Exoplanet code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Rectangle\n",
    "from matplotlib.colors import ListedColormap\n",
    "import seaborn as sn\n",
    "import scipy.stats as scs\n",
    "import scipy\n",
    "from statsmodels.tools import add_constant\n",
    "from statsmodels.discrete.discrete_model import Logit\n",
    "from sklearn.preprocessing import normalize\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import LogisticRegression, LinearRegression\n",
    "from sklearn import metrics\n",
    "from sklearn import tree\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "# from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import train_test_split\n",
    "import itertools\n",
    "plt.style.use(\"ggplot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_classification_thresholds(ax, X, y, inode, tree, xlim, ylim):\n",
    "    threshold = tree.threshold[inode]\n",
    "    if tree.feature[inode] == -2:\n",
    "        color = ['r', 'b'][np.argmax(tree.value[inode])]\n",
    "        ax.add_patch(Rectangle((xlim[0], ylim[0]), xlim[1]-xlim[0], ylim[1]-ylim[0], fc=color, alpha=0.3))\n",
    "        return\n",
    "    if tree.feature[inode] == 0:\n",
    "        ax.plot((threshold, threshold), ylim, 'k', lw=0.5)\n",
    "        plot_classification_thresholds(ax, X, y, tree.children_left[inode],  tree, (xlim[0], threshold), ylim)\n",
    "        plot_classification_thresholds(ax, X, y, tree.children_right[inode], tree, (threshold, xlim[1]), ylim)\n",
    "    else:\n",
    "        ax.plot(xlim, (threshold, threshold), 'k', lw=0.5)\n",
    "        plot_classification_thresholds(ax, X, y, tree.children_left[inode],  tree, xlim, (ylim[0], threshold))\n",
    "        plot_classification_thresholds(ax, X, y, tree.children_right[inode], tree, xlim, (threshold, ylim[1]))\n",
    "\n",
    "def plot_classification_tree(ax, X, y, model=None, fit = True):\n",
    "    ax.plot(X[y==0, 0], X[y==0, 1], 'r.', label='False Positive')\n",
    "    ax.plot(X[y==1, 0], X[y==1, 1], 'b.', label='Confirmed')\n",
    "    ax.set_title(\"Classifying Exoplanets with Decision Trees\")\n",
    "    ax.set_xlabel(\"\")\n",
    "    ax.set_ylabel(\"\")\n",
    "    ax.legend(loc='upper left')\n",
    "    \n",
    "    if model is None:\n",
    "        model = tree.DecisionTreeClassifier()\n",
    "    if fit:\n",
    "        model.fit(X, y)\n",
    "    \n",
    "    xlim = ax.get_xlim()\n",
    "    ylim = ax.get_ylim()\n",
    "    plot_classification_thresholds(ax, X, y, 0, model.tree_, xlim, ylim)\n",
    "    \n",
    "    ax.set_xlim(xlim)\n",
    "    ax.set_ylim(ylim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lee/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "FALSE POSITIVE    3922\n",
       "CONFIRMED         2281\n",
       "CANDIDATE         1792\n",
       "Name: koi_disposition, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_data = pd.read_csv('data/cumulative.csv')\n",
    "pd_trim = pd_data[['kepid','koi_disposition','koi_pdisposition','koi_score','koi_period','koi_time0bk',\n",
    "                   'koi_impact','koi_duration','koi_depth','koi_prad','koi_teq','koi_insol','koi_model_snr']]\n",
    "pd_trim.dropna(inplace=True)\n",
    "pd_trim['koi_disposition'].value_counts()\n",
    "# pd_trim.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FALSE POSITIVE    3922\n",
       "CONFIRMED         2281\n",
       "Name: koi_disposition, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indexNames = pd_trim[pd_trim['koi_disposition'] == 'CANDIDATE'].index\n",
    "pd_known = pd_trim.drop(indexNames)\n",
    "pd_known['koi_disposition'].value_counts()\n",
    "# pd_known.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11138155, 11818800, 11918099, ..., 11911561,  8765560, 10128825])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_cand = pd_trim[pd_trim['koi_disposition'] == 'CANDIDATE']\n",
    "pd_cand['koi_disposition'].value_counts()\n",
    "np_cand = pd_cand['kepid'].to_numpy()\n",
    "# name_cand = pd_cand['kepler_name'].copy()\n",
    "np_cand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>rowid</th>\n",
       "      <th>kepid</th>\n",
       "      <th>koi_score</th>\n",
       "      <th>koi_fpflag_nt</th>\n",
       "      <th>koi_fpflag_ss</th>\n",
       "      <th>koi_fpflag_co</th>\n",
       "      <th>koi_fpflag_ec</th>\n",
       "      <th>koi_period</th>\n",
       "      <th>koi_period_err1</th>\n",
       "      <th>koi_period_err2</th>\n",
       "      <th>...</th>\n",
       "      <th>koi_steff_err2</th>\n",
       "      <th>koi_slogg</th>\n",
       "      <th>koi_slogg_err1</th>\n",
       "      <th>koi_slogg_err2</th>\n",
       "      <th>koi_srad</th>\n",
       "      <th>koi_srad_err1</th>\n",
       "      <th>koi_srad_err2</th>\n",
       "      <th>ra</th>\n",
       "      <th>dec</th>\n",
       "      <th>koi_kepmag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9564.000000</td>\n",
       "      <td>9.564000e+03</td>\n",
       "      <td>8054.000000</td>\n",
       "      <td>9564.000000</td>\n",
       "      <td>9564.000000</td>\n",
       "      <td>9564.000000</td>\n",
       "      <td>9564.000000</td>\n",
       "      <td>9564.000000</td>\n",
       "      <td>9110.000000</td>\n",
       "      <td>9110.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>9081.000000</td>\n",
       "      <td>9201.000000</td>\n",
       "      <td>9096.000000</td>\n",
       "      <td>9096.000000</td>\n",
       "      <td>9201.000000</td>\n",
       "      <td>9096.000000</td>\n",
       "      <td>9096.000000</td>\n",
       "      <td>9564.000000</td>\n",
       "      <td>9564.000000</td>\n",
       "      <td>9563.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4782.500000</td>\n",
       "      <td>7.690628e+06</td>\n",
       "      <td>0.480829</td>\n",
       "      <td>0.188206</td>\n",
       "      <td>0.231598</td>\n",
       "      <td>0.194898</td>\n",
       "      <td>0.120033</td>\n",
       "      <td>75.671358</td>\n",
       "      <td>0.002148</td>\n",
       "      <td>-0.002148</td>\n",
       "      <td>...</td>\n",
       "      <td>-162.265059</td>\n",
       "      <td>4.310157</td>\n",
       "      <td>0.120738</td>\n",
       "      <td>-0.143161</td>\n",
       "      <td>1.728712</td>\n",
       "      <td>0.362292</td>\n",
       "      <td>-0.394806</td>\n",
       "      <td>292.060163</td>\n",
       "      <td>43.810433</td>\n",
       "      <td>14.264606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2761.033321</td>\n",
       "      <td>2.653459e+06</td>\n",
       "      <td>0.476928</td>\n",
       "      <td>0.390897</td>\n",
       "      <td>0.421875</td>\n",
       "      <td>0.396143</td>\n",
       "      <td>0.325018</td>\n",
       "      <td>1334.744046</td>\n",
       "      <td>0.008236</td>\n",
       "      <td>0.008236</td>\n",
       "      <td>...</td>\n",
       "      <td>72.746348</td>\n",
       "      <td>0.432606</td>\n",
       "      <td>0.132837</td>\n",
       "      <td>0.085477</td>\n",
       "      <td>6.127185</td>\n",
       "      <td>0.930870</td>\n",
       "      <td>2.168213</td>\n",
       "      <td>4.766657</td>\n",
       "      <td>3.601243</td>\n",
       "      <td>1.385448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.574500e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.241843</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.172500</td>\n",
       "      <td>...</td>\n",
       "      <td>-1762.000000</td>\n",
       "      <td>0.047000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.207000</td>\n",
       "      <td>0.109000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-116.137000</td>\n",
       "      <td>279.852720</td>\n",
       "      <td>36.577381</td>\n",
       "      <td>6.966000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2391.750000</td>\n",
       "      <td>5.556034e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.733684</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>-0.000276</td>\n",
       "      <td>...</td>\n",
       "      <td>-198.000000</td>\n",
       "      <td>4.218000</td>\n",
       "      <td>0.042000</td>\n",
       "      <td>-0.196000</td>\n",
       "      <td>0.829000</td>\n",
       "      <td>0.129000</td>\n",
       "      <td>-0.250000</td>\n",
       "      <td>288.660770</td>\n",
       "      <td>40.777173</td>\n",
       "      <td>13.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4782.500000</td>\n",
       "      <td>7.906892e+06</td>\n",
       "      <td>0.334000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.752831</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>-0.000035</td>\n",
       "      <td>...</td>\n",
       "      <td>-160.000000</td>\n",
       "      <td>4.438000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>-0.128000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.251000</td>\n",
       "      <td>-0.111000</td>\n",
       "      <td>292.261125</td>\n",
       "      <td>43.677504</td>\n",
       "      <td>14.520000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7173.250000</td>\n",
       "      <td>9.873066e+06</td>\n",
       "      <td>0.998000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.715178</td>\n",
       "      <td>0.000276</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>...</td>\n",
       "      <td>-114.000000</td>\n",
       "      <td>4.543000</td>\n",
       "      <td>0.149000</td>\n",
       "      <td>-0.088000</td>\n",
       "      <td>1.345000</td>\n",
       "      <td>0.364000</td>\n",
       "      <td>-0.069000</td>\n",
       "      <td>295.859160</td>\n",
       "      <td>46.714611</td>\n",
       "      <td>15.322000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9564.000000</td>\n",
       "      <td>1.293514e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>129995.778400</td>\n",
       "      <td>0.172500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.364000</td>\n",
       "      <td>1.472000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>229.908000</td>\n",
       "      <td>33.091000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>301.720760</td>\n",
       "      <td>52.336010</td>\n",
       "      <td>20.003000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             rowid         kepid    koi_score  koi_fpflag_nt  koi_fpflag_ss  \\\n",
       "count  9564.000000  9.564000e+03  8054.000000    9564.000000    9564.000000   \n",
       "mean   4782.500000  7.690628e+06     0.480829       0.188206       0.231598   \n",
       "std    2761.033321  2.653459e+06     0.476928       0.390897       0.421875   \n",
       "min       1.000000  7.574500e+05     0.000000       0.000000       0.000000   \n",
       "25%    2391.750000  5.556034e+06     0.000000       0.000000       0.000000   \n",
       "50%    4782.500000  7.906892e+06     0.334000       0.000000       0.000000   \n",
       "75%    7173.250000  9.873066e+06     0.998000       0.000000       0.000000   \n",
       "max    9564.000000  1.293514e+07     1.000000       1.000000       1.000000   \n",
       "\n",
       "       koi_fpflag_co  koi_fpflag_ec     koi_period  koi_period_err1  \\\n",
       "count    9564.000000    9564.000000    9564.000000      9110.000000   \n",
       "mean        0.194898       0.120033      75.671358         0.002148   \n",
       "std         0.396143       0.325018    1334.744046         0.008236   \n",
       "min         0.000000       0.000000       0.241843         0.000000   \n",
       "25%         0.000000       0.000000       2.733684         0.000005   \n",
       "50%         0.000000       0.000000       9.752831         0.000035   \n",
       "75%         0.000000       0.000000      40.715178         0.000276   \n",
       "max         1.000000       1.000000  129995.778400         0.172500   \n",
       "\n",
       "       koi_period_err2  ...  koi_steff_err2    koi_slogg  koi_slogg_err1  \\\n",
       "count      9110.000000  ...     9081.000000  9201.000000     9096.000000   \n",
       "mean         -0.002148  ...     -162.265059     4.310157        0.120738   \n",
       "std           0.008236  ...       72.746348     0.432606        0.132837   \n",
       "min          -0.172500  ...    -1762.000000     0.047000        0.000000   \n",
       "25%          -0.000276  ...     -198.000000     4.218000        0.042000   \n",
       "50%          -0.000035  ...     -160.000000     4.438000        0.070000   \n",
       "75%          -0.000005  ...     -114.000000     4.543000        0.149000   \n",
       "max           0.000000  ...        0.000000     5.364000        1.472000   \n",
       "\n",
       "       koi_slogg_err2     koi_srad  koi_srad_err1  koi_srad_err2           ra  \\\n",
       "count     9096.000000  9201.000000    9096.000000    9096.000000  9564.000000   \n",
       "mean        -0.143161     1.728712       0.362292      -0.394806   292.060163   \n",
       "std          0.085477     6.127185       0.930870       2.168213     4.766657   \n",
       "min         -1.207000     0.109000       0.000000    -116.137000   279.852720   \n",
       "25%         -0.196000     0.829000       0.129000      -0.250000   288.660770   \n",
       "50%         -0.128000     1.000000       0.251000      -0.111000   292.261125   \n",
       "75%         -0.088000     1.345000       0.364000      -0.069000   295.859160   \n",
       "max          0.000000   229.908000      33.091000       0.000000   301.720760   \n",
       "\n",
       "               dec   koi_kepmag  \n",
       "count  9564.000000  9563.000000  \n",
       "mean     43.810433    14.264606  \n",
       "std       3.601243     1.385448  \n",
       "min      36.577381     6.966000  \n",
       "25%      40.777173    13.440000  \n",
       "50%      43.677504    14.520000  \n",
       "75%      46.714611    15.322000  \n",
       "max      52.336010    20.003000  \n",
       "\n",
       "[8 rows x 45 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "stat_conf = pd_data[pd_data['koi_disposition'] == 'CONFIRMED']\n",
    "conv_sum = stat_conf.describe()\n",
    "conv_sum.to_csv('confirmed.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "stat_cand = pd_data[pd_data['koi_disposition'] == 'CANDIDATE']\n",
    "conv_sum = stat_cand.describe()\n",
    "conv_sum.to_csv('candidate.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "stat_fpos = pd_data[pd_data['koi_disposition'] == 'FALSE POSITIVE']\n",
    "fpos_sum = stat_fpos.describe()\n",
    "fpos_sum.to_csv('false_pos.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7995 entries, 0 to 9563\n",
      "Data columns (total 13 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   kepid             7995 non-null   int64  \n",
      " 1   koi_disposition   7995 non-null   object \n",
      " 2   koi_pdisposition  7995 non-null   object \n",
      " 3   koi_score         7995 non-null   float64\n",
      " 4   koi_period        7995 non-null   float64\n",
      " 5   koi_time0bk       7995 non-null   float64\n",
      " 6   koi_impact        7995 non-null   float64\n",
      " 7   koi_duration      7995 non-null   float64\n",
      " 8   koi_depth         7995 non-null   float64\n",
      " 9   koi_prad          7995 non-null   float64\n",
      " 10  koi_teq           7995 non-null   float64\n",
      " 11  koi_insol         7995 non-null   float64\n",
      " 12  koi_model_snr     7995 non-null   float64\n",
      "dtypes: float64(10), int64(1), object(2)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "# pd_trim.describe()\n",
    "pd_trim.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9564 entries, 0 to 9563\n",
      "Data columns (total 50 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   rowid              9564 non-null   int64  \n",
      " 1   kepid              9564 non-null   int64  \n",
      " 2   kepoi_name         9564 non-null   object \n",
      " 3   kepler_name        2294 non-null   object \n",
      " 4   koi_disposition    9564 non-null   object \n",
      " 5   koi_pdisposition   9564 non-null   object \n",
      " 6   koi_score          8054 non-null   float64\n",
      " 7   koi_fpflag_nt      9564 non-null   int64  \n",
      " 8   koi_fpflag_ss      9564 non-null   int64  \n",
      " 9   koi_fpflag_co      9564 non-null   int64  \n",
      " 10  koi_fpflag_ec      9564 non-null   int64  \n",
      " 11  koi_period         9564 non-null   float64\n",
      " 12  koi_period_err1    9110 non-null   float64\n",
      " 13  koi_period_err2    9110 non-null   float64\n",
      " 14  koi_time0bk        9564 non-null   float64\n",
      " 15  koi_time0bk_err1   9110 non-null   float64\n",
      " 16  koi_time0bk_err2   9110 non-null   float64\n",
      " 17  koi_impact         9201 non-null   float64\n",
      " 18  koi_impact_err1    9110 non-null   float64\n",
      " 19  koi_impact_err2    9110 non-null   float64\n",
      " 20  koi_duration       9564 non-null   float64\n",
      " 21  koi_duration_err1  9110 non-null   float64\n",
      " 22  koi_duration_err2  9110 non-null   float64\n",
      " 23  koi_depth          9201 non-null   float64\n",
      " 24  koi_depth_err1     9110 non-null   float64\n",
      " 25  koi_depth_err2     9110 non-null   float64\n",
      " 26  koi_prad           9201 non-null   float64\n",
      " 27  koi_prad_err1      9201 non-null   float64\n",
      " 28  koi_prad_err2      9201 non-null   float64\n",
      " 29  koi_teq            9201 non-null   float64\n",
      " 30  koi_teq_err1       0 non-null      float64\n",
      " 31  koi_teq_err2       0 non-null      float64\n",
      " 32  koi_insol          9243 non-null   float64\n",
      " 33  koi_insol_err1     9243 non-null   float64\n",
      " 34  koi_insol_err2     9243 non-null   float64\n",
      " 35  koi_model_snr      9201 non-null   float64\n",
      " 36  koi_tce_plnt_num   9218 non-null   float64\n",
      " 37  koi_tce_delivname  9218 non-null   object \n",
      " 38  koi_steff          9201 non-null   float64\n",
      " 39  koi_steff_err1     9096 non-null   float64\n",
      " 40  koi_steff_err2     9081 non-null   float64\n",
      " 41  koi_slogg          9201 non-null   float64\n",
      " 42  koi_slogg_err1     9096 non-null   float64\n",
      " 43  koi_slogg_err2     9096 non-null   float64\n",
      " 44  koi_srad           9201 non-null   float64\n",
      " 45  koi_srad_err1      9096 non-null   float64\n",
      " 46  koi_srad_err2      9096 non-null   float64\n",
      " 47  ra                 9564 non-null   float64\n",
      " 48  dec                9564 non-null   float64\n",
      " 49  koi_kepmag         9563 non-null   float64\n",
      "dtypes: float64(39), int64(6), object(5)\n",
      "memory usage: 3.6+ MB\n"
     ]
    }
   ],
   "source": [
    "pd_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lee/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "FALSE POSITIVE    4723\n",
       "CONFIRMED         2292\n",
       "Name: koi_disposition, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_wider = pd_data[['koi_disposition','koi_period','koi_time0bk','koi_impact',\n",
    "                   'koi_duration','koi_depth','koi_prad','koi_teq','koi_insol',\n",
    "                   'koi_model_snr','koi_steff','koi_slogg','koi_srad','koi_kepmag']]\n",
    "pd_wider.dropna(inplace=True)\n",
    "\n",
    "indexNames = pd_wider[pd_wider['koi_disposition'] == 'CANDIDATE'].index\n",
    "pd_wide = pd_wider.drop(indexNames)\n",
    "pd_wide['koi_disposition'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2ZunSpTRr1ozWrVsXuf2wYcNo06YNV69eBWDUqFE4OTmRnJzM8uXLiYvTDpABAwZQr149tmzZQkJCAjExMVSoUAF/f39++eUXJk2aRGpqKsuWLSM6OhozMzOGDBlC9erVSUlJYdGiRSQnJ1O7du0ye2F8qSny31SpNVyPSuL7Xi3IzFUzYNMZmjhXorqtFav7tsLR2pz49Cw+3X6OGnZWNHMpu0/sFQpF4V8W7ANFtNFotOcMVWxcg8tT1pBw4Q6NZvSnzvA3uDFvKxfGLKfxzP68MvZtIvedR52da6AMnqEkuQGZR0+QefQEpp5NqDBkEHEjx5dBcCWjKNiZoMT1yatW56aEn7ulP8xQaWxElUY1OPjFOiJD7uA7LYCWQ3twcsG20gq9sCLrUZI2TxspTIxw6tKM60GbCjWrM6onmlw1j3Wf2JanosZVwX1r5Sr2+HbvyIheY8oqrGf7q/uCAreretenXr+O7HprBgBunTzJiE0m9nIYzm3ql1Kwz1GCv/+z+mONjo2JvnqfTf1mUal6Ffr8NJE1f9wkW3eeqomlGT2/G8XB6ev1vzOYEozvEu3Hi1GjZ5syW+0C/nZt2n/5AadmbdKel1sEE0szXv1+FMenrSenHGpT4n3aM7a9se4AF7/5LxoNNJ3QixZfvM/JwBV/O9znKcl+61n9sf2XH3CymNpsf3s6aVGJWNjb0HPDRBLuhBP++81SifuFIl+gXH4Tr/DwcL755huGDh3KkiVLGDx4MA0aNGDz5s1s27aNgQMHluhxLC0tmT17NkePHmXNmjVMmjSJ1atX0717d+rVq0dsbCxBQUF8/fXXANy9e5cZM2Zgamqqn7ABbNmyBXd3dyZMmMCVK1dYsmQJ8+fPZ+vWrdSrV49evXoRHBzMgQMHiozjwIED+vtmjBvy9/44LxFHazOiUp7u3KNSM3GwMivQxpxKFiZYmBhjYQJe1Wy5FZNCdVsrHK3NAe3hh361HbkamVSmE6+M8Ph8q1EWznZkRibka5MZHo9FVbvCbTQaMiPiSdB9qhj+v9+pM+INAFJDwzndbw4AVjWdqOLf1NCpFKKOjsHI0VF/28ixMqrY2GLbZ4dcwrhaVZQVbVAnld9hEp79/WnyrvYTwchLd6ng/LQ+FZzsSI1KzNc+Iz4FMxtLFEZKNCo11s52pEblr2G9Hm24vuvpm6uUiHhSIuKJ1H1af2v3H7T6rIehUgKe9KOnuZg72xfR1+Iwf0Z/dPTzJOnyPbJjk/Jt59KnA46dm3Kmd5CBon++twe8yRvvay+OcT3kJo5Vn/Y9R2cHYqPi8rWv06g2LjWqsfnkegDMLczYfOJH+rYLKLOYGw7wp57u0+eYi3exyvO3t3K2I71AX8uMT8G0QF9Lz1Mfu/qudJj3Eb8FzCcrUXt4m1OLulTv4oWbnwdGZiaYVLDAb/FnHBr5banm0rS/P036PR03NlXteXJsQQUnO1Kjnz1uKuQZN417d+T3ZdoLHSTejyLpYQx2tZyJvHgXpbERPb8bxbWdp7i951yp5lCU9Ih4LPPsfy2d7cgoMG7SI+KxqmpPjO62VVU7MgrUrigKIyWur7Xgt27/Ks2QC2k8wJ8Gun1a9MW7WOfpZ9bOdqQV0c8K7tPSdLVxbOJO16XDATC3q0B1Xw/UKjX39p5HaWzEq8tHcWvnKe6WUW2squY/pDY9quja5G+TiNLUuNhtM2Ofvv7c/ukwndaW7PSVv6LxAH8almJtuhWojUal5u7e8/rHyYhL5s6e81TxrPVyTrxE+RxqmJyczLx58xgxYgSOjo6kpaXRoEEDADp27Mj169dL/Fht27bV/3/r1i0ALl++zKpVqxg/fjxz584lPT2djAztm//mzZtjalr4Qg03btygQ4cOADRq1IjU1FTS09O5fv067du3B8DLywsrK6si4/D392fOnDnMmTOnxLH/f9DQqSIPEtJ5nJROjkrN3puR+NR0zNfGp5YjFx4nkKtWk5Gj4kpkEu52VmTk5JKmWwnKyMnl9P04alUu25PsE0PuYFXTCUs3BxQmRlTr2YbIfefztYncdx7XPto+YutVm5yUDLKiE8mKSSLjcRzWuitjObRvpL8oh2llG+3GCgWvjHmLsHVFT+gNKfv6DYxdq2Hk7ATGxlj6+5F5PP8nu0YuVfU/m9Stg8LEpFwnXQAh6w6w7tWprHt1KqF7z9PwnXYAODetRVZKOmnRhd9QPTx9jbqvtQSgYa/23NkXrL/PtIIFLq3r5ftdekwSKRHx2NbU1q5624bE3X6MIT3paxa6vla1yL4WrO9rlbxqk5OSTlaefKu9VfgwQwdfD2oP78HZAf9GlZFt0ByeZcfaXQzsMoSBXYZwbO8JuvXqDEBDr/qkJqcVOpzw9MHfeaNpL3q1fo9erd8jMyOrTCddAFfXHmB716ls7zqVsD3nqdtL29ccvWqRnZJOehF9LfzUNWq+ru1rdXu3J0zXr6yr2tNlxWgOj/qOpHuR+vZ/zNnCTy1GsqHNGA4MW0r4yWulPukCuLDuAGtfm8ra16Zye1/Jxs2D09d4RTduGr3Tntv7tbkkP46letuGAFhWtsGupjNJD7SHjnab9xFxoeGcW/lbqedQlLiQu1Rwd8LK1QGliRE13mzNozxjGeDRvmDcdbWr7FWL7OR0MorItyCn9o1IDg0nPcKwh7peXnuAzd2msrnbVO7uPU89XW2qNC2+nz0+dY3aun5Wr1d77upyXtd2LOu8x7DOewx3dv/B0alruLdXux/xm/8R8bfDCVlRNrWJDbmLjbsT1rrauL/ZmocFavNwXzC1dLVxyFObZ21rkee8KrdXm5N485HBcri89gCbuk1lk6429UtQm0cFanMvT23Weo9hra42R6au4e7e8xhbmGFipf2Q2djCDLcOjYgzYE7lSaNRl9m/F1W5rHhZWlpib2/PzZs38fb+e5cHz7v0++RnjUZDUFBQkRMsM7Oiz9N41iGERR6mUI7GfzmHsxcukZiYTKeeHzD0wwDe6dG1vMMqkrFSyUS/BgzdcQ61RsObDV2oVbkCWy9qL6Pc28ONmvbWeNdwoM+PJ1EqFLzVyIXalSvwKDGdsb9cALSHI75az5m2NQqfg2RIGpWaS1PW0GbjJO0lvjceIeXmY2r07wRA2LqDRB0IoUonT/zPfI0qI4sLo59eCODS1LU0WzYMhYkx6fej9fe59PTGfZD2jWfE7rM82Hi0TPMCQKUmccF/qPzNXFAakfa/38i9F4blW9qVnfT//oKFTwcsX+2CJjcXsrKI/3y6fnPbrz7HzMsDZaWKOO3aTPLKNaT/UjYv6E/cPRSCu68HHx1fQE5GNnvGLdff9/aaceyduJK0qESOzd5E9yXDaTe+N9FXw7i8+Yi+XZ2uzbl/7DI5GfnPPTz4xVpeX/wZRibGJD6IzvfYhqBRqbkyZQ2tN05GYaTk4cYjpN58RPX+/gDcX3eA6AMXcOzkid+Zb1BlZBGSp68ZWZji0KExl8avzPe4jWcNRGlqQuvNUwBIOB/K5YmrDJrL85w++Dtt/Fqx5eR6MjMymTV2nv6+f6+bzZzx/y60AlbeHhwKwc3Pg34nFpCbmc2RsU/7w6vrxnF0/ErSoxL5fdYm/JcNp8WE3sReCeOG7gItXmPewrySNe1mDQRAk6tiRzldMvruoRBq+nrw8bEF5GZk81uevv3OmnHsnbCS1OhEjs7exBtLhtN+XG+i8oyb04t38uqCTxi0dzYo4OiczWQkpFKteV0avdOe6OsPGLBbu7p6fP4W7h423HmFGpWas1PX0mnDBBRGSu5sOkrSrcfUCfAD4PaPh3h8MISqnTx485Q239NjnubbbtkwqrSpj5mdNW+dW8ylBdu5o9sf13izddkeZgjcPxRCdT8PAk5oYz0Y+DTW7mvHcXiCdp92avYmui4dTqvx2n52TdfPiuPcoi71erUn9voD+u7R1ubM3C3cN3Btzny+ls4bJmgv9b/5KIm3HvOKrjY3fzzEo4MhVPPz4O2TC1BlZHNCN66K2xag+ef9sGtQHY1GQ+qjWE5P/HMXSfurwnS16X9C+3qTtzY91o7jUJ7adFs6nNbjexNzJYyrz6mNpYMNr68YDYDCyIhbu07x4IjhLiwnypdCU8YnLUVHRzN37lyCgoIICgqia9eu7Nq1i8GDB1O/fn22bNlCeno6AwcOLNE5Xp07d6Znz54cO3aMU6dOMWnSJBYtWoS7uztvvKE9rCssLIwaNWqwZcsWzM3N9b+/evWq/hyvH374ARsbG3r16sXVq1dZu3Yt8+bN44cffqBixYq88847XLhwgdmzZ7Ny5UpsbGyKzTEn9m7p/+HKSc62ReUdQqnaPy3m+Y3+IZq7Rz6/0T/IxsdVn9/oH+KV7Jfru0rmGJXNhQXKQn/Fy9PPAJKMyjuC0lM1p7wjKF2JL1FtrF/cBYS/JOWFvrTcnzfi4fryDqFEsm4XfeEnQzCrU/bf+1oS5XaOl7m5OZMmTWLmzJm0bNmS9evXk5WVhaOjI0OHDi3x4+Tk5DBlyhQ0Gg2jRo0CYNCgQaxatYpx48ahUqmoX78+Q4Y8+7yrPn36sGzZMsaNG4eZmRnDhmm/U6Z3794sWrSIiRMnUr9+fSpXLnzlLSGEEEIIIYR4ljJf8SpNw4YNY/bs2c9cfSoPsuL14pIVrxeXrHi9uGTF68UlK14vLlnxenHJilf5yLpVdlfVNavbrsye6894ybqeEEIIIYQQQrx4yvV7vEpq/vz5ROf5oleA999/n6VLl5ZTREIIIYQQQogSU79cR4P8Ff+Iidf48S/OF7YKIYQQQgghxJ/1j5h4CSGEEEIIIf7BXuDv1yorco6XEEIIIYQQQhiYTLyEEEIIIYQQwsDkUEMhhBBCCCGEYanlUENZ8RJCCCGEEEIIA5MVLyGEEEIIIYRhycU1ZMVLCCGEEEIIIQxNVryEEEIIIYQQhiXneMmKlxBCCCGEEEIYmqx4CSGEEEIIIQxKo1GVdwjlTla8hBBCCCGEEMLAZMVLCCGEEEIIYVhyVUOZeBlCzrZF5R1CqTHpNaq8QyhVmmmfl3cIpSYuwqq8QyhVJuUdQCnKVLxcBxNUNDIv7xBKz0v2um+kKe8ISk+OorwjKF3WL1FfU71ktanwEtVG/LPIxEsIIYQQQghhWHJVQznHSwghhBBCCCEMTVa8hBBCCCGEEIYl53jJipcQQgghhBBCGJqseAkhhBBCCCEMSy3f4yUrXkIIIYQQQghhYDLxEkIIIYQQQggDk0MNhRBCCCGEEIYlF9eQFS8hhBBCCCGEMDRZ8RJCCCGEEEIYlnyBsqx4CSGEEEIIIYShyYqXEEIIIYQQwrDkHC9Z8RJCCCGEEEIIQ5MVLyGEEEIIIYRhyTlesuIlhBBCCCGEEIYmK14voJNhMcw/ch21Gno2cmFwy5qF2px7GMf8ozfIVWmoZGHCqj6tAHht1RGsTIxRKhUYKRRseN+7rMP/Uz6ftZBjJ//AzrYSO9d/V97hPJejbxMaz+iPwkjJ/Z8Oc3vJL4XaNJ7ZnyqdPFFlZBM86juSLocBYGJjiefCj7F5xRU0GoLHLCfh/O0yziA/645eVPviYzBSEr95PzHfbst3f6U3O+Lw6TsAqNMzefz5MjKvhwHgMm8kNn4tyI1L4lbX4WUdul7HrwKo4etJbkYW+wKXE3MlrFAbG1cHXl0yDPNK1kRfCWPv6G9R56io1ro+PVaOIflhDAChe87yx6KdAJjaWOI/7yPs67qARsP+8SuIDA41aC5VfJvQdHoACiMldzcc4WYR/ctzRn+cO3mQm5HN2dHfk6jrXwAoFfjvmUlGZAIn+/8bgCb/ehfnLl6os3NJux/F2dHLyUlON2geRfnkq09o4duCrIwsFgYu5M6VO4XajJo3ijpN6qBQKHh87zELxy4kMz2T1p1bEzAuALVajVql5vuvvufa2WtlnoP39ADc/LR97ciY5cQW0dcquDrQaZm2r8VeDuPQKG1fq/2WN55DuwOQk5bJ8clriL/+QL+dQqng7d0zSItMYM/ABQbPxferANx142ZP4HKiixk33fOMm926cQPg0ro+vl9+gNLEiIz4FLb0CcK2pjPdlz7dF1R0czBZQWkAACAASURBVOTUwm0Er9prkBxaTQ/ARVePE2OWE1dEDtauDvgsG4aZrTVxl8M4NvJpDsVt33bBx7j6e5IZm8zOTpP1j9X883dx7dwUdXYuKfejOTF2Odl/cSxV82lCy+kBKJRKbm88wuWlhcd6ywLxxeviK25b00pW+Hw7HGtXB1IfxnDk0/+QnaSNz7a+K23mDsbE2gLUGv73+heosnLotnUqFlUqocrMBmDfu3PJjEv+SzmVROvpAbjqcjr2jJr5LhuGWSVtzY7qxlDFWs50WDgE+0Y1ODdvK1e+322wOJ8wRJ2aju+Faxcv0GjIiE3mxJjvyYhKxMzWGp/lI6nsUZPQLcf4/fN1Bs+vzMmKl6x4vWhUag1zDl1jSc/mbB/Qjj03I7gTl5qvTUpmDrMOXeObN7zYPqAd87t75rt/ee+WbP6g7Qs/6QLo+Vpnvls4s7zDKBmlAo/Zgzj93jwOdhiPy1veVKhbLV+TKp08sa7pxIE2YwkZtxKPuYP19zWe2Z/oQxc52H4chzpNIvX247LOID+lkmrTP+XewGnc6jyMSm90wKy2a74m2Q+juNt3MrdfHUn0fzbjMvvpm6qEbQe5N2BaGQedXw1fDyrVcGJth0AOTlqFX9DAItu1ndyPCyv3sLbjOLKS0mjY10d/X/jZm2x4dSobXp2qn3QBdJwWwP0jl/jRbwI/dZtCfGi4YZNRKvCaNZDj789jT8cJuPVsU6h/Ofl5YF3Tid+8Azk/fhVecwblu7/Ox91IuZ0/zqhjV9jnM5H9nSaTcieSeiPeMGweRWju25xqNarxUYePWDxpMcODip6oL5++nOHdhjOs6zBiHsfQY2APAEJOhjCs6zBGvDqCr8d9zai5o8oyfABc/Tyo6O7EpnaBHJu4inazBxbZrtWUflxesYdN7bV9rV4/HwBSHsTwc6+ZbOs8heBFO+kwb3C+7Rp92I0EQ/cxHXdfD2xrOPFDh0D2T1qFfzHjpsPkfpxfuYcfOo4jMymNxrpxY2ZjiX/QQHZ+uJC1/pP45bP/AJBwN4IfX53Kj69OZf3rn5ObkcXtPecMkoOLnwc27k5sbxfIqYmraFNMPZpP7cfVFXvY3k5bjzrv+jx3+9Atx9j//vxCjxV+7DI7/Saxq/MUku9G0GR4j78Uu0KpoFXQAPZ/MI+dvhNw79mainWq5mtTTRffjnaBnM4T37O2bTysBxEnrrGj3TgiTlyj8TBtfAojJe0Xf8bpSavZ5TeJPb2DUOfk6p/r2PBl/NxlKj93mWrQSdeTv/nWdoGcmLgK72Jq1mKKtmbbdGOorm4MZSWmcfqLH7lcBhMuMFydrnz7Kz93nsLPXaby6MAFPMe8BYAqM4cL87ZxbsaGMslPlI9SnXhFR0cTGBhYorabN2/m0qVLxd7/66+/kpWVpb89e/Zs0tLS/naMAP/9738ZMWIEo0aNIiQkRP/7gICAIttPmzaNO3cKfzprCFciE3GtZIlLJUtMjJR0fcWJI3ei8rX57WYEnWpXwdnGAgA7S7Myic0Qmns2pqJNhfIOo0Rsm9Ym9V4U6Q+i0eSoeLTzNE5dm+Vr49S1GQ+2HAcgITgUExtLzBwrYWxtgX3retzfcAQATY6qXFYd8rL0rEP2/QiyH0ahyckl8Zdj2HRpla9NevANVMlp+p9NnCrr70v74yq5SSllGnNBNbs04/r2EwBEXriDmY0Vlo6VCrVz9W7A7d1/AHBt23FqFahbQabWFlRr+QpXNx0BQJ2j+sufbJeUXdNapIZFkfYgBk2Oioe7zlCtQJxVuzXj/lZt/4oPDsXUxhJzXb4WznY4d/Lk7obD+baJOnoZjUr7KWNccCgWVe0MmkdRWndpzcHtBwG4eeEmVjZW2DraFmqXkZqh/9nU3BSNRgNAZnqm/vfmlub635elGl2acWubtq9FBxff16q2bcDdX7V97dbW49TQ1TDq/G396kNUcCjWzk/rYOVsR/VOntzYcMSwSejU6tKMa7pxE6EbN1ZF5OLm3YBbunFzddtxautyqfemN7d/O0tKeBwAGUW8WXdr25DEB9GkPI4zSA5uXZsRqqtHTPAdTCtaYVFEDs5tGxCmq0fo1uNU1+XwrO2jfr9JVmJqoccKP3ZFP5aig+9g6fzXxlLlprVICYsi9UEM6hwV93adwa3AWHfr2ow7RcT3rG3dujYjVLd/CN16HLduzQGo2rExCdcfknBNu8KalZCKRl32Y6h6lwJ/c5uia1a1bQPuFVGzzLhkYi/eRZ2rKpN4DVWnnDz7OWNLM/3+LDcji+izt1Bl5ZRJfuVBo1GV2b8XVbmtePXt25cmTZoUe//u3bvzTbwmT56MlZXV337eR48ecerUKRYuXMjUqVNZtWoV6hdo6TM6NYsqFSz0t6tYmxOTmpWvzf2ENJKzcvlo6++899Mpfrn2dOVEgYKhO87x3k+n2H7pYZnF/f+BhbMtGeFP30RkRsRjUeCFV9smvkAbW6yqO5Idl4LXok/w2T8LzwUfY1TOE2aTKvbkhMfqb+dExGFSxb7Y9rZ9u5By5HxZhFZi1k62pEY8rUlqZDzWTvnf0JvbWpOVnK5/w5QaEY9VnjZOXrV5b08Qb64dj51uhcnGzYGM+BQ6LxjCu7tn0mnuRxhbGLZeFk52pOd5k5oeEY9FgVwsnOxIDy/QxlnbxnN6AJdmboRnvKFy79eRyEMXSzny56vsVJmYiBj97djIWCrnmcTnNebfY/jp/E+41HLhl9VPD+tp07UN3x/