{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d6134f24",
   "metadata": {},
   "source": [
    "# Does Ride-Share Frequency Predict Regular Transit?\n",
    "\n",
    "### Random Forest follow-up to the geography → transit memo\n",
    "\n",
    "**Course:** PSYCH 755 · University of Wisconsin–Madison  \n",
    "**Project:** CA persona / PRCA research framework  \n",
    "**Notebook role:** Geo-memo follow-up — alternative predictors of regular transit  \n",
    "\n",
    "---\n",
    "\n",
    "#### Research question\n",
    "\n",
    "> Does ride-share use (Q28 days; Q29 typical rides) predict whether a matched respondent takes public transportation regularly?\n",
    "\n",
    "| Element | Specification |\n",
    "|---|---|\n",
    "| **Outcome** | `regular_transit`: `Q26` ∈ {`4-8 days a month`, `8 or more days a month`} |\n",
    "| **Features** | `Q28`, `Q29` |\n",
    "| **Model** | Balanced Random Forest + stratified 5-fold CV (`seed=42`) |\n",
    "| **Benchmarks** | Chance (AUC = 0.50); geo RF (≈ 0.551); CA RF (≈ 0.590) |\n",
    "\n",
    "Companion memo: [`memos/rideshare_predicts_transit.md`](../memos/rideshare_predicts_transit.md)  \n",
    "Supporting code: [`src/ca_personas/transit_covariate_rf.py`](../src/ca_personas/transit_covariate_rf.py)  \n",
    "CLI: `ca-personas covariate-transit-rf --specs rideshare`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a0418a3",
   "metadata": {},
   "source": [
    "## 1. Analytic roadmap\n",
    "\n",
    "| Section | Content |\n",
    "|---|---|\n",
    "| **2. Setup** | Imports, paths, styling |\n",
    "| **3. Sample** | Matched File A/B/C cohort; complete-case features |\n",
    "| **4. Descriptive associations** | Regular-transit prevalence by feature level |\n",
    "| **5. Random Forest CV** | ROC-AUC and classification metrics vs benchmarks |\n",
    "| **6. Importances** | Gini (aggregated) + permutation importance |\n",
    "| **7. Written results** | Plain-language findings for the memo |\n",
    "| **8. Artifacts** | Export tables / JSON under `outputs/transit_covariate_rf/` |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "623c8526",
   "metadata": {},
   "source": [
    "## 2. Setup\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e173064a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:41:39.848429Z",
     "iopub.status.busy": "2026-07-25T15:41:39.848300Z",
     "iopub.status.idle": "2026-07-25T15:41:40.881311Z",
     "shell.execute_reply": "2026-07-25T15:41:40.880638Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data source: ../sibling_data File A/B/C\n",
      "Prolific: ['/tmp/sibling_data/PRCAProlificExport_FileA.csv', '/tmp/sibling_data/PRCAProlificExport_FileB.csv']\n",
      "Qualtrics: /tmp/sibling_data/PRCAQualtricsExport_FileC.csv\n",
      "Spec: rideshare {'features': ['Q28', 'Q29'], 'label': 'Ride-share frequency (Q28/Q29)', 'research_question': 'Does ride-share use (Q28 days; Q29 typical rides) predict whether a matched respondent takes public transportation regularly?'}\n"
     ]
    }
   ],
   "source": [
    "from __future__ import annotations\n",
    "\n",
    "from pathlib import Path\n",
    "import sys\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "ROOT = Path.cwd().resolve()\n",
    "if ROOT.name == \"notebooks\":\n",
    "    ROOT = ROOT.parent\n",
    "if str(ROOT / \"src\") not in sys.path:\n",
    "    sys.path.insert(0, str(ROOT / \"src\"))\n",
    "\n",
    "from ca_personas.load import load_full_cohort\n",
    "from ca_personas.paths import default_prolific_paths, default_qualtrics_path, sibling_data_available\n",
    "from ca_personas.transit_covariate_rf import (\n",
    "    FEATURE_SPECS,\n",
    "    plot_comparison_memo_figure,\n",
    "    plot_family_memo_figure,\n",
    "    run_all_followup_analyses,\n",
    "    run_feature_family_analysis,\n",
    "    save_feature_family_artifacts,\n",
    "    save_followup_bundle,\n",
    ")\n",
    "\n",
    "plt.rcParams.update({\"axes.grid\": True, \"grid.alpha\": 0.25})\n",
    "plt.rcParams[\"figure.dpi\"] = 120\n",
    "\n",
    "if sibling_data_available():\n",
    "    PROLIFIC = default_prolific_paths()\n",
    "    QUALTRICS = default_qualtrics_path()\n",
    "    source = \"../sibling_data File A/B/C\"\n",
    "else:\n",
    "    staged = Path(\"/tmp/sibling_data\")\n",
    "    PROLIFIC = [\n",
    "        staged / \"PRCAProlificExport_FileA.csv\",\n",
    "        staged / \"PRCAProlificExport_FileB.csv\",\n",
    "    ]\n",
    "    QUALTRICS = staged / \"PRCAQualtricsExport_FileC.csv\"\n",
    "    source = \"/tmp/sibling_data File A/B/C\"\n",
    "\n",
    "SEED = 42\n",
    "N_SPLITS = 5\n",
    "N_PERM = 30\n",
    "print(\"Data source:\", source)\n",
    "print(\"Prolific:\", [str(p) for p in PROLIFIC])\n",
    "print(\"Qualtrics:\", QUALTRICS)\n",
    "\n",
    "SPEC_KEY = 'rideshare'\n",
    "print('Spec:', SPEC_KEY, FEATURE_SPECS[SPEC_KEY])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08bfbb14",
   "metadata": {},
   "source": [
    "## 3. Sample & outcome\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "28dab89a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:41:40.883692Z",
     "iopub.status.busy": "2026-07-25T15:41:40.883504Z",
     "iopub.status.idle": "2026-07-25T15:41:41.192747Z",
     "shell.execute_reply": "2026-07-25T15:41:41.192123Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matched analytic rows: 241\n",
      "{'n_prolific_raw': 262, 'n_prolific_unique': 262, 'n_qualtrics_raw': 273, 'n_qualtrics_with_pid': 255, 'n_qualtrics_complete_ca': 260, 'n_joined': 252, 'n_matched_both': 252, 'n_analytic': 241, 'n_dropped_missing_pid': 0, 'n_dropped_incomplete_ca': 11, 'n_dropped_unscorable_ca': 0, 'n_dropped_unjoined': 31, 'n_prolific_only': 10, 'n_qualtrics_only': 21, 'n_qualtrics_missing_pid': 18, 'waves': {'B': 153, 'A': 99}, 'notes': ['Normalized 19 DATA_EXPIRED Student status values to missing.', 'Full Prolific waves (File A/B) omit Ethnicity / Nationality / Language; demos tier uses Age, Sex, Country of residence, and Student status.', 'Analytic sample = Prolific∩Qualtrics with complete scorable PRCA group + interpersonal items.', 'Merge coverage (pre-CA filter): 252 matched Prolific∩Qualtrics; 21 Qualtrics-only (incl. 18 blank Q0 test rows; disregard); 10 Prolific-only (disregard).']}\n"
     ]
    }
   ],
   "source": [
    "participants, report = load_full_cohort(\n",
    "    prolific_paths=PROLIFIC,\n",
    "    qualtrics_path=QUALTRICS,\n",
    "    join_how=\"inner\",\n",
    ")\n",
    "print(\"Matched analytic rows:\", len(participants))\n",
    "print(report)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8611a17b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:41:41.194656Z",
     "iopub.status.busy": "2026-07-25T15:41:41.194554Z",
     "iopub.status.idle": "2026-07-25T15:41:45.727631Z",
     "shell.execute_reply": "2026-07-25T15:41:45.726838Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Does ride-share use (Q28 days; Q29 typical rides) predict whether a matched respondent takes public transportation regularly?\n",
      "n                233.000000\n",
      "n_regular         99.000000\n",
      "n_not_regular    134.000000\n",
      "prevalence         0.424893\n",
      "dtype: float64\n"
     ]
    },
    {
     "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>participant_id</th>\n",
       "      <th>Q28</th>\n",
       "      <th>Q29</th>\n",
       "      <th>Q26</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ebab12e9878344fdc93a8c1cad27ff2f709e12b90f0496...</td>\n",
       "      <td>0-1 days a month</td>\n",
       "      <td>3-4 rides in a typical day</td>\n",
       "      <td>2-4 days a month</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b6954799ca47bc836ac02ab86e4710c3b0e096d7ace449...</td>\n",
       "      <td>0-1 days a month</td>\n",
       "      <td>1-2 rides in a typical day</td>\n",
       "      <td>Never</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6ac07c3845b0ce5cac4ab3569ea70236b7cfa8fd51576e...</td>\n",
       "      <td>Never</td>\n",
       "      <td>1-2 rides in a typical day</td>\n",
       "      <td>Never</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c67b3266ecd4af12979c1f67d1efffd0d3e9bfbc567d21...</td>\n",
       "      <td>Never</td>\n",
       "      <td>1-2 rides in a typical day</td>\n",
       "      <td>Never</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1cefb8d34b92489e8ffd9cf34c469964f0527b1cae33e0...</td>\n",
