analyser.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "from pandas import DataFrame, Series\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "from scipy import stats\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrixMalEX=\"data_GG.csv\"\n",
    "matrixMal=\"data_GM.csv\"\n",
    "matrixIt=\"data_L.csv\"\n",
    "n_qty=2 #CAMBIAR SEGUN LA CANTIDAD DE NODOS USADOS\n",
    "n_groups= 2\n",
    "repet = 3 #CAMBIAR EL PRIMER NUMERO SEGUN NUMERO DE EJECUCIONES POR CONFIG\n",
    "\n",
    "p_value = 0.05\n",
    "values = [2, 10, 20, 40]\n",
    "dist_names = ['null', 'BestFit', 'WorstFit']\n",
    "\n",
    "labelsP = [['(2,2)', '(2,10)', '(2,20)', '(2,40)'],['(10,2)', '(10,10)', '(10,20)', '(10,40)'],\n",
    "          ['(20,2)', '(20,10)', '(20,20)', '(20,40)'],['(40,2)', '(40,10)', '(40,20)', '(40,40)']]\n",
    "labelsP_J = ['(2,2)', '(2,10)', '(2,20)', '(2,40)','(10,2)', '(10,10)', '(10,20)', '(10,40)',\n",
    "              '(20,2)', '(20,10)', '(20,20)', '(20,40)','(40,2)', '(40,10)', '(40,20)', '(40,40)']\n",
    "positions = [321, 322, 323, 324, 325]\n",
    "positions_small = [221, 222, 223, 224]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfG = pd.read_csv( matrixMalEX )\n",
    "\n",
    "dfG = dfG.drop(columns=dfG.columns[0])\n",
    "dfG['S'] = dfG['N']\n",
    "dfG['N'] = dfG['S'] + dfG['%Async']\n",
    "dfG['%Async'] = (dfG['%Async'] / dfG['N']) * 100\n",
    "\n",
    "if(n_qty == 1):\n",
    "    group = dfG.groupby(['%Async', 'Groups'])['TE']\n",
    "else:        \n",
    "    group = dfG.groupby(['Dist', '%Async', 'Groups'])['TE']\n",
    "\n",
    "#group\n",
    "grouped_aggG = group.agg(['mean'])\n",
    "grouped_aggG.rename(columns={'mean':'TE',}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfM = pd.read_csv( matrixMal )\n",
    "dfM = dfM.drop(columns=dfM.columns[0])\n",
    "\n",
    "dfM['S'] = dfM['N']\n",
    "dfM['N'] = dfM['S'] + dfM['%Async']\n",
    "dfM[\"TR\"] = dfM[\"TC\"] + dfM[\"TS\"] + dfM[\"TA\"]\n",
    "dfM['%Async'] = (dfM['%Async'] / dfM['N']) * 100\n",
    "\n",
    "if(n_qty == 1):\n",
    "    groupM = dfM.groupby(['%Async','NP', 'NS'])['TC', 'TS', 'TA', 'TR']\n",
    "else:\n",
    "    groupM = dfM.groupby(['Dist', '%Async','NP', 'NS'])['TC', 'TS', 'TA', 'TR']\n",
    "\n",
    "#group\n",
    "grouped_aggM = groupM.agg(['mean'])\n",
    "grouped_aggM.columns = grouped_aggM.columns.get_level_values(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:12: FutureWarning: set_axis currently defaults to operating inplace.\n",
      "This will change in a future version of pandas, use inplace=True to avoid this warning.\n",
      "  if sys.path[0] == '':\n"
     ]
    }
   ],
   "source": [
    "dfL = pd.read_csv( matrixIt )\n",
    "dfL = dfL.drop(columns=dfL.columns[0])\n",
    "\n",
    "if(n_qty == 1):\n",
    "    groupL = dfL[dfL['NS'] != 0].groupby(['Tt', '%Async', 'NP', 'NS'])['Ti', 'To']\n",
    "else:\n",
    "    groupL = dfL[dfL['NS'] != 0].groupby(['Tt', 'Dist', '%Async', 'NP', 'NS'])['Ti', 'To']\n",
    "\n",
    "#group\n",
    "grouped_aggL = groupL.agg(['mean', 'count'])\n",
    "grouped_aggL.columns = grouped_aggL.columns.get_level_values(0)\n",
    "grouped_aggL.set_axis(['Ti', 'Iters', 'To', 'Iters2'], axis='columns')\n",
    "\n",
    "grouped_aggL['Iters'] = np.round(grouped_aggL['Iters']/repet)\n",
    "grouped_aggL['Iters2'] = np.round(grouped_aggL['Iters2']/repet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_aggL.to_excel(\"resultL.xlsx\") \n",
    "grouped_aggM.to_excel(\"resultM.xlsx\") \n",
    "grouped_aggG.to_excel(\"resultG.xlsx\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>%Async</th>\n",
       "      <th>Groups</th>\n",
       "      <th>Dist</th>\n",
       "      <th>Matrix</th>\n",
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       "      <th>Time</th>\n",
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       "      <td>475</td>\n",
       "      <td>1000000000</td>\n",
       "      <td>50.0</td>\n",
       "      <td>10,10</td>\n",
       "      <td>2,2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1,10</td>\n",
       "      <td>2.962400</td>\n",
       "      <td>500000000</td>\n",
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       "    <tr>\n",
       "      <td>476</td>\n",
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       "      <td>1,1</td>\n",
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       "      <td>0.2</td>\n",
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       "      <td>4.608352</td>\n",
       "      <td>500000000</td>\n",
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       "      <td>478</td>\n",
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       "      <td>1,1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1,10</td>\n",
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       "      <td>500000000</td>\n",
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       "<p>480 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              N  %Async Groups Dist  Matrix  CommTam  Time Iters        TE  \\\n",
       "0    1000000000     0.0  20,40  2,2  100000        0   0.2  1,10  4.652589   \n",
       "1    1000000000     0.0  20,40  2,2  100000        0   0.2  1,10  5.018492   \n",
       "2    1000000000     0.0  20,40  2,2  100000        0   0.2  1,10  4.493219   \n",
       "3    1000000000    75.0  10,40  1,1  100000        0   0.2  1,10  4.105919   \n",
       "4    1000000000    75.0  10,40  1,1  100000        0   0.2  1,10  4.245686   \n",
       "..          ...     ...    ...  ...     ...      ...   ...   ...       ...   \n",
       "475  1000000000    50.0  10,10  2,2  100000        0   0.2  1,10  2.962400   \n",
       "476  1000000000    50.0  10,10  2,2  100000        0   0.2  1,10  2.961127   \n",
       "477  1000000000    50.0  40,20  1,1  100000        0   0.2  1,10  4.608352   \n",
       "478  1000000000    50.0  40,20  1,1  100000        0   0.2  1,10  4.589667   \n",
       "479  1000000000    50.0  40,20  1,1  100000        0   0.2  1,10  4.489824   \n",
       "\n",
       "              S  \n",
       "0    1000000000  \n",
       "1    1000000000  \n",
       "2    1000000000  \n",
       "3     250000000  \n",
       "4     250000000  \n",
       "..          ...  \n",
       "475   500000000  \n",
       "476   500000000  \n",
       "477   500000000  \n",
       "478   500000000  \n",
       "479   500000000  \n",
       "\n",
       "[480 rows x 10 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>Dist</th>\n",
       "      <th>%Async</th>\n",
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       "<p>160 rows × 1 columns</p>\n",
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      ],
      "text/plain": [
       "                          TE\n",
       "Dist %Async Groups          \n",
       "1,1  0.0    10,10   2.627098\n",
       "            10,2    2.899689\n",
       "            10,20   2.888230\n",
       "            10,40   3.993755\n",
       "            2,10    2.944463\n",
       "...                      ...\n",
       "2,2  100.0  20,40   5.341889\n",
       "            40,10   4.388462\n",
       "            40,2    4.029106\n",
       "            40,20   5.118012\n",
       "            40,40   5.099804\n",
       "\n",
       "[160 rows x 1 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_aggG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>%Async</th>\n",
       "      <th>NP</th>\n",
       "      <th>NS</th>\n",
       "      <th>Dist</th>\n",
       "      <th>Matrix</th>\n",
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       "      <td>2.739422</td>\n",
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       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "      <td>2,2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1,10</td>\n",
       "      <td>1.268700</td>\n",
