{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# All packages used in this notebook are imported here.\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn import (\n",
    "    linear_model, metrics, neural_network, pipeline, preprocessing, model_selection, tree\n",
    ")\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import cross_val_score, cross_val_predict\n",
    "import plotly\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "from scipy import stats\n",
    "import statsmodels as sm\n",
    "import statsmodels.formula.api as smf\n",
    "from statsmodels.iolib.summary2 import summary_col\n",
    "from patsy.builtins import *\n",
    "from patsy import dmatrices\n",
    "\n",
    "#Jellyfish is not included in syzygy. Uncomment if necessary.\n",
    "#!pip install jellyfish\n",
    "import jellyfish as jf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prep data for prediction\n",
    "def prep_data(df, continuous_variables, categories, y_var, test_size=0.15):\n",
    "\n",
    "    ohe = preprocessing.OneHotEncoder(sparse=False)\n",
    "\n",
    "    y = df[y_var].values\n",
    "    X = np.zeros((y.size, 0))\n",
    "\n",
    "    # Add continuous variables if exist\n",
    "    if len(continuous_variables) > 0:\n",
    "        X = np.hstack([X, df[continuous_variables].values])\n",
    "\n",
    "    if len(categories) > 0:\n",
    "        X = np.hstack([X, ohe.fit_transform(df[categories])])\n",
    "\n",
    "    X_train, X_test, y_train, y_test = model_selection.train_test_split(\n",
    "        X, y, test_size=test_size, random_state=42\n",
    "    )\n",
    "\n",
    "    return X_train, X_test, y_train, y_test\n",
    "\n",
    "# This function will allow us to compare the MSE's of each model\n",
    "def fit_and_report_mses(mod, X_train, X_test, y_train, y_test):\n",
    "    mod.fit(X_train, y_train)\n",
    "    return dict(\n",
    "        mse_train=metrics.mean_squared_error(y_train, mod.predict(X_train)),\n",
    "        mse_test=metrics.mean_squared_error(y_test, mod.predict(X_test))\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Authoring Judge</th>\n",
       "      <th>Court Location</th>\n",
       "      <th>Number of Judges</th>\n",
       "      <th>Circuit</th>\n",
       "      <th>Year</th>\n",
       "      <th>Decision Ideology</th>\n",
       "      <th>Case Type</th>\n",
       "      <th>Case Category</th>\n",
       "      <th>Year of Appointment</th>\n",
       "      <th>Appointing President</th>\n",
       "      <th>Judge Party</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Race</th>\n",
       "      <th>Year of Birth</th>\n",
       "      <th>ABA Rating</th>\n",
       "      <th>Congress</th>\n",
       "      <th>Unity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Avis, John Boyd</td>\n",
       "      <td>Camden, NJ</td>\n",
       "      <td>1</td>\n",
       "      <td>3RD CIRCUIT</td>\n",
       "      <td>1932</td>\n",
       "      <td>100</td>\n",
       "      <td>alien petitions</td>\n",
       "      <td>Civil Liberties/Rights Cases</td>\n",
       "      <td>1929</td>\n",
       "      <td>HOOVER</td>\n",
       "      <td>Republican</td>\n",
       "      <td>male</td>\n",
       "      <td>white/caucasian</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>78TH</td>\n",
       "      <td>Unified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Avis, John Boyd</td>\n",
       "      <td>Camden, NJ</td>\n",
       "      <td>1</td>\n",
       "      <td>3RD CIRCUIT</td>\n",
       "      <td>1932</td>\n",
       "      <td>100</td>\n",
       "      <td>alien petitions</td>\n",
       "      <td>Civil Liberties/Rights Cases</td>\n",
       "      <td>1929</td>\n",
       "      <td>HOOVER</td>\n",
       "      <td>Republican</td>\n",
       "      <td>male</td>\n",
       "      <td>white/caucasian</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>78TH</td>\n",
       "      <td>Unified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Strum, Louie W.</td>\n",
       "      <td>Jacksonville, FL</td>\n",
       "      <td>1</td>\n",
       "      <td>5TH CIRCUIT</td>\n",
       "      <td>1932</td>\n",
       "      <td>100</td>\n",
       "      <td>(non)conv-criminal case</td>\n",
       "      <td>Criminal Justice Cases</td>\n",
       "      <td>1931</td>\n",
       "      <td>HOOVER</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>male</td>\n",
       "      <td>white/caucasian</td>\n",
       "      <td>1890.0</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>81ST</td>\n",
       "      <td>Divided, House</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Moscowitz, Grover</td>\n",
       "      <td>Brooklyn, NY</td>\n",
       "      <td>5</td>\n",
       "      <td>2ND CIRCUIT</td>\n",
       "      <td>1932</td>\n",
       "      <td>100</td>\n",
       "      <td>criminal court motions</td>\n",
       "      <td>Criminal Justice Cases</td>\n",
       "      <td>1925</td>\n",
       "      <td>COOLIDGE</td>\n",
       "      <td>Republican</td>\n",
       "      <td>male</td>\n",
       "      <td>white/caucasian</td>\n",
       "      <td>1886.0</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>80TH</td>\n",
       "      <td>Unified</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cochran, Andrew</td>\n",
       "      <td>Maysville, KY</td>\n",
       "      <td>1</td>\n",
       "      <td>6TH CIRCUIT</td>\n",
       "      <td>1932</td>\n",
       "      <td>-100</td>\n",
       "      <td>voting rights</td>\n",
       "      <td>Civil Liberties/Rights Cases</td>\n",
       "      <td>1901</td>\n",
       "      <td>MCKINLEY</td>\n",
       "      <td>Republican</td>\n",
       "      <td>male</td>\n",
       "      <td>white/caucasian</td>\n",
       "      <td>1854.0</td>\n",
       "      <td>Not Rated</td>\n",
       "      <td>73RD</td>\n",
       "      <td>Unified</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Authoring Judge    Court Location  Number of Judges      Circuit  Year  \\\n",
       "0    Avis, John Boyd        Camden, NJ                 1  3RD CIRCUIT  1932   \n",
       "1    Avis, John Boyd        Camden, NJ                 1  3RD CIRCUIT  1932   \n",
       "2    Strum, Louie W.  Jacksonville, FL                 1  5TH CIRCUIT  1932   \n",
       "3  Moscowitz, Grover      Brooklyn, NY                 5  2ND CIRCUIT  1932   \n",
       "4    Cochran, Andrew     Maysville, KY                 1  6TH CIRCUIT  1932   \n",
       "\n",
       "   Decision Ideology                Case Type                 Case Category  \\\n",
       "0                100          alien petitions  Civil Liberties/Rights Cases   \n",
       "1                100          alien petitions  Civil Liberties/Rights Cases   \n",
       "2                100  (non)conv-criminal case        Criminal Justice Cases   \n",
       "3                100   criminal court motions        Criminal Justice Cases   \n",
       "4               -100            voting rights  Civil Liberties/Rights Cases   \n",
       "\n",
       "   Year of Appointment Appointing President Judge Party Gender  \\\n",
       "0                 1929               HOOVER  Republican   male   \n",
       "1                 1929               HOOVER  Republican   male   \n",
       "2                 1931               HOOVER    Democrat   male   \n",
       "3                 1925             COOLIDGE  Republican   male   \n",
       "4                 1901             MCKINLEY  Republican   male   \n",
       "\n",
       "              Race  Year of Birth ABA Rating Congress           Unity  \n",
       "0  white/caucasian         1875.0  Not Rated     78TH         Unified  \n",
       "1  white/caucasian         1875.0  Not Rated     78TH         Unified  \n",
       "2  white/caucasian         1890.0  Not Rated     81ST  Divided, House  \n",
       "3  white/caucasian         1886.0  Not Rated     80TH         Unified  \n",
       "4  white/caucasian         1854.0  Not Rated     73RD         Unified  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decision_data = pd.read_csv('Carp Manning with Attributes.csv')\n",
    "\n",
    "# We only need some of this data\n",
    "judge_decisions = decision_data[['Authoring Judge','Court Location','Number of Judges','Circuit','Year','Decision Ideology',\n",
    "                                'Case Type','Case Category','Year of Appointment','Appointing President','Judge Party',\n",
    "                                'Gender','Race','Year of Birth','ABA Rating','Congress','Unity']].copy()\n",
    "\n",
    "# We begin by converting the decision ideology, which is either liberal or conservative, into a dummy.\n",
    "def libcon_dummy(ideology):\n",
    "    if ideology == 'Conservative':\n",
    "        return 100\n",
    "    if ideology == 'Liberal':\n",
    "        return -100\n",
    "    else:\n",
    "        return np.nan\n",
    "judge_decisions['Decision Ideology'] = judge_decisions['Decision Ideology'].apply(libcon_dummy)\n",
    "\n",
    "# Here is what the data looks like\n",
