30_EMA_Workbench_run.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ginkel\\AppData\\Local\\Continuum\\anaconda3\\envs\\urban-SETP\\lib\\site-packages\\ema_workbench\\em_framework\\evaluators.py:21: UserWarning: ipyparallel not installed - IpyparalleEvaluator not available\n",
      "  warnings.warn(\n",
      "C:\\Users\\Ginkel\\AppData\\Local\\Continuum\\anaconda3\\envs\\urban-SETP\\lib\\site-packages\\ema_workbench\\em_framework\\optimization.py:48: ImportWarning: platypus based optimization not available\n",
      "  warnings.warn(\"platypus based optimization not available\", ImportWarning)\n",
      "C:\\Users\\Ginkel\\AppData\\Local\\Continuum\\anaconda3\\envs\\urban-SETP\\lib\\site-packages\\ema_workbench\\analysis\\prim.py:30: ImportWarning: altair based interactive inspection not available\n",
      "  warnings.warn((\"altair based interactive \"\n"
     ]
    }
   ],
   "source": [
    "#Workbench imports\n",
    "import math\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from scipy.optimize import brentq\n",
    "\n",
    "from workbench_version import * #load the model\n",
    "from pathlib import Path\n",
    "\n",
    "from ema_workbench.analysis import regional_sa\n",
    "from numpy.lib import recfunctions as rf\n",
    "\n",
    "from ema_workbench import(ema_logging, perform_experiments)\n",
    "from ema_workbench import MultiprocessingEvaluator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### LOAD ADAPTATION STRATEGIES ####\n",
    "from mayors import *\n",
    "allMayors = [Reactive(),Economicus(),Sentiment(),Lawkeeper()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "500\n"
     ]
    }
   ],
   "source": [
    "# Load all SurgeHeight (Realisations of extreme value distribution)\n",
    "transient_paths = list(Path('SurgeHeight','five_hundred').glob('*.csv'))\n",
    "print(len(transient_paths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ema_workbench import(RealParameter,ScalarOutcome, Constant, BooleanParameter, CategoricalParameter, IntegerParameter,\n",
    "                          Model, TimeSeriesOutcome)\n",
    "\n",
    "model = Model('Rotty',function=run_model_workbench)\n",
    "\n",
    "model.uncertainties = [CategoricalParameter('SLR',categories=['01','02','03','04',\n",
    "                                                              '05','06','07','08','09']),\n",
    "                       #CategoricalParameter('SLR',categories=['01','03','06','09']),\n",
    "                       CategoricalParameter('transient',categories =transient_paths), \n",
    "                       CategoricalParameter('Housing_market', #only for control of temp scalar outcome\n",
    "                                            categories=['rational','boundedly_rational']),\n",
    "                       CategoricalParameter('implementation_time', #only for small measure, large measure is calculated proportionally from it\n",
    "                                categories=[4,5,6,7,8,9,10,11,12,13,14])]\n",
    "                                \n",
    "model.levers = [CategoricalParameter('Mayor',categories=allMayors)]\n",
    "\n",
    "model.outcomes = [ScalarOutcome('HP_hp_2200'),\n",
    "                  ScalarOutcome('CC_hp_2200'),\n",
    "                  ScalarOutcome('HP_first_SETP'),\n",
    "                  ScalarOutcome('CC_first_SETP'),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99000\n"
     ]
    }
   ],
   "source": [
    "#Determine the number of uncertainties and levers to completely sample all possibilities\n",
    "number_of_uncertainties = 1\n",
    "for unc in model.uncertainties:\n",
    "    number_of_uncertainties *= len(unc._categories._data)\n",
    "print(number_of_uncertainties)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run without parallel processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiments, outcomes = perform_experiments(models=model,scenarios=10,policies=4)\n",
    "results = experiments, outcomes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run with parallel processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[MainProcess/INFO] pool started\n",
      "[MainProcess/INFO] performing 99000 scenarios * 4 policies * 1 model(s) = 396000 experiments\n",
      "[MainProcess/INFO] 3960 cases completed\n",
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      "[MainProcess/INFO] experiments finished\n",
      "[MainProcess/INFO] terminating pool\n"
     ]
    }
   ],
   "source": [
    "ema_logging.log_to_stderr(ema_logging.INFO)\n",
    "\n",
    "with MultiprocessingEvaluator(model) as evaluator:\n",
    "     results = evaluator.perform_experiments(scenarios=number_of_uncertainties,policies=4,reporting_frequency=100)\n",
    "experiments, outcomes = results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[MainProcess/INFO] results saved successfully to D:\\Python\\Urban-SETP\\temp\\experiments\\2021_2_23_EMA_experiments_five_hundred.tar.gz\n"
     ]
    }
   ],
   "source": [
    "from ema_workbench import save_results\n",
    "\n",
    "target = Path(\"temp\",\"experiments\")\n",
    "\n",
    "today = datetime.date(datetime.now())\n",
    "date = \"{}_{}_{}\".format(today.year,today.month,today.day)\n",
    "\n",
    "if not target.exists(): target.mkdir()\n",
    "\n",
    "save_results(results,Path(target,date + '_EMA_experiments_five_hundred.tar.gz'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[MainProcess/INFO] results loaded succesfully from D:\\Python\\Urban-SETP\\temp\\experiments\\2021_2_23_EMA_experiments_five_hundred.tar.gz\n"
     ]
    }
   ],
   "source": [
    "from ema_workbench import load_results\n",
    "\n",
    "target = Path(\"temp\",\"experiments\")\n",
    "experiments, outcomes = load_results(target / \"2021_2_23_EMA_experiments_five_hundred.tar.gz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Housing_market</th>\n",
       "      <th>SLR</th>\n",
       "      <th>implementation_time</th>\n",
       "      <th>transient</th>\n",
       "      <th>Mayor</th>\n",
       "      <th>scenario</th>\n",
       "      <th>policy</th>\n",
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       "      <th>395995</th>\n",
       "      <td>rational</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_430.csv</td>\n",
       "      <td>Lawkeeper</td>\n",
       "      <td>98995</td>\n",
       "      <td>3</td>\n",
       "      <td>Rotty</td>\n",
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       "    <tr>\n",
       "      <th>395996</th>\n",
       "      <td>rational</td>\n",
       "      <td>3</td>\n",
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       "      <td>Lawkeeper</td>\n",
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       "      <td>Rotty</td>\n",
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       "      <td>boundedly_rational</td>\n",
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       "      <td>Lawkeeper</td>\n",
       "      <td>98997</td>\n",
       "      <td>3</td>\n",
       "      <td>Rotty</td>\n",
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       "      <td>boundedly_rational</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_32.csv</td>\n",
       "      <td>Lawkeeper</td>\n",
       "      <td>98998</td>\n",
       "      <td>3</td>\n",
       "      <td>Rotty</td>\n",
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       "      <th>395999</th>\n",
       "      <td>rational</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_308.csv</td>\n",
       "      <td>Lawkeeper</td>\n",
       "      <td>98999</td>\n",
       "      <td>3</td>\n",
       "      <td>Rotty</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Housing_market SLR implementation_time  \\\n",
       "395995            rational   1                   4   \n",
       "395996            rational   3                  13   \n",
       "395997  boundedly_rational   3                  10   \n",
       "395998  boundedly_rational   5                  11   \n",
       "395999            rational   6                   4   \n",
       "\n",
       "                                            transient      Mayor scenario  \\\n",
       "395995  SurgeHeight\\five_hundred\\five_hundred_430.csv  Lawkeeper    98995   \n",
       "395996  SurgeHeight\\five_hundred\\five_hundred_453.csv  Lawkeeper    98996   \n",
       "395997  SurgeHeight\\five_hundred\\five_hundred_157.csv  Lawkeeper    98997   \n",
       "395998   SurgeHeight\\five_hundred\\five_hundred_32.csv  Lawkeeper    98998   \n",
       "395999  SurgeHeight\\five_hundred\\five_hundred_308.csv  Lawkeeper    98999   \n",
       "\n",
       "       policy  model  \n",
       "395995      3  Rotty  \n",
       "395996      3  Rotty  \n",
       "395997      3  Rotty  \n",
       "395998      3  Rotty  \n",
       "395999      3  Rotty  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiments.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Housing_market</th>\n",
       "      <th>SLR</th>\n",
       "      <th>implementation_time</th>\n",
       "      <th>transient</th>\n",
       "      <th>Mayor</th>\n",
       "      <th>scenario</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>rational</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_267.csv</td>\n",
       "      <td>Sentiment</td>\n",
       "      <td>9900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>rational</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_196.csv</td>\n",
       "      <td>Sentiment</td>\n",
       "      <td>9901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rational</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_16.csv</td>\n",
       "      <td>Sentiment</td>\n",
       "      <td>9902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>boundedly_rational</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_153.csv</td>\n",
       "      <td>Sentiment</td>\n",
       "      <td>9903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>boundedly_rational</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_87.csv</td>\n",
       "      <td>Sentiment</td>\n",
       "      <td>9904</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Housing_market SLR implementation_time  \\\n",
       "0            rational   6                   6   \n",
       "1            rational   6                   8   \n",
       "2            rational   2                  13   \n",
       "3  boundedly_rational   3                   9   \n",
       "4  boundedly_rational   2                  10   \n",
       "\n",
       "                                       transient      Mayor scenario  \n",
       "0  SurgeHeight\\five_hundred\\five_hundred_267.csv  Sentiment     9900  \n",
       "1  SurgeHeight\\five_hundred\\five_hundred_196.csv  Sentiment     9901  \n",
       "2   SurgeHeight\\five_hundred\\five_hundred_16.csv  Sentiment     9902  \n",
       "3  SurgeHeight\\five_hundred\\five_hundred_153.csv  Sentiment     9903  \n",
       "4   SurgeHeight\\five_hundred\\five_hundred_87.csv  Sentiment     9904  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiments2 = experiments.drop(['policy','model'],axis=1)\n",
    "experiments2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Housing_market</th>\n",
       "      <th>SLR</th>\n",
       "      <th>implementation_time</th>\n",
       "      <th>transient</th>\n",
       "      <th>Mayor</th>\n",
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       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>9902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>153</td>\n",
       "      <td>2</td>\n",
       "      <td>9903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>87</td>\n",
       "      <td>2</td>\n",
       "      <td>9904</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Housing_market SLR implementation_time transient  Mayor scenario\n",
       "0               0   6                   6       267      2     9900\n",
       "1               0   6                   8       196      2     9901\n",
       "2               0   2                  13        16      2     9902\n",
       "3               1   3                   9       153      2     9903\n",
       "4               1   2                  10        87      2     9904"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiments3 = experiments2.copy()\n",
    "map_house_market = {'rational' : 0, \n",
    "             'boundedly_rational' :1}\n",
    "#map_mayor = {'H. Economicus': 0,\n",
    "#             'Sentiment' : 1,\n",
    "#             'R. Active' : 2,\n",
    "#             'Lawkeeper' : 3}\n",
    "map_mayor = {'H. Economicus': 1,\n",
    "             'Sentiment' : 2,\n",
    "             'R. Active' : 0,\n",
    "             'Lawkeeper' : 3}\n",
    "\n",
    "experiments3['Housing_market'].replace(map_house_market,inplace=True)\n",
    "experiments3['Mayor'].replace(map_mayor,inplace=True)\n",
    "experiments3['transient'] = experiments3['transient'].apply(lambda s : int(s.split('_')[-1].split('.')[0]))\n",
    "experiments3[['Housing_market', 'SLR', 'transient', 'Mayor']].astype('float')\n",
    "experiments3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "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>HP_hp_2200</th>\n",
       "      <th>CC_hp_2200</th>\n",
       "      <th>HP_first_SETP</th>\n",
       "      <th>CC_first_SETP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>349603.388474</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>349603.388474</td>\n",
       "      <td>9999</td>\n",
       "      <td>9999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9999</td>\n",
       "      <td>2101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>348459.280872</td>\n",
       "      <td>2056</td>\n",
       "      <td>2115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>227363.672293</td>\n",
       "      <td>2053</td>\n",
       "      <td>2096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395995</th>\n",
       "      <td>0</td>\n",
       "      <td>277261.757208</td>\n",
       "      <td>9999</td>\n",
       "      <td>2101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395996</th>\n",
       "      <td>0</td>\n",
       "      <td>347211.587672</td>\n",
       "      <td>2059</td>\n",
       "      <td>2089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395997</th>\n",
       "      <td>0</td>\n",
       "      <td>152563.671702</td>\n",
       "      <td>2032</td>\n",
       "      <td>2107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395998</th>\n",
       "      <td>287227</td>\n",
       "      <td>349858.775190</td>\n",
       "      <td>2085</td>\n",
       "      <td>9999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395999</th>\n",
       "      <td>0</td>\n",
       "      <td>349827.357336</td>\n",
       "      <td>2100</td>\n",
       "      <td>9999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>396000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        HP_hp_2200     CC_hp_2200  HP_first_SETP  CC_first_SETP\n",
       "0                0  349603.388474           9999           9999\n",
       "1                0  349603.388474           9999           9999\n",
       "2                0       0.000000           9999           2101\n",
       "3                0  348459.280872           2056           2115\n",
       "4                0  227363.672293           2053           2096\n",
       "...            ...            ...            ...            ...\n",
       "395995           0  277261.757208           9999           2101\n",
       "395996           0  347211.587672           2059           2089\n",
       "395997           0  152563.671702           2032           2107\n",
       "395998      287227  349858.775190           2085           9999\n",
       "395999           0  349827.357336           2100           9999\n",
       "\n",
       "[396000 rows x 4 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(outcomes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scenario discovery with first_SETP indicator\n",
    "\n",
    "What is SD doing? It finds subspaces in the input space, where high concentrations of outcomes of interest are found. In our case: we search for points where the first SETP in the City Centre happens before the year 2200."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[MainProcess/INFO] 396000 points remaining, containing 85496 cases of interest\n",
      "[MainProcess/INFO] mean: 1.0, mass: 0.05259090909090909, coverage: 0.24359034340787872, density: 1.0 restricted_dimensions: 5\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from ema_workbench.analysis import prim\n",
    "x = experiments3\n",
    "y = outcomes['CC_first_SETP'] < 2200\n",
    "prim_alg = prim.Prim(x,y,threshold=0.99)\n",
    "box1 = prim_alg.find_box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZRsRh6c9xfYnOzKwaIuJ5AEn7RsS+ZYfOkHQfcFalc2R6myxpX0lD09+Pk3SOpLEZ6h0i6Z+SnpF0RjdlDpD0aDqD7d1Z4jEz68ZQSfut2pC0DzA0S8Ws/QzPB3aVtCvJUnwXkTRD9++uQjqO8Jck3XLmAA9Luj4iZpaV2RA4DzgkIl6QtGnGeMzMunIScLGkDUge5b0OZBoYkjUZtkdESDocODciLpL06Qp19gSeiYhnASRdBRwOzCwrcwxwbUS8ABAR8zLGY2Y1LFrbaH12bvWvGzGNpOE2AlBEvJ61btZO10skfR04DrgpbfU1VqizJcnq9qvMSfeV2xYYKekuSdMkHd/ViSRNkjRV0tRXX301Y8hmtr6RtJmki4CrI+J1STtKOilL3awtw0+StOJOioiX0+eFP64UVxf7Or/6bgD2AN5LMhvO/ZIeiIinV6uUTAE+GaClpaXb1+dmeVraupLG+nqaGuqLDqXm1Zea2WDxhExl+7n9eCnJ9IL/nW4/DVxN8mivR1nnM3wZOKds+wXgsgrV5gBjyrZHAy92UWZ+RCwFlkq6B9iV5D/ArCZMf24u3772rzz/2kLqVMcH3jGBb33kvQxtbio6NFvTqIj4fXonS0S0SyplqZj1bfKRkv4l6fVerEX6MDBB0jhJTSRT6VzfqcyfgfdIakinA9uLZEows5rw/PxFTLr4Wp59dQGljqCtVOIvT/yLL17e+atsNWKppI1J70IlvZvkJUpFWW+TfwR8qDfT/acZ+TTgNqAeuDginpR0cnr8goh4StKtwAygA7iwfJEXs6L99r7ptJVWb1isbC/x6AsvMevVBYzbZKOCIrNufIWk0TU+7V+4CfDxLBWzJsNX+rLuSUTcDNzcad8FnbZ/TOXnj2aFeGbea7R3rPmYuqG+jjkLFjsZ1p4nSbr8bUfy3uKfZLwDzpoMp0q6GvgTyer2QLYVp8zWZbuO3YJHn3+JlV20DrfZbOOCorIe3B8Ru5MkRQAkTQe6XFy+XNZkOIJkfN/7y/YFGVacMluXHbvPO7n6wRm0r+igI5IW4qDGBg7eaRu22HB4wdHZKpI2J+m6N1jSO3mrN8sIYEiWc2R9m3xinyI0W8dtMnwoV596DD+95e/c/8wLDGtu4ph9duPE9+xRdGi2ug8AJ5D0WjmnbP9i4BtZTpB1QahtSYbkbRYRO0vaBfhwRHyvV+GarYPGbrwh5x73ocoFrTAR8RuS5UQ/GhHX9OUcWUeg/Br4OtCWXngGSVcZM7Naskc65wEAkkZKytRoy5oMh0TEQ532tWeNzsysSiZGxKJVGxGxEDg0S8WsyXC+pPG81ZHxY8BLPVcxM6u6eknNqzYkDQaaeyj/pqxvk08lGRu8vaS5wCySGWTNzGrJb4E7JF1C0nj7DPCbLBUrrY73lbLNm0lWpq8DlgIfZfW3NmZmhYqIH6XrobyXpHvNdyPitix1K7UMV3Wk2g54F8lYYgGfAu7prpKZWX+RNIZkYpjNSYbtTo6Ic7srHxG3kKzq2SuV1kD5ThrMX4DdI2JJun0m8IfeXszMrA/agf+MiOmShgPTJN3eadb8eyNiP0lLWH2qQJGs8jmi0kWyPjMcC6ws214JbJWxrplZn0XES6QvbCNiiaSnSEabzCwrs1/6s8/DgrImw8uBhyRdR5J1jyDjQ0mz9cF9Tz/PBXc+wNyFi9l59GZ84eB9mLD5qKLDWleMkjS1bHtyOqHzGiRtBbwTeLCLY3XAjIjYuS9BZB2O931JtwDvSXedGBGP9OWCZgPNDY88xZnX/ZUVbUnX23lPvcGUZ17gdyd/ku222KTg6NYJ8yOipVIhScOAa4D/iIg15lONiA5Jj0kau2pdpd7I2jIkIqYD03t7AbOBrKMj+NFNd7+ZCAEiYMXKNn52271ccMIRBUY3cEhqJEmEv6swW9YWwJOSHiLp9QJARHy40jUyJ0MzW9OCpctY2rpyjf0BPD775eoHNABJEskaJk9FRKXufN/p63WyjkAxsy4MH9RM12ufwSYjhlU3mIFrX5LufAdJejT9dDfE7tCIuLv8Qz8PxzOzLjQ3NnBEy04Malz9JmtwYwMnH7hnQVENLBFxb0QoInaJiN3Sz83dFD+4i30Ts1zHt8lma+n0D+5PqVTi+keeoq6ujjqJ0963N4fssl3Roa03JJ0CfJ5k7ZMZZYeGA/dlOYeTodlaamqo58wjD+ZrH9yfBUuXsdmI4V5bufquIBl18gPgjLL9SyJiQZYTOBma9ZOhzU1eS7kgEfE68LqkbwIvR0SrpAOAXSRdVj6tV3f8zNDMBpJrgJKkbUjeQI8jaTVW5GRoZgNJR0S0A0cCP4+IL5P0PazIydDMBpI2SUcDxwM3pvsas1R0MjSzgeREYG/g+xExS9I4kglfK/ILFDMbMCJipqTTSWbaIiJmAT/MUtctQzMbMCR9CHgUuDXd3k3S9Vnq5poMJR0i6Z+SnpF0Rg/l3iWplC40ZWbWV2cCewKLACLiUZI3yhXllgwl1QO/JBkKsyNwtKQduyl3NpBpnQIzsx60p30Oy0WXJTvJ85nhnsAzEfEsgKSrgMMpm5029QWSvkHvyjEWs5oREcydu5COjmDMmI1IJmUZWDraVrL85dlFXPoJSceQLBk6AfgiMCVLxTyT4ZZA+b/GHGCv8gKStiSZNfsgekiGkiYBkwDGjh3b74GaVcv/PTuPM79zHfNfW4IkRowYzLe/+RF22OFtRYc2UHwB+G+glaSz9W3A97JUzDMZdvXnrnNz9efA6RFR6umvYzoF+GSAlpaWTE1es1qzYkUbX/nqFSxZsmK1fV874yquuPwURowYXGB0/at5ZSNbzdk0U9lZ/XTN9JHb9RHxPpKE2Ct5vkCZA4wp2x4NvNipTAtwlaTngI8B50n6SI4xmRXm3vuepr29tMb+UqmDO//W+emR9VZElIBlkjboS/08W4YPAxPSTo9zgaOAY8oLRMSbb3kkXQrcGBF/yjEms8K8tuAN2trWTIatre3Mn/9GARENSCuAxyXdzurT/n+xUsXckmFEtEs6jeSevR64OCKelHRyevyCvK5tVot23mk0DQ31tLd3rLZ/8KBGdtllTDe1rJduSj+9lusIlHQ22ps77esyCUbECXnGYla0HXd4G7u8YwyPzXiB1tZkAanm5gbGj9+Ulj0ydYWzCiKiz0sYezieWZVI4ntnfZQbbnqUW255jFJH8IGDd+Yjh+9BXd3A616zrnEyNKuihoZ6jjh8D444fI+iQ7FOPDbZzNZ5ki5Pf36pr+dwMjSzgWAPSW8HPiNppKSNyj9ZTuDbZLOCRAQzHp/NK6+8zoRtNmfcuE2KDmlddgHJTDVbA9NYfdBHpPt75GRoVoCFC5fy5a9ewauvLiYiSYy7v3MrzvzWETQ2emW93oqIXwC/kHR+RJzSl3P4NtmsAD/88U3MnbuQ5cvbWLGijdbWdqY/8hxX//7BokNbp0XEKZJ2lXRa+tkla10nQ7MqW758JY888hyl0uqdr1tb27nhpkcKimpgkPRF4HfApunnd5K+kKWub5PNqqyrIXmrrFzZXsVIBqTPAntFxFIASWcD9wP/U6miW4ZmVTZixGBGb7nmC876+jr23WfbAiKqbZIuljRP0hNZigPlf21KdD2D1hqcDM0KcPrXPsjgwU00NSUvSwYNamSjkUP5zIn/VnBkNelS4JCMZS8BHpR0pqQzgQdIFpOvyLfJZgXYbrstuOySSdx0y2PMnv0aO+80mvcfvDODBzcVHVrNiYh7JG2Vsew5ku4C9iNpEZ4YEZkexDoZmhVk442Hcfxx+xYdRi0YJWlq2fbkdELnPomI6cD03tZzMjQrQETwpz9P57dXTGHRoqWMHr0Rnz/5vey15/iiQyvC/IhoKToIPzM0K8BVv3+QX190FwsXLiUCZs9ewJlnXcf0R54rOrT1lpOhWZWVSh387ooprFjRttr+1tZ2LrrknoKiMidDsypbvHg57d30NZwze0GVo6l9kq4k6Su4naQ5kk7qZf1Mzx/9zNCsykaMGExDYz0ru0iIo8dkmmBlvRIRR2cpl66O98WI+FmnQ7/KUt8tQ7Mqq6+v49ij92ZQc+Nq+5ubGzjpBPcz7Kt0dbzDu9g/LUt9twzNCnDUJ9/NsmUr+eM1D9O6sp0Rwwdx2qkHs/vuWxUd2rruPkn/C1zN6qvjVexq42RoVoBHHn2eP177MG3pOsrLV7TxP7+8nZ132pLNN9+w4OjWafukP88q2xfAQZUqOhmaVVlE8NNzbnlzhTxIJm8olTq48OK7+eY31rjTs4wi4sC+1nUyNKuyxYuXd7lofEdHMHXac9UPaACR1Ax8FNiKsvwWEWd1V2cVJ0OzKmtubiS6OTZ0aHNVYxmA/gy8TjL1f2tvKjoZmlXZoEGN7LvPBO6975+USm+lxabGeo48ovBRaeu60RGRdYab1bhrjVkB9h6/MbFgCZRK0NYOpQ465rzKbuO9KNRamiLpHX2p6JahWZV1dHRw0VcvQy8vom5wMwxugjeWQ1s7l/z3FXzvhq8XHeK6bD/gBEmzSG6TBUREVFwLJddkKOkQ4FygHrgwIn7Y6fixwOnp5hvAKRHxWJ4xmRVt8WtLWLIweYGi5a2wPHm0FcDM+58uMLIBYWJfK+aWDNOhMb8EDgbmAA9Luj4iZpYVmwXsHxELJU0EJgN75RWTWS0YMnwwUtcz0Y/cbIMqRzOwRMTzfa2b5zPDPYFnIuLZiFgJXEWnoTIRMSUiFqabDwCjc4zHrCY0DWri4E/vT1OnWa0HDWnmqDOOKCgqy/M2eUtgdtn2HHpu9Z0E3JJjPGY14/M//wwrl7dx19VTaGiqJzqCY75xJO87bmCMTS6VVrJk8dyiw+iVPJNhV/cBXXavknQgSTLcr5vjk4BJAGPHju2v+MwK09TcyH9dehqn/OwEFry0kM3HbUrz4AHUx3DZCuoeeqroKHolz2Q4BxhTtj0aeLFzoXTF+wuBiRHxWlcnStdDmAzQ0tLSXX9Vs3XO8JHDGD5yWNFh9Lt6GhihkdkK18j/o/N8ZvgwMEHSOElNwFHA9eUFJI0FrgU+FRF+jWZmhcmtZRgR7ZJOA24j6VpzcUQ8Kenk9PgFwLeAjYHz0rdr7bWwMIyZrX9y7WcYETcDN3fad0HZ758FPptnDGZmWXg4npkZToZmZoCToZkZ4GRoZgY4GZqZAU6GZmaAk6GZGeBkaGYGOBmamQFOhmZmgJOhma0DJB0i6Z+SnpF0Rh7XcDI0s5pWtoTIRGBH4GhJO/b3dZwMzazWVVxCpD84GZpZ0UZJmlr2mdTpeFdLiGzZ30F43WQzK9r8CvOYZl5CZG24ZWhmtS7TEiJry8nQzGpdxSVE+oNvk82spnW3hEh/X8fJ0MxqXldLiPQ33yabmeFkaGYGOBmamQFOhmZmgJOhmRngZGhmBjgZmpkBToZmZoCToZkZkHMyrDQ7rRK/SI/PkLR7nvGYmXUnt2SYcXbaicCE9DMJOD+veMzMepJnyzDL7LSHA5dF4gFgQ0lb5BiTmVmX8pyooavZaffKUGZL4KXyQunMt6tmv22V9ET/hrpWRgHziw6ik1qLyfH0rNbiAdhubSovYeFtf40/jspYvCb+2/NMhllmp800g21ETAYmA0iaWmFW3KqqtXig9mJyPD2rtXggiWlt6kfEIf0VS7XkeZucZXbaqsxga2ZWSZ7JMMvstNcDx6dvld8NvB4RL3U+kZlZ3nK7Te5udlpJJ6fHLyCZrPFQ4BlgGXBihlNPzinkvqq1eKD2YnI8Pau1eKA2Y8qVIvp9kSkzs3WOR6CYmeFkaGYG1HAyrLWhfBniOTaNY4akKZJ2LTKesnLvklSS9LE848kak6QDJD0q6UlJdxcZj6QNJN0g6bE0nizPrNcmnoslzeuun2wB3+lK8VT1O124iKi5D8kLl/8DtgaagMeAHTuVORS4haSv4ruBBwuOZx9gZPr7xKLjKSt3J8mLqo/VwP9mGwIzgbHp9qYFx/MN4Oz0902ABUBTjjH9G7A78EQ3x6v2nc4YT9W+07XwqdWWYa0N5asYT0RMiYiF6eYDJH0m85Ll3wfgC8A1wLwcY+lNTMcA10bEC/KRf1YAAAQ0SURBVAARkWdcWeIJYLgkAcNIkmF7XgFFxD3pNbpT1eGpleKp8ne6cLWaDLsbptfbMtWMp9xJJH/h81IxHklbAkcAF+QYR69iArYFRkq6S9I0SccXHM//AjuQdPR/HPhSRHTkGFMl1fxO91be3+nC1eoi8v02lK+fZL6WpANJvjj75RRL1nh+DpweEaWk4ZO7LDE1AHsA7wUGA/dLeiAini4ong8AjwIHAeOB2yX9PSIW5xBPFtX8TmdWpe904Wo1GdbaUL5M15K0C3AhMDEiXssplqzxtABXpYlwFHCopPaI+FOBMc0B5kfEUmCppHuAXYE8kmGWeE4EfhjJQ7FnJM0CtgceyiGeLGpueGoVv9PFK/qhZVcfkiT9LDCOtx5+79SpzAdZ/WHzQwXHM5ZkJM0+tfDv06n8peT/AiXLv9EOwB1p2SHAE8DOBcZzPnBm+vtmwFxgVM7/TlvR/QuLqn2nM8ZTte90LXxqsmUY+Q3lyzOebwEbA+elrbH2yGkmkozxVFWWmCLiKUm3AjOADuDCiMhlOraM/0bfBS6V9DhJAjo9InKbTkrSlcABwChJc4BvA41l8VTtO50xnqp9p2uBh+OZmVG7b5PNzKrKydDMDCdDMzPAydDMDHAyNDMDnAzNzAAnQ6sySTXZt9XMydDeJOn4dO66xyRdLuntku5I990haWw6B+BzkurSOkMkzZbUKGm8pFvTSRj+Lmn7tMylks6R9DfgbEl7pvPjPZL+3K7sXL9Pr3e1pAcltaTH3i/pfknTJf1B0rDC/qFsYCp6CIw/tfEBdgL+STocDdgIuAH4dLr9GeBP6e9/Bg5Mf/8kyUgSSIbaTUh/3wu4M/39UuBGoD7dHgE0pL+/D7gm/f2rwK/S33cmmU6rhWRs9T3A0PTY6cC3iv4382dgfXzLYqscBPwx0uFoEbFA0t7Akenxy4Efpb9fTZIE/0ayBOx5aUttH+APZbPkNJed/w8RUUp/3wD4jaQJJLOyNKb79wPOTa//hKQZ6f53AzsC96XnbgLu74//aLNVnAxtFVF5uqhVx68HfiBpI5Ipue4EhgKLImK3buouLfv9u8DfIuIISVsBd5XF0F1st0fE0RXiM+szPzO0Ve4APiFpY4A00U0hafkBHAvcCxARb5BMc3UucGNElCKZA3CWpI+n9dXDmhkbkMwQA3BC2f57gU+k9XcE3pHufwDYV9I26bEhkrZdu/9cs9U5GRoAEfEk8H3gbkmPAecAXwROTG9XPwV8qazK1cBx6c9VjgVOSus/SddLEUByu/0DSfeRzCizynnAJun1TieZ3eb1iHiVJGlemR57gGTeQbN+41lrrGZIqgcaI2KFpPEkrdVtI1nDxCxXfmZotWQI8DdJjSTPCU9xIrRqccvQzAw/MzQzA5wMzcwAJ0MzM8DJ0MwMcDI0MwPg/wNSR8Le4/BN0gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "box1.show_tradeoff()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "box1.inspect(4,style='graph')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "box1.inspect(6,style='graph')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "box1.inspect(7,style='graph')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "box1.inspect(8,style='graph')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "box1.inspect(10,style='graph')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "box1.inspect(11,style='graph')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coverage    0.442629\n",
      "density      0.93488\n",
      "id                11\n",
      "mass         0.10222\n",
      "mean         0.93488\n",
      "res_dim            5\n",
      "Name: 11, dtype: object\n",
      "\n",
      "                                                                box 11  \\\n",
      "                                                                   min   \n",
      "Mayor                                                                0   \n",
      "Housing_market                                                       1   \n",
      "SLR                                                          {1, 2, 3}   \n",
      "implementation_time                   {6, 7, 8, 9, 10, 11, 12, 13, 14}   \n",
      "transient            {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...   \n",
      "\n",
      "                                                                        \\\n",
      "                                                                   max   \n",
      "Mayor                                                                2   \n",
      "Housing_market                                                       1   \n",
      "SLR                                                          {1, 2, 3}   \n",
      "implementation_time                   {6, 7, 8, 9, 10, 11, 12, 13, 14}   \n",
      "transient            {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...   \n",
      "\n",
      "                                                      \n",
      "                                           qp values  \n",
      "Mayor                                    [-1.0, 0.0]  \n",
      "Housing_market                           [0.0, -1.0]  \n",
      "SLR                                      [0.0, -1.0]  \n",
      "implementation_time  [3.5543173448635126e-255, -1.0]  \n",
      "transient                 [0.3981219527586406, -1.0]  \n",
      "\n"
     ]
    }
   ],
   "source": [
    "box1.inspect(11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Density = hoeveel procent van de punten in de box is een SETP?\n",
    "Coverage = hoeveel procent van alle SETPs zit in de box?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature scoring\n",
    "Feature scoring is een soort gevoeligheidsanalyse. Die doe ik hier op 6 verschillend output indicatoren.\n",
    "\n",
    "HP_hp_2200_obj : de huizenprijs in de Heijplaat, volgens een rationele huizenmarkt, in het jaar 2200\n",
    "\n",
    "HP_hp_2200_sub : de huizenprijs in de Heijplaat, volgens een boundedly rational housing market, in het jaar 2200\n",
    "\n",
    "CC__           : idem voor het stadscentrum\n",
    "\n",
    "CC__           : idem voor het stadscentrum\n",
    "\n",
    "HP_first_SETP  : Heijplaat: het jaartal waarin de eerste SETP plaatsvindt (nu alleen op basis van criterium 'rapid change')\n",
    "\n",
    "CC_first_SETP  : City Centre: idem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Housing_market</th>\n",
       "      <th>SLR</th>\n",
       "      <th>implementation_time</th>\n",
       "      <th>transient</th>\n",
       "      <th>Mayor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>267</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>196</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>153</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>87</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395995</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>164</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395996</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>130</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395997</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>192</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395998</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>142</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395999</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>209</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>396000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Housing_market SLR implementation_time transient  Mayor\n",
       "0                    0   6                   6       267      2\n",
       "1                    0   6                   8       196      2\n",
       "2                    0   2                  13        16      2\n",
       "3                    1   3                   9       153      2\n",
