notebook_plot_PF_E1000.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from pylab import *\n",
    "from matplotlib import colors\n",
    "from matplotlib.colors import ListedColormap\n",
    "from argparse import Namespace\n",
    "from pymoo.indicators.hv import HV\n",
    "from pymoo.config import Config\n",
    "Config.warnings['not_compiled'] = False\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "conf = !python  parameters.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "configs = eval(conf[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Experiment config\n",
    "configs.name=\"E1000v2\"\n",
    "configs.n_devices=999\n",
    "configs.n_jobs=9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getWeithDecomposition(val):\n",
    "    print(val)\n",
    "    if val[0]==\"0\":\n",
    "        return \"(0,1.)\"\n",
    "    elif val[0]==\"1\":\n",
    "        return \"(1.,0)\"\n",
    "    return \"(%s,%s)\"%(float(val[0:2])/100,float(val[2:])/100)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DRL - average all samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
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       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>1716.0</td>\n",
       "      <td>-260.00</td>\n",
       "      <td>562.0</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>0.690426</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7525</td>\n",
       "      <td>0</td>\n",
       "      <td>305.0</td>\n",
       "      <td>624.0</td>\n",
       "      <td>-384.75</td>\n",
       "      <td>562.0</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>0.690806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5050</td>\n",
       "      <td>0</td>\n",
       "      <td>527.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>-351.50</td>\n",
       "      <td>562.0</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>0.688146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2575</td>\n",
       "      <td>0</td>\n",
       "      <td>1009.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>-476.50</td>\n",
       "      <td>562.0</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>0.693997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0100</td>\n",
       "      <td>0</td>\n",
       "      <td>2450.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>-279.00</td>\n",
       "      <td>562.0</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>0.687160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      0  1       2       3       4      5       6         7\n",
       "0  1000  0   260.0  1716.0 -260.00  562.0  1620.0  0.690426\n",
       "1  7525  0   305.0   624.0 -384.75  562.0  1620.0  0.690806\n",
       "2  5050  0   527.0   176.0 -351.50  562.0  1620.0  0.688146\n",
       "3  2575  0  1009.0   299.0 -476.50  562.0  1620.0  0.693997\n",
       "4  0100  0  2450.0   279.0 -279.00  562.0  1620.0  0.687160"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## previou eval_trained_PF_ppo.py\n",
    "#df = pd.read_pickle('logs/log_eval_PF_'  + str(configs.name) + \"_\" + str(configs.n_jobs) + '_' + str(configs.n_devices)+'.pkl', 'infer')\n",
    "configs.name=\"E1000v9\"\n",
    "df = pd.read_pickle('logs/log_eval_PF_'  + str(configs.name) + \"_\" + str(configs.n_jobs) + '_' + str(configs.n_devices)+'_v3.pkl', 'infer')\n",
    "df = pd.DataFrame(df)\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>sample</th>\n",
       "      <th>time</th>\n",
       "      <th>cost</th>\n",
       "      <th>reward</th>\n",
       "      <th>iTime</th>\n",
       "      <th>iCost</th>\n",
       "      <th>execTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>1716.0</td>\n",
       "      <td>-260.00</td>\n",
       "      <td>562.0</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>0.690426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7525</td>\n",
       "      <td>0</td>\n",
       "      <td>305.0</td>\n",
       "      <td>624.0</td>\n",
       "      <td>-384.75</td>\n",
       "      <td>562.0</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>0.690806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5050</td>\n",
       "      <td>0</td>\n",
       "      <td>527.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>-351.50</td>\n",
       "      <td>562.0</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>0.688146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2575</td>\n",
       "      <td>0</td>\n",
       "      <td>1009.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>-476.50</td>\n",
       "      <td>562.0</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>0.693997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0100</td>\n",
       "      <td>0</td>\n",
       "      <td>2450.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>-279.00</td>\n",
       "      <td>562.0</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>0.687160</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>1554.0</td>\n",
       "      <td>-259.00</td>\n",
       "      <td>562.0</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>0.684845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>7525</td>\n",
       "      <td>0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>608.0</td>\n",
       "      <td>-353.75</td>\n",
       "      <td>562.0</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>0.683001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>5050</td>\n",
       "      <td>0</td>\n",
       "      <td>507.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>-335.50</td>\n",
       "      <td>562.0</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>0.688669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>2575</td>\n",
       "      <td>0</td>\n",
       "      <td>826.0</td>\n",
       "      <td>328.0</td>\n",
       "      <td>-452.50</td>\n",
       "      <td>562.0</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>0.681462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>0100</td>\n",
       "      <td>0</td>\n",
       "      <td>2755.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>-225.00</td>\n",
       "      <td>562.0</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>0.685047</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    model  sample    time    cost  reward  iTime   iCost  execTime\n",
       "0    1000       0   260.0  1716.0 -260.00  562.0  1620.0  0.690426\n",
       "1    7525       0   305.0   624.0 -384.75  562.0  1620.0  0.690806\n",
       "2    5050       0   527.0   176.0 -351.50  562.0  1620.0  0.688146\n",
       "3    2575       0  1009.0   299.0 -476.50  562.0  1620.0  0.693997\n",
       "4    0100       0  2450.0   279.0 -279.00  562.0  1620.0  0.687160\n",
       "..    ...     ...     ...     ...     ...    ...     ...       ...\n",
       "495  1000       0   259.0  1554.0 -259.00  562.0  1620.0  0.684845\n",
       "496  7525       0   269.0   608.0 -353.75  562.0  1620.0  0.683001\n",
       "497  5050       0   507.0   164.0 -335.50  562.0  1620.0  0.688669\n",
       "498  2575       0   826.0   328.0 -452.50  562.0  1620.0  0.681462\n",
       "499  0100       0  2755.0   225.0 -225.00  562.0  1620.0  0.685047\n",
       "\n",
       "[500 rows x 8 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.columns = [\"model\",\"sample\",\"time\",\"cost\",\"reward\",\"iTime\",\"iCost\",\"execTime\"]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>time</th>\n",
       "      <th>cost</th>\n",
       "      <th>iTime</th>\n",
       "      <th>iCost</th>\n",
       "      <th>execTime</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0100</th>\n",
       "      <td>2493.959961</td>\n",
       "      <td>257.200012</td>\n",
       "      <td>562.0</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>0.686687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>259.500000</td>\n",
       "      <td>1630.010010</td>\n",
       "      <td>562.0</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>0.686690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2575</th>\n",
       "      <td>976.690002</td>\n",
       "      <td>315.970001</td>\n",
       "      <td>562.0</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>0.686415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5050</th>\n",
       "      <td>531.679993</td>\n",
       "      <td>183.229996</td>\n",
       "      <td>562.0</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>0.686810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7525</th>\n",
       "      <td>292.160004</td>\n",
       "      <td>568.309998</td>\n",
       "      <td>562.0</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>0.686533</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              time         cost  iTime   iCost  execTime\n",
       "model                                                   \n",
       "0100   2493.959961   257.200012  562.0  1620.0  0.686687\n",
       "1000    259.500000  1630.010010  562.0  1620.0  0.686690\n",
       "2575    976.690002   315.970001  562.0  1620.0  0.686415\n",
       "5050    531.679993   183.229996  562.0  1620.0  0.686810\n",
       "7525    292.160004   568.309998  562.0  1620.0  0.686533"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfmodel = df.groupby(\"model\").agg({\"time\":mean,\"cost\":mean,\"iTime\":mean,\"iCost\":mean,\"execTime\":mean})\n",
    "dfmodel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(type(dfmodel))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0100\n",
      "2493.9599609375\n",
      "1000\n",
      "259.5\n",
      "2575\n",
      "976.6900024414062\n",
      "5050\n",
      "531.6799926757812\n",
      "7525\n",
      "292.1600036621094\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(dfmodel)):\n",
    "    print(dfmodel.iloc[i].name)\n",
    "    print(dfmodel.iloc[i].time)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0100\n",
      "1000\n",
      "2575\n",
      "5050\n",
      "7525\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "\n",
    "ax.scatter(dfmodel.time, dfmodel.cost, s=30, facecolors='none', edgecolors='blue',label=\"PPO model\")\n",
    "\n",
    "ax.scatter(dfmodel.iTime[0],dfmodel.iCost[0],color=\"red\",label=\"Cloud\")\n",
    "for i in range(len(dfmodel)):\n",
    "    ax.annotate(getWeithDecomposition(dfmodel.iloc[i].name), (dfmodel.iloc[i].time, dfmodel.iloc[i].cost))\n",
    "\n",
    "plt.title(\"Average solutions for each decomposition model\")\n",
    "plt.xlabel(\"Time\", fontsize=14)\n",
    "plt.ylabel(\"Cost\", fontsize=14)\n",
    "ax.legend()\n",
    "# plt.savefig(\"images/pf_nsgaII-0.pdf\",dpi=300,bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 2)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_pf = np.vstack((dfmodel.time.values,dfmodel.cost.values)).T\n",
    "avg_pf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2493.96,  257.2 ],\n",
       "       [ 259.5 , 1630.01],\n",
       "       [ 976.69,  315.97],\n",
       "       [ 531.68,  183.23],\n",
       "       [ 292.16,  568.31]], dtype=float32)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_pf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HV 68938.44889273681\n"
     ]
    }
   ],
   "source": [
    "ref_point = np.array([500., 900.])\n",
    "ind = HV(ref_point=ref_point)\n",
    "print(\"HV\", ind(avg_pf)) "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DRL one sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'logs/log_eval_PF_E1000v9_9_999.pkl'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[64], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39;49mread_pickle(\u001b[39m'\u001b[39;49m\u001b[39mlogs/log_eval_PF_\u001b[39;49m\u001b[39m'\u001b[39;49m  \u001b[39m+\u001b[39;49m \u001b[39mstr\u001b[39;49m(configs\u001b[39m.\u001b[39;49mname) \u001b[39m+\u001b[39;49m \u001b[39m\"\u001b[39;49m\u001b[39m_\u001b[39;49m\u001b[39m\"\u001b[39;49m \u001b[39m+\u001b[39;49m \u001b[39mstr\u001b[39;49m(configs\u001b[39m.\u001b[39;49mn_jobs) \u001b[39m+\u001b[39;49m \u001b[39m'\u001b[39;49m\u001b[39m_\u001b[39;49m\u001b[39m'\u001b[39;49m \u001b[39m+\u001b[39;49m \u001b[39mstr\u001b[39;49m(configs\u001b[39m.\u001b[39;49mn_devices)\u001b[39m+\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39m.pkl\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39minfer\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[1;32m      2\u001b[0m df \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mDataFrame(df)\n\u001b[1;32m      3\u001b[0m df\u001b[39m.\u001b[39mcolumns \u001b[39m=\u001b[39m [\u001b[39m\"\u001b[39m\u001b[39mmodel\u001b[39m\u001b[39m\"\u001b[39m,\u001b[39m\"\u001b[39m\u001b[39mcase\u001b[39m\u001b[39m\"\u001b[39m,\u001b[39m\"\u001b[39m\u001b[39mtime\u001b[39m\u001b[39m\"\u001b[39m,\u001b[39m\"\u001b[39m\u001b[39mcost\u001b[39m\u001b[39m\"\u001b[39m,\u001b[39m\"\u001b[39m\u001b[39mreward\u001b[39m\u001b[39m\"\u001b[39m,\u001b[39m\"\u001b[39m\u001b[39miTime\u001b[39m\u001b[39m\"\u001b[39m,\u001b[39m\"\u001b[39m\u001b[39miCost\u001b[39m\u001b[39m\"\u001b[39m]\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.7/envs/DGLenv/lib/python3.9/site-packages/pandas/io/pickle.py:190\u001b[0m, in \u001b[0;36mread_pickle\u001b[0;34m(filepath_or_buffer, compression, storage_options)\u001b[0m\n\u001b[1;32m    124\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    125\u001b[0m \u001b[39mLoad pickled pandas object (or any object) from file.\u001b[39;00m\n\u001b[1;32m    126\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    187\u001b[0m \u001b[39m4    4    9\u001b[39;00m\n\u001b[1;32m    188\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    189\u001b[0m excs_to_catch \u001b[39m=\u001b[39m (\u001b[39mAttributeError\u001b[39;00m, \u001b[39mImportError\u001b[39;00m, \u001b[39mModuleNotFoundError\u001b[39;00m, \u001b[39mTypeError\u001b[39;00m)\n\u001b[0;32m--> 190\u001b[0m \u001b[39mwith\u001b[39;00m get_handle(\n\u001b[1;32m    191\u001b[0m     filepath_or_buffer,\n\u001b[1;32m    192\u001b[0m     \u001b[39m\"\u001b[39;49m\u001b[39mrb\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m    193\u001b[0m     compression\u001b[39m=\u001b[39;49mcompression,\n\u001b[1;32m    194\u001b[0m     is_text\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[1;32m    195\u001b[0m     storage_options\u001b[39m=\u001b[39;49mstorage_options,\n\u001b[1;32m    196\u001b[0m ) \u001b[39mas\u001b[39;00m handles:\n\u001b[1;32m    197\u001b[0m \n\u001b[1;32m    198\u001b[0m     \u001b[39m# 1) try standard library Pickle\u001b[39;00m\n\u001b[1;32m    199\u001b[0m     \u001b[39m# 2) try pickle_compat (older pandas version) to handle subclass changes\u001b[39;00m\n\u001b[1;32m    200\u001b[0m     \u001b[39m# 3) try pickle_compat with latin-1 encoding upon a UnicodeDecodeError\u001b[39;00m\n\u001b[1;32m    202\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m    203\u001b[0m         \u001b[39m# TypeError for Cython complaints about object.__new__ vs Tick.__new__\u001b[39;00m\n\u001b[1;32m    204\u001b[0m         \u001b[39mtry\u001b[39;00m:\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.7/envs/DGLenv/lib/python3.9/site-packages/pandas/io/common.py:865\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    856\u001b[0m         handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39m(\n\u001b[1;32m    857\u001b[0m             handle,\n\u001b[1;32m    858\u001b[0m             ioargs\u001b[39m.\u001b[39mmode,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    861\u001b[0m             newline\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m    862\u001b[0m         )\n\u001b[1;32m    863\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    864\u001b[0m         \u001b[39m# Binary mode\u001b[39;00m\n\u001b[0;32m--> 865\u001b[0m         handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39;49m(handle, ioargs\u001b[39m.\u001b[39;49mmode)\n\u001b[1;32m    866\u001b[0m     handles\u001b[39m.\u001b[39mappend(handle)\n\u001b[1;32m    868\u001b[0m \u001b[39m# Convert BytesIO or file objects passed with an encoding\u001b[39;00m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'logs/log_eval_PF_E1000v9_9_999.pkl'"
     ]
    }
   ],
   "source": [
    "df = pd.read_pickle('logs/log_eval_PF_'  + str(configs.name) + \"_\" + str(configs.n_jobs) + '_' + str(configs.n_devices)+'.pkl', 'infer')\n",
    "df = pd.DataFrame(df)\n",
    "df.columns = [\"model\",\"case\",\"time\",\"cost\",\"reward\",\"iTime\",\"iCost\"]\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'case'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[19], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m CASE \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m \n\u001b[0;32m----> 2\u001b[0m dfDRL \u001b[39m=\u001b[39m df[df\u001b[39m.\u001b[39;49mcase\u001b[39m==\u001b[39mCASE]\n\u001b[1;32m      3\u001b[0m dfDRL\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.7/envs/DGLenv/lib/python3.9/site-packages/pandas/core/generic.py:5902\u001b[0m, in \u001b[0;36mNDFrame.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5895\u001b[0m \u001b[39mif\u001b[39;00m (\n\u001b[1;32m   5896\u001b[0m     name \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_internal_names_set\n\u001b[1;32m   5897\u001b[0m     \u001b[39mand\u001b[39;00m name \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_metadata\n\u001b[1;32m   5898\u001b[0m     \u001b[39mand\u001b[39;00m name \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_accessors\n\u001b[1;32m   5899\u001b[0m     \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_info_axis\u001b[39m.\u001b[39m_can_hold_identifiers_and_holds_name(name)\n\u001b[1;32m   5900\u001b[0m ):\n\u001b[1;32m   5901\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m[name]\n\u001b[0;32m-> 5902\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mobject\u001b[39;49m\u001b[39m.\u001b[39;49m\u001b[39m__getattribute__\u001b[39;49m(\u001b[39mself\u001b[39;49m, name)\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'case'"
     ]
    }
   ],
   "source": [
    "CASE = 0 \n",
    "dfDRL = df[df.case==CASE]\n",
    "dfDRL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'dfDRL' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[72], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m avg_pf \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mvstack((dfDRL\u001b[39m.\u001b[39mtime\u001b[39m.\u001b[39mvalues,dfDRL\u001b[39m.\u001b[39mcost\u001b[39m.\u001b[39mvalues,dfDRL\u001b[39m.\u001b[39mmodel\u001b[39m.\u001b[39mvalues))\u001b[39m.\u001b[39mT\n\u001b[1;32m      2\u001b[0m avg_pf\n",
