{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7a8d8f9f",
   "metadata": {},
   "source": [
    "\n",
    "# Getting Started: Lagrangian Stochastic (LS) Footprint Model\n",
    "\n",
    "This notebook shows how to use the **`ls_footprint_model.py`** module to compute a simple\n",
    "2‑D flux footprint from eddy‑covariance tower data. It uses the attached example\n",
    "configuration (`US-UTE.ini`) and data file referenced therein.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68fa8701",
   "metadata": {},
   "source": [
    "\n",
    "## 1) Setup\n",
    "\n",
    "This section adjusts `sys.path` so you can import from your local source tree, then imports\n",
    "the LS model. If you've installed the package, the first `import` will also work.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "358db358",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-31T00:50:05.236950Z",
     "start_time": "2025-08-31T00:50:03.572392Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imports ready.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# --- Standard library\n",
    "import os, sys, math, pathlib, warnings\n",
    "\n",
    "# --- Third‑party\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sys.path.append(\"../../src\")\n",
    "from fluxfootprints.ls_footprint_model import LSFootprintConfig, BackwardLSModel\n",
    "\n",
    "plt.rcParams[\"figure.dpi\"] = 120\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "print(\"Imports ready.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b811e1a9",
   "metadata": {},
   "source": [
    "\n",
    "## 2) Load site configuration and data\n",
    "\n",
    "We read `US-UTE.ini` to discover the time column, wind variables, and file names. Then we load the\n",
    "CSV it references and make a minimal set of variables needed by the LS model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ae5371a3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-31T00:55:32.910237Z",
     "start_time": "2025-08-31T00:55:32.004735Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 3,283 rows from: input_data/US-UTE_HH_202406241430_202409251400.csv\n"
     ]
    },
    {
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         "name": "CO2",
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         "type": "float"
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         "name": "H2O_SIGMA",
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       "      <td>1765.9350</td>\n",
       "      <td>25.52736</td>\n",
       "      <td>22.41975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-24 15:30:00</th>\n",
       "      <td>2024-06-24 15:30:00</td>\n",
       "      <td>202406241600</td>\n",
       "      <td>426.4163</td>\n",
       "      <td>1.965228</td>\n",
       "      <td>14.87533</td>\n",
       "      <td>0.808026</td>\n",
       "      <td>1.081928</td>\n",
       "      <td>NaN</td>\n",
       "      <td>154.11530</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>30.24634</td>\n",
       "      <td>28.28498</td>\n",
       "      <td>9.838229</td>\n",
       "      <td>30.69433</td>\n",
       "      <td>28.63222</td>\n",
       "      <td>10.41335</td>\n",
       "      <td>30.40344</td>\n",
       "      <td>1495.7350</td>\n",
       "      <td>25.12511</td>\n",
       "      <td>22.32785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-24 16:00:00</th>\n",
       "      <td>2024-06-24 16:00:00</td>\n",
       "      <td>202406241630</td>\n",
       "      <td>426.0534</td>\n",
       "      <td>2.665907</td>\n",
       "      <td>15.61140</td>\n",
       "      <td>1.002919</td>\n",
       "      <td>0.519664</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135.56180</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>30.75179</td>\n",
       "      <td>28.75255</td>\n",
       "      <td>10.538220</td>\n",
       "      <td>31.14621</td>\n",
       "      <td>29.16225</td>\n",
       "      <td>11.09066</td>\n",
       "      <td>30.90061</td>\n",
       "      <td>1491.0620</td>\n",
       "      <td>24.63557</td>\n",
       "      <td>22.18172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-06-24 16:30:00</th>\n",
       "      <td>2024-06-24 16:30:00</td>\n",
       "      <td>202406241700</td>\n",
       "      <td>427.8476</td>\n",
       "      <td>1.102921</td>\n",
       "      <td>15.21034</td>\n",
       "      <td>0.703084</td>\n",
       "      <td>1.147608</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.06287</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>29.16274</td>\n",
       "      <td>30.77158</td>\n",
       "      <td>10.165810</td>\n",
       "      <td>29.57434</td>\n",
       "      <td>30.96792</td>\n",
       "      <td>10.63069</td>\n",
       "      <td>29.30510</td>\n",
       "      <td>341.9711</td>\n",
       "      <td>24.14865</td>\n",
       "      <td>22.03216</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          datetime_start  TIMESTAMP_END       CO2  CO2_SIGMA  \\\n",
       "TIMESTAMP_START                                                                \n",
       "2024-06-24 14:30:00  2024-06-24 14:30:00   202406241500  427.0199   0.628133   \n",
       "2024-06-24 15:00:00  2024-06-24 15:00:00   202406241530  425.9499   1.019297   \n",
       "2024-06-24 15:30:00  2024-06-24 15:30:00   202406241600  426.4163   1.965228   \n",
       "2024-06-24 16:00:00  2024-06-24 16:00:00   202406241630  426.0534   2.665907   \n",
       "2024-06-24 16:30:00  2024-06-24 16:30:00   202406241700  427.8476   1.102921   \n",
       "\n",
       "                          H2O  H2O_SIGMA        FC  FC_SSITC_TEST         LE  \\\n",
       "TIMESTAMP_START                                                                \n",
       "2024-06-24 14:30:00  17.26862   1.019290  0.069210            NaN  156.40850   \n",
       "2024-06-24 15:00:00  15.18936   0.703052  0.285446            NaN  138.30920   \n",
       "2024-06-24 15:30:00  14.87533   0.808026  1.081928            NaN  154.11530   \n",
       "2024-06-24 16:00:00  15.61140   1.002919  0.519664            NaN  135.56180   \n",
       "2024-06-24 16:30:00  15.21034   0.703084  1.147608            NaN   95.06287   \n",
       "\n",
       "                     LE_SSITC_TEST  ...  TA_1_2_1  RH_1_2_1  T_DP_1_2_1  \\\n",
       "TIMESTAMP_START                     ...                                   \n",
       "2024-06-24 14:30:00            NaN  ...  29.95141  33.26877   12.052600   \n",
       "2024-06-24 15:00:00            NaN  ...  30.02516  29.22197   10.155240   \n",
       "2024-06-24 15:30:00            NaN  ...  30.24634  28.28498    9.838229   \n",
       "2024-06-24 16:00:00            NaN  ...  30.75179  28.75255   10.538220   \n",
       "2024-06-24 16:30:00            NaN  ...  29.16274  30.77158   10.165810   \n",
       "\n",
       "                     TA_1_3_1  RH_1_3_1  T_DP_1_3_1  TA_1_4_1     PBLH_F  \\\n",
       "TIMESTAMP_START                                                            \n",
       "2024-06-24 14:30:00  30.32464  33.45364    12.46181  30.06976  1665.4670   \n",
       "2024-06-24 15:00:00  30.35956  29.77183    10.72635  30.13765  1765.9350   \n",
       "2024-06-24 15:30:00  30.69433  28.63222    10.41335  30.40344  1495.7350   \n",
       "2024-06-24 16:00:00  31.14621  29.16225    11.09066  30.90061  1491.0620   \n",
       "2024-06-24 16:30:00  29.57434  30.96792    10.63069  29.30510   341.9711   \n",
       "\n",
       "                     TS_2_1_1  SWC_2_1_1  \n",
       "TIMESTAMP_START                           \n",
       "2024-06-24 14:30:00  25.72815   22.44161  \n",
       "2024-06-24 15:00:00  25.52736   22.41975  \n",
       "2024-06-24 15:30:00  25.12511   22.32785  \n",
       "2024-06-24 16:00:00  24.63557   22.18172  \n",
       "2024-06-24 16:30:00  24.14865   22.03216  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "import configparser\n",
    "from pathlib import Path\n",
    "\n",
    "# Point to the example INI and CSV (adjust paths as needed)\n",
    "ini_path = Path(\"US-UTE.ini\")\n",
    "if not ini_path.exists():\n",
    "    ini_path = Path(\"./input_data/US-UTE.ini\")  # fallback for this shared environment\n",
    "\n",
    "cfg = configparser.ConfigParser(interpolation=None)\n",
    "cfg.read(ini_path)\n",
    "\n",
    "# Pull the essentials\n",
    "date_col = cfg.get(\"DATA\", \"datestring_col\")\n",
    "date_fmt = cfg.get(\"METADATA\", \"date_parser\")\n",
    "skiprows = cfg.getint(\"METADATA\", \"skiprows\", fallback=0)\n",
    "missing_val = cfg.getfloat(\"METADATA\", \"missing_data_value\", fallback=-9999.0)\n",
    "\n",
    "# Columns for wind speed and direction\n",
    "ws_col = cfg.get(\"DATA\", \"wind_spd_col\", fallback=\"WS\")\n",
    "wd_col = cfg.get(\"DATA\", \"wind_dir_col\", fallback=\"WD\")\n",
    "\n",
    "# Resolve the climate/data CSV next to the INI, or in the same folder\n",
    "csv_rel = cfg.get(\"METADATA\", \"climate_file_path\")\n",
    "csv_path = ini_path.parent / csv_rel\n",
    "if not csv_path.exists():\n",
    "    csv_path = Path(\"/mnt/data\") / csv_rel  # fallback\n",
    "\n",
    "# Load data\n",
    "parse = lambda s: pd.to_datetime(s, format=date_fmt, errors=\"coerce\")\n",
    "df = pd.read_csv(csv_path, skiprows=skiprows)\n",
    "df[date_col] = df[date_col].apply(parse)\n",
    "df = df.dropna(subset=[date_col]).set_index(date_col).sort_index()\n",
