{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d9fcc9ab",
   "metadata": {},
   "source": [
    "# Does Employment Status 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 employment status 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** | `Employment status` |\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/employment_predicts_transit.md`](../memos/employment_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 employment`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36f68372",
   "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": "d109731f",
   "metadata": {},
   "source": [
    "## 2. Setup\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "55d09db6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:41:30.704505Z",
     "iopub.status.busy": "2026-07-25T15:41:30.704380Z",
     "iopub.status.idle": "2026-07-25T15:41:31.738973Z",
     "shell.execute_reply": "2026-07-25T15:41:31.737870Z"
    }
   },
   "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: employment {'features': ['Employment status'], 'label': 'Employment status', 'research_question': 'Does employment status 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 = 'employment'\n",
    "print('Spec:', SPEC_KEY, FEATURE_SPECS[SPEC_KEY])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "617063dd",
   "metadata": {},
   "source": [
    "## 3. Sample & outcome\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "45b0d54a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:41:31.740787Z",
     "iopub.status.busy": "2026-07-25T15:41:31.740601Z",
     "iopub.status.idle": "2026-07-25T15:41:32.051744Z",
     "shell.execute_reply": "2026-07-25T15:41:32.050463Z"
    }
   },
   "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": "29dd2572",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:41:32.052982Z",
     "iopub.status.busy": "2026-07-25T15:41:32.052872Z",
     "iopub.status.idle": "2026-07-25T15:41:36.154394Z",
     "shell.execute_reply": "2026-07-25T15:41:36.153274Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Does employment status predict whether a matched respondent takes public transportation regularly?\n",
      "n                241.000000\n",
      "n_regular        101.000000\n",
      "n_not_regular    140.000000\n",
      "prevalence         0.419087\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>Employment status</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>Part-Time</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>Other</td>\n",
       "      <td>Never</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6ac07c3845b0ce5cac4ab3569ea70236b7cfa8fd51576e...</td>\n",
       "      <td>Full-Time</td>\n",
       "      <td>Never</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c67b3266ecd4af12979c1f67d1efffd0d3e9bfbc567d21...</td>\n",
       "      <td>Other</td>\n",
       "      <td>Never</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1cefb8d34b92489e8ffd9cf34c469964f0527b1cae33e0...</td>\n",
       "      <td>Full-Time</td>\n",
       "      <td>Never</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      participant_id Employment status  \\\n",
       "0  ebab12e9878344fdc93a8c1cad27ff2f709e12b90f0496...         Part-Time   \n",
       "1  b6954799ca47bc836ac02ab86e4710c3b0e096d7ace449...             Other   \n",
       "2  6ac07c3845b0ce5cac4ab3569ea70236b7cfa8fd51576e...         Full-Time   \n",
       "3  c67b3266ecd4af12979c1f67d1efffd0d3e9bfbc567d21...             Other   \n",
       "4  1cefb8d34b92489e8ffd9cf34c469964f0527b1cae33e0...         Full-Time   \n",
       "\n",
       "                Q26  y  \n",
       "0  2-4 days a month  0  \n",
       "1             Never  0  \n",
       "2             Never  0  \n",
       "3             Never  0  \n",
       "4             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": "f6823b22",
   "metadata": {},
   "source": [
    "## 4. Descriptive associations\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "07b79a67",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:41:36.156310Z",
     "iopub.status.busy": "2026-07-25T15:41:36.156157Z",
     "iopub.status.idle": "2026-07-25T15:41:36.233135Z",
     "shell.execute_reply": "2026-07-25T15:41:36.232630Z"
    }
   },
   "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>0</th>\n",
       "      <td>Employment status</td>\n",
       "      <td>Full-Time</td>\n",
       "      <td>148</td>\n",
       "      <td>67</td>\n",
       "      <td>81</td>\n",
       "      <td>0.452703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Employment status</td>\n",
       "      <td>Other</td>\n",
       "      <td>62</td>\n",
       "      <td>19</td>\n",
       "      <td>43</td>\n",
       "      <td>0.306452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Employment status</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>31</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>0.483871</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             feature      level    n  n_regular  n_not_regular  pct_regular\n",
       "0  Employment status  Full-Time  148         67             81     0.452703\n",
       "1  Employment status      Other   62         19             43     0.306452\n",
       "2  Employment status  Part-Time   31         15             16     0.483871"
      ]
     },
     "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": "863423f7",
   "metadata": {},
   "source": [
    "## 5. Random Forest cross-validated performance\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "334dbf01",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:41:36.235472Z",
     "iopub.status.busy": "2026-07-25T15:41:36.235310Z",
     "iopub.status.idle": "2026-07-25T15:41:36.310490Z",
     "shell.execute_reply": "2026-07-25T15:41:36.309878Z"
    }
   },
   "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_employment</td>\n",
       "      <td>241</td>\n",
       "      <td>0.528076</td>\n",
       "      <td>0.424449</td>\n",
       "      <td>0.559512</td>\n",
       "      <td>0.585714</td>\n",
       "      <td>0.247240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>prevalence_prob</td>\n",
       "      <td>241</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.419087</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.243453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>majority_class</td>\n",
       "      <td>241</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.419087</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.419087</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      model    n   roc_auc  average_precision  \\\n",
       "0  random_forest_employment  241  0.528076           0.424449   \n",
       "1           prevalence_prob  241  0.500000           0.419087   \n",
       "2            majority_class  241  0.500000           0.419087   \n",
       "\n",
       "   balanced_accuracy        f1     brier  \n",
       "0           0.559512  0.585714  0.247240  \n",
       "1           0.500000  0.000000  0.243453  \n",
       "2           0.500000  0.000000  0.419087  "
      ]
     },
