kdd_explorations_2021/model_selection/fairness_model_selection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "import os\n",
    "import yaml\n",
    "import sqlalchemy\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from IPython.display import display \n",
    "pd.options.display.max_columns = None\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('db_default_profile.yaml') as fd:\n",
    "    config = yaml.load(fd)\n",
    "    dburl = sqlalchemy.engine.url.URL(\n",
    "        \"postgres\",\n",
    "        host=config[\"host\"],\n",
    "        username=config[\"user\"],\n",
    "        database='johnson_county_ddj',\n",
    "        password=config[\"pass\"],\n",
    "        port=config[\"port\"],\n",
    "    )\n",
    "    engine_old = sqlalchemy.create_engine(dburl, poolclass=sqlalchemy.pool.QueuePool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "disp_cons_sql = \"\"\"\n",
    "WITH dist_res AS (\n",
    "  SELECT DISTINCT * FROM  kit_bias_class_figs.model_adjustment_results_race_2way\n",
    ")\n",
    ", mg_rns_value AS (\n",
    "  SELECT *,\n",
    "         row_number() OVER (PARTITION BY train_end_time, list_size, metric, parameter ORDER BY base_value DESC, base_max_recall_ratio ASC, RANDOM()) AS rn_base_value\n",
    "  FROM dist_res\n",
    "  WHERE past_train_end_time = train_end_time\n",
    "    AND base_max_recall_ratio < {disparity_constraint}\n",
    ")\n",
    ", mg_rns_disp AS (\n",
    "  SELECT *,\n",
    "         row_number() OVER (PARTITION BY train_end_time, list_size, metric, parameter ORDER BY base_max_recall_ratio ASC, base_value DESC, RANDOM()) AS rn_base_disparity\n",
    "  FROM dist_res\n",
    "  WHERE past_train_end_time = train_end_time\n",
    ")\n",
    ", base_mgs_hard_cons AS (\n",
    "  -- Best-performaing (in terms of precision) model groups meeting the hard constraint on disparity\n",
    "  SELECT * FROM mg_rns_value WHERE rn_base_value = 1\n",
    ")\n",
    ", base_mgs_soft_cons AS (\n",
    "  -- For time/size/metric combinations with no model groups meeting the hard constraint, fill in with lowest-disparity model group\n",
    "  SELECT\n",
    "      COALESCE(h.model_group_id, d.model_group_id) AS model_group_id,\n",
    "      COALESCE(h.train_end_time, d.train_end_time) AS train_end_time,\n",
    "      COALESCE(h.list_size, d.list_size) AS list_size,\n",
    "      COALESCE(h.metric, d.metric) AS metric,\n",
    "      COALESCE(h.parameter, d.parameter) AS parameter\n",
    "  FROM (SELECT * FROM mg_rns_disp WHERE rn_base_disparity = 1) as d\n",
    "  LEFT JOIN base_mgs_hard_cons h USING(train_end_time, list_size, metric, parameter)\n",
    ")\n",
    "-- Simple model selection on last time period, baseline with no recall adjustments\n",
    "SELECT 'Disparity Constraint {disparity_constraint} - Unadjusted'::VARCHAR(128) AS strategy,\n",
    "       r.train_end_time, r.past_train_end_time,\n",
    "       r.list_size, r.metric, r.parameter,\n",
    "       r.base_value AS value,\n",
    "       r.base_max_recall_ratio AS max_recall_ratio,\n",
    "       r.base_recall_white_to_nonwhite AS recall_white_to_nonwhite,\n",
    "       r.base_recall_nonwhite_to_white AS recall_nonwhite_to_white,\n",
    "       r.base_frac_white AS frac_white,\n",
    "       r.base_frac_nonwhite AS frac_nonwhite\n",
    "FROM dist_res r\n",
    "JOIN base_mgs_soft_cons b\n",
    "  ON r.model_group_id = b.model_group_id\n",
    "  AND r.past_train_end_time = b.train_end_time\n",
    "  AND r.list_size = b.list_size\n",
    "  AND r.metric = b.metric\n",
    "  AND r.parameter = b.parameter\n",
    "WHERE r.train_end_time > r.past_train_end_time\n",
    "ORDER BY train_end_time\n",
    ";\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "value_cons_sql = \"\"\"\n",
    "WITH dist_res AS (\n",
    "  SELECT DISTINCT * FROM  kit_bias_class_figs.model_adjustment_results_race_2way\n",
    ")\n",
    ", mg_rns_value AS (\n",
    "  SELECT *,\n",
    "         row_number() OVER (PARTITION BY train_end_time, list_size, metric, parameter ORDER BY base_value DESC, base_max_recall_ratio ASC, RANDOM()) AS rn_base_value\n",
    "  FROM dist_res\n",
    "  WHERE past_train_end_time = train_end_time\n",
    ")\n",
    ", best_mgs AS (\n",
    "  SELECT * FROM mg_rns_value WHERE rn_base_value = 1\n",
    ")\n",
    ", value_dist_cons AS (\n",
    "  SELECT r.*,\n",
    "         row_number() OVER (PARTITION BY r.train_end_time, r.list_size, r.metric, r.parameter ORDER BY r.base_max_recall_ratio ASC, r.base_value DESC, RANDOM()) AS rn_base_disparity\n",
    "  FROM dist_res r\n",
    "  JOIN best_mgs b USING(train_end_time, list_size, metric, parameter)\n",
    "  WHERE b.base_value - r.base_value <= {value_constraint}\n",
    ")\n",
    ", base_mgs_value_cons AS (\n",
    "  -- Lowest-disparity model groups within hard constraint distance from best model group in terms of metric value\n",
    "  SELECT * FROM value_dist_cons WHERE rn_base_disparity = 1\n",
    ")\n",
    "-- Simple model selection on last time period, baseline with no recall adjustments\n",
    "SELECT 'Accuracy Constraint {value_constraint} - Unadjusted'::VARCHAR(128) AS strategy,\n",
    "       r.train_end_time, r.past_train_end_time,\n",
    "       r.list_size, r.metric, r.parameter,\n",
    "       r.base_value AS value,\n",
    "       r.base_max_recall_ratio AS max_recall_ratio,\n",
    "       r.base_recall_white_to_nonwhite AS recall_white_to_nonwhite,\n",
    "       r.base_recall_nonwhite_to_white AS recall_nonwhite_to_white,\n",
    "       r.base_frac_white AS frac_white,\n",
    "       r.base_frac_nonwhite AS frac_nonwhite\n",
    "FROM dist_res r\n",
    "JOIN base_mgs_value_cons b\n",
    "  ON r.model_group_id = b.model_group_id\n",
    "  AND r.past_train_end_time = b.train_end_time\n",
    "  AND r.list_size = b.list_size\n",
    "  AND r.metric = b.metric\n",
    "  AND r.parameter = b.parameter\n",
    "WHERE r.train_end_time > r.past_train_end_time\n",
    "ORDER BY train_end_time\n",
    ";\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "res_dfs = []\n",
    "\n",
    "for c in [1.0, 1.05, 1.1, 1.2, 1.3, 1.4, 1.5, 2.0, 2.5]:\n",
    "    df = pd.read_sql(disp_cons_sql.format(disparity_constraint=c), engine_old)\n",
    "    res_dfs.append(df)\n",
    "\n",
    "for c in [0.6, 0.5, 0.25, 0.2, 0.15, 0.10, 0.05, 0.0]:\n",
    "    df = pd.read_sql(value_cons_sql.format(value_constraint=c), engine_old)\n",
    "    res_dfs.append(df)\n",
    "\n",
    "df = pd.concat(res_dfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['constraint_type'] = df['strategy'].map(lambda x: x.split('-')[0].split(' ')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5, 1.8)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6852ed8748>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['dataset'] = 'joco'\n",
    "ts_df_1k = df.copy()\n",
    "fig, ax = plt.subplots(figsize=(12,5))\n",
    "sns.lineplot(data=ts_df_1k.groupby(['constraint_type', 'strategy'])[['value', 'recall_white_to_nonwhite']].mean().reset_index(), \n",
    "              x='value', y='recall_white_to_nonwhite', \n",
    "              hue='strategy',\n",
    "#              hue='dataset', \n",
    "#              marker=\"o\", \n",
    "              style='constraint_type',\n",
    "              markers=True,\n",
    "#              color='dodgerblue',\n",
    "              markersize=10,\n",
    "              dashes=None,\n",
    "              ci=None,\n",
    "              ax=ax)\n",
    "\n",
    "\n",
    "x_coords = list(ts_df_1k.groupby(['constraint_type', 'strategy'])[['value', 'recall_white_to_nonwhite']].mean().reset_index()['value'].values)\n",
    "y_coords = list(ts_df_1k.groupby(['constraint_type', 'strategy'])[['value', 'recall_white_to_nonwhite']].mean().reset_index()['recall_white_to_nonwhite'].values)\n",
    "labels = list(ts_df_1k.groupby(['constraint_type', 'strategy'])['strategy'].min())\n",
    "\n",
    "prec_errors = 1.96*ts_df_1k.groupby(['constraint_type', 'strategy'])['value'].sem().values\n",
    "disp_errors = 1.96*ts_df_1k.groupby(['constraint_type', 'strategy'])['recall_white_to_nonwhite'].sem().values\n",
    "colors = sns.color_palette().as_hex()[0]\n",
    "ax.errorbar(x_coords, y_coords, \n",
    "            xerr=prec_errors, \n",
    "            yerr=disp_errors,\n",
    "            ecolor=colors, \n",
    "            fmt=' ', zorder=-1, capsize=5)\n",
    "\n",
    "ax.hlines(1.0, 0, 1, linestyles='dashed')\n",
    "\n",
    "ax.set_xlim((0.3,0.75))\n",
    "ax.set_ylim((0.5,1.80))\n",
    "#ax.get_legend().remove()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>dataset</th>\n",
       "      <th>strategy</th>\n",
       "      <th>value</th>\n",
       "      <th>recall_white_to_nonwhite</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>joco</td>\n",
       "      <td>Accuracy Constraint 0.0 - Unadjusted</td>\n",
       "      <td>0.554308</td>\n",
       "      <td>1.518707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>joco</td>\n",
       "      <td>Accuracy Constraint 0.05 - Unadjusted</td>\n",
       "      <td>0.530400</td>\n",
       "      <td>1.452619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>joco</td>\n",
       "      <td>Accuracy Constraint 0.1 - Unadjusted</td>\n",
       "      <td>0.507800</td>\n",
       "      <td>1.438104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>joco</td>\n",
       "      <td>Accuracy Constraint 0.15 - Unadjusted</td>\n",
       "      <td>0.469800</td>\n",
       "      <td>1.302689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>joco</td>\n",
       "      <td>Accuracy Constraint 0.2 - Unadjusted</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>1.276797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>joco</td>\n",
       "      <td>Accuracy Constraint 0.25 - Unadjusted</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>1.276797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>joco</td>\n",
       "      <td>Accuracy Constraint 0.5 - Unadjusted</td>\n",
       "      <td>0.435600</td>\n",
       "      <td>1.318078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>joco</td>\n",
       "      <td>Accuracy Constraint 0.6 - Unadjusted</td>\n",
       "      <td>0.435600</td>\n",
       "      <td>1.318078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>joco</td>\n",
       "      <td>Disparity Constraint 1.0 - Unadjusted</td>\n",
       "      <td>0.435600</td>\n",
       "      <td>1.318078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>joco</td>\n",
       "      <td>Disparity Constraint 1.05 - Unadjusted</td>\n",
       "      <td>0.435600</td>\n",
       "      <td>1.318078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>joco</td>\n",
       "      <td>Disparity Constraint 1.1 - Unadjusted</td>\n",
       "      <td>0.442800</td>\n",
       "      <td>1.324425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>joco</td>\n",
       "      <td>Disparity Constraint 1.2 - Unadjusted</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>1.372456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>joco</td>\n",
       "      <td>Disparity Constraint 1.3 - Unadjusted</td>\n",
       "      <td>0.513400</td>\n",
       "      <td>1.443448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>joco</td>\n",
       "      <td>Disparity Constraint 1.4 - Unadjusted</td>\n",
       "      <td>0.535200</td>\n",
       "      <td>1.467991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>joco</td>\n",
       "      <td>Disparity Constraint 1.5 - Unadjusted</td>\n",
       "      <td>0.537800</td>\n",
       "      <td>1.444482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>joco</td>\n",
       "      <td>Disparity Constraint 2.0 - Unadjusted</td>\n",
       "      <td>0.554308</td>\n",
       "      <td>1.518707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>joco</td>\n",
       "      <td>Disparity Constraint 2.5 - Unadjusted</td>\n",
       "      <td>0.554308</td>\n",
       "      <td>1.518707</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dataset                                strategy     value  \\\n",
       "0     joco    Accuracy Constraint 0.0 - Unadjusted  0.554308   \n",
       "1     joco   Accuracy Constraint 0.05 - Unadjusted  0.530400   \n",
       "2     joco    Accuracy Constraint 0.1 - Unadjusted  0.507800   \n",
       "3     joco   Accuracy Constraint 0.15 - Unadjusted  0.469800   \n",
       "4     joco    Accuracy Constraint 0.2 - Unadjusted  0.449000   \n",
       "5     joco   Accuracy Constraint 0.25 - Unadjusted  0.449000   \n",
       "6     joco    Accuracy Constraint 0.5 - Unadjusted  0.435600   \n",
       "7     joco    Accuracy Constraint 0.6 - Unadjusted  0.435600   \n",
       "8     joco   Disparity Constraint 1.0 - Unadjusted  0.435600   \n",
       "9     joco  Disparity Constraint 1.05 - Unadjusted  0.435600   \n",
       "10    joco   Disparity Constraint 1.1 - Unadjusted  0.442800   \n",
       "11    joco   Disparity Constraint 1.2 - Unadjusted  0.470000   \n",
       "12    joco   Disparity Constraint 1.3 - Unadjusted  0.513400   \n",
       "13    joco   Disparity Constraint 1.4 - Unadjusted  0.535200   \n",
       "14    joco   Disparity Constraint 1.5 - Unadjusted  0.537800   \n",
       "15    joco   Disparity Constraint 2.0 - Unadjusted  0.554308   \n",
       "16    joco   Disparity Constraint 2.5 - Unadjusted  0.554308   \n",
       "\n",
       "    recall_white_to_nonwhite  \n",
       "0                   1.518707  \n",
       "1                   1.452619  \n",
       "2                   1.438104  \n",
       "3                   1.302689  \n",
       "4                   1.276797  \n",
       "5                   1.276797  \n",
       "6                   1.318078  \n",
       "7                   1.318078  \n",
       "8                   1.318078  \n",
       "9                   1.318078  \n",
       "10                  1.324425  \n",
       "11                  1.372456  \n",
       "12                  1.443448  \n",
       "13                  1.467991  \n",
       "14                  1.444482  \n",
       "15                  1.518707  \n",
       "16                  1.518707  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_df_1k.groupby(['dataset', 'strategy'])[['value', 'recall_white_to_nonwhite']].mean().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## JoCo Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [],
   "source": [
    "original_sql = \"\"\"\n",
    "WITH dist_res AS (\n",
    "  SELECT DISTINCT * FROM  kit_bias_class_figs.model_adjustment_results_race_2way\n",
    ")\n",
    ", mg_rns_value AS (\n",
    "  SELECT *,\n",
    "         row_number() OVER (PARTITION BY train_end_time, list_size, metric, parameter ORDER BY base_value DESC, base_max_recall_ratio ASC, RANDOM()) AS rn_base_value\n",
    "  FROM dist_res\n",
    "  WHERE past_train_end_time = train_end_time\n",
    ")\n",
    ", best_mgs AS (\n",
    "  SELECT * FROM mg_rns_value WHERE rn_base_value = 1\n",
    ")\n",
    "-- Simple model selection on last time period, baseline with no recall adjustments\n",
    "SELECT 'Original'::VARCHAR(128) AS strategy,\n",
    "       r.train_end_time, r.past_train_end_time,\n",
    "       r.list_size, r.metric, r.parameter,\n",
    "       r.base_value AS value,\n",
    "       r.base_max_recall_ratio AS max_recall_ratio,\n",
    "       r.base_recall_white_to_nonwhite AS recall_white_to_nonwhite,\n",
    "       r.base_recall_nonwhite_to_white AS recall_nonwhite_to_white,\n",
    "       r.base_frac_white AS frac_white,\n",
    "       r.base_frac_nonwhite AS frac_nonwhite\n",
    "FROM dist_res r\n",
    "JOIN best_mgs b\n",
    "  ON r.model_group_id = b.model_group_id\n",
    "  AND r.past_train_end_time = b.train_end_time\n",
    "  AND r.list_size = b.list_size\n",
    "  AND r.metric = b.metric\n",
    "  AND r.parameter = b.parameter\n",
    "WHERE r.train_end_time > r.past_train_end_time\n",
    "ORDER BY train_end_time\n",
    ";\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [],
   "source": [
    "j_res_dfs = []\n",
    "\n",
    "letters = list(reversed(list('ABCDEFGH')))\n",
    "\n",
    "for i, c in enumerate([1.0, 1.05, 1.1, 1.2, 1.3, 1.5, 2.0, 5.0]):\n",
    "    df = pd.read_sql(disp_cons_sql.format(disparity_constraint=c), engine_old)\n",
    "    df['Level'] = letters[i]\n",
    "    j_res_dfs.append(df)\n",
    "\n",
    "for i, c in enumerate([0.6, 0.5, 0.25, 0.2, 0.15, 0.10, 0.05, 0.0]):\n",
    "    df = pd.read_sql(value_cons_sql.format(value_constraint=c), engine_old)\n",
    "    df['Level'] = letters[i]\n",
    "    j_res_dfs.append(df)\n",
    "\n",
    "df = pd.read_sql(original_sql, engine_old)\n",
    "df['Level'] = 'Original'\n",
    "j_res_dfs.append(df)\n",
    "\n",
    "j_df = pd.concat(j_res_dfs)\n",
    "j_df['Constraint Type'] = j_df['strategy'].map(lambda x: x.split(' ')[0])\n",
    "j_df['dataset'] = 'joco'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5, 1.9)"
      ]
     },
     "execution_count": 371,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f685051ee10>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts_df_1k = j_df.copy()\n",
    "disp_col = 'recall_white_to_nonwhite'\n",
    "\n",
    "colorlist = ['maroon', \n",
    "             'red',\n",
    "             '#001c7f', #Original\n",
    "             ]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "sns.lineplot(data=ts_df_1k.groupby(['Constraint Type', 'Level'])[['value', disp_col]].mean().reset_index(), \n",
    "              x='value', y=disp_col, \n",
    "              hue='Constraint Type',\n",
    "              hue_order=['Accuracy', 'Disparity', 'Original'],\n",
    "#              palette=sns.color_palette(\"nipy_spectral\", 8) + list(reversed(sns.color_palette(\"nipy_spectral\", 11))),\n",
    "#              palette=sns.color_palette(\"nipy_spectral\", 3),\n",
    "              palette=colorlist,\n",
    "#              hue_order=dc3_sort_ord,\n",
    "#              hue='dataset', \n",
    "#              marker=\"o\", \n",
    "              style='Level',\n",
    "              markers=['o', 'v', '^', '<', '>', 's', 'd', 'p', 'X'],\n",
    "              style_order=['Original'] + list(reversed(letters))[:8],\n",
    "#              color='dodgerblue',\n",
    "              markersize=10,\n",
    "              dashes=None,\n",
    "              ci=None,\n",
    "              ax=ax)\n",
    "\n",
    "\n",
    "for i, t in enumerate(['Accuracy', 'Disparity', 'Original']):\n",
    "    tmp_df = ts_df_1k.loc[ts_df_1k['Constraint Type'] == t].copy()\n",
    "\n",
    "    x_coords = list(tmp_df.groupby(['Constraint Type', 'Level'])[['value', disp_col]].mean().reset_index()['value'].values)\n",
    "    y_coords = list(tmp_df.groupby(['Constraint Type', 'Level'])[['value', disp_col]].mean().reset_index()[disp_col].values)\n",
    "    labels = list(tmp_df.groupby(['Constraint Type', 'Level'])['Level'].min())\n",
    "\n",
    "    prec_errors = 1.96*tmp_df.groupby(['Constraint Type', 'Level'])['value'].sem().values\n",
    "    disp_errors = 1.96*tmp_df.groupby(['Constraint Type', 'Level'])[disp_col].sem().values\n",
    "    colors = colorlist[i]\n",
    "    ax.errorbar(x_coords, y_coords, \n",
    "                xerr=prec_errors, \n",
    "                yerr=disp_errors,\n",
    "                ecolor=colors, \n",
    "                fmt=' ', zorder=-1, capsize=5)\n",
    "\n",
    "\n",
    "ax.hlines(1.0, 0, 1, linestyles='dashed')\n",
    "\n",
    "ax.legend(loc='lower right', fontsize=14)\n",
    "\n",
    "ax.set_ylabel('Recall Disparity', fontsize=32)\n",
    "ax.set_xlabel('Precision at Top-K', fontsize=28)\n",
    "\n",
    "ax.tick_params(axis='x', labelsize=24)\n",
    "ax.tick_params(axis='y', labelsize=24)\n",
    "\n",