6nq/WfMU3478xeMwFWTnZkpbnb58WEY9lEX0t+xl97Yl6/Xx4cPjpB4/e0z7gTNDGMptQWjvZkpJn3KQUMW4sbK3JLJDLkza2NZ0wr2hFn81T+eDXGTR4p12h56j3Rhtu7DptsBwsS1APM1trspOe5pCep01Jtn+WOv068Ohw8R8ePz/2p68TRT13cfE9a1uLyjZkRCcCkBGdiLm9DQAVazoBGjr/NIEee2bS6LPX8z1Xu4VDeGNfEE1G9/xL+ZRUwZzSixgfZgXGUFoxY6gsGKpOAE0n9qb32UXUfMubC/O3GzAL8aIx2DleUVFRLFiwgCFDhrB69WqysrKoUqUKn332GdbW1ixdupRmzZrRunXrQtvu3r2b+Ph4vvrqK2xsbPjyyy8ZNmwYs2fPJjMzk1mzZlGvXj1u375N9erV8fHxYevWrSQlJTFy5Ehq165NZmYmP/zwAw8fPkSlUtG7d29atGjB2bNn8fb2xsTEBEdHR5ycnAgNDaVu3boArFu3jqtXr2JlZcXo0aOxsbHRx6VWq1m2bBmVK1emX79+hvrTFabIf1Ol1nA9Konve7UgM1fNgE1naOJcieq2Vqzu2wpHa3Pi07P4dPs5athZ0cyl7D/hfikpFIV/V/CNUhFtNBpQGCup2LgGl6asIeHCHRrP6E/d4W9wfd5WAwVbAiXJR8eqTWPs+nbmTq+JBg7qz3p+Dooi89T+F3MljNVtRpOTnkUNXw96rBjD2o7jUBob4dioBke+WEdUyB06TAug+dAenFmwrfBjlZKiy1Ewl6LbOPs3JTM2icRLYTi0qV/k49cb9SYalYoH20+WRrh/W3GTjK/HfY1SqeTT6Z/SoUcH9m/dD8Dpvac5vfc0jVo2ImBcAFPfm1qW4f718V/gdlXv+tTr15Fdb80AwK2TJxmxycReDsO5mNqVNkUR46ZQPYrZlwEojZQ4NnZn67uzMTE34d2d04gIDiXhXqT2fhMjanX24vjczaUe+7PiK0k99AX5E/u/gpqMfANNrpq7O/7iWHpWXM9soynZtgUfysgIxxZ1+d9rX5CbkU3XLZOJuxxGxImrHBuxjPTIBIytzPFdMYpavdrpV3BKW1H74sL7uOePoTJjwDpdmLuVC3O30nh4D+oP6kzIgh1/K9R/jBdooSMkJITVq1ejVqvp1KkTPXvm/+AhPT2dxYsXExcXh0qlokePHvj6+v7t5zXIxCs8PJxvvvmGoUOHsmTJEgYPHkyDBg3YvHkz27ZtY+DAgc/c/rXXXuPXX3/lyy+/zDfxeSIyMpKxY8fi4uLC5MmTOXHiBNOnT+fcuXPs2LGDCRMmsGPHDho1asTQoUNJS0tjypQpNG7cmPj4eOrUqaN/LDs7O+LjtZ9KZGVl4e7uTv/+/dm2bRtbt27lww8/BEClUrF48WLc3Nx4++23C8V04MABDhw4AMAXNf7iHw5wtDYjKuXpMnRUaiYOVmYF2phTycIECxNjLEzAq5ott2JSqG5rhaO1uTYvSzP8ajtyNTJJJl6lJCM8HouqT1eEzJ3tyIhMKKKNXb42mZEJoNGQGRFPwgXtIavh//udOuVwrk1eOZGxmFR9uupg4mxPTnR8oXbm9WrgMmcE9wZOQ5VYvocWAjTp70+jd7U7v6hLd7F2floTayc7UqMS87XPiE/BzMYShZESjUqNtbMdaVHaumXnOeQj7PBFfGcOxNzWmtSIeFIj4okK0dYrdPcfNP/sr53PUVLpEfFYVnuai6WzHZkFckmPiMeyqj1xedtEJuLSvRVVuzTDuZMnRmYmGFewoOWSz/hj+LcAVO/dnqr+TTnaZ5ZBc8ire//udH23KwC3L93GwdlBf19lp8rERRV/CJparebYL8fo9Wkv/cTriSt/XMHZzRkbWxuSEwx3PgpAwwH+1HtP29diLt7FKs/4t3K2I71AfTLjUzAt0NfS8+wj7Oq70mHeR/wWMF9/KJtTi7pU7+KFm58HRmYmmFSwwG/xZxwa+W2p5uLZ35/GunETeekuFfKMmwpOdqQVMW7Mixk3qZEJZCRcIjcji9yMLB79fgOHBm76iZe7jwdRV8JIjy3d+tQb4E/d97U5xIY8vx5Z8SmYVnyag6WzHem6HNIj4p+7fVFq926Pq39T9vSZ/Zfz0D53/kNNn8SVv03h+JSmxsVumxGbjIVjJTKiE7FwrKQ/Xys9Ip6oMzfIStD2uUeHLmLXqAYRJ67q+2duWib3dp6ismfNUp141R/gzyu6MRRbYAxZlmAMWRUYQ2XJUHXK6+5/T+G/btz/n4nXC0KtVrNq1So+//xz7O3tmTx5Ms2bN8fFxUXfZs+ePbi4uDBp0iSSk5MZNWoU7du3x9j4702dSv1Qw+TkZObNm8eIESNwdHQkLS2NBg0aANCxY0euX7/+t5/D0dERNzc3lEolrq6uNG7cGIVCgZubGzEx2sNZLl26xK5duxg/fjzTpk0jOzub2NjYZx7KoVAo8PbWXpCiffv23LhxQ3/fihUrip10Afj7+zNnzhzmzJnzt3Jr6FSRBwnpPE5KJ0elZu/NSHxqOuZr41PLkQuPE8hVq8nIUXElMgl3OysycnJJy9aeMJuRk8vp+3HUqmz9t+IRTyWG3MG6phOWbg4oTIxw6dmGyH35D72L3Hcetz7tAbD1qk1uSgZZ0YlkxSSR/jgO61rOADi0b0TKrfK9uEb6xduY1qiKiUsVFCbGVOrRgeT9f+RrY1LVgerfTebhmIVk3yubE/+f59K6A/qLYdzZe576usOcnJrWIislnfTowm+eHp2+Rp3XWgLQoFd77u4LBsDSoaK+TRWPmiiUCjITUkmPSSIlIp5KNbX1cm3bkHgDXwwlIeQu1u5OWLpq+5frm60J35u/f4XvDaZ6b23/svOqTU5KBpnRiVyZtZlfm41gd8vRnPl0CdEnruknXVV8m1BveA9ODFyAKiPboDnk9b91/2PEqyMY8eoITu89Tad3OgHwStNXSEtJIyG68JsQ5+rO+p9b+bfiYejDQr+v1agWxqbGBp90AVxde4DtXaeyvetUwvacp24vbV9z9KpFdjF9LfzUNWq+ru1rdXu3J0zX16yr2tNlxWgOj/qOJN0EBeCPOVv4qcVINrQZw4FhSwk/ea3UJ10AIesO6C98Ebr3vP7wQGfduEkrIpcHp69RVzduGvZqT6gul9B956nW8hUURkqMzU1xblqLuDwXdan3pmEOM7yx9oD+IhAP9p6ntq4eDl61yE5O1x9ml1fEqWvU0NWjdu/2PNDl8GBfcIm2z6uaTxMaD+3OgYEL9VcB/CtiQ+5i4+6EtasDShMj3N9szUNdXE883BdMrSLie9a2D/cFU1u3f6jduz0PdPuPx0cvYVvfDSNzUxRGSpxa1yPp9mMURkrMbLXvERTGRrj4NyXx5qO/nFdRrq89wM6uU9nZdSr39+SvWU5K8TVzL6JmZc1QdargXkW/vWsXL5LuRJRdUuVNoy67f88QGhqKk5MTVapUwdjYGG9vb86ePZuvjUKhIDMzE41GQ2ZmJtbW1iiVf3/aVOorXpaWltjb23Pz5k39JKa0mZiY6H9WKBT62wqFQn++lkajITAwkKpV81+Bxt7enri4p5+0xsfHY2dX9IpQ3iXvunXrcvXqVbp3746pqWmp5VKQsVLJRL8GDN1xDrVGw5sNXahVuQJbL2pPiu3t4UZNe2u8azjQ58eTKBUK3mrkQu3KFXiUmM7YXy4A2sMRX63nTNsaDs96unI3/ss5nL1wicTEZDr1/IChHwbwTo+u5R1WkTQqNZemrMF74yTt5eQ3HiHl5mNq9Ne+oQxbd5CoAyFU6eRJ5zNfk5uRxYXR3+u3vzx1Lc2WDUNpYkz6/WiC89xXLlRqwr/4jprrvgIjJQlbDpB1+wF273cDIP6nPVQZ2Q8jWxuqzfwMAE2uitA3xgLgtngcVq0bY2xrQ73Tq4n6egMJW/YX+3SGEHYohBq+Hgw4voDcjGz2j1uuv+/NNeM4MHElaVGJnJi9iVeXDKfN+N7EXA3j6uYjANR+rSVNAjqhzlWRm5nDb8OX6rc/8sVaui3+DCMTY5IeROd7bEPQqNRcmLKGDhsnojBScm/TUZJvPaamrn/dXXeQyIMhOHfy5NXTC1FlZHN2zPP7kFfQAJSmJnTcpL0sdlxwKMETfzBoLgWdPXSWFr4tWHV8FVkZWXw97mv9fV+t+YpFExeREJ1A4NeBWFpbggLuXbvHkqlLAGj7Wls6vdOJ3JxcsjOzmTPs733A9Vc8OBSCm58H/U4sIDczmyNjn/aHV9eN4+j4laRHJfL7rE34LxtOiwm9ib0Sxg3dBVq8xryFeSVr2s0aCGjH0o7XvyjzPADuHQqhpq8HHx5fQE5GNnvz9O231oxjn27cHJ+9ideXDKft+N5EXw3jim7cxIeGE3bkEgP2zUajVnN50xHibmnfsBubm1K9fSP2TzZsH3t0MAQXPw/eOan9QOF4nnp0XjeOE+NXkhGVyLmgTfgsG47XhN7EXQ3j1sYjz92+49JhOLWpj7mdNX3OLebCv7dze9NRWs8cgJGZMV03TQIgJjiU05NW/+nYNSo1Zz5fS+cNE1AolYRuPkrirce8EuAHwM0fD/HoYAjV/Dx4WxffCV18xW0LcHnpL3T8bgR13u1I6uM4jnyyGIDspHSuLv+N7rung0bDo0MXeXQwBGMLMzpvmIjS2AiFkZKI41e59dPhooMuBQ8Paf/mvXVjKO/fvIuuZulRiZydtQnfZcNpNqE3cVfCuKkbQxYOFXlz9wxMrC3QqNU0+qgb230n5rtYRWkyVJ2aTe5LxVrOaNQa0h7H5utDvc58jYm1BUpTY9y6NWffu3NIuv1ifOj5MomPj8fe/ulKpb29Pbdv5/96n27dujFv3jw++eQTMjIyGDNmTKlMvBSaUjybNzo6mrlz5xIUFERQUBBdu3Zl165dDB48mPr167NlyxbS09MZOHDgM8/xAggMDGTixIk4OmpXe/Ke4zV37lwWLNB+z0nex3ny/AsWLGDDhg1kZGQwePBgFAoF9+7dw93dnYcPH7J48WJmzZpFQkIC06dPZ/HixSiVSvr06cOoUaNo27Yt27dvJykpicGDBzNt2jQCAgK4fv06V65cYfz48RgZGRX7d0j/ruwvdWwoJr1enlwAfm30eXmHUGpqmpf/YX+l6bC64vMb/UNUzSm3sxIMYrVJ+RzqYwhvql+uQ69TX6IvhbF7cS9E9pcUcZbPP5bqZUoGMHq5dtEMfLy+vEMokYzDK8vsuU6qauhPAQLtkWn+/v4AnD59mosXL/Lpp58CcOzYMUJDQxk8+OnXfJw5c4YbN24wYMAAoqKimDFjBvPnz8fS0vJvxWWQc7zMzc2ZNGkSM2fOpGXLlqxfv56srCwcHR0ZOnRoiR7D39+fWbNmYWtry5dffvmnY+jVqxdr1qxh3LhxADg4ODBp0iRcXV1p06YNY8eORalU8uGHH+pnsGZmZjx8+JCJEydiaWnJmDFj8j1m9+7dSU9P5z//+Q8jR44slZmvEEIIIYQQL70yvLhG3olWQQWPfouLi8PWNv8VKw8fPkzPnj1RKBQ4OTnh6OhIeHg4tWvX/ltxleqKl9CSFa8Xl6x4vbhkxevFJSteLy5Z8XpxvUyLRLLi9WL7x6x4HTTsIft5WXQaUux9KpWKUaNG8cUXX2BnZ8fkyZMZOXIkrq6u+jYrVqygYsWK9OnTh8TERCZOnMj8+fOLvOjfn2Gwy8kLIYQQQgghBPDci16UFSMjIwYPHkxQUBBqtRpfX19cXV3Zt28fAF26dOGdd95h2bJlBAYGAvD+++//7UkXvAATr/nz5xMdHZ3vd++//z6enp7lFJEQQgghhBDiZeXl5YWXl1e+33Xp0kX/s52dHZ9/XvpHSZX7xGv8+PHlHYIQQgghhBDCkF6gL1AuLy/R0eFCCCGEEEII8WIq9xUvIYQQQgghxEvuBTnHqzzJipcQQgghhBBCGJiseAkhhBBCCCEMS87xkhUvIYQQQgghhDA0WfESQgghhBBCGJaseMmKlxBCCCGEEEIYmqx4CSGEEEIIIQxLrmooK15CCCGEEEIIYWiy4iWEEEIIIYQwLDnHS1a8hBBCCCGEEMLQZMXLAPZPiynvEEqNZtrn5R1CqXr9yszyDqHU7Gj8r/IOoVRVfIk+Buo+v0Z5h1CqEicpyjuEUmOsKe8ISpedqrwjKD1G5R1AKYt5iRKqmvtyDZx05cuzTxP/LDLxEkIIIYQQQhiWXFxDDjUUQgghhBBCCEOTFS8hhBBCCCGEYcnFNWTFSwghhBBCCCEMTVa8hBBCCCGEEIYl53jJipcQQgghhBBCGJqseAkhhBBCCCEMS87xkhUvIYQQQgghhDA0WfESQgghhBBCGJaseMmKlxBCCCGEEEIYmqx4CSGEEEIIIQxLoynvCMqdrHgJIYQQQgghhIHJipcQQgghhBDCsOQcL1nxEkIIIYQQQghDkxUvIYQQQgghhGHJipeseAkhhBBCCCGEocmK1wvC0bcJjWf0ByMlD346zO0lvxRq03hmfxw7eaLKyObCqO9IuhwGgLGNJU0XfkyFV1xBo+HCmOUknL+NTQM3POZ9iLGVGekPYzk/dCm5qRllnNnT3BRGSu4/I7cqutyC8+RmYmOJ58KPsdHlFqzL7UX1+ayFHDv5B3a2ldi5/rvyDqdITr5NaDo9AIWRkrsbjnCjiHo0ndEf504eqDKy+WP09yRcDkNpZoLff/+FkakxCmMjHv7vD67+ezsAlRpWp/ncwSjNTNCoVJyftJr4kLulFnM1nya0nB6AQqnk9sYjXF5aOOaW0wNw8fMkNyOLE2OWE38l7Jnbmlaywufb4Vi7OpD6MIYjn/6H7KR0zGyt8Vk+ksoeNQndcozfP1+nfw6liRGtZg7Aybs+qDUEz93K/d1nSy3PvE6GRjJvbwhqjYa3mrozuG29Qm3OhkUzf99FclUabC1NWTXAB4Cffr/Njgv30GjgbS93PmhVxyAxPk+b6QG46mpydMxy4nQ1yauCqwN+y4ZhVsma2MthHBn1LeocFRVrOdNx4RAqN6rB2Xlbufz9bv02DT/sSr13fVAoFNzYcJgrq/YaLIdWBfpVUTlYuzrgs2wYZrbWxF0O49hIbQ7P216hVNDjtxmkRyZwYMAC/e/rD+pM/UFdUOeqeHQwhHNBm17oXExtLGn774+o9IoLaDScCFxBzPlQanRviefYt6lUpyq/vP4lcZfulUoeBVX1aUIL3RgP3XiEK0XsH1pMD6CanyeqjCxO5tk/eC/4mGr+nmTGJvNLp8n69s0+fxeXzk1RZ+eScj+ak2OXk5OcbpD4C/L9KgB3X+3feU/gcqKLqJONqwPdlwzDvJI10VfC2D36aZ1cWtfH98sPUJoYkRGfwpY+QQCY2VjSZd5HVK7rgkajYe/4FUQEhxo0F2efJnjN0NbmzsYjXC/i9cZrRn+q+mlfb86M0b7eWFa1o/WizzB3rAhqDaHrD3ErzzivM7gLdQd1RpOrJvxgCCEzNxosh9Z59mPHnjFufHX7sbjLYRwd9bQexW3f8KNuvPKuD2g0xN94xPHA5aiycvSP2eiT12j1r/dY3/hTshJSDZZfmdLIipeseL0IlAqazB7E6ffmcajDeKq95U2FutXyNXHs5IlVTScOthnLxXEr8Zg7WH9f45n9iTp0kUPtx3G40yRSbj8GwHPhx1wL2shh30lE/HaW2kO7l2laACgVeOhyO9hhPC5F5FalkyfWNZ040GYsIUXkFn3oIgfbj+NQp0mk6nJ7UfV8rTPfLZxZ3mEUS6FU0GzWQI69P489HSdQvWcbbArUw9nPgwo1ndjtHci58atoNmcQAOqsHI70CmKv/xT2+k/B2bcJ9l61AfD417tcWbiDfZ2ncGXeNjz+9W6pxtwqaAD7P5jHTt8JuPdsTcU6VfO1qebngY27EzvaBXJ64irazB743G0bD+tBxIlr7Gg3jogT12g8rAcAqswcLszbxrkZGwrF0mTkm2TGJfPf9uP5r89EIk9fL7U881KpNczec4Gl77Vjx2dd2XPlIXdikvO1Sc7MZvZvF1jUty07PuvC/F6tAQiNTmLHhXus/9CPLZ/4c/x2BPfjUgwS57O4+nlQ0d2JLe0COTFxFe10NSmo5ZR+XF6xhy3tvVU/dgAAIABJREFUx5GdlMYr/XwAyEpM49QXP3Ipz4QLwPYVF+q968PO7l+yvcsU3PybYuNexSA5uOj61fZ2gZzK068Kaj61H1dX7GF7u3FkJaVR512fEm3f4KNuJN4Oz/c7J+/6uHVtxk7/yez0m8SV7/Ln/yLm0mp6AI8OX+K/HSewq/MUknQ5Jdx4xKGPFxF55map5FCUJ2P84Afz+Nl3AjWesX/Yqds/tMoTe+iWYxx8f36hxw0/dpmf/SbxS+cpJN+NoPHwHgbLIS93Xw9sazjxQ4dA9k9ahX/QwCLbdZjcj/Mr9/BDx3FkJqXRuK8PoJ1c+QcNZOeHC1nrP4lfPvuPfhvfaQGEHbnEar8JrOs2hfjQ8CIfu7Q8eb058v48dvtMoPqbbbCpU8TrjbsT/2sbyB8TVtF8tu71JlfNhek/sbvjBPZ1/5I6Azvrt3X0boBL12b81mkyu30ncv3bXw2Ww5N+v1W3H/MuZty0mKIdN9vaa8dNXd1+rLjtLZ1saTi4C7te/xc7/CejMFJS843W+sezcrajWvtGpD6KNVhuonyU2cQrOjqawMDAErXdvHkzly5dKvb+7777jkePHpVWaCV29epVbt4s/RcQ26a1SbsXxf+xd9/hTZXtA8e/SboXtKWlLVAKZc/SIks2ZbpwgDhAxI2iIpSNolJBEHwdqKC+IihbXwXZBYsge5QNpYUyOqGhM+lK8vsjobRpWhCStj+8P9flJU2eJ7nvPOPknOecE82lNAyFOhJ/24Nf/7BSZfz7h3F51U4Arh+Ow97DBUffmti5OePdqRmXlkUDYCjUUWQ6KucW7E/6njMApO04TsCD91k99lvxbNeInBK5XbGQm1//MC5VkNvFZdGAMbfKOuJ4p9qHtKaGh3tVh1Eur3bBZCekknvpKvpCHZd+30sds/aoMyCMhNXG9kg3tYeTb00AijT5gHHlR2mvwmD6TQ6DwYC9mzNgXKXUpmRYLeZapphzTDFf+H0vgWYxB/YPI37NLgCuHo7HoYYrzr41K6wb2D+MOFOecat3EjigvTFHbT5pB2JLHXm8ofGwHhz/wnTE1mCw2VHIE0lq6nm6UdfTDXuVkv4t6xF9tvSXpI0nLtO7WR38a7gA4OXqBMD5a9m0qeOFs70ddkolYYG12H7Wtl+wLKnfL4xzpjZJOxyPg4exTcwF3N+CC+v3AxC7eidBpvbJS8/i2tHz6It0pcrXbBRA2pF4dHkFGHR6kveeIcjUdtYW2D+MOAv9ypz//S1IMOUQt3on9Uv2sXLqu/h7UbdPCOeWR5d6rWYjwjm2YB36giLA+DlU51zs3Zyp3bFpcR76Qh0Fpnk6My6JrPhkq8RfHm+zMZ7w+17qmc0P9UrMD9fMck/bd5b8jLLjOPmvExh0xqPzVw/H4+LvZdM8bgjuF8apX4yxJh+Jx9HDFVcL7RTYpQWxG4ztdHLNThqZcm72SBfObTxAdlI6AFpT/3Fwc6Zuh6YcXxENGNsp38bbU692weSYbW/qmrVN3f5hJKy5ub1xqGHc3uSlZXDddOZLUW4eWXFJuPh7AtB4RB9Ofbm2eIzkW2mMWFK/n1m/v415rOS4qai+wk6FyskBhUqJnbMDmtTrxa/XccazHIhcUbyNFfeOanmq4ZNPPlnh86+++molRVLayZMncXJyomnTplZ9XSd/T7SmSRJAm6zG07SSULqMulQZZ39P9EV6CtKzaffZK3i0qE/msQscn74EnSaf7DNX8OsfRsrmQ9R5qBPOAd5Wjft2OJvllmchN2ez3PJMuRlMuYWacssokZu4M85+XmgTb7aHJlmNd7vgMmU0Zv3R2d+TvLQMFEoFfTdH4tagNnE/bEV9JB6AI+8upcfyiYS8+zQoFWx7+H2rxezi50luif6Rm6zGxyxmY5n0UmVc/DwrrOtcywNtmnEHUZuWgZO3R4VxOHgYd3DaTXgCv87Nyb6Yyt6pP5J3zfob/bQsLX4ezsV/1/Zw5niiulSZi+nZFOkNvLAkGk1+EU93aMxDbevTyMeDL/88QYYmH0d7FbviUmgR4Gn1GG/F1c+THLM2cfXzLP7MARw93cjP0hR/wb3RbhW5fvYK900cgmNNN4ryCqjXuy3XbHQKW3n9yjyHgsybOWhK5FBR/Y7vP8vBmcuLD1jc4NHQj9odmhI2YQi6/EIOfLica0fv/rRdW+Vi0OnIS8+m66cv49UikPRjCex7dylF2sqZp83HuCZZTS0L80PJOU1jIfeKNBrWnYS1+6wT8C24+XmSnXwz1uwUNW5+nuSWiNXZ0428EuMmJ9lYBsCzoR8qOxVDV07Fwc2Jw//dzKlfdlEj0AeNOpv+817Gt3kgqccT2D7Dtu3kYrYt0SSr8Q4tu70p2a80Sca2ySuRr2vdWni2qs+1w8btjXuwPz4dm9Fm4lD0+YUc+WAZaiuMEcs5lO73mnLmsQKzecy1nHFzo/61Yxc4sXADw/Z9RlFeAYl/HSfxrxMABPYNRZNyHfXpSzbJqUrJzTWq5lTD1NRUJkyYQFxcHFOnTmX8+PHMnTuXnBzjUacFCxawd+/ecuvPmDGD+HjjABw+fDg//fQTEydO5MMPPyQuLo4ZM2bwxhtvcPDgQQCio6OZM2cOkZGRvPXWW6xevbr4tebMmcPEiRN55513iIqKKn48JiaGiRMnEhERwQcffEBaWhpbt25l/fr1REREcPq09U4xUigUZR80P8phoYzBAEo7JTVaB5GwOIodfadQpMmn8RsPA3Bk7CIaPN+XHpsjsXNzKj46VKnuIjeFKbcLi6OI7jsFnSafJqbcxB2y0Bxl26P8Mga9gS19p7AudAxe7YKp0bQuAI1GhBPz3k+sa/8mMe/9xH3zXrJizJb60O2UMdxe3dsNQ6XENcCbtAOxrBswjbRDcdz37tN39mK3YClE81R0egOnk6/z5bCufPVMNxbtOs3F9Gwa+njwfJemvPrzTl5ftosmtWuiUlpqVBu7jbFvce67hYy4JI5+9QeDlk9i4E8TUJ+6VGZVzGrucP4qbsBy6tcND0F7LYt00xH9kpQqJY41XPnjoRkcmLmcnt+88Y/DtshGuShUKrxbB3FmyTbW9p9GkSa/0k7LM4Z1Z/PD7a4ktH7zYQxFei78+vcdRPfPKSxMwGViLWebCcb+49u6Ab+O/IRfnv2YTm8OxrOBH0o7FbVbBXF06TaWDppGoTafDqNt3E53uL0pma+diyNdv3ubw+8uLb5GXaFS4lDDla0PvseRD5dx/8IxVgzaLLzb6DsWy9yivkMNFwL7hbKq81iWh43B3tmR4MfuR+XkQNs3H+bQJ2usEr+ofip9xSspKYn//Oc/jB49mi+//JJRo0bRokULVq5cyZo1axg5cuQ/er38/HxatmzJs88+y9y5c1mxYgXTpk3jypUrLFiwgPbtjaegxMXFMW/ePBwdHZk8eTKhoaEEBwczevRo3NzcKCgoYPLkyXTs2BGDwcDChQt5//338fX1JScnBzc3N/r27YuTkxMPP1z2y39UVFTxjlvnf/iZaJPUpVajnP29yEu5XqpMXpIa5wCvsmUMBvKS1Vw3rTwk/bGPxmOM8eXEJbFn2GwAXBv6UTu83T+M7O6Z5+bk74XWLDetWW5Ot5GbuDPaZDXOdW62h4u/F9rUjDJlXMz6o/mpg4VZGq7uPo1frzZknr1C0NBuHJluvAnF5XX7rLrjpUlW41qif7j6e5U6JeNmGW+zMhkoHezKrau9loWzb020aRk4+9a85Sld+ddzKNTkcXGj8YBOwh/7aDysx13nZ0ltD2dSsm7eCCc1S4uP2cpIbQ9naro44Oxgh7ODHWGBtTibmkl9b3cebdeAR9s1AODz7cepbVqts7UWz4XT7OleAFw9eh63AG9STc+5+nuRa9bX8tTZOHq4oFApMej0xvYxmx8sObtiB2dX7ACg/cSh5Carb1Hj9jV7LpwmzxhzuBZz3mK/KilfnY1DjZs5uJToY+X1y6AHOhDYL5S6vduicrTHwd2Z7p+/xl9vfk1u8vXiPnYt5jwGvQFHL3fy1f/8Or3KyAWDgdxkNddM83TC+v2VuuOVazY/uJQzP5Sc0yzNe5Y0HNKNuuHt2DJ0lvUCtiBkRDitnzK2U8qx87j734zV3a/suNGqs3EqMW7c/L3INeWck3Id7fVjFGnzKdLmc2XfGXxaBHJl/1myk9WkxBjbKXbDfjq8Ztt2svi5p5Td3rgGeHPjSiaXgJtto7BT0fW7t0n49W+umMbEjTpXTDc1Ut/lGLGk+XPhNDXNY9eOlh43LhbGTZ46G4dy5rFcs3Fzo35A11ZkX75KninmhI0HqR3WGPWpi7jX8+HRLR8BxnE2eNNM1j74HtqrmVbJr0rJqZOVu+KVlZXFnDlzGDNmDL6+vuTm5tKiRQsAevTocUerSHZ2doSEhAAQGBhIixYtsLOzIzAwkKtXrxaXa9OmDe7u7jg4ONChQwfOnDFe+7RhwwYiIiKYOnUq165dIzk5mdjYWJo3b46vry8Abm5ut4wjPDyc2bNnM3v27H+cQ0ZMPK4N/XAJ9EFhr6LO4M6kbDlUqkzKlkPUG9oNAM/QRhRma8lPyyD/aibaxHTcgv0B8OnWiuxY4w0oHGqZTp1SKGg69lESlkRR2TJi4nErkVvdcnILLJFbUYncNOXkJu6MOuY87g38cK3ng9JeReAjnUjcXLo9EjcfJmiIsT28TX0tLy0DR2937E1f4FVO9tTu3pKsOOP1G3mp1/Hp3BwA364tyb6QYrWYr8Wcx6OBH26mmBs80onLWw6XKnN5y2GCn+gKgE9oMAVZGrRpGRXWvbzlMI1MeTYa0o1LZp+DJVe2HjHe0RAI6NqSTBvd7KVlgCeX1DkkXs+lUKdn88nL9GjiX6pMzyYBHLl0jSK9Hm1hEccT1TSsZby+UJ2bB0BypobtZ5IY2LKeTeI0d+rHKH7tP5Vf+08lYdMhGpvaxDc0mIJsjcVTu5J2n6LBAx0AaDKkGwlmbWvJjdNCXQO8aTCwPfG/77ZaDmd+jGJtv6ms7TeVS5sP0chCvzKXvPsUQaYcGg3pxiVTDpe2HLZY/9DsVaxq/yZrOo1lx+gFJP99ir/e/NpYZ/NB/O83bhc9GvqhcrC74y+UlZGL9momuUlqPEzztH/XlmRU4jydbprTbozxoFvMD7VCgyksJ/eSAnq2odXoB9k+cj66vAKbxQ8QsySKpQOnsnTgVOI2H6LF48ZY/dsFk5+tKXWa4Q2X9pyiySBjO7V8ohtxppzjthyiToemxuuGnBzwbxdM+rkkNFczyU5W49nQ2E6B97ck3cY3q7K0vblitv1P3HKYoCdKbG+ytMWnGXac9xJZ5xI5u2hjqTpXNh2idlfjGHFv6IfyLsaIJad/jOK3/lP5rf9ULm4qPW4Ky5nHkkvMYxWNmxv1c5PS8W3XCJWTA2DcnmTEJXL9zBWWhbzOqs5jWdV5LLnJan4bMO3e2OkSQCWveLm4uODt7c3Zs2fp0qWLVV5TpVIVL+UqFArs7IwpKZVKdLryTz9RKBScPHmS48ePM3PmTBwdHZkxYwaFhWUvqLc1g07PsSmL6bx8EgqVkkvLo8k+m0jQiD4AJCzZRmpUDLX7hBC+91N02nyOvL2wuP6xqT8S9tXrKOzt0FxMK36u7uAuNHi+LwDJGw5wafmOKsutiym3ixXk1nfvpxSZ5XbclJvSlNvhEs9VRxHvzebAkWNkZGTRZ/CzjH5hOI8/1L+qwypm0Ok5PGUxPZZPNN5OfsUOsmITCTa1R/ySbSRvi8G/TwgP7JlPkbaA/WONn7mTb006fvYqCpUShVLBpbX7SI46AsCB8d/R7sMRKFVKdPmFHIz4zqox7532I32XTTDeLnrlDjJiE2k6vDcAZ5du58q2GOr0bstjf89Dpy1g1zuLKqwLcHzBOnp8M4bGT/UgJzGd6Fc+L37PJ/Z+ir2bM0oHOwIHtGfLU7PJPJfEwcgVdPv8NRxmPEueOpu/xy6yWp4l2SmVTBoQwmvLdqI3GHikbRCNfGuw+pDxaPWQsGAa+njQJdiPoQu3olAoeLRdAxr51gBg3Oo9ZGoLsFMqmTwwBA9nB5vEWZHL22Oo17stT+6aR1FeATveuflZ9V8ynp0R36FJzWD/Ryvo/dUbtJ8whPQTCZw1Xfzv7FODwRs+xMHNGYNeT6sXB7Cm10QKc7T0XfQWjp5u6IuK+HvqjxRk2uYmAVe2xVC3d1seN/WrnSVy6LtkPLsivkObmsHByBX0/OoNQicMIf1kArGmG01UVL8851bsoOu8lxm8bRb6Qh07rTTn2TKXfdN/pMcXr6G0tyP7Ulrx+Asc0J5OM0fg5OVO3yXjUZ+8yJZn5lglnxsMOj37p/1IeIkxnhmbSBPT/BC7dDuJpvnh0b/nUaQtYHeJ2LsteJ3anZvj5OXG4wc/5+gnvxC3YgcdZj6HytGOvismAXD1cBz7Jv1g1dgtubA9hoa92vLCznkUagvYPP5mrI8uHs+Wid+Rm5rBzlkreODLN7g/YghpJxM4sTIaAHVcEgnRx3huyywMej3HV0STHmu8Gdn2d39k0OevobK3I/NSGpvG22b+usGg03Nw6mJ6Liu9vWk03Li9iVu6jSTT9ubB3fPRaQvYZ9re1OrQhAZDupFx6hIDthpXf47OWkny9qOcXxFNx/kvM3D7bPSFRex7y3Y/3XJ5u7HfDzHNYyX7fT/TuNGkZnDgoxX0+uoNwszmsfLqXz0Sz4UN+xm8aSaGIh3pJy9y5uc/bZZHtSHXeKEwVNItU9LS0vj444+JjIwkMjKS/v378/vvvzNq1CiaN2/OqlWr0Gg0jBw5kgULFhAWFkanTp0svtaMGTMYPnw4wcHBDB8+nKVLlwKwatWqUqcC3nguOjqa5cuXM2/ePBwcHJgyZQqvvfYaarWabdu2MWnSJBITE5kwYQJTpkyhXr16TJw4scyphuvWrUOr1TJ06NAKc/3dzzbXfVSFe21R+IET1fdW7//Ur62nV3UIVqWtiuuQbOTJj+tXdQhW9dOki1UdgtXY3WuT2j1EVdUBWNnVeyihgKJ7a+Bo7qHtDcALV36q6hBui/aHCZX2Xs7PW/cgj7VU+jVeTk5OTJo0iZkzZ9KhQwd++ukn8vPz8fX1ZfTo0TZ736ZNm/LFF1+QkpJC165dCQ4OJjAwkK1btzJ+/HgCAgJo3Nj4I6MeHh68/PLLfPLJJxgMBjw8PJg+fTphYWHMnz+fAwcOFO8wCiGEEEIIIW5BVrwqb8WrKkVHRxMfH88LL7xQKe8nK17Vl6x4VV+y4lV9yYqXqAz30AIRICte1ZmseFUN7ffjK+29nF/4pNLe65+olr/jJYQQQgghhLiHGGTFq1rveM2dO5e0tLRSjz3zzDPFdzG8XT179qRnz55WjEwIIYQQQgghbl+13vGKiIio6hCEEEIIIYQQd8mgv7dOWb0Tlfo7XkIIIYQQQgjxb1StV7yEEEIIIYQQ9wC5q6GseAkhhBBCCCGErcmKlxBCCCGEEMK25K6GsuIlhBBCCCGEELYmO15CCCGEEEIIYWNyqqEQQgghhBDCtuR28rLiJYQQQgghhBC2JiteQgghhBBCCNuS28nLipcQQgghhBBC2JqseAkhhBBCCCFsS1a8ZMfLFto3SKnqEKwmPdm1qkOwql9bT6/qEKzmseMfVnUIVrW+1bSqDsFq1kYkVHUIVnXUsaiqQ7CatkX2VR2CVQUU6qo6BKtRcW9deJ9gp6rqEKymUKGo6hCsKu3eaRrx/4zseAkhhBBCCCFsy3BvHVy5E3KNlxBCCCGEEELYmKx4CSGEEEIIIWyrGl3jFRMTww8//IBer6dPnz4MHjy4TJmTJ0+yePFidDod7u7uvP/++3f9vrLjJYQQQgghhPhX0Ov1fP/990ybNg1vb28mT55M+/btqVu3bnGZ3NxcvvvuO6ZOnUqtWrXIzMy0ynvLjpcQQgghhBDCtvTV4xqvuLg4/Pz8qF27NgBdunThwIEDpXa8du3aRceOHalVqxYANWrUsMp7y46XEEIIIYQQ4l9BrVbj7e1d/Le3tzfnzp0rVSY5OZmioiJmzJiBVqtl0KBB9OjR467fW3a8hBBCCCGEELZlqLxrvKKiooiKiir+Ozw8nPDwcGMYFu6uqDD7yQSdTseFCxeYPn06BQUFTJs2jcaNGxMQEHBXccmOlxBCCCGEEOKeUXJHy5y3tzfp6enFf6enp+Pp6VmmjLu7O05OTjg5OdG8eXMuXrx41ztecjt5IYQQQgghhG3pDZX3XwWCg4NJTk4mLS2NoqIidu/eTfv27UuVad++PWfOnEGn05Gfn09cXBx16tS5649AVryEEEIIIYQQ/woqlYpRo0YRGRmJXq+nV69e1KtXjy1btgDQr18/6tatS0hICOPHj0epVNK7d28CAwPv+r1lx0sIIYQQQgjxrxEaGkpoaGipx/r161fq74cffpiHH37Yqu8rO15CCCGEEEIImzJUox9QripyjZcQQgghhBBC2JiseAkhhBBCCCFsq5r8gHJVkhUvIYQQQgghhLAxWfESQgghhBBC2FYl/oBydSU7XtWQY6f7qPH2GyhUSnLXbiBn6fJSzzt164LHy89j0BtApyPzPwsoOHYCgJpTI3Dq0gn99QzSnn2hKsIvw61HKHXefQlUStQrt3L16zWlnq/5SA98Xn0cAL0mj8RpX5F3OgGAunPexKP3fRSlZxLb/43KDh0Av15taPfBcBQqJeeXRXPmy3VlyrT7cAT+fdqi0xaw/+2FXD+egNLRnt7/m47KwQ6FnYrLf+zn5Ce/AFCzZX3afzwKpaM9Bp2OQ5N+QB1zvpIzq9i0j+bz19/78fKsyW8/fVPV4Vjk26sNrT8cgUKl5OLPf3LOQtu0njmC2n1C0GkLOPzWN2QeTwDA3sOFkPkv4dG0HhgMHB67iOuHzhHwUEeajX8c98YB7Bg4nYyjFyotn39TX3vivZG07NWOAm0+S8d/zZWTZT/n5/4zhsDWDdEV6bh4NI7lU75FX6SrgmiNOn8wnHq9QyjS5rNj7CLSTySUKeNez4feX72OY003rh1PIPqtr9EX6qgR7E+P+S9Tq1UQB+as5vjCDcV1Wr7Qn2ZP9UShUHBm2Z+c+H6zTfPw6dWWFjON4+byz38S/8XaMmVaRD6Hr2ncHH3za7KOJ+Aa7E+7RW8Wl3Gp70vsnDUkLNqIR8v6tJr7grGfFek5Mem/ZB6Jt2keN9Tq1ZbmM58DlZIrP2/ngoV8mkc+R60+7dBr8zluygeg/ksDqftsbwCu/Lydi4s2AtBo4lBqDwjDoDdQcC2L429+TX7q9UrJx1z/GSNo3KsthdoCfh+/kBQL/e6+5/rScdQAvIL8mBvyCtrrOQB4B/vzyCev4NcyiD8/WcWeRRvK1LW2gJ5tuO+D4SiUSuKWR3NiQdl57L4PhlOndwg6bT5/j12E2pRTeXU9WwTSafbz2Lk4kXPlKrve+JrCHC0KOxVdPnkRr1ZBKOyUnF+zixMW5k1r6TtjOMG9QijU5vPH+EWkWmiLGvV8GPzF6zjVdCPlRALrxhrnACcPFwbNfRnP+r4U5ReyPuJbrsVeAeC1XZ9SkJuHQadHr9Ox+KF3bZaDqFpWO9UwLS2NcePG3VbZlStXcuzYsdsqe/LkSWbPnn03oZWyfv168vPzi/+eNWsWubm5Vnv9u6ZUUnPcW6S/M4nUp57HpW9v7ILqlyqSf/AwacNf4upzL3M9ci41p4wvfk6zfjPpYydVdtTlUyqp88GrXBg5g9i+r1Pz4e44NqpXqkjB5VTOPzmZcwPfJO2LldSddXMH6/qabVx4bkYlB32TQqkg7KOR/PXMHDb1mED9wZ3xaFL6B/T8e7fFvaEfG7qM42DE94TNfh4AfX4h0U9Esjl8CpvDp+Dfqw3eoY0AaDv9KU7M/5UtfadwYs4a2k5/qtJzu5XBg/ryzfyZVR1G+ZQK2s56nj1Pz2Fb9wjqPtoFd7O2qd0nBLeGfkR1foeY8d/R9uNRxc+1njmCtO1H2dZtPNv7TCLnXCIAWWcus3/Up6TvPVOp6fyb+lqLniH4NPDj/Z5vsXzKtwyLtHyQ6MBvO/mwz1g+6j8eeycHugzrXcmR3lSvd1tqNPBjVddx7Jr4PV1njbRYrsOUYRz/dhOruo2nIDOXpsN6ApCfkcvud5dybGHpL76eTevS7Kme/Pbge/zSbwqB4e3waFDbdokoFbSc/Tz7n/6YHd3GE/BoF9zM+plPnxBcG/gR3Wksx8d/S6s5xvbJjU9mV5/Jxv/6TkGnLSB1wwEAmr37NOc++YVdfSYTO2c1zac/bbsczPJpMXsUB5+eza5u4/B/9H5czfKp1ScElwb+7Oz0NifGf0uLOS8C4NasLnWf7c2eAVPZ3XsiPn1DcWngB8CFBev4u9dEdveZxNWthwke91jl5GOmUa+2eDfw48se4/hj8vc8MPN5i+UuH4xl6TOzyLh8tdTj2oxcNr23hD3frq+McFEoFXSMfI5tz85hba8JBA3uRI3GAaXK1OndFo8GfvzWdRx7Jn5PR9NYqqhu57kvcvijlawLn8zljQdp+doDAAQ92AGlgx3rwiezfsB0mjzbG9e6tWySW3Cvtng28OObHuPYOPl7BswcabFcr0nD2P/9Jhb2HE9eZi5tn+xpzOGNR0g7dZHvB0xh3Tvf0HfG8FL1lg2L5L+Dpt7bO13V5AeUq1KVXOP15JNP0qZNG5u8tsFgQF/B7So3bNhQasdr8uTJuLq62iSWO+HQohlFVxLRJSVDURGaqO04de9SqoxBm1f8b6WzExhudrCCmGPos7IqLd5bcQlpTMHFZAoup2IoLCJj3V949OtYqozm8Bl0WbnF/7b3uzlp5u4/SVFmdqXGXJJXu2CyE1LJvXQVfaGOS7/vpU7/sFJl6gyXLZ2YAAAgAElEQVQII2H1TgDSD8dh7+GCk29NAIo0xr6mtFehtFdhMLWVwWDA3s0ZMK68aFMyKiul29Y+pDU1PNyrOoxyebZrRM6FVDSX0jAU6rjy2x78zNrGr38Yl1YZ2+a6qW0cfWti5+aMd6dmXFwWDYChUEdhlgaAnHNJ5MQnV2YqwL+rr7Xpdx/7f/0LgIQj53B2d8XDp2aZcqeiY4r/ffFoHJ5+XpUWo7n6/cI4t2YXAGmH43HwcMXZt2zMAfe34ML6/QDErt5JkKkN89KzuHb0fJkVu5qNAkg7Eo8urwCDTk/y3jMEDWhvszxqhjZCcyEF7UXjuEn6bQ+1zd6v9oAwEk39LOPQzXFTUq1urdAkpKK9cs34gMGAnfvNfpZXSatD5vmk/LbbQj7tSVpt7G+ZJfJxbVyHjEPn0GuNn/313aepPeg+AHQ52uL6KhdHqKLvcU37hnH0F2NbJB6Jw9HDBTcL/S7l5EUyb7RFCZr0LJKOnUdfWDkrxd6meSzHNI8l/L6XembzWL3+YcSbxtK1w/E41DCOpYrqegT7k2o6GJa08wSBpnYyGMDOxRGFSomdswP6wiIKS7SdNTXuG8aJX4xxJx2Jx9HDFVcLbVG/SwvObDDOASd+2UmTfsYcajWuQ8LfJwFQxydTo24tXGp52CRWUX3Z5FTD1NRU5s2bx8svv8wPP/xAfn4+tWvX5rXXXsPNzY0FCxYQFhZGp06dLNaPiYlh8eLFuLu706BBg+LHV61ahZOTU/GPmY0bN46JEycCxpWrli1bEhsbS0REBL/99hvx8fEUFBTQqVMnhg4dyoYNG1Cr1bz//vt4eHjw3nvv8frrrzNr1iw8PDz4448/+PPPPwHo3bs3DzzwAGlpacyaNYumTZsSGxuLl5cXEyZMwMHBwRYfHUqfWujS0or/1qVdw6Fl8zLlnHp0xeO1F1F51iR93BSbxGIN9rW9KUy6uTEoTE7HJaRJueU9n+xHdvShygjttjj7eaFNTC/+W5OsxrtdcJkymqSbZbTJapz9PclLy0ChVNB3cyRuDWoT98NW1KZTb468u5QeyycS8u7ToFSw7eH3Kyehe4izvyfaEp97XrIaT9MqT+ky6lJlnP09MRTpKUjPJvSzV/BoUZ+MYxc4Pn0JOk0+VeXf1Ndq1vbkeok8MlLSqennRdZVyzuFSjsVHR7tzpr3F1dShGW5+nmSUyLm3GQ1rn6eaNNuxuzo6UZ+lgaDTl9cxsXPs8LXvX72CvdNHIJjTTeK8gqo17st147Z7vRWJz+zcZOUTk2zcePkX7ov5iWrcfL3Ir9ErgGPdiHpf7uL/z41fQkdVkym+XvPolAq2P3gezbLoSRHPy+zfNTUMMvH0UI+jv5e5Jy5TJPJw7D3dEOXV4BPeAiZR2+ehtt48pMEDOlOUbaG/Y99YPtkLHD38yKrRH7ZKWrca3uSk1b1B1AscfHzJLfEnKtJVlPLbB5z8fMsNY9pTOOkoroZZy9Tr18ol7ccpv6DHXENMB6Eubh+P/X6hzLkyJeonB04OONnCjJscxaTu5+nxbbILdEWzmZzQFayGnfTHJB26hJNB97HlYOx+LdtSI06tfDw80JzLQswMOynSRgMBmJ+3k7M8j9tkkOVk9/xsv6KV1JSEvPmzWP06NF88803PPPMM3zyyScEBgayZs2aW9YvKChg4cKFTJw4kQ8++ICMjNubXJKSkujevTtz5szBx8eHp556itmzZ/PJJ59w6tQpLl68yKBBg/Dy8uK9997jvfdKbxTOnz/Pn3/+SWRkJJGRkWzbto0LF4wbv+TkZAYMGMD8+fNxcXFh7969//yDuV0KRdnHDGUPteXt2EXasJGkT3wX95ctn3pQLdxmPgCunVvj9WRfkmcvtm1M/4SF8MvEX0EZg97Alr5TWBc6Bq92wdRoWheARiPCiXnvJ9a1f5OY937ivnkvWTfuf4Pb6VsWyhgMoLBTUqN1EBcWRxHddwo6TT5N3rDur9P/Y/+mvmaxXcpfUnjywxeI23+a+AOVe/pnKbfR3xSWytxCRlwSR7/6g0HLJzHwpwmoT12y7XVstxGjwkJHK9k+CnsVtfuFkbxuX/FjgSP7curdpWwPfYNT7y6lzacvWyfeW7GYzm0sTxkM5J5L4vyXa2m/airtl08m6+RFDEU3vxiem7WSHaGvk/zLLuqP6m+1kP8Ji81VwVipahbHgHm45Yz/iurufudbmo7sywMbP8Te1Ql9YREAtUIaYtDpWR06hv91eocWrwzCLdDnLrMox11scwD2fL0OJw9XRm2IpP3IfqSevIjetIO29LEP+OGBaax6bi6hI8Kp16GptaMX1YRVV7yysrKYM2cO48aNw9vbm9zcXFq0aAFAjx49+PTTT2/5GklJSfj6+uLv7w9A9+7diYqKumW9WrVq0aTJzZWU3bt3s23bNnQ6HdevX+fKlSvUr1+/3PpnzpyhQ4cOODk5AdChQwdOnz5N+/bt8fX1JSgoCICGDRty9erVMvWjoqKK4xxzy2jLp0+7isrXt/hvlW8tdNfKnj5wQ0HMMezqBKCs4YE+s/qcYnhDYco17ANunjpo7+9NYZq6TDmnZkHUnT2GCyNnoMuoulMLzWmT1TjX8S7+28XfC21qRpkyLgE3yzj7e5U5naswS8PV3afx69WGzLNXCBrajSPTlwBwed2+6vFl+P8ZbZIa5xKfu5O/F9qU6xbKeJUqk5dyHQwG8pLVXDetCiX9sY/GY6p2x+te72vdh/ejy1N9ALh4NB7PEnnU9PMms5xT0wa+9QRu3h5898q8SomzpBbPhdPs6V4AXD16HrcAb1JNz7n6e5Fr1j556mwcPVxQqJQYdHpc/b3QpNz6lLuzK3ZwdsUOANpPHEpuctk50lryks3GTYC3cUyUoE1Ox7mONzcedfL3Ir9EGd8+IWQev0DB1czix+oO7c6pqT8CkLx2L63nV04/yy+TT+lYi8vU8eZGa5XMJ3HZnyQuM64uNJ4yjLwSKxo3JP36N2E/TyRu7q0PHltD+xF9CR1m7HdJx87jUSI/dz8vsqvpaheYVoJLzLku/l5ozMa2xmweuzHXKR3syq2bFZ9M1NMfA+De0I+6fUIAaPBoF5Kij2Eo0pGXnsXVA7F4t21IzqWy39PuROiIcEJMbZF8G22hNZsDPPy9yDHlUJCjZX3EouKyr+36tPiavBsrmJr0LGI3H8I/JJjL+89aJYdqpRpfe1VZrLri5eLigre3N2fP2qazqFSqUkfdCgoKiv99Y4cJjDf6WLduHdOnT+eTTz4hNDSUwsLCCl+7oqOt9vb2xf9WKpXodGWPRoaHhzN79uy7vhFIwekz2NWrg8rfD+zscAnvTd7OPaXKqOrevFDVvkljFPb21XKnC0Bz9BwOQQHY162Nwt6Omg91J2vr/lJl7AN8qP/NZC6PnU/BhaQqitQydcx53Bv44VrPB6W9isBHOpG4ufSpkImbDxM0pBsA3qGNKMzWkpeWgaO3O/YeLgConOyp3b0lWXHGa4fyUq/j09l4Cqlv15ZkX0ipxKzuDRkx8bg19MMl0AeFvYq6gzuTsqV026RsOUTgUGPbeIY2oihbS35aBvlXM9EkpuMWbDzA49OtFdmxiZWeQ0n3el/7a+kWZg+ayOxBEzm25QAdHusOQFC7xmizNRZPM+z8ZG+ad2/D4jGfVThH28qpH6P4tf9Ufu0/lYRNh2j8RFcAfEODKcjWlDrN8Iak3ado8EAHAJoM6UbClsO3fB8nb+N1Hq4B3jQY2J7433ffosadyzwSj2tDP5xN4yZgcGdSzfpZ2ubD1DH1s5phjSjK1lR4miFAfsp1vLoY+5l3t5ZozldOP8s8Eo9LiXz8BnchrUw+hwgYYuxvNcIaUVgiHwfTNTZOdbypPeg+kk153bjJBoBv/zByz1Xetungkq0sGjSFRYOmcHbLQdo+bmyLOu0akZ+trbanGQKkm+YxN9M8FvRIJy6bjYHLWw4TbBpLtUKDKcwyjqWK6t4YIygUtHnrEWKXbgMgNzEdv/tbAmDn7Eit0EZkxlmvrQ4vieK/g6by30FTid1yiFaPG+MOaBdMfram1GmGN1zcc4pmg4xzQKvHu3FuqzEHRw8XlPYqANoO68nl/WcoyNFi7+yIg6vxO6y9syMNurfi2tkrVstBVC9WXfGys7MjIiKCyMhInJyccHNz4/Tp0zRv3py//vqL5s3LXqtkLiAggLS0NFJSUvDz82PXrl3Fz/n4+HD4sLEDnz9/nrQS10KVpNFocHJywsXFhYyMDGJiYmjZ0jgwnZycyMvLw8Oj9AWNzZs356uvvmLw4MEYDAYOHDjAG29Uwe3LdXoy5n1Brf98DEoVuX9spOhCAi6PPgSA5n/rcO7ZHZeB/TAUFUF+PuppN88993x/Go6hbVHWrIHf7yvJ+m4xmnUbKz+PG3R6kt79hoZL3geVkuurosg/dwmvZwYAoP55E7XfHIbK04M6M18DwFCkI+7hdwAI/Hw8rp1aY+fpQbM9P5D66TKur9paaeEbdHoOT1lMj+UTjbf4XrGDrNhEgkcYj9zHL9lG8rYY/PuE8MCe+RRpC9g/diEATr416fjZqyhUShRKBZfW7iM56ggAB8Z/R7sPR6BUKdHlF3Iw4rtKy+l2Rbw3mwNHjpGRkUWfwc8y+oXhPP5Q1ZxuY4lBp+fYlMV0WT7JeDv55dFkn00kyNQ2CUu2kRoVQ+0+IfTd+ylF2nyOvL2wuP7xqT8S9tXrKO3t0FxM47DpOf+B7WkT+RwO3h50+mkCmScusucp691ZtaJ8/i197eSfR2jZqx3v7fiMQm0BP0V8Xfzcaz9MYtnEhWSmXWdY5IuoE68y7n/Gu2vGbNrPps9/qZKYL2+PoV7vtjy5ax5FeQXseOfmkev+S8azM+I7NKkZ7P9oBb2/eoP2E4aQfiKBsyuiAXD2qcHgDR/i4OaMQa+n1YsDWNNrIoU5WvouegtHTzf0RUX8PfVHCjI1NsvDoNNzYvJiOqyYjEKl5MryaHLOXiFwRDgAl5ZEkRZ1BJ8+IfTc9x902nyOvXVz3CidHajVvTXHx5fuR8fGfUvLmSNQ2KnQ5RdybHzl9DODTs+pyT/QfsUUUz5/knP2CvVM+VxeEsXVqCPU6hNC932fodPmc/ytmz+PEfL9Ozh4uqEv0nFq8g8UZRqvD2oy7SlcGwWAXo/2yjVOVtG4Obc9hka9Qnjjr/kUagtYO/5mWzy1OIJ1E74lJy2DDiP70+XVB3HzqcGrm2dz7s8Y/pj4Ha4+NXhp3UwcTf2u46iBfBU+gQIb3YDCoNOzf9qPhC+bYLwl/ModZMYm0mS48Y6ksUu3k7gthjq92/Lo3/Mo0haw2zSWyqsLEDS4M81GmvrohoPErTTeLOXs4q10+fRlHt4+GxQK4lf+RcbpyzbJLX57DMG92vLqX/Mo1BawfvzNOWDo4vFsmPAdOWkZRM9awSNfvkGP8UNIOZnA0ZXRANRqFMCD81/FoNNzLS6RDRHfAuBay4PHFr0NGK9nPfX7bs7vuL07f/+/I7/jhcJgpcOIaWlpfPzxx8ybN4/c3FxmzpxJaGgoMTEx5Ofn4+vry+jRo//xzTWaNWvG5cuXmTRpEgUFBcyZM4fMzEyCg4M5e/YskydPBih+7xsWLFhAXFwcvr6+2Nvb0759e3r27MnGjRvZvHkznp6et31zjZKvvXbtWvLy8hg6dGi5n0Vi56q75bG1pSdXnzs+WsPp/HvnDkKPHf+wqkOwqvWtplV1CFaTfwfX+lRnOx2LqjoEq2lbZH/rQv+PBFTS3eoqg6qqbh1oI/udVFUdgtUEFd1bc1riPfYrtpMv/lTVIdyW3Onlf3e2NtcPV1Xae/0TVtvxEjfJjlf1JTte1ZfseFVfsuNVfcmOV/UlO17Vl+x4VY3cqUMq7b1cI1dX2nv9E1XyO15CCCGEEEII8W9Spfv8c+fOLXOd1jPPPENISEgVRSSEEEIIIYQQ1lelO14RERFV+fZCCCGEEEKISmCQH1CWUw2FEEIIIYQQwtbuscsLhRBCCCGEENWO/ICyrHgJIYQQQgghhK3JipcQQgghhBDCtmTFS1a8hBBCCCGEEMLWZMVLCCGEEEIIYVsGuauhrHgJIYQQQgghhI3JipcQQgghhBDCtuQaL1nxEkIIIYQQQghbkxUvIYQQQgghhE0ZZMVLVryEEEIIIYQQwtZkxUsIIYQQQghhW7LiJTtetrA8MaCqQ7Aa+6oOwMpq3ENrvOtbTavqEKzqgRMzqzoEq/m57btVHYJV9c1TVXUIVnPtHtvq9Rxz77TNvi8KqjoEq6pXdO90Nq2iqiOwrqb5cltzUTXunVlBCCGEEEIIUT3pZYf3Hjr+L4QQQgghhBDVk+x4CSGEEEIIIYSNyamGQgghhBBCCNuSm2vIipcQQgghhBBC2JqseAkhhBBCCCFsS1a8ZMVLCCGEEEIIIWxNVryEEEIIIYQQNmUwyIqXrHgJIYQQQgghhI3JipcQQgghhBDCtuQaL1nxEkIIIYQQQghbkxUvIYQQQgghhG3JipeseAkhhBBCCCGErcmKlxBCCCGEEMKmDLLiJSteQgghhBBCCGFrsuJVjfR+fzgNeoVQpM1n47hFpJ1IKFOmRj0fHvzydZxqupF6IoENb3+NvlDHfa88QPPBXQBQ2inxalSHr0JeIy8zF0cPF/rPeRHvJnXBYGBTxLckH46zeT493h9OkCmfLeMWcdVCPh71fBhoyiftRAKbTfnU6dSch74bS9blqwDEbTrA/s9+A8DBw4XwEvlsjfiWFCvkU6dnGzp8MByFUsm55dEcX7CuTJkOHwynbm9jTrvGLkJtyqm8ug41Xen59Ru41fMh5/JVol/9goJMDY6ebvRc9Ca12jYkbtVf7Ju2pPg9lPYqOs58Dr8uzUFv4PDHq7m44cBd53eDb682tP5wBAqVkos//8m5L8vm2XrmCGr3CUGnLeDwW9+QedyYp72HCyHzX8KjaT0wGDg8dhHXD50j4KGONBv/OO6NA9gxcDoZRy9YLV5rmfbRfP76ez9enjX57advqiyO6tDPVE4O9Fz0Jh71fdHr9FzZeoRDs1ZaNc8b/QyVkksV9DNfUz87UqKf2Xm40G7+S7ib+tkRUz9rv3AMbsH+ANjXcKUwM5fo8ClWjdtcpw+GU8/UFn+NXUS6hXnMrZ4Pvb56HceabqQfT2DHW8Z5rKL6LV8cQNOneoLBgPrMFXaOW4Quv5B27zxG06d7kpeeDcDBj1dxZftRm+WnatgGh/7DQaGkKCaawt2l20lZvzlOQ8aizzDOxbqzByjc+RsKL38cH3vjZjlPXwp2rKFo/2abxXo7vHq1pfHM51GolCT/vI2LX/xe6nmXRgE0/2w07q0bED9rBZe/NubrGOBNiy9fx8GnJga9gaSforjy7cZKi7uj2Zgvr5/1/Op1HD2N/eyvN2/2s/LqP7H3U4py8tDr9RiKdKwb9C4Ani0C6TL7eexdnMi+cpW/3viawhytTXLr8sFwAk2xRY9dxDULubnX86HPV8bvAteOJ7DdNIYaPdqFkNEPAlCYm8fOyYtRn74EQI9PXqJ+eAjaa1msDp9sk9hLqt2rDW1M286En/8k1sKc1mbmCPxMc9qht74hwzSn9T/wGUU5Wgw6PQadnj/7TwOgxYQh+A8Iw6DXk38ti0NvfUNeaobNc6kysuIlO17VRYNebfEM8uP77uPwbxdM38iR/PzIjDLluk8exsHvNnF23V7CP3qe1k/25OhP2ziwcD0HFq4HoGF4O9q/MIC8zFwAes8YzoXoY6x99XOU9irsnR1tnk9Qr7bUDPLjx+7j8GsXTO/Ikay0kM/9k4dx5LtNxK7bS++Pnqflkz05/tM2AJIOnGXt8/PK1OkxYzgXo4+xwZSPnRXyUSgVdIx8ji1PzUaTrObBDR9wacshMs8lFZep07stHg38+LXrOHxCg+k8ayTrH5pRYd3Wrz9E8q5THF+wjtavP0Tr1x/i0Ecr0eUVcmTOGjyb1aVm07qlYmnz5iPkpWfxv24RoFDgWNP1rvMrplTQdtbz/D10FtrkdHpumknKlsNkxyYWF6ndJwS3hn5EdX4Hz9BGtP14FH+ZNtatZ44gbftRDrz4GYoSn33WmcvsH/UpIXNfsF6sVjZ4UF+efvxhpnz4SZXFUJ362clv1pOy+zRKexX9V06hTq82JP55zDqJKhW0mfU8u039rIeFfubbJwTXhn5sK6efpZboZypTPzv4yhfF9VvOeIbCLI114i1HXVNbrDa1RZdZI1n30Iwy5e6bMoyT327i/Nq9dJn1PE2G9eTM0m3l1nfx86TlqH780nsiurxCen09hoYPd+Lc6p0AnPh2EycWbrBpbgAoFDgMfI68n2djyFLj9MIHFMUewnAtqVQx3eWz5K8sPRcb1MnkfTe1+HWc3/oC3dmDto+5IkoFTWe/wJGhM8lPSqf95llc3XwQTYl+V5iRQ+zUH/AZeF+pqoYiHefeW0rO8QuoXJ24b+ts1DuOlaprKzf6yS8lxvwfFvpZ+6nGfnZh7V46z36exk/15OySbbesv3FIJPnXc0q91v1zX+TAh8tI3XuGxk92p9VrD3Bk7hqr51avd1tqNPBjRddx+IYG03XWSH6zkFvHKcM4/u0m4tfupdus52k2rCenlm4j+9JV1j4xk4JMDfV6taH7nFHF9WNX/8XJxVvp9Z9XrB53GaZt5y7TnNZr00ySy9l2bjHNaSEfjyLaNKcB7Hw8kgJ1dqmXjf3qD07NWQ1A8Av9afbOY8RM/K/t8xFVpkpPNUxLS2PcuHG3VXblypUcO3Z7XwpOnjzJ7Nmz7yim3NxcNm++ecTubl7rn2jUL4yTv+wCIPlIPI4errj61ixTrl6XFsRu2G+Mbc1OGvUPK1Om+cOdOb12DwAObs7U7dCU4yuiAdAX6si38ZcVgIb9wjhtyifFlI9LOfmcM+Vzas1Ogi3kU5KDmzN1OjTlZIl8CqyQT612wWQnpJJz6Sr6Qh0Xft9LoFksgf3DiF9jzOnq4Xgcarji7FuzwrqB/cOIM32Zilu9k8AB7QEo0uaTdiAWXX5hmVgaD+vB8S9MR9IMhjIbzLvh2a4RORdS0VxKw1Co48pve/Azy9OvfxiXVhljvn44DnsPFxx9a2Ln5ox3p2ZcXBZtDK1QV/zFN+dcEjnxyVaL0xbah7Smhod7lcZQXfqZLq+AlN2nAeMYSj+egIu/l9Xy9GzXiNwS/SzRQj/z7x/G5Qr62aVl0YCxnxVZGON1HupE4v/2WC1mS+r3CyOuZFt4GNvCXMD9Lbiw3jiPxa3eSX1TrhXVV9ipUDk5oFApsXN2QJN63aa5WKIMCEavTsWQcRX0OnQn92LXpOI52BJVg5YYrqdhyEy3QZS3zyO0EZoLKeRdNPa7tN924zOg9A5W4bUssmPiMZhWim4oSMsg57hxpV6Xm0fuuUQc/aw3JioS2N+sn9Sw3M/8729BgoV+drv1S6