Project_Carmignac/repair.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6c12c301",
   "metadata": {},
   "source": [
    "# Level Shift Repair \n",
    "\n",
    "**Three improvements over first attempt:**\n",
    "- Gap stability tolerance is now **relative** (1%) instead of absolute (1e-6)\n",
    "- Detection is now **iterative**: multiple level shifts per trajectory are corrected\n",
    "- Minimum relative gap threshold lowered to **2%** to capture smaller persistent anomalies\n",
    "\n",
    "**Sections:**\n",
    "1. Imports & Data Loading\n",
    "2. Build Panel\n",
    "3. Improved Repair Algorithm\n",
    "4. Rebuild Stocks File\n",
    "5. Validation & Diagnostics\n",
    "6. Figure"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26e87f83",
   "metadata": {},
   "source": [
    "## 0. Imports & Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "80b3f4a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stocks: (4880297, 19)\n",
      "Flows:  (2574461, 25)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "stocks = pd.read_csv(\"stocks.csv\", low_memory=False)\n",
    "flows  = pd.read_csv(\"flows.csv\",  low_memory=False)\n",
    "\n",
    "stocks[\"Centralisation Date\"] = pd.to_datetime(stocks[\"Centralisation Date\"])\n",
    "flows[\"Centralisation Date\"]  = pd.to_datetime(flows[\"Centralisation Date\"])\n",
    "\n",
    "print(f\"Stocks: {stocks.shape}\")\n",
    "print(f\"Flows:  {flows.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17760729",
   "metadata": {},
   "source": [
    "## 1. Build Panel (Account x ISIN x Date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "939e717e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Panel size:  (4865922, 5)\n",
      "Accounts:    12,470\n",
      "ISINs:       491\n"
     ]
    }
   ],
   "source": [
    "KEY   = [\"Registrar Account - ID\", \"Product - Isin\", \"Centralisation Date\"]\n",
    "GROUP = [\"Registrar Account - ID\", \"Product - Isin\"]\n",
    "\n",
    "stocks_panel = stocks[KEY + [\"Quantity - AUM\"]].copy()\n",
    "\n",
    "flows_panel = (\n",
    "    flows[KEY + [\"Quantity - NetFlows\"]]\n",
    "    .groupby(KEY, as_index=False)[\"Quantity - NetFlows\"]\n",
    "    .sum()\n",
    ")\n",
    "\n",
    "df = stocks_panel.merge(flows_panel, on=KEY, how=\"left\")\n",
    "df[\"Quantity - NetFlows\"] = df[\"Quantity - NetFlows\"].fillna(0)\n",
    "df = df.sort_values(KEY).reset_index(drop=True)\n",
    "\n",
    "# Remove negative AUM (data quality filter)\n",
    "df = df[df[\"Quantity - AUM\"] >= 0].copy()\n",
    "\n",
    "print(f\"Panel size:  {df.shape}\")\n",
    "print(f\"Accounts:    {df['Registrar Account - ID'].nunique():,}\")\n",
    "print(f\"ISINs:       {df['Product - Isin'].nunique():,}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5931079a",
   "metadata": {},
   "source": [
    "## 2. Improved Repair Algorithm\n",
    "\n",
    "**Fix 1 — Relative gap stability tolerance**\n",
    "The original algorithm used `GAP_TOL = 1e-6` (absolute), requiring the gap to be perfectly constant.\n",
    "In practice gaps fluctuate slightly due to valuation effects and rounding.\n",
    "We now use a relative tolerance of 1%:\n",
    "`|gap(t+k) - gap(t)| / |gap(t)| < REL_STABILITY_TOL`\n",
    "\n",
    "**Fix 2 — Iterative detection**\n",
    "The original algorithm stopped after the first detected shift.\n",
    "We now loop until no more shifts are found, correcting multiple successive migrations.\n",
    "\n",
    "**Fix 3 — Lower detection threshold**\n",
    "Lowered from 5% to 2% to capture smaller but clearly persistent anomalies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3754dcaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ============================================================\n",
    "# PARAMETERS\n",
    "# ============================================================\n",
    "\n",
    "REL_GAP_THR       = 0.02   # minimum relative gap to trigger detection (2%, was 5%)\n",
    "REL_STABILITY_TOL = 0.05   # relative tolerance on gap stability (1%, was absolute 1e-6)\n",
    "MIN_PERSISTENCE   = 2      # minimum consecutive stable periods\n",
    "MAX_ITERATIONS    = 10     # maximum repair iterations per trajectory\n",
    "\n",
    "# ============================================================\n",
    "# REPAIR FUNCTION (IMPROVED)\n",
    "# ============================================================\n",
    "\n",
    "def repair_group(g):\n",
    "    \"\"\"\n",
    "    Iteratively detect and correct persistent level shifts.\n",
    "    Improvements: relative stability tolerance, iterative detection, lower threshold.\n",
    "    \"\"\"\n",
    "    g = g.copy()\n",
    "    obs    = g[\"Quantity - AUM\"].values.copy()\n",
    "    flows_ = g[\"Quantity - NetFlows\"].values\n",
    "    n      = len(obs)\n",
    "\n",
    "    corrected   = obs.copy()\n",
    "    repair_flag = np.zeros(n, dtype=bool)\n",
    "    n_repairs   = 0\n",
    "\n",
    "    for _ in range(MAX_ITERATIONS):\n",
    "\n",
    "        # Build expected path from current corrected series\n",
    "        expected = np.empty(n)\n",
    "        expected[0] = np.nan\n",
    "        for t in range(1, n):\n",
    "            expected[t] = corrected[t-1] + flows_[t-1]\n",
    "\n",
    "        gap     = corrected - expected\n",
    "        rel_gap = np.abs(gap) / np.maximum(np.abs(expected), 1.0)\n",
    "\n",
    "        # Search for first persistent level shift\n",
    "        idx   = None\n",
    "        shift = None\n",
    "\n",
    "        for i in range(1, n - MIN_PERSISTENCE):\n",
    "            if rel_gap[i] <= REL_GAP_THR:\n",
    "                continue\n",
    "\n",
    "            # Check gap stability with RELATIVE tolerance (Fix 1)\n",
    "            window = gap[i:i + MIN_PERSISTENCE]\n",
    "            ref    = gap[i]\n",
    "\n",
    "            if abs(ref) < 1.0:\n",
    "                stable = np.all(np.abs(window - ref) < 1.0)\n",
    "            else:\n",
    "                stable = np.all(np.abs(window - ref) / np.abs(ref) < REL_STABILITY_TOL)\n",
    "\n",
    "            if not stable:\n",
    "                continue\n",
    "\n",
    "            idx   = i\n",
    "            shift = ref\n",
    "            break\n",
    "\n",
    "        # No more shifts found: stop (Fix 2 — iterative)\n",
    "        if idx is None:\n",
    "            break\n",
    "\n",
    "        # Safety: do not create new negative AUM\n",
    "        candidate = corrected[idx:] - shift\n",
    "        if ((candidate < 0) & (corrected[idx:] >= 0)).any():\n",
    "            break\n",
    "\n",
    "        # Safety: avoid extreme corrections\n",
    "        if abs(shift) > 2 * np.nanmax(np.abs(corrected)):\n",
    "            break\n",
    "\n",
    "        # Apply correction\n",
    "        corrected[idx:] = candidate\n",
    "        repair_flag[idx:] = True\n",
    "        n_repairs += 1\n",
    "\n",
    "    if n_repairs == 0:\n",
    "        return g\n",
    "\n",
    "    # Rebuild expected path after all corrections\n",
    "    expected_corr = np.empty(n)\n",
    "    expected_corr[0] = np.nan\n",
    "    for t in range(1, n):\n",
    "        expected_corr[t] = corrected[t-1] + flows_[t-1]\n",
    "\n",
    "    g[\"corrected_aum\"]       = corrected\n",
    "    g[\"expected_stock_corr\"] = expected_corr\n",
    "    g[\"repair_flag\"]         = repair_flag\n",
    "    g[\"n_repairs\"]           = n_repairs\n",
    "\n",
    "    return g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5041d1cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_7300/2675817932.py:11: FutureWarning:\n",
      "\n",
      "DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== REPAIR SUMMARY ===\n",
      "   Before repair  After repair  Repaired points  Multi-shift trajectories\n",
      "0         553084        148599           230833                      1020\n"
     ]
    }
   ],
   "source": [
    "# Apply repair\n",
    "df_repair = df.copy()\n",
    "df_repair[\"corrected_aum\"]       = df_repair[\"Quantity - AUM\"]\n",
    "df_repair[\"expected_stock_corr\"] = np.nan\n",
    "df_repair[\"repair_flag\"]         = False\n",
    "df_repair[\"n_repairs\"]           = 0\n",
    "\n",
    "df_repair = (\n",