      "\u001b[0;31mNameError\u001b[0m: name 'dfDRL' is not defined"
     ]
    }
   ],
   "source": [
    "avg_pf = np.vstack((dfDRL.time.values,dfDRL.cost.values,dfDRL.model.values)).T\n",
    "avg_pf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2493.96, 257.2)\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'dfDRL' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[20], line 10\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39mlen\u001b[39m(avg_pf)):\n\u001b[1;32m      9\u001b[0m     \u001b[39mprint\u001b[39m((avg_pf[i,\u001b[39m0\u001b[39m], avg_pf[i,\u001b[39m1\u001b[39m]))\n\u001b[0;32m---> 10\u001b[0m     ax\u001b[39m.\u001b[39mannotate(getWeithDecomposition(dfDRL\u001b[39m.\u001b[39miloc[i]\u001b[39m.\u001b[39mmodel), (avg_pf[i,\u001b[39m0\u001b[39m]\u001b[39m+\u001b[39m\u001b[39m12\u001b[39m, avg_pf[i,\u001b[39m1\u001b[39m]\u001b[39m-\u001b[39m\u001b[39m2.5\u001b[39m))\n\u001b[1;32m     11\u001b[0m ax\u001b[39m.\u001b[39mlegend()\n\u001b[1;32m     12\u001b[0m plt\u001b[39m.\u001b[39mtitle(\u001b[39m\"\u001b[39m\u001b[39mObjective Space. Case code: \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m%\u001b[39mconfigs\u001b[39m.\u001b[39mname)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'dfDRL' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "\n",
    "ax.scatter(avg_pf[:,0], avg_pf[:,1], s=30, facecolors='none', edgecolors='blue',label=\"PPO model\")\n",
    "\n",
    "\n",
    "ax.scatter(dfmodel.iTime[0],dfmodel.iCost[0],color=\"red\",label=\"Cloud\")\n",
    "\n",
    "for i in range(len(avg_pf)):\n",
    "    print((avg_pf[i,0], avg_pf[i,1]))\n",
    "    ax.annotate(getWeithDecomposition(dfDRL.iloc[i].model), (avg_pf[i,0]+12, avg_pf[i,1]-2.5))\n",
    "ax.legend()\n",
    "plt.title(\"Objective Space. Case code: %s\"%configs.name)\n",
    "plt.xlabel(\"Time\", fontsize=14)\n",
    "plt.ylabel(\"Cost\", fontsize=14)\n",
    "# plt.savefig(\"images/pf_nsgaII-0.pdf\",dpi=300,bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GA PF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Experiment config\n",
    "configs.name=\"E1000v9\"\n",
    "configs.n_devices=999\n",
    "configs.n_jobs=9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_pickle('logs/log_ga_pf_'  + str(configs.name) + \"_\" + str(configs.n_jobs) + '_' + str(configs.n_devices)+'_0.pkl', 'infer')\n",
    "df = pd.DataFrame(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = [\"case\",\"time\",\"cost\",\"executionTime\"]\n",
    "dfga = df[df.case==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_pickle('logs/log_ga_pf_'  + str(\"E1000v150\") + \"_\" + str(configs.n_jobs) + '_' + str(configs.n_devices)+'_0.pkl', 'infer')\n",
    "df2 = pd.DataFrame(df2)\n",
    "df2.columns = [\"case\",\"time\",\"cost\",\"executionTime\"]\n",
    "dfga150 = df2[df2.case==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = pd.read_pickle('logs/log_ga_pf_'  + str(\"E1000v200\") + \"_\" + str(configs.n_jobs) + '_' + str(configs.n_devices)+'_0.pkl', 'infer')\n",
    "df3 = pd.DataFrame(df3)\n",
    "df3.columns = [\"case\",\"time\",\"cost\",\"executionTime\"]\n",
    "dfga200 = df3[df3.case==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4 = pd.read_pickle('logs/log_ga_pf_'  + str(\"E1000v250\") + \"_\" + str(configs.n_jobs) + '_' + str(configs.n_devices)+'_0.pkl', 'infer')\n",
    "df4 = pd.DataFrame(df4)\n",
    "df4.columns = [\"case\",\"time\",\"cost\",\"executionTime\"]\n",
    "dfga250 = df4[df4.case==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5 = pd.read_pickle('logs/log_ga_pf_'  + str(\"E1000v300\") + \"_\" + str(configs.n_jobs) + '_' + str(configs.n_devices)+'_0.pkl', 'infer')\n",
    "df5 = pd.DataFrame(df5)\n",
    "df5.columns = [\"case\",\"time\",\"cost\",\"executionTime\"]\n",
    "dfga300 = df5[df5.case==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "df6 = pd.read_pickle('logs/log_ga_pf_'  + str(\"E1000v400\") + \"_\" + str(configs.n_jobs) + '_' + str(configs.n_devices)+'_0.pkl', 'infer')\n",
    "df6 = pd.DataFrame(df6)\n",
    "df6.columns = [\"case\",\"time\",\"cost\",\"executionTime\"]\n",
    "dfga400 = df6[df6.case==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2493.96,  257.2 ],\n",
       "       [ 259.5 , 1630.01],\n",
       "       [ 976.69,  315.97],\n",
       "       [ 531.68,  183.23],\n",
       "       [ 292.16,  568.31]], dtype=float32)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_pf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6j/7gfvt_29797dypw8t1wttblw0000gn/T/ipykernel_63285/543872840.py:5: UserWarning: You passed a edgecolor/edgecolors ('blue') for an unfilled marker ('+').  Matplotlib is ignoring the edgecolor in favor of the facecolor.  This behavior may change in the future.\n",
      "  ax.scatter(dfga.time, dfga.cost,       s=30,  marker=\"+\", edgecolors='blue', label=\"NSGA-II: 100 generations\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0,1)\n",
      "(1,0)\n",
      "(.25,.75)\n",
      "(5.,5.)\n",
      "(.75,.25)\n"
     ]
    },
    {
     "data": {
      "image/png": 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SX/aKigoUFhZKjV7n5eVxv+x6enpISkqSOkf1aiJ1/UEoKSlBSUmpMboiO1VlkpU/Bq8H5JXf3B4AOvYFCho4CtCxD1DyCHiRD7T7/6/Pngj+vTmy2tNbQN85NQ5/k/Hm4zHWdCxi78XiztM76Kndkz2hkWGYFunWGkcAkqkg1aPXV79yQDvFpv3/2caNG9G3b996Ta2o/ve4pKQEcnJycHd3x759+7Bq1arXzrsGgKdPn+L333/HoUOH0Lt3b65cJBJh8ODBOHPmDJycnGBsbPzuHWqAHj16QE9PD+fOneOS6eLiYiQmJmL27NkAADs7OxQWFiIlJQX9+vUDAJw/fx5isRgDBw6s9bympqa4f/8+8vLyuA8Lr85ZtrGxweHDh6GrqwsNjYZ9a2xtbQ2RSIT8/HwMGfJ20yPj4uLw888/c98o3L9/H//8I71croKCAkSi1y91q6GhAQMDA8TFxUkNfMbFxdUYqX+Trl27YtasWZg1axaWL1+OwMDAZp9ct6hpIYqKihgwYECNT8V//fUXDA0NAUjmdikoKEittZiRkYF79+7Bzs4OgOQPIi0tTeoTZ3R0NDQ0NGok7gwAIxfgfsy/r0WVkiRdLJJsVWWSsmqHhwNXVkv+W8sIMLAHYv8rWQ3kcRKQvh+w8Pm3fXEOUPoP0KVh86j4cnwM7z4chWWFGN59OEusGYZpkdopyv//xn+pjM+VNxVLS0t4eHhg+/btUuWzZ8/G2rVrERcXh5ycHCQkJMDT0xMdO3bk/n1dv349OnfuDFtbWwQHByM1NRVZWVmIiIhAfHw8N4q6d+9eaGtrw93dnRsJt7CwgJWVFcaMGfNONzZGRETAzKzu+4SEQiEEAgE3Mp6dnQ2BQMBNfeDxeFi4cCG++eYbHD9+HGlpafD09ISBgQG3pra5uTmcnJwwY8YMJCUlIS4uDnPnzsXEiRNf+8Fi5MiRMDIygpeXF1JTUxEXF4evvvqKuyYgWd5PR0cHY8eORWxsLLKzs3HhwgXMnz//tTebvsrExAQeHh7w9PREeHg4srOzkZSUhA0bNuDkyZN1HtuzZ0/s3bsX6enpSExMhIeHh9S3C4BkxZBz584hNzf3tcs3+vn5YdOmTTh8+DAyMjKwbNkyCAQCLFiwoF59AICFCxciKioK2dnZuHbtGmJiYmBubl7v42WGmpnnz5/T9evX6fr16wSAvv32W7p+/Trl5OQQEVF4eDgpKCjQrl276M6dO7Rjxw7i8/kUGxvLnWPWrFnUrVs3On/+PF29epXs7OzIzs6Oq6+qqiILCwsaNWoUCQQCioyMpI4dO9Ly5cvfKtaioiICQEVFRY3T+aZ26RKRvj5RYKDkdWCg5PWlS9LtRFVEu7oTPUmTvD7tRbQV0ttpr3/bB/Ygyj7z7+viB0T/cyL6vh3RL12IbuySPv+VNUTn5r9zdy5kX3jnczAMwzREaWkp3bp1i0pLS9/5XCXllWS49A8yXPoHlZRXNkJ0dfPy8qKxY8dKlWVnZ5OioiK9nCb873//ozFjxpC+vj4pKiqSgYEBubm5UWpqqtSxhYWFtHz5cjIzMyMlJSVSUVGhPn360MqVK+np06dERGRpaUlz5sypNZ7Dhw+ToqIiPXnypF7xvvo6JCSE3pTexMTEEIAam5eXF9dGLBbTypUrqVOnTqSkpEQjRoygjIwMqfM8ffqUJk2aRGpqaqShoUFTp06l58+f13nt9PR0sre3J0VFRTIzM6MTJ04QAIqMjOTaPH78mDw9PUlHR4eUlJTogw8+oBkzZnD5Rm0/swULFtCwYcO41xUVFbRq1Srq3r07KSgokL6+Prm6unI/r5CQENLU1KwR37Vr16h///6krKxMPXv2pCNHjpChoSF99913XJvjx4+TsbExycvLk6GhIRERBQQEkJWVFddGJBLR6tWrqXPnzqSgoEBWVlZ0+vRprj47O5sA0PXr17myZ8+eEQCKiYkhIqK5c+eSkZERKSkpUceOHWnKlCn0zz//1Pq+1vU32NT5WrNLruvzCx8UFETGxsakrKxMVlZWdOzYMalzlJaW0pw5c6h9+/bUrl07cnV1pcePH0u1uXv3Lo0ePZpUVFRIR0eHvvzyS6qsfLv/ibXI5LriBVGkD9GFaCJlZSIejwggmjJFsufxJOWvJti3DhAdd3/z+YvuEu0fVP94youJdhsRleS/XT9qcfrO6Tc3YhiGeQ8aM7lm2pbLly8TAMrMzJR1KC1ac0queURU8/nRTL0UFxdDU1MTRUVFDZ4X1eSqyoGkDcBnvwIP84Dafvw8HqCnB9SxCH5z9FPST/C1rd/jcxmGYRpTWVkZsrOz0aNHD6mHmjHMqyIiIqCmpoaePXsiMzMTCxYsQPv27bl7x5iGqetvsKnztRY155ppBPJKwH9WAwFra0+sAUn5mjVNGlZjGGc2TtYhMAzDMEydnj9/Dl9fX5iZmcHb2xsDBgzA77//LuuwmEbEkuu2pvIFcGIC4PU5MGVK7W2mTAGmT2/auBrB2ktrZR0CwzAMw9TJ09MTf/31F8rKyvDgwQOEhoZCW1tb1mExjYgl120Njw/o2wGhYcDevbW32bsX2L27aeNqBL98/IusQ2AYhmEYpo1jyXVbI68E9F8smRbCq/3R4eDxgFWrmjauRuBy0EXWITAMwzAM08ax5LqtqSwB/ucIHAgFlJT+TbCrp4jweJLyw4dlFmJDnZh0QtYhMAzDMAzTxrHkuq2RUwBMJgBDhgNnzkhWBQkMBPbskez19CTlb/lEp+Zg1h+zZB0CwzAMwzBtHFuK7x20yKX4WrGHxQ/RWaOzrMNgGKYNYkvxMYxssaX4GNmpLAEO2En2rcyx28dkHQLDMAzDtEmrV69G3759ZR1Gs8CS67ZGThHot1iyb2WMOhjJOgSGYZgWx9vbGzweDxs3bpQqP3bsGHiv3PgeGBgIKysrqKmpQUtLC9bW1tiwYYNUm+LiYqxcuRK9e/eGiooKtLW1MWDAAGzevBnPnj2rcf2DBw+Cz+fD17d+DwHz9vbGuHHjXvu6Pi5dugQXFxcYGBiAx+Ph2LFjtV6Hx+NJbU5OTlJtCgoK4OHhAQ0NDWhpacHHxwdCofCtYmmJanvPlixZgnPnzskmoGaGJddtDV8BMJ0g2bcyKvIqsg6BYRimRVJWVsamTZtqTX6rBQcHY+HChZg/fz4EAgHi4uLg7+8vlUwWFBRg0KBBCAkJwZIlS5CYmIhr165h3bp1uH79Og4cOFDjvEFBQfD398fBgwdRVlb2Xvr3qpKSElhZWeGnn36qs52TkxMeP37MbQcPHpSq9/DwwM2bNxEdHY0//vgDly5dwsyZM99n6O+NSCSCWCxu8PFqampsve7/x5LrtqZCCIT2luxbmaSHSbIOgWEYpkVycHCAnp5ejVHolx0/fhzu7u7w8fGBsbExevfujUmTJmHdunVcm//+97+4d+8ekpKSMHXqVPTp0weGhoYYNWoUDh48iDlz5kidMzs7G1euXMGyZctgYmKC8PDw99bHl40ePRrffPMNXF1d62ynpKQEPT09bmvfvj1Xl56ejsjISOzevRsDBw7E4MGDsWPHDhw6dAiPHj167Tlv376NwYMHQ1lZGb169cLZs2drjATfv38f7u7u0NLSQocOHTB27FjcvXuXq68erd+6dSv09fWhra0NX19fVFZWcm3Ky8uxZMkSdO7cGaqqqhg4cCAuXLjA1YeGhkJLSwvHjx9Hr169oKSkhHv37iE5ORkjR46Ejo4ONDU1MWzYMFy7do07rnv37gAAV1dX8Hg87vWr00LEYjHWrFmDLl26QElJCX379kVkZCRXf/fuXfB4PISHh+PDDz9Eu3btYGVlhfj4eK5NTk4OXFxc0L59e6iqqqJ37944depUnT+z5oAl122NvDIw/FvJvpXxsfGRdQgMwzDvrKREBB7vGni8aygpETXJNfl8PtavX48dO3bgwYMHtbbR09NDQkICcnJyaq0Xi8U4fPgwJk+eDAMDg1rbvDrNJCQkBM7OztDU1MTkyZMRFBT0bh2BJGl89ToNdeHCBejq6sLU1BSzZ8/G06dPubr4+HhoaWmhf//+XJmDgwPk5OSQmJhY6/lEIhHGjRuHdu3aITExEbt27cKKFSuk2lRWVsLR0RHq6uqIjY1FXFwc1NTU4OTkhIqKCq5dTEwMsrKyEBMTg7CwMISGhiI0NJSrnzt3LuLj43Ho0CGkpqZiwoQJcHJywp07d7g2L168wKZNm7B7927cvHkTurq6eP78Oby8vHD58mUkJCSgZ8+eGDNmDJ4/fw4ASE5OBiD52T1+/Jh7/aoffvgB27Ztw9atW5GamgpHR0d88sknUtcHgBUrVmDJkiUQCAQwMTHBpEmTUFVVBQDw9fVFeXk5Ll26hLS0NGzatAlqamqv/Xk1G8Q0WFFREQGgoqIiWYfCEJFnhKesQ2AYpo0qLS2lW7duUWlp6TufSyisIiCFgBQSCqsaIbq6eXl50dixY4mIaNCgQTRt2jQiIoqIiKCX04RHjx7RoEGDCACZmJiQl5cXHT58mEQiERER5ebmEgD69ttvpc5vY2NDqqqqpKqqShMnTuTKRSIRde3alY4dO0ZERE+ePCFFRUX6+++/6x1vba/Dw8PJ1NS03v0HQBERETXKDx48SL///julpqZSREQEmZub04ABA6iqSvIzWbduHZmYmNQ4rmPHjvTzzz/Xeq3Tp0+TvLw8PX78mCuLjo6WimHv3r1kampKYrGYa1NeXk4qKioUFRXF9dnQ0JCLhYhowoQJ9NlnnxERUU5ODvH5fHr48KHU9UeMGEHLly8nIqKQkBACQAKBoM73RyQSkbq6Op04cYIrq+09CwgIICsrK+61gYEBrVu3TqrNgAEDaM6cOURElJ2dTQBo9+7dXP3NmzcJAKWnpxMRkaWlJa1evbrO+KrV9TfY1PkaG7luayqeA792kexbmbBxYbIOgWEYpsFKSkT/v4lfKhNz5U1h06ZNCAsLQ3p6eo06fX19xMfHIy0tDQsWLEBVVRW8vLzg5ORU51zdiIgICAQCODo6orS0lCuPjo5GSUkJxowZAwDQ0dHByJEjERwcDACIjY2Fmpoat+3fv79efXB1dcXt27ffptu1mjhxIj755BNYWlpi3Lhx+OOPP5CcnCw1teJtZWRkoGvXrtDT0+PKbG1tpdrcuHEDmZmZUFdX5/reoUMHlJWVISsri2vXu3dv8Pl87rW+vj7y8/MBAGlpaRCJRDAxMZF6Dy9evCh1DkVFRfTp00fq+nl5eZgxYwZ69uwJTU1NaGhoQCgU4t69e/XuZ3FxMR49egR7e3upcnt7+xq/Wy9fX19fHwC4fsyfPx/ffPMN7O3tERAQgNTU1HrHIEvysg6AaWLyKoDLEcm+lXE56MKe0sgwTIulpnajRlmnTmncfxPZvPcYhg4dCkdHRyxfvhze3t61trGwsICFhQXmzJmDWbNmYciQIbh48SKGDRsGLS0tZGRkSLXv1q0bAEBdXR2FhYVceVBQEAoKCqCi8u+/R2KxGKmpqfj666/Rv39/CAQCrq5Tp06N1s+G+OCDD6Cjo4PMzEyMGDECenp6XBJYraqqCgUFBVLJ89sSCoXo169frR8mOnbsyP23goL0wgQ8Ho/7kCMUCsHn85GSkiKVgAOQmlahoqJSYwqNl5cXnj59ih9++AGGhoZQUlKCnZ2d1JSUxvRyP6pjqe7H9OnT4ejoiJMnT+LMmTPYsGEDtm3bhnnz5r2XWBoLG7lua+TkAQM7yb6VYYk1wzDMu9u4cSNOnDghdWPZ6/Tq1QuAZPUNOTk5uLu7Y9++fXXe0AcAT58+xe+//45Dhw5BIBBw2/Xr1/Hs2TOcOXMGKioqMDY25jZ1dfVG6V9DPXjwAE+fPuVGV+3s7FBYWIiUlBSuzfnz5yEWizFw4MBaz2Fqaor79+8jLy+PK3t1zrKNjQ3u3LkDXV1dqf4bGxtDU1OzXrFaW1tDJBIhPz+/xjnelPjHxcVh/vz5GDNmDHr37g0lJSX8888/Um0UFBQgEr3+2xQNDQ0YGBggLi6uxrmrf2fqq2vXrpg1axbCw8Px5ZdfIjAw8K2OlwWWXLc15cXADg3JvpVZFLlI1iEwDMM0mFBoBaHQCnl5llxZXp4lV95ULC0t4eHhge3bt0uVz549G2vXrkVcXBxycnKQkJAAT09PdOzYEXZ2dgCA9evXo3PnzrC1tUVwcDBSU1ORlZWFiIgIxMfHc6Ooe/fuhba2Ntzd3bmRcAsLC1hZWWHMmDHvdGNjREQEzMzM6mwjFAq5hB6QrFoiEAi4qQ9CoRB+fn5ISEjA3bt3ce7cOYwdOxbGxsZwdHQEAJibm8PJyQkzZsxAUlIS4uLiMHfuXEycOPG1N3SOHDkSRkZG8PLyQmpqKuLi4vDVV18B+HfU1sPDAzo6Ohg7dixiY2ORnZ2NCxcuYP78+a+92fRVJiYm8PDwgKenJ8LDw5GdnY2kpCRs2LABJ0+erPPYnj17Yu/evUhPT0diYiI8PDykvl0AJCuGnDt3Drm5ua9dvtHPzw+bNm3C4cOHkZGRgWXLlkEgEGDBggX16gMALFy4EFFRUcjOzsa1a9cQExMDc3Pzeh8vKyy5bmsUVIFJ8ZJ9K+NrW78HEDAMwzRHqqr8/9/kXiqT48qb0po1a2rMo3ZwcEBCQgImTJgAExMTuLm5QVlZGefOnePWN9bW1kZSUhI8PT2xZcsW2NrawtLSEqtXr8Znn33GjToGBwdzS7m9ys3NDcePH68xWlpfRUVFNaamvOrq1auwtraGtbU1AGDx4sWwtrbGqlWrAEhWT0lNTcUnn3wCExMT+Pj4oF+/foiNjYWSkhJ3nv3798PMzAwjRozAmDFjMHjwYOzateu11+Xz+Th27BiEQiEGDBiA6dOnc6uFVD+yu127drh06RK6deuG8ePHw9zcHD4+PigrK3urR3eHhITA09MTX375JUxNTTFu3DgkJydz03ReJygoCM+ePYONjQ2mTJmC+fPnQ1dXV6rNtm3bEB0dja5du3Lv4avmz5+PxYsX48svv4SlpSUiIyNx/Phx9OzZs959EIlE8PX15T7ImJiY4Oeff6738bLCIyKSdRAtVVM/q56pW/D1YEyznibrMBiGaYPKysqQnZ2NHj16cElSQ5WUiLj510KhVZMn1kzTiouLw+DBg5GZmQkjI/ak4Yaq62+wqfO11jfxlqlbeTHwoyYwtwhQal0fCNort39zI4ZhmGZOVZXfJDcvMrIREREBNTU19OzZE5mZmViwYAHs7e1ZYt2KsOS6rVFUA2bel+xbmc4anWUdAsMwDMPU6fnz51i6dCnu3bsHHR0dODg4YNu2bbIOi2lELLluc3iAooZk38pEZUbBtrPtmxsyDMMwjIx4enrC09NT1mEw7xG7obGtqXgumRbSCh8i8+V/vpR1CAzDMAzDtHEsuW5rFNUl860VZbte6Pvgfcxb1iEwDMMwDNPGseS6zSGgoliyb2V+m/CbrENgGIZhGKaNa3bJ9aVLl+Di4gIDAwPweDwcO3bstW1nzZoFHo+H77//Xqq8oKAAHh4e0NDQgJaWFnx8fCAUCqXapKamYsiQIVBWVkbXrl2xefPm99CbZqhCCOzqKtm3Mi4HXWQdAsMwDMMwbVyzS65LSkpgZWWFn376qc52ERERSEhIqPUpSB4eHrh58yaio6Pxxx9/4NKlS5g5cyZXX1xcjFGjRsHQ0BApKSnYsmULVq9eXefC762GkgbwJbW6ZfgA9vhzhmEYhmFkr9mtFjJ69GiMHj26zjYPHz7EvHnzEBUVBWdnZ6m69PR0REZGIjk5Gf379wcA7NixA2PGjMHWrVthYGCA/fv3o6KiAsHBwVBUVETv3r0hEAjw7bffSiXhrZJYBBTcBjqYAXKt68EE/z33X6wfsV7WYTAMwzAM04Y1u5HrNxGLxZgyZQr8/PzQu3fvGvXx8fHQ0tLiEmtA8shWOTk5JCYmcm2GDh0KRUVFro2joyMyMjLw7Nmz1167vLwcxcXFUluLU1kCHLST7FuZSRaTZB0CwzAMw7RJ3t7eGDdunKzDaBZaXHK9adMmyMvLY/78+bXW5+bmQldXV6pMXl4eHTp0QG5uLtemU6dOUm2qX1e3qc2GDRugqanJbV27dn2XrsiGkgYwr7hVTgtJy0+TdQgMwzAtjre3N3g8HjZu3ChVfuzYMfB40s9ECAwMhJWVFdTU1KClpQVra2ts2LBBqk1xcTFWrlyJ3r17Q0VFBdra2hgwYAA2b95c6wDWwYMHwefz4evrW+94X07iGpLUbdiwAQMGDIC6ujp0dXUxbtw4ZGRkSLUpKyuDr68vtLW1oaamBjc3N+Tl5Um1uXfvHpydndGuXTvo6urCz88PVVVVbxVLS3P37l3weDwIBAKp8h9++AGhoaEyiam5aVHJdUpKCvfDe/UPviksX74cRUVF3Hb//v0mj+GdiauAR/GSPcMwDMMAUFZWxqZNm+r89jY4OBgLFy7E/PnzIRAIEBcXB39/f6kFAwoKCjBo0CCEhIRgyZIlSExMxLVr17Bu3Tpcv34dBw4cqHHeoKAg+Pv74+DBgygrK3sv/XvVxYsX4evri4SEBERHR6OyshKjRo1CScm/3+ouWrQIJ06cwJEjR3Dx4kU8evQI48eP5+pFIhGcnZ1RUVGBK1euICwsDKGhoVi1alWT9KGxVVRUvNPxmpqa0NLSapxgWjpqxgBQREQE9/q7774jHo9HfD6f2wCQnJwcGRoaEhFRUFAQaWlpSZ2nsrKS+Hw+hYeHExHRlClTaOzYsVJtzp8/TwCooKCg3vEVFRURACoqKmpQ/2SivJjol86SfSuTmpsq6xAYhmmjSktL6datW1RaWvrO5xIKywnYQsAWEgrLGyG6unl5edHHH39MZmZm5Ofnx5VHRETQy2nC2LFjydvbu85zffHFF6SqqkoPHz6stV4sFku9/vvvv0lFRYUKCwtp4MCBtH///nrF+/K/4a++boj8/HwCQBcvXiQiosLCQlJQUKAjR45wbdLT0wkAxcfHExHRqVOnSE5OjnJzc7k2O3fuJA0NDSovf/3PLS4ujqysrEhJSYn69evHvc/Xr1/n2qSlpZGTkxOpqqqSrq4uTZ48mZ48ecLVDxs2jObNm0d+fn7Uvn176tSpEwUEBEhd59mzZ+Tj40M6Ojqkrq5OH374IQkEAq4+ICCArKysKDAwkLp37048Ho+IiE6fPk329vakqalJHTp0IGdnZ8rMzOSOg2QtX24bNmwYEdX8OZSVldG8efOoY8eOpKSkRPb29pSUlMTVx8TEEAA6e/Ys9evXj1RUVMjOzo5u377NtREIBDR8+HBSU1MjdXV1srGxoeTk5Frf17r+Bps6X2tRI9dTpkxBamoqBAIBtxkYGMDPzw9RUVEAADs7OxQWFiIlJYU77vz58xCLxRg4cCDX5tKlS6isrOTaREdHw9TUFO3bt2/aTjU1RXXgiwet8iEyB/88KOsQGIZhWiQ+n4/169djx44dePDgQa1t9PT0kJCQgJycnFrrxWIxDh8+jMmTJ9e6kheAGt86h4SEwNnZGZqampg8eTKCgoLerSNAg77dLioqAgB06NABgOSb8srKSjg4OHBtzMzM0K1bN8THxwOQ3L9laWkpNc3U0dERxcXFuHnzZq3XKS4uhouLCywtLXHt2jWsXbsWS5culWpTWFiIjz76CNbW1rh69SoiIyORl5cHd3d3qXZhYWFQVVVFYmIiNm/ejDVr1iA6OpqrnzBhAvLz83H69GmkpKTAxsYGI0aMQEFBAdcmMzMTR48eRXh4ODfNo6SkBIsXL8bVq1dx7tw5yMnJwdXVFWKxGACQlJQEADh79iweP36M8PDwWvvq7++Po0ePIiwsDNeuXYOxsTEcHR2lrg8AK1aswLZt23D16lXIy8tj2rRpXJ2Hhwe6dOmC5ORkpKSkYNmyZVBQUKj1es1Kk6Twb+H58+d0/fp1un79OgGgb7/9lq5fv045OTm1tjc0NKTvvvtOqszJyYmsra0pMTGRLl++TD179qRJkyZx9YWFhdSpUyeaMmUK/fnnn3To0CFq164d/frrr28Va4scuRZVEmVHSvYMwzBMo2iMkWuhsJyEwnLKyxNyI9d5eUKu/H15ecRx0KBBNG3aNCKqOXL96NEjGjRoEAEgExMT8vLyosOHD5NIJCIiotzcXO7f7ZfZ2NiQqqoqqaqq0sSJE7lykUhEXbt2pWPHjhER0ZMnT0hRUZH+/vvvesdb2+vw8HAyNTWtd/9FIhE5OzuTvb09V7Z//35SVFSs0XbAgAHk7+9PREQzZsygUaNGSdWXlJQQADp16lSt19q5cydpa2tL/Z4EBgZKjVyvXbu2xnnv379PACgjI4OIJCPXgwcPrhHb0qVLiYgoNjaWNDQ0qKysTKqNkZERl+sEBASQgoIC5efn1/7G/L8nT54QAEpLSyMiouzs7Boj7UTSPwehUEgKCgpS30RUVFSQgYEBbd68mYikR66rnTx5kgBw74+6ujqFhobWGV81NnJdh6tXr8La2hrW1tYAgMWLF8Pa2vqt5jDt378fZmZmGDFiBMaMGYPBgwdLrWGtqamJM2fOIDs7G/369cOXX36JVatWtf5l+ACgqgy4sFiyb2XYQ2QYhmnJ1NS2Q01tOzp12smVdeq0kytvCps2bUJYWBjS09Nr1Onr6yM+Ph5paWlYsGABqqqq4OXlBScnJ25UszYREREQCARwdHREaWkpVx4dHY2SkhKMGTMGAKCjo4ORI0ciODgYABAbGws1NTVu279/f7364Orqitu3b9e7z76+vvjzzz9x6NCheh/TUBkZGejTpw+UlZW5MltbW6k2N27cQExMjFTfzczMAABZWVlcuz59+kgdp6+vj/z8fO4cQqGQuxmzesvOzpY6h6GhITp27Ch1njt37mDSpEn44IMPoKGhge7duwOQ3LxZX1lZWaisrIS9vT1XpqCgAFtb2xq/Wy/3Q19fHwC4fixevBjTp0+Hg4MDNm7cKBV7c9bs1rkePnw4iOr/aO67d+/WKOvQoUOtN028rE+fPoiNjX3b8Fo+RTXAu/avq1o69hAZhmGYdzN06FA4Ojpi+fLl8Pb2rrWNhYUFLCwsMGfOHMyaNQtDhgzBxYsXMWzYMGhpadVYdaNbt24AAHV1dRQWFnLlQUFBKCgogIqKClcmFouRmpqKr7/+Gv3795dakeLVVb4aw9y5c7mHzXXp0oUr19PTQ0VFBQoLC6Vu0svLy4Oenh7XpnqKxMv11XUNJRQK4eLigk2bNtWoq04+AdSYHsHj8bgPOUKhEPr6+rhw4UKNc7zcH1VV1Rr1Li4uMDQ0RGBgIAwMDCAWi2FhYfHONzy+zsv9qJ7OU92P1atX4/PPP8fJkydx+vRpBAQE4NChQ3B1dX0vsTSWZjdyzbxnokog44hk38q4H3F/cyOGYZhmSiicD6FwPvLyZnNleXmzufKmsnHjRpw4cYKbW1yXXr16AZDM05WTk4O7uzv27duHR48e1Xnc06dP8fvvv+PQoUNS91Fdv34dz549w5kzZ6CiogJjY2NuU1dvvHuFiAhz585FREQEzp8/jx49ekjV9+vXDwoKCjh37hxXlpGRgXv37sHOzg6A5P6ttLQ0bpQVkIzGa2hocO/Lq0xNTZGWloby8nKuLDk5WaqNjY0Nbt68ie7du0v139jYuNZkuDY2NjbIzc2FvLx8jXPo6Oi89rinT58iIyMDX331FUaMGAFzc/MaK8hUPyNEJBK99jxGRkZQVFREXFwcV1ZZWYnk5OTXvjevY2JigkWLFuHMmTMYP348QkJC3up4WWDJdVsjrgBSvpXsW5nQcaGyDoFhGKbBVFUV/39TeKlMgStvKpaWlvDw8MD27dJTUWbPno21a9ciLi4OOTk5SEhIgKenJzp27MglnOvXr0fnzp1ha2uL4OBgpKamIisrCxEREYiPjwefL3ky8N69e6GtrQ13d3duJNzCwgJWVlYYM2bMO93YGBERwU2jeB1fX1/s27cPBw4cgLq6OnJzc5Gbm8tNW9HU1ISPjw8WL16MmJgYpKSkYOrUqbCzs8OgQYMAAKNGjUKvXr0wZcoU3LhxA1FRUfjqq6/g6+sLJSWlWq/7+eefQywWY+bMmUhPT0dUVBS2bt0K4N9RW19fXxQUFGDSpElITk5GVlYWoqKiMHXq1DoT2pc5ODjAzs4O48aNw5kzZ3D37l1cuXIFK1aswNWrV197XPv27aGtrY1du3YhMzMT58+fx+LFi6Xa6OrqQkVFhbvRsvpm0Jepqqpi9uzZ8PPzQ2RkJG7duoUZM2bgxYsX8PHxqVcfSktLMXfuXFy4cAE5OTmIi4tDcnIyzM3N63W8LLHkuq1RUAU+j5fsW5ltV7bJOgSGYZhWYc2aNTXmUTs4OCAhIQETJkyAiYkJ3NzcoKysjHPnzkFbWxsAoK2tjaSkJHh6emLLli2wtbWFpaUlVq9ejc8++wyBgYEAJGtmu7q61rqqh5ubG44fP45//vmnQbEXFRXVmJryqp07d6KoqAjDhw+Hvr4+tx0+fJhr89133+Hjjz+Gm5sbhg4dCj09PamVMfh8Pv744w/w+XzY2dlh8uTJ8PT0xJo1a157XQ0NDZw4cQICgQB9+/bFihUruHvKqudhGxgYIC4uDiKRCKNGjYKlpSUWLlwILS0tyMnVL23j8Xg4deoUhg4diqlTp8LExAQTJ05ETk5OndNr5OTkcOjQIaSkpMDCwgKLFi3Cli1bpNrIy8tj+/bt+PXXX2FgYICxY8fWeq6NGzfCzc0NU6ZMgY2NDTIzMxEVFVXvVdn4fD6ePn0KT09PmJiYwN3dHaNHj8bXX39dr+NliUdvM8GZkVJcXAxNTU0UFRVBQ6OFPPFQVAHc3AP09gT4TTcS0hSSHibBtrPtmxsyDMM0srKyMmRnZ6NHjx5SN6sxzJvs378fU6dORVFRkdT8c+bt1PU32NT5WrO7oZF5z8SVwF9HAPNJrS65flj8EOgs6ygYhmEY5vX27NmDDz74AJ07d8aNGzewdOlSuLu7s8S6FWHJdVujoAp8GiXrKN6LZ2Wvf2wvwzAMwzQHubm5WLVqFXJzc6Gvr48JEyZg3bp1sg6LaUQsuW5rqsoBwU9AX19AvvYbLlqqoYZDZR0CwzAMw9TJ398f/v7+sg6DeY/YDY1tDYmAx/GSfSvzU9JPsg6BYRiGYZg2jo1ctzUK7QCXI7KO4r34zuk7WYfAMAzDMEwbx0au25qqcuDKasm+lWGPP2cYhmEYRtZYct3WkBh4/kCyb2XY488ZhmEYhpE1lly3NQoqgONuyb6V8TrmJesQGIZhGIZp41hy3dZUlQEXFkv2rcx3jmzONcMwDMMwssWSa6bVCLoWJOsQGIZhGKZN8vb2xrhx42QdRrPAkuu2Rl4ZGP6tZN/KsEefMwzDvD1vb2/weDxs3LhRqvzYsWPg8XhSZYGBgbCysoKamhq0tLRgbW2NDRs2SLUpLi7GypUr0bt3b6ioqEBbWxsDBgzA5s2b8exZzYd9HTx4EHw+H76+vvWO9+UkriFJ3YYNGzBgwACoq6tDV1cX48aNQ0ZGhlSb4cOHg8fjSW2zZs2SanPv3j04OzujXbt20NXVhZ+fH6qqqt4qlpbm7t274PF4EAgEUuU//PADQkNDZRJTc8OS67amshSImi7ZtzKlVa2vTwzDME1BWVkZmzZtqjX5rRYcHIyFCxdi/vz5EAgEiIuLg7+/P4RCIdemoKAAgwYNQkhICJYsWYLExERcu3YN69atw/Xr13HgwIEa5w0KCoK/vz8OHjyIsrKmmbJ48eJF+Pr6IiEhAdHR0aisrMSoUaNQUlIi1W7GjBl4/Pgxt23evJmrE4lEcHZ2RkVFBa5cuYKwsDCEhoZi1apVTdKHxlZRUfFOx2tqakJLS6txgmnpiGmwoqIiAkBFRUWyDqX+KsuI4gIk+1bmx8QfZR0CwzBtVGlpKd26dYtKS0vf+VxCYTkBqwlYTUJheSNEVzcvLy/6+OOPyczMjPz8/LjyiIgIejlNGDt2LHl7e9d5ri+++IJUVVXp4cOHtdaLxWKp13///TepqKhQYWEhDRw4kPbv31+veMeOHfva1w2Rn59PAOjixYtc2bBhw2jBggWvPebUqVMkJydHubm5XNnOnTtJQ0ODystf/3OLi4sjKysrUlJSon79+nHv8/Xr17k2aWlp5OTkRKqqqqSrq0uTJ0+mJ0+eSMU2b9488vPzo/bt21OnTp0oICBA6jrPnj0jHx8f0tHRIXV1dfrwww9JIBBw9QEBAWRlZUWBgYHUvXt34vF4RER0+vRpsre3J01NTerQoQM5OztTZmYmdxwAqW3YsGFEVPPnUFZWRvPmzaOOHTuSkpIS2dvbU1JSElcfExNDAOjs2bPUr18/UlFRITs7O7p9+zbXRiAQ0PDhw0lNTY3U1dXJxsaGkpOTa31f6/obbOp8jY1ctzXySsB/Vre6R58DwDizcbIOgWEYpkXi8/lYv349duzYgQcPHtTaRk9PDwkJCcjJyam1XiwW4/Dhw5g8eTIMDAxqbfPqNJOQkBA4OztDU1MTkydPRlDQu987ExoaWuM6b1JUVAQA6NChg1T5/v37oaOjAwsLCyxfvhwvXrzg6uLj42FpaYlOnTpxZY6OjiguLsbNmzdrvU5xcTFcXFxgaWmJa9euYe3atVi6dKlUm8LCQnz00UewtrbG1atXERkZiby8PLi7u0u1CwsLg6qqKhITE7F582asWbMG0dHRXP2ECROQn5+P06dPIyUlBTY2NhgxYgQKCgq4NpmZmTh69CjCw8O5aR4lJSVYvHgxrl69inPnzkFOTg6urq4QiyVL+CYlJQEAzp49i8ePHyM8PLzWvvr7++Po0aMICwvDtWvXYGxsDEdHR6nrA8CKFSuwbds2XL16FfLy8pg2bRpX5+HhgS5duiA5ORkpKSlYtmwZFBQUar1es9IkKXwr1SJHritKiI5/Ktm3Ml+c+ELWITAM00Y1xsi1UFhOQmE55eUJuZHrvDwhV/6+vDziOGjQIJo2bRoR1Ry5fvToEQ0aNIgAkImJCXl5edHhw4dJJBIREVFubi4BoG+//Vbq/DY2NqSqqkqqqqo0ceJErlwkElHXrl3p2LFjRET05MkTUlRUpL///rve8db2Ojw8nExNTevdf5FIRM7OzmRvby9V/uuvv1JkZCSlpqbSvn37qHPnzuTq6srVz5gxg0aNGiV1TElJCQGgU6dO1XqtnTt3kra2ttTvSWBgoNTI9dq1a2uc9/79+wSAMjIyiEgycj148GCpNgMGDKClS5cSEVFsbCxpaGhQWZn0t9RGRkb066+/EpFk5FpBQYHy8/PrfH+ePHlCACgtLY2IiLKzs2uMtBNJ/xyEQiEpKChIfRNRUVFBBgYGtHnzZiKSHrmudvLkSQLAvT/q6uoUGhpaZ3zV2Mg1Izs8PqBvJ9m3Mr98/IusQ2AYhmkwNbUNUFPbgE6dtnJlnTpt5cqbwqZNmxAWFob09PQadfr6+oiPj0daWhoWLFiAqqoqeHl5wcnJiRvVrE1ERAQEAgEcHR1RWvrvvTHR0dEoKSnBmDFjAAA6OjoYOXIkgoODAQCxsbFQU1Pjtv3799erD66urrh9+3a9++zr64s///wThw4dkiqfOXMmHB0dYWlpCQ8PD+zZswcRERHIysqq97lflZGRgT59+kBZ+d9FBWxtpW/Gv3HjBmJiYqT6bmZmBgBS1+7Tp4/Ucfr6+sjPz+fOIRQKoa2tLXWe7OxsqXMYGhqiY8eOUue5c+cOJk2ahA8++AAaGhro3r07AMnNm/WVlZWFyspK2Nvbc2UKCgqwtbWt8bv1cj/09fUBgOvH4sWLMX36dDg4OGDjxo3v9N43JXlZB8A0MXkloP9iWUfxXrgcdGFPaWQYhnkHQ4cOhaOjI5YvXw5vb+9a21hYWMDCwgJz5szBrFmzMGTIEFy8eBHDhg2DlpZWjVU3unXrBgBQV1dHYWEhVx4UFISCggKoqPz7UDOxWIzU1FR8/fXX6N+/v9SKFC9Pv2gsc+fOxR9//IFLly6hS5cudbYdOHAgAMlUCiMjI+jp6XFTJKrl5eUBkEyhaSihUAgXFxds2rSpRl118gmgxvQIHo/HfcgRCoXQ19fHhQsXapzj5ZsOVVVVa9S7uLjA0NAQgYGBMDAwgFgshoWFxTvf8Pg6L/ejejpPdT9Wr16Nzz//HCdPnsTp06cREBCAQ4cOwdXV9b3E0lhYct3WVJYAv48HxoYDCjX/qFoyllgzDNOSCYXLAQAlJZXc6HVe3hKoqjbtHNONGzeib9++MDU1fWPbXr16AZDM05WTk4O7uzv27duHVatWvXbeNQA8ffoUv//+Ow4dOoTevXtz5SKRCIMHD8aZM2fg5OQEY2Pjd+9QLYgI8+bNQ0REBC5cuIAePXq88ZjqRL86wbWzs8O6deuQn58PXV1dAJLReA0NDe59eZWpqSn27duH8vJyKClJ7n1KTk6WamNjY4OjR4+ie/fukJdvWJpmY2OD3NxcyMvLcyPP9fH06VNkZGQgMDAQQ4YMAQBcvnxZqo2ioiIAyc/qdYyMjKCoqIi4uDgYGhoCACorK5GcnIyFCxe+VV9MTExgYmKCRYsWYdKkSQgJCWn2yTWbFtLWyCkAJhMk+1Zm1h+z3tyIYRimmVJVVfz/TeGlMgWuvKlUT4PYvn27VPns2bOxdu1axMXFIScnBwkJCfD09ETHjh1hZ2cHAFi/fj06d+4MW1tbBAcHIzU1FVlZWYiIiEB8fDz4fMmUxL1790JbWxvu7u7cSLiFhQWsrKwwZsyYd7qxMSIigptG8Tq+vr7Yt28fDhw4AHV1deTm5iI3N5ebtpKVlYW1a9ciJSUFd+/exfHjx+Hp6YmhQ4dy0xhGjRqFXr16YcqUKbhx4waioqLw1VdfwdfXl0ucX/X5559DLBZj5syZSE9PR1RUFLZulXyQqh619fX1RUFBASZNmoTk5GRkZWUhKioKU6dOrTOhfZmDgwPs7Owwbtw4nDlzBnfv3sWVK1ewYsUKXL169bXHtW/fHtra2ti1axcyMzNx/vx5LF4s/W23rq4uVFRUuBstq28GfZmqqipmz54NPz8/REZG4tatW5gxYwZevHgBHx+fevWhtLQUc+fOxYULF5CTk4O4uDgkJyfD3Ny8XsfLEkuu2xq+ItBnumTfyqwculLWITAMw7QKa9asqTGP2sHBAQkJCZgwYQJMTEzg5uYGZWVlnDt3Dtra2gAAbW1tJCUlwdPTE1u2bIGtrS0sLS2xevVqfPbZZwgMDAQgWTPb1dW11lU93NzccPz4cfzzzz8Nir2oqKjG1JRX7dy5E0VFRRg+fDj09fW57fDhwwAko7Nnz57FqFGjYGZmhi+//BJubm44ceLfb0j5fD7++OMP8Pl82NnZYfLkyfD09MSaNWtee10NDQ2cOHECAoEAffv2xYoVK7h1savnYRsYGCAuLg4ikQijRo2CpaUlFi5cCC0tLcjJ1S9t4/F4OHXqFIYOHYqpU6fCxMQEEydORE5OTp3Ta+Tk5HDo0CGkpKTAwsICixYtwpYtW6TayMvLY/v27fj1119hYGCAsWPH1nqujRs3ws3NDVOmTIGNjQ0yMzMRFRWF9u3b16sPfD4fT58+haenJ0xMTODu7o7Ro0fj66+/rtfxssQjIpJ1EC1VcXExNDU1UVRUBA0NDVmHUz+VJcARB2DC2VY3LeSnpJ/ga1u/J3wxDMM0prKyMmRnZ6NHjx5SN6sxzJvs378fU6dORVFRkdT8c+bt1PU32NT5WrMbub506RJcXFxgYGAAHo+HY8eOcXWVlZVYunQpLC0toaqqCgMDA3h6euLRo0dS5ygoKICHhwc0NDSgpaUFHx8fqSdIAUBqaiqGDBkCZWVldO3aVeqpS62anCLQb7Fk38oYdTCSdQgMwzAMU6c9e/bg8uXLyM7OxrFjx7B06VK4u7uzxLoVaXbJdUlJCaysrPDTTz/VqHvx4gWuXbuGlStX4tq1awgPD0dGRgY++eQTqXYeHh64efMmoqOjubuAZ86cydUXFxdj1KhRMDQ0REpKCrZs2YLVq1dj165d771/MsdXAEwnSPatjIo8+x8TwzAM07zl5uZi8uTJMDc3x6JFizBhwoS2kX+0Ic16WgiPx0NERATGjRv32jbJycmwtbVFTk4OunXrhvT0dPTq1QvJycno378/ACAyMhJjxozBgwcPYGBggJ07d2LFihXIzc3l7npdtmwZjh079lZrY7bIaSEVQuDAQODzREBRTdbRNKotcVvgZ+8n6zAYhmmD2LQQhpEtNi2kERUVFYHH43HrNsbHx0NLS4tLrAHJTRhycnJITEzk2gwdOpRLrAHJI0szMjLw7Nmz116rvLwcxcXFUluLI68MDP9Wsm9lfGzqdwcywzAMwzDM+9Kik+uysjIsXboUkyZN4j6J5ObmcutNVpOXl0eHDh2Qm5vLtXn1btnq19VtarNhwwZoampyW9euXRuzO01DTh7o7ijZtzKLohbJOgSGYRiGYdq4FptcV1ZWwt3dHUSEnTt3Nsk1ly9fjqKiIm67f/9+k1y3UVU8B37tItm3MmHjwmQdAsMwDMMwbVyLTK6rE+ucnBzuaUjV9PT0uGfSV6uqqkJBQQH3OFI9PT3uEaXV6vPIUiUlJWhoaEhtLY68CuByRLJvZVwOusg6BIZhGIZh2rgWl1xXJ9Z37tzB2bNnuYXrq9nZ2aGwsBApKSlc2fnz5yEWizFw4ECuzaVLl1BZWcm1iY6Ohqmpab0XN2+x5OQBA7tWOS2EPf6cYRiGYRhZa3bJtVAohEAggEAgAABkZ2dDIBDg3r17qKysxKeffoqrV69i//79EIlE3CNLKyoqAADm5uZwcnLCjBkzkJSUhLi4OMydOxcTJ06EgYEBAMnjRxUVFeHj44ObN2/i8OHD+OGHH2o84rNVKi8GdmhI9q3Mokg255phGIZhGNlqdkvxXbhwAR9++GGNci8vL6xevRo9evSo9biYmBgMHz4cgOQhMnPnzsWJEycgJycHNzc3bN++HWpq/y49l5qaCl9fXyQnJ0NHRwfz5s3D0qVL3yrWFrkUn1gEFNwGOpgBcnxZR9OoMgsyYdzBWNZhMAzTBrGl+Ji2ztvbG4WFhVIP/2tKbCm+OgwfPhxEVGMLDQ1F9+7da60jIi6xBoAOHTrgwIEDeP78OYqKihAcHCyVWANAnz59EBsbi7KyMjx48OCtE+sWS44P6PRudYk1AFzKuSTrEBiGYVocb29v8Hg8bNy4Uar82LFj4PF4UmWBgYGwsrKCmpoatLS0YG1tjQ0bNki1KS4uxsqVK9G7d2+oqKhAW1sbAwYMwObNm2td7vbgwYPg8/nw9fWtd7wvP//i1df1sXPnTvTp04e7f8rOzg6nT5+WalNWVgZfX19oa2tDTU0Nbm5uNe7XunfvHpydndGuXTvo6urCz88PVVVVbxVLS3P37l3weDxuhkG1H374AaGhoTKJqblpdsk1856VFwPbeK1yWkh75VY+X55hGOY9UVZWxqZNm+p81kNwcDAWLlyI+fPnQyAQIC4uDv7+/hAKhVybgoICDBo0CCEhIViyZAkSExNx7do1rFu3DtevX8eBAwdqnDcoKAj+/v44ePAgysrK3kv/XtWlSxds3LgRKSkpuHr1Kj766COMHTsWN2/e5NosWrQIJ06cwJEjR3Dx4kU8evQI48eP5+pFIhGcnZ1RUVGBK1euICwsDKGhoVi1alWT9KGxVU+vbShNTU3umSNtHjENVlRURACoqKhI1qHUn1hEVHxfsm9lEh8kyjoEhmHaqNLSUrp16xaVlpa+87lKSspIX38h6esvpJKSskaIrm5eXl708ccfk5mZGfn5+XHlERER9HKaMHbsWPL29q7zXF988QWpqqrSw4cPa60Xi8VSr//++29SUVGhwsJCGjhwIO3fv79e8Y4dO/a1rxuqffv2tHv3biIiKiwsJAUFBTpy5AhXn56eTgAoPj6eiIhOnTpFcnJylJuby7XZuXMnaWhoUHl5+WuvExcXR1ZWVqSkpET9+vXj3ufr169zbdLS0sjJyYlUVVVJV1eXJk+eTE+ePOHqhw0bRvPmzSM/Pz9q3749derUiQICAqSu8+zZM/Lx8SEdHR1SV1enDz/8kAQCAVcfEBBAVlZWFBgYSN27dycej0dERKdPnyZ7e3vS1NSkDh06kLOzM2VmZnLHAZDahg0bRkQ1fw5lZWU0b9486tixIykpKZG9vT0lJSVx9TExMQSAzp49S/369SMVFRWys7Oj27dvc20EAgENHz6c1NTUSF1dnWxsbCg5ObnW97Wuv8GmztfYyHWbwwMUNST7ViYqM0rWITAMw7RIfD4f69evx44dO/DgwYNa2+jp6SEhIQE5OTm11ovFYhw+fBiTJ0/mFhB41avTTEJCQuDs7AxNTU1MnjwZQUFB79YRAKGhoTWuUxeRSIRDhw6hpKQEdnZ2AICUlBRUVlbCwcGBa2dmZoZu3bohPj4egORpz5aWllIPpXN0dERxcbHUCPjLiouL4eLiAktLS1y7dg1r166tMS21sLAQH330EaytrXH16lVERkYiLy8P7u7uUu3CwsKgqqqKxMREbN68GWvWrEF0dDRXP2HCBOTn5+P06dNISUmBjY0NRowYgYKCAq5NZmYmjh49ivDwcG6aR0lJCRYvXoyrV6/i3LlzkJOTg6urK8RiMQAgKSkJAHD27Fk8fvwY4eHhtfbV398fR48eRVhYGK5duwZjY2M4OjpKXR8AVqxYgW3btuHq1auQl5fHtGnTuDoPDw906dIFycnJSElJwbJly6CgoFDr9ZqVJknhW6kWOXJdVkS0FZJ9K1NSUSLrEBiGaaMaY+S6pKSMSkrK6MmTYm7k+smTYq78fXl5xHHQoEE0bdo0Iqo5cv3o0SMaNGgQASATExPy8vKiw4cPk0gk+SY0NzeXANC3334rdX4bGxtSVVUlVVVVmjhxIlcuEomoa9eudOzYMSIievLkCSkqKtLff/9d73hrex0eHk6mpqZv7HdqaiqpqqoSn88nTU1NOnnyJFe3f/9+UlRUrHHMgAEDyN/fn4iIZsyYQaNGjZKqLykpIQB06tSpWq+5c+dO0tbWlvo9CQwMlBq5Xrt2bY3z3r9/nwBQRkYGEUlGrgcPHlwjtqVLlxIRUWxsLGloaFBZmfTvjZGREf36669EJBm5VlBQoPz8/NrfoP/35MkTAkBpaWlERJSdnV1jpJ1I+ucgFApJQUFB6puIiooKMjAwoM2bNxOR9Mh1tZMnTxIA7v1RV1en0NDQOuOrxkauGdlRVAfmFkn2rYz3MW9Zh8AwDNNgxsbLYGy8DH36/Dtnt0+fVVx5U9i0aRPCwsKQnp5eo05fXx/x8fFIS0vDggULUFVVBS8vLzg5OXGjmrWJiIiAQCCAo6MjSktLufLo6GiUlJRgzJgxAAAdHR2MHDkSwcHBAIDY2Fioqalx2/79++vVB1dXV9y+ffuN7UxNTSEQCJCYmIjZs2fDy8sLt27dqtc1GiojIwN9+vSRWs3C1tZWqs2NGzcQExMj1XczMzMAQFZWFteuT58+Usfp6+tzD9G7ceMGhEIhdzNm9ZadnS11DkNDQ3Ts2FHqPHfu3MGkSZPwwQcfQENDA927dwcguXmzvrKyslBZWQl7e3uuTEFBAba2tjV+t17uh76+PgBw/Vi8eDGmT58OBwcHbNy4USr25qz1PUmEeQMCKooBRTW0tqkhv034TdYhMAzDtGhDhw6Fo6Mjli9fDm9v71rbWFhYwMLCAnPmzMGsWbMwZMgQXLx4EcOGDYOWlhYyMjKk2nfr1g0AoK6ujsLCQq48KCgIBQUFUFH594nBYrEYqamp+Prrr9G/f3+pFSlenn7RGBQVFWFsLFm+tV+/fkhOTsYPP/yAX3/9FXp6eqioqEBhYaHUTXp5eXlST3uuniLxcn11XUMJhUK4uLhg06ZNNeqqk08ANaZH8Hg87kOOUCiEvr4+Lly4UOMcL/dHVVW1Rr2LiwsMDQ0RGBgIAwMDiMViWFhYvPMNj6/zcj+qp/NU92P16tX4/PPPcfLkSZw+fRoBAQE4dOgQXF1d30ssjYWNXLc1FUJgV1fJvpVhjz9nGKYly8zciMzMjUhNXcOVpaau4cqbysaNG3HixAlubnFdevXqBUAyT1dOTg7u7u7Yt28fHj16VOdxT58+xe+//45Dhw5xD44TCAS4fv06nj17hjNnzkBFRQXGxsbcpq7+fr9xFYvFKC8vByBJthUUFHDu3DmuPiMjA/fu3ePmZdvZ2SEtLY0bZQUko/EaGhrc+/IqU1NTpKWlcdcBgOTkZKk2NjY2uHnzJrp37y7Vf2Nj41qT4drY2NggNzcX8vLyNc6ho6Pz2uOePn2KjIwMfPXVVxgxYgTMzc1rrCCjqKgIQDJX/XWMjIygqKiIuLg4rqyyshLJycmvfW9ex8TEBIsWLcKZM2cwfvx4hISEvNXxssCS67ZGSQP4kiT7VoY9/pxhmJasXTul/98UXypT5MqbiqWlJTw8PLB9+3ap8tmzZ2Pt2rWIi4tDTk4OEhIS4OnpiY4dO3IJ5/r169G5c2fY2toiODgYqampyMrKQkREBOLj48HnS56xsHfvXmhra8Pd3Z0bCbewsICVlRXGjBnzTjc2RkREcNMoXmf58uW4dOkS7t69i7S0NCxfvhwXLlyAh4cHAMmycj4+Pli8eDFiYmKQkpKCqVOnws7ODoMGDQIAjBo1Cr169cKUKVNw48YNREVF4auvvoKvry+UlGr/eX3++ecQi8WYOXMm0tPTERUVha1btwL4d9TW19cXBQUFmDRpEpKTk5GVlYWoqChMnTq1zoT2ZQ4ODrCzs8O4ceNw5swZ3L17F1euXMGKFStw9erV1x7Xvn17aGtrY9euXcjMzMT58+drPL1aV1cXKioq3I2WRUVFNc6jqqqK2bNnw8/PD5GRkbh16xZmzJiBFy9ewMfHp159KC0txdy5c3HhwgXk5OQgLi4OycnJMDc3r9fxssSS67ZGLAL+uSnZtzL/PfdfWYfAMAzTKqxZs6bGPGoHBwckJCRgwoQJMDExgZubG5SVlXHu3Dloa2sDALS1tZGUlARPT09s2bIFtra2sLS0xOrVq/HZZ58hMDAQgGTNbFdX11pX9XBzc8Px48fxzz//NCj2oqKiGlNTXpWfnw9PT0+YmppixIgRSE5ORlRUFEaOHMm1+e677/Dxxx/Dzc0NQ4cOhZ6entTKGHw+H3/88Qf4fD7s7OwwefJkeHp6Ys2aNbVdEgCgoaGBEydOQCAQoG/fvlixYgW3Lnb1PGwDAwPExcVBJBJh1KhRsLS0xMKFC6GlpQU5ufqlbTweD6dOncLQoUMxdepUmJiYYOLEicjJyalzeo2cnBwOHTqElJQUWFhYYNGiRdiyZYtUG3l5eWzfvh2//vorDAwMMHbs2FrPtXHjRri5uWHKlCmwsbFBZmYmoqKi0L59/Z5Jwefz8fTpU3h6esLExATu7u4YPXo0vv7663odL0vN7vHnLUmLfPx5eTGwqwsw80GrG71Oy0uDZSdLWYfBMEwbxB5/zjTU/v37MXXqVBQVFUnNP2feTnN6/Dm7obGtUdIA5rW+pzMCQFo+S64ZhmGY5m3Pnj344IMP0LlzZ9y4cQNLly6Fu7s7S6xbEZZctzXiKiA3GdAbAMixHz/DMAzDNKXc3FysWrUKubm50NfXx4QJE7Bu3TpZh8U0IpZdtTVVpcCJCcDU9Fa31rWlLhu1ZhiGYZo3f39/+Pv7yzoM5j1iNzS2NYrqwBcPWl1iDQAH/zwo6xAYhmEYhmnjWHLd1oirgLtRkn0rs37EelmHwDAMwzBMG8eS67amqgy4sFiyb2XYQ2QYhmEYhpE1Nue6rVFUA7xvyjqK94I9RIZhGIZhGFljI9dtjagSyDgi2bcy7kfcZR0CwzAMwzBtHEuu2xpxBZDyrWTfyoSOC5V1CAzDMAzDtHEsuW5rFFSBz+Ml+1Zm25Vtsg6BYRiGYdokb29vjBs3TtZhNAssuW5rRBVA6m7JvpVxNHaUdQgMwzAtjre3N3g8HjZu3ChVfuzYMfB4PKmywMBAWFlZQU1NDVpaWrC2tsaGDRuk2hQXF2PlypXo3bs3VFRUoK2tjQEDBmDz5s149uxZjesfPHgQfD4fvr6+9Y735STuXZO6jRs3gsfjYeHChVLlZWVl8PX1hba2NtTU1ODm5oa8vDypNvfu3YOzszPatWsHXV1d+Pn5oaqq9a3G9bK7d++Cx+NBIBBIlf/www8IDQ2VSUzNDUuu2xpxJfDXEcm+lXlY/FDWITAMw7RIysrK2LRpU63Jb7Xg4GAsXLgQ8+fPh0AgQFxcHPz9/SEUCrk2BQUFGDRoEEJCQrBkyRIkJibi2rVrWLduHa5fv44DBw7UOG9QUBD8/f1x8OBBlJU17UpWycnJ+PXXX9GnT58adYsWLcKJEydw5MgRXLx4EY8ePcL48eO5epFIBGdnZ1RUVODKlSsICwtDaGgoVq1a1ZRdaDQVFe826KapqQktLa3GCaalI6bBioqKCAAVFRXJOhSGiIKuBck6BIZh2qjS0lK6desWlZaWvvO5hEIhASAAJBQKGyG6unl5edHHH39MZmZm5Ofnx5VHRETQy2nC2LFjydvbu85zffHFF6SqqkoPHz6stV4sFku9/vvvv0lFRYUKCwtp4MCBtH///nrFO3bs2Ne+rq/nz59Tz549KTo6moYNG0YLFizg6goLC0lBQYGOHDnClaWnpxMAio+PJyKiU6dOkZycHOXm5nJtdu7cSRoaGlReXv7a68bFxZGVlRUpKSlRv379uPf5+vXrXJu0tDRycnIiVVVV0tXVpcmTJ9OTJ0+4+mHDhtG8efPIz8+P2rdvT506daKAgACp6zx79ox8fHxIR0eH1NXV6cMPPySBQMDVBwQEkJWVFQUGBlL37t2Jx+MREdHp06fJ3t6eNDU1qUOHDuTs7EyZmZnccdW/m9XbsGHDiKjmz6GsrIzmzZtHHTt2JCUlJbK3t6ekpCSuPiYmhgDQ2bNnqV+/fqSiokJ2dnZ0+/Ztro1AIKDhw4eTmpoaqaurk42NDSUnJ9f6vtb1N9jU+RobuW5rqsqBq99K9q3MUMOhsg6BYRimReLz+Vi/fj127NiBBw8e1NpGT08PCQkJyMnJqbVeLBbj8OHDmDx5MgwMDGpt8+o0k5CQEDg7O0NTUxOTJ09GUFDQu3UEQGhoaI3r1MbX1xfOzs5wcHCoUZeSkoLKykqpOjMzM3Tr1g3x8fEAgPj4eFhaWqJTp05cG0dHRxQXF+PmzdqXvC0uLoaLiwssLS1x7do1rF27FkuXLpVqU1hYiI8++gjW1ta4evUqIiMjkZeXB3d36RWxwsLCoKqqisTERGzevBlr1qxBdHQ0Vz9hwgTk5+fj9OnTSElJgY2NDUaMGIGCggKuTWZmJo4ePYrw8HBumkdJSQkWL16Mq1ev4ty5c5CTk4OrqyvEYjEAICkpCQBw9uxZPH78GOHh4bX21d/fH0ePHkVYWBiuXbsGY2NjODo6Sl0fAFasWIFt27bh6tWrkJeXx7Rp07g6Dw8PdOnSBcnJyUhJScGyZcugoKBQ6/WaE5ZctzUkAh7HS/atzE9JP8k6BIZhmAYrKSnhtrrK3hdXV1f07dsXAQEBtdYHBARAS0sL3bt3h6mpKby9vfHbb79xSdeTJ09QWFgIU1NTqeP69esHNTU1qKmpYdKkSVy5WCxGaGgoJk+eDACYOHEiLl++jOzs7Hfqh6amZo0YXnXo0CFcu3atxnzxarm5uVBUVKwxzaFTp07Izc3l2rycWFfXV9fV5sCBA+DxeAgMDESvXr0wevRo+Pn5SbX58ccfYW1tjfXr18PMzAzW1tYIDg5GTEwM/vrrL65dnz59EBAQgJ49e8LT0xP9+/fHuXPnAACXL19GUlISjhw5gv79+6Nnz57YunUrtLS08L///Y87R0VFBfbs2QNra2tuaoybmxvGjx8PY2Nj9O3bF8HBwUhLS8OtW7cAAB07dgQAaGtrQ09PDx06dKjRz5KSEuzcuRNbtmzB6NGj0atXLwQGBkJFRaXGB6h169Zh2LBh6NWrF5YtW4YrV65w04Pu3bsHBwcHmJmZoWfPnpgwYQKsrKxqfW+bk2aXXF+6dAkuLi4wMDAAj8fDsWPHpOqJCKtWrYK+vj5UVFTg4OCAO3fuSLUpKCiAh4cHNDQ0oKWlBR8fH6k5YQCQmpqKIUOGQFlZGV27dsXmzZvfd9eaB4V2gMsRyb6V+c7pO1mHwDAM02DVCejLCVunTp248qawadMmhIWFIT09vUadvr4+4uPjkZaWhgULFqCqqgpeXl5wcnLiEuzaREREQCAQwNHREaWlpVx5dHQ0SkpKMGbMGACAjo4ORo4cieDgYABAbGws13c1NTXs37+/Xn1wdXXF7du3X1t///59LFiwAPv374eysnK9ztlYMjIy0KdPH6nr2traSrW5ceMGYmJipPpuZmYGAMjKyuLavTpPXF9fH/n5+dw5hEIhdzNm9ZadnS11DkNDQy5Zrnbnzh1MmjQJH3zwATQ0NNC9e3cAkkS3vrKyslBZWQl7e3uuTEFBAba2tjV+t17uh76+PgBw/Vi8eDGmT58OBwcHbNy4USr25qzZJdclJSWwsrLCTz/VPgq5efNmbN++Hb/88gsSExOhqqoKR0dHqZsgPDw8cPPmTURHR+OPP/7ApUuXMHPmTK6+uLgYo0aNgqGhIVJSUrBlyxasXr0au3bteu/9k7mqcuDK6lY5LYQ9/pxhGObdDB06FI6Ojli+fPlr21hYWGDOnDnYt28foqOjER0djYsXL6Jjx47Q0tJCRkaGVPtu3brB2NgY6urqUuVBQUEoKCiAiooK5OXlIS8vj1OnTiEsLAxisRj9+/eHQCDgtk8++aRR+piSkoL8/HzY2Nhw17148SK2b98OeXl5iEQi6OnpoaKiAoWFhVLH5uXlQU9PD4Bkmsyrq4dUv65u0xBCoRAuLi5SfRcIBLhz5w6GDv13+uOr0yN4PB73IUcoFEJfX7/GOTIyMqRGylVVay7L6+LigoKCAgQGBiIxMRGJiYkA3v2Gx9d5uR/V03mq+7F69WrcvHkTzs7OOH/+PHr16oWIiIj3EkdjanaPPx89ejRGjx5dax0R4fvvv8dXX32FsWPHAgD27NmDTp064dixY5g4cSLS09MRGRmJ5ORk9O/fHwCwY8cOjBkzBlu3boWBgQH279+PiooKBAcHQ1FREb1794ZAIMC3334rlYS3SiQGnj+Q7FsZ9vhzhmFasupvWEtKSrjR67y8vFoToPdp48aN6Nu37xunVgBAr169AEhilpOTg7u7O/bt24dVq1a9dt41ADx9+hS///47Dh06hN69e3PlIpEIgwcPxpkzZ+Dk5ARjY+N379ArRowYgbS0NKmyqVOnwszMDEuXLgWfz0e/fv2goKCAc+fOwc3NDYBk1PnevXuws7MDANjZ2WHdunXIz8+Hrq4uAMlovIaGBve+vMrU1BT79u1DeXk5lJSUAEhWLHmZjY0Njh49iu7du0NevmFpmo2NDXJzcyEvL8+NPNfH06dPkZGRgcDAQAwZMgSAZIrJyxQVFQFIflavY2RkBEVFRcTFxcHQ0BAAUFlZieTk5BpLHr6JiYkJTExMsGjRIkyaNAkhISFwdXV9q3M0tWY3cl2X7Oxs5ObmSt1goKmpiYEDB0rdYKClpcUl1gDg4OAAOTk57tNXfHw8hg4dyv2CAJKbEDIyMupchqhVUFABHHdL9rWJjQUMDIDduyWvd++WvI6NbboYG8jrmJesQ2AYhmkwVVVVbqur7H2ztLSEh4cHtm/fLlU+e/ZsrF27FnFxccjJyUFCQgI8PT3RsWNHLuFcv349OnfuDFtbWwQHByM1NRVZWVmIiIhAfHw8+Hw+AGDv3r3Q1taGu7s7LCwsuM3Kygpjxox5pxsbIyIiuGkUtVFXV5e6poWFBVRVVaGtrQ0LCwsAktzCx8cHixcvRkxMDFJSUjB16lTY2dlh0KBBAIBRo0ahV69emDJlCm7cuIGoqCh89dVX8PX15RLnV33++ecQi8WYOXMm0tPTERUVha1btwL4d9TW19cXBQUFmDRpEpKTk5GVlYWoqChMnTq1zoT2ZQ4ODrCzs8O4ceNw5swZ3L17F1euXMGKFStw9erV1x7Xvn17aGtrY9euXcjMzMT58+exePFiqTa6urpQUVHhbrQsKiqqcR5VVVXMnj0bfn5+iIyMxK1btzBjxgy8ePECPj4+9epDaWkp5s6diwsXLiAnJwdxcXFITk6Gubl5vY6XpRaVXFffIFDbDQQv32BQ/Qmymry8PDp06PBONyEAQHl5OYqLi6W2FqeqDLiwWLJ/VWwsMGoUkJsLzJgBeHpK9rm5kvJmnmB/58jmXDMMwzSGNWvW1JhH7eDggISEBEyYMAEmJiZwc3ODsrIyzp07B21tbQCSm9ySkpLg6emJLVu2wNbWFpaWlli9ejU+++wzBAYGApCsme3q6lrrqh5ubm44fvw4/vnnnwbFXlRUVGNqSkN89913+Pjjj+Hm5oahQ4dCT09PamUMPp+PP/74A3w+H3Z2dpg8eTI8PT2xZs2a155TQ0MDJ06cgEAgQN++fbFixQpuXezqedgGBgaIi4uDSCTCqFGjYGlpiYULF0JLSwtycvVL23g8Hk6dOoWhQ4di6tSpMDExwcSJE5GTk1Mj/3mZnJwcDh06hJSUFFhYWGDRokXYsmWLVBt5eXls374dv/76KwwMDLiZBK/auHEj3NzcMGXKFNjY2CAzMxNRUVFo3759vfrA5/Px9OlTeHp6wsTEBO7u7hg9ejS+/vrreh0vU02y4F8DAaCIiAjudVxcHAGgR48eSbWbMGECubu7ExHRunXryMTEpMa5OnbsSD///DMREY0cOZJmzpwpVX/z5k0CQLdu3XptPAEBATXWd0RLW+e6spQoZpFk/yp9fSIejwioufF4kvpmbPPlzbIOgWGYNqox17lm2pZ9+/aRgoICvXjxQtahtGhsnesGqr5BoLYbCF6+waD6LtNqVVVVKCgoeOebEJYvX46ioiJuu3///rt1SBbklYHh30r2r1qzRpJK14ZIUt+M2Xa2fXMjhmEYhpGhPXv2cEsOHjt2DEuXLoW7uztUVF4zXZNpcVpUct2jRw/o6elx6zgCkpU/EhMTpW4wKCwsREpKCtfm/PnzEIvFGDhwINfm0qVLqKz89xHg0dHRMDU1rfPrCiUlJWhoaEhtLU5lKRA1XbJ/1fTpwJQptR83ZYqkvhkrraqlTwzDMAzTjOTm5mLy5MkwNzfHokWLMGHChLaxWlkb0uySa6FQyC0ZA0huYhQIBLh37x54PB4WLlyIb775BsePH0daWho8PT1hYGCAcePGAQDMzc3h5OSEGTNmICkpCXFxcZg7dy4mTpzI3bn8+eefQ1FRET4+Prh58yYOHz6MH374ocak/VaJJweod5HsX7V7N7B3b+3H7d37702OzVRWQctY/5JhGIZpu/z9/XH37l2UlZUhOzsb3333Hdq1a33PnmjLeESvmwcgGxcuXMCHH35Yo9zLywuhoaEgIgQEBGDXrl0oLCzE4MGD8fPPP8PExIRrW1BQgLlz5+LEiROQk5ODm5sbtm/fLrUIf2pqKnx9fZGcnAwdHR3MmzevxiNI36S4uBiampooKipqmaPYrzIwkNy8WNuvBI8H6OkBjx41fVz19LD4ITprdJZ1GAzDtEHViVKPHj2a/MEkDMPU/TfY1Plas0uuW5IWmVxXvgAivQCnsJpPaaxeLaS8XJJgT5kiGbHm8QAlJeDMGeD/171sjmb9MQu/fPyLrMNgGKYNYsk1w8hWc0qum920EOY94/EBfTvJ/lVDhkgSaD09IDAQ2LNHstfTa/aJNQCWWDMMwzAMI3MsuW5r5JWA/osl+9oMGSKZ+lF98+L06ZLXzTyxBtjjzxmGYRiGkb0GJ9cffPBBjac3veqnn37CBx980NBLMO9DZQnwP0fJvpVhjz9nGIZhGEbWGpxc3717F4WFhXW2KSwsRE5OTkMvwbwPcgqAyQTJvpWZ9ccsWYfAMAzDMEwb916nhRQVFUFJ6TXTDxjZ4CsCfaZL9q3MyqErZR0CwzAMw8jE6tWr0bdvX1mHweAtk+tLly5xGyAZvX65rHqLiYnBnj17sH//fqkl8phmoLIEOGDXKqeFHLt9TNYhMAzDtEi5ublYsGABjI2NoaysjE6dOsHe3h47d+7EixcvarTfsGED+Hw+tmzZ0qDrXbp0CS4uLjAwMACPx8OxY8dqtCEirFq1Cvr6+lBRUYGDgwPu3Lkj1aagoAAeHh7Q0NCAlpYWfHx8IBQKGxRTS1Lbe7ZkyRKph+wxsiP/No2HDx8OHo8HQPKDDQsLQ1hYWK1tiQg8Hg8bN2589yiZxiOnCPRbLNm3MkYdjGQdAsMwTIvz999/w97eHlpaWli/fj0sLS2hpKSEtLQ07Nq1C507d8Ynn3widUxwcDD8/f0RHBwMPz+/t75mSUkJrKysMG3aNIwfP77WNps3b8b27dsRFhaGHj16YOXKlXB0dMStW7e4pdY8PDzw+PFjREdHo7KyElOnTsXMmTNx4MCBt38jZEwkEoHH40FOrmGTCtTU1KSe58HIEL2FgIAAWr16NQUEBBCPx6Phw4fT6tWra2xr166ln376iW7duvU2p29xioqKCAAVFRXJOhSGiC5kX5B1CAzDtFGlpaV069YtKi0tfbcTXbpEpK9PFBgoeR0YKHl96dK7B/kajo6O1KVLFxIKhbXWi8ViqdcXLlygzp07U0VFBRkYGFBcXNw7XR8ARURE1Limnp4ebdmyhSsrLCwkJSUlOnjwIBER3bp1iwBQcnIy1+b06dPE4/Ho4cOHr71eeno62dvbk5KSEpmbm1N0dHSNGO7du0cTJkwgTU1Nat++PX3yySeUnZ3N1Xt5edHYsWNpy5YtpKenRx06dKA5c+ZQRUUF16asrIy+/PJLMjAwoHbt2pGtrS3FxMRw9SEhIaSpqUm///47mZubE5/Pp+zsbEpKSiIHBwfS1tYmDQ0NGjp0KKWkpHDHGRoaEgBuMzQ0JCJJjmZlZcW1E4lE9PXXX1Pnzp1JUVGRrKys6PTp01x9dnY2AaCjR4/S8OHDSUVFhfr06UNXrlzh2ty9e5c+/vhj0tLSonbt2lGvXr3o5MmTr31vZamuv8GmztfeKrl+2fDhwyksLKwxY2lxWmRyXf6cKKSXZN/KbL68WdYhMAzTRjVKcn3pEpGyMhGPRwQQTZki2fN4kvL3kGD/888/xOPxaMOGDfU+ZsqUKbRkyRIiIvryyy9p2rRpUvUhISH0NmN3tSXXWVlZBICuX78uVT506FCaP38+EREFBQWRlpaWVH1lZSXx+XwKDw+v9VpVVVVkampKI0eOJIFAQLGxsWRraysVQ0VFBZmbm9O0adMoNTWVbt26RZ9//jmZmppSeXk5EUmSaw0NDZo1axalp6fTiRMnqF27drRr1y7uWtOnT6f//Oc/dOnSJcrMzKQtW7aQkpIS/fXXX9z7pKCgQP/5z38oLi6Obt++TSUlJXTu3Dnau3cvpaen061bt8jHx4c6depExcXFRESUn59PACgkJIQeP35M+fn5RFQzuf72229JQ0ODDh48SLdv3yZ/f39SUFDgrl+dXJuZmdEff/xBGRkZ9Omnn5KhoSFVVlYSEZGzszONHDmSUlNTKSsri06cOEEXL16sz4+1ybWK5Jppocm1qJIoO1Kyb2Wevngq6xAYhmmjGiW51tf/N7F+dePxJPWNLCEhgQDUSEa1tbVJVVWVVFVVyd/fnysvKioiFRUVEggERER0/fp1UlNTo+fP/x2wCQ8PJ1NT03rHUFtyHRcXRwDo0aNHUuUTJkwgd3d3IiJat24dmZiY1Dhfx44d6eeff