oR7E/q3jMAJO08QdCg+yosf6eC+oURa4ot7XD53wUC7m/BeVNusat3EmTKLfXQOQoyjeM/9XAcbiXmqeR9Z8nLsN72sSJeZnPald/24G82pwVY2HY63aIdikqsMqpcbH9QvMrpK/G/aur/zTVeTz75JG3atLH5++Tm5rJlyxabv485Nz9PspNvbrSyU9S4+XmWKuPs6UZ+lgaDztijcpLVuJuVsXNyIKhnG86ZTk2rEeiDRp3NgHkvM3zDTPp9/GKlrHi5+XmSUyKfHAv5OFnIx7VEGb/QRjy9KZJHfozAq0kdADwCfdCqs+k772We2jCTPh+/aJUVLxc/T3KT1MV/5yarcTGL11gmvUyZiuo61/JAm2Y8bUCbloGTt0eFcTh4uADQbsITPLRpJj0XjsGpVsV1/glnf0+0JXLIS1bjbPaF21hGbVbGE9f6vhSkZxP62Sv03PoRIfNe+ndsKKyouvSzkhw8XKjXtx3Ju07eUU6WOJn1M22yGiezfuZk1s+0pn7mYupn7T57hR7l9DPvTs3Iv5ZJ7oUUq8VsiXlbaMzmKABHTzcKSsxjuSXKlFdfk3KdEws3MGzfZzx1+EsKsjUk/nWiuFyLkX15dOtHdPvkJRxquNgsP4W7J4asm21gyFajcPcsU05VpxFOL0XiOCwCRa06ZZ9v0Zmik7bdCb4djn5e5Jf4vPOT0u9o58mpng/urRqQVQmn5EP5Y74kR083CjJv9jNNiTIV1jcY6L98Eg9t/JAmz/QqLpNx9jKB/UIBCHqwI64BttnJdL2N3JzMxpD5d4Ebmg3rySVrrcr/Q5bmNPNtp6U5zcn/Zjt0XTGJXpsjCXq2d6l6LSYNZcChL6j3+P3Fq1/i3lVtdrxSU1OZMGECcXFxTJ06lfHjxzN37lxycoxHMxYsWMDevXvLrR8TE8Pbb7/N9OnT2bdvX/HjeXl5fPXVV0yePJkJEyZw4IBxhyQ6Opo5c+YQGRnJW2+9xerVxs6+bNkyUlJSiIiIYOnSpcWvMW/ePN5++20+//xzDAbrn6OqQFH2QfP3UZQtY14kuG87kg7GFp9mqLRTUbtVEDFLt7F00DQKtfl0GP2QtcKuwK3zUVjIB1ORqycS+KHz2ywbMJWji7fw0LdjAWM+vq2COLZ0G8tN+bS3Rj4VxFJxGcPt1b3dMFRKXAO8STsQy7oB00g7FMd97z59Zy9m8Q3uvJ8p7JTUaB3EhcVRRPedgk6TT5M3HrZebP8G1aSfFb+VSkn3Ba9z+r+bybl09e5erOTr3kU/U5r6WcLiKHb0nUKRJp/GZv2szqNduPK/3VaLtzyW8jCf/y2WuUV9hxouBPYLZVXnsSwPG4O9syPBj90PwOklUay+/x3+128qmrQMOk5/5u4TKY+ldjKjT05A88Xb5H07laIDW3AaOrZ0AaUKu3HaLJIAACAASURBVCahFJ3eZ7F+pbLYFv9skKhcHGn1/TjOTV+MzkbXPJVxh+OlREcrt/76wR+wdsA0tj47l+Yjw6ndsSkAu975lmYj+/LQxg+xd3VCV1h0FwlU4E7nArO/A7o0p9mwHuyLXGG92P6B25nTKvpOs+OhGWzvN5Xdz3xMw+f74t2pWXGRU7NXsSlsDJd/+ZvgUf2sGbaohqrFNV5JSUn85z//YfTo0Xz55ZeMGjWKFi1asHLlStasWcPIkSMrrF9QUMDChQt599138fPz49NPPy1+7tdff6VVq1aMHj2a3NxcpkyZQuvWrQGIi4tj3rx5ODo6MnnyZEJDQ3n66ae5fPkyc+fOBYynGl64cIH58+fj6enJ9OnTOXv2LM2aNSsVQ1RUFFFRUQDUus28Q0aE0+Yp4xGolGPncff3Ln7O3c+LHLMLLLXqbBw9XFColBh0etz8vcgxOz2l2UOdOf37zSOP2clqspPVpMTEAxC7YT8dX7PNjlebEeG0MuWTeuw8biXycbvNfHJN+RSU2OAl/HmUXjNH4uTpRk6ympxkNammfOI27Ke9FfLRJKtLHfFz9fcqc+qPsYy3WZkMlA525dbVXsvC2bcm2rQMnH1rkpeeVWEc+ddzKNTkcXGj8VqJhD/20XhYj7vO7wZtkhrnEjk4+XuhTbluoYxXqTJ5KdfBYCAvWc31I8bPPumPfTQeIzte/0R16Wc3dJnzAlkXUjj1nXVviGDez5xv9KES8sz6mfNt9jOFSon/oPvY0W+qVWO+oflz4TR92jiPXTt6vlRbuJjaolQe6mwcSsxjrv5eaEy55pq15Y36AV1bkX35Knmm6z0SNh6kdlhj4n/9m7xrN9vu7LI/6bf49k7HvxOGLDUKj5ttoHD3wpBtdspjwc25WBd/FJQjwdkNtMaDoqpGbdGnJEDu7fU5W8pPTsexxOftGOBNQcrtn8KpsFPR6r/jSP1lJ1dNp8DbSrPnwotXoK7FnLc45kvKV2fjUONmP3MpMf7LmzMAtKb/56VncXHjIXxCgkndd5bM+GS2PP0xAB4N/ajbJ8RqubV8LpxmpjF09eitczMfQ24lxhCAV/N6dJ/zIhuHzyW/kk4tNGdpTrvVtrPkvHfjhhn517JI3ngQr3bBpJtO9bzh8v920+WnCE7P/cVWaVQ5uZ18NVjxysrKYs6cOYwZMwZfX19yc3Np0aIFAD169OD06dO3fI2kpCR8fX3x9/dHoVDQvXv34ueOHTvG77//TkREBDNmzKCgoIBr164B0KZNG9zd3XFwcKBDhw6cOXPG4us3atQIb29vlEolQUFBpKWllSkTHh7O7Nmz/9H1YDFLolgycCpLBk4lbvMhWj7eFQD/dsHkZ2vITSt7Z5vLe07RZFAHAFo+0Y34LYeLn3Nwd6Zup2alHtNczSQ7WY1nQ+OdwOrf35L0c7a5WPjYkiiWDZzKsoFTid98iOamfPxM+Wgs5HNlz/+xd+dxUZVrAMd/MwPIJggIAoob7hube66IaLuVlmWWWfdaYqmJu2YuqGmadcPS8l41yyUztSw1VFxyV3DJUlERlU1ABGHYZub+MRM7LjXDID3fz6dPMvOeM88z73vOnHeec86co6khn1YDu3PZELutq2Nhmzo+jVEoFeTculOYTy1DPl6PtCbNCPmkRF/GoZE79l6uKC1VNHq6M9eKvY8A13aexHugPidXf2/yMrJRJ6ffddlrO0/SZFB3AJoM6k7cjhP3jOX6L1H6OxoCnt1ac9uI/ZUefQn7xu7Y1ndFYami3oAuJO4sGVPizhPUf14fs5N/Ewoy1eQmp5N78zbZN1IL7yrn2r1NiQuLxb1VpXHmN2EgljVtODpjjZGz1I8zu2LjrG4F48yr2DjLLzbO1HcZZ6492nAnJp6chDRM4fdVEWzuN5XN/aZydfsJmhTri/zM7MJTOotLOHiORo/r92NNBnUnztAvcTtPlrt8Vnwqbn5NUFlbAfrtPD1Gn2Pxa3Ma9G/PrfPXTZIngDb+MkpndxS1XEGpQtW6MwUXSo5HhV3Rvljp2VhfnVAXHfxatK4apxkCZEZdwraxB9aGcec2oCspO+7/hh8tPnqT7Is3uGa4UZUp/bEqgq3BU9kaPJW4HSXH2Z/bfGkJB8/R8D7G2Z/LW9jUwMLOGgALmxrU7dmmcDwVno6sUOAz+mnOf7XLaLn9tiqC7/pN5bt+U4ndfoJmhtjc/L3Jq+BYIP7gORobcms2qDuxhtzsPV0I/mIMe0Z/zm0Tn1p8N7fK+exMKLVPSyj12ZmfqSYnOR2VbVE/qGxr4NazLRl/XAPArpF74fIe/fy5E1Pyxjai+jF7xcvW1hYXFxfOnz9P165djb5+nU7HuHHj8PT0LPF4TEzZc7fLLRMDlpaWhf9WKpVotca/au/y7mga9fbhjf2LyFfnsT10eeFzz64MZcfEL8lKSmffvHU88ekouo0fRPJvsZxZH1nYrmm/9lzdd4Z8dW6Jde96bxWPf/IWKksL0uOSS6zbVGJ3R9Owtw+v7l9EgTqPX4q95tMrQ4kw5HNg3joe/XQUXcYP4uZvsfxmyKfJYx1pN7QP2gINBTn5/DwqvHD5yPdW0d+Qz+245BLr/qt0Gi2Hp62i7zcTUCiVxKzfS/qFGzQfqj8X+/xXu7m+K5q6gT48++siNOo8Dry7/K7LApwJ/4Gen79N0xd7cudGKpEjPil8zYGHP8LS3gallQX1+7dn54vzuX0xnuNh6+j+yVtYvf8yOWmZ/DrWeP2l02g5PWUlXddO0t9Ofm0kmedv0PCVPgDErt5FUkQ0dfr40vfwRxSoc4kas6xw+TNTVxGwNASlpQXZV5M5aXjO49H2tAt7FSsXBzqvmcDts1c59KLpb0rzIMbPmM+xqNOkp2fQZ8DLjHx9KM892a9SY6gq4yz/Tg4+oweQfvEGT+2YA8Dv//uFi2sjjZbn6Skr6WIYZ3F3GWdBhz9CU2qcnTaMM4VhnBV/ru6ALtyohNMMAa7tjqZeoA+DDiyiICeP/e8WbYvBq0M5MP5LspPSOTZ3Hb2XjiJgwiBSz8Zy3nDzn4qWvxl1iSs/HWXA9jnoCjSk/naVP77eA0DHqYNxbt0AdDoyr6Xw6yQT3uFMpyVv+yqsX5wASiUF0XvRpdzAwl8/HgtO7kbVsiOWAX3QaTWQn0/u90X7YiysUDVqQ+5PVeMubDqNlguT/4vvuqkoVEri1+4h6/x1PF/pC0D86l+wcnWk/c75WNS0QafV4fXvxzjS/V3sW9XH4/me3Dl3lQ67FgBwee5aUndFmTzu67v04+Q5wzZffJz1NYwzdVI6x8PW0WvpKPwnDCL1t1guGLbXipa3dnWgz4oxAChUKi5vPsiNSP01Uo0HdKHFsCAArv50nIvr95kkt7jd0dQP9GGwYRuILJbbo6tD2WvYho7MXUfQ0lF0mDCIlLOx/GHYhvzHPoN1LXu6zR0G6O8+uelx/Z0C+3wagkeXllg72zPk2CccX/Qd59ftNUkeOo2W6CkreaTUZ2cjwz7tyupdJBr2acGGfdoJw36rRm1HOv+v6HKJa5t+JclwrVqbqYOxb+IBWh3Z11OImrDCJPFXGVLxQqEzxQVL9yk5OZkPPviAsLAwwsLC6NevH1u2bGH48OG0bNmSDRs2kJ2dzbBhwwgPDycgIIDOnTuXWU9eXh6jR49mxowZuLu7s2TJEnJycpg0aRLffPMNarWa4cOHo1AouHLlCo0aNSIyMpK1a9eyaNEirKysmDJlCm+99RZubm5MnDiRpUuXAvpTDX/44QcmTZoEwIoVK/D29qZXr14V5vVh/ZdN8n6Zg+W9mzxUHDX3bvOwqKWpwrft+QsePzvH3CEYzdc+79270UPEqRqNtRQLs5/oYVSD3zR3BMZz5D955g7BqK5aWJk7BKPJu/flgA8V14Lqs08DeDbxG3OHcF/SX+x970ZGUmvtnkp7rQdh9ooXgLW1NZMmTWLOnDl07NiRNWvWkJubi5ubGyNHjrzn8lZWVowYMYL58+dTs2ZNWrRowbVr+jLuwIEDWblyJaGhoQC4uroWTqKaN2/Of/7zHxITE+nWrRve3t6Fj48bNw5fX1/8/f1NlLUQQgghhBD/ENVrvvuXmLXiZU6RkZFcunSJ1183/g++SsWr6pKKV9UlFa+qSypeVZdUvKouqXhVXVLxMo/0Fyqx4rW+ala8qtcnkBBCCCGEEKLK0Wl1lfbfvURHRzN69GjefvttNm/eXGG7mJgYXnjhhbv+pNWDqBKnGj6IhQsXlrmr4JAhQ/D1fbBbofbq1euu12kJIYQQQgghqhetVsuKFSuYNm0aLi4uTJ48mfbt21OvXr0y7b7++usHnmPczUM38Ro/fry5QxBCCCGEEEI8iCpyhmdMTAzu7u7UqVMHgK5du3Ls2LEyE6+ff/6ZTp06cenSJaO9tpxqKIQQQgghhPhHSEtLw8Wl6AexXVxcSEtLK9Pm6NGjBAcHG/W1H7qKlxBCCCGEEOLhcj/XXhlLREQEERERhX8HBQURFKT/7bry7itY+rd8V65cyZAhQ1AqjVujkomXEEIIIYQQotooPtEqzcXFhdTU1MK/U1NTcXJyKtHm0qVLfPzxxwBkZGQQFRWFUqmkY8eOfysumXgJIYQQQgghTKuKXOPl7e1NQkICycnJODs7c/DgQd55550SbcLDw0v8OyAg4G9PukAmXkIIIYQQQoh/CJVKxfDhwwkLC0Or1dK7d2+8vLzYuXMngNGv6ypOJl5CCCGEEEIIk9JVkYoXgL+/P/7+/iUeq2jCFRISYrTXlbsaCiGEEEIIIYSJycRLCCGEEEIIIUxMTjUUQgghhBBCmFYVOtXQXKTiJYQQQgghhBAmJhUvIYQQQgghhElVpZtrmItUvIQQQgghhBDCxKTiZQLN8zTmDsFochTVa27+xMKG5g7BaLaOjzV3CEb1tc975g7BaIacmmXuEIxqTTXqG5XO3BEY14bPzB2B8RRYWpk7BKNKrEaDrW1u9ckFIMmieh3bPDSk4iUVLyGEEEIIIYQwNal4CSGEEEIIIUxKrvGSipcQQgghhBBCmJxUvIQQQgghhBAmJRUvqXgJIYQQQgghhMlJxUsIIYQQQghhUlLxkoqXEEIIIYQQQpicVLyEEEIIIYQQpqVTmDsCs5OKlxBCCCGEEEKYmFS8hBBCCCGEECYl13hJxUsIIYQQQgghTE4mXkIIIYQQQghhYnKqoRBCCCGEEMKkdFq5uYZUvIQQQgghhBDCxKTiJYQQQgghhDApubmGTLyqDNfePrSZ/QoKlZK4r/cQ8+nWMm1az3mVOn180ajziB79GbfPxGLn7UHAsncK29g2cOP8go1c+eJnWr73Eu59/dHma8iKTSJ6zOcUZGRXSj51erfDb9ZQFColl7+J5PynP5Rp4zv7FTz6+FCgzuPYmGWkn4ktelKpIGj7HNSJt/j1lQ8BaDf9RTyC/dHmFZB1NYljY5aTX0n5/OnXmEQW7IhGq9PxjF8jhj/SokybY7HJLNx5igKNDidbK1a82guAr49cZFPUFXQ6eNa/ES93alqpsf/JvVTf/FFO3/gZ+kajzuPomGXcOhOLsoYlgd9PR2VlgcJCxbUfj/Lbh98BUKt1A9p/MBxlDUt0Gg0nJv2PtOjLRou5bq92dJw1FIVSycW1kZwJLxtzx1lDqRfoS4E6lwNjl5N2Nvauy1rVsqPXZ6Ow93LlzrWbRL75H/JuZ1PDyZ5ey9+htk9jYjbs48i01QCorK3otfwdHBq4odVouf5LFCfmrTdajg9i2tzF7Pv1KM5Otdi85nOzxFCRTqX6IdXQD8XZe7nSa2kINZzsST0Ty753PkObr7nn8gqlgid/nk124i0iXl0EgN/4gdQP9ken05GTksH+sctQJ6UbPS9TjEG/8QPxCvYHnQ51SgYHTBT7n6pr3wB0mTUUL0NseyvIraaXK4FLQ6hRy56UM7FEjtbn5ujtQc/F/6Z2m4YcW/AtZ5b9VLiMlYMt3Re+gXPzeuh0OvaN+4LkkzEmyaEi/d5/hSa9fchX57E1dBmJ5eTW/tW+dBreH+eG7nzoOwL1rTsAuHh78NSHI3Bv3ZA9H27g8PKfyixrSm6929HWcGxz9es9XCzn86btnFcKj21Ojv6c24ZjAUsHW3wX/wuH5l6g03Fy7HJunbhIywmDcO8fAFotuSkZnBz9OTkm3G6K6zprKPUN4yxy7HJSKhhnfZaGYG0YZ7sN46zJM13xHfkEAPlZOeyfvJK03+MKl1MoFTz702yyEm+xfdiiSslHVD451bAqUCpoO+81jrz0AXt6hOL5TFfsm9Ut0cStjy/2jd3Z3WUsp0K/oO0HrwOQdSmBfUGT9f8FT0GjziPx52MApOw9Q2SvCewNnEjW5QSavvN0peXjP3cY+4csYHvPCdQf0IWapfJxD/TBvrE7P3cdx4nxK/Cf/1qJ55v+qz+ZF+NLPJa07yw7e03klz6TybyUSIu3nzJ5KsVptDrmbY8i/KVubHqrH9vPXuPSzYwSbTJy8pj3cxQfv/AIm94KZuHAzgDEJN9mU9QV1rweyIYRQey/mMDV1MxKjR/0O/aAucPYZ+ibBgO64FCqbzwCfajZ2J2fuo7j+PgVBBj6RpubT+TAMHYETWFH0BQ8erfDxb8JAD7TX+Ts4k3s7DuFsws24jP9RaPG3CnsVX55eQGbe0+g0YDOODb1LNGmbqAPDo3c2dRtHIcmrqDLvGH3XLZtyJMkHDjHpm6hJBw4R9uQJwHQ5OQTtWAjx2d/UyaW3z7fxvc9J/BDv6m4dWhG3d7tjJbngxjwWF8+XzzHLK99N/UM/fBdt3EcLNYPpbWfOpjfvtjOd91Cyb2dRdMXe93X8q3e6E96qf3C2c+2saXvFLYGT+VaRBS+Y58xel6mGoNnP9vGVkPs100U+5+qa98AeAX64NjInQ3dxnFg4gq6VZBbxymDOfPFdjZ0DyXvdhbNB/cCIDc9i4PvfcXpZWUnJV1mDuV65Gm+7TWBTcFTSI+JL9PGlJr09sG5kTvhPcexbfIKHpvzWrntrh+/wJoh80i/drPE4+r0LLbPWM3hL7ZVRrglKRX4zHuNQy8tYFeP8dR7pmuZY4E6hmObiC7vEh36JT4fDC98ru2cV0jefYpd3UPZ3WcSdy7eAODi0h/ZEziJPUFTSPwliubvPlsp6fw5ztZ1G8e+u4yzToZxtq67fhtqYRhnmXE32TpwDhv7TuHkx5vpsWB4ieXavN6fW5U8viqbTqeotP+qqio58UpOTmbcuHH31Xb9+vWcPn3axBEVef/997l06ZJR1+nk14SsK4lkxyWjy9cQv/kQ7v3al2jj3i+Aaxv2A5B+MgZLB1tquNUq0ca1exuyY5NQX08B4ObeM+g0+rrurRMXsfZwNmrcFXH28+ZObBJZcTfR5Wu4tuUwdfsFlGjj2T+Aq9/q80k7GYOVgy3WhnxsPJzx6OPL5W/2lFgmqVg+qSdjsPGsnHz+dDY+DS8ne+o52WOpUtKvtReR50vuJH8+e43AFnXxcLQFwNnOGoDLKZm0q+uMjaUFFkolAfVrs/t85e9gnf28yTT0jTZfQ1w5fVO3fwCxhr5JNYy1P/umIDsXAKWlCqWlCp1OB4BOp8PS3gbQf0upTjTet4+1DTHfMcR8Zcth6peKuX6/AC5tPADAzZOXsHK0w8at1l2Xrd8vgBhDnjHf7qd+f/02V6DOJfnYBTS5+SVeQ5OTR+LB3wHQ5mtIPROLbSVtU6W1922Lo0NNs7z23dTvF0BMOf1QmscjrYjddhTQv/cNivdJBcvbejhTr48vF9dGllhX/h114b8tbGuAYUwak6nGYOnYdSaIvXh81bFvABoEB3DREFvyyUtYOZSfm+cjrbhiyO3Ct/tpaMgtJzWDlFOX0RZoSrS3tLfBo1Nzzhvy0uZryKvksyya9Q3g9Hf6/dSNqBisHWyxLye3xN+uctvw2V9cdmoGCacvo8nXlHnO1Jz8mnDnSlLhsc31zYdwL7XduPcLIM5wbHOr2LGNhb0NLp1bcPWbSAB0+ZrCM1wKio0rlW2NSskFoGFwABeKjbMaDnbYVjDOLpczzpJOXCTvtj6HpJMx2Bf7/LDzcKZBH1/++CbStEkIs3voTzV84YUX/vY6NBoNKpXKCNH8NdYeTqjjUwv/zklIpZahklDUxpmcYm3UCWlYeziTm1x0gOs5oCs3Nh8s9zW8XuxF/JbDRo68fDbuzmTfKIo1OyENFz/vsm3iS7ax8XAiJzkd31lDOT1nLZZ2NhW+RqPBPbm2tXLy+VNyhhp3h6KY6jjYcOZGWok2V1MzKdDqeH11JNm5BbzUsSlP+jSgiasDn+45S3p2LjUsVRyISaSVp1Olxg/69139gH2jLtY3CqWCvjvCsG9Uh5j//UJalP5LiKj3vqLn2on4vvcSKBXsemqm0WK2dXciK77ofc5KSMO1VMz6Nqkl2ti6O911WZvaDqgN2486OR1rF4f7jsnKwRavvn6cW7H9L+VUXVXUD+pi+6kaTvbk3c4u/BIl29DmXst3mvkyx+esLZzgF+c/cRBNBnYjLyObnwfNNVFexh+DAH7FYt9ugtjvFd/D3jcAdu5O3CkVm105ueVmFOWWVSy3itSs74o6LZOei/+Nc6v6pJyJ5dB7X1GgzjVJHuXG4O5MRrHcMhLTqFnHiTvJlXNq3d9hU+bYJg2nUsc2+jZpJdrYeDihK9CSl5qJ/8cjcGjVgPTTVzgzfTUaw5d/LSc9j9eg7hRkZnPgucqp/ttVsA1kF+sLayd78oqNszuGsVhai8G9iNtTVDTo+v7LHA4rfxuqTuQarypa8SouKSmJCRMmEBMTw9SpUwkNDWXhwoXcuaM/fzk8PJzDhys+AA8JCWHNmjVMnjyZyZMnk5iYWLjcqlWrmDlzJl9//TUxMTFMmzaNCRMmMG3aNOLj9dWIvLw8lixZQmhoKB999BF5eXnGT1JRTkm09BeD5bYpaqSwVOEeHED81iNlmjUdPQBdgZYb3x34m4Hen/JD1d1XG48gP3JSbpN+OrbC9bcY/TQ6jYa47379m5E+mPK+qy2dh0ar4/eEW3w6uBtLh3Rn+YHfuZqaSWNXB17r2pw3v95PyDcHaFanFiqlGUrh5b1k6W+h79JGp9Wxs+8UfvB/G2c/bxyb1wOgyStBRM9Yww/t3yF6xho6LPqXEWP+G9vH/Sz7oOGolPQID+H3/+7gTtzNey/wT3KP/VTFbe6+fL0gX9QpGaQWvw60mJMffMuGDqO59P1BWr7W94FCvi8mHINRH3zLtx1Gc9lUsd8rvnu2ufvyZu+bu8RWssmD72+VFipqt2nIua928X3/aRRk5+JjOCW5stzP52mV9RfHnE4HCgsljm0bcmVlBJF9p6DJzqXZqKLLC36fv4GdAW9z7btfaTw82NiRl++v5lPqb8+uLWkxuCdHwtYBUL+PfhtKqWAbEtVLla54xcfHs2TJEkaOHMmnn37K8OHDadWqFevXr2fjxo0MGzbsvtZja2vLvHnz2Lt3LytXrmTSpEkAJCQkMH36dJRKJdnZ2cycOROVSsXp06f55ptvCA0NZefOnVhZWfHhhx9y9epVJk6cWO5rREREEBERAcAjD5hnTnwaNp4uhX9be7iQk3irVJtUrIu1sfFwLtHGLdCX22eukJdyu8Ry9Z7vgVtfPw4PCnvAqP667IQ0bOsWxWrr4VzmwtfshDRsPV1ILd4mMZ16T3TCMzgAjz6+qGpYYlHTho6fvsXRUZ8B0GBQdzyD/Nj7vOm+Ga5IHQcbEjOKTnFIylDjWurbqToONtSytcLGygIbKwsC6tfmfNJtGrjU5Bm/Rjzj1wiAT3afoY6DbaXGD4bqVam+KX2xu9rQN3+y8XAuc+pgfkY2Nw/+jnvvdtw+f52Gz3cnarr+JhTXfjhi1IlXdkIadp4lT8nITrpVThuXUm3SUVpZVLisOiUDG7daqJPTsXGrRU5qyev1KtJ1wetkXEnk3Jc7/k5a1UaLV4NoNqQ3ACnRl8vth+Jy0zKxcrRFoVKi02ixLdYnFfVjw8c7Uj/Yn3qBPqhqWGJV04Yen7zFvnc+K7Huy98fpO/qUKIXbTJqjqYag6VjDzJy7NW5b1q9GkSLl/S53Tx1GXtPF5KKxZZVKrectExqOBTlZufhTHZi2X4oLishjayENG4aKvtXth2tlIlX+1f64jdYn1v86cs4FHvfHdydH4pqF4C6zLGNM+pS77m+jXOJNjmJt0CnIychjVuG9z7+xyM0Lee67uvfH6TLmvH8sfA7k+TQutQ4u9c2lJOWiVWxcWZfapw5t/Six4I3+HnoQnLT9QUE9w7NaBDsT33DNmRZ04bAT95id6ltqDqQ3/GqwhWvjIwMFixYwNtvv42bmxtZWVm0atUKgJ49e/L777/f97oeeeSRwv9fuHCh8PHOnTujVOrfguzsbBYvXsy4ceNYtWoV169fB+DcuXP06NEDgAYNGtCgQYNyXyMoKIj58+czf/78B841PfoSdo3dsanvisJSheeALiTuPFGiTeLOk3g93x2AWv5NyM/MLnGaYd1nyp5m6NrbhyajnuTYqx+iUZugUleBW9GXsW/kjq2XPh+vpzsTv6NkPvE7TtJgkD4fZ/8m5GeqyUlO5+zc9WwLeJufOo7h8JufknzgXOGkq07vdrQY9SQHhi2q1Hz+1NrTibi0O9y4lUW+RsuO367Rs5lHiTa9mnkSFZdCgVaLOr+AMzfSaFxbfy1OWlYOAAm3s9n9RzyPtvaq9BzSoi9Ts5E7dl6uKC1V1H+6MzdK9c2NHSdpaOgbl2J9U8OlJpaGyaLK2pI6PVqTEZMAQE7SLVy7tATArVtrMq8kGi3mlOjLODRyx94Qc6OnO3Nt58kSba7tPIn3wG4AuPp7k5eRjTo5/a7Lwf+2cQAAIABJREFUXtt5kiaGPJsM6k5cqfehPH4TBmJZ04ajM9YYLb+H3R+rItgaPJWtwVOJ23GCJuX0Q2kJB8/R8PGOgOG9N/RJ3M6T5S5/Yv4GNrR/h42dx7J3ZDgJv54rPLB3aFSncL31g/25fSnB6DmaagzWLBa7lwlir859c25VBJv6TWVTv6nEbj9BU0Nsbv7e5GWWn1v8wXM0MuTWbFB3Ykv1YWnqm7fJik/DsbF+P+/ZrTW3DDd4MKXjq3/hi8em8MVjUzi/8zjtntPvp+r6NSEnU/3QTLzSoy9h39gdW8OxTb1yj21OUN9wbOPk34SCTDW5yenk3rxN9o1U7L31771r9zZkXtC/93aN3AuX9+jnT6YJb0jx26oIvus3le8M46xZqXGWXcE4a1zOOLP3dCH4izHsGf05t4t9Rh6dv4GvO7zDN13GEhESTvyv56rlpEvoVdmKl62tLS4uLpw/f56uXbv+rXUVP8Wg+L+tra0L/71+/Xpat27N+PHjSU5OZuZM412jci86jZazU1bSee1kFCol19ZGcuf8dRq8EgTA1dURJEdE4dbHl8DDS9Coc4kes6xweZWNFa492nJ6/Jcl1tt27jCUVpZ0Xj8FgFsnYjgzcUWl5BM1ZSU91k5EoVJyZd1eMi7coPErfQC4vHoXibui8ejjy6OHFqNR53Fs7LJ7rBX8w15FaWVJz3WTAf2NH05O/K9JcynOQqlkUn9f3vpmP1qdjqd9GtLEzZFvT+i/kRsU4E1jVwe6ervz/LJfUCgUPOPXiCZujgCM+/YQt9V5WCiVTH7UFwcbq0qL/U86jZaTU1bS09A3lw19423om0urd5Fg6JvHDy2mQJ3HUUPfWLvVotPHb6JQKVEoFcRtPUJCRBQAx0K/xG/2KyhVSjS5+RwvNRb/bsyHp62i7zcTUCiVxKzfS/qFGzQfGgjA+a92c31XNHUDfXj2V/2k/MC7y++6LMCZ8B/o+fnbNH2xJ3dupBI54pPC1xx4+CMs7W1QWllQv397dr44n/w7OfiMHkD6xRs8tUN/TcHv//ulzA0FKsP4GfM5FnWa9PQM+gx4mZGvD+W5J/tVehylXd8VTb1AH54z9MN+Qz8A9F0dyoHxX6JOSud42Dp6LR2F/4RBpP4WywXDe3i35SsSMPkFHL090Gl13LmRwqFJ/zN6XqYag8VjzzJR7H+qrn0DcG13NF6BPrxwYBEFOXnsLRZbv9Wh7B//JdlJ6Rydu47ApaNoP2EQqWdjOb9On5uNqyMDfpqNlb0NOq2WNm/0Z2PvieTfUfPr9FX0/s9bKK0syLyazN5x987bmGJ2R9Okty8h+/T7462hRZ+Vg1eO58cJX3AnOZ0Ow/rR9c0nsHd1ZMSO+cTsiebHiV9i5+rIGz/MoYYht07DH+WzoAnkFbtBhanoNFpOT1lJ17WT9LeTXxtJ5vkbNDR83sSu3kVSRDR1+vjS9/BHFKhziSp2bHNm6ioCloagtLQg+2oyJw3PtZ46GPsm+nGlvp5C9ATTH9cAxO2Opn6gD4MN4yyy2Dh7dHUoew3j7MjcdQQtHUWHCYNIORvLH4Zx5j/2Gaxr2dNt7jD9+1OgYdPj71VK7FXFw3KWrCkpdFXwZOHk5GQ++OADwsLCCAsLo1+/fmzZsoXhw4fTsmVLNmzYQHZ2NsOGDSM8PJyAgAA6d+5c7rpCQkLo27cvAwYMYN++fRw8eJBJkyaVWW7hwoV0796dzp07s2HDBvbu3Ut4eDg//vgj169f58033yQuLo4JEyYQFhaGt7d3ua8H8IO78W6lbW45iipbFP1LnljY0NwhGM3W8bHmDsGo1Oa45s1EhpyaZe4QjGqNT/U5OKg+o0yvyn2A/w0F1axzElXVp3fa5lafXACSLKrXsc2I6w/HWRhx7ftU2mvVP76r0l7rQVTZihfoK1KTJk1izpw5dOzYkTVr1pCbm4ubmxsjR4687/Xk5+czZcoUdDodo0ePLrfN008/TXh4ONu2baN169aFjwcHB7N06VJCQ0Np2LAhTZo0KXd5IYQQQgghRPnkGq8qWvEyppCQEObNm4eDw/3fKvrvkopX1SUVr6pLKl5Vl1S8qq7q9AEuFa+qSypeVdvDUvG66h9Uaa/V4GREpb3Wg6jSFS8hhBBCCCHEw08qXtVo4rVw4UKSk5NLPDZkyBDCw8PNFJEQQgghhBBC6FWbidf48ePNHYIQQgghhBBClKvaTLyEEEIIIYQQVVP1vqvE/aleVxcKIYQQQgghRBUkFS8hhBBCCCGEScnNNaTiJYQQQgghhBAmJxUvIYQQQgghhEnpdFLxkoqXEEIIIYQQQpiYVLyEEEIIIYQQJqXTmjsC85OKlxBCCCGEEEKYmFS8hBBCCCGEECallWu8pOIlhBBCCCGEEKYmFS8hhBBCCCGEScldDaXiJYQQQgghhBAmJxUvE5ivSjJ3CEbjqLI2dwhGlT6p+nzbcqpGgblDMKq+OSpzh2A0a3zeM3cIRvXyqVnmDsFoskJeN3cIRvXzfk9zh2A0HRxTzB2CUXWPv2buEIzm/HN1zR2CUY2MqGnuEIxqhLkDuE86bfU5BvurpOIlhBBCCCGEECYmFS8hhBBCCCGESel05o7A/KTiJYQQQgghhBAmJhMvIYQQQgghhDAxOdVQCCGEEEIIYVJycw2peAkhhBBCCCGEyUnFSwghhBBCCGFSWvkBZZl4CSGEEEIIIf45oqOj+d///odWq6VPnz4MGDCgxPP79+9ny5YtAFhbW/PGG2/QsGHDv/26MvESQgghhBBCmJSuilS8tFotK1asYNq0abi4uDB58mTat29PvXr1Ctu4ubnx/vvvY29vT1RUFMuXL2fu3Ll/+7XlGi8hhBBCCCHEP0JMTAzu7u7UqVMHCwsLunbtyrFjx0q0ad68Ofb29gA0bdqU1NRUo7y2TLyEEEIIIYQQJqXTVd5/d5OWloaLi0vh3y4uLqSlpVXYfvfu3fj5+RnlPZBTDYUQQgghhBDVRkREBBEREYV/BwUFERQUBICunJmZQlH+aZBnz55lz549zJo1yyhxycRLCCGEEEIIYVKVeVfD4hOt0lxcXEqcOpiamoqTk1OZdlevXmXZsmVMnjyZmjVrGiUuOdVQCCGEEEII8Y/g7e1NQkICycnJFBQUcPDgQdq3b1+iTUpKCh9++CGjRo3C09PTaK8tFS8hhBBCCCGESVWVuxqqVCqGDx9OWFgYWq2W3r174+Xlxc6dOwEIDg5m48aN3Llzhy+//LJwmfnz5//t15aJ10NizKxRdAnsRI46h7CxC7hw9mKFbcfOfpvHXuhP32aPV2KEdzdi5gg69O5ArjqXxeMWc+nspTJtRi8YTdN2TVEoFNy4coPF7y4mJzuHzn07MzR0KFqtFq1Gy7KZyzh37Fylxt9l1lC8An0pUOeyd+xyUs/GlmlT08uVwKUh1KhlT8qZWCJHf4Y2X4Ojtwc9F/+b2m0acmzBt5xZ9lPhMq1f70eLF3uhUCj445s9nF2xoxKzKmvgjGG07u1HnjqXr0I/4/pvV8q0eXXJ29Rv2xhNgYarp2JYO+ULtAUaM0QLbr3b0Xb2K6BSEvf1Hi5++kOZNm3nvIJbH1806jyiRn/O7TOxAFg42OK3+F/UbO4FOh1RY5dz68RF2i97G3tvDwAsHe3Iv51FZNAUk+bRadZQ6hnG14EKxpe9lyu9loZQw8me1DOx7HtHP77utbxCqeDJn2eTnXiLiFcXAeA3fiD1g/3R6XTkpGSwf+wy1EnpJs3xbqbNXcy+X4/i7FSLzWs+N1sc98vCpwM2w0aBUkXe7m3kbllbbjuVd3Ps54STvWQW+Uf2oXBxxTZkMspazqDVkbvrR/J+/q6Soy/Jo1c7/GcPRaFUcmltJL+Xsw35z34Fz0AfNOo8Do9dxq0zsShrWBK0aTpKKwuUFirith3l7IfmzQXAtlt7ak9+E1QqMjb+TPqXG0o8b/9Eb5xefx4AbXYON2f9h7zzlwFQ1rTDbdZYrJo2BJ2O5GmLyTn1e2WnUMbsD6bQp28P1Go1Y0ZO4Uw5MS1ZGkaXRzqQkXEHgDEjp/DbmT8Kn/fxa8O2iLWMeG0c27burLTYi1O1aY/1iyNRKJTk7f+ZvJ/Xl9tO2bAZdlM/Qf15GAUn9gNg/8FX6HLUoNWCVkPW7JDKDL1CQ99/Hd/e/uSqc1ke+imxZy+XafPWx2No3NabggINl09d5L+TP0dj+Mxs2bk1L783HJWlisy0TMJemF7ZKfxj+fv74+/vX+Kx4ODgwn+/+eabvPnmm0Z/XZl4PQS6BHaiXqO6vNBtKK39WxI6bwz/frL8nU6Lds2wd7Sv5Ajvrn3v9tRtWJc3erxBc7/mjAobxdinx5Zpt3zWctR31AD8a/q/eHLYk3y79Fuif43m8C+HAWjYoiGTl05mROCISovfK9AHx0bubOg2Djd/b7rNG8aWJ98v067jlMGc+WI7l7ceptu812g+uBe/f7WL3PQsDr73FQ37BZRo79S8Hi1e7MXmJ2agzS/g0TUTiNsdTcaVpErKrKRWvXxxbeTOzF6jaejXlMFhr/PhgGll2h3bvJ9VY/4DwLBP3qHr4EAOrPmlssMFpYJ2817j4PPzUCek0nP7HBJ3niTzwo3CJm59fLFr7M6uLu/i5N8Enw+Gs++x9wD9hCxp9ymOvfExCksVKpsaABwf8Z/C5Vu/P4T8jGyTplEv0AeHRu58120crv7edJk3jB/LGV/tpw7mty+2c2XrYbrMf42mL/bi/Opd91y+1Rv9Sb8Yj1VNm8LHzn62jaiFGwFoOTwY37HPcGjS/0ya590MeKwvLz33FFNmf2i2GO6bQonN8NFkhY1Hm3qTmvM+J//4QbQ3rpZpZ/3Svyk4VewWxRoNOV99hubKRbC2oea8ZRScPl522UqiUCoImDuMPYPnoU5II/in2dzYcZKMi0XbkEegDzUbufPjI+Nw8W9C+3mv8csTM9Dm5rN7UBgF2bkoLFQEbX6PhN2nSD0ZY5ZcAFAqcZ0Wwo03JlOQlILX+v+Qtecw+ZfiCpsUXE/ixqvj0WbcwbZ7e9xmjub64NEA1J78FtkHjpM4dg5YWqC0rmGuTAoF9u1B48YN6OrfH//27Zi/aAaPBw0ut+2s6R+WO6lSKpVMm/kukbt+NXW4FVMosRnyNlmLJqK7lYLd9E8piD6ENiGuTDvrgW9QcPZEmVVkLwxFdyejkgK+N5/e/rg38mBczxC8/ZoxbM6/eX/ApDLtDm7ex2ejlwAQ8slYeg0OYteaHdg62DJszr9Z8MpsUuNTcHBxrOwUKt297jb4T/BQXeOVnJzMuHHj7qvt+vXrOX36dIXPb9u2jdzcXGOFZlLd+nVl+0b9ge1vJ3+npqM9Lm7OZdoplUpCpo9g6ZxllR3iXXUO7syu73YBcD7qPHYOdji5lb2I8c9JF4CVtVXhXWdysnMKH7e2tS73bjSm1CA4gIsbDwCQfPISVg522LjVKtPO85FWXNl2FIAL3+4vnGjlpGaQcupymapQrSaeJEddQpOTh06jJeHwHzTs377MeitLu+AOHN20D4DYqIvY1LTDwbVsnuciowv/ffVUDE7uZcdiZXDya0LWlSSy45LR5Wu4sfkQ7qUmtx79Ari2Qf+N6a2TMVg62FLDrRYW9ja4dG5B3DeRAOjyNRSUM8Gq+2Rnbnx/yKR51O8XQIxhfN08eQkrx/LHl8cjrYg1jK+Yb/fTwJDr3Za39XCmXh9fLq6NLLGu/GLbmoVtDbN/Grb3bYujg3EuXDY1VZMWaJPi0SYngKaAvIO7sezwSJl2NR59hvwj+9HdLqok6tLT9JMugBw12htxKJ1rV1boZTj7eXMnNomsuJto8zXEbTlMvVLbUL1+AcRu1G9DqSdjsHK0xdowvgqy9Z+hSksVSkuV2ceRddvm5MfFU3A9EfILuPNzJPaBXUq0yYk+h9ZQFco59QcWdfTvv8LOFpv2bcn4bru+YX4B2sysSo2/PP0fC+TbdVsAOHn8NA6ONXGr82Bj5vURQ9i29RdSUozzO0R/hapxc7TJ8ehSEkFTQP7RSCz8upZpZ9XnafJPHECXab4K/P0K6NuRA99FAnAp6gJ2DnbUKufY5tSek4X/vnTqIs4e+tuYd326B8e2HyY1PgWAjNTbpg9amN1DNfF6EC+88ALt2rWr8PmffvrpoZl4ubrXJjk+ufDv5ISbuLqX3fE+99oADuw8RGpyxb9FYA613WtzM+Fm4d8piSnULid+gLEfjuXrE19Tz7seP/yv6JSXLv26sGz3MmaunMmS8UtMHnNxdu5O3Ikv+sDKSkjDzr3kzrWGkz25GdnoNNrCNrbuZXfAxd06fx2PTs2pUcselbUVXoE+2Hu63HUZU6pVx4lbxfJMT0yl1l0mVUoLFR2f6cG5vacqI7wyrD2cUBeLV52QhrWHczlt0kq0sfFwwraBG3mpmfh9PIKev8zFd9G/UNmW/HbbpXMLclNuk3Ul0aR52Lo7kVVqfJUeOzWc7Mm7XTS+sou1udvynWa+zPE5a9Fpyx4Q+08cxPPHPsb7ma6cXGj+U8QeFkrn2mhTi/bH2tSbKJ1K7s8UTrWx7NCdvF+2Vrwe1zqoGjWhIMZ8p7LZujuTXWzsZBu2j+Js3J1LjK/s+KLxpVAq6P/LXJ45/RmJ+86SGlX2FPLKpKrjQn5i0WdNQWIKKreKJykOz/Una7++Imnp5Y4m7TZuYePw+i4c11ljUNiYv+Ll7uFG/I2ifVBCfBIeHnXKbTtp+mh2/fo9M+dOxMrKsnD5R58IYvV/yz+tr7IoatVGm1bUN7pbKShrldpuarlg4d+N/Mgfy65Ap8P23fnYTQ/Hssdjpg73vji5OxdOmgDSElNxqlPxZ6bKQkW3Z3txOjIKAPdGntg52jN13Sxm/7iQbs/2MnXIZqfVKSrtv6rqoZ14JSUlMWHCBGJiYpg6dSqhoaEsXLiQO3f032SFh4dz+PDhcpf96aefSEtLY+bMmcycOROAU6dOMXXqVCZOnMjixYvJydFXWaKjoxkzZgzTp0/nv//9r1EurHtQ5f22QOmqT+06LvR+oicb/7upssL6WyqqWn0U+hFDOwzlWsw1ejzZo/DxQzsOMSJwBLPfmM3Q0KGVFaZeeb/tUCr+in7/4W7SY+I5tfRHHls7iUfXTCDtXJzZrpUCys3zbtXFF2a/TszR37l07I8K25hSue956XjLzQmUFkoc2zYkdmUEe/tOoSA7l6ajnirRru4zXbn+/UFjhly+v5gHurs9p6NekC/qlAxSDde0lXbyg2/Z0GE0l74/SMvX+j5QyP9o5W7rJfvLZlgI6m+WgU5b/jpqWGP77izUq8JBbdpTWe+q3FRKj73ymujb6LQ6tvedwpaAt3Hx9caxeT3jx/gg7qNv/mTT0QeHZ/uRumiFflGVihqtmnB7/Y9cey4EnToHpzdeMGGw9+d+Pv8B5s78iO4dHufR3s9Ty8mRkDFvADBr3mTmzFiEVlvBWKws99E31i+OJHfjl+VuN1nzx5I1ayTZS6ZiFfgUqmZtTRTo/bvfvvnTsDn/5o8j5zh/TP9li9JCSaM23nz4WhgfDJ3FgHcG4t7Iw2TxiqrhobzGKz4+niVLljBy5Eg+/fRThg8fTqtWrVi/fj0bN25k2LBhd13+scceY9u2bcyYMQMHBwcyMjLYtGkT06dPx9rams2bN/Pjjz/y1FNPsWzZMt577z3c3d356KOPKlxn6R9q+7ueffVpnhqivznG79HncfN0K3zOzcOVlKSSpww0bdOEeg3rsv7XNQBY29Rg/YGveKFbJU9SDJ545Qn6vdgPgIunL+Lq4Vr4XG332qQmVXzKg1arZd8P+xj45kB++bbktUNnj57Fo74HDk4OZNwy3bnerV4NosVLvQG4eeoy9p4u/HnllZ2HM1mlbkSQk5ZJDQdbFColOo0WOw9nshNv3fN1zq/by/l1ewFoP/F5shIqt1rZY2gwXV/sA8DVU5dwKlZxq+Xuwu2k8nN4dPRA7F0c+HLEokqJszzq+DRsisVr4+FMTqn3PCc+DRtP57JtdDpyEtK4ZfiGPv7HIzR9u2jipVAp8XisA3uDp5ok9havBtFsiH58pURfxq5YHnYezmSXGl+5aZlYORaNL1sPZ7INfZOdkFbu8g0f70j9YH/qBfqgqmGJVU0benzyFvve+azEui9/f5C+q0OJXvRwfGljbtrUmyhdivbHShdXtLdK7s9UjZtj947+WkKFgyMWfp1AoyH/+K+gUmE3bhb5ByLIP7q/UmMvLTshDdtiY8fWwxl1YsmxpzaMrz+/17f1dC5zI5b8jGySD/2OR+923D5/3dRhV0iTmIKle9FnjYV7bTTJZT9rrJo1wm3WGOJHTEN7OxOAgqQUCpJuknv6PAB3dh7A6Y3nKyfwUoa98SJDXh0EwKmTZ/Cs6174nIdnHRITk8ssk5yk76G8vHzWff09b416DQAfv9Z8/l/9ftrZ2Yk+fXug0WjYvm2XqdMoQXfrJkrnor5RONVGm15qu2nQFJsR+hsZKewdsWjbgRythoKog+gMbXWZ6RSc/BVVo+ZoLpypvAQMgl7pT+/B+i+qLp+OwcWzqGrn7O5CenL5n5nPjH6ems4O/HfygsLHbiWkcjotk1x1LrnqXP44eo76LRuSeCXBtEmYUVW5q6E5PXQTr4yMDBYsWMC4ceNwcXEhKyuLVq1aAdCzZ8+7To4qcvHiRa5fv8706fq7yRQUFNCsWTPi4+Nxc3PDw0P/DUSPHj0qnFwV/6G2R74K/CuplbBp1RY2rdKf192lTyeeGzaAiC27ae3fkjsZWWVOJzy06whP+Q0s/PuXC9vMNukC+HH1j/y4Wn+6QIfADjz56pPs3bqX5n7NycrM4lY5OyePBh4kXNXvcDoFdeJazLUyj3u38cbCysKkky6Ac6siOLdK39degb60fq0vl7Ycws3fm7zMbNTJZc8/jz94jkaPd+Ty1sM0G9Sd2J0ny7QpzdrFgZzUDOw8XWj0aHu2PP2+sVO5q31f7WTfV/qLsVv39qPHq/04sfUgDf2aos7MJuNm2Ty7vBBIyx7t+M9Lsyv9ervi0qMvYdfYHdv6rqgT0qg7oAsnRn5aok3izhM0Gh7Mjc2HcPJvQn6mmlxD36lvpGLv7cGdSwm4dm9T4qYcrj3acCcmnhwTTYT/WBXBH4bxVa+PLy2H9eXKlkO4+nuTl1H++Eo4eI6Gj3fkytbDNBnUnTjD+IrbebLc5U/M38CJ+fo7url3aUmbNx8rnHQ5NKpTeBOX+sH+3L5UfT/ojU1z6Q+U7nVRurqjTUvBqmsgWZ/MKdEm8+2XCv9t+9ZE8k8e0k+6ANs3J6C9cZXcbd9WatzlSYu+TM1G7th5uaJOTKP+0505GBJeos2NnSdp+lowVzcfwsW/CfkZanKS06nhXBNtgYb8jGxU1pbU6d6a38PLOUWsEuWcPY9lg7pY1K1DQXIq9o/2ImlCybNULDxccf/kPZImLST/atE2r0m5RUFiCpYN65Efex3bzr7kXYor/RKVYuWXa1n5pf5OmX2CezD8X0PY/N1P+LdvR2ZGZuEkqzi3OrULH3/08T788bv+WsJOPkV3aVuyNIxftu+t9EkXgObKeZR16qKo7Y7uVgqWHXuhXj6vRJs7k14p/Lf18PEUnDpMQdRBsLIGpQJy1GBljap1ALk/rKnsFACIWL2diNX66wB9AwPo++qjHNp6AG+/ZmRnZpc78eo1OIi2PX2Z9+L7JT4zT/xylFdn/QulSomFpQXevs3Y/qV5tyFheg/dxMvW1hYXFxfOnz9P165lL8z8K3Q6HW3btmXMmDElHo+NjTXK+v+uQ7uO0CWwExt+XUOOOoe57xZ9Y/Lh6nnMH/9hmQpYVXJs9zE69O7Aiv0ryFXn8lFo0eR45sqZfDzxY24l32LcR+OwtbcFBVw5d4VPp+oPoh957BH6PNeHgvwC8nLymB9Suad7XtsdjVegDy8cWERBTh57311e+Fy/1aHsH/8l2UnpHJ27jsClo2g/YRCpZ2M5vy4SABtXRwb8NBsrext0Wi1t3ujPxt4Tyb+jpu/y0dRwskdbUMCvU1eRd9t8px39tieK1r39mLH3Y/LVeawZX1QZeet/k/hm4jJuJ99icNgbpN24ybjv9Qeb0duPsv2Tyr9GSKfRcnrKSrqsnYRCpSRubSSZ52/Q8BV9BS929S6SIqKp08eXoMMfoVHnEjWm6MYzp6euImBpCApLC7KvJpd4ru6ALtyojNMMgeu7oqkX6MNzvy5Co85jf7Hx1Xd1KAfGf4k6KZ3jYevotXQU/hMGkfpbLBcMN8y42/IVCZj8Ao7eHui0Ou7cSDHrHQ0Bxs+Yz7Go06SnZ9BnwMuMfH0ozz3Zz6wxVUirRf3fT7CbsgCUSvIif0Z7PRaroCcByIsoezv2P6mat8GqRzCaq5eo+cEXAKjXfklB9JFKCb00nUbL8akr6fXNRBQqJZfX7SXjwg2aDNVvQzFf7SJ+VzQefXx54uBiNOo8jozVbyc2dWrR+eM3USiVoFQQ98MR4iOizJJHIY2Wm2HheH4xF4VSScb3O8mLuYrDC/qzRzLWb8PprSGoHGvi+t4oAHQFGq4//zYAN8PCqbNgIgpLC/KvJ5I81XwV/T/t2rmPPn17cChqO+rsHMaGFFXh12z4nHHvTCcp8SbhXyzAxcUZhULBb2f+YMK7M80YdTm0WnK+/hTbsfNQKJXkHdiBNv4qlj2fACB/b8UTDoVDLWxHva//Q6ki/8geNGePV0LQdxe9+wQ+vf1ZtG8peYbbyf8pdOVUvpywlPTkW7wWNoKUGzd5/3v9RPPY9sNs/uRb4mNucHpvFPN2fIRWqyNyXQTXL5hnsi8qj0Jnzq+sH1BycjIffPABYWFhhIWF0a9fP7Zs2cK311OEAAAgAElEQVTw4cNp2bIlGzZsIDs7m2HDhhEeHk5AQACdO3cud13jxo1j4sSJuLm5kZGRwcSJE5kxYwbu7u7k5uaSmppK7dq1GT16dOHjS5YsIScnh0mTyt4utLhH6v79ildV4aiyNncIRvWM1jx34DOFUxb55g7BqPrmqMwdgtGkqR7ay2fL9fKpWeYOwWiyQl43dwhG9fN+T3OHYDQdapWt5DzMusdfM3cIRnP+ubrmDsGoRkY8HHdRvV9rrj4cp4of8Xy20l6rU3zVfE8euooXgLW1NZMmTWLOnDl07NiRNWvWkJubi5ubGyNHjryvdQQFBTF37lycnJyYMWMGISEhfPzxx+Tn6w9mBw8ejKenJyNGjGD+/PnUrFmTFi1acO1a9dmRCiGEEEIIISrHQ1XxMrfffvuNH374QSpeDzGpeFVdUvGquqTiVXVJxavqkopX1SUVL/M4XIkVr85VtOJVvY4OhBBCCCGEEKIKeihPNXwQCxcuJDm55K1XhwwZgq+v7wOvq3Xr1rRu3dpYoQkhhBBCCPGPUJV/2LiyVPuJ1/jx480dghBCCCGEEOIfrtpPvIQQQgghhBDmJT+gLNd4CSGEEEIIIYTJScVLCCGEEEIIYVJacwdQBUjFSwghhBBCCCFMTCpeQgghhBBCCJPSIdd4ScVLCCGEEEIIIUxMKl5CCCGEEEIIk9LqzB2B+UnFSwghhBBCCCFMTCpeQgghhBBCCJPSyjVeUvESQgghhBBCCFOTiZcQQgghhBBCmJicamgCryg8zR2C8VSzX7uzqEYXdvoUWJo7BKNKqUZ7I1U1GmcAWSGvmzsEo7ELX2HuEIwq2/c9c4dgNNvuuJo7BKN6xLGGuUMwGqWzytwhGJWvztbcIfwjye3kpeIlhBBCCCGEECZXjb5jFkIIIYQQQlRF1ewkqr9EKl5CCCGEEEIIYWJS8RJCCCGEEEKYlFzjJRUvIYQQQgghhDA5qXgJIYQQQgghTEqu8ZKKlxBCCCGEEEKYnFS8hBBCCCGEECYlFS+peAkhhBBCCCGEyUnFSwghhBBCCGFScldDqXgJIYQQQgghhMlJxUsIIYQQQghhUlopeEnFSwghhBBCCCFMTSpeQgghhBBCCJPSyjVeUvESQgghhBBCCFOTiZcQQgghhBBCmJicaliFdJ01lPqBvhSoc4kcu5yUs7Fl2tT0cqXP0hCsa9mTciaW3aM/Q5uvockzXfEd+QQA+Vk57J+8krTf4wqXUygVPPvTbLISb7F92CLJ5z50mjWUeob4D4xdTmo58dt7udJraQg1nOxJPRPLvnf08d9reYVSwZM/zyY78RYRrxbF3/K1vrR8LRhtgYbru6I5HrbOJLl1mTUUL0NseyvIraaXK4FLQ6hh6JtIQ984envQc/G/qd2mIccWfMuZZT8VLtP69X60eLEXCoWCP77Zw9kVO0wSf+di8e+7S9/0NsSfeiaWvaOL+qai5Vu/0Z/mL/YCnY60P66zf9xyNLn5+L37LM1f6kVOaiYAxz/YwPXdp4yeV91e7eg4aygKpZKLayM5E/5DmTYdS42rNEPsFS3rN34gXsH+oNOhTsngwNhlqJPSjR773Vj4dMBm2ChQqsjbvY3cLWvLbafybo79nHCyl8wi/8g+FC6u2IZMRlnLGbQ6cnf9SN7P31Vq7A9q2tzF7Pv1KM5Otdi85nNzh1Ohv7MNOXp70GPxv3Fp05DjC77lbLF9QPcP/4VXkC85KRlsCppcafn0nDmUhr31+ewct5yb5eTj4OXKo5/qP2+Sz8ayY4w+n7qdW/Lkl2PJuHYTgJjtxzj68WZqNfbgsfBRRcvXd+Pw4o1Em2i/VpHh7/8Lv97tyVPn8mnoEq6cvVymzeiP36Vx2yZoCjTEnLrIssnhaAo02DnYMXLhO7g38CAvN4+l4z/h2oW4cl7F9FTN/ajx1OugVJJ/NIL8PZtKPt+4NdbDJqO9lQxAwZnD5EdsQOHoQo3Bo1HWdEKn01Jw5BfyD/xojhQInDmURoZx9vO45SSXM84cvVx5wjDOks7G8pNhnHUY8TgtB3QFQGmhxLlJXZb6vkV+Th6Dv52GysoCpYWKCz8d5eDiTWXWWx3ozB1AFSAVryrCK9AHx0burOs2jn0TV9Bt3rBy23WaMpgzX2xnXfdQcm9n0WJwLwAy426ydeAcNvadwsmPN9NjwfASy7V5vT+3YuJNnEWRhz2feoE+ODRy57tu4zg4cQVdKoi//dTB/J+9+w5vqvofOP5O0t1SaEtLW8ouq4wCLXsWShmKon4RVNYXFZWlUHadSAHxCy5QFBAZylbUnwhYkSV7lL3aUhndLaUrncnvj4TOtBRN0qqf1/P0eUhybvL55Jxzb849514urtzF9h66+Js+06dS2/u8MJDU6yXjd+/WkvoD/NgROIcdfWdzYcVOTOF+3WzpEcyhCuqmk75utvScTu69TJrr6yYnNZPDb67n3Ocl43Nq7kWLZ/qw49G32B40l/qB7XFsVMfo8d//brfq4+9WTvwd5+rqZpu+bTXTx1/e9nbuTrQaF8T3j7zBt4FzUKiUNH6sS+H7XVi5ix0DQtgxIMQkgy6FUkHn0DH8MnIxOwJm0mhoF2o29SxRpq4+9m97BHOkWLuqaNsLn/3ED/3n8kNQCLfDztBu6hNGj/0BiWE77lUyF84mfdpYrLr3Q1m3gcFyNs+OJ//siaLnCgrIXv8Z6dPGkv76BKyDHje8bTUydHB/ViydX9VhVOiv9qGc1EyOvLm+xEmX+65vPcDuke+bMPqyGgb4UquhO2t7BfPr7NX0DR1rsFz3OSM4s2oXa3vr8mk1vE/hazEnrvLNoBC+GRTC8Y92AJAaFVv43MZHXidfnUPkrpNmyKhI+wA/PBp5Mrn3S6yYs5zx818xWO7Ajv282ncC04ImY2VtRb8RQQA8OWkY0ZduEDxwCp9M+4Bxb79ozvCLKJRYPzEe9ep3yfrfFCza9UDh5lWmWMGNy6g/mIb6g2nkhW3RPanRkPt/X5H1v8mol83Cstsgg9uaWqMAX5waurO6VzB7Zq+mfzntrNecEZxctYvVvaeTfS+TNvp2duLzn1g3KIR1g0I48N4Wbh+9TPa9TApy8tgyYgHrBoawbmAIjXq3xaN9E/MlJsyq2g28EhISCA4OrlTZzZs3c+7cuXJfX7FiBbdv3zZWaFy8eJFFixYZ7f2Kaxjkx7VthwBIOB2JtaM9dm61ypTz7O5D1E/HAbi29SANB/gBEH/qOrn3snT/Ph2Bg4dz4Tb2Hs406NeOK9/sM0nshvzd86k/wI8IffyJpyOxqmmPrYH4Pbr7EK2PP2LrQRro469oezsPZ7z6teP6xpLxtxgdyLnlP6LJzQcgOznNJLk1CPLjerG6sXI0nJtndx9uGKib7OQ0ks5GockvKFG+lrcnCWciKcjORVugIfboFRoO9DdJ/CW+20rEX7xuKtpeYaFCZWOFQqXEwtaKrPi7Ro+/PLXbNyE9Op6Mm4lo8gq48f1R6utjvq/+AD8iDbSrirbNy1AXbm9hZ41Wa95zjirvFmjiY9AkxEJBPrmH92LZsXuZctaDniDv2EG094pm47SpKRTcuK57kK1Gc+cmSufa5gr9T/Fv14aajjWqOowK/dU+VN4+ACDu2FVyUjNMGH1ZjYP8uLxdl0/cmfKPN/W6+XB9py6fS9sO0qRU/6pIve6tuHczgfQ7ycYJupI69u/Mvu2/AXD9zFXsHO2p5eZUptyZ304V/jvi7DVcPFwA8Gpaj/O/604UxUTewdXLjZq1y343pqas3xRNUizalHgoyCc//BAWrTpValtt+l00d/SzfDnZaBJuo6zpYsJoDfMO8uOivp3F6tuZfTnt7Jq+nV3cdhBvA+2s5WNdufzDkcLHeVk5ACgtVCgtLDDzbtpsNGb8q66q3cDrYQwfPpy2bduW+/rLL7+Ml5f5z4r8GfbuTmTGFO3QM2NTsHMvuXO1cXIgNy0LbYGuSWXEpmDvXnYH3GJEH27+VjQg7fb2SI6GbjTrD66/ez52lYjf2smB3HtF8WcVK1PR9p3fGcnJ+RvRakrG79jYnTqdmvPoj28zaFsItX0bmyQ3e3cnMkrFVvp7t3ZyIKdY3RjKv7S7V2/j0bk51rUcUNlYUa+vLw6exj84lv5us8qJP7dU/Pbl1M397bPi7nLh852MOPYRz5xeRm56FncOXCgs5zO2P0/8soCe/3sRq5p2JsorpfCxoe+8vHb1oG3bzxrGsBMf0fiJbpx537xL9ZTOtdEkJxQ+1iQnonQqOXhSONXGsmNPcn/5ofz3ca2DqpE3+RGXTRbrv8Vf7UPVjYO7ExmxRflkxKXgYOB4k1PB8ca9gzfP7grl8bUzcG5Wt8xnNHusK1e/P1LmeVNzcXchOSax8HFKXDIudcrfr6osVPR6MoDwfacB+ONSNJ0HdQXA27cprnXdcHE3/6BF4eiMNjWp8LH2XjIKA4MnVYPm2E5dis3zb6CsU6/s+zi5ovRsRMHNayaN1xAHdyfSi7WzdAPtzNZAO6tRqoyFjRUN+7Tl+s6i2X2FUsHon0OZcOZT/jh0nrjwSBNmIqpStR54xcfHM3PmTCIiIggJCWH69Om8//77ZGTozqYtX76co0ePlrv922+/TWSkrvGOGjWKjRs3MmPGDEJCQkhN1Z1VPXLkCMHBwcyYMYO33noLgNzcXD799FOCg4OZOXMmFy5cKPczjEZh4BabpQcWBsqUHnp4dmtJixG9Oaa/Nqh+v3aok9JIOh9tnDgr6++ez5+MvzCBcrb3CtTFn2wgfqVKiXVNe/5vyNucmL+RPismlX0PY6hEbgpDZR4gNSKGs5/+H4M3zmbQhpmkXLpp8Iz4X2UottKDcINlHrC9VU076gd1YEvXqWz0m4ylrTVNntTNzFxeF8bW7tP4LiiErIRUOr/x3F9PpLSK2lOFZbQP3PbMe1vZ2vFVor47TMv/9v9LYT40g22pZGK2Yyei/uZz0JZzntLaBrtp81CvXQ7qLOPH+C/zV/tQ9fMn92n6IokXolnT9TW+GRjC2a/2MGTl1BLFlJYqGvfvQMRPx4wVcOUZTK38mnhx/stcOnaRyycuAfDdZ9uwd3Tg/Z0fMmjso9y4GEVBgfH3yw9UieNOwZ0oMheM1y0z/P0nbMbMLlneygab0bPI+eFLyFFjbopKtDODv2tKFWnSvz0xJ6+RfS+zqIxGy7pBIXzeeQruvk2o3ezvMWnwsDQKhdn+qqtqe3ONmJgYPvzwQyZMmMCyZcsYN24cPj4+bN68mW3btjF27NiHer+cnByaNm3KM