    "ax.set_xlim((0.35,0.72))\n",
    "ax.set_ylim((0.5,1.90))\n",
    "#ax.get_legend().remove()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Donors Choose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('db_default_profile.yaml') as fd:\n",
    "    config = yaml.load(fd)\n",
    "    dburl = sqlalchemy.engine.url.URL(\n",
    "        \"postgres\",\n",
    "        host=config[\"host\"],\n",
    "        username=config[\"user\"],\n",
    "        database='donors',\n",
    "        password=config[\"pass\"],\n",
    "        port=config[\"port\"],\n",
    "    )\n",
    "    engine_donors = sqlalchemy.create_engine(dburl, poolclass=sqlalchemy.pool.QueuePool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "dc_disp_cons_sql = \"\"\"\n",
    "WITH dist_res AS (\n",
    "  SELECT DISTINCT * FROM hemank_bias_2way.model_adjustment_results_plevel\n",
    ")\n",
    ", mg_rns_value AS (\n",
    "  SELECT *,\n",
    "         row_number() OVER (PARTITION BY train_end_time, list_size, metric, parameter ORDER BY base_value DESC, base_max_recall_ratio ASC, RANDOM()) AS rn_base_value\n",
    "  FROM dist_res\n",
    "  WHERE past_train_end_time = train_end_time\n",
    "    AND base_max_recall_ratio < {disparity_constraint}\n",
    ")\n",
    ", mg_rns_disp AS (\n",
    "  SELECT *,\n",
    "         row_number() OVER (PARTITION BY train_end_time, list_size, metric, parameter ORDER BY base_max_recall_ratio ASC, base_value DESC, RANDOM()) AS rn_base_disparity\n",
    "  FROM dist_res\n",
    "  WHERE past_train_end_time = train_end_time\n",
    ")\n",
    ", base_mgs_hard_cons AS (\n",
    "  -- Best-performaing (in terms of precision) model groups meeting the hard constraint on disparity\n",
    "  SELECT * FROM mg_rns_value WHERE rn_base_value = 1\n",
    ")\n",
    ", base_mgs_soft_cons AS (\n",
    "  -- For time/size/metric combinations with no model groups meeting the hard constraint, fill in with lowest-disparity model group\n",
    "  SELECT\n",
    "      COALESCE(h.model_group_id, d.model_group_id) AS model_group_id,\n",
    "      COALESCE(h.train_end_time, d.train_end_time) AS train_end_time,\n",
    "      COALESCE(h.list_size, d.list_size) AS list_size,\n",
    "      COALESCE(h.metric, d.metric) AS metric,\n",
    "      COALESCE(h.parameter, d.parameter) AS parameter\n",
    "  FROM (SELECT * FROM mg_rns_disp WHERE rn_base_disparity = 1) as d\n",
    "  LEFT JOIN base_mgs_hard_cons h USING(train_end_time, list_size, metric, parameter)\n",
    ")\n",
    "-- Simple model selection on last time period, baseline with no recall adjustments\n",
    "SELECT 'Disparity Constraint {disparity_constraint} - Unadjusted'::VARCHAR(128) AS strategy,\n",
    "       r.train_end_time, r.past_train_end_time,\n",
    "       r.list_size, r.metric, r.parameter,\n",
    "       r.base_value AS value,\n",
    "       r.base_max_recall_ratio AS max_recall_ratio,\n",
    "       r.base_recall_highest_to_not_highest AS recall_highest_to_not,\n",
    "       r.base_recall_not_highest_to_highest AS recall_not_to_highest,\n",
    "       r.base_frac_highest AS frac_highest,\n",
    "       r.base_frac_not_highest AS frac_not_highest\n",
    "FROM dist_res r\n",
    "JOIN base_mgs_soft_cons b\n",
    "  ON r.model_group_id = b.model_group_id\n",
    "  AND r.past_train_end_time = b.train_end_time\n",
    "  AND r.list_size = b.list_size\n",
    "  AND r.metric = b.metric\n",
    "  AND r.parameter = b.parameter\n",
    "WHERE r.train_end_time > r.past_train_end_time\n",
    "ORDER BY train_end_time\n",
    ";\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "dc_value_cons_sql = \"\"\"\n",
    "WITH dist_res AS (\n",
    "  SELECT DISTINCT * FROM  hemank_bias_2way.model_adjustment_results_plevel\n",
    ")\n",
    ", mg_rns_value AS (\n",
    "  SELECT *,\n",
    "         row_number() OVER (PARTITION BY train_end_time, list_size, metric, parameter ORDER BY base_value DESC, base_max_recall_ratio ASC, RANDOM()) AS rn_base_value\n",
    "  FROM dist_res\n",
    "  WHERE past_train_end_time = train_end_time\n",
    ")\n",
    ", best_mgs AS (\n",
    "  SELECT * FROM mg_rns_value WHERE rn_base_value = 1\n",
    ")\n",
    ", value_dist_cons AS (\n",
    "  SELECT r.*,\n",
    "         row_number() OVER (PARTITION BY r.train_end_time, r.list_size, r.metric, r.parameter ORDER BY r.base_max_recall_ratio ASC, r.base_value DESC, RANDOM()) AS rn_base_disparity\n",
    "  FROM dist_res r\n",
    "  JOIN best_mgs b USING(train_end_time, list_size, metric, parameter)\n",
    "  WHERE b.base_value - r.base_value <= {value_constraint}\n",
    ")\n",
    ", base_mgs_value_cons AS (\n",
    "  -- Lowest-disparity model groups within hard constraint distance from best model group in terms of metric value\n",
    "  SELECT * FROM value_dist_cons WHERE rn_base_disparity = 1\n",
    ")\n",
    "-- Simple model selection on last time period, baseline with no recall adjustments\n",
    "SELECT 'Accuracy Constraint {value_constraint} - Unadjusted'::VARCHAR(128) AS strategy,\n",
    "       r.train_end_time, r.past_train_end_time,\n",
    "       r.list_size, r.metric, r.parameter,\n",
    "       r.base_value AS value,\n",
    "       r.base_max_recall_ratio AS max_recall_ratio,\n",
    "       r.base_recall_highest_to_not_highest AS recall_highest_to_not,\n",
    "       r.base_recall_not_highest_to_highest AS recall_not_to_highest,\n",
    "       r.base_frac_highest AS frac_highest,\n",
    "       r.base_frac_not_highest AS frac_not_highest\n",
    "FROM dist_res r\n",
    "JOIN base_mgs_value_cons b\n",
    "  ON r.model_group_id = b.model_group_id\n",
    "  AND r.past_train_end_time = b.train_end_time\n",
    "  AND r.list_size = b.list_size\n",
    "  AND r.metric = b.metric\n",
    "  AND r.parameter = b.parameter\n",
    "WHERE r.train_end_time > r.past_train_end_time\n",
    "ORDER BY train_end_time\n",
    ";\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "dc_res_dfs = []\n",
    "\n",
    "for c in [1.0, 1.05, 1.1, 1.2, 1.3, 1.4, 1.5, 2.0, 2.5, 3.0, 5.0]:\n",
    "    df = pd.read_sql(dc_disp_cons_sql.format(disparity_constraint=c), engine_donors)\n",
    "    dc_res_dfs.append(df)\n",
    "\n",
    "for c in [0.6, 0.5, 0.25, 0.2, 0.15, 0.10, 0.05, 0.0]:\n",
    "    df = pd.read_sql(dc_value_cons_sql.format(value_constraint=c), engine_donors)\n",
    "    dc_res_dfs.append(df)\n",
    "\n",
    "dc_df = pd.concat(dc_res_dfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "dc_df['constraint_type'] = dc_df['strategy'].map(lambda x: x.split('-')[0].split(' ')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5, 1.9)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6852c78438>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dc_df['dataset'] = 'donors_choose'\n",
    "ts_df_1k = dc_df.copy()\n",
    "fig, ax = plt.subplots(figsize=(12,5))\n",
    "sns.lineplot(data=ts_df_1k.groupby(['constraint_type', 'strategy'])[['value', 'recall_not_to_highest']].mean().reset_index(), \n",
    "              x='value', y='recall_not_to_highest', \n",
    "              hue='strategy',\n",
    "#              hue='dataset', \n",
    "#              marker=\"o\", \n",
    "              style='constraint_type',\n",
    "              markers=True,\n",
    "#              color='dodgerblue',\n",
    "              markersize=10,\n",
    "              dashes=None,\n",
    "              ci=None,\n",
    "              ax=ax)\n",
    "\n",
    "\n",
    "x_coords = list(ts_df_1k.groupby(['constraint_type', 'strategy'])[['value', 'recall_not_to_highest']].mean().reset_index()['value'].values)\n",
    "y_coords = list(ts_df_1k.groupby(['constraint_type', 'strategy'])[['value', 'recall_not_to_highest']].mean().reset_index()['recall_not_to_highest'].values)\n",
    "labels = list(ts_df_1k.groupby(['constraint_type', 'strategy'])['strategy'].min())\n",
    "\n",
    "prec_errors = 1.96*ts_df_1k.groupby(['constraint_type', 'strategy'])['value'].sem().values\n",
    "disp_errors = 1.96*ts_df_1k.groupby(['constraint_type', 'strategy'])['recall_not_to_highest'].sem().values\n",
    "colors = sns.color_palette().as_hex()[0]\n",
    "ax.errorbar(x_coords, y_coords, \n",
    "            xerr=prec_errors, \n",
    "            yerr=disp_errors,\n",
    "            ecolor=colors, \n",
    "            fmt=' ', zorder=-1, capsize=5)\n",
    "\n",
    "\n",
    "ax.hlines(1.0, 0, 1, linestyles='dashed')\n",
    "\n",
    "ax.set_xlim((0.2,0.75))\n",
    "ax.set_ylim((0.5,1.90))\n",
    "#ax.get_legend().remove()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Donors Choose (All Train End Times)\n",
    "\n",
    "Looks like above was from an earlier run that only had a subset of the train_end_times; below is the full set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('db_default_profile.yaml') as fd:\n",
    "    config = yaml.load(fd)\n",
    "    dburl = sqlalchemy.engine.url.URL(\n",
    "        \"postgres\",\n",
    "        host=config[\"host\"],\n",
    "        username=config[\"user\"],\n",
    "        database='donors_kit',\n",
    "        password=config[\"pass\"],\n",
    "        port=config[\"port\"],\n",
    "    )\n",
    "    engine_donors3 = sqlalchemy.create_engine(dburl, poolclass=sqlalchemy.pool.QueuePool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "dc3_disp_cons_sql = \"\"\"\n",
    "WITH dist_res AS (\n",
    "  SELECT DISTINCT * FROM kit_bias_all.model_adjustment_results_plevel\n",
    "      WHERE list_size=1000\n",
    "      AND train_end_time >= '2011-03-01'\n",
    ")\n",
    ", mg_rns_value AS (\n",
    "  SELECT *,\n",
    "         row_number() OVER (PARTITION BY train_end_time, list_size, metric, parameter ORDER BY base_value DESC, base_max_recall_ratio ASC, RANDOM()) AS rn_base_value\n",
    "  FROM dist_res\n",
    "  WHERE past_train_end_time = train_end_time\n",
    "    AND base_max_recall_ratio < {disparity_constraint}\n",
    ")\n",
    ", mg_rns_disp AS (\n",
    "  SELECT *,\n",
    "         row_number() OVER (PARTITION BY train_end_time, list_size, metric, parameter ORDER BY base_max_recall_ratio ASC, base_value DESC, RANDOM()) AS rn_base_disparity\n",
    "  FROM dist_res\n",
    "  WHERE past_train_end_time = train_end_time\n",
    ")\n",
    ", base_mgs_hard_cons AS (\n",
    "  -- Best-performaing (in terms of precision) model groups meeting the hard constraint on disparity\n",
    "  SELECT * FROM mg_rns_value WHERE rn_base_value = 1\n",
    ")\n",
    ", base_mgs_soft_cons AS (\n",
    "  -- For time/size/metric combinations with no model groups meeting the hard constraint, fill in with lowest-disparity model group\n",
    "  SELECT\n",
    "      COALESCE(h.model_group_id, d.model_group_id) AS model_group_id,\n",
    "      COALESCE(h.train_end_time, d.train_end_time) AS train_end_time,\n",
    "      COALESCE(h.list_size, d.list_size) AS list_size,\n",
    "      COALESCE(h.metric, d.metric) AS metric,\n",
    "      COALESCE(h.parameter, d.parameter) AS parameter\n",
    "  FROM (SELECT * FROM mg_rns_disp WHERE rn_base_disparity = 1) as d\n",
    "  LEFT JOIN base_mgs_hard_cons h USING(train_end_time, list_size, metric, parameter)\n",
    ")\n",
    "-- Simple model selection on last time period, baseline with no recall adjustments\n",
    "SELECT 'Disparity <= {disparity_constraint}'::VARCHAR(128) AS strategy,\n",
    "       r.train_end_time, r.past_train_end_time,\n",
    "       r.list_size, r.metric, r.parameter,\n",
    "       r.base_value AS value,\n",
    "       r.base_max_recall_ratio AS max_recall_ratio,\n",
    "       r.base_recall_highest_to_not_highest AS recall_highest_to_not,\n",
    "       r.base_recall_not_highest_to_highest AS recall_not_to_highest,\n",
    "       r.base_frac_highest AS frac_highest,\n",
    "       r.base_frac_not_highest AS frac_not_highest\n",
    "FROM dist_res r\n",
    "JOIN base_mgs_soft_cons b\n",
    "  ON r.model_group_id = b.model_group_id\n",
    "  AND r.past_train_end_time = b.train_end_time\n",
    "  AND r.list_size = b.list_size\n",
    "  AND r.metric = b.metric\n",
    "  AND r.parameter = b.parameter\n",
    "WHERE r.train_end_time > r.past_train_end_time\n",
    "ORDER BY train_end_time\n",
    ";\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "dc3_value_cons_sql = \"\"\"\n",
    "WITH dist_res AS (\n",
    "  SELECT DISTINCT * FROM  kit_bias_all.model_adjustment_results_plevel\n",
    "      WHERE list_size=1000\n",
    "      AND train_end_time >= '2011-03-01'\n",
    ")\n",
    ", mg_rns_value AS (\n",
    "  SELECT *,\n",
    "         row_number() OVER (PARTITION BY train_end_time, list_size, metric, parameter ORDER BY base_value DESC, base_max_recall_ratio ASC, RANDOM()) AS rn_base_value\n",
    "  FROM dist_res\n",
    "  WHERE past_train_end_time = train_end_time\n",
    ")\n",
    ", best_mgs AS (\n",
    "  SELECT * FROM mg_rns_value WHERE rn_base_value = 1\n",
    ")\n",
    ", value_dist_cons AS (\n",
    "  SELECT r.*,\n",
    "         row_number() OVER (PARTITION BY r.train_end_time, r.list_size, r.metric, r.parameter ORDER BY r.base_max_recall_ratio ASC, r.base_value DESC, RANDOM()) AS rn_base_disparity\n",
    "  FROM dist_res r\n",
    "  JOIN best_mgs b USING(train_end_time, list_size, metric, parameter)\n",
    "  WHERE b.base_value - r.base_value <= {value_constraint}\n",
    ")\n",
    ", base_mgs_value_cons AS (\n",
    "  -- Lowest-disparity model groups within hard constraint distance from best model group in terms of metric value\n",
    "  SELECT * FROM value_dist_cons WHERE rn_base_disparity = 1\n",
    ")\n",
    "-- Simple model selection on last time period, baseline with no recall adjustments\n",
    "SELECT 'Accuracy Loss <= {value_constraint}'::VARCHAR(128) AS strategy,\n",
    "       r.train_end_time, r.past_train_end_time,\n",
    "       r.list_size, r.metric, r.parameter,\n",
    "       r.base_value AS value,\n",
    "       r.base_max_recall_ratio AS max_recall_ratio,\n",
    "       r.base_recall_highest_to_not_highest AS recall_highest_to_not,\n",
    "       r.base_recall_not_highest_to_highest AS recall_not_to_highest,\n",
    "       r.base_frac_highest AS frac_highest,\n",
    "       r.base_frac_not_highest AS frac_not_highest\n",
    "FROM dist_res r\n",
    "JOIN base_mgs_value_cons b\n",
    "  ON r.model_group_id = b.model_group_id\n",
    "  AND r.past_train_end_time = b.train_end_time\n",
    "  AND r.list_size = b.list_size\n",
    "  AND r.metric = b.metric\n",
    "  AND r.parameter = b.parameter\n",
    "WHERE r.train_end_time > r.past_train_end_time\n",
    "ORDER BY train_end_time\n",
    ";\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "dc3_res_dfs = []\n",
    "# dc3_sort_ord = []\n",
    "\n",
    "for c in reversed([1.0, 1.05, 1.1, 1.2, 1.3, 1.4, 1.5, 2.0, 2.5, 3.0, 5.0]):\n",
    "    df = pd.read_sql(dc3_disp_cons_sql.format(disparity_constraint=c), engine_donors)\n",
    "    dc3_res_dfs.append(df)\n",
    "#    dc3_sort_ord.append('Disparity <= %s' % c)\n",
    "\n",
    "for c in [0.6, 0.5, 0.25, 0.2, 0.15, 0.10, 0.05, 0.0]:\n",
    "    df = pd.read_sql(dc3_value_cons_sql.format(value_constraint=c), engine_donors)\n",
    "    dc3_res_dfs.append(df)\n",
    "#    dc3_sort_ord.append('Accuracy Loss <= %s' % c)\n",
    "\n",
    "dc3_df = pd.concat(dc3_res_dfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "dc3_res_dfs = []\n",
    "# dc3_sort_ord = []\n",
    "\n",
    "for c in reversed([1.0, 1.05, 1.1, 1.2, 1.3, 1.5, 2.0, 5.0]):\n",
    "    df = pd.read_sql(dc3_disp_cons_sql.format(disparity_constraint=c), engine_donors)\n",
    "    dc3_res_dfs.append(df)\n",
    "#    dc3_sort_ord.append('Disparity <= %s' % c)\n",
    "\n",
    "for c in [0.6, 0.5, 0.25, 0.2, 0.15, 0.10, 0.05, 0.0]:\n",
    "    df = pd.read_sql(dc3_value_cons_sql.format(value_constraint=c), engine_donors)\n",
    "    dc3_res_dfs.append(df)\n",
    "#    dc3_sort_ord.append('Accuracy Loss <= %s' % c)\n",
    "\n",
    "dc3_df = pd.concat(dc3_res_dfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "dc3_df['constraint_type'] = dc3_df['strategy'].map(lambda x: x.split(' ')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5, 1.9)"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6850aadd30>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dc3_df['dataset'] = 'donors_choose'\n",
    "ts_df_1k = dc3_df.copy()\n",
    "fig, ax = plt.subplots(figsize=(12,5))\n",
    "sns.lineplot(data=ts_df_1k.groupby(['constraint_type', 'strategy'])[['value', 'recall_not_to_highest']].mean().reset_index(), \n",
    "              x='value', y='recall_not_to_highest', \n",
    "              hue='strategy',\n",
    "#              palette=sns.color_palette(\"nipy_spectral\", 8) + list(reversed(sns.color_palette(\"nipy_spectral\", 11))),\n",
    "              palette=sns.color_palette(\"nipy_spectral\", 8) + list(reversed(sns.color_palette(\"nipy_spectral\", 8))),\n",
    "#              hue_order=dc3_sort_ord,\n",
    "#              hue='dataset', \n",
    "#              marker=\"o\", \n",
    "              style='constraint_type',\n",
    "              markers=True,\n",
    "#              color='dodgerblue',\n",
    "              markersize=10,\n",
    "              dashes=None,\n",
    "              ci=None,\n",
    "              ax=ax)\n",
    "\n",
    "\n",
    "x_coords = list(ts_df_1k.groupby(['constraint_type', 'strategy'])[['value', 'recall_not_to_highest']].mean().reset_index()['value'].values)\n",
    "y_coords = list(ts_df_1k.groupby(['constraint_type', 'strategy'])[['value', 'recall_not_to_highest']].mean().reset_index()['recall_not_to_highest'].values)\n",
    "labels = list(ts_df_1k.groupby(['constraint_type', 'strategy'])['strategy'].min())\n",
    "\n",
    "prec_errors = 1.96*ts_df_1k.groupby(['constraint_type', 'strategy'])['value'].sem().values\n",
    "disp_errors = 1.96*ts_df_1k.groupby(['constraint_type', 'strategy'])['recall_not_to_highest'].sem().values\n",
    "colors = sns.color_palette().as_hex()[0]\n",
    "ax.errorbar(x_coords, y_coords, \n",
    "            xerr=prec_errors, \n",
    "            yerr=disp_errors,\n",
    "            ecolor=colors, \n",
    "            fmt=' ', zorder=-1, capsize=5)\n",
    "\n",
    "\n",
    "ax.hlines(1.0, 0, 1, linestyles='dashed')\n",
    "\n",
    "ax.set_xlim((0.2,0.7))\n",
    "ax.set_ylim((0.5,1.90))\n",
    "#ax.get_legend().remove()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Migrate the `kit_bias_all` tables from the extracted DB to the current working DB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ohio.ext.pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp_df = pd.read_sql('SELECT * FROM kit_bias_all.model_adjustment_results_plevel', engine_donors3)\n",
    "exp_df.to_csv('dc__kit_bias_all__model_adjustment_results_plevel.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp_df.to_sql('model_adjustment_results_plevel', engine_donors, schema='kit_bias_all', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp_df = pd.read_sql('SELECT * FROM kit_bias_all.composite_results_plevel', engine_donors3)\n",
    "exp_df.to_csv('dc__kit_bias_all__composite_results_plevel.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp_df.to_sql('composite_results_plevel', engine_donors, schema='kit_bias_all', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine_donors3.dispose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Donors Plot Updates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "dc3_original_sql = \"\"\"\n",
    "WITH dist_res AS (\n",
    "  SELECT DISTINCT * FROM  kit_bias_all.model_adjustment_results_plevel\n",
    "      WHERE list_size=1000\n",
    "      AND train_end_time >= '2011-03-01'\n",
    ")\n",
    ", mg_rns_value AS (\n",
    "  SELECT *,\n",
    "         row_number() OVER (PARTITION BY train_end_time, list_size, metric, parameter ORDER BY base_value DESC, base_max_recall_ratio ASC, RANDOM()) AS rn_base_value\n",