    "    df_repair\n",
    "    .groupby(GROUP, group_keys=False)\n",
    "    .apply(repair_group)\n",
    "    .reset_index(drop=True)\n",
    ")\n",
    "\n",
    "# Rebuild expected stock before repair\n",
    "df_repair = df_repair.sort_values(KEY).reset_index(drop=True)\n",
    "df_repair[\"prev_aum\"]        = df_repair.groupby(GROUP)[\"Quantity - AUM\"].shift(1)\n",
    "df_repair[\"prev_flow\"]       = df_repair.groupby(GROUP)[\"Quantity - NetFlows\"].shift(1).fillna(0)\n",
    "df_repair[\"expected_stock\"]  = df_repair[\"prev_aum\"] + df_repair[\"prev_flow\"]\n",
    "\n",
    "df_repair[\"gap_before\"]     = df_repair[\"Quantity - AUM\"] - df_repair[\"expected_stock\"]\n",
    "df_repair[\"gap_after\"]      = df_repair[\"corrected_aum\"]  - df_repair[\"expected_stock_corr\"]\n",
    "df_repair[\"rupture_before\"] = df_repair[\"gap_before\"].abs() > 10\n",
    "df_repair[\"rupture_after\"]  = df_repair[\"gap_after\"].abs()  > 10\n",
    "\n",
    "multi_shift = (df_repair.groupby(GROUP)[\"n_repairs\"].max() > 1).sum()\n",
    "\n",
    "print(\"=== REPAIR SUMMARY ===\")\n",
    "print(pd.DataFrame({\n",
    "    \"Before repair\":              [int(df_repair[\"rupture_before\"].sum())],\n",
    "    \"After repair\":               [int(df_repair[\"rupture_after\"].sum())],\n",
    "    \"Repaired points\":            [int(df_repair[\"repair_flag\"].sum())],\n",
    "    \"Multi-shift trajectories\":   [int(multi_shift)]\n",
    "}))\n",
    "\n",
    "df_repair[[\n",
    "    \"Registrar Account - ID\", \"Product - Isin\", \"Centralisation Date\",\n",
    "    \"Quantity - AUM\", \"corrected_aum\", \"Quantity - NetFlows\",\n",
    "    \"expected_stock\", \"expected_stock_corr\",\n",
    "    \"gap_before\", \"gap_after\", \"repair_flag\", \"n_repairs\"\n",
    "]].rename(columns={\n",
    "    \"Quantity - AUM\":      \"aum_raw\",\n",
    "    \"corrected_aum\":       \"aum_repaired\",\n",
    "    \"Quantity - NetFlows\": \"flows\",\n",
    "    \"expected_stock\":      \"expected_aum_raw\",\n",
    "    \"expected_stock_corr\": \"expected_aum_repaired\"\n",
    "}).to_csv(\"df_repaired.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56ac9cc7",
   "metadata": {},
   "source": [
    "## 3. Rebuild Stocks File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "542189d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Share of repaired observations: 7.3211%\n",
      "Number of repaired rows: 408,335\n",
      "Trajectories with 2+ repairs: 1,020\n"
     ]
    }
   ],
   "source": [
    "stocks_repaired = stocks.copy()\n",
    "stocks_repaired[\"Centralisation Date\"] = pd.to_datetime(stocks_repaired[\"Centralisation Date\"])\n",
    "\n",
    "repair_map = df_repair[KEY + [\"corrected_aum\", \"repair_flag\", \"n_repairs\"]].rename(\n",
    "    columns={\"corrected_aum\": \"Quantity - AUM repaired\"}\n",
    ")\n",
    "\n",
    "stocks_repaired = stocks_repaired.merge(repair_map, on=KEY, how=\"left\")\n",
    "stocks_repaired[\"Quantity - AUM original\"] = stocks_repaired[\"Quantity - AUM\"]\n",
    "stocks_repaired[\"Quantity - AUM\"] = np.where(\n",
    "    stocks_repaired[\"repair_flag\"] == True,\n",
    "    stocks_repaired[\"Quantity - AUM repaired\"],\n",
    "    stocks_repaired[\"Quantity - AUM\"]\n",
    ")\n",
    "\n",
    "# Recompute monetary values (NAV per share unchanged)\n",
    "stocks_repaired[\"nav_ccy\"] = stocks_repaired[\"Value - AUM CCY\"] / stocks_repaired[\"Quantity - AUM original\"]\n",
    "stocks_repaired[\"nav_eur\"] = stocks_repaired[\"Value - AUM €\"]   / stocks_repaired[\"Quantity - AUM original\"]\n",
    "stocks_repaired[\"Value - AUM CCY\"] = stocks_repaired[\"Quantity - AUM\"] * stocks_repaired[\"nav_ccy\"]\n",
    "stocks_repaired[\"Value - AUM €\"]   = stocks_repaired[\"Quantity - AUM\"] * stocks_repaired[\"nav_eur\"]\n",
    "stocks_repaired = stocks_repaired.drop(columns=[\n",
    "    \"Quantity - AUM repaired\", \"Quantity - AUM original\", \"nav_ccy\", \"nav_eur\"\n",
    "])\n",
    "\n",
    "print(f\"Share of repaired observations: {stocks_repaired['repair_flag'].mean():.4%}\")\n",
    "print(f\"Number of repaired rows: {stocks_repaired['repair_flag'].sum():,}\")\n",
    "print(f\"Trajectories with 2+ repairs: {(stocks_repaired.groupby(GROUP)['n_repairs'].max() >= 2).sum():,}\")\n",
    "\n",
    "stocks_repaired.to_csv(\"stock_repaired.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9644cf0e",
   "metadata": {},
   "source": [
    "## 4. Validation & Diagnostics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "76cd990d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Negative AUM before repair: 27,047\n",
      "Negative AUM after repair:  22,630\n",
      "Series where repair created negatives: 213\n",
      "\n",
      "Ruptures before: 553,084\n",
      "Ruptures after:  148,599\n",
      "Reduction rate:  73.1%\n"
     ]
    }
   ],
   "source": [
    "# Safety check: no new negative AUM\n",
    "df_compare = stocks.merge(\n",
    "    stocks_repaired[KEY + [\"Quantity - AUM\"]],\n",
    "    on=KEY, how=\"inner\", suffixes=(\"_raw\", \"_repaired\")\n",
    ")\n",
    "\n",
    "neg_raw = (df_compare[\"Quantity - AUM_raw\"] < 0).sum()\n",
    "neg_rep = (df_compare[\"Quantity - AUM_repaired\"] < 0).sum()\n",
    "created_neg = df_compare[\n",
    "    (df_compare[\"Quantity - AUM_raw\"] >= 0) &\n",
    "    (df_compare[\"Quantity - AUM_repaired\"] < 0)\n",
    "].groupby([\"Registrar Account - ID\", \"Product - Isin\"]).size()\n",
    "\n",
    "print(f\"Negative AUM before repair: {neg_raw:,}\")\n",
    "print(f\"Negative AUM after repair:  {neg_rep:,}\")\n",
    "print(f\"Series where repair created negatives: {len(created_neg):,}\")\n",
    "\n",
    "n_before = int(df_repair[\"rupture_before\"].sum())\n",
    "n_after  = int(df_repair[\"rupture_after\"].sum())\n",
    "print(f\"\\nRuptures before: {n_before:,}\")\n",
    "print(f\"Ruptures after:  {n_after:,}\")\n",
    "print(f\"Reduction rate:  {1 - n_after/n_before:.1%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "895ec10a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Accounting scatter: Flow vs ΔAUM (before / after)\n",
    "\n",
    "def build_accounting_df(stocks_df):\n",
    "    flows_agg = flows[KEY + [\"Quantity - NetFlows\"]].groupby(KEY, as_index=False).sum()\n",
    "    d = stocks_df.merge(flows_agg, on=KEY, how=\"left\")\n",
    "    d[\"Quantity - NetFlows\"] = d[\"Quantity - NetFlows\"].fillna(0)\n",
    "    d = d.sort_values(KEY)\n",
    "    d[\"prev_aum\"]  = d.groupby(GROUP)[\"Quantity - AUM\"].shift(1)\n",
    "    d[\"flow_lag\"]  = d.groupby(GROUP)[\"Quantity - NetFlows\"].shift(1).fillna(0)\n",
    "    d[\"delta_aum\"] = d[\"Quantity - AUM\"] - d[\"prev_aum\"]\n",
    "    return d[d[\"prev_aum\"].notna()]\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
    "for ax, stocks_df, title in zip(axes, [stocks, stocks_repaired], [\"Raw\", \"Repaired\"]):\n",
    "    s = build_accounting_df(stocks_df).sample(min(20000, len(stocks_df)), random_state=1)\n",
    "    ax.scatter(s[\"flow_lag\"], s[\"delta_aum\"], alpha=0.2, s=4)\n",
    "    lim = s[\"flow_lag\"].abs().quantile(0.99)\n",
    "    x = np.linspace(-lim, lim, 100)\n",
    "    ax.plot(x, x, color=\"red\", linewidth=1.5, label=\"Perfect identity\")\n",
    "    ax.set_xlim(-lim, lim); ax.set_ylim(-lim, lim)\n",
    "    ax.set_xlabel(\"Flow (t-1)\"); ax.set_ylabel(\"Δ AUM\")\n",
    "    ax.set_title(title); ax.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4ec5038d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gap persistence: raw vs repaired\n",
    "\n",
    "def build_gap_df(stocks_df):\n",
    "    flows_agg = flows[KEY + [\"Quantity - NetFlows\"]].groupby(KEY, as_index=False).sum()\n",
    "    d = stocks_df.merge(flows_agg, on=KEY, how=\"left\")\n",
    "    d[\"Quantity - NetFlows\"] = d[\"Quantity - NetFlows\"].fillna(0)\n",
    "    d = d.sort_values(KEY)\n",
    "    d[\"prev_aum\"]  = d.groupby(GROUP)[\"Quantity - AUM\"].shift(1)\n",
    "    d[\"flow_lag\"]  = d.groupby(GROUP)[\"Quantity - NetFlows\"].shift(1).fillna(0)\n",
    "    d[\"gap\"]       = d[\"Quantity - AUM\"] - (d[\"prev_aum\"] + d[\"flow_lag\"])\n",
    "    return d\n",
    "\n",
    "def compute_gap_sequences(df, tol=1.0):\n",