671WqdPnyZ5eXl6/PgxV/bqyPXevXvJ1NRUasS+vLycVFRUKCoqiogkybWhoSFVVVVJxfbZZ58REVFOTg7x+fwaI+gjRoyg5cuXE9G/H0Kq38vXEYlEpK6uTidOnODKanvPXk2uDQwMaN26dVJtBgwYQHPmzCGif5Pr3bt3c/U3b94kAJSenk5ERJaWlrR69eo642sumlNy3eDVQu7fv4/z589L3eggFouxadMm2Nvbw8HBASdPnmzo6Zn3RU4e6O4o2bcyi6IWyToEhmGYhluzRpJK14ZIUt9EkpKSIBAI0Lt3b5SXl3PlBw8ehJGREaysrAAAffv2haGhIQ4fPsy1cXV1xe3bt5ss1reRkZGBrl27Qk9PjyuztbWVanPjxg1kZmZCXV2dm8fcoUMHlJWVISsri2vXu3dv8Pn/Pu1YX18f+fn5AIC0tDSIRCKYmJhw51BTU8PFixelzqGoqIg+ffpIXT8vLw8zZsxAz549oampCQ0NDQiFQty7d6/e/SwuLsajR49gb28vVW5vb4/09HSpspevr6+vDwBcP+bPn49vvvkG9vb2CAgIQGpqar1jaMsanGGtXLkSJ06cQG5uLle2bt06BAQEcK8vXryIK1euYMCAAe8WJdN4Kp4DIebA1HRAUV3W0TSqsHG131zLMAzTIkyfDly6BOzdW7NuypR/n5zbiIyNjcHj8ZCRkSFVXv0AOBUVFanyoKAg3Lx5E/Ly/6YPYrEYwcHB8PHxabS4qpPfvLw8LuGrfl293Jyenh6XBFarqqpCQUGBVPL8toRCIfr164f9+/fXqOvYsSP33woK0s+L4PF4EIvF3Dn4fD5SUlKkEnAAUjcdqqiocAtFVPPy8sLTp0/xww8/wNDQEEpKSrCzs0NFRUWD+1SXl/tRHUt1P6ZPnw5HR0ecPHkSZ86cwYYNG7Bt2zbMmzfvvcTSWjR45DouLg4ODg7cD4WI8OOPP8LMzAz37t1DUlISVFVVG7xMD/OeyKsALkck+1aGPf6cYZgWbffu2hNrQFK+e3ejX1JbWxsjR47Ejz/+iJKSupdoTUtLw9WrV3HhwgUIBAJuu3DhAuLj4xt1tLpHjx7Q09OTWlquuLgYiYmJsLOzAwDY2dmhsLAQKSkpXJvz589DLBZj4MCBtZ7X1NQU9+/fR15eHleWnJws1cbGxgZ37tyBrq4ujI2NpTZNTc16xW9tbQ2RSIT8/Pwa53hT4h8XF4f58+djzJgx6N27N5SUlPDPP/9ItVFQUIBIJHrtOTQ0NGBgYIC4uLga5+7Vq1e9+lCta9eumDVrFsLDw/Hll18iMDDwrY5vixqcXOfn58PQ0JB7LRAI8OTJE8ybNw9dunRB//79MW7cuBq/tIyMyckDBnatcloIe/w5wzAt2qpVwCujmBweT1L/Hvz888+oqqpC//79cfjwYaSnpyMjIwP79u3D7du3uZHXoKAg2NraYujQobCwsOC2oUOHYsCAAQgKCgIAREREwMzMrM5rCoVCLjkHgOzsbAgEAm7qA4/Hw8KFC/HNN9/g+PHjSEtLg6enJwwMDDBu3DgAgLm5OZycnDBjxgwkJSUhLi4Oc+fOxcSJE2FgYFDrdUeOHAkjIyN4eXkhNTUVcXFx+Oqrr7hrApLl/XR0dDB27FjExsYiOzsbFy5cwPz58/HgwYN6vacmJibw8PCAp6cnwsPDkZ2djaSkJGzYsOGNU2Z79uyJvXv3Ij09HYmJifDw8KjxDUL37t1x7tw55Obm4tmzZ7Wex8/PD5s2bcLhw4eRkZGBZcuWQSAQYMGCBfXqAwAsXLgQUVFRyM7OxrVr1xATEwNzc/N6H99WNTi5FovF3NcGAHDhwgXweDx89NFHXFnnzp2lpo0wzUB5MbBDQ7JvZRZFsjnXDMO0YIcPA0pK/ybYU6ZI9jyepPylec2NycjICNevX4eDgwOWL18OKysr9O/fHzt27MCSJUuwdu1aVFRUYN++fXBzc6v1HG5ubtizZw8qKytRVFRUY5rJq65evQpra2tYW1sDABYvXgxra2useukDhL+/P+bNm4eZM2diwIABEAqFiIyM5Na4BoD9+/fDzMwMI0aMwJgxYzB48GDs2rXrtdfl8/k4duwYhEIhBgwYgOnTp2PFihUAwJ23Xbt2uHTpErp164bx48fD3NwcPj4+KCsrg4aGRv3eVAAhISHw9PTEl19+CVNTU27AsVu3bnUeFxQUhGfPnsHGxgZTpkzB/PnzoaurK9Vm27ZtiI6ORteuXbn38FXz58/H4sWL8eWXX8LS0hKRkZE4fvw4evbsWe8+iEQi+Pr6ch9kTExM8PPPP9f7+LaKR/S6uyfq1rt3b2hra3NPaxw2bBiysrKkPtX5+Pjg1KlTePz4ceNE28wUFxdDU1MTRUVFb/UHJ1NiEVBwG+hgBsjx39y+BcksyIRxB2NZh8EwTBtUVlaG7Oxs9OjRQyr5e2uxscBnn0luXpw+XTIVZNUqSWI9ZEjjBcxw4uLiMHjwYGRmZsLIiD2MrKWq62+wqfO1Bs8NcHNzw7p16/Dpp59CWVkZly9fxty5c6Xa3Lp1i7spgmkm5PiATm9ZR/FeXMq5xJJrhmFatiFDgEeP/n09ffp7uZGxLYuIiICamhp69uyJzMxMLFiwAPb29iyxZhpNg6eFLFmyBAMGDEB4eDgOHDgAS0tLrF69mqvPyclBUlIShg8f3ghhMo2mvBjYxmuV00LaK7eXdQgMwzBMM/f8+XP4+vrCzMwM3t7eGDBgAH7//XdZh8W0Ig0eudbQ0EBCQgL+/PNPAJIbC15dbiY8PBz9+/d/twiZxqWoBsy8L9m3Mp01Oss6BIZhGKaZ8/T0hKenp6zDYFqxd14ywsLCotZyQ0NDqdVEmOaCByhqSPatTFRmFGw72765IcMwDMMwzHvSKOuxxcXFQSAQoLi4GBoaGujbt2+NpwIxzUTFc+BHTWBuEaDUQm7CrKcv//OlrENgGIZhGKaNe6fk+sqVK5g6dSoyMzMBSB4kU71OZM+ePRESEsIt9s40E4rqksS6lT2dEQC8j3njtwm/yToMhmEYhmHasAYn1zdv3sSoUaPw4sULjBw5Eh9++CH09fWRm5uLmJgYnDlzBo6OjkhISHjrpwEx7xMBFcX/P+e6dU0NYYk1wzAMwzCy1uDkes2aNaioqMCpU6fg5OQkVbd06VJERkbik08+wZo1a3Do0KF3DpRpJBVCYFfXVjktxOWgC3tKI8MwDMMwMtXg5PrChQv49NNPayTW1ZycnPDpp5/i3LlzDQ6OeQ+UNIAvG/TcoGaPJdYMwzAMw8hag9e5LioqQo8ePeps06NHDxQVFTX0Esz7IBYB/9yU7FuZ/577r6xDYBiGYZqxCxcugMfjobCwsN7HdO/eHd9///17i4lpfRqcXBsYGCAhIaHONomJiTAwMGjoJWolEomwcuVK9OjRAyoqKjAyMsLatWvx8lPciQirVq2Cvr4+VFRU4ODggDt37kidp6CgAB4eHtDQ0ICWlhZ8fHwgFAobNdZmqbIEOGgn2bcykywmyToEhmGYFsfb2xs8Hg88Hg8KCgro1KkTRo4cieDgYIjFYqm23bt359q2a9cOlpaW2L17t1SbhiSwDNOaNDi5/uSTT3DhwgWsXLkSZWVlUnVlZWUICAhATEwMxo4d+85BvmzTpk3YuXMnfvzxR6Snp2PTpk3YvHkzduzYwbXZvHkztm/fjl9++QWJiYlQVVWFo6OjVJweHh64efMmoqOj8ccff+DSpUuYOXNmo8baLClpAPOKW918awBIy0+TdQgMwzAN9+wOkHft9duzO28+RwM5OTnh8ePHuHv3Lk6fPo0PP/wQCxYswMcff4yqqiqptmvWrMHjx4/x559/YvLkyZgxYwZOnz793mJjmBaHGuiff/4hIyMjkpOTo44dO5KzszNNmzaNnJ2dSVdXl3g8HhkZGdHTp08beolaVV/nZePHjycPDw8iIhKLxaSnp0dbtmzh6gsLC0lJSYkOHjxIRES3bt0iAJScnMy1OX36NPF4PHr48GG9YykqKiIAVFRU9C5dalqiSqKHVyT7VmZ/6n5Zh8AwTBtVWlpKt27dotLS0oadoOAvoq1481bwV+MGTkReXl40duzYGuXnzp0jABQYGMiVGRoa0nfffSfVrkOHDrRo0SLudUxMDAGgZ8+e1ev61e0jIyOpb9++pKysTB9++CHl5eXRqVOnyMzMjNTV1WnSpElUUlLCHVdWVkbz5s2jjh07kpKSEtnb21NSUpLUuU+ePEk9e/YkZWVlGj58OIWEhNSILTY2lgYPHkzKysrUpUsXmjdvHgmFwjr7zDQ/df0NNnW+1uCRa21tbSQkJMDLywtCoRCnTp1CSEgITp06hefPn2Pq1KlISEhAhw4dGuVDQLX//Oc/OHfuHP766y8AwI0bN3D58mWMHj0aAJCdnY3c3Fw4ODhwx2hqamLgwIGIj48HAMTHx0NLS0vq0ewODg6Qk5NDYmLia69dXl6O4uJiqa3FqSoFTkyQ7FsZS11LWYfAMAzTMBXPG7ddI/joo49gZWWF8PDwWuvFYjGOHj2KZ8+eQVFR8Z2vt3r1avz444+4cuUK7t+/D3d3d3z//fc4cOAATp48iTNnzkh9S+3v74+jR48iLCwM165dg7GxMRwdHVFQUAAAuH//PsaPHw8XFxcIBAJMnz4dy5Ytk7pmVlYWnJyc4ObmhtTUVBw+fBiXL1/G3Llz37k/TNvV4OQaAHR0dBAcHIyioiLcuHEDsbGxuHHjBoqKihAUFAQdHZ3GipOzbNkyTJw4EWZmZlBQUIC1tTUWLlwIDw8PAEBubi4AoFOnTlLHderUiavLzc2Frq6uVL28vDw6dOjAtanNhg0boKmpyW1du3ZtzK41DUV14IsHrfIhMgf/PCjrEBiGYVoVMzMz3L17V6ps6dKlUFNTg5KSEj799FO0b98e06dPf+drffPNN7C3t4e1tTV8fHxw8eJF7Ny5E9bW1hgyZAg+/fRTxMTEAABKSkqwc+dObNmyBaNHj0avXr0QGBgIFRUVBAUFAQB27twJIyMjbNu2DaampvDw8IC3t7fUNTds2AAPDw8sXLgQPXv2xH/+8x9s374de/bsqTHllWHq662T63Xr1uG///0vKisruTIFBQVYWlrC3t4elpaWICKsWLECGzdubNRgAeC3337D/v37ceDAAVy7dg1hYWHYunUrwsLCGv1ar1q+fDmKioq47f79++/9mo1OXAXcjZLsW5n1I9bLOgSGYZhWhV568nI1Pz8/CAQCnD9/HgMHDsR3330HY2Pjd75Wnz59uP/u1KkT2rVrhw8++ECqLD8/H4BkxLmyshL29vZcvYKCAmxtbZGeng4ASE9Px8CBA6Wu8epTo2/cuIHQ0FCoqalxm6OjI8RiMbKzs9+5T0zb9FbJ9dmzZ7Fq1Spoa2tDQUHhte0UFRWhra2NFStWcJ8yG4ufnx83em1paYkpU6Zg0aJF2LBhAwBAT08PAJCXlyd1XF5eHlenp6fH/YFWq6qqQkFBAdemNkpKStDQ0JDaWpyqMuDCYsm+lXE56CLrEBiGYVqV9PT0Gsvu6ujowNjYGEOGDMGRI0cwf/583Lp1652v9XJeUb1yyct4PF6N1UvelVAoxBdffAGBQMBtN27cwJ07d2BkZNSo12LajrdKrvfs2YP27dvXay6Sr68vOnTogJCQkAYHV5sXL15ATk46bD6fz/3B9ejRA3p6elIPrykuLkZiYiL3idXOzg6FhYVISUnh2pw/fx5isbjGp9xWR1EN8L75/48/b13YQ2QYhmEaz/nz55GWlgY3N7fXtunatSs+++wzLF++vAkjA4yMjKCoqIi4uDiurLKyEsnJyejVqxcAwNzcHElJSVLHvbqEsI2NDW7dugVjY+MaW2PMI2faprd6QuOVK1fg4OAAJSWlN7ZVUlKCg4OD1C9+Y3BxccG6devQrVs39O7dG9evX8e3336LadOmAZB8sl24cCG++eYb9OzZEz169MDKlSthYGCAcePGAZD8wTk5OWHGjBn45ZdfUFlZiblz52LixImNvi53syOqBDKPAcbjAP7rv31oidyPuOO3Cb/JOgyGYZgWp7y8HLm5uRCJRMjLy0NkZCQ2bNiAjz/+GJ6ennUeu2DBAlhYWODq1atSCwWkpaVBXf3f+3t4PB6srKwaJV5VVVXMnj0bfn5+6NChA7p164bNmzfjxYsX8PHxAQDMmjUL27Ztg5+fH6ZPn46UlBSEhoZKnWfp0qUYNGgQ5s6di+nTp0NVVRW3bt1CdHQ0fvzxx0aJlWl73iq5fvTokdT8pzfp0aMHfv/997cOqi47duzAypUrMWfOHOTn58PAwABffPEFVq1axbXx9/dHSUkJZs6cicLCQgwePBiRkZFQVlbm2uzfvx9z587FiBEjICcnBzc3N2zfvr1RY22WxBVAyrfAB2NaXXIdOi5U1iEwDMO0SJGRkdDX14e8vDzat28PKysrbN++HV5eXjW+LX5Vr169MGrUKKxatQqnTp3iyocOHSrVjs/n11gz+11s3LgRYrEYU6ZMwfPnz9G/f39ERUWhffv2AIBu3brh6NGjWLRoEXbs2AFbW1usX7+eG4wDJPO8L168iBUrVmDIkCEgIhgZGeGzzz5rtDiZtodH9NKjDd9ATU0Ns2fPxpYtW+rV3s/PDzt37my1Tz4sLi6GpqYmioqKWub861Zm7cW1WDlspazDYBimDSorK0N2djZ69OghNZBTb8/uAMEmb2437S+gfc+3Pz/DtHJ1/Q02db72ViPXBgYG+PPPP+vd/s8//0Tnzp3fOijmPRJVADf3AL09AX7rmk/maOwo6xAYhmEapn1PSeJc1zrWiuossWaYFuCtkushQ4Zg3759uHv3Lrp3715n27t37+L8+fNvnKvFNDFxJfDXEcB8UqtLrh8WPwTYZzmGYVoqljgzTKvwVquF+Pr6orKyEp9++in++eef17Z7+vQpJkyYgKqqKsyePfudg2QakYIq8GmUZN/KPCt7JusQGIZhGIZp495q5NrGxgYLFy7E999/j169emHWrFn48MMP0aVLFwDAw4cPce7cOezatQtPnjzB4sWLYWNj814CZxqoqhwQ/AT09QXk37zqS0sy1HDomxsxDMMwDMO8R2+VXAPAtm3boKysjC1btmDdunVYt26dVD0Rgc/nY/ny5fjmm28aLVCmkZAIeBwPWM2SdSSN7qekn/Cd03eyDoNhGIZhmDbsrVYLeVlWVhZCQkJw5coV5ObmApA8+dDe3h7e3t5t4slGbLUQhmEYBmiE1UIYhnknLXa1kJcZGRmxkemWqKocSNoA2C5vddNCXA66sKc0MgzDMAwjU291QyPTCpAYeP5Asm9lWGLNMAzDMIysseS6rVFQARx3S/atjNcxL1mHwDAMwzBMG8eS67amqgy4sFiyb2W+c2Q3MzIMw7wPPB4Px44de+/X6d69O77//vv3fh2GeZ9Ycs20GkHXgmQdAsMwTIuUm5uLefPm4YMPPoCSkhK6du0KFxcXnDt3TtahMUyL0+AbGpkWSl4ZGP6trKN4L2w728o6BIZhmHcmEosQey8Wj58/hr66PoZ0GwK+HP+9Xe/u3buwt7eHlpYWtmzZAktLS1RWViIqKgq+vr64ffv2e7s2w7RGLLluaypLgfPzgI92tPh51y8qqtBrVRT3ulQuBdmr7dFOkf1aMwzTMoWnh2NB5AI8KH7AlXXR6IIfnH7AePPx7+Wac+bMAY/HQ1JSElRV/316b+/evTFt2rRaj0lLS8OCBQsQHx+Pdu3awc3NDd9++y3U1NQAAMOHD0ffvn2lpniMGzcOWlpaCA0NBQDk5+fDx8cHZ8+ehZ6eHluBjGk12LSQtoYnB6h3kexbmSreY1mHwDAM02Dh6eH49LdPpRJrAHhY/BCf/vYpwtPDG/2aBQUFiIyMhK+vr1RiXU1LS6tGWUlJCRwdHdG+fXskJyfjyJEjOHv2LObOnftW1/b29sb9+/cRExOD//3vf/j555+Rn5/f0K4wTLPBhvjaGnkl4D+rZR3FO3lRUQUAeCoslypXEQ3CU2EFIBk4YSPYDMO0GCKxCAsiF4BQ87luBAIPPCyMXIixpmMbdYpIZmYmiAhmZmb1PubAgQMoKyvDnj17uIT8xx9/hIuLCzZt2oROnTq98Rx//fUXTp8+jaSkJAwYMAAAEBQUBHNz84Z1hGGaEZZ9tDWVL4BIL8ApDFBoJ+toGuTlqSAvK1I4hCGbdbjXdzc6N1VIDMMw7yT2XmyNEeuXEQj3i+8j9l4shncf3mjXbchDmtPT02FlZSU10m1vbw+xWIyMjIx6Jdfp6emQl5dHv379uDIzM7NaR8oZpqVpfXMDmLrx+IC+nWTfymhXvt1XkgzDMM3F4+f1m9ZW33b11bNnT/B4vEa/aVFOTq5G4l5ZWdmo12CY5ool122NvBLQf7HMH31eUiICj3cNPN41lJSI3urYW2sccWuNI2L9h0uV5yt+LfW6evoIwzBMc6evrt+o7eqrQ4cOcHR0xE8//YSSkpIa9YWFhTXKzM3NcePGDan2cXFxkJOTg6mpKQCgY8eOePz43w8CIpEIf/75J/fazMwMVVVVSElJ4coyMjJqvR7DtDQsuW5rKkuA/zlK9i1UO0V5tFOUh7aa9AcE3YoAqdevzslmGIZproZ0G4IuGl3AA6/Weh546KrRFUO6DWn0a//0008QiUSwtbXF0aNHcefOHaSnp2P79u2ws7Or0d7DwwPKysrw8vLCn3/+iZiYGMybNw9TpkzhpoR89NFHOHnyJE6ePInbt29j9uzZUomzqakpnJyc8MUXXyAxMREpKSmYPn06VFRa9ipWDAOw5Lrt+b/27jy8qSr9A/g3XdI2KWkLbVOKLK0gUMomIFZkUToURAYEHVQElE0ZkBFwAQdxQIf6Y1DcGFxQUUcWGUEFFWFAFqHsIJSlgBQBoSmytCSlTZfz++OSm6RNmqTN0pDv53nyJLn33NyT2xDenr7nPUGhwG0PSfc+YDCU37hVWGyrkLfXxqXQd62ed5+7CUXGMo5gE1GdFxwUjLf6vgUAVQJs0/M3+77pkXrXycnJ2LdvH+655x5MnToVqamp+NOf/oQNGzZg4cKFVdqrVCr8+OOPuHz5Mrp06YIHH3wQvXv3xrvvmr+DR40ahZEjR2LEiBHo2bMnkpOTcc8991i9zieffILExET07NkTgwcPxrhx4xAfH+/290fkbQpRk9kMBAAoLCxEVFQUCgoKoNFoPHei1Q8BnaYAiZVGEK5fAlb0Nj8vKwKungLG5wMR9YHlvYDC34CwKGl/m5FAp8mOz7f1ReDESil1JCgUuPufQLMMad/2fwAHFgCRjaTnDdoA/b+QHu9/FzAWAl1ftPvSCsW+ak8txO2O+2eh2bTv5Mdl+AMhiLXZjpMbiciTiouLkZubi6SkJISHh9f4dWzVuW6saYw3+77psTrXRDeD6v4Nei1eu4HVQuq6C7uA4stVA2sAiGgAjDhgfr57HnBusxRYm/SaD7QYZH5eagBWpAMP/Q8IrVrTFABwS3fgzpekRWbyfwGW9wCeOm9u33oYcM+bVY9rNw74pDXQYYI5oPewPTOkXy46v7oB14N3oF75/V45LxGRJwxuPRgDWw706gqNROReDK7ruoPvA60eda5t9kfA3ZnVtwlSSqPgQUr7bZL6mR/HtQUggKKLQJSdYNwkWAk06wMcXQJ0GG+ziV7fHgBw+nQJUlOtZ6fv2tUS+flGAEB8fDX9sxAbKf12uvX5Xug8b6/Vvj0z0qFS8j8kIvIvwUHBbi23R0TexZzruu7sJqBhV8ftft8OFF8Bbq00crt1GvBpW2D1UCllJDgUaPmQdO+M7E+AqGRA09S87fgK4LP2wJf3Amd+sm7fMA04s8Huy6nVwVCrg6sE1gBwxx050GqzodVm2ziyehHKYChgHZCrlMHy5EciIiIib2BwXdfpzwEqxwX5kf0RkDICCLIIJPt9Dow6Bow4KKV6rLofMOqBxW2ke0d+2wBkzQLuXw4obkywaf8UMOY0MOIXoNsrwJqhUl63iTpB6rOXxUaGY2r/YByZneH1cxMRERGZ+GVw/fvvv+Oxxx5DgwYNEBERgbZt22LPnj3yfiEEZs6ciYYNGyIiIgLp6ek4ceKE1WtcvnwZw4YNg0ajQXR0NEaPHg293omA09tCVEBZcfVtjHog50sgdZT1dk1j6V6hADpOBApOSTnXvd4AQhxMuDm7GfjxCWDQaqB+S/N2dYJ51LtRNyC+I5BnvvYoKwZCHJdS0ulSodOlIjvbvNRtdnZreXtNjL59NFTKEJx+rT9Ov9afI9ZERETkdX4XXF+5cgXdunVDaGgofvjhBxw5cgSvv/46YmJi5DZz587F22+/jffeew87d+6EWq1GRkYGiovNQeqwYcNw+PBhrF+/HmvWrMGWLVswbtw4X7yl6sW1A67kVN8mZzkQ1x5o0Mq8raIMMOjMz49/JY2Aq7VS5Y9tL0nVPWw5twX4YTgw8Bsgvr31vmsWo9JXTgAXDwCxbc3bLh+V+uJAfLwS8fFKxMWZc6Lj4oLl7TUx+UcnKqEQEXlQRUWF40ZE5HZ16d+e35XimzZtGrZt24atW7fa3C+EQGJiIqZOnYpnn30WAFBQUACtVovFixfj4YcfxtGjR5GSkoLdu3ejc+fOAIC1a9fivvvuw7lz55CYmOhUX7xS2mXfO8C1M0DPf0nPT34L/PotkLHI3GbJXUC7sUDqE+ZtpQZgeU+gvARQBAERsUDPN4DoZKmiR4MUqbrHbQ8C+vPAyvvMlUc+aiGV1FNbrATW73NpcuMPIwHdXin9RBEMdJ0uvYbJl/cAd70C3HK3/fe0dSswdCgwezby/zwCL2pfwWy8h4hvliPmz/fW+pIREXlbRUUFTpw4geDgYMTFxUGpVEKhsL0gDBG5jxACRqMRFy9eRHl5OVq0aIGgIOuxY2+X4vO74DolJQUZGRk4d+4cNm/ejEaNGuGvf/0rxo4dCwA4deoUbr31Vuzfvx8dOnSQj+vZsyc6dOiAt956Cx9//DGmTp2KK1euyPvLysoQHh6OFStW4IEHHrB57pKSEpSUmFf9KywsROPGjT37wzLqgaV3AY9m2S+d54qKMuD8DmDTM8CwXVLg7S6XjgDrnwQetv2LDwApsO7TBygpAYQAhg8HPv9cSl0JCwPWrQO612wFsgFLB2D1I6tr2HkiotoxGo24cOECioqKfN0VooCjUqnQsGFDKJVV//rNOtcOnDp1CgsXLsSUKVPw4osvYvfu3Zg0aRKUSiVGjhyJvLw8AJCXYDXRarXyvry8vCqrQIWEhKB+/fpyG1syMzMxa9YsN78jB5SRUq3qglwgtma5yFaCQqRR5cf2OG7rqmtngT+9X32boUPNgTUgBdaA9LykRNp//nyNTs/Amoh8SalUokmTJigrK0N5ee1WnCUi5wUHByMkJKTO/LXI74LriooKdO7cGXPmzAEAdOzYEdnZ2XjvvfcwcuRIj557+vTpmDJlivzcNHLtcU17O27jrJJC4INbgHHngCIdYLxmv62yHhDTwvnXbuZEpY7Zs4Ebf2WoQghpfw1NXjsZ8/vOr/HxRES1pVAoEBoaitBQJ8udEtFNx++C64YNGyIlJcVqW+vWrfHVV18BABISEgAAOp0ODRuac4Z1Op2cJpKQkID8/Hyr1ygrK8Ply5fl420JCwtDWFiYO96G74SqgUeypDzrxa0dtx913LUA25ExY4AtW8wj1paGD5f219CEOybUomNEREREted31UK6deuGnBzr6hnHjx9H06bSIidJSUlISEjAhg3mhUwKCwuxc+dOpKVJS4inpaXh6tWr2LvXvKLfxo0bUVFRga5dnViwxZ8FBQOxbYAyJ3MCqxvZrolFi2wG1gKQti9aVGWfs7b8tqXm/SIiIiJyA78LridPnowdO3Zgzpw5OHnyJJYsWYIPPvgAEyZIo5YKhQLPPPMMXn31VXz77bc4dOgQRowYgcTERAwaNAiANNLdt29fjB07Frt27cK2bdswceJEPPzww05XCvFbJYXA6wrnFpHxhJkzzQvS3CAAKAAIhULaX0Mx4TGOGxERERF5kN8F1126dMGqVauwdOlSpKam4pVXXsGbb76JYcOGyW2ef/55PP300xg3bhy6dOkCvV6PtWvXIjzcvHDKF198gVatWqF379647777cPfdd+ODDz7wxVvyLmUkMO4sEKryzfmXLwfCwqRAGsD1h4ZBAaACUrWQ64uXwGCo2USgRppGbuwoERERkev8rhRfXeLt0i5uIYSU6nHlBPBFZ8ftH9sLaG93bx+2bsX5HoMxE0/hIzyA0ViF2XgPQ/EafkbHG910/ZyvbH4FL/V8yb19JSIiIr/GOtd+xC+D65JC4N0o4C+bgS97Om7vieAagEKxr9r9NQmui0qLAKFEyswfAQBHZmdwCXQiIqIA5+14ze/SQqiWlPWAiQXuWZCmFvT69tDr20OnMy+drtO1lbfXxONfP+6m3hERERHVDIf1Ao6QljaHb/9goVYH29gWZHO7M4qMZVg8cAmKjOUW28yPOYJNRERE3sCII9AY9cAHjYHHqk/LkCnrebY/bpIy80fkK2ch3viyvK3zq/+TH59+rb8vukVEREQBhsF1oAnTAFNvjFqPOu7eFRprQK0Ohl7fHpGRvyAy8hfo9e1rPHptGVgTERER+QKD60BTUQ5cPgbUb+XxwNmbjszOwMubZuCFu2bJI9Z7ZqRDpaxZoE5ERERUE5zQGGhKDcDSNOnexwyG8hu3CottFfJ2V6iUIRjRfphVMK1SBkOlDGG+NREREXkNo45AE6YBni70dS8AAJGRv1TZptUekh+7Wo7vUP4h3BrTWn5eZCxjWT4iIiLyKkYbgaaiDMjbDSR0AYJuvh+/ShkiT14sMpb5uDdEREQUaJgWEmjKrgOrH5LufcxWrWsA0OlSa1Trum289DpFxrIbN+uyfKbtRERERJ5y8w1dUvWU9YAnz/m6FwBs17o2MaWMuFI9ZGn2UrTVtpVTQSyxLB8RERF5A0euA01FGXD6R+m+DrA1edFygqMr5vSe41S7ZtO+Q7Np3+EPfXGNzkNERERkD0euA01ZMbBpCvDoTkAZ6eve2JzUmJx8RH5sGWg7GsEesHQAVj+yGkdmZwCQUkEql+W7pC9B97mb3NBzIiIioqoYXAcaZSTw+GFf98JprlQPWf3IagC2lzq/biwDIHC91BysXzaUyo9jI8Nr2VMiIiIiBteBp7wUOPk10HwQEBzq697IExcNhgqrQLom/rLiL/jyoS9t7rM1Wt1n/hb5MfOwiYiIyB2Ycx1oKozA3jek+zpArQ6+cTN/FE+daiM/1unaylVFHFk8aLHVc1NZPgbORERE5C0cuQ40oWrg0Sxf96JaarXC4nGQ09VCXt/+Ol7q+ZLNfXtm9AYgpYKYRqzXTe6B+mrfj94TERHRzYPBdaApNwKHPwPajACClb7ujUytDpZzql1d+twko3mG3X22cqrrq0OZa01ERERuxbSQQFNRChxfId3XUaZAW4jbnR61BoDfC393+VxFxjK5NB8XmCEiIqLa4sh1oAlVAw9WXWSlLjEYymu0iMyV4isO28RGhlvlYDOgJiIiInfiyHWgKSsB9rwh3d9kejTt4XRbLpFOREREnsCR60AjyoELWUD7p3zdkypMudaWC8e4sojMgl0LML/vfKfO5WiJ9COzM2zWyyYiIiKqDqOHQBOqAgas8HUvbLK1WqMri8g4G1gTEREReQrTQgJNWQmw/R83ZVrIgKUDnG57ZHYGjszOwJ4Z6fK2dZPNaSWX9EZOdCQiIiKXceQ60IgK4No56b6OsbVao07X1mqBmeqYlj93hq2UD8sVG7vP/clqX5GxTE4lYcoIERER2eP3I9evvfYaFAoFnnnmGXlbcXExJkyYgAYNGiAyMhJDhgyBTqezOu7MmTPo378/VCoV4uPj8dxzz6GsLABGKEMjgIxF0n0dY2u1RlOQ7UzFkJFfj/RIv0wTHYmIiIgc8evht927d+P9999Hu3btrLZPnjwZ3333HVasWIGoqChMnDgRgwcPxrZt2wAA5eXl6N+/PxISErB9+3ZcuHABI0aMQGhoKObMmeOLt+I9ZcXAzy8Cd88BQm6uBVTmZ7iec61ShuDIbGnxmSJjudWkRpPK2ywrjADgiDYRERHJ/HbkWq/XY9iwYfjwww8RExMjby8oKMBHH32EN954A/feey86deqETz75BNu3b8eOHTsAAOvWrcORI0fwn//8Bx06dEC/fv3wyiuvYMGCBTAajb56S37PYDBCoZgHhWIeDIaaXUdTxRCdrq3FtgoYDOUOV278aN9HNTqnShly4+ZcPe3Or/4PKTN/tFlxhIiIiAKb3wbXEyZMQP/+/ZGenm61fe/evSgtLbXa3qpVKzRp0gRZWVkAgKysLLRt2xZarVZuk5GRgcLCQhw+fNjuOUtKSlBYWGh18zsh4UCvN+rsqHVk5C+IjPzFqkqIVntI3l6dOxrd4bZ+bH2+l1PtnKmTzVUgiYiIAodf/g172bJl2LdvH3bv3l1lX15eHpRKJaKjo622a7Va5OXlyW0sA2vTftM+ezIzMzFr1qxa9t7HSq8DG58G7n3HbXnXplFqg6HUYpv5sVqtdMt5HLledr1Wx6uUIfLqjZZB8J4ZvQEo5PSQPTPS5ceWKSOWjy1XgSQiIqLA4XfB9dmzZ/G3v/0N69evR3i4d0dfp0+fjilTpsjPCwsL0bhxY6/2odYUQUC9W6R7N4mMfLvKNq12ofxYiGedfq3aVAz59fKvTp/HFZXzqJ1NHzEF6JVHt+29bp21+iGg0xQgMc16e1kxsOZh4PIRICQCiIgH0hcCMc2l/ct7AYW/AWFR0vM2I4FOkx2f75sHgIJc8/OLB4GBXwPN/yyVkTywAIhsJO1r0Abo/4X0eP+7gLEQ6PpiLd4sERFR7fjJ/+5me/fuRX5+Pm6/3bygSHl5ObZs2YJ3330XP/74I4xGI65evWo1eq3T6ZCQkAAASEhIwK5du6xe11RNxNTGlrCwMISFhbnx3fhASBhw1z983Qu7bFUFMQfZqXJqiF7fvkrbQa0Gua0flqPYAKqkc9iaBLlnRrpV4O1oFUi/GN2+sAsovlw1sDZpNw5I6gcoFFJwu24MMHSTeX+v+UCLQa6dc+Aq8+O8PcBXfYGkvuZtrYcB97xpuy+ftAY6TDAH9ERERF7mdznXvXv3xqFDh3DgwAH51rlzZwwbNkx+HBoaig0bNsjH5OTk4MyZM0hLkwKEtLQ0HDp0CPn5+XKb9evXQ6PRICUlxevvyatKi6SRyNIit72kXj8Jev0k6HTjq+yztc1TXtnyisde2xRsn36tv8UESOtJkCplsLz9pnHwfaDVo7b3hYQDyfdJgTUANLwTKDzt3vNnfwSkPAYEO5FaFKwEmvUBji5xbx+IiIhc4HdRQL169ZCammq1Ta1Wo0GDBvL20aNHY8qUKahfvz40Gg2efvpppKWl4c477wQA9OnTBykpKRg+fDjmzp2LvLw8zJgxAxMmTPD/kWlHFMFAwzTp3k1MOdWWedaWTDnZruZe6/XtrdJDLl40p1SYRrBPnUpBUpKUHvTe/e+51nEPs1fib8+M3v4TgJ/d5FwqBwDsewu4daD1tq3TgO0vAfVTgO6ZQHSy8+cuvQ4cWwoM3Wq9/fgK4OxPQHgD4M6XgCb3mPc1TAN+/Rbo4L1f6oiIiCz5yf/wrpk/fz6CgoIwZMgQlJSUICMjA//+97/l/cHBwVizZg3Gjx+PtLQ0qNVqjBw5ErNnz/Zhr70kJAzoPMVxuxqwzLO2tc2V3GtblUFSU49W2ZacfARCSClCA5YOcGmVRneonD5SeZ+97dUF13VqNUj9OUClddxu5xzg6kngIfNfjNDvc0DTGBBCypNedT/wxBHnz33iv0DMbUCcuSwj2j8FdP07EBwK/L5Nys9+bDegaSrtVydIfSYiIvKRmyK43rRpk9Xz8PBwLFiwAAsWLLB7TNOmTfH99997uGd1UKkB+GYwMHAlEKr2dW/cIj9fGhn3dmDtDNul+Zyf1Jgy80ffBtghKmniYnV2zwNOrAQe/B8QqjJv19yY7KtQAB0nApufBa5fAiIaOHfuQx8BqaOtt6kt5kQ06gbEd5Tysk3BdVmxNLmSiIjIR26K4JpcEBQK3PaQdO9mev0kAFJ6iOWItU43Hmq1a+ezVTXEHq02GwAwatX7+GjQ+y6dx9NcmdRoq7oIAFzSlyBlro9GsuPaAVdyzIFyZXvekFI3HvofEB5t3l5RJgXS6huj3se/kkbATYH11ulSxY+OE22/7pWTgG4PMOhb6+3XzknVbgDgygng4gEg1mJk+/JRIK69q++SiIjIbRhcB5pgJdBujEde2l5OtVod6nK+ta2qIY5Mu/PvLh9Tl9hb8bH73E3e7YilFg8Cp38Emt5YlOnkt1JOc8YiKdDdPBWISga+vJH3HBwGDNsJlJcAq/pL94ogICLWOlC++Aug7SQ91p8HVt4HjDhg3p/9MdBiCBCmse7Pz38HdHuBoBBp3kDvBUD928z7T68F7vLcxFYiIiJHFEII4etO+KvCwkJERUWhoKAAGo3G8QF1QakBWJEujTR6IC3EYDBWGbk20enGIz7etXMaDOVy/vWuXbfhjjuOW+3/+uskDBok1UTO3PAznu46AUDNgnNPsByNtlWyz3IUutm07xy+3p4Z6QAEOr+6wWq7x0a0jXpg6V3Ao1nu+7xUlANL7pSCcDfWW8elI8D6J4GHtzpuS0REAcPb8RqD61rwy+C6vBQ4+TXQfJA0KczNFIp51e53ZVJj1dfeV32D5tuBk3fdOM/t1betra1bgaFDgdmzgTFjgEWLgJkzgeXLge7dqzR3ZpKiZRtXVRtcXzkBGK/ZP1hZD4hpYX//bxuk9I7YVPtt6oLTPwL1GgMNbvJymkRE5BJvx2tMCwk0waFAy4d8dvqaluVzSqmXyihu3Qr06QOUlABjxwJbtgCffy5N3OvTB1i3zmaA7YhKGYIjszNQZCyrMjLtiCko3zOjN2IjLVYuvXIC+Pg2O0dZGHXcfoDdtLdLffGZZhm+7gEREZH/LSJDtWTUA4vbSPceYFpQxp7IyLdtLpfu3Gu3h17fHjqdeQJbdnZrc4NGh+WHCsU+5OcboVDsg0KxDwaD9STBWhk6VAqsTX/0+fxz6V4IafvQoVUOqbwIjT22yvRtfb6X012rEpRXN2Jdk3ZERERULQbXgSYkHOj1hnTvAWq10jOj0pDyqKWb+WOrUinMDfZbL2BiMFR4pB+YPdscWFcmhLS/Fkwj2CYRStfyx22V/yMiIiLvYHAdaIJCpD+fB3k2I8jesudr1w5GdvZI5OZedct5kpMtFiXJeN3uvsjIX9w3ij1mDDB8uO19w4dL+2vJ3kj3nhm9sfX5e6o5UkoRMaWJXCkqqXVfiIiIyHnMuQ40xmvAJ62BJ45KE9k8xF5VkL59V8qPazq5Ua0OlicsWk1y/NpLK2wuWmROBans88+BHj3cEmCbVF4F0pVR6cc+2oXvYtzWFSIiInKAwXWgCYkABqy4aVaxMy02Exn5C/DIM8DSNx0ec/q0EXFxwXKaictmzpQmL9pKDVEopP1uDK5dYVnmzxXXS8twc3wiiIiIfIul+GrBL0vxeZEp9ePs2UL07PmlzTZ6/SS35Gjn5xvllRpdpde3dy3ItqwWIoSUCmKqFhIWVuNqITVVXZm/K7lZiFl5l8PX6H/lTXz36t9qfB4iIqK6ytvxGnOuA01JIfCORrr3sKSkaCQlRaNxY89/kNXq4Co51x7TvbsUQCckAB9+CHz2mXSfkOD1wNqRGJWXyhMSERERAAbXgSdUDTzixtX2nFBUVGp3X35+0Y1VHY21OodaHYwTS2bJaSIAcOqUc4uJ5OeXwmAod22iY/fuwPnz5vSPMWOk5z4IrKst8+dkXv1/J/Wxu89UfaTIWG6xrbxKVZIiYxmaTfsOzaZ9x2olRGR2bBmw+i/uf13jNWDRrUDRH+5/baJa4N91A01QMBDbxqunTE391O6+5ORF8uParN4IAFt+24JRHZu7vDqjZVURj6/s6G0xLaQFYizqWF8vLcOD72UBAP77VBoi1DGIqGaFRlurRpqWcgdgNdmSiMiKqAB+fhEY+I30vKQQ+N9TwKk10tyfDhOBtJfsH7/tJWlV4UtHgY4TgXveNO9T1gNSRgA7/wncM9+T74LIJQyuA01JIfBuFDCxAAi7ufLEY8KrlsXQ69vDYCh3Oh/bYCiv2STHuqxS4CyMZThcdlF6HH87UMvcadModeWRbRPmZhP5kSsnql9USlnP/mqutpz6HgivD8TdWPxr49NA8WVg7BmgKB/4bzqgaQq0GWH7+OjmQI+5wMEPbe9vMxL4vANw9z+BUJXz/SLyIP6vF2iUkcC4s9K9l5hWbMzPL7IaqTbR6cZDrQ6t9XkaaRpV2WaqCKLTpdoNsLOzWyE19ZjVNoOhXKpAghpMeKzjKpf2c8S0oE2RsVwesbasSsKRbaKbxJUTwMe3OW436rjzAfav