    "  FROM dist_res\n",
    "  WHERE past_train_end_time = train_end_time\n",
    ")\n",
    ", best_mgs AS (\n",
    "  SELECT * FROM mg_rns_value WHERE rn_base_value = 1\n",
    ")\n",
    "-- Simple model selection on last time period, baseline with no recall adjustments\n",
    "SELECT 'Original'::VARCHAR(128) AS strategy,\n",
    "       r.train_end_time, r.past_train_end_time,\n",
    "       r.list_size, r.metric, r.parameter,\n",
    "       r.base_value AS value,\n",
    "       r.base_max_recall_ratio AS max_recall_ratio,\n",
    "       r.base_recall_highest_to_not_highest AS recall_highest_to_not,\n",
    "       r.base_recall_not_highest_to_highest AS recall_not_to_highest,\n",
    "       r.base_frac_highest AS frac_highest,\n",
    "       r.base_frac_not_highest AS frac_not_highest\n",
    "FROM dist_res r\n",
    "JOIN best_mgs b\n",
    "  ON r.model_group_id = b.model_group_id\n",
    "  AND r.past_train_end_time = b.train_end_time\n",
    "  AND r.list_size = b.list_size\n",
    "  AND r.metric = b.metric\n",
    "  AND r.parameter = b.parameter\n",
    "WHERE r.train_end_time > r.past_train_end_time\n",
    "ORDER BY train_end_time\n",
    ";\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [],
   "source": [
    "dc3_res_dfs = []\n",
    "# dc3_sort_ord = []\n",
    "\n",
    "letters = list(reversed(list('ABCDEFGH')))\n",
    "\n",
    "for i, c in enumerate([1.0, 1.05, 1.1, 1.2, 1.3, 1.5, 2.0, 5.0]):\n",
    "    df = pd.read_sql(dc3_disp_cons_sql.format(disparity_constraint=c), engine_donors)\n",
    "    df['Level'] = letters[i]\n",
    "    dc3_res_dfs.append(df)\n",
    "#    dc3_sort_ord.append('Disparity <= %s' % c)\n",
    "\n",
    "for i, c in enumerate([0.6, 0.5, 0.25, 0.2, 0.15, 0.10, 0.05, 0.0]):\n",
    "    df = pd.read_sql(dc3_value_cons_sql.format(value_constraint=c), engine_donors)\n",
    "    df['Level'] = letters[i]\n",
    "    dc3_res_dfs.append(df)\n",
    "#    dc3_sort_ord.append('Accuracy Loss <= %s' % c)\n",
    "\n",
    "df = pd.read_sql(dc3_original_sql, engine_donors)\n",
    "df['Level'] = 'Original'\n",
    "dc3_res_dfs.append(df)\n",
    "\n",
    "dc3_df = pd.concat(dc3_res_dfs)\n",
    "dc3_df['Constraint Type'] = dc3_df['strategy'].map(lambda x: x.split(' ')[0])\n",
    "dc3_df['dataset'] = 'donors_choose'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5, 1.9)"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f685ccbccc0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts_df_1k = dc3_df.copy()\n",
    "\n",
    "colorlist = ['maroon', \n",
    "             'red',\n",
    "             '#001c7f', #Original\n",
    "             ]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "sns.lineplot(data=ts_df_1k.groupby(['Constraint Type', 'Level'])[['value', 'recall_not_to_highest']].mean().reset_index(), \n",
    "              x='value', y='recall_not_to_highest', \n",
    "              hue='Constraint Type',\n",
    "              hue_order=['Accuracy', 'Disparity', 'Original'],\n",
    "#              palette=sns.color_palette(\"nipy_spectral\", 8) + list(reversed(sns.color_palette(\"nipy_spectral\", 11))),\n",
    "#              palette=sns.color_palette(\"nipy_spectral\", 3),\n",
    "              palette=colorlist,\n",
    "#              hue_order=dc3_sort_ord,\n",
    "#              hue='dataset', \n",
    "#              marker=\"o\", \n",
    "              style='Level',\n",
    "              markers=['o', 'v', '^', '<', '>', 's', 'd', 'p', 'X'],\n",
    "              style_order=['Original'] + list(reversed(letters))[:8],\n",
    "#              color='dodgerblue',\n",
    "              markersize=10,\n",
    "              dashes=None,\n",
    "              ci=None,\n",
    "              ax=ax)\n",
    "\n",
    "\n",
    "for i, t in enumerate(['Accuracy', 'Disparity', 'Original']):\n",
    "    tmp_df = ts_df_1k.loc[ts_df_1k['Constraint Type'] == t].copy()\n",
    "\n",
    "    x_coords = list(tmp_df.groupby(['Constraint Type', 'Level'])[['value', 'recall_not_to_highest']].mean().reset_index()['value'].values)\n",
    "    y_coords = list(tmp_df.groupby(['Constraint Type', 'Level'])[['value', 'recall_not_to_highest']].mean().reset_index()['recall_not_to_highest'].values)\n",
    "    labels = list(tmp_df.groupby(['Constraint Type', 'Level'])['Level'].min())\n",
    "\n",
    "    prec_errors = 1.96*tmp_df.groupby(['Constraint Type', 'Level'])['value'].sem().values\n",
    "    disp_errors = 1.96*tmp_df.groupby(['Constraint Type', 'Level'])['recall_not_to_highest'].sem().values\n",
    "    colors = colorlist[i]\n",
    "    ax.errorbar(x_coords, y_coords, \n",
    "                xerr=prec_errors, \n",
    "                yerr=disp_errors,\n",
    "                ecolor=colors, \n",
    "                fmt=' ', zorder=-1, capsize=5)\n",
    "\n",
    "\n",
    "ax.hlines(1.0, 0, 1, linestyles='dashed')\n",
    "\n",
    "ax.legend(loc='lower right', fontsize=14)\n",
    "\n",
    "ax.set_ylabel('', fontsize=32)\n",
    "ax.set_xlabel('Precision at Top-K', fontsize=28)\n",
    "\n",
    "ax.tick_params(axis='x', labelsize=24)\n",
    "ax.tick_params(axis='y', labelsize=24)\n",
    "\n",
    "ax.set_xlim((0.2,0.80))\n",
    "ax.set_ylim((0.5,1.90))\n",
    "#ax.get_legend().remove()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## San Jose Housing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('db_big_profile.yaml') as fd:\n",
    "    config = yaml.load(fd)\n",
    "    dburl = sqlalchemy.engine.url.URL(\n",
    "        \"postgres\",\n",
    "        host=config[\"host\"],\n",
    "        username=config[\"user\"],\n",
    "        database='san_jose_housing',\n",
    "        password=config[\"pass\"],\n",
    "        port=config[\"port\"],\n",
    "    )\n",
    "    engine_sj = sqlalchemy.create_engine(dburl, poolclass=sqlalchemy.pool.QueuePool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "sj_disp_cons_sql = \"\"\"\n",
    "WITH dist_res AS (\n",
    "  SELECT DISTINCT * FROM kit_bias_adj.model_adjustment_results_median_income \n",
    "      WHERE list_size=500 AND train_end_time >= '2013-06-01'\n",
    ")\n",
    ", mg_rns_value AS (\n",
    "  SELECT *,\n",
    "         row_number() OVER (PARTITION BY train_end_time, list_size, metric, parameter ORDER BY base_value DESC, base_max_recall_ratio ASC, RANDOM()) AS rn_base_value\n",
    "  FROM dist_res\n",
    "  WHERE past_train_end_time = train_end_time\n",
    "    AND base_max_recall_ratio < {disparity_constraint}\n",
    ")\n",
    ", mg_rns_disp AS (\n",
    "  SELECT *,\n",
    "         row_number() OVER (PARTITION BY train_end_time, list_size, metric, parameter ORDER BY base_max_recall_ratio ASC, base_value DESC, RANDOM()) AS rn_base_disparity\n",
    "  FROM dist_res\n",
    "  WHERE past_train_end_time = train_end_time\n",
    ")\n",
    ", base_mgs_hard_cons AS (\n",
    "  -- Best-performaing (in terms of precision) model groups meeting the hard constraint on disparity\n",
    "  SELECT * FROM mg_rns_value WHERE rn_base_value = 1\n",
    ")\n",
    ", base_mgs_soft_cons AS (\n",
    "  -- For time/size/metric combinations with no model groups meeting the hard constraint, fill in with lowest-disparity model group\n",
    "  SELECT\n",
    "      COALESCE(h.model_group_id, d.model_group_id) AS model_group_id,\n",
    "      COALESCE(h.train_end_time, d.train_end_time) AS train_end_time,\n",
    "      COALESCE(h.list_size, d.list_size) AS list_size,\n",
    "      COALESCE(h.metric, d.metric) AS metric,\n",
    "      COALESCE(h.parameter, d.parameter) AS parameter\n",
    "  FROM (SELECT * FROM mg_rns_disp WHERE rn_base_disparity = 1) as d\n",
    "  LEFT JOIN base_mgs_hard_cons h USING(train_end_time, list_size, metric, parameter)\n",
    ")\n",
    "-- Simple model selection on last time period, baseline with no recall adjustments\n",
    "SELECT 'Disparity Constraint {disparity_constraint} - Unadjusted'::VARCHAR(128) AS strategy,\n",
    "       r.train_end_time, r.past_train_end_time,\n",
    "       r.list_size, r.metric, r.parameter,\n",
    "       r.base_value AS value,\n",
    "       r.base_max_recall_ratio AS max_recall_ratio,\n",
    "       r.base_recall_over55k_to_under55k AS recall_over55k_to_under55k,\n",
    "       r.base_recall_under55k_to_over55k AS recall_under55k_to_over55k,\n",
    "       r.base_frac_under55k AS frac_under55k,\n",
    "       r.base_frac_over55k AS frac_over55k\n",
    "FROM dist_res r\n",
    "JOIN base_mgs_soft_cons b\n",
    "  ON r.model_group_id = b.model_group_id\n",
    "  AND r.past_train_end_time = b.train_end_time\n",
    "  AND r.list_size = b.list_size\n",
    "  AND r.metric = b.metric\n",
    "  AND r.parameter = b.parameter\n",
    "WHERE r.train_end_time > r.past_train_end_time\n",
    "ORDER BY train_end_time\n",
    ";\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "sj_value_cons_sql = \"\"\"\n",
    "WITH dist_res AS (\n",
    "  SELECT DISTINCT * FROM kit_bias_adj.model_adjustment_results_median_income \n",
    "      WHERE list_size=500 AND train_end_time >= '2013-06-01'\n",
    ")\n",
    ", mg_rns_value AS (\n",
    "  SELECT *,\n",
    "         row_number() OVER (PARTITION BY train_end_time, list_size, metric, parameter ORDER BY base_value DESC, base_max_recall_ratio ASC, RANDOM()) AS rn_base_value\n",
    "  FROM dist_res\n",
    "  WHERE past_train_end_time = train_end_time\n",
    ")\n",
    ", best_mgs AS (\n",
    "  SELECT * FROM mg_rns_value WHERE rn_base_value = 1\n",
    ")\n",
    ", value_dist_cons AS (\n",
    "  SELECT r.*,\n",
    "         row_number() OVER (PARTITION BY r.train_end_time, r.list_size, r.metric, r.parameter ORDER BY r.base_max_recall_ratio ASC, r.base_value DESC, RANDOM()) AS rn_base_disparity\n",
    "  FROM dist_res r\n",
    "  JOIN best_mgs b USING(train_end_time, list_size, metric, parameter)\n",
    "  WHERE b.base_value - r.base_value <= {value_constraint}\n",
    ")\n",
    ", base_mgs_value_cons AS (\n",
    "  -- Lowest-disparity model groups within hard constraint distance from best model group in terms of metric value\n",
    "  SELECT * FROM value_dist_cons WHERE rn_base_disparity = 1\n",
    ")\n",
    "-- Simple model selection on last time period, baseline with no recall adjustments\n",
    "SELECT 'Accuracy Constraint {value_constraint} - Unadjusted'::VARCHAR(128) AS strategy,\n",
    "       r.train_end_time, r.past_train_end_time,\n",
    "       r.list_size, r.metric, r.parameter,\n",
    "       r.base_value AS value,\n",
    "       r.base_max_recall_ratio AS max_recall_ratio,\n",
    "       r.base_recall_over55k_to_under55k AS recall_over55k_to_under55k,\n",
    "       r.base_recall_under55k_to_over55k AS recall_under55k_to_over55k,\n",
    "       r.base_frac_under55k AS frac_under55k,\n",
    "       r.base_frac_over55k AS frac_over55k\n",
    "FROM dist_res r\n",
    "JOIN base_mgs_value_cons b\n",
    "  ON r.model_group_id = b.model_group_id\n",
    "  AND r.past_train_end_time = b.train_end_time\n",
    "  AND r.list_size = b.list_size\n",
    "  AND r.metric = b.metric\n",
    "  AND r.parameter = b.parameter\n",
    "WHERE r.train_end_time > r.past_train_end_time\n",
    "ORDER BY train_end_time\n",
    ";\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "sj_res_dfs = []\n",
    "\n",
    "for c in [1.0, 1.05, 1.1, 1.2, 1.3, 1.4, 1.5, 2.0, 2.5, 3.0, 5.0]:\n",
    "    df = pd.read_sql(sj_disp_cons_sql.format(disparity_constraint=c), engine_sj)\n",
    "    sj_res_dfs.append(df)\n",
    "\n",
    "for c in [0.6, 0.5, 0.25, 0.2, 0.15, 0.10, 0.05, 0.0]:\n",
    "    df = pd.read_sql(sj_value_cons_sql.format(value_constraint=c), engine_sj)\n",
    "    sj_res_dfs.append(df)\n",
    "\n",
    "sj_df = pd.concat(sj_res_dfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "sj_df['constraint_type'] = sj_df['strategy'].map(lambda x: x.split('-')[0].split(' ')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5, 2.0)"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WAAEBAWzfvp1Dhw4xcODACu3169ePvLw8Vq1apR07duwYUVFR9OrVi7CwMIqKirh69Sp79+6lW7duVY4tJyeHrKwsBg8ezOLFi4mPjwegWbNmZGdnV1kvKyuLtm3bAlS5AkhVqmt74MCB7Ny5k4yMDDIyMti5c2el18Df31/rd+3atTz99NMVynzxxRfExcVVCLABfHx8CAsLIz8/HyjJJS/Nfa5KaVDcsmVLcnJytA86lpaWWFpaEh0dDVDuPS6rQ4cOWgrRkSNHOHfuHACXLl3C3Nyc559/nsDAQK1M2ev05JNPsm/fPs6ePQvAzZs3OX36NF27diUlJYXk5GSAer+R904kyBZCCCEeUqGhoVrqR6lhw4YRGhrKoEGD8Pf3x8PDA71er92k9tVXX7Fs2TJcXV3p0aMHly9fpl27dowYMQJnZ2dGjBiBm5tblX2+//77eHl50bNnT7p27aodX7p0KXv27MHFxQWDwaCt5tGoUSP69u3LiBEjMDY2rtCeoiiEh4eza9cuOnfujJOTE3/7299o06YNAQEBuLq6otPp6NevHwsWLKBNmzZVji07Oxs/Pz9cXV3x9vbmk08+AUrSaBYuXIibm5sWwJU1b948nn32WQwGQ7kZ4JpwdXXF2NgYnU5X4cZHKysr5syZg6enJ56ensydOxcrKyugZIa/NCc8KCiIH3/8EXt7e3bt2nXHFU3+l5+fH7169cJgMKDX69m3bx8ff/xxtXUsLS2ZNGkSzs7ODBw4EE9PT+3cF198wWuvvYZer68wY1+ahjJs2DDS09NxcnJi+fLldOlSsnTn8ePH6datG3q9nvnz5zN79mwAJk+ezKBBg+jbty+tWrUiJCSE0aNH4+rqSvfu3UlKSsLMzIxVq1YxZMgQ3N3dK53Rv5eUmnxdcdeNK8rngB/wq6qqzpWctwD+DTxGSerKIlVVv7hTux4eHmrpD5YQQogHy8h/HSj3POzl7nfVToegkhujUoKH/OExNZSTJ0/i4ODQ0MO4rxUXF2srk9jb2zf0cMQf8N1337F58+Zaz/bfS5X9TiqKEquqqkdt26rvnOwQYDnwZRXnXwMSVVX9i6IorYBTiqJ8rapqfj2PSwghxAPiTjtGlgbbslPkn09iYiJ+fn4EBARIgP2A27x5M7NmzeLzzz9v6KHcM/UaZKuquldRlA7VFQGaKSXfHTQF0oGqV3UXQghx36jpdul3G/zWd/vi/ufo6MjPP//c0MMQdcDf3x9/f/+GHsY91dCriywHNgOXgGbASFVVi6uvIoQQ4n7whm+XcsFtaRrI3aZ/VNX+nYLtpbvPsHT3GQm2hRD3lYYOsgcCcUA/oDPwo6IoUaqq3vjfgoqiTAYmAzz22GP3dJBCCCEaTmmwXRrEJ6aV/BeRnVvyxeeDnJMthPjzaujVRcYDG9QSZ4FzQNfKCqqqukpVVQ9VVT1atWp1TwcphBBCCCFEbTR0kP0L0B9AUZTWwBOAJF8JIYQQQogHWr0G2YqihAIHgCcURUlVFGWCoihTFEWZ8luR94EeiqIcB3YDM1VVvVafYxJCCCFEeRs3bkRRFJKSkhp6KHfl8uXLjBo1is6dO2MwGBg8eDCnT5+us/Y3btyordtdG5s3byY4OLjaMikpKfznP/+p8vzatWuxt7fH3t6+yqXv0tPT8fX1xd7eHl9fX233xsjISCwsLNDr9ej1et57771ajT8yMhI/P79yx8aNG3dXO2xWxsfHR1vre/DgwWRmZta6jcjISPbv31/reh06dODatfoNOes1yFZVdbSqqjaqqpqqqmqnquoaVVVXqqq68rfzl1RVfUpVVRdVVZ1VVf13fY5HCCHE/anLrIq70FXl5d6diH/Th6jXvNg7yUBxVg5qfgHFN/LqvK+HRWhoKN7e3vW+Q15RUVGdt6mqKgEBAfj4+JCcnExsbCwfffQRV65cqbM+qguyCwurXhTN39//jhvDVBdkp6enM3/+fA4ePEhMTAzz588vt/15qeDgYPr378+ZM2fo379/ucC+V69exMXFERcXV2F78vvJtm3bsLS0rHW9uw2y74WGThcRQgghyC+q2cZoz3s9xqu6thTuOEXbJk149B9fUhh9FDW7gMIdpyjOyK2zvh4WOTk5REdHs2bNGtatW1fu3Mcff4yLiws6nU4LFs+ePcuAAQPQ6XS4u7uTnJxcYcZz6tSphISEACUzhjNnztQ2lPnss8/w9PREp9MxbNgwbdv2K1euEBAQgE6nQ6fTsX//fubOncuSJUu0dmfNmsXSpUvLjXHPnj2YmpoyZcoU7ZhOp6NXr16oqkpgYCDOzs64uLgQFhYGlARmPj4+DB8+nK5duzJmzBhtZ8KgoCAcHR1xdXXlrbfeYv/+/WzevJnAwED0ej3Jycn4+PgwY8YMPDw8WLp0Kd9//z1eXl64ubkxYMAALcAPCQlh6tSpQMkM8PTp0+nRowedOnXSZoODgoKIiopCr9dX2PFxx44d+Pr6YmVlRYsWLfD19WX79u0V3sNNmzbx4osvAvDiiy9q297Xtw4dOvDuu+/i7u6Oi4uL9k1ITEwM3bt3x83NjR49enDq1CkAbt++zahRo3BwcCAgIIDbt2+Xa+vatWukpKTg7Pz7/oWLFi1i3rx5ACxbtkx7b0aNGkVKSgorV65k8eLF6PV6oqKiuHr1KsOGDdN2ydy3bx8A169f56mnnsLJyYmJEydW2ImyPjT06iJCCCFEjTzjbsuQdi3IX74P9epNjDpbY/rOZBSlUZljLVHNH8W48YP331vB5lUUp9XtbUlGNp0w9Z9cbZlNmzYxaNAgunTpgrW1NbGxsRgMBn744Qc2bdrEwYMHMTc3Jz09HYAxY8YQFBREQEAAubm5FBcXc+HChWr7sLa25siRI0BJsDNp0iQAZs+ezZo1a5g2bRrTp0+nT58+hIeHU1RURE5ODra2tjzzzDPMmDGD4uJi1q1bR0xMTLm2ExISMBgMlfa7YcMG4uLiiI+P59q1a3h6etK7d28Ajh49yokTJ7C1taVnz57s27cPBwcHwsPDSUpKQlEUMjMzsbS0xN/fHz8/P4YPH661nZ+fr6U6ZGRk8NNPP6EoCqtXr2bBggX8/e9/rzCetLQ0oqOjSUpKwt/fn+HDhxMcHMyiRYvYsmVLhfIXL16kXbt22nM7OzsuXrxYodyVK1ewsbEBoE2bNuVm8Q8cOIBOp8PW1pZFixbh5ORU6bW6Wy1btuTIkSP885//ZNGiRaxevZquXbsSFRWFiYkJu3bt4p133uG7775jxYoVmJubc/LkSY4dO4a7u3ut+goODubcuXM0btxYe2+mTJlC06ZNeeuttwB47rnneOONN/D29uaXX35h4MCBnDx5kvnz5+Pt7c3cuXPZunUra9asqdPrUJkH718hIYQQD6Vnn2hD4Y5TqFdvAlAQGkejCd3IDzukHSvcdZpGna3gAQyyG0poaCivv/46AKNGjSI0NBSDwcCuXbsYP3485ubmAFhZWZGdnc3FixcJCAgAwMzMrEZ9jBw5UnuckJDA7NmzyczMJCcnh4EDBwIQERHBl1+WbBBtbGyMhYUFFhYWWFtbc/ToUa5cuYKbmxvW1tY1fm3R0dGMHj0aY2NjWrduTZ8+fTh06BDNmzenW7du2NnZAaDX60lJSeHJJ5/EzMyMCRMm4OfnVyEfuarXlJqaysiRI0lLSyM/P5+OHTtWWmfo0KEYGRnh6OhYp+ksZSmKQskef+Du7s758+dp2rQp27ZtY+jQoZw5c+cNnsq2dafjzzzzDAAGg4ENGzYAkJWVxYsvvsiZM2dQFIWCggIA9u7dy/Tp0wFwdXXF1dW1Vq/N1dWVMWPGMHToUIYOHVppmV27dpVL7blx4wY5OTns3btXG9+QIUNo0aJFrfq+G/KvkBBCiPtC6fboVYl6zYu2/e0x6mxNQWgc3C4gf/k+7bzSqgmNJnTjen4+nkG763u4de5OM871IT09nYiICI4fP46iKBQVFaEoCgsXLqxVOyYmJhQX/76XXG5u+bSdJk2aaI/HjRvHxo0b0el0hISEEBkZWW3bEydOJCQkhMuXL/PSSy9VOO/k5HRXN+I1btxYe2xsbExhYSEmJibExMSwe/du1q9fz/Lly4mIiKi0ftnXNG3aNN588038/f2JjIzU0huq67Mm6Qpt27Ytd31SU1Px8fGpUK5169akpaVhY2NDWloajz76KADNmzfXygwePJhXX32Va9eu0bJlS+14eHg48+fPB2D16tV4eHho56ytrSvkgKenp5erX/qaSq8hwJw5c+jbty/h4eGkpKRUOuaqVPeztHXrVvbu3cv333/Phx9+yPHjxyvULy4u5qeffqrxB8D6JDnZQgghHghqfiH5H3yK8lhJMP2/TMd5Upx0mts3bjXA6B5M69evZ+zYsZw/f56UlBQuXLhAx44diYqKwtfXly+++ELLmU5PT6dZs2bY2dlpOb95eXncunWL9u3bk5iYSF5eHpmZmezeXfWHnOzsbGxsbCgoKODrr7/Wjvfv358VK1YAJTdIZmVlARAQEMD27ds5dOiQNutdVr9+/cjLy2PVqlXasWPHjhEVFUWvXr0ICwujqKiIq1evsnfvXrp1q/izUyonJ4esrCwGDx7M4sWLiY+PB6BZs2ZkZ2dXWS8rK4u2bdsCVLkCSFWqa3vgwIHs3LmTjIwMMjIy2LlzZ6XXwN/fX+t37dq1PP3000DJqiulwXxMTAzFxcUVvgkICAjQbowsG2AD2Nvbc+nSJU6ePAnA+fPniY+PR6/XV/uayl6P0tx8gN69e2s3eSYkJHDs2LEKdVu3bs2vv/7K9evXycvL09JoStOS+vbty8cff0xWVhY5OTkVrt9TTz3Fp59+qj2Pi4ur0PcPP/xQ6Q2kdU1msoUQQtSJg+dKcnbvNCPdrLEJMwd0xKd9E0wppvhGOpO7t+Wdp6v/jzvl3BWM+ncvycEOO1ThfOHWk5iO0mNnXFTtLpB3Gt/DJDQ0lJkzZ5Y7NmzYMEJDQ1mxYoUWeDVq1IjBgwfzf//3f3z11Ve8/PLLzJ07F1NTU7799ls6derEiBEjcHZ2pmPHjri5uVXZ5/vvv4+XlxetWrXCy8tLC5CWLl3K5MmTWbNmDcbGxqxYsYLu3bvTqFEj+vbti6WlJcbGxhXaUxSF8PBwZsyYwccff4yZmRkdOnRgyZIleHt7aznJiqKwYMEC2rRpU+VShdnZ2Tz99NPk5uaiqiqffPIJUJJGM2nSJJYtW1bprPm8efN49tlnadGiBf369ePcuXM1fg9cXV0xNjZGp9Mxbtw43njjDe2clZUVc+bMwdPTE4C5c+diZWUFlMzwT5kyBQ8PD4KCghgxYgRr1qyhffv2fPPNN0DJh6gVK1ZgYmLCI488wrp166pMAalM48aN+fe//8348ePJzc3F1NSU1atXY2FhUW29t99+mxdffJEPPviAIUN+/1185ZVXGD9+PA4ODjg4OFTIpVcUBVNTU+bOnUu3bt1o27YtXbuW7FFYVFTE888/T1ZWFqqqMn36dCwtLfnLX/7C8OHD2bRpE59++inLli3jtddew9XVlcLCQnr37s3KlSt59913GT16NE5OTvTo0eOe7B6u3Iu7K+uah4eHWnqzgRBCiIaz+MfTvOHbBYCR/zrAwXPpWoBbGsyWDXiLC3IpuJmBcvsG5N6G4wcojt9HocOTNPEdjWJRdb5tRMIlfGwstZscK2P6kifZHa1o0eKRKtvpELT1vtmK/eTJkzg4ODT0MO5rxcXF2sok9vb2DT0cUQ+Kiop49NFHuXz5Mqampg06lsp+JxVFiVVV1aOKKlWSdBEhhBB3benuym+iWvzj7xuBdAjaylOL/0vqxV/4NXIJv4QMJ+Xfz3F+02tkNLqG8fh3MP0lkbx//hU1q+rNIXwes6Yw4qwWYCutmtAo0Acjx9ZamcJdZ7AwqvlMnbi/JSYm8vjjj9O/f38JsP/ESpfVa+gAu65JuogQQog694ZvFy0ATwkewpXLqdzc+DK3si9rZdSCXLKOb+Rmyk/YjViAuupdCvd8i8mQCSimjSq0Ofzfh/hmlAGjzi1LVhGZ0I3ipNOYjtJTnJJO4a4zNJrQDSNVtkBdAAAgAElEQVSLhr/hSdQNR0dHfv65bpc1FPefB3Wn0TuRIFsIIUSdSM0ouUHuf3OeP9wcxwuNtlFYJsAuqzD7MhkJ4bQw9KPo0I9ccfPnr9t/qVDuyC9ZPL4ggvBJXuhefpLr+fncbtMGO+MiFMdHadTZukYBdiNjmekWQtQ/CbKFEEL8ISP/dQCAG7kly3c1Myv5ryX7t+cjXC3J3Vj9zYY3kn6ghf+nsH8rFBeSmHajyrIvfBVbrn0tv9qsZl81n/5wcI3KCSHEHyFBthBCiDrlaFOyNm/paiPmpnCzoPrtztWCXDAyAtPGYGSitVFWaXtCCPEgkCBbCCH+5MquAFIfwl7uTlFBLr/8+iuFRQU8Zt0ck0bN0H/wX7LzCmlt0YRzpmYlgXQVFFMzKC7G2MOXtratCHu5bYUysvSeEOJBIquLCCHEn1xVK4DUhS6tm5J381eSDyzhwrbRXNw6goP/GUrygSV8P9mBLq2bYtS4GY90rX7JvOZd/x+cPY5J3xGV3vQo6tfGjRtRFOWBvQHt8uXLjBo1is6dO2MwGBg8eDCnT5++c8Ua2rhxY7mtumtq8+bNBAcHV1smJSVF2ySlMmvXrsXe3h57e/sqN7r59ttvcXJywsjIiLtZ4njevHksWrSo3LEOHTpw7VrVq/3URtOmTQG4dOkSw4cPv6s2QkJCuHTpUq3qpKSk4OzsfFf91QUJsoUQQty1VSM6cmTDeC6d+JbiwpKZ6uLCXC6d+Ja0iFf414gOGJmaYap/AZNmbSptw6RZG6wMYzD1+ku162SL+hMaGoq3tzehoaH12k9RUVGdt6mqKgEBAfj4+JCcnExsbCwfffQRV65cqbM+qguyS7cSr4y/vz9BQUHVtl1dkJ2ens78+fM5ePAgMTExzJ8/v9KdCp2dndmwYQO9e/eutq+GZmtrW+lmPjVxN0F2Q5MgWwghxF0pKsgl59S/ycupfNWQvJzL3Dz9H4oK81i49xrmQ1di4TaiJDWEkhQRC/2ztHtuDSaWdigWVtX219ZSluarDzk5OURHR7NmzRrWrVtX7tzHH3+Mi4sLOp1OCxbPnj3LgAED0Ol0uLu7k5ycTGRkJH5+flq9qVOnattpd+jQgZkzZ2obynz22Wd4enqi0+kYNmyYtm37lStXCAgIQKfTodPp2L9/P3PnzmXJkiVau7NmzWLp0qXlxrhnzx5MTU2ZMmWKdkyn09GrVy9UVSUwMBBnZ2dcXFwICwsDIDIyEh8fH4YPH07Xrl0ZM2aMtv14UFAQjo6OuLq68tZbb7F//342b95MYGAger2e5ORkfHx8mDFjBh4eHixdupTvv/8eLy8v3NzcGDBggBbgh4SEMHXqVADGjRvH9OnT6dGjB506ddKCzaCgIKKiotDr9SxevLjca9uxYwe+vr5YWVnRokULfH192b59e4X30MHBgSeeeKImb3etpaSk4ODgwKRJk3BycuKpp57i9u3bAFW+l+fOnaN79+64uLgwe/bscm2VziyXvTYAfn5+REZGUlRUxLhx47T3bPHixaxfv57Dhw8zZswY9Ho9t2/fJjY2lj59+mAwGBg4cCBpaWkAxMbGaj9D//jHP+rlmtSU5GQLIcRDoHQFkLoU7NeW9OTq86TTz27hfJcx7Ei8QnTyNV71HsqTQ0fSSCkmXzXiy8QbnFj/C7cL7rwN9Y3cQhoZK9zKq3rm8EH2a8Qi8n6tuxQHgMaPduHRfm9VW2bTpk0MGjSILl26YG1tTWxsLAaDgR9++IFNmzZx