       "4                    1   2                  10        87      2\n",
       "...                ...  ..                 ...       ...    ...\n",
       "395995               0   2                   8       164      0\n",
       "395996               1   3                   7       130      0\n",
       "395997               1   4                  13       192      0\n",
       "395998               1   9                   9       142      0\n",
       "395999               1   6                  11       209      0\n",
       "\n",
       "[396000 rows x 5 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiments3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ema_workbench.analysis import feature_scoring\n",
    "x = experiments3\n",
    "y = outcomes\n",
    "\n",
    "fig, ax = plt.subplots(1,1)\n",
    "fs = feature_scoring.get_feature_scores_all(x, y)\n",
    "ax = sns.heatmap(fs, cmap='viridis', annot=True,ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "foldername = \"Visualisations\"\n",
    "\n",
    "today = datetime.date(datetime.now())\n",
    "date = \"{}_{}_{}\".format(today.year,today.month,today.day)\n",
    "\n",
    "target = Path(foldername,date)\n",
    "\n",
    "if not Path.exists(target): Path.mkdir(target)\n",
    "\n",
    "fig.savefig((target / \"Five_hundred_feature_scoring.png\"),bbox_inches=\"tight\",dpi=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dimensional stacking: \n",
    "This is a more visual approach to scenario discovery, indicating which combinations of input factors (X) explain outcome metrics of interest (M).\n",
    "\n",
    "### What explains if SETPs in the City Centre occur before 2200?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ema_workbench.analysis import dimensional_stacking\n",
    "\n",
    "x = experiments3\n",
    "y = outcomes['CC_first_SETP'] <2200\n",
    "#y = outcomes['CC_first_SETP'] \n",
    "\n",
    "fig = dimensional_stacking.create_pivot_plot(x,y, nr_levels=2, nbins=4,categories=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 'H. Economicus', 2: 'Sentiment', 0: 'R. Active', 3: 'Lawkeeper'}"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "map_mayor\n",
    "invert_map_mayor = {}\n",
    "\n",
    "for key,value in map_mayor.items():\n",
    "    invert_map_mayor[value] = key\n",
    "invert_map_mayor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold_year = 2200\n",
    "\n",
    "x = experiments3\n",
    "y = outcomes['CC_first_SETP'] <threshold_year\n",
    "#y = outcomes['CC_first_SETP'] \n",
    "\n",
    "fig = dimensional_stacking.create_pivot_plot(x,y, nr_levels=2, nbins=4,categories=True)\n",
    "\n",
    "horizontal_texts = fig._axstack._elements[2][1][1].texts\n",
    "for i,text in enumerate(horizontal_texts):\n",
    "    if i % 12 == 0: #it's a mayor text\n",
    "        text._text = invert_map_mayor[i//12]\n",
    "        \n",
    "    else:\n",
    "            values = [4,5,6,7,8,9,10,11,12,13,14]\n",
    "            index = int(text._text)-1\n",
    "            text._text = str(values[index])\n",
    "\n",
    "vertical_texts = fig._axstack._elements[1][1][1].texts\n",
    "for i, text in enumerate(vertical_texts):\n",
    "    if i % 3 == 0: #number can be divided by three\n",
    "        pass #don't change the SLR labels\n",
    "    else:\n",
    "        if text._text == '0': text._text = 'R'\n",
    "        elif text._text == '1': text._text =  'BR'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "foldername = \"Visualisations\"\n",
    "\n",
    "today = datetime.date(datetime.now())\n",
    "date = \"{}_{}_{}\".format(today.year,today.month,today.day)\n",
    "\n",
    "target = Path(foldername,date)\n",
    "\n",
    "if not Path.exists(target): Path.mkdir(target)\n",
    "\n",
    "fig.savefig((target / \"Dimstack_fivehundred_CC_below_{}.png\".format(threshold_year)),bbox_inches=\"tight\",dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "threshold_year = 2090\n",
    "\n",
    "x = experiments3\n",
    "y = outcomes['CC_first_SETP'] < threshold_year\n",
    "#y = outcomes['CC_first_SETP'] \n",
    "\n",
    "fig = dimensional_stacking.create_pivot_plot(x,y, nr_levels=2, nbins=4,categories=True)\n",
    "\n",
    "# horizontal_texts = fig._axstack._elements[2][1][1].texts\n",
    "# for i,text in enumerate(horizontal_texts):\n",
    "#     if i % 12 == 0: #it's a mayor text\n",
    "#         text._text = invert_map_mayor[i//12]\n",
    "        \n",
    "#     else:\n",
    "#             values = [4,5,6,7,8,9,10,11,12,13,14]\n",
    "#             index = int(text._text)-1\n",
    "#             text._text = str(values[index])\n",
    "\n",
    "# vertical_texts = fig._axstack._elements[1][1][1].texts\n",
    "# for i, text in enumerate(vertical_texts):\n",
    "#     if i % 3 == 0: #number can be divided by three\n",
    "#         pass #don't change the SLR labels\n",
    "#     else:\n",
    "#         if text._text == '0': text._text = 'R'\n",
    "#         elif text._text == '1': text._text =  'BR'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspect some values manually"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "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>HP_hp_2200</th>\n",
       "      <th>CC_hp_2200</th>\n",
       "      <th>HP_first_SETP</th>\n",
       "      <th>CC_first_SETP</th>\n",
       "      <th>Housing_market</th>\n",
       "      <th>SLR</th>\n",
       "      <th>implementation_time</th>\n",
       "      <th>transient</th>\n",
       "      <th>Mayor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>198063</th>\n",
       "      <td>0</td>\n",
       "      <td>340107.108737</td>\n",
       "      <td>2075</td>\n",
       "      <td>2100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>410</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198483</th>\n",
       "      <td>0</td>\n",
       "      <td>340107.108737</td>\n",
       "      <td>2075</td>\n",
       "      <td>2100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>342</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198767</th>\n",
       "      <td>0</td>\n",
       "      <td>340107.108737</td>\n",
       "      <td>2075</td>\n",
       "      <td>2100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199121</th>\n",
       "      <td>0</td>\n",
       "      <td>340107.108737</td>\n",
       "      <td>2075</td>\n",
       "      <td>2100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>348</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199138</th>\n",
       "      <td>0</td>\n",
       "      <td>340107.108737</td>\n",
       "      <td>2075</td>\n",
       "      <td>2100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>130</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295747</th>\n",
       "      <td>0</td>\n",
       "      <td>340107.108737</td>\n",
       "      <td>2075</td>\n",
       "      <td>2100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>468</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296121</th>\n",
       "      <td>0</td>\n",
       "      <td>340107.108737</td>\n",
       "      <td>2075</td>\n",
       "      <td>2100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296450</th>\n",
       "      <td>0</td>\n",
       "      <td>340107.108737</td>\n",
       "      <td>2075</td>\n",
       "      <td>2100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>134</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296831</th>\n",
       "      <td>0</td>\n",
       "      <td>340107.108737</td>\n",
       "      <td>2075</td>\n",
       "      <td>2100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>274</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296908</th>\n",
       "      <td>0</td>\n",
       "      <td>340107.108737</td>\n",
       "      <td>2075</td>\n",
       "      <td>2100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>142</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>505 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        HP_hp_2200     CC_hp_2200  HP_first_SETP  CC_first_SETP  \\\n",
       "198063           0  340107.108737           2075           2100   \n",
       "198483           0  340107.108737           2075           2100   \n",
       "198767           0  340107.108737           2075           2100   \n",
       "199121           0  340107.108737           2075           2100   \n",
       "199138           0  340107.108737           2075           2100   \n",
       "...            ...            ...            ...            ...   \n",
       "295747           0  340107.108737           2075           2100   \n",
       "296121           0  340107.108737           2075           2100   \n",
       "296450           0  340107.108737           2075           2100   \n",
       "296831           0  340107.108737           2075           2100   \n",
       "296908           0  340107.108737           2075           2100   \n",
       "\n",
       "        Housing_market SLR implementation_time transient  Mayor  \n",
       "198063               0   1                   5       410      1  \n",
       "198483               0   1                   5       342      1  \n",
       "198767               0   1                   5        22      1  \n",
       "199121               0   1                   5       348      1  \n",
       "199138               0   1                   5       130      1  \n",
       "...                ...  ..                 ...       ...    ...  \n",
       "295747               0   1                   5       468      1  \n",
       "296121               0   1                   5        99      1  \n",
       "296450               0   1                   5       134      1  \n",
       "296831               0   1                   5       274      1  \n",
       "296908               0   1                   5       142      1  \n",
       "\n",
       "[505 rows x 9 columns]"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(outcomes)\n",
    "df = df.merge(experiments3,left_index=True,right_index=True)\n",
    "sel = df.loc[(df.Mayor == 1) & (df.SLR==1) & (df.Housing_market == 0) & (df.implementation_time == 5)]\n",
    "#sel.CC_first_SETP.unique()\n",
    "sel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dimensional stacking: Heijplaat tips before 2100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[MainProcess/INFO] model dropped from analysis because only a single category\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = experiments2.drop('policy',axis=1)\n",
    "y = outcomes['HP_first_SETP'] <2100\n",
    "#y = outcomes['CC_first_SETP'] \n",
    "\n",
    "fig = dimensional_stacking.create_pivot_plot(x,y, 2, nbins=4,categories=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpretation: in a boundedly rational housing market, there will almost certainly be a tipping point before 2100 in the Heijplaat. With a low degree of SLR (scenario 6-9), the city might survive, but this depends on the transient storm surge scenarios.\n",
    "\n",
    "Note that this does not necessarily mean that in the other scenarios the house price does not go to zero!!!! It just indicates that this does not go ABRUPT, tipping-point like.\n",
    "\n",
    "In a rational housing market, there will only be tipping points under very high sea level rise scenarios and Mayor 0 (Economicus)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parallel coordinate plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'tuple' object has no attribute 'loc'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-38-f4c5c7735643>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mema_workbench\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0manalysis\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mparcoords\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mo\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutcomes\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'loc'"
     ]
    }
   ],
   "source": [
    "from ema_workbench.analysis import parcoords\n",
    "data = results.loc[:, [o.name for o in model.outcomes]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "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>Housing_market</th>\n",
       "      <th>SLR</th>\n",
       "      <th>transient</th>\n",
       "      <th>Mayor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>boundedly_rational</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>H. Economicus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>rational</td>\n",
       "      <td>6.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>H. Economicus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rational</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>H. Economicus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>rational</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>H. Economicus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rational</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>H. Economicus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3995</th>\n",
       "      <td>rational</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Lawkeeper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3996</th>\n",
       "      <td>rational</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Lawkeeper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3997</th>\n",
       "      <td>boundedly_rational</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Lawkeeper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3998</th>\n",
       "      <td>boundedly_rational</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Lawkeeper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3999</th>\n",
       "      <td>rational</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Lawkeeper</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Housing_market  SLR  transient          Mayor\n",
       "0     boundedly_rational  6.0       18.0  H. Economicus\n",
       "1               rational  6.0       19.0  H. Economicus\n",
       "2               rational  4.0       12.0  H. Economicus\n",
       "3               rational  1.0        5.0  H. Economicus\n",
       "4               rational  2.0        7.0  H. Economicus\n",
       "...                  ...  ...        ...            ...\n",
       "3995            rational  8.0        9.0      Lawkeeper\n",
       "3996            rational  3.0        5.0      Lawkeeper\n",
       "3997  boundedly_rational  5.0       13.0      Lawkeeper\n",
       "3998  boundedly_rational  7.0        1.0      Lawkeeper\n",
       "3999            rational  1.0       16.0      Lawkeeper\n",
       "\n",
       "[4000 rows x 4 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp_par = experiments.copy().drop(['policy','model','scenario'],axis=1)\n",
    "exp_par['SLR'] = exp_par['SLR'].astype('float')\n",
    "exp_par"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "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>Housing_market</th>\n",
       "      <th>SLR</th>\n",
       "      <th>transient</th>\n",
       "      <th>Mayor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{boundedly_rational, rational}</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>{Lawkeeper, Sentiment, R. Active, H. Economicus}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{boundedly_rational, rational}</td>\n",
       "      <td>9.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>{Lawkeeper, Sentiment, R. Active, H. Economicus}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Housing_market  SLR  transient  \\\n",
       "0  {boundedly_rational, rational}  1.0        0.0   \n",
       "1  {boundedly_rational, rational}  9.0       19.0   \n",
       "\n",
       "                                              Mayor  \n",
       "0  {Lawkeeper, Sentiment, R. Active, H. Economicus}  \n",
       "1  {Lawkeeper, Sentiment, R. Active, H. Economicus}  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "limits = parcoords.get_limits(exp_par)\n",
    "limits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'ix'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-60-cab47053b02e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mparaxes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparcoords\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mParallelAxes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlimits\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\urban-SETP\\lib\\site-packages\\ema_workbench\\analysis\\parcoords.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, limits, formatter, fontsize, rot)\u001b[0m\n\u001b[0;32m    181\u001b[0m                 self.recoding[column] = CategoricalDtype(categories=cats,\n\u001b[0;32m    182\u001b[0m                                                          ordered=False)\n\u001b[1;32m--> 183\u001b[1;33m                 \u001b[0mlimits\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcats\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    184\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    185\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalizer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpreprocessing\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMinMaxScaler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\urban-SETP\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5272\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5273\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5274\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5275\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5276\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'ix'"
     ]
    }
   ],
   "source": [
    "paraxes = parcoords.ParallelAxes(limits)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regional sensitivity analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiments2['SLR'] = experiments2['SLR'].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hGPj9fvr3709+fn6l9Vw3/tVaRkYG9913H/fddx+lpaVcfHH154P36dMHgA4dOvD555+zadMmunfvTqtWrYD42IeH07dvXwCCwWBi8O1gMEg4HKZt27b8/ve/54MPPiAjI4NY7NC1Hl27dk2U//a3v9GuXTscJ35qfc2aNaxYsYKVK1cCjfd1nTS8jRs3kpGRwfTp0wFYtWoVN954I/37969x+YOXBIRCIW666SbatGmDz6fR32oz+8vZLNu+LJF41pSMRuwIUSd62G0NbD+wCSI+vNdee438NV9z95SphMNhLrjgAjp37kzv3r1Zu3YtpaWlPPXUU3Tu3JlnnnmGxYsXY9s248aNY+zYscyZM4e3334bn8/HoEGDuPvuu5k1axZbtmyhoKCAbdu28bOf/YwhQ4bw/vvvM3v2bHJycohGo3Tr1o2VK1cya9YsBg8ezLp165gxYwZPPvkkkydP5te//jVvvPEGc+fOxbIsOnTowKRJk1i/fj0PPPAAlmWxYcMGRo0axdVXX82qVav45S9/iW3bZGRkcMMNN9CpUydmz57N8uXLad++PXv27MG2418jW5aVuGTAsizC4bCXPwppxkaOHMmDDz5Ily5dGDRoEACmaeL3+5k0aRJpaWns2LGDWCzGWWedxSOPPEJBQQFLly5l0qRJPProo5x99tlAPH/o3r07mzdvBip/Xsvx45g+iVNTU1m2bBnXXHMN0WiUL774gksvvZRAIMDu3bsB+Pe/4xfR7tq1i7y8PJ5++mnC4TDnnHMOl1xySaXtGVWudzzxxBP59ttvCYVCBAIBVq5cSbdu3Y463jlz5jBgwADGjx/PP//5T/7617/WuO9Ro0YxatQobrvtNhYuXEi3bt3o0KEDN910E6FQiNmzZ5OVlYVhGImEXJLT6tWrmT9/Pr/97W9JSUmha9euZGZmHvZrpNTUVGbOnMmoUaMYOHBgpee9yyGvrn4VB4dTWp9CtpVNwArgN/0ErAABMxCvW/5EOWCW1yv0H2zrld2rxn289957FBcXN1jMwWCQ4cOH13u9fv36MXXqVJ588knefvttfvCDH7BkyRIWLlxIJBLhF7/4BatXr+bdd9/l5Zdfxufzceutt/LRRx8BEAgEePbZZ1m6dClz5sxhyJAhPPHEEyxcuJDWrVtz4403VtrfmWeeSY8ePZg8eTJ+vx/HcYhEIvz6179m3rx5FBcX849//IM//OEPDBkyhG3btrFgwQI2bdrEhAkTGDVqFPPmzePGG2+kY8eO7Nixgzlz5nDLLbeQl5fHCy+8QCAQ4PzzzyczMxOo/h4M8STj4B/vIkeqS5cuHDhwgHnz5jFp0iQ2b95MaWkpixcvZuHChZSVlTF69Ghc18UwDEaOHMkjjzzC97//ffx+P927d+ezzz5j2LBhlJaWsmbNGnJzc4Gaf0+l5TumhDUtLY3c3FyuuOIKotEow4cPp2/fvlx22WXce++9vPXWW5x88skAtGvXjt27dzNq1CjS0tK47rrrDnvmKicnhxtuuIHx48fTunVrwuHwMZ3tOu+883jwwQd56623aN26NZZl1XrHbI8ePbj44ouZPn069913H9OmTeOqq66itLSU8ePHY5om/fv3Z+bMmeTm5urxiEnq/PPPJz8/n8suu4y0tDRc12Xy5MksXryYRx55hF/96ldA/C/2qpcKtG3blsmTJ3P//ffz8ssvY5qe36OYlM7JPYcHz36w0bZ/NMllQ6n4B+mpp54KxL8B2rNnD+vXr6dfv35YlkWrVq2YNm0a7777Lv3798fv9wMwaNAg1q5dC1T+BikSibBnzx4yMjLIzs4GKn+DVNMfw6ZpsmfPHgKBAOFwmEAgQP/+/fn4448ZMmQIPXv2xOfzkZqaSmpqKpZl8dVXX7Fp0yZ8Ph9+vx/btsnPz2fAgAGUlJRg2za5ubmkpKTU+hq0adOGaDTKE088wd13390Ar6ock2gIvpgHoX1gx3BiEVw7imPHcOwYVhKNujNixAjefPNNunbtyubNmxP/V0aPHk0gEKBdu3aJG5dHjx7Nueeey5tvvgnEH1N83333MW7cOMLhMLfccgtt2rTx8nDEY0eU/Z111lmcddZZldruuuuuWpc/7bTTeOutt6q1P/TQQ9XaPvzwQyB+89ZBQ4cOZejQocRiMXbt2pUYPuvKK6+kY8eOte533rx5iXLFyxemTp2aKL/33nt1rlfxhq6f/OQnifLjjz9ebb2xY8cyduzYWuOR5HDzzTdz8803V2r70Y9+VOOyVZ8df/HFF9d4+Yq0XCkpKezevQeAvLy8Wpfr1q0b8+fPx3EcbNvmxhtv5J577mHu3LnEYjEsy2L58uWMGjWKb775ptpZodatW1NSUkJhYSE5OTmsWrWKDh06APGEtUePHqSkpJCVlZVoGzhwIJFIhIyMDNq3b8/zzz9Pz549CQaDGIZBampq4luo9u3b07t3b6677joGDhxIfn4+y5cvp1evXrz88st06dKFUCjEtm3byMjIAKj0TUJOTk4iyT6YRIj3nA2f8Ke1LhGrE45hYQCGa2NiY7oO7VO8PRteMV84OEQgHPpcr41t25xxxhmJkz+BQIDHHnus2nIVP6/l+JLUF+f5fD7Kysq49NJL8fv99OvXj06dOlW6+eug7373u/zP//yPB1GKSEsyZMgQXnpxHuPGjaNv376kp6fXuFyfPn0YMmQI48aNw3Ecxo0bR+/evbngggsSbWeccQY/+tGPahyv2efzMX36dCZOnEhWVlaN3x6dfvrpTJ48mYcffhiIJ60///nPufXWWzEMg6ysLKZPn544i1vVPffcw4MPPkg4HCYUCjF16lT69OnD8OHDGTNmDO3bt9dZq2YmFosSttLI6FZISfAEiqLFlERKKI7E51/s+oIruc7rMOvl/fff5ze/+Q2PPPKI16FIEjPcOi7CHD16dIM8HECOX4/Nn8E945p+sGj97jaN/1jwHwzJHdKglwR8/fXXiTN7XnHK9mG2au3JvtetW5e4YdRLyfBzkOqWLZvNug1BZoQOfSvpN/0EA0EyA5kEU4K8OOLFJo9L77neenbVszz1+VOsuGoFAat5jm3/t7kPMOTa/1drf1KfYRUREZFDQk78GtUnvnMzA/uMITOQSaqVqhuRpMVTwioiItLMdE5tR/u09l6HIdJkdNuziIiIiCQ1nWEVkaRzcGxG8YbGl/ZYuARKdoIdAScKdjRetiOwdwPQ1usIRZqcElYRSSqpqakUFBTQpk0bJa0ecF2XgoICUlNTvQ7luLV17rXsdlvjYOEaFk755BoW+3xpuGkupNQ8eoVIS6WEVUSSSm5uLlu2bEk8La++ajs7WJ+zhm4shBVIO6r9H6vdu3cTjR7+UbaNKTU1NfFUIWl636b0JSU1jdadu2OaPgzLh2lZmJafogNb+d2G+fTPmnH4DYm0IEpYRSSp+P3+o35WeGFhIX/5y1/w+XyJs7OGYVQr19UHYIRLOP/iMcd6KEdl6tSpGh7oOFdqGOwK7ObrNh0OjbEajs+3lW5jk7PJ6xBFmpwSVhFpMWKxGG3btuW88847tg19807DBCRyFFYGLJYd+Ja1n72PgUFGIINgIEgwEKRNahtO7X4q3VvrceByfFHCKiItR9k+KNkO/3gGXAdcu3zugOPU0FahXLFt7wboPcLro5HjlIPB2SnteX7U82T4MzANDegjooRVRFqONe9BwT74/BeHWdAA0wLDBKN8blpgGIfqIh5KxSQYCHodhkjSUMIqIi2HY8fn92woT0arJqQH2zT6gIhIc6KEVURanlbZXkcgUiu3eDsHvvkQJxYhFAtTGi2jNBaiNBZmfyxMqtHO6xBFko4SVpHj0NKtS9laupWoEyViR+KTEyFqR4k4kTrbEus4EQpCBV4fikiz89r7TxByTqfUDRHDwcGHTSscUrBdG5sIWRkZXocpklSUsIocZ4rCRdy0+KZq7QYGAStAwAzgt/yHyma87Lf8BMwAmYHMxDJ92/RlVI9RHhyFSPO10S6jyFlN8OQAwZQsgimtyUxpTTA1m2Bqa4KBICcFT/I6TJGkooRV5DgTdeKD0t828DbGnDImkYz6DJ+eLCXSRAIu3Hn2/V6HIdJsKGEVOU5l+jNpndra6zBEREQOS2O3iIiIiEhSU8IqIiIiIklNlwSIiIg0FscBJwp2BOxofHIcr6MSaXaUsIqIiDSgyK6veePdF4iYrbCxsA0Lx7BwDQvXMMkyv8t+O9/rMEWaFSWsIiIiDeiLjX/FST2NhZH5pBgmqaZJK8NHK8tHmuUn3QowoPMAr8MUaVaUsIqIiDSgmGvj4vJA/8sY2O8qr8MRaRGUsIqI50pLS9m6dSuu6wLguu4RlyvWD5RlASVNfwAiNTA1rrFIg1HCKiKe27p1Kxs3bqR9+/YYhpF4gEHVsmmaldqrLpcZKKP17kWABmQXEWlJlLCKSFJo3749AwYc43V9WxbCgbUNE5CIiCQNJawiIiL19NGmj/hy95eUREoojhRXmu87sIf/9v0PBrokQKShKGEVERGpp5mfPE4rO51MK5V0M4UsM5WORntaGZ1pZdmYuJyaeZLXYcrxwoVuZjd2bN+BzzyU2h28xr+mel199anXZ726+vb72lAXJawi0vy4bvlkg+uAUz6Phb2OTI4TZzOYE81Muh3YjeHamNiYro3hlmG6Nq2c9/BnPeR1mHKcsEIW/xn4T9bnr690fT9QqVzf+rGsW99tnVC2mrooYRVpRhzXIWJHiDgRInaEqB1NlCNOeb1Cf01tReEirw+juvV/g62rYPG7h5JP144npZXqzqGpNqa/6eKW45aBwV4jj++PnARWAExffG4FwPKDvxX4UrwOU44XLuxx9zD2B2MJWAGvozk6740Enq61WwmrSDOxbu86xr09jpAdOuZt+QwfXTK7NEBUDWTvhnhi2usCMEwwrPjcLJ8fnBL1g/0V+g62tT3F66OR44UvBU7o63UUIscFJawizcT2/dsJ2SGu6HUFJ2aeSMAKELAC+E1/vGwGjqytvGwapteHVFlKJoyc7nUUIiKShJSwijQzF3e/mH7t+nkdhkiLtm7vOl5f93q1EQCKw/H5cGM4SfYnn0iLpoRVRESkivnfzGfBmgW0T2tPMBAkGAjSIa0DPbN7khnIJHd1iO+0au91mCKVOTYQA8pvTK02p46+CsvUe50K85rWP5J1HLvOQ1PCKiIiUoWDQ9tWbfnLZX+p1ue6LkvzHqCt2cqDyERqUPgtkA2PdPA6kkajhFVERKSKVvsiXOmOZ9GCebiA45o4GLgYOIaFkdaHnsY6r8MUiQsXA9kw5E7wpQIG8edWGGAYdc/hMMvU0H/YdWrYRl3bM4z4jbN1UMIqIiJSha+okEInlfEF72OaPkzLxDBNTMuHafkwLD+cOcnrMEUq+8Ed8RtYWyAlrCIictw5ED3A71b+jl0HdlW6sepguZvbjX5GO9Ju/cTrUEUEJawiInIc+mrPVzz31XO0a9WONq3aEAwEOSl4UuIGq5z1m2hfVscDKkSkSSlhFRGR445bfmfy40MfZ1CHQdX6N215mK379ahfkWShhFVEjto333zDhg0bcMuHLnFdt1q5rr6DZcf8Dqe6yz04AhERaQ6UsIrIUdu3bx8nnXQSnTt3BsAwDIzyu0BrKtfat2ACZmirB0cgIiLNgRJWkSTmui4RJ0LEjlAaLfU6nBqlpaURDAaPcSu6VlCa3gBrAHs37SWvIA/HcXAcJ37G33EosU8gwCavQxSRckpYRRrZ+qL1/D7v9xyIHSBqRxMJaMSOEHWi8XJ5W6V+J0LMiVXbnt/0e3AUIi2Le2AvIwMjcb/5M65rYzoxfE4E04li2FGC0RLaOIVehyki5ZSwijSyP2/8M39c+0dyM3JJsVIIWAH8lp+AGSAzkEnALK9bAQJmIN5vltertOWk5tArp5fXhyTSLByIHmBr6dbEkFUVh65at/kfnOleSE7k73wnvQuYPrAC5VMaWO0g9wqvD0FEyilhFWkiiy5dpLOjIk1o/NvjyS/Kr7Ev3UrlTP8IugyZAr0uauLIRKS+lLCKiEiLVBgq5Pudvs/Vp15NMCU+vmpmIJPMQCbW+jmzXDwAABJYSURBVE9Y+NkOTkjJ9jpMETkCSlhF5Og5MSjZCbtD4DrxybHLyza4boX6wbaDy7iH2kp3en0k0kLlZuZyduezq7W7HsQiIkdPCauIHL38D2H3J1C87Ni31XXosW9DRERaJCWsInL0YmFo1wuG3QiGGZ9Mq7xcPjfNKvUq/YYRb8vu6vXRSAtjYGA4BuFwODFc1cGhq+yy6iNwiEjyUsIqIscmox2cNsbrKESqOY9z6L6lG+9uewMDF9Nw43NcTDtC+wObgS5ehykiR0AJq4iItEgBx2RH8Uvcvm1JzQu0yoasq5s2KBE5KkpYRUSk5crsAHf8Gyx/+VQ+1qrpi1+OIiLNghJWERFpuUwfZHX2OgoROUam1wGIiIiIiNRFZ1hFRKRZitgR8gryKAoXVXrsanE4/hjWgD7iRFqMuv83710PL18ZL7sVh1muUHarDr9cW98RtDfqOrUtf4T78STmI32da2tPguNsp2dxJ6ONGzdSXFyMW/6zc103MVWs11Y+WN+TchId2eHBEYjA7C9n8+yqZ6u1p/vTyQxkMtw9m15WpgeRiRwh1y3/DD2C+cHla+pzbG/ib0J1J6yhIti7oUqjUWOxcnvVC9lr6zuC9iZZxzjUXtM1+EkZc9X2w/XVd50GijlScxjirX//+9+ccMIJpKamYhhGYgIOW67Y1unzmXRIz/XmIOS4FyrcyejAJXzP3w6fa2C5Zvl1bgaODUVGK07zb/M4SmkW/jAKdv27wgmYuhLImvoPt04NyWdDancatOkWH9u6hao7YTVMuHlpE4UiLdL8x7yOQGrRo0cPgsHgsW2k7CswOjZMQCL1lFKwjzROoc+OjzFNE8MwME0D0zAxTAPTtMg5/Sqvw5Tm4NuPocNp0Hkgh05iVZhD9bZK8xr6D7tObfuhHuuUL7s7Hw4QHwmjhdIFPiIi0ky5lLhFnHTL614HIi1Bz+Hww6leR3F0ls2F9Xu9jqJRKWEVEZGk5LoueQV57Ny/89ANVZHixA1W+2LFnGic4HWYItIElLCK1KI4UsxXe74iakeJOBEidnyKOtF4uY62iutsKN7g9aGINEtfF37NuLfHVWozMMgMZBIMBOlNezrZjkfRiUhTUsIqUovHPn2MRfmLDrucaZgEzAB+y0/ADBCw4pPf9BOwArRJbcOZHc7EZ+i/m0h9HIgeAOCB7z3A9zp9j2AgSLo/HbP8xpLNbz7C5sIyL0MUkSaiT1CRWhyIHiA3I5eZ58yslIweTEQPzn1mM/xv5MRg9zdQ6gfHAdcB147PHbty3XWrtx2slycUIo2pS2YXOmfoaVUix7M6P2ltI0BhYWGi7rpHPhRDYy2rbTftto81jgwjo17rJ5tUXyp92/b1OoyGFS6BwvXwyl0QaYAxVNPaHPs2RGrRxmhDqDjELmsXruviOE5ivsfOAHSGVY5dmS+LsmgKbkFBoq3q51l96sey7tFsy9pv0dLVmbCW+NuxYsWKSm1GYhiFw6vPstp2y9l2xWV3ujvrFZM0gWgZ4ELf0dB7aHz4OsOKD5FiWhXqZoW6WUPditezT/b6iKSlipTyk5SfsPeLVXzlrsR0Y5iujeHGMJ0YZuwAncpWex2ltACfdrqW/SUd8H/+ebXPu7rq9Vm2MbdlRSzyYnm0ZHUmrD43zLBhw5oqFmmBblpzEw/xkNdhSE3angK9LvA6CpHaFW7ApBUd9s5lcOoJ8TEmrQD4AmD64uXvDPc6SmkBXMPkjIxdnDCseY7bm7cqj093fup1GI2qGV58JyIiLZHrupTFyhLDV63dvxWTHnDmT+CMG70OT0Q8pIRVREQ8M33ZdJZuW0pxOD6+asyNVeqfljqNDKuVR9GJSLJQwioiIp75YOMHpPnSGHbSMIIpQYKBYGKc1eDezWxa49I3s4vXYYqIx5SwioiIp87seCb3fe++GnqWsonNTR6PiCQf0+sARERERETqojOsIi2c67qJcftc18W1baB+Q6CJNJZ+znc4eWsn3n9zIU7576rjujguuLaN5erRqyKihFWk2XAch/fff59IJALUkIjWUoZD4/UZhoEBmL4gPtNu4iMQqa69k01J0d+5ZNey+BirOOVjrdqYro3PAOM/PvQ6TBHxmBJWkWbCcRz279/PRRddBJQnnxUT0cOUE0p3wcxToMcvmi54OW7tDe3l480fJ4aqKomUHJqHi+lGV6KtLFpf92r5OKvlY61a5WOt+luBL8XrwxARjylhFWlGDMMgNTXV6zBEjtjcvLnM/WouAKZhkhnIJNOfSTAlPhrACY5L39R2cMKpHkcqIslMCauIiDSaqB0l3Z/O4jGLSfOnYRqV7/X9x9xpdE5P8yg6EWkulLCKiEijMjHJCGR4HYaINGMa1kpEREREkprOsIqISKMxHINTjVPJz8/HcZz4sFWOkygX+U+gM/u8DlNEkpwSVpHmInoA7Ch8cB+4zqHJscvLdoU2p4a28nK0zOsjkeNIq8IyBhtnUbjsFUw3hulEMcrnphPlxAN7aBfs5HWYIpLklLCKNBebl4ETg2W/jQ/3Y1hgmGCa8XmiXj4/OFWql5c7D4Lc73p9RHI8KN3Jbsfgu2VLDw1b5QscGr4qOxNOv9zrKEUkySlhFWkuyh8CwLXvQu4gb2MRqa+J73sdgYg0Y7rpSkRERESSmhJWEREREUlquiRARESOWsyJ8fq619mxfwfF4WJKovFHrh58/GpGJIXvmB28DlNEmjklrCIictS+Lviah/7xUOKxq0FfkKyULIL+IJ0yTqBjKExu2PY6TBFp5pSwiojIUbP37+KnKT8lxwiC4cOwHYz9NqZrY2JjuDYnRf7ldZgi0swpYRURkaNXsoMsI4tO0Vf4fptTMXzlw1UdHMLKCsAJY7yOUkSaOSWsIo0sHA6Tl5eH4zgAuK6LWz5E1cFy1XpNZae0FHCb/gBEjoDRcxjGmT/1OgwRaaGUsIo0spKSErZv306vXr0wDAPDMAAS5ZraaixvKyWw8gH4wVxvDkRERMQjSlhFmkBqaio9evQ4to2UfgWhTQ0TkIiISDOicVhFREREJKnpDKuIiNTpo00f8dnOzyiOFFcba7UoVMjt/jsxDZ3/EJHGo4RVRETq9IelvyPH6Ui64aO9kU4XgqRwIgEs/AaYGPTPOMnrMEWkBVPCKiIiderhdKed7XJuaBumAaZhYJbfFGiaJgG2k9blQa/DFC+V7YUvXwFccN2a51B7n1tL/2HXqW0/1GMdAKOxXhlpIEpYRUTksEoDhfS8WiNUSC32bYTXb/Q6iiNkgGFUnp94O7Tu4nVgUgclrCKNbc862LMGXpgNrgOuHZ87TuW664BToVypbkNkv9dHIiJSu1s/j8+rJoN1zqm7r97rHG5btfj4Y+jap0FeBmkcSlhFGtuWT6GsFMK7wfSBYYJpVZj7D9WNiu1G9bbULDihr9dHJCJSXZvuXkcgLZgSVpGmcv3i+KMqRUREpF40DomIiIiIJDWdYZUWKebG2Fa6jYgdIeJEiNpRIk4kXq+lLepEiTrRRH3N3jUErIDXhyLS6Nw96zjw+UIcO4pj2zhODNe2cRwHx7ZpZbQFCrwOU5KYY/goLS2t1OYm7sA/9npDbqumeiQSQZKbElZpkdYUruHHf/zxUa/vN/0ErADDTx7egFGJJKed/3yVT0q74LeLcHBwcbBxsXHikxkjmqaPC6ldUUonPv74Y4wqNzYdS70pt5Wamkp6ejqSvPQOJC1SK18r7j/7fvyWn4AZIGAFCJiBeL28XFub3/RXezMTackWh3awwY3xSuyVGvvTfGlM6jmpiaOS5sTnhLnooou8DkNaMCWs0iKlWClcesqlXoch0izscyKYwBPnPEEwEExMmYFMMgOZ+Ex9VIiIt/QuJHKEDl7z5LpuneWq9ZCjexsl+RkuugRGRJKWElaRWmzatIlPP/20UhIK8WufDl4ycLiyYRgQzqFDeHXTH4CIiDRrjutUm2zXrlR2XZfSSOnhN9bMKWEVqUU4HKZr166cfvrpQOWEtF6WPAFf/QH4ZcMGKCIiDWLuV3NZt29dtYSwaqLoum6d/Yk6Do5Tvg4utmNXaneoYZ0a9lMflmG16PsvlLCK1MEwDExTX+lLC1C2D8LFYEfBjpRPsfg8FvI6OhFPPbniSTL8GWSlZGEaJqZhJhJAy7BqbfOZvkRfbetU7KvURnm7aWFQvo55qL3GderYT25mLn6z5T6cRgmriEhLt3cDyxf+kgO+bBzDwjUsHMPCwcI2LLL9g9nnbvc6ShFPje8znltOv8XrMKQWSlhFRFq4suJt5GedSZ7zISU+KHVClNohSuwyQm4EJ+pg+Fym8YDXoYqI1KjOhNUBvt33LS7ldz+X/4MKd0xX7Su/N6Vqe6UnVlTsc2vZZsU+l2r7rlquum7FdSotX7HvCOOrsb3iPmuLu5ZYK8VdJfaqx1Rj3EcY38Hlj2p/VfZd48+7tu1U6KvvNTgi0vDWlW7FxmCluZl2WScRDHQgt8LQVcFAkF45vbwOU0SkVnUmrPsNg0vevKSpYpF6Mii/E738X3ljop7oq/i0j6rrGFRapuK6VZc/WK+4TtXlq65zcvDkpnkxROSwJp14Eed87zavwxARqbc6E1YDeHzo4+Vlo+ZkqKkSpRrKVZepKYaDy1fdR9XjqbrdqklZxZiqHU8dx1n10XCHja+mGKrG14LvAkwqBwpgVz4s/wJc59Dk2OVlu0KbU0NbeXnLcq+PREREpFk77DWsF3S9oCniEEk+a96Dgh3wz/n1WMkA0wLDBOPg3IROA+N1ERERqTfddCVSG8eG1Cy4c0086TQtMIzKiWil5NSITyIeKAoXsb5oPcWR4vgUjs9LIiVs2rWKwfzY6xBFRI6aElaRuhgmZJ7gdRQih3Xrn25k/4EYJuVjN5b/SzX8pJGC5TPplqbfZRFpnpSwioi0AD1D3elotadTaBd+N0bAsQm4MXyujeHaBPgLuR0meR2miMhRUcIqItJC7HW/ZuKl94IVKJ/8h+amrqEWkeZLCauISEthmpB9ktdRiIg0OD0kXURERESSmhJWEREREUlquiRAWqSAHeDvf/97pcfnVi3X1QcQojud+caD6EVERKQiJazSIrm4dOnS5dBTxAyjWrmuPgBj8etkHMjzIHqRGkQOwKZ/QCwMdgTsKDhRsCO4sSgaAVhEWjIlrNIiOYZDly5djnErReCGGyQekWO1b8n/snRHCrbhwzUsbMPCMXy4RgqukUZ3Xz82mku8DlNEpFEoYZWWybFh7Z/jc9cBt3yeqDtV6hX73Xi94Fuvj0Ik4cv9BWw0MvnY/w+K7P0Ux0oIOxEcHGxsAK7odYXHUYqINA4lrNIyxULw4phj307P4ce+DZEGkBfdS5GZxokn9CAYCJIZyDw0TwkS9Afp376/12GKiDQKJazSMpkWXP+X+KNVDTNeP1g2ysumWUNbxeVMSMn0+khEEgxg5jkzvQ5DRKTJKWGVFsqA3EFeByEiIiINQOOwioiIiEhS0xlWEZFk4tqwf0/50FXlw1fZkfhwViIixyklrCIiSSS0ZxN5c2/HMSwcw8Itn2ekdaHY73V0IiLeUMIqIpJEXH8a7innY5omPsvCNH0YpkmoaDVLi/7udXgiIp5QwioikkRClsPyzjYlkX0UR4oT09rQWva5+7wOT0TEE0pYRUSSyI7IPn654pdYhlVprNXuWd3pf4rGWRWR45MSVhGRJNIxpTVvjV9Mmi8NwzC8Dkek0bmui+M6OK6D7dqJsoOD48TbXFxsx67U7lC+fHk5sV75dlzXrbS9mtoOTi6u1y+DHIYSVhGRJGJhku5P9zoMkXopMwxGvDai5sTzMIlksiSLrVNaex2C1EEJq4iIiBwTB+jXrh8mJqZxaLIMC8MwsAyrzraq69TUVuM6mJhm+TpHsO/a9uEzfZwUPMnrl1HqoIRVREREjtmMITO8DkFasDqfdOUQa6o4RBqUqUv/pJny+VK8DkGk3hyiXocgLVydCWvH7M5NFYdIg2qX3cXrEESOSqtUXUcnzY/yBWlsdSashlFnt0gS0ylWEZGmonxBGpt+w0REREQkqSlhFREREZGkpoRVRERERJKaElYRERERSWpKWEVEREQkqRmu69b6TLSzzjqLzp01VIUcm+zsbJ577rkm3ad+d+VYefF7C/rdlWOn91xprur63a0zYRURERER8ZouCRARERGRpKaEVURERESSmhJWEREREUlqSlhFREREJKkpYRURERGRpPb/AeZmKrde1g7PAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('white')\n",
