32_EMA_Scenario_discovery.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "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 pathlib import Path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load results from EMA Workbench run (see notebook 30), inspect and clean the raw results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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"
     ]
    },
    {
     "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>implementation_time</th>\n",
       "      <th>transient</th>\n",
       "      <th>Mayor</th>\n",
       "      <th>scenario</th>\n",
       "      <th>policy</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395996</th>\n",
       "      <td>rational</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_453.csv</td>\n",
       "      <td>Lawkeeper</td>\n",
       "      <td>98996</td>\n",
       "      <td>3</td>\n",
       "      <td>Rotty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395997</th>\n",
       "      <td>boundedly_rational</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_157.csv</td>\n",
       "      <td>Lawkeeper</td>\n",
       "      <td>98997</td>\n",
       "      <td>3</td>\n",
       "      <td>Rotty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395998</th>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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\")\n",
    "experiments.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>implementation_time</th>\n",
       "      <th>transient</th>\n",
       "      <th>Mayor</th>\n",
       "      <th>scenario</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>boundedly_rational</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_119.csv</td>\n",
       "      <td>R. Active</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>boundedly_rational</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_90.csv</td>\n",
       "      <td>R. Active</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>boundedly_rational</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_220.csv</td>\n",
       "      <td>R. Active</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>boundedly_rational</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_141.csv</td>\n",
       "      <td>R. Active</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rational</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>SurgeHeight\\five_hundred\\five_hundred_7.csv</td>\n",
       "      <td>R. Active</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Housing_market SLR implementation_time  \\\n",
       "0  boundedly_rational   4                  12   \n",
       "1  boundedly_rational   4                  14   \n",
       "2  boundedly_rational   2                  10   \n",
       "3  boundedly_rational   9                   9   \n",
       "4            rational   5                  13   \n",
       "\n",
       "                                       transient      Mayor scenario  \n",
       "0  SurgeHeight\\five_hundred\\five_hundred_119.csv  R. Active        0  \n",
       "1   SurgeHeight\\five_hundred\\five_hundred_90.csv  R. Active        1  \n",
       "2  SurgeHeight\\five_hundred\\five_hundred_220.csv  R. Active        2  \n",
       "3  SurgeHeight\\five_hundred\\five_hundred_141.csv  R. Active        3  \n",
       "4    SurgeHeight\\five_hundred\\five_hundred_7.csv  R. Active        4  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiments2 = experiments.drop(['policy','model'],axis=1)\n",
    "experiments2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "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 (R1)</th>\n",
       "      <th>SLR scenario (X1)</th>\n",
       "      <th>Implementation time (X3)</th>\n",
       "      <th>Transient storm surge (X2)</th>\n",
       "      <th>Management strategy (L1)</th>\n",
       "      <th>scenario</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>220</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Housing market (R1) SLR scenario (X1)  Implementation time (X3)  \\\n",
       "0                    0                 4                        12   \n",
       "1                    0                 4                        14   \n",
       "2                    0                 2                        10   \n",
       "3                    0                 9                         9   \n",
       "4                    1                 5                        13   \n",
       "\n",
       "   Transient storm surge (X2) Management strategy (L1) scenario  \n",
       "0                         119                        0        0  \n",
       "1                          90                        0        1  \n",
       "2                         220                        0        2  \n",
       "3                         141                        0        3  \n",
       "4                           7                        0        4  "
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiments3 = experiments2.copy()\n",
    "\n",
    "map_house_market = {'rational' : '1', \n",
    "             'boundedly_rational' :'0'}\n",
    "\n",
    "map_house_market_invert = {'1':'R', \n",
    "                           '0':'BR'}\n",
    "\n",
    "map_mayor = {'H. Economicus': '2',\n",
    "             'Sentiment' : '1',\n",
    "             'R. Active' : '0',\n",
    "             'Lawkeeper' : '3'}\n",
    "\n",
    "map_mayor_invert = { '2' : 'Economic',\n",
    "                    '1' : 'Sentiment',\n",
    "                    '0' : 'Reactive',\n",
    "                    '3' : 'Proactive'\n",
    "                    }\n",
    "\n",
    "\n",
    "rename_cols = {'Housing_market' : 'Housing market (R1)',\n",
    "               'SLR' : 'SLR scenario (X1)',\n",
    "               'implementation_time' : 'Implementation time (X3)',\n",
    "               'transient' : 'Transient storm surge (X2)',\n",
    "               'Mayor' : 'Management strategy (L1)'}\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",
    "cols_convert = ['transient','Housing_market','implementation_time','transient']\n",
    "experiments3[cols_convert] = experiments3[cols_convert].astype('int')\n",
    "experiments3 = experiments3.rename(columns=rename_cols)\n",
    "experiments3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 396000 entries, 0 to 395999\n",
      "Data columns (total 6 columns):\n",
      " #   Column                      Non-Null Count   Dtype   \n",
      "---  ------                      --------------   -----   \n",
      " 0   Housing market (R1)         396000 non-null  int32   \n",
      " 1   SLR scenario (X1)           396000 non-null  category\n",
      " 2   Implementation time (X3)    396000 non-null  int32   \n",
      " 3   Transient storm surge (X2)  396000 non-null  int32   \n",
      " 4   Management strategy (L1)    396000 non-null  object  \n",
      " 5   scenario                    396000 non-null  category\n",
      "dtypes: category(2), int32(3), object(1)\n",
      "memory usage: 12.7+ MB\n"
     ]
    }
   ],
   "source": [
    "experiments3.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scenario discovery\n",
    "Scenario discovery seeks to find (orthogonal) subspaces with high density of points of interest. Here, we search for the the uncertainties (X1-X3) and policies (L1) with a high density of tipping points before 2200.\n",
    "\n",
    "The optimisations towards high density of tipping points has a trade-off: the coverage, i.e. the number of tipping points inside the box (as fraction of total number of tipping points) goes down."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "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": [
    "%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=0.97)\n",
    "box1 = prim_alg.find_box()\n",
    "fig = box1.show_tradeoff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SLR scenario (X1) [1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