     "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": "iVBORw0KGgoAAAANSUhEUgAAAjkAAAIzCAYAAADicAgLAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjExLjEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvctoD+AAAAAlwSFlzAAASdAAAEnQB3mYfeAAAnPlJREFUeJzs3XdYFFfbBvB7WXbpUgUUBRvYFQV7L3SxxF5TjImaZj5NjOnFJG8STTMxJpoYuzFqjAiICoIae6+oWFBRlN5h2d35/iCMbkAEdpdd4P5dV673nTMzZ54dKTdTzpEIgiCAiIiIqI4xMXQBRERERPrAkENERER1EkMOERER1UkMOURERFQnMeQQERFRncSQQ0RERHUSQw4RERHVSQw5REREVCcx5BAREVGdxJBDREREdRJDDhEREdVJDDlENaxJkyaQSCQV/rd582ZDl6nB3NwcHTp0MHQZRERVwpBDRKQjEokEvr6+taZ/fddLZGimhi6AqL5KSUmBk5OTocsgIqqzeCWHiIiI6iSGHCIjl5ubC4lEgr59+yI9PR0zZsyAq6srrKys0L9/fxw9ehQAkJeXh7lz58Ld3R3m5ubw9vZGVFTUY/tKTU3F9OnT4eLiAgsLC/j4+GDjxo1Vri86OhqBgYFwcHCAmZkZPD09MX/+fGRmZorbPHjwAGZmZmjfvv1j+wkKCoJEIsHFixd1/rlLxcfH45lnnkHTpk0hl8vRsGFDjB49GidPnnzsecrJycGrr76Kxo0bi59h5cqVGttv3rwZEokEAHDixAmN56s+/PDDJ57DI0eO4KmnnoK7uzssLCzQsmVLjB07Fv/880+V+k9ISMBbb72FLl26oEGDBrC2tka3bt2wbNkyCIJQpXrj4+MhkUgwZcqUcmv29fUV+6jKZyGqUQIR1Sg3NzcBgJCSklKp7XNycgQAQteuXYVOnToJADT+s7GxEa5cuSL06NGjzDqZTCZcuXKl3L7at29fZnsAwtdff12mBjMzM6F9+/Zl2pctWyZIJJJy+2nTpo2QlpYmbjtp0iQBgLB///4y/dy8eVMwMTERBg0apJfPLQiCEBkZKVhYWJRbq1wuF6Kiosoc28fHR/D19S13n1WrVonb//nnn+VuA0D44IMPKvz3jYuLE0xNTR+7f1X6b9my5WO3mzVrVpXqvXTpkgBAmDx5crl1+/j4iPVV5bMQ1SR+1RHVsNKQU9F/xcXF4valv3ABCJ6enkJUVJSQnZ0tXL9+XfD39xcACC4uLkLTpk2Fbdu2CZmZmcLt27eFsWPHCgCEF154ody+XFxchM2bNwsZGRnCjRs3hPnz5wsSiUSQy+XCzZs3NWouL+TcvHlTkMvlAgDhtddeE65evSpkZWUJkZGRQrNmzQQAwrPPPituf+DAAQGAMHXq1DLn5N133xUACJs3b9bL505PTxfs7OwEKysr4bvvvhMSExOFwsJC4c6dO8JXX30lmJqaCk2bNhUUCkWZYzdv3lzYvn27kJGRISQnJwv/93//JwAoN/SVBqOqmDZtmgBAmDlzpnD9+nWhqKhISExMFLZs2SL06dOnSv2HhoYKP/30k3Dx4kUhPz9fSElJEbZs2SI0adJEACBcuHCh0v1VJ+RU5bMQ1QSGHKIaVt2QI5VKhYsXL2r0deXKFXGfgwcPaqzLyMgQTExMhG7dupXpC4Cwe/fuMrWV/pJauHChRnt5IWfhwoUCAGHChAll+jl37pwglUoFuVwu5Ofni+2dO3cWLCwshIyMDLFNqVQKbm5uQpMmTfT2ub/99lsBgLBy5coytQqCILz22msCAOGff/7ROLaJiYlw7tw5jW3VarV4xSQnJ0djnTYh5/bt20/ctjr9C4IgbN68WQAgfPPNN5XuT5uQU5nPQlQT+EwOkYGkpKRAKPlDo8x/pqZlX3xs1aoV2rZtq9HWsmVLmJqawtHREb169dJYZ2dnBxcXF9y5c6dMX66urhg6dGiZ9mnTpgEATp069cT6S59jefrpp8us69ChA3r06AGFQiE+YwMAs2bNQkFBAdasWSO2hYeHIykpCS+++KLePvehQ4cAAM8//zxMTU0hlUohlUphYmICiUSC7777DgBw8+bNMsf+7/hAEokE7dq1AwBkZGSUf3KqYNy4cQCAiRMnYtOmTbh79261+3rw4AHmzZuH9u3bw8rKSnzOZsyYMQBQ7teCLunysxDpAkMOUS1R3uvmJiYmkEqlj30VXS6XQ6FQlGl3c3Mrd/vGjRsDALKysp5YT+k2j+urSZMmAKDxAPKUKVPQoEED/PLLL2LbL7/8ArlcjhkzZpTbjy4+d1paGgBApVJBpVJBrVZDrVZrPIwLoMy5atiw4WP7L+1PWyEhIYiIiIBcLseUKVPg5uYGNzc3vPDCC7h69Wql+0lNTYWvry8WL16MixcvIj8/v8w2hYWFWtdbEV19FiJdYcghqoeSkpLKbS/9y9vW1vaJfZRu87i+Sq8aPNqXlZUVpk2bhvPnz+PQoUO4c+cOdu7ciTFjxsDFxaVKn6EqSmuIiYl57NUzQRDwzDPP6K2GigQFBSE6OhpZWVk4evQo/u///g8RERHw8fHBtWvXKtXHsmXLcPv2bfj5+eHw4cPIyMiASqWCIAjYvXt3lWuSSqUAyga/UqmpqXr7LES6wpBDVA8lJycjJiamTPvatWsBAF26dHliH6XbrFq1qsy6ixcv4ujRo5DL5WVeG589ezYAYPny5fj111+hUqnw0ksvVfkzVEXPnj0BlAQBfZJKpSgqKqr2/hYWFujWrRvmzp2Lr7/+Gjk5OVi/fn2l+k9ISAAALFy4ED169ICdnR1MTEp+xIeFhVW5Xnt7ewDQuN1Y6vTp00hMTNTqsxDVBIYconpqypQp+Ouvv5CZmYlbt27hnXfewerVqyGTyTB58uRK7S+TybBx40a8/vrruHbtGrKzsxEVFYXQ0FAolUpMmjQJFhYWGvu1bdsWAwcOxB9//IHly5fD29sbvXv31tfHBAA8++yzsLOzw6ZNmzBu3DgcPnwYWVlZyM7OxtmzZ7FkyRL06NFD6+O4uLjg6tWrOHbsGJRKZaX2GT9+PN5//30cPXoUaWlpKCoqwrlz57B8+XIAgFqtrlT/Hh4eAIBFixbh2rVrKCoqwtWrV/Hmm29iyZIlVa7XyckJLVq0wIULF/Dee+8hJSUFmZmZ+PvvvzFq1CitPwtRjTDAw85E9Vpl3q56dOyT0jd9HvcKrpmZmdC6dety13l4eAiOjo5l+urSpYvQrl27co+9ePHico9R3ivTP/7442M/g5eXl5CamlpuXZs2bRK3W7FiRbnb6PJzC4Ig7NmzR7CxsXlsvWZmZpU+9ujRowUAwo0bNzTan3/++Qr/LctT3jg/pf85ODhoHKOi/q9fvy5YW1uX28/06dMFAMJLL71UpXp//vnncvvr1auX0LFjxzJvV1XlsxDVBF7JIaqHLC0tERsbi6effhoNGzaEmZkZunTpgvXr1+P//u//Kt3P7NmzERUVBT8/P9jZ2UEul6Nly5aYN28eDh8+DEdHx3L3GzVqFGxtbWFvb49Jkybp6mNVaMiQITh79ixeeukltGrVCmZmZrC3t4e3tzdef/11HDt2TOtjfPXVV3j22Wfh6uoq3ip6kj///BOffPIJevToAQcHB1hZWaF169Z47bXXcOrUKTRr1qxS/Tdv3hxxcXEICAiAg4MDrK2t0atXL2zZsgXPP/98tep94YUXsGzZMrRp0wYymQyNGjXCK6+8gl27dokPX1f3sxDVBIkg/Of1AiKqs3Jzc2FjY4M+ffrgwIEDBqvj1KlT6Nq1K15//XV8/fXXBquDiOo2Xskhohp169Yt8XXx6dOnG7gaIqrLyo68RUSkBzt37kRQUJC4PGrUqAon7CQi0hZDDhHVKBsbGwQEBOCnn34ydClEVMfxmRwiIiKqk/hMDhEREdVJDDlERERUJzHkEBERUZ3EkENERER1Et+uqoTMzEzExcWhadOmMDMzM3Q5RERE9VJRURFu376NAQMGwM7O7onbM+RUQlxcHEaOHGnoMoiIiAjAtm3bMGLEiCdux5BTCU2bNgVQclJbtWqls36VSiVycnJgY2MDU1P+U+gaz69+8fzqF8+vfvH86pe+zm9CQgJGjhwp/l5+Ev7LVkLpLapWrVrpdIRWpVKJzMxM2NnZ8ZtMD3h+9YvnV794fvWL51e/9H1+K/voCB88JiIiojqJIYeIiIjqJIYcIiIiqpMYcoiIiKhOYsghIiKiOokhh4iIiOokhhwiIiKqkxhyiIiIqE5iyCEiIqI6iSGHiIiI6iSGHCIiIqqTGHKIiIioTjLaWckKCgoQHh6OHTt2IDc3F4sWLUKzZs0qvb9KpcKGDRsQGxsLpVIJX19fTJ8+HRYWFvormoiIiIyGUV7J2bRpEzw8PLBx40akp6djy5YtyMzMrPT+CoUCQUFBeOONN9ChQwf06dMHS5cuRffu3ZGRkaG/womIiMhoGGXI6dWrF27cuIHNmzeje/fuVd5/yZIl2L17N8LCwjBnzhzMmDEDMTExuHnzJhYsWKCHiomIiMjYGGXIadq0KaysrKq9/4oVK+Dj4wNfX1+xzdXVFSNHjsTatWtRWFioizKJiIioHKnZuTh9846hyzDOkKONrKwsxMfHw8fHp8w6X19f5OXl4dy5cwaojIiIqO47e/M2pn67Au9t2oEb91MMWovRPnhcXUlJSQAAFxeXMutK25KSktCtW7dy93/w4AFSUjT/URISEgAASqUSSqVSZ7UqlUqoVCqd9kkP8fzqF8+vfvH86hfPr35sPXwSX/4VCaVKDQCYu3ITVr82HdYW5jrpv6r/XnUu5CgUCgCAqWnZjyaTyQCgwttVS5cuxUcffVTuupycnCo9AP0kKpUKubm5AACpVKqzfqkEz69+8fzqF8+vfvH86pZCqcKSnbHYdeYiVMLD9pTsHJy6eh0d3Rvr5Dg5OTlV2r7OhRxra2sAQH5+fpl1eXl5AAAbG5vH7j979myMHTtWoy0hIQEjR46EjY0N7OzsdFZraSK1tbUtN5SRdnh+9YvnV794fvWL51d37mdmY/76rTDJzcTY5s7YlpiKQpUabva2WPTMOHi6uersWBX9/i5PnfuX9fDwgEwmQ2JiYpl1pW2enp6P3d/Z2RnOzs7lrjM1NdX5N4NUKtVLv1SC51e/eH71i+dXv3h+tXfi2k28sXITmptL0MPVDgDg38QeGVYOmBs8CE1cXXR6fqvaV537l5XJZOjfvz/2798PtVoNE5OHz1bHxMTAw8MDXl5eBqyQiIiodhMEARv3H8HibVHwcbKGj1PJFZZ8pQpuLVvj3cDByM7OMnCVdeDtqo0bN2LMmDGIj48X2+bPn4+kpCR89913YltkZCTi4uLw1ltvGaJMIiKiOqFQUYz31v+FL7ZGomdDG42A075bT7w4PAAmJhIDV1nCKK/kXL16VRy0rzS8vPHGG7C1tUWDBg3w22+/idueP38eW7ZswZw5c8Q2Pz8/LF26FHPnzsWmTZtgaWmJAwcO4M0338TMmTNr9LMQERHVFXfTMzH3t42Iv3MPAxvZoZ19yZh2+SoBAUHBaNvcw8AVajLKkOPo6IgJEyaUu87MzExjeeLEifD29kbbtm012mfNmoVJkybh6NGjUCqVWLduHVxddffwExERUX1y5Mp1zF/1J7Lz8jHEzR5etpYAAAVMMGncU3B2dDBwhWUZZchxcHDAmDFjKrVt+/bt0b59+3LX2draws/PT5elERER1SuCIGD13oP4Lmw31IIAO7kpmln/O+6NzAzPjhuj1SwF+mSUIYeIiIgMr6BIgQ83/o2oU+fFtkyFEnsf5GJUG3eMGTEc5ua6GehPHxhyiIiIqIzbqen4v1834uq9+xrtXo1d8fVz4+HmaA+JxDgeMH4chhwiIiLScODiVSxYsxk5BYUwM5EgoKkjjj7IRpd2rfH++OGwkMsNXWKlMOQQERERAEClVuPnqFgs37UPgiDAQmqCUA8nOJnL0KSlC8aEDK41AQdgyCEiIiIAaTm5WLB6M45evQEAsDI1wXAPJ9iblcz72KJZM51ObVQTGHKIiIjquWNXb2DBms1IzS6ZtNRGJsVwDyfYyktigpeXF/r3768xi0BtwJBDRERUT6nVavwWfQBLI2KgFkqmD7eTm2K4hxOsZSWzs7dr1w59+vQx+oeMy8OQQ0REVA9l5Obh3bVb8U98gtjmaGaK4R4NYWFacsWmc+fO6N69e60MOABDDhERUb1z+sYtzF/1J+5nZmu0D3V/GHB8fX3RpUuXWhtwAIYcIiKieqN09OIlO/ZAqVZrrBvVsytmBg/C7p070aZNG3Tq1MlAVeoOQw4REVE9kJ1fgPfX/4XY85c12s1lMrw9dhiGd/cGADz11FMwNa0b8aBufAoiIiJ6rPO3kvDm75twNz1To71HE2eM7+mNwf8GHAB1JuAADDlERER1liAI2Lj/CBb/vQtKlUpj3Vjv1nAuzkPCxQtwsbd77GTXtRlDDhERUR2UU1CIjzb+jT1nLmq0y01NMWdQd+QmJUIAIJVKYWNjY5gi9Ywhh4iIqI6Jv3MPb/y+CbdT0zXamzo5YM7Abrh2sWRWcVNTUwQEBMDNzc0QZeodQw4REVEdIQgCth4+gS+2REKhVGqs8/Nuj7EdWuD0yZMAALlcjsDAQLi6uhqi1BrBkENERFQH5BcVYeGmHYg4cVaj3VQqxdwR/mhlIcWpfwOOmZkZQkJC4OTkZIhSawxDDhERUS2XcO8B3lj5B248SNVob+xghy+fGQdLZRH27NkDALCwsEBISAgcHBwMUWqNYsghIiKqxbYfPY3P/tyBwuJijfaBHdrg40kj0cDSAoIgoFWrVkhOTkZISAhsbW0NVG3NYsghIiKqhbLzC7B4WxT+PnpKo93UxASvhfphysBe4pQMEokEAwcORGFhISwtLQ1RrkEw5BAREdUigiAg7NgZfLt9F9Jz8zTWudg1wBdPj0VHdzecOXMGHTt2hFRaMpu4iYlJvQo4AEMOERFRrXHlbjI+3xyOU9dvlVnXp60nFk4eBRtzM+zatQu3b9/GgwcPMHToUJiYmBigWsNjyCEiIjJyOQWF+ClyL/44cBSq/0ysKTc1xczAgXhmcB8olUpERkbi3r17AICCggIolUrI5XJDlG1wDDlERERGShAERJw4i6//3oW0nNwy6/u09cT8p4Lg3tARRUVFiIyMxIMHDwAAjRs3RkBAAGQyWU2XbTQYcoiIiIzQ1bv38fnmcJy8nlhmXSN7O7z5VBAGdmgNiUSCgoICREREIC0tDQDQtGlT+Pn51anJNqujfn96IiIiI5NbWIhlkbHYsP9ImVtTMqkUTw/ug+l+/WDx7y2o/Px8hIeHIyMjAwDQvHlzDB48WHzguD5jyCEiIjICgiAg8uQ5fPP3LqRk55RZ37tNK8wfHQyPho5im0qlQlhYGLKysgAAnp6eGDBgQL190Pi/GHKIiIgMLOHeA/xvSziOJ9wss87VzhZvjArE4E5txXFvSkmlUnh7eyMuLg5t27ZF3759y2xTnzHkEBERGUheYRF+jorF+rjDUP7n1pSpVIppg3pjhl9/WJg9/u2o1q1bw9bWFi4uLgw4/8GQQ0REVMMEQcCuUxew6O+dSMkqe2uqZ+uWeOupYDRzKTuBZmpqKuRyORo0aCC21eWZxLXBkENERFSDrien4H9bwnH06o0y61zsGmDeyEAM7dyu3KsyycnJiIyMhLm5OYYPHw4rK6uaKLnWYsghIiKqAflFRfglKg5rYw+VvTVlYoKpg3pjhn9/WJqZlbt/UlISoqKioFQqUVxcjLt378LT07MmSq+1GHKIiIj0SBAE7DlzEYu27cT9zOwy67t7Nsdbo0PQwrXhY/u4desWdu/eDZVKBYlEgv79+zPgVAJDDhERkZ7cvJ+K/22NwOHL18qsa2hrg3kjA+Hv3b7CB4avX7+OmJgYqNVqSCQSDB48GC1bttRn2XUGQw4REZGOFRQpsHz3PqzeexBKlUpjnamJCSYN6IkXAwbCyrz8W1Olrly5gri4OAiCABMTEwwdOhTNmjXTY+V1C0MOERGRjgiCgJizl/DVXzuRnJlVZr1vq2Z4a3QIWjVyfmJfly9fRlxcHICS8XACAgLQpEkTnddclzHkEBER6UBiShq+2BKBg/EJZdY1bGCD/xsRgMCuHSo9lk3Dhg1hZmYGtVqNwMBANGrUSNcl13kMOURERFooUCjw6+79WBXzD4r/c2tKamKCSf174MXAgbA2N69Svw4ODggODoYgCHB2fvKVHyqLIYeIiKgaBEHA3nPx+OqvnbiXkVlmvU9LDywYE4JWjVwq3V9GRgYcHBzEtoYNH//GFT0ZQw4REVEV3UpJw5d/ReLAxatl1jnaWOP/RgQg2KdjpW9NCYKAgwcP4tKlSwgMDOSzNzrCkENERFRJ2fkF+D36ANbGHYZCqdRYZyKRYEK/HpgVNAg2FpW/NaVWq7Fv3z5cuXIFAHDo0CGMHj2aM4nrAEMOERHRExQoFNiw7whWRh9ATkFhmfVdWrhjwZgQeDWu2hxSarUaMTExuH79OgDAzs4OwcHBDDg6wpBDRET0GMUqFf46dAK/7IpDanZumfUO1lZ4fbg/hnXrXOUZwJVKJfbs2YNbt24BABwdHREcHAwLCwud1E4MOURERGWo1WrsPHUeP0Xuxe3U9DLrZVIpxvXthhcDBqKBZdVDSXFxMXbt2oWkpCQAJQ8YBwUFwbyKb2BRxRhyiIiI/iUIAvZfvIofwqNx5W5ymfUmEgmGdeuMmYGD0NjBrlrHUCgU2LlzJ5KTS/pv1KgRAgICIJfLtSmdysGQQ0REBODU9UR8v2MPTl2/Ve76wZ3a4qXgwWjpqv2YNep/ZyFv0qQJ/P39YWrKX8f6wLNKRET12pW7yViyIxr7L14pd303z+Z4NWQoOjbTzWvdcrkcQUFBOH36NHx9fSGVSnXSL5XFkENERPXS3fQsLArfi6jTFyAIQpn17Zo2xivDhqKnV4sqP1T8X0VFRZDL5WI/ZmZm6NGjh1Z90pMx5BARUb2SkpWDn3fuxV9HTkH1722jR3k0dMTLIUMwtHM7rcMNAGRnZ2PHjh3w9PREt27dtO6PKo8hh4iI6oXs/AL8HvMP1scdRmFxcZn1LnYNMDNwIEK7ecNUR7eQMjIyEB4ejvz8fJw6dQqNGjXiaMY1iCGHiIjqtAKFAhv3HcFvjxnIz87KEs8N7YtxfbrDXC7T2XFTU1MRERGBwsKSY3p7e8PNzU1n/dOTMeQQEVGdVKxSYdvhk/glKg4p2Tll1pvLZJgyoCeeHtK3StMwVMaDBw8QEREBhUIBAOjWrRu6dOmi02PQkzHkEBFRnaJWqxF16gKWRsaUO5CfqVSKMb18MLpbRzR3a6zz17fv3r2LqKgoFP97S6xXr17o2LGjTo9BlcOQQ0REdYIgCDhw6SqW7Ch/ID+JRIJhvp0xM3AgXGxtkJmZqfMabt++jV27dkGlUgEA+vfvjzZt2uj8OFQ5DDlERFTrnbp+C0t27MHJ64nlrh/UsQ1eCh6CVo1KBvJT/mcGcV25e/cuVCoVJBIJBg4cCE9PT70chyqHIYeIiGqtK3eT8UN4NPZdKH8gP99WzfDqsKHo1KxpjdTTvXt3KJVKNG7cGM2bN6+RY9LjMeQQEVGtczs1Hct27kXEiXPlDuTXtkkjvDJsKHq1bqmTsW4qolarYWJiAqDkllifPn30ejyqPIYcIiKqFYpVKsSdv4yth07g0OVr5YYb94aOeCl4MPw6txODhz6dP38eN27cQFBQEOefMkL8FyEiIqN2OzUdfx06gb+PnkZaTm652zjbNsCLgQMwvHsXyGpoLqhTp07h2LFjAIDY2FgMHTq0Ro5LlceQQ0RERqdYqUTMuXhsPXQCR65cf+x2tpYWeG5oP4zvq9uB/CoiCAKOHz+OU6dOAQDMzc3h7e1dI8emqmHIISIio5H4IBVbDp1A2LEzyMjNe+x2LVwbYnQvHwzv3kXnA/lVRBAEHDp0COfPnwcAWFpaIiQkBPb29jVWA1UeQw4RERlUUXExos9ewtZDJ3A84eZjtzOTmcLfuwNG9/ZB52ZN9f5A8X+p1WocOHAA8fHxAABra2sMGzYMDRo0qNE6qPIYcoiIyCCuJ6dg66ETCDt2Gln5BY/dzquxC57q5YNgn05oYGlRgxU+pFarERsbi4SEBACAra0tQkJCYG1tbZB6qHIYcoiIqMYUKoqx58xFbDl0HKeu33rsduZyGYK6dsRTvXzQwd2txq/a/FdqaiquXbsGALC3t0dISAgsLS0NWhM9GUMOERHp3dW797H10AnsOH6m3JnAS7Vp0gije/kgyKcjrM1r7lmbJ3F2dsaQIUNw5swZBAUFwdyIaqPHY8ghIiK9KChSIOr0BWw9eBxnE+88djtLMzmCunbE6N6+aNe0cQ1WWDUtWrRAs2bNamT8HdINhhwiItKp+Dv3sOXQCUSeOIvcwqLHbtfe3Q2je/kgsGsHWJqZ1WCFT1ZUVITY2Fj06NEDdnZ2YjsDTu3CkENERFrLLyrCzpPnseXQCVy4lfTY7azNzRDs2wlP9fRBmyaNarDCyisoKEBERATS0tKQmpqKESNG8AHjWoohh4iIqu3i7bvYcvA4Ik+eQ36R4rHbdWrWFE/18kGAd3tYmMlrsMKqycvLQ3h4ODIzMwEALi4usLAwzBtdpD2GHCIiqrJryQ/w/vptFV61sbEwxzDfzniqlw88G7vUYHXVk5OTgx07diAnJwcA4OXlhf79+/MWVS1mtCFHpVJhw4YNiI2NhVKphK+vL6ZPn17pRJ2amopVq1bh8uXLUCqVaNGiBaZMmYJmzZrpt3AiojouMy8fM39ajZSsnHLXd2nhjtG9fDG0c7sam2pBW5mZmQgPD0deXskoy+3atUOfPn0M/uo6acco46lCoUBQUBDeeOMNdOjQAX369MHSpUvRvXt3ZGRkPHH/uLg4NGvWDBs2bEDXrl3Rp08f7N+/H56enli3bl0NfAIiorpJEAR88sf2MgHH1tICUwb0wta3XsbKV6djWLfOtSbgpKenIywsTAw4nTp1YsCpI4zySs6SJUuwe/duHDt2DL6+vgCA0NBQeHp6YsGCBVi2bFmF+7/99tuQyWTYu3cvbGxsAADPPPMMOnfujLlz52Ly5Ml6/wxERHXR30dOIfrsJXHZXCbDe+OHY2jntjCT1Y5Q8yhBELB3714UFJSMuOzj44OuXbsy4NQRRnklZ8WKFfDx8REDDgC4urpi5MiRWLt2LQoLHz+QFABkZGTAzc1NDDgAIJVK0bp1a2RmZkIQBL3VTkRUV91KScMXWyM12uaNDECIb6daGXAAQCKRYMiQIbCwsEDPnj3h4+PDgFOHGF3IycrKQnx8PHx8fMqs8/X1RV5eHs6dO1dhH+PGjcPly5dx9OhRsS0xMRGxsbEYM2YMv4CJiKqoWKXCO2u3okDx8A2qgR1aY3Rv3wr2qh3s7Owwbtw4dOrUydClkI4Z3e2qpKSSJ/VdXMo+iV/alpSUhG7duj22jw8++ABOTk4ICQlB+/btIZfLcezYMcyePRvvv/9+hcd/8OABUlJSNNpKJ2RTKpVQKpVV+jwVUSqVUKlUOu2THuL51S+eX/0ytvP7885YnHtk1GJHGyu8MyYEKpXKgFVVz61bt1BUVAQHBwfx/EqlUqM513WBvr5+q9qf0YUcxb9/JZiali1N9u/l0Cfdrjp79iy+++47uLu7Y+zYsZDL5ZBKpVi+fDkGDBgAf3//x+67dOlSfPTRR+Wuy8nJEcdO0AWVSoXc3FwAJd9gpFs8v/rF86tfxnR+z9++i9+iD2i0zR02BBJlsU5/JtaEO3fu4NixYwCAjh07onnz5gY/v3WRvr5+S1/vryyjCzmlo0rm5+eXWVf65Pujz9r8lyAI4i2pgwcPwuzfocJnzJiB4OBgjBs3DtevX4eDg0O5+8+ePRtjx47VaEtISMDIkSNhY2OjMby3tkoTqa2tbbmhjrTD86tfPL/6ZSznN7ewCF9uj4b6kWcZx/ftBn+fzgarqbquXr2KY8eOQRAEmJiYwMLCwuDnt67S19dvRb//y2N0/7IeHh6QyWRITEwss660zdPT87H737p1CwkJCXjllVfEgFNqxIgRiIyMxPHjxx97NcfZ2RnOzs7lrjM1NdX5N4NUKtVLv1SC51e/eH71yxjO76K/t+NuRqa43NLVGa8PD6h1/+YXLlzAP//8A6DkZ/mQIUNgZWVl8PNbl+nj67eqfRndg8cymQz9+/fH/v37oVarNdbFxMTAw8MDXl5ej92/NNhkZWWVWVfaZm5ursOKiYjqpqiT57Hj2BlxWSaV4rOpo2vN+Delzpw5IwYcmUyG4OBguLm5GbgqqglGF3IAYP78+UhKSsJ3330ntkVGRiIuLg5vvfWWxrYbN27EmDFjEB8fD6DkVfM+ffpg8+bNOHPm4TdnUlISfvzxRzRp0gQ9evSomQ9CRFRLJWdkYeGfYRptrw4bitZurgaqqOoEQcDx48dx5MgRACV/BA8bNgyurrXnM5B2jDLk+Pn5YenSpXjnnXfQq1cvDBkyBCNHjsSbb76JmTNnamx7/vx5bNmyBampqWLbxo0bMXDgQHTr1g39+/fH0KFD0aZNGzRu3Bjh4eFlbmMREdFDKrUa767bipyChy959PBqgckDehqwqqq7cuUKTp48CQCwsLBAaGgoGjZsaOCqqCYZ7Y3IWbNmYdKkSTh69CiUSiXWrVtXbvqeOHEivL290bZtW7GtSZMmCA8Px+3bt5GQkIDi4mI0b968wmd5iIioxOq9B3E84aa4bGtpgY8njap1E1W2bNkSV65cQXZ2NoYNGwZbW1tDl0Q1zGhDDlDyVLafn1+F27Rv3x7t27cvd13Tpk3RtGlTfZRGRFQnXbp9Fz9GxGi0vTd+OFzsGhioouozNTVFQEAAFAqF+OYu1S+1K5YTEZHeFCgUWLBmC5SPDPA3skcXDO3czoBVVZ5KpcKlS5c0pu6Ry+UMOPWYUV/JISKimvP137tw88HD5xubOjngzaeCDFhR5SmVSuzZswe3bt1CRkYGevXqxSl8iCGHiIiAfRcu489/jonLUhMTfDZlNCxrwYsaxcXFiIqKwt27dwGUTM+jUqk4/g0x5BAR1XdpObn4YMPfGm0v+A9Ax2ZNDFRR5SkUCkRGRuL+/fsAgEaNGiEgoPYNVkj6wa8CIqJ6TBAEfLBhGzJy88S2zs2bYrpfPwNWVTmFhYWIiIgQhxBp2rQp/Pz8GHBIxK8EIqJ67I8DR3Hg4lVx2crMDJ9OfgqmRj5pZX5+PsLDw5GRkQEAaNasGYYMGcLJNkkDQw4RUT11LfkBvtm+S6PtrdHBaOJU/gTGxkKhUCAsLEycqqdVq1YYOHBgrRvHh/SPXxFERPWQQqnEgtVbUFSsFNv8vdtjWDfjn11cLpejZcuWAIA2bdow4NBj8UoOEVE99EN4NK7cTRaXXewa4N1xobXmtWsfHx84OTnBw8Oj1tRMNY/Rl4ionjl65TrWxB4SlyUSCRZOfgoNLC0MWFXF0tLSUFj4cC4tiUSCZs2aMeBQhRhyiIjqkay8fLy77i+NUYGfHtQb3TybG7Cqit2/fx9hYWGIjIyEQqEwdDlUizDkEBHVE4Ig4JNNYXiQlS22tWnSCC8FDzZgVRW7e/cuwsPDoVAokJKSgjt37hi6JKpF+EwOEVE9sf3oaew5c1FcNpOZ4rMpoyEz0nFlbt26hd27d0P171xa/fv3R4sWLQxcFdUmxvmVTUREOnU7NR1fbI3QaJs7IgAtXBsaqKKK3bhxA9HR0VCr1ZBIJBg0aBBatWpl6LKolmHIISKq45QqFd5ZswX5RQ+fZ+nf3gtj+3QzYFWPd/XqVcTGxkIQBJiYmGDo0KFo1qyZocuiWoghh4iojlu+ax/OJj58lsXB2gofThhhlG8mxcfHY9++fQAAqVQKf39/NG3a1MBVUW3FkENEVIeduXEby3fFabR9NGkkHGysDVRRxWxsbCCVSmFiYoLAwEA0atTI0CVRLcaQQ0RUR+UVFuGdtVugfuR18fF9u6NfOy8DVlUxNzc3+Pn5wdzcHM7OzoYuh2o5hhwiojrqi60RuJOWIS63cGmI14f7G7CisgRBQF5eHqytH15Zcnd3N2BFVJdwnBwiojpo1+kL2H70tLhsKpXis6mjYS6XGa6o/xAEAQcPHsSWLVuQlpZm6HKoDmLIISKqY+5nZmHhpjCNtpdDBqNNE+N5vkWtVmPfvn24cOECioqKsH//fo1RmIl0gberiIjqELVajffW/YXs/AKxrZtnc0wb2NuAVWlSq9XYu3cvrl27BgCwtbXF0KFDjfJtL6rdGHKIiOqQNbGHcPTqDXG5gaUFPpk0CiYmxnHhXqlUIjo6GomJiQAABwcHBAcHw9LS0sCVUV3EkENEVEfE37mHJeHRGm3vjguFq72tgSrSpFQqERUVhaSkJABAw4YNERQUBHNzcwNXRnUVQw4RUR1QoFDg7TVboPx3nicACO3mDX/v9gas6iGFQoGdO3ciOTkZAODq6orAwEDI5XIDV0Z1GUMOEVEd8O323bh+P0VcdnO0x/zRQQasSJNSqUR+fj6AkrFw/P39IZMZz5teVDcx5BAR1XL7L17BHweOistSExN8NmU0rI3oNpClpSVCQkJw+vRp9OrVC6ZGOvM51S3G8SQaERFVS3pOLj5Yv02j7Xm//ujc3PDzPSmVSo1lGxsb9OvXjwGHagxDDhFRLSUIAj7Y8DfSc/PEtk4eTTDDv78BqyqRnZ2NP//8E/Hx8YYuheoxhhwiolrqz3+OYf/FK+KypZkcn04dDVOp1IBVAZmZmQgLC0NOTg727duHBw8eGLQeqr94zZCIqBa6cT8FX/+9S6Nt/lPBaOrkYKCKSqSlpSE8PByFhYUAgM6dO6Nhw4YGrYnqL4YcIqJaplipxII1W1BYXCy2De3cDsO7exuuKAAPHjxAZGQkioqKAAC+vr7o0qULRzImg2HIISKqZX6MiEH8nXvisrNtA7w3LtSgYeLevXvYuXMniv8NXj179kSnTp0MVg8RwJBDRFSrHLt6A6v2HtRo+2TyKNhaGW5ahDt37iAqKgqqfwci7NevH9q2bWuweohKMeQQEdUS2fkFeHfdVo3ZuqcN6o0eXi0MWBVw5coVqFQqSCQSDBw4EJ6engath6gUQw4RUS0gCAIWbgrD/cxssa21myteDhliwKpKDBgwAEqlEq1atUKLFoYNXESPYsghIqoFdhw7g12nL4jLZjJTfDZ1NOQGGlhPEATxGSCpVAo/Pz8+YExGh+PkEBEZuaS0DPxvS4RG2+vD/dHS1dkg9Zw/fx579+6FWq0W2xhwyBjxSg4RkRFTqtR4e+0W5P37WjYA9GnrifF9uxukntOnT+Po0ZJ5sszNzdG7d2+D1EFUGQw5RERG7PeYAzhz47a4bG9thY8mjqjxKyeCIOD48eM4deoUgJKA4+XlVaM1EFUVQw4RkZG6eCcZy3fv02j7aOIIODWwqdE6BEHA4cOHce7cOQAPZxS3t7ev0TqIqoohh4jICOUVFuF/f++CSv3wdfGxvX3Rv33rGq1DEATs379fnGjT2toaISEhsLW1rdE6iKqDIYeIyAgt3r4LdzOyxOVmzk74v5EBNVqDWq1GbGwsEhISAAANGjTAsGHDYG1tXaN1EFUXQw4RkZHZc+Yith89LS6bSqX4fOpoWMjlNVrHnTt3xIBjb2+PkJAQWFoabmRloqriK+REREbkfmY2Pv5ju0bbS0GD0LZp4xqvxd3dHT169ICTkxNCQ0MZcKjW0UnISUtLw4cffohBgwZpTMj2/fffIzc3VxeHICKq89RqNd5f/xey8wvEtq4tPDBtcB+D1dS5c2eMGDEC5ubmBquBqLq0Djm3bt2Ct7c3Fi9ejOLiYvHpewBITEzEypUrtT0EEVG9sC7uMI5cuS4uW5nJ8fHEEZCa1MxF96KiIuzduxf5+fka7VKptEaOT6RrWn/nzJ8/H23atMHNmzdx4MABjXUTJ07E6tWrtT0EEVGddzkpGd/v2KPRNid4EFzta+YtpsLCQuzYsQNXr15FREQEih4ZfJCottL6weOoqCgcPnwYjo6OZdZ5eXnh/Pnz2h6CiKhOK1QUY8GazShWqcS2YJ+OGNS+Zgbby8vLQ0REBDIyMgAAtra2MDXQnFhEuqT1V3Fubi5cXV3F5UdH4SwoKIBJDV1mJSKqrb4L243rySnicmMHO7w5KgjKwoIK9tKNnJwchIeHIzu7ZHZzT09PDBgwgD+7qU7Q+qu4RYsWiI6OFpcfDTl79uxB69Y1O3AVEVFt8s+lq9iw/4i4bCKR4NMpo2Ftbqb3Y2dlZWH79u1iwGnbti0GDhzIgEN1htZfyZMnT8bs2bMRHh4O1SOXWmNiYjBv3jxMmzZN20MQEdVJ6bl5eH/9No225/36o0sLd/0fOz0d27dvR15eHgCgU6dO6Nu3L2cTpzpF69tVb775Jvbt24dhw4bBysoKarUaDRs2RGpqKgIDA/Hyyy/rok4iojpFrVbjg/XbkJbzcJiNDu5umBEwoEaOvWvXLhQUlNwO69q1K3x8fBhwqM7ROuSYmZkhMjIS69evR1hYGJKTk8WBo6ZNm8aH14iIyvHTzr3Yf/GKuGwhl+OzqaMhq4HXtU1MTDBw4EBERETAx8cHnTt31vsxiQxBJwnE1NQU06ZN460pIqJK2HX6Apbv0pxdfP7oYLg3LPuWqr64urpi/PjxsLKyqrFjEtU0rZ/JmTdvnlbriYjqk/g79/D++r802sb26YaRPbro9bi3bt3CvXv3NNoYcKiu0zrkLF68WKv1RET1RXpuHl7/dSMKFcVim09LD7w5KlCvx71+/Tp27dqFnTt34sGDB3o9FpEx0esDM9nZ2ZDX8Ky5RETGqFilwhsrN+FeRqbY1sjeFl89Mw4yPT67eOXKFcTFxUEQBAAQHzYmqg+q9Z21YsWKCpeBkjlQoqOj4enpWb3KiIjqkEV/7cSJazfFZXOZDN9MnwgHG2u9HfPixYvidDumpqbw9/dHkyZN9HY8ImNTrZAzY8aMCpdLOTk5cYJOIqr3thw6jj8OHNVo+2jiSLRp0khvxzx79iwOHz4MAJDJZAgKCtIYnZ6oPqhWyLl69ar4/z09PTWWS1lZWcHV1ZXjLhBRvXbqeiI+3xyh0Tbdrx8CunbQy/EEQcDJkydx4sQJACXDfAQHB6Nhw4Z6OR6RMatWyGnVqpX4/3fv3q2xTEREJZIzsjD3tz+gfGQ0+P7tvfBS0GC9HfPChQtiwLGwsEBISAgcHBz0djwiY6b1025Dhw4FACiVSiQmJqK4uLjMNm3atNH2MEREtUqBQoHXf92A9Nw8sa25sxM+nTJar3NDtWrVCpcuXYJCoUBISAjs7Oz0diwiY6d1yCkoKMDrr7+O3377rdyAA0B8qp+IqD4QBAEf/7Edl+48HJfG2twc3z4/ETYW5no9trm5OUJCQqBUKtGgQQO9HovI2Gn958SHH36Ibdu24fPPPwcALFu2DPPnz4e7uztGjx7NB4+JqN75PeYfRJ44Jy6bSCT44ukx8HB20vmx1Go1bt68qdFmaWnJgEMEHYSczZs347fffsPcuXMBAC+++CL+97//ISEhAaamprCwsNC6SCKi2mL/xSv4fscejbbXQv3Qp63uh9NQKpXYtWsXdu3ahfPnz+u8f6LaTuuQc+vWLfTr1w8AIJFIxFtWMpkMX331FT755BNtD0FEVCvcvJ+KBau3aNyiD/bphGmDeuv8WMXFxYiKisKtW7cAAAkJCVCr1To/DlFtpnXIUSqVsLGxAQA0aNBA/IYDAFtbW1y7dk3bQxARGb2cgkLM+XUDcgsLxbZ2TRvj/fHDdT6UhkKhQEREBJKSkgAAjRo1QnBwsF4faCaqjXT6HdG1a1d888034l8x33zzDZo2barLQxARGR2VWo2312zBzQepYpujjTW+mT4B5nKZTo9VWFiI8PBw3L9/HwDQpEkTBAUFcQodonLodMKUuXPnIjQ0FBs2bIBcLkdycjKWLl2qy0MQERmdH8Ojsf/iFXHZVCrFomfHw8XOVqfHyc/PR3h4ODIyMgAAzZo1w5AhQyCVSnV6HKK6QuuQ8+hr4yEhIdi5cyfWrl0LABg5ciSeeuqpavWrUqmwYcMGxMbGQqlUwtfXF9OnT6/Sg8z379/HunXrcO7cOdjY2CAkJAQBAQHVqoeIqDyRJ87ht+gDGm3vjA1BlxbuOj1OYWEhwsLCkJWVBaBkPJyBAwfyFhVRBbQOObGxsbCzs4Ovry8AwN/fH/7+/lr1qVAoMGzYMJw7dw7z58+HlZUVvvnmG/z888/Yt28f7O3tn9hHREQEJk6cCH9/fwQFBQEAlixZggMHDvBhaCLSiUu37+KjjX9rtE3o1x2jevro/FhmZmZwdnZGVlYW2rRpg759+zLgED2B1iEnMDAQx44d00UtoiVLlmD37t04duyYGJ5CQ0Ph6emJBQsWYNmyZRXuf+XKFYwZMwbvvfceFixYILY/99xzSE5O1mmtRFQ/pefk4vVfN6LwkavZvq2aYe7IQL0cTyKRYMCAAXBzc4OnpyfnBSSqBK3/DGjZsiWcnZ11UYtoxYoV8PHxEQMOALi6umLkyJFYu3YtCh95e6E8CxcuhK2tLd54440y6zgLLxFpq1ipxLyVm5CcmSW2NbK3w1fPjINMh8/H5OTkQPXIvFcmJibw8vJiwCGqJK1DzltvvYWvvvpKF7UAALKyshAfHw8fn7KXe319fZGXl4dz586Vs2cJQRCwY8cODBgwAMePH8frr7+OSZMmYf78+Thz5ozO6iSi+uvLrZE4eT1RXDaXy/Dt8xNhb22ls2M8ePAAsbGx2Lt3L8e/IaomrW9XyWQyJCYmonv37hg+fDjc3Nwgk2m+MjllypRK91c67oOLi0uZdaVtSUlJ6NatW7n7p6SkICMjA4cPH8bQoUMxd+5cdOjQAZs3b8bixYuxfPlyPPvss489/oMHD5CSkqLRlpCQAKBkTCClUlnpz/IkSqUSKpVKp33SQzy/+lVfz+/mgyfw58HjGm0fTRiBli5OOjsX9+7dw+7du6FUKnHr1i0kJiZyOA4dq69fvzVFX+e3qv1pHXKmTp0q/v/HPZtTlZCjUCgAAKamZUsrDU8V3a7Kz88HACQmJmL79u0IDQ0FUPI8zuDBg/HSSy9h2LBhaNiwYbn7L126FB999FG563JycpCZmVnpz/IkKpUKubm5AMBXQPWA51e/6uP5PZuYhK+27dRom9K3G3zcG+nsZ0NycjIOHz4sXr3p3LkzbGxsdPqzh+rn129N0tf5zcnJqdL2Woecim4dVYe1tTWAh2HlUXl5eQAgjrBcntJ1dnZ2YsABSh7amzZtGmJjYxEbG4uxY8eWu//s2bPLrEtISMDIkSNhY2MDOzu7Kn2eipQmUltb23JDHWmH51e/6tv5vZeeiU+27oTqkVtH/dt74dXhATAx0c0zMjdv3hQDjkQiQZcuXdChQ4d6cX5rWn37+q1p+jq/Ff3+L4/WR+7QoYO2XWjw8PAQb4H9V2mbp+fjJ7pzdHSEg4NDuWHE0dERACr8i8jZ2fmxD1Kbmprq/JtBKpXqpV8qwfOrX/Xl/BYoFJi36k9k5D3846uFa0N8NnU05Doa0TghIQF79+6FIAgwMTHBwIEDYWdnVy/Or6HUl69fQ9HH+a1qX0Y3yIJMJkP//v2xf//+Mg/bxcTEwMPDA15eXhX2ERAQgDt37ohXfkpdvnwZQMkbYURElSEIAj7c8DcuJz0cfsLGwhzfTp8Ia3NznRwjPj4eMTExEAQBUqkUAQEBaNasmU76JqrPjC7kAMD8+fORlJSE7777TmyLjIxEXFwc3nrrLY1tN27ciDFjxiA+Pl5jf7Vajffff19su337Nr777jt07NgRAwYM0P+HIKI64bc9+xF16ry4bCKR4Iunx8K9oaPOjlE6qJ9MJkNQUBAfMibSEaO8Rufn54elS5di7ty52LRpEywtLXHgwAG8+eabmDlzpsa258+fx5YtWzBnzhyxrXPnzti0aROef/55REREoGnTpjhy5AjatWuHP/74gw+ZEVGl7LtwGT9ExGi0vT7CH73btNLpcby8vKBWq+Hg4KDzcceI6jOjDDkAMGvWLEyaNAlHjx6FUqnEunXryh3Ib+LEifD29kbbtm012keNGoWgoCAcOXIEWVlZ+Prrr3X+/BAR1V3Xk1OwYPUWCIIgtg3r1hlTBvTSum9BEFBUVATzR253tWnTRut+iUiT0YYcoOSpbD8/vwq3ad++Pdq3b1/uOnNzc96aIqIqy84vwJxfNyCvqEhsa+/uhvfGhWo92rAgCDh06BASExMRGhoqvlFKRLqnk2dy0tLS8OGHH2LQoEHo1KmT2P7999+L78kTEdUGKrUaC1Zvxq2UNLHNqYE1vn5uAsxk2r1JpVarsX//fpw/fx45OTnYt2+ftuUSUQW0Djm3bt2Ct7c3Fi9ejOLiYo1xcxITE7Fy5UptD0FEVGO+37EH/8QniMsyqRRfPzcBLnYNtOpXrVYjNjZWfEnC1tYW/fv316pPIqqY1iFn/vz5aNOmDW7evIkDBw5orJs4cSJWr16t7SGIiGpE+PGzWBXzj0bbO+NC0amZdm87qVQq7NmzR5wixt7enreqiGqA1s/kREVF4fDhw+JAe4/y8vLC+fPny9mLiMi4XLiVhI//+FujbVL/nhjZo4tW/SqVSuzevRu3b98GADg5OSE4OFjjoWMi0g+tQ05ubq7GW0+PPpRXUFAgjv9ARGSsUrNz8H+/bURR8cPJ/7p7Nsf/jfDXql+FQoGoqCjcu3cPQMkkw0FBQZDL5Vr1S0SVo3UCadGiBaKjo8XlR0POnj170Lp1a20PQUSkNwqlEnNX/oH7mdlim5ujPb58ZhxMtRxTKz8/H+np6SV9urkhODiYAYeoBmkdciZPnozZs2cjPDwcKpVKbI+JicG8efMwbdo0bQ9BRKQXgiDg883hOHPjtthmIZfj2+kTYWdlqXX/dnZ2CAkJQatWrRAQEACZlm9nEVHVaH276s0338S+ffswbNgwWFlZQa1Wo2HDhkhNTUVgYCBefvllXdRJRKRzfxw4ir8On9Ro+3TKU/Bs7FLtPtVqtcZteicnJwwePLja/RFR9WkdcszMzBAZGYn169cjLCwMycnJcHJyQmhoKKZNm8bZXYnIKB27egNf/bVTo21m4EAM7tT2MXs8WXZ2Nnbu3IlevXpx/ikiI6CTBGJqaopp06bx1hQR1QpJaRl44/dNUKnVYtvgTm3xgn/1R0jPzMxEeHg48vLysGvXLowePRp2dnY6qJaIqkvrZ3KmTZvGUTuJqNYoKFLg9V83IDMvX2xr1cgZn0waVe23QdPS0hAWFoa8vDwAJdPN2Nra6qReIqo+rUPO9u3bMWDAAHh5eeF///uf+KokEZGxEQQB763/C1fu3hfbbC0t8O30ibAyN6tWnykpKdixYwcKCgoAAD4+PujRo4fWc1wRkfa0Djn37t3DmjVr4ObmhrfffhtNmzbF8OHD8ffff0OpVD65AyKiGrJi9z7sOXNRXJaamODLZ8ahiZNDtfpLTk7Gjh07UPTvRJ49e/aEj48PAw6RkdA65FhYWGDKlCnYu3cvEhIS8NZbb+HUqVMYOXIkmjRpgvnz5+uiTiIirew9F48fI2I02uaOCEAPrxbV6u/OnTuIiIhAcXExAKBv374aExQTkeHpdDjiFi1aYOHChUhMTERYWBjkcjm+/PJLXR6CiKjKEu49wDtrt2i0jejeBRP796hWf4Ig4PTp01AqlZBIJBg4cCDatWuni1KJSId0/n53QkICVq5cid9//x13795Fo0aNdH0IIqJKy8rLx+u/bkB+kUJs6+TRBO+MG1bt20oSiQR+fn6IjIxEp06d0KJF9a4GEZF+6STk5OfnY/Pmzfjtt98QFxcHU1NTBAcHY/r06QgJCdHFIYiIqkypUmH+6s24nZoutjW0tcHi5yZAruUYXmZmZhgxYgSfvyEyYlqHnBdffBEbN25EdnY2PD098fnnn+OZZ57RmLSTiMgQvgvbjcOXr4nLclNTfP3cBDS0talyXxcuXEBeXh66d+8utjHgEBk3rUPOmjVrMHr0aDz//PMYMKD6A2kREelS2NHTWBN7SKPtvXGh6OjRpMp9nTlzBkeOHAFQcgWnc+fOOqmRiPRL65Bz7949DnpFREblXOIdfLIpTKNt6sBeCO3uXaV+BEHAiRMncPJkyfxWZmZmaNy4sa7KJCI90zrkMOAQkTFJycrB//22EYpHxunq2bolXgv1q1I/giDgyJEjOHv2LICS4TJCQkLg4FC9MXWIqOZVOeQsW7YMADBz5kyN5YqUbktEpE8KpRJzf9uIlKwcsa2pkwO+mDYGplJppfsRBAEHDhzApUuXAABWVlYYNmwY/6gjqmWqHHJmzZoF4GFwKV2uCEMOEembIAj45I8wnE28I7ZZmsnxzfSJsLWyrHQ/arUacXFxuHr1KgCgQYMGCAkJgY1N1R9WJiLDqnLIuX37doXLRESG8EtUHMKOndZo+2zqaLRq5Fylfq5duyYGHHt7e4SEhMDSsvIhiYiMR5VDTpMmTSpcJiKqaWFHT+OnnXs12l4KHoyBHdpUua9WrVrh/v37uH//PoKDg2FhYaGrMomohmk9rcO8efO0Wk9EpI0jV67jo41/a7QN69YZz/v1r1Z/EokEffr0QWhoKAMOUS2ndchZvHixVuuJiKor4d59zP1tI5RqtdjW3bM5Phg/vNID9RUVFeHgwYPiRJtASdCRy+U6r5eIapbO5656VHZ2Nn9QEJFePMjKxss/r0NuYZHY1tLVGYueHQ9ZJadsKCwsREREBFJTU5GRkYGAgACYajndAxEZj2p9N69YsaLCZaDkr6Po6Gh4enpWrzIiosfILyrCq8vXIzkzS2xr2MAGP7w4GQ0sK3eLKT8/H+Hh4cjIyAAAyGQyTtNAVMdUK+TMmDGjwuVSTk5OWLlyZXUOQURULqVKhTdX/Yn4O/fENgu5HEtemIxG9naV6iM3Nxc7duxAdnY2gJKHjQcOHAgTE63v4BOREalWyCl9vRIAPD09NZZLWVlZwdXVlX8ZEZHOCIKA/22JwIGLD3/mSE1M8OUzY9GmSaNK9ZGVlYXw8HDk5uYCANq0aYN+/frxZxVRHVStkNOqVSvx/+/evVtjmYhIX36PPoDNB49rtC0YHYx+7bwqtX96ejrCw8NRUFAAAOjQoQN69erFgENUR2n9hN3QoUN1UQcRUYV2njyH73bs0Wh7dkhfjOnTrVL7K5VKREREiAGnS5cu8PX1ZcAhqsM4dxURGb2T1xLx3rq/NNoCu3TAKyFDKt2HqakpevfujejoaHTr1g3e3t46rpKIjA3nriIio3bzfirm/LoBxSqV2NalhTs+mjSyyg8Kt2jRAg4ODrCzs9NxlURkjDh3FREZrfScXLz0y1pk5xeIbR4NHfHN9Ikwk8meuP/t27dhY2OjEWoYcIjqD85dRURGqUChwKvL1yMpLUNss7e2wo8vToFdJWYVv3HjBqKjo2Fubo7hw4ejQYMG+iyXiIyQXgaFuH//Pnbv3o2UlBR9dE9EdZxKrcbba7bg/K0ksc1cJsP3z09CEyeHJ+5/9epV7NmzB2q1GoWFhcjMzNRjtURkrLQOOWfPnsUzzzwjLp8+fRqenp7w9/eHl5cXLl26pO0hiKieWbwtCnvPxYvLEokEn00djY7Nnnzl+OLFi9i7dy8EQYBUKkVgYCDc3d31WS4RGSmtQ86HH36IUaNGicuffvopnJ2d8eeff6Jv37749NNPtT0EEdUj6+IOYf2+wxpt80YGYHCntk/c9+zZszhw4ACAkmkagoODeUudqB7Tepycw4cP45dffgEAqNVq7NmzB4sWLcKYMWPQpUsXDBw4UNtDEFE9EXP2EhZti9Jom9S/JyYP6FXhfoIg4NSpUzh+vGSgQDMzMwQFBcHZ2VlvtRKR8dM65GRlZcHComRCvDNnziAzMxODBw8GALi5uSE1NVXbQxBRPXDu5h0sWLMZgiCIbYM7tcXckQFP3PfMmTNiwLGwsEBwcDAcHR31VisR1Q5a367y8PBAVFTJX17r16+Hu7s7mjdvDgBITExEs2bNtD0EEdVxt1PT8eqK9SgqVoptHT2a4NMpT0FaibFwmjdvDktLS1hZWSE0NJQBh4gA6OBKznPPPYfJkyejbdu2OH36NN59911xXXR0NPz9/bU9BBHVYZl5+Xj557XIyM0T25o42uO75yfCQi6vVB+2trYICQmBVCrlq+JEJNI65MydOxcymQyxsbEYPnw4FixYIK7bv38/Fi5cqO0hiKiOKiouxpwVG5CYkia22Vpa4McXp8DBxvqx+6nVaty/fx+NGj2cedze3l6vtRJR7aN1yJFIJHjttdfw2muvlVm3YcMGbbsnojpKrVbjvXV/4fSNW2Kb3NQU3z4/ER7OTo/dT6lUIjo6Grdu3cLQoUPF2+NERP