    "\n",
    "# model is the same across experiments\n",
    "x = experiments2.copy()\n",
    "x = x.drop(['model','policy'], axis=1)\n",
    "y = outcomes['CC_first_SETP'] <2200\n",
    "fig = regional_sa.plot_cdfs(x,y)\n",
    "sns.despine()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sobol-indices (does not work)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[MainProcess/INFO] pool started\n",
      "[MainProcess/INFO] terminating pool\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'sample'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-36-9ec77551b732>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mMultiprocessingEvaluator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mevaluator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m     sa_results = evaluator.perform_experiments(scenarios=1000, policies=['Sentiment'],\n\u001b[0m\u001b[0;32m      6\u001b[0m                                                uncertainty_sampling='sobol')\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\urban-SETP\\lib\\site-packages\\ema_workbench\\em_framework\\evaluators.py\u001b[0m in \u001b[0;36mperform_experiments\u001b[1;34m(self, scenarios, policies, reporting_interval, reporting_frequency, uncertainty_union, lever_union, outcome_union, uncertainty_sampling, levers_sampling, callback)\u001b[0m\n\u001b[0;32m    164\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    165\u001b[0m         '''\n\u001b[1;32m--> 166\u001b[1;33m         return perform_experiments(self._msis, scenarios=scenarios,\n\u001b[0m\u001b[0;32m    167\u001b[0m                                    \u001b[0mpolicies\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpolicies\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mevaluator\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    168\u001b[0m                                    \u001b[0mreporting_interval\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreporting_interval\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\urban-SETP\\lib\\site-packages\\ema_workbench\\em_framework\\evaluators.py\u001b[0m in \u001b[0;36mperform_experiments\u001b[1;34m(models, scenarios, policies, evaluator, reporting_interval, reporting_frequency, uncertainty_union, lever_union, outcome_union, uncertainty_sampling, levers_sampling, callback, return_callback)\u001b[0m\n\u001b[0;32m    397\u001b[0m     \u001b[1;32melif\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscenarios\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mIntegral\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    398\u001b[0m         \u001b[0msampler\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSAMPLERS\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0muncertainty_sampling\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 399\u001b[1;33m         scenarios = sample_uncertainties(models, scenarios, sampler=sampler,\n\u001b[0m\u001b[0;32m    400\u001b[0m                                          union=uncertainty_union)\n\u001b[0;32m    401\u001b[0m         \u001b[0muncertainties\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscenarios\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\urban-SETP\\lib\\site-packages\\ema_workbench\\em_framework\\samplers.py\u001b[0m in \u001b[0;36msample_uncertainties\u001b[1;34m(models, n_samples, union, sampler)\u001b[0m\n\u001b[0;32m    494\u001b[0m     '''\n\u001b[0;32m    495\u001b[0m     \u001b[0muncertainties\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdetermine_parameters\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'uncertainties'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0munion\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0munion\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 496\u001b[1;33m     \u001b[0msamples\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msampler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgenerate_designs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0muncertainties\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_samples\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    497\u001b[0m     \u001b[0msamples\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mScenario\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    498\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\urban-SETP\\lib\\site-packages\\ema_workbench\\em_framework\\salib_samplers.py\u001b[0m in \u001b[0;36mgenerate_designs\u001b[1;34m(self, parameters, nr_samples)\u001b[0m\n\u001b[0;32m    108\u001b[0m         '''\n\u001b[0;32m    109\u001b[0m         \u001b[0mparameters\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0moperator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mattrgetter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'name'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 110\u001b[1;33m         \u001b[0msampled_parameters\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgenerate_samples\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnr_samples\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    111\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    112\u001b[0m         \u001b[0mnr_designs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msampled_parameters\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\urban-SETP\\lib\\site-packages\\ema_workbench\\em_framework\\salib_samplers.py\u001b[0m in \u001b[0;36mgenerate_samples\u001b[1;34m(self, uncertainties, size)\u001b[0m\n\u001b[0;32m     73\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m         \u001b[0mproblem\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_SALib_problem\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0muncertainties\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 75\u001b[1;33m         \u001b[0msamples\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mproblem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     76\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     77\u001b[0m         \u001b[0mtemp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\urban-SETP\\lib\\site-packages\\ema_workbench\\em_framework\\salib_samplers.py\u001b[0m in \u001b[0;36msample\u001b[1;34m(self, problem, size)\u001b[0m\n\u001b[0;32m    136\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    137\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0msample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproblem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 138\u001b[1;33m         return saltelli.sample(problem, size,\n\u001b[0m\u001b[0;32m    139\u001b[0m                                calc_second_order=self.second_order)\n\u001b[0;32m    140\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'sample'"
     ]
    }
   ],
   "source": [
    "from SALib.analyze import sobol\n",
    "from ema_workbench.em_framework.salib_samplers import get_SALib_problem\n",
    "\n",
    "with MultiprocessingEvaluator(model) as evaluator:\n",
    "    sa_results = evaluator.perform_experiments(scenarios=1000, policies=['Sentiment'],\n",
    "                                               uncertainty_sampling='sobol')\n",
    "\n",
    "experiments, outcomes = sa_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "problem = get_SALib_problem(model.uncertainties)\n",
    "Si = sobol.analyze(problem, outcomes['max_P'],\n",
    "                   calc_second_order=True, print_to_console=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Old stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ginkel\\AppData\\Local\\Continuum\\anaconda3\\envs\\urban-SETP\\lib\\site-packages\\ema_workbench\\analysis\\prim.py:30: ImportWarning: altair based interactive inspection not available\n",
      "  warnings.warn((\"altair based interactive \"\n",
      "[MainProcess/INFO] no time dimension found in results\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 36 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ema_workbench.analysis import pairs_plotting\n",
    "\n",
    "fig, axes = pairs_plotting.pairs_scatter(experiments, outcomes, group_by='policy',\n",
    "                                         legend=False)\n",
    "fig.set_size_inches(8,8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'<' not supported between instances of 'Reactive' and 'Lawkeeper'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-8-009b6f81d739>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutcomes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mfs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfeature_scoring\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_feature_scores_all\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheatmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'viridis'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mannot\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\urban-SETP\\lib\\site-packages\\ema_workbench\\analysis\\feature_scoring.py\u001b[0m in \u001b[0;36mget_feature_scores_all\u001b[1;34m(x, y, alg, mode, **kwargs)\u001b[0m\n\u001b[0;32m    349\u001b[0m     \u001b[0mcomplete\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    350\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;32min\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 351\u001b[1;33m         \u001b[0mfs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0malgorithms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0malg\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    352\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    353\u001b[0m         \u001b[0mfs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\urban-SETP\\lib\\site-packages\\ema_workbench\\analysis\\feature_scoring.py\u001b[0m in \u001b[0;36mget_ex_feature_scores\u001b[1;34m(x, y, mode, nr_trees, max_features, max_depth, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, max_leaf_nodes, bootstrap, oob_score, random_state)\u001b[0m\n\u001b[0;32m    270\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    271\u001b[0m     '''\n\u001b[1;32m--> 272\u001b[1;33m     \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muncs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_prepare_experiments\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    273\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    274\u001b[0m     \u001b[1;31m# TODO\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\urban-SETP\\lib\\site-packages\\ema_workbench\\analysis\\feature_scoring.py\u001b[0m in \u001b[0;36m_prepare_experiments\u001b[1;34m(experiments)\u001b[0m\n\u001b[0;32m     60\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     61\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mx_nominal_columns\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 62\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     63\u001b[0m             \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     64\u001b[0m             _logger.info((\"{} dropped from analysis \"\n",
      "\u001b[1;32m<__array_function__ internals>\u001b[0m in \u001b[0;36munique\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\urban-SETP\\lib\\site-packages\\numpy\\lib\\arraysetops.py\u001b[0m in \u001b[0;36munique\u001b[1;34m(ar, return_index, return_inverse, return_counts, axis)\u001b[0m\n\u001b[0;32m    261\u001b[0m     \u001b[0mar\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masanyarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mar\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    262\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 263\u001b[1;33m         \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_unique1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mar\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_inverse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_counts\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    264\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0m_unpack_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mret\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    265\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\urban-SETP\\lib\\site-packages\\numpy\\lib\\arraysetops.py\u001b[0m in \u001b[0;36m_unique1d\u001b[1;34m(ar, return_index, return_inverse, return_counts)\u001b[0m\n\u001b[0;32m    309\u001b[0m         \u001b[0maux\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mar\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mperm\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    310\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 311\u001b[1;33m         \u001b[0mar\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    312\u001b[0m         \u001b[0maux\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mar\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    313\u001b[0m     \u001b[0mmask\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maux\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbool_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: '<' not supported between instances of 'Reactive' and 'Lawkeeper'"
     ]
    }
   ],
   "source": [
    "from ema_workbench.analysis import feature_scoring\n",
    "\n",
    "x = pd.DataFrame(experiments)\n",
    "y = pd.DataFrame(outcomes)\n",
    "\n",
    "fs = feature_scoring.get_feature_scores_all(x, y)\n",
    "sns.heatmap(fs, cmap='viridis', annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "Selected KDE bandwidth is 0. Cannot estiamte density.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\urban-SETP\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py\u001b[0m in \u001b[0;36mkdensityfft\u001b[1;34m(X, kernel, bw, weights, gridsize, adjust, clip, cut, retgrid)\u001b[0m\n\u001b[0;32m    450\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 451\u001b[1;33m         \u001b[0mbw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    452\u001b[0m     \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'scott'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-21-57a387edcdb5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpairplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'policy'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvars\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutcomes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\urban-SETP\\lib\\site-packages\\seaborn\\axisgrid.py\u001b[0m in \u001b[0;36mpairplot\u001b[1;34m(data, hue, hue_order, palette, vars, x_vars, y_vars, kind, diag_kind, markers, height, aspect, corner, dropna, plot_kws, diag_kws, grid_kws, size)\u001b[0m\n\u001b[0;32m   2119\u001b[0m             \u001b[0mdiag_kws\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"shade\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2120\u001b[0m             \u001b[0mdiag_kws\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"legend\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2121\u001b[1;33m             \u001b[0mgrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap_diag\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkdeplot\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mdiag_kws\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2122\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2123\u001b[0m     \u001b[1;31m# Maybe plot on the off-diagonals\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\urban-SETP\\lib\\site-packages\\seaborn\\axisgrid.py\u001b[0m in \u001b[0;36mmap_diag\u001b[1;34m(self, func, **kwargs)\u001b[0m\n\u001b[0;32m   1488\u001b[0m                     \u001b[0mdata_k\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove_na\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_k\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1489\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1490\u001b[1;33m                 \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_k\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabel_k\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1491\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1492\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_clean_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\urban-SETP\\lib\\site-packages\\seaborn\\distributions.py\u001b[0m in \u001b[0;36mkdeplot\u001b[1;34m(data, data2, shade, vertical, kernel, bw, gridsize, cut, clip, legend, cumulative, shade_lowest, cbar, cbar_ax, cbar_kws, ax, **kwargs)\u001b[0m\n\u001b[0;32m    701\u001b[0m                                 cbar, cbar_ax, cbar_kws, ax, **kwargs)\n\u001b[0;32m    702\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 703\u001b[1;33m         ax = _univariate_kdeplot(data, shade, vertical, kernel, bw,\n\u001b[0m\u001b[0;32m    704\u001b[0m                                  \u001b[0mgridsize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcut\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclip\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlegend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    705\u001b[0m                                  cumulative=cumulative, **kwargs)\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\urban-SETP\\lib\\site-packages\\seaborn\\distributions.py\u001b[0m in \u001b[0;36m_univariate_kdeplot\u001b[1;34m(data, shade, vertical, kernel, bw, gridsize, cut, clip, legend, ax, cumulative, **kwargs)\u001b[0m\n\u001b[0;32m    291\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0m_has_statsmodels\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    292\u001b[0m         \u001b[1;31m# Prefer using statsmodels for kernel flexibility\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 293\u001b[1;33m         x, y = _statsmodels_univariate_kde(data, kernel, bw,\n\u001b[0m\u001b[0;32m    294\u001b[0m                                            \u001b[0mgridsize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcut\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclip\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    295\u001b[0m                                            cumulative=cumulative)\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\urban-SETP\\lib\\site-packages\\seaborn\\distributions.py\u001b[0m in \u001b[0;36m_statsmodels_univariate_kde\u001b[1;34m(data, kernel, bw, gridsize, cut, clip, cumulative)\u001b[0m\n\u001b[0;32m    365\u001b[0m     \u001b[0mfft\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkernel\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"gau\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    366\u001b[0m     \u001b[0mkde\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msmnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mKDEUnivariate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 367\u001b[1;33m     \u001b[0mkde\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkernel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfft\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgridsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgridsize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcut\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcut\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclip\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mclip\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    368\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mcumulative\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    369\u001b[0m         \u001b[0mgrid\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkde\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msupport\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkde\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcdf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\urban-SETP\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, kernel, bw, fft, weights, gridsize, adjust, cut, clip)\u001b[0m\n\u001b[0;32m    136\u001b[0m                 \u001b[0mmsg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"Weights are not implemented for fft\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    137\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 138\u001b[1;33m             density, grid, bw = kdensityfft(endog, kernel=kernel, bw=bw,\n\u001b[0m\u001b[0;32m    139\u001b[0m                     \u001b[0madjust\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0madjust\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mweights\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgridsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgridsize\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    140\u001b[0m                     clip=clip, cut=cut)\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\urban-SETP\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py\u001b[0m in \u001b[0;36mkdensityfft\u001b[1;34m(X, kernel, bw, weights, gridsize, adjust, clip, cut, retgrid)\u001b[0m\n\u001b[0;32m    451\u001b[0m         \u001b[0mbw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    452\u001b[0m     \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 453\u001b[1;33m         \u001b[0mbw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbandwidths\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mselect_bandwidth\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkern\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# will cross-val fit this pattern?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    454\u001b[0m     \u001b[0mbw\u001b[0m \u001b[1;33m*=\u001b[0m \u001b[0madjust\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    455\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\urban-SETP\\lib\\site-packages\\statsmodels\\nonparametric\\bandwidths.py\u001b[0m in \u001b[0;36mselect_bandwidth\u001b[1;34m(x, bw, kernel)\u001b[0m\n\u001b[0;32m    172\u001b[0m         \u001b[1;31m# eventually this can fall back on another selection criterion.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    173\u001b[0m         \u001b[0merr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"Selected KDE bandwidth is 0. Cannot estiamte density.\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 174\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    175\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    176\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mbandwidth\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mRuntimeError\u001b[0m: Selected KDE bandwidth is 0. Cannot estiamte density."
     ]
    },
    {
     "data": {
      "image/png": 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SpEYM25IkSVIjhm1JkiSpEcO2JEmS1IhhW5IkSWrEsC1JkiQ1YtiWJEmSGjFsS5IkSY0YtiVJkqRGDNuSJElSI4ZtSZIkqRHDtiRJktRI87CdZHOS/UkOJLl8xP1vS/L+/v2bkjy+dU2SJEnSUmgatpOsAXYAW4BNwLYkm4aaXQzcU1XfC7wVeHPLmiRJkqSl0vrN9rnAgao6WFUPAruArUNttgLX9j9/EHhWkjSuS5IkSWruhMbPXwfcOXA+D5y3UJuqOpzkXuAM4J8GGyXZDmzvnz6Q5NYmFR/dWobqWuX9Lmffy9XvrVX1pGk8aIWMWTj+/g6Pt35h9Y3b4+3v8Hj8b7xjttv9LmffnRuzrcP2qDfUdQxtqKqdwE6AJHNVNfvwy5vM8dbvcva9nP1O61krYcwuZ9/2u7R9T+tZK2Hc2u/q79sx2+1+l7PvLo7Z1tNI5oENA+frgbsWapPkBOB04MuN65IkSZKaax229wEbk5yV5CTgImD3UJvdwEv6n58H/HlVPeTNtiRJktQ1TaeR9OdgXwLsBdYAV1fVbUmuBOaqajfwO8B1SQ7Qe6N90RiP3tmsaPtdKX2vtn79O7TfLvZ9vP27PN76Xc6+HbPd7nc5++5cv/ElsiRJktSGO0hKkiRJjbTe1ObqJHcvtAxPet7W3z3yliRPaVmPJEmStJRav9m+Bth8lPtbgI39Yzvwzsb1SJIkSUumadiuqo9z9GX8tgLvqZ5PAY9K8tiWNUmSJElLZbnnbI/aYXLdqIZJtieZSzJ39tlnF72Nbzw8pn1MjWPWYwmPqXHceizRMTWOWY8lOo7ZcoftsXaPBKiqnVU1W1WzJ598cuOypIfPMasuctyqaxyzWumWO2yPs8OkJEmS1EnLHbZ3Ay/ur0ryNODeqvqHZa5JkiRJmoqmO0gmeR9wPrA2yTzwWuBEgKp6F7AHuAA4AHwVeFnLeiRJkqSl1Hq79m2L3C/g51rWIEmSJC2X5Z5GIkmSJK1ahm1JkiSpEcO2JEmS1IhhW5IkSWrEsC1JkiQ1YtiWJEmSGjFsS5IkSY0YtiVJkqRGDNuSJElSI4ZtSZIkqRHDtiRJktSIYVuSJElqxLAtSZIkNWLYliRJkhoxbEuSJEmNGLYlSZKkRgzbkiRJUiOGbUmSJKkRw7YkSZLUiGFbkiRJasSwLUmSJDVi2JYkSZIaMWxLkiRJjTQP20k2J9mf5ECSy0fcPzPJDUluTnJLkgta1yRJkiQthaZhO8kaYAewBdgEbEuyaajZa4Drq+oc4CLgHS1rkiRJkpZK6zfb5wIHqupgVT0I7AK2DrUp4LT+59OBuxrXJEmSJC2JExo/fx1w58D5PHDeUJvXAR9JcilwCvDsxjVJkiRJS6L1m+2MuFZD59uAa6pqPXABcF2Sh9SVZHuSuSRzhw4dalCqNF2OWXWR41Zd45jVStc6bM8DGwbO1/PQaSIXA9cDVNUngUcCa4cfVFU7q2q2qmZnZmYalStNj2NWXeS4Vdc4ZrXStQ7b+4CNSc5KchK9H0DuHmrzeeBZAEmeSC9s+7+mkiRJ6rymYbuqDgOXAHuBO+itOnJbkiuTXNhv9krg5Uk+C7wPeGlVDU81kSRJkjqn9Q8kqao9wJ6ha1cMfL4deHrrOiRJkqSl5g6SkiRJUiOGbUmSJKkRw7YkSZLUiGFbkiRJasSwLUmSJDUy9mokSR4J/Cfgh+jtAvmXwDur6v5GtUmSJEmdNsnSf+8BvgK8vX++DbgOeP60i5IkSZJWg0nC9r+tqu8fOL+hvxGNJEmSpBEmmbN9c5KnHTlJch7wiemXJEmSJK0Oi77ZTvI5enO0TwRenOTz/fPHAbe3LU+SJEnqrnGmkfxY8yokSZKkVWjRsF1Vfw+Q5Mz25UiSJEmrxyQ/kPxjetNHAjwSOAvYD5zdoC5JkiSp88YO21X1fYPnSZ4C/MzUK5IkSZJWiWPeQbKqPgP8wBRrkSRJklaVSXaQvGzg9BHAU4FDU69IkiRJWiUmmbN96sDnw8AfAb8/3XIkSZKk1WOSOduvP/I5ySOA76iq+5tUJUmSJK0CY8/ZTvLeJKclOYXeZjb7k/xSu9IkSZKkbpvkB5Kbquo+4MeBPcCZwP/RpCpJkiRpFZgkbJ+Y5ER6YfvDVfUv9NbdliRJkjTCJGH73cD/Ak4BPp7kccB9LYqSJEmSVoOxw3ZVva2q1lXVBVVVwOeBZx65n+QlLQqUJEmSuurhbGpTVXV44NLPT6EeSZIkadU45rA9QkZeTDYn2Z/kQJLLF2jzgiS3J7ktyXunWJMkSZK0bCbZ1GYxD/mxZJI1wA7gR4B5YF+S3VV1+0CbjcCrgKdX1T1JHj3FmiRJkqRl0/rN9rnAgao6WFUPAruArUNtXg7sqKp7AKrq7inWJEmSJC2baYbtT4y4tg64c+B8vn9t0BOAJyT5RJJPJdk8xZokSZKkZTPWNJIkz6G3vvY6etNF7qK31vafHmlTVZeM+uqIa8PTTU4ANgLnA+uBv0jypKr656EatgPbAc4888xxypaWlWNWXeS4Vdc4ZrXSLfpmO8lv0Ftp5GPAVcBb+p9fkeQ3F/n6PLBh4Hw9vaA+3ObDVfUvVfV3wH564ftbVNXOqpqtqtmZmZnFypaWnWNWXeS4Vdc4ZrXSjfNm+4KqesLwxSTvB/4HR1/ybx+wMclZwBeAi4AXDrX5ELANuCbJWnrTSg6OUZckSZK0oo0zZ/v+JOeOuP4DwP1H+2J/He5LgL3AHcD1VXVbkiuTXNhvthf4UpLbgRuAX6qqL439J5AkSZJWqHHebL8UeGeSU+lN+YDe1JD7+veOqqr2AHuGrl0x8LmAy/qHJEmStGosGrar6jPAeUkeQ+8HkgHmq+qLrYuTJEmSumzc1UhOB57BwGokSfYOrxgiSZIk6ZvGWY3kxcBn6C3N9+3AKcAzgU/370mSJEkaYZw3268Gnjpi3et/A9wEvKdFYZIkSVLXjbMaSXjoRjQA/8roTWskSZIkMd6b7TcCn0nyEb659fqZwI8Ab2hVmCRJktR1i77ZrqprgVl6u0Y+ADwI3AjMVtU1LYuTJEmSumys1Uiq6h5gV+NaJEmSpFVlnNVINiTZleQvkvxqkhMH7n2obXmSJElSd43zA8mr6U0buRR4LPCxJGf07z2uUV2SJElS540zjWSmqt7V/3xpkhcBH09yIaNXKZEkSZLEeGH7xCSPrKr7Aarq95J8EdhLb4MbSZIkSSOMM43kt4HzBi9U1Z8BzwdubVGUJEmStBos+ma7qt66wPWb6a21LUmSJGmEcd5sk+Q5SS5O8vih6z/doihJkiRpNRhn6b83Aa8Gvg/4aJJLB25f0qowSZIkqevGebP9Y8APV9UvAE8FtiQ5MrUkzSqTJEmSOm6csH1CVR0GqKp/Bp4LnJbkA8BJLYuTJEmSumycsP23SZ5x5KSqvl5VFwP7gSc2q0ySJEnquHHC9vOBvxq+WFWvATYcOU9y9hTrkiRJkjpv0bBdVV+rqq8tcO8LA6fXTa0qSZIkaRUYa+m/MfljSUmSJGnANMN2TfFZkiRJUudNM2xLkiRJGjDNsP3gqItJNifZn+RAkssX+nKS5yWpJLNTrEmSJElaNidM0jjJTwA/RG/KyF9W1X87cq+qnjai/RpgB/AjwDywL8nuqrp9qN2pwCuAmyb+E0iSJEkr1NhvtpO8A/hZ4HPArcDPJNmxyNfOBQ5U1cGqehDYBWwd0e4NwFXA/ePWI0mSJK10k0wjeQbwnKr63ar6XeAC4PxFvrMOuHPgfL5/7RuSnANsqKo/OtqDkmxPMpdk7tChQxOULS0Px6y6yHGrrnHMaqWbJGzvB84cON8A3LLId0YtB/iNVUuSPAJ4K/DKxTqvqp1VNVtVszMzM2OUKy0vx6y6yHGrrnHMaqWbZM72GcAdSY7sJvkDwCeT7AaoqgtHfGeegV0mgfXAXQPnpwJPAm5MAvAYYHeSC6tqboLaJEmSpBVnkrB9xTE8fx+wMclZwBeAi4AXHrlZVfcCa4+cJ7kR+EWDtiRJklaDscN2VX1s0odX1eEklwB7gTXA1VV1W5Irgbmq2j3pMyVJkqSuGDts95f9ezPwaHpzsQNUVZ12tO9V1R5gz9C1kW/Jq+r8ceuRJEmSVrpJppFcBTy3qu5oVYwkSZK0mkyyGsk/GrQlSZKk8S36Zrs/fQRgLsn7gQ8BDxy5X1V/0Kg2SZIkqdPGmUby3IHPXwV+dOC8AMO2JEmSNMKiYbuqXjbOg5K8qqre9PBLkiRJklaHSeZsL+b5U3yWJEmS1HnTDNujtmaXJEmSjlvTDNs1xWdJkiRJneebbUmSJKmRscN2krWLNPnAw6xFkiRJWlUWDdtJnpvkEPC5JPNJfnBUu6r6z1OvTpIkSeqwcd5svxH491X1WOAnAZf3kyRJksYwTtg+XFV/A1BVNwGnti1JkiRJWh3G2UHy0UkuW+i8qn59+mVJkiRJ3TdO2P4tvvVt9vC5JEmSpBHG2a799UtRiCRJkrTaLBq2k7ztaPer6hXTK0eSJElaPcaZRvLpgc+vB17bqBZJkiRpVRlnGsm1Rz4n+YXBc0mSJEkLm3S79mpShSRJkrQKTRq2JUmSJI1pnB9IfoVvvtH+9iT3HbkFVFWd1qo4SZIkqcvGmbPtmtqSJEnSMXAaiSRJktRI87CdZHOS/UkOJLl8xP3Lktye5JYkH03yuNY1SZIkSUuhadhOsgbYAWwBNgHbkmwaanYzMFtVTwY+CFzVsiZJkiRpqbR+s30ucKCqDlbVg8AuYOtgg6q6oaq+2j/9FLC+cU2SJEnSkmgdttcBdw6cz/evLeRi4E9G3UiyPclckrlDhw5NsUSpDcesushxq65xzGqlax22M+LayI1xkrwImAXeMup+Ve2sqtmqmp2ZmZliiVIbjll1keNWXeOY1Uq36NJ/D9M8sGHgfD1w13CjJM8GXg08o6oeaFyTJEmStCRav9neB2xMclaSk4CLgN2DDZKcA7wbuLCq7m5cjyRJkrRkmobtqjoMXALsBe4Arq+q25JcmeTCfrO3AN8BfCDJXyfZvcDjJEmSpE5pPY2EqtoD7Bm6dsXA52e3rkGSJElaDu4gKUmSJDVi2JYkSZIaMWxLkiRJjRi2JUmSpEYM25IkSVIjhm1JkiSpEcO2JEmS1IhhW5IkSWrEsC1JkiQ1YtiWJEmSGjFsS5IkSY0YtiVJkqRGDNuSJElSI4ZtSZIkqRHDtiRJktSIYVuSJElqxLAtSZIkNWLYliRJkhoxbEuSJEmNGLYlSZKkRgzbkiRJUiOGbUmSJKkRw7YkSZLUSPOwnWRzkv1JDiS5fMT9b0vy/v79m5I8vnVNkiRJ0lJoGraTrAF2AFuATcC2JJuGml0M3FNV3wu8FXhzy5okSZKkpdL6zfa5wIGqOlhVDwK7gK1DbbYC1/Y/fxB4VpI0rkuSJElqrnXYXgfcOXA+3782sk1VHQbuBc5oXJckSZLU3AmNnz/qDXUdQxuSbAe2908fSHLrw6ztWKwF/uk46nc5+16ufm+tqidN40ErZMzC8fd3eLz1C6tv3B5vf4fH43/jHbPd7nc5++7cmE3VQ3Lt1CT5d8Drquo5/fNXAVTVmwba7O23+WSSE4AvAjN1lMKSzFXVbLPC7XfZ+15t/fp3aL9d7Pt4+3d5vPW7nH07Zrvd73L23cV+W08j2QdsTHJWkpOAi4DdQ212Ay/pf34e8OdHC9qSJElSVzSdRlJVh5NcAuwF1gBXV9VtSa4E5qpqN/A7wHVJDgBfphfIJUmSpM5rPWebqtoD7Bm6dsXA5/uB50/42J1TKO1YHG/9Lmffq61f/w7tt4t9H2//Lo+3fpezb8dst/tdzr4712/TOduSJEnS8az1pjZXJ7l7oV8Gp+dt/d0jb0nylJb1SJIkSUup9Q8krwE2H+X+FmBj/9gOvLNxPZIkSdKSaRq2q+rj9H70uJCtwHuq51PAo5I8tmVNkiRJ0lJp/WZ7MePsMClJkiR10nKH7bF2j4TeDlFJ5pLMnX322dVv5+Ex7WNqHLMeS3hMjePWY4mOqXHMeizRccyWO2zPAxsGztcDd41qWFU7q2q2qmZPPvnkJSlOet9dEDgAACAASURBVDgcs+oix626xjGrlW65w/Zu4MX9VUmeBtxbVf+wzDVJkiRJU9F0U5sk7wPOB9YmmQdeC5wIUFXvorfZzQXAAeCrwMta1iNJkiQtpdbbtW9b5H4BP9eyBkmSJGm5LPc0EkmSJGnVMmxLkiRJjRi2JUmSpEYM25IkSVIjhm1JkiSpEcO2JEmS1IhhW5IkSWrEsC1JkiQ1YtiWJEmSGjFsS5IkSY0YtiVJkqRGDNuSJElSI4ZtSZIkqRHDtiRJktSIYVuSJElqxLAtSZIkNWLYliRJkhoxbEuSJEmNGLYlSZKkRgzbkiRJUiOGbUmSJKkRw7YkSZLUiGFbkiRJasSwLUmSJDXSPGwn2Zxkf5IDSS4fcf/MJDckuTnJLUkuaF2TJEmStBSahu0ka4AdwBZgE7AtyaahZq8Brq+qc4CLgHe0rEmSJElaKq3fbJ8LHKiqg1X1ILAL2DrUpoDT+p9PB+5qXJMkSZK0JE5o/Px1wJ0D5/PAeUNtXgd8JMmlwCnAsxvXJEmSJC2J1m+2M+JaDZ1vA66pqvXABcB1SR5SV5LtSeaSzB06dKhBqdJ0OWbVRY5bdY1jVitd67A9D2wYOF/PQ6eJXAxcD1BVnwQeCawdflBV7ayq2aqanZmZaVSuND2OWXWR41Zd45jVStc6bO8DNiY5K8lJ9H4AuXuozeeBZwEkeSK9sO3/mkqSJKnzmobtqjoMXALsBe6gt+rIbUmuTHJhv9krgZcn+SzwPuClVTU81USSJEnqnNY/kKSq9gB7hq5dMfD5duDpreuQJEmSlpo7SEqSJEmNGLYlSZKkRgzbkiRJUiOGbUmSJKkRw7YkSZLUiGFbkiRJamTRpf+SXF9VL0jyOR661XoBXwZ+o6o+3KJASZIkqavGWWf75/v//LEF7q8F/itg2JYkSZIGLDqNpKr+of/PvwceAL4feDLwQFX9fVV9GviPTauUJEmSOmjsOdtJ/k/gr4CfAJ4HfCrJTwP0A7ckSZKkAZNs1/5LwDlV9SWAJGcA/w9wdYvCJEmSpK6bZDWSeeArA+dfAe6cbjmSJEnS6jHOaiSX9T9+AbgpyYfprUKyld60EkmSJEkjjDON5NT+P/+2fxzh6iOSJEnSUSwatqvq9YPnSU7tXa7/t1lVkiRJ0iowyWokT0pyM3ArcFuSTyc5u11pkiRJUrdN8gPJncBlVfW4qnoc8Ergt9qUJUmSJHXfJGH7lKq64chJVd0InDL1iiRJkqRVYpJ1tg8m+b+A6/rnLwL+bvolSZIkSavDJG+2fxqYAf6gf6wFXtaiKEmSJGk1GPvNdlXdA7xioftJ3l5Vl06lKkmSJGkVmOTN9mKePsVnSZIkSZ03zbAtSZIkaYBhW5IkSWpkmmE7Iy8mm5PsT3IgyeULtHlBktuT3JbkvVOsSZIkSVo2kyz9t5jfHL6QZA2wA/gRYB7Yl2R3Vd0+0GYj8Crg6VV1T5JHT7EmSZIkadks+mY7yelJ/kuSv0nypf5xR//ao460q6prRnz9XOBAVR2sqgeBXcDWoTYvB3b0Vzuhqu4+5j+NJEmStIKMM43keuAe4PyqOqOqzgCe2b/2gUW+uw64c+B8vn9t0BOAJyT5RJJPJdk86kFJtieZSzJ36NChMcqWlpdjVl3kuFXXOGa10o0Tth9fVW+uqi8euVBVX6yqNwNnLvLdUfO4a+j8BGAjcD6wDfjtwTfmA33urKrZqpqdmZkZo2xpeTlm1UWOW3WNY1Yr3Thh+++T/HKS7zpyIcl3JfkVvvWt9SjzwIaB8/XAXSPafLiq/qWq/g7YTy98S5IkSZ02Ttj+KeAM4GNJ7klyD3Aj8J3ACxb57j5gY5KzkpwEXATsHmrzIXrTUkiylt60koNj/wkkSZKkFWrR1Uj6P1z8lf4xkao6nOQSYC+wBri6qm5LciUwV1W7+/d+NMntwNeBX6qqL03alyRJkrTSjLX0X5LnAD9O78eNRW8qyIer6k8X+25V7QH2DF27YuBzAZf1D0mSJGnVWDRsJ/kNelM73kNvfjX05l6/IsmWqvr5hvVJkiRJnTXOm+0LquoJwxeTvB/4H4BhW5IkSRphnB9I3p/k3BHXfwC4f8r1SJIkSavGOG+2Xwq8M8mpfHMayQbgvv49SZIkSSOMsxrJZ4DzkjyG3g8kA8wPbnIjSZIk6aHGWo2k70vDATvJ2qr6pynXJEmSJK0Ki87ZTvLMJPPAXUk+kuTxA7c/0qowSZIkqevG+YHkVcBzqmoG2An89yRP699Ls8okSZKkjhtnGslJVXUbQFV9MMkdwB8kuZzeBjeSJEmSRhgnbP9Lksccma/d3279WcAfAd/TtDpJkiSpw8aZRnI58F2DF6pqHngG8F9aFCVJkiStBuMs/fdnC1y/F3jj1CuSJEmSVolxViM5LcmbklyX5IVD997RrjRJkiSp28aZRvK79FYd+X3goiS/n+Tb+veetvDXJEmSpOPbOGH7e6rq8qr6UFVdCHwG+PMkZzSuTZIkSeq0cVYj+bYkj6iqfwWoqjf2N7n5OPAdTauTJEmSOmycN9t/CPzw4IWquhZ4JfBgi6IkSZKk1WDRsF1VvzxqRZKq+tOq2njkPMlLpl2cJEmS1GXjvNke189P8VmSJElS500zbGeKz5IkSZI6b5phu6b4LEmSJKnzfLMtSZIkNTLNsP2JKT5LkiRJ6ryxw3aSM5K8Pclnknw6yW8ObmxTVZcs8L3NSfYnOZDk8qM8/3lJKsnsZH8ESZIkaWWa5M32LuBu4CeB5wGHgPcf7QtJ1gA7gC3AJmBbkk0j2p0KvAK4aYJ6JEmSpBVtkrD9nVX1hqr6u/7xfwOPWuQ75wIHqupgVT1IL7BvHdHuDcBVwP0T1CNJkiStaJOE7RuSXJTkEf3jBcAfL/KddcCdA+fz/WvfkOQcYENV/dEEtUiSJEkr3iRh+2eA99Lbov3IW+rLknwlyX0LfGfUCiXfWCIwySOAt9Lb+v2okmxPMpdk7tChQxOULS0Px6y6yHGrrnHMaqUbO2xX1alV9YiqOqF/PKJ/7dSqOm2Br80DGwbO1wN3DZyfCjwJuDHJ/wKeBuwe9SPJqtpZVbNVNTszMzNu2dKyccyqixy36hrHrFa6EyZpnGQd8LjB71XVx4/ylX3AxiRnAV8ALgJeOPDde4G1A8+/EfjFqpqbpC5JkiRpJRo7bCd5M/BTwO3A1/uXC1gwbFfV4SSXAHuBNcDVVXVbkiuBuarafcyVS5IkSSvcJG+2fxz4t1X1wCQdVNUeYM/QtSsWaHv+JM+WJEmSVrJJfiB5EDixVSGSJEnSarPom+0kb6c3XeSrwF8n+SjwjbfbVfWKduVJkiRJ3TXONJIjP1b8NOAca0mSJGlMi4btqrp2nAcl+f2q+smHX5IkSZK0OkwyZ3sx3z3FZ0mSJEmdN82wXYs3kSRJko4f0wzbkiRJkgZMM2xnis+SJEmSOm/RsJ1kJsmmEdfPTjIzcOlXplqZJEmS1HHjvNl+OzAz4vp64DePnFTVR6ZVlCRJkrQajBO2v6+qPjZ8sar2Ak+efkmSJEnS6jBO2D7aFu1u3y5JkiQtYJyw/T+TXDB8MckW4OD0S5IkSZJWh3G2a/8F4I+TvIDelu0As8C/A36sVWGSJElS140Ttgv4aWAj8KT+tY8BvwN8vVFdkiRJUueNE7Z/A/jVqvrdwYtJZvv3ntuiMEmSJKnrxpmz/fiqumX4YlXNAY+fekWSJEnSKjFO2H7kUe6dPK1CJEmSpNVmnLC9L8nLhy8muZhv/mBSkiRJ0pBxVyP5b0n+I9+6GslJwH9oVZgkSZLUdYuG7ar6R+AHkzyTb65G8sdV9edNK5MkSZI6bpw32wBU1Q3ADQ1rkSRJklaVceZsS5IkSToGhm1JkiSpkeZhO8nmJPuTHEhy+Yj7lyW5PcktST6a5HGta5IkSZKWQtOwnWQNsAPYAmwCtiXZNNTsZmC2qp4MfBC4qmVNkiRJ0lJp/Wb7XOBAVR2sqgeBXcDWwQZVdUNVfbV/+ilgfeOaJEmSpCXROmyvA+4cOJ/vX1vIxcCfNK1IkiRJWiKtw3ZGXKuRDZMX0dss5y0L3N+eZC7J3KFDh6ZYotSGY1Zd5LhV1zhmtdK1DtvzwIaB8/XAXcONkjwbeDVwYVU9MOpBVbWzqmaranZmZqZJsdI0OWbVRY5bdY1jVitd67C9D9iY5KwkJwEXAbsHGyQ5B3g3vaB9d+N6JEmSpCXTNGxX1WHgEmAvcAdwfVXdluTKJBf2m70F+A7gA0n+OsnuBR4nSZIkdcrY27Ufq6raA+wZunbFwOdnt65BkiRJWg7uIClJkiQ1YtiWJEmSGjFsS5IkSY0YtiVJkqRGDNuSJElSI4ZtSZIkqRHDtiRJktSIYVuSJElqxLAtSZIkNWLYliRJkhoxbEuSJEmNGLYlSZKkRgzbkiRJUiOGbUmSJKkRw7YkSZLUiGFbkiRJasSwLUmSJDVi2JYkSZIaMWxLkiRJjRi2JUmSpEYM25IkSVIjhm1JkiSpEcO2JEmS1IhhW5IkSWqkedhOsjnJ/iQHklw+4v63JXl///5NSR7fuiZJkiRpKTQN20nWADuALcAmYFuSTUPNLgbuqarvBd4KvLllTZIkSdJSaf1m+1zgQFUdrKoHgV3A1qE2W4Fr+58/CDwrSRrXJUmSJDV3QuPnrwPuHDifB85bqE1VHU5yL3AG8E+DjZJsB7b3Tx9IcmuTio9uLUN1rfJ+l7Pv5er31qp60jQetELGLBx/f4fHW7+w+sbt8fZ3eDz+N94x2+1+l7Pvzo3Z1mF71BvqOoY2VNVOYCdAkrmqmn345U3meOt3Oftezn6n9ayVMGaXs2/7Xdq+p/WslTBu7Xf19+2Y7Xa/y9l3F8ds62kk88CGgfP1wF0LtUlyAnA68OXGdUmSJEnNtQ7b+4CNSc5KchJwEbB7qM1u4CX9z88D/ryqHvJmW5IkSeqaptNI+nOwLwH2AmuAq6vqtiRXAnNVtRv4HeC6JAfovdG+aIxH72xWtP2ulL5XW7/+HdpvF/s+3v5dHm/9Lmffjtlu97ucfXeu3/gSWZIkSWrDHSQlSZKkRlpvanN1krsXWoYnPW/r7x55S5KntKxHkiRJWkqt32xfA2w+yv0twMb+sR14Z+N6JEmSpCXTNGxX1cc5+jJ+W4H3VM+ngEcleWzLmiRJkqSlstxztkftMLluVMMk25PMJZk7++yzi97GNx4e0z6mxjHrsYTH1DhuPZbomBrHrMcSHcdsucP2WLtHAlTVzqqararZk08+uXFZ0sPnmFUXOW7VNY5ZrXTLHbbH2WFSkiRJ6qTlDtu7gRf3VyV5GnBvVf3DMtckSZIkTUXTHSSTvA84H1ibZB54LXAiQFW9C9gDXAAcAL4KvKxlPZIkSdJSar1d+7ZF7hfwcy1rkCRJkpbLck8jkSRJklYtw7YkSZLUiGFbkiRJasSwLUmSJDVi2JYkSZIaMWxLkiRJjRi2JUmSpEYM25IkSVIjhm1JkiSpEcO2JEmS1IhhW5IkSWrEsC1JkiQ1YtiWJEmSGjFsS5IkSY0YtiVJkqRGDNuSJElSI4ZtSZIkqRHDtiRJktSIYVuSJElqxLAtSZIkNWLYliRJkhoxbEuSJEmNGLYlSZKkRpqH7SSbk+xPciDJ5SPun5nkhiQ3J7klyQWta5IkSZKWQtOwnWQNsAPYAmwCtiXZNNTsNcD1VXUOcBHwjpY1SZIkSUul9Zvtc4EDVXWwqh4EdgFbh9oUcFr/8+nAXY1rkiRJkpbECY2fvw64c+B8HjhvqM3rgI8kuRQ4BXh245okSZKkJdH6zXZGXKuh823ANVW1HrgAuC7JQ+pKsj3JXJK5Q4cONShVmi7HrLrIcauuccxqpWsdtueBDQPn63noNJGLgesBquqTwCOBtcMPqqqdVTVbVbMzMzONypWmxzGrLnLcqmscs1rpWoftfcDGJGclOYneDyB3D7X5PPAsgCRPpBe2/V9TSZIkdV7TsF1Vh4FLgL3AHfRWHbktyZVJLuw3eyXw8iSfBd4HvLSqhqeaSJIkSZ3T+geSVNUeYM/QtSsGPt8OPL11HZIkSdJScwdJSZIkqRHDtiRJktSIYVuSJElqxLAtSZIkNWLYliRJkhoZO2wn+e4kf5jkn5LcneTDSb67ZXGSJElSl03yZvu99HZ6fAzwvwEfoLcutiRJkqQRJgnbqarrqupw//g9wM1nJEmSpAUsuqlNku/sf7whyeXALnoh+6eAP25YmyRJktRp4+wg+Wl64Tr9858ZuFfAG6ZdlCRJkrQaLBq2q+qspShEkiRJWm3GebMN/P/t3XuUZGV57/HvDziARgSFEZUhgjrEjMgRHPGueAcvoPECqBESItGIaNQohnUIwbjCxYg3joaVqKBRRDzqKCgSGCSHgDJcAswQdBx1MSHKqATjQW7ynD/2biiavlT31O7q6v5+1qo1td96936f6n665tm73r03JHnjRO1VdfrgwpEkSZIWjr6LbeDJPc+3Bp4PXAFYbEuSJEkT6LvYrqq39S4n2Rb47MAjkiRJkhaITbmD5K3AskEFIkmSJC00M5mz/XXuva72ZsBympvcSJIkSZrATOZsf7Dn+V3AT6pqw4DjkSRJkhaMmRTbq4HfVNXdSXYD9krys6q6s6PYJEmSpJE2kznbFwFbJ9kJOB/4I+AzXQQlSZIkLQQzKbZTVbcCfwB8rKpeSTNvW5IkSdIEZlRsJ3ka8Hrg7LZtJtNQJEmSpEVlJsX224H3AV+pqjVJHg2s6iYsSZIkafTN5KY2F9HM2x5bXg8cObac5GPjb3wjSZIkLWabclOb8Z4xwG1JkiRJI2+QxfaEkuyb5Pok65IcNUmf1yZZm2RNks93HZMkSZI0Fzo9wTHJ5sApwAuBDcBlSVZW1dqePsto5oI/o6puTvKwLmOSJEmS5sogj2xngra9gXVVtb6q7gDOAA4Y1+dNwClVdTNAVd00wJgkSZKkoRlksf2RCdp2Am7oWd7QtvXaDdgtycVJLk2y7wBjkiRJkoZm2mI7ybZJjk/y70l+0T6ua9u2G+tXVZ+ZaPUJ2mrc8hbAMmAf4GDgH3q32xPH4UlWJ1m9cePG6cKWhs6c1SgybzVqzFnNd/0c2T4TuBnYp6q2r6rtgee2bV+aZt0NwM49y0uBGyfo87WqurOqfgRcT1N830dVnVpVK6pqxZIlS/oIWxouc1ajyLzVqDFnNd/1U2zvUlUnVNVPxxqq6qdVdQLwu9OsexmwLMmuSbYEDgJWjuvzVZrinSQ70EwrWd/vG5AkSZLmq36K7Z8keU+SHccakuyY5L3cdz72/VTVXcARwLnAdcCZ7d0nj0uyf9vtXOAXSdbS3JHyL6rqF7N5M5IkSdJ80s+l/w4EjgK+01Nw/5TmCPVrp1u5qs4BzhnXdkzP8wLe2T4kSZKkBWPaYru9JN9724ckSZKkPvV1U5skLwZeQXPZvqI5yfFrVfWtDmOTJEmSRtq0xXaSD9OctHg6zZVDoLmqyJFJ9quqt3cYnyRJkjSy+jmy/ZKq2m18Y5IvAt8HLLYlSZKkCfRzNZLbkuw9QfuTgdsGHI8kSZK0YPRzZPtQ4BNJtuHeaSQ7A79qX5MkSZI0gX6uRnIF8JQkD6c5QTLAht6b3EiSJEm6v76uRtL6xfgCO8kOVfXzAcckSZIkLQjTztlO8twkG4Abk3w7yS49L3+7q8AkSZKkUdfPCZInAi+uqiXAqcB5SZ7avpbOIpMkSZJGXD/TSLasqjUAVXVWkuuA/5PkKJob3EiSJEmaQD/F9p1JHj42X7uq1iR5PvAN4DGdRidJkiSNsH6mkRwF7NjbUFUbgOcAx3cRlCRJkrQQ9HPpv3+epP0W4AMDj0iSJElaIPq5GsmDk/xtks8med241/53d6FJkiRJo62faSSfprnqyJeBg5J8OclW7WtPnXw1SZIkaXHrp9h+TFUdVVVfrar9gSuAC5Js33FskiRJ0kjr52okWyXZrKruBqiqD7Q3ubkIeFCn0UmSJEkjrJ8j218HntfbUFWnAe8C7ugiKEmSJGkhmLbYrqr3THRFkqr6VlUtG1tOcsigg5MkSZJGWT9Htvv19gFuS5IkSRp5gyy2M8BtSZIkSSNvkMV2DXBbkiRJ0sjzyLYkSZLUkUEW2xdP1Jhk3yTXJ1mX5KjJVk7y6iSVZMUAY5IkSZKGpu9iO8n2ST6W5Ioklyf5SO+NbarqiAnW2Rw4BdgPWA4cnGT5BP22AY4EvjubNyFJkiTNRzM5sn0GcBPwKuDVwEbgi9OsszewrqrWV9Ud7TYOmKDf+4ETgdtmEI8kSZI0r82k2H5oVb2/qn7UPv4G2G6adXYCbuhZ3tC23SPJnsDOVfWNqTaU5PAkq5Os3rhx4wzClobDnNUoMm81asxZzXczKbZXJTkoyWbt47XA2dOsM9FJk/dctSTJZsDJNHejnFJVnVpVK6pqxZIlS2YQtjQc5qxGkXmrUWPOar6bSbH9p8DnaW7RPjYl5J1J/jvJryZZZwOwc8/yUuDGnuVtgN2BC5P8GHgqsNKTJCVJkrQQbNFvx6raZhbbvwxYlmRX4D+Ag4DX9WzzFmCHseUkFwLvrqrVsxhLkiRJmlf6LrYBkuwEPKp3vaq6aLL+VXVXkiOAc4HNgU9V1ZokxwGrq2rl7MKWJEmS5r++i+0kJwAHAmuB37bNBUxabANU1TnAOePajpmk7z79xiNJkiTNdzM5sv0K4Peq6vaugpEkSZIWkpmcILke+B9dBSJJkiQtNNMe2U7yMZrpIrcCVyU5H7jn6HZVHdldeJIkSdLo6mcaydiVQS4HPKFRkiRJ6tO0xXZVndbPhpJ8uapetekhSZIkSQvDTOZsT+fRA9yWJEmSNPIGWWzX9F0kSZKkxWOQxbYkSZKkHoMstjPAbUmSJEkjb9piO8mSJMsnaH98kiU9Te8daGSSJEnSiOvnyPbHgCUTtC8FPjK2UFXfHlRQkiRJ0kLQT7H9hKr6zvjGqjoX2GPwIUmSJEkLQz/F9lS3aPf27ZIkSdIk+im2f5DkJeMbk+wHrB98SJIkSdLC0M/t2t8BnJ3ktTS3bAdYATwNeFlXgUmSJEmjrp9iu4A/BpYBu7dt3wH+EfhtR3FJkiRJI6+fYvvDwF9W1ad7G5OsaF97eReBSZIkSaOunznbu1TV1eMbq2o1sMvAI5IkSZIWiH6K7a2neO0BgwpEkiRJWmj6KbYvS/Km8Y1JDuPeEyYlSZIkjdPv1Ui+kuT13PdqJFsCr+wqMEmSJGnUTVtsV9XPgKcneS73Xo3k7Kq6oNPIJEmSpBHXz5FtAKpqFbCqw1gkSZKkBaWfOduSJEmSZqHzYjvJvkmuT7IuyVETvP7OJGuTXJ3k/CSP6jomSZIkaS50Wmwn2Rw4BdgPWA4cnGT5uG5XAiuqag/gLODELmOSJEmS5krXR7b3BtZV1fqqugM4Azigt0NVraqqW9vFS4GlHcckSZIkzYmui+2dgBt6lje0bZM5DPjmRC8kOTzJ6iSrN27cOMAQpW6YsxpF5q1GjTmr+a7rYjsTtNWEHZM30Fy/+6SJXq+qU6tqRVWtWLJkyQBDlLphzmoUmbcaNeas5ru+L/03SxuAnXuWlwI3ju+U5AXA0cBzqur2jmOSJEmS5kTXR7YvA5Yl2TXJlsBBwMreDkn2BP4e2L+qbuo4HkmSJGnOdFpsV9VdwBHAucB1wJlVtSbJcUn2b7udBDwI+FKSq5KsnGRzkiRJ0kjpehoJVXUOcM64tmN6nr+g6xgkSZKkYfAOkpIkSVJHLLYlSZKkjlhsS5IkSR2x2JYkSZI6YrEtSZIkdcRiW5IkSeqIxbYkSZLUEYttSZIkqSMW25IkSVJHLLYlSZKkjlhsS5IkSR2x2JYkSZI6YrEtSZIkdcRiW5IkSeqIxbYkSZLUEYttSZIkqSMW25IkSVJHLLYlSZKkjlhsS5IkSR2x2JYkSZI6YrEtSZIkdcRiW5IkSeqIxbYkSZLUkc6L7ST7Jrk+ybokR03w+lZJvti+/t0ku3QdkyRJkjQXOi22k2wOnALsBywHDk6yfFy3w4Cbq+qxwMnACV3GJEmSJM2Vro9s7w2sq6r1VXUHcAZwwLg+BwCntc/PAp6fJB3HJUmSJHWu62J7J+CGnuUNbduEfarqLuAWYPuO45IkSZI6t0XH25/oCHXNog9JDgcObxdvT3LtJsY2GzsAP19E4w5z7GGNe21V7T6IDc2TnIXF9ztcbOPCwsvbxfY7XIyf8ebsaI87zLFHLmdTdb+6dmCSPA04tqpe3C6/D6Cq/ranz7ltn0uSbAH8FFhSUwSWZHVVregscMcd+tgLbVx/h447imMvtp/lYht3mGObs6M97jDHHsVxu55GchmwLMmuSbYEDgJWjuuzEjikff5q4IKpCm1JkiRpVHQ6jaSq7kpyBHAusDnwqapak+Q4YHVVrQT+EfhsknXAL2kKckmSJGnkdT1nm6o6BzhnXNsxPc9vA14zw82eOoDQZmOxjTvMsRfauP4OHXcUx15sP8vFNu4wxzZnR3vcYY49cuN2OmdbkiRJWsy8XbskSZLUkXldbA/rVu99jPvOJGuTXJ3k/CSPmotxe/q9OkklGcjZuP2Mm+S17Xtek+Tzgxi3n7GT/G6SVUmubH/eLxnAmJ9KctNkl4dK46NtTFcn2WsG215UOdvP2D39FkTeDiNn2+12krfDytk+x/azdg7GHbWcbdddVJ+1iy1n+xm7i7ztLGeral4+aE6o/CHwaGBL4N+A5eP6/Bnwyfb5QcAX52jc5wIPbJ+/Za7GbfttA1wEXAqsmKP3uwy4EnhIu/ywOfwdnwq8pX2+HPjxAMZ9NrAXzTUzJ3r9JcA3aa4B/1Tgu+aseTvMnO0qb4eVs8PMW3N2tHN2mHlrzlof8bCKtgAACyRJREFUzDZn5/OR7WHd6n3acatqVVXd2i5eCizdxDH7Grf1fuBE4LYBjNnvuG8CTqmqmwGq6qY5HLuAB7fPtwVu3NRBq+oimivfTOYA4PRqXApsl+QRfWx6seVsX2O3FkreDiVnobO8HVbO9jW2n7Xm7CQW22ftYsvZfscemfpgPhfbw7rVez/j9jqMZi9nU007bpI9gZ2r6hsDGK/vcYHdgN2SXJzk0iT7zuHYxwJvSLKB5qo2bxvQ2Jsa12zXW0g529fYCyxv52vOwuzydlg52+/Yvfys7W7cYxmdnO13vYX0WbvYcrbfsY9lROqDzi/9twkGdqv3DsZtOiZvAFYAz9nEMacdN8lmwMnAoQMYq+9xW1vQfFW0D81e+r8k2b2q/msOxj4Y+ExV/V2aO5J+th377k0ce1Pjmu16Cylnpx17AebtfM3ZfmObzTpd5OyMtutnrTk7i/UW0mftYsvZfscemfpgPh/Z3gDs3LO8lPt/RXBPnzS3et+WqQ//D2pckrwAOBrYv6pu38Qx+xl3G2B34MIkP6aZK7RyACdB9Ptz/lpV3VlVPwKup/nj2lT9jH0YcCZAVV0CbA3sMICxNzWu2a63kHK2n7EXWt7O15ztN7bZrNNFzvY7tp+15uxs11tIn7WLLWf7HXt06oPpJnUP60Gzt7Qe2JV7J8c/flyft3LfEyDOnKNx96SZuL9sLt/vuP4XMpgTIPp5v/sCp7XPd6D5CmX7ORr7m8Ch7fPfb5M6Axh7FyY/AeKl3PcEiO+Zs+btsHO2i7wdVs4OM2/N2dHO2WHmrTlrfTDrnB1UMnTxoDnr8/tt4h7dth1Hs7cIzV7Ml4B1wPeAR8/RuP8M/Ay4qn2snItxx/UdyB9Tn+83wIeAtcA1wEFz+DteDlzc/qFdBbxoAGN+AfhP4E6avdTDgDcDb+55v6e0MV0zk5/zYsvZxZi3w8jZLvN2WDk7zLw1Z0c7Z4eZt+as9cFsctY7SEqSJEkdmc9ztiVJkqSRZrEtSZIkdcRiW5IkSeqIxbYkSZLUEYttSZIkqSMW25IkSVJHLLYHIMmvxy0fmuTj7fNjk/xHkquSXJtk/ym285kkr97EWB6Y5Owk/55kTZLje157Z5K1Sa5Ocn6SR/W8dkiSH7SPQ3ran5TkmiTrknw0yUS3Kp0upgsnupNVkv2THDWb96nZM19nFecuSa4dxLY0e0kenuSMJD9sc+OcJLu1j3Pa3/t1Sc5MsuMk27gn3zcxlpPavL06yVeSbNe2vzDJ5W0eXp7keT3rTJifSR6a5Lw2n89L8pBZxHNskndP0P7IJGdtynvV7Jivs4rzx0nm4u6lc8pie26cXFVPBF4DfCpJ1z/3D1bV42juZPWMJPu17VfSXIB9D+As4ERo/nCAvwKeAuwN/FXPH88ngMNpbr+6jOZuUQNRVSur6vjpe2qOma+ad9r/6L8CXFhVj6mq5cBfAjsCZwOfqKrHVtXv0+TBko5DOg/Yvc3P7wPva9t/Dry8qp4AHAJ8tmedyfLzKOD8qloGnN8uD0RV3VhVm7RTrJkzX9XLYnsOVdV1wF00tzSdzLOT/GuS9WNHDZPsk+Sidm90bZJPTlYAVdWtVbWqfX4HcAWwtF1eVVW3tl0vHWsHXgycV1W/rKqbaf4o903yCODBVXVJNXc/Oh14xWSBJ3likkt79px793bf0L6va5Ps3fYfyB67urEI8vXI3Hvk/Iy27T5HB9t83aVd3CLJaW3/s5I8cIqfiwbvucCdVfXJsYaquoqmCLikqr7e076qqqb6JuKRSb7VHpk7cawxya+T/F2SK9J8mzJpAVRV366qu9rFe/Kzqq6sqhvb9jXA1km2miY/DwBOa5+fxtR5+9AkX23z8NIke/S8/D+TXNC+rze1/f1WZjjM1ybG56T5pvSqJFcm2ab9P+IbPX0+nuTQntX+Isn32sdjp/i5jAyL7cF4QE8yXUVzO9H7SfIU4G5g4xTbegTwTOBlQO9R372BdwFPAB4D/MF0QbVfE72cZs9zvMOAb7bPdwJu6HltQ9u2U/t8fPtkTgfe2+45X0Nz9HHM71TV04E/Az41XezqlPnaOArYs83XN08XH/B7wKlt/1/R5LLmzu7A5TNon8oTgQNp8vPAJDu37b8DXFFVewHf4b6fYVP5Y+7Nz16vAq6sqtuZOj93rKr/BGj/fdgUY/11u809aI6Unt7z2h7AS4GnAcckeWSf8WvwzNfGu4G3tt+WPgv4TR/x/aqq9gY+Dny4j/7znsX2YPymqp449gCOGff6n7dFzQeBA9u9xMl8tarurqq1NF83jfleVa2vqt8CX6ApcCaVZIu230erav24194ArABOGmuaYBM1RftE420LbFdV32mbTgOe3dPlCwBVdRHw4Law0nAs+nxtXQ38U7v9u6boN+aGqrq4ff45pnlPmtfOr6pbquo2YC0wdj7A3cAX2+d9/Y6THE2TP/80rv3xwAnAn441TbD6VPk5mWfSftVfVRcA27efvwBfq6rfVNXPgVU0O70afaOcrxcDH0pyJE2N0M9n7Rd6/n3aLMacdyy258bJbWHzrKr6l2n63t7zvDfZxyf5dEl/KvCDqrrPXmGSFwBHA/u3e6/Q7LHu3NNtKXBj2750gvbZmGn8Gp7Fkq8vBU4BngRc3hb8d3Hfz8Wtp3gP5vDcWkPzu+q3fSq9eftbYItJ+k35O05zcu7LgNf37pQmWUozX/eNVfXDtnmq/PxZ+7U97b83TTXsFHGao/OH+QpUc17WnwAPAC5N8jim/pwd/z4WRA5bbI+OvZPs2s59PRD4v5N1TPI3wLbAO8a17wn8PU3h0vvHcS7woiQPaedZvwg4t/166L+TPDVJgDcCX5tozKq6Bbg5ybPapj+k+VprzIFtDM8Ebmn7a+Ga1/naxrVzO1/8PcB2wIOAHwN7tX32AnbtWe13k4wdZTl4qvekTlwAbDU2FxkgyZOBdcDTk7y0p33fJE+YxRibAWMnE76OqfN2X+C9NPl5a0/7djQnwL2v55sQpsnPlTQnp9H+O2Heti4CXt+OtQ/w86r6VfvaAUm2TrI9sA9w2XRvWJ0xX5vtP6aqrqmqE4DVwOOAnwDL27nh2wLPH7fagT3/XjLZtkeJxfbouIRmTuy1wI9o9kLvp91DPRpYDlzRzsv9k/blk2gKii+17SsBquqXwPtpPpgvA45r2wDeAvwDzQfED5l4nteYQ4CTklxNM8esdy7wzUn+FfgkzfzbMQtir1X3M9/zdXPgc0muobnqyclV9V/Al4GHttNo3kJz1v6Y64BD2vx+KM2Z+poj7ZG4VwIvTHMptTXAsTRH214GvC3NCWRrgUOZ+ujwZP4f8PgklwPPY5LzGVofB7YBzmvzc+xEuCOAxwL/q+fciLE5rZPl5/Ht+/oB8ELue/7DeMcCK9o8PJ57ix6A79EUTpcC7+858c3P2Tlmvt7jHWlONP83mvna36yqG4Azaafy0XwG99oqyXeBtwN/PvWPYDRk6umYmg/aoxfvrqqXDTuWQUryLpqznfs9qUMjYKHmqxa+JL+uqgcNO45BSvIk4ENV9Zxhx6LBWoj5ulB5ZFtDkeTNNHvznxtyKJK0IKW5mdgXgI8MOxZpMfPI9hC0ZwO/Zlzzl6rqAzPczneBrcY1/2FVXbMp8fUx7inAM8Y1f6SqPt3luBoO81WjKMmLaa6u0OtHVfXKGW5nKPmT5I9ovkbvdXFVvbXLcTUc5uvCZrEtSZIkdcRpJJIkSVJHLLYlSZKkjlhsS5IkSR2x2JYkSZI6YrEtSZIkdeT/Az7Sqvqr+rh/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "policies = experiments['policy']\n",
    "for i, policy in enumerate(np.unique(policies)):\n",
    "    experiments.loc[policies==policy, 'policy'] = str(i)\n",
    "\n",
    "data = pd.DataFrame(outcomes)\n",
    "data['policy'] = policies\n",
    "\n",
    "\n",
    "sns.pairplot(data, hue='policy', vars=list(outcomes.keys()))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<mayors.Reactive object at 0x000001D502353A60>,\n",
       "       <mayors.Sentiment object at 0x000001D502353A90>,\n",
       "       <mayors.Economicus object at 0x000001D502353CD0>,\n",
       "       <mayors.Lawkeeper object at 0x000001D502353E80>], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiments.Mayor.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'RCP'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\urban-SETP\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2645\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2646\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2647\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'RCP'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-14-e4a723279a16>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mexperiments2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexperiments\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mexperiments2\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'SLR'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m  \u001b[0mexperiments2\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'RCP'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"_\"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mexperiments2\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'collapse'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"_\"\u001b[0m  \u001b[1;33m+\u001b[0m \u001b[0mexperiments2\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'PDF'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mexperiments2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\urban-SETP\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2798\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2799\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2800\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2801\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2802\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\envs\\urban-SETP\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2646\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2647\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2648\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2649\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2650\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'RCP'"
     ]
    }
   ],
   "source": [
    "experiments2 = experiments.copy()\n",
    "experiments2['SLR']=  experiments2['RCP'] + \"_\" + experiments2['collapse'].map(str) + \"_\"  + experiments2['PDF'].map(str)\n",
    "experiments2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        RCP85_True_50\n",
       "1        RCP85_True_17\n",
       "2       RCP26_False_17\n",
       "3       RCP26_False_17\n",
       "4        RCP85_True_17\n",
       "             ...      \n",
       "3995     RCP26_True_17\n",
       "3996    RCP26_False_50\n",
       "3997    RCP26_False_17\n",
       "3998    RCP85_False_50\n",
       "3999     RCP26_True_50\n",
       "Name: slr_scenarios, Length: 4000, dtype: object"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slr_scenarios = experiments2['SLR']\n",
    "#unique = np.unique(slr_scenarios)\n",
    "#for i, slr in enumerate(unique):\n",
    "#     experiments2.loc[slr_scenarios==slr, 'SLR'] = str(i) #replace  by integer\n",
    "data = pd.DataFrame(outcomes)\n",
    "data['slr_scenarios'] = slr_scenarios\n",
    "#data['slr_scenarios']\n",
    "data.iloc[:,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.sort_values(by='slr_scenarios',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x13262343040>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 836x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data, hue='slr_scenarios',vars=list(outcomes.keys()),diag_kind='hist',palette='Paired')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, policy in enumerate(np.unique(policies)):\n",
    "    experiments.loc[policies==policy, 'policy'] = str(i)\n",
    "\n",
    "data = pd.DataFrame(outcomes)\n",
    "data['policy'] = policies\n",
    "\n",
    "\n",
    "sns.pairplot(data, hue='policy', vars=list(outcomes.keys()))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[MainProcess/INFO] no time dimension found in results\n"
     ]
    },
    {
     "data": {
      "image/png": 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01Vc51cMoIvtEZLuIvCciW0KPVYjIn0Tkg9DPcr3LSUQEhNYqtNijfuYPBc1kQeUlt6P2rgZUXnI7NJMFQP4NtSqxoNcSAMtnMzU+UbLCiThWbffi/lddsBiBVVf2Pqd+8Lo7q8+r2DE9fizM1dgxfGRt1OccPrJW7yKlSP7XW+k65nKxh3GaUqq12+/3AtiolPqRiNwb+v0efYpGRBSU7wvbi2aAfdJFUXNB7JMuQseWdXoXLeWYGp9ocE64g0lu7KbgxeyM/3HEPKc+PJa9F+7xYnrA1Rk3Fna4vTkXO4aPrMXhY51Ri9sfblyG4SNrcehAk97FG5RCqLe4rEZ8XwSwIvT/FQDm6FgWIiIA+b+wvfK60bXjVVTMuBW1dzWgYsat6NrxKpQ3i6+EBshujp2m3G7WqUBEOUYphcOdCh8eUzi/1oAJVVrccypbz6u4MV0zxI2FuRg7Pm5ui/k5P25u07tog1YI9Va6jrlcazAqABtE5B0RuSX02FCl1McAEPpZE+uFInKLiGwRkS0tLS0ZKi5RfuN5FV++LxAsZiuqLr8z6qKi6vI7IWar3kVLOb8fuHmdE5v2+uD1K2za68PN65zw+/t+7UDwvKJ8U15ZBTHbIkNT7/+cBfNf7H1OGQTo8qSnDIM9r+LGdK87bizMdOxIhXyuuwqh3krXMZdrDcbzlFJTAFwK4BsickGyL1RK/VIpNVUpNbW6ujp9JSQqILlwXlmstqi5GBarLSPbzfcFgpXHFeeiIn+yzXV3zKFQ/5wDliUdqH/OgWOO9A2dy4XziihZ5ZVVOOEOQHmDiTfmNTgxrFjwgwstuO4FByoe7sD03zqgCXDbp8xpm983kPOqvLIqUn/Ej+nWhLEwk7EjFfK57sqGeuvr37gNZvsQiGgw24fg69+4LeXbSMcxl1MNRqXUR6GfzQDWAPgUgCMicgoAhH4261dCIsomFqsNPqMVNfWLUbtwDWrqF8NntGak0ag8TrS+9Bhc+7dB+X1w7d+G1pcey4u7tAAgJkvsiwpTlmdzGACXHygrEjR8yQb34hI0fMmGsiKBK4t7CYj0pImg1CrQRBDoaoPqOgZT5UjUXPsgjnQqzHnWgZvWuqCJ4MVrg+fVb2YXYeV2b9bM7ws3dMP1R/yY7oobC3MxduRz3aV3vfX1b9yGX65YiYrZ30HtwjWomP0d/HLFypQ2GtN1zOVMg1FE7CJSEv4/gJkAdgB4EcBNoafdBCB/Zq4S0aB4lcSci+FVkvZti7kI9okX4ugrT6Hpx/U4+spTsE+8MC/u0gKACvjR+odHoy8q/vAoVCCLr4QGqMQCrJgTnXVuxZzsz3RIpAdNBEOLBWuvCV6wrr3GhqHFAl9rE9peegw11z6IstvXosQCPFOfvRlE2zudUfVH/JhujRsLczF25HPdpXe99eunf4uqHtckVbMW4ddP/zZl20jXMZdLWVKHAlgjIkCw3CuVUi+LyNsAnhOR+QCaAFytYxmJKIvoNRdDE0GJBeh461mUWACfAipm3IqWxmV5cZc2SBDwutH28hPwtR+BsXQoAl43gPQ3xjONWVKJkldiAVaGGoIAMG2MESvrizDnWQfMF9wYzFDZ2gRBdp9XPesP5XFGxfSijiZ0vNUEBQ0BgylmLMy12GEyaCgxqW6fU/Kq7pI49ZZkqN7yOjpjXpN4HZ0p20a6jrmcaTAqpT4EcGaMx9sATM98iYgo24m5CC3rfgT3gR0IuLqgWe2wjDwjrXdKw3fXV9afXDR3XoMTR1bfB+OwT0J582SOn98LBHosdhzwBx/PM+Gsc+ELYCD7Mx0S6SXRBevI08/D0ZefwLBigcOrYp5X2dL71rP+EKiYsf1wZyAY+7oLxcJcih0mg4YqG7Cy3pa3dVdpaQnau3p8joAfpaUlGdm+yVYc85rEZCtO2TbSdczlzJBUIqL+Uh4n3B/9E9Vz7kPtwjWonnMf3B/9M613SrvfXQ8PB1lZHxwO4u9oy5ukMGIuQsmUKyDGYKNRjKbg73kwbKmnXMx0SKSXcCbU7sINwaYfz0Wx5sLK+iJABRPgdD+v5jU44UpTltT+6ll/xIvtQyyIGwtzKXbYTCrv6672TmfMfdXemZneU5Mg5jWJKYUdnOk65nKmh5GIqL+6r5sFIDKHsblhSdq2mejues3cRWhueCBt284k5XGi7LzrUH7BDScf8/tw4q3ndCxVerj8QLtL4SuNTuw9rjCmTNDuUlmduIJIL+FMqD174jrcwKh7GtH00BU4v9aATi9w/WQjrnzOgeMuoMwKXHuGCU9tyY5RCj3rj4Q9p3FioQu5EzsKoe7Su95yev2oqf9OWq9JvEpiHnODzd3ABiMR5S095jCG767HGmaVT8tOhFOvhys+IH9Sr/cUUAqaCBQUlAJaHAod7uDjRBQtfL7MedaBDncwJp5wA7UL1wI4GSMnVGm4eqIJT15+MmZs2uvDM9uzo8HYs/5IFNvjxcKAuytnYkch1F1611uZuCbx+gMwGTQox8ljzuEVeP2BQb0vG4xElLf0mMPY4QZmrXTAZATaXUCpFfD6AIcPKAqt2ZUPlMeJIy8sgWgC5XZALDaogAK8uZ8YIZZsvMAjylYKgg5VBAUnOlQRYPBFLtS7xI55DQ7MP9uEOasd0LToWNnl07v0Qb3nMMaP7ScSxMJciR0Or8T5fJI3dZfe9VamrkkG2ziMhQ1GIspb3eegWEZMhPvgzoxkeyux9k6M4OhUoW3n/l1aAIAYoJktqJ61KOq7DfiyZAISEelCRINmL+sRGx5Gc8NS1NTfj+G3PYMDj8zBsjc9qLD3jpVdndnRwOpZfxx4ZE7M2N7VqeLGQhENSqX+4j0d/AGFEluiuitLU7v2h871lvK6Y1+TeLP/u2XSGyLKW93noHRfhzGdPYyJkt4E3I60bTfTxGSK/d2aTH2/mIjylpitMWLD3YAAret/jKZH5qLEAljN8ZPIZIOe9UeipDfxYqFmL4NIblxq9113ZUdDfjD0rrfEZImz/Sw56BPIjaOYiGgA9JjD2OEG/nenFxUPnYD2gxOoeOgE/nenFx1uYOhV38uLYT0AoDyuON9tnvSgElG/GU2W+LHB7cCIb6yAhBoeHW7gyuccuP0PJ+NxNq1R2LP+SBTb48XCdN+gTKV8rLsm1dVBs9iDvd4Wu+71ll5rQ6cCh6QSUV4S0XSZ4G7SgBXveWEORVeF4O8mLb8aVHonDyCi7BPQjBB4cPyJOehwA1YDYDQAnR5giAVoeugKDLEAHR5gYrWGOeONWP6uF4ATT1xWlGXrMFqjYlyi2B4vFuZKYwDIv7prUl0ddn2wFwZbKXxeFwy2UvgRf19lQi7Xm+xhJKL8ZDBBeZxofekxuPZvg/L74Nq/Da0vPZbeClwBQ6yChi/Z4F5cgoYv2TDEKoAKVwy5d5c2FuVxoqVxWdR3m4n5oUSUvZTHgaHFgrXX2LBijhXVxYJ11wZj4e2fMmNosWDNNTa47i/BE5dasXK7F/PPNuGZ7d7IOozZ08Poiqo/EsX2eLEwVxoDAPKu7tq5aw80iw2Vl9yO2rsaUHnJ7YDBiJYXH+69rzI0h1CXa5IUEZUj2ZtSaerUqWrLli16F4NIVyLyjlJqaqreL9vOK81cBIO9HKbqUb0ykrn2b0PA3ZWW7ZZagxdL3VOTb9rrw5xnHeg0liPQdTxnkiD0JdyLqzzOyM98+WwDle/nFVEi3ePfGU924olLrZFY2PN3IBgbb3/JhZ0tAZRYEFmGo90VfW2qx3mlWWywjjozUn8Msai4sf2EWyBmK5THFYmFQ69dGkyokiMxP9/qLs1chJorvxvVm+favw1H/vd7kNANZTEXQXndUIHMpObVLHZYR03O6DVJX5I9tzgklYjykvK6YR19NhwfvJXRLKmJFj+21p4O1/5tadt2puXSxQMRpV/3+LerNRAVC3v+DgSfu6s12Fhce40N8xqcOJI1WVJdvbKkxovt4VjYPcFNc8OSnLqJlm91l/K6Y84XhN+nWzZvvTK3pwKHpBJRXhKzFa597+qSJfXNJn/UY+F5Oa6m7TlRMRAR9ZeIISr+TajSomJhz9+BUGw0A6NLo7NyZoOe2V4TxfYwpQIIuLuifuaKvuquU2oqdSrZwITnoHan99BaPTK3pwobjESUl5THBd/xw7pkSZ3X4MSmvT54/SpqXo4YuOQEEeUfEQM0e2lU/LvnPDNuXncyFs4Zb+wVG697wQmTBvzjayUAsi1LqqtXltR4sT0f9FV3lZWV6l3EfikyxZiv+OLDKDLpN7iSWVKJiLKNwQTRDBnPSFZiAWxGhTnPOiLzcaqKgj+LZt+N5oYlads2EVGm9JzDbDn1dNTUL0bTQ1dE4p/VAHxxtQOdHuC0csGZNRqufM6B4y6g1Ao4PID7v4ZE3jObs6Qmiu35oK+6a1eO1V0OlwcI+CNDg4PzFT1waIa+X5wmzJKaQSJiEJF3RWR96PcxIvI3EflARJ4VEbPeZSSiLOD3AaKh9Q+P9siI9nDaexgdvmDyAPfiEqy9xgaHTyJrdeXCnUQiokRENGj2MtTUL0btwjWoqV8M90f/xNE/PYXae9aj/I71GLlwLZx+QYdHAABdXuCe8y04srAEG2+0wWoUeAPo1aN1Ikt67JTHFZX9NF5sz5byDlbe1V1+LzRbcdQxqtmKAb9XtyLlcnbxtPQwikgFAKWUOpaGt/8WgF0AwrekHgLwqFJqtYg8BWA+gJ+nYbtElEPEbEFN/f3wdx3D0VeegrftIIylQxFwOdI6rySgFDSRqLu0HW6g9p71cO3flhN3EomIEhEoFPuOoXn1fZEYV3Ptg2hZ+yAqLr4VQLDnBKJBzBYoTzCZTc+4CCDqsRNuZE2MFLMVxZNnRuoPU1UtjrQ29foMYrbpXdSUyLe6S8wWVM+6O9KbF5wveDeaGx7QrUxKBSCiRfd65khipJQ1GEWkFsDDAKYDOB58SIYAeBXAvUqpfSnYxggAlwNYCuDbIiIALgIwL/SUFQC+DzYYiQpeeP6JGIywT/x88DG/D02PzIWIAUr5+3iHgdFEUGMXrLqyCOfXGvBmkx/XveBE00NXABZ7TtxJJCLqyWiyIKAZoTwODCsWrKw/GePmNThxZPV9UBAovw/ugzvRvPa/odlKUD3r7kiW0Z56xsp5DU4c7syOGKk8LpSddx3KL7gBAND00BUxY/uRLofOJU0Nm9WcsO6CTplFB6rnHFQg3FPq0qlEwIyZM6HZy1A9a1FUltQZM2filQ0bdCtXMlI5JPVZAGsADFNKjVVKfRLAKQDWAlidom38BMDdAMJN8UoAx5VS4QVUDgIYHuuFInKLiGwRkS0tLS0pKg5RYcvm8ypRhrR0ZkkrNgOrrizCtDFGmAzBzH+rrixCsRnQzFaAiW+oD9l8XlFhMposUBY7auoXY4gFWFkfHePC2U3FXISmR+ai7eUnABWI9PAceGQOhhZHD3ccWixo7lK93mdImuYE9ve86lmHxIvtJXkyEcqovAnrLiW5NYstG7OkbnpjM8RoQXPDEjQ9MhfNDUsgRgs2vbFZtzIlK5V7v0op9azqdtteKeVXSq1GsGE3KCJyBYBmpdQ73R+O8dSYC/gopX6plJqqlJpaXV092OIQEbL7vFIeF1pfeixqrkDrS49BeVxp7eXr9MRey6rTA1RdfhdEy61KlzIvm88rKkwBzRhZDiDRen3h2Hrqgp9DuR2RHp6SBI3MWO+TDv09r3rWIYliez7It7or0TWAXgJuJ5TPHTWvUvncCLizo1c9kVTu/XdE5EkR+bSInBr692kReRLAuyl4//MAzBaRfQj2WF6EYI9jmYiEh9aOAPBRCrZFRDlOzFbYJ16Io688haYf1+PoK0/BPvFCGIor0joXw26OvZaV3RwaDuPNkwwJRFQwui8HkGi9PqUCkUyQmtUe6eFJ1MiM9T7ZoGcdkii254N8q7viXQNwHcaBSWWD8UYA2wH8AMAfAWxAcD7hDgA3DPbNlVLfUUqNUEqNBnAtgFeVUtcD2ATgqtDTbgKwbrDbIqLcpzwudG77Iypm3IrauxpQMeNWdG77I4yVI9Paw+j3I2rtsU17fbh5nRN+f2g4jEm/yoqIaAG9PWkAACAASURBVCDCjUAg/np94Wyh4UyQlpFnRDJCJmpkZuu6hj3rkESxPR/kW90V7xpAzx5GrsMIQCnlQTDZTKYTztwDYLWILEGwJ3N5hrdPRFlIzEUonvyFkxnuKkegePIX0LF1fVrv5rn8QLtL4SuNTuw9rjCmTNDuUnD5AfeLD+fcXVoiIi3gQ0vjMlTPWoSRC9fiwCNz4mYLDWeCdO3fBuVxhjJCBhuZPRPldLiBuc86cKLb+yhDdnTZ9axDRMWP7fnAE0hcd1104fl6F7Ff4l4DvNOoa5lydR3GlC6rISJfQHBY6CtKqf3dHv8/SqnfpGo7SqnXALwW+v+HAD6VqvcmovygPM6oDHdAMEvqibeeS+t2w6nJFRSUAlocKpQq3oaAoz0n0mcTEXXn87phNFkiywGYKmthPvca1IYyUJeFMlCHdY9zIhpq4zQySyxAe2gpjRNuFyovvxNtv380458vllh1SNNDV/SK7UrFTJ2Rc3wBBaMWp+5ydmR9Fs+e9LoGSGTCuNOwO3TjpXuW1AnjTtOtTMlK2ZBUEXkQwP0A6gC8KiK3d/vzbanaDhFRMroPoQoLZ0jLdKNNgNCiwaWQHMs0R0SFSUSDiKDUKtBEYDd4oDwOiLkIFRffGlmuCEjcSxKOxbX3rEf5HetRe896FM19EDAVocONYFZUjxNDLAptv/+/us4x6y5WHdLrOUBexfRAj7ZvpO4qKoHFmlvrTca/BtCvN+/97dth9LmisqQafS68v327bmVKViqP8lkALlJK3QHgHACXikj4NlGsbKZERGkTnkfTPUNaS+MyKH8grRW8JhI7ffzq+1A9++6cmwdCRIVHRINAYViPWDasWKA8jtixNc48rPBw1ujnPwx4nb1iZfD9s2M+V886pOmhK2LGdomdnD/n9FV3eXNscIzyumMfpzpOC5lUVwef0RqVJdVntGJSXZ1uZUpWKoekGsPrISqljovILAC/FJH/BZAdA9KJqGAoFYBoRjSvWQrldkCz2mGfeCFs485Fc8OStG23e/p4AJH08XOedYQyzek34Z6IKBliLkKJOOLGshNdxyPDU8VcBOVxxh25cXI46wNQHldwlIfHFbWeY8/3zwZiLoLl1NPRsvZBBFxdWV/ewcq3uksFfMFrgO7HqdcNFfD1/eI02bXnQ9TUL47MYQxnSd2VxmuSVEllg/HfIvJ5pdTrQHANRgDzQ8lorkzhdoiIkqMCGHn7MxDDyVCn/L603sHucAPfesmJ/SdUZI7OqCGCDjdQdHAnxJQlOeOJiOJQHic6gKg5h6NLBVtuKQ4mpunnsH5ft16d6spyeLqcCWNlNlAeJ6q/eG+k/mh66IqsLu9g5WPdpWfjMBZmSQ26OtaDSqnFIpLpzKlERBBzEVrW/QjuAzsQcHVBs9phGXlGWucwaABanMDaa2xR2QA1AK1/eBQqkGPjeoio4AgUhhZLr6ym43/aMah1EsPDHp+7xoaLf+uIGyuzQc/6w4D4sT0fsO5KPz2uSVIllctqJGoel6RqO0REyVIeJ9wf/RPVc+6LykiWzrt5dgsw/2wTbn/JhV2tAUyo0jD/bBOe+LsHHW4H4PembdtERKlQkiCOnUiiR00TicqE2uEGiuwlUcMeE8XKbNCz/jj+xJysLu9g9VV3lZXm3qX8jJkzsemNzQi4ndAsRZh2wXm6ZnvV45okVVK6rEYCGwDUZmhbREQQMUDMRaietajXfIF0zmHscAMrt3uxfPbJO/PzXwwOv1JwcFkNIsp6CeNYH8tIhHsRe/ZOHunsAACcX2vocxvZQMzWqPoj28s7WIk+X2mFFcfaWvUuYr/MmDkTm/7yNqrnLo40zjY1PowZM2fq1mjU45okVVK5rMbjcf49AaAsVdshIkqGmC26zBcosQDLZwfvoJsMgmljjFg+uwgllv7P+yEiyrRw72C8OFZeWRX3tSKGqF7E8GtX1gdfW2IB3mwKrnSfaBvZQHlcUfVHtpd3sBJ9vlxrLALAq6+/iepZd8M6ajLEYAw1zu7Gq6+/qVuZOIcx6MsA7gIQ617LdSncDhFRn4LZ+ILrMIXv5gEn12JMlw73yTvoYefXGvLmLjQR5a9w7+CRThU/jsWZgSRigGYvRUfXsbivFZMV8xqcsBlV1sdKMVuj6o9sL+9g5dvnUx4Xuna/Gclyq1ntsI2/AMqjX7bXnscUkP5rklRJ5VzdtwHsUEqt6PkPQEcKt0NE1DfRAAha//Bor/W/lCd9NWD3O+hhbzb58+YuNBHlr3Dv4MRqLW4ci9cbEh7CmSgGSlEJmt0WtDqB08olq2Ol8kSv45fvsT3vPp9ocHzwFqrn3IfahWtQPec+OD54K3RtoI+ex9TJNUyzv1Weym/tKgDvxfqDUmpMCrdDRNQnMZlRU38/yj9/M46+8hSaflyPtpefgPL5Iab0LQ3b4Qbmv+jEpr0+eP0Km/b68mqeCxHlr3Av0/2fs/SKY/MagnEsXkbH8HC7DjcwryH2awMuBzR7OTrcwPc+33sb172QPbFSzBYUT54ZqT/yPbbn2+cTkzkyX/DkkNRFaa3/kylTeG3PpkfmomXtg7CcerquZUpWKrOkHk3VexERDVZ4/okYjLBP/HzwMb8PTT+uB/pI2jAYJRZgXl10prl5dfmTSY+I8le4l+m6OhMAROJYsRk44QY0ezmGWGL3NYSnANTesx5ND10RtYZjhxsIKBXJWlliAUYM0bD0ImtkG2PKBCfcCgqSyY8cl/K4UHbedSi/4AYAwLGfXJHXsT3f6q6ec1CB8HxB/YakKq87am1PIHRd8shc3cqUrFQmvRkiIv8tIv8jIvN6/O3JVG2HiCgZ4YuX7twHd8JYOjTtcxiXv+vFE5da4bq/BE9casXyd705e5eWiApH997BqyYa8cSlVtTYJdSzaMMQixY3AYryuCLD7UYuXIuiuQ9C7OU44dYQCN2ke2XDBvhdXZHtDCsWvPtVO165wYYuL+DyIWvmc4Xnm4Xle2x3+Q0xP5/Lb+j7xVko3jWAnmse9jymgNyZwyh9pUdO+o1EXgDwAYC/Avg/ALwA5iml3CKyVSk1JSUbSoGpU6eqLVu26F0MIl2JyDtKqamper9sO69ENGj2UlTPujuSUrv1D48i4HJAedK7vEWsNcgCaezVpOyR7+cV5TcRDQLVK37BYody9x03g8sZWaE8Toi5CMrjglL+Xs+zWG3wup0xt2NCAG6Xo8f7Zv68CtYhZaietShShzSvvi+vY7vFZITV4I98PpffALfXp3exBiSchKn7/mtpXIZAV3vMYzITJtXVYffeg1HXJS2ND2P8mBF4f/t2XcqU7LmVyiypn1BKXRn6/1oRuR/AqyIyOxVvLiJWAG8AsCBY7ueVUt8TkTEAVgOoALAVwA1KqdzsPyeilFEqABENzQ0PBDOmmixQgQDg96Z9eYt8uoAgosIRjpsdASsUXOgIWKAMASCJxmLw9cldiLtdDlisNnQEELUdc4zGol5O1iFLTjaAIWh35e/ySLnaOIxFKT9EDNH7L84NjEx5f/t2TKqrw65uZZow7jTdGov9kcoGo0VENBWKKEqppSJyEMFGXnEK3t8N4CKlVKeImAC8KSIvAfg2gEeVUqtF5CkA8wH8PAXbI6Icx3UPiYj6J1NxM1sahomwDsltejYO48mFxmEsqcyS2gjgou4PhJbUuAvAoHv8VFBn6FdT6J8KbfP50OMrAMwZ7LaIKD+IaNAs9qifRESFJltj4fCRtVHlGj6yVu8iRcnW742Sk+3HVy5JZZbUu+M8/jKAseHfReSmUEOy30TEAOAdAJ8E8DMA/wZwXCkV7kM/CGB4nNfeAuAWAKit5QFDlArZfF7Fmn/S0rgMIhrvGlNWy+bzinJPtsbC4SNrcfhYJ2rqF0fKdbhxGYaPrMWhA00p315