       "      <td>2-4 days a month</td>\n",
       "      <td>1-2 rides in a typical day</td>\n",
       "      <td>Never</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      participant_id               Q28  \\\n",
       "0  ebab12e9878344fdc93a8c1cad27ff2f709e12b90f0496...  0-1 days a month   \n",
       "1  b6954799ca47bc836ac02ab86e4710c3b0e096d7ace449...  0-1 days a month   \n",
       "2  6ac07c3845b0ce5cac4ab3569ea70236b7cfa8fd51576e...             Never   \n",
       "3  c67b3266ecd4af12979c1f67d1efffd0d3e9bfbc567d21...             Never   \n",
       "4  1cefb8d34b92489e8ffd9cf34c469964f0527b1cae33e0...  2-4 days a month   \n",
       "\n",
       "                          Q29               Q26  y  \n",
       "0  3-4 rides in a typical day  2-4 days a month  0  \n",
       "1  1-2 rides in a typical day             Never  0  \n",
       "2  1-2 rides in a typical day             Never  0  \n",
       "3  1-2 rides in a typical day             Never  0  \n",
       "4  1-2 rides in a typical day             Never  0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analysis = run_feature_family_analysis(\n",
    "    participants,\n",
    "    spec_key=SPEC_KEY,\n",
    "    n_splits=N_SPLITS,\n",
    "    n_perm_repeats=N_PERM,\n",
    "    random_state=SEED,\n",
    ")\n",
    "frame = analysis[\"frame\"]\n",
    "print(analysis[\"summary\"][\"secondary_rq\"])\n",
    "print(pd.Series(analysis[\"summary\"][\"sample\"]))\n",
    "frame[[\"participant_id\", *analysis[\"features\"], \"Q26\", \"y\"]].head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb6854fc",
   "metadata": {},
   "source": [
    "## 4. Descriptive associations\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b50deffa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:41:45.729192Z",
     "iopub.status.busy": "2026-07-25T15:41:45.729068Z",
     "iopub.status.idle": "2026-07-25T15:41:45.881353Z",
     "shell.execute_reply": "2026-07-25T15:41:45.880857Z"
    }
   },
   "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>feature</th>\n",
       "      <th>level</th>\n",
       "      <th>n</th>\n",
       "      <th>n_regular</th>\n",
       "      <th>n_not_regular</th>\n",
       "      <th>pct_regular</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q28</td>\n",
       "      <td>2-4 days a month</td>\n",
       "      <td>65</td>\n",
       "      <td>34</td>\n",
       "      <td>31</td>\n",
       "      <td>0.523077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Q28</td>\n",
       "      <td>Never</td>\n",
       "      <td>62</td>\n",
       "      <td>9</td>\n",
       "      <td>53</td>\n",
       "      <td>0.145161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Q28</td>\n",
       "      <td>0-1 days a month</td>\n",
       "      <td>47</td>\n",
       "      <td>11</td>\n",
       "      <td>36</td>\n",
       "      <td>0.234043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Q28</td>\n",
       "      <td>4-8 days a month</td>\n",
       "      <td>42</td>\n",
       "      <td>29</td>\n",
       "      <td>13</td>\n",
       "      <td>0.690476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Q28</td>\n",
       "      <td>8 or more days a month</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0.941176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Q29</td>\n",
       "      <td>1-2 rides in a typical day</td>\n",
       "      <td>206</td>\n",
       "      <td>82</td>\n",
       "      <td>124</td>\n",
       "      <td>0.398058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Q29</td>\n",
       "      <td>3-4 rides in a typical day</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>0.588235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Q29</td>\n",
       "      <td>5-6 rides in a typical day</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Q29</td>\n",
       "      <td>7 or more rides in a typical day</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  feature                             level    n  n_regular  n_not_regular  \\\n",
       "1     Q28                  2-4 days a month   65         34             31   \n",
       "4     Q28                             Never   62          9             53   \n",
       "0     Q28                  0-1 days a month   47         11             36   \n",
       "2     Q28                  4-8 days a month   42         29             13   \n",
       "3     Q28            8 or more days a month   17         16              1   \n",
       "5     Q29        1-2 rides in a typical day  206         82            124   \n",
       "6     Q29        3-4 rides in a typical day   17         10              7   \n",
       "7     Q29        5-6 rides in a typical day    9          6              3   \n",
       "8     Q29  7 or more rides in a typical day    1          1              0   \n",
       "\n",
       "   pct_regular  \n",
       "1     0.523077  \n",
       "4     0.145161  \n",
       "0     0.234043  \n",
       "2     0.690476  \n",
       "3     0.941176  \n",
       "5     0.398058  \n",
       "6     0.588235  \n",
       "7     0.666667  \n",
       "8     1.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4sAAAFnCAYAAAAPCtyzAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjExLjEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvctoD+AAAAAlwSFlzAAASdAAAEnQB3mYfeAAAgstJREFUeJzt3Xl8TFf/B/DPTPY9soogizSxRCISS6y1E/tatSUERVCttdo++Flaqq2i1oRSbVpRpdrSCmqnVLSkthBLEBFZJJFtkvP7wzPzmNyZbBIT8nm/Xnkx555z7vfeubmZ79xzz5UJIQSIiIiIiIiIniHXdQBERERERERU9TBZJCIiIiIiIgkmi0RERERERCTBZJGIiIiIiIgkmCwSERERERGRBJNFIiIiIiIikmCySERERERERBJMFomIiIiIiEiCySIRERERERFJMFkkIiIiIiIiCX1dB0BERFXPp59+iv3796tey2QymJiYoG7duujcuTN69uwJmUymwwhLb9WqVfjll1+watUqvPbaa7oO56UWHByMnJwcfP/997oO5aVw8uRJLFiwAMHBwXjzzTd1HU6l++233/D5558jLCwMvXv31lkcjx8/xs6dO3H8+HEkJSXB3NwcDRs2xIABA9CgQQOt7RISErBr1y78+++/uHv3LqysrNCiRQsMHz4c1tbWWtvdvHkTW7duxfXr1/HkyRO4urqiR48e6NixYyVsHdELJoiIiIoIDg4WALT+dO7cWWRnZ+s6zFJ56623BABx5swZnaz/zTffFCNGjNDJuiuai4uLsLW1lZS/SttYkX788UcBQCxcuFDXobwQGzduFADEqlWrdBZDVFSUsLe313jeksvlYvz48SI3N1fSbs6cOUImk2ls5+DgII4fP65xfUuXLhUGBgYa2/Xs2VM8efKksjeZqFLxyiIREWk1Z84ctG/fHgCQkpKCY8eOYcOGDYiOjsaXX36J6dOn6zjCqu/gwYPQ1381/txu2bIFBQUFkvJXaRvp5fXdd99h2LBhEELA398fI0aMgJubG9LT0/H7778jMjISGzZswN27d7Fnzx610RF37tyBs7Mz+vXrh4YNG8LJyQk3btzAqlWrcPPmTfTv3x9xcXGwsLBQtfn7778xZ84cCCEwZMgQ9OnTB8bGxjh79qxqRMOnn36KDz74QBe7g6hC8MxORERa+fr6onv37qrXw4YNg42NDRYvXozo6Ggmi9WM8osDoqrm/v37GD9+PIQQeOedd/Dpp5+qJYOjRo1CaGgoevXqhV9++QXr1q3DxIkTVcs//vhjODs7S4bXjxo1Cj4+Prh//z7279+PAQMGqJbt378fQggMGDBAbWj2wIEDUb9+fYSEhOC3335jskgvNSaLRERUJvXr1wcAKBQKjcvPnTuH7du349q1awCAhg0bIjg4GB4eHpK6+fn52Lx5Mw4dOoT8/HwEBATgrbfewsmTJ7Fy5Uq888476Natm6r+O++8g0uXLuHXX3+FXK4+R9uePXtUVzu7dOlSqm25c+cOvv/+e1y6dAnJycmoVasWevTogd69e0s+NC5evBhHjx7F5s2bkZmZiU2bNuHy5cuoX78+PvroI0nfsbGxmD59OlJTUyGTydSS7p49e2LKlCkAgP79+6NGjRrYtGkTDhw4gKioKCQkJGD06NEYOHAgAOD06dP4+eefERcXh9zcXHh6euLNN9+Er6+vZL3P9nfkyBF88803SExMhIuLC0JDQzW2yc/Px3fffYcTJ04gMTERjo6O8PHxwciRI9WupBS9Z7G026jNs7H++uuv+OGHH/Do0SN4eHhg3Lhx8PLyKraNtv2VmJiIr7/+GufOncOTJ09Qp04dDBgwQO0esu+//x6bN2/GxIkT0bdvX437ZNCgQbCwsMC2bdtU5ZmZmdixYwf++usv3LlzBxYWFmjVqhVGjhwJc3PzYre3qNLECajfC9ixY0ds3LgRJ06cgBACrVu3xvjx42FqaqpxHUlJSdi2bRv++usvZGdno169eujXrx9at25d7nhKIzU1FevXr8dff/0FfX19vP766xgzZgwMDAxUdXbs2IHw8HBMmDAB/fr1k/SRl5eHwYMHw9raGlu2bCl2fV9++SUyMjIQGBgoSRSVOnTogEWLFuHdd9/FsmXL1JLF2rVra+zXzs4OQUFBiIiIwIMHD9SWmZiYAHh6jiuqUaNGanWIXlo6HQRLRERVkvKexcjISLXy1NRU0aZNGwFAzJ07V21ZYWGhePvttzXeu2NgYCC2bNmiVj89PV00b95cUtfV1VV8+OGHAoDYuHGjWpsWLVoIACI/P18S86pVqwQAsXnzZrVybfcsbty4Ucjlco3x9u3bVygUCrX6AwcOFADEl19+KUxMTFR1O3XqpHEfHj16VOs9n2+99ZaqnpmZmahXr56YO3euWp1PPvlECCHE4MGDNfYhk8nEypUrJetV9rd48WKN78Pvv/+uVj8lJUU0aNBA4zqsrKxESkqKqm7RexZLu43aKGOdPXu2pL2RkZHYvXu31jba9ldUVJQwMzPTGFNISIgoLCwUQghx6dIlAUC8/vrrGmNT3m84efJkVdnly5eFhYWFxr7d3d3FrVu3NPah6Z7F0sYpxP/uBZw3b55o2LChpH7z5s013of3/fffa13Hxx9/XO54tFHGOXfuXOHq6qoxzvT0dFX9W7duCT09PdGyZUuN/UVGRgoA4v333y9x3U2aNBEAxHfffVdsvaysLGFqaioAiH///bfEfoUQon///gKAiI6OViu/ceOGMDIyEq6urmrv/ZMnT8TQoUMFALFmzZpSrYOoqmKySEREEspk0dfXV3Tr1k1069ZNNG/eXPUhy9fXVzx69EitzSeffCIACDc3N7Fw4UKxfft28d1334lp06YJExMTYWBgIC5fvqyqP378eAFA2NjYiEWLFokdO3aIpUuXCgcHB2FkZFTpyeLChQtF/fr1xbx588S2bdvEjh07xOLFi4WLi4vGD3nKZNHIyEi0aNFCrFmzRvz6669aJ85JTU0Ve/fuFTVq1BB2dnZi7969qp/Y2FhVPTMzM2FoaChkMpkYMWKE2LJli9i7d6+Ij48XQgjRpk0b0bVrV/HJJ5+IHTt2iG+++UZMnTpVmJiYCD09PXH16lW19Sr7k8vlIjg4WHz99dfi66+/Fp06dRIARKNGjdTqf/TRRwKAeO2118SKFSvErl27RHh4uJgyZYqwtLQU9+/fV