    "judge_decisions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1974eceeb8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x612 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the list of case types with more than 1000 cases\n",
    "high_vol_types = judge_decisions[['Case Type','Authoring Judge']].groupby('Case Type').count().reset_index()\n",
    "high_vol_types['High Vol'] = [1 if count >= 1000 else 0 for count in high_vol_types['Authoring Judge']]\n",
    "high_vol_types = high_vol_types[high_vol_types['High Vol'] == 1]\n",
    "high_vol_types = high_vol_types.drop(columns = ['Authoring Judge','High Vol'])\n",
    "\n",
    "ideology_by_type = (judge_decisions[['Case Type','Decision Ideology']].groupby('Case Type')\n",
    "                    .mean().sort_values('Decision Ideology').reset_index())\n",
    "ideology_by_type = ideology_by_type.merge(high_vol_types, on = 'Case Type', how = 'right')\n",
    "\n",
    "plt.style.use(\"fivethirtyeight\")\n",
    "fig, ax = plt.subplots(figsize=(11,8.5))\n",
    "ax.set_facecolor('white')\n",
    "ax.grid(False)\n",
    "ax.set_title(\"Average Ideological Result by Case Type\")\n",
    "ax.set_xlabel(\"Average Ideological Score\")\n",
    "colors = [\"b\" if x < 0 else \"r\" for x in ideology_by_type['Decision Ideology'].values]\n",
    "\n",
    "ideology_by_type.plot(kind = 'barh', x = 'Case Type', y = 'Decision Ideology', ax = ax, color = colors, legend = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Mean Decision Ideology Score')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x612 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ideology_by_party_year = (judge_decisions[['Year','Judge Party','Decision Ideology']].groupby(['Year','Judge Party',])\n",
    "                          .mean().reset_index())\n",
    "ideology_by_party_year = ideology_by_party_year[(ideology_by_party_year['Judge Party'] == 'Democrat') |\n",
    "                                                (ideology_by_party_year['Judge Party'] == 'Republican')]\n",
    "# 1933 is first year where both parties are present\n",
    "ideology_by_party_year = ideology_by_party_year[ideology_by_party_year['Year'] >= 1933]\n",
    "democrats = ideology_by_party_year[\"Judge Party\"] == \"Democrat\"\n",
    "republicans = ideology_by_party_year[\"Judge Party\"] == \"Republican\"\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(11,8.5))\n",
    "ax.set_facecolor('white')\n",
    "ax.grid(False)\n",
    "\n",
    "ax.plot(ideology_by_party_year[republicans][\"Year\"],\n",
    "        ideology_by_party_year[republicans][\"Decision Ideology\"],\"-o\",label=\"Republicans\",color=\"#f23417\")\n",
    "\n",
    "ax.plot(ideology_by_party_year[democrats][\"Year\"],\n",
    "        ideology_by_party_year[democrats][\"Decision Ideology\"],\"-o\",label=\"Democrats\",color=\"#0e44f5\")\n",
    "\n",
    "ax.legend()\n",
    "ax.set_title(\"Decision Ideology By Judge Party and Time\")\n",
    "ax.set_xlabel(\"Year\")\n",
    "ax.set_ylabel(\"Mean Decision Ideology Score\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x190784b6a0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x612 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ideology_by_app_pres = (judge_decisions[judge_decisions['Year of Appointment']>=1933]\n",
    "                        [['Appointing President','Decision Ideology']]\n",
    "                        .groupby(['Appointing President'])\n",
    "                        .mean().reset_index().reindex([9,12,2,0,10,1,5,8,6,7,3,11,4]))\n",
    "fig, ax = plt.subplots(figsize=(11,8.5))\n",
    "ax.set_facecolor('white')\n",
    "ax.grid(False)\n",
    "ax.set_title(\"Average Ideological Result by Appointing President\")\n",
    "ax.set_xlabel(\"Average Ideological Score\")\n",
    "colors = [\"b\" if x in ['F. ROOSEVELT','TRUMAN','KENNEDY','JOHNSON','CARTER','CLINTON','OBAMA'] \n",
    "          else \"r\" for x in ideology_by_app_pres['Appointing President'].values]\n",
    "\n",
    "ideology_by_app_pres.plot(kind = 'barh', x = 'Appointing President', y = 'Decision Ideology',\n",
    "                          ax = ax, color = colors, legend = False)"
   ]
  },
  {
   "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>Gender</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Race</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>African-American/black</th>\n",
       "      <td>1.870748</td>\n",
       "      <td>5.016212</td>\n",
       "      <td>4.439944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asian-American</th>\n",
       "      <td>-14.754098</td>\n",
       "      <td>5.413105</td>\n",
       "      <td>0.211416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latino/Hispanic</th>\n",
       "      <td>5.091650</td>\n",
       "      <td>26.355388</td>\n",
       "      <td>24.271457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white/caucasian</th>\n",
       "      <td>12.628256</td>\n",
       "      <td>15.822735</td>\n",
       "      <td>15.497959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>10.976939</td>\n",
       "      <td>15.695166</td>\n",
       "      <td>15.190996</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Gender                     female       male        All\n",
       "Race                                                   \n",
       "African-American/black   1.870748   5.016212   4.439944\n",
       "Asian-American         -14.754098   5.413105   0.211416\n",
       "Latino/Hispanic          5.091650  26.355388  24.271457\n",
       "white/caucasian         12.628256  15.822735  15.497959\n",
       "All                     10.976939  15.695166  15.190996"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ideology_by_gen_race = (judge_decisions[judge_decisions['Race'].isin(\n",
    "                                        ['African-American/black','Asian-American','Latino/Hispanic','white/caucasian'])]\n",
    "                       .pivot_table(index='Race', columns='Gender', values='Decision Ideology', margins=True))\n",
    "ideology_by_gen_race"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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t1THDbN++fYSFhZEnTx6r8id91pkyZcLPz4/169dz4MABvL29rdY5Z84csmXL9sqNFJaclIojiSooKAgHBwf8/f0tZeYDmblj2KPM082ePduq/ODBg+zcuZOKFStafuhiYmKYNGkS7u7uVkE9QKpUqRgwYABAvD33BwwYECeoB8iZM6fNH+natWtTsGBBS3BkZk5t6devnyWoB3BxceGLL76wue4JEyaQOnVqxowZYxXUA/Tq1euJlzIT4v79+yxcuBBnZ2e+/PJLq9fefPNNmjZtGmeeK1euMG/ePN566604gUO6dOn4+uuvAev30xwY9OjRw+qHwdHRkQEDBpAuXTpmzZpFTEzME+trXs53331n1cKXNm1aBg8eDBiXk82WL1/OzZs3ef/99+OcAPbo0SPB/TcePHjA1KlTSZcuHWPGjImzPzg5OVltl3mUmsflzZuXBg0acODAAc6fP5+gdT+JyWRiyJAhDBkyhIEDB9K+fXuqVq3K3bt3La1vj5oyZQpRUVEMGTIkzg90lSpVqFmzJjt37rSkjZg7a9ra/52cnCzB0sumWrVqcVo4O3XqhKenJ2vXruXixYuA0dm0bdu23L9/n5kzZ1pNHxQURExMzDO11p85c4a1a9eSL18+SyCXIUMGGjZsyLVr11i6dGm8827atIlhw4YxbNgwRowYwfTp07l8+TJ16tShcuXKNucxHxsfDfrMVyqfNx1n2bJlln2qZ8+e+Pj4sHHjRooUKWLzOJUnTx6bAXLbtm3JkCFDnOPgfzVs2DCrY6Gbmxt16tQhMjKSY8eOWcrNvw1ffPGF1X6aJk0a+vXr98zrzZs3b5ygHrB0VI5vO5s3b24V1MPDVuhdu3ZZyk6dOsWmTZvw8vKKk/r17rvv2jypSogTJ05YPs9vvvmGxo0b0717d8Bo3Hj0N+zxoB6MY7r56vKmTZtsrqNbt25xgvqEMDekmVvnzRYtWkRERAQBAQHK/09CarGXRLNlyxYOHTpExYoVrQ4s9erVI1OmTCxevJjBgweTOXNmy2v169cnU6ZMzJ8/n/79+1taacwH80dPEA4dOkRERAT58+dn+PDhNuuQLl06/v333zjlr732GkWKFLE5T2xsLHPmzGH27Nn8888/REZGWgWmj4+q8vfff+Po6IiPj0+cZdlqEbx58yb79u0je/bs8fYnSJs2LRcuXCAyMtLq/UmogwcPcu/ePd58802b81esWDHOCU94eLhlOx9t4TeLiooCsHo/9+zZA2AzQHF3d+eNN95gz549HD169IkdV/fu3YujoyNvv/12nNdKlixJlixZOHToEHfv3iV9+vTs3bsXsP3+Ojs7U7Ro0QS1nB88eJCbN29SunRpcuXK9dTpwbhK8fPPP/PXX39x+fJly/tidv78eatUrecRGxtr8yrH999/T7du3eKUb9++HYDNmzfbHE726tWrgPHZFSxYkEqVKuHp6cnIkSPZuXMnNWrUwMfHhxIlSlilO71sKlasGKcsTZo0lC1bliVLlvD333/j5uYGGIHwgAEDCAwM5LPPPsPBwcHSSp0xY0abaQLxCQoKwmQy4e/vbxUEBgQEMHPmTAIDA3n//fdtzvvVV19ZOs+aTCYuXLjAsmXL+Prrr1m3bh3r16+3Cp7MnWadnZ1p0KCBpdzLy4tKlSoRFhb2XJ1oly9fzvLly63K3nrrLZYuXWoz5e/Bgwf8+uuvLF682PI9MZlMltcT4wTWLFu2bDZHy/H09ASwyuk3f/dtBcTlypWLd4Sh+Ny6dYuJEyeyYsUKjh49yq1bt6zy+uPbTnN63tPqaz5Gli9f3uaJUsWKFZ/rKt/Jkyctx4hUqVKRLVs2atWqRfv27eOc/J48eZKxY8eyceNGzp49y927d61ej28bH72i/ixq165N7ty5mTt3Lt9//73lN9OcXqY0nKT18h7RJcUxtyw9GoyD0UrYtGlTpk2bxrx58/jkk0+sXmvcuDFBQUGsWbOGunXrYjKZmDdvHhkzZrS6fHft2jXA6JT0pFQPWzmXrq6u8U7fq1cvJk+ejIeHh6WTnrl1YebMmXEOgjdv3iRbtmw2A6IcOXLEKbt+/TpgtJA/LUXl9u3bzxXY37hxA4h/O22Vm9/PnTt3xts501ynhK7H3d2dPXv2WKaLz82bN8mSJYvVFY/Hl3P9+nVu3rxJ+vTpLZ2L41uvrffdlsjISODhD/LT/P7773z88cekT5+eqlWrkjdvXjJkyICDgwNhYWFs3br1iWObJ1SqVKkswfidO3f466+/6N69O99//z158+aNcxnb/Nk9bWQT82eXOXNmQkJCGDp0KKtWrbK0Srq4uNC6dWv69u1rszU/ucX3uZr3g0f3s8yZM9OsWTOmTZtGWFgYVapUYfXq1Zw5c4aPPvoowVd1zJ1mHR0drdIAASpUqMDrr7/Oli1b+Pfff5866pKjoyOenp506NCB8+fPM3r0aEaPHs2oUaMs08yfP5/bt2/TunXrOI0IAQEBhIWFERgY+MyB/aRJk2jevDkxMTGcOnWKH374gVmzZtGhQwdmzJgRp9W6devWBAcHky9fPurVq4erq6vl+zlhwoRE2c/N4vsszMfURxtWzN99W/tC6tSpcXFxsXyvnyYqKop69eqxe/duihQpQpMmTciePTtOTk6YTCZ++OGHeLfTVp1t1fd5jsUJUaVKFZYsWfLU6Y4dO0a1atW4ceMGFSpUoFq1amTKlIlUqVJx4sQJ5s6dG+82Pm/dHB0d+fjjj/n+++9ZsGABH3zwAfv27WPHjh1Uq1bN5hUEeXEU2EuiuH79uuWg07lzZ0tu9OOmTZtmFdiDcSIQFBTErFmzqFu3LuvXr+f8+fP4+/uTIUMGy3TmA2u9evUsqRwJZevSKxijyEyZMoVixYrZ7LxqK63H2dmZ69evEx0dHSe4v3z5cpzpzfUuUaIEYWFhz1TvhDKv49KlSzZft1VunqdTp06W9JdnWY+tVr8LFy5YTRcfZ2dnIiIiiIqKshncm5djTtMx/49v+2y977aYT5oS2vo4aNAg0qVLR2hoKAULFrR67ezZsy8kv/61116jUqVKLFy4kPLly9OlSxcqVKhgFdiY399Tp04lOGD19PRk7NixxMbGcujQITZv3szUqVMZN24cN2/eZPTo0cDD70p8nRITGkQlhvg+V/N+8Pi2t2vXjmnTpjF16lSqVKliGaHrWVoMg4ODOXfuHECcFKhHBQYGJvh7A1ju5fH4SbS5jr/99lu89yv4L51oU6VKRb58+Rg/fjwXL15kxYoVBAYGWr0nf/75J8HBwVSvXp25c+daHddiYmL48ccfn3m9icX83b98+bLV7wEYVxkiIiLiPb4/btmyZezevZvWrVvHGRTgzJkziXI32Oc5FiemcePGERERYTmxe9ScOXOeeJOphL6PtnzwwQeWgTI++OADS3qZWuuTnnLsJVHMmjWL+/fvU7x4cVq3bm3zL1euXOzfvz/OMIE+Pj4UKFCANWvWcO3aNZtpOGB0ZDSPJBPfcIjP6vjx48TGxlK9evU4geqpU6c4depUnHmKFy+OyWSypEM8ytZwey4uLhQqVIiDBw9aWu8TW+HChUmXLp0llehxW7ZsiVNWpkwZy2gHCVWyZEkAmzmaFy9e5NChQzg7O5M/f/4nLqdEiRKYTCab9dq7dy/Xr1+ncOHCllZk80gitt5fc6pTQpj3oX379sU7lOmjjh8/jre3d5ygPiYmxubnn5jy5s1Lly5duHHjRpxUqXLlygE814mFg4MDhQsXpl27dqxatYrUqVNbpWyY85htvT8RERFW+c9P4+joSGxsbLzDpj6Nrf0jKiqK8PBwHBwc4gTeRYsWxdfXl5UrVxIeHk5ISAhlypSx7LcJYb7yWLNmzXiPZalSpWLOnDnP1IptTtd49L3YuXMne/fuxc3NLd51vfnmm0RFRTFr1qwEr8sWBwcHhg4dipOTE4MGDbIaYtf8mdatWzdOY8WOHTvipJ/Bw34bT+tP81+Zv/u29vUdO3ZYpQs9jXk7H015MrO1rz0P8762bds2m+9NYq0nPi9iGxPyWWfLlo1GjRqxa9cutmzZwrx58/D09NQwl8lAgb0kCvOP4bBhwxg3bpzNP3PnpPg60UZFRfHrr7+yYsUKvLy84uTXpk6dmvbt23PhwgV69eoVJ28QjHSXv//+O8H1Nue6bt26Nc7l3+7du9v80TBfnh84cKDVD15kZCQjR460uZ7PPvuMqKgoOnfuHGdMaDAu39rKlU6otGnT0rRpU27evMnQoUOtXtu1axcLFy6MM4+7uzvvv/8+u3btYvjw4TZbaM+cOWM1brd5fO0ffvjBquUpNjaWb775hnv37uHv7//UUSrM42l///33Vqk+UVFRlg5x5mnAuErj7OzM/Pnz43y+I0eOfGrqj5mTkxMff/wxd+/e5X//+1+ccatjYmIsHTIBcufOzeHDh63KYmNjGTx48HONZ/6sunTpQpYsWfjtt984fvy4pbx9+/akTp2avn372qxHdHS01cnXvn37OHnyZJzprl27RnR0tFUnxsKFC+Ps7MyyZcusWswfPHhAr169nimYzZYtGyaTKUEnUbasX7+edevWWZVNmDCBs2fP8s4771jy6x/Vrl07Hjx4QKtWrTCZTM/UYnj69GlCQkLInDkzQUFB8R7LatSowbVr1xKUGgHG6CC//vorgFW/EvOxsEOHDvGuy9xa/qT7SiRUwYIFadmyJVeuXGHChAmWcvNxcPPmzVbTX7p0Kc7Qk2bmqwdnzpz5T3V6mpYtWwLG9/zRRouoqCgGDhz4TMsyb+fjDRPHjx//TyOSPb6OSpUqcfLkScu9UsxWrFjxwkbRenT9EHcb16xZ89wnhwn9rM2daNu2bcuNGzdo3br1S92Hx17pHZf/7NF808dHDXhUixYt+O677/j9998ZMmSIVS55ixYtGDhwIMOHD+fBgwdWY9c/qnfv3uzfv59p06YRHBxs6RR49epVjh8/zrZt2/j000+feAn9UTlz5qRhw4YsWbKEypUrU7VqVW7cuMGGDRvIkCEDRYoUiXMDklatWrF48WLLkJd16tThwYMHLFu2jDfffJMjR47E6dD1wQcfsHfvXqZMmUKpUqWoXr06uXPnJjIykpMnT/LHH39Qs2ZNq5FgnpV5HP+JEyeye/duyzj2ixcvpkaNGqxatSrOPCNGjOD48eMMHjyYOXPm4Ovri6urKxcuXODIkSOEh4czbNgwS4t1hQoV6Nq1K2PHjsXX15dGjRqRMWNGNmzYwN69eylWrFiCRqpo1qwZwcHBLF68GB8fH+rVq0eqVKkIDg7m6NGjVKtWjQ4dOlimd3FxYfjw4XTq1IlatWpZjWO/f/9+fH192bp1a4I60vXp04fdu3ezZs0a3nrrLcv49OfOnWPTpk20adPG0vmxU6dO9OzZk0qVKtGgQQNSpUrF1q1bOXr0KLVq1WL16tUJ/XieS6ZMmejWrRvfffcdgwcPZvLkyYARfP/000906dIFX19fqlevToECBYiOjubMmTNs374dk8lkab1bv34933zzDeXKlaNQoULkyJGDCxcusHLlSmJjYy2ja4BxktipUyeGDRtGpUqVqFevHmAMO5gqVSqb34n4VKtWjT179hAQEMA777xDunTp8PLySnBH1jp16tCiRQvLTd12797N+vXryZ49e7wd6Bs0aICbmxsXLlzAxcWFJk2aJGhdYIzEZDKZaNas2RP7HHz00UcEBwcTGBgYZ1seHe7SZDJx6dIl1q5dy7lz53j99df5/PPPAeNkftGiRTg5OT1xCMRSpUpRqlQpdu/ezaZNm+IdWSehevXqxdy5cxk/fjyffPIJWbNmpVy5cpQtW5bFixdz7tw5fHx8uHjxImvXrsXb29tm7nXVqlVxdHTkp59+4sqVK5ZUsU8//TRBY9knVJUqVWjVqhUzZszA19eX+vXrkzp1aoKDg8mSJQuurq4JvhJat25dvLy8GDt2LPv27aNYsWKcPn2a1atXU6tWLRYtWpQodR45ciQ1a9akT58+bNiwgWLFinHs2DGWL19O7dq1CQ4OTpT12NKuXTvmzJlD69atadiwIW5ubhw4cIB169bRuHFcLbkmAAAgAElEQVTj59rGhH7WZcuWpWTJkuzZs4dUqVLx4YcfJtp2ScKpxV7+M3OO6AcffPDE6VxcXGjQoAF37tyJk+dnvtvpgwcP4oxd/6jUqVMzc+ZMfvnlF9544w3Wrl3L+PHjWbt2Lffv3+eLL754pmHtwGgB/N///sedO3eYMmUKGzZsoE6dOqxevdpm/rKjoyOzZs2iR48eREVF8csvv7Bq1SoCAgIsreW25hsxYgRz587Fx8eHsLAwfvrpJ5YvX87Vq1dp3759nNvQP6scOXKwdu1a/P39+ffff5k4cSL//PMPo0ePtgqSH5UpUyZWrlzJiBEjcHV1ZdmyZYwfP56wsDBSp07Nd999F+eSbv/+/Zk6dSqFChVi3rx5/Pzzz9y7d4+ePXsSHBycoB91BwcHfv31V0aOHEmOHDmYPn06U6dOJX369AwYMCBOni8YLXezZ8+maNGiLF68mKlTp5I5c2bWrl1r6XSYkHzztGnTsmDBAkaMGIGnpyfz5s1j4sSJbNu2jbfffptatWpZpv3kk08YN24cOXLkYObMmcyfP588efIQEhKS4JPH/6p9+/a4ubmxYMEC/vnnH0t58+bNCQ0NpXnz5hw4cIDJkycze/Zsjhw5Qq1atfj5558t077zzjt8+umn3L9/n5UrV/LTTz8RFhZmuRfD4/d9+PLLL+nfvz+vvfYaQUFBLF++nCpVqhAcHJzgnH4wxib/5JNPuHr1KmPGjGHQoEFxhqN8kkaNGhEUFMTJkyctIxM1atSIkJCQeDvkpU6d2hJst2zZMsGdgs2dZoGnBiQ1atQgZ86c/PHHH3FOch4d7vKHH35gwYIFZM2alZ49e7JhwwZL66e502zt2rVtXnl4lLk+5mPtf5E7d24++ugjbty4YenEa04tatOmDWfPnmXSpEns2LGDNm3asGDBAputrm+88Qa//PIL+fLl47fffmPQoEEMGjQowVfPnsXYsWMZMGAAr732GtOmTWPhwoX4+fmxYMECbt68meB90tnZmeXLl/Pee++xb98+Jk2axP79+/nyyy/jHbHseRQqVIiQkBDq16/Ptm3b+Pnnnzl37hyzZ8+mbt26ibYeW0qWLMnSpUspW7Ysq1evJjAwkNu3bzNz5syn/kbH51k+a/NV3dq1ayd4kAJJXA4RERH/7dqeiFisXbuW999/n+bNmzNp0qTkrs4rITo6mhIlSnD58mVOnz4d5z4B8uqpX78+mzZt4s8//4zTP0Lsx6FDh/Dx8cHX19fmFUlJep07d2bmzJksWrQoSe7KLXGpxV7kOZhHbXnU1atX+f777wF0l70XICIiIk6/CvP47+fOnaNWrVoK6oXw8HA2bdqEn5+fgno7cfHixTj9C27fvk3fvn0BHW9fFqdPn2bBggUULFgQPz+/5K7OK0s59iLPoW/fvuzbt4+yZcuSPXt2zp49y9q1a4mIiKB+/fov/HLrqyg8PJyPP/4YPz8/vLy8uHXrFjt27OCff/4he/bsljsPy6vp559/5sKFC8yePRtHR0e++uqr5K6SJJKJEyeycOFCKlasiLu7OxcvXmTjxo2cO3eOsmXLPnP6pSSu2bNnc/ToURYtWsT9+/fp16/ffxo6U/4bBfYiz6FBgwZcu3aNtWvXcv36ddKkSUPBggVp0aJFnHxlSRwFChSgTp06/Pnnn6xbt46oqCjc3d1p06YNX3zxRYLvJCv2aezYsVy8eJF8+fIxaNCg576Lprx8/Pz8OHDgAJs2beLq1as4ODiQL18+PvroIz777LN4b3QnSSMoKIjt27eTM2dOhgwZoisoyUw59iIiIiIidkA59iIiIiIidkCBvYiIiIiIHVBgLyIiIiJiB175zrOP3v30VRceHq4OZ/JU2k8kIbSfSEJoP5Gn0T5iLTIy8omvq8VeRESS1unT4OcH3t5QtCiMGWOU9+wJhQtDiRLQuDFERBjlUVHQpg0ULw4lS0JoqFF+5w68+64xT9Gi8OWXD9dx/z40bw4FCoCPD5w4kZRbKCKSLBTYi4hI0nJygpEj4cAB2LYNxo+H/fuhRg345x/YuxcKFYIhQ4zpJ082/v/9N6xdC198ASaTUdajBxw8CLt2wZYtYL4D6a+/QpYscOQI/O9/0Lt30m+niEgSU2AvIiJJy8MD3nrLeOzsbLTcnz0LNWsaQT9A+fJw5ozxeP9+qF7deOzqCi4uEB4Or71mtPwDpEljLNM8z5Il8OGHxuP33oN16yBWozuLiH1TYC8iIsnnxAmjtd3Hx7p86lSoU8d4XLKkEahHR8Px4/DXX0Y6z6MiImDZsocnAGfPQu7cxmMnJ8icGa5efaGbIiKS3F75zrPxiYqK4tq1a8TGxr4yt0ZOly4dFy5cSO5qJBvzZ501a1bdyVAkKdy6BU2bwujRkCnTw/JBg4xgPCDAeP7xx0baTpky4OUFFSo8bNkHI+Bv2RK6doXXXzfKbLXOvyLHchF5dSmwtyEqKopLly6ROXPmVyaoB3BwcCBDhgzJXY1kFRsby6VLl3B1dVVwL/IiPXhgBPUBAdCkycPyoCBYvtxInTEff52c4McfH05ToQIULPjwefv2xvPu3R+W5cpltOrnymUE/pGRkDXri90mEZFkplQcG65du/bKBfVicHBwIHPmzFzVJXuRFyc2Ftq2NXLrP//8YXlwMAwbBkuXGvnzZnfuwO3bxuO1a41Av0gR43m/fkbQPnq09ToaNDBOEgAWLIBq1dRiLyJ2Ty32NrxK6TcSlz57kRdsyxb47Tdj+MpSpYyywYONVJr7943RccDoQPvzz3DpEtSqBY6OkDOnMS8YHWUHDTKGuzR3xv3sM2jXzjhxaN3aGO4ya1aYMyfpt1NEJIkpsLdBgZ1oHxB5gd5+23YOfN26tqfPmxcOHYpbnitX/CPdpEsH8+c/dxVFRFIipeKIiIiIiNgBBfYiIiIiInZAqTjPwNk5Y5Ku7+bNW880fYcOHZg1axYATk5OuLi44O3tTaNGjWjTpg2pU6d+EdVMdidPnqRYsWJs3LiRt8x5tiIiIiKvGLXY2xk/Pz+OHDnCvn37WLJkCXXq1GHQoEHUrFmT2+ZRJVKI6OhoYnWnSBEREZEEUWBvZ9KkSYObmxuenp6UKFGCLl26sGrVKvbs2cPo/x8OLioqiq+//po33ngDNzc3qlSpQkhIiGUZmzZtwtnZmTVr1lCpUiVy5MhBzZo1OXv2LJs3b8bX1xd3d3fee+89q2EhTSYTw4YNo3DhwmTLlg0fHx+WL19uVb/z58/Ttm1b8uTJg6urKxUqVCAsLAyAwYMHU65cOWbMmEGJEiXIli0bt2/fZu3atdSsWZPcuXOTJ08eGjVqxMGDBy3LLFasGABVqlTB2dmZOua7VYqIiIi8QhTYvwKKFCnCO++8w5IlSwDo2LEjmzdv5tdff2Xbtm34+/vTrFkz9u3bZzXf4MGDGTp0KBs2bCAiIoIPP/yQoUOHMnbsWFauXMnBgwcZMmSIZfoJEyYwZswY+vfvz7Zt26hfvz4BAQHs3bsXgNu3b1OnTh1OnjzJrFmz2LZtG71797Za58mTJ5k/fz7Tp09n69atpEuXjjt37tC5c2c2bNjAypUryZQpE82bNycqKgqA0NBQABYvXsyRI0eYOXPmi3orRURERF5ayrF/RRQuXJjQ0FCOHTvG/Pnz2bdvH7lz5waM3PwNGzYwffp0ypUrZ5mnX79+VKxYEYC2bdvSo0cPNm3aRKn/H3fa39+f33//3TL92LFj6dq1K82aNbPMv2XLFsaOHcuUKVOYN28eFy9eJCQkhOzZswPwuvn27/8vKiqKyZMn4+rqailr2LCh1TQTJ07E09OT8PBwKlSoYFlW1qxZcXNzS5T3S0RERCSlUWD/ijDfdGvPnj3ExsZStmxZq9fv37/P22+/bVVmTnEBLIF20aJFrcouX74MwI0bNzh//jzly5e3Woavry9r1qwBYO/evRQtWtQSiNuSM2dOq6Ae4NixYwwcOJDw8HCuXLmCyWTCZDJx5syZhG6+iIiIiN1TYP+KOHjwIHnz5sVkMuHg4EBoaGicUXIe76jq5PRw9zDfsOnReRwcHDCZTFbz2Lqxk7ksIR1hX3v0NvL/r1mzZnh6ejJmzBg8PDxwcnKibNmyllQcEREREVGO/Sth//79hISE0KhRI0qUKEFsbCyXLl0if/78Vn8eHh7PvY5MmTLh4eHB1q1brcq3bt1K4cKFAShZsiT79u3jypUrCV7u1atXOXToED169MDPz4/ChQtz69YtoqOjLdOkSZMGgJiYmOeuv4iIiEhKpxZ7OxMVFcXFixcxmUxcuXKF0NBQRowYQalSpejatSsZMmSgefPmfPrppwwePJiSJUty/fp1Nm3ahIeHhyU//nl069aNQYMGkT9/fkqVKsXcuXP5448/2LRpE2C0vP/444+0bNmS7777jpw5c7Jv3z6cnZ2pXLmyzWVmyZKFbNmyERgYSM6cOTl//jz9+vWzupqQI0cO0qdPz7p16/Dy8iJt2rRkzpz5ubdDRP67Mo+l+yUbDZkrIq8QtdjbmQ0bNlCgQAG8vb2pV68eK1eupE+fPqxevZoMGTIARufTVq1a8fXXX1O6dGnef/99tmzZQq5cuf7Tujt27Ei3bt34+uuv8fHxYdmyZZahKwEyZMjAqlWr8PT0pHnz5pQrV47BgwfbTN8xc3R0JCgoiH/++QcfHx8+//xz+vXrR9q0aS3TODk5MXz4cIKCgihYsCAtWrT4T9shIiIikhI5REREvNLNGbZadi9cuEDGjEl7l9mXwe3bty3B/6vu1q1buLu7J3c1Xkrh4eGUKVMmuashL7snnLAnKbXYv9R0PJGn0T5iLTIy8omvq8VeRERERMQOKLAXEREREbEDCuxFREREJGX6+GNwdYVH7r3D7t1QvjyUKgVlysCOHUb5kiVQosTD8s2bH87TqxcULQre3tC1a4pN41NgLyIiIiIp00cfQXCwdVmvXvDtt0aA37+/8RygenXYs8conzoV2rUzyv/4A7Zsgb174Z9/4M8/YePGJN2MxKLAXkRERERSpsqVIWtW6zIHB7hxw3gcGQmensbjjBkfduy/ffvhYwcHuHcPoqLg/n148ADc3JKm/olM49iLiIiIiP0YPRpq1YIePcBkMlrkzRYvhj594NIlWLHCKPP1BT8/8PAwUnA++8xIyUmBUkyL/ahRo/Dz8yN37tzkz5+f5s2bs3//fqtpOnbsiIuLi9XfO++8k0w1FhEREZEkN3Ei/PgjnD5t/G/b9uFrjRvDwYPw++/w9ddG2ZEjcOAAnDkDZ8/C+vUQFpY8df+PUkxgv3nzZtq2bcvq1atZunQpTk5ONGrUiOvXr1tNV7VqVQ4dOmT5mz9/fjLVWERERESSXFAQNGliPH7//YedZx9VuTIcPQpXrhit+OXLG6k6GTNCnTqwbVvS1jmRpJjAftGiRbRq1YoiRYpQtGhRJk2axJUrV9j22BufNm1a3NzcLH9ZsmRJphqLiIiISJLz9HzY+XX9eihY0Hh85MjD0W527jRy6rNlgzx5jOmjo438+o0blYqT1G7duoXJZMLFxcWqfOvWrRQoUIDSpUvTtWtXLl++nEw1fLUNHjyYcuXKPdM0CZlHRERExKJlSyNH/tAhyJULfv0VJk+GL76AkiWhb1/45Rdj2oULjWExS5WCzp1h7lyj4+x770H+/FC8uDFPyZJQv37ybtdzcoiIiEiRA3V+9NFHHD16lNDQUFKlSgXAwoULSZ8+PV5eXpw6dYqBAwdiMpkIDQ0lbdq0Npdz+PDhOGXp0qUjR44cccrd3N0TdyOe4uKFC880fdeuXZk3bx4AqVKlwt3dnerVq9O3b984J0Av2g8//MDy5cvZ+IThoh6f5vbt29y/f5+sj/duTwaXL1/m3r17yV0NkRSrTNmyyV0FAML//DO5qyAikmgKmq8+xCNFjorTt29ftm3bRnBwsCWoB2jatKnlcdGiRSlVqhTFixdn9erVNGjQwOayypQpE6fswoULZMiQIfEr/oyetQ5OTk74+fkxefJkoqOjOXjwIJ06deLOnTtMmzbtqfPfvn070bY7TZo0ODo6PnF5j0/zMrznZrGxsbgn8YlcShEeHm7zeyPyMtK++nLT8USeRvuItcjIyCe+nuJScfr06cPChQtZunQpefPmfeK0Hh4eeHp6cuzYsaSp3EsgTZo0uLm5kTNnTqpXr06TJk1Yt26d5fXIyEi6dOlCvnz58PDwoHbt2uzcudPy+owZM3B3d2flypWUKlWK7NmzU7duXY4fP26ZxlbKjHm+xwUGBuLt7U2OHDlo0aIFV65cibfutpY7c+ZMfHx8yJYtG6+//jodOnSwvDZu3DjKly+Pm5sbhQoVonPnzkRERMSpU2hoKOXKlcPNzY26dety4sSJp7+RIiIiIilMigrse/fuzYIFC1i6dCmFChV66vRXr17l/PnzuKXQmwz8V8ePHyckJITUqVMDRiv0e++9x7lz55g/fz6bN2+mYsWK1KtXjwuPpP3cv3+foUOHMnHiRNatW0dMTAz+/v7EPuPtlU+dOsXcuXOZM2cOS5cu5ejRo3Tq1CnB80+dOpVu3brRqlUrtm7dysKFC/F+pDOLo6Mjw4YNY8eOHUydOpW//vqLnj17Wi3j/v37jBw5kgkTJrBu3ToiIyPp3r37M22HiIiISEqQYlJxevTowdy5c5kxYwYuLi5cvHgRMNI3MmbMyK1btxg6dCgNGjTAzc2NU6dO0b9/f3LkyEG9evWSufZJJyQkBHd3d2JiYiw54kOGDAEgLCyMv//+m+PHj5M+fXoAvv76a1atWsXs2bNp3749ANHR0QwbNgxfX18AJk+eTPHixQkNDcXPzy/Bdbl79y6//PILuXPnBmDMmDHUqlWLI0eOUKBAgafOP2zYMDp16kSXLl0sZW+++ablcefOnS2Pvby8GDBgAC1atGDSpEk4OjpatmXkyJGWE8GuXbvSsWNHTCaTZRoRERERe5BiAvspU6YA0LBhQ6vy3r1706dPH1KlSsX+/fuZM2cOkZGRuLm5UalSJaZNm4azs3NyVDlZVKxYkbFjx3Lv3j0CAwM5duwYHTt2BGDXrl3cuXOHfPnyWc1z7949q1QbR0dHq3y2PHny4OHhwcGDB58psPf09LQE9QBly5bF0dGRQ4cOPTWwv3z5MufOnaNq1arxTrNx40ZGjhzJoUOHuHHjBjExMURFRXHx4kU8PDwAY/jTR6/ueHh48ODBAyIiIl6KTroiIiIiiSXFBPaP5k7bkj59ehYtWpREtXl5pU+fnvz58wPGqDN169Zl2LBh9O3bF5PJhKurK6tXr44zX6ZMmRK8DkdHxzhpOQ8ePPhvFX/M09J+Tp06xXvvvcdHH33EV199RdasWdmzZw9t2rQhKirKMp2Tk/Uu7uDgAIDJZErU+oqIiIgkN+Ui2Lk+ffrw448/cv78eUqVKsWlS5dwdHQkf/78Vn+PDu9pMpn466+/LM9Pnz7N+fPneeONNwDInj07ly5dsgq+//777zjrPnfuHGfOnLE8Dw8Px2QyWZbzJK6urnh6ehIaGmrz9V27dhEVFcXQoUPx8fGhYMGCnD9//qnLFREREbFXCuztXKVKlfD29mb48OH4+flRvnx5WrRowZo1azhx4gTbt29n0KBBbNmyxTKPk5MTX375Jdu3b2fv3r106NABb29vSxrO22+/zfXr1xkxYgTHjh0jKCiI33//Pc6606dPT4cOHdi7dy/bt2+ne/fu1KpVK0H59QA9e/ZkwoQJ/PTTTxw+fJi9e/cyduxYAPLnz4/JZGL8+PGcOHGC+fPnM2HChER4x0RERERSJgX2r4DPPvuM6dOnc/r0aRYuXEiVKlXo0qULb731Fh9++CGHDx+25KSDkZfeo0cPOnTogJ+fHyaTiZkzZ1rSWAoXLszo0aOZNm0avr6+bNiwgR49esRZb548eWjatCnNmjWjXr165M2bl4kTJya43u3atWPkyJEEBgbi4+ND48aNOXDgAADFihVj+PDh/PTTT5QtW5agoCAGDRr0H98pERERkZQrxd55NrFkzpw5TtmFCxfImDFjMtQmed2+fZvFixfTo0cPq+EvX0W3bt3SDarioZuFSIL8f0NAsnvGYXolael4Ik+jfcSa3d2gSkRERF4Bp0+Dnx94e0PRojBmjFF+7RrUqAEFCxr/r19/OE9oKJQqZUxfpYpRduiQUWb+y5QJRo82Xtu9G8qXN8rLlIEdO5J0E+XpypQtazQUvAx/KYACexEREXn5ODnByJFw4ABs2wbjx8P+/TB0KFSvDocPG/+HDjWmj4iATp1g6VLYtw/mzzfK33jDCOB374a//oLXXoPGjY3XevWCb781Xuvf33gukoIpsBcrrVq1euXTcERE5CXg4QFvvWU8dnY2Wu7PnoUlS+DDD43yDz8E8+ANs2ZBkyaQJ4/x3NU17jLXrYP8+cHLy3ju4AA3bhiPIyPB0/PFbY9IEkgx49iLiIjIK+rECdi1C3x84OJFI+gH4/+lS8bjf/+FBw+galW4eRO6dYMPPrBezpw50LLlw+ejR0OtWtCjB5hM8McfSbE1Ii+MWuxteNrNkcT+aR8QEXlJ3LoFTZsaQfiTbqYYHW2k2qxYAatXw4ABRrBvFhVlpOm8//7DsokT4ccfjXz+H3+Etm1f3HaIJAEF9jY4ODgosHuF6bMXEXlJPHhgBPUBAUaaDYCbG5hvSHj+/MOUm1y5oHZtyJABsmeHypVhz56Hy1q1ykjtcXN7WBYU9HC577+vzrOS4imwtyFr1qxERkYqwHsFxcbGEhkZSbZs2ZK7KiIir7bYWKMF3dsbPv/8YXmDBkZADsb/hg2Nxw0bwqZNRsv9nTuwfbsxr9ns2dZpOGDk1G/caDxev94YaUckBVOOvQ1p0qTB1dWVq1evAlhuzGTvrly58kqfzJi33dXVlTRp0iRzbUREXnFbtsBvv0Hx4sZwlACDB8OXX0KzZvDrr0ZHWfPoN97eRot9iRLg6Ajt2kGxYsZrd+7A2rUwaZL1OiZPNnLxo6MhXTr45Zek2z6RF0A3qLJxg6pXlW4CIQmh/UQS5GVpEHmFGytSAh1P5KlelmMJvBTHE92gSkRERETkFaDAXkRERETEDiiwFxERERGxAwrsRURERETsgAJ7ERERERE7oMBeRERERMQOKLAXEREREbEDCuxFREREROyAAnsRERERETugwF5ERERExA44JXcFRERE5NVUpmzZ5K6CITY2uWsgkijUYi8iIiIiYgcU2IuIiIiI2AEF9iIiIiIidkCBvYiIiIiIHVBgLyIiIiJiBxTYi4iIiIjYAQX2IiIiIiJ2QIG9iIiIiIgdUGAvIiIiImIHFNiLiIiIiNgBBfYiIiIiInZAgb2IiIiIiB1QYC8iIiIiYgcU2IuIiIiI2AEF9iIiIiIidkCBvYiIiIiIHVBgLyIiIiJiBxTYi4iIiIjYAQX2IiIiIiJ2QIG9iIiIiIgdUGAvIiIiImIHFNiLiIiIiNiBFBPYjxo1Cj8/P3Lnzk3+/Plp3rw5+/fvt5omNjaWIUOGULhwYdzd3Xn33Xc5cOBAMtVYRERERCTppJjAfvPmzbRt25bVq1ezdOlSnJycaNSoEdevX7dMM2bMGMaPH8+wYcNYv349OXLkoHHjxty8eTMZay4iIiIi8uI5JXcFEmrRokVWzydNmkSePHnYtm0bderUITY2lokTJ9K9e3caNmwIwMSJEylYsCALFiygTZs2yVFtEREREZEkkWJa7B9369YtTCYTLi4uAJw8eZKLFy9SrVo1yzTp06enQoUKbN++PbmqKSIiIiKSJFJMi/3jvvzyS4oXL065cuUAuHjxIgA5cuSwmi5HjhycP38+3uWEh4e/uEqmQHo/JCG0n8jTlEnuCvw/7asvN+0n8jQvyz4CL8d+UrBgwSe+niID+759+7Jt2zaCg4NJlSqV1WsODg5Wz2NjY+OUPapMmZdpl0le4eHhej/kqbSfSEqifVUSQvuJJMTLsJ9ERkY+8fUUl4rTp08fFi5cyNKlS8mbN6+l3M3NDYBLly5ZTX/lypU4rfgiIiIiIvYmRQX2vXv3ZsGCBSxdupRChQpZvebl5YWbmxsbNmywlN27d4+tW7fi4+OT1FUVEREREUlSKSYVp0ePHsydO5cZM2bg4uJiyanPkCEDGTNmxMHBgY4dOzJy5EgKFixIgQIFGDFiBBkyZOC9995L5tqLiIiIiLxYKSawnzJlCoBlKEuz3r1706dPHwC6devG3bt36dmzJxEREZQuXZpFixbh7Oyc5PUVEREREUlKDhEREbHJXYnklDlz5uSuwktDnSIlIbSfSII8YdCCJBX7Sv/Evfy0n8jTvCz7CLwU+4nddZ4VEREREZG4FNiLiIiIiNgBBfYiIiIiInZAgb2IiIiIiB1QYC8iIiIiYgcU2IuIiIiI2AEF9iIiIiIidkCBvYiIiIiIHVBgLyIiIiJiBxTYi4iIiIjYAQX2IiIiIiJ2QIG9iIiIiIgdUGAvIiIiImIHFNiLiIiIiNgBBfYiIiIiInZAgb2IiIiIiB1QYC8iIiIiYgcU2IuIiIiI2AEF9iIiIiIidkCBvYiIiIiIHVBgLyIiIiJiBxTYi4iIiIjYAQX2IiIiIiJ2QIG9iIiIiIgdUGAvIiIiImIHFNiLiIiIiNgBBfYiIiIiInZAgb2IiIiIiB1QYC8iIiIiYgcU2IuIiIiI2AEF9iIiIiIidkCBvYiIiIiIHVBgLyIiIiJiBxTYi4iIiIjYAQX2IiIiIiJ2QIG9iIiIiIgdUGAvIiIiImIHFNiLiIiIiNiBBAf2pUuXZtWqVfG+vmbNGkqXLp0olRIRERERkWeT4MD+2LFj3Lp1K97Xb968yfHjxxOlUiIiIiIi8myeKRXHwcEh3iNFam4AACAASURBVNeOHTtGxowZ/3OFRERERETk2Tk96cU5c+Ywd+5cy/NRo0Yxc+bMONNFRETw999/U6NGjcSvoYiIiIiIPNUTW+wjIyM5efIkJ0+exMHBgatXr1qem/9OnTpFdHQ0/v7+jB49OqnqLcnh9Gnw8wNvbyhaFMaMsX59xAhwcIArV4zn169D48ZQogSUKwf//PNw2jFjoFgxYznab0RERET+sye22Hfo0IEOHToAULRoUYYPH867776bJBWTl5CTE4wcCW+9BTdvQunSUKMGFCliBP1r10KePA+nHzwYSpWCxYvh4EHo3BnWrTMC/MmTYccOSJMGateGd9+FggWTb9tEREREUrgE59jv27dPQf2rzsPDCOoBnJ2NlvuzZ43n//sfDB9utNib7d8P1asbjwsXhhMn4OJFOHAAypeH114zThaqVDGCfxERERF5bhrHXp7PiROwaxf4+MDSpZAzJ5QsaT1NyZKwaJHxeMcOOHkSzpwxUnDCwuDqVbhzB1auNFr8RUREROS5xZuKkz179ieOgmOLg4MDly5d+s+VehVFREC7dkaWioMDTJ1qxLtLloCjI7i6QmAgeHpCaCg0bAj58hnzNmkC33xjPM6b12hMT5XKaAwPD38Blb11C5o2NXLjnZxg0CBYsybudF9+Cd26Gek4xYvDm28a03t7Q+/eRhpPxozGCYDTE7PCREREROQp4o2munfvnpT1SJAtW7Ywbtw49uzZw/nz5xk/fjwBAQGW1zt27Mjs2bOt5ilTpgwhISFJXdVn1q2bkWq+YAFERRkN2UWLwoABxutjx0L//vDzz8bzSpVg+XLby9qwAbJnf0EVffDACOoDAowzir//huPHH7bWnzljpOvs2AHu7jBtmlEeG2uciZjPRtq2Nf4A+vaFXLleUIVFREREXg3xBvb9+vVLynokyO3btylSpAgtW7bk008/tTlN1apVmTRpkuV5mjRpkqp6z+3GDSMzJTDQeJ4mjfH3qNu3rdPXk0VsrBGMe3vD558bZcWLw6NXafLmNS4TZM9uXIZ47TVjY6ZMgcqVIVMmY7pLl4zLEKdOGek6W7cm+eaIiIiI2JMUlf9Qs2ZNatasCUCnTp1sTpM2bVrc3NySslr/2bFjkCMHtGkDe/YYg82MGQMZMvB/7N13eBTl+sbxe6kSBJJQQklAICjdKKFIU0ARpCNwKEcQBBSVY0ERLCAKAsfokSZKOSIKKNJBEESagAqhCijFECASgpREEuBQsr8/5kdiTJvAbvbN5vu5rr2SnZld77BPxmcm77yj116TZs+WihWzzsTf8MMP1knysmWtWSZr1LCWOxxSy5bW1yeflAYOdGHQLVukzz6zmvmQEGvZO+9IjzyS9va//CL17m2NC6peXZo5M3ndo49aY+zz55emTJH8/FwYFAAAIPex3dh/9dVXtrbr2rXrTYdxhR9++EHBwcEqVqyYGjVqpDfeeEMlS5b0aKbMXLsm7dwpTZpkXYv63HPSuHHWMJwxY6zH2LHS5MnSqFHWSJdjx6zh6StXSh07SocPW++1ZYvV7J8+bQ1hr1rVOlHuEo0bW2ftMxIZmfz9ffclB/u77793USgAAABIWWjsB2Zw6vevF9l6srF/8MEH1a5dO1WoUEHHjx/X6NGj1b59e23YsEEFCxZM8zXhbrm6NGvOnMmnUqWqKW/enxUeLtWsebs+/bS0wsOPJG1Ts2YBPf98FbVrtz/Fa0uVkhISamnt2l/k63tNknTypLWufv2yWrjwunx8YmxnMeHfA+ajTpCZUE8H+H/UqtmoE2TGlBqRzKiTKpnc88d2Y79z585Uy65fv65jx45p+vTpOn36tCZPnpz1hC706KOPJn1fo0YNhYSEqFatWlq9erXat2+f5mtCQ80omcqVpSJFQnXXXdZFsffdJxUrFpp0z6ZJk6xJZUJDQ3XqlBQQYA232bbNmlCmRYsQXbwoJSZas+IkJFgz7IwYIYWGBtnKEB4ebsy/B8xFnSAnoVZhB3UCO0yok7i4uAzX227sK96YzeRvgoOD1aJFC3Xs2FGzZ8/WuHHjspbQjcqUKaOyZcsqIiLC01EyNWmSNdHMlStSpUrWZDL9+0sHD1rTXVaokDwjzoIF0tSpVkNfqJD0xRdWkx8TI3XqZG1z7ZrUs6c10w4AAAC8n8sunm3Tpo3Gjx9vVGN/9uxZRUdH54iLaUNCUs85v3Bh2ts++6z1+LtKlayLbwEAAJD7uKyx/+OPP3Tx4kVXvV2a4uPjk86+JyYmKioqSnv37pWfn5/8/Pw0btw4tW/fXgEBATp+/LjeeustlSxZUm3btnVrLgAAAMDTbDf20dHRaS6Pi4vT999/rylTpqhRo0YuC5aWXbt2qV27dknPx44dq7Fjx6pHjx56//33deDAAX3xxReKi4tTQECAmjRpok8++URFihRxay4AAADA02w39tWrV08x+81fOZ1O1a1bV++9957LgqWlSZMmio2NTXf9okWL3PrfBwAAAExlu7GfMGFCqmUOh0O+vr6qVKmSaty4QxIAAACAbGe7se/du7c7cwAAAAC4Bbd08eyVK1e0cuVKxcbGqmXLlipbtqyrcgEAAADIAtuN/dChQ/Xjjz9q06ZNkqybU7Vu3Vq7du2S0+lUkSJFtHr1alWrVs1tYeFeoXXrejpCMqfT0wkAAABylDx2N1y/fr1atGiR9Hzx4sXauXOnxo0bp1WrVsnX11f//ve/3RISAAAAQMayNN3lX+8+u3LlSlWvXl0DBw6UJPXr10/Tpk1zfUIAAAAAmbJ9xj5fvny6dOmSJGt6y02bNunBBx9MWu/v769z5865PiEAAACATNlu7KtVq6YFCxbozz//1Lx583Tu3LkUjf3x48dVokQJt4T0dg6HGQ8AAADkXFm6eLZ79+664447JEmhoaFq3Lhx0vpvv/1W99xzj8sDAgAAAMic7ca+WbNmWr9+vdatW6eiRYuqS5cuSXeiPX/+vOrVq6d27dq5LSgAAACA9DliY2Nz9byCxYoV83QEY4bBOGVIEInpLg0WHh6u0NBQT8eA6YzZsbEvMRp1gsyYUiOSEXUSFxeX4fos36AqPDxcmzZt0h9//KH+/furcuXKunjxog4dOqTg4GDdfvvtNx0WAAAAwM2x3dhfvXpVAwYM0LJly+R0OuVwONSqVStVrlxZDodDHTt21ODBgzVkyBB35gUAAACQBtuz4owbN04rVqzQ2LFj9eOPP8r5lz9HFCpUSB07dtSqVavcEhIAAABAxmw39vPnz9fjjz+uJ598UiVLlky1/s4779TRo0ddGg4AAACAPbYb+5iYGNWuXTvd9QULFlRCQoJLQgEAAADIGtuNfUBAgI4dO5bu+l27dql8+fIuCQUAAAAga2w39u3atdMnn3yiiIiIpGU35rH/9ttv9eWXX6pTp06uTwgAAAAgU7bnsY+Li9MjjzyiY8eOqWHDhlq7dq2aNWumhIQEbdu2TSEhIfr6669VqFAhd2d2KeaxT8Y89rCDeexhizE7NvYlRqNOkBlTakQyok4ym8fe9hn7YsWKae3atRo8eLCioqKUP39+bdq0SWfPntXLL7+cI5t6AAAAwFtw51nO2CfhjD3s4Iw9bDFmx8a+xGjUCTJjSo1IRtSJy87YZ+batWv6/PPPXfV2AAAAALLglhv7S5cu6aOPPlJISIj+9a9/uSITAAAAgCzKtLGfM2eOGjdurMDAQNWsWVMjR47UtWvXJEnTp09X7dq1NXz4cBUpUkQTJ050e2AAAAAAqeXLaOX8+fP17LPPysfHR5UrV1Z0dLQmTZqkixcv6o8//tDSpUvVqFEjffjhh3rooYeyKzMAAACAv8mwsZ8+fbqCg4P19ddfq1SpUrp27ZoGDRqkmTNnqkiRIpo3b55atWqVXVkBAAAApCPDoTi//PKLevfurVKlSkmS8uXLpxdeeEFOp1MvvfQSTT0AAABgiAwb+4SEBJUuXTrFshvPa9as6b5UANLUr59UqpT011+/r76SatSQ8uSRwsNTbj92rBQcLN11l7R6tbXs4EEpJCT5UbSo9MEH2fczAAAA98j04llHOvOH5s2b1+VhAGTs8celb75JuaxmTWnRIqlp05TLDxyQvvhC2r/fes3TT0vXr1tN/u7d1mPHDsnHR+rUKdt+BAAA4CYZjrGXpEmTJmnBggVJz69duyaHw6HRo0fL398/xbYOh0Pz5s1zfUoAkqzmPTIy5bJq1dLedulSqXt3qWBBqWJF68z9tm3Sffclb/Pdd1LlylKFCm6LDAAAskmGjX2ZMmV05swZnTlzJtXy33//Xb///nuK5emd3QeQ/X7/XWrQIPl5YKC17K+++ELq0SN7cwEAAPfIsLE/cOBAduUA4GJp3fn6r8feV65Iy5ZZ4/ABAEDOd8t3ngVgpsBA6cSJ5OdRUVLZssnPV62S7r1XCgjI/mwAAMD1aOwBL9W+vTXU5n//k44elQ4flurVS14/bx7DcAAA8CaZXjwLwBw9ekgbNkhnzlhn5EeNkvz9pcGDpT/+kNq0saawXL3amgKzWzepenUpXz5pyhTpxmRWFy9K334rffyxR38cAADgQo7Y2Ng0RuLmHsWKFfN0BJlyzbFThgSR0h4gDiOEh4crNDTU0zFgOmN2bOxLjEadIDOm1IhkRJ3ExcVluJ6hOAAAAIAXoLEHAAAAvACNPQAAAOAF0r149r333svymzkcDr344ou3FAgAAABA1qV78ayfn1/W38zh0Llz5245VHbi4tlkXDwLO7h4FrYYs2NjX2I06gSZMaVGJCPqJLOLZ9M9Y3/mzBmXhwEAAADgHuk29nlvTHgNAAAAwHhcPAsAAAB4gSzdefbQoUOaNm2adu/erbi4OCUmJqZY73A4FB4e7tKAAAAAADJn+4z99u3b9cADD2jx4sXy9fXVkSNHVKZMGfn5+eno0aMqWLCg7r33XndmBQAAAJAO24396NGjFRAQoPDwcH388ceSpJdffllr167VihUr9Pvvv6tXr15uCypJW7ZsUffu3VWtWjX5+vpqzpw5KdY7nU6NHTtWVatWVenSpdWmTRv98ssvbs0EZAeHw5wHAAAwk+3GfufOnerTp4/8/PyUJ4/1shtDcRo2bKjHHntMb7/9tntS/r+EhARVr15d48aNU6FChVKtnzBhgqZMmaLx48dr3bp1KlmypDp16qQLFy64NRcAAADgaVm6ePbG3PY+Pj6SpPPnzyetu/POO3XgwAEXRkutZcuWGjFihDp06JB0cHGD0+nU1KlT9fzzz6tDhw6qXr26pk6dqvj4eC1YsMCtuQAAAABPs93YBwUFKSIiQpJUsGBBlS9fXhs2bEha/+OPP97UTa1c5dixY4qJiVHz5s2TlhUqVEgNGzbUTz/95LFcAAAAQHawPStOs2bNtHTpUo0aNUqS9Nhjj2nMmDGKioqS0+nUxo0bNXjwYLcFzUxMTIwkqWTJkimWlyxZUtHR0em+zoxZfLiL59+Z8bmYxKwa4fNBZkypWGrVbNQJMmNKjUhm1EmVKlUyXG+7sR8yZIg6deqkq1evKn/+/BoyZIgSExO1ZMkS5cmTR0OGDNHLL798y4FvleNvV/c5nc5Uy/4qNNSkksENfC5m4/NBTkGtwg7qBHaYUCdxcXEZrrfd2Pv7+8vf3z/pucPh0NChQzV06NCbT+dCAQEBkqTTp08rMDAwafmZM2dSncUHAAAAvI3tMfaXLl3KcEhLdHS0Ll265JJQN6NChQoKCAjQ+vXrk5ZdvnxZP/zwg+rXr++xXAAAAEB2sH3Gfvjw4dq+fbu2bNmS5vpu3bqpfv36CgsLc1m4v4uPj0+6gDcxMVFRUVHau3ev/Pz8FBQUpEGDBum9995TlSpVFBwcrLCwMBUuXFhdunRxWyYAAADABLYb+3Xr1qlnz57prm/Xrp3mzp3rklDp2bVrl9q1a5f0fOzYsRo7dqx69OihqVOn6rnnntOlS5f08ssvKzY2VnXq1NGiRYtUpEgRt+YCAAAAPM12Yx8TE6MyZcqkuz4gIECnTp1ySaj0NGnSRLGxsemudzgcGj58uIYPH+7WHAAAAIBpbI+xL168uA4ePJju+l9//VXFihVzSSgAAAAAWWO7sX/wwQc1a9Ys7dixI9W6nTt36tNPP1WLFi1cGg4AAACAPbYb++HDh6tYsWJ6+OGH1atXL40ePVpjxoxRr1691LJlSxUrVkyvvfaaO7MCAAAXOHFCatZMqlZNqlFDmjAh5fqwMMnhkM6csZ6fPy916iTVri3Vqyft22ctP3hQCglJfhQtKn3wgbXu3DnpoYekKlWsr+fPZ9/PB+RWjtjYWKfdjU+dOqURI0Zo5cqVSkhIkCQVLlxYjzzyiN58802VLVvWbUHdxYThQxncPytbOWVIEEly2i7LXMGUGpGk7dvDjbhJBwxnStGyL0lTdLT1uPde6cIFqU4dackSqXp1q+nv31/69Vdpxw6pRAnp5Zel22+XRo60lj/zjPTddynf8/p1qVw56aefpAoVpKFDJX9/adgwadw4q7EfP/5vQagTZMaUGpGMqJPMblBl+4y9JJUuXVrTpk3TsWPHdODAAe3fv1/Hjh3TtGnTcmRTD8AN0jsVmN7pu6VLrdOAISFSaKi0ebPnsgO5RJkyVlMvSUWKWL+uv/9uPX/hBenf/07ZTx04IN0YbVu1qhQZKcXEpHzP776TKle2mnrJ+tXu08f6vk8f68ABgHtlqbG/IW/evCpTpozKli2rvHnzujoTgJwsXz7pvfekX36RfvxRmjLF6grGjbM6g8OHra/jxlnbt2gh7dkj7d4t/fe/1qlCANkmMlLatUuqX19atsw663733Sm3uftuadEi6/tt26Rjx6SoqJTbfPGF1KNH8vOYGOsAQrK+nj7tth8BwP9Ld7rLr776SpLUtWvXFM8zc2N7ALlUmTLJ/zf/66nApUulDRus5X36SA88YP1d/vbbk1+bkGDWn10BLxcfLz36qDUuPl8+acwYac2a1NsNGyY995z1h7VataR77rG2v+HKFeugYOzY7MsOILV0x9j7+fnJ4XDo1KlTKlCggPz8/DJ/M4dD586dc3lId2KMfTLG2JvLlBqRsjjGPjJSatrUutKufHnpr/eh8PNLHo6zeLE0fLh1Su/rr6X77nN5bmQzU4qWfUm6rl6V2raVHn5YevFF6eefrT+g+fhY66OipLJlrTP0pUsnv87plCpWlPbutS6Wlazj9ilTUh4U3HWXdSxfpow1nv+BB6yLbVOgTpAZU2pEMqJOMhtjn+4Z+507d0qSChQokOI5ANjy11OBN/7vn55OnazHpk3SG29Ia9dmT0Ygl3I6pSeesP6g9uKL1rJatVIOl7njDik83Lp4NjbWavgLFJBmzLCO1//6az1vXsphOJLUvr306afW2f5PP5U6dHD7jwXkeuk29hUrVszwOQCk6+pVq6nv1Uvq3NlaFhBgnba7cfquVKnUr2vaVPrtN2uOvRIlsjczkIts2SJ99pnVzIeEWMveeUd65JG0t//lF6l3bylvXmvmnJkzk9ddvCh9+6308ccpXzNsmNStm7Vt+fKSzRG9AG5Buo09ANyUtE4FSumfvjtyxJpKw+GQdu60BusWL+6Z7EAu0bhx5qMKIiOTv7/vPuu697T4+Ehnz6ZeXrx46ikxAbiX7ca+U6dOGa53OBwqWLCgypUrp6ZNm6pt27bKk+emJt0BkJOldyowvdN3CxdKs2dL+fNLhQpJX35p1phKAAByCNs3qGrVqpViYmIUGRmpIkWKKCgoSE6nU1FRUbpw4YIqVqyowoUL69ixY4qPj1dISIgWLVokX19fd/8Mt4SLZ5Nx8ay5TKkRiRtUwSZTipZ9idmoE2TGlBqRjKgTl92gasyYMYqLi9MHH3ygiIgIbdmyRVu3blVERITef/99xcXFaeLEiTp69KjCwsK0Z88ejR49+pZ/AAAAAACZs33GvnXr1goJCdHYdCapHTZsmPbs2aNVq1ZJkp555hmtX79eBw4ccF1aN+CMfTLO2JvLlBqROGMPm0wpWvYlZqNOkBlTakQyok5cdsZ+z549Cg4OTnd9cHCw9uzZk/Q8NDRUZ86csfv2AAAAAG6B7cbez89Pa9K6Hd3/W7NmTYqbWMXGxhpxNhwAAADIDWw39r1799aaNWvUq1cvbdy4UcePH9fx48e1YcMG9ezZU2vXrlXv3r2Ttl+zZo1q1arlltAAAAAAUrI93eXQoUOVkJCgqVOnJo2jvyFPnjx65pln9Morr0iSLl++rB49eqh27dquTQsAAAAgTbYvnr0hJiZG69atU1RUlCQpKChIzZo1U0BAgFsCupsJw4VMuS6Ei2fNZUqNSFw8C5tMKVr2JWajTpAZU2pEMqJOMrt4Nst3ng0ICFCPHj1uOhAAAAAA18tyY79lyxatWbNGx48flySVL19eLVu2VKNGjVweDgAAuJZRJ0A9HQDwMrYb+6tXr2rgwIFaunSpnE6nbr/9dklSfHy8Jk2apA4dOmj69OnKly/LxwoAAAAAbpHtWXHeffddLVmyRP3799e+fft04sQJnThxQvv379eAAQO0ZMkShYWFuTMrAAOE1q1rnfLz9AMAAKRg++LZkJAQhYaGasaMGWmu79+/v8LDw7V7926XBnQ3Lp5NxsWz5jKlRiSD6oQaMZspRUudpGLKRyOxP4ENRhWs5+vEZXeejY6OVsOGDdNdf9999yk6Otp+MgAAAAAuY7uxL1WqlHbu3Jnu+t27d6tkyZIuCQUAAAAga2w39p06ddLcuXP17rvvKj4+Pml5fHy8wsLCNGfOHHXp0sUtIQEAAABkzPYY+0uXLqlnz57asGGD8uXLp9KlS0uSTp06pWvXrumBBx7QvHnzdNttt7k1sKsxxj6ZMWMdJSPGsZnElBqRDKoTasRsphQtdZKKKR+NxP4ENhhVsJ6vk8zG2Gf5zrMrVqxINY/9ww8/rDZt2tx8Sg+isU9mzA5WMuKXxySm1IhkUJ1QI2YzpWipk1RM+Wgk9iewwaiC9XyduLyx9zY09smM2cFKRvzymMSUGpEMqhNqxGymFC11koopH43E/gQ2GFWwnq8Tl82KAwAAAMBc6d4mtlOnTll+M4fDoUWLFt1SIAAAAABZl25jf+nSJTlM+vMHAAAAgHQxxp4x9kmMGesoGTGOzSSm1IhkUJ1QI2YzpWipk1RM+Wgk9iewwaiC9XydMMYeAAAAyAXSHYqTni1btqSa7rJly5Zq1KiRy8MBAAAAsMf2UJyrV69q4MCBWrp0qZxOp26//XZJ1p1nHQ6HOnTooOnTpytfviwfK3gUQ3GSGfMnUcmIP3eZxJQakQyqE2rEbKYULXWSiikfjcT+BDYYVbCerxOXDcV59913tWTJEvXv31/79u3TiRMndOLECe3fv18DBgzQkiVLFBYWdsuBAQAAAGSd7TP2ISEhCg0N1YwZM9Jc379/f4WHh2v37t0uDehunLFPZsyZE8mIo2KTmFIjkkF1Qo2YzZSipU5SMeWjkdifwAajCtbzdeKyM/bR0dFq2LBhuuvvu+8+RUdH208GAAAAwGVsN/alSpXSzp07012/e/dulSxZ0iWhAAAAAGSN7ca+U6dOmjt3rt59913Fx8cnLY+Pj1dYWJjmzJmjLl26uCUkAAAAgIzZHmN/6dIl9ezZUxs2bFC+fPlUunRpSdKpU6d07do1PfDAA5o3b55uu+02twZ2NcbYJzNmrKNkxDg2k5hSI5JBdUKNmM2UoqVOUjHlo5HYn8AGowrW83WS2Rj7LN95dsWKFVq9erVOnDghyZrH/uGHH1abNm1uPqUH0dgnM2YHKxnxy2MSU2pEMqhOqBGzmVK01Ekqpnw0EvsT2GBUwXq+Tlze2HsbGvtkxuxgJSN+eUxiSo1IBtUJNWI2U4qWOknFlI9GYn8CG4wqWM/Xictmxbnh5MmTWrBggaZOnarff/9dkpSYmKjz58/r+vXrN5fSRcaOHStfX98UjzvvvNOjmQAAAIDskKXbxI4YMUJTp07VtWvX5HA4VL16dZUrV04XLlxQrVq19Nprr2nQoEHuympLlSpVtGLFiqTnefPm9WAaAAAAIHvYPmM/efJkTZo0SQMHDtSCBQvk/MufI4oVK6Y2bdpo+fLlbgmZFfny5VNAQEDSo0SJEp6OBAAAALid7cZ+1qxZ6tq1q8aMGaN77rkn1fqaNWvqt99+c2m4mxEZGalq1aqpdu3a6tevnyIjIz0dCQAAAHA724398ePH1ahRo3TXFylSRLGxsS4JdbNCQ0P14Ycf6quvvtLEiRMVExOjli1b6ty5cx7NBQAAALib7TH2/v7+iomJSXf9gQMHkua295SHHnooxfPQ0FCFhIRo7ty5evbZZ9N8TXh4eHZEy0SopwMYx4zPxSTUyN9RI2YzpWKpk7SY8umYgzoxl0nVakKdVKlSJcP1thv7hx56SLNnz9aAAQNSrdu3b58+//xz9enTJ+sJ3ej2229X1apVFRERke42oaEmlQxu4HNBZqgR2EGdwA7qBHaYUCcum+7ytddek9PpVMOGDfX222/L4XBo3rx5GjhwoFq0aKGAgAANHTr0lgO70uXLl3X48GEFBAR4OgoAAADgVrYb+9KlS2v9+vVq3ry5Fi5cKKfTqS+//FIrV65U586dtWbNGvn5+bkza6Zef/11bd68WZGRkQoPD1efPn108eJF9ejRw6O5AAAAAHe7qTvPOp1OxcTEKDExUQEBAcbMFd+vXz9t3bpVZ8+eVYkSJRQaGqrXXntNVatWTfc13Hk2mTF3AJSMuLubSUypEcmgOqFGzGZK0VInqZjy0UjsT2CDUQXr+TrJbCjOTTX23oTGPpkxO1jJiF8ek5hSI5JBdUKNmM2UoqVOUjHlo5HYn8AGowrW83WSWWOf7sWz7733BfEEfwAAIABJREFUXpb/Yw6HQy+++GKWXwcAcI1+/aQVK6RSpaR9+6xlu3dLTz0lXb4s5csnffihVK+eNGeONH68tc3tt0tTp0p33209nzBBmj7d+v/YgAHS888n/zcmTZImT7beq00b6d//zt6fEQCQtnTP2Kc1Xt7x/0dNzr8dsTgcDjmdTjkcjhw3Zzxn7JMZc+ZEMuKo2CSm1IhkUJ1QI2natMlq0nv3Tm7sW7aUXnhBat1aWrnSasQ3bJC2bpWqVZP8/KRVq6Q335R++sl6Xffu0rZtUoECUqtWVtNfpYq0fr00Zoz09ddSwYLS6dPWQUQqphQtdZKKKR+NxP4ENhhVsJ6vk5ueFefMmTMpHvv371eNGjXUsWNHrVmzRkePHlVERIRWr16tDh06qFatWtq/f7/LfwAAgH1Nm0r+/imXORzSn39a38fFSWXLWt83bGg19ZLUoIEUFWV9/8sv1nMfH+us/P33S4sXW+umTpWGDbOaeimdph4A4BG2x9j37dtXV65c0Zw5c9Jc37NnTxUsWFCffPKJSwO6G2fskxlz5kQy4qjYJKbUiGRQnVAj6YqMlNq2TT5j/8sv0sMPW/9kiYnWmfoKFVK+JixM+vVXacYMa/sOHaQffpAKFZJatJBCQ60hOCEh1rpvvpFuu816Xd26aYQwpWipk1RM+Wgk9iewwaiC9XyduGwe+++++07NmzdPd32LFi303Xff2U8GAMgWU6dK//mPdOKE9fWJJ1KuX79emjkzebx9tWrSK69IDz1kDcO5+27rzL0kXbsmnT8v/fij9O67UrduRvy/DgCgLDT2efLk0YEDB9Jdv2/fPuXJY/vtAADZ5NNPpc6dre+7drXGzt+wd6/Uv7+0dKlUvHjy8ieekHbutMbs+/tb4+slKTDQei+Hw7oAN08e6cyZ7PtZAADps92Jt23bVrNmzdKECROUkJCQtDwhIUEffPCBZs+erbZt27olJADg5pUtK23caH2/bl1yk378uNWkf/aZdOedKV9z+nTyNosWSTfu89exo/UeknTokHTlilSihPt/BgBA5myPsf/zzz/VvXt3/fDDD8qfP78CAgLkcDh06tQpXb16VQ0aNNAXX3xhxJj1rDAhrynDx4wZ6yjxt/2/MaVGJIPqhBpJU48e1ow3Z85IAQHSqFHSXXdJzz1nDaO57TZruss6dawz9QsXJo+3z5dPCg+3vm/SRDp7VsqfX3r/fWucvWQ18v36WVNoFihgjbFPc5SmKUVLnaRiykcjsT+BDUYVrOfrxOU3qFq+fLnWrFmjEydOyOl0qnz58mrZsqXatm2bNB1mTkJjn8yYHaxkxC+PSUypEcmgOqFGzGZK0VInqZjy0UjsT2CDUQXr+TrhzrOZoLFPZswOVjLil8ckptSIZFCdUCNmM6VoqZNUTPloJPYnsMGogvV8nbhsVhwAAAAA5sqX0cru3btn6c0cDofmzZt3S4EAAAAAZF2Gjf3evXuzNG4+J46xBwAAALxBho19RvPWAwAAADAHY+wBAAAAL0BjDwAAAHgBGnsAAADAC9DYAwAAAF6Axh4AAADwAhnOigMAyDlMmnHY8/dnBIDc56bO2J86dUo///yzEhISXJ0HAAAAwE3IUmO/atUq1atXT9WrV9f999+v8PBwSdLZs2fVuHFjff31124JCQAAACBjthv7b7/9Vv/85z9VpEgRDRkyRE5n8h9aixcvrjJlymjOnDluCQkAAAAgY7Yb+/Hjx6tu3bpau3atnnrqqVTr69Wrp59//tml4QAAAADYY7ux379/vx599FE5HA450rhCKyAgQH/88YdLwwEAAACwx3ZjX6BAAV25ciXd9VFRUSpatKhLQgEAAADIGtuNff369bVkyZI01124cEFz585V48aNXRYMAAAAgH22G/tXXnlFe/fuVZcuXbRmzRpJ0r59+zR79mw1a9ZM58+f18svv+y2oAAAAADS54iNjbV9H5GNGzfqhRde0NGjR1Msv+OOOzR58mQ1atTI5QHdrVixYp6OYMxNZZwyJIgkObm9zV+ZUiOSQXVCjaRCnaSBOkmFOkkDdWIuowrW83USFxeX4fosNfaS5HQ6tWfPHh05ckSJiYmqWLGi6tSpozx5bupeVx5HY5/MmB2sZMQvj0lMqRHJoDqhRlKhTtJAnaRCnaSBOjGXUQXr+TpxeWPvbWjskxmzg5WM+OUxiSk1IhlUJ9RIKtRJGqiTVKiTNFAn5jKqYD1fJ5k19vnsvtFPP/2U4XqHw6GCBQuqXLlyKlGihN23BQAAAOACthv7Vq1apTl/fVqqVq2qV199VW3btr3pYAAAAADssz0UZ+3atXrrrbcUHx+v3r17q3LlynI6nfrtt9/02WefqWjRonrhhRd07NgxffLJJzp+/LjmzJmjVq1auftnuCUMxUlmzJ9EJSP+3GUSU2pEMqhOqJFUqJM0UCepUCdpoE7MZVTBer5OXDbG/q233tK6dev0zTff6Lbbbkux7uLFi2rdurUefPBBvfHGG7p48aLuv/9++fv7a/Xq1TefPhvQ2CczZgcrGfHLYxJTakQyqE6okVSokzRQJ6lQJ2mgTsxlVMF6vk4ya+xtT2Uzd+5cde/ePVVTL0k+Pj7q3r275syZk/T8H//4h/bt25fFuAAAAABuhu3GPi4uLsOjhL+vL168uO0x+QAAAABuje3GPjQ0VB9//LH27NmTat3u3bv18ccfq27duknLDhw4oDJlyrgmJQAAAIAM2Z4VZ/z48WrTpo2aNWumevXqqVKlSpKkiIgIbdu2TUWLFtXYsWMlSZcvX9bmzZuZFQcAAADIJlm6QdXJkyf1/vvva+3atYqKipIkBQYG6qGHHtLzzz+vcuXKuS2ou3DxbDJjLmKSjLhAxSSm1IhkUJ1QI6lQJ2mgTlKhTtJAnZjLqIL1fJ1w59lM0NgnM2YHKxnxy2MSU2pEMqhOqJFUqJM0UCepUCdpoE7MZVTBer5OXDYrDgAAAABz2R5jL0lXrlzRypUrtXv3bsXFxSkxMTHFeofDoQ8++MClAQEAAABkznZjHx0drQ4dOujw4cMqUqSI4uPjVbRoUf35559yOp3y8/OTj4+PO7MCAAAASIftoTgjR47UqVOntGLFCu3atUtOp1OffvqpoqKiNHz4cBUpUkQrVqxwZ1YAAAAA6bDd2K9bt079+vVTo0aNUtx4ysfHR0OHDlX9+vX16quvuiUkAAAAgIzZbuzj4+OT5q4vWLCgJOnChQtJ6++77z5t2bLFxfEAAAAA2GG7sS9durROnjwpSSpcuLD8/Py0f//+pPW///678ubN6/qEN2HGjBmqXbu2AgICdP/992vr1q2ejgQAAAC4le3G/r777tP69euTnrdt21YTJ07Uf/7zH7333nv66KOP1LRpU7eEzIpFixZp2LBhGjJkiDZt2qR69eqpa9euOnHihKejAQAAAG5j+wZVP//8s9atW6cnn3xSt912m86fP68+ffro+++/lyQ1aNBAn3zyiUqXLu3WwJlp0aKFatSooYkTJyYtu/fee9WhQweNHDky1fbcoCqZMTcKkYy4CYRJTKkRyaA6oUZSoU7SQJ2kQp2kgToxl1EF6/k6yewGVbanu6xVq5Zq1aqV9NzPz0/Lli3TuXPnlCdPHvn6+t58She5cuWKdu/ercGDB6dY3rx5c/30008eSgUAAAC4n60z9pcuXVLPnj3VpUsX9erVKzty3ZTo6GhVq1ZNX3/9tRo1apS0fPz48frqq68UHh6e6jWHDx/OzogAAADGq1s31NMRJEnbt6fu3XKzKlWqZLje1hn7QoUKaceOHerYsaNLQrmb429/tnE6namW3RAaakbhmiA8PJx/D2SKOoEd1AnsoE5gBzWSLLOhOLYvnm3YsKF+/PHHWw7kTsWLF1fevHl1+vTpFMvPnDmjkiVLeigVAAAA4H62G/vx48dr27ZtGjVqlLEzzBQoUEAhISEpZu+RpPXr16t+/foeSgUAAAC4n+2LZxs1aqTr169rwoQJmjBhgvLly5d0o6obHA6Hjh8/7vKQWfHMM8/oySefVJ06dVS/fn3997//1alTp9S3b1+P5gIAAADcyXZj/8gjj7gzh8t07txZ586d07vvvquYmBhVq1ZN8+fPV/ny5T0dDQAAAHAb2439tGnT3JnDpfr376/+/ft7OgYAAACQbWyPsQcAAABgriw19lFRUXrxxRcVGhqqChUqaMuWLZKks2fPaujQodqzZ49bQgIAAADImO2hOIcPH1arVq109epV3XPPPYqIiNC1a9ckWdNMbt26VZcvX9bEiRPdFhYAAABA2mw39iNHjlShQoW0ZcsW5c+fX8HBwSnWP/zww1q2bJnLAwIAAADInO2hOJs3b9aAAQNUunTpNO/iWr58eZ08edKl4QAAAADYY7uxv3btmooUKZLu+vPnzytv3rwuCQUAAAAga2w39lWrVtXmzZvTXOd0OvX111/r7rvvdlkwAAAAAPbZbuwHDRqkxYsX6/3331dcXJwkq6GPiIjQwIEDtWPHDj3zzDNuCwoAAAAgfbYvnu3atasiIyP1zjvvaMyYMZKkRx99VE6nU3ny5NGoUaPUqlUrtwUFAAAAkD7bjb0kvfzyy+rWrZuWLVum3377TYmJiapYsaI6duyoihUruisjAAAAgExkqbGXpAoVKmjw4MHuyAIAAADgJtkeY9++fXvNnj1b58+fd2ceAAAAADfBdmMfHR2t5557TnfddZe6deumL7/8UhcuXHBnNgAAAAA22W7st2/fro0bN+rpp5/WoUOH9NRTT+nOO+9U7969tXTpUl2+fNmdOQEAAABkwHZjL0m1a9fWm2++qd27d+vbb7/V448/rh07dujxxx9XlSpVNHDgQHflBAAAAJCBLDX2fxUaGqqxY8dq//79mjBhghwOhxYsWODKbAAAAABsyvKsODdERUVp8eLFWrRokfbs2aM8efKoadOmrswGAAAAwKYsNfanT5/W4sWLtXjxYm3fvl1Op1N169bVuHHj1KlTJ5UsWdJdOQEAAABkwHZj3759e23dulXXr19XrVq1NGLECHXu3FlBQUHuzAcAAADABtuNfUxMjF566SV16dJFwcHB7swEAAAAIItsN/Y//fRTptscOHBA1atXv6VAAAAAALLupmfFueHUqVOaNGmSmjRposaNG7siEwAAAIAsuqlZceLj47Vs2TLNnz9fmzdv1vXr11WtWjU9//zzrs4HAAAAwAbbjf3169e1du1azZ8/X6tWrdKlS5fkcDjUv39/PfPMM6pQoYI7cwIAAADIQKaNfXh4uL788kstXrxYZ8+eVbVq1TRkyBCFhoaqY8eOeuCBB2jqAQAAAA/LsLGvU6eOjh49qsDAQD322GPq0qWLatSoIUk6fvx4tgQEAAAAkLkMG/uIiAhVqFBBr7/+uh555BH5+PhkVy4AAAAAWZDhrDhTpkxRxYoV9eSTT+rOO+/UE088oZUrV+rq1avZlQ8AAACADRmese/Zs6d69uypmJgYzZ8/X/Pnz1evXr1UtGhRNW7cWA6HQw6HI7uyAgAAAEiHrXnsAwICNHjwYH3//ffaunWr+vbtqz179sjpdOrZZ5/VoEGDtGzZMiUkJLg7LwAAAIA0ZPkGVdWqVdObb76pffv2admyZWrdurW+/vpr9enTR8HBwe7ICAAAACATt3Tn2SZNmmjy5Mk6fPiw/vvf/+qBBx5wUSwAAAAAWXFTd579u4IFC6pTp07q1KmTK94OAAAAQBbd0hl7AAAAAGagsQcAAAC8AI09AAAA4AVo7AEAAAAvQGMPAAAAeAEaewAAAMAL0NgDAAAAXoDGHgAAAPACNPYAAACAF6CxBwAAALwAjT0AAADgBWjsAQAAAC9AYw8AAAB4Aa9q7Nu0aSNfX98Uj379+nk6FgAAAOB2+TwdwNV69eqlESNGJD2/7bbbPJgGAAAAyB5e19j7+PgoICDA0zEAAACAbOVVQ3EkaeHChapUqZIaNGig119/XRcuXPB0JAAAAMDtvOqMfdeuXRUUFKTSpUvr119/1ahRo7Rv3z4tWbLE09EAAAAAt3LExsY6PR0iI6NHj1ZYWFiG2yxfvlxNmjRJtXzHjh1q0aKFNmzYoJCQkDRfe/jwYZfkBAAA8BZ164Z6OoIkafv2cE9HMEqVKlUyXG98Y3/27FmdPXs2w20CAwPl4+OTanliYqJKliyp6dOnq3Pnzmm+tlixYi7J6Q3Cw8MVGmrGLzLMRZ3ADuoEdlAn5nI4PJ3Asn07NfJXcXFxGa43fihO8eLFVbx48Zt67f79+3X9+nUupgUAAIDXM76xt+vo0aOaP3++WrZsKX9/fx08eFCvv/66ateurQYNGng6HgAAAOBWXtPY58+fXxs3btRHH32khIQElStXTi1bttSwYcOUN29eT8cDAAAA3MprGvvAwECtXLnS0zEAAAAAj/C6eewBAACA3IjGHgAAAPACNPYAAACAF6CxBwAAALwAjT0AAADgBWjsAQAAAC9AYw8AAAB4ARp7AAAAwAvQ2AMAAABegMYeAAAA8AI09gAAAIAXoLEHAAAAvACNPQAAAOAFaOwBAAAAL0BjDwAAAHgBGnsAAADAC9DYAwAAAF6Axh4AAADwAjT2AAAAgBegsQcAAAC8AI09AAAA4AVo7AEAAAAvQGMPAAAAeAEaewAAAMAL0NgDAAAAXoDGHgAAAPACNPYAAACAF6CxBwAAALwAjT0AAADgBWjsAQAAAC9AYw8AAAB4ARp7AAAAwAvQ2AMAAABegMYeAAAA8AI09gAAAIAXoLEHAAAAvACNPQAAAOAFaOwBAAAAL0BjDwAAAHgBGnsAAADAC9DYAwAAAF6Axh4AAADwAjT2AAAAgBegsQcAAAC8AI09AAAA4AVo7AEAAAAvQGMPAAAAeIEc09jPmjVLbdu2Vfny5eXr66tjx46l2iY2NlYDBw5U+fLlVb58eQ0cOFCxsbEeSAsAAABkrxzT2F+8eFHNmzfXsGHD0t2mf//+2rt3r7766istWLBAe/fu1ZNPPpmNKQEAAADPyOfpAHY9/fTTkqRdu3aluf7gwYNau3atvvnmG9WvX1+S9J///EetW7fW4cOHVaVKlWzLCgAAAGS3HHPGPjPbtm3T7bffntTUS1KDBg1UuHBh/fTTTx5MBgAAALhfjjljn5nTp0+rePHicjgcScscDodKlCih06dPp/u68PDw7IiXY/DvATuoE9hBncAO6sRUoZ4OkIQaSZbZCBSPNvajR49WWFhYhtssX75cTZo0sfV+f23qb3A6nWkuvyE01JzC9bTw8HD+PZAp6gR2UCewgzqBHdRIsri4uAzXe7SxHzRokLp165bhNoGBgbbeq1SpUjpz5kyKRt7pdOrs2bMqWbLkLWcFAAAATObRxr548eIqXry4S96rXr16io+P17Zt25LG2W/btk0JCQkpxt0DAAAA3ijHjLGPiYlRTEyMjhw5IsmaBScuLk5BQUHy8/PTXXfdpQcffFAvvPCCJkyYIKfTqRdeeEEPP/wwM+IAAADA6+WYWXH++9//qmnTphowYIAkqVu3bmratKlWrlyZtM306dNVs2ZNde7cWY8++qhq1qypjz/+2FORAQAAgGyTY87YDx8+XMOHD89wGz8/P02bNi2bEgEAAADmyDFn7AEAAACkj8YeAAAA8AI09gAAAIAXoLEHAAAAvACNPQAAAOAFaOwBAAAAL0BjDwAAAHgBGnsAAADAC9DYAwAAAF6Axh4AAADwAjT2AAAAgBegsQcAAAC8AI09AAAA4AVo7AEAAAAvQGMPAAAAeAEaewAAAMAL0NgDAAAAXoDGHgAAAPACNPYAAACAF6CxBwAAALwAjT0AAADgBWjsAQAAAC9AYw8AAAB4ARp7AAAAwAvQ2AMAAABegMYeAAAA8AI09gAAAIAXoLEHAAAAvACNPQAAAOAFaOwBAACQgtNpxgNZQ2MPAAAAeAEaewAAAMAL0NgDAAAAXoDGHgAAAKn06yeVKiXVrJly+aRJ0l13STVqSEOHWsu+/VaqU0eqVcv6um5d8vbz5lnLa9eWWrWSzpzJvp8ht6GxBwAAQCqPPy59803KZevXS0uXSnv3Svv3Sy+9ZC0vUUJavlz6+Wfp00+lxx6zll+7Jj33nPW6vXut5n7y5Gz9MXIVGnsAAACk0rSp5O+fctnUqdKwYVLBgtbzUqWsr/fcI5Uta31fo4Z0+bL0v/8lz26TkGB9/fPP5O3gejT2AAAAsOXQIen776X69aX775e2b0+9zcKFVqNfsKCUP791MFCrltXQHzggPfFE9ufOLWjsAQAAYMu1a9L589KPP0rvvit165Zyvvn9+6VXXpE+/th6fvWq1djv2iWdPGkNxRk71jPZcwMaewAAANgSGCh17iw5HFK9elKePMkXw0ZFSZ06SbNnS5UrW8t277a+Vq5svaZbN2nrVs9kzw1o7AEAAGBLx47JM94cOiRduWJdOBsbK7VpY52Nb9Qoefty5azhN3/8YT3/9lupWrXsz51b5PN0AAAAAJinRw9pwwbrjHxgoDRqlDUFZr9+1hSYBQpYM+A4HNZMN0eOSG+/bT0kac0aa1z9yJHWhbj580sVKkizZnnyp/JuNPYAAABIZd68tJd//nnqZa+/bj3S8tRT1gPux1AcAAAAwAvQ2AMAAABegMYeAAAA8AI09gAAAIAXyDGN/axZs9S2bVuVL19evr6+OnbsWKptatWqJV9f3xSPN998M/vDAgAAANksx8yKc/HiRTVv3lyPPPKIXn311XS3Gzp0qJ74y72KCxcunB3xAAAAAI/KMY39008/LUnatWtXhtsVKVJEAQEB2REJAAAAMEaOGYpj16RJk1SxYkU1btxYYWFhunLliqcjAQAAAG7niI2NdXo6RFbs2rVLzZo10549e1ShQoUU6yZPnqzatWvL399fO3fu1Jtvvqk2bdpo0qRJ6b7f4cOH3R0ZAAAAuGVVqlTJcL1HG/vRo0crLCwsw22WL1+uJk2aJD3PqLH/u8WLF6tv376KiIiQv7+/SzIDAAAAJvLoGPtBgwapW7duGW4TGBh40+9fp04dSaKxBwAAgNfzaGNfvHhxFS9e3G3v//PPP0sSF9MCAADA6+WYWXFiYmIUExOjI0eOSJIOHjyouLg4BQUFyc/PT9u2bdP27dvVpEkTFS1aVLt27dKrr76q1q1bKygoyMPpAQAAAPfKMRfPjh07VuPHj0+1fMqUKerVq5d2796tl156SYcOHdKVK1cUFBSkzp0767nnnpOPj48HEgMAAADZJ8c09gAAAADSl2OG4uDWnThxwva2DF8CAAAmOHXqlK5evUpvYgNn7HMRPz8/ORwOW9ueO3fOzWlgqtq1a9uukz179rg5DQAgt6tXr56OHDlCb2IDZ+xzkfXr1yd9f+TIEY0cOVJ9+/ZV3bp1JUnbt2/XrFmz9Oabb3ooIUwwYMCApO8TEhL04Ycf6t57701RJzt37tQzzzzjqYgwBAeBcIV69erpt99+09mzZz0dBYYaMWKE/vzzT0/HyBE4Y59LPfLII3ryySfVoUOHFMuXLl2qjz76SKtWrfJQMphk0KBBCg4O1pAhQ1Isf//99/Xrr79q2rRpHkoGE/z1rt6ZHQS+8sornooJw02bNk3nzp3TsGHDPB0FyPFo7HOp0qVLa/PmzQoODk6x/MiRI2rSpImio6M9lAwmCQoK0saNG1WpUqUUyyMiInT//fdn6boNeDcOAgHA8xiKk0uVL19eM2bM0Lhx41IsnzFjBhenIImPj482b96cqrHfvHmzChUq5KFUMNGKFSu0cePGVMs7duyo+++/3wOJAOQEDRs2tL3t1q1b3ZjEO9DY51LvvPOO/vnPf+q7775TaGioJGnHjh06fvy4PvvsMw+ngymefvppvfTSS9q1a1dSnYSHh2vevHn82RwpcBCItHTv3t32tl988YUbk8BU7du393QEr0Jjn0s9+OCD2rFjh2bOnKlDhw7J6XSqXbt26tu3rwIDAz0dD4Z47rnnVL58eX300UdavHixJOmuu+7S1KlT1alTJw+ng0k4CERa/P39PR0BhmP/4FqMsQcAuMTixYv10Ucf6eDBg5Ksg8CnnnqKg0AAyCY09rnY/v37NWvWLEVGRmrSpEkqXbq0VqxYoaCgIN19992ejgdDXL58WatXr9bRo0f1+OOPy9fXV0ePHpWvr6/8/Pw8HQ8A4EU+//xzLVy4UFFRUbpy5UqKdUybmzmG4uRS69atU48ePfTggw9q48aNunz5siTp6NGjmjt3rubOnevhhDBBRESEOnTooISEBMXFxaljx47y9fXVzJkzFRcXl2K6Q4CDQGRm06ZN6TZty5cv91AqmGLixIl6//331bdvX23dulVPPPGEIiIitHXrVg0ePNjT8XKEPJ4OAM8YM2aMxowZozlz5qhAgQJJy5s0aaKdO3d6MBlMMnz4cDVv3lyHDx9OcQFk69at9f3333swGUwTERGhunXr6oUXXtDbb7+t2NhYSdLMmTM1YsQID6eDCebMmaOuXbsqPj5emzdvVokSJRQbG6s9e/aoatWqno4HA3z66aeaMGGCRo4cqfz582vgwIH64osv9OyzzzK9sk009rnUr7/+qoceeijVcl9fX50/f94DiWCin376SYMHD1bevHlTLA8MDNSpU6c8lAom4iAQmZk8ebLeffddzZw5U/nz59fIkSP1/fff6x//+IcKFy7s6XgwwMmTJ3XvvfdKkm677baku8126dJFy5Yt82S0HIPGPpfy9fVN8yZUe/bsUdmyZT2QCKa6evVqqmVRUVEqWrSoB9LAVBwEIjORkZFJ9zQoUKCA4uPjJUkDBgxg+CckSaVKldLZs2clWTdI3L59uyTrL4IOh8OT0XIMGvtcqkuXLhoxYoR+//0eubtYAAAgAElEQVR3ORwOXbt2TZs3b9Ybb7yRpXmH4d2aN2+uKVOmpFj2559/auzYsWrZsqWHUsFUHAQiI/7+/knNfJkyZfTLL79Iks6dO5d0nRdyt6ZNm2rVqlWSpMcee0yvvfaa2rZtq379+qldu3YeTpczMCtOLnX16lU9/fTTWrhwoZxOp/LkySOn06kuXbpo6tSpqc66IXeKjo5O2plGRkaqdu3aioiIUKlSpbRy5UqVKFHCwwlhin79+snHx0eTJ09WYGCgNm/eLH9/f/Xs2VN33HGHJk+e7OmI8LD+/fvr7rvv1uDBgxUWFqYPP/xQrVq10qZNm3Tvvfdq9uzZno4ID0tMTFRiYqLy5bPmdlm0aJF+/PFHBQcHq2/fvsqfP7+HE5qPxj6Xi4yM1J49e5SYmKjatWurcuXKno4Ew1y6dEkLFizQ3r17lZiYqLvvvltdu3blbqJIgYNAZOb8+fO6fPmyypQpo8TERE2cODGpaXvppZfk6+vr6YhAjkdjn0uNHz9egwcPlo+PT4rlly5d0sSJE/XKK694KBmAnIqDQAC3Yvfu3RmuDwkJyaYkOReNfS7l7++vgwcPqmTJkimWnzt3TsHBwTp37pyHksHTli1bptatWyt//vyZzkLQvn37bEoFIKfLbMY17nUAPz8/ORwOOZ3JrelfL5qlN8kcN6jKpZxOZ5pXmO/du5eday7Xp08fHTp0SCVLllSfPn3S3c7hcLCTzeU4CERWVKpUKcOZTdif4O93lr127Zr27t2rsLAwjRw50kOpchbO2OcygYGBcjgcSkhIkI+PT4qd7PXr13X58mX169dPYWFhHkwJICfw8/NLOgjM6IQAB4GQpM2bN6d4fqNpmzlzpl5//XV17drVQ8lgunXr1mn8+PFavXq1p6MYj8Y+l5k7d66cTqeeffZZjR07NsU0dAUKFFD58uVVr149DyaEp919991av369/P39070WAwBcZenSpfrss8+0YMECT0eBoX777Tc1adJEJ0+e9HQU49HY51KbN29W/fr1mToKqZQuXVrh4eEKDAxM91oMQOIgEK5x9OhRNWrUiKYNqa7DcDqdOnXqlMaNG6fIyEht2rTJQ8lyDsbY51KNGzdO+j4mJkZXrlxJsT4oKCi7I8EQtWvX1rPPPqsGDRrI6XRq0qRJ6d7undmTcreYmBhdvHgxqbG/MZc9YFd8fLw+/PBDlStXztNRYIC0rsNwOp0qV66cPvnkEw+lylk4Y59L/fnnnxo6dKiWLFmSqqmXuIgpNzt8+LBGjx6tiIgI7d+/X8HBwUk3C/m7rVu3ZnM6mKRly5by8fFRgwYNks7YcxCI9Ny4xusGp9OpixcvqnDhwpo2bZpat27twXQwwd+vw8iTJ49KlCihSpUqpfv/IaREY59L/etf/9LOnTs1atQoPfbYY5o8ebJOnjypjz76SGPGjFGHDh08HREG+OvFkcDfcRCIrJgzZ06Kxv5G0xYaGsrNqQAXobHPpapXr64ZM2aoYcOGCgoK0saNG1WpUiUtWLBAn3/+uZYsWeLpiAByEA4CAdyqXbt2acGCBTpy5Ijy58+vKlWq6B//+IeqVq3q6Wg5Bn/XyKXi4uKSxtEXLVpU586dU6VKlVS3bl3961//8nA6eBJzk+NmZHbzISA6OlrLli1L0bR16tSJs/WQJL311lv6z3/+o8KFC+uOO+6Q0+nUhg0bNHHiRL3xxht6/vnndfnyZW3fvl1NmjTxdFxj0djnUnfccYciIyMVFBSkO++8UwsXLlSdOnW0fPlyblCVy3GDKtjFQSDs+uSTTzR8+HD973//U5EiRSRJFy5c0GuvvaZJkybp0UcfldPp1M8//6zatWt7OC2y2/z58zVlyhS98847euKJJ1SgQAFJ0pUrVzR9+nS9/fbbqlixombMmKH/a+/eo6Iq9/+BvwfkqnFmBBUTuYj3MCAiLzCkhWYXUI83XFoHjwodV4p5CtQUwUvq8ZiSVHghOZUuAS3rqNnJy4LRsVJIUzMwBhAEzLj2JUcYmd8fLuYXDSia+ezNvF9r+cc8e7vn7Wprn+fhs5+tVqtZ2N8GW3Es1DvvvANra2u8/PLLyMrKQkREBBobG9HU1IS1a9ciKipKdEQikji+oIra4/Dhw5g6dSqioqIwf/589OzZE8CtFfxNmzYhNTUVBw4cQGpqKry9vfmgtQUKDQ1FeHh4mx0DSUlJSExMxMCBA/HZZ5/BxcXlASeUDxb2BAAoKSnBt99+C29vbzzyyCOi4xARUQcRHh6Oxx57DAkJCa0eT0hIwJYtW6BSqbB//3706dPnwQYk4Xr16gWNRtPmf3udToeAgABcunSJRf0dsBWHANzat7655760tBRubm6CE5Eod2qp+C22VxDRnZw5cwbr169v8/i0adOQlJTEot6CKRQKGI1trzMbjUY4OjqyqG8HFvZkcvXqVaxfvx4fffQRKioqRMchQW7XV/9bbK8gTgKpPQwGA+zt7ds8bm9vD3t7exb1Fmzw4ME4cOBAm604+/fvZzdBO7GwtzA1NTV4/fXXcfToUdjY2GDBggWIjo7GunXrkJSUhIEDByI5OVl0TBKIu5tQe3ESSO3Rt29fHDt2DJGRka0eP3r0KPr27ftgQ5GkzJkzB/PmzYOdnR1mzZpleh9GY2MjUlNTsWbNGmzevFlwSnlgYW9hVq5cCa1Wi2nTpuHIkSNYsmQJjh07hl9//RWZmZkIDg4WHZGIZIKTQGqP6dOnIyEhAT169DB7u+zBgweRmJiIJUuWCEpHUjB58mScP38eixYtwurVq+Hh4QGFQoHCwkLU19dj/vz5mDx5suiYssCHZy2Mj48PkpOTMXLkSBQVFcHf3x/R0dFYu3at6GgkUQaDATk5OSgtLUVDQ0OLY9OmTROUiojkwmg0YtasWfjkk0/Qt29f9O/fHwqFAnl5eSgoKMCECROQmpoqOiZJQE5ODjIyMqDT6QAA3t7emDJlCh577DHByeSDhb2FcXFxwblz50zbjfXs2RNHjx7FoEGDBCcjKcrPz0dERASKi4thNBphbW0Ng8EAGxsb2NnZoaSkRHREkhBOAul2PvnkE2RmZqKgoADA/y/axo8fLzgZUcfBVhwL09TUBBsbG9Nna2trODg4CExEUrZ48WL4+flBo9FgwIAB0Gg0qK2txT//+U8sXbpUdDySkDtNAlnY04QJEzBhwgTRMUgmRowYgYyMDO7Sd5dY2FsYo9GIqKgo01vd9Ho9YmJizIr73bt3i4hHEpObm4sDBw6gc+fOsLKygsFggJ+fHxITExEbGwutVis6IkkEJ4F0N6ZMmYK3334brq6uoqOQRF2+fBkGg0F0DNlhYW9hfr9qNmXKFEFJSA6a9w4GAGdnZ5SVlaFfv37o1asXCgsLBacjKeEkkO6GVquFXq8XHYOow2Fhb2Heffdd0RFIRgYNGoRz587B09MTAQEBSEpKgrW1NT744AN4eXmJjkcSwkkgEd1Pw4cPv+37D6h1LOyJqE2vvfYa6uvrAQBLly7F1KlTERYWBmdnZ+zYsUNwOpISTgLpbvTu3du0VzlRazIzM0VHkCXuikNEd6W6uhpKpRIKhUJ0FJKQI0eOoL6+HuHh4SgqKsLUqVORn59vmgSq1WrREYlIgvgG6/uLhT0REf0pOAmkM2fOtPtcPz+/PzEJSZVKpWrXeXyDdfuwsCeiNkVERNz2OHdPIqLbUalUUCgUMBpvX2qwaCO6P9jgZqFKSkrg5uZmtpJmNBpRWlqK3r17C0pGUtK1a9cWnw0GA86fP4/S0lKEhYUJSkVSxEkgtebs2bOiIxBZFBb2FsrX1xd5eXno1q1bi/Hq6mr4+vpy5YQAtL2L0htvvIEuXbo84DQkZZwEUmvc3d1FRyCJY4/9/cVWHAulUqlw6dIluLi4tBi/fPkyhg0bhrKyMkHJSA5+/PFHjB07Fj/++KPoKCRxzZPAxYsXi45CArDHnu6EPfb3F1fsLUxsbCyAW39BEhMTW7xxtqmpCTk5ORgyZIioeCQTly5dEh2BZGLmzJkYO3YsC3sLNWrUKPbY021VV1eLjtChsLC3MN9//z2AW730+fn5sLGxMR2ztbWFr68v5s2bJyoeSUzzRLCZ0WjE1atXcfjwYUyfPl1QKpITTgItG3vsiR4sFvYWZv/+/QCAuXPnYu3atXBychKciKSseSLYzMrKCi4uLnjzzTcxY8YMQalIijgJpNawx57u1pdffolt27ahqKgIH3/8Mdzc3PDBBx/Aw8MDTz75pOh4ksfC3kI1PxSp1+uh0+mgUCjg5eXF1zdTC80TQaI74SSQ2uPChQtIS0tDYWEhkpOT4erqiv3796N3797w9fUVHY8Ey8jIwMKFC/Hiiy8iOzsbBoMBAHDz5k0kJSWxsG8HFvYWymAwIDExEdu2bUNDQwOMRiPs7OwQFRWFZcuWtWjRIctWW1sLnU4HGxsbeHh44KGHHhIdiSRo//79vFfoto4ePYpp06YhNDQU2dnZ0Ov1AIDCwkLs2rULu3btEpyQREtKSkJSUhImTpyIDz/80DT++OOP48033xSYTD6sRAcgMeLj45GRkYG33noLOTk5yM3NxYYNG5Ceno7ExETR8UgCSkpKMHXqVPTp0wdPP/00QkJC4O3tjVmzZuHatWum827cuCEwJUkB7xVqj9WrV2P16tXYuXMnbG1tTeNqtRq5ubkCk5FU6HQ6BAYGmo136dIFv/zyi4BE8sMVewu1Z88eJCcnY8yYMaYxLy8vuLi4YP78+Vi1apXAdCRaWVkZRo8eDSsrKyxZsgQDBgyA0WhEXl4eUlNTTStuWq0WeXl5WLBggejIJAjvFWqvH374AaNHjzYbVyqV3BmFAACurq4oKCgwezbjxIkT8PLyEpRKXljYW6i6urpW/5J4eXmhtrZWQCKSknXr1sHDwwP79u1rsSVqWFgY5s6di7/+9a+YNm0acnJysHXrVoFJSTTeK9ReSqUS5eXl8PDwaDF+9uxZPPzww4JSkZRERkYiLi4Ob7/9NgCgtLQUWq0Wy5cvx6JFiwSnkwe24lgoHx8fbNmyxWw8JSWF+9gTvvzySyxbtqxFodbM0dERb7zxBk6ePImVK1di3LhxAhKSVPBeofaaNGkS4uPjceXKFSgUChgMBhw/fhzLli1DRESE6HgkATExMQgLC8OECRNQX1+PsLAwLFy4EDNnzsScOXNEx5MFvnnWQp04cQJTpkyBq6srAgMDoVAocOrUKVRUVCAzMxPDhw8XHZEE6t69O7799lv06tWr1eNXrlyBr68vfv755wecjKSG9wq1V2NjI+bOnYu9e/fCaDTCysoKRqMRkyZNwnvvvQdra2vREUkifv31V+Tl5aGpqQkDBgxAly5dREeSDa7YW6igoCCcPn0a48ePR319PX755ReMHz8ep06dYlFP6NatG3Q6XZvHCwoK0KNHjweYiKSK9wrdiUajQUNDA2xsbLBt2zbk5uYiLS0N27dvx6lTp7B161YW9RZu586duHz5sumzo6Mj/P39ERAQwKL+LnHFnojMxMTEIC8vD59++ins7OxaHNPr9Rg/fjwGDhyITZs2CUpIUsF7he5EpVLB3t4egYGBCAkJQUhICAICAljMk0n37t1hMBjQu3dv0z2iVqvh6uoqOprssLC3MO3deUClUv3JSUjKysvLMWrUKFhbW2POnDno168fFAoFfvjhB6SmpuLmzZs4duwYevbsKToqCcZ7he5Ep9NBo9Hg+PHjOH78OCoqKtC5c2cMGzbMVMD5+flBoVCIjkqCXL9+HV999RWOHz8OjUaDb7/9Fjdv3kTfvn1N94harUbXrl1FR5U8FvYWRqVS3fEfT4VCgcrKygeUiKSquLgYr732Go4cOQKj8dY/EwqFAqGhoVi/fr3ZzhZkuXiv0N3Iz8+HRqPBiRMncPz4cfz8889wcnJCUVGR6GgkEfX19Th58qSp0P/uu+9gNBr5rE47cLtLC/Pf//63zWNHjhxBSkoKOnXibUGAh4cHMjMzUVNTg4KCAgCAt7c3lEql4GQkNbxX6G70798fKpUKKpUKTk5O+Pjjj1FfXy86FkmIg4MDVCoVlEollEolbGxs4OTkJDqWLHDFnnDmzBkkJCRAq9UiMjISsbGxcHFxER2LiIg6iKqqKlM7TnZ2NoqKiuDn54egoCAEBQVh2LBh6Ny5s+iYJIjRaMTZs2eh0Wig0Whw8uRJPPTQQxgxYgSCg4MRFBSEfv36iY4pCyzsLVhRURFWrVqFffv2ISwsDPHx8XyzGxER3VdBQUEoKCiAv7+/qZAfOnQoHB0dRUcjifD09IS9vT1CQkJMhby3t7foWLLE7S4tUFVVFeLi4vDEE0/g6tWr+PLLL7Fjxw4W9UREdN/pdDoolUp4eHjA09MTXl5eLOqpBaVSierqapSWlqK0tBRXrlxBQ0OD6FiyxBV7C7NhwwYkJSXB3d0dCQkJCA0NFR2JiIg6sMbGRpw+fdrUZnH69Gk4OzsjODjY9MvT01N0TBLsypUrpntEo9Hgp59+wmOPPWa6R4YOHWq2pS6ZY2FvYVQqFRwcHKBWq2+7O87u3bsfYCqSqpKSEri5uZndK0ajEaWlpejdu7egZEQkVzdu3MA333xj2vEkNzcX3bp1w7lz50RHIwkpLi423SMHDx5EY2MjysvLRceSPG5/YmEiIiK4VzC1m6+vL/Ly8tCtW7cW49XV1fD19UVVVZWgZEQkV1ZWVqZfCoUCRqMRV65cER2LJKS0tBRarRZarRYnTpzAL7/8AgcHB9GxZIGFvYV57733REcgGTEaja1OBP/v//4P9vb2AhIRkdwYDAbk5OSYWixOnToFvV4PNzc3qNVqvPjii1Cr1aJjkkDl5eWm1XmNRoPi4mLY2tri8ccfx4wZM6BWqxEYGCg6piywsCciM7GxsQBuvWQoMTGxxUpJU1MTcnJyMGTIEFHxiEhGPDw8cP36dbi6uiI4OBjr1q2DWq1mXz2ZDB48GLa2tvD398fEiROhVqsxdOhQLiDdAxb2RGTm+++/B3BrxT4/Px82NjamY7a2tvD19cW8efNExSMiGVm1ahVCQkK4fSG1ac+ePRg+fDh3S7oP+PAsEbVp7ty5WLt2Ld/4R0REJAMs7InojvR6PXQ6HRQKBby8vPjjUSIiIgniC6qIqE0GgwHLli2Dp6cngoODMWLECHh6eiI+Ph6NjY2i4xEREdFvsMeeiNoUHx+PvXv34q233sLw4cMBAFqtFitWrEBTUxNWrVolOCERERE1YysOEbWpf//+SE5OxpgxY1qMf/HFF5g/fz7y8vIEJSMiIqLfYysOEbWprq4OXl5eZuNeXl6ora0VkIiIiCxNRUUFSkpKRMeQBRb2RNQmHx8fbNmyxWw8JSWF+9gTEdEDER4eDl9fX9ExZIE99kTUpsTEREyZMgXHjh1DYGAgFAoFTp06hYqKCmRmZoqOR0REFiA+Ph51dXWiY8gCe+yJ6LbKy8uxfft25Ofnw2g0YuDAgZg1axZ69uwpOhoRERH9Bgt7IiIiIpKMwsJC0+YMAwYMaPVZL2odW3GIyEx1dXW7zlOpVH9yEiIishRVVVV45ZVX8Pnnn8PK6tZjoEajEc888wzeeecddO3aVXBC6eOKPRGZUalUUCgUtz1HoVCgsrLyASUiIqKObvr06dDpdNi4cSMef/xxAMDp06excOFC9OnTBx999JHghNLHwp6IzBw/frzNY0eOHEFKSgo6derE7ceIiOi+6dmzJz799FM88cQTLca/+eYbjB8/HmVlZYKSyQdbcYjITHBwsNnYmTNnkJCQAK1Wi8jISMTGxgpIRkREHZWzszMcHR3Nxh0cHNiG007cx56IbquoqAizZ89GaGgoVCoVvv76a/zrX/+Ci4uL6GhERNSBxMbGYvHixS1W5svKyrB06VIuJrUTW3GIqFVVVVVYt24dduzYgaFDh2LFihXw9/cXHYuIiDqoESNG4PLly9Dr9aYtlcvLy2Fvbw93d/cW52q1WhERJY+tOERkZsOGDUhKSoK7uzt27dqF0NBQ0ZGIiKiDCw8PFx1B9rhiT0RmVCoVHBwcoFarb7s7zu7dux9gKiIiIrodrtgTkZmIiIg7bndJRER0v+n1enzxxRcoLCxEZGQklEolCgsLoVQq+e6UduCKPREREREJp9PpMG7cONTX16O2thY5OTnw9PTE0qVLUVtbi82bN4uOKHncFYeIiIiIhFu8eDGeeuopXLp0CQ4ODqbxZ599FhqNRmAy+WArDhEREREJ9/XXX+Pw4cOwtrZuMe7m5oaKigpBqeSFK/ZEREREJAmNjY1mY6WlpXBychKQRn5Y2BMRERGRcE899RTeeeedFmN1dXVYs2YNxowZIyiVvPDhWSIiIiISrry8HGFhYQBuvfX80UcfhU6nQ/fu3XHw4EG+8bwdWNgTERERkSRcv34de/bswXfffYempib4+vpi8uTJLR6mpbaxsCciIiIi6gDYY09EREREwq1cuRLvv/++2fj777+PVatWCUgkPyzsiYiIiEi49PR0PProo2bjfn5+2L17t4BE8sPCnoiIiIiEu3btWqsPyHbt2hXXrl0TkEh+WNgTERERkXBubm7QarVm4ydOnMDDDz8sIJH88M2zRERERCRcZGQklixZgsbGRoSEhAAAsrKykJiYiAULFghOJw8s7ImIiIhIuHnz5qGqqgpxcXFoaGgAANja2uLll19GTEyM4HTywO0uiYiIiEgy6uvrkZeXB6PRiAEDBqBLly6iI8kGC3siIiIiEq64uBgeHh6tHvv6668xdOjQB5xIfvjwLBEREREJp1arkZ6e3mKsqakJq1evRlhYmKBU8sLCnoiIiIiES0hIwMKFCzF79mzU1dWhsLAQo0ePxs6dO5GRkSE6niywFYeIiIiIJCE/Px9z5sxBZWUlamtrMXLkSLz99ttQqVSio8kCV+yJiIiISBK6d+8Od3d3/PTTT7h+/TpCQ0NZ1N8FFvZEREREJNyJEycQHByMsrIyfPXVV0hOTsayZcswffp0VFVViY4nC2zFISIiIiLhunXrhpiYGCxatAidOt161VJxcTGio6NRVFSEH374QXBC6eMLqoiIiIhIuH379iEoKKjFmIeHBw4ePIgNGzYISiUvXLEnIiIiIuoA2GNPRERERMKMGTMGNTU1ps+JiYmorq42fa6srISPj4+IaLLDwp6IiIiIhDl16hQaGxtNn7dv347a2lrT55s3b+LKlSsioskOC3siIiIikgyj0bxLXKFQCEgiPyzsiYiIiIg6ABb2RERERCSMQqHgivx9wu0uiYiIiEgYo9GIqKgo2NraAgD0ej1iYmLg4OAAAGhoaBAZT1a43SURERERCTN37tx2nffuu+/+yUnkj4U9EREREVEHwB57IiIiIqIOgIU9EREREVEHwMKeiIiIiKgDYGFPRESyolQqsWbNGtExiIgkh4U9EZFM7Nq1C0qlEv7+/m2eM2TIECiVStOv7t27w8/PD6+//jquXr36h67dmjVr1rT4PhcXFwwZMgSxsbGoqam5q2v9Vnp6OnfAICK6S9zHnohIJjIyMuDu7o7CwkJ88803eOKJJ1o975FHHsH8+fMBADdu3MC5c+fwn//8B9nZ2dBqtbC2tr7na7dl/fr1cHJywq+//oqsrCxs3boVZ8+exaFDh+7pxTMZGRnIz89vdRu8iooKdOrE/30REf0e/2UkIpKBiooKZGdnIyUlBStXrkRGRkabxberqyumTp3aYuwvf/kL/v3vf+PcuXPw8/O752u3JTw8HD169AAAzJw5EzNnzsQnn3yC3NxcBAQE3NW17sTe3v6+Xo+IqKNgKw4RkQxkZmbC3t4ezz33HCZOnIiPP/4YjY2N7f79zUW3jY3Nfb92a0aMGAEAKCwsbDG+c+dOjBs3Dv3790f37t0REBCATZs2oampyXTO888/jyNHjqCkpKRFm0+z3/fY79y5E0qlElqtFitWrMCAAQPg6uqKCRMmoKioyCxbWloa/P390aNHDwQHB+PQoUP4xz/+gSFDhvyhPzMRkWhcsScikoH09HQ888wz6NKlCyZNmoRNmzbh8OHDePbZZ83ObWxsRGVlJYBbrTjnz59HUlIS/Pz8MHjw4D907fa6fPkyAEClUrUY37ZtG/r164fQ0FA4ODjg2LFjSEhIQF1dHeLj4wEAr732GmpqalBRUYE333yz3d+5ZMkSODg44NVXX0VlZSWSk5MRFRWF//3vf6Zz0tLSsGDBAgQGBiIqKgo///