888w4YNG/j111956qmn2LZtGyEhITg7O5OZqesEu3frLpxdsmQJd+7cYf78+Xz00UcVvn9YWBhhYWEANKpkTK3GBNLi2QAAEs9GYV9sdsDew5msUhfAZ6ekY+Voh0KlRFugwcHDmay4oqVQzi3r0WvxC/w86v3CpR7uHZvRIKgD9fv6orK2xLKGLX0/foW9Uz6rZJSV93fPp8WYQJo9p4s/KfzB8eekpGNVsyh+Ow/nwqVpWbEpBrdv+Egn6gd1wEsfv1UNW3p9/AoHpnxGZuxd/vj5ZOHnazVarJ1rkJOS/pdz8ylVNw6eLsQXiy3TQN1YF6sb+1J1U56rm/ZzddN+APxnPU1mbMoDtqiclmMCaa6PP6lU27KrRNsqHn9mqbq5v71nj9ak30okW/99R/98kjp+TYn89neyk4qWfV795jeCvqrccuiHoWszJZfUll7qWF67UlpZPHBbgKjvDhO4bjrhS8x34bYmORGli1vhY6WLK5q7JZdrqRo3x37KmwAoHGti0b4zFBSQd/J3UKmwD55H3qEw8o4fNFvc/zTG7EPVQdvRgbR+RpdP/LkoHDyK8nFwdyajVD7qUvs0Bw9nMvV9JLfYctzo384SMH8sNk4OZN/VHXca9vEl4UI0WUmmWf5d2sDRgwuv0Yo8dx0XT1dAN9Pr7O5CSoLh/eqwV0fg6FyTz+csLHxOnaHm0xkfFz7+9NBKEm7FG9rcpLT3klHUKprpVtR0QZtWKo9ig6mCK6fhiZfArgZkpYNShc3omeSfOUDBhfJPuBtbu9GBtNW3s7hzUdQo1s5qVLKdZZTaF7cY0pXL5cye5qRlcevoZRr2aUvSNeNdKiOqj2o545WWlsbixYuZPHkybm5uZGZm4uPjA0Dv3r25fPnhl5pYWFjg56dbZ9u4cWMSE3VT982bN2f58uWEhYWh0ejOtl65coVevXoBULduXVxdXYmNja3w/QMDA1m0aNFDXQN2cW0Y2weEsH1ACNG7TtHsPz0AcOvQhNz0LLISyt55LObwJRo/olsX3WxYT6L36JYTOHi6ELTyNX57dQX3bsQVlj++aAtfd5zCN12nEjZxOTG/XzLJoOufkM+VtWH8EBTCD0Eh3Nx9Cm99/K4dmpCbloXaQPyxhy/RUB+/97Ce3NTHf3PPaYPbn1q0hS3+U9jWZSr7Jywn9vdLHNDHf3P3STy669q5Y2N3VFYWRhl0AVxaG8a3A0L4Vl83TUvVjaHcYg5fopGBuqmIjYsjAPaeLjQa5E/k94eNEv/ltWGFN7b4Y1fJuskrJ/7YYvFXVDf3t8+MScatvTcqGysAPHu0IjXiDkCJ618aDPTn7lXjHxCTwqNwbOSOQz1XlJYqGj3ehVulvvNbe07TxEC7qmjbGsVucFIvqAP3IivelxlbQeQVlO51Ubq6g8oCq259yTtZsl2kT36WtMnPkDb5GfKO7ke9+kPdoAuwe3kmmjt/kPPTVrPG/U9jzD5UHZxbF1Z444vI3ado+ZQuH/f2Tcgp53hz+8glmg7W5ePzn55E6fOxc61ZWKaOb2MUSkXhoAug2eNduWbGZYa71u1kxuDXmDH4NY7vOUafp3Q//Ju2b05WehapCWUHwP1G9Kdd7/Z8OPl/JWbE7BztsbDUnWMPHBHE5eMXUWeYf7ZIc+s6ytoeKJzcQGWBRbseFFw6UaKMokbRflZZr6lu9ihLdwy0fnoimoTb5B0ofzmyKYSvCyu8IUbE7lO00rczD307yzTQzm4duUQzfTtr9Z+eRBbrN1Y1bPHq0qLEc7bONbB21C1ft7C2pEGP1qREmu9maOakNeNfdVUtZ7zs7OxwcXHh6tWrdOvWzSjvqVKpCpcaKJXKwqn28ePHc/36dU6fPs3MmTNZvHix2S8+B7i5N5z6fX0ZcWgJ+dm57Jv2ReFrg9ZNZ/+MVWTFp3JswSYCP51Ex5nDSLoQzZVN+wDoMPUJbGo50GPBWAC0+QV8+8ibZs/jvr97Prd/Dcerry9P/b6EAnUuB4vF33/ddA7NWIU6PpWToZvo8+kkOswcRvLFaK7pb5hR0fblub5pPz2WjGforwvR5BVw8LXPTZLbrb3h1Ovry3B93ewvFtuAddM5qK+b4ws20ffTSfjPHEbyhWiu6uvG1rUmQ3e+i5WDLVqNhtYvDGRbwCzyMtT0/+JVrJ0c0OTn83vI2sIbpBg7fq++vgzTx1/8uw3S101WfConFmwi4NNJ+JWKv7ztE89EcmPncYbumo82v4Dki39w5WvdRe2dQkbg3KoBaLWk30ri99lfGj0vbYGGo6+vpf83M1EolURs3k/qtTs0H9UXgKvr93L713Dq9vXlSX27OqSPvbxtAfzmDKdmEw+0Gi2Zd5I4MnuN0WOvkEaD+suPsZ+7GJRKcvf9jOZ2NFaBQwDIDSt7y/z7VM1bY9UriII/Iqnx3koA1BtXkR9eBUu+KmnGW4s4ceYcqalp9Bs6kgnPj+KpIQOqOqwS/mofsnWtyeM738Wy2D5gu34f0GfZRDy6tsTG2YERJz7m9JLtXNPPgptK9N5wGgb4MubgEvLVufwyvSifx7+aTtisVWTGp3Jo4SYGLZtE1xnDSLwYzcXNuny8B3ei7ah+aPILyM/O4+dJywu3t7Cxon7P1uydY/w+Xxmn956kQ4Afyw58To46h0+nF81ezf3qTT6buYy7CSmMD51A4p0EQr9bDMCxXUfY9vFmvLy9mLx0KpoCDbcjbpWY/TIrjYacHSuxffEt/e3kf0UTfwuLLrq+kX90NxZtumLRdSBoCiAvl+yvdf/VirJhSyz9AiiIjcZ26lIAcn/eoJsVM6OoveE0CvDlhYNLyFPnsqtYO3vyq+ns1rezAws38eiySfSYMYyEi9Gc17czgKYD/PnjwHny1DmFz9m71WLQ0pdQqpQolAqu/t8xon4NN2dqwowU2qoYZVQgISGB9957j9DQUEJDQxkwYADff/8948aNo2XLlmzZsoWsrCzGjh3L8uXL8fPzo0uXLgbf6+2332bUqFE0adKEUaNGsX79egCOHj3KqVOnmDhxInFxcbi7uwMwc+ZMJkyYwIULF7h16xavvPIKMTExhUsNr127xo8//sjs2bMNft59n3uNNO6XIozGqlq19r8mv/ouYf5TquX0+5+k+ge1M4ChPe5UdQhGY798dVWHYFTr2lXdCTZjy/gn7QSAgwrzLE00h6+eVlV1CEa1YrN9VYdgVNNvbqjqECpls4cJro8ux/DYr832WQ+jWs54AdjY2DB79mzmz59Pp06d2LBhAzk5Obi5uTFhwgSjfc6GDRsKlxG2bt2aBg0a4OnpycqVKwkODkalUjFhwgQsLS2N9plCCCGEEEKIf5dqN+P1TyAzXtWXzHhVX/+kk90y41V9yYxX9SUzXtWXzHhVb3+XGa+Nnuab8XompnrOeP3DdnNCCCGEEEIIUf1U26WGD+P9998nISGhxHPPPfcc7dq1q6KIhBBCCCGEEPdpDP1faP8y/4iB14wZM6o6BCGEEEIIIYQo1z9i4CWEEEIIIYSovv5hlz//KXKNlxBCCCGEEEKYmMx4CSGEEEIIIf41wsPDWbNmDRqNhn79+jF06NASr2u1WtasWcOZM2ewtrZmwoQJNG7c+C9/rsx4CSGEEEIIIUxKozDfX4VxaDSsXr2auXPn8sEHH/D7779z+/btEmXOnDlDXFwcH3/8MePHj2fVqlVG+Q5k4CWEEEIIIYT4V4iIiMDd3Z06depgYWFBt27dOHHiRIkyJ0+epFevXigUCpo1a0ZmZiZ37979y58tAy8hhBBCCCGESWnM+FeRlJQUXFxcCh+7uLiQkpJSpkzt2rUrLPNnyDVeQgghhBBCiH+MsLAwwsLCCh8HBgYSGBgI6K7fKk2hKLk+sTJl/gwZeAkhhBBCCCFMypy3ky8+0CrNxcWF5OTkwsfJyck4OTmVKZOUlFRhmT9DlhoKIYQQQggh/hWaNGlCbGwsCQkJ5Ofnc/jwYfz9/UuU8ff358CBA2i1Wq5du4adnZ1RBl4y4yWEEEIIIYQwqQfdbdBcVCoV48aNIzQ0FI1GQ0BAAPXq1WPPnj0ABAUF0b59e06fPs2UKVOwsrJiwoQJRvlsGXgJIYQQQggh/jU6dOhAhw4dSjwXFBRU+G+FQsELL7xg9M+VgZcJ3FNVdQTGozLnglwzcC6o6giMxzPvH5QM0GfyP6fjbPmsqiMwrp8PelZ1CEaT1e7Nqg7BqEaHz6vqEIzmRJsZVR2CUSktHKs6BKPZuLGqIzCujH/aj5u/iQfdbfDfQK7xEkIIIYQQQggTkxkvIYQQQgghhEnJjJfMeAkhhBBCCCGEycmMlxBCCCGEEMKktNXkroZVSWa8hBBCCCGEEMLEZMZLCCGEEEIIYVJyjZfMeAkhhBBCCCGEycnASwghhBBCCCFMTJYaCiGEEEIIIUxKlhrKjJcQQgghhBBCmJzMeAkhhBBCCCFMSlvVAVQDMuMlhBBCCCGEECYmM15CCCGEEEIIk9LIf6AsM15CCCGEEEIIYWoy4yWEEEIIIYQwKbmrocx4CSGEEEIIIYTJyYxXNdLv7VE0DmhHnjqHn6d/QfyF6DJlatZzZcgnE7Gt5UD8hWj+b+pnaPIKsKphy6MfvoKjpwtKCxXHv9jJha0HqOHhzCMfvIy9a020Gi1nv/mNU2t2myWfgHdG0SigHfnqHHYFf0GCgXwc67ny6LKJ2NRyIOFCNDtf0+UD4NWlJQFvjURpqUKdks6Wp0NxauzBo8snFX0f9d04vHQbp1cbP6fO80bh1VcX/6GpX5BsIH6Heq70+XQi1k4OJJ+P5sCUovjL297K0Y7u/3uBWs29QKvlUPBKEk9F0PDRTrSb9iS1mnry4yNvkXzuhtFzAnAN8MVn/mgUKiW3vv6NyE9+KFPGJ3QMbv3aUaDO5eyUz0g7H419Ew/afzGlsIxdAzeuLd5G9Bc/49iqAa3ffx6ltSXafA0XZn/JvTORJom/PKrGbbEaMAoUSvLD95F3+McSrysbtMRm2FQ0qYkAFFw9Qd7BHSicPbB+sqhNKZ3cyN2/jfzj5uknYLq2BqBQKhjy87tkxd0lbMwSANrP+A/1gzqg1WrJTkrj4NTPUcenmiw/jz5t6fDuKBRKJZEb93F52Y9lynR4dzSefX0pUOdydOrn3D0fjdLaksBv30BpZYHSQsXNn45z4X/bTRbng3SZN4p6+u/5QAX1FPDpRKxr6epp/6u6eqrZxINeS8fj0rohJxdv5cLnOwu36fm/F6kX2I7spDS+DZxjxowe7PUFSznw+3GcnWqxY8OKqg7ngWoFtKPRvHGgUpLwza/cWfZdiddtvevi/cFE7Ns05uaib4hZUbT/63D8Mwoy1FCgQVtQwLmBs8wdPgC93hlFA307C5v2BYnlHDsHLNcdOxMvRLPn1aL9AYCbb2OGff82uyZ8QuTOE4Du2NNv8Qu4NPdCq9Xy6/SVxJ2OMHk+XYv1m/3l9Jsa9Vzpq+83Seej2Ves3/ReOp7arRtyYvFWzhfrN6Dbvw3dqdu/7R67xOS5lDbo7dE0DfAlT53LjumfE2sgt05j+tNl3ECcG7qzuN1LZN3NAKDN0G70eHkIALlZ2fxfyCBPa5QAACAASURBVBriL980Z/hmJzNeMuNVbTQO8MWpkTsreweze85q+s8fa7Bc79kjOLl6Fyv7TCf7XiZth/cBoMPo/iRfv8NXg0LYODyUgNefRWmpQlOg4bf537C63yw2DH2b9qMDcWnqafJ8GgX44tTQnS97BfPL7NUEhhrOp9ecEZxatYsve+vyaaPPx9rRjsDQsex4filrA2fz4yufAHA3Kpb1g0JYPyiEDY+8Tr46h+u7Tho9fq++vjg2cmd7j2AOz1pN14WG4/cPGcHFlbvY3mM6OfcyafpMnwdu33neKG7/do7ves/k+/5zuXc9RpfbldvsffEj4o5eNXo+hZQKWi36L8effY/9Pafj+UQ3HJrVLVHEtV877Bu5s6/LVM5PX0nrxc8DkBkZy6F+c3R//edSoM4lXn9Ab/Hms1z/33YO9ZvDtcVbafnGs6bLwRCFAqtBY8jeuBj1ipmoWnVBUbtsOy+4dZXsVSFkrwoh7+AOALQpsYXPZa9+HW1eDgVXjd+mymPKtgbg88JAUvVt7L4Ln/3E9/3n8kNQCLfCztBu6hMmyExHoVTgt2As+55bzM4+M2nweFccm5Zscx59fanRyJ3/6x7M8Zmr8V/4XwA0OXnsHRbKrv5z+bn/XDz6tMWlg7fJYq3I/e95a49gDs1aTbdy6qnjXF09beupq6dmI/oAkJOayZE315f54QhwfesBdo9834TR/3lDB/dnxdL5VR1G5SiVNF7wIpeeCyW892vUHtoD22ZeJYrk303nxuurSwy4irv4n7c42396lQ26GgT4UquRO+t7BrN31mr6LBhrsFy3OSMIX7WL9b2mk52aiY++nYGuz3WbM5yb+8+V2KbX26P4Y985NgTMZOOAuaRExGBq9fr6UrORO1v0/aZHOf2m09wRnF+5iy09p5N7L5PmxfrN4TfXc85AvwFo/fxAUs2QhyFNA3xxbuTOx72D+XHOah6Z/1+D5W6evMa65xaSeiuxxPOptxJZ8/S7fDZwDvs/3sGQhc+bI2xRxR448EpISCA4OLhSb7Z582bOnTv34IJGsHz5co4ePfqXy1QX3v39uLj9EACxZyKxcbTH3q1WmXL1u/lwdedxAC5sP0jTID8AtFotVg62AFjZ25CdmokmX0NmQmrhzFluZjbJETE41HE2eT5Ngvy4VCwf6wryuabP5+K2g3gP0OXT4vFuXP/5BOkxyQCok9PKbtu9Fak3E0i/k2z0+OsP8CNimy7+xNORWNW0x9ZA/B7dfYj+SRd/xNaDNNDHX972lg621OncnOsb9wGgySsgNy0LgHsRMaRFxho9l+JqdfAm60Yc6j8S0OYVELPjCHUG+pcoU2egH3e2HgQg9VQElo52WJfKvXbP1mRFx6O+naR7QqvFooau/Vk62pEdf9ekeZSm9GyCJiUebWoiaAoouHgUi2Z+D/0+qkat0N5NQHvP+G2qPKZqawB2Hs549WtX2N7uy8tQF/7bws4atKb731Wc2zchIzqezJuJaPIKuPn9UbwGlKwbrwF+RG/Ttbnk0xFY1bTDRp9DflYOAEpLFUpLlUljrUiDoFLfs6PhevLs7sMNA/WUnZxG0tkoNPkFZbaJO3aVnNQME0b/5/m3a0NNxxpVHUalOLT3Rh0dR87NeLR5+SR9fwjnAR1LlMlLTiPjbCTavPwqirJijYP8uKw/dsbrj512BtqZV3cfIvTt7Mq2gzQu1qfa/jeIyJ9PlDhuWjrY4tm5OZc27QNKHntMqUGQH9f1/Sahkv3m2taDNKxEv7H3cKZev3Zc/WafyeKvSPP+fpzdrttv3T4TgY2jHQ4Gcou7+Aep94+Vxdw6dZ1sfR3cPn0dRw/T/zaraloz/lVXRl1qOHz4cGO+XbWl0WhQKo07WVjD3Ym0mKIfe+lxKdSo40RmQtHyH1snB3LSstAW6CZr02NTcHB3AuDM2l94cvU0JpxYhpW9DT9MWlbmB4qjV23qtGpAbLjpl4A5uDuRHlsyHwf3svlkF8sno1g+To3dUVmoeHpzCFYONpz+cnfhQO6+Fo915cr3R0wSv527E5nF6iMzNgU7dyfUxeK3dnIg915R/Fn6MhVtry0oIDs5nR4fjMfZpz7J56I59uZ68tU5JsmjNBt3J9TF4sqOSaZWqRkEGw9n1MUGs9mxKdh4OJNTLHfPJ7oR893hwseX3lhHp01zaPnWSBRKBYcffcuEWZSlqOGENi2l8LE2PQWlZ5My5VR1vbF5MRRteiq5Yd+gTbpT8nWfruRfNE2bKo+p2po6IZXO74zk5PyNWOpPyhTXYdYwvP/Tg9y0LH4etsBU6WHn7kxWsfiyYlNw6VCybmzdnUvkkBWjyyE7IRWFUsGA3aE4NKzD9a9+IdnMS1jvK/09Z8WmYG+onort0zL1ZYR5WLs7k3un6AdubmwKDu2bVv4NtFp8Nr0JWi3x638hfsMvJoiyYvbuTmQUa2f3j4tZxdqZTanfAsWPnfbuTjQZ6M93wxdQx7dx4TY167uSnZJO4NLx1G5Zn4Tz0Rx4y/THntL5ZJbTb3JK9Ru7SvSbLm+P5Hio4f2bOTi6O5f43ZYWl4JjHScyEh5+2XaHEX2I2HfWmOGJauqhBl7x8fEsWbKE8ePHs2bNGnJycqhTpw6vvPIKDg4OLF++HD8/P7p06WJw+4kTJ9K9e3cuXrxIQUEB48ePZ+PGjcTFxTFkyBCCgoLQarVs2LCB8PBwAJ566im6deuGVqvlyy+/5MKFC7i5uZV436ioKNauXUt2djaOjo5MmDABJ6cHd9qvv/6akydPolKpaNu2LaNHj2b58uXY2toSFRVFamoqI0eOpEuXLly8eJFt27ZRq1YtoqOj+eCDDx7mq3swRdn/3EBb+syugTL3h/UNe7ch4eIfbBqxgFoN6vD017P46vhVcvVnti3trBm64lV+nbeh8DlTUvDn8rlfRKlS4tamEVufWYiljSXP7Hib2NMR3L0Rp3vdUkWT/h04+N5mo8deXmxlzrRXUB/lba9QqXBp05Cjb6wj6Uwknd8ZRZtJQzjz/ra/HHKlGIqrdJEH1J3CUkWdID+uhG4qfK7+2P5cenM9cT8dx+OxLrT9YDzHTPhjvoxK5KWJjSbrk9cgLwdVE19snp6K+tPpRQWUKiyadSDrNxO1qfKYqK15BbZDnZRG8vlo3Lu2LFPk9HtbOf3eVtpMGkLL//YnfMm3Dx97ZRiqmjL5GSqiK6PVaNnVfy6Wjnb0XD2Vms29uHf1tvHjfABFJfbRBsuYLCJRRmX6UgXOPxZCXvxdLF0c8dn8FuqIO6QdvWTEAB+sMu2somNnz7dG8vuCTWg1JbdRWqhwbd2Q/W+sIz48kp5vj8Jv4hCO/c/Ex55K1ImhnB+kfj/dNZFJ56PxMLB/M4sK9lsPo2FXH9oP78OXT80zQlDVm/w/Xg8x8IqJieHDDz9kwoQJLFu2jHHjxuHj48PmzZvZtm0bY8eOrdT71K5dm9DQUL766is+/fRT3n33XfLy8pg2bRpBQUEcO3aM6Oho3n//fdLS0pgzZw4tW7bk2rVrxMTEsGTJElJTU5k2bRoBAQHk5+fz5ZdfMnPmTBwdHTl8+DAbN25kwoQJFcaRkZHB8ePH+fDDD1EoFGRmZha+lpqayrx584iJieG9994rHEhGRESwZMmSMgM/gLCwMMLCwgCo7GRx+9GBtB0RAEDcuSgcPV24f/69hrtzmbMm6pR0rB3tUKiUaAs01PBwJkO/pKvNsN4c+1R3wXrqH/Hcu5WIcxMP4s5GobRQMXTFq1zacdgk10Pd1250IG2eKcqnhodL4Ws13J3JjC+bj02xfBw8nMnU55MRdxf13XPkq3PIV+dw+9gVXH3qFw68GvXxJf5CNFlJZZcg/lktxgTS7Dld/EnhUdh7FsVv7+FMVqn4c1LSsapZFL+dhzNZ+vizYlMMb6/VkhmbQpL+rH30T8dpM2mI0XJ4kOzYFGyLxWXj6UJ2XMllgerYZGzrunD/WRsPZ3KKlXHr145752+Qm3iv8Dmvp3txKWQtALE/HKXN0hdNl4QB2rQUFI5FPU9RwxlteqnljrlFJxwKIs+CcizYOoBat8RL5e2LJi4aMo3XpspjjrbW8JFO1A/qgFdfX1TWlljVsKXXx69wYMpnJd476rvD9F833WQDr6zYFOyKxWfn4Yw6rtS+QJ/D/bkKO0/nMjf7yEvLIuHIZTwC2ppt4NVyTCDNn9XX09mS9WRnoJ6yU9KxKrZPs/dwJivOvMtu/81yYpOxqlu78LGVhzO58SkVbFFSnr5P5SWnkfLzMRzaeZtl4NVmTCCt9MfOhLNROBRrZ7rjYtl2Zl3OsdOtbSMG6m9AZeNcgwYBvmgLNMSdjiAjNoV4/YqXyJ3H8ZtgmmOPz5hAWuj7TaI+n3j9a/aVyKcy/aZOx2bUD+pAvWL7tz4fv8K+Uvs3Y+s4uj9++t9td/S/2+5zdHcm/SFnu+q0qMdj773A12MWo66my42FcVVqvVxaWhqLFy9m8uTJuLm5kZmZiY+PDwC9e/fm8uXLlf5Af3/d9ST169fH29sbW1tbHB0dsbS0JDMzkytXrtC9e3eUSiW1atXCx8eHyMhILl++XPi8s7MzrVu3BnQDwlu3bvHuu+8yY8YMtm/fTkrKg3e0tra2WFlZsWLFCo4dO4a1tXXhax07dkSpVOLl5cW9e0U/Lr29vQ0OugACAwNZtGgRixYtqvR3cWZdGGsHh7B2cAjX95yi1VM9APBo34Sc9KwSy/Luu3nkEs0HdwKg9VM9uf7LaQDS7iTRoHsrAOxqO+Lc2IN7NxMAGLj4BZIjYji56udKx/ZnhK8LK7zxRcTuU/hUMp9m+nxa/acnEXt0+UTsOUXdTs1RqJRY2Fjh0b4JycVuENDiceMvM7yyNowfgkL4ISiEm7tP4f0fXfyuHZqQm5ZVYmnEfbGHL9HwEV383sN6clMf/809pw1ur068R2ZMCo5NPADw6NGK1Gt3yryvqdw7E4l9Y3ds67uisFThObQr8btPlSiTsPs0dYf1BKCWnzf56VkVLjMEyIm7i3M33VlHl56tyIqKM3EmJWliolA6u6Oo5QpKFapWXci/drpEGYV9zcJ/Kz0b687EqosOdBatzLfM0Bxt7dSiLWzxn8K2LlPZP2E5sb9fKhx0OTaqU/i+9YM6cM+E1xamhEdRo5E79vVcUVqqqP94F27vKdnm7uw5TcP/6NqcSwdv8tLUZCekYu1cA0tHOwBUNpbU6dmKtAjTXgdZ3OW1YewYEMKOASH8satkPeWll19PjQzUkzC9jPAIbBt5YF3PDYWlBbUf70HK7sqdbFTaWqO0tyn8d83evmRdNc8d5s6vDWPTwBA2DQwhavcpWuqPnXXaNyE3PavEMsP7bh++hLe+nbX4T09u6NvZuu7TWNttKmu7TSVy53H2hXxF1O5TZCXeIyM2hVqNdccer+6tSLlummPPpbVhfDsghG8HhBC96xRN9f3GrYMuH0P9JqZYv2k2rCfRD+g3JxZtYWPHKWzqOpW9E5cT8/slkw+6AE6s+4UVg+eyYvBcruw5ie9Tuv2WV3tvctLVD7XMsKanC8M/f43vpn5G8g3zHjOrisaMf9VVpWa87OzscHFx4erVq3Tr1u2vfaCF7iOVSiWWlpaFzyuVSgoKyl48WVx509FeXl6EhoY+VBwqlYoFCxZw/vx5Dh8+zK5du3jrLd11KcXjKj5tXHxwZmxRe8NpHODLiweWkK/O5efpXxS+9tRX09k9cxUZCansX7iJx5ZNouf0YcRfjOb85n0AHPl4B4OWvMR/dy8EBexftBn13Qzq+jej9VM9Sbh8kzE7dd/Rwfe3EPWbadcS39Dn8/zBJeSpc9ldLJ8nvprOnlmryIxP5eDCTTyybBLdZwwj4WI0F/T5pETEEL3vHGP2LESr0XB+0z6Sr+nOclvYWNGgZ2t+mfOlyeK//Ws4Xn19eer3JRSoczk4rSj+/uumc2jGKtTxqZwM3USfTyfRYeYwki9Gc01/E4OKtj/2xlp6f/IKSksL0m8mcEj/Wv2B/nSZPxob5xr0XzedlIt/sOe5xUbNS1ug4cKcr+i0aQ4KlZLbG/eRcfU29UcHAnBzXRgJYWdw7deOPsc+pECdw7lXPy/cXmlrRe1ebTg/fVWJ9z0XvJJW80ejsFBRkJPHuVKvm5xWQ+6utdg8MxOUSvLD96NNuoNFh74A5J/ei6plJyz9+qHVFEBeHjnfLS/a3sIKVaPW5Ow0XZsqjynbWnn85gynZhMPtBotGXeSODJ7janSQ1ug4WTIV/T5ZhYKlZKoTftJu3YH71H9AIhY/ysxv4bj0a8djx5eSoE6l2NTdW3Otk4tunz0MgqlEpQKbv54jJiwMyaLtSK39uq+52GHlpCfXfJ7DtLXU1Z8KicWbCLg00n4zRxG8oVorupvZmDrWpPHd76LpYMtWo2G1i8MZHvALPIy1PRZNhGPri2xcXZgxImPOb1kO9c27a+SPEub8dYiTpw5R2pqGv2GjmTC86N4asiAqg7LsAINUXNX4bPxDRQqJfGb9qK+dos6o4MAiF+3B0vXWrTdtRhVDVvQaPF48VHCe7+KhbMjLb6cCYDCQkXidwdJ/S3c7ClE7w2nQV9fRh/SHTt/DS5qZ0PWTmfvTN2x8/DCTQxcPokuM4aReCGai/p2VpH9b6wl6JNXUFlakHYzgbDgB+8r/qpbe8Op19eX4fp+s79YvxmwbjoH9f3m+IJN9P10Ev4G+s3Qne9iVazfbNP3m6p2fW84TQPaMeXAUvLUuXw/vehY+dxXM/hh5krSE1LpPHYA3V9+FAfXmryyexHXfwvnh1mr6P3qE9g61eCRd/V3cS0o4Ishb1RVOsJMFNoHLEhNSEjgvffeIzQ0lNDQUAYMGMD333/PuHHjaNmyJVu2bCErK4uxY8dW6hqvhQsX4ujoyL59+4iMjOT5558v8drly5cJCwtjzpw5ZGRkMHv2bBYsWMDVq1cLn7937x7Tpk3jpZdewt/fn6lTpzJ58mSaNWtGfn4+sbGx1KtXr8J4srOzycnJoWbNmmRkZDB58mTWrFlTZptRo0axfv16Ll68yI8//sjs2bMf+KUubjDygWX+LlT/sAsUnCse2/+tuBm4y9PfWZ/JqqoOwWi2mP7Eq1nZVNGdBE0hS/nPushgdPg/57qQE21mVHUIRnXKompu+mAKNv+cXQAAd/5hP27e/uPrqg6hUhaZ8ffx7D82mO2zHkalr/GysbFh9uzZzJ8/n06dOrFhwwZycnJwc3N74PVUD6NTp05cu3aNGTN0O+CRI0dSq1YtOnXqxIULFwgODsbDw4OWLXXLmiwsLAgODmbNmjVkZWVRUFDA4MGDqVevXoWfo1arWbx4MXl5eWi1WsaMGWO0HIQQQgghhBBF/lnD3T/ngTNe4uHJjFf1JTNe1ZfMeFVfMuNVfcmMV/UlM17Vl8x4VY2FZvx9POfvPuMlhBBCCCGEEH+GRua8TDPwev/990lISCjx3HPPPUe7du1M8XF/u3iEEEIIIYQQ/y4mGXjdvz6ruqhu8QghhBBCCPFvUp1v824ulfp/vIQQQgghhBBC/HlyjZcQQgghhBDCpOQKL5nxEkIIIYQQQgiTkxkvIYQQQgghhEnJNV4y4yWEEEIIIYQQJiczXkIIIYQQQgiT0vyz/v/6P0VmvIQQQgghhBDCxGTGSwghhBBCCGFSGrmvocx4CSGEEEIIIYSpyYyXEEIIIYQQwqRkvktmvIQQQgghhBDC5GTGywQ886o6AuPJ+4fdgUZV1QEYkeofdu7o2Ce5VR2C0eRbWlV1CEbVsWZSVYdgND9luFZ1CEZ1os2Mqg7BaDqef7+qQzCqnf6vV3UIRmOh/Wf9GDipTa3qEMS/lAy8hBBCCCGEECYl/4GyLDUUQgghhBBCCJOTGS8hhBBCCCGEScnt5GXGSwghhBBCCCFMTma8hBBCCCGEECYl810y4yWEEEIIIYQQJiczXkIIIYQQQgiTkrsayoyXEEIIIYQQQpiczHgJIYQQQgghTEruaigzXkIIIYQQQghhcjLjJYQQQgghhDApme+SGS8hhBBCCCGEMDmZ8RJCCCGEEEKYlNzVUGa8hBBCCCGEEMLkZMZLCCGEEEIIYVLav8lVXhkZGXzwwQckJibi6urK1KlTcXBwKFEmKSmJ5cuXk5qaikKhIDAwkMGDBz/wvWXgVU149GlLx3dHoVAqidi4j4vLfixTxv/dUdTt2458dQ5Hpn5ByvloALosfRGvwHZkJ6Xxf33nFJbvsWISjk08ALBytCM3LYud/UNMmkfneaPw0sd4aOoXJF+ILlPGoZ4rfT6diLWTA8nnozkw5TM0eQUVbt99yYvU0+e4o19Rjv6vP0O9/u3R5OaT/kcCh6Z9QW5allFz8uzTlo7ziurmwvKyddNxnq5uCtQ5/D71C1L0cXdb8iJ19XH/WCxuv9efwatY3L9P+4I8I8ddntoBvrScPwZUSm5/vZcbn/xQpkzL0DHU7tcejTqH81M+I03f1hq8OAivkX0BuP31Xv744mcAvGc9TZ2Bfmg1WnKT0jg/5TNy4u+aJZ/7nAN8aTr/vyhUSmK//pU/Pvm+xOt23p60/GgCNdo0InLhJm59pqtHa08XfJZNxMq1FlqNlpgNYdxe+bNZYy+u67xR1NP3gf3l9KEa9Vzp++lErGs5kHQ+mn2v6vpQzSYe9F46ntqtG3Ji8VbOf76zcBsrRzt6vv8Czs290Gq1HAheScLpCLPkZNfDn9pzXgaVirRtP5O6akuJ1x0eDcDp+acB0GRlkzjvE3KvRgGgrGGP27ypWDVtCFotCa8vJfvsZbPEXVrvd0bRMEBXN3uCvyDRQN041nNl0LKJ2NRyIOFCNLtf09VN3S4tGbJqKmm3EgGI2HWC4x/toFZjDwYvn1S0fX03ji7dRvjq3WbJqVZAOxrNGwcqJQnf/MqdZd+VeN3Wuy7eH0zEvk1jbi76hpgVRfuLDsc/oyBDDQUatAUFnBs4yywx/1mvL1jKgd+P4+xUix0bVlR1OJX2yFujaRbQjjx1LtunryD2YnSZMp1HB9Ft3EBcGrqzoP1LZN1NB6BFfz8Cpw1Dq9Wgydewc956/jh51cwZFBn09miaBviSp85lx/TPiTXQhzqN6U+XcQNxbujO4nYvkXU3A4A2Q7vR4+UhAORmZfN/IWuIv3zTnOGXMf6dl/AP8CdHncOHwR8QeSGyTJkpi1+laVtvUCiIuXGHD6Z9QHZWNp37d2Hk9JFoNVoKCgpY+c4XXDpxqQqyEKXt2LGDNm3aMHToUHbs2MGOHTsYOXJkiTIqlYpRo0bRuHFj1Go1s2fPpm3btnh5eVX43rLUsBpQKBV0WjCGvc8t5sc+M2n4eBdqNvUsUcazry81Grnzffdgjs1cTaeFYwtfi9p8gL3PvV/mfQ+9vIyd/UPY2T+Emz+d4NbOEybNw6uvL46N3NneI5jDs1bTtViMxfmHjODiyl1s7zGdnHuZNH2mzwO3j9hygF8M5Bhz4Dw7+s7m+/5zSYuKpe2kIUbNSaFU0Dl0DL+OXMwPATNpOLRs3dTVx72jRzBHZq2mc6m4fy0n7h/6zuZHfdxtjBx3uZQKfBaN4+SzizjUMxiPJ7pj36xuiSK1+7XDrpEHB7u8xoXpK/FZ/AIADi288BrZlyMDQzjcdxau/Ttg18gdgBvLf+T3gFkc7jebxF9O0yT4SfPkUyyv5oue5+yzCzjWcypuT3THrlReeakZXAtZw83PSg6ctfkFXH9rPcd6TuPU4BC8/jugzLbmUq+vLzUbubOlRzCHZq2mRzl9qNPcEZxfuYstPaeTey+T5iP6AJCTmsnhN9dzrtiA676u74zi9r5zbO0zk2+D5pIaEWPCTIpRKnF9fSIxL73OzSEvUmNwAJZN6pcokn87njtjZnDriVe4u+Jr3N55tfC12nNeIevQSW4++gI3n3yF3Kiq+aHVMMCXWg3dWdsrmF9nr6Zv6FiD5brPGcGZVbtY21u3f2s1vE/hazEnrvLNoBC+GRTC8Y92AJAaFVv43MZHXidfnUPkrpNmyAhQKmm84EUuPRdKeO/XqD20B7bNSv5oyL+bzo3XV5cYcBV38T9vcbb/9Go/6AIYOrg/K5bOr+owHkqzPu1waeTOB32msWPuKh4LHWew3M1TV1kzcgF3byeWeD7q9wssGzSb5YPn8u3Mzxn63ovmCNugpgG+ODdy5+Pewfw4ZzWPzP+vwXI3T15j3XMLSb1VMpfUW4msefpdPhs4h/0f72DIwufNEXa5/AP88WzoyfheL7Js9idMCJ1osNzKeV8weeBkJg+YROKdRB4dqzven/09nMkDJjFl0GQ+mv4hk9+bYs7wRQVOnDhB7969AejduzcnTpT9/ezk5ETjxo0BsLW1pW7duqSkpDzwvavlwCshIYHg4OBKld28eTPnzp17qPc/fvw4t2/ffmC5tLQ05s6dy8yZM7l8+TJHjhxh6tSpvPPOOw/1eQ/i0r4J6dHxZNxMRJNXQPT3R/Ea4FeiTL0BftzYdgiApNORWNW0x9atFgAJx66Soz8jVJ4Gj3UmescRo8ZdWv0BfkToY0wsFWNxHt19iP7pOAARWw/SQJ9rRdvHH7tKTmrZHGMOXEBboLtcM+F0JHYezkbNyVDd1DNQN5EV1Y2BuGOLxZ1ogrjLU6uDN1k34lD/kYA2r4C4HYepM9C/RJk6A/2J2XoAgHunIrB0tMParRb2TeuSeuo6GnUu2gINdw9fps7gjgC6s956Kjtrs98z1lGfV7Y+r4Qdh3Ed2LFEmbykNNLDI9HqZ1fvy01IJeP8DQAKMrPJvH4Ha3fz1EdpDYL8uK5vSwmnI7FyNNyHPLv7cEPfh65tPUhDfZvMTk4j6WwUmvySOVo62OLRuTlXN+4DQJNXYPSZTsBTogAAIABJREFU4fLYtGlO3s0Y8m/HQV4+GT/vw6Fv1xJlssMvoUnT9ZPss1ewqFMbAIW9Hbb+bUjbvktXMC8fTXqmWeIurXGQH5e36+om7kwk1o722Bmom3rdfLi+U1c3l7YdpEmp/UVF6nVvxb2bCaTfSTZO0A/g0N4bdXQcOTfj0eblk/T9IZwHlOo3yWlknI1Em5dvlphMyb9dG2o61qjqMB5KyyA/wr89CMDtMxHY1LDDwbVsu4u9+Aept5PKPJ+blVP4bys7G7Taqlvq1by/H2e3F8vF0Q4HA30orpxcbp26TrZ+v3X79HUczXTcLE/noC7s3b4XgKtnrmLvaI+Tm1OZcupix0crG6vCOsjOyi583sbO5l9xr3WNGf/+inv37uHkpKtLJycn0tLSKiyfkJDAjRs38Pb2fuB7/+2XGg4fPvyhtzlx4gR+fn4PnA48f/48np6eTJqkWwYSGhrK888/T+vWrf9UrOWxc3ciK6ZolJwVm0LtDk1KlLF1dyIzpuhgnBmTgq27E+qE1Ae+v1vn5mQn3iP9RrzxgjbArnSMsSnYlYrR2smB3HtZhYOOLH2Zym5fkaYjenHjh2PGSKWQLqZSddO+SZkyWcXiznrIuL1H9CLayHGXx9rdGXWxWLNjUqjZoeSOwtrDGXWxH37ZsSlYeziTceUWzeaMwNLJgYLsXFwD23HvbFRhuaZzhuM5rBf56Vkcf3Ke6ZMpHrO7MznF8sqJScaxQ9OHfh+beq7UaN2INDMtwSvN3t2JjFJ9wN5AH8pJK+pDmcX6UHlq1HdFnZJO76XjcfapT9L5aI68uZ58dU6F2xmDqo4LeXFFZ67z45Kwbtui3PKOTw0k86Du7KJlPXcKUu7hFhqMdYvGZF+8TtLCz9CaIe7SHNydyIgtqpuMuBQc3J3IKlY3NqXqJkNff/e5d/Dm2V2hZMancjD0G1Ku3SnxGc0e68rV7017gqw4a3dncu8U/cDNjU3Bof1D9ButFp9Nb4JWS/z6X4jf8IsJovx3q1HHiXvFjkFpcSk4ujuRkVi54wtAywH+BM0cgb2LI+vHlV2BYS6O7s6kFdu/pcWl4FjHiYxKHiuL6zCiDxH7zhozvIfm4u5CUmzRvi05LgkXdxfuJpRdZv/q/17DP8CfW9dvsfrd1YXPdx3QldGzxlCrdi3eGfu2OcL+1wgLCyMsLKzwcWBgIIGBgYWP3333XVJTy7a9ESNGPNTnZGdns2TJEsaOHYudnd0Dy1f7gVd8fDxLlixh/PjxrFmzhpycHOrUqcMrr7yCg4MDy5cvx8/Pjy5duhjc/uuvv+bkyZOoVCratm1L586dOXnyJJcuXWL79u2FM2urV68mLS0Na2trXnrpJfLy8tiwYQO5ubnMmDGDTp06ceXKFRISEvD392fUqFHGS1KhKPNU6ZNSCgNlyhQqR8OhXU0+2wUYzMNAIgbKPMT25Wg75TG0+Rqivv29UuUry/D3XqZQ2SKVjLuNPu4bRo67XAbSqdRpNq2WzOsxRC37Af8tIRRkZpN28Q+0+UXnla4v3Mz1hZtpPOVxGowbQMT724wW9gMZqoOHPH2osrOm9epgrr/xVYkZPLOqRB8w2CYfQGmhonbrhhx+Yx2JZyLp+s4ofCcO4dT/zFBHBuM1XDe2nXxxfHIAt0dO022qUmHt403iguXknLtK7Tkv4/TCcFI+WWfCgMvzJ+tGXyTxQjRrur5GXlYODQN8GbJyKmt7Ty8sprRU0bh/Bw6/t9mYQVfsL+xzAc4/FkJe/F0sXRzx2fwW6og7pB2Va1SMqVLHoAe4vPskl3efpGGnFgROG8aakQuME9zDMtjcHn6ap2FXH9oP78OXT5n3BF9pCgMJlZfOR9M/RKlU8tK8l+k5pCdhW3UDgiO7j3Bk9xFadWrFyOmjeP1Z016HX9U0ZpzWKz3QKu2NN94o97WaNWty9+5dnJycuHv3Lo6OjgbL5efns2TJEnr27Ennzp0rFVe1HnjFxMTw4YcfMmHCBJYtW8a4cePw8fFh8+bNbNu2jbFjx1a4fUZGBsePH+fDDz9EoVCQmZmJvb09/v7+JQZr8+bN48UXX8TDw4Pr16+zatUq3nrrLYYPH05kZCTPP69bR3zx4kVGjRpFkyZNynxW8ZH1w86HZcWmYOdZNGVu5+GMOu5umTL2ni7cP7di7+mMOv7BZ4kUKiX1Bnfk54HlN7C/osWYQJo9FwBAUngU9p4uha/ZeziTVSrGnJR0rGraoVAp0RZosPNwJkt/E4b7OVa0vSHew3pSL7A9u55eaIyUSsiMTcG+VN1kxZetG7ticdt5VK5uGg/riVdge/aYIO7y5MSmYFssVhtPZ3JKtbWc2BRs67pwPwMbj6Iyd775jTvf/AZA07kjyI4puyQq5tvf8ft6llkHXjmxyVgXy8va04XcuMrf3ENhoaL1l8HEbz9Ion6ZmLn4jAmkxbO6PpR4NgoHTxfuz03beziTWaotZaekY+1Y1IfsPZzJekCumbEpZMamkHhGd+H3jZ+O4zvRPNcVFsQlYenuWvjYwr02BQll241Vs0a4zXuNmJdeR3NPd2OA/Pgk8uMTyTmnuxlAxp5DOL3wtFniBmg7OpDWz+jqJv5cFA4eRW3Mwd2ZjFJ1oy5VNw4ezmTq9xe5xQbz0b+dJWD+WGycHMjWLxNv2MeXhAvRZCVVvKTFmHJik7GqW7vwsZWHM7nxD75G4b48fW55yWmk/HwMh3beMvAygs6j+uOvb3d3zkZRs9gxyNHdmbQ/eeOi6ONXcG7ghp1TjcKbb5hax9H98Ruhz+VcFI7F9tOO7s6kP+RsV50W9XjsvRf4esxi1AaW8ZvaI6MfYcAzAwG4fu4atT2K9m0u7rVJiS9/mbBGo+Hgjwd48uWnCgde9108fhH3+u44OjmSdtd8+wBhmL+/P/v372fo0KHs37+fjh07limj1WpZsWIFdevW5dFHH630e1fLa7xAd33V4sWLmTx5Mm5ubmRmZuLj4wPoLnS7fPnBd7WytbXFysqKFStWcOzYMaytrcuUyc7O5urVqyxdupQZM2bwxRdfGJx6fJDAwEAWLVrEokWLHnrb5PAoajRyx76eK0pLFQ0f78LtPadLlLm95zSN/tMDgNodmpCbllWppWzuPVuTFhFDVmzlD6YP48raMH4ICuGHoBBu7j6Ftz5G1wpijD18iYaPdAJ0g6ab+lxv7jldqe2Lq9unLW0mPErY2KUUZOcaMzWgqG4citXNrVJ1c2vPaZoUq5u8SsTt2actrSc8yl4TxV2ee2cisWvsjm19VxSWKtyHdiNh96kSZRJ2n8JzWC8Aavp5k5eeRY4+H6vaurM+NnVdqDO4I7HfHQb4f/buO66p633g+CcJskQQEAQnKu4quBUnLpytrbW1tY6qdY86cLbVqjirtlaso7+62jq61FpX3asORMRVnKgIgjJkhZXk90dSRIZiJYH6fd7fV19fSc5JnpNz7r0597n3JHORDQBnnwYkXTfRwg0GCedvYl3ZFUtDu5x7ePFob/4XKKixdBjJ1+9zb9UfRowyd1fW7+dXn+n86jOd0D3nqGoYS871q5CWkPtYCj95hUqGbahar5aEZhuT2akfPiYpPAa7yvpVTsu0qE3s9fvPrFNQUi6FUKxiWczKloZiZth0bkPSoVNPlTFzdcJl2WdETllE+p0ncWkexZLx4BHF3PSXhVs39STtpukW1wjesD9z4Yube89Rs6e+b1zqVSE1Ifmpywz/EfbXFap20fdNrbdbcsvQN9ZOdpllSntURqFUZE66AKq90YxrJrzMECAx6AZWlVyxKO+MopgZpd5oQUw+txullQXK4paZ/7Zr7UFySOGuMPeqOL3xT/y7TMO/yzSu7AvA862WAJSr505qgvqFLjN0qFg689+utd1QFTMz2aQL4OyGP1nZZRoru0zj730BePTM1pYXmHjZlXHk3VUf89u4b4i+/cBYIT/THxv+YEzn0YzpPJq/9p6ibU/9Kr/V61UnOSEp18sMXSu6Zv67cfsmhN0Iy/F4ldeqUMzc7JWfdOlM+N/L6NGjB8HBwYwZM4bg4GB69OgBQExMDPPm6U+Wh4SEcPToUS5duoSvry++vr4EBj77WAxFOONlbW2No6MjISEheHl5/avXUKlUzJ07l4sXL3Ly5En27NnDjBkzniqj1WopXrw4ixYV3nXPOo2Ws9PX0+7HSShUSm5uPsLja/ep2le/QV/feJD7B4Io086DN04uJkOdxl/jVmfWb7FiJKWb1cTCwYY3A5YRvPgXbm46AoDbG01Nc5khEHYgiHJtPeh5YjEadRrHxj+JscOGiRz3/RZ1ZBwBfptps2IU9Sf1IvpyKNcMN/w/q35r/5G4NKuJpYMN7wQs4/wXv3B98xGazumPysIMn81TAHgYeIO/pqwtsDbpNFrOfLKe9j9O0i8nv0XfN9UMfXPN0Ddl23rw5gl935zMEndLf33fWDrY0DNgGRe++IUbm4/Q2BB3hyxxny7AuJ/VnitT19Jw8zQUKiVhmw6RGBJG+X76dPy9Dft5uP88pdp50ur0V2jUqVwc+2TZZc//G4+5vQ3aDA1Xpq4l47F+oYNqn7xHcfcyoNWiDnvEZd9vjd6W7O26NvU7PDdPR6FSEr7pEEkhYZTp1wGA8A1/Yu5kR8N98zErYYVOq6P8kC6cbjkem1oVcH2nNYlX7tDowEIAbs3dRPSB8yZtA8C9g0GUb+vBu8cXk5GSxpEsY8lnw0SO+X5LcmQcZ+Zupu2KUTSc1IvoS6GEbD4MgJWTHT12zcbcxgqdVstrgzvxs/dk0hPVnPh0Pd5fD0dpbkbCnSiOTFidRxQFTKPloZ8/ZdbMRaFUEv/bPtJu3MH23a4AxG/5A/vhfVDZlcDpM/39tLoMDWHvjAbgoZ8/pRdORlHMjPSwB0RNX2yauLMJPRiEm7cH/Y/pt/M/Jz75/N5YN5H9k78lKTKO4/M203n5KJr59uLh5VAubzkMgHuXxtTt2w5thoaMlHR2j/LPrG9maU6Flq9xcOp3pm2URsutad9Sa9OnKFRKIjcfRH3tHqX7dQQgcsM+ijmVpO6ehahKWIFWh+tH3QhqPRYzB1tqfDcJ0GeMH/52jLhDQaaN/wX5zpjP2fPBxMXF067HB4wY1Jee3X0KO6xnunYoiGrenow/spQ0dSq/+q7KfK7v2klsm7yahKg4mg7woeXQbtg4lWTUnvlcOxTEtilrqN25MZ5vtUSbkUF6SjpbRn1daG25fjCIqt6ejDm6hHR1GtsnPmlLn3W+7Ji0hoSoOJoM8KH5sG7YONkxfO98rh8KYsfkb2k99k2s7EvQdbZ+NUStRsPq7sa5mic/Ag6epaF3Q9Yc+1a/nPzEpZnPzVw3k2WTlxEbFcu4peOxtrFGoYDbV27jP12/7Xt1aU7bnm3RpGtIS0llwcgFhdUUkU2JEiX47LPPcjzu4ODA1Kn6nwaqUaMGW7duzVHmeRS6wlziJg9RUVEsWLAAPz8//Pz88PHxYfv27QwcOJCaNWuydetWkpOTGTBgwDPv8UpJSSE1NRU7OzsSExMZPXo0a9eu5bvvvqNSpUp4e+vT35988gldu3alWbNm6HQ67ty5g5ubG4cPH37qUsOZM2fmealhVt+X+eCZz/+XpL/47SRFmqqwAyhAzhn//VXGsjJ/6XWIio6bxcwLO4QC5W378PmF/iP+SHR6fqH/kEbphXQ/ohE0ulh4J0CN4fOGnxR2CAXGTPdqfRkI0L34lU1F2c67pr9a498Y6tbLZO+1KvQnk73XiyiyGS8AS0tLpkyZwpw5c2jcuDHff/89qampODs7M2LEiOfWV6vVLFy4kPT0dHQ6Hf379wfAy8uLVatWsXv3bsaPH8+YMWNYs2YNv/76KxkZGTRv3hw3Nzcjt04IIYQQQgjxv6JIZrz+6yTjVXRJxqvokoxX0SUZr6JLMl5Fl2S8ii7JeBWOj0yY8VpTRDNeRXZxDSGEEEIIIYR4VRTpSw1fxKJFi4iKinrqsT59+uDp6VlIEQkhhBBCCCHgxX9f81X0yky8fH19CzsEIYQQQgghhMjVKzPxEkIIIYQQQhRNr86d3P+e3OMlhBBCCCGEEEYmGS8hhBBCCCGEUck9XpLxEkIIIYQQQgijk4mXEEIIIYQQQhiZXGoohBBCCCGEMCpZXEMyXkIIIYQQQghhdJLxEkIIIYQQQhiVVieLa0jGSwghhBBCCCGMTDJeQgghhBBCCKOSfJdkvIQQQgghhBDC6CTjJYQQQgghhDAqreS8ZOJlDHGqwo6g4Ni8Ymt/PnyF+ibU7BVqDFA+49XZHT1QvVoHl5bh9wo7hALT3M6isEMoUEoz28IOocDsavhJYYdQoGYEzCnsEAqMVZmWhR1CgYodWKewQxD/o16dbzpCCCGEEEKIIkknGS+5x0sIIYQQQgghjE0yXkIIIYQQQgijesXuXvlXJOMlhBBCCCGEEEYmGS8hhBBCCCGEUcmqhpLxEkIIIYQQQgijk4yXEEIIIYQQwqhkVUPJeAkhhBBCCCGE0cnESwghhBBCCCGMTC41FEIIIYQQQhiVLCcvGS8hhBBCCCGEMDrJeAkhhBBCCCGMSqeTxTUk4yWEEEIIIYQQRiYZLyGEEEIIIYRRyQ8oS8ZLCCGEEEIIIYxOMl5FSMvP+1KxrScZ6lQOjF/Nw0uhOcqUKO+Ej/9ILEva8PBSKH+O/QZtuibzeWePyry9fSZ7R3zNzV1nsXF1oP2Xw7B2skOn1XH5x0MEf7e3QOIt26YujWf1RaFUcn3TYS76/56jTONZfSlnaNPxcauJMbQpr7rmJYvT5ptR2JR3IvHeQw4P+5q0x8kA2NcsT7MFAylmYwVaHTu7foYmNZ1OP03HqnRJNClpAOx7bwEp0fEF0sZ/eH/el0re+nbsmbCaqFz6xra8E92W6/sm6lIouz5+0jflmtbEe8YHKIupUMcksPUdPwAsbK3puHAwpaqVQ6fTsdd3DRGBNwo09ufxmdmPqt4epKvT2D5xFQ9yaVuj/h1oMrATDm4uLPIcijo2EQDHKq688cVQXGq7ceiLrfy1epdJYm6SbVxF5xKzTXkn2qwYiYW9DdEXQzk65kl/5FX/7VNLyUhMQavVosvQ8HuXzwCwr1UBr/kfUszakoSwhxwd9Q3piWqjt9NnZj/cDX2zI4++aZilb77I1jevZ+mbUybqm7zMXjCNdh1aoVar+XjENC5euJqjzJcr/GjWvBHx8fo2fDxiGpcv/p35vEe91/hj/yaGfjiBP3bsM1nsuRk48yPqeTckTZ3K8olfcvvSrRxlxn41nsp13NFkaLhx4TqrpvqjydBQ3LY4IxaNwaWiK2mpaazwXca9a3dNGn+rLMeb/Xkcb2yzHW/25XK86bV9JnsMxxsAc1tr2i0cjGN1/T7twMQ1PDDxPq3rjH5U8/YkXZ3GLxNXEnE5NEeZJv064jWwE45uLsytN5Tk2AQAanRoQPvxvdDptGgztOyatZE7ASEmjT+/Ppm7hKMnzuBgX5Jt368s7HDyZemSWXTu1JZktZpBg8ZxPuhSruVmz5pMz57d0Gg0rFq1geX+39G6VTN+/eU7bofeA2Dbtl3M8fvSlOFnUtVqgOU7w0GpJP3EHtL2bs21nLJiNawnLyXl23lkBB5HUbocVoOnPnm+lAupv28k/eA2U4VeqGRVQ5l4FRkVvT0oWcmF71tOoHS9KrSeO4CfX5+Zo5zX1N5c+HYP13ecos3cD6nVuw2XNh4AQKFU4DX1Xe4eCc4sr9VoOTH7Rx5eCqVYcUve3TWbe8cuEns9/KXiVSgVNPHrz7735pMcEUO3XbO4u+8cj7O8btm2HthWcuHXFhNwql+FZvMG8Ef3mc+sW2dkdyKOX+Gi/+/UGdmdOiO7c27uFhQqJS2XDefY2JXEXrmLhb0N2vSMzPc6OmoF0cG3X6pNeank7YG9mwvftZqAa70qtPcbwI9vzMxRrtXU3pz7dg8hv5+i/dwPqfNuGy58fwALW2va+w3gl74LSQiPxsrRNrOO98y+hB4O5vdhy1AWU1HMysIobciLu7cHjpVcWN56AmXrudN1zof8X48ZOcrdC7jGtQPn6b/5k6ceV8clsWfGBqr7NDBVyJQzjKtfsoyrnd1n5ijXcHpvLq/Zw+0dp2g2/0OqvteGkA0Hnlt/dy8/Ug2Tl380XzSYs7N/JPLU31R9txWvDe/K+UU/G7Wd7t4eOFRywd/QN13mfMh3ufRNWMA1rh84T788+qaGCfsmL207tKJy5Yp41e9E/YZ1mb94Bl3b98617KxPv8h1UqVUKvnk8/EcPnDC2OE+Vz3vBrhWKsPo1kOpWq86Q+YMZ2oP3xzljm47wldjlwDw8bKJtOvdkX3f7+atUb0IvXKbRUPnUaZKWT6aPYzP3//UZPH/c7zZaDjetJk7gJ/yON4EvcDxBqDVzL7cORzMbsM+zczE+7RqbTxxrOTC0jbjKVfPndf9BrKqx2c5yt09F0LIwUAGbX76c7914hLL/zwHQOka5entP5av2k00SewvqkeXDrzf83Wmzf6isEPJl86d2lLVvRI1arWgSeP6+C+fh1eL7jnK9e/3DuXKlaH2a63Q6XQ4OTlmPnf8+BneeLO/KcPOSaHE8r2RJH81DV3sI6ynLiMj+BTaiLs5ylm8ORDNlXOZD+kiw0j2G5n5fPH535MRdNKEwYvCViQuNYyKimLChAn5KrtlyxaCg4OfXzCLrVu3smPHjn8TmslU6tiAv385DkDk+ZtY2BbH2rlkjnLlmtfixh9nAPj752NUzvKlqu6HHbm5+yzqLNme5Ki4zDOZ6UkpxNwIx8bF4aXjLVWvCgmhkSTefYg2XcPt7aeokO0LXgWfBtz8Wd+mh4E3MbcrjpVzyWfWreDTgBs/HQPgxk/HqNCpIQBlWtch9uo9Yq/od2ypsYnotKa5VrhKxwZcMfRNhKFviufSNxW8anFtl75vLv98DHdDm2q84cX13WdJCI8GyOwfcxsryjWuzsXNhwHQpmtIjU82dnOeUr1DAy78ov+875+/gYWtNTa5tO3B5Ts8DnuU4/Hk6HjCg289dRbc2Cr4NOBGLuMqO9fmtQg1bCs3fjpGxaxjLB/1s7Kr4krkKX3mJfzYJdy6NCqw9uSlWocGBGfpG8t/0TcRwbfQmLBv8tKpS1t+2rwdgMCAYGztSuBcutQLvcagoX34Y8efPHoUbYwQX0ijDk04/MshAK6fD8Hatjglne1zlDt/6MkXrhsXruHoqv8CWa5qeS6euABA+M37OJVzxq7Us8dgQarcsQFXjXC8KWZjRZkm1bmSZZ+WZuJ9Ws2ODQj6Vb/dhJ2/gWUJa2yccrYt4vId4nLZbtKSUzP/bW5tWaRXYWvoWQc72xKFHUa+de/uw8Yf9CesTp8JxK6kHS4uzjnKDRvajzl+SzM/+4cPC3+bz0rpVh1tVAS6Rw9Ak0HG2SOY1W2Wo1wx79fJOH8CXcLjXF9HVcMT3aMIdDFRxg65yNCZ8H9FVZGYeL2Id999l7p16xZ2GAXOxsWexPAnO5fEiBhsXJ4+kFva25Aan4xOo80sU9xQpriLPZU7Ncw8G5mbEuVK4VS7Ig/O33zpeK1d7EkKj8n8OykiButs8erLROco86y6VqVsUUfFAaCOisPSkB2yq+wC6OjwwyS675nDa8O7PvVeLZYM4fV9ftT9uMdLty07Gxd7EiKetCPhQc6+sbK3ISVb3/xTxr6yC5Z2xXlny3Q++GM2tXq20LepghPJMQn4LB5C311z6LhgsMnPDpdwcSA+/Om2lSid8wtkUZLXuMrKwt6GtMdP+iM5S5ln1tfp8Nk0he67Z1Otj3dmmbiQe1ToWB8At25NKF7m5U9ePE/2von/D/RNXlxcnQm//yDz74jwSFxdS+dadsqnYzlw4jc+nzsZc/NimfU7d2vPhu+2mCTe53F0cSQ6/GHm3zEPonEs7ZhneZWZilZveRN0OBCAO1dCadJZ/0XN3aMqTmWdcXTJu35BK/4vjzc2WY43VXI53thVcCIlJoH2S4bQe/cc2i4shH1aaXseZzm+xD+Iwdblxbabmj4NGXvgC/p+58tvk1YXdIj/s8qWcSHs3pOrYu6HRVC2jEuOcpUru/FOr9c59dcudu7YiLt7pcznmjZtwLmAP9m5YyO1alUzSdzZKe0d0cY+2f61cY9Q2D+9/SpKOmLm6UX60T/yfJ1iDVuTfvawscIURVSRu9QwMjKSxYsXM2TIENauXUtqaiqlS5dm+PDh2NjY4O/vT4MGDWjatGmu9X/44QcCAgJQqVTUrVuXfv36PfV8aGgoa9asyfG6N27cYOXKlVhYWFCjRg2CgoJYvHgxqamp+Pv7Ex4eTtmyZXn48CGDBg2iSpUqBdtwhSLHQznOtOVS5p9JfcsZH3By7uY8s0DFrC3ovGosx2Z+XzD3pjwjlmeX0eWvbvaXUqlwblSNnV0+I0Odhs/WqURfDCXi+GWOjl5B8oNYzIpb4r1mLFXebpGZaSsICv5d3/xTRKlS4lynEj+9N49ilsV4b9tMIgJvoDRTUfo1Nw5+toEHQTfxntmXxiO6c3KxcS9he07YTwIvqvIaV88t8/z6f/SYhTpSP+H32TyZxzfCiTwdwvHxa2gyux8e497k3r5ANFkuczWW3MMs4n2TB0V+9m/A3M+XEhX5CHPzYiz66nNGfjyYpQu/Yda8qcyZsRittojcIfCCffPRnGFcOX2Zq2evAPDbNz/z4YyPWLTrS+6G3OH25VtoNKbLTOarP56xT2s54wNO5HK8UZqpcHrNjSOfbiAy6CYtZ/alwcjunP7ClPu0Fz++ZHd1bwBX9wbg1rgG7cf3Yu0HcwsmuP9x+d0PWFiYk5KSStNmXejRozPfrl5Mm7ZvEXj+IpXdG5OUlEznTm355ac6y/hSAAAgAElEQVTvqFm7hSlCz+b5xyCLXsNI/e070OWxz1KZofJoSuq2tUaIr+iSVQ2L2MQrPDycL7/8khEjRrB8+XIGDhxIrVq12LJlCz///DMDBgx4Zv3ExETOnDnDl19+iUKhICkpKUeZvF73m2++YciQIVSvXp0ffvghs/zevXuxsbHhiy++4O7du0yaNCnX996/fz/79+8HoFw+21unf3tqvac/qx514RY2ZZ6cMbFxdSApMu6p8ikxCVjYWqNQKdFptIYysQA4162Ej/8oACwdSlDR2wOtRsvtvedQmqnovHos17ad5NaegHxG92zJETFPnfUv7upAsiGWp8s4ZisTh9LcLM+66kfxWDmXRB0Vh5VzycxFMpIjYog89XfmvTdhBy/g8JobEccvk/xAXzcjKYXb205SyrPyS0+8PPu1p46hbx4E36KE65N2lHDJ2TfqmAQs8+ibxAexqGODyVCnkqFOJez03zjVqkDYmRASImJ4EKTPQF7bdYbGw3Ne717QGvbrQP3e+raFB9/CtszTbUuIisuraqGp0b99ZgbqUdCtXMdVVqkxCZjbPekP6yxjLK9xCaA2/H9KdDx3dp/DybMKkadDeHwzgn3vLwDAtrIL5dp5GqWdDft1oF4efWPr4kBiEeybvAwY/B59+vcC4ELgRcqUfXJm27VMaR48yHl5TVSk/tKvtLR0Nv/wG8NHfQiAR73arPxuMQAODva069AKjUbDnj/yzvAXtE79utCud0cAbgZfx7GME6BfIMTBxZGYqJhc6/Ua2xtbBztWTZ2X+Zg6Uc0K32WZf684voaoe5HGCx798aZ2AR5vOmU73ug0Wh4E3iAxIoZIwz7t5q4zNBhh/H1ak74daGho2/0Lt7DLcnyxdXEgPtuxKb9Cz/yNQ0VnrO1LZC6+IV7M8GH9GTSoDwABAUGUK18m87my5VwJj8g57sPuR/Drb/pM0bZtu/m/Nfr7JBMSntx7u3vPQb5eNhdHR3uio/9d//5b2thHFLN3yvxbWbIUurint39VxaqZi2goituiqt0INBoyLvwFgNlrDdHevYEu4b+zTxcFo8hcahgfH8/ChQsZPXo0zs7OJCUlUatWLQBat27N1as