8OBBzM3NSU8vufF0zJgxBAUFERAQQG5uLsXFxVy4cKHaPqytrTly5AgA169fZ9KkSQDMnj2bNWvWMG3aNKZPn06fPn0IDw+nqKiInJwcbG1teeaZZ5gxYwbFxcWsW7eOmJiYcm0nJCRU2J671IYNG4iLiyM+Pp5r167h6empzfYePXqUEydOYGtrS8+ePdm3bx8ODg6Eh4eTlJSEoihkZmZiaWmJv78/fn5+5VId8vPztdSMjIwMfvrpJxRFYfXq1SxYsIC///3vFcaTlpZGdHQ0SUlJ+Pv7M3z4cIKDg1m0aBFbtmypUP7ixYu0a9dOe25nZ8fFixervdb14cyZM4SGhvLZZ58xYsQIvvvuO55//nmeeeaZSt/L119/nVdeeYUXXnih1oFuXFwcFy9eJCEhAUB7D5YvX86iRYvw8PCgoKCAadOmsWnTJlq1akVYWBizZs3i888/Z/z48SxfvpzevXsTGBhY59eiNiTIFkIIcVeMlWItRaQqxYW5mCjFANzMK2JrYjoLd5dfnq+ylUSq0tjU+E8bZDeU0NBQXn/9dQBGjRpFaGgoBoOBXbt2MX78eMzNzQGwsrIiOzubixcvEhAQAICZWc2+XRg5cqT2OCEhgdmzZ5OZmUlOTg4DBw4EICIigi+//BIAY2NjLCwssLCwwNramqNHj3LlyhXc3Nywtq55SlF0dDSjR4/G2NiY1q1b06dPHw4dOkTz5s3p1q0bdnZ2AOj1elJSUnjyyScxMzNjwoQJ+Pn5lZudr+41paamMnLkSNLS0sjPz6djx46V1hk6dChGRkY4OjrWaTrLH6Uola8dX3q8Y8eO6PV6AAwGAykpKUDV7+W+ffv47rvvABg7diwzZ86s8Vg6derEzz//zLRp0xgyZAhPPfVUhTKnTp0iISEBX19foCQNycbGhszMTDIzM7UPUmPHjuWHH36ocd91TYJsIYR4CIS93L3O28y7eZUUE7NqA20jEzNaWzTRAumwl7tXmFWv6dhK65WuoV26TvafxZ1mnOtDeno6ERERHD9+HEVRKCoqQlEUFi5cWKt2TExMKC4u1p7n5pb/mWjSpIn2eNy4cWzcuBGdTkdISAiRkZHVtj1x4kRCQkK4fPkyL730UoXzTk5Od5Xn27hxY+2xsbExhYWFmJiYEBMTw+7du1m/fj3Lly8nIiKi0vplX9O0adN488038ff3JzIyknnz5t2xz9L0lOq0bdu23PVJTU3Fx8fnjvUqM2vWLLZuLfnmKS4urtw5a2trLd2iVHZ2NpaWlmRnZ1e4VqXpItW9l1UF7qWq+plp0aIF8fHx7Nixg5UrV/LNN9/w+eefl6urqipOTk4cOFD+35LMzMxq+7zXJCdbCCHEXTFp1Iw2T/yl2jJWj/th0rjmM9Xi3lq/fj1jx47l/PnzpKSkcOHCBTp27EhUVBS+vr588cUXWp5teno6zZo1w87Ojo0bNwKQl5fHrVu3aN++PYmJieTl5ZGZmcnu3bur7DM7OxsbGxsKCgr4+uuvteP9+/dnxYoVQMnMZFZWFgABAQFs376dQ4cOaTOlZfXr14+8vDxWrVqlHTt27BhRUVH06tWLsLAwioqKuHr1Knv37qVbt25Vji0nJ4esrCwGDx7M4sWLiY+PB6BZs2ZkZ2dXWS8rK4u2bUuWnaxqBZCqVNf2wIED2blzJxkZGWRkZLBz585Kr0FNfPjhh8TFxVUIsAF69+7N5s2btXFs2LABnU6HsbFxtW1W9V727NlTy+8ve7ysDh06EBcXp6UblaYBXbt2jeLiYoYNG8YHH3ygpRmVvU5PPPEEV69e1YLsgoICTpw4gaWlJZaWlkRHR1fb970iQbYQQoi7YmxqRnvDSzRuWvmqIY2btqFplzEYmzSu9LxoeKGhoVrqR6lhw4YRGhrKoEGD8Pf3x8PDA71ery3x9tVXX7Fs2TJcXV3p0aMHly9fpl27dowYMQJnZ2dGjBiBm5tblX2+//77eHl50bNnT7p27aodX7p0KXv27MHFxQWDwaCt5tGoUSP69u3LiBEjKg36FEUhPDycXbt20blzZ5ycnPjb3/5GmzZtCAgIwNXVFZ1OR79+/ViwYAFt2lT+8wolQaOfnx+urq54e3vzySefACVpNAsXLsTNzY3k5OQK9ebNm8ezzz6LwWCgZcuW1VzxilxdXTE2Nkan01W48dHKyoo5c+bg6emJp6cnc+fOxcqq5AbhiRMnajnh4eHh2NnZceDAAYYMGVLrQNzV1ZWpU6fi7e2NXq9n5cqVrF69+o71qnsv//GPf+Di4lIhh7x0hrtnz5507NgRR0dHpk+fjru7O1CSh+7j44Ner+f555/no48+AkpmzadMmYJer6eoqIj169czc+ZMdDoder2e/fv3A/DFF1/w2muvodfra/RtQX1SGnoAd8PDw0O9m3UghRDiYdQhaOvvW4/Xg7ycXzl/5Asun9pMcWEuRiZmtHnCn0adRzP5m3PsfKOPlupRH+ki9fna6tvJkydxcHBo6GHc14qLi7WVSezt7Rt6OOIPiI2N5c033+S///1vQw+lSpX9TiqKEquqqkdt25KcbCGE+JN7vX/9BiaNmz5K5x4zMOk8GhOlmNYWTTBp3Bzd+5HkyE2K4g9ITEzEz8+PgIAACbAfcIcPH+a555674+Y8fyYSZAshxJ9cfW6pXsrYpDF/21LytXDpzHTZADs14xZ2Lcz/UB9tmpsx7slOvBZ2WJvF7mBtTl6uSmOz6m+yqon63n5e1J6joyM///xzQw9D1AEPD4863YXzQSA52UIIIerdxczql/qrib/2dSAnpTFzBpVsZtHB2pzVo704fDCf3NvFd6h9Z/W5/bwQ4uEjM9lCCCHueznZxezbUcyNTJXuA5rzXLfHeNa1PYd2w43MQtraGdOug4Kx8d3NaC/+8eGaYRNC1D+ZyRZCCHFfy8tViT9SwI3Mkhv1Y/cWE9Cl428BdsmxI4cKKMi/+xv5ZRZbCFHXajyTrSjKBFVV1/zPsWBVVYPqflhCCCH+bBLTbjDyXwe01UFK3WnLd4/HWjDJ2x7LR42IicwnPx/+u7VIO9/cUqHPwEaEHPyZPaeu1npcqRm3al1HCCHupDYz2cMURRlT+kRRlH8AraqroCjK54qi/KooSkI1ZXwURYlTFOWEoij375ouQggh7rk2zc14sVdnvkss5KesYrwHVlxz+8n+jfn6eAE97Kte/7g6dZEv/qDbuHEjiqKQlJTU0EO5K5cvX2bUqFF07twZg8HA4MGD6/Qmu40bN2rrdtfG5s2b77iaRkpKCv/5z3+qPL927Vrs7e2xt7evcqObb7/9FicnJ4yMjCi7xHFKSgqPPPIIer0evV7PlClTajX+lJQUnJ2dyx2bN2+etmb6HzVu3Dhtt86JEyfe1TWOi4tj27Ztta7n4+NDfS8HXasgGxinKMpoRVHWAoWqqk64Q50QYFBVJxVFsQT+CfirquoEPFuL8QghhPgTa9PcjCkDOhN44CidbXII6GrK4aj8CuUSYwt4TmfCmr2S8nG3QkND8fb2JjQ0tF77KSoqunOhWlJVlYCAAHx8fEhOTiY2NpaPPvqIK1eu1Fkf1QXZhYVVL1Pp7+9PUFD1X/hXF2Snp6czf/58Dh48SExMDPPnzycjI6NCOWdnZzZs2EDv3r0rnOvcubO20+PKlSurHUtDWr16NY6OjrWud7dB9r1wxyBbURQrRVGsgEeAicDbQDYw/7fjVVJVdS+QXk2R54ANqqr+8lv5X2s6cCGEEH9uz3V/jK0X0rhVWMRjZubs2JSr5WCXlZpSxK+XirCxMGuAUT74cnJyiI6OZs2aNdpW2KU+/vhjXFxc0Ol0WrB49uxZBgwYgE6nw93dneTkZCIjI/Hz89PqTZ06lZCQEKBk++yZM2dqG8p89tlneHp6otPpGDZsmLZt+5UrVwgICECn06HT6di/fz9z585lyZIlWruzZs1i6dKl5ca4Z88eTE1Ny83S6nQ6evXqhaqqBAYG4uzsjIuLC2FhYQBERkbi4+PD8OHD6dq1K2PGjNF2BwwKCsLR0RFXV1feeust9u/fz+bNmwkMDESv15OcnIyPjw8zZszAw8ODpUuX8v333+Pl5YWbmxsDBgzQAvyQkBCmTp0KlMzaTp8+nR49etCpUydtBjcoKIioqCj0en2FHR937NiBr68vVlZWtGjRAl9fX7Zv317hPXRwcOCJJ56oydtdp3x8fJg5cybdunWjS5cuREVFASUfHHr16oW7uzvu7u7aboyqqjJ16lSeeOIJBgwYwK+//lqurdKZ5aZNm2rH169fz7hx44CSGXtnZ2d0Oh29e/cmPz+fuXPnEhYWhl6vJywsjJs3b/LSSy/RrVs33Nzc2LRpEwC3b99m1KhRODg4EBAQwO3bt+v9+tQkJzsWUAGlzN9DfvujAp3+QP9dAFNFUSKBZsBSVVW/rKygoiiTgckAjz322B/oUgghxL10p5zrqqzck8zzPdvzXKf2JB+FG5kly/Q1t1R4sl9jEo8UkJpSMjOaFF/M0J7tOHohs87Gfa+diV5EzvW6XeWkqXUX7L3fqrbMpk2bGDRoEF26dMHa2prY2FgMBgM//PADmzZt4uDBg5ibm5OeXjJnNmbMGIKCgggICCA3N5fi4mIuXLhQbR/W1tYcOXIEgOvXrzNp0iQAZs+ezZo1a5g2bRrTp0+nT58+hIeHU1RURE5ODra2tjzzzDPMmDGD4uJi1q1bR0xMTLm2ExISMBgMlfa7YcMG4uLiiI+P59q1a3h6emqzvUePHuXEiRPY2trSs2dP9u3bh4ODA+Hh4SQlJaEoCpmZmVhaWuLv74+fnx/Dhw/X2s7Pz9eCwoyMDH766ScURWH16tUsWLCAv//97xXGk5aWRnR0NElJSfj7+zN8+HCCg4NZtGgRW7ZsqVD+4sWLtGvXTntuZ2dXYZvyOzl37hxubm40b96cDz74gF69etWq/p0UFhYSExPDtm3bmD9/Prt27eLRRx/lxx9/xMzMjDNnzjB69GgOHz5MeHg4p06dIjExkStXruDo6MhLL71U477ee+89duzYQdu2bcnMzKRRo0a89957HD58mOXLlwPwzjvv0K9fPz7//HMyMzPp1q0bAwYM4F//+hfm5uacPHmSY8eOadu416c7Btmqqnas5/4NQH9KZsoPKIryk6qqFf6VUVV1FbAKSrZVr8cxCSHEfa/LrG2c/nBwQw+jShm5ORyc+yQFagEmigmF+UZczCpgZcT5GrdxK7+IVXt+ZnQ3O3w8bLFqY0xSXCEe/Rrxz5h8/BxM6GlvzKn4QvoMbMS38efq8RX9eYWGhvL6668DMGrUKEJDQzEYDOzatYvx48djbl6yiZCVlRXZ2dlcvHiRgIAAAMzMavbtwciRI7XHCQkJzJ49m8zMTHJychg4cCAAERERfPllyTybsbExFhYWWFhYYG1tzdGjR7ly5Qpubm5YW1vX+LVFR0czevRojI2Nad26NX369OHQoUM0b96cbt26YWdnB4BeryclJYUnn3wSMzMzJkyYgJ+fX7nZ+epeU2pqKiNHjiQtLY38/Hw6dqw8dBo6dChGRkY4OjrWaTpLVWxsbPjll1+0D09Dhw7lxIkTNG/evEb1FaXyJTHLHn/mmWcAMBgMpKSkAFBQUMDUqVOJi4vD2NhYy4/fu3ev9n7Y2trSr1+/Wr2enj17Mm7cOEaMGKH1+7927tzJ5s2btbzx3NxcfvnlF/bu3cv06dMBcHV1xdXVtVZ9341ar5OtKEpHwA04oarqqT/YfypwXVXVm8BNRVH2AjpAFiwVQohq5Bfdn3MNE707kHYzk3+fjcGhRTPejV/J483s+NB9GtFZx1nynDeTQ+LK1SndIbIqGbeLWB9fwK/ZENC7ESFx+Yz2UNiaUIiCQkDvRqw5WkDvDi0J3n6KRsZKrT6AdAjaCtT/9vN3cqcZ5/qQnp5OREQEx48fR1EUioqKUBSFhQsX1qodExMTiot/3xAoN7f8zaRNmjTRHo8bN46NGzei0+kICQkhMjKy2rYnTpxISEgIly9frnTW08nJSUu9qI3GjX+/idbY2JjCwkJMTEyIiYlh9+7drF+/nuXLlxMREVFp/bKvadq0abz55pv4+/sTGRnJvHnz7thnaXpKddq2bVvu+qSmpuLj43PHemX7K+3TYDDQuXNnTp8+jYeHh1bm4MGDvPzyy0DJTLG/v792ztraukIOeHp6erkPEaXtl15DgMWLF9O6dWvi4+MpLi6u8YexUmWD+LI/SytXruTgwYNs3boVg8FAbGxshbqqqvLdd981SPrM/6pJTvbGMo+fBiKAvwDfK4oy7g/2vwnwVhTFRFEUc8ALOPkH2xRCiIfG/bSJimcHS5w7PMIHcT/g2aojRjRhRPsBzHadzOLjezC07MCx9FSecqp2YapyrtzKZUZ0LDaP3qBbR5WQI/mMMMCSE8dx65xNn8eNCDmSzwSvRjzZyZKU4CF3PcP/MG6pvn79esaOHcv58+dJSUnhwoULdOzYkaioKHx9ffniiy+0nOn09HSaNWuGnZ0dGzeWhAZ5eXncunWL9u3bk5iYSF5eHpmZmezevbvKPrOzs7GxsaGgoICvv/5aO96/f39WrFgBlNwgmZWVBUBAQADbt2/n0KFD2qx3Wf369SMvL49Vq1Zpx44dO0ZUVBS9evUiLCyMoqIirl69yt69e+nWrVuVY8vJySErK4vBgwezePFi4uPjAWjWrBnZ2dlV1svKyqJt27YAVa4AUpXq2h44cCA7d+4kIyODjIwMdu7cWek1qMrVq1e1m01//vlnzpw5Q6dO5bN8vby8tBsjywbYUJIbbWNjo33QSE9PZ/v27Xh7e1fbb1ZWFjY2NhgZGfHVV19pY+jdu7f2fqSlpbFnz55K67du3ZqTJ09SXFxMeHi4djw5ORkvLy/ee+89WrVqxYULFypcv4EDB/Lpp59qH2KOHj2q9V16g2lCQgLHjh2r/uLVgZqsLtK+zOOZQD9VVccDPYA3qquoKEoocAB4QlGUVEVRJiiKMkVRlCkAqqqeBLYDx4AYYLWqqlUu9yeEEKK8+2kTlXG97NjySwJTHPoSc/UqKsZ4tfJg5cn92rFCVeUv7q1r1N6N/AK+PvULpzJzeD82gYMZKbzhY8KeixcZ79CRT44lcTAzhaABjbBuYsSN/LtfuaJ0Fvt++tByL4SGhmqpH6WGDRtGaGgogwYNwt/fHw8PD/R6vfb1+1dffcWyZctwdXWlR48eXL58mXbt2jFixAicnZ0ZMWIEbm5uVfb5/vvv4+XlRc+ePenatat2fOnSpezZswcXFxcMBoO2mkejRo3o27cvI0aMwNjYuEJ7iqIQHh7Orl276Ny5M05OTvztb3+jTZs2BAQE4Orqik6no1+/fixYsIA2bape6jE7Oxs/Pz9cXV3x9vbmk08+AUrSaBYuXIibmxvJyckV6s2bN49nn30Wg8FAy5Ytq7niFbm6umJsbIxOp6tw46OVlRVz5szB09MTT09P5s6di5VVyZoTEydO1HLCw8PDsbOz48CBAwwZMkQLxPfu3Yurqyt6vZ7hw4ezcuVKrX5Nffnll7z//vvo9Xr69evHu+++S+fOnaut8+qrr7J27Vp0Oh1JSUnarH9AQAD29vY4Ojrywgsv0L17+W+xSmewg4OD8fPzo0ePHtjY2GjnAwMDcXFxwdnZmR49eqDT6ejbty+JiYnajY9z5syhoKAAV1dXnJycmDNnDgCvvPIKOTk5ODg4MHfu3Crz+OuScqevKxRFOaKqqvtvj2NUVe1W5txRVVWr/k2qJx4eHmp9r20ohBD3s9IUh/vJ4Xd7cLNQ5c0Du7lw8wZz3Hty+dY1/NrrefunXZzKuk5wt364t2zJoZSb/DU0nhu5VS9/1tzMhDlPO9K4mTEbUi7yhpM9//jxLLP8nfkuOQO31qZYGZuwYEsSb/s5setiNo8ZqwSGxVXZZlVSgocAJde19PG9cPLkSRwcHO5Zfw+i4uJibWUSe/uGTekR9cfFxYXNmzdXmc9+r1T2O6koSqyqqh5VVKlSTWaydYqi3FAUJRvQK4pi81uHjYCKHymFEEI8lFRMCPv5JBduluzo+M3PSQxsp+Pfp8/wbCcnALZfSOZ2oRFxNy8z5+nqg8sbuYUEhh1jQ1Qqkzt3ZMGWJF7zfYLA6AusP5OOWmzKnhO/8rafEx/GXua7sxmYNTXD54map6OI+1tiYiKPP/44/fv3lwD7T8zX1xcXF5cGD7DrWk1WF6kqkDYHXq7b4QghhKiplOAhdAjailfHkq9/D55L1x43hGI1H0Mrczxa9uOL0/G87erNm/v2cyEnh9kGdyZ1dWPIY12YFh3NhZwcZula4NO1FZFJ5bdCb2tphl0Lc+357fwilu48w8t9H2fXxWxSc0o2pAk+dIlZHjZ8GHtZO7bxXCYznnqC27VIHTl4rrrtHERDcnR05Oeff27oYYh69uOPPzb0EOpFrVcXKaWqaiYl+dZCCCEERfkK7ZvaEHHxDP/n2Y91Z5O5kJMDwJJjxwn28mJ69D7t2IZfkpnS15XYlPKrF9zILSQx7UaF9t8MPcIcf2f+ZmjDp8d+JaegmL8d+H3NYLumjZjh+ihB38TxS/qtWo39btfyFkKIqtRkdZGXyjy2UxRlt6IomYqi7FcU5eG7FVsIIe4zYS93v+MyePfChawC/nspme6t7XknJoKuLZoy2+BOU1NTcgoKmPrbDDZAu6ZN+aujnvc31XxBqezcQt7+Jo5DiWm842FT4XygW2s+2JxQ6wAbIDHtRqWBvRBC3K2azGRPBT7/7fEnQBjgCzwNrKBkIxkhhBAPmdSMW1zM/H0N2/c3FbLyJR2v79/BhZs3eP/IXp7t6MBcD3fePnCwXN1AFzfe+TaRE5d+D2z/N03kfyWm3eAxK3OGd2vPB7GXK5xfdyadyX06syqy4uoP1Tl4Lh1Hm+baYyGEqAu1TRfpoqrqiN8ehyuKMreuBySEEOLOGhlXvhNbQ85o38jP47OTR7UbH7u1suW5x115fd++CmW/PXeGf0/2xKLM5hx38nroUaYM6MK7By9pOdhlHbh8k6GPW/GfSU9ibFT59alMh6Ct2nW7H1dtEUI8mGqyuoidoijLFEX5FGilKIppmXOmVcyCYn4AACAASURBVFUSQghRf+7HLdWbN2rMxK56Fj45AB+b9gTpe/H6vt9zsMuKunyZ+OvXKSqzS+CdPNe9PdvOZ2kBtl3TRnza5zH+P3t3HlZVtT9+/L0E08wBwRwQb+LMdM7Bw6CCiAPqVS7fcEDN4eJ000Iyvw50NcPSX+asF795HbGukYUypDmkQoIWKAmGShl6uopCGkOHnGH//kD2hRiPQeZtvZ6Hx3P23mutz9774LPOYu318bRuph4Tfv4GxvuPvl72H1V0dDRCCDIyMh53KI8kOzubsWPH0rlzZ/R6PcOGDVNTedeF6Ohodd1uU8TGxrJ8+fJqjzEYDGqSlMrs3LmTrl270rVr1yoT3cybN48ePXqg0Wjw9/cnPz/fpDhDQ0PVddBLdezYkZs3b5pUT1WaNm0KwLVr1xg1atQj1REeHs61a9dMKmMwGHB0dHyk9upCbTrZ84AU4DTwd6ApgBCiLRBbf6FJkiRJNXncqcB/qUWjxni26UCIzpOIi9+Vm4O9o39/+pZJBLI9IwPj/fu1rntT3Hfompnzd31berRszBJ3ayJPXmahuzXveHagR8vGvO35JywamfZH2t/bNXwcIiIi8PT0JCIiol7bKc38V5cURcHf3x9vb28yMzNJSUnh7bffJicnp87aqK6TXZpKvDJ+fn6EhIRUW3d1nezc3FyWLFlCUlISycnJLFmypEKacyhZAq80i2G3bt14++23q23zcbG2tiYyMvKRyj5KJ/txq7GTrSjKTuBfgKWiKDsVRcl7uD1bUZS/13eAkiRJUtV+j6nAzRo0wLJxYzpiyUKtCz0sLHirpzvvx13ldRcX3nJxo4eFBe/06oWFCdNFbt0rYnN8Jp8mG/hbD0s2HfmWSzd/pkUjc/pYN2ON93O0bmL6H1h/j9fwt1RYWEhiYiLbtm3jww8/LLfvnXfewcnJCa1Wq3YWv/vuOwYNGoRWq6Vnz55kZmYSHx+Pr6+vWi4oKIjw8HCgZER0wYIFakKZLVu24OrqilarZeTIkWra9pycHPz9/dFqtWi1Wk6ePMnixYtZt26dWu/ChQtZv359uRjj4uJo2LAhM2bMULdptVr69u2LoijMmzcPR0dHnJyc2L17NwDx8fF4e3szatQoevTowfjx49U03CEhIdjb26PRaJg7dy4nT54kNjaWefPmodPpyMzMxNvbm9mzZ+Pi4sL69ev55JNPcHd3x9nZmUGDBqkd/PDwcIKCggAIDAwkODiYPn360KlTJ7WzGRISQkJCAjqdrkLGx0OHDuHj44OlpSUtW7bEx8eHgwcPVriHgwcPxty85Mtlr169uHr1ao33vbYMBgN2dnZMnz4dBwcHBg8ezO3btwGqvJeXL1+md+/eODk5sWjRonJ1lY4sl702AL6+vsTHx1NUVERgYKB6z9auXUtkZCSnT59m/Pjx6HQ6bt++TUpKCv369UOv1zNkyBCuX78OQEpKivoZ2rhxY51dh0dRq6/7iqIUCSHGAWtrPFiSJEl6LH5vI7Lzdn+Nd49nWT/Okzf2nif7pzu0aNSIgR3a49a2tUkd7LISLt4k4eJN9WFFALMGwuQR7Mo8zmt4Jmkl+bl1m9bdwrIbzu7zqj0mJiaGoUOH0q1bN6ysrEhJSUGv13PgwAFiYmJISkqiSZMm5OaWPBQ6fvx4QkJC8Pf3586dOxQXF3PlypVq27CysuKrr74C4Mcff2T69OkALFq0iG3btjFr1iyCg4Pp168fUVFRFBUVUVhYiLW1NSNGjGD27NkUFxfz4YcfkpycXK7u9PT0KlNk7927l9TUVNLS0rh58yaurq54eXkBcObMGc6dO4e1tTUeHh6cOHECOzs7oqKiyMjIQAhBfn4+FhYW+Pn54evrW26qw71799S05nl5eXz55ZcIIdi6dSsrVqxg9erVFeK5fv06iYmJZGRk4Ofnx6hRo1i+fDmrVq1i3759FY7PysqiQ4cO6nsbGxuysrIqHFfW9u3bGTNmTLXHmOrixYtERESwZcsWAgIC2LNnDxMmTGDEiBGV3stXXnmFmTNnMmnSJJM7uqmpqWRlZZGeng6g3oOwsDBWrVqFi4sL9+/fZ9asWcTExPDss8+ye/duFi5cyPbt25k8eTJhYWF4eXkxb171n/36Zsr/SCeEEGGUrC7yc+lGRVG+qvOoJEmSJJP9Hkdk4zNuYNGoEdk//WcVErMGDR65g13ffo/XsL5FRETwyiuvADB27FgiIiLQ6/UcOXKEyZMn06RJyYovlpaWGI1GsrKy8Pf3B6Bx48a1aqNspy89PZ1FixaRn59PYWEhQ4YMAeDYsWO89957AJiZmdGiRQtatGiBlZUVZ86cIScnB2dnZ6ysrGp9bomJiYwbNw4zMzPatGlDv379OHXqFM2bN8fNzQ0bGxsAdDodBoOBXr160bhxY6ZOnYqvr2+50fnqzunq1auMGTOG69evc+/evSozFz7//PM0aNAAe3v7Op3OUmrZsmWYm5szfvx4k8oJUfmDwqXbbW1t0el0AOj1egwGA1D1vTxx4gR79uwBYOLEiSxYsKDWsXTq1IlLly4xa9Yshg8fzuDBgysc880335Ceno6Pjw9QMg2pXbt25Ofnk5+fr36RmjhxIgcOHKh123XNlE627uG/b5bZpgAD6i4cSZIkSfpjqmnEuT7k5uZy7Ngxvv76a4QQFBUVIYRg5cqVJtVjbm5OcZmHWO/cuVNu/zPPPKO+DgwMJDo6Gq1WS3h4OPHx8dXWPW3aNMLDw8nOzmbKlCkV9js4ODzSPN9GZb7omZmZ8eDBA8zNzUlOTubo0aNERkYSFhbGsWPHKi1f9pxmzZrFnDlz8PPzIz4+ntDQ0BrbLJ2eUp327duXuz5Xr17F29u70mPDw8PZt28fR48erbTTvHDhQvbvL1k9JzU1tdw+KysrdbpFKaPRiIWFBUajscK1Kp0uUt29rKrjXqqqz0zLli1JS0vj0KFDbNq0iY8++ojt27eXK6soCg4ODnzxRfkkUqY+8FnfavPgIwCKovSv5Ed2sCVJkiTpCRUZGcnEiRP5/vvvMRgMXLlyBVtbWxISEvDx8WHHjh3qPNvc3FyaNWuGjY0N0dHRANy9e5dbt27x3HPPcf78ee7evUt+fj5Hjx6tsk2j0Ui7du24f/8+u3btUrcPHDiQd999FygZmSwoKADA39+fgwcPcurUKXWktKwBAwZw9+5dNm/erG47e/YsCQkJ9O3bl927d1NUVMSNGzc4fvw4bm5uVcZWWFhIQUEBw4YNY+3ataSlpQHQrFkzjEZjleUKCgpo3749QJUrgFSlurqHDBnC4cOHycvLIy8vj8OHD1d6DQ4ePMiKFSuIjY1V//LwS8uWLSM1NbVCBxvAy8uL2NhYNY69e/ei1WoxMzOrNvaq7qWHh4c6v7/s9rI6duxIamqqOt2odBrQzZs3KS4uZuTIkSxdulSdZlT2OnXv3p0bN26onez79+9z7tw5LCwssLCwIDExsdq2fyu17mQLIdoIIbYJIQ48fG8vhJhaf6FJkiRJklSfIiIi1KkfpUaOHElERARDhw7Fz88PFxcXdDqdusTb+++/z4YNG9BoNPTp04fs7Gw6dOhAQEAAjo6OBAQE4OzsXGWbb731Fu7u7nh4eNCjRw91+/r164mLi8PJyQm9Xq+u5vHUU0/Rv39/AgICKu30CSGIioriyJEjdO7cGQcHB1577TXatm2Lv78/Go0GrVbLgAEDWLFiBW3LrHDzS0ajEV9fXzQaDZ6enqxZswYomUazcuVKnJ2dycysmOwoNDSU0aNHo9fradWqVTVXvCKNRoOZmRlarbbCg4+Wlpa8/vrruLq64urqyuLFi7G0tARKRvhL54QHBQVhNBrx8fFBp9OVewi0tjEEBQXh6emJTqdj06ZNbN26tcZy1d3LjRs34uTkVGEOeekIt4eHB7a2ttjb2xMcHEzPnj2Bknno3t7e6HQ6JkyYoK6UEhgYyIwZM9DpdBQVFREZGcmCBQvQarXodDpOnjwJwI4dO3j55ZfR6XS1+mtBfRK1DeBh53oHsFBRFK0Qwhw4oyiKU30GWBkXFxel9IMlSZIk/T6VJnYxLB/OmH+WjDj9mmQ5pXWUpj8vffDx95BS/lFduHABOzu7xx3G71pxcbG6MknXrr+vh3sl06SkpDBnzhw+//zzxx1KlSr7nRRCpCiK4mJqXbUeyQZaKYryEVAMoCjKA0Cu+C9JkiRJUr04f/48Xbp0YeDAgbKD/YQ7ffo048aNUx+y/SMw5cHHn4UQVpQ87IgQohdQUC9RSZIkSZL0h2dvb8+lS5cedxhSHXBxcanTLJxPAlM62XMoyfDYWQhxAngWeLTcmJIkSZIkSZL0X6zWnWxFUb4SQvQDugMC+EZRlNrnw5UkSZIkSZKkP4gaO9lCiBFV7OomhEBRlL11HJMkSZIkSZIkPdFqM5L9l4f/tgb6AKWrsvcHTgKyky1JkiRJkiRJZdS4uoiiKJMVRZkMNATsFUUZqSjKSMDh4TZJkiRJkp5g0dHRCCHIyMh43KE8kuzsbMaOHUvnzp3R6/UMGzasTh+yi46OVtftNkVsbCzLly+v9hiDwcAHH3xQ5f6dO3fStWtXunbtWmWim3nz5tGjRw80Gg3+/v5q5kODwcDTTz+NTqd7pPWzDQYDjo6O5baFhoaqa6b/WoGBgWq2zmnTpj3SNU5NTeXTTz81uZy3tzf1vRy0KUv4dVAUpWzOzRzgT3UcjyRJkiRJv7GIiAg8PT2JiIio13aKiup+5V9FUfD398fb25vMzExSUlJ4++23ycnJqbM2qutkP3jwoMpyfn5+hISEVFt3dZ3s3NxclixZQlJSEsnJySxZsoS8vLwKx/n4+JCens7Zs2fp1q2bmsAFoHPnzmqmx02bNlUby+O0detW7O3tTS73qJ3s34IpneyjQohDQohAIUQgsB84Uj9hSZIkSZL0WygsLCQxMZFt27apqbBLvfPOOzg5OaHVatXO4nfffcegQYPQarX07NmTzMxM4uPj8fX1VcsFBQURHh4OlKTPXrBggZpQZsuWLbi6uqLVahk5cqSatj0nJwd/f3+0Wi1arZaTJ0+yePFi1q1bp9a7cOFC1q9fXy7GuLg4GjZsWG6UVqvV0rdvXxRFYd68eTg6OuLk5MTu3bsBiI+Px9vbm1GjRtGjRw/Gjx+vZgcMCQnB3t4ejUbD3LlzOXnyJLGxscybNw+dTkdmZibe3t7Mnj0bFxcX1q9fzyeffIK7uzvOzs4MGjRI7eCHh4cTFBQElIzaBgcH06dPHzp16qSO4IaEhJCQkIBOp6uQ8fHQoUP4+PhgaWlJy5Yt8fHx4eDBgxXu4eDBgzE3L5kB3KtXL65evVrjfa8L3t7eLFiwADc3N7p160ZCQgJQ8sWhb9++9OzZk549e6rZGBVFISgoiO7duzNo0CB++OGHcnWVjiw3bdpU3R4ZGUlgYCAAH3/8MY6Ojmi1Wry8vLh37x6LFy9m9+7d6HQ6du/ezc8//8yUKVNwc3PD2dmZmJgYAG7fvs3YsWOxs7PD39+f27dv1/v1MWV1kaCHD0H2fbhps6IoUfUTliRJkiT9sXyespIbeXW7jvCzLbvRTz+v2mNiYmIYOnQo3bp1w8rKipSUFPR6PQcOHCAmJoakpCSaNGlCbm4uAOPHjyckJAR/f3/u3LlDcXExV65cqbYNKysrvvrqKwB+/PFHpk+fDsCiRYvYtm0bs2bNIjg4mH79+hEVFUVRURGFhYVYW1szYsQIZs+eTXFxMR9++CHJycnl6k5PT0ev11fa7t69e0lNTSUtLY2bN2/i6uqKl5cXAGfOnOHcuXNYW1vj4eHBiRMnsLOzIyoqioyMDIQQ5OfnY2FhgZ+fH76+vowa9Z+Vi+/du6d2CvPy8vjyyy8RQrB161ZWrFjB6tWrK8Rz/fp1EhMTycjIwM/Pj1GjRrF8+XJWrVrFvn37KhyflZVFhw4d1Pc2NjYV0pT/0vbt2xkzZoz6/vLlyzg7O9O8eXOWLl1K3759qyltugcPHpCcnMynn37KkiVLOHLkCK1bt+azzz6jcePGXLx4kXHjxnH69GmioqL45ptvOH/+PDk5Odjb2zNlypRat/Xmm29y6NAh2rdvT35+Pk899RRvvvkmp0+fJiwsDIC///3vDBgwgO3bt5Ofn4+bmxuDBg3in//8J02aNOHChQucPXtWTeNen0xZJ7t0JRH5oKMkSZIk/ZeIiIhQs/CNHTuWiIgI9Ho9R44cYfLkyTRp0gQAS0tLjEYjWVlZ+Pv7A9C4ceNatVG205eens6iRYvIz8+nsLCQIUOGAHDs2DHee+89AMzMzGjRogUtWrTAysqKM2fOkJOTg7OzM1ZWVrU+t8TERMaNG4eZmRlt2rShX79+nDp1iubNm+Pm5oaNjQ0AOp0Og8FAr169aNy4MVOnTsXX17fc6Hx153T16lXGjBnD9evXuXfvHra2tpWWef7552nQoAH29vZ1Op2l1LJlyzA3N2f8+PEAtGvXjn//+9/ql6fnn3+ec+fO0bx581rVJ4SocfuIESWL0On1egwGAwD3798nKCiI1NRUzMzM1Pnxx48fV++HtbU1AwYMMOn8PDw8CAwMJCAgQG33lw4fPkxsbKw6b/zOnTv8+9//5vjx4wQHBwOg0WjQaDQmtf0oat3JfjiK/Q4lq4yIhz+Koii1u1OSJEmSJFWpphHn+pCbm8uxY8f4+uuvEUJQVFSEEIKVK1eaVI+5uTnFxcXq+zt37pTb/8wzz6ivAwMDiY6ORqvVEh4eTnx8fLV1T5s2jfDwcLKzsysd9XRwcFCnXpiiUaNG6mszMzMePHiAubk5ycnJHD16lMjISMLCwjh27Fil5cue06xZs5gzZw5+fn7Ex8cTGhpaY5ul01Oq0759+3LX5+rVq3h7e1d6bHh4OPv27ePo0aNqJ7hRo0Zqm3q9ns6dO/Ptt9/i4uKilktKSuLFF18ESkaK/fz81H1WVlYV5oDn5uaW+xJRWn/pNQRYu3Ytbdq0IS0tjeLi4lp/GStVthNf9rO0adMmkpKS2L9/P3q9npSUlAplFUVhz549dO/e3aQ264Mpc7JXAH6KorRQFKW5oijNaupgCyG2CyF+EEKk13CcqxDigRBCZpCUJEmSyln72bd0DNlP0uVcki7nYrzzAOOdB+r7jiH76Riyn7Wf/bFSNteFyMhIJk6cyPfff4/BYODKlSvY2tqSkJCAj48PO3bsUOdM5+bm0qxZM2xsbIiOjgbg7t273Lp1i+eee47z589z9+5d8vPzOXr0aJVtGo1G2rVrx/3799m1a5e6feDAgbz77rtAyQOSBQUFAPj7+3Pw4EFOnTqljnqXNWDAAO7evcvmzZvVbWfPniUhIYG+ffuye/duioqKuHHjBsePH8fNza3K2AoLCykoKGDYsGGsXbuWtLQ0AJo1a4bRaKyyXEFBAe3btweocgWQqlRX95AhQzh8+DB5eXnk5eVx+PDhSq/BwYMHWbFiBbGxsepfHgBu3LihPmx66dIlLl68SKdOncqVdXd3Vx+MLNvBhpK50e3atVO/aOTm5nLw4EE8PT2rPaeCggLatWtHgwYNeP/999UYvLy81Ptx/fp14uLiKi3fpk0bLly4QHFxMVFR/5mZnJmZibu7O2+++SbPPvssV65cqXD9hgwZwj/+8Q/1S8yZM2fUtksfMC19SLS+mdLJzlEU5YKJ9YcDQ6s7QAhhRskI+WET65YkSZL+AF716YZh+XDcbS0r/TEsH45h+XBe9en2uEN94kRERKhTP0qNHDmSiIgIhg4dip+fHy4uLuh0OvXP7++//z4bNmxAo9HQp08fsrOz6dChAwEBATg6OhIQEICzs3OVbb711lu4u7vj4eFBjx491O3r168nLi4OJycn9Hq9uprHU089Rf/+/QkICMDMzKxCfUIIoqKiOHLkCJ07d8bBwYHXXnuNtm3b4u/vj0ajQavVMmDAAFasWEHbtm2rjM1oNOLr64tGo8HT05M1a9YAJdNoVq5cibOzM5mZmRXKhYaGMnr0aPR6Pa1atarmilek0WgwMzNDq9VWePDR0tKS119/HVdXV1xdXVm8eDGWlpZAyQh/6ZzwoKAgjEYjPj4+5ZbqO378OBqNBp1Ox6hRo9i0aZNavrbee+893nrrLXQ6HQMGDOCNN96gc+fO1ZZ56aWX2LlzJ1qtloyMDHXU39/fn65du2Jvb8+kSZPo3bt3uXKlI9jLly/H19eXPn360K5dO3X/vHnzcHJywtHRkT59+qDVaunfvz/nz59XH3x8/fXXuX//PhqNBgcHB15//XUAZs6cSWFhIXZ2dixevLjKefx1SdTmzxUAQoj1QFsgGrhbur2mjI9CiI7APkVRHKvYPxu4D7g+PK7Gv/m4uLgo9b22oSRJkvTrdAzZD4Bh+XDG/PMLAHa/2Lu6IpVa+9m3rD96scbjXhnY9YnraF+4cAE7O7vHHcbvWnFxsboySdeuXR93OFI9cXJyIjY2tsr57L+Vyn4nhRApiqK4VFGkSqY8+NgcuAUMLrNN4Vc8CCmEaA/4U5I90vVR65EkSZL+e73q041XfbpV6Kj/mo679GQ4f/48vr6+6gio9N/Jx8cHJyenx97BrmumLOE3uR7aXwcsUBSluKonWEsJIf4G/A3gT3+SOXAkSZIk6b+dvb09ly5detxhSPXss88+e9wh1AtTVhfZQcnIdTmKotR+gcOKXIAPH3awWwHDhBAPFEWJrqSdzcBmKJku8ivalCRJkupBt4Wfcq+o4n/PpdNGSl8/ZSb4dtmw3zI0SZKk35wpDz7uoyTL437gKCXTRwp/TeOKotgqitJRUZSOQCTwUmUdbEmSJOn3b6Z3lzo9TpIk6UlmynSRPWXfCyEigMTqyjw8xhtoJYS4CrwBNHxY3yZTg5UkSZJ+v0rnTlfmUeZPV/XAY9mR8dLjnrQHHiVJ+u9nUsbHX+hKSWKaKimKMq62lSmKEvgrYpEkSZL+y1TXaYf/dNxlB1uSpN+jWk8XEUIYhRA/lf4AnwAL6i80SZIkSZJ+C9HR0QghyMjIeNyhPJLs7GzGjh1L586d0ev1DBs2TE3lXReio6PVdbtNERsby/Lly6s9xmAwqElSKrNz5066du1K165dq0x0ExoaSvv27dHpdOh0Oj799FOT4gwNDVXXQS/VsWNHbt68aVI9VWnatCkA165dY9SoR8s7GB4ezrVr10wqYzAYcHSsdAXp34Qp00WaVbdfCOGgKMq5Xx+SJEmSJFVU2+kjT+J62Y9bREQEnp6eREREsGTJknprp6ioqNKEMr+Goij4+/vz17/+lQ8//BCAtLQ0cnJy6Natbj4H0dHR+Pr6Ym9vX2FfaTr2yvj5+VXIovhLpZ3sF154ocK+3NxclixZwunTpxFCoNfr8fPzo2XLlhWOffXVV5k7d24tz+jxsLa2JjKyxnQolQoPD8fR0RFra+s6jqr+/JrpIr/0PtCzDuuTJEmSnlD10SGuafqI9GgKCwtJTEwkLi6Ov/zlL+U62e+88w7/+te/aNCgAX/+859Zvnw53333HTNmzODGjRuYmZnx8ccfc+XKFVatWsW+ffuAkgyELi4uBAYG0rFjR8aMGcNnn33G/PnzMRqNbN68mXv37tGlSxfef/99mjRpQk5ODjNmzFCX7Hv33Xc5ePAglpaWzJ49G4CFCxfSunVrXnnlFTXGuLg4GjZsqGY5BNBqtUBJB3z+/PkcOHAAIQSLFi1izJgxxMfHExoaSqtWrUhPT0ev1/Ovf/0LIQQhISHExsZibm7O4MGDGTFiBLGxsXz++ecsXbqUPXv2MHXqVHQ6HYmJiYwbN45u3bqxdOlS7t27h5WVFbt27aJNmzaEh4dz+vRpwsLCCAwMpHnz5pw+fZrs7GxWrFjBqFGjCAkJ4cKFC+h0Ov7617/y6quvqudx6NAhfHx81CyNPj4+HDx4kHHjaj0b91czGAz8+c9/xtPTk5MnT9K+fXtiYmJ4+umn2bJlS6X38vLly7zwwgsUFhbyP//zP+Xq8vX1JT09vdy1AfD19WXu3Ln07duXqVOnql8spkyZQocOHTh9+jTjx4/n6aef5osvvuD8+fPMmTOHwsJCWrVqRXh4OO3atSMlJYUpU0oWvhs8eHCl5/RbqctOdvULXUuSJEl/GLJDbLqP0lZytaDupjgA2LToRoB2XrXHxMTEMHToULp164aVlRUpKSno9XoOHDhATEwMSUlJNGnShNzcXADGjx9PSEgI/v7+3Llzh+LiYq5cuVJtG1ZWVnz11VcA/Pjjj0yfPh2ARYsWsW3bNmbNmkVwcDD9+vUjKiqKoqIiCgsLsba2ZsSIEcyePZvi4mI+/PBDkpOTy9Vd2kmuzN69e0lNTSUtLY2bN2/i6uqKl5cXAGfOnOHcuXNYW1vj4eHBiRMnsLOzIyoqioyMDIQQ5OfnY2FhgZ+fH76+vuWmOty7d09Na56Xl8eXX36JEIKtW7eyYsUKVq9eXSGe69evk5iYSEZGBn5+fowaNYrly5eX+4JSVlZWFh06dFDf29jYkJWVVem5hoWF8d577+Hi4sLq1asrHe1+VBcvXiQiIoItW7YQEBDAnj17mDBhAiNGjKj0Xr7yyivMnDmTSZMmsXHjRpPaSk1NJSsri/T0dAD1HoSFhbFq1SpcXFy4f/8+s2bNIiYmhmeffZbdu3ezcOFCtm/fzuTJkwkLC8PLy4t586r/7Nc3U5bwq4lcu1qSJEmSnjARERGMHTsWgLFjxxIREQHAkSNHmDx5Mk2aNAHA0tISo9FIVlYW/v7+ADRu3FjdX50xY8aor9PT0+nbty9OTk7s2rWLc+dKZpoeO3aMmTNnAmBmZkaLFi3o2LEjVlZWnDlzhsOHD+Ps7IyVlVWtz610pNnMzIw2bdrQr18/oy/pdQAAIABJREFUTp06BYCbmxs2NjY0aNAAnU6HwWCgRYsWNG7cmKlTp7J3795qz63sOV29epUhQ4bg5OTEypUr1XP6peeff54GDRpgb29PTk5Orc+jJjNnziQzM5PU1FTatWvH//7v/5pUvqqEgKXbbW1t0el0AOj1egwGA1D1vTxx4oQ62j5x4kSTYunUqROXLl1i1qxZHDx4kObNm1c45ptvviE9PR0fHx90Oh1Lly7l6tWr5Ofnk5+fr36RMrXtulaXI9mSJEmSJD2imkac60Nubi7Hjh3j66+/RghBUVERQghWrlxpUj3m5uYUFxer7+/cuVNu/zPPPKO+DgwMJDo6Gq1WS3h4OPHx8dXWPW3aNMLDw8nOzlanAZTl4ODwSPN8GzVqpL42MzNT51YnJydz9OhRIiMjCQsL49ixY5WWL3tOs2bNYs6cOfj5+alTUWpqU1FqHpts3759uetz9epVvL29KxzXpk0b9fX06dPx9fWtcMzChQvZv79kulZqamq5fVZWVly/fr3cNqPRiIWFBUajscK1un37NlD9vawpk3dVn5mWLVuSlpbGoUOH2LRpEx999BHbt28vV1ZRFBwcHPjiiy/Kbc/Pz6+2zd9aXY5k36vDuiRJkiRJqmeRkZFMnDiR77//HoPBwJUrV7C1tSUhIQEfHx927NjBrVu3gJIOebNmzbCxsSE6uiRv3N27d7l16xbPPfcc58+f5+7du+Tn53P06NEq2zQajbRr14779++za9cudfvAgQN59913gZIHJAsKCgDw9/fn4MGDnDp1iiFDhlSob8CAAdy9e5fNmzer286ePUtCQgJ9+/Zl9+7dFBUVcePGDY4fP46bm1uVsRUWFlJQUMCwYcNYu3YtaWlpADRr1gyj0VhluYKCAtq3bw9Q5QogVamu7iFDhnD48GHy8vLIy8vj8OHDlV6Dsh3kqKioSlfUWLZsGampqRU62ABeXl7ExsaqcezduxetVlvjQ6pV3UsPDw/1IdSy28vq2LEjqamp6nSj0mlAN2/epLi4mJEjR7J06VJ1mlHZ69S9e3du3LihdrLv37/PuXPnsLCwwMLCgsTExGrb/q2Y1MkWQvgJIVY9/PlL2X2KovSq29AkSZIkSapPERER6tSPUiNHjiQiIoKhQ4fi5+eHi4sLOp1OXeLt/fffZ8OGDWg0Gvr06UN2djYdOnQgICAAR0dHAgICcHZ2rrLNt956C3d3dzw8POjRo4e6ff369cTFxeHk5IRer1eXzHvqqafo378/AQEBlXb6hBBERUVx5MgROnfujIODA6+99hpt27bF398fjUaDVqtlwIABrFixgrZt21YZm9FoxNfXF41Gg6enJ2vWrAFKptGsXLkSZ2dnMjMzK5QLDQ1l9OjR6PV6WrVqVc0Vr0ij0WBmZoZWq2Xt2rXl9llaWvL666/j6uqKq6srixcvVh+CnDZtmjonfP78+Tg5OaHRaIiLi6tQT21iCAoKwtPTE51Ox6ZNm9i6dWuN5aq7lxs3bsTJyanCHPLSEW4PDw9sbW2xt7cnODiYnj1L1s7IysrC29sbnU7HhAkTePvtt4GSUfMZM2ag0+koKioiMjKSBQsWoNVq0el0nDx5EoAdO3bw8ssvo9PpavXXgvokahuAEOJtwA0o/VowDjilKMrf6ym2Krm4uCilHyxJkiRJelJduHABOzu7xx3G71pxcTE9e/bk448/pmvXro87HOlXSElJYc6cOXz++eePO5QqVfY7KYRIURTFxdS6TBnJHg74KIqyXVGU7cBQoOKkH0mSJEmSpDpw/vx5unTpwsCBA2UH+wl3+vRpxo0bV275xf92pj74aAHkPnzdoo5jkSRJkiRJUtnb26vrZktPNhcXlzrNwvkkMKWT/TZwRggRR8ma2F7Aa/USlSRJkiRJkiQ9wUxJqx4hhIgHXB9uWqAoSna9RCVJkiRJkiRJT7Baz8kWQhxVFOW6oiixD3+yhRBVr9EjSZIkSZIkSX9QNY5kCyEaA02AVkKIlvwnfXpzoH09xiZJkiRJkiRJT6TajGS/CKQAPR7+W/oTA4TVX2iSJEmSJP0WoqOjEUKQkZHxuEN5JNnZ2YwdO5bOnTuj1+sZNmxYnT5kFx0dra7bbYrY2FiWL19e7TEGg4EPPvigyv07d+6ka9eudO3atdpEN//4xz/o0aMHDg4OzJ8/36Q4Q0ND1XXQS3Xs2JGbN2+aVE9VmjZtCsC1a9cYNWrUI9URHh7OtWvXTCpjMBgqTczzW6mxk60oynpFUWyBuYqidFIUxfbhj1ZRFLWTLYTwqddIJUmSJEmqFxEREXh6ehIREVGv7RQVFdV5nYqi4O/vj7e3N5mZmaSkpPD222+Tk5NTZ21U18l+8OBBleX8/PwICQmptu7qOtm5ubksWbKEpKQkkpOTWbJkCXl5eRWOi4uLIyYmhrS0NM6dO8fcuXOrbfNxsba2JjIy8pHKPkon+3Gr9ZxsRVH+UcMh7/zKWCRJkiRJ+o0VFhaSmJjItm3b1FTYpd555x2cnJzQarVqZ/G7775j0KBBaLVaevbsSWZmJvHx8fj6/id1RlBQEOHh4UDJiOiCBQvUhDJbtmzB1dUVrVbLyJEj1bTtOTk5+Pv7o9Vq0Wq1nDx5ksWLF7Nu3Tq13oULF7J+/fpyMcbFxdGwYUNmzJihbtNqtfTt2xdFUZg3bx6Ojo44OTmxe/duAOLj4/H29mbUqFH06NGD8ePHq9kBQ0JCsLe3R6PRMHfuXE6ePElsbCzz5s1Dp9ORmZmJt7c3s2fPxsXFhfXr1/PJJ5/g7u6Os7MzgwYNUjv44eHhBAUFASUZC4ODg+nTpw+dOnVSO5shISEkJCSg0+kqZGo8dOgQPj4+WFpa0rJlS3x8fDh48GCFe/juu+8SEhJCo0aNAGjdunWN9722DAYDdnZ2TJ8+HQcHBwYPHszt27cBqryXly9fpnfv3jg5ObFo0aJydZWOLJe9NgC+vr7Ex8dTVFREYGCges/Wrl1LZGQkp0+fZvz48eh0Om7fvk1KSgr9+vVDr9czZMgQNbV8SkqK+hnauHFjnV2HR2HqOtnVETUfIkmSJElSZVal7+Lbn/5dp3V2a/4n5jqOr/aYmJgYhg4dSrdu3bCysiIlJQW9Xs+BAweIiYkhKSmJJk2akJtbkiZj/PjxhISE4O/vz507dyguLubKlSvVtmFlZcVXX30FwI8//sj06dMBWLRoEdu2bWPWrFkEBwfTr18/oqKiKCoqorCwEGtra0aMGMHs2bMpLi7mww8/JDk5uVzd6enp6PX6Stvdu3cvqamppKWlcfPmTVxdXfHy8gLgzJkznDt3Dmtrazw8PDhx4gR2dnZERUWRkZGBEIL8/HwsLCzw8/PD19e33FSHe/fuqWnN8/Ly+PLLLxFCsHXrVlasWMHq1asrxHP9+nUSExPJyMjAz8+PUaNGsXz5clatWsW+ffsqHJ+VlUWHDh3U9zY2NhXSlAN8++23JCQksHDhQho3bsyqVatwdXWtcNyjunjxIhEREWzZsoWAgAD27NnDhAkTGDFiRKX38pVXXmHmzJlMmjTJ5I5uamoqWVlZpKenA6j3ICwsjFWrVuHi4sL9+/eZNWsWMTExPPvss+zevZuFCxeyfft2Jk+eTFhYGF5eXsybN6/OrsGjqMtO9uNNEC9JkiRJkskiIiLULHxjx44lIiICvV7PkSNHmDx5Mk2aNAHA0tISo9FIVlYW/v7+ADRu3LhWbYwZM0Z9nZ6ezqJFi8jPz6ewsJAhQ4YAcOzYMd577z0AzMzMaNGiBS1atMDKyoozZ86Qk5ODs7MzVlZWtT63xMRExo0bh5mZGW3atKFfv36cOnWK5s2b4+bmho2NDQA6nQ6DwUCvXr1o3LgxU6dOxdfXt9zofHXndPXqVcaMGcP169e5d+8etra2lZZ5/vnnadCgAfb29nU6neXBgwfk5uby5ZdfcurUKQICArh06RJC1G78s6rjSrfb2tqi0+kA0Ov1GAwGoOp7eeLECfbs2QPAxIkTWbBgQa3PpVOnTly6dIlZs2YxfPhwBg8eXOGYb775hvT0dHx8SmYqFxUV0a5dO/Lz88nPz1e/SE2cOJEDBw7Uuu26VpedbEmSJEmSHlFNI871ITc3l2PHjvH1118jhKCoqAghBCtXrjSpHnNzc4qLi9X3d+7cKbf/mWeeUV8HBgYSHR2NVqslPDyc+Pj4auueNm0a4eHhZGdnM2XKlAr7HRwcHmmeb+nUCijp1D948ABzc3OSk5M5evQokZGRhIWFcezYsUrLlz2nWbNmMWfOHPz8/IiPjyc0NLTGNkunp1Snffv25a7P1atX8fb2rnCcjY0NI0aMQAiBm5sbDRo04ObNmzz77LPqMQsXLmT//v1AyWhxWVZWVup0i1JGoxELCwuMRmOFa1U6XaS6e1lTB7+qz0zLli1JS0vj0KFDbNq0iY8++ojt27eXK6soCg4ODnzxxRfltufn51fb5m/NlHWyK1wtIUSjMm8NdRGQJEmSJEm/jcjISCZOnMj333+PwWDgypUr2NrakpCQgI+PDzt27FDn2ebm5tKsWTNsbGyIjo4G4O7du9y6dYvnnnuO8+fPc/fuXfLz8zl6tOo0GkajkXbt2nH//n127dqlbh84cCDvvvsuUDIyWVBQAIC/vz8HDx7k1KlT6khpWQMGDODu3bts3rxZ3Xb27FkSEhLo27cvu3fvpqioiBs3bnD8+HHc3NyqjK2wsJCCggKGDRvG2rVrSUtLA6BZs2YYjcYqyxUUFNC+fcmqxtWtAFKZ6uoeMmQIhw8fJi8vj7y8PA4fPlzpNXj++eeJi4sDSqaO3Lt3j1atWpU7ZtmyZaSmplboYAN4eXkRGxurxrF37160Wi1mZmbVxl7VvfTw8FDn95fdXlbHjh1JTU1VpxuVTgO6efMmxcXFjBw5kqVLl6rTjMpep+7du3Pjxg21k33//n3OnTuHhYUFFhYWJCYmVtv2b6XWnWxgW9k3QoimwKel7xVFGVFXQUmSJEmSVP8iIiLUqR+lRo4cSUREBEOHDsXPzw8XFxd0Op26xNv777/Phg0b0Gg09OnTh+zsbDp06EBAQACOjo4EBATg7OxcZZtvvfUW7u7ueHh40KNHD3X7+vXriYuLw8nJCb1er67m8dRTT9G/f38CAgIq7fQJIYiKiuLIkSN07twZBwcHXnvtNdq2bYu/vz8ajQatVsuAAQNYsWIFbdu2rTI2o9GIr68vGo0GT09P1qxZA5RMo1m5ciXOzs5kZmZWKBcaGsro0aPR6/UVOrc10Wg0mJmZodVqKzz4aGlpyeuvv46rqyuurq4sXrwYS0tLoGSEv3RO+JQpU7h06RKOjo6MHTuWnTt31nqqSGkMQUFBeHp6otPp2LRpE1u3bq2xXHX3cuPGjTg5OVWYQ14al4eHB7a2ttjb2xMcHEzPnj2Bknno3t7e6HQ6JkyYwNtvvw2UjJrPmDEDnU5HUVERkZGRLFiwAK1Wi06n4+TJkwDs2LGDl19+GZ1OV6u/FtQnUdsAhBBvAq0URXnpYVKa/cAWRVF21GeAlXFxcVFKP1iSJEmS9KS6cOECdnZ2jzuM37Xi4mJ1ZZKuXbs+7nCkXyElJYU5c+bw+eefP+5QqlTZ76QQIkVRFBdT6zJlCb/FQKEQYhNwGFj9ODrYkiRJkiT9MZw/f54uXbowcOBA2cF+wp0+fZpx48apD9n+EdQmrXrZaSBJwOtAMqAIIUYoirK3voKTJEmSJOmPy97enkuXLj3uMKQ64OLiUqdZOJ8EtVld5C+/eH8GaPhwuwLITrYkSZIkSZIklVFjJ1tRlMmPWrkQYjvgC/ygKEqF5PFCiPHAAkoS2RiBmYqipD1qe5IkSZIkSZL0e1DjnGwhRKtfvJ8ghNgghJhe2bJ+vxAODK1m/2Wgn6IoTsBbwOZqjpUkSZIkSZKkJ0JtHnw8XPpCCLEImAikAIOBNdUVVBTlOJBbzf6TiqLkPXz7JWBTi3gkSZIkSZIk6XetNp3ssqPVI4ARiqLsBF4ABtVhLFOBx5f7UpIkSZL+gMzMzNDpdDg4OKDValm9erWaie/06dMEBwfXa/uLFy/myJEjAKxbt05NflNbhYWFvPjii3Tu3Bm9Xo+3tzdJSUl1Fl98fLy6BrMpanPt8vPz+b//+78q90+ZMoXWrVvj6Fhhxq1KURSCg4Pp0qULGo1GTd5SW+Hh4QQFBZXb5u3tTV0tldyxY0du3rwJQJ8+fR6pjujoaHXddFM0bdr0kdqrK7XpZD8thHAWQugBM0VRfgZQFOU+UFQXQQgh+lPSya4yub0Q4m9CiNNCiNM3btyoi2YlSZIk6Q/v6aefJjU1lXPnzvHZZ59x4MABlixZApSsCLFhw4Z6a7uoqIg333yTQYNKxuwepZM9bdo0LC0tuXjxIikpKezYsUPt1NWF6jrZDx48qLJcba5dTZ3swMBADh48WG0dBw4c4OLFi1y8eJHNmzczc+bMao9/nB7lywo8eif7catNJ/s6JdNCVgG5Qoh2AEIIK6DqT1ctCSE0wFbgfxRF+bGq4xRF2awoiouiKC7PPvvsr21WkiRJkqRfaN26NZs3byYsLAxFUYiPj8fX1xeAzz//HJ1Oh06nw9nZGaPRSHx8PF5eXgwfPpzu3bszY8YMdRR85syZuLi44ODgwBtvvKG20bFjRxYsWKAmmAkMDCQyMpINGzZw7do1+vfvT//+/dm+fTuzZ89Wy23ZsoVXX321XLyZmZkkJSWxdOlSGjQo6dLY2toyfPhwANasWYOjoyOOjo6sW7cOAIPBgJ2dHdOnT8fBwYHBgwdz+/ZtADZs2IC9vT0ajYaxY8diMBjYtGkTa9euRafTkZCQoGYedHd3Z/78+SQnJ9O7d2+cnZ3p06cP33zzDUC5axcaGsqUKVPw9vamU6dOauc7JCSEzMxMdDod8+bNq3A/vLy81AyPVYmJiWHSpEkIIejVqxf5+flcv369Nre7Vpo2bcrChQvRarX06tWLnJwcAD755BPc3d1xdnZm0KBB6vYff/yRwYMH4+DgwLRp08plXSwdWS57bQCCgoIIDw8HSq5J6T2YO3cuJ0+eJDY2lnnz5qHT6cjMzCQzM5OhQ4ei1+vp27cvGRkZAFy+fJnevXvj5OTEokWL6uwaPKrarC7Sv4pd+YDXr2lcCPEnSpYAnKgoyh9r8URJkiRJKmP12c/4tuCHOq2zW4vW/K/Gx6QynTp1oqioiB9+KB/LqlWr2LhxIx4eHhQWFtK4cWMAkpOTOX/+PM899xxDhw5l7969jBo1imXLlmFpaUlRUREDBw7k7NmzaDQaAKysrNRpDaUjtcHBwaxZs4a4uDhatWpFYWEhy5YtY+XKlTRs2JAdO3bwz3/+s1xM586dQ6fTVZpuvXRUOykpCUVRcHd3p1+/frRs2ZKLFy8SERHBli1bCAgIYM+ePUyYMIHly5dz+fJlGjVqRH5+PhYWFsyYMYOmTZsyd+5cALZt28bVq1c5efIkZmZm/PTTTyQkJGBubs6RI0f4+9//zp49eyrEk5GRQVxcHEajke7duzNz5kyWL19Oeno6qampJt2jsrKysujQoYP63sbGhqysLNq1a/fIdZb1888/06tXL5YtW8b8+fPZsmULixYtwtPTky+//BIhBFu3bmXFihWsXr2aJUuW4OnpyeLFi9m/fz/btm2rdVs//vgjUVFRZGRkIIRQ74Gfnx++vr6MGjUKgIEDB7Jp0ya6du1KUlISL730EseOHeOVV15h5syZTJo0iY0bN9bJ+f8atc74WEoI0VQI0RNopihKtX/TEUJEAF8A3YUQV4UQU4UQM4QQMx4eshiwAv5PCJEqhJC50iVJkiTpd8jDw4M5c+awYcMG8vPzMTcvGadzc3OjU6dOmJmZMW7cOBITEwH46KOP6NmzJ87Ozpw7d67cn/vHjBlTY3tNmzZlwIAB7Nu3j4yMDO7fv4+Tk1Ot401MTMTf359nnnmGpk2bMmLECBISEoCS0W6dTgeAXq/HYDAAoNFoGD9+PP/617/U86vM6NGj1Y59QUEBo0ePxtHRkVdffZVz585VWmb48OE0atSIVq1a0bp1a3Xk93GraqG40u1PPfWUOupc9lpdvXqVIUOG4OTkxMqVK9XzPn78OBMmTABKzrlly5a1jqVFixY0btyYqVOnsnfvXpo0aVLhmMLCQk6ePMno0aPR6XS8+OKL6sj9iRMnGDduHAATJ06sdbv1pTYZH/9PUZSXHr72BD4AMoEuQogXFUX5tKqyiqKMq65uRVGmAdNMC1mSJEmS/vuYOuJcXy5duoSZmRmtW7fmwoUL6vaQkBCGDx/Op59+ioeHB4cOHQIqdtKEEFy+fJlVq1Zx6tQpWrZsSWBgIHfu3FGPeeaZZ2oVy7Rp0/h//+//0aNHDyZPrpi2w8HBgbS0NIqKiiodza5Ko0aN1NdmZmbqdJH9+/dz/PhxPvnkE5YtW8bXX39dafmy8b/++uv079+fqKgoDAYD3t7etWqzuvncpmjfvj1XrlxR31+9epX27duXO2bjxo1s2bIFgE8//RRra2t1n5WVFXl5eeWOz83NpVWrkhWcGzZsqN7jsnHPmjWLOXPm4OfnR3x8PKGhobWO2dzcXJ1WBKifDXNzc5KTkzl69CiRkZGEhYVx7NixcmWLi4uxsLCocvS/5tWlfzu1GcnuVeb1W8DzD6eQ9APerJeoJEmSJEn6zd24cYMZM2YQFBRUobOSmZmJk5MTCxYswNXVVZ0Hm5yczOXLlykuLmb37t14enry008/8cwzz9CiRQtycnI4cKB2i4c1a9YMo9Govnd3d+fKlSt88MEH6ghlWZ07d8bFxYU33nhDnftrMBjYv38/ffv2JTo6mlu3bvHzzz8TFRVF3759q2y7uLiYK1eu0L9/f9555x0KCgooLCysENMvFRQUqJ3a0nnFtVVT3bXh5+fHe++9h6IofPnll7Ro0aLCVJGXX36Z1NRUUlNTy3WwAVxdXTlx4gTZ2dlAyaood+/eLTcFpTJlz3vnzp3qdi8vLz744AOg5KHMX3bgAZ577jnOnz/P3bt3yc/P5+jRo0DJKHVBQQHDhg1j7dq1pKWV5Ccse52aN2+Ora0tH3/8MVCyukrpcR4eHnz44YcA7Nq1q6ZLV+9MnS7SXFGUrwAURbn0COUlSZIkSfoduX37trqE36BBgxg8eHC5BxVLrVu3DkdHRzQaDQ0bNuTPf/4zUNJJCwoKws7ODltbW/z9/dFqtTg7O9OjRw9eeOEFPDw8ahXL3/72N4YOHUr//v95HCwgIAAPD48qpx1s3bqVnJwcunTpgqOjI4GBgbRu3ZqePXsSGBiIm5sb7u7uTJs2DWdn5yrbLioqYsKECTg5OeHs7ExwcDAWFhb85S9/ISoqSn3w8Zfmz5/Pa6+9hrOzs8mj01ZWVnh4eODo6Fjpg4/jxo2jd+/efPPNN9jY2Kjzmzdt2sSmTZsAGDZsGJ06daJLly5Mnz692tVKKtOmTRvWr1/PsGHD0Ol0zJ49m4iICPVB0qqEhoYyevRo9Hq9OuoN8MYbb3D8+HEcHBzYu3cvf/rTn9R9pV/cOnToQEBAAI6OjgQEBKj3xWg04uvri0ajwdPTkzVrStKxjB07lpUrV+Ls7ExmZia7du1i27ZtaLVaHBwciImJAWD9+vVs3LgRJycnsrKyTLoO9UGUfeqz0gOEuAV8R8l62R2BPymKkieEaACcrSxden1zcXFR6mr9RkmSJEl6XC5cuICdnd3jDuORxcfHs2rVKvbt21dvbfj6+vLqq68ycODAemtDqn8//vgjPXv25Pvvv3/coVSrst9JIUSKoiguptZV45xs4Je//YUP/7Wk5MFFSZIkSZKkOpWfn4+bmxtarVZ2sJ9w165dw9vbW12h5Y+iNkv4fS+EMAPeUxRlfJntNylZfk+SJEmSpD8gb2/vKh/0+7UsLCz49lu5uu9/A2tr6z/kvazVnGpFUYqA54QQT9VzPJIkSZIkSZL0xKvNdJFSl4ATQohY4OfSjYqirKnzqCRJkiRJkiTpCWZKJzvz4U8DoFn9hCNJkiRJkiRJT75ad7IVRVkCIIRoUlOmR0mSJEmSJEn6I6v1OtdCiN5CiPNAxsP3WiGEaYsxSpIkSZL0u2JmZqauk63Valm9erWaje/06dMEBwfXa/uLFy/myJEjQMla3LdumTaOV1hYyIsvvkjnzp3R6/V4e3uTlJRUZ/HFx8dz8uRJk8vV5trl5+dXu671lClTaN26NY6OVa+WrCgKwcHBdOnSBY1Gw1dffaXuK723Op0OPz8/k8+hadOm5d6Hh4cTFBRkcj2VCQ0NZdWqVUD5z4ApDAaDmvjGFIGBgURGRppczlSmTBdZBwwBYgEURUkTQnjVS1SSJEmSJP0mnn76aTVF9Q8//MALL7zATz/9xJIlS3BxccHFxeTlgWutqKiIN9/8T/LodevWMWHCBJo0aVLrOqZNm4atrS0XL16kQYMGXL58mfPnz9dZjPHx8TRt2pQ+ffpU2PfgwQPMzSvvStXm2pV2sl966aVK9wcGBhIUFMSkSZOqrOPAgQNcvHiRixcvkpSUxMyZM9UvGWXv7e9Z2c+AKUo72S+88EIdR1Q3TMrYqCjKlV9sKqrDWCRJkiRJeoxat27N5s2bCQsLQ1EU4uPj8fX1BeDzzz9XR0WdnZ0xGo3Ex8fj5eXF8OHD6d69OzNmzFBHwWfOnImLiwsODg7lMkh27NiRBQsW0LNnTz7++GN1VHHDhg1cu3aN/v37079/f7Zv387s2bPVclu2bOHVV18tF29mZiZJSUksXbpUzVBoa2vL8OHDAVizZg2Ojo44Ojqybt06oKRjZmdnx/Tp03FwcGDw4MHcvn0bgA0bNmBvb49Go2Hs2LEYDAY2bdrE2rVr1YzEXjKZAAAgAElEQVSPgYGBzJgxA3d3d+bPn09ycjK9e/fG2dmZPn368M033wCUu3ahoaFMmTIFb29vOnXqxIYNGwAICQkhMzMTnU5XacZHLy8vLC0tq71nMTExTJo0CSEEvXr1Ij8/n+vXr9fmdv8qgYGBBAcH06dPHzp16qSODBcWFjJw4EB69uyJk5OTmo0RYNmyZXTr1g1PT0/1OpXWVVq+Y8eO/H/27j0uqjp//PjrNBBeSLktffOSgJUgwzDDRcJBBPVLKupPDFGUajJScFkTv6VkeWujr654idLI++6apqnYKljfLAiNDW+LJjcJwbyFonLzlg7n9wdxVgR0xiBt+zwfDx87c875nM/7nM/Qvuczn/P5VFRUAPW/BjRMEdnc5y8hIYE9e/ag1WpZsmQJRqOR1157DV9fXzQaDR9++CFQ39sfFxdHr169GDRoEOfOnWvz+wPm9WSflCSpLyBLkmQJvAIUtE1YgiAIgvD7svjItxyrutCq53yqsz3TNE+bVcbFxQWj0dgkEUlKSmLZsmXo9Xpqa2tp164dAPv27SM/P58ePXowePBgtm3bRnh4OImJidjZ2WE0Ghk4cCBHjhxBo9EA9cuJNwxr+OyzzwCYMmUKixcvJiMjAwcHB2pra0lMTGThwoVYWlqydu1aJWlqkJeXh1arRaVSNbmOgwcPsnbtWnJycpBlGT8/P/r374+trS3FxcVs3LiRlStXEhERwdatW4mKimL+/PmUlpZiZWVFZWUlNjY2xMTEYG1trSyksnr1ak6dOkV2djYqlYrq6mr27NmDhYUFu3fvZubMmWzdurVJPIWFhWRkZFBTU0OvXr2IjY1l/vz5HD169Bf1Np8+fZru3bsr77t168bp06d57LHHuHbtGj4+PlhYWJCQkMDIkSPvuZ7mnD17lr1791JYWMiIESMIDw+nXbt2pKam0qlTJyoqKnj66acZMWIEhw4d4uOPPyY3N5ebN2/i5eWFt7e3yXU19/mbP39+oxVHV6xYQefOndm/fz/Xr19Hr9cTEhLCv/71L4qKisjPz6e8vJzevXszYcKEVr0XzTGnJzsG+CPQFTgNaH9+LwiCIAjCfzi9Xs+0adNITk6msrJSGSbRp08fXFxcUKlUREZGsnfvXgA2b96Ml5cXOp2OvLy8RkM4xowZc9f6rK2tGTBgADt37qSwsJAbN27g4eFhcrx79+4lLCyMjh07Ym1tzahRo9izZw9Q39ut1WoB8Pb2pqysDACNRsP48eNZv359i8NAAEaPHq0k9lVVVYwePRq1Wk18fDx5eXnNlgkNDcXKygoHBwccHR0pLy83+Vru1YkTJzhw4AAbNmxg6tSplJSU/OJzSpKkvB45ciQPPfQQvXv3Vq5HlmVmzpyJRqNh0KBBnD59mvLycvbs2UNYWBgdOnSgU6dOZo8Rb+nzd6v/+7//429/+xtarRY/Pz8uXLhAcXExWVlZREZGolKp6NKlCwMGDPhlN8FE5vRkS7eu+CgIgiAIQusxt8e5rRw/fhyVSoWjoyMFBf/+wTohIYHQ0FDS09PR6/V8/vnnQOOkq+F9aWkpSUlJ7N+/H1tbWwwGA9euXVOO6dixo0mxREdH88477+Dq6sqLL77YZL+7uzuHDx/GaDQ225vdEisrK+W1SqVShoukpaWRlZXFjh07SExM5Lvvvmu2/K3xz5o1i+DgYFJTUykrK2txBczb67x586bJ8d5J165dOXny36N5T506RdeuXZV9UP/rRFBQEP/617/o2bOncuzJkycZPnw4ADExMcTExDQ6d/v27fnpp594+OH6tQgvXryIg4NDs9ckyzIAH330EefPn+fgwYNYWlri5OTUqO3vxsLCQhlydGu5lj5/t5Jlmffee49nnnmm0fb09HST629N5vRkfyNJ0v9JkvSSJEk2bRaRIAiCIAj3xfnz54mJiSEuLq5J8lxSUoKHhwczZszA19eXwsJCoH64SGlpKXV1dWzatImAgACqq6vp2LEjnTt3pry8nF27dplU/yOPPEJNTY3y3s/Pj5MnT7JhwwYiIyObHN+zZ098fHyYM2eOkuSVlZWRlpZGv3792L59O1euXOHy5cukpqbSr1+/Fuuuq6vj5MmTBAcHs2DBAqqqqqitrW0S0+2qqqqUZHbdunUmXWdL13svRowYwd/+9jdkWebbb7+lc+fOPPbYY1y6dInr168DUFFRwTfffEPv3r0ble3evTu5ubnk5uY2SbAB+vfvz/r16wG4evUqmzdvJjg4+I7xVFVV4ejoiKWlJRkZGZw4cQKoH1++fft2rl69Sk1NDTt27Gi2vJOTEwcPHgRoNOymuc/f7ffvmWee4YMPPuDGjRsAHDt2jMuXLxMYGMimTZswGo2cPXuWjIyMO15DazE5yZZl+SngTcAdOCRJ0k5JkqLaLDJBEARBENrc1atXlSn8Bg0aREhISKMHFRssXboUtVqNRqPB0tKSIUOGAODr60tcXBxubm44OzsTFhaGp6cnOp0OV1dXxo0bh16vNymWiRMnMnjw4EaJXEREBHq9Hltb22bLrFq1ivLycp544gnUajUGgwFHR0e8vLwwGAz06dMHPz8/oqOj0el0LdZtNBqJiorCw8MDnU7HlClTsLGxYfjw4aSmpioPPt5u+vTpvP766+h0OrN7p+3t7dHr9ajV6mYffIyMjMTf35+ioiK6devG6tWrAUhJSSElJQWAoUOH4uLiwhNPPMHLL7+sTAlYUFCAj48Pnp6eBAcHk5CQ0CTJvpt3332Xbdu2odVqefrppxk9ejSBgXeeWG78+PEcOHAADw8P/va3v+Hq6gqAl5cXY8aMwdPTkyFDhuDr69uoXMOXujlz5vDKK6/g4+PT6NeJ5j5/Go0GlUqFp6cnS5YsITo6mt69e+Pl5YVarWbSpEncvHmTsLAwnnzySXr37s3zzz+Pv7+/WffhXkkN3/zMKiRJDsBiYLwsy6b/PtNKfHx85AMHDvza1QqCIAhCqyooKMDNze1+h3HPMjMzGz141haGDRtGfHw8AwcObLM6hPtr+PDhTJs27a695L+G5v4mJUk6KMuy2XNZmrMYTSdJkl6QJGkXkA2cBfqYW6EgCIIgCMLdVFZW8tRTT9G+fXuRYP8HmzBhAleuXCEgIOB+h9LqzHnw8TCwHXhLluV/tlE8giAIgiD8RgQFBbX4oN8vZWNjw7Fjx9rk3MKDY82aNfc7hDZjTpLtIsuyLEmStSRJ1rIs17ZZVIIgCIIgCILwG2bO7CLukiT9C8gD8iVJOihJkrqN4hIEQRAEQRCE3yxzkuwVwDRZlnvIsvw48D8/bxMEQRAEQRAE4RbmJNkdZVlWJhaUZTkTMG02eUEQBEEQBEH4HTEnyT4uSdIsSZKcfv73JnC8rQITBEEQBKHtqVQqZZ5sT09PFi1apKy4d+DAAaZMmdKm9c+ePZvdu3cD9XMhX7lyxazytbW1TJo0iZ49e+Lt7U1QUBA5OTmtFl9mZibZ2dlmlzPl3lVWVirzWjdnwoQJODo6ola3PDq3sLAQf39/rKysSEpKMjvOzMxMhg0b1mibwWBgy5YtZp+rOUFBQTRMuzx06FAqKyvvKcZ7aQMnJycqKirMLtdazEmyJwB/ALYBWwGHn7cJgiAIgvAb1b59e3Jzc8nLy+OLL75g165dzJs3DwAfHx+Sk5PbrG6j0chbb73FoEGDgHtLsqOjo7Gzs6O4uJiDBw+ydu3aVk2s7pTg3WnxGVPu3d2SbIPBwGeffXbHc9jZ2ZGcnMyrr756x+MeBOnp6djYmL9o+L0m2febOSs+XpJleYosy16yLHvLsjxVluVLDfslSXqvbUIUBEEQBOHX4OjoyIoVK3j//feRZblRL+fXX3+NVqtFq9Wi0+moqakhMzOTwMBAQkND6dWrFzExMUoveGxsLD4+Pri7uzdaQdLJyYkZM2bg5eXFJ598ovSaJicnc+bMGYKDgwkODmbNmjVMnTpVKbdy5Uri4+MbxVtSUkJOTg5vv/02Dz1Un9I4OzsTGhoKwOLFi1Gr1ajVapYuXQrUL7vu5ubGyy+/jLu7OyEhIVy9ehWA5ORkevfujUajYezYsZSVlZGSksKSJUuUFR8NBgMxMTH4+fkxffp09u3bh7+/Pzqdjr59+1JUVAQ07iGeO3cuEyZMICgoCBcXFyX5TkhIoKSkBK1W2+yKj4GBgdjZ2d21zXx9fbG0tDSlic3m5OTEnDlz8PLywsPDg8LCQoAWr/vq1auMHTsWNzc3wsLClHvbcK6KigrKysoa9c4nJSUxd+5cwLQ2OH/+PM8++yy+vr74+vryzTffAHDhwgVCQkJwd3cnOjqae1lwsTWZM4Xf3TRZM1WSpDXAMOCcLMtNfuuQ6tfQfBcYClwBDLIsH2rFmARBEAThN2Hx4cMU38NP6XfypI0N0zw9zSrj4uKC0Wjk3LlzjbYnJSWxbNky9Ho9tbW1tGvXDqhPtvLz8+nRoweDBw9m27ZthIeHk5iYiJ2dHUajkYEDB3LkyBE0Gg1Qv5z4oUP1/3ff0FM7ZcoUFi9eTEZGBg4ODtTW1pKYmMjChQuxtLRk7dq1fPjhh41iysvLQ6vVNlp+u0FDr3ZOTg6yLOPn50f//v2xtbWluLiYjRs3snLlSiIiIti6dStRUVHMnz+f0tJSrKysqKysxMbGhpiYGKytrZWe4tWrV3Pq1Cmys7NRqVRUV1ezZ88eLCws2L17NzNnzmTr1q1N4iksLCQjI4Oamhp69epFbGws8+fP5+jRo+Tm5prVRr82BwcHDh06xPLly0lKSmLVqlW4uro2e90ffPABHTp0oKCggCNHjuDl5WVWXaa0wbhx44iPjycgIIAffviBZ555hoKCAubNm0dAQACzZ88mLS1NWYb+fmnNJLs564D3gb+1sH8I8OTP//yAD37+X0EQBEEQHiB6vZ5p06Yxfvx4Ro0aRbdu3QDo06cPLi4uAERGRrJ3717Cw8PZvHkzK1as4ObNm5w9e5b8/HwlyR4zZsxd67O2tmbAgAHs3LkTNzc3bty4gYeHh8nx7t27l7CwMDp2rJ+jYdSoUezZs4cRI0bg7OyMVqsFwNvbm7KyMgA0Gg3jx49n5MiRjBw5ssVzjx49Wknsq6qqeOGFFyguLkaSJG7cuNFsmdDQUKysrLCyssLR0ZHy8nKTr6Ut1fd33nn7qFGjgPp7tW3bNqDl687KylLGoms0GqXNTWVKG+zevZv8/HzlfXV1NbW1tWRlZSnxhYaGYmtra1bdra1Nk2xZlrMkSXK6wyH/D/ibXN+f/60kSTaSJD0my/LZtoxLEARBEB405vY4t5Xjx4+jUqlwdHSkoKBA2Z6QkEBoaCjp6eno9Xo+//xzoGmSJkkSpaWlJCUlsX//fmxtbTEYDFy7dk05piHxvZvo6GjeeecdXF1defHFF5vsd3d35/DhwxiNxmZ7s1tiZWWlvFapVMqQhrS0NLKystixYweJiYl89913zZa/Nf5Zs2YRHBxMamoqZWVlLa6AeXuddxrP3dpSU1OVcfarVq3Cx8dH2Wdvb8+lS5caHX/x4kUcHByU9w2x3xq3qdfdHAsLC2VYEdDos2FKG9TV1fHtt98qv6Y8qFozyW7+q9CddQVO3vL+1M/b7phkFxUVNWrMN998k0GDBpGbm9to/FaDd955h759+5Kdnc3MmTOb7F+6dClarZbdu3fz9ttvN9n/4Ycf0qtXL3bs2MGiRYua7P/73/9O9+7d2bRpEx988EGT/Vu2bMHBwYF169axbt26JvvT09Pp0KEDy5cvZ/PmzU32Z2ZmAvU/1e3cubPRvvbt27Nr1y4A/vznP/Pll1822m9vb6/8bPX666/zz3/+s9H+bt26sX79egCmTp3a5Cerp556ihUr6qdDnzhxYpMlbrVarTLOLSoqilOnTjXa7+/vz//+7/8C8Oyzz3LhwoVG+wcOHMisWbMAGDJkSKOxWwDDhg1Tfh5q7g84IiKCyZMnc+XKFYYOHdpkv8FgwGAwUFFRQXh4eJP9sbGxjBkzhpMnT/Lcc8812f8///M/DB8+nKKiIiZNmtRkv/jsic+e+OyJz94v+eydO3dOGUvcoHPnzvzXf/0XgDLO9Va2trY4OjpiNBr5/vvvm+y3t7fHwcGBGzducPx400nA/vCHP2BnZ8dPP/1EaWkpsiwr9Vy8eJE33niDuLg4rl+/zg8//EBtbS1FRUX88MMPPP7448TGxrJ//34OHz7M9evXycnJ4csvv6RLly6sWbOG2NhYqqurad++PT/++CP5+fns3LmTXr16UVRUpIyTra6u5uzZs1RVVXH69GmKiop4+OGHqaiowMHBQRkq8P3337Nv3z4+/fRTioqKcHZ25uGHH+bixYvcvHmTXr16ERcXxyuvvIIkSTz00EMcO3YMtVrN5MmTGTVqFLIss2nTJhYsWIDRaFTu/aVLlzh37hxXrlyhoKCAM2fOMHDgQAICAtiwYQP/+te/uHr1KufPn6eoqKjRF4ozZ85QU1PD6dOn8fLyoqioiGXLlin7z58/r9y7iooKrly5wvHjx5Ve/9OnT6NSqbh06ZJy/62srHBycgLqx45fv36dU6dO8dNPP1FUVET79u15/PHHgfovQ7f2mldUVDRK3L///nvlWnv37s2mTZt45JFH6NKlCwDHjh1DlmVkWebEiROkp6fj5eXF9evXOXz4MB07dqSoqIgbN27w/fffc+HCBS5evAjUP7B663W/99573Lhxg4qKCgIDA1m/fj1du3bl2LFjHDlyhLKyMh555BElsba1teXs2bN8++23dOzYkS1bthAQEMDFixeprq7G39+fP/zhD6xfv75RG1RXV9OpUycGDBjA7NmzeemllwAoKCjAzc0Nf39/NmzYwNSpU9m4cSOXLl1SYgfo3r07HTp0UD57t+vRowdAi//dM1drJtnvtuK5mpAkaSIwERp/GxQEQRAE4d5du3aNkSNHcvPmTVQqFePHj+eNN97gp59+anTcX//6V/bt28fDDz+Mh4cHISEh7NixAw8PD/785z9z4sQJ/Pz8GD58OJ06dUKj0TBkyBAee+wxk8flRkREMGLECLp27UpqaioAgwcPprCwkM6dOzdb5u2332bBggWEhITQrl07HnvsMZKSknB2diYsLIyIiAgAwsPD6d27d4t1G41Gpk+fzk8//YQsy7z00kt06tSJ4OBgXnnlFb766ivly9GtXnrpJRISEkhJSTGrNxfqk02dTsfw4cPp169fk/NPmzaN/fv3c+nSJfr378+0adOYPn06KSkpVFRUMHr0aM6fP094eDi1tbWoVCrWrVvXaCjF3Tz88MMsXLiQmTNncvPmTdq3b8+qVat45JFH7lju1uvu37+/sj02NpYXXniBoUOH0rNnT9zd3RuVkyQJS0tLJk+eTEREBI8++ijOzs5AfRtERUVRWVnJTz/9xHPPPdeoDfbs2cOyZctISkoiJiaGESNGYDQa8fHxYd68eSQkJPDyyy/z0Ucf4eHhoXyhuF+kuz15KUnSDqDFg2RZHnGX8k7AzhYefPwQyJRleePP74uAoLsNF/Hx8ZEb5lwUBEEQhN+qhh6436rMzMxmf21oTcOGDSM+Pp6BAwe2WR1C2zMajTg6OvLjjz+22UworaG5v0lJkg7KsuzTQpEWmdKTbf7M5qb7BxAnSdLH1D/wWCXGYwuCIAiCUFlZSZ8+ffD09BQJ9n+Ahmn1HuQEu7XdNcmWZfnrez25JEkbgSDAQZKkU8AcwPLn86YA6dRP3/c99VP4NX2qQRAEQRCEB1JQUJDZQyRMZWNj02Q8vPDb1TC/9u/JXZNsSZK+487DRVqcm0WW5cg7nfvnWUX+eLcYBEEQBEEQBOG3xJThIsPufoggCIIgCIIgCA1MGS5y4tcIRBAEQRAEQRD+Uzx090PqSZL0tCRJ+yVJqpUk6SdJkoySJFW3ZXCCIAiCIAiC8FtkcpJN/fLokUAx0B6IBpbdsYQgCIIgCA80lUqFVqvF3d0dT09PFi1apCwacuDAAWWJ7LYye/Zsdu/eDdQvknTlyhWzytfW1jJp0iR69uyJt7c3QUFB5OTktFp8mZmZZGdnm13OlHtXWVnJ8uXLW9w/YcIEHB0dUaubzIKs+Oijj9BoNHh4eNC3b18OHz5sVpyZmZkMG9Z4ZLDBYGDLli1mnaclQUFBNEy7PHToUCorK80+x722gZOTExUVFWaXay3mJNnIsvw9oJJl2SjL8lpgcNuEJQiCIAjCr6F9+/bk5uaSl5fHF198wa5du5QluH18fEhOTm6zuo1GI2+99RaDBg0C7i3Jjo6Oxs7OjuLiYg4ePMjatWtbNbG6U4J3p6XRTbl3d0uyDQYDn3322R3P4ezszNdff813333HrFmzmDhx4h2Pv5/S09OxsbExu9y9Jtn3mzlJ9hVJkh4GciVJ+oskSfFmlhcEQRAE4QHm6OjIihUreP/995FluVEv59dff41Wq0Wr1aLT6aipqSEzM5PAwEBCQ0Pp1asXMTExSi94bGwsPj4+uLu7M2fOHKUOJycnZsyYgZeXF5988onSa5qcnMyZM2cIDg4mODiYNWvWMHXqVKXcypUriY+PbxRvSUkJOTk5vP3228ry9M7OzoSGhgKwePFi1Go1arWapUuXAvXLlbu5ufHyyy/j7u5OSEgIV69eBSA5OZnevXuj0WgYO3YsZWVlpKSksGTJErRaLXv27MFgMBATE4Ofnx/Tp09n3759+Pv7o9Pp6Nu3r7JE+q33bu7cuUyYMIGgoCBcXFyU5DshIYGSkhK0Wi2vvfZak/YIDAzEzs7ujm3Wt29fbG1tAXj66ac5derUXdvZHE5