       "      <td>0.951857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1000000000</td>\n",
       "      <td>2.220557</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1000000000</td>\n",
       "      <td>75.0</td>\n",
       "      <td>10</td>\n",
       "      <td>40</td>\n",
       "      <td>1,1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1,10</td>\n",
       "      <td>1.229340</td>\n",
       "      <td>0.152475</td>\n",
       "      <td>1.039797</td>\n",
       "      <td>250000000</td>\n",
       "      <td>2.421612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1000000000</td>\n",
       "      <td>75.0</td>\n",
       "      <td>10</td>\n",
       "      <td>40</td>\n",
       "      <td>1,1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1,10</td>\n",
       "      <td>1.282071</td>\n",
       "      <td>0.150512</td>\n",
       "      <td>1.328807</td>\n",
       "      <td>250000000</td>\n",
       "      <td>2.761390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>475</td>\n",
       "      <td>1000000000</td>\n",
       "      <td>50.0</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>2,2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1,10</td>\n",
       "      <td>0.552342</td>\n",
       "      <td>0.086523</td>\n",
       "      <td>0.606111</td>\n",
       "      <td>500000000</td>\n",
       "      <td>1.244976</td>\n",
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       "    <tr>\n",
       "      <td>476</td>\n",
       "      <td>1000000000</td>\n",
       "      <td>50.0</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>2,2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1,10</td>\n",
       "      <td>0.559686</td>\n",
       "      <td>0.039759</td>\n",
       "      <td>0.584163</td>\n",
       "      <td>500000000</td>\n",
       "      <td>1.183608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>477</td>\n",
       "      <td>1000000000</td>\n",
       "      <td>50.0</td>\n",
       "      <td>40</td>\n",
       "      <td>20</td>\n",
       "      <td>1,1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1,10</td>\n",
       "      <td>1.062025</td>\n",
       "      <td>0.495980</td>\n",
       "      <td>1.637047</td>\n",
       "      <td>500000000</td>\n",
       "      <td>3.195052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>478</td>\n",
       "      <td>1000000000</td>\n",
       "      <td>50.0</td>\n",
       "      <td>40</td>\n",
       "      <td>20</td>\n",
       "      <td>1,1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1,10</td>\n",
       "      <td>1.068324</td>\n",
       "      <td>0.476552</td>\n",
       "      <td>1.684447</td>\n",
       "      <td>500000000</td>\n",
       "      <td>3.229323</td>\n",
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       "    <tr>\n",
       "      <td>479</td>\n",
       "      <td>1000000000</td>\n",
       "      <td>50.0</td>\n",
       "      <td>40</td>\n",
       "      <td>20</td>\n",
       "      <td>1,1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1,10</td>\n",
       "      <td>1.111539</td>\n",
       "      <td>0.378755</td>\n",
       "      <td>1.518684</td>\n",
       "      <td>500000000</td>\n",
       "      <td>3.008978</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>480 rows × 14 columns</p>\n",
       "</div>"
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      "text/plain": [
       "              N  %Async  NP  NS Dist  Matrix  CommTam  Time Iters        TC  \\\n",
       "0    1000000000     0.0  20  40  2,2  100000        0   0.2  1,10  1.386783   \n",
       "1    1000000000     0.0  20  40  2,2  100000        0   0.2  1,10  1.451572   \n",
       "2    1000000000     0.0  20  40  2,2  100000        0   0.2  1,10  1.268700   \n",
       "3    1000000000    75.0  10  40  1,1  100000        0   0.2  1,10  1.229340   \n",
       "4    1000000000    75.0  10  40  1,1  100000        0   0.2  1,10  1.282071   \n",
       "..          ...     ...  ..  ..  ...     ...      ...   ...   ...       ...   \n",
       "475  1000000000    50.0  10  10  2,2  100000        0   0.2  1,10  0.552342   \n",
       "476  1000000000    50.0  10  10  2,2  100000        0   0.2  1,10  0.559686   \n",
       "477  1000000000    50.0  40  20  1,1  100000        0   0.2  1,10  1.062025   \n",
       "478  1000000000    50.0  40  20  1,1  100000        0   0.2  1,10  1.068324   \n",
       "479  1000000000    50.0  40  20  1,1  100000        0   0.2  1,10  1.111539   \n",
       "\n",
       "           TS        TA           S        TR  \n",
       "0    0.975962  0.000000  1000000000  2.362745  \n",
       "1    1.287850  0.000000  1000000000  2.739422  \n",
       "2    0.951857  0.000000  1000000000  2.220557  \n",
       "3    0.152475  1.039797   250000000  2.421612  \n",
       "4    0.150512  1.328807   250000000  2.761390  \n",
       "..        ...       ...         ...       ...  \n",
       "475  0.086523  0.606111   500000000  1.244976  \n",
       "476  0.039759  0.584163   500000000  1.183608  \n",
       "477  0.495980  1.637047   500000000  3.195052  \n",
       "478  0.476552  1.684447   500000000  3.229323  \n",
       "479  0.378755  1.518684   500000000  3.008978  \n",
       "\n",
       "[480 rows x 14 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
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       "      <th>NP</th>\n",
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       "      <td rowspan=\"5\" valign=\"top\">1,1</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.547969</td>\n",
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       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.303257</td>\n",
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       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.471601</td>\n",
       "      <td>0.515365</td>\n",
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       "      <td>10</td>\n",
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       "      <td rowspan=\"4\" valign=\"top\">40</td>\n",
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       "      <td>2.377278</td>\n",
       "      <td>2.817690</td>\n",
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       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.948845</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.783927</td>\n",
       "      <td>2.732773</td>\n",
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       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.288021</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.137097</td>\n",
       "      <td>3.425118</td>\n",
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       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1.442273</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.960198</td>\n",
       "      <td>3.402471</td>\n",
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       "<p>160 rows × 4 columns</p>\n",
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      "text/plain": [
       "                         TC        TS        TA        TR\n",
       "Dist %Async NP NS                                        \n",
       "1,1  0.0    2  2   0.223021  0.324948  0.000000  0.547969\n",
       "               10  0.303257  0.441400  0.000000  0.744658\n",
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       "            10 2   0.281236  0.425874  0.000000  0.707110\n",
       "...                     ...       ...       ...       ...\n",
       "2,2  100.0  20 40  1.444816  0.000000  2.211954  3.656770\n",
       "            40 2   0.440411  0.000000  2.377278  2.817690\n",
       "               10  0.948845  0.000000  1.783927  2.732773\n",
       "               20  1.288021  0.000000  2.137097  3.425118\n",
       "               40  1.442273  0.000000  1.960198  3.402471\n",
       "\n",