    "\n",
    "# Basic cleaning for common missing value flags\n",
    "df = df.replace({missing_val: np.nan, -9999: np.nan, -9999.0: np.nan})\n",
    "\n",
    "print(f\"Loaded {len(df):,} rows from:\", csv_path)\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef972c4c",
   "metadata": {},
   "source": [
    "\n",
    "## 3) Choose a time slice and prepare LS model inputs\n",
    "\n",
    "The LS model needs:\n",
    "- `zm` (receptor height, m)\n",
    "- `ustar` (friction velocity, m s⁻¹)\n",
    "- `L` (Monin–Obukhov length, m) — **not used** in this minimal implementation but kept for extension\n",
    "- `h` (boundary‑layer height, m)\n",
    "- `wind_dir_deg` (meteorological *from* direction, degrees)\n",
    "- surface roughness `z0` and a numerical grid/domain\n",
    "\n",
    "If your dataset has a `USTAR` column, we use that. Otherwise we estimate \\(u_*\\) from wind speed\n",
    "by the neutral log‑law (requires assumptions for `zm` and `z0`).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dbc6685a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-31T00:55:46.143805Z",
     "start_time": "2025-08-31T00:55:46.134236Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected time: 2024-06-24 14:30:00\n",
      "u* = 0.285 m s⁻1 | wind_dir = 83.8° | zm=4.0 m, z0=0.1 m, h=1000.0 m\n"
     ]
    }
   ],
   "source": [
    "\n",
    "KAPPA = 0.4\n",
    "\n",
    "# Assumptions you can tune for your site\n",
    "zm_default = 4.0   # receptor height [m]\n",
    "z0_default = 0.1   # roughness length [m]\n",
    "h_default  = 1000.0\n",
    "L_default  = -50.0  # kept for API compatibility; not used in the core model\n",
    "\n",
    "# Pick a representative timestamp with non‑missing wind + speed\n",
    "needed = [wd_col]\n",
    "if \"USTAR\" in df.columns:\n",
    "    needed.append(\"USTAR\")\n",
    "else:\n",
    "    needed.append(ws_col)\n",
    "\n",
    "sub = df.dropna(subset=needed).copy()\n",
    "if sub.empty:\n",
    "    raise RuntimeError(\"No rows with required variables present. Check column names and missing values.\")\n",
    "\n",
    "row = sub.iloc[0]  # take the first valid row; feel free to select by date/time\n",
    "\n",
    "# Derive ustar if needed\n",
    "if \"USTAR\" in df.columns and pd.notna(row.get(\"USTAR\")):\n",
    "    ustar = float(row[\"USTAR\"])\n",
    "else:\n",
    "    # Estimate u* with neutral log profile: u* = κ U / ln(zm / z0)\n",
    "    U = float(row[ws_col])\n",
    "    ustar = KAPPA * U / math.log(zm_default / z0_default)\n",
    "\n",
    "wind_dir_deg = float(row[wd_col])\n",
    "zm, z0, h, L = zm_default, z0_default, h_default, L_default\n",
    "\n",
    "print(f\"Selected time: {row.name}\")\n",
    "print(f\"u* = {ustar:.3f} m s⁻1 | wind_dir = {wind_dir_deg:.1f}° | zm={zm} m, z0={z0} m, h={h} m\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99622368",
   "metadata": {},
   "source": [
    "\n",
    "## 4) Run the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a69635e2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-31T00:55:59.619615Z",
     "start_time": "2025-08-31T00:55:52.986886Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(0.0), np.float64(0.00039797840676442895), np.float64(0.0025))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "cfg = LSFootprintConfig(\n",
    "    zm=zm,\n",
    "    ustar=ustar,\n",
    "    L=L,\n",
    "    h=h,\n",
    "    wind_dir_deg=wind_dir_deg,\n",
    "    z0=z0,\n",
    "    n_particles=20_000,   # increase for smoother fields\n",
    "    dt=0.25,\n",
    "    t_max=600.0,\n",
    "    domain=(2000.0, 2000.0),\n",
    "    dx=20.0,\n",
    "    dy=20.0,\n",
    "    seed=42,              # reproducible runs\n",
    ")\n",
    "model = BackwardLSModel(cfg)\n",
    "model.run()\n",
    "\n",
    "x, y, F = model.footprint()\n",
    "xx, fx = model.crosswind_integrated()\n",
    "\n",
    "F.min(), F.max(), F.sum()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22ca4d31",
   "metadata": {},
   "source": [
    "\n",
    "## 5) Visualize the footprint\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4ea993c2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-31T00:56:04.971897Z",
     "start_time": "2025-08-31T00:56:04.352097Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(6, 5))\n",
    "plt.pcolormesh(y, x, F, shading=\"auto\")\n",
    "plt.colorbar(label=\"Footprint weight (m$^{-2}$)\")\n",
    "plt.xlabel(\"Cross‑wind distance y (m)\")\n",
    "plt.ylabel(\"Upwind distance −x (m)\")\n",
    "plt.title(\"2‑D Lagrangian flux footprint\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(6, 4))\n",
    "plt.plot(-xx, fx)  # positive axis for downwind distance\n",
    "plt.xlabel(\"Upwind distance (m)\")\n",
    "plt.ylabel(\"f(x) (m$^{-1}$)\")\n",
    "plt.title(\"Cross‑wind integrated footprint\")\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45322449",
   "metadata": {},
   "source": [
    "\n",
    "## 6) Optional: 80% source area contour\n",
    "\n",
    "Below we compute a density threshold such that the **integral** of the footprint inside the contour\n",
    "is ~80% of the total. We then plot that as a single contour line.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "de1d284b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-08-31T00:56:28.128223Z",
     "start_time": "2025-08-31T00:56:27.879003Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wn3Lnzp3Do0eP0L17dzRo0EBtH+bPn6/T+46oOmNwXIPIx4nZ2NioPS7ff/fu3XKdc/36ddy7d69cwbG1tbXa/Q0bNlRqV92x8lLXLwODoltfJpNpVOfzunbtClNTU8WUcikpKahVqxZef/11xVjhlJQUNG/eHL/++itat26t8fWoo41rzMjIwIABA7Bs2TKlcaJXrlzBunXrsHXrVqxcuRJWVlYYNmwYPv74Y6VrKCgowLvvvotbt27h6NGjimNmZmYICwvDzZs3sWzZMnzxxReKWVXkQXJJYxjv37+vVK40q1evxsGDB5GWlgYDAwMsWbIEjo6OiImJgUQiQdeuXbF3714sXboU3333Xal12dvb448//sDcuXORlJSEbdu2ASgKYmbMmIEpU6Yo9bs8v1MVoe6ekddtZ2f3wvOzs7MBFH0QioqKKrFcbm5umfozePBgbN++HU2bNsWAAQPQsGFDSKVSAMCyZcvw+PFjteepuw5N+nb58mV07twZd+7cQY8ePeDt7Y26detCX18fV69eRXx8fIl9eFZ5XsOKvOcl/R1pYGCg9KFCl33QhDb/riJ6FTA4rkHkAUZmZqba4zdu3FAq9/w56qZLU3dOWdy8eVPtfnnf1NVXlafjMjQ0xOuvv449e/bgxo0bSElJgbu7O0xMTBQZ9uTkZHTo0AEPHjzQatZYW7p27ap2hb4mTZpg3rx5L3yw6dy5c7h48SI6dOig9h9TDw8PLFu2TOlhupYtWwIoyhqq89dffwEo+paiNPKHOMPCwhQPL509exZ9+vRR3DempqZo2bIlTp8+XWpdcq1atcIPP/yAgoIC/Pnnn0hOTsaKFSswdepUmJqaYsyYMRr9TunpFa29VFBQoPace/fulfj7pO53QB5wlSVDKq/Xz89PEfBr6tixY9i+fTv69OmD3bt3w9DQUHGssLAQixcvLvFcddehSd8+//xzZGdn49tvv1U88Cq3adMmxMfHl6keTV7D8rzn2qbt++5FQXRV/ruXSBe4Ql4NUrt2bTRr1gwZGRmKoONZBw4cAAB06NBBsU/+FaO6FdouXryIf/75B02aNFHKiOjr678wW3n9+nW1q9DJ23nRV5u6Iv/KU5OMsqenJwBg8+bNSE9PV/wMFH3NefDgQezbt0+prC77U15GRkYVOl+eobt9+7ba47du3QIApRlGunbtCmNjY/z+++948OCBUvnCwkLFXMbyp/hLMn78eLz22muYM2eO2j7J5efnl+FKlBkYGKBjx46YOXMmNm3aBABISEgAoNnvVP369QEAf//9t0r5ixcvljvb5+TkhHr16uG///0v/v333zKVTU1NxdOnT8vVzvMuXrwIoGgIyrOBMQD88ccfKsMbXkSTvsn78M4776gcO3ToUJnbNjU1hbOzM27evPnCoQiavOeaKO13v2XLljAxMcHJkyfVDpsq73137NixCvWVqLphcFzDjB49GkIIhISEKP2le/v2bUVmcPTo0UrlAWD+/PmK4AYo+gt7xowZKCwsxJgxY5TaMDc3x61bt0r9x1Emk2HmzJlKXyVeuXIFX375JQwMDJSmAatM8um5rl+/Xu5z5dngRYsWQQihEhzfu3cPsbGx0NPTU5uh1XZ/KpuzszPq1auH69evY82aNUrH7t69i6VLlwJQ/mBgZmaGESNG4OHDhyoL2KxcuRJXr17Fm2++WeqUd99//z2SkpKwbt06pcC7devW+PXXXxVfQV++fBlnzpxBmzZtXngtf/zxh9pvN+T7nl3JsLy/U05OTqhTpw527NiBrKwsxf68vDzFcI3y0NfXR1BQEPLy8jBhwgSVDwRPnjxR/O4aGBhg8uTJuHHjBqZMmaL2d/TGjRs4c+bMC9uVDxd6/oNzVlYWJk2aVO7r0KRvJfVhz549Kvfgi8hf+/Hjx6sM8yksLFRkY4Hyv+eaKO13v1atWnjvvfeQm5uLiIgIpWOXLl3Cl19+CUNDQ4wYMUKxv1OnTtDT08PGjRvx6NEjxf6cnByEhoZWqK9E1c5LfBiQtAjPLQ5RksePH4vXX39dABBt2rQRISEhYtKkScLKykplyiy50NBQAUBYWVmJoKAgERISoniy+/XXXxePHz9WKi+fJu2NN94Q4eHhYt68eeLnn39W6mvbtm2Fvb29cHV1FaGhoWL8+PGiXr16AoBYvHixUn0verpfiBcvnKAO1DydLZ9WqVmzZmLmzJli3rx5ZZ7tQSaTiQYNGggAwszMTOmJ+7///lvxHnXq1KnM/Tlz5ozQ09MT1tbWYvr06WLevHli3rx5iuOlza5RltdN2+Li4oREIhEAhKenp5gxY4YYM2aMsLS0FABE165dVaYau337tmjRooUAIHr37i3CwsLEgAEDFPecuqm65DIzM0WDBg3EjBkzVI7t2LFDcZ9PmzZNMeXbf//73xdex9SpU4WBgYHw9PQU48ePF2FhYSIgIEAYGRkJqVQqDh8+rCirye/UnDlzBABha2srJk2aJMaPHy+aNm0qXn/9dWFra1vue/nx48fC09NTABCNGzcWQUFBYubMmWLYsGHCwsJC6bwnT56It99+WwAQdnZ2YsSIESIsLEyMHj1a9OjRQ+jp6YmFCxe+8DUqKCgQ3bt3FwCEu7u7CAkJEe+//76wtLTU+DrK27c///xT1KpVS0ilUjFs2DAREhIifH19hUQiEYMHDy7X/V9YWCjef/99AUBYWlqKsWPHio8//liMHDlSNGrUSKme8r7nzy4Coo580Y9n5eTkCBMTE1GnTh3x4YcfKn735dPMZWdnKxbd6dKliwgLCxNjx44VderUERKJRMTExKi0M2LECAFANG/eXAQHB4vRo0eLhg0binfffVej2XKIqisGx9VEWYNjIYTIy8sT0dHRok2bNsLIyEiYmZmJ7t27i40bN5Z4zqZNm0T37t2FmZmZkEqlonXr1mL+/PkiLy9PpWxubq6YMGGCsLOzE/r6+ir/KMj/Es7IyBDvvfeesLS0FFKpVLRv3/6FK+SVRFvBsRBFU605OTmJWrVqKaZfKit/f38BQGm1Njl5AKguWCqtP99//71o166dMDIyUnmfq1pwLIQQhw4dEn5+fqJhw4bCwMBAmJqaig4dOoiFCxeqvV+EKPqHfsqUKaJx48bC0NBQNGzYUIwaNUr8/fffpbb1zjvviObNmyvNO/ysb775RjRt2lQYGhqK1q1bK6bae5HU1FQxYcIExcpv8tXmAgMDxalTp1TKl/d3qrCwUCxcuFDRt0aNGomQkBDx8OFDje5lIYrmtl6xYoVwc3MTpqamwsTERDg6Oopx48aJv/76S6X97777TrESnKGhobC1tRXdu3cX0dHRKis6liQ7O1tMnDhR2NvbC6lUKpo2bSo+/vjjCl1Hefv2+++/Cw8PD1GvXj3F6759+3aN7//169eLN954Q9SpU0dIpVLh4OAghg0bJv7zn/8olSvPe65JcCyEEImJiaJr167C1NRU8bv/7O/6nTt3RGhoqHB0dBS1atUSdevWFX369BF79uxR205+fr6YMWOGsLOzE4aGhqJZs2ZiwYIF4unTpwyOiZ4hEaKUub+IdEAikaBnz55qxzETERERvUwcc0xEREREVIzBMRERERFRMQbHRERERETFGBxrSW5uLoKDg2FrawsjIyO4urpi8+bNL7tbVZIQguONiYiIqEriCnla4u/vj7S0NCxatAgtWrTAxo0bMXToUBQWFmLYsGEvu3tEREREVAacrUILdu/ejX79+ikCYjlvb2+cPn0a169fV6x2RERERERVF4dVaMH27dthZmaGgIAApf2jRo3Cv//+i6NHj76knhERERFReXBYhRakp6ejVatWMDBQfjnbtm2rON6tWze152ZlZSktywwA9+/fx4ULF+Di4gKpVKqbThMREenQ48eP8ffff6Nnz56oV69epbT577//4s6dOzqrv379+rC1tdVZ/VQ1MDjWguzsbDRt2lRlf4MGDRTHSxIbG4uoqCid9Y2IiOhlSkhIwIABA3Tezr///gunlo3wILdQZ23Url0b586dY4BczTE41hKJRKLRsaCgIJXhGGfOnMG7776LtnCHCcy01keiMpE8N9pKqPmH5rkyEr2S7/Hnz5FIAPmTDhI9PUgMisbjP/v4g6SWoXI78jH70uL9JkYAAJnp/75ZeVK/FgDgcd2isvdaFO1v3PoGAODrJr8CAPqmFf8jLQReCz3/4n4TkUYeIRf/xRE0atSoUtq7c+cOHuQWYtu3NnBsYqj1+i9eeQr/UTdw584dBsfVHINjLTA3N1ebHc7JyQHwvwyyOlZWVrCyslJ7zARmMJPU1U4nicrq+eAYZQiOS/kA+Ow5QgigsLBoAyDRl0JPv+gfMVH4v3YkBtLieovbKQ6gUatovzAyBgDITIwV5zyuU3RMr35R2byGRfvNmjwBALRpWXS8VqY1AMB68WWY8veLSHeKP+9W9vDApk0M0Kql9oPjQnD+gpqCD+RpgYuLC86ePYuCggKl/adOnQIAODs7v4xuEVUZQggImQyQFSgCYwDK/1+JDK/nwfTE/ZfSNhERVW0MjrXAz88Pubm52Lp1q9L++Ph42NraokuXLi+pZ0QvhxBCaUNhofLwDIkEEsNakLykKQ7r7b714kJE9EoqFIWQ6WArVDfEjKolDqvQAl9fX3h5eWHixIm4f/8+HB0dsWnTJiQlJWH9+vWc45iqHpWhE6UUVXf/ys8XhUplRGFxMCwKiwYWSyRFm5yeHvRq1SoaQywfY1x8rp6x0f/KFRYfk49llhaNJ0aBrKi9u7kAAIPiPwHAILPoa1TTx48BAOY78ov6VPzzm3ntAABNcbKsl05ERDUQg2Mt2bZtG2bPno2IiAjk5OTAyckJmzZtwpAhQ15214gqlzwwBor+VDx9J4GkVi1Iiqc8rKz1h4RMBvH0aaW0RUQvXyGETsYHc8xxzcHgWEvMzMywfPlyLF++/GV3hejlKmEcsaRWrbI9uKdFQiaDePKkUtskIqJXG4NjItINfX1I9PQhCmWQ6OlDole5jziIggIUPnxYqW0S0ctXWPyfLuqlmoHBMRFVmHyIhNJQCYmkaB7j4qBYCKH1zLHiYT89vaLhGwUFEI8eQeTlK2eMDQyA52aTISIiUofBMdGr6rmH4tQeK091Gjw4KtHXhygs/N+YXv3//ZXybCAsCgogZAUQEgkkUmnRsUJ5QF0UxCqNC5YVFge+RQ/goXg1WFEoH8P8zFjmsmBgTFRjFApApoNnGgo55LjGYHBMRBoTQkA8G3gWKk/XVjS/cQEgk8lPAGQyCD09iCdPAYiioNzAQBFMF9X59H/nEBERVSIGx0SkuVKyM6KgQHm2Cvn+52aOEAUFRcMh9PSKpn1jUExEFcDZKqiiGBwTkcYkenqAvn7R6ndA0dhf+dCH57LIEj29/5V7Zr8ieH5+lguJRGWoh2JWuOJ/pFTqI6IarxACMgbHVAEMjomoQiT6+v8ba1xYCKGnX7RMtJy+PiT6BkUBrTxAVgS+EkBSHOTKZEXRr54eJHr6RX8+/4+RfNBfJc2RTERENQ+DY6KqrqwP12n7IbznH/R7tv7iY0L23DFRCAkAIc8I6+lDItEDCgtR+GyWVwiIwuceknsmgyyKs8gMgYmovDisgiqKwTERaZ1iTmMNAnYiIqKXicExERERVRuFQuhoKjdmjmsKpnWIiIiIiIoxc0xERETVRmHxpot6qWZgcExUFZS22p183/Pjd0var1Rt8cIaJSzt9OxUaC8qC6iZNk3R3+enaJNfj/KcxkRERFUdg2MiIiKqNmQ6mudYF3VS1cQxx0RERERExZg5JiIiomqjUAAyHSR5SxxxRtUOM8dERERERMWYOSZ6GZ5/AE/dg3jPK6mMugfz5CvYFZbyoN/z1ah53k5jZbkeIiId4GwVVFHMHBMRERERFWPmmIiIiKqNQkggg0Qn9VLNwMwxEREREVExZo6JXgZdjMktbQERIqIaolDoZmYJzlZRczBzTERERERUjJljIiIiqjY45pgqipljIiIiIqJizBwTERFRtSHTUeZYF3VS1cTMMRERERFRMWaOiYiIqNoohASFgmOOSXMMjomIiKja4AN5VFEcVkFEREREVIyZYyIiIqo2ih7I037ujw/k1RzMHBMRERERFWPmmIiIiKoNIXTzQJ7QQZ1UNTFzTERERERUjJljIiIiqja4CAhVFDPHRERERETFmDkmIiKiaqNQ6EEmtJ/7K9RBnVQ18Z0mIiIiIirGzDERERFVG4WQoFAHuT+ukFdzMHNMRERERFSMmWMiIiKqNgp1NFsFM8c1BzPHRERERETFmDkmIiKiakOmo9kqdFEnVU18p4mIiIiIijFzTERERNVG0WwVHHNMmmPmmIiIiIioGINjIiIiqjaKZqvQ0/pWkcxxbm4ugoODYWtrCyMjI7i6umLz5s1lOjcrKwuBgYGwsLCAiYkJ3N3dkZKSorZscnIy3N3dYWJiAgsLCwQGBiIrK0ul3NOnTxEVFQUHBwdIpVI4OTlhxYoVKuU2bdqEN954A9bW1pBKpbC1tUX//v1x+PBhlbIODg6QSCQq24QJE8p0nVUJh1UQERER6ZC/vz/S0tKwaNEitGjRAhs3bsTQoUNRWFiIYcOGlXje48eP4enpibt372L58uWwsrJCTEwMfHx8kJycjJ49eyrKHjp0CL6+vujXrx927NiBrKwszJw5E56enjh27BikUqmibFBQEL7//nvMmzcPbm5u2LNnD6ZOnYoHDx5g1qxZinLZ2dno3r07pk6dCgsLC9y4cQOff/453njjDaSkpCi1DwDdu3fH0qVLlfZZW1tX9OWrdDUqON6/fz/Wr1+Pw4cP4++//0a9evXQqVMnREREoGPHjkplAwMDER8fr1JHy5Ytce7cOZX9K1asQExMDK5cuQJbW1sEBgZi1qxZMDQ01Nn1EBERkbJCHc1WUahhnbt378a+ffsUATEAeHh44Nq1awgJCcHgwYOhr6+v9ty1a9ciPT0dhw8fhru7u+Lcdu3aITQ0FEePHlWUDQkJQYsWLbBlyxYYGBSFd02aNEH37t2xbt06TJw4EQBw+vRprF27FtHR0QgJCQEA9OrVC9nZ2Zg/fz4mTJiABg0aAAA+/PBDlT75+vrC0tISa9euVQmO69Wrh65du2r0OlUlNWpYxVdffYWrV69i6tSp2L17N5YvX46srCx07doV+/fvVylvbGyMI0eOKG0//PCDSrno6GhMnToV/v7+2LNnD4KCgrBgwQJMmjSpMi6LiIiIqqjt27fDzMwMAQEBSvtHjRqFf//9VynAVXduy5YtFYExABgYGGD48OH4448/kJGRAQDIyMhAWloaRowYoQiMAaBbt25o0aIFtm/frtiXkJAAIQRGjRql0p+8vDwkJSWVej21a9eGkZGRUjvVTfW9MjViYmJgZWWltM/HxweOjo5YsGABevfurXRMT0/vhZ+A5J+0xo0bhwULFgAo+gT29OlThIeHIzg4GK1bt9buhRAREZFahdBDoQ5yf/I6L168qHLM0tJSJb6QS09PR6tWrVSCybZt2yqOd+vWrcRze/ToobJffu7p06dhZ2eH9PR0pf3Pl/3999+V6rS0tETDhg1L7M/zZDIZCgsLkZGRgYULF0IIoTYB+Ouvv6J27drIz89H8+bNMWbMGAQHB5eYGa+qalRwrO7GNTMzQ+vWrfH3339rVGdSUhLy8/PVfgKbPXs2EhISGBwTERFVEpkAZEL7067JRNGfAwcOVDkWGRmJuXPnqj0vOzsbTZs2VdkvH7qQnZ1dYpvZ2dmKcqWdK/+zpLLPtlFSnaampqhVq5ba/rRp0wbnz58HANjY2CApKUllOGq/fv3QqVMnNGvWDHfu3MFPP/2EGTNm4OTJk/j+++9LvMaqqEYFx+rcu3cPx48fV8kaA0BeXh4aNmyIW7duwcbGBgMHDsQnn3yidFPJP2G5uLgonWtjYwMLCwu1n8CelZWVhVu3bintU/eplIiIiF6+hIQEODo6Ku2ztLQs9RyJpORgvbRj5T23pLJlLVfSsa1bt+Lhw4e4fv06vv76a/j6+uLnn39Gr169FGViYmKUzhkwYADq16+PlStXYvr06Wjfvn2JbVY1NT44njRpEh4+fIjZs2cr7W/Xrh3atWsHZ2dnAEVPgX7xxRdISUlBWloazMzMABR9ApNKpTA1NVWp+/lPa+rExsYiKipKS1dDRERUsxUWT72mi3oBwNHREW3atCnzeebm5mpjgZycHADqs73lPdfc3ByA+ix0Tk6OUhvm5uY4efKkSrmHDx/iyZMnavsjv97OnTtj4MCBaN++PaZOnYo///yzxL4DwPDhw7Fy5Uqkpqa+UsHxK/tA3sGDB9XOp6duU3cTAMCcOXOwYcMGfPHFFypfD0ybNg3Tpk2Dl5cXvLy8MH/+fHz33Xc4d+4cVq9erVS2Ip8Ig4KCkJ6errQlJCSU6TUgIiKiqs3FxQVnz55FQUGB0v5Tp04BgCIJV9K58nKlnSv/s6Syz7bh4uKCW7duITMzs9z9AYoeCOzQoQMuXLhQajkAEKJoLIqe3qsVbr6ymeOWLVuqBKklady4scq+qKgozJ8/H9HR0WqnKlHHz88PpqamSE1NVewzNzdHfn4+Hj16BBMTE6XyOTk5KkH386ysrEocxE9ERETlUwg9jadde1G9mvDz88Pq1auxdetWDB48WLE/Pj4etra26NKlS6nnBgUF4ejRo4pyBQUFWL9+Pbp06QJbW1sAgJ2dHTp37oz169djxowZigfgUlNTcf78eQQHByvqHDBgAMLDwxEfH4+ZM2cq9sfFxcHY2Bg+Pj6lXk9+fj5SU1NVhpao89133wHAKze92ysbHNvY2GDs2LEanRsVFYW5c+di7ty5SpNdl4UQQukTkHys8alTp5Ru8MzMTNy+ffuFn8CIiIio+vL19YWXlxcmTpyI+/fvw9HREZs2bUJSUhLWr1+vCGTHjBmD+Ph4XLp0Cfb29gCA0aNHIyYmBgEBAVi0aBGsrKwQGxuL8+fPIzk5WamdTz/9FF5eXggICEBQUBCysrIQFhYGZ2dnpUkD2rRpgzFjxiAyMhL6+vpwc3PD3r17sWrVKsyfP19pWEW3bt3w9ttvo1WrVqhbty6uXr2Kr776CpcuXVKaHm7jxo3Ytm0b+vXrB3t7e9y9exc//fQTNm/ejMDAQLRr106XL7HWvbLBsabmzZuHuXPnIjw8HJGRkeU6d8uWLXj06JHSJyAfHx8YGRkhLi5OKTiOi4uDRCJR+1QrERER6YZ8+Whd1Kupbdu2Yfbs2YiIiEBOTg6cnJywadMmDBkyRFFGJpNBJpMphiIAgFQqRUpKCkJDQzF58mQ8evQIrq6uSExMVFmAo1evXti9ezciIiLQv39/mJiY4K233sKSJUuUVscDip53srOzw4oVK5CZmQkHBwcsX74ckydPVirXrVs3bN68GVevXsXDhw9hYWEBd3d3fPHFF0rTzzVt2hR3797FrFmzkJ2dDUNDQ7Rp0waxsbEYP368xq/byyIRz74L1dxnn32GGTNmwMfHR21gLA96r127hmHDhmHIkCFwdHSERCLBoUOHsGzZMjRr1gxHjx5VegAvOjoac+bMwccffwxvb2+kpaUhPDwc77//PlatWlXufp4+fRrOzs7oCi+YSepqfsFEREQvSa64h1TsQ3p6erkeYNOU/N/OT3c747XmJi8+oZz++esRZvZNr7TroZenRmWOf/nlFwBFcxOrWwFG/jmhTp06sLa2xueff46bN29CJpPB3t4eU6ZMwaxZs1Rmppg9ezZq166NmJgYLF26FA0bNkRYWJjKDBhERESkWzIh0dE8x9qvk6qmGhUcHzx4sEzl6tevj23btpWr7ilTpmDKlCka9IqIiIiIqooaFRwTERFR9SZ0tHy0eHVnv6Vy4jtNRERERFSMmWMiIiKqNmRCDzIdzHOsizqpauI7TURERERUjJljIiIiqjYKUbE5iUurl2oGZo6JiIiIiIoxc0xERETVRqGOxhwXcsxxjcF3moiIiIioGDPHREREVG3IoAeZDnJ/uqiTqia+00RERERExZg5JiIiompDCAkKhfZnqxA6qJOqJmaOiYiIiIiKMXNMRERE1YYMEh2NOWbmuKZgcExERETVhhB6Opl2TXAqtxqD7zQRERERUTFmjomIiKjaKBpWof0hEBxWUXMwc0xEREREVIyZYyIiIqo2CiHRyZjjQmaOawxmjomIiIiIijFzTERERNVGoY7GHDNzXHMwc0xEREREVIyZYyIiIqo2CnU0z7Eu6qSqie80EREREVExZo6JiIio2igUepAxc0wVwHeaiIiIiKgYM8dERERUbRRCNzNLFGq9RqqqmDkmIiIiIirGzDERERFVGxxzTBXFd5qIiIiIqBgzx0RERFRtFAoJCoUOxhzroE6qmpg5JiIiIiIqxswxERERVRsy6EGmg9yfLuqkqonvNBERERFRMWaOiYiIqNoQQjfjg4XQepVURTFzTERERERUjJljIiIiqjYKoYdCHeT+dFEnVU0MjomIiKjaKIQEMl1M5aaDJampauLHICIiIiKiYswcExERUbXBRUCoopg5JiIiIiIqxswxERERVRtFmWMdPJDHzHGNwcwxEREREVExZo6JiIio2iiEBDIdzCzB2SpqDmaOiYiIiIiKMXNMRERE1QZnq6CKYuaYiIiIiKgYM8dERERUbRQKPR3NVsF8Yk3Bd5qIiIhIh3JzcxEcHAxbW1sYGRnB1dUVmzdvLtO5WVlZCAwMhIWFBUxMTODu7o6UlBS1ZZOTk+Hu7g4TExNYWFggMDAQWVlZKuWePn2KqKgoODg4QCqVwsnJCStWrFApt2nTJrzxxhuwtraGVCqFra0t+vfvj8OHD6ttf/PmzXB1dYWRkRFsbW0RHByM3NzcMl1nVcLgmIiIiKoNgaKZJbS9iQr0yd/fH/Hx8YiMjERiYiLc3NwwdOhQbNy4sdTzHj9+DE9PT6SkpGD58uXYsWMHrK2t4ePjg0OHDimVPXToEHx9fWFtbY0dO3Zg+fLlSE5OhqenJx4/fqxUNigoCAsXLsSkSZOwZ88e+Pn5YerUqViwYIFSuezsbHTv3h2xsbHYu3cvPv/8c9y8eRNvvPGGSvsbNmzA0KFD4ebmhsTERERGRiIuLg7+/v4VeOVeDokQoiLv9yvl4MGD8PDwUHvsyJEj6Nq1q9K+48ePIzQ0FKmpqTAwMEDv3r2xdOlSNG3aVOX8FStWICYmBleuXIGtrS0CAwMxa9YsGBoalrufp0+fhrOzM7rCC2aSuuU+n4iI6GXLFfeQin1IT09HmzZtdN6e/N9O/81vo36z+lqv/86lO9g25OdyX8/u3bvRr18/bNy4EUOHDlXs9/b2xunTp3H9+nXo6+urPTc2NhaTJk3C4cOH4e7uDgAoKChAu3btYGZmhqNHjyrKdu7cGQ8fPsSff/4JA4OiUbOHDx9WBLcTJ04EUPQ6ubi4IDo6Gh9//LHi/A8++ADr16/HP//8gwYNGpR4Pffu3YOlpSWGDBmC7777DgAgk8nQqFEjuLi4YM+ePYqyGzduxHvvvYfdu3fD19e3zK/Zy1YjM8cLFizAkSNHlDZnZ2elMufOnUOvXr3w5MkT/Pjjj1i3bh0uXLiAHj164NatW0plo6OjMXXqVPj7+2PPnj0ICgrCggULMGnSpMq8LCIiohpPJiQ62zSxfft2mJmZISAgQGn/qFGj8O+//yoFuOrObdmypSIwBgADAwMMHz4cf/zxBzIyMgAAGRkZSEtLw4gRIxSBMQB069YNLVq0wPbt2xX7EhISIITAqFGjVPqTl5eHpKSkUq+ndu3aMDIyUmonNTUVN27cUKkzICAAZmZmSu2/CmrkA3nNmzdXyRI/LyIiAlKpFDt37kSdOnUAAB07dkTz5s2xdOlSfPrppwCKvnKYP38+xo0bp/g6olevXnj69CnCw8MRHByM1q1b6/aCiIiIqFJcvHhRZZ+lpSWsrKzUlk9PT0erVq2UgkkAaNu2reJ4t27dSjy3R48eKvvl554+fRp2dnZIT09X2v982d9//12pTktLSzRs2LDE/jxPJpOhsLAQGRkZWLhwIYQQSgnAkto3NDSEk5OT2jqrshqZOX6RgoIC7Ny5E++8844iMAYAe3t7eHh4KH0CSkpKQn5+vtpPYEIIJCQkVFa3iYiIajwBPcWMFdrcRHHINHDgQDg7OyttsbGxJfYnOztb7TAF+b7s7OwKnyv/s6Syz7ZRUp2mpqaoVauW2v60adMGtWrVQpMmTfDLL78gKSkJHTt2VKqzrO2/Cmpk5njSpEkYMmSI4qnPOXPm4PXXX1ccv3TpEvLy8kr8BLZv3z7k5+fDyMhI8WnIxcVFqZyNjQ0sLCxe+GkpKytLZZiGuk+lRERE9PIlJCTA0dFRaZ+lpWWp50gkJQ/JKO1Yec8tqWxZy5V0bOvWrXj48CGuX7+Or7/+Gr6+vvj555/Rq1cvjdqv6mpUcFy3bl1MnToVvXr1grm5OS5evIglS5agV69e2LVrF958800AL/4EJITAnTt3YGNjg+zsbEilUpiamqot+6JPS7GxsYiKitLC1REREZGuV8hzdHQs1wN55ubmamOBnJwcAOpjjfKea25uDkB9FjonJ0epDXNzc5w8eVKl3MOHD/HkyRO1/ZFfb+fOnTFw4EC0b98eU6dOxZ9//qnSvrW1dantvwpe2WEVBw8ehEQiKdMmvwnat2+PZcuWYeDAgejRowdGjRqFw4cPw8bGBqGhoSptlPWTVUU+EQYFBSE9PV1p41AMIiKi6sHFxQVnz55FQUGB0v5Tp04BgMqEAM+fKy9X2rnyP0sq+2wbLi4uuHXrFjIzM8vdH6DogcAOHTrgwoULSnWqa7+goADnzp17YZ1VzSubOW7ZsiVWr15dprKNGzcu8Vi9evXw1ltv4euvv0ZeXh6MjY1f+AlMIpGgXr16AIo+LeXn5+PRo0cwMTFRKfvsmBx1rKysShzET0REROUjn5dYF/Vqws/PD6tXr8bWrVsxePBgxf74+HjY2tqiS5cupZ4bFBSEo0ePKsoVFBRg/fr16NKlC2xtbQEAdnZ26Ny5M9avX48ZM2YopoZLTU3F+fPnERwcrKhzwIABCA8PR3x8PGbOnKnYHxcXB2NjY/j4+JR6Pfn5+UhNTVUaWtKlSxfY2NggLi5O6Rq3bNmC3NzcV26u41c2OLaxscHYsWO1Upd8qmd5lrdZs2YwNjYu8ROYo6MjjIyMACh/Wnr2Bs/MzMTt27dfuU9LREREpD2+vr7w8vLCxIkTcf/+fTg6OmLTpk1ISkrC+vXrFYHsmDFjEB8fj0uXLsHe3h4AMHr0aMTExCAgIACLFi2ClZUVYmNjcf78eSQnJyu18+mnn8LLywsBAQEICgpCVlYWwsLC4OzsrDRpQJs2bTBmzBhERkZCX18fbm5u2Lt3L1atWoX58+crDYHo1q0b3n77bbRq1Qp169bF1atX8dVXX+HSpUtKkxPo6+tj8eLFGDFiBMaPH4+hQ4fir7/+QmhoKLy8vF4YcFc1r2xwrC137tzBzp07FcsdAkVfGfTv3x/btm3D4sWLUbt2bQDA9evXceDAAUybNk1xvo+PD4yMjBAXF6cUHMfFxUEikWDgwIGVej1EREQ1mRDQyZjjiiyZtm3bNsyePRsRERHIycmBk5MTNm3ahCFDhijKyGQyyGQyPLs2m1QqRUpKCkJDQzF58mQ8evQIrq6uSExMRM+ePZXa6NWrF3bv3o2IiAj0798fJiYmeOutt7BkyRJIpVKlsrGxsbCzs8OKFSuQmZkJBwcHLF++HJMnT1Yq161bN2zevBlXr17Fw4cPYWFhAXd3d3zxxRcq088NHz4c+vr6WLRoEeLi4tCgQQO8//77iI6O1vyFe0lq1Ap5w4YNQ+PGjdGpUydYWFjgr7/+wmeffYZLly4hMTERffr0UZQ9d+4c3Nzc0KFDB4SFhSE/P19xU588eVLpydTo6GjMmTMHH3/8Mby9vZGWlobw8HC8//77WLVqVbn7yRXyiIjoVfeyVsh7c30A6jbV/gNg9y7nYM/wnyrteujlqVGZ47Zt2+KHH37A119/jdzcXDRo0ACvv/46vv/+e7i5uSmVdXJywsGDBzFz5kwMGjRIafno56dsmT17NmrXro2YmBgsXboUDRs2RFhYGGbPnl2Zl0dEREREFVSjguOwsDCEhYWVuXzHjh1VxvSUZMqUKZgyZYqmXSMiIiIt0PVUblT9vbJTuRERERERaVuNyhwTERFR9SZ0NJWb0EGdVDUxc0xEREREVIyZYyIiIqo2OOaYKoqZYyIiIiKiYswcExERUbVRCN1keQu1XiNVVcwcExEREREVY+aYiIiIqg2hozHHgmOOawxmjomIiIiIijFzTERERNUGZ6ugimLmmIiIiIioGDPHREREVG0U6miFPF3USVUTM8dERERERMWYOSYiIqJqg7NVUEUxc0xEREREVIyZYyIiIqo2OFsFVVSFg+MLFy4gIyMDeXl5sLCwQMuWLVG3bl1t9I2IiIiIqFJpFBynpqbi66+/RmJiIm7fvg0AEEJAIpFAT08Prq6uGD58OAIDAxkoExERUaUpFLrJ8hYKrVdJVVS5guOTJ08iODgYv/76K1q1aoVBgwahQ4cOsLKygpGREXJycnD58mWkpqZi9uzZmDNnDmbNmoXp06ejVq1auroGIiIiIiKtKFdw3KVLF7z33nv4/PPP0aFDh1LLPnz4EJs3b8bixYtRUFCA8PDwCnWUiIiI6EUEdDRbBec5rjHKFRynp6ejefPmZSpramqKMWPGIDAwEH///bdGnSMiIiIqDyEkOpl2jVO51RzlmsqtrIHxs/T19eHg4FDu84iIiIiInlVYWIhHjx7ptA3Oc0xERETVhnz5aF1sVPny8/MRFxeHgIAA2NraolatWqhduzZMTEzQqVMnhIaG4s8//9RqmxWayi0hIQEbNmzAtWvXkJ+fr3RMIpFovbNEREREVP3l5eVh8eLFWL58Oe7duwcnJyd4enqqTAKxevVqfPbZZ+jWrRsWL14Md3f3CretcXC8ZMkSzJw5E5aWlnB0dISpqWmFO0NERERUEVw+unpo3rw5TE1NER4ejvfeew/W1tZqywkhcODAAXz77bfw8PDAypUrMXbs2Aq1rXFwHBsbi9GjR+Obb76Bvr5+hTpBRERERCT3ySefYOTIkS+MMSUSCXr37o3evXsjKioK169fr3DbGgfH2dnZGDZsGANjIiIiqjI4W0X1MHr06HKf07RpUzRt2rTCbWv8QF737t1x9uzZCneAiIiIiKiq0Dg4XrZsGWJiYvDzzz/jyZMn2uwTERERkUbky0drf3vZV1Yz5Ofn49SpU2qna/v9998rpQ8aB8eOjo7o06cP/Pz8YGJigjp16ihtdevW1WY/iYiIiKgaO3LkCBo1aoRevXrB0tISixYtUjru6+tbKf3QeMxxaGgoVq5cCVdXV7Rq1Qq1atXSZr+IiIiIyo1jjl9dH330ET777DO8//77OH/+PEaOHIkLFy5gzZo10NPTgxCVk77XODiOi4vDzJkzsXDhQm32h4iIiIhqoDNnzuD9998HALRs2RIHDx7EoEGDMGjQIGzevLnS+qHxsAqZTAYvLy9t9oWIiIioQgR0Md5YAsEV8nSubt26yMjIUPxsZGSEhIQEGBkZwcfHB4WFhZXSD42DY29vb6SmpmqzL0RERERUQ/Xp0wfffvut0j4DAwNs2LABzZo1Q15eXqX0Q+NhFXPmzMHgwYNhamqKfv36oUGDBipl1O0jIiIi0hUhijZd1Eu69dVXX6GgoEBlv0QiwerVqzFnzpxK6YfGwXG7du0AANOnT8f06dPVlpHJZJpWT0REREQ1SK1atUqd4KFx48aV0g+Ng+OIiAhIJBx/Q0RERFVHISQo1MH4YF3USS82e/ZsREdHV2qbGgfHc+fO1WI3iIiIiIiUffHFF69OcExERERU1XCe4+qlsuY2fla5ZqtYvHhxuZ8U/M9//oNdu3aV6xwiIiIiopcxhLdcwfG3336Lpk2bIjw8HOfOnSuxXH5+PrZu3Yp+/fqhW7duuHfvXoU7SkRERPQiQgdzHBfqKBtNVVO5hlWcOnUKMTExWLp0KRYuXAgrKyt06NABVlZWMDIyQk5ODi5duoRTp06hoKAA/fr1w/Hjx9GmTRtd9Z+IiIiISGvKFRwbGBhg6tSp+PDDD7Fjxw7s3r0bR44cweHDh5GXlwcLCws4OTlhzpw5GDZsGJo2baqrfhMRERGp4DzH1cvLGHOs0QN5+vr68Pf3h7+/v7b7Q0RERKQxAd08PMfY+OXo3r17pbep8fLRRERERES6lJycXOltcio3IiIiqjY4lRtVFDPHRERERDqUm5uL4OBg2NrawsjICK6urti8eXOZzs3KykJgYCAsLCxgYmICd3d3pKSkqC2bnJwMd3d3mJiYwMLCAoGBgcjKylIp9/TpU0RFRcHBwQFSqRROTk5YsWKFSrk1a9Zg4MCBcHBwgLGxMRwdHTFx4kTcuHFDpayDgwMkEonKNmHChDJdZ1XCzDERERFVG/Kp13RRr6b8/f2RlpaGRYsWoUWLFti4cSOGDh2KwsJCDBs2rMTzHj9+DE9PT9y9exfLly+HlZUVYmJi4OPjg+TkZPTs2VNR9tChQ/D19UW/fv2wY8cOZGVlYebMmfD09MSxY8cglUoVZYOCgvD9999j3rx5cHNzw549ezB16lQ8ePAAs2bNUpSLjIyEh4cHFixYADs7O5w/fx7z5s3Djh07cOLECVhbWyv1t3v37li6dKnSvufLaCohIQEbNmzAtWvXkJ+fr3RMIpHgzz//1Eo7QA0LjgMDAxEfH1/i8SNHjqBr166llm3ZsqXaOZ5XrFiBmJgYXLlyBba2tggMDMSsWbNgaGiovQsgIiKiV8ru3buxb98+RUAMAB4eHrh27RpCQkIwePBg6Ovrqz137dq1SE9Px+HDh+Hu7q44t127dggNDcXRo0cVZUNCQtCiRQts2bIFBgZF4V2TJk3QvXt3rFu3DhMnTgQAnD59GmvXrkV0dDRCQkIAAL169UJ2djbmz5+PCRMmoEGDBgCAEydOwMrKStFGz5490aFDB7i5uWH16tUIDw9X6m+9evUUcZQ2LVmyBDNnzoSlpSUcHR1hamqq9TaeVaOC4zlz5qhN7/fv3x9SqRRubm5K+42NjbF//36Vfc+Ljo7GnDlzEBYWBm9vb6SlpSE8PBwZGRlYtWqVdi+CiIiISqajqdw0na5i+/btMDMzQ0BAgNL+UaNGYdiwYTh69Ci6detW4rktW7ZUBMZA0bS6w4cPx6xZs5CRkQE7OztkZGQgLS0NCxcuVATGANCtWze0aNEC27dvVwTHCQkJEEJg1KhRKv1ZvXo1kpKSFNnsZwNjuY4dO0JfXx9///23Zi+IBmJjYzF69Gh88803JX6Q0CaNg+OUlBR4enqWePzLL7/ElClTNK1eJ5o1a4ZmzZop7Tt06BBu376N8PBwlRdcT0/vhZ+A5J+0xo0bhwULFgAo+gT29OlThIeHIzg4GK1bt9buhRAREdFLcfHiRZV9lpaWagNJAEhPT0erVq2UglYAaNu2reJ4ScFxeno6evToobJffu7p06dhZ2eH9PR0pf3Pl/3999+V6rS0tETDhg1L7E9pDh06BJlMpnaBt19//RW1a9dGfn4+mjdvjjFjxiA4OLjCAW12djaGDRtWKYExUIEH8t58803MnTtXZXLme/fuwc/PD9OmTatw5yrD2rVrIZFIMHr0aI3OT0pKQn5+vtpPYEIIJCQkaKGXREREVBby2Sp0sQHAwIED4ezsrLTFxsaW2J/s7GzFMIVnyfdlZ2dX+Fz5nyWVfbaNkuo0NTVFrVq1Su3PgwcPEBQUhEaNGqnETf369cPy5cuxa9cubNmyBR06dMCMGTMQGBhYYn1l1b17d5w9e7bC9ZSVxpnj2bNnY/78+fjtt9+wceNGWFtbIy0tDYMHD8adO3fw448/arOfOnHv3j1s2bIFnp6eaNKkicrxvLw8NGzYELdu3YKNjQ0GDhyITz75ROmmkn/CcnFxUTrXxsYGFhYWL/wElpWVhVu3bintU/eplIiIiF6+hIQEODo6Ku2ztLQs9RyJpOSH+Uo7Vt5zSypb1nKlHcvPz4e/vz+uXbuG/fv3w8zMTOl4TEyM0s8DBgxA/fr1sXLlSkyfPh3t27cvsc0XWbZsGfz8/NCoUSP4+PigVq1aGtdVFhoHx1FRUejZsyeGDRsGV1dXvPfee1i5ciVcXFyQkpKiNtisajZt2oS8vDyMGTNG5Vi7du3Qrl07ODs7Ayj6GuGLL75ASkoK0tLSFDdFdnY2pFKp2sHhz39aUyc2NhZRUVFauBoiIiLS9TzHjo6OaocUlMTc3FxtLJCTkwNAfba3vOeam5sDUJ+FzsnJUWrD3NwcJ0+eVCn38OFDPHnyRG1/Hj9+DD8/P/zf//0fdu7ciS5dupTY52cNHz4cK1euRGpqaoWCY0dHR/Tp0wd+fn6QSCQwMTFROi6RSHDv3j2N639ehR7I6927Nw4ePIgOHTrgiy++QOfOnfHbb7+pjKvRhYMHD8LDw6NMZU+cOAFXV1eV/WvXroW5uTn8/PxUjj0/LMTLywvt27fHoEGDsHr1aqXjFflEGBQUpDJI/+LFixg4cGCp5xEREVHV5+Ligk2bNqGgoEApPjp16hQAKJJwJZ0rL/es58+V/3nq1Cn07dtXpeyzbbi4uGDz5s3IzMxUGndcUn8eP36MgQMH4sCBA9ixY0epz5s9Tz70Vk+vYstqhIaGYuXKlXB1dUWrVq2qbuYYAI4fP453330XtWrVgqenJ3bt2oUPPvgAMTExamd10KaWLVti9erVZSrbuHFjlX3//e9/cezYMUydOlVp7r/S+Pn5wdTUFKmpqYp95ubmyM/Px6NHj1Q+yeTk5KBjx46l1mllZVXiIH4iIiIqHwGNJ5Z4Yb2a8PPzw+rVq7F161YMHjxYsT8+Ph62tralZmH9/PwQFBSEo0ePKsoVFBRg/fr16NKlC2xtbQEAdnZ26Ny5M9avX48ZM2YoHlxLTU3F+fPnERwcrKhzwIABCA8PR3x8PGbOnKnYHxcXB2NjY/j4+Cj2yTPG+/fvx7Zt2/Dmm2+W69q/++47AKjw9G5xcXGYOXMmFi5cWKF6ykrj4Dg2NhYfffQRWrdujT179qBZs2ZYs2YNpk6dimPHjuGnn35Cy5YttdlXJTY2Nhg7dqzG569duxYAyl2HEELpE5B8rPGpU6eUbvDMzEzcvn271E+EREREVL35+vrCy8sLEydOxP379+Ho6IhNmzYhKSkJ69evVwSyY8aMQXx8PC5dugR7e3sAwOjRoxETE4OAgAAsWrQIVlZWiI2Nxfnz55GcnKzUzqeffgovLy8EBAQgKCgIWVlZCAsLg7Ozs9KkAW3atMGYMWMQGRkJfX19uLm5Ye/evVi1ahXmz5+vNKxi0KBBSExMxOzZs2Fubq6UHKxTp45iNq6NGzdi27Zt6NevH+zt7XH37l389NNP2Lx5MwIDA9GuXbsKvYYymQxeXl4VqqM8NA6OP/zwQ0yYMAHLli1TpLfHjh2LLl264N1330WnTp3w4MEDrXVUmx4/foz169ejc+fO5Qpet2zZgkePHil9AvLx8YGRkRHi4uKUguO4uDhIJBIOjyAiIqpEAjoacwzN69y2bRtmz56NiIgI5OTkwMnJCZs2bcKQIUMUZWQyGWQymdIsYFKpFCkpKQgNDcXkyZPx6NEjuLq6IjExUWl1PKBoGtndu3cjIiIC/fv3h4mJCd566y0sWbJE5Rvy2NhY2NnZYcWKFcjMzISDgwOWL1+OyZMnK5XbuXMngKL1HKKjo5WO9ezZEwcPHgQANG3aFHfv3sWsWbOQnZ0NQ0NDtGnTBrGxsRg/frzGr5uct7c3UlNT0bt37wrXVRYS8fxcbGX0ww8/KH098KyHDx9i4sSJinR6VfPDDz9gyJAhWLVqFcaNG6dy/Nq1axg2bBiGDBkCR0dHSCQSHDp0CMuWLUOzZs1w9OhRpQfw5IuAfPzxx0qLgLz//vsaLQJy+vRpODs7oyu8YCapW6FrJSIiehlyxT2kYh/S09PL9QCbpuT/djb+4kNIG2l/uOLjv7NwfdrKSrse+p9Tp05h8ODBGD9+PPr161fq9HbaoHHmuKTAGCiaK6+qBsZA0ZAKU1NTpU9sz6pTpw6sra3x+eef4+bNm5DJZLC3t8eUKVMwa9YslZkpZs+ejdq1ayMmJgZLly5Fw4YNERYWhtmzZ1fG5RAREZFcVRt0TBUmH5Yxffp0TJ8+XW0ZmUymtfZq1PLRcnv37i31eP369bFt27Zy1TllypQqtyIgERER0asuIiLihbN/aZNWgmN/f398+OGHlTYWhIiIiEgdIaCjeY61XiWV0dy5cyu1vYpNPFcsISEBV69e1UZVREREREQvjVaCYyIiIqIqQcizx9rdOOa4cjk7O2P79u1lLn/jxg1MmTIFixYtqnDbDI6JiIiIqEp599138f7776Nx48b4+OOPsWfPHty6dUsx1V1eXh7S09OxZs0a9O/fH/b29vjPf/6Dt99+u8Jt18gH8oiIiKh6EkJH8xzroE4qWUREBMaNG4dly5ZhzZo1+PTTTyGRSCCRSGBoaIgnT54AKFqcrUePHti8eTP8/f210jaDYyIiIqo+hKRo00W9VKlsbGzw6aefYv78+Th69CiOHDmCf//9F3l5ebCwsICTkxN69eqF1157TavtMjgmIiIioirL0NAQr7/+Ol5//fVKaY/BMREREVUbArqZdo3P49UcfCCPiIiIiKgYM8dERERUfXD5aKogrWSOe/bsCRsbG21URURERET00mglc3zgwAFtVENERERUIZzKjSqKY46JiIiIqMr69ddfkZubq/ZYbm4ufv31V622x+CYiIiIqg+hw41eCg8PD5w5c0btsfPnz8PDw0Or7TE4JiIiIqIqS5QyN9/Tp0+hp6fdcJazVRAREVG1wTHH1cP9+/dx9+5dxc+ZmZm4fv26Upm8vDzEx8ejYcOGWm2bwTERERERVSlffPEFPvnkEwCARCKBn5+f2nJCCMyaNUurbTM4JiIiouqD8xxXC97e3jAzM4MQAqGhoZg8eTIaN26sVEYqlcLFxQU9e/bUatsVDo7v3buH1NRU3L59G3379kX9+vW10S8iIiIiqqHc3d3h7u4OAHj48CHGjRsHW1vbSmm7QsHxvHnzsGjRIuTl5UEikSAtLQ3169eHp6cnvLy8EBYWpq1+EhEREZWBpHjTRb30MkRGRir+/9atW8jLy1Mp83xWuSI0frwvNjYWUVFRGDNmDHbt2qX0JOFbb72FXbt2aaWDRERERFRzPXjwAGPHjoWpqSkaNmyIJk2aqGzapHHmeOXKlZg+fToWL14MmUymdKx58+b466+/Ktw5IiIionLj+OBqJTg4GBs3bsSYMWPQtm1bSKVSnbancXB8+fJlvPnmm2qP1a5dW2n6DSIiIiIiTezatQuLFi3C1KlTK6U9jYPjunXr4ubNm2qPXb16FVZWVhp3ioiIiEgjnK2i2snPz4eLi0ultafxmGNPT08sXrwYDx8+VOyTSCQoKCjAV199VWJWmYiIiIiorPr27Yvffvut0trTOHP8ySefwM3NDa1bt4afnx8kEglWrlyJEydO4Pr16/jxxx+12U8iIiKiFxMAdLGaHTPHlSonJ0fx/+Hh4Rg0aBBq166N/v37w9zcXKV8gwYNtNa2xsGxo6Mjfv/9d0yfPh2xsbEQQuC7776Dh4cHNmzYoNUpNYiIiIio5rCwsIBE8r8POUIIhISEICQkRG355yeHqIgKzXPcunVrJCUl4fHjx8jOzkb9+vVhbGysrb4RERERlYsQRZsu6qXKExERoRQcVyatLB8tlUorbdUSIiIiohLxgbxqYe7cuS+tbY0fyJs+fTree+89tceGDx9eYtqbiIiIiKiq0jg4/vnnn+Ht7a32mLe3N3bs2KFxp4iIiIg0IiS626hG0Dg4zsjIgIODg9pj9vb2+OeffzStmoiIiIgIAKCnpwd9fX21m4GBASwsLODj44MDBw5opz1NTzQ1NcXff/+t9tj169dhZGSkcaeIiIiINCEBIBE62F72hdVgERERsLe3R4MGDTBy5EiEhoZixIgRaNCgARo3bozhw4fjn3/+gZeXF/bt21fh9jR+IM/d3R2fffYZBg8eDENDQ8X+p0+f4osvvkC3bt0q3DkiIiIiqtkaNGiAhg0b4tSpUzA1NVXsz83NhZeXF+zs7HDy5El4eXkhOjoaXl5eFWpP48xxeHg4zpw5A2dnZyxevBgbNmzAp59+CmdnZ5w5cwYREREV6hgRERFRuQkdbvRSfPnll5gxY4ZSYAwAZmZmmDFjBmJjY2FgYIAJEybg+PHjFW5P48xxly5d8PPPP2PSpEkICwtT7G/WrBl+/vlndO7cucKdIyIiIqKa7Z9//lEapfAsAwMDZGZmAgBsbGzw9OnTCrdXoXmO33zzTVy8eBF//fUXbt26BUtLSzRv3rzCnSIiIiLSiK5mluBsFS9Ny5YtsXz5cvj6+sLA4H+ha0FBAZYvX46WLVsCAG7cuAFLS8sKt6eVRUCaN2/OoJiIiIiItO6TTz7BO++8A0dHRwwcOBDW1ta4efMmEhISkJGRga1btwIA9u3bB3d39wq3V6Hg+MGDB0hMTMS1a9eQl5endEwikWDOnDkV6hwRERFRuXCFvGpnwIAB2LlzJyIiIrBixQoIISCRSNCpUyd88803ePPNNwEAa9as0Up7GgfHR48eRb9+/ZCTk6P2OINjIiIiItIGHx8f+Pj44NGjR7hz5w7q168PExMTnbSl8WwV06ZNg52dHf744w/k5+ejsLBQaZPJZNrsJxEREdGLcbaKas3ExAR2dnY6C4yBCmSOT506hY0bN6JTp07a7A8RERER1XDXr1+HjY0NDA0Ncf369ReWb9y4sdba1jg41sbTgERERERaxTHH1UKTJk1w5MgRdO7cGQ4ODpBISp8tRJsjFjQOjidPnoyvv/4ab7311gs7TERERERUVuvWrUOzZs0U/1+ZsabGwXFhYSHOnTuH9u3bo1+/fjA3N1c6LpFIMG3atAp3kIiIiKjsdDTPMZgIrEwjR45U/H9gYGCltq1xcBwSEqL4///+978qxxkcExEREZG23Lt3D6mpqbh9+zb69u2L+vXr66QdjYPjK1euaLMfRERERBUmEUWbLuqll2fevHlYtGgR8vLyIJFIkJaWhvr168PT0xNeXl4ICwvTWlsaT+Vmb2//wo2IiIiopsvNzUVwcDBsbW1hZGQEV1dXbN68uUznZmVlITAwEBYWFjAxMYG7uztSUlLUlk1OToa7uztMTExgYWGBwMBAZGVlqZR7+vQpoqKi4ODgAKlUCicnJ6xYsUKl3Jo1azBw4EA4ODjA2NgYjo6OmDhxIm7cuKG2/c2bN8PV1RVGRkawtbVFcHAwcnNzy3SdpYmNjUVUVBTGjBmDXbt2QYj/fVJ56623sGvXrgq38SyNg+Oq4sGDBwgNDYW3tzcsLS0hkUgwd+7cEssfP34cffr0gZmZGerVqwd/f39cvnxZbdkVK1bAyckJUqkUTZo0QVRUFJ4+fapSrjw3LhEREelQFZzn2N/fH/Hx8YiMjERiYiLc3NwwdOhQbNy4sdTzHj9+DE9PT6SkpGD58uXYsWMHrK2t4ePjg0OHDimVPXToEHx9fWFtbY0dO3Zg+fLlSE5OhqenJx4/fqxUNigoCAsXLsSkSZOwZ88e+Pn5YerUqViwYIFSucjISJiZmWHBggVISkpCaGgodu7ciY4dO+LmzZtKZTds2IChQ4fCzc0NiYmJiIyMRFxcHPz9/TV/4YqtXLkS06dPx5dffglvb2+lY82bN8dff/1V4TaeVaHlo3/99Vd8+eWXOHv2rNrloy9dulShzpVFdnY2Vq1ahXbt2mHgwIGlLh147tw59OrVC66urvjxxx+Rn5+PiIgI9OjRAydPnlSani46Ohpz5sxBWFgYvL29kZaWhvDwcGRkZGDVqlWKcvIb9+7du1i+fDmsrKwQExMDHx8fJCcno2fPnjq9fiIiIqq6du/ejX379mHjxo0YOnQoAMDDwwPXrl1DSEgIBg8eDH19fbXnrl27Funp6Th8+DDc3d0V57Zr1w6hoaE4evSoomxISAhatGiBLVu2wMCgKLxr0qQJunfvjnXr1mHixIkAgNOnT2Pt2rWIjo5WPD/Wq1cvZGdnY/78+ZgwYQIaNGgAADhx4gSsrKwUbfTs2RMdOnSAm5sbVq9ejfDwcABF06iFhITA29sbq1evVvSzdu3aeO+995CYmAhfX1+NX8PLly8rloh+Xu3atXH37l2N61ZH48zx//3f/8HT0xP37t3D2bNn4eTkBDs7O1y/fh0GBgZ44403tNnPEtnb2+POnTs4dOgQFi5cWGrZiIgISKVS7Ny5E3379oW/vz927dqFW7duYenSpYpy8htk3LhxWLBgAXr16oWQkBBERkZizZo1OHPmjKKs/Mb98ccf8d5778HLywtbtmxBixYtEBoaqrPrJiIioqpv+/btMDMzQ0BAgNL+UaNG4d9//1UKcNWd27JlS0VgDAAGBgYYPnw4/vjjD2RkZAAAMjIykJaWhhEjRigCYwDo1q0bWrRoge3btyv2JSQkQAiBUaNGqfQnLy8PSUlJin3PBsZyHTt2hL6+Pv7++2/FvtTUVNy4cUOlzoCAAJiZmSm1r4m6deuqZKrlrl69qrafFaFxcBwZGYlRo0YpXsT58+fjt99+w/Hjx5Gbm6uVNHpZSCSSMs19V1BQgJ07d+Kdd95BnTp1FPvt7e3h4eGh9MYlJSUhPz9f7Y0jhEBCQoJiX1lvXCIiItI9+QN5utgA4OLFizh9+rTSpm5cr1x6ejpatWqlFLQCQNu2bRXHSztXXk7duadPn1aqo6Syz7aRnp4OS0tLNGzYsNz9AYqGb8hkMrRp00apTnXtGxoawsnJ6YV1voinpycWL16Mhw8fKvZJJBIUFBTgq6++KjGrrCmNh1Wkp6djxowZisBUvjJJ27ZtMWfOHHzyySfo37+/dnqpBZcuXUJeXl6JN86+ffuQn58PIyMjxZvo4uKiVM7GxgYWFhYqN1mPHj3U1gkU3bh2dnYl9isrKwu3bt1S2nfx4sWyXxgRERFVmoEDB6rsi4yMLPF5p+zsbDRt2lRlv3zoQnZ2doltZWdnK8qVdq78z5LKPttGSXWampqiVq1apfbnwYMHCAoKQqNGjTB69GilOktr/+rVqyXWWRaffPIJ3Nzc0Lp1a/j5+UEikWDlypU4ceIErl+/jh9//LFC9T9P4+D40aNHMDMzg56eHqRSKW7fvq045uTkpDT0oCp40RsnhMCdO3dgY2OD7OxsSKVSmJqaqi1blpusLDc98L8nMImIiEgLhI4WASmuMyEhAY6OjkqHnn1mSZ3SvuF+0bff5Tm3pLJlLVfasfz8fPj7++PatWvYv38/zMzMynxuRVe3c3R0xO+//47p06cjNjYWQgh899138PDwwIYNG9C4ceMK1f88jYPjxo0bK8Z/tG7dGrt27VIMtj506JDKinllcfDgQXh4eJSp7IkTJ+Dq6lruNsp6Q2jjZnzRMaDoidHnxyFdvHhR7SdTIiIierkcHR2VhhS8iLm5udpEWU5ODgD1SbvyniuPuUoq+2wb5ubmOHnypEq5hw8f4smTJ2r78/jxY/j5+eH//u//sHPnTnTp0kWln/L2ra2tS21fU61bt0ZSUhIeP36M7Oxs1K9fH8bGxhWuVx2Ng+NevXrh4MGDGDRoEMaNG4egoCCcPXsWUqkUe/fuxUcffVTuOlu2bKl4yvFFyvsp4UU3jkQiQb169RRl8/Pz8ejRI5iYmKiU7dixo1K9mt70QNFgd20PJCciIqqxKjjtWqn1asDFxQWbNm1CQUGB0rjjU6dOAQCcnZ1LPVde7lnPnyv/89SpU+jbt69K2WfbcHFxwebNm5GZmak07rik/jx+/BgDBw7EgQMHsGPHDnh6eqrtp7yO1q1bK/YXFBTg3Llzilk6tEEqlcLW1lZr9amjcXAcFRWlCAAnTJiAR48eYcOGDZBIJAgPD8fs2bPLXaeNjQ3Gjh2raZdK1axZMxgbG5d4kzk6OsLIyAiA8pv87KejzMxM3L59W+UmK8uNS0RERDWPn58fVq9eja1bt2Lw4MGK/fHx8bC1tVXJwj5/blBQEI4ePaooV1BQgPXr16NLly6KINHOzg6dO3fG+vXrMWPGDMXUcKmpqTh//jyCg4MVdQ4YMADh4eGIj4/HzJkzFfvj4uJgbGwMHx8fxT55xnj//v3Ytm1biQ++denSBTY2NoiLi1O6xi1btmhlkgY3Nzd4eXnB09MTr7/+OqRSaYXqexGNg2MLCwtYWFgofp4+fTqmT5+ulU7pgoGBAfr3749t27Zh8eLFqF27NgDg+vXrOHDgAKZNm6Yo6+PjAyMjI8TFxSndtHFxcZBIJEpDHsp64xIREVElqUJLPfv6+sLLywsTJ07E/fv34ejoiE2bNiEpKQnr169XBLJjxoxBfHw8Ll26pFhlePTo0YiJiUFAQAAWLVoEKysrxMbG4vz580hOTlZq59NPP4WXlxcCAgIQFBSErKwshIWFwdnZWWn2rTZt2mDMmDGIjIyEvr4+3NzcsHfvXqxatQrz589X+sZ70KBBSExMxOzZs2Fubo7U1FTFsTp16iiyxPr6+li8eDFGjBiB8ePHY+jQofjrr78QGhoKLy8vpYBbEzY2NoiNjcWiRYtgZGSE7t27o0+fPujTp4/St/naonFw3Lt3b8TGxsLJyUnl2IULFzBhwgTs37+/Qp0rq8TERDx8+BAPHjwAAJw5cwZbtmwBAPTt21cxNCIqKgpubm546623EBYWplgExMLCQmkYSIMGDRAeHo45c+agQYMGikVA5s6di7Fjxyp9ZVCeG5eIiIhqnm3btmH27NmIiIhATk4OnJycsGnTJgwZMkRRRiaTQSaTKS2NLJVKkZKSgtDQUEyePBmPHj2Cq6srEhMTVRYZ69WrF3bv3o2IiAj0798fJiYmeOutt7BkyRKVTGtsbCzs7OywYsUKZGZmwsHBAcuXL8fkyZOVyu3cuRNA0cJo0dHRSsd69uyJgwcPKn4ePnw49PX1sWjRIsTFxaFBgwZ4//33Vc7TxM8//wyZTIajR48iOTkZKSkpiIiIwKxZs1C/fn307t1bqzNWSMSz70I56OnpITU1FZ07d1Y59p///AedO3dWTO+maw4ODrh27ZraY1euXIGDg4NS32bOnIkjR47AwMAAvXv3xtKlS9GsWTOVc7/88kvExMTg6tWraNiwIUaNGoXZs2fD0NBQqdzNmzcVSyrKb9x58+ahT58+Gl3P6dOn4ezsjK7wgpmkrkZ1EBERvUy54h5SsQ/p6enleoBNU/J/O18LCUGt5+bw1YYnmZn4Z8mSSrseKt0ff/yBiIgI7N27FxKJRKsxZ4WWjy7JjRs3VB5k06XyzJ/XsWPHMmd0p0yZgilTprywnLW1NeLj48vcByIiIiIqu8zMTCQnJ2Pfvn1ISUnBjRs30KhRI4waNUrjZGRJyhUc79ixAzt27FD8PG/ePJW5/fLy8nDw4EG0b99eOz0kIiIiKqsqNlsFVZyLiwvOnDmD+vXro1evXggPD4enpyeaN2+uk/bKFRyfOXMGP/30E4Ci+Xv3798PPT3lFailUilcXFywfPly7fWSiIiIiGqk06dPw9jYGIMGDYKPjw969+6NOnXq6Ky9cgXHH3/8MT7++GMARWOODxw4oHbMMREREdFLwcxxtXPs2DEkJycjOTkZw4YNQ0FBATp16gQvLy94eXnB3d1dMeuHNmg85riwsFBrnSAiIiIiUqdDhw7o0KEDQkND8fjxY/zf//0f9u3bh507d2L+/PkwMzPDvXv3tNae3ouLqPfvv//i/Pnzip8LCgqwePFiDBkyBOvWrdNK54iIiIjKQyJ0t9HLl5mZiatXr+LatWv4+++/IYTAw4cPtdqGxpnjDz74APb29oiJiQEAzJ8/H5988gnq1auHn376CbVq1cLw4cO11lEiIiIiqnm2bt2qGFZx+fJlCCHQokULvPvuu/D09ETv3r212p7GmeMTJ07Aw8ND8fPq1asxbdo05OTk4IMPPlAEzURERESVRkh0t9FLERAQgJ9//hnu7u5Yt24d/v77b5w7dw4rV66En58f6tbV7poQGmeOs7Oz0bB4ku2zZ8/ixo0bCAwMBAC88847+OGHH7TSQSIiIiKqudLT05VWJ9Y1jYPjunXrIisrCwDw66+/okGDBnBxcQFQNM3bkydPtNNDIiIiovLg+OBqpTIDY6ACwXHnzp3x6aefwtDQEMuXL4e3t7fi2OXLl2Fra6uVDhIRERERVRaNxxzPmzcPly9fxoABA3Dz5k3Mnj1bcSwhIYHzHxMREVHl09VMFcxG1xgaZ45dXV1x7do1nDt3Do6OjkorlQQFBelsST8iIiIiIl3ROHMMACYmJujQoYPKEn79+vVDixYtKtQxIiIionITOtzopfj111+Rm5ur9lhubi5+/fVXrbZXrszx9evXYWNjA0NDQ1y/fv2F5Rs3bqxxx4iIiIjKS1cLdnARkJfHw8MDR44cUTtk9/z58/Dw8IBMJtNae+UKjps0aaLonIODAySS0uf802ZHiYiIiKjmEaLkTyZPnz6Fnl6FBkKoKFdwvG7dOjRr1kzx/y8KjomIiIgqla6GQDBzXKnu37+Pu3fvKn7OzMxUGbWQl5eH+Ph4xbob2lKu4HjkyJGK/5cv+EFEREREpE1ffPEFPvnkEwBF62f4+fmpLSeEwKxZs7TatsazVRARERFVOcwcVwve3t4wMzODEAKhoaGYPHmyyrNsUqkULi4u6Nmzp1bbLldwLI/gy0IikWDOnDnl7hARERER1Wzu7u5wd3cHADx8+BDjxo2rtAXmyhUcz507V+lniUSiMkj62XHIDI6JiIioMkmgo9kqtF8llVFkZGSltleux/sKCwsV2/nz59GkSRNER0fjypUryMvLw5UrVzBv3jw0adIE586d01WfiYiIiKgGunXrFq5fv66yaZPGY46nTp2K999/Hx9//LFin729PWbNmoWnT59iypQpSExM1EoniYiIiKhmun//PqZPn45NmzYhPz9fbZmXNs/xs3777Td89NFHao91794dS5cu1bhTREREREQAMG3aNGzcuBFjxoxB27ZtIZVKddqexsGxVCrFsWPH4OnpqXLs2LFjqFWrVoU6RkRERFRunK2i2tm1axcWLVqEqVOnVkp7GgfHfn5+iIqKgpmZGYYNG4b69evjzp072LBhAz755BO899572uwnEREREdVA+fn5cHFxqbT2NF5v7/PPP0fXrl0xefJkWFhYQCqVwsLCAlOmTEHnzp3x+eefa7OfRERERC8kEbrb6OXo27cvfvvtt0prT+PMce3atbF//34kJSXh4MGDyM7Ohrm5OTw8PODt7c2lpYmIiIiowsLDwzFo0CDUrl0b/fv3h7m5uUqZBg0aaK29Cq+Q5+PjAx8fH230hYiIiKhiOOa42nF2dgYAhISEICQkRG2ZKjFbBRERERGRrkVERFTqiAQGx0RERFR9MHNc7Ty/QrOuafxAHhERERFRdcPMMREREVUbuppZgrNVVK7r16/DxsYGhoaGZVoeunHjxlprm8ExEREREVUpTZo0wZEjR9C5c2c4ODi8cMwxH8gjIiIiKgmzvK+8devWoVmzZgCAb7/9tlLbLldw3Lt37zKXlUgkSElJKXeHiIiIiKhmGzlyJAAgLy8Ps2bNwtdff43+/ftXStvlCo4LCwuV0trnz59HZmYm7O3t0bBhQ2RmZuLatWuwsbFBy5Yttd5ZIiIiotJwzHH1YmxsjLy8PJiamlZam+UKjg8ePKj4/6SkJIwbNw6///473N3dFfsPHz6MwYMHY8aMGVrrJBEREVGZcCq3asfT0xPJycnlGsFQERqPOQ4PD8fcuXOVAmMA6NatGyIjIzF79mz07du3wh0kIiIioppr1qxZeOedd2BkZAR/f3/Y2NioPKBXJZaPPn36NBo1aqT2WOPGjXHu3DmNO0VERESkEWaOq52OHTsCKFoMJCoqSm2ZKjFbhbW1NbZu3Qpvb2+VYz/99BOsra0r1DEiIiIioldm+eigoCCEhYUhJycHw4YNUzyQt2HDBmzfvh0LFy7UZj+JiIiIXkxHD+Qxc/zyVPby0RoHx6GhoXj06BEWL16Mbdu2AQCEEDAyMsLs2bMRGhqqtU4SEREREVWGCi0CMnfuXEybNg1HjhxBdnY2zM3N0bVrV9SrV09L3SMiIiIqB445rpZkMhl+/PFHHDhwQBFzenh4ICAgAAYG2l3TrsK11a1bFz4+PtroCxERERGRktu3b8PHxwfHjx+HgYEBzM3NkZ2djTVr1mDp0qXYs2cPLCwstNZehYJjIQTS0tJw7do15OXlqRx///33K1I9ERERUfkxy1utTJs2DefPn8eGDRvw7rvvQl9fHzKZDD/88AMmTJiAadOm4fvvv9daexoHxxcuXMDbb7+Nv/76C0Ko3oUSiYTBMRERERFVyC+//IL58+dj6NChin36+voYNmwYsrKytP7AnsbB8aRJk5Cfn48ffvgBbdu2hVQq1Wa/iIiIiMqNy0dXP0IItGnTRu0xZ2dntUnaitA4OP7jjz+wevVqDBo0SJv9ISIiIiJS6NOnD5KTk9GnTx+VY/v27UOvXr202p6epieamZmhTp062uwLERERUcUIHW4ays3NRXBwMGxtbWFkZARXV1ds3ry5TOdmZWUhMDAQFhYWMDExgbu7O1JSUtSWTU5Ohru7O0xMTGBhYYHAwEBkZWWplHv69CmioqLg4OAAqVQKJycnrFixQqXc6dOnERQUBHd3d5iamkIikeDgwYNq23ZwcIBEIlHZJkyYUKbrLM2cOXPw448/IiQkBCdOnMCNGzdw4sQJzJgxAz/99BOioqKQk5Oj2CpK4+B41KhR2LhxY4U7UFEPHjxAaGgovL29YWlpCYlEonbsiUwmw+effw4fHx+89tprMDExQatWrRAWFoa7d++qlFf3BkskEixatEilbHluXCIiIqpZ/P39ER8fj8jISCQmJsLNzQ1Dhw59YRz1+PFjeHp6IiUlBcuXL8eOHTtgbW0NHx8fHDp0SKnsoUOH4OvrC2tra+zYsQPLly9HcnIyPD098fjxY6WyQUFBWLhwISZNmoQ9e/bAz88PU6dOxYIFC5TKHTt2DAkJCWjQoAE8PT1feJ3du3fHkSNHlLaZM2eW8VUqWYcOHXD16lV89tln6NSpE1577TV06tQJn3/+Oa5evYqOHTvC0tJSsVWUxsMqnJ2dsWnTJrz99tvo378/zM3NVcr4+/tXqHNlkZ2djVWrVqFdu3YYOHAg1qxZo7ZcXl4e5s6di6FDh2Ls2LGwsLDA8ePHMX/+fPzyyy84duwYjI2Nlc4ZNGgQPvroI6V9jRs3VvpZfuPevXsXy5cvh5WVFWJiYuDj44Pk5GT07NlTuxdMREREJapqY453796Nffv2YePGjYoHyjw8PHDt2jWEhIRg8ODB0NfXV3vu2rVrkZ6ejsOHD8Pd3V1xbrt27RAaGoqjR48qyoaEhKBFixbYsmWLYt7fJk2aoHv37li3bh0mTpwIoCgbvHbtWkRHRyMkJAQA0KtXL2RnZ2P+/PmYMGECGjRoAAAYMWIERo4cCQDYsmULfvnll1KvtV69eujatatmL1QpXpnlo4cNGwYAuHLlCnbu3KlyXCKRQCaTad6zMrK3t8edO3cgkUhw+/btEoNjY2NjXLlyRSmI79WrFxo3boyAgABs3boVw4cPVzrH2tr6hW9yeW5cIiIiqlm2b98OMzMzBAQEKO0fNWoUhg0bhqNHj6Jbt24lntuyZUtFfAEABgYGGD58OGbNmoWMjAzY2dkhIyMDaWlpWLhwodKCGN26dUOLFi2wfft2RXCckJAAIQRGjRql0p/Vq1cjKSlJEePp6Wk8wECrXpnlow8cOKDNfmisrJ8k9PX11Wa3O3fuDAD4+++/NWq/rDcuERERVQIdr5B38eJFlUOWlpawsrJSe1p6ejpatWqlsopb27ZtFcdLCo7T09PRo0cPlf3yc0+fPg07Ozukp6cr7X++7O+//65Up6WlJRo2bFhifzT166+/onbt2sjPz0fz5s0xZswYBAcHl5gZL0+9HTp0gJmZmcqx3NxcHD9+HG+88UaF2niWxsFxdRkusH//fgBQO0XIxo0bsXbtWhQWFsLZ2Rkffvihyietst64JcnKysKtW7eU9qn7xSMiIqKXb+DAgSr7IiMjS8xuZmdno2nTpir75UMXsrOzS2wrOztbUa60c+V/llT22TZKqtPU1BS1atUqtT+l6devHzp16oRmzZrhzp07+OmnnzBjxgycPHmywgt0eHh44MiRI4qE5rPOnz8PDw8PrY5W0O5i1K+YjIwMhIWFoVOnTnjrrbeUjg0bNgz9+vVDo0aNkJWVhbVr12L06NG4fPky5s2bpyhX1hu3JLGxsYiKitLC1RAREZGuM8cJCQlwdHRUOvSih8BK+5b7Rd+Al+fcksqWtVxZ+lOSmJgYpZ8HDBiA+vXrY+XKlZg+fTrat2+vUb0ASp3H+OnTp1of/lGu4Hj06NGYM2cOmjRpgtGjR5daViKRYO3ateXqzMGDB+Hh4VGmsidOnICrq2u56n9WTk4O+vbtCyEEfvjhB5UXdsOGDUo/v/POO+jfvz8WLVqEKVOmKP0iVOQmCwoKUhmHdPHiRbWfTImIiOjlcnR0LHFBCnXMzc3VJsrkU46pS7CV91z5sNGSyj7bhrm5OU6ePKlS7uHDh3jy5Emp/Smv4cOHY+XKlUhNTS13cHz//n2l2cQyMzNx/fp1pTJ5eXmIj49XGSJSUeUKjg8cOICpU6cCKBqOoO1PHi1btsTq1avLVPb5WSPK486dO/Dy8kJGRgb279+v9usOdYYPH46dO3fi2LFj8PX1BVCxmx4ArKysShynREREROUjKd50Ua8mXFxcsGnTJhQUFCiNOz516hSAotm/SjtXXu5Zz58r//PUqVPo27evStln23BxccHmzZuRmZmpFFSWpT/lJc/4apLZ/eKLL/DJJ58AKIop/fz8Smxj1qxZmndSjXIFx1euXFH8/9WrV7XaEQCwsbHB2LFjtV7vs+7cuYM+ffrgypUrSElJUTt4vSTq3uSy3rhERERU8/j5+WH16tXYunUrBg8erNgfHx8PW1tbdOnSpdRzg4KCcPToUUW5goICrF+/Hl26dIGtrS0AwM7ODp07d8b69esxY8YMxQNwqampOH/+PIKDgxV1DhgwAOHh4YiPj1eagzguLg7Gxsbw8fHR2rV/9913AKDR9G7e3t4wMzODEAKhoaGYPHmySmJUKpXCxcVF68/B1agxx/LA+PLly9i3b1+5U/zff/89DA0N0bFjR8W+st64REREVEl0MeZYQ76+vvDy8sLEiRNx//59ODo6YtOmTUhKSsL69esVgeyYMWMQHx+PS5cuwd7eHkDRcNaYmBgEBARg0aJFsLKyQmxsLM6fP4/k5GSldj799FN4eXkhICAAQUFByMrKQlhYGJydnZUmE2jTpg3GjBmDyMhI6Ovrw83NDXv37sWqVaswf/58pW+8Hz16hN27dwMoCrSBosVGbt++DVNTU8W36Bs3bsS2bdvQr18/2Nvb4+7du/jpp5+wefNmBAYGol27duV+3dzd3RUzgT18+BDjxo2rtJhK4+A4ICAAXl5e8PT0RLNmzbTZp3JLTEzEw4cP8eDBAwDAmTNnsGXLFgBA3759YWJigry8PLz55ps4ceIEli1bhoKCAsUbDRQNppdfx5IlS3DmzBl4enritddeUzyQt3fvXsydOxcWFhaK88pz4xIREZFuVbVFQABg27ZtmD17NiIiIpCTkwMnJyds2rQJQ4YMUZSRyWSQyWRKD59JpVKkpKQoMqePHj2Cq6srEhMTVbKlvXr1wu7duxEREYH+/fvDxMQEb731FpYsWQKpVKpUNjY2FnZ2dlixYgUyMzPh4OCA5cuXY/LkyUrlsrKyVJ6Lks/KYW9vrxhF0LRpU9y9exezZs1CdnY2DA0N0aZNG8TGxmL8+PGav3DFIiMjFf9/4cIFZGdnw8LCAs2bN69w3epIRGmPAJaibdu2OH36NICi8b99+vRBnz594OnpqRQ8VgYHBwdcu3ZN7bErV67AwcEBV69eRZMmTUqsY+TIkYiLiwMA/PLLL1i8eDHOnz+PO3fuwNjYGK6urggKClK6keVu3ryJ0NBQ7Ny5U3Hjzps3D3369NHoek6fPg1nZ2d0hRfMJHU1qoOIiOhlyhX3kIp9SE9PL9cDbJqS/9vZYngojMy1+4AWAORnZ+LC+sWVdj2kTD413D///KPY99prr+Gzzz7DoEGDtNqWxpnj//73v7h16xb27duH5ORk7Nu3D2vXroWenh5cXFzg5eWFxYsXa7OvJSrL+GcHB4dSpwJ5Vv/+/dG/f/8yt29tbY34+PgylyciIiId0fFUblT5du/ejSFDhqBNmzb48MMPYWtri4yMDKxfvx5DhgzBL7/8ohjioQ0aZ47VOXbsGMLDw7F3795KWz66OmLmmIiIXnUvLXP8ng4zxxuYOX4Zunfvjjp16mDXrl1KkyIIIeDr64sHDx4orQJYURV6IE8mk+HIkSOK7HFaWhr09fXh4eGh8ZACIiIiIo0xc1ztnDx5Eps3b1aZEk4ikSAoKAjDhg3TansaB8f9+/fHoUOHkJeXh3bt2qFPnz6YO3cuevToASMjI232kYiIiIhqKH19fTx58kTtsZe+Qt6zdu3aBRMTE3z00UcYMmRIhVarIyIiItKGqjhbBVWMm5sbFi9ejL59+8LY2Fix//Hjx1i6dGmpc0VrQuPgeMuWLUhOTsa2bduwZMkSWFhYwNPTE15eXujTpw8aNWqkzX4SERERUQ0UFRUFT09PNG3aFAEBAWjYsCFu3LiBbdu2ITs7G/v379dqexoHx/7+/vD39wcAXLt2TTFjRVhYGG7fvo0WLVrg7NmzWusoERER0QtxzHG18/rrr2Pv3r0ICwtDTEwMhBDQ09NDly5dsGnTJnTr1k2r7WllhbyGDRvCwcEBTZo0wV9//YVbt27hwoUL2qiaiIiIiGq4nj174siRI3j06BHu3LmD+vXrw8TERCdtaRwc/+c//0FycjKSk5Px+++/4/HjxzA1NcUbb7yB4cOHc7YKIiIieik4Prj6+ueff5CdnY1Hjx7pbIU8jYNjNzc3GBoaokuXLpg5cyb69OmDLl26wMBAK8loIiIiIiIAr8gKeTt37kTPnj1hamqqzf4QERERaY5jjqudyl4hT+PguG/fvlrrBBERERGROtHR0fD29lZZIS8kJAS+vr6YP3++VoPjCs2afPXqVYwfPx4tWrSAubk5WrRogfHjx+PKlSva6h8RERFRmcnnOdbFRi/HyZMnERQUVOIKeX/++adW29M4OD558iTat2+PuLg42NnZwdvbG3Z2doiLi0P79u1x8uRJLXaTiIiIiGqiyl4hT+PagoODYWlpib/++gsHDhzApk2bcODAAVy4cAFWVlaYNm2aNvtJRERE9GJChxu9FPIV8vLy8pT2V7kV8v744w+sXbsWjRs3Vtpvb2+PuXPnYuzYsRXuHBERERHVbK/MCnl169ZF3bp11R6rV68e6tSpo3GniIiIiDTC2SqqnVdmhbxhw4ZhzZo1ametWL16NYYOHVqhjhERERERAa/ICnkdOnTAli1b0LlzZwwdOhQNGzZEZmYmNm3ahKysLAQEBGDbtm2K8v7+/lrpMBEREVFJdDWzBGerqFyhoaGYMmUKXnvtNcW+wsJCmJiY6CwoltM4OB4xYgQA4O+//8axY8fUHhei6E6SSCSQyWSaNkVERERENYh85Tt5cCyTyVCrVi2kpaWhQ4cOOm1b4+D4wIED2uwHERERkXYwy/vKkydYX7RPFzQOjnv27KnNfhARERFVmEQISHQQROmiTqqatDtrMhERERHRK6xcmePevXuXeMzQ0BDW1tbw8vLC0KFDYWCgcVKaiIiISDOcyq3aOH/+vCKelD+7du7cObVltTkOuVwRbFZWFiQSidpjBQUFOHr0KNavX4+YmBjs379f508TEhEREVH1FBgYqLJPPiGEnBBC6xM/lCs4Tk9Pf2GZxMREDB48GNHR0YiOjta4Y0RERETlxancqodvv/32pbWt9bEPvr6+mDlzJr7//nsGx0RERERUbiNHjnxpbevkgbzOnTvj2rVruqiaiIiIqGRChxvVCDoJjvPy8vhAHhERERG9cnQSwe7evRutW7fWRdVEREREJeKYY6qocgXHOTk5JR6TyWTIyMjADz/8gDVr1iA2NrbCnSMiIiIiqkzlCo4tLCxKnMrtWR9++CE++OADjTtFREREpDFmeakCyhUcR0RElBgcGxgYwNraGr1790bTpk210jkiIiIiospUruB47ty5OuoGERERkRboaMwxs9E1h05mqyAiIiIiehVxvjUiIiKqPnQ1JzEzxzUGM8dERERERMWYOSYiIqJqg/McU0Uxc0xEREREVIyZYyIiIqo+hCjadFEv1QjMHBMRERERFWPmmIiIiKoNCXQ05lj7VVIVxcwxEREREVExZo6JiIio+uA8x1RBDI6JiIio2pAUFm26qJdqBg6rICIiIiIqxswxERERVR8cVkEVxMwxEREREVExBsdERERUbciXj9bFpqnc3FwEBwfD1tYWRkZGcHV1xebNm8t0blZWFgIDA2FhYQETExO4u7sjJSVFbdnk5GS4u7vDxMQEFhYWCAwMRFZWlkq5p0+fIioqCg4ODpBKpXBycsKKFStUyp0+fRpBQUFwd3eHqakpJBIJDh48WGJfN2/eDFdXVxgZGcHW1hbBwcHIzc0t03VWJa98cPzgwQOEhobC29sblpaWkEgkmDt3rtqygYGBkEgkKpuTk5Pa8itWrICTkxOkUimaNGmCqKgoPH36VKVceW5cIiIiqln8/f0RHx+PyMhIJCYmws3NDUOHDsXGjRtLPe/x48fw9PRESkoKli9fjh07dsDa2ho+Pj44dOiQUtlDhw7B19cX1tbW2LFjB5YvX47k5GR4enri8ePHSmWDgoKwcOFCTJo0CXv27IGfnx+mTp2KBQsWKJU7duwYEhIS0KBBA3h6epba1w0bNmDo0KFwc3NDYmIiIiMjERcXB39//3K8UlXDKz/mODs7G6tWrUK7du0wcOBArFmzptTyxsbG2L9/v8q+50VHR2POnDkICwuDt7c30tLSEB4ejoyMDKxatUpRTn7j3r17F8uXL4eVlRViYmLg4+OD5ORk9OzZUzsXSkRERGWgo+WjNRx0vHv3buzbtw8bN27E0KFDAQAeHh64du0aQkJCMHjwYOjr66s9d+3atUhPT8fhw4fh7u6uOLddu3YIDQ3F0aNHFWVDQkLQokULbNmyBQYGReFdkyZN0L17d6xbtw4TJ04EUJQNXrt2LaKjoxESEgIA6NWrF7KzszF//nxMmDABDRo0AACMGDECI0eOBABs2bIFv/zyi9p+ymQyhISEwNvbG6tXr1b0s3bt2njvvfeQmJgIX19fjV6/l+GVzxzb29vjzp07OHToEBYuXPjC8np6eujatavS1q5dO6Uy8htk3LhxWLBgAXr16oWQkBBERkZizZo1OHPmjKKs/Mb98ccf8d5778HLywtbtmxBixYtEBoaqvXrJSIiolfH9u3bYWZmhoCAAKX9o0aNwr///qsU4Ko7t2XLlorAGAAMDAwwfPhw/PHHH8jIyAAAZGRkIC0tDSNGjFAExgDQrVs3tGjRAtu3b1fsS0hIgBACo0aNUulPXl4ekpKSFPv09MoWJqampuLGjRsqdQYEBMDMzEyp/VfBKx8cy4dGaFNSUhLy8/PV3jhCCCQkJCj2lfXGJSIiIt3T9Zjjixcv4vTp00qbunG9cunp6WjVqpVS0AoAbdu2VRwv7Vx5OXXnnj59WqmOkso+20Z6ejosLS3RsGHDcventH6qa9/Q0BBOTk4a1fkyvfLBcXnl5eWhYcOG0NfXx2uvvYYPP/wQOTk5SmXkb6KLi4vSfhsbG1hYWKjcZGW5cUuSlZWl8kt28eJFja6NiIiIdGvgwIFwdnZW2mJjY0ssn52drRim8Cz5vuzs7AqfK/+zpLLPtlFSnaampqhVq1ap/Smtn2Vt/1Xwyo85Lo927dqhXbt2cHZ2BlA0eP2LL75ASkoK0tLSYGZmBqDoTZZKpTA1NVWpo6w3WVluegCIjY1FVFSUxtdEREREz9DxPMcJCQlwdHRUOmRpaVnqqaV9w/2ib7/Lc25JZctariz9KU1Z26/qqlRwfPDgQXh4eJSp7IkTJ+Dq6lqu+qdNm6b0s5eXF9q3b49BgwZh9erVSse1cTO+6BhQ9MTo8+OQLl68iIEDB5Z6HhEREVU+R0dHtGnTpszlzc3N1SbK5N9aq0uwlfdcc3NzAOoTcjk5OUptmJub4+TJkyrlHj58iCdPnpTan9L6KW/f2tq61PZfBVUqOG7ZsqXiKccXady4sVba9PPzg6mpKVJTUxX7zM3NkZ+fj0ePHsHExESpfE5ODjp27KhUVtObHgCsrKxgZWVVkUsgIiIiuQrOSVxavZpwcXHBpk2bUFBQoDTu+NSpUwCg+Da7pHPl5Z71/LnyP0+dOoW+ffuqlH22DRcXF2zevBmZmZlK447L0p/S+imvo3Xr1or9BQUFOHfunGKWjldFlQqObWxsMHbs2EpvVwih9ETms29yly5dFPszMzNx+/ZtlZusLDcuERER1Tx+fn5YvXo1tm7disGDByv2x8fHw9bWVinOUHduUFAQjh49qihXUFCA9evXo0uXLrC1tQUA2NnZoXPnzli/fj1mzJihmBouNTUV58+fR3BwsKLOAQMGIDw8HPHx8Zg5c6Zif1xcHIyNjeHj41Pua+zSpQtsbGwQFxendI1btmxBbm7uKzfXcZUKjl+GLVu24NGjR+jatatin4+PD4yMjBAXF6d008bFxUEikSgNeSjrjUtERESVQOhonmMN6/T19YWXlxcmTpyI+/fvw9HREZs2bUJSUhLWr1+vCGTHjBmD+Ph4XLp0Cfb29gCA0aNHIyYmBgEBAVi0aBGsrKwQGxuL8+fPIzk5WamdTz/9FF5eXggICEBQUBCysrIQFhYGZ2dnpdm32rRpgzFjxiAyMhL6+vpwc3PD3r17sWrVKsyfP1/pG+9Hjx5h9+7dAKD4hv3QoUO4ffs2TE1NFXMX6+vrY/HixRgxYgTGjx+PoUOH4q+//kJoaCi8vLw0CrhfpmoRHCcmJuLhw4d48OABAODMmTPYsmULAKBv374wMTHBtWvXMGzYMAwZMgSOjo6QSCQ4dOgQli1bhjZt2ihlrBs0aIDw8HDMmTMHDRo0UCwCMnfuXIwdO1bpK4Py3LhERERU82zbtg2zZ89GREQEcnJy4OTkhE2bNmHIkCGKMjKZDDKZDOKZIFwqlSIlJQWhoaGYPHkyHj16BFdXVyQmJqosMtarVy/s3r0bERER6N+/P0xMTPDWW29hyZIlkEqlSmVjY2NhZ2eHFStWIDMzEw4ODli+fDkmT56sVC4rK0vluSj5KsT29va4evWqYv/w4cOhr6+PRYsWIS4uDg0aNMD777+P6Ojoirx0L4VECJ0sI1OpHBwccO3aNbXHrly5AgcHB9y5cwdjxozBiRMncPPmTchkMtjb28PPzw+zZs1C3bp1Vc798ssvERMTg6tXr6Jhw4YYNWoUZs+eDUNDQ6VyN2/eRGhoKHbu3Km4cefNm4c+ffpodD2nT5+Gs7MzusILZhLVfhEREVV1ueIeUrEP6enp5XqATVPyfzs79JoO0zoNX3xCOT28n4njBz+vtOuhl6daZI6f/eRSkvr162Pbtm3lqnfKlCmYMmXKC8tZW1sjPj6+XHUTERERUdVTLYJjIiIiIoVX/jtxeplq3Ap5REREREQlYeaYiIiIqg2JjuY51sncyVQlMXNMRERERFSMmWMiIiKqPgpF0aaLeqlGYHBMRERE1YeAbh7IY2xcY3BYBRERERFRMWaOiYiIqNrgA3lUUcwcExEREREVY+aYiIiIqhfBNC9pjpljIiIiIqJizBwTERFRtcExx1RRzBwTERERERVj5piIiIiqD85zTBXEzDERERERUTFmjomIiKjakAgBiQ5mq9BFnVQ1MXNMRERERFSMmWMiIiKqPgqLN13USzUCM8dERERERMWYOSYiIqJqRDdjjjldRc3BzDERERERUTFmjomIiKj64DzHVEHMHBMRERERFWPmmIiIiKoPIYo2XdRLNQIzx0RERERExZg5JiIiompDIoo2XdRLNQMzx0RERERExZg5JiIiouqDY46pghgcExERUbUhEYBEB0s9c1hFzcFhFURERERExZg5JiIiouqDwyqogpg5JiIiIiIqxswxERERVR9cPpoqiJljIiIiIqJizBwTERFRtVG0CIj207ycraLmYOaYiIiIiKgYM8dERERUfXC2CqogZo6JiIiIiIoxc0xERETVhwCggxXyOFtFzcHMMRERERFRMWaOiYiIqNqQCKGj2SqYOq4pmDkmIiIiIirGzDERERFVH5ytgiqImWMiIiIiomLMHBMREVH1wcwxVRAzx0RERERExZg5JiIiouqjELqZ51gXdVKVxMwxEREREVGxVz44fvDgAUJDQ+Ht7Q1LS0tIJBLMnTtXbVmJRFLi5uTkVKayixYtUqk3KysLgYGBsLCwgImJCdzd3ZGSkqKLyyUiIqJSSCAUcx1rdavAEnm5ubkIDg6Gra0tjIyM4Orqis2bN5fp3PLEGMnJyXB3d4eJiQksLCwQGBiIrKwslXJPnz5FVFQUHBwcIJVK4eTkhBUrVqit8/Lly/D390e9evVgZmYGLy8vHD9+XKWcg4OD2rhpwoQJZbrOquSVH1aRnZ2NVatWoV27dhg4cCDWrFlTYtkjR46o7Dt69CiCg4Ph5+encmzQoEH46KOPlPY1btxY6efHjx/D09MTd+/exfLly2FlZYWYmBj4+PggOTkZPXv21PDKiIiIqDrw9/dHWloaFi1ahBYtWmDjxo0YOnQoCgsLMWzYsBLPK0+McejQIfj6+qJfv37YsWMHsrKyMHPmTHh6euLYsWOQSqWKskFBQfj+++8xb948uLm5Yc+ePZg6dSoePHiAWbNmKcrdunULPXr0QP369bFu3ToYGRlh4cKF6NWrF9LS0tCyZUul/nbv3h1Lly5V2mdtbV3Rl6/SvfLBsb29Pe7cuQOJRILbt2+XGhx37dpVZd8333wDiUSCMWPGqByztrZWe86z1q5di/T0dBw+fBju7u4AAA8PD7Rr1w6hoaE4evRoOa+IiIiINCago9kqNDtt9+7d2LdvnyIgBorihGvXriEkJASDBw+Gvr6+2nPLE2OEhISgRYsW2LJlCwwMisK7Jk2aoHv37li3bh0mTpwIADh9+jTWrl2L6OhohISEAAB69eqF7OxszJ8/HxMmTECDBg0AAEuWLMGtW7dw+PBh2NvbAwBef/11NGvWDBEREfjhhx+U+luvXr0Xxk2vgld+WIU8ba+JBw8e4KeffkLPnj3h6OioUR3bt29Hy5YtFTctABgYGGD48OH4448/kJGRoVG9RP/f3r2HRV3lfwB/T3KZGZA7JKh4AQHlIqZ5oVQQKbyQQtqTpnmvFS2rda3MAkyttFrN1VxdS1dQi03dvIsi/nZVSlMLvC7euxCKN0BQRj+/P4SJrzPAIIzC+H49zzw+c77ne77nM8eZ+czhzBkiIroH5Vu5meN2D9auXQt7e3sMHjxYUT5q1Cj8+uuvVU6imZpj/PLLL9i3bx+GDx+uT4wBICwsDH5+fli7dq2+bN26dRARjBo1yqA/xcXF2LJli+L6vXr10ifGAODg4IC4uDisX78eOp2uho9Gw9Dgk+PaWL16NYqKijB27Fijx1euXAmNRgNbW1t07NgRX375pUGd7OxshISEGJSXlx0+fLjKPuTl5eHw4cOKW05Ozj1EQ0REROaWk5Nj8L5tbF1vuezsbLRt21aRtAJ/5AnZ2dlVnmtKjlHeRmV1K14jOzsb7u7uaNKkSZX9KS4uxsmTJytts7i4GKdOnVKU/9///R8aN24Ma2trtGvXDp988glu3bpVaXz1VYNfVlEbS5cuhZOTE5599lmDY0OHDkW/fv3QvHlz5OXlYenSpRg9ejROnTqF999/X18vPz9f/+eHisrL8vPzq+zDwoULkZSUVMtIiIiICIDZfwRk4MCBBocSEhIq3QwgPz8frVu3Nig3JU8wNcco/7eyuhWvUVmbdnZ2sLGx0de9fPkyRMTkHKdfv37o1KkTfHx8cPnyZaSmpmLy5Mk4dOgQVqxYUWmM9VG9So4zMjIQERFhUt2DBw8iNDT0nq91+PBhfPfdd5gwYQLUarXB8ZSUFMX9Z599FjExMfjwww/x6quvwt3dXX+sqmUd1S35iI+PN/hTS05OjtEnHxERET1Y69atM1iKWTEnMKY2eUJNzq2srqn1alN3wYIFimMDBgyAs7Mz/va3v+GNN95Ahw4dKm2nvqlXybG/vz+WLFliUt27d42oqaVLlwJApUsqjBk2bBg2bNiA/fv3o0+fPgAAV1dXo5/6Ll26BMD4p7iKPDw84OHhYXIfiIiIqApm/hEQX19fBAYGmnxabfIEU891dXUFYHwW+tKlS4pruLq64tChQwb1ioqKcPPmTX1dZ2dnqFSqWuU4w4YNw9/+9jdkZmYyOb5Xnp6eNUpW79XNmzexYsUKdOzYsUazz1L2J5VHHvljqXZwcDCysrIM6paXBQUF1a6zRERE1GAFBwdj1apV0Ol0inXHpuQJpuYY5f9mZWWhb9++BnUrXiM4OBirV69Gbm6uYt3x3W1qNBr4+vpWen2NRmN0uUhFxvKmhqBh9baOfPvtt7h48aLR7duqsmLFClhbW6Njx476stjYWBw7dkzxbVOdTofk5GR06dIFXl5eddZvIiIiqo55fgTkXvdyi42NRWFhIb755htF+fLly+Hl5YUuXbpUea4pOUbTpk3RuXNnJCcnK74Al5mZiePHjyMuLk5fNmDAAKhUKixfvlxxrWXLlkGj0SA6Olpx/fT0dJw/f15fVlBQgDVr1uCZZ54x+JLh3f75z38CML6Vbn1Wr2aO79XmzZtRVFSEgoICAMCRI0fwr3/9CwDQt29faLVaRf2lS5dCo9FUuvH2nDlzcOTIEURGRqJZs2b6L+Rt27YNiYmJcHNz09cdPXo0FixYgMGDB+PDDz+Eh4cHFi5ciOPHj2P79u1mipiIiIgagj59+iAqKgrjx4/HtWvX4Ovri1WrVmHLli1ITk7W73E8ZswYLF++HCdPntRvnVaTHOOjjz5CVFQUBg8ejPj4eOTl5eGtt95CUFCQYtu2wMBAjBkzBgkJCWjUqBEef/xxbNu2DYsXL8aMGTMUSyUmT56MFStWoF+/fpg+fTpsbW3x4YcfoqSkRPEFxJUrV2LNmjXo168fWrRogStXriA1NRWrV6/GyJEj0b59ezM+wnXPIpLj8ePH4+zZs/r7qampSE1NBQCcPn0aLVu21B87f/48tm3bhmHDhsHR0dFoewEBAfj222+xceNGXL58GRqNBqGhoVi1ahWef/55RV1bW1vs2LEDU6ZMwSuvvILr168jNDQUmzdv5q/jERER3W9m3q3iXqxZswbvvPMO3nvvPVy6dAkBAQEGOcWtW7dw69Yt/VIEoGY5Rnh4ODZt2oT33nsPMTEx0Gq16N+/P+bMmaP4dTzgzk5ZTZs2xfz585Gbm4uWLVti3rx5eOWVVxT13N3d8Z///AeTJ0/GiBEjoNPp0K1bN2RkZCAgIEBfr3Xr1rhy5QqmTp2K/Px8WFtbIzAwEAsXLsTLL798z4/bg6ISMcf/IKqNw4cPIygoCF0RBXuV8QSeiIioPiuUq8hEGrKzs2v0BbZ7Vf7e+USrMWhsW/XuEfei4MYF7D699L7FQw+ORcwcExEREQEAbsudmznapYfCQ/mFPCIiIiIiYzhzTERERJZDYKY1x3XfJNVPnDkmIiIiIirDmWMiIiKyHPVwtwpqWDhzTERERERUhjPHREREZEHMNHPMRccPDc4cExERERGV4cwxERERWQ7uc0y1xJljIiIiIqIynDkmIiIiyyG379zM0S49FDhzTERERERUhjPHREREZDn4C3lUS0yOiYiIyHKImb6Qxx8BeWhwWQURERERURnOHBMREZHl4M9HUy1x5piIiIiIqAxnjomIiMhycOaYaokzx0REREREZThzTERERJaDM8dUS5w5JiIiIiIqw5ljIiIishy3Bbhthp96NsfeyVQvceaYiIiIiKgMZ46JiIjIgphpzTF/P/qhwZljIiIiIqIynDkmIiIiy8HdKqiWOHNMRERERFSGM8dERERkOW6LeXaW4G4VDw3OHBMRERERleHMMREREVkOuQ0RM+xzbI42qV7izDERERERURnOHBMREZHlEJhnfTCXHD80OHNMRERERFSGM8dERERkObjPMdUSk2MiIiKyHLdvAyozfHnuNr+Q97DgsgoiIiIiojKcOSYiIiLLwWUVVEucOSYiIiIiKsOZYyIiIrIYIgIxw/pg4czxQ4Mzx0REREREZThzTERERJaDa46pljhzTERERERUhjPHREREZDluC8zyW8/m+Elqqpc4c0xEREREVIYzx0RERGQ55PadmznapYcCZ46JiIiIiMpw5piIiIgshgggZlgfzM0qHh6cOSYiIiIiKsOZYyIiIrIcchsA1xzTvWvwM8fp6ekYPXo0AgICYGdnh6ZNm2LAgAH44YcfjNY/cOAAevfuDXt7ezg5OSEuLg6nTp0yWnf+/PkICAiAra0tWrVqhaSkJJSWlhrUy8vLw8iRI+Hm5gatVotu3bphx44ddRonERERNUyFhYV47bXX4OXlBbVajdDQUKxevdqkc2uSY2zfvh3dunWDVquFm5sbRo4ciby8PIN6paWlSEpKQsuWLWFra4uAgADMnz/faJunTp1CXFwcnJycYG9vj6ioKBw4cMBo3dWrVyM0NBRqtRpeXl547bXXUFhYaFKc9UmDT44///xznDlzBpMmTcKmTZswb9485OXloWvXrkhPT1fUPXbsGMLDw3Hz5k18/fXX+OKLL3DixAl0794dFy5cUNSdOXMmJk2ahLi4OGzduhXx8fGYNWsWJkyYoKh348YNREZGYseOHZg3bx7+/e9/49FHH0V0dDR27dpl9viJiIjoD3JbzHa7V3FxcVi+fDkSEhKwefNmPP744xgyZAhWrlxZ5Xk1yTF27dqFPn364NFHH8W///1vzJs3D9u3b0dkZCRu3LihqBsfH48PPvgAEyZMwNatWxEbG4tJkyZh1qxZinoXLlxA9+7dceLECXzxxRf4+uuvUVJSgvDwcBw/flxRNyUlBUOGDMHjjz+OzZs3IyEhAcuWLUNcXNw9P24PikqkYS8xz8vLg4eHh6KssLAQvr6+CAoKwvbt2/Xlzz33HHbu3ImTJ0/CwcEBAHD27Fm0adMGr7/+Oj766CMAQH5+Ppo1a4YXX3wRf//73/Xnz5o1C9OmTUN2djbatWsHAFi4cCEmTJiAPXv2oFu3bgAAnU6H9u3bw97eHt99912NYzp8+DCCgoLQFVGwVznW+HwiIqIHrVCuIhNpyM7ORmBgoNmvp3/vVD1tlvfOQrmKTNla43g2bdqEfv36YeXKlRgyZIi+/KmnnsLhw4dx7tw5NGrUyOi5NckxOnfujKKiIvz444+wsrqzanbPnj144oknsHDhQowfPx7AnccpODgYM2fOxNtvv60//6WXXkJycjJ+/vlnuLi4AACmTJmCuXPn4n//+x9atGgBALh27Rp8fHzQq1cvfPXVVwCAW7duoXnz5ggODsbWrVv1ba5cuRIvvPACNm3ahD59+pj8mD1oDX7m+O7EGADs7e3Rrl07nD9/Xl+m0+mwYcMGPPvss/rEGABatGiBiIgIrF27Vl+2ZcsWlJSUYNSoUYp2R40aBRHBunXr9GVr166Fv7+//j8tAFhZWWHYsGH4/vvv8csvv9RFmERERGSK8n2OzXG7B2vXroW9vT0GDx6sKB81ahR+/fXXKifRTM0xfvnlF+zbtw/Dhw/XJ8YAEBYWBj8/P0WOs27dOoiI0RynuLgYW7ZsUVy/V69e+sQYABwcHBAXF4f169dDp9MBADIzM/Hbb78ZtDl48GDY29srrt8QWOQX8q5evYoDBw6gV69e+rKTJ0+iuLgYISEhBvVDQkKQlpaGkpISqNVqZGdnAwCCg4MV9Tw9PeHm5qY/DgDZ2dno3r270TaBO5/QmjZtWmlf8/LyDJZ0HDlyBABwHYVm+QVMIiIic7uOO2tN7/6TvvmvW2SW987rKAIA5OTkGBxzd3c3OlkH3MkT2rZtq0hagT/yhOzsbISFhVV6rik5RnleUlmOs3v3bkWb7u7uaNKkSaX9AYDi4mKcPHkSsbGxRtssLi7GqVOn4OfnV+n1ra2tERAQoMibGgKLTI4nTJiAoqIivPPOO/qy/Px8AND/qaAiFxcXiAguX74MT09P5Ofnw9bWFnZ2dkbrlrdV3m5lbVa8bmUWLlyIpKQko8d+wt4qzyUiIqrvsrKy8Nhjj5n9Os7OzmjcuDF+KthjtmvY2Nhg4MCBBuUJCQlITEw0ek5+fj5at25tUG5KnmBqjlFdjmNK3mJnZwcbGxt93cuXL0NE6uT6Z86cqTTG+qheJccZGRmIiIgwqe7BgwcRGhpqUP7uu+8iJSUF8+fPR8eOHQ2Oq1SqStuseMzUejWte7f4+HiDP7UcOnQIw4YNw9dff61f22wJcnJyMHDgQKxbtw6+vr4Pujt1ylJjs9S4AMuNzVLjAiw3NkuN68iRI3juuefg5+d3X67n5eWFY8eO4fLly2a7xq1bt4yuD3Z3d6/yvNrkCXWRj9QmbzHH9eu7epUc+/v7Y8mSJSbV9fb2NihLSkrCjBkzMHPmTEycOFFxzNXVFYDxT2iXLl2CSqWCk5OTvm5JSQmuX78OrVZrULdi0u3q6lppm4DxT1EVeXh4VPqnmHbt2t2XLzHcb76+vhYZF2C5sVlqXIDlxmapcQGWG5ulxlXxez7m5uXlBS8vr/t2PVPUJk8w9dzqcpyK13B1dcWhQ4cM6hUVFeHmzZv6us7OzlCpVDW+/qOPPlrl9RuCepUce3p6YuzYsfd0blJSEhITE5GYmIipU6caHPfx8YFGo0FWVpbBsaysLPj6+kKtVgP4Y61xVlYWunTpoq+Xm5uLixcvIigoSF8WHBxcaZsAFHWJiIjo4RIcHIxVq1ZBp9Mp1h2bkieYmmOU/5uVlYW+ffsa1L07b1m9ejVyc3MV647vblOj0cDX17fS62s0Gv1ykYp5U8W/eOt0Ohw7dkyxS0dD0OB3qwCA999/H4mJiZg2bRoSEhKM1rGyskJMTAzWrFmDgoICffm5c+ewc+dOxT580dHRUKvVWLZsmaKNZcuWQaVSKdYbxcbG4tixY4pvm+p0OiQnJ6NLly717hMsERER3T+xsbEoLCzEN998oyhfvnw5vLy8FJNwxs41Jcdo2rQpOnfujOTkZNy6dUtfNzMzE8ePH1fkOAMGDIBKpcLy5csV11q2bBk0Gg2io6MV109PT1fs/lVQUIA1a9bgmWee0Sf7Xbp0gaenp0He9K9//QuFhYUNb69jaeA+/vhjASDR0dGyd+9eg1tFR48eFXt7e+nRo4ds2rRJ1qxZI0FBQeLl5SV5eXmKujNmzBCVSiVTp06VjIwMmTNnjtja2sq4ceMU9UpKSiQwMFCaN28uKSkpkpaWJrGxsWJlZSUZGRn3FFN2drYAkOzs7Hs6v76y1LhELDc2S41LxHJjs9S4RCw3NsZl+aKiosTZ2VkWL14s6enpMm7cOAEgycnJ+jqjR4+WRo0ayZkzZ/RlNckxdu7cKVZWVhIbGytpaWmSkpIizZs3l6CgICkpKVHUHTt2rNja2sqcOXMkIyNDpk6dKiqVSmbOnKmol5eXJ56enhIcHCxr166VTZs2SY8ePaRx48Zy9OhRRd0VK1YIAHnppZdk586dsnjxYnFycpKoqKi6ehjvmwafHPfs2VNwZ9MWo7e77d+/XyIjI0Wr1YqDg4MMHDhQcnJyjLY9b9488fPzExsbG/H29paEhAS5efOmQb3c3Fx58cUXxcXFRdRqtXTt2lXS0tLuOabff/9dEhIS5Pfff7/nNuojS41LxHJjs9S4RCw3NkuNS8RyY2Nclq+goEBeffVVadKkidjY2EhISIisWrVKUWfEiBECQE6fPq0or0mOsW3bNunatauo1WpxcXGRF1980ejjf/PmTUlISBBvb2+xsbERPz8/+eyzz4y2mZOTIwMHDhQHBwfRarUSGRkpP/zwg9G6K1eulJCQELGxsZEmTZrIq6++KgUFBSY8QvVLg/+FPCIiIiKiumIRa46JiIiIiOoCk2MiIiIiojJMjomIiIiIyjA5JiIiIiIqw+TYzNLT0zF69GgEBATAzs4OTZs2xYABA/DDDz8YrX/gwAH07t0b9vb2cHJyQlxcHE6dOmW07vz58xEQEABbW1u0atUKSUlJKC0tNaiXl5eHkSNHws3NDVqtFt26dcOOHTtqHVtBQQGmTJmCp556Cu7u7lCpVJX+tvzIkSOhUqkMbgEBAfUutprEBTSsMTMmIyPD6NioVCpkZmYa1DdHvPdLYWEhXnvtNXh5eUGtViM0NBSrV69+YP2pSk3GpT6PSU2eTw3tuWRqbA3p9a8m71kNbbyITPagt8uwdIMGDZKIiAhZuHChZGRkSGpqqnTt2lWsrKxkx44dirpHjx6Vxo0bS/fu3WXjxo3yzTffSGBgYJX7ML/99tuyc+dOmT17ttjY2BjdhzkoKEiaNWsmycnJsm3bNhkwYECt9mEud/r0aXF0dJQePXrI2LFjBYAkJCQYrTtixAjRaDQG+1AfOnTIoO6Djq0mcTW0MTNm586dAkBmzZplMD53b8Fjjnjvp6ioKHFycpJFixZJenq6fnxTUlIeWJ8qY+q41PcxMfX51BCfS6bG1pBe/0x9z2qI40VkKibHZmZsf8GCggJ59NFHJTIyUlE+ePBgcXNzk6tXr+rLzpw5I9bW1jJlyhR92cWLF0WtVstLL72kOH/mzJmiUqnk8OHD+rIFCxYIANmzZ4++rLS0VNq1ayedO3euVWy3b9+W27dvi4jIhQsXqk2O7ezsqm2zPsRWk7ga2pgZU56EpaamVlvXHPHeLxs3bhQAsnLlSkV5VFSUeHl5iU6nu+99qoqp41Lfx8TU51NDfC6ZGltDev0z9T2rIY4XkamYHD8gERER4ufnp79fWloqGo1GXn75ZYO6Tz31lLRp00Z/Pzk5WQAY/ALgr7/+KgAUv3DTu3dv8ff3N2hz1qxZAkB+/vnnuginzpLj+hZbVXE19DErZ2oSZq5475exY8eKvb29lJaWKspXrlwpAGT37t33vU9VMWVcGtqYVPZ8soTnUl0kx/U1NhHle5YljBdRVbjm+AG4evUqDhw4gMDAQH3ZyZMnUVxcjJCQEIP6ISEhyMnJQUlJCQAgOzsbABAcHKyo5+npCTc3N/3x8rqVtQkAhw8frn1AJiouLkaTJk3QqFEjNGvWDBMnTsSlS5cUdRpSbJY2ZhMmTICVlRUcHBzw9NNP47///a/iuLnivV+ys7PRtm1bWFlZKcrL43kQfTJFVePS0MeknKU9l4xpyK9/d79nPQzjRQ83q+qrUF2bMGECioqK8M477+jL8vPzAQAuLi4G9V1cXCAiuHz5Mjw9PZGfnw9bW1vY2dkZrVveVnm7lbVZ8brm1r59e7Rv3x5BQUEAgF27duGvf/0rduzYgX379sHe3l7fn4YSm6WMmaOjIyZNmoTw8HC4uroiJycHc+bMQXh4ODZu3Iinn35acd26jvd+yc/PR+vWrY32p/x4fWLKuDT0MSlnKc+lyjT017+737MsfbyImBzXQEZGBiIiIkyqe/DgQYSGhhqUv/vuu0hJScH8+fPRsWNHg+MqlarSNiseM7WeqXXrIraqvP7664r7UVFR6NChAwYNGoQlS5YojtdlbOaOy5Q+1LReTetWdC/xdujQAR06dNCXd+/eHbGxsQgODsaUKVP0yXFN+3avMZhTfexTZWoyLg15TEztw/1+LtWlB/X6Vxeqes+y1PEiYnJcA/7+/liyZIlJdb29vQ3KkpKSMGPGDMycORMTJ05UHHN1dQVg/JPxpUuXoFKp4OTkpK9bUlKC69evQ6vVGtSt+ALm6upaaZvAH5/IaxvbvYiNjYWdnZ1iW6q6js2ccT3oMTOmruJ1cnJC//79sWjRIhQXF0Oj0Zgt3vulNo9rfXH3uDT0MSlXH59L5nY/Xv9qq7L3rIdxvOjhwuS4Bjw9PTF27Nh7OjcpKQmJiYlITEzE1KlTDY77+PhAo9EgKyvL4FhWVhZ8fX2hVqsB/LF2KysrC126dNHXy83NxcWLF/V/uiuvW1mbAPR1axNbbYgIHnnkj6XvdR2bOeN60GNmTF3GKyIA/pitMVe890twcDBWrVoFnU6nWHdsyuNan1Qcl4Y+JuXq43PpfjD3619tVPWe9bCOFz1EHtQ3AR8m06dPFwAybdq0Kus999xz4uHhIdeuXdOXnT17VmxsbOTNN9/Ul+Xn54tarZY//elPivM/+OADg61xFi5cKAAkMzNTX1ZaWiqBgYHSpUuX2oamV91uFcZ89dVXAkDmzp2rL6tvsVUXV0Mes6pcunRJmjZtKqGhoYpyc8R7v2zatEkAyOrVqxXl0dHR9XIrN2OMjUtDGpOqnk8N/blU09fA+vz6Z8p7VkMfL6KqMDk2s48//lgASHR0tMEG8HdvbXP06FGxt7eXHj16yKZNm2TNmjUSFBRU5abqU6dOlYyMDJkzZ47Y2toa3VQ9MDBQmjdvLikpKZKWliaxsbF1tqn6pk2bJDU1Vb744gsBIIMHD5bU1FRJTU2VoqIiEbmz92VYWJh89tlnsmnTJtm8ebO89dZbolarJTAwUAoLC+tdbKbEJdIwx+xuQ4YMkTfffFNSU1Nl586dsnjxYvH39xcrKytJS0tT1DVHvPdTVFSUODs7y+LFiyU9PV3GjRsnACQ5OfmB9akypo5LQxgTU55PDfW5VF1sDe31z9T3rIY6XkSmYHJsZj179hQAld7utn//fomMjBStVisODg4ycOBAycnJMdr2vHnzxM/PT2xsbMTb21sSEhLk5s2bBvVyc3PlxRdfFBcXF1Gr1dK1a1eDpOdetWjRotLYTp8+LSJ3ZrtiY2OlZcuWotFoxMbGRtq0aSNTpkyRK1eu1MvYTImrXEMbs7t98MEHEhoaKo6OjtKoUSNxd3eX2NhY+f77743WN0e890tBQYG8+uqr0qRJE7GxsZGQkBBZtWrVA+tPVWoyLvV9TEx9PjXE51J1sTW017+avGc1xPEiMoVKpGwBGxERERHRQ44/AkJEREREVIbJMRERERFRGSbHRERERERlmBwTEREREZVhckxEREREVIbJMRERERFRGSbHRERERERlmBwTEREREZVhckxEREREVIbJMZEZ/PTTTxg1ahRatWoFtVoNe3t7PPbYY5g9ezYuXbr0oLtndi1btsTIkSPrtM3w8HCEh4ff8/kqlQqJiYn6+8uWLYNKpcKZM2dq1M6sWbOwbt26e+6HpSktLUVAQAA+/PDDOmvzxIkTsLGxwYEDB+qsTSIiU1k96A4QWZolS5YgPj4e/v7++Mtf/oJ27dqhtLQU+/fvx6JFi7B3716sXbv2QXfTrNauXQsHB4cH3Y0q9evXD3v37oWnp2eNzps1axYGDRqEgQMHmqdjDczChQtx+fJlvPLKK3XWpp+fH1544QW8/vrr2LVrV521S0RkCibHRHVo7969GD9+PKKiorBu3TrY2trqj0VFReHPf/4ztmzZUmUbxcXF0Gg05u6qWXXo0OFBd6Fa7u7ucHd3f9DdaNB0Oh3mzJmD0aNHw87Ork7bnjhxIjp16oQ9e/YgLCysTtsmIqoKl1UQ1aFZs2ZBpVJh8eLFisS4nI2NDZ555hn9/ZYtW6J///5Ys2YNOnToALVajaSkJABAdnY2BgwYAGdnZ6jVaoSGhmL58uWK9m7fvo0ZM2bA398fGo0GTk5OCAkJwbx58/R1Lly4gJdeegnNmzeHra0t3N3d8cQTT2D79u1VxrJx40aoVCrs27dPX/bNN99ApVKhX79+irohISF49tlnFXFVXFaRkZEBlUqFVatW4Z133oGXlxccHBzQu3dvHD9+XNGWiGD27Nlo0aIF1Go1HnvsMWzevLnKvlZ07do1jBs3Dq6urrC3t0d0dDROnDhhUM/YsoqDBw+if//+8PDwgK2tLby8vNCvXz/8/PPPAO4szSgqKsLy5cuhUqmgUqn0Sz0uXLiA+Ph4tGvXDvb29vDw8ECvXr3wn//8R3HdM2fOQKVS4eOPP8ann36KVq1awd7eHt26dUNmZqZBP7/77jvExMTA1dUVarUaPj4+eO211xR1/ve//2Ho0KH6frdt2xYLFiyo9rGKjIxEQEAARERRLiLw9fU1GOe7ffvtt/jll18wfPhwRXliYiJUKhV++uknDB48GI6OjnBxccEbb7wBnU6H48ePIzo6Go0bN0bLli0xe/Zsg7Y7duyItm3bYtGiRdXGQURUlzhzTFRHbt26hfT0dHTs2BHNmzc3+bwDBw7g6NGjmDZtGlq1agU7OzscP34cYWFh8PDwwGeffQZXV1ckJydj5MiR+P333zFlyhQAwOzZs5GYmIhp06ahR48eKC0txbFjx3DlyhV9+8OHD8eBAwcwc+ZM+Pn54cqVKzhw4ADy8/Or7FfPnj1hbW2N7du34/HHHwcAbN++HRqNBrt27UJpaSmsra2Rl5eH7OxsjB8/vtpYp06diieeeAL/+Mc/cO3aNbz55puIiYnB0aNH0ahRIwBAUlISkpKSMGbMGAwaNAjnz5/HuHHjcOvWLfj7+1fZvohg4MCB2LNnD9577z08/vjj2L17N/r06VNt34qKihAVFYVWrVphwYIFePTRR5Gbm4udO3eioKAAwJ2/DPTq1QsRERF49913AUC/fKR8LXlCQgKaNGmCwsJCrF27FuHh4dixY4fBeukFCxYgICAAc+fOBQC8++676Nu3L06fPg1HR0cAwNatWxETE4O2bdvi008/hbe3N86cOYNt27bp2zly5AjCwsLg7e2NTz75BE2aNMHWrVvx6quv4uLFi0hISKg05kmTJmHAgAHYsWMHevfurS/fvHkzTp48ic8++6zKx2zjxo3w8PBAu3btjB5/7rnnMGzYMLz88stIS0vD7NmzUVpaiu3btyM+Ph6TJ0/GypUr8eabb8LX1xdxcXGK88PDw5GamgoRgUqlqrIvRER1RoioTuTm5goAef75500+p0WLFtKoUSM5fvy4ovz5558XW1tbOXfunKK8T58+otVq5cqVKyIi0r9/fwkNDa3yGvb29vLaa6+Z3KeKnnzySenVq5f+vq+vr/zlL3+RRx55RHbt2iUiIikpKQJATpw4oYhrxIgR+vs7d+4UANK3b19F+19//bUAkL1794qIyOXLl0WtVktsbKyi3u7duwWA9OzZs8r+bt68WQDIvHnzFOUzZ84UAJKQkKAv+/LLLwWAnD59WkRE9u/fLwBk3bp1VV7Dzs5OEVtldDqdlJaWSmRkpCKe06dPCwAJDg4WnU6nL//+++8FgKxatUpf5uPjIz4+PlJcXFzpdZ5++mlp1qyZXL16VVE+ceJEUavVcunSpUrPvXXrlrRu3VoGDBigKO/Tp4/4+PjI7du3q4yxbdu2Eh0dbVCekJAgAOSTTz5RlIeGhgoAWbNmjb6stLRU3N3dJS4uzqCdJUuWCAA5evRolf0gIqpLXFZB9ICFhITAz89PUZaeno7IyEiDGeiRI0fi+vXr2Lt3LwCgc+fO+PHHHxEfH4+tW7fi2rVrBu137twZy5Ytw4wZM5CZmYnS0lLFcRGBTqdT3MpFRkZi9+7dKC4uxtmzZ5GTk4Pnn38eoaGhSEtLA3BnNtnb2xtt2rSpNtaKS0rKYweAs2fPArgzM1tSUoIXXnhBUS8sLAwtWrSotv2dO3cCgMH5Q4cOrfZcX19fODs7480338SiRYtw5MiRas+526JFi/DYY49BrVbDysoK1tbW2LFjB44ePWpQt1+/fvrZcsDwsThx4gROnjyJMWPGQK1WG71eSUkJduzYgdjYWGi1WsUY9u3bFyUlJUaXapR75JFHMHHiRGzYsAHnzp0DAJw8eRJbtmxBfHx8tbO1v/76Kzw8PCo93r9/f8X9tm3bQqVSKWbyrays4Ovrq4+7ovK2f/nllyr7QURUl5gcE9URNzc3aLVanD59ukbnGdstIT8/32i5l5eX/jgAvP322/j444+RmZmJPn36wNXVFZGRkdi/f7/+nK+++gojRozAP/7xD3Tr1g0uLi548cUXkZubCwBYvnw5rK2tFbdyvXv3xo0bN/Df//4XaWlpcHNzQ4cOHdC7d2/9muW7/yRfFVdXV8X98nXZxcXFiriaNGlicK6xsrvl5+fDysrK4DqmnOvo6Ihdu3YhNDQUU6dORWBgILy8vJCQkGDwgcKYTz/9FOPHj0eXLl3wzTffIDMzE/v27UN0dLQ+voqqeywuXLgAAGjWrFml18zPz4dOp8P8+fMNxrBv374AgIsXL1bZ79GjR0Oj0ejX9i5YsAAajQajR4+uNubi4uJKE3cAcHFxUdy3sbGBVqs1OMfGxgYlJSUG55fXM/b4ERGZC9ccE9WRRo0aITIyEps3b8bPP/9cZVJTkbHZOVdXV/z2228G5b/++iuAO4k4cGfW7Y033sAbb7yBK1euYPv27Zg6dSqefvppnD9/HlqtFm5ubpg7dy7mzp2Lc+fO4dtvv8Vbb72FvLw8bNmyBTExMYov3VXUpUsX2NvbY/v27Thz5gwiIyOhUqkQGRmJTz75BPv27cO5c+dMTo6rU54wlifuFeXm5qJly5bVnq/T6ZCfn69IPo21Z0xwcDBWr14NEcFPP/2EZcuWYfr06dBoNHjrrbeqPDc5ORnh4eH4/PPPFeXl65VrqnwnjfIvAxrj7OyMRo0aYfjw4ZgwYYLROq1ataryOo6OjvoPT5MnT8aXX36JoUOHwsnJqdo+urm5mXXf7vK2y/+/ExHdD5w5JqpDb7/9NkQE48aNw82bNw2Ol5aWYv369dW2ExkZifT0dH0yXO6f//wntFotunbtanCOk5MTBg0ahAkTJuDSpUtGf9zC29sbEydORFRUlP4HFlxdXdGpUyfFrZy1tTV69OiBtLQ0pKenIyoqCgDQvXt3WFlZYdq0afpkuS507doVarUaKSkpivI9e/YY/bP73SIiIgDA4PyVK1fWqB8qlQrt27fHX//6Vzg5OSl+jMLW1tboTKZKpTLYoeSnn37SL4GpKT8/P/j4+OCLL77AjRs3jNbRarWIiIjAwYMHERISYjCOnTp1MpihNqb8y3uDBg3ClStXMHHiRJP6GBAQgJMnT9Yorpo4deoUHnnkkWq/iElEVJc4c0xUh7p164bPP/8c8fHx6NixI8aPH4/AwECUlpbi4MGDWLx4MYKCghATE1NlOwkJCdiwYQMiIiLw3nvvwcXFBSkpKdi4cSNmz56t380gJiYGQUFB6NSpE9zd3XH27FnMnTsXLVq0QJs2bXD16lVERERg6NChCAgIQOPGjbFv3z5s2bLFYGeAykRGRuLPf/4zAOhniDUaDcLCwrBt2zaEhIRUue60JpydnTF58mTMmDEDY8eOxeDBg3H+/HkkJiaatDTiqaeeQo8ePTBlyhQUFRWhU6dO2L17N1asWFHtuRs2bMDChQsxcOBAtG7dGiKCNWvW4MqVK/oPBcCd2eWMjAysX78enp6eaNy4Mfz9/dG/f3+8//77SEhIQM+ePXH8+HFMnz4drVq1UqzjrokFCxYgJiYGXbt2xeuvvw5vb2+cO3cOW7du1X8AmDdvHp588kl0794d48ePR8uWLVFQUICcnBysX78e6enp1V7Hz88P0dHR2Lx5M5588km0b9/epP6Fh4dj+vTpuH79OrRa7T3FWJXMzEyEhobC2dm5ztsmIqrUg/0+IJFlOnTokIwYMUK8vb3FxsZG7OzspEOHDvLee+9JXl6evl6LFi2kX79+RtvIysqSmJgYcXR0FBsbG2nfvr18+eWXijqffPKJhIWFiZubm9jY2Ii3t7eMGTNGzpw5IyIiJSUl8qc//UlCQkLEwcFBNBqN+Pv7S0JCghQVFZkUy48//igApE2bNory8h0g3njjDYNzKtutIjU1VVGvfOeGinHdvn1bPvjgA2nevLnY2NhISEiIrF+/Xnr27FntbhUiIleuXJHRo0eLk5OTaLVaiYqKkmPHjlW7W8WxY8dkyJAh4uPjIxqNRhwdHaVz586ybNkyRfuHDh2SJ554QrRarWIHjRs3bsjkyZOladOmolar5bHHHpN169bJiBEjpEWLFgYxz5kzx6Dvd/dRRGTv3r3Sp08fcXR0FFtbW/Hx8ZHXX3/d4HEcPXq0NG3aVKytrcXd3V3CwsJkxowZ1T5e5ZYtWyYAZPXq1Safk5OTIyqVSr7++mtFefluFRcuXFCUjxgxQuzs7Aza6dmzpwQGBirKCgoKRKvVGux4QURkbiqRu3Z/JyKih86zzz6LzMxMnDlzRvGlzOrExMRAp9PV6IdaTLF06VJMmjQJ58+f58wxEd1XXFZBRPSQunHjBg4cOIDvv/8ea9euxaefflqjxBgAPvjgA3To0AH79u3T/1hMbel0Onz00Ud4++23mRgT0X3H5JiI6CH122+/ISwsDA4ODnj55Zfxyiuv1LiNoKAgfPnllybvCGKK8+fPY9iwYfq17kRE9xOXVRARERERleFWbkREREREZZgcExERERGVYXJMRERERFSGyTERERERURkmx0REREREZZgcExERERGVYXJMRERERFSGyTERERERURkmx0REREREZZgcExERERGV+X8QfEol0IlUOwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 720x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Convert density (m^-2) to probability mass per cell\n",
    "cell_mass = F * cfg.dx * cfg.dy\n",
    "flat = cell_mass.ravel()\n",
    "order = np.argsort(flat)[::-1]\n",
    "csum = np.cumsum(flat[order])\n",
    "\n",
    "# Find threshold where cumulative mass first exceeds 0.8\n",
    "idx_thr = int(np.searchsorted(csum, 0.8))\n",
    "thr_mass = flat[order][idx_thr]\n",
    "thr_density = thr_mass / (cfg.dx * cfg.dy)\n",
    "\n",
    "plt.figure(figsize=(6, 5))\n",
    "plt.pcolormesh(y, x, F, shading=\"auto\")\n",
    "plt.colorbar(label=\"Footprint weight (m$^{-2}$)\")\n",
    "cc = plt.contour(y, x, F, levels=[thr_density])\n",
    "plt.clabel(cc, fmt=\"80% area\", inline=True, fontsize=8)\n",
    "plt.xlabel(\"Cross‑wind distance y (m)\")\n",
    "plt.ylabel(\"Upwind distance −x (m)\")\n",
    "plt.title(\"Footprint with ~80% source area contour\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f6f2b1b",
   "metadata": {},
   "source": [
    "\n",
    "## 7) Save outputs (optional)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "590edbff",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "out_dir = Path(\"outputs\")\n",
    "out_dir.mkdir(exist_ok=True)\n",
    "\n",
    "# Save grids\n",
    "np.save(out_dir / \"footprint_density.npy\", F)\n",
    "np.savetxt(out_dir / \"footprint_density.csv\", F, delimiter=\",\")\n",
    "np.savetxt(out_dir / \"x_centers_m.csv\", x, delimiter=\",\")\n",
    "np.savetxt(out_dir / \"y_centers_m.csv\", y, delimiter=\",\")\n",
    "\n",
    "# Save figures\n",
    "plt.figure(figsize=(6, 5))\n",
    "plt.pcolormesh(y, x, F, shading=\"auto\")\n",
    "plt.colorbar(label=\"Footprint weight (m$^{-2}$)\")\n",
    "plt.xlabel(\"Cross‑wind distance y (m)\")\n",
    "plt.ylabel(\"Upwind distance −x (m)\")\n",
    "plt.title(\"2‑D LS Footprint\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(out_dir / \"footprint_2d.png\", dpi=200)\n",
    "plt.close()\n",
    "\n",
    "plt.figure(figsize=(6, 4))\n",
    "plt.plot(-xx, fx)\n",
    "plt.xlabel(\"Upwind distance (m)\")\n",
    "plt.ylabel(\"f(x) (m$^{-1}$)\")\n",
    "plt.title(\"Cross‑wind integrated footprint\")\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.savefig(out_dir / \"footprint_fx.png\", dpi=200)\n",
    "plt.close()\n",
    "\n",
    "print(\"Saved outputs to:\", out_dir.resolve())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b00d4376",
   "metadata": {},
   "source": [
    "\n",
    "## 8) Tips & next steps\n",
    "\n",
    "- Increase `n_particles` for smoother fields (e.g., 50,000–200,000).\n",
    "- Adjust `domain`, `dx` and `dy` to control the spatial extent and resolution.\n",
    "- If you have measured `USTAR`, use it directly; otherwise, provide a better local estimate for `z0` and set your actual `zm`.\n",
    "- The current `L` parameter is kept for future extensions (e.g., stability‑dependent turbulence); it is not used in the core update step here.\n",
    "- For climatological footprints, repeat runs across many time steps and average the resulting `F` fields (watch memory and runtime).\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "footprints",
   "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": "3.13.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}