OTsyZMwcvLy88PDyUqfhauu6rV68yduxY3NzcCAsLU+5tw7kqKiooKytr1DuflJTE3LlzAdPa4Pz58zz77LP4+vri6+vLN998A8CFCxcICQlR5uS+24KLbc2cZdWfoz6pjgPige7As20RlCAIgiD83izJPcaxytpWPedTNtbEa58yq4yLiwtGo5Fz58412p6UlMSyZcvQ6/XU1tbSrl07oD7Zys/Pp0ePHgwePJht27YRHh5OYmIidnZ2GI1GBg4cyJEjR9Bo6mf9tbe359ChQwBKT+2UKVNYvHgxGRkZODg4UFtbS2JiIgsXLsTS0pK1a9fy4YcfNoopLy8PrVaLSqVqch0Nvdo5OTnIsoyfnx/9+/fH1taW4uJiNm7cyMqVK4mIiGDr1q1ERUUxf/58SktLsbKyorKyEhsbG2JiYrC2tubVV18FYPXq1Zw6dYrs7GxUKhXV1dXs2bMHCwsLdu/ezcyZM9m6dWuTeAoLC8nIyKCmpoZevXoRGxvL/PnzOXr0KLm5uWa1UUtWr17NkCFDWuVct3JwcODQoUMsX76cpKQkVq1ahaura7PX/cEHH9ChQwcKCgo4cuQIXl5eZtVlShuMGzeO+Ph4AgIC+OGHH3jmmWcoKChg3rx5BAQEMHv2bNLS0li9enWr3wtzmJNkVwA/ybJ8DZgnSZIKsGqbsARBEARBeJDo9XqmTZvG+PHjGTVqFN26dQOgT58+uLi4ABAZGcnevXsJDw9n8+bNrFixgps3b3L27Fny8/OVJHvMmDF3rc/a2poBAwawc+dO3NzcuHHjBh4eHibHu3fvXsLCwujYsSMAo0aNYs+ePYwYMQJnZ2e0Wi0A3t7elJWVAaDRaBg/fjwjR45k5MiRLZ579OjRSmJfVVXFCy+8QHFxMZIkcePGjWbLhIaGYmVlhZWVFY6OjpSXl5t8LabIyMhg9erV7N2716xykiTddfuoUaOA+nu1bds2oOXrzsrKUsaiazQapc1NZUob7N69m/z8fOV9dXU1tbW1ZGVlKfGFhoYqPfz3izlJ9pfAIKDha3Z74P+Avq0dlCAIgiD83pjb49xWjh8/jkqlwtHRkYKCAmV7QkICoaGhpKeno9fr+fzzz4GmSZokSZSWlpKUlMT+/fuxtbXFYDBw7do15ZiGxPduoqOjeeedd3B1deXFF5suCu3u7s7hw4cxGo3N9ma3xMrq332EKpVKGdKQlpZGVlYWO3bsIDExke+++67Z8rfGP2vWLIKDg0lNTaWsrKzFFTBvr/NO47nNdeTIEaKjo9m1axf29vZN9qempirj7FetWoWPj4+yz97enkuXLjU6/uLFizg4ODSJ/da4Tb3u5lhYWCjDioBGnw1T2qCuro5vv/1W+TXlQWXOmOp2siwrv2P9/LpD64ckCIIgCML9cP78eWJiYoiLi2uSPJeUlODh4cGMGTPw9fVtNDa3tLSUuro6Nm3aREBAANXV1XTs2JHOnTtTXl7Orl27TKr/kUceoaamRnnv5+fHyZMn2bBhA5GRTReR7tmzJz4+PsyZM0cZf1tWVkZaWhr9+vVj+/btXLlyhcuXL5Oamkq/fv1arLuuro6TJ08SHBzMggULqKqqora2tklMt6uqqqJr164ArFu3zqTrbOl678UPP/zAqFGj+Pvf/85TTzX/RS0sLIzc3Fxyc3MbJdgATz75JGfOnFG+UJ04cYLDhw8rPf0taem6AwMD2bBhAwBHjx7lyJEjTco++uijnDt3jgsXLnD9+nV27twJmN4GISEhvPfee8r7huE2t9a9a9euJl8efm3mJNmXJUlSBtZIkuQNXL3D8YIgCIIgPOCuXr2qTOE3aNAgQkJCGj2o2GDp0qWo1Wo0Gg2WlpbK2F9fX1/i4uJwc3PD2dmZsLAwPD090el0uLq6Mm7cOPR6vUmxTJw4kcGDBxMcHKxsi4iIQK/Xt/jT/6pVqygvL+eJJ55ArVZjMBhwdHTEy8sLg8FAnz598PPzIzo6Gp1O12LdRqORqKgoPDw80Ol0TJkyBRsbG4YPH05qaqry0N3tpk+fzuuvv45OpzO7d9re3h69Xo9arW72wcfIyEj8/Vg41QwAACAASURBVP0pKiqiW7duyhjjlJQUUlJSAHjrrbe4cOECkydPRqvVNkmi78bKyor169fz4osvotVqCQ8PZ9WqVXTu3PmO5Vq67tjYWGpra3Fzc2P27Nl4e3s3KidJEpaWlsyePZs+ffrw3//937i6ugKmt0FycjIHDhxAo9HQu3dv5V7MmTOHrKws3N3d2bZtG48//rhZ96K1SaY+eSlJki/wMXAGkID/AsbIsnyw7cJrno+Pj9ww56IgCIIg/FYVFBTg5uZ2v8O4Z5mZmSQlJSk9kW1h2LBhxMfHM3DgwDarQ2h7RqMRR0dHfvzxRywtLe93OC1q7m9SkqSDsiyb9+0FM8Zky7K8X5IkV6DXz5uKZFlufnS/IAiCIAjCL1BZWUmfPn3w9PQUCfZ/gIZp9R7kBLu1mZxkS5LUAZgG9JBl+WVJkp6UJKmXLMtt9/VVEARBEIQHVlBQkFkPvJnDxsaGY8eOtcm5hV9fwxj+3xNzxmSvBX4C/H9+fxp4u9UjEgRBEARBEITfOHOS7J6yLP8FuAEgy/IV6sdmC4IgCIIgCIJwC3OS7J8kSWoPyACSJPUErrdJVIIgCIIgCILwG2bSmGypfrLMFOAzoLskSR8BesDQdqEJgiAIgiAIwm+TST3Zcv08f68Bo6hPrDcCPrIsZ7ZZZIIgCIIgtDmVSqXMk+3p6cmiRYuU1fgOHDigLJHdVmbPns3u3buB+rm4r1y5Ylb52tpaJk2aRM+ePfH29iYoKIicnJxWiy8zM5Ps7Gyzy5ly7yorK1m+fHmL+ydMmICjoyNqtbrFYz799FM0Go0yR7a5y6qvW7eOuLi4RtuCgoJoramSnZycqKioAKBv33tbJHz79u2NllE3lbW19T3V11rMGS5yCHCRZTlNluWdsixXtFVQgiAIgiD8Otq3b09ubi55eXl88cUX7Nq1S1mC28fHh+Tk5Dar22g08tZbbzFo0CDg3pLs6Oho7OzsKC4u5uDBg6xdu1ZJ6lrDnZLsOy0+Y8q9u1uSbTAY+Oyzz+54joEDB3L48GFyc3NZs2YN0dHRdzz+frqXLytw70n2/WZOku0H/FOSpBJJko5IkvSdJElN18oUBEEQBOE3ydHRkRUrVvD+++8jyzKZmZkMGzYMgK+//hqtVotWq0Wn01FTU0NmZiaBgYGEhobSq1cvYmJilF7w2NhYfHx8cHd3b7SCpJOTEzNmzMDLy4tPPvkEg8HAli1bSE5O5syZMwQHBxMcHMyaNWuYOnWqUm7lypXEx8c3irekpIScnBzefvttHnqoPqVxdnYmNDQUgMWLF6NWq1Gr1SxduhSoX3bdzc2Nl19+GXd3d0JCQrh6tX4B6+TkZHr37o1Go2Hs2LGUlZWRkpLCkiVLlNUGDQYDMTEx+Pn5MX36dPbt24e/vz86nY6+fftSVFQE0OjezZ07lwkTJhAUFISLi4uSfCckJFBSUoJWq212xcfAwEDs7Ozu2GbW1tbUj+qFy5cvK69bi7W1NW+88Qaenp48/fTTlJeXA7Bjxw78/PzQ6XQMGjRI2X7hwgVCQkKUebFvXfSwoWf51nsDEBcXpyzNnpCQoLTBq6++SnZ2Nv/4xz947bXX0Gq1lJSUUFJSwuDBg/H29qZfv37K9IClpaX4+/vj4eHBm2++2ar34V6YPE828EybRSEIgiAIv3NLD52l+NK1Vj3nk7btmOr1mFllXFxcMBqNnDt3rtH2pKQkli1bhl6vp7a2lnbt2gGwb98+8vPz6dGjB4MHD2bbtm2Eh4eTmJiInZ0dRqORgQMHcuTIETQaDVC/nPihQ4cAlJ7aKVOmsHjxYjIyMnBwcKC2tpbExEQWLlyIpaUla9eu5cMPP2wUU15eHlqtFpVK1eQ6Gnq1c3JykGUZPz8/+vfvj62tLcXFxWzcuJGVK1cSERHB1q1biYqKYv78+ZSWlmJlZUVlZSU2NjbExMRgbW3Nq6++CsDq1as5deoU2dnZqFQqqqur2bNnDxYWFuzevZuZM2eydevWJvEUFhaSkZFBTU0NvXr1IjY2lvnz53P06FFyc3PNaqPbpaam8vrrr3Pu3DnS0tJ+0blud/nyZZ5++mkSExOZPn06K1eu5M033yQgIIBvv/0WSZJYtWoVf/nLX1i0aBHz5s0jICCA2bNnk5aWpiwFb4oLFy6QmppKYWEhkiQpbTBixAiGDRtGeHg4UN97n5KSwpNPPklOTg6TJ0/mq6++4pVXXiE2Npbnn3+eZcuWtep9uBcm92TLsnyiuX9tGZwgCIIgCA8GvV7PtGnTSE5OprKyEguL+n66Pn364OLigkqlIjIyUhkTvHnzZry8vNDpdOTl5TX6uX/MmDF3rc/a2poBAwawc+dOCgsLuXHjBh4eHibHu3fvXsLCwujYsSPW1taMGjWKPXv2APW93VqtFgBvb2/KysoA0Gg0jB8/nvXr1yvX15zRo0criX1VVRWjR49GrVYTHx9PXl5es2VCQ0OxsrLCwcEBR0dHpee3NYSFhVFYWMj27duZNWuWWWVb6vlu2P7www8rvc633qtTp07xzDPP4OHhwcKFC5XrzsrKIioqCqi/ZltbW5Nj6dy5M+3ateOll15i27ZtdOjQockxtbW1ZGdnM3r0aLRaLZMmTeLs2bMAfPPNN0RGRgLw3HPPmVxvWzGnJ1sQBEEQhDZibo9zWzl+/DgqlQpHR0cKCgqU7QkJCYSGhpKeno5er+fzzz8HmiZpkiRRWlpKUlIS+/fvx9bWFoPBwLVr/+6l79ixo0mxREdH88477+Dq6sqLL77YZL+7uzuHDx/GaDQ225vdEisrK+W1SqVShoukpaWRlZXFjh07SExM5Lvvvmu2/K3xz5o1i+DgYFJTUykrK2txBczb67zTeO57FRgYyPHjx6moqMDBwUHZvmzZMlauXAlAeno6Xbp0UfbZ29tz6dKlRue5ePGiUt7S0lJp41vj/tOf/sS0adMYMWIEmZmZzJ071+Q4LSwslGFFgPLZsLCwYN++fXz55Zds2bKF999/n6+++qpR2bq6OmxsbFrs/W/t4TK/hDljsgVBEARB+A92/vx5YmJiiIuLa5KslJSU4OHhwYwZM/D19VXGwe7bt4/S0lLq6urYtGkTAQEBVFdX07FjRzp37kx5eTm7du0yqf5HHnmEmpoa5b2fnx8nT55kw4YNSg/lrXr27ImPjw9z5sxRxv6WlZWRlpZGv3792L59O1euXOHy5cukpqbSr1+/Fuuuq6vj5MmTBAcHs2DBAqqqqqitrW0S0+2qqqro2rUrgDKu2FR3O7cpvv/+e+XaDx06xPXr17G3t290zB//+Edyc3PJzc1tlGAD+Pr68s033/Djjz8C9bOiXL9+ne7du9+x3luv+69//auyPTAwkA0bNgCwa9euJgk8QI8ePcjPz+f69etUVlby5ZdfAvW91FVVVQwdOpQlS5Zw+PBhoPF96tSpE87OznzyyScAyLKsHKfX6/n4448B+Oijj+4Y/69BJNmCIAiC8Dt29epVZQq/QYMGERIS0uhBxQZLly5FrVaj0WiwtLRkyJAhQH2SFhcXh5ubG87OzoSFheHp6YlOp8PV1ZVx48ah1+tNimXixIkMHjyY4OBgZVtERAR6vb7FYQerVq2ivLycJ554ArVajcFgwNHRES8vLwwGA3369MHPz4/o6Gh0Ol2LdRuNRqKiovDw8ECn0zFlyhRsbGwYPnw4qampyoOPt5s+fTqvv/46Op3O7N5pe3t79Ho9arW62QcfIyMj8ff3p6ioiG7duinjm1NSUkhJSQFg69atqNVqtFotf/zjH9m0aZNZvbmPPvoo7777LkOHDkWr1TJ16lQ2btyoPEjakrlz5zJ69Gi8vb0b9ZrPmTOHrKws3N3d2bZtG48//riyryGu7t27ExERgVqtJiIiQmmXmpoahg0bhkajISAggMWLFwMwduxYFi5ciE6no6SkhI8++ojVq1fj6emJu7s7n376KQDvvvsuy5Ytw8PDg9OnT5t8D9qKdOtTn78VPj4+cmvN3ygIgiAI90tBQQFubm73O4x7lpmZSVJSEjt37myzOoYNG0Z8fDwDBw5sszqEtnfhwgW8vLw4ceLBfpyvub9JSZIOyrLsY+65RE+2IAiCIAgPnMrKSp566inat28vEuzfuDNnzuDv76/M0PJ70eYPPkqSNBh4F1ABq2RZnn/b/seBvwI2Px+TIMtyelvHJQiCIAjCLxMUFNTig36/lI2NDceOHWuTcwu/ri5duvwu27JNe7IlSVIBy4AhQG8gUpKk3rcd9iawWZZlHTAWaHnpI0EQBEEQBEH4DWjr4SJ9gO9lWT4uy/JPwMfA/7vtGBno9PPrzsCZNo5JEARBEARBENpUWyfZXYGTt7w/9fO2W80FoiRJOgWkA39q7kSSJE2UJOmAJEkHzp8/3xaxCoIgCIIgCEKreBAefIwE1smy3A0YCvxdkqQmccmyvEKWZR9Zln3+8Ic//OpBCoIgCIIgCIKp2jrJPg3cOpt5t5+33eolYDOALMv/BNoBDgiCIAiC0OZUKpUyT7anpyeLFi1SVuM7cOAAU6ZMadP6Z8+eze7du4H6ubivXLliVvna2lomTZpEz5498fb2JigoiJycnFaLLzMzk+zsbLPLmXLvKisrWb685UfRJkyYgKOjI2q1+q717d+/HwsLC7Zs2WJWnOvWrSMuLq7RtqCgIFprqmQnJycqKioA6Nu37z2dY/v27eTn55tdztra+p7qay1tnWTvB56UJMlZkqSHqX+w8R+3HfMDMBBAkiQ36pNsMR5EEARBEH4F7du3Jzc3l7y8PL744gt27drFvHnzAPDx8SE5ObnN6jYajbz11lsMGjQIuLckOzo6Gjs7O4qLizl48CBr165VkrrWcKck+06Lz5hy7+6WZBsMBj777LO7xmg0GpkxYwYhISF3PfZ+upcvK3DvSfb91qZJtizLN4E44HOggPpZRPIkSXpLkqQRPx/2P8DLkiQdBjYCBvm3uEKOIAiCIPzGOTo6smLFCt5//31kWSYzM5Nhw4YB8PXXX6PVatFqteh0OmpqasjMzCQwMJDQ0FB69epFTEyM0gseGxuLj48P7u7ujVaQdHJyYsaMGXh5efHJJ59gMBjYsmULycnJnDlzhuDgYIKDg1mzZg1Tp05Vyq1cuZL4+PhG8ZaUlJCTk8Pbb7+trFDo7OxMaGgoAIsXL0atVqNWq1m6dClQv+y6m5sbL7/8Mu7u7oSEhHD16lUAkpOT6d27NxqNhrFjx1JWVkZKSgpLlixRVnw0GAzExMTg5+fH9OnT2bdvH/7+/uh0Ovr27UtRURFAo3s3d+5cJkyYQFBQEC4uLkrynZCQQElJCVqtttkVHwMDA7Gzs7tru7333ns8++yzODo63vVYc1lbW/PGG2/g6enJ008/TXl5OQA7duzAz88PnU7HoEGDlO0XLlwgJCQEd3d3oqOjuTWla+hZvvXeAMTFxSlL0ickJCht8Oqrr5Kdnc0//vEPXnvtNbRaLSUlJZSUlDB48GC8vb3p168fhYWFAJSWluLv74+Hhwdvvvlmq98Lc7X5PNk/z3mdftu22be8zgdMW29VEARBEP5Drc25TunFulY9p7PdQ7zoZ2VWGRcXF4xGI+fOnWu0PSkpiWXLlqHX66mtraVdu3YA7Nu3j/z8fHr06MHgwYPZtm0b4eHhJCYmYmdnh9FoZODAgRw5cgSNRgPULyd+6NAhAKWndsqUKSxevJiMjAwcHByora0lMTGRhQsXYmlpydq1a/nwww8bxZSXl4dWq0WlUjW5joZe7ZycHGRZxs/Pj/79+2Nra0txcTEbN25k5cqVREREsHXrVqKiopg/fz6lpaVYWVlRWVmJjY0NMTExWFtbKwuprF69mlOnTpGdnY1KpaK6upo9e/ZgYWHB7t27mTlzJlu3bm0ST2FhIRkZGdTU1NCrVy9iY2OZP38+R48eJTc316w2utXp06dJTU0lIyOD/fv33/N5WnL58mWefvppEhMTmT59OitXruTNN98kICCAb7/9FkmSWLVqFX/5y19YtGgR8+bNIyAggNmzZ5OWlqYsBW+KCxcukJqaSmFhIZIkKW0wYsQIhg0bRnh4OAADBw4kJSWFJ598kpycHCZPnsxXX33FK6+8QmxsLM8//zzLli1r9XthrgfhwUdBEARBEB5wer2eadOmkZycTGVlJRYW9f10ffr0wcXFBZVKRWRkJHv37gVg8+bNeHl5odPpyMvLa/Rz/5gxY+5an7W1NQMGDGDnzp0UFhZy48YNPDw8TI537969hIWF0bFjR6ytrRk1ahR79uwB6nu7tVotAN7e3pSVlQGg0WgYP34869evV66vOaNHj1YS+6qqKkaPHo1arSY+Pp68vLxmy4SGhmJlZYWDgwOOjo5Kz+8vNXXqVBYsWKD05JtLkqQ7bn/44YeVXudb79WpU6d45pln8PDwYOHChcp1Z2VlERUVBdRfs62trcmxdO7cmXbt2vHSSy+xbds2OnTo0OSY2tpasrOzGT16NFqtlkmTJnH27FkAvvnmGyIjIwF47rnnTK63rbR5T7YgCIIgCHdnbo9zWzl+/DgqlQpHR0cKCgqU7QkJCYSGhpKeno5er+fzzz8HmiZpkiRRWlpKUlIS+/fvx9bWFoPBwLVr15RjOnbsaFIs0dHRvPPOO7i6uvLiiy822e/u7s7hw4cxGo3N9ma3xMrq3/dapVIpw0XS0tLIyspix44dJCYm8t133zVb/tb4Z82aRXBwMKmpqZSVlbW4Aubtdd5pPLc5Dhw4wNixYwGoqKggPT0dCwsLRo4cqRyzbNkyVq5cCUB6ejpdunRR9tnb23Pp0qVG57x48SIODvVzUFhaWiptfGvcf/rTn5g2bRojRowgMzOTuXPnmhyzhYWFMqwIUD4bFhYW7Nu3jy+//JItW7bw/vvv89VXXzUqW1dXh42NTYu9/y19abgfRE+2IAiCIAgAnD9/npiYGOLi4pokKyUlJXh4eDBjxgx8fX2VcbD79u2jtLSUuro6Nm3aREBAANXV1XTs2JHOnTtTXl7Orl27TKr/kUceoaamRnnv5+fHyZMn2bBhg9JDeauePXvi4+PDnDlzlLG/ZWVlpKWl0a9fP7Zv386VK1e4fPkyqamp9OvXr8W66+rqOHnyJMHBwSxYsICqqipqa2ubxHS7qqoqunatXwKkYVyxqe52blOUlpZSVlZGWVkZ4eHhLF++vFGCDfDHP/6R3NxccnNzGyXYAL6+vnzzzTf8+OOPQH3Sfv36dbp3786d3Hrdf/3rX5XtgYGBbNiwAYBdu3Y1SeABevToQX5+PtevX6eyspIvv/wSqO+lrqqqYujQoSxZsoTDhw8Dje9Tp06dcHZ25pNPPgFAlmXlOL1ez8cffwzARx99dLdb1+ZEki0IgiAIv2NXr15VpvAbNGgQISEhjR5UbLB06VLUajUajQZLS0uGDBkC1CdpcXFxuLm54ezsTFhYGJ6enuh0OlxdXRk3bhx6vWmPXk2cOJHBgwcTHBysbIuIiECv17c47GDVqlWUl5fzxBNPoFarMRgMODo64uXlhcFgoE+fPvj5+REdHY1Op2uxbqPRSFRUFB4eHuh0OqZMmYKNjQ3Dhw8nNTVVefDxdtOnT+f1119Hp9OZ3Tttb2+PXq9HrVY3++BjZGQk/v7+FBUV0a1bN2V8c0pKCikpKWbV1ZJHH32Ud999l6FDh6LVapk6dSobN2686/CTuXPnMnr0aLy9vZVeb4A5c+aQlZWFu7s727Zt4/HHH1f2NXxx6969OxEREajVaiIiIpR2qampYdiwYWg0GgICAli8eDEAY8eOZeHCheh0OkpKSvjoo49YvXo1np6euLu78+mnnwLw7rvvsmzZMjw8PDh9+vYZo3990m9xIg8fHx+5teZvFARBEIT7paCgADc3t/sdxj3LzMwkKSmJnTt3tlkdw4YNIz4+noEDB7ZZHULbu3DhAl5eXpw4ceJ+h3JHzf1NSpJ0UJZlH3PPJXqyBUEQBEF44FRWVvLUU0/Rvn17kWD/xp05cwZ/f39lhpbfC/HgoyAIgiAI9yQoKKjFB/1+KRsbG44dO9Ym5xZ+XV26dPldtqXoyRYEQRAEQRCEViaSbEEQBEEQBEFoZSLJFgRBEARBEIRWJpJsQRAEQRAEQWhlIskWBEEQhN8xlUqlzJPt6enJokWLlNX4Dhw4wJQpU9q0/tmzZ7N7926gfi7uK1eumFW+traWSZMm0bNnT7y9vQkKCiInJ6fV4svMzCQ7O9vscqbcu8rKSpYvX97i/gkTJuDo6Iharb5jfJ07d0ar1aLVannrrbfMijMzM1NZNr2BwWBgy5YtZp2nJUFBQTRMuzx06FAqKyvNPse9toGTkxMVFRVml2stYnYRQRAEQfgda9++vbJE9blz5xg3bhzV1dXMmzcPHx8ffHzMnh7YZEajsVFSuHTpUqKioujQoYPJ54iOjsbZ2Zni4mIeeughSktLyc/Pb7UYMzMzsba2pm/fvk323bx5EwuL5lMpU+5dQ5I9efLkZvcbDAbi4uJ4/vnn73iefv36telc5a0lPT39nsrdqQ0eZKInWxAEQRAEABwdHVmxYgXvv/8+siw36uX8+uuvld5SnU5HTU0NmZmZBAYGEhoaSq9evYiJiVF6wWNjY/Hx8cHd3b3RCpJOTk7MmDEDLy8vPvnkE6XXNDk5mTNnzhAcHExwcDBr1qxh6tSpSrmVK1cSHx/fKN6SkhJycnJ4++23lRUKnZ2dCQ0NBWDx4sWo1WrUajVLly4F6pddd3Nz4+WXX8bd3Z2QkBCuXr0KQHJyMr1790aj0TB27FjKyspISUlhyZIlyoqPBoOBmJgY/Pz8mD59Ovv27cPf3x+dTkffvn0pKioCGvcQz507lwkTJhAUFISLiwvJyckAJCQkUFJSglarbXbFx8DAQOzs7H5hq/4yTk5OzJkzBy8vLzw8PCgsLARo8bqvXr3K2LFjcXNzIywsTLm3DeeqqKigrKysUe98UlISc+fOBUxrg/Pnz/Pss8/i6+urLAsP9QvehISE4O7uTnR0NPd7wUXRky0IgiAID4Bvvr7OhfN1rXpO+z88hL6/lVllXFxcMBqNnDt3rtH2pKQkli1bhl6vp7a2lnbt2gH1yVZ+fj49evRg8ODBbNu2jfDwcBITE7Gzs8NoNDJw4ECOHDmCRqOpj8venkOHDgHw2WefATBlyhQWL15MRkYGDg4O1NbWkpiYyMKFC7G0tGTt2rV8+OGHjWLKy8tDq9WiUqmaXMfBgwdZu3YtOTk5yLKMn58f/fv3x9bWluLiYjZu3MjKlSuJiIhg69atREVFMX/+fEpLS7GysqKyshIbGxtiYmKwtrZWFlJZvXo1p06dIjs7G5VKRXV1NXv27MHCwoLdu3czc+ZMtm7d2iSewsJCMjIyqKmpoVevXsTGxjJ//nyOHj2q/JJwr/75z3/i6elJly5dSEpKwt3d/Red73YODg4cOnSI5cuXk5SUxKpVq3B1dW32uj/44AM6dOhAQUEBR44cwcvLy6y6TGmDcePGER8fT0BAAD/88APPPPMMBQUFzJs3j4CAAGbPnk1aWpqyDP39IpJsQRAEQRDuSq/XM23aNMaPH8+oUaPo1q0bAH369MHFxQWAyMhI9u7dS3h4OJs3b2bFihXcvHmTs2fPkp+fryTZY8aMuWt91tbWDBgwgJ07d+Lm5saNGzfw8PAwOd69e/cSFhZGx44dARg1ahR79uxhxIgRODs7o9VqAfD29qasrAwAjUbD+PHjGTlyJCNHjmzx3KNHj1YS+6qqKl544QWKi4uRJIkbN240WyY0NBQrKyusrKxwdHSkvLzc5Gu5k4alyq2trUlPT2fkyJEUFxebXF6SpLtuHzVqFFB/r7Zt2wa0fN1ZWVnKWHSNRqO0ualMaYPdu3c3GhJUXV1NbW0tWVlZSnyhoaHY2tqaVXdrE0m2IAiCIDwAzO1xbivHjx9HpVLh6OhIQUGBsj0hIYHQ0FDS09PR6/V8/vnnQNMkTZIkSktLSUpKYv/+/dja2mIwGLh27ZpyTEPiezfR0dG88847uLq68uKLLzbZ7+7uzuHDhzEajc32ZrfEyurf91qlUilDGtLS0sjKymLHjh0kJiby3XffNVv+1vhnzZpFcHAwqamplJWVtbgC5u113rx50+R476RTp07K66FDhzJ58mQqKipwcHBQtqempjJv3jwAVq1a1WisuL29PZcuXWp0zosXLzYq3xD7rXGbet3NsbCwUIYVAY0+G6a0QV1dHd9++63ya8qDSozJFgRBEAQBgPPnzxMTE0NcXFyT5LmkpAQPDw9mzJiBr69vo7G5paWl1NXVsWnTJgICAqiurqZjx4507tyZ8vJydu3aZVL9jzzyCDU1Ncp7Pz8/Tp48yYYNG4iMjGxyfM+ePfHx8WHOnDnK+NuysjLS0tLo168f27dv58qVK1y+fJnU1FT69evXYt11dXWcPHmS4OBgFixYQFVVFbW1tU1iul1VVRVdu3YFYN26dSZdZ0vXey9+/PFH5dr37dtHXV0d9vb2jY4JCwsjNzeX3NzcJg9jPvnkk5w5c0b5QnXixAkOHz6s9PS3pKXrDgwMZMOGDQAcPXqUI0eONCn76KOPcu7cOS5cuMD169eVhzZNbYOQkBDee+895X3DcJtb6961a1eTLw+/NpFkC4IgCMLv2NWrV5Up/AYNGkRISEijBxUbLF26FLVajUajwdLSkiFDhgDg6+tLXFwcbm5uODs7ExYWhqenJzqdDldXV8aNG4derzcplokTJzJ48GCCg4OVbREREej1+hZ/+l+1ahXl5eU88cQTqNVqDAYDjo6OeHl5YTAY6NOnD35+fkRHR6PT6Vqs22g0EhUVhYeHBzqdjilTpmBjY8Pw4cNJTU1VwRSnZwAAIABJREFUHrq73fTp03n99dfR6XRm907b29uj1+tRq9XNPvgYGRmJv78/RUVFdOvWTRljnJKSQkpKCvx/9u47LKpr+xv49zBDB6mCCCJImUpHEBAVW1CJUbEgKIFY8Rpii3pvctUYTVMMEk00GDXqNRp7YomvRhRLbIioNBEdQUUUhIEBBGY47x8486MqeEHwZn2exyczp+y9TsmwZs8+ewPYu3cvxGIxXFxcEB0djV27djXbBaQpmpqa2LFjByIjI+Hq6oqxY8di06ZNMDAweOl+zR13VFQUZDIZBAIBlixZAg8Pj3r7MQwDdXV1LFmyBF5eXhgyZAj4fD6All+DuLg4XL16Fc7OzhAKhapzsXTpUiQmJkIkEmH//v2wtrZu8XloD0xHP3n5Ojw9PVnlmIuEEELI2yo9PR0CgaCjw3htp0+fxurVq9t1+LigoCDMnTsXgwYNarc6SPtTKBQwMzPD48ePoa6u3tHhNKup/ycZhkliWbbVY1lSSzYhhBBCOp3i4mI4OjpCW1ubEuz/Acph9Tpzgt3W6MFHQgghhLyWAQMGtOqBt9YwNDTE7du326Vs8uYp+/D/nVBLNiGEEEIIIW2MkmxCCCGEEELaGCXZhBBCCCGEtDFKsgkhhBBCCGljlGQTQgghf2McDkc1TraLiwtiYmJUs/FdvXpVNUV2e1myZAlOnjwJoHYs7vLy8lbtL5PJMGPGDNjZ2cHDwwMDBgzApUuX2iy+06dP48KFC63eryXnrri4GN9//32T65STsgiFQohEIqxdu7bJ7ViWRXR0NOzt7eHs7Ixr1661Ks6tW7di9uzZ9ZYNGDAAbTVUso2NDQoKCgAAvr6+r1XGwYMH602j3lJ6enqvVV9bafckm2GYQIZhMhmGucMwzOJmthnPMEwawzCpDMPsbO+YCCGEEFJLW1sb169fR2pqKk6cOIFjx46ppuD29PREXFxcu9WtUCiwfPlyDB48GMDrJdlTp06FsbExsrKykJSUhC1btqiSurbwsiT7ZZPPtOTcvSzJ5nK5iImJQVpaGi5evIj169c3mWgeO3YMWVlZyMrKwo8//oioqKiX1tmRXufLCvD6SXZHa9ckm2EYDoD1AIYBEAKYyDCMsME2DgD+CcCPZVkRgDntGRMhhBBCmmZmZoYff/wR69atA8uyOH36NIKCggAAZ86cgaurK1xdXeHm5obS0lKcPn0a/fr1w4gRI8Dj8TBz5kxVK3hUVBQ8PT0hEonqzSBpY2ODRYsWwd3dHXv27EFERAT27t2LuLg4PHr0CAEBAQgICMDmzZsxZ87/pQTx8fGYO3duvXizs7Nx6dIlrFixAmpqtSmNra0tRowYAQBYs2YNxGIxxGIxYmNjAdROuy4QCDBt2jSIRCIMHToUFRUVAIC4uDgIhUI4OzsjJCQEEokEGzZswLfffquabTAiIgIzZ86Et7c3Fi5ciMuXL8PHxwdubm7w9fVFZmYmANQ7d8uWLcMHH3yAAQMGoFevXqrke/HixcjOzoarq2ujGR8tLCzg7u4OoHb6dYFAgIcPHza6ZocOHUJ4eDgYhkGfPn1QXFyMvLy8Vl/75ujp6eGTTz6Bi4sL+vTpg/z8fADA77//Dm9vb7i5uWHw4MGq5YWFhRg6dKhqXOy6kx4qW5brnhsAmD17tmpq9sWLF6uuwYIFC3DhwgX89ttv+Pjjj+Hq6ors7GxkZ2cjMDAQHh4e8Pf3Vw0PeO/ePfj4+MDJyQmffvppm52D19Xe42R7AbjDsuxdAGAYZheA9wDU/ToyDcB6lmWLAIBl2SftHBMhhBDS6dw9VomyxzVtWqZuNzX0GqbZqn169eoFhUKBJ0/q/zlevXo11q9fDz8/P8hkMmhpaQEALl++jLS0NPTs2ROBgYHYv38/xo4di5UrV8LY2BgKhQKDBg3CjRs34OzsDKB2OnFlt4Y//vgDABAdHY01a9YgISEBpqamkMlkWLlyJVatWgV1dXVs2bIFGzdurBdTamoqXF1dweFwGh2HslX70qVLYFkW3t7e6N+/P4yMjJCVlYVffvkF8fHxGD9+PPbt24dJkybhq6++wr1796CpqYni4mIYGhpi5syZ0NPTw4IFCwAAP/30Ex48eIALFy6Aw+GgpKQEZ8+eBZfLxcmTJ/Gvf/0L+/btaxRPRkYGEhISUFpaCh6Ph6ioKHz11Ve4desWrl+//tJrIpFIkJycDG9v70brHj58iB49eqjeW1lZ4eHDh7CwsHhpmS1VVlaGPn36YOXKlVi4cCHi4+Px6aefom/fvrh48SIYhsGmTZvwzTffICYmBp999hn69u2LJUuW4MiRI6qp4FuisLAQBw4cQEZGBhiGUV2DkSNHIigoCGPHjgUADBo0CBs2bICDgwMuXbqEWbNm4dSpU/joo48QFRWF8PBwrF+/vk2O/7/R3t1FLAHk1nn/4MWyuhwBODIMc55hmIsMwwS2c0yEEEIIaSU/Pz/MmzcPcXFxKC4uBpdb207n5eWFXr16gcPhYOLEiTh37hwA4Ndff4W7uzvc3NyQmppa7+f+CRMmvLI+PT09DBw4EIcPH0ZGRgaqq6vh5OTU4njPnTuH0aNHQ1dXF3p6ehgzZgzOnj0LoLa129XVFQDg4eEBiUQCAHB2dkZYWBh27NihOr6mjBs3TpXYS6VSjBs3DmKxGHPnzkVqamqT+4wYMQKampowNTWFmZmZquX3VWQyGYKDgxEbG4suXbq09PBbjGGYly7X0NBQtTrXPVcPHjzAO++8AycnJ6xatUp13ImJiZg0aRKA2mM2MjJqcSwGBgbQ0tLClClTsH//fujo6DTaRiaT4cKFCxg3bhxcXV0xY8YMVcv9+fPnMXHiRADA5MmTW1xve+kMMz5yATgAGADACkAiwzBOLMsW192IYZjpAKYDgLW19ZuOkRBCCGlXrW1xbi93794Fh8OBmZkZ0tPTVcsXL16MESNG4OjRo/Dz88Px48cBNE7SGIbBvXv3sHr1aly5cgVGRkaIiIjA8+fPVdvo6uq2KJapU6fiiy++AJ/PR2RkZKP1IpEIKSkpUCgUTbZmN0dT8//ONYfDUXUXOXLkCBITE/H7779j5cqVuHnzZpP7143/3//+NwICAnDgwAFIJJJmZ8BsWOfL+nMrVVdXIzg4GGFhYRgzZkyT21haWiI39//aMx88eABLy/rtmevXr0d8fDwA4OjRo+jevbtqnYmJCYqKiupt/+zZM5iamgIA1NXVVde4btwffvgh5s2bh5EjR+L06dNYtmzZK49HicvlqroVAVDdG1wuF5cvX8aff/6JvXv3Yt26dTh16lS9fWtqamBoaNhs639zXxo6Qnu3ZD8E0KPOe6sXy+p6AOA3lmWrWZa9B+A2apPueliW/ZFlWU+WZT27du3abgETQgghf1dPnz7FzJkzMXv27EbJSnZ2NpycnLBo0SL07t1b1Q/28uXLuHfvHmpqarB792707dsXJSUl0NXVhYGBAfLz83Hs2LEW1a+vr4/S0lLVe29vb+Tm5mLnzp2qFsq67Ozs4OnpiaVLl6r6/kokEhw5cgT+/v44ePAgysvLUVZWhgMHDsDf37/ZumtqalQjenz99deQSqWQyWSNYmpIKpWqklplv+KWelnZLMtiypQpEAgEmDdvXrNljBw5Etu2bQPLsrh48SIMDAwadRX5xz/+gevXr+P69ev1EmwA6N27N86fP4/Hjx8DqB0VpbKysl4XlKbUPe6ff/5Ztbxfv37YubN2DItjx441SuABoGfPnkhLS0NlZSWKi4vx559/AqhtpZZKpRg+fDi+/fZbpKSkNDpPXbp0ga2tLfbs2aM6T8rt/Pz8sGvXLgDAf/7zn5fG/ya0d5J9BYADwzC2DMNoAAgB8FuDbQ6ithUbDMOYorb7yN12josQQgghACoqKlRD+A0ePBhDhw6t96CiUmxsLMRiMZydnaGuro5hw4YBqE3SZs+eDYFAAFtbW4wePRouLi5wc3MDn89HaGgo/Pz8WhTL9OnTERgYiICAANWy8ePHw8/Pr9luB5s2bUJ+fj7s7e0hFosREREBMzMzuLu7IyIiAl5eXvD29sbUqVPh5ubWbN0KhQKTJk2Ck5MT3NzcEB0dDUNDQ7z77rs4cOCA6sHHhhYuXIh//vOfcHNza1HrdF0mJibw8/ODWCxu9ODj+fPnsX37dpw6dUr1wOnRo0cBABs2bMCGDRsAAMOHD0evXr1gb2+PadOmNTtaSXPMzc2xdu1aDB8+HK6urpgzZw5++eUX1YOkzVm2bBnGjRsHDw8PVas3ACxduhSJiYkQiUTYv39/vd4Hyi9uPXr0wPjx4yEWizF+/HjVdSktLUVQUBCcnZ3Rt29frFmzBgAQEhKCVatWwc3NDdnZ2fjPf/6Dn376CS4uLhCJRDh06BAAYO3atVi/fj2cnJyafEj0TWPqPvXZLhUwzHAAsQA4ADazLLuSYZjlAK6yLPsbU3vGYwAEAlAAWMmy7K6Xlenp6cm21fiNhBBCSEdJT0+HQCDo6DBe2+nTp7F69WocPny43eoICgrC3LlzMWjQoHarg7S/wsJCuLu74/79+x0dyks19f8kwzBJLMt6trasdu+TzbLsUQBHGyxbUuc1C2Dei3+EEEIIISguLoaXlxdcXFwowX7LPXr0CAMGDFCN0PJ30RkefCSEEELIW2jAgAHNPuj33zI0NMTt27fbpWzyZnXv3v1veS1pWnVCCCGEEELaGCXZhBBCCCGEtDFKsgkhhBBCCGljlGQTQgghhBDSxijJJoQQQv7GOByOapxsFxcXxMTEqGbju3r1KqKjo9u1/iVLluDkyZMAasfiLi8vb9X+MpkMM2bMgJ2dHTw8PDBgwABcunSpzeI7ffo0Lly40Or9WnLuiouLmx3XWjkxjlAohEgkwtq1a5uNz8DAQDWW9vLly1sV5+nTp1XTpitFRERg7969rSqnOQMGDIBy2OXhw4ejuLj4FXs0HePrXAMbGxsUFBS0er+2QqOLEEIIIX9j2traqimqnzx5gtDQUJSUlOCzzz6Dp6cnPD1bPTxwiykUinpJYWxsLCZNmgQdHZ0WlzF16lTY2toiKysLampquHfvHtLS0tosxtOnT0NPTw++vr6N1snlcnC5TadSLTl3yiR71qxZjdZxuVzExMTA3d0dpaWl8PDwwJAhQyAUChtt6+/v365jlbcV5WQ6rfWya9CZUUs2IYQQQgAAZmZm+PHHH7Fu3TqwLFuvlfPMmTOq1lI3NzeUlpbi9OnT6NevH0aMGAEej4eZM2eqWsGjoqLg6ekJkUhUbwZJGxsbLFq0CO7u7tizZ4+q1TQuLg6PHj1CQEAAAgICsHnzZsyZM0e1X3x8PObOnVsv3uzsbFy6dAkrVqxQzVBoa2uLESNGAADWrFkDsVgMsViM2NhYALXTrgsEAkybNg0ikQhDhw5FRUUFACAuLg5CoRDOzs4ICQmBRCLBhg0b8O2336pmfIyIiMDMmTPh7e2NhQsX4vLly/Dx8YGbmxt8fX2RmZkJoH4L8bJly/DBBx9gwIAB6NWrF+Li4gAAixcvRnZ2NlxdXRvN+GhhYQF3d3cAtdOKCwSCDpnF0MbGBkuXLoW7uzucnJyQkZEBAM0ed0VFBUJCQiAQCDB69GjVuVWWVVBQAIlEArFYrFq+evVqLFu2DEDLrsHTp08RHByM3r17q6aFB2onvBk6dChEIhGmTp2K9p5w8VWoJZsQQgjpBMr/UwlFTk2blsmxVoNOmGar9unVqxcUCgWePHlSb/nq1auxfv16+Pn5QSaTQUtLC0BtspWWloaePXsiMDAQ+/fvx9ixY7Fy5UoYGxtDoVBg0KBBuHHjBpydnQHUTid+7do1AMAff/wBAIiOjsaaNWuQkJAAU1NTyGQyrFy5EqtWrYK6ujq2bNmCjRs31ospNTUVrq6u4HA4jY4jKSkJW7ZswaVLl8CyLLy9vdG/f38YGRkhKysLv/zyC+Lj4zF+/Hjs27cPkyZNwldffYV79+5BU1MTxcXFMDQ0xMyZM6Gnp6eaSOWnn37CgwcPcOHCBXA4HJSUlODs2bPgcrk4efIk/vWvf2Hfvn2N4snIyEBCQgJKS0vB4/EQFRWFr776Crdu3VL9ktAciUSC5ORkeHt7N7n+r7/+gouLC7p3747Vq1dDJBK9tLzWMjU1xbVr1/D9999j9erV2LRpE/h8fpPH/cMPP0BHRwfp6em4ceOG6otCS7XkGoSGhmLu3Lno27cvcnJy8M477yA9PR2fffYZ+vbtiyVLluDIkSP46aef2vQ8tBYl2YQQQgh5JT8/P8ybNw9hYWEYM2YMrKysAABeXl7o1asXAGDixIk4d+4cxo4di19//RU//vgj5HI58vLykJaWpkqyJ0yY8Mr69PT0MHDgQBw+fBgCgQDV1dVwcnJqcbznzp3D6NGjoaurCwAYM2YMzp49i5EjR8LW1haurq4AAA8PD0gkEgCAs7MzwsLCMGrUKIwaNarZsseNG6dK7KVSKd5//31kZWWBYRhUV1c3uc+IESOgqakJTU1NmJmZIT8/v0XHIZPJEBwcjNjYWHTp0qXReuVU5Xp6ejh69ChGjRqFrKysFpUNAAzDvHL5mDFjANSeq/379wNo/rgTExNVfdGdnZ1V17ylWnINTp48Wa9LUElJCWQyGRITE1XxjRgxAkZGRq2qu61Rkk0IIYR0Aq1tcW4vd+/eBYfDgZmZGdLT01XLFy9ejBEjRuDo0aPw8/PD8ePHATRO0hiGwb1797B69WpcuXIFRkZGiIiIwPPnz1XbKBPfV5k6dSq++OIL8Pl8REZGNlovEomQkpIChULRZGt2czQ1/+9cczgcVZeGI0eOIDExEb///jtWrlyJmzdvNrl/3fj//e9/IyAgAAcOHIBEIml2BsyGdcrl