5V8DKzsrKCnNzc1auXMnp06exsHj6Eofk5ORcXzcpKQm1Wk316tUBaNHiyRmUv//+Gy8vLwAqVKhAxYoVc33v9u3bM3/+fObPn5/vNl9cv58tnaazpdN0bu09Rw3DJWil61UhLSGZ5Fy+ZN0/eQX3ro0BqPF2S27t01+6sqH5eDZ4jWOD1zhu7jrDkenruL1Xf39B20WDibkeTtCa3fmO7XkeBd3CtpILNuWdUBZTUemNptwzxPKPe/sCqfK2vk1O9auQFp+MOirumXXv7QvEvVdLANx7teSuoQ33jwRjX7MCKktzFColLk1r8Pj6fRQqJRb2NgAozFSUa1+PuJCwl25f0Ib9bOw8nY2dp3Nj77nMywNd61UhNSGZpFz65u5fV6jWRd83td9uyQ1Dm27sO0fZxtVRqJSYWZrjWq8K0dfDSX74mISIGOwruwJQoXltoq/ff+nYnydgw5+s7jKN1V2mEbIvAI+e+s+7bD13UhPURfLL/d/r97Oj43R2dJzO3b3ncM9lXGUXcfIKboZtxb1XS+4a+uPuvsBc65tZWWBW3BIAMysLyrZ+jVjDWPrnklcUCjzGvkHIMy7pfRkBG/5kTZdprDH0Td0sfZNSRPsmL+u+3USHlm/RoeVb7P7jAL16vwFA/YZ1SYhPyJxkZZX1vq/OXdvx99XrADTx6Ejjuh1oXLcDO3fsZcqE2SaddAHs2bAL3y4f49vlY87sO02bnvov+lXrVSc5IZm4qJxf/tr17oBn63p8OfqLp87sW9sWx6yY/rxn+94duXrmMmojr5J5cf1+NneazmbD8aZmPo43YdmON7ezHG/We41jveF4c3j6Om7tPUfyw8ckRsRQ0rBPK9e8NjEm2Ked3vgn/l2m4d9lGlf2BeD5ln67KffPPu1h/rcbh4pPLoF1re2GqpiZTLpewjcr19OwUUcaNurIjh176dvnbQCaNK5P/OP4XE/A7NixB+82zQFo3aoZ167rVwwtXfrJZKdRQ0+USqXJJ10A2jshKJ3LoHAsDSozzBq1JiP41FNlkj4ZQNL0/iRN70/G+eOkbl6eOekCMGvY5n/yMkOdTmey/4qqIpPxsra2xtHRkZCQkMyJzotSqVTMnTuXixcvcvLkSfbs2cOMGTlXAcuuKHTQnYNBVGzrQd/ji8lQp3FgwpPryrutn8ihSd+SFBnHyXmb8fEfRRPfXjy6FJp5E3NeXBtVo8bbLXl09S7v7tEvYX5qwVbuHLrwUvHqNFpOfbKeDj9OQqFUcmPLEeKu3ad637YAhGw8SNiBIMq29eCtE4vRqNM4Pn71M+sCXPT/ndYrR1P1vdYk3o/m8FD9WeG0x8lcXr2bbrtmgU5H2MELhB0IwszKgg4/TkZppkKhUhJx7DLXfjj0Um3L7vbBICp7ezDo2GLS1Wnsnfikb95cN5F9k/V9c2zeZrouH0Vz315EXQ7l0pbDAMTcCCf0cDD9981Dp9VycfNhoq/pv9Af/Gw9XZYNR1XMjMd3o9gz0bT3E1w/GIS7tyejji7JXLL8H++t8+X3SWtIjIqj8QAfvIZ1w8bJjmF753P9UBA7J39LcSc7Pvp9DhY2Vui0WpoM7MyK9pNIM+KXyLADQZRr60FPw7g6Nv7JZ9Zhw0SO+36LOjKOAL/NtFkxivqTehF9OZRrmw4/s76lky3t/u9jQH9p661tJ7l/WL+QT+UezagxoD0Ad3YFcH3LUaO17x83DH0z8ugSMrL1Te91vuw09E2jLH0zdO98bmTpm8HZ+uYbI/dNXg7sO0q7Dq346/we1MkpjBs5PfO577euZMKYT4l88BD/NQtxdHRAoVBw+eLfTBr/ucljzY/AgwHU927A8qOrSFWnsmLik+zVtHWf8c2k5cRGxTDEbwQP70fh99tCAE7v+Yufl22hnHs5Ri8Zh1ajJezGvaeyX6YQajje9Duu36dlPd50Xz+Rg1mON538R9HUtxcPL4Vy+TnHG4Ajn66n49f6fVr83Sj2TzDtPu3aoSCqeXsy/shS0tSp/Or7ZLvpu3YS2yavJiEqjqYDfGg5tBs2TiUZtWc+1w4FsW3KGmp3boznWy3RZmSQnpLOllFfmzT+F+E7Yz5nzwcTFxdPux4fMGJQX3p29ynssPK0a/cBOnVqS8jVEySr1QwePD7zud+3b2DIMF8iIiJZsNCfjeuXM3bsRyQlJjN0mH7F0J5vdWXo0H5kZGhIUafQ54MRhdMQrZaULSuwHuOnX07+5D60EXco1rILAOnHnvPTHcUsMKtZn5QfTLvdi6JBoSsCs46oqCgWLFiAn58ffn5++Pj4sH37dgYOHEjNmjXZunUrycnJDBgw4Jn3eKWkpJCamoqdnR2JiYmMHj2atWvXsnXrViwtLXn99dfx9fXN9XUnTJjA0KFDqVatGj/++CPnzp1j8eLF7Nixg8jISD766CPCwsLw9fVlzpw5z7zHa3n5D4z5cZmUTRG5paKgRKsKO4KCk6Qo9E23QJXPyO2Gs/+mMLNXq29WPD5f2CEUmOZ27oUdQoFqrbV9fqH/iEjVq3XAmREwp7BDKDBWZVoWdggFKnZgncIOoUCVWLmnsEPIF5/ynU32XnvvFdxVXgWpyGS8ACwtLZkyZQpz5syhcePGfP/996SmpuLs7MyIEc8/s6FWq1m4cCHp6enodDr698/5Ww8jR47MXFwj6+sOGzaMVatWYWFhQe3atbG2tgagY8eO+Pv7M3HiRNzc3KhQoULmc0IIIYQQQgiRH0Ui41UUpKSkYGmpv79j27ZtxMbG8uGHH6LVasnIyMDc3JwHDx4we/ZsvvrqK8zM8p6zSsar6JKMV9ElGa+iSzJeRZdkvIouyXgVXZLxKhwdy3cy2Xvtu1c0P5MilfEqTIGBgfz2229otVpKlSrFyJH6XxZPTU3l888/R6PRoNPpGDx48DMnXUIIIYQQQgiR3X92BrFo0SKiop5eDadPnz54ev67ZZ69vLxyXdTDysrqhVYqFEIIIYQQQojs/rMTL19f38IOQQghhBBCCJEP8gPKReh3vIQQQgghhBDiVfWfzXgJIYQQQggh/htkPT/JeAkhhBBCCCGE0UnGSwghhBBCCGFUco+XZLyEEEIIIYQQwugk4yWEEEIIIYQwKp1kvCTjJYQQQgghhBDGJhkvIYQQQgghhFFpZVVDyXgJIYQQQgghhLFJxksIIYQQQghhVP+VfFdiYiJLly7l4cOHODk5MW7cOGxsbHItq9VqmTJlCg4ODkyZMuW5ry0ZLyGEEEIIIYQAtm3bRp06dVi2bBl16tRh27ZteZbdtWsXZcuWzfdry8RLCCGEEEIIYVRadCb772WcPXuW1q1bA9C6dWvOnj2ba7no6GgCAwNp165dvl9bLjU0AhttYUdQcDSKwo6gYJXJ+K8kup8vXfFqdY76FWpOndRXZ5wBhPTM/9m8ok7poCrsEArUpk2FHUHBMdO9QjsBwKpMy8IOocCow48VdggFanLDaYUdQoFaUtgBvGIeP36Mvb09APb29sTHx+dabt26dXzwwQeo1ep8v7ZMvIQQQgghhBBG9bKZqBexf/9+9u/fn/l3+/btad++febfs2fPJi4uLke93r175+v1z507h52dHZUrV+by5cv5jksmXkIIIYQQQohXRvaJVnaffvppns/Z2dkRGxuLvb09sbGx2Nra5igTEhJCQEAA58+fJy0tDbVazbJlyxgzZswz45KJlxBCCCGEEEIADRs25MiRI/To0YMjR47QqFGjHGXef/993n//fQAuX77M77///txJF8jiGkIIIYQQQggj0+l0JvvvZfTo0YPg4GDGjBlDcHAwPXr0ACAmJoZ58+a91GtLxksIIYQQQgghgBIlSvDZZ5/leNzBwYGpU6fmeLx27drUrl07X68tEy8hhBBCCCGEUZlycY2iSi41FEIIIYQQQggjk4yXEEIIIYQQwqh0kvGSjJcQQgghhBBCGJtkvIQQQgghhBBG9bKrDb4KJOMlhBBCCCGEEEYmGS8hhBBCCCGEUcmqhpLxEkIIIYQQQgijk4yXEEIIIYQQwqjkHi/JeAkhhBBCCCGE0UnGq4go26YujWf1RaFUcn3TYS76/56jTONZfSnX1pMMdSrHx60m5lLoM+t6jn+Lqu+3ITUmAYBz87dy/+AFk7Upq6az+lLeEPvRcauJNsSelU15J7xXjMSipA3RF0M5MvYbtOka7Kq40mrJEBxfcyNg4U9cWrXLpLG7tqlL/dn6z/fmpsNcXZ6zb+rP7keZth5o1GmcGreK2IuhWJdxoOlXw7F0tgOtjhvfH+Ta/+3NrFN1YEeqfdgBXYaW8ANBBM3ZZLQ2lGlTl0aGMXJj02Eu5TK+Gs3qS9m2nmjUqZzIMr7yqmtfqwJN53+ImbUliWEPOT7qG9IT1SjMVHh9MRiH19xQmCm59fNxLuXymRUUr1l9qWAYW4fHreZRLmOrRHkn2q0YiWVJGx5dDOWgYWy5v+mF54huAKQnpXBs6jpirt4FoPUXH1GxvSfqR/H81H6q0eLPytm7LnVm90OhUnLnh0Ncz+VzqzOnH6XbeaJRpxE4diWPL4YCUMzWGs8lH2FbvTzodASOW03suevUnNQLl04NQKsl9VE8gWNXkhIZZ5L2/EP1WkMs3xuBQqEk7dhu0nZvybWc0q0axacvQ73Sj4xzxwCwWbARXYoatFrQakiaPdKUoedKVb0eFq8PAqWS9DP7ST/069PPV66N5YCpaGOjAMi4eIr0/VtR2Dli0XssyhL26HRaMk7/SfrxnYXRBJpl2ScfyWOfXKK8E20N++RHF0M5nGWf3HrJEEq95sbZhT9xMds+WaFU0GPXbJIfxLJ3wGITteiJzjP7UdXbg3R1GtsmriIil7Y17t+BpgM74eDmwkLPoSTHJgJQp4cXLYZ1ByAtOYWd09cSadgnFJalS2bRuVNbktVqBg0ax/mgS7mWmz1rMj17dkOj0bBq1QaW+39H61bN+PWX77gdeg+Abdt2McfvS1OGny+fzF3C0RNncLAvybbvVxZ2OPn25oz+1PSuR5o6lU0Tv+H+5dAcZVr086HVwM6UcnPh03ofkRSr/07mPaQb9Xu0AECpUlHavSyf1f+I5MdJpmyCSck9XjLxKhIUSgVN/Pqz7735JEfE0G3XLO7uO8fj6+GZZcq29cC2kgu/tpiAU/0qNJs3gD+6z3xu3Str9nDZxBOV7MoZYv/JELvXvAH83n1mjnKNpvXm8po93NpxCq95H1Ktdxv+3niA1Lgk/vpsIxV9Gpg8doVSQYO5AzjUex7qiBg67prN/b2BxF+/n1nGta0HJSq5sLP5BBzru9Nw3of82W0G2gwt52f9QOzFUMyKW+KzZw4Pjl4i/vp9nL1qUc6nAbvbTUWbloGFo61R29DErz9/GsZIl12zuJfH+NrWYgKl6lehybwB7M4yvnKr22zRYM7N/pHIU3/j/m4rag/vStCin3Hr1hiluRm/t5+KytKcNw4v4Pa2v0gKe1TgbSvf1gO7Si5sbjEB5/pVaDFvANtyGVtNpvXm4po93NxxipbzPqRG7zZc2XiAhLsP2fH2HNIeJ1Peuy6tFg7MrH/tp6NcXvcn3l8OLfC4c6VU4DHvQ068Mw91RDRt9szhwb5AEq49GWul23liU9mF/c3GY1/fHY8FAzna5TNAPyGLOniBs4O/QlFMhZmVBQDXV+zk6sKfAKg8yIfq49/iwuTvTNMmAIUSqz6jSVo8GV3sI4p/upyMoL/QRtzNUc7y7cFkXDqX4yWSF01ElxhvooCfQ6HE4s0hqFfPRPc4GqsxC8m4fAZdVNhTxTS3r5Ky1u/pulotaTvXob1/CywssR67mIxrQTnqGts/283WLNvN9ly2m8aG7ebWjlO0mPch1Xu34aphn3zys4245bFPfm1QJ+JuhGNuY2XkluRU1dsDh0ouLGs9gXL13Ok650O+7TEjR7m7Ade4duA8AzZ/8tTjcfcesvad2aTEJ+PexoPu8wblWt9UOndqS1X3StSo1YImjevjv3weXi265yjXv987lCtXhtqvtUKn0+Hk5Jj53PHjZ3jjzf6mDPuF9ejSgfd7vs602V8Udij5VrONJ6UquTK3zcdUrOfO236D+arHJznK3T4XwuWDgYzc/NlTjx9avZNDq/UnXmq1q0/rQV1e6UmX0CuylxpGRUUxYcKEfJXdsmULwcHBRo5Ib+bMmdy8ebNAX7NUvSokhEaSePch2nQNt7efokK2A1oFnwbc/Pk4AA8Db2JuVxwr55L5qlvYKnZswI2ssdvqY8+uTPNa3P7jDAA3fjqWOdFKiY7n0YVbaDM0pgvawKFeFRJDI0kyfL53t5+iXLbPt5xPA0J/1p+djw68gbmdNZbOJUmJiiPWkI3ISEoh/kY41q72AFTt144ry3egTcsAIDXaeF8qHbONkdDtpyifrQ3ls4yvR1nG17Pq2lZxJfLU3wCEH7tEhS6NANDpwMzaAoVKiZmVOdr0DNIT1UZpm1vHBlwzxB0VeBML2+JY5zG2bhnG1rWfjmV+YYw8d520x8n6fwfewMbVIbNOxOkQUuISjRJ3buzruZN4O5Lku1Ho0jWEbfsLl2z95OLTgLtb9WMtNvAGxWytsXAuiZmNFY5Na3Dnx8MA6NI1pMfr25WR5bNXWVuYpC1ZqSpXRxsVju7RA9BkkH7mMGb1vHKUM2/3BunnjqNLMG027kUpK1RF+ygCXUwkaDLICDqOWe3G+aqrS4jVT7oAUlPQRoWhtHN8diUjqNixAdezbDf52Sdn3W6etU8u7upA+XaehPx42GjxP0v1Dg248It+Gwk7fwNLW2tscmnbg8t3iMvlZNC9c9dJMWw7YYHXsc2yTygM3bv7sPGHnwE4fSYQu5J2uLg45yg3bGg/5vgtzbyH5uHDaJPG+bIaetbBzrZEYYfxQl7r2JCAX48CcOf8DaxKWFPCKedYu385lNiwh898rfqvN+f8jpNGibMo0Znwf0VVkZ14vYh3332XunXr/uv6Go3pv9BnZe1iT1J4TObfSRExWLvY51ImOkeZ59Wt+WEHXv9zLs0Xf4S5nbURW5G37LEnR8RQPFv7LOxtSItPRqfRAvp2ZC9TGKxdHEjOFruV69NxWbk4PN2+8Jz9V7xcKexfq8ijQP2kvUQVV5ya1KDDzs9p98snOHhUNmIbnh4jyXmMr+ztzG18Za0bF3KP8h3rA1CxWxOKl9F/QbnzxxkyklPpdX45b535kssrd5EWZ5yzeMXz2C6yssw2thLzGFs1erfh7iHTnMDJjZWrPeosbUmJiMEq25c+fZmYbGXsKV7RmbToBOp/NZQ2f87Fc/FHT02yak55h47nvqZ8z+aZ2S9TUZQshTbmyZcOXewjlCVLZSvjiFn9FqQfzuWyO50O6/HzKf6pP8VadTF2uM+lsHVAF/fkC7vucTSKXCZPqorVsRq3BMtBn6IsXT7n69g7oSxTCc3da0aNNzfFXexJzLbd5LZPTs22T86+beWm6cwPOOO3qdBuord1cSA+S9viH8RgW/rfHUvq927DjcOFc3n+P8qWcSHs3pOrE+6HRVC2jEuOcpUru/FOr9c59dcudu7YiLt7pcznmjZtwLmAP9m5YyO1alUzSdz/C2xLOxCXZazFPYjBzuXFJ+rFLM2p0dqD4N2nCzI8UUT9Jy41jIyMZPHixQwZMoS1a9eSmppK6dKlGT58ODY2Nvj7+9OgQQOaNm2aa/0ffviBgIAAVCoVdevWpV+/fvj7+2NjY0NoaCiVKlXCy8uLdevWkZaWhrm5OSNGjKBMmTKkpaWxYsUKwsLCKFu2LGlpaQXfQIUi52PZj1m5ltE9s+7fG/Zz4cvf0Omg3qS3afRZH05MWPPS4b4oRS4xZj8o51rGaBG9gFw+XrJ/oci1a56UMbO2oMW3HxP42cbM7INCpcTcrjh/dpuBg2dlmq8aze9NxxVg4FnC+5fjS6fTPbPuyfFraDy7H3XHvcm9fYFo0/XZu1KeldFptPxUfzQWdsXx+e1TIo5dIvHus8/4/St5bRfPKZO9+WW8alKjd2u2vzm74GJ7Uf+2LTpQmCmxq+NG8LR1xJ6/SZ3Z/ag26vXMSdbV+Vu5On8rVUe/TuWBHfl70S/GaEHucmtXth6wfG8EqT9/CzptjpJJ88ehi4tGUaIk1hPmo31wD821i0YKNh/y0U+a+7dImjsE0lJQ1aiPZf8pJC/Mcm+auSWW/SaTuuM7SDVONviZ8tGGXLf956jQzpOUR/E8uhiKa7Oa/za6l/Oc/XF+uTWrRb132/Bdz1kFENS/l5/jJ4CFhTkpKak0bdaFHj068+3qxbRp+xaB5y9S2b0xSUnJdO7Ull9++o6atVuYIvRXXu67thcfa7XbN+B2QIhcZvg/oshPvMLDw/nyyy8ZMWIEy5cvZ+DAgdSqVYstW7bw888/M2DAgGfWT0xM5MyZM3z55ZcoFAqSkp4M7IiICD799FOUSiXJycl8/vnnqFQqgoOD+fHHH5k4cSL79u3D3NycL774gjt37jB58uRc32f//v3s378fgBov2MbkiJjMbAHoL9VIjozNpYxjtjJxKM3N8qyb8ujJ5WvXfzhEu/X5u3SzINTs357q73sD8OjCraditzbEnlVKTALmttYoVEp0Gq2+HQ+e/gwKQ3JEDNbZYlc/eDp2taFv/jkHbl3GAbWhfQozFS2+/ZjQX08QtjvgqTphu84CEBN0C51Wh4VDicyFUApSUrbxZZ3H+MrRzlzGV9a68Tcj2P/+AgBKVHahXDtPACq96UX44WB0GRpSouN5ePYajh6VC2ziVbt/e2oYxtbDbGOreD7Glk22seVQszytFg5md99FpJrw0sLs1OExWGVpi6WrA+ps24C+jMNTZVIexIJOR0pEDLHn9RnV8J2nqTr69RzvEfbbSZp972vSiZcu9iFKB6fMvxX2pdDGPX0ZlKpiVayGTtM/b2OHWZ1GpGg1ZJw/ic5QVpcQR0bgCVSVqhfqxEv3OBpFloydws4RXXzM04WyTKY0fwfCm0PBugQkJ4BShWW/SWScP4rm0ilThU2tbNuNTRlHIg3PFXd1ICmX7cbiBffJpRtVo0LH+pRv64HKohjmJaxos2w4h8d8Y4wmZWrUrwMNeuvbdj/4FrZZtiNbFwcSol7s8tXSNcrz+oLB/NB/IepC2CcMH9afQYP6ABAQEES58mUynytbzpXwiMgcdcLuR/Drb38AsG3bbv5vzRIAEhKexL97z0G+XjYXR0d7oqML//j6X9S8b0eavtcWgHsXblIyy1gr6eLA48gX/1zrdW/2P3GZIYBWlpMv2pcaxsfHs3DhQkaPHo2zszNJSUnUqlULgNatW3P16tXnvoaVlRXm5uasXLmS06dPY2Hx5PKbpk2bolTqP4Lk5GSWLFnChAkTWL9+PWFh+pudr1y5QqtWrQCoWLEiFStWzPV92rdvz/z585k/f/4Lt/NR0C1sK7lgU94JZTEVld5oyr19gU+VubcvkCpv689SOdWvQlp8MuqouGfWzXrNfoXODYkLMd0N3FfX72ebz3S2+Uznzp5zuGeJPT1BH3t2ESevUKmr/l4J914tuZvtMygMMUG3KFHJheKGz7fCG00J2/f0zf/39wXi9nZLABzru5MerybF0L4miz8i/vp9QlbvfqpO2J5zlG6hH8slKrugNDczyqQLINrQhn/GiNtzxlep+lVIN4yvZ9W1/GdBEIWCumPf4NrGAwAk3Y/GpXltAMysLChV353HN8IpKJfX7+cXn+n84jOd0D3nqGaI27l+FdISkknOZWyFn7xCZcPYqtarJV7qsEgAACAASURBVKGGNtiUcaTjmo85NHYlj28/KLAY/424oJvYVHbBuoITimIqyvVoxoNsY+3BvnNUeEc/1uzru5ORoCY1Ko7Uh49Jvh+NTRVXAJxavpa5KEfxSk8uS3L1qU9CAfZFfmhuh6AsXRZFKRdQmVGscRsygv56qkzilH4kTu5L4uS+pJ87Rsr3X5Nx/iSYW4KlYYEGc0tUtRuguR9q0viz0967jrKUKwp7Z1CZYebZAs2Vs0+VUZR4su9Vlq+qPzWerN++Ld4ZiTYqjPSjO0wa95X1+/nVZzq/Grabqtm2m9z2yeFZ9slZt5u8nJ2/lU2NxrC52TgOjvQn/MQVo0+6AM5u+JOVXaaxsss0/t4XgEdP/TZSrp47qQlqEl9g4mVXxpF3V33Mb+O+IbqQ9gnfrFxPw0YdadioIzt27KVvn7cBaNK4PvGP43nwICpHnR079uDdpjkArVs149p1/b2EpUs/OenRqKEnSqVSJl0v4cTGfSzuMoXFXaZwcV8ADd8yfD+s505KQjIJD19skm9ZwooqTWpx6c+A5xcWr4QinfGytrbG0dGRkJAQvLxy3oydHyqVirlz53Lx4kVOnjzJnj17mDFDv0KRpaVlZrktW7ZQu3ZtfH19iYqK4vPPPy+QNuSHTqPl1Cfr6fDjJP2S3VuOEHftPtX76s+qhGw8SNiBIMq29eCtE4vRqNM4Pn71M+sCNPykNw61KqLT6UgMe8RfplzJLIt7B4Mo19aDXscXk5GSxjFD7AAdN0zkuO+3JEfGcXbuZrxXjKLBpF5EXwolZPNhAKyc7Hhj12yK2Vih02p5bXAnfvGebLQFG7LSabQETF9Hmx8no1ApubX5CPHX7uPetx0ANzYeIPxAEK7tPOl2cgkadRqnx60CoFTjalTq1ZK4K3fp9OdcAC7M20LEwQvc2nyYJkuG0PngfLTpGZwea7zlc3UaLWc+WU/7LGPk8bX7VDOMr2sbD3LfML7ePLGYDHUaJ7OMr9zqArj1aEaNAe0BuLsrgBtb9DcZh6z7E6+lQ3j94HxQKLi55ShxV+8ZpW13DwZRoa0HvQ1j63CWsdV5w0SOGMbW6bmbab9iFI0m9eLRpVD+Noyt+uPexLKkDS3mDtC3N0PDr131K0+1Wz4S12Y1sXSwoc/ZZQQs/oWQzUeM0g7Qf9bB09bhtWmKfjn5TYdJCLmPWz/9WAvdcIDI/UGUbudJh1NLyVCncv7jVZn1L05fT4MVI1EWMyP5ThSBhudqT++NjbsrOq0Oddgjgib9n9HakCutlpQflmM9bh4KpZK043vRht+hWGvDMv5H8l5OXWFbEutRM/V/KFWknz6E5lIhf0HRakndtgarj2YYlpM/gDbyHmZNfQDIOLUXszrNMGvWCbQaSE8j5Qf9kupKt5oUa+CNJiIUq3H6jETa7u/1WTETuncwiPJtPXjXsN0cybLd+GyYyDHDdnNm7mbarhhFw1z2yT12zcY8yz75ZxPtk5/n+sEgqnp7MuboEtLVaWyf+GQb6bPOlx2T1pAQFUeTAT40H9YNGyc7hu+dz/VDQeyY/C2tx76JlX0Jus7+EACtRsPq7p8WVnPYtfsAnTq1JeTqCZLVagYPHp/53O/bNzBkmC8REZEsWOjPxvXLGTv2I5ISkxk6zBeAnm91ZejQfmRkaEhRp9DngxGF1ZRn8p0xn7Png4mLi6ddjw8YMagvPbv7FHZYz3T10Hlqensy7chXpKtT2eT75Dj+0drJbJm8mvioWFoO6IT30O6UcCrJxD0LuHooiK1T9NtcHZ/GhBwLJk2dWljNMKmivOiFqSh0RfRnpKOioliwYAF+fn74+fnh4+PD9u3bGThwIDVr1mTr1q0kJyczYMCAZ97jlZKSQmpqKnZ2diQmJjJ69GjWrl2bo86iRYto2bIlTZs2ZevWrRw5cgR/f3927txJWFgYw4YN4+7du0yaNAk/Pz+qVKmSZ+zryn5gtM/F1DQvfpl/kWatLZLD/V9J/xf3YBRl6leoOaUzct6r9F/WtqsR7s8rJEoHm8IOoUBt2lS8sEMoMPdVr87+GWBOxOHCDqHAqMOPFXYIBWpyw2mFHUKBWhK6ubBDyJfapZuY7L0uRxbNxUqKdMYL9FmpKVOmMGfOHBo3bsz3339Pamoqzs7OjBjx/DM3arWahQsXkp6ejk6no3//3H/L4o033sDf358//viD2rVrZz7esWNHVqxYwcSJE3Fzc8Pd3b3A2iaEEEIIIcT/ArnHqwhnvP7LJONVdEnGq+iSjFfRJRmvoksyXkWXZLyKLsl4FY6azvn7zcOCcDXqjMne60UU+YyXEEIIIYQQ4r9N7vF6xSZeixYtIirq6dV++vTpg6enZyFFJIQQQgghhBCv2MTL19e3sEMQQgghhBBCZCP3eBXx3/ESQgghhBBCiFfBK5XxEkIIIYQQQhQ9co+XZLyEEEIIIYQQwugk4yWEEEIIIYQwKrnHSzJeQgghhBBCCGF0kvESQgghhBBCGJXc4yUZLyGEEEIIIYQwOpl4CSGEEEIIIYSRyaWGQgghhBBCCKPS6bSFHUKhk4yXEEIIIYQQQhiZZLyEEEIIIYQQRqWVxTVk4mUMCa9QHrHEK5YVTlYqCjuEAhOlKuwIClb11FdnsEWavUI7AWDE/hKFHUKB8tRZF3YIBSZR9Wp9kQnQxRV2CAUmdmCdwg6hwKR8MoIZe0oWdhgFZkHA3MIOQfyPkomXEEKI/xmv0qTrVfMqTbpeNa/SpEsUHp38gLLc4yWEEEIIIYQQxiYZLyGEEEIIIYRRyT1ekvESQgghhBBCCKOTjJcQQgghhBDCqOQeL8l4CSGEEEIIIYTRScZLCCGEEEIIYVRayXhJxksIIYQQQgghjE0yXkIIIYQQQgij0smqhpLxEkIIIYQQQghjk4yXEEIIIYQQwqhkVUPJeAkhhBBCCCGE0cnESwghhBBCCCGMTC41FEIIIYQQQhiVVhbXkIyXEEIIIYQQQhibZLyEEEIIIYQQRiWLa8jEq0hp9XlfKrb1JEOdyv7xq3l4KTRHGdvyTvj4j8SypA0PL4Wyb+w3aNM1mc87e1Sm1/aZ7BnxNTd3nQWg/8mlpCWloNNo0Wo0bO36mVHiL9umLo1n9UWhVHJ902Eu+v+eo0zjWX0pZ2jj8XGriTG0Ma+69XzfpnzH+qDToX4Uz/Fxq1BHxmFhb0Ob1WMo5VGZG1uPcvqTDQXenqaz+lLeEOvRcauJzqU/bMo74b1iJBYlbYi+GMqRLP2RV/3agztR/b02oNMR83cYxyasRpOanvmarw3tQpNP3+f7OsNIjU0s8HYBdJjZlyrenqSrU9k5cTWRubTNrrwTPb7Wj7UHl0L5fZy+bZa21nRZNAT7is5kpKbzh+8aHl0LA2D48afH2rruxhlr/yjtXZe6s/uhUCkJ/eEQ15bnHHN15/TDpZ0nGnUa58auJO6ivq0+Z78iI1GNTqNFp9FyyOcTAGpN6oVrpwbotFpSH8VzbuxKUiLjjNqOrLxm9aWCYdwcHreaR7n0TYnyTrRboe+bRxdDOWgYd+5veuE5ohsA6UkpHJu6jpirdzPrKZQK3to1m6QHsewZsNhUTQKg78xBeHrXJ1WdyuqJywm9dCtHmeFffUzlOlXIyNBw68J1vpu6Ek2Gfnuq2bQ2H3w2EFUxFQkxCfi9+6lJ4wdo+3lfKnnr+2b3hNVE5bHddFuu75vIS6Hs+ljfN42GdqVmDy8AlGZKHNzLssJzOOkpafT+6RNU5mYozVRc23WGk0t+NWm7Os/sR1VvD9LVaWybuIqIXNrVuH8Hmg7shIObCws9h5Js2DfV6eFFi2HdAUhLTmHn9LVEZhlzhWHI50Np6N2QVHUqX05Yys1LN3OUGbNwLFXruoNCQfjt+ywdv5SU5BSadGjKBxM/QKfVodFoWPP5aq6cvVIIrQBVrQZYvjMclErST+whbe/WXMspK1bDevJSUr6dR0bgcRSly2E1eOqT50u5kPr7RtIPbjNV6Hl6c0Z/anrXI02dyqaJ33D/cmiOMi36+dBqYGdKubnwab2PSIpNAPj/9u48LKqyfeD4d9hFREURQUXFDXEB11wTzaV8fctKTds0yywtKzfc+oXmvqaFmdWraVpqpZlLFrnmvmYuoIIboCwKsgz7zO+PgREEF5DxcI7357q8kjNnxvvpnDmc+zzPcz90frsXzXt3AMDK2hq3utX4v+ZD0N9KeZRNeCCTps9n995DuFSswIbvlygdjihFZKhhKVGzsy8ValdlZcdRbA/4Fv/pgwrdr934/pz45ndWPjmatIQUfPr7m1/TWeloN/4lruw6WeB96/tN48enJ1os6dJZ6Xhi2kD+fHU2GzqPpXbvNpSv55Fvn2pdfHGuXZVfOoxif8C3tJ0x6L7vPfXlZjZ2m8DG7hOJCD6O30fPA5Cdlsnx2T9x5NPVFmlP9ZxY13UYxd8B39IuJ9Y7tZrQn9Nf/85PHUeTfiuF+jnH427vd6xakUaDu/Prfz7ml67j0Vlb4fVsG/PnlXV3oVrHxiRHxFmkXQB1OvtSsXZVlnQaxdbx3/L01MLb1nlcfw59+ztf+Y8m7VYKvi+Z2tb2veeIOXOZb5+ewG8jl9At8LV871vdfxr/6znR4kkXVjp8Z7zB3pdn8+eTY6j+fDvK1a+Wbxe3p/xw8qrKH21Hcmz0N/jNGpzv9T0vTmN71wnmpAvg3OJN/NVlHNu7TuD6n8fxHvmCZduRR40uvpSvXZUfO4xid8C3dLjLeffEhP78+/Xv/Jhz3nnnnHdJV2LZ2GcqP3WbwLGFG3hydv72Nn7zaeIvRFm4FQX5dm5O1drujOo0nG/HL2HQ1LcL3W/fht2M6fI+47t/iJ29Hf79uwLg6OzIoKlvM/+tGYzr9iGfD5v7KMMHoHZnXyrWqsq3T47ij3Hf0m3aoEL3e3J8f4588zvfdjJ9b5rkfG8Of7WZFc9MZMUzE9k9ay0RB86SdiuF7PRM1vafzoqnJ7Li6YnU7tQU92Z1Hlm76nX2xaV2VRZ1GsVv47/lP1PfKHS/K0fOseKVGSRcjc23PeFqLMv6fcqXT49n16IN/HfGm48i7Ltq2bklHrU8ePvJIXwx7nOGTRte6H5fT1nK+0+/z/s93iM2MpZeg0zJ4z97T/B+j/cY8cz7LBz9Ge/PGvEow79NZ4XDgOHov5hEyuS3sWnlj5W7Z6H72T8/mOwzR82bjNER6KcNN/2Z/j7GjHSyTux7hMEXrqG/H5VruzPd/0PWTfiaPtPeKnS/i0dD+fLVadyMyH+u7Vi6iXk9xzGv5zg2z/6BsINnSmXSBdC7ZzeWzJ+qdBiljsFofGR/SqsST7xiYmIYNWrUA+27Zs0aTp4smCTkGj58OImJiSUVWqnm1b0FZ3/+G4Do42HYO5fFsUqFAvtVb+/Dhc2HAAj5aQ9ePVqYX2v6RnfCth4m9caj/39WuVkdki5Fk3wlFkNmNhd/PYBnntgAPHu0IOwnUxtjj4VhV74sZapUuOd7M5NTze+3cbQ3d1NnpaYTc/hcvp6iklSzewsu5I3V2RTrnTza+3Ax53hcWLeHmjlx3+v9OhtrrB3s0FlbYVPGDn10vPnzngh8lcPTfrRod3y9bi04lXOuReWca2ULaVvNdj6EbDG17dTPe6jf3dS2yvWqcWnvaQBuhl2jfPXKOFZ2tli8d+PSrC4pF6PRX4nBmJlNxIb9uN9xznn0aMGVtXsAiD92AVtnRxwKaWteWXnOOWtH+5IP/B5qdW/BuZzzJubY3a8DHu19CM85786t20OtnHZHHz1Pxi296e/HLuDk7mJ+T1l3F2o+5UfI6p2WbUQhWnRrzd8/m/7dsOPnKOtclgpVKhbY758dx8x/D/vnPC7ulQBo99yTHP79ADeiTA8kEm/csnzQd6jbvQWnc7431+7xvanRzodzOd+b0z/toe4d5yRAw2fbcnbjfvPPmfp0AKxsrLGyseFR3jM06NaCf342fUcijl/AwdkRp0Ladf30ZRIKeSB09eh50hJN51zEsfM45znnlPBE9zZs/3k7AKHHQynrXJaKhZxrqXm+53YOduZrbpo+zbzdwdEBpWoBWNVqgCHmGsa465CdRdbhXdg0bVtgP9vOz5J1fC/GpMK/E9befhjjrmG8GWPpkO+rcfeWHPllNwCXj1+gTDlHyrkWPNciT18i/o6k607Nn23P8Y3KJ5N309KvCeWdyykdhiiFFO3xeumll2jatKmSIZQaZatWJDnqhvnn5Gs3caqa/5eFQ0Un0hP1GLMNBfYpW7UidZ5uyamVfxX4bKPRyHOrxvHS5k9p9HJni8TvWLUiKVE3zT+nXLuJ4x3xm/a5UWCf+723WUBf+h5eiNfz7Tg+52eLxH+nO2PVX7tJ2TvaY1/RiYw8xyMlzz53e7/+ejynvtpC/4MLGXDsCzKS9ETuPgWAZ7fm6K/H5xsaZgnlqlYkMU9sSddvUs4tf9vK3HGuJV67SbmctsWcuUKDZ1oB4O7rRflqlXGumnuzZaT/9+MYtOlT/AZY5lzL5eBekdQ87Ui9dpMyd9z0mfa5mW8fB/ecthqNdPhxHJ23TaPWq13yvc9nXD+ePvo5NV5sz5nZ6yzXiDuUvct3JC+HO8675ELOTQDv/v5c2XH7wVa7wFc5MO0HRcbYV6zqYk6aAG5ev0FFt7vfoFvbWNPhBX9O7jwOQNXaHpQt78TEH6fw6aY5dHjB39IhF+BUtSJJ1/J/b+68Rt/5vUnO873JZeNgRy3/ppzPGQoOpl7/17dOY9jxxVz++1+unyg4NM5SnKu65LseJF6/ibNbwfPpQTTv78+Fnf+UVGjFUqlqJeKu3b5pv3E9jkpVKxW67wdzP2Tl0e+pXqcGm5bdHqbctkdbvty+hE+WB7JwzGcWj7kwVhUrYYi/3Q5DQhy6ivnboatQCRu/dmTu3nzXz7Ft2YnMwzstFWaROLu5kJDnXEu4fpPyVYueqNs62OHdyZeTWw+WZHjiETAajY/sT2ll0Tle0dHRzJs3j7fffptly5aRnp6Om5sb7777Lk5OTgQFBdGiRQvatGlzz8/JyMhgzpw5PPHEE3To0IH//e9/XL16lezsbPr27UurVq3YuXMnhw4dIjMzk5iYGDp06EDfvn2JiYlh+vTpeHt7c/78eWrWrIm/vz/r1q3j1q1bjBgxgrp163LhwgWWL19ORkYGdnZ2DBs2DA8PD9LT0wkKCiIqKopq1aoRGxvLm2++SZ06JTsURKfTFdhW4MQpdB/Tfzt+8ip7p/+I0VDwZPv5hSmkRCdQppIzvVcHEB8WRdTB0BKJ+16xFXhSWOg+xvu+9/isdRyftY4m7/2Xhm9048Q8y89/eJDjUeg+93m/XXlHPLs3Z23bj0hP1PPUkvep80J7Lm05jO+IZ/n95VklEv893e043Gef3F32f/kb3T55jcFbphEbepXo05cx5NxornxhCskxCThWcqb/9wHcCIvi6qESPtfMId6/HYXvY/rPrv8GkhadgH1lZ9qvGU/ShShuHAgB4MzMtZyZuZb67z9LncHdOfuIEv5iH5s7fvZo1xDv/p349flPAfB8yo/UuETi/r2Ee9uGJRTsg3ug61seg6a+TcjBM4QePguY5kTVblyHGS9/gq2DHYHrZ3DheCjXL16zWMx30vFw35tcdbo1I+rIOdLyDJEyGoyseGYi9s6OPLf0QyrXr26eN2lxhTar6Dcttdr60Owlf/734pQSCKr4CjtOd2vOwtGfYWVlxdAp79Dxvx0JXhcMwP5t+9m/bT+NWjfi1dGvMenliZYM+S7uf77Z932H9PX/A6Oh8I+wtsHatw3pG5ZZIL6iK+zyVpzu3UZdW3DxSGipHWYoxL1YLPGKioris88+Y9iwYXzxxRcMHjwYHx8f1qxZw08//cSgQYMe6HPS0tJYuHAhTz75JJ06dWL16tU0btyYYcOGkZKSwoQJE2jSpAkAFy5cYN68edjb2zN+/HiaN29OuXLluH79OiNHjqR69eqMHz+ev//+mylTpnDkyBF++eUXxo4di4eHB5MnT8ba2pqTJ0+yevVqRo8ezbZt23BycmLu3LlcuXKFsWPHFhpncHAwwcGmi3a1QvcoqMnArjTK6RWI+SccJ4/bT7Oc3F1IuWNCf9rNJOydHdFZW2HMNuTsYxqmVqVpbZ4Oeg8AB5dy1OzsizHbQPi2o+bPSb2RSNjvR3Hzq1PiiZf+2k3KeuQf1pR3CN3tfSrdsU8CVnY2930vQPj6fXRdMdpiiVfDgV1pkNMjGPdPeL5YHXNizSvtZhJ2eY5HWXcX9NdNcafc0dbc93t0aEzS1VjSbpomC1/aegS3FvW4eeYy5Wq48vwf0wHT/4Pev09lY69PSI19+GFVzV/vil9/U9uunQzHOU9s5aq6kBSTv22pd5xrzu4uJOcck4zkVDaPWWre992/F5jnfSTnfI7+RiLnth3F3a+OxRKv1KiblMnTjjLuLqRejy9kH5d8+6Tl7JNbMCM9LpFrW4/g0qyOOfHKdXX9Ptp9P8aiiVejgV3xzjnvYu8478o+wHnnlOe8A3BpWIMnZ7/F1tfmkJ5gKoBQtVV9anZvjmcXX6ztbbEtV4Yui95l+4gvLdaurq8/Tef+3QAIP3mBSh6Vb8dYtRIJMQW/4wDPf9CPci7O/G/8bPO2+Gs3OHkzifTUdNJT0wk5dAbPhrUsnnj5vd6VpjnX6Osnwynnnv97kxx97++NU57vTS7v/7bl7K/7KUx6op6rB85Sy7+pRROvVq93o0XO9SDyjuuBcyHXg/tx867Bs7PeYtXA2aQmWKYg0L385/X/0GPA0wCcP3mOyu6u5tcqVa3Mzegbd3srBoOBPb/t5oV3XjQnXrlOHzpNVc+qOFd0JjH+0Q7hN8THYVvxdjusKlTGmHAz3z7WNeuZi2joyjpj3agVZGeT9Y/p/LJp3BLDlQsYkx5dcaA7tX+tO20GmEYUXP0njAp5zrUKVV24Vcjv+vtp9t+2pXqYobg7tazjlZyczIIFC4iNjcXV1ZWPPvoIJyenAvulpKSwZMkSrl69ik6n491336V+/fr3/GyLJF6JiYnMnj2bUaNGUalSJVJSUvDx8QGgU6dOLFiw4IE/a86cOTz77LN07NgRgJMnT3L06FF++800LCAjI4O4ONMQlqZNm1KunGlMbevWrQkJCaFVq1ZUqVIFT0/TpNQaNWrQpEkTdDodnp6exMaabhj1ej1BQUFcv34dgOxsUyWtkJAQevbsCYCnpyc1a9YsNM6uXbvStatpIvjnq159oLb9+10w/35nutDX6uJH00HdOP/rftya1SEjSY++kF9+EfvOUPc/rTm/8QDefTpy8Q/TnIgV7UfejmX+21wMPk74tqPYlLFHZ6UjMyUNmzL2eD7ZmEMLS76yUdyJcJxrV8Wphiv66zep/Vwbdg9fnG+fq38cw3tQNy7+uh/X5nXISNSTGpNA2o3Eu763XG03ki5GA1Cje3NuhVnuRuvsd8GczTkeNbr40fCNboTnxJqZZIr1Ttf2naH2f1oTvvEAdft25ErO8bjyx7FC358SdYMqzepi7WBHdloGHh0aEXcynPiQCFb73Z4E3m//An7t+XGJVTU8tiKYYytMbavTxY8WA7txZuN+PJrVIT1JT0ohbbu8/wzePVtz9rcDNH6xI+f/NLXN3tmRzNR0DJnZ+Pb35+qhEDKSU7HNOdcyUtKwLWNP7Scbs9cC51qu+BNhOHlVxdHTldRrN6neuy2Hh32Rb59rfxzFa3B3Ijbsp2LzumQmpZIWk4C1oz06nY6slDSsHe2p0qkJITmV5MrWrkrKRdN1wL1Hc5ItXIzi9HfBnM457zy7+NHojW6E/bqfKs3vfh2I2ncGr/+0JmzjAer37cilnPPOyaMS3b/+kB0fLOFWThsADs1cy6GZpopo7m0b4ju0p0WTLoDgFb8TvOJ3APy6tKDbwGfYv/Fv6jSrjz5JX2ji5d+/K006+TFjQGC+Xpejfx5i4JQhWFlbYWNrQx2/+vz+zSaLxg9wYkUwJ3K+N15d/Gg2sBshG/fjfo/vzdX9Z6jfszWhvx2gUZ+OhP1xe96aXbkyVG/jzZYPbv+/L+NSDkNWNumJemzsbanZoTGHvixYnbMkHV7xJ4dX/AlAvS5+tB7YnVMb91O9WV3Sk1LND1AeRHmPSrz01Yes/+hLbuQ55x6lzSs2s3mFabhdyy6t6DWwF7s37qJBswbok1KIL+Rcc6/pzrXLpt8nrbs+QcSFiALb6zSug62dzSNPugAMl0OxquKBrpIbxoQb2LTqRNq3+UdEpEwaZP67w8BRZP170Jx0Adi09Fd8mOHelX+wd+UfADTs3IwOA3twfOM+ajarS1qSnqTYoiWFDuXKUOcJH1Z9GGSJcIUAYMOGDTRp0oTevXuzYcMGNmzYwKuvFry3X7ZsGX5+fowaNYqsrCzS09Pv+9kWSbwcHR2pVKkSoaGhtGvX7qE+q0GDBhw/fpwOHTqg0+kwGo2MGjUKD4/8FfMuXLhQ4L25w1tsbW3zbcv9WafTYTCYuujXrFlDo0aNGDNmDDExMUyePPmh4i6qS9tPULOLL6//PY/M1Az+GnW7R+G/341m+9hvSIlOYN+MH3k66D3ajOlL7KlLnP5x5z0/19HVmf98/SEAOmtrzv26jys7717QpLiM2QYOTPqObqvHorOy4sKaXSSci6TBa6YnXaErtxPx1wmqdfHlhb3zyE7N4O+RS+/5XoAW41+ifB13jAYjKZFx7B93e8hEnwMLsHUqg5WdDZ5Pt+SPATO5db5kbpKvbj9B9S6+9P17HllpGewZeft4dF8xmr/HfIM+OoHD03+k8+L3aDG2LzdOXSI053jc7f2xx8O4uOUQvX+fijErmxunLxOyakeJxPygwrafoE5nX97ZbTrXNo++3bZ+exgZUwAAIABJREFUy0ezZew3JMcksHPGjzz3xXt0Gt2X66cv8c8aU9sq1/Wg1/x3MGYbiLsQyZYxXwNQtrIzLyw1nWtWNtac+XUf4YVU2CwpxmwDJyYsp/0P49BZW3H5h50khUZS+/WnALi44i+uB5/A7Sk/uh9YQHZqOkc//AoA+8rlabPsI3OsV3/ZS3TOfKjGE/vjVNcdDEb0EXEcH/utxdpwpyvbT+DZxZf+OefNzjzn3TMrRrMr57w7OP1Hui5+j1Zj+xJ36hIhOedd84+ex6GCEx1yqqIas7L5xUKVTIvixPaj+HZuzrzdi8nIKSefa/TyiXwzdjEJMfG8MW0ocZGxBK6fAcDh3w+wYdE6oi5EcnLXcWZsW4DBYGTnj8FEnHu0JcvDt5+gdmdf3tpj+t78nud788Ly0WwLMF2jd8/4kV5fvEeHMX2JOX2Jf3O+NwD1erTk8u5/yUy9/cu5bJUKPDN/KFbWVuisdIRuOkj4XyceWbvObz9Bvc5+jNg9n8zUDH4d/ZX5tVeWj2Hj2K9JikngiUE9aP9OL5xcy/Putpmc33GCjQHf0OmD5ylTsRz/+dRUDdGQnc3S/z76Uv+5jmw/TMvOLfl6zzemcvKjbz/oDVweyKKARcTHxPPRgpE4Ojmi08HFMxcJmmi6kW/Xsz1dXuxCdmY2GWnpzBr+CIZ/F8ZgIG3NYhxHTDOVk9/3B4Zrl7HtaHoQnLlny73fb2uPTcPmpK1a9AiCfTBndxynYWc/JuxaSGZqOj+MuV1mfciyANYELCUxJp6Og56m89D/Us61AqN/n8XZHSdYO870fWvSozWhe06SkXr/G1wljflkJoePnyQhIZGner/KsDdf48X/9lA6LMWV5rlXeR0+fJjAwEDA1GEUGBhYIPHS6/WcPXuW4cNND81tbGywsbl/WqUzlvD/hZiYGGbNmsW0adOYNm0aPXr04Ndff2Xw4ME0bNiQtWvXotfrGTRo0H3neA0fPpwZM2bw888/k5WVxZAhQ1i9ejWpqakMHjwYnU7HxYsXqV27Njt37uSHH35g3rx52NnZMWHCBN59913KlSvHrFmzmDfPtGZN3n8zN9Z58+YxZ84cOnbsSJs2bVi7di27du0iKCiIjRs3Eh0dzZAhQ4iIiGDMmDFMnTr1nnO8Pq/xYD1ealDuLkPH1Sq7sDHmKhVjrXQEJatBunZOtlgbba3Uscdar3QIJcbP6Kh0CCUqWaeOG5kHccSo3JA4S/ihZ/b9d1KJT36/dzVYtZl1ZLrSIZQo28peSofwQJzLPro4E1MKrhX5oAYNGsTy5cvNP7/xxhssW5Z/ruSlS5f46quvqF69OpcvX8bLy4tBgwbh4OBwz8+22BwvBwcHxo0bx9SpU2ndujXff/896enpVKlShWHDhhXpswYNGsSXX37J999/T79+/Vi+fDmjR48GwNXVlXHjxgGm3rHPP/+c69ev06FDB+rUqUNMzIOVUH3uuecICgpi8+bNNGrUyLy9e/fuBAUFMXr0aGrVqoWnpyeOjtr6xS2EEEIIIYQlPcr1tfLWXoD8U4IAPv30UxISCj7s6d+//wN9fnZ2NhcvXmTw4MHUq1ePZcuWsWHDhvu+v8R7vJSyc+dOwsLCePPNkl280WAwkJWVhZ2dHdevX+fTTz9l4cKF9+xOlB6v0kt6vEov6fEqvaTHq/SSHq/SS3q8Si/p8VKGk2PtR/ZvJesvFvu9H3zwAYGBgVSsWJH4+HgCAwNZuHBhvn0SEhKYOHEiQUGmYcpnz55lw4YNjB8//p6fbdFy8lqQnp7O5MmTyc7Oxmg08tZbbz3QGE4hhBBCCCGEiVElVQ1btmzJrl276N27N7t27aJVq1YF9qlQoQKVKlUiKioKDw8P/v33X6pXr37fzy4VGcScOXMKDAl85ZVX8PPze+DP8Pf3x9/fv4QjgzJlyjBz5swS/1whhBBCCCFE6dK7d28WLFjA9u3bqVy5MiNHmiqH37x5k6+++srcqzV48GAWLVpEVlbWA0+l0sxQw9JEhhqWXjLUsPSSoYallww1LL1kqGHpJUMNSy8ZaqiMso61Htm/laK/9Mj+raIoFT1eQgghhBBCCO16lMU1SittPZYVQgghhBBCiFJIeryEEEIIIYQQFiWzm6THSwghhBBCCCEsTnq8hBBCCCGEEBallnLyliQ9XkIIIYQQQghhYdLjJYQQQgghhLAomeMlPV5CCCGEEEIIYXHS4yWEEEIIIYSwKOnxkh4vIYQQQgghhLA46fESQgghhBBCWJT0d0mPlxBCCCGEEEJYnM4oAy5VKzg4mK5duyodRonQUltAW+3RUltAW+3RUltA2lOaaaktoK32aKktoK32aKktomRIj5eKBQcHKx1CidFSW0Bb7dFSW0Bb7dFSW0DaU5ppqS2grfZoqS2grfZoqS2iZEjiJYQQQgghhBAWJomXEEIIIYQQQliYdWBgYKDSQYji8/LyUjqEEqOltoC22qOltoC22qOltoC0pzTTUltAW+3RUltAW+3RUlvEw5PiGkIIIYQQQghhYTLUUAghhBBCCCEsTBIvIYQQQgghhLAwSbyEKCEhISEPtE0IIYQQQjx+bJQOQDyY5OTke77u5OT0iCIpWWvWrOGll14y/2wwGPjiiy8YMWKEglEVz7Jly5g1a9Z9t6lFZmYmBw8eJCYmBoPBYN7ep08fBaMqvtDQUGJjY8nOzjZv69Spk4IRFV14ePg9X1fjJO6ZM2ei0+nu+npAQMAjjKb4tNKOXAaDgTFjxjBv3jylQ3loU6ZM4f/+7//4/vvvefXVV5UOp0QUdi1wdHTE1dUVa2trBSIqHi1e07RybIRlSOKlEgEBAeh0OoxGI3FxcTg5OWE0GklJSaFy5coEBQUpHWKxxMXFsX79ep5//nkyMzOZP38+tWvXVjqsIjl37hyhoaEkJiayadMm83a9Xp8vYVGb2bNn4+joiJeXF7a2tkqH81A+//xzoqOjqVWrFlZWtzv61ZZ4rVy5EoCMjAzCw8OpWbMmRqORK1euULduXT799FOFIyw6Nzc3EhIS6NixIwB79+7F1dUVPz8/hSMrmmeffVbpEEqUlZUVNWvWJC4ujsqVKysdzkOJj4/nzJkzHD16lPbt23NnTTE13tx/++23+a4BV69epWbNmiQlJTFkyBB8fX2VDvGBaPGappVjIyxDEi+VyE2sli5dSsuWLWnevDkAx48f599//1UytIcybNgwFi1axPr16zl9+jR+fn706tVL6bCKJCsri7S0NLKzs0lNTTVvd3R0ZOTIkQpG9nBu3rzJxIkTlQ6jRISHhzN//vx79kiowSeffALAZ599xtChQ/H09ATgypUr/Pbbb0qGVmyXLl1i8uTJ5p9btmzJJ598wssvv6xgVEXn4+Nj/ntWVhZRUVEAeHh4YGOjzl+18fHxjBw5krp162Jvb2/errbeu5deeokNGzZw48YNVqxYUeD13O+Vmri6uvLOO+9Qo0YNACIiIti4cSMvvvgic+fOVc3NvRavaVo5NsIy1Pnb4DEWFhbG22+/bf65WbNmrFmzRsGIiidvV3zPnj1ZunQpDRo0wMfHh/DwcFU9gfTx8cHHxwd/f39cXV2VDqfE1K9fnytXrph/EapZjRo1SEhIoGLFikqHUiIiIyPzHRdPT08uXbqkXEAPITExkejoaNzc3ACIiYkhMTFR4aiK7/Tp0wQFBZmvBXFxcQwfPjxfYqYWffv2VTqEElGhQgUmTJjATz/9pNqh0neKjIw039gDVK9enYsXL5q/R2qjpWua1o6NKFmSeKmMs7MzP//8Mx07dkSn07Fnzx7KlSundFhFlju8IJeTkxORkZHm7Wp8ArlkyRJGjhxJ2bJlAdO8vIULF6q21ygkJISdO3dSpUoVbG1tMRqN6HQ65s6dq3RoDyx33k1aWpr5yX3e3ge1PbnPVa1aNZYsWWK+DuzevZtq1aopHVaxDBw4kMDAQPNNSWxsLEOGDFE4quJbsWIFkyZNwsPDA4CoqCgWLlyoyrmeakwWC5M71/bw4cOaSbw8PDz4+uuvad++PQD79u3D3d2dzMxMVfawaumaprVjI0qWLKCsMsnJyaxbt46zZ88C0LBhQ/r27ava4hpaMnbsWGbPnn3fbWoRGxtb6HY19eqdOXPmnq+r9cYyIyODP/74I991oHv37tjZ2SkcWfFkZmYSGRkJmG7A1DyncPTo0QUeThS2TQ0OHjzIqlWruHXrFoD54ct3332ncGRFM3HiRKpVq8bx48dp27ZtgdcHDx6sQFQPJyMjg23bthESEoLRaMTb25sePXpga2tLRkYGDg4OSodYJFq6pmnt2IiSJYmXSun1eqysrFT/BU5KSmLdunWEhoYC4O3tTZ8+fVTZixcQEMCYMWPME9FjY2OZO3euKp9057p06ZK5JL63tze1atVSNqBiKqyamZYqnKlZeno6mzZtIjY2lnfeeYdr164RFRVFixYtlA6tWBYvXoxOp+PJJ58EYM+ePRgMBoYNG6ZwZEX3/vvvExAQQPXq1ZUO5aEkJiby77//smrVKvr161fgdX9//0cflBDisSR9nipz5coVvvjiC3N5+XLlyjF8+HDVzsP57LPPaNiwIaNGjQJMNymfffYZH3/8scKRFd2AAQP4+OOPzb0oZ8+ezTcfT222bNnCX3/9RevWrQFTZcCuXbvyzDPPKBxZ0RVWgObEiROqTbyuXbvG6tWriYiIIDMz07z9iy++UDCq4lm8eDFeXl6cP38egEqVKjF//nzVJl5Dhgxh27ZtbN26FaPRSMOGDenRo4fSYRVLhQoVVJ90gWmIfvv27alWrZpqHx7dadSoUQWKBeVWoX3xxRdV9/BSS9c0LbVFlDxJvFRm6dKlvP766zRu3BgwTeReunQpU6dOVTiy4klOTs435v7FF1/k8OHDCkZUfH5+fsyaNYvz589jNBoZOHAgzs7OSodVbNu3b2fatGnmXtXnnnuOSZMmqSrx+uOPP9i2bRsxMTGMHj3avD01NZUGDRooGNnDWbx4Mf369eO7775jwoQJ7NixQ+mQii06OpqPPvqIvXv3AqhyaFFetra29OrVi169epGcnMyNGzdUO3TSy8uLBQsW0KpVq3xteOKJJxSMqvjs7OyYMmUKt27dYt68eVy+fJkjR47w4osvKh1akTVr1gwrKys6dOgAmJZhMBqNODo6EhQUxLhx4xSOsGi0dE3TUltEybO6/y6iNElPTzcnXQCNGjUiPT1dwYgeTqNGjdi7dy8GgwGDwcC+ffvMpfLVyMrKivLly+Po6EhERMR95xiVZkajMd+aV1ZWVgXWvyntOnToQEBAAC1atCAgIMD8Z9asWapcpDtXRkYGTZo0wWg04urqSr9+/Th16pTSYRWLjY0NGRkZ5qf3169fV/UE9MDAQPR6PcnJyYwZM4bFixerbk5UrtTUVOzt7Tl58iRHjx41/1Grr776ipdfftm8iG3NmjXZt2+fwlEVT2hoKC+//DKenp54enoyYMAAzp49S+/eve86P7c009I1TUttESVPvb/dHlNVqlThp59+yjd/QE3FDnK9/vrr5gWhN2/ezOeff45Op8NgMODg4FDoOPzS7q+//mLLli3cvHmTWrVqce7cOerXr6/KCo0AnTt3ZuLEibRq1QqAw4cP06VLF4WjKhpHR0ccHR358MMPCQ8PJyQkBJ1OR4MGDVRdkMbOzg6DwYC7uzu///47Li4u5gIIatO3b1+mTZtGXFwcixYtIjQ0lHfffVfpsIpNr9fj6OjIX3/9RefOnenXr1++3lY1UeO8tHvJyMigbt26+bblfbikJmlpaZw/f5569eoBcOHCBdLS0gDMiaWaaOmapqW2iJIniZfKvPvuu6xdu5Z58+aZ5w+o8ZdjYYtYqt2WLVuYMWMGEydO5JNPPiEyMpK1a9cqHVax9erVCx8fH3NxjWHDhlG7dm2Foyqen376if3795uHSH355Ze0adNGlUOMwFSCPSMjgzfeeIM1a9Zw6tQphg8frnRYxeLr62ue42U0Ghk0aJCqh+hmZ2cTHx/P/v376d+/v9LhPJTvv/+eF154ATs7O6ZPn87ly5cZOHCg+cGf2pQrV47r16+be1cPHDig2rX9hg4dypdffmlOtsqUKcM777xDWloavXv3Vji6otPSNU1LbRElTxIvlXFyclJl6dvCGI1GLly4wM2bN9HpdFSsWJG6desWmDCsFnZ2dub5KZmZmVSrVo2oqCiFoyq63Cf2ycnJVKlShSpVqphfS05OVmVP0d69e5k1a5b5+PTu3ZuAgABVJl4Gg4H9+/fz2muv4eDgoMoHL3lNmTKF//u//8s3xDh3mxr16dOHadOm4e3tTd26dYmOjqZq1apKh1Us//zzD6+++iqHDh3CxcWFkSNHMnnyZNUmXm+++SZLly4lMjKSoUOHUqVKFdUOOa5bty7z5s1Dr9djNBrN60cCtGvXTsHIik5r17TcXlUttEWUPEm8VGL58uUMGjTIvCDsndS2EOw///zDN998g7u7Oy4uLgDcuHGD69ev89Zbb+Hr66twhEXn4uJCSkoKrVq1YurUqZQtW9bcNjVZtGgR48aNIyAgIN+5lruGjxorM7m6upKZmZkvMc5dsFdtrKysCA8PNx8PtcrIyCAjI4OkpCRzlVYwJf7x8fEKRvZw2rZtm2+tKDc3N9UONczOzgbg2LFjdOjQQZUPXfLS6XR8/PHHpKWlYTQaKVOmDDExMUqHVSx6vT7fmp4+Pj706dMHR0dHhSMrOq1c03KFhYXxyy+/EBcXZ/4OAapcy0+UPFnHSyXCw8Px8vK6a7EGtS0E+9FHHzF+/Ph8vSkAMTExzJgxgwULFigUWck4c+YMer0ePz8/VRcK0IrZs2cTFhZG06ZN0el0nDx5Em9vb/OQNrX1Iq9YsYJr167Rtm1b7O3tzdvVVG1uy5YtbN68mfj4eFxcXMyFWxwdHXnqqad4+umnFY6weBITEwkODiY2NjbfTZcan3yvWrWKw4cPm4ca6vV6Zs6cyfTp05UOrVhyC+vcb5sazJ07F09PTzp16gTA7t27uXz5smqTfC1c03J98MEHvPbaa3h6euZLJNU4H1+UPLkjVAkvLy8MBgPBwcGqHRqRV3Z2NpUqVSqw3cXFhaysLAUiKhl3FnBQc9JV2HAvtQ4Ba926tXk9MlDfg4o7JScnU65cuQKVstR0k9KzZ0969uzJ1q1b77lEwcmTJ2natOkjjOzhzJ49G29vb5o0aaLawg25XnnlFZ577jkcHR2xsrLCzs6OsWPHml9Xy7GJjIzk6tWr6PV6Dh48aN6empqab50lNYmOjs6XZPXt25cxY8YoGNHD0cI1LZezszMtW7ZUOgxRSqn3rvAxZGVlRVJSEllZWaq+oQdTxbzx48fTrl07KleuDEBcXBz79u1TXeW8XFop4KDFIWD+/v5kZGQQFxeHh4eH0uE8NDX2ntzN/daFW7VqlSpu7nOlp6erdmHuwuQdXujg4GBe1w/Uc2yioqI4duwYKSkp+crhOzg4MHToUAUjKz47OztCQkLw9vYGICQkRNVr4GnpmtavXz+WLFlC48aNNbH+nShZ6r57fwy5urry8ccf06JFi3y/AHv16qVgVEX3/PPP06pVK44cOcK5c+cwGo1UqlSJESNGUL16daXDKxatFHAIDg42DwEbN25cviFgPXr0UDi64jly5AgrV64kKyuLoKAgLl26xJo1a1Q3NzJXdHQ0y5Yt4/z58+h0OurXr8+gQYMKDN3VArWNhm/RogXHjh1T9XqED0otx6ZVq1a0atXKvMSHFgwZMoSgoCBzcQ0nJydVJy9aqqC5Y8cOoqKiyMrKytfrLYmXAEm8VKdixYpUrFgRo9FIamqq0uE8lOrVq1O9enXN9ERopYDDgw4BU5N169YxY8YMAgMDAahVq5ZqJ9WDqQBKjx49zEOL9u7dy2effabauTf3orbJ9lu2bGH9+vXY2NhgY2NjLhig1kWU70Vtx+bQoUNUr15dEzf3tWrVYs6cOej1egBVFtXIS0sVNC9fvsy8efOUDkOUUpJ4qUzfvn0B0+KJeXu81EpLPRE2NjaMHDmyQAGH//3vf4D6Cjg888wzXLlyhYiIiHzzIHInc6uJtbV1gRsTtd005mU0GvPdkDz55JNs27ZNwYhELi2uUagVWri537Rp0z1fV9vol1xaqqBZr149IiIiVDt6R1iWJF4qc+7cOfOiiV9++SWXLl0iODiYt956S+nQiqWwnojY2FhlgyomrRVwWLduHWfOnCEiIoJmzZpx/PhxvL29VZl41ahRg7///huDwcC1a9fYunWrqoccNWrUiA0bNtCuXTt0Oh379u2jWbNm5jl5ar5puZNaKoFFRkZSrVo1wsPDC33dy8vrEUdkeWo5Nrm0cHOv9pEud9OiRQs+/PBD7OzseOutt0hMTMw3P0pNQkND2bVrF1WqVMHW1tbc6y3l5AVI4qU6y5cvZ+LEicyePRswJSq563ioUWE9EWrl7++vdAgl6sCBA8yZM4eAgACGDRtGQkICS5YsUTqsYhk8eDC//PILtra2LFy4EF9fX9XNvctr3759APz555/5tu/YsUM1a62dOnWKxo0b56syl1fufAi1lMfetGkTQ4cOZeXKlYW+/sknnzziiIrvbsckl9qOTS4t3NznjnrRGq1U0ASYMGGC0iGIUkwSLxXKrQKYS80li7XUE3H06FHWrFlDbGwsBoNB9XM77OzssLKywsrKCr1eT/ny5VU7L8re3p4BAwYwYMAApUMpEUFBQfd8XQ03KWfOnKFx48b5qszlpbaJ6LnV8e6XYKnh2NztmORS27HJpaWbey0Vo8ilhQqaYOoJvnNpGS32eIvikcRLZSpVqkRoaCg6nY6srCy2bNlCtWrVlA6r2LTUE7F8+XJGjx5dYNFEtapTpw4pKSk89dRTjBs3DgcHB+rWrat0WEUyc+bMex4LNc4lfBBquEnp168foK0y0g9CDcdGy8dEKzf3WpivVhRqqaAJ2llaRliGJF4qM2TIEJYvX87NmzcZOnQovr6+vPnmm0qHVWxa6omoXLkyNWrU0ETSZTQa6d27N2XLlqV79+74+fmRmppKzZo1lQ6tSJ599lnANHQqISGBjh07AqYqgGqbn1IUarpJ0ev1rFu3zjxk2sfHhz59+mhmCPKd1HRsEhIS+OGHH4iPj2fChAlERERw7tw51a61eD9qOjZamK9WFGr6vaqVpWWEZUjipTLOzs6MGDFC6TBKTFhYGOvXryc2Ntb8iwRQ5STUV155hRkzZuDj45Nv3oAaq0zpdDrmzJnDrFmzAFS7PlRugZM1a9YwefJk8/aWLVuqas5NUanpJmXx4sV4enry0UcfAbB7924WL16suvlDD0ptx8bf35/169cD4O7uzoIFCzSbeKnp2GhhvppWaWVpGWEZknipTGELpw4cOFC1X+pFixbx2muvaWJ43o8//oiDgwOZmZlkZWUpHc5Dq1evHhcuXFDd8MLCJCYmEh0dbf6exMTEkJiYqHBUAkzXtLxJVt++fc3rkwllJSUl0a5dOzZs2ACYiiGpeU6xlmhpvtqDUNMIBa0tLSNKliReKlPYwqkLFy5U7cKpzs7OtGzZUukwSkRycjKTJk1SOowSc/r0aYKDg3F1dcXe3l7VJXEHDhxIYGCgOfGKjY3l7bffVjgqy1HTTYqdnR0hISF4e3sDEBISYn5SrEVqOjb29vYkJSWZH4qdO3dOs0NAQV3HBrQxX01r1U1Be0vLiJKlM6ppULNgwoQJBZKsiRMnMm3aNIUiejj//vsve/fupXHjxvmGSaixataqVato3Lgxvr6+SodSIu62nprabk5yZWZmEhkZCUC1atXynW9qeTr8oDcpahIWFsaSJUvQ6/UAlC1bltatW9OnTx+FIysaLR6b8PBwli1bxpUrV/D09CQxMZGRI0eqbq6nFo/N/YwdO9a87ExptnbtWvr168fixYsLfV2thV4yMjKIi4vDw8ND6VBEKSM9XiqjtYVTd+zYQVRUFFlZWfmGsKjxF+G2bdvYuHEjNjY22NjYqL6cvNqHft7J1taWWrVqFfqaWp4Oa60EO8DSpUt57733zAn9sWPH2LJli+oSLy0eGy8vLwIDA4mKisJoNOLh4YGNjfpuG7R4bO5HLddvLVY3PXLkCCtXriQrK4ugoCAuXbrEmjVrNFtFVxSN+q6gjzktLJya1+XLl5k3b57SYZSIFStWKB1CiZoxYwY6nQ6j0UhmZiYxMTF4eHgwf/58pUMrcWrp+NfiTcrIkSOZP38+I0aM4OzZs+zZs0eVQ3a1dGzu1jN07do1QH2JipaOjVZpqbrpunXrmDFjBoGBgQDUqlVLtWtgipIniZfKaGHh1Lzq1atHREQE1atXVzqUYouMjKRatWqEh4cX+rpaF068MyEODw8nODhYoWgsSy1Ph3Np6SbFzc2NDz74gDlz5lC5cmUmTpyo6jleWjg2uT1Dt27d4ty5czRq1Agwzfts1KiR6hKvXFo4Ng9KbUPCtVTd1NrausA5pbbfMcJyJPHSGLUMmcoVGhrKrl27qFKlCra2tqos4LBp0yaGDh3KypUrC31dK2XLvby8CAsLUzoMgTZuUkaNGpXvZiQ5ORmDwcCECRMAdS4pAdo4Nrk9QzNnzmT+/PlUrFgRgPj4eL799lslQ3soWjg2WixGAdqqblqjRg3+/vtvDAYD165dY+vWrdSvX1/psEQpIYmXxqhlyFSu3JssNRs6dChw/wRLbb2RmzZtMv/dYDBw8eJFnJ2dFYzIctT2dFgLNynjxo1TOgSL0MKxyRUbG2tOugDKly9vHm6oRlo4Nlqdr6al6qaDBw/ml19+wdbWlkWLFtG0aVPVzVkVliOJl8aopTtbr9fj6OhImTJllA7lkVFbb2QNAJkQAAAIVklEQVRqaqr579bW1jRv3lx1v9S1+nRYCzcpakt2H5QWjk0uHx8fpk2bRvv27QHTHOPcYYdqpIVjo9X5aoMHDy60uqka7d27lwEDBjBgwADztlWrVvHKK68oGJUoLSTxEopYtGgR48aNIyAgwFzAIZcai4Q8CLX1Rvbt21fpEB6aVp8Oa+kmRWu0dGzefPNNDh06xJkzZwDo2rWratsC2jo2WpuvppXqpgAHDhzA1taWjh07AvDNN9+QmZmpcFSitJB1vDRm7ty5qnt6/7gICAhg1qxZSodxXzNnzrxnz6mUxFVeQEAAw4YNK3CTotaF1LVEa8cmISGBCxcuoNPpqFu3LuXLl1c6pGLT0rGZO3cunp6edOrUCTDNV7t8+bJqf/9HR0cXqG4aEBCgykQyIyODWbNm0blzZ06cOIGTkxODBg1SOixRSlgH5ta7FKXaqVOnqFKlCgcPHiQyMrLAn9yqgO3atVM40qKZMmWK+RfHvbZpQXBwMN26dVM6jPtycXGhQYMGJCQkkJmZib+/P15eXly7dg13d3eaNGmidIhFptfrWb16NT/++CPBwcFERUVRr169fIsoq0nTpk1ZtGgRvr6+HD9+nB07djBhwgTVtkdLtHRs9u3bx4IFC0hPTyciIoI1a9ZQqVIl1Vah1dKxWb9+PR999BFOTk44OTnRqFEj1q9fT/fu3ZUOrVhy2zB37lzi4+MZN24cDg4OSodVJMnJyWRkZJCdnY2fnx/Lli2jatWq9OvXj4yMDNUNaxWWIUMNVUJrQ6YyMjLIyMggKSnJvPgzmG6Q4+PjFYzMctQyp8XHxweANWvWMHnyZPP2li1bqrZCoxaqmeWltRLsWqKlY7N+/XpmzJhh7uVKTEzk008/pU2bNgpHVjxaOjZamK8G2qpumnfqRO5/jx07xrFjxzQ7hUIUnSReKqG1CbXBwcFs3rzZ/GQrd8Sro6MjPXr0UDi6otFqAYfExESio6Nxc3MDICYmhsTERIWjKh4tVDMDbd2kaI0Wj43BYMg3tNDJyQmDwaBgRMWjxWOjlflqWqpuer91VnOprcKxKFmSeKmMVibU9uzZk549e7J161aeeeaZu+6nhguU1nojcw0cOJDAwEBz4hUbG8vbb7+tcFTFo5Wnw1q6SdEaLR4bPz+/AlUNmzVrpnBURafFY6OVYhRqGQlSktRW4ViULCmuoTJam1B7P2opSKFVmZmZREZGAlCtWrV8cyHUkBTnCgsLK/TpsNpuUoR41A4cOEBoaChGoxEfHx9V9qpokZaKUTxuxo4dy+zZs5UOQyhEerxURitDph6Ump4LaKU3Mi9bW1tq1apV6GtqemqnlafDQjxqbdq0oWnTpuYhhsnJyTg5OSkcldDSfLXHjVrWWxWWIYmXymhlyNSDUtMFSmsFHO5HTUnxyJEjCzwdnjRpktJhCVGq/fnnn6xduxY7O7t8RQOkSIBytDhfTYjHiSReKqOVCbVa9Lj1RqopKZanw0IU3W+//ca8efNwdnZWOhSRQ4vz1R43j+O8NnGbJF4q87gNmVLTBepx641UA3k6LETxubm5YW9vr3QYIg81/U583Gi1wrEoWZJ4qYxWhkxp8QL1uPVGquEGQJ4OC1F8L7/8MpMmTaJevXrY2Ny+XRg8eLCCUQlROmm1wrEoWVLVUIWioqLMQ6bGjBmjyl6VtWvX0q9fPxYvXlzo62pcrywgIIBhw4YV6I2cPn26wpEVzYMmxUIIbRs/fjze3t54enrm6zn29/dXLighhFAxSbxU4s4hU7du3cLR0dFc3luGTClPK+V9tZgUCyGKbtKkSUydOlXpMIRQFS1WOBYlRxIvlYiNjb3n62oY9lUYrV2gtNAbKYQQAD/88AOurq60aNEi3xp+Uk5eiLt73NZbFUUjiZdQlBYuUFrtjdRaUiyEKJrhw4cX2Cbl5IW4tzFjxjBnzpz7bhOPJymuIRSlhRLsWi3g8LitSyaEyC8oKOier588eVI1i6gL8ahIhWNxL5J4CUVp4QKl1mGe96OFpFgIYTmrVq2SxEuIOzxuFY5F0UjiJRQlF6jSSwtJsRDCcmSmghAFPW7rrYqikcRLKEouUKWXJMVCiHvJO7dVCGGilfVWhWVI4iUUJReo0kuSYiGEEKJo3Nzc+OCDD8wVjidOnCijRYSZVDUUipMS7KWTVtYlE0JYxty5c6XYjhA5tFrhWJQsSbyEIuQCpQ6SFAvx+Dl16hSNGzfm4MGDhb7+xBNPPOKIhCj9tLreqihZkngJRcgFqvSSpFiIx9vatWvp168fixcvLvT1YcOGPeKIhBBCGyTxEkLkI0mxEEIIIUTJk8RLCCGEEAXo9XrWrVvH2bNnAfDx8aFPnz4yz1MIIYpJEi8hhBBCFDB37lw8PT3p1KkTALt37+by5ctSUEMIIYrJSukAhBBCCFH6REdH069fP9zc3HBzc6Nv375ER0crHZYQQqiWJF5CCCGEKMDOzo6QkBDzzyEhIVLZVAghHoIsoCyEEEKIAgYPHsySJUvQ6/UAlC1bltatWysclRBCqJckXkIIIYQoYOnSpbz33nvmSqbHjh1jy5Yt9OnTR+HIhBBCnaS4hhBCCCEKiI6OZv78+YwYMYKzZ8+yZ88eAgICpKqhEEIUkyReQgghhChUVFQUc+bMoXLlyowZM0bmeAkhxEOQxEsIIYQQZqNGjUKn05l/vnXrFo6Ojtja2gKmMvNCCCGKThIvIYQQQpjFxsbe8/XcOV9CCCGKRhIvIYQQQgghhLAwWcdLCCGEEEIIISxMEi8hhBBCCCGEsDBJvIQQQgghhBDCwiTxEkIIIYQQQggLk8RLCCGEEEIIISzs/wFwvSd9e9kadQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrMatrix = pd_wide.corr()\n",
    "plt.figure(figsize=(15,10))\n",
    "sn.heatmap(corrMatrix, annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FALSE POSITIVE    4723\n",
       "CONFIRMED         2292\n",
       "Name: koi_disposition, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_wide['koi_disposition'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1792.,    0.,    0.,    0.,    0., 3922.,    0.,    0.,    0.,\n",
       "        2281.]),\n",
       " array([0. , 0.2, 0.4, 0.6, 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.hist(pd_trim['koi_pdisposition'], label=\"guess\")\n",
    "plt.hist(pd_trim['koi_disposition'], label=\"conclusion\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FALSE POSITIVE    3922\n",
       "CONFIRMED         2281\n",
       "CANDIDATE         1792\n",
       "Name: koi_disposition, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_trim['koi_disposition'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_f = pd_wide['koi_disposition'].apply(lambda x: 1 if x == \"FALSE POSITIVE\" else 0)\n",
    "y_c = pd_wide['koi_disposition'].apply(lambda x: 1 if x == \"CONFIRMED\" else 0)\n",
    "# X = pd_wide.drop(columns=['koi_disposition']).copy().values\n",
    "X = pd_wide.drop(columns=['koi_disposition']).to_numpy()\n",
    "X_const = add_constant(X, prepend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_normalized = normalize(X)\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y_c,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.26208686565162265"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_regularization_strengths = np.logspace(\n",
    "    np.log10(10e-6), np.log10(10), num=100 )\n",
    "\n",
    "l_try = Lasso(alpha=0.01)\n",
    "l_try.fit(X_train, y_train)\n",
    "l_try.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lee/anaconda3/lib/python3.7/site-packages/statsmodels/discrete/discrete_model.py:1747: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.369220\n",
      "         Iterations 15\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.835495367070563"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logit_model = Logit(y_c, X).fit()\n",
    "# logit_model = Logit(y, X_normalized).fit()\n",
    "model = LogisticRegression(max_iter=10000).fit(X, y_c)\n",
    "model.predict(X)\n",
    "model.predict_proba(X)\n",
    "model.score(X, y_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lee/anaconda3/lib/python3.7/site-packages/statsmodels/discrete/discrete_model.py:1747: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>koi_disposition</td> <th>  No. Observations:  </th>  <td>  7015</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td>  7002</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>    12</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Fri, 28 Aug 2020</td> <th>  Pseudo R-squ.:     </th>  <td>0.4156</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>13:44:15</td>     <th>  Log-Likelihood:    </th> <td> -2590.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -4432.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th>  <td> 0.000</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>      <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th>  <td>   -0.0156</td> <td>    0.001</td> <td>  -18.566</td> <td> 0.000</td> <td>   -0.017</td> <td>   -0.014</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th>  <td>    0.0041</td> <td>    0.001</td> <td>    4.395</td> <td> 0.000</td> <td>    0.002</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th>  <td>   -0.5830</td> <td>    0.111</td> <td>   -5.264</td> <td> 0.000</td> <td>   -0.800</td> <td>   -0.366</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th>  <td>   -0.0821</td> <td>    0.010</td> <td>   -8.515</td> <td> 0.000</td> <td>   -0.101</td> <td>   -0.063</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x5</th>  <td>   -0.0001</td> <td> 1.52e-05</td> <td>   -6.969</td> <td> 0.000</td> <td>   -0.000</td> <td> -7.6e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x6</th>  <td>   -0.1084</td> <td>    0.010</td> <td>  -11.082</td> <td> 0.000</td> <td>   -0.128</td> <td>   -0.089</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x7</th>  <td>   -0.0025</td> <td>    0.000</td> <td>  -17.007</td> <td> 0.000</td> <td>   -0.003</td> <td>   -0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x8</th>  <td>-6.533e-05</td> <td> 4.19e-05</td> <td>   -1.560</td> <td> 0.119</td> <td>   -0.000</td> <td> 1.67e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x9</th>  <td>    0.0018</td> <td>    0.000</td> <td>    9.357</td> <td> 0.000</td> <td>    0.001</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x10</th> <td>    0.0003</td> <td> 4.95e-05</td> <td>    5.886</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x11</th> <td>    0.3120</td> <td>    0.119</td> <td>    2.617</td> <td> 0.009</td> <td>    0.078</td> <td>    0.546</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x12</th> <td>    0.2458</td> <td>    0.025</td> <td>    9.729</td> <td> 0.000</td> <td>    0.196</td> <td>    0.295</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x13</th> <td>    0.0014</td> <td>    0.033</td> <td>    0.044</td> <td> 0.965</td> <td>   -0.062</td> <td>    0.065</td>\n",
       "</tr>\n",
       "</table><br/><br/>Possibly complete quasi-separation: A fraction 0.17 of observations can be<br/>perfectly predicted. This might indicate that there is complete<br/>quasi-separation. In this case some parameters will not be identified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:        koi_disposition   No. Observations:                 7015\n",
       "Model:                          Logit   Df Residuals:                     7002\n",
       "Method:                           MLE   Df Model:                           12\n",
       "Date:                Fri, 28 Aug 2020   Pseudo R-squ.:                  0.4156\n",
       "Time:                        13:44:15   Log-Likelihood:                -2590.1\n",
       "converged:                       True   LL-Null:                       -4432.3\n",
       "Covariance Type:            nonrobust   LLR p-value:                     0.000\n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1            -0.0156      0.001    -18.566      0.000      -0.017      -0.014\n",
       "x2             0.0041      0.001      4.395      0.000       0.002       0.006\n",
       "x3            -0.5830      0.111     -5.264      0.000      -0.800      -0.366\n",
       "x4            -0.0821      0.010     -8.515      0.000      -0.101      -0.063\n",
       "x5            -0.0001   1.52e-05     -6.969      0.000      -0.000    -7.6e-05\n",
       "x6            -0.1084      0.010    -11.082      0.000      -0.128      -0.089\n",
       "x7            -0.0025      0.000    -17.007      0.000      -0.003      -0.002\n",
       "x8         -6.533e-05   4.19e-05     -1.560      0.119      -0.000    1.67e-05\n",
       "x9             0.0018      0.000      9.357      0.000       0.001       0.002\n",
       "x10            0.0003   4.95e-05      5.886      0.000       0.000       0.000\n",
       "x11            0.3120      0.119      2.617      0.009       0.078       0.546\n",
       "x12            0.2458      0.025      9.729      0.000       0.196       0.295\n",
       "x13            0.0014      0.033      0.044      0.965      -0.062       0.065\n",
       "==============================================================================\n",
       "\n",
       "Possibly complete quasi-separation: A fraction 0.17 of observations can be\n",
       "perfectly predicted. This might indicate that there is complete\n",
       "quasi-separation. In this case some parameters will not be identified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logit_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pd_wider[['koi_disposition','koi_score','koi_period','koi_time0bk','koi_impact',\n",
    "#                    'koi_duration','koi_depth','koi_prad','koi_teq','koi_insol',\n",
    "#                    'koi_model_snr','koi_steff','koi_slogg','koi_srad','koi_kepmag']]\n",
    "pd_try = pd_wide[['koi_disposition','koi_period','koi_time0bk','koi_impact',\n",
    "                    'koi_depth','koi_prad','koi_teq',\n",
    "                    'koi_model_snr','koi_steff','koi_srad']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrMatrix = pd_try.corr()\n",
    "plt.figure(figsize=(12,8))\n",
    "sn.heatmap(corrMatrix, annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd_try.drop(columns=['koi_disposition']).to_numpy()\n",
    "X_const = add_constant(X, prepend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_normalized = normalize(X)\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y_c,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22560050671101994"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_regularization_strengths = np.logspace(\n",
    "    np.log10(10e-6), np.log10(10), num=100 )\n",
    "\n",
    "l_try = Lasso(alpha=0.01)\n",
    "l_try.fit(X_train, y_train)\n",
    "l_try.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.379317\n",
      "         Iterations 15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lee/anaconda3/lib/python3.7/site-packages/statsmodels/discrete/discrete_model.py:1747: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8245188880969352"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logit_model = Logit(y_c, X).fit()\n",
    "# logit_model = Logit(y, X_normalized).fit()\n",
    "model = LogisticRegression(max_iter=10000).fit(X, y_c)\n",
    "model.predict(X)\n",
    "model.predict_proba(X)\n",
    "model.score(X, y_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lee/anaconda3/lib/python3.7/site-packages/statsmodels/discrete/discrete_model.py:1747: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>koi_disposition</td> <th>  No. Observations:  </th>  <td>  7015</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td>  7006</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>     8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Fri, 28 Aug 2020</td> <th>  Pseudo R-squ.:     </th>  <td>0.3997</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>13:44:24</td>     <th>  Log-Likelihood:    </th> <td> -2660.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -4432.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th>  <td> 0.000</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>     <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th> <td>   -0.0187</td> <td>    0.001</td> <td>  -23.387</td> <td> 0.000</td> <td>   -0.020</td> <td>   -0.017</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th> <td>    0.0071</td> <td>    0.001</td> <td>    8.139</td> <td> 0.000</td> <td>    0.005</td> <td>    0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th> <td>   -0.6506</td> <td>    0.107</td> <td>   -6.085</td> <td> 0.000</td> <td>   -0.860</td> <td>   -0.441</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th> <td>-9.375e-05</td> <td> 1.37e-05</td> <td>   -6.820</td> <td> 0.000</td> <td>   -0.000</td> <td>-6.68e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x5</th> <td>   -0.1128</td> <td>    0.009</td> <td>  -11.959</td> <td> 0.000</td> <td>   -0.131</td> <td>   -0.094</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x6</th> <td>   -0.0025</td> <td> 9.11e-05</td> <td>  -26.940</td> <td> 0.000</td> <td>   -0.003</td> <td>   -0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x7</th> <td>    0.0017</td> <td>    0.000</td> <td>    9.224</td> <td> 0.000</td> <td>    0.001</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x8</th> <td>    0.0004</td> <td> 3.04e-05</td> <td>   13.598</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x9</th> <td>    0.1714</td> <td>    0.015</td> <td>   11.569</td> <td> 0.000</td> <td>    0.142</td> <td>    0.200</td>\n",
       "</tr>\n",
       "</table><br/><br/>Possibly complete quasi-separation: A fraction 0.16 of observations can be<br/>perfectly predicted. This might indicate that there is complete<br/>quasi-separation. In this case some parameters will not be identified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:        koi_disposition   No. Observations:                 7015\n",
       "Model:                          Logit   Df Residuals:                     7006\n",
       "Method:                           MLE   Df Model:                            8\n",
       "Date:                Fri, 28 Aug 2020   Pseudo R-squ.:                  0.3997\n",
       "Time:                        13:44:24   Log-Likelihood:                -2660.9\n",
       "converged:                       True   LL-Null:                       -4432.3\n",
       "Covariance Type:            nonrobust   LLR p-value:                     0.000\n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1            -0.0187      0.001    -23.387      0.000      -0.020      -0.017\n",
       "x2             0.0071      0.001      8.139      0.000       0.005       0.009\n",
       "x3            -0.6506      0.107     -6.085      0.000      -0.860      -0.441\n",
       "x4         -9.375e-05   1.37e-05     -6.820      0.000      -0.000   -6.68e-05\n",
       "x5            -0.1128      0.009    -11.959      0.000      -0.131      -0.094\n",
       "x6            -0.0025   9.11e-05    -26.940      0.000      -0.003      -0.002\n",
       "x7             0.0017      0.000      9.224      0.000       0.001       0.002\n",
       "x8             0.0004   3.04e-05     13.598      0.000       0.000       0.000\n",
       "x9             0.1714      0.015     11.569      0.000       0.142       0.200\n",
       "==============================================================================\n",
       "\n",
       "Possibly complete quasi-separation: A fraction 0.16 of observations can be\n",
       "perfectly predicted. This might indicate that there is complete\n",
       "quasi-separation. In this case some parameters will not be identified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logit_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# instantiate the model (using the default parameters)\n",
    "logreg = LogisticRegression(max_iter=1000)\n",
    "\n",
    "# fit the model with data\n",
    "logreg.fit(X_train,y_train)\n",
    "\n",
    "#\n",
    "y_pred=logreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[789, 171],\n",
       "       [ 94, 349]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
    "cnf_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ACoXC7nX1/v4AgFAHX9PdHBmzt+OYfQPH7MTtWmsgCILFYzKZrNO23333HYqKirBs2bJOn1er1VCr1eJyXV2drf00o1Kp7FrXVFwA4Wg5EHu7w6/pbvaOWQo4Zt/AMdsnIiKiy+es1tCVSqVZCUWn06F///4W7U6fPo23334bCxYsQHBwsEMddRUeqkhEvsBqoA8ZMgQ1NTWora2FwWDAgQMHEB8fb9amrq4Oq1atQnp6+jXfPdyKhyoSkcRZLbn4+flhxowZyMrKgslkQlJSEqKjo1FYWAgA0Gg02L59OxoaGrBp0yZxnezsbNf2nIiIzNh0HHpcXBzi4uLMHtNoNOLXaWlpSEtLc27PiIjILpI/U5RXViQiXyH5QOcOUSLyFZIPdADcIUpEPkHSgc5yCxH5EskGuqm4AMLW9QBYbiEi3yDZQBdr51NnsdxCRD5BsoEOgLVzIvIpkgx01s6JyBdJMtB5qCIR+SJJBjoAlluIyOdILtBZbiEiXyW5QGe5hYh8leQCHQDLLUTkk6QZ6EREPoiBTkQkEZIKdO4QJSJfJqlA5w5RIvJlkgp0ANwhSkQ+SzKBznILEfk6SQQ6L5VLRCSBQDcLc14ql4h8mMLdHeguXvfcswiCgObmZphMJshksm5t6/z582hpaXFSz7wDx+wbrI1ZEATI5XIEBgba9Xfk1YEu1s25I9RjNDc3w9/fHwpF93+1FAoF/Pz8nNAr78Ex+wZbxmwwGNDc3IzevXvbvF2vLrnwMEXPYzKZnBLmRL5OoVDAZDLZtY5XBzoAzs49THfLLET0/+z9e/L+QCciIgAMdJK41atX46233rpmm4KCAvzwww92bbeqqgrjx4/H4MGDrW6/pwmCgCVLluDee++FWq3Gt99+22m7ffv24YEHHkBKSgpSU1Nx6tQpAMCGDRuQkpKClJQUjBkzBtHR0fjll18AAPPmzcOdd96JMWPGWGzvb3/7G0aPHo2kpCS89tprAAC9Xo+JEyciJiYGixYtMmvf2tqKF198Eb/+9a+RmJiIf/3rXwCAd999F8nJyWK/rv7ZXLp0CSNGjDDbniAIyM7Oxm9+8xvcd999yMvLAwDU19fjiSeegFqtRlJSEj744AMAbT+/9jGmpKRg6NCh2LhxIwAgLS1NfDwhIQEpKSkAgPLycvFxtVqN3bt3i68/ceJEjB49Wny+rq4OAHDo0CE88MADuOGGG/DPf/5TbH/mzBmMHTsWKSkpSEpKwrvvvtv1D9QOLHaSzysoKIBarUZsbKzN6/Tr1w+vvvoqCgoKXNgzx+zduxenTp1CSUkJysrKsHDhQrMwabdw4ULk5+cjJiYGmzdvRk5ODtauXYtnnnkGzzzzDACgsLAQGzduRP/+/QEAkyZNwvTp0/H888+bbWv//v347LPPoNVqERAQIAZaYGAgXnzxRRw7dgzHjx83Wyc3NxdKpRIHDx5Ea2srLly4AAB49NFH8ac//Ul8/czMTGzbtk1cb+XKlRg5cqTZtj788ENUV1ejuLgYcrlcfP3NmzcjNjYWW7ZsgU6nQ2JiIh599FHcfPPN2LNnDwDAaDRixIgRGDduHACYvUFnZmaib9++AIBhw4Zh9+7dUCgUOH/+vBje7fuM3nzzTdx1111m/YqMjMSaNWss3vTDwsKwc+dOBAQEoLGxEWPGjIFGo0F4eLjFz8keDHRyGdP7GyGcOeX4+jIZBEEwe0wWPRjyP/z5muvl5ORg+/btiIiIgFKpxJ133gkA2LZtG7Zt24bW1lYMHjwYubm5+O6777Bnzx4cOnQIOTk52LhxI/bv32/R7uojDVQqFVQqFf7973/bPJ41a9Zgz549aG5uRnx8PF5//XXIZDJMnDgRS5YswV133QWdTgeNRoPS0lIYjUZkZWXhiy++gEwmw+TJkzFjxgyrr/PZZ59h4sSJkMlkGDFiBC5evIjz588jLCzM/Hspk+HSpUsA2ma9Vz8PADt37kRqaqq4PHLkSJw5c8ai3bvvvotnn30WAQEB4vcHAIKCgnDPPfeIs/+O3n//fRQXtx3YIJfLERoaCgAIDg4W21y+fNmsjvzNN9/g559/xv33349vvvnG7PXffPNNyOVys9eXyWRoaGiAIAhobGxEv379LHbal5SUYNCgQYiKijJ7XBAEfPLJJ/jwww8BwOx3oKWlxab6dnR0tDi+jnr16iU+1tLSYvfOz654baB3PGSRqN0333yDXbt2obCwEAaDAWPHjhUDfdy4cZgyZQoA4PXXX8d7772HGTNmiB+hH374YQBA3759O23XXdOmTcPcuXMBAM899xz27NkDjUbTZfu///3vOHPmDD777DMoFAqx7PHKK6/gwIEDFu0nTJiA9PR0nDt3DhEREeLjAwcOxLlz5ywCe9WqVZg6dSoCAwMRHByMTz75xOz5pqYmfP7552L55FpOnjyJw4cPY8WKFQgICMCSJUtw9913d9n+4sWLAIAVK1bg0KFDuOGGG5CVlYXrr78eQNvM+p133kFra6sYqCaTCcuWLUNOTg5KSkrMtvfjjz9i165dKCgogFKpxLJly3DTTTdh+vTpmDZtGuLi4tDQ0IANGzZYhOvVb1rtSktLcf311+Omm24SHysrK8P8+fNx9uxZ5Obmmr05zJs3D3K5HA8++CDmzJljNfB/+uknPPHEEzh16hSWLFnS7dk54MWBzkMWPZ+1mbQ1CoUCBoPBrnVKS0sxduxYcTbVXv8EgOPHj2PFihWor69HY2Mj7rvvvk63YWs7ex04cAAbNmxAU1MTLly4gKFDh14z0EtKSjB16lQxNNrLHpmZmdd8nas/1QCdHy2xceNGbN26FXFxcdiwYQMyMzOxatUq8fnCwkLEx8eLr3stRqMRFy9exCeffIKKigqkpaXh4MGDXYaa0WhETU0NfvWrX+G1117DunXrsGzZMrzxxhsA2t78pk2bho8//hg5OTnIycnBli1bMGbMGERGRlpsr7W1FQEBAdi9ezc+/fRTzJ8/Hx9//DE+//xz3HbbbfjHP/6BH3/8EY8//jgSEhLETwGtra0oLCzEwoULLba5Y8cOTJgwweyxuLg4FBUVobKyEnPmzEFSUhICAwPxxhtvYODAgWhoaMCf//xnbN++Hb/73e+u+T2LjIyEVqvFuXPn8OSTT+Khhx4S39AcZVOgV1RUID8/HyaTCcnJyRbvZoIgID8/H+Xl5QgICMCsWbPM3tVchocsUie6CpG5c+ciLy8Pt912Gz744AMcPHiwW+3s0dzcjIyMDHz66aeIjIzE6tWrxTMF/fz8xI/cHc8e7CyYAesz9IEDB6K6ulp8vKamxmJ2rtPp8P333yMuLg4A8Mgjj4ifStrt2rWr05lrZwYOHIhx48ZBJpNh+PDhkMvl0Ov1UCqVnbbv378/evfuLdatH374Ybz//vudjqk9bL/66iuUlpZiy5YtaGxsxJUrV3DdddchIyMDAwcOxEMPPQSg7ZPYvHnzAAAffPAB0tPTIZPJMHjwYERHR6OqqgrDhw8HABQVFeGOO+6wCFKDwYDdu3eb7fjsKCYmBr1798bx48dx1113YeDAgQCAPn36IDU1FRUVFVYDvV14eDhiY2NRWloqfkp0lNWjXEwmE/Ly8pCRkYE1a9Zg//79OHv2rFmb8vJynDt3Drm5uXjqqaewadOmbnXKmsuFO3hlRerUyJEjUVBQgKamJjQ0NIg7vgCgoaEBYWFhuHLlCj7++GPx8T59+qCxsdFqO1tNmjQJNTU1Zo+1B3VoaCgaGxvFIzqAtjprez24Y9kjMTERW7duFT+ltJdcMjMzsWfPHot/6enpAACNRoPt27dDEAR89dVX6Nu3r0Wgh4SEoL6+HidOnAAAFBcXIyYmRny+vr5ePELDFg888AD2798PADhx4gRaW1vFmnhnZDIZUlJSxDemkpIS8fVPnjwpttNqtRg8eDCAtp2OR44cQWlpKZYsWYKJEyciIyMDADB27Fjx9Q8ePChOKCMjI8XyzM8//4yTJ09i0KBB4vZ37NjR6ZvWvn37cPPNN5uVrv773/+KP4uzZ8/i5MmTiI6OhsFggF6vBwBcuXIFWq0WQ4cOveb3q7q6Gk1NTQCACxcu4MiRIxgyZMg117GF1Rl6VVUVwsPDxV+IUaNG4ciRI2Y7EL788kskJiZCJpMhNjYWjY2N+OWXX2z6qGYv0/sbcenfbb/0LLfQ1e644w6MHz8eGo0GUVFRSEhIEJ9bsGABHn74YURFRWHYsGFoaGgA0DYLXLBgAfLy8vDOO+902a6j2tpajBs3Dg0NDZDL5di4cSM+//xzXHfddfjxxx/Rr18/s/YhISGYPHky1Go1oqKizI6GSEtLQ1paGj766COMHj1afHzy5Mk4efIk1Go1FAoFpkyZgunTp1v9HiQnJ2Pv3r2499570bt3b/z1r38Vn5s6dSpWrlyJ8PBwrFy5Ek899RRkMhn69euH1atXi+12796NxMREBAUFmW171qxZOHjwIPR6PUaMGIEXXngBjz/+OP7whz9g/vz5GDNmDPz9/bF27Vrxk1JCQgIaGhrQ2tqKgoICvPfee4iNjcWiRYswe/ZsLF26FKGhoVizZg2Atvr5vn37oFAoEBISgrVr11od87PPPov09HRs3LgRQUFBWLlyJQBgzpw5mDt3LpKTkyEIAjIyMsQ3mqamJhQXF+P111+32N7OnTstyi2HDx/GunXroFAoIJfLsXz5coSGhuLy5cuYPHkyDAYDjEYjRo8eLX7aqaiowJNPPomLFy9iz549WL16tViyeeWVV8Rtp6Wl4ZZbbrE6TmtkQlef6/7n0KFDYk0MaHsnr6ysxJNPPim2yc7ORmpqKoYNGwYAWLZsGaZMmWLxjqPVaqHVasV1Wltb7e7wpby1MPxYhYDRagRpbPs4KAWO1JPd4fz58+KRDr7oP//5D9577z0sW7bM3V0hCWhpabH4dNWrV68u21udoduyg8XWnTBqtRpqtVpcbj9W1C4T/giVSoW6ujpcdmR9L9U+Zk/X0tLitAstecubWEcxMTF4+eWXHe63N465uzjmrrW0tFj83XcsA13Nag1dqVRCp9OJyzqdzqKUolQqzV60szZERORaVgN9yJAhqKmpQW1tLQwGAw4cOID4+HizNvHx8SguLoYgCPjhhx8QFBTEQPdRVip4RGQHe/+erJZc/Pz8MGPGDGRlZcFkMiEpKQnR0dEoLCwE0LZHffjw4SgrK8Ps2bPRq1cvzJo1y7Hek9eTy+UwGAy8hC5RNxkMBouToKyxulPUlToeK2sPb6knO5O3jNmZdywKCAjwuTvZcMy+wdqYr3XHomvV0DmNIqeSyWR23WHlWrzlTcyZOGbf4Kox8/K5REQSwUAnIpIIBjoRkUS4dacoERE5j1fO0F966SV3d6HHccy+gWP2Da4as1cGOhERWWKgExFJhN/SpUuXursTjuiRG2h4GI7ZN3DMvsEVY+ZOUSIiiWDJhYhIIhjoREQS4dHXcvHYm1O7kLUx79u3Dzt37gQABAYGYubMmbjxxhvd0FPnsTbmdlVVVVi0aBHmzp2LkSNH9nAvncuWMR89ehSbN2+G0WhEcHAwMjMz3dBT57E25suXLyM3Nxc6nQ5GoxHjx49HUlKSm3rbfevXr0dZWRlCQkLMbu/XziX5JXgoo9EopKenC+fOnROuXLkivPDCC8KZM2fM2nz11VdCVlaWYDKZhOPHjwsLFy50U2+dw5YxHzt2TLh06ZIgCIJQVlbmE2Nub7d06VJh+fLlwsGDB93QU+exZcwNDQ3CnDlzhJ9//lkQBEG4cOGCO7rqNLaM+aOPPhK2bt0qCIIgXLx4UZg2bZpw5coVd3TXKY4ePSqcOHFCmDdvXqfPuyK/PLbk0vHm1AqFQrw5dUdd3ZzaW9ky5qFDh6JPnz4A2m531vFuUt7IljEDbTctTkhIQN++fd3QS+eyZcwlJSVISEiASqUC0HaTaW9my5hlMhmam5vFSzD36dPH7uuBe5Jbb71V/FvtjCvyy2O/W3q9HkqlUlxWKpXQ6/UWbdp/4btq401sGXNHe/fuxfDhw3uiay5j68/58OHD0Gg0Pd09l7BlzDU1NWhoaMDSpUvxl7/8BV988V9CPfUAAAIpSURBVEVPd9OpbBnz2LFj8dNPP+Hpp5/G/PnzMX36dK8OdGtckV8eW0MXnHhzam9hz3i+++47FBUVef3d5W0Z8+bNmzFlyhTJ/HHbMmaj0YhTp05hyZIlaG1txeLFixETE3PNmxt4MlvG/PXXX2PQoEF4+eWXcf78ebz66qsYNmwYgoKCeqqbPcoV+eWxge6LN6e2ZcwAcPr0abz99ttYuHAhgoODe7KLTmfLmE+cOIGcnBwAQH19PcrLyyGXy3HPPff0aF+dxdbf7eDgYAQGBiIwMBC33HILTp8+7bWBbsuYi4qKkJqaCplMhvDwcAwYMADV1dW4+eabe7q7PcIV+eWxUx5fvDm1LWOuq6vDqlWrkJ6e7rV/3B3ZMuZ169aJ/0aOHImZM2d6bZgDtv9uHzt2DEajES0tLaiqqkJkZKSbetx9toxZpVLh22+/BQBcuHAB1dXVGDBggDu62yNckV8efaZoWVkZtmzZIt6c+re//a3ZzakFQUBeXh6+/vpr8ebUQ4YMcXOvu8famN966y2UlpaKtTc/Pz9kZ2e7s8vdZm3MHa1btw4jRozw+sMWbRnzrl27UFRUBLlcjjFjxuChhx5yZ5e7zdqY9Xo91q9fL+4YnDBhAhITE93Z5W5Zu3Ytvv/+e1y6dAkhISGYNGkSDAYDANfll0cHOhER2c5jSy5ERGQfBjoRkUQw0ImIJIKBTkQkEQx0IiKJYKATEUkEA52ISCL+D7TTZEWrPHJLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_proba = logreg.predict_proba(X_test)[:,1]\n",
    "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_proba)\n",
    "auc = metrics.roc_auc_score(y_test, y_pred_proba)\n",
    "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree_depth = 9\n",
    "dtc = tree.DecisionTreeClassifier(max_depth=tree_depth)\n",
    "dtc = dtc.fit(X, y_c)\n",
    "tree.plot_tree(dtc) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_dt = tree.DecisionTreeClassifier(max_depth = 5, min_samples_leaf = 10)\n",
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "plot_classification_tree(ax, X, y_c, model_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.879543834640057\n",
      "Precision: 0.7686274509803922\n",
      "Recall:    0.8848758465011287\n"
     ]
    }
   ],
   "source": [
    "y_pred = model_dt.predict(X_test)\n",
    "print(\"Accuracy: \",metrics.accuracy_score(y_test, y_pred))\n",
    "print(\"Precision:\",metrics.precision_score(y_test, y_pred))\n",
    "print(\"Recall:   \",metrics.recall_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrMatrix = pd_known.corr()\n",
