{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d357be88",
   "metadata": {},
   "source": [
    "# Helper methods for InputVar"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "090219e9-ed92-4c1e-8059-9da9992dd743",
   "metadata": {},
   "source": [
    "This tutorial complements the general tutorial on the uncertainty and sensitivity analysis module [unsequa](climada_engine_unsequa.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c1a119e-8506-47dd-8b2c-7476b3f1170a",
   "metadata": {},
   "source": [
    "The InputVar class provides a few helper methods to generate generic uncertainty input variables for exposures, impact function sets, hazards, and entities (including measures cost and disc rates)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1d804ea",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Helper-methods-for-InputVar\" data-toc-modified-id=\"Helper-methods-for-InputVar-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Helper methods for InputVar</a></span><ul class=\"toc-item\"><li><span><a href=\"#Exposures\" data-toc-modified-id=\"Exposures-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Exposures</a></span><ul class=\"toc-item\"><li><span><a href=\"#Example:-single-exposures\" data-toc-modified-id=\"Example:-single-exposures-1.1.1\"><span class=\"toc-item-num\">1.1.1&nbsp;&nbsp;</span>Example: single exposures</a></span></li><li><span><a href=\"#Example:-list-of-litpop-exposures-with-different-exponents\" data-toc-modified-id=\"Example:-list-of-litpop-exposures-with-different-exponents-1.1.2\"><span class=\"toc-item-num\">1.1.2&nbsp;&nbsp;</span>Example: list of litpop exposures with different exponents</a></span></li></ul></li><li><span><a href=\"#Hazard\" data-toc-modified-id=\"Hazard-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Hazard</a></span></li><li><span><a href=\"#ImpactFuncSet\" data-toc-modified-id=\"ImpactFuncSet-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>ImpactFuncSet</a></span></li><li><span><a href=\"#Entity\" data-toc-modified-id=\"Entity-1.4\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>Entity</a></span><ul class=\"toc-item\"><li><span><a href=\"#Example:-single-exposures\" data-toc-modified-id=\"Example:-single-exposures-1.4.1\"><span class=\"toc-item-num\">1.4.1&nbsp;&nbsp;</span>Example: single exposures</a></span></li><li><span><a href=\"#Example:-list-of-Litpop-exposures-with-different-exponents\" data-toc-modified-id=\"Example:-list-of-Litpop-exposures-with-different-exponents-1.4.2\"><span class=\"toc-item-num\">1.4.2&nbsp;&nbsp;</span>Example: list of Litpop exposures with different exponents</a></span></li></ul></li><li><span><a href=\"#Entity-Future\" data-toc-modified-id=\"Entity-Future-1.5\"><span class=\"toc-item-num\">1.5&nbsp;&nbsp;</span>Entity Future</a></span><ul class=\"toc-item\"><li><span><a href=\"#Example:-single-exposures\" data-toc-modified-id=\"Example:-single-exposures-1.5.1\"><span class=\"toc-item-num\">1.5.1&nbsp;&nbsp;</span>Example: single exposures</a></span></li><li><span><a href=\"#Example:-list-of-exposures\" data-toc-modified-id=\"Example:-list-of-exposures-1.5.2\"><span class=\"toc-item-num\">1.5.2&nbsp;&nbsp;</span>Example: list of exposures</a></span></li></ul></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e446c327-d6cf-457f-b181-fef150bdbe81",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:29.292044Z",
     "start_time": "2022-01-10T20:10:29.289389Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore') #Ignore warnings for making the tutorial's pdf. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ef9636c-c8a0-434a-b136-af54085141d8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-18T12:22:14.943204Z",
     "start_time": "2021-08-18T12:22:14.938441Z"
    }
   },
   "source": [
    "## Exposures"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c92d6f3-8326-458c-9931-bce0ccb94d0b",
   "metadata": {},
   "source": [
    "The following types of uncertainties can be added:\n",
    "\n",
    "- ET: scale the total value (homogeneously)\n",
    "        The value at each exposure point is multiplied by a number\n",
    "        sampled uniformly from a distribution with\n",
    "        (min, max) = bounds_totvalue\n",
    "- EN: mutliplicative noise (inhomogeneous)\n",
    "        The value of each exposure point is independently multiplied by\n",
    "        a random number sampled uniformly from a distribution\n",
    "        with (min, max) = bounds_noise. EN is the value of the seed\n",
    "        for  the uniform random number generator.\n",
    "- EL: sample uniformly from exposure list\n",
    "        From the provided list of exposure is elements are uniformly\n",
    "        sampled. For example, LitPop instances with different exponents.\n",
    "\n",
    "\n",
    "If a bounds is None, this parameter is assumed to have no uncertainty."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60601195-ef75-492f-b7f2-371928575249",
   "metadata": {},
   "source": [
    "### Example: single exposures "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0fd2b3ec-c0e8-4ae6-8439-39b4501e6196",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:32.886682Z",
     "start_time": "2022-01-10T20:10:29.504756Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-01-10 21:10:32,810 - climada.entity.exposures.base - INFO - Reading /Users/ckropf/climada/demo/data/exp_demo_today.h5\n"
     ]
    }
   ],
   "source": [
    "#Define the base exposure\n",
    "from climada.util.constants import EXP_DEMO_H5\n",
    "from climada.entity import Exposures\n",
    "exp_base = Exposures.from_hdf5(EXP_DEMO_H5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b062b610-787c-41ab-a355-bbb6291361bd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:33.458079Z",
     "start_time": "2022-01-10T20:10:32.888874Z"
    }
   },
   "outputs": [],
   "source": [
    "from climada.engine.unsequa import InputVar\n",
    "bounds_totval = [0.9, 1.1] #+- 10% noise on the total exposures value\n",
    "bounds_noise = [0.9, 1.2] #-10% - +20% noise each exposures point\n",
    "exp_iv = InputVar.exp([exp_base], bounds_totval, bounds_noise)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "87cc5928-3a53-4cef-acc9-097e85da398a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:33.471516Z",
     "start_time": "2022-01-10T20:10:33.459502Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03700231587024304"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The difference in total value between the base exposure and the average input uncertainty exposure \n",
    "#due to the random noise on each exposures point (the average change in the total value is 1.0).\n",
    "avg_exp = exp_iv.evaluate()\n",
    "(sum(avg_exp.gdf['value']) - sum(exp_base.gdf['value'])) / sum(exp_base.gdf['value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "53e56665-c74f-49f3-a355-135fd0e5ed40",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:33.696309Z",
     "start_time": "2022-01-10T20:10:33.474333Z"
    }
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#The values for EN are seeds for the random number generator for the noise sampling and\n",
    "#thus are uniformly sampled numbers between (0, 2**32-1) \n",
    "exp_iv.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3c5bd7e-09d5-4c94-a72a-8691641bf837",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-18T12:22:14.943204Z",
     "start_time": "2021-08-18T12:22:14.938441Z"
    }
   },
   "source": [
    "### Example: list of litpop exposures with different exponents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "802ac379-39a0-476d-b068-36520d03a459",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:33.703989Z",
     "start_time": "2022-01-10T20:10:33.697855Z"
    }
   },
   "outputs": [],
   "source": [
    "#Define a generic method to make litpop instances with different exponent pairs.\n",
    "from climada.entity import LitPop\n",
    "def generate_litpop_base(impf_id, value_unit, haz, assign_centr_kwargs,\n",
    "                          choice_mn, **litpop_kwargs):\n",
    "    #In-function imports needed only for parallel computing on Windows\n",
    "    from climada.entity import LitPop\n",
    "    litpop_base = []\n",
    "    for [m, n] in choice_mn:\n",
    "        print('\\n Computing litpop for m=%d, n=%d \\n' %(m, n))\n",
    "        litpop_kwargs['exponents'] = (m, n)\n",
    "        exp = LitPop.from_countries(**litpop_kwargs)\n",
    "        exp.gdf['impf_' + haz.tag.haz_type] = impf_id\n",
    "        exp.gdf.drop('impf_', axis=1, inplace=True)\n",
    "        if value_unit is not None:\n",
    "            exp.value_unit = value_unit\n",
    "        exp.assign_centroids(haz, **assign_centr_kwargs)\n",
    "        litpop_base.append(exp)\n",
    "    return litpop_base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ac28063d-83aa-43f8-a75d-f91fe20e44e8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:33.740545Z",
     "start_time": "2022-01-10T20:10:33.706259Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-01-10 21:10:33,708 - climada.hazard.base - INFO - Reading /Users/ckropf/climada/demo/data/tc_fl_1990_2004.h5\n"
     ]
    }
   ],
   "source": [
    "#Define the parameters of the LitPop instances\n",
    "tot_pop = 11.317e6\n",
    "impf_id = 1\n",
    "value_unit = 'people'\n",
    "litpop_kwargs = {\n",
    "    'countries' : ['CUB'],\n",
    "    'res_arcsec' : 300, \n",
    "    'reference_year' : 2020,\n",
    "    'fin_mode' : 'norm',\n",
    "    'total_values' : [tot_pop]\n",
    "}\n",
    "assign_centr_kwargs={}\n",
    "\n",
    "# The hazard is needed to assign centroids\n",
    "from climada.util.constants import HAZ_DEMO_H5\n",
    "from climada.hazard import Hazard\n",
    "haz = Hazard.from_hdf5(HAZ_DEMO_H5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a0370352-db8d-4507-90e2-931108fd0854",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:38.921653Z",
     "start_time": "2022-01-10T20:10:33.742286Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Computing litpop for m=0, n=0 \n",
      "\n",
      "2022-01-10 21:10:33,998 - climada.entity.exposures.litpop.litpop - INFO - \n",
      " LitPop: Init Exposure for country: CUB (192)...\n",
      "\n",
      "2022-01-10 21:10:33,999 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-01-10 21:10:35,544 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
      "2022-01-10 21:10:35,545 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2022-01-10 21:10:35,545 - climada.entity.exposures.base - INFO - cover not set.\n",
      "2022-01-10 21:10:35,546 - climada.entity.exposures.base - INFO - deductible not set.\n",
      "2022-01-10 21:10:35,546 - climada.entity.exposures.base - INFO - centr_ not set.\n",
      "2022-01-10 21:10:35,549 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 2500 centroids.\n",
      "2022-01-10 21:10:35,551 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
      "2022-01-10 21:10:35,577 - climada.util.interpolation - WARNING - Distance to closest centroid is greater than 100km for 77 coordinates.\n",
      "\n",
      " Computing litpop for m=0, n=1 \n",
      "\n",
      "2022-01-10 21:10:35,824 - climada.entity.exposures.litpop.litpop - INFO - \n",
      " LitPop: Init Exposure for country: CUB (192)...\n",
      "\n",
      "2022-01-10 21:10:35,825 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-01-10 21:10:37,238 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
      "2022-01-10 21:10:37,239 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2022-01-10 21:10:37,239 - climada.entity.exposures.base - INFO - cover not set.\n",
      "2022-01-10 21:10:37,239 - climada.entity.exposures.base - INFO - deductible not set.\n",
      "2022-01-10 21:10:37,240 - climada.entity.exposures.base - INFO - centr_ not set.\n",
      "2022-01-10 21:10:37,243 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 2500 centroids.\n",
      "2022-01-10 21:10:37,245 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
      "2022-01-10 21:10:37,269 - climada.util.interpolation - WARNING - Distance to closest centroid is greater than 100km for 77 coordinates.\n",
      "\n",
      " Computing litpop for m=0, n=2 \n",
      "\n",
      "2022-01-10 21:10:37,499 - climada.entity.exposures.litpop.litpop - INFO - \n",
      " LitPop: Init Exposure for country: CUB (192)...\n",
      "\n",
      "2022-01-10 21:10:37,501 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-01-10 21:10:38,888 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
      "2022-01-10 21:10:38,889 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2022-01-10 21:10:38,889 - climada.entity.exposures.base - INFO - cover not set.\n",
      "2022-01-10 21:10:38,890 - climada.entity.exposures.base - INFO - deductible not set.\n",
      "2022-01-10 21:10:38,891 - climada.entity.exposures.base - INFO - centr_ not set.\n",
      "2022-01-10 21:10:38,894 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 2500 centroids.\n",
      "2022-01-10 21:10:38,895 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
      "2022-01-10 21:10:38,919 - climada.util.interpolation - WARNING - Distance to closest centroid is greater than 100km for 77 coordinates.\n"
     ]
    }
   ],
   "source": [
    "#Generate the LitPop list\n",
    "\n",
    "choice_mn = [[0, 0.5], [0, 1], [0, 2]] #Choice of exponents m,n\n",
    "\n",
    "litpop_list = generate_litpop_base(impf_id, value_unit, haz, assign_centr_kwargs, choice_mn, **litpop_kwargs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cfa54889-cb6c-4b4d-afc7-81a20dd77eb8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:38.926915Z",
     "start_time": "2022-01-10T20:10:38.923116Z"
    }
   },
   "outputs": [],
   "source": [
    "from climada.engine.unsequa import InputVar\n",
    "bounds_totval = [0.9, 1.1] #+- 10% noise on the total exposures value\n",
    "litpop_iv = InputVar.exp(exp_list = litpop_list,\n",
    "                         bounds_totval=bounds_totval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ee5cfd3d-9f71-4ccc-95e1-dc8e6ea9d8b3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:38.934028Z",
     "start_time": "2022-01-10T20:10:38.928323Z"
    }
   },
   "outputs": [],
   "source": [
    "# To choose n=0.5, we have to set EL=1 (the index of 0.5 in choice_n = [0, 0.5, 1, 2])\n",
    "pop_half = litpop_iv.evaluate(ET=1, EL=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "15c78c6a-d1ee-4eb0-982a-2920eda04f25",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:38.949269Z",
     "start_time": "2022-01-10T20:10:38.937644Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>geometry</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>region_id</th>\n",
       "      <th>impf_TC</th>\n",
       "      <th>centr_TC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1383</th>\n",
       "      <td>1713.015083</td>\n",
       "      <td>POINT (-78.29167 22.45833)</td>\n",
       "      <td>22.458333</td>\n",
       "      <td>-78.291667</td>\n",
       "      <td>192</td>\n",
       "      <td>1</td>\n",
       "      <td>431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1384</th>\n",
       "      <td>1085.168934</td>\n",
       "      <td>POINT (-79.20833 22.62500)</td>\n",
       "      <td>22.625000</td>\n",
       "      <td>-79.208333</td>\n",
       "      <td>192</td>\n",
       "      <td>1</td>\n",
       "      <td>476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1385</th>\n",
       "      <td>950.764517</td>\n",
       "      <td>POINT (-79.62500 22.79167)</td>\n",
       "      <td>22.791667</td>\n",
       "      <td>-79.625000</td>\n",
       "      <td>192</td>\n",
       "      <td>1</td>\n",
       "      <td>524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1386</th>\n",
       "      <td>1129.619078</td>\n",
       "      <td>POINT (-79.45833 22.70833)</td>\n",
       "      <td>22.708333</td>\n",
       "      <td>-79.458333</td>\n",
       "      <td>192</td>\n",
       "      <td>1</td>\n",
       "      <td>475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1387</th>\n",
       "      <td>300.552289</td>\n",
       "      <td>POINT (-80.79167 23.20833)</td>\n",
       "      <td>23.208333</td>\n",
       "      <td>-80.791667</td>\n",
       "      <td>192</td>\n",
       "      <td>1</td>\n",
       "      <td>617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            value                    geometry   latitude  longitude  \\\n",
       "1383  1713.015083  POINT (-78.29167 22.45833)  22.458333 -78.291667   \n",
       "1384  1085.168934  POINT (-79.20833 22.62500)  22.625000 -79.208333   \n",
       "1385   950.764517  POINT (-79.62500 22.79167)  22.791667 -79.625000   \n",
       "1386  1129.619078  POINT (-79.45833 22.70833)  22.708333 -79.458333   \n",
       "1387   300.552289  POINT (-80.79167 23.20833)  23.208333 -80.791667   \n",
       "\n",
       "      region_id  impf_TC  centr_TC  \n",
       "1383        192        1       431  \n",
       "1384        192        1       476  \n",
       "1385        192        1       524  \n",
       "1386        192        1       475  \n",
       "1387        192        1       617  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_half.gdf.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b11722a1-0609-4fdc-9f92-e735a14f5c56",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:41.243291Z",
     "start_time": "2022-01-10T20:10:38.951093Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x936 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pop_half.plot_hexbin();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4d6d5ea3-4733-4bff-88c8-e9fc14090497",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:41.251711Z",