       "[160 rows x 4 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_aggM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
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       "      <td>20</td>\n",
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       "      <td>0.2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.199800</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>10</td>\n",
       "      <td>0.199741</td>\n",
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       "      <td>5277</td>\n",
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       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>10</td>\n",
       "      <td>0.199756</td>\n",
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       "      <td>5279</td>\n",
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       "      <td>40</td>\n",
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       "      <td>1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.199748</td>\n",
       "      <td>0.0</td>\n",
       "      <td>224.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5280 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               N  %Async  NP  N_par  NS  Dist  Compute_tam  Comm_tam  Time  \\\n",
       "0     1000000000     0.0  40     20   0     2       100000         0   0.2   \n",
       "1     1000000000     0.0  40     20   0     2       100000         0   0.2   \n",
       "2     1000000000     0.0  40     20   0     2       100000         0   0.2   \n",
       "3     1000000000     0.0  40     20   0     2       100000         0   0.2   \n",
       "4     1000000000     0.0  40     20   0     2       100000         0   0.2   \n",
       "...          ...     ...  ..    ...  ..   ...          ...       ...   ...   \n",
       "5275  1000000000    50.0  20     40   0     1       100000         0   0.2   \n",
       "5276  1000000000    50.0  20     40   0     1       100000         0   0.2   \n",
       "5277  1000000000    50.0  20     40   0     1       100000         0   0.2   \n",
       "5278  1000000000    50.0  20     40   0     1       100000         0   0.2   \n",
       "5279  1000000000    50.0  20     40   0     1       100000         0   0.2   \n",
       "\n",
       "      Iters        Ti   Tt     To  \n",
       "0        10  0.221581  0.0  224.0  \n",
       "1        10  0.200142  0.0  224.0  \n",
       "2        10  0.199802  0.0  224.0  \n",
       "3        10  0.199798  0.0  224.0  \n",
       "4        10  0.199800  0.0  224.0  \n",
       "...     ...       ...  ...    ...  \n",
       "5275     10  0.199738  0.0  224.0  \n",
       "5276     10  0.199741  0.0  224.0  \n",
       "5277     10  0.199924  0.0  224.0  \n",
       "5278     10  0.199756  0.0  224.0  \n",
       "5279     10  0.199748  0.0  224.0  \n",
       "\n",
       "[5280 rows x 13 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Ti</th>\n",
       "      <th>Iters</th>\n",
       "      <th>To</th>\n",
       "      <th>Iters2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tt</th>\n",
       "      <th>Dist</th>\n",
       "      <th>%Async</th>\n",
       "      <th>NP</th>\n",
       "      <th>NS</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"5\" valign=\"top\">0.0</td>\n",
       "      <td rowspan=\"5\" valign=\"top\">1</td>\n",
       "      <td rowspan=\"5\" valign=\"top\">0.0</td>\n",
       "      <td rowspan=\"4\" valign=\"top\">2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.199723</td>\n",
       "      <td>1.0</td>\n",
       "      <td>224.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.199689</td>\n",
       "      <td>1.0</td>\n",
       "      <td>224.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.199723</td>\n",
       "      <td>1.0</td>\n",
       "      <td>224.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.199708</td>\n",
       "      <td>1.0</td>\n",
       "      <td>224.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0.199149</td>\n",
       "      <td>1.0</td>\n",
       "      <td>223.333333</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"5\" valign=\"top\">1.0</td>\n",
       "      <td rowspan=\"5\" valign=\"top\">2</td>\n",
       "      <td rowspan=\"5\" valign=\"top\">100.0</td>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "      <td>0.587325</td>\n",
       "      <td>3.0</td>\n",
       "      <td>224.000000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">40</td>\n",
       "      <td>2</td>\n",
       "      <td>0.336983</td>\n",
       "      <td>5.0</td>\n",
       "      <td>223.333333</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.445416</td>\n",
       "      <td>3.0</td>\n",
       "      <td>224.000000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.525447</td>\n",
       "      <td>3.0</td>\n",
       "      <td>224.000000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.526898</td>\n",
       "      <td>3.0</td>\n",
       "      <td>224.000000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>288 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Ti  Iters          To  Iters2\n",
       "Tt  Dist %Async NP NS                                     \n",
       "0.0 1    0.0    2  2   0.199723    1.0  224.000000     1.0\n",
       "                   10  0.199689    1.0  224.000000     1.0\n",
       "                   20  0.199723    1.0  224.000000     1.0\n",
       "                   40  0.199708    1.0  224.000000     1.0\n",
       "                10 2   0.199149    1.0  223.333333     1.0\n",
       "...                         ...    ...         ...     ...\n",
       "1.0 2    100.0  20 40  0.587325    3.0  224.000000     3.0\n",
       "                40 2   0.336983    5.0  223.333333     5.0\n",
       "                   10  0.445416    3.0  224.000000     3.0\n",
       "                   20  0.525447    3.0  224.000000     3.0\n",
       "                   40  0.526898    3.0  224.000000     3.0\n",
       "\n",
       "[288 rows x 4 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_aggL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TIEMPO EJECUCCION\n",
      "Distribución BestFit -------------------------\n",
      "Para  2  padres\n",
      "EX numC= 2 p = 0.0 Diff = 0.303 Asíncrono\n",
      "EX numC= 10 p = 0.0 Diff = 0.422 Asíncrono\n",
      "EX numC= 20 p = 0.0 Diff = 0.494 Asíncrono\n",
      "EX numC= 40 p = 0.002 Diff = 0.398 Asíncrono\n",
      "Para  10  padres\n",
      "EX numC= 2 p = 0.0 Diff = 0.415 Asíncrono\n",
      "Para  20  padres\n",
      "EX numC= 2 p = 0.0 Diff = 0.458 Asíncrono\n",
      "EX numC= 10 p = 0.015 Diff = 0.057 Asíncrono\n",
      "EX numC= 20 p = 0.008 Diff = 0.145 Síncrono\n",
      "EX numC= 40 p = 0.002 Diff = 0.34 Síncrono\n",
      "Para  40  padres\n",
      "EX numC= 40 p = 0.001 Diff = 0.672 Síncrono\n",
      "Distribución WorstFit -------------------------\n",
      "Para  2  padres\n",
      "EX numC= 2 p = 0.0 Diff = 0.362 Asíncrono\n",
      "EX numC= 10 p = 0.002 Diff = 0.408 Asíncrono\n",
      "EX numC= 40 p = 0.016 Diff = 0.26 Asíncrono\n",
      "Para  10  padres\n",
      "EX numC= 2 p = 0.0 Diff = 0.337 Asíncrono\n",
      "EX numC= 10 p = 0.016 Diff = 0.152 Síncrono\n",
      "Para  20  padres\n",
      "EX numC= 2 p = 0.011 Diff = 0.417 Asíncrono\n",
      "EX numC= 10 p = 0.045 Diff = 0.274 Síncrono\n",
      "EX numC= 20 p = 0.043 Diff = 0.295 Síncrono\n",
      "EX numC= 40 p = 0.02 Diff = 0.62 Síncrono\n",
      "Para  40  padres\n",
      "EX numC="
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:12: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  if sys.path[0] == '':\n",
      "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:13: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  del sys.path[0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 10 p = 0.042 Diff = 0.228 Síncrono\n",