    "sn.heatmap(corrMatrix, annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X = pd_known[['koi_period','koi_time0bk',\n",
    "#                    'koi_impact','koi_duration','koi_depth','koi_prad','koi_teq','koi_insol','koi_model_snr']].copy().values\n",
    "X = pd_known[['koi_period','koi_time0bk',\n",
    "                   'koi_impact','koi_duration','koi_depth','koi_prad','koi_teq','koi_insol','koi_model_snr']].to_numpy()\n",
    "X_const = add_constant(X, prepend=True)\n",
    "y_f = pd_known['koi_disposition'].apply(lambda x: 1 if x == \"FALSE POSITIVE\" else 0)\n",
    "y_c = pd_known['koi_disposition'].apply(lambda x: 1 if x == \"CONFIRMED\" else 0)\n",
    "# y.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>kepid</th>\n",
       "      <th>koi_disposition</th>\n",
       "      <th>koi_pdisposition</th>\n",
       "      <th>koi_score</th>\n",
       "      <th>koi_period</th>\n",
       "      <th>koi_time0bk</th>\n",
       "      <th>koi_impact</th>\n",
       "      <th>koi_duration</th>\n",
       "      <th>koi_depth</th>\n",
       "      <th>koi_prad</th>\n",
       "      <th>koi_teq</th>\n",
       "      <th>koi_insol</th>\n",
       "      <th>koi_model_snr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>11138155</td>\n",
       "      <td>CANDIDATE</td>\n",
       "      <td>CANDIDATE</td>\n",
       "      <td>1.000</td>\n",
       "      <td>4.959319</td>\n",
       "      <td>172.258529</td>\n",
       "      <td>0.831</td>\n",
       "      <td>2.22739</td>\n",
       "      <td>9802.0</td>\n",
       "      <td>12.21</td>\n",
       "      <td>1103.0</td>\n",
       "      <td>349.40</td>\n",
       "      <td>696.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>11818800</td>\n",
       "      <td>CANDIDATE</td>\n",
       "      <td>CANDIDATE</td>\n",
       "      <td>0.999</td>\n",
       "      <td>40.419504</td>\n",
       "      <td>173.564690</td>\n",
       "      <td>0.911</td>\n",
       "      <td>3.36200</td>\n",
       "      <td>6256.0</td>\n",
       "      <td>7.51</td>\n",
       "      <td>467.0</td>\n",
       "      <td>11.29</td>\n",
       "      <td>36.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>11918099</td>\n",
       "      <td>CANDIDATE</td>\n",
       "      <td>CANDIDATE</td>\n",
       "      <td>0.993</td>\n",
       "      <td>7.240661</td>\n",
       "      <td>137.755450</td>\n",
       "      <td>1.198</td>\n",
       "      <td>0.55800</td>\n",
       "      <td>556.4</td>\n",
       "      <td>19.45</td>\n",
       "      <td>734.0</td>\n",
       "      <td>68.63</td>\n",
       "      <td>13.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>9579641</td>\n",
       "      <td>CANDIDATE</td>\n",
       "      <td>CANDIDATE</td>\n",
       "      <td>0.871</td>\n",
       "      <td>3.435916</td>\n",
       "      <td>132.662400</td>\n",
       "      <td>0.624</td>\n",
       "      <td>3.13300</td>\n",
       "      <td>23.2</td>\n",
       "      <td>0.55</td>\n",
       "      <td>1272.0</td>\n",
       "      <td>617.61</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>3246984</td>\n",
       "      <td>CANDIDATE</td>\n",
       "      <td>CANDIDATE</td>\n",
       "      <td>0.773</td>\n",
       "      <td>1.626630</td>\n",
       "      <td>169.820171</td>\n",
       "      <td>1.260</td>\n",
       "      <td>2.11450</td>\n",
       "      <td>1620.6</td>\n",
       "      <td>32.43</td>\n",
       "      <td>1547.0</td>\n",
       "      <td>1355.88</td>\n",
       "      <td>103.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9550</th>\n",
       "      <td>8488381</td>\n",
       "      <td>CANDIDATE</td>\n",
       "      <td>CANDIDATE</td>\n",
       "      <td>0.532</td>\n",
       "      <td>4.736816</td>\n",
       "      <td>131.787600</td>\n",
       "      <td>0.218</td>\n",
       "      <td>2.84000</td>\n",
       "      <td>35.3</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1137.0</td>\n",
       "      <td>395.05</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9551</th>\n",
       "      <td>8489260</td>\n",
       "      <td>CANDIDATE</td>\n",
       "      <td>CANDIDATE</td>\n",
       "      <td>0.386</td>\n",
       "      <td>130.235324</td>\n",
       "      <td>218.271900</td>\n",
       "      <td>0.075</td>\n",
       "      <td>5.67800</td>\n",
       "      <td>750.1</td>\n",
       "      <td>2.44</td>\n",
       "      <td>332.0</td>\n",
       "      <td>2.86</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9557</th>\n",
       "      <td>11911561</td>\n",
       "      <td>CANDIDATE</td>\n",
       "      <td>CANDIDATE</td>\n",
       "      <td>1.000</td>\n",
       "      <td>8.870416</td>\n",
       "      <td>137.481093</td>\n",
       "      <td>1.206</td>\n",
       "      <td>1.28640</td>\n",
       "      <td>873.1</td>\n",
       "      <td>39.46</td>\n",
       "      <td>1151.0</td>\n",
       "      <td>414.26</td>\n",
       "      <td>43.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9558</th>\n",
       "      <td>8765560</td>\n",
       "      <td>CANDIDATE</td>\n",
       "      <td>CANDIDATE</td>\n",
       "      <td>1.000</td>\n",
       "      <td>47.109631</td>\n",
       "      <td>144.131720</td>\n",
       "      <td>1.230</td>\n",
       "      <td>5.74100</td>\n",
       "      <td>752.2</td>\n",
       "      <td>78.98</td>\n",
       "      <td>751.0</td>\n",
       "      <td>75.40</td>\n",
       "      <td>35.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9561</th>\n",
       "      <td>10128825</td>\n",
       "      <td>CANDIDATE</td>\n",
       "      <td>CANDIDATE</td>\n",
       "      <td>0.497</td>\n",
       "      <td>1.739849</td>\n",
       "      <td>133.001270</td>\n",
       "      <td>0.043</td>\n",
       "      <td>3.11400</td>\n",
       "      <td>48.5</td>\n",
       "      <td>0.72</td>\n",
       "      <td>1608.0</td>\n",
       "      <td>1585.81</td>\n",
       "      <td>10.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1792 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         kepid koi_disposition koi_pdisposition  koi_score  koi_period  \\\n",
       "37    11138155       CANDIDATE        CANDIDATE      1.000    4.959319   \n",
       "58    11818800       CANDIDATE        CANDIDATE      0.999   40.419504   \n",
       "62    11918099       CANDIDATE        CANDIDATE      0.993    7.240661   \n",
       "63     9579641       CANDIDATE        CANDIDATE      0.871    3.435916   \n",
       "72     3246984       CANDIDATE        CANDIDATE      0.773    1.626630   \n",
       "...        ...             ...              ...        ...         ...   \n",
       "9550   8488381       CANDIDATE        CANDIDATE      0.532    4.736816   \n",
       "9551   8489260       CANDIDATE        CANDIDATE      0.386  130.235324   \n",
       "9557  11911561       CANDIDATE        CANDIDATE      1.000    8.870416   \n",
       "9558   8765560       CANDIDATE        CANDIDATE      1.000   47.109631   \n",
       "9561  10128825       CANDIDATE        CANDIDATE      0.497    1.739849   \n",
       "\n",
       "      koi_time0bk  koi_impact  koi_duration  koi_depth  koi_prad  koi_teq  \\\n",
       "37     172.258529       0.831       2.22739     9802.0     12.21   1103.0   \n",
       "58     173.564690       0.911       3.36200     6256.0      7.51    467.0   \n",
       "62     137.755450       1.198       0.55800      556.4     19.45    734.0   \n",
       "63     132.662400       0.624       3.13300       23.2      0.55   1272.0   \n",
       "72     169.820171       1.260       2.11450     1620.6     32.43   1547.0   \n",
       "...           ...         ...           ...        ...       ...      ...   \n",
       "9550   131.787600       0.218       2.84000       35.3      0.60   1137.0   \n",
       "9551   218.271900       0.075       5.67800      750.1      2.44    332.0   \n",
       "9557   137.481093       1.206       1.28640      873.1     39.46   1151.0   \n",
       "9558   144.131720       1.230       5.74100      752.2     78.98    751.0   \n",
       "9561   133.001270       0.043       3.11400       48.5      0.72   1608.0   \n",
       "\n",
       "      koi_insol  koi_model_snr  \n",
       "37       349.40          696.5  \n",
       "58        11.29           36.9  \n",
       "62        68.63           13.7  \n",
       "63       617.61            8.7  \n",
       "72      1355.88          103.6  \n",
       "...         ...            ...  \n",
       "9550     395.05            6.9  \n",
       "9551       2.86            9.7  \n",
       "9557     414.26           43.8  \n",
       "9558      75.40           35.1  \n",
       "9561    1585.81           10.6  \n",
       "\n",
       "[1792 rows x 13 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_cand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1792, 9)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Nine astype('float32') assignments below added to resolve this error message:\n",
    "# Input contains NaN, infinity or a value too large for dtype('float32')\n",
    "#\n",
    "\n",
    "# pd_cand.koi_period = pd_cand.koi_period.astype('float32')\n",
    "# pd_cand.koi_time0bk = pd_cand.koi_time0bk.astype('float32')\n",
    "# pd_cand.koi_impact = pd_cand.koi_impact.astype('float32')\n",
    "# pd_cand.koi_duration = pd_cand.koi_duration.astype('float32')\n",
    "# pd_cand.koi_depth = pd_cand.koi_depth.astype('float32')\n",
    "# pd_cand.koi_prad = pd_cand.koi_prad.astype('float32')\n",
    "# pd_cand.koi_teq = pd_cand.koi_teq.astype('float32')\n",
    "# pd_cand.koi_insol = pd_cand.koi_insol.astype('float32')\n",
    "# pd_cand.koi_model_snr = pd_cand.koi_model_snr.astype('float32')\n",
    "X_cand = pd_cand[['koi_period','koi_time0bk','koi_impact','koi_duration','koi_depth',\n",
    "                  'koi_prad','koi_teq','koi_insol','koi_model_snr']].to_numpy()\n",
    "# X = pd_known[['koi_period','koi_time0bk','koi_impact','koi_duration','koi_depth',\n",
    "#               'koi_prad','koi_teq','koi_insol','koi_model_snr']].to_numpy()\n",
    "\n",
    "X_cand.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1792 entries, 37 to 9561\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   koi_period     1792 non-null   float64\n",
      " 1   koi_time0bk    1792 non-null   float64\n",
      " 2   koi_impact     1792 non-null   float64\n",
      " 3   koi_duration   1792 non-null   float64\n",
      " 4   koi_depth      1792 non-null   float64\n",
      " 5   koi_prad       1792 non-null   float64\n",
      " 6   koi_teq        1792 non-null   float64\n",
      " 7   koi_insol      1792 non-null   float64\n",
      " 8   koi_model_snr  1792 non-null   float64\n",
      "dtypes: float64(9)\n",
      "memory usage: 140.0 KB\n"
     ]
    }
   ],
   "source": [
    "X_cand_check = pd_cand[['koi_period','koi_time0bk','koi_impact','koi_duration','koi_depth',\n",
    "                  'koi_prad','koi_teq','koi_insol','koi_model_snr']].copy()\n",
    "X_cand_check.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_cand = X_cand_check.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_normalized = normalize(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y_c,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.28061390111589424"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_regularization_strengths = np.logspace(\n",
    "    np.log10(10e-6), np.log10(10), num=100 )\n",
    "\n",
    "l_try = Lasso(alpha=0.01)\n",
    "l_try.fit(X_train, y_train)\n",
    "l_try.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.388890\n",
      "         Iterations 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lee/anaconda3/lib/python3.7/site-packages/statsmodels/discrete/discrete_model.py:1747: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8379816217959052"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logit_model = Logit(y_c, X).fit()\n",
    "# logit_model = Logit(y, X_normalized).fit()\n",
    "model = LogisticRegression(max_iter=1000).fit(X, y_c)\n",
    "model.predict(X)\n",
    "model.predict_proba(X)\n",
    "model.score(X, y_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lee/anaconda3/lib/python3.7/site-packages/statsmodels/discrete/discrete_model.py:1747: RuntimeWarning: overflow encountered in exp\n",
      "  return 1/(1+np.exp(-X))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>koi_disposition</td> <th>  No. Observations:  </th>  <td>  6203</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td>  6194</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>     8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Fri, 28 Aug 2020</td> <th>  Pseudo R-squ.:     </th>  <td>0.4087</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>13:44:51</td>     <th>  Log-Likelihood:    </th> <td> -2412.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -4079.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th>  <td> 0.000</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "   <td></td>     <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x1</th> <td>   -0.0132</td> <td>    0.001</td> <td>  -14.109</td> <td> 0.000</td> <td>   -0.015</td> <td>   -0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x2</th> <td>    0.0154</td> <td>    0.001</td> <td>   17.429</td> <td> 0.000</td> <td>    0.014</td> <td>    0.017</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x3</th> <td>   -0.4325</td> <td>    0.108</td> <td>   -4.015</td> <td> 0.000</td> <td>   -0.644</td> <td>   -0.221</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x4</th> <td>   -0.0498</td> <td>    0.009</td> <td>   -5.656</td> <td> 0.000</td> <td>   -0.067</td> <td>   -0.033</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x5</th> <td>   -0.0001</td> <td> 1.62e-05</td> <td>   -7.168</td> <td> 0.000</td> <td>   -0.000</td> <td>-8.44e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x6</th> <td>   -0.1101</td> <td>    0.009</td> <td>  -12.663</td> <td> 0.000</td> <td>   -0.127</td> <td>   -0.093</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x7</th> <td>   -0.0008</td> <td>    0.000</td> <td>   -5.739</td> <td> 0.000</td> <td>   -0.001</td> <td>   -0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x8</th> <td>   -0.0005</td> <td> 6.52e-05</td> <td>   -7.122</td> <td> 0.000</td> <td>   -0.001</td> <td>   -0.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>x9</th> <td>    0.0019</td> <td>    0.000</td> <td>    9.214</td> <td> 0.000</td> <td>    0.001</td> <td>    0.002</td>\n",
       "</tr>\n",
       "</table><br/><br/>Possibly complete quasi-separation: A fraction 0.19 of observations can be<br/>perfectly predicted. This might indicate that there is complete<br/>quasi-separation. In this case some parameters will not be identified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:        koi_disposition   No. Observations:                 6203\n",
       "Model:                          Logit   Df Residuals:                     6194\n",
       "Method:                           MLE   Df Model:                            8\n",
       "Date:                Fri, 28 Aug 2020   Pseudo R-squ.:                  0.4087\n",
       "Time:                        13:44:51   Log-Likelihood:                -2412.3\n",
       "converged:                       True   LL-Null:                       -4079.9\n",
       "Covariance Type:            nonrobust   LLR p-value:                     0.000\n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "x1            -0.0132      0.001    -14.109      0.000      -0.015      -0.011\n",
       "x2             0.0154      0.001     17.429      0.000       0.014       0.017\n",
       "x3            -0.4325      0.108     -4.015      0.000      -0.644      -0.221\n",
       "x4            -0.0498      0.009     -5.656      0.000      -0.067      -0.033\n",
       "x5            -0.0001   1.62e-05     -7.168      0.000      -0.000   -8.44e-05\n",
       "x6            -0.1101      0.009    -12.663      0.000      -0.127      -0.093\n",
       "x7            -0.0008      0.000     -5.739      0.000      -0.001      -0.001\n",
       "x8            -0.0005   6.52e-05     -7.122      0.000      -0.001      -0.000\n",
       "x9             0.0019      0.000      9.214      0.000       0.001       0.002\n",
       "==============================================================================\n",
       "\n",
       "Possibly complete quasi-separation: A fraction 0.19 of observations can be\n",
       "perfectly predicted. This might indicate that there is complete\n",
       "quasi-separation. In this case some parameters will not be identified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logit_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# instantiate the model (using the default parameters)\n",
    "logreg = LogisticRegression(max_iter=10000)\n",
    "\n",
    "# fit the model with data\n",
    "logreg.fit(X_train,y_train)\n",
    "\n",
    "#\n",
    "y_pred=logreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[645, 131],\n",
       "       [ 69, 396]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
    "cnf_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3dDoddDodACAtLQ1qtbqv/QUAKJVKs30v5e7ExRPl8JoUCl8bj+nqrq5ZDlizPLBmOx7XUgNBELq8plAoum1bXl6OvXv34vXXX+/2fY1GA41GI27X19db208zarXabF/jl7sBAIawu20+pqu7umY5YM3ywJr7JiAgoMf3LE65qFQqsykUvV6PESNGdGl36tQpvPfee1i8eDGGDh1qU0f7hdMtRCRzFgM9ODgYNTU1qKurg8FgQGFhIcLDw83a1NfX4+2330ZycnKvPz2IiMhxLE65eHp6IjExEampqTCZTIiJiUFQUBByc3MBAFqtFjt27EBTUxO2bNki7pOWlubYnhMRkRmr1qGHhYUhLCzM7DWtViv+PSkpCUlJSfbtmZW4/pyIqIPbXynK9edERB3cPtAB8IQoERGkEuhERMRAJyKSCgY6EZFEMNCJiCSCgU5EJBFuHejiGnQiInLvQOcadCKi/3HrQAfANehERL9w/0AnIiIADHQiIslgoBMRSQQDnYhIItw20LlkkYjInNsGOpcsEhGZc9tAB8Ali0REnbh3oBMRkYiBTkQkEW4Z6Jdyd/KEKBHRVdwy0FsL8gDwhCgRUWdKZ3fAZjwh6pIEQUBraytMJhMUCkW/jlVbW4u2tjY79cw9sGZ5sFSzIAjw8PCAj49Pn76P3DfQySW1trbCy8sLSmX//2kplUp4enraoVfugzXLgzU1GwwGtLa24pprrrH6uG455UKuy2Qy2SXMieROqVTCZDL1aR8GOtlVf6dZiOh/+vr9xEAnIpIIBjpJ2tq1a/Huu+/22iYnJwcnTpzo03ErKysxffp0jB071uLxB5ogCFi+fDmioqKg0Whw9OjRbtvt378f999/P2JjY/HCCy/AYDBY3D8iIgJxcXGYOnUqpk2bJr7+r3/9CzExMQgMDMSRI0fE1wsKChAfH4+4uDjEx8dj//79Xfoxb948xMbGituHDh3C/fffjxtuuAGfffaZWdugoCBMnToVU6dOxVNPPdXlWCkpKRg/fry4vWnTJrF9bGwsgoKCcO7cuV5rOXfuHGbPno2oqCjMnj0b58+fF9/729/+hqioKNx777346quvAAAtLS2YN28eoqOjERMTgzVr1ojtV6xYIX79e+65BzfddJP43l/+8hfExMRgypQpWL58OQRB6FJPX3Gyk2QvJycHGo0GISEhVu9z3XXXYfXq1cjJyXFgz2yTn5+Pqqoq7N+/HyUlJViyZEmXYDSZTHjxxRfx8ccfIzg4GG+99RY++eQTPP744xb3/+STT+Dr62t2vIkTJ2Lz5s149dVXzV739fXF1q1b4e/vj+PHj2Pu3Lk4fPiw+P7u3btx7bXXmu0zatQorFu3rtsflD4+PsjLy+u27iNHjqCxsdHsteeeew7PPfccACA3NxebN2/GiBEjeq0lPT0d99xzD5KTk/HOO+8gPT0dy5Ytw4kTJ5CdnY38/HzU1tZi9uzZ+PrrrwEASUlJiIqKwuXLl/HYY48hPz8fsbGxWLVqlXjcv//97ygv77h+pri4GMXFxdDpdACAhIQEHDx4EJGRkd3WZi0GOjmM6f82QzhdZfv+CkWXUYsiaCw8Zv++1/02bNiAHTt2ICAgACqVCr/+9a8BAB9++CE+/PBDXL58GWPHjsXGjRtRXl6OvLw8HDp0CBs2bMDmzZtx4MCBLu2uXmmgVquhVqvx5ZdfWl3PunXrkJeXh9bWVoSHh+ONN96AQqHAzJkzsXz5ctx6663Q6/XQarUoKiqC0WhEamoq9u3bB4VCgTlz5iAxMdHi19mzZw9mzpwJhUKB22+/HY2NjaitrYWfn5/Y5ty5c/D29kZwcDAAIDo6Gu+88w4ef/xxq/a/WudRcWeTJ08W/z5hwgS0traira0N3t7eaG5uxvvvv4+1a9fi97//3//ToKAgAICHh/UTCEajEatXr0Z6enqPP2Szs7ORkJBg8Vh79uzBjh07AAC//e1vMXPmTCxbtgx79uzBjBkz4O3tjRtuuAFjxoxBaWkpwsPDERUVBQAYNGgQbrnlFtTU1HQ57s6dO/HKK6+I221tbbh8+TKAjhUtv/rVr6yutyduN+ViKshB+7FSZ3eDXNR3332HXbt2ITc3F1u2bDH79X/atGnYvXs3dDodxo0bh+3bt+OOO+7A1KlTkZKSgry8PIwZM6bbdvbw1FNPYffu3cjPz0dLS0uPI80rPvjgA5w+fRp79uyBTqfDb37zGwDmv8Z3/vPOO+8AAM6ePYuAgADxONdffz3Onj1rdmxfX1+0t7eLn8/nn3+O6upqi/srFAo8/vjjiI+PxwcffNCn+j///HNMnjwZ3t7eAIA333wTzz77bJ+W5bW1tWHatGl46KGHzII7KysLWq22xx86LS0t+Oqrr/DAAw+Ir/VUS319vXgcPz8/6PV6ANZ9ro2NjcjLy8M999xj9vqZM2dw+vRpMfjvuOMOREZGIiwsDKGhoZgyZUqPPxT7wu1G6LxtrvuwNJK2RKlUivO61ioqKkJ8fLwYElOnThXf++GHH/Dmm2/iwoULaG5uxpQpU7o9hrXt+qqwsBCbNm1CS0sLzp8/jwkTJkCr1fbYfv/+/Zg3b564DPTKVEHnX+O7091c7NWrJRQKBTIyMrBy5UpcvnwZ0dHR4rro3vbfuXMn/P39UV9fj9mzZ2PcuHG46667eu0P0PGZrlmzBh999BEAoLy8HCdPnsSqVavEHyTW+Oabb+Dv749Tp05h1qxZmDhxInx8fPDZZ5+Jo+ru5ObmIjw83Gy6pa+1WPpcDQYDnn/+eSQmJmL06NFm7bKzs/Hggw+Kn3FVVRUqKirw7bffAgBmz56NQ4cOWfVZ9saqQC8rK0NWVhZMJhPi4uK6/NoiCAKysrJQWloKb29vLFy4EDfeeGO/OtYbr0mhMPEqUepBT0u9XnrpJWRmZmLSpEn4+OOPcfDgwX6164vW1lYsXboUu3fvxqhRo7B27VrxSkFPT09xvXHnqwd7Okm2YsUKFBYWdnl9xowZSE5OxvXXX28WkjU1Nd2OXMPDw/HPf/4TALBv3z789NNPANDr/v7+/gA6ppymTZuGsrIyiyFUXV2Np59+Ghs2bMCYMWMAAIcPH8bRo0cREREBo9GI+vp6zJw5s9dQ7vz1R48ejbvvvhvl5eXw8fHByZMnxdFvS0sLoqKicODAAXG/Xbt2dcmtnmpRq9XiFFNtbS1UKpXFzwUA/vSnP2Hs2LFm00dXZGdnIzU1VdzevXs3wsLCxPMHsbGxKCkp6XegW5xyMZlMyMzMxNKlS7Fu3TocOHAAZ86cMWtTWlqKs2fPYuPGjXjmmWewZcuWfnWKyFZ33XUXcnJy0NLSgqamJrNpjaamJvj5+aG9vV0MMgAYMmQImpubLbaz1qxZs7rMoV4Jal9fXzQ3N+Pzzz8X3wsKCsJ3330HoGO1yBXR0dHYtm2b+FvKldUZq1atQl5eXpc/ycnJAACtVosdO3ZAEAQcPnwYw4YN6zbQ6+vrxb6lp6dj3rx5ve5/6dIlNDU1AQAuXbqEffv2YcKECb1+Fo2NjXjyySexZMkS3HHHHeLrv/vd71BSUoKioiLs2rULN954o8UwP3/+vPg5NjQ0oLi4GCEhIdBoNCgrK0NRURGKiopwzTXXmIX5hQsXxJUzV/RWi1arxSeffAKg46Tplf20Wi2ys7PR1taG//znP6iqqkJoaCgA4I033sDFixe7/e2psrISjY2NCA8PF18bNWoUDh06BIPBgPb2dhw8eBDjxo3rtX5rWByhV1ZWwt/fX/wHERkZieLiYgQGBoptvv32W0RHR0OhUCAkJATNzc04d+6c2a83RAPhlltuwfTp06HVahEYGIiIiAjxvcWLF+Ohhx5CYGAgJk6cKH5Dz5gxA4sXL0ZmZibef//9Htt1VldXh2nTpqGpqQkeHh7YvHkzvvrqK1x77bU4efIkrrvuOrP2w4cPx5w5c6DRaBAYGIhbb71VfC8pKQlJSUn49NNPce+994qvz5kzBz/99BM0Gg2USiXmzp2L+fPnW/wM4uLikJ+fj6ioKFxzzTX461//Kr43b948vPXWW/D398emTZug0+lgMpnw5JNPivO+Pe3/888/4+mnnwbQcRIyISEBMTExAIAvvvgCKSkpaGhowJNPPolJkybho48+QlZWFk6ePIn169dj/fr1AIDt27dDrVb32P+ysjI8/fTT4nz02rVrsXfvXlRUVODVV1+F4peT5cnJyVatTPriiy8QHR2NwYMHi6/1Vsvzzz+PpKQkbN++HaNGjcJ7770HoOOk7vTp0xETEwNPT0+kpqbC09MT1dXV2LhxI8aNGyeG//z58zFnzhwAHaPzGTNmmP3mOH36dBQUFCAuLg4KhQL33Xdfr9Nv1lIIFhY/Hjp0CGVlZUhKSgLQsa60oqJC/DAAIC0tDQkJCZg4cSIA4PXXX8fcuXPFM+hX6HQ6cZlOWlqaeIa3Ly5mrofCwwND5i/q877uzJb5ZGeora0VT3rJ0b///W9s374dr7/+urO7QhLQ1tbW5berQYMG9dje4gjdmhMs1rQBAI1GA41GI25f+ZWvT2Y8AbVabdu+bsxdam5ra7PbjZbc5YdYZ+PHj8drr71mc7/dseb+Ys09a2tr6/J933mlzdUszqGrVCpx2Q4A6PX6LlMpKpXK7It214aIiBzLYqAHBwejpqYGdXV1MBgMKCwsNJvcBzrOlhcUFEAQBJw4cQKDBw9moMuUPS5fJqIOff1+sjjl4unpicTERKSmpsJkMiEmJgZBQUHIzc0F0HHmNzQ0FCUlJVi0aBEGDRqEhQsX2tZ7cnseHh4wGAy8hS5RPxkMhj5dLQtYcVLUkfpyQUFn7jKfbE/uUrM9n1jk7e0tuyfZsGZ5sFRzb08s6m0OncMosiuFQtGnS7l74y4/xOyJNcuDo2p2u3u5EBFR9xjoREQSwUAnIpIIp54UJSIi+3HLEfrVT0WRA9YsD6xZHhxVs1sGOhERdcVAJyKSCM+VK1eudHYnbOHIB2i4KtYsD6xZHhxRM0+KEhFJBKdciIgkgoFORCQRLn0vF1d7OPVAsFTz119/jezsbACAj48PFixYID54111ZqvmKyspKLFu2DC+99FK/H6brbNbUfOzYMWzduhVGoxFDhw7t9nmV7sRSzZcuXcLGjRuh1+thNBrFx725q4yMDJSUlGD48OFYu3Ztl/cdkl+CizIajUJycrJw9uxZob29XXjllVeE06dPm7U5fPiwkJqaKphMJuGHH34QlixZ4qTe2oc1NR8/fly4ePGiIAiCUFJSIouar7RbuXKlsGbNGuHgwYNO6Kn9WFNzU1OT8OKLLwo///yzIAiCcP78eWd01W6sqfnTTz8Vtm3bJgiCIDQ2NgpPPfWU0N7e7ozu2sWxY8eEH3/8UfjjH//Y7fuOyC+XnXLp/HBqpVIpPpy6s54eTu2urKl5woQJGDJkCICOx511fpqUO7KmZqDjQb8REREYNmyYE3ppX9bUvH//fkRERIgPUx4+fLgzumo31tSsUCjQ2toq3oJ5yJAhfb4fuCu5+eabxe/V7jgiv1z202poaIBKpRK3VSoVGhoaurTp/PTw7tq4E2tq7iw/Px+hoaED0TWHsfb/8zfffGOXp6K7AmtqrqmpQVNTE1auXIk///nP2Ldv30B3066sqTk+Ph7//e9/8eyzz+Lll1/G/Pnz3TrQLXFEfrnsHLpgx4dTu4u+1FNeXo69e/e6/dPlral569atmDt3rmS+ua2p2Wg0oqqqCsuXL8fly5eRkpKC8ePH9/pwA1dmTc1HjhzB6NGj8dprr6G2tharV6/GxIkTMXjw4IHq5oByRH65bKDL8eHU1tQMAKdOncJ7772HJUuWYOjQoQPZRbuzpuYff/wRGzZsAABcuHABpaWl8PDwwJ133jmgfbUXa/9tDx06FD4+PvDx8cFNN92EU6dOuW2gW1Pz3r17kZCQAIVCAX9/f4wcORLV1dUYN27cQHd3QDgiv1x2yCPHh1NbU3N9fT3efvttJCcnu+03d2fW1Jyeni7+ueuuu7BgwQK3DXPA+n/bx48fh9FoRFtbGyorKzFq1Cgn9bj/rKlZrVbj6NGjAIDz5yGsClwAAADcSURBVM+juroaI0eOdEZ3B4Qj8sulrxQtKSnBP/7xD/Hh1I888ojZw6kFQUBmZiaOHDkiPpw6ODjYyb3uH0s1v/vuuygqKhLn3jw9PZGWlubMLvebpZo7S09Px+233+72yxatqXnXrl3Yu3cvPDw8EBsbiwcffNCZXe43SzU3NDQgIyNDPDE4Y8YMREdHO7PL/bJ+/Xp8//33uHjxIoYPH45Zs2bBYDAAcFx+uXSgExGR9Vx2yoWIiPqGgU5EJBEMdCIiiWCgExFJBAOdiEgiGOhERBLBQCcikoj/B4SAE4WU4WCWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred_proba = logreg.predict_proba(X_test)[:,1]\n",
    "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_proba)\n",
    "auc = metrics.roc_auc_score(y_test, y_pred_proba)\n",
    "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x864 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, ncols=1,figsize = (20,12))\n",
    "ax1.scatter(stat_conf[\"koi_period\"], stat_conf[\"koi_prad\"], color=\"green\")\n",
    "ax1.set_title(\"Confirmed\")\n",
    "\n",
    "ax2.scatter(stat_cand[\"koi_period\"], stat_cand[\"koi_prad\"], color=\"blue\")\n",
    "ax2.set_title(\"Candidate\")\n",
    "\n",
    "ax3.scatter(stat_fpos[\"koi_period\"], stat_fpos[\"koi_prad\"], color=\"red\")\n",
    "ax3.set_title(\"False Positive\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, ncols=1,figsize = (20,12))\n",
    "ax1.scatter(stat_conf[\"koi_duration\"], stat_conf[\"koi_prad\"], color=\"green\")\n",
    "ax1.set_title(\"Confirmed\")\n",
    "\n",
    "ax2.scatter(stat_cand[\"koi_duration\"], stat_cand[\"koi_prad\"], color=\"blue\")\n",
    "ax2.set_title(\"Candidate\")\n",
    "\n",
    "ax3.scatter(stat_fpos[\"koi_duration\"], stat_fpos[\"koi_prad\"], color=\"red\")\n",
    "ax3.set_title(\"False Positive\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(220.82677970852018, 206.56799999999998, 'X[5] <= 13.48\\ngini = 0.465\\nsamples = 6203\\nvalue = [3922, 2281]'),\n",
       " Text(138.85098094170405, 184.824, 'X[0] <= 2.319\\ngini = 0.495\\nsamples = 4113\\nvalue = [1858, 2255]'),\n",
       " Text(74.64501121076233, 163.07999999999998, 'X[1] <= 133.008\\ngini = 0.276\\nsamples = 1046\\nvalue = [873, 173]'),\n",
       " Text(44.94652466367713, 141.336, 'X[3] <= 1.953\\ngini = 0.198\\nsamples = 863\\nvalue = [767, 96]'),\n",
       " Text(27.399551569506727, 119.592, 'X[6] <= 1899.0\\ngini = 0.323\\nsamples = 371\\nvalue = [296, 75]'),\n",
       " Text(15.764125560538117, 97.848, 'X[8] <= 14.9\\ngini = 0.438\\nsamples = 201\\nvalue = [136, 65]'),\n",
       " Text(7.506726457399103, 76.10399999999998, 'X[0] <= 2.255\\ngini = 0.155\\nsamples = 71\\nvalue = [65, 6]'),\n",
       " Text(6.005381165919283, 54.360000000000014, 'X[7] <= 2703.31\\ngini = 0.133\\nsamples = 70\\nvalue = [65, 5]'),\n",
       " Text(3.0026905829596413, 32.615999999999985, 'X[2] <= 0.821\\ngini = 0.089\\nsamples = 64\\nvalue = [61, 3]'),\n",
       " Text(1.5013452914798207, 10.872000000000014, 'gini = 0.062\\nsamples = 62\\nvalue = [60, 2]'),\n",
       " Text(4.504035874439462, 10.872000000000014, 'gini = 0.5\\nsamples = 2\\nvalue = [1, 1]'),\n",
       " Text(9.008071748878924, 32.615999999999985, 'X[8] <= 11.2\\ngini = 0.444\\nsamples = 6\\nvalue = [4, 2]'),\n",
       " Text(7.506726457399103, 10.872000000000014, 'gini = 0.0\\nsamples = 4\\nvalue = [4, 0]'),\n",