      "Management strategy (L1) ['1', '2', '0', '3']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1e89f2008b0>"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 601.25x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "box1.show_pairs_scatter(i=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZkAAAEwCAYAAABltgzoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8GearUAAAgAElEQVR4nOzdd3hURdvA4d/sphdCCikkgYSSRg2BQEAEBJEiCEqVqqiAlU9RXhWxAL4q2AuKCtKbSAcRpSkIoSM1oYWahBRSSdnsfH9s4ouYwBKyOSlzX9deZHfPmfPskuyzc87MM0JKiaIoiqJYgk7rABRFUZSqSyUZRVEUxWJUklEURVEsRiUZRVEUxWJUklEURVEsRiUZRVEUxWKstDz4vn37PK2srL4DGqMSnqIoSkmMwBGDwfBEREREotbB3AlNk4yVldV33t7eobVq1UrV6XRqwo6iKEoxjEajuHr1alh8fPx3QG+t47kTWvceGteqVStdJRhFUZSS6XQ6WatWrTRMZ30qFa2TjE4lGEVRlNsr/KzU+jP7jlW6gBWlOnrxxRdrT5o0yaus2uvQoUODpKQkfVJSkv69996rVVbtKsrNVJJRqpX8/HytQ6gQtm3bdsrDw6MgOTlZ//3333tqHU9pvfPOO54ZGRklfo4NHDiw7r59++xKen7t2rXOmzZtcrzT4/r6+ja5cuWKWde0b/yCMG7cuNorV650vtPjVWbVPsmcPHnSJjAwsNHAgQPrNmzYsFHv3r0DV65c6dyiRYuQunXrNt6yZYtDenq6rn///gGNGzcODQ0NDZs/f37Non0jIiKCw8LCQsPCwkKLflnXrl3rHBkZGdytW7d6gYGBjXr37h1oNBo1fx1btmxxCA8PDwkNDQ0LDw8POXTokC3A3r177Zo0aRIaEhISFhQUFPbXX3/Zpqen6zp27NggODg4rGHDho2+/fZbV4u+AOCLL75wDwoKCgsODg7r06dPYExMjE1UVFRQUFBQWFRUVFBsbKxNcnKy3tfXt0lBQQEAGRkZOm9v76a5ubni6NGjtu3bt2/YqFGj0IiIiOADBw7YATzyyCMBTzzxhF/r1q2Dnn76ab+S3oeMjAxdjx496gUFBYX17NmzXtOmTUO2b9/uAPDTTz/VaN68eUhYWFho9+7d66WlpVn8b2fChAneAQEBjdu2bRsUGxtrC3Cr1zhy5Ej/8PDwED8/vyazZ892BYiLi7Nu2bJlcEhISFjDhg0b/fzzz07wvw/Jl156ye/ChQu2ISEhYaNHj/br06dPYNHvN0Dv3r0DFyxY4GLp11rEaDRS9H9rjm+++cYrMzOz2P8Lg8HAkiVL4iIiInJK2n/z5s3Ov//+u1MpQi2VTz755HKfPn0yyut4FYHQsgrzoUOHzjVr1iwJ4OUfD/nHxGc4lGX7Qd7O2dP6Nbtwq21Onjxp06hRoyY7d+48FhERcb1p06ahYWFh15csWXJu4cKFNX/44Qf3kJCQnLCwsOtPP/10SlJSkr5ly5ahhw8fPiaEQK/XSwcHB/nXX3/ZDh48uN6RKR0yU2N3OR85EWvfqnmTLFtba7n/8DGH+gF1cmvWcDb/r+dGnmHZ9Pnyrl/H0qVLzzo7Oxutra1ZuXKl84wZMzw3btx4esSIEf5t2rTJGjt2bMpr21/zP3XtlMO1lGtWaalp+noN6+UCGAoMWOlLNxixgWuD7MntJt8y/r1799r169evwZ9//nnCx8fHkJCQoB88eHBg3759U5977rnkTz75xH3t2rU1f/3119OdO3euP27cuMRevXplfPvtt66//vprjSVLlsRFRUUFzZw5M65Jkya5mzdvdnzttdd8d+3aFfPII48EpKSkWG3atOmUlZUVKSkpuuLeh0mTJnmdOnXKbuHChXF79uyxi4qKarR58+bjvitW1jq8caNrk5CQ63q9nvOXL9lIo6Sun19eqd4QwLZhw+za704t8T35/fffHUaNGhWwb9++E/n5+TRv3jxs5MiRVzdt2uRS0mvMzs7WrV279szBgwft+vbt2+D8+fNH3nzzTa+cnBzx/vvvxxsMBjIyMnSurq5GX1/fJnv37j2enp6ue/DBBxvGxsYe/W3ucf+zxy45X7pyyTosJCzHUGDg4KEDjhHhLbOEEKV6nW6+Ttmdh4fe9ne3e/fuDdu2bZuxb98+px49eqRu3LixZl5enujZs+e1jz/++HJ6erqud+/e9a5cuWJjNBrFK6+8cjkhIcH67bff9gsMDMxxdXU17N69O8bBwSH8qaeeSti8eXONadOmXXzjjTd8p0+ffuHee+/N/vHHH2tMmjTJt6CgQLi5uRl++OGHc+3atQvV6XTSzc3N8Mknn5xv2rRpzmOPPVb30qVLNgAfffTR+a5du2bFx8frH3nkkXopKSnW4eHhWVu3bq2xb9++4z4+PobiXtOECRO8lyxZ4lG7du08d3f3/PDw8Ox33nkn4ZFHHgl48MEH0x577LFUX1/fJn379k35448/nA0Gg/j666/j/vOf//jGxcXZPvfccwmvvPLK1ZvbPXTokEezZs0CSvWfoRFNhzBXFL6+vrmRkZHXAYKCgq7fd9996TqdjhYtWmRPmTKldnx8vM3GjRtrfvbZZ94Aubm54tSpUzZ169bNHzVqVN1jx47Z63Q64uLibKFDJoCzk2OBna2NBHBydCjIyckV1LBsL/l2ryMlJUU/cODAwHPnztkJIWR+fr4AiIqKypo+fbrPxYsXbTKiMvR6nR4HR4eC8+fO28adi5Oubq4FNWrUKF2CNNPGjRtr9OrVK7Xoj9bLy6vgwIEDjhs2bDgNMHbs2JS3337bD6B///6pixYtcu3Vq1fG0qVL3Z5++umraWlpugMHDjj179+/flGbeXl5f38yPvzww6lWVqZf95Leh507dzq98MILiQCtWrXKCQoKyga4nJBgdT0nR3fw2FEHAGmUODs5WfT92LJli1OPHj2uOTs7GwG6du16LScn55avsXfv3tf0ej0RERE5ycnJ1gBt2rTJGj16dEB+fr6uX79+qW3btr1+q+O6uLgUnD57yjY/P18kJSdZubu5G0qbYO7EuXPn7L799ttzDz/88LVly5a5Hj58+LiUki5dujTYsGGDU0JCgpW3t3f+1q1bTwEkJyfr3d3dC2bMmOG1bdu2mKLfm+vXr+saN258/ZNPPrkM8MYbbwBw+fJlq2effTZg69atJ0JCQvISEhL0Xl5eBcOHD7/q5ORU8M477yQA9OrVK/DFF19MeOCBBzJjY2NtHnjggYZnzpw5+p///Kd2VFRU5vTp068sXrzYZdGiRR4lvZbff//dYcWKFW5//fXXsaIvCOHh4dnFbevv75938ODBE6NGjfJ//PHHA3bv3n2i8DU0Ki7JVEYVJsncrsdhSTY2Nn9353Q6HXZ2dhJAr9dTUFAg9Hq9/PHHH081a9Ys98b9Xnzxxdqenp75y5cvP2s0GrG3t4+gz5cXdqxd6/zh+g+9tny65RTA1OHD67T0bZn1/FPPJ2v5OiZMmODboUOHjE2bNp0+efKkzX333RcMMGbMmJT27dtnrVixwmXOmDnOX3311bne/XtnJNyboF++fLnL9x9+Xyu0U2j69OnTr1gqdiklQgizutWDBw++9s477/gmJCTojxw54tCrV6/09PR0nbOzs+HEiRPHitvHycnp7/OVJb0PJfXqLzzYM2lRRrpxzZo1Z0vx0krt5g93o9HIrV5j0f83/O+1dO/ePXP79u0nly9f7jJy5MjA559/PuHZZ58t9vewqMdx8PWV3rEywbh8y3K3WbNmnWvZsmWJp5vKio+PT17nzp2znnrqKb/t27fXCAsLCwPIzs7WnThxwq5z584Zr7/+uv/YsWN9H3roobRu3bplFteOXq9n5MiRqTc/vnXrVsfIyMiMkJCQPDB9iSlu/x07dtSIjY21L7qfmZmpT01N1e3atcv5p59+OgUwaNCgtNGjR5f4JaO4LwglbTtgwIBrAE2aNMnOysrSubq6Gl1dXY22trbGpKQkvYeHh0W/zJSHan9NxhydOnVK//DDD72Krqvs2LHDHiAtLU3v4+OTr9fr+eqrr9zv5FyyFtLT0/V+had4vvnmm7+/iR07dswmNDQ0d+LEiYldu3a9dvDgQftz585ZOzs7G59++umUcePGJRw8eLBMT2XerFu3bumrV692i4+P1wMkJCTow8PDs7777jvXwnjdWrZsmQng4uJibNasWdbo0aPrdO7cOc3Kygo3Nzejn59f3qxZs1zB9IH8559/2hd3rJLeh7Zt22YuXrzYFWDfvn12MTEx9gAdO3bM2rt3r9ORI0f+vnZz+PBhW8u9G3Dfffdlrlu3rmZmZqZITU3Vbdq0qaaDg4PZr7FITEyMja+vb/5LL72UNHTo0KT9+/f/4//RxcWlICsr6x+fA2PGjEn65ptvvADKI8EAODg4GMGUHMeNG3flxIkTx06cOHHs/PnzR/7v//4vqWnTprn79+8/1qRJk+uvv/667/jx432Ka8fGxsZY1GO9UeGXmNvGIaVk7969x4uOn5iYeNjV1dUIpi9u5jK391f0xUCn0/3rS2JRD7uyU0nGDO+9995lg8Egii6eTpw40Rdg3LhxiYsWLXJv1qxZSExMjJ29vb1lr+7fpQkTJsS/9dZbfi1atAi5MSHOmzfPLSgoqFFISEhYbGys3ejRo5P37dtn37x589CQkJCw999/32fSpEkW68WA6cPspZdeutK+ffuQ4ODgsKefftp/xowZ5+fNm+cRFBQUtmjRIvevvvrq797ugAEDUletWuU2ePDglKLHFi1adGb27NkeRYMVli9fXrO4Y5X0Prz88stXk5OTrYKCgsKmTp3qHRwcfN3V1bWgdu3ahm+++ebcoEGD6gUFBYVFRESE/PXXXyWOWCoL99xzT3bfvn1TGjdu3OjBBx+sHxkZmXknr7HIxo0bncPCwhqFhoaGrVq1yvWVV15JuPF5b2/vgoiIiMyGDRs2Gj16tB+Av7+/oX79+jlDhw61aM+7ON27d0+fN2+eR9HAirNnz1pfunTJqqQvPY6OjgXmDMLo1KlT1u7du51PnDhhA6YvMQDOzs4FGRkZ+qLt7rnnnvT333//79F2O3futAdo06ZNxqxZs9wBli5dWiM9PV1PCYr7glC6d6NqqDAX/hVFawaDgby8POHg4CCPHj1q27Vr16DTp08fufE0VHWQkZGhCwsLCzt48OBxd3d3i3fPT548aVM0+ABg8uTJnvPmzfMAUw9nwYIFZ0+cOGH76quv+ul0OqysrORXX30Vd++992ZPnTrV87vvvqvl6emZX3ThPzs7+0BR25GRkcFFF/6XLl1a48033/QzGo24u7vn79y5M/bw4cO2/fr1q6/T6fjkk0/ON2vWLOeJJ56oExsba1dQUCBat26dsXDhwvNFF/5TU1OtoqKiMjds2FDTnAv/vr6+ubVr184PDQ29XtyF/7179x738fExfPbZZ+579+51nDt37nkwjf4reu7GdivjhX+VZBSlUGpqqq59+/bB+fn5QkrJlClTLg4YMCBd67jK08qVK53Hjh0bMHbs2IRJkyZVqkKM1YFKMndIJRlFURTzVcYkU2FGlymKolQm8fHx+o4dOwbf/PjWrVtPent7V+xRQOVIJRlFUZRS8Pb2LihpOLnyP1qPLjMajcYqMUxPURTFkgo/Kyv0CNbiaJ1kjly9etVFJRpFUZSSFS5a5gIc0TqWO6Xp6TKDwfBEfHz8d/Hx8Wr5ZUVRlJL9vfyy1oHcKU1HlymKoihVm+o9KIqiKBajkoyiKIpiMSrJKIqiKBajkoyiKIpiMSrJKIqiKBajkoyiKIpiMSrJKIqiKBajkoyiKIpiMSrJKIqiKBajkoyiKIpiMSrJKIqiKBajkoyiKIpiMSrJKIqiKBajkoyiKIpiMSrJKIqiKBajkoyiKIpiMSrJKIqiKBZz2yQjhJglhEgUQhS7trQw+UwIcUoIcVgI0aLsw1QURVEqI3N6Mj8A3W7xfHegYeHtKWDG3YelKIqiVAW3TTJSyu1Ayi02eQiYK012ATWFED5lFaCiKIpSeVmVQRu+wIUb7l8sfOzKzRsKIZ7C1NvB3t4+wt/f/44PlpufTILMKl2kyl0TCHRChx49VsIKvdCjR2/6V+ixwvSYQGgd6j8YjUZ0usp5CVLFXv4qctwxMTFJUspaWsdhrrJIMsV9msjiNpRSzgRmArRs2VLu3bv3jg92PT+bY/NG43tpHYQPhY6vgqhYH2i38ueffxIVFWWx9qWUFMgCjNL49+3m+8U9Vtz9m9vaf3Q/Ln4uJGYnkpCVQGJ2IonZiWTkZ/wrDmdrZ7wcvfB08MTTwRMvB69//uvohautK6Kc/u+2bt1Kx44dy+VYZU3FXv4qctxCiDitY7gTZZFkLgI3dkn8gMtl0G6x7K0dyAh8HO/a/rDrS7BxgfvfqTSJxtXKFW9Hb63DKBWH8w50DO/4r8ez87NJyP5f0knITvhHEjqVeoqknCSM0viP/ax11n8nIT8nP4aEDqGRR6NyejWKopSHskgyq4FnhRCLgdZAmpTyX6fKypQQ8MBUMOTAzs/A2h46vWbRQyolc7B2INAlkECXwBK3MRgNJF1P+mciuiExbbu4jTVn1tAjsAfPt3geXyffcnwFimJZ+bl5nNrzFxd37Sf7yBFqtmtLhycHah1WubhtkhFCLAI6Ah5CiIvAm4A1gJTya2A90AM4BWQDj1kq2JsCgx7ToSAPtr0Pehu4d3y5HFq5c1Y6K7wdvUvsxWXmZTLryCzmHpvLprhNDAkdwhNNnsDF1qWcI1WUu2PIN3B63xHO/2lKKLanY/C+eh7bgnz8gGxrO6741NY6zHJz2yQjpRx8m+cl8EyZRXQndDro9akp0WyeDFZ20PZZTUJR7o6TjRPPt3ieAcED+OLAF8w5OocVp1YwuuloBgUPwlpvrXWIivIvBYYCzhw4zvld+8k8/Be2p0/imRiHvSEPP+C6lQ0JXgHEteuGc9PG1ImKoHmzECKs9FqHXm7K4nSZtnR6eOgrMOTCL6+DlS1EPql1VEopeTt6M+WeKQwLG8aHez/kgz0fsPD4QsZFjKNr3a7lNlBAUW5mNBo5dziGczv3knHoL2xOn8Qz/hwOhlxqA7l6a+I963KhdRccmzamblQETcPDsLKu/B+zd6NqvHq9FTzyHRTkw/rxpkTTYrjWUSl3IdgtmJldZ7Lj0g6m753O+G3jaVqrKeNbjifcM1zr8JQqThqNxB2J5eyOvaQdOoz1qZN4XjmLY34OPoC7zor4WnW42KojDk2a4N8mgkYRYTS3tdE6dIQQdsB2wBbTZ/yPUso3b9pGAJ9iutSRDYyUUu63RDxVI8kA6K2h/2xY/Cisfh70ttCselxYq8ra+bajjU8bVp9ezRcHvmD4huF0rtOZcS3GEeASoHV4ShX0x5wV2H80lezcLLwAN6EnvpYfl1q0x75xY3xbhxMa2ZRmdrZah1qSXOA+KWWmEMIa+EMIsaFwsnyRGyu1tMZUqaW1JYKpOkkGTD2YgfNh4QBYOQasbKBRX62jUu6SXqenb8O+PBDwAHOPzWX2kdlsu7CN/sH9GdNsDG52blqHWCklXczk9P5Eix4jMc7I7vQzFj1GWUqMu4Rxwy6yAntDcAjOvj64+XriYm1N0RCUzGtw4JdLd3UcnwYu1Alzv/uAi1F4nTyz8K514e3muYt/V2oBdgkhagohfCwxMrhqJRkwDWcevBjmPwLLnzCNOgvpqXVUShlwsHZgTLMx9Avqx4yDM1h6cilrTq9hVJNRDA0dip2VndYhVirbF5/kyqm04qdTlxUJV4+ds+ABypAEpBH87zeNXk0XpKbnc/743SWU4rToWvdukoyHEOLGmewzCye6/00IoQf2AQ2AL6WUu29qw+xKLXer6iUZABtHeHQpzOsLS0fA4EXQ8H6to1LKiIe9B29EvcGQ0CF8vO9jPt3/KUtOLuG58Od4sN6D6ETFLAdSkaQnX+fKqTRa965Hyx4BFjtORZ45f6PsjCz+6NkPj5TL2M34nsSCzIocd5KUsuWtNpBSFgDNhRA1gRVCiMZSyhsr6ZtdqeVuVd2/RrsaMHQ5eIXB4iFwZqvWESllrF7Nenze+XNmPTALNzs3Xv/jdQatHcSuK7tuv3M1F7snAYCgSC+NI9FegaGAX0c8g29iHFnj3ySs/S0/vysVKeU1YCv/rqRfbpVaqm6SAbCvCcNWgnsDWDgIzu3QOiLFAlp5t2JRz0W81/490nLTePKXJxn761hiU2O1Dq3CiolOwLueCzU87LUORXPrxk+m4bHdnHt4JO1HPqx1OHdNCFGrsAeDEMIe6AKcuGmz1cDwwvXA2mDBSi1VO8kAOLjB8JVQ0980IODCHq0jUixAJ3T0rNeT1X1X81LESxxKPES/Nf14a+dbXM2+qnV4FUrSxUxSLmepXgyw+ct5NPx5CTEtOtJ9SpWpGOIDbBFCHAb2AJuklGuFEGOEEGMKt1kPnMFUqeVb4GlLBVM1r8nczMkThq+G2d1NAwJGrILaaq5FVWSrt2Vk45H0adCHbw5/w+KTi1l/dj09nXvSQXZQkzmBmOh4dDpBg5aeWoeiqQMbf8f9yw845xtEt+8/rrCl/e+UlPIw8K8PuMIyYEU/l1ullqrxrpqjhg+MWAN2LqYBAfHFriatVBE17WoyIXICqx9aTSvvVvyY+iPv7n4Xg9GgdWiakkZJ7J4E/Bu5Ye+k/cRBrVw4cYacCS+R5liTVnNmYmuvRiZaSvVJMmA6ZTZiNVjZw9yH4OpJrSNSLMy/hj+f3/c5nWt0ZvHJxbyw5QWy87O1Dkszl09dIzM1t1qfKktPSePkqNFYF+Tj8+WXePhV3/eiPFSvJAPgFmjq0QgdzOkNyae1jkixMJ3Q0ce1D2+0eYMdl3Yw4ucRJGQlaB2WJmKiE7Cy1RPYtNIsrFimDPkGtg8fi3fKZfInTqFhZFOtQ6ryql+SAfBoYOrRGPNhTi9IPad1REo5GBA8gM/v+5zz6ecZsn4IJ1OqV0+2IN/I6f2J1GvugbVt9akCfKP1z02k/qkDXBwyhqiBapJ2eaieSQbAMxSGr4K8LFOPJu2i1hEp5aC9X3vmdp+LRDJ8w3D+uPSH1iGVm7ijyeRmGwiKrJwrs96tX6bNpOHWVcREPUC3ic9pHU61UX2TDIB3Exi2Aq6nmhJNRrzWESnlINgtmIU9FlKnRh2e/e1Zlp5cqnVI5SImOh57Z2v8Q1y1DqXc7Vn1Kz6zPuVMQGN6fP2B1uFUK9U7yQD4tjBVBsiINyWaTDWnojrwcvTih24/0LZ2WybvmsxH+z7CKI1ah2UxudcNnDucTIOWXuj01evP/szBE8g3JpDk4knbuV9jXQHK8Vcn1eu3rST+kTBkKVw7D/P6QHaK1hEp5cDR2pHP7vuMgcEDmX1kNuO3jSfHkKN1WBZx5kAiBQZjtRtVlpqYTNyYsQAEfPM1rp6WqXyslEwlmSIB95gKaSbFmubRXL+mdURKObDSWfF669cZ33I8v8b9yqhfRpGSU/W+ZMREJ1Cjlj1eATW0DqXc5OfmsXPYaNzTriImv09gs2CtQ6qWVJK5Uf1OMHAeJByFBf0gN0PriJRyIIRgRKMRfNTxI06mnGTIuiGcSas8a6DcTta1XC6eTCUo0qtaVTxYP/oV6sUdJWHUC7R6qIvW4VRbKsncLOgB0wqbl/bDggGm0WdKtdClbhdmPTCLbEM2w9YPY0981ahzF7s3ASQEV6NRZRsmf0bQro3EdnyI+8c/qXU41ZpKMsUJ7QWPfAsXdsGiwZB/XeuIlHLStFZTFvRYgLu9O09teoo1p9doHdJdi4lOwLOuMzW9HLQOpVz8uXgd/gu+5lTDFvT4fIrW4VR7KsmUpPEj8NBXcHY7