3wKN75UelxYBOcuAh7cB4dHSrePTQPZH9oPrNiOl+5zltvdHNQPCGwDnNgNJ/ZzrE5GHMec64CgApUa69xLTqo3x8dWPKtQ27/rHk1WDPHMf7AfHKpV5n8FQ4Xr+9U3OtCS7ZYk/lTLY5lLtROTHqhuxrkk7ALh4AKjfSnp8OQcoNwLxHcz74zoAFw86/3q2NEgB8g/U7jWI3Ij/MwYa47U6lxai1S6UH9cm73rqXVPt7jMtPGM5Im2SnHzYoi+Hqhxraq/TpSI+3jNLu/szRyPbRBTAiq+Y/68p1UuT6S1XCA6Pdi1Yt0Wpkc5DVEdw5DrQKOtJgbUHV2e0xx31rKvz+NePO9EHKciuycTFmtbRvlnYq0rCkW0isis8xlz6NTRSSg2psKgmVOKG/4+MhdJ5iOoIBtcBR0hfRPDN2kF6/STo9ZOg0423ub82qSFfPmR7oRr7fWkPvb49dLq2Th9jmTJiMJRDodgHhWIf00iIiGyJ6wBcvjGnpX5LIDgUuGjx18P8A0Cs89/BNl06Yp1qQuRjDK4DjVEPfNBYuvcBU/61OyYwVjZg6QAX+2JaAt38z0CnS7WqlV1ZZOQvVdJKLAVywF1tvW0iCky3DgDO/iQ9DlUBLYdK5fVKCqQJlPvfAdqOMbdf+7h0MykvBcqKgYpy6VZWLG0zKfwNuP4HcEsPb7wbIqcwuA40YRpgqvB5vrXBYHthmfz8IuTmXoVCMQ8KxTyXRrJXP7K61v0yBdyOSCPYFRbPORGSiKiKpPuk4PePG2l1974LKKOA928BlnYD2o62rhRSeAZI7GZ+vn4s8FYEcPQ/wIF3pcfrx5r3H/4MaPO4VxdGI3JEIYTwTX7ATcDba9W7RUW59Ce6+q2kBWV8RKGY53RbvX6SU/naL254EXN6z6lNt2T5+cYqOdY6XVuo1UHVjlzbwomQROQXdPuA/3Ry3O6xvYDWhXkrR5dKC8EMsFNOz6SsBPisHTAyW0ofccR4Dfi8o7TqsCrO+f5QwPF2vMaR60BTagCWpkn3LjIYjFAoZkGhmFXrsnmunbfUqfM9kvqI9YZaLLkbH6+EELdbpYio1UFOjWoHKcpxcOpf0Co+FwAnQhKRn3B2YqGrExBbP+I4sAaAkDBgVI5zgbWpH6NPMrCmOoeJkYEmTAM8XVijQy0DXIPBWKvqH3r9pBtBc6nNhWUsmUr1ORrBPpR/CG21NybGVF5yd3kv4EIWEGTxpT3qOBCZ6FR/fxo/DqoPDgHBoah4U9p2aeBhxCVdqtK2QgRj3ubhmNPvXQz+9HUAkNNF7AXnN/OiNUTkJ2JaSN+L7lyhkSgA+eXIdWZmJrp06YJ69eohPj4egwYNQk5OjlWb4uJiTJgwAQ0aNEBkZCSGDBkCnU5n1ebMmTPo378/VCoV4uPj8dxzz6GsrAw3tYoy4HyWdSkkBwwG442bOU9aq30dublXajyCLS0qo3a4sIylyMi3nT9B5SV3AaD7/wGT9OabE4G1qXRfr56RUPSQjlf8TbpFxN0it6s8EfK/B9PRu8VuNI6+cKPv1U+EtCU/3yhPjszP995fCogogMW0kFI+7N0YWBM55JfB9ebNmzFhwgTs2LED69evR2lpKfr06QODwZzqMHnyZKxevRorVqzA5s2bcf78eQwePFjeX15ejv79+8NoNGL79u349NNPsXjxYsycOdMXb8l7yq4Dqx+S7p0UGZmJyMhMJCdbB7fJyW8jMjLT3T2ssbbxFoG05ZK7XlB5ImSRMQK7z6agf+ufqz3ONAnS9uTIimqOJCIiorroppjQePHiRcTHx2Pz5s3o0aMHCgoKEBcXhyVLluDBBx8EABw7dgytW7dGVlYW7rzzTvzwww+4//77cf78eWi1WgDAe++9hxdeeAEXL16EUuk45cEvJzTWgEIxq9r9Ot1UxMdHuuVcBoMR+flFVVJFzhy8B0Glegz48yoAwLafH0FEhEWKh7IeXtz3iXlC4xd3AO2eAtqOkp4v73VjtnoFUK8p0Gmy9Qx1R5w83nIi5NuD5kIZXIoH/r1MLvdXOd1Dodjn1Omzs1sjLk461tHkSKaYEBERmXk7Xrspcq4LCgoAAPXr1wcA7N27F6WlpUhPT5fbtGrVCk2aNJGD66ysLLRt21YOrAEgIyMD48ePx+HDh9GxY8cq5ykpKUFJSYn8vLCwZrnLPlVRBpzZADTpbb0EbS1ota9DiJfd8lpqtbLKCHnz2ItovK4zAGDf5Bsbv3qryrFzRh03P7FcchcAumcCDVKAEBVwdqM00VFZD2jxgHMdc/J400RIg6Ecbz6oRou4M05PhKxOaupR+XFNVpckIiIi7/DLtBBLFRUVeOaZZ9CtWzekpqYCAPLy8qBUKhEdHW3VVqvVIi8vT25jGVib9pv22ZKZmYmoqCj51rhxYze/Gy8oKwY2TZHunaTXT/dghxyrF1biuBGAAd+MNj+xXHIXABLTgLAoaRZ6swyg3ZNAjhOz12txvCbcgCtF1f+GXJNVIu2pLsXEMn+btbiJiIg8x+9HridMmIDs7Gz8/HP1ua3uMH36dEyZMkV+XlhY6H8BtjISePywS4eo1UrodFNx8WIRUlMX2mxz+HA+2rSJd0cPoddPAiCV4DNVCnHG6n5vmp9YLrlri6KWv1c6OF6tDsbTj14EWjwIVDNqbWtE+9SpFKjVQbh4sVwesbZMC7HH1oRJrfZQtccwhYSIiMi9/HrkeuLEiVizZg1++ukn3HKLuXJDQkICjEYjrl69atVep9MhISFBblO5eojpualNZWFhYdBoNFY3v1NeCuSssF4+1gnx8ZFo1iza7v5z5wqQm3ullp2T1HSJ9L+se8H8xHLJ3eKrUvWQ0iJpEZ3fNgAH3wNaDLH/YpZL8Nbk+NIiIG83kHyfS+8BAOLjQxEfr7QKpuPighEfr3TLYjRcTZKIiMhz/DK4FkJg4sSJWLVqFTZu3IikpCSr/Z06dUJoaCg2bNggb8vJycGZM2eQlpYGAEhLS8OhQ4eQn58vt1m/fj00Gg1SUlK880Z8ocII7H1DundRdTWm+/ZdguTkt32yyIzJwrQZ5ieWS+5WlAJZs4D3EoAFMcCmyUDPN4CWD5nbf9UP2GmxuqPlErw1Of7EV0DjewBNU6f6bir5J8TtNR49djbFRKs9JJcG5BLuRERE7uWX1UL++te/YsmSJfjmm2/QsmVLeXtUVBQiIiIAAOPHj8f333+PxYsXQ6PR4OmnnwYAbN++HYBUiq9Dhw5ITEzE3LlzkZeXh+HDh2PMmDGYM8e5JbQDpVqIJUeVQ0z0+um1WmTGipNL8s5sPg6zB75v3uDskruVuboEb2WiAvisA3D/MmkSpJdZpnrUFCdNEhHRzYLLnzth4cKFKCgoQK9evdCwYUP5tny5OYiaP38+7r//fgwZMgQ9evRAQkICVq5cKe8PDg7GmjVrEBwcjLS0NDz22GMYMWIEZs+e7Yu35D3lRuDgIum+Bk6dmuRUu9Onr8qLz3hL9wadkZ9vgEIxDwrFPBiaDHE9sAZcX4K3MkUQMPKgTwLrykyL21iOZrtj8iQRERHZ5pcj13WFX45clxqAbwYDA1cCoWoAwI4dJzF48AIAwMqVE3Dnnc2rfYnDh/Nx7lwB+vZd4tQpa12mz8mR6+RV/ZG7rZf83NFy6YGk8sRF8/YKedKjTtfWbj1uIiIif8U61+RZoWrgwR9r9RJt2sRDparhqG5NKOs51cwQZJ0rbLlcO4Nsa7aCZ3fU4yYiIgp0DK4DTVkJcGAB0GECduw5CwA4fPh3effgwQuwcOFj0Gqjqh3BTkqKwa5dY3DHHYvstjHJzb2CpKSYmvc5pgUw6jhgvCZvun699MZ9GdL/tALXSsKQX6GwOsyyjJ8Qz8JgMCIyUlqgJtBGtU0TJomIiMizmBZSC/6ZFlIErB0J9P0UiU3/Xm3T8+fnO/WSzkxydNcKjpVZBszI+Bb48c92zh/YwTUREVGg4oRG8qxQFTBghXTvwJkzl5x6SZ1uqsM2psmNublX5HJ97qqLLffjsx+g04236Nd46PWToNONv3F+c5pIfn6ReeKjD8oGEhER0c2JwXWgKSsBtv8DKCvBypUTqm16552vOvWS8fGR0OmmVltJJDIyE5GRmVW2Hz6cLwfbhw/n2ziyemq1EkI8CyGexegND1stPKNWh0KtVkKrXYjIyLet0kSSkx2ns7iTwWBkME9ERBQAmHMdaEQFcO0cICocVgVxRXx8pFNB49mzBVaPTbnT7rD6kdU1Clw58ZGIiIjchTnXteCXOdeVJCZOdtjGmfJ8Jrm5V5Cc/LbVNp3uWWi18xweu2zZEDz88FcAarYIzcivR+LTQZ9W2W4KuA2GUqvRa1uEeNalczpi69w63Xh5hJ3BPBERkWcx55o8q6wY2DRFunfS4MELkJg4GYmJk/HHH9eqbZuUFAMhXoZeP13eZpmqUR1TYO0qKeViFj57NMHmyLVarbxx82L5wBsiI9+ukpJiSlORJ2ISEVHdtXUrkJgILLqRTrhokfR861bf9ovqLKaFBLiDB6UVKdu1m+nR85jysc+eLUDPnlVHlyurUapGx/0u9GeMnHdtOZJMREQk27oV6NMHKCkBxo4FtmwBPv8cUCik7evWAd27+7qXVMcwuA40IeFArzfkp7Gx0gItS5aMw6OPfuDw8EuX9CgqMsqTHU+efA0qVViVdtJEQ3P5vaSkqgHy2rWPori4DIMGfVlln2Uaib0yfpYpFwCA3xtVG5SbJj9aHittD/VYeoZeP0nuo620ECIiqsOGDpUCa1MG7eefS/dCSNuHDgXOn/dd/6hOYnAdaEqvAxufBu59BwiNkDenpt7i1OH33DPXbV257bZYFBXVfEJjleojoaVOBeXeZCto92QwT0REbjR7tjRibYsQ0n6iShhcBxpFEFDvFoRFqGC0WC1cCIHz5+djx46TGDx4gdMvV1RkHgG2NYJdmSkn28Sy/N7mzSPllBGd7lnXR3djnK+bbTmKTUREZNOYMeZUkMqGD5f2E1XCaiG14M/VQhQK66XCK38MiopK0Lz5NADADz9MRr9+jldrdHZFR3ukFRSl0WhnqoVYV+KQRqwtg3KODhMRUa0sWmR/5BoAPvyQAbYfYLUQ8iiFQlElsK5uOwDExKg93a0asVUFxJRywcCaiIhqbeZMafKiLQqFtJ+oEqaFkE0qVZg8Em1Zfm/HjhlQqZRydZGDB2dDpXJPIFt5EiQREZFPLV9urhYihJQKYqoWEhYm7SeqhME1ORQbW88q5aOoqER+rFIpncq19iQG5URE5BHdu0vl9oYOlSYvjhkD9OghjVgvX84yfGQTg2siIiIie7p3ty63N2YM86ypWgyuA4xp4qKjCY3VsUwZISIiIiIzTmgkIiIiInITjlwHKFZgJCIiInI/jlwTEREREbkJg2siIiIiIjdhcE1ERERE5CYMromIiIiI3ITBNRERERGRmzC4JiIiIiJyEwbXRERERERuwuCaiIiIiMhNuIhMLZgWYiksLPRxT4iIiIjIFlOc5q0F9Bhc18KlS5cAAI0bN/ZxT4iIiIioOpcuXUJUVJTHz8Pguhbq168PADhz5oxXfljuUFhYiMaNG+Ps2bPQaDS+7k61/Kmv/orX2LN4fT2P19izeH09j9fY8woKCtCkSRM5bvM0Bte1EBQkpaxHRUX53T8IjUbjN332p776K15jz+L19TxeY8/i9fU8XmPPM8VtHj+PV85CRERERBQAGFwTEREREbkJg+taCAsLw8svv4ywsDBfd8Vp/tRnf+qrv+I19ixeX8/jNfYsXl/P4zX2PG9fY4XwVl0SIiIiIqKbHEeuiYiIiIjchME1EREREZGbMLgmIiIiInITBtdERERERG7C4LqGFixYgGbNmiE8PBxdu3bFrl27fNaXzMxMdOnSBfXq1UN8fDwGDRqEnJycKu2ysrJw7733Qq1WQ6PRoEePHrh+/bq8//Llyxg2bBg0Gg2io6MxevRo6PV6t/b1H//4BxQKhdWtVatW8v7i4mJMmDABDRo0QGRkJIYMGQKdTmf1GmfOnEH//v2hUqkQHx+P5557DmVlZW7tpz/ZsmULBgwYgMTERCgUCnz99ddW+4UQmDlzJho2bIiIiAikp6fjxIkTVm2c+dkfPHgQ3bt3R3h4OBo3boy5c+d6+q3VCY6u7+OPP17lM923b1+rNry+9jnz/eWu74VNmzbh9ttvR1hYGJo3b47Fixd7+u3VCc5c4169elX5HD/11FNWbXiN7Vu4cCHatWsnLwSTlpaGH374Qd7Pz3DtOLq+de7zK8hly5YtE0qlUnz88cfi8OHDYuzYsSI6OlrodDqf9CcjI0N88sknIjs7Wxw4cEDcd999okmTJkKv18tttm/fLjQajcjMzBTZ2dni2LFjYvny5aK4uFhu07dvX9G+fXuxY8cOsXXrVtG8eXPxyCOPuLWvL7/8smjTpo24cOGCfLt48aK8/6mnnhKNGzcWGzZsEHv27BF33nmnuOuuu+T9ZWVlIjU1VaSnp4v9+/eL77//XsTGxorp06e7tZ/+5Pvvvxd///vfxcqVKwUAsWrVKqv9r732moiKihJff/21+OWXX8Sf//xnkZSUJK5fvy63cfSzLygoEFqtVgwbNkxkZ2eLpUuXioiICPH+++976236jKPrO3LkSNG3b1+rz/Tly5et2vD62ufM95c7vhdOnTolVCqVmDJlijhy5Ih45513RHBwsFi7dq1X368vOHONe/bsKcaOHWv1OS4oKJD38xpX79tvvxXfffedOH78uMjJyREvvviiCA0NFdnZ2UIIfoZry9H1rWufXwbXNXDHHXeICRMmyM/Ly8tFYmKiyMzM9GGvzPLz8wUAsXnzZnlb165dxYwZM+wec+TIEQFA7N69W972ww8/CIVCIX7//Xe39e3ll18W7du3t7nv6tWrIjQ0VKxYsULedvToUQFAZGVlCSGkQCcoKEjk5eXJbRYuXCg0Go0oKSlxWz/9VeXgr6KiQiQkJIh//etf8rarV6+KsLAwsXTpUiGEcz/7f//73yImJsbqGr/wwguiZcuWHn5HdYu94HrgwIF2j+H1dU3l7y93fS88//zzok2bNlbnGjp0qMjIyPD0W6pzbP0f0bNnT/G3v/3N7jG8xq6LiYkRixYt4mfYQ0zXV4i69/llWoiLjEYj9u7di/T0dHlbUFAQ0tPTkZWV5cOemRUUFAAA6tevDwDIz8/Hzp07ER8fj7vuugtarRY9e/bEzz//LB+TlZWF6OhodO7cWd6Wnp6OoKAg7Ny50639O3HiBBITE5GcnIxhw4bhzJkzAIC9e/eitLTU6tq2atUKTZo0ka9tVlYW2rZtC61WK7fJyMhAYWEhDh8+7NZ+3gxyc3ORl5dndU2joqLQtWtXq2vq6GeflZWFHj16QKlUym0yMjKQk5ODK1eueOnd1F2bNm1CfHw8WrZsifHjx+PSpUvyPl5f11T+/nLX90JWVpbVa5ja1JXvbW+qfI1NvvjiC8TGxiI1NRXTp09HUVGRvI/X2Hnl5eVYtmwZDAYD0tLS+Bl2s8rX16QufX5DXD4iwP3xxx8oLy+3+gEBgFarxbFjx3zUK7OKigo888wz6NatG1JTUwEAp06dAiDlO8+bNw8dOnTAZ599ht69eyM7OxstWrRAXl4e4uPjrV4rJCQE9evXR15entv617VrVyxevBgtW7bEhQsXMGvWLHTv3h3Z2dnIy8uDUqlEdHS01TFarVbuQ15ens1rb9pH1kzXxNY1s7ymjn72eXl5SEpKqvIapn0xMTEe6b8/6Nu3LwYPHoykpCT8+uuvePHFF9GvXz9kZWUhODiY19cFtr6/3PW9YK9NYWEhrl+/joiICE+8pTrH1jUGgEcffRRNmzZFYmIiDh48iBdeeAE5OTlYuXIlAF5jZxw6dAhpaWkoLi5GZGQkVq1ahZSUFBw4cICfYTewd32Buvf5ZXB9k5kwYQKys7OtRqUrKioAAE8++SSeeOIJAEDHjh2xYcMGfPzxx8jMzPRa//r16yc/bteuHbp27YqmTZviyy+/vOm/GOjm9PDDD8uP27Zti3bt2uHWW2/Fpk2b0Lt3bx/2zP/Y+v4i97J3jceNGyc/btu2LRo2bIjevXvj119/xa233urtbvqlli1b4sCBAygoKMB///tfjBw5Eps3b/Z1t24a9q5vSkpKnfv8Mi3ERbGxsQgODq4yy1en0yEhIcFHvZJMnDgRa9aswU8//YRbbrlF3t6wYUMAkH/DM2ndurWckpGQkID8/Hyr/WVlZbh8+bJH31d0dDRuu+02nDx5EgkJCTAajbh69apVG8trm5CQYPPam/aRNdM1qe7z6szPntfdecnJyYiNjcXJkycB8Po6y973l7u+F+y10Wg0AfOLvb1rbEvXrl0BwOpzzGtcPaVSiebNm6NTp07IzMxE+/bt8dZbb/Ez7Cb2rq8tvv78Mrh2kVKpRKdOnbBhwwZ5W0VFBTZs2GCV++NNQghMnDgRq1atwsaNG6v8eblZs2ZITEysUnrp+PHjaNq0KQAgLS0NV69exd69e+X9GzduREVFhfwh9QS9Xo9ff/0VDRs2RKdOnRAaGmp1bXNycnDmzBn52qalpeHQoUNWwcr69euh0Wiq/PJAQFJSEhISEqyuaWFhIXbu3Gl1TR397NPS0rBlyxaUlpbKbdavX4+WLVsGTMqCs86dO4dLly7Jv9Ty+lbP0feXu74X0tLSrF7D1MZX39ve5Oga23LgwAEAsPoc8xq7pqKiAiUlJfwMe4jp+tri88+vy1MgSSxbtkyEhYWJxYsXiyNHjohx48aJ6Ohoq1mo3jR+/HgRFRUlNm3aZFWGpqioSG4zf/58odFoxIoVK8SJEyfEjBkzRHh4uDh58qTcpm/fvqJjx45i586d4ueffxYtWrRweym+qVOnik2bNonc3Fyxbds2kZ6eLmJjY0V+fr4QQipX1KRJE7Fx40axZ88ekZaWJtLS0uTjTeV0+vTpIw4cOCDWrl0r4uLiAroU37Vr18T+/fvF/v37BQDxxhtviP3794vffvtNCCGV4ouOjhbffPONOHjwoBg4cKDNUnzV/eyvXr0qtFqtGD58uMjOzhbLli0TKpUqIErFVXd9r127Jp599lmRlZUlcnNzxf/+9z9x++23ixYtWlQpc8nra5sz31/u+F4wldl67rnnxNGjR8WCBQsCpoyZo2t88uRJMXv2bLFnzx6Rm5srvvnmG5GcnCx69OghvwavcfWmTZsmNm/eLHJzc8XBgwfFtGnThEKhEOvWrRNC8DNcW9Vd37r4+WVwXUPvvPOOaNKkiVAqleKOO+4QO3bs8FlfANi8ffLJJ1btMjMzxS233CJUKpVIS0sTW7dutdp/6dIl8cgjj4jIyEih0WjEE088Ia5du+bWvg4dOlQ0bNhQKJVK0ahRIzF06FCrAP/69evir3/9q4iJiREqlUo88MAD4sKFC1avcfr0adGvXz8REREhYmNjxdSpU0Vpaalb++lPfvrpJ5s//5EjRwohpHJ8L730ktBqtSIsLEz07t1b5OTkWL2GMz/7X375Rdx9990iLCxMNGrUSLz22mveeos+Vd31LSoqEn369BFxcXEiNDRUNG3aVIwdO7bKL9q8vvY58/3lru+Fn376SXTo0EEolUqRnJxc5TvyZuXoGp85c0b06NFD1K9fX4SFhYnmzZuL5557zqpOsBC8xtUZNWqUaNq0qVAqlSIuLk707t1bDqyF4Ge4tqq7vnXx86sQQgjXx7uJiIiIiKgy5lwTEREREbkJg2siIiIiIjdhcE1ERERE5CYMromIiIiI3ITBNRERERGRmzC4JiIiIiJyEwbXRERERERuwuCaiCgA9OrVCwqFwtfdICK66YX4ugNEROQaV4NkrhVGROQ9DK6JiPzMyy+/XGXbm2++iYKCApv7AOCzzz5DUVGRp7tGRBTwuPw5EdFNoFmzZvjtt984Sk1E5GPMuSYiCgC2cq4XL14MhUKBxYsXY/Xq1ejatStUKhUaNWqEl156CRUVFQCATz/9FO3bt0dERASaNGmCf/3rXzbPIYTAxx9/jG7dukGj0UClUqFz5874+OOPPf7+iIjqCqaFEBEFuFWrVmHdunUYNGgQunXrhu+++w6vvvoqhBCIiorCq6++ioEDB6JXr1746quv8Pzzz0Or1WLEiBHyawghMGzYMCxduhQtWrTAo48+CqVSifXr12P06NE4cuQI5s2b58N3SUTkHUwLISK6CThKC+nVqxc2b95stX/x4sV44oknEBoaim3btqFLly4AgGvXrqF58+bQ6/XQaDTYtm0bkpOTAQBnz55F8+bN0bJlSxw8eFB+rQ8//BDjxo3DE088gffffx+hoaEAAKPRiAcffBCrV6/Gnj170KlTJ09dAiKiOoFpIUREAe6xxx6TA2sAqFevHu6//34UFRVh/PjxcmANAI0bN8bdd9+NI0eOoKysTN7+7rvvQq1WY8GCBXJgDQBKpRL//Oc/AQBLly71wrshIvItpoUQEQW4Dh06VNnWsGHDaveVl5dDp9OhUaNGKCoqwqFDh5CYmIj/+7//q9K+tLQUAHDs2DG39puIqC5icE1EFOA0Gk2VbSEhIQ73mYLmK1euQAiB33//HbNmzbJ7HoPB4I7uEhHVaQyuiYioVkwBeKdOnbBnzx4f94aIyLeYc01ERLVSr149tG7dGkePHsXVq1d93R0iIp9icE1ERLU2adIkFBUVYezYsTbTP3Jzc3H69Gnvd4yIyMuYFkJERLX25JNPYseOHfj000+xbds2pKenIzExETqdDseOHcPOnTuxZMkSNGvWzNddJSLyKAbXRERUa6aVHu+77z58+OGHWLNmDfR6PeLj49GiRQvMmzcP6enpvu4mEZHHcREZIiIiIiI3Yc41EREREZGbMLgmIiIiInITBtdERERERG7C4JqIiIiIyE0YXBMRERERuQmDayIiIiIiN2FwTURERETkJgyuiYiIiIjchME1EREREZGbMLgmIiIiInITBtdERERERG7C4JqIiIiIyE0YXBMRERERucn/Az1U4JosAPSwAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "\n",
    "\n",
    "\n",
    "ax.scatter(dfga.time, dfga.cost,       s=30,  marker=\"+\", edgecolors='blue', label=\"NSGA-II: 100 generations\")\n",
    "ax.scatter(dfga150.time, dfga150.cost, s=30,  marker=\"+\", color='mediumblue', label=\"NSGA-II: 150 generations\")\n",
    "ax.scatter(dfga200.time, dfga200.cost, s=30,  marker=\"+\", color='darkblue', label=\"NSGA-II: 200 generations\")\n",
    "ax.scatter(dfga250.time, dfga250.cost, s=30,  marker=\"+\", color='navy', label=\"NSGA-II: 250 generations\")\n",
    "ax.scatter(dfga300.time, dfga300.cost, s=30,  marker=\"+\", color='midnightblue', label=\"NSGA-II: 300 generations\")\n",
    "ax.scatter(dfga400.time, dfga400.cost, s=30,  marker=\"+\", color='black', label=\"NSGA-II: 400 generations\")\n",
    "\n",
    "ax.scatter(dall.time, dall.cost, s=30, marker=\"X\", facecolors='red', color='red', label=\"GA: 100 generations\") #MONO solutions\n",
    "\n",
    "ax.scatter(avg_pf[:,0], avg_pf[:,1], s=30,  marker=\"s\", color='darkorange',label=\"DRL model\")\n",
    "\n",
    "#ax.scatter(dfmodel.time, dfmodel.cost, s=30,  marker=\"s\", edgecolors='darkorange',label=\"PPO: average cases\")\n",
    "\n",
    "ax.scatter(dfmodel.iTime[0],dfmodel.iCost[0],color=\"green\",label=\"Cloud\")\n",
    "\n",
    "#mlabel = [\"(1,0)\",\"(.75,.25)\",\"(.5,.5)\",\"(.25,.75)\",\"(0,1)\"]\n",
    "mlabel = [\"(0,1)\",\"(1,0)\",\"(.25,.75)\",\"(5.,5.)\",\"(.75,.25)\"]\n",
    "for i in range(len(dfmodel)):\n",
    "    print(mlabel[i])\n",
    "    if i in [0]:#(0,1)\n",
    "        ax.annotate(mlabel[i], (avg_pf[i,0]+80, avg_pf[i,1]-15),color=\"darkorange\",fontsize=9)\n",
    "    elif i in [1]:#(1,0)\"\n",
    "        ax.annotate(mlabel[i], (avg_pf[i,0]+60, avg_pf[i,1]-55),color=\"darkorange\",fontsize=9)\n",
    "    elif i in [2]:#\"(.25,.75)\"\n",
    "        ax.annotate(mlabel[i], (avg_pf[i,0]+60, avg_pf[i,1]+40),color=\"darkorange\",fontsize=8)\n",
    "    elif i in [3]: #\"(5.,5.)\n",
    "        ax.annotate(mlabel[i], (avg_pf[i,0]+40, avg_pf[i,1]),color=\"darkorange\",fontsize=9)\n",
    "    else:\n",
    "        ax.annotate(mlabel[i], (avg_pf[i,0]-160, avg_pf[i,1]+40),color=\"darkorange\",fontsize=8)\n",
    "\n",
    "ax.legend()\n",
    "ax.vlines(x=dfmodel.iTime[0], ymin=100, ymax=dfmodel.iCost[0], colors='green', ls=':', lw=0.6)\n",
    "ax.vlines(x=avg_pf[1,0], ymin=100, ymax=avg_pf[1,1], colors='darkorange', ls=':', lw=0.8)\n",
    "# plt.title(\"Objective Space. Case code: %s\"%configs.name)\n",
    "plt.xlabel(\"Time\", fontsize=14)\n",
    "plt.ylabel(\"Cost\", fontsize=14)\n",
    "plt.xticks(list(plt.xticks()[0]) + [avg_pf[1,0]])\n",
    "plt.savefig(\"images/pf_ppo_nsgaII_c0.pdf\",dpi=300,bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NSGAII + DRL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "ax.scatter(dfga.time, dfga.cost, s=30, facecolors='none', edgecolors='blue',label=\"NSGA\")\n",
    "\n",
    "ax.scatter(avg_pf[:,0], avg_pf[:,1], s=40, facecolors='none', edgecolors='red',label=\"DRL\")\n",
    "\n",
    "plt.title(\"Objective Space. Case code: %s\"%configs.name)\n",
    "plt.xlabel(\"Time\", fontsize=14)\n",
    "plt.ylabel(\"Cost\", fontsize=14)\n",
    "plt.legend()\n",
    "# plt.savefig(\"images/pf_nsgaII-0.pdf\",dpi=300,bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ga_pf = np.vstack((dfga.time.values,dfga.cost.values)).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H - GA  164877.0\n"
     ]
    }
   ],
   "source": [
    "ref_point = np.array([900., 900.])\n",
    "ind = HV(ref_point=ref_point)\n",
    "print(\"H - GA \", ind(ga_pf)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 2 is out of bounds for axis 0 with size 2",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[45], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mH - DRL \u001b[39m\u001b[39m\"\u001b[39m, ind(avg_pf)) \n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.7/envs/DGLenv/lib/python3.9/site-packages/pymoo/core/indicator.py:15\u001b[0m, in \u001b[0;36mIndicator.__call__\u001b[0;34m(self, F, *args, **kwargs)\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, F, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m---> 15\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdo(F, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.7/envs/DGLenv/lib/python3.9/site-packages/pymoo/core/indicator.py:30\u001b[0m, in \u001b[0;36mIndicator.do\u001b[0;34m(self, F, *args, **kwargs)\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[39m# do the normalization - will only be done if zero_to_one is enabled\u001b[39;00m\n\u001b[1;32m     28\u001b[0m F \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnormalization\u001b[39m.\u001b[39mforward(F)\n\u001b[0;32m---> 30\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_do(F, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.7/envs/DGLenv/lib/python3.9/site-packages/pymoo/indicators/hv/__init__.py:43\u001b[0m, in \u001b[0;36mHypervolume._do\u001b[0;34m(self, F)\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[39m# calculate the hypervolume using a vendor library\u001b[39;00m\n\u001b[1;32m     42\u001b[0m hv \u001b[39m=\u001b[39m _HyperVolume(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mref_point)\n\u001b[0;32m---> 43\u001b[0m val \u001b[39m=\u001b[39m hv\u001b[39m.\u001b[39;49mcompute(F)\n\u001b[1;32m     44\u001b[0m \u001b[39mreturn\u001b[39;00m val\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.7/envs/DGLenv/lib/python3.9/site-packages/pymoo/vendor/hv.py:56\u001b[0m, in \u001b[0;36mHyperVolume.compute\u001b[0;34m(self, front)\u001b[0m\n\u001b[1;32m     53\u001b[0m dimensions \u001b[39m=\u001b[39m \u001b[39mlen\u001b[39m(referencePoint)\n\u001b[1;32m     54\u001b[0m \u001b[39mfor\u001b[39;00m point \u001b[39min\u001b[39;00m front:\n\u001b[1;32m     55\u001b[0m     \u001b[39m# only consider points that dominate the reference point\u001b[39;00m\n\u001b[0;32m---> 56\u001b[0m     \u001b[39mif\u001b[39;00m weaklyDominates(point, referencePoint):\n\u001b[1;32m     57\u001b[0m         relevantPoints\u001b[39m.\u001b[39mappend(point)\n\u001b[1;32m     58\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39many\u001b[39m(referencePoint):\n\u001b[1;32m     59\u001b[0m     \u001b[39m# shift points so that referencePoint == [0, ..., 0]\u001b[39;00m\n\u001b[1;32m     60\u001b[0m     \u001b[39m# this way the reference point doesn't have to be explicitly used\u001b[39;00m\n\u001b[1;32m     61\u001b[0m     \u001b[39m# in the HV computation\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.7/envs/DGLenv/lib/python3.9/site-packages/pymoo/vendor/hv.py:47\u001b[0m, in \u001b[0;36mHyperVolume.compute.<locals>.weaklyDominates\u001b[0;34m(point, other)\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mweaklyDominates\u001b[39m(point, other):\n\u001b[1;32m     46\u001b[0m     \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39mlen\u001b[39m(point)):\n\u001b[0;32m---> 47\u001b[0m         \u001b[39mif\u001b[39;00m point[i] \u001b[39m>\u001b[39m other[i]:\n\u001b[1;32m     48\u001b[0m             \u001b[39mreturn\u001b[39;00m \u001b[39mFalse\u001b[39;00m\n\u001b[1;32m     49\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mTrue\u001b[39;00m\n",
      "\u001b[0;31mIndexError\u001b[0m: index 2 is out of bounds for axis 0 with size 2"
     ]
    }
   ],
   "source": [
    "print(\"H - DRL \", ind(avg_pf)) "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Violin plot del PF según pesos del modelo DRL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>model</th>\n",