     "start_time": "2022-01-10T20:10:41.244959Z"
    }
   },
   "outputs": [],
   "source": [
    "# To choose n=1, we have to set EL=2 (the index of 1 in choice_n = [0, 0.5, 1, 2])\n",
    "pop_one = litpop_iv.evaluate(ET=1, EL=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ced52885-d233-4c55-b84b-b2821847f281",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:41.264409Z",
     "start_time": "2022-01-10T20:10:41.253039Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>geometry</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>region_id</th>\n",
       "      <th>impf_TC</th>\n",
       "      <th>centr_TC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1383</th>\n",
       "      <td>56.491498</td>\n",
       "      <td>POINT (-78.29167 22.45833)</td>\n",
       "      <td>22.458333</td>\n",
       "      <td>-78.291667</td>\n",
       "      <td>192</td>\n",
       "      <td>1</td>\n",
       "      <td>431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1384</th>\n",
       "      <td>22.670204</td>\n",
       "      <td>POINT (-79.20833 22.62500)</td>\n",
       "      <td>22.625000</td>\n",
       "      <td>-79.208333</td>\n",
       "      <td>192</td>\n",
       "      <td>1</td>\n",
       "      <td>476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1385</th>\n",
       "      <td>17.402300</td>\n",
       "      <td>POINT (-79.62500 22.79167)</td>\n",
       "      <td>22.791667</td>\n",
       "      <td>-79.625000</td>\n",
       "      <td>192</td>\n",
       "      <td>1</td>\n",
       "      <td>524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1386</th>\n",
       "      <td>24.565452</td>\n",
       "      <td>POINT (-79.45833 22.70833)</td>\n",
       "      <td>22.708333</td>\n",
       "      <td>-79.458333</td>\n",
       "      <td>192</td>\n",
       "      <td>1</td>\n",
       "      <td>475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1387</th>\n",
       "      <td>1.739005</td>\n",
       "      <td>POINT (-80.79167 23.20833)</td>\n",
       "      <td>23.208333</td>\n",
       "      <td>-80.791667</td>\n",
       "      <td>192</td>\n",
       "      <td>1</td>\n",
       "      <td>617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          value                    geometry   latitude  longitude  region_id  \\\n",
       "1383  56.491498  POINT (-78.29167 22.45833)  22.458333 -78.291667        192   \n",
       "1384  22.670204  POINT (-79.20833 22.62500)  22.625000 -79.208333        192   \n",
       "1385  17.402300  POINT (-79.62500 22.79167)  22.791667 -79.625000        192   \n",
       "1386  24.565452  POINT (-79.45833 22.70833)  22.708333 -79.458333        192   \n",
       "1387   1.739005  POINT (-80.79167 23.20833)  23.208333 -80.791667        192   \n",
       "\n",
       "      impf_TC  centr_TC  \n",
       "1383        1       431  \n",
       "1384        1       476  \n",
       "1385        1       524  \n",
       "1386        1       475  \n",
       "1387        1       617  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_one.gdf.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "33d4b6b5-643e-47e3-bf62-a28a104c6bc0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:43.527411Z",
     "start_time": "2022-01-10T20:10:41.265824Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jOXz4MMuWLSMgIACw/6DJnDHAZDIhlUpdgreHLf8wOAe7jh+kZrOZ4sWL58tfOZWPiIigbNmyLn09J11vz549+f3339myZQvXrl3j/PnzpKSkZLlWTpQuXVoMUh1+cWgeHe85+mxkZKS4wMZB/fr12bZtGwARERGipADsfc5Z/vCwzzM3/z3cAeB/FG9vbypWrJijPbvVoY6HluP/UqnUJXhzBBGZR/icKVGiRK7XBbhx4wYGg4Hbt28TFRVFYGCgaMscjNhsNrEO2dXZUSebzUZISAh79uzhjz/+4I8//mDevHmsWbMmx6lxh6bHQebzW61W3nrrrSwpXfIz/Zmf+uZ0TH6oUKECvr6+nDlzJttgfMaMGVSoUIEhQ4bg7e1NampqtufJbkFQ+fLlxSC8cuXK1KlTh0aNGrF9+3Zx9CE/XLlyheeff57GjRvToEEDevXqxalTp1i7dm2+yt+4cYOoqChatGghvletWjVsNhtJSUkEBAQQFxfnUubu3bvI5XKKFSuWp72g9fX39+fnn3/m8OHD7N+/n19++YUVK1awatUqatSokWd7unfvzpgxY9DpdGzfvp0pU6YA9n5YpkyZbFfnFytWjLi4OPr06UPlypVp0aIFHTp0ICEhQZzqdVDQwCo1NZWXX36ZS5cusWzZMpdR7YCAABfNHEBcXJxL8P6w5R+W7EZeBUHIt79yKp/f+xvsEpwDBw7Qs2dPunXrxpQpU+jZs2e+y2f3LM38g9lBdp+vzWbDarUC9pX5mX/oObfxYZ9nbv57uDWATylVq1bl1KlTLg+MkydPolAoKF++PGAXdDumH8AuulepVA/1i9FoNPLee+8xcOBAunbtynvvvSc+wAAuXbokLkQBOHXqlPjlWrVq1Sz6uxMnTlCsWDF8fHxYvnw5x48fp3PnzsyaNYsff/yRiIgILl26hEKhQK/Xu/wyj4mJybWuVatW5ebNm1SsWFH827JlC1u3bs1XW/Oq78Mgk8no2rUr33//fZYFDdeuXePHH38UvzCCg4OJiYnJontLSEjg2rVr1K5dO9drORZhOH9O+WHDhg3UqFGDL7/8khdeeIGGDRty8+bNPDVgDo4fP84bb7yBTqcT3ztz5gweHh6UKVOG0NBQEhMTiYyMdCkTHByMSqXK017Q+u7fv59Vq1bRpEkTJk6cyPbt2/H09OSPP/7IV3uaNGmCh4cHS5cuRa/Xi6NKVatW5e7du3h4eIj9TC6XM3PmTOLj4/ntt9+Qy+UsX76c4cOH06xZM3E6Mr++zIzVamXUqFFcvXqVVatWZZE0hIaGEhER4aJvPH78uHjcw5Z/nDysvypXrsytW7dITEwU31u3bh39+vVzOS41NZWNGzcye/Zs3nzzTbp06YJSqSQlJeWBP5fcqFKlSpbnycmTJ6lSpQoAgYGBnDp1SrSZTCaX5/fDPs/c/PdwB4BFmHPnzomjWY6/7MTrHh4eXL9+nTt37uT73A7N3IwZM4iMjGT//v3MmjWLnj17uowITZ48mXPnznHo0CHmzJnDc889l2suruTkZOLi4rL8OUag5s+fj16v5/XXX+edd97h+vXrLit94+Li+PDDD7l69SobNmxg/fr14rTGsGHD+PPPP/nqq6+4du0aW7Zs4ZtvvmHw4MFIJBLu3LnD9OnTOXr0KNHR0WzYsAFPT08qV65MSEgIRqORJUuWEBMTw+LFi/MUao8YMYIffviBtWvXcuPGDdatW8dXX33lMkWTG3nV92F57bXXsFgsvPDCC/z1119ER0ezY8cOhg8fLq7yA/uXcUhICK+//jpHjx4lJiaGgwcPMmrUKGrWrJll6jE+Pl783K5evcrUqVOx2WwueSbzg6+vL1evXuXYsWNER0ezfPlyfvrpJ5dpWa1WS0REhMuXrYN27drh4+PDpEmTuHr1Knv27OGTTz5h1KhRSKVSypYtS6tWrZgwYQLnz59n586dLF26VJwmy8te0PrabDbmzp3Lli1bxBQzcXFx4vR0YmJirgtCZDIZnTt3ZsmSJXTo0EEc/W3atCnVq1dn3LhxnD17losXL/Luu+9y8+ZNypYti6+vL/fu3WPfvn3ExMSwYcMGcRovOy2jg7i4uBwTJ69evZojR44wY8YMihcvLn7ejmnb8PBwqlatyjvvvMPly5dZt24dW7ZsYfDgwY+kvKN+D5NVICce1F8OmjVrRoUKFXjvvfe4fPkyR44c4auvvuLZZ591OU6lUqHRaNi1axfR0dEcP36cMWPGIAhCvq5TUEaMGMH69etZu3YtUVFRrF69mp9//pkhQ4YA8OKLL7Ju3To2btzI1atXmTZtGgkJCS7lc3uemUwm4uLiHkvd3RRRBDdFksDAwGz/Nm3aJERHRwuBgYHCpUuXBEEQhH379gmNGjUSGjZsKFitVmHQoEHCrFmz8rzG0aNHheeee06oVauW0KxZM+Gzzz4TjEajIAiCcOjQISEwMFD47rvvhEaNGgmNGjUSZs+eLZhMpgLXOTAwUJgyZYpw5swZoWbNmsKOHTvEMt9//70QEhIiXLt2TZg/f77QrVs34YMPPhDq1q0rtG3bVtiwYYPLNXbt2iV07dpVqFWrltCmTRth2bJlgs1mEwRBEPR6vTB16lShSZMmQu3atYW+ffsKR48eFct+9913QtOmTYXQ0FDh3XffFT799FNhzJgxgiAIws8//yw0bNgwS5t+/PFHoX379kKtWrWE9u3bC+vWrcux/Xv37hUCAwPzXd/58+cLvXr1yvF8giAI48ePF+uYHXFxccK0adOEVq1aCbVr1xbatm0rzJ07V0hJSXE5LikpSZg6darQokULoVatWkLz5s2FqVOnComJieIxjs/c+S8sLEx48cUXXfzoTE5+EwRB0Ol0wptvvinUr19fCA8PF4YMGSKsW7dOCAoKEm7fvi0IgiAsWLBACAkJEUaNGpXtOa5cuSIMHTpUCA0NFZo3by58/fXXov8EQRASEhKEMWPGCCEhIULz5s2F7777zqV8XvaC1nf58uVCmzZthFq1aglt27YVfvjhB7H8oEGDhPHjx+d4fkEQhFOnTgmBgYHCoUOHXN6/ffu2MHr0aCEsLEyoX7++MHr0aPGaVqtVmD59utCwYUMhLCxMeO6554StW7cKQUFBwrFjx7I8DxwEBgYK8+fPz7Ye/fr1y/Y+df4so6OjhRdffFGoU6eO0LZtW+HXX399ZOUd9fv555+zrd+lS5eEwMBAITo6WhCEjL6Zmpoqlt27d694vLM9L3/lVV4QBOH69evCiBEjhJCQEKFp06bC559/LlitVkEQBKFVq1bCqlWrBEGw3/MdOnQQ6tSpI7Rp00aYP3++8NJLLwmTJk0SBEHI9Vmc+f7P7nMcM2aMS5/68ccfhXbt2gm1atUSOnfuLGzcuNHlnD/88IPQqlUroW7dusKkSZOEYcOGufSB3J5nDh9k7ptu/rtIBOExjFW7+ddz+PBhhgwZwokTJ1wWZTxOFixYwO+//+6y1dnTzoQJE9DpdMyfP7+wq5ItGzZsYPbs2e7cYUB0dDRLlizhww8/zPGYPXv2MH36dPbu3Zuj1utp4eeff0atVtOlS5fCroobN08lT/cTyI2bfwGOqZnMi1YKm/j4+HzlwHtaWLp0KR07dszWFh0dzbZt2/j888957rnnnvrgz2azsXHjxofaR9qNGzcPh3v5jxs3RZzff/+dZs2asWnTpmz3zC0sevbsyZ07dx5Jwun/ApMmTcoxJ2BsbCyTJk0iPDw8x+TMTxNSqZTvvvsuXzkU3bhx83hwTwG7cePGjRs3btw8ZTzd8xBu3Lhx48aNGzdPIe4A0I0bN27cuHHj5inDHQC6ceMmT27dukWvXr3EhSgREREMGDCAunXr0qVLl3wnQ3bw888/06lTJ0JDQ8XttBzs3LmTiRMnPtL6ZyYtLY2goKAHXr08ePBgZs+eXeByW7ZsoXfv3g90zQdhwYIFj+x6QUFB2f61a9cuX+UnTJiQZT9eN27cFB7uRSBu3LjJk+nTp/PSSy8hl8sxGAy89NJLtGrViunTp7Nz507GjBnDli1bxF1kcmPPnj1MmzaNDz/8kHr16rF3715Gjx7NDz/8QJ06dejQoQNLlizh6NGjNGjQ4Am0ruAsWLCgwFto/f3330yePFncueHfyJw5c2jSpInLe7ltDenGjZuii3sE0I0bN7ly/vx5zpw5I6Y42bFjBxaLhUmTJlG1alVGjRpFSEgIP/zwQ77Ot379enr16kXPnj2pUKECL774Ig0bNmTz5s3iMQMHDuTLL798LO15FPj6+uLp6Znv42fPns3IkSPzFSAXZby9vSlRooTLn7+/f2FXy42bx4LJZKJbt24uMxR5YbVamTt3Lk2bNiU8PJxx48Zlu9NRUcAdALp56gkKCmLDhg307t2bOnXq0KtXL06fPi3a7927x/jx42nSpAn16tXjjTfeELfki4mJISgoiIULF9KwYUNeeeUVDh8+TNOmTdm6dSstWrQgLCyMGTNmEBUVxcCBA6lbty7/+9//iIqKyncd//zzT3r27ElISAhdunTh559/Fm1Dhw6lV69e4l69W7ZsoXbt2ly8eJHDhw8THh7OTz/9RLNmzQgPD2fKlCkuW3C1bt2aCRMm5HjtVatW0bZtWzF33YkTJwgNDXUZAWvQoAHHjx/PV1tGjRrF0KFDs7zvvKdx69atOX78OFeuXMnXOe/fv8/48eNp3LgxtWvXpm3btvz000+iXa/Xi2lYmjVrxvbt213KDx48mMWLF/PKK68QEhJC586dOXfuHIsXL6Zx48Y0atTIJSB1ngJesGABo0ePZtasWTRs2JAGDRowc+ZMl32njx8/zsqVK2nfvn2+2pOZtWvX0qFDB7HvOO8Ju3LlSjp27Ejt2rVp2LAh7777rsv+yc4cPnyY559/nrp169KuXTsXH+XVD/LCYrEwb9482rRpQ61atWjSpAkfffRRtntIp6am8tZbb9GoUSNCQ0MZPny4y/1w5swZBgwYQEhICO3bt2fJkiUu/nTj5nFjMBgYO3Ysly9fLlC5uXPnsnHjRj755BNWrVpFVFQUU6dOfUy1fEgKdyMSN24Kn8DAQKFly5bCvn37hAsXLggDBgwQt2gym81Cly5dhP79+wunT58WTp8+LTz//PNC3759BZvNJm7f1L9/f+HatWtCRESEcOjQISE4OFh48cUXhcuXLwu//vqrEBgYKLRq1UrYtWuXcOHCBaF79+7C6NGj81W/K1euCKGhocK6deuE69evC1u3bhUaNGggbN26VRAE+xZSoaGhwsqVK4V79+4JjRo1Er755htBEOzbO9WsWVPo3LmzcPz4ceHIkSNC69athQkTJojnv3//vpCcnJzj9Rs3bizs2rVLfP3yyy8L06ZNczlm9erVwrPPPps/h2fi/PnzQo0aNYRffvnF5f1+/foJS5Ysydc5hg4dKgwfPly4ePGiEBUVJXz88cdCrVq1hLi4OEEQ7FvqdejQQTh+/Ljwzz//CN26dXPZ9mrQoEFC3bp1hY0bNwrXrl0T+vfvLzRo0EB44403hCtXrgjLly8XAgMDhQsXLojHO7b4mj9/vlCrVi1h8uTJQmRkpLB+/XqhRo0awp49e7LUMz/b/2Vmw4YNQkhIiPDTTz+JbQsPDxeSkpKEzZs3C+Hh4cL+/fuFmJgYYffu3UK9evXELe+cr3f16lWhdu3awpw5c4SrV68KGzduFGrVqiX6IK9+kHn7tMx8/fXXQqtWrYSjR48K0dHRwsaNG4Xg4GDht99+Ez8Dx7aGM2bMEHr06CFcuHBBuHr1qvDSSy8J/fr1E+sRHh4ufPPNN0JUVJSwb98+4dlnnxX7tBs3j5uzZ88KXbp0Ebp3755nv3cmJSVFqF27tsvz8uDBg0KHDh1y3Ua1sHBrAN24AQYMGEDLli0BGDZsGKNGjcJsNnPgwAGuXbvG3r17KVWqFABffPEFbdq04e+//6ZixYoADBkyhEqVKgH20SiLxcI777xD9erVqV69Oh9//DFt27albdu2AHTv3p1169blq25LliyhW7du/O9//wOgQoUK3Lhxg6VLl9K5c2fKlSvHm2++yfz58zlw4ACVKlVi+PDhYnmr1cr06dOpV68eABMnTmTs2LFMmjQJT0/PXKfwbt68yf3796latar4nl6vR6lUuhynVCofaBP5O3fuMGbMGOrWrUvnzp1dbNWqVePMmTP5Os+zzz5Lq1atxCnWUaNGsXz5cqKiolCr1WzZsoUvv/xS9MG0adPo16+fyzkaN25Mz549AejatauoU/T29qZq1ap88cUXXLlyhRo1amS5vkqlYsqUKSiVSqpUqcLq1as5deoUrVu3LqhLsrB69Wr69etHnz59AHj33XeRSqUkJSVRokQJZs6cSYsWLQAoW7YsDRs2zHbUYv369VSrVo133nkHgMqVK5OSkiKOrOVnKveNN97IovnbsWMHpUqVolq1anz88ceEh4cDUK5cOZYtW8bly5ezLBSJiYlBq9VSrlw5PD09mT59Ojdv3hTbGxISwsiRIwGoWLEib775Jh999JH4nhs3j5NDhw7Rpk0bXn31VerWretiu3//PjNmzGD//v1otVpatmzJhAkT8PLy4tixY8hkMvG7BOCZZ55hx44dT7oJ+cIdAD7FVKr0BW+/3YTRoxu6vB8VlUjlyvM4c+ZVatcuWUi1e7I4gjdA1HaZzWauXLlC2bJlxeAPICAggLJlyxIRESEGgGXLls1yznLlyon/VqvVLq9VKhVmszlfdbt8+TKXL1/m119/Fd+zWCwuU7CDBg1iy5Yt/PHHH2zbts3lS1qhUBAaGiq+rlOnDmazmatXrxISEpLrte/fvw+An5+fS1syB3smkwm1Wp2v9jiIjo5m+PDhKBQKvvrqqyyLKnx9fbl27Vq+zjVgwAB27NjBihUriIqK4ty5c4A9+L169Spms5ng4GDx+Dp16mTZji3z5+Xj44O3t7f4Xm5BbpkyZVyCYk9PzwcKiLPjypUrvPjii+JrmUzG+PHjAShfvjznzp1j/vz5XL16lYiICCIjI8VANvN56tSp4/Le4MGDC1SXyZMnZ9m+rXjx4gC0bduWI0eOMHfuXK5du8bFixeJjo7OdpXwq6++yksvvUSTJk1o2LAhrVu3pkePHoB9hfmhQ4cICwsTj7fZbBgMBhISElz6ohs3jwPnH9CZGT16NAEBAfz4448YDAbmzJnDuHHj+Pbbb7l+/ToBAQHs2bOHr776ioSEBJo3b87EiRPx8vJ6gi3IH+4A0I0byHZLKkEQsox0ObDZbAhOm+ioVKosx2QeKXnQ/V+tViuDBw/OMmLlTHJyMjExMUgkEo4cOULlypVdrut8bUe9C7J601l/VapUKe7du+dij4uLcwmS88IR1Pj5+fHdd99lO/pktVqRSCR5nksQBF566SVu375Nly5deO6556hWrRqdOnXKcpwDmUyWpf2ZA9D8XNvB49zSLLfVxps2bWLq1Kn07t2bpk2b8vLLL7NgwYICnye/lChRQvzRk5mFCxeyfPly+vTpQ9u2bRk3bhxvvfVWtseGhISwZ88e/vjjD/744w/mzZvHmjVr+Omnn7BYLLRv356xY8dmKVcUv0TdPD0cPnyYixcvsmLFCvG74dNPP6VFixZERESQlpbG3bt3WbRoERMnTkQmk/Hxxx/z5ptvsmTJkkKufVbcAaAbN7lQtWpVbt26xZ07d8QAJzY2ltu3bz+xdB5Vq1bl+vXrLl+8P/74I1euXGHSpEkAfPTRR5QqVYqxY8cya9YsWrZsSUBAAGBfXBEREUH16tUB+Oeff1CpVC5BYk6ULGkfAU5ISBCDtLCwMD7//HOsVqsYRB07dsxlxCY3YmNjefHFFyldujRLlizJcS/hhIQESpQokef5IiMj+fvvv9m9e7c4BexYxCMIAlWqVEGhUHD69GlxNOrSpUv5HoEtbCpXrsz58+fp2rUrYG9Tx44deeedd/j+++8ZPnw4b7zxhmi7fv06tWvXzvY8mfMevvvuu5QoUUKcFn4Yvv/+e8aPH0/fvn0B+6jwrVu3XAJvB8uXL6dy5cp07tyZzp07ExUVRYcOHbh06RJVq1bljz/+cOnvu3fvZseOHcyZM+eh6+nGzYMSERGBXq/PMgoOcPXqVeRyOWlpacyaNUuUinz88cf06tUryzO8KOBeBewmV9LSTIwZs41y5T5DoZhO+fKf8/HHfwKwZ89VFIrp3L+fseLw9u0UZLIPOXHiNhaLjalTf6dKlXkoFNMpVepTxo7dgdVqH0364IN99O79I2+9tRN//9n4+c3mzTd3YrPZvzDyKv8kaNKkCYGBgbz11lucPXuWM2fO8Oabb1KpUiUaN278yK4TFxfnsjLXmWHDhrFv3z4WLVrE9evX2blzJ7NmzRID0v3797NlyxamTZtG3759CQwM5P3333c5x+TJkzl37hyHDh1izpw5PPfcc2i1WgDi4+NJSUnJ9tqlSpWiRIkSXLx4UXyvffv22Gw2pk6dSmRkJF9//TVnzpwRRyitVitxcXE5ToHOmDEDq9XKrFmzMJvNxMXFERcXR3Jysstxly5dEqcsDQYDcXFx2Z7Py8sLmUzG1q1buXnzJn/99Zc4RWoymfD09OR///sfM2fO5PDhw5w7d46pU6cWaISvMHnxxRdZu3YtW7Zs4fr168yePZvU1FTCw8Px8fHh8OHDXLlyhYiICKZMmcKVK1ey9X3//v25dOkSCxYsICoqik2bNrF9+3ZatWoF5N4P8oOPjw/79+/n+vXrnDt3jjfeeIOkpKRs63Lnzh2mT5/O0aNHiY6OZsOGDXh6elK5cmUGDhzI9evXmTFjBlevXuXvv//mgw8+wMvL64FH0d24eRRYLBbKlCnDpk2bXP5+++03mjZtKv5grlatmljGMVBw69atQqlzbrjvJje5Mm7cTg4ejGHTpn5cujSaN95oxKRJezl58jatWlWmRAktmzZlBAc//XSe6tX9qVevNHPm/MXKladYubIXERFj+OSTdixceJTNmy+Jx2/ZcpmUFBMHDw5n7tz2zJt3mK1b7QL2/JR/3EilUhYuXIivry+DBw9m6NChBAQEsHz58hynhx+EZs2asW3btmxttWvXZv78+Wzbto0uXbowa9YsXnnlFYYPH05qairvv/8+/fv3p06dOkgkEt5//30OHDjAL7/8Ip6jU6dODB8+nLFjx9KxY0cxQALo27cvH330UbbXlkgkNG/enKNHj4rveXh4sGTJEi5fvkzPnj3Ztm0bX331lTj6dvv2bZo1a+aSqsSByWRiz549xMfH07lzZ5o1ayb+TZ48WTwuJSWFS5cu8eyzzwKwbds2mjVrlm0dS5UqxYcffsi6devo1KkTM2bMYMCAAQQFBYlawIkTJ9K6dWvGjBnD8OHD6dGjBxqNJtvzFTW6dOnCG2+8wWeffUb37t05ffq0OHI6adIkJBIJffr0YejQoRiNRl5++WXOnz+f5TzlypVj0aJF7N27l27durFo0SJmz54tLtrIrR/kh1mzZhETE0O3bt147bXXKFu2LH379hU/A2feeOMNmjZtytixY+ncuTMHDx7km2++wdvbm4CAAL799lvOnj1Ljx49ePfdd+nWrdtj3x3GjZu8qFq1Knfv3sXDw4OKFStSsWJF5HI5M2fOJD4+nvr16wO43H8RERGAq8a4yFBo64/dFDoVK34uKJXTBQ+Pj1z+tNqPBPhAOHPmjrB8+Unh+PFbLuV8fGYKK1f+IwiCIIwbt0Po0GGVaGvWbJkwbdo+QRAEYePGC8LevVddyoaEfC18+KHd/v77vwve3jMFo9Ei2sPCFgnvvbc7X+X/S/z000/Cli1bHvl5Dx06JAQGBgqpqakPfI5Tp04JjRo1KlAag3nz5gknT5584GuuXbtWGDJkiMt7Q4cOfeDzuXHjxs2D4JwGxmq1Cj179hQGDRoknDlzRkwb1rVrV8FisX+Pvf7660LXrl2FkydPCmfOnBF69uwpvPTSS4XZhBxxjwA+5Uyc2Ix//nnF5W/btgGifciQuty8mczbb/9G9+5rKVfuM5KSjFit9mnaAQPqsGfPNe7f13HrVgp//x1N//52/VHPnjWQSiVMnLib3r1/pGrV+Zw+fUcsC1Cxog9KZYYY39tbhclkzXf5/wI2m42NGzdmqyspCoSEhFCrVq0syZNzIjU1lYMHD7qsui0IgiDwww8/8Oqrr4rvHTt2TNQ0unHjxk1hIJVK+frrr/H19WXIkCEMGjQIf39/lixZIuqhZ82aRf369Rk5ciQvvPAC1atXZ+7cuYVc8+xxLwJ5yileXEu1aq4rMOXyjN8FI0ZsZufOSIYMqcuAAXVYsKAToaHfiPbw8DJUrerHpk0XSUszU79+aapXLwbA9On7+eyzQwwbFkrPnjX4+OM29O//s8u1nIM/Bw7NeH7K/xeQSqV89913j3Ul6cPywQcfMHr0aDp37pznalJPT09Wrlz5wO3ZsWMHgYGBPPPMM+J7oaGh4vTKf4nXXnuNv//+O0d7586dH2pa1o0bNw/HpUuukqOAgIAcV9oDaDQaPvjgAz744IPHXLOHxx0AuskRQRBYvvwUu3YNpnVr+4rR2NhUkpIMLiv7+vevzS+/XCIhwcCAARl5xhYsOMLcue0ZNsy+OtRotHD9emK2qwKz42HL/5t4XMFfo0aNsjzAHoTy5cu7aArz4mHa06lTpywpXB5FCpOiyPvvv49er8/R7uHh8QRr48aNm6eJ/+ZT1YnExERSU1MLuxpFEqvVSmJiIjExMS7v375tXwkYG3sHrVbOypVHUasN3Lmj4+OPjyAIEBt7TyzXunVJZs06gNUq8MUXTcX3vb0V/PTTaapXV5GaauLzz0+SkGDg3j37NZOTkzGZzC7XNxqNpKSkEBMTk2d5N27+C+SWj9FgMLj7uhs3+cTT0zPHtFJFIRbIrX6FgUT4Lw6npJOYmEj79u1JSkoq7KoUSa5e7Y6f30X8/Fy3jTKbPbh2rQcVK27FbPYgLi4Mi8UDuVyPl1cURqM/MpmegIAjYpkbN9ojlVooV26v+J5eX4y7dxtgMnkjkxnx9IzBZpNhsXhQrtzv3LtXh7S0MlSsuFMsEx3dBrU6nhIlTuZZ3o0bN27cuHHg4+PDb7/9liXISkxMJDw8vEDJ7x8HOdWvsPhPjwCmpqaSlJTE6tWri7yA3GAwFHgrrcdPztvh5MykfB432enf7+Z6ZHa+2bz5fyxceJQrV+IpXVrJM8/IKF06mdTUVMqWLUutWrVISUkhOjqaW7duYbVaKV68OBUqVKBEiRKUKFECX19fduzYQWJiIg0aNBD3JfX09CzQg0IQBG7fvo2/vz8qleqh8suZTCaWLl0q5owqVaoU9erVo0GDBjlOq2b2j8FgIDExkYULFwL2ZM49e/bEZDJx6tQppFIpp0+fxsPDg0aNGrmkV5k6darLFLtEIkEQBF577bUifw9lR9G8r4oGbt/kztPkn+TkZD755BN69+7Nhg0bAGjevDmXLl1iyJAhfPHFF7z33nviM6go+iY2NpaBAweSmpqaJcBKTU1FJpNx48YNLBZLodRPLpdToUKFbOtXWPynA0AHAQEBRTMHjxN6vf5fk5fsSePsG4vFwowZv/D115fo3VtGx47g7V2dJUvuMnlye1566ZkswZtjG7PsksjWrVuX06dPc+rUKfbu3Utysj2I9PLyomzZskRHR2M0GpFIJGJg5ziP4z2LxYJarSY5ORlBEKhZsyaDBg3Cx8cnz7bZbDbi4+OJi4tj7969Ys60QYMG0bhxYzFZc378YzAY2LJlC3v32kdhHT5LSUlBoVDQuHFjcZNys9mMTCZDKpVy7tw5Fi5ciFqtZsCAAbRu3Rqwb9d2+fJlGjRowL1799i8eTNnz54F7IsTHDtT2Gw2bt++jZ+fX77q+yRx31c54/ZN7jxN/jGZTPTs2ZO9e/eKbQ4MDKR48eJs2rSJWrVqcfHiRVGb+2/1jdViwVpIAWBRTDv/yAPA2NhYPv74Yw4fPoxcLqdly5ZMmDABb29vMbv7sWPH8PDwoFevXrzxxhuiwHvr1q18+umnlC5dmrlz51K6dGliYmJo06YNAwcOZOrUqS7X+v3333nllVceicjdTdHn1q1bLFu2jMWLYcyo8ngbzjB43Dv4lSxFw3LHeWvGn7zySlMArGYrhvtpaIp5IFXIMOvNmExG1D4aBEHAlGZEppQjV8qpW7cutWrWwmqyoPRQ2Uf0om8TezeW559/Hg8PD6wmC2aDGYWHPfmzMc2IRCpBprQHUXKrDJWnGhs2duzYwYzpM+jbpy+NGmef2kWn07FmzRrOnz+PRqMhISEBsCeEfv755ws8VXHw4EFWrVoFgFqtpnLlyiQmJhIXF0e1atXEbYkcOI8mOvZXlUgkJCYmigFuYGAggYGBxMbGiqOJAKVLl6ZYsWLi62+XfMs/p/6hePHiTJgwAa1Wy/nz56lcufK/8kvCjZunDaVSSY8ePXj22WdZtmwZERERrFmzhubNm1OtWjWOHDnC9evX0el0tG/f/t+7KEsisf8V1rWLGI/0U7TZbLz22mv4+fmxcuVKjEYjH3zwAePHj+fLL7/kpZdeIjg4mJ9//pl79+7xzjvvoFAoeP311zEajSxatIjPP/+cixcvMn/+fGbOnCmee+3atfTo0YO6des+yiq7+Rdw7do1zp49y9GjR+nVqxfvv7+b3h1r8P0HSzGmpQKlCC+p5tL1DK2nYLVhTjOh8tUgVciwmq1YjBZxetNisiKRSkBpvwVsFisWkxWlhz0Q8vPypXjx4qg8VACYMCGxStF6aJFIJEjNUmRyKSovNVaLFX2CDpvNhlwpp2vXrpQqVor9B/bTqHEj7t27x9WrV7l79y6xsbHExsZy//59mjZtyocffohWo+XsyTNs2bmVI0eOYLVaGThwYLYjlrdu3eL06dNER0fj6elJqVKlSE1NZceOHRQrVoyXX35ZHO0WBAGdTodWa6+zzWZDr9cjk8lcpm8cwaBer6dHjx5ZrlmsWDG6d+9O9erVqVKlSpYp7ppBNbh46SL379/ns88+o0WLFvzwww/Ur1+foUOHurfvcuPmX4KPjw/jxo3j2rVrLF26lD///FO0Pf/880RHR/PBBx9Qs2ZNatWqRWho6L/qR56Uwtv+rCg+BR9pAHjhwgXOnj3LgQMHxE3cJ02axIABA7h79y41a9bkww8/xNvbmypVqtCxY0dxc3Kz2YxarSYwMBAgS26s0qVLM2XKFDZs2PDv/fXhpkCkpqZy9OhRDh48SEhICJMmTcLX15eaNf/hjq4kH2//Qzz2rKeWmsElxNdytQLfqsXF12pvNXhnBD0e/q7pNZRaFUqtSnytzWzXKFFqMrZ+0/plTHXK5DI8S3i5HK/x0qBUKbl37x6zZ88mKCiIEiVKEBISQvv27SlVqhQqVcb1QsLrEhJeF4PBwKJFi/jxxx/p16+fS7BlsVhYsGABoaGh1K1bl7S0NM6fP09CQgIffvghxYtntDc1NZVbt25x8+ZNbt68ya1bt7h9+zZSqRSLxYKvry+lS5fGaDRy48YNALp165atzlChUNCxY8cs7zto/mwLyleqwMqVK7l16xbr16+nTZs23LhxQ7zfe/fuTaVKlXI8hxs3booOlStX5v3332fPnj3s3r0bnU7HkSNHKFeuHJ07dyY1NZV//vlHvNc7duxY6Ass8oN7ANCVRxpJlSlThiVLlojBn8uF5HLmzZsnvr548SJ79uwRRxw8PT1p0qQJDRo0wNPTk6VLl7qUf++993jzzTdZtmwZI0eOfJTVdlOEMJlMGAwG9u7dy19//UXVqlXp16+fuKE2wKRJzRk+fDNLl3anWbMKHDhwg+HDN/PRR60LseaunDlzhooVK/LPP/9Qr149+vfvn69yarWal19+mXnz5rF582aXEbm//vqL0qVL8/zzz4vvNWnShJkzZxIVFcXp06e5ePGiqFssW7YsZcqUoUKFCjRu3JgyZcqg0Wiw2WzcuXOH2NhY5HI51apV4/Lly6xZs4bixYvToEGDAre3UqVKjB49mmPHjuHv78/vv/9OQkICer2eu3fvIpfLuX79Otu3b6dHjx6ULl26wNdw48bNk8Pxw69jx47odDpiYmK4efMmMTExxMXFYTQaGTt2LJs2beKLL77g1VdfLXIa4MxIJRKkhRSJFdZ1c+ORBoB+fn60aNHC5b3ly5dTsWJFl6CwS5cuXLlyhVq1ajF06FDx/XHjxjF06FC0Wi1KpdLlPBUqVODVV19l4cKFdOrUSdx43k3RxGaziYskBEEgOjoanU6HSqVCqVSiUqmwWCzEx8fbF0HcuUts7B0uX7mMIAiE1w/nrdFvUqp8KQxGI8lbT2GJT8N/cBP6969DbOwd+vRZSlKSkpo1izO2VzGCo/cB9kTUVoOZlGsJeFX2R6aWYzaYsRotqNJHAY0pBuQqBXKV/RawGC1YjGZUXun2VCNypQy5yj4iZjFZsBjsdolEgjHVgFQuQ6FWYLPaMKYYUHmqkMpl6HQ6/vzzT0qWLElycjKvvPwK+mQ9Sq0Smdz+K9mUZgSJfeRRsAkYUg0oNUpkChkajYaRw0Yy/6v5xMXF0a5dO1JSUti2bRtjx47FarZi0ptQe6pRqVR06dKFQ38fwtfPl0aNGtG3T1881Z5ovNRIpFIEQbC3V5o+umcDX60vpUJKIZXZ7UFVgnhl5CssWryI4ODgB0pA7O/vT/v27QEIDw/n1q1b3Lt3j6CgIFQqFZ999hlXrlzh+vXrfPzxxw+1WtqNGzdPDq1WK2qCwa5h3rt3L6tXr2bs2LFs2bKFL7/8kjFjxhT5KWH3UyeDxzqXunjxYnbt2sWiRYtc3p8zZw7Jycl8/PHHvPbaa6xYsUK05bY8esSIEWzdupUPPvggywihmyeH1WrlxIkTaLVaKleuTHR0NNWrV0cqlXLlyhW+/PJLTCYTGo2GYsWKkZiYiIeHBz4+PphMJoxGIyaTCalUir+/P/7+/nh7eBNSuw4vDH0BrVaLxWjBmKwXt4Uz37iP5V5GEs9mzXyoWfN3Vq5cSdWqVTnz2SISzkWKdpvFhiXViM1iQ4Zd42e1WDPsVhs2q831tcXptcWKTSoBVU52W8aYviBgM1sRbPbKarVaJr87mbh7cVQPro5apUafpBftjvM5EBCwWWwudq1Gy7hRYzly6ijLli3DarXy4osvUrp0aSxGi/14QUCChFq1alGrajAKjQKFRonFZMGYakQQMh52NosNm9yWXl3Bfn2nDKA2i5WypctQr149Nm/enO8Ry9woU6YMZcqUEV83b95cTGycnJycr1XSbty4KXpIJBK6dOlCUlISy5Yt49VXX+XHH3/kq6++YvTo0S7ylqKETEKhRYCyIhh5PrZE0AsXLmT+/PlMnjyZwYMHZ3vMmTNn6Nu3L7/++qv4yyIzjlXAjmOOHz/OwIED+eSTT/D09Mx1FbCj7J49e9xpYApAfHw8x44dIzU1ldq1axMYGEhycjLHjx8nOjqayMhIvL290el03L59G4BevXrRtm1bPvroI7p06UJYWBipqancv38fHx8ffHx8HnjEpyj5pijyKP2j0+n48MMP6dKlC3Xr1sXLyyvHz82xqMRoNLJv3z5OnDhBQkICr732GsHBwTle49atW5QuXfqJjAC6+07OuH2TO27/5IzDN1arlU8++YSOHTsSEhLC999/T1xcHOPGjXvii79y+7532O5GXy+0NDAyuZyS5SsWqXjksYwAfvTRR6xatYqpU6cycOBAAO7evcs///wjThEBVKtWDUBMgZEf6tevz3PPPcfMmTN5993cEwg7MBgMue63WRQwGo2FXQVMJhMHDhzgxIkTBAcH4+XlxapVq6hZsyaRkZFUr16datWq0axZMzEp8PHjx9m2bRt79+7FarXi6elJUFCQuNq0ZMmSgP0zeFCKgm+KMo/SPxKJhFdffZUdO3awb98+BEGgdOnSNGzYMMuPtA0bNoh5AZ955hkGDhzIypUriYmJoXLlyjlew8/P76H6Q0Fw952ccfsmd9z+yRln3zRo0IDIyEiCgoKoU6cO69atQ6/XP/EAMD/PFPciEFceeQA4b948Vq9ezaxZs+jZs6f4flRUFGPGjGHv3r2ULVsWsI8ASqVSqlatWqBrvP322+zdu5cFCxbk63i1Wv2v+CVXWHVMSUnhwJ8H+PPPP6lWrRpjxozBQ+1BcmIyO3bs4OLFi9SpU4eu7bug1KqQKWRYTBbMejONGzcmJSWFP//4k8iISPo/398+hZtuV3urRR2gIdmAUqNAppTbdWw6I2ovDRKpBGOa/YHiSLvisDs0eRgEBIsNbQlPACx6M2m3k/As54dMKSPtegKWZAM+dUqL5fXxOjT+WmQKGWa9CavZmqHxSzEgU8pRqO26OLPBjMVkQe1sV8hQpK/8NRvNWAwWsT2GFANSuRSlRmnXAKYaUHqoRI2fMdWARCpBqVVhs9k1gg7fAS7tFWwChhS9i92kMyLYBFSeatF3Co0CeTa+EwQBTCBXy1GoFC6+k6ZrAA0pBuTp7bVZrBjTjKg81aIG0O4PGQq1kvLlyjOk32BUnmpS01K5fOESP6z9ga7dutK0aVOx3zRt2lTcaWX//v1cunSJpKQkoqKiaN++PYIgsGzZMrRaLc8++6y48CM2Npa7d+9Sp04dMT3NgQMHOHz4MFarlVq1auHp6cmBAwfw9PRk5MiRD6RJdPBvuPcLC7dvcsftn5xx+KZGjRp8+eWXdOnSBa1WS0pKChKJ5In7Lj87k0ih0KaA//NpYM6fP8+iRYsYMWIETZs2JS4uTrTVq1ePWrVqMXHiRCZPnkx8fDxTp06lX79+Lukr8oO3tzfvvfce48aNe5TVfyq5ffs28+fPp1ZwLQb3G0xgcCAyhQyT3oSQrlOrV68ev/32G+1btBNToQg2G4LNhkwmo3PnznTs2BFDoh6lpyrdLiDY0nVmEkCwl7HZBGTYdWiCVUDArmNz1r/ZT2C3O3RqQibNniAI2Mw2HCJBm9GK1WDNVN4q2gVBcNXg2QSkTuqHzHbBJrhsiYaQ3h5nu/PxTnV12DNeOOxCtnaBdF9ksovHCA5/O7XFyXf2421O5xey1sdqE88vOOrjhP16Tr5Ir4+3tzchdepS3K84y9esYMuWLaK+R6vV0qVLF+rUqUNqaqqY49CRJNohG6hXrx7z5s2jYsWKNGrUiOPHj3Py5EkaNmzIiy++yJ07d/jhhx/EujjS0pQtW5arV69y9uxZGjXKPqG2GzduCpeyZcsSGBjI9u3b6dWrF7Vr1+bs2bM0bNiwsKuWBWkhDgH+51cB79y5E5vNxuLFi1m8eLGL7ddff+Xrr7/mo48+YuDAgchkMnr06MFbb731QNfq3Lkzv/zyC/v27XsENX86cSTf7t69O40bN3axKTVKipctQdeuXTl27Bjjx4/Hp5SvaFeolSjUGSu1pVKpS+48hVohjq4BSKQStH4ZdrlSjtw/o/s5Rt4cyJRytE52bXFPF7tCq8QvqKT42ivQ9UeETCnHq0xGfZVaFThlKND6uqYryJznT5PJnrm9Gp+MX7dSmTRL3kC1dx52p/Zm9h2AyjPDnpfvJBIJGue8hApX30kkruVlCpnL9SQSiUt7ZXJXu1KrpFKgPS9YfHy8uJdmXFwca9eu5a+//iIsLIxixYpRqlQpbt26xe7du4mKiqJx48b07NkTlUrFkSNHOHjwIACTJ0/mq6++IiIigmrVqlGsWDEaNWpE+fLlkUqlrFu3jps3bwL2FcXOCILgXkHsxk0Ronfv3syYMYOWLVuKCwOLYgAoK8QpYGkRfGQ9tkUgRQH3IhBX/vnnH3bt2kVwcDBt27bljz/+ICoqipdeeumxXvdhcYuxc6cw/WMymThy5AgXLlwQ90IOCAigRo0ahISEZEnn5Mzx48fZvn07EydOzJJENjIyknXr1uHr64unpyf169cnODiYa9eu8cknnzBz5sx8rSJ2952ccfsmd9z+yZnsfLNkyRJOnjxJ3759uXr1KiNGjHiidcrPIpDU2GgEayHtBSyT4xlQvkjFI+4tNf7DHDt2jMuXL2MymagXVo/vv/+egf0Hsu+PfWzduhVPD09GvTwKQMxlp/S069jMBhMWowW1t8au03LYPew6NbPBjMVgRu1jtws2AYMj151SbtcA6kz28tIMu0KrtOvYTBZMTnZjql3A6xj5spqtGFMMqNNH2oypRgSbTRxZs1qsmFLTdW4yKSadPeWLw26zWDGmpuvc5FJMehNWk9W+IwhgSDYgV8nEUT17e13tMqVMHBXM3F5Dst6uAdSqsvgOwJBi1wCqPFRZfGdvT0Z7M/sO7BpBwSag9sqwyzUKu8Yvk+8EQUCfqEOePupqNVsxpWX4xq4h1CNXyVGolVl859AYOtprs9g1jdn5zqUveNpzOjYKb0j9OvWy9BWZRJa971L0SGVS6tWrx/79+/nuu+/o3bs3/v7+ACQmJrJy5Uri4uKIjo4G7NNMwcHBfPvttwB8v/J7+g/sL5Zx48ZN4dK9e3ciIiK4fv06JpOpsKuTLYU5CFcEBwDdAeB/FZ1Ox5o1a+jWrRvr16/nyJEjdO7Qmbp1Q6gWWI2dO3fS4pnm+Pr7imWyDAZnkeXlNlicScOWw7GiDi2vBghCpmNcNW0IedUnkz3zoULW85FH+/O8Vi7H56euBR+Kdwgs83E9AVdNYL4+y5yPyeLbgvSVdJNjxfGuXbv46KOPAHvuwISEBOLj4wkLC0Oj0RAUFCROA9evX5/du3dz/uJ5Pv30U1566aVcVxy7cePmyVCqVCl69erFqlWraNasWWFXJ1tk0qyP+SeFpAiuAnFPARcRHna6wWazcf78eSIiItDr9dy+fRt/f3+GDh2K0WgkKSlJTMnyb8M9FZM7/wX/mEwmbt26xc6dO2nSpAnr1q3jpZdeokKFClmO/eeff9i0aRN3796lSpUqvP322zme97/gm8eF2ze54/ZPzuTmm3v37uHj45PtvuIPi8lkYv369Vy8eBGJRELnzp1p1KgREokkX1PA5rgYKKQpYGRyFCXKFal4xD0C+C/HseXa6NGjAejatSvFihWjSpUq1K5dGwCVSvWvDf7cPB0olUoqVarEyy+/DNj1gTt37qRjx474+Pjg6ekp5hULDQ2lYsWKnD59mpCQEAwGA5cvX+bevXuUK1eO6tWruxeJuHFTSBQ0q0dBOHv2LH/99ReTJk3CYDCwfv16Nm3ahL+/PzVr1syzvET8jxtwB4D/Sq5fv87ff//NqX9OkZKaQsWKFQHw9vKmY4eOGbquJD0mnQmlVonNYsWQYkDtpUaarlPTJ+mRKTJ0bIZkPSpPtZg3z2K0iLqtLHaDGbPehMZXK+ZyMyTpUXqokCvlmI1mzDoTGh+tqAHUJ+lQaJUoVAosJgumNCMaHw0SqRRjqgFByFgdazVbMaYaRE2fMc2IYHXSADrZpTIpprR0DaBPhkbQ6NRek86E1WQR7YYkPTKVXNT4mfQmrEYne7IemUKOUpuDBjApXQPokdV3jvISmVTUANrtKmQK+y1nSDEgkTg0gDb0Tr4Du0ZQsIHaW52j79Q+GjHPny4hDYVGKWoAnX3j6AtylRyFRpmjXaaUZ9tXnH2XbV8oYF8xJOmRyKVizsfMfcWQbKBP995s372D5cuXk5KSgsVsoVGjhvTo2RO1Wo2fnx8tW7YEENNKOejUqRPdunV7tDedGzduCp06depQvnx5IiMjadGiBePHjychIYG7d++ybNmyPMvbt4IrrEzQhXPZ3HAHgP8SBEHg0qVL7Ny5k7t379KsWTNGjRxFyYCSXLtxDYVcQdkSZVzKSKQSp74uSR8VkTjZcX0tkWS8zJwvSYLrqEqm15L08zveE//tXMTFjmt9JBIkmYRkzuUlgJBrfSQumTYd9RHbILH7w/n4zMWd38hc9yxI8zheIsl4mbmtWa7n8F2m8lLBpXxm30py+uxyqI+LLzI9BHPrK5LMvst8/mxya+XWV5BKXF5n7isSKShVKp577jkArCYL9+7cY9e+3cyYMYMBAwYQHBxMcnIyJpOJShUrIZfJadioIX/99Rfbt29HrVbTrl073Lhx899BoVAwbNgw5s6dS9WqVSlbtqy4n/yLL77IL7/8kmv5QtwK+AE03o8ftwawiJBZU2Gz2fj777+5evUqOp2O2NhYpFIp7du3Jzw8HLn86Ynd3Vqc3Hma/HP+/HnWrFlDmTJluHTpEhKJhJEjR4p7D0dGRjJ37lwA+vTpQ+PGjdFqtbmd8qnlaeo3D4LbPzlT2L45dOgQu3btYvz48WKqqejoaNq2bZurBlB2PwaJzZrdKR87glSGtZhbA+gmHaPRiEQiccmVZrVa2bt3L+fOnUOv19OyZUu0Wi3+/v6UK1fuie+v6MZNUSI4OJg333yTffv28fzzz3P06FHOnz8vBoBVq1Zl7NixfPHFF/z8889ERUVRo0YNatWqha+vb+FW3o0bN4+ERo0aceHCBb777juGDRuGTCbLl+5XJnWdOXmSCFIJhRN65ow7migk9u/fz7hx45gwYQJnz54F7NuyzZ07l/PnzyOXy+nTrTcNwsIJDQ2lbEAZDIl6cTs0h+7LpLfnW7KaraTFp2GzONkTdZh09j1nrRa73WrJ6IL6JJ24J63NYrPbzXa7SW9Cl5AmpvOwWW3o4tOwmuwrqMx6E7p4J7vNbrc47Aaz3e7YvswmkHovhRPHTvDbb79x9MhR7ty4gy19ezVDigFDcsZm3laTBV18mtheY4oBQ5I+w2622u3p7TWmGdEn6TLsmdpr0hnRJers6VYcvtNl5Koy6TLam9l3Ynud/JGb78CuATSmGFx85/CN3Z7R3sy+E9ubrBd9p4tPw2wwA2Bx+Mbm9FnHp2F29IVMvnvYvuLsu0fRV5x9B1n7irPvACxG1/Zq5Vo6t+1MsWLFqF69OhERETgTGBjIwIEDUSrt+zSfO32W6R9O588//8wzHY8bN26KPhKJhP79+3Pq1Clmz57N+++/j9FozLsgGdPAT/qvKOIeASwkfv/9dwAMBgMrVqzgmWee4fDhw3Tt2pXmzZvbbSkGJLL0GF0qsf/bWUolk4ojghKJ/bWrXSJqtyQSCVKZ1FV7JZVmaLvSyzvMUqnUfj7xYOzXd5xPJkUilzqZ7fVznF+aqb4CAqt+/J6k5CSqB1bnSsQV1kauxdfXl+rVq1O5QmWsViuxcbH4+/tTtkxZoqOiOXPhDCVLlqRUiVIU9y9GmlHH/fv30aXpKFOqDBUqV8Am2Lh39x4JCQlYBXtQItgEbBYbVatXpVr1akikEqSyjLZLZdIsr5FASkoKOp2OtKQ0SpYqYd9CztFeJ3/k5ju7XYLE6bORZPa907Uz+06028QDkMikSJ0+S4lM6qoBlEsfrq+41Ne1r0ikEvHa2bW3wH3F2Xdk7St23zn3U9f2OvuuYsWK3L17N8uUVNOmTalQoQIbN27k1s1b6A161q5dy/Hjx2nVqhW1atV6qmQUbtz819Dp7D/4vb298fb25vPPP8+zjEwiKbQ9eYtiZgK3BrAQuH37NhcvXqRSpUp88skntGnTBh8fH+rXr4+fn19hV++xcPfuXT7//HM+/PBDMT+UzWYjJiaGiIgIIiIikMlklCtXjvj4eKKjo/Hz86NJkyakpqYSGRnJ/fv38fPzw9/fH5VKRVRUFHfv3kUmk+Hn54evry9qtX0VsUQiwWw2c/XqVW7dukWlSpWoX78+derUISEhgdjYWO7cuUNsbCxxcXEkJyej0+nQaDR4eHggl8uJj48nKCiIevXqUadOHfHcRY3C1uMUNl988QVt2rShTp06WWw6nQ6dTodUKuXnn3/m5MmTlC9fHkEQGDNmDF5eXvm+ztKlSzl+/DhgD3qLFy9OQEAAQUFBPPvss0XyAZ8bT3u/yQu3f3KmKPjm1KlT/PXXX7z66qtIJBLOnDlD3759c9UAeiTdQlpIGkCbVEaaT5kiFY+4fwIXAhs3buT27dvEx8czZMgQnnnmmSJxQz1O7t+/T0BAgEtyUKlUSoUKFahQoQJt2rTJUubevXvYbDaqVatG48aNH/jaer2eS5cucejQITZu3Ejx4sUpVaoUAQEBNGjQgBIlSuDt7Y2np6fLnrQ6nY7Tp09z9OhR1q5dK+5IERoammXvWjeFR1hYGMuWLaNp06b06NHDpY9JJBIxL9mwYcNYvXo1N27cAGDevHlZ9iE2mUzs3r2b1NRU/P39adu2rWhr27YtKSkpxMbGkpyczN27d7l79y6nT5+mZs2aBAQEPKEWu3HzdHPv3j2++eYbVCqV+MMrP4MnUkl6KpjCoAj+PsxXABgbG8vHH3/M4cOHkcvltGzZkgkTJuDt7Z2rLTu2bdvGuHHjXN6rXr06W7ZsAWD58uUsW7aMmjVr8umnn+Ll5cXhw4cZMmQI7777LsOHD3cp+/3337Ns2TL27t37IO1/oty5c4fLly8TERHBpEmTUEvUqDwyFoAYUw1YLTa0vtp03ZYOhUaBUqPEYrJgTDGg8dWKudv0Cfb9X5Vae243Q7IejY8GqVyWsT+sUo7SQyXa1d4aMVedLlGHTC5D5anCZrGiT8qwm3QmLAYzGj+tmNtNn6hD5aVGrpRj0psw681oM9s91chVcswGMyadCa2vPbfbtWvXCjy6uWfPHvbv388nn3zyUH7XaDSEhoYSGhpaoHJarZZnnnmGZ555Bp1Ox6lTp9i/fz+7du2iX59+lC4dgMpTjc1iQ5+kQ+2tFvP86ZN0SKRS1F7qLL4DRH2f2luTbrfnCZSrMvIECjYbGh+tXQOYqEOpVaBQK7EYLRhTjWh8M/L4pcWnPba+YkozYjFZMnI+PmRf0SfqkMql4r7PZoMJk84s9hVn34FdA2hMS2+vVOriO4CmjZsSWDGQzTs3s3jxYl599dVsF0vJZDIGDx7M8ePH2b9/P5GRkaxevZohQ4aIxyQkJIjPovr167uUr1ixImPHjhVf22w2lixZQs2aNSlVqlSB+pYbN24ejNOnT7N8+XIAl3s3P8iQICu0SKzoRYB5LgKx2Wy89tpr6HQ6Vq5cyddff83FixcZP358rraciIyMpFmzZhw4cED8W7VqFWAfpt2+fTtff/01wcHBrFixwqXsggULiImJecgmFw46nY5PPvmEqKgounfvjr+/P3KFDKnT6INULkPmpJWSyTO0VVKpBJnCdaWTVCFFmn68JN3uLESTymVZ7M7aKpk8ozwS1/NL5VKkCifdlsS1vEwmdalrZrtUJkXAxrbt25g2bRpHjx51GU3JD47puZs3bxao3ONAq9XSuHFjxo0bR5MmTVi4eCHHTtqnA5GS7rsMf9g/S/tnm9k3drvTZyeRIFNIs/ls0vuGxNEX0s8nTT9e4nq8TJbps86pr0hy6isy1/JSp74gdxrxzPxZ59BXpE59wbmvZD6fVJbe7x2awkz3gUQqQSbP0AA6+85h9/P3Zfiw4RiNRv766y9yQiKREB4ezltvvUX37t2zTNuWKlWKl18ayeujXs/yYzMzUqmUl19+mRYtWvzrpn/duPk38ttvv7Fu3Tr8/f1p0KABYWFhBSovISNt6RP/ezwueSjyHAG8cOECZ8+e5cCBA5QoUQKASZMmMWDAgFxtycnJ2Y4CRkREEBgYKB7vjE6nw9/fn8DAQKKiosTVsQ78/f2ZNm0aS5YseaDGPmkOHjxIYmIily9f5ubNm5QpU4bBgweLdscIhgOFWgHYp68kEomLXSqXubyWSCSovZzsMmk2dnWOdkAcgcnOLlfKxdEqsH/JOttlSjmyXOwpaSksWboELy8vXnjhBSpUqFDgFDZms33Va0pKSoHKPU4kEgktWrSgWrVqLF68mOvR1wkLC6N69eouQYljlwvI6htAXFySk12hyRgZztwXZAoZMoUmZ7tchiy3viIvWF+RqxTIVYqc7Xn0lcx2536XXXucfZed3dl3mdvbvXt3Vq1aRZMmTfKcpu/YsWO279cNC821nBs3bp48f/zxBwcPHqRWrVpERkby/PPPF/gcMgkUmninCEaAeX4jlylThiVLlmQbsBUvXjxHW05Lsq9cuUKVKlWytQUGBuLl5UXdunWZO3cugwYNcrG///77/Pnnn2zbti2vahcJjh49yq+//krjxo3RaDRUqlSpsKv0RLh8+TKzZ8+mTp06jBw5kkqVKj1Q/sK0tDQAKlSo8Kir+NCUKVOG8ePHI5PJWLlyJbt27SrsKrnBngfQz8+PHTt2FHZVssVisafGMZlMnD9/nrt375KamppnucjISH7++We2bdvGxYsXxZQ4btw8LRw9ehQ/Pz8uXrzImDFjHijBuwQJ0kL6yyn/YGxsLK+//jqNGjWiadOmvPfeeyQnJz+su/JFniOAfn5+tGjRwuW95cuXU7FiRUqVKpVF++KwZRcUms1mrl+/zqFDh1i6dClGo5HmzZvzzjvviNN9c+bMYcKECfj4+GT5BR8eHk7fvn35+OOPadasWY46w6JC/fr18fT0pGHDhjRo0ABDsh59og6Nb7quKyENpVYpjvYYUgzYLFa0fh5ibjeFRolSm67rSjag8cvQdekcui+tCqvJgiHZgNpXg8yh60rQIVPJUTl0XUl61D5OGsCENGQKGSpPNVaLFUOi3q5jU8ox6Yx23Za/R4auK0GHyttJ16UzofX3AOwjxfv27SMmJgZBEBg8eLCYnPdBad26NSEhIXh6ej7cB/GY0Gg0PP/886SkpKCSKrGaLOKoqD4xXcfmrc7wnZda1Pjpk/RIALWPxkk/qRJH2gzJegSbkG1fsRjN6RrAjL6Qdj8VhVaZoQEsYF/RJeiQ59BXjGlGLEaLqOErcF/RmTDrTaJdl2DXADpGHc16k10v6udh1wAm6lxGETO315CkRwA0Pg57uuYxvb39+/bjy0ULkUql1KpVCw8PD/z9/Qt1mvbUqVP8+eefnD9/nvHjx/Pll19iNBrx9PTEYDCgUqoIqxdG+/bt8fLywmg0IpVKkclkHDx4kO3bt9OiRQv0ej0/r/+J23dimTx5snvhiZunAkEQiImJoXz58owdOxYfH58HOk9hjgAK2Tx+HDI6Pz8/Vq5cidFo5IMPPmD8+PF8/fXXj71OBV4FvHjxYnbt2sWiRYsKZAOIiorCYrEgl8v5/PPPuXv3LrNmzWLs2LEsXbpUPM7f3z/H67/zzjv8/vvvfPrpp3z44YcFrf4Tw2w2s3v3bvr06QPYp9nkKoWY7BaJfWrMWQslV8qwOenA5Cq5GKxJpVJkKrnLl5hclVFeIku3O+WKkynlGTo0qcTFbr+e3FUjqJKLueSkchlyVUaGIInEbneM5MnkMmxKGadPn2bHjh2YTCbatWvH888/j4+PzyPJsRYQEEBAQAB6vT7vgwuR2Nu3adm0pUueQJky02ehkrvkHZQrMz53iVSCXCnPUj6nviKRSV3OD+l9Rf5gfcVen4y+lrmvyOQyEDLyWBW4ryhkCDa507VkLtPlUrnMPkUsyeq77NorU7o+wqUyiUt7/YsXY9Qro9i4eSPnz58nLi6OSpUq8fLLLxdaEHj69GnOnz8P2L/M0tLSUKvVqNVq3h77Ngajgf0H9jN9+nQMBgNqtRqr1YrFYqFcuXKMHTuWUqVKIQgCndp24q0Jb/HRRx+xYMGCQmmPGzdPEolEwmuvvUaVKlUeajcsmaTwFoEI2Tx7HkRG9ygp0Lf0woULmT9/PpMnT+bZZ5/Nt81B9erVOXTokLgatGbNmvj7+9O3b1+ioqLyNUXq4+PDxIkTefvtt+nRo0dBqv/YEQSBlJQUbt26xe7duylbtiy1atUS7XaNnx2JRILKy1ULJVcpQOVkd9boOY2YZGuXudoBVJ6qXO1KJ62VVOpqz1bX5WS/e+8ua9euxWAw0KlTJ+rWrfvUblOXmpaGb3Ffl2TISq2Thi+T7yCrxi9zX8itr8jkMmReMlf7Q/QVuz3nviJXycWRSyh4X7Fr+DLqq8xW4+dkd/Jddu119p29vTLUzna1gtLlSjNq1Cj0ej1KpZJPPvmEP//8M8tsxpOiXbt2lCtXjpCQEDHdU926dTl8+DAR1yJo1KgR/fv3p3///giCIAaqzv8G+2el8dbw+uuvs2zZMi5evEiNGjUKpU1u3DxJqlWr9tDnkFB4259lF3bmJrHL784mD0O+A8CPPvqIVatWMXXqVAYOHJhvW2YypwJxfKh37tzJt0aua9eubNy4kalTp9K3b988jzcYDI99FMlgMDBnzhzAni6iZs2aNGzYEIPBkEdJO0/iw35UREVF8dNPP9GyZUvCw8ORSCSPtf5F2Td///03UqkUhUJRaCOVRdk/hY3DNwMGDGD58uVUrVo11xmGx4WPjw/PPPOM+Hru3LnYbDYCAgLYs2cP+/btIyAggHLlytGgQQOXXIYOjEYjSqUSiURCxYoVGT58OOvWraN79+4EBgYWuE7ufpM7bv/kTFH0TX6+awtzCtiWTQSYm8Quu6AwOzL/SCwI+QoA582bx+rVq5k1axY9e/bMty0zu3btYvLkyezbt0/8FXzu3DmkUmmBF0h88MEHdOvWje+//z7PY9Vq9WNPsnzz5k2SkpJ4tvmzPNf/OVEHJVfIxdEcQ7Iem02w5/lL399V4WHXbQFIzPYca1r/bDSARguGFANaXy1SeYauS65WoPJQYTFZMCTq0fhp7VNuDrtKbtf4me252zTOGsD4NKQKGWovuwZQn67rkivlmNKMmA1Oui6LDV2ijvjUeL777jtGjBjxQF86D0pRSJJttVo5c+YMt2/f5v79+8TGxqLX63lz3JvILXIUWoU4EqZL1CFN17HZrDZ0CTrULhpAHSBB49AAJuhQeqpy6Cs2dPE6sa9YjGaMKUYXjZ8tzWrXAOa3ryQb0DhrAOPTkKsUqDxVWfqKMdVg1wCm94XMfcWYZsSSTV9xtFfUAKZr/HQJaUhlMtTe9lFCk86EKc2IRzEPJFKpi+/AvlewKTWjvfr0PaGdNYCGFAPadLuL79J9E1AigNatWzN//nwGDx5M7dq1n2TXyZEOHTrQvn17YmNjuXnzJidPnuTPP/+kSZMm+Pn5IVgEUpKSuXE7mqNHj9KgQQOe6/Y/VJ4qgoKCeP5/z/Pt0m8ZPGRwtjuh5EVRuK+KMm7/5ExR801+dmoqzDyAtnxcNy8ZHdg3Nti0aRP79+/nzJkzJCQkIJVK8fPzo06dOrRq1YouXbrka5FMngHg+fPnWbRoESNGjKBp06bExcWJttu3b+do8/PzQy6Xk5iYiEwmw8vLi/DwcORyORMnTmTMmDHExcXx/vvv06dPnwInUi1fvjyvvfYan376KWXLli1Q2UeNXq9n9erVdO7YmbZt7LnuJFIJcrXcNa+fUo5McNJ1qRUuU19ylRyb0/FyVYZdKpeiUGfSbakzAg6pTIpcoxB1ZhKJBIVagdSh65JJ7OVlruUz8gzaz+94LVPKcd4jUCK1l/9l7S906tTpiQZ/j4K7d+8SGRmJRCJBqVQil8tRKBQoFAru3LlDVFQUWq0WuVxOcnIyer2eChUq4OXlRWJiInfu3OHixYuULFmSsmXLUqlSJerVq0f16tWRy+wLIZyngBWZ9JgKtdxF9+acViXvviJx6StSmRR5Nn2hIH1FoZY77Q2ce1+RKe36uox9nl37ivP0r6M9zu21awAV4hyIXKVw8ZVMIbMHvunnV6jkLjkKZXLX9jpPR7u0N72MXCnHeYdLh2+aNWtG6dKl+fbbb2nevDkdO3YsErIFiURC6dKlKV26NOHh4Vy8eJHTp08TExOD2WRGrVKLC6FiYmI4e/kcMbdiuH7jOjdu3MBkMnH8+HEqVarEpUuXOH/+PBUqVECn04mzEUWhnW7cFDYSSSFOAecR/+Ulo7NYLCxevJjly5dTrlw5WrRoQdeuXfH19cVqtZKQkMClS5f48ccf+fTTTxk8eDAjR45EqVRmvZijTnntBfz555/nGI126NCBnTt3Zmv79ddfCQwMZPDgwZQtW5ZZs2YBcPHiRWbNmsXp06dRqVR07dqVd955J9dKOnYCOXHiBB4eHi4O6dOnDykpKdnuBPKk9gL+7rvvUKlUDBgw4IHP8W/YCu7cuXOsX7+eKVOmPNGt0B7GN4IgsGbNGk6fPk2NGjXEPYIdfxaLBX9/fypXroxer8dms+Hl5YVKpSI6OprU1FR8fX0pVaoUVapUKZI7Pvwb+k5hkZ1vkpKS+Pbbb9FoNPTp04fixYsjCEKBFy7pdDrMZrO4d/TjZOvWrWzduhVPT08qVapE5cqVqVSpEvHx8axfvx6TyQRAjRo1qFmzJn///Tc1a9YkMjISuVzOiy++mGVKyd1vcsftn5wpir7J7fveYQvU30UpFM5ewCaJjMuaktnWzyGjmzJlSo4yut69e/PMM88waNAgypQpk+u1rl27xpo1azhy5Ai//PJLjsflGQD+m3kSAWBkZCTffvst06ZNyzWIzYuieEM5Y7Va+fjjj+nRowchISFP9NoP45tbt24xf/58pk2bhkqlyrvAv5Ci3ncKk5x8Y7FY+O2339i/fz8GgwEPDw9GjBiBj48Pt27domzZsvj5+WGxWFi7di1Wq5UWLVogk8k4e/Ysx48fJzExEaVSiU6nQ6PR4Ovrm+VPLpdjNBopU6YMFSpU4MiRI/j6+lKjRo0C/YjS6XTodDqKFSvmovd59913SU1NJSAggKSkJIYNG+ay8Mxms7Fv3z527drFuHHjKFmyZJ6+cWOnMP2j1+tJTEykdOnShXL9vCiKfSc/AWANfVyhBoAXNSWy1G/evHl88803fPzxx7nK6O7cuVPgAYi8yjzen63/cbb8uoXff/+dXr16oVQq0SfqAND4au26r/g0VF5qUdelT9Ij2Gz2PH82G2n301B6qMRVj4ZkPVaLDQ9/DwSbQFp8KkqtEqVWZdc5JevtOqd03VbafXtuN5WH3a5P1KH19xA1gGn3UpGrFXaNn9kq5iB0TBWm3U9FppS72NU+GlHXZdab7bosiYS///4bLy+vB9IZFSbFixdHLpez+vvVlPAvQViDMMqWLYvufhrydN8BLu0FXPSTAKY0Iya9CY9inkgkEtLi05Cl76hhtVjRJ2T4Duw5FiVSqV3jZ7GhS0iza+bSp34L0ldsNhs6p75iNpgxphjQ+nuIGsDUeyn57ysmC4YkvdgXBEGw+0PtpBd16ivGVAMWgwVtel/Iq6/YLFa75tFbk64BNGLSmfDw90QilZB2LxWpQiZq+IxpRozJBjxLeiGVSUm7n4pUKkXjZ9ewZG6vs+/AnifQkGxA6+eBVC51vc8EgdS4lGzvs86dO9OuXTtS76UQGX2VxYsXYzAYKF+uPHfu3gFAoVBQzLcYtWrXYs2aNchkMqpUqsL/evyPoDpBSKVSUu+loDcb0Jv1xN+PJ+52HDqjjitXrmCxWLBZbGzcsJH3Jr3H6tWrAagVXIuXRr6U7x+NWq02W03PnDlzuH37NgEBAZw8eZKFCxcyavirlKtUHt9ivlhNVhrWaQACrFy5krfeesu9bd2/gIMHD/LTTz/xxRdfPNTAghtXpIW4CESazW2Xm8TOIaNzkFMgl5SUhJeXV7Yyj7wCRncA+IDcu3ePffv38c5b71CqjN3JcrWrrkuhVYq52QAUajnieKtEglKrdMkHJ1crkFnTM/xLQKlRiomFpXIpCq0SidQ51YgCmSLDrvRQitoqSfr5paJuzF4fZw2gUqvMyOUms59fQODy5ctIkFA2wD7MHBkZya+//sqYMWP+dV8eSqWS8ePHc/TwES5cvMiZC2d4++237Z+Ns/5SKRc1GhKJBIVGmUWT5/wYVmoUGb6T2n3nogHUKDPy5qX73iXnYwH6iiRTX5EpZOl9wemzLEhfkbnWVyJJv77C0RfS6yvL0NRJpBl7D2cu7+w7e3vS7fIMPalSQNQAZvaVXCkHT5XYHqVWmUkD6NpeZ9/Z2ytz6dsKtcJFA5jbfSaXy/Hw9iCsXhih9UJJSkrC18sHi9mK3qwnLS2NYj7+KFRKOnTqAIDVbMVqtiJN94nKQ41WrkWmlFOhfHlMQWYUGoX4QDbrTcxfuIDbt2/z7jvv8vnnnyOTyZg9ezbPPPMM9erVo1ixYjwojlGiEydOALB87QqkEinj3hxHqZKlUGiVtHy2JX8e+JOrV69StWrVB76WmydD7dq1+emnn/jrr79o1apVYVfnP4MMCm0RSHaB586dO7HZbCxevJjFixe72Bwyupz49ttvWbp0KUlJSezcuZP58+fj4+PDhAkT8i1JcQeAD8ihQ4do2LAhAWUzMvFnyd2WKd+Zi/BfIsmSD80ll1omu1Qmdd1fViJx2RNVKpNm2WNV6bIfrTRLfVxy0Ukl2KQ2Nvy8gRMnTuDv709cXBxKpRKz2cywYcMoX758Dt4o2nh5edG6bRtatWnN8uXLefvttylfvjwDBw4UtRR55qLLZM/suyy+zaMvPExfyVdfKIA9c3sz9xX7vs842V3rm9k3WexymUtwWxDfgj2gVckzzqfIHABmao/zIpHMbYfc7zNHICZTylGhwtfXl8zk1ldyus/q1a/HypUradGiBaUCSjHy5ZFcvHiRkydPMnv2bPz9/alXrx716tWjePHiWa6ZHxwav6ZNm3Ls2DHOnz9P6dKlxfoEBgYSExPjDgD/BZQsWZKhQ4fmqfVyUzCkFN4ikOyuO27cOMaNG1fgc3377besWbOGSZMmMXnyZMC+JsMhdXrnnXfydR53APiA/PPPP/Tv37+wq/FIWbp0KREREQwePJjw8HDS0tIwmUx4e3s/0UUfjwuJRMLQoUMZOHAgR48e5fPPPyckJITGjRtTtWrVf93oppt/D/Xq1ePvv/9m+/btjB49GqlUSnBwMMHBwfTr14+IiAhOnjzJnDlz6NixI61bty7wNbp27UpYWBhqtZoTJ05w7do1fv/9dxQKBaVKlaJkyZJcvnyZFi1a8MUXXyCXy2nXrp1LIun79+/zzTffULlyZXr27FnkdF5PEw0aNCjsKvznKGpTwA/K+vXrmTZtGs2bN2fKlCkAtG3bFq1Wy4QJE9wB4OMkLS2N+Ph4ypUuR+rdFLTF7NokXYJdm6T1S9d13U9D5e2k60rUIdgEtP5ZdV0AhiQ9VosVj2Kedg1g+v6udo2fGUOSAY2/VszdlnYv1a7r8lRnmwcwNS4VhVpu16mZLOgSdWh9teJUYdr9VPtIhlLCtm3biIqK4r333hN1Ax4eHi6rrv8rKJVKmjZtSkhICIcOHWLt2rWYjCbq1a1H81bNKVasGGn37RpAx44WxjQjZl26BlAqEX2n9tZgNVvRJaSh8dGKo09p8WlIZRI0Plq7Ji5eZ9cApveFvPpKclwSsbGxVAqqjFwuz6oBTDaI/c6hc8vcV7T+ThrAPPpK2v1U5Kp0vWimvmJIMWAxmkX9o9Wcrnn0TdcAphrsGsDinhkawHgdah+75jFb38llqNM1gKY0I8YUAx4l0jWA91KQSKXiPtOZ2+vsOwCLIV0DmN5efaIOm02wa2lFDaBSHAk0JOuxmrO/z4D09lpEPaiz7wCX9gKuvjNb0CVkvc9UCiWTJk3CoDdgSTFjMVrEvmJIMlCxdEVq1KhB+7btmPPJJ1hMZtq2b1eg9C0ymYwKFSoAMHHiRI4ePcrt27cxm83s27cPQRCIjY3l22+/RSaTcevWLZYuXcq4cePEkSa5XE5MTAwxMTFcvHiRyZMnZ5uU2o2bfyNSJEgLaQr4UV43NjY229zJpUuXJikpKd/ncQeAD8D9+/cpXrw4CpUCCRJX7VI6EqkEpafKdWpLq4T0/V0dU0/O01EKjQKZY89UCel2R243GUpPV+2U0kMl7osqlUtd7I7tvTJyw8lQeapcdGhKDxVWq4X58+dTvHhxpk6d+tj3HixKeHl50a5dO9q2bUvU1SgOHjrI7NmzKVOmDA3qhdOgYUPxWIVjr9n0e1jpoUIqzdDEqTxVSBWuW8GJeQBlUpSeKlGP6bA7yK6vLP1+GYmJiVgsFsaMGYO/t7/YV2QKGUonzZyjPpn7iqQgfcUjo29k7isKtQKZ3FUDqPTM0PjJ1QoXvaFEtMvy9B04pmzVGfeRp9pFU5i5vZmnkKUOe3p7FFplxj7Kom+c7jO1QgzOMt9nmdsrCEKWz8aRx9DhDxffybK