LB0OhlytI1LKiZ+zH/O6z6OFZwte++M1ZhyagameYOWTGp/F1fMZNGxVPS74x+w+hM3UicS7+9Jx7gysrKtHDeCKTCWZW2k+GB78GGJ/gR8fh4J8rSNSyomLrQtfd/ma3vV789XBr5i4YyL5lfD/PyY6ASGoFkkm6WIC8c8+S57emuDvv8HZtfoMcqjIVJK5nZaPQfcP4MRa+OlJKKjeVXyrE2u9NVPaTeHp5k+z+vRqxvw6hrTcNK3DMpuUkpjoeHyDXXF0sdU6HIvKyb7OnhFP4ZJ1Dbv3P8Q/pJ7WISmFVJIxR+vRcP9kOLoCVj0Dxqo7aU/5JyEEY5uN5d173mV/4n6GbRjGxYzKUYIo4Ww66Uk5Vb6MjNFoZOOo/yPgUgzJz7xC+APttQ5JuYFKMuZq9zx0mgiHF8PaF1SiqWZ61e/FzPtnknw9mSHrh3D46mGtQ7qtmN3x6K111A+v2hWXN0ycRtCBbcR2G8h9zwzTOhzlJirJ3IkOL0P78bB/Lvw8ASrpxWCldFp5t2J+j/k4WDnw+MbH+TXuV61DKlFBgZHYfYkENPHAxr7qXvzePutHAn6aQ2xYa3pOf0PrcJRiqCRzp+6bCFHPQvRM+GWiSjTVTKBLIAt6LiDYLZgXt77InKNzKuTIswvHUsjJzK/SZWSObt+D84eTueRZly5zvkRvVT2XL6joVJK5U0JA1ynQ6kn48wvYrMbhVzdudm583/V7utTtwvS903l397sVLtHERCdg62BF3cZVs1ZX/JkLXBv3PNm2DjT9YSYOzo5ah6SUoOr2oy1JCNOIs4Jc+H06WNmZTqUp1YadlR3TO0zno70fMefYHAJcAhgSOkTrsADIyzFw9tBVglp7o7eqOt8jjUYjx7bt4cySFXjs3oJDXg7WX36Ldz1/rUNTbkElmdLS6eDBT8GQB1umgJUNtHtB66iUcqQTOl5q+RJxGXFM3zud5rWa08ijkdZhcfZQEoY8I8FV5FTZqX1HOD5/Oc47N+OVlkig0BFXrwk+Y56iccdIrcNTbkMlmbuh08FDX0JBHmyaZOrRtB6tdVRKORJCMKXdFPqt6cf4beNZ2mspzjbOmsYUE52Ak6stPvVrahrH3bh48iyH5izDZvuv+CVdoB6COL9gzvcdTMthD9PE15mqkA0AACAASURBVFPrEBUzqSRzt/RW8PBMU6LZ8ArobUwTOJVqw8XWhWn3TmPkzyN5a+dbTO8wXbNqx9npeVw4nkL4/f4IXeWquHz1whX2zfsJ+dsvBFyKoR5wwTOAswOeoPmw/jRqWEfrEJVSMCvJCCG6AZ8CeuA7KeV7Nz3vAswH6hS2OV1KObuMY6249NbQbxYsGQpr/w+sbKH5o1pHpZSj5p7Neb7F83y872OWxSxjQPAATeI4vT8RaZSVZgJmWlIqe+avJGfjBgLOHaWuNHKlpjenHnyURkP707V5iNYhVjpCCH9gLuANGIGZUspPb9qmI7AKOFv40E9SyncsEc9tk4wQQg98CdwPXAT2CCFWSymP3bDZM8AxKWUvIUQt4KQQYoGUMs8SQVdIVrYwYB4sGmiqCqC3gSb9tI5KKUcjG41kT/we3o9+n6a1mhLiVv4fkDHR8bj7OuLu61TuxzZXdmY2exavIW3deuqc3I+v0UCSoxtnOj1Eg8EP07FdC3S6qjNgQQMG4CUp5X4hhDOwTwix6abPbIDfpZQPWjoYc3oykcApKeUZACHEYuAh4MaAJeAsTOcInIAUTC+0erG2g0ELYUF/+OkpU+IJ7aV1VEo50QkdU++ZSv/V/Xl528ssfnAxjtblN7Q27ep14s+kE9W3frkd01z5uXnsXbGJxJWr8TsajWd+DrZ2zsS16UKdfn1o1629SixlREp5BbhS+HOGEOI44Ms/P7PLjTlJxhe4cMP9i0Drm7b5AlgNXAacgYFSyupZd8XGER5dYlrCedljMGiBaSE0pVpws3Pj/XvfZ9Qvo5i8azL/vee/5XZ9JnZPPFBxKi4XGAqIPxDDqjkr8Tqwk5o5GdhY23GxcWs8+/SiZZ/7sba10TrMyshDCLH3hvszpZQzi9tQCBEAhAO7i3k6SghxCNPn9ngp5dGyDhRA3G4SmRCiP/CAlPKJwvvDgEgp5XM3bNMPaAe8CNQHNgHNpJTpN7X1FPAUgJeXV8TixYtLFXRmZiZOThX3dACAVX4mzQ5NwjHrPH81mUiqW3OgcsReEhW7+X6+9jPr0tbxqPujRDlF3VVb5sQupeTUBomVLQR21rZHkHohkczfdlL3cDS1slPJ1VkRG9CI3MhWeLZpgo1dxU8sFfl3vVOnTvuklC1vt50QwgnYBkyVUv5003M1AKOUMlMI0QP4VErZ0CIBSylveQOigI033H8VePWmbdYB7W+4vxlTIiqx3YiICFlaW7ZsKfW+5SorWcqv2ko52UvKs79LKStR7MVQsZvPUGCQozaOki3ntZSxKbF31ZY5sSfGpcsvRv8mj2y/eFfHKq2M1DS56eNZcl3nPvJYcIg8Ehwq1z7QT855ZYpMS76mSUx3oyL/rgN75e0/t62BjcCLt9u2cPtzgIc5297pzZyvPHuAhkKIQCGEDTAI06mxG50HOgMIIbyAYOBMKfNe1eHgBsNWQs06sGAAnC+ux6pURXqdnvfav4ejtSPjt40nOz/bosc7GR2PTi+o36L85o8YjUYObNjOqqHPcPKee/H9+gMc05I53WsoLqvW0fPnZdTp3o4abi7lFpMChdfGvweOSyk/KmEb78LtEEJEYioxlmyJeG57TUZKaRBCPIspK+qBWVLKo0KIMYXPfw1MBn4QQvwFCGCClDLJEgFXOk61YMRqmN0dFvTDudEkoKPWUSnlwMPeg/+2/y+jN43mv9H/ZXK7yRY5jtEoid2TQN3G7tg5WlvkGDeKP3OBfd8txGHzz3hfi6eO3obzTaPwHdSf9r06qQv42msHDAP+EkIcLHzsNUxTTIo+s/sBY4UQBuA6MKiwl1TmzJonI6VcD6y/6bGvb/j5MtC1bEOrQpy9YcQamN2dpoffglatwaep1lEp5SCqdhRPNn2SmYdnEukdSa/6ZT/a8FJMKtlpeRadG5N7PYddC9eQ8dNPBJw5TD1pJM6nARf7vUjrxwcQrnorFYaU8g9MX/Zvtc0XmAZsWZya8V9eXPxgxBoKvr4P63l9YOQ68AzVOiqlHIxtNpZ9CfuYvGsyjTwaUc+lbJcGjolOwNpOT0CTsq+4fHznfmJmL8I7egueuVlY27tw5r4+hD3+KN0itK/Tplhe4eCvn6VpOPREoAUwRUq535z9Vb+2PLkGcKjZFNBZwZzekHRK64iUcmCls+L99u9jp7fj5W0vk2PIKbO2DfkFnNmfSP3mtbCyKZv1VFLjk/l56hdsat8NHh9C4B8bSKjXiJSJ79Nq13Z6fzmVBirBVCdvFCaYe4AHgDnADHN3VkmmnF13qA3DV4M0wpxekHL29jsplZ6XoxdT75lKTGoMH+z5oMzaPXc4mbycgrs+VWbIN/DnknWs7vc45+/rSN15XyKMBZwbPJram36j94o5tBvaW81rqZ4KCv/tCcyQUq4CzP5FUKfLtOAZAsNXwZwHTT2ax9ZDTbUmRlXX3q89jzV+jNlHZhPpHUm3wG533WZMdDwONWzwDXEt1f5nD53kr1kLcPvjV9yzUrGyceBc1P3UHzaILqqMvmJySQjxDdAFeF8IYcsddFBUktGKd2MYtgLmPGTq0Ty2AWr4aB2VYmHPhT/H/oT9vPXnW4S5h1GnRukrC+dk5RN3NJkm9/qhu4OKy6mJyUTPXobxlw0EXIqhPoKzgY0x9HmONsP60MrBvtQxKVXSAKAbpsLH14QQPoDZqzSq02Vaqh0OQ5dD1lWY2xsyE7WOSLEwa5010+6dhl7oGb9tPHkFpa8he3p/IkaDJKj17cvI5F7PYfvsH1nTZzgXOnagzuxPsctM43TvYdRcvZ4HNyyl4+jB2KkEo9xESpmNqWJzlhCiDqaJnifM3V/1ZLTm3wqGLIP5j8Dch2DEWnCsmuuyKyY+Tj5MaTeF57c8z4d7P+TV1q+Wqp2Y6ARqejlQq07xi6QZjUYObfyD80uW47P/D2rlZWNj58zZtt2oN/gROnRqrea0KLclhHgOeBNIwLR0AJiKIps1D0MlmYqgblsYvMhUFWBeH9PkTfvSnWNXKodOdToxNHQo84/PJ9I7ks51O9/R/hkpOVyOvUZkr8B/FeA8c/AER+csocYfv+GZcZW6emvOh7Yiv89DtOr3ADZ2tmX5UpSq7wUgWEpZqooAKslUFPU6mio2Lxps6tUMWwl2NbSOSrGgFyNe5EDiAd7Y+QYh7iH4OvmavW/sngTgfxWXky4msHfOMsSmDdSJP2NarrhOKBcHjyBy+MM091BfWpRSuwCklXZnlWQqkob3w4A5sHS4aU2aocvBtmJWglXunrXemmkdpjFgzQBe3vYyc7rNwVpvXlmYmOgEatVx4uDK1WSsWUPAqYPUlUYuu9XmzMMjaT5yII2CAiz7ApTq4gywVQixDsgterCkumg3UydkK5qQnvDwt3AxGhYNgvzrWkekWJC/sz9vt32bv5L+4rMDn912+wJDAdvn/0bypUwcts7D66O3cb94irMdeiG/nUfnnb/R890J+KoEo5Sd85iWb7HBtF5Y0c0sqidTETV+GAryYcVoWDzEdL3GSp1Hr6q6BnRlYPxAfjj6A628W3Gv373/2iY2+jDH5y3FdecW0j3vgTpdyHOH9Cc/JrJPF6ys1Z+yYhlSyrcBCpdyllLKzDvZX/1mVlTNBoIhB9Y8D0tHwIC5YKVmW1dVL7d6mUNXD/HaH6/xY68f8Xb0xmg08uv0bzGu+hFD8kXqFc5nudygM76BrvR58XOtw1aqASFEY2Ae4FZ4PwkYLs1cSVOdLqvIIkZAj+kQswF+egIKDFpHpFiIrd6WafdOI78gnwnbJ2AwGtj0wdf4z/oEYZScHfgUHht+IeKzmRjyrQht56d1yEr1MRPT4md1pZR1gZeAb83dWSWZii7ySeg6FY6tgpVjwFhw+32USinAJYBJUZPYn7ifz+a+is/crzgT2ATrKa/S4+3/wzvQj5joeKxsdAQ289A6XKX6cJRSbim6I6XcCjiau7NKMpVB22eh8yT4a5np9JnRePt9lEqpZ72ePOzRnVZfrSXNwYE2332BTm+qrlxgMHJqfyKBzWphY6fOdCvl5owQ4g0hREDhbSJgdmVflWQqi/YvQYcJcGA+rB8PllnETtGYId9A5GencMmCGYN0GF3/N9Hy/NFkcrMMBEXevoyMopShx4FawE/AisKfHzN3Z/V1qDLp+KppMMCOT02jzR54F4T5hRGVim/9+HdoGHecY8OGccpnJf/5/T8MsRkCmObG2DlZ4x/mpnGUSnUipUwFni/t/irJVCZCQJe3wZALu74CvQ10eUslmirij7kraLhxGTEtOtL31f+gPxPGGzve4LTVaf7YthOPQ1E0aFMLvV6dgFAsTwjxiZRynBBiDaZaZf8gpextTjsqyVQ2QkC390yJZscnYG0PHf+jdVTKXTp3OAbbaZO56OHP/d9MR6fT0adBH3RCx+J9izmx7xLtDIJp1yby/Vp72vi0oY1PG8I9w7GzstM6fKVqmlf47/S7aUQlmcpICOj5ERTkwdb/mno07V/UOiqllLIzsjj19LO4CEHQ11/i4Py/gTu96/emxoUapF2uwVXXdB6+pwe743cx99hcZh2ZhY3OhnCvcNr4tCHKJ4oQtxD0urJZhlmp3qSU+wr/3Vb0mBDCFfCXUh42tx2VZCornQ56f266RvPb22BlB1FPax2VcoeMRiObnnyRBkkXuTbxPeo2bvivbfKvSy6dvEaLbnVp07wjYxlDdn42+xL2sevKLv688ief7v+UT/kUF1sXIr0j/046fs5+/6rSrCh3QgixFeiNKV8cBK4KIbZJKc36ZquSTGWm00Pfb0w9mo2vmioCtHpC66iUO7Dpg68JOrid2G4D6T20+FPc6edNgwmDIr3/fszB2oH2fu1p79cegKTrSey+sptdV3ax68ouNsVtAsDXyffvU2uRPpG42alBA8odc5FSpgshngBmSynfFEKonky1obeGR2bBkqGw7iXQ20KLYVpHpZjh8K87/55w2WPaxBK3uxYn8fB3ws2n5PlvHvYe9KzXk571eiKlJC497u+E88u5X1geuxyAELcQonyiTNdzvMKxt1IrYSq3ZVW45PIA4PU73rns41HKnZWNqbbZokGw+jnTqbOm/bWOSrmFpIsJpE8Yj7R3oc13X5RY4PJaQjY5KdCio3exzxdHCEGASwABLgEMChmEwWjgePLxv5PO/OPzmX10NtY6a1p4taCDXwc6+nfE39m/rF6eoiEhhD8wF/DGtJLlTCnlpzdtI4BPgR5ANjBSSrm/hCbfATYCf0gp9wgh6gGx5sajkkxVYW0HgxbCwgGm6s16a2jUR+uolGIY8g3sHvUM/tfTEZ9/i7uvZ4nbnoyOB/63OFlpWOmsaFKrCU1qNeHJpk9y3XCd/Qn7+fPyn+y4vIMP9nzAB3s+oL5LfTr6d6Sjf0eaeDRRAwgqLwPwkpRyf2Hl5H1CiE1SymM3bNMdaFh4aw3MKPz3X6SUy4BlN9w/AzxibjAqyVQlNg4weDHMfxiWjzJN2AzurnVUmju24zKXdhv57fxxrUMB4MKu/dSwC+dQjyHUvlST3+aWHFfckWQcPcHJteyWerC3sqedbzva+bZjPOO5kHGBbRe2sfXCVuYcncP3R77Hzc6N9r7t6eTfiajaUThYO5TZ8RXLklJeAa4U/pwhhDgO+AI3JpmHgLlSSgnsEkLUFEL4FO77D0KIWsCTQAA35Awp5ePmxKOSTFVj6wRDlsHcPqYVNgcvggZdtI5KMzlZ+WxbdBIEGK6laB0OuVnX0WXbkujVDHtrVy4ev3VMVtY6XIMtOzrM39mfoWFDGRo2lPS8dHZc2sHWC1vZfGEzq06vwkZnQ6RPJB39OtLBvwPejuafulMswkMIsfeG+zOllDOL21AIEQCEA7tvesoX07LKRS4WPvavJAOsAn4HfgXuuEKvSjJVkZ0LDPsJ5vQyLXo2ZBkE/nshrOrgzIGrGA2Sel0F3R9up2ks5w7HcHXIIK65eNBuw4p/zIe5la1bt1o2sBvUsKlB98DudA/sTr4xn4OJB9l6YStbL2xlyu4pTNk9hVC3UDr6mxJOmFuYGiJd/pKklC1vt5EQwglYDoyTUqbf/HQxu5RUENFBSjnhDmP8m0oyVZW9KwxbCT88CAsHwtCfoG6U1lGVu5joeGp6OWDnqu0y1reacFlRWeusaeXdilberRjfcjxn08+y9cJWtl3YxjeHv2HGoRl42nvSwd80cCDSO1JVH6gghBDWmBLMAinlT8VschG4caSHH3C5hObWCiF6SCnXlyYWlWSqMkcPGL4KfugBC/qbfvaL0DqqcpOZmsOl2GtEPhhIlojTLA6j0cimp1665YTLik4IQT2XetRzqcfjjR8nNSeV3y/9ztYLW1l3Zh3LYpZhb2Uqd9PJvxPt/drjYa/WvNFC4cix74HjUsqPSthsNfCsEGIxpgv+acVdjyn0AvCaECIPyMPUC5JSyhrmxKOSTFXn7AUj1sDs7jC/r+lnn2ZaR1UuYvYkgDSNzDp4TLsks+mDmQQd2HbLCZeVjaudK73r96Z3/d7kFeSxJ36PqZdzcRtbLmxBIGhSqwmhBaG0zGuJk42T1iFXJ+2AYcBfQoiDhY+9BtQBkFJ+DazHNHz5FKYhzCWW7pdSOt9NMCrJVAc1ahcmmh6mAQEj14JXI62jsriY6AS8AmtQ09Phn+NqypFpwuUXt51wWZnZ6G3+Hq32mnyNmNQYtl7Yyi9xv7AkdQmrl62mR2AP+gX1o5F7I3UNx8KklH9Q/DWXG7eRwDPmtFfYMxoCBEopJxfOw/GRUkabs7+qGV5d1KxjOl1mZQtzH4KrMVpHZFHJlzNJvpip6QJfRRMu024z4bIqEUIQ7BbM6Gaj+bHXj4z3Hk/3wO6sP7uewesGM3DtQJaeXEpmXqbWoSrm+wqIAh4tvJ8JfGnuzirJVCfu9WH4atPPc3pB8mlt47GgmOgEhE7QIEKbJFM04dL5egY1p310ywmXVZUQgrq2dXm77dts7r+Zia0nYpRGJu+azH3L7uOtnW9xNOkoUq3yWtG1llI+A+TA34uY2Zi7s1lJRgjRTQhxUghxSghR7OIlQoiOQoiDQoijQohtxW2jVAC1gkyJpiDP1KO5dl7riMqcNEpioxPwD3XFoYbZfwtlav34d6gXd5SEkc/S5L5iJ1JXK042TgwMGciyXstY2GPh372bQesGqd5NxZcvhNBTOMS5cHKm0dydb5tkChv/ElMZgjBgsBAi7KZtamLqUvWWUjYCVOGsiswrDIavhNx0U48m7ZLWEZWpK2fSyEjJ+UfV4vJ04wqXXV8ZrUkMFZUQpgEBb7d9m9/6/1Zi70apUD4DVgCeQoipwB/Au+bubM5J4kjgVGG9GgqHvD3EPy+lPgr8JKU8DyClTDQ3AEUjPs1g6ApTb2Zubxi53jQSrQqIiU7AykZHYLPyH0J78wqXSsmcbZwZGDKQAcEDOJJ0hGUxy1h/dj3LY5cT6hZKv6B+9KzXE0frij+nqCqTUi4QQuwDOmMaUNBHSml2jSZxu/OhQoh+QDcp5ROF94dhOkf37A3bfAJYA40AZ+BTKeXcYtp6CngKwMvLK2Lx4sXmxvkPmZmZODlVziGRFS32GmnHaXboLXLsanGw+VTybVxK3LaixV4cY4EkZpXEyRv82v6vo14esedfz8X4zjRcM1O4/Mp/qOlfNtdhKsP7XpI7jf268Tp7svawI2MHl/MvYyNsaOnYknZO7ahjW8eCkf5TRX7PO3XqtM+cGf9lqWhFTP5Zu6ykqs3/JKW85Q3Tqa/vbrg/DPj8pm2+AHYBjoAHpjLQQbdqNyIiQpbWli1bSr2v1ipk7Ge2STnZU8oZ7aTMSi5xswoZ+03OHLoqvxj9mzx7+Oo/Hrd07AUFBXLloNHySHCo/GPeqjJtuzK87yUpbexGo1EeSjwk3/jjDdlqfivZ+IfGsv/q/nLJiSUyMy+zbIMsRkV+z4G98jaf22V5AyZjqnO2FdhSeNts7v7mXPg3p/zAReBnKWWWlDIJ2A5Ujxl/VUHgvTBoAVw9CfMfgZw0rSMqtZjoeOycrPEPK98VIIsmXJ7uNoB2VWTCpZaEEDSt1ZR32r3Db/1/4/XWr1MgC5i8azKdlnbirZ1vcSHjwu0bUsrCAKC+lLKjlLJT4e0+c3c2J8nsARoKIQKFEDbAIEwlCW60CmgvhLASQjhgKlNQMeqqK+Zp0MW08Fn8YVMJmtzKN9InL8fAuUNJNIjwRK8vv9H5RRMuT1fhCZdacrZxZlDIIH7s9SMLeiygW0A31p1ZxwtbXlDDn8vHEaBmaXe+7YV/KaVBCPEsppXR9MAsKeVRIcSYwue/llIeF0L8DBzGNLTtOynlkdIGpWgkuDv0mwXLHjOtsvnoUtMaNZXE2YNXMeQby3VU2Y0rXEZVkwmXWinq3TSt1ZRwz3Am7ZxEdHw0rX3UEHEL+y9wQAhxBMgtelBKaVaX3ay/CGmqvrn+pse+vun+NGCaOe0pFVjYQ9D3G/jpSVgyBAYtMq26WQnERCfg7G6Hdz2z6vbdtQsnzhD72BO4X89A98V31XLCpVZ61OvBJ/s/Yf6x+SrJWN4c4H3gL+5gfkwRNeNf+bem/eGhL+D0Zlg2Agx5Wkd0W9npeVw4nkJQK69yqY11fOd+zj86hBqZqeRN/UhNuCxntnpbBgQPYNvFbcSla1f8tJpIklJ+JqXcIqXcVnQzd2eVZJTihQ+Fnh9BzM+w/HEoMGgd0S3F7k1ASsrlVFn0T7+QPXoUAI7ffE+rvvdb/JjKvw0MHohep2fh8YVah1LV7RNC/FcIESWEaFF0M3dndQJZKVmrUabyMz//B1aMBvdHb7+PRmKiE/Dwd8KttmUn7m2ZsQD3z/9LkosnQT98j19woEWPp5TMw96DHoE9WHFqBc+EP0MNm/I5TVoNhRf+2+aGxyRg1ggzlWSUW2szFgy58OubBHunQIdOoKtYHeBrCdkknkun7cMNLHqc9ZM+JHDpd5zzDaLNglm4ertb9HjK7Q0JHcLq06tZEbuCEY1GaB1OlSSl7HQ3+6sko9zePePAkIvP1ndh3Yvw4MdQgdYEidmTAMK0OJklFBgKWPv0qwRtX0NsaCRd5s3AwanyjLqrysLcw4jwimDh8YUMCR2ClU59pJUVIcRQKeV8IcSLxT0vS1518x8q1ldSpeLq8ApxdfrBvtmm02cVZH6ClJKY6Hh8g2ri5Gpb5u3nZF9n3aAnCdq+hph23emx9HuVYCqYYaHDuJx1ma0XtmodSlVTdO7ZuYSbWVTaV8wjBGcDh1K3thfs+hL0NnD/O5r3aBLjMkhLvE6LB+qWedvXrqaw89FRNLxwgjMPP0avKePRVbBThQp09O+Ir5Mv847No0vdLlqHU2VIKb8p/Pftu2lHJRnFfELAA1PBkAM7PwNre+j0mqYhxUTHo7MS1A+vVabtXjl9nqPDR+GfcpnLT0+g5/Mjy7R9pezodXoeDXmUaXuncTT5KI3cq/7S4uVBCPHZrZ6XUj5vTjvqa5lyZ4SAHtNNQ5y3vQ/btStnbywwErs3kYAmHtg6WJdZu7HRhzk9YBBuaVfJensanVWCqfD6NuyLg5UDC44t0DqUqmRf4c0OaIGp8HEs0BwoMLcR1ZNR7pxOB70+M03S3DwZrOyg7bO336+MXTyZyvX0PIIiy+6C/761WzC+Nh693gqbL2bSomNkmbWtWI6zjTN9G/Zlyckl/F/E/1HLoWx7ttWRlHIOgBBiJNBJSplfeP9r4Bdz21E9GaV0dHroM8NUhuaX1yH623IPISY6ARt7K+o2LpuhxNu+X4b+lefJsnei9rz5NFYJplJ5NORRCowFLDm5ROtQqpra/PNCv1PhY2ZRSUYpPb0VPPI9BPeA9eNh/7/WqbOY/LwCzhy4Sv0WtbCy1t91ez9P/QKPaW+S4FmHxsuXUrdxwzKIUilPdWrUoYN/B5aeXEpuQe7td1DM9R6mApk/CCF+APZzB8svqySj3B29NfT/wbRUwOrn4VD5fIs8dziJ/NyCuy4jYzQaWf3sROrO+5IzDcO5Z9USPPyqxjLU1dGw0GGk5qay/sz622+smEVKORvT8i0rCm9RRafSzKGSjHL3rGxh4HwIuAdWjoGjKyx+yJjoBBxr2uLbsNTLXJCXk8uaR8fQ8NflxLTqQrcfZ+PoUjGX3FXM08q7FUGuQcw7Pq/arjUjhJglhEgsLM1f3PMdhRBpQoiDhbdJt2tTShkvpVxVeIu/k3hUklHKhrU9PLoE/FvD8ifgxDqLHSonM5/zR5Jp2MoLoSvdPJ30lDQ29R1G0MHfOfXgo/Sa8ynWtjZlHKlS3oQQDA0dSmxqLNHx0VqHo5UfgG632eZ3KWXzwts7lgxGJRml7Ng4mhY682kGS0dA7CaLHObU/kSMRlnqUWUJ5y6z+6GB1D13lItPvEiv6W+oSZZVSI96PXCzc2P+sflah6IJKeV2IEXrOIqovyylbNnVgKHLwTMUFg+BM1vL/BAx0fG4+jji4Xfnp7ZOHzjOyX4D8Ei5Qtob/+X+8U+WeXyKtmz1tvQP6s+2i9s4n35e63AswUMIsfeG21OlaCNKCHFICLFBCFHs7FUhhNutbuYeSCUZpezZu8KwleBeHxYOgnM7yqzp9OTrXDmVRlDknS9OdmjTDpJGDsMmPxf9pzNo96hZq8cqldDfa82cqJJrzSRJKVvecJt5h/vvB+pKKZsBnwMrS9huH7C38N+rQAymyZhXCx8zi0oyimU4usPwVeDiBwsHwIU9ZdJs7J4EAILusOLyxd8PYhz3NDk2DnjOmUfTLm3LJB6lYqrlUIvuAd1ZEbuCjLwMrcOpUKSU6VLKzMKf1wPWQgiPYrYLlFLWAzYCvaSUHlJKd+BB4Cdzj6eSjGI5Tp4wYjU41oL5j8DlA3fVNLMavgAAIABJREFUnKnicgI+9V2o4WFv9n6/TJtJ84XfctXdh9DlS6jXPOSu4lAqhyFhQ8g2ZLMi1vKjHSsTIYS3KDwNIISIxJQHkm+xS6vCZASAlHID0MHc46kko1hWjdowYg3YucC8vhBf7KhKsyRfyiLlcpbZF/yNRiNrxk/G//uPifUNImrVUjzr+JT6+Erl0si9ES08W7DwxEIKjGaX2qr0hBCLgD+BYCHERSHEKCHEGCHEmMJN+gFHhBCHgM+AQfLW472ThBAThRABQoi6QojXuXVS+geVZBTLq+lv6tFY2cPch+DqyVI1ExMdj04nqB/hedttDfkG1jz+fzRYu5CY5vfiPOEZnF3V8rzVzbCwYVzKvFSt1pqRUg6WUvpIKa2llH5Syu+llF9LKb8ufP4LKWUjKWUzKWUbKeXO2zQ5GKjF/yZj1ip8zCwqySjlwy3Q1KMROpjTG5JP39Hu0iiJ3ZOAfyM37J1uPZ8lOzObDf0eJ2jXL8Te15deC2dgZa1qwVZHnfw7mdaaOT5P61AqLSllipTyBaC9lLKFlHKclNLsIdIqySjlx6OBqUdjzIc5vSD1nNm7Xj51jczU3NueKktNTGbbQ4NpcHIPZwc9Re+v3lVzYKoxvU7P4JDB7EvYx7HkY1qHUykJIdoKIY4BxwrvNxNCfGXu/uqvTylfnqGmUWd5WaYeTdpFs3aLiU7AylZPYNOSS7hfjv3/9u48voryauD472QlQCAJSwhJICwBZJMdEURQVEAroKAogrQoVXF5rbbV11attdZW+7YuVUGruNUNERUBxQVxD5tsImEJS4CEsBO2kNzz/jETucQk3IQkk5uc7+czn8ydO8u5yWUO88wz59nC0pFXkrh9AztuvYfh999eUVGbIHZZ6mXOWDNrbKyZcvoncBHufRhVXQ4MDHRjSzKm6jXrAuNnwpG9TqI5WHoppILjPjYs3Unrbo0Jjyy+4vK6RavYOHYsMQd2ceTPj3LeTddURuQmCEVHRDOy7UjmZMxh15FdXocTlFR1a5FFAfeksCRjvJHYE8bNcBLMi5dCbk6Jq25evZtjh/NLrLi87MMv2DvpWkIL8ol8chp9R5+qbJOpba4+w8aaOQ1bReRsQEUkQkTuBNYEurElGeOdFn1h3Juwb4vT6+xw8fcS09OyiIoOJ7lD7M/e++qVWchvpnCkTj0SXrKBxkzxWjZoyblJNtZMOd0ATAESgUyc4ZdvCnRjSzLGWykD4Kr/wu71znM0R/ad9PaxI/lsWrGbtr3iCQk9+es6/x/P0fAv95AT15wzZrxBStd2VRm5CTLXdLyGPUf32FgzZddeVceparyqNlXVa4AzAt3YkozxXpvz4IqXIHs1vDoajp0oA7Jx2U4K8n0n9Srz+XzM/t1DJD37Dza37MhZ79lDlubU+jTrQ2psKq+seaXWjjVTTk8EuKxYlmRM9dB+KIx+HrYthVevcHqf4fQqa9AkivgU50HK/OP5vH/dHbR572XWdenPkHdetocsTUBEhPFnjCd9bzqLsiqmll5NJiL9ROQOoImI/MZvuh8IeMxzSzKm+uh4KVw2DbZ+C69dxaFd+8lcu/enistHDx9hzhXX0e7reaQP/AUXvzaVyKg6Xkdtgsjw1sOJjYy1hzMDEwHUB8KAaL/pAE5pmoDYY9CmeukyGgryYNaNrPvP46D9ad+nGfty9vD1VZNIzfyRjNGTGPHgnV5HaoJQZGgkY9qP4dkVz7LlwBZaNGjhdUjVlqp+DnwuItNVdXN592NXMqb66XY1XPIv0rc2o2l0DkcPZLN45JUkb0tn+02/Z7glGHMaavhYM5XhORGJKXwhIrEi8mGgGweUZERkqIisFZH1InJXKev1FpECEQn4UsqY4uxNuoKc/Da0PjqbnLGX0mj/TnLv+zvn3zrR69BMkGtatylDU4baWDOBa6yqP3X7VNW9wKmr1LpOmWREJBT4NzAM6AhcJSIdS1jvbzgD3BhzWtLTnMHJfN9sJDL/GM0ntKPfFcM8jsrUFNd0vMbGmgmcT0R+alcUkZZAwN3zArmS6QOsV9WNqpoHvA6MKGa9W4C3gZ2BHtyY4qgqKz7dSMO9azmCEHvHKOIPfwSzbwOfz+vwTA1QW8eaKad7gC9F5GUReRlYCNwd6MaBJJlEwL9uTaa77CcikgiMAp4J9MDGlGT2w/8l72gIUYfW0e6t12ky4W9wzp2w9CWY93uwZxxMBbim4zXOWDOZC7wOpVpT1XlAD+AN4E2gp6oG3GIVSO8yKe64RV7/C/i9qha4o3oWvyORycBkgPj4eBYsWBBgmCfLzc0t97Zes9hLpj4fW9/4kAbb60BCE6J+NYQfN6/nx83rIWQAbZLWk5w2ja3bd7KhzUQo5btWlP3evVGdYw/REOJC43jy6ycJ3XjyYx/VOe6qIiIdVPVHEenhLtru/mwhIi1UdWlAO1LVUiegH/Ch3+u7gbuLrJMBbHKnXJwms5Gl7bdnz55aXp999lm5t/WaxV68/OP5Ouu6O3VVh4761PUf6AdPf//zlXw+1dl3qN7XQPXjB8q0f/u9e6O6xz591XTtPL2z/rDrh5OWV+e4gcV6ivN2RUzANPfnZ8VMnwa6n0CayxYBqSLSSkQigLHAe0USVStVTVHVFGAGcJOqzgooy5la73DuYT4YO5l2X8xmZf/x+ELqcEa/5j9fUQSG/R16TIAvHoXPH6n6YE2NMip1FFFhUbyy5hWvQ6l2VHWyiIQAf1DVwUWm8wLdzymTjKrmAzfj9BpbA7ypqqtF5AYRuaHcn8AYYMXHX/PtkItJXfU1G0ZOIK7fJUTWC6NFp0bFbxASApf8C7qOhc8ehK8eq9qATY3SIKIBI9uOZG7GXBtrphiq6gMePZ19BPScjKrOUdV2qtpGVf/iLntGVX92o19VJ6rqjNMJytR8x44c5f3b7iPkluuJzDvKvvsf5aI//Y6M5bto26MpoWGlfDVDQmHEv6HTKJh/L3w3teoCNzXOuDPGcdx3nDfXvul1KNXVRyJyuZR2w70UVlbGVLk1Xy9l2+/uou2uraR3O4eBj/+V2KaNSE/LIj/PV+LgZCcJDYPLnoX8PJj7OwiNgF6/rPzgTY1TONbMG2vfYFKXSUSGRnodUnXzG6AekC8iR3E6g6mqBlSZ1srKmCpz/Fges+/6K8cnTaB+7j5yfvdnRrw+jdimTtNYelo29WMjSWjTMLAdhobDmBcg9UKYfTt8b2VCTPkUjjUzN2Ou16FUO6oaraohqhqhqg3c1wGXPrckY6rE+iWr+fSiUbSZ9RKb2/egzfvvMvBXJ6oPHTmYx5Yf9jgVl0PKcFUeFglXvAytz4V3p8BKa6k1Zde3WV/axrTllR9srJmiROSTQJaVxJKMqVQF+QXM/dO/yJ1wFXF7sthxy/9y8czpNEk+eZCx9Ut2oj4NrKmsqPA6MPa/0KIfzJwMa96voOhNbSEijO84nrV717I4e7HX4ZwWEXleRHaKyKoS3hcRedytRbnC7zmYouvVEZE4oLFbFDPOnVKAYrp/Fs+SjKk0m1ev58Oho0l5bSqZLTuS9M47nDdlPCEhP//apadl0SixHo0S65fvYBH14Oo3ILEHvPVLSLcSeqZshrcaTkxkDC//EPRjzUwHhpby/jAg1Z0mA0+XsN6vgSVAB/dn4fQuTj3LgNiN/yK+eWcDOVsrrzLrnj0+3lvxfaXtvzIFHLsquzdlEr59KzS6gOwuk2iUksiiD/YAe4pdP2vjAfqNanN6AUZGw7gZ8NIIeGM8XP26M7SzMQGoE1aHMe3G8NzK5zin+Tleh1NuqrrQvdooyQjgJffBzm9FJEZEElR1R5H9PAY8JiK3qGrAwy0XZUnGz76dh1n64WYaNo2iTr3wSjmG7zjkHcmvlH1XtkBiP370GLmbtlDn6CGORjWkbkpLoqPqkHe09CKESR1i6dAvodR1AhIVA+PfgRd/Aa9dDdfMgJQBp79fUyuM7TCWF1a9wMKDCxnDGK/DKUljEfFv05umqtPKsH1J9Sh3FL86WSISraoHReQPOHXMHtQAy8pYkvGzblE2CIy8vTv1YytnWN8FCxYwaFCvStl3ZSstdp/Px4KnXiH61ccILzjOjrHXceHdvyQ0LOChwCtO3TgYPwumXwyvXuEkHWMC0LRuUy5qdRHzM+bz7IpnGZoylOQGyV6HVdQuVT2dk0gg9Sj9/VFV3xKRAcBFOA9nPg30DeRgdk/Gpaqkp2WTmBpTaQmmpsrZuoMPLptIwpN/ZXdcAtGvvMGwP97qTYIpVL8JXPseRMfDq6OJPrDWu1hMUJnSbQpJEUk8vuxxhr8znKtmX8WLq18k61CW16FVlEzAP3MmcaL4ZXEKmyEuBp5W1XeBiEAPZknGlbPlIPuyD5evd1MttvD5GWy45FJapi9l42UTGTJ/Fm26n+F1WI7oZnDt+xAVS/dl9zhDBRhzCsnRydze7HY+uvwj7uh5Bz58PLr4US6YcQHXzr2W1358LdhL0LwHTHB7mZ0F7C96P6aIbSIyFbgCmCMikZQhd1hzmSv9u2xCwoQ2PZp4HUpQ2LtzN1/ccjepy78gs3EyMX9/lovP7uZ1WD/XMAmu/5R9z11G3Hu3wNbvYPijEB7ldWSmmkuon8DEzhOZ2HkiWw5sYd6meczNmMtD3z3Ew2kP07tZb4alDGNIyyE0jAzwAeIqICKvAYNw7t1kAvcB4eCUAwPmAMOB9cBh4FSlMq7A6a32qKruE5EE4LeBxmNJBvD5lHWLs0np3JjIupVzw78m+eb1D/D9/UFaHznA+mFjueivdxFRpxqX4qjXmBVd72OQfgMLH4Edy50HOONaeR2ZCRItGrRgctfJTO46mXV71zFv0zzmZczj/m/u58FvH+TsxLMZmjKUwcmDqR9Rzm74FURVrzrF+wpMKcP+DovITmAAsA7Id38GxJIMsO3HvRw+kEe7PvFeh1KtHc09wrsTbqFd2sfsiE1AH/0XvzgvoHt/3pNQGPwHSOoNM6+HaefCqKnQfpjXkZkgkxqbSmpsKjd3u5k1e9YwL2Me8zbNY2HmQiJCIhiYNJChrYYyMGkgUWHBf8UsIvcBvYD2wAs4V0WvAP0D2d6SDM6DgBF1QmnZpYTy8rWcz+fjm9dnE/WPv5F8aC/p543kgr//kbr163odWtm1uwgmfw5vToDXxsI5d8Dge5zKzsaUgYjQsVFHOjbqyP/0/B9W5Kxg3qZ5fLjpQz7e8jFRYVEMSh7EsJRh9E/sT0RowPfKq5tRQHdgKYCqbheR6EA3rvVJJj+vgA3f59CmR1PCwu1E468gv4AvX5zJsRf/Q/LOzWTVb8yxfzzFiIsHeR3a6YlrBZM+gjm/hS/+AZmLYfTzUK+x15GZIBUiIXRr2o1uTbvx216/ZUn2EuZtmsf8zfOZmzGX6PBozm95PkNThtInoQ/hIUHVLJ+nqioiCiAi9cqyca1PMhkrdnH8aIE1lfnJO3qMhVP/S8hrL5GwL4udDZqwddLtSNcUelw0yOvwKkZ4FIx4EpL7wAd3wtSBMOZFSO7tdWQmyIWGhNInoQ99Evpwd9+7+W7Hd8zNmMvHmz9m1vpZxEbGcsOZN3D1GVd7HWqg3nR7l8WIyPXAr4BnA9241ieZ9LRs6jaMILFdrNeheO5w7mEWPj6dejP/S2LubrbHJbLj1ns4Z9IVhEdGsGDBAq9DrHg9JkDCmU4ZmheGwUUPQZ/rnaGejTlN4SHhDEgcwIDEARwrOMZX275iXsY8oiMCbm3ylDtQ2Rs49csO4NyXuVdV5we6j1qdZI4eOs6W1bvpMjiJkLKUl69hDu49wBf/mEbMB2/R8sgBtsS3ZvctdzB4/Ihii1nWOAlnwq8/h3dugLm/dbo5X/q4U3TTmAoSGRrJeS3O47wWwVNPz20mm6WqPYGAE4u/Wp1k1i/Zia9AaV9LH8DcsyOHrx55mviP36VV3mEyWnQk7NeTuWDUBbUjufiLioWxr8GX/wef/QWyV8OVL0PjVK8jM8Zr34pIb1VdVJ6Na3WSSU/LIrZZXRone9uvvaplbdxK2iNPkfTFXNrmH2N9ux5ETLmR4RfV8kKSISEw8E5I7AlvT4Jpg2DEv6HTSK8jM8ZLg4Ffi8hm4BAnhl/uGsjGtTbJHNh9hB3r99P30lZILWl/37x6Pd8/8gQpaZ/S2lfAxi79SL19Cr84u9gxi2qvNoPh1wvhrYnw1rWQeTMMud8Z7tmY2ue0HiartUlm3aJsAFJ71/ymsnVpK/jh/56kzfKvaIWQ0WsQXe64mUu7dfA6tOqrYRJMnAMf/QG+eRK2LYUxLzj10IypRVR18+lsX6uTTLPWDWjYJPifyC3Jyk+/Y9Pj/6b1j4tpERrOhnOG0+vOKXRtl+J1aMEhLAKG/93p5vzeLfDMOU6isfFpjAlYrUwyu7flsnvbIQaObed1KJViyfufkvXUM7TOWElCeB02XDSafnfeSPfkChgUrDbqMhriOzndnF+8FIbcB2ffat2cjQlArUwy6WlZSIjQtmdTr0OpMMeP5ZH21hwOPv88Lbevo1FkfTaMnMA5d95Ar8b2DNBpa3oGTP4M3p0C8++FrWkw8imoU32q7xpTHdW6JKM+Z3CyFh3jiIoO2lpCgFP25ft5C8l8+13il31N3NGDaL1YNl19AwNvm0S9hrWr11yli4x2qgJ8+xR89EeYNtjp5hzfyevIjKlSIjJNVScHsm6tSzI7Nuwjd+8x+o1q43Uo5eLz+Vj92XdsfHMWjRctJO7wPlqGhLGlfU+ODR/GWdeMIDLKRvasNCLQbwo07w5v/RKePR9+8RiceaXXkRlT4UQkFLhVVf9Z5K2pge6j1iWZtWnZhEWG0urM4Bqc7Mevvyf99Zk0/GYBTQ/m0EpC2dKmK8eGDqXX1SPoFmfNNlWq5dlON+cZv4J3JjtVAob+FcKq8bg6xpSRqhaIyAjgn0WWLwl0H7UqyRTk+9iwZCetz2xMeGT1r7i8Ydka1rz6NnW/+oyEvdtpjbC5ZUfyrhxPr2suo2szG5rAU9HxMOFd+PQB+Oox2PG905wWk3zqbY0JHl+JyJM4NcwOFS5U1aWBbFyrkszmVbs5djifdtW4jMzWHzey4pW3ifj8E5JyNtMG2JTYjs2XTKH7uMvonNLc6xCNv9AwuOABZzC0WTc51Zwvfw7anu91ZMZUlLPdnw/4LVMgoCJstSrJpKdlExUdTvIZ1au3VVZGJstemYl8Op+WO9bTGtjaNIWMMdfR9ZrLGNbehgmu9s74BTTtCG9cA69cDoP/F8650ylVY0wQU9XBp7N9rUkyeUfy2bRyFx37Nyck1Pt/+Ht25LD45Znkf/wRLbasIQVle1wiGy4dT6dxl3Phme29DtGUVaM2cN3HMPt2p8jm1jS4bBrUjfM6MmPKTUQigcuBFPxyhqo+UNI2/mpNktmwLIeC4z5PByfbsyOHrfO+YfYjz9By40qS1cfOBk3IuGgM7a4axflndfMsNlNBIurBqKmQ3Bfm3QVTz4UrX3J6oxkTnN4F9gNLgGNl3bjWJJn0tCwaNIkivlWDKjvmgT37WTnnc3Z98RV1Vy+j+a5MeqHsrhdLxqBLaX3FSM45t3ftK6tf04lA70mQ0M0psPmfC2H4I9DjWqsSYIJRkqoOLe/GtSLJHNp/jG1r99JzWEqlVlw+fPAQqz76kqzPvyJixVKSsjOIUx/RIaFkNk9l4yVXcahFAmNu+iWhYdW/d5s5TUk9YfLnMPM6eP822LoILn7UGfrZmODxtYh0UdWV5dk4oCQjIkOBx4BQ4DlVfbjI++OA37svc4EbVXV5eQKqDOsWZaNKhTeV5R09xupPv2Xbp18QsnwJidvWE+3Lp66EsC0+hY1DRhF/7gC6XHQOXaOdURYXLFhgCaY2qdcIxs2Az//mTDuWO81nca29jszUYAGcswfhNINluItmlnKPZQAwUUQycJrLKnY8GfeJz38DFwCZwCIReU9Vf/BbLQM4V1X3isgwYBrQN5AAqkJ6WjZNWkQT2+z0htPNP57Pj18uYfMnC9Gli2m+5Uei8vNoA2xrnMymAcNoPPBsOg89l85WL8wUCgl1epsl9oKZ18PUQTDqGegw3OvITA0U4Dkb4AtVvSSAXVb6eDJ9gPWquhFARF4HRgA/BayqX/ut/y2QdDpBVaS9WYfI2XKQ/qPblnlbn8/HurQVbPzoc/IXL6JZxg/UP36E1kBWTDO29hpMTP9+dB4+mDMSa06xTVNJ2l3oVAl4cwK8fhUM+A0Mvsd51saYinPKc3ZZnO54MqKqpa8gMhoYqqrXua/HA31V9eYS1r8T6FC4fpH3JgOTAeLj43u+/vrr5Qo6NzeX+vUDK/64c6WPnNXQboQQHlX6/RhfQQH7tu4kd0U6ddLXkpy5npijBwHIqRfHthapHO/Qnthu7YmOL1+31LLEXt1Y7BUjpCCPtuufo/mOD9kb05UfOt7B8YiYEtevTrGXVbDGXp3jHjx48BJV7VXS+4Gcs93msrdxrnS2A3eq6urKiDeQ/0IVd2YuNjOJyGBgEk4b3s83Up2G05RGr169dNCgQYFFWcSCBQsIZFtV5ZWPvyGpQxQXDDu5C2lBfgGbVqaz+Zsl5K5YRdj6tcRnbyLh+FEA9kU1ICe1K7l9+9Ju6CAGdk4tV6zljb06stgr0PkXwrJXif3gN/RfeTeMmQ4tim9hrnaxl0Gwxl7N424sIov9Xk9zz62FAjlnLwVaqmquiAwHZgEVc5IrIpAkkwn4F2NKwsl8JxGRrsBzwDBV3V0x4Z2e7IwDHNh1lJ7DWrLx+x/Z/M0SDi5fSdiGtTTN2kS940dJAI6HhJLVOJnMHucQ1akTrQb1o2+vzta12FSu7uOgWRen+Wz6cLjwL9D319bN2ZzKrtKuZAjgnK2qB/zm54jIUyLSWFV3VWyogSWZRUCqiLQCtgFjgav9VxCRFsBMYLyqpld0kGXh8/nYvHIdm75ezJblxxFfcw5MGY0c3UczoFFIKFmNk9jmJpTmfbrTvm9Xulp5fOOFhK4weQHMuhHm/d6p5nzpExBZPZtqTFAI5JzdDMhWVRWRPkAIUCkXB6dMMqqaLyI3Ax/idId7XlVXi8gN7vvPAPcCjYCn3OdQ8k+RaSvETwnlmyUcWLGS8PVrabJjE/WPH6GphLCu30PUO7qe7K69ONC5syUUUz1FxcCVr8LXj8EnD0D2amcwtCZWWsiUXYDn7NHAjSKSDxwBxuqpbtCXU0DdWlR1DjCnyLJn/OavA352o78yrP32ezKfm8Gch56gSdYm6ucd/ukKJbtREtu796dOp07USe7C8YXHGHLrGFp3C66xY0wtFBICA26HxJ7OGDXPnudc0XS+zOvITBAK4Jz9JPBkVcQSdH0nc9Iz6LpkAdmNk9h+5tnU6dyJxD7daX/WmSddocx/fjWRdXfTspONuWKCSKuBbjfna2HGLyFzEeEhfUHV7tWYoBR0Sab3mGEsjK/HBRddWOI6x48VsHH5Ltr1jic03G7emyDToDlM/ADm3wvfPkV/noLF9SGulVMpoOhUv5kNKWCqraBLMpFRdQiPjCh1nYzlOeQfK/C04rIxpyUsAoY9DJ0vY93nb5LaKBT2bHTu1/z4Afjy/daN8ktArSCuzYkE1CDREpDxVNAlmUCkp2VTPzaS5m1LfsDNmKCQ3IdtSYdJ9X9moyAfDmQ6SWfPRtiT4fzctQ7WfQQFeSfWDY2E2BS/Kx+/q6GGyVZtwFS6GvcNO3Iwjy0/7KHbkGQkxNqwTQ0UGuYkjtgUaFNkBFxfARzY7peA/BLRxgWQf+TEuiFhENPSSTyxrU5OQjEtIdx6YZrTV+OSzPolO1Gf0q5PM69DMabqhYRCTLIztT735PdU4WCWX+LZcCIBbU2DYwf8Vhbn3lBca78rITcBxbaCOlU3LpMJbjUuyaSnZRPXvB6Nk+xhNmNOIgINEpwppf/J76nC4d1OwtmbcXIzXPo8OJRz8vp1G51IOIUJqHC+XmPrCWd+UqOSzIFdR8jauJ+zRtpYHcaUiYiTHOo1huTeP3//2EG/BOQmn70ZsOVbWPkWJ5XGiqj/U9JpnRsG0ZtP3AeKTrCOCLVMjUoy6WnZAKT2tl5lxlSoyGinBE5CMeNU5R+DfVtOXP0UXgnt/IGkPZtg68wT64bVKbkjQoMk64hQA9WYv6iqkp6WRULbhjRoZMPbGlNlwiKhcaozFbHws08Y1K2tXxOcXzPchs+KdEQIh9iWfk1wflNMC6dbtwk6NSbJ7Nqay96sw5x7tdV7MqbakFA3cbSE1oNOfs/ng9yskxNP4bTlW8g76LefEKfLtf/VT+vB0KxzVX4aUw41Jsmkp2UREiq07WkjVBoTFEJCnB5sDZpDSpEhqFTh0K6TE0/h1dDqd+DIXkCg2zg47w9OZwZTLdWIJOPzKesWZdOiUyPq1Av3OhxjzOkSgfpNnKm4wdxyc+Drx+G7Z2D1TOh/G5x9C0TUq/pYTalqRDeP7el7ObQ/z8rIGFNb1G8CF/4ZpqRB6oWw4K/wRE/4/r9OM5ypNmpEkklPyyY8MpRWXRt7HYoxpirFtYIrXoRffeg0u826EZ4dBBlfeB2ZcQV9ksk/XsCGpTtp3b0JYRGhXodjjPFCi7Ng0sdw+X/g8B548RJ47WrYtd7ryGq9oE8ym1fuJu+oVVw2ptYLCYEuo+HmRXD+vZDxOTzVF+be5SQe44mgTzLpadlENYggqX2s16EYY6qD8Cg45w64dRl0Hw9pU+HxbvDNvyE/79TbmwoV1Enm6KHjbFq1i9ReTQkJDeqPYoypaPWbwi/+BTd8BYm94MP/da5s1rzvdJE2VSKoz8wbl+Xgy7eKy8aYUsR3hPEzYdzbzvg6b1wD0y+G7cu8jqxWCOokk56WRcOmUTRtGe11KMaY6i51CNzwJVwdAiL+AAAMPElEQVTyT8hZC9MGwcxfw/5tXkdWowVtksnde5Rt6/bRrk8zxMqKG2MCERoGvX7l3K8ZcLtTPeCJnvDpX+BYrtfR1UhBm2TSF2WDYr3KjDFlV6cBDLnf6YnW4WJY+Hd4ogcsfckZXdRUmOBNMmnZNE1pQEzTul6HYowJVrEtYfR/nGdsYlrCe7fA1IE02vWd84zN4T2WdE5TUNYuO7pf2Z2Zy4Arfl5a3Bhjyiy5N0z6yGk++/g+uqx6CFY95L4pUKch1I2DqFiIijt5PirW73XhfJwzBo9HTfkiMhR4DAgFnlPVh4u8L+77w4HDwERVXVoZsQRlktm/SZEQIbWXNZUZYyqICHS+DNoPZ/n7T3Fmm0Sn2vORPc4VTeH8oRzYle68Pnag5P2FhJ1IPCclplhoMxjaDqmkjyGhwL+BC4BMYJGIvKeqP/itNgxIdae+wNPuzwoXdElGfcr+zZDcIZa6DWwQI2NMBQuvw964HnDmoFOvW3Acjuxzks+RvW4yKmF+31bYsdyZD6tTaUkG6AOsV9WNACLyOjAC8E8yI4CXVFWBb0UkRkQSVHVHRQcTdElmx8b9HD9sN/yNMdVAaPiJIQnKonIrRScCW/1eZ/Lzq5Ti1kkELMkIUL8ZtOpWxj+qMcZUFyGn1eeqsYgs9ns9TVWn+b0u7kZQ0RIHgaxTIYIuySS0jaHloBAi6gRd6MYYUxF2qWqvUt7PBJL9XicB28uxToUI2i7MxhhjirUISBWRViISAYwF3iuyznvABHGcBeyvjPsxEIRXMsYYY0qmqvkicjPwIU4X5udVdbWI3OC+/wwwB6f78nqcLsy/rKx4LMkYY0wNo6pzcBKJ/7Jn/OYVmFIVsVhzmTHGmEoTUJIRkaEislZE1ovIXcW8LyLyuPv+ChHpUfGhGmOMCTanTDJ+T48OAzoCV4lIxyKr+T89Ohnn6VFjjDG1XCBXMj89PaqqeUDh06P+fnp6VFW/BWJEJKGCYzXGGBNkAkkyJT0ZWtZ1jDHG1DKB9C6rsKdHRWQyTnMaQK6IrA3g+MVpDOwq57Zes9i9YbF7I1hjr85xt/Q6gLIIJMlU2NOjbumDaUWXl5WILD7FE6/VlsXuDYvdG8Eae7DGXR0F0lxWrZ4eNcYYEzxOeSVT3Z4eNcYYEzwCeuK/Oj096jrtJjcPWezesNi9EayxB2vc1Y44+cEYY4ypeFZWxhhjTKUJuiRzqhI31ZWIJIvIZyKyRkRWi8htXsdUFiISKiLLRGS217GUhTus7AwR+dH93ffzOqZAicjt7ndllYi8JiJ1vI6pJCLyvIjsFJFVfsviRGS+iKxzf8Z6GWNJSoj9Efc7s0JE3hGRGC9jDGZBlWQCLHFTXeUDd6jqGcBZwJQgih3gNmCN10GUw2PAPFXtAJxJkHwGEUkEbgV6qWpnnE43Y72NqlTTgaFFlt0FfKKqqcAn7uvqaDo/j30+0FlVuwLpwN1VHVRNEVRJhsBK3FRLqrpDVZe68wdxTnZBURVBRJKAi4HnvI6lLESkATAQ+A+Aquap6j5voyqTMCBKRMKAulTSyIUVQVUXAnuKLB4BvOjOvwiMrNKgAlRc7Kr6karmuy+/xXn2z5RDsCWZGlG+RkRSgO7Ad95GErB/Ab8DfF4HUkatgRzgBbep7zkRqed1UIFQ1W3Ao8AWYAfOs2cfeRtVmcUXPi/n/mzqcTzl9StgrtdBBKtgSzIBla+pzkSkPvA28D+qesDreE5FRC4BdqrqEq9jKYcwoAfwtKp2Bw5RfZtsTuLevxgBtAKaA/VE5Bpvo6p9ROQenKbuV72OJVgFW5IJqHxNdSUi4TgJ5lVVnel1PAHqD1wqIptwmifPE5FXvA0pYJlApqoWXjHOwEk6wWAIkKGqOap6HJgJnO1xTGWVXViN3f250+N4ykRErgUuAcapPetRbsGWZAIpcVMtiYjg3BtYo6r/53U8gVLVu1U1SVVTcH7fn6pqUPyPWlWzgK0i0t5ddD7wg4chlcUW4CwRqet+d84nSDot+HkPuNadvxZ418NYykREhgK/By5V1cNexxPMgirJuDfiCkvcrAHeVNXV3kYVsP7AeJwrge/dabjXQdUCtwCvisgKoBvwkMfxBMS9+poBLAVW4vxbrbZPoYvIa8A3QHsRyRSRScDDwAUisg64wH1d7ZQQ+5NANDDf/bf6TKk7MSWyJ/6NMcZUmqC6kjHGGBNcLMkYY4ypNJZkjDHGVBpLMsYYYyqNJRljjDGVxpKMKTMRuV9E7nTnHxCRIV7HBCAim0SkcSXtu5t/l3MRubSiqoCLyEQRaV6O7W4QkQnl2C5GRG4q63bGlIclGXNaVPVeVf3Y6ziqQDecIcYBUNX3VLWinvuYiFM65mfcyuPFUtVnVPWlchwvBihTkiktDmNKY0mmlhCRFHd8jBfdMTJmiEhd972eIvK5iCwRkQ/9SoG0EZF57vIvRKRDMfudLiKj3flNIvInEVkqIisL1xeRJu54IktFZKqIbC7uikNEnhaRxe4YKn/yW17SfhuJyEdu8cupFF/bDhHJFZF/uNt/IiJN3OXXi8giEVkuIm/7/T7GiDOGy3IRWehWl3gAuNJ9MO9K9+rjSb/fweMi8rWIbPT7fYSIyFPu55ktInMK3/OLbTTQC+eB0e9FJMr9vPeKyJfAmFLi9L+iLPZvJSLx4oyHstydzsZ5KLKNe7xHxPGI+5lXisiV7raDxBkD6b/AShH5s/iNgyQifxGRW0v52hkDqmpTLZiAFJxiov3d188DdwLhwNdAE3f5lcDz7vwnQKo73xenpAzA/cCd7vx0YLQ7vwm4xZ2/CXjOnX8SuNudH+rG0biYGOPcn6HAAqDrKfb7OHCvO39xKftVnPpTAPcCT7rzjfzWedDvGCuBRHc+xv05sXC7oq/d38FbOP9p64gzHAXAaGCOu7wZsLfwd1UkvgU448bg93l/5/e6pDj9/w4l/a3ewCnGWvh7bYjzXVjlt8/LccZPCQXicUraJACDcIqKtvL7Di1150OADf6x2WRTcVMYpjbZqqpfufOv4AyKNQ/ojFM+A5wTzQ5xqkWfDbzlLgeIDOAYhYU/lwCXufMDgFEAqjpPRPaWsO0VIjIZp3pyAs4Je0Up+x1YOK+qH5SyXx/OyRacz124r84i8iBO81F9nHJFAF8B00XkTb91T2WWqvqAH0Qk3l02AHjLXZ4lIp8FuC/84i0tTuCnyt4l/a3OAyYAqGoBsF9+PkLlAOA19/1sEfkc6A0cANJUNcPdfpOI7BaR7jjJaJmq7i7DZzK1kCWZ2qVoDSHFaWJaraonDUsszoBf+1S1WxmPccz9WcCJ71exzVhFjtcK58qqt6ruFZHpgP9ww8Xtt/AzlFXhNtOBkaq6XEQm4vzPHVW9QUT64lwdfS8igfwOjvnNS5Gf5XHIb77YOP2EUL6/VaHS4jxU5PVzOFdxzXCuho0pld2TqV1ayIkx7q8CvgTWAk0Kl4tIuIh0UmesmwwRGeMuFxE5s5zH/RK4wt3PhUBxY703wDmh7XevBIYFsN+FwDh3v8NK2C843/PCeyFXu/GAUwBxhzhDMIwrXFlE2qjqd6p6L7ALZ3iJg+76ZfElcLl7byaenyeHQqfad7FxFjrF3+oT4EZ3eaj7n4eix1uIc78p1L1fNRBIKyGWd3CaPHtT5IrKmOJYkqld1gDXilOROA5nMK88nBPw30RkOfA9J8YtGQdMcpevpvxDXf8JuFBEluIkjx04J7qfqOpyYJl7nOdxmqwC2e9Ad78X4txLKM4hoJOILMFpPnrAXf5HnNFJ5wM/+q3/iHsDfBXOCXg58BnQsfDGfwCxgTN2UCawCpjqHmt/MetNB54pvPFfzPslxQknrspK+lvdBgwWkZU4TY2d3Caur9wb/Y/gJI4V7uf8FOd+UFZxH8j9vnyGUwG94BSf3xirwlxbiDPk82xV7ezBsSOBAlXNd6+Ynj6Npp3yHD9XVetX1fGKHLu+quaKSCOcq4P+JZ3Ay7HvJ3BuxL9QEfsL8JghOMMPjFHVdVV1XBO87J6MqQotgDfdE1QecL3H8VSl2SISA0QAf67ABPNnnF5k91fE/gI8ZkdgNvCOJRgTKLuSMcYYU2nsnowxxphKY0nGGGNMpbEkY4wxptJYkjHGGFNpLMkYY4ypNJZkjDHGVJr/B0tVD7FaEOGBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 18,