8lfGWV1djeDgYNUXm6bUTbyHDx+OWbNmoaCgAKampqrlBw4cwGeffQYA2LRpU72+4iYmJigqKqpX5rNnz+rtr4y9btwtPe6mcLlcVbciAPXujZZcg5qaGly8eFH1a0pnRX2yCSGEEAIAePr0KWbOnInZs2c3Sp6zs7Ph5OSERYsWoXfv3vX65t67dw81NTXYvXs3+vbti5KSEujq6sLAwAD5+fk4duxYi+rX19dHaWmp6r23tzdyc3Oxc+dOTJw4sdH2dnZ28PT0xNKlS1X9byUSCY4cOQJ/f38cPHgQ5eXlKCsrw4EDB+Dv799s3TU1NaoRPb7++mtIpVLIZLJGMTUklUphaWkJANi6dWuLjrO5462LZVlMmTIFAoEA8+bNa7aMx48fq4798uXLqKmpgYmJSb1tRo8ejevXr+P69euNHsZ0cHDAo0ePVF+o7t+/j5SUFFVLf3OaO+5+/fph586dAIBbt27hxo0bjfY1NzfHkydPUFhYiMrKStVDmy29BkOHDsV3332neq/sblO37mPHjjX68vCmUZJNCCGE/I1VVFSohvAbPHgwhg4dWu9BRaXY2FiIxWI4OztDXV0dw4YNAwD07t0bs2fPhkAggK2tLUaPHg0XFxe4ubmBz+cjNDQUfn5+LYpl+vTpCAwMREBAgGrZ+PHj4efn1+xP/5s2bUJ+fj7s7e0hFosREREBMzMzuLu7IyIiAl5eXvD29sbUqVPh5ubWbN0KhQKTJk2Ck5MT3NzcEB0dDUNDQ7z77rs4cOCA6qG7hhYuXIh//vOfcHNza1HrdF0mJibw8/ODWCxu9ODj+fPnsX37dpw6dUr1wKlydI4NGzZgw4YNAIC9e/dCLBbDxcUF0dHR2LVrV7NdQJqiqamJHTt2IDIyEq6urhg7diw2bdoEAwODl+7X3HFHRUVBJpNBIBBgyZIl8PDwqLcfwzBQV1fHkiVL4OXlhSFDhoDP5wNo+TWIi4vD1atX4ezsDKFQqDoXS5cuRWJiIkQiEfbv3w9ra+sWn4f2wHT0k5evg2GYUgCZHR0H6XRMAXTcgJiks6L7gjSlU9wXJ06ccOrWrVvrMrNO5NKlS2pbt25V/+GHHyrbq46oqCjNyZMnV/v6+rbtU6FNUCgUXA6H89Zej85MoVCgf//+OgkJCeXq6uodHU6zHj9+zB0yZEjDPio8lmX1W1vW29onO5Nl2fYbuJO8lRiGuUr3BWmI7gvSlM5yX6SkpEjEYnGHJ/uvSyKR6HO5XHOxWHynrcsuKCjgeHp6CgQCQcn06dPvtnX5Tbl165ZALBanv3pL0lq2traiiRMnPnZzc3vz4xC2gkKhMG342cAwzNXXKettTbIJIYQQ0sGCgoJKg4KCmu+w/F8wNTVVSCSSW+1RNnnz7t27l9rRMbxp1CebEEIIIYSQNva2Jtk/dnQApFOi+4I0he4L0hS6L0gjpqamTzs6BtIpvdbnxVuZZLMsSx+OpBG6L0hT6L4gTaH7gjSlW7dub23/eNJ+Xvfz4q1MsgkhhBBCCOnMOnWSzTBMIMMwmQzD3GEYZnET6yMYhnnKMMz1F/+mdkSc5M161X3xYpvxDMOkMQyTyjDMzjcdI3nzWvB58W2dz4rbDMMUd0Sc5M1qwX1hzTBMAsMwyQzD3GAYZnhHxNmROByOB5/PF9rb24t4PJ5w6dKl5gqFAgCQmJioExER0aM9658zZ073gwcP6gPA8uXLzUpLS1uVm0ilUrXQ0NCePXr0EItEIoGXlxfv1KlTzU4p+ezZsy43btwQ37hxQ/zgwYNuDdc/f/5cIz093fHmzZvC9PR03qFDhwxPnDjRsikq62jJuSsoKOB89dVXXZtaV15ezjg5OQl4PJ7Q3t5eNHfu3O5NbVdRUcGMGDGil7W1tdjZ2ZmfmZmp0Zo4582b133JkiXmdZdZWlo65eXltcngGDo6Om4AIJFI1AMDA3u9ThlxcXEmEomkVeP+ZWZmajg4OIhaun12drZNcnKyy9OnT5s8z0ytuBefJTcYhnF/VZmdNslmGIYDYD2AYQCEACYyDCNsYtPdLMu6vvi36Y0GSd64ltwXDMM4APgnAD+WZUUA5rzxQMkb1ZL7gmXZucrPCgDfAdj/5iMlb1IL/458CuBXlmXdAIQA+P7NRtnxNDU1azIyMtLu3LmTeurUqdsnTpwwWLBgQXcA6NevX/nWrVtz26tuuVyO2NjYR6NGjSoFgI0bN5rLZLJW5SZhYWE2RkZGcolEcis1NTV927Zt9548edJkgsiyLB48eGDt4OBwWywWpxYXFxuXlZXVm5s7JyfHytjYuNDJySnNwsLi0fHjx7ufPXtWr6nyqqurm42rJeeusLCQ89NPP5k1tU5LS4s9d+5cZmZmZlpqamran3/+2eXPP/9slOyvXbvW1MDAQJ6Tk3Nr9uzZ+fPmzbN6WZ0dxcbGpvqPP/54raEYd+zYYZqTk9Oug2ubmpoW2NvbZ71kk2EAHF78mw7gh1eV2WmTbABeAO6wLHuXZdkqALsAvNfBMZGO15L7YhqA9SzLFgEAy7JP3nCM5M1r7efFRAC/vJHISEdqyX3BAujy4rUBgEdvML5Ox9LSUr5p0ybJli1bzGpqanD48GH9gIAAewA4cuSIHp/PF/L5fKFAIBAWFRWpHT58WN/T05M3YMAAexsbG3FoaKi1shU8LCzMWiwWCxq2wlpaWjpFRUVZCoVCwebNm42Cg4NttmzZYrRixQqzJ0+eqPfv39/R29vbMTY21uSDDz5QtQTHxMSYTpkypV7LcGpqqmZycrLu2rVrH3I4HAAAn8+vCgkJkQLAsmXLzB0cHEQODg6iF63kuvn5+VUikcghNDTU+r333lMfPHiwvUwmYwBgxYoVZoGBgYb+/v7dgoKCej1+/Ljy119/1d6wYYM5n88X/vHHH3rBwcE2oaGh1s7OzvyoqCirhIQEHVdXV75AIBC6ubnxU1JSNAHUO3fz5s3rPm7cOBsvLy+elZWV04oVK8wAYP78+Va5ubmafD5fOGPGjHrJsZqaGgwMDGoAoKqqipHL5UxTMzkePnzY8IMPPigEgMjIyKILFy7o19S0zbw9mZmZGr169RKFhIT0tLe3F/n5+Tkoz1VMTIypWCwW8Hg84TvvvGOn/AUiIyNDw9XVle/o6CiMjo7uXrcsZctyXFycSXh4uGo6xoCAAPvDhw/ry+VyBAcH2zg4OIgcHR2Fn332mdmWLVuMbt26pRMeHt6Lz+cLZTIZc/bsWZ3evXvzRCKRoG/fvg73799XB4CzZ8/q8Hg8IY/HE65Zs6bJLy/NMTAwkKmrq79sIqL3AGxja10EYMgwjMXLyuzM42RbAqj7DfABAO8mtgtmGKYfgNsA5rIs227fuEmn0JL7whEAGIY5D4ADYBnLsn+8mfBIB2np5wUYhukJwBbAqTcQF+lYLbkvlgH4fwzDfAhAF8DgNxNaY5Vbc3qwj57rtGWZTHetcs0I61b9XRQKhVUKhQIPHz6slyPExMR0i4uLuz906NAyqVSqpqOjUwMAN2/e1E1OTr7l6OhY1a9fP4dt27YZRUZGFq1Zs+ahubm5Qi6Xw9fXl3fp0iVtb2/vCgAwMTGRp6WlpQPA8ePHDQDg008/ffLDDz+Ynzlz5raFhYVcKpWqicVii8rKygeamprsjh07TDdu3Hi/bkzXr1/XEgqF5Vxu43Tm7NmzOjt37jRJSkpKZ1kWHh4eAm9vb0ZDQ6M6JydHf8eOHXft7e1LJ0+e3H3btm1Gs2bNehYXF9ft1KlTRcbGxmUaGhqFAPTHjBkDMzOzpytWrMgDgPj4eNO8vDyNa9euZXC5XDx79kztypUrGerq6jh48KD+woULrY4fP57dMJ47d+5oXbhwIbO4uJgjEAjEH3/88dOYmJgHQUFB2hkZGWlNXQu5XA6xWCzMycnRfP/9958MHDiwrOE2+fn5Gra2tlUAoK6uDj09PUV+fj7XwsKiTWauzMnJ0dqxY8ddX1/f+8OHD++lPFdhYWFF8+fPLwBXilz1AAAgAElEQVSA6Ojo7nFxcaaffPLJk1mzZllPnTr16ezZswu//PLLJrvCNOevv/7SycvLU8/KykoFarvTmJqaKn744Qez1atX5/br16+8srKSiY6Otj5y5Mid7t27y+Pj440WLFhguWfPHsmUKVNs1q5dmzNs2DBZwy8tbaCpzxNLAHnN7dCZW7Jb4ncANizLOgM4AeDnDo6HdA5c1P6cMwC1LZbxDMMYdmhEpDMJAbCXZVlFRwdCOoWJALayLGsFYDiA7QzDvO1/G9tFnz59ZAsWLOixYsUKs4KCAo5yamwnJ6cyoVBYxeVyMX78+GfKrhU///yzsVAoFAiFQmFWVpZWSkqKqltGeHh40avqMzAwqPHz8yvdvXu3QXJyslZ1dTXj5eVV0dJ4T58+rTd8+PDiLl261BgYGNSMGDGi6Pz589oAYGlpWenr61sBACKRSC6RSDQBgMfjVcyfP5+zbds2k+zsbF5paak+wzCNmoXHjBlTpEzsnz17xhk+fLidg4ODaOHChT1u376t1XB7ABg6dGixtrY2a2FhITc2Nq5+8ODBKxs6uVwuMjIy0nJycm5cu3ZN98qVK02W/d9gGIZ92fK658rNza1cea6SkpK0PTw8eI6OjsJ9+/aZpKamagHAtWvX9KZNm/YMAGbMmFHYmlj4fH5lbm6u5vvvv99j7969XYyMjBp9Tt+4cUMzKytLe+DAgY58Pl+4atUqi0ePHqkXFBRwSktLOcOGDZMBgLJ1vyN15pbshwDq/ixk9WKZCsuydU/gJgDfvIG4SMd65X2B2m+Xl1iWrQZwj2GY26hNuq+8mRBJB2jJfaEUAuAf7R4R6Qxacl9MARAIACzL/sUwjBYAUwBvvJtZa1uc20taWpoGh8OBpaWlPCUlRbX8iy++eDxq1CjpoUOHDPz9/flHjhzJAoCGXRgYhkFGRobGunXrzJOSktK7du2qCA4Otnn+/Lnqy4u+vn6L+jNMnz69YOXKld0cHR2fT5o0qdHweq6urs/T09N15HI5mmrNbkhNTU0ul8t1NDQ0WACoqqrS4HK5crlczgBAQkJC1rFjx/QPHTpk8OOPPxqkpqY+BGCqpqZWL149PT3V+0WLFln279+/9MSJE9mZmZkaAwcO5DVVt6ampiqZ5XA4UNbZEqampgp/f//S33//3aB3797P664zNzevunfvnoadnV11dXU1ZDIZx9zcvF4r9ocffmh54sQJAwBo2GpuYmIiz8vLq/ewZFlZGcfU1FQhlUo5ynP1Im62oqJCDQCmT59uu3fv3js+Pj4VcXFxJmfOnNFXbqemptZk4q7E5XLZul1aKisr1QCga9euilu3bqUdOHCgy4YNG7ru3r3beM+ePZK6+7Isy9jb21dcv349o+7ygoICzsvqbAOt+TsDoHO3ZF8B4MAwjC3DMBqo/cP4W90NGvSFGQkg/Q3GRzrGK+8LAAdR24oNhmFMUdt95LUetiBvjZbcF2AYhg/ACMBfbzg+0jFacl/kABgEAAzDCABoAfjbTkjy6NEj7rRp03pGRkY+UVOrnyKkpqZqenl5VaxcufKxs7Nz2a1bt7SA2u4iGRkZGgqFAnv37jX29/cvLSoq4mhra9cYGxsrcnNzuadPnzZoSf26uroKqVSqqnjgwIFleXl5GgcOHDCZMmXKs4bbi0SiSmdn57J58+Z1VyZtmZmZGrt27TIICAiQHT161LC0tFStpKRE7ejRo0aDBw8urK6u1mRZlqmpqWGKi4uNuVxuBQAoFApkZ2drBAYGVqxbt+6hTCbjZGVldTcwMJCVlpY2m8CVlJRwrKysqgBg48aNpi05TiUDAwNFWVlZk7nYo0ePuMrEUSaTMQkJCV0EAsHzhtuNGDGiePPmzSYAsGXLFiMfH5/Shtfuu+++e5iRkZHWVLeUQYMGyY4fP25QVFSkBgA///yzIZ/Pb7ILTl3l5eVq1tbW1ZWVlcyuXbuMlcvd3d1l8fHxxgAQHx9v0tS+dnZ2VampqToKhQJ37txRv3Hjhi4A5OXlcRUKBSIiIoq//PLLhzdv3tQBAD09PYVUKuUAgLOz8/Nnz55xT548qQsAlZWVzNWrV7VMTU0V+vr6iuPHj+sBwNatW42bqvu/8BuA8BejjPQBIGVZttmuIkAnbslmWVbOMMxsAMdR2692M8uyqQzDLAdwlWXZ3wBEMwwzEoAcwDMAER0WMHkjWnhfHAcwlGGYNAAKAB83+NWD/I9p4X0B1CZZu1iWfWkrC/nf0ML7Yj5qu5TNRe1DkBF/t/ujsrJSjc/nC+VyOcPhcNgJEyYULl26NL/hdt98843ZhQsXujAMw/J4vIqxY8dK//zzTz2xWFw2c+ZMa4lEouXr61syefLkYg6HA7FYXG5nZye2sLCo8vDwkLUklvfff78gMDDQ0dzcvOrSpUu3AWDUqFFFN27c0OnatWuTXbx27NghmTVrVo+ePXuKtbS0WCMjI/mqVaty+/btWx4aGlro7u4uAIDJkyc/7du3b8XVq1cfsSzb89atWyJjY+MCLpfLVlZWGj558sQgNDS0W0lJiUZNTY16SEiIQldXlxMaGpo7fvz4XseOHTOMjY3NaVj/okWLHk+dOtX266+/7j5kyJBWDQ3arVs3hYeHh8zBwUE0cOBA6caNGx8o1+Xm5qpHRETYKhQKsCzLvPfee88mTpwoBWqHPezdu3dZWFiY9KOPPioIDg62tba2FhsYGCh2797dqD/4y3h7e1dMmzbtSZ8+ffgMw8DExKR68+bNklftt3jx4kdeXl4CY2Njubu7u0wmk3EA4Pvvv88JCQnpFRsb2y0wMLDe+VB2QRkyZIhs/fr1lfb29iJ7e/vnQqGwHKgd5m/KlCk2NTU1DAAsX778AQCEh4cXfPjhhz0//vjjmqtXr6bv2rUrOzo62rq0tJSjUCiYqKiofE9Pz+c//fSTZOrUqTYMw2DAgAElrTkPWVlZtmVlZfpyuVydYZgHAJYCUAcAlmU3ADiK2i5ldwCUA4h8VZnM3+yzhBBCCOk0UlJSJC4uLm/tLIOHDx/Wj4mJMU9ISLjTXnUEBATYz5kzJ/+9994rba86SPs7e/aszrx583pcuXIls6NjeZmUlBRTFxcXm7YoqzN3FyGEEELI31RBQQHHxsZGrKWlVUMJ9tstMTFRZ/Lkyb1mz57d6BeS/2XUkk0IIYR0kLe9JZuQ/zXUkk0IIYQQQkgnRkk2IYQQQgghbYySbEIIIYQQQtoYJdmEEPI3wDBMi4ZQI4QQ0jYoySaEEEL+xjgcjgefzxfa29uLeDyecOnSpeYKRe2Q1ImJiToRERE9XlHEf2XOnDndDx48qA8Ay5cvNystLW1VbiKVStVCQ0N79ujRQywSiQReXl68U6dO6bZVfIcPH9Y/ceJEq8trybkrKCjgfPXVV12bW29paenk6Ogo5PP5QrFYLGhqm5qaGkRERPSwtrYWOzo6Cs+dO6fTmjjj4uJMwsPDresu8/Ly4iUmJraqnOZYWlo65eXlcQHAzc2N/zplbN++3TApKanVU8rr6Oi4vU59baXTTkZDCCGkeQzDfAUgl2XZ9S/eL0PtxFwBqJ3VUh3ApyzLHmqw3wAAC1iWDXrxfh1qJ2bZyjCMB4A1APQAFKB2YpaXzmhG3n6ampo1ypkAHz58yB03blyvkpISzrfffvuoX79+5f369Stvr7rlcjliY2MfKd9v3LjRfNq0ac9aOu06AISFhdn07NmzUiKR3OJwOMjIyNC4fv26dlvFeOrUKX09PT3FkCFDyhquq66uhrq6epP7teTcFRYWcn766SezxYsXNzvL6JkzZ25bWFjIm1u/Z88eg7t372pJJJJbCQkJurNmzbK+ceNGRnPbd6Tk5OTXiuvgwYOGcrlc6uHh0WjGy86MWrIJIeTttBvA+DrvxwP4GcBolmXdUZtsxzAMw7SkMIZh1AF8B2Asy7IeADYDWNm2IZPOztLSUr5p0ybJli1bzGpqanD48GH9gIAAewA4cuSIHp/PF/L5fKFAIBAWFRWpHT58WN/T05M3YMAAexsbG3FoaKi1shU8LCzMWiwWC+zt7UVz587tXqcOp6ioKEuhUCjYvHmzUXBwsM2WLVuMVqxYYfbkyRP1/v37O3p7ezvGxsaafPDBB6qW4JiYGNMpU6bUaxlOTU3VTE5O1l27du1DDqd25nM+n18VEhIiBYBly5aZOzg4iBwcHETLly83A2qnXe/Vq5coJCSkp729vcjPz89BJpMxALBixQozOzs7kaOjozAoKKhXZmamxrZt27pu2LDBnM/nC//44w+94OBgm9DQUGtnZ2d+VFSUVUJCgo6rqytfIBAI3dzc+CkpKZoA6p27efPmdR83bpyNl5cXz8rKymnFihVmADB//nyr3NxcTT6fL5wxY4bV61yzQ4cOGYaFhRWqqalh0KBBZSUlJdz79+83nfm/Bh0dHbcPP/zQksfjCV1cXPi5ublcANi5c6eBs7MzXyAQCH19fR2Vyx8/fszx8/NzsLe3F02YMKFn3aGilS3Ldc8NAISHh1vHxcWZAMCsWbMslddg+vTpVidOnNA9efKk4aeffmrF5/OFqampmqmpqZr+/v4OIpFI4OHhwUtOTtYCgIyMDA1XV1e+o6OjMDo6ujs6GLVkE0LIW4hl2WSGYcwYhukOoCuAIgCPAXzLMEw/ADUALAGYv1j+KjwAYgAnXuTlHADUiv0GVe241qMmr6RNfqJXUrPoUq4xyT23NfsIhcIqhUKBhw8f1ssRYmJiusXFxd0fOnRomVQqVdPR0akBgJs3b+omJyffcnR0rOrXr5/Dtm3bjCIjI4vWrFnz0NzcXCGXy+Hr68u7dOmStre3dwUAmJiYyNPS0tIB4Pjx4wYA8Omnnz754YcfzJUtt1KpVE0sFltUVlY+0NTUZHfs2GG6cePG+3Vjun79upZQKCznchunM2fPntXZuXOnSVJSUjrLsvDw8BAMGjSo1NTUVJGTk6O1Y8eOu76+vveHDx/ea9u2bUazZs16FhcX1+3+/fs3tbW12YKCAo6pqakiPDz8qZ6enmL58uX5ABAfH2+al5ence3atQwul4tnz56pXblyJUNdXR0HDx7UX7hwodXx48cbTW1+584drQsXLmQWFxdzBAKB+OOPP34aExPzICgoSFv5S0JTBg0a5MAwDCIjI58uWLCg0ZjqeXl56jY2NlXK9xYWFlX3799X79mzZ/VLL3QLVVRUqPn4+Mi+++67hzNnzrT67rvvun7zzTd5Q4YMkYWEhGSoqalhzZo1psuXL+8WHx//YPHixd19fHxkq1evztu1a5fBr7/+atrSuh4/fsw5evSo0d27d2+pqalBeQ0GDx5cHBQUJI2MjCwCAB8fH8cff/zxvpOTU+WpU6d0o6KirC9evHh71qxZ1lOnTn06e/bswi+//LLZbjhvCiXZhBDy9toDYCyAbqht2Q5DbcLtwbJsNcMwEgAN+zHKUf9XTOV6BkAqy7I+7RoxeWv16dNHtmDBgh7jx49/NnHixCI7O7saAHBycioTCoVVADB+/PhnZ8+e1YuMjCz6+eefjbdu3Woql8uZp0+fqqekpGgpk+zw8PCiV9VnYGBQ4+fnV7p7924DJyen59XV1YyXl1dFS+M9ffq03vDhw4u7dOlSAwAjRowoSkhI0B83blyxpaVlpa+vbwUAuLm5lUskEk0A4PF4FaNHj7YdOXJkcVhYWHFzZY8ZM6ZImdg/e/aMM2HCBFuJRKLFMAxbXV3d5K9HQ4cOLdbW1ma1tbXlxsbG1Q8ePHhlDnbu3LkMW1vb6ocPH3IHDhzoKBKJng8bNqxNH2Ju7scu5XJ1dXVW+cuAh4dH2cmTJ7sAwL179zRGjRpl9fTpU/Wqqiq1Hj16VALAxYsX9ffv338HAEJCQqQzZsxQtDQWExMThaamZs2ECRNsgoKCiidMmCBtuI1UKlVLTk7WGzdunJ1yWVVVFQMA165d0zt27Fg2AMyYMaPw888/f61fB9oKJdmEEPL22g0gHoApgP6o7TLy5EWCHQCgZxP73AcgZBhGE4A2gEEAzgHIBNCVYRgflmX/etF9xJFl2dQ3cSAEaG2Lc3tJS0vT4HA4sLS0lKekpKiWf/HFF49HjRolPXTokIG/vz//yJEjWUDjJI1hGGRkZGisW7fOPCkpKb1r166K4OBgm+fPn6u+3LW0z/X06dMLVq5c2c3R0fH5pEmTGrXiurq6Pk9PT9eRy+VoqjW7ORoaGqo+DBwOh62oqFADgISEhKxjx47pHzp0yGD16tUWmZmZTd7/enp6qvgXLVpk2b9//9ITJ05kZ2ZmagwcOJDX1D6ampp164RcLn9lVy5bW9tqoLYbz4gRI4r/+usv3YZJtoWFRbVEItFQvs/Ly9No2Ir95Zdfdv3555+7AsAff/yRZWNjo1pvamoqLy4u5tTdvri4mGNubi4HAC6Xy6qp1V46Lperinv27NnWH3300eOwsDDp4cOH9ZcvX97i7hnq6upsTc3/3QKVlZXMi+W4fv16+m+//dZl7969Rj/88IPZxYsXb9fdV6FQQF9fX95c67+amlqnmcqc+mQTQshb6kUCrA/g4YsHFP8DwJNhmJsAwgE0esiIZdlcAL8CuPXiv8kvllehtlX8a4ZhUgBcB+D7Jo6DdB6PHj3iTps2rWdkZOQTZWKllJqaqunl5VWxcuXKx87OzmW3bt3SAmq7i2RkZGgoFArs3bvX2N/fv7SoqIijra1dY2xsrMjNzeWePn3aoCX16+rqKqRSqarigQMHluXl5WkcOHDAZMqUKc8abi8SiSqdnZ3L5s2b112ZtGVmZmrs2rXLICAgQHb06FHD0tJStZKSErWjR48aBQQElDZXt0KhQHZ2tsa7775bun79+ocymYwjlUo5+vr6itLSUk5z+5WUlHCsrKyqAGDjxo0t7hoBAAYGBoqysrImc7GSkhK1oqIiNeXrhISELs7Ozo1a8keOHFn8n//8x6SmpgZ//vmnrr6+vqJhkv3Pf/7zaUZGRlpGRkZa3QQbAPr27VuWlJSkl5OTwwVqR0WpqqpSs7Ozq8JLlJaWcqytrasBYOvWrSbK5X369ClVvv/111+7lJSUNDp3dnZ2lXfu3NGuqKhgCgoKOOfOnesC1LZSv/hlQLphw4bcjIwMHQDQ09NTlJSUqAGAsbFxjZWVVdXmzZuNgNrRVf766y9tAHB3d5fFx8cbA0B8fLxJw3rfNGrJJoSQtxjLsk51XhcAaLK7B8uyenVeLwSwsIltrgPo1w5hkk6ssrJSjc/nC+VyOcPhcNgJEyYULl26NL/hdt98843ZhQsXujAMw/J4vIqxY8dK//zzTz2xWFw2c+ZMa4lEouXr61syefLkYg6HA7FYXG5nZye2sLCo8vDwaFEXh/fff78gMDDQ0dzcvOrSpUu3AWDUqFFFN27c0OnatWuT3Q527NghmTVrVo+ePXuKtbS0WCMjI/mqVaty+/btWx4aGlro7u4uAIDJkyc/9fPzq8jMzNRoqhy5XM6EhobalpaWcliWZaZOnfrE1NRUERwcXDx27Fi7Y8eOGcbGxuY03G/RokWPp06davv11193HzJkSLNdTJrSrVs3hYeHh8zBwUE0cOBA6caNGx8o1z148IA7evRoewBQKBRMcHBw4dixY0sA4JtvvukKAAsXLnw6fvx46ZEjRwx69uwp1tbWrtm0aZOkNTH06NFD/vXXX+cGBgY61NTUMLq6uoodO3bcVT5I2pxPPvnk0cSJE+0MDAzkffv2Lc3JydEEgK+++upRcHBwL3t7e5Gnp6fMwsJClawrf/Wwt7evfvfdd4v4fL7IysqqUiQSlQO1LehBQUH2ypbtzz//PBcAwsLCnkVFRdls2LDBfO/evdm//PLL3WnTpvX8+uuvLeRyOTN69OhnPj4+Fd9//31OSEhIr9jY2G6BgYGtuhbtgan71CchhBBC3pyUlBSJi4tLo24Qb4vDhw/rx8TEmCckJNxprzoCAgLs58yZk//ee+812wpNOr/Hjx9z3N3dhY8ePbrZ0bG8TEpKiqmLi4tNW5RF3UUIIYQQ0ukUFBRwbGxsxFpaWjWUYL/dJBKJep8+fQT/+Mc/Gv1C8r+MWrIJIYSQDvK2t2QT8r+GWrIJIYQQQgjpxCjJJoQQQgghpI1Rkk0IIYQQQkgboySbEEIIIYSQNkZJNiGEEEIIIW2MkmxCCCGEtLnFixd3e539JkyY0DMpKUnrZdts377d8FXbxMXFmUgkEvXXiYGQtkBJNiGEEELaXFxcnEVTy2tqaqBQNDl5IwBg9+7d9z08PJ6/rOyDBw8a3rhxQ/tl2+zYscM0JyeHkmzSYSjJJoQQQv7G1q1bZ+Lo6Cjk8XjCUaNG2WZmZmr06dPH0dHRUejj4+OYlZWlAQDBwcE2ERERPdzc3PhWVlZOW7ZsMQKA+/fvq3t6evL4fL7QwcFB9Mcff+jNmjXLUjld+8iRI20zMzM1bGxsxKNHj7ZxdHQUZWdna4SFhVmLxWKBvb29aO7cud2V8Xh5efESExN1AEBHR8ftww8/tOTxeEIXFxd+bm4u98SJE7onT540/PTTT634fL4wNTVVs+ExbdmyxejWrVs64eHhvfh8vnDXrl0GgwcPtlOuP3DgQJchQ4bYKeuYMmVKD3t7e5GPj4/jo0ePuACQmpqq6e/v7yASiQQeHh685OTkl7acE9IQJdmEEELI39TVq1e1Vq9ebXHmzJnbmZmZaRs3bsyJioqyDgsLK7x9+3bahAkTCqOionoot8/Pz1e/evVqxqFDh7KWLl1qCQCbN282HjRokDQjIyMtPT091dvbu/z7779/qKmpWZORkZH222+/3QOAnJwczdmzZz+9c+dOqqOjY9WaNWse3rp1Kz0jIyP1/Pnz+pcuXWrUMl1RUaHm4+Mjy8zMTPPx8ZF99913XYcMGVI2ePDg4hUrVjzIyMhIE4lElQ33i4yMLBKLxeXbtm27m5GRkTZ+/Hhpdna2ljKB3rx5s0lkZGSBsg5PT8+yO3fupPr5+ZUuXry4OwBMnTq15/fff5+TmpqavmrVqgdRUVHW7XMVyP8qSrIJIYSQv6njx493effdd4ssLCzkAGBubq5ITk7WnT59+jMAiIqKepaUlKSn3H7kyJHFHA4HHh4ezwsLC9UBoE+fPmW//PKL6bx587pfvnxZ28jIqKapuiwsLKoGDRpUpnz/888/GwuFQoFQKBRmZWVppaSkNGopVldXZ0NCQqQA4OHhUXb//n2N1zlONTU1jB8/vjA+Pt64oKCAc+3aNb1x48ZJleumTp36DAA++OCDwsuXL+tJpVK15ORkvXHjxtnx+XzhrFmzej558oS6npBW4XZ0AIQQQgh5O2hpabHK1yxb+3LYsGGyxMTEzH379hl88MEHtrNnz86fPXt2YcN9dXR0VMl3RkaGxrp168yTkpLSu3btqggODrZ5/vx5o4Y/LpfLqqmpKV9DLpczrxt7VFRU4YgRI+y1tLTYd999t0hdvemcmWEYKBQK6OvryzMyMtJetz5CqCWbEEII+Zt65513Sn7//Xejx48fcwAgPz+f4+bmVrZp0yYjANi4caOxp6en7GVl3L59W8PKyqp6/vz5BeHh4U+vXbumA9QmyJWVlU0mxUVFRRxtbe0aY2NjRW5uLvf06dMGrYlbT09PUVJS8tIcRk9PTyGVSjnK9zY2NtXm5ubVMTExFtOnTy9QLq+pqYGyf/nWrVtNvLy8So2NjWusrKyqNm/ebKTc5q+//nrpg5aENERJNiGEEPI35enp+Xz+/Pl5/v7+fB6PJ5w1a1aPDRs25Gzfvt3U0dFR+Msvv5h8//33uS8r4/jx4/oCgUAkEAiE+/btM164cGE+AISFhT0VCATCkSNH2jbcx8fHp0IsFpfb2dmJx48f38vDw+OliXxDYWFhz+Li4roJBIImH3wEgPDw8IIPP/ywJ5/PF8pkMgYAQkJCCi0sLKrc3d1Vo5doa2vXXL58WdfBwUGUmJio/+WXX+YBwC+//HJ3y5YtpjweT+jg4CDat2+fYWtiJIRR/txDCCGEkDcrJSVF4uLiUvDqLUlbCA8Pt3ZzcyufO3eu6pzr6Oi4lZeXJ3dkXKTzSElJMXVxcbFpi7KoTzYhhBBC/ueJRCKBtrZ2zcaNG1/aMk9IW6EkmxBCCCFvrcmTJ1tfuXJFr+6yqKio/I8++qjew5epqanpTe1PrdikvVCSTQghhJC31vbt23M6OgZCmkIPPhJCCCGEENLGKMkmhBBCCCGkjVGSTQghhBBCSBujJJsQQgj5m9u+fbshwzAeycnJjaY2J4S8HkqyCSGEkLfEvn37jAMDA508PT09AgMDnfbt22fcFuXu2rXL2N3dXbZt27Y2Ka8pcrm8vYompFOiJJsQQgh5C+zbt884JiamZ0FBgQYAFBQUaMTExPT8bxNtqVSqduXKFb0tW7ZIDhw4oCrrk08+6ebo6Ch8MROkJQDcunVL09fX15HH4wmFQqEgNTVV8/Dhw/oBAQH2yv3Cw8Ot4+LiTADA0tLSKSoqylIoFAo2b95sFBMTYyoWiwU8Hk/4zjvv2JWWlqoBQG5uLnfIkCF2PB5PyOPxhCdOnNCdM2dO9+XLl5spy/3www8tP//8czMQ8pagIfwIIYSQTmD58uU97ty5o9Pc+tu3b+vK5XKm7rKqqiq1VatW2Rw6dKhrU/vY29uXL1my5KWTr+zcudNwwIABUmdn50ojIyP52bNndfLy8rhHjx41TEpKytDX16/Jz8/nAEBoaKjtggULHoeHhxeXl5czCoWCuXfvnsbLyjcxMZGnpaWlA8Djx4858+fPLwCA6Ojo7nFxcaaffPLJk5kzZ1r7+/uXLlmyJFsul0MqlXKsra2rR48ebbdkyZInCoUCBw8eNLpy5UqTY10T0hlRkk0IIVqCZ8oAAAiPSURBVIS8BRom2K9a3lK//vqrcXR09BMACA4OfrZ9+3ZjlmUxadKkAn19/RoAMDc3VxQVFanl5+drhIeHFwOAjo4OC4B9Vfnh4eFFytdJSUnaS5YssSwtLeWUlZVx+vfvLwWACxcu6O/du/ceAHC5XJiYmChMTEwUhoaG8vPnz2vn5eWpi0Si8m7duin+m2Ml5E2iJJsQQgjpBF7V4hwYGOik7CpSl6mpadW2bdsyX6fO/Px8zsWLF/UzMzO1Z8+eDYVCwTAMwwYFBRW9eu9a6urqbE1Njep9ZWVlvaRfmagDwPTp02337t17x8fHpyIuLs7kzJkz+i8rOzIysmDTpk2mT548UY+MjCx82baEdDbUJ5sQQgh5C0ybNu2hhoZGTd1lGhoaNdOmTXv4umVu377daPTo0c8ePXp08+HDhzcfP358w8rKqsrAwECxY8cOU2Wf6fz8fI6RkVFNt27dqrZv324IABUVFUxpaamanZ1d5Z07d7QrKiqYgoICzrlz57o0V195ebmatbV1dWVlJbNr1y5V/28/P7/SVatWdQVqH5AsLCzkAMDkyZOLExISDFJSUnSDg4Olr3uchHQESrIJIYSQt0BwcPCz+fPn3zc1Na0Caluw58+ffz84OPjZ65a5Z88e4zFjxtRrtX7vvfeK8vLy1IcNG1bs6uoq4PP5ws8//7wbAOzYsePe+vXrzRwdHYWenp783Nxcrr29ffW7775bxOfzRe+9914vkUhU3lx9ixcvfuTl5SXw9PTkOzg4PFcu/+GHH3LOnDmj7+joKBSLxULlUIJaWlqsr69vyciRI59xufTjO3m7MCz7yu5UhBBCCGkHKSkpEhcXl4KOjqOzUigUEIlEwj179mQ7OTlVdnQ85H9fSkqKqYuLi01blEUt2YQQQgjpdJKSkrR69uzp5O/vX0IJNnkb0W8vhBBCCOl0PDw8nj948OBmR8dByOuilmxCCCGEEELaGCXZhBBCCCGEtDFKsgkhhBBCCGljlGQTQgghhBDSxijJJoQQQv7GOByOB5/PF9rb24t4PJ5w6dKl5gpF7ezliYmJOhERET3as/45c+Z0P3jw/7d3fyFNrnEcwH/v3rnN2c7w75yjHLUz53te9aLQMLTsIrprYRT0F7Oi7hK88CKKTLsIVhKhCJIV3QRBJ5gXEf1TCSRCVtsaOg6rzmzpTjs7aup69nouamKHc06Gr87F93P1sj/P73ngHXz327v3+VVHRNTc3JyX2AAHINXhRAYAAEgRvb29up07dwqhUEg5/3gxY6rVasnn83n9fr/n4cOHQ/fv39c3NjYWEBFVV1d/vHbt2v9u974YjDFqa2sbsdvt40REnZ2dhomJCWQT+CHgRAYAAEgBvb29uqamJkswGNQ0NTWtTRy3t7cXyFXDZDKxrq6uQHd3d54kSeR0OnU1NTUWIqKenp5VNptNsNlsQnFxsRCJRBROp1O3YcOGoi1btljMZrO4d+/eNYku+L59+9aIolhssVh+aWhoKJhXo+TEiRMmQRCKr169mllbW2vu7u7ObGlpyRsdHU3bvHmztaKiwtrW1pZ9+PDhuS66w+HIqa+vX9KuOoCccJ9sAACAFaC5uXm13+/X/tfzfr9fG4vFFEREHo9Hl9ix+d69ezmBQCD9395jsVg+nj59+rs60YIgxOLxOAWDwa8ygsPhyL98+fLrbdu2TUajUYVWq5WIiF6+fJkxODjotlqtserq6p9v3LiRWVdXF7l48WLQYDDEGWNUWVlZNDAwkF5RUTFFRJSdnc28Xu+rL/PXExGdOnVqtKOjw/DkyZMho9HIotGoQhRF48zMzO9qtXr25s2bOZ2dna+/Zy0AyYRONgAAQAooLCyc0mq1cY7jKBGwOY6j/Pz8ZdkNcePGjRONjY2rW1pa8sLhMJ+WlkZERCUlJZOCIMSUSiXt3r37Q19f3yoiouvXr2cJglAsCIIwPDyscblcmsRYBw8ejHyrnl6vlzZt2jR+69Yt/eDgoObTp09ceXn51JItEEBm6GQDAACsAN/qOCcuF0kEbCIinudnS0tLx8+dO/dGrnl4vV4Vz/NkMpmYy+Wae/z8+fMhu90evXv3rr6qqsrW09MzTPQ56M/HcRz5fD7VlStXDM+fP3+Vm5sbr62tNU9PT8819nQ6nbSQuRw7dizc2tqab7Vap/fv3x+WZ4UAywOdbAAAgBRw6dKl1YwxjohIpVJJSqVyljHG9ff3Z8pVY2RkRHn06NHCurq6UYXi64jg8XjU5eXlU62traHS0tJJt9utIfp8uYjP51PF43G6fft2VlVV1XgkEuHT09OlrKys+Nu3b5WPHz/WL6R+RkZGPBqNzhXeunXr5Lt371R37tzJrq+v/yDXOgGWAzrZAAAAKaCjo2Oovb29oK+vL7OhoeHNs2fPdP39/Zlnz579bTHjzszMKGw2m8AY43ien92zZ88fZ86cef/P1124cCHv6dOnP3EcN1tUVDS1a9eu6IMHD1aJojh5/PjxNYFAQFNZWfnXgQMH/uR5nkRR/Lhu3TrRaDTG1q9fP7GQuRw6dCi8fft2q8FgiA0MDAwREdnt9siLFy+0ubm58cWsE2C5cfN/dgIAAIDl43K5AmVlZSl7GYTT6dQ5HA7Do0eP/EtVo6amxnLy5Mn3O3bsGF+qGgAJLpcrp6yszCzHWLhcBAAAAFaccDjMm81mUaPRSAjYkIrQyQYAAEiSVO9kA/xo0MkGAAAAAFjBELIBAACSR5Ikifv2ywBgqX35LC7o9pILgZANAACQPO6xsTE9gjZAckmSxI2NjemJyC3XmLiFHwAAQJIwxo6EQqGuUCgkEhpfAMkkEZGbMXZErgHxx0cAAAAAAJnhWzMAAAAAgMwQsgEAAAAAZIaQDQAAAAAgM4RsAAAAAACZIWQDAAAAAMjsb9H3lDBFlyVnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6851427470>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sj_df['dataset'] = 'san_jose_housing'\n",
    "ts_df_1k = sj_df.copy()\n",
    "fig, ax = plt.subplots(figsize=(12,5))\n",
    "sns.lineplot(data=ts_df_1k.groupby(['constraint_type', 'strategy'])[['value', 'recall_over55k_to_under55k']].mean().reset_index(), \n",
    "              x='value', y='recall_over55k_to_under55k', \n",
    "              hue='strategy',\n",
    "#              hue='dataset', \n",
    "#              marker=\"o\", \n",
    "              style='constraint_type',\n",
    "              markers=True,\n",
    "#              color='dodgerblue',\n",
    "              markersize=10,\n",
    "              dashes=None,\n",
    "              ci=None,\n",
    "              ax=ax)\n",
    "\n",
    "\n",
    "x_coords = list(ts_df_1k.groupby(['constraint_type', 'strategy'])[['value', 'recall_over55k_to_under55k']].mean().reset_index()['value'].values)\n",
    "y_coords = list(ts_df_1k.groupby(['constraint_type', 'strategy'])[['value', 'recall_over55k_to_under55k']].mean().reset_index()['recall_over55k_to_under55k'].values)\n",
    "labels = list(ts_df_1k.groupby(['constraint_type', 'strategy'])['strategy'].min())\n",
    "\n",
    "prec_errors = 1.96*ts_df_1k.groupby(['constraint_type', 'strategy'])['value'].sem().values\n",
    "disp_errors = 1.96*ts_df_1k.groupby(['constraint_type', 'strategy'])['recall_over55k_to_under55k'].sem().values\n",
    "colors = sns.color_palette().as_hex()[0]\n",
    "ax.errorbar(x_coords, y_coords, \n",
    "            xerr=prec_errors, \n",
    "            yerr=disp_errors,\n",
    "            ecolor=colors, \n",
    "            fmt=' ', zorder=-1, capsize=5)\n",
    "\n",
    "\n",
    "ax.hlines(1.0, 0, 1, linestyles='dashed')\n",
    "\n",
    "ax.set_xlim((0.5,1.0))\n",
    "ax.set_ylim((0.5,2.0))\n",
    "#ax.get_legend().remove()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## San Jose Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [],