/zxztValUSAVJjn3Fv3gxxr4+llVrVnH67Bm6detGtWrVCjwCr9VqadmypfjaZrNx8OBB1q5dy8mTJwH7NKPFYnHRS/r4+LBgwQK+/PJLLl26xN69e+nQoUOBru3m0SIIdl12YGCge5biIZFReCOAj/K6tWvXZufOnYwYMcLl/dWrVxMcHJzv87gDwAfg9u3bFCtWzP5F6PRllFV75PpFlUV7lFkLpZSLnSSz3X6tzLouJ7s0w37jxg0iIiJo06ZNxvHSrFoohVrB2ZNnkclkDB069Kl9uEgkEipXrUzlqpX533P/4+zZs/z++++cPneGV155BalUag+q5K5Bglg+B986nz9LX8ijr8TGxvLuu+9y+vRpcfWm1GnBjksAKZGIu8lk2PPXV8T6ZtI0Otuz0/i52DNp/LJoDnPxXb7sudxn2doz32ea/N9nmdubbfk89KC59oV89JXS5Urz9ttvc/jwYX7++Wfu379P6dKlCQgIwMfHh5IlS9KoUSMKglQqpWnTpoSFhXH27Fnu3bvHn3/+Sbt27ew7jjhtJyWTyfD29qZ69eocP37cHQAWMikpKcybN4+JEyf+a3XYTwKLxZLnMdJC3AnkUY4ATpgwgeHDh3Po0CHMZjNffPEFV69eJSoqiqVLl+b7PO4AsIBYrdYi/avYZrOxcuVKMZdZThvFR0REEBcXx9atW2nfvv1TG/xlRqFQEBYWRkhICDNmzCAiIoKgoKAnXo+aNWty9uxZnn32WU6ePMlvv/1Gp06dnng93BQOUqmUxo0b07hxY1JSUrh9+zaxsbGcPHmS7du34+vr+0D9UqvV0rBhQ3755ReUSiWbNm1i27ZtVKlSBS8vL+RyOQEBAbRq1YqFCxeSlpZGTEzMY02k7yZ35HL7rj2RkZHuADATx48f58iRI6jVaiIiIvI8vqgtAnlQ6tSpw44dO1i9ejVarRaDwUDz5s1ZtGhRgXIFugPAAvLtt98SHR1NaGgoZr0JQ4oBj2Ke6dqkNAB77jarjbT7qai91OIIgV2bZMPD3xObzUbavVRUnipxRECfpMdmtuJR3K5NSr1nz1+m8lBhMZjRJ+vtef4cuq64VBTadF2X0UJqfAobtm3E19eXMmXKsHLFCia8MxFvf28sJgv6BB0aPy0Wm4XPP/8cAF8fH5o1a1Y4zizCyGQyunTpwurVq3nzzTfRKDSY0ox4FvfiyNEjxN26S8WKlVB6KFm1ahWCTaBpk6bUC69HQEAA927G8cfff1KhcgXC6oaKefEUogYwu76iQaGx22tUDeLoiWO0bNmS6tWrk5KQgklnRKlVYdabMaToxX4nCAIpd5PFvmI2mO15AHPpK/okHVq/7HNGOvcVuUMDaMjQ+GXWPBpTDZh1ZjxKeIp7Bevi09B4a5Cr7RpAh++yywNo0hkxphrxKO6JVColLT49D2B6nr/c7jNAbK+Hv0eGBtBqs2v80n2T3/sMwJCix2KwZGj8nHwH2WgAnfNtmuwaQIfvAFLvpSJX2DWP2epF76cildnb6/Cdo68Y04ygF6herTqBgYHUqV2HrxZ+xbJly/joo48KvAOJ1Wpl3759/PXXX4wbNw4/bz+MKQYmfviey3Ht27enb9++rFixgs/mfsbrY16nUpVKBbqWm0eDVqvF29ubdevWUbNmzSK5G1FhsXnzZsLDw9m1axcpKSl5Hl+YewE/6uv6+/szZsyYhzqHOwAsII0bN+b69eucO3eOWsG1UHs5aZdc0q5IUHupM7RG2LU+zrndsrXbbBgMBuLi4pDYwEPmiSATuHPnDrNnz2bIkCEEBQVx7949Ym/FEp8Qz+WIyzRs0JA///wT/2L+jBw5kmvXrhF/P54PP/6QChUq4Ofnh0alwWA0EB0TTaNGjWjV/FnKlCv7n1jh+zgIDw8nPj6eTz/9lLohdfH18WXjLxsBCK4ZzOWrESCxp92oVrkax/85zrx58/Dx8SEtNY2KFSty8beL7N69G28vb6QyKWXKlKFJkyb4evuK18noKxmfg5evN1abPWP93bt3qRlUU0wNI1PKXPodYC+fnudPppSh8lK76EVVXmoxz59MIUPlqRY1fBKJxH68I2+fXMaZy2dJTE4kNS2VS5cu8c6b72RoAOX2lEOOaVCFWoFUJnPRCKq81EiVDg2gwq6BkzjqonLRDMpVCpe9hlUemTSA6cnKs7vPnNvr0AA632eib7K5z+yNd/WdvT1KZIqMvYWdfQfpe2jLZS52cXpebq+L85S42qm9mX0HoHLSPDp85+gLCrWr73z9fHn37XdYumIZ3333Hb179y5QDsGkpCQ2btzI4MGD8fX1RalSIpVKmTlzJqtWreL8+fP4+/vTqlUrvL29ibwSyYG/DjDn0zl8+OGHD5yixs3DkZqaCsDevXv/c9knHoa4uDj27NlDy5YtqVatGn///Xeux8uQIP+XBoB9+vTJ90zdTz/9lK/j3AFgAQkJCcFoNLJz506Cg4Nd9D+ZtUeZ85nJ8rIrZBw8eIR169bh7++P1WrFYDBgMBhQKBTUrVuXlStX4u3tTfHixSlevDglSpTg2rVrpKWl0bVrV+rXr49EIiEoKIigd4JITEzk5s2bJCQkkJaWRoBHAHVD6xIcHOwO/PJB+/btqVq1KpGRkcQnxFO9enX69OkjrrZ0pnqN6nTr1o1NmzYRHh5OYGAgVquVK1euYDAYALhy5QqzZs1ixIgR4vR85r4gCAJypRwBexQjlUqxYXPRAEo1rhpA5/JSqdRFt2a3Z9ahufa9zDq1XXt3oVKpKFu2LFKplC8WfMGgQYMoV65cVk2hXOay6CGLJlEuRSp31vBl1fg5tyc7jZ80h/ssu/bm5z5zSLJzsrtq/Fzrm1nzmFXjl1nvWUA9aCY9Z3af9bBhw9i+fTuzZ88mMDAQhUJBaGgotWvXzvW+9vf354UXXmD9+vUYjUZatGiBUqNEqVHy2muviRqiL7/8kpo1a9L3f33x8fXhxIkTpKWluQPAQuLll19m8+bNhSJHKcp4eHjQuXNnWrVqRUxMTJ7HS6CQwr+Hv+7jSAju3gv4AbDZbCxatIikpCTatm1LYGAgPj4+D3VOvV7PgQMH2L9/P6NHjyYgICDb42w2W4FSQ/wXKIr7Tj4Mv//+Ozdu3OCFF15wef/IkSPs3r2bpKQkevXqxcGDBxk3bhznzp1jw4YNTJo0KdvP/nH459dff+XUqVO88MILlClThkmTJtGlSxeaN2/+SK/zuPmv9Z3MxMXFcfXqVYxGI0ePHuXWrVsUL14crVZL6dKl6d27d7bTxPfu3WPhwoV07dqVtLQ0tmzZQpUqVRg+fDhSqZSzZ8/yzTffMGvWrKcqM4Az//W+8zAURd/kZy/gdsZEtIKtUOqnk0jZpfJ95PHIw/DII4nY2Fhef/11GjVqRNOmTXnvvfdITk7O0wawdetWWrVqxYABA7h9+zZg/+CCgoL48MMPs1zr999/L5RfRFKplFdeeYWOHTty+PBhJk6cSGRkJLr4NHTxdn2SzWoj5U4yZr1JLKdL0JF23z6Ub7PZ7cY0IwDJycls376dt956K8fgz3FtN/9uwsLCOH36NMl37fmaLGYLSbGJrFq1it69e/PMM8+wbes2rGb7qrbg4GAxuS/YNXEpd5KxpW9nJgiCS18yG8yk3EnGarFm2O8mY0ixj0JajBa73ZzJnqwX7S3qN6fVs62YP38+N27cAODo0aPs37+flMRkkm8nYTGaAXvevJS7yWI6EavFau/7BrvdmGok5U6ymJol9V6KuJ8v2PMAptxJxpY+LZt2P1XM9Zdde53vs+za63yfZfYNgD5JR2qcXS8k2NLtqQbRbkjWk3I3BUEQsvguc3uz+M5k963FlLEiMTUuBX2S3W41233j8J2jvQ5/ZPFdmqvvMj9XfDy8Ca5Uk2ZNm/HWW28xdcpUenbsQesWrThz5gxRUVFkR/Hixenfvz87d+zkhx9+oEqVKty/f59Tx06hT9Bx7tw52rRp89QGf27+u0gK6e9Rs2nTJp5//nnCwsIIDw9n0KBB7N69u0DneKTRhM1m47XXXkOn07Fy5Uq+/vprLl68yPjx43O1ARiNRhYtWsTnn39O9+7dmT9/vsu5165dy6lTpx5ldR8KqVRKWFgY3bp1Q6FQ4OXlhcpThcrTrk+SSCWofTTInKZ3VJ4qVOmJhSUSu90xfXT16lVq1qyJn5/fk2+MmyeKr68vZUqXYdP2Xzh37hxvvvUm36xYjFQqJTAwkFatWnHv/j0xdbxEIqFbt2788ssvpKamIlPJUftoXDWATn1JrrTbnfP8abw14tSjI4G1swZQ4+Nq1/ppadKkCQMGDGDFihVMmDABX19ffvzxR75YMA+Nr0bUzSk1SjTeGhcNoNpHI07VKjQKe9Ln9OqqvTUuOj652m531tQ57iMgS3ud7zN7e2Uu7XW+zzL7BkCpVaH2Th+9kKTbnaZZFVolmvT6iL5zsis1mew+WX3rogH01qD0sNulcqnd7qQ5VHmpRX9k8Z3a1XeZnytylcLFN94+3lSrWZ1aIbVp0qQJn332GaNGjeLu3btkpnz58owfP55GDRtx8eJFgoODWbZqGePfn8CZM2ewWCysW7eOyMjILGXduPk3Ik/XABbW36NixYoVvP/++9StW5fp06czdepUAgMDeeutt/jll18K4I9HyIULFzh79iwHDhwQ96WcNGkSAwYMyNWWnJyMVCpFrVaLmx9nFnOWLl2aKVOmsGHDhgKvfHuclClThubNm7Nu3TpGjx7N+fPnqVSpElqtNks+M5dcahKJaLfZbJw7d84d/P0HuHDhAqmpqYSGhua6g8KIl0awb98+1q1bR4cOHcSV21KpFK3WvvrVebS3Ro0aNGzYkPfff58KFSpQtWpVmjdvjo+Pj0tfAnuQkLnvyTPbM+naXHRq0gxdXFhYGH/88Qfnz5/nhRde4ObNm9y+fRudSY+PJiOocQSTQJb6SGVSlwUpWTR8edmlUqTqDLssk10ilaJwtudwn+XbLpe5PBnlWfIWurY3s8Yvq28zaxIz2TNrFnPxXUHsnTp1om3btmzcuJHZs2ej0WhQKpWMGTNGfNbI5DJ8fH2QyWScOnWK4OBgPDw8OHfuHFqtFpVKxdKlS2nZsiV37tzBw8ODjh074uHhQWJiIhqNBpXKdVGOGzdFFZkgQSb8OxeBOPPdd98xffp0unfvLr7XvXt3ateuzVdffUWPHj3ydZ5HGkmVKVOGJUuWiAGeM8WLF8/RZjQaKVGiBE2aNKFBgwZ4enpmSWb43nvv8eabb7Js2TJGjhz5KKudK2azmXv37uHn54danTGqYLPZ0Ol0eHp6EhYWxrp16zh//jxffvkl48ePp2LFivm+xi+//EJkZCQlS5Z8HE1w8wT5/vvvSUhIoFKlSrzzTsbK2cz4+PjQo0ePbG/U/fv3A/ZcgM5069aNli1bEh0dzdmzZ5k+fTohISFUq1YNLy8vfHx8xH2bExMTSUpKIjEx0eXfNpsNrVaLRqNBq9WKfxKJBIvFgsViwWq1iv+vWLEi1atX5/vvv+f7778H7DtIeHl5PWLPuXkcKBQKnnvuOfr27cutW7c4fPgwW7ZsYfDgweIxJUuWpGTJkrz00kucPHmS1NRUxo8fLz6rg4ODWb58OSEhIaSlpTF9+nRCQ0P5+++/KVasGG+88Qa+vr6F1EI3bvKPhEebkLmg135UJCUlUatWrSzvh4WFZTvanxOPNAD08/OjRYsWLu8tX76cihUrUqpUqSz5ixw2x4Nm3LhxDB06FK1Wi1LpulKuQoUKvPrqqyxcuJBOnTo9kYSYFy5cYPXq1VitVpKSkpg5cyaCILBz506OHTuG2WymdKnS9Ovfj9TUVL788kvAPrVis6bn+fNSi9NDuoQ0bFYBz+LpeQDjUlF6qrhz5w5du3a1v5eW9p/cf/dpoX379uzdu5eoqChMJtMDjY40bdqUChUqZLvS2Nvbm1q1alGrVi06derEsWPHiI6O5t69eyQnJ5OcnIzFYsHHxwdfX1/x/+XLl6dOnTrIZDJ0Oh16vR6dTkdqaip37961rzyWy13+JBIJu3fvFn+cGAwGypUrx0vDRpB6JwWNn32U25BiwKwz4VnSKyMP4P00cerVmGrAlGbCs4Q9D2BqXAoyhUzM82dMM2Jy5AGUSUm9l4pUJhHz/Jn0JozJBtGedj8NEPAoZs/DZzaYMSTp0Raz5z10vs8cGkClZ0aeP32iDqvZimcJL3sewPS9gh15/gzJeswGM54l7EFu6l37PsuOfaGd2yvaNQrU3posORTBrgGUKWVofLT2PIDxaWh8NeLIYeq9FKQy+7R7Xr5zPFfU3vb8opl957A72mvWmzCk+65cuXL4+fox8+OZbN28hdbt7DsFNW7cmJiYGBYsWMCYl8cgl8rYsHUj9+/f5399+uKn8WPi+InIFDL0STrqBYcREX2FyZMn8/fff7Np0yZefPHFAvdzN26eNPY8gIV37UdFnz59+OKLL/jkk0/EgSmr1Sqmhsovj3UudfHixezatYtFixbl25bbL8kRI0awdetWPvjggwJtd1JQ9Ho9Gzdu5Ny5cwwcOJBq1arxww8/MG3aNCQSCQ0bNmTixIn4ePvw14EDfLnwS2rUqMHx48epV68eUqk9Oa+zTgrsWh+c8gBqfLVIFVJatGjBqlWrSEpKolKlSu4A8F9My5YtadGiBRaLJdcp4NzQarX5Wtzk7e1N69atH/uKvKSkJDw9Pbl9+zbffPMNZy+cI6RWiBjgKLVK5I79fiE9qXFG31do7Hn1HM8/TSb9okKjQCaXie9pnDRvkJ5H0Ddjz221d8ZIPNinUDW+ThpAp/sMEO8zBypPVcZewZJ0u9OUrlKrsu9vnN4ejZ/WdQ/uTO11ttsDW43LNLNze6VyaZbngsZHK7Y3L99JpJJM9vR9ix160XS7Y99puUqBxsl3Wg8tb457kzlz51C7bh0qVqyIRCLhueeeY9myZWz9bSspKSmcPXcWgB/XrWPcG2NF/6g8VFSrWY2guvYURoGBgezYsQM3bv4NSAUJ0kLKe/IoRx5v3rzJgQMHaNmyJcHBwcjlci5dukRcXBxBQUH07dtXPDa3nICPLQBcuHAh8+fPZ/LkyTz77LP5tuWGQqFg2rRpDBw4kF9//RVPT89HWudbt26xf/9+jh07RlhYGJMmTRL1WEOGDKFfv37itjwOWjzbEqPZxD///MOECRPE0UyJRJIl31nm/VId2iAvLy/8/f0JCgqibNmyj7RNbp48EonkgYO/oogjxVG5cuUYNmwYX3/9NcrBSurUqQNkr0Nz7vuZ7Zk1fFKpFKkqew0f2IMa5/Plac/hPhOvl6ddipQH0yxme9/nZVdkrk/uGj8XeybfZbZn9o1EIsG/hD/NmzfnwIEDLlKVnj17sn37dkoFlEKukJOUlET16tVdryd39f2BAwcIDQ3FjZt/A/ZE0IV37UeFYxbImbp16xb4PI/FFx999BGrVq1i6tSpDBw4MN+2/FC/fn2ee+45Zs6cybvvvpuvMgaDAb1en60tJSWFyMhITp06RXx8PPXq1WP8+PGixilzOavVmuUczZo1c9lOLadr5cTGjRtp3Lgx9evXx2g05l0gFy5evEhKSgplypShTJky/4k9fh/WJ/91nqR/AgICGDlyJGvXrkUQBKpXr/7Erv0guPtO9jRv3pwVK1bw559/irMWGo2G3r17YzKZOH/+POfOnePGjRtERERQqlQpDAaDi/bz3r173Llzh+eff77Az7x/A+6+kzNF0TeOZPu5YV8E8gQqk921H2EAOHr06EdynkceAM6bN4/Vq1cza9YsevbsmW9bQXj77bfZu3cvCxYsyNfxKpUKtVotBkOJiYns27eP06dOk5ycTI2aNQgPD6dqmSqoNGq0fpm0SSXse5SmxqUglUnR+qdrk3QmjCnO2iR77jGPYp7ZagBvXbuJXq+nUlBlJEhErU5qaipGo/GRTOP9/PPPxMfHi6+/+uqrhz5nXsTExHD37l18fX3x8/PDx8cn3/kKDQYDe/fuJTExkUaNGlGlSpVsg9ailnS0qPEk/VOtWjUGDRrE119/zeDBg6leqRpmnTlDA5iuc8tJx5ZyNxmZQp71PnNoAAtwnwGizk3UAManYbPa7Bo/QcCSbHbRAOoSdFjNFrxKemerAdQn6bEYzK4aP60CtZfdx4ZkPWa9q12uVqDxcdIA+mbs9ZtyNzl9mlqbxTeAS3tFDaC3fV/oLBpAi33faMdzJS8NoOi7Yp5I5VKxvSoPJV26dOHnn3/myJEjvPrqq+Jsx+bNm8WFSAD379/n/v37qFQq+vfth7+PPzfv36JEiRLcvn1bfL7+F3E/d3KmqPkmP31QiuTRJz/OJ4968cn+/fv55ptviIyMxGq1UqVKFV544QW6dOmS73M80gDw/PnzLFq0iBEjRtC0aVPi4uJE2+3bt3O0+fn5FSi1i7e3N++99x7jxo3L1/FTp06lcuXKvPzyy+zfv5+jR4/yzDPPMGjwIMqXKSfuRWrUG4m6HsXFPy5y9epV4uLiSEtLo2TJktStW5eOHToilWR0H4VagdRJf6P20YgfsUQqQeOnRSaXER0dzU8//cTtW7dRq9VYrBbatWtHw/CGJKUm5bvd+WHGjBnodDqMRqP4QH+cREdHM3PmTPG1t7c3aWlpeHt74+fnR6lSpahSpQpVqlShVKlSWQJDx7Z6AQEBHD16FA8PD5cRWDdFk8qVK/Pqq6/y9ddfM2jgIGoFB7vsFezo+2DPqydXZujYtH4eLhpApUaJTOGkAfTTInF6WGa+zzQ+GmeJn13n5pcxVar21ojb6DnO55qXT+2iAdT6aZHIXTWCSq1SbI/WX+uyd7HSQ4VC42zPaI9MIbNfz2la17m9om+cNYJO7ZXKsvpO5uQ7iUziYs/sO+fnjovvZI4TpGsW5VJKly7N22+/zc8//8wXX3zBmDFj8PLy4urVqwAMGDCAwMBASpYsidls5tq1a6xYsQK5TE7cPfvzWy6Xk5CQQMmSJTl48CAmk4lmzZplWcTnxk1RQFqII4CPMgDctGkTkydPpm/fvvTv3x+bzcaJEyeYMGECVqvVJT1MbjzSAHDnzp3YbDYWL17M4sWLXWwdOnTI0fbrr7+K+f/yS+fOnfnll1/Yt29fvo6XSCTMmjWL5s2bM2XKFDHDvc1m4+zZs/z1119cvnyZkiVLEhwcTIcOHShVqhRarZa4uDhWrFiBr6+vy1SvRCpx0f84HroREREcPXoUgISEBKKjo+nSpQtNmjRBJpNx/fp1tm/fzqZNm5DL5XTs2JHGjRsXqP254Ujt8STw8vJCq9Wi0+kICQnhlVdewWKxkJiYSEJCArdv3yYiIoIdO3ZgMBioWrUqQUFB1KhRg4CAAHx8fHj55ZfZuXMnCoWC+Ph4kpOT3QHgvwDnILBfv37Uq1cPSNehOd0XUqkUlHlo/LK5j3KyZ9ahZbVn0sRlo+FzLAWUSCRZNYlOdnt987Jn0vBlOl+e9syaxEy+kypzbk/mthfYLpHQp08fNm/ezLx585g4caK4I0tYWJi4IE2hUBAYGEhoaCi///47crmct99+mwoVKmC1Wvnmm2/EWYxDhw4xcuRI977BboocckGCvJDyAD7KYOubb75h4sSJLjK6Hj16EBgYyJIlS/IdAD4VewGPGjWKrl27Urx4cby9vcVf7pcuXWLdunXI5XKeffZZ6tSpk+PCkn/++Yd9+/bxxhtvuExRCoKARCLBZDJx8OBBTp8+zZ07d2jVqhUKhQKNRkNISEi26UDMZjMymQypVFok91bMi3v37vHpp5/StGlT2rVrh0qlytY3DhISErhy5QoXL17kwoULeHt788ILL1C6dOlcr/Nv9M2TpLD9c+PGDRYvXkxoaChNmzYVtzLMqS84HjlPwq7X613kH9mVz9xPH8T+qM+X2330qF47+o3j9bx58wgLC2Pv3r34+/ujVCr5f3vnHR9F1f3hZ8qW9AKhJoQaOhHpHcSCNFGwocKLWF9EBQuKitjRFwQLgijSVH5WVLpIUZGigJTQe5FeUrfvzO+Pyc6WVCCYAPP4yUd2z8yde0/uJif3fu85999/PyaT/0S02+1my5YtNGrUSF/l++abb1i+fDmvvfYacXFx/PLLL6xbt46RI0dyOVPan6uyTFn0TXFqAQ/KchFTShFPhgDToswlUgs4NTWVuXPn5kkVdvDgQXr16sXmzZuL1U7ZKalxCbnttttITEwk+2QWDux4RC/fffcde/bs4fbbb6dJkyZFHpY4efIkFeIrYDtrI6JcsDbJjoNPp35KbGwsrVu3JjU1tVhbIJfzSdHs7GxmzZpFhw4d6NGjB4pHIftkll+blO3ElROsTZJdEk0aNKFFixY4sh2sWLZcLzNmcPlSrVo1RowYwfz58/nggw+QJZnrOnShXYf2mMIC8gT68gCe1PLi+fL8+XRuusbvdDaCKOb5nPl0bLYzOahAZPmCNIA2VEXx5/k7GaLxS7fhdXmJqujXAAbl+cu043F48s3zB+DMcuSjAZQJiwnH4/RgTw/RAB7PRLbImsbP5cF2zhaiAcxGlEUi4iP8GsHcPH+hviu2BjBCK5dXkAbQFG4GOcB38RH07duXd999F1VVefrpp/nmm28YPnw4VouV++69l9SmWnUb30pv4Pe/Ro0afPrppwy69z+0atKSJUuWcPr0aWMV0KBMIaliKR4CKTmqV6/OqlWr8gSAq1evLnJBJZCrIgDUa3bGhbHmz7X89NNPtGrVihdffLFYwlFFUVizZg19b+1LWKz/rx5TmIkdu3bwxewv6NatG507d74iTt0Wxf79+/nkk09o3rw53bp1AzRtUnh8hL79pudKC9AmhcdH+HO1RVho3LQJf/y1Kv+HGFxWREVFcdddd3HnnXeyf/9+Zs6cye6De6hfvz41atSgYkJFfS5ElIsMyvNnDrdoOlxfrrr44DyYpjCTpnOT/BrBQGSriXBZ0udWWGyYPw+gQNC8A00jqOfjDJmXkFuLOMK/ahZeLgIxQLNoibRgCtQIlgvQAJqloM8BQERCpF8facrHHh/hzwMoi0F2S4QFk9XvmzyfszBNX1nQ5yzUd4F2h9Ph950skpiYyJNPPoksy0RGRjJo0CDuuecedu3Yxfc/fE9q02vIj1atWtGiRQvmz5/Pex+9z4MPPMgNN9zAuHHj6N+/v54uyMCgtBEpxTyAJRgaPPzww4wYMYLdu3fTtGlTAP7++2+++eYbXnrppWK3c1UEgKtXr0ZRFHbt2oXFYmHo0KHnVUlk/fr1hIeH06CRX+iuKAoLFixg1apVPPTQQ9SqVetSdb/M8cMPP9CjRw/atWunvycIQrDWSRSQRKlguyCwe+/uq8pvVwOCIFCzZk1GjBjB2rVr2bdvH4sWLcJisZCamkpycjL169cPWiEPnSuBwZivzcC5cz72UFt+9+exiyKBRwXzahJFpALs+T2vKHuoZvG8PkfF+Jzl6U8h9tAVBbPZTINGDZg2YxrZ2dkFSmREUaRXr15UqVKFyR9P5umnnyY5OZnPP/+cv//+m9tvv73MbRkaXH2IioiklNKzS7Ct7t27oygK06ZN47vvvsNisVCjRg3Gjx9P165di93OVREAnj59mtq1a9O/f39q1KhR7BQloOX9mz9/PnfddReg6WdsNhvTp0/H6XQyYsQIPVHu1YDb7ebQoUM8/PDDhWqZCtJpBdpr1arFggUL2Lp1a751DQ0uX8LCwvQk74qisG/fPrZt28bSpUtZtGgRw4cP1yUQxdHUFfba996F3n8pXpeUBrA4n6MLtRfHd75/16xZk/nz53PTTTcRHR2N2+3OV9fcrFkzsrOzmTBhAi6Xixo1arBmzRq2bdvGf/7zH+rVq5fnHgODf4tSPQRSwsctevbsSc+ePS+qjasiAOzVq9cFiy43b96sl+ZKP5bOuk3rWP77cq699lpuvfVWJKm0KguWDh6PR/u/w0N2VlaANslLzpkcXZvkynEG6br82iRNi+W2u4k1x/Dg4AeZNmMarVu3pmvXrkYZvCsQURSpXbs2tWvXRlVV/jfmHf5eu4GW7VsB5Jkr2ae0WsD+PH/uIB2brxZwZPmoXLtfxyaZ/HkA9Tx/56sBzLDjcRasAXRkOfCEagAtWp4/j9ON7ayN8Lhw5FyNX9axDCSLTES5yAI0gFot4IhyeTWAzmxnoRrA4nzOiqsB1HxnQ/F6iaqgZUnw1Vnuf9fdzF+4gNdff10/9NalSxdsNpueLispKYkbb7yRTp06IQgC27dvJzMzE1mWadCgATNnzqRmzZq0bduWOnXqXNYaaIPLE60UXOkEgNrWc8kFgZs2bWLatGns37+fyZMnM2/ePKpVq8ZNN91U7DauigDwYqhSpQonT57kjTfe4Ny5c9SoUYOHHnqI6tWrl3bXSoWwsDCqVavG/kP7adyocYA2SdS0Url7Y+Zc7ZLvtShpon4hUJtklqhdoTbPPfccs2bN4sUXX+SGG26ge/fupTM4g0uOIAjUa1CfE+dOkpmZicvloly5cvpcSU9P52zWWarXqK7f45srPh1beH4aQFOABjAuPEjjFzjvIDePYJA9MigvoTU6LGhVLI890ooa4T/xHlHer/GTzLIWjAVs60ZWjPZr/ExS0OfE136QBrBcsFbWHGYO1gBewOcsUAPoG4+uAQzyXVjQ7yjZIuvPu/fee7n77rs5d+4cTqeTv/76i6pVq3LNNdcQERHB9u3bGTduHNHR0Zw4cSKoatKaNWto2bIlSUlJzJ8/n2PHjtGoUSP69u17Ve2gGJQuoiIhKaUUAKICnhJp69dff+WJJ56gd+/e7N+/H4/Hg8vl4qmnniInJ4fbbrutWO0YAWARVKxYkWeffRaXy0V0dLSeP/Bqpm3btixctJDGTfzibkEQ8midBDlU6yTle310dDRDhgwhIyODV199lQ4dOhh5AK9g6tWvx6xZs1i4cCGgfcaSkpL0ZKZhYWGMGzdOvz50buWrAQy0h0g88uYNDE7JGhisaXYhKBF1kfbz0ByG9rUouyAKQcHnxXzO8htPUb4LtUuSpJ/sDa1bnpycTJs2bZgxYwadOnUiNTWVXbt28dlnnwHw559/0rFjR7p27Up2djZLly5l7NixPProo1SpUgUDg0tN6a8AlgzvvfceI0eO5I477mDu3LkADBkyhISEBD755JNiB4ClVRXlsqJChQokJiYawV8uLVu2xGw28/vvvwe9ryqqriFSVdVfbaEYdlVViY6OpnLlyuzdu/cSj6B0sdvtrFq1iqysrNLuSqmQkpISlErk7rvvplGjRjRu3JihQ4fidDrZtm0boOkHz507h81m069XVTVIx5bfXAqyK/lcX5L2fK4P5GKed76fo4u1n69vQ8cbHR3N0KFD6dChA9HR0SQlJREREcGQIUPo1KkTmzdvRlVUIiMjueWWW+jRowcTJkzgt99+w+VyYWBwKZFUAUkRS+erBAPPffv25Vs8ok2bNvzzzz/FbsdYATQ4bwRB4K677mLChAk0bdqU6OjoC9AAunBmOYN0XWvXreX48ePUr1+/tId4yfj777/5/PPPsdvtdL+xOz37XJyI93KlT58+NGnSBLPZHHQi/8SJEyiKwrq166hfvz5Tp05l9+7duN1uKlasSP369amaUAWrxUpU+WhkWSYuOg6vzVOwBjC01u85G173eWgAT2Uhh5kI82kAMx15agX7NYC+PIBhyBaTbvflPSxYAyhqGsFiawAtmMMtF60B9DjcBeonATxOD44MO+Fx4UhmOUg/qdnd2NPtet7DQN8JgkBUhJaLsVxsOTp37sy4ceNondqKitW1ZOENazSgwv0JzF+ygOXLlzNo0KA8J5ENDEoK0SsheUtn3askS8FVqlSJ7du358lm8scff5zXaroRABpcEFWqVKF169Z8//33/Oc//0GUJU0Llbtd5auXGqRNCrCbwszIFr92KSI+gl37d5GSkpLv6cIrgfXr1zNz5kzcbjctW7bkhm43lHaXSg1BEPJNARQdHU2zZs3YvHUzU6ZM4fDhw7zxxhsIgsC+ffvYsWMHf/y1CpfLhdfrxeVy4XA4GPHsCH1rMzwuQq8FLIhC0LwDCIsN9592zceeRwOYx25FjSxIAyhpGjvJf32gXTQVYPfVApaDPyd6XWIxf3vRnzMTskXW7YHjdTg0DWBEQB7BQN+BpgEM7G9YTHjQCqBkDraH+i4sIox27dqxcvVK+vXrR/v27Xl30nhuvPFGrr/+eiLKRxKZEMXQekNZt24dH374IV27duX666+/6g7YGVx6RFVEVEspACzBLeD//ve/vPTSSxw4cABFUVi+fDn//PMP//d//8eLL75Y7HaMANDggunevTuvvfYau3ZpgVuolinwlxzk1ToF2n2/gLKysnC5XP9qMXmv14sgCLr+SVVVtm3bxvLly9m2bRujR4+mQoUKF9T2yZMn2b59O9WqVWPq1KmAdnz/5ptvviqShp8vYWFhDB48mOzsbDZs2EDnzp3106IpKSn51gyfPXs28+bP4/bbb8dsNusaPbfbDeSjGSxEw1cse8jczTOv5SLmfahdLORzcZ6fo4u1h449tL8XYm/WvBlTpkyhZs2aXHPNNbRq1YoPP/yQxMTEoLQwzZs3p2bNmnz++ef89ttvVKtWjc2bNzNu3LhiJew3MCgKSRGRvKXzh4VUgj/ue/fuTbly5Zg8eTJhYWF88MEH1KpVi3HjxnH99dcXux0jADQoEEVROHLkCImJifnmTrRarfTr14+vvvqK559/HlEU9aorPq1Q4H2KohRoV1WV2/vdzpwf5vDkk09y77330rZt20s+RlVVmTBhAtHR0Vx//fUsWLCA+Ph4Xd/YuHFjypUrd97tZmdlI5tkPvroI7xeL2fOnAHgqaeeMpJfF4PIyEg6duxYrGt79erFjBkzGDFiBMnJybRr144waxgfTfqIkSNHUrVKFcidd5Cb6071/9GhKioIlIjdN68D5/nJkyf5888/OXbsGM2aNSM1NRVJkvzthbQf+jlBVRGK+Tkqjl1VFAgYS+jnMGhsxbQH9zfYnpiYyA033MBff/3F0aNHsVqtqKpKenp6nrHHxcUx9LGhHD12lC1btrBp0yYjXYxBiSGU4gqgUMJ5ANu1axdUjOFCKFYAePz4cd58803Wrl2LLMt06tSJ5557LuhQhMvlom/fvgwfPpwuXboU2NaCBQsYNmxY0Ht16tRh3rx5AEyfPp3PPvuM+vXrM3bsWKKioli7di0DBgzg2WefZfDgwUH3fv7553z22WcsW7as2IM2KB4ZGRmMGTOGpk2bcs899xAeHp7nmmuuuYZVq1Yx5/vvaX1NaypUrYglojjapFANYDZWLDz44IOMGzdOX725VOzfv5/ff/+dXbt2cfbsWSpVqoQsy2zdulW/5oknnqBu3brn3favv/7KV199pb+OiIigT58+dO3a1djWugRERkYyZMgQ7HY7u3fv5rtvv+PcuXPUrFmTn378ibtuuZOI2IgiNIAmrFG5Gr/z1QCe8msAszKyWLxgMRk5GWRlZ3H06FEsFgsNGjSgSZMmLPl5Cct+WcaDDzxIbLlY7f7Tl1IDGPw508abXWIaQK/LU6gG0Ov2Yj9no22rtnTq1Imc9BymzfiMw4cPs3TpUsIEK3sO7iUmPoZGjRoRGxGD2+amSuUqVKlShT179vDtt99yyy23YDabzyuJv4FBKJIiISmltAJYwhVI/vrrL2bPns3u3bsRRZH69etz77330qhRo2K3UWQAqCgKQ4YMIS4ujpkzZ+J0Ohk9ejQjRoxg0qRJADgcDoYPH86uXbuKfODevXtp3749Y8aM8XdC1rpx5MgRFi5cyKRJk/jll1+YMWMGjz32mH7dBx98wE033XTBSZ0Nzo/Y2FgSExPZv38/r7/+Ovfeey8NGjQIukYQBAYMGMCIESNYvmIFN954I3369NG0SeH+H9iiJAbVRM2rAfSXmOrRowcffvghbrf7vJazi0NWVhYrVqxg4cKF+opFUlIS9913H4mJiQwfPpyDBw/SvHnzC85P5jvdGx4eTuvWrenSpcsFrSIanB9hYWE0adKEBvUbYLPZiIyK5Ouvv+Z/E8dSr149RFHk3LlzpKenExMTwz333EP58uWD5iWANUbLhed7LzIhEgRBXxE/d+4cXo+H7J05rFy5ErfbTUJCArGxsRw6dIjy5cvTJLWJfqrdN48yMzOpU6cOS39ZyqwvZ/HYY48hCIKW0Dr38ZJJyqsBjChCA3genzNBFHR7oAbQZw+Pj8iTBzDweWGx4UF2X95Dnz3Ud6HjCY8JRzJJVKlShfr16zPjq5lIkkSzZs2YMGECVatWpVOnTjRKaIQoiDRr1oxZs2axcuVKkpKSeOCBB4iPj7/YqWJwlSKpEpJaOhufUgkuAM6fP59nnnmGLl260LNnTxRFYdOmTdx1111MmjSJDh06FKudIj2xfft20tLSWLlyJQkJCQC88MIL9O/fn8zMTA4fPsyIESOKvbKxe/duUlJS9LYCsdlsxMfHk5KSwoEDB0hLSwuyx8fH88orr/DJJ58U61kGF4cgCLRv354DBw7QsmVLPvvsM/773/8GJcFWFIVff/1Vfx1YSipU41ao1ilAZF+zZk0SEhKw2+0lOp61a9cyY8YM/fV1111H27ZtqVy5sv6er2LFxdC9e3duvvlmY7WvlJBNMtEx2grVLbfcQqdOndi7dy+CIBAbG0tMTAw7d+7knXfeoXPnzjRs2JCkpCR9vgqCQOCBPbfHw9q1a1m6dCmCIJCQkIAkSYSHh9O5c2ciIyNRFIXMzEwSExNp3bp1vhrWzz//nLS0NKxWKw6HA4/Hg8lkCpr7kI9GL/RzVJS9kM9ZUfbQsV8K+30DBrBq1Sp++eUXmjVrBkBCQgKjRo1i8+bNzJ8/nwULFtCjRw9mzZoFwLvvvsvSpUuZOHEiTz31VL67EQYGRSGUYh5AoQSf+/777zNixAgGDhwY9P7UqVP53//+V3IBYJUqVfjkk0/yDdicTidr1qyha9euPProo6Smphb5wD179hTYuZSUFKKiokhNTaVSpUr6h9/Hyy+/zMMPP8yCBQuMahH/Eg0bNmTevHn07duXO++4k4kTJ9KiRQuqVauGIAisXbsWr9fLoEGDmDZtGjfeeCPgzx8W+MtK8SqaSNynTQqw+3KJZWVn8emnn5KUlFTi3+MdO3YgiiI33ngjN9xwwyUrTm9sU5UtKleuHBTkAyQmJlK/fn3++OMPZs6cSWZmJnXr1iUxMVEP6LKysjh48CB79+6lVq1a3HPPPdSuXfuCD+80atSIQ4cOkZmZSUJCAl9//TU33HADCeXLA1rCZ9/noqDPCRT+OTofO+T9nGqfQ7/mME9/FBWVYE1gYf3Nzx4eFs6NN95Is2bN2LBhA7Is8/fff+Nyubj55ptp1aoVP/74o77DNPSxoZhMJm666SYyMjKYMmUKjz32mL5zZGBQXCRkpFI6+lCSywEnTpzIVyPdtWtX3n///WK3U6Qn4uLi8jxo+vTpJCcnk5CQkEeTVxhut5uDBw+yZs0apk6ditPppEOHDjzzzDN65Yd33nmH5557jpiYmDwrKM2bN6dfv368+eabtG/f3kjM/C9Qrlw5WrRowQcffEDfHn15etjTrN+4nrTNaXg9XqpXr06PHj0QVKhXrS4mQRNsn482CRHW/L6aHbt2sn3Xdjp27EiPHj1KPJAaOHBgnr+YDK5eqlSpwu233w7AuXPn2LFjBydOnODUqVNIkkRkZCStW7fmvvvuK5HKNB07dqRjx444HA5OnTrFli1bGDt2LLf37keD+g2IKBeJ4lEuuQYw53QhGsBzRWgAMy5MA+jLexhYRzk+Pp7WTVqRactk48aN+s97QRC45ZZbkGWZzRs3k1ypmv5+75t7MXXaVD7//HMGDhxonKQ3OC9EJMRSCgDFEqwD3LVrV2bNmsWoUaOC3p87dy5du3Ytdjvn7YkpU6awZMkSJk+efL63cuDAATweD7IsM378eE6ePMmYMWN48skn9RQZQKEaj2eeeYbly5czduxYXn311fPug8H507dvX1auXMknsz6hX79+dO/ePc9pP4DIBL+WyRyh/YLStUuSGGQ3hZlBEli1ZhVLliwhMjKSli1bcstttxh6OYN/nbi4uHwz618KrFYrSUlJJCUlUb9+fSZNmsSjVR+lRrlIJJMU9DmxRFqwRFiCNIDn+zkzWQNqCYuCbvdpACMt/m3o8PiIoL7KFlkPPiF/DWCgPVQDKOfaff2xRlkh0hqgr4xizuc/6NvpPnz2Bo0aaLWdfb6LCuOBhx5kwnsTmD9/Pj17Xp2J1A0uDDH3v9J6dkkRERHBN998w5o1a7j22mv1A4xpaWm0a9eOJ554Qr/2vffeK7Cd8woAJ06cyPvvv8+LL74Y9GEtLnXq1GHNmjXExcUBUL9+feLj4+nXrx8HDhwI0pYVRExMDM8//zxPP/00t9xyS7Ge63A4SlxPVtI4nc7S7kKhtGjRguTkZGbOnElsbGyeDOTng8vlYt26daxdu5aKFSty991361vKQJ7vVVn3TWlj+KdgyrpvKlWqxF133cXXX3/Nf//73391W7Os+KZOnTps27YNr9erf/YVRWH//v106tQJh8OR554HHniAqVOnUq5cOa655ppL0q+y4p+ySFn0TX7zJBRBlBHF0kkrJJRgJmi3263/8eP1evF6vRekXy/2T5s33nhDX3K85557zq+3AfiCPx++Dp84caJYASBoiXTnzJnDqFGj6NevX5HXW63WS6b3KknKeh+Tk5O59dZbmTZtGt26dQOgYsWKpKSkaPoer4IgiX4tkFfVqzOoqsrhw4dZt24da9asoV69egwYMKDYJ7rLum9KG8M/BVPWfXPttdeyZs0aNmzYQMeOHYv8HBVmB1A8CoIUoMkLuD7QDtrPxkC7oiigEqThU7wKou95uXWES8queBWaXduMhQsX8tprr5Genk7lypWx2+2UL1+e+vXqI8tysGbRqxJWPowBAwYwYcIErFYrTZs2vSTfm7I+d0qTsuab4iQLFwURUSilFcASfO5bb71VIu0UKwB87733+OKLLxgzZgx9+vS54IctWbKEF198kRUrVuiTZ+vWrYiiWOzgz8fo0aPp1asXn3/++QX3x+D8adSoEXfccQfbtm1DEARWrVpFVlaWtm2rgMlsok3bNlSpUJkzJ87gxMU/R//h77//BrRfds8880y+h4oMDK5WunTpwrx582jftn2hGkDVqxZdc/tM/rWABVkLwHLOFKwBtJ+znacG0Jtb+zhXA5hZuAbQme3AbffnUMw5nY1skXnhhRfIzMxk9hez+efoPwx+YDApKSlknchEMkn61nTgeCtXrsx/7h7IzP+bxfbt27npppsM+YhBoYiijCiVkgZQvLhEgC+88AKPP/44FStWLNb1R48e5YMPPig0WCzSE9u2bWPy5Mk88MADtGvXjlOnTum2uLi4Ircs0tPTkSSJqKgomjdvjizLPP/88wwdOpRTp07x8ssv07dv32IPykdSUhJDhgxh7NixVK1a9bzuNbg4mjZtGvQX9/Hjx8nMzMTr9WKz2Vi8eDF2u52YmBhiY2P1w0KBqTYMDAz8JCcnc/ToUQRJ0IMjyNUABtQdFmUxyG6OsGCOCLBLwXZTmAlTmMmf4kb0t69rAK1+e6gG0GTVagnrtX1jg9OvyBZZD/YgVwNIiAYwwG6JsmKJCtAA5vbFl15n6BNDg+4PHEt+462XWp8X67zIokWLGDNmDElJSSQnJ1OtWjUaN25snBQ2CEI0ld4WsHiRmaA7dOjAPffcQ2pqKjfccAPt27cnMjIy6JozZ87w119/8dNPP7F161ZGjBhRaJtFfjoWL16MoihMmTKFKVOmBNnmzp2bb23OQIYOHUrVqlUZM2YMcXFxTJ06lTFjxnD77bdjsVjo2bMnzzzzTFHdyJdBgwYxb948PfGuQelQqVIlKlWqpL/25fYyMDAoHuHh4SQkJLB161YaN26sv5/fH0yB75W0vbSfdyGvIyIi6Nu3L927d2f37t0cOnSIn3/+mXnz5vHcc88ZpeQM/IgiQimVguMis1p069aNjh078vXXXzNu3DiGDRtGQkICcXFxKIrCuXPnOHPmDImJidx555288847eQLEUARVLeECdWWII0eO0LVrV5YuXVrmq4fY7fYyp6koKxi+KRzDPwVzOflm69atfPXVV4x8fiQWq0XXzCmKgiRrKVJUVUXxKIiyGGQXAzSBilfR6nLn5hUMvB7A6/EiiiIOpwOr1RpkV7zaKkWQhi/UrhKkSQyyKwooKmIh/VUVRbcrHgVEf+7M0PEqHgUEgvIUBo43sL+Bz9qyZYueqeLWPrfS9NqmxMXFIQhCsdNLXU5z59+mLPqmsN/3PtuH566lglK0VvBScFJ08FjchhKLR/bs2UNaWhqnT5/WV9AbNmx4XrXmjfVxAwMDgzJAw4YNiYyIZMem7TRMbahpAHPyagBtZwM0gDYXrhynVm5N0gIs25mcQjWAtjM5BWsAM+woHm+BGkBHpgOvy1OgBtCZ5cDjOA8N4JlsvY4ykGe8OWeykcwS4XG5GkCbXwMoSAK2szkIokhEuQgUr0Lahi0cO30Mh9t/SvWfw/+w5Jcl2Gw2KleuTOfOnUlKSiIxMdFI2n6VIYgiQimlgRFKeK6VRNUqIwA0MDAwKCNUTaxKhjNTy5OJljcvPw2grqnz1QoO0QD6XpvDzflqAAVBwG63a7WCAzWAccEav7wawOBVnzwawOgwiD5/DaCP0PGG2i2R1iANYER5/xbXb7//xvLly0lNTdXtzz33HElJSSiKgs1mY/fu3WzatIlffvkF0CRKRm3hqwdRlhHVUtIACp5SeW5hGAGggYGBQRmhZs2apKWl0alTJ/29S6GbK+h1aWsAL6a9HTt20KlTJ1wuF3PnzgW0kn+CICBJElarlcaNG3PttdeiqirTpk1jzpw551XNyuDyRpBlhFIKe0rruYVR9npkYGBgcJVSt25d5syZo+ncRBFVUTRNnyz5NX5ur/ZazNUAeryIpvOwe7xBGj+v24uUa1e8CqqqBmkOz9uuqEim4tm9bi+CKARo/BQUb4Dd49V0e4EawIDxBNq7dOnChx9+SPPmzQEt5RQ+zaAsMn36dP7++2969+rNuvXrOHr0KLIs8+0339AkNZU6deoYWQqucLQt4JKsynsezy6lrefCMAJAAwMDgzJCbGwsVapUYdSoUbRu3ZrrO3XFbXPr5dRUr4rtnM2vAbS7cGWHaADP2TDnlpBzO4I1gKhgO2vDHGEGCTwOD45MO+HlIpBkCUeGHcWraM8jQAMYH45kKkQDGBeObJZxZjvxONz6tq+SqwEMiw1Dtphw5TiDNIC2sznIVhNhueljnDmuIA2g7WwOkknWt6Z1zWNCpLaNfc6GKImEx0dQp3YdRo94mZjyMdx7773IskzO6WwQIKJcJBnpGQD8NPcn/vvf//LRRx/RoW0HTIKJ2bNnYzabOXz4MI0aNeLgwYNERkZq2+QmE7IsI8syMTExXHvttbRq1coIFi9DBFkutVrAxgqggYGBgUGBiKLIHXfcwcqVK1m2bBktW7akQsUKfrss6sEXgCVCC/R0uxRsN4ebMYeb9deCKOh2TQOo5Qn0kV8eQJM1wB6XNw9g4PPCYsIgxq8TlMzBdmt0mKYTzCXQBpoG0BrlP6XpO4yijzc3L6IPX6AKIMkS5ZOCE8wHagTvG3Aff/75J7t27eKHH34gJiaGvQf20rdvX3re2ospU6Zw+PBh0tLSeP311/VVWI/Hg8fjwe12c+rUKX755Rd+++032rZtS9u2bY2DJJcRgiwhCKUUAKoSeAu2u1wu+vbty/Dhw+nSpUuR7S1cuJCpU6dy4MAB5syZw6xZs6hcuTKDBg0qdp+MANDAwMCgDPHbb7/x22+/kZqaSvny5Uu7O1cMFStWpFevXqiqyvr161m2bBkHDhxg/PjxDBo0iHr16pGWlkZ0dDS//vor1atXJzExkQoV/AF4cnIyTZs2Zdu2bSxZsoQNGzYwcOBAYmJiSnFkBsVFECUEoZS2gAsJAB0OB8OHD2fXrl3Fauv777/n7bff5v777+ejjz4CoFatWrz99tt4PB4efPDBYrVjBIAGBgYGZQhFUejTpw833ngjilfB4/Qgmf0aPq8rV1Mnann3FLeSxy6axFwNYa4GL9Qu+zV1gXavxwsqwRo+lwfJrJ0EVjy5GsCC7F4F1asgmeUC7YpXQc61e10eBEkMqg2seBVdM+h152r8cvsbOl6v2wuCtvoX6hvQNIagjSfQ3rx5c5o3b47D7mD4U8OZNm0akZGRPPLII1SuXJkVK1bw9ddfk5GRwZOPP0lKvRRdzyhKIo0bN6ZB/QbMmzeP1157jcaNG9OiRQvq1q2LJJVOgGFQNIIsI4iltAKoyODO+76vYsf5zJtp06YxevRobr75Zj3f5Z133klsbCxvv/12sQNAY+3awMDAoAzRvHlzVq9ejdfrxW13YU+3QW66ftWrYk+34XZqv0ncdjf2dBuqol2gKrl2u2b3OIPtqOTaXZrd5cGebtMTKjuzHNgzbHpfvC4P9nS7lpAZcGY7sJ0LsLu92NPteqDlynFiO2fDV19A8dldWgoMl82FPdeuqppe0Znjz9nntmt233jt52w4sx0B9tzx5LZvz7DhzNLsilfBnm7D4/Sn23Bk2nFk2oN94/D/FlbsXvr27gtAfHw8v/76K6tWrWLZsmUkJycz5KEhVI6v5PfdOb/vFI/CdW268MLzL1CtWjXmzp3LCy+8wDfffMOhQ4cK/yYblA6ioFXkKJWv/DWja9asoWvXrnz11VfFHsahQ4do1KhRnvfr16/P6dOni92OsQJoYGBgUIaoU6cOUVFR/Pnnn7Rp0wZLpF8Td74aQFOYWc8pCPloAPNo/II1gLLFRFRFvz1PLeAiNH55NIChGr+KoRo/a9B48+QBDBlvZPlgDWBoexHl/BrAUN+Apnns2q0rXbt1xePx8MEHH7Bz507efPNNLBZLULWLQN9BsD6yS5cudOnShRMnTvDXX3/x8ccfU6tWLW6//XaiooLHYFB6CJKMIJXSCqA3/+deSBqimjVr8ueff5KUlBT0/sKFC6lZs2ax2zECQAMDA4MyhCAIXHfddfz222+0adOmtLtz1SDLMsOGDdNf2+32826jYsWK9OzZkxtvvJH/+7//46effuKee+4pyW4aXASCSSq9AFAsOWnA8OHDefzxx9m8eTNer5fZs2dz6NAhVqxYwXvvvVfsdowtYAMDg6uKEydOsGXzFrKzs0u7KwVSq1YtDh48iOJVcDvc+panqqq4HW6t5i4UbM/d0lWUwu2qogbZvW4vHpd/CzWP3RNiV4PtiscbtAWr23O3oDVNo38L1uP0oHj8ynjFq+AJaM/j8mi6xAB7UH9cHn37OfRZvvGE2n2+0+254wn1Teh4VVXFU4jvtPFodrPZTKdOndizZw/ni6IoLFmyhKlTp3Lo0CFsNhtnz54lMzMz6FkGF4BQWtu/ovbsEqJDhw58++23uFwu6tSpw6pVq7BYLHz11Vd07dq12O2UeCh8/Phx3nzzTdauXYssy3Tq1InnnnuO6Gj/0nlBx53nz5/P2LFjqVy5MuPGjaNy5cp6Eed77rmHUaNGBT1r+fLlPPLII+zcubOkh2FgYHAFYrPZeOWVVwAICwvjjjvuoFWrVqXcq7yEh4fjdDpx2fx58RC0oMORYccabUUMM+NxuHGG5AF0ZNhzS8RZ8Do9ODIduh0VHBl2zBEWLQ+gKzfPX24eQGe2E9WrIOemT/G6fXkAtVrBrhwnXpdXT7/idXtxZNj1PIAumwu3Q8tb6Ds04siw67WC9fFU0H712DNsmKwmfdvYbXfhyvGP15FhRzJJ+taz2+HW8h7m5gF0ZDkQRJHwuHAUr/YsX91hQLOjbfWG+g7QxqsoRJSLRFU1uy+HIoAr24nXnTteFewZdr3OcqjvVFXFnm7HFG7GGmUlLi6O7Oxs7HY7v//+O1lZWbRu3ZqqVavq32dFUVi8eDG7du2icuXKNG/enO+++w6TyUSDBg14++23sVgsWK1W3G4teO3du3dQpRiD4iPIEoJcWomgS/a5tWrV4q233rqoNko0AFQUhSFDhhAXF8fMmTNxOp2MHj2aESNGMGnSJKDg485Op5PJkyczfvx4duzYwfvvvx80uNmzZ3PLLbeQmppakl02MDC4iggPD+exxx7j448/xm63M2PGDFwuFx06dCjtrgUhSRKSJCGYBaKi/H8858nzF6EFegXZz18DGJrnL0QDGHOeGkCTVLgGME+evxANYELhGsBAjV++GsCAvIb5agADxiuKee2BmsfCNICgbd0H2iMjI4mIiODFF1+kUaNGVKhQgQkTJtC1a1c2btxIWFgYWVlZhIeHc9NNN7F69WrGjh1LixYtGDhwIKIo0r59eywWi35C9MSJE0yYMIEaNWpQrVo1DM4TUUAorbyNBRwCuRDeeeedQu3PPvtssdop0QBw+/btpKWlsXLlShIStIScL7zwAv379yczM5PDhw8XeNzZ7XZjtVpJSUkBYNWqVUH2ypUr89JLL/H9998jy4Z00cDA4MJo0KAB/fr1Y/bs2YD2x2XFihX1nz1lhYiICLKysrBYLEVfbFDmEEWRxx9/nGPHjtGwYUMA9u/fz08//QTA0KFDMZvNVK9eHUmS9KAuMLl0eHhwwF2xYkWuueYa/u///g+v10vVqlW59dZbjYMmxUWWwVRa8UPJPXfLli1Br71eL0eOHCEzM5Pu3buXQo+AKlWq8Mknn+jBXyBOp1M/7vzoo4/mWcmLjIykbdu2tGjRgsjISKZOnRpkHzlyJMOHD+ezzz7joYceKsluGxgYXGV06NCBSpUqsWfPHmJjY6lRo0ZpdykPVapU4cD+A8RExCBbZT2Pn8fhRjLLiJKI4lXwujzIVtN52LW8gqDt2nidfrvX7UFVtAofoG05e5zuALsXVVGQLaZgu8WEIGp5BBWPoq+M+frjsyseL94Au9vhRpRFvbaw1l+vPl6P040ginrewdDxeJweBBEkk+wfu0XWAyiffk82y3l8A5qGUFW18eZrDxhvoO9ESczjO994JFlElCVURSUqLJK4Bg307+m9d99LRnYGbrebWjVr4XG6EQV/sHddpy76ClVBvuvSpQs7duygatWq/LnmTz766COefvppI/9gMSjVLWC15J47a9asfN9/4403zmuBrETXQuPi4ujYsWPQe9OnTyc5OZmEhAQGDx7MsGHDsFqt+d4/bNgw/vjjD37//fc8OW6qVavGo48+ysSJEzl8+HBJdtvAwOAqpE6dOtx88820adMGk8lU9A3/Mm3btmXW57OYM2eOfhBCVdTcerzaa4/TjSPLEZQH0JHl0A9aeF1eHJmOoDyAjiy7ngvPZ/cdbHDluILy7nndufbcPIAumwtHVoDd47Nr/XHbXTizHAGHQhStv7kHMdwON45Mu54H0JnlwG1z6e25HW4cWXY9D6AjS9NB+tDHm9u+M9uBK0ezq14lyDegafhcuXkG/b7zH1Jx2fzjVdVg3/nH6yyW71RVxZEZYA/xnaqqyIpExfiK1K5dW/dd4CEXR5YDV0Cewfx8l5CQQMeOHameXJ0eXbsTHhbOggULMCgGQinmASxG7eidO3cWqwxcQQwYMIDvvvuu2Ndf0rXQKVOmsGTJEj1TdXGIjY0t0PbAAw8wf/58Ro8enWeF0MDAwOBiSU9PZ9GiRfzzzz8kJSXRu3fvAv9gvdRce+211KlTh5kzZ/Lu+Hf573//S2RkZEitXwvm8BANYIVADWBwrV9BFHR7fhrAPHn+8qv1G2jPk+cvDAJ2I0M1gOer8YsMqOWb33iD8vzlowEML0IDGDheURTzaBIDxxvoOyhAAxhgD/WdIAhB4w31nTbegLyGRfhOkiWiK8UwYOAAXnrpJTp37mxsBReFLGlfpYFy6Z+7fv3686pNfckCwIkTJ/L+++/z4osv0rlz5xJp02Qy8corr3DPPfcwd+5cIiMji74J7eDJheR0+jdxOp1FX3SVYvimcAz/FMz5+iYnJ4dNmzYBcPr0af7++2/69u2ra7j+bWRZZtCgQSxevJg5c+bQr1+/EmvbmDeFc7n4Z9++fdSuXRtJkv6133Nl0TcOh6PIawRRLLVDICX53L59++qyAx/Z2dkcPHjwvCRylyQAfOONN5g1axajRo0q8SSYzZo144477uCtt94q9kkXq9UalNG9rHI59LG0MHxTOIZ/CuZ8fFO1alUeeughvvrqK3Jycjhz5gyfffYZPXv25Oabb87zQ/ffokePHrz66qvs2bOHurXqIpklJFnSawWbwvwaP7fdjWzxawDztefW4rWYLUF2j8sDqqpr/BRFwePwYLLmavzcHhSv6tf45ebC89tzNYBhoXYZQRQ1jaDbq59MdttzNYA+jZ/Hi8fl1fvjdrgRRUGvLRw6Ho/TDYKga/wCxw7oOQl9Gr889oDx5mc3SyYURRuvqqq4bS4ks4xk0nzvtrkwR1gQRM23rhwXsllGMkua7+xuTGFmvTZx4HhDfQe5mkgp1B7su0A956njp/jxxx+57bbb8hwYudSUtZ85xVqpl8TSWwH0llwA2Llz5zw/i0wmE02aNDmv5PElHgC+9957fPHFF4wZM4Y+ffqUdPMAPP300yxbtowPPvjgkrRvYGBw9VKjRg2ee+45fv/9d/2k8Lx583C5XJfsZ1pRmM1mBg4cyCeffILD7kBFJTU1lVt73YpFMAcdEnFla6sz5nAzXrcXV7YT2SLreQBdOU5NQydqAZcz24ls1sTxbrsLxRsQALoVnNlOJJOEJEq47W68bq8eAGr3O5BkEcks43a4cw+NaP1RvArObAeiHI5sFvE43FqQlXu/M8eBbDHpAaDb6cFtc2ntC1pfJVnSA0CP040r26W378xxIYq5AWDuswQxTA/gfPpB2SKjKirObCeCICCGaXa3zYWi+ANAzXeqvs3strvxenLHq4Ijw4ElyoJkkvC6PDgzHchWE1JulQdHug1zpJUwcxhKrgZQMktIohaABo431HegaRYls4RkCvP7TgpHtuT1nepVmD59Otc2vZYmTZpcusl3JVGaW8Deknvu0KFDS6SdEg0At23bxuTJk3nggQdo164dp06d0m1xcXEllr4lOjqakSNHBpXtMTAwMChJOnTogMPhYM6cOQD8/PPPSJJEr169SqU/derU4Y033kBVVRRFYdGiRfxv/P+46667uKbCNYCmYwusn5tHpyb6dWh2u13L81ehkDx/FpmogPYCc/xBbq3fCsF5/gjI8yeZpCB7Hg1g+eLn+YN8NICBGj9ZyqPhC8rzJ4lBY4G8GsDQ2sPWEA1gdJUY/bUpzIypakCORUEgJjFOfy1bTERXjgmyB2n8QnwHEBGgeQz0ndvtZvoXM4iKiqJOnTq0aNECu9PB0RNHefKpJzEoJr5KIKX17IvgiSeeKPa1xS0HV6IB4OLFi1EUhSlTpjBlypQg29y5c0s0z1b37t358ccfWbFiRYm1aWBgYBDIDTfcgCAIzJ07F7fbzb59+1i9ejUpKSmUK1fuX+9P4GnlPn360LhxY2bOnMmWLVu49957S22L2uDSsmrVKl2bumbNGmrWrMn06dPp0KEDZrO5iLsNfAiyWHppYOSLCwAvxRZ/iQaAw4YNK/aqXHHLtyUmJhZ47ccff1zsvhkYGBhcCNdffz2NGzdmzpw5bN68mX379mEymWjcuDGVK1emWbNmpRIMglYOauTIkYwdO5atW7fSsGFD3DaXpmPzaQQdbkzhZr8G0OZCsgRo6gLsHqcHVfVr/Hy1d825OjaPy4PqVXQNn6oouOx+u6/2rjncZ1dx2V2YwkyIoqjbfRo+l92lbYHmJuf1erTavKYwrT9uuwtBEnXNoq/WsK+/bocbQRD8Gj+bC9lq8mv8HG4Q8Gv8Qu0B49Xtub4DgsarqiouW67GzySheHI1gJEBGsDc7XbJLGt1mAN8A9qWtGSSdI2fy+7CHGbSDwgEjlf3ndXEvn37qFChAvfddx/jxo3jpZdeQhAE2rVui9PhxGI1koUXC0mEiwzELurZF8HFln3LD6OkhoGBgUERVKxYkUceeYRdu3bx5ZdfIiAgCALbt2/nyJEj3H///aXWN4vFQrdu3fj2229JTk5GcACCgFmWNA1gbtCjawBtLswCIGgBlSs36BFkCbfDrQU8gQGgL+gRJTyOXA1gbgDoC4J0u9OtHRrJDfB890smCdEs4nF5cOcGhKqq4s5xoVhkfwDo9OCyuTBZzZoGMPdeXwDocWn9NYWZwBcgiqIWAHq1AE2URT3A8+Xkky0mLaCyaQFWoF1VFD0AdNlcuu+A4PHm6idB2571ur04sxzIYX4NoCtb01dKZjmPb3wBpmoNPkQimyUk0a9ZlMzaeAN917t3b1599VXee+89mjRpQqVKlQBYvXo1P82fy0033UTHjh2NFeCiEEtxC7gEn6uqKkuXLmX37t14vQF5L10utm7dWuw0eUYAaGBgYFBMUlJSePHFF1m2bBlr167l2LFjDBw4kB9++IH69etTt27dUulXs2bNWL9+Pe+++y5NmjThuuuuw4y5WBrAyIRADWDePH+Buevy0wAG2jWNX4DdJAXbQzV+oXn+Qmob59UAmvXVRYDwuEANoJgnr2CQxk/Kxx4wXlHMaw8cb548gGEmTFVj/XZBICpA8xfqO0EQ8mj8Qp8XVNs41/7333/j9Xpp3bo1FouFKlWqcPToUU6dOkVc+Xh69O7J1KlTqVmzJklJSRgUglyKp4BLcOXxrbfe4ssvv6RWrVrs2rWLevXqcfToUTIzM88rVVQphcIGBgYGlyeyLHPjjTcycuRIHnvsMdLS0khLS+OLL77g1KlTnDhxAkVR/vV+XXfddTRu3BiPx8Nbb73Fnj17/vU+GJQ8U6dO5ZtvvuHw4cNs2LCBLVu2EB4eTosWLXA4HKxatYpGjRoxfvx4tm3bVtrdLdsIAoil9FWCq7Pz58/njTfe4McffyQxMZF3332X33//nS5dupxX4npjBdDAwMDgApAkiQYNGrB8+XLCw8M5evQoL7/8MhazhYqVKvLkk0/+q1VEateuTe3atQFo2LAhU6ZM4YmhT1A+rjzmCL8G0Kdjg9wtXrsbc3iuxs/pRlVU/xZvHrsHxavoq3CKouC2uTGFaxo/j8uD4vHqJ3V9266mME1353XnavxyNXy+LVtff7xuLx6XR3uezy6Jem1ir8eLx+HRx6NtAQtBW7y+Z4F/C1jf4s1xYbIGaPwCxqvn8bPKem3iwPHm0QCG+MafB1BLWxNqB3DZnIhy7hZviO80u98fqqJiz7ITGRnJsGHDqFixYp7vec2aNfn+++/ZunUrDoeDDes28M8//9ChQ4dSq2BTltEOgZRSIugSfG5GRgYtWrQAoF69emzevJkaNWowZMgQhg4dygsvvFCsdowVQAMDA4OL4KGHHiI5OZn69etTqWIlVFQOHTrEmjVrSq1PDRo0oFu3bnw/5/tcnZu/FrAnN5cf5Gr4Auwep0cPmsB3CMSlr2h6XB7tYEUuqlfF7XCherX7vS4PbrvbXwtYyb3f67/f176WeNkVVJvX6/bgsfvb9zjcejJnze7V7s8djtuhaQ719hwuvfau//7c5+UmVvYG2T24fe2ruXa3X1PlcQaMV/edJ8B3Lr9v0Q5xeHJrEYf6zpdo2jfeUN/p48ntj6IoHNh/gIjwiHyDv40bN7J48WLuvfdeYmJiKF++PIIKe/bs4csvv8xzvQEgSiCV0pdYclvPlSpV4sSJE4CWt3THjh0AREVFcfbs2WK3Y6wAGhgYGFwEJpOJvn376q+9Xi/Lli0jIiKCL7/8krCwMDp27PivnxTu2LEjq1ev5vNvP6dRo0a0bt0aWZZ1HZqmAZSJDBDthWr8ZLMclLvOGhW8qiSZpCB7Hg2gHGI/zzx/gbV8AcxhZsxhBWgAJTFPXsE8GsAQzWFwnr987NHWAHuwhk/zXbDGL7TWb2B/BEHIq/EL6W9gXkNJlnAoDuLLxRPK0aNH+frrr4mNjdW3Ad1uN63bt6FixYq8+eab7Ny5s9Q0qWUW33ZsaT27hOjVqxdPP/00b731Fh06dGDIkCHUqVOHFStWULNmzWK3YwSABgYGBiWIJEnccMMNHD16lFmzZpGUlMSqVavo3r17viWcCsJms/Hrr78iSRLVqlWjXr1659UPWZYZPnw4GzduZPXq1ezevZvevXsTHR0dlE/Qh8vlYvPmzZw5c4aYmBiqVKlCUlKScbK0FMnIyODcuXOoqqp/H9avX89XX31F3759SUxM5JNPPuG5555j0aJFfPPNN5w4cQKn08mPP/5IZGQkhw4dQlEU4uLiaNWqFR07dkSWtbQ5NpsNVVXJzMwkISEh33lxRSELpZcGRr64z9Hq1av1Mm+PPfYYVquVrKwsunbtysCBAxk7diwxMTG8/fbbxe/SRfXIwMDAwCBfqlSpwiOPPMKMGTMIDw9n7ty5tGjRgsjIyELv27BhA3/++Sd79uzBZrPp748bN+6866+GhYXRpk0bmjVrxvjx43nppZdodm0zBtw3AMjdxrS7yHJkM/njyURFRZGYmMixY8dYuHAhUVFR9OnZh5p1aiBKWpoXxaPoJ3V9qUpM4WZESdMAet1efZVPURTcOS5Ei8T2HdvZsWMH//zzDzk5OdSoUYN2rduRmJjo1/i5vXicbq2+riBomjlJ1EvT5bVrGkCTNVcDmOPEFGZGlP159QC/xi/bqaVtydX4+ba/dY1fjjOoNJ3H6c7VAFry2H2+y1sLOEADaHNhijDrGj9njlNPaxPqO9DSzIiymKtpVGh9bSst1ctPP9GtWzcOHDjAd999x9ChQ0msmsiaP9Zw8uRJhg8fzpNPPknHjh0JDw9n9+7dTJo0CYBbb72VVq1acfSfo3zxxRcsWbKELl268Msvv+DxeHA4HJqPTCYSypen6bXXcvPNN+t9vqK4jCuB3H///VSoUIHevXvTp08fHn74Yd02ZMgQhgwZct5tGgGggYGBwSWiQYMGvPzyy2zbto1q1aoVGfypqsqnn34KaFuGlSpVokaNGrRt2/a8g79AzGYzQ4cOJW1LGj8v/pnnRj5HcnIyGekZuF0ucuw2evToQZcuXfSVJkVRWLF8BdNmTiMzKxOr1YogCEiSROPGjREEAYvZQptrW3My/RRLly8lOjKa1EappDZPRZZl9u3dx8b1G9mYtpGYmBhSU1Np0qQJ4eHhbN26lY8+/oihjw4lqYaWvkQL8Dx6gOlxerRDE74AMDcRtDncAoKmKRR9AaCq4nF6kMyyHgD69HimME0DqNn99WC9Lo+m4QvX8vx5nB4ESfQHgC5v0KEWj9ODIGp2RVG0pNQBiZ49TjeCqKXHUb2aXQ4zgYjeP0A/5OFxerTavrnyMI/Lg6Rq41UUFcWt8MCgwcxdMI/Ro0fjcrm46667SEpKQvEqNKrbkFdGv8LLo19m2rRpZGdnExkZiSRJWK1WHA4Hp0+fJjo6mjCTlTNnzwDwww8/AHDz9d3o0KkD1ogwVI/CF198yfz588nOzqZBgwZ4PB6Sk5P5+eefOXPmDFlZWZw9e5aIiAhq1qxJmzZt9INHlwWSeNEJmS/q2RfB8uXLmTdvHgsWLOCTTz6hQYMG9OnThx49elywvERQfWrdK5AjR47QtWtXli5dSmJiYml3p1DsdvtF/YC/kjF8UziGfwqmLPtGURT27t1LUlISsizj9Xoxm7XTrfv27cPr9VKnTp1L8uzMzExOnTqF1WpFlmUtQCjET263G4fDgdvtJjs7m3379iEIAqdOnWLFihVUrlyZm266iZycHNatW8fBgwcxmUxER0eTmJhI7dq16dSpU552f/31V1atWsXw4cNxuVxERUXl8/TSoazNnaNHj7J//37atm2bZ1t+165d/PHHH7Rs2ZKKFSty5swZUlJSSEtLo2rVqsTHazrCZcuW8e2339KoUSOqVavGggULAHjmmWeoUaMGhw8fZvny5Zw4cQJZljl69Cg5OTmAlgx9wIABxMfH43Q62bhxI4sWLaJLly54vV6qV6/ONddc86/6JJDCft/7bJ/3fZ1KkaVTted49hnu/e7FEolH9u/fz/z581m4cCEHDx6kXbt23HLLLVx//fXnVRrQCADLCGXth01ZwvBN4Rj+KZiy7JvDhw/z1ltvIUmSns3/5ptvplevXv/K80vKN3a7XV8d9OFyuXA6nUUGdKqq8vHHH3PgwAGys7OpWbMmd9xxR5n4eV2W586Foqoqp06d4vfff9dXBlVV5cYbb6R8+fJ5rt+9ezdbtmyhfv36JCcn6/Vofb7Zs2cPmzZtIiwsjLVr15Kamsott9yCJP37yZaLFQD2e51KUXnH+W9wPOs0935bMgFgINu3b2f+/PksWbKEM2fO0K1bN15//fVi3VusLeDjx4/z5ptvsnbtWmRZplOnTjz33HNER0fjdrt56623mD9/PgB9+/blqaeeKnACLFiwIE+94Dp16jBv3jwApk+fzmeffUb9+vUZO3YsUVFRrF27lgEDBvDss88yePDgoHs///xzPvvsM5YtW1asARsYGBiUBZKSknj22Wcxm81s3LiRP//8k1atWpV2t86b/IIks9lcrJUIQRAYPHgwP/74Ix07dmTnzp28+eabjB49mgoVKuDMdiDKkl7NxOv24ra7sERpAaczx4koilppOUXFme3AFG7WNX4um6YBNIeb/fYws77F67a7URQFS4Sm8XNmhdgdbhSPF0uk1W+3mnJLvWll6SyRVl0D6MytBezT+LlynJgjLYii6LebZWRLgD3ComsAA8erKAqubL/d4/LgcXqwRGr6R994zOFmRFnSUvA43LpvQv3h811CQgJ9+/bNM17dt7njcdldJFdN1lehFY8XR6YdS6RV911S5SR9C7h92/Z89tlURo0aRcWKFYmJjiEmKppqNZJp0KBB2chLeBlvARdE/fr1SUpKol69enz22Wd89913JRcAKorCkCFDiIuLY+bMmTidTkaPHs2IESOYNGkS7777LqtXr2bKlCnk5OTw7LPPEhMTEyRQDGTv3r20b9+eMWPG+Dsha904cuQICxcuZNKkSfzyyy/MmDGDxx57TL/ugw8+4KabbioTfx0aGBgYXCzVq1cHtAMj3bt3z2NXVZXZs2dz6tQpbrjhBho0aPAv9/DSYzKZ6NevH4qisHXrVipXrkxsbCyAlrNPBXy1iT1eLU+fil7LWBVVTGgaQK/bixyQl0/x+HP66XaLgk9053V7UdXcvICq9loye/UAUHF78Xr9eQO9HgXRqyABiqLm3q8iIOjtiZIIZk1z6HV7QVH1jLuK24tXFLTaxb77FVXXAHrdvmeZIMSueJSgHIW+8SiKigh4vUpQjkN9vF4VZFC8wb7zj1dBMkn+/uSORwl4FiHj1XzhRQ2oeBMRHs4DAx4gw55BemYGp4+f4uzZcyxdupRPP/2U11577V9PhRSKIJViIugSDgCzs7NZtmwZCxcu5I8//qBy5cr06tWL9957r9htFBkAbt++nbS0NFauXElCQgIAL7zwAv379+fUqVPMnj2bCRMmkJqaCsBTTz3FuHHjePDBB/M9RbR7925SUlL0tgKx2WzEx8eTkpLCgQMHSEtLC7LHx8fzyiuv8MknnxR7gAYGBgaXKx6Ph7/++gun08nOnTsZOHDgZblKWBSKovD222/j9Xp56KGH9NXD8IA8fqCd5jUF5AEMiwnO8xeaVzAwr2H+9pA8fyF2S0DeQ0EQgvL0yWYZOeD6ULtkkoLaEwQhKK9hqB0gPC5gPHKwPbQOcuh4QnMkhtpNVjMmq98eOl7ZIiMXkRMycLx5ckLm5j2MJIqqJEJD7f19+/YxduxYLBYLpc5lngcwJyeHpUuXsmjRIlauXEl4eDjdu3dn5syZF6S/LDIArFKlCp988km+Ads///yD3W6nefPm+nvNmzfn1KlTHDp0SP/rNpA9e/bQoUOHfJ+VkpJCVFQUqampVKpUiVmzZgXZX375ZR5++GEWLFiQ71/LBgYGBlcSJpOJd999lx07dvDBBx8wY8YM9u7dS1paGj179qRt27al3cUSQVVV/vnnH8aOHVs2tgoNSoyaNWuSkpLCL7/8wi233FK6eSUv4y3gRx99lFWrVqGqKp07d2b8+PF06tRJ30G9EIrsUVxcHB07dgx6b/r06SQnJ3PixAnCw8ODUhv4AsXjx4/nacvtdnPw4EHWrFlDt27d6NKlC6NGjSIrK0u/5p133mHlypUsWbKEqlWrBt3fvHlz+vXrx5tvvklmZub5jdTAwMDgMkQQBOrXr6/X91y5ciWNGjZizpw5nD59upR7VzJIkkSbNm0YM2YM//3vfxkyZAjLly8n/dQ5XLm5/EBL22LPtOvbkM5sp67zUxUVe4Ydb8C2rzPHiSvHGWwP2Np02Vw4fXZVxZ5pDypN57K7cGY7dLsj044n1+71eLFn2INKvTky7f5Sbh5Fs3sD7FkOvTSd4g22AziyHHruQt2eu63rcXpwBIxdVZSg8Xpcofbg8Xrdwb7Tx6vbtfH4Stu5cpy6bwLHq9ttft9o4/UGjSfQd3369GH7tu28/vrrzJs3D5fL/z39VxHxrwL+618X1/WsrCxefPFF/vjjD95//326du16UcGfzx3nxZQpU1iyZAkjR47EbrfnEfr6Xuf3DT5w4AAejwdZlhk/fjyjR4/mr7/+4sknnwy6Lj4+vsBDJM888wyqqjJ27Njz7bqBgYHBZUvVqlUZNGgQtWrUYuu2rciyzN69e0u7WyVG//79ufPOO4mJiaFr167s3buXt8e9EyQFUhQV1acLBFSvghoQYCleJag2r+pVUXJr7aqo2vVBdv/9qKB6FJRAu6IGBWiKJ+B6ny0gj4biVYICvjx2jzePPTARh+oNeL5KkF1RlKC+qLl2cq9XvUpQHWRVzR2v735vsO/08fr6o+ReT8D1gc9Tgvur+S6g777+qAHX5/YnOTmZoQ89xu233c727duZN2+eHjj/q4iifxXw3/66yATUn3/+ObfffnuJpko6r/Bx4sSJvP/++7z44ot07tyZRYsW5Qn0fK/zOxlWp04d1qxZQ1xcHKCdXomPj6dfv34cOHAg3y3jUGJiYnj++ed5+umnueWWW86n+wYGBgaXNS1atKBFixZs376d9evXX1GHQnwrnW+99Zb+3p49e/j000+JiI6gbt26mKwm/UQwBNfyFSUxSIMHwRo/URTz1BYO0viJQl57QN3iPBo+s0xEvBxsjwvR+AVcn8cuS3n6G1S7WA4eT74avwB7qD5SlILHG+q70PFqGkD/eAJ9B7kawIDxWvKpCx3Yn1DfRZaPol75elSsUpFPP/2UCRMmcOedd+bZ6bukyGIploIre5VVih0AvvHGG8yaNYtRo0Zxzz33AFCpUiVsNhs5OTlERGjf+FOnTgFa0sj88AV/PnxHyE+cOFGsABCgZ8+ezJkzh1GjRtGvX78ir3c4HNjt9mK1XVo4nc6iL7pKMXxTOIZ/CuZK9U316tX1n5cX+rPtcvBN1apV6d+/Pz/88AOPPPLIeSW5vVguB/+UFhfjG6vVypAhQ1i/fj2fffYZ7du3p3Xr1hfdJ19Ju0IRBO2rNCiDNbWLFQC+9957fPHFF4wZM4Y+ffro79erV4+wsDDWr1+v6wTXrVtH+fLlqVatWp52lixZwosvvsiKFSv0FcKtW7ciimKxgz8fo0ePplevXnz++edFXmu1Wi+LhJ6XQx9LC8M3hWP4p2AM3xTM5eCbJk2a8Oeff7J69Wquv+563HYX1ugwLQ9gtgNBFDCHW1AUBWemA3OERU/j4tOwWSIsuibOEmFGMsu6XVVUrFFWXcNnCjMj59olVULxKgF2B7JVxmQx4XV7ceU4sURb9Tx/jkwHskXW8vh5vDiznViirIiSqOfdk0wSpjAzilfBmeXQ7aBpAEVZxBxgN0dakGQJt8ONx+nBGq3l+Qsdr9vpxuPw21VFG4/P7nF5gnynKiqOLP94vW4vLpsTa1QYgigE+Q4IGq/mGxFVUfW8gF6PF1fAeF02V4G+A2jdvBV1qtfh3Q/epWHDhlSoUOGi5kmxDg9JpbgCWFqHTwqhyB5t27aNyZMnM3jwYNq1a8epU6f0L1mW6devH6+99hrr169n9erVjBs3joEDB+r3p6en64c8mjdvjizLPP/88+zdu5c1a9bwwgsv0Ldv3wJXDAsiKSmJIUOGcOTIkfMcsoGBgYHB5UR2djYJCQmaTk1Rg3RmaoBmTrMFa/h8dhXt34G1r/K7P0gjqIa8VpQ87Qdq/FRVDThkQdC9+vN0DZ6vPyHtKfm3H9h20HiDXgdo9lCD+xPiO39//IdCVEXVNYChvvD1N1/f59OfIn2nQkxUNDfeeCMzZszA4/EfvrlUaAuAQil9XfLhnTdFrgAuXrwYRVGYMmUKU6ZMCbLNnTuXZ555BqfTyYMPPojFYqFfv3488MAD+jVDhw6latWqjBkzhri4OKZOncqYMWO4/fbbsVgs9OzZk2eeeeaCOj9o0CDmzZsXdIrYwMDAwODKYdWqVZw+fZomTZpgMoVoAEPy/IVq+AJz1YmiSES5gu2CGKzRg3w0gCEav/BQjV+Ahi8/e7DGT8rT37CYgseTnwawMI2fKIpB/c1XAxhgl82yvvIJ+ef5Cw/UAEbm1QCGF6IBDPWtOdwM4Wauu+46du3axTfffMOdd96Zb/7gEkMSQC6lSEwqexFgkQHgsGHD8pRuC+W1117jtddey9cWmsuvXr16TJ8+vfg9BFq1asXOnTvzvC/LMj/++ON5tWVgYGBgcPmwZs0a7r77bkwmU9EXG1x2iKLIf/7zHyZMmMDcuXMv7eHOyzgP4KWg7PXIwMDAwMAgF5vNRkxMDABupxt7uk3fWnRkOXStmuJVsKfbgvL8ObMd/jx+iortbI6ex89nd2Tadbs93YY7N08fgMvm9NvVXLtDs3vdXuzptqC0Lppdy4Th9eRjz7DreQ0Vj4LtnC2oXJ0j047L5h+P7ZxNz/Pndri0setpXYLH63a4g+yqkmvPHW+o70LH63V5guyBvstvvM4cJ46sYLvtnN9emO9AyxNoz9DyEoaFhfHAfYPZtHET8+bNC97qLklKLQdgKVYgKYSLyyJoYGBgYGBwCcjKyuKnn34iJyeH+Pj4Yt1zqeKGC33epe5PSbR/PpU5LuV4oqKieOzRIXw0ZRI5OTk0b96c5OTki052HIRYiiuAl3Jr+wIxAkADAwMDgzJFRkYGb775Ji1atGDUqFH6aWWTxaSfIoUQjZ8kBtXShWCdWr55/kLsYSG1h83hwTq2sFCNX8Dz8tjlfOyBGj85b3/zaBoD7KG1fPPaQzV+YlB/Qn0XOl7JLBNmLkLjF/C8QI1ffvbCfAeappGAQ+hhseGEEc6TTz7J/Pnz+eabbzh79iw9evSgQ4cOJaINFCQBoZS0eKX13MIwAkADAwMDgzLFjh07SE5OLlaeV4Mri8jISO68804Ajhw5wuzZs9m7dy8DBw4MqhDm8Xj4/fff+eOPP8jKyiIxMbHoxn2l4EqDsrcAWBa7ZGBgYGBwoXg8CmPGrKRu3Q+xWF6nQoX/0b//d+zde7bEnuF2e/noo7/016NHr6B58ymF3FE4Z86c4YknnuCpp54iLS2NH374geuuuw5Hhj2oHq3b4cZ2LsevAcy06zo1TTOXg9fl4emnf6Zz5+k4shy6Tk1VNHthGkDbuRxdp7Z/fzr/GTiHqlXHERHxJk2aTGLC2JW6hk/TvOXk0QAG2pOrjeeD99cG220+DaAX29mcoNrF9gy7XrtY8SqaPVfj57K7sIVoAG3ncoI0gLZzOUGl4wLHG+o7fby5GkCPy5Nrz9X4BfhOG48naLyBvtP9cTYnSCNoz/BrADXf+iuHuWx+TWOo73zjLRcZz+OPP87Zs2d5ZfQrPPnkk4waNYqPP/6Y0aNHs3njJu68805GjBgBfjcWzGVcCu5SYKwAGhgYGFxBjBy5lO++287773ejYcMKnDiRzejRv9KhwzS2bRtCbGwxEuYWwZdfbmHUqOX8978tAHj66bYMHdrygtv76aefaNy4MQcOHGDBggXcdddd1KtXD0eWI0ijJogCgiiAEPDaZ8/N8UbI9X7y5mMTBMG/DOLLEScKbNhwjOuvn0n3brX5v89vpUq1WFasOMDw4Ys5cDiLCe/d7L8/6BH+/uQnrdP6G3CtKCAQ2F/y9t83vIC2g8Yb+DogyBAQgu7RfRUyfn9/ff/OvUAUCB5C8P1C6EqaEPyez5f+sYgEPlwIuZ6Q8fm+l2azmdtuu43lS5dzS+/euLxuTpw4wfXXXU+VClX0xNc3d7uZDyd9SKGIQt5+/1sYh0AMDAwMDC4lU6f+zYQJN9GjRwoA1avH8u23t1Ox4lh++GEH//nPNRf9jNDDAJGRZiIjz79M26lTp9izZw9paWm89tprhIeHaOKi8qlHW4BOTQzRvEFILrr8NH4ReXVqqqoyYMAcevSozaxZfXV7rVrxhIWZuO++OQx5rCV16pQLai+vxk/yB6y59lCNX2h/rFEF2/PL83c+Gr9Q3+XRAJqkoNf5afwC7YEaP9A0j8F2/3wI9Q34ahf7X+exB4y3Zs2a1KxZU7dVqVKFUKyRxakEIpRePr4yqAEse2uSBgYGBgYXjCgKLFt2AI/HXxUiIsLMxo2PcOut9QBtm/jVV3+nZs33MJleo2LFsTz55CK8udt3o0ev4LbbvuKppxYTH/82cXFvM3z4YhRFZcWKAwwa9CNnztgRhFdYseJAni3g2bO30LTpx1itrxMd/Ra33PJ/nDiRDWjbgdOmrSAx8XWqVJnIgAHLcTrb0r371/r9GzYco2vXmURHv0XlyuMYMWIJbnfBe3yLFu2hceNJhIW9Qe/es8nICK4Lu27dUTp0mEZY2BvUqfMB77zzB4qS/5HWtWv/YevWUzz1VKs8tjvvbMgvv9xHtWpaWhpBeIV583bp9hUrDiAIr5Cd7d/K3L37DK1bf4rV+jrt2n3G7t1ndNuePWe57baviIt7G4vldRo1+iioPYOSRRBFBKmUvsrgFnDZ65GBgYGBwQXz1FNtmD59I4mJ7/Kf//zAjBkbOXEim9q144mJ0VZJ3nnnD778ciszZ97K7t1D+d//bmDixL/46Sd/wv1583aRleVi9erBjBt3I++9t5b583fRtm0SEybcRHx8GMeOPUXbtklBz1+16jD/+c+PPPtsW3bufIwffriLjRuP8/rrv/Lbit8YPuxVHnroV667LonVvw3kkYc68vHH2wBw211sWXeEjh2nUbduOX795V4++qAbs2ZtZuTIpZqO7WyABjDDTtqGo/TuPZu+t9Vj1YoBtGldlalT/9bsmQ4O7zvLDTfMonv32vz5+0DGj9PG+r///aHp3AI1gGdzWPfnEaKizNStWy5Xx2bT7KqKM8NO+9ZVsVhkPb9eoAbQkRWsiVMVlY8/Xs9DDzVjw4aHqFwhnOu6zMDl8uJxe+h+8xdERphYs2YwGzc+TMN65Rn0nx9wubwoHoWcUA1goMbPm2v3afzsLs03ARrAnLMhGsCzIRrAs37No8fp0TR8uRpAR4Zd1/CBphHMCdT4ZTlwZASPN+dsDoonQAOY7ved7WwO7kCNn82paxp9GkGfPlKz+8erqiq2dJueIzG/8Toyg4P+fDHyAAZhbAEbGBgYXEE891x76tSJZ9KkdXz55RZmzNiEJAkMGdKCd9+9CUkSadAggY8/vpn27asB2jbxuHGrSUs7ya231gcgLMzEhx92x2yWqFu3PB9++Cdr1hyhV6+6xMRYEQSoVCkyz/MtFolPPunF3Xc3BiA5OZbrr6/KvHl/kVKnHFmZKTRunMmMGQNwZjtJbVqVP9cd58SJbARJZOqMzVSvHsvEid1x5Thp2LgiLq/KfffN4aWR7THJol+HJglM/3wLqamVePnlzjiyHDRpVoUVvx7C6fQgSAIff7qBli2r8tyI9jiyHDRIrcxbb7l54olFDBva0l97VwBBFknPcBIdrW1vCqKAGJA3TpS11RzthV97qNsDNXi5erb772/K/fc3RVVVPvqwG7XqTeKnn3Zy0401+c+Axjz8aAvKldfS0wx7shVff7edEyeyqVolClES9XZEUQzqiyBo28S+fgiSiCgHawCD7xcQAnznG68oBWgEJVHXJGr/9uPzhS65lEQQ/KvMen8C9ZkF+S4/30pika+DNIWh4y3OFqtEKW4Bl85jC8MIAA0MDAyuMPr2bUDfvg3IznaxYsUBZs7cxPvv/0nlylE891x7+vSpx88/7+T5539h584zbNp0gn37zulbxADJyTGYzf7fWtHRFlyuoo9aNmtWhehoC6+//hs7dpxm8+bjpKWd5Jpr4hn6+FB69vySFi2qaJq4XI1f27aJzJmzA9kss2vvOdq0SUQQBF3j1759NdxuhX0HM7nmmkr6syyRVrbvPMO111bSNG25OrJWrary228HsURY2LHrLMuW7Scq+i39PkVRsds9ZNk9lCun6dYEQSAsOowKFSNJT9dWkwJr74Zq+CRZ840v6BMEIUhTKMpawNKmTaJur1A1ljp14tm+/RT9+jVg2NPt+PLLLWzYcIwdO86wfv1RALxeVdMABujiZIuMbAnU8IXY89P4BdhD8/z5xqvbTVLQ9ZbIfDR+AfZAjZ823hB7Ib4DX15D/+tQe+h4i9SDhmgW8yPPQZp/kdJ6bmEYW8AGBgYGVwibN5/giScW6q8jI8307JnC11/fzp13NmTRoj0AvPbar9x55xxcLi99+tRj/vz+QYEVEBT8+ShOJYhly/bTuPEktm07gdV6ikaNDtCrVyWio6MBkGWxQP0dgMWSd13Cd31B94X2K7DvHo9C37712bjxEf1r8+ZH2b17qL4lHkiLFlXIyXGzffvpPDaPR6Fbt89ZunRfvv0I1F36kEIqTyiKisUiY7O5adXqUyZPXk/VqtEMG9aaH364K992DQpHURS2bt1a9IWSAHIpfV0Nh0COHz/O448/TqtWrWjXrh0jR44kMzMTALfbzauvvkqrVq1o1aoV77zzDl6v/y/K+fPn06VLF/r378+xY8cALRFk3bp1efXVV/M8a/ny5dStW7ekh2BgYGBwWeL1Krz//p/8+uuBPLboaAsJCdpW4wcf/Mlbb3Vh3LibGDAglRo1Yjl4ML3YNVgLW8yYPHkdvXrVpFq19Vx/fQzj3hyGyxWJqmq6r7q14li//piu+3LmOFm3Tvt577K7qF09hjVr/tF1X84sB6tWHcZkEkmqEkFOgI7NnmGjQb1y/PnnP5om7kw2HpeHDRu09hyZdurUjGXHjtPUrBlL5Tgz1ZOi2br1JKNHr8AVqPFTVHLO5tCofjkaN67A+PF/4sxyYAvQsX328V8sXryXxMRoPC4PJpNIero/192OLcf934tcDeDGv4/p9n/2nWH37rPUr1+epb/sY+fOM6xYNoCRIzvQs2cKh/ed1a/1erxkn8rSNX6uHCc5Z7KDNYC54/X5LudsTl6706cBdGn3B2gAc85kB2kAc85k6xo/R6Y9KM+fxxViz3LovtPGm2vPzWvozA72Xc7ZnKA8f84cZ7DG72xOkMbPZXPqGr/AuaLbA8b7/fffs3z58oInZS6ldgBECt7+LiuUaI8URWHIkCHYbDZmzpzJpEmT2LFjh5akEXj33XdZvXo1U6ZMYfz48fz00098+umnADidTiZPnsz48ePp3bs377//flDbs2fPZtOmTSXZXQMDA4MriqZNK3PbbfW5/fZvmDJlPXv3nmXjxuOMHbuKL77YwvDhrQGIjw9j0aJ97Nlzlg0bjnHHHd9y7pwDp7M42XS1lcXsbBfbtp3C4fAE2WJjLaxYsZvatTtwTWpXJny4mUWL9uJ0ehBliQceaMq2bad46aXl7N2fwafTNvJ//5eGIAhIksgjj1zL/v3nePzxhezZe47FS/fz1FM/M3BgKvHlIpACdGyiLPHQg9eye/dZRjz3C/sOZvDRpHX6SVpRlnj00ebs3n2WJ55YzN4D6Sxdvp9HH51PTIwFk1lGzN3KRQBJFhElicmTe/L99zt5aMhCNm05ya5dZ5gwYQ3Dnl3Ks8+0pW7d8oiiQPNmlfn44/Xs2HGaZcv2897EdboffDn8Jn6kaTG3bj3Jw48tonpyDN2716FcuTBcLi9ff7uNgwfTmTNnOyNe0IIYp9Or+cMs66dHRVlCMvlXNgVBQDJJiLm6OEkSNd+E2H3aOFHKvT9AAyiZJF1nJ4i51/s0gybJ75t87JIcYhdErf3c/oiyFNQfyRSs6ZNkUUuVU8DzRFlCNAW/DmxPlEQkk/Z679693HLLLRSJL09kaX2VMUo0ANy+fTtpaWm89dZb1K1blyZNmvDCCy+wbNkyTp06xezZsxkxYgSpqam0bduWp556ilmzZqEoCm63G6vVSkpKCvXq1SMnJyeo7cqVK/PSSy/h8XgKeLqBgYGBwZdf3sbjj7fivffW0rjxJDp0mMaiRXtYtOge2rTRTuxOn96HAwfSadx4En36/B/JyTEMHtyU9euPFesZXbvWpGnTyjRt+jHz5+/i5MmTHDlyhBkzZiCKv1G5spUnn9xEh47T2bH7HGPH3sC2bafwKFCnfgXmzLmT77/fTvM2n/HNt9u5774mmM0SklmmVt0KLFx4D+vWHaNF22k8OmQhAwemMnFiDySThDU6TA8aLBEW6tRLYPHie1m27AAt2k5jzg87eewxLSm1OdxMzTrlWbz4XtavP0rLdtMZdP9P9O/fmPHju2EKM+naMZ9OTTJJtG2bxJIld5OV7eLW27+ladOPmTFjE++9140xb18PaAHJR5N64nR6SU2dzNNPL+Htd27QfeQ7gPHMM215550/aNbsE+xOLz/NvRtJEmnbrhpvvHEdI0cuo0GDj3j11d8YO+5G4uKsrF9/VNcA+g46yBY5aOyCqPXXFzRJ5vztPq1iqO8Cxxtkzw3gzGHmIJ2fJAfbA32n+UPU+pMbsJqsJl3Dqek9w4I0e7LFpOv6fHrQILtZRrJKHDp0iD/++IOlvy1l2a/LWblyJUePHtX6GxXGypUrOXfuHAkJCUXOW+1gSil9lcFTwIJa3DX/YnDu3Dm2bNlCx44d9ffWr19P//79+eqrr7jzzjtZv349kZHaybHDhw9z/fXXs3jxYqpXr8748eP59NNPiYyMZOrUqTRq1IgjR47QtWtXJk6cyPDhw3nsscd46KGHAG0L+JFHHmHnzp359sd379KlS4tXJ7AUsdvtesFzg2AM3xSO4Z+CMXxTMCXpmxMnTvDKK6/Qvn17unTpQuXKlQu8Ni3tJE6nh2bN/Ml8H3lkHi6Xl88+K8Yqzr9Eac2d2bO38MYbv7N9+2nq1y/PCy900E9U/9scOnSIzZs307ZtW+Lj4/X3L8Q3NpsNWZYxm7Wg0u12c+zYMRISEpBlmSNHjrB//37279/PiRMnsNlsZGVlkZCQQLVq1YiLi8Pj8ZCZmcnevXvJyMjAbDYTGRnJo48+isvlKvD3vS8W+GbibCpXqJRf9y45x04e5/Yhd5epeKRETwHHxcUFBX8A06dPJzk5mRMnThAeHq4Hf4AesR8/fpzq1aszbNgwBg0aRHh4uD5JfFSrVo1HH32UiRMncvPNN5OUFJx7ysDAwMDg38ftdrNw4UKSk5PpcVMPZEFGVVUEQcDr8eJIt2ONtiKZZVw2J9s2HuWB/y5k9uy+1EqMZMPmU3zxxRa+/fZ2XDYXbruL8PgIBEHAdi4HUZb0lSKX3YXb5iI8LgJBFLCn2/SVLsWrYD9nwxJl1U+P+vLYhcUE2i3IuRUzHJl2VEXVKoDk1sY1h5sx5Z5gdWQ5UDxewuMidJ2aKXdlzOPy4Mx0EBYbjiiLAXYT5nALXpcHR6YDa6y2CqeqKrYzOchWGUukFa/bg/2cjbC4cCST1t9pU9bx6psr+WzaLbRpVZVli3bz2FO/AHBbrzq47W4iykciCAKKR6v1a40OQ7bIft/l+iZ0vG67C1eA77TayDZ9vB6nG2eWk7C4cNweN+dOnOXDDz8kPDycihUrEh0RjTPLQVhcuO47RVEJz63+4fdHGIf/OcLWzVs5d+4cp8+d5vjx4zgcDhRFISoqigoVKnDkyBEiIiJIT09HURQqVapE9erVadiwIddffz2RkZFERUXliQV8OBwO3G43YWFhegBZFNpqXOlo8YqVpuZf5pKmgZkyZQpLlixh8uTJpKen5/lG+l67XH5haGxsbIHtPfDAA8yfP5/Ro0czderUS9JnAwMDA4Pi4fV6+fDDD4mMjGTYsGGIiHjd3qBtSMki6790RVnilj712Xs4iyFDFnD8eDY1qsfy4Yc3c9NNtXMPTgRuE8ohujEJ1SLrOjZNI+ff0gy9XjYHa9jkgL747lcD8gDKFjlIhyabJZSAX9yyRda3TEVR1NoTA+0m/X5BErWxB/bPKuvBniCKyBZTUIWIsRPWMuXjHnTpUgNFUbiua00++aQXTzy5iNv71vf3NWA8vjx+kknSfCcE2wN9L5v9dgQBOUADKUjaeLakbWH69Ok4nU6u63wdoiyyb98+mjVtprXn0wCaZcSA/oiSiEtxM+vTzzly5AhNGjchIaE8TZs3pVKlSsTFxaEoCmfOnOHkyZNUqFCBChUqoCgKHo+nwECvIKxWK1br+dW19m0BlwZlcQv4kgWAEydO5P333+fFF1+kc+fOLFq0KCjQA3/gV9ylZJPJxCuvvMI999zD3Llzg1YTDQwMDAz+XVauXInH42Hw4MG69itQxyWKYlD+Nl/utuef78Dzz3fI055kCj7okKfebB57QK45UcASkivOFJKLLo89oLZuYN5Bvb+Wgu2iLGKJsqKqKqdOnSIzMxOXy4XJZCIuLo6YmJg8ueuC7pdErCH1b3fsPEPn67Sat6Ko2Tt2Smb79tP55vmzhuTxCz0kEjjeIu2yhBQl8eeff1K7dm1SU1OpU6cOn3zyCT179tTHm5/vAHJsOUyZNoWkpCReeeUVJClvGiFJkvTAT/eDKJ538HehBNZm/re5agLAN954g1mzZjFq1CjuueceACpVqoTNZiMnJ4eICC0VwalTpwCoWLFisdtu1qwZd9xxB2+99RbPPvtsse5xOBzY7faiLyxFnE5n0RddpRi+KRzDPwVj+KZgLtY3Bw8e5LfffmPQoEFXpJ8LGpPb7ebcuXMcPXqUAwcOcODAAVRVJTY2FlmW8Xg8ZGVlkZWVRVhYGLGxsSQmJpKYmEi1atXyLFy4XC7Onj1LdnY2tWpFs3jxDrp2ranbf/31IPXqlfvXfoclJCSwdu1afUs1OjqamjVrBj3f55tNmzZx6NAhnE4nBw4coHnz5nTq1CnPYs+/gcNRjFJwkqh9lQZlMA1MiQeA7733Hl988QVjxoyhT58++vv16tUjLCyM9evX6zrBdevWUb58eapVq3Zez3j66adZtmwZH3zwQbGut1qtl4UQ/HLoY2lh+KZwDP8UjOGbgrlQ3xw6dIjPPvuMgQMGEmuNwSybkUwSzmwHHqdH1/B5PV7suRpA2SzjzHHicbgJj9cWAWxnc5Atsr4y5rI5cdvchJfL1QCezdUARgdoAHNyNYKigC3dhhigAbSdy9FOm/o0gOk2ECAsJhyP28OZo2dw48bhdmCz2VCdCmaLhdiEWKwWC167QkRMBOcyz7F161Y2bNjA8ePHcbvd+thFUaRcuXJUrVqVunXr0rVrVxISEvJUelAUhczMTE6ePMm+fftYu3Yts2fPJiwsjJo1a1KtWjV2797Nzp07KVeuHNHR0Vx7bTb33PMV/ftHcM015di+LYfPv8ikR48wZkyfQf069WjXuT2yLKN4vNjO5GCNCUO2mnDlOHHlOIkoH6VrAG2ns7HEhGGymnDZXbhyXETEhyOIIoqiYDtrwxxhxhxm5tzpc0ycNJGjx7RqJCaTCbfbTUZGBjt37qRpk6Y4shy65u/Q3oP89ONPdO/RHYvFws0330z58uUvaD6VBMXZDtbKzxkrgD5KNADctm0bkydP5oEHHqBdu3b6Ch9oB0T69evHa6+9xpgxY3C5XIwbN46BAwee93Oio6MZOXIkw4YNK8nuGxgYGBgUg8WLF1OvXj3qN6iP2+bSdVU+TV5g/VqT1a/Lk81ygARNwBRmCtLcSSYZNSAmla2mYA2gSUK1mnQdmylEg5e2cytp29OIiIigSpUq2HPsrFu/jsysTGw2G1arVT9cEBYWhtvlwuFw4HA69Z0it9tN+fLlSU1NpU+fPlSrVi1oi1IQhKCavwUhiiKxsbHExsaSkpICaEHhyZMn2bt3L0eOHKF+/frcf//9evuPPw4zZmzgjTd+Y8qUf6ieHMnTT11L71tqkJWRxR+r/mDpb8vo378/dVPqYgo3B6WBMSlqkAbQFG7Wc+dJJknbtg1IA2OympBMEqdOneKVV15BlmQaN27Mli1baN++vZ5cWVVVRFnEZDXp/v599Upat2pF+/bti/RFmaE08/GVwTyAJRoALl68GEVRmDJlClOmTAmyzZ07l2eeeQan08mDDz6IxWKhX79+PPDAAxf0rO7du/Pjjz+yYsWKEui5gYGBgUFRbN68mZ9++gmPx8PTTz+NKIpBujbZLEOITi3QHqhDU1WVjJxMPB4PcXFxZGRkcODAAWJiYjhx4gTVq1enWrVqQStrkiwhRfoDRv20rsPB//3f/+npPhwOB0eOHEEQBO66+y4qVqxIeHh4vrq0UBRFQRTFS5IGRhRFKlWqRKVKBaciGTjwWgYOvDZfW6s2rdi6dSszZ86kUaNG9OrVC4us+bdYGr/IEHtuvd9NmzahKAouxUV6ejrVqlVj06ZNDBs2jA8++ID9+/fz1VdfccMNN2A2m9m1S8v9OGDggIvyx7+NcQo4mBLNA1jWMPIAXhkYvikcwz8FY/imYM7XN4qi8Oabb9K2bVs6dOiAyWQq+qYCOHv2LP/73/9QVZWwsDDS09NxOp3Url2bkydP0qhRI/bs2YMgCLRq1YoaNWogyzJOpxOXy6X/3+Vycfz4cTZt2kRqaiq33357iR0oKMtzx2azMXfuXP766y9SU1NxOBwkJCTQuXNnYmJiEAQBVVVZtWoVu3fvJjU1lWrVqiHLsr7SabfbycnJ4fDhw2zYsIEzZ84A0LNnT+bNm8d1113HmTNn9ApcjRo1ArTdvLp169K4ceOLmgMlTWG/7322H2fPoUqlKgW0cGk5evwot9x9a5mKRy5pGpiywj///FPaXSgSh8Nx3kfarxYM3xSO4Z+CMXxTMOfrm40bN+J2uklMqMqJEycAUNxeHFmO3IoUIi6bE6/TizU2TM9V58i0I4ebQAR7tp3Mc5nM+moWdrudFi1a0Lt3b1wubeUp8HRop06dOHz4MJs3b2bt2rV4vV7MZjMmkwmz2ax/xcbGcu+99xIfH8/JkydLzT//Nu3ataNRo0Zs3bqVcuXKsX//fl588UW8Xq8e6MXFxXHNNdewePFijh49iqIoWCwWPYWK1WqlcuXKdO3alSpVqhAeHo6qqmRnZ+N0Oqlevbq+EhsVFaU/2+Fw6HOgrHD8+PEirzl15nRQ2p1/k1NnTpfKcwvjil4BzMzMpEWLFqXdDQMDAwMDA4NLTExMDD///HOefMLp6enceOONZGRklE7Hcimof6XFFR0AghYEZmZmlnY3DAwMDAwMDC4hkZGRBQZX6enpZGdn/7sdCqGw/pUGV3wAaGBgYGBgYGBgEEzZy0xoYGBgYGBgYGBwSTECQAMDAwMDAwODqwwjACwhsrOzGTlyJK1bt6Z169aMGDGCc+fO5bnu3Xff5brrrgt6b/78+XTp0oX+/ftz7NgxvF4vzZo144svvgi67quvvqJu3bq8//77Qe/PnTuXJk2alEr5nUAK88Hx48d5/PHHadWqFe3atWPkyJGFajMXLFhA3bp1g7569uyp26dPn07Hjh15+OGHycrK4uzZs9StWzdPXsh3332XunXr8t133wW9P3nyZLp27Vpyg78IijN3XC4XvXr10hOz+rgS5s7333+f53vt+/rrr79IT0/n6aefplWrVnTo0IEPP/wQRVEKbO9qmDtF+ayoOXUlzBso2g9er5dx48bRrl07mjdvzrBhw0hPTy+wPWPu/MXmzZvzvN+0aVP9/itl7hgYAWCJ8corr7B7926mTp3K1KlT2b17Ny+99FLQNWlpaUydOjXoPafTyeTJkxk/fjy9e/fm/fffR5IkmjVrpudf8rF69WoqVqzI6tWrg97/+++/adq06b9WULsgCvKBoigMGTIEm83GzJkzmTRpEjt27GDEiBEFtrV3717at2/PypUr9a9Zs2YBWk6nhQsXMmnSJBo0aMCMGTOIj4+ndu3a5+WzVq1albwTLoCi5o7D4eDJJ59k165dQfddKXOne/fuQd/nlStX0qFDB6655hqaNm3K0KFD2bFjB5MmTWLixIn8/PPPjB8/vsD2roa5U5TPCptTV8q8gaL9MG7cOObMmcP//vc/Zs2axYEDBxg1alSB7Rlzpyl79uyhdu3aQfZffvkFuLLmjoERAJYYy5YtY+DAgTRs2JCGDRsyaNAg/vjjD93ucrl4/vnng/6SAq2wuNVqJSUlhXr16pGTkwNAy5Ytgz5Qqqqydu1aBg8ezObNm4NOM5WFHypQsA+2b99OWloab731FnXr1qVJkya88MILLFu2rMBVwN27d5OSkkJCQoL+FRcXB2hJUOPj40lJSaF27doF+iwrK4utW7cyePBg1qxZE9T+xo0by4TPoPC5s3XrVvr165dvLssrZe5Yrdag7/PGjRtZu3YtY8eOZefOnfz555/873//49prr6VJkya89tprTJ8+Pag4fSBXw9wpzGeyLBc6p66UeQOF+8HhcDBr1ixGjx5N27ZtqV+/PiNGjGDXrl1BtX0DMeaOzO7du6lVq1bQNeXKlQOurLljYASAJUZcXBzz5s0jKyuL7Oxs5s2bp2dOB5g4cSLVqlWjW7duQfdFRkbStm1bWrRowcMPP8xDDz0EaB+oAwcO6NsVO3fuxGazcddddxEZGcm6desA7YfSzp07ad269b8z0EIoyAdVqlThk08+ISEhIc89Tqcz37b27NlDzZo187WlpKQQFRVFamoq48aN49577wWgRYsWbN68Gd/B9rVr11KlShX69OnDmTNn2L17N6D9lZ+enl4mfAaFz501a9bQtWtXvvrqqzz3XUlzx4fH42Hs2LEMGjSIpKQkDh06hNVqpX79+vo1devWxeVysWXLlnzbuJrmDuT1GRQ+p67EeQN5/bBu3TokSaJTp076Na1bt2bRokUFVrAw5k7hPrhS587VihEAlhCvvvoqaWlptGjRgubNm7Nv3z59m2rr1q18/fXXjB49Ot97hw0bxh9//MHvv/+u/5Bu2LAhERER+l9Vq1atolmzZlgsFlq2bMmqVasArYajxWKhcePGl36QRVCQD+Li4ujYsWPQtdOnTyc5OTnfoNDtdnPw4EHWrFlDt27d6NKlC6NGjSIrK0u/5p133mHlypUsWbKEqlWrAtCqVSuysrLYt28foG1BtGnThpiYGOrXr6/77O+//6Z69epUrFjxUrnivChs7gwePJhhw4YVWJHgSpk7PhYvXszJkycZPHgwAOXLl8fhcHD27Fn9Gt9qaOB7Pq62uQN5fQaFzym48uYN5PXDwYMHqVSpEkuXLqV379506NCBkSNHBs2FQIy5o7Fnzx527dql+2z48OFBFVauxLlztWIEgCXE/v37qVGjBrNmzWLatGlYrVZGjhyJy+Xiueee49lnn8032PERGxsbpInw6So2b94MaD9UfH81tW7dWtdVbNiwgWbNmpWJmowF+SCUKVOmsGTJknxtAAcOHMDj8SDLMuPHj2f06NH89ddfPPnkk0HXxcfHBxV3L1euHLVq1dJ/CPl+EIP2QzrQZ2XpL9Di+q0groS54+PLL7/ktttuIyYmBoDU1FSSkpJ4+eWXycjI4Ny5c7z55pvIspzvNt7VNncgr8+geHPqSpo3kNcPOTk5nDx5ksmTJ/P8888zbtw41eriGgAABhJJREFUtm3bxvDhw/O935g7ms+OHTuGy+Xi9ddfZ9y4cRw9epTBgwcHfd6utLlz1aIaXDQHDx5U69Wrp+7fv19/b/fu3WpKSoo6ePBgdfDgwfr7s2bNUrt06VKsdj/++GN18ODBqsvlUq+55hp18+bNqqqq6p49e9S6deuqGRkZ6uDBg9UpU6aU6HguhMJ8sGnTJv29Dz/8UE1JSVFnzpxZaHtnz54Ner1582Y1JSUlqP38GDVqlDpq1Cj1xIkTat26ddXTp0+rqqqqK1asUFu2bKkqiqJ269ZNnT9//vkN8BJRXL+pqqqmpKSoy5YtK1a7l9Pc8XH8+PF8x719+3b15ptvVuvWratec8016qeffqq2adOmQF9cLXNHVfP32fnMqVAux3mjqvn74eOPP1ZTUlLU7du36+9t3bpVTUlJUQ8cOJBvO1f73FFVVc3MzFTdbrf++tSpU2q9evXUlStXFtre5Tp3rmaMFcASIC0tDbPZTPXq1fX3ateujdVqZd++faxdu5amTZvStGlTxowZw9GjR2natKmuiyiIVq1asWXLFjZt2oQsyzRs2BCAWrVqUb58eTZs2MDmzZvLxF+VhfngyJEjALzxxht88MEHjBo1ivvuu6/Q9nzC68C2gCILkPt8tmbNGlJSUnTxcvPmzcnJyWHDhg0cOHCgTPgMiue3C+Fymjs+fvvtNypXrkyTJk2C3q9Xrx4LFizgjz/+YM2aNdx9992cOXNG1yyFcrXMHcjfZxczpy7HeQP5+6FChQqA//sP6Nq2o0eP5tvO1T53AKKiopBlWX9dvnx5YmNji+2Dy23uXM0YAWAJULFiRRwOBwcOHNDfO3LkCA6HgzFjxjBv3jx++OEHfvjhBx5++GEqVKjADz/8EHRIJD8aNmyIy+Vi7ty5tGrVClH0f7tatWrFwoULURSFBg0aXKqhFZvCfFCtWjXee+89vvjiC8aMGcM999xTaFtLliyhVatWQac8t27diiiKQb/U8qNly5bs3r07aBsGICIigkaNGvH1119Tu3Zt4uPjL2icJU1RfrtQLqe54+Pvv/+mefPmQe9lZGTQv39/jh49Srly5bBYLKxYsYKEhARq1aqVp42rae5A/j67mDl1Oc4byN8PzZo1A2Dbtm36e74DGYmJiXnaMOYObNmyhaZNm3L48GH9vaNHj3L27Nl8P2+BXK5z52rGCABLgNTUVBo2bMjzzz/P1q1bSUtL4+mnn6Zly5a0bNmS5ORk/SsuLg5ZlklOTi5Q2O9DlmWuvfZafvrpp6AfKqDpKhYvXkyLFi2C9CilRWE+EEWRyZMnM3jwYNq1a8epU6f0L4/HA2iFun1i6+bNmyPLMs8//zx79+5lzZo1vPDCC/Tt27dIAXX58uWpWrUqixYtKtBnZSkFQWF+K+oPhMK4nOaOj507d1KnTp2g92JiYnA6nYwZM4aDBw/y22+/8corr/DYY48hCAJw9c4dyN9nFzOnLsd5A/n7ISkpiW7duvHCCy+wceNG0tLSGDVqFJ06ddJXj425E+yzevXqUblyZV544QV27NjB5s2befLJJ2nbti2pqamFtne5zp2rGSMALAFkWebjjz+mSpUqPPDAAzz00EPUqFGDDz/88KLbbtWqFTabLc+yeevWrbHb7WXmh0phPli8eDGKojBlyhTat28f9OU7OTd06FDeeOMNQNuGmTp1Kunp6dx+++0MGzaMjh07FprANZCWLVvicrny/HXr81lZ2oIw5o6fM2fOBB1k8DFhwgRycnLo06cPr7zyCo8//jh33XWXbr9a5w7k77OLnVOX27yBgufOmDFjaNasGQ899BADBw6kTp06jBs3TrcbcyfYZyaTiSlTphAdHc2AAQO4//77qVGjBhMmTChWm5fj3LmaEVQ1N3mRgYGBgYGBgYHBVYGxAmhgYGBgYGBgcJVhBIAGBgYGBgYGBlcZRgBoYGBgYGBgYHCVYQSABgYGBgYGBgZXGUYAaGBgYGBgYGBwlWEEgAYGBgYGBgYGVxlGAGhgYGBgYGBgcJVhBIAGBgYGBgYGBlcZRgBoYGBgYGBgYHCV8f96vAZIJuZRvwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 648x936 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pop_one.plot_hexbin();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2294a2ae-b312-483e-bab7-f8c30469d6be",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:43.739566Z",
     "start_time": "2022-01-10T20:10:43.529732Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#The values for EN are seeds for the random number generator for the noise sampling and\n",
    "#thus are uniformly sampled numbers between (0, 2**32-1) \n",
    "litpop_iv.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f78643e-7343-4d1d-843b-969884aefae5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-18T12:31:26.382961Z",
     "start_time": "2021-08-18T12:31:26.379295Z"
    }
   },
   "source": [
    "## Hazard"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6ebdffd-6a08-4278-a83b-24a3c488b706",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-01T14:00:53.406863Z",
     "start_time": "2021-11-01T14:00:53.401328Z"
    }
   },
   "source": [