    "    lengths = []\n",
    "    for _, g in df.groupby(GROUP):\n",
    "        gaps = g[\"gap\"].values\n",
    "        run = 1\n",
    "        for i in range(1, len(gaps)):\n",
    "            if np.isfinite(gaps[i]) and np.isfinite(gaps[i-1]) and abs(gaps[i]-gaps[i-1]) < tol:\n",
    "                run += 1\n",
    "            else:\n",
    "                lengths.append(run); run = 1\n",
    "        lengths.append(run)\n",
    "    return np.array(lengths)\n",
    "\n",
    "raw_lengths   = compute_gap_sequences(build_gap_df(stocks))\n",
    "clean_lengths = compute_gap_sequences(build_gap_df(stocks_repaired))\n",
    "\n",
    "max_len = 20\n",
    "raw_h   = np.bincount(np.minimum(raw_lengths,   max_len), minlength=max_len+1) / len(raw_lengths)\n",
    "clean_h = np.bincount(np.minimum(clean_lengths, max_len), minlength=max_len+1) / len(clean_lengths)\n",
    "x = np.arange(max_len + 1)\n",
    "\n",
    "plt.figure(figsize=(9, 4))\n",
    "plt.plot(x, raw_h,   marker=\"o\", label=\"Raw\")\n",
    "plt.plot(x, clean_h, marker=\"o\", label=\"Repaired\")\n",
    "plt.xlabel(\"Gap persistence length (20 = 20+)\")\n",
    "plt.ylabel(\"Share of sequences\")\n",
    "plt.title(\"Persistence of accounting gaps — Raw vs Repaired\")\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2011955e",
   "metadata": {},
   "source": [
    "## 5. Figure — Rupture intensity distribution (Before vs After)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "250f2125",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "domain": {
          "x": [
           0,
           0.48
          ]
         },
         "hole": 0.45,
         "labels": [
          "Clean (<=1%)",
          "Moderate (1-10%)",
          "High (10-30%)",
          "Severe (>30%)"
         ],
         "name": "Before repair",
         "textinfo": "percent",
         "type": "pie",
         "values": {
          "bdata": "zczMzMxMSEAAAAAAAIA8QDMzMzMzMyZAzczMzMzMJ0A=",
          "dtype": "f8"
         }
        },
        {
         "domain": {
          "x": [
           0.52,
           1
          ]
         },
         "hole": 0.45,
         "labels": [
          "Clean (<=1%)",
          "Moderate (1-10%)",
          "High (10-30%)",
          "Severe (>30%)"
         ],
         "name": "After repair",
         "textinfo": "percent",
         "type": "pie",
         "values": {
          "bdata": "zczMzMxMSEAzMzMzM7M8QAAAAAAAACdAZmZmZmZmJkA=",
          "dtype": "f8"
         }
        }
       ],
       "layout": {
        "annotations": [
         {
          "showarrow": false,
          "text": "Before repair",
          "x": 0.22,
          "y": 0.5
         },
         {
          "showarrow": false,
          "text": "After repair",
          "x": 0.78,
          "y": 0.5
         }
        ],
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
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k9qljAY5KoB70F1PNkAkss+o2pjyxfyJqi2hatE5SqPoYG4yRaqc2TnXZV5Vu9AdTwXAI3/7wq2ovQ0Eu2mMUdREpxVKdP7GuEo5JnLdxUwhHW+UHW6KvwfJDsQwmG8s8dZzU5TyNPuPmQjn3uNZgpgB9iqWt1EUCViFgamcTEHEXjpdWsG58Xk6d4NQJD+WIliEKivXaEhxXOLBHDtr7UeaaTjJ1bQ/24OQPZwsOD+zECQn5SFi/5s6J+gsK2I5HQvu42CEaiEiHsY2zTzlKf8km2JuRiNwhGeXDWf99yUoJfANXjZfiGUwPTsg1XfARrcHU+VffLhC8WIV9JJiewDxMH8JJwtS7KgMH7Fe4EKi8YMvNW3eKehtZlcMhgZ3K8auJr6oTyRI24SKl6ihbe3XroL8JrfLRPuyAPSoP44UIstrGMhL7wBQvuWC6ErpQHwnryEMZZJBn1YRjDs8qwnnH4xnoB6LXiFjj+MT5BHkqYRtToWo72FLVx7kF5xjIqHawi+HZeQAAEABJREFUjhsULGtLsToe1T7/wpsf68c3nvk0th3tMYo+wRnDvhioE/qxb+DYxLmutpu7UMe7YhntWMCOaBKOk7qep/EVEPRd7VfK+TbevOF4xpclcOxGYyfrkICVCJje2QRMTJ3i7VAclOqCr/JwQVXP8uBAxvQN6gQm1DU++6OenTFOR+Ekg0gqIhB4Ngp6xz74oq4q0vb0SkH+QA8+zYEi1QbawXpudlZUL85AV7gJjyYguqpY4HNHuHCg75gixPNEYAObVML0Gy5a4bah5ALbgj7oCvb2p6qDJS4ymJrDRQZ18EC9unCjHLYiYoPISSR24QKIN/bhbEMvEk72cJwQbYXuUAlTX7gxMbKBQxH4JnFgnxXfUHpryodNDz39duUztBgzREowTmCg6gbyQr9QBoZYGlMk9kEv+oefZIROJKwjD2VGvWZfh+NuHDv0Bccc7MbxiOMS60g4J+A8ovY/yCJhvJXzALlQCfWNx5FqB59Lwj4Yqp4xH+OLcTbqgQ1IOIbC3e/RHvYZ6A72uEng/qD013aM4rlRJByHsBX6A5O6oVQ3mIHlahv7EvZVxdt4vINlXcZCtRHpEvsD9gvUw/iBCxLWg52njdchyOEcY2SIcQh27nl83BX6M8Noh4kE7EzANM4mDka8JICTSzDgOCHheSXjN9hUHvKRUBd5RhmlC45J4HNYcCBUuVoqO6APCTpVGXQjTyWUIS+wPeSjL9Cl6hqXODnjQqL0qCXqGeWCreMkiPaMtqNesPYgA1nUUboC24YdyFPlqKPsUUujbvQJ22hT1cESOqALCevGPKUHS9SFDpQjob1AG1U+5JECyxERwAUXF1GjLtSrLUEeNkAvEtbVGGKp6gezC31GHZUC7UJd9B0MlAzWkYcxgDz0Qs6YlE3Qr/IhB/nPJz+if41B6TPKKFksIa9ksMQ2EvoH/ZBBgi2wCTJIWEceZNEe7IScSthGPmSRsI48VY5lqLrgiTpYQi5ZCfbBjmBJ9T/QNtgcKG/sO5ihbrjjAVnUMbYDu4w6jWVqHTKBdgSOqZINtYSN0IE+BcrAJtiGcpXC0Q9dkId9gTrVNvYT9E/JYBlKN8qgDwl1UFfpUW2hTE+zXxOjDPZv6EU/VZ1wlpBHPdQPJh+MDdpHvUB5OMsoUymYXrSDfKPMPt07CvLQf6UT68b+IR9tQg46sM1EAlYjYBpn02rgaG/yCOCbn8apz+RZwpZJgARIgARIgARqI0BnszZC1i23peUqqokpbWP0w5adZadIgARIgARIwAYEUsLZ5BSEDfbUPV3ANBKmkzCme7K4IAESIIGkEMANL6a8MfVduwGUIIHUJZASzmbqDi97TgIkQAIkQAIkQALJJUBnM7n82XoQAswiARIgARIgARKwDwE6m/YZS/aEBEiABEiABGJNgPpIoM4E6GzWGSEVkAAJkAAJkAAJkAAJhCJAZzMUGeaTQKQEKE8CJEACJEACJFCNAJ3NakiYQQIkQAIkQAIkYHUCtN88BOhsmmcsaAkJkAAJkAAJkAAJ2I4AnU3bDSk7RAKREqA8CZAACZAACcSPAJ3N+LGlZhIgARIgARIgARKIjIANpels2nBQ2SUSIAESIAESIAESMAsBOptmGQnaQQIkECkBypMACZAACViAAJ1NCwwSTSQBEiABEiABEiABcxMIbR2dzdBsWEICJEACJEACJEACJFBHAnQ26wiQ1UmABEggUgKUJwESIIFUIkBnM5VGm30lARIgARIgARIggQQTMLmzmWAabI4ESIAESIAESIAESCCmBOhsxhQnlZEACZCAjQmwayRAAiQQBQE6m1FAYxUSIAESIAESIAESIIHwCNDZDI9TpFKUJwESIAESIAESIAES0AjQ2dQg8B8JkAAJkICdCbBvJEACySRAZzOZ9Nk2CZAACZAACZAACdicAJ1Nmw9wpN2jPAmQAAmQAAmQAAnEkgCdzVjSpC4SIAESIAESiB0BaiIBWxCgs2mLYWQnSIAESIAESIAESMCcBOhsmnNcaFWkBChPAiRAAiRAAiRgSgJ0Nk05LDSKBEiABEiABKxLgJaTgJEAnU0jDa6TAAmQAAmQAAmQAAnElACdzZjipDISiJQA5UmABEiABEjA3gTobNp7fNk7EiABEiABEiCBcAlQLi4E6GzGBSuVkgAJkAAJkAAJkAAJgACdTVBgIgESiJQA5UmABEiABEggLAJ0NsPCRCESIAESIAESIAESMCsBc9tFZ9Pc40PrSIAESIAESIAESMDSBOhsWnr4aDwJkECkBChPAiRAAiSQWAJ0NhPLm62RAAmQAAmQAAmQQEoRqMHZTCkO7CwJkAAJkAAJkAAJkEAcCNDZjANUqiQBEiCBmBOgQhIgARKwKAE6mxYdOJpNAiRAAiRAAiRAAlYgYEdn0wrcaSMJkAAJkAAJkAAJpAQBOpspMczsJAmQAAkkiwDbJQESSHUCdDZTfQ9g/0mABEiABEiABEggjgTobMYRbqSqKU8CJEACJEACJEACdiNAZ9NuI8r+kAAJkAAJxIIAdZAACcSIAJ3NGIGkGhIgARIgARIgARIggeoE6GxWZ8KcSAlQngRIgARIgARIgARCEKCzGQIMs0mABEiABEjAigRoMwmYjQCdTbONCO0RKS0R7/LFUvblNCn5vxel+JUnpGjSA1L42O1S8MAYyb9jtOy+8XzZfeUZsuuSf0veiMPkybtXyrW3lcut95TLPQ975OGJHnn6RY+8/KZX3n7PK1M/9cq383yyfKVfdu0mZBJILQL+bZvFs2CelE57W4rfnCRFLz4qRRPvlcKHb5WCe6+R/Nsuld3Xj5Rdo0+VvAv/JTuuPU9G31AuY+4ol9vvL5f7H/PIY//1yKRXPPLa215550OvfDLTJz8t8Mm69X4pK08tnuwtCZBAZASckYlTmgRiSMDnE9+GtVI+72speeclKXzkNtl91QjJO+8YzaEcJUUvPCwlH7wupZ+9L2WzZ0j5j3PE8/t8zRFdJL51q8S3dZP483cJ/vP6/FJYJLJth8jfG/y6U/n7Ir/M+8knX3/rk48/98nr/+fVndAb7iyXK27CBdQjz79e4Yj+8LNPrwddTCRgWQJFheJZ+oeUzvxQil56VPLvukJ2XXi87LridCl46Cbd0YTDWfbFR1L23Uwp//k78SxaIN6VS/Vj0b9jq4imQ3xe8XhEdueLbNay1qzzy5/L/bLgd79896NPvpjtkw8/8erHz72PeOSKG8sFxxVu8nCcffaVT35fzBs7y+5HNJwEYkyAzmaMgVJdzQQ8f/wkxf97RvJvvUTyzjpCj6YUPnGXlLz/mpT/9K34tmysWUGMSsvKRHABRWQGjuhLb3j1iOg1t5bL0y945POvfbJ6rV80fzhGLVINCcSeAJxDOI64Uds1+hRBVLJAczCLX3pMymZ+JF7N8fQXFUTesMMZcR3MGGDmADMI703z6scRHNBb7y2XV97y6k7qlm0Rq2WFVCLAvtqWQORnFNuiYMfiQcCvTd/hoofpurzzj5WC+6+X0un/J95Vy+LRXJ11FhWLHpGZMtUrDzzukatuKZfHJ3lkuhYZXa5NwSPaU+dGqIAEoiXg1SKOS36T4ree1R8l2aVNe2NKHDdq/h0x9OT8vmgtrFZv23aR7+f79On3sfdVREAxozDrO59s/MdfTZ4ZJEAC9iNAZ9N+Y5rcHmnemGfhz3r0cveYc/XpO0znYbpOSjRPLrnWRdw6IqBLlvnlo08rpuAR+XxBm3rHFKF23Y9YX4wqUE0KEfDvzpOy2Z9K4eN3St4lJ0jBuKukdOpb+qMkVsSACChmFN6a4pW7HvLITXeVy7sfeWXt33Q8rTietJkEwiFAZzMcSpSplYBv4zopfvVJ2XXZSVJw33V69NK3fk2t9awmgBch5i/w6VOEeHkCz6ctW+EXP6+TVhtKc9ur7VDlv8yVgvE3yq5LT5SiSeOl/IdZFc9TmtvyiK3buUtk5iyf3PeoR38ZadpnPtmyNWI1rEACSSTApmsjQGezNkIsD02gvEzKvvlMfwlh93XnSOmM98RfmB9a3mYleCEJz6c98oxHfzkCU+8782zWSXYnoQT8Bbv1qOXuq0dI4YRbxPPbDwltP9mN4WWkaTO8Mvb+crn3YY/+IhJmF5JtF9snARKoGwE6m3Xjl5K1fZvWS/FrT8mu/wyTomfu119CSEkQhk7jrV28VHTzuHL9Dd31GxnqNOCpXOVKcALeNSuk6L8PyK5Rp+jPY/q2/BNcMIVy123w659YulGbZv9gulfyo3jPKYVwsaskYGoCdDZNPTzmMq587peCN113X3OWlH46JaWimOGOhDb7qX97cNwEjzyqRTwX/UmnM1x2KSeHmYHZMwTfuMy/+SIpmzNDRMtLOQ61dBgv7X36hU9/tvO1yV5OsdfCK9xivKA1VYsih5twQx2ubspZhkDCDKWzmTDU1m0Iz47hZZ/Cp8bp3/Czbk8Sa/nSFX558jmP/hIE3sb1+hLbPlszKQHtjqRs1ieyS5sqx48V4BuXJrXUVGZ5vCLf/eDTp9jxeTJ8HcJUBlrMmDlzffLxZ+Gn3fnxu3Ee++CLgpQMhIuXrZFDT7xCXp78aTKaj7hNq9mrOkhnU5HgshoBTO3l33m5/uyYHV/2qdbhOGXg8y74ziB+ieWX3+N3wo6T+VQLAjFKniW/6p8sKnr2QYnpp4piZJ9V1OBrEA9PrJg92MDPJ1li2IqKS+Ti6ydI7yEXVqZkOZgKGGx67Ll3ZPzYy+SSs09Q2XFZwpkN1l/kKSazvv+1sm04lcP/M062bq/6IkDvHh3lpcduls9mza9WVlnZhCt0Nk04KMk2Cd/GRBQTU3veZQuTbY5t2sf3Bp99xSMPPumRNfzMi23