lemon (not cream or meringue)",
          "pumpkin"
         ],
         "marker": {
          "colors": [
           [
            0.6196078431372549,
            0.00392156862745098,
            0.25882352941176473,
            1
           ],
           [
            0.8853517877739331,
            0.3190311418685121,
            0.29042675893886966,
            1
           ],
           [
            0.9873125720876587,
            0.6473663975394078,
            0.3642445213379469,
            1
           ],
           [
            0.9971549404075356,
            0.9118031526336025,
            0.6010765090349866,
            1
           ],
           [
            0.9288735101883892,
            0.9715494040753557,
            0.6380622837370243,
            1
           ],
           [
            0.6334486735870821,
            0.8521337946943485,
            0.6436755094194541,
            1
           ],
           [
            0.2800461361014994,
            0.6269896193771626,
            0.7024221453287197,
            1
           ],
           [
            0.3686274509803922,
            0.30980392156862746,
            0.6352941176470588,
            1
           ]
          ],
          "line": {
           "color": "#FFF",
           "width": 2
          }
         },
         "name": "Flavors of Pie",
         "showlegend": false,
         "textinfo": "label+percent",
         "type": "pie",
         "uid": "a31ce595-4599-4049-90d5-f835a7a91734",
         "values": [
          1,
          2,
          2
         ]
        }
       ],
       "layout": {
        "autosize": false,
        "height": 600,
        "title": {
         "text": "Pie Consumption Patterns in the United States"
        },
        "width": 1000
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sources_pie = go.Pie(labels=source_counts.index, values=source_counts, marker=dict(colors=colors\n",
    "                                                            , line=dict(color='#FFF', width=2)), \n",
    "                                                            domain={'x': [0.0, .4], 'y': [0.0, 1]}\n",
    "                                                            , showlegend=False, name='Sources of Pie', textinfo='label+percent')\n",
    "\n",
    "flavor_pie = go.Pie(labels=flavor_counts.index, values=flavor_counts, marker=dict(colors=colors\n",
    "                                                            , line=dict(color='#FFF', width=2)), \n",
    "                                                            domain={'x': [.6, 1], 'y': [0.0, 1]}\n",
    "                                                            , showlegend=False, name='Flavors of Pie', textinfo='label+percent')\n",
    "\n",
    "layout = go.Layout(height = 600,\n",
    "                   width = 1000,\n",
    "                   autosize = False,\n",
    "                   title = 'Pie Consumption Patterns in the United States')\n",
    "fig = go.Figure(data = [sources_pie,flavor_pie ], layout = layout)\n",
    "\n",
    "\n",
    "py.iplot(fig, filename='basic_pie_chart')\n",
    "\n",
    "#https://stackoverflow.com/questions/39629735/how-to-plot-pie-charts-as-subplots-with-custom-size-with-plotly-in-python\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "megapixels",
   "language": "python",
   "name": "megapixels"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}