    "The following types of uncertainties can be added:\n",
    "\n",
    "- HE: sub-sampling events from the total event set\n",
    "            For each sub-sample, n_ev events are sampled with replacement. HE is the value of the seed\n",
    "            for  the uniform random number generator.\n",
    "- HI: scale the intensity of all events (homogeneously)\n",
    "            The instensity of all events is multiplied by a number\n",
    "            sampled uniformly from a distribution with (min, max) = bounds_int\n",
    "- HF: scale the frequency of all events (homogeneously)\n",
    "            The frequency of all events is multiplied by a number\n",
    "            sampled uniformly from a distribution with (min, max) = bounds_freq\n",
    "\n",
    "If a bounds is None, this parameter is assumed to have no uncertainty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "21876972-5120-44ce-81f3-6bb52a32cc97",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:43.765948Z",
     "start_time": "2022-01-10T20:10:43.741273Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-01-10 21:10:43,742 - climada.hazard.base - INFO - Reading /Users/ckropf/climada/demo/data/tc_fl_1990_2004.h5\n"
     ]
    }
   ],
   "source": [
    "#Define the base exposure\n",
    "from climada.util.constants import HAZ_DEMO_H5\n",
    "from climada.hazard import Hazard\n",
    "haz_base = Hazard.from_hdf5(HAZ_DEMO_H5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0adedfaf-bd2b-4ec4-9d28-28c0c69d56c4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:43.772087Z",
     "start_time": "2022-01-10T20:10:43.767951Z"
    }
   },
   "outputs": [],
   "source": [
    "from climada.engine.unsequa import InputVar\n",
    "bounds_freq = [0.9, 1.1] #+- 10% noise on the frequency of all events\n",
    "bounds_int = None #No uncertainty on the intensity\n",
    "n_ev = None \n",
    "haz_iv = InputVar.haz(haz_base, n_ev=n_ev, bounds_freq=bounds_freq, bounds_int=bounds_int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "187b9c74-3ab3-403d-95ec-20ab148f0b4e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:43.780479Z",
     "start_time": "2022-01-10T20:10:43.774073Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10000000000000736"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The difference in frequency for HF=1.1 is indeed 10%.\n",
    "haz_high_freq = haz_iv.evaluate(HE=n_ev, HI=None, HF = 1.1)\n",
    "(sum(haz_high_freq.frequency) - sum(haz_base.frequency)) / sum(haz_base.frequency)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "040bcec7-406e-4007-94a1-0650c3686920",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:43.786970Z",
     "start_time": "2022-01-10T20:10:43.782373Z"
    }
   },
   "outputs": [],
   "source": [
    "bounds_freq = [0.9, 1.1] #+- 10% noise on the frequency of all events\n",
    "bounds_int = None #No uncertainty on the intensity\n",
    "n_ev = round(0.8 * haz_base.size) #sub-sample with re-draw events to obtain hazards with n=0.8*tot_number_events\n",
    "haz_iv = InputVar.haz(haz_base, n_ev=n_ev, bounds_freq=bounds_freq, bounds_int=bounds_int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ffbde49-fc7c-49bf-9e41-831fb5b18afc",
   "metadata": {},
   "source": [
    "Note that the HE is not a univariate distribution, but for each sample corresponds to the names of the sub-sampled events. However, to simplify the data stream, the HE is saved as the seed for the random number generator that made the smaple. Hence, the value of HE is a label for the given sample. If really needed, the exact chosen events can be obtained as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3cdd89a1-831d-49a6-bcdd-6d650150ecb8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:43.792766Z",
     "start_time": "2022-01-10T20:10:43.788430Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "HE = 2618981871 #The random seed (number between 0 and 2**32)\n",
    "rng = np.random.RandomState(int(HE)) #Initialize a random state with the seed\n",
    "chosen_ev = list(rng.choice(haz_base.event_name, int(n_ev))) #Obtain the corresponding events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c21c99a6-0e13-4e56-bab3-36fd5af1f387",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:43.797524Z",
     "start_time": "2022-01-10T20:10:43.794056Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1998209N11335'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The first event is \n",
    "chosen_ev[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "afc1d32a-63cb-4a8f-911c-a9e539b21a61",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:43.992504Z",
     "start_time": "2022-01-10T20:10:43.799061Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#The values for HE are seeds for the random number generator for the noise sampling and\n",
    "#thus are uniformly sampled numbers between (0, 2**32-1) \n",
    "haz_iv.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3102f7ea-8a23-4c2d-9c27-e6bd8f3c63f0",
   "metadata": {},
   "source": [
    "The number of events per sub-sample is equal to n_ev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4f7252dd-b1ba-43c4-ba56-160faf16de43",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:44.005540Z",
     "start_time": "2022-01-10T20:10:43.994260Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The number of events per sample is equal to n_ev\n",
    "haz_sub = haz_iv.evaluate(HE=928165924, HI=None, HF = 1.1)\n",
    "#The number for HE is irrelevant, as all samples have the same n_Ev\n",
    "haz_sub.size - n_ev"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ca01a5d-bf43-4ca5-b69c-9a8a9f3b683d",
   "metadata": {},
   "source": [
    "## ImpactFuncSet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b76d5c86-2f98-4f56-a1eb-90b8a5b3aa8f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-01T14:01:03.864028Z",
     "start_time": "2021-11-01T14:01:03.858880Z"
    }
   },
   "source": [
    "The following types of uncertainties can be added:\n",
    "- MDD: scale the mdd (homogeneously)\n",
    "            The value of mdd at each intensity is multiplied by a number\n",
    "            sampled uniformly from a distribution with\n",
    "            (min, max) = bounds_mdd\n",
    "- PAA: scale the paa (homogeneously)\n",
    "            The value of paa at each intensity is multiplied by a number\n",
    "            sampled uniformly from a distribution with\n",
    "            (min, max) = bounds_paa\n",
    "- IFi: shift the intensity (homogeneously)\n",
    "            The value intensity are all summed with a random number\n",
    "            sampled uniformly from a distribution with\n",
    "            (min, max) = bounds_int\n",
    "\n",
    "If a bounds is None, this parameter is assumed to have no uncertainty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1b44d6e2-1905-4191-a91d-f4dd80c3d4ae",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:44.010158Z",
     "start_time": "2022-01-10T20:10:44.007159Z"
    }
   },
   "outputs": [],
   "source": [
    "from climada.entity import ImpactFuncSet, ImpfTropCyclone\n",
    "impf = ImpfTropCyclone.from_emanuel_usa()\n",
    "impf_set_base = ImpactFuncSet()\n",
    "impf_set_base.append(impf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c26d664f-82eb-478d-9f4d-644ef45ac91d",
   "metadata": {},
   "source": [
    "It is necessary to specify the hazard type and the impact function id. For simplicity, the default uncertainty input variable only looks at the uncertainty on one single impact function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c9d1c458-6af7-4103-8b8c-99edd9306e89",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:44.017097Z",
     "start_time": "2022-01-10T20:10:44.012007Z"
    }
   },
   "outputs": [],
   "source": [
    "from climada.engine.unsequa import InputVar\n",
    "bounds_impfi = [-10, 10] #-10 m/s ; +10m/s uncertainty on the intensity\n",
    "bounds_mdd = [0.7, 1.1] #-30% - +10% uncertainty on the mdd\n",
    "bounds_paa = None #No uncertainty in the paa\n",
    "impf_iv = InputVar.impfset(impf_set_base,\n",
    "                           bounds_impfi=bounds_impfi,\n",
    "                           bounds_mdd=bounds_mdd,\n",
    "                           bounds_paa=bounds_paa,\n",
    "                           haz_id_dict={'TC': [1]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "376e5b42-5a5f-4a3b-8316-90bd1ac638e1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:46.868271Z",
     "start_time": "2022-01-10T20:10:44.019021Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the impact function for 50 random samples (note for the expert, these are not global)\n",
    "n = 50\n",
    "ax = impf_iv.evaluate().plot()\n",
    "inten = impf_iv.distr_dict['IFi'].rvs(size=n)\n",
    "mdd = impf_iv.distr_dict['MDD'].rvs(size=n)\n",
    "for i, m in zip(inten, mdd):\n",
    "    impf_iv.evaluate(IFi=i, MDD=m).plot(axis=ax)\n",
    "ax.get_legend().remove()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18f00c4e-e2fc-4565-9ca0-ff7136326508",
   "metadata": {},
   "source": [
    "## Entity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba8cb3e2-2a85-4ee4-a565-f9351768997a",
   "metadata": {},
   "source": [
    "\n",
    "The following types of uncertainties can be added:\n",
    "- DR: value of constant discount rate (homogeneously)\n",
    "            The value of the discounts in each year is\n",
    "            sampled uniformly from a distribution with\n",
    "            (min, max) = bounds_disc\n",
    "- CO: scale the cost (homogeneously)\n",
    "            The cost of all measures is multiplied by the same number\n",
    "            sampled uniformly from a distribution with\n",
    "            (min, max) = bounds_cost\n",
    "- ET: scale the total value (homogeneously)\n",
    "            The value at each exposure point is multiplied by a number\n",
    "            sampled uniformly from a distribution with\n",
    "            (min, max) = bounds_totval\n",
    "- EN: mutliplicative noise (inhomogeneous)\n",
    "            The value of each exposure point is independently multiplied by\n",
    "            a random number sampled uniformly from a distribution\n",
    "            with (min, max) = bounds_noise. EN is the value of the seed\n",
    "            for  the uniform random number generator.\n",
    "- EL: sample uniformly from exposure list\n",
    "            From the provided list of exposure is elements are uniformly\n",
    "            sampled. For example, LitPop instances with different exponents.\n",
    "- MDD: scale the mdd (homogeneously)\n",
    "            The value of mdd at each intensity is multiplied by a number\n",
    "            sampled uniformly from a distribution with\n",
    "            (min, max) = bounds_mdd\n",
    "- PAA: scale the paa (homogeneously)\n",
    "            The value of paa at each intensity is multiplied by a number\n",
    "            sampled uniformly from a distribution with\n",
    "            (min, max) = bounds_paa\n",
    "- IFi: shift the intensity (homogeneously)\n",
    "            The value intensity are all summed with a random number\n",
    "            sampled uniformly from a distribution with\n",
    "            (min, max) = bounds_int\n",
    "\n",
    "\n",
    "If a bounds is None, this parameter is assumed to have no uncertainty."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d23eda3-bda4-402f-9f07-3b8646794704",
   "metadata": {},
   "source": [
    "### Example: single exposures "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3b83130a-4f80-445b-9be8-b7851560cc03",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:47.187916Z",
     "start_time": "2022-01-10T20:10:46.870375Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-01-10 21:10:47,184 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2022-01-10 21:10:47,185 - climada.entity.exposures.base - INFO - geometry not set.\n",
      "2022-01-10 21:10:47,185 - climada.entity.exposures.base - INFO - region_id not set.\n",
      "2022-01-10 21:10:47,185 - climada.entity.exposures.base - INFO - centr_ not set.\n"
     ]
    }
   ],
   "source": [
    "from climada.entity import Entity\n",
    "from climada.util.constants import ENT_DEMO_TODAY\n",
    "ent = Entity.from_excel(ENT_DEMO_TODAY)\n",
    "ent.exposures.ref_year = 2018\n",
    "ent.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "402cf2f7-8b50-450f-a878-321272b3fd64",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:47.205620Z",
     "start_time": "2022-01-10T20:10:47.198309Z"
    }
   },
   "outputs": [],
   "source": [
    "from climada.engine.unsequa import InputVar\n",
    "ent_iv = InputVar.ent(\n",
    "    impf_set = ent.impact_funcs,\n",
    "    disc_rate = ent.disc_rates,\n",
    "    exp_list = [ent.exposures],\n",
    "    meas_set = ent.measures,\n",
    "    bounds_disc=[0, 0.08],\n",
    "    bounds_cost=[0.5, 1.5],\n",
    "    bounds_totval=[0.9, 1.1],\n",
    "    bounds_noise=[0.3, 1.9],\n",
    "    bounds_mdd=[0.9, 1.05],\n",
    "    bounds_paa=None,\n",
    "    bounds_impfi=[-2, 5],\n",
    "    haz_id_dict={'TC': [1]}\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "50b9cd64-1c42-42ef-9955-762615fefb02",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:47.840005Z",
     "start_time": "2022-01-10T20:10:47.206997Z"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1728x720 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ent_iv.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "498373e1-cc10-4b14-b938-22c9a0b11832",
   "metadata": {},
   "source": [
    "###  Example: list of Litpop exposures with different exponents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f9bcf449-5d5d-4fac-a10a-e28cf1581773",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:47.847448Z",
     "start_time": "2022-01-10T20:10:47.842240Z"
    }
   },
   "outputs": [],
   "source": [
    "#Define a generic method to make litpop instances with different exponent pairs.\n",
    "from climada.entity import LitPop\n",
    "def generate_litpop_base(impf_id, value_unit, haz, assign_centr_kwargs,\n",
    "                          choice_mn, **litpop_kwargs):\n",
    "    #In-function imports needed only for parallel computing on Windows\n",
    "    from climada.entity import LitPop\n",
    "    litpop_base = []\n",
    "    for [m, n] in choice_mn:\n",
    "        print('\\n Computing litpop for m=%d, n=%d \\n' %(m, n))\n",
    "        litpop_kwargs['exponents'] = (m, n)\n",
    "        exp = LitPop.from_countries(**litpop_kwargs)\n",
    "        exp.gdf['impf_' + haz.tag.haz_type] = impf_id\n",
    "        exp.gdf.drop('impf_', axis=1, inplace=True)\n",
    "        if value_unit is not None:\n",
    "            exp.value_unit = value_unit\n",
    "        exp.assign_centroids(haz, **assign_centr_kwargs)\n",
    "        litpop_base.append(exp)\n",
    "    return litpop_base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9adcdda7-5488-42cc-abfd-d098e359d0f2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:47.874958Z",
     "start_time": "2022-01-10T20:10:47.849449Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-01-10 21:10:47,851 - climada.hazard.base - INFO - Reading /Users/ckropf/climada/demo/data/tc_fl_1990_2004.h5\n"
     ]
    }
   ],
   "source": [
    "#Define the parameters of the LitPop instances\n",
    "impf_id = 1\n",
    "value_unit = None\n",
    "litpop_kwargs = {\n",
    "    'countries' : ['CUB'],\n",
    "    'res_arcsec' : 300, \n",
    "    'reference_year' : 2020,\n",
    "}\n",
    "assign_centr_kwargs={}\n",
    "\n",
    "# The hazard is needed to assign centroids\n",
    "from climada.util.constants import HAZ_DEMO_H5\n",
    "from climada.hazard import Hazard\n",
    "haz = Hazard.from_hdf5(HAZ_DEMO_H5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6afb089a-a268-40dd-ae7d-8e12e23ae925",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:55.048825Z",
     "start_time": "2022-01-10T20:10:47.877094Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Computing litpop for m=1, n=0 \n",
      "\n",
      "2022-01-10 21:10:48,109 - climada.entity.exposures.litpop.litpop - INFO - \n",
      " LitPop: Init Exposure for country: CUB (192)...\n",
      "\n",
      "2022-01-10 21:10:48,111 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-01-10 21:10:49,491 - climada.util.finance - WARNING - No data available for country. Using non-financial wealth instead\n",
      "2022-01-10 21:10:50,565 - climada.util.finance - INFO - GDP CUB 2020: 1.074e+11.\n",
      "2022-01-10 21:10:50,570 - climada.util.finance - WARNING - No data for country, using mean factor.\n",
      "2022-01-10 21:10:50,582 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
      "2022-01-10 21:10:50,583 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2022-01-10 21:10:50,583 - climada.entity.exposures.base - INFO - cover not set.\n",
      "2022-01-10 21:10:50,584 - climada.entity.exposures.base - INFO - deductible not set.\n",
      "2022-01-10 21:10:50,584 - climada.entity.exposures.base - INFO - centr_ not set.\n",
      "2022-01-10 21:10:50,589 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 2500 centroids.\n",
      "2022-01-10 21:10:50,592 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
      "2022-01-10 21:10:50,624 - climada.util.interpolation - WARNING - Distance to closest centroid is greater than 100km for 77 coordinates.\n",
      "\n",
      " Computing litpop for m=0, n=1 \n",
      "\n",
      "2022-01-10 21:10:50,872 - climada.entity.exposures.litpop.litpop - INFO - \n",
      " LitPop: Init Exposure for country: CUB (192)...\n",
      "\n",