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YLCIYDOLRo0e4fPmyKkeg2rVu6euEbc5MIP78888xPT2NtbU1iaG1XC6jVCrh3Llz2NzcxNDQENLpdNcdyi7UdD06OsLBwQGKxSI2NzfR399/KnSVTy65Puvr6xAEAel0GqVSCTzP49133+15fU4LtOy+uLgIn8+H/v5+ye7FYhHnzp3ruu3lJxkEQQDw6qXa/Px8mx7Pnj2DIAjIZrMSuejExITpvvTsEwqF0Gg00Gg0MDEx4cg8OzOBuFKpQBAEeDwecBzXwqZLzhkeHBwgnU735DEcGigdkuil1P806MrzvDSpyGkI+dgRht2DgwNkMpme1+e0gHypKYqirt09Hg9KpVLP+pJ8Lsh9pa+vr+UDllKpRPXSUXl8TRRF7O7uYmBgoKWeU/PsTARikrZOL30lydlwlr4uIl9OESeo1+sA0PWXKjQg1EB6nyWTJO7dXpGdJRCb6tndzLzqJTQaDTSbTQQCAce3UkgiqWg06sr59d63rgmQLFt6zhIKhc5cdiolEaLP55OC8WkAYZ8wyg1Bktp0MhvWWYaZmx/wYybA05LnRJmgx8m5Tp4O3Iofpz4Qk2xbRp+6kq9fzkpqSfmjIwHHcS2ffPYyCBOK2aRLZJVPCD4ZrIHc/MzaXR6Me33eyLdZSDY6J9um4cejxakOxISvymyaO4/H0/E8o26BHDBXwu/393ywIsnvabOkBYNB6Vw4Az1ob34Ep2ERo5TL5/M56ickyLNArID8sZYGXq8XHo+n54OVEer1uupesNfr7ekVsd19x2AwiHq97uhq53WAnBTTyh4nyfHcq8FY7enQSTlJIHZrbp3KQGx1RUUQCATQbDZP7WQmDqalO0lo3Ysgibqtvvwhq7NqtdrTN5xegvzmZ+dFE3mi7MVtCq2v25ySk21NqIDnedvU4mSL4jRO5lqtpnsyIhAI9ORLO8IxZjd15Wnat+w2nD754PV64ff7e872yhUx8EpWJxdbbGtChlqtBq/Xa/sY2mmezFr7wwRGPF7dQK1WA8dxjh2tOw37lt0GCcJ2Fy1K9NopFq3xd3qfmK2I/x/yc4JOgOM46VHrtIB8tGEEv9/fM6vier3u6LgRkEdlFozbQbbv3Do7TzgVeyEYq62GAedXsG7mvz41gZh8vOA0HYrX65XSZZ4G1Ot1UwGtV/aJBUFAo9FwjcbG6/UiEAicqptpJ1CtVuH3+11lMCH0VN2eO0YEn6fhJn0qArHdN75G8Pv9p+JYFJHRzIqYMFh0c1Vs9oMNu+i1R+Vug+d5aYHhNgKBAERR7GowrtfrmnPCiTkgiqLrseFUBOLj42PXySGDwSDK5XJP3z1FUaSaXD6fr2v6iKKIo6OjjrFk+/1+6TPU1xknJycAOvuZOzlS2K2nEr3Y4ETcIFuYgHur6zORa4KBgeEVyDGrbvQLuLuP2gs4OjpCLBZzXE8WiBkYGBi6jK5z1vUSI3GvyOKEHGbasKpHr9hJDb0smx10mlXYCty2PU37gPE2gpYc3fChrgfiXmIk7hVZnJDDTBtW9egVO6mhl2Wzg06zCluB27anbd+q/8v70QvKjUZDasNuUO56IAa0mWSBV+kri8UiEolEx2ThOA7FYrGFCZrneZycnODtt992XYYbN24gn88jHo9LXwYtLS1JA83zPD788EPDNqLRaJtNOY5DMpnE4OCgbZsqbQUAGxsb0kubd99911b7duSS+1MoFML9+/cRCoWQTqcts/r2AmZnZ9sYi1dWViCKIgKBAH7yk590XT4tVuhqtYo333zTVsCanZ1Ff3+/NDc8Hg+ePn0KURSRSCRQq9Vw+fJlTVlqtRpevnyJo6MjTf+8ceMGkskkstkswuEwUqkUPv74Y4yNjSGRSKBSqaBWq+FXv/qVdI3dm1/XWZy12JfJkbLR0VFks9mOsMgSWWZnZ1tk2dvbw/HxMaanpxEOh12XJRgMolAo4JNPPpHyYpCkIzzPY2ZmxpCZNhgM4ttvv1VlRS4WixgcHLSsB7nuL3/5C9LpNA4PD3F8fCydGb548SJmZma6wvyr5k/b29vS2eu+vj4kk8meYCWmgZx1WclYDLy6OY+OjlpiLHZSRq25HIvF8NZbb9li4ybtf/HFF1L7L1++lE4HFQoFye8A4LPPPkMqlZL8c2dnB41GA/39/bhy5YqmHMFgEOPj4/jkk08wOTmJ27dvo16vIxKJoFqtYmJiAslkEvfv38f6+jpEUUQkErFl855YESvZUqenp9FoNLC5uUlNceKWLIuLi3j69CnS6bTrMqRSKdy4cQMDAwMQRbFFjtXVVSwvL2NwcNCwjcnJSWxtbeHg4AAApDYePnzoiB6XL1/G1tYWBgYGIAgCRkdH0Wg08PjxY3g8Hrzxxhu2+7ACrTFcWlrC1tYWRkZGuiKXXVy7dg1bW1sYHx8HAGmBQpiKDw4ODP3CbejZfn5+Hu+9954r7a+trSGbzbbUVfrn9PQ0eJ7H2toa8vm8Zh/y+QcAH3zwgWo9eRt2V8RdPzXRS4zEvSKLE3KYacOqHr1iJzt99gIrMQ1OA9u127anaR8wt0esVkderkfUG4vFcHR0hOHhYdsEol1fEV+7dg2FQgFra2uIRCIYGBjA0tISAoEARkZG8OTJE4yNjWFoaMh1WTiOw+eff46RkRE8f/5cMvza2hrC4TCSySQ2NjZQKBSkVYkbIDZZX19HOBzGwMAA1tbWMDw8jHK5jEAggEKhoHuWUd5GJBJBKpXC7u6u9Kj1y1/+0vJZSC07EQbg4eFhvHjxAsPDw67aSQ1qtltZWQEAZDIZbG9vA0DL/t5pgNo82d7exvHxMQYGBqSnnlwu17UVv9wv1tbWEI1GJb8Ih8PIZrO2/EKt/Uwmg42NDWSzWaytrWFkZARDQ0O4evWq5AfRaBTJZBLLy8sYHx/H6uoq3n//fU3/l/czPj6OUCiECxcugOd5lEolDAwM4Ny5c1Iypf39ffsfjXR7RUxAUiQeHh4ilUpJmaPC4TBKpZLELtsplMtlqe9oNNpCxyRnu3UT5NNunueRTCalz7xp+280GtIEAJyTX/7p+f7+PpLJJCqVCiKRiKP9WAFJEHVycoJUKtWSMKqbctmB3pwgWdYODg56QjeSerNUKiEUCrV85u6E/UlCo/39fUSjUekFoFrboiji+fPnGBoakgInTf/lchkHBwctW6RO+1BPBGL5hJbD6dSJZqFkR9aSz20oWZmtJveu1+uoVquOM9DyPC9l4SIwyyHoNsiNVK6vWtlpAs/zmvmc5UG62/op54sbPiHfPjDasmg2m9LYW8lEZ3arww56ItdEtVpVzc5FUjl2+l6hZEfuVn5fZVYpq/lV5aSKTkEURYii2ObYvcCZJwgCvF5vW0ByOj9tJ0Hk1gpmHMchEAj0ROIjYn8Cp1Oyqs1DvbnpNt+cE+h6IJYfzVKCOFcnM4ipsSMDnQ8wat/u2w3ETjpitVpVTcfJcVzXU3Aqb6QEvZSjmQYku5lRKlESpLt9s1HyKTq9kFEuUIzGlQViE9BaDROQ4NMpI2qxXzhNz20EtRyrVh2aJIJxihaK3Ky0HvMCgUDXVsWCIEgpQJXohZuEFdDwMwaDQdRqta5m3QPak/84+TSinBtGlEhuE386ga4GYjJpjB6biXN1AlrsyIDzHFh60Ep2bWVV5zTxodHqjAS8bpCzGsnWzZuEFRBqMLNbS3IKqW5AuS1B4AZtkdpvNTSbTcv+36kbWlcDsdFqmIBQxLttFKNUfp3antCTw2qAc+rRTBRFzckmRzcCHlnx6E1McuM/DQzehGmclmLK4/F0ba9eayHj1PYECapK6I2p3a2JTrz87FogbjQaqi9UtEBYl92E1t4iAVmVuH1D0FuVW3VopwKxkY0IyPZAJwOeWSqtTj5hWQU5eWA1LwNJlN9J+xstZJxYFWvNDb227bys7lR+564FYrMTmoAEEjf3ecys9Hw+n+svfIzksLLadGJrgqyGzR5D6mTAI6c4aGikennPkLwMtRMESKKdTr5f0fNbJwKxFlEoeWrWglU7ylfgbtqxK4G4Xq/D5/NRG8fNVTFZoRvB7SNQZpgOjJzOqG2r0Fupq6GTAU/rFIcWeuWolxqMjqqZBaH46ZSeRv5hN6CZmRtOB0uyInbyHYsaOh6IRVGkntAEHo/Htcdds+zIZI/RreBC8+hPI4PdxytCoEg7bp0IBEanONTQqW0mWpg9qmYWZPvP7ZMiZqmS7CxkzKy4nY4NJBC7ffyt44G4UqlYWg0TEJJPJ0HSN5qVye/3SySNTqNarZp6vPZ6vdR2sLOlQsaNFuSG4eZ2TrlctnRjd3McrYB8Hec06zVZ/bu5X2z2Zuvz+Syf6KhUKrqB2Ov1On5ahBwFdHtF3PHvUL1er61PlslHHk6C4ziqzxc9Ho9rnztGIhFTk9DszazZbEor51gsZlkurU9rzSAUCrnqxH6/35JsXq/XcV+yAzJOTn8FKf8wysonvmZgli2Z4zgpFwktIpGIrvwej6etbfl7JXLyiuYmR+Zjo9HQPFLqBHoi1wSDexAEAbVa7VSlfGRgcArkKSMSidjKNdJoNCAIgmNbRkqwQMzAwMDQZTi+znabbdYOw2qn2JGN2jDbjh2WWStMtm6w13bSXjSydZqpl2ZeONkvbf9ujgONzxvVdctv3WQ/14Pjgdhttlk7TLGdYkc2asNsO2ZYZpUgjkTeTBvtC8v7cIOFt5P2opHNDV2d6M/pfmn7d3McaHzeqK6e38qDqfIF5fHxcUtaBWVQlbelFZSdZG8mcO1l3ezsLBqNBiYnJ9vYXO3yVinbDgQCePjwIQAYshsrmV1jsZjEsfbTn/7UdP9KJt3l5WWk02kcHx+bYtKVM8XKdQiFQshms9jc3JTYaNWuzWazSCaTLbZNpVI4d+4cstksPv74Y3z00Ue4c+eOxI6gHIe+vr42NofZ2dmW+sArZuZkMon9/X387Gc/M2UjuaxyRmp5/+TFiVl/0GLlXV5eNhx3s+0RpmtRFDEzM+M4e7iShVg+7oA5hm47fcv19Xg8WFtbk/5OMw5y9u5YLIbl5WVEIhFDpnOlDIFAADzPY3l5GaFQSPJ5LRbyUCiEQCCAmZkZzXblzMsfffSRNB/UWJjVbhhkfo+PjyOZTOoyODt1w3ScxVnO8DswMCAxqB4dHcHn86FQKCAej7fUpW2fkPvJmWwTiQRisRhSqZSUYUvZthrL7ObmJmq1Gur1OhKJhCFLM2lDyaTLcRzq9ToymYwpluBgMNjCFr2zsyPVJ1Qyeiyz8v53d3elt+KDg4OYm5tDvV5HoVBALpdTrZtMJjE4OAifzyf1oabb3t4eOI5DqVTC5OQkNUuwkpGaZNKr1Wrw+XyYmpoyxYytxRBcLpfRaDSQTqcRDAZNy6bHONxoNDA1NeUoe7ichVnOQiwf9/feew/FYrGlvlNQ0/fly5fSCaCxsTGqcZD77ubmJkRRRKlUwtjYmKaPNBqNNmblWq2Gzc1NvPnmm5icnJSuU2MhbzabaDQaGBkZQSgUavNbols2m8WtW7dQr9fx4sUL3Lt3TyL6LBaLEsNJoVCAIAgtchIZCdu3FoPzxsYG+vv7HRsn1wJxtVrFzs4OwuEwBEHA+Pg4YrEYnj59ilKpZNnJG40G7ty5g0ajgWQyKRmH7Cs9e/YMw8PDmo6gdm0ymUStVsPGxgZGRkYMA7FaG7FYDHt7e6hUKsjn84a6+Xw+VKtV7O/vS4l8SDsPHz5EPp9Hs9lUbePmzZsQRbGt/1qthmg0igsXLuDixYvwer2asm5ubqJYLLb0oafb8fExDg4OMDQ0RDVuPp9PklfpzLu7u/B4PMhkMqYCgJ5sL168MBw7ZXvxeBwnJydtY5BMJrGzs4Nz5861TVSrIHLNzc2p6lCr1bCysiL9zY1ArKYv4WR78OCBamBSa0drHHiex9jYGDwej+b8azabLXFhZGQEyWQSa2trOHfunOSPaj6eTCbRbDbbFilKmdLpNC5evIiLFy9iZGQEly9fxsjICEZGRhCLxZBMJlEsFqVjacpATGQsFAot7ZDrE4kE+vv7W8bVLhw/NeE226wdpthOsSObqWeHZVnvWp7n8be//Q1DQ0NIpVItPG31P+kAACAASURBVFtGfbjBwttJe3WzntPtOM3E3AvjQOPzNHIo637xxRcScejOzg5mZmawvLyMRCIhEfFOTExgdXUVP//5z1v2eOVtkbk0MjIikZBmMhmsrKwgk8kgFothaGioN/eItZhcnz17hmaziWw2i1KphHw+b4mZWclOnE6ncXh4KLGr7uzsoFgsqratdu36+jo8Hg9GRkZQrVap2JEJS/D+/j7K5TKSyaT0qGekm5osKysriMViqFQquiyzagzNKysrGBsbw/r6unT3Bl7RgZ8/fx7b29sSE3W5XMbw8DBu377dwmasptvh4SEODw+Ry+Xw4sULDA0NUY2b3B9Iu5lMBqVSCZVKBdlsFl6v17BNeTurq6uIx+MSw/bMzAwWFhao2L7V5Orv78fS0hImJyexv7+PWCzmGCOycl4QJub19XXJDtvb2/D5fMjn844zMWvpu7GxgeHhYTx//hzZbNawX6XvZTIZrK2tSQzT+Xwe+Xxe1XfV/HZ5eRmTk5NYXFxsYRZX+mImk8Hy8rIk/89//nNN3WKxGDiOQywWQ7FYxMrKCmZmZjAyMoIXL17gX/7lXwC8YvVWBlE5W3Y0GsXY2BgWFxfBcRwGBwexvb2NX/ziFwiFQtjf33fu9IToIiqViigIgri3tyc2m02xXC5Lf9vb27PdviAI4sbGRku7ZttWu5ZGJqLP3t6eWK/XxVqtZqkdURTFcrksbm5uivV6nfr6er0urq6utpRpXX9yciJubGwY1pPrRuQzusYMSLvr6+um5NVrg1znlGzlclnc2tqSxsBue3qoVqtivV6X2hcEQaxUKq73KwfR147f8jwv7u7uijzPi41Gg6qNZrMprq6uioIgGF6n9BmjPuz6mCi2xq56vS5Wq1VL7ZiFax90iLIvWgic/o6evPhpNBrUj3FkL0gQBEuPgHJ6dtEmy3OlUpFeZNF+uVOv11Gr1Ux9Gl0ul019zq1k3SXU5XY/vSU5PZrNpmVbKZmtnZBNFEVUq1XpRZ1bX0+RvpTzAnB+bhjJwPO8lKbU6qqOPMaTcaWxmyAIUr5lvc+Wiawkk5wZ+9hlXVaOkdaYOQnXkv6o5a11OoUkOYID0GfRkmfrsnIvkm/SO5Gv1GpGN5Hi23mz9dxi4SW5ZO0kUFF+7+8EEwjxo07wmmnlK+gkownxfacyilmxG/Fbo/6Jz3SSc04Zu9zOuAi4GIjVUl06nVTdzsSWTz4rzqgMgHZzBNtJXO2kk6jJ4xT/nDwAWGlPTTYndCc3nk6sRrVSwBIbu/SA2gL5AsYq5AnTrdhNNElfZNdnrEBtjNy+UboSiLWCixuJwglRpZU2rfKXqfFmWb3JEIe0CrMObXaCawUKJwIesZvVtoyCmB25nM54pgYjejByFtxtKJ94rD4RKleNNO2Y9Vty07A6x2mhFbtO5YpYL7m5G85m525pxcBqAcHqytqJIGB2EphZuWgxlTi1IrBz49RKDO7E1ol8ddetpP9k664Tq2Kir9UAoxwL8hGVWZBAbNQ3efJ0Ox8wQbdY3F0JxHqZ9N1Qxs7dysq1WsHTyiSWPyZadTazK2KjQCzf6rHSh1lY0VNvC8dJ2dyabGScjcaAvDDsFOzoK9eF9v0P8TWnFhBqsLrS14pdbm5POB6IzRBw0t491SAPLLQDZfdareusTCJ5ULe6sjf70sNIVyMKKztPHnYDpVuyKe3iViA2S3/kNjmtchysPJ2ojaWVYOnGdpB8flp5ga93s3TipbwWHLdErVYz/OTPie0JOy8c7Fyrl6XfTiAl11vd66ZZEWvVNbqJ2tkCoDndoQYjclerqxWlzm5wk5Fgb5bBwk2CWqW+Vl90a42Fk7ZTa8usn1vRywyXpls3akcDMVHcKMg5cWehJYtUXutGILarl52XjmYcVO9ub8YmdoKU2niZbctopQJYt72ZJzi7oGWY9vv9rj0CO6Gv1hxw+oWa8kW22T1lq6ehzNjGqaOcSjgaiGk4newqpFzV0uzPqq0KaO5yegGB5o6pdBTaPWZRFKmCmd5KwSyzttVVf6PRaBkvGjuZlc3KakVry8bp44A0N37yQtPNvWolaPTVuml7vV7qOa3nv1Z8xsjPtWD2BuXW9oSjgdiIZVUOr9cLnuct96V8dKNhCla71syjYL1eN3QEGiZZtX5pH0nNTlbyNl4r2FerVVNj5/P5LLFoK8dGFEXT40UjG21+WDX2bkEQHAuCx8fHlggn3WKYVtNXFEWqxYOWj5JUsLTyaEHNZ4yeFMiXm7SBuFwum45dHo/H8cT9jn7iTPvI36nzm68Djo6OpDzPeiCsA4xMtDOw4+NndX6Ioojj42NT/moVjUYDPM/bYi7vJBh56BmB2cf3ZrOJZrPpGi04A4MZmPVXqzhtfm4pENsl2KMl+9Oqr3ycknNRyetolRtdp/U3pXxm9bIqCykHzO1NadU1Yy9S7hZpqZ6uWvZxSja9fUAr4222X6M2lOgE0acZfUk9vUd2Wr80M5/V/EJLVlqf6UTsMjPGSli6XZh5tHWSgFOrvlvJ1WnalsNIL6uyyPuisZudxPhqoBl3K4n1rdicRrbj42PH+6bd5jGzt0g7P6y0Y9YX9Gwml8HN+eykz3QidlnZP7YUiG/cuIFQKISijDzw8ePH0t/NkBAqSRQjkQiePn0qpSR86623Wh5dlIShsVgM165dw9TUlNSGnAwxm81ieXkZFy5cwPz8fBthaK1Ww/z8vCoJaCAQwPnz5zE/P69JgAqgjdxTaRc5uSUhRlSSc0YiEdy4cQPnz5+XyuT25HkeHo8H//AP/yDZgeM4TE9PtyQ8IvUSiYS0giB9jYyMtKS1VBI4AsDS0hLC4TCazSauXLmiO25Km6yurraNuxbR58LCAhKJRJtNiZzz8/PgOK7Fhqurq0in0wCgmfhdSagai8WwuLiIZrOJWq2GZrOJd955R5M8tlgstvkJIbbc3t42JJZVI+bc3d2VXkjTEuaqEdRubm6C53mk02lMTk6abkc+XsArMtiJiQnMz88jlUq12IyMBUn7uLm5iXQ63TZHyFEyMgfU5ufW1hYODg7w3nvvged5CIKASqUCQRBU/cPn82F+fr4lLhCf1Zqr29vbOHfuHIaHhxEIBKQXtkS+aDTa8nKt2Wyq2vbRo0cIBoPgeR7vvvuupj1v3LgBr9erGROsEiNb4qwLBoP4+9//3kIeGIlEJNJFswScchLFra0t6TFnamoK6XRaakONMHRzcxP5fB6Tk5MSMaUgCKjX6+A4DpcuXUKxWITH41ElDO3v78fg4GAbCWitVoMoisjlcvjiiy9UCVAvXLigSu6ptMvu7i729vYAABcvXoTP58Nnn32GRCIhtbm5uYlCoYDvv/++RT4SOM+fP4/Lly9Lb5cJcePIyAjOnTuH3//+96jX62g0GtjZ2cHz588lJyFEoKTuxsYGUqkU4vF4iz329vaQSCRwdHSEDz74QHPs1MZhd3cXHPcjE7NcTiUx597eHgqFAu7evdtG2BkIBJDP5/HZZ59JrCvE5jzPo/j/rCtmCVU3NzfBcRx4nscbb7yBCxcuoFar4fPPP28hr9zb20MymUQqlUJ/f3+LzDs7O9jb24PX60UikYDX61XlYSOkoEpiTpJzl3C6yevrQYugttlsgud55PN5UySuauNFyGAJldaXX37ZRqQLAIVCAePj4wgEAm1EvYRs980335S2LdTmZ61WA8/z6Ovrg8/nQzQaxe9+9zsUCgV8/fXXLeNwdHQksZrL48Lu7i7S6TS++uor9PX1SfUJm8nMzIxEMprL5ZDJZPDHP/5ROpH04sULiRqJnM/+/PPP29oSRRHvvvsuxsbGdO0aDAZb5iohNRUEAWNjY4jH45Z47CztEdvZEjB7vbye3Ud6N7cm5HWstmlla6JSqWBubg5bW1sYGBgAAExPT6PRaGB1dRVXrlyRElmTuoVCAYVCQbcfp+WkacPN8QRePWZrvUW32rcbXHSd4Jej2ZrQO3lgdmviyy+/lOjMCEO2WZnM6qE3JzY3N/HOO+8Y6kPTn5XrtWA5EGspfHx8jKmpKV3GCqMgIggCPvjgg5ZArFY/GAwimUzi2rVrbX/jeR7r6+v4yU9+onntDz/8oCrD2toaisUinjx5ovr33d1dvPnmm6hWq22BWM8RZmZmcOvWLSpZtra28Pbbb0sOT9r/xS9+oerMarKQunrylUolHB4e4v3339edEFrXLy0t4Wc/+5m0Itaq5/V6sbi4qPo3v9+Px48fa9qPPBnQykb40MrlMm7fvk0tF7leOd6k33A4rOqDxC7NZhPFYhEjIyOmJ7KWLuQJ4uLFi6YCsVY7Pp9P178vX74MjnvFBXd0dKQ7B/TGm7QljwfyoGxGpvn5eZTLZcM5ZTQngFc3Fi0fWFtbw7vvvqsbu7Rk393dxcnJCX7+859bCsSWtia++uorjI2NodlsIpFIYGhoCKurq0gmk1hZWUGxWNRdnqtdf3h4KJFBEopt0oZa/Z2dHYRCIczNzWFkZKTlb0tLS6jX68jn8wiHw1hbW8PFixdxfHyMRCKBUCiE2dlZzMzMtOmwvb2N/v5+3L17F5OTky1/f/z4MRKJBHZ2duDz+RAOh1t0VJPz8ePHSCaTePnyJZ4+fdr291KphEgkgq2trTYZV1dXkUgkpABw9erVluuvXr2Kg4MDLCws4Pnz5yiXy7h79650dGd3dxcXL15Eo9HAN998g2w2q9rPy5cvwfM8kskk9vf3cXR0JO3JGo3b06dPkclksL+/j0AgIDmgms1fvnyJhYWFNrsvLS3B6/Xihx9+0LT54eEhRFFENBpV9Ss9n6zX66jX65ifn8f4+HhLnbW1NYTDYYTDYVXbEDLRu3fvIpfLqW5NXL9+vc0HiS8NDg7i+PgYh4eHqj6jBhIUlLKQR+yNjQ0IgqBpCzM28Xq92N7ebutja2sL6XQa9+7dQ6FQwJMnT3DlypWWOpubm5iYmMDc3BxyuVybXw4NDeHJkycoFArY3d1FvV7HrVu3UK1WcffuXRweHkqrU9JmqVTCw4cPMTEx0TY/dnZ28Pbbb7fZYmhoCF9//XWLP5E5cefOHayurqLZbOLmzZtYX19Ho9HA7du323xgdXUVQ0NDePHiBQqFgm7sWlxcxOXLl9t8pFqtYnh4GCcnJ6bGWAnbx9eazSb29vbQ398PANjf30cqlTJ9BEQQBBwcHCCZTEqfJirbUB4ZIftGpIzcTUVRxMHBATKZjCRLIpFoOd4iCAJKpRKSyaR0rd/vx97eHtLptFSvXC63cGTt7+8jnU63/Vvr+JogCNjf30cmk5F0kssiiiJevnyJ/v5+ifNLfubx8PAQqVSqpS8ALXYnehC7PXv2DKOjo+A4riW/gSiKaDabqFarUgYw8pvskcodp1QqIZfLGY67lk3kcsptnslkpBdGZBUqH69yuYxQKCS9aDFjczXZRFHE7u6utGqR+4KcHkteh4yB3++XPuMmMuv1rfRN+bgArZ8268mv1yaRxawt1Noh9kylUpJ+Sn339/fb5rH8+JpaHS2/7Ovrk14mq81nkhxMEATs7u5icHCwRSZRFKW+tMqBVp8hY9rf3y/1C0Cy0/7+PsLhcJufyOtbjV1knM2OsRK2P+ggG/fRaNTS9VYICE9OTuDz+drqks8U9drQqqN3baVSgc/nkzJjGYGcYvB6vdJ/StTrdVSr1bb9Kq1yNZnIKQcjAkitr4zsfH1EbCKKomFCGy3SUj0yU5r2lSBv56PRqGZeEKM6ZolW1eSWn1l14gvGSqUiBSGrH0HwPA+fzwdBEDR9HNDf6zVbJxwOo1arafq+mTZpy4FX8+7k5ASRSMTUF4mkfjgcpk6EJI9XVrYilLD9/aSd9IbyhCs0KSD1viM3c1+xci1NshuyitBLUkLspuxTq1xLTpqk8Fb60oPZbFtG+V214PP5LCXfMZNrwEh3Kz5N2jKb8tJsm04kARJF0XW6Ibkt3UzlqQUy7mZ1tOP/yidYux8oOxKIrQoi/8yRNqtRNwKx2QE2Q3ioTPEnLzdyDrU9LL36WmwINCwJarATxN1sn9jWik3sQC2rn5PEpnZldermoAUlyUGnWJcJzIy7HHZ8QK6rE5nyur4ipk1zp9WfGTmsXCtf5ZiFGVoc0qfSWcnKxSgQyye80erD6RWxFZt0EmafKpy+kShXSU7kru1EvmSnQLtAcBq0Y2rXB4j/O7H6dyS1k5W7n9pkNqOQnUCs7M9MudUMWGZuCmp3Y7POQWM3vb6srAhobr5WVxt2gryZm5lV3Y36pU1kbgSlLaw+Iej92yko50qnV8Vmxl1Z34oPKMfECT9yJBBbuauo3entkGCaCQ5aE1zvWjtMIPL21aBlNz07qN0YzAQtrTatOiINHbuWzfXal+to9UZhNRA7EewA55KIk3as7vHK7en2dpIcnd4npg2stIGbQIsAw45duxaI9dg8zDxSmi03U0dvBaZkWTZL/02gt92i5TR69qzX66p2M5JN6+9W9jHlNjFa9eiNl9aThjzQW11VmZmQWm1b2W7TSuvoJNOGHYJU+X6m2naY2m+aOmrMH2Z8w4nf8jLarQmnKJXsvgh1bGvCyqpFbSIa7avpveQy2kKwukdMc7JDOUh6E1HLafScSWuFbmQ3K31pgealjBWbyye1HR4/o+u0dJfLZtY2Wnu5dlaFygWC1aCuvLEp2yD6mnmnoVVHbWFldqtQbRxoy4mMNNuIdlawSt3srv67siLWq2vkbHZXxFaCOM2joXJloBeoaFepenYzCohOBmJyHWBsE3kwkfejVa5s3y4ztpU68qBDYxu19uzsH6qdwrACo2OixP+NjoUSm6iNt9a7FL0boha/HClX2o7I4OTePq1NtZ6e7e6HdyUQ631CSLO9oDZIynKnrgXMBQWlQ+rpQ/viUO8NupH8tH2ZhdHKU2vymJ1UndzTJDATmOTQIuRUtkkLJ09MkHHWW8TojaUZm6i1TbInarWp5xtaAbobPkEgT82pBqty2QrEJKcBrWHq9bph1n+twZP3J3carXI55Jn+5fJqlTebzZZ/m3ncVbtjKtuxCvJZqBY8Ho+lxyOaO7nSvkY2IQwKSttqTTba9o3kM4LRHrGZ1bjWvj2Bz+ezdIxNLcDT6qdWX+mPZvTVmiN61wD6PqnVr5Zv6MlA9KKBlRWsXuziODo2eDlsBWLaPRkCo+NJRrTcWntVRvtc8jpqA6l1Z3YCtC9E1GDFblb70oJVm6gFXLLCkk8IJ21uFbTH55S070p4PB7b54kJrLxcNYJ8pWkEtScYPf3NrLK1ytWuNVq5uw0937C6EAJsBmKv1yt9Y03jaEa5Dfx+v+a32/V6HaIotn0frlUuRyQSkQZRLi/5Np0kllfTj8Aop4ba3wlLAAH5Tp3IbVROYGQ3n88n5SAmaDabqm3Ky2nyhHg8nrY+9K4n4yEIQgsVOhkvpc1p21eC4zipvpZPyvtUa5vIFgwGTeW5iMVihjdIK7lY1K4Jh8NUhJhye8jblctL9PX7/Zp5WoLBIPx+v5TbRPk3vfwuWn5LmFOU/ZJyURRbxpDIoCyX6yXXxwi09fV0AYBAIECd7IfAMRZnqx8+ONUPTf/kbqqsr1XuBoi8Srm1yt3sqxMQRbFlBST/vNTqk5UR9PSz+rezCFp9nbKPmblsZb53Sx87oOKsc4p92Wp9I5ZX5d+6VU7+pizT0lsOj8fTdTvL6wPa50bl9czYRCkH8CodJ037ZuXT6tNNpm2rshm9lDPbt5PjZXd8teRQyqOnjxm7GNmDhmVdeY0TrOVasihBFYjJo78RjNhardanpVLpVrnR3+R11NBtOyvrO2UTZbt6srjFbO20jzkhmxmm5E6Pl9uM42aprey0oZxfZvzeKuOzXWZnqq2Jv/71r8jn8xLDaiKRwG9/+1vk83lkMhkcHR3B7/fj/fffl86X3r59GxzHofj/rLyBQAClUgmbm5uIx+MSDRChRFKytUajUdTrdfzhD3/A0NCQxGAbCAQQCATwn//5n8jlcsjn822Mwbdv38bQ0BByuRwCgQD8fj8+/vhjDA0NYWRkpK38N7/5DVZWVlrkJYzPi4uLbey/1WoVT58+VWUrDoVCLdcEAgH893//N/7xH/8RfX19+P3vf49CoYB33nkHf/rTnzA8PCz9/s1vfoPV1VVVFl+Oe5Ujd3Jyss1u8r7+67/+CyMjI5iamsJf/vIXjIyM4O23325pX01uwjj93nvvSc6jZPsle5RLS0ttzMWBQACPHz+Gz+fDxMQEPB4PPv30U/zzP/8zYrEY/vCHP+Cjjz5COp3Gp59+qqkrAFWbr66uolQqYXBwECMjI23szZFIBL/97W9RLBYRj8fB8zz8fj9+9rOf4Xe/+x1+/etf49mzZ6o+9vvf/x5DQ0O4dOmS5OOxWAzLy8vI5XLIZrOqLNZqLL5y2yl1K5VK2N7eRiAQwHvvvYfj42PcuXOnhb3Y4/mRkbxQKLTZmvQbCATg8XgwNTUlyaDWJ8dxePLkSQur9yeffIJf//rXbb4QCoXQbDaxuLjYUv/TTz/F4OAgZmZmWnyB6M/zvMRWTuSQzyWSd5zYOZPJSHPZ5/Ph448/xkcffYS+vj58+umn+Nd//VfJN4r/TwT86aef4vz584jH4zg8PEQul2thcP74449RLBaRSCSkdxAffPABOI7D9evXJQZmMu//4z/+A2NjY4hGo/B6vahUKvjwww+ll8taTO4A8OGHH7aMszLOLS8vA3j1zuvChQu6wZqKKikYDKJQKEisyalUCj6fD0dHR4hEIggEArh06RIqlQq2t7eRSCSQyWQwOzvbxgIbCoXQ39/fQomkxl579+5dLC8vY2xsDMViUWIAzuVyuH//PjjuFWPzrVu3WphVT05OUCgU8N1337XU93g8uHTpEubm5lAul7GysoInT55gfHwcAwMD+Prrr1tYhF+8eIH+/n589dVXLayz+/v7SCaTbeWEEkfJCpzL5eD1etHf34/l5WV4vV4MDAxgcHAQoVAIx8fHGB0dRSgUQl9fH4aGhlRZfOv1Ovr6+lTtpuyrVCphYmJCs30ly3Kz2UQymcTFixfh9Xqll3mfffYZIpFIi44DAwP48ssvW3Qn9DGEoTedTuPRo0cAgMnJSTx9+hRerxdTU1NYXFyE1+tFsVjE119/3cKqe3h42NKOnHFZEARcvHgRw8PDaDQaiMViGBkZkXwyk8kgEAjg6OgI8Xhc8knyCBmPx9tsG4lE8OTJE8k35MzgPM+D53mkUinEYjHE4/E2FutEIoFGo9FCpS5ns1ZjCvd6vSgUCojH46jVashkMi3sxS9fvgTHcUgmkxgYGEA6nW4bLxJgx8bGkMlkWsZLac94PI5cLiexepNgQuwvt/PBwQFSqRS++uorJBKJlnG8dOmSxJosZ+EGgHfffVeiGiK6y+d+NBptsbN8Lj948EDyDbK4kfuGXIZMJgOPx4OZmRncvHmzrQ3y9wsXLiCTyWBpaQkDAwOIxWISAzOJB16vV2LmmZmZwcDAALa3t9HX1yfZUs7kvrOzg2w2K90M5DaXxw3CDD06OoqpqSlDZmeqrQlCmT0wMNByt9HC8fEx7t27h0QigUgkgoODgxbiv/Pnz7ddo6z/4YcfSkapVCrY3d3F1tZWS9/y8oODAwA/knJOTk7q1pcTDlYqFVy+fFkiFhQEQWqHppzneVQqFTx58kTShfRfqVRamJQrlUqLXKTO3Nycqt1evHihSpAot5tSV5r2yYpTHlTUdPR6vYY2AaArhxWbExmJT5j1ST3di8Viizzy9ohtyFt9LX9eXV3FN998g1wuh6mpKU1/lpNNkhUd0YP4qtyHG40GTk5OJO4+eTs8z2N5eVmic9IbL0JQS+iijOwfCAQ0x1FtrjUaDXz77bcttlfqrrSz1lxWk00uA8GXX36p6vNK5PN5VCoVPHjwwFR90vf169cxODgonaiamJiQyGTHxsYMbU64B3d3dzExMaHZF0C5NaHck/n8888xPT2NtbU1xGIx9Pf3Y3V1FalUCvF4HH19faZoeOSP2Mrlu7yPqakpiWOsUqng5s2bKBaLCAaDhvTcWvW12tfT26hcz1bBYBCPHj1CsViUBumtt95q+a2lj1m7yXWy2z5gfc9Radv79+9L9ic2MDN2NPKp+eTi4iL29vbw9ttvq/ZnxwdoZNOCGcp6J/eIafU1U9+MHFpzUFmu5htq4+r1enX/vrq6Co/HI21pymUyil3K+lZsrmVPNVBtTRDGWo7jcO/ePUxOTiKRSKCvrw+ZTAYvX77EzMyM9Lj13Xffwe/34969e9jf34cgCLh16xb8fj+ePHmCer3extbs8/la6pPH8L6+PszNzSEQCODevXsIBAIoFArSOclvv/1Wkotc+/XXXyMSibTVv3XrltQO0SGTyeDmzZtSuVzehw8fIhgMtpRvbGwgFArh5s2bbf3eunULfX19+P7779tsxXEc8vk8fD4f8vk8RkdH4ff7cf78een33Nxcm93IY9f8/Dz8fr/0OKxmN7KiyefzGBoaQigUMmx/eXkZq6urOD4+xtOnT6W9aAAtY65mW1J29+5d/PDDDwiFQi06x+NxaSsqHo9L+mrJcufOHVWb37p1Czs7O1hcXESxWJQeC9V8MplMIp1O4+DgAKOjo5iYmJDOed64caNFn+npafh8PmSzWXi9Xvz9739v01cURXz33XeqY721tYWTkxM8ffpUsl00GsXVq1fb/PnWrVvgOA4vXryQ9hDVZCL2FEURjUYDc3Nzqn0Hg0Hcv39f2uvW6vPevXsIh8NtPh8IBFTnTr1el/rMZDKIRqPIZrP49ttv28ZlYWEBfr8fCwsLLVsTSr8JhUJIp9MIBoP4/vvvpXbI3PT5fAiFQshmswgGgy12kctAeOLItUpfmckQrgAAGCZJREFUi8fjqFarGB0dRS6XA8/ziEQiLbaRx65z585hZ2enJXYp6xNdHz58iFgshrm5OUlXLZuvrKygWq3i5OTEkHGbakWsZMoVRVGXwVlZnzAGkyMpRvVfvnyJVCol1VcyK5O+yYcQShbkRCIh3UB8Pl8bKyypb6dc3j7pl7DfEnJB8pmpGgOuGUZipR1o7UZbXy4fqUP+32w2VW27t7cnPZbJ7UHK1eRQ05UwKyttq9WOXD6gnWlYaWs1hmQ9VmBBEHB0dNSiL2E+3t3dbbOblu0AqNZXskvL7UDYheU2JS+RtHRUjhdph3zAIx8XtXJRFHF4eCht/cgZ0tXKSTscx1GNi3wucxynyqYsP75GxuLw8FDSUz5WyvbNsqzrsc+bra9lc/I3M8zOlj/oaDQaaDab0ttQIwiCIP3nRH0t9mja8nq9jlqt5lo50YXYi5btlbygM3utlfq1Ws3UUR3C+qz8Mou23Kn2lRBFUTqXbEZ3UXzFtg2oP1oKgjbTs9G1tH250S9pR8lqrFXebDZRLpdtl+vpHgqF2nQy0w6po/XlLNkiUGtfDW7VF0VrzNCWPyeh/RqFHMlxqj5tDgX5HUsO8jWd2XKtdrTKSVtWv9yR28HMPVPel9n68lWHHoxyA5gtN2pfzbZmUh86rbv86z8n+jKb0Y3oq5dukqYds/bU0ler3Oy4iKJ+WlE9fc30pZU200ges6kraeqbtYkStgKx2Qlstb6eo+s5mdqAOxVEjD651HIUwsRrJUOUlZSMbtTXyrRHytXy3NLorDd2ZuQjupidYEb19folY2o2cT2pb3Yy6/VLZJZ/5UbTjlPlZjMvGtnKzPgaBWKzNgGs+4mZsbaSjRKwEYjdvKuQ+lYmiZUArVduNriYvWNbDcQ0E96t+lorF9oVllH7dlbEtMFRT3e9lZp8MtNOfjMB1OimTjNeNHNEr1xr3I3kMNLdjJ+4cVOk9ROasaOd57YzXdB2Skt9o1dfa/D0ytXa0iunaV/vTshxnC3aHxo7k/pmAgRNfSs3P5pATPtorCWfWd3lN3u1+nryW12ImF1VOXFTdyqw6rVjhnxTHsj0bnh67ejZRP5URxNYaeqTJ1qr20FGsB2IaYMLTYAwqq9lSL1ymgBKW240ALS627mW9gZptr6Vmx+NUzrRDu2jod7epdHNlVYuJyaz2XZIwKEJrLTlZm1NZFaDmeBl1iZmQOR2o35HA7G8EzN3FWV9M45opn63AivNilipCw3sXOtWfdqnAStBkebmqtUGDfTqW6FbdwJWHm+12nF7+86ufcy041RfboPYlhaWArH8xZsZp5e/4HKyfjcCK205cSAroL2WPLa61T7g3E3OqfYJaCcoTX29m6vTfRGojYtT7eiVawUR2nJamGnHbF9uj4+TfkhgORDTDIA8cDvZfjcCKyk3u2qj1V15La2daerTHikkcDsQ05QTqOluZs/RqL5av7R9qa2SrExYtRvnaVghymW2cnLIqH05aG/YtPXNbDtYWXhZCsRqE1hPONr6asFLq76b5XrBRQ1q5bS6y6G0g5HTWKkvl83MJLG6uncbSt3NvPyhqa+8Vm43o20EpQ9Y3XZQymz15W8n4ZTuWqC1iZX6SvmtvufRg6VArEZQqUeaR1tfyYkliq0cVfIJI2+HlOutGtTq65WrtWMkvxy1Wq1NHrP8ftVqteVaURTbbGOnfq1Wa3EypZ3l0NLZjC3M2MpuO9Vqta09ZZkcaj6mV195rXJMjewsB8dxmvX17KAmn5bMdsaLFkZzWbki1rMVLWhsYqU+7VhbtS1VGkyCYDDYMoFJcmon68uVJ3lXCcgdjONa6UfkdzZ5MJUHaHl9o3Kz7Wj1q6aLXuIPJWivVbOb3ipPaVej+kRnZR218mbzR8p2M4SKcpsqbW6mHaVPEZJJLZB8EVr15f0q21G7Vm9V5ff7W1ZhPp9P0/+17EB0tNuOXvta+hqV642LUne1bTqt9rXqyLc7lOPu9Xp1CV9p6yvH2qi+3LY072AsBWKlIEassk7XbzabkuPLg5O8XP74IAiCan0z5fJ2aPs1o4se7NrNaG+apj7HcZLOch21ykVRVLWVHtTa0bK5HV3M1pePqXLyy0HOyJrty6i+3A7yfpX6221HXi6KomR32nK9cTGSWat9OZR15FDOCSObuF1fzYfNwDEWZwYGBgYGazBcnukxmZKoTw6265UrV1E05fK/aZUry8wwsWrJT9uOvD7Q/oilpaOyjtqqzIwdnLIzrf317AYYv8A1WrUKAh2jsp5v0PikGTlp/dOpci259XQx0lO51WYGavOhEzJozUOnYxHtNVr+Y1Y3wxWxFaZitXK7bLFkAARBaGEzUOYMJZA/4pD6arloY7GYarleO3r9Kuvp6SiHFktDrzBUG9lf7VFMj3VCqa+WPZ2widbftHR0Qwa7drari9oc0PJzM78BOj+X/01v7gCtp33UTv7YHQ8nWdlJudGCT/7dhVpQNmToaDQauHnzJnZ2dgAApVIJPM9jfn4eDx48wObmpsRMWy6XVcuPj49RrVYRj8cxOzuLFy9ewOPxoFQqYW5uDtVqFfV6vaWNeDyOO3fuYHd3V1Lwj3/8IwqFAu7cuSO1QcorlQqSyST+93//F9VqVfpN6hNq7ng8jt/97neoVCp49OiRRAIqL9drRymPsj5pk+jXbL7Ko3rnzp02G96/fx8bGxvY3t5GJpPBd99912KbcrkM4BVDrPJaQXjFAvDs2TPJzrVaDdVqFbdv326rT1it5fXL5TJ4nsfCwoJmv5VKRXpLrGf/QqGAdDqNTz75RLLD8fExRFGU7HDnzh0sLS1he3sbi4uLLePu9Xrb7Knsi/jS9evX0Wg02nwmmUxKLB5y3Z8+fYpbt24BePXGm5TzPG/oS2oymLGblp2J/2i1o7Tz8fFxm54PHjxAqVSS+iVBYH5+Xqov10Xuk0bzxexvPT9X6vjgwQPcuXMHBwcHkgxkvin9plwuY319HZFIRCqX11EbD7n+yrG/du1ai5zyusS3iZzVarXN5sRvw+Ew7ty503INz/P45ptvsLe3J1Es+f1+KY4cHx9jbW0Na2trmJiYgN/vR71eV91TNxWI1ZhoBUHA8PCwxMQqZ5YdGhrCwsJCCyNtOp1GLBbDX/7ylxbG2Gw2i6mpKYk1t9FoQBRFpNNpZLNZqd9sNotms4lYLNbCQEzKt7e3MTQ0BAAtv0n9L7/8EoVCAQsLC6jX64hEIhgeHsatW7faygkDgFo7SnmU/c7MzLQwSm9ubiKVSrXoQmxYrVYRDocxNDQEv9+PTCbTxtTb19enei0plzMKb29vI5lMqtYn5UoG4lQqhVwu19ZvIpFANpuVmIWN7B+LxXD37t0W2xLGXGIHjuPwi1/8Avl8HoFAAKOjo9K4p9PpNnsq+yK+lM/nVX1GS/dms4l8Po/r16+3lBMf0/MlNRl2d3fbrpXbc3BwsM3OhLFbLtvu7i4ymYyqDHK/Vc6vfD6P+fn5Fl1Iv6S+XBe5TxIGcS0/N/tbzc/7+/tVdaxWqygUCi0y/PDDD8jn88hkMrh9+7bkN9VqVWIe//7771Gv11EoFKQ6hUJBlXW6r68Pf/3rX1vYqw8ODjAyMtLihzs7OxKjtZw1m4xdNpttsfnm5iZqtRrS6XTbNTs7OxgeHsbt27elGPLdd98hn88DeMWQHY/Hsbe3h62tLQwNDWmyOZvampibm5MYSoEfWVs9Hg+ePn3a9jfCGCsvv3LlCu7evWuqrlr51NQUstlsmzyknMgqJwslxJqkvpyxWa5boVBoY1bWa8eoX6W9tHQnzBNjY2M4OTnB7du3qeyTTCZx7do1R+2s1T6xnZYdlI9uen6zt7eH0dHRFn3V7KnVBq0ujUYD33//PRqNhikfc0MGu34u12Vubg71ep1KF7U5oObnZn9/+eWXKJVKpu0fCATw6NEjXRnk0JOBZjzIDUteFggEkEqlTM8dpfxq7Stjixa09D01e8RyFmS5wjSMxXK2WDkjrbLcDPOxVr9qLLdnYY/YyP5Kdmojtl+lvmr2pGV4trtH7KYMdu1sRxf5WBjNF7O/af1cixVa3qacTXlnZwdXrlwx1S+t3d3YI1bGEKLL2toaeJ5HNptFKBTCuXPnrAVinufxt7/9DSMjI1hfX0c4HEZ/fz82NjZwdHSEc+fO4fDwENFoFP39/Xj+/LnEiLqzsyOVA5Do1OXlt27dwtjYGNbX1zE4OIh0Oo2VlRWcnJxgeHhYetweHR0F8OrA9OzsLIaGhrC9vY3JyckW5lU5YSchOJydncX58+cRiUQQj8fh8/lQq9UwPz+PfD6PWCwmlQuCID06kEP+yna2trYwNTWFWCyGZrMJv98PQRAQDAZx9epV5PN5bG1tIRaLoa+vT2KaVerO8zxCoRCeP3+OWq2G0dFRrK+vIxqNIpVKYWVlBVeuXMGNGzcwMjIiXTswMACv14v79++32V/LzgCkx0F5/e+//x5vvPEGlpaWJPsvLS3hJz/5Ca5du4aJiQnJzolEAqIo4ttvv8Xw8HCLHYBXZy6J/dXsUKlU4PV6EYvFkM1mcf36dcmvxsfHJfbdcDgsjcP169clm5N2/H4/7t69i6GhoRZdCBt0LpfD5uYmkskkBgYG8OzZM+kxVV6fsAmPj49jZ2dH8iXCj0fej4yMjGBlZQXJZBKZTAalUglPnjzBpUuXsLKygmg0iuHhYbx48QKlUgnnz5/HixcvpPLl5WX89Kc/xVdffYXh4WG8fPkSfX19UkB59OgRzp07h93dXVy8eFFi+/7222+Ry+Wwvb0t6b6zs4OjoyNks1lpy6m/vx+RSAS3bt1CNpvFwcGBpEuj0cDf//53DA8PSzrGYjHUajWJf03u52Z/K8c3kUggGAzi4cOHGBwcxMHBAaLRKDKZDE5OTvD48WOMjY2hVCpJvsTzvBSUyJwj2zhq/fp8PtV+eZ7HkydPcOHCBcnnM5kMlpeXcXBwgKmpqZZyjuPw4MED5HK5FjlDoRDm5uYwNjYm2TyTyWBnZ0famimVSlL5yckJFhcXMTU1hWg0Ku0R12o1hMNhbGxsYHh4WDpRoUsiKlLi+PhY3NzclP69t7fnaPnz58/FarVqulyrHT35X7x4odqOWjltO2bqnJycGF67trYm8jxv+lor5RsbG23la2trYqVSMV1fq1wOK3XU6pmVgfyNVuaTkxNxfX1dV861tTWxXC639UVbruzLqXIjXczoSItyuazaJm25lX7X1tZU2++lcjOg/qCjXC7D6/UiGAz2TLnH4zF9DlKrvlPtONUX7bXlchkcx7U99nSrvJN1aMsJI6/ZcjvXnpZyO+iWDL1mQzt62U4M38lyre/5Ke8l1P3StuNUX2p1nMgI14nyTtaxIxsDQy+AKhCTAKAMBE6WqwUaUq4G2snmdjtO9cWCCAPD6wPqQOxUBn+9zwHNlusFaDWc5pWw3TYZGBh6F9SBWC3waVH0aJVrteNUuRbcbsdsnU49huvpqwY3bgBu1+mkLgwMbsGRQExb7nbg1oLWil5vpa/VjtVAbIZXrhM3MBo72LG/U3WA7u93d+tmcFZvNk7J02v2tKKXI1sTTk1gpwK3FtwO6E711YtPHlZvYE4F4m7prqxjZAf5JJS32clyQDsDnla5VRjJpnznQ37bfQei1Y6Zci153Cg3C9dXxG4GaNoASjtpteDUpKVtv5NPHnYDgbyOWpAy068d2eQ6mimnkUFtEpJyNd1pyrXaMepXTUez5XZAZFBy/unJTHS3I4O8fSVDjlG5GTkB6JarcRxqlZtBV7Ym3F5Za4F20tK2Y6aOneDl9EpKb6LKoVWuFTis1DGanFp1aCeYVuAgOupNJK1AZlUGtWBBdNQqd6pfpwOxXd072a8df6D1AbOgCsROrTKslGsFJpq7q5kVhxloBSatNmkHjHY1Qcq1ViU0qxUnVl5auuidA3fDJk7obkYGuR3cWJ257Q92YGb1bUZ3WpzGuaAH6g86tDqjVYZco1auZiy9ciuB2M7dzExwsfN4aOQ0dh8DjYKXlYBCawfaOlZsYlV32psK7eS0urJ2yx/swCmbWO23E3PBzhwxC+oVsSi+YidVEjzqlQPthJAEauVq7dCU68lP6stpr7XKzbSj1a+RjspyOUi5sn1CrmilXK6XXrlWvzTlarqY0VevDpHTrmzycqXuZNJp+QCpoyczzW+O41rKSb9a5cp2tMrN/LZK+66EU7rTwind9crJWGuVm+nLLKgCMdkeUO7jGpUTwTtVbkZ++TVa5UbteL1ezfp2+tKzJ1kF0JYrZbDajplytTp6drVTh5QpZSD19MrlbZHfemOqd62anY3KlfrYKdeTV63c7JwxAxrZnOrbSG+1vzltc2W5GT/W1Ed06hmFgYGBgcESLCX9YWBgYGBwDj69PzrF+Or2bznkj6ly9lQz8qu1Ia+nbEdeX+8FpJHMRi8Gev23mi5u1emULsoytfpadcxc2ws6Au2+bpQG1kkWaBoZTnssMrKtLnlovV7HH/7wB4nxNZVKqbKqmvn95z//WWKClbcjL9dijtXql1xbLpexsbGBnZ0drK6uSgwXtVoN0WhUYovVkkGNRfb58+eYmJiQGHHD4bBl1lk9mY+PjxEMBvHnP/8ZPM+bZtOlHQta+9O0L9cnGo1S11HaZHd3F+FwGJlMxhF/o9GLyBEMBlvKtJi91epolTvx24mxk9vaDMOwMiaozQXa37Qy2O3XjP+bKXdaLwLDrYnx8XFsb29DEARcu3ZNosWh/a3Vjrxc67dRmwcHB6jVaigWixK1D7l7mpGB/BZFEQcHBxgdHcX4+DgePnwo2cGofigUorIDkXlwcBDBYNCUHeyMBa39ado3Ywe9OkqbjIyMIBwOO+ZvNHoROcnqRW8sarVay3XycrO+bWfc7epI/HxhYQE0sDMuWjLMz8+72q8ZG9LECrN6yWOILkQdyCleevm3Fo6Ojmy3Uy6XTdW3I3Mv2NAp+7tdp1O6KMvU6mvVMXNtN39rgbZOJ2XoBbu5aVvdUxNffPEFCoWCRO64sLCAbDaLvr4+3L17FxMTEwiFQrh37x5GR0cRDodx7949jI2NIRwO4/79+xgdHcXdu3clMtHp6Wncu3cP2WwWjx8/xvT0NF6+fIm9vT1cuHABKysrqFarmJycxIsXL3B0dITp6Wk8f/4c5XIZU1NTWF1dRbValX6nUinE43E0m03E43GsrKzgl7/8Jb755hsQQk4t+YPBoCTn6uoqEokEBgYGUK/XcXh4iKGhIeTzeYlA1efzYWFhAWNjY1heXkYsFsPg4CASiYRERBmLxaQ6d+7cadM9l8vh5cuX8Pv9KBQK4DhOIvy8d++eRHZYKpUwMzOD5eVlXLlyBQsLC8jlckilUi3yE5ur2XlwcBBPnjyR7Dw5OSmVE+JDuf3X19dx6dIl1b7UdCEklqFQCIVCAV6vF3fu3MH4+Dii0Sju3r0r2YrY9sGDBygWi9jd3cXh4aGkYyKRQF9fH16+fImjoyMMDQ1hd3cXR0dHUp0333wTCwsLOH/+PB49etSm1/nz55FMJrGwsCD55w8//GDKJwcHB7G/vw+/34833ngD169fR7FYxNbWVouclUpF6u/ChQvY2tpCMBjEpUuXMDs7i/fff18ifCUr/YsXL2JlZQWVSgVTU1NYXl5GtVqV2IxJm6urq+B5HtPT01hdXcXJyQkuXLiA58+fS9cSUllih8ePH0vjSOTKZrNIJpP44YcfVP08Ho/j3Llz2NzchN/vRz6fx9DQkKk9YjIX5ubmVH31rbfekvynr69PkkE+FvK5trGxgWAwqCuDvF/5HJTrFYlEpPbv3r2LCxcuYHt7G6VSSYotRLZsNqvp24eHh5ienm6pn8vlkEwmpfoLCwuYnp5um9d7e3vw+XwoFApYW1tDOp1GLBYztq1hKJdBToa3t7fX8u+lpSXTv+XXav3WasNsHafkN9OWVn21Olr6al2rpa8Z+c3Y2Yz9tfqi1UXLVlp/N1PHjC5a9qHVRatM3oaWrMr2ldcq69D6v1072CERdcJXrchgdXxpfdvJuaAFdo6YgYGBoctg54gZGBgYugwWiBkYGBi6DBaIGRgYGLoMFogZGBgYugwWiBkYGBi6DBaIGRgYGLoMFogZGBgYugwWiBkYGBi6DBaIGRgYGLoMFogZGBgYugwWiBkYGBi6DBaIGRgYGLoMFogZGBgYuoz/A4CkHIefx1MaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree_depth = 9\n",
    "dtc = tree.DecisionTreeClassifier(max_depth=tree_depth)\n",
    "dtc = dtc.fit(X, y_c)\n",
    "tree.plot_tree(dtc) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_dt = tree.DecisionTreeClassifier(max_depth = 5, min_samples_leaf = 10)\n",
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "plot_classification_tree(ax, X, y_c, model_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.8879935535858179\n",
      "Precision: 0.8234126984126984\n",
      "Recall:    0.8924731182795699\n"
     ]
    }
   ],
   "source": [
    "y_pred = model_dt.predict(X_test)\n",
    "print(\"Accuracy: \",metrics.accuracy_score(y_test, y_pred))\n",
    "print(\"Precision:\",metrics.precision_score(y_test, y_pred))\n",
    "print(\"Recall:   \",metrics.recall_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0   1]\n",
      " [950 842]]\n"
     ]
    }
   ],
   "source": [
    "y_decision = model_dt.predict(X_cand)\n",
    "unique_elements, counts_elements = np.unique(y_decision, return_counts=True)\n",
    "print(np.asarray((unique_elements, counts_elements)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.895245769540693\n",
      "Precision: 0.8252427184466019\n",
      "Recall:    0.9139784946236559\n"
     ]
    }
   ],
   "source": [
    "exo_r = RandomForestClassifier(n_estimators=300, max_depth=6, random_state=0)\n",
    "X_rf, y_rf = make_classification(n_samples=2000, n_features=8, shuffle=False)\n",
    "exo_r.fit(X_train,y_train)\n",
    "\n",
    "y_pred=exo_r.predict(X_test)\n",
    "print(\"Accuracy: \",metrics.accuracy_score(y_test, y_pred))\n",
    "print(\"Precision:\",metrics.precision_score(y_test, y_pred))\n",
    "print(\"Recall:   \",metrics.recall_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.9331184528605962\n",
      "Precision: 0.8804780876494024\n",
      "Recall:    0.9505376344086022\n"
     ]
    }
   ],
   "source": [
    "exo_r.fit(X_test,y_test)\n",
    "\n",
    "y_pred=exo_r.predict(X_test)\n",
    "print(\"Accuracy: \",metrics.accuracy_score(y_test, y_pred))\n",
    "print(\"Precision:\",metrics.precision_score(y_test, y_pred))\n",
    "print(\"Recall:   \",metrics.recall_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0   1]\n",
      " [950 842]]\n"
     ]
    }
   ],
   "source": [
    "y_decision = model_dt.predict(X_cand)\n",
    "unique_elements, counts_elements = np.unique(y_decision, return_counts=True)\n",
    "print(np.asarray((unique_elements, counts_elements)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[   0    1]\n",
      " [1013  779]]\n"
     ]
    }
   ],
   "source": [
    "y_random = exo_r.predict(X_cand)\n",
    "unique_elements, counts_elements = np.unique(y_random, return_counts=True)\n",
    "print(np.asarray((unique_elements, counts_elements)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([882.,   0.,   0.,   0.,   0., 199.,   0.,   0.,   0., 711.]),\n",
       " array([0. , 0.2, 0.4, 0.6, 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_likely = y_decision + y_random\n",
    "plt.hist (y_likely)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>kepid</th>\n",
       "      <th>decision_tree</th>\n",
       "      <th>random_forest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11138155</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11818800</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11918099</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9579641</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3246984</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1787</th>\n",
       "      <td>8488381</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1788</th>\n",
       "      <td>8489260</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1789</th>\n",
       "      <td>11911561</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1790</th>\n",
       "      <td>8765560</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1791</th>\n",
       "      <td>10128825</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1792 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         kepid  decision_tree  random_forest\n",
       "0     11138155              1              1\n",
       "1     11818800              1              1\n",
       "2     11918099              0              0\n",
       "3      9579641              0              0\n",
       "4      3246984              0              0\n",
       "...        ...            ...            ...\n",
       "1787   8488381              0              0\n",
       "1788   8489260              0              0\n",
       "1789  11911561              0              0\n",
       "1790   8765560              0              0\n",
       "1791  10128825              0              0\n",
       "\n",
       "[1792 rows x 3 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "possibles = pd.DataFrame({'kepid': np_cand, 'decision_tree': y_decision, 'random_forest': y_random},\n",
    "                         columns=['kepid', 'decision_tree', 'random_forest'])\n",
    "possibles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
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 "nbformat_minor": 4
}