GNZyO4GYNb5YXjLtafH+vDqcKZcIggNkDPLKCr0JwmjcMYEkSgcN02iV3ScvmjWXx7NcqU25OlsCpSpJZ8uOvf+pNH9yvl76Mxx84kHAmH3/+3Wrq0feComL5acZz8u7z4+TDGd8KHGAIvvXBF3LFhcOkWZOG2KyS4HAef+QAmTbz+yr5Zt6ItbNp5r7SttoIFBXq38fcdc1ZguczaxNneXQEVq72y/2PeuTF/3mFb69Hx9AqtfCNzKLnJ+jRzFR7szxRY+TXJgvwVYjb7i2XT2b6pLw8US2znXAJwClq3aKJ3HHdeVWqjL3mXIHjVCVT24Bzevw5N1dGQOGwadn6P0QI4bypZCxDlPCaOydWRlAxPQ6HTq8Y5M8Xc36WQQP6SnZWZpDSvVnQAV2IzCpncG9pzWtHDuynO9djRo2oJrhl+07Jzc7S22/etKEUFBRLYVGJ7oDDCa3JCT6k/z7y+5K/Kp3TaspNlkFn02QDkhRzPOVS+vFk2XXlGfr3MUXbToodKdboj7/45Lb7yvXfmC4tS7HO2727ZaVSMuUV2XXVcCn76mNJ3u+e2h303v7hGPrwEwQevUQAABAASURBVK/+TCeOLTihe0u5liwCcM7mzl8YllMHG+Fonnf1eBl+0hDdSUPEb9JrU/UpY+gqKCzWI4GLZ78mcOAmPDNZL0NdpJ9+XSo3jD5Tr3vpyBMF0+SohzJjQh4cOjhtxnzjOmyB03vDPZNk2uvj5ZUnbtEdQ8goB1Q5vcYlnF7I1JaaN2kksAG2bNmWJ7m5WZKTnSmIap469PDKtoLpgXP6z+YdsnrdpmDFpsujs2m6IUmsQd6N62T3DedL8Rv/5dvliUWvt+bxiB6NQVTm90VaiEbP5R8rE/D89afsuu4c3dm04q9mWZk9bMdsAWYNxj/hkS3bkMNkBgKdO7QKy4xFy1brcicfN1BfdmrfUupp0+3IRwTy2ktPr3TC4Cj6tbsKOGq6sPbnyEH9KqOlNbWJCCIiiXDatGpV/sH5QxQTTu8bT4+Vz95+uNp0NiKy86ZP0p1aOL7GNP62y6roC7UBHYhsHjR0tIwYNU7gYMJ5hAPap0enGiO0cEpbtWgcSrXp8uls7hmSVFyUfTlV8m++UHz//J2K3TdVn/G82dMveuTNd72cBjTVyERgjM8rJe+8JAV3jBI89xxBTYrGgcDqtX4ZN6FcMMUeB/VUGSGBVWvD/3bs3xu3yJDTr9On0eGI/bjn2Uo0aYwowkHbnV+I7JglRDMvuPYhPToazMlUDRntMEY1sR5uZBO64JgqRxVT7iqqCedaPeOKF5hCRWihwwqJzqYVRinGNvpLiqTwkduk6IVHRPjzHDGmWzd1s+f6ZNzD5fL3BkY560YysbV9WzdJ/u2jpOT91zhlHhv0MdGC0xteHnrmJY8UFvGYignUCJUgGonnIjGVjohhONXxrCJemlFOGJZwxODgXXrDw/rb48jDFHv9ejnhqKwmg8ggpq2NUVEI4YWc15+6VZ9+P/6cm6tM0aNcJUQl6xrZVLrUEv1DVBP9NzrnmG6HrUrOikunFY2mzdET8KxaKvk3nC/lP30bvRLWjCuBzVtE7n/MIzNn+USbIYprW1RedwL4ZubuG84Tfi+z7izjpeG3hX6560GPLF3Bj93Gi3FNejElvnHzdrn/yTeqiI2f+KbAwTJmYvoYspM/+royGy8BISEDziWcL6zjBZtoI5twgjGF/cOCJVBVJaEMz2diCh1T6TU5nVUq1nFDRTXRvvERAPQTU/5G9ZhuxzYeM8DS7InOptlHqDb7IigvnfqWFGjRF0RhIqhG0SQQ8HpF3v3Iq/8KUd4uRmSSMAS1NukvzJeCCTcLvpnJZzNrxZV0gV27RR77r1fem+YVHF9JNyiFDEC08IOX75VNW3boU+O993xrEy/7IEJoRAFZOHlTPp5dKYuXgOCEQvaAfbvrzzdCx0NPvy1wPo31I1k/dvCBUlPEFbZgKv2xu6+Qky8Yqz9DGW50VtkBJxm24tNHUz+fq/cJeapcLVUeIrjIU0vUHTv+RX1aH04oypDgJO+3T9fK51eRZ+ZEZ9PMoxMr24qLpOCBMfpvLvMsGyuoidGz7C88d+aRdevpcCaGeHiteJf+IYhmen6xznfuwuvZHimHPS8NmCn47CufTJjokaLi+BxTewhyEUAAjhKihZj+VgnPK0IMSySsIyknT8nB4UMeyiCn8j+f/IggwQlVZSjHOhIcNrSJtrEdmDBdjTzjM6HYDkzQjynzmnQF1lHbsEHZq5bIU+VqiTyj7cjHNuqgbdiAPCREg/FRd0SMsW2FZM8zihXIJ8hGX9522X37f8Tz+/wEtchmYk2goFDkoSc9smQZpwBjzTYafWXzvpb8e6629y8A+e29r61a45cHHvfIjrzUdTgHD3LKSceHn+rXc0RzuJi6DpxQfCYJkUN8v9PUxu4xDo4mnlvFR92VA76nyNQLOpumHp66GYfPGuXfeqng10vqpiketakzEgJl5SJPPOuVH362txMQCZNkyJbO/FCKnrhLOEOQDPqxbVN/NvpRj6Tqz10eeZhThg11hZ3q14stf7NoQ8QQkcN4/1xlrPprNXtVv+lsKhI2W3qWL5L82y7Toi9bbdaz1O0OpgBfesMrH39OhzMZe0Hx/56W4pceS0bTbDNOBPDJMXyPk7MGIhInxlRLAiBAZxMUbJbwpnnBuKtFirX5V5v1jd0RmfqpV1592yM+X+pOASZyP/B7PFL4+J1SOv2dRDbLthJEoLSUswYJQs1mUpgAnU2bDT6m+fANTf7kZNwG1hSK5/7ol4kveKW0jA5nPAfEj5fr7rtOyn+YFc9mqDvJBNSswfSZnDVI8lCweZsSoLNpl4HVzpbFr03kNJ9dxjOMfiz60y+P/5cOZxioohLx7d4p+bf/R7x//hZVfVayHoGPPvHqn0aynuWpbDH7bgUCdDatMEph2Fj03ENS+um7YUhSxE4EVq7RIpzPe8XjYYQzluPq3bRB+HJdLIlaRxc+jTSdz0VbZ8BoqSUI0Nm0xDDVbCQ+1o5fMalZiqV2JbDsL79MesUrXm/NDqdd+x/rfuFzYQV3Xi7+bZtjrZr6LELgo0+98uUcr0Wsjc5MPHJV8u7LEm7y5+2IriHWIgGNAJ1NDYKV/+Gbf8VvPWvlLtD2GBD4Y7Ffnn/dy5eG6sjSX1IsBfePEf8uXljriNLy1f/vA5/M/dG+z3CWzfxISt57NezkM9kxgV/cSdTPSEa7M4998EWpwcZo1cat3tbtebq9sDvWjdDZjDXRBOorX/SLFD11TwJbZFNmJrDgd7+8Ntne0Zh48vf7fIKX63zrVsazGeq2EAEcT7/8Zl+HM1FDgQ+m9x5yocBBNLYJpyZYvlEmGeuwC6kubaOv+HlO/Exn4MfXoRtMItEPedQLrIN2wBAp0LHFB+APPfEK/ScyL75+ghh/ahO6oNOoD3bip0JXrN4gqGssq+s6nc26EkxSfe+6VVI44RYRH52LJA2BKZv9fr5fJr9vs30iQaRL/veMeBb+nKDW2IwVCPj9os8Y/L6IDmddx6ttq2by4YxvKx0eODPLV62X+rnZdVVtuvpw6t6YMlPOG36c4FeKlIFw7uAUTv18rsqqdamcycefr/5OBhg+9PTb8u7z4wQ/azn8pCFyy/3PVzJ+64MvZPzYy/Syls0bi/pZTtQrKCqWs085qlr7cDivuHCYoG61wjpk0NmsA7xkVfVt2ywF910rUlqSLBPYrokJfPWNT76cw4tjJENUOusTvmAXCbAUktUC3vLsK15Zv1HzPFOo37Hu6gH7dpfc7KxKhwfOzPATB0uD+rlVmkLEDQ4ZEtaNhcpZQ9lVY58yFukOFqJ3KEOCrBKAnmvunCiqHGVIkFMJTh3ksYQziIQy1IHziIR15CGhPuSDpdXrNkl+YbH06dGpSjF+pQhO4bB/DaqSX9MGfjMddcaMGlFN7IcFSwRc8atCKDyk/z6yYdM2QfuwFw5l8yaNUCRdOraRVWv/0dfB/tShh1dxhPWCPX9gN6KymFbfk1VlEc0Gnc1oqCWxjr8wXwruuVr8u3Ym0Qo2bXYC73zolZVr6HCGM07lf/wkxc9NCEeUMilKwKNNFkx83qM5NHQ467ILjDztWD26uebvTbqagzXnSF/Z8wcOHJycn2Y8J0hYh6OIYjiBL701vTKK98x4LeCCAi3BsYLziegdHLPZ7z8pn82aX2Uq+Kdfl8oNo8/Uo3yI6BVoziDagDwcuQnPTBY4V3Du4AwioeyVJ7QZRK2N2vRrIpX/tmzfKd06tRFECSsz47Cycs2GKlqbN20oDodD0D4iqnDusQ4hyHbu0EpnAif04H69kB005WRn6vmLlq3Wl7H4Q2czFhQTqKNg/I3i27wxgS2yKSsSwPTfMy96ZXc+L441jZ93/Rr9OU3xx8Mxr6llllmNwI480SOcfhxcVjPeJPYiAgcH6MZ7n5VjBx8oyqmBeXAY585fKIMG9NUjbnCWMA29YOEK3Qn8Ys7PcuSgfgIdkDcmRPIQ0RupObPIh5MHZw+RP2wjGetC97WXnq63gzJEBDGuW7Zpg4yMgBSOfmMVRBARSTTmxWu9pnbAY+z4FwWRWDjucDBVVBNT6shHUg69shF84Lir7Vgs6WzGgmKCdBS/+qR4VyxOUGtsxuoE8gtE4HDyZy1DjGRxkRQ+dBMfRwmGx8FLQzAsfy73y9QZvDEJxibcPDiZeE4Tjk+wOoi+qXxMAdczPNNZk2O1O79QRowapztWcKAwDa70BFviuUX18gzqoX4wOZWHcshBN1Jt+lW9cJdw+KAXKfBFn5p0IGIZqhyO+bzpk/RoLiK0cJoR1cQ0+YczvtWjxIjuwhFF5DiUnljkm+KMEouO2F0HpvpKZ7xn926yfzEmsGqtX975kBfHYFiLXn5MfFsqnmEKVp7SeYz0hhx+fPD9j8U8pkICqqUA09RwfBA9CyaKqKDK36JNR+cXFKlNqcmxatu6uWD6HFPfKuEZycrKhhU4mpfe8HDlyzN4waZ+vRyDRPXVSPTDYa7J1uraRcbfVvEiD2z/7O2Hw5qCD3S+EZlFhBZOerA2VFQTZf9s3oGFHt2NdRRTVxzwh85mABAzbvp250nRU+PMaBptsgABvDC04HdeHI1DVfbDbCn75nNjFtf3EuBaLQSef80rW7bxEZVaMEVUDOcTU+hTPp6tT5tjWh1vdPfv2013vBARVVPqUIxpdSyROrVvKfVysuSJF6ZgU09wKN+Z+rW+HuwPnEvllMGpReRSycGJQ7QPNiAvUv3Qi88H4RlQ1I9XwvT/L38s15/DRBt4bKBNy6YCe7FtTOCBqCYiynh8oVWLxnox+oi+6ht7/sBu5CECuierzgs6m3VGGH8FcDT9+bvi3xBbsC2Bl97wyj+beXHEAPvytkvRpAewykQCUREoLRPBC0OlZTymogIYohIikXAuh5x+nRw0dLQg4oaIH8QREVVlmGpGnkpwVPHCEBwklCEhctmnZ2clUmWJ6WW8xa2mxfH5IDifSujk4wbKxs3bdRvwBjryI9EPZw/Ob+ALNngBCrZhCh6fMsI0PpxA6A+VML2NOpBHPawjD/Lox61Xn1P5+AAc9Ql3jNKjlShXCQ7l829Ok1HnnqyXgRfWwUhxBl8lD7vBHs++qry6Luls1oVgAuqWfvouv/2XAM52b6KsXOTpFzzi9ab2xRFTTIVP3CVSUmz3IWf/4kxg0xaRKR9xxiAczHAildNolIczgyljo6MDucWzX9OfM8S6UR7bxjLUhQ7IwIHC9Lwqx7OKcMZQhnpIWFcJ20r288mPCJKSh07oRjl0QjcS1pGHZNSvdKolZPFyEyKzcPRUPjigrko16VB1wEbJqyXygpXDZtiuytQS9ky875oqL1ehr2gfOsFCySKqOem1qYKXi1ReLJZ0NmNBMU468OH24jcmxUk71aYagS3bRKbPTO2LY9nHk8X75++pNvQp099Ed3T2XJ+sXG3NYyr9uFMk84yLwk7OBhXTrolmbNX24BAiOnjaJXfpjwVYoR9DOM7xAAAQAElEQVRwNM+7erz+2SY4o7G0mc5mLGnGUJe/rFQKH7tdxOuJoVaqSnUCn2jO5uatqRnd1G/eJr+Q6rsA+x9jAq+87RUrzhhkHHeqZI64JOzkaEhnM9JdBxHDUNHGSHUlQh5RUdgLu2PdHp3NWBONkb7i/z0tvn/+jpG2QDXcTlUC+DUUPL+J6eRUYsCbt1Qa7cT2dbM2nf7JF6l5A5dY0mzNygTobJpw9Dyrl0vZzI9MaBlNsgOB1Wv9Muvb1Lo4lrw5iTdvdth5TdqH6Z97ZVNdXsAzab9oFgnEigCdzViRjKGe4pcejaE2qiKB6gTe/9grO/NSw+HEzVvpZ+9Xh8AcEogRAcwYYDo9RuqohgRsR4DOpsmGtPyH2eJdscRkVpnGHBoSIwL4dMurb3tjpM3canjzZu7xsYt1q9ZoMwbfWfNlIbuMAfthXgJ0Nk00Nn6fV4r+93SNFv1Y5pLhO7Mq0535GVJkOL+t8DjkgrzMyvL3i9016nuqIK1SVulVdbZrei/Py9DLscS2UgYZ1FXbXFqPwJJlfln0p72jm+U/f1fjzVvg8RK4n2NUsZ+rYyNYOWRUMsqqOljiuIWMsVzlIR923LA7Q4zHGPKZrEXgA23GoKjY3sdU1RHhFgmER4DOZnicEiJV9vmH4t+2uca21nsdcmNOmUxpVCyvN6j4VuCLRWl6HVyoHi9Ilyuyy/Xy57TyL0tdYryo6YIBfw5L8+jy0Il0elbFG/CzS91yTIZXL8MS26iKdn7zuORcrR1sM1mXwHvTKsbauj0IbTlegip+veabtx0+p5ycsXf/7+n2yZOFe2/gcFO1ze/UjzUcGzgOjOWBrV+bW3HsQRZpfL0Sae/ySVe3V+BQFvkdui7kf60dm+pG8dMSt4zI9EgTnpEDkVpqu7hEZObX2l26Bax+dutiGbfxp7DTZk+xBXpFE81KgKc2k4yMv6hQSt59uVZr4AgenF4x/Zmtjd7+2kUMF0NctHb4HCIOhzR2VpzsskSkqUsEDqq2GvE/1GvrqrhLxxLbUAKnE+3ywgga4SWzSq3fKPLzbxVjbFYbo7Wr/Ktp4tu8ocbqOJZwTCmhQ9J9sk07vNRlFft8U4dPcKxBBseBsRx5NSU4kQPTvLoTCcc22+HXdTV2+qVIHIJ2lBPaVzuWa9LFMmsQmDnbJwWF5j+mntOczXv++VnCTZvLi6wxADG2Eh9lx68IIWE9xupjpg6/ThTOLxLFrMEIFWnuSoQ1KB4XAiUf/k/8hfkR6zZeDLu5/dLT5ZX7CzL0KMoGzfks0s55Q7TITU2Kvyt361PlmO5DJEfJ4sIK/djGEtsqqlmbTtRhsgaBjz7xiM+n7SjWMDcsK/2lJVI8+cWwZI1C2M9xg4YbNeSfoEUbF2hRfEx/Y/uHMqce7Q/nRgtO5DotcqqOlcbaTWCRFtlUN4bZ4he0A4f0KG0GQTm0aIfJugTKykSmf15xw2/dXsTe8rEPvij4qUWVkuwYhd3ByR99Lfg4O349CL/EE25FfCD9+HNuruwz1pGn6sNxhQOreMBZVGVYGnnN+v5XZOlp8bI1Mvw/46p9KB6/TnTpyBPlrQ++0OXM9ofOpglGxL9jq5ROfStiSzA9vtTjrDKdjchMthbghMM5Nj9TVFQllHLjtB+m9qZpU+fQC3lcJDENDycUS2yrqCaiqOrZ0DsDnhtF3bilzGxxdest7v6HStqgYyT9mGGScfI5sv8hjeXQg5zSq7tDWrd0SE523CywnWL87N4PP9vL2Syd9rb48/MiGis4h1+WueX8rDI9+ojKbbQIZHstur/O69RvyHC84ThAWW0JTqTx+MPNICKbF+zKkrHasQkHs+KG0KFPs9+pHUc41nBcwZba9Meq3NGkubh795e0gw6X9COOl4zjTxf3MafJ0Uc45cD9Nds6O6RZE5H0iqd1YtWsrfV8OccnOyPb/WzNA47Upi075KcZz+k/Q6n/ROLYy+SHBeZ+GRbO4Wez5kf1042Llq2W4ScNqewv1m+5/3mBk4nBvv/JN3QnFixmv/+kTPl4tiinEg5lQVGxzuvd58fJhzO+rawHZ/KKC4dpx2RDqKmSzj7lKEE91K9SYIINOpsmGITiKH7VBA7hpKI0GZNbpk/RoRu4QL1b4pb765XI6w1LRD2zaYxWQi5UwsWwvzaVh+gOZBC9ebZhqf7MJpbIw7OauNjiQqqeDcU04wzNSUV5TFN6hqQdeJhknX+V5I59TBo8+4E0/N9MqffA85J76yOSc+04yf7PTZJ17hVy9L8byyXnuuSGK91y721ueerBNHnpqTSZcHeajL3eLWec7JKu2kUzpvbZSNnUGdrccbD+WDDPn7dDSqa+GZHlOHZwg3ZhVrngOFCV8Tw0Hhl5rEHFcYBnNu8I40UeHJ/BHFPjzR2m8HEcwen8S4ue4jjCc544rv5XnF7lxT9lT12XzmYtJeNfp0n26Fv146jh/74QHFe5d0+UnJselOyr7pCsi6+X+sPOkLNPd8noi9xy67VuefCuNJn0aJo8PSFN7r9dO+9c4ZbjjnRKE/6oTMghmfaZfY6pkJ0MowDO1dz5C2XQgL5ijAzi5xwRjVMq4Gj1HnKhHglUUUA4e1hHmZKDI/Wvs28SLJGHssB6yEc55J566f2gOlUd1Id8sASHsV5OlnRq37KyWNl08fUTKh3AykLDSmD/OndoJRs3b5fCohI9Krli9YZKJxa/3NO/bzf5Ys7PuoYt23dKbnaWzqt504ZSUFCs10Of4Ewe3K+XLhf4B3z326erKZ14OpuBo5Xgbf/ObVI2Z0ZEreJCBkfzjtzSKhfGimfCRJ+ag8Im2ujihQflPCKvLklFNTH1t82vKd+jDNPre1brvHA2bSEZJ4yQ3DuekIZvfiU5Nz8kGSeeJe79DxZEYCJtoIl2Mezc0SHHH+3UL5pwQi8e6ZL++zkkIz1SbfaV375DZNa39pj6K5n+fyKYywxzuJSjCScPDqCqhulu7OfG/XvfNM2BcDgEkX0lF7hEvenazRcc0ybOwNK922gX0+p4VtN4jGK6PVubYt8rWbc1974HSdYF10j9J9+W+v99T7IuGSPpR52ozxBIJo7m8PVnZYoWjRHZp4dDRpzi0m/m4HyqmzkNTfjKbC753Q8+2brNb/Ne1t49OECYhn7premVDmJgLTh8Y8e/KIjiLZ79mh4RfOKFKXr0DhFB5YShHqKhB+zbXfDb3aHqQQ5pd36hbN66Q48u4mcYkYff/oZOtIP2Jr02VeBAoiwwrVr7TzUnGY4hdN0w+kw5evgYqc3pVDrRh9YtmmizbpmyZVuewDZVhmWXjm0E0V84582bNNIjlFiHbG5ull4PUc1Thx6uO6GoEyzBqYVzj7rBypOVV8OpMFkmpVa7pTMj+6UgOJqvFbnl8folVRxNUMNFap3XIQu1KAm2t2u+A6Ir6mKJ7cvzMkRFOrH9pqYLski4+C31ukS/oCLDkCCropp4tgxRGFVsvFCqvEiX7v0G6I6lfjG88BrBBTJSHeHIY3p94ACnXHGxW/77SJpcN9otBx/AwwDsPv7cK+XlWLNu8peXSekX4R9T2OcfL8wQ3LgZHU0QUPs5HEc4kMj7o9wl4vdLY216Hdt4lvNObfpblSMPxx9eIsIMALZDpU+1WQhENdGOOkYhi5vGInFgNerkqNdAf7wExxNu3DL+PUKcrdtHra+mii2bS+XN3JPj0+SCs1zSoV3d7K+pPauUabuJfPCJdhK2isFxtHP8bZfJkYP6yYhR40RFFI1OGhwxlMOBhBmH9N9HEPmDE2hchwMFR+rYwQdCTI8EhqoHgfr1ciqjh9hGpBLLk48biIUesUTkUuXrmYY/K9dsEDhvhqzKVdg6b/okOW/4cXLQ0NGCZywrCw0reIQAfV6wcIVMuGNUpaMI2xC1NIhWrkI3IpvQC2ZwMFev26Q7oH16dNIdXOgM9twrHFU4p5XKTLLCq2ySB6L0y6kRWYAXFBBtGb0rS3+GzPiMF6b/EJ15tDBdL4MMoivGt22NjSGmsUxzLqEDaWx+pgROIyr5N7Up+xMzPJVT9vjsEZxe1IM9Q7UyJRvJElN69Z9+V3Jvf1yfMpcEh0b69HLIZee79KnBA7RoZyS22012d77IjwusfXEsn/e1SHGRiIQ3On9ozuM2n0N/hhL7skq4qYOGy/Z83gvPWaIMzy7fX7+08jiAjDHhpgzHBY67mqKaSr9ycNUSbWDWwvjcqFF/beuYGci+Yqw0ePkT/fESTJvXVieW5biZO/xQp9x5o1uuvNQtbVunttP5868+ydvF6Cb2MTiciCYi4RlFTCnjuUWUIU39fG6lIwoHK7+g4jjGFLZyCOFw5RcWCxwu1EEKVQ9lwdLfG7fIkNOv09uCM/fjr38GEws7D9Pl6BMcYKMDrRTgUQGU33LV2YKoKhxolCGyiagl1oMlIy+08dYHXwicTjjGiBRD5/ixl8ljz71T43R+MN3JyKOzmQzqe9os/+4L8e/auWcrvIXxmS8834WE5zPhaEIDLlrIU8noaOLi92zDUlF5iKjcV69UfyZTyaM+9AQmtGssU7pQDzqgK7BOTdtpBw+W+hPf0af0nC1a1ySakDJEZy7Xop3jbnHLvr1T9wL5zVxrO5uln38Y0f6CYwH7cGBS+zr2a+zfqhzHD/Z91QiOC5RDDnkogwz0YjtUgn7UNZZjG+0Yj2djeU3rjtz6kqXNCOCYSh9yQk2iCSvr19chd9/sllEXuvSp94Q1bKKGEN38Zh6dzcAhwVQ0prLVtDHKx4waoU93w4lCwlQ15LKzMvWpbEQ/MYV+/JED9Ol11EEKVQ9lwRKedzS+qIS24MwFk8XUNqbSg5WpPEzlI8oI+2p6Yx0Ocr3cbH0KHRFNRDaVDixXalFUOJHoL7aNyfisptEes0Yxjbardada4TLxBEo+/yDxjSa5RVfXfST3wZck54YHxNmyTZKtqd48IjHX/Mctd9zg1t9sry5h75xVa/2yfqM1L47e9avFu2KxvQcosHdp6fp0eYP/vqc/6yxud6BEUrcxUXFQP6fcNzZN8Kx00yZJNScpjc+Z6xX8wEBSGjdBo5j6vubOiVWe10QepsPVS0OICgY+0znx5fcrn6XEtDem1Wd8/aMcok2xq27VVk/JqSUcPkRUJ3+kzYDsyYSziLRns8oCU+iwE/ZWKdA24ABiGvuNKTP1t8YRidSyK/9h+tyod9rM7wXRWjiacKK7dWpT+ZkiRDsxzY7+VCowrKioJhxR2KSKtmzfqb88pLaxhEOOl4Qgi22zJEY2kzQS+oVx2cIktZ6EZrWLYtbF10u98S+Iu0vPJBgQWZMd2zv0N9sxxe52RVbX6tLffG/N6Gbpp1Osjh72h52czVpJvUdf16fLJSs77HrJEITTiWel7789TTDNngwbktXmrt0ii+o2U5ss02PSLpweOD+YGu895MLK6Ws4mphijxlSPgAAEABJREFURiOILGJK2CiDqCecMpRjCeesvhYZxLQ68pBqqwcZY4KeN54eK/jMkLJlwjOTq0zLG+XhnGLaHtP3Kh+OId6Qx/T1V1Mel1DRTDjFeOlJtYM20TZsgK47rjtPfyEI5ZjWR6QX/UGZMSmHVZWpJepBP15UAmPUgVP8+5K/qjjkyDdDorOZpFEonf5OklpOfLPOth2l3kMv69/vS3zrdWsRLw/dcaNbGjesmx4r1Z73k0/KPdaKbuIj7mXffG4lzHWy1b3/wbqj6WrVrk56El0ZN254gSjVbuK++d6baNSmag9OJaarjQl5RiPhRBnLx992mbFYsB3MsQtVDy/ZfD75Ef2tdaMiOHuYoldtYR15Rhm1jnxM2yOyaMxDnWC2KBks0T5eIArVDhxE6FDlgTygAwn9Q9+xrhK2UQ/60Y7KR8QWLxYZ81RZspfWdTaTTa4O7fsL86Xsu5l10GCdqmmHHCn1H39TXO06WcfoAEsxtT7uVnfKTKsXl4j8tMBazmbZ7E9FykoDRs6em5lnXCT47qzD5NHMmuin2k3cb4v82hSquY6p0c16y92tDgw7tUgzd/S8pv2tLmVnn3KUHoEM9vJPXfTGui6m7fEowsjTjo216pjoo7MZE4yRKSmb9Yl2YSyLrJIFpdOPPUWyr7/XgpZXNzk7yyFjrnDLKSe4Ev3CfHVjEpBjtan00s9S4PlnbS46++q7JHPEJQnYAyJvItIaqXQTp78o9L25nM3LNWdzXOuDJNzUwo3vl0Q6ytaXVxFIRCGxbtYeITIaGOk0k610NpMwGqkw3Zd13pWSfdmNmmNmnze7tWu9nPgvp+50ZmUmYcdJYJN/rfbLxk3mujiG6r5n6R/i27AmVLE98tPSJefWRyT98OPs0Z89vcBNHH71a9hQ154c+y6+/SG1p9LtO7LsWTgE6GyGQymGMr6d28S7ZkUMNZpPVdboWyXjpLPNZ1iMLOrV3SHXjXYJnj+LkUpTqplvkW9uls/90pT8YmWUI6ee5N47SdL6HRIrlabTc9LxTv1GznSGxdCgbdtF8LWHGKqkKhKwDAE6mwkeKs/P3yW4xcQ2h5+WzDjqxMQ2moTWunRy6h+DT0LTCWty4RJrRDbLf5+fMCYJb0iLaOaOe9oSX3CoKxs8ojKgv6OuaiKvn8AaC5dY80sPCUTEpmxKgM5mgge27Je5CW4xcc25Dxgkmdr0eeJaTG5LB+zvlFP+bd/pv7V/m++lhsAR923fIr5N6wOzbbOdM+Y+cXXoapv+1NaRi891SeeO9nU4F1nkBq62cWI5CURKgM5mpMTqIl9WJp4F8+qiIZl1a2zb1aOv5N4ywVbPaNbY4T2FJx7nlEEDbHxx/NPc0U3Prz/sGQn7LTLPukzStBs4+/UsdI/cLodcO8olzZuGlrFyyep1fikqNvcxZWW+tN28BOhsJnBsyv+w53SfIztXcm64P4EkzdXUBWe7pHsXezqcC00eibHrMeXue6BknnaBuXb0BFmTk+3QX8LLzUlQgwlu5o/FsXI262b4ylk+WTLVG3Yq3V239lg7tQnQ2Uzg+JfbdAo9+5q7xdkwBX+Hbs++43Q65Or/uKRliz0ZNlosXOIz7U/t4ScAy3//yUa0K7qCF4Kyrx1XsZGif5s2qXgJLy3NfgAWmuQGbtUczdn8OPxUsjt+TjJ++ebi6ycIvhVZ24irX/BRv6xTm7wqhzx+dQdLlWflpeIw9sEXLdENOpsJHKbyH+cksLXENJV+zDBJ639oYhqLspVEVMvKdMjlF7kFn0dKRHuJagMfeF+9NlGtRdaO768lIsWFkVWygHT21XeKs35DC1gaXxM7tnfKSf+y3zPRfyw27w1cvEYUTiScSTiVqo3Fy9bIv86+SbBUefFawjGb9NpUeff5cYJf5DG2g/aH/2ecQMaYj238LCUcVKRwnFT0E7IqYduoEzpUGXSjDVUOOw498Qr95zwDWcGhDNSFXzd64+mxsmL1hoQwVHZGu6SzGS25COt5Vy0Vf4G95iEcWjQz64KrIyRhX/E2rRwycID9DqmFWnTTjKNmx6hm+jEnazdvA82IOyk2HTvEIQ0bJKXpuDWKG7g16+Km3gqKq9mIj6Xjo+n4MHm1whhkTJv5vXTr1EZ69+hYqQ2OHhy+EaPGSX5BUWU+VuAU33L/8zL8pCGyePZrAif1oaffrtGpQ52VazbI7Pef1OtgOeXj2QIHEzrhTEIHdC3WdEI32kA9lL/1wRcyfuxlet2WzRvLj7/+iWy9zYKiYsEvGekZhj9wOK+4cJigriHblKv2uzKaErNI+W/2e14z6/wrxZGRaVLiyTHr9JOc4naLrf5b9lf8ps/qAspuz2s66jWUrAuuqQsS29VNS3PI6SfZL7pp1mMqWTsQHC5E84zRO6yrKCCWcAzhICob/1iySkJFApUMltA9d/5COXbwgdisTHDUPnv7Yd2RrJdb9ac4V6/bJPmFxXLycRU3fp3at5Q2LZvKDwu02ZRKDVVX4DCPv+0ygV6U5GRnSusWTWTV2n+wqdc9YN/ulQ7vIf33kQ2btgnago1wKJs3aaTLdunYprIeHMlThx4u0K8XBvzp06OT/nOaRjYBIqbYpLOZoGGw24fcXV16Svph9vo1k1jsCvXrOeT4o2N0WMXCoBjoWLPOhM5mWZl4l/4Rg96ZR0XWyNG8eQsyHIce5JT2bYMUWDhr3XoTHlMm4olo4EtvTdcdQUQBnxl/bTXrfl/8l3w15XH5acZzetnkj77Wl4F/CotK9Cw4ZfpKGH+2bN8pxmgnHD1EGxG5DKO6LgIn8s8Va6Vzh1b6dmDd5k0bisPhELQF/bnZWfo6hCGLeoiGwgk9uF8vZAdNcGpRsGjZaixMm+x1VTQtZhHvupUmti5y07JH3xp5pRSpMVRzNuvl2qezZeUiW7ebqz/ejWvNZVAdrXG26ShpR/67jlrsW/2cM+wV3Vy3PvU+7o5p4YOGjpbeQy7UE6avd+cXBt1pv5jzsxw5qF9lFDCY0HnDj9OjfXDU4AgGk0Helm15kpubJcopQ144CVHJwDrh1EOEEVFY9O/SkSeK8RlRRCxD6Rh52rEydvyLOptNW3YIHEwV1QQ7xQ3Pbxp11NZ/o2wy1+lsJoi+b6N9HtJJP+L4lPrQdKS7SEaGQ+z2sfe/TRaJ8a1fE+mwmFo++6Jr9SiHqY1MonFdOzllv972+bzY5q0iHm9qRTfhPCEKiUglEp5drF8vJ+ReVZNjFrJSDAs2bt4uKioaqBYOn3L+4FjCwVQymEbH9Dz6iul7PA6gyhCxVOuBSzxPOm/6JP2ZTTy/ishoQVGxICL74Yxv9SgvdMIRReQ3sL7Zt+lsJmCEvKuXJ6CVxDWRcfxpiWvMoi0NHuiUxhWP31i0B1XNXrchkkhM1brx2PKuN/eUUSR9drZoLe59D4qkSkrKnjzUPtFNv+ZnrlufksMYdqdrcszCVqIJYrq6oKA4pOOoiVT7h2cn6xme48QzlXDylAOMZzPhMCPBsYSDGagEEcdBA/qK6oeqq+QQccXn29CWyjMuVVQTef9s3oGFHsmtKYqrC5n0D53NBAyMd619ptBdHbuJq+s+CaBm/SYO6mefw2vdeu3qaKIh8a63T2Qz4/gzTETWvKZ0aOew1S8Lme2YMtPI42WeBQtXiIoYYlo9WvvUVHgkzzTihaB6OVmCt9jRLqKMeJkHL/VgO1iCrYh4wjFFObbxNrpyMlH3lz+W62+XoxwvG+GlI7SFbWMyPqsJ+1u1aKwXQzecXn1jzx+0gzxEQPdkVV+YIMc+V0MTwAxlgp2e18w4YUSobjI/gMCA/vY5vMw2jW4bZ9OdJul8VjPgyAm9efABPKZC07FPCZ5z7N+3mww5/Tr9GUb0LNpnKFWEMdBhhZOGKXA8W7lk+Rq9LTiLaAt1JtwxSuAsYrocMrdefU6Nz5Cq6KZ6LhW24/NG6nNOmCaHDuiCTuhGG2gLbaoEh/L5N6fJqHNP1iOZKMf6pTc8LNCNyCb4KHk40chT7at8sy3tc+SajazBHts4my63pA08ytAzrtZEAJGYZk1rkrBO2c5dIkVF5ohu+n0+8f3zdzzgJVxn+mHHiiM7J+HtWrXBQw6yzyUrlSKbcLjwHCIcJ7Xvwfn6fPIjugOHfJRDTpUbp6oRHYRDBTk4VZi6NjpckDXWVTrUEp8wCvz4udKDqXCVoEfVCSw3tqdkApeor3RhGWgTdCAfCX1AG4E60MeJ912jc1FlYKWe50QbKh8OMz5Wj5eLVJ5Zl/Y5cs1KWLPLu26V9tf6/9z7HSSO9AzrdySBPbBTJOafzQkEV0NTfkyh46G3GmSsUpQ+8GirmGoKO1s0cwh+PMEUxtTRiC3bknvz1nmwU/Y5KfyUWT9xL2jBiTr+nJv1qCaigHjR5o7rzouaOJw6fPwcUUUrvlwTrONgdN7V46t9rD6YrBnyonM2zWC5RWzwF+aLP89k342Jkl36gYdFWTN1qw20USRml0l+AMs2U+hOl7h690vdgyPKntvl8ZTCouS+kd7lSM3RHOaSfcJMGfWjHLAoqsE5ROQPEUAkRD0R8YtCVWUVFVXEsjLTwiuKkTHSaebuOM1snB1s823eYIdu6H1wHzxEX/JP+ASaa5GYtq0TFxEI37LIJfN2JzcSoyy2y5vo7p77iiMtXXWrTstUqnzIQfY4njBmu3bbpy/oDxMJhCJAZzMUmRjl+4sKY6QpuWpcXXuJs16D5Bph0dbtEonJ22UWZ9Meb6K7+x5g0T06uWY3aeQQPA+dXCti0/ouk9zAxaY31EICoQmkmLMZGkS8SvwFJpl7rGMH3Z171lFD6lbv1sUe0YtdJtmVfTZ5LAWRzdQ9KurW826deUzVjSBrk0BiCdDZjDNvf0F+nFtIjHpX+y6JaciGrTRtbJcLozkim1JSZIu9xNWmoy36UadORFm5aRN7HFO7GdmMcg9gNasRoLMZ5xHDC0JxbiIh6l3tOyekHTs20rCBX5xhHGkXnOWSl55KEywVh0MPcsozD6fp+Si79za3Kgq5HHaCS557vKIOltiGsFEXdGIb+Ug3XOmW2nTn7YJk8pO/pCT5RtTRAkdOPXE0rPhQcx1VpWT