      "2022-01-10 21:10:50,874 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-01-10 21:10:52,432 - climada.util.finance - WARNING - No data available for country. Using non-financial wealth instead\n",
      "2022-01-10 21:10:52,863 - climada.util.finance - INFO - GDP CUB 2020: 1.074e+11.\n",
      "2022-01-10 21:10:52,867 - climada.util.finance - WARNING - No data for country, using mean factor.\n",
      "2022-01-10 21:10:52,877 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
      "2022-01-10 21:10:52,878 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2022-01-10 21:10:52,878 - climada.entity.exposures.base - INFO - cover not set.\n",
      "2022-01-10 21:10:52,879 - climada.entity.exposures.base - INFO - deductible not set.\n",
      "2022-01-10 21:10:52,879 - climada.entity.exposures.base - INFO - centr_ not set.\n",
      "2022-01-10 21:10:52,884 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 2500 centroids.\n",
      "2022-01-10 21:10:52,886 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
      "2022-01-10 21:10:52,914 - climada.util.interpolation - WARNING - Distance to closest centroid is greater than 100km for 77 coordinates.\n",
      "\n",
      " Computing litpop for m=1, n=1 \n",
      "\n",
      "2022-01-10 21:10:53,159 - climada.entity.exposures.litpop.litpop - INFO - \n",
      " LitPop: Init Exposure for country: CUB (192)...\n",
      "\n",
      "2022-01-10 21:10:53,161 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-01-10 21:10:54,584 - climada.util.finance - WARNING - No data available for country. Using non-financial wealth instead\n",
      "2022-01-10 21:10:54,997 - climada.util.finance - INFO - GDP CUB 2020: 1.074e+11.\n",
      "2022-01-10 21:10:55,001 - climada.util.finance - WARNING - No data for country, using mean factor.\n",
      "2022-01-10 21:10:55,011 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
      "2022-01-10 21:10:55,012 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2022-01-10 21:10:55,012 - climada.entity.exposures.base - INFO - cover not set.\n",
      "2022-01-10 21:10:55,013 - climada.entity.exposures.base - INFO - deductible not set.\n",
      "2022-01-10 21:10:55,013 - climada.entity.exposures.base - INFO - centr_ not set.\n",
      "2022-01-10 21:10:55,018 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 2500 centroids.\n",
      "2022-01-10 21:10:55,020 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
      "2022-01-10 21:10:55,046 - climada.util.interpolation - WARNING - Distance to closest centroid is greater than 100km for 77 coordinates.\n"
     ]
    }
   ],
   "source": [
    "#Generate the LitPop list\n",
    "\n",
    "choice_mn = [[1, 0.5], [0.5, 1], [1, 1]] #Choice of exponents m,n\n",
    "\n",
    "litpop_list = generate_litpop_base(impf_id, value_unit, haz, assign_centr_kwargs, choice_mn, **litpop_kwargs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c3bff058-b814-4059-8893-9de07fb8b360",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:55.144154Z",
     "start_time": "2022-01-10T20:10:55.050611Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-01-10 21:10:55,139 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2022-01-10 21:10:55,140 - climada.entity.exposures.base - INFO - geometry not set.\n",
      "2022-01-10 21:10:55,140 - climada.entity.exposures.base - INFO - region_id not set.\n",
      "2022-01-10 21:10:55,141 - climada.entity.exposures.base - INFO - centr_ not set.\n"
     ]
    }
   ],
   "source": [
    "from climada.entity import Entity\n",
    "from climada.util.constants import ENT_DEMO_TODAY\n",
    "ent = Entity.from_excel(ENT_DEMO_TODAY)\n",
    "ent.exposures.ref_year = 2020\n",
    "ent.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "99939159-2a7c-4f32-8f78-8fcbe067b1b5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:55.154796Z",
     "start_time": "2022-01-10T20:10:55.146177Z"
    }
   },
   "outputs": [],
   "source": [
    "from climada.engine.unsequa import InputVar\n",
    "ent_iv = InputVar.ent(\n",
    "    impf_set = ent.impact_funcs,\n",
    "    disc_rate = ent.disc_rates,\n",
    "    exp_list = litpop_list,\n",
    "    meas_set = ent.measures,\n",
    "    bounds_disc=[0, 0.08],\n",
    "    bounds_cost=[0.5, 1.5],\n",
    "    bounds_totval=[0.9, 1.1],\n",
    "    bounds_noise=[0.3, 1.9],\n",
    "    bounds_mdd=[0.9, 1.05],\n",
    "    bounds_paa=None,\n",
    "    bounds_impfi=[-2, 5],\n",
    "    haz_id_dict={'TC': [1]}\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d786ada6-bb2d-4542-894b-dff7ba12a377",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:57.415665Z",
     "start_time": "2022-01-10T20:10:55.157499Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<GeoAxesSubplot:title={'center':\"LitPop Exposure for ['CUB'] at 300 as, year: 2020, financial mode: pc,\\nexp: (0.5, 1), admin1_calc: False\"}>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x936 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ent_iv.evaluate().exposures.plot_hexbin()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25dbb8ab-bbf4-4b2e-a8f5-86d8d61bf106",
   "metadata": {},
   "source": [
    "## Entity Future"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61212965-7e5e-4df8-b0ba-4b39b14524f9",
   "metadata": {},
   "source": [
    "The following types of uncertainties can be added:\n",
    "- CO: scale the cost (homogeneously)\n",
    "            The cost of all measures is multiplied by the same number\n",
    "            sampled uniformly from a distribution with\n",
    "            (min, max) = bounds_cost\n",
    "- EG: scale the exposures growth (homogeneously)\n",
    "            The value at each exposure point is multiplied by a number\n",
    "            sampled uniformly from a distribution with\n",
    "- EN: mutliplicative noise (inhomogeneous)\n",
    "            The value of each exposure point is independently multiplied by\n",
    "            a random number sampled uniformly from a distribution\n",
    "            with (min, max) = bounds_noise. EN is the value of the seed\n",
    "            for  the uniform random number generator.\n",
    "- EL: sample uniformly from exposure list\n",
    "            From the provided list of exposure is elements are uniformly\n",
    "            sampled. For example, LitPop instances with different exponents.\n",
    "- MDD: scale the mdd (homogeneously)\n",
    "            The value of mdd at each intensity is multiplied by a number\n",
    "            sampled uniformly from a distribution with\n",
    "            (min, max) = bounds_mdd\n",
    "- PAA: scale the paa (homogeneously)\n",
    "            The value of paa at each intensity is multiplied by a number\n",
    "            sampled uniformly from a distribution with\n",
    "            (min, max) = bounds_paa\n",
    "- IFi: shift the impact function intensity (homogeneously)\n",
    "            The value intensity are all summed with a random number\n",
    "            sampled uniformly from a distribution with\n",
    "            (min, max) = bounds_impfi\n",
    "\n",
    "\n",
    "If a bounds is None, this parameter is assumed to have no uncertainty."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d90593a9-efe5-471f-b894-6a90db970bd2",
   "metadata": {},
   "source": [
    "### Example: single exposures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "36d59a4c-eb0a-4b11-a19b-89b968240c29",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:57.511248Z",
     "start_time": "2022-01-10T20:10:57.417175Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-01-10 21:10:57,507 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2022-01-10 21:10:57,507 - climada.entity.exposures.base - INFO - geometry not set.\n",
      "2022-01-10 21:10:57,508 - climada.entity.exposures.base - INFO - region_id not set.\n",
      "2022-01-10 21:10:57,509 - climada.entity.exposures.base - INFO - centr_ not set.\n"
     ]
    }
   ],
   "source": [
    "from climada.entity import Entity\n",
    "from climada.util.constants import ENT_DEMO_FUTURE\n",
    "\n",
    "ent_fut = Entity.from_excel(ENT_DEMO_FUTURE)\n",
    "ent_fut.exposures.ref_year = 2040\n",
    "ent_fut.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "46115ff5-ddea-4ceb-9653-578b74c07297",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:57.519160Z",
     "start_time": "2022-01-10T20:10:57.513470Z"
    }
   },
   "outputs": [],
   "source": [
    "entfut_iv = InputVar.entfut(\n",
    "    impf_set = ent_fut.impact_funcs,\n",
    "    exp_list = [ent_fut.exposures],\n",
    "    meas_set = ent_fut.measures,\n",
    "    bounds_cost=[0.6, 1.2],\n",
    "    bounds_eg=[0.8, 1.5],\n",
    "    bounds_noise=None,\n",
    "    bounds_mdd=[0.7, 0.9],\n",
    "    bounds_paa=[1.3, 2],\n",
    "    haz_id_dict={'TC': [1]}\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb14ecfc-cc9b-4dea-9f67-bc52a8a79a95",
   "metadata": {},
   "source": [
    "### Example: list of exposures "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5766a082-03dc-4e8c-896a-88e6411774a9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:57.525368Z",
     "start_time": "2022-01-10T20:10:57.521104Z"
    }
   },
   "outputs": [],
   "source": [
    "#Define a generic method to make litpop instances with different exponent pairs.\n",
    "from climada.entity import LitPop\n",
    "def generate_litpop_base(impf_id, value_unit, haz, assign_centr_kwargs,\n",
    "                          choice_mn, **litpop_kwargs):\n",
    "    #In-function imports needed only for parallel computing on Windows\n",
    "    from climada.entity import LitPop\n",
    "    litpop_base = []\n",
    "    for [m, n] in choice_mn:\n",
    "        print('\\n Computing litpop for m=%d, n=%d \\n' %(m, n))\n",
    "        litpop_kwargs['exponents'] = (m, n)\n",
    "        exp = LitPop.from_countries(**litpop_kwargs)\n",
    "        exp.gdf['impf_' + haz.tag.haz_type] = impf_id\n",
    "        exp.gdf.drop('impf_', axis=1, inplace=True)\n",
    "        if value_unit is not None:\n",
    "            exp.value_unit = value_unit\n",
    "        exp.assign_centroids(haz, **assign_centr_kwargs)\n",
    "        litpop_base.append(exp)\n",
    "    return litpop_base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8f505ca7-1133-450b-96a0-e8133ba285ee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:10:57.551457Z",
     "start_time": "2022-01-10T20:10:57.527285Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-01-10 21:10:57,529 - climada.hazard.base - INFO - Reading /Users/ckropf/climada/demo/data/tc_fl_1990_2004.h5\n"
     ]
    }
   ],
   "source": [
    "#Define the parameters of the LitPop instances\n",
    "impf_id = 1\n",
    "value_unit = None\n",
    "litpop_kwargs = {\n",
    "    'countries' : ['CUB'],\n",
    "    'res_arcsec' : 300, \n",
    "    'reference_year' : 2040,\n",
    "}\n",
    "assign_centr_kwargs={}\n",
    "\n",
    "# The hazard is needed to assign centroids\n",
    "from climada.util.constants import HAZ_DEMO_H5\n",
    "from climada.hazard import Hazard\n",
    "haz = Hazard.from_hdf5(HAZ_DEMO_H5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6f6bc7a9-3779-4ae7-94e2-91808cad51d8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:11:03.982167Z",
     "start_time": "2022-01-10T20:10:57.552955Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Computing litpop for m=1, n=0 \n",
      "\n",
      "2022-01-10 21:10:57,798 - climada.entity.exposures.litpop.litpop - INFO - \n",
      " LitPop: Init Exposure for country: CUB (192)...\n",
      "\n",
      "2022-01-10 21:10:57,799 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-01-10 21:10:57,800 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-01-10 21:10:59,239 - climada.util.finance - WARNING - No data available for country. Using non-financial wealth instead\n",
      "2022-01-10 21:10:59,680 - climada.util.finance - INFO - GDP CUB 2020: 1.074e+11.\n",
      "2022-01-10 21:10:59,683 - climada.util.finance - WARNING - No data for country, using mean factor.\n",
      "2022-01-10 21:10:59,693 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
      "2022-01-10 21:10:59,693 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2022-01-10 21:10:59,694 - climada.entity.exposures.base - INFO - cover not set.\n",
      "2022-01-10 21:10:59,694 - climada.entity.exposures.base - INFO - deductible not set.\n",
      "2022-01-10 21:10:59,695 - climada.entity.exposures.base - INFO - centr_ not set.\n",
      "2022-01-10 21:10:59,698 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 2500 centroids.\n",
      "2022-01-10 21:10:59,700 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
      "2022-01-10 21:10:59,726 - climada.util.interpolation - WARNING - Distance to closest centroid is greater than 100km for 77 coordinates.\n",
      "\n",
      " Computing litpop for m=0, n=1 \n",
      "\n",
      "2022-01-10 21:10:59,981 - climada.entity.exposures.litpop.litpop - INFO - \n",
      " LitPop: Init Exposure for country: CUB (192)...\n",
      "\n",
      "2022-01-10 21:10:59,982 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-01-10 21:10:59,983 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-01-10 21:11:01,391 - climada.util.finance - WARNING - No data available for country. Using non-financial wealth instead\n",
      "2022-01-10 21:11:01,816 - climada.util.finance - INFO - GDP CUB 2020: 1.074e+11.\n",
      "2022-01-10 21:11:01,819 - climada.util.finance - WARNING - No data for country, using mean factor.\n",
      "2022-01-10 21:11:01,829 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
      "2022-01-10 21:11:01,829 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2022-01-10 21:11:01,829 - climada.entity.exposures.base - INFO - cover not set.\n",
      "2022-01-10 21:11:01,830 - climada.entity.exposures.base - INFO - deductible not set.\n",
      "2022-01-10 21:11:01,830 - climada.entity.exposures.base - INFO - centr_ not set.\n",
      "2022-01-10 21:11:01,833 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 2500 centroids.\n",
      "2022-01-10 21:11:01,835 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
      "2022-01-10 21:11:01,861 - climada.util.interpolation - WARNING - Distance to closest centroid is greater than 100km for 77 coordinates.\n",
      "\n",
      " Computing litpop for m=1, n=1 \n",
      "\n",
      "2022-01-10 21:11:02,111 - climada.entity.exposures.litpop.litpop - INFO - \n",
      " LitPop: Init Exposure for country: CUB (192)...\n",
      "\n",
      "2022-01-10 21:11:02,113 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2040. Using nearest available year for GPW data: 2020\n",
      "2022-01-10 21:11:02,113 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n",
      "2022-01-10 21:11:03,498 - climada.util.finance - WARNING - No data available for country. Using non-financial wealth instead\n",
      "2022-01-10 21:11:03,929 - climada.util.finance - INFO - GDP CUB 2020: 1.074e+11.\n",
      "2022-01-10 21:11:03,933 - climada.util.finance - WARNING - No data for country, using mean factor.\n",
      "2022-01-10 21:11:03,944 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n",
      "2022-01-10 21:11:03,945 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2022-01-10 21:11:03,945 - climada.entity.exposures.base - INFO - cover not set.\n",
      "2022-01-10 21:11:03,945 - climada.entity.exposures.base - INFO - deductible not set.\n",
      "2022-01-10 21:11:03,946 - climada.entity.exposures.base - INFO - centr_ not set.\n",
      "2022-01-10 21:11:03,950 - climada.entity.exposures.base - INFO - Matching 1388 exposures with 2500 centroids.\n",
      "2022-01-10 21:11:03,951 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n",
      "2022-01-10 21:11:03,979 - climada.util.interpolation - WARNING - Distance to closest centroid is greater than 100km for 77 coordinates.\n"
     ]
    }
   ],
   "source": [
    "#Generate the LitPop list\n",
    "\n",
    "choice_mn = [[1, 0.5], [0.5, 1], [1, 1]] #Choice of exponents m,n\n",
    "\n",
    "litpop_list = generate_litpop_base(impf_id, value_unit, haz, assign_centr_kwargs, choice_mn, **litpop_kwargs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c7d61fe7-7496-4e89-9393-6e8ca5e9f080",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:11:04.107072Z",
     "start_time": "2022-01-10T20:11:03.984164Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-01-10 21:11:04,102 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2022-01-10 21:11:04,103 - climada.entity.exposures.base - INFO - geometry not set.\n",
      "2022-01-10 21:11:04,103 - climada.entity.exposures.base - INFO - region_id not set.\n",
      "2022-01-10 21:11:04,104 - climada.entity.exposures.base - INFO - centr_ not set.\n"
     ]
    }
   ],
   "source": [
    "from climada.entity import Entity\n",
    "from climada.util.constants import ENT_DEMO_FUTURE\n",
    "\n",
    "ent_fut = Entity.from_excel(ENT_DEMO_FUTURE)\n",
    "ent_fut.exposures.ref_year = 2040\n",
    "ent_fut.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b63f8e63-052d-4ac7-9d9b-56e070e54990",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-01-10T20:11:04.117569Z",
     "start_time": "2022-01-10T20:11:04.109427Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from climada.engine.unsequa import InputVar\n",
    "entfut_iv = InputVar.entfut(\n",
    "    impf_set = ent_fut.impact_funcs,\n",
    "    exp_list = litpop_list,\n",
    "    meas_set = ent_fut.measures,\n",
    "    bounds_cost=[0.6, 1.2],\n",
    "    bounds_eg=[0.8, 1.5],\n",
    "    bounds_noise=None,\n",
    "    bounds_mdd=[0.7, 0.9],\n",
    "    bounds_paa=[1.3, 2],\n",
    "    haz_id_dict={'TC': [1]}\n",
    "    )"
   ]
  }
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