       "      <th>time</th>\n",
       "      <th>cost</th>\n",
       "      <th>case</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>260.0</td>\n",
       "      <td>1716.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>7525</td>\n",
       "      <td>352.0</td>\n",
       "      <td>646.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>5050</td>\n",
       "      <td>305.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>2575</td>\n",
       "      <td>1158.0</td>\n",
       "      <td>347.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>0100</td>\n",
       "      <td>3107.0</td>\n",
       "      <td>342.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    model    time    cost  case\n",
       "0    1000   260.0  1716.0     0\n",
       "30   7525   352.0   646.0     0\n",
       "60   5050   305.0   198.0     0\n",
       "90   2575  1158.0   347.0     0\n",
       "120  0100  3107.0   342.0     0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtvio = dfDRL.loc[:,[\"model\",\"time\",\"cost\"]]\n",
    "dtvio[\"case\"]=np.zeros(len(dtvio),dtype=int)\n",
    "dtvio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = list(range(len(dtvio)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['(.1,.0)', '(.7,.5)', '(.5,.0)', '(.2,.5)', '(.0,.1)'], dtype='<U7')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def getWeithDecomposition(val):\n",
    "    if len(val)==3:\n",
    "        if val[0]==\"0\":\n",
    "            return \"(0,1.)\"\n",
    "        else:\n",
    "            return \"(1.,0)\"\n",
    "    else:\n",
    "        return \"(.%s,.%s)\"%(val[0],val[1])\n",
    "    \n",
    "fticks = np.vectorize(getWeithDecomposition)\n",
    "ticks = fticks(dtvio.model)\n",
    "ticks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, sharey=True)\n",
    "axes[0].barh(x, dtvio.time, align='center', color='red', zorder=10)\n",
    "axes[0].set(title='Time')\n",
    "axes[1].barh(x, dtvio.cost, align='center', color='blue', zorder=10)\n",
    "axes[1].set(title='Cost')\n",
    "\n",
    "axes[0].invert_xaxis()\n",
    "axes[0].set(yticks=x, yticklabels=ticks)\n",
    "axes[0].yaxis.tick_right()\n",
    "axes[1].yaxis.tick_left()\n",
    "\n",
    "\n",
    "\n",
    "for ax in axes.flat:\n",
    "    ax.margins(0.03)\n",
    "    ax.grid(True)\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.subplots_adjust(wspace=0.23)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1000\n",
       "50     7525\n",
       "100    5050\n",
       "150    2575\n",
       "200    0100\n",
       "Name: model, dtype: object"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtvio.model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['0100', '1000', '2575', '5050', '7525'], dtype='object', name='model')\n"
     ]
    }
   ],
   "source": [
    "dfmodel #average DRL all cases\n",
    "print(dfmodel.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfrindex = dfmodel.reindex(['010', '19', '28', '37', '46', '55', '64', '73', '82', '91', '100'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "shape mismatch: objects cannot be broadcast to a single shape.  Mismatch is between arg 2 with shape (11,) and arg 3 with shape (5,).",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[295], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m fig, axes \u001b[39m=\u001b[39m plt\u001b[39m.\u001b[39msubplots(ncols\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m, sharey\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[0;32m----> 2\u001b[0m axes[\u001b[39m0\u001b[39;49m]\u001b[39m.\u001b[39;49mbarh(x, dfrindex\u001b[39m.\u001b[39;49mtime, align\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mcenter\u001b[39;49m\u001b[39m'\u001b[39;49m, color\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mred\u001b[39;49m\u001b[39m'\u001b[39;49m, zorder\u001b[39m=\u001b[39;49m\u001b[39m10\u001b[39;49m)\n\u001b[1;32m      3\u001b[0m axes[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mset(title\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mTime\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m      4\u001b[0m axes[\u001b[39m1\u001b[39m]\u001b[39m.\u001b[39mbarh(x, dfrindex\u001b[39m.\u001b[39mcost, align\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mcenter\u001b[39m\u001b[39m'\u001b[39m, color\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mblue\u001b[39m\u001b[39m'\u001b[39m, zorder\u001b[39m=\u001b[39m\u001b[39m10\u001b[39m)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.7/envs/DGLenv/lib/python3.9/site-packages/matplotlib/axes/_axes.py:2629\u001b[0m, in \u001b[0;36mAxes.barh\u001b[0;34m(self, y, width, height, left, align, data, **kwargs)\u001b[0m\n\u001b[1;32m   2519\u001b[0m \u001b[39m\u001b[39m\u001b[39mr\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   2520\u001b[0m \u001b[39mMake a horizontal bar plot.\u001b[39;00m\n\u001b[1;32m   2521\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2626\u001b[0m \u001b[39m:doc:`/gallery/lines_bars_and_markers/horizontal_barchart_distribution`.\u001b[39;00m\n\u001b[1;32m   2627\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   2628\u001b[0m kwargs\u001b[39m.\u001b[39msetdefault(\u001b[39m'\u001b[39m\u001b[39morientation\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mhorizontal\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m-> 2629\u001b[0m patches \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbar(x\u001b[39m=\u001b[39;49mleft, height\u001b[39m=\u001b[39;49mheight, width\u001b[39m=\u001b[39;49mwidth, bottom\u001b[39m=\u001b[39;49my,\n\u001b[1;32m   2630\u001b[0m                    align\u001b[39m=\u001b[39;49malign, data\u001b[39m=\u001b[39;49mdata, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   2631\u001b[0m \u001b[39mreturn\u001b[39;00m patches\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.7/envs/DGLenv/lib/python3.9/site-packages/matplotlib/__init__.py:1433\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1430\u001b[0m \u001b[39m@functools\u001b[39m\u001b[39m.\u001b[39mwraps(func)\n\u001b[1;32m   1431\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39minner\u001b[39m(ax, \u001b[39m*\u001b[39margs, data\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m   1432\u001b[0m     \u001b[39mif\u001b[39;00m data \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m-> 1433\u001b[0m         \u001b[39mreturn\u001b[39;00m func(ax, \u001b[39m*\u001b[39;49m\u001b[39mmap\u001b[39;49m(sanitize_sequence, args), \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1435\u001b[0m     bound \u001b[39m=\u001b[39m new_sig\u001b[39m.\u001b[39mbind(ax, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m   1436\u001b[0m     auto_label \u001b[39m=\u001b[39m (bound\u001b[39m.\u001b[39marguments\u001b[39m.\u001b[39mget(label_namer)\n\u001b[1;32m   1437\u001b[0m                   \u001b[39mor\u001b[39;00m bound\u001b[39m.\u001b[39mkwargs\u001b[39m.\u001b[39mget(label_namer))\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.7/envs/DGLenv/lib/python3.9/site-packages/matplotlib/axes/_axes.py:2397\u001b[0m, in \u001b[0;36mAxes.bar\u001b[0;34m(self, x, height, width, bottom, align, **kwargs)\u001b[0m\n\u001b[1;32m   2394\u001b[0m     \u001b[39mif\u001b[39;00m yerr \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m   2395\u001b[0m         yerr \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_convert_dx(yerr, y0, y, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconvert_yunits)\n\u001b[0;32m-> 2397\u001b[0m x, height, width, y, linewidth, hatch \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39;49mbroadcast_arrays(\n\u001b[1;32m   2398\u001b[0m     \u001b[39m# Make args iterable too.\u001b[39;49;00m\n\u001b[1;32m   2399\u001b[0m     np\u001b[39m.\u001b[39;49matleast_1d(x), height, width, y, linewidth, hatch)\n\u001b[1;32m   2401\u001b[0m \u001b[39m# Now that units have been converted, set the tick locations.\u001b[39;00m\n\u001b[1;32m   2402\u001b[0m \u001b[39mif\u001b[39;00m orientation \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mvertical\u001b[39m\u001b[39m'\u001b[39m:\n",
      "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mbroadcast_arrays\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.7/envs/DGLenv/lib/python3.9/site-packages/numpy/lib/stride_tricks.py:540\u001b[0m, in \u001b[0;36mbroadcast_arrays\u001b[0;34m(subok, *args)\u001b[0m\n\u001b[1;32m    533\u001b[0m \u001b[39m# nditer is not used here to avoid the limit of 32 arrays.\u001b[39;00m\n\u001b[1;32m    534\u001b[0m \u001b[39m# Otherwise, something like the following one-liner would suffice:\u001b[39;00m\n\u001b[1;32m    535\u001b[0m \u001b[39m# return np.nditer(args, flags=['multi_index', 'zerosize_ok'],\u001b[39;00m\n\u001b[1;32m    536\u001b[0m \u001b[39m#                  order='C').itviews\u001b[39;00m\n\u001b[1;32m    538\u001b[0m args \u001b[39m=\u001b[39m [np\u001b[39m.\u001b[39marray(_m, copy\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m, subok\u001b[39m=\u001b[39msubok) \u001b[39mfor\u001b[39;00m _m \u001b[39min\u001b[39;00m args]\n\u001b[0;32m--> 540\u001b[0m shape \u001b[39m=\u001b[39m _broadcast_shape(\u001b[39m*\u001b[39;49margs)\n\u001b[1;32m    542\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mall\u001b[39m(array\u001b[39m.\u001b[39mshape \u001b[39m==\u001b[39m shape \u001b[39mfor\u001b[39;00m array \u001b[39min\u001b[39;00m args):\n\u001b[1;32m    543\u001b[0m     \u001b[39m# Common case where nothing needs to be broadcasted.\u001b[39;00m\n\u001b[1;32m    544\u001b[0m     \u001b[39mreturn\u001b[39;00m args\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.7/envs/DGLenv/lib/python3.9/site-packages/numpy/lib/stride_tricks.py:422\u001b[0m, in \u001b[0;36m_broadcast_shape\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m    417\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Returns the shape of the arrays that would result from broadcasting the\u001b[39;00m\n\u001b[1;32m    418\u001b[0m \u001b[39msupplied arrays against each other.\u001b[39;00m\n\u001b[1;32m    419\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    420\u001b[0m \u001b[39m# use the old-iterator because np.nditer does not handle size 0 arrays\u001b[39;00m\n\u001b[1;32m    421\u001b[0m \u001b[39m# consistently\u001b[39;00m\n\u001b[0;32m--> 422\u001b[0m b \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39;49mbroadcast(\u001b[39m*\u001b[39;49margs[:\u001b[39m32\u001b[39;49m])\n\u001b[1;32m    423\u001b[0m \u001b[39m# unfortunately, it cannot handle 32 or more arguments directly\u001b[39;00m\n\u001b[1;32m    424\u001b[0m \u001b[39mfor\u001b[39;00m pos \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39m32\u001b[39m, \u001b[39mlen\u001b[39m(args), \u001b[39m31\u001b[39m):\n\u001b[1;32m    425\u001b[0m     \u001b[39m# ironically, np.broadcast does not properly handle np.broadcast\u001b[39;00m\n\u001b[1;32m    426\u001b[0m     \u001b[39m# objects (it treats them as scalars)\u001b[39;00m\n\u001b[1;32m    427\u001b[0m     \u001b[39m# use broadcasting to avoid allocating the full array\u001b[39;00m\n",
      "\u001b[0;31mValueError\u001b[0m: shape mismatch: objects cannot be broadcast to a single shape.  Mismatch is between arg 2 with shape (11,) and arg 3 with shape (5,)."
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, sharey=True)\n",
    "axes[0].barh(x, dfrindex.time, align='center', color='red', zorder=10)\n",
    "axes[0].set(title='Time')\n",
    "axes[1].barh(x, dfrindex.cost, align='center', color='blue', zorder=10)\n",
    "axes[1].set(title='Cost')\n",
    "\n",
    "axes[0].invert_xaxis()\n",
    "axes[0].set(yticks=x, yticklabels=ticks)\n",
    "axes[0].yaxis.tick_right()\n",
    "axes[1].yaxis.tick_left()\n",
    "\n",
    "\n",
    "\n",
    "for ax in axes.flat:\n",
    "    ax.margins(0.03)\n",
    "    ax.grid(True)\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.subplots_adjust(wspace=0.23)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NSGAII -single objective"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0100', '2575', '5050', '7525', '1000']"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weigthRange = list(np.arange(0,11,2.5))\n",
    "combinationsWeightTC = list(zip(weigthRange,weigthRange[::-1])) #[(0, 10), (1, 9), (2, 8), (3, 7), (4, 6), (5, 5), (6, 4), (7, 3), (8, 2), (9, 1), (10, 0)]\n",
    "allCombination = combinationsWeightTC\n",
    "allCombination\n",
    "\n",
    "combinations = [\"0100\",\"2575\",\"5050\",\"7525\",\"1000\"]\n",
    "combinations\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "case = \"1000v9v100\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w0100\n",
      "\t (1, 6)\n",
      "w2575\n",
      "\t (1, 6)\n",
      "w5050\n",
      "\t (1, 6)\n",
      "w7525\n",
      "\t (1, 6)\n",
      "w1000\n",
      "\t (1, 6)\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>case</th>\n",
       "      <th>time</th>\n",
       "      <th>cost</th>\n",
       "      <th>constraing</th>\n",
       "      <th>exec</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:12:00</td>\n",
       "      <td>0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>761.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:12:23</td>\n",
       "      <td>2575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>494.0</td>\n",
       "      <td>524.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:12:16</td>\n",
       "      <td>5050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>1160.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:12:10</td>\n",
       "      <td>7525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>304.0</td>\n",
       "      <td>1887.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:12:38</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  case    time    cost constraing            exec model\n",
       "0    0  2600.0    99.0          0 0 days 00:12:00  0100\n",
       "0    0   761.0   289.0          0 0 days 00:12:23  2575\n",
       "0    0   494.0   524.0          0 0 days 00:12:16  5050\n",
       "0    0   359.0  1160.0          0 0 days 00:12:10  7525\n",
       "0    0   304.0  1887.0          0 0 days 00:12:38  1000"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dall = pd.DataFrame([],columns= [\"case\",\"time\",\"cost\",\"constraing\",\"exec\",\"model\"])\n",
    "for combi in combinations:\n",
    "    print(\"w%s\"%combi)\n",
    "    df = pd.read_pickle('logs/log_ga_mono_'  + str(case) + \"_\" + str(configs.n_jobs) + '_' + str(configs.n_devices)+'_0_w%s.pkl'%combi, 'infer')\n",
    "    df = pd.DataFrame(df)\n",
    "    df.columns = [\"case\",\"time\",\"cost\",\"constraing\",\"exec\"]\n",
    "    df=df[:1]\n",
    "    df[\"model\"]=[combi]*len(df)\n",
    "    print(\"\\t\",df.shape)\n",
    "    dall = pd.concat([dall, df], axis=0)\n",
    "    \n",
    "dall.shape\n",
    "dall.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w0100\n",
      "\t (1, 6)\n",
      "w2575\n",
      "\t (1, 6)\n",
      "w5050\n",
      "\t (1, 6)\n",
      "w7525\n",
      "\t (1, 6)\n",
      "w1000\n",
      "\t (1, 6)\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>case</th>\n",
       "      <th>time</th>\n",
       "      <th>cost</th>\n",
       "      <th>constraing</th>\n",
       "      <th>exec</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2760.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:22:10</td>\n",
       "      <td>0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>412.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:29:37</td>\n",
       "      <td>2575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>295.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:28:57</td>\n",
       "      <td>5050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>258.0</td>\n",
       "      <td>437.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:28:31</td>\n",
       "      <td>7525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>1778.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:25:06</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  case    time    cost constraing            exec model\n",
       "0    0  2760.0    81.0          0 0 days 00:22:10  0100\n",
       "0    0   412.0    90.0          0 0 days 00:29:37  2575\n",
       "0    0   295.0   198.0          0 0 days 00:28:57  5050\n",
       "0    0   258.0   437.0          0 0 days 00:28:31  7525\n",
       "0    0   250.0  1778.0          0 0 days 00:25:06  1000"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "case = \"1000v9v200\"\n",
    "dall200 = pd.DataFrame([],columns= [\"case\",\"time\",\"cost\",\"constraing\",\"exec\",\"model\"])\n",
    "for combi in combinations:\n",
    "    print(\"w%s\"%combi)\n",
    "    df = pd.read_pickle('logs/log_ga_mono_'  + str(case) + \"_\" + str(configs.n_jobs) + '_' + str(configs.n_devices)+'_0_w%s.pkl'%combi, 'infer')\n",
    "    df = pd.DataFrame(df)\n",
    "    df.columns = [\"case\",\"time\",\"cost\",\"constraing\",\"exec\"]\n",
    "    df=df[:1]\n",
    "    df[\"model\"]=[combi]*len(df)\n",
    "    print(\"\\t\",df.shape)\n",
    "    dall200 = pd.concat([dall200, df], axis=0)\n",
    "    \n",
    "dall200.shape\n",
    "dall200.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6j/7gfvt_29797dypw8t1wttblw0000gn/T/ipykernel_51595/3379096194.py:6: UserWarning: You passed a edgecolor/edgecolors ('blue') for an unfilled marker ('+').  Matplotlib is ignoring the edgecolor in favor of the facecolor.  This behavior may change in the future.\n",
      "  ax.scatter(dfga.time, dfga.cost, s=30,  marker=\"+\", edgecolors='blue', label=\"NSGA-II: 100 generations\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "\n",
    "ax.scatter(dall.time, dall.cost, s=30, facecolors='none', color='navy', label=\"NSGA-II $mono$: 100 generations\") #MONO solutions\n",
    "ax.scatter(dall200.time, dall200.cost, s=30,  marker=\"p\",facecolors='none', color='darkblue', label=\"NSGA-II $mono$: 200 generations\") #MONO solutions\n",
    "\n",
    "ax.scatter(dfga.time, dfga.cost, s=30,  marker=\"+\", edgecolors='blue', label=\"NSGA-II: 100 generations\")\n",
    "\n",
    "\n",
    "ax.scatter(avg_pf[:,0], avg_pf[:,1], s=30,  marker=\"s\", color='darkorange',label=\"PPO\")\n",
    "ax.scatter(dfmodel.iTime[0],dfmodel.iCost[0],color=\"green\",label=\"Cloud\")\n",
    "\n",
    "\n",
    "\n",
    "ax.vlines(x=dfmodel.iTime[0], ymin=100, ymax=dfmodel.iCost[0], colors='green', ls=':', lw=0.6)\n",