      "EX numC= 40 p = 0.029 Diff = 0.385 Síncrono\n",
      "SINC: 14 || ASINC: 18\n"
     ]
    }
   ],
   "source": [
    "print(\"TIEMPO EJECUCCION\")\n",
    "sinc = 0\n",
    "asinc = 0\n",
    "for dist in [1,2]:\n",
    "    print(\"Distribución \" + dist_names[dist] + \" -------------------------\")\n",
    "    dist_v = str(dist)+\",\"+str(dist)\n",
    "    for numP in values:\n",
    "        print(\"Para \", numP, \" padres\")\n",
    "        for numC in values:\n",
    "            #if numP != numC:\n",
    "                group = str(numP) + \",\" + str(numC)\n",
    "                v1 = dfG[(dfG[\"%Async\"] == 0.0)][(dfG.Groups == group)][(dfG[\"Dist\"] == dist_v)]['TE']\n",
    "                v2 = dfG[(dfG[\"%Async\"] == 100.0)][(dfG.Groups == group)][(dfG[\"Dist\"] == dist_v)]['TE']\n",
    "                res = stats.ttest_ind(v1, v2)\n",
    "                diff = grouped_aggG['TE'].loc[(dist_v, 0.0, group)] - grouped_aggG['TE'].loc[(dist_v, 100.0, group)]\n",
    "                if diff > 0:\n",
    "                    mejor = \"Asíncrono\"\n",
    "                    asinc+=1\n",
    "                else:\n",
    "                    mejor = \"Síncrono\"\n",
    "                    sinc+=1\n",
    "                    \n",
    "                if res[1] < p_value:\n",
    "                    print(\"EX numC=\", numC, \"p =\", round(res[1],3), \"Diff =\", abs(round(diff,3)), mejor)\n",
    "print(\"SINC: \" + str(sinc) + \" || ASINC: \" + str(asinc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TIEMPO MALLEABILITY\n",
      "Distribución BestFit -------------------------\n",
      "Para  2  padres\n",
      "TR numC= 2 p = 0.0 Diff = 0.076 Síncrono\n",
      "TR numC= 20 p = 0.002 Diff = 0.098 Síncrono\n",
      "TR numC= 40 p = 0.03 Diff = 0.162 Síncrono\n",
      "Para  10  padres\n",
      "TR numC= 2 p = 0.0 Diff = 0.191 Síncrono\n",
      "TR numC= 10 p = 0.001 Diff = 0.154 Síncrono\n",
      "TR numC= 40 p = 0.013 Diff = 0.572 Síncrono\n",
      "Para  20  padres\n",
      "TR numC= 2 p = 0.0 Diff = 0.176 Síncrono\n",
      "TR numC= 10 p = 0.0 Diff = 0.353 Síncrono\n",
      "TR numC= 20 p = 0.0 Diff = 0.514 Síncrono\n",
      "TR numC= 40 p = 0.0 Diff = 0.88 Síncrono\n",
      "Para  40  padres\n",
      "TR numC= 2 p = 0.024 Diff = 0.999 Síncrono\n",
      "TR numC= 10 p = 0.0 Diff = 0.803 Síncrono\n",
      "TR numC= 20 p = 0.002 Diff = 0.782 Síncrono\n",
      "TR numC= 40 p = 0.0 Diff = 1.212 Síncrono\n",
      "Distribución WorstFit -------------------------\n",
      "Para  2  padres\n",
      "TR numC= 2 p = 0.003 Diff = 0.082 Síncrono\n",
      "TR numC= 10 p = 0.001 Diff = 0.181 Síncrono\n",
      "TR numC= 20 p = 0.0 Diff = 0.511 Síncrono\n",
      "TR numC= 40 p = 0.004 Diff = 0.594 Síncrono\n",
      "Para  10  padres\n",
      "TR numC= 2 p = 0.019 Diff = 0.199 Síncrono\n",
      "TR numC= 10"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:9: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  if __name__ == '__main__':\n",
      "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " p = 0.0 Diff = 0.481 Síncrono\n",
      "TR numC= 40 p = 0.002 Diff = 0.558 Síncrono\n",
      "Para  20  padres\n",
      "TR numC= 10 p = 0.0 Diff = 0.889 Síncrono\n",
      "TR numC= 20 p = 0.001 Diff = 0.636 Síncrono\n",
      "TR numC= 40 p = 0.0 Diff = 1.14 Síncrono\n",
      "Para  40  padres\n",
      "TR numC= 2 p = 0.0 Diff = 1.179 Síncrono\n",
      "TR numC= 10 p = 0.0 Diff = 0.872 Síncrono\n",
      "TR numC= 20 p = 0.006 Diff = 0.84 Síncrono\n",
      "TR numC= 40 p = 0.001 Diff = 1.01 Síncrono\n"
     ]
    }
   ],
   "source": [
    "print(\"TIEMPO MALLEABILITY\")\n",
    "for dist in [1,2]:\n",
    "    print(\"Distribución \" + dist_names[dist] + \" -------------------------\")\n",
    "    dist_v = str(dist)+\",\"+str(dist)\n",
    "    for numP in values:\n",
    "        print(\"Para \", numP, \" padres\")\n",
    "        for numC in values:\n",
    "            #if numP != numC:\n",
    "                v1 = dfM[(dfM[\"%Async\"] == 0.0)][(dfM.NP == numP)][(dfM.NS == numC)][(dfM[\"Dist\"] == dist_v)]['TS']\n",
    "                v2 = dfM[(dfM[\"%Async\"] == 100.0)][(dfM.NP == numP)][(dfM.NS == numC)][(dfM[\"Dist\"] == dist_v)]['TA']\n",
    "                res = stats.ttest_ind(v1, v2)\n",
    "                diff = grouped_aggM['TS'].loc[(dist_v, 0.0, numP, numC)] - grouped_aggM['TA'].loc[(dist_v, 100.0, numP, numC)]\n",
    "                if diff > 0:\n",
    "                    mejor = \"Asíncrono\"\n",
    "                else:\n",
    "                    mejor = \"Síncrono\"\n",
    "                if res[1] < p_value:\n",
    "                    print(\"TR numC=\", numC, \"p =\", round(res[1],3), \"Diff =\", abs(round(diff,3)), mejor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TIEMPO Iters\n",
      "Distribución BestFit -------------------------\n",
      "Para  2  padres\n",
      "Para  10  padres\n",
      "Ti numC= 40 p = 0.002 Diff = 0.1302 Síncrono\n",
      "Para  20  padres\n",
      "Ti numC= 40 p = 0.001 Diff = 0.1491 Síncrono\n",
      "Para  40  padres\n",
      "Ti numC= 2 p = 0.0 Diff = 0.152 Síncrono\n",
      "Ti numC= 10 p = 0.003 Diff = 0.1596 Síncrono\n",
      "Ti numC= 20 p = 0.0 Diff = 0.2193 Síncrono\n",
      "Ti numC= 40 p = 0.0 Diff = 0.3601 Síncrono\n",
      "Distribución WorstFit -------------------------\n",
      "Para  2  padres\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:12: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  if sys.path[0] == '':\n",
      "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:13: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  del sys.path[0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ti numC= 20 p = 0.0 Diff = 0.1312 Síncrono\n",
      "Ti numC= 40 p = 0.0 Diff = 0.1105 Síncrono\n",
      "Para  10  padres\n",
      "Ti numC= 2 p = 0.034 Diff = 0.013 Síncrono\n",
      "Ti numC= 20 p = 0.0 Diff = 0.1755 Síncrono\n",
      "Ti numC= 40 p = 0.0 Diff = 0.1934 Síncrono\n",
      "Para  20  padres\n",
      "Ti numC= 2 p = 0.0 Diff = 0.1546 Síncrono\n",
      "Ti numC= 10 p = 0.004 Diff = 0.1578 Síncrono\n",
      "Ti numC= 20 p = 0.001 Diff = 0.3209 Síncrono\n",
      "Ti numC= 40 p = 0.0 Diff = 0.3876 Síncrono\n",
      "Para  40  padres\n",
      "Ti numC= 2 p = 0.0 Diff = 0.1378 Síncrono\n",
      "Ti numC= 10 p = 0.0 Diff = 0.2457 Síncrono\n",
      "Ti numC= 20 p = 0.0 Diff = 0.3257 Síncrono\n",
      "Ti numC= 40 p = 0.0 Diff = 0.3271 Síncrono\n"
     ]
    }
   ],
   "source": [
    "print(\"TIEMPO Iters\")\n",
    "for dist in [1,2]:\n",
    "    print(\"Distribución \" + dist_names[dist] + \" -------------------------\")\n",
    "    dist_v = str(dist)+\",\"+str(dist)\n",
    "    for numP in values:\n",
    "        print(\"Para \", numP, \" padres\")\n",
    "        for numC in values:\n",
    "            #if numP != numC:\n",
    "                #exp = dfL[(dfL[\"Tt\"] == 0)][(dfL[\"Dist\"] == 1)][(dfL[\"%Async\"] == 0.0)][(dfL.NP == numP)][(dfL.NS == numC)]\n",
    "                #TimeOp = exp['Ti'] \n",
    "                #print(TimeOp)\n",
    "                v1 = dfL[(dfL[\"Tt\"] == 0)][(dfL[\"Dist\"] == dist)][(dfL[\"%Async\"] == 100.0)][(dfL.NP == numP)][(dfL.NS == numC)]['Ti']\n",
    "                v2 = dfL[(dfL[\"Tt\"] == 1)][(dfL[\"Dist\"] == dist)][(dfL[\"%Async\"] == 100.0)][(dfL.NP == numP)][(dfL.NS == numC)]['Ti']\n",
    "                res = stats.ttest_ind(v1, v2, equal_var = False)\n",
    "                diff = grouped_aggL['Ti'].loc[(0, dist, 100.0, numP, numC)] - grouped_aggL['Ti'].loc[(1, dist, 100.0, numP, numC)]\n",
    "                if diff > 0:\n",
    "                    mejor = \"Asíncrono\"\n",
    "                else:\n",
    "                    mejor = \"Síncrono\"\n",
    "                if res[1] < p_value:\n",
    "                    #and abs(diff) > grouped_aggL['Ti'].loc[(0, dist, 0.0, numP, numC)]\n",
    "                    print(\"Ti numC=\", numC, \"p =\", round(res[1],3), \"Diff =\", abs(round(diff,4)), mejor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.0\n"
     ]
    }
   ],
   "source": [
    "auxIter = pd.DataFrame(dfM['Iters'].str.split(',',1).tolist(),columns = ['Iters0','Iters1'])\n",
    "auxIter['Iters1'] = pd.to_numeric(auxIter['Iters1'], errors='coerce')\n",
    "iters = auxIter['Iters1'].mean()\n",
    "print(iters)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribución BestFit -------------------------\n",
      "Para  2  padres\n",
      "NC=2 Es mejor Asíncrono con una diff de 0.323\n",
      "NC=10 Es mejor Asíncrono con una diff de 0.437\n",
      "NC=20 Es mejor Asíncrono con una diff de 0.502\n",
      "NC=40 Es mejor Asíncrono con una diff de 0.373\n",
      "Para  10  padres\n",
      "NC=2 Es mejor Asíncrono con una diff de 0.408\n",
      "NC=10 Es mejor Asíncrono con una diff de 0.046\n",
      "NC=20 Es mejor Asíncrono con una diff de 0.109\n",
      "NC=40 Es mejor Síncrono con una diff de 0.037\n",
      "Para  20  padres\n",
      "NC=2 Es mejor Asíncrono con una diff de 0.423\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n",
      "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:16: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NC=10 Es mejor Asíncrono con una diff de 0.047\n",
      "NC=20 Es mejor Síncrono con una diff de 0.114\n",
      "NC=40 Es mejor Síncrono con una diff de 0.278\n",
      "Para  40  padres\n",
      "NC=2 Es mejor Síncrono con una diff de 0.2\n",
      "NC=10 Es mejor Síncrono con una diff de 0.203\n",
      "NC=20 Es mejor Síncrono con una diff de 0.182\n",
      "NC=40 Es mejor Síncrono con una diff de 0.61\n",
      "Distribución WorstFit -------------------------\n",
      "Para  2  padres\n",
      "NC=2 Es mejor Asíncrono con una diff de 0.318\n",
      "NC=10 Es mejor Asíncrono con una diff de 0.42\n",
      "NC=20 Es mejor Asíncrono con una diff de 0.091\n",
      "NC=40 Es mejor Asíncrono con una diff de 0.277\n",
      "Para  10  padres\n",
      "NC=2 Es mejor Asíncrono con una diff de 0.335\n",
      "NC=10 Es mejor Síncrono con una diff de 0.081\n",
      "NC=20 Es mejor Asíncrono con una diff de 0.092\n",
      "NC=40 Es mejor Asíncrono con una diff de 0.045\n",
      "Para  20  padres\n",
      "NC=2 Es mejor Asíncrono con una diff de 0.358\n",
      "NC=10 Es mejor Síncrono con una diff de 0.289\n",
      "NC=20 Es mejor Síncrono con una diff de 0.234\n",
      "NC=40 Es mejor Síncrono con una diff de 0.537\n",
      "Para  40  padres\n",
      "NC=2 Es mejor Síncrono con una diff de 0.177\n",
      "NC=10 Es mejor Síncrono con una diff de 0.271\n",
      "NC=20 Es mejor Síncrono con una diff de 0.238\n",
      "NC=40 Es mejor Síncrono con una diff de 0.407\n"
     ]
    }
   ],
   "source": [
    "#iters = dfM['Iters'].mean()\n",
    "resultados = [0,0]\n",
    "for dist in [1,2]:\n",
    "    print(\"Distribución \" + dist_names[dist] + \" -------------------------\")\n",
    "    dist_v = str(dist)+\",\"+str(dist)\n",
    "    for numP in values:\n",
    "        print(\"Para \", numP, \" padres\")\n",
    "        for numC in values:\n",
    "            #if numP != numC:\n",
    "                Titer = dfL[(dfL[\"Tt\"] == 0)][(dfL[\"Dist\"] == dist)][(dfL.NP == numC)]['Ti'].mean() #Tiempo por iteracion\n",
    "                i=0\n",
    "                for adr in [0.0, 100.0]:\n",
    "                \n",
    "                    auxExp = dfM[(dfM[\"Dist\"] == dist_v)][(dfM[\"%Async\"] == adr)][(dfM.NP == numP)][(dfM.NS == numC)]\n",
    "                    Tr = auxExp['TS'].mean() + auxExp['TA'].mean() #Tiempo de redistribucion\n",
    "                    M_it = dfL[(dfL[\"Tt\"] == 1)][(dfL[\"Dist\"] == dist)][(dfL[\"%Async\"] == adr)][(dfL.NP == numP)][(dfL.NS == numC)]['Ti'].count()/3 #Iteraciones asincronas\n",
    "                    #No se presupone una diferencia temporal entre iteraciones normales y asincronas\n",
    "                    if(M_it > iters):\n",
    "                        M_it = iters\n",
    "                    resultados[i] = (iters - M_it) * Titer + Tr\n",
    "                    i+=1\n",
    "\n",
    "                if resultados[0] > resultados[1]:\n",
    "                    mejor = \"Asíncrono\"\n",
    "                else:\n",
    "                    mejor = \"Síncrono\"\n",
    "                diff = abs(round(resultados[0] - resultados[1], 3))\n",
    "                print(\"NC=\"+ str(numC) + \" Es mejor \" + mejor + \" con una diff de \"+  str(diff))\n",
    "                #TODO Comprobar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A partir de aquí se muestran gráficos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for dist in [1,2]:\n",
    "    v1 = grouped_aggL.loc[(0,dist,100.0,slice(None))]\n",
    "    v2 = grouped_aggL.loc[(1,dist,100.0,slice(None))]\n",
    "    aux_aggL = v1['Ti'].values / v2['Ti'].values\n",
    "\n",
    "    colors = ['r', 'orange', 'g', 'm', 'y']\n",
    "    markers = ['+', 'x', '1', '2', 'X']\n",
    "\n",
    "    f=plt.figure(figsize=(10, 7))\n",
    "    ax1 = f.add_subplot(111)\n",
    "    plt.xlim(0, max(values)+1)\n",
    "    plt.ylim(0, 1.2)\n",
    "    plt.xticks(values)\n",
    "    ax1.set_ylabel('Incremento de velocidad')\n",
    "    ax1.set_xlabel('Procesos hijo')\n",
    "    ax1.set_title(\"Incremento de velocidad al realizar redistribuciones asíncronas - \" + dist_names[dist])\n",
    "\n",
    "\n",
    "    for i in range(len(values)):\n",
    "        numP = values[i]\n",
    "        c = colors[i]\n",
    "    \n",
    "        mini = i * len(values)\n",
    "        maxi = (i+1) * len(values)\n",
    "        array_values = aux_aggL[mini:maxi]\n",
    "        indexes = np.arange(len(values))\n",
    "        aux_j=0\n",
    "        for j in range(len(values)):\n",
    "            indexes[aux_j] = values[j]\n",
    "            aux_j+=1\n",
    "    \n",
    "        x = indexes\n",
    "        y = array_values\n",
    "        label = str(numP) + ' padres'\n",
    "        ax1.axvline(numP)\n",
    "        plt.plot(x, y, color=colors[i], label=label, marker=markers[1], markersize=10)\n",
    "    \n",
    "    ax1.axhline(1, color='k')\n",
    "    f.legend()\n",
    "    #f.tight_layout()\n",
    "    f.savefig(\"Images/\"+\"Iters\"+ dist_names[dist] +\"_SpeedUp\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.19967587612208262, 0.19967587612208262, 0.19967587612208262, 0.19967587612208262, 0.1999576544502616, 0.1999576544502616, 0.1999576544502616, 0.1999576544502616, 0.19993147795414454, 0.19993147795414454, 0.19993147795414454, 0.19993147795414454, 0.2007191721611723, 0.2007191721611723, 0.2007191721611723, 0.2007191721611723]\n",
      "[1.597407008976661, 1.3997035811518312, 1.3995203456790117, 1.405034205128206, 1.3977311328545783, 1.7996188900523542, 1.5994518236331563, 1.405034205128206, 1.3977311328545783, 1.5996612356020927, 1.5994518236331563, 1.405034205128206, 1.1980552567324958, 1.3997035811518312, 1.3995203456790117, 1.405034205128206]\n",
      "[0.6347463333333334, 0.8381213999999999, 1.0813388666666666, 1.8329176, 0.8863835999999998, 0.5820358000000001, 0.9037563333333334, 2.330260466666666, 1.0072358000000001, 0.8873689333333334, 1.1739236666666666, 2.9118454666666667, 2.2802995999999998, 2.3853660666666663, 2.754362, 3.621757266666666]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:21: FutureWarning: set_axis currently defaults to operating inplace.\n",
      "This will change in a future version of pandas, use inplace=True to avoid this warning.\n",
      "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:34: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n"
     ]
    }
   ],
   "source": [
    "TP_data=[0]*len(values)*(len(values))\n",
    "TH_data=[0]*len(values)*(len(values))\n",
    "TM_data=[0]*len(values)*(len(values))\n",
    "\n",
    "TP_A_data=[0]*len(values)*(len(values))\n",
    "TH_A_data=[0]*len(values)*(len(values))\n",
    "TM_A_data=[0]*len(values)*(len(values))\n",
    "#ACTUALMENTE NO SE DIFERENCIAN LOS TIEMPOS DE ITERACIONES DE PADRES E HIJOS CUANDO COINCIDE EL NUMERO DE PROCESOS\n",
    "if(n_qty == 1):\n",
    "    groupM_aux = dfM.groupby(['NP', 'NS'])['TC']\n",
    "    groupL_aux = dfL[dfL['Tt'] == 0].groupby(['NP'])['Ti']\n",
    "else:\n",
    "    groupM_aux = dfM.groupby(['NP', 'NS', 'Dist'])['TC']\n",
    "    groupL_aux = dfL[dfL['Tt'] == 0].groupby(['Dist', 'NP'])['Ti']\n",
    "\n",
    "grouped_aggM_aux = groupM_aux.agg(['mean'])\n",
    "grouped_aggM_aux.columns = grouped_aggM_aux.columns.get_level_values(0)\n",
    "\n",
    "grouped_aggL_aux = groupL_aux.agg(['mean'])\n",
    "grouped_aggL_aux.columns = grouped_aggL_aux.columns.get_level_values(0)\n",
    "grouped_aggL_aux.set_axis(['Ti'], axis='columns')\n",
    "\n",
    "\n",
    "dist=1\n",
    "dist_v = str(dist)+\",\"+str(dist)\n",
    "i=0\n",
    "r=0\n",
    "for numP in values:\n",
    "    j=0\n",
    "    for numC in values:\n",
    "        \n",
    "        tc_real = grouped_aggM_aux.loc[(numP,numC,dist_v)]['mean']\n",
    "        for tipo in [0, 100]:\n",
    "            iters_aux=dfM[(dfM[\"NP\"] == numP)][(dfM[\"NS\"] == numC)][(dfM[\"Dist\"] == dist_v)][(dfM[\"%Async\"] == tipo)]['Iters'].head(1).tolist()[0].split(',')\n",
    "            itersP_aux = int(iters_aux[0])\n",
    "            itersS_aux = int(iters_aux[1])\n",
    "            iters_mal_aux = 0\n",
    "            if tipo != 0:\n",
    "                iters_mal_aux = grouped_aggL['Iters'].loc[(1,dist,tipo,numP,numC)]\n",
    "            \n",
    "            t_iterP_aux = grouped_aggL_aux['Ti'].loc[(dist,numP)]\n",
    "            t_iterS_aux = grouped_aggL_aux['Ti'].loc[(dist,numC)]\n",
    "            \n",
    "            \n",
    "            p1 = t_iterP_aux * itersP_aux\n",
    "            p2 = t_iterS_aux * max((itersS_aux - iters_mal_aux),0)\n",
    "                \n",
    "            array_aux = grouped_aggM[['TS', 'TA']].loc[(dist_v,tipo,numP,numC)].tolist()\n",
    "            p3 = tc_real + array_aux[0] + array_aux[1]\n",
    "                \n",
    "            if tipo == 0:\n",
    "                TP_data[i*len(values) + j] = p1\n",
    "                TH_data[i*len(values) + j] = p2\n",
    "                TM_data[i*len(values) + j] = p3\n",
    "            else:\n",
    "                TP_A_data[i*len(values) + j] = p1\n",
    "                TH_A_data[i*len(values) + j] = p2\n",
    "                TM_A_data[i*len(values) + j] = p3\n",
    "        j+=1\n",
    "    i+=1\n",
    "print(TP_A_data)\n",
    "print(TH_A_data)\n",
    "print(TM_A_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f=plt.figure(figsize=(20, 12))\n",
    "#for numP in values:\n",
    "\n",
    "x = np.arange(len(labelsP_J))\n",
    "\n",
    "width = 0.35\n",
    "sumaTP_TM = np.add(TP_data, TM_data).tolist()\n",
    "sumaTP_TM_A = np.add(TP_A_data, TM_A_data).tolist()\n",
    "\n",
    "ax=f.add_subplot(111)\n",
    "\n",
    "ax.bar(x+width/2, TP_data, width, color='blue')\n",
    "ax.bar(x+width/2, TM_data, width, bottom=TP_data,color='orange')\n",
    "ax.bar(x+width/2, TH_data, width, bottom=sumaTP_TM, color='green')\n",
    "\n",
    "ax.bar(x-width/2, TP_A_data, width, hatch=\"\\\\/...\", color='blue')\n",
    "ax.bar(x-width/2, TM_A_data, width, bottom=TP_A_data, hatch=\"\\\\/...\", color='orange')\n",
    "ax.bar(x-width/2, TH_A_data, width, bottom=sumaTP_TM_A, hatch=\"\\\\/...\", color='green')\n",
    "\n",
    "ax.set_ylabel(\"Time(s)\", fontsize=20)\n",
    "ax.set_xlabel(\"NP, NS\", fontsize=20)\n",
    "plt.xticks(x, labelsP_J, rotation=90)\n",
    "\n",
    "sync_patch = mpatches.Patch(color='white',label='Synchronous V')\n",
    "blue_Spatch = mpatches.Patch(color='blue', label='Parents')\n",
    "orange_Spatch = mpatches.Patch(color='orange', label='Resize')\n",
    "green_Spatch = mpatches.Patch(color='green', label='Sons')\n",
    "async_patch = mpatches.Patch(color='white',label='Asynchronous V')\n",
    "blue_Apatch = mpatches.Patch(hatch='\\\\/...', color='blue', label='Parents')\n",
    "orange_Apatch = mpatches.Patch(hatch='\\\\/...', color='orange', label='Resize')\n",
    "green_Apatch = mpatches.Patch(hatch='\\\\/...', color='green', label='Sons')\n",
    "\n",
    "handles=[sync_patch,blue_Spatch,orange_Spatch,green_Spatch,async_patch,blue_Apatch,orange_Apatch,green_Apatch]\n",
    "\n",
    "plt.legend(handles=handles, loc='upper right', fontsize=21)\n",
    "    \n",
    "ax.axvline((3.5), color='black')\n",
    "ax.axvline((7.5), color='black')\n",
    "ax.axvline((11.5), color='black')\n",
    "    \n",
    "ax.tick_params(axis='both', which='major', labelsize=24)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=22)\n",
    "    #ax.axvline(4)\n",
    "    \n",
    "f.tight_layout()\n",
    "f.savefig(\"Images/EX_Partitions_\"+dist_names[dist]+\".png\", format=\"png\")\n",
    "j = (j+1)%5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.23330666666666666, 0.3000864, 0.46798120000000004, 1.0002442666666667, 0.2698616, 0.3514898000000001, 0.538622, 1.2814994666666664, 0.3155408, 0.39067860000000004, 0.613136, 1.4615621333333333, 0.4981289333333334, 0.8814400666666666, 1.0837216666666667, 1.4631459333333334]\n",
      "[0.23330666666666666, 0.3000864, 0.46798120000000004, 1.0002442666666667, 0.2698616, 0.3514898000000001, 0.538622, 1.2814994666666664, 0.3155408, 0.39067860000000004, 0.613136, 1.4615621333333333, 0.4981289333333334, 0.8814400666666666, 1.0837216666666667, 1.4631459333333334]\n"
     ]
    }
   ],
   "source": [
    "TC_data=[0]*len(values)*(len(values))\n",
    "TS_data=[0]*len(values)*(len(values))\n",
    "TA_data=[0]*len(values)*(len(values))\n",
    "\n",
    "TC_A_data=[0]*len(values)*(len(values))\n",
    "TS_A_data=[0]*len(values)*(len(values))\n",
    "TA_A_data=[0]*len(values)*(len(values))\n",
    "\n",
    "#FIXME El TC actual no es la media de todos los del mismo tipo\n",
    "\n",
    "if(n_qty == 1):\n",
    "    groupM_aux = dfM.groupby(['NP', 'NS'])['TC']\n",
    "else:\n",
    "    groupM_aux = dfM.groupby(['NP', 'NS', 'Dist'])['TC']\n",
    "\n",
    "grouped_aggM_aux = groupM_aux.agg(['mean'])\n",
    "grouped_aggM_aux.columns = grouped_aggM_aux.columns.get_level_values(0)\n",
    "\n",
    "dist=1\n",
    "dist_v = str(dist)+\",\"+str(dist)\n",
    "i=0\n",
    "r=0\n",
    "for numP in values:\n",
    "    j=0\n",
    "    for numC in values:\n",
    "        \n",
    "        test_tc_real = grouped_aggM_aux.loc[(numP,numC,dist_v)]['mean']\n",
    "        #print(test_tc_real)\n",
    "        for tipo in [0, 100]:\n",
    "            \n",
    "            test=grouped_aggM.loc[(dist_v,tipo,numP,numC)][['TS', 'TA']]\n",
    "            test=test.tolist()\n",
    "                    \n",
    "            if tipo == 0:\n",
    "                TC_data[i*len(values) + j] = test_tc_real\n",
    "                TS_data[i*len(values) + j] = test[0] \n",
    "                TA_data[i*len(values) + j] = 0\n",
    "            else:\n",
    "                TC_A_data[i*len(values) + j] = test_tc_real\n",
    "                TS_A_data[i*len(values) + j] = test[0]\n",
    "                TA_A_data[i*len(values) + j] = test[1]\n",
    "        j+=1\n",
    "    i+=1\n",
    "                    \n",
    "                    \n",
    "##########################\n",
    "\n",
    "print(TC_data)\n",
    "print(TC_A_data)\n",
    "#print(TS_data)\n",
    "#print(TA_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f=plt.figure(figsize=(20, 12))\n",
    "\n",
    "x = np.arange(len(labelsP_J))\n",
    "width = 0.35\n",
    "sumaTC_TS_A = np.add(TC_A_data, TS_A_data).tolist()\n",
    "\n",
    "ax=f.add_subplot(111)\n",