/z6ts/d4oOZk+vvKdHrdKvjXQFyql/EqpswCMAPApABNiPS3Oa3+plJqqlJpaXV090CIQUTfZfF6JuSj2Gkw6ZkgjSkY2n1eUe7I1Fn7c3BazXB83t6Vle/09r7L1e6PkZPr4ynd6NBgHvcCOUuo4gNcAfAZAmYiEe0pHAPhosO9PRLkvvIh0d8E1mJw6lYiIKPMSxULNYofRZMm6cmWDbC8fJcb9l1p6NBgHlD5QRKpFpCz0/yIAMwDsArAJwFWhp90EYF0qCklEuS2X1zsiIkqVeOvRaVY7auoXQ+nUaMz2GJ3t5aPEuP9SK5d6GE8BsElEtgF4G8CflFLrAdwD4Nsi8i8AlQCWp6aYRJTLlNcbWURa+X1w7d+GlsZlUF6v3kUjIsoY5XHGjIX2iRdGhukFtFQmzU+yXFkeo7O9fJQY919qpTxCiMgYpdTeBI9tHsj7KqW2ATg7xuMfIjifkYjoJOWHbexn0bL2QQRcXdCsdtjGX4DO917Su2RERBnTcz1BzWqHfeKFqLj4VgA6DtPL9hid7eWjxLj/Uiodt5ReADClx2PPAzgHAJRSt6Vhm0REUcRcBPfhPVCBYDY7FQjAfXgPExYQUd4TMUDM1sji4IAg4O6CZrGjes59sI6aHHlucJhe5uLipLo67NrzIQCga+drsJx6OoZe8wAAwLV/Gxy7XstYWRJhHZLbNEvs/adZuP8GImUNRhEZD2ASgFIRqe/2pyEAOGCYiDJKeZzwd7RFpdRuaVzGCe9ElNdEDNDspTGWgzDAYDSipXFZr79pAV/fb5wCk+rqsHvvoV5x+fDqxSj77JfQ0rgM0y44LyNl6QvrkNxmNRrgirH/rEaD3kXLSamcw3g6gCsAlAGY1e3fFABfSeF2iIj6xJToRFSIxGyNE/us8HndEHcXmhuWoOmRuWhuWAJxd8HndWekbLv2fBizbJ6P/4mWNUsw7dypeGXDhoyUpS+sQ3Kb0+uPuf+cXr/eRctJKethVEqtA7BORD6rlHorVe9LRIVBRIOYiyJDqJTHOajFkZlSm4gKUV+xr3vjUDOYEDCaI/EXPg8C/vQlBeletoNPfhkBd1co5lsRcDuxa9futG27PzSDCSrgT/g9Dh9Zi4+b2yJ11ik1lVwQPov0tZwM91v/pCNL6lwRGSIiJhHZKCKtIvKfadgOEeUJEQ2avQw19YtRu3ANauoXQ7OXQWTgISpeKnneHSaifJZs7NMMJkhRSVTclaISaAZT2st28MkvQwV83bb9X9DsZfjo4CEMH1mbtu0nI/y9JPoeh4+sxeFjnVHf3eFjnbqXnU6Ktf+Ob14FzV7K/TYA6WgwzlRKnUBweOpBAOMALErDdogoT6Rj6E+8VPLsYSSifKY8rjixzxX9RKM5ZtyF0Zy2sk0YdxpaGpch4O6KO2z24+a2tG0/KaHvxTikJu73+HFzW8zy6152iggfa933X8fWRlTPupv7bQDSkSU1fGvqMgCrlFJHRQa69CIRFYJ0DB/tmUo+FcNciYiynVJ+iBh6xD4XlIqeu6XHsP33t2/HpLo67NzxftZOGQh/L6fO/yk+Wn5bt+/RCuVxh75fLWvLT0HhY21Xj2sA7reBSUcPY6OI7AYwFcBGEakG4OrjNURUwNI1fFSpQFRFIeaiQQ1zJSLKBUr5e8Q+a6/Yp9ew/fe3b0+4bb2nDXQvm6/zaPe/AFC9nhPGKQ/Z5/3t24PzZFUAAXdXVuy38soqaBZ7cCqOxY7yyqqMbXswUn7lpJS6F8BnAUxVSnkBdAH4Yqq3Q0T5I13DR9MxN5KIKNslFft8nphxFz5P2st3Sk1l3OGeKkMZW+MKfS/7/+81EIMh5ncYr/yn1FTqW3ZKSO/9Vl5ZhRPuQNQxdcIdyIlGY8qvmkTkRgSX07g+9P+rAMxM9XbopDVr1kBEsHt3+rOLJbutffv2YeXKlSnb7tq1a7Fz586Ez1m0aBHGjx+PyZMnY+7cuTh+/DgAoK2tDdOmTUNxcTFuu+22yPMdDgcuv/xyjB8/HpMmTcK9994b+Zvb7cY111yDT37yk/j0pz+Nffv2peyzUG9KBRDoOh6V6j3QdXzQw0eZFp2ICk15ZVVSsS/g90I5O6LirnJ29JkldcbMmTBYgz0kBqsdM2b2/xLv0IGmHjH/AQQcHRjy2ashJku/3y+VAn4vAl3HIaLifoeHDjTB6HNFfXdGn4vZNrOc3vutvdMZ85hq78z+IbHpuM3+H93+fQ7A9wHMTsN2KGTVqlU4//zzsXr16l5/8/tTu95Mom11l6jB6PP1f4HgZBqMF198MXbs2IFt27Zh3Lhx+O///m8AgNVqxQMPPIBHHnmk12sWLlyI3bt3491338XmzZvx0ksvAQCWL1+O8vJy/Otf/8Kdd96Je+65p99lpuSJGCAWGwy2UkAEBlspxGKDyOAW2OV8BSIqJOEejGRin4gBMJqj5jkm01jc9JctqJ4b7CGpnrsYm/6yJW6jsa/hdxUX3wpT1UgorxumylNhqhiue3wur6yCWGwJv8MZM2fCCy2qzvJCG1DjmTJH7/2Wy9ck6RiSenu3f18BcDaA9KXcKnCdnZ3YvHkzli9fHmnEvfbaa5g2bRrmzZuHuro6+P1+LFq0CP/xH/+ByZMn4xe/+EXktdOnT8eUKVNQV1eHdevW9Xtb8dx7773485//jLPOOguPPvoonn76aVx99dWYNWsWZs6ciddeew1XXHFF5Pm33XYbnn766chrJ06ciMmTJ2PhwoX4y1/+ghdffBGLFi3CWWedhX//+98xtzlz5kwYjcE8Tp/5zGdw8OBBAIDdbsf5558Pq9Ua9XybzYZp06YBAMxmM6ZMmRJ5zbp163DTTTcBAK666ips3LgRSqmEn5kGwWCEZrGh8pLbUXtXAyovuR2axQYYBpeXKxvmKxARZUq4B6Ov2Cdi6LW8gGYv7fMm3aY3NsfsIdn0xuZez+1z+J3BhON//h9UzLgVtXc1oGLGrTj+5/8B0risRzKOt3dAs9gSfocbX309Zp218dXXdSo1JUPv/ZbL1ySZmMjjADA2A9spSGvXrsUll1yCcePGoaKiAlu3bgUA/P3vf8fSpUuxc+dOLF++HKWlpXj77bfx9ttv41e/+hX27t0Lq9WKNWvWYOvWrdi0aRPuuuuuhI2ieNuK5Uc/+hE+97nP4b333sOdd94JAHjrrbewYsUKvPrqq3Ffd/ToUaxZswbvv/8+tm3bhsWLF+Pcc8/F7NmzsWzZMrz33nv4xCc+0ef38pvf/AaXXnppn88LO378OBobGzF9+nQAwKFDhzBy5EgAgNFoRGlpKdramHY5XUTTUHXZnVEXIVWX3QnRBheiuLQGERWScA+G8vsSxj4xW+Mua5FIwB27hyTg7h1T+xp+l664P1jhckEzxv0Os7XslJje+620uCjmMVVanP0NxpQvqyEijQinkQo2SCcCeC7V26GgVatW4Y477gAAXHvttVi1ahUuv/xyfOpTn8KYMWMAABs2bMC2bdvw/PPPAwDa29vxwQcfYMSIEbjvvvvwxhtvQNM0HDp0CEeOHMGwYcOS3taUKVOSLuvFF1+MioqKhM8ZMmQIrFYrFixYgMsvvzyqFzJZS5cuhdFoxPXXX5/U830+H6677jp885vfxGmnnQYAMRvOXB4mfZTXHXuYxiCTH3BpDSIqJOEejFELG7D/kfq4sW+gQ+M0S/D9raMmRx5zH9wJzdL7grevbaQr7g9WuFy131qJpsfm9VhWwxWpV7Kx7JSY3sfcsbZWlFdWRZ2XpcVFONbWmpHtD0Y61mHsPlHMB2C/UurgYN9UREYC+C2AYQACAH6plHpMRCoAPAtgNIB9AL6klDo22O3lgra2Nrz66qvYsWMHRAR+vx8igssuuwx2uz3yPKUUnnjiCXzhC1+Iev3TTz+NlpYWvPPOOzCZTBg9ejRcrtgroMTb1sMPP5x0Q6p7mYxGIwKBkxft4e0ajUb8/e9/x8aNG7F69Wr89Kc/Tdgj2dOKFSuwfv16bNy4Mely3XLLLRg7dmykMQwAI0aMwIEDBzBixAj4fD60t7f32dilgROzNeZFSF93u5PBxiERFYpwD0b1rEWovfM5uA/uREvjsmASF3MRJtXVRS1r0TvmJu7pmHbBedgUen/LiImR9592wXm9npvMNg48Pu9kg9bvw9Crv5+SuD8Y3euj2m8FczG49m9Dc8MDkfokUZ2lWeyYMO40vL99uy7lp/jSea2RrFxoHMaS0j5YCQ5+/y+l1Ouhf5tT0VgM8QG4Syk1AcBnAHxDRCYCuBfARqXUWAAbQ78XhOeffx433ngj9u/fj3379uHAgQMYM2YM3nzzzajnfeELX8DPf/5zeL3Byex79uxBV1cX2tvbUVNTA5PJhE2bNmH//v2D3lZYSUkJOjo64r7fqFGjsHPnTrjdbrS3t2Pjxo0AgvMk29vbcdlll+EnP/kJ3nvvvaTeDwBefvllPPTQQ3jxxRdhs9kSPjds8eLFaG9vx09+8pOox2fPno0VK1ZEPvtFF13EHsY0Uh4XWhof7jFM42EoD5dwJSJK1rG2VgyxaNGZTwMqModw995DmFRXF4q5sZe1SOSVDRsw7dypaFkTfP+WNUsw7dypeGXDhl7PTTT8zmK19V72w2rHkdX3A77EiXfSLan6yOeN8xxP1PdMWSbOftP7mMsFKe1hVEr5RcQhIqVKqfYUv/fHAD4O/b9DRHYBGI7gGo8Xhp62AsBrAAoipeWqVauiloIAgCuvvBI///nPo+b5LViwAPv27cOUKVOglEJ1dTXWrl2L66+/HrNmzcLUqVNx1llnYfz48f3e1sqVK/G5z32u1/MnT54Mo9GIM888EzfffDPKy8uj/j5y5Eh86UtfwuTJkzF27FicffbZAICOjg588YtfhMvlglIKjz76KIDgENivfOUrePzxx/H888/HnMd42223we124+KLLwYQTHzz1FNPAQBGjx6NEydOwOPxYO3atdiwYQOGDBmCpUuXYvz48ZGhtbfddhsWLFiA+fPn44YbbsAnP/lJVFRU9JnkhwZHzEWwnDoeLWsfRMDVBc1qh2XkGXDt36Z30YiIckq4B0Oz2FFTvzjSmxKeQ7irYQmU8kPE0GPIqgtK9Z1ZPVbjMF454g2/0yx21ITmN3YvW3PDkgF+6tQRcxECjq7o78brje59NZrj1lndv2fKMgn2GyUmqc78KCLPIdgD+CcAXeHHlVLfTOE2RgN4A8AZAJqUUmXd/nZMKVUe56UAgKlTp6otW7akqjhEOUlE3lFKTU3V+w3mvBLRULtwDaRbVlTl96HpkbkcUko5JZvOKyps2RxXE5UN6D2VIJPnVTLfW6LnjLqnMWu+Z4qWzeeEXpI9t9KRFuj3AP4LwQbdO6F/KavtRKQYwAsA7lBKnejH624RkS0isqWlpSVVxSEqaKk6r3I51TRRqrG+olTI5riaqGzpKl+y51Uy31tfz8mW75miZfM5ke3SkfSmTCn1WPcHRORbqXhjETEh2Fh8RinVEHr4iIicopT6WEROAdAc67VKqV8C+CUQvLOUivLko7a2tsjSEt1t3LgRlZWVvR7fvn07brjhhqjHLBYL/va3v6WtjN/4xjeweXP0mk/f+ta38OUvfzlt26TYUnVeKY8TR57/AcRghHI7gosm+32Az5OyshLlCtZXlCwRQyh7Z+9hpRPGnYbdMRLUTBh3mm7lLa+sQnunE8rjRHPDkuDwzS/eGymb8jgx8YxJadl2sufVhHGnYefq+yOZZcPZUUUzRD8nRp1lLBsWmaup5/dMscXbbxPHc/W/vqRjSOpWpdSUHo+9q5Q6e5DvKwjOUTyqlLqj2+PLALQppX4kIvcCqFBK3Z3ovTjEhyi7hs6JaNDsZb0ubAJdxwt2mAjlpmw6ryi/iRig2UtjxM32SKNxUl0ddu35MNKg1DN7Z3llFU64AzHjfHieoGYrxhCL1iuTZCbPq0TlLKuoiMzNTPRZmCU1O8Xbb7GOuUKR8SGpInJdaA3GMSLyYrd/mwCkYsXz8wDcAOAiEXkv9O8yAD8CcLGIfADg4tDvRJRDxFwUZxFpDhMhIopFzNbYcdNkgWaxR5bQCLi7oFQAAXeXro2Y9k5nnDhvhfJ6AOWHGC1o70y8FqR+5SyKlC3RZ6HsFW+/6X3M5YJUDkn9C4JZTKsA/Ljb4x0ABp1+SCn1JoB46xr0HkNJRDljoItIExEVqrhx0+sCTLbI0g7Z0tOVqLyarQwBtwP+Ey2AzqNKkqmP4j/HhaHXLsXuxmVZ9d1TEK81Bi5lPYxKqf1KqdeUUp/ttg7j60qprUopX/h5IvJWqrZJRPmBE9GJiJI3qa4ubtw0lg2DiAou7bDnw7iv1yz24HSAUG9kusUrr6lyZLB3zmCEmCy6x/349ZE1UrZEdVZkWY043z3ph9caA5eOLKl9YX89EUVRHidaX3osajHd1pce410/IqIeJtXVYffeQ1B+P1pejF6EvO2lx1B63nWRnpRYMTT8+pr6xahduCZzC837PGhpXNa7vJ+9JlhWtwPK49Q97sevj1yRRGx91VnstcpOvNYYuHRkSe0LM74RURQxF8E+8UIcfeUpeNsOwlQ5AvaJF6LjnUa9izYgmghKLECHG5GfgRQnGCOi3DeQWLFrz4eoqV8M66jJ2P9IPdpefgK+9iMwVY5A2QU3wmAvj/SkxOo56f56AJlbaN5oRvHkmWhuWALldcJUORLK3QnPhmVoa1yGIaHPD7MtveXoQ9z6aOt6wN2FUqtAANhdR9C8+j4o0XrVWbnaazVq5HAcb/kocjyWVZ+K/QcO6V2slMm3a41M0qOHkYgoivI40bltAypm3IrauxpQMeNWdG7bkJN3/TQRDC0WrL3GBvfiEqy9xoahxQJN4k3BJqJCNNBY0X0elpisCHhdGHrNEpxy8+Mw2MuDy1Moibu0Q7LzuGbMnAmDNThs1WC1Y8bMmYP6vMrjRNl516H4jIuCcxa7jqHa6Oj1VWEFzgAAIABJREFU+ZXHMajtDFa8+gjurpj7S1Qgqs7K1WU1Ro0cDs/xj6M+n+f4xxg1crjeRUuZfLrWyDQ9ehh51UREUcRsheXU09Gy9kEEXF3QrHZYRp4B1/5/6F20fiuxACvrizBtTDC8ThtjxMr6Isx5Vt+LICLKHuWVVQOOFeHeQ+uoyaj91ko0PTYv2GsXWYsx+HP8mOExk650f31Yzx6xGTNnYtNftqB67uLI8gObGpdhxsyZeGXDhgF95vB2Ky6+Nfj7++ux8mpbFsZKDQFnJ5rXLIVyO6BZ7bBPvDBY3jj7q2Xtg5E6q7lhSU4uq3G85SOsvSbW/vhI55KlTj5da2Ra2nsYReQ8EflZt4duiPtkIipIyu+H6+BOaNYSQASatQSugzuh/H69i9ZvHW7g/FpD1GPn1xqCQ62IiBBM758oVnRPSCMS/ZwJ406LmgtYM+deiMmCiWdMSmoJjZ6vj9UjtumNzTGXH9j0xuaY71leWRVV5vLKqoTbLb9oQdbGSjGbUfSJc0K/AAZ7BcRiT7y/utVZei9fMlDZuj9SKZ+uNTItLT2MInIWgHkAvgRgL4CG8N+UUjvSsU0iymF+LzRjKSovuT1yN7v194/C7+zQu2T9VmIB3mzyR+7SAsHfSyw6FoqIsoryODEkQawo6taz19K4DCIGKBW8qH1/+3ZMqqvDrm69iv3p0Urm9QF37GGrAXfvoXvhxdBr6qPLXF5ZhWNtrXjmmZVY/P0fYv+HHwBiQHPDA1AeV8LPryflccHTvBc1c++P+jyJYnvRJbfrUmfNmDkTm97YjIDbCc1ShGkXnDfgHuCCqLvy6Foj01LWwygi40TkuyKyC8BPARwAIEqpaUqpJ1K1HSLKP2K2ouryO6PuZlddfuegF0E+eYf+5M9063AD8xqc2LTXB69fYdNeH+Y1OPPqLi0RJU/E0KvHUMxFCWNF7wXho6/a39++PanexHgSvX7GzJkQkzX28gOm3jE50WLozzyzEl+7cxGcU2/CyG83oObqH0CzFqPy8juzNlaK2YqqS78F66jJ+PjpO9DcsASBruN97q9U1Fn90X3YcO3CNaieuxib/rJlwHNNy6pPjfn5yqpPTXHJM6fn8jHputYoBKnsYdwN4M8AZiml/gUAInJnCt+fiPKU8rjiLoI8UCIaNHsZqmct6nGnXoNK48LQAaWgiWDOsw5mSSUqcCIGaPbSXnEo0HUcmr0cRzqPRcWKE26gduHaqPcYbCzsr01vbIYKBND6h0dRddmdJ3ti/vAoVKB37EyURGfx938I2/TbojKyVl12J46+8hQUBK1SjjnPHo18foe5AgrHMvI54wnXRx8tvw1+Z3tUz+mR1ff1iu2196wHoM9+qp7bO9vtpjUDy3a7/8AhjBo5HHOezY8sqd2Xjzm5/+5P+bVGoUjl7fYrARwGsElEfiUi08EEN0SUBDHHuZs9iLt+Yi6Kedc7E6nOA0qh3aWifhJR4RGzNW4cGmLRALMNJ9wCmG3Q7JUJF43PlIDbCfg9KPvcDTj6ylNo+nE9jr7yFMo+dwPg9/R6fqLF0Pd/+EHMC3Rv20GI2YrKyxei/I71qL1nPcrvWI/Kyxfq3tsTro98J5p77buaax9Eh7IB5iIUzX0w0lgE9NlPyQ4bTtb+A4ei6q5cbSwCweVjeu4/Y9kw3c+vXJWyHkal1BoAa0TEDmAOgDsBDBWRnwNYo5Qa2KBqIsp7yutF85qlMBQNga/9CIylQ+F3noDyegf+nkmmjiciSpdEcej40eD8QbPFCrerCwAgmhEtjct69Ugqb++GWrpoliIoBRhLqnDq/Ccjj7v2b4Nm6X3DrbS4KGaZS4uLUFYxEs4YGVlNlSPgbT3QLburFcrrg1htuvf2KK8nmCE1UR0ixjh1li9j5dQssbPdxtpHhSjW/is97zo0NyyFwabffstVKZ/Qo5TqUko9o5S6AsAIAO8BuDf8dxEpT/U2iSjHKT/EYELlJbej9q4GVF5yO8RgAtTAM5cluutNRJQJieJQ7cI1qKlfDJ/RCos1uFj91752KwIuB5rXLEXTI3PRvGYpAi4Hvva1WzNW5mkXnAfl88bMpDrtgvN6Pf9YWyuGWDQ0NywJlrlhCYZYNBxra8WS738Xjo0/jXqf1j88Cr/bAc1ehpr6xaHv4b+g2YqhXF2AQY8V304SEYjBlLgO0SRmnWU2Z67s0y44L+l9VIhi7T/v0UMQo777LVelNQOEUuqoUuoXSqmLuj28MZ3bJKLcI+YiVM++O3rY1uy7B9W4Ux5nzMqUPYxElC49l5eIF4eMQ2qihqh6VXAGz5M/+ym+9tUFMBqCl2dGg4avfXUBnvzZTzP2GV7ZsAHTp1+IgOMEmhseiDQCp507Fe+8szXm8hnH2lqjkugca2sFAFx//Tz4HR1RjUn/iVYod1fsobomM0TnBiOMZlTPvhvGITVx6xAxGGLWWeH92B/JLEkSyysbNmDauVPRsib43basCe6jgWZJzTexlo/p2NqYsv1WaPQ4K7lXiChKOoaPKhWAiNZrQet0JrwhosIVXl4CBjPEjEjcCXQdjxp6aRhSg1Pnn2wA9ox1T/7spxltIMYSq9HR1/IZsZRXVsEFI2rqv9PtNQ8j0NWetVMGwvXRqfN/iv2P1EcPm/W4InVLvPJrBhMC/uSmUwzkO+2OjcP43t++HRarrdc1QLYed9ku/TnmextQ9gcR+Y2INIvIjm6PVYjIn0Tkg9BPDnclykFpSXoTypJ6csjT4v/P3t3Hx13Vef9/fSZzl0nSlt4KhN4ABdqSXhUDLquopRQULTdFpcAKrLCIN7i6FwiLuPtgr8qF4oq7iKwojy36kwJiW6giglBUuFQMyFJoC1WgpQXatPQmt5NM5vz+mMw0k/nOdJLMbfJ+Ph55TOZ7e875zvmeOXPO9xx8dRNKMrWGiIw9+9q7cHGH+SzjvpOsaOBg0mmfSduvWrrK55o+Y2j7fCVnd08LlHfiv2TY3rzrC/jCdendZusm9E+Lkr3MstoGfDWBvM41nDSV/MxraiLmD6flRQuVf1CpalVN35xWAB8etOw64HHn3GwSXV2vG7yTiFQ+19OdpevPCKbVKOMoqSIy9riexOii3ved/ucUfT52PXxr+r3uoW8SsMofSXk4rTO59snW3dPFyjsASTJsXqOkJq9lrjJrypJrwB/M+1xq8SoOr1FSw9PnF/y7xlhRNV1SnXO/NbOZgxafDXyo//+7gSeBa4cZLhEpEwuGqZ9/Ou/8+r/o3b2NwKRG6uefTtuzDw37mCqIRaSUDtblrTdYy9SlX6WvY0/qXucfP414tJNorMyz1ech2fI2eFTOXD/C5drH+56/Fsr82EAybPt/f3/Wa5mrzBpKOTOcNJX8eOXF2DtvFvy7xlhRtAqjmU0FUm28zrmt/f8uKuBppjnn3uo//lv95xSRKuN6upnwvgs45AOfOrCsL8b+398/7GOqIBaRUhpfX8u+drLed5JfYK3GT93cDwKJ+9zWb51briAPSa7pM3Lv802mLPnKgX0e/Aaup6vg9/xCSYat7dm1Oa9ltvAPpZwZTppKfry+A/S+8waHvu8/K/JzV+kK3iXVzM4ys83Aa8BvgNeBXybXO+feKfQ58wzXFWbWYmYtra2t5QiCyKhTqHxVjGcYNUqqVCuVV9Vpz+5dOe473UW5z5VSrukzsrlg2TLiHfvYuXo5W//9XHb9/NuY359jAvVaLFCc9Mg3XyWvU9ZRUnt7c17L1rW3QCy/eTOHk6aSH69RUi2gZxiHy5wrbL95M/sf4FTg1865d5vZQuAC59wVBTj2TODnzrnj+9+/DHyov3XxUOBJ59yxBztOc3Oza2lpGWlwRKqamT3rnGsu1PFGkq/MH6KmbjyTz/xy6lfWXQ/fSl/HPtwIumqZ+VK/BmuUVCmFSspXUh7JQVEO3He6ca6vaPe5ShasG8fEs/6ZHfd+DQuGwMWZet6/0Nexh72//RGTPvKPaS1r8c42rKaGeG/6M2WlzFcDr9PuX3+fvv2tB0ZJ7etjxtWr2PKtpVmvpbl43qOkSnHNa2pi4yuvHsiLfTHP6xbv3J/xmRsr8s1bxeiS2uuc221mPjPzOefWmdk3inAegIeAS4Cb+18fLNJ5RKSY+nqZcMqn0p4rmHDKp9j9i1tHdFhVDkWk1Jzr815RpPtcJevtbCfUOJcZ1ya+nm35xpJUt1wglRbmD+LicSad+cXyp8eA6xTrv07j3vtxdv/iVmZcuzZjm8HXMq5yp2K8tH592nsz35jLg4VSjArjXjOrB34H/MTMdgIjHvLKzFaSGOBmspltA/6VREXxfjO7DNgKfGKk5xGR0rNgLf6GyRx22fdSy7q3vKDnDUVk1AhE6j3vc4FIfRlDVVyBSH3ac2S+cF3qfd3cD1I394OJrp5rbmL6P91bEemR7Tr5wnWp9xaKjLlrORqMxTxYKMWYVuNsoBP4EvAI8FdgyUgP6py7wDl3qHMu4JxrdM7d5Zzb7Zxb5Jyb3f9alucjRWRkAuY8nxWphqHmRUTycfmlF7Nr0H1u19pbuPzSi8sdtKIZHOfQEcd73usjx32gYtLD6zq1rr2F0BHHp967vtiYu5ajwVjMg4VS8BZG51yHmc0AZjvn7jazCFBT6POIyOgR7e4kFI6wc9Xy1LMGAXNEuzvLHTQRkYL43u3fBeCHK/4vvZ3tBCL1XHHpxanlo5FXnI87cgabH0q8rwlHcL29tD//S6Kv/K4i0iNrmF99ia3fOpdApJ7PXnF5xjaVEHbJbSzmwUIpxqA3/wBcAUx0zh1lZrOB/3LOFXI6jRHRIAIiGpxDpBiUr0QKT/lKpDjyzVvF6JL6eeB9wH4A59xmQPMjioiIiIiIVJliVBijzrnUBDRm5gf0IJKIiIiIiEiVKUaF8Tdmdj1Qa2aLgZ8Ca4twHhERERERESmiYlQYrwVagfXAZ4CHgRuKcB4REREREREpooKOkmpmPuAF59zxwA8KeWwREREREREprYK2MDrn4sD/mNn0Qh5XRERERERESq/g8zAChwIvmdkzQEf/MuecO7sI5xIREREREZEiKcYzjDcCHwP+Dfg28AxwdBHOIyKjiJkPX6gu7VVEpBx8NYG0+5GvJlDuIFWVw4+YnpZ+hx/h3fEs3+1KLZ/rX6lhl9x03Yan4N/InHO/AfYBHwVWAIuA/yr0eUSk+vjMGB+2tFdIVBbxB6H/PWbgD6rSKCIl56sJYLUNhGfMxxeuw/V0jZn7kc8s4z7t91le+4bCkdQX8bd27iY8Yz7Tr17N1KU38Pae9owv5mY+3tr2Bg3WieFosE7e2vZG2b/A+2oCOF9NWnnkfDUZadPe+gb0dKa2efPtnWUPeyH5fcP7HFSyw4+Yzptv70y7tqPtuhVLwbqkmtkxwDLgAmA3cB9gzrmFhTqHiFQvnxnT6o17ltby/uk1PLW1jwtXdSUqjebHF4owZck1hBrnEt22gda1txDv6yt3sEVkrPEHCR12LNE3X2bKOden3ZPMfCSGaxh9kj/ged2n/T4jFs8+pXYoHCHmDzN10D18zxM/ZOLiK5my5BreWrU8bXvDeZ7rrW1vFD2uuThHRnm0897rAe+02dHumHruV2ldewtvbn+rrGEvFL/PmFI39M9BpXtz+1v4Ig0Z3zVGy3UrpkL+XLaJRGviEufc+51ztwH6ticiADSE4J6ltSyc5SdQYyyc5eeepbU0hMACAaYsuYbwjPlYjZ/wjPlMWXINFlA3MBEpLdfTRfSNF73vScHacgevaBpC2e/TdcHc+/Y680yvjg1PAhBqnJtoqR2wfa4yoZy8yqNcadMQYtSVWXXB4X0OKp2+awxfIQe9OY9EC+M6M3sEuBeo/vZrESmItihs2x/n+O+1s3FXnDmTfVz7viBtUXB0E2qcm7Z94gtGd5lCKyLVwu8z6oKJe0xDCDp6GFEriAVriXd3ZLkndWXZq/q1RROv759ek1q2cn0vX/9dlLYojA9b1rR1PV2e6RXvTox9GN22Ia2y7Xq6aCN7mVBOrqeLrtXXs7P/8zQwPNnC2/aNj9EQAtdTvnAXUq7yupq5Hn3XGK6CVRidc6uB1WZWB5wDfBmYZmZ3AKudc48W6lwiUn3CNXDDuigrzj7QxeXSB7sI10B3TZjotg2EZ8xPbZ/4ghEuY4hFpNIVpetcrAcL1ma5J43uFkaAp7b2sXCWn5Xre/nqE93cddbB0zZbevnCdXRveYHWtbdw6NRJaduH+zqzlgnl4vcZ7/LodrqvKxHfbOHd988No6bbJuQur6uZBfVdY7iKMehNh3PuJ865jwGNwPPAdYU+z0Bm9mEze9nM/mJmRT2XiAyPvwZWnJ3exWXF2bX4axK/+rWuvYXuLS/g+mKpLxj61U9EcilG17l4Xy+upyvLPWl0tzC2ReHCVV2sey3G138X5a6z8kvbgDnP9Ip3d7Bz1XLedUg929/YmrZ9rjKhXLJ9nvw1ucuw0dRtE3LHtZoFDM/PaUD9IQ+qGPMwpjjn3gG+3/9XFGZWA9wOLAa2AX8ys4eccxuKdU4RGbr2nvSuTpB4394DzsUx87Fz1XJcTxcWrMX1dI3awSVEpDDaot73lZF2nRuL96S4c/jM2NHuOOe+ziGlbbS7k1A4kpZeAXNEs6RXtLsTn1nWMqFcssU5GaaDhbcQn71KkKu8rmZZP6fdneUOWsUbDWNEnwT8xTn3qnOuh8Szk2eXOUwiMkhDKNHVaaCntvalukE5Fyce7Uh7FRHJ5WD3lZEYi/ekuHPEXaI75VDTNtrdmZZeB/sSXsxrN1y5wpRPeMsd/kKpxGtTKEP9nErCaKgwHg4MHIN5W/+yNGZ2hZm1mFlLa2tryQInMpoNJV8N7OrU2+dY91qMC1d1jYpfY0UKSeVV/jp6vO8rHVXeElJuHT3Z79mFSttSX7t88lWuMGVb1x5l1H32lK9kMHOuuh/MNbNPAGc45y7vf/8p4CTn3FXZ9mlubnYtLS2lCqJIRTKzZ51zzYU6Xj75ymeWGnUu+Rqv8nuQyEDlyFdjXaFHSZUEv8+IO9Lu2YVO23yvXSnzVa4wDV43+HU0ffaUr8aGfPPWaGhh3AYcMeB9I/BmmcIiIh7MfPhCdaioEZFCi8Ud+7oTXSn3dTt9qS2QWDyRpr0EUpWiuiBEwrlHdfGZMT5saa9J/kAIX6gO6183+FJVwqWLxR0dfQfiGHeJKUXMMsMbDgXY1+0IhwJp22ZLowPxT5SJ/kDl9vGsxGsj5TMaKox/Amab2SwzC5KYC/KhModJRPqZ+fDVTYCeTt5Vb6w5P0L0hgbWnB9hWn36lwkREakckXCQ8YFY2n17fCCWtULkM2Nalvu8PxDChepwWcqC7h4qokzwB0LEYz0Z8TDIWDY+EEtUjPNIo2T8py69gelXr2bq0htwFVppzHUdZWyq+gqjcy4GfAH4FbARuN8591J5QyUiSRasZcqSa2gIwWXvDnDVL7sJf72Nq37ZzWXvDoyKh+hFpHRCAX9aC1YoUNQB38e0AL2e00wE6PW8Dg0h72kpGkIQj/UwZck1jMuyTThIRZQJ8VgPDSHY2eG46pfdPLAhxsJZ/pxlmNfyAL3px/X5mbLkGsIz5mM1fsIz5jNlyTXEfZX3+VV5LYNV3qd0GJxzDwMPlzscIpLJ9XQRapzLzijcs743bRLoyx7SoDcikr9QwM/EcJx7lkbSJlYPBfxEe2PlDt6ok2tqjWn1mddhR3uu7Y0d990ALvs296zvLWuZEAr4mVZv3LM0vZwCUuHzKsPyKduSZWHa+RrnVuTcnvnGScaOqm9hFJHKZsFaots20BAiYxLou86q1S+WIgL0d38c0GLl1e0xXNPn3TpV0+dxRPGSK50HP39YH8w+vUK2lsRc2zcE4zm3KXeZ4PX5uuusWr7+u2jOMiyfsi1ZFg4U3bYBC9aOKMzzmprSnouc19Q0ouMBKq8lgyqMIlJUrqeL1rW3FG2CbRGpfvk+K6f7yMjkSmev59Z8Bhf8zHs6pGzXwWs6ht7YgfX789imXLLFa+OueM445/OZ9MVjtK69he4tL+D6YnRveYHWtbfgiw+/ZXxeUxObXtue9lzkpte2j7jSqHwmg42KLqkiUrksWEvosGMJbP0DT23tY+GsA7ed0TIRsIiMTOJZuUjq/pBssTrnvvRJtZOtU7qPZBcJBwnQm5oOoZcAnd2JCfRypXNgQKthct3q8yN89CednHNfZ9rxGkK9Wa/DjnaXvn0MOm8Yx7rXEhWjcQfZppzXMtvnqz544H+vOOfzmYz1RvEHQuxctRzX04UFa/HFY8R6h18L2/jKq9DTSdfq69nZn5YuChtfGVnNTvlMBlOFUUSKyvV0ETnmb3ln8x9Yel8nkyLGa3sdsyYYuzudfrEUkbxbNLr7arhwVVfaM2YXruqiuy9937Eq2YI4+NnCgXPgXvXLbr56SogLmhJTQQxM54HX4KqHu/jJ+l66+yDghxqfsa87DiSe9fO6DsnjdPeS9izgutdiLPpRZ9qy0PK2tDkMfTfuZ3z/+3JJtpBmi1e2MizbPuPDhvPXsr898cNHsnI4r6mJja+8Sl+sF1+ojjnHHMlL69cPObzJEWcHn/vt9s6D73yQdFB5LQOpwigixVUTYO/vfkwNEPIbP1hyoGC74Gdd1EQ1uZPIWJerRaMmXEc82oUvVMvChafyu3VPpLVOdffVaMCbfl4tiH9zuI8/bI97DuRyQVMgreUoeQ2ueriLBzbG+Nkn0yuegRofvX1xor0xaszSrkNbFB6/+MD2S+5Jb5kc+NxjzY37MwaXSQya4yhn1b8hBH9zuI/z7u9kb3fi/akza7j/ExEiy9uylmF/c7gvLa7JfZLxGlcfSVUaB3YjDTXOJbptA5vW3sK8pqYhVxrHebQKe7XMD5XKaxlMzzCKSFFZTQ2Tz/wykRCsPC/9IfqV59USURcXkVHrtNNPpyacGJCjJlzHaaef7rldLwHP59raojDl3MTzWVPOvYF1/6+FUxaeyr7uxKTy+7rdqK8seqVhtnT1aqn9zZbsA7mknh8kkPb84U/We0+nEQm4VJioO4Tac2/iiKvXAInK4sDt114YAUhdp4Fhq88xaE45y4SOHvjD9jg/+2SEuVN8rDk/wupldQRqLGcZ9oft8f6uuqTtk4yXxQ6MhLrxlVc9p9fY+MqrQw5vsZ41VHktg6mFUUSKyvV0p6bV0EP0ImPHaaefzrr/18KUcw+0pKxbewunnX46v3700bRtO7t7iISDGS1WU5fdRHjGfIDUF+t1q5eXIzpl4ZWGj9//r/hq6z3T1auldm+39713Q2ucc+7rpLsXxk2oo7N7D74BrYZprbg90P7VhlQ3y+TynffdwPSvPJRXxWVg2Cp1UJU+X4gd7dFU3AeGMVeY26KOhlBvXvEq5PQaxXrWsFKvj5SPWhhFpKgsGE5Nq5FtKHURqU65hvR/4rHHqI/tYee917P3tnNo/dmNiQrfb5/2PFZnd09ay6HDPL9Yx6OVN2/d4Okokq/j6iNDOs64+kja/k889lhGa5T5A54tVE889hjdPZkjkGa7944Pw65rGnjk7yL4e/alPec4tS59tNSJdUZoeVvGKKrT6hxbv3lWzvt78jMxsAUz1/blLBPMZwB0RskIY64whwfUrSbf0kZk+f6MbZIj/hZyeo1so9KOtGKn8loGU4VRRIrK9XTTuvabdGQp2Dr0i6XIsOTb3bNYcg3pP64+klG5mBrqofVnN+Zd4fOFvL9Y+0Ijm7duKPJJ4+R0FL0xMuJcZ915VxrH1Se2T6uQ1RutP7sxbTuvFqqdP/0XptUbj3wqwrnH+Vl6fyeh5W2pljKve+/fNQUyuoP6DOqCmd0Rk+s9u5EG47THQyz6USfjb96fGLzm5v0s+lEnbbEAGze/hi8YwZE+Omq2ik45ywTX2820+kT301Nn1qSFMVcZZpZIO0i8mkFk+f7UNqfOrGF8IIbPjNpAjef0GnOOOXLI4Y07l0rT5PXe0Z740WUkVF7LYKowikhx1QSId3cRBwKWXrAFzBEvd/hEqtDArooDn+8rZqUxFPCntX5tfPHFrM9iWazLs3JR74vmXeFb+IH3eX6xXviB9xUtjgPlm8bJilTA712hikW70tItFPB+GihXmqVt59FC1eCPpfb93kdr2XPtOB6/OJIK365BlYpzj/Nz25kHrkOyu+GqT0bo7M3sjvjRn3QSd3Dajzs5/nvtrFzfm7afi8czWx/rDXD4QhGmnvc1xoXg7GNriASMxy+OeFZ0gLKWCeNCcNm7E89z3v+JCB+f4+e8/sp3rjJsXNhY9clE3Fd9MsK4sNEXh/Pu7+Tjc/ysXlaXqnR34ydMjJ2rlrP1W+eyc9Vyjpt1+LBGSQUYP3ESbS6Cw2hzEcZPnDTidIiDd0V0xEeWaqUKo4gUmaOmto6wH7piMCVimCVeu2IQ1pPUIkO27rdPe1bWsnX3HKlQwM/EcDyjQrDzvhvSt+t/FivXM1D5Vvh+/eijLPzbZlpXJ75Y77j3a7jeKI8/9uuM7q/FkG8aJ+PqFedt++OMCw3q3hmOe1Yac6XZ1ls/yZZvLGHrrZ/E9XRlVKQP9sxZsnNhoL8V7BNzA2nbJrsbLpzlZ85kX1p3xMjy/YyvNR5aFqH7qw3c9pEwX32im5Xrew90I/V7D5LT4I8R399K1+rraYsmBuBJVl4BDLj+lCC7rmng8YuTlczyaYvCPet7GR9OpMltZ9byzrXjiP/ruJxlmFfcw0F459pxqYp5qnLdsRd/rA16OhkXcgTi0ayVxYO1cB8yaTL7o/G0Vv790XhG1+hsP1Lk4v0DgIxVqjCKSFGZr4bJH7sa5yASTAzT3f3VBn6wpJZI0BhhzxmRMSke9R44o1jP94VrMkfaTHZHHCj5LFauZ6AGD3ixV0unAAAgAElEQVSTy68ffZS+7g7mHj8Po48G68RwNFgnG198saiVxnzTOBlXrzjf+JuoZ/fOcE36dgOPM1DyuAMrBL66CcQ796cq0jtXLffc95M/7cz40j+xzrJ2B33vYYkK51dPCXHZQwfWe7Wc3nVWLV9b153qRpqrwtoQAlzidW83TB4QptXnR7hnfS8PbIildX0tl4YQ3HVWLX/XlDlqb64yLJ8BYp7a2kfAYFo9ef2AkE8L9772rowfNVzH3szrnuUcudIh2yi2MjapwigiReV6o4Qa5+KvgRVnpxdAK86uxa/5tkWGrNTP9+WqEHg9i+X8tZ6VEucfXvg2vviiZ4vHxhdfLET0POWbxskKWG8ssyL26h6X92iT2dKsLRbIaOW0QIi+7g4AjvjiPbTF/Bn7PvF6lkp+CGJ96d0Nd3U4/vmURG3ggqYAXz81zD+s7SK0vC3rtX91j0t1I81V2V1zfoRI8MDIqtmm+MiVNqWSjOttZ9amdUc9577OnGVYtrgPvpbhYJbWSI8fEPJp4fZ6nrUh5PI+x8HSYaByXxspL1UYRaSoLBAium0DHT3eBVBHT5kCJlLFSv18X64KgdezWPvbO+lw4bRKSYcLpyYvH875S93ikW8aJwceCfgzn/uqD+Y/2qRXmr3d7jjiyz9N227gFAzJ5xmnX70m49y5vvTf/4kIkYDRNNXYd904Ar70yu676o2O3sT9O9e1v/6UIA2h7JXdmePTr1W2MG3cFc+ZNqUyMK7J7qjJZ0FzlWFecd/fPzXJwGcAh1IRy6eF2+t51kJU9jRKqgxWFRVGM/uEmb1kZnEzax607p/N7C9m9rKZnVGuMIqINxePs+vhW7N+caoPlilgIlVs8PN9rauXs/Bvm4fU3XMouvtqPL8Ud/fVEI924FyceLQj7Vms/e2dadNkDLeyCOVp8RhKGifjOPg1Gs+ebl4Gp5kFIzmnYAiYO1CpNT/t/kOYuuwmJly1JueX/nPu6yTid7RcUc+612KMr7WMCufOaJAp5/1r1i6svTH41w+GaIsmwj14/6kR+J/PNgDp3VO9wjRnsq9gU0KMRFuUtO64616LcdlDiTDlKsMyBojp8mPBCId86edMv/bnHPKlnzN12U1Dqojl08I9vr4240eNQlT2ijVdh1Qvc1XwAJGZzSExaNP3gaudcy39y+cCK4GTgMOAXwPHOOdytrs3Nze7lpaW4gZapMKZ2bPOueaDb5mfbPnKzIfVjsPXtY/JdcbK82p5//QantraxwU/62JXhyNWBfchkXyUKl+VQyjgJ1zTd2Ay974aor2xkpx7fDjRHXXgBOXrXotxzn2JClYlG0m6hcIRYv4wU5ZcQ6hxLtFtG2hdewv+WDfR7s7UNr3OEq2O/iBW48dFO4FES+E9Sw/ccy9c1cXb7Q7DaAi5VJjaouCfPJO+rn0Z5zpu1uG8tvllAvSmtu+NQecN49KuwbymJjZseJlxgd6s16ozmniGcWCYLvhZFzs7XCocDSEyrmmp8tX4sHHVSUHWbIqxcVecOZN9nHOcn9ue6aEjStYyrA+YtuymtHQLTJ7Bu5YtTx3b9cXY+q1zPK/JO92+jM9E6hnGQddj8I8Wh0yazL72LlxPV+KHhJ7Es6v5nCMbvxlxSLsmbdFEK5PK69El37xVFeMTOuc2AphljNB0NnCvcy4KvGZmfyFRefx9aUMoItlYsJZw4xy6N/+BdzoPzMHVEIKunvIOoS4i+StV5dBLsG4CF67al/ElOFg3oWxhytdI0i3a3UkoHGHHff+KBfy4nm4sGOayyz+dtk1SovKY+EJvwQg72rvS7rltUcMCtWDG/miigtHmCzL92v8PgDfv+gI7Vy1PVT7mHHNkqtV4yqRDmBY4cA2SrU7Ja/DS+vWJSuOLL3Lhqq6Ma5UMw9vtblCYEq+TayESMGLB8cNOr5EK1k3grj9nfs5w4MCzDHOBMIal0s0XqiXe08OEkz+ZduxEy3CEHe2dacfo7qvh6OPm4AvVZaT7aaefzrrVy4lHE8dd+IH3ZbRw79m9KyMeoYA/4xxD+RzGAf+gr9x+g5jqimNWVXRJzeFw4I0B77f1L8tgZleYWYuZtbS2tpYkcCKjXT75yvV0EX3zZaYuuymjsIk5RjzBsMhoM1rKq1xTAoyrj6QN+3+wye1bd+8hFhyf1u0vFhxP6+49xY5GyQ1Om95oF75IPVOXfq1/pNSvcefd9/C5z38BSKTzwG0brJOGujBXXv73+OomUHvuTRxx9Rpqz70JX90ErvyHT6e6EV95+d/j81mqS+PE067AFwjx2c99LqOLcT7X4KX16/ns5z6f0UXz7fYD9/mB9ZC2nppUpXFXF0W9pvnkq9bde7znhzSwcJ1nGWbB2rQ07evu4LOf/Qy7BnUV3bX2Fq68/O8zui0ffdwcNr22PW0k3E2vbWdeU1NqlODkcfPtch7tjaV3jR7ijxZzjj/eM65zjj9+SMeRUcQ5VxF/JLqTvujxd/aAbZ4Emge8vx34uwHv7wLOO9i53vOe9ziRsQ5ocQXMw9nylQVr3bRlNzkD9656c09cHHE9NzS4Jy6OuHfVmzMoZjRFSqpU+arSLVq82PnqDnHTlt3kpl+9xk1bdpPz1R3iFi1e7Brqaj3vBQ11teUOdtllSxurCbgZ1/489Tdt2U0uEGlwixYvznpvbairdZ/93OddINLgwFwg0uA++7nPZ5wzn22GavAxc93/k3HJpVT5yu8zz3D6AqEhl2H5pqsFI27aspsyrq8FIznTpJhUXo8d+eatimlhdM6d5pw73uPvwRy7bQOOGPC+EXizuCEVkaFwPd2EGudqXieRMSTXlAAW6/K8F1isOHNIVpNsadPg703bLtQ4l97Odtb99ums91aLdfG9279LT8d+nIvT07Gf793+3Yxz5rPNUA0+Zq77fzIulSAS8J6Sot4XHXIZlm+6ek2NMXAk3HJQeS2DVUyFcZgeApaZWcjMZgGzgWfKHCYRGSA5rYbmdRIZO3JNCaB7QXb5pk102wYCkfqqSc9cYUzGpRLkCmex0tlraoyBI+GWQzV8pqS0qqLCaGbnmtk24GTgF2b2KwDn3EvA/cAG4BHg8+4gI6SKSGklp9XQvE4iY0euKQF0L8guV9oMng/y8ksvrpr0zBXGXf1xqQS5wlmsdJ5zzJGe833OOebIkR14BKrhMyWlVRUVRufcaudco3Mu5Jyb5pw7Y8C6rzvnjnLOHeuc+2U5wykiHvpixKOdmtdJZAzJNel9tknenb98LSqVIlvatEWN1jU3sfVb57Jz1XLedUg937v9uyz8wPuy3lsrKT07ey3r/f+KSy4sSDfYQsgVzv1FKsNeWr+e42Ydzs5Vy1PX97hZh6cNOFRq2dKhszdjtgIZI6piHsZCq6R5rUTKpZTzxZnVYMEwrqeTcYPmddIoqTKajOZ5GIfqtNNPZ91vn/acEmBcfQSLHZhqwflr2d/eeZAjjg2D06az14gHarNOrXDa6afzxGOPpU1RUYnpGajxEQm4tHj19uU3sVIp89XgcO6PJqYocT1dGOnzRY7mMmwk10uqR755qypaGEWk2o3OAlWkks1rasIXSkxr4QvVMa+pqaTnzzUlwP72xGTvvv4Gi/aOLsaHDb9v9LVgDPU6JNMmOSVCb18859QKv3700YypGoZSWYyEg2nTeETCwdQ6v8/S1vnM8orHwO2Tr7198Yx4VaJj5s6jzdWmSi0DGqwTy1KOlTOPFVNf3OV8L2OLKowiUlRmPnx1E6Cnk3fVG2vOjxC9oYE150eYVp/4IiEihTWvqSnr3G6Vwu8zptSl3xOm1I2uSmOlX4dIOMj4QCztGowPxIiEg57XZ1q9YTj8Ew/LGg+fGdOq9F6fuF7boLcno7wCPONFb7Qir+1IVPM1lOJQhVFEisqCtUxZco2G6RYpoY2vvOo5rcXGV14td9BS6oLe94S64MH3rRaVfh0C9HpegwC9Wa9PQwhi77yZNR7VfK9PXK+v0BDsy4hD1ngF+yry2o5ENV9DKQ5VGEWkqJJzTGmYbpHSGcrcbuXqujoW7gmVOMfeQMOdRiIZL9fTlfH5qebrmqu8yideXte23F3Dh6Oar6EUhyqMIlJUFgwT3bZBw3SLlFC+c7uVs8vkWLgnVOIcewMNdxqJZLwsGGbTa9vx1Y7DVzeBqUtvqOrrmqu8yideg69tpXdJzqaar6EUhyqMIlJUrq+PXQ/fqmk1REoo37ndytllsqPH+57Q0VP0U5dMJc6xN1AvAc9r0Esg5z3bP/EwWtfeguvrY8qSa4h37U99jtp6fFV7r0+VVz01GXHImh49NVmvbaV3Sc4m2xQi+6vgGkpx+MsdABEZ5fp6mXDKp9j/xwd4e9dWzrmvc0wMSS5STi+tX8+8piY2rlqemA4gWMucY47MmNutnF0mY3GH32dp94SOnsTy0SLf61Aund09RMLBtGvQS4DO7h7MfOzoqkm/Z8f8OPqIvfMmc445kg0vvpT6vCQ/R9O/8hBbv3lWdd7rc5RXADvaXUa8CIbY+q1zPa9tpXdJzs7Y0ZGeN9t61MY0lqnCKCJFZcFa/A2TOeyy76WW1W55gZ2rlhN3HWUMmcjolk+lJNm1MDxjfmpZKbtMjqbKYTaVUjnMprPbu0nXgrVMXXoD4RnzOaR/WereHU3cu32hutTnZeDnaPpXHqJ7ywu0rVrOvu7quc8PLq+23vpJpi5LT4OhxKvc+Wu4cl17GZv0c4GIFJXr6fLsklX5v7CKjH6V3mVSyiefz0ZyG1/tuFHxORoc55HGq1rzV7WGW4rHXDV0ESiw5uZm19LSUu5giJSVmT3rnGsu1PFy5SszHxasTXXJcj1dOFeZkzaLjEQp81WhzGtqYuMrr1Zkl0kpr3w+G4O3KcbnqJT5anB8AubodTbseFVr/qrWcMvQ5Ju31CVVRIpOlUORyqUvgZJNPp+N0fb5KXR8qjV9qjXcUhzqkioiIiIiIiKeVGEUERERERERT6owioiIiIiIiKcxOeiNmbUCW8odjiGaDOwqdyCGqZrDDqM3/DOcc1MKdZIB+ara0ysbxav6lCNuxcpXSaP5emUz1uI81uILB49zsfPVUMNTaaopvNUUVqiu8A4nrHnlrTFZYaxGZtZSyBHCSqmaww4Kf6Wfr1QUr+ozGuM2GuN0MGMtzmMtvlB5ca608BxMNYW3msIK1RXeYoZVXVJFRERERETEkyqMIiIiIiIi4kkVxupxZ7kDMALVHHZQ+Cv9fKWieFWf0Ri30RingxlrcR5r8YXKi3Olhedgqim81RRWqK7wFi2seoZRREREREREPKmFUURERERERDypwigiIiIiIiKeVGEUERERERERT6owioiIiIiIiCdVGEVERERERMSTKowiIiIiIiLiSRVGERERERER8aQKo4iIiIiIiHhShVFEREREREQ8qcIoIiIiIiIinlRhFBEREREREU+qMIqIiIiIiIinqqkwmtmxZvb8gL/9ZvYlM5toZo+Z2eb+10PKHVYREREREZHRwJxz5Q7DkJlZDbAdeC/weeAd59zNZnYdcIhz7tpc+0+ePNnNnDmz+AEVqWDPPvvsLufclEIdT/lKRPlKpBiUr0SKI9+85S9FYIpgEfBX59wWMzsb+FD/8ruBJ4GcFcaZM2fS0tJS1ACKVDoz21LI4ylfiShfiRSD8pVIceSbt6qmS+ogy4CV/f9Pc869BdD/OtVrBzO7wsxazKyltbW1RMEUGd2Ur0QKT/lKpPCUr0SGr+oqjGYWBM4CfjqU/Zxzdzrnmp1zzVOmFKxXg8iYpnwlUnjKVyKFp3wlMnxVV2EEPgI855zb0f9+h5kdCtD/urNsIRMRERERERlFqrHCeAEHuqMCPARc0v//JcCDJQ+RiIiIiIjIKFRVFUYziwCLgVUDFt8MLDazzf3rbi5H2ESy8ZkR8RsNIcNnxrhQ4n1y+cB148MHXkM1mcsCvgPbNoSMSCBzG5+l/y8iIlJKByuXkq8hX+ay0cYr3iLFVIzPXFVVGJ1znc65Sc65fQOW7XbOLXLOze5/faecYRQZKFFBhCn1xkPLIkRvaODBZRGm1BtXNgeoC6avW3N+hJkTjKtOCjKxNrHvmvMPrJtcZ/zje4NEb2jgoWUR6gIwtc7StplWbwSM1P8qnEREpFR8ZkyrTy+XxoUSZdWpM2uYVp94jfhhokf5NZrKLK+0GG1xlMpSrM9cVVUYRapNQwgmR4wVZ9eycJafQI2xcJafFWfXcu+LvYwPZ66766xa1myKcc95tUyOWNq6e5Ym1iXfjw8bK8+rzdgmHCT1f0Oo3KkgIiJjRUMI7lmaXi5NjiTKqt9s6eOepYnXgD9zu9FWZnmlxWiLo1SWYn3mqnUeRpGq0BaF9h7H+6fXpC1///Qa9nbDvqj3uo274rx/eg2v7XWe65Je2+u9f1s0838REZFia4uSUS4ly6q93QfKP8jcbrSVWV5pMdriKJWlWJ85tTCKFFFDCGZNMJ7a2pe2/KmtfUwIZ183Z7KPp7b2MWuCea5LyrZ/8pekgf+LiIgUW0OIjHIpWVZNCB8o/8aHM7cbbWWWV1qMtjhKZSnWZ04VRpEiaovCrk7HpQ92se61GL19jnWvxbj0wS6WHR9gX3fmusse6uKc4/xc+LMudnW6tHUXrkqsS77f1+244GddGdt095D6X79kiohIqbRF4cJV6eXSrs5EWfXBGTVcuCrx2hvL3G60lVleaTHa4iiVpVifOXPOHXyrUaa5udm1tLSUOxgyRvjMCNdATQ109EB9EGJ90N0H4f5eA8l1DaFEZm8IQXcv9MbTl3X2QDiQ2LYuCH1x6I6lb5O8KST/j2fJ42b2rHOuuVDxVL4SUb4SAfpH8s5eLiVfAwbhYPoyrzKrmvOVV1pkK5dFCmEon7l885aeYRQpMhUMIiIylqjcO0BpIaVWjM+cuqSKiIiIiIiIJ1UYRURERERExJMqjCIiIiIiIuJJFUYRERERERHxpAqjiIiIiIiIeFKFUURERERERDypwigiIiIiIiKeVGEUERERERERT6owioiIiIiIiCdVGEVERERERMSTKowiIiIiIiLiSRVGERERERER8VRVFUYzm2BmD5jZJjPbaGYnm9lEM3vMzDb3vx5S7nCKiIiIiIiMBlVVYQT+A3jEOXcc8L+AjcB1wOPOudnA4/3vRUREREREZISqpsJoZuOADwB3ATjnepxze4Gzgbv7N7sbOKc8IRQRERERERldqqbCCBwJtAL/bWZ/NrMfmlkdMM059xZA/+tUr53N7AozazGzltbW1tKFWmQUU74SKTzlK5HCU74SGb5qqjD6gROAO5xz7wY6GEL3U+fcnc65Zudc85QpU4oVRpExRflKpPCUr0QKT/lKZPiqqcK4DdjmnPtj//sHSFQgd5jZoQD9rzvLFD4REREREZFRpWoqjM65t4E3zOzY/kWLgA3AQ8Al/csuAR4sQ/BERERERERGHX+5AzBEVwE/MbMg8Crw9yQqvfeb2WXAVuATZQyfiIiIiIjIqFFVFUbn3PNAs8eqRaUOi4iIiIiIyGhXNV1SRUREREREpLRUYRQRERERERFPqjCKiIiIiIiIJ1UYRURERERExFPJKoxmNt7MbjazTWa2u/9vY/+yCaUKh4iIiIiIiOSnlC2M9wN7gA855yY55yYBC/uX/bSE4RAREREREZE8lLLCONM59w3n3NvJBc65t51z3wCmlzAcIiIiIiIikodSVhi3mNlXzGxacoGZTTOza4E3ShgOERERERERyUMpK4znA5OA35jZO2b2DvAkMBH4ZAnDISIiIiIiInnwl+pEzrk9wLX9fyIiIiIiIlLhSjqthpkdZ2aLzKxu0PIPlzIcIiIiIiIicnClnFbji8CDwFXAS2Z29oDVN5UqHCIiIiIiIpKfknVJBf4BeI9zrt3MZgIPmNlM59x/AFbCcIiIiIiIiEgeSllhrHHOtQM45143sw+RqDTOQBVGERERERGRilPKZxjfNrMFyTf9lcePAZOBphKGQ0RERERERPJQygrjxcDbAxc452LOuYuBD5QwHCIiIiIiIpKHUk6rsS3H6v8pVThEREREREQkPyWdViOHDflsZGavm9l6M3vezFr6l000s8fMbHP/6yHFDaqIiIiIiMjYULIWRjP7p2yrgPohHGqhc27XgPfXAY875242s+v63187zGCKiIiIiIhIv1K2MN4EHAI0DPqrH2E4zgbu7v//buCcERxLRERERERE+pVyWo3ngDXOuWcHrzCzy/M8hgMeNTMHfN85dycwzTn3FoBz7i0zm+q1o5ldAVwBMH369OGEX0QGUb4SKTzlK5HCU74SGb5StjD+PbA1y7rmPI/xPufcCcBHgM+bWd6jqzrn7nTONTvnmqdMmZLvbiKSg/KVSOEpX4kUnvKVyPCVrMLonHvZOdeaZd2OPI/xZv/rTmA1cBKww8wOBeh/3VmYEIuIiIiIiIxtJaswmlmNmX3GzP6Pmb1v0Lob8ti/zswakv8DpwMvAg8Bl/RvdgnwYGFDLiIiIiIiMjaVskvq94EPAruB/zSzbw9YtzSP/acBT5nZ/wDPAL9wzj0C3AwsNrPNwOL+9yIiIiIiIjJCpRz05iTn3HwAM/su8D0zWwVcQGJqjZycc68C/8tj+W5gUYHDKiIiIiIiMuaVsoUxmPzHORdzzl0BPA88wdDmYRQREREREZESKGWFscXMPjxwgXPu34D/BmaWMBwiIiIiIiKSh1KOkvp3/c8cDl7+Q+dcoFThECkGMx++UOTAqy+AmQ/zh/AFk8vrUsvMH8QXrE3fL1iL+YOJ9anj+A9s138cERGRSjawzDvwGvF4rclYXo284iuVSddqeEr5DCMAZhYAPgsk51D8DfBfzrneUodFpBDMfPjqJjBlyTWEGucS3baB1rW3EJg8g9ie7Uw+88uEGuey9+mVtD33c/DV4AuEUsuj2zaw6xe3Unf8qXS89ATx3igTT/0Cve9sp/2FR9OP+9A3MfPhXLzc0RYREcmQrUysaZhEX9vujOWhw45lytnXpd5XWxmXLb7VFo+xQNdq+MpRrb4DeA/wvf6/E/qXiVQlC9YyZck1hGfMx2r8hGfMZ8qSa+h56xUmn/nl1PKuzb+npnYcNaG6tOXhGfOZ/NEv07X590w+88vUhOrY/8cH6Nr8+8zjnvUVLFhb7iiLiIh4ylYmxt5503N59I0X095XWxmXLb7VFo+xQNdq+Erewgic6JwbONrpE/1TZYhUJdfTRahxbtqyUOPcjOW9u7elrR+8fe/ubYQa5xLbtyPndq6nq5DBFxERKZh8y8Tk8nh3R8Z21SRXfKWy6FoNXzlaGPvM7KjkGzM7EugrQzhECsKCtUS3bUhbFt22IWN5YFIj/vHT8I+f5rl9YFIj0W0b8I+fRmBSY+q913FFREQqUb5lYnK5L1yXsV01yRVfqSy6VsNXjgrjNcA6M3vSzH5DYlqNq8sQDpGCcD1dtK69he4tL+D6YnRveYHWtbcQPPQYdj18a2p57eyT6evaT1+0I21595YX2PWLW6mdfTK7Hr6VvmgH4977cWpnn5x53Ie+qV/CRESkYmUrE/0TD/NcHjri+LT31VbGZYtvtcVjLNC1Gj5zzpX2hGah/n+PBQzYBOCci5YqDM3Nza6lpaVUp5MxwMyHBcO4nu7Ea28vuD6oCWC+GlxvNxasTdyUagKA618ePbBfIISL9wEGfb39x+nB/IHEdoEwrre7YA9mm9mzzrnmghwM5SsRUL4SgWSZmCjzDrwOKCNTr1EsGEpb7lXGVXq+8oqvBlGpTLpW6fLNW+V4hvH3zrkTgBeSC8zsORKD34hUpbF8sxERERlorJWJYy2+1UzXanhKVmE0s3cBhwO1ZvZuEq2LAOOASKnCISIiIiIiIvkpZQvjGcClQCPw7QHL24DrSxgOERERERERyUPJKozOubuBu83sPOfcz0p1XhERERERERmekj7DaGZnAIvN7BLAAW8Ca5xzvyplOEREREREROTgSvkM43eAY4AfAckZzBuBfzSzM51z/1iqsIiIiIiIiMjBlbKF8Uzn3DGDF5rZfcArgCqMIiIiIiIiFcRXwnN1m9lJHstPBLpLGA4RERERERHJQylbGC8F7jCzBg50ST0C2N+/TkRERERERCpIKUdJfQ5474D5GA3Y5px7eyjHMbMaoAXY7pz7mJnNAu4FJgLPAZ9yzvUUNvQiIiIiIiJjTym7pGJm44EP9v+dAnzQzCYM8TD/CGwc8P4bwK3OudnAHuCyQoRVRERERERkrCtZhdHMLibRAvghIALUAQuBZ/vX5XOMRuCjwA/73xtwKvBA/yZ3A+cUNOAiIiIiIiJjVCmfYfwq8B7n3N6BC83sEOCPJKbbOJjvAF8BGvrfTwL2Oudi/e+3kejumsHMrgCuAJg+ffqQAy8imZSvRApP+Uqk8JSvRIavlF1SDXAey+P963LvbPYxYKdz7tlBxxzM6xw45+50zjU755qnTJmST3hF5CCUr0QKT/lKpPCUr0SGr5QtjF8HnjOzR4E3+pdNBxYD/yeP/d8HnGVmZwJhYByJFscJZubvb2VsBN4seMhFRERERETGoJK1MDrn7gaagd8AUaAHeBJods6tyGP/f3bONTrnZgLLgCeccxcB64CP9292CfBgwQMvIiIiIiIyBpWyhRHn3B4SU2AU0rXAvWa2HPgzcFeBjy8iIiIiIjImlbTCmI2ZrXfONeW7vXPuSRKtkzjnXgVOKk7IRERERERExq6SVRjNbGm2VcC7ShUOERERERERyU8pWxjvA36C9yim4RKGQ0RERERERPJQygrjC8C3nHMvDl5hZqeVMBwiIiIiIiKSh1LOw/glYH+WdeeWMBwiMgatXr0aM2PTpk0Vc67XX3+d2tpaFixYkDWkhJ0AACAASURBVPrr6elhxYoVTJkyhQULFjB37lx+8IMfALBixQq+8IUvpB2js7OTj370oxx33HHMmzeP6667LrXut7/9LSeccAJ+v58HHnggbb+7776b2bNnM3v2bO6+++7U8meffZampiaOPvpovvjFL+KcS537zTcPPmvRRRddxLHHHsvxxx/Ppz/9aXp7ewHYtGkTJ598MqFQiG9961up7d944w0WLlzInDlzmDdvHv/xH/+RWvfOO++wePFiZs+ezeLFi9mzZ89Bzy8iIiKFVcppNX7nnNuaZV1LqcIhImPTypUref/738+992YO1NzX11eycw121FFH8fzzz6f+gsEgAOeffz7PP/88Tz75JNdffz07duzIeoyrr76aTZs28ec//5mnn36aX/7ylwBMnz6dFStWcOGFF6Zt/84773DjjTfyxz/+kWeeeYYbb7wxVRn77Gc/y5133snmzZvZvHkzjzzyCDC0CuOmTZtYv349XV1d/PCHPwRg4sSJ/Od//idXX3112vZ+v59///d/Z+PGjfzhD3/g9ttvZ8OGDQDcfPPNLFq0iM2bN7No0SJuvvnmg55fRERECquULYyY2RlmdpmZzRy0/NOlDIeIjC3t7e08/fTT3HXXXalK3JNPPsnChQu58MILaWpqoq+vj2uuuYYTTzyR+fPn8/3vfz+176JFizjhhBNoamriwQdzT/Xqda6RmDp1KkcddRRbtmzxXB+JRFi4cCEAwWCQE044