    "ax.vlines(x=avg_pf[0,0], ymin=100, ymax=avg_pf[0,1], colors='darkorange', ls=':', lw=0.8)\n",
    "\n",
    "\n",
    "ax.set_xlabel(\"Time\", fontsize=14)\n",
    "ax.set_ylabel(\"Cost\", fontsize=14)\n",
    "ax.legend()\n",
    "plt.savefig(\"images/mono_nsgaII-0.pdf\",dpi=300,bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>case</th>\n",
       "      <th>time</th>\n",
       "      <th>cost</th>\n",
       "      <th>constraing</th>\n",
       "      <th>exec</th>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>105.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:03:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:03:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:03:29</td>\n",
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       "      <th>85</th>\n",
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       "      <td>54.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>105.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:03:29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>88 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    case   time  cost  constraing            exec\n",
       "0      0  105.0  54.0           0 0 days 00:03:29\n",
       "1      0  105.0  54.0           0 0 days 00:03:29\n",
       "2      0  105.0  54.0           0 0 days 00:03:29\n",
       "3      0  105.0  54.0           0 0 days 00:03:29\n",
       "4      0  105.0  54.0           0 0 days 00:03:29\n",
       "..   ...    ...   ...         ...             ...\n",
       "83     0  105.0  54.0           0 0 days 00:03:29\n",
       "84     0  105.0  54.0           0 0 days 00:03:29\n",
       "85     0  105.0  54.0           0 0 days 00:03:29\n",
       "86     0  105.0  54.0           0 0 days 00:03:29\n",
       "87     0  105.0  54.0           0 0 days 00:03:29\n",
       "\n",
       "[88 rows x 5 columns]"
      ]
     },
     "execution_count": 475,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_pickle('logs/log_ga_pf_mono_'  + str(configs.name) + \"_\" + str(configs.n_jobs) + '_' + str(configs.n_devices)+'_0_w5050.pkl', 'infer')\n",
    "df = pd.DataFrame(df)\n",
    "df.columns = [\"case\",\"time\",\"cost\",\"constraing\",\"exec\"]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w010\n",
      "   0       1     2               3\n",
      "0  0  2874.0  99.0 0 days 02:24:40\n",
      "\t (1, 5)\n",
      "w27\n",
      "   0      1      2               3\n",
      "0  0  732.0  321.0 0 days 02:23:15\n",
      "\t (1, 5)\n",
      "w55\n",
      "   0      1      2               3\n",
      "0  0  456.0  586.0 0 days 02:26:05\n",
      "\t (1, 5)\n",
      "w72\n",
      "   0      1      2               3\n",
      "0  0  312.0  987.0 0 days 02:21:38\n",
      "\t (1, 5)\n",
      "w100\n",
      "   0      1       2               3\n",
      "0  0  283.0  1555.0 0 days 02:36:46\n",
      "\t (1, 5)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        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>case</th>\n",
       "      <th>time</th>\n",
       "      <th>cost</th>\n",
       "      <th>exec</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2874.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0 days 02:24:40</td>\n",
       "      <td>010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>732.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>0 days 02:23:15</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>586.0</td>\n",
       "      <td>0 days 02:26:05</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>312.0</td>\n",
       "      <td>987.0</td>\n",
       "      <td>0 days 02:21:38</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>283.0</td>\n",
       "      <td>1555.0</td>\n",
       "      <td>0 days 02:36:46</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  case    time    cost            exec model\n",
       "0    0  2874.0    99.0 0 days 02:24:40   010\n",
       "0    0   732.0   321.0 0 days 02:23:15    27\n",
       "0    0   456.0   586.0 0 days 02:26:05    55\n",
       "0    0   312.0   987.0 0 days 02:21:38    72\n",
       "0    0   283.0  1555.0 0 days 02:36:46   100"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "combinations = [\"010\",\"27\",\"55\",\"72\",\"100\"]\n",
    "case = \"E1000v9\" #HERE\n",
    "\n",
    "dall = pd.DataFrame([],columns= [\"case\",\"time\",\"cost\",\"exec\",\"model\"])\n",
    "for combi in combinations:\n",
    "    print(\"w%s\"%combi)\n",
    "    df = pd.read_pickle('logs/llog_GA_'  + str(case) + \"_\" + str(configs.n_jobs) + '_' + str(configs.n_devices)+'_0_w%s.pkl'%combi, 'infer')\n",
    "    df = pd.DataFrame(df).T\n",
    "    print(df)\n",
    "    df.columns = [\"case\",\"time\",\"cost\",\"exec\"]\n",
    "    df=df[:1]\n",
    "    df[\"model\"]=[combi]*len(df)\n",
    "    print(\"\\t\",df.shape)\n",
    "    dall = pd.concat([dall, df], axis=0)\n",
    "    \n",
    "dall.shape\n",
    "dall.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/6j/7gfvt_29797dypw8t1wttblw0000gn/T/ipykernel_63285/3448456907.py:5: UserWarning: You passed a edgecolor/edgecolors ('blue') for an unfilled marker ('+').  Matplotlib is ignoring the edgecolor in favor of the facecolor.  This behavior may change in the future.\n",
      "  ax.scatter(dfga.time, dfga.cost, s=30,  marker=\"+\", edgecolors='blue', label=\"NSGA-II: 100 generations\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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lR6dOndC2bVvpJsbdu3fD09PzqcfMycmRCnOg9GbF+Ph4qZNMoVBgwoQJWLhwIfbu3Yvz589j+PDhcHFxQf/+/QGUdqoFBARg9OjROHXqFKKjoxESEoIhQ4bAxcWl3OO+9dZbaNCgAYKCgpCQkIDo6Gh88skn0jGB0in8atasiX79+uH48eNITk7G0aNHMW7cONy69eTpXx/VqFEjBAYGYvjw4di1axeSk5Nx6tQpLFmyBAcOHHjqa93d3fGf//wHiYmJiI2NRWBgoMZ/DurVq4fDhw8jNTUVmZmZ5e5n6tSpWLp0KX744QckJSVhxowZiI+Px/jx4yt0DgAwYcIEHDx4EMnJyTh79iwiIyPh5eVV4de/koSOuX//vjh37pw4d+6cACBWrVolzp07J27cuCGEEGLXrl3C2NhYfP311+LKlSvi888/F4aGhuL48ePSPj788ENRp04dceTIEXHmzBnh4+MjfHx8pPaioiLRtGlT0b17dxEfHy/Cw8PFa6+9JmbOnPlcuWZnZwsAIjs7WzsnX0HFxSXiiy9ihYfH5wKYJ4yMFoh33tkhEhJSqzUPIiIq9fDhQ3Hp0iXx8OHDSu8jM/OhmDQpXNjafiaAecLKarEICTkg0tNztJhp+e7cuSNCQkKEm5ubMDY2FpaWlqJdu3Zi+fLl4sGDByI/P1/Y29uLZcuWlfv6pUuXCgcHB1FQUCBCQ0PFs8qLyMhIAUDjERQUJMWUlJSI2bNnC0dHR2Fqaiq6desmkpKS1PZz7949MXToUGFpaSmUSqUYOXKkuH///lOPnZiYKHx9fYWJiYnw9PQU+/btEwBEeHi4FHP37l0xfPhwUbNmTWFqairq168vRo8eLf2+DwoKEv369VPb7/jx48Wbb74pPS8oKBBz5swR9erVE8bGxsLZ2VkMGDBAJCQkCCGECA0NFdbW1hr5nT17VrRp00aYmZkJd3d3sXPnTlG3bl2xevVqKWbv3r2iYcOGwsjISNStW1cIIcTcuXNF8+bNpZji4mIxb948UatWLWFsbCyaN28ufvnlF6k9OTlZABDnzp2TtmVmZgoAIjIyUgghREhIiGjQoIEwNTUVr732mnjvvffE33///dSvr1ye9h6sznpN5wrrirzZNm7cKBo2bCjMzMxE8+bNxZ49e9T28fDhQ/HRRx8JW1tbYWFhIQYMGCDu3r2rFnP9+nXRo0cPYW5uLmrWrCkmT54sCgsLnytXOQrrkpISMWrUT0KhmCf+8Y+d4j//+V2sWBEtGjZcJ2rUWCRiY29VWy5yGb57uNwpEBGp0UZhXaaoqFhkZj4UhYXFWsiMnuW3334TAMTVq1flToVegK4U1gohhOZa0VQhKpUK1tbWyM7Orrbx1lFRN/Dmm2HYuLEv3n+/pbT9wYMCdO68GQoFcOrU6GrJRS4ZDzNgZ24ndxpERJK8vDwkJyfDzc1NbcEy0j27d++GpaUl3N3dcfXqVYwfPx62trb47bff5E6NXsDT3oPVWa/p1RhrAsLC4uHuboeRI1uoba9RwwSzZ3fC6dN3nnhn+cti49mNzw4iIiIqx/379xEcHAxPT0+MGDECbdu2xU8//SR3WvSS0Kvp9ghITc1B48avlbuqVZMmpdMA3b17H40blz9h/MugXa12zw4iIiIqx/DhwzF8+HC506CXFHus9Yybmw3i4u6iqEhzoYDY2NsAgHr1bKo5q+r1sOih3CkQERERaWBhrWc++KAVbt1S4bPP1MeCpaXlYN68o+ja1Q0NGrzc44+vZVx7dhARERFRNeNQED3TsqUz5s17E7NnR+LAgSvo1csdaWk52LLlPExNjbBhQy+5U6xy/T37y50CERERkQb2WOuhuXM7Y9++obCyMsHatbHYt+8PfPBBK8TFjYG7u73c6VW5T6M+lTsFIiIiIg3ssdZTvXs3Qu/ejeROQxYbem+QOwUiIiIiDeyxJr3TZ1sfuVMgIiKiR8ybNw8tWrSQOw3ZsbAmvbNv6D65UyAiemmMGDECCoUCn332mdr2PXv2aEzt+s0336B58+awtLSEjY0NWrZsiSVLlqjFqFQqzJ49G02aNIG5uTns7e3Rtm1bLFu2DJmZmRrH37ZtGwwNDREcHFzhfPv37//E5xURFRWFPn36wMXFBQqFAnv27NGIEUJgzpw5cHZ2hrm5Ofz8/HDlyhW1mIyMDAQGBkKpVMLGxgajRo1CTk7Oc+Wij8r7mk2ZMgWHDx+WJyEdwsKa9M6H+z+UOwUiopeKmZkZli5dWm7hW2bTpk2YMGECxo0bh/j4eERHR2PatGlqhWRGRgZef/11hIaGYsqUKYiNjcXZs2exaNEinDt3Dlu3btXY78aNGzFt2jRs27YNeXl5VXJ+j3vw4AGaN2+O9evXPzFm2bJlWLduHTZs2IDY2FjUqFED/v7+ajkGBgbi4sWLiIiIwP79+xEVFYUxY8ZUxyloXXFxMUpKNKfyrShLS0vY27/893k9U5Uvmv4Sq8615+l/bmXfkjsFIiI1Dx8+FJcuXRIPHz6UO5XnFhQUJHr37i08PT3F1KlTpe27d+8Wj5YJ/fr1EyNGjHjqvv75z3+KGjVqiNu3b5fbXlJSovb8zz//FObm5iIrK0u0b99ebNmypUL59uvX74nPnxcAsXv3bo08nZycxPLly6VtWVlZwtTUVGzbtk0IIcSlS5cEAHH69Gkp5pdffhEKheKJ5y+EEImJicLX11eYmpoKLy8vERERoZFDSkqKeOedd4S1tbWwtbUVffv2FcnJyRrnvHz5cuHk5CTs7OzERx99JAoKCqSYvLw8MXnyZOHi4iIsLCxEu3btRGRkpNQeGhoqrK2txU8//SS8vLyEoaGhSE5OFqdOnRJ+fn7C3t5eKJVK0alTJxEXFye9rm7dugKA9Khbt64QQoi5c+eK5s2bS3HFxcVi/vz5olatWsLExEQ0b95c/PLLL1J7cnKyACD++9//is6dOwtzc3PRrFkzceLECSnm+vXronfv3sLGxkZYWFiIxo0biwMHDpT7dX3ae7A66zX2WJPe2XN5j9wpEBFVmdyCItSbcQD1ZhxAbkFRtRzT0NAQixcvxueff45bt26VG+Pk5ISTJ0/ixo0b5baXlJTghx9+wLBhw+Di4lJuzONDS0JDQ9GrVy9YW1tj2LBh2Lhx44udCICwsLByVyd+HsnJyUhNTYWfn5+0zdraGu3bt0dMTAwAICYmBjY2NmjTpo0U4+fnBwMDA8TGxpa73+LiYvTv3x8WFhaIjY3F119/jVmzZqnFFBYWwt/fH1ZWVjh+/Diio6NhaWmJgIAAFBQUSHGRkZG4du0aIiMjsXnzZoSFhSEsLExqDwkJQUxMDLZv346EhAS88847CAgIUBvOkpubi6VLl+Lbb7/FxYsX4eDggPv37yMoKAi//fYbTp48CXd3d/Ts2RP3798HAJw+fRpA6bW7e/eu9Pxxa9euxcqVK7FixQokJCTA398fffv21RhOM2vWLEyZMgXx8fFo1KgRhg4diqKi0u/74OBg5OfnIyoqCufPn8fSpUthaWlZ/kXTESysSe80sGsgdwpERC+dAQMGoEWLFpg7d2657XPnzoWNjQ3q1asHDw8PjBgxAjt27JCGD/z111/IysqCh4eH2utat24NS0tLWFpaYujQodL2kpIShIWFYdiwYQCAIUOG4LfffkNycvILnYe1tbVGDs8rNTUVAODo6Ki23dHRUWpLTU2Fg4ODWruRkRHs7OykmMdFRETg2rVr+O6779C8eXN06NABixYtUov54YcfUFJSgm+//Rbe3t7w8vJCaGgoUlJScPToUSnO1tYWX3zxBTw9PdG7d2/06tVLGuOckpKC0NBQ7Ny5Ex07dkSDBg0wZcoUdOjQAaGhodI+CgsL8eWXX+KNN96Ah4cHLCws0LVrVwwbNgyenp7w8vLC119/jdzcXBw7dgwA8NprrwEAbGxs4OTkJD1/3IoVKzB9+nQMGTIEHh4eWLp0KVq0aIE1a9aoxU2ZMgW9evVCo0aNMH/+fNy4cQNXr16VzsPX1xfe3t6oX78+evfujU6dOpV7PF3Bwpr0jrmRudwpEBFpXW5B0f8/ih/ZVixtrw5Lly7F5s2bkZiYqNHm7OyMmJgYnD9/HuPHj0dRURGCgoIQEBDw1LG5u3fvRnx8PPz9/fHw4UNpe0REBB48eICePXsCAGrWrIm33noLmzZtAgAcP35cKsgtLS2xZcuWCp3DgAEDcPny5ec57WqTlJQEV1dXODk5SdvatWunFvP777/j6tWrsLKyks7dzs4OeXl5uHbtfysPN2nSBIaGhtJzZ2dnpKenAwDOnz+P4uJiNGrUSO1reOzYMbV9mJiYoFmzZmrHT0tLw+jRo+Hu7g5ra2solUrk5OQgJSWlwuepUqlw584d+Pr6qm339fXV+N569PjOzs4AIJ3HuHHjsHDhQvj6+mLu3LlISEiocA5y4TzWpHdO3T6FN+u9KXcaRERa1XjOQY1tbRb+Kn1+/bOqX1m3U6dO8Pf3x8yZMzFixIhyY5o2bYqmTZvio48+wocffoiOHTvi2LFjePPNN2FjY4OkpCS1+Dp16gAArKyskJWVJW3fuHEjMjIyYG7+v86SkpISJCQkYP78+WjTpg3i4+Oltsd7j6tSWeGblpYmFXtlz8umlHNycpIKwDJFRUXIyMhQK5yfV05ODlq3bl3uHxKP9g4bGxurtSkUCukPnJycHBgaGiIuLk6t+AagNpTC3NxcY9hMUFAQ7t27h7Vr16Ju3bowNTWFj4+P2jAUbXr0PMpyKTuPDz74AP7+/jhw4AAOHTqEJUuWYOXKlfj444+rJBdtYI816Z1RrUbJnQIR0Uvrs88+w759+6SxxE/TuHFjAKWzbBgYGGDw4MH4/vvvcefOnae+7t69e/jpp5+wfft2xMfHS49z584hMzMThw4dgrm5ORo2bCg9rKystHJ+FeHm5gYnJye16eNUKhViY2Ph4+MDAPDx8UFWVhbi4uKkmCNHjqCkpATt27cvd78eHh64efMm0tLSpG2Pj1Fu1aoVrly5AgcHB7Xzb9iwIaytrSuUf8uWLVFcXIz09HSNfTyr6I+Ojsa4cePQs2dPNGnSBKampvj777/VYoyNjVFcXPyEPQBKpRIuLi6Ijo7W2HfZ90xFubq64sMPP8SuXbswefJkfPPNN8/1+urGHmvSOxMPTsTm/pvlToOISKsuLfAHUDr8o6yn+swnfrAwMXzay7TO29sbgYGBWLdundr2sWPHwsXFBV27dkXt2rVx9+5dLFy4EK+99ppUbC5evBhHjx5Fu3btsGDBArRp0wY1atRAQkICYmJi0LRpUwDAf/7zH9jb22Pw4MEaPaY9e/bExo0bERAQUKn8d+/ejZkzZz51OEhOTo40jhcovVkxPj4ednZ2qFOnDhQKBSZMmICFCxfC3d0dbm5umD17NlxcXKQ5s728vBAQEIDRo0djw4YNKCwsREhICIYMGfLEmzffeustNGjQAEFBQVi2bBnu37+PTz75BMD/emsDAwOxfPly9OvXDwsWLEDt2rVx48YN7Nq1C9OmTUPt2rWf+TVo1KgRAgMDMXz4cKxcuRItW7bEX3/9hcOHD6NZs2bo1evJ//1wd3fHf/7zH7Rp0wYqlQpTp05V+68CANSrVw+HDx+Gr68vTE1NYWtrq7GfqVOnYu7cuWjQoAFatGiB0NBQxMfHV3hIDwBMmDABPXr0QKNGjZCZmYnIyEh4eXlV+PVyYI816R0W1UT0MrIwMfr/h+Ej2wyl7dVpwYIFGuOm/fz8cPLkSbzzzjto1KgRBg4cCDMzMxw+fFiav9je3h6nTp3C8OHDsXz5crRr1w7e3t6YN28e/vGPf0i9jZs2bcKAAQPKnb1j4MCB2Lt3r0YvaUVlZ2drDEd53JkzZ9CyZUu0bNkSADBp0iS0bNkSc+bMkWKmTZuGjz/+GGPGjEHbtm2Rk5OD8PBwmJmZSTFbtmyBp6cnunXrhp49e6JDhw74+uuvn3hcQ0ND7NmzBzk5OWjbti0++OADaVaQsv1aWFggKioKderUwdtvvw0vLy+MGjUKeXl5UCqVFf46hIaGYvjw4Zg8eTI8PDzQv39/nD59Whqa8yQbN25EZmYmWrVqhffeew/jxo3TuElz5cqViIiIgKurq/Q1fNy4ceMwadIkTJ48Gd7e3ggPD8fevXvh7u5e4XMoLi5GcHCw9EdMo0aN8OWXX1b49XJQCCGE3EnoK5VKBWtra2RnZz/XNzu9mD7b+nD1RSLSKXl5eUhOToabm5ta4VUZuQVF0njrSwv8q72opuoVHR2NDh064OrVq2jQgLNeVdbT3oPVWa/x3Up6h0U1Eb3MLEyMquVGRZLH7t27YWlpCXd3d1y9ehXjx4+Hr68vi+qXBIeCkN6ZGD5R7hSIiIgq5f79+wgODoanpydGjBiBtm3b4qeffpI7LdIS9liT3gluFyx3CkRERJUyfPhwDB8+XO40qIqwx5r0TtSNKLlTICIiItLAwpr0jq2Z5rQ+RERERHJjYU16p5ayltwpEBEREWlgYU165+BVzWV/iYiIiOSmc4V1VFQU+vTpAxcXFygUCuzZs+eJsR9++CEUCgXWrFmjtj0jIwOBgYFQKpWwsbHBqFGjkJOToxaTkJCAjh07wszMDK6urli2bFkVnA1VhclvTJY7BSIiIiINOldYP3jwAM2bN8f69eufGrd7926cPHmy3GVDAwMDcfHiRURERGD//v2IiorCmDFjpHaVSoXu3bujbt26iIuLw/LlyzFv3rynrpZEumPEnhFyp0BERESkQeem2+vRowd69Ojx1Jjbt2/j448/xsGDBzXWu09MTER4eDhOnz6NNm3aAAA+//xz9OzZEytWrICLiwu2bNmCgoICbNq0CSYmJmjSpAni4+OxatUqtQKcdNOOd3bInQIRERGRBp3rsX6WkpISvPfee5g6dSqaNGmi0R4TEwMbGxupqAYAPz8/GBgYIDY2Vorp1KkTTExMpBh/f38kJSUhMzPzicfOz8+HSqVSe1D167Otj9wpEBG9NEaMGAGFQgGFQgETExM0bNgQCxYsQFFREY4ePSq1KRQKODo6YuDAgfjzzz/V9nHixAn07NkTtra2MDMzg7e3N1atWoXi4mKZzopIHjrXY/0sS5cuhZGREcaNG1due2pqKhwcHNS2GRkZwc7ODqmpqVKMm5ubWoyjo6PUZmtb/nRuS5Yswfz581/0FOgFcUlzInopZV4BCu4/ud3ECrB1r5JDBwQEIDQ0FPn5+fj5558RHBwMY2Nj+Pj4AACSkpJgZWWFK1euYMyYMejTpw8SEhJgaGiI3bt3Y/DgwRg5ciQiIyNhY2ODX3/9FdOmTUNMTAx27NgBhUJRJXkT6Rq9Kqzj4uKwdu1anD17VpY36cyZMzFp0iTpuUqlgqura7Xn8ar71+F/YXG3xXKnQUSkPZlXgE2Nnh33/h9VUlybmprCyckJADB27Fjs3r0be/fulQprBwcH2NjYwNnZGXPmzEFgYCCuXr2K2rVrY/To0ejbt6/afUoffPABHB0d0bdvX+zYsQP/+Mc/tJ4zkS7Sq6Egx48fR3p6OurUqQMjIyMYGRnhxo0bmDx5MurVqwcAcHJyQnp6utrrioqKkJGRIf3QcHJyQlpamlpM2fOymPKYmppCqVSqPaj6DW06VO4UiIi062k91ZWJe0Hm5uYoKCh4YhsAFBQU4NChQ7h37x6mTJmiEdenTx80atQI27Ztq9JciXSJXhXW7733HhISEhAfHy89XFxcMHXqVBw8WDq3sY+PD7KyshAXFye97siRIygpKUH79u2lmKioKBQWFkoxERER8PDweOIwENId59PPy50CEdFLSQiBX3/9FQcPHkTXrl012u/evYsVK1agVq1a8PDwwB9//AEA8PLyKnd/np6eUgzRq0DnhoLk5OTg6tWr0vPk5GTEx8fDzs4OderUgb29vVq8sbExnJyc4OHhAaD0zR0QEIDRo0djw4YNKCwsREhICIYMGSJNzffuu+9i/vz5GDVqFKZPn44LFy5g7dq1WL16dfWdKBERkY7Yv38/LC0tUVhYiJKSErz77ruYN28eTp8+DQCoXbs2hBDIzc1F8+bN8d///ldtAgAhhFypE+kUnSusz5w5gy5dukjPy8Y0BwUFISwsrEL72LJlC0JCQtCtWzcYGBhg4MCBWLdundRubW2NQ4cOITg4GK1bt0bNmjUxZ84cTrWnJ7wdvOVOgYjopdKlSxd89dVXMDExgYuLC4yM1MuD48ePQ6lUwsHBAVZWVtL2Ro1Kx4UnJibijTfe0NhvYmIiGjduXLXJE+kQnSusO3fu/Fx/+V6/fl1jm52dHbZu3frU1zVr1gzHjx9/3vRIB2y7sA3ejiyuiYi0pUaNGmjYsOET293c3GBjY6OxvXv37rCzs8PKlSs1Cuu9e/fiypUr+PTTT7WdLpHO0qsx1kQAOCMIEZGOqFGjBv7973/jp59+wpgxY5CQkIDr169j48aNGDFiBAYNGoTBgwfLnSZRtWFhTXqHC8QQEemOQYMGITIyEikpKejYsSM8PDywevVqzJo1C9u3b+cc1vRK0bmhIETPwgViiOilY2L17JjniXsOT7t/qaLDMzt27Ijw8HAtZkWkn1hYk94ZvHMwdryzQ+40iIi0x9a9dPEXmVZeJCLtYGFNeiesf5jcKRARaR+LZiK9xzHWpHdWnlgpdwpEREREGlhYk97xb+gvdwpEREREGlhYk965rbotdwpEREREGlhYk97JzMuUOwUiIiIiDSysSe90qttJ7hSIiIiINLCwJr2z/tR6uVMgIiIi0sDCmvTO6oDVcqdAREREpIGFNekdLmlORFS9FAoF9uzZU+XHqVevHtasWVPlxyGqKiysSe9wSXMiIu1KTU3Fxx9/jPr168PU1BSurq7o06cPDh8+LHdqRHqFKy+S3gnaE4TN/TfLnQYRUZUoLinG8ZTjuHv/LpytnNGxTkcYGhhW2fGuX78OX19f2NjYYPny5fD29kZhYSEOHjyI4OBgXL58ucqOTfSyYY816Z3V/hxjTUQvp12Ju1BvbT102dwF7+56F102d0G9tfWwK3FXlR3zo48+gkKhwKlTpzBw4EA0atQITZo0waRJk3Dy5MlyX3P+/Hl07doV5ubmsLe3x5gxY5CTkyO1d+7cGRMmTFB7Tf/+/TFixAjpeXp6Ovr06QNzc3O4ublhy5YtVXF6RNWKhTXpnY1nN8qdAhGR1u1K3IVBOwbhluqW2vbbqtsYtGNQlRTXGRkZCA8PR3BwMGrUqKHRbmNjo7HtwYMH8Pf3h62tLU6fPo2dO3fi119/RUhIyHMde8SIEbh58yYiIyPx448/4ssvv0R6enplT4VIJ7CwJr3TrlY7uVMgItKq4pJijA8fDwGh0Va2bUL4BBSXFGv1uFevXoUQAp6enhV+zdatW5GXl4fvvvsOTZs2RdeuXfHFF1/gP//5D9LS0iq0jz/++AO//PILvvnmG7z++uto3bo1Nm7ciIcPH1b2VIh0Agtr0jsPi/iDl4heLsdTjmv0VD9KQOCm6iaOpxzX6nGF0CzknyUxMRHNmzdX6+H29fVFSUkJkpKSKrwPIyMjtG7dWtrm6elZbg85kT5hYU1651rGNblTICLSqrv372o1rqLc3d2hUCi0foOigYGBRtFeWFio1WMQ6SIW1qR3+nv2lzsFIiKtcrZy1mpcRdnZ2cHf3x/r16/HgwcPNNqzsrI0tnl5eeH3339Xi4+OjoaBgQE8PDwAAK+99hru3v3fHwHFxcW4cOGC9NzT0xNFRUWIi4uTtiUlJZV7PCJ9wsKa9M6nUZ/KnQIRkVZ1rNMRtZW1oYCi3HYFFHBVuqJjnY5aP/b69etRXFyMdu3a4b///S+uXLmCxMRErFu3Dj4+PhrxgYGBMDMzQ1BQEC5cuIDIyEh8/PHHeO+99+Do6AgA6Nq1Kw4cOIADBw7g8uXLGDt2rFrR7OHhgYCAAPzzn/9EbGws4uLi8MEHH8Dc3Fzr50dUnVhYk97Z0HuD3CkQEWmVoYEh1gasBQCN4rrs+ZqANVUyn3X9+vVx9uxZdOnSBZMnT0bTpk3x1ltv4fDhw/jqq6804i0sLHDw4EFkZGSgbdu2GDRoELp164YvvvhCinn//fcRFBSE4cOH480330T9+vXRpUsXtf2EhobCxcUFb775Jt5++22MGTMGDg4OWj8/ouqkEJW5c4EAACqVCtbW1sjOzoZSqZQ7nVdGn219uPoiEemUvLw8JCcnw83NDWZmZpXez67EXRgfPl7tRkZXpSvWBKzB215vayNVopfS096D1VmvceVF0jssqonoZfW219vo59GvWldeJCLt0bmhIFFRUejTpw9cXFygUCiwZ88eqa2wsBDTp0+Ht7c3atSoARcXFwwfPhx37txR20dGRgYCAwOhVCphY2ODUaNGqa0IBQAJCQno2LEjzMzM4OrqimXLllXH6ZEWfLj/Q7lTICKqMoYGhuhcrzOGeg9F53qdWVQT6RGdK6wfPHiA5s2bY/369Rptubm5OHv2LGbPno2zZ89i165dSEpKQt++fdXiAgMDcfHiRURERGD//v2IiorCmDFjpHaVSoXu3bujbt26iIuLw/LlyzFv3jx8/fXXVX5+9OJmd5otdwpEREREGnRuKEiPHj3Qo0ePctusra0RERGhtu2LL75Au3btkJKSgjp16iAxMRHh4eE4ffo02rRpAwD4/PPP0bNnT6xYsQIuLi7YsmULCgoKsGnTJpiYmKBJkyaIj4/HqlWr1Apw0k17Lu9BcLtgudMgIiIiUqNzPdbPKzs7GwqFQlqtKSYmBjY2NlJRDQB+fn4wMDBAbGysFNOpUyeYmJhIMf7+/khKSkJmZuYTj5Wfnw+VSqX2oOrXwK6B3CkQERERadDrwjovLw/Tp0/H0KFDpbs8U1NTNabrMTIygp2dHVJTU6WYsrk2y5Q9L4spz5IlS2BtbS09XF1dtXk6VEHmRpznlIh0EyfaIpKHrrz39LawLiwsxODBgyGEKHeezaowc+ZMZGdnS4+bN29Wy3FJ3anbp+ROgYhIjbGxMYDSe4GIqPqVvffK3oty0bkx1hVRVlTfuHEDR44cUZuT0MnJCenp6WrxRUVFyMjIgJOTkxSTlpamFlP2vCymPKampjA1NdXWaVAljWo1Su4UiIjUGBoawsbGRvr9Y2FhAYWi/FUUiUh7hBDIzc1Feno6bGxsYGgo7yw6eldYlxXVV65cQWRkJOzt7dXafXx8kJWVhbi4OLRu3RoAcOTIEZSUlKB9+/ZSzKxZs1BYWCj9ZRMREQEPDw/Y2tpW7wnRc5t4cCI2998sdxpERGrKOmYe79whoqpnY2Pz1M7R6qJzKy/m5OTg6tWrAICWLVti1apV6NKlC+zs7ODs7IxBgwbh7Nmz2L9/v9o4aTs7O+lmxB49eiAtLQ0bNmxAYWEhRo4ciTZt2mDr1q0ASm949PDwQPfu3TF9+nRcuHAB77//PlavXv1cs4Jw5UUiInpccXExCgsL5U6D6JVhbGz81J7q6qzXdK6wPnr0KLp06aKxPSgoCPPmzYObm1u5r4uMjETnzp0BlC4QExISgn379sHAwAADBw7EunXrYGlpKcUnJCQgODgYp0+fRs2aNfHxxx9j+vTpz5UrC2t5cElzIiIiqqhXurDWJyysiYiIiHRbddZrejsrCL26JoZPlDsFIiIiIg0srEnvcNVFIiIi0kUsrEnvRN2IkjsFIiIiIg0srEnv2JpxSkQiIiLSPSysSe/UUtaSOwUiIiIiDSysSe8cvHpQ7hSIiIiINLCwJr0z+Y3JcqdAREREpIGFNemdEXtGyJ0CERERkQYW1qR3dryzQ+4UiIiIiDSwsCa902dbH7lTICIiItLAwpr0zr6h++ROgYiIiEgDC2vSO/86/C+5UyAiIiLSwMKa9M7QpkPlToGIiIhIAwtr0jvn08/LnQIRERGRBhbWRERERERawMKa9I63g7fcKRARERFpYGFNemfbhW1yp0BERESkgYU16Z3F3RbLnQIRERGRBhbWpHe4QAwRERHpIhbWpHe4QAwRERHpIhbWpHcG7xwsdwpEREREGlhYk94J6x8mdwpEREREGlhYk95ZeWKl3CkQERERaWBhTXrHv6G/3CkQERERaWBhTXrntuq23CkQERERaWBhTXonMy9T7hSIiIiINOhcYR0VFYU+ffrAxcUFCoUCe/bsUWsXQmDOnDlwdnaGubk5/Pz8cOXKFbWYjIwMBAYGQqlUwsbGBqNGjUJOTo5aTEJCAjp27AgzMzO4urpi2bJlVX1qpCWd6naSOwUiIiIiDTpXWD948ADNmzfH+vXry21ftmwZ1q1bhw0bNiA2NhY1atSAv78/8vLypJjAwEBcvHgRERER2L9/P6KiojBmzBipXaVSoXv37qhbty7i4uKwfPlyzJs3D19//XWVn191KCkRuHo1A1eu3ENxcYnc6Wjd+lPlf28QERERyUkhhBByJ/EkCoUCu3fvRv/+/QGU9la7uLhg8uTJmDJlCgAgOzsbjo6OCAsLw5AhQ5CYmIjGjRvj9OnTaNOmDQAgPDwcPXv2xK1bt+Di4oKvvvoKs2bNQmpqKkxMTAAAM2bMwJ49e3D58uUK56dSqWBtbY3s7GwolUrtnnwlbdp0DosXH8e1a6XDJerUscbUqW8gOLgtFAqFzNkRERERVa/qrNd0rsf6aZKTk5Gamgo/Pz9pm7W1Ndq3b4+YmBgAQExMDGxsbKSiGgD8/PxgYGCA2NhYKaZTp05SUQ0A/v7+SEpKQmbmk8fv5ufnQ6VSqT10ybJl0Rg1ai/atHHBL78E4tChYejcuR4+/vgXzJ4dKXd6WsMlzYmIiEgXGcmdwPNITU0FADg6Oqptd3R0lNpSU1Ph4OCg1m5kZAQ7Ozu1GDc3N419lLXZ2tqWe/wlS5Zg/vz5L34iVeDevVzMmROJyZN9sGJFd2n7W281QIMGtliw4Bg+/LANatfWjZ71F8ElzYmIiEgX6VWPtdxmzpyJ7Oxs6XHz5k25U5Ls3n0ZhYUlmDbNV6NtwoTXYWJiiJ07L8qQmfYF7QmSOwUiIiIiDXpVWDs5OQEA0tLS1LanpaVJbU5OTkhPT1drLyoqQkZGhlpMeft49BjlMTU1hVKpVHvoiszMh6hRwxgODjU02pRKU9jbWyAzM6+cV+qf1f6r5U6BiIiISINeFdZubm5wcnLC4cOHpW0qlQqxsbHw8fEBAPj4+CArKwtxcXFSzJEjR1BSUoL27dtLMVFRUSgsLJRiIiIi4OHh8cRhILquSRMH3L9fgNOnNRdPuXz5b9y6pULjxq/JkJn2bTy7Ue4UiIiIiDToXGGdk5OD+Ph4xMfHAyi9YTE+Ph4pKSlQKBSYMGECFi5ciL179+L8+fMYPnw4XFxcpJlDvLy8EBAQgNGjR+PUqVOIjo5GSEgIhgwZAhcXFwDAu+++CxMTE4waNQoXL17EDz/8gLVr12LSpEkynfWL8/dvADc3GwQH/4x793Kl7SpVPj78cD8cHWtgwABPGTPUnna12smdAhEREZEGnbt58cyZM+jSpYv0vKzYDQoKQlhYGKZNm4YHDx5gzJgxyMrKQocOHRAeHg4zMzPpNVu2bEFISAi6desGAwMDDBw4EOvWrZPara2tcejQIQQHB6N169aoWbMm5syZozbXtb4xNDTAzp3voHv371Gv3lr07esBQ0MFfvopCUII/PxzIExNde5yV8rDoodyp0BERESkQafnsdZ1ujiP9d2797FhwxkcPHgNQgBdu9bD2LFtUaeOtdypac36U+sR3C5Y7jSIiIhID1RnvVbpwrp+/fqYMGECxo0b98SY9evXY+XKlfjzzz8rnaAu08XC+lVwW3UbtZS15E6DiIiI9IBeLBBz/fp1ZGVlPTUmKysLN27cqOwhiMr1adSncqdAREREpKFKb17Mzs6GqalpVR6CXkEbem+QOwUiIiIiDc91N1tUVJTa8+vXr2tsA4Di4mLcvHkTW7ZsQaNGjV4sQ6LH9NnWh6svEhERkc55rjHWBgYGUCgUFYoVQkChUCAsLAzvvfdepRPUZRxjTURERKTbqrNee64e6zlz5kChUEAIgQULFuDNN99E586dNeIMDQ1hZ2eHLl26wMvLS1u5EgEAPtz/IYeDEBERkc6p9KwgXbp0wciRIzF8+HBt56Q32GMtD84KQkRERBWlsz3Wj4qMjNRmHkQVtufyHs5jTURERDqn0rOC3Lx5E0eOHEFu7v+Wzy4pKcHSpUvh6+sLPz8/HDhwQCtJEj2qgV0DuVMgIiIi0lDpHuvZs2dj3759SE1NlbYtWrQIc+fOlZ4fO3YMJ06cQNu2bV8sS6JHmBuZy50CERERkYZK91hHR0fDz88PxsbGAEpnAfniiy/g6emJlJQUnDp1CjVq1MDy5cu1liwRAJy6fUruFIiIiIg0VLqwTk9PR926daXn8fHx+Ouvv/Dxxx+jdu3aaNOmDfr374/Tp09rJVGiMqNajZI7BSIiIiINlS6sS0pKUFJSIj0/evQoFAoFunbtKm2rVauW2lARIm2YeHCi3CkQERERaah0YV2nTh2cOvW/f8nv2bMHzs7O8PDwkLalpqbCxsbmhRIketzm/pvlToGIiIhIQ6UL64EDByI6OhqDBg3CsGHD8Ntvv2HgwIFqMZcuXUL9+vVfOEmiR/XZ1kfuFIiIiIg0VHqBGJVKhe7du0u91s2aNUNkZCRsbW0BADdu3ED9+vUxY8YMLFq0SHsZ6xAuEENERESk2/RigRilUomTJ0/iwoULAAAvLy8YGhqqxezatQtt2rR5sQyJHjMxfCJWB6yWOw0iIiIiNZXusSb2WMvlasZVNLRrKHcaREREpAf0osf6UdHR0YiPj4dKpYJSqUSLFi3g6+urjV0TaYi6EcXCmoiIiHTOCxXWJ06cwMiRI3H16lUApYvEKBQKAIC7uztCQ0Ph4+Pz4lkSPcLWzFbuFIiIiIg0VLqwvnjxIrp3747c3Fy89dZb6NKlC5ydnZGamorIyEgcOnQI/v7+OHnyJBo3bqzNnOkVV0tZS+4UiIiIiDRUurBesGABCgoK8PPPPyMgIECtbfr06QgPD0ffvn2xYMECbN++/YUTJSpz8OpBtKvVTu40iIiIiNRU+uZFR0dHdOvWDVu3bn1izLvvvovDhw8jLS2t0gnqMt68KI/cwlxYGFvInQYRERHpgeqs1yq9QEx2djbc3NyeGuPm5obs7OzKHoKoXCP2jJA7BSIiIiINlS6sXVxccPLkyafGxMbGwsXFpbKHICrXjnd2yJ0CERERkYZKF9Z9+/bF0aNHMXv2bOTl5am15eXlYe7cuYiMjES/fv1eOMlHFRcXY/bs2XBzc4O5uTkaNGiATz/9FI+OaBFCYM6cOXB2doa5uTn8/Pxw5coVtf1kZGQgMDAQSqUSNjY2GDVqFHJycrSaK1UNLmlOREREuqjSY6zv3buH9u3bIzk5Gfb29mjXrh0cHR2RlpaG06dP46+//kL9+vVx6tQp2NnZaS3hxYsXY9WqVdi8eTOaNGmCM2fOYOTIkVi0aBHGjRsHAFi6dCmWLFmCzZs3w83NDbNnz8b58+dx6dIlmJmZAQB69OiBu3fv4t///jcKCwsxcuRItG3b9qljxh/HMdZEREREuq0667UXWnnx77//xrRp07B9+3a1XmszMzMMHToUS5cuRc2aNbWSaJnevXvD0dERGzdulLYNHDgQ5ubm+P777yGEgIuLCyZPnowpU6YAKB0P7ujoiLCwMAwZMgSJiYlo3LgxTp8+LS25Hh4ejp49e+LWrVsVHr7Cwloe/zr8LyzutljuNIiIiEgP6MXNiwBQs2ZNbNq0CdnZ2fj9999x/Phx/P7778jOzsbGjRu1XlQDwBtvvIHDhw/jjz/+AAD8/vvv+O2339CjRw8AQHJyMlJTU+Hn5ye9xtraGu3bt0dMTAwAICYmBjY2NlJRDQB+fn4wMDBAbGys1nMm7RradKjcKRARERFpeO55rBctWoQHDx5g/vz5MDY2BgAYGxvD29tbiikoKMCsWbNgZWWFGTNmaC9bADNmzIBKpYKnpycMDQ1RXFyMRYsWITAwEACQmpoKoHQ6wEc5OjpKbampqXBwcFBrNzIygp2dnRRTnvz8fOTn50vPVSqVVs6Jns/59PPwdvR+diARERFRNXquHutff/0Vc+bMgb29vVRUl8fExAT29vaYNWsWIiMjXzjJR+3YsQNbtmzB1q1bcfbsWWzevBkrVqzA5s2btXqc8ixZsgTW1tbSw9XVtcqPSURERET64bkK6++++w62trYICQl5ZmxwcDDs7OwQGhpa6eTKM3XqVMyYMQNDhgyBt7c33nvvPUycOBFLliwBADg5OQGAxqI0aWlpUpuTkxPS09PV2ouKipCRkSHFlGfmzJnIzs6WHjdv3tTmqVEFeTuwt5qIiIh0z3MV1idOnICfnx9MTU2fGWtqago/Pz9ER0dXOrny5ObmwsBAPW1DQ0OUlJQAKF2UxsnJCYcPH5baVSoVYmNj4ePjAwDw8fFBVlYW4uLipJgjR46gpKQE7du3f+KxTU1NoVQq1R5U/bZd2CZ3CkREREQanmuM9Z07d1C/fv0Kx7u5ueGnn3567qSepk+fPli0aBHq1KmDJk2a4Ny5c1i1ahXef/99AIBCocCECROwcOFCuLu7S9Ptubi4oH///gAALy8vBAQEYPTo0diwYQMKCwsREhKCIUOGcEEbPcAZQYiIiEgXPVdhbWBggMLCwgrHFxYWavQuv6jPP/8cs2fPxkcffYT09HS4uLjgn//8J+bMmSPFTJs2DQ8ePMCYMWOQlZWFDh06IDw8XJrDGgC2bNmCkJAQdOvWDQYGBhg4cCDWrVun1VypavTZ1gf7hu6TOw0iIiIiNc81j3WjRo3QoEED/PLLLxWK79GjB/78808kJSVVOkFdxnmsiYiIiHSbzs5j3bFjRxw5cgTXr19/Zuz169dx5MgRdOrUqbK5EZVr8M7BcqdAREREpOG5Cuvg4GAUFhZi0KBB+Pvvv58Yd+/ePbzzzjsoKirC2LFjXzhJokeF9Q+TOwUiIiIiDc9VWLdq1QoTJkzA2bNn0bhxY8yZMweRkZG4cuUKrly5gqNHj2L27Nlo3Lgx4uLiMHHiRLRq1aqqcqdX1MoTK+VOgYiIiEjDc42xBgAhBGbNmoXly5dLU9w93m5oaIhp06Zh4cKFUCgUWktW13CMtTxO3T6FdrXayZ0GERER6YHqrNeee0lzhUKBxYsXY9SoUQgNDcWJEyekZcCdnJzg6+uLESNGoEGDBlpPlggAbqtuA7XkzoKIiIhI3XMX1mUaNGiAhQsXajMXogrJzMuUOwUiIiIiDdqdZJqoGnSqy5lmiIiISPewsCa9s/7UerlTICIiItLAwpr0zuqA1XKnQERERKSBhTXpnT7b+sidAhEREZEGFtakd/YN3Sd3CkREREQaWFiT3gnaEyR3CkREREQaWFiT3lntzzHWREREpHsqPY81UXXILShC4zkH1baN7nkBszpNlykjIiIiovKxx5r0ThuXtnKnQERERKSBPdakk3ILigAA93LyNdrS76ukdgsTfgsTERGRbmBVQjrp8eEfj5rw40HM/sEYAHD9s17VlRIRERHRU3EoCOkd8+LX5U6BiIiISAMLa9JJlxb449ICfxyf1lmjLdt4u/R5vRkHpGEhRERERHLiUBDSSdLYaUvNNvvCELXn93LyYWHHb2UiIiKSF3usSe+km8xXe95x2VHkFhSx55qIiIhkpRBCCLmT0FcqlQrW1tbIzs6GUqmUO52XWr0ZByoUx5sZiYiI6FHVWa+xx5r0wplPuuHMJ90AAPeMv5A5GyIiIiJNHJhKeqGmpRkA4Pi0zvBZ9rda25lP/GBhYihHWkREREQS9liTXjE3McRDw5Nq2yxMDGFhYsTFYoiIiEhWLKxJr9S0NMOWkf1waYG/3KkQERERqdHLwvr27dsYNmwY7O3tYW5uDm9vb5w5c0ZqF0Jgzpw5cHZ2hrm5Ofz8/HDlyhW1fWRkZCAwMBBKpRI2NjYYNWoUcnJyqvtUqBLMjcxhYWKE65/1wvXPerGnmoiIiHSC3hXWmZmZ8PX1hbGxMX755RdcunQJK1euhK2trRSzbNkyrFu3Dhs2bEBsbCxq1KgBf39/5OXlSTGBgYG4ePEiIiIisH//fkRFRWHMmDFynBI9p1O3T8mdAhEREZEGvZtub8aMGYiOjsbx48fLbRdCwMXFBZMnT8aUKVMAANnZ2XB0dERYWBiGDBmCxMRENG7cGKdPn0abNm0AAOHh4ejZsydu3boFFxeXCuXC6fbU3blzH199dRo//3wVxcUl6NSpLkJC2qFRI3utHifjYQbszO20uk8iIiJ6OXG6vafYu3cv2rRpg3feeQcODg5o2bIlvvnmG6k9OTkZqamp8PPzk7ZZW1ujffv2iImJAQDExMTAxsZGKqoBwM/PDwYGBoiNjX3isfPz86FSqdQeVOr331PRvPkGrF0bi6ZNHdCuXS1s334BzZtvQHj4Va0ea+LBiVrdHxEREZE26F1h/eeff+Krr76Cu7s7Dh48iLFjx2LcuHHYvHkzACA1NRUA4OjoqPY6R0dHqS01NRUODg5q7UZGRrCzs5NiyrNkyRJYW1tLD1dXV22emt4qKREYMuS/qFPHGsnJ47F5c398/XUf3LgxAX5+9TFkyI+4fz9fa8fb3H+z1vZFREREpC16V1iXlJSgVatWWLx4MVq2bIkxY8Zg9OjR2LBhQ5Ufe+bMmcjOzpYeN2/erPJjPlXmFSDt7JMfmVeevQ8tOHbsOi5f/htr1vjD3t5C2m5ubowvv+yJ+/cLsH37Ba0dr8+2PlrbFxEREZG26N10Cs7OzmjcuLHaNi8vL/z3v/8FADg5OQEA0tLS4OzsLMWkpaWhRYsWUkx6erraPoqKipCRkSG9vjympqYwNTXVxmm8uMwrwKZGz457/w/A1r1KU0lM/BuGhgp06FBHo83V1RqNGtkjMfHvcl5ZOfuG7tPavoiIiIi0Re96rH19fZGUlKS27Y8//kDdunUBAG5ubnBycsLhw4eldpVKhdjYWPj4+AAAfHx8kJWVhbi4OCnmyJEjKCkpQfv27avhLLSg4L52416Avb05iosFUlKyNdpycwtx58592Nuba+14E8M5xpqIiIh0j94V1hMnTsTJkyexePFiXL16FVu3bsXXX3+N4OBgAIBCocCECROwcOFC7N27F+fPn8fw4cPh4uKC/v37Ayjt4Q4ICMDo0aNx6tQpREdHIyQkBEOGDKnwjCD0P716NYJSaYqFC6Pw+CQzn38ei/v38zF0qLfWjhfcLlhr+yIiIiLSFr0bCtK2bVvs3r0bM2fOxIIFC+Dm5oY1a9YgMDBQipk2bRoePHiAMWPGICsrCx06dEB4eDjMzMykmC1btiAkJATdunWDgYEBBg4ciHXr1slxSnrP0tIEy5e/hX/+cz/u3s3BqFEtYWZmhO3bL+K7737HlCk+qF/f9tk7qqCoG1FoaNdQa/sjIiIi0ga9m8dal8g6j3XaWeD71s+OGxYHOLaq+nwA7Nx5EZ9+GoXz50vHr9erZ4PJk30QHNwWCoVCa8fZnbgbA7wGaG1/RERE9PKqznpN73qsSXe9804TDBrUGLduqVBcLODqqoShofZHG9VS1tL6PomIiIhelN6NsSbdplAo4OpqjXr1bKqkqAaAg1cPIregCPVmHEC9GQeQW1BUJcchIiIieh4srEnvTH5jstwpEBEREWlgYa2vTKy0G6cncguK8N6uIOQWFD+yrRi5BUXsuSYiIiJZ8ebFFyDrzYtA6SIxT5un2sSqyheHqW71Zhx4avv1z3pVUyZERESkD3jzIlXMS1Y0V1S6yXw4FMyVOw0iIiIiNSysSa9cWuAPwB+5BcVos/BXAMCZT/xgYWIob2JERET0yuMYa9IrFiZGWHh8jlohbWFiCAsTI1iY8O9EIiIikg8La9I7Q5sO1djG6feIiIhIbuziI71zPv08vB291W5UZDFNREREcmNhTXqtrKB+fPq9MhweQkRERNWFVQfpHW8Hb+nzxnMOarSX3dQIAGc+6YY2Cw9Ln9e0NKv6BImIiOiVxDHWpHe2XdgmdwpEREREGthjTXpncbfF0uel0+9BY/q9h/8/RCTjQaEU++jn7LkmIiIibWNhTXqnz7Y+2Dd0H4Dyx1BbmBiqDQcp0311lPQ5V2gkIiIibeNQENI7ZUU1ERERkS5RCCGE3Enoq+pce57+Z/DOwdjxzo6nxvydkwegdPhHWU/1oYmdYFfDGACHghAREb0qqrNe41AQ0jth/cOeGVNe4WxXw5gFNREREVUZDgUhvbPyxMoXej1XaSQiIqKqwB5r0jv+Df0rHFvT0ow3KhIREVG1YGFNeue26jZQ6/lfx1UaiYiIqCqxkiC9k5mXWanXPWuVRvZsExER0YvgGGvSO53qdqqS/XK8NREREb0IFtakd9afWl+p111a4I9LC/xx5hM/advxaZ014nhzIxEREVUGh4KQ3lkdsLpSryt/DLVC+qxsvDWLaSIiIqoMve+x/uyzz6BQKDBhwgRpW15eHoKDg2Fvbw9LS0sMHDgQaWlpaq9LSUlBr169YGFhAQcHB0ydOhVFRSyo9EGfbX20tq+OyyKlz9ss/BWN5xxEm4WHpW25BcX4OyePPdhERET0THrdY3369Gn8+9//RrNmzdS2T5w4EQcOHMDOnTthbW2NkJAQvP3224iOjgYAFBcXo1evXnBycsKJEydw9+5dDB8+HMbGxli8eLEcp0LP4UWXNH+e2T8evbmRiIiI6Gn0tsc6JycHgYGB+Oabb2Brayttz87OxsaNG7Fq1Sp07doVrVu3RmhoKE6cOIGTJ08CAA4dOoRLly7h+++/R4sWLdCjRw98+umnWL9+PQoKCuQ6JaqgoD1BL7yP8sZbV0RuQTFyC4rUeq45JpuIiIgAPS6sg4OD0atXL/j5qRdGcXFxKCwsVNvu6emJOnXqICYmBgAQExMDb29vODo6SjH+/v5QqVS4ePFi9ZwAVdpq/8qNsX6UhYnR/z8MpW1nPun2zGK7bLhIeVP3ERER0atNL4eCbN++HWfPnsXp06c12lJTU2FiYgIbGxu17Y6OjkhNTZViHi2qy9rL2p4kPz8f+fn50nOVSlXZU6AXsPHsRkz1nar1/ZYV2xX1vAvO5BYUSQX5pQX+XJCGiIjoJaN3v9lv3ryJ8ePHIyIiAmZmZtV67CVLlmD+/PnVekzS1K5WO63ty8LE6KkLw5z5pBsAhTTW+swnflIvNxecISIiokfp3VCQuLg4pKeno1WrVjAyMoKRkRGOHTuGdevWwcjICI6OjigoKEBWVpba69LS0uDk5AQAcHJy0pglpOx5WUx5Zs6ciezsbOlx8+ZN7Z4cVcjDoodVuv+yYvv6Z71Q09JMbbiIhYlhpXq2Sx/qPduPj9UmIiIi/aZ3PdbdunXD+fPn1baNHDkSnp6emD59OlxdXWFsbIzDhw9j4MCBAICkpCSkpKTAx8cHAODj44NFixYhPT0dDg4OAICIiAgolUo0btz4icc2NTWFqalpFZ0ZVdS1jGtypwCgdDgHUFokl9ejXYY920RERK8GvSusrays0LRpU7VtNWrUgL29vbR91KhRmDRpEuzs7KBUKvHxxx/Dx8cHr7/+OgCge/fuaNy4Md577z0sW7YMqamp+OSTTxAcHMzCWQ/09+xfrcd70nCR8nqty3q0iYiI6NXzUlYAq1evhoGBAQYOHIj8/Hz4+/vjyy+/lNoNDQ2xf/9+jB07Fj4+PqhRowaCgoKwYMECGbOmivo06lNs6L1B7jQqrLye7TKlY7iJiIjoZaAQQgi5k9BXKpUK1tbWyM7OhlKplDsd0nGPzgpSpqKzg7zIa4mIiF5l1Vmv6d3Ni0TaXNK8OpV3oyJvYiQiInp5sMf6BbDHmp5HvRkHntpe3jjusoL7Xk4+Oi47Wu7rznzSDTUtq3fqSSIiIn3BHmuip/hw/4dyp1BtylZ5fFJRDQBtFh6uvoSIiIjoiThAk/TO7E6z5U4BACCEwIkTN/Hnn5lwcrJEly5uMDJ68t+qFZ2erzIeHUrCsdhERETy4G9b0jt7Lu9BcLtgWXM4c+YOhg/fjcTEv6VttWsrsWFDL/Tq1ajc11Rmer6yYvxpQ0EAqC2V/rgKLaWeeQUouP/E/cPECrB1f3I7ERERsbAm/dPAroGsx//zz0z4+X2HRo3sceTIcLz+em1cuvQX5sw5iv79f0BkZBA6dKijlWNJRbBlxeLv5RRobMsoZ5uazCvApvL/GFDz/h8sromIiJ6CY6xJ75gbmct6/NWrY2BqaoRffx2OLl3cYG5ujNatXbBnzz/g7e2ARYuOP/X1jy6Zro0hGmc+8ZM+77gsUqO9+5r/5VPuLCRP66l+VEXjiIiIXlHssSa9c+r2KbxZ703Zjr937x8IDPSGUqm+SqexsSE++KAVQkJ+Rn5+EUxNtff2enz1x0eHdzzPGG0upU5ERFR1WFiT3hnVapSsxy8oKIalpUm5bVZWJhACKCwsgalpuSFV4n9jsQvK7bV+mszcfNhWRVJERESvGA4FIb0z8eBEWY//xhuu2LUrESUlmlPA//hjIpo1c3xi4a0tjw8nKXvYl3Pc49M6S5+f+cQPlxb4q93kOGzjqSrNlYiI6FXBwpr0zub+m2U9/sSJr+PSpb/w0UcHkJ2dBwDIyyvC4sXHsXdvEiZNel3W/B5n/shQkbJZSCoztvthIVeIJCIiehoOBSG902dbH+wbuk+243foUAdff90HY8cewPffJ6Bx49dw7VomMjIeYtasjhg+vLlsuT0+Fhsofyn1R30/qh2w69n7HrQhBheL/nqusdkVmuqPiIjoJcHfcqR35Cyqy3zwQSv07OmOzZvj8eefmfD3b4Dhw5vD3d1e7tQ0lFdsP8rWohoHgxMREb3EWFiT3pkYPhGrA1bLnQZcXKwwc2ZHudN4cSZWFQr7cVx3CJuKzWNd1kueW1D8yLb/fV5ezzV7t4mISN/xNxfpHblXXXzp2LqXLv7y2DzVDwuLMGhDDIDSotrcwavCu3x8WXWAU/0REdHLj4U16Z2oG1FoaNdQ7jReLuWsqCgKinCx6K/SzyvYU10ZlendJiKqFplXnr44lokVV6QlNfyNRXrH1oyzLleHZ43Nfpqy6fxyC4qlnuozn/iVu5gNe7eJSCdlXgE2NXp23Pt/sLgmCQtr0ju1lLXkToGeobxe5rKp/oiI9MLTeqorE0evBP6WI71z8OpBtKvVTu40SEuep3ebiIhIl7GwJr0z+Y3JcqdAFVSR4STs3SYiopcFV14kvTNizwi5UyAiIiLSwC4h0js73tkhdwpUBV7kZkkiIiJdwB5r0jt9tvWROwUiIiIiDSysSe/owpLmRERERI9jYU1651+H/yV3CkRE9LIzsdJuHL0SOMaa9M7QpkPlToGIiF52tu6li7+UzVNdkAPs7gkM+BkwsSzdxpUX6TF62WO9ZMkStG3bFlZWVnBwcED//v2RlJSkFpOXl4fg4GDY29vD0tISAwcORFpamlpMSkoKevXqBQsLCzg4OGDq1KkoKiqqzlOhSjiffl7uFIiI6FVg6w44tip9uHYCxuWUfizbxqKaHqOXhfWxY8cQHByMkydPIiIiAoWFhejevTsePHggxUycOBH79u3Dzp07cezYMdy5cwdvv/221F5cXIxevXqhoKAAJ06cwObNmxEWFoY5c+bIcUpERESky0qKgDsxpR+JnkAhhBByJ/Gi/vrrLzg4OODYsWPo1KkTsrOz8dprr2Hr1q0YNGgQAODy5cvw8vJCTEwMXn/9dfzyyy/o3bs37ty5A0dHRwDAhg0bMH36dPz1118wMTF55nFVKhWsra2RnZ0NpVJZpef40su88vRlYR/5d9v5tPPwdvSupsSIiIhQ+jsq1AsYmchx1XqmOuu1l2KMdXZ2NgDAzs4OABAXF4fCwkL4+flJMZ6enqhTp45UWMfExMDb21sqqgHA398fY8eOxcWLF9GyZUuN4+Tn5yM/P196rlKpquqUXi2ZV4BNjZ4d9/4fgK07tl3YxsKaiIiql4kV8M9bcmdBOk4vh4I8qqSkBBMmTICvry+aNm0KAEhNTYWJiQlsbGzUYh0dHZGamirFPFpUl7WXtZVnyZIlsLa2lh6urq5aPptX1NN6qsuJW9xtcRUmQ0REVI6SIuD6QQ4FoafS+8I6ODgYFy5cwPbt26v8WDNnzkR2drb0uHnzZpUfkzRxgRgiIqp2RXnA0UmlH4meQK+HgoSEhGD//v2IiopC7dq1pe1OTk4oKChAVlaWWq91WloanJycpJhTp06p7a9s1pCymMeZmprC1NRUy2dBz4sLxBARUbUzsQRGXJQ7C9JxetljLYRASEgIdu/ejSNHjsDNzU2tvXXr1jA2Nsbhw4elbUlJSUhJSYGPjw8AwMfHB+fPn0d6eroUExERAaVSicaNG1fPiVClDN45WO4UiIjoVVNcCCTtLP1I9AR62WMdHByMrVu34qeffoKVlZU0Jtra2hrm5uawtrbGqFGjMGnSJNjZ2UGpVOLjjz+Gj48PXn/9dQBA9+7d0bhxY7z33ntYtmwZUlNT8cknnyA4OJi90jourH+Y3CkQEdGrpqQAiFsF1O8JGBrLnQ3pKL3ssf7qq6+QnZ2Nzp07w9nZWXr88MMPUszq1avRu3dvDBw4EJ06dYKTkxN27doltRsaGmL//v0wNDSEj48Phg0bhuHDh2PBggVynBI9h5UnVsqdAhERvWqMawDvxpR+JHqCl2Iea7lwHmstSTsLfN/62XHD4gDHVjh1+xTa1WpX9XkRERGVKS4ALn4HNBkOGD57rQvSHdVZr+lljzW9ZCo60f7/x91W3a7CZIiIiMpRUgj8sbP0I9ET6OUYa3rJ2LqXLv5SwZUXM/MyqykxIiKi/2dcAxh0UO4sSMexsCbd8P9Fc0V0qtupChMhIiIqR1E+EL8eaBEMGHGSAyofh4KQ3ll/ar3cKRAR0atGFAN3Y0o/Ej0Bb158Abx5kYiIiEi38eZFoqfgkuZERFTtivKBE/NKPxI9AQtr0jtc0pyIiKqdKAHu3yr9SPQELKxJ7wTtCZI7BSIietUYmwP+35Z+pOdWVFSCnTsvon//7ejYMRSjR+9FXNwdudPSOhbWpHdW+6+WOwUiInrVFOUBRyeVfqTn8vBhIXr02ILBg3/EX3/lws3NBhERf6JNm2+wfHm03OlpFafbI72z8exGTPWdKncaREREVAFz5x5FdHQKDh8ejq5d3QAAxcUlmDv3KKZN+xU+Pq7o0KGOzFlqB3usSe9wOXMiIqp2RmZA51WlH6nC8vKK8M03ZxES0k4qqgHA0NAACxZ0QaNG9li//rSMGWoXC2vSOw+LHsqdAhERvWoKHwIHPyj9SBV2+7YKWVl58PdvoNFmYKCAv38DnD+fJkNmVYOFNemdaxnX5E6BiIheNQoDwKp26UeqMCur0lUqb91Sldt+86YK1tYvz38B+N1Beie4XbDcKRAR0avGyBR4Yx6XM39ODg410LWrG9asiUVeXpFa26VLf2H//j8wZEgTmbLTPhbWRERERFRlFi/uisuX/0anTqH48cdLOHfuLlatikHnzmHw8qqJkSNbyp2i1nBWECIiIiKqMu3b18bhw8MxZcohvPPOTgCAsbEBBg9ugjVrAmBpaSJzhtqjEEIIuZPQV9W59jwRERGRvvvzz0xkZDyEm5sN7O0tquWY1VmvsceaiIiIiKpF/fq2qF/fVu40qgzHWBMRERERaQELayIiIiIiLWBhTURERESkBSysiYiIiIi0gIU1EREREZEWsLAmIiIiItICFtZERERERFrAeaxfQNnaOiqVSuZMiIiIiKg8ZXVadayJyML6Bdy/fx8A4OrqKnMmRERERPQ09+/fh7W1dZUeg0uav4CSkhLcuXMHVlZWUCgUcqejd1QqFVxdXXHz5k0uCa/jeK30C6+XfuH10i+8Xvql7HpdunQJHh4eMDCo2lHQ7LF+AQYGBqhdu7bcaeg9pVLJH056gtdKv/B66RdeL/3C66VfatWqVeVFNcCbF4mIiIiItIKFNRERERGRFrCwJtmYmppi7ty5MDU1lTsVegZeK/3C66VfeL30C6+Xfqnu68WbF4mIiIiItIA91kREREREWsDCmoiIiIhIC1hYExERERFpAQtrIiIiIiItYGFNWjNv3jwoFAq1h6enp9Sel5eH4OBg2Nvbw9LSEgMHDkRaWpraPlJSUtCrVy9YWFjAwcEBU6dORVFRUXWfykspKioKffr0gYuLCxQKBfbs2aPWLoTAnDlz4OzsDHNzc/j5+eHKlStqMRkZGQgMDIRSqYSNjQ1GjRqFnJwctZiEhAR07NgRZmZmcHV1xbJly6r61F5Kz7peI0aM0Hi/BQQEqMXwelWPJUuWoG3btrCysoKDgwP69++PpKQktRht/fw7evQoWrVqBVNTUzRs2BBhYWFVfXovnYpcr86dO2u8vz788EO1GF6v6vHVV1+hWbNm0oI8Pj4++OWXX6R2nXtvCSItmTt3rmjSpIm4e/eu9Pjrr7+k9g8//FC4urqKw4cPizNnzojXX39dvPHGG1J7UVGRaNq0qfDz8xPnzp0TP//8s6hZs6aYOXOmHKfz0vn555/FrFmzxK5duwQAsXv3brX2zz77TFhbW4s9e/aI33//XfTt21e4ubmJhw8fSjEBAQGiefPm4uTJk+L48eOiYcOGYujQoVJ7dna2cHR0FIGBgeLChQti27ZtwtzcXPz73/+urtN8aTzregUFBYmAgAC191tGRoZaDK9X9fD39xehoaHiwoULIj4+XvTs2VPUqVNH5OTkSDHa+Pn3559/CgsLCzFp0iRx6dIl8fnnnwtDQ0MRHh5ereer7ypyvd58800xevRotfdXdna21M7rVX327t0rDhw4IP744w+RlJQk/vWvfwljY2Nx4cIFIYTuvbdYWJPWzJ07VzRv3rzctqysLGFsbCx27twpbUtMTBQARExMjBCitJAwMDAQqampUsxXX30llEqlyM/Pr9LcXzWPF2olJSXCyclJLF++XNqWlZUlTE1NxbZt24QQQly6dEkAEKdPn5ZifvnlF6FQKMTt27eFEEJ8+eWXwtbWVu16TZ8+XXh4eFTxGb3cnlRY9+vX74mv4fWST3p6ugAgjh07JoTQ3s+/adOmiSZNmqgd6x//+Ifw9/ev6lN6qT1+vYQoLazHjx//xNfwesnL1tZWfPvttzr53uJQENKqK1euwMXFBfXr10dgYCBSUlIAAHFxcSgsLISfn58U6+npiTp16iAmJgYAEBMTA29vbzg6Okox/v7+UKlUuHjxYvWeyCsmOTkZqampatfH2toa7du3V7s+NjY2aNOmjRTj5+cHAwMDxMbGSjGdOnWCiYmJFOPv74+kpCRkZmZW09m8Oo4ePQoHBwd4eHhg7NixuHfvntTG6yWf7OxsAICdnR0A7f38i4mJUdtHWUzZPqhyHr9eZbZs2YKaNWuiadOmmDlzJnJzc6U2Xi95FBcXY/v27Xjw4AF8fHx08r1lVJkTIypP+/btERYWBg8PD9y9exfz589Hx44dceHCBaSmpsLExAQ2NjZqr3F0dERqaioAIDU1Ve0bv6y9rI2qTtnXt7yv/6PXx8HBQa3dyMgIdnZ2ajFubm4a+yhrs7W1rZL8X0UBAQF4++234ebmhmvXruFf//oXevTogZiYGBgaGvJ6yaSkpAQTJkyAr68vmjZtCgBa+/n3pBiVSoWHDx/C3Ny8Kk7ppVbe9QKAd999F3Xr1oWLiwsSEhIwffp0JCUlYdeuXQB4varb+fPn4ePjg7y8PFhaWmL37t1o3Lgx4uPjde69xcKatKZHjx7S582aNUP79u1Rt25d7Nixgz9AiLRsyJAh0ufe3t5o1qwZGjRogKNHj6Jbt24yZvZqCw4OxoULF/Dbb7/JnQpVwJOu15gxY6TPvb294ezsjG7duuHatWto0KBBdaf5yvPw8EB8fDyys7Px448/IigoCMeOHZM7rXJxKAhVGRsbGzRq1AhXr16Fk5MTCgoKkJWVpRaTlpYGJycnAICTk5PGnbxlz8tiqGqUfX3L+/o/en3S09PV2ouKipCRkcFrqAPq16+PmjVr4urVqwB4veQQEhKC/fv3IzIyErVr15a2a+vn35NilEolOy8q4UnXqzzt27cHALX3F69X9TExMUHDhg3RunVrLFmyBM2bN8fatWt18r3FwpqqTE5ODq5duwZnZ2e0bt0axsbGOHz4sNSelJSElJQU+Pj4AAB8fHxw/vx5tWIgIiICSqUSjRs3rvb8XyVubm5wcnJSuz4qlQqxsbFq1ycrKwtxcXFSzJEjR1BSUiL90vHx8UFUVBQKCwulmIiICHh4eHBYQRW7desW7t27B2dnZwC8XtVJCIGQkBDs3r0bR44c0Rheo62ffz4+Pmr7KIsp2wdVzLOuV3ni4+MBQO39xesln5KSEuTn5+vme+v578UkKt/kyZPF0aNHRXJysoiOjhZ+fn6iZs2aIj09XQhROiVOnTp1xJEjR8SZM2eEj4+P8PHxkV5fNiVO9+7dRXx8vAgPDxevvfYap9vTkvv374tz586Jc+fOCQBi1apV4ty5c+LGjRtCiNLp9mxsbMRPP/0kEhISRL9+/cqdbq9ly5YiNjZW/Pbbb8Ld3V1t+rasrCzh6Ogo3nvvPXHhwgWxfft2YWFhwenbKuFp1+v+/ftiypQpIiYmRiQnJ4tff/1VtGrVSri7u4u8vDxpH7xe1WPs2LHC2tpaHD16VG16ttzcXClGGz//yqYEmzp1qkhMTBTr16/n9G2V8KzrdfXqVbFgwQJx5swZkZycLH766SdRv3590alTJ2kfvF7VZ8aMGeLYsWMiOTlZJCQkiBkzZgiFQiEOHTokhNC99xYLa9Kaf/zjH8LZ2VmYmJiIWrVqiX/84x/i6tWrUvvDhw/FRx99JGxtbYWFhYUYMGCAuHv3rto+rl+/Lnr06CHMzc1FzZo1xeTJk0VhYWF1n8pLKTIyUgDQeAQFBQkhSqfcmz17tnB0dBSmpqaiW7duIikpSW0f9+7dE0OHDhWWlpZCqVSKkSNHivv376vF/P7776JDhw7C1NRU1KpVS3z22WfVdYovladdr9zcXNG9e3fx2muvCWNjY1G3bl0xevRotemkhOD1qi7lXScAIjQ0VIrR1s+/yMhI0aJFC2FiYiLq16+vdgyqmGddr5SUFNGpUydhZ2cnTE1NRcOGDcXUqVPV5rEWgterurz//vuibt26wsTERLz22muiW7duUlEthO69txRCCPH8/dxERERERPQojrEmIiIiItICFtZERERERFrAwpqIiIiISAtYWBMRERERaQELayIiIiIiLWBhTURERESkBSysiYiIiIi0gIU1EdEroHPnzlAoFHKnQUT0UjOSOwEiIno+z1sgcx0wIqLqwcKaiEjPzJ07V2PbmjVrkJ2dXW4bAHz33XfIzc2t6tSIiF5pXNKciOglUK9ePdy4cYO900REMuIYayKiV0B5Y6zDwsKgUCgQFhaGffv2oX379rCwsECtWrUwe/ZslJSUAAA2b96M5s2bw9zcHHXq1MHy5cvLPYYQAps2bYKvry+USiUsLCzQpk0bbNq0qcrPj4hIF3AoCBHRK2737t04dOgQ+vfvD19fXxw4cAALFy6EEALW1tZYuHAh+vXrh86dO+O///0vpk2bBkdHRwwfPlzahxACgYGB2LZtG9zd3fHuu+/CxMQEERERGDVqFC5duoQVK1bIeJZERFWPQ0GIiF4CzxoK0rlzZxw7dkytPSwsDCNHjoSxsTGio6PRtm1bAMD9+/fRsGFD5OTkQKlUIjo6GvXr1wcA3Lx5Ew0bNoSHhwcSEhKkfX3zzTcYM2YMRo4ciX//+98wNjYGABQUFGDQoEHYt28fzpw5g9atW1fVl4CISHYcCkJE9IobNmyYVFQDgJWVFXr37o3c3FyMHTtWKqoBwNXVFR06dMClS5dQVFQkbf/iiy9Qo0YNrF+/XiqqAcDExASLFi0CAGzbtq0azoaISD4cCkJE9Ipr0aKFxjZnZ+enthUXFyMtLQ21atVCbm4uzp8/DxcXFyxdulQjvrCwEABw+fJlreZNRKRrWFgTEb3ilEqlxjYjI6NntpUVzJmZmRBC4Pbt25g/f/4Tj/PgwQNtpEtEpLNYWBMR0QspK75bt26NM2fOyJwNEZF8OMaaiIheiJWVFby8vJCYmIisrCy50yEikg0LayIiemHjxo1Dbm4uRo8eXe6Qj+TkZFy/fr36EyMiqkYcCkJERC/sn//8J06ePInNmzcjOjoafn5+cHFxQVpaGi5fvozY2Fhs3boV9erVkztVIqIqw8KaiIheWNkKjj179sQ333yD/fv3IycnBw4ODnB3d8eKFSvg5+cnd5pERFWKC8QQEREREWkBx1gTEREREWkBC2siIiIiIi1gYU1EREREpAUsrImIiIiItICFNRERERGRFrCwJiIiIiLSAhbWRERERERawMKaiIiIiEgLWFgTEREREWkBC2siIiIiIi1gYU1EREREpAUsrImIiIiItICFNRERERGRFvwfiTShMJhorq4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "\n",
    "ax.scatter(dall.time, dall.cost, s=30, facecolors='none', color='navy', label=\"GA: 100 generations\") #MONO solutions\n",
    "\n",
    "ax.scatter(dfga.time, dfga.cost, s=30,  marker=\"+\", edgecolors='blue', label=\"NSGA-II: 100 generations\")\n",
    "\n",
    "\n",
    "ax.scatter(avg_pf[:,0], avg_pf[:,1], s=30,  marker=\"s\", color='darkorange',label=\"PPO\")\n",
    "ax.scatter(dfmodel.iTime[0],dfmodel.iCost[0],color=\"green\",label=\"Cloud\")\n",
    "\n",
    "ax.vlines(x=dfmodel.iTime[0], ymin=100, ymax=dfmodel.iCost[0], colors='green', ls=':', lw=0.6)\n",
    "ax.vlines(x=avg_pf[0,0], ymin=100, ymax=avg_pf[0,1], colors='darkorange', ls=':', lw=0.8)\n",
    "\n",
    "\n",
    "ax.set_xlabel(\"Time\", fontsize=14)\n",
    "ax.set_ylabel(\"Cost\", fontsize=14)\n",
    "ax.legend()\n",
    "plt.savefig(\"images/ppo_ga_nsgaII-0.pdf\",dpi=300,bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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