     "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.show_ppt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhgAAADrCAYAAAArHe+OAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8GearUAAAgAElEQVR4nO3deXwV1f3/8dcnJCQsClYQlUWoUKgLsgRkCwmoiMqmFaug1dpKtS51K4pbrX7rVjewagsoLq1QixVRK/SnEBZZZMddKMtDcAFaQJCwhHx+f9xJvIn3JjcwyQ3J+/l43If3zpw558wIzPueOTPX3B0RERGRMKUkuwMiIiJS/ShgiIiISOgUMERERCR0ChgiIiISOgUMERERCZ0ChoiIiIQuNdkdECmpUaNG3rJly2R3I679+/ej27tFYjMzatWqlexulGrJkiVb3L1xWeXq1Knz1e7du5tURp/ClJGRUbB79+5KG0DIyMj4Oi8v7+iSyxUwpMpp2bIlixcvTnY34tq0aRO1a9dOdjdEqqS9e/dy1FFHJbsbpTKz9YmU2717d5ND8cuEmaUU9vuGG25g8eLFdOrUidGjRxeV2b17N1dffTVr167lxBNP5IknnmDcuHE888wzAFx33XUMGzaM7du3M3z4cHbu3MmQIUO4/vrrY7UXM4TpEomIiEgSFRQUVEi9S5cu5dtvv2XOnDns3buXRYsWFa0bM2YMw4YNY8aMGTzxxBMAnHHGGSxYsIA5c+bwyCOPADB27FiGDx9Obm4us2fPZsuWLQm3r4AhIiKSoIKCAn75y1+SnZ3NWWedxdtvv023bt3o1q0bb7/9Nhs2bOCiiy4CIpdT+/TpA8D48ePJysoiKyuLpUuXAnDKKadw8cUX89BDDzFhwgRycnLIzMzk3//+NwALFy6kU6dOXHTRRXTq1AmAzZs3M2jQIPr06cOvf/3rUvs6f/58Tj/9dABOP/10FixYULQuNzeXqVOnkpOTw9SpU4HI6DFAampq0WWuNWvW0L59ewBOPPHEco0u6xKJiIhIgl577TWOOuooxo8fT0FBAb179y4KBP3792fevHls2bKFvLw8Fi5cSO/evdmyZQtTp05l9uzZbN26lcsvv5wpU6awYcMG5s2bR7169di1axc///nP2b59O0OHDqVfv37ce++9TJ06lSOOOILjjjsOgAceeIBRo0bRvXt3brnlFubPn0/37t1j9nXbtm0cf/zxADRo0IAPP/ywaN1//vMfrr/+eu677z5ycnI4++yzSU2NRII///nPDBkyBIC2bdsya9Ys2rVrx+zZsznhhBMSPlYKGCIiIgn67LPP6NGjBwApKSmYGYcffjhA0bf+/v37M23aNGbMmMGIESNYs2YNK1asKBrNKNS2bVvq1asHwPTp0xk9ejTuzqZNmwD45ptvaNasGQBt2rQB4OOPP+bWW2/FzNi5cyddu3aN29eGDRvyzTffFNXVsGHDonUNGjQgOzub9PR0Wrduzddff03Tpk1ZuHAh//rXv5gyZQoAV1xxBb/61a+YMmUKxx57LE2aJD7nVQFDREQkQW3btmXBggUMGDCAgoICCgoKik7i+/fvB+D888/ntttu48svv+Tkk09m8+bNdOnShcmTJwOwb98+IBJQCt1///3MmjWLPXv20LNnTwAOP/xwvvjiCxo2bMjq1auL2r/44ovp3LkzAPn5+XH72r17d/7yl79wwQUX8Pbbb3PZZZcVrevRowcrV66kU6dOrFu3jsaNG7Nx40Zuuukmpk6dWhSW6tWrx1//+lf279/PsGHD6NatW8LHSgFDREQkQYMGDeL111+nd+/e1K9fn7vuuot+/frh7txzzz0AHHfccaxZs4a+ffsC0LhxY8455xx69+5NrVq16Nu3L3feeWexegcMGEDv3r3p2rVr0UjDnXfeycCBA2ndujXNmzcH4LbbbmPEiBFs376dlJQUxo0bR6tWrWL2tVOnTmRkZJCVlcUpp5xC165dufbaa3niiSe45ZZbuPTSS/nmm2+44oorqF27Nvfccw9ff/015513HgBvvfUWH330ETfffDNmxsiRI6lbt27Cx8oOxVtwpHrLzMx03aYqcmg6RG5TXeLumQmU82SeI/Pz80lNTeXbb7+lX79+vPvuuwltZ2aV+qyeoD0ruVx3kYiIiFRB7777LtnZ2WRlZfHb3/422d0pN10iERERqYKys7OZNWtWsrtxwBQwREREYsjIyCgws0NupD8jIwOz712xqMj2Yj4p7JA7cCIiIpVh9+7dKe5OvNeECROYMGFC3PVlva655pqiegp/4yiM1+7du4veX3/99fTq1YvrrruuWJnf/OY3ZGdnk52dTcOGDXF31q9fT58+fcjKyuLvf/877s7MmTNp0aIF2dnZXHLJJfHai5klFDBEyik9PZ20tLSYr8IH1VQXqampcfe1rJeORfU9FhKOwkd0P/fccxXyuPDSHhX++OOPk5uby2OPPcY555wDwIMPPsgf/vAHZs6cyfjx44tugb3kkkvIzc3lhRdeKFf7Chgi5VRQUBD3m0NlDktWhsLZ6Afy0rGovseiJtu7dy+DBw+mf//+TJ8+HYB77rmHnJwc+vbty7p161i3bh19+/bl/PPPp3PnzmzYsIHVq1fTo0cP+vTpw3333QdAr169eO+991i+fDmnnXYazz//PGeccUZRW3379i16ZsaBKO1R4YVeffXVottSCx8LXqtWLZo0aVL07I2JEyeSlZXFxIkTy9W+AoaIiEiCpkyZQteuXZk2bRqHHXYYO3bsYOPGjeTm5vLkk09y//33A7B161ZefvllbrzxRl555RVyc3MZMWIEM2fOZNSoUUX1de3alQ4dOvDOO+9w6aWXctxxx7F69Wo+/fRTWrduTVpa2gH3ddu2bUVPGW3QoAFbt279Xplp06bRv39/4LvHgu/atYsFCxawdetWMjMz+eSTT5g2bRpjxoxh8+bNCbevgCEiIpKgNWvW0LFjRwA6d+5MXl4eubm55OTkcNVVVxU91fOEE04gJSWFpk2bsm3bNi644AJWrlzJ8OHDmTZtWtz6hw8fzqRJk5g0aVLRj6YdqNIeFQ6watUqmjZtWvTwrFGjRjF27FiGDh1Ku3btaNKkCfXr1yctLY169erRu3dvVq1alXD7ChgiCZo2Ddq2hc6dD+eRRw78W0V1cNVV6bRqVZeuXeskuytJtWGDcfbZGXTuXJcuXerw1FM1+89FTdCqVStWrFgBwLJly0hPT6dfv37k5uYWm6cQfVnM3UlLS+PRRx9lwoQJ3HXXXcXqTEtLK3rMeHZ2NnPmzGHOnDlkZ2cfVF+7d+/OO++8A1D0q6/RXn31Vc4999yiz02aNGHKlClMnjyZ9PR0WrVqVewx6IsWLSr6xdVEKGCIJGD/frj6anjrLZg//xsmT07lk09q7nX14cP38eqru5PdjaRLTYX77tvLkiW7mDEjj7Fj02r0n4uaYMiQIcybN48zzzyTbdu20aBBA44++mhycnLo06cPEyZMiLnd1KlTycrKonv37gwbNqzYunPOOYchQ4bwyiuvkJKSQvv27Tn55JOL/VbJgYh+VHhKSkrRo8ILvfHGGwwcOLDo85tvvkmfPn0YOHAgo0aNwsx4+eWX6dq1Kz179mTw4MEce+yxCbevR4VLlVMVHxU+fz7cfTdMnx65tjp6dGRI8eabi0/AMrODmpRV1aSlpcV95PD69cbQoRm8915ezPU16VgU+ulPM/jVr/bRt+/+Ysur27EojR4VfvBGjhzJ0KFD6dKlywFtr0eFixxCNm6E4LeGAGja1PnyS31Tle+sX2+sXJlCZub+sguLxHHXXXexfv36Aw4XVYluzhZJQKwvA7rzUArt3AkXX5zBAw/sIZi0L3JACn+RtTpQwBBJQLNm8Pnn333euNE4+mhdXhTYty8SLi64IJ/BgzV6UZ1kZGR8bWZNkt2P8qrsR5xnZGR8HWu5AoZIArp0gVWrYO1aqFsXXnkllWef3ZPsbkmSucPVV6fTtm0B115bM+ZY1CR5eXlHJ7sPhzLNwRBJQGoq/OlPcOaZ0K3b4Zx3Xj4//nH4j/Y9VPz85+mcdlodVq1KoW3bujz/fM38rjJ/fgoTJ6Yxa1YtevSoQ48edZg+vVayuyVSJeguEqlyquJdJNG2bt0a9+l61e1ugUTunIhHx+I71e1YlKY63UUiB0cjGCIiIhI6BQwREREJnQKGiIiIhE4BQ0REREKngCEiIiKhq5n3lokchJSUlGK/lBitut2V5e5x9zWRbasTHQuR8ilzBMPM3MxejPqcamabzeyNiu1a8phZSzMbVnbJYtvcdoBtXW9mdQ9k23K2M8TM7gre321mN8co86yZbTKzD0osf9jM+pZS9+Nm1jt438rMFprZKjP7u5nVDpYPMLPfh7tXybFnzx727dsX85Wfn5/s7oUqPz8/7r6W9dKxqL7HQiQRiYxgfAucZGZ13D0POAPYWLHdSrqWwDDgpXJscxtwX8mFFvnKY+4e76lM1wN/BXaVs4/lNRIYVEaZ54A/AS+UWP4EMA6YUXIDM/sB0M3drw8WPQg85u6TzOzPwC+Ap4E3gXvN7EF3r+h9rRBTlm3kj9M/pXmdvaSm1ebCLs3p1aZxsruVFHNXbWbSos/57869HFlfx0LHQuT7Ep2D8RZwTvD+ImBi4Qoz62pm88xsWfDftsHyy8zsn2Y2Lfg2+1DUNk+b2WIz+zD6W62ZnW1mn5jZXDMbUzhKYmb1gm/Xi4J2Bke1McXMXjeztWZ2jZndGJRZEJz8MLPjg34sMbM5ZtYuWP5c0M48M1tjZucHXXkAyDKz5WZ2Q/SBMLNjzGx2sO4DM8sysweAOsGyvwUjIB+b2VPAUqB5rH02s+uAY4GZZjYzWNbPzOab2VIz+4eZ1Y93bMwsJTi2jYMyKWa22swalejzj4A97r6ltP/J7j4b+F+M5euBI80s1mNzzwemBe0Y0BeYHKx7HhgS1OFALjCgtD5UVVOWbWTUP99n47Y8HNiycy/j5qxl7qrNye5apZu7ajPj5qxly869OhY6FiJxJRowJgEXmlkG0B5YGLXuE6C3u3cE7qL4t/gOwE+Bk4GfmlnhD17fHjxFrT2QbWbtg7r/Apzl7r2A6K8AtwMz3L0L0Af4o5nVC9adRGS0oSvwB2BX0Jf5wM+CMmOBa929M3Az8FRU3ccAvYic+B4Ilt0KzHH3Du7+WIljMQyY7u4dgFOA5e5+K5AXlB8elGsLvODuHYMT9Pf22d3HAF8Afdy9TxAM7gBOd/dOwGLgxnjHJhgV+StQ2ObpwIoYQaInkaBzMJYG9ZTUE1gSvD8S2ObuhePBG4CmUWUXA1kH2Y+k+OP0T8nbV/yHrPbkFzBp0edxtqi+Ji36nD35xQfkdCy+U1OPhUhJCU3ydPeVZtaSyOjFv0qsbgA8b2ZtAAein6H8jrtvBzCzj4DjgM+BC8xsRND+McAJRMLOGndfG2w7ERgRvO8HDIqaN5ABtAjez3T3HcAOM9sOvB4sfx9oH4wA9AD+Yd9N0EqP6uOU4ET9kSX2q3mLgGfNLC3YdnmccuvdfUHU51j7vLLENt2C5e8Gfa1NJCi1I/6xeRZ4DXgcuByYEKMvxwAH+5VqE5HRltLqjjUDLnp2W7w6CI7NCIAWLVrEKpJUX2zLi7n8vzv3VnJPki/ePutYlL1cpCYpz22qU4GHibo8EriXyEn+JGAgkZN/oeifm9wPpJpZKyKjCKe5e3si1+YziH1yKmTAT4IRgg7u3sLdP47RRkHU5wIiJ/MUIt+qO0S9fhynj2VOEQ8uI/QmMg/lRTP7WZyi3xZVGn+fY+3n/4vq5wnu/ovS+uXunwNfB5MwTyVyOaukvDjtlUdGUE9pdW8BGppZYXBtRmSEpqw6cPex7p7p7pmNG1e969fHNqwTc/mR9WtXck+SL94+61iUvVykJilPwHgWuMfd3y+xvAHfTfq8LIF6Didy8t0ejBicFSz/BPhhMFICkUsrhaYD1wbX+DGzjol22t2/Adaa2dBgWzOzU8rYbAdwWKwVZnYcsMndxwHPAJ2CVfuCUY1Y4u1zybYWAD3NrHXQVt1g/kRpxwZgPJFLJS+7+36+72OgdZy+JepHwAcxlhfVHcyzmElkXgbApURGV8qqo8r77ZltqZNW/Fcy01NTuLBL8zhbVF8XdmlOemrxfzp0LL5TU4+FSEkJBwx33+Duo2Osegi438zeBcr8nWJ3XwEsAz4kElreDZbnAb8GppnZXOBrYHuw2b1ELr2stMgtlPcm2u/AcOAXZrYiaHdwGeVXAvlmtqLkJE8gB1huZsuAnwCFx2Rs0L+/laws3j5HbfeWmc10981EQtpEM1tJJHC0K+PYQGR0qT6xL48AzAY6Fga0wB1mtqHwBWBmE4lckmkbLP9FsDyNSIiI9ROnbwbHpNAtROaNrCYyJ+OZqHV9gvKHnCEdm3L/eSfTtGEdDGhUvzZXZLWqkXcL9GrTmCuyWtGofm0dCx0Lkbiq1M+1m1l9d98ZnAifBFbFmGRZI5V2bMwsk8itoXEnUJrZaOB1d3/7ANo+F+jk7nfGWT8XGODu20qpownwkrufVlZ7Vf3n2jdt2kTt2hoCF4lFP9cuharao8KvMLPlRL7pNyBy54RExDw2ZnYr8Aowqozt7wMO9IFeqcAjpay/ie8m3cbTIignIiI1QJUawRABjWCIHMo0giGFqtoIhoiIiFQDChgiIiISOgUMERERCZ0ChoiIiIROAUNERERCp4AhIiIioVPAEBERkdApYIiIiEjoFDBEREQkdAoYIiIiEjoFDBEREQmdAoaIiIiETgFDREREQqeAISIiIqFTwBAREZHQKWCIiIhI6BQwREREJHQKGCIiIhI6BQwREREJnQKGiIiIhE4BQ0REREKngCEiIiKhU8AQERGR0ClgiIiISOgUMERERCR0ChgiIiISOgUMERERCZ0ChoiIiIROAUNERERCp4AhIiIioVPAEBERkdApYIiIiEjoFDBEREQkdAoYIiIiEjoFDBEREQmdAoaIiIiETgFDREREQqeAISIiIqFTwBAREZHQKWCIiIhI6BQwREREJHQKGCIiIhK61GR3QEQSl5qaipkddD3uTn5+fgg9EhGJTQFD5BBiZrh7KPWIiFQkXSIRERGR0ClgiIiISOgUMESqkb174dpr0+nQoS6dOtXltddqJbtLIlJDaQ6GSDXyxz/WpnFjZ/nyXRQUwP/+l+weiUhNpYAhUo28+GIqS5bsAiAlBRo1SnKHRKTG0iUSkWpi27bIf++9tza9etXhkksy2LRJd4uISHIoYIhUE/n5sHFjCt2772fu3Dy6dt3P7bfXTna3RKSGUsAQqSaOPBLq1nUGDtwPwLnn5rN8uf6Ki0hy6F8fkWrCDM46K585cyJ3juTm1qJdu4N/KJeIyIHQJE+RauSee/ZyxRUZ3HILNGrkPP30nmR3SURqKAUMkWqkRQtn+vS8ZHdDRESXSERERCR8ChgiIiISOl0iESmn9PR0UlNT9ZPnIlFSU1MxM/1SrxTRCIZIORUUFODu+odUJIqZ4e4UFBQkuytSRShgiIiISOh0iUTkEBLWyIm7no8hIhVLIxgicQwaNIiTTjop2d0oJj8/n3379h30S3NHpLLdfvvtNG/enPr168dcP3nyZMyMxYsXV3LPpKIcUMAws51hNG5mOWb2Rhh1HUDbLc1sWHnLmVmmmY0JqQ+3lfg8L4x6Y7TT0czGB+9vNLNnotYNN7M3g/dXmtn7ZrbczOaa2QnB8sZmNq2U+o+J/v9oZqPMbLWZfWpmZ0Ytf9vMjqiIfQzbP//5z7j/EIpI+Q0cOJD33nsv5rodO3YwZswYTj311ErulVSkmjyC0RIoM2CULOfui939upD6UCxguHuPkOqN1c4TwfsxQGcz62lmDYH/A64N1r3k7ie7ewfgIeDRoF+bgS/NrGec+m8ExgEEoeRC4ESgP/CUmdUKyr0I/DrUPasAO3fu5NFHH+WOO+5IdldEqo1u3bpxzDHHxFx35513MnLkSDIyMiq5V1KRDipgBCMQs8zsZTP7zMweCL4Rvxd8Ez4+KPecmf3ZzOYE5QbEqKuemT1rZovMbJmZDQ6WX2ZmU8zsdTNba2bXBN/Cl5nZAjP7QVDueDObZmZLgnbaRbU9xszmmdkaMzs/aPIBICv4tn5DMFIxx8yWBq8eccoVjbqY2Q+Cvq0M+tI+WH53sC+5QZvfCyRm9gBQJ6j3b8GyneU8ro3N7JXgmC2KFQDM7DCgvbuvAHD3fCIn+SeJhIhn3X1NsO6bqE3rAdEX6qcAw+P8UfgJUDjCMRiY5O573H0tsBroGqybClwUp44q48477+Smm26ibt26ye6KSLW3bNkyPv/8cwYM+N5pQQ5xYUzyPAX4MfA/YA0w3t27mtlviHwzvj4o1xLIBo4HZppZ6xL13A7McPfLg2/W75nZ28G6k4COQAaRE9Yt7t7RzB4DfgY8DowFrnT3VWZ2KvAU0DfY/higF9COyEluMnArcLO7DwAws7rAGe6+28zaABOBzBjlcqL6/HtgmbsPMbO+wAtAh2BdO6APcBjwqZk97e77Cjd091vN7JpgtOBAj+to4DF3n2tmLYDpwTbRMoEPohe4+zwz+xg4vWR5M7uayIhE7ajjB7CYyGgHJcq3Ara6e+GPXjQFFkQV2RAsw923mlm6mR3p7v+Ns99JtXz5clavXs1jjz3GunXrkt2dcnvo7TV8+vW3ye6G1EA9Wzdi2659NGuQzoi+iV0JLSgo4IYbbuC5556r2M5JUoQRMBa5+5cAZvYf4N/B8veJnGALvezuBcAqM1tD5AQcrR8wyMxuDj5nAC2C9zPdfQeww8y2A69HtdHezOoDPYB/2Hcz7NOj6p4StP2RmTWJsx9pwJ/MrAOwH/hRAvvei8i3d9x9hpkdaWYNgnVvBifdPWa2CWhC5GSbqESO6+nACVH7fLiZHRYcq0LHAJujKw6OVyaRfW4c3S93fxJ40iLzTu4ALg1WbQKOjdHPkvXHusUheiSksJ5iAcPMRgAjAFq0aEGyzJ8/nyVLltCyZUvy8/PZtGkTOTk55ObmJq1PItXVjh07+OCDD8jJyQHgq6++YtCgQUydOpXMzMzkdk4OWhgBI/rnGguiPheUqL/kfXElPxvwE3f/tNjCyGhEWW2kANtKGQ2I3j7ePX43AF8TGTlIAXbHKVeyzyUV7ld0m/sp/7FO5LimAN3dvbRft8ojEtai/R74K5H9fQwYGmO7ScDTUZ8zgrrKqn8D0DzqczPgi7LqcfexREahyMzMTNo9lFdddRVXXXUVAOvWrWPAgAGHVLgYefoPk90FqaHS0tJwd/bt21d24UCDBg3YsmVL0eecnBwefvhhhYtqojIneQ41s5Rg/sAPgU9LrJ8OXGvB13Ez65hoxcHcgbVmNjTY1szslDI220Hk8kWhBsCXwUjHJUCtOOWizSaYlxBcOtlSYh5DWfaZWVo5ypf0b+Cawg/B6EtJHwOto8qcDJwDPEjkhH6cmZ0RrGsTtd05wKqozz+ixKWWwGdELn8VmgpcGFwKaQW0Ad4L6jfgaGBdQnsnItXGyJEjadasGbt27aJZs2bcfffdye6SVLDKfNDWp8AsIpcKrgzmOkSvv5fIXIqVwYloHVCeWT/DgafN7A4iQ/+TgBWllF8J5JvZCuA5InM2XglCykzg2zjllkXVcTcwwcxWArv47nJCosYS2d+l7h5vAmVpriNyOWMlkf+Xs4Erowu4+ydm1iCY7LmTyKjEDe6+G8DMfg28EISTa8zsdGAfsLXE/vQB3izZAXf/1sz+Y2at3X21u39oZi8DHwH5wNXuvj8o3hlYEEw0rfJatmzJBx/EylQiUl4PPfQQDz30UKllDqXRQimbVcYT/czsOeANd59c4Y3J95jZDcAOdx9/EHXMBga7+9YY684FOrt7qfd1mtloYKq7v1NauczMTK/KD9vZunUraWlpmFm5hoNFqrPoSyRHHFG1H3djZkvcXddhKlhNfg5GTfI0xed0lIuZNQYejRUuANz9VRK77PFBWeFCRESqh0q5ROLul1VGOxJbcDnkxYPYfjOR52CUVqbM0RF3H3egfRARkUOLRjBEREQkdAoYIiIiEjoFDJFySklJwcz0k+ciUdwdMyMlRacViajM21RFqoU9e/YoXIiUkJ8fuft87969Se6JVBWKmiIiIhI6BQwREREJnQKGiIiIhE4BQ0REREKngCEiIiKhU8AQERGR0ClgiIiISOgUMERERCR0ChgiIiISOgUMERERCZ0ChoiIiIROAUNERERCp4AhIiIioVPAEBERkdApYIiIiEjoFDBEREQkdAoYIiIiEjoFDBEREQmdAoaIiIiETgFDREREQqeAISIiIqFTwBAREZHQKWCIiIhI6BQwREREJHQKGCIiIhI6BQwREREJnQKGiIiIhE4BQ0REREKngCEiIiKhU8AQERGR0ClgiIiISOgUMERERCR0ChgiIiISOgUMERERCZ0ChoiIiIROAUNERERCp4AhIiIioVPAEBERkdClJrsDIoea9PR0UlNj/9Vxd/Lz8xOqJzU1FTMrd/vlaUNEJFkUMETKqaCgAHePua48gcHM4tZT1nYiIlWdLpGIiIhI6BQwREREJHQKGCIJuvxyOOoo6NHj8Aqpf8MG4+yzM+jcuS5dutThqafSKqQdEZHKoIAhkqDLLoNp0yqu/tRUuO++vSxZsosZM/IYOzaNTz7RfAsROTQpYIgkqHdv+MEPKq7+o492OnQoAOCww6Bt2wK++EJ/RUXk0KR/vUSqoPXrjZUrU8jM3J/sroiIHBAFDJEqZudOuPjiDB54YA+HV8x0DxGRCqeAIVKF7NsXCRcXXJDP4MEavRCRQ5cChkgV4Q5XX51O27YFXHvtvmR3R0TkoChgiCTooouge3dYvTqFtm3r8vzz4T4Id/78FCZOTGPWrFr06FGHHj3qMH16rVDbEBGpLHpUuEiCJk6M/Hfr1m2kpYX/jIoePQrYsWNn6PWKiCSDRjBEREQkdAoYIiIiEjoFDBEREQmdAhrzmWcAAAd0SURBVIaIiIiETgFDREREQqe7SETKKSUlBbPYP0Lm7gnX4+5x6ylrOxGRqk4BQ6Sc9uzZE8pJPj8/P4TeiIhUTdXyEomZ3W5mH5rZSjNbbmanBstzzSyzRNkcM9tuZsvM7BMzezg5vU6MmY03sxPKuc1kM/uhmR1mZv8xszbB8jQzez/q+DxrZpvM7IMS2z9sZn1Lqf9xM+sdvG9lZgvNbJWZ/d3MagfLB5jZ78u7v1XJlGUb6fnADK6buJRrXlrK3FWbQ29j7qrNXPPSUi4au6DC2hARqQzVLmCYWXdgANDJ3dsDpwOfl7HZHHfvCHQEBphZzwru5gExs1ru/kt3/6gc25wI1HL3Ne6+AxgFPBmsvhmY5+4Lg8/PAf1jVPMEcGuc+n8AdHP32cGiB4HH3L0NsBX4RbD8TWCQmdVNtO9VyZRlGxn1z/fZuC0PB7bs3Mu4OWtDDQBzV21m3Jy1bNm5t8LaEBGpLNUuYADHAFvcfQ+Au29x9y8S2dDd84DlQNOS68zsRDN7LxgRWRk1CvCz4PMKM3sxWNbYzF4xs0XBq2ew/O5glCDXzNaY2XVR9U8xsyXByMuIqOU7zeweM1sIdI8ehTGzi4IRiA/M7ME4uzUceC1qH18GCsxsJHAlkcBRuG428L8Yx2U9cKSZHR2j/vOBaUF/DOgLTA7WPQ8MCepwIJdI+Dvk/HH6p+TtK/7jY3vyC5i0qKzsmrhJiz5nT35BhbYhIlJZqmPA+DfQ3Mw+M7OnzCw70Q3N7AigDTA7xuorgdHu3gHIBDYEowO3A33d/RTgN0HZ0US+xXcBfgKMj6qnHXAm0BX4nZkVPnP6cnfvHNR9nZkdGSyvB3zg7qe6+9yovh5LZLSgL9AB6GJmQ2L0uyewpMSy64Nt/8/dvxco4lga1FVa/UcC29y9cHLBBoqHtcVAVqzKzWyEmS02s8WbN1e9b+xfbMuLufy/O/eG1ka8usJsQ0SkslS7gOHuO4HOwAhgM/B3M7usjM2yzGwl8BXwhrt/FaPMfOA2M7sFOC4Y7egLTHb3LUHbhSfr04E/mdlyYCpwuJkdFqx70933BNtsApoEy68zsxXAAqA5kaADsB94JUZ/ugC57r45OKH/Degdo9wxwXGI1h/4EjgpzvGIZRNwbBn1x7olIno2ZLw6cPex7p7p7pmNGzcuR7cqx7EN68RcfmT92qG1Ea+uMNsQEaks1S5gALj7fnfPdfffAdcQGUUozZxgvsbJwFVm1iFGnS8Bg4A8YHow6dEofgItlAJ0d/cOwatpMP8BYE9Uuf1AqpnlEAkl3YORkGVARlBmt7sXH5uPSPT+xryougpHPq4jMoJytpm1T7CejKCu0urfAjQ0s8K7k5oB0Zen4tVR5f32zLbUSSv+y6bpqSlc2KV5aG1c2KU56anF/0qG3YaISGWpdgHDzNoWzo8IdADWJ7Ktu38G3A/cEqPeHwJr3H0MkVGJ9sA7wAWFlzOCCY8QuUxzTdS23wssJTQAtrr7LjNrB3RLoLsLgWwza2RmtYCLgFkxyn0MtI76/Bhwn7tvAG4EnrTEHsbwI+CDGMuL6g/mWcwkMi8D4FKi5n+UUkeVN6RjU+4/72SaNqyDAY3q1+aKrFb0ahPeaEuvNo25IqsVjerXrrA2REQqS3V8DkZ94AkzawjkA6uJXC4p9KaZ7Qvez+e7OyoK/Rm42cxaufvaqOU/BS4Otv0KuMfd/2dmfwBmmdl+IiMPlxEZIXgyuOySSmROx5Wl9HkacGVQ/lMil0lK5e5fmtkoIid0A/7l7q/FKPomkAO8bWZnAC2AZ4I6XjezK4CfAc+b2cSgbCMz2wD8zt2fCeaJtCYyhyJW/b/iu3kmtwCTzOz/guPxTFTZPkRNKj3UDOnYlCEdm7Jp0yZq166Yyxa92jRWoBCRasH0VMDqzczqEAkhPeNcakmkjnOJ3PZ7Z5z1c4EB7r6tlDqaAC+5+2lltZeZmemLF8fKMlVDRQYMkUPd3r17Oeqoo5LdjVKZ2RJ3zyy7pByManeJRIoLJqP+jhi33pZDKvBIKetvIjIyUpoWQTkREakBquMlEinB3acf5Pb/KGP9wtLWB2UWHUwfRETk0KIRDBEREQmdAoaIiIiETgFDREREQqeAISIiIqFTwBAREZHQKWCIiIhI6BQwREREJHQKGCIiIhI6BQwREREJnQKGiIiIhE4BQ0REREKngCEiIiKhU8AQERGR0ClgiIiISOgUMERERCR0ChgiIiISOgUMERERCZ0ChoiIiIROAUNERERCp4AhIiIioVPAEBERkdApYIiIiEjoFDBEREQkdAoYIiIiEjoFDBEREQmdAoaIiIiETgFDREREQqeAISIiIqEzd092H0SKMbPNwPpk96MUjYAtye5ECLQfVYv2o/Ic5+6Nk92J6k4BQ6SczGyxu2cmux8HS/tRtWg/pLrRJRIREREJnQKGiIiIhE4BQ6T8xia7AyHRflQt2g+pVjQHQ0REREKnEQwREREJnQKGiIiIhE4BQ0REREKngCEiIiKhU8AQERGR0P1/Ew5meotaFaoAAAAASUVORK5CYII=\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": "markdown",
   "metadata": {},
   "source": [
    "## Make more insightfull graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = ['#61217B','#0057A5','#008ABE','#008D36','#7DAF23','#DABB00','#E29000','#CA280E','#942A36']\n",
    "color_dict = {0:'#0057A5',\n",
    "              1: '#CA280E',\n",
    "              2: '#008D36',\n",
    "              3: '#61217B'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "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": [
    "df = box1.peeling_trajectory\n",
    "df['color'] = df.res_dim.apply(lambda x: color_dict[x])\n",
    "df['step'] = df.index\n",
    "\n",
    "def label_point(x, y, val, ax,offset=0,size=10,only_uneven = False):\n",
    "    a = pd.concat({'x': x, 'y': y, 'val': val}, axis=1)\n",
    "    for i, point in a.iterrows():\n",
    "        if (only_uneven and i%2 != 1):\n",
    "            pass\n",
    "        else:\n",
    "            ax.text(point['x']+offset, point['y'], str(int(point['val'])),size=size,verticalalignment='center')\n",
    "\n",
    "fig, ax = plot_tradeoff(df)\n",
    "label_point(df['coverage'],df['density'],df['step'],offset=-0.08,size=11,ax=ax,only_uneven=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'prim': <ema_workbench.analysis.prim.Prim at 0x1e8a0075f70>,\n",
       " 'peeling_trajectory':     coverage   density  id      mass      mean res_dim    color  step\n",
       " 0   1.000000  0.172687   0  1.000000  0.172687       0  #0057A5     0\n",
       " 1   1.000000  0.194273   1  0.888889  0.194273       1  #CA280E     1\n",
       " 2   1.000000  0.222026   2  0.777778  0.222026       1  #CA280E     2\n",
       " 3   1.000000  0.259030   3  0.666667  0.259030       1  #CA280E     3\n",
       " 4   0.998874  0.310486   4  0.555556  0.310486       1  #CA280E     4\n",
       " 5   0.987409  0.383653   5  0.444444  0.383653       1  #CA280E     5\n",
       " 6   0.927966  0.480742   6  0.333333  0.480742       2  #008D36     6\n",
       " 7   0.834991  0.576768   7  0.250000  0.576768       2  #008D36     7\n",
       " 8   0.690264  0.715197   8  0.166667  0.715197       2  #008D36     8\n",
       " 9   0.661631  0.754159   9  0.151500  0.754159       3  #61217B     9\n",
       " 10  0.629051  0.795153  10  0.136614  0.795153       3  #61217B    10\n",
       " 11  0.585956  0.832658  11  0.121523  0.832658       3  #61217B    11\n",
       " 12  0.339714  0.960872  12  0.061053  0.960872       3  #61217B    12\n",
       " 13  0.302805  0.982678  13  0.053212  0.982678       3  #61217B    13,\n",
       " 'box_lims': [  Housing market (R1)            SLR scenario (X1)  Implementation time (X3)  \\\n",
       "  0              {1, 0}  {1, 2, 3, 4, 5, 6, 7, 8, 9}                         4   \n",
       "  1              {1, 0}  {1, 2, 3, 4, 5, 6, 7, 8, 9}                        14   \n",
       "  \n",
       "     Transient storm surge (X2) Management strategy (L1)  \n",
       "  0                           0             {1, 2, 3, 0}  \n",
       "  1                         499             {1, 2, 3, 0}  ,\n",
       "    Housing market (R1)         SLR scenario (X1)  Implementation time (X3)  \\\n",
       "  0              {1, 0}  {1, 2, 3, 4, 5, 6, 8, 9}                         4   \n",
       "  1              {1, 0}  {1, 2, 3, 4, 5, 6, 8, 9}                        14   \n",
       "  \n",
       "     Transient storm surge (X2) Management strategy (L1)  \n",
       "  0                           0             {1, 2, 3, 0}  \n",
       "  1                         499             {1, 2, 3, 0}  ,\n",
       "    Housing market (R1)      SLR scenario (X1)  Implementation time (X3)  \\\n",
       "  0              {1, 0}  {1, 2, 3, 4, 5, 6, 9}                         4   \n",
       "  1              {1, 0}  {1, 2, 3, 4, 5, 6, 9}                        14   \n",
       "  \n",
       "     Transient storm surge (X2) Management strategy (L1)  \n",
       "  0                           0             {1, 2, 3, 0}  \n",
       "  1                         499             {1, 2, 3, 0}  ,\n",
       "    Housing market (R1)   SLR scenario (X1)  Implementation time (X3)  \\\n",
       "  0              {1, 0}  {1, 2, 3, 4, 5, 6}                         4   \n",
       "  1              {1, 0}  {1, 2, 3, 4, 5, 6}                        14   \n",
       "  \n",
       "     Transient storm surge (X2) Management strategy (L1)  \n",