gW3btgEwc+ZM5s+fj8+Xfqv/1a9+xeLFi5k4cSKHHHIIixcv5pFHHuGtt95i//79nHzyyZgZF198MWvWrOGBBx6gpaWFiy66iAULFtDV1cXjjz/Ou9/9bpqamvj0pz9NNBoF4Mwzz8TMMDNOOumkVFimTp3KiSeeSCAQSAvLoYceygknnABAQ0MDc+bMYfv27QA8+OCDXHLJJQBccsklrFmzZsTpKSIiIkNTsgqjmd0EfBVoAh43s6sGrP6C914iIiO3Zs0aPvzhD3PMMccwceJEnnvuOQCeeeYZvv71r7Nhwwbuuusuxo8fz5/+9Cf+9Kc/8YMf/IDXXnuNcDjM6tWree6551i3bh3/+3//71Q3zaGcK5u//vWvqe6on//85zPWv/rqq7z66qscffTRB43n3r17Wbt2LYsWLcq53fbt2zniiCNS7xsbG9m+fTvbt2+nsbExY/nHP/5xmpub+clPfsLzzz+PmXHppZdy3333sX79emKxGHfccUfaOXp7e/nxj3/Mhz/84YOGO+n111/nz3/+M+9973sB2LFjB4ceeiiQqFju3Lkz72OJiIhIYZSyhXEJcKpz7kvAe4CPmNmt/eushOEQkTFm5cqVLFu2DIBly5axcuVKAE466SRmzZoFwKOPPsqPfvQjFixYwHvf+152797N5s2bcc5x/fXXM3/+fE477TS2b9+es3totnNlM7BL6u23355aft9997FgwQIuuOACvv/97zNx4sScx4nFYlxwwQV88Ytf5Mgjj8y5rVeF18yyLh/s5ZdfZtasWRxzzDFAovXvt7/9bdo2n/vc5/jABz7AKaeckjMsSe3t7Zx33nl85zvfYdy4cXntIyIiIsVXylFS/c65GIBzbq+ZLQHuNLOfAsEShkNExpDdu3fzxBNP8OKLL2Jm9PX1YWaceeaZ1NXVpbZzznHbbbdxxhlnpO2/YsUKWltbefbZZwkEAsycOZPu7u4hneub3/ymZ8Url/PPP5/vfve7eW9/xRVXMHv2bL70pS8ddNvGxkaefPLJ1Ptt27bxoQ99iMbGxlQX0uTyww47LGP/XC2sADfeeCOtra2pbr0H09vby3nnncdFF13E0qUHpuydNm0ab731FoceeihvvfUWU6dOzet4IiIiUjilbGH8q5l9MPnGOdfnnLsMeBmYU8JwiMgY8sADD3DxxRezZcsWXn/9dd544w1mzZrFU089lbbdGWecwR133JEa1fOVV16ho6ODffv2MXXqVAKBAOvWrcv6LOFQzlVoN9xwA/v27eM73/lOXtufccYZPProo+zZs4c9e/bw6KOPcsYZZ3DooYfS0NDAH/7wB5xz/OhHP+Lss88GEs8XtrW1AXDcccfx+uuv85e//AWAH//4x3zwg4nb+w9/+EN+9atfsXLlyoxnJ70457jsssuYM2cO//RP/5S27qyzzkqN4Hr33XenwiIiIiKlU8oK4yeAZwYvdM7dAByRubmIyMitXLmSc89Nn7nnvPPO45577klbdvnllzN37lxOOOEEjj/+eD7zmc8Qi8W46KKLaGlpST3Dd9xxx434XCOxYsUKGhsbU3/btm1LPYd5wgknsGDBgtTIpH/6059obGzkpz/9KZ/5zGeYN28ekBix9Gtf+xonnngiJ554Iv/yL/+S6vJ6xx13cPnll3P00Udz1FFH8ZGPfASASy+9lCuvvJIFCxbgnOO///u/+cQnPkFTUxM+n48rr7wSgCuvvJIdO3Zw8skns2DBAv7t3/4NgLfffpvGxka+/e1vs3z5chobG9m/fz9PP/00P/7xj3niiSdSz3I+/PDDAFx33XU89thjzJ49m8ceeyxtyhAREREpDTtY16KSBMJsq3NueqnO19zc7FpaNJOHjG1m9qxzrrlQx1O+ElG+EikG5SuR4sg3b5V0Wo0cNOiNiIiIiIhIhSnloDe5lL+ZU0QkT7t37/acuuLxxx9n0qRJGcvXr1/Ppz71qbRloVCIP/7xj0ULo4iIiEghlKzCaGa34V0xNGBCqcIhIjJSkyZN4vnnn897+6ampiFtLyIiIlIpStnCmKuzuDqSi4iIiIiIVJiSVRidc3eb2RRgBvAX59zeUp1bREREREREhq5kg96Y2eXAS8BtwCYzO2uI+4fN7Bkz+x8ze8nMbuxfPsvM/mhmm83sPjMLFiH4IiIiIiIiY04pR0n9EjDPOXcy8LfAPw9x/yhwqnPufwELgA+b2d8A3wBudc7NBvYAlxUwzCIiIiIiImNWKSuMPc65VgDn3KtAaCg7u4T2/reB/j8HnAo80L/8buCcwgRXRERERERkbCvloDeNZvaf2d475754sAOYWQ3wLHA0cDvwV2Cvcy7Wv8k24PAs+14BXAEwffr0YUVARNIpX4kUnvKVSOEpX4kMXykrjNcMev/sUA/gnOsDFpjZBGA1MMdrsyz73gncCdDc3Kx5H0UKQPlKpPCUr0QKT/lKZPhKWWH8yYCWwBFxzu01syeBvwEmmJm//9iNwJuFOIeIiIiIiMhYV8pnGJ9J/mNmtw11ZzOb0t+yiJnVAqcBG4F1wMf7N7sEeHDkQRUREREREZFStjDagP/fN4z9DwXu7n+O0Qfc75z7uZltAO41s+XAn4G7Rh5UERERERERKWWFcUT9xZ1zLwDv9lj+KnDSSI4tIiIiIiIimUpZYTzOzF4g0dJ4VP//9L93zrn5JQyLiIiIiIiIHEQpK4xeI5qKiIiIiIhIhSpZhdE5tyWf7czs9865k4sdHhEREREREcmtlKOk5itc7gDI6Gfmw/whfMFazHz4ghHMfP3ralLvfcFazB9K/B+qS3ud19SE+QL4Qv3bDnpNP35t4ripbeo8X0VERCpBrrLKF6rDHwgN2r7mwPoBZadnORmsxXz+QeVipOLLwYOV26OlXB8t8ZDCKWWX1HxpMlUpKjMfForgC0WYfOaXCTXOJbptA60PfTNxc4yMZ8pZX0kt3/WLfyfe083Uc796YNu132TDSy/jizQQOuxYom++zJQl1xBqnMvep1fS9tzPM4+/9hZqGibR17ab+vmn07HhSSZ/5B/T1pv5cC5e7iQSEZExzMyHr25CWrnW/sKjqffJMssfCBHrjSYqfnXj09bvevhW/IccTu+uLYky78UnmPzRA2Xijvu+lrFPJZeDg9NkcHgPtr5ajJZ4SGGZc5VVPzOz55xzJxTzHM3Nza6lpaWYpxjVfGY0hKAtSuo1XmGfI/AO58DXjijEObBs6rKb2LlqOVOX3sDOe68nXAM1NdDRA/VBaO85cOyAD0L+xLK6IPTFoSeWfrykweeeuuwm3vn1fzHxtCsJzzgw1lP3lhfYuWo58WhHSdLHzJ51zjUX6njKVyLKVzI0lVieZis726OJX/SnX/tztn7jY55lW3jGfLZ+42OpMjNUA4GaA2Vldy/UBgaUnX3Q56A3PqDsDEYyysFKyFe+UB30dGbEOxleX6iOqUtvKGu5PlJe1376tf8/e28eJkd13vt/36qurt5mXyRgGCHyQJBA2IkV+xKwuAKhaxYJaXBAkq8xz0Ni+2JjTAyCIGHHQeaCRK5McGximxsgQQLb2jEQNhmM4OdETnwFlmRwUCTEolmk2Xqp3s7vj+qqruqu6u6Z6XX0fp5nnuqpqnPOe9b3vHW2pxsuHkzplFq36nGMmYq/wtQKiQgzQoTt1wWgrW3C9usCmBEiSFRf2WbIefMnvTij1S7vGa36/a4Q4bwuMuPQ/+RdEPEo+p+8C80q0BUi7Fyhu9uxIoDuoB7HgAdo8xN2ZJ7tXBFAV1BvZK3+AXBMq/4n70Ji6CjUnrk2mdWeuRDxaNXTimEYhqk+9ahPDZkg8vVXd4ggAThy/1Wuuu3I/VehO6jrx8eW+dAdyurKWz7lRWcwR3eGCAEF+PJ8xfRHxCM1i38hRDziGG9DXhGPNrRedyuPR+6/qqHiwVSGmhuMRHQhEf295dbnayYMU5QmFdjU58fC2R4oMmHhbA829fnRpBZ3W00MObcfTOKRpXZ5H1mq39/U58fhUWGLA3n1a2eA8OjVdnebr9GfKR5g8zX2Z49e7UdHgPL8c0srpaMH2tH9Npm1o/tBXn+NUoxhGIapJvWoTw2ZQM6yBdXCcjepWf14/564TY8aetdJdz75VsL0p7nO+hMGzS7xNuQlr7+h9XqhfG2keDCVoSYGIxF9nIjWE9F/AVgH4KDxTAjxVi1kYkpjTAMu6pVt9y7qlW1TMOsBQ84Dg2lHeY37htzGbxGPYkwDDg0L13iOxJzT4NCwyPPPzQ//WRdg8NkHETu8DyKVROzwPgzs2sBf8BiGYU4S6lGfGjIVkq3UZ7n6100fHxoWGI7Z/alHiuWXiEcxsGtDw+r1QvFrpHgwlaFqBiMRnU1E3ySiAwC+B+A96GsoFwohHqqWHMzUaFKB146kbPdeO5KqyxHG146kMKdTcpTXuG/IbfwWIo0mFZjdSq7xbPE5p8HsVsrzz82PsV/vQmqkH/1b1+HIA8v1tQHhYV5QzjAMc5JQj/rUkKmQbKU+y9W/bvp4diuh1Wf3px4pll9CpJEODzesXi8Uv0aKB1MZqjnCeBDApQCWCCEuyhiJqSJumDpjTANWbY1i96EkEimB3YeSWLU1WndfBA05l53jwY077fLeuFO/v2prFLOaKS8OYxowGBG4YYfd3cot+juJJLByi/3ZDTuiGIqIPP/c0iqthXXlknNlGIZhTg7qUZ8aMkE4yxbWCss9pmX14x0Xem161NC7TrpzxXlKXcS/EKXkVyPr9ULxa6R4MJWharukEtFyACsA/CmA5wA8CeDHQojZVRHAAu86NzXqcVc3Jya6S6o1DhJRxXZJrZe0qodd5xhmusH1ipkI9ahPi+2SmhaioNwS0ZR2SXWKf73Uq3rMr3Iy3ePH5FNq3araOYxCiG0AthFREMAyALcCmEFEPwCwTQjxfLVkYaZGozQeU5GzUeLIMAzDNC71qGtKkanQO/UYp3IxneMGTP/4MZOn6pveCCHCQognhBBXAegB8BsAdxrPiait2jIxDMMwDMMwDMMw+dT0WA0hxHEhxD8IIS6x3H6pZgIxDMMwDMMwDMMwJjU/h9GB+joBnmEYhmEYhmEY5iSlHg1GnkDNMAzDMAzDMAxTB9SjwcgwDMMwDMMwDMPUAfVoMDpOSSWi04loNxEdIKLfEtEtmfvtRPQCEb2TufKmOQzDMAzDMAzDMGWgZgYjEXUTUa/xZ3l0qYuTJIBvCCHmAPhvAL5CRHOh77D6khDiLOgb5tzp4p5hGIZhGIZhGIaZAFU3GIloKRG9A+AQgFcA/BeAZ43nQojjTu6EEB8KIf4983sMwAEApwG4GsBjmdceg37GI8MwDMMwDMMwDDNFajHCeA/0EcK3hRCzoY8o7pmIB0R0BoA/AvArADOEEB8CulEJoNvFzReJaC8R7R0YGJi89AzDmHC9Ypjyw/WKYcoP1yuGmTy1MBgTQoghABIRSUKI3QA+XqpjIgoB2ALg60KI0VLdCSF+KISYL4SY39XVNXGpGYbJg+sVw5QfrlcMU364XjHM5PHUIMzhjNH3SwBPEFE/9PWJRSEiBbqx+IQQYmvm9jEiOkUI8SERnQKgvyJSMwzDMAzDMAzDnGTUYoTxagARAF8H8ByA/wSwpJgjIiIAjwA4IIT4P5ZHOwF8IfP7CwB2lFVahmEYhmEYhmGYk5SqjzAKIcJENAvAWUKIx4goAEAuwemFAD4P4E0i+k3m3l0A7gPwEyK6EcARAH9WCbkZhmEYhmEYhmFONqpuMBLRXwD4IoB2AH8AfafTh+F+nAYAQAjxGlzOaCzmlmEYhmEYhmEYhpk4tZiS+hXoo4WjACCEeAcuO5syDMMwDMMwDMMwtaMWBqMmhIgb/xCRB4CogRwMwzAMwzAMwzBMAWphML5CRHcB8BPRZQB+CmBXDeRgGIZhGIZhGIZhClALg/EOAAMA3gTwJQDPAFhbAzkYhmEYhmEYhmGYAlR10xsikgDsE0KcB+BH1QybYRiGYRiGYRiGmRhVHWEUQqQB/D8i6q1muAzDMAzDMAzDMMzEqfqxGgBOAfBbIvpXAOHMPSGEuLoGsjAMwzAMwzAMwzAu1MJg/LblNwG4CMDKGsjBZCCSQF4/RDxqXvXB4MLPqh2ucQXJIEWBiMccZSKSQIoPIhEDKSpEOgWkBSARSJIhEpr+PJ0CUoms/7luUmlAlkGSlHWTiIG8vkzY+hWykn3HfOYsu/WqkIAWi5QlLRmGYZj6gEjO6AI3HRCDEKmKywCSLLrSBxGPA0jrvxMJQKRAXj+kdBLJhGZxKznru0QcEGkIkQaRlKOLM/oQZA/Xomshe0Cyx/Z+nj4FytbHKAeV7AMxlace868eZSqFqhuMQohXiOjjAFYBuBbAIQAPV1sORodIghRsRdeS26H2zIV2dD8Gdm3QlQHg+myqhXsy4cpNHVBnno3IO2+4ykQkQQq0oGvpavP54M//FqnoOORAMzqvuNV2v3XBF5A4/j7G9/0LupZk3Rzbcg/II0FSA6ab4T2bMb7veVvYgz/fiHRSQ9PHL0d4/y/QefktNrnUU/8QXVff6f6/L8BGI8MwzDSBSIYUbMnTUaHzF6P1wpUWnSVXzGgkkgGPAkkN5MmhdM5C6wXXYmDXBgTOugDBcy7CwK4N8CgqkgnN1KGhj/0PR52WjkV0Pe3xOvqfDo9ACjTZ9eQzG+E/808QffffbDrYKV3S4eGy9DHKk47u/ZR6kI8pTD3mXz3KVCpVW8NIRGcT0TeJ6ACA7wF4DwAJIRYKIR6qlhyMHfL60bXkdvhmnQ+SPfDNOh9dS24Hef0Fn9Ui3OTxDxA5+GpBmcjrR9fS1bbnnVd+AyR70HnFrXn3R3/1M0TfeQNdS+xuSJIg+5ttbqIZQ9Xux62Q1SCi77yBzstvyZNLe++tgv8nBE05LRmGYZj6gLw+Rx0VfeeNHJ3lq6gMJHsc5Yh/+Lb5O3LwVfN3WvJk3Oo61E2nkcej62kX/53i33nFrYgcfDVPBzuni78sfYxyUMk+EFN56jH/6lGmUqnmCONBAL8EsEQI8XsAIKJbqxg+44CIR6H2zLXdU3vm6lM/M7/dntUiXFFEJld/tYjj/cTQUcfwhBZBMsevxNBRRz+SI8dcZU7HwgX/L0daMgzDMPWBmw4ydI3xfyXb/lL0t1UfOelQN30ntIjtfyf/3XRhrdNlohTrpzD1TT3mXz3KVCrV3CX1GgAfAdhNRD8iokuhr2Fkagh5/dCO7rfd047uN7/yuT2rVbiSL1hQJle3asDxvtLRA6WjJ+8ZqQF4WmbY7ju9px3dD0/LDNdnki9Y8P9G+KrEMAzDlIabDlI6emz/V7LtJ6/fVecZ4Vr1kZMOddNppAaK+u+mC0tNl3rRi5XsAzGVpx7zrx5lKpWqGYxCiG1CiOsAnAPgFwBuBTCDiH5ARIurJQdjR8SjGNi1AbHD+yBSScQO78PArg36aF6BZ7UI19N+KgLnLCgok4hHMbBzve354M//FiKVxOAzG/PuN3/qs/CfdQEGdtndiHQaqeiozY3+3oYcPzYipYXhP+sCDD77YJ5c6unnFfxfITHltGQYhmHqAxGPOeoo/1kX5OisWEVlEKmkoxzeU842fwfOWWD+ltLJjFtdh7rpNJFM6nraxX+n+A8+sxGBcxbk6WDndInWzWhLJftATOWpx/yrR5lKhYSoXYeViNoB/BmA64QQl1Qr3Pnz54u9e/dWK7i6h3dJPTl3SSWiXwsh5pfLP65XDMP1iuFdUiuxS2qt6lWj7mjJ6NRj/tWbTKXWrVocq2EihDgO4B8yf0yNKFRQK1mIKxVurRsDhmEY5uSl0sZgpWUoRYeeLHr2ZInndKUe868eZSqFaq5hZBiGYRiGYRiGYRoINhgZhmEYhmEYhmEYRxrGYCSi/0tE/UT0luVeOxG9QETvZK5ttZSRYRiGYRiGYRhmOtEwBiOARwF8JufenQBeEkKcBeClzP8MwzAMwzAMwzBMGWgYg1EI8SqA4zm3rwbwWOb3YwCWVVUohmEYhmEYhmGYaUzDGIwuzBBCfAgAmWu324tE9EUi2ktEewcGBqomIMNMZ7heMUz54XrFMOWH6xXDTJ5GNxhLRgjxQyHEfCHE/K6urlqLwzDTAq5XDFN+uF4xTPnhesUwk6fRDcZjRHQKAGSu/TWWh2EYhmEYhmEYZtrQ6AbjTgBfyPz+AoAdNZSFYRiGYRiGYRhmWtEwBiMRbQbwBoA/JKKjRHQjgPsAXEZE7wC4LPM/wzAMwzAMwzAMUwY8tRagVIQQK10eXVpVQRiGYRiGYRiGYU4SGmaEkWEYhmEYhmEYhqkubDAyDMMwDMMwDMMwjrDB2IAQSZDUQPYqKSAqPStVXwCSGgSRBPKodr9IhuT16/8bV9vz7NXuNuh4dX4WyPrvUUEkW+Jlf5dhGIZhqo0kKyBJydN7xWjr6LTpsbaOznx/PapFzwby3i1FF547b172HW+uTs3Xt8a9c+fNM92TlC+LJCsAgEWLF+fofV2+to5OEHls9xctXqzft/pl6ZuU1ofIylkPOMqb06cp1K9RfYGSwypWZgqxaPFiyD7drezT84IpDPc1J0fDrGFkdIgkSMFWdC25HWrPXGhH92Ng1waktQiIJAiRLuhe9QWQ9PjQveR2JMcGMfzLf0LnFbdC7ZmL4T2bMb7vebvfO9dDbu5EamzIdn/w5xuRTmrw9cyF9sHvEDp/McL7f4HOy2/Jk6u7by3UnrkY2HEftA8OomvJ6qw/z2w0ZXeKVylxYhiGYZhyIckKhCRDUgMT0kltHZ0Y1dKmzjPctHV04sTQoG6MKSpkNWDqXeOd0PmLMbrv+ZJ04bnz5uHgofchUilIgRZ0Lc3XqU1/fBVaL1xpuocko+vKv8TBXRug+gKIp9Igr26w2WTZud7sQDv2NcLDefd371qPdCwKOdhi86t/23dAsmLKZ+1j5PY/rP7XWu+79rPCwyA1gO7la3DsyTWu78jNnUhoEai+ALRYpGBYxcpMIRYtXozdr+9F1/K1lrzYgEWLF+PF558vZ5JMG7ivOXnYrG4wyOtH15Lb4Zt1Pkj2wDfrfHQtuR0ke0Bef1H3CUGm+9Ff/QydV9xq+hV95418v5euRvL4B3n3O6+8FbIahPbeW+hacjui77yBzstvcZTLuKe/u9ruzxW3QvY3u8erhDgxDMMwTNnweEGyZ8I6aWQ86uhmZDxq+iv7m21613jH0L+l6MIDb7+b0a8yupY669ToO2/Y3AstYv5OCALJHmdZlq4Gef0F5ci/vxrk8eT5JfubbfJZ+xi5/Q+r/7XW+4XiLvub9fsF3jHyICGoaFhFy0wBdr+6x9Ht7lf3lCMZpiXc15w8PMLYYIh4FGrPXNs9tWcuhFb4K5aT+8TQUZtfuf+bfruEmRw5BggBtWeuu1uLXOlYuKg/TmEzDMMwTLUw9M5EdZKrfs64EfEokomY4zuGDi3mhzWcgrrZxb31d7JIWBPpDwgtknc/OXLMtY9RqL9RawrlQTIRK/qOtV8zlbCKkdac3aa12qdhvTKV9D7Z4RHGBoO8fmhH99vuaUf3g9RASV9IrO6Vjh6bX7n/m367hOlpmQHJF4R2dL+7WzU7j99418kf13jxVx+GYRimipDXD1IDE9ZJxfQYef3wtMxwfMfQoaXoQuOdQrpZ6ehxdG/8JjXgKosxyjeR/oBTeuX6b+0nFOpv1FrvF4q7p2VG0Xes/ZqphFUMSXV2K6ncb3KD+5qThw3GBkPEoxjYtQGxw/sgUknEDu/DwK4NEKlkSV9IFBKm++ZPfRaDz2w0/fKfdUG+3zvXw9N+at79wZ9vREoLQz39PAzs2gD/WRdg8NkHHeUy7unvrrf788xGpKKj7vHirz4MwzBMNUnGIVLJCeuklpDf0U1LyG/6m4qO2vSu8Y6hf0vRhXPOPjOjX1MY2OmsU/1nXWBzT2rA/K2QgEglnWXZuR4iHi0oR/799RDJZJ5fqeioTT5rHyO3/2H1v9Z6v1DcU9FR/X6Bd4w8UKj4CGPRMlOAhQsudHS7cMGF5UiGaQn3NScPiRKGzKcb8+fPF3v37i34jkSEJhUY02Be05m0mugz4xr0AuF4/n1FAlQPMB7X30mlgFgq62euvyEvkMy806QCoxoAUMkLdlVfAAlBegWRnRNU5wAAIABJREFUFZAsQ8RjIK8PIq6BFC9EQgMpqn71+izPs1e7Wz9EPJp3BeDwLOOPokKk00AqCSFS+q5jOe/yIuTKQUS/FkLML5d/pdQrhpnuTOd6pSoe+OSUqb9iKRlaIun6fnMoAEpGzfeFx4/R8YjjszFN/4IdtOjHBBREYnHTv66ONsTDw4761XoNx4Fkemp9G0lWIARAimLTe246qZDuVzyyLd1GNYAUX0bP+iASug5tCfmR0iIYD0dtfoxqQG5f7dx583Dg7Xd1fan4IBJRNFvcxBJAIg34PIBH0vsXbn0I411A748k0tl3fTLgke3urX0Xn2KX0ypDWAPSyL5v4PMAsaTutyzr+RXy6mEI5McVqG69cspLa9+sUNmzXgH7PaM859ajMQ2AN2D2fVpC/qIb3hgsWrwYL7/wQtEyV6yulpoO6WlgM3Bf006pdYtHGB2QiDAjRNh+XQDa2iZsvy6AGSGCRDThZ2e0Em7+pBdntBIkIO95d5DgV4AdK/R7O1cE0BUiNKu6HE7y7Mi889gyH7ZfF8DMEIFQeiXWYhGktTCESEMkNaS1CIRIZ64ppDOVx7zanmevdrdhx6vzs0jW/6QGIVIA4PguwzAMU3tUxYN2X9qmv9p9aaiK81YIzaEAghSzvR+kGJpDAcdn3UFCSIXtXouSRMDnBaAbi574CC45Q3bUwTd/0mv+3xUkeKTiG44UIp1KQKQTeXrPCUNHJ5L5Or5FBdpUe7rNDBEUkcjowawOTWkRhMPRPD9mhgjNIfsxDb99801TT4a8wMwcN+0BwqLZMrqCZPYvjD7J1z7ltfUh2gOEgEfvECbS2Tg8tsyHrlC++5s/6UWTF2gP5Mt5yRlyNh9ChPO6yMwjAGhRdWOxWQW6QoSdK7L9mu4ggZDt+9QCt37cLZ/yIpbKz98ZIcKY5nwfgPn7kjNktChJSER59WhGiKCkNbMclGosAsDLL7xQUpkrVFdLTQejr9vocF9zcrDB6ECTCmzq82PhbA8UmbBwtgeb+vxoUif+7JGlfmw/mMQjS/0A5T/ffI0fnQGy3Xv0av1ek+ouz6NX+3H/nrgtfIZhGIapBD455aj7fHLK8X1KRh3fp2TU8ZmTLtzU54eCBAAgHh7Gpj4/XjnsLMf2g0nb/0Fv9dLG0NGKJ19XdwQIm68pLd0oGUXIpY9BSfcpc25p/asPUnj0auc+SW4fQvHoo7vW/sb9e+Ku7iXJWc5XDqds/x8eFbZ+Skemb9MZoDy/N1+T7UvVikL9uEL9P7f71nQp9K5bPZqMvBMpcxPxl/uaJzdsMFowDvEc04CLemXbs4t6ZYxpmNSzA4Np8x2n54eGheM9Y0pDIX+t4TMMwzAnD8UO7bYd7m45tD2XUt4rpPuc3E5GVzrpwjENkH1ZvTwcK6wPre7KQSkHoxuyOcXr0LBwTQcnfwqnc/Yg+dNO7zXlKuSmUJ/E2ocYieWHb7zn5H7EJR+GY87xNH4bfZtC6VLL/kyhNJtoOlvjbpTbidajYuVvqmVuounAfc2TFzYYMxiHeXb3rUWTCrx2xP4l5rUjKfPL10SfzemUzHecns9uJcd71hFGN3+t4TMMwzAnB9ZDu3tv24au5Wux+/W9ZofSONy9u09/3t23FgcPvZ9nDJb6XiHd5+R2MrrSSRc2qUDX8qxebvUV1odWd1OlWBobGLI5xWt2K7mmQy7F+hjdfXdn0vlufHRiHKE/XoKOK28t6KZQn8Tah2jx5YdvvOfkvsUlH1p9zvE0fht9m0LpUusRRrc4TzSdrXE3yu1E6lEw1Fy0/E21zE00HbivefLCBmMG62GeYxqwamsUuw8lkUgJ7D6UxKqtUfPL10Se3bgzimXneHDjzigg8p+v3BLFYETY7t2wQ79nHWHMdXfDjijuuNBrC59hGIY5OSh2aLdxuHvu8wNvv2vzp9T3YinZRfeRo9uJ6konXbhqaxRjcdmmly+e5SzHsnM8tv/DcUyZUg9GN2RLJPPjNRQRWLnFOR1yER4/xt3SLankyRF95w2M/upnGEsqjm4+daqMG3Y490ly+xCJpL5JjTVv7rjQ6+o+nXaW8+JZsu3/Wc1ki/NQpm8zGBF5fq/cEq2LEUa3flyhMu1235ouhd/Nr0fRRKpo+XPyz63MxVJygZgXTwfua57c8C6pGYgk9N62DSR7cHj9UpBI5+0OJaB//SSIvGe9dzwNkUriyAPLbLuETWWXVGHZ+bTQLqnTZecqprpM590cGaZWVKteWXWWga6DlkOIdNHnpfpjJXd3x1EN6L1tu6NbAJAUL0KSZr4/nlaRTiYAIdAU9BXdJdXq/+H7l0D2BRAU4arskjrRtHHbJXVU0/1p8qazz+IShBCOm200hwKOu6Q6pvPf9gEAer+xFUcfvM6W1rXYJTUvHya5S6rTru/VrFeAyOvHlWOXVCMf3/vbPlt5KFSPipU/IsmxTypg37F2orukuvtb+o78TGNQat0qfcukaY5xmKdv1vlQOnrQvujL8M06H22Z5/7D+9C/dR0AfdrA8RcfRvdy+zva0f3wtJ6CqEfJe2a4716x1vF+W99atM8635QnZgkPYIOQYRiGyWIc2u2z6A3rod1WnWZ9nntAdanvAcjrcEpq0NUtkT6VNFffDT33ENLREYyOhyH7gnk6MXZ4H8a3rcNITH9u+E9ePzqXrXHUn2kRnmDqlUaxNLbipqMlNYjuPme974Rx7IjVPankKIfS0WP+Pv0vtwAA2qCnYfTFh4HRQTRnwm7PCbv7z9Yi+eLDaLvx+7b+RlvmfatfQ889BGAE/uVr0ZbzrH/rOvTe8RO73w55OrZ1nZkORzZeC7m1Bc2fuRm+WeejI/NeoEC6VAvy+iEHWuD/zM1oy8hqxMfAiE/b139ipkPkuYfQ9qUf5fftMu8AwNh3r4N2dD96V+8EkE3Dsa3rXOtRsfJHXr9r+RqJTb5eFPKXOTnhKakZrId5Fj5QNuZ6UP3gz/8WKS3ifoi9w4Ghg88+CE/7qXyQKMMwDFMyxQ7tNg53z30+5+wzbf6U+p4ThdwuXHCh66HyhozF4mB97j3l7EnLOVnKcTD6VNIX0PsmIpV09MN/1gWO/ZXBZzai+VOfde1beNpPNd8x7kH2Qm7ucs0zubnL1a9i/Zy8+4k4UtHR/H7WzvX6WZc17PuIeNQuW4H4WOVORUfz30kkbPdEWjj6BdnrnL+KXLz8JePOfianNiebD7hn8tCnRTT2H4DPAPgdgN8DuLPY+5/4xCeEEwAJ8gYEQAKyV5DXb7uXfU8y72WvfgHZm/2dd5V0fx2f5/plD49hKgGAvaKM9dCtXjHMyUQ169Wll10mJFXXF5IaEJdedpnt+dzzzrPplbnnnefoT6nvTdTtpZddltVzik9AVvNkLBYH63NAnrSck6WYfKUwlfQVQu+bgBRb3+HUntOz6SJ7BSnWdM7ti+T0MRRrf8X+DJDy/SJPRgbZ5idJHlMG535P9prb/8nK7cvKlLnvnAbVq1e5shXut/kt72TTMhBsEv/rpq8IJdBkT1/yOOaLV/U7lpFSyh9JHptbkjwTKl+F0oH7pdOfUutWw69hJCIZwNsALgNwFMC/AVgphNjv5obXWjEMr2FkmErA9Yphyg/XK4apDKXWrekwJfWTAH4vhHhXCBEH8CSAq2ssE8MwDMMwDMMwTMMzHQzG0wC8Z/n/aOYewzAMwzAMwzAMMwWmg8FIDvfy5tkS0ReJaC8R7R0YGKiCWAwz/eF6xTDlh+sVw5QfrlcMM3mmg8F4FMDplv97AHyQ+5IQ4odCiPlCiPldXV1VE45hpjNcrxim/HC9Ypjyw/WKYSbPdNj0xgN905tLAbwPfdObVUKI3xZwMwDgcHUkLBudAAZrLcQkaWTZgekr/ywhRNm0pqVeNXp6ucHxajxqEbdK1SuD6ZxfbpxscT7Z4gsUj3Ol69VE5ak3GkneRpIVaCx5JyNrSXWr4Q1GACCiKwB8F4AM4P8KIb5TY5HKDhHtLecOYdWkkWUHWP56D69acLwaj+kYt+kYp2KcbHE+2eIL1F+c602eYjSSvI0kK9BY8lZSVk8lPK02QohnADxTazkYhmEYhmEYhmGmE9NhDSPDMAzDMAzDMAxTAdhgbBx+WGsBpkAjyw6w/PUeXrXgeDUe0zFu0zFOxTjZ4nyyxReovzjXmzzFaCR5G0lWoLHkrZis02INI8MwDMMwDMMwDFN+eISRYRiGYRiGYRiGcYQNRoZhGIZhGIZhGMYRNhgZhmEYhmEYhmEYR9hgZBiGYRiGYRiGYRxhg5FhGIZhGIZhGIZxhA1GhmEYhmEYhmEYxhE2GBmGYRiGYRiGYRhH2GBkGIZhGIZhGIZhHGGDkWEYhmEYhmEYhnGEDUaGYRiGYRiGYRjGETYYGYZhGIZhGIZhGEfYYGQYhmEYhmEYhmEcYYORYRiGYRiGYRiGccRTawFqQWdnpzjjjDNqLQbD1JRf//rXg0KIrnL5x/WKYbheMUwl4HrFMJWh1Lp1UhqMZ5xxBvbu3VtrMRimphDR4XL6x/WKYbheMUwl4HrFMJWh1LrFU1IZhmEYhmEYhmEYR9hgZBiGYRiGYRiGYRxhg5FhGIZhGIZhGIZxpGEMRiL6QyL6jeVvlIi+TkTtRPQCEb2TubbVUk6PLKHFR5CI8q5NqvN9VSY0q9l3Ah79t81fKft+s+WdFh+BiHDa6b0ly3jTV74Kb7AZRBIkxQfJG9B/e/0gyQNJzfyvBnP+z1xz4kBE+ruGm8y1raMT586bZ7snyUrWH68f5FFzwsq+a43TosWLIfv0ZyQpNhmd4q76Aq7yGlcP2e8RSfqfx5txm+/O6Z5bvpLL+7n55+aHKuu/A55s+XArW7lX6+96IFs28mXNfyd7LUe4trKbuVaaQnVkOlGJPKtXnOoZwzDTm/w2Lr9td34v2xZWuu1o6+jM63tNFrd+hDWujYRTvtRab1Uj/EqUuYbR7kKI3wkhPi6E+DiATwCIANgG4E4ALwkhzgLwUub/muCRJUhCYEwDZoQI268LQFvbhO3XBTAjRAjHnd35FWDHCv3dnSsC6AoRmlWYGeyRCF3BrH87Mu88tsyH7dcFMDNE+ODoeyUZjTd95av44WOb0L70r9Bx5a0QqRRgFCSSAJEGYC9Yvlkfg9zSje6+u+FpasuL28wQAfFI1gFJkPzNGNXS2P/Wb21+iXQK3X13o/e2bei+5puQgy3ouPJWNH1iCSCE7d0Pjr6P007vxaLFi7H79b3oWr4WoY9fDinQlPWjby0+OjFuizuRhAQIIh6F3yPQHbTLe0Yr4eZPetEVIpzXRWb+EPTw5WArkIhjZk48AeTdM/LVKb8p8/4lZ8i257n516xm49wVyN5vDxC8EuCRgfE40OoDzm4nm183f9LrGLZCMH/XumOrN4Z6GXGSxDDW3d1OJVwAJJfV32JIRK51RAq2ThujikiCFGxFd99asy5Op/hZccrTeqhbDMNUjmxbpvcNRDyCoAeQMtVeIiDogf5x2OuHp/3UvLaw0m1HW0cnRrW0rR0e1dKTMhqtsvrk/H7NzFBjGY1OOgqgmuqtaujNSpW5RtXslwL4TyHEYQBXA3gsc/8xAMuqJYR1pM4bbAbSAh1BQpMKbOrzY+FsDxSZsHC2B5v6/ACcDYtRDbjyiQg6N4zh0scjGI4JjGpAU8aQCHrz/Xv0aj/u3xM3/W5WgQ/7h4rK/ONHH4f31D/EwPZ7MfTzjZACIbPgNn1iSX5BDjQj+s6/ovPyW+CbdT4C8eOOcTNkBREge5Aa6Uc6Mu5YMY799K9Bsge+Weej84pbMfqrn2H0/9sCKdiS824LPjh6FC+/sgdyUwd8s85H5OCr6FpyO3yzzjf96Fpyuxl31ReAFGwBtChmhginNhE2X2OX95Glfmw/mMSmPj8OjwpbHMjrR+cVtyKkpPLi6Zavxe6/cjjfLyP/PhoXaPERdmY+GPxoiR/f+oWGj8YFNvX54bN8TNhybQDvjQJCCCz6pwj+6B/CeOLNhGPYPi/y86aGSIFmAMgz3ruD2QbMqaxMGY835wPD3eXxtwCFykPXkttBXl9Fw68W5PU51sXpEj8rhfKUYZjpi66XdP3hlYAmlbD1Wl2Hbb02gCZV/7Db3bcWqbEh9P/s27a2cKpth3V2lewLYtHixbbnI+NRx3Z4ZDw64bjaZPU4y93cQG2ek45y11v+mslUbr1ZKX3VqAbjCgCbM79nCCE+BIDMtdvJARF9kYj2EtHegYGBKQtgHanrvW0b2pf+FYIqMDgukBbAon+K4Lzvj2PzmwkAwEW9smsmhrxAiz/fkBzT9LDGNN29lYt6ZRwYTJu/xzRAxIs3EInIOLQPfoeuZXdB6TgdXUtWmwU3+s4bLgVZhdozt6AsYxr0DvnyNZDUAEASSFGc/ZOzp7moPXORGDrq/q7XZzbEx566G+lY2JTF6ocR94QgdC1ZjSZVN7gODQvXtDPkzk3D/ifvwng8P565cf/YD8aw7KkIxjTgmp9EcPMzUVsYxvvDMff8u+vlGB69Ot+g/c4vNd2POGzPNl/jR8hLiK1pwkOX+3DYJX658aoUpdQrUnzoWroaIS/yjHc9PpVpRMnrA8meqhs1Yxrw0/0JtN8/Cunbo2i/fxQ/3Z/AmGaU1VjFwq4mIh5zqYvTI35WCrV7laDc+ophmInXq1y9pHqATTk6bNM1fqgemLpF++B3ALJtYaG2wzot8dx58/LCt86u6r1tG7qWr8Xu1/fajEYRj7q0wxH7Ehel+Cl6Vlmr3eZVAicd5Z5eEzewyyVTufVmpfKu4QxGIvICWArgpxNxJ4T4oRBivhBiflfX1M9+/fGjj6MzpyM6pgGdIX20yOjQr3k5hs1vJvDakZRrJo7HC38NaFKB146kbO5eO5LCnE7J/G2MjhWDvH6zAUwcP2oruImho64FWTu6v6AsTSoQ+d0eHH/xYaQjwyBFKaliakf3Q+noKViJrA2x5Auaslj9MOJuhGmk9ZxOyTXtDLlz07BJBc5sozx31rh3rR/F4RGB8Tgwt0vCTX/ixc8OJE2j0fDvtSMptPqc06y3hVwNvgODaVseW58dGhZmOXGS0ylelaKUeiUSet46GeFG+a9EIyriMQgtUnWjRpGAzW8m0O4nEAHtfsLmNxNQJKOsTo8ROPL6MLDjPrz34Aocvn8J3ntwBQZ23Ddt4melULtXCcqtrxiGmXi9ytVLhXQYkO3fWNvCgm0HEUIfvxzdfWtx8ND7eUbj7lf3OH7w3P3qHvMd8vrx3gPLcOK7V+HI/VfhxHevwnsPLMtbPtPuSxc1Gq2yVrvNqwTk9eX1F8nrd+lDVkdvOclU7vArlXcNZzACuBzAvwshjmX+P0ZEpwBA5tpfaQEUWYI/NYb+J+/Cie9ehcP3X4X3HlzhOIL4yFI/7t4dw6qtUddMDHoLfw0Ix4FVW6PYfSiJREpg96EkbtgRxR0XerH7UBKrtkYxqgGndHcUld1qxCkdPbaCm/s/kDXGBp99ELHD+xDxtufJsmprFGEKYvjVx9G+6Mvo/UZ26t/Ajvsc/ROpJGKH92HwmY1o/tRnC1Ri3RA0GuLAOQswsGsDYof3mX4M7Npgxt3wx0jrNZ9WceNOu7w37oxi2TkerNoaxaxmMuNgjDCOacC7JwSWPRWB9O1RtNynjxSNaXo+yN8ehSwRtl2X/TCw6c0EbvwjBU+8mbD5d+POKC6eJTvmXzLlbJi+diSF2a2ElVt0OXOfWY3Ib12sYuWW/PyIxWGTo5aQokI7uh9Br3v5r0QjSl4fSA1geM9mfPDITTi8fik+eOQmDO/ZXFHlIBPQ7CP8aIkfsTX6NONmH0EmYGDXBoh4A32iLYCIx8zZCr23bUPXsrugffC7aTnCGHNog416xjDM9IS8Ppv+KKTDAOMD+Om2trBQ29G9fA0i77yByNuvQ27qwIG337WNOKY154/uaU3/ML1o8WKIeCRvqVOzy2w2n2yXPRejj7P7UBKJpLPcte5PTASRSmLwmY22/qKIxxz7kNXSW9UI35aPZcw7EjkbjdQ7RPQkgH8RQvxj5v8NAIaEEPcR0Z0A2oUQqwv5MX/+fLF3795Jha/IEjoDemW8qFfGa0dSWLU1imNRD5BKQFvbBEXOrstKpATUdWPm/zNClOd2JCrw888FsHB21jjYfSiJZU9FMBLT88cjEYJevSCEvEAyBcRS+peEUQ04ted0vP/ekaLye4PNaF/6V/DNOh/h/a9g+NXH0XH5LVB75mJ4z2aM73seXUtuh9ozF9rR/RjYtQHp8AgAAfL6IOIxEASaVF2WJhUIS02Qgm1oX/Rl+Gadb4YVO7wP/VvXobtvbY5/wwB0Q0Kk00BKn7YrBVvzwibJg56b/tH0S8SjAMmZEcwYyOvHKd0dZtxVXwBJjw8iPIwZIT2fjo6m8e1XNLx7IkduDUgD5r3uFfdC7ZmL9x5Y5phPx8b1vGhS9Q1lcvPr5mdj2D+Q1r/iCH0551c/6cX2g0nzfm7+PbbMh2/9QsMjS7NhrdwSxagmkEgBHQF9Dabx7IYdUdx3qQ8r5ylmuFc8EYHXk42X0SgYv9MudZyIfi2EmF+00JSIW70ijwo52AJveABdIcKjV9vjMzAuEIUXcrAFnVfcaub/4DMbkQqPQCQn18oRSfoGTIFmhzI9DCHSU42yI02qPssgt3wsfTKC8YT+YUikkxUJu5pIagDdfXc71Pl7kNYiBVw2HvpuxYDiAUZiQIsPSCSBSDK/flWrXjHMyUQt6pWxQYmhPwa+uwxdQQcdFhZo/uy9GHr2QbQuuB7BuRebbSHiUde2o/eOpxE7vA/HnrrbRU+NYsaKe/La2IFt65CKhSH7ggghktcfkf9mFLE1zn1Rt/4AAAQ8ZPYrc/sRxlUmIJFuDLuBSELHlfo+GYmho1A6etD8qc9i6Ocb9YGLeNS8AqhYn8CKpAbQ9ImliL7zhimT/6wLMPbrnWXTm8bmNrl5Bzj3B0utW8UnNdcRRBQAcBmAL1lu3wfgJ0R0I4AjAP6skjIEFIFNfdnKaXy5WfZUBPDoX5usFdcYBh65sxnSt0fRPy7MdW9NKjCu6ftvrdoazTNQRi395GSZKuif33A9fvjYBnQuuR2BP7wQ8cEj6N+2DkKLgtQARCqN/i1/A5HQdIMuEUfnkm9g5I2nMoX7dCSG3sNoZs3iiZd/DPG7PUgMvec6/dQw9Mjrh0ICmkulPO30XnxoeReyFz3/60fmF5g5Z5+J3775ZsH4abEIVF8ACa8fH41HbGk9uxX4z1uazXcNo3xMBNC9Yq3ZKFtHioGcPEbhNaXWCjqqAY/8RyIvXz8aF+i9bTuGH1qGnmYJ37nEh5ufjeHAYBqzWwmKBETWNGP3oSQ+88/ZOIS8+q5sM0OEREqY/mkpIJqs4wY8lUBaiyCWAkZiAn+xK4pDwwKzWwkjMYFYCgASaP3053H8xYfNRrT105/H0M83Tj5cWQF5smtjgew6k/6t95Qlak6EXaYthePAjBX3oH/L31Qs7GpyMq1hDHmBS2fLeOWw/oWeAFz2BzJeOlT4iz3DMI2LdQ0jAMSSwLqFqqmv53RKWLdQxRe2x5B84WHTWASybWFTgbbjyP1X6UthBFz01DoM7NqQZ0guXHAhACCtRTGGfH1jLLlx6osWQksBzSrQGSAAAjdnPngfGEzj9GYJy87x4KF/bZxpFaSo8DR14tQbv2/eix3eB0/rTJz2pR/Z7vVvXVcVmUQ8htYLV6Jtweez91JJjL7xk7KF0aQStl/ndxiEmto6zYYyGIUQEQAdOfeGoO+aWhWKLSZ1MvyM6YFuI1PLnorgoxxDclQDyBuoSBxSWgT9274DoUV0IzGZRPMF1yKy/xfmaKPeMK2HSGi2UUjrfe3ofrRf9mUAwPhbL0M7ut/2JcyYUprWwuY947wgwyhsCflxYmgQAGwjpOfOm4cDb7+LIw8sB3n9JRmLZrix7Fca2RdE94q16H9yDQKKnt7WvBnTAAH7tI9ieWysScxtjEOZEWC59RSMjnyE3tu24f3vfQ7LngpnRzUpCKWzA9rR/ea0gU19fvzHl4J47UgKN+6MYv1lPjPMRBrQ7tSN3N2Hklj8uN0ILjSCWE+IuAa/Ry/XAgJCAAMR/QganweIST7Hhn1KU1IlCSJe/TWMIa97+VB75kIkGmhOTwGMacT5dX76rWEcjwO//jCNLdcGzPbjC9uj5tolhmGmH7kfxUJeoKdZwls3hcx7uw8lEfICgcu+7NgWjsejrm2HtrYpO4PpqbXoXb3TdG98cL/k4guxe9s6pLUoJNWPhQsuxIvPPw8AkFQ/Qojk6Ztl53iwckvUNjtp1dYoYqn8I6astLe34sTxYQjouvlbF6tYd0m2PU+kBO79ZeM0eiKdxuDTD6DzqttsBrdIxhE7vM92T6Hq9KOMZVNOfeVyccGCS7Bq68t5tsgFCy6Zkr8NZTDWA8baOLcvN8dyDD/DyOj7ScRmIOQaLc22oWP9nBiyGFrl4sePPo4Zn/2WrbAevn8Jom+/gY7M0RmA8YVrNfq3rnO8f3zbPeaXr7ZL/hykBh2/hLWEspXAel6Q9Z22jk7TaDQo1TgsxsIFF2L3rg1ouuBaHHvjqby88QebEE2kbBW4WB5fPEvGqi1RbMqZKmpM/mj99P/E0PPfh3Z0P3pueUqPO4D3/+Ev0PmZm5EKn8DQsw+ie8W9OPbkXaZMZ7YRvnNJdrrpa0dS+vQVy2hiCsCoJkGIxhnZ0He6vRv9T97l+DyWBARijuVnSpveJDSQGqi6URNNONfzaAIITCODSiQSznmWSNRatLLTpALXf0yxjSxc/zGlob62MwwzMYz194b+KNS2jz95V94UQEEeNBdoO6zrC40ZTAaGEWEYh04sXHAhXnrhhTyZHvmPBPrD9r5oLCVDSxRl3CRkAAAgAElEQVReCjEwdAJdHW2Ih4eL9oMagUsvuRgvv/I6hp57CMmRY/C0zIBIxCESsfyZb7HqLKPwK7Kj3vQrhY35ifDc8y/iM4sXYdlTL2NME2hSCRcsuATPPf/ilPxtuDWM5aCUuesSUV7lTwtRdA2j29o3c9QQyJu2ONPFTaGRo9NO78WH/UNmgbeu4ysEkYTe27bZjrY4svFaiEQUvd+w3xepJI48sDzvfZFK4r3/04c5c+fiwNvvZqeQJuOAx+s4egjoW0h39611XOeYroBxbIabk5fG6K1fkREeH4VHUSHUoFmBi61hnBEixJMCbX7CoWGBOZ0S1nxaxcwQYdlTEYxTCFKoA+nICLqWrka/gyJpv/IvMbJnM5LDH2XWhkYdy8HxsEBCWBQQymcsVm0NI0mQAi0QkWH3dPX6yz6v31gr4DRyno6FIZKV6ew3qYRbPpWdyjOnU5/K8+Cv4oiqXUiNDVZlrUSl0deIeswdkcnr141FkZwW8bMiEaE7aF9PvHJLFP3h/Haa1zAyTPmpyRrGzPp7Y2398b9b5tq2h7z5uu2jcQEC3NuOb+mzh4z1hcY+CoYRcc7s04p+PF+0eDFeeuGFnEGHqc88cuvrDkaARKpx2vdFixdj96t7HEdoa0Uw1IxoImXqTaMvWitKrVtsMDogEbl2bA2jMaCIPAOkifIXHxtTTv3L70X/lnuQjts7vy0+fXerK5+IQPGUVuFPO70XH50Yz/tCMbMtVNRotG56Y3Dk7/4nkI6je7mzMedk5Pn3PoZD7xwsGFYuTsaqYZRWqoNZLC8NPIqKtCRnNtLRDbjcBvj027brU2Shu3Pb4Kh7xb3mgnVCyjH8j8YFyBuwGdVEZAtzVAMqWT+raTBC8qBZSbjWj1GNyl42jPJ24uUfI7z/F0jHwpB8QaRj+seJSpY5t7LRdMF1ZV3cXkv0DSFa0LVktd0YD49MS4PRWNtjrL8djAiMOrTTbDAyTPmpdr3yKCpSyXheP8CtbX/pemfdZswgc2w7vtVsf1cETCNiIstwKoFEBAIQssTf2HOjEZbBMKVTat1qxGM1Ksa58+ZBUoOOx2NYz0VMpNIYiekGx0hMQAk0obtvbcGD3aPb7gIS0bxzdsY04MonImjx27dFnhEic6ejXD7sH3I8m+fD/qGicfzzG67HYM6WvpJEEFoUA7vW523161dkRF76nu1+5KXvYd1ff3PC6Vvs6IxyQyQXzUuDZEJDWotkOroEUoPwL78XvXc8jbavP43eO57WZVVUwBsw16lZMdapZQ+H97qG36wCaS1sG4EVmfJklKtp8zGHZEj+EMY04JZno+YxJS33jeKWZ/Up2ZU5VsOP4T2bEf3Pf7Md/SAFWwFZmWqsXClUNsb+/elpM2VTP9N1dU47tLpi9bmWNKnAtusC+P3XmpD6ZjN+/7UmbLsu0FDTsxiGKQ2PoiKdjOedZViobXfb96BQ22E7ZiMlI62FIUQaaS1cU2MR0OV+8foARu5sRvpbzRi5sxkvXs9t3skMG4wZzp03DwcPve9o+AH2TU9ySUTGofbMtZ2z+LEfjKE/gjwj8MBbb6HFpxuDLT59qqTiKc2oMRDxKAa232seln38hYfNBdLF+P7ffw9f/MIqHN/5v3HkgeU4vvN/44tfWAUh0mhWZfRvXYcjDyxH/9Z1aFYlhMdH8YONG9D/s7/Ge3+3EseeXIPxkRP46te+NqH0BYCWkN/x/BnrOsdyQSRDCrZMOC8BPX2Dcy7Ok3XwmY1oX3wTuvvWIpkCbtjhfLYikN1YZTLhTzdI0XcqlQAMRO11YiCqN0IiHsPAzpwPFjvXT20NYzyKsX/fZa7BtX5cIbl86wVySbiUjUQKkBQf0EDrTwthPdPVoNR2qNHgeswwJw9pyeP4sbdQ2+52UHqhtkNdpw8qHI9JRdcXVhtu85hceNObDAfeftecejnRxb5KIJS36+V/jQjb9Dvrwubt12V3y1qyKYJIsvSKqfoCjucVJscGS/6y//2//x6+//ffy7ufu/GMwVe/9jWQZY1foc1qCnFiaBBtHZ22xca56xzLhbEddnTbXRNeuE1eP4LnXASSJAxsvxfpWBie1plou/gGc8vs/pT79tpAduS0ifJ3MGu0heNTxdhpLljguJJRTYJIJeyL01MJTOWblhBpEElV3yVVK1A2uq/6RtW276401djtrV6YDhtAMAxTGiLufFxFobbdaTOcz81T8MSbCffj1mL1O4uI2zwmFx5hzGD9Wj4Wl7Bqq/0rkrGbqRPGNE8Bsu2S6mYEWr9Y7VoVsI1MGrhVzIQgx+moscP7cEp3R76DMjAyHnUMc2R84iMJJ4YGbdMuKmEsAtn8NIz4UvPScDuwawMCZ/8per76zwABp/75D0xjEdAbU2N77dQ3m/HWTSH0NEv6bmSH9+lHj8Sjkwp/umFMNy1UJ8jrRffyNTjtSz/CrNU7cdqXfoTu5WtAXm9ZwrZS6V1SrVuvW8uGeazGNBmBM+pJ7oyB6RI/K1yPGebkgbx+x35Z0KVtD3qzO+Sr68bQ95MIPjvHg+9f6W/YtmPURe7ROpebqRw8wpjB+rW8d/VOHFm/tOTz7ozRuod//I/wtJ+KseMfoEktbWTJ6DC7HbeRS6FpYKXskjoZGnHqmZGfvXc8jSP3XzWhswuNkSnrSGjuSIpbno1qwNi2dRBa1Nz4QyJqyLMTy4WI60dmFPpiOapV5hD4Whz9MB5H3hlYK7fo524FptEInFM9EfHotNvwBtDr68lejxnmZEFKJzGWzNfxqbQ+JfXRq+1HaqXSQMeS2zHy+lOAdgT9t2U3xkl/qxnSt0fz2o45551X41gWhrwBHBuP5shNFTsfnKl/2GDMYHRqjY5l93Xr9I6lNoKRWPE1R9//++/hlVdfMddBDmz5tqNBkTtN3fiCFUkIXPp4xHZ2o1NnpBbTwBpx6pk1P0+/bTu0o/sxvmsDRsMjJR1LYXR6iSQAAseeXANSAwjOuVifrhpsw0fjJ2yNqbFbrpRKImnpNHOnEkiHhzEGYMkmfTfgkRj0MyaTQCRZwTImUhCJmJ5/igqRFvrZNhU0atyMC2P33EpOh60209E4dIPrMcOcHCQTGjyKio/G4/azDJPAsTH7+YZaAkjIKrrnXgw52Oa4DOal6wNY9lQEvXc8DQDwH96HA3W+NGHO2Wfi4KH34V9yO1ozH1vHM0d9MCcnbDCaCEj+ZtvXcrm5C+nwcMk+/PbNN3HuvHk4sHUdRCKOYwmR12mcESLsPpQ0jchLH484HrtwPOY8W1gh4ThiolDlOjPGZjW5YU52s5pqnIsjRApEcs7oR6ygsUgkmaMk+jWS2VLbOGIjjMg7b2D8/z0PiPT02cW0CkjBVojwCTT58st6ZFxkpjeuzzuiYSqj2PqxD/nrfdPh4aobOgLIlMXCZZBhGIapPcmEBiIJ455WdC/XdcjYA8vR5nQOdzhhLkVJu8w+Gk+raMv4Xe8ztICc/mydHPXB1BY2GDOQ14+ORV9yPIdwIuRWJlXxoElNmUajdY1jkwrbTlyAfSMQJ7RYBKovYDOEFBLQYpU7162cm9UsWrwYu1/fi67la81O/O5dG7Bo8eKKGI2lkmtcvPfAMsx0PL/xBMgbQFoLl1XW6Yx1AyK3sv4nCxbh5Vdet296k0jg0ssWTTlco04ba2/7t94Dj6IimajMYgz3sz8j/JGBYRimQcjVIU2qwKY+580Mh557KKOzLsO/vv6afbAgqaD3ti2mv/U+Q8uAjUPGChuMGeacfSYOWkbRhvdsxti/74KIRyGpwZK/rHR1tCEeHjanleodx0DO1yhC7x07IVJJvPfAsglvXVwp47CtoxMj41FHo7Bcm9PsfnUPupavzevE795W2+kZ+plyVsVQaEfP+v4yWG8Yu6T2F9j0Zvere8xdig1ih/dNqVwY4Vox1kWmK6isJ/oRiGEYhqk/RDyK6La70G+ZJeamw3q//iPEDu/Dy1vXIa3pbX3A5wWQAFIJnPjuVRhLetD9Z3+DgV0bMOfsM00/CvW9GKZe4F1SM/z2zTdxzuzTzHMIx/f9C7qXr0XvbdvQ3bcWBw+9j3PnzSvoR1dHGzzxEWy/LgCF9AamPyxw87Mx/Gx/Mnu+olefDqcd3T+hHVIrSVtHJ0a1NLr7snEe1dJo6+gsazhpzXkDnXSNjTDrxj7h/a8U2dGz/r8M1hPk9ePDf76tYFmvRLkotENqJacDFSo7HoX3JGcYhql3ujraMDNEtnODi/XXjKmm586bh4DPixYlaT+L259C/5N34ZzZp5kDENXqezHMVGGD0cJv33wTaS2cGW1anXeMxIG33y3oPh4exqY+P658IoL2oN7QxNY04aHLfVjzcgyb30yYHUdjC/pxl62LY6nKHSzuRDmPziiEpPodO/GSWlsjzNh0Jbz/FQy/+nhBxVDvaw/qDRGPIjU2VHB7cSP9rUx12o6xQ2resQ+JREWN/oJlRw2y0cgwDFPnGP056zFoxY7IMHTWgbffhYJEnvtNffpxHdbZatXqezHMVOEpqRbOnTcPB95+FyIeRf+Td5lTEIyrABV0b4wsKJ78KWmPLPXj5mdjmBkiNKnAwLZ15mYvquLJ2xzHp1bXlq/W0RkLF1yI3Q4b6CxccGFZwzFwm+pBRJmNbJAxAoGBXRsgKT50XH4L+p+8y/XYjJNpZ8hyYF3D+N9Ok3DNTyIYjgGtPuDiWTJe/q8URrWo8/EXUyl/IoW0FkH/tu9AaBGQGoBIJgGRgpROFnc/ScY04KpNEXgtu8HGk0A0CXQvv33C66IZhmGY6uI0U6RJBXqaYOuvnd1OiCSAI+uvRpM3BcSLT1+10ojHljEnJzzCmOHcefPMIzFIljEjZyrCjBBB3+vQHWNkwa2hODCYNkcPU7GwucmLLEsIKISXrg9g8PYmvHR9AC1KMjP/XUciQouPbNdyUuoIz6LFiyH7giCSIPuCWLR48YTCefH557HwT+djYJs+9Xdg2zos/NP5Zd/wBshO9UA8gmZVAPEI0uEhSBlj8ex2MvN3ZoiQDp9AcvgjqD1z0XvH07aDeJc9FcGxccGblkwCYy3hmAb0zVFwapMEIuDUJgl9cxRzhDEdj5tTwvu3rkM6Hp/SSGBrezuQjEMk4vo0VC0C8ngA2YtUsnIGIwAkUkA6U1TSQv8f4I4AwzBMI+A0U2RMA37zob0P8JsPhX5sUiCd12e89qf2detOy40qMbuGYSoBG4wZDrz9rjktoMmTdJxK0FxkJpk32IpVW6OuU9JCXn2XVJ+cMg0/RZZcpy4o0A8XN3ZdzG2Mymk0Gkdn5E3fi8cgqbqBSB4Vu/f8G7oyazu7lq/F7tf3TspoTMXCECJtM5zLzch4FCJ8Ii/tuoMEvwf4/XGBJZsitvy1Nt69dzyNtq8/je4V92JMBPgctklinrEoA2t3a3jocp85VXvtbg0+WT8oWVIU2zoOSVGmNBI4PDIGSB5I/hC6++7W/V2+BpIvAFDlmj4JQHvAXubaAwQJmY6A4qtY2AzDMMzUGdP0kUTp26NouW8Uf/DgKAhAZ05/ojNEIACbr8nvw738X6m86asJKLZw3Ppekz22jGEqBRuMGazTAkqdSpDLwNAJJL0tRee52xqbQPHwrLsuWhujoBdo8RE80tQNxxNDg2hWJfsIT3gEUrDF7MR7mjrQtTR/befuV/dMOfxyQiRB8gYg4hHHtNt8jR8hL6HZR3g1Y9gb6S2lk46NdyWnME53RDyKgZ3r4ZGBR6+258WjV/vhkYE0yflla+lqpGnya3lJkkAej+P6EFKU4h5MkoA3v/Ow+Ro/Al5g8JmNEGk+h5FhGKZeUWQJM0KEiGWZ0GAUCLn0xUKqex/OOktpJOFBJBa3vefU92pWJd4llak72GDMYB1ZmsrOpQNDJ5AWAgNh+3TGkajAS9frI4Of+adI3iLoQuG5GZSRhG58dgXLZzSmNX3kT9/8x2frbCdHjtXlDqdWiCRIgRYgEcXMEGE87px2h4YFHr3aDzlTA4z0TiY0kBa2Nd6khSt2Zt9JAckQqYRrXozHAZFwOQIjEZt0sCKhFTxao1IUimfrpz8PpBIVC5thGIaZGgFFYHBc5I0mFvq479aHG4kJpIXASEzkGYsGuX0vNhaZeoQNxgxzzj7THFkaS3ocRwhHS7AZzp03D5IaREoQhABeuj6AkTubEVnbbBqIlqWJZmPjFJ4xdcHNoJzTKdlGG8tN7mJspaOnLnc4tUJeP7qWrjZHFud0Sq5pd1GvjHAcefmbTGi2xpuNxalBioKmP76q4IcRUlTndRxT2FGUvD54Wme6Hq1RKYJe53gGvYCnqZPXpjAMw9QxYxoQyBlN/GhcFNRhix6PoOW+UXMK66WPR/KmnzJMI9NQBiMRtRLRz4joIBEdIKILiOivieh9IvpN5u+KyfhtPYdRpFJ5G558NC5w6WWXFfTDunGOpHhdRxqsU1tfO5JCqw+48Y8U16kLTgbljTujWPNp1dHPcpG7GLvlgusw+MzGvOmaldrhdDIYRq7xJXDNp1XcuNM57YxOvLGhDYrsgstMDhGPIbz/FxjT4JgXYxog0um8sqVP35z8jrQiHkPLhSsx9OyD9jK7c31FRxhTKeCGHfZ43rAjilQKU9/5lWEYhqkoTrucfueXGqJx54/70TjQ7bDPhMfDBxEw04dGK80PAnhOCPFZIvICCAD4HwA2CiEemKrn1rNxcqd4yoSCm7Pc9JWv4sDb76K7by18s85HSNLQ1UZ47UjKPF4DyH6NSqSEeVTD5+Yp+NbFKu79ZdxxY5W0EJCIzK2cz2wjfOcSH1bOU2x+lhtjMbZx1IEcbNOPKdhyD0QiBkn1m0eD1AuGkWt8CTTS6HNbo/hoXODMNsK3/7uKmSHCyi1RjMcB8gYgEOXjMioEeX3ovPwWRLfdhVXzFNz8bAwHBtOY0ylh1TwFD/1rHFKwCSPhCIaeewjJkWPwtMxAWougtaVpCuH64WnqROuC63H8xYeRGDoKT8sMiFSqoqN8KaFPQ/qLXVEcGhaY3UoYiQmkBJAOD3M5YxiGqWMiCUKTKmz9twODaagSMBYT6PtJxDwyKZEEPFL+UWqb+vxY9lSkUDAM01A0jMFIRM0AFgC4AQCEEHEAcSrz8RIAEPB50RWkvDP4Aj6v4xz0m77yVfzwsU15G+d873J9dOuRpVl/Vm7RR1TUdWNo9QGfm6fgoSv82H0oWdDoMwxJiQjhBDAzRDajsxIjjCeGBtHW0amPuuacY1gJJFkBPF4zLCTjSE9wvZexwYrIjMpu6vPjs3M9mBnyY8mmCAYiAl/YHsuerck7n1YcYx1hvwY88h+JvHo1pgHp2CCIJKSEAIRAKjICEY/iRGx8CuFGMbBrPbqWrMYpN/wdtKP7MfjMRkCkKzrKl0gDSgoYigoIoV+TKf0+G4sMwzD1TSKVhkRkO4t5divh3RMCzSrQ4SeMagIdfsJgRGB0khslMkwj0TAGI4AzAQwA+Eci+hiAXwO4JfPsq0R0PYC9AL4hhDgxlYD0Yy4CJX8t+vGjj6Nz6V+hf+s6/fiAWefrB7w2S/jOJT5zRGV2q97IAMCMUNYgzd1FtRjGdNkxyw5elaLcxiGRrJ+JlzEKRTwGIVKQZAXkb8o7uF2SlQkZjUKk9SNAFD8+Go/a0imSBNIJNhCrDXl90I7uhyI5l11F0j8WSIEWdC1dnc3/nesnnP9WjLLQv/UeiHgMpKj6FNdUoqKGmzEjoEk2/s+UPf44wTAMU/dIsgIKtOLY+LCpr3wy0B0k/P/s3X18lPWd7//XZ24yk3vuAq0igq1UwFDWZatdPZ6imFoVRGyLslt1T61nH2rX+jtSrcuebU+t3RZ3dbdWratd7R4RugoobuvBG1wr9maxWlCg2hVRtJWAEHI7mZvP74+ZhAyZhIQkM5nk/Xw88kjmmuua6ztwfeZ7feZ7t6fZcdJfBta3eGdd1lNvMpGRopjGMIaAU4C73f2PgGbgJuBu4CPAHOD3wN/nOtjMrjKzzWa2ub6+vtcT9XdZjXhLE5HJMwlVTeycOKct09f9QxXGMRVGRQm8ud8JB2H8uDE5F4Xvyw1lT/sU4ma0Y4IfswCBSDmzamt73d8smLVMx8TFywmUV2MWhFBJzuUPCPV/Nh/3FKn2FjwzM1mqy28ZXH2JK29vo379CvDuHzgBAAeC4ZzLahAc2KQB6YmLWjLXRCueiOWllS+la08GoD/1lYj0TZ/jKlMfAexdVknqb9MTF+5pdoI5OrW19TC2sYdJUUWKUjEljLuB3e7+y8zjR4BT3P19d096+i7wn4FP5DrY3e9197nuPrempqbXE/V3WY1wWQWx3ds45ot3EiytZs+qm4k7HGxzPv2vLWytT2UNhg61N3R7jWik7zfGw+FmtOsEPx3J346d7/aaNB6+TEfnmniZFsfcyx+kuw6GwpGs5DQ0gNkzZfD0Ja4sHCHVfJC4QzQEHcODA5Z+HPehWVYDdN1IcepPfSUifdPXuOqojw6/FxxfCpFgdh1WlqnDDmbGNkZuaWTxj1s42OYESzRLqowcRZMwuvsfgHfM7GOZTWcD28zsw112uwh4daDnihPudZmLw115xWXszbQsJmNNVEbSy2m4Q2lJ94VeTzs2vShs1ySyOpygLDoEa2MMke2vv5kz+dv++ps9HtNbUnj4jKzQsfxBKaFwBI+UZyWnrpv/ouHxGIHyKsIBqIwaaz6fvu7XfL6MyqgRDgzNshq6bkREpL866qPDZ6g/GOu5Dksk08MPIP3bg+Ee110UKUZFkzBmfBl4yMy2kO6CeivwXTPbmtk2D7h+oCdpaWunIR7qcZmLw931/Tu56vKlfPD4t/FYc2eX1hPHW87urf+xK9ktiVy5uJQwxbOg95FaBHPpLSkk0d7Znbfrkh0k2kkFQjmT01SgmIbgjl6dLcvh7l+erFxcSjQ8NMtq6LoREZH+6qiPKCnLGj7UWx32sQnp+72Onl9KFmWkKao7J3d/BZh72OYvDMW5+hvsd33/Tu76/p2YBaiKwEl3NtKSsG6Dob/8k1biKZj/ry3MmBDgr/9bhEtrw0U3o1ZH8hc9fnbnts7krwcdY9kOn9im68Q3XWdk7Zgl1SyQlZx+8NQ9NG97Dm9vJRApx9u1JMZw5u1tJBr30hiD3QdTnHxXU+eyGjeeXpK+7i091vDwZTUY0DqMrTTveIH6dbeSamsmEC2n7KQztQ6iiIj0LJlIj31vbwWM5kgNHqunMeY91mH1rcXXAiPSH0WVMBYDKymlMdYKOOuWlPL5f2vpnJr537bFWbsjweOXlHUuK/DFx9M3rx+qsKKaUWvG9BPYkSP5mzH9hB6PcU9iFsxKCjuSRaDH2TC7JqcfPHUPzb/dRM2im7POaxZQ0jhcBcMc+Nm/Eg3C8o0xHrjw0LIaVzzWSjQI02acxLYdb5BsPZheVqP1IJ5MMHPWSUd/XgvS8sbPu10rWPDIx4qIyKiUvlcJgAUJlFYw4byvEJk8k/rbF/VYh2ndRRnp9IXIILn6mmspKa/C21uojKSnWl60uoWDbZBIprs0PLQ13q07w/0LS/mbjW29jpEcjl7bupWTph3LnjW38PZtF7FnzS2cNO1YXtu6NWu/w2dSnXnyTFKx5szslc2dyWJvAqlEZ3fV5m3P9TBxztAtxC4DY4EggbJqQkH4wuwwX/5pG9FvNfLln7bxhdlhQsH09VQSDEDHBE7ulAQD3a6nfp03HM59rYSLJ85ERCT/Zp48K12HLPwqLa+/yO47/7zXOqzYeomJ9JdaGAfB1ddcy70PriTV0sSHuqyv2LEw+QfNTux/VxH4xsGcy3W8ud+JRopvgPSRbua7zqTa0cKzY/0KZtXW8trWren1EktKu7Q25u5amojHCIUjnS2T/R07KYXl8TaSjftoaoeVW+Pcv7A0q4W9qT19rSRCUSYe1mLdca0c1Xnb0zPdvb/6b4i999usVm0REZFcOu5dvL2V1rU309SxbnAvdZjWXZSRTi2Mg+C+B37EhAXLqIx47gHRJbBxZ6LX5TqKLVnsi95mUjULECgfc9iajGPS3UDIrNnYpWUymUiQijX3PnGODEtWUkrNgmVURuD+hd1b2CsjRzfrbl/O++4//yXt9W/1eJ2JiIh0tf31NwlXjuNDh81m31sdtnRNKx7SfYiMXGphHATxliYik2eyJ8eMqB3dFBatbqElM0Xz4S2QTSMvVwSOvIxGR4IAdCYIe9bckk4Wy6u7jY80CwLew8Q5amEcrjqug97iw2ml/t//Ac9MNGAlpVikbED/r97eSgqYuHh5zutMRETkcN7eSmT/f7FySRkXPtyCBejsbtpjHRaKcrBJYxhl5FLCOAjCZRXEdm/rNiMqZHdTSAKNmcVdG9qgOgrxxKFhWyNNrplU3/n+FQTKx5BqPnBUyWQq1oxZ4LCJczRL6nBmJVFiu7dRUZI7PipK0l19SCWzui/Xr18xwPOWqguziIj0W2MMLny4hfLIoWFG47/b2GMdpmRRRjr1yxoEV15xGXvXr6AxZlmLvG7cmWDpmtbOb6YqI7B+aRn7b6wi9bdV7L+xivVLy4q+3/vhE9vMqq0F0jOpHr62Isn2zklqeupaeqSb/EMT5hz6LcNXx3IqiRRc8Vh2fFzxWCuJVJe1GrtNZhQdwHlbCUTL1YVZRET6zEqiVEbAAtnrLvZWh4mMdAVpYTSzcYC7+/5CnH+gqirKsEQ6EayMgIdKuery/8F9D/yIPzQ1smh1S+dzZ00N8uPPlfHC20nO/lFLj90ZitWRJraZVVvL9sNaAyOTZ+LJRI9dS49mjUcZvqwkSrByPG3N+/nYuEBWfJx6TJB3GpI4bT18SXD0E9RYSSmR405WF2YREekzb28jfvxs2nZtybpna0vQYx0mMtLlLWE0synAdzTfGe4AACAASURBVIGzgQPpTVYFPAvc5O5v5assA1FVUUa5tbFySVnWOMT/++APaW9uwSzAmC+vhe8tYt2Sss6uC/OmhaiO9t5ltRhtf/1N3C27i6hb52Qlh89wGYikW3yOv2ENu25bnLNrqVmwh5t8zW5ZjLy9jao/XkjjT/+BrfUp1h0WO+EAxEOlHNj0MK1v/Jz4vt2Ex0+m9MRPDuhLAm9vI/beb4kc8zHq191Kqq1ZXZhFRKRXVlJK+JgZhP+wJeueLRygxzpMZKTL52W+GlgLfMjdT3T3jwIfBtYBq/JYjgGxRGvOmVAtkW6x6Biv1Zhjgo9LTw732mW1GHl7K4GSEiYuXs74868nWFYN8RZwz0xSk626orSzm+qU63/cOWtldUVp5028e5JUc0PWGo+p5oY+rdkow1AwzIGf/SvRMLlnEQ6nr6PGXz+BJ+IAeCKefjyQSW8y11Hbri1dksU2JYsiItKjjvqoMTNRYcc9W291mMhIl88uqRPcfXXXDZ7OAFaZ2TfzWI4ByZUIdu1W2jFeK9cEOJ+bGeb+l+JZ3RkaY5Aq4llvOiaoSTbvp+GFhxj/meu6zWzaNdHbv28vY8dPyGpZrK4oZf++vVmvq+Rw5LBAgAnnXc+eVTf3HDvBMIFIGePP/XLn9bP3J7eTzCSQR0vXkYiI9EumPppw0V/z/qqbO+/ZoPf7P5GRLJ8tjC+Z2V1mdqqZHZP5OdXM7gJezmM5BqS3tRQ7pNqau30z1dGaGHf4kzPPIeVOQ5sXdbI4q7a2c0xiw89XM/4z1/Vp0pL9+/ZmTVpzeLIoI4vHY0Qmz+w1djqSyq7Xz4TzrscC6usjIiL507U+cowxX17HlBuf6NP9n8hIlc+7scuArcA3gP8HbAC+DrwKfCGP5RiQnhLBjm+YrKSUSZ/7OlNufIL3W4MsWt1C5Jb0RDjvtwaZeMmtbHxxM/Pr6gr7RgaoY7Kbjglq4vt2a/kCycnC6W7aTalIztjxUGlnUtlVZPJMPK6vbkVEJH+61kcd9RfQax0mMtLlLWF093Z3v9vdz3X3Wnc/2d0/4+53uXvR3BWedc45vN/k2Ylgk3PWOecAEDbvHKN33PWP0OhlTLzkVsZ+5Qmm3LCus+Vt4/ObCvxOBmb7629Ss2AZBELUr19BqHqSli+QnDyVZO9Pbqfm4r9lT6wkK3aaPb3YcddKuUNs9zYsfPTLaoiIiPRX1/rIUyn2/uR22nZtYfJ1q3m/NZSzDhMZ6fLa38vMPm1mXzSz4w/b/j/yWY6BeHrDBs465xyaKMMxGj39+/lNv+Dqa64l1tZCKNHWOWFLT2sKpmLF3fLW8b6mXLcSTyZJNO6j/vHvZq25qJlNBYBknNIT/iQ9U2m8nSaroHzOeRyMWWdF6/G27tfP49/F47p+REQkf7rWRyTbO+uvt2+7CAtHSM04D8doaHMlizJq5HNZjVuBM4BfAzeb2R3u/r3M09cCP8xXWQbq6Q0buPqaa7n3wZVM6LL0w70Prui2b09rCgYixd3y1vV9TbluJQC7brv4sGUy2jTpiEAwTOub/0nNopuzJrQheGhqOSspJRVrzb5+kkm1UIuISF5ZSSmplvRM7X2pv0RGg3y2MC4AznL3rwB/DHzGzG7PPGd5LMeAza+r4577/oUJC5ZlT9KxYBn33PdDEqEoExcvZ8oNawmNO6azi2rXlreq8uK+EZ4x/YRu7ysQLWPG9BM6J7NRsigAFgzmntAmeGi2uRnTTyAQLeuMm4mLl3deTyIiIvni7a0EyscwcfFyLBjKWX8FQvlcZECk8PJ5xYfcPQHg7gfMbAFwr5n9G1CSx3IMyPy6Oja+uLnHrqbe3sbExX/T2aJ4zOV38N6DX8lqOQmNO4aD+98rRPEHzWtbtzKrtpbtXd7XjOkn8NrWrYUumgwz3t7WY6x00PUkIiLDgZVEqck0CIzUYUUi/ZXPFsb/MrP/3vHA3ZPu/kXgt8CMPJZjQDY+v4maBcsIRMt7mOQl2u3D5cN/fhve3srxN65nyvU/5sN/ftuI+LB5bevWrOUxdHMvuVhJDxPaHLbkiq4nEREptK5fcvZ0rxcuqyhE0UQKJp8J4+eAXx2+0d2XA8flsRwDkoq1Uv/oN6jwJvasupn9d1zAO/9wMW27trB3/Qo8njjibKHFNobx6muupaS8CrMAJeVVXH3NtYUukhQRj8c7uy+/8w8Xs/+OC9iz6mYqrZWqirLO/XSdiYhIoXX9krN85qeyht+8fdtFtK69mURLI9VRy6rDREayfC6r0eruPTWrVearHANlOBMj7axbUkZseSXrlpQxMdLOnlU3c9XlSykpCeUcs+jxeNbjeWeeXui30icdk/uEjp1FIFpOvKWJe+77F8y0oLr0kSdJxVrYs+rmbrFTbm1UVZRx9TXXcvcP7iORTAGQSKa4+wf3KWkUEZG88vY29qz9Fu/+4Es0vvwTPN6emfl+EZNKEznrMJGRzty90GXAzN529yl92G8McB9wMuDA/yDdpXU1MBV4C/i8u+/v7XXmzp3rmzdvPqqyVkeNdUvKmDft0PDPjTsTLFrdQkNb+t8yEi0j7oa3t1AVgcYYVEbgYAwCkTLmnXk6T2/YcFTnz7eS8ipCx84i9t5vqekyI2z9+hWkmhs0sU0RM7OX3H3uYL1eT3E1v66OZ556mqqI9xg7B9uDBMqqul9jLQfxVGKwiigy5PIVVyKjST7jygIhzJNUdrl/6/h9pPs/kWLT19jKWzORmf1TDz/fA8b08WX+EXjS3U8CPg5sB24CnnH3E4FnMo+HTGMMzpgSzNp2xpQgjbFDj2NtLVSEnQ9VWNY3UR+qMMpDXjTJIkC8pYnYO692DgDvmCWsZsGybmPQRHJ5esMGzj5nfq+xY+GS3NdYuGjmwxIRkRHAPMmkw+7fJlVYn+7/REaqfPYr/AvgVeClw342A+1HOtjMqoAzgfsB3L3d3Q8AFwIPZnZ7EFg06CXvojICL7yd3ar2wtvpb6KyyptoZeXiUuZNCxEOGvOmhVi5uBRLFNdkN+GyClJtzT3Mcllc70UK5+kNG3qNnb7MpCoiIjLUKiPkvH/r6/2fyEiUz2U1/hN41d1fPPwJM/t6H44/AagH/sXMPk462bwOmOTuvwdw99+b2cRcB5vZVcBVAFOmHLH3a488VMrSNelk8IwpQV54O8nSNa14KHsSm5HyTdSVV1zGPff9C7Hd2zqXCnn7OxdQGUkvnlkdTX/rlhoGXZsl//oTV40xcsZOY+zQJAMd1xjknkm1WATMunVnUoxIXw1WfSUih/Q1rnq7fzv7Ry3dPtsrinxdbZG+yGcL42eBV3I94e7T+nB8CDgFuNvd/whoph/dT939Xnef6+5za2pq+npYNwebWmj2KItWtxC5pZFFq9OPDza1ZO03Ur6Juuv7d+LtrZ0T+bz9nQtydtUImBW6qFIA/Ymrygi0tHtW7LS0e6aFMUb9+u8eNlnUd/H2IvuGhXSyqBiRgRis+kpEDulrXPV0/wbk/GxvalZvKxn58jlL6gfu3nLkPXu0G9jt7r/MPH6EdAL5vpl9GCDze8/ASnpkB5vSA5xT7jS0ebdkEQ61RG7cmSCedDbuTORsiSwG7ilSzQ3sWfPNXrtqiPTGQ6WUlWRXtmUlhodKGTNuLB6Ps+/J7/H23y9m35Pfw+NxxowbW+hi95tiRESkeHX0hjn8/k2f7TKa5a1LamYM4teAycBP3X1ll+fucverezve3f9gZu+Y2cfc/bfA2cC2zM/lwN9lfj82VO+hPw42tVBVUcai1S2dXRc8VJozuRxODs3w2oqVlBI2J9bWgnuSQKR8xHS1lfzrLSYCkXIq/3gBrW/8HAALhan84wtoeGl9gUvdf4oREZEiVlLG+82xrLqqsT0IntRnu4xa+eyS+i+kh709ClxiZo+aWcf3Mqf18TW+DDxkZluAOcCtpBPFc8zsDeCczONhoS8tkYXQdYF0C0UIRMo6/04EI0xcvJwpN6xl4uLlJEJRItH0GkPe3jpiutpKYfQUE97eStOWDYyb/5dM+V9rGDf/L2nasqEoJ1ZSjIiIFC9vb8XKqii96FaOu2EdpRfdipVV6bNdRrV8Jowfcfeb3H2duy8Efg08a2bj+/oC7v5Kpv/5bHdf5O773X2fu5/t7idmfn8wdG+h+F19zbXc++BKxi38GuPPv55geTUTF/8NU25YS6hyPDULv9ptaYO4p8deWUlpj1019A2bDISFoz0sq1F8k94oRkREildP9ZE+22U0y+csqREzC7h7CsDdv2Vmu4HngYo8lmNUu++BHzFh4deIHj+b9+6/mgnnXd85M2Wi4f1el8+orijlYCzC+037s7tqaAZIGSCP97CsRrz4ltVIuRMwU4yIiBShHusjjPebXJ/tMirls4VxPXBW1w3u/iDwv+jDOoz5Nqu2lkCkHLMAgUg5s2pr+3V8JByiOpqeGbE6akTC+czNexZvaer8IIzv2531oRgeP5nY7m1Z+7/7z3+JlZRiFqChqZVU8wEoKeNgzKCkjED5eH1YyhEdiifLGRcWjnS79mK7t2HhgfX1MQtmxbFZ8MgHDYKOLrddf4uIyPDXtT567/5r2XXbYt75x0ty7hsO5adOESm0fM6S+lV3fzrH9ifd/cSOx2Z2eb7K1JNZtbXs2Plu1li+HTvf7XPSGAmHGBdNZc0GOS6aGhZJY7isovOD8PAEsfqTS9j7k9s7lzbYfc+VeCKW9e8QKB+Dt7elZ06NNbN/395CvRUpEh3xRHsrHzpsSvKOuPBUKuvaa9u1hb0/uR1PpY76vGZBAuXVh12/1XlLGkVEpPh01Ee777mSxMH69NCdi/83lmPJpOFybycy1PLZwthX1xW6ANtffzNn//Xtr7/Zp+OjwWTOqZejweSRDx5iV15xGXszaypWnfrZrJv0YPlYUrEW9jz6Td6+7SJSrQdzjysr0sXUpTA64qky4j3GhXmKVKwla1mNVKwF8wEkjCU9jIvU9SsiIj3oqI9SrQcJllV1Dt2pLEkN23s7kaE2HL8WKfjq1t7e2utYviMZztPq3/X9OwG474FvE29pgmCYPWu+ibe3HVpGoz09c6VZYED/DiJwKJ729BIXKY8TCIZJegO4k2xpgEQ7qWR8wOftSteviIj0JpVM10eeSpLoMp5xON/biQy14djCWPDBPlZSmns8VUlpn44f7lMv3/X9O2lvPoh7Ck/E8PYYVlKKt7cSd+vssjfQfwcROHQdHSkuUsk4qVhzZ3fngSSLHec9sOlh3rv/anZ9dyHv3X81BzY9rOtXRER6lUrGsZJSguVjO++Dhvu9nchQGo4JY8FbGGdMP4H6TLfNjvFU9etXMGP6CX06vi0Z7Db18qWPthJPQlVF2RCXvn96HucVwNtbc/47eHvxzVwphdMRT40x49JHu8dFWzL9BcXY8ROyJqgZO37CgM47ktZ2FBGR/Bk7fgLe3koq3tY5dKexPdBrHSYykuW9S6qZTXP3nb1s25TvMh3uta1bmVVby/Y1t6QXcC0pZcb0E3ht69Y+HR+LJ4iEQ1y4qoWmdjhhrLHinAiTqwIsXdNKVUVZ54LlhdZ1nBfQOc5rz5pbsHCEVPMB9nT5d0hPeKP++tJ3nfH06n5iCfjS+lZ2HnCmjTFiCScSKWHs+AkcjKWYuHg5kckzie3eRv36FYwdP+GoJ1ayktIer20REZFcOuojKyll4kV/TbJ5Px88fQ/uqR7rMJGRrhBjGB8FTjls2yPAHwO4+7V5L1EOfU0OexKLJ6iOGs9cVsa8aYf+mVcuLmXR6uGRLELv47wmLl7OnjW3kIo1F6h0MlK8tnUr1VFjzZLseNi4M8Gi1S00xI2Ji5cPanKnMYwiItJfDU3p+5/3V/01kckzsWCIfetXUBWhxzpMZKTLW8JoZicBs4BqM1vc5akqYEROW1gMA6Q7xpd13KjDoXGKurmWwdRbPDiDn9z1dm2LiIjk0vFlY0cdsmfVzUyqMN5v8mF/TycyVPI5hvFjwAXAGGBBl59TgC/lsRx5UwwDpL29Lec4xUCkXDfXMqh6i4ehmGCpp2tbY3BFRKQnHfVRIFJO/foVVEbSvcOK4Z5OZKjkrYXR3R8DHjOzT7r7z/N13kJqjMHSNa2sXFzKGVOCvPB2kqVrWofVt1HuScyCWUtrWKSMCedfT/36FVRXKGGUwdFbPFSPK6V+/QpqFizLGsM4kOvv0LWtMbgiItI31RWH6qP6f/+Hzt4xnspdh3lI90ky8hViDONFZvYa0Ao8CXwc+Iq7/98ClGVIVUagodVZtLqFxlj6cTzBsPs2quMGeuz4CTQ0tZJq3MeeNbdQXVF61BOOiByut3jYv28vY8dPyEruBuP6U3IoIiL9cXh9VJVpWTx4cxVVtx7MqsM8VDpsJjEUGUqFWFajzt0Pku6euhuYDiwrQDmGXGMMqkuNdUvKiC2vZN2SMqpLbVi1MHa1f9/erHXwlCzKYDpSPOj6ExGR4aBrfdTRO2bjzgT7bkzXXZMq0nWXkkUZLQrRwhjO/D4PeNjdPzAr+NKLQyLlTsAs69uoxlh6u8hoo3gQEZFio7pLpDAJ43oz20G6S+rVZlYDjNhZKPSBInKI4kFERIqN6i4Z7fLeJdXdbwI+Ccx19zjQDFyY73KIiIiIiIhI7/Lewmhml3X5u+tTP8p3WURERERERKRnhZj05k+6/Pw34OvAwgKUY0jNr6sjGC3HLEAwWs78urpCF0mkYBQPIgKwdu1azIwdO3YM6XmCwSBz5szh4x//OKeccgovvvhij/u+9dZbrFy5ctDOvW7dOrZt29brPsuWLeOkk05i9uzZXHTRRRw4cACAffv2MW/ePCoqKrj22ms7929paeH888/npJNOYtasWdx0002dz8ViMZYsWcJHP/pRTj31VN56661Bey+i+ksECtMl9ctdfr4E/BFQku9yDKX5dXVsfHEzNRctZ8oNa6m5aDkbX9w8aB8yZgECkfKs3yLDVV/jQde1yMj38MMPc8YZZ7Bq1apuzyWTg7cMTmlpKa+88gq/+c1v+Pa3v83Xvva1HvftLWFMJBL9PndfEsZzzjmHV199lS1btjB9+nS+/e1vAxCNRvnmN7/Jbbfd1u2YG264gR07dvDyyy+zadMmfvrTnwJw//33M3bsWH73u99x/fXXc+ONN/a7zJKtaz00lPdzIsViONyRtQAn9mVHM3vLzLaa2Stmtjmz7etm9m5m2ytmdt6QlrYPNj6/iZoFy4gePxsLhogeP5uaBcvY+PymAb+2WYBA+RgmLk5/eE1cvJxA+RjdXMuw1Zd40HUtMvI1NTWxadMm7r///s6E8bnnnmPevHksXbqU2tpakskky5Yt40/+5E+YPXs2P/jBDzqPPfvssznllFOora3lscce6/N5Dx48yNixY3t8/qabbuJnP/sZc+bM4fbbb+eBBx7gc5/7HAsWLKCuro7nnnuOCy64oHP/a6+9lgceeKDz2JkzZzJ79mxuuOEGXnzxRR5//HGWLVvGnDlz+K//+q+c56yrqyMUSo8KOu2009i9ezcA5eXlnHHGGUSj0az9y8rKmDdvHgAlJSWccsopncc89thjXH755QB89rOf5ZlnnsE1SctR61ofWUnpkN3PiRSTQoxhXA90fJIFgJnAj/vxEvPc/fAF2m539+5fxxVIKtZKZPLMrG2RyTNJxVoH/NpdP7yAzg+vPWtuIRAMk0rGB3wOkcHUl3iwkmgP1/U381pWERk669at49xzz2X69OmMGzeOX//61wD86le/4tVXX2XatGnce++9VFdX85//+Z/EYjFOP/106urqOO6441i7di1VVVXs3buX0047jYULFx4+F0Kn1tZW5syZQ1tbG7///e959tlneyzX3/3d33HbbbfxxBNPAPDAAw/w85//nC1btjBu3Diee+65nMd98MEHrF27lh07dmBmHDhwgDFjxrBw4UIuuOACPvvZz/bp3+WHP/whS5Ys6dO+AAcOHGD9+vVcd911ALz77rscd9xxAIRCIaqrq9m3bx8TJkzo82vKIV3rI28fuvs5kWJSiK/vbwP+PvPzbeDMzMypI0YgUkpsd3Z3lNjubQQipQN+7Z4+vLy9FUIjqmevjBB9iQdvb+vhuh6xK+6IjDoPP/wwl1xyCQCXXHIJDz/8MACf+MQnmDZtGgAbNmzgRz/6EXPmzOHUU09l3759vPHGG7g7N998M7Nnz2b+/Pm8++67vP/++z2eq6NL6o4dO3jyySe57LLL+tXqds455zBu3Lhe96mqqiIajXLllVeyZs0aysrK+vz6Hb71rW8RCoX4sz/7sz7tn0gkuPTSS/mrv/orTjjhBICc72ukrm+dD13rIysZuvs5kWKS14TRzILA37j7f2R+Nrn77n68hAMbzOwlM7uqy/ZrzWyLmf3QzHrud5In8848nfr1K2jbtQVPJmjbtYX69SuYd+bpA37tHj+8ouXppFFkmOlLPFhJNOd1bSXRw19ORIrQvn37ePbZZ7nyyiuZOnUqK1asYPXq1bg75eXlnfu5O9/73vd45ZVXeOWVV9i5cyd1dXU89NBD1NfX89JLL/HKK68wadIk2tr69oXSJz/5Sfbu3Ut9fX2fy9u1TKFQiFQq1fm447yhUIhf/epXXHzxxZ2tp/3x4IMP8sQTT/DQQw/1OcG76qqrOPHEE/nKV77SuW3y5Mm88847QDqhbGhoOGKyKz3rWh9FjvnYkN3PiRSTvCaM7p4EWsys+ihf4nR3PwX4DHCNmZ0J3A18BJgD/J50y2U3ZnaVmW02s839qTSOxtMbNjDvT+dSv/YW3r7tIurX3sK8P53L0xs2DPi1vb0154dX5LiTsRJ94yX51Ze46ks8eDye87r2uLpYy+iTz/oqXx555BEuu+wydu3axVtvvcU777zDtGnTeOGFF7L2+/SnP83dd99NPBP7r7/+Os3NzTQ0NDBx4kTC4TAbN25k165dfT73jh07SCaTjB8/PufzlZWVNDY29nj88ccfz7Zt24jFYjQ0NPDMM88A6XGVDQ0NnHfeedxxxx288sorfXo9gCeffJLvfOc7PP74431umVy+fDkNDQ3ccccdWdsXLlzIgw8+CKT/nc866yy1MObQ17jqWh9N/OzfEqwcz541g38/J1JMLN8Do83sx8BpwFNAc8d2d/+rfr7O14GmrmMXzWwq8IS7n9zbsXPnzvXNmzf353TDilkAKynF21sJRMuJHHcysfd+i7c2agyj9JmZveTucwfr9QYSV4FgGA8EsWAIj7VgkTI8mcBSSV3TUlSGU1wNJ5/61Ke46aabslrh/umf/om7776bj3zkI53jB1OpFMuXL2f9+vW4OzU1Naxbt454PM6CBQuIx+PMmTOnc5bQqVOn5jxfMBiktrYWSLda3nrrrZx//vk5943H45x77rns3buXK664grFjx7J582buvPPOzn2++tWv8thjj3HiiSdSUlLCwoUL+fSnP82FF15IW1sb7s4NN9zA5ZdfzqZNm/jSl75EJBLhkUce4SMf+Ui3c370ox8lFot1JrGnnXYa99xzDwBTp07l4MGDtLe3M2bMGDZs2EBVVRXHHXccJ510EpFIBEhPvnPllVfS1tbGF77wBV5++WXGjRvHqlWrOrurjhT5jCvVRzKa9DW2CpEwXp5js7v7j45wXDkQcPfGzN9PAf8H+I27/z6zz/XAqe5+SW+vNRIq4EAwDKESvL013bKYaNcHmfTLcLux1TUtI8FwiyuRkSDfcaX6SEaLvsZW3mdJBca4+z923WBm1/XhuEnA2kw3ixCw0t2fNLN/NbM5pMc3vgX8z0Eu77CkDy4ZaXRNi4jIcKD6SCRbIRLGy4F/PGzbFTm2ZXH3N4GP59j+hUErmYiIiBSFffv2cfbZZ3fb/swzz+Qcr7h161a+8IXsW4ZIJMIvf/nLISvjNddcw6ZN2Wv2XXfddfzFX/zFkJ1TRGSw5S1hNLNLgaXANDN7vMtTlcC+fJVDREREit/48eM7J5rpi9ra2n7tPxi+//3v5/V8IiJDIZ8tjC+SnsV0AtkzmTYCW/JYDhEREREREemDvCWM7r4L2AV8srf9zOzn7t7rPiIiIiIiIjL08roOYx9ppW4REREREZFhYDgmjPld50NERERERERyGo4Jo4iIiIiIiAwDwzFhtEIXQERERERERAqcMJrZBDM7PEHUuooiIiIiIiLDQN4SRjM7zcyeM7M1ZvZHZvYq8Crwvpmd27Gfu7+arzKJiIiIiIhIz/K5DuOdwM1ANfAs8Bl3/4WZnQQ8DDyZx7KIiIiIiIjIEeSzS2rI3Te4+78Bf3D3XwC4+448lkFERERERET6KJ8JY6rL362HPaelNI4gFDCqo0bA0r9DAc0NJMVH17GIiAxnqqdEustnl9SPm9lB0rOglmb+JvM4msdyFJ1QwKgpN1YuLuWMKUFeeDvJ0jWthAJGIqVcW4qDrmMRERnOVE+J5Ja3FkZ3D7p7lbtXunso83fH43DHfmY2Nl9lKhblJbBycSnzpoUIB41500KsXFxKeUmhSybSd7qORURkOFM9JZLbcFyH8ZlCF2C4aYzBGVOCWdvOmBKkMVagAokcBV3HIiIynKmeEsktn11S+2rUdRaPhENEg0kaY1AZgbZkkFg80fl8ZQReeDvJvGmH/rteeDtJZaQQpRU5OpUR+MZ/xFi3I8H2vSlmTAiw6KSQrmMRERkWVE+J5DYcE8ZR1Uk8Eg4xLppi5eKyrP7ykXCoM2lsboela1q79alvbi9w4UX6oTEG978c73Yd65tbEREZDlRPieQ2HLukFrVQMJA9u1aw93/iaDCZs798NJjs3CeRcuqbnUWrW4jc0sii1S3UN7sGYEvRmFVbS2Uk99gQfXMrIiLDgeopkdyGYwtj0XZJDQUD1JTRrbUwptr97wAAIABJREFUFAyQSKZyHtPX/vJKDqVYzaqtZcfOd3GNDRERkWFMYxhFcitYC6OZTTSzKR0/XZ46u1BlGqjysOeeXSvcc7LXMT6xK41PlJFk++tvUrNgma51EREZ1lRPieSW94TRzBaa2RvATuA/gLeAn3Y87+4f5LtMg+VI30zNr6sjGC3HLEAwWs78ujrakkGWrmll484E8aSzcWeCpWtaaUsGc5xBpPh4eyuRyTNpjJHzWtc3tyIiMhyonhLJrRBdUr8JnAY87e5/ZGbzgEv7cqCZvQU0Akkg4e5zzWwcsBqYSjr5/Ly77x+Cch9Rb7OZzq+rY+OLm6m5aDmRyTOJ7d7GxvUrmDfvLH628VkWrW7pcZZUkWJmJaXEdm+jMgKnHRvg4h+3cKANxkThvx8f5Nm3kkd+ERERkSGmekokt0J0SY27+z4gYGYBd98IzOnH8fPcfY67z808vgl4xt1PJL2G402DXN4+a45bzm+mmuPGxuc3UbNgGdHjZ2PBENHjZ1OzYBkbn99ELJ6goc1JudPQ5koWZUTx9hj161fQlIryi3dTPPr5MmLLK3n082X84t0UJeVjCl1EERERDsbIWU8dVAujjHKFaGE8YGYVwM+Ah8xsDzCQDOlC4FOZvx8EngNuHEgBj1YimSIUDGS1FjbHjUQyhVmAyOSZWftHJs8kFWstRFFF8ihFeMLxtP/+df7Q5FnxUVI+hvp9BekQICIichjj/WbLqqca27WggEghEsYLgVbgK8CfAdXA/+njsQ5sMDMHfuDu9wKT3P33AO7+ezObmOtAM7sKuApgypQpuXYZFD3NhhqIpLvlRY+f3bkttnsbgUjpkJVFZKj1Ja4CkVLGfPLzWdd+6a4t1K+9haSSRZFu8lVfiYwmfa2vai5aTvT42YzNbOuor0RGs7wnjO7ebGbHAye6+4NmVgb0dYaX0939vUxS+JSZ7ejHee8F7gWYO3du3teomHfm6Wxcv4KaBcuITJ7JO39/MZUlSTwG1VEjTpiWtvZ8F0tkQPoSV12v/T3/9r+pDCU6v7kti5bouhc5TKHrK5GRqK/11bOrbk63LHa0MMbgrHPOyWtZRYabQsyS+iXgEeAHmU3HAuv6cqy7v5f5vQdYC3wCeN/MPpx57Q8Dewa7zIPh6Q0bmPenc6lfewtv37aISeUp1i1J95Fft6SM6nCCsmhJoYspMug6rv09q25mUmlS172IiAxLLz7/HJMqLKuemlRhvPj8c4UumkhBFaJj9jXA6cBBAHd/A8jZjbQrMys3s8qOv4E64FXgceDyzG6XA48NRiFzLYExUE9v2ECyrZmqCDnXawwTH4SSiwxPlbruRURkGAsTVz0lkkMhEsaYu3f2QTOzEOmxiUcyCXjBzH4D/Ar4d3d/Evg74JzM2o7nZB4PyPy6Op596ikqaMFwKmjh2aeeGpSkEY68XqPISNKxpIyuexERGc56q6dCAStQqUQKrxAJ43+Y2c1AqZmdA/wbsP5IB7n7m+7+8czPLHf/Vmb7Pnc/291PzPz+YKAFfPapp3J2SXj2qacG+tLAofUau+pYr1FkpOlYUkbXvYiIDGe91VM15aYhFDJqFWKW1BuBK4GtwP8EfgLcV4By9Khr1zmgs0vCotUtAFRVlGGJ1s4B0R4q5WBTS59fP06YpWtaWbm4lDOmBHnh7SRL17QSJzwk70ekkFKxViKTZ7I3BhetbmFCmbHzgDNtjLG3xWlWC6OIiAwDccI566mp1cYd5x66DxQZbfKaMJpZANji7icD/5zPc/dHb10SqirKKLc2Vi4py0r2qirK+pw0trS1UxYtyVrnR7OkykjVsaRMCoiGjH9ecOiLkksfbeVgTJNAiohI4YVCIaIkutVT7zW6hlDIqJbXLqnungJ+Y2bDemGp3rokWKI154BoS7T26xwtbe00tDkpdxraXMmijFjzzjyd+vUrqIzAwxdnx87DF5eqS6qIiAwLlmjNWU+1pzSEQka3Qoxh/DDwmpk9Y2aPZ34GZWbTwdLRZXTjzgTxpLNxZ6Kzy6gm7hDpn45lNRQ7IiIynPVWT2nokIxmhRjD+I0ufxtwBnBpAcrRo966jFZHjRfeTnaObwR96yRyJE9v2KDYERGRYa2jh1mueqohHlJvMBm18t7C6O7/ATQA5wMPAGcD9+S7HEfSU5dRD5XmbH30UGmBSywyvCl2RERkOOutnlKyKKNZ3loYzWw6cAnp1sR9wGrA3H1evsowGA42tRAOBrJaH1viRjzZ84Q38+vq2Pj8JlKxVgKRUuadeTpPb9iQx1KLFNbY8RNoihuN7Z4VO/2dYVhERGSoHGxqIWCWVU81xiDlqqdkdMtnC+MO0q2JC9z9DHf/HpA8wjHDzvy6OlKlYyi96FaOu2EdpRfdSqp0DPPr6rrtW1VRRnU0vX5jBS0EwiXUXLScjS9uzrm/yEg0dvwEDsZSTFy8nEBY/U9FRGR4Gjt+Qr+2i4wW+UwYLwb+AGw0s382s7NJj2EsKh2LkEePn40FQ0SPn03NgmVsfH5T1n4dy2+sW1JGbHkl65aUMTHSTv2j38i5v8hI1dDUSs2CZdQ/+g0mRtqzYqLc2qiqKCt0EUVERGj4YB+TKiyrnppUYTR8sK/QRRMpqLwljO6+1t2XACcBzwHXA5PM7G4zK5rmto5FyLuKTJ5JKpa9rEZPy29UBGI59xcZqbw9HTMVgdigLEkjIiIyFCoj5KynNDmbjHaFmPSm2d0fcvcLgMnAK8BN+S7H0epYhLyr2O5tBCLZE3f0NjVzrv1FRiorSceMltUQEZHhTPWUSG6FWIexk7t/4O4/cPezClmO/uhYhLxt1xY8maBt1xbq169g3pmnZ+3XMTVzVx1TM+faX2Skqq4opX79il5jQkREpNBUT4nkVtCEsRh1LEJev/YW3r7tIurX3sK8P53bbdbTnqZmboyRc3+RkWr/vr1URQKdCx9rWQ0RERmOtPyTSG55W1ZjJOlLsnewqYVgIHtq5qZ2SLnnoYQiw8sxx3yYhqZW/tDUomU1RERkWOrp3i2ZUj0lo5taGIfIrNpaKBubtfwGZWPT20VGkVm1tezY+S4TFy9nSiYWrHwsk088WcmiiIgMG7p3E8lNCeMQ2f76mzmX39j++puFLppIXikWRESkGKi+EslNCeMQ6VhKoKvI5Jl4u5YQkNFFsSAiIsVA9ZVIbkoYh0jHUgJdxXZvw0o0cFpGF8WCiIgUA9VXIrlp0puh0t5C69qb2ZMZNN0YM6x8DDOmn1Dokonk1YzpJ7Bj/QpqFixjz+rlVJakOicTiIRDxOKJQhdRRESEGdNPYPuqmzP3bXT+nnHyyYUumkhBqYVxCETCISZVGOuWlBFbXsm6JWVMqgBv3s9rW7cWungiefXa1q2cNO1Y9qy6mUnlnhUX46IpImF9byUiIoX3ux3bc9y/Gb/bsb3QRRMpqKJKGM0saGYvm9kTmccPmNlOM3sl8zOn0GUEiAaTrFxcyrxpIcJBY960ECsXl2rhVxm1Xtu6lcoIOeMiGkwe+QVERESGWE/3b6qnZLQrtq/2rwO2A1Vdti1z90cKVJ6cGmNwxpRg1rYzpgRpjBWoQCLDgOJCRESGM9VTIrkVTQujmU0GzgfuK3RZjqQyAi+8nf1t1AtvJ9XCKKOa4kJERIYz1VMiuRVNwgjcAXwVSB22/VtmtsXMbjezYRHSbckgS9e0snFngnjS2bgzwdI1rbQlg0c+WGSEUlyIiMhwpnpKJLei6JJqZhcAe9z9JTP7VJenvgb8ASgB7gVuBP5PD69xFXAVwJQpU4a0vLF4gkg4xKLVLZ2zbLUlg5oNUkac/sSV4kKkb/JZX4mMFn2JK9VTIrkVSwvj6cBCM3sLWAWcZWb/191/72kx4F+AT/T0Au5+r7vPdfe5NTU1Q17gWDxBQ5uTcqehzfVhIyNSf+NKcSFyZPmur0RGg77Gleopke6KImF096+5+2R3nwpcAjzr7n9uZh8GMDMDFgGvFrCYIiIiIiIiI0pRdEntxUNmVgMY8ArwlwUuj4iIiIiIyIhRFC2MXbn7c+5+Qebvs9y91t1Pdvc/d/emgbz2rNpaApFyzAIEIuXMqq0dnEKLjHKKLRERKQaqr0S6K/YWxkEzq7aWHTvfZeLi5UQmzyS2exs71q9gVm0tr23dWujiiRQtxZaIiBQD1VciuRVdC+NQ2f76m9QsWEb0+NlYMET0+NnULFjG9tffLHTRRIqaYktERIqB6iuR3JQwZnh7K5HJM7O2RSbPxNtbC1QikZFBsSUiIsVA9ZVIbkoYM6yklNjubVnbYru3YSWlBSqRyMig2BIRkWKg+kokNyWMGTOmn0D9+hW07dqCJxO07dpC/foVzJh+QqGLJlLUFFsiIlIMVF+J5KZJbzJe27qVWbW1bF9zC97eipWUMmP6CRrkLDJAii0RESkGqq9EclPC2IU+EESGhmJLRESKgeorke7UJVVERERERERyUsIoIiIiIiIiOSlh7MGs2loCkXLMAgQi5cyqrS10kUSKluJJRESKgeorke40hjGHWbW17Nj5LhMXLycyeSax3dvYsX4Fs2pr1bddpJ8UTyIiUgxUX4nkphbGHLa//iY1C5YRPX42FgwRPX42NQuWsf31NwtdNJGio3gSEZFioPpKJDcljDl4eyuRyTOztkUmz8TbWwtUIpHipXgSEZFioPpKJDcljDlYSSmx3duytsV2b8NKSgtUIpHipXgSEZFioPpKJDcljDnMmH4C9etX0LZrC55M0LZrC/XrVzBj+gmFLppI0VE8iYhIMVB9JZKbJr3J4bWtW5lVW8v2Nbfg7a1YSSkzpp+gAc8iR0HxJCIixUD1lUhuShh7oA8HkcGjeBIRkWKg+kqkO3VJHSLz6+oIRtPr+ASj5cyvqyt0kUQKRvEgIiLFQPWVSHdqYRwC8+vq2PjiZmouOrSOz8b1K5hfV8fTGzYUungieaV4EBGRYqD6SiQ3tTAOgY3Pb8q5js/G5zcVumgiead4EBGRYqD6SiQ3JYxDIBXLvY5PKqZ1fGT0UTyIiEgxUH0lklvRJYxmFjSzl83siczjaWb2SzN7w8xWm1lJocsYiORexycQ0To+MvooHkREpBiovhLJregSRuA6YHuXx98Bbnf3E4H9wBcLUqou5p15es51fOadeXqhiyaSd4oHEREpBqqvRHIrqklvzGwycD7wLeD/MzMDzgKWZnZ5EPg6cHdBCpjx9IYN6YHTa28hFWslECll3pmna8C0jEqKBxERKQaqr0RyK6qEEbgD+CpQmXk8Hjjg7onM493AsbkONLOrgKsApkyZMsTFRB8uMir0Na4UDyJ9l+/6SmQ0UH0lcvSKpkuqmV0A7HH3l7puzrGr5zre3e9197nuPrempmZIyigy2iiuRAaf4kpk8CmuRI5eMbUwng4sNLPzgChQRbrFcYyZhTKtjJOB9wpYRhERERERkRGjaFoY3f1r7j7Z3acClwDPuvufARuBz2Z2uxx4rEBFFBERERERGVHMPWcPzmHNzD4F3ODuF5jZCcAqYBzwMvDn7h47wvH1wK4hL+jgmgDsLXQhjlIxlx1GbvmPd/dB65fTJa6K/d+rJ3pfxacQ722o4qrDSP7/6sloe8+j7f3Ckd/zUMdVf8sz3BRTeYuprFBc5T2asvYptooyYRyNzGyzu88tdDmORjGXHVT+4X6+fNH7Kj4j8b2NxPd0JKPtPY+29wvD7z0Pt/IcSTGVt5jKCsVV3qEsa9F0SRUREREREZH8UsIoIiIiIiIiOSlhLB73FroAA1DMZQeVf7ifL1/0vorPSHxvI/E9Hcloe8+j7f3C8HvPw608R1JM5S2mskJxlXfIyqoxjCIiIiIiIpKTWhhFREREREQkJyWMIiIiIiIikpMSRhEREREREclJCaOIiIiIiIjkpIRRREREREREclLCKCIiIiIiIjkpYRQREREREZGclDCKiIiIiIhITkoYRUREREREJCcljCIiIiIiIpKTEkYRERERERHJSQmjiIiIiIiI5KSEUURERERERHIKFboAhTBhwgSfOnVqoYshUlAvvfTSXnevGazXU1yJKK5EhoLiSmRo9DW2RmXCOHXqVDZv3lzoYogUlJntGszXU1yJKK5EhoLiSmRo9DW21CVVREREREREclLCKCIiIiIiIjkpYRQREREREZGclDCKiIiIiIhITkoYRYZYOBigLGRURoyAGVURoyyU/rssZFnPVUcP/Y4Eu28LBw7tWxkxysLd9wnYob8/Xjur0G9fZETIFWci0n/n1s2nOhrIqqsUVyLDmxJGkSEUDgYoDzs1Fcbjl5QRW17JY5eUUVNh/OXcMOUlZD23bkkZU8cYX/5ECeNKjaoIrFty6LkJ5cZ1p5YQW17J45eUUR6GieWWtc+kCiNs6eP2vLVdSaPIAAXMmFTRPc50cyvSP+fWzec3P3+WdUtKARRXIkVCCaPIECoLOxPKjAcuLGXetBDhoDFvWogHLixl1atxqqPdn7t/YSnrdiRYeXEpE8os67mVi9PPdTyujhoPX1zabZ9oCZ1/v/XGtkL/M4gUtcoIrFzcPc4qI4UumUhx+fnzz3bGkuJKpHjkJWE0s6CZvWxmT2QeTzOzX5rZG2a22sxKMtsjmce/yzw/tctrfC2z/bdm9uku28/NbPudmd2Uj/cj0leNMdh5wDljSjBr+xlTghxo6/m57XtTnDElyM4DnvO5Dj0d3xjr/reIHJ3GGL3GmYj0TWPsUJ2luBIpHvlqYbwO2N7l8XeA2939RGA/8MXM9i8C+939o8Dtmf0ws5nAJcAs4FzgrkwSGgS+D3wGmAlcmtlXZFiojMC0McYLbyeztr/wdpIx0Z6fmzEhwAtvJ5k2xnI+16Gn4zu+oe36t4gcncoIvcaZiPRNZeRQnaW4EikeQ54wmtlk4HzgvsxjA84CHsns8iCwKPP3hZnHZJ4/O7P/hcAqd4+5+07gd8AnMj+/c/c33b0dWJXZV2RYaIkbe1ucKx5rZePOBPGks3Fngisea+WSk8M0tHV/7ouPt7LopBBLH21lb4tnPbd0Tfq5jscNbc6lj7Z226etnc6/p56o71BEBqIxBkvXdI8ztYSI9M8nzzyrM5YUVyLFI5SHc9wBfBWozDweDxxw90Tm8W7g2MzfxwLvALh7wswaMvsfC/yiy2t2Peadw7afmqsQZnYVcBXAlClTBvB2RPounkwRDgZoTzgLV7XQ3A4VJZBIwj2b40SD0Nx+6LnKSPrm9Hu/aqctDvEULFrdQmMs/VxLO/zjL9u59WftlJdAMgVtCc/ap6OyXbS6haknzuQ3W18bsvenuJLRIOVOwKxbnKXcj3zwUVBcyUj15IanObduPotWPwvA+03d6y/FlcjwM6QJo5ldAOxx95fM7FMdm3Ps6kd4rqftuVpIc37SuPu9wL0Ac+fOHZpPI5Ec4snUkXcqUoorGS2G6iY2F8WVjGRPbni6IOdVXIkcvaFuYTwdWGhm5wFRoIp0i+MYMwtlWhknA+9l9t8NHAfsNrMQUA180GV7h67H9LRdREREREREBmBIxzC6+9fcfbK7TyU9ac2z7v5nwEbgs5ndLgcey/z9eOYxmeefdXfPbL8kM4vqNOBE4FfAfwInZmZdLcmc4/GhfE8iIiIiIiKjRT7GMOZyI7DKzG4BXgbuz2y/H/hXM/sd6ZbFSwDc/TUz+zGwDUgA17h7EsDMrgX+HxAEfujuQzdgS0REREREZBTJW8Lo7s8Bz2X+fpP0DKeH79MGfK6H478FfCvH9p8APxnEooqIiIiIiAj5W4dRREREREREiowSRhEREREREclJCaOIiIiIiIjkpIRRREREREREclLCKCIiIiIiIjkpYRQREREREZGclDCKiIiIiIhITkoYRUREREREJCcljCIiIiIiIpKTEkYRERERERHJSQmjiIiIiIiI5KSEUURERERERHIa0oTRzKJm9isz+42ZvWZm38hsf8DMdprZK5mfOZntZmb/ZGa/M7MtZnZKl9e63MzeyPxc3mX7H5vZ1swx/2RmNpTvSQbP2PETCETKMQsQiJQzdvyEQXvtWbW1BCJl6dcuKcUCYSxUkv7bAgRKyrBQpPPcs2pruxx3qExmQcwCBKPlzK+rwwKhQ68b6foaZV2OyfwOhLIel1dUUVJehVmAkvIqrr7m2kF7v9Kzbv8vpu/JREQKobyiKl3/dtTNWfVnmT6fRYap0BC/fgw4y92bzCwMvGBmP808t8zdHzls/88AJ2Z+TgXuBk41s3HA3wJzAQdeMrPH3X1/Zp+rgF8APwHOBX6KDGtjx0/gYCzFxMXLiUyeSWz3NurXr2Ds+Ans37d3QK89q7aWHTt3M3Hx3xCZPJMDmx6m8ddPEIiUMeG86zvPt/ff/54xZ15OqHICO9avIBItIxGKditToLSK8fP/J8+s+msC5WOoWbAs6zVSiQSVc86ledtzTPjMdVnHRo75GDUX3nTo8bEf48OZx/c+uAKAu75/52D8k0oOZoFu/2f161dgFsA9VejiiYiMGuUVVbR5kIkXf62zbm7askGfzyJFYEi/yvG0pszDcObHeznkQuBHmeN+AYwxs/+fvXuPk6quHz/+es9lZ2ZvwLK7hCwgKsTFNSLU/OnXwguahSJWon1TSzPzVn7LMsP6WtpXpVK7maYG9k2wDLmUKaaYX80bmoECiYHAIsJy3fvszOz798c5MzuzO5cF9jK7vJ+Pxzxm5n0+55zPmTmfM+cz55z3GQ6cATylqrvdTuJTwJnusFJVfVFVFXgImNljC2S6zb6GZipmXE9w9DGI10dw9DFUzLiefQ3NBz3ttW9voGLGNxPTbl7/It5QKeVnXZcyv/JPfp26lx9NzDuikrZOsbpaJ1YQ6jS8/JNfxxsopHn9i5R/4qudxg1veTPj+/IZ13P/vIe64dM0maT7zipmXI8UhPq6asYYc0hpjsSoODv1t9m2z8b0Dz1+7F9EvCLyBrADp9P3sjvoVve00ztFJODGRgBbkkavcWPZ4jVp4ibPaWszgaqJKbFA1US09eA7jB2nHdlVQ3Tf9rTzi+yqSZl3tjplGh7dt53Irpq0w9paGrO+jzQ1YHpOT65nxhhjui7db7Ntn43pH3q8w6iqMVWdDFQBx4nI0cC3gfHAsUAZ8C23eLrrD/UA4p2IyOUislJEVtbW1u7nUpjuJgUhwjVrUmLhmjXd8s9ix2n7h1bhGzQs7fz8Q6tS5p2tTpmG+wYNwz+0Ku0wT7Ao63t/YfFBLGnfy/d21ZPrmTE9Jd/blTEHIt1vc29un61dGXPgeu3qYlXdCzwLnKmq29zTTsPAb4Dj3GI1wMik0aqA93LEq9LE083/PlWdqqpTKyoqumGJzMEYVByidtlcWjatQmNRWjatonbZXAYVH/wPxYRxR1C77I7EtENjTyDWXMfOx+9Mmd/OP/+Y0uM/nZi3XzRtnbylFU6stbnT8J1//jGxcBOhsSew8y93dxo3MPLojO93LpvLZZdc1A2fZt/J93aV7jurXTbX/sE2eS3f25UxByLk91K7NPW3uTe3z9aujDlw4lz610MTF6kAIqq6V0RCwHLgduA1Vd3mZjS9E2hR1RtE5JPA1cBZOElvfqqqx7lJb14D4llTXwc+oqq7ReRV4BrgZZykNz9T1cez1Wvq1Km6cuXK7l9gs1+GDC1nX0Mz2tqMFIQYVBw66IQ3cZOqq1n79r/R1hbEH0CjUfAI4vGikTDiD6JtMYhFkIIQE8YdwVurV7vjbUjUSVtbAMUTCDHt5BN5+q/PIP4CZ7oFQTQWn0bQjYXax42EnXm770N+LxGFSFMD/sJiLrvkoj5NeCMir6nq1O6aXr62KxFP6vfS2mwJFUyPOVTalTEHoqi4lObWKBpxf5sj4aTfT+c53fbZ2pUxPaOrbauns6QOB+aLiBfnaObvVfVPIvKM25kU4A3gCrf84zidxXeAJuALAG7H8AfAq26576vqbvf1V4B5QAgnO6plSO0nuqtzmM5bq1f36ngmf1nn0Bhj8kNjQ11fV8EYcwB6tMOoqquAD6eJn5KhvAJXZRj2IPBgmvhK4OiDq6kxxhhjjDHGmI7sDqnGGGOMMcYYY9LK2mEUkUEicpuIrBORXe5jrRsb3FuVNMYYY4wxxhjT+3IdYfw9sAf4uKoOVdWhwDQ39oeerpwxxhhjjDHGmL6Tq8N4uKrerqrvxwOq+r6q3g6M6tmqGWOMMcYYY4zpS7k6jJtE5JsiMiweEJFhIvItYEvPVs0YY4wxxhhjTF/K1WE8HxgK/E1EdovIbuBZoAz4bA/XzRhjjDHGGGNMH8p6Ww1V3QN8y30YY4wxxhhjjDmE5LythoiMF5FTRaSoQ/zMnquWMcYYY4wxxpi+luu2GtcCS4BrgLdE5JykwT/syYoZY4wxxhhjjOlbWU9JBb4EfERVG0TkcOBRETlcVe8GpKcrZ4wxxhhjjDGm7+TqMHpVtQFAVd8VkY/jdBpHYx1GY4wxxhhjjBnQcl3D+L6ITI6/cTuPnwLKgeqerJgxxhhjjDHGmL6Vq8N4EfB+ckBVo6p6EXByromLSFBEXhGRf4rIWyJysxsfIyIvi8h6EXlERArceMB9/447/PCkaX3bjf9LRM5Iip/pxt4RkRu6vOTGGGOMMcYYY7LK2mFU1RpVfT/D4H92Yfph4BRV/RAwGThTRD4K3A7cqapjgT3ApW75S4E9qnoUcKdbDhGZCMwGJgFnAr8UEa+IeIFfAJ8AJgIXuGWNMcYYY4wxxhyknLfVyGJNrgLqaHDf+t2HAqcAj7rx+cBM9/U57nvc4aeKiLjxhaoaVtWNwDvAce7jHVXdoKqtwEK3rDHGGGOMMcaYg5Q16Y2I/FemQUBxV2bgHgV8DTgK52jgv4G9qhp1i9QAI9zXI4At4Jz6KiL7gKFB+Wb3AAAgAElEQVRu/KWkySaPs6VD/Piu1MsYY4wxxhhjTHa5jjD+EBgClHR4FHdhXABUNaaqk4EqnCOCE9IVc5/TZV7VA4h3IiKXi8hKEVlZW1ubu+LGmJysXRnT/axdGdP9rF0Zc+By3VbjdWCxqr7WcYCIXLY/M1LVvSLyLPBRYLCI+NyjjFXAe26xGmAkUCMiPmAQsDspHpc8TqZ4x/nfB9wHMHXq1LSdSmPM/rF2ZUz3s3ZlTPezdmXMgct1lPALwOYMw6bmmriIVIjIYPd1CDgNWAusAD7tFrsYWOK+Xuq+xx3+jKqqG5/tZlEdA4wFXgFeBca6WVcLcBLjLM1VL2OMMcYYY4wxuWU9wqiq/8oybHsXpj8cmO9ex+gBfq+qfxKRNcBCEbkF+AfwgFv+AeC3IvIOzpHF2e683hKR3+Mk2okCV6lqDEBErgaeBLzAg6r6VhfqZYwxxhhjjDEmh1xJb7zAZTinej6hqi8kDZujqrdkG19VVwEfThPfgHM9Y8d4C/CZDNO6Fbg1Tfxx4PFs9TDGGGOMMcYYs/9ynZJ6L/AxYBfwUxH5SdKwWT1WK2OMMcYYY4wxfS5Xh/E4Vb1QVe/CuV1FsYgsEpEA6TOUml4QCBbiCRQi4nGePX5GjBzV5fGvvOpqCopKnfH9wdRpiRdPQch5XxCiqLgUT6AoaXj7s/gCSbGiTs9DhpYzqbo6Jebx+tvHKQghvgCnTZ8OwIiRo1LK7s8y9bV034nzGQXxFBSmfCYeXwDxFbR/zimfRwHiCyRNx5f0fRQyqbq6rxe1x6Rbj0x+s+/MGLO/nN9LZ3uRbt/CGJN/cmVJLYi/cDOaXi4i3wWeoYv3YTTdKxAsJOoLUjnjegJVEwnXrKF22Vzee38HI0aOYuuWTDmKHFdedTX3zX+Y8rO/TbR+J3v/77eUn3UdgaqJ7H1hAQ2rllORPO2ld+ArG0asfldKfOef76QtGiZYNZHwe/+i+JjpNK55lvJPfDWlXnvXrWfYp79HoGoitUtuI/zeOipmfLN9Oo/fyTPP/b19uWbNSQx7f9ncLi1TX8v0nfjLRxPdszXl8933+p+gIIjXH0jE459n0dGn0PjWM7RFwpSdcjWR3Vs7fR/rlt7BpOpq3lq9uq8Xu1uJePAUDU5d95bNRcSDaltfV8+kYd+ZMWZ/xX8vS46ZTt2Lv7dtiDH9RK6/claKyJnJAVX9PvAb4PCeqpTJLKJCxYzrCY4+BvH6CI4+hooZ1yNeH9t27Mo5/v3zHqLcHb/u5UcpP+u6xLSa17/Yedpnf5Po7vc6xcs/eR3eQBHhLW9SMeN6mte/SPknvpq2XvGYU/abqdM56zq8odKMy9WVZeprmereuu3tTp+vN1SKN1CUEo9/ns3rX3Q+j0ARdS8/mvH7WPv2hr5e5G4nBaH063VBqK+rZjKw78wYs7/iv5fN61+0bYgx/UiuLKn/mSF+P3B/j9TIZKWtzQSqJqbEAlUT0XBTl8aPNDUkxo/sqkmZVsf3iWlnmGd033ZQJVA1MfO4SfVqa2nMOZ108853Gb+TDvHIrpqU4R3Lxz/D6L7tWcv1h89kf2X7DE1+su/MGLO/4tuNyK6afv27b8yhpksni4uIX0SuFZFH3cc1IuLv6cqZzqQgRLhmTUosXLMGCRR26V85f2FxYnz/0KqUaXV8n5h2hnn6Bg3DEywiXLMm87iBwsT7eNl008m4XP3gn8Zsde/4+foGDcM3aFja8vHP0DdoGP6hVVm/j4GmP3//hyr7zowx+yu+3fAPrbJtiDH9SFevLr4H+AjwS/cxxY2ZXuYXpXbZXFo2rUJjUVo2raJ22Vw0FmV45dCc4192yUXsdMcvPf7T7Hz8zsS0QmNP6DztpXegbUrt0jtS4jv/fCexcCOBkUdTu2wuobEnsPMvd6etVzzmlO0wncfvJNZcl2G57kBbm/EEiigqLsUbdC+S9/hTku10TI6TkoDGTawjvoLERfXxJDvJTps+vT05TXyceGKfpKQzIl7EF0xJBqSxaKfPp3bZXAqGj+v0+caa64iFG1Pi8c8zNPYE5/MIN1J6/Kczfh8Txh3RbetTvtDW5vTr9UH+02xJWXpOT31nxpiBSyNhdiy6lbaWRtuGGNOPiKrmLiTyT1X9UK5YfzF16lRduXJlX1fjgAWChUQUtLUFKQiikQiHjRje5eQwV151NffPe4hIUwN4/YjXh0aa8ZeNxFd2GC2bV6HhZsQfAPFSOes7ROt3su+FBUT3vu/Ms7XFHdfr1iOEtjanPA8qDnHYYcNZ+/aGRIxoK/j8zjj+ANrWxqmnfIy/Ll/OiJGj2LZjl1M2EKJkygwGn3hB4kJ4b8lQAh8YR5N7bV/yRfIfGFLM1i2b3QvqA50S6ziJZC7DV1JO7bI7mPb/juWvy5cDTmdxxQuvUnF2+zjb//gDxON0MJOT09QuvYO25gY8hcUp89ix5HY8Xh+xht2J7wSNgbcA8XjQSEviM9m3rx5FEY8XjYQTn6fzecQAgVjEnU4r4vM75fxBJnzwyG5LeCMir6nq1G6ZGAffrkQ8ndajg0l8kCkpS1vjXkuo0E26+zsbCPKtXRmTL06bPp2n//osnsIiKmZ8k2j9Tnb9+S73NzC+DWkBtNN2xNqVMT2jq22rqx3G14HPqOq/3fdHAI+q6pSDrmkfsA1FO0+giMpZcwiOPiYRa9m0ih2LbgHIOKwt3NgndRKPh4qZN2asU6Zxdz3xM8Tn57BLf+n8i/nYLcRanGXwBouoODd1nM13nY83VMrQM69JO69089j9119RdtoVPf75dJeB/gOcbT3qD9+P6Z8Gersy5kB5g0Wotu9XvPfAlZSddkWXttHWrozpGV1tW7luqxF3PbBCRDbg3H9xNPDFg6ifyRO5Elf0xQXp2eqkOeqUNUFP0vu2cPsytIU7j6PhJqJdTGYTj8eT1tjpNPnBkrIYY0z+iP/uZkq8Fx9m22hj8k9XL+h5HhgLXOs+Pgi80FOVMr0n20XnfXVBerb5ZkqcE69TtgQ9/qFVifeeQPsyeAKdx5FAYcbkNJnmEU9SYxfs5wdLqGCMMfnDE0jdrziUErsZ0991tcP4oqqGVXWVqv5TVcPAiz1ZMdM7Jow7Iu1F5xPGHZF1WF/UyVd2GIXjT047LJ7wx0mekyaxjptIJp5MZ9rJJybmN+3kEzslrdG2NmLNdZ2S09QuvQONtHaex1/uTiSpGYhJafojS6hgjDH5Y9rJJ6KRSGK73DHxnm2jjclfWa9hFJEPACOA/wUuxDkdFaAU+JWqju/xGvYAO3c91aTq6pTENBPGHZFIrJJtWG/WKeT30hKNOae0iBfx+xPJdoZXDk1J+JOSFMhNrAMKsShSEOSUj52USHgTd9r06Tzz7PNOcpr4OLGIk9jH40kkndFIGLx+CoMFNEdinRL99Nbn0x0OhWtCLCmL6W2HQrsy5kCdNn06Tz/1DFJQkCZ5npMALt022tqVMT2ju65hPAO4BKgCfpIUrwdu7EIlRgIPAR8A2oD7VPVuEflv4EtArVv0RlV93B3n28ClQAy4VlWfdONnAncDXuB+Vb3NjY8BFgJlwOvA51W1NVfdTLtsHZy+6vwczHzDLU37PU7HDqQZGKxzaIwx+cN+a43pn7J2GFV1PjBfRM5T1T8ewPSjwNdV9XURKQFeE5Gn3GF3quqPkguLyERgNjAJOAz4q4iMcwf/AjgdqAFeFZGlqroGuN2d1kIR+RVOZ9PuEWmMMcYYY4wxBynnNYwicgZwuogsFZElInKPG8tJVbep6uvu63pgLc4prpmcAyx0r5fcCLwDHOc+3lHVDe7Rw4XAOSIiwCnAo+7484GZXambMcYYY4wxxpjssnYYReQu4KvAs8AdwFzgb8BXReTu/ZmRiBwOfBh42Q1dLSKrRORBERnixkYAW5JGq3FjmeJDgb2qGu0QTzf/y0VkpYisrK2tTVfEGLOfrF0Z0/2sXRnT/axdGXPgch1hPEtVz1LVhar6vPtYCHwSOKurMxGRYuCPwNdUtQ7nlNEjgcnANuDH8aJpRtcDiHcOqt6nqlNVdWpFRUVXq26MycLalTHdz9qVMd3P2pUxBy5Xh7FFRI5LEz8WaOnKDETEj9NZ/J2qLgJQ1e2qGlMnI8WvcU45BecI4cik0auA97LEdwKDRcTXIW6MMcYYY4wx5iDlypJ6CXCPm7Cmxo2NBOrcYVm51xg+AKxV1Z8kxYer6jb37bnAm+7rpcDDIvITnKQ3Y4FXcI4kjnUzom7FSYxzoaqqiKwAPo1zXePFwJJc9TLGGGOMMcYYk1uuLKmvA8cn3Y9RgBpVfb+L0z8R+DywWkTecGM3AheIyGSc00ffBb7szu8tEfk9sAYnw+pVqhoDEJGrgSdxbqvxoKq+5U7vW8BCEbkF+AdOB9UYY4wxxhhjzEHKdYQRERkEfAynw6jAeyLypKruzTWuqj5P+usMH88yzq3ArWnij6cbT1U30H5KqzHGGGOMMcaYbpIrS+pFwOvAx4FCoAiYhnM/xYt6vHbGGGOMMcYYY/pMriOM3wE+0vFoonsbjJeBh3qqYsYYY4wxxhhj+lauLKlC+ttUtJH+VFNjjDHGGGOMMQNEriOMtwKvi8hyYIsbGwWcDvygJytmjDHGGGOMMaZvZT3CqKrzganA34Aw0Ao8C0xV1Xk9XTljjDHGGGOMMX0nZ5ZUVd2Dc49DY4wxxhhjjDGHkFzXMGYkIqu7syLGGGOMMcYYY/JL1iOMIjIr0yDgA91fHWOMMcYYY4wx+SLXKamPAL8jfabUYPdXxxhjjDHGGGNMvsh1Suoq4Eeq+oWOD2BvjnGNMcYYY4wx++mxxx5DRFi3bl2Pzsfr9TJ58mQ+9KEPMWXKFP7+979nLPvuu+8SCoWYPHly4tHa2sq8efOoqKhg8uTJTJw4kV//+tcAzJs3j6uvvjplGk1NTXzyk59k/PjxTJo0iRtuuCEx7LnnnmPKlCn4fD4effTRlPHmz5/P2LFjGTt2LPPnz0/EX3vtNaqrqznqqKO49tprUdXEvN97772cy/+5z32OD37wgxx99NF88YtfJBKJALBu3TpOOOEEAoEAP/rRjxLlt2zZwrRp05gwYQKTJk3i7rvvTgzbvXs3p59+OmPHjuX0009nz549OeffX+TqMH4NqMsw7NxurosxxhhjjDGHvAULFnDSSSexcGHnvJOxWKzb5hMKhXjjjTf45z//yf/8z//w7W9/O2v5I488kjfeeCPxKCgoAOD888/njTfe4Nlnn+XGG29k+/btGafxjW98g3Xr1vGPf/yDF154gb/85S8AjBo1innz5nHhhRemlN+9ezc333wzL7/8Mq+88go333xzojP2la98hfvuu4/169ezfv16nnjiCWD/Oozr1q1j9erVNDc3c//99wNQVlbGT3/6U77xjW+klPf5fPz4xz9m7dq1vPTSS/ziF79gzZo1ANx2222ceuqprF+/nlNPPZXbbrst5/z7i1y31fg/Vd2cYdjKnqmSMcYYY4wxh6aGhgZeeOEFHnjggUSH8dlnn2XatGlceOGFVFdXE4vFuP766zn22GM55phjuPfeexPjnnrqqUyZMoXq6mqWLFnS5fnW1dUxZMiQg6p7ZWUlRx55JJs2bUo7vLCwkGnTpgFQUFDAlClTqKmpAeDwww/nmGOOweNJ7Z48+eSTnH766ZSVlTFkyBBOP/10nnjiCbZt20ZdXR0nnHACIsJFF13E4sWLefTRR1m5ciWf+9znmDx5Ms3NzTz99NN8+MMfprq6mi9+8YuEw2EAzjrrLEQEEeG4445L1KWyspJjjz0Wv9+fUpfhw4czZcoUAEpKSpgwYQJbt24FYMmSJVx88cUAXHzxxSxevPigPst8kjNLqoicISKXisjhHeJf7MK4I0VkhYisFZG3ROSrbrxMRJ4SkfXu8xA3LiLyUxF5R0RWiciUpGld7JZfLyIXJ8U/IiKr3XF+KiLS9cU3xhhjjDEmfyxevJgzzzyTcePGUVZWxuuvvw7AK6+8wq233sqaNWt44IEHGDRoEK+++iqvvvoqv/71r9m4cSPBYJDHHnuM119/nRUrVvD1r389cZpmOs3NzUyePJnx48dz2WWXcdNNN2Wt27///e/E6ahXXXVVp+EbNmxgw4YNHHXUUTmXc+/evSxbtoxTTz01a7mtW7cycuTIxPuqqiq2bt3K1q1bqaqq6hT/9Kc/zdSpU/nd737HG2+8gYhwySWX8Mgjj7B69Wqi0Sj33HNPyjwikQi//e1vOfPMM3PWO+7dd9/lH//4B8cffzwA27dvZ/jw4YDTsdyxY0eXp5XvsnYYReSHwHeAauBpEbkmafDV6cdKEQW+rqoTgI8CV4nIROAG4GlVHQs87b4H+AQw1n1cDtzj1qMM+B5wPHAc8L14J9Mtc3nSeF3/po0xxhhjjMkjCxYsYPbs2QDMnj2bBQsWAHDccccxZswYAJYvX85DDz3E5MmTOf7449m1axfr169HVbnxxhs55phjOO2009i6dWvW00Pjp6SuW7eOJ554gosuuihrBzP5lNRf/OIXifgjjzzC5MmTueCCC7j33nspKyvLuozRaJQLLriAa6+9liOOOCJr2XT1EZGM8Y7+9a9/MWbMGMaNGwc4R/+ee+65lDJXXnklJ598Mv/xH/+RtS5xDQ0NnHfeedx1112UlpZ2aZz+LFeW1BnAh1U1KiL/DTwsIkeo6nU4t9bISlW3Advc1/UishYYAZwDfNwtNh94FviWG39InTXgJREZLCLD3bJPqepuABF5CjhTRJ4FSlX1RTf+EDAT+EuXlt4YY4wxxpg8sWvXLp555hnefPNNRIRYLIaIcNZZZ1FUVJQop6r87Gc/44wzzkgZf968edTW1vLaa6/h9/s5/PDDaWlp6dK8TzjhBHbu3EltbS2VlZX7Ve/zzz+fn//8510uf/nllzN27Fi+9rWv5SxbVVXFs88+m3hfU1PDxz/+caqqqhKnkMbjhx12WKfxs3WAAW6++WZqa2sTp/XmEolEOO+88/jc5z7HrFntdyAcNmwY27ZtY/jw4Wzbtm2/P8N8luuUVJ+qRgFUdS9OB7JURP4AFOzPjNxTWj8MvAwMczuT8U5l/BMdAWxJGq3GjWWL16SJp5v/5SKyUkRW1tbW7k/VjTEZWLsypvtZuzKm+/WXdvXoo49y0UUXsWnTJt599122bNnCmDFjeP7551PKnXHGGdxzzz2JrJ5vv/02jY2N7Nu3j8rKSvx+PytWrMh4LWE669atIxaLMXTo0G5dpo7mzJnDvn37uOuuu7pU/owzzmD58uXs2bOHPXv2sHz5cs444wyGDx9OSUkJL730EqrKQw89xDnnnAM41xfW19cDMH78eN59913eeecdAH7729/ysY99DID777+fJ598kgULFnS6djIdVeXSSy9lwoQJ/Nd//VfKsLPPPjuRwXX+/PmJugwEuT6Zf4vIx+JvVDWmqpcC/wImdHUmIlIM/BH4mqpmyroK6Y9a6gHEOwdV71PVqao6taKiIleVjTFdYO3KmO5n7cqY7tdf2tWCBQs499zUGxGcd955PPzwwymxyy67jIkTJzJlyhSOPvpovvzlLxONRvnc5z7HypUrE9fwjR8/Puv84tcwTp48mfPPP5/58+fj9Xq7bXnmzZtHVVVV4lFTU5O4DnPKlClMnjw5kZn01Vdfpaqqij/84Q98+ctfZtKkSYCTsfSmm27i2GOP5dhjj+W73/1u4pTXe+65h8suu4yjjjqKI488kk984hMAXHLJJVxxxRVMnjwZVeU3v/kNn/nMZ6iursbj8XDFFVcAcMUVV7B9+3ZOOOEEJk+ezPe//30A3n//faqqqvjJT37CLbfcQlVVFXV1dbzwwgv89re/5Zlnnkl8bo8//jgAN9xwA0899RRjx47lqaeeSrllSH8n2Q7TikgIQFWb0wwboapbc85AxA/8CXhSVX/ixv4FfFxVt7mnnD6rqh8UkXvd1wuSy8UfqvplN34vzmmszwIrVHW8G78guVwmU6dO1ZUrLcmrObSJyGuqOrW7pmftyhhrV8b0BGtXxvSMrratXLfVaE7XWXS92IVKCPAAsDbeWXQtBeKZTi8GliTFL3KzpX4U2OeesvokMF1EhrjJbqbjdEC3AfUi8lF3XhclTcsYY4wxxhhjzEHIlfQmm67cvuJE4PPAahF5w43dCNwG/F5ELgU2A59xhz0OnAW8AzQBXwBQ1d0i8gPgVbfc9+MJcICvAPOAEE6yG0t4Y4wxxhhjDE4inXS3rnj66afTXq+4evVqPv/5z6fEAoEAL7/8co/V0eS3g+kwZk85BKjq82TuWHZac93sqJ1v6uIMexB4ME18JXB0rroYY4wxxhhzqBk6dChvvPFG7oKu6urq/SpvBr6sHUYR+RnpO4YCDO6RGhljjDHGGGOMyQu5jjBmuyLYrhY2xhhjjDHGmAEsa4dRVeeLSAUwGnjHvRejMcYYY4wxxphDQNYsqSJyGfAW8DNgnYic3Su1MsYYY4wxxhjT53Kdkvo1YJKq1orIEcDvcG59YYwxxhhjjDFmgMt6hBFoVdVaAFXdAAR6vkrGGGOMMcYYY/JBriOMVSLy00zvVfXanqmWMcYYY4wxxpi+lqvDeH2H96/1VEWMMcYYY4wxxuSXXB3G36lqtFdqYowxxhhjjDEmr+S6hvGV+AsR+VkP18UYY4wxxhhjTB7J1WGUpNcn9mRFjDHGGGOMMcbkl1wdRu2VWuSJSdXVeAJFiHgQEQYFBU/Sc5zf60kZJiL4gkV4CoJpx4s/lwTSxwNeoTTQXqbQlzo/AJ+nvXxpUplBQcHnkY6LYnIYMrQc8QXwFIQQ8eApKCQQLATgtOnT8RQUuvGQU87rT6wbnkARk6qr+d3vHqYgWIgn4Jbt8Jw6/RAi3qQyRZ2eR4wc1cefSs9pX9bM7WogSvddG2PMQJRrH6rjvpNHOu8TyQD/TTCmv8q19zJeRFaJyOqk16tEZLWIrMo1cRF5UER2iMibSbH/FpGtIvKG+zgradi3ReQdEfmXiJyRFD/Tjb0jIjckxceIyMsisl5EHhGRgv1b/HaTqqtZt3ErlbPmICJ8oFhYfH4h4TklLD6/kGHF7Ru88kJShn2gWGiLRiAS5gPFQksrDEszfmOaeGWREPLDktlObOnsQiqKhdIAiZ1pn0eoKGofb4lbZv7MIIvPL6SiyDqN+2PI0HL2NbbgLRpE5XnfZdQ3HqPyvJuIegOIx8eKF16l8ryb3Ph38RaWgD9A5aw5TmzWHNZtrOE/L7qEmC9IcPSH8BQNpnKWM07JR85GAoUdpv9dPEWD8JWNwFM0mJKPzMA7qDJlmu/vaRiQnUYRD56iwdDalLFdDUTx5U7+jj1Fg63TaIwZcHLtQwmk7Dtdc5yzu9Zxn+gDxdZpNCYfiWrmg4giMjrbyKq6KevERU4GGoCHVPVoN/bfQIOq/qhD2YnAAuA44DDgr8A4d/DbwOlADfAqcIGqrhGR3wOLVHWhiPwK+Keq3pOtTgBTp07VlStXpsQ8gSIqZ80hOPoY9tz1KRafX8i0Me05gVZsjDLzkSaAnMNmPtKUsUy6+JeWNfPOtSWdYrVNyr4WZVBQ0o53zV9aePPK4sS097X0zgHh0SNHsLf2PerDUBKAwRWHsWnL1l6Z9/4I+H0EvbFEPTs+N4ahjfZY5ewfsmPRLVTOmsOOhTcS9ILXC42tUFwADTEf4vGikTB+DwR80NAKRQUQa4PWaOr0xB9EI2FKApoy78rZP2T3X39F2WlXEBx9TKK+LZtWsWPRLbSFG3vl8xGR11R1andNL127gva21fzYjRnbRW+tu73FOVsgdZ0b9a0/9fp3bHpfb7UrY/JJrn2oQbfVpcSO/mUDW+rauvybYO3KmJ7R1baV9a9uVd2U7ZE0sxczjP8csLuLdT4HWKiqYVXdCLyD03k8DnhHVTeoaiuwEDhHnL+gTgEedcefD8zs4rw617W1mUDVRMDZuTtplDdl+EmjvNSHuzYsW5l08Y17NW2sPkzW+qzd2ZYy7d4weuQIWvduS/lHsHXvNkaPHNE7FeiigN9HWbCNa44r4PDBqf9gHj5YuOa4AiqKhaMrJHGka8fCG9HWZnYsvJHSAFQUC0tntx/VrQzG0EiYQh8MCUnqUeEip4OQPD2NtDCsmE5H1HYs/A6RXTWJ9S1R56qJaGtzH31iPSfetrK1i4HEI5L2DIPNt39qwH7HxphDW659qI6xtTvbDpnfBGMGgu46Nyq4n+Wvdk9tfVBEhrixEcCWpDI1bixTfCiwN+m2H/F4WiJyuYisFJGVtbW1nYcXhAjXrAGcIwLPb46lDH9+c4ySQNeGZSuTLj5msKSNlQTIWp8J5Z6UafeGvbXv8fCsENPG+PB7hWljfDw8K8Te2vd6pwJdFPTGeHhWiMXrojxwdmp9HzjbiT88K8SmOk0sQ0nAWQ9KAlBeKMw7J3W8Bec5w/w+WHBe6rB554QYWiidppfusyoJKP6hVYn1LS5cswYpCPXRJ3ZgcrUraG9b2drFQJL5e++f37HpfV1pV8bkk1z7UB1jE8o9vf6bYO3KmAPXXR3G/Tmf7B7gSGAysA34sRtPd9K6HkA8fQVV71PVqao6taKiotPwCeOOoHbZXFo2raK+1cOFi5pZsTFKJKas2BjlwkXN1IehKSLph0X91IfhwkXNtLSScfyO8Qv+2MzOJk2JXbLEicX/ZWtMM71LljTzrRMLEtNubM3yiXej/vKPYLyea3e2ZTw6m1zv+GuNhKkPw8a9mnE597VkPlLccXqZphEaewI7/3I3LZtWobEoLZtWUbtsLsMrh3bvB9HDcrUrcP55rl02N+36H28XA0m277122Vw7wmhy6kq7Miaf5NqHauiw/Z853tfrvwnWrow5cL7cRbqXqm6Pv0e4dnsAACAASURBVBaRXwN/ct/WACOTilYB8cNW6eI7gcEi4nOPMiaX329vrV7NpOpq1i66BVXl/QZl5iNNKdcgtbnXe/q9npRhdWHwBvy0eTy83xCmNADb04xfVJA+7vfAOQub2q+Hi0FLrH1+0TbF55HEeMUFEI3BxYtbnGvxWp0yvSH+j2DyNQf5eJQoXs8J5Z609Y3H4/WOv97XEmVQUKgolIzL6ZH0n8GYwUJtk6aUzTSN+teWOae/LroFbW1GCkIMrxzK1i2be/iT6X2qbU7WvIJC3m9oytiuBops33td415U2/qwdsYY0/1y7UMpsLOJlFhJAHY3ppZraSXv9ieMMd13hLHLKa1EZHjS23OBeAbVpcBsEQmIyBhgLPAKTpKbsW5G1AJgNrBUnWw9K4BPu+NfDCw5mIV4a/Vq2sKNqLah6iScaUt6jovE2lKGqSrRlkbaWlvSjhd/rg+nj4djSl24vUxTVDvtREfb2svXJZXZ16K91lkEJ8FNun8EB1cc1mt16IqWmJcLFzUzc7yPS5em1vfSpU78wkXNjC6VTv9qNrbCziblkiWdjwbXhyEShQv+2PmI764m7TS9dJ9VU0QS61ny80DsLMa1L2vmdjVQZPvX3DqLxpiBKtc+VCTmXLc4rNjJI1AfhrKi1Ou9y4pkwJ11YsxAkDVLatoRRMqBXZo0oogcrapvpim7APg4UA5sB77nvp+M84fTu8CXVXWbW/47wBeBKPA1Vf2LGz8LuAvwAg+q6q1u/AicJDhlwD+A/1TVnJsay451cAZqltTkzovPIxR4UrOkRsWPz+ejobG5S1lSh5YNZu/efRT627OkNkWESCw/Og2Wda7npMuSOhA7x6Yza1fGZJe8fYTOv8/ptpXWrozpGV1tW1lPSRWRjwK34WQ6/QHwW5zOn0dELlLVJwDSdRbd+AVpwg9kmp/bEbw1Tfxx4PE08Q04WVRNL8rHzmE64Ug0d6EMevOorRl4rHNojDHp2fbRmP4n1zWMPwduBAYBzwCfUNWXRGQ8zj0Tn+jh+hljjDHGGGOM6SO5rmH0qepyVf0D8L6qvgSgqut6vmrGGGOMMcYYY/pSrg5j8sVWHXPB2zkFxhhjjDHGGDOA5Tol9UMiUoeTBTXkvsZ9H+zRmhljjDHGGGOM6VNZO4yq6s02PE5Ehqjqnu6pkjHGGGOMMcaYfNBd92F8upumY4wxxhhjjDEmT3RXh1G6aTr9XsDvY1BQ8IgwKCgE/LnO+jXm0GHtwxhjDm32O2BM/9NdHUZLgIOzESwLtrH4/ELCc0pYfH4hZcE22xgag7UPY4w51NnvgDH9U3d1GA0Q9MZ4eFaIaWN8+L3CtDE+Hp4VIuiN9XXVjOlz1j6MMebQZr8DxvRPdkpqN6oPw0mjUvMEnTTKS324jypkTB6x9mGMMYc2+x0wpn/arw6jiFSKyKj4I2nQqd1cr36pJADPb079l+z5zTFKAn1UIWPyiLUPY4w5tNnvgDH9U5c6jCJytoisBzYCfwPeBf4SH66qu3ukdv1MS8zLhYuaWbExSiSmrNgY5cJFzbTE2v9Ni1/knfxszKGgJeblgj+mto8L/pjaPowxxgxc9WE6/Q503E8yxuSfrh5h/AHwUeBtVR2Dc0TxhVwjiciDIrJDRN5MipWJyFMist59HuLGRUR+KiLviMgqEZmSNM7Fbvn1InJxUvwjIrLaHeenIn3b+wpHouxu8TDzkSYCt9Qz85Em3m9QIp4AQ4aW4xFhWLGkXOw9rNjpNE6qru7Lqmd02vTpeINFiHjwBos4bfr0vq6S6Yc8Xj+RaIxwVPnSsmaCt9bzpWXNhKNKJOr822zrmjHGDFzxfaDLpvi55i8tBG+t59xHmtjeoLRG2/AEihgytLyvq2mMSaOrHcaIqu4CPCLiUdUVwOQujDcPOLND7AbgaVUdi3P/xhvc+CeAse7jcuAecDqYwPeA44HjgO/FO5lumcuTxus4r14XjkTxFA1FioYQOveHjPrGYipnzaEu3EZJgLQXe5cEYN3GrXnXaTxt+nRW/H0lFefOYdQ3HqPi3Dms+PtK25E3+8Xj9SOhEkoCsOj8Qt65toTYd0t559oSFp1fSEnA1jVjjBno4vtAt5wS5M0ri4l9t5TH3N+AUd94LLGvZJ1GY/JPVzuMe0WkGPg/4HcicjcQzTWSqj4HdDxd9Rxgvvt6PjAzKf6QOl4CBovIcOAM4ClV3a2qe4CngDPdYaWq+qKqKvBQ0rT61L6GZipmXE9w9DGI10dw9DFUzLg+68XeFTOuZ+3bG/qoxumteO6FtMux4rmcB5eNaecryLn+27pmjDEDW7bfgOTt/r6G5j6qoTEmk652GM8BmoCvAU8A/wZmHOA8h6nqNgD3udKNjwC2JJWrcWPZ4jVp4mmJyOUislJEVtbW1h5g1btGW5sJVE1MiQWqJma92DtQNRFtza+NZFs4/XK0hfOrnqbvdKVdxdtDtvXf1jVj2vXm75UxvaUrCW96cl/I2pUxB65LHUZVbQRGAh9X1fnA/UBrN9cl3fWHegDxtFT1PlWdqqpTKyoqDrCKXSMFIcI1a1Ji4Zo11IdJmxSnPuwMl4JQj9Zrf3kC6ZfDE8ivepq+05V2FW8P2dZ/W9eMadebv1fG9JZsvwFxPbkvZO3KmAPX1SypXwIeBe51QyOAxQc4z+3u6aS4zzvceA1OpzSuCngvR7wqTbzPDSoOUbtsLi2bVqGxKC2bVlG7bC6DyoayvUFTkuJsb1AqZ/+Q2mVzmTDuiL6ueoppJ5+YdjmmnXxiX1fN9CfRVmqXzaVy9g/Trv9tqrauGWPMABcqKsm4D5Syr1RsfxQak298XSx3FU7CmZcBVHW9iFRmHyWjpcDFwG3u85Kk+NUishAnwc0+Vd0mIk8CP0xKdDMd+Laq7haRehH5qFuvi4CfHWCdutWeXTsZMrScHYtuQVubkYIQg4pD7Nm1M1FmUnU1a9/egNLMjkW3MGHcEby1enUf1rqzvy5f7iQjeewW2sLNeAIhpp18In9dvryvq2b6kbZYBI/X77QHhHoNgaeVfS2RRBlb14wxZmBrbKijqLiU+kgMpZl6DSX2gTLtKxlj8kNXO4xhVW2N37VCRHxkOf0zTkQWAB8HykWkBifb6W3A70XkUmAz8Bm3+OPAWcA7ONdLfgGcezyKyA+AV91y30+67+NXcDKxhnDuC5m4N2Rfy7XBy7fOYSa2w266Q1sskrOMrWvGGDOwNTbU9XUVjDEHoKtJb/4mIjcCIRE5HfgDsCzXSKp6gaoOV1W/qlap6gOquktVT1XVse7zbresqupVqnqkqlar6sqk6Tyoqke5j98kxVeq6tHuOFe72VL7lN1LzpjOrF0YY4wB+z0wpj/qaofxW0AtsBr4Ms7RwDk9Van+yu4lZ0xn1i6MMcaA/R4Y01/l7DCKiAdYraq/VtXPqOqn3dd9fjQv39i95IzpzNqFMcYYsN8DY/qrnB1GVW0D/ikio3qhPv2a3UvOmM6sXRhjjAH7PTCmv+rqKanDgbdE5GkRWeo+luQc6xBj95IzpjNrF8YYY8B+D4zpr7raYbwZ+BTwfeAnwCvAUT1Vqf7K7iVnTGfWLowxxoD9HhjTX3Xpthqq+jcRmQxcCHwW2Aj8qicr1h8k7qXo3j9owrgjmPb/ptq95IxJ8tflyykqLk2511bI77V2YYwxh5Dkfab474HtJxnTP2TtMIrIOGA2cAGwC3gEEFWd1gt1y2uTqqtZt3ErlbPmEKiaSLhmDeuWzQUg1tLYx7U7MEOGlrOvodluoGu61aTqalrwUTnr24m2UrtsLpOqq1PuR2rrnzHG5LcD3U6n22eqXTaX8WNGWGfRmH4g1ymp64BTgRmqepKq/gyI9Xy18t/atzekzfS19u0NfV21AzJkaDl14TYqZzmpritnzaEu3MaQoeV9XTXTz3Wlrdj6Z4wx+e1gttMDbZ/JmENNrg7jecD7wAoR+bWInApIz1cr/2lr+kxf2po909ek6mo8AeeGtZ5AEZOqq3uyml22r6E57cZ8X4NlLjMHpyttxdY/Y4zJbweznc72O5Av+0HGmMyynpKqqo8Bj4lIETATuA4YJiL3AI+p6iF7HoEUOJm+gqOPScTCNWsQlEFBoT4MJQFoiXkJR6JA5tNYO56a1xcOtANsTC7JbWXzHWdTUtBGfRhKAxDw+whHorb+GWNMnjvQ7XTA76M0oGz50UxKAlDf6mHUN5c6+0wFIdZt3JoX+0HGmMy6lCVVVRtV9Xeq+imgCngDuKFHa5bnJow7olOmrx0Lb2RYsbD4/ELCc0pYfH4hZcE2An6nX57Pp2TEd+qTxTfmxhwMbQ1Tu2wum2+fwbAiTds+bP0zxpj8diDb6YDfR1mwLWW7P6xI2Xz7DGqXzSVw2AfzZj/IGJNZV2+rkaCqu1X1XlU9pScq1F+8tXo148eMYMeiW9j8o3PZsegWSgLw8KwQ08b48HuFaWN8PDwrRNDrXPaZ7d85nz+Qcqqqzx/o1eUZVBxKm+p6ULHtsJuD1QYeLyUBzdg+bP0zxpj8diDb6aA3lna7XxJQ2prq8JeNsLNJjOkHunRbjZ4gIu8C9ThJdKKqOlVEynAysR4OvAt8VlX3iIgAdwNnAU3AJar6ujudi4E57mRvUdX5vbUMHU+f8Ihw0ihvSuykUV7qw87rTKexIl40UETljOtTsof5/AGikXCPLwfAnl07GTK0POXWB5al0nQL8UJbjPowGdtHW4utf8YYk88OZD8h23Z/2OwfULtsLtH6nXY2iTF5br+PMHazaao6WVWnuu9vAJ5W1bHA07Sf9voJYKz7uBy4B8DtYH4POB44DvieiAzpxfqnKAnA85tTk8g+vzlGiXuwMN1prLXL5iJ+f9pTVds8vduf37NrJ23hRlTbaAs32s56N/GIUOgTSgKCR4TSgPM+Hk8eNijY/hzwdo75Pe1lSwJCob9zGY+0v/ZK3+eoiq/fudqHrX8mn6VrZ8Ycarq6nY63E4DyufV4bq7Dc3Mdg26rI3BLPSUB2LHwRnxNe/Bvfglam6xdGZPH+rrD2NE5QPwI4XycRDvx+EPqeAkYLCLDgTOAp9zTZPcATwFnHujMDzaDaUvMy4WLmlmxMUokpqzYGOXCRc3Uh+G06dPTnsY6fswItLXFEn4MUE4HESqKhaWznWs4lswupKJYuGKqn6KC1GGLzy/k8MHCNccVUBZyxk2+9qO8SPjq8QWE55SwdHYhRX6oLEq9bnZYseAXZ7zK4r7vNMbX7/pWT9r20RLz5p6IMX3II9Lp+vRhxbZzawx03ndK115KA85v1SmHexlW7DwX+qAsze+XtStj8k9fdhgVWC4ir4nI5W5smKpuA3CfK934CGBL0rg1bixTfL8lZzCN318onrmrq8KRKNsblJmPNBG4pZ6ZjzSxu1EBePW5p/B5hLdWr075d+6t1ast4ccAVhKA8kJh3jmp13DMOyfEwjcjDAp2HvbA2SEWr4vy8Hkhygul07Ufi9dFE+8HBYUF56W5LrCAxOvi3r0cthMpCBKuWcOoby7t1D62N2gii7Ax+SrT9eklfdy2jOlthcGCTkfaO+47pWsv5YXOb9XfNjnXNP5tUwy/z9qVMf1FX3YYT1TVKTinm14lIidnKZvu7ybNEu88AZHLRWSliKysra3tNPxgM5heedXVFBSVogj1FAHO6YJPfL79n7OKIqEwWNBpXE9bNO2pqp4225Hu7+rDsHGvpr2GY29L5mFrd7Zx0igvG/dq2mFxmcaPXzeb/Lon5GpXABqJOFlS7zgn7VGadG3CmHyS7TqsntCVdmVMbysMFjDIH+20DdemupR9p3TtJf5btbel/fdvX4u1K2P6iz7rMKrqe+7zDuAxnGsQt7unmuI+73CL1wAjk0avAt7LEk83v/tUdaqqTq2oqOg8/CDuA3flVVdz3/yHKTv7284/bOd+J2NGSD+RTuNHI2Ek3JhyqqqEG3st4Y3pOSUBGDNY0l67NziYediEcg/Pb44xZrCkHRaXafz4P7TJr3tCrnblFIrR1hqmpCB9trx0bcKYfJLr+tvu1qV2ZUwv8xNJf0SwILVtpGsv8d+qwcH2379BQWtXxvQXfdJhFJEiESmJvwamA28CS4GL3WIXA0vc10uBi8TxUWCfe8rqk8B0ERniJruZ7sb2v04HcVro/fMeorzD0cls/0gnn85RWlwIOJ3G5FNVrbM4MNSHYWeTcsmS1Gv3LlnSzOyj/exr6Tzs0qXNzBzv48I/NrOzSTtd8zdzvC/xfl+LcsEf01wX2EridUMfr0pSEGLYeXOoD8Mf1kQou91JflB2ex1/WBPp0SOgxnSH+jAZr0835lCR60j7lp+cx567PpW2vexscn6rPjbayfXwsdFeIlFrV8b0F311W41hwGPO3TLwAQ+r6hMi8irwexG5FNgMfMYt/zjOLTXewbmtxhfAuSekiPwAeNUt931V3X0gFZow7gjWLZtLRYdbW0wYd0TOcSNNDZ2OTsb/YZs2pv0j/uwfmhhWLDw8K8RJo7w8vznGhYuaKS0upK6h6UCqbfKEiAcpCCVSjWtrs9P5V8UjQmtUOXthE42tUFwA0Rj8amWEoBcaW9uHlQScH+WfvdJKSwQibTDzkSbqw86wpla4++VWfvh/rRQVQKwNWqKaUib+YzvzkSYawhDTtGdp95r40Xu/wGProvzxs4Up679/AOc3yLRemP4l3o47trO2Pm5bxvSmdPs18SOCm390LsNCUR6e5Wzf49epJ7eXfWHlmXedWyw9826MpihEGjv/flm7Mib/9EmHUVU3AB9KE98FnJomrsBVGab1IPDgwdbprdWrmVRdzdqk+wtNGHdEp3stpuMvLE7cX3H7IzcRfu9faCuc+lBTYgMYf376osLExjZ+OseZ/9tEaUBoaMXpBMQAn5+mltaDXaxeMam6mrVvb0j7uWUbNlCIxA/Ua/uzeJ2414/4QzRHWhBvCKWZ+pgfUMTvpTkSRgqCaGsL4g9QF42BV6gLR5x4pJX6Nj9KmPq2IKph6sOxDDXJT/7CYrb97zcIFZBIepPcJuKnHwWChURUEuuKX5RwS//9I0XEg6docKc/oUQ8A6bTeCh1iG0n1gx0zu2a6NR5KwwW4Mc5G2TmI02ccriX33+mMPGnn1M+wsOz2vdv2r5XyrkLGxMdxPj06sKCFASpC7cgBUFaW8NENIDSQr0GUVr6+FMwxqTTV0cY89KBdmQuu+Qi7ps/F0/JUGL1u6icNYcdC29MezTx1IeaaPteaWLcP6yJMCTkZA+Ll7tkSTP7WiIUBgt6rdPo8frBV5DY8SPaSlss97Vlydll4zvF65bNTWSXzTRsoHUa03UM/OWjie7ZSvlZ1xGomsjeFxZQ//qfwOPF4w8k4uGaNez8850UHX0KjW89Q1skTNkpVxPZvZWGVctTp7v0DkS8qPafTmNbaxit3+XcqDlNm9jeoE5nEQ/ewlKikRa8hYOINNcRCBb2206jFIQSibSARCKtHYtu6eOadY9DoUNszKEifiuMjtvn9njqmSHxeylG8NOmrXhEUk5XvebxZl7a2sbi8ws7be8rZ92Uss0IHPZBKs65wbYhxuSxfLsPY97Yn3sy/vIXP+fyiy8kuvu9xA5iV9OwL3gz0um2CPPOcW6nkJwMxOdJvWm0z9N95/F5vH4kVJKSFltCJU4nMods2WUPNvNsfyEFwbTL2brtbcrPui4Rb17/It5QKd5AUUo8OPoYyj95Hc3rX6T8rOvwBoqoe/lRmte/2Hm6Z38TKQj29SLvlzaPj4oZ12dtE63RGJ5AIUPPvIZRX1/E0DOvwRMopDXafzrGHR1MIq3+ILlDnLze2+2AjOl/sm2fM8Wrxh6d+FO7Y6Kb363OkCAnQKdtRnjLm7YNMSbPWYcxjQO5J+Mvf/HzlB3EbBeHJ1/gnSmt9Ma9mrgWzecRKjrc3LaiqBs7jb6CtDt++HLf7iDbTvFA32GOi9+YPll8OZPjkV01RPdtJ7pve9rykV01BKomEt23nciumsT7dNPtT+KfQ7Y2IR5P5070Wdchnv67iRro91c9VNq3MYeCbNvnTPHk/SL1hVIS2Ozt4i0zAlUTaWtpTHlv2xBj8o+dkprG2rc3UDlrTqdTydbmOJUsvoMYP8KY6eLw5Gu4MpUbM1iobXKumSkqaP+HD9qvfZz5SBOeQCEaiXDYiOFs3bK5S8t35VVXc/+8h4g0NSC+AvB42L7wO+61dGHEX4BGwog/QFFxKc2RmHuqqnutnfuM148UBNn8o3M7XccUf9766ytoa65Lijnji3gQfwBta+PUUz7GX5cvZ8TIUWzbsStRdnjl0C4vU1+K35g+vr5Ae8cgOe4fWoVGI4nhHcv7h1YRrlmDb9AwxOfPWK6/dTjin0O2NlEXDrvrYOr6059pazO1aRJp9ffliuu4fkP/XD+NMdkT2mTcbjfuZe3bYU6bPh1voJD3dzen7N9kGi+ucc3f2Pt//wvA5js/S+CwDzLoo5+xbYgxeaj//n3fgw7kn/Mrr7oaVNm+8DtsvfdLWdOwJ//DVh+m020RLlni3E4hgj9RJtM/dZWzbsJTWMJ77+9gxMhROZct+Z6RQz95HZ7CUipn3cSobzxGyUfOxlM0iMrzvuscWT3vu7SoF1/ZYXiKBifKVc66CW9JOVIQJDj6Q3iKBlPykRl4B1WmHJX1FA0m1rgnEQuOPgZPUfv8Ks/7Lt6iQTzz3N8JBAt5f09Dyvjv72no0jL1NW1toXbZXFo2rUJjUVo2raJ22VwKho9j5+N3JuKhsScQa64jFm5MibdsWsXOP99JaOwJ7Hz8TmLhRkqP/zShsSd0nu7SO5zOej+irc3seOzWnLcmcNax1PWnP1Nto61xb8r9Vdsa9w6Ya3PiHeKO6/1A6RAbM9CUFhemXtoi7a+zbZ8vXZom3upNbKtX/P1V9u5rwFtaQejcH1L2yf/KOr2WTatoePMZ9vxtnnMZgjud1tp32f7779k2xJg8JHoIZn6bOnWqrly5MuNwT6AIIs6NxhPZvVq94A/QFm6ktLgQiTYnhrXEvMQCpZTPuJ4dC2/slGUs/lxUQMqtE+LPfg8EfKRkSW2JtWflGxQUGsJQ7JaP35ahJZZ0G4VAEajSFm7MuFwABUWllJ39bYKjj+G9B66k7LQrEkcIOr4HZ8O+Y9EtKUdcN9/+qU7L4CsflX7cx27l/7d37mFyVNWi/61+znsmk0xCIE8UhRi8ChwQUY4KRFHAgHgInCtwlIsfqIDIkaeHoyCKgIDo8XFFxXuJRCFAeCigRL1yOCgqkPB+hxyUPCDMZB4909Pr/lG7eqq7q6t7hmSmu7N+39dfV+1XrV219161d+299rwzVgDw0tXL6Fl6XkmYzb+6htGB1wuuEbx2pTyVfY4hFt+C//0ZyDHmpgiqORLJNLnsME1xiMfJb4WxddizgyqEPLMcDGeL0/PCBq/ZmwGIIal0wdda72urv/5DYHTMSqokku6LbxM6ktlmBm9E5M+qus82SYzy9UokRqylk9bsFraGlP+2FGylmZlHfSnk+V9ELlN/Rm+Ky15vBiTV0nBWRHckK6nVMln1yqhNit8PNNFMJjNMU3y04J0hM5IFylsmfaPX9bdlakpAIkZB21v8DjE0Atmcp7NC2+eid5Pgu0tTsryO7ctAOg7J+FgaQyPQnPTOmxIQj43p2EzWkzks/1avDGP7UG3dsi+MYQwPMKs1V7BmcFZrDoYH6GhroVWGCvy6m3LkhrbmLaMG/RZ0CZ/bN8WCLiEGJf4zW4XmJNy6zHNbtayFnjahIw0tTd4awr4MzAzEu9WFuW5pkydbm6CZ/qpG5YJ7RhavkYtaM+e7r7v0sJI8zGoTRjatC48beNnPDfWHhsm+/so2Xw/lW3bz733YM+lpExb3SD4PgiISI5cdpiMNPW3CqmVj93xmqyBASwKmNUvhM2v1lH5heqXPe6c2QcjRvvcR7ovs2NfWWFMbjGbRbMb7OpUZQHNZcv6ejsMDdWUd1UdSTfQc8UW2Dpfej1lt3nYy5deB1tfXVBgre8XPneEBYq1dgW1Y6h+vnPYX/BvGjkrY+0E8O8i0dK7knSGdTIS2FbPaxr76+f8dbS3jvm53i3Dwwjg9rWO6ytd/p+2XKniH6G4Rmt3MUd+adVMcZrZK6LvJBxbEaU9Bd0up7G/plrzOvW5pEzPbxtI4fb8UM1ya1y1toqe1UMd2NwstCXB7dBuGUUM0zpvLNiTKKphkB8P9EiOh8a49oplbnshy7RHNIKX+P/uYZxE1ykpqWLo/+Wgzl943nL9+R5qq5v37e0YC+TVzeb+icyhcixd1bzrShMdNjym6WFNraJhE56xtbiDEl9O/92HPZPlRzbzYq4XPN+X9z2gRfvLR0mfVnoZkglDLttNbpCS9cuVo8On7mXHo6TuEhUm/MxhZrxrIQExUPhv1GRvGjk5MJPT9YHqLlOiL5Uc10xQfjWwrgh2xVhmK7DSWey954OXREj3m67/id4hkYsyuwvKjmhkaDddzM1qE3704SiwWLvtTr2pe515633DB9X29G+b3/oUJln/Mk6MjXTarhmFMEdZhDOBvpRG1ZnAifo9vykVaG3t+i4a6+Wu7otINXn/2zOkV87jbrvPz64469ju6ZI1d2Jq5RPfOefeo/G/65dWl65ncsY5mSc9dzMbbvlG4du/OKxkd7CUpGroeqpo8heHL6d/7sHsXtNjmH+uwN6Xn+S1aNp/VWLatZF2uUSygVoOkmll3+ZGR96OR1sNF5bNRn7FhNCoHL1lCvMnbYive1MrBS5aUhPG/FIbV/ShdEtVWFHfEJDtIqrUDkRip1g52mTsvL9d4LZz6+i/4DvH6EAUyRb2vbBkqrweD8Yr1b/C8nG725TAMo7YwK6mO4FYaRoNdlwAAH89JREFUgzefF2nda7x+e8yIRVobW9hVOP2i2EpquXh7zIgVXL8ai6JPP/ci6V3exsZbLvFMWcdTbLjpInTEraPLZtlw01fyVlJ1JONNkRweYsPKi6Itpr2+gQ0rL86vZ+psa2bnhbvxeMCN7DAbVl4UWLeXg9ERRlLNqDMQosOD3nq93Cgvr99CLN1aaDlT4kgy6dYAlq6bEonRkS6891HPJJiHvlya9tgQPS1SNp8xqWzZtpJ1ucH2OTuMhUkdHiTW2kVb9rWy96NPm/MGYoqt7b4RvOmfMSSVKlgzuj2nTkY990Z9xobRiBy8ZAmr//NBeo68IG/pePVtl3HwkiX8+u678+H8r3JLVwyU1P2FXeV1iX8cZUkUAkbulp2bl+Pvt11G216Hk+zehZFff3NcFk59/Rd8h+hsgpxWjruwS3h1UNEI2X33Yv0bPC+nm4NyGIZRO9gXRkdwk/lKFk7H4/epVYMs3T3Bp1YNgpb6H3uTZxE1zEqqP8rWP1wa78RbBzn7gFSJpclKjAxspeej5zD39BuYf/ZtgCKpJmYt+ypzT7+BWcd8xb3QCjqSKbGOGpX/RYvfVrCe6bXNm3h0zZoCt9zoSN6IiaSamfXxfy+wiul9fRFPpiK/RPfOtL3jUGIt7QGZLihYF+YbWPHl9O992DM5buUg8zukIA/dS071vpYOKCfeWvqs+jIwkg23bLt5QEvSK3evmnfbP/yLbAN+ffI3eI+6Hzo8iKRbiLd0ggjxlk4k3fKG7odIDGKJQsu8R31pu68jjMpnoz5jw2hEVv/+vtA9ilf//r6CcP5XNc2V1v3NA1qiLyq9T/QX6XO/I1Ysx+DT99P7wI30ZZOh6ey3c7xEj/n6r/gdYiQLA8NjMjXFy1tw/8f5cXIheT1u5SBv6Za8zj37gFTB9X29G+a3+vksx93kydFrXxgNo+YwK6kOkRjzzroZiSd47arDylr96s2UWr7sy4Ak00g8QW6of5tZSVXAfz6JmNCairCSSnWW1VKtHSR2eRuZl9aSG+pHUk1lrVMC4X4hlmBJt6KZgcBejd6ei4sWL+Lxp54r+mrUVD7tlRcDWtZPYrFQS6u+NdVYujVvbTXMmmuUldR5Z98OeHtDbb7tslArqT7VWEn1Kb62ImX3rZxMoyGTaSV13lk3s/HKpQVl1v9visPgaBzIhezDGEM1OyF5YulWgG1ufbeqa+8gVlKNUsya4/gI6rb2tNfeZmvgE1MsnkRzo/n3Ah8dzbLu8iML6nFnk2fk5f0LE3Rc0ovEiuq+xGhP5QJW12N53S5oSZs4q01YflQz75kX5w/rRjlu5SAbMinmnnlToRxXHAXAvC+sZP3Vx9AWy0zYSmpHGjYPetd+ZasWhJ1MK6nDOa+tLG6frV4Zxvah2rplU1IdwU2oyzXYr2xVJNUCw6Vm/ueeeROvXXUYpArdf3N8S37KRezLvWQuaCcZH5uCOjKqdHytj5mtQv+IMr8zxvnvTbNTm7B0RfXbCWgiTSyeJDc6Ehlut13n88TzT9Kz9DyyfZvYfMeVkdYpQ/0Qtia6aN9rCZrpR568j7a3L6H/sd8y49DTCzYpf2zto8xa9lXScxax7oqPE2vtpOfwL/LKDedHruEr56cRflC4h6bfAexyCt5X/NNcvC6ncHuHYd5ZK/PptS76RzbddjmDOWHe51cy3b0s9H3jCOZ9YWXBy8O0oNI+a8xvGmMvFtPOuC1/3nv5ke7LbviLRyMiqSYy6x8jHofz3p3ilieyPL4px9yOGEt3T3D1A8MwmiPW2lWyyX2uf8uEr1upLG1PJmIW3zDGy8FLlrD69/eRywwSSzfz/gMPKJgq+bY99ywYsNvjLbvy6Jo1JelUGy6dTJRsD/Hm3fcoGzcZj9GS1ILO00GHHMKv7747vw2E3zfsboJObywxP+AyQpJ3H/i+fB5B6YgYBNxWnc5YPInG4kgiFbp0IJYunFbuf5VbflQzm89uL3hnSAa2nPJ1T/OLj7Dhpq8w9/Qb8m1d+15L6Lt/BbPahIFhZemKgYJ8zVx2YcE1M+sfIzl9Tv7Y70z2XXoYm/619D0jfXEftxzTwtIVA2yVNuac9n/d2nIteC+JfbmX9rTXoVvU472PHLtnktXPZ1m6YoDZ7fDs6R35tH33aWfcTtdoli3XLC147wmG6frcLXkd2c1Yp3f+2auAgE60WRiGUXM0xJRUEfmQiDwpIs+IyDkTSSNodCXKcpkOD4SawfaNerSkxrZyKF403tXkTS0J8k+/GKCzSfjfhzczdH471xzaxPn3DrG+N5f/SpWICT2thdtqdDZ7jfu0M25n5rJLvEY4UdRbDeHp517MT7HpfeDGSOuUUX49h/8rg0/dT/9jv81PjSln9dN3k2SSnsO/6J1P8LrlLK3668LKxk23hLonp88JtQ4r6RYSnbOqsiKb6JxV1i/W1Bp5viOsZ9PhITbedhn9w7B8zQjXHNqUL+vL14zQP+y23giZ+uV/jZ4IlcqSYdQzwfV18866mZ4jL2D1fz6YN8oSXJfvT99/4vn/5m177lmQTrXh0skE3U2l20M8vnZtaNxkPMaMFkq2l7n3nntIxmMl20Ak4sLmwcLwncks995zDz1HXoCIsFOb8IEF8VAd/Ll9U/nznlYhEXsDWzMkUkg8UWDwLbh04P0HHlAQPKfKK1u9Tl764j6Wrhjgla3KvLNvp3P/YwqMy/nG3jSXK5le6r97vH5uB6+f00Huwg5uOaaF9jShcjTvtn+J8Tp/DWEQf0qrvxayddH7vEG8jp6S8LkLO+jLwND57aw9tY1j90wCAeN2RX25oPG4zPrHIg3wlNPBwXNJNVn7bBg1SN13GEUkDnwHOBRYBBwrIouiY5UyooIk0mxYeXFkg9dRrjMZ2FbD38qhuIP4z3uWrjO494XREtPV1x7RzJd/l8kvfG9Nle/Awth+hxPZhzHe0ROqiBIdM0l0zCzrl56ziJFXX8rvrViN1c+CvRZjidC0iSXKX7d7Z1p2PzDSmqYOD7JxVZEl1juuQEezpUr7jivo2O9oZx22MI7mcowO9la0IrvpjisZzfSXXZOYnrs48ryzbcdQjLn+LbSnCd3ixBuI2fb7MOrwIJodaRjrq4YRpNL6uuC6/KD/4089V5BOteGa4qNl9VBY3Jaklg0f5he2xVQw/fZUjuVHNfO7F8PluOWJbMF5a+Xx07Lo8CCaGWD2/7ycVM8CNqy8mHWXH+lNZe/fUvAV1yenSm9GkPYZbE1MY+ayS9DRLPHWaeQyA2y6/QrWXXEUm391DbmhAZLTds7H9XVo1LtHrn8LG2+5hHWXH+kZrevfQt+fV7H5jisZ7e9lw00X5Qeuw9YWam6s49jylnezcdU3GO3dGBo+qtNZbC/Bd/fb1qi4xTrY7/QWts1D1j4bRg3SCFNS9wWeUdXnAETkBuCjwGORsYrQ4UF2OW15fg1jWWuOEQ26f+ybi/Y7iP7U1qP2SPLjv45wxA0DDIx4FsPKpffca2PTaaKuOY3AfodVTIPz92Fsmv92ktPn0H3wp3n11z8osE6Z6JjJzp/6NgDrv3dSqN/Qi49403MZGyWsZPUzOO133unLWXf1cYVWMVWYf+YKAF6+9rMlFjN1JMPI358FiY9ZWi1a+6eaQyRWaPk1Nwo5ZbR/S8ACrGeFdfMdV+bTCMYhN4qO5BjNjhTGGRkKXNtbr0k8Sd+Dt6IjmRK5hl58hHWXHxl63tnWzGubN1V8ZvWOJNPM/Ni/seGG88qWY3/aamn5mfgXRr8s5Pp7S55Zo08DNhqfXGYwdJDFm7pZNEAX8C9+Ga82XCU9VBy3j8q6stgvbIspP33/+lvKbOfgbxFRfJ2JIKlmECGz/jFmHXNR3n1snX35eD0fOZNNd1xZor9yqqDK6MDrxJo78joWxnRoc9+6CMvac9n5U/9RIsesZV8tWaP9yg3nFUxp1RzcemwLx60cpDcDfSsvQkfc8pVUC69sHSgI3xuYYhtcljPg/Fc/ny1wz6c5PEQv4XF7M0B2S0lb3PfnVfTe//MxfQrWPhtGDVL3XxiBXYCXAufrnVsBInKyiDwoIg9u3LixJJHg1LUoK4dRo2e+n28u+poPN3P0Hgk+9vOxaSqZrLctw68/0cLaU9tCp6n+Yd0obSmIuSmmUdcM7ndIdphKnHTi8Wwq2oex++CTmXvacjr2/yckmab74JPHvrSNDKEjI85a6gXMPvEq78vaL6+mad6e6MhwfpSwnNVP301HRgq+5M1ceg6xpta8NcxYKpX36z74ZM+QUCxeYGVVNYfmPEurQbcgqjlyw85/eBDNDqO5ETQ77J37/tlMQRolcTSLZjOFcTQXuLb3XxCmSK6o/0boLFaqVwCay7Hpzisjy7E/bbW0/Ez8CyN4ZUE1W/LMDKOWqaZexdLh06399XXVTseuNlxU/Q2LW42uLPYL22LKT9+PU05n+ltElJNrXGSH0dHsuGcndLY1s/G2y5jxkc8z97TlzFr2VSSZpqu7m1ymH4nFQQR0NPRLWyXL2sVy6Oho6YyaO6+EdCvsdUy+I7l1BJauGOC1tl2JtU5jj7e8CXQUSbcQa2pl5rJL6PrcLcxcdglb411ILFEyxXbTVmWG22sy6J5NdaKq+bY1nkiFTs+NJ1KeDs6E69G8Pt2O7XM19cowjDKoal3/gI8DPwycfwK4JirO3nvvrcUsWrxYY63TdNayS3T6R85UAe1IU/C/aPFiTcREd2oTvff4Fh2+oF3vPb5Fd2oTTcRE487v/PemdGFXYZh5naJdLh2BfBqn7JMMTU8gL1tcCA0DqKSaFUmoxBIleSrHKad+RpMt7QqixFNeGoj7j6skm7zzZJOm0s16yqmfUSReFG4svHfcEvIf00WLF4f4NefTJ57Sru7pqqoqsURB2PHkyRg/wIO6DetiWL3yriMq6daCch9W1sPKkWHUG5NVrw465JC8zpp31i06a9klGmudpgcdcoiqFuq0oP+ixYsL0qk2XCoRL1t/w+KW05UCoX6zWkU705RNXySmO7WJLn1ruBznvzdVcB4X9I0gsYSnW4v0XiW6uqcXtGO+fitIN54K6NnmgrDtrc0l7x5hbWOBbk0W6tQwfeu7BZ8rxEpkCerdREwKZEnEqmuT44lUgbzxRKrKu16eyapXhrGjUW3dqvttNURkf+DfVfWD7vxcAFX9Wrk45cwpBy3FEU8i8Xh+emGU5beBEWFk1BsVS8RjtAb8/O00RnMwmB0zrR80pV1smnpoNE5mpHArgVo1PW7UL5Npplwk5qZmDZRYOTSLokYjMZn1qhGspOanTSaayWazJBnJu02VlVSj9rBtNQxj+7AjbavxJ2A3EVkI/DewDDhuIgmFKcgw/M5hGNkIvzeCKT+jnrFpoIax7QkzvhKkWp1WbbjigcxKROnK3q3VbxtlGIZhTC1132FU1ayIfBa4C4gDP1LVR6dYLMMwDMMwDMMwjLqn7juMAKp6J3DnVMthGIZhGIZhGIbRSDSClVTDMAzDMAzDMAxjO2AdRsMwDMMwDMMwDCOUureSOhFEZCPw4lTLMU5mAPW6cV89yw6NK/98Ve3ZVhcJ1Kt6v1/lsHzVH1ORt+1Vr3wa+XmVY0fL846WX6ic5+1dr8YrT61RT/LWk6xQX/JORNaq6tYO2WGsR0TkwW1pUnoyqWfZweSv9etNFpav+qMR89aIearEjpbnHS2/UHt5rjV5KlFP8taTrFBf8m5PWW1KqmEYhmEYhmEYhhGKdRgNwzAMwzAMwzCMUKzDWD/8YKoFeAPUs+xg8tf69SYLy1f90Yh5a8Q8VWJHy/OOll+ovTzXmjyVqCd560lWqC95t5ustobRMAzDMAzDMAzDCMW+MBqGYRiGYRiGYRihWIfRMAzDMAzDMAzDCMU6jDWOiHxIRJ4UkWdE5JyplqcSIjJXRFaLyOMi8qiInO7cu0XkHhF52v1Pm2pZyyEicRH5q4jc7s4XisgDTvYVIpKaahnLISJdInKjiDzhnsH+k3Xv662slqMRynAl6rmMl2Mqy/72pBGfVRQi8oKIrBGRh0TkQedW988xikYtu2GIyFvds/V/vSJyRi3ld6p0mYj8SEQ2iMjagFvofRGPbzkZHxGRvQJxTnDhnxaREwLue7u69YyLK29A1nHpyRqQt0lE/igiDzt5v+zcQ9tTEUm782ec/4JAWuc69ydF5IMB921abqpt+ydNVlW1X43+gDjwLLArkAIeBhZNtVwVZJ4N7OWO24GngEXAN4BznPs5wKVTLWtEHs4ElgO3u/OfA8vc8feAU6ZaxgjZrwNOcscpoGsy7n09ltWIvNR9Ga4ij3VbxiPyNCVl357VNs/vC8CMIre6f44V8tyQZbeKfMeBvwPzayW/U6nLgAOBvYC1AbfQ+wJ8GPglIMC7gAecezfwnPuf5o6nOb8/Avu7OL8EDn0Dso5LT9aAvAK0ueMk8ICTI7Q9BU4FvueOlwEr3PEiVybSwEJXVuLbo9xQZds/WbJOemW037gKy/7AXYHzc4Fzp1qucebhVuAQ4ElgtnObDTw51bKVkXcO8BvgA8DtrpHZBCTCnkkt/YAO4HmcMauA+3a/941QViPyVldluIr81G0Zj8jTlJV9e1bbPM8vUNphrOvnWCG/DVl2q8z7EuC+WsrvVOsyYAGFHcbQ+wJ8Hzi2OBxwLPD9gPv3ndts4ImAe0G4bSB3pJ6sJXmBFuAvwH7l2lPgLmB/d5xw4aS4PPjhtnW5GU/bP1my2pTU2mYX4KXA+XrnVhe4z+LvxBvJmaWqfwNw/zOnTrJIrgK+COTc+XRgi6pm3XktP4NdgY3Aj900hh+KSCuTc+/ruqyWo07LcCXquYyXYyrL/vakEZ9VJRS4W0T+LCInO7d6f45RNGrZrYZlwM/cca3kt9Z0Wbn7Uk7OKPf1Ie5vmCr15JTL66Z4PgRsAO7B+8pWrj3Ny+X8X8drf8ebj4kynrZ/UmS1DmNtEzZfuy72QRGRNuAm4AxV7Z1qeapBRA4DNqjqn4POIUFr9Rkk8KazfFdV3wn0400JmQzq6T5VRT2W4Uo0QBkvx1SW/e1CAz+rShygqnsBhwKfEZEDp1qg7UzDld1qcOuvjgB+MdWyFFEvdaycnON1f2NCVK8np1xeVR1V1Xfgfb3bF9gj4hpTJu8E2v5JkdU6jLXNemBu4HwO8PIUyVI1IpLEa0CuV9WVzvkVEZnt/GfjjfDUGgcAR4jIC8ANeFMBrgK6RCThwtTyM1gPrFfVB9z5jXgvIpNx7+uyrJajjstwJeq9jJdjKsv+9qJRn1Ukqvqy+98A3Iz3YlfPz7ESjVh2q+FQ4C+q+oo7r5X81pouK3dfyskZ5T4nxH3CjFNPTrm8Pqq6Bfgt3hrGcu1pXi7n3wm8OoF8TITxtv2TIqt1GGubPwG7OctIKbzpG6umWKZInBWra4HHVfWbAa9VwAnu+AS8+e41haqeq6pzVHUB3r2+V1X/GVgNHO2C1aTsAKr6d+AlEXmrczoIeIzJufd1V1bLUc9luBL1XsbLMcVlf7vQqM8qChFpFZF2/xhvjdta6vg5VqIRy26VHMvYdFSonfzWmi4rd19WAceLx7uA190U0LuAJSIyTTwLpUvw1qv9DegTkXc5HXc8b+AeT0BPTrW8PSLS5Y6bgYOBxynfngbzcTRe+6vOfZmzTLoQ2A3POM82KzcTaPsnR9aJLsi03+T88CxLPYU31/r8qZanCnnfg/dp+xHgIff7MN586t8AT7v/7qmWtUI+3seYZapdXSV7Bm/6THqq5YuQ+x3Ag+7+34JndWxS7n29ldWIfDREGa4in3VZxiPyM2Vl357VNsvnrngW+x4GHvXbkUZ5jhH5btiyWya/LcBmoDPgVjP5nSpdhteB/hswgvcV6FPl7gvetMLvOBnXAPsE0vmkax+eAf4l4L4P3gDMs8C3KTK0NE5Zx6Una0DetwN/dfKuBf7NuYe2p0CTO3/G+e8aSOt8J9OTBCy3bo9yQxVt/2TJKi6iYRiGYRiGYRiGYRRgU1INwzAMwzAMwzCMUKzDaBiGYRiGYRiGYYRiHUbDMAzDMAzDMAwjFOswGoZhGIZhGIZhGKFYh9EwDMMwDMMwDMMIxTqMDYCIHCkiKiK718q1RGSBiBy3Da+7VEQWVQhzmYg8ISKPiMjNgT13povIahHZKiLfDoRvEZE7XJxHReTrAb+0iKwQkWdE5AERWbCt8mJMDTVcTwZF5KHALyUiJ4rIRnf+mIj8Lxf+xGAZdm5R5fhAEfmLiGRF5OiieCeIyNPud0LAfW8RWePK/rfcHlj+tXeuIu/Xi8iTIrJWRH4k3ubOiMjuInK/iGRE5KxA+Lmufj7u5D894NctIvc4Ge9x+3QZdcxk1UMRGXX152FXB94dEdb0lVFz1HBdMZ21A+os6zA2BscCf8DbfLMAEYlP1rWKWACEKmARSUzgukuBSAUM3AMsVtW34+0vc65zHwK+BJwVEudyVd0deCdwgIgc6tw/Bbymqm8GrgQunYDMRm1Ri/UE4FlVfUfgN+zcV6jqO/D2YbpERGZFpFGuHK8DTgSWBwOLSDdwIbAfsC9wYUCxfRc4GW+T392ADzn3E4GKyhe4Htgd2BNoBk5y7q8CpwGXF4XPAl9Q1T2AdwGfCbxsnwP8RlV3w9vT65wqrm/UNpNVDwddffofeLrgaxFhF2D6yqg9arGugOmsHVJnWYexzhGRNuAAPIWxzLm9z41+LAfWiEjcjWb+yY1mftqPKyK/caM5a0Tko+O9VgRfB97rRps+70Z6fiEitwF3OxlvD6T9bRE50R1/3Y1QPSIil7vRriOAy1x6bwq7oKrerapZd/pfwBzn3q+qf8BTxMHwA6q62h0PA3/x4wAfBa5zxzcCB/mjVkb9UcP1pCKqugFvc935ZfzLlmNVfUFVHwFyRdE+CNyjqq+q6mt4L68fEpHZQIeq3q/eJr0/BZa6kd59gOtdHWwWkYNE5K/unvxIRNLumneqA28TYV+WDar6J7xNqYPy/01V/+KO+4DHgV2cd7AeXof3Im7UKZNZD4voAF6L8Dd9ZdQUNVxXKmI6qzF11kRGzozaYinwK1V9SkReFZG9nPu+eKOXz4vIycDrqvoProLcJyJ3Ay8BR6pqr4jMAP5LRFa5SlPVtfxKE8I5wFmqehh4UwOA/YG3q+qrIvK+sEhuFOlIYHdVVRHpUtUtIrIKuF1Vb6zyvnwSWFFlWMSbDnQ4cLVz2gXv/qCqWRF5HZgObKo2TaOmqNV6AvAmEXnIHd+nqp8JeorIrsCuwDNU+GoRUo7LkS/fjvXObRd3XOCuqjeKyGfx6vSDItIE/AQ4yOXzp8ApwFUBWZLAJ4DTqRLxptK9E3jAOc1S1b+Bp6RFZGa1aRk1yWTWw2ZXr5qA2cAHIuQyfWXUGrVaV8B0VlD+BewgOsu+MNY/xwI3uOMb3DnAH1X1eXe8BDjeVfAH8BTJboDgTRt4BPg1XsWLmkJQ7lrVco+qvlohTC/eyOoPReQoYGCc10BEzsebMnB9leETwM+Ab6nqc75zSNByja1R+9RyPQlO7wkq3mOcLD8DPl2p7pQpx2WDh7hphHsxbwWeV9Wn3Pl1wIFFYf4D+L2q/r8KsngCeSPqNwFnqGpvNXGMumMy66E/zW53vClqPx3nVzfTV8ZUUst1xXQWO57Osi+MdYyITMcbCVosIgrE8SrKnUB/MCjwOVW9qyj+iUAPsLeqjojIC3gjTFVfS0S+GDFqVUxQpiyFAxZNkB8d3Rc4CG8axmepPNoVlPME4DC8UaRq5foB8LSqXhVwWw/MBda7Rq0Tbz67UWfUYT3xWaGqnx1H+LByXI71eOtMfOYAv3Xuc4rcXw6JH/niLSIX4t2zT1chiz+yexNwvaquDHi9IiKz3UjtbGBDNekZtcdk1sNiVPV+96Wlh+rLkOkrY0qow7riYzqrgXWWfWGsb44Gfqqq81V1garOBZ4H3lMU7i7gFBmz/PQWEWnFUyobXIPyfsrMNx/ntXz6gPaI9F4EFoln3a0TT+H6IzadqnoncAbwjirTQ0Q+BJwNHKGqVY30isjFePfhjCKvVYBvheto4N4JvPAbtUEt15NtQkQ5LsddwBIRmSae4YAlwF1uKk2fiLzLjTAfD9zq4gTr4BPAAhF5szv/BPA7J8tJeOtNjlXV4nUoYbILcC3wuKp+s8g7WA9PCMhi1B+TWQ8LEM/KZBzYXCaI6SujlqjlurJNMJ1Vh6iq/er0hze68qEit9PwFuDeHnCLAZcAa4C1wGq8ijoDuB94EPihi7dgnNf6bpnwSTwLUQ8Dn8ezVvXtojDfAJ4EbgdWujCz8RYdP+LkPcGFPQB4DPgr8KYy13wGb477Q+73vYDfC3gjrlvxRqQW4Y1Eqcu3H+ckF74J+IVL84/ArlP9vO03sV+N15MFwNoQ95L6EnD3y7D/iyrH/+DC9OO9ADwaSOuTrnw/A/xLwH0fl/9ngW8D4tw/5urrQ3iW5A5y9XEN8CMg7cJlXVxfln9z7js5WXqBLe64A+8lSF2d9+N82MWZjteOPO3+u6e6PNlvYr/JrIcundFAeXoY+EhEWNNX9quZX43XlQWYztohdZZ/Uw3DMAzDMAzDMAyjAJuSahiGYRiGYRiGYYRiRm+MAtxi69+EeB2kqiVz2kVkT+D/FDlnVHW/7SGfu+Z38Kb8BLlaVX+8va5pGEHqoZ4YRqNTD/XQ9JVRC9RDXTFqG5uSahiGYRiGYRiGYYRiU1INwzAMwzAMwzCMUKzDaBiGYRiGYRiGYYRiHUbDMAzDMAzDMAwjFOswGoZhGIZhGIZhGKFYh9EwDMMwDMMwDMMI5f8DNB7G4idlk6kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x1080 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ema_workbench.analysis import pairs_plotting\n",
    "\n",
    "fig, axes = pairs_plotting.pairs_scatter(experiments, outcomes, group_by='collapse',\n",
    "                                         legend=True)\n",
    "fig.set_size_inches(15,15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Area_A_trust_2120': array([45.08716481, 55.53645285, 51.64224843, ..., 53.90578306,\n",
       "        11.96904438, 34.07296084]),\n",
       " 'Area_A_FPL2100to2120': array([24.76569913, 67.62419361, 46.17610852, ..., 46.17610852,\n",
       "         5.92469669, 24.76569913]),\n",
       " 'Area_B_trust_2120': array([69.92675009, 69.64431336, 69.1611392 , ..., 69.45103983,\n",
       "        69.81192545, 65.99996165]),\n",
       " 'Area_B_FPL2100to2120': array([1741.85920216, 4818.09541286, 3278.66657571, ..., 3278.66657571,\n",
       "        2403.91616273, 1741.85920216])}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'ema_workbench.analysis.feature_scoring' has no attribute 'CLASSIFICATION'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-26-2aa08795bb09>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0moutcomes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Area_B_trust_2120'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m50\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mfs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfeature_scoring\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_ex_feature_scores\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfeature_scoring\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCLASSIFICATION\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[0mfs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mascending\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mby\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'ema_workbench.analysis.feature_scoring' has no attribute 'CLASSIFICATION'"
     ]
    }
   ],
   "source": [
    "from ema_workbench.analysis import feature_scoring\n",
    "x = experiments\n",
    "y = outcomes\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}