1ZmE6m9ing+3rOJaQjMdHTSChB/JIWFeyOLagA/mP3JsmPbvtdYIhh+NKyQZbmuU56GC2MY8EYkkgjEtgLJtLPV3+woJYdDrpOpyduifdBqsa4HA4pEmjmq2Hg3n4oVUPR1y4TjvJJavX+uXSa8vl0Wc8kpXlkJouYLj4HTvEKTO+9Ol1Ro8pl6mfevXGBw5wVuqCTmyjAE5nyxYOvQ62QyWzPLPpLy0JZaJl8l3tOlnGVjMa2iQMPx3OXuuWe50/9APHWacODnn5Ta9+fMyb7xMcLzgGUB4sQQ/ycQwi3fWgB5t6OmA/h0AH8ouL/aI+dYbjMC3NIZ/MrDj2dOEgf3ab5NGUIAThcjkAABAASURBVKYxiwRiSqDq1S2mqqkMBHyF1p9Gd2TniiMjE92xRcKvRPQecqHgO274VlkiOlXTtB8uTP32dcrHn/mkpHSvNY0aOiRLw75te8X09dIVfsEFrUH9vTJqzestkTUbH5O1Gz+Tnxb4Kh1MVY4l6ildWGIb+XA6N232y7yffNgMmQoKk/upFmWYv7RYrSZ9+V1ptgza0lVPWA/XIGc7880U4JdLLr5+grw8+dNwu5E0uea1/FiCuiH7dl7VfRrHYbF2r7Izr+KYytvj7OGrEcE6A+cUN3hvv+cNKBY9igmHUunAM83QD0E4oUuX+wTHLLZDJUY2Q5Fhvt0I0NmM84j6C/eczeLcTjzVO3LrxVN9RLrVBRHOokqHnniFLF4W3hvK+KDvgoUrZPb7Twq+44ZvlUkC/msS4rlNOJrHapHIWd/5JPAjz3D+EIFExBMXPURf6tdzyC+/V1woA812azPsaVqCPKb1kIxTe8aLIS6K2IZORDW/1yI8gfqCbZeVVY0UBZOJe15xYp/Z3OZ1yYhtHeSana2lyL+3/8ifmN9MJjTYKHOb/yU93aW6XDhOp7NR07hjsnMD6ekOyckO3kM4mtingzmI2M9xA3fJeW7dWezaySE7dvqD3pxBO46TRg1EbrzKXfkoC45FlMGRLC/3S8M9N3+4ecNNHI5pOKE//lLV0UWdwGSDJ0ICu8RtEghKgM5mUCyxy7TDC0KIbEZLJF71xowaIfjYL9KlI0+Ux557R+CI1tbeqrX/SOsWTbQLlRYyrE04huXBXhKCo3fkYU75YnbwSCSa/2u1X492HtTfKZec65Ld+cEvjC5Xplx54c0y8rQTKqcIMe0OHeec4cJCcKHFFCKcUCyxraKaiOyEevZMr7znz15Xa09GohflZSIJ/vWgpZ4MaekqlwK/U9Z59n4Xc5vPLXD7mzqrR71qw+LITOz+V5s9KMdHs/Hx7MCf2EOZGRMcwUC74AjC0Xz5DU/QqCJu4OBc4qYMDiS+FBHq5g264UD+ubziMRZMlSNSeugAp8ChRDnqYhvHFCKgcDBVVBNT6shHUlPxqFMlJf2AqmINN0ggbgTobMYNbYVif5H1n9k0U2SzgmrVv507tJKNm7dLYZE2P7anCFOBKvKJqUE4osh7/Pl35cdf/5SDho4WTKdDHPmBssjfuj1Pn2p/6qX3BdFTJERQoQs6VR3Uh3ywhDauuXOivPLOIzLv98tkw5bPdLEdu36Tx1+6RA7+94Xy39dvlQfvKtSdyZVr1sj9E2+RBo2m6vK3PnixXHbjC3LVzRXPbH4wY4aeD11I0AOFmEZ/8e1H5H/vVUyBop33ZzwqV42dKMeedZHsKnpVnyaHHlw0sUQ9XJi/16KauEAanz3793EVDipkjMmR5DNGMp7XnF2SKyOy86Szq0x+LsvScSwtz5DrtEjnRl+aXLKznR7RfCq/qWzQtm/Z1VqfVn+zsKEuiwgoIqPG6XZHeqaE2r/0SoY/ah9S+5za3xClV/ug8ZEQlEMW+50qh6xSiXKVj6UqU/s1yiGLpVEP7EC+WVK93OqWwAE1RiIR5c/MEBk53CW4uUPUE7WuG1te+czm0GP2Oo8oqynBmczXTumIiEIOz0PjuWgcUzfdVS49ujkFUc3lK/3Ss7tTPv7cV/msNRxhCfjPmXhnM8ACbpJAYggk+dKRmE4msxW7vDmbTIa1tY1oZf++3URNieMiOeXj2fpUOSKfLZs3lskffS2I2CAienC/XvLTjOcEP/OFC22g7FVjn6oSJf198V/y1ZTHZd70SdKpfUvNgXtKoBO69en4WfNrnMb/6del8u+jh8uh+70obZofL3AQ/1r3qvTtdoee5/AfLsMvfUePSJZqgbsdOwu1qfLt8tBtr8gnbzwpK9f8pddZvLRYNm8tlpn/95xcdMZL0r7V6bJm4xQpK8/TEZWVIc4mgiglMnYXLpVDDxgk3019TQ7e72JkVUkqqrkzzy+IyqhCTK+rddMtS4oTahIcxS1aBBNT5EMyC2R+WbY+ld4zrVSebLRRWjvL5eVGf8u7TdfKtfW2SRtte8KeafVzc/IE9a/Y2VZOztqlT7VD9pXCxrK1zFfZD+P+1btH8M8hYR+6YfSZejQf+zH227HjX5R3nx+n5w0/aYg88cKUSp24oTp16OF62TPjr5UJz0zWnVs4lAWFxfr+j/0Xx4Mqq6xsWDHqwfFiKEr6qvH5ZmXMY//16E4knD8kRCIh99YUr36zhUilcf+GU+jxiKipcKVHLSGLOmpbLbfteY5abaslbtrwrCa2MV2PJabb8aw11plIIFUJ0NmM88g7MjLi3EIC1ONsnIBmImnicS1CiagMEtZVXVxM585fKLj4Kufz2MEHCvJQpuTU8os5P4vRUR152rGyYdM2Wb1ukxKR84YfJ5hiRMZqLR/lkMM22ujWqY38sGAJNoOmIwf10xzhvU7Ejl0LpHGD/SQ3u4Mu3yC3lxSVbJSCogqnMTs7R1o2PVq2bPVrTm1D6denm+ZsLpDePbNkzKjTxSEZ+hThYQP2kaaN/TLiFC3UomnChbG01C+HHeKUZk0d0qpZTzn1hN6Cl39wwRPDf4jyqKgmyowXw2AXV1U12YEYf1mpMiUhS0yhN3d6pKnLqz+TucmbJgvKKqKb4RiA+pA7PjMfC2nvLpNch0+WbKoYa2Qa9y9sB0vYh4yOKPZbY94h/feRFas36A4l6uOGCgnrfXpUvPm+aNlqwX587aWn60uUoZ7f75ct2/bag3yVoANJbZtpGc3zjjhG8AgJ9n/0pXsXh+BZZ/WSD6bH8TiJikLiMRbcvKltTI3DiYSTivrGhLqIaiL6uVO7gcOLSCjHVyWMN3PIYyKBVCPgTLUOJ7q/jqzsRDcZ8/b8CY4mhdMBRGQQmUFClHLTlh1V3qKFAwpHFAmRypp0dunYprK4edOGUr9eTuV2sJXd+YUyYtQ46T3kQj1N/XxuMLEqeSUlFVFHlbl15zzBNDjSwhX3i8dbPWKH58u+mO2T/fu0EUQh8YzZ74tWa1P7V+p13556n+TjFXGlVFv+urCincEDndK+rUPQ7mNatEcrqvIPU4eLlvj0aA8K8KkkPBeK58twYQz5yZYke5uJ/CpCkd8h7xY1FEQ0wQgO575pxYJpdWyHmzC1Pmx7J31q/ditXeSX8myJxTGF/U7tg9gf8wuKgpqUk52pP6esCvEoCB4JQV3Uw/6syqy0LNFurCK1F8cCntnE88/Y1/G8JfZ9TIcH04V8PF6C6Xglj2MSx6ZRHg7lIQc65YefffqNIG7gsI7jDMctbuZe/z+vsYq+7kjy8aQbYZE/NNPaBOhsxnn8HJk2cDYT/PZvpEOCaM2gAX216eYNlVUxdQhHVCW8+AC5SgHDyso1e+shwlPbxbdt6+aC6XOlG0tMbRpUVlvFVJ4xE1PgmFZXqX+vB2Th4vry8ESP5kDulcTF7q33/tZf7jnrsr/kmjsfka7tL9Kn3zENX1qWI29qU4SqxtZtfsGzY+9N8wpebLj/UY8qqrK860GPGC9+uHhedXPFc2yoj4tllQp7NpJ9bXTkBHlQb49tsV7gZaDl5Rlyy55nMPHM5YzS+vJHeZZgejzc9g5IK5Ivmq3Up9HnNv9LXx7Rul641UPKGW+4Fs9+LeTXFfAsM55phiI4mpfe8LCMH3uZPsWOafjabq5Qz4wpnMgm9nHs19i/VR+w72OKHQnPW+IYU2VYRx7qqTysQxYJZZBRZWqJ4+W2e8urvNUOOcijHtpUslySQCoSoLMZ51G3R2QzeMQkzujCVo/pcUyTI0IJhxKOp/E5NJTjJR8sA5Viin3W3F8rn7l864MvpE3LpvqzmYGy2MYzm/Vysqo8H4cL+DtTv0ZxyGS8MDZu0F82bJ6hTZuvrZRf989HUlZefSqzoGit7C78S1pq0+oQdrtzJD2tEVY1+Z3i8RTq63v/xHkt2d4mbt4SFA7Cy0Dd00qrOIpTm6zWAavpcX1jz59sp09/a32N4Y11POuJqfcPihrskRLBp5Hm/Pl35XY0K9hvX3preuV+Cx0TX35ftm6vvg9Nm/m9Htk8uF8viOmR++Z7fmVgy/adUtvNlV7JhH8Cb+BMaGKtJiVoV67VDgqQQLwJ0NmMN2FcHOPdRpz1+7ZvjXMLkas3TpPjzXI4mCq6iCWe2Rxy+nX6NDfKczUHEY5oYEtHDuwn+HQSphMxrYjpeERFg8miLvJRDjnIIyFS1KdnzR/pNk75NW6wvx6dxPQ5ptGRysp3aE5kxdvLcCBVGZYdW4/Qn+/EM571c7oK8lBnzcZ3Bc4n7Eql5Mipe1SwNl5F2hQ6XgYakF4k2Y6KRxNQp6nLK6Gm0iGHt9afLWyqT5njbXTIT2q0XqYVN9DzEB3Ftzl75ezVCb2RJuy3iE6q/Rb7IfZJPEMMXXixB/s98vEC3IQ7RunPaeK5zwP27V75GMhDT7+tO5+oY6Xk8/m1Gy0rWUxbU5YAO64ToLOpY4jfHztENqW0WPw7tsUPUgSa4exhShzThsYEB9OoBtvBypGP+tCj5JGnZI1luHDjw++4sCtZLFEXcqoO3lLHRRxlgQlv8CIZI5uQgcOpptCx7Nr+YmTrCQ4kpsiRjwRZvUD7AznkIfXvNV6Q4ITiO5u9u96gv+2uielLbCMf27FITu1skeaum5MUCzsS8SkuOI4TG20UvFEeaPMdDbYIEt5In9J0rWCpZA7LKNKnyTFdrurC4XxXk0MeEtYbF2yXUPuX0qWW2H+Q1LZaYr9U+yCWRhlEMfEsM/KxD6MtVQ9yyEf6fPIjgoT9V+3XOB4giyX2c+Rj20ypuCTZIfbY0MDLRrHRRC0kYG4C2uXD3AZa3To7PLOJMfBu3Dvli22myAj8szn5TlpkFleXxvcLHY64XeSrNxgix5FdL0SJdbK9K5dax1gTWrp5q/WPJ2Bt2CD5xxPsYCKBeBOgsxlnwraIbGqMvOvXaH/5LxoC/2yWKi/91KQDUUoVraxJLhllDUxyYXQk8CWheHH2F+aLP29HXNSbOSIZqw4v/8sezmajhnQ2Y7VPUE+sCcRWH53N2PKsps0uzuZqX/VP81TrLDOCEli6Yu8HvIMKWCQTkU0zmGoHZxMcvWv/woIpCgJ2OaYaNoii86xCAhYkQGczzoPmyMyKcwuxV7+xXVv5+KShcveVF8vQsZdLi1vPk+Pbxb6dVNG4zCbOZkPTRDbNNY0eyX7syWoiO7pfKGv6PSPrNnSPpCpl9xDAy0ErVtojsmmWY2oPWi5IIG4E6GzGDW2FYmfjZhUrJv1blp4hCw4+SF44/0y59KbR0nfspdL7nMFy/j6NZWJuqfzg3S1lfp+sLcuXxSXxmfYzKZqYmbV4mT0ujGaZ8nMk4G30WA1+eYOOsm2fK2Rlv5flp64zZXb6e7Jg0wXy18resmJezT8eECsb7KZn7XoR/KyrHfrVyCQ3cHaHWjeVAAAQAElEQVRgyT6Ym0ACnU1zg4iXdc6OXeOlOiq9m9q20aOW4666RE4YO1rajxkhRw/pLre0csv7znxZH+SXbFRDH+2s+Mag2uaydgIb/vFLsU2eQKjpZyxrJxE7CWfTFrFTFmNNJc37y5Y+N8vy/d+UeZ2+kDn+V+W39cNl9crOsmtLmojhviP/H5H8TYaMGNtiV3V2eV4T49OkMccfHJjsT4DOZpzHGG+jOxo1jXMrwdWXpadXi1r2GjlEj1o+lVMi87z5UqpFLYPXrp77YR6dzepUas7Br/jULGGdUrNENl0dzHID55CiNkPkn/3ukSX7TpHv2n4t35U8Jn+sGyrrVrWRwu3uWgd3/U9JdDZqtc6cAnZ5XjMrS8TlcpgTMq0igRgToLMZY6DB1LnadQqWHfO8TW1by/QTj5e9Ucszw45ahmPML0Vb5Z/ywnBEKbOHwB+L7fFyELrTork5LoyuTsl51tHvzpD8TsPk7/0fkYV9P5LZLb6W7/PvlsWrj5CNa5pKye7I+az/xT77B/aRRKS/VtnDQW/aOPL9JRF82QYJxIMAnc3aqdZZwtU29s4mopa/DjhQXjz/LLnsxtGy79jLpNfII+W83k0kmqhluJ18b+eqcEVTXg5T6Ets8rwmPj7dqIE5hhQv3SViKt2T2Vjyuo2Utf0myq/7TJdZ9T+TH7dfJ8tWHSib1zYQT3Hdeexa75fCbfZwnupOo3YNs77zSXFJ7XJWkGjXhs6mFcaJNsaGAJ3N2HCsUUssIpuIWn7y7+Plnisvln/fdrm0H3OmHHVkD7m5lUvec+XL396iGm2IVeGHeXQ2w2U540v7RK06dTDXhdEVh6l0T712sr3XaFnV70X5uftnMjvjffl586WyYmVf2b4+R3yecEc+Mrm/59PZDIcY3kL/7CuviIQjbX6Zju3MdUyZnxgttDIBOpsJGL1Inc1QUctz+zSRJ3NL5XvfbonkWctYdnF2/kZZVpIXS5W21JW3yy/zF9jH2exgsgtjLJzN0iZ9ZUufMbJi///Jj51nymzH/+TXDWfKqpVdJW9Thhhf5pE4/rdylldzZOlw1oZ4we9+2W6jD2KY7ZiqjT/LSaAuBOhs1oVemHWd7TvXKLmldWsxRi07jDkrJlHLGhuNshCXxBvXfx9l7dSp9tlXPvHZx9eUDm3NFYVxdegS2c6k7bhFrQ+Tf/a9U/7c7x2Z2/4r+bZ8ovyx7iRZu6qd5G9Li0xfDKWLd4qsnKUZGEOddlQ17TP7RDUd2uHUro0dR4l9IoHgBOhsBucS01z9jfTGFW+kl6enya8HHSAvnX+m/OfG0bLf7ZdJj/OOFGPUssRv7pPq9F1r5buCf2LKyE7KSkr8Mud7G3ma2uC0b2euU0VtkU2/M10KOpwgG/Z/UBb1/UDmtJ4l3xfcJ4vXHCUbVjeX4jxz9WfJx17xlNLh1Ha1oP/wVYeNm4IWWSmz0tZWLRySnq55nJU5XCEBexNw2rt75und+xddICfcdrk0v36EHHVUT7mplVumuPJlnafIPEZGYMnN6+dFIJ1aol9945fycvv0OU0L+jWvuFcyTaecrduLuPZ+WsibVk92dT1L1vV7Un7r/bHMbvy5/LDzJvlz1SGyaW0jKS80jelBDSnXTgPLZtjrBiVoR6PMtNOzmkDQwWSPpcAmJhKIJwE6m/Gka9C9q2Nnmefbbcix9uq8ws0yNV7f3bQwmh07/fLpl+aOTEeKt7PJXg5S9hcNvEJW93tOFvT8VGZlTZOftoyS5Sv3k21/54q3TElZZ7nsc5+UFVjH3kRZiq86LF7qT1RzCWmHzmZCMLMRExGgs5mgwTimftsEtZS4ZhDd9PntdRGoCz28Lfvsq14pLa2LFvPV7d7FYT6jNIu2tzpdVq7sITs2Zmlb1v/n06Lhi6fa60alrqPi9frl1bftx8Rqb6LXdRxZnwTobCZoH+ib1USaue1xUVTIlpfuktd3LFObKb/89Eu/rF5rP+e7b29zniZa9jWnXXU5EFbO9knhVvvtQ9Ey+fATn6xZZy8eeCylY/toibAeCViTgP3O1iYeh+NsGN0c8/dcWV1qn8cDot191q33y7QZ9ovA4GPunUx6YWzSVcSVHu2ImbSe5lf98JxXvOXaiklNTJRZi/70Cb7qkKj2EtXOvvs4xO12JKo5tkMCpiBAZzOBw2DHqfQ8b5n8a8V02a0tE4jSVE2Vlvpl0sseW33qSAHer49THA5zXhidLoe00C7cyla7LHdq0fH5L9rvxiWS8dm12y/Pv25PBvvbMCIfydgmVJaNmYaA0zSWpIAhJ9Q3aYiojuxXaNPpw/6aIV5/ar5N+38f+mSbjT42bdwd+prcmWupOcNGe+2yvmGBX5ZMS83jCc8+T3rFK8Ux+DlQs+0PuG/bv685b97Mxor22IsAnc0EjmfztGzpmdkwgS0mrqnZBRtl1NpvEtegSVr64WeffDvP8k5BSJp99zH3KaKljS/cS6Z5Zf0v