       "  0                           0             {1, 2, 3, 0}  \n",
       "  1                         499             {1, 2, 3, 0}  ,\n",
       "    Housing market (R1) SLR scenario (X1)  Implementation time (X3)  \\\n",
       "  0              {1, 0}   {1, 2, 3, 4, 5}                         4   \n",
       "  1              {1, 0}   {1, 2, 3, 4, 5}                        14   \n",
       "  \n",
       "     Transient storm surge (X2) Management strategy (L1)  \n",
       "  0                           0             {1, 2, 3, 0}  \n",
       "  1                         499             {1, 2, 3, 0}  ,\n",
       "    Housing market (R1) SLR scenario (X1)  Implementation time (X3)  \\\n",
       "  0              {1, 0}      {1, 2, 3, 4}                         4   \n",
       "  1              {1, 0}      {1, 2, 3, 4}                        14   \n",
       "  \n",
       "     Transient storm surge (X2) Management strategy (L1)  \n",
       "  0                           0             {1, 2, 3, 0}  \n",
       "  1                         499             {1, 2, 3, 0}  ,\n",
       "    Housing market (R1) SLR scenario (X1)  Implementation time (X3)  \\\n",
       "  0              {1, 0}      {1, 2, 3, 4}                         4   \n",
       "  1              {1, 0}      {1, 2, 3, 4}                        14   \n",
       "  \n",
       "     Transient storm surge (X2) Management strategy (L1)  \n",
       "  0                           0                {1, 2, 0}  \n",
       "  1                         499                {1, 2, 0}  ,\n",
       "    Housing market (R1) SLR scenario (X1)  Implementation time (X3)  \\\n",
       "  0              {1, 0}         {1, 2, 3}                         4   \n",
       "  1              {1, 0}         {1, 2, 3}                        14   \n",
       "  \n",
       "     Transient storm surge (X2) Management strategy (L1)  \n",
       "  0                           0                {1, 2, 0}  \n",
       "  1                         499                {1, 2, 0}  ,\n",
       "    Housing market (R1) SLR scenario (X1)  Implementation time (X3)  \\\n",
       "  0              {1, 0}            {1, 2}                         4   \n",
       "  1              {1, 0}            {1, 2}                        14   \n",
       "  \n",
       "     Transient storm surge (X2) Management strategy (L1)  \n",
       "  0                           0                {1, 2, 0}  \n",
       "  1                         499                {1, 2, 0}  ,\n",
       "    Housing market (R1) SLR scenario (X1)  Implementation time (X3)  \\\n",
       "  0              {1, 0}            {1, 2}                         5   \n",
       "  1              {1, 0}            {1, 2}                        14   \n",
       "  \n",
       "     Transient storm surge (X2) Management strategy (L1)  \n",
       "  0                           0                {1, 2, 0}  \n",
       "  1                         499                {1, 2, 0}  ,\n",
       "    Housing market (R1) SLR scenario (X1)  Implementation time (X3)  \\\n",
       "  0              {1, 0}            {1, 2}                         6   \n",
       "  1              {1, 0}            {1, 2}                        14   \n",
       "  \n",
       "     Transient storm surge (X2) Management strategy (L1)  \n",
       "  0                           0                {1, 2, 0}  \n",
       "  1                         499                {1, 2, 0}  ,\n",
       "    Housing market (R1) SLR scenario (X1)  Implementation time (X3)  \\\n",
       "  0              {1, 0}            {1, 2}                         7   \n",
       "  1              {1, 0}            {1, 2}                        14   \n",
       "  \n",
       "     Transient storm surge (X2) Management strategy (L1)  \n",
       "  0                           0                {1, 2, 0}  \n",
       "  1                         499                {1, 2, 0}  ,\n",
       "    Housing market (R1) SLR scenario (X1)  Implementation time (X3)  \\\n",
       "  0              {1, 0}               {1}                         7   \n",
       "  1              {1, 0}               {1}                        14   \n",
       "  \n",
       "     Transient storm surge (X2) Management strategy (L1)  \n",
       "  0                           0                {1, 2, 0}  \n",
       "  1                         499                {1, 2, 0}  ,\n",
       "    Housing market (R1) SLR scenario (X1)  Implementation time (X3)  \\\n",
       "  0              {1, 0}               {1}                         8   \n",
       "  1              {1, 0}               {1}                        14   \n",
       "  \n",
       "     Transient storm surge (X2) Management strategy (L1)  \n",
       "  0                           0                {1, 2, 0}  \n",
       "  1                         499                {1, 2, 0}  ],\n",
       " 'qp': [{},\n",
       "  {'SLR scenario (X1)': [3.350731666296113e-244, -1.0]},\n",
       "  {'SLR scenario (X1)': [0.0, -1.0]},\n",
       "  {'SLR scenario (X1)': [0.0, -1.0]},\n",
       "  {'SLR scenario (X1)': [0.0, -1.0]},\n",
       "  {'SLR scenario (X1)': [0.0, -1.0]},\n",
       "  {'SLR scenario (X1)': [0.0, -1.0], 'Management strategy (L1)': [0.0, -1.0]},\n",
       "  {'SLR scenario (X1)': [0.0, -1.0], 'Management strategy (L1)': [0.0, -1.0]},\n",
       "  {'SLR scenario (X1)': [0.0, -1.0], 'Management strategy (L1)': [0.0, -1.0]},\n",
       "  {'SLR scenario (X1)': [0.0, -1.0],\n",
       "   'Implementation time (X3)': [2.0689805171095086e-102, -1.0],\n",
       "   'Management strategy (L1)': [0.0, -1.0]},\n",
       "  {'SLR scenario (X1)': [0.0, -1.0],\n",
       "   'Implementation time (X3)': [0.0, -1.0],\n",
       "   'Management strategy (L1)': [0.0, -1.0]},\n",
       "  {'SLR scenario (X1)': [0.0, -1.0],\n",
       "   'Implementation time (X3)': [0.0, -1.0],\n",
       "   'Management strategy (L1)': [0.0, -1.0]},\n",
       "  {'SLR scenario (X1)': [0.0, -1.0],\n",
       "   'Implementation time (X3)': [0.0, -1.0],\n",
       "   'Management strategy (L1)': [0.0, -1.0]},\n",
       "  {'SLR scenario (X1)': [0.0, -1.0],\n",
       "   'Implementation time (X3)': [0.0, -1.0],\n",
       "   'Management strategy (L1)': [0.0, -1.0]}],\n",
       " '_resampled': [],\n",
       " 'yi_initial': array([     0,      1,      2, ..., 395997, 395998, 395999], dtype=int64),\n",
       " 'boxes_quantitative': Empty DataFrame\n",
       " Columns: [name, lower, upper, minimum, maximum, qp_lower, qp_upper, id]\n",
       " Index: [],\n",
       " 'boxes_nominal': Empty DataFrame\n",
       " Columns: [item, name, n_items, x, id]\n",
       " Index: [],\n",
       " '_cur_box': 13,\n",
       " 'yi': array([    20,     77,     85, ..., 296924, 296960, 296992], dtype=int64)}"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "box1.__dict__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "today = datetime.date(datetime.now())\n",
    "date = \"{}_{}_{}\".format(today.year,today.month,today.day)\n",
    "target = Path('Visualisations') / date\n",
    "\n",
    "if not Path.exists(target): Path.mkdir(target)\n",
    "\n",
    "fig.savefig((target / \"SD_PRIM_tradeoff_EMA_2021_2_23.png\"),bbox_inches=\"tight\",dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Slightly adapted from EMA-workbench\n",
    "\n",
    "import matplotlib as mpl\n",
    "import numpy as np\n",
    "\n",
    "def plot_tradeoff(peeling_trajectory, cmap=mpl.cm.viridis):  # @UndefinedVariable\n",
    "    '''Visualize the trade off between coverage and density. Color\n",
    "    is used to denote the number of restricted dimensions.\n",
    "    Parameters\n",
    "    ----------\n",
    "    cmap : valid matplotlib colormap\n",
    "    Returns\n",
    "    -------\n",
    "    a Figure instance\n",
    "    '''\n",
    "\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111, aspect='equal')\n",
    "\n",
    "    boundaries = np.arange(-0.5,\n",
    "                           max(peeling_trajectory['res_dim']) + 1.5,\n",
    "                           step=1)\n",
    "    ncolors = cmap.N\n",
    "    norm = mpl.colors.BoundaryNorm(boundaries, ncolors)\n",
    "\n",
    "    p = ax.scatter(peeling_trajectory['coverage'],\n",
    "                   peeling_trajectory['density'],\n",
    "                   c=peeling_trajectory['res_dim'],\n",
    "                   norm=norm,\n",
    "                   cmap=cmap)\n",
    "    ax.set_ylabel('density')\n",
    "    ax.set_xlabel('coverage')\n",
    "    ax.set_ylim(bottom=0, top=1.2)\n",
    "    ax.set_xlim(left=0, right=1.2)\n",
    "\n",
    "    ticklocs = np.arange(0,\n",
    "                         max(peeling_trajectory['res_dim']) + 1,\n",
    "                         step=1)\n",
    "    cb = fig.colorbar(p, spacing='uniform', ticks=ticklocs,\n",
    "                      drawedges=True)\n",
    "    cb.set_label(\"nr. of restricted dimensions\")\n",
    "\n",
    "    return fig, ax"
   ]
  },
  {
   "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
}