   "source": [
    "sj_original_sql = \"\"\"\n",
    "WITH dist_res AS (\n",
    "  SELECT DISTINCT * FROM kit_bias_adj.model_adjustment_results_median_income \n",
    "      WHERE list_size=500 AND train_end_time >= '2013-06-01'\n",
    ")\n",
    ", mg_rns_value AS (\n",
    "  SELECT *,\n",
    "         row_number() OVER (PARTITION BY train_end_time, list_size, metric, parameter ORDER BY base_value DESC, base_max_recall_ratio ASC, RANDOM()) AS rn_base_value\n",
    "  FROM dist_res\n",
    "  WHERE past_train_end_time = train_end_time\n",
    ")\n",
    ", best_mgs AS (\n",
    "  SELECT * FROM mg_rns_value WHERE rn_base_value = 1\n",
    ")\n",
    "-- Simple model selection on last time period, baseline with no recall adjustments\n",
    "SELECT 'Original'::VARCHAR(128) AS strategy,\n",
    "       r.train_end_time, r.past_train_end_time,\n",
    "       r.list_size, r.metric, r.parameter,\n",
    "       r.base_value AS value,\n",
    "       r.base_max_recall_ratio AS max_recall_ratio,\n",
    "       r.base_recall_over55k_to_under55k AS recall_over55k_to_under55k,\n",
    "       r.base_recall_under55k_to_over55k AS recall_under55k_to_over55k,\n",
    "       r.base_frac_under55k AS frac_under55k,\n",
    "       r.base_frac_over55k AS frac_over55k\n",
    "FROM dist_res r\n",
    "JOIN best_mgs b\n",
    "  ON r.model_group_id = b.model_group_id\n",
    "  AND r.past_train_end_time = b.train_end_time\n",
    "  AND r.list_size = b.list_size\n",
    "  AND r.metric = b.metric\n",
    "  AND r.parameter = b.parameter\n",
    "WHERE r.train_end_time > r.past_train_end_time\n",
    "ORDER BY train_end_time\n",
    ";\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [],
   "source": [
    "sj_res_dfs = []\n",
    "\n",
    "letters = list(reversed(list('ABCDEFGH')))\n",
    "\n",
    "for i, c in enumerate([1.0, 1.05, 1.1, 1.2, 1.3, 1.5, 2.0, 5.0]):\n",
    "    df = pd.read_sql(sj_disp_cons_sql.format(disparity_constraint=c), engine_sj)\n",
    "    df['Level'] = letters[i]\n",
    "    sj_res_dfs.append(df)\n",
    "\n",
    "for i, c in enumerate([0.6, 0.5, 0.25, 0.2, 0.15, 0.10, 0.05, 0.0]):\n",
    "    df = pd.read_sql(sj_value_cons_sql.format(value_constraint=c), engine_sj)\n",
    "    df['Level'] = letters[i]\n",
    "    sj_res_dfs.append(df)\n",
    "\n",
    "df = pd.read_sql(sj_original_sql, engine_sj)\n",
    "df['Level'] = 'Original'\n",
    "sj_res_dfs.append(df)\n",
    "\n",
    "sj_df = pd.concat(sj_res_dfs)\n",
    "sj_df['Constraint Type'] = sj_df['strategy'].map(lambda x: x.split(' ')[0])\n",
    "sj_df['dataset'] = 'san_jose_housing'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5, 2.0)"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6850d87b00>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts_df_1k = sj_df.copy()\n",
    "disp_col = 'recall_over55k_to_under55k'\n",
    "\n",
    "colorlist = ['maroon', \n",
    "             'red',\n",
    "             '#001c7f', #Original\n",
    "             ]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "sns.lineplot(data=ts_df_1k.groupby(['Constraint Type', 'Level'])[['value', disp_col]].mean().reset_index(), \n",
    "              x='value', y=disp_col, \n",
    "              hue='Constraint Type',\n",
    "              hue_order=['Accuracy', 'Disparity', 'Original'],\n",
    "#              palette=sns.color_palette(\"nipy_spectral\", 8) + list(reversed(sns.color_palette(\"nipy_spectral\", 11))),\n",
    "#              palette=sns.color_palette(\"nipy_spectral\", 3),\n",
    "              palette=colorlist,\n",
    "#              hue_order=dc3_sort_ord,\n",
    "#              hue='dataset', \n",
    "#              marker=\"o\", \n",
    "              style='Level',\n",
    "              markers=['o', 'v', '^', '<', '>', 's', 'd', 'p', 'X'],\n",
    "              style_order=['Original'] + list(reversed(letters))[:8],\n",
    "#              color='dodgerblue',\n",
    "              markersize=10,\n",
    "              dashes=None,\n",
    "              ci=None,\n",
    "              ax=ax)\n",
    "\n",
    "\n",
    "for i, t in enumerate(['Accuracy', 'Disparity', 'Original']):\n",
    "    tmp_df = ts_df_1k.loc[ts_df_1k['Constraint Type'] == t].copy()\n",
    "\n",
    "    x_coords = list(tmp_df.groupby(['Constraint Type', 'Level'])[['value', disp_col]].mean().reset_index()['value'].values)\n",
    "    y_coords = list(tmp_df.groupby(['Constraint Type', 'Level'])[['value', disp_col]].mean().reset_index()[disp_col].values)\n",
    "    labels = list(tmp_df.groupby(['Constraint Type', 'Level'])['Level'].min())\n",
    "\n",
    "    prec_errors = 1.96*tmp_df.groupby(['Constraint Type', 'Level'])['value'].sem().values\n",
    "    disp_errors = 1.96*tmp_df.groupby(['Constraint Type', 'Level'])[disp_col].sem().values\n",
    "    colors = colorlist[i]\n",
    "    ax.errorbar(x_coords, y_coords, \n",
    "                xerr=prec_errors, \n",
    "                yerr=disp_errors,\n",
    "                ecolor=colors, \n",
    "                fmt=' ', zorder=-1, capsize=5)\n",
    "\n",
    "\n",
    "ax.hlines(1.0, 0, 1, linestyles='dashed')\n",
    "\n",
    "ax.legend(loc='lower right', fontsize=14)\n",
    "\n",
    "ax.set_ylabel('', fontsize=32)\n",
    "ax.set_xlabel('Precision at Top-K', fontsize=28)\n",
    "\n",
    "ax.tick_params(axis='x', labelsize=24)\n",
    "ax.tick_params(axis='y', labelsize=24)\n",
    "\n",
    "ax.set_xlim((0.7,0.9))\n",
    "ax.set_ylim((0.5,2.0))\n",
    "#ax.get_legend().remove()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## El Salvador"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('db_big_profile.yaml') as fd:\n",
    "    config = yaml.load(fd)\n",
    "    dburl = sqlalchemy.engine.url.URL(\n",
    "        \"postgres\",\n",
    "        host=config[\"host\"],\n",
    "        username=config[\"user\"],\n",
    "        database='el_salvador_mined',\n",
    "        password=config[\"pass\"],\n",
    "        port=config[\"port\"],\n",
    "    )\n",
    "    engine_mined = sqlalchemy.create_engine(dburl, poolclass=sqlalchemy.pool.QueuePool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "mined_disp_cons_sql = \"\"\"\n",
    "WITH dist_res AS (\n",
    "  SELECT DISTINCT * FROM bias_analysis_1year_all_models.model_adjustment_results_ovg \n",
    "      WHERE list_size=10000 \n",
    "    AND train_end_time >= '2011-01-01'\n",
    ")\n",
    ", mg_rns_value AS (\n",
    "  SELECT *,\n",
    "         row_number() OVER (PARTITION BY train_end_time, list_size, metric, parameter ORDER BY base_value DESC, base_max_recall_ratio ASC, RANDOM()) AS rn_base_value\n",
    "  FROM dist_res\n",
    "  WHERE past_train_end_time = train_end_time\n",
    "    AND base_max_recall_ratio < {disparity_constraint}\n",
    ")\n",
    ", mg_rns_disp AS (\n",
    "  SELECT *,\n",
    "         row_number() OVER (PARTITION BY train_end_time, list_size, metric, parameter ORDER BY base_max_recall_ratio ASC, base_value DESC, RANDOM()) AS rn_base_disparity\n",
    "  FROM dist_res\n",
    "  WHERE past_train_end_time = train_end_time\n",
    ")\n",
    ", base_mgs_hard_cons AS (\n",
    "  -- Best-performaing (in terms of precision) model groups meeting the hard constraint on disparity\n",
    "  SELECT * FROM mg_rns_value WHERE rn_base_value = 1\n",
    ")\n",
    ", base_mgs_soft_cons AS (\n",
    "  -- For time/size/metric combinations with no model groups meeting the hard constraint, fill in with lowest-disparity model group\n",
    "  SELECT\n",
    "      COALESCE(h.model_group_id, d.model_group_id) AS model_group_id,\n",
    "      COALESCE(h.train_end_time, d.train_end_time) AS train_end_time,\n",
    "      COALESCE(h.list_size, d.list_size) AS list_size,\n",
    "      COALESCE(h.metric, d.metric) AS metric,\n",
    "      COALESCE(h.parameter, d.parameter) AS parameter\n",
    "  FROM (SELECT * FROM mg_rns_disp WHERE rn_base_disparity = 1) as d\n",
    "  LEFT JOIN base_mgs_hard_cons h USING(train_end_time, list_size, metric, parameter)\n",
    ")\n",
    "-- Simple model selection on last time period, baseline with no recall adjustments\n",
    "SELECT 'Disparity Constraint {disparity_constraint} - Unadjusted'::VARCHAR(128) AS strategy,\n",
    "       r.train_end_time, r.past_train_end_time,\n",
    "       r.list_size, r.metric, r.parameter,\n",
    "       r.base_value AS value,\n",
    "       r.base_max_recall_ratio AS max_recall_ratio,\n",
    "       r.base_recall_0_to_1 AS recall_not_to_over,\n",
    "       r.base_recall_1_to_0 AS recall_over_to_not,\n",
    "       r.base_frac_0 AS frac_not,\n",
    "       r.base_frac_1 AS frac_over\n",
    "FROM dist_res r\n",
    "JOIN base_mgs_soft_cons b\n",
    "  ON r.model_group_id = b.model_group_id\n",
    "  AND r.past_train_end_time = b.train_end_time\n",
    "  AND r.list_size = b.list_size\n",
    "  AND r.metric = b.metric\n",
    "  AND r.parameter = b.parameter\n",
    "WHERE r.train_end_time > r.past_train_end_time\n",
    "ORDER BY train_end_time\n",
    ";\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "mined_value_cons_sql = \"\"\"\n",
    "WITH dist_res AS (\n",
    "  SELECT DISTINCT * FROM bias_analysis_1year_all_models.model_adjustment_results_ovg \n",
    "    WHERE list_size=10000 \n",
    "    AND train_end_time >= '2011-01-01'\n",
    ")\n",
    ", mg_rns_value AS (\n",
    "  SELECT *,\n",
    "         row_number() OVER (PARTITION BY train_end_time, list_size, metric, parameter ORDER BY base_value DESC, base_max_recall_ratio ASC, RANDOM()) AS rn_base_value\n",
    "  FROM dist_res\n",
    "  WHERE past_train_end_time = train_end_time\n",
    ")\n",
    ", best_mgs AS (\n",
    "  SELECT * FROM mg_rns_value WHERE rn_base_value = 1\n",
    ")\n",
    ", value_dist_cons AS (\n",
    "  SELECT r.*,\n",
    "         row_number() OVER (PARTITION BY r.train_end_time, r.list_size, r.metric, r.parameter ORDER BY r.base_max_recall_ratio ASC, r.base_value DESC, RANDOM()) AS rn_base_disparity\n",
    "  FROM dist_res r\n",
    "  JOIN best_mgs b USING(train_end_time, list_size, metric, parameter)\n",
    "  WHERE b.base_value - r.base_value <= {value_constraint}\n",
    ")\n",
    ", base_mgs_value_cons AS (\n",
    "  -- Lowest-disparity model groups within hard constraint distance from best model group in terms of metric value\n",
    "  SELECT * FROM value_dist_cons WHERE rn_base_disparity = 1\n",
    ")\n",
    "-- Simple model selection on last time period, baseline with no recall adjustments\n",
    "SELECT 'Accuracy Constraint {value_constraint} - Unadjusted'::VARCHAR(128) AS strategy,\n",
    "       r.train_end_time, r.past_train_end_time,\n",
    "       r.list_size, r.metric, r.parameter,\n",
    "       r.base_value AS value,\n",
    "       r.base_max_recall_ratio AS max_recall_ratio,\n",
    "       r.base_recall_0_to_1 AS recall_not_to_over,\n",
    "       r.base_recall_1_to_0 AS recall_over_to_not,\n",
    "       r.base_frac_0 AS frac_not,\n",
    "       r.base_frac_1 AS frac_over\n",
    "FROM dist_res r\n",
    "JOIN base_mgs_value_cons b\n",
    "  ON r.model_group_id = b.model_group_id\n",
    "  AND r.past_train_end_time = b.train_end_time\n",
    "  AND r.list_size = b.list_size\n",
    "  AND r.metric = b.metric\n",
    "  AND r.parameter = b.parameter\n",
    "WHERE r.train_end_time > r.past_train_end_time\n",
    "ORDER BY train_end_time\n",
    ";\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "mined_res_dfs = []\n",
    "\n",
    "for c in [1.0, 1.05, 1.1, 1.2, 1.3, 1.4, 1.5, 2.0, 2.5, 3.0, 5.0]:\n",
    "    df = pd.read_sql(mined_disp_cons_sql.format(disparity_constraint=c), engine_mined)\n",
    "    mined_res_dfs.append(df)\n",
    "\n",
    "for c in [0.6, 0.5, 0.25, 0.2, 0.15, 0.10, 0.05, 0.0]:\n",
    "    df = pd.read_sql(mined_value_cons_sql.format(value_constraint=c), engine_mined)\n",
    "    mined_res_dfs.append(df)\n",
    "\n",
    "mined_df = pd.concat(mined_res_dfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "mined_df['constraint_type'] = mined_df['strategy'].map(lambda x: x.split('-')[0].split(' ')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 5.3)"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6852e90940>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mined_df['dataset'] = 'el_salvador_mined'\n",
    "ts_df_1k = mined_df.copy()\n",
    "fig, ax = plt.subplots(figsize=(12,5))\n",
    "sns.lineplot(data=ts_df_1k.groupby(['constraint_type', 'strategy'])[['value', 'recall_over_to_not']].mean().reset_index(), \n",
    "              x='value', y='recall_over_to_not', \n",
    "              hue='strategy',\n",
    "#              hue='dataset', \n",
    "#              marker=\"o\", \n",
    "              style='constraint_type',\n",
    "              markers=True,\n",
    "#              color='dodgerblue',\n",
    "              markersize=10,\n",
    "              dashes=None,\n",
    "              ci=None,\n",
    "              ax=ax)\n",
    "\n",
    "\n",
    "x_coords = list(ts_df_1k.groupby(['constraint_type', 'strategy'])[['value', 'recall_over_to_not']].mean().reset_index()['value'].values)\n",
    "y_coords = list(ts_df_1k.groupby(['constraint_type', 'strategy'])[['value', 'recall_over_to_not']].mean().reset_index()['recall_over_to_not'].values)\n",
    "labels = list(ts_df_1k.groupby(['constraint_type', 'strategy'])['strategy'].min())\n",
    "\n",
    "prec_errors = 1.96*ts_df_1k.groupby(['constraint_type', 'strategy'])['value'].sem().values\n",
    "disp_errors = 1.96*ts_df_1k.groupby(['constraint_type', 'strategy'])['recall_over_to_not'].sem().values\n",
    "colors = sns.color_palette().as_hex()[0]\n",
    "ax.errorbar(x_coords, y_coords, \n",
    "            xerr=prec_errors, \n",
    "            yerr=disp_errors,\n",
    "            ecolor=colors, \n",
    "            fmt=' ', zorder=-1, capsize=5)\n",
    "\n",
    "\n",
    "ax.hlines(1.0, 0, 1, linestyles='dashed')\n",
    "\n",
    "ax.set_xlim((0.1,0.9))\n",
    "ax.set_ylim((0.0,5.30))\n",
    "#ax.get_legend().remove()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## El Salvador Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [],
   "source": [
    "mined_original_sql = \"\"\"\n",
    "WITH dist_res AS (\n",
    "  SELECT DISTINCT * FROM bias_analysis_1year_all_models.model_adjustment_results_ovg \n",
    "    WHERE list_size=10000 \n",
    "    AND train_end_time >= '2011-01-01'\n",
    ")\n",
    ", mg_rns_value AS (\n",
    "  SELECT *,\n",
    "         row_number() OVER (PARTITION BY train_end_time, list_size, metric, parameter ORDER BY base_value DESC, base_max_recall_ratio ASC, RANDOM()) AS rn_base_value\n",
    "  FROM dist_res\n",
    "  WHERE past_train_end_time = train_end_time\n",
    ")\n",
    ", best_mgs AS (\n",
    "  SELECT * FROM mg_rns_value WHERE rn_base_value = 1\n",
    ")\n",
    "-- Simple model selection on last time period, baseline with no recall adjustments\n",
    "SELECT 'Original'::VARCHAR(128) AS strategy,\n",
    "       r.train_end_time, r.past_train_end_time,\n",
    "       r.list_size, r.metric, r.parameter,\n",
    "       r.base_value AS value,\n",
    "       r.base_max_recall_ratio AS max_recall_ratio,\n",
    "       r.base_recall_0_to_1 AS recall_not_to_over,\n",
    "       r.base_recall_1_to_0 AS recall_over_to_not,\n",
    "       r.base_frac_0 AS frac_not,\n",
    "       r.base_frac_1 AS frac_over\n",
    "FROM dist_res r\n",
    "JOIN best_mgs b\n",
    "  ON r.model_group_id = b.model_group_id\n",
    "  AND r.past_train_end_time = b.train_end_time\n",
    "  AND r.list_size = b.list_size\n",
    "  AND r.metric = b.metric\n",
    "  AND r.parameter = b.parameter\n",
    "WHERE r.train_end_time > r.past_train_end_time\n",
    "ORDER BY train_end_time\n",
    ";\n",
    "\"\"\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [],
   "source": [
    "mined_res_dfs = []\n",
    "\n",
    "letters = list(reversed(list('ABCDEFGH')))\n",
    "\n",
    "for i, c in enumerate([1.0, 1.05, 1.1, 1.2, 1.3, 1.5, 2.0, 5.0]):\n",
    "    df = pd.read_sql(mined_disp_cons_sql.format(disparity_constraint=c), engine_mined)\n",
    "    df['Level'] = letters[i]\n",
    "    mined_res_dfs.append(df)\n",
    "\n",
    "for i, c in enumerate([0.6, 0.5, 0.25, 0.2, 0.15, 0.10, 0.05, 0.0]):\n",
    "    df = pd.read_sql(mined_value_cons_sql.format(value_constraint=c), engine_mined)\n",
    "    df['Level'] = letters[i]\n",
    "    mined_res_dfs.append(df)\n",
    "\n",
    "df = pd.read_sql(mined_original_sql, engine_mined)\n",
    "df['Level'] = 'Original'\n",
    "mined_res_dfs.append(df)\n",
    "\n",
    "mined_df = pd.concat(mined_res_dfs)\n",
    "mined_df['Constraint Type'] = mined_df['strategy'].map(lambda x: x.split(' ')[0])\n",
    "mined_df['dataset'] = 'el_salvador_mined'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 5.3)"
      ]
     },
     "execution_count": 375,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f68510c7e80>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts_df_1k = mined_df.copy()\n",
    "disp_col = 'recall_over_to_not'\n",
    "\n",
    "colorlist = ['maroon', \n",
    "             'red',\n",
    "             '#001c7f', #Original\n",
    "             ]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "sns.lineplot(data=ts_df_1k.groupby(['Constraint Type', 'Level'])[['value', disp_col]].mean().reset_index(), \n",
    "              x='value', y=disp_col, \n",
    "              hue='Constraint Type',\n",
    "              hue_order=['Accuracy', 'Disparity', 'Original'],\n",
    "#              palette=sns.color_palette(\"nipy_spectral\", 8) + list(reversed(sns.color_palette(\"nipy_spectral\", 11))),\n",
    "#              palette=sns.color_palette(\"nipy_spectral\", 3),\n",
    "              palette=colorlist,\n",
    "#              hue_order=dc3_sort_ord,\n",
    "#              hue='dataset', \n",
    "#              marker=\"o\", \n",
    "              style='Level',\n",
    "              markers=['o', 'v', '^', '<', '>', 's', 'd', 'p', 'X'],\n",
    "              style_order=['Original'] + list(reversed(letters))[:8],\n",
    "#              color='dodgerblue',\n",
    "              markersize=10,\n",
    "              dashes=None,\n",
    "              ci=None,\n",
    "              ax=ax)\n",
    "\n",
    "\n",
    "for i, t in enumerate(['Accuracy', 'Disparity', 'Original']):\n",
    "    tmp_df = ts_df_1k.loc[ts_df_1k['Constraint Type'] == t].copy()\n",
    "\n",
    "    x_coords = list(tmp_df.groupby(['Constraint Type', 'Level'])[['value', disp_col]].mean().reset_index()['value'].values)\n",
    "    y_coords = list(tmp_df.groupby(['Constraint Type', 'Level'])[['value', disp_col]].mean().reset_index()[disp_col].values)\n",
    "    labels = list(tmp_df.groupby(['Constraint Type', 'Level'])['Level'].min())\n",
    "\n",
    "    prec_errors = 1.96*tmp_df.groupby(['Constraint Type', 'Level'])['value'].sem().values\n",
    "    disp_errors = 1.96*tmp_df.groupby(['Constraint Type', 'Level'])[disp_col].sem().values\n",
    "    colors = colorlist[i]\n",
    "    ax.errorbar(x_coords, y_coords, \n",
    "                xerr=prec_errors, \n",
    "                yerr=disp_errors,\n",
    "                ecolor=colors, \n",
    "                fmt=' ', zorder=-1, capsize=5)\n",
    "\n",
    "\n",
    "ax.hlines(1.0, 0, 1, linestyles='dashed')\n",
    "\n",
    "ax.legend(loc='lower right', fontsize=14)\n",
    "\n",
    "ax.set_ylabel('', fontsize=32)\n",
    "ax.set_xlabel('Precision at Top-K', fontsize=28)\n",
    "\n",
    "ax.tick_params(axis='x', labelsize=24)\n",
    "ax.tick_params(axis='y', labelsize=24)\n",
    "\n",
    "ax.set_xlim((0.1,0.85))\n",
    "ax.set_ylim((0.0,5.3))\n",
    "#ax.get_legend().remove()\n"
   ]
  },
  {
   "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",
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