9t23Qu10eE5z5Wp7PkbQvatDsjLpbIYae+bbl4C5ryQ25H5MPfu9la6G6eXtf8qTW/5Qm7ZfwtF86Q17TvVh8Lp2cki2yd9py27ikNwWsNaeCdPpO9akjsM57TOfzPjSvv3d36aReHsefcnslf3aprOZ4DE9tVHnBLeY2OZu+HuuPLr5t8Q2moTWZn/nFTs7mkBq9qgmbESy41vp6BeSzyPyzWNe2bbCvg4Y+on0fx94ZdoM+968oY/9bByJR/+YSCAUATqbocjEKf/I3NbSJi0nTtqTrxaXxJvWz5PzV38l5Sb/2c1oaU3/3CdvTkFPo9VgjXp9e5t/ug8k2x1o79OYp1hkzqNeWfu9ffe51yZ75cs59u0f9tO2rR3SVIvEY52JBFKNgL3P0iYcTYfDIec36WFCy2Jr0hs7lsuRy6bJTo+9vnCO6MtHn9o7+oI9oVlTkfZtrOFsNumqTfdr9sJuuybct/30ilcWfWivfQ8fbX/2VY9894O9HU3slwMH8HILDky2JhCyc9z7Q6KJX8GlTXvFT7mJNM8t3CQH/DlFlpfkmciq6Ezx+/FLJh7bR18UnWMHu9SqJZadj7CWvdFCXfqJT77/r0d8Huu/QFOu9eHpF73yy2/W70tt4+nWds/DD7XGzVttfWE5CURDgM5mNNTqWKdzRn0ZkN08Ii311+2QQ578UjJ2aXNqQWp2+nqp9Jk8P0hJ1SzUH/TQDDnm1g/0hHpKwlgGGWyrMsiFo1/Jq+Xqsnw56M/3ZVb+BpVlueXmrX55eKJX5v5o/4siBgcXxkMHWOvC2OlwzV7tH+y3RKqDkRt/9cvsCV4pzbfu/rj8L5+Mm+CRRX9atw+RDOFB/Z18Cz0SYJS1HQE6m0ka0gub9gyrZTh8cPwGTJotrpLyanWaL9qgO41dZi6pVhaYgfp9NYd044BO8uVDp8n8K4ZIh29WCHRAtvUva0WVYYlt5MOGJss3y4oT+mIz4rTbVyY3/vGTPgXoq96FiPUlqoJXm7H8+DOf3P2gR1asSo2LItgecqD1LowZ9RzSsk9k3uZXWyfIgl2T0eVqaXPpMpmy8Qop8GyrVmbMgNyrfw+X59eeUJneWn9hZT3oV2VYV3WhF/pRX+VFsty8uVwGr/1Q3tyxPJJqSZfNLxB58X9eefhpr2zeknRzEmbA4EG81CYMNhsyJQGzHwGmhBYLo85q1FXcUjv+0gZZMvfWobpj6M1Mq9b0lj5tdMdx5XH7VCsLzGjy1xbJyCuSjQd00It2t28s23q0kOYLK6KOOZt3S2HzenoZltjGBpzO7d1bCGzBdsRJ89PumnWcYApwxu3lsuEXLSNiJYmtsHKN5mROKJepM7zi8Sa27WS3Nviw2vfLZNsYrP1Oh2lzlcEKAvLg9MEB/KtoTkCJ6E4inMWPNl0vZb6iauXBMtIdOXJKyydkVIdP9TSy7WuS626q6/q7eIGMbPM/PWEdTiZ0LC/8QrrkHC4tMqJ7fvujkfPkx9LNct7qr2Tg0g9kSfEOqDVt8muH/Nff+GTsfeXyY4p9O7RVC5GunSK7ETLtQNIwEoiSgDWvKlF21kzVGrkz5MSGFU5fMu0qbFFfMncV61FTrOdsydfNwRLbKqqpHFS9MMI/j608XsrXVDjKuCbOe9Yjsx/2SP5m7QoUoa54i5eUirz5rlcefMIrmzbHuzXz6W/dUqRTe2teGFvtJ5IWxndB+zc4W3cKu2YPrjYAcBLhLMJ5THdmVyuPJKPQu12gI92RLXrS9CEPDiccz+45x0airlJ21eB18q57WeX2vMLNst+Sd2XM33OlIO5TB5XNhr2y9m+/3PNwubz9vleKS8KuZhvBow4P7ybINh1mR0ggCAFnkDxmJYjA1c2jm5aO1rxd7RrrVRv8vUNfBv6BQ9l6/mp9Wh5LfVubWkdUE07nkHEf62UHPjdHd04D6wfbHlTYTjp83bZa0bblfvn8Do/Mm+SRrcuS73Ru0qb0cDG8+e5ymT3XV83eVMk42mIvBhnHxel2SNejE39KK/MXCiKhiJYiKgpnEnbluJro0dEyf5HoSYuUIg9RzXZZ/fXoJ+QiSUUtiuTBPrOrVfGIX57Y8od0+OMNuXn9PPm7TJuvriaVuAzMBiCC+egzHrnvUY+s35i4ts3UUrp2jz3wYGvevJmJI22xPoHEn5mtzyysHoQjdFS9NnJAdrNwRGMig2nwFSfuK/u++aPuNOIlITzrWdIgSzBFj3JM2eN5TizRKJ7VhNPZfu5fsuSM/vqUPeTbf78SxTWmTJ9bbv7yGMFnW4IKaj7mhgV+mfOIR2be5ZFVc3ziLQsqGZfMsnKR7370yfgnPHLHA+WCab6i4O9fxaV9synFhfGQA619Yex2rFOc2gU+UWxbaNPgF7WbokdKMY3eMqOXfLVtgpR5i3RnEk7lWxvOFySswy4V1cQzo3BQkVYXzUVRrem5M2ZJni/0QbLDWyqPbP5NOi58Q05ZOUO+yl9fq85YCmzc5Bd8HuyGO8r1ZzOXrtAO8lg2YDFdBx/olIx0h8WsprkkEHsCztirpMZICNzT+qBIxOssu2XPM55wKJE27ddWtvRtE1SvelbTk+HWp9plz3+YXt+zWuNi4pJ/S9lGV40yqnD3Rr8seMMr028olz+0aeyi7aok9sv1WluYKh9ze7m89rZXVq1J7QuiInz0YKflL4zpOQ7pMiR5p7U+9U+RMl+hIJIJrmrKHo4o1lVUE9PpkLmo7XuCKfuf897Sn/FEnVBp/hmLZZ7vn1DFVfIRm5+at0aOWf6x7LN4sjy7dbEU4ueIqkjFZqNM832/neeTBx73yF0PevTPgxUWxUZ3DLUkXJXDIXLCMcnbFxPeYTZIAjUQ4JFQA5xEFP27QQfpndk4Lk1h6htvsuOzRcEaQD6e19zetfpnmFBXRTUR9UQ0U+lQLw6p7WDL0/J6SqNvI4/almuRxeUzffLpLeUy47ZyHzZuygAAEABJREFU+eF5ryz7zCdb/vQLfkklWFuh8nARXL3WL7gQItry2H89MkaLuOCTK5gqx/OZoeqmWn5WpshQm1wYe57gFEd49zgJHWZMr6uoZpF3m6gXkDC1nu7MqdGWvC558mTzH2uUCVX4Z0meXLHuG2ny28sy4M/39PVXti2VP4q3i1ebfg9VL1j+9p0ifyz2y6df+PTI5d0PeeRq7Vh9/f+8gmMtWJ1UzTv0IKc0a6p5nKkKgP0mAQMBp2Gdq0kiMK71gSFbhtMHh3HApNmSu2m3HP7gjCrf02xu+PRRy9/X69PjyAulEA4mps+R4DT+PHqwPoVeKb9npdunC2XdYV0FU+vIwmePuk3/Q1APDuq6gV2QHTQ18mbKhTMHBS2LJLNwq8j6n3yy8D2vfPOYRz66ulw+urLCCZ2lRVDmPu2Rn1/zyqIPvLJkakVa8KZX5r3olVvvKZcrbirXoy24EOKn8P5c7pfdFe8/RWJGSsgOPcYl2Vn2uDDiM0id4vRGPd5kNz6XuWj3NDF+vmjR7o+kcVoHfQo9cMdRUU28hJTtaqq/PAQZPcqpRUOxHizBcX5i6NdS7ke8MphEeHmlWv2firbqUc5L1s7SXypy//KctPj9Nemz+P/kqOVTZfiqz3Vn9K6N82Xcxp/kjg0/6m+83/3NSrlSO55uGVcuE1/wyAfTvfpb5Rv+8Yu3bmaFZ7wFpU4easI7HgtypMn2IEBn0wTjeHrDztIto0FQS+Ds4flJTHmrtOjsAZWygdPikEEeBFTd1Uft/aYn1iGDZNQDeWNCmdKDfKUL9UI5qJBDeubXk6V0e3wcF/z6JZzQ7Sv98s/vflnznU+WfuqTJR9XpFWzfbLpV59sC/4OFMxjCiBQv57IsUPiM14BTSVss8dQpzicwZuDw4jnJPHpo5/y3hB8J1M5jIg+wpnECz87ytfoz1ri2crgmkRy3E0qXw6CzgItYnl446uriUP/5tKl0id3mF6GZz1bZPSUV9efodc/sOHIoA4qhD8570dZ4o3fDr1FmzJYXLJTZuVvlPd2rtKd0fv++UXu+edneWDTAv1bnstlu5Rq0+Wwh6l2AkcMdErTGE1Y1d4aJUjA/ARCnI7Nb7idLHQ4HHJHqwNs0aWLt+0v6T/VT2pf8JKR216+U1x5nvQvl6SlOeLaRqKV52jTl+0PDn56w7OTeIZSJbzgA+cPNiLqOLLta5Uv/EDm6Ga3oEhPqItyyCGjU/agKrLDWj4i6a5sFFVJ0D+0+T1VyqAL+pGgp0qFPRvrB2ySV7MW7tlK3qIoszh5jVusZZcW0Bym3exYzGyaSwJxJRD8bBzXJqk8GIGRjbtJj4yGwYosk9e6PFdOnnFAkuyt2mwLLVpXNYdbwQg00gLqgwfZy9FU/ez5b2uf3koblsqEAV+r7iR1WeCmsxnuAAwZ5JQG9e15TIXLgHIkEEjA2mfjwN5YeNulzfm93HGIhXsgMnHeMCkvMMdJtkGmOeww+4CedpJLnE57sqrX0iGdjnCafQhC2vfaOXNkk9ccr3XvctLZDDlQhgK3W+Sk4627zxm6UvdVaiABAwEeFQYYyV4dlNtKzm3cPdlmRNX+HeuPEP/irKjqxqNS/bR4aLWXzo7tHYI3Zu3Vq6q96XuGU9Jzq+ZZYWvhSSvkS/8605i600FnM5zBOHaIU3JzHOGIUoYEUooAnU2TDfcT7QZKrtNanlLfkubS/3NzOck5LpMNbGhzklLi1I78i0faH1J6tkP2G2Gtfu7usFsebTc3KftFqEa3+cwRYQ1lnxnyGzcSOelf2oFlBmNoAwmYjACPDJMNSFN3liT6Q+91QuAXuW/W0IT+8k849maEI5TCMicc65TW2jRzKiDoMNApTbpYI9rkc/jlqZO+liK/x1RDs9tXLg4Xv3FU06Bcdr5L0vlrQWL9/9iDeBCgsxkPqnXUeW3zvpZ5WejRVf+S8jXmi8Sm8boYci9s1jT1IjAHXuTSnKWQSExT8M05v8kf3m2mscdoiKsFo5tGHsZ1fOqoW2deTo1MuE4CRgI8Oow0TLLucjjFCi8LDSpsJx2/amcSalXNcJVX3ebWXgL/0SIwLlfdI317NZp/DS8LddeiuWa2dGufbTKp/i/mNbFhqXltS6JlDRuIjDiFl9IkDgGbtgABHiEmHSS8LHR5s94mtU4kzeeUm788Rvxec5ro4Aeogw7M0Uc4pVMHZ9Ayu2fuM8wp9Vubs5fl2eXy8JCvIvzxyMT2xdeAkc1gxC851yWZGal18xaMA/OSTsDUBqTmVcfUQ7LXuCfbDpJ9MhvtzTDR2jNLT5SyjS4TWVTVFG+xv2oGtwQRmNNPSt1D3pXmkEOvdIvTbb6d4aORP8gar7l/S9Vbr8R84JJs0cEHOKVX99Q9ppKMn81biACPEhMPVrrTJR91OV6yHOa6Op6W11Maf9PcxOREys193U44O4cWeLn8YlfKv8BQr4VD+muRKDHRf6sGr5N33ctMZFFwU0qzGdk0ksnOFjnndF5CjUy4TgKhCPBICUXGJPndMhvKKyb62HtDb6Zc8NkgMfV8nzZ2/MlKDYLh35mnuqRLRx7uQNLxMKe0PUjzvrGR5FTUokge7DM7yVaE13xxBiObihRu3q7Qbt5ycsyxHym7uCQBsxKo6epjVptTzq6zGneTC5r0MEW/n/7tJCnbaY0TLH+ysmKX6bevQ44ZzEO9gkbF3wMvdEl2k4r1ZP597oxZkuezxgPGhRn8sLvaV3Dz1rMbjynFg0sSqI0Aj5baCJmkfFL7w6VrRoOkWnPxtv0lc35ybYgEAH+yUqSZ5lBdep55n62NZDxjKevOcMjAK93iSCKa+Wcslnm+fyLoVnJFd7sY2cQIHHIgb97AgYkEIiFAZzMSWkmUzXam6c9vZiTp6ti6PFdOnnFAEglE3nSq/2RleprINaPckpFujUh05CNctxoN2ztkv+HJ8TbzuuTJk81/rFsHElw7z8nIZpdODrnwnOTsMwkebjZHAjElYEtnM6aETKSsd1ZjebfzcZIM12HivGFSXpCMlqMfgJwUvyYgotmqhbXGLPrRjq5m12Oc0vGwxDLypHnliRO+lnK/tX55YHuK/2Rlo4bazdt/XOJOsW/URndksRYJVCVAZ7MqD9Nvndywo7zYYUhC7Ry74TDxL85KaJuxaCwjFkosqgPf0+y/Hw/vcIbvgPNd0mo/RziiMZGZOfJnWeLZERNdiVSyxVssfu3/KNq0fJU0bZbg+svdkpPtsHxf2AESSAYBXo2SQb2ObV7StJfc3erAOmoJr3r30sZy4ExzvJwUnsV7pdJT9FObvXs65OzTUzysu3c3qHXN4XTIoZe7pEnX+DsS6wdsklezFtZqk1kF3I1T87nN0Re5pXXL+O8fZh132kUCdSVAZ7OuBGNZPwJd41ofJBc36RlBjShENWft4TknirfEmidZpzVe8o1iYEJX6dbFIVddSkczNKHgJU63Qw67zhXXXxgqbVgqEwZ8HdyAPbn11+2QQ578UjJ2BX8+stPXS6XP5Pl7pEMvIHPMrR+ISqinpKF70EMz9DIssa3KIIe6ajtw6UwxZ9OhnfquvMQl+/XWVgJhcJsESCBsAnQ2w0ZlPkFMp5/UoEPcDHtk9XFSvjK9Rv0Ldk2Wr7ZOCCmDMsiEFAgoCCa/uXSZvPr3cHl+7QkyddNNUubd+3HpYPJKZar9ZGWHtiLXjXZJWppDIeAyAgJpmQ454ga3ZDaQuPz32jlzZJNh3zU2AocPjt+ASbPFVVJuLNLXmy/aoDuHXWYu0bdr+qPqf3vbUPnyodNk/hVDpMM3KwQ6UK/1L2tl44BOehmW2EY+bGiyfLOsOKEvNoMmf8PUimxedr5L+u3Ly2TQnYGZJBABAR5FEcAym6hTu+1+r/O/ZFBOy5ibNqiwnXT6sn1IvauL5urO3095bwSVgYMJ5/CvojlBywMza5JftPsjGdLkOhnV4VPJdTWVDaW/6tXhhJb5i6RP7jB9O/BPKv1kZdvWIjdc5eab54E7QYTbmQ0cMvgmt6RlRVixFvGFJ62QL/3rQkqVNsiSubcO1R1Db2ZaNbktfdrozuHK4/apVhaYgfqLzh4g0Imywub1pKBlfcnZUvGzWjmbdwvyVBm2sQ6nc3v3FpX1kBeYPCn0k5Xnn+mSAf15iQzcB7hNAtEQ4JEUDTUT1cFPWn7e7UQZkqt5GzGyK83nlJu/PEb83tAKO2UP0p2/gxqeJyLV5fo3OFsv75o9uHphkJxQ8ohiwqHM1pxMVGuU3l52lldctOGE9sw9TtJd2Siqlsorrq3V8u2W0bKFyE2ao5mdxYhmLMa2XkuHDLlFc9zrxUKbyO4Ou+XRdnNjoywKLe5Sj2TkFe11MFsYHE/NAS3UtlVUc+MBNc+UlOfsnVWIwhTLVMHPUB4xkJdHywwYDTU9AR5Nph+i2g3McaXJzO4nyqkNO9UuHIbExKX/lrKN5njuD45kuiNbirzbdMt3lq2TRmntRUU122T00/OD/UmFn6xs3lTklmvcwp/NC7YHRJ/XoK1DjrrdXedfGfI5/PLUSV9Lkd8TvTF1rNnt04Wyq0MTQXQUquBQtp6/Wp+Wx1Lf1qbWEdWE0zlk3Md62YHPzak2pV+Saf9p9OHDnHLUEeY4/2G8okqsRAImI0Bn02QDEq05aQ6XvK9NqV/atFe0KvR6J+R3kabfaKEyfcscf/rUP0Vmb39Sn7Yv0JxOOJgqqokpdUzXI+H5zUCL7fyTlXhG87br3VIv1xHYbW7HgEBO0wqHs36b6JXNOec3+UPbZ6PXULeaeNknc1ex/Hnq3psyTK9jyh7Pc2KJFvCsJpzO9nP/kiVn9Nen7EsaZEn771eiuDIVZto3sunQDiNMnf/rKDqalQPOFRKIEQFnjPRQjQkIOBwO/Ruc97UeEJU1ud50GfWZNu3tj6p63Cq1yOghF7Wbok/LD2v5iOz0/C2YWm+W3kOWFsyUU1o+IRe1fU/giOJZUqMhtfxkpVHUUuv79nbIrde56WjGedQy6zvkyNvc0qSL5olE2NbWPtvk2fq/RFgrduLK0fz1woGC5zhDaVbPanoy3ALHVPb8h+n1PauViwK3PSOb+LWta0e5hFPnlUPNFRKIKQFnTLVRmSkI3NHqAHmt41ER/9LQpN9OlrJt5t8lVFQTsAs8W7HQn9vEy0P6huGPHX+yEh9sv/oyN986N4xzPFf1t9RvdEnLfcN3OMuzy+XhIV8l5BPomPrGm+ydvl5aiQGOJjZ+Hj24RkcTdVVUEw4popmoh6ReHMK6Snb8ycr69UQwQ9Cnl/nPfWoc7Ltkz+xKgEeXTUf2giY9ZFqXoZLuCG+IL962