+ldcgBgKKiIqxZswbR0dFITU2Fk5MThgwZgmnTpkFeycvNRFS/fL9jD3advqDR9smkUejSwuOx+yiVSkRFRSEpqWSQwHPnzqFZs2acSZyIyqWTt6sGDRqEU6dOQS6Xo2HDhkhJScHGjRvx888/Izo6mvfIiUjDn/8cw+8x/2i0vRbqh4CuHR67j0KhwM6dO5GcnAwAcHV1RWBgIAMOET2W1m9Xvffee7h//z62bduGgoIC3LlzBwUFBfj777+RlJSE999/Xxd1ElEdse/CFXy+OVyjbWxvXzwzuM9j9yksLER4eLgYcJo0aYLg4GBeKSaiCmkdcv766y/8/vvvGDFiBEz+fdXTxMQEw4cPx++//46tW7dqXSQR1Q0Xb9/F/FV/Qv3IWDj92nlh/ujgx16Ryc/Px44dO8S58Dw8PBAQEABTU53cbSeiOkzrnxLJycno2bNnuet69uwp/uVFRPXb3fRMvLp8HQoUCrGtbZNG+OLpMTCVSsvdJz8/H2FhYcjKygIAtGzZEoMGDRL/oCIiqojWPymcnZ3FkUb/68SJExxWnYiQnV+Al39Zi9TsXLGtkb0tvp8xGZZmZo/dTy6Xw9q65FXy1q1bM+AQUZVo/dNi+PDhePrpp7F7926N9ujoaDzzzDMYMWKEtocgolqsWKnE3JV/4HpyithmbW6OJS9MQUNbmwr3NTU1hb+/P3r37o3+/fsz4BBRlWj9E+OTTz6BtbU1/P39YWNjA09PTzRo0ABDhw5FgwYN8Mknn+iiTiKqhQRBwEcbt+PY1Rtim6lUiq+fG49Wjcq/ypuTk6Mxf5VMJkOHDh34FhURVZnWz+Q4OTnh2LFj+PXXXxEdHY309HS0b98eQ4YMwfTp02FpaamLOomoFvpp517sOH5Go+2D8cPR3atFuds/ePAAkZGRaNWqFXr37s1gQ0Ra0cnrCVZWVnj11Vfx6quv6qI7IqoDth05hV+i4jTaZgUOQmh373K3v3fvHnbu3Ini4mJcuHABLVu2hKuraw1USkR1lU5vcOfk5CAxMRE5OTm67JaIapnDl69h4R/bNdqGd/fGCwEDyt3+zp07iIiIQHFxMQCgX79+DDhEpDWdhJzo6Gj06NEDtra2aNasGWxtbdGjRw/s3btXF90TUS1y9e59zFv5B5RqtdjWs3VLvDd+eLm3n27evImdO3dCpVJBIpFg0KBBaNu2bU2WTER1lNa3qyIjIxEaGormzZvjtddeg4uLC+7fv4+wsDD4+/sjPDwc/v7+uqiViIzc/cxsvPzLWuQWFoltno1csOjZcZCVMxZOQkIC9u7dC0EQYGJigiFDhnDCTSLSGa1DzjvvvINp06ZhxYoVGq93Llq0CNOnT8fbb7/NkENUD+QVFuGVX9bhfma22NbQ1gY/vDgZ1ubmZbaPj4/Hvn37AABSqRR+fn5wd3evsXqJqO7TOuRcuHAB27dvLzN+hVQqxccffwwvLy9tD0FERq5YpcIbv2/ClbsPRzi3NJNjyYzJcLGzLXef/Px8ACVj4QQGBqJx48Y1UisR1R9ah5zGjRtD+pgh2U1NTeHm5qbtIYjIiAmCgM82h+NgfILYJjUxwaJnxqNNk0aP3a9Lly4QBAFNmjSBi4tLTZRKRPWM1g8eL1iwAIsXLy533VdffYW3335b20MQkRFbGfMP/jp8UqPtnbHD0LttK402QRDEt6cAQCKRwMfHhwGHiPRG6ys5lpaWSEhIQK9evRAaGio+eLx9+3a4urrCx8cHa9eu1dhnypQp2h6WiIxA9PnLWBqp+RbldL9+eKqXj0abIAg4dOgQ7t+/j5CQEMjl8posk4jqKa1DztSpU8X/f/jw4TLr//777zJtDDlEtd+Ja4lYFLZHoy3IpyNeDh6i0aZWq3HgwAHEx8cDAPbv348hQzS3ISLSB61Dzrlz53RRBxHVIteTUzDv900oVj0cC8enZTN8NHGkxlg4arUasbGxSEgoeV6nQYMG6NGjR43XS0T1k9Yhp0OHDrqog4hqibScXLz8y1rkFBSKbc2dnfDN9AmQmz78kaJSqRAdHY2bN28CAOzt7RESEsL57Iioxuhk7ioiqh8KihR45Zd1uJueKbY5WFvhhxenoIGlhdimVCqxe/du3L59G0DJRL7BwcEwL2e8HCIifWHIIaJKUanVeGv1Zly8fVdsM5eZ4pvnJsDN0V5sUygUiIqKwr179wAALi4uCAoK4sPGRFTjGHKI6IkEQcBXf0Ui7sJlsc1EIsHbowLQ3l1zEL/MzEw8ePAAQMk4WgEBAZDJZDVaLxERYMQhR6VSYcOGDYiNjYVSqYSvry+mT58OCwuLJ+/8H7/++isiIyPRtm1bfPLJJ3qolqhuWxt7CBv3H9VomzsiAL29WpTZ1tnZGX5+foiPj8fgwYNhamq0P2aIqI4zyp8+CoUCw4YNw7lz5zB//nxYWVnhm2++wc8//4x9+/bB3t7+yZ386+zZs3jppZegVCqRnJz85B2ISMOeMxfx9fZdGm3TBvXG+L7dkJmZCaDkSs+jb1W5u7tzHioiMjitRzwGgLS0NHz44YcYNGgQOnXqJLZ///33yM3NrXJ/S5Yswe7duxEWFoY5c+ZgxowZiImJwc2bN7FgwYJK96NQKDB16lRMmDABTk5OVa6DqL47c+M23lm7BYIgiG1DO7fDnFA/cTk3Nxfbtm1DamqqIUokInosrUPOrVu34O3tjcWLF6O4uFhj3JzExESsXLmyyn2uWLECPj4+8PX1FdtcXV0xcuRIrF27FoWFhRXs/dB7772H+/fv4+uvv65yDUT13a2UNMxZsR5FxUqxrVOzplg4+SlxQt7c3FyEh4cjJSUFERERyMvLM1S5RERlaB1y5s+fjzZt2uDmzZs4cOCAxrqJEydi9erVVeovKysL8fHx8PHxKbPO19cXeXl5lRqA8MCBA1i0aBGWLFkCBweHKtVAVN9l5ObhpZ/XIiMvX2xr6uSA756fCHN5yUPEGRkZiIuLE4ONp6cnx8AhIqOi9TM5UVFROHz4MBwdHcus8/Lywvnz56vUX1JSEgCUO2lfaVtSUhK6dev22D5yc3Px9NNPIzQ0FGPHjq3S8R88eICUlBSNttLRWpVKJZRKZXm7VYtSqYRKpdJpn/QQz2/1FBYX47UV63E7NV1ss7W0wPfPT4SNuRmUSiVSU1MRFRWFoqIiAIC3tze6dOkClUplqLLrHH796hfPr37p6/xWtT+tQ05ubi5cXV3F5UcfPiwoKBAva1eWQqEoKaycNzJKX0N90u2q119/HWlpaVi6dGmVjg0AS5cuxUcffVTuupycHPFBS11QqVTiM0tSqVRn/VIJnt/q+eyvKJy9eUdclkml+HhsCGxMTZCZmYm0tDT8888/4g+bdu3aoUWLFsjKyjJUyXUSv371i+dXv/R1fnNycqq0vdYhp0WLFoiOjsaoUaMAaIacPXv2oHXr1lXqz9raGgCQn59fZl3pZXEbG5vH7r9z506sWLECy5cvR+PGjR+73ePMnj27zNWfhIQEjBw5EjY2NrCzs6tyn49T+kvC1taWr9nqAc9v1e2/eAUxF66IyxIJ8MmkkejTsS0A4O7duxoBp1OnTvD29ub51QN+/eoXz69+6ev8VvT7vzxaH3ny5MmYPXs25HI5AgMDxfaYmBjMmzcP8+fPr1J/Hh4ekMlkSExMLLOutM3T0/Ox+x8+fBhSqRSRkZHYuXOn2J6ZmYn4+HiMGTMGo0aNwuTJk8vd39nZGc7OzuWuMzU11fk3g1Qq1Uu/VILnt/IKFcVY9Lfmq+KvDvNDoE/JG5NqtRpHjx6FUqmERCJB37590bBhQ55fPeLXr37x/OqXPs5vVfvS+shvvvkm9u3bh2HDhsHKygpqtRoNGzZEamoqAgMD8fLLL1epP5lMhv79+2P//v1Qq9Uat7tiYmLg4eEBLy+vx+4/bty4cicNjY6ORsOGDTFhwgS0adOmSjUR1Qe/Re9HUlqGuNypWVM8Pai3uGxiYoLAwEDs2LED3bp1g4eHh05v3xIR6ZrWIcfMzAyRkZFYv349wsLCkJycDCcnJ4SGhmLatGnVSnDz58+Hv78/vvvuO7z++usAgMjISMTFxZV5zmbjxo3YvHkzFi5ciDZt2qBdu3Zo165dmT5ffvllODo6YsyYMdX7oER12K2UNPwe/Y+4bCKR4O0xIWWeqbO2tsbYsWMhlUr5wCYRGT2dXEMyNTXFtGnTMG3aNF10Bz8/PyxduhRz587Fpk2bYGlpiQMHDuDNN9/EzJkzNbY9f/48tmzZgjlz5ujk2ET1jSAI+GJrJBSPhJbxfbujTZNGuHjxIiQSCdq2bSuu40OaRFRbGO2NyFmzZmHSpEniMwDr1q3TeIur1MSJE+Ht7a3xQ7g8K1asEB9qJqKH9p6Lxz+XrorLjjbWmBU0CGfPnsXhw4cBlNxGbtWqlaFKJCKqFq1DzrvvvvvEbRYuXFitvm1tbeHn51fhNu3bt0f79u2f2NewYcOqVQNRXVZQpMCXWyM12uaE+uHqpYs4ceIEgJJb0ra2toYoj4hIK1qHnE8//fSJ21Q35BCRfi3fvQ/JmQ/Ht+nawh0N1YU4cfosAMDCwgIhISEcNZyIaiWtQ05xcbHGsiAISEpKwo4dO3D+/HnOG0VkpG7cT8HqvQfFZamJCUa3boqzZ0sCjpWVFUJCQnQ6NhQRUU3Seu6q0nfgS/+TyWRo1qwZXn75ZQwePLhKs4YTUc0QBAH/2xIB5b/TMEgAPOfdCkmJNwGUDLg1fPhwBhwiqtW0DjkVCQgIwNq1a/V5CCKqhl2nL+DIlevics9GDpAVlowobmdnh+HDh1d5ZFEiImOj17erLly4IM5FRUTGIa+wCIu27dRoGz5kICSp95CXl4fg4GBYWFgYqDoiIt3ROuTExsaWacvJycG5c+fw/fffw9/fX9tDEJEO/RwVi5Ssh5PcdfdsjkCfTlCp2kOlUsHMzMyA1RER6Y7WIWfQoEGPXTd8+PBqzQRORPqRcO8B1scdhtxEgo4O1jibkY+3RodAIpFwDh8iqnO0/okWGRlZps3W1hYtWrSAi4uLtt0TkY4IgoDPN++AVAKEujvB2UIOH4/GaO7iZOjSiIj0QuuQY2pqCjs7O/j6+uqiHiLSk4gT53Dx5m2M9HCCo7kMANC8oQPUajWnaiCiOknrt6sCAwP5A5LIyOUUFOKnHbswqllDMeBYOzZEgL8/v3+JqM7SOuS0bNkSzs7OuqiFiPRk2Y5dGOhkCTuzkou3aYIU40cOLzPLOBFRXaL1T7i33noLX331lS5qISI9OBl/FaYPktBAXhJwzqXnYeKoEbyCQ0R1XrWeyTl//jw6dOgAoGR24sTERHTv3h3Dhw+Hm5sbZDKZxvZTpkzRvlIiqrKCggIcjNsLK1lJoDmRkoNO3p3RzJkPGxNR3VetkNOxY0cIggAAmDp1qth+7NixcrdnyCEyjN3nLuPY/Sz0cbXF4QdZSFab4lu//oYui4ioRmj9dtW5c+d0UQcR6Vh+URG+37Ebqdm5uJdfhAeFxfj2+YmwkMsNXRoRUY3QOuSU3rYiIuOQlJQEe3t7rIw9hNTsXADAg8Ji9G3niYEd2hi4OiKimsPhTYnqkBs3biA6OhpW1jb449RVsd3UxARzRwQYsDIioppX7ZDTt2/fSm974MCB6h6GiCrp6tWriI2NhSAIyM7OQgNTE2QXlawb26cbmrs0NGyBREQ1rNohJz4+Xpd1EJEWLl26hP379wMAJCYmCLvxAHfyShKOjYU5XgwcaMDqiIgMo9ohJzU1VZd1EFE1nTt3DocOHQJQMqTDiRwlbv8bcADghYABsLOyNFR5REQGw+FOiWqxkydPigFHLpfDoWVrHLh+R1zv3tARE/p2N1R5REQGxZBDVEudPHkSx48fBwCYm5vDPyAQy/Ye1tjm9eH+kJny/QIiqp8YcohqKTc3N5iamsLS0hKhoaGIOH8F9zKyxPXdPJtjYIfWBqyQiMiwqvUn3l9//aXrOoioilxcXBAUFAQrKysoIMFve/aL6yQSCeaOCIBEIjFghUREhlWtKzkjR47UcRlE9CRqtRrp6ekabY0aNUKDBg3wY0QM8osUYvvIHl3Qpkmjmi6RiMio8HYVUS2gUqmwe/dubNu2Dffv39dYF3/nHrYdOSUuW5rJ8VLw4JoukYjI6DDkEBk5pVKJqKgoJCYmQqlUig8bA4AgCFj8d5Q4YS4APDe0H5wa2BiiVCIio8LXLoiMmEKhQFRUFO7duwcAcHV1hZ+fn7g+9vxlHLt6Q1xuZG+LKQN61XidRETGiCGHyEgVFRUhIiICKSkpAErepvL394dMJgMAFCuV+PrvKI19Xgv1g7lcVuO1EhEZI4YcIiNUUFCAiIgIpKWlAQDc3d0xdOhQmD4y5s3G/UdxO/Xhg8idmjVFQJcONV4rEZGxYsghMjJ5eXkIDw9HZmYmAKBFixYYPHgwTEwePkIXfeYivt+xR2O/N0YG8pVxIqJHMOQQGRmpVCqGFS8vL/Tv318j4Px95BQ+2vg31I88bBzk0xEdmzWp8VqJiIwZQw6RkTE3N0dISAji4+PRpUsXjasza2MPYdG2nRrbt3BpiDdGBdV0mURERo8hh8gIFBQUwMLCQly2tLRE165dxWVBEPDTzr34JSpOY792TRvjxxenwN7aqsZqJSKqLThODpGBPXjwAJs2bcLZs2fLXa9Wq/Hl1sgyAcenZTP88tLTDDhERI/BkENkQPfu3UN4eDiKiopw+PDhMtM2KFUqvL9hGzbsP6LR3r+9F358cQqszc1rslwiolqFt6uIDOTOnTuIioqCSqUCAPTt2xcODg7i+qLiYry1ejP2novX2C/IpyM+njQKMqm0RuslIqptGHKIDODmzZvYs2cP1Go1JBIJBgwYAC8vL3F9flER5qzYgKOPjGYMAGP7dMOC0cEab1sREVH5GHKIati1a9cQExMDQRBgYmKCwYMHo0WLFuL6rLx8vPzLOpxLvKOx33ND++GVkCEcC4eIqJIYcohq0OXLl7Fv3z4IggCpVAo/Pz+4u7uL61OycjBr2Wok3Hugsd+c4X54ZnDfmi6XiKhWY8ghqkHJyckQBAGmpqYICAiAm5ubuC4pLQMvLl2FO2kZYptEIsG744ZhdC9fQ5RLRFSrMeQQ1aB+/fpBIpHAy8sLrq6uYnvCvQeY9dNqpGTniG2mUik+nfIU56MiIqomhhwiPRIEQXz2BgBMTEzQv39/jW3O30rCS8vWICu/QGwzl8mw+Lnx6NPWs0brJSKqS/iKBpGeCIKAI0eOYNeuXVCr1eVuc+zqDbzw4+8aAcfa3Aw/zZrKgENEpCWGHCI9EAQBBw4cwNmzZ3Hr1i0cPny4zDax5+Px0s9rkV+kENvsra2w4uVn0aWFR02WS0RUJ/F2FZGOqdVqxMXF4erVqwCABg0aoGPHjhrbhB8/g/fXb4PqkSs8rna2WDZrGpq5ONVovUREdRVDDpEOqVQqxMTE4MaNkkH87O3tERISAktLS3GbjfuP4H9bIjT282joiGWzp6GRvV1NlktEVKcx5BDpiFKpxO7du3H79m0AgKOjI4KDg8XZxQVBwIrd+/BjRIzGfq3dXPHTzKlwsLGu8ZqJiOoyhhwiHSguLkZUVBTu3r0LAHBxcUFgYCDMzMwAlAScb7bvwuq9BzX2827uju9nTEIDS4sar5mIqK5jyCHSgXv37okBp3HjxggICIBMJgMAqNRqLNwUhr8On9TYp0+bVlj03HhYyOU1Xi8RUX3AkEOkA+7u7ujXrx9u3rwJPz8/mJqWfGsplEq8vWYL9py5qLG9n3d7fDblKchM+S1IRKQv/AlLpCNt27ZFmzZtxAk0C4oUmLvyDxyMT9DYblTPrnh3XCiknEmciEiv+FOWqBpyc3MRHh6O3NxcjfbSgJOdX4BZy9aUCThPD+6D98cPZ8AhIqoB/ElLVEVZWVnYvn07kpKSEB4ejqKiIo31aTm5mPHj7zh945ZG+yshQzAn1E8MQkREpF+8XUVUBenp6QgPD0dBQck0DO7u7pA/8uDwvYxMvLh0NW6lpGnst2BMCMb37V6jtRIR1XcMOUSVlJqaqnHlpmvXrvDx8RGvzNy4n4KZP63G/cxscR+piQk+njQKIb6dDFIzEVF9xpBDVAn3799HZGQkFIqSeaa6d+8Ob29vcf2l23cxe9kaZOTli21yU1N89cw4DOjQuqbLJSIiMOQQPVFSUhKioqKgVCoBAL1790aHDh3E9SevJeLV5euQW/jw2RxLMzm+e34Sunk2r/F6iYioBEMOUQVUKhXi4uKgVCohkUjQv39/tG798MrMgYtXMW/lHygsLhbb7Kws8eOLU9De3c0QJRMR0b/4dhVRBaRSKfz9/WFubo7BgwdrBJyok+cxZ8V6jYDT0NYGv77yLAMOEZER4JUcoidwcnLChAkTNN6i2nLoOBZu2gFBEMS2pk4OWDZrGtwc7Q1RJhER/QdDDtF/XLp0CVZWVnB3dxfbHg04K6MP4Luw3Rr7tGrkjJ9mTkNDW5saq5OIiCrGkEP0iLNnz+Lw4cOQSqUICgpC48aNxXWCIGDJjj34LfqAxj4dPZrghxcmw9bKsqbLJSKiCjDkEKEkwJw6dQrHjx8HAJiamoqTbAKAWq3G55vD8efB4xr79fBqgW+mT4ClmVmN1ktERE/GkEP1niAIOHr0KM6cOQMAsLCwQHBwMBwdHQEAxSoV3l//FyJPnNPYb3CntvjftDGQcyZxIiKjxJ/OVK8JgoCDBw/iwoULAAArKyuEhITAzs4OAFCoKMabqzZh34UrGvuFdvPGBxOGw1QqremSiYiokhhyqN5Sq9XYt28frlwpCTA2NjYICQlBgwYNAAC5hYV4bfkGnLh2U2O/Sf17Yt7IAJhwJnEiIqPGkEP11unTp8WAY2dnh5CQEFhZWQEAMnLz8NLPa3Hx9l2NfWYGDsSLAQM5kzgRUS3AkEP1VocOHZCYmAiVSoWQkBBYWFgAAO5nZmHWT2tw/X6KxvZvjArE5AG9DFEqERFVA0MO1VtyuRxBQUEAAHNzcwDArZQ0vLh0Ne5lZIrbmUgk+HDiSAzv7m2AKomIqLqMNuSoVCps2LABsbGxUCqV8PX1xfTp08W/tisiCAJiYmKwd+9e3Lp1C40bN8bgwYPh7+9fA5WTsVIoFEhISEDbtm3F202l4QYArtxNxqyf1iAtJ1dsk0ml+OLpsRjcqW2N10tERNoxyicnFQoFgoKC8MYbb6BDhw7o06cPli5diu7duyMjI+OJ+7dq1QojRoxAbm4uhg4dCgAYNWoUhg0bhqKioifsTXVRYWEhwsPDceDAAZw6darM+jM3bmP6kpUaAcdCLseSFyYz4BAR1VJGeSVnyZIl2L17N44dOwZfX18AQGhoKDw9PbFgwQIsW7aswv1tbGywe/dutGjRQmzr1asXRo4ciaVLl+L111/Xa/1kXAoKChAVFYX09HQAQEpKCtRqtfh21KHL1/D6rxtQqHg40aaNhTl+fHEKOjVrapCaiYhIe0Z5JWfFihXw8fERAw4AuLq6YuTIkVi7di0KCwsr3D8uLk4j4ABAcHAwJBIJ9u3bp5eayTjl5+cjIiJCDDgtW7aEn5+fGHCiz1zEq7+s0wg4Tg2s8dsrzzHgEBHVckYXcrKyshAfHw8fH58y63x9fZGXl4dz586Vs+dDtra2ZdquXr0KQRDEQd6o7svOzsa+ffuQlZUFAGjdujUGDRokBpy/j5zCG79vQrFKJe7T2MEOv73yHDwbuxikZiIi0h2ju12VlJQEAHBxKftLprQtKSkJ3bp1q1K/b731FgBgypQpFW734MEDpKRovjqckJAAAFAqlVAqlVU6bkWUSiVUKpVO+6QSmZmZ2LlzJ/Lz8wEA7dq1Q48ePaBWq6FWq7F+3xF8vX2Xxj7NnZ3w44uT4WzbgP8mlcCvX/3i+dUvnl/90tf5rWp/RhdyFAoFAGhMjlhKJpMBwBNvV/3X559/jrCwMDz33HMYMmRIhdsuXboUH330UbnrcnJykJmZWaVjV0SlUiE3t+RBVymnB9CZgoICxMTEiA+Ze3p6onXr1sjKyoIgCFi97wjW7D+msY9XI2d8PnE45IJap//GdRm/fvWL51e/eH71S1/nNycnp0rbG13Isba2BgDxL/BH5eXlASh5sLiyfvnlF7z99tsIDg7GTz/99MTtZ8+ejbFjx2q0JSQkYOTIkbCxsdHp7a7SRGpra1tuqKPqsbW1RcuWLXHx4kW0adMG3bt3h6mpKdRqAYu3R+GPA5oBx6elBxY/Ox7W5pxJvCr49atfPL/6xfOrX/o6v1X5/Q8YYcjx8PCATCZDYmJimXWlbZ6enpXqa/Xq1Zg5cyYCAgKwdetWyOXyJ+7j7OwMZ2fncteZmprq/JtBKpXqpd/6rk+fPmjatCmsrKxKzq1Ego//3I4dx85obDegfWt88fRYmMtlBqq0duPXr37x/OoXz69+6eP8VrUvo3vwWCaToX///ti/fz/UarXGupiYGHh4eMDLy+uJ/fzxxx947rnnMHToUGzbtg1mZvwrvS5LTk5GcfHDN6QkEgnc3NwAAIXFxXjj901lAk6wTycsem48Aw4RUR1ldCEHAObPn4+kpCR89913YltkZCTi4uLEB4hLbdy4EWPGjEF8fLzY9vfff2PKlCkYPHgwtm/frjGqLdU9N2/exI4dO7Br164yD6Vl5OVj1rI12HsuXqN9XN9uWDh5FGS8F09EVGcZ5TU6Pz8/LF26FHPnzsWmTZtgaWmJAwcO4M0338TMmTM1tj1//jy2bNmCOXPmACh5bXj8+PFQKpWQy+Vl3qby8vLCZ599VlMfhfQsISEBe/fuhSAIuHfvHlJSUtCoUSMAwI37KXhl5Z9IzszW2Ge6Xz+8HDyEM4kTEdVxRhlyAGDWrFmYNGkSjh49CqVSiXXr1sHV1bXMdhMnToS3tzfati0Zet/c3Bxr1659bL+Ojo56q5lqVnx8vDi4o1QqhZ+fnxhwjl29gf/7bSNyCh6+iSeRSPB/I/wxdWBvg9RLREQ1y2hDDlDyVLafn1+F27Rv3x7t27cXl+VyOcaMGaPv0sjAzp8/j4MHDwIoeRAtMDAQjRs3BgBsP3oaH/+xHcpHBvkzl8nw2dTRnIeKiKgeMeqQQ1Se06dP4+jRowBKQm1QUBBcXFwgCAJ+ityLX3bFaWzvaGOF72ZMRgd3N0OUS0REBsKQQ7XKiRMncOLECQAltyaDg4Ph5OQEhVKJDzZsQ+QJzSk/mjV0wJIZk9HU2ckQ5RIRkQEx5FCt4uDgAIlEAgsLC4SEhMDe3h6Zefl4/dcNOHX9lsa23T2b4+0RfmjkYGeYYomIyKAYcqhWad68OYYMGQJHR0fY2toiMSUNr/yyDrdS0jS2G9WzK+aPCkRuFYcAJyKiuoMhh4yaWq1GXl6exlDeLVq0AACcup6IOSs2ICu/QGOfV4cNxbND+kL1yIPHRERU/zDkkNFSqVSIjo7GgwcPEBoaCltbW3Fd5IlzeH/9Xyh+JMjITU3xyeRRCOjSwRDlEhGRkTHKEY+JlEoldu3ahZs3byI/Px9HjhwBAAiCgOW74rBgzWaNgGNvZYlfXnqaAYeIiES8kkNGR6FQICoqCvfu3QMAuLi4YODAgShWKvHJpjBsP3paY/tmzk5Y8sJkNHVyMEC1RERkrBhyyKgUFRUhMjISDx48AAA0btwYAQEBKChWYu7K9Th29YbG9j4tm+Hr58bD1srSEOUSEZERY8gho1FQUICIiAikpZW8KeXu7o6hQ4fiflYOXv55LW48SNXYfphvZ7w/YTjkpvwyJiKisvjbgYxCXl4ewsPDkZmZCaDkVfHBgwfj4u17eHXFemTk5mlsPzNwIF4MGMhJNomI6LEYcsgoqFQqKBQKAICnpycGDBiAmHPxeGftFhQVK8XtTKVSfDRxBEJ8OxuqVCIiqiUYcsgoNGjQACEhIbhy5Qq6deuG1bEH8e323ZrbWFrgm+kT4NOymWGKJCKiWoUhhwymuLgYMplMXLa3t0dXX198+ucObDl0QmPbJo72+OGFKWjmwjmoiIiocjhODhlESkoKNm7ciOvXr4ttuYWFePWXdWUCTufmTbH69RkMOEREVCW8kkM1Ljk5GZGRkSguLkZMTAycnJyQp1Lj1V/W4+q9+xrbBnTpgI8njYTZI1d8iIiIKoMhh2pUUlISoqKioFSWPEzcq1cv3MnKxavL1yE1O1dj2+l+/fBS0GCYmPCCIxERVR1DDtWYxMRE7NmzByqVChKJBAMGDMBdhRpvLfkNhYpicTtTExO8Oy4UI3t2NWC1RERU2zHkUI24fv06oqOjIQgCJBIJBg8ejCN3UvDVtp0QBEHcztrcDIueHY+erVsasFoiIqoLGHJI765cuYK4uDgIggCpVIrBQ4Zg04mL2LD/iMZ2jeztsOSFyWjVyNlAlRIRUV3CkEN6JQgCrl27BkEQYGpqigGDBuGb3f9g34UrGtu1d3fD9zMmwdHG2kCVEhFRXcOQQ3olkUjg5+eHPXv2wKOVJ97ZEoX4O/c0thncqS0+nfIULORyA1VJRER1EUMO6VzpMzal80qZmpqieUdvvLp8He5nZmtsO21Qb8wJ9eMbVEREpHMMOaRTgiDgyJEjUCqV6NOnDyQSCf65dBVv/L4J+UUKcTsTiQRvjQ7GuL7dDVgtERHVZQw5pDOCIOCff/7BxYsXAQBWVlZIyFfif1sioFKrxe0szeT48ulx6NvO01ClEhFRPcCQQzqhVqsRFxeHq1evAgBsbGwQe+MuVu07qrGds20DLHlhMlq7uRqiTCIiqkcYckhrKpUKe/fuFeehsrW1xYlcFXYd0Qw4bZo0wnfPT4KLXQNDlElERPUMQw5pRalUYs+ePbh16xYAwNbODpF3MnH6VpLGdv3be+F/08bA0szMEGUSEVE9xJBD1VZcXIyoqCjcvXsXANDAzh7r4u/gVnqmxnYT+nXHG6OCIOUbVEREVIMYcqjabty4IQYcK1s7LDuVgMyCQnG9RCLBGyMDMWlAT0OVSERE9RhDDlWbl5cXMjMzcfHadXx/9BKKVA/foDKXy/C/aWMwsEMbA1ZIRET1GUMOVZtarcbh+1n4/cglqB9pd2pgje9nTEa7po0NVhsREREfkqBKy83NxZ49e6BQKFBUXIwFa7bgt+gDGgHHs5EL1rw+gwGHiIgMjldyqFKysrIQHh6O3NxcZGVnY8ftNJy6cUdjm16tW+KrZ8fB2tzcQFUSERE9xJBDT5SRkYHw8HDk5+cDAI7dSsapm/c1thndywdvjQmBTCo1RIlERERlMORQhVJTUxEREYHCwpK3ps5lFmD/3XSNbeYM98PTg/qIE3ISEREZA4Yceqz79+8jMjISCkXJxJpHU3JwPOXhLOJmMlMsnPwU/LzbG6pEIiKix2LIoXLdvXsXO3fuhFKpBADsT87EufQ8cb29tRW+e34iOjVraqgSiYiIKsSQQ2UoFArs3r0bSqUSAoDYuxm4lJkvrm/u7IQfXpwCN0d7wxVJRET0BHyFnMqQy+Xo0bsPVAKw+066RsDp5tkcq+Y8z4BDRERGj1dyqIzbqemYvykCD9IzUPDIKMbDu3vjvXGhkJnyy4aInuz69es4evSouGxqagonJyf4+PjAxsbmids/ytXVFQMHDqzUce/fv4+9e/eiYcOGGDJkSLnbnDx5EmfOnMHEiRNhbW1dZv2ePXuQl5eHESNGlFmnUqlw+vRpJCUlwdzcHO7u7vD09ITUAG+XFhcX49ChQ0hPT0erVq3QoUOHSu2XmpqKPXv2lLuuV69e8PDwKNOekJCAy5cvo0GDBmjbti2cnJwe239aWhqOHDkCpVIJd3d3dO7cGTKZrHIfSof424oAAJcvX4ajoyPu5OTj9RUbkJGXr7H+peDBeN6vP9+gIqJKi4mJwYwZMzBgwAC4urqiqKgIZ86cQXJyMj755BPMnTu3wu0f1bFjx0qHnK+//hpffvklzM3NcffuXdjbl73y/Pvvv+Onn35CUFBQuSHn3Xffxc2bNzVCjlqtxpdffolFixbBwsIC3t7eUKlUOH78OORyOV555RXMnz+/UjXqwsGDBzF+/HhYWVmhdevW2L9/P7p27YotW7bA1ta2wn3j4+MxceJE9O3bF25ubhrrmjRpohFy1q1bh88//xy3b99G3759kZ6ejlOnTmH27Nn44osvNMKLWq3Gm2++iR9//BFeXl5wd3fH6dOnIZFI8P3332PkyJE6PQdPwpBDOHfuHA4dOgQTU1NsSkhGRn6RuE4mleLjSaMQ5NPRgBUSUW321ltvITAwEACgVCoxYcIEzJs3D926dUP//v0r3L6qiouLsWrVKgQHByMqKgpr167FK6+8olX9ACAIAiZMmICwsDD8/PPPmDp1qvhHX3FxMX788UcsWbKkxkJOWloaQkNDMWDAAPz555+QSqW4d+8eunbtiueeew5btmypVD9z5859YvD4/fff0bt3byxatAgNGjQAAGzfvh0jR46EiYkJFi1aJG67cuVKLF68GF9++SWeffZZ2NnZQa1WIzAwEBMnTsTNmzfh4uJS7c9dVXwmp547efIkDh06BADIL1IA6oe3p2wtLfDLS08z4BCRzpiamuLll18GAISHh+u8/7CwMNy/fx+ffvopQkJCsGLFCp30u2rVKvz5559YtGgRpk2bpnFVWyaTYc6cOVi/fr1OjlUZy5cvR3p6OhYuXCjeJmvUqBFeffVVbN26FVeuXNHZsT744AP88ssvYsABgOHDh6Nbt25Yt26dxrbHjx8HAEycOFFsk8vlGDt2LAoLC3H+/Hmd1VUZvJJTTwmCgGPHjuH06dMAgHylCmGJqUgrKnllvKmTA354cQo8GjoasEoiqossLS0BQBxkVJeWL1+OXr16wdvbG7Nnz0ZgYCCOHj2K7t27a9Xv0qVLYWtri+eff/6x2/To0aPCPkqfFaqMHj16oHnz5o9dv2fPHjRs2BDt2rXTaB80aBAAYPfu3fDy8nriceLj47F161ZYWFigS5cuZW4TAkDfvn3L3be4uFgcCb9Uly5dAAAXL15E165dxfbz58/DzMysTL36xpBTDwmCgEOHDomJOrdYhe2JqchUlAScLi3c8c30ibCzsjRkmUT11mvL1+N2ahpUKjWkUhPACJ6Fa+rogO9mTNJJX7t27QIAdOvWrdz1cXFxyMzM1GgLCAgo99maR926dQu7du3CqlWrAAD+/v5o1aoVli9frlXIUSgUOHHiBPr37w8zM7Nq93Pu3DmNKxwVWblyZYUhJyEhAe7u7mXaS5+lSUhIqNRxVq1ahVatWuHGjRu4dOkSJk+ejJ9++glWVlYV7nfw4EGcOnUKo0aN0mh/7rnncPr0aUyfPh1TpkxBy5YtcfToUWzfvh2rVq1Co0aNKlWXrjDk1DNqtRr79+/H5cuXAQDZCiW2J6Yiu1gFAAjy6YiPJo6EnG9QERnM7bR0XL+faugydKY0tBQVFeHYsWP4+eef8dRTT2HChAnlbn/o0CHcuHFDo6179+5PDDm//fYbHBwcMHbsWACARCLBzJkz8eGHH+Kbb74p9wHjysjIyIBarYajo3ZXtl1dXTF+/PhKbVtRwAGA3Nzcct+AKg0nOTk5Fe7fuHFjHDlyRCP8/fHHH5g0aRIUCgU2btz42H3T09MxdepUWFpa4rPPPtNYZ2pqCl9fX+zatQvh4eFwd3fHmTNn0LFjR3h6elZYkz7wN1k9c+zYMTHgZBQVIywxDbnKkoDzgv8AzAoaxDeoiEinSkNLXl4eDh48iCZNmuDzzz+H6WP+mKrOg8dqtRq//fYbOnXqhL/++ktst7CwQG5uLjZu3FjhraaKlAaHgoKCau1fqkOHDhWGh6qQy+XilDuPKm0zNzevcP8WLVqgRYsWGm3jx4/H9u3bsX79enz99ddo3Lhxmf3y8vIwbNgwJCYm4o8//kCbNm001i9duhQvvfQSfvjhB4wfPx52dnZQqVSYNGkS+vXrh/Pnzz8xwOkSQ049k2VihpxiFRQqNbYnpqJApYapVIr3xw/H8O7ehi6PiFByawiCYHS3q6rr0dCSkZGBAQMGYOjQoTh9+jQcHKrf76N27tyJBw8eoHfv3ti2bZvGOl9fXyxfvlwj5MjlcgAlz5WUp6ioSLw1ZW1tDQ8PD1y4cEGrGnX5TE6TJk1w//79Mu3JyckAUOa18Mrq0qUL1q9fj2vXrpUJOQUFBQgNDcWRI0ewZs0ajB49usz+v/zyC9zc3PDiiy+KtxzNzMzw3nvvYevWrVizZg3ef//9atVWHQw59cj6uMP4attOWJuaQKEWUKRSw8bCHF8/NwHdPGsuWRNRxb6bMQlKpRKZmZmws7N77BWP2sje3h4rVqxAz5498fbbb2PZsmU66XfFihXo169fuVdKjh49ih49euDcuXPo2LHkbdHS51lu3LhRJkyo1WrcvHkTnTp1EtsmTJiAL774Avv370e/fv3KrSErK6vC8Wl0+UxOz5498cMPPyAtLU3jNlrp2029evWq1HH+6969ewBQJnwWFRVh1KhRiIuLw6pVqzBpUvnPZ2VlZZU70GNp23+ftdI3vkJexykUCty8eRMxZy/hy78iIQgCcopVKFKp4eZoj1WvPc+AQ0Q1qnv37njqqafw66+/4urVq1r3l5ycjLCwsMfe4vL19YWTkxOWL18uto0YMQIymQy//vprme3//PNPZGZmYty4cWLb/Pnz0bx5czz//PO4e/dumX3i4+Px1FNPVVhn6TM5lfnvSbd0nn32WQAlV05KCYKAn3/+GV5eXhrjDz148AAbN27UeH07MTGxTJ/37t3DqlWr0KZNG423oJRKJcaNG4fdu3dj5cqVmDJlymPr6t27N65evVrmqtfWrVsBAH369Knwc+la3fnzgMooLCxEZGQkUlJScOCB5kNonTya4NvnJ8LBpnoP4hERaeOTTz7BX3/9hffffx8bNmzQqq/ff/8dSqXysSHHxMQEAQEBWLt2rTgSsoeHBxYtWoR58+YhOTkZw4YNg7W1NY4fP45ff/0V48ePx8yZM8U+7O3tsXfvXowfPx7t2rXD1KlT0aVLF6hUKhw8eBAbN2584tUTXT6T06VLF3z00Ud4//33kZ6ejvbt22PTpk24ePEidu3aBROTh9cwLl68iIkTJ+KDDz4Qp3346KOPcPPmTQwaNAhNmjTB9evX8fPPP8Pc3BwbN27UeDZz1qxZ2L59O0aMGAG5XF7mM4wZM0a82vjpp5/i8OHD8Pf3x3PPPYcWLVqI53TChAll3sbSN4acOqqgoADh4eFIT08HADQ2N8XZf9d5N3fHslnTYC6v+XlEiKj+aNmyJcaPH1/ua8Nt27bFp59+irNnzyIjIwP29vYVbl+RnJwcTJ8+He3bt3/sNs888wyUSiXi4+Ph7e0NAJg0aRKCg4OxdetWnD9/HsXFxXBzc8OePXvKnULCw8MDhw8fRmxsLPbs2YP9+/fD3NwcrVu3Rmxs7BPHydG19957D4MHD8bmzZuxd+9e9OrVC7/88guaNGmisZ2zszPGjx+vMa/Vb7/9hiNHjiAyMhKxsbGwsrLCF198gbFjx5Z5C83R0VF8K+y/zzsBwKhRo8SQ06xZM8THx2Pt2rU4duwYDhw4AA8PD+zdu/ext/n0SSIIglDjR61lLly4gA4dOuD8+fMVfhNVlb7uuefl5WHHjh3IysoCAFzNykd0UgbUAOytLLHxjZlwsat4XpO6oK4+02AseH71i+dXv3h+9Utf57eqv4/5L1vHZGdnIzw8XBwj4VJGHmLvZUJAyZgRC6c8VS8CDhEREUNOHZKZmYnw8HDk5eUBAC5lFWDvvUxx/fSh/dCnbc0PxkRERGQIDDl1RHZ2NsLCwsTBqm4WqrE3KV1c79OyGWYGDjRQdURERDWPr5DXEdbW1nB2dgYA5JpbI+L6PXGdg7UV/jdtDEz/namWiIioPmDIqSNMTEwwdOhQyBu5Y/XJy2K7RCLB59PGoKFt2cGZiIiI6jKGnFosNTUVarVaXF6/7wi+jT6ksc2LAQPQw6vFf3clIiKq8xhyaqnExERs27YNcXFxEAQBq2L+weK/ozS26dPWEzP8BxioQiIiIsPig8e10LVr1xATEwNBEJCQkIAbBUp8v+uAxja927TC4mfHQ2rCHEtERPUTQ04tc/nyZezbtw+CIEAqlQING5UJOH3atMLX0yfATMYRjYmIqP5iyKlFLly4gH/++QcAYGpqCrVTI/wQc1hjm77tPLH42fEMOEREVO8x5NQSp0+fxtGjRwEAcrkcCgdXLI3RfMi4XzsvLH5uPOQcopyIiIgPHtcGx48fFwOOmZlZuQGnf3sGHCKq3d59911IJBIolUpDl0J1BENOLWBmZgYAsLCwQIG9S5mAM7BDayx6lgGHiIzT5cuXMXPmTHh6esLS0hIODg7w9vbGnDlzEB8fb+jyqA5jyKkFOnbsiD59+iDX1hnLygScNvjqmXEMOERklNatW4fOnTvj9u3bWLlyJVJSUnDz5k3873//w7FjxzB06FBDl0h1GH8zGiG1Wg2FQgFzc3MAgCAIiLt1H7/EHNTYbnCntvhi2hjIGHCIyAidPHkSzz77LEaPHo3169dDIpGI6wIDA+Hv74+FCxcasEKq63glx8ioVCpER0cjLCwMhYWFEAQBSyNj8EtUnMZ2Qzu3wxdPj2XAISKj9dlnn0GtVmPx4sUaAaeUiYkJ3n///TLtRUVFePXVV+Ho6AgbGxuMHj0aKSkpGtusXbsWEolE/M/a2hq9evXCn3/+qbFd6XM+eXl5Gn2OGzcOqampZY597do1PP3003Bzc4OFhQU6deqEH3/8ESqVSmObqVOnwtXVFXK5HK1atcJnn32msQ0ZB6MNOSqVCmvXrsXzzz+PZ555Bj/88IM4w3ZN7G8ISqUSu3btwo0bN5CRkYGomL1YsGYLlu/ap7Hd0M7t8Pm0MZBxwk0iMlJqtRq7du1C586d0bhx4yrtO2/ePPTv3x83btxAZGQk9u/fj1mzZmlsM2XKFAiCAEEQoFarcfnyZfj5+WHChAmIiYl5Yp8HDhzAG2+8obHNhQsX4OPjg4SEBGzduhUpKSnYsGEDLl26hGPHjgEA4uPj0a1bN9y9exdRUVFIT0/H999/j2+//RYvvvhiFc8S6ZtRXgZQKBQYNmwYzp07h/nz58PKygrffPMNfv75Z+zbtw/29vZ63d8QiouLERUVhbt37wIActUSrNhzBAq1oLGdn3d7fDZ1NAMOUT2QmJiIgwcPlnsVpJSjoyN69+4tLqempuLQoUOP3b5UaGioxnJYWNhjt/Xy8kLr1q0rUfFDGRkZyMnJgbu7e5X2A4BOnTphzJgxAIC+ffvi5Zdfxocffoi0tDQ4OjqW2V4ikcDNzQ0ff/wxIiMjsXz5cgwePLjCPmfPno2PP/4YaWlpcHFxAQDMmTMHMpkMkZGRaNCgAQCgffv2+OGHH8R+/u///g9mZmbYtm0bbGxKJj4ODg7GV199hWeeeQZz585F27Ztq/yZST+MMuQsWbIEu3fvxrFjx+Dr6wug5BvS09MTCxYswLJly/S6f00rKirCrl27xMuxd/KKEHErDUpBM+AEdOmAT6c8BVMGHKJ6IS8vD8nJyVXaR6FQ4N69e1U+VkX7NGrUqMr9aSMkJERjuUOHDhAEATdu3BBDjkKhwJdffok//vgD165de+KV+v/22b59e7FPFxcXFBQUYO/evZg6daoYcP6rsLAQu3fvxpQpU8SAU6r0Aep9+/Yx5BgRoww5K1asgI+PjxhQAMDV1RUjR47E2rVr8e2334oP5epj/5qUlJKGg4cOQqosBgAk5hRi5500qB7JN/ZWlpg8sBeeGdyHAYeoHrGysoKrq+sTr+Q8Si6XVyuUVLTPf3+hV4a9vT1sbGxw69YtrWspDR2ZmZli2+zZs7FhwwasWLECQ4cOhYODA6RSKQYNGoSMjIxK95mVlQUASE9Ph0qlgpub22PrSktLg1KpxKpVq7BmzRoI//4hWnrbrHQbMh5GF3KysrIQHx+PF154ocw6X19frF27FufOnUO3bt30sn9NOZd4B+tjDsA2Jw0O5iVTMFzLLsDuO+lQ/7tNS1dnTB7QE8E+nWAu5zQNRPWNh4cHOnfuDNMqvGDg5ORU5lZUZVRnn4qYmJjA398ff//9N+7evVul53IqCnVAyfM+a9euxXPPPYeJEydqrLtx4wbs7Oyq3GdpSEpKSnrsNvb29pBKpZg1axaWLFlSYX9kHIwu5JR+gZXeI31UaVtSUtJjQ4q2+z948KDMU/wJCQkASh4M1sVInEqVGvN++wPqogKM8HACAFzOzEfM3QwIAHq3bolJ/Xugh1cL8RuTI4BWnVKphEql4rnTE55f/aoL5/fNN9/E9u3bMW/ePKxevbrMerVajc8++wzvvvuuuAyU/XlX+tZS6flQq9WQSCSQyWQa2x44cACJiYmwtbUV2yvbp0wmw6BBg7Bjxw6kp6eXe8tKLpeL23z66aewtLSs1nmpD/T19VvV/owu5CgUCgAo9y8X2b+TThYWFupt/6VLl+Kjjz4qd11OTo7G5VJthPp0wIqYgwi/nYYWNhY4lpaL4C7t8VQPb3g4OQB4eBmVqkelUiE3NxcASmZsJ53i+dWvunB+W7Roge+//x6vvfYa0tPT8corr6Bjx45Qq9U4evQoFi1ahDt37uDll18G8PBnc2ZmpsbP8NLzkJubK/4MHjp0KFavXo3BgwfD29sbx48fx+effw5fX18UFBSI2z2uz7y8PPF/S7f94IMPEBISgsDAQCxcuBCtW7fGnTt3sHLlSowdOxY+Pj746KOPMGzYMISEhOCdd95B27ZtkZOTg/Pnz+O3337DZ599hmbNmunrlNYa+vr6zcnJqdL2RhdyrK2tAQD5+fll1pV+UVZ0f1jb/WfPno2xY8dqtCUkJGDkyJGwsbEp9zJodUwa2Btr9x9DkYkpOnTujI/6dIOdFf8q0KXSxG9ra1uly/1UOTy/+lVXzu/zzz+Pvn37imEnKSkJ5ubmcHd3x8CBAzFjxgzx52rps5J2dnYan7n057q1tbW47a+//op58+ZhxowZKCgoQJ8+fbBmzRrMmTMHycnJT+zTyspK/N/SbXv27IkjR47g448/xtSpU5GdnQ1PT0/MmDEDAwcOhFQqRffu3XH8+HF89tlnePHFF3Hv3j24uLjA29sbr776Kjp37vzEW2P1gb6+fqv8fJhgZBQKhSCTyYQJEyaUWffxxx8LAITLly/rbf/ynD9/XgAgnD9/vkr7PcmpazeFu8nJQnFxsU77pRLFxcVCSkoKz6+e8PzqF8+vfvH86pe+zm9Vfx8b3WCAMpkM/fv3x/79+8V7qaViYmLg4eEBLy8vve1fkzq4u3G8GyIiIj0xupADAPPnz0dSUhK+++47sS0yMhJxcXF46623NLbduHEjxowZozGTbVX2JyIiorrJKG/0+vn5YenSpZj7/+3de1BU5/kH8O+yCyssKIgIjSbGBK2IIkHECwooIMZURRchiimJWsVop+nYpFbTsTHGZKqtjbZmbCtYUzRTNVLjLXgBrxHQ0BqjRvEWTLwEBBTk4i7P74/82GZdbsoeZY/fzwzj8J73eXnOcxQez425c/Gvf/0Lbm5uOHToEN544w2kpqZazT158iQ2b96M11577YHiiYiISJ3aZJMDALNmzcLkyZORl5cHk8mEjIwM+Pn52cybNGkSgoODbd4w2dJ4IiIiUqc22+QA39+VHRsb2+ScwMBABAYGPnA8ERERqVObvCeHiIiIqLXY5BAREZEqsckhIiIiVWKTQ0RERKrEJoeIiIhUiU0OERERqRKbHCIiIlIlNjlERESkSmxyiIiISJXY5BAREZEqsckhIiIiVWKTQ0RERKrUpn9BZ1tRU1MDACgsLLTruiaTCbdv34aHhwd0Oh4Ke2N9lcX6Kov1VRbrqyyl6lv/c7j+53JzeGRboKioCAAQHx//aBMhIiIiFBUVISQkpNl5GhGRh5CPQysrK8P+/fvx5JNPQq/X223dwsJCxMfHIzMzE/7+/nZbl77H+iqL9VUW66ss1ldZStW3pqYGRUVFiIyMhKenZ7PzeSanBTw9PTFu3DjF1vf390dgYKBi6z/uWF9lsb7KYn2VxfoqS4n6tuQMTj3eeExERESqxCaHiIiIVIlNDhEREakSm5xHyMfHBwsXLoSPj8+jTkWVWF9lsb7KYn2Vxfoqq63Ul09XERERkSrxTA4RERGpEpscIiIiUiU2OURERKRKbHKIiIhIlfjGY4Vs27YN27dvR0VFBfr06YPp06fD29v7ocWrmYhg8+bNyMrKQnV1NYKDgzF9+nS0b9++RfGHDh3C7t27cfHiRfj6+mLo0KGKvtHa0ZjNZmzYsAE5OTkwmUwIDQ3FtGnT4Orqet9rrVmzBjt37kRAQADefvttBbJ1PLW1tVi3bh2OHDkCEUF4eDhSUlLg7Ozc4jWKioqQkZGB06dPw9vbG+PHj8ewYcMUzNpxVFVVIT09HXl5edBqtYiKisLkyZOh1WpbFH/58mV8+OGHuHDhAnQ6HXr27ImUlJRH/pRQW1FcXIzNmzcjOzsbTk5OWL9+/X3F37p1C2vWrEFBQQHatWuH2NhYJCQkQKPRKJIvz+QoYPr06Zg0aRK6du2KmJgYfPLJJ+jbty/Onz//UOLVrK6uDklJSZgxYwZ69OiBqKgorF+/HsHBwfj222+bjR84cCBiYmJw48YNxMTEwGAw4OWXX0ZERARu3779EPagbautrcXzzz+P119/HX369EF4eDhWrVqFsLAwlJaW3tdaJ06cwOzZs5GZmYns7GyFMnYsFRUViIiIwDvvvIP+/ftjwIABWLJkCSIjI3Hnzp0WrbFhwwb07t0bp06dwogRI9CrVy8sXLgQ77//vsLZt32lpaUICwvDihUrMGTIEPTr1w/z5s3DqFGjUFtb22z8P//5T/j7++PgwYMIDw9HSEgIPvroI3Tv3h379+9/CHvQts2fPx9BQUH4/PPPcebMGXz88cf3Ff/tt98iODgY69atQ2RkJHr27IlZs2YhMTERdXV1yiQtZFeZmZkCQD766CPLWFVVlXTv3l2io6MVj1e7tWvXCgDZtWuXZay8vFx8fX3FaDQ2Gz9o0CA5efKk1dihQ4cEgMyfP9/u+TqaZcuWCQDJz8+3jF29elXc3d1l5syZLV6npqZGgoKCJCUlRXx9fSU8PFyJdB3OvHnzRKfTyblz5yxj586dE51OJwsWLGg2Pi8vT3Q6nfz1r3+12Xb16lW75uqIUlNTxWAwyDfffGMZKygoEI1GI0uXLm023tfXVwICAsRkMlnGbt26JV5eXvw7LCJnz56Vu3fviojICy+8IHq9/r7iExISxMfHR0pLSy1je/bsEQCSnp5ux0z/h02Onf3kJz+Rjh07itlsthr/3e9+JwDkwoULisar3dChQ6Vbt24246+99ppotVr57rvvmowvKytrcNzT01OGDh1qjxQdWq9evaR///4241OmTBGDwSBVVVUtWueNN94QX19fKSkpYZPz/+rq6qRz584SFxdns23UqFHi5+cndXV1Ta7x/PPPS69evZRK0aFVV1eLu7u7JCUl2WwbNGiQ9OzZs8n4uro60ev1Mm7cOJttQUFBEhAQYK9UVeF+m5zi4mLR6XQye/Zsm23+/v4yZMgQe6ZnwctVdpabm4vg4GA4OVmXNjQ0FABw9OhRRePVzGw249ixY+jfv7/NttDQUJjNZuTn5ze5RocOHWzGrl69ilu3bsHT09NeqTqk8vJynDlzptH6VlZW4osvvmh2nUOHDmHZsmVYuXIlOnbsqESqDunixYu