wzoqOj0atXr3v+sxIRSQULeyIiibt48SLOnz+PuLg4AICPjw8GDRqEjIyMVovv7OxseHt7txgLCAjA7t27zfrd7/babamurkanTp1QX1+P7OxsbN++HT169EBQUFCL8w4ePAhHR0fT59mzZ2PevHnYsmUL4uLiYGdnh1GjRsHV1RV1dXVmLUW34+joiP3798PK6tYPo1UqFZYsWYKLFy9i0KBBaGxsxMqVK+Hj44MDBw7A1tYWABASEoJx48ahd+/e7f4uIiIpYisOEZHEpaenw8nJCWPGjDGNTZw4EZ9//jnq6urMzvf398e+ffuwb98+pKenIz4+HgUFBZgxYwauX7/+h67dlmHDhsHb2xuPPvooXnnlFfTr18/U4vNbzUX9zZs3UVNTg8rKSgQHB6O+vh6XLl1q9/e1ZubMmaaiHoBpUtHcjpObm4vKykpERkaainoAePLJJzFo0KA/9N1ERFLAFXsiIgkzGo3Ys2cPgoKCUFFRYRoPDAyEXq/HZ599hhkzZrT4PV27dsXIkSNNn5955hn069cPL774Ij744ANER0ff87XbkpaWBqVSiWvXrmHr1q0oLi6GnZ2d2XknT57EihUrkJOTg4aGhhbHamtr2/Vdbfn9intzX351dTUAoDIcODYAAANwSURBVKSkBADMfprRPHb27Nk/9P1ERKKxsCcikjCNRoPS0lKUlpbi888/NzuekZHRruI7JCQEAKDVak2F/f26NgAMHz7c9IDuc889h6CgIMyePRtZWVmmVfSioiJMmDABffr0wZo1a+Dm5gY7OzucPXsWy5cvb/EA7b1obRtP4NYE5k7acw4RkdSxsCcikrCMjAyoVCps3rzZ7FhWVhZSU1NRVlaGhx9++LbXMRgMAID6+vr7fu3f69y5MxYvXozo6Gjs3bsXkydPBnCrv16v12P37t1wd3c3nV9cXGx2jXvZ+/5Omlf0CwoKMGrUqBbHdDrdff8+IqIHjYU9EZFENbfDjB07Fi+88ILZcR8fH2zbtg179uwxvZCqLc07w/j4+Nz3a7dm4sSJWLlyJTZu3IhJkyZBoVCYVtR/uzp+48YNbN261ez3Ozo6/uHWnN/z9/eHs7Mz0tLS8NJLL5n67LOysnDx4kU+PEtEsseHZ4mIJKr5Adbnnnuu1eOenp4YNGgQ0tPTW4xXVFQgPT0d6enp+PDDD7Fo0SK8+uqrcHZ2RlRU1B+6dnt16tQJL7/8Mr7//nscOnQIAPD000/D1tYWERER2LJlCzZv3oynnnqqxQOvzfz9/VFXV4e4uDhkZmZi796995Tjt2xtbfHGG2/g/PnzeP7555GSkoJVq1bhpZdewuDBg/+UnxIQET1ILOyJiCQqPT0dtra2ePrpp9s8Z+zYsbhw4QLOnz9vGrtw4QKio6MRHR2NmJgY7N27Fy+88AIOHz5saqu512vfjb/97W9wcnLCxo0bAQB9+/bFzp070alTJyxfvhwpKSkYO3YsVqxYYfZ7o6KiMHXqVGRkZCAqKgqzZs26pwy/9/e//x1vvfUWrl27huXLl+PQoUPYsmUL+vbtyzfaEpHsKWpqavjEEBERWbSgoCB069YN+/btEx2FiOieccWeiIgshl6vN9sBJysrCxcuXDDtHEREJFdcsSciIouh0WgQFxeH8PBwuLq64uLFi0hLS4OzszO0Wq1p73siIjnirjhERGQx3N3d4eHhgR07dqCqqgpOTk544YUXEB8fz6KeiGSPK/ZERERERB0Ae+yJiIiIiDoAFvZERERERB0AC3siIiIiog6AhT0RERERUQfAwp6IiIiIqANgYU9ERERE1AH8P4cEdj1DNw5NAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 792x612 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ideology_by_rating = (judge_decisions[judge_decisions['ABA Rating'].isin(\n",
    "                                      ['Not Rated','Well-Qual.','Qualified','Not Qualified','Excep. Well-Qual.']) &\n",
    "                                      judge_decisions['Judge Party'].isin(['Democrat','Republican'])]\n",
    "                      .groupby(['ABA Rating','Judge Party'])[['Decision Ideology']].mean()\n",
    "                      .unstack().reset_index().reindex([2,1,3,4,0]))\n",
    "ideology_by_rating.columns = ['ABA Rating','Democrat','Republican']\n",
    "\n",
    "# Add counts of each rating-party pair\n",
    "count_by_rating = (judge_decisions[judge_decisions['ABA Rating'].isin(\n",
    "                                      ['Not Rated','Well-Qual.','Qualified','Not Qualified','Excep. Well-Qual.']) &\n",
    "                                      judge_decisions['Judge Party'].isin(['Democrat','Republican'])]\n",
    "                      .groupby(['ABA Rating','Judge Party'])[['Decision Ideology']].count().unstack().reset_index())\n",
    "count_by_rating.columns = ['ABA Rating','Democrat Count','Republican Count']\n",
    "ideology_by_rating = ideology_by_rating.merge(count_by_rating, on = 'ABA Rating')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(11,8.5))\n",
    "ax.set_facecolor('white')\n",
    "ax.grid(False)\n",
    "ideology_by_rating.plot(kind = 'bar', x = 'ABA Rating', y = ['Democrat','Republican'], ax = ax, color =['b','r'])\n",
    "ax.set_title(\"Average Ideological Result by ABA Rating and Party\")\n",
    "ax.set_ylabel(\"Average Ideological Result\")\n",
    "for i, v in enumerate(ideology_by_rating['Democrat'].values):\n",
    "    text = ideology_by_rating[ideology_by_rating['Democrat'] == v]['Democrat Count'].iloc[0]\n",
    "    if v > 0:\n",
    "        ax.text(i - 0.25 , v + 1, text, color='b', fontdict = {'size': 10})\n",
    "    else: \n",
    "        ax.text(i - 0.25 , v - 1, text, color='b', fontdict = {'size': 10})\n",
    "for i, v in enumerate(ideology_by_rating['Republican'].values):\n",
    "    text = ideology_by_rating[ideology_by_rating['Republican'] == v]['Republican Count'].iloc[0]\n",
    "    if v > 0:\n",
    "        ax.text(i + 0.05, v + 1, text, color='r', fontdict = {'size': 10})\n",
    "    else: \n",
    "        ax.text(i + 0.05, v - 1, text, color='r', fontdict = {'size': 10})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "decisions = decision_data[list(judge_decisions) + ['House Democrats','House Republicans','Senate Democrats',\n",
    "                                                   'Senate Republicans','House Independents','Senate Independents',\n",
    "                                                   'Recess Appointment']]\n",
    "decisions = decisions.drop(columns = ['Authoring Judge'])\n",
    "decisions = decisions.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We begin by converting the decision ideology, which is either liberal or conservative, into a dummy.\n",
    "def libcon_dummy(ideology):\n",
    "    if ideology == 'Conservative':\n",
    "        return 1\n",
    "    if ideology == 'Liberal':\n",
    "        return 0\n",
    "    else:\n",
    "        return np.nan\n",
    "decisions['Decision Ideology'] = decisions['Decision Ideology'].apply(libcon_dummy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "categories_dec = ['Court Location', 'Circuit', 'Case Type', 'Case Category', 'Appointing President', 'Judge Party', 'Gender',\n",
    "              'Race', 'ABA Rating', 'Congress', 'Unity', 'Recess Appointment']\n",
    "\n",
    "# Categorical variables \n",
    "continuous_variables_dec = list(filter(lambda col: col not in categories_dec + ['Decision Ideology'], list(decisions)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_dec_train, X_dec_test, y_dec_train, y_dec_test = prep_data(\n",
    "    decisions, continuous_variables_dec, categories_dec, 'Decision Ideology'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "alphas = np.exp(np.linspace(-2., -12., 25))\n",
    "lr_model = linear_model.LinearRegression(n_jobs = 100)\n",
    "logistic_model = linear_model.LogisticRegression(solver=\"liblinear\", max_iter = 1000, n_jobs = 100)\n",
    "lasso_model = linear_model.LassoCV(cv=6, alphas = alphas,  max_iter=500, n_jobs = 100)\n",
    "forest_model = RandomForestRegressor(n_estimators = 100, n_jobs = 100)\n",
    "nn_scaled_model = pipeline.make_pipeline(\n",
    "    preprocessing.StandardScaler(),  # this will do the input scaling\n",
    "    neural_network.MLPRegressor((150,),activation = \"logistic\",\n",
    "                                solver=\"adam\",alpha=0.005)) # we tried a few alphas chose this one \n",
    "                                                            # based on minimizing mse_test ... but it was somewhat arbitrary\n",
    "models = { \"OLS\": lr_model, \"Logistic\": logistic_model, \"Lasso\": lasso_model,\n",
    "          \"Random Forest\": forest_model, \"Neural Network\":nn_scaled_model}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "MSE_by_model = pd.DataFrame(index = models.keys(),\n",
    "                            columns= [\"Train MSE\",\"Test MSE\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:1297: UserWarning:\n",
      "\n",
      "'n_jobs' > 1 does not have any effect when 'solver' is set to 'liblinear'. Got 'n_jobs' = 100.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n",
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ian\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:492: ConvergenceWarning:\n",
      "\n",
      "Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ideo_mse_list = [fit_and_report_mses(model,X_dec_train, X_dec_test, y_dec_train, y_dec_test) for model in models.values()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Train MSE</th>\n",
       "      <th>Test MSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>OLS</th>\n",
       "      <td>0.216954</td>\n",
       "      <td>3045509606943.511719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic</th>\n",
       "      <td>0.348573</td>\n",
       "      <td>0.351613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lasso</th>\n",
       "      <td>0.217721</td>\n",
       "      <td>0.219440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.063205</td>\n",
       "      <td>0.240012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neural Network</th>\n",
       "      <td>0.174729</td>\n",
       "      <td>0.230434</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Train MSE              Test MSE\n",
       "OLS             0.216954  3045509606943.511719\n",
       "Logistic        0.348573              0.351613\n",
       "Lasso           0.217721              0.219440\n",
       "Random Forest   0.063205              0.240012\n",
       "Neural Network  0.174729              0.230434"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MSE_by_model['Train MSE'] = ['%f' % result[\"mse_train\"] for result in ideo_mse_list]\n",
    "MSE_by_model['Test MSE'] = ['%f' % result[\"mse_test\"] for result in ideo_mse_list]\n",
    "MSE_by_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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