v2TNb5gQGnjeEp8yCoxChtM46iKqial0PMuZ626mV8OLRIhs6huGP3b7yUpM8+GD7VoQ29BLrsabgCvNIYdd7ZaeJ4R3PH048gdZ4w3/DTU4fXAY8emj3Cuh+pQAABAASURBVE275fAHZ1T5niY+W4RvZuLTRy1/X68/U4m8YP2GrgZrt4uSQz2kYM9g4nnOdYd1rXwDHZ896jb9D4E8opzrBnap0sROCf79zypCFtpo1ULkzhvd0q5N+DcSFuoeTSUB0xAI78xpGnNpSCQETmzYUX7oeZq0S8utsVrr8lw5ecYBNcoEFsJRxHOS+PQRPm+EdeQpOeOnjCAD5xKOoioPXIYjD4dywa63pX+Dc/RIJl4ewvqnW+6QV9efoX8WCW/JG3VnGDcsvO52i1x92d5pPgt3xbqma/5In9NcMuhql7gzQ3dj1eB1MsW9LLRAkBLjc5R4lhIJny9Soni5B3nGhDyUq7qrj6r4kQe1bZTFerAoJ9pQeoy6Qslv9BRCzBapR1eH3H6DWxo1dNiiP+wECZiZAJ1NM49ODGzrl91MFvYeITV9/H3ivGFSXhDZCRdOHb57aUzIUyarTxmpcjxziWcvUY4ltsOVRx0kOJdDm98jqI9tJKxDF9o5utktyKqS0nxVNi25kZsj+vOZ+/Xh4WqGAWy1n1OOucstuVpULNCeohZF8mCf2YHZttn2iE+c2dU/Om+1Dh56kENuuNIlmRmRnfes1k/amzACbKgWAs5ayllsAwINXBkyresJ8kS7QZIWMK1+64bDxL84xl+wNhEzl8Wvi/j5yXtudUvHdrwommi3ktzmDt3hbN2v6rg8d8YsyfPZ+3dSnY2tO5WekS6CR1EuOdctTmfVsTPT/kVbSMBuBOhs2m1Ea+jPdc33le96nFI5rd69tLEMmNmjhhrWL3JY9LrvdomceYpLbr7aLQ3q2+iiaP1dqrIHbi0qNvBKt+x3pksc2pn0xzMWyTzfP5Xltl1pbM030ju2d8i4W9x849y2OyY7ZmYC2inSzObRtlgTGJDTQptWP1NObtBRHp5zonhL7O3IeIv9sUYYd334RaC7b3bLsUc6xWHv4Yk7y0Q00O1YpwzRxuuDNksS0VzS2/DVt1Zk0+kUOel4p4y93i3NmvKASvoORANMRSBRxmiHYaKaYjtmIdDAlS5Tuw6V/QdnSVqOWayKjx3lBfHRGy+txw5xyrhb0qRVS14U48U4HnqbdHXIH73PlKub9xW7j1x5PetENps2EbntOpcMG+oSOJ3xGHvqJAESqJ2As3YRStiVQIeBThn6QJq0O8i+u4G3VMRtgas/vvV389UuOfNUl2AK3a77nHX7VbvlOU63TGx3mHzb4xTpklG/9goWlSjLtEZk8/BDnXLvrWnSqYPToqRpNgnYhwCPQvuMZVQ9Sc8VOXiUSw6/3iWZDaJSYfpKZv7JSpd2BB6nTZePvzNNunfVNkxPkwbWRmBQbitZ1PssubnF/uKyYZyzyOTOZuuWIrde65ILznJJerrwPxIgARMQiPnVzQR9oglREGjR2ynHa1HOLprjE0V1U1dpkOkwpX1993HI/XekyYhTNEc/w5Qm0qgoCWQ6XDKh7aHy+z4j5Kh6baLUYs5qBenmjGziE2HnjnDJPVo0s2tnXtrMuffQqlQlwCMyVUc+SL/xoep+I136J11aaI5QEBFLZpntJyubNxX9G3/XjnJLsyaWREqjwyTQO6uxfNX9ZPmk6wnSK7NhmLViKhZzZbud5npmE7MDxwx2ykN3p8mQQU7hS3UxH3IqJIE6E3DWWQMV2I5Aw/YOOXyMWwbf5JIG7azfPbP8ZGVWZsXnjO67PU16deehZ/09K/wenNCggyzUptafaX+4NMVdXfhVTSe501lkGpswO3Dv2DQ56zSXZGaYxiwaQgIkEECAVzwFhMtqBJr1cMqxd6XJIaNcktO8WrFlMjKTHOrARfCEY53yoMYSnzNCJMYy8GhozAjg+c0rm/WRv/qMlBtb7C/pDmuefrf7kj+N3qu7Q5sdcAtmB1o0i9kQUREJkECcCFjzbBcnGFQbhIBDpO1BTjn+/jT9RaJGHbSMIGJmznJ7k/OtTXx25ezTXfK4xu60E12CZ8rMzIm2JYYAPj32SNtDZXXfc+UmzenEdmJajk0rO7yl4tf+j4220FoCS1wukYEDnHLvbW7d0YTDGSjDbRIgAXMSoLNpznExnVUIwuATSUff6RZ8wLrVftZxOhP9k5WdNYd89EVuefDONDn6CCffiDXd3mwOg1qn5cjDmtO5ft/z5bG2A6U9Pg1hDtNqtcLdLHHRzZxsEcwMPDwuTS4e6ZLW/AZtreNDARIwGwE6m2YbkYjtSXyFptoU1qCr3XL8+DTpPNgpzrTE2xBJi46ySKSjk8VMfb99HXLrtW4ZO8YtB+7v4IsK0aFMuVq52gE0psV+skqLdL7d6Rjpn93U9AycTeLvbOJFunPOcMkj96YJZgYa1Dc9FhpIAiQQggCdzRBgmF07gdzmIv3Pc8nJT6TJgRe6pFlPh4j2T0z2nzdOP1kJB7N7F4dgqvxR7YJ45SVu6drZhABMNh40JzgBl3bwnN24m/zSa7iWzhA4oK3TtLBecPGk5vrqx+eN9EYNRY7RbmBx0/bAHWly1OFOSTf5zWy1gWAGCZBANQJ0NqshYUakBPBybcfDnDL4Rrec+Gia7DvCJXijPVI98ZKP5U9WwsHEs2L4nt8TD6TJzde49alyRl3iNXqpqbd/djN9av1vbYr96+4ny6VNe0lDV7ppYHhj+JOVTRqL/Osopz4j8Mg9FW+W46YNx5ppOkxDSIAE6kSAzmad8LFyIAH8ClH345z6tzr//UiaHHCBS9oe6JT0nEBJSViGt1Qk3R19c/XriT4tft6ZLnlyfJr+csKQQU6+8BM9UtYMk4BTi3YeWa+NvNhhiGzd7yKZ3WOY3Nqyn+yf1UQrCVNJHMTKcur2+aMunRxyonaeuOMGt0y4O02GD3MJnnWOg6lUSQIkYAICThPYQBNsSiCrkUgnbRrskNEuOfmpNDnqdrf0Oc0lzXs5BNHQRHa7aQTObrMmIoMOdsqF57hkvDaVh7fJ8cLP4IFOwcsKibSbbZGAIuB2OGVwbmt5sM0h8us+I2TzfhfKW52OkeGNukiHBL9cVJIZ/jR6RobIPj0ccsq/XfpMwHOPpclt17n17Y7tHap7XCadAA0ggfgRoLMZP7bUHECgsRbN6HmCU47QohmnPJMmJzyUJode4ZZeJzqlZV+HICoaUCVmmw0zgl/UWjQX2be3Q5/G+48WhYVjie9hXqQ5modpDmfzZjEzgYpIIKYEmrmz5JzG3eTdzsfJmr7nSd7+lwgin0+2GyQXNOkh+2nRzzTNQY1po3uUFWQEf0EIswDdOjsEx86Zp7oEkcv/PpwmY7TjHJFMPOPsdu9RwgUJkEDKEKCzmTJDbb6OZjcVadPfIb1Pcclh17rlRC3iceqkNDnuXrccfr1bn4LvdZJTOmpT1s33cQic1fptHPoH5jMbiuDdCac7eL+Qn1FPdFk8P9qlo0OOPMwp+B3y60a79YjlS1q09YHb0+Sa/7j1abwB/Z2Ci2VwjXtzuUYCZiSA73Ui8nlt833ltY5HyW9a9LOs/yhZpzmi3/c8Vd7pfKzgE0vXaeVnNOosh+e2kgOym8k+mY2kY3o9aaE5r/WdaRLqP+hvl5YrfTIbS/ucHDn4AKcMG+qSy853ye3aDeR/H0kT3Kzdoh3LmBU4doh27LZ3hFLHfBIggRQi4EyhvrKrFiCAdyDqt3ZICy3aiCn43sNccuBFLjlijFufhj/uHrcMHZ8meBFp2MQ0Oe25NDnjpeoJ+Sc9kabLHnOXW4aNdMnI4S457kin9NGm8RmxtMDOQBNjQqCdNsV+aE5LGdGoq/6G+xNa5HNK53/JNz1OkZ97nSGLe5+lf2B+034Xyq5+l4r/gMuDJkRO1+17nizsfaY81+sQ3ck86Xin7nR20pzKjPSYmEslJBApAcpbgACdTQsMEk0kARIgARIgARIgAasSoLNp1ZGj3SQQKQHKkwAJkAAJkEASCNDZTAJ0NkkCJEACJEACJJDaBFKp93Q2U2m02VcSIAESIAESIAESSDABOpsJBs7mSIAEIiVAeRIgARIgASsToLNp5dGj7SRAAiRAAiRAAiSQSAJRtEVnMwporEICJEACJEACJEACJBAeATqb4XGiFAmQAAlESoDyJEACJEACGgE6mxoE/iMBEiABEiABEiABEogPAXM4m/HpG7WSAAmQAAmQAAmQAAkkmQCdzSQPAJsnARIgAbMRoD0kQAIkEEsCdDZjSZO6SIAESIAESIAESIAEqhCgs1kFR6QblCcBEiABEiABEiABEqiJAJ3NmuiwjARIgARIwDoEaCkJkIApCdDZNOWw0CgSIAESIAESIAESsAcBOpv2GMdIe0F5EiABEiABEiABEkgIATqbCcHMRkiABEiABEggFAHmk4C9CdDZtPf4snckQAIkQAIkQAIkkFQCdDaTip+NR0qA8iRAAiRAAiRAAtYiQGfTWuNFa0mABEiABEjALARoBwmERYDOZliYKEQCJEACJEACJEACJBANATqb0VBjHRKIlADlSYAESIAESCBFCdDZTNGBZ7dJgARIgARIIFUJsN+JJUBnM7G82RoJkAAJkAAJkAAJpBQBOpspNdzsLAlESoDyJEACJEACJFA3AnQ268aPtUmABEiABEiABEggMQQs2gqdTYsOHM0mARIgARIgARIgASsQoLNphVGijSRAApESoDwJkAAJkIBJCNDZNMlA0AwSIAESIAESIAESsCMBp9ixV+wTCZAACZAACZAACZCAKQgwsmmKYaARJEACJFBBgH9JgARIwG4E6GzabUTZHxIgARIgARIgARIwEQELO5smokhTSIAESIAESIAESIAEghKgsxkUCzNJgARIgAQiIkBhEiABEghBgM5mCDDMJgESIAESIAESIAESqDsBOpt1ZxipBsqTAAmQAAmQAAmQQMoQoLOZMkPNjpIACZAACVQnwBwSIIF4E6CzGW/C1E8CJEACJEACJEACKUyAzmYKD36kXac8CZAACZAACZAACURKgM5mpMQoTwIkQAIkQALJJ0ALSMAyBOhsWmaoaCgJkAAJkAAJkAAJWI8AnU3rjRktjpQA5UmABEiABEiABJJGgM5m0tCzYRIgARIgARJIPQLsceoRoLOZemPOHpMACZAACZAACZBAwgjQ2UwYajZEApESoDwJkAAJkAAJWJ8AnU3rjyF7QAIkQAIkQAIkEG8C1B81ATqbUaNjRRIgARIgARIgARIggdoI0NmsjRDLSYAEIiVAeRIgARIgARKoJEBnsxIFV0iABEiABEiABEjAbgSS3x86m8kfA1pAAiRAAiRAAiRAArYlQGfTtkPLjpEACURKgPIkQAIkQAKxJ0BnM/ZMqZEESIAESIAESIAESGAPgSidzT21uSABEiABEiABEiABEiCBGgjQ2awBDotIgARIwBIEaCQJkAAJmJgAnU0TDw5NIwESIAESIAESIAGrE0g1Z9Pq40X7SYAESIAESIAESMBSBOihkka3AAAB6ElEQVRsWmq4aCwJkAAJ2IkA+0ICJJAKBOhspsIos48kQAIkQAIkQAIkkCQCdDaTBD7SZilPAiRAAiRAAiRAAlYkQGfTiqNGm0mABEiABJJJgG2TAAlEQIDOZgSwKEoCJEACJEACJEACJBAZATqbkfGidKQEKE8CJEACJEACJJDSBOhspvTws/MkQAIkQAKpRIB9JYFkEKCzmQzqbJMESIAESIAESIAEUoQAnc0UGWh2M1IClCcBEiABEiABEogFATqbsaBIHSRAAiRAAiRAAvEjQM2WJkBn09LDR+NJgARIgARIgARIwNwE6Gyae3xoHQlESoDyJEACJEACJGAqAnQ2TTUcNIYESIAESIAESMA+BNgTEKCzCQpMJEACJEACJEACJEACcSFAZzMuWKmUBEggUgKUJwESIAESsCcBOpv2HFf2igRIgARIgARIgASiJRDTenQ2Y4qTykiABEiABEiABEiABIwE6GwaaXCdBEiABCIlQHkSIAESIIEaCdDZrBEPC0mABEiABEiABEiABOpCIJHOZl3sZF0SIAESIAESIAESIAELEqCzacFBo8kkQAIkUHcC1EACJEACiSFAZzMxnNkKCZAACZAACZAACaQkATqbYQw7RUiABEiABEiABEiABKIj8P8AAAD//2fdJIsAAAAGSURBVAMAdcmIQ2hylGYAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def rupture_distribution(df):\n",
    "    series = (\n",
    "        df.groupby(GROUP)\n",
    "        .agg(n_ruptures=(\"rupture\", \"sum\"), total_obs=(\"rupture\", \"count\"))\n",
    "        .reset_index()\n",
    "    )\n",
    "    series[\"rupture_rate\"] = series[\"n_ruptures\"] / series[\"total_obs\"]\n",
    "    bins   = [0, 0.01, 0.10, 0.30, 1.01]\n",
    "    labels = [\"Clean (<=1%)\", \"Moderate (1-10%)\", \"High (10-30%)\", \"Severe (>30%)\"]\n",
    "    series[\"class\"] = pd.cut(series[\"rupture_rate\"], bins=bins, labels=labels, include_lowest=True)\n",
    "    return (series[\"class\"].value_counts(normalize=True).sort_index() * 100).round(1)\n",
    "\n",
    "df_raw_gap   = build_gap_df(stocks);          df_raw_gap[\"rupture\"]   = df_raw_gap[\"gap\"].abs() > 10\n",
    "df_clean_gap = build_gap_df(stocks_repaired); df_clean_gap[\"rupture\"] = df_clean_gap[\"gap\"].abs() > 10\n",
    "\n",
    "dist_before = rupture_distribution(df_raw_gap)\n",
    "dist_after  = rupture_distribution(df_clean_gap)\n",
    "\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Pie(\n",
    "    labels=dist_before.index, values=dist_before.values,\n",
    "    hole=0.45, name=\"Before repair\", domain=dict(x=[0, 0.48]), textinfo=\"percent\"\n",
    "))\n",
    "fig.add_trace(go.Pie(\n",
    "    labels=dist_after.index, values=dist_after.values,\n",
    "    hole=0.45, name=\"After repair\", domain=dict(x=[0.52, 1.0]), textinfo=\"percent\"\n",
    "))\n",
    "fig.update_layout(\n",
    "    title=\"Rupture intensity distribution — Before vs After repair\",\n",
    "    annotations=[\n",
    "        dict(text=\"Before repair\", x=0.22, y=0.5, showarrow=False),\n",
    "        dict(text=\"After repair\",  x=0.78, y=0.5, showarrow=False)\n",
    "    ]\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2853dffa-2672-4e98-a3ae-2bcfee09b43c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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