9UtmiyWdhu1UcYKQAwdOlR8/fXXIjw8XHTo0EEAEJaWluLevXsa22jaX3/99ZcwMDAQ5ubmYsqUKeLrr78WO3fuFB9//LGoW7euKtlXatGihZDJZOLmzZuS2Pr16yc5bs6cOSNq1KghpkyZIjZs2CB27dol1qxZo/oCpU+fPmp9aEsWyxqnMgkzNjYWtWrVEkuXLhU//PCDWLp0qahRo4bG35XTp08LfX19AUC0adNGrFy5Uvzwww9i+fLlonXr1mL69OnljkcbZZxGRkbC1tZWLFmyRERFRYmFCxcKa2trAUCMHTtWrU2fPn0EAPHPP/9I+uvQoYPQ09MTt2/fLna9hYWFqm1NSEgoMc5WrVoJACIqKqrEurGxscLQ0FDUr19f8gWSEEL89NNPwsHBQRgYGIiAgADRtm1bYWNjIwwNDcWMGTNEQUFBiesgqsqYLBIRkURxs6F26dJF5OTkqNXPyckRNWrUEO7u7iItLU3SX1RUlAAgZs6cKYQQIi0tTRgZGQlDQ0NJsnP79m3VFY7KTBaTkpI0Xi25f/++MDAwEO3atVMrVyaL7dq10/ihURtHR0fh7OysdblyW7XNmPngwQON5du2bRMAxJIlSzT2t2LFCrXyvLw84e7uLgCoJYBTp04VAMTJkycl60hLS1O7YqVtNtSStlEbZayzZs1SKy8sLBS9evUSAMT//d//aWyjaX/1799fyGQy8eeff0qW3b9/X5ibm4vGjRurytauXauxr+TkZGFoaChJrDMyMkRWVpakb4VCIVq0aCHkcrnIyMhQlWtLFssapzIJc3BwEElJSWr1d+zYIQCIN954Q61cuf8mTpwoWYcQQjx8+LDc8WijjNPExERcv35dbVlsbKwwMDAQBgYGIjk5WVW+b98+yRVcIYS4du2akMlkon///iWuNz09XXV+ysvLK7H+gAEDBACxfv36YuulpKQILy8vYWBgoPH3Q4in+/Hdd98Venp6aufJzp07a9yfRC8b3rNIRERaKWdDVSgUuHz5Mj7//HPs378fK1aswOzZs1X1zpw5g9TUVFhYWGD48OEAAPH0C0kAQG5uLoCn97gp6+fm5uKNN96QPPuwTp06GDZsGDZu3Fip22Zvb4+bN2/iu+++w4ULF5CWlqaa6dPAwABXr17V2O6dd96Bnp6e2utLly6p1RkzZgyGDBlS6lhkMhneffddjcscHBxw4sQJ/PTTT7h+/ToyMzMhhMCTJ08AQGOchoaGCAsLUyszMDBA69atcePGDdy7dw81a9YEALRq1QorV67E5s2b4ebmBkdHR1UbKyurUm9DeclkMsydO1dSNmfOHPz88884evSoxjZF95cQAvv374eRkREWLFigKlP+K4SAXC7HpUuXIISATCbD0KFDMW3aNGzZskVtEpLIyEjk5eUhODhYbR3m5ubIzMxEREQETp48iaSkJOTl5QF4OsFKYWEhrl+/rvG+0OeJU2nEiBGwt7dX669Dhw4AgHv37qnKCgsLVTPULlu2TGMcdnZ2zx2PNkOHDoW7u7taWcOGDdG3b1/s2LEDp0+fRlBQEACga9eu8PDwwLZt27Bs2TLVPX4bNmyAEAKTJ08ucX1GRkaq/+fl5andF6mJ8nxkaGiotU5GRgaCgoJw7do1bNmyBS1btpTUSUpKQmBgIOLj4zF48GAEBQXBzMwM586dw5dffok2bdpg586d6NmzZ4nbQFRVMVkkIiKtnp0NtVevXhgwYAB8fX0xb948DB48WPWBMCEhAQBw+/Zt3L59W2t/ygQnKSkJAODp6amxnqaJTSraN998gzFjxqg+7BdVdAIdpaIT9Zw8eRKnT59WK3v99dfLFEutWrW0TlAybtw4hIeHa22bmZkpKatdu7bGR1lYWloCgNo2v/HGG7h06RI++eQTbNy4EV5eXmjWrBm6deuGQYMGqX0QrwyOjo4ak1LlREqJiYmSZZr2V3p6umpf/PLLL8WuMy8vD0ZGRrC2tkbfvn2xfft2nDhxAq1atQLw9BEhenp6GDFihFq7y5cvo3Pnzrh7967WvjW9H88bp5Krq6ukjqb3NC0tDU+ePIGnp2eJk+48TzzaaPv91fSeymQyTJgwATNmzEBUVBRGjRqF/Px8bNmyBQ0aNCjV5DpGRkawsbFBSkoKrl27hiZNmhRbX/kFi4uLi8bl6enp6N69O06fPo0NGzZIjgOljz76CDdu3MDMmTPVkvJBgwahd+/eaNWqFaZMmcJkkV5qTBaJiKjU3N3d8e677+L//u//sHTpUqxfvx4AVIlJUFBQsTNg2tjYAPjfN/qPHz/WWE9bufKqhvLKx7OUiWhpZGRk4K233kJ+fj6Cg4PRtm1b2Nvbq+IaPXo0MjIyNLY1NjZWe71ixQqkpaWplZU12S3ap9Kvv/6K8PBwWFlZYdKkSfD29oaVlRX09PRw9+5djB07VuO+KM3Vn2fNnz8fc+bMwdGjR3H27FkcP34co0ePxoIFC3DixAnVVajKoG0/p6enA4DG5ETT/lIegw4ODiXOnPnslafg4GBs374dW7ZsQatWrfDvv//i7Nmz6N69O5ycnNTaTZ06FXfv3kWbNm0wZMgQODs7w8TEBDKZDF9++SV+/vlnje9HRcQJlP59Ve6f1NTUEus+TzzaaPv91faejh49Gh9++CE2bNiAUaNGYdeuXUhKSsJ//vOfUq0PeHqF/Oeff8bu3buLTRYvXbqEa9euwdjYGM2bN5csT0lJQbdu3fDXX39h7dq1GDt2rNa+Tpw4AeDpFy5FBQYGwsXFBfHx8UhMTFRdySd62TBZJCKiMpkyZQqWLVuGrVu3YtGiRbC3t4e3tzcAIC4uDp06dSrxQ6Xy6pymIYYAcPjwYY3lyqskCQkJcHNzU1tW9OpecWJiYpCVlYXg4GB89dVXastSUlLw8OFDrQlcUZqGpz1LLpdrfJB9aRw7dgwAsG7dOgwdOlRt2bPPdasIxsbG6NKli+qxI1FRURgyZAg+++wzLFmypNi2z7ONWVlZ+PPPPyUf3P/44w8ApU+8zc3N4eLiglu3bsHV1VV1Fask3bp1g5OTE77//nt88cUXqoQpJCREUvfYsWNwcnLC4cOHJVeelUM4KyvOsjA1NYWrqytu3ryJ06dPo0WLFi80nkOHDknKhBCq3+ui76mNjQ3eeOMNfPXVV/j333+xYcMGmJubY9SoUaVe56hRo/Dzzz/j888/x+jRo1G3bl2NMcyYMQPA06GyZmZmassfPnyIzp07459//sGaNWvw1ltvFbvOwsJCVbuiFAoFUlJS1OoRvYw0j7EhIiLSws7ODsOHD0dOTg7WrFkD4OnwsubNm+Pq1asYMWKEZOjgs/d5AYCPjw/c3Nxw7tw5LFiwQO3D1Keffqo1WVQ+z+zjjz9WtRFCYP369di5c2ept6FGjRoAng4rfPaK5P379zF06NByJz6aWFtbIzk5GY8ePSpzW2WcMTExauV//vmn1nscy2r9+vU4cOCA5IrYrVu3AKDYIZdKz7ONADBhwgTcuXNH9TomJkZ1D2H//v1L3Y8yuRgwYAD++usvtWWFhYXYt2+f5OqZcrhpeno6du7ciW3btsHKykrjsxdr1KiBx48fIy4uTlWWm5uLDz74AKdOnarUOMtKOXRyxIgROHPmjNqyM2fO4Mcff6y0eE6ePIklS5aojqnCwkJ88MEH+Oeff+Di4gI/Pz9JG+U9tnPnzsWBAwcwatQotWd8lmTAgAFo3bo10tPT0aFDB8kXUQ8ePMDQoUPx66+/ok6dOli6dKna8sTERLRv3x7//PMPVq9erfYMRm2UX3C89957asdvdnY2pkyZgoyMDDg7O6NWrVql3g6iKucFTaRDREQvEW3PWVS6cOGCanZG5cyoFy5cEFZWVgKAMDQ0FN7e3uL1118Xr732murxCM/2t337dtXMgTVr1hRt2rQRzs7OAoDqmWmbNm1SW+/Zs2dVz0asU6eOaNeunapNQEBAqWdDzc/PFx4eHgKAsLa2Fu3atRN+fn7CyMhI1KpVS9SsWVOYmZmp9aOcDfXatWvl2pf29vaiQ4cOolu3bmrPR1Q+N1DbfjYwMBAAhIuLi3j99deFl5eXACD8/f0FADFw4EC1NsX1FxYWJpn5tG/fvgJ4+kxFPz8/0aFDB7Vn5D37eAFts6GWtI3amJmZCQcHB1GzZk1hbGwsAgICROPGjVUzS7Zv317y6IHiti8rK0s0a9ZMFbu7u7to37698PHxUT0fMTg4WNLu4sWLAoBwcnISAMT48eM19j9hwgQBQOjr64vmzZuLVq1aCRsbGyGXy1XH7NGjR1X1tc2GWtY4lbOMrlq1ShJTfn6+ACBatGihVp6RkSEaN24sgKfP5KxXr55o166dqFOnjgCg9uiM8u63opRx+vr6qvZnmzZthKOjo6rvHTt2aG2v/B0GUKpHrxSVkJCg9sxQNzc30aFDB+Hv7696tEbdunVFTEyMpK3yER5WVlaqxwUV/Sn6rNi4uDhhaWmpOuc1bdpUtG7dWtjY2KhiCA8PL/N2EFUlTBaJiEiipGRRCKF6bl9ERISq7OrVq6Jnz56Sh91bWlqKcePGSR5aHh4erkow8d/ns/3nP/8R8+fP1/octPXr16s9PNzU1FQsX768zI/OOH/+vPD29laL09fXV1y4cEF4eXlVWLJ4/fp1VYKn/Hn2gfXFJT9CCLF161bVs/QACLlcLoYMGSL+/vvvCkkW9+7dKzp37ix5z1xdXcWGDRvU2mtLFkvaRm2Usf7111+q5F3507dvX5Gamqq1jTaZmZni7bffljxgXk9PTwQFBYlDhw5pbPdsonL8+HGNdR4/fqx67ILyx87OTnz77bfi7bffLnWyWNY4y5MsCvH0OZghISGqLxyUPy1atBAnTpyokP32rGfjfP/991VfEim/lCn6u1nU5s2bBQDRoUOHEtelTVpampgxY4ba74zyp3PnziIzM1NjO+X5rLif999/X9IuJiZGBAYGSuo6OzurnRuJXlYyIUq4E5uIiKqdCxcu4O7du2jSpInWiRlu3ryJy5cvo2bNmpIJJdLS0nDp0iXk5eWhdu3aqFu3rtb7GHNycnD+/HkoFAp4e3vD2toaXbt2xf79+/H333/Dx8dH0iYzMxPnz5+HTCaDj48PLCwscPv2bfz7779o3LgxnJ2dVXX//fdf3L59G61atVLNHKlUWFiIuLg43Lt3D7Vq1VLNznrs2DHk5OSgc+fOqrrnz59HYmIi2rVrp3XmUm2EEPj3339x//59KBQK1K1bVzWkNjo6GkZGRmjbtq3W9tnZ2bh8+TIyMzPh6ekJR0dHZGdn4/Dhw5L9X1x/ly5dwq1btxAYGCiZgfTx48eqR3PUqlULbm5ukvvyDh8+jIKCAo0zVBa3jdqYm5ujZs2aiIuLgxACFy5cwKNHj+Dh4YE6depobFOa/QU8nbkzNjYWqampcHR0hKurq+QetWdduXIF8fHxkMvl6Nq1a7F937t3D3FxcTA3N0fjxo1hYGCAy5cv4+bNm2jZsiWsra0BPJ3199y5c/D09JQ8SqIscSYkJODixYto2LCh5F48IQR+++03WFtba71/NjMzExcvXkR+fj7q1atX7LDIsu634uJMTU1FbGws9PT04OfnV+J9wOvXr8eECROwY8cODBw4sFTr1KagoACXLl1SvQczZ86EhYUF/v77b8n9zgBw9uxZJCcnF9tnvXr1JI/6Ubp//z7i4uKQn5+PWrVqwcvLq8wTTRFVRUwWiYhIJ7KysrBt2zaEhISoZkdUKBT47LPPMHv2bNSpUwc3b97U+ggLevk9myxS9ZacnAw/Pz/k5ubi7t27pZ55tbRCQkJUM94eOXJE7VmpRKQd/wITEZFO5OfnY8KECbC1tYW/vz9atWqFmjVrYvbs2QCAxYsXM1EkesV9+umn6NKlCxo0aICEhARMmTKlwhNFAFi9ejU8PDxw4sQJLF68uML7J3pV8dEZRESkE+bm5pgwYQK2bNmCc+fOqcodHR2xZMkSjBw5UofREdGLcOHCBURHRwMAOnXqpHq0RUUzNzfH/v37cfny5UpJRoleVRyGSkREOpWdnY3r168jKSkJ9vb2aNSoEa8oVhOlvf+QXl0XLlzAvXv34OzsrHpeKxFVHUwWiYiIiIiISIJf3RIREREREZEEk0UiIiIiIiKSYLJIREREREREEpwNlUjH0tLScPjwYdSpU0f1rDkiIiIiooqWm5uLO3fuoH379rC2ti6xPpNFIh07fPgw+vXrp+swiIiIiKia2LVrF/r27VtiPSaLRDpWp04dAMCOHTtQv359HUdDVY1CoUBGRgYsLCygr89TNv1PRRwbMes/RHp8LKzcGsHvrYUVHCHpEs8dpA2PjeotLi4O/fr1U33+LAmPECIdUw499fDwQKNGjXQcDVU1CoUCaWlpsLa25h91UlMRx0a6Yw2kpBrDxrEGzz+vGJ47SBseGwSg1Lc+cYIbIiIiIiIikmCySERERERERBJMFomIiIiIiEiCySIRERERERFJ8K5WIiKiasrKxUvtXyIiomcxWSQiIqqmGo2YpesQiIioCuMwVCIiIiIiIpJgskhEREREREQSHIZKRERUTaXfuoz8JxkwMLWAlUt9XYdDRERVDJNFoipi+KcboG9jp+swqIoxkMvhbGmKu4+fIL+wUNfhUBVSEcdGaPpRuCkeIV7fFhFWbSs4QtIlnjtIGx4bund+xQJdh1BqHIZKREREREREEkwWiYiIiIiISILJIhEREREREUkwWSQiIiIiIiIJJotEREREREQkwWSRiIiIiIiIJJgsEhERERERkQSTRSIiIiIiIpLQ13UAREREpBsRVm11HQIREVVhvLJIREREREREEkwWiYiIiIiISILJIhEREREREUkwWXxJzJgxA23b8t6SqqBPnz4YM2aMrsMgInpuoelHsejRLoSmH9V1KEREVAWVe4Kb+/fv44svvsCpU6fw6NEjODk5oVu3bpgwYQLMzMwqMkYCkJiYiPj4eF2HUW0MHjwYhoaG+OabbyTLbt++jby8PB1ERURERET04pQrWbxz5w6aNm0KOzs7LFu2DK6urjh37hzeffddfPPNNzh16hQMDQ0rOlaiF+bOnTswNjbWdRhERERERDpTrmRxy5YtSE5Oxo4dO9C+fXsAQOPGjZGamop33nkHhw4dQrdu3So0UCIiIiIiInpxynXPokwmAwDY2Nioldva2pa6j/v37+Pdd99Fs2bN4O3tjQEDBmD//v1qdSIiIuDh4YHU1FSsW7cObdq0QYMGDbT2qayfkpKCVatWoWXLlmjRogXWrl0LABBCYOXKlQgMDISfnx8WLVqEwsLCSoktNjYW48ePR9OmTdG4cWOMGDEC//zzT6n2zY4dO9C5c2c0btwYI0eOxM2bNzXWmzZtGjw8PODh4QFPT08EBgZi5syZSEpKUtUJCwtD8+bNoVAoJO0//PBDeHt7IysrCwBw7do1TJw4ES1btkTTpk0xfPhwnDx5ssR4SxOHNsr7/5KSkjBu3Dj4+vqiV69eiImJAfB0+O1bb70FX19fdOzYEQcPHtTYz8GDBzFo0CB4e3sjICAAU6dORUJCgsZ1paSkICwsDL6+vmjbti22bt2qVi8wMBDnz5/H6dOnVdsVGBgoWWdJ/RARERERvczKlSyOGjUKdnZ2WLRoETIzMwEA9+7dw+eff44mTZqgQ4cOxba/desWmjZtit9//x3z589HREQEatWqha5du2L16tWqeqmpqbh+/TrmzZuHhIQEfPbZZ+jbt6/WfpX1/+///g+PHz/GmjVrMHz4cEyZMgWbNm3C3LlzkZ6ejtWrV2Ps2LFYsGABPv/88wqP7ddff4W/vz9SUlLw6aefIjw8HKampmjZsiWOHTtW7L5ZvXo1Bg8eDB8fH2zatAmDBg1CSEgI0tPTJXXfe+897Nu3D/v27cOuXbswa9YsREdHo2PHjsjNzQUABAUF4cyZM9i9e7da26ysLKxatQre3t4wMzPDw4cP0aZNGyQkJGDZsmX46quv0KdPH8yaNQtXrlwpNubSxKHN7du3ER8fj9DQUAQFBSE8PByGhobo1KkT4uLiMHLkSHTt2hURERGwtbVFUFAQbt++rdZHeHg4OnfuDBsbG4SHh2PhwoU4evQomjZtiri4OLV13bx5E6NHj0b79u2xefNmBAQEIDg4GHv27FHVi4yMRIMGDeDj46ParsjISLV15uXlldgPEREREdHLTCaEEOVpGB8fj4EDByI2NhZ2dnZ48OABgoKCsG3bNlhaWhbb9s0338SuXbsQHx+PmjVrqsqHDBmCPXv24NatW3BwcMDy5csxc+ZMTJkyBStXrgTw9Oqg8spmUcr6c+bMwUcffaQq79u3L06fPo1Ro0Zh2bJlqvKBAwfi7NmzuHXrVoXFlpOTA1dXVzRu3BjR0dFq8XXu3BkpKSk4d+6cxvjT0tLg7OyMfv36qU2scuXKFXh7e8PR0VFytayoGzduoF69eoiKisKgQYNQWFgIDw8PuLu7q8WzceNGjB8/Hr///ju6dOmCXbt2oX///oiNjUXDhg3V+lQoFNDXL9uI5aJxaNOkSRNcuHABMTEx8PHxAQDVhEn29vbYs2cPmjZtqto/NWvWxPTp07F48WIAQHp6OmrXro3XX39dLVF79OgR3Nzc0KFDB1WirFzX2bNn4efnp6rbsGFDODk54cCBA6qyli1bwtjYGH/88YfWmEvTT1FJSUl4+PChWllcXBz69esH3+DJMLCx09qWqicDuRy1LE1w73E28jWMhKDqqyKOjeC0I3DNT8ZNAztssW5XwRGSLvHcQdrw2NC9k8ve19m6Y2Nj0aRJE1y8eBGNGjUqsX657lm8d+8eevXqBQDYvn073NzcEBMTg9mzZ+ONN97A7t27i53g5pdffkHXrl3VkjEACAkJQVRUFA4cOIA333xTVT506FDV/7Ulis8aPHiw2ms/Pz/89NNPkqTFz88PO3fuRHZ2NkxMTCoktsOHDyMpKQmjR4+WxNW3b19MnToVSUlJcHBwkCw/fPgwnjx5gmHDhqmVe3l5wd/fX5IoZmRkYPXq1di/fz8SExPVZui8fPkyAEAul2PChAmYM2cOrly5Ai8vLwDA2rVr4eLigk6dOgEA6tSpAwBYtGgR5s+fD09PT1VfJSWKpYmjOF5eXqpEEXg6nLlu3bqQy+WqRBEArK2t4erqqnal8/Dhw8jMzMSoUaPU+rS1tUWvXr2wc+dOtWTX09NTLcEDgGbNmhWb4GlS3n7WrFmDBQsWaFzmYGYMU0vTMsVBrz59mRx2pkaAkEEh+Eed/qcijg2jDDmQDxjpyeHM888rhecO0obHhu6lpaXpbN0ZGRllql+uZPHDDz/EpUuXcOPGDbi6ugIAfHx8YG9vj549e2Ljxo0ICwvT2DYrKwsZGRmq5ORZdevWBfD0nkFN5aXl7Oys9tra2rrY8pSUFDg7O1dIbMohknPnzsXChQshhIDy4q3yzXnw4IHGZDExMREAULt2bcmy2rVrqyWLCoUCHTt2RGJiIhYtWgQfHx+Ym5sjNzcXjRs3Rk5OjqpuaGgo5s2bh7Vr12LFihU4efIkYmJiMH/+fMjlT0ci+/v7Y9WqVVi4cCEiIyPh7OyMjh07Yvz48WjTpo0knvLEoU3R9wV4+t5YWFhoLH/06JHqtfL90Pae5ebmIiUlRbW/Ne3bGjVqqPVZGuXtZ9KkSZIvM5RXFpOycmBg9KRMcdCrz0AuB2QC9zL4DTCpq4hjI7egUPXv3cc8/7xKeO4gbXhs6J4yB9EFTZ+vi1OuZPHcuXNwdnZWJYpKrVu3BgD89ddfWtuamJhAX18fKSkpkmXJyckAIBnGWtZHGOjp6ZWpXJnMVURsyjdg4cKFaNWqlcb1aUt+lW01rb9oEnL8+HGcPXsW27dvV0s+rl69Kmlra2uLIUOGYMuWLViyZAnWrl0LuVyOkJAQtXqTJ09GWFgYzp8/j2PHjmHr1q1o27YtfvzxR/Tr109jzGWJQ5vyvl9A8fssOTkZMpkM5ubmZeqzNMrbj4ODg8YvCgBAUVgI8KRNGigKBfILC/lHnSSe99j42dQbJiIf2TIDHl+vIJ47SBseG7pV1tu7dLnuck1wY2dnh4cPHyI7O1utXDlrp729vfYVyuVo2bIljhw5InmwuXLGUW1JVmWriNjat28PfX19XLhwQTWTZtEfbUN0AwMDIZPJJDN+pqenS+5zVM5g6ujoqFa+c+dOjX1PmjQJaWlpWL16NaKiotCpUye4uLhI6slkMvj5+WHKlCk4evQoTExMsGvXLq3bW9Y4KppynxW9P7SgoACHDh2Cn58fTE3LPrTK2NhY4wyyRESvkkR9a8Qb2CNR31rXoRARURVUrmTxrbfeQm5uLiZMmKBKFu7fv4/JkyfDyMgIwcHBxbb/z3/+g3v37mHy5MmqYYp79+7FypUr8cYbb0gmWHmRnje2WrVqYe7cuVixYgXWrl2L/Px8AEB+fj7++OMPvPPOO1rburm54c0338TKlStVyU9WVhYmT56sdg8hAAQEBMDc3BwrV65Ui/P48eMa+27RogX8/f0xd+5c5OTkIDQ0VG15VFQUvvjiC7UrdEeOHEF2dja8vb21xlzWOCqam5sbgoODsW7dOtVENrm5uZg2bRpu3LiB+fPnl6tfDw8PXL16VTXbLxERERFRdVOuZHHQoEGIiorCX3/9BRsbG9SqVQsuLi6Qy+X4448/SkyounTpgh9//BFHjhyBpaUlbGxsMGTIEIwdOxZbtmwp14ZUlIqIbcGCBVi3bh1WrFgBU1NT1K5dG9bW1li0aBG6d+9ebNvw8HAMHDgQQUFBsLOzg6enJ/r166eamEbJwcEBkZGROHXqFGxtbWFra4tVq1Zhw4YNWvsOCwtDQUEBbGxsJMNKW7dujVu3bsHLywsODg6wt7fHyJEjMW/ePEybNk1rn+WJo6KtX78eYWFhGDVqFGrUqAFLS0vs27cPO3bsQO/evcvV5+zZs2FtbQ1HR0fUq1dP43MWiYiIiIheZeV+dIZSTk4OHj16BEdHx3KNv01OTkZ2djacnJwk7dPS0pCcnAx3d3fVRCzF0VY/PT0dDx8+hJubm9q9ZtrKKzK21NRUZGVloVatWqXaBqWsrCykpqbCyckJenp6ePDgAbKzsyX3iQJPJ8YxNTVV3U8ZFxcHGxsb2NjYqNU7ceIEWrdujalTp+KLL77Quu6HDx9CT09P0r4kpY3jWXfu3IG+vj6cnJzUyhMSEiCXy1GrVi1JuUwm0zgpjkKhwP3792FsbKxxKLS2dSUnJ+Px48dwd3eXtElKSkJGRgb09PRU+748/RQnNjYW3t7eaDRyEvT56AwqwkD+dJbKu4+f8N4SUlMRx0ZQ1j9wUqTjvr4VfjXzKbkBvTR47iBteGzo3vkVmmfGfxGUnzsr9dEZzzI2Ntb4wb207Oy0fzi2trYu02xB2upbWVnBysqq1OUVGVuNGjVQo0aNEusVZWZmBjMzM9XrovcEPqvoYz48PDw01ouKigIAyRDUooq757Q4pY3jWZpmMQU0zzZaXDnw9IZdbf0Vty47Ozut77WmCWnK0w8RUVXkpEiHm6Jss0ETEVH1Ua5hqPTySUxMxNdff43u3burPdOQiIiIiIhIEyaLr7iCggJ4eHjA3d0dDg4OWLt2ra5DIiIiIiKil4DuHvJBL4Senh727dsHU1NTODk5QSaT6TokIiIiIiJ6CTBZrAZKc+8gERERERHRszgMlYiIiIiIiCSYLBIREREREZEEk0UiIiIiIiKS4D2LRERE1dQ5o7qIN7BDqtxU16EQEVEVxGSRiIiomooxdtF1CEREVIVxGCoRERERERFJMFkkIiIiIiIiCQ5DJaoivpk+Hr6+vroOg6oYhUKBtLQ0WFtbQ1+fp2z6n4o4Nu4c2Y0nyfdgalcLddr1reAISZd47iBteGxQWfDKIhERUTV15+hPuPbjOtw5+pOuQyEioiqIySIRERERERFJMFkkIiIiIiIiCSaLREREREREJMFkkYiIiIiIiCSYLBIREREREZEEk0UiIiIiIiKSYLJIREREREREEnwSJxERUTVl5eKl9i8REdGzmCwSVRHDP90AfRs7XYdBVYyBXA5nS1PcffwE+YWFug6HqpCKOzZqAw+zgLPzKiw20r3qeu44v2KBrkMgeqVwGCoRERERERFJMFkkIiIiIiIiCQ5DJSIiqqZqKtJgIvKRLTNAor61rsMhIqIqhskiERFRNdUz6wLcFI8Qr2+LCKu2ug6HiIiqGA5DJSIiIiIiIgkmi0RERERERCTBZJGIiIiIiIgkmCwSERERERGRBJNFIiIiIiIikmCySERERERERBJMFomIiIiIiEiCySIRERERERFJ6Os6ACqdGTNm4PTp0zh69KiuQ6n2+vTpAzs7O2zatEnXoRARPZcIq7a6DoGIiKowXlksxpQpU+Dh4YHg4OAytTt69CiGDx+OZs2aoWnTphg8eDD27dv3XLEkJiYiPj7+ufqg0hs8eDCGDx+ucdnt27dx7969FxwREREREdGLxWRRi127diE8PBy3b9/G3bt3S91uw4YNaNeuHfT19bF69Wps2LABTk5O6NGjBz766KNKjJgq0p07d8r0vhMRERERvWqYLGqQlJSE8ePH4z//+Q9MTU3L1Pazzz6Di4sLNm/ejBYtWiAgIAArV66Ev78/Pvvss0qKmIiIiIiIqGIxWdRg3LhxcHZ2xsyZM8vcViaTwcrKCnK5+q61tbWFEKJUfezYsQOdO3dG48aNMXLkSNy8eVNjvWnTpsHDwwMeHh7w9PREYGAgZs6ciaSkJFWdsLAwNG/eHAqFQtL+ww8/hLe3N7KysgAA165dw8SJE9GyZUs0bdoUw4cPx8mTJ0uMtzRxaNOnTx+MGTMGSUlJGDduHHx9fdGrVy/ExMQAeDr89q233oKvry86duyIgwcPauzn4MGDGDRoELy9vREQEICpU6ciISFB47pSUlIQFhYGX19ftG3bFlu3blWrFxgYiPPnz+P06dOq7QoMDJSss6R+iIiqutD0o1j0aBdC03k/PBERSTFZLCIiIgK//PILIiIioK9f9vl/3n33XVy8eBHffPONquzXX3/FoUOHMH369BLbr169GoMHD4aPjw82bdqEQYMGISQkBOnp6ZK67733Hvbt24d9+/Zh165dmDVrFqKjo9GxY0fk5uYCAIKCgnDmzBns3r1brW1WVhZWrVoFb29vmJmZ4eHDh2jTpg0SEhKwbNkyfPXVV+jTpw9mzZqFK1euFBtzaeLQ5vbt24iPj0doaCiCgoIQHh4OQ0NDdOrUCXFxcRg5ciS6du2KiIgI2NraIigoCLdv31brIzw8HJ07d4aNjQ3Cw8OxcOFCHD16FE2bNkVcXJzaum7evInRo0ejffv22Lx5MwICAhAcHIw9e/ao6kVGRqJBgwbw8fFRbVdkZKTaOvPy8krsh4iIiIjoZSYTpb3cVQ3cvHkTPj4+mDhxIpYuXQoAsLa2RkBAAKKjo0vdz3fffYfx48dDX18fenp6ePz4MT777DOEhYUV2y4tLQ3Ozs7o16+fWrJ55coVeHt7w9HRUXK1rKgbN26gXr16iIqKwqBBg1BYWAgPDw+4u7urbcPGjRsxfvx4/P777+jSpQt27dqF/v37IzY2Fg0bNlTrU6FQlDlxLhqHNk2aNMGFCxcQExMDHx8fAMCjR4/g5OQEe3t77NmzB02bNlXtn5o1a2L69OlYvHgxACA9PR21a9fG66+/rpaoPXr0CG5ubujQoYMqUVau6+zZs/Dz81PVbdiwIZycnHDgwAFVWcuWLWFsbIw//vhDa8yl6aeopKQkPHz4UK0sLi4O/fr1g2/wZBjY2GltS9WTgVyOWpYmuPc4G/mFhboOh6qQijg2gtOOwDU/GTcN7LDFul0FR0i6VF3PHSeXva/rEKo8hUKB9PR0WFlZlevCCL3cYmNj0aRJE1y8eBGNGjUqsT6PkP8qLCxEcHAwatasiQULFhRbd/ny5Vi3bp3qtaWlJc6dOwfg6RDS0aNH480338TYsWOhr6+P7du3Y9q0aTA0NMS4ceO09nv48GE8efIEw4YNUyv38vKCv7+/JFHMyMjA6tWrsX//fiQmJiIvL0+17PLlywAAuVyOCRMmYM6cObhy5Qq8vLwAAGvXroWLiws6deoEAKhTpw4AYNGiRZg/fz48PT1VfZV0IilNHMXx8vJSJYrA0yG7devWhVwuVyWKwNPE3dXVVe1K5+HDh5GZmYlRo0ap9Wlra4tevXph586dasmup6enWoIHAM2aNSs2wdOkvP2sWbNG6/HlYGYMU8uy3SNLrz59mRx2pkaAkEEhqs8HPipZRRwbRhlyIB8w0pPDmeefV0p1PXekpaXpOoQqr6CgAJmZmQAAPT09HUdDL1pGRkaZ6jNZ/K+NGzfi6NGjOHToEIyNjYutm5ycjOvXr6teW1lZAQCys7Mxbtw4NGvWTO0ZfM2bN8e9e/cwZcoU9O3bFw4ODhr7TUxMBADUrl1bsqx27dpqyaJCoUDHjh2RmJiIRYsWwcfHB+bm5sjNzUXjxo2Rk5OjqhsaGop58+Zh7dq1WLFiBU6ePImYmBjMnz9fdW+lv78/Vq1ahYULFyIyMhLOzs7o2LEjxo8fjzZt2mjdF2WJQxtnZ2dJmbW1NSwsLDSWP3r0SPX6/v37AP6X7D6rbt26yM3NRUpKimqfa9q3NWrUUOuzNMrbz6RJkzB48GC1MuWVxaSsHBgYPSlTHPTqM5DLAZnAvYzqdXWASlYRx0ZuQaHq37uPef55lVTXc4e1tbWuQ6jylPNY8Mpi9aTp83VxeIT8V3x8PPT09BAaGqpWnpGRgRMnTsDDwwNTp07F1KlTMXPmTIwdO1ZVR/mtzI0bN5CWlobWrVtL+m/dujUiIyNx8eJFdOzYUWMMyjcvJSVFsqxoEnL8+HGcPXsW27dvV0s+rl69Kmlra2uLIUOGYMuWLViyZAnWrl0LuVyOkJAQtXqTJ09GWFgYzp8/j2PHjmHr1q1o27YtfvzxR/Tr109jzGWJQxtt32ppK3925HRx+yw5ORkymQzm5uZl6rM0ytuPg4OD1i8LFIWFQDX6g06lpygUyC8srFYf+Kh0nvfYUJ6zhBA8vl5B1fHcweSndPT09KCvr8/9VQ2V9T3nEfJfRRNApaZNm6Jx48bYsmULbGxsADxNvmxtbSV17eye3m9269YtyTLljKb29vZaYwgMDIRMJsPBgwfRoUMHVXl6ejrOnTun9k2AcgZTR0dHtT527typse9JkyZh69atWL16NaKiotCpUye4uLhI6slkMvj5+cHPzw/jxo2DjY0Ndu3apTVZLGscFU25z6KjoxEUFKQqLygowKFDh+Dn51fmx58AgLGxscYZZImIiIiIqgvOhvpftra2qsckPPsjl8thYmICDw8PVbKojaOjI/r27YuoqCjs2LFDVR4dHY21a9eiVatWaNy4sdb2bm5uePPNN7Fy5UrVZDRZWVmYPHmy2j2EABAQEABzc3OsXLlSNdRz7969OH78uMa+W7RoAX9/f8ydOxc5OTmSK6hRUVH44osv1K7QHTlyBNnZ2fD29tYac1njqGhubm4IDg7GunXrVBPZ5ObmYtq0abhx4wbmz59frn49PDxw9epV1Zh+IiIiIqLqhsliBdu2bRveffddjBs3DtbW1rCxsUH//v0xevToUj1WITw8HAMHDkRQUBDs7Ozg6emJfv36qSamUXJwcEBkZCROnTqlutK5atUqbNiwQWvfYWFhKCgogI2NjeRKYevWrXHr1i14eXnBwcEB9vb2GDlyJObNm4dp06Zp7bM8cVS09evXIywsDKNGjUKNGjVgaWmJffv2YceOHejdu3e5+pw9ezasra3h6OiIevXqaXzOIhERERHRq4yPzihBfHw8jIyMUKtWrTK3TUlJQUFBQbFDT7XJyspCamoqnJycoKenhwcPHiA7Oxuurq6SuomJiTA1NYWlpSWApxOm2NjYSK6EnjhxAq1bt8bUqVPxxRdfaF33w4cPoaenV+KV1PLG8aw7d+5AX18fTk5OauUJCQmQy+WS/Z6QkACZTKZxUhyFQoH79+/D2NhY4z7Xtq7k5GQ8fvwY7u7ukjZJSUnIyMiAnp6eat+Xp5/ixMbGwtvbG41GToI+H51BRRjIn85Seffxk2p13xGVrCKOjZqKNJiIfGTLDJCob12xAZJOVddzx/kVxc9oT08/L6WlpcHa2pr3LFZDys+dfHRGBXFzcyt327ImW88yMzODmZmZ6nXRewKfVbNmTbXXHh4eGutFRUUBgGQIalHlSW7LEsezNM1iCmiebbS4cuDpDbva+ituXXZ2dqr7TYvSNCFNefohIqqKmCASEVFxOAy1mkhMTMTXX3+N7t27qz3TkIiIiIiISBMmi6+4goICeHh4wN3dHQ4ODli7dq2uQyIiIiIiopcAh6G+4vT09LBv3z6YmprCyckJMplM1yEREVEVEZT1D5wU6bivb4VfzTjqhIiI1DFZrAZKc+8gERFVP06KdLgpHuk6DCIiqqI4DJWIiIiIiIgkmCwSERERERGRBJNFIiIiIiIikmCySERERERERBJMFomIiIiIiEiCySIRERERERFJMFkkIiIiIiIiCT5nkaiK+Gb6ePj6+uo6DKpiFAoF0tLSYG1tDX19nrLpfyri2LhzZDeeJN+Dr10tTGnXt4IjJF3iuYOIKgLPHkRERNVUHSaIRERUDA5DJSIiIiIiIgkmi0RERERERCTBYahERETVlPKeRVO7WhySSkREEryySEREVE3dOfoTrv24DneO/qTrUIiIqApiskhEREREREQSTBaJiIiIiIhIgskiERERERERSTBZJCIiIiIiIgnOhkpURQz/dAP0bex0HQZVMQZyOZwtTXH38RPkFxbqOhyqQiri2AhNj4cbgL/i4jFp2ryKDfAldH7FAl2HQERUpfDKIhEREREREUkwWSQiIiIiIiIJDkMlIiKqpu7rW6n9S0RE9Cwmi0RERNXUr2Y+ug6BiIiqMA5DJSIiIiIiIgkmi0RERERERCTBYahERETVVE1FGkxEPrJlBkjUt9Z1OEREVMUwWSQiIqqmemZdgJviEeL1bRFh1VbX4RARURXDYahEREREREQkwWSRiIiIiIiIJJgsEhERERERkQSTRSIiIiIiIpJgsviSmDFjBtq25eQDVUGfPn0wZswYXYdBRERERFSpmCw+48GDB1i2bBl69uwJHx8f9OjRA5999hlycnLK3eeUKVPg4eGB4ODg54otMTER8fHxz9UHld7gwYMxfPhwjctu376Ne/fuveCIiIiIiIheLD4647/y8vJQp04duLu74/3334evry8uXLiA2bNnIzw8HCdPnoSVlVWZ+ty1axfCw8NRUFAAV1fXygmcKsWdO3dgbGys6zCIiIiIiHSGVxb/q7CwEAMHDkRMTAxGjhwJHx8fDB8+HN9++y0uXbqElStXlqm/pKQkjB8/Hv/5z39gampaSVETERERERFVDiaL/2VkZIRvv/0WJiYmauUBAQEAgNjY2DL1N27cODg7O2PmzJlljmXHjh3o3LkzGjdujJEjR+LmzZsa602bNg0eHh7w8PCAp6cnAgMDMXPmTCQlJanqhIWFoXnz5lAoFJL2H374Iby9vZGVlQUAuHbtGiZOnIiWLVuiadOmGD58OE6ePFlivKWJQxvl/X9JSUkYN24cfH190atXL8TExAB4Ovz2rbfegq+vLzp27IiDBw9q7OfgwYMYNGgQvL29ERAQgKlTpyIhIUHjulJSUhAWFgZfX1+0bdsWW7duVasXGBiI8+fP4/Tp06rtCgwMlKyzpH6IiKq6CKu2+MC2HyKseE88ERFJMVn8L5lMBplMJik/fvw4AKB27dql7isiIgK//PILIiIioK9ftpG+q1evxuDBg+Hj44NNmzZh0KBBCAkJQXp6uqTue++9h3379mHfvn3YtWsXZs2ahejoaHTs2BG5ubkAgKCgIJw5cwa7d+9Wa5uVlYVVq1bB29sbZmZmePjwIdq0aYOEhAQsW7YMX331Ffr06YNZs2bhypUrxcZcmji0uX37NuLj4xEaGoqgoCCEh4fD0NAQnTp1QlxcHEaOHImuXbsiIiICtra2CAoKwu3bt9X6CA8PR+fOnWFjY4Pw8HAsXLgQR48eRdOmTREXF6e2rps3b2L06NFo3749Nm/ejICAAAQHB2PPnj2qepGRkWjQoAF8fHxU2xUZGam2zry8vBL7ISIiIiJ6mcmEEELXQVRV2dnZaN68Oa5cuYILFy7Ay8urxDY3b96Ej48PJk6ciKVLlwIArK2tERAQgOjo6GLbpqWlwdnZGf369cM333yjKr9y5Qq8vb3h6OgouVpW1I0bN1CvXj1ERUVh0KBBKCwshIeHB9zd3dXWv3HjRowfPx6///47unTpgl27dqF///6IjY1Fw4YN1fpUKBRlTnqLxqFNkyZNcOHCBcTExMDHxwcA8OjRIzg5OcHe3h579uxB06ZNVfunZs2amD59OhYvXgwASE9PR+3atfH666+rJWqPHj2Cm5sbOnTooEqUles6e/Ys/Pz8VHUbNmwIJycnHDhwQFXWsmVLGBsb448//tAac2n6KSopKQkPHz5UK4uLi0O/fv3gGzwZBjZ2WttS9WQgl6OWpQnuPc5GfmGhrsOhKoTHRsU7uex9XYdQYRQKBdLT02FlZVXmv+H0auOxUb3FxsaiSZMmuHjxIho1alRifR4hWhQWFiIkJAQXL17EihUr1BLF5cuXY926darXlpaWOHfuHAoLCxEcHIyaNWtiwYIFZV7n4cOH8eTJEwwbNkyt3MvLC/7+/pJEMSMjA6tXr8b+/fuRmJiIvLw81bLLly8DAORyOSZMmIA5c+bgypUrqu1Yu3YtXFxc0KlTJwBAnTp1AACLFi3C/Pnz4enpqeqrpBNJaeIojpeXlypRBABbW1vUrVsXcrlclSgCT5NuV1dXtSudhw8fRmZmJkaNGqXWp62tLXr16oWdO3eqJbuenp5qCR4ANGvWrNgET5Py9rNmzRqtx4aDmTFMLXl/K6nTl8lhZ2oECBkUggkB/Q+PjYqXlpam6xAqTEFBATIzMwEAenp6Oo6GqhIeG9VbRkZGmeozWdRACIEJEyZg+/btmDt3Lt5++2215cnJybh+/brqtXKW1I0bN+Lo0aM4dOhQuWbSTExMBKB5yGvt2rXVkkWFQoGOHTsiMTERixYtgo+PD8zNzZGbm4vGjRurPe4jNDQU8+bNw9q1a7FixQqcPHkSMTExmD9/PuTypyOR/f39sWrVKixcuBCRkZFwdnZGx44dMX78eLRp00ZrzGWJQxtnZ2dJmbW1NSwsLDSWP3r0SPX6/v37AP6X7D6rbt26yM3NRUpKChwcHABo3rc1atRQ67M0ytvPpEmTMHjwYLUy5ZXFpKwcGBg9KVMc9OozkMsBmcC9DF49InUVcWwEpx2Ba34ybhrYYYt1uwqO8OVjbW2t6xAqjHKuAl49oqJ4bFRvmj5fF4dHiAZvv/02Nm7ciFmzZqmGOz5r5syZGDt2rOq18luZ+Ph46OnpITQ0VK1+RkYGTpw4AQ8PD0ydOhVTp07VuF7lm5eSkiJZVjQJOX78OM6ePYvt27erJR9Xr16VtLW1tcWQIUOwZcsWLFmyBGvXroVcLkdISIhavcmTJyMsLAznz5/HsWPHsHXrVrRt2xY//vgj+vXrpzHmssShjbZvtbSVPztyurh9lpycDJlMBnNz8zL1WRrl7cfBwUGVuBalKCwEmAyQBopCgfzCQiaLJPG8x4bynCWE4PGFkkfSvGz09PSgr6//ym0XPT8eG9VXWd9zHiFFzJw5E6tWrcLMmTNV9xwWZWtrC1tbW41tn00ilZo2bYrGjRtjy5YtsLGx0bruwMBAyGQyHDx4EB06dFCVp6en49y5c2rfBChnMHV0dFTrY+fOnRr7njRpErZu3YrVq1cjKioKnTp1gouLi6SeTCaDn58f/Pz8MG7cONjY2GDXrl1ak8WyxlHRlPssOjoaQUFBqvKCggIcOnQIfn5+5Xp0ibGxscYZZImIiIiIqgvOhvqM+fPnY/ny5ZgxYwaWLVtW5va2traqRy08+yOXy2FiYgIPD49ik0U3Nze8+eabWLlypWoymqysLEyePFntHkLg6SM9zM3NsXLlStVQz71796pmby2qRYsW8Pf3x9y5c5GTkyO5+hkVFYUvvvhC7QrdkSNHkJ2dDW9vb60xlzWOiubm5obg4GCsW7dONZFNbm4upk2bhhs3bmD+/Pnl6tfDwwNXr15VjeknIiIiIqpumCz+1/3797FgwQLIZDL8+OOPkoRv+PDhLySO8PBwDBw4EEFBQbCzs4Onpyf69esnmYnVwcEBkZGROHXqlOpK56pVq7BhwwatfYeFhaGgoAA2NjaSK4WtW7fGrVu34OXlBQcHB9jb22PkyJGYN28epk2bprXP8sRR0davX4+wsDCMGjUKNWrUgKWlJfbt24cdO3agd+/e5epz9uzZsLa2hqOjI+rVq6fxOYtERERERK8yPjrjvwoKChAfH691uYmJicaJWEojPj4eRkZGqFWrVqnbZGVlITU1FU5OTtDT08ODBw+QnZ0NV1dXSd3ExESYmprC0tISwNMJU2xsbCRXMU+cOIHWrVtj6tSp+OKLL7Su++HDh9DT0yv2KqgmpY3jWXfu3IG+vj6cnJzUyhMSEiCXyyX7LCEhATKZTON7oVAocP/+fRgbG8Pe3r7U60pOTsbjx4/h7u4uaZOUlISMjAzo6emp9n15+ilObGwsvL290WjkJOjz0RlUhIFcDmdLU9x9/IT3lJGaijg2QtOPwk3xCPH6toiwalvBEb58zq8o+0zmVZVCoUBaWhqsra15Xxqp4bFRvSk/d/LRGWWkp6cHDw+PSunbzc2tzG3MzMxgZmamel30nsBn1axZU+21tu2IiooCAMkQ1KI0JVqlUdo4nqVpFlNA82yjxZUDT2/Y1dZfceuys7ODnZ3mJE3ThDTl6YeIiIiI6GXDYajVRGJiIr7++mt0795d7ZmGREREREREmjBZfMUVFBTAw8MD7u7ucHBwwNq1a3UdEhERERERvQQ4DPUVp6enh3379sHU1BROTk6QyWS6DomIiKqIX8waw0TkI1tmoOtQiIioCmKyWA1U1r2YRET0ckvUt9Z1CEREVIVxGCoRERERERFJMFkkIiIiIiIiCQ5DJSIiqqaCsv6BkyId9/Wt8KsZZ8omIiJ1TBaJiIiqKSdFOtwUj3QdBhERVVEchkpEREREREQSTBaJiIiIiIhIgskiERERERERSfCeRaIq4pvp4+Hr66vrMKiKUSgUSEtLg7W1NfT1ecqm/6mIY+PE4lCkXH4Efw83THl/QQVHSERELzteWSQiIiIiIiIJJotEREREREQkwWSRiIiIiIiIJHgDDBERUTVVp20f2DYIgKldLV2HQkREVRCTRSIiomqqTru+ug6BiIiqMA5DJSIiIiIiIgkmi0RERERERCTBYahERETV1J0ju/Ek+R5M7WpxSCoREUnwyiIREVE1defoT7j24zrcOfqTrkMhIqIqiFcWiaqI4Z9ugL6Nna7DoCrGQC6Hs6Up7j5+gvzCwgrv//yKBRXeJxEREb0aeGWRiIiIiIiIJJgsEhERERERkQSTRSIiIiIiIpJgskhEREREREQSTBaJiIiIiIhIgskiERERERERSfDRGURERNWUlYuX2r9ERETPYrJIRERUTTUaMUvXIRARURXGYahEREREREQkwWSRiIiIiIiIJDgMlYiIqJpKv3UZ+U8yYGBqASuX+roOh4iIqhgmi0RERNVU7LZPkHL5LGzqB6DV+xG6DoeIiKoYDkN9ScyYMQNt27bVdRgEoE+fPhgzZoyuwyAiIiIiqlRMFot4+PAhpk2bBn9/fzRp0gRvvfUW7ty5U6Y+zp8/j7lz58LX1xceHh64cePGc8eVmJiI+Pj45+6HSmfw4MEYPny4xmW3b9/GvXv3XnBEREREREQvFpPFZzx8+BAtWrTAqVOnsGLFCqxfvx43b95EQEAAbt26Vao+li9fjpCQEFhYWKBp06a4fv068vLyKjlyqmh37tzB3bt3dR0GEREREZHOMFl8xsKFC3Hv3j38+OOPaNu2LVq0aIEdO3agoKAAc+bMKVUfU6ZMwfnz5/Hee+/B2dm5kiMmIiIiIiKqHEwW/0sIgcjISLz++utwcnJSlVtYWKBPnz748ccf8eTJkxL7MTIyeu5YduzYgc6dO6Nx48YYOXIkbt68qbHetGnT4OHhAQ8PD3h6eiIwMBAzZ85EUlKSqk5YWBiaN28OhUIhaf/hhx/C29sbWVlZAIBr165h4sSJaNmyJZo2bYrhw4fj5MmTJcZbmji0Ud7/l5SUhHHjxsHX1xe9evVCTEwMgKfDb9966y34+vqiY8eOOHjwoMZ+Dh48iEGDBsHb2xsBAQGYOnUqEhISNK4rJSUFYWFh8PX1Rdu2bbF161a1eoGBgTh//jxOnz6t2q7AwEDJOkvqh4iIiIjoZcZk8b/u3LmD5ORkeHt7S5Y1btwYubm5iI2NrfQ4Vq9ejcGDB8PHxwebNm3CoEGDEBISgvT0dEnd9957D/v27cO+ffuwa9cuzJo1C9HR0ejYsSNyc3MBAEFBQThz5gx2796t1jYrKwurVq2Ct7c3zMzM8PDhQ7Rp0wYJCQlYtmwZvvrqK/Tp0wezZs3ClStXio25NHFoc/v2bcTHxyM0NBRBQUEIDw+HoaEhOnXqhLi4OIwcORJdu3ZFREQEbG1tERQUhNu3b6v1ER4ejs6dO8PGxgbh4eFYuHAhjh49iqZNmyIuLk5tXTdv3sTo0aPRvn17bN68GQEBAQgODsaePXtU9SIjI9GgQQP4+PiotisyMlJtnXl5eSX2Q0RERET0MuOjM/5LeRXM1tZWskxZVporZc8jLS0Ns2fPxrBhw/DZZ5+pyuvXrw9vb284Ojqq1Xd0dFQra9iwIXx9fVGvXj3s2bMHgwYNQo8ePeDm5oa1a9di4MCBqrrffvst0tPTERoaCgA4fvw4kpKSsHTpUjRs2BAA4OPjgzfeeEPjVcmyxlGcI0eOICYmBj4+PgCAjRs3wsnJCe3bt8eePXvQtGlTVXnNmjWxfv16LF68GACQnp6Od955Bz179sSGDRtUfTZv3hxubm6YPn26WqJ8+PBhnD17Fn5+fgCApk2b4rfffsOKFSvQu3dvAICrqyuMjIxgbGwMDw8PjTGXph9NkpKS8PDhQ7UyZUKrL5fDQM7vb0idgVwOfbms0o6Nkn6/qepSKBQoKCh4rvdQCKH6l8fCq6Uijg96NfHYqN7K+r4zWfyvgoICAIBcwwcyZZmyzvLly7Fu3TrVcktLS5w7d+65Yzh8+DCePHmCYcOGqZV7eXnB399fMqwyIyMDq1evxv79+5GYmKg2kc7ly5dVsU+YMAFz5szBlStX4OXlBQBYu3YtXFxc0KlTJwBAnTp1AACLFi3C/Pnz4enpqepLX7/4w6Q0cRTHy8tLlSgCT5PzunXrQi6XqxJFALC2toarq6valc7Dhw8jMzMTo0aNUuvT1tYWvXr1ws6dO6FQKFTb4OnpqUrwlJo1a4YDBw6UGOezytvPmjVrsGDBAo3LHMyMYWppWqY46NWnL5PDztQIEDIoRGGF95+WllbhfdKLUVBQgMzMTACAnp5eufpQfmhQKBQ8Fl4xFXF80KuJx0b1lpGRUab6TBb/y9raGoDmHagsU9ZJTk7G9evXVcutrKwqJIbExEQAQO3atSXLateurZYsKhQKdOzYEYmJiVi0aBF8fHxgbm6O3NxcNG7cGDk5Oaq6oaGhmDdvHtauXYsVK1bg5MmTiImJwfz581WJsL+/P1atWoWFCxciMjISzs7O6NixI8aPH482bdpojbkscWijaSIga2trWFhYaCx/9OiR6vX9+/cB/C/ZfVbdunWRm5uLlJQUODg4ANC8b2vUqKHWZ2mUt59JkyZh8ODBamVxcXHo168fkrJyYGBU8n2xVL0YyOWATOBeRjbyCys+WVSe1+jlo0z0rKysSvxST5vWH2yqyJCoCqmI44NeTTw2qjdNn6+LwyPkv9zd3WFkZKR2j5vStWvXAAANGjQAAMycORNjx45VLa+ob2WUb15KSopkWdEk5Pjx4zh79iy2b9+ulnxcvXpV0tbW1hZDhgzBli1bsGTJEqxduxZyuRwhISFq9SZPnoywsDCcP38ex44dw9atW9G2bVv8+OOP6Nevn8aYyxKHNtr2n7Zy5bApoPh9lpycDJlMBnNz8zL1WRrl7cfBwUGVuBalKCwEKiEZoJefolAgv7CwUpJFflB4uenp6UFfX5/vI2nE44O04bFRfZX1PecNUv9lYGCArl274uDBg2rDKIUQ2Lt3L1q2bAl7e3sAT5Mv5SyZHh4ecHNzq5AYAgMDIZPJJDN+pqenS4a5KmcwLXof486dOzX2PWnSJKSlpWH16tWIiopCp06d4OLiIqknk8ng5+eHKVOm4OjRozAxMcGuXbu0xlzWOCqacp9FR0erlRcUFODQoUPw8/ODqWnZh3YaGxtzLD8RERERVWtMFp8xb948pKenY/r06VAoFCgsLMTixYtx9epVLFq0qNLX7+bmhjfffBMrV65UJT9ZWVmYPHmy2j2EABAQEABzc3OsXLlSNdRz7969OH78uMa+W7RoAX9/f8ydOxc5OTmqiW2UoqKi8MUXX6hdoTty5Aiys7M1zhBb3jgqmpubG4KDg7Fu3TrVRDa5ubmYNm0abty4gfnz55erXw8PD1y9elU1pp+IiIiIqLrhtedn+Pv7Y8+ePZgyZQrs7Oygp6cHc3NzfP/996qJYEpy+vRpDB8+HMD/hkZ269YNBgYGcHJywtGjR4ttHx4ejrCwMAQFBcHS0hJGRkZYuXIlfvzxR9X9ecDT4YyRkZGYMGECbG1tYWxsjBYtWiAiIgK1atXS2HdYWBjGjBkDGxsbybDS1q1bY/ny5fDy8oJMJoMQAnK5HPPmzcO0adO0xlueOCra+vXrYWdnh1GjRkEul+PJkyeoW7cuduzYUezMpMWZPXs2jhw5AkdHR9SsWRMODg6leuYkEdHL5MTiUKRcPgub+gFo9X6ErsMhIqIqRibKerNWNZGUlISCggLUrFkTMpms1O2ys7Nx9+5djcv09fXh6upaqn6ysrKQmpoKJycn6Onp4cGDB8jOztbYPjExEaamprC0tATwdMIUGxsb2NjYqNU7ceIEWrdujalTp+KLL77Quu6HDx9CT09P0r4kpY3jWXfu3IG+vj6cnJzUyhMSEiCXyyUJZ0JCAmQymcZJcRQKBe7fvw9jY2PVkOHSrCs5ORmPHz+Gu7u7pE1SUhIyMjKgp6en2vfl6ac4sbGx8Pb2RqORk6BvY1emtvTqM5DL4WxpiruPn1TKPYvnV2ienZeqPuUMptbW1uW+74jJ4qurIo4PejXx2KjelJ87L168iEaNGpVYn0eIFtomISmJiYmJ1mfzlYWZmRnMzMxUr4veE/ismjVrqr3Wtv6oqCgAkAxBLUpTolUapY3jWZpmMQU0zzZaXDnwNBnX1l9x67Kzs4OdneYkTdOENOXph4iIiIjoZcN7FquJxMREfP311+jevbvaMw2JiIiIiIg0YbL4iisoKICHhwfc3d3h4OCAtWvX6jokIiIiIiJ6CXAY6itOT08P+/btg6mpKZycnMp0/yUREREREVVfTBargYq4h5KIiIiIiKoXDkMlIiIiIiIiCSaLREREREREJMFhqERERNVUoxEzkf8kAwamFroOhYiIqiAmi0RERNWUlUt9XYdARERVGIehEhERERERkQSTRSIiIiIiIpLgMFQiIqJqKnbbMqTfugIrFy80GjFL1+EQEVEVw2SRqIr4Zvp4+Pr66joMqmIUCgXS0tJgbW0NfX2esqlipd+6gpTLZ3UdBhERVVEchkpEREREREQSTBaJiIiIiIhIgskiERERERERSTBZJCIiIiIiIgkmi0RERERERCTBZJGIiIiIiIgkmCwSERERERGRBB/aRUREVE3VadsHtg0CYGpXS9ehEBFRFcRkkYiIqJqq066vrkMgIqIqjMkiURUx/NMN0Lex03UY1d75FQt0HQIRERFRlcB7FomIiIiIiEiCVxaJiIiqqTtHduNJ8j2Y2tXikFQiIpLglUUiIqJq6s7Rn3Dtx3W4c/QnXYdCRERVEJNFIiIiIiIikmCySERERERERBJMFomIiIiIiEiCySIRERERERFJMFkkIiIiIiIiCSaLREREREREJMFkkYiIiIiIiCT0dR0AERER6YaVi5fav0RERM9iskhERFRNNRoxS9chEBFRFcZhqFQl5efnIycnBwqFQuPy3Nxc5Ofnv+CoiIiIiIiqDyaLVCUNHjwYJiYm8PHxQUFBgWS5o6MjBg8erIPIiIiIiIiqByaLVKVdunQJERERug6DiOiVlH7rMpIvnUH6rcu6DoWIiKogJotUZVlZWaFdu3aYN28esrKyyty+sLBQUpaXl4e8vDyN9QsKCpCTkwMhRKn7U/b57JDYwsJCDpElopdC7LZPcGrJWMRu+0TXoRARURXEZJGqtE8++QQPHjzA8uXLS1V/9+7daNWqFQwNDWFkZIQmTZpg586dquXBwcFwdHRETk6OpO2YMWNQs2ZNtWUl9QcAzZs3R+/evXHhwgW0b98eJiYmGDduXDm3mIiIiIioamCySFVa8+bNMXjwYCxfvhwPHjwotu6aNWvQv39/dO3aFffu3UNqairGjBmDwYMH4/vvvwcAhIaGIi0tTZLwZWRkYMeOHXjzzTdhYmJS6v6UUlJSMGfOHHz55Zd4+PAh+vTpU4F7gYiIiIjoxeOjM6jK++ijj7Br1y7MmzcP69at01gnNTUVs2bNwrBhwzB//nxV+dSpU3Hy5Em89957eOONN9CpUye4ubkhIiICw4YNU9WLjIzEkydPEBoaWqb+lP766y9cvnwZr732GgBgwIABGuNMSkrCw4cP1cri4uIAAPpyOQzk/P5G17TNwKsrCoUCBQUFVS4u0r2KODaUw+6FEDzGXjE8d5A2ymPj+vXrqF+/PtatW6f6/FOdLVmyBPPmzUNGRgaMjY11HU6lKes5gckiVXnu7u6YOHEivvzyS0ybNg3169eX1Pnjjz+QlZWFvn37QqFQQAih+hDUtm1bfPfdd0hISEDt2rUxevRozJs3D/Hx8XBzcwMAbNq0CT4+PggICChzfwDg6empShSLs2bNGixYsEDjMgczY5hampZ9B1GFSktL03UIagoKCpCZmQkA0NPT03E0VJVUxLGh/NCgUCiq3LFPz4fnDtJGeWw8fvwYAPDkyRP+/gPIzs4G8PRzwKucLGZkZJSpPpNFeil8+OGH+OqrrzBnzhzs2rVLsjwpKQkAMGLECMhkMslyIyMjpKamqpLF+fPnY/Pmzfi///s/xMbG4vTp0/jiiy/K1R8A1KpVq1TbMWnSJMkjP+Li4tCvXz8kZeXAwOhJqfqhymNtba3rENQoP8xbWVlBX5+nbPqfijg2lO309fWr3LFPz4fnDtLm2S+JAMDU1JS//4DqNiRra+tXOlm0sLAoU32ePeilYGtri/feew9z5szB0aNHJcvt7e0BALt27UKPHj2K7at27dro1q0bvvrqK8yfPx8REREwMjLCiBEjytUfABgYGJRqOxwcHODg4KBxmaKwENAy4yq9OFXxQ5Wenh709fWrZGykW897bCi/DJPJZDy+XkE8d5A2enp6qivOyuOkupP/91agV/13pqzbxhuk6KXx9ttvo06dOpg5c6ZkWceOHWFhYYHIyMhS9TV27FjcuXMHv/zyC7Zt24Z+/frBxsam3P0RERFR9SOEwLJly9CwYUOYmZmhdu3aGDx4MC5evKiqk5OTA5lMpvoxNDSEh4cH3n//fbUZ2M+fPw+ZTIbvvvsO4eHhqFevHszNzdG7d2/VJH8bNmyAh4cHjI2N0a5dO1y7dk0tnmf72LBhA9zd3WFsbIxmzZohOjq6VNuUm5uLBQsWoH79+jAyMoKdnR1GjhyJu3fvFtuuLOt+tu7mzZvh5eUFfX19/PHHHwCAq1evYtiwYXB0dIShoSE8PT2xbNky1WPM1q9fD5lMhlOnTkni+PbbbyGTyXDo0CEAQHh4uNr+t7CwQOvWrTWOVNOkpFgAYMaMGdDX10d2djYmTpwIGxsbWFpa4o033kBKSorGPkeMGIFatWrBxMQEvr6+2LBhg1qfpVnvi8BkkV4axsbGWLRoEU6fPo309HS1ZdbW1li9ejW++eYbhIWF4eLFi0hLS8OFCxewevVq9OrVS61+79694eDggAkTJuDhw4eSG7vL2h8RERFVP5999hnmz5+PJUuW4MGDB4iJicGwYcPw2WefqeoYGxur5j4QQuDhw4f49NNPsWHDBsyYMUPSZ1RUFG7duoWTJ0/i7NmzuHbtGoYPH45Nmzbh5s2bOH78OP755x8kJydj+PDhGuP6/vvvcfXqVRw/fhxXrlxB/fr10aNHDxw5cqTY7VEoFOjRowfWrVuHjz76CA8fPsSxY8dw9+5dtGnTplT3NpZl3du3b8fFixdx8OBBnDhxApaWlrhw4QKaNWuGlJQUREdHIyUlBcuXL8fSpUsxdepUAMCwYcNgZmaGiIgISZ+bNm2Cu7s7Xn/9dQBPLxAo931BQQEuXbqEdu3aYeDAgTh27Fix21KaWJ71zjvvoFu3brh58yZ2796N/fv3S+qdP38eAQEBSEhIwE8//YTk5GRs27YN586dwz///FOu9VYmmdD2BHIiHRoyZAiOHDmCxMREtfLCwkK0aNECFy5cQJ8+fbB9+3a15SdOnMCnn36KU6dOISsrC66urnj99dcxdepUuLu7q9WdM2cOVqxYgbp16+Ly5cuq4Qdl7a9FixZwdHTETz/9VK5tjY2Nhbe3NxqNnAR9G7ty9UEV5/wKzRMQ6Ypy4hFra+tXelgMlR2PDSrOy3R83DmyG3eOFv831MrFC41GzFK9Tr91GbHbPimx71bvqycTJxZrnvWzTts+qNOubymiVae86vfnn3+Wue3SpUsxb948ZGVlQU9PD+fPn4efnx86d+6M/fv3q+pt3rwZY8aMQa9evbBnzx5V+datWxEcHIy///4bPj4+AKDqo3Xr1mqJkEKhgKenJ5ycnHD48GGkpaUhLS0Nr732GjZu3IixY8cCeJpohYaGYu/evejevbuq/cOHD+Hq6or33nsPH3zwgcbtKc26jx8/rlY3MDAQJ06cUOunY8eOiIuLw+XLl2Fq+r+J/9avX49Jkybh2rVrcHd3x+jRo/HDDz/g/v37MDMzAwDcvHkT7u7uWLhwId5///1i93/jxo0REBCAzZs3AwAWLVqEDz/8ENnZ2ap7Fksby4wZM/Dpp59i06ZNGD16tKre3LlzsWzZMqSmpqruFWzbti2uX7+Oa9euqeIuqrTrLQ/l586LFy+iUaNGJdav2mcPqraKJoFKcrkcZ86c0dquVatWaNWqVanW8fHHH+Pjjz8utk5p+jt9+nSp1kdERERST5LvIeXy2TK1yX+SUeY2ALS2sW0QUOa+AMDX1xdLlizB7NmzMWzYMDRu3Fjjl88///wzPvvsM8TExCA9PR3PXqtJSEiAi4uL6nXRuRKUs8C3bdtWrbxBgwYAgBs3bqiSRaWiz3vW19dHz549sWbNGjx5on0yvT179sDS0hJdu3ZVK7e3t4ePjw8OHz6sNVks7bqfTX6K1s3IyMDhw4cxfvx4tXoA0LlzZxQWFuLo0aNwd3dHaGgovvrqK0RFRSEkJATA08RaLperXgNPhwEvXboU27dvx40bN9SG/hY3sU9ZYlHq2bOnWj1vb28UFBTg1q1b8Pb2Rnp6Oo4fP4633npLa6JYnvVWJiaLRERERKQzpna1YFO/+GTNysVL7bWBqUWJbTTR1sbUrnSzmhf1wQcfQAiBrVu3YtmyZahRowa6du2KDz74AN7e3gCAvXv3ok+fPpgyZQo2btyIOnXqwNDQEF9++SUmT56M/Px8tT6dnJzUXiuvSGkr1zQ01NHRUWNZYWEhUlNTtSYqiYmJePz4MQwNDQFAbfgsAPj5+ZW0S0pc97MJkLOzs1q9pKQkFBYWYsOGDdi4caPas2CV/3/06BEAoE2bNvDy8kJERARCQkJQWFiIr776Ct26dVPrd+zYsdi9ezc2bdqEDh06wMbGBnK5HIGBgZJ9X95YgKcXNIpOYmhpaQngf+9RcnIyhBCS7X6e9VY2JotEREREpDN12vUt8xBQK5f6kiGmpVGeNsUxNjbG4sWLsXjxYly/fh0HDx7ERx99hLZt2+LatWuws7PD119/DXt7e7VHdAFAfHy8xj41PbKruHJNlBPiFC2Ty+WoUaMG8vLyNLazs7NDzZo1cf/+/VKvq6zrflbR2eRtbW0hk8nw7rvv4pNPSh5mHBoailmzZuHq1au4efMmbt++jc8//1y1PC8vD99//z2mTJkieXRZfHw8XF1dtfZd1lhK8/7Y2dlBJpMVO1lQWddb2TjBDRERUTV1YnEofh7pq/U+LiIqvXr16mHcuHFYsmQJ0tLSVJOVAFBdqVNSJjGV5dl7GwGgoKAAv/zyC1q2bCkZ2vis3r17IzExscSJcCpj3cDTYaFt2rTBTz/9pDZcVJvg4GAYGBhg06ZNiIiIgL29PXr37i2pZ2RkpPb6999/15jUPk8spWFlZYU2bdpg9+7dyMrKemHrfR5MFomIiIiIyuGNN97AunXrcO3aNeTm5uLGjRvYtm0batSoAV9fXwBP78tLSEjARx99hIyMDFy5cgWDBg1CixYtKi0uW1tbzJ49G4mJibhz5w7GjBmDO3fuYPHixcW2Gz16NDp27Ig333wTkZGRSEpKwuPHj3H27Fm8++67+PLLLytt3UqrV6/GgwcP0LdvX5w5cwZZWVm4e/cufv75Z3Tv3l3tqpyDgwN69eqFzZs3Y/fu3Rg1apTa1UpDQ0N069YNEREROH78ODIzM7Fv3z7Mnz8f/v7+FRpLaX3xxRfIyMhAz549cfbsWWRlZeHixYuYOHEizp8/X2nrLS8mi0RERERE5bBo0SLExsaiV69esLKyQrt27WBlZYVjx47B1tYWADB06FCsWLECERERcHBwwKBBgzBy5Ei12UYr2tChQ+Hq6orAwEC89tpriI2NxS+//KJ6nIQ2BgYG2LdvH6ZNm4alS5fCxcUFLi4umDx5MlxcXBAcHFxp61by8fFBTEwMateujQEDBqBGjRoIDAxEeHg43n33Xcn9fmPHjkVSUhJyc3Mlj0IDnk5607VrV/Tr1w+1atVSPRrN3Ny8wmMpDT8/P5w5cwY1a9ZEUFCQ6jmWfn5+qomKKmO95cVHZxDpGB+dUbXw0Rn0sqiIY+PE4lCkXD4Lm/oBFX4vF+kWzx3Vk/KRFJGRkRg6dKjGOpV1bJRm3aR7ZX10Bq8sEhERERERkQSTRSIiIiIiIpJgskhEREREREQSHMRORERERPQKaNKkCXQ1HYku102Vh1cWiYiIiIiISIJXFomIiKqpRiNmIv9JBgxMLXQdChERVUFMFomqiG+mj1c9wJeI6EWwcqmv6xCIiKgK4zBUIiIiIiIikmCySERERERERBIchkpERFRNxW5bhvRbV2Dl4oVGI2bpOhwiIqpimCwSERFVU+m3riDl8lldh0FERFUUh6ESERERERGRBJNFIiIiIiIikmCySERERERERBJMFomIiIiIiEiCySIRERERERFJMFkkIiIiIiIiCT46g0jHcnNzAQBxcXHQ1+evJKlTKBTIyMiAhYUFjw9SUxHHxo0HqUhPy0Hag1RYxcZWcISkSzx3kDY8Nqq3uLg4AP/7/FkSHiFEOnbhwgUAwKBBg3QcCRFVX1eBVT/qOggiInpB7ty5g6ZNm5ZYj8kikY55enoCALZv346GDRvqOBqqauLi4tCvXz/s2rULHh4eug6HqhAeG1QcHh+kDY+N6i03Nxd37txB+/btS1WfySKRjllaWgIAGjZsiEaNGuk4GqqqPDw8eHyQRjw2qDg8PkgbHhvVV2muKCpxghsiIiIiIiKSYLJIREREREREEkwWiYiIiIiISILJIpGO2dvbY968ebC3t9d1KFQF8fggbXhsUHF4fJA2PDaoLGRCCKHrIIiIiIiIiKhq4ZVFIiIiIiIikmCySERERERERBJMFomIiIiIiEiCySIRERERERFJ6Os6AKJX2c8//4xffvkFmZmZ8Pb2xtixY2Fra/vC2lPV9eDBA0RERODff/+FlZUV+vbti65du5aqbUZGBn744QfExMQgIyMD9erVw/Dhw+Hq6lq5QdML88svv+Dnn39GZmYmGjVqhLFjx8LOzq7M/ezbtw/h4eGwtLTEpk2bKiFSetGSkpJU5w4LCwv06dMH3bt3L1MfZ8+exQ8//ICEhAS4u7sjODgY7u7ulRQxvUi//fYbfvrpJ6Snp6Nhw4YIDQ2Fo6NjqdpmZ2dj27Zt+Pvvv/H48WPUqVMHAwYMgL+/fyVHTVUZrywSVZKxY8fizTffRO3atdG5c2fs2bMHjRs3xvXr119Ie6q6Ll68iEaNGiE6Ohpdu3aFo6Mj+vXrh+nTp5fY9o8//kCtWrXw0UcfoXbt2mjTpg3Onj0LLy8vrFmz5gVET5VtwoQJeOONN+Ds7IzOnTvj119/hbe3N65du1amfh4+fIjg4GDs3r0bP/30UyVFSy/Sv//+C29vb+zbtw+dO3eGk5MTBg4ciLfffrtU7YUQeOedd9C5c2cUFhaiR48esLS0RJ8+ffDnn39WcvRU2d5++23069cPDg4O6Nq1K6Kjo+Ht7Y1///23xLY3b96Ep6cnFi1aBDc3N3Tu3Bn37t1DQEAA5syZ8wKipypLEFGF27VrlwAgvvvuO1VZdna2cHNzE506dar09lS1BQQEiAYNGoi8vDxVWXh4uAAgDh06VGzbyMhIERYWJnJyctTKhw4dKvT19cWtW7cqI2R6QX7++WcBQGzbtk1VlpOTIzw8PET79u3L1Fe/fv1EYGCgaNu2rbC1ta3gSEkXWrZsKby8vERubq6q7KuvvhIAxP79+0ts/9lnnwljY2Px999/q5Xn5uaKlJSUCo+XXpzo6GgBQISHh6vK8vLyRIMGDUSLFi1KbD9p0iQBQPzzzz9q5UOHDhUymUzcvXu3wmOmlwOvLBJVgvDwcNjY2GDw4MGqMmNjYwQHB+PAgQOIj4+v1PZUdf399984e/YsxowZAwMDA1X5yJEjYWZmho0bNxbbvkePHli9ejWMjIzUynv16gWFQoGTJ09WStz0YoSHh8PKygpDhw5VlRkZGSEkJASHDx8u9dXFzZs349dff0V4eDjkcv6pfxXExsbi1KlTCAkJgaGhoap82LBhsLCwKPHckZOTg0WLFiE4OBg+Pj5qywwNDVGjRo1KiZtejPDwcJiammLkyJGqMgMDA4SGhuL06dO4cOFCse1TU1Mhk8lQv359tfKGDRtCCIG0tLTKCJteAvwLQlQJTp8+jSZNmkg+pAUEBAAATp06Vantqeo6ffo0AEjuATE0NETjxo1LfG+trKw0ll+5cgUAYG1t/fxBks6cPn0avr6+0NPTUysvy+/+rVu3MG3aNHzwwQdo2LBhpcRJL562c4eBgQF8fX1LPDaOHTuGlJQUvP7669i2bRtCQ0MxatQofPrpp0hNTa20uOnFOH36NLy9vdW+SABKf+4YPHgwhBD4+uuvVWWZmZn44Ycf4OPjAy8vr4oPml4KTBaJKlh+fj4ePnyo8YZyZdndu3crrT1VbQkJCQCg9f0tz3t79+5drFy5ErVr18brr7/+vCGSjhQWFuL+/fvP9btfWFiI4OBguLi48D6jV8zznjuuXr0KAJgzZw4+/vhjNG3aFE2bNsWqVavg7e2NuLi4ig+aXpiEhITnOnf0798fu3btwsKFC9GsWTMEBQXB3d0dPj4+OHDggOQLLKo+OBsqUQXLzc0FAOjrS3+9lMMOc3JyKq09VW15eXkAtL+/ubm5EEJAJpOVqr/s7GwMHDgQGRkZ+O677yTDU+nlUdKxAZT8u//555/j2LFjOHnypNowZ3r5lXR8FBQUQKFQaFwOAE+ePAEAZGVl4Z9//oGlpSUAYODAgfD09MSUKVOwd+/eSoqeKpMQAvn5+c917khMTMSnn36KgoICDBkyBPb29nB0dMSOHTvQokULhIWFVUrsVPUxWSSqYKamppDL5ao/zM/KysoCAFhYWFRae6razM3NAUDr+2tubl7qRDEvLw8DBgzAn3/+ifXr15d5+nyqWoyNjaGvr1/u3/0rV67g/fffx7Rp09CsWbNKi5N0o6Rzh/L40UZ57PTt21eVKAJAnTp10LFjR/z+++/Iy8uTDGOkqk8mk8HMzOy5PjeEhYXhr7/+wuXLl1GnTh0AQEhICGrWrIkpU6agRYsWqiGtVL1wGCpRBZPL5XB3d8etW7cky5Rlr732WqW1p6pN+d5pe39L+94qFAoMGTIE+/btw5dffolx48ZVaJykGx4eHuX+3Y+JiUFubi7+/fdfDBo0SPUTGxuLjIwMDBo0CMuWLau02KlyPe+5w9PTEwA0PqvX1tYWCoVClVjQy+e11157rs8NBw8ehL+/vypRVOrXrx+EEDh48GDFBUsvFSaLRJWgU6dO+PvvvyWTBhw8eBDGxsZo27Ztpbanquv111+Hnp4e/vjjD7Xy+/fv4/Lly+jSpUuJfRQUFGD48OHYvXs3Vq9ejYkTJ1ZStPSiderUCRcvXsSjR4/Uyg8ePAhDQ0O0b99ea9s2bdogKioKY8aMwdChQ1U/Dg4OMDIywtChQ3nueIm1b98e+vr6knNHUlISYmNjSzx3BAYGwsLCQjUZ1rOuXLkCW1tbTpD1EuvUqROuXLmC+/fvq5UfPHgQenp66NChQ7HtjYyMkJ6eLilXlhkbG1dcsPRy0emDO4heUZcvXxaGhoYiLCxMVfbPP/8IExMTMX36dLW6hw4dEgMHDlR7vl5Z2tPLZ8yYMcLCwkJcvnxZCCFEYWGhCAkJEWZmZuLOnTtqdSdPnizGjx+vel1YWCiCg4MFALFq1aoXGjdVvmvXrgkjIyPx1ltvqcouXrwoTE1Nxdtvv61W98iRI2LgwIEiOjq62D7bt2/P5yy+IsaPHy/Mzc3Fv//+qyobO3asMDU1FTdv3lSrO3XqVDF27Fi1sgULFgg9PT1x5MgRVdn27dsFAPGf//yncoOnSnXz5k1hamoqQkJCRGFhoRBCiCtXrggLCwu184kQQpw8eVIMHDhQ7N27V1UWFhYmAIjt27eryjIyMkT79u2FoaGhuHHjxovZEKpymCwSVZLdu3cLGxsb0bhxY9G9e3dhYmIihg8frvYgdiGE2Lx5swAgNm/eXK729PLJysoS/fv3F2ZmZqJHjx6iYcOGwsHBQeNDtevVqyecnZ1Vr5UP4LazsxMDBw6U/Pz8888vclOoEuzZs0fY2toKb29v0b17d2FqaiqGDh2q9iB2IYT4+uuvBQCxcePGYvtjsvjqePLkiRg0aJAwNTUVPXr0EI0aNRL29vZi3759krpeXl7C0dFRraygoEBMmTJFGBkZibZt24qWLVsKAwMDMWXKFKFQKF7UZlAl+e2334SDg4No2LCh6NGjhzAzMxMDBw4UT548UasXFRUl+cIxKytLjBs3Tujr6wt/f3/RvXt3YW9vLzw8PMSvv/76ojeFqhCZEELo8MIm0SstOzsbp0+fRmZmJry9veHq6iqpc+vWLZw5cwbNmjWDi4tLmdvTyysuLg6XL1+GpaUlWrRooXEm03379qGwsBBBQUEAgOvXryMmJkZrn02aNIGHh0elxUwvRk5ODk6dOoXMzEw0bNgQ7u7ukjq3b9/Gn3/+iYCAgGLPDYcPH8bjx4/Ru3fvSoyYXqTr16/j0qVLsLCwQMuWLTWeO5QT1vTq1UuyLDExUXUeadq0qcZHLtDLKTc3F6dOnUJGRgbq16+v8e/B3bt3cfLkSfj5+aFevXpqy5KTk3HlyhWkp6ejbt26qF+/frETJ9Grj8kiERERERERSXCCGyIiIiIiIpJgskhEREREREQSTBaJiIiIiIhIgskiERERERERSTBZJCIiIiIiIgkmi0RERERERCTBZJGIiIiIiIgkmCwSERERERGRBJNFIiIiIiIikmCySEREryR9fX3MmDFD12G81EaMGAFXV1ddh1ElVeS+eeutt9CuXbsK6aui9erVC97e3iXW08WxMnLkSHTv3v2FrpOoumGySEREL43MzEx89NFH8PX1hZWVFezt7dGmTRusXLkSaWlpug6PnsOgQYPg4eGh6zCeS3m24cKFC4iIiMCiRYsqKaqXk7e3N6ZNm1Zsnfnz5+PAgQPYu3fviwmKqBrS13UAREREpZGfn4+OHTvi2rVrWLt2Lbp06QJ9fX3s2LEDs2fPxo0bN7BixQpdh0nVxLZt2yqkn6VLl6JRo0ZV9spiVVavXj1069YNS5YsQY8ePXQdDtEriVcWiYjopbB//36cOXMG8+bNw9ChQ2FrawsrKyuEhoYiJiYGXl5eug6RqEwePXqEHTt2YOTIkboO5aU1YsQIHDt2DBcvXtR1KESvJCaLRET0UkhJSQEAODs7S5bVqVMHEydO1NjuxIkTaN68OYyNjVGvXj1s2rRJUsfb2xsymQwymQz6+vpwdnbGmDFjkJiYqFZPeR/kH3/8gRYtWsDY2BjLly9Xxff222/D1dUVhoaGcHZ2xtSpU5GRkVHsdn333XeQyWSIiYnB4sWLUbduXcjlctW6T548iZ49e6JGjRowNjaGr6+vxqta69atg6enJ4yNjdGkSRP8/vvvmDNnDmQymVq9Jk2aaLzPa8aMGdDXL3nA0YwZM1T7Si6Xo0aNGujevTtOnjypVk95r9u9e/cwYMAAWFlZoXPnzhr7DAgIwA8//IDr16+r+pbJZFAoFCX2VdZ4kpKSMHDgQFhYWMDOzg5TpkxBXl6eWt2///4bffr0Qc2aNWFubg4/Pz+sXr0a+fn5qjpF79EraRs0+f3335Gbm4uOHTuqlW/YsAEymQy3bt1SlX3//feQyWSSul26dIG/v79aWWmPmdLWK+rAgQOoUaMGevXqhczMTI11Zs6cCUNDQ9y/f1+y7P3334eenh4SEhJKXFdJlPtjz549z90XEUkxWSQiopdCixYtoK+vjy+++ELjB1BNrl69ilWrVuGbb77BvXv30KtXL4SGhuLMmTNq9S5evAghBIQQyMjIwPbt2/HXX39h4MCBKCgoUKt76dIlrFy5El9//TWuXLmChg0bIjU1FYGBgYiOjsaWLVuQkpKCH3/8EQcOHECPHj0kfWjy0UcfwdTUFOfOncOOHTsgl8vx888/o127dqhZsyb++usvPHjwAFOmTEFoaChWr16tartixQpMmjQJISEhSEhIwM6dOxEREYELFy6Uaj+VxfLly1X7Ki8vD3/++Sfs7OzQvXt3teQGAPLy8jB+/Hi8/fbbiI+PR3BwsMY+z549i4EDB6JevXqqvoUQasmrtr7KEk9+fj4mT56MadOm4d69e1i9ejXWrVuHZcuWqepkZmaiS5cuMDIywqlTp5CcnIytW7ciLi4OR48e1bpfSrMNRR07dgyGhobw8fFRK+/SpQuAp8mkUnR0NExMTHD8+HE8efIEAJCTk4Njx46p6gMo9TFT2npFhYeHo3v37hg5ciR2794Nc3NzjfUmTpyIgoICbNy4Ua08Pz8fERER6Nq1K2rXrq11PaXl4OAAFxcXHDly5Ln7IiINBBER0UsiIiJCWFpaCj09PdGyZUsxYcIEERkZKdLS0iR19fT0hKOjo3jy5ImqLCcnR1hbW4tx48aVuK7o6GgBQJw/f16tT2tra5GZmalWd+bMmUJfX19cvXpVrfzs2bMCgIiKitK6nsjISAFAjB49Wq1coVCI2rVrizZt2ojCwkK1ZdOmTRPW1tYiOztbZGdnC2tra9G/f3+1Ok+ePBEODg6i6J96X19f0a1bN0kc06dPF3p6emplw4cPFy4uLlpjV8rLyxMmJibi448/VpX17NlTABAHDhwosb0QQgwcOFDUq1dP47Ky9lVcPCdOnFCr279/f1GnTh3V61OnTgkA4rfffit2HZr2TXHboEmvXr1ErVq1NC6rV6+eGDRokOq1i4uLmD17tpDL5eLXX38VQgixf/9+AUBER0cLIUp/zJS2nhBP91ujRo1EQUGBmDFjhtDT0xOrV68u1f7o0aOHqF27tlAoFKoy5fH+7O/EJ598IgCU6icmJkay7pYtW4oGDRpo3I9E9Hx4ZZGIiF4aY8aMwb1797Bz50506dIFt27dQkhICOrVq4effvpJUr9jx44wMTFRvTYyMsJrr72GGzduqNU7f/48BgwYgJo1a0JfXx8ymUw1zDEuLk6tbqdOnWBmZqZWtmfPHjRp0gSvvfaaWrm/vz9q1KiBw4cPl7htffr0kcSUkJCAQYMGSYaSdu7cGWlpafj7778RExODtLQ09O7dW62OiYmJ1mGfz+PRo0d4++23Ua9ePRgZGUEmk8HQ0BDZ2dmSfWVmZiYZNlle2voqSzw1atRAYGCgWpm3tzcSEhJUQ1Hr1asHMzMzzJo1Cz/88EOlzrKblpYGCwsLjcu6dOmCAwcOoLCwENeuXcOtW7fwxhtvwN/fX3XFcf/+/TAxMUGbNm0AlP6YKW09pZycHAwYMADr16/Hnj17EBYWVqrtCwsLQ0JCgtrv5po1a2BnZyc53p+HpaUlZ0MmqiRMFomI6KViZmaGPn364P/+7//w66+/4vLly7CwsMDw4cNV9zUqOTk5SdoX/WB569YttGnTBjk5Ofj999+RkZEBIQROnToFAGr3qQGa75lMTEzEX3/9BX19fejr60NPTw9yuRwymQypqal49OhRidtVtF/lPYvvvvuuqk9lv7169QLwNFFS9u3g4CDpU1OZNkKIUtXp3r07du3ahfXr1yMpKQmFhYUQQsDa2rpU+6q8NPVV1ni0HQ9CCDx+/BgAYGdnh99++w02NjYYOnQobGxs4O/vjy+++KLY+w/Lw9raWrXeorp06YLU1FScOXMG+/fvh729PZo0aYIuXbpg//79AJ4mi23btoWRkRGA0h8zpa2nlJSUhH379qFZs2ZlSv579OgBNzc3rF27FgAQGxuLo0ePYuTIkTA0NFTVmzFjhtrQXSEEGjVqhLfffltS3qRJE8l6Hj9+jBo1apQ6LiIqPSaLRET0UnN1dUVISAgyMzNx/vx5tWVFr5po8sMPPyArKwvr1q2Dj4+P6kpkfHy8xvoGBgaSMjs7O7Rv3x4KhQIKhQIFBQWqpEUIgW+//bbEOIr2a2dnB+DpZCfKPov2GxQUBFtbWwBPP9AXpanMyspK46Q7d+/eLTHGS5cu4ezZs3j//ffRuXNnWFlZQSaTIS0tTeOVHU37qrw09VXWeEpzPABA69atcfDgQaSlpSE6OhpNmjTBtGnTKvxZiC4uLnj06JEkqQWeXhXX09PD77//jv3796Nz586QyWTo2rUrYmNjVVcIn71fsbTHTGnrKdWtWxe//PIL/vzzT/To0UPrpDZFyeVyTJgwAdHR0bh27RrWrFkDAAgNDS33PtPk/v37cHFxqdA+iegpJotERPRS2LNnj9qEH89SJjrW1tbl7l95dUZp69atpW7bu3dvnD59WjKhyvPw9/dHrVq1sH379mLr+fn5wdraGr/88otaeU5ODg4cOCCpX69ePVy5ckUtQXny5AkOHTpU6tieZ19pY2Zmhtzc3HK1rYx4gP8NfY2IiEC9evVKnESlrNvQpk0b5OXlaZyIyNraGgEBAdi7dy8OHTqkSgpbtWoFc3NzzJ49G0IItWSxtMdMaes9q1OnToiOjsbff/+Njh07lupqOfA0MTQyMsInn3yCbdu2oUWLFmjUqFGp11uSpKQk3Lp1C23btq2wPonof5gsEhHRSyE9PR3du3fH+PHj8ffffyM3Nxd3797F8uXLsWnTJnTu3Bl+fn5l7rd79+4wMDDA9OnTkZSUhLt372LGjBmS+xKL88EHH6Bu3bro2bMn9u3bh7S0NDx69AhHjx5FSEgIfv311zLHpa+vj/Xr1+PQoUMICQnBv//+i+zsbMTHxyMyMlI1HNDY2Bjz5s3Dzp07sXTpUjx69Ajx8fEYM2aM5JEKADBu3Dg8evQIc+fORWpqKq5du4bRo0ejRYsWJcbk5eUFLy8vfPLJJ4iNjUV6ejq2bduGffv2lWnIqybe3t64f/8+zpw5g8LCwlK1qYx49uzZg9GjR+Po0aNITU1FZmYmvv/+e9y+fRsdOnSo0G1Qzrp68OBBrctPnjyJ9PR0VVJoYGCA9u3b47fffoODg4PaTKqlPWZKW6+oFi1a4MiRI7h79y7atWuHe/fulbiNtra2GDJkCDZu3IjHjx9X+FVF5b4res8uEVUMJotERPRSGDJkCH744QekpqZi4MCBsLS0RP369fHdd99h4cKF2LNnT6mHGT6rYcOG2LlzJ2JjY+Hq6orWrVvDzs4Oc+fOLXUfNjY2OH36NHr16oW3334bjo6OaNiwIf7zn/+gU6dOald/yqJXr144efIkMjMz0aFDB1hbW6NLly7Yu3cvPvnkE1W9adOm4csvv0R4eDicnZ3Rv39/hISEwNPTU+3eMAAIDAzEhg0b8MMPP6BmzZoYNmwYpk6dCk9PzxLj0dPTw549e1C3bl20bt0a7u7u2L9/P7799tty7ftnTZo0CQMGDEDXrl2hp6dX4jMKKyuebt26oWPHjvjwww9Rr149ODs7Y9myZfj888/x/vvvV+g22NraYvDgwfj66681LlceNw0aNFB7zISyvFOnTpLtLO0xU9p6RTVq1AjHjh1Dbm4u2rRpg+vXrxe7TwCoJsQxMzPD0KFDS6xfFtu2bUObNm3g7e1dof0S0VMyUZo72omIiOilM3jwYPz5558VOjyWKtbFixfRpEkTHDx4EO3atdN1OJXi8uXLaNCgAUJCQrB58+YK6/f69euoX78+fvrpJ/To0aPC+iWi/+GVRSIiolfQ48ePER0djfbt2+s6FCqGt7c3xo4diw8//FDXoVSa77//HsDTIdAVacGCBejUqRMTRaJKxCuLREREL7mLFy8iPDwcY8eOhZubG65du4Z3330Xp06dwtmzZ9GwYUNdh0jVVFxcHNq2bQsvLy/88ccfug6HiMpIX9cBEBER0fNp0KAB6tWrh5EjR+Lq1aswNDREq1atcPToUSaKpDM1a9ZEWloaWrZsiS1btug6HCIqB15ZJCIiIiIiIgnes0hEREREREQSTBaJiIiIiIhIgskiERERERERSTBZJCIiIiIiIgkmi0RERERERCTBZJGIiIiIiIgkmCwSERERERGRBJNFIiIiIiIikmCySERERERERBJMFomIiIiIiEiCySIRERERERFJ/D8OMryCOdbcXgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 864x336 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x336 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "assoc = analysis[\"associations\"].copy()\n",
    "display(assoc)\n",
    "for feat in analysis[\"features\"]:\n",
    "    sub = assoc.loc[assoc[\"feature\"] == feat].sort_values(\"pct_regular\")\n",
    "    fig, ax = plt.subplots(figsize=(7.2, max(2.8, 0.45 * len(sub))))\n",
    "    ax.barh(sub[\"level\"].astype(str), sub[\"pct_regular\"], color=\"#2F6F7E\")\n",
    "    ax.axvline(frame[\"y\"].mean(), color=\"#B35C2E\", ls=\"--\", label=\"sample prevalence\")\n",
    "    ax.set_xlabel(\"Share regular transit (weekly+)\")\n",
    "    ax.set_title(f\"Regular-transit prevalence by {feat}\")\n",
    "    ax.legend(frameon=False)\n",
    "    plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e83badd6",
   "metadata": {},