4ceNGo/W9du0aLl261Gh8VVUV9uzZg+joaGRnZ+PnP/85kpOTsWDBApw9e1bBzB3Dl19+iYqKikbre/bs2SYvuWo0GiQkJODAgQO4fPmyZTw/Px+nT59GYmKiInk/Lo4fPw6TydTo8Tl27BhMJpPdvy6bHDu6e/cuvvvuO/j6+tpsqx/75ptvFItXu5KSElRXV9u9PvPnz0ddXR2mTJnS6hwdWX3tWlPfiooKpKSkYMyYMZg4caL9k3RgV65cAfDg9b148SLu3r2L7du3Y8KECfDz80NUVBQOHjyIoKAgZGZmKpK3o2htfQEgLS0Nc+bMwXPPPYfY2FhERUUhLi4Oy5Ytw8KFC+2f9GOkueNTW1uL4uJiu39dPl1lRzU1NQAAnc62rPVPTlRXVysWr3b1Nw7asz5/+9vfsHbtWowdOxZJSUmtT9KB2aO+v/zlL1FSUoJVq1bZP0EH19r61t+YfOnSJRw+fBhDhgwBAEydOhUhISGYNm0aRo4cCTc3N3un7hDs8fd3//79WL16Nfr374/ExETU1taiqqoKK1asQEREBIKDg+2e9+NCie/fLcEzOXbk5uYGJyenBp+SqKysBAB4eHgoFq927u7uAGC3+mzcuBGzZs1CeHj4fT8GqUatre+uXbvw97//HcuWLcMTTzyhTJIOrLX1rd/27LPPWhocANBqtZgyZQpu3ryJ3Nxce6bsUFpb37KyMkycOBGBgYHYvXs3fvazn2H27Nk4dOgQ9Ho9Jk6cqNwTQI8Be3//bik2OXbk5OSEZ555xup6br36sR49eigWr3aenp7o1KmTXeqzdetWJCcnIywsDDt37oTBYLBrro6oW7ducHZ2fuD6Hj16FFqtFjt37kRCQoLlo6ysDGfOnEFCQgIyMjIUy7+tq6/dg9a3e/fucHZ2bvB9WfVjZWVldsjUMTVXX51Oh+7duzcaf/z4cZSXlyM+Pt5q3NnZGaNHj0ZhYWGT90xR05o7Pl5eXoq8C46Xq+wsOjoaaWlpKC0thZeXl2V83759aNeuXbMv7GptvNpFR0djx44dqKqqsno53b59++Dp6Wm5Qbspn376KRITExESEoJdu3Y91mfHfsjZ2RkRERE4ePAg6urqrG5+37dvH7p164aePXs2Gp+YmIg+ffrYjO/duxc+Pj548cUX0atXL0VydwTe3t7o168fcnJybLZlZ2cjJCSkyRu1XVxcEBUVhYKCAphMJqvT/l999RWA78/yPK6effZZPP300zb1FRHk5OQgIiICLi4ujcbr9XoA39+Af6/6sXbt2tkv4cdMSEgIvLy8kJOTg1/84heW8ZqaGhw5cgQjR45U5gsr8szWY+zMmTPi4uJi9ZjciRMnxNXVVebOnWs1Nzs7W4xGo2RnZz9Q/OMoPz9ftFqt1Tttjhw5IjqdTt5++22rudu2bROj0Sh5eXmWsZycHHF1dZWBAwdKeXn5Q8vbUWRlZQkA+eMf/2gZ27Fjh2g0Gvnggw+s5m7YsEGMRqOcPn26yTX5CPn/rF+/XgDIunXrLGMffvihAJCNGzdazV29erUYjUard75kZ2eLRqORd955xzL21Vdfiaenp0RGRiqef1u3atUqASBbt261jK1cuVIASFZWltXc5cuXi9FotLyzpaamRp588knp0qWLFBUVWeadOHFC3N3dZfDgwQ9lHxxFc4+Qv/XWW2I0Gq1eh7Jo0SLRarVy4MABy9jChQtFq9XKsWPHFMmTTY4C/v3vf0vHjh2lb9++MmrUKHF1dZXk5GSpra21mpeent7gS5BaGv+4ysjIEA8PDwkJCZHY2FjR6/Uyc+ZMm3cLLV++XADIli1bLGPu7u4CQGJjY8VoNFp9zJkz5yHvSdu0atUqcXV1lUGDBsmIESPExcVFfv3rX9vMW7BggQCQgwcPNrkemxxrS5YsEb1eL8OGDZNhw4aJXq+X9957z2bezJkzBYBNE5meni4eHh4SFBQk0dHR4ubmJtHR0XL9+vWHtQtt2rx588TFxUWGDx8ugwcPFldXV/nLX/5iMy8pKUkAWL1E8b///a8MHjxYDAaDjBgxQoYNGyYuLi4yevRoq8bncVX/H0ej0Sh+fn7i5ORk+fzdd9+1mhsdHS0ALC8PFBExm82Smpoqer1eYmNjJTQ0VDw8PCQjI0OxnDUiIsqcI3q8VVVVITc31/K7p55++mmbOZcvX0Z+fj4GDBiAbt263Xf846yyshJHjx5FdXU1+vXrh65du9rMKSwsxH/+8x8MGTLEciPspk2bGl3T3d0do0aNUixnR1JeXo68vDyYTCY899xz8PPzs5nz5Zdf4vTp0xg+fHiT19K3bdsGd3d3REVFKZixYykpKUF+fj40Gg1CQ0MbrF9BQQHOnz+PuLg4m0uqlZWVyM3NRWVlJXr27Ikf//jHDyt1h3D9+nV8/vnn0Gq1CAsLa/AdWLm5uSgqKsKYMWMsl6rqFRYW4uLFi9BqtfD398dTTz31kDJv2+q/pzbkRz/6EcLDwy2fHzx4ENevX4fRaLT5vVRXrlzBiRMnoNfrMXDgQMtNyUpgk0NERESqxKeriIiISJXY5BAREZEqsckhIiIiVWKTQ0RERKrEJoeIiIhUiU0OERERqRKbHCIiIlIlNjlERESkSmxyiIiISJXY5BARAODNN9+0ef26Wt3vvj5OtSFSEzY5RA6uU6dO0Gg0DX4cO3bsUafnMH71q19Bp9M96jTsTq37RdQSbHKIVCAyMhIiYvMRGhr6qFNrkxYvXoz7+bV99zufiNoGNjlERESkSmxyiFTuz3/+s9UlrPbt2yMiIgLbt29vNvbSpUtITk5Gly5d4ObmhsDAQCxatAiVlZVW806dOoXExET4+PjAxcUFvXr1wvLly5s9+zFnzhy4u7ujrKwMycnJ6NChA7y8vJCSkoLi4mKb+Tk5ORgxYgQ8PDzg5uaGsLAwbNy48b5zvvcem4SEBPzhD3+A2Wy2qlVhYWGD899//31oNBoUFBTY5JiWlgaNRoPPPvvMbvW5desWpk6dCm9vb/To0QNAy45rc/vVmtyIHAGbHCKVmzNnjuXyldlsxsmTJzFgwADEx8cjPz+/ydjRo0ejqKgIe/fuRWlpKT7++GMAwJYtWyxzjh8/jrCwMFRVVSEnJwclJSVYsmQJFi1ahNdff71FOc6aNQuTJk1CUVERMjMzkZ2djZEjR6K2ttYyZ+fOnYiJiUGXLl3wxRdf4MKFC4iJiUFiYiJWrVp1Xznfa9OmTZg7dy60Wq3V5T5/f/8G57/00kvQ6/VIS0uz2ZaWloaAgAAMHjzYbvVJTU1FfHw8CgsL8Zvf/AZAy45rc/tlj9yI2jQhIofm7e0tAGw+jEZjk3H+/v6Smppq+XzBggXyw28J165dEwCyevXqJtcZPHiwPPPMM1JdXW01/qc//Um0Wq0UFRU1Gjt79mwBIOnp6Vbju3btEgCSlpZmGevdu7f06NFDTCaT1dy4uDhp3769VFZWtjjne/dVRGTu3Lmi1WpbPP/FF18ULy8vq/0+c+aMAJBly5ZZxuxRnzVr1jS5Pz9073Ftar9akxuRI+CZHCIVaOjG402bNgEA7ty5g9/+9rcICAiAq6ur1SWLH162uFenTp3wxBNP4N1338U//vEPXL9+3WZOcXExPvvsM4wdOxZ6vd5qW0xMDMxmMw4fPtxs/mPHjrX6PC4uDq6urti3bx8A4Nq1azh16hTGjRsHrVZrNTchIQG3bt1Cfn5+i3K2l2nTpqG0tNTqDFFaWhqcnZ3x0ksvAVCuPsCDH9d69sqNqC1jk0Okcj/96U+xcuVKLFmyBFeuXIHZbIaIIDg4GHfv3m00TqvVIisrC3379kVqair8/PzQu3dvLFq0CHfu3AEASxOxYsUK6HQ6aLVaaLVaODk5oU+fPgCAkpKSJvPT6XTo2LGjzXjnzp0t9+XUr+Hn52czr36suLi4RTnbS3R0NLp37441a9YAAEwmE9atW4cxY8agc+fOAOxTHxcXF3Tq1Mlm/EGPaz175EbU1rHJIVKxiooKbNmyBa+++irGjx8Pb29vODl9/8/+0qVLzcYHBgZi69atKCsrw+HDh/HCCy/grbfewquvvgoAlh++8+fPh8lkgtlshtlsRl1dneWMUv3cxphMJty8edNm/MaNG/D29gYAy58NnZmpH6vPpbmc7UWj0eCVV17B3r17cfnyZezYsQPXrl3DtGnTLHPsUR9nZ2ebsdYeV3vlRtTWsckhUjGNRgMRsbkcUd8EtJRer8eQIUOwdOlSDB8+HAcOHAAA+Pr6YsCAAdiyZUuLzh405pNPPrH6PCsrC1VVVYiOjgbw/dmagIAAbN26FXV1dVZzN2/ejPbt29u8E6ixnBtjMBhQV1cHk8nU4rxfeeUVaDQapKenY82aNejSpQvi4uIs2+1Vn3vdz3FtbL+Uyo2oLWGTQ6RiBoMBI0aMwOrVq3H06FFUVFRg+/bt+P3vf4+goKAmYwsKCjBhwgTs3r0bN27cQFVVFXbv3o3jx49j+PDhlnkffPABvv76a4wfPx7Hjx/HnTt3cOXKFWzduhWxsbHNXvIwGAz49NNPsWPHDty+fRsHDhzAjBkzEBQUhMmTJ1vmLV26FOfOncPUqVNx+fJlXL9+HW+++SZ27tyJxYsXw2AwtDjnhvTp0wcigm3btsFsNregukDXrl0RFxeH1atXY8eOHXj55Zdt7hlqbX0aq1lLj2tT+6VEbkRtysO/15mI7Mnb21siIyMb3X716lVJSkoSb29vad++vYwbN06+/vprGThwoFXcvU8Qmc1myczMlFGjRknnzp3FYDBI7969ZfHixTZP45w7d05SUlKkS5cu4uzsLE899ZRMmDBB9u7d22Tus2fPFoPBIDdv3pSkpCTx8PCQDh06yJQpU+TGjRs28/fs2SNRUVFiMBikXbt2EhoaKhs2bLjvnBt6WspsNsuMGTPEx8dHNBqNAJBz5841Or/e5s2bBYBoNBo5f/58g3NaW5+GtPS4NrVfrcmNyBFoRPjGJyJ6NObMmYO1a9eioqLiUadCRCrEy1VERESkSmxyiIiISJXY5BAREZEq8Z4cIiIiUiWeySEiIiJVYpNDREREqsQmh4iIiFSJTQ4RERGpEpscIiIiUiU2OURERKRKbHKIiIhIldjkEBERkSqxySEiIiJVYpNDREREqsQmh4iIiFTp/wDdc8Zmm/UvAAAAAABJRU5ErkJggg==",
      "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": "d929e86c",
   "metadata": {},
   "source": [
    "## 6. Feature importances\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7a5cb78a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:41:36.311950Z",
     "iopub.status.busy": "2026-07-25T15:41:36.311835Z",
     "iopub.status.idle": "2026-07-25T15:41:36.358973Z",
     "shell.execute_reply": "2026-07-25T15:41:36.357950Z"
    }
   },
   "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>Employment status</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             feature  importance_gini_sum\n",
       "0  Employment status                  1.0"
      ]
     },
     "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>Employment status</td>\n",
       "      <td>0.059478</td>\n",
       "      <td>0.035538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             feature  importance_perm_mean  importance_perm_std\n",
       "0  Employment status              0.059478             0.035538"
      ]
     },
     "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": "3d19ae22",
   "metadata": {},
   "source": [
    "## 7. Written results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0b1b78b1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:41:36.360399Z",
     "iopub.status.busy": "2026-07-25T15:41:36.360286Z",
     "iopub.status.idle": "2026-07-25T15:41:36.363374Z",
     "shell.execute_reply": "2026-07-25T15:41:36.362835Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Employment status Random Forest CV ROC-AUC = 0.528. Comparable to or weaker than the geo benchmark (≈0.55). Does not exceed the CA-score benchmark (≈0.59).\n",
      "\n",
      "Sample: {'n': 241, 'n_regular': 101, 'n_not_regular': 140, 'prevalence': 0.4190871369294606}\n",
      "CV metrics: {'roc_auc': 0.5280763790664781, 'average_precision': 0.42444850464903056, 'balanced_accuracy': 0.5595120226308345, 'f1': 0.5857142857142857, 'brier': 0.24724032068607005}\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": "7c7f19e1",
   "metadata": {},
   "source": [
    "## 8. Artifacts\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a4a4f72c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:41:36.364392Z",
     "iopub.status.busy": "2026-07-25T15:41:36.364284Z",
     "iopub.status.idle": "2026-07-25T15:41:36.546465Z",
     "shell.execute_reply": "2026-07-25T15:41:36.545320Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wrote 11 artifacts to /workspace/outputs/transit_covariate_rf/employment\n",
      "Memo figure: /workspace/memos/figures/employment_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": "8a6c8517",
   "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
}