   "source": [
    "## 5. Random Forest cross-validated performance\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "db8aead9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:41:45.883238Z",
     "iopub.status.busy": "2026-07-25T15:41:45.883066Z",
     "iopub.status.idle": "2026-07-25T15:41:45.958317Z",
     "shell.execute_reply": "2026-07-25T15:41:45.957123Z"
    }
   },
   "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>model</th>\n",
       "      <th>n</th>\n",
       "      <th>roc_auc</th>\n",
       "      <th>average_precision</th>\n",
       "      <th>balanced_accuracy</th>\n",
       "      <th>f1</th>\n",
       "      <th>brier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>random_forest_rideshare</td>\n",
       "      <td>233</td>\n",
       "      <td>0.745138</td>\n",
       "      <td>0.654822</td>\n",
       "      <td>0.731079</td>\n",
       "      <td>0.70852</td>\n",
       "      <td>0.197184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>prevalence_prob</td>\n",
       "      <td>233</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.424893</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.244359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>majority_class</td>\n",
       "      <td>233</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.424893</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.424893</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     model    n   roc_auc  average_precision  \\\n",
       "0  random_forest_rideshare  233  0.745138           0.654822   \n",
       "1          prevalence_prob  233  0.500000           0.424893   \n",
       "2           majority_class  233  0.500000           0.424893   \n",
       "\n",
       "   balanced_accuracy       f1     brier  \n",
       "0           0.731079  0.70852  0.197184  \n",
       "1           0.500000  0.00000  0.244359  \n",
       "2           0.500000  0.00000  0.424893  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Benchmarks — geo AUC 0.551 · CA AUC 0.590 · chance 0.500\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 624x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics = analysis[\"metrics_table\"].copy()\n",
    "display(metrics[[\"model\", \"n\", \"roc_auc\", \"average_precision\", \"balanced_accuracy\", \"f1\", \"brier\"]])\n",
    "print(\"Benchmarks — geo AUC 0.551 · CA AUC 0.590 · chance 0.500\")\n",
    "roc = analysis[\"roc\"]\n",
    "fig, ax = plt.subplots(figsize=(5.2, 5.0))\n",
    "ax.plot(roc[\"fpr\"], roc[\"tpr\"], color=\"#2F6F7E\", lw=2.2,\n",
    "        label=f\"RF AUC = {analysis['metrics']['roc_auc']:.3f}\")\n",
    "ax.plot([0, 1], [0, 1], \"--\", color=\"#999999\", label=\"Chance\")\n",
    "ax.set_xlabel(\"False positive rate\")\n",
    "ax.set_ylabel(\"True positive rate\")\n",
    "ax.set_title(FEATURE_SPECS[SPEC_KEY][\"label\"])\n",
    "ax.legend(frameon=False, loc=\"lower right\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a86790d",
   "metadata": {},
   "source": [
    "## 6. Feature importances\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5bb69c8b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:41:45.959810Z",
     "iopub.status.busy": "2026-07-25T15:41:45.959708Z",
     "iopub.status.idle": "2026-07-25T15:41:46.011625Z",
     "shell.execute_reply": "2026-07-25T15:41:46.011236Z"
    }
   },
   "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>feature</th>\n",
       "      <th>importance_gini_sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Q28</td>\n",
       "      <td>0.906433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q29</td>\n",
       "      <td>0.093567</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  feature  importance_gini_sum\n",
       "0     Q28             0.906433\n",
       "1     Q29             0.093567"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>feature</th>\n",
       "      <th>importance_perm_mean</th>\n",
       "      <th>importance_perm_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Q28</td>\n",
       "      <td>0.291085</td>\n",
       "      <td>0.037258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q29</td>\n",
       "      <td>0.014732</td>\n",
       "      <td>0.007175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  feature  importance_perm_mean  importance_perm_std\n",
       "0     Q28              0.291085             0.037258\n",
       "1     Q29              0.014732             0.007175"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 780x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(analysis[\"gini_raw\"])\n",
    "display(analysis[\"permutation_importance\"])\n",
    "perm = analysis[\"permutation_importance\"].sort_values(\"importance_perm_mean\")\n",
    "fig, ax = plt.subplots(figsize=(6.5, max(2.5, 0.55 * len(perm))))\n",
    "ax.barh(perm[\"feature\"], perm[\"importance_perm_mean\"], xerr=perm[\"importance_perm_std\"],\n",
    "        color=\"#1F4E5F\", ecolor=\"#8FA3A8\")\n",
    "ax.set_xlabel(\"Permutation importance (mean AUC drop)\")\n",
    "ax.set_title(\"Permutation importance\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "225fcc0a",
   "metadata": {},
   "source": [
    "## 7. Written results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fc665ac9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:41:46.012947Z",
     "iopub.status.busy": "2026-07-25T15:41:46.012802Z",
     "iopub.status.idle": "2026-07-25T15:41:46.015619Z",
     "shell.execute_reply": "2026-07-25T15:41:46.015203Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ride-share frequency (Q28/Q29) Random Forest CV ROC-AUC = 0.745. Stronger discrimination than the geo benchmark (≈0.55). Exceeds the CA-score benchmark (≈0.59).\n",
      "\n",
      "Sample: {'n': 233, 'n_regular': 99, 'n_not_regular': 134, 'prevalence': 0.4248927038626609}\n",
      "CV metrics: {'roc_auc': 0.7451379466304839, 'average_precision': 0.6548216401037097, 'balanced_accuracy': 0.731079451228705, 'f1': 0.7085201793721974, 'brier': 0.19718437562060037}\n",
      "Caveats:\n",
      "- Same-wave self-reports; association ≠ causal effect on transit use.\n",
      "- Complete-case analysis for the listed features; N may be smaller than the full matched cohort when items are missing.\n",
      "- Regular transit is a thresholded Q26 label (weekly+), not continuous ridership.\n"
     ]
    }
   ],
   "source": [
    "summary = analysis[\"summary\"]\n",
    "print(summary[\"verdict\"][\"interpretation\"])\n",
    "print()\n",
    "print(\"Sample:\", summary[\"sample\"])\n",
    "print(\"CV metrics:\", {k: summary[\"cv_metrics\"][k] for k in\n",
    "      [\"roc_auc\", \"average_precision\", \"balanced_accuracy\", \"f1\", \"brier\"]})\n",
    "print(\"Caveats:\")\n",
    "for c in summary[\"caveats\"]:\n",
    "    print(\"-\", c)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbc1a971",
   "metadata": {},
   "source": [
    "## 8. Artifacts\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7b8f7aac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:41:46.017114Z",
     "iopub.status.busy": "2026-07-25T15:41:46.016992Z",
     "iopub.status.idle": "2026-07-25T15:41:46.212559Z",
     "shell.execute_reply": "2026-07-25T15:41:46.212138Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wrote 11 artifacts to /workspace/outputs/transit_covariate_rf/rideshare\n",
      "Memo figure: /workspace/memos/figures/rideshare_predicts_transit_memo.png\n"
     ]
    }
   ],
   "source": [
    "out = ROOT / \"outputs\" / \"transit_covariate_rf\" / SPEC_KEY\n",
    "paths = save_feature_family_artifacts(analysis, out)\n",
    "fig_path = ROOT / \"memos\" / \"figures\" / f\"{SPEC_KEY}_predicts_transit_memo.png\"\n",
    "plot_family_memo_figure(analysis, output_path=fig_path)\n",
    "print(\"Wrote\", len(paths), \"artifacts to\", out)\n",
    "print(\"Memo figure:\", fig_path)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e976c883",
   "metadata": {},
   "source": [
    "## 9. Limitations\n",
    "\n",
    "- Complete-case analysis for the listed items can shrink *N* relative to the full matched cohort (especially `Q20`/`Q21`).\n",
    "- Same-wave self-reports cannot establish whether mobility constraints *cause* transit use.\n",
    "- The outcome is a weekly+ threshold on `Q26`, not continuous ridership intensity.\n",
    "- Compare AUCs carefully across families when analytic *N* differs.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}