    "\n",
    "ax.bar(x+width/2, TC_data, width, color='chocolate')\n",
    "ax.bar(x+width/2, TS_data, width, bottom=TC_data, color='purple')\n",
    "\n",
    "ax.bar(x-width/2, TC_A_data, width, hatch=\"\\\\/...\", color='chocolate')\n",
    "ax.bar(x-width/2, TS_A_data, width, bottom=TC_A_data, hatch=\"\\\\/...\", color='purple')\n",
    "ax.bar(x-width/2, TA_A_data, width, bottom=sumaTC_TS_A, hatch=\"\\\\/...\", color='red')\n",
    "\n",
    "ax.set_ylabel(\"Time(s)\", fontsize=20)\n",
    "ax.set_xlabel(\"NP, NS\", fontsize=20)\n",
    "plt.xticks(x, labelsP_J, rotation=90)\n",
    "\n",
    "sync_patch = mpatches.Patch(color='white',label='Synchronous V')\n",
    "brown_Spatch = mpatches.Patch(color='chocolate', label='Spawn')\n",
    "purple_Spatch = mpatches.Patch(color='purple', label='Synchronous Comm')\n",
    "async_patch = mpatches.Patch(color='white',label='Asynchronous V')\n",
    "brown_Apatch = mpatches.Patch(hatch='\\\\/...', color='chocolate', label='Spawn')\n",
    "purple_Apatch = mpatches.Patch(hatch='\\\\/...', color='purple', label='Synchronous Comm')\n",
    "red_Apatch = mpatches.Patch(hatch='\\\\/...', color='red', label='Asynchronous Comm')\n",
    "\n",
    "handles=[sync_patch,brown_Spatch,purple_Spatch,async_patch,brown_Apatch,purple_Apatch,red_Apatch]\n",
    "\n",
    "plt.legend(handles=handles, loc='upper left', fontsize=24)\n",
    "\n",
    "    \n",
    "ax.axvline((3.5), color='black')\n",
    "ax.axvline((7.5), color='black')\n",
    "ax.axvline((11.5), color='black')\n",
    "    \n",
    "ax.tick_params(axis='both', which='major', labelsize=24)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=22)\n",
    "    #ax.axvline(4)\n",
    "    \n",
    "f.tight_layout()\n",
    "f.savefig(\"Images/Malt_Partitions_\"+dist_names[dist]+\".png\", format=\"png\")\n",
    "j = (j+1)%5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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m62qt17bulFIm9tJ9vp5kdJL3Jbmwl+4BAAAAADAo9IuH/NVal/X2PUop70kyLcmPaq0/7O37AQAAAAAMdP1lBXOvKqWMSvKNJC8kObmPywF6yGmnnZavfvWrSZLZs2dn8uTJfVwRwLrhYUoAAEB/MSgC5qzou/z6JJ+rtf5PXxezpnrjL4/92br4i+3EiRPzyCOPJEkee+yxTJgwodOxy5Yty+abb57nnnsuSXL88cfnkksu6XL+Cy64IJ/85CeTJKecckrOP//8Hqq87yxYsCAXXriio8xee+2V97znPT0y77HHHpsrrrhijcdvvvnmmT9/frfu8dRTT+Xv//7vkyT77LNPDj/88G5dDwAAAEDX+kWLjN5USnlzVqxa/k2SL/dxOfSxKVOmtG3PmjWry7F33XVXW7i8JuNXHjN16tTuF9gPLViwIGeeeWbOPPPM/PCH61d3maeeeqqt9v/8z//s63IAAAAABpwBvYK5lDIkybeSDE3y8VrrSw3nOSnJSUmy7bbb9lyBrHNTp07NZZddlmRFGPxXf/VXnY5dOVD+n//5n/z+97/P61//+lWOr7Xm1ltvTZIMGTIkBx54YA9VPfCdcsopHcL/Vdlwww1fc+y8887Leeed11tlAQAAALAaAzpgTvKxJG9J8v1a641NJ6m1Xpzk4iSZPHmyBoXrse6sYG49f9BBB+XnP/95XnzxxcyaNSvHHnvsKsffc889efrpp5Mke+yxRzbddNMeqnrg23vvvTNt2rS+LgMAAACAbhqwLTJKKVsnOTvJ4iSf6uNy6Cd22GGHtlXoc+fOzZNPPrnKccuXL29bjXzooYdmn332SZLMnDmz07nbnxso7TEAAAAAoCsDNmBO8sUkY5Ocm+S5Usro9q8krU/QG97uGIPAmqxivvvuu/PMM8+0jW9td9HVquc17b9ca82VV16Z9773vXn961+fESNGZNNNN82ee+6ZU089NQ899FCX9d90000ppaSUkrPPPjtJ8sADD+Tkk0/OpEmTMnbs2JRScvnll3e4bu7cufnkJz+ZvfbaK5tsskk22GCDjBs3LjvvvHMOPfTQfOUrX8n999/fNv7BBx9MKSVveMMb2o790z/9U9u9278ee+yxLmvuLaeddlpbDXPmzGk7fs8996SUkje+8Y1tx775zW+usvbuPjgQAAAAgFcN5IB5u5b3LyRZtIpXazPl/9vuGIPAmgTMrcc32mijTJ48uS1gfvDBB/P444+v8ppbbrklSdf9l5944onsu+++OeaYY3LNNdfksccey4svvphnn302d999d772ta9ll112yVe+8pU1/jyXXHJJ3vzmN+frX/965s6dm0WLXvu/8j/8wz/kjW98Yy644ILceeedee655/LKK6/k6aefzm9+85vceOONmT59eo477rg1vi8AAAAADPQezPAa7VcXry5g3nfffbPBBhtkv/32y7Bhw/LKK69k1qxZOeaYYzqMv++++zJv3rwknfdfXrhwYQ444IC2FcoTJkzIBz/4wey2225ZsmRJfvzjH+df//Vf8/LLL2f69OmptWb69OldfpZbbrklM2bMyAYbbJATTzwx++23XzbccMPcf//92WKLLZIks2fPzsc+9rEsX748w4YNy5//+Z/nwAMPzPjx4/Pyyy/niSeeyB133JGf/OQnHebeaqutcs011+TJJ5/MRz/60STJIYccko997GOvqWPcuHFd1rmubbfddrnmmmvy6KOP5hOf+ESS5PDDD8+JJ574mrFjx45d1+UBAAAADBgDNmCutU7t6nwp5eGsWOV8Zq31jHVQEv3EjjvumG222SaPPfZY7rvvvjz11FN53ete13a+1tq2Grl1tfPo0aPzpje9KXPmzMnMmTNfEzCvSf/l9u0vpkyZkh/+8Icdws0Pf/jDuf7663PkkUfmxRdfzGc/+9kcfvjh2X333Tv9LDfeeGO23nrr3HzzzZk0adIqx/zTP/1Tli9fniS56qqr8t73vneV41555ZXcfvvtbfujRo3KtGnT8uCDD7Yd22677daLh/GNGTMm06ZNyz333NN2bPvtt18vagcAAABYn/SbFhmllHGtryTtl39u0v5cKWXIStfVltcZ67Rg1mvt22S0hsmt7rnnnjz99NNJ0qHVRVd9mFfXf/nJJ5/MZZddliTZZJNN8v3vf3+VK2ff+c535owzzkiSvPzyyznvvPNW+1m+/e1vdxouJ2kLiDfbbLNOw+UkGTZsWPbbb7/V3q83HHfccavsj9z6GjZswP5bGAAAAMB6rd8EzEnmtXvd0e74L1Y6t+1rL4Xu6apNRuv+hhtumH333bfteGvAPHfu3Dz55JMdrlld/+Uf/ehHeemll5IkH/zgBzusmF7Zxz/+8YwaNSpJcu2116bW2unYHXfcMYcffnin55MVfaST5Lnnnuuzh/EBAAAAMDBZFsig1NWD/lr33/KWt2TEiBFtxw844ICUUtpaaBx11FFJOgbOnfVfbt964tBDD+2yttGjR2e//fbLjTfemOeeey4PPPBAdtlll1WO3X///bucK0ne8Y535LrrrsuyZcsyZcqUfPrTn860adOy+eabr/badeWUU07p8N9kZUOG9Kd/CwMAAACgVb8JmGutZR1fN7HJdQwMb3jDGzJhwoT84Q9/aGuJ0Rq4rtx/udVmm22WXXfdNffee29mzpzZFjCvSf/lJ554om17p512Wm19O+20U2688ca2azsLmCdMmLDauU466aRcffXVufXWW/O73/0uJ5xwQk488cTsvvvu2W+//TJ16tQcfvjhffqwu7333lt/ZAAAAID1kGWBDFqtAXL7h/q1PvQvySpbXayqD/Pq+i8nyaJFi9q2W9tfdGX06NGrvHZlI0eOXO1cG264YW666aace+652W677ZKs+My//vWv861vfSvHHHNMtthii5x88sld3gsAAAAAViZgZtBaVZuM1vfOHnjXGjDfd999mTdvXodrOuu/nCRjxoxp216yZMlqa1u8ePEqr21q+PDhmT59eh5++OHcc889+da3vpX3v//9bSugly5dmq9//euZOnVqli5dutb3AwAAAGBw6DctMmBdW9WD/lrf99prrw6riFu1D5BvueWW7Lnnnnn88ceTdN5/OUm22mqrtu3f/va32WGHHbqs7be//W3b9tZbb72aT9I9u+22W3bbbbecdNJJSZKbb745H/rQh/Loo4/mjjvuyHe+85185CMf6dF7AkB/Vs5s1HFtterpnT+oFwAABgormBm0dtppp7bg9+67784zzzzTFjB3thJ56623zo477phkRe/lNem/nCT77LNP23Zrb+XOLFmyJD//+c+TJBtvvHF23nnn1X6WtXHwwQfnwgsvbNv/2c9+1uF8+wfs1bp+/UV5fa4dAAAAYH1gBTOD2pQpU3LllVdm+fLl+fa3v50nn3yy7XhnDjjggDz00EOZNWtWnn322bbjXQXM73rXuzJ8+PC89NJLueSSS/K3f/u3GTdu3CrHfvOb32xrkXHkkUemlN5ZVdXexIkT27ZfeeWVDufar+Rek/Ye/cn6XDsAAL2nN35zwW8tADBYWcHMoNY+FP7qV7+aZMWq1/3337/Ta1pXN99zzz254YYb2q7pbNVzkmy55Zb5wAc+kCRZsGBB/vIv/3KVD9T7yU9+ktNPPz3Jij7Qp556avc+0Cp88pOfzG233dblmIsuuqhte8899+xwbvz48W1B7Z133rnW9axLW2+9dTbccMMk61/tAAAAAOsDK5gZ1NoHzE899VSSFb2UN9lkk06vaQ2Sa62ZP39+2zWd9V9udd5552XGjBl56KGHMmPGjOy666750Ic+lF122SXPP/98fvKTn+T73/9+WyuHv/u7v8vuu+++Nh8vSXL11VfnggsuyA477JBDDjkke+yxR8aPH58XX3wxjz76aK6++ur86le/SrIiTD7hhBM6XF9KyUEHHZTrrrsuv/nNb3L00Udn2rRpHb6jgw46qC3I7U+GDRuWAw88MDfeeGPuvvvuHHfccXnXu96VjTfeuG3MwQcfnA022KAPqwQAAABYfwmYGdR23nnnbLnllm2tMZLO+y+32nHHHTNhwoT84Q9/aDvWVXuMVmPHjs2tt96aI444IrNnz85jjz2Ws8466zXjNthgg5x99tmZPn36mn+QLrT2If7d736Xiy++uNNx22+/fa655pqMHz/+NedOP/303HjjjVm6dGmuuuqqXHXVVR3O//73v88222zTI/X2tDPPPDOzZs3KSy+9lMsvvzyXX355h/Pz5s3rtF0JAAAAAF0TMPdjenitG1OmTOkQmK4uYE5W9GG+8sor2/bXJGBOkq222iq33XZbW0g7e/bszJs3LyNGjMi2226bd7zjHfnrv/7r/NEf/VG3P0dnfvWrX2XmzJmZMWNGbr/99vzud7/Ls88+m6FDh2b8+PHZY489Mm3atBx33HEZMWLEKufYe++9M2fOnJx//vm59dZb89hjj+X555/vsRp709ve9rbMnj07559/fn72s5/l8ccfX29qBwAAAOjvSuuv47NmJk+eXOfMmdPo2vvvvz+77LJLD1cErGv+LDfT+g8xM2fO7NM6YCDwcKqe1RvfZ9Lz36mfo9BzBvPP0V55hvgZ/f/79DMU6Anry8/QpHd+js6aNeu/a62TVz7nIX8AAAAAADQiYAYAAAAAoBEBMwAAAAAAjQiYAQAAAABoRMAMAAAAAEAjw/q6AAAAAABg/VDOLD0+Zz299vicrDtWMAMAAAAA0IiAGQAAAACARgTMAAAAAAA0ImAGAAAAAKARAfM6Vqum5bA+82cYAAAA4FUC5nVoyJAhWb58eV+XAayF5cuXZ8gQPzoBAAAAEgHzOjVy5MgsWbKkr8sA1sKSJUsycuTIvi4DAAAAoF8QMK9DY8aMyaJFi/q6DGAtLFq0KGPGjOnrMgAAAAD6BQHzOjR27Ng8//zzeeaZZ/q6FKCBZ555Js8//3zGjh3b16UAAAAA9AvD+rqAwWTo0KHZbrvt8sgjj+T555/PmDFjMmrUqAwZMiSllL4uD1hJrTXLly/PkiVLsmjRojz//PPZbrvtMnTo0L4uDQAAAKBfEDCvY8OHD88OO+yQhQsX5tlnn80TTzzhwX/Qjw0ZMiQjR47MmDFjsuWWWwqXAQAAANoRMPeBoUOHZtNNN82mm27a16UAAAAAADSmBzMAAAAAAI0ImAEAAAAAaETADAAAAABAIwJmAAAAAAAaETADAAAAANCIgBkAAAAAgEYEzAAAAAAANCJgBgAAAACgEQEzAAAAAACNCJgBAAAAAGhEwAwAAAAAQCMCZgAAAAAAGhEwAwAAAADQiIAZAAAAAIBGBMwAAAAAADQiYAYAYL1RSs+/AACA5gTMAAAAAAA0ImAGAAAAAKARATMAAAAAAI0ImAEAAAAAaETADAAAAABAIwJmAAAAAAAaETADAAAAANCIgBkAAAAAgEYEzAAAAAAANCJgBgAAAACgEQEzAAAAAACNCJgBAAAAAGhEwAwAAAAAQCMCZgAAAAAAGhEwAwAAAADQiIAZAAAAAIBGBMwAAAAAADQiYAYAAAAAoBEBMwAAAAAAjQiYAQAAAABoRMAMAAAAAEAjAmYAAAAAABoRMAMAAAAA0IiAGQAAAACARgTMAAAAAAA0ImAGAAAAAKARATMAAAAAAI0ImAEAAAAAaETADAAAAABAI8P6uoAkKaWMSXJQkrckmdzyvnnL6V1qrQ80nHd8kvcmOSTJXkkmJFmW5NEkNye5oNb64NpVDwAAAAAwOPWLgDnJwUmu6YV5H0/Hz7g4yfAkk1peHy6lfKjW+r1euDcAAAAAwIDWn1pkPJXkP5OcmeSkHppzWJJbknwgyVa11jFJNkqyf5K7koxIclkpZY8euh8AAAAAwKDRX1YwX1drvbZ1p5QysYfmnVJrvaX9gVrrsiT/VUo5NMk9SV6X5JNJPthD9wQAAAAAGBT6xQrmltC3N+a9pYtz87JixXSS7N0b9wcAAAAAGMj6RcDch55ueR/ap1UAAAAAAKyHBnvAPKXl/Z4+rQIAAAAAYD00aAPmUsoRSSa37F7Sl7UAAAAAAKyPBmXAXEqZkOTilvfIrsAAACAASURBVN0f1lpvWM34k0opc0opc+bNm9f7BQIAAAAArAcGXcBcShmd5Nokr0vySJIPr+6aWuvFtdbJtdbJ48eP7+0SAQAAAADWC4MqYC6ljEjyg6xojTEvyZ/UWuf3bVUAAAAAAOunQRMwl1KGJ/nXJH+c5Nkkh9Za5/ZtVQAAAAAA669BETCXUoYl+V6SP02yOMnhtda7+rYqAAAAAID124APmEspQ5JcmuS9SV5I8p5a6y/6tioAAAAAgPXfgA6YSyklycVJ/irJS0neW2v9ad9WBQAAAAAwMPSbgLmUMq71lWTTdqc2aX+uZUVy++tqy+uMVUz7tSQfTvJKkqNqrTf02gcAAAAAABhkhvV1Ae3M6+T4yu0stk/y8OomK6Vsm+SUlt2a5FullG91Nr7WuuUa1AgAAAAAQIv+FDD3tPYrnTdIskVfFQIAAAAAMBD1m4C51lp68rpa68NJGs0JAAAAAMDq9ZsezAAAAAAArF8EzAAAAAAANCJgBgAAAACgEQEzAAAAAACNCJgBAAAAAGhEwAwAAAAAQCMCZgAAAAAAGhEwAwAAAADQiIAZAAAAAIBGBMwAAAAAADQiYAYAAAAAoBEBMwAAAAAAjQiYAQAAAABoRMAMAAAAAEAjAmYAAAAAABoRMAMAAAAA0IiAGQAAAACARgTMAAAAAAA0ImAGAAAAAKARATMAAAAAAI0ImAEAAAAAaETADAAAAABAIwJmAAAAAAAaETADAAAAANCIgBkAAAAAgEYEzAAAAAAANCJgBgAAAACgEQEzAAAAAACNCJgBAAAAAGhEwAwAAAAAQCMCZgAAAAAAGhEwAwAAAADQiIAZAAAAAIBGBMwAAAAAADQiYAYAAAAAoBEBMwAAAAAAjQiYAQAAAABoRMAMAAAAAEAjAmYAAAAAABoRMAMAAAAA0IiAGQAAAACARgTMAAAAAAA0ImAGAAAAgAGolJ5/wcoEzAAAAAAANCJgBgAAAACgEQEzAAAAAACNCJgBAAAAAGhEwAwAAAAAQCMCZgAAAAAAGhEwAwAAAADQiIAZAAAAAIBGBMwAAAAAADQyrK8LAAancmbp8Tnr6bXH5wQAAACgc1YwAwAAAADQiIAZAAAAAIBGBMwAAAAAADQiYAYAAAAAoBEBMwAAAAAAjQiYAQAAAABoRMAMAAAAAEAjAmYAAAAAABoRMAMAAAAA0IiAGQAAAACARgTMAAAAAAA0ImAGAAAAAKARATMAAAAAAI0ImAEAAAAAaETADAAAAABAIwJmAAAAAAAaGdbXBQCw9sqZpcfnrKfXHp8TAABgXfP3JehdVjADAAAAANCIgBkAAAAAgEa0yAAAAOjH/Go3ANCf9YsVzKWUMaWU95RSvlBKub6UMr+UUltek3pg/rGllLNLKfeXUp4vpTxdSrm5lPLnPVE/AAAAAMBg1F9WMB+c5JremLiUsk2SW5Js33JocZKxSf44yR+XUv6h1vrR3rg3AAAAAMBA1i9WMLd4Ksl/JjkzyUk9MWEppST516wIlx9O8vZa65gkY5JMT7I8yUdKKSf2xP0AAAAAAAaT/rKC+bpa67WtO6WUiT007xFJ3poVQfKRtda7kqTWujTJV0opWyc5JclZpZRLa60v9dB9AQAAAAAGvH6xgrnWuqyXpn5fy/tNreHySs5LUpNsmRUtMwAAAAAAWEP9ImDuRVNb3n+8qpO11j8kubdlV8AMAAAAANANAzZgLqW8Lsm4lt17uxh6X8v7rr1bEQAAAADAwDJgA+YkW7XbfryLca3ntupiDECPKaXnXwAAAAB9YSAHzKPabb/QxbjnW95HdzaglHJSKWVOKWXOvHnzeqQ4AAAAAID13UAOmNuv6atrM1Gt9eJa6+Ra6+Tx48evZVkAAAAAAAPDQA6YF7fb3qiLca3nFncxBgAAAACAlQzkgLl93+WtuxjXeu6JXqwFAAAAAGDAGbABc611XpL5Lbu7dTF015b3+3q3IgAAAACAgWXABswtftry/o5VnSylTMir4fPN66QiAAAAAIABYqAHzN9teT+0lLLnKs5/KiseBvhEXg2jAQAAAABYA/0mYC6ljGt9Jdm03alN2p8rpQxZ6bra8jpjFdP+IMltWfE5ryml7NtyzYallFOTnNIy7vRa60s9/qEAAAAAAAawYX1dQDvzOjn+i5X2t0/y8JpMWGutpZQ/T3JLy3W/KKUsTjIir372f6i1frv75QIAAAAADG79ZgVzb6m1PpbkTUnOSfJAVgTLi7KiJcZRtdaP9mF5AAAAAADrrX6zgrnWWnrrulrrwiSfaXkBAAAAANADBvwKZgAAAAAAeoeAGQAAAACARgTMAAAAAAA0ImAGAAAAAKARATMAAAAAAI0ImAEAAAAAaETADAAAAABAIwJmAAAAAAAaETADAAAAANCIgBkAAAAAgEYEzAAAAAAANCJgBgAAAACgEQEzAAAAAACNCJgBAAAAAGhEwAwAAAAAQCMCZgAAAAAAGhEwAwAAAADQiIAZAAAAAIBGBMwAAAAAADQiYAYAAAAAoBEBMwAAAAAAjQiYAQAAAABoRMAMAAAAAEAjAmYAAAAAABoRMAMAAAAA0IiAGQAAAACARgTMAAAAAAA0ImAGAAAAAKARATMAAAAAAI0ImAEAAAAAaETADAAAAABAIwJmAAAAAAAaETADAAAAANCIgBkAAAAAgEYEzAAAAAAANCJgBgAAAACgEQEzAAAAAACNCJgBAAAAAGhEwAwAAAAAQCMCZgAAAAAAGhEwAwAAAADQiIAZAAAAAIBGBMwAAAAAADQiYAYAAACgXyil519A7xIwAwAAAADQiIAZAAAAAIBGBMwAAAAAADQiYAYAAAAAoBEBMwAAAAAAjQiYAQAAAABoRMAMAAAAAEAjw7p7QSllTJK3J9k2ybgkLyR5KsldtdZ7e7Y8AAAAAAD6qzUKmEspI5Mck+TDSfbJqyufS8t7bRk3P8m/Jbmo1vrrni0VAAAAAID+pMuAuZQyLMnJST6TZNMkS5P8MsnsJE8mWZBkZJLNk0xKsm+SjyT5P6WUm5KcWmu9p9eqBwAAAACgz6xuBfMDSbZPckOSS5P8oNb6YlcXlFJ2SnJ8kvcnubOU8uFa62U9UCsAAAAAAP3I6gLm+5L8Wa31V2s6Ya31N0k+XUo5I8lfZ8UKZwAAAAAABpguA+Za63uaTlxrfSnJBU2vBwAAAACgfxuy+iEAAAAAAPBaq2uRsUZKKZOSvDPJ80murLU+1xPzAgAAAADQf3VrBXMp5fOllCdKKZu1O3ZIkjuTnJfk75PcUUrZvGfLBAAAAACgv+lui4x3Jnmg1rqg3bEvJqlJTk9yUZLtk3yiZ8oDAAAAAKC/6m7APDHJ/a07pZQJSfZO8ve11rNrrR9PMiPJtB6rEAAAAACAfqm7AfOmSdqvXn57Vqxe/lG7Y/+dZNu1rAsAAAAAgH6uuwHzvCQT2u0flOTlJLe1Oza8wbwAAAAAAKxnhnVz/F1J3lNK2T3J0iR/meRntdYX2o2ZmOSJnikPAAAAAID+qrsrjb+cZOMkv0oyt2X7q60nSykjkkxNMqeH6gMAAAAAoJ/q1grmWuutpZR3JTkxK3ovX1Frvb7dkP2SPJzkmh6rEAAAAACAfqm7LTJSa70hyQ2dnJuR5M1rWxQAAAAAAP2fh/EBAAAAANBIlwFzKWXy2kxeShlRStllbeYAAAAAAKB/Wt0K5ttLKdeUUt7anUlLKRuXUj6R5HdJ/qJxdQAAAAAA9Fur68F8QpIvJPl5KeW3Sa5M8l9J5tRan2kdVEoZmmTnJPsm+ZMk704yIsnVSS7phboBAAAAAOhjXQbMtdZ/LqVcleQTSf5Pks8nqUlSSnk5yTNZESSPbbmkJFmW5LokX6m1/qKX6gYAAAAAoI+t9iF/tdYltdZzkmyf5J1Jvpbk9iRPJ9m0ZdiDSf49yd8kmVhrfW+TcLmUsmUp5f+VUh4qpSwtpfxvKeW6UsrB3Z1rpXmPLKX8qJTyRCnl5VLKolLKXaWUL5VStlibuQEAAAAABqvVtchoU2tdnuTHLa8eV0rZI8mMJJu3HFqYZFySdyX501LKp2utX+rmnEOSXJbkfe0OL0qyUZI9W14nllIOq7XOXsuPAAAAAAAwqKx2BfO6UEoZmeSHWREu35lk91rrxlmxQvqrWdF644ullEO7OfWJeTVc/n9Jtqi1js2Kth6HJXk0yWZJvtcSRgMAAAAAsIb6S6j6f5Jsl2RxknfXWu9NklrrwlrraUmubRn3xW7O+1ct7z+ttZ5Sa32qZd5Xaq0/TvKBlvM7JtljbT4AAAAAAMBg018C5tZVxt+ttf5hFee/0vK+VyllUjfmbe2vfEcn5/+73faobswLAAAAADDo9XnAXEoZk2Tvlt3O+jv/MslzLdt/3I3pH255f3Mn51vv+2KS+7oxLwAAAADAoNfnAXOSXbKix3KS3LuqAS0PGJzbsrtrN+b+dsv7H5dSzi+lvC5JSinDSil/kuTSlvNn1Vqf6V7ZAAAAAACDW38ImLdqt/14F+Naz23VxZgOaq3/luQzSZYlOSXJ/5ZSFiZZmuSGJIuSfLDWek5X85RSTiqlzCmlzJk3b96a3h4AAAAAYEDrDwFz+97HL3Qx7vmW99HdnP+LWfEwvyUt+2OSDG1373GllC6/h1rrxbXWybXWyePHj+/m7QEAAAAABqb+EDCX1Q9pOPGK/s7XJbk8yU+TvDUrAuaJST6SZGxWPEDwit6qAQAAAABgoGoUMJdS9iilfKmU8oNSyk3tjk8spRxVStm0G9Mtbrc9sotxG61i/Op8LcmfJrmp1vruWuvttdbFtdZHaq3fSnJkkprk6FLKO7sxLwAAAADAoNftgLmUclaSO5JMT/LuJAetNN/3khzbjSnb913euotxreeeWJNJSyljk3ywZfeCVY2ptd6SFZ8lSY5Yk3kBAAAAAFihWwFzKeXoJJ9NcmOSN2VFf+M2tdbfJZmT5D3dmPaBrFhFnCS7dXLfIUl2btm9bw3nfUNe7bX8P12M+13L+8Q1nBcAAAAAgHR/BfPJSR5MckSt9e4kL61izP1ZEe6ukVrroqwIpZPkHZ0Me2uSjVu2b17DqZe32962i3HbtbwvWsN5AQAAAABI9wPmNyb5ca11VcFyq8eTbNHNeb/b8v6+UspWqzh/Wsv7f9da567hnA8kWdqyfdKqBpRS9kqyV8vubWs4LwAAAAAA6X7AXNJxZfCqbJFXg9019a0kjyQZk+RHpZRdk6SUMqaU8uUk720Z9+nXFFRKbXmd0f54rfWFJJe17B5ZSvl2KeX1LdeMKKUckeTaJMOSLEzynW7WDAAAAAAwqA3r5vjfJtmvs5OllKFJ9k9yb3cmrbW+0BL43pwVK4rvLaUsTDI6K0LwmuTTtdafdLPeU5Ps2lLTCUlOKKUsSTIyr4bri5L8Ra11fjfnBgAAAAAY1Lq7gvn7SfYqpZzayfn/m+SP8mrLizVWa/1Vkt2TXJgVD97bMMnTSf4jyTtqrV9qMOfiJFOTfCjJT5LMa5n3hST3JDk/yRsbBNcAAAAAAINed1cwX5DkL5J8uZRyVFasLE4p/5+9ew+T7azrRP/9JZtLbgQkwVzkoqhAiKAhDDqIYjDRw8Ug+IxoHMcjCl5QEHBG4yggKggDCl4TFUElXkAkCIzcjyiKGASEcPEAY3wOBExAEwIJkOR3/lir3Z2d3r271169q2r35/M866mqXu96+63fXvVW72+tWqv+V5IHJDkzyVuTXDhlMN39sSSPH5etblMHWH9Dkt8dFwAAAAAAZrKtgHk8lcU3JHlekvOSHDmuemKGczP/QZLHdff1s44SAAAAAICls90jmNPdVyX5nqp6YpL7Jrl9kquSvK27r5h5fAAAAAAALKltB8xruvuTSV4z41gAAAAAAFgh273IHwAAAAAAJJlwBHNVHZ3k0Um+MskXJbnFBs26ux90kGMDAAAAAGCJbStgrqp7JXltkhOT1CZN+2AGBQAAAADA8tvuKTJ+OUO4/JQkd0lyi+4+YoPlyLkHCgAAAADActnuKTK+OsmfdvfP7cRgAAAAAABYHds9gvmaJJftxEAAAAAAAFgt2w2Y35jkfjsxEAAAAAAAVst2A+bzk9yjqn6iqja7yB8AAAAAAIe5bZ2Dubs/XFVfm+Rvknx/Vb0zyVUbN+1HzzFAAAAAAACW07YC5qr6oiQXJ7nduHzxfpp2EgEzAAAAAMBhbFsBc5JfTvLlSV6Q5EVJPprk+rkHBQAAAADA8ttuwHxWktd09/ftxGAAAAAAAFgd273I3xFJ3r0TAwEAAAAAYLVsN2B+a5LTd2IgAAAAAACslu0GzD+V5IFV9aidGAwAAAAAAKtju+dgfkiSNyZ5cVX9QJK3J7lqg3bd3U8/2MEBAAAAALC8thswP3Xd/a8bl410EgEzAAAAAMBhbLsB8zfsyCgAAAAAAFg52wqYu/svd2ogAAAAAACslu1e5A8AAAAAAJIImAEAAAAAmGjTU2RU1Y1JbkxyWnf/0/i4t9Bvd/d2z+8MAAAAAMAKOVAI/OYMgfJn9nkMAAAAAMAut2nA3N0P3OwxAAAAAAC71wHPwVxV311V9zoUgwEAAAAAYHVs5SJ/L0zy8B0eBwAAAAAAK2YrATMAAAAAANyMgBkAAAAAgEkEzAAAAAAATLJni+1uW1V32k7H3f0vE8YDAAAAAMCK2GrA/Phx2areRt8AAAAAAKygrYbAVyf5950cCAAAAAAAq2WrAfMvdffP7uhIAAAAAABYKS7yBwAAAADAJAJmAAAAAAAmETADAAAAADCJgBkAAAAAgEkOeJG/7hZCAwAAAABwM8JjAAAAAAAmETADAAAAADCJgBkAAAAAgEkEzAAAAAAATCJgBgAAAABgEgEzAAAAAACTCJgBAAAAAJhEwAwAAAAAwCQCZgAAAAAAJhEwAwAAAAAwiYAZAAAAAIBJBMwAAAAAAEwiYAYAAAAAYBIBMwAAAAAAkwiYAQAAAACYRMAMAAAAAMAkAmYAAAAAACYRMAMAAAAAMImAGQAAAACASQTMAAAAAABMImAGAAAAAGASATMAAAAAAJMImAEAAAAAmETADAAAAADAJAJmAAAAAAAmETADAAAAADCJgBkAAAAAgEkEzAAAAAAATCJgBgAAAABgEgEzAAAAAACTCJgBAAAAAJhkz6IHsF5VnZTkJ5M8NMmpSa5K8rYkv9zdbzjIvu+Q5PFj33dJcmSSy5NckuSPuvvig+kfAAAY1NNq9j77KT17nwAAHLylCZir6l5J3pjk9uOPrk5yQoZA+CFVdX53P3Ni3w9O8gdJbjf+6NokNyT50nE5MYmAGQAAAABgG5biFBlVdVSSV2QIl9+R5PTuPj5DIPycJJXkGVV1zoS+H5Dkz8a+/jjJV3T30d193Pj7HpHk1bM8EQAAAGZTtTMLADCfZTmC+bFJ7pzkmiQP6+6PJEl3X53kyVV11yQPT/KMJK/daqdVdeskv5vklkku7O7Hrl/f3Z/MED4DAAAAALBNS3EEc5LzxtuL1sLlfTx7vD2jqu6+jX6/Pcldk/xbkicdxPgAAAAAANjHwgPmqjouyX3Gh6/ZT7O3ZrjgX5KctY3u14Lrl3b3NROGBwAAAADAfiw8YE5yjwznWE6SSzdq0N03JvnA+PC0rXRaVZXkfuPDv66qM6rqZVV1RVVdV1UfrKpfq6o7H8zgAQAAAAB2q2UImE9ed/+jm7RbW3fyJm3W+8Iktxnv3zPDUdDfmuToJJ/PcOqMH0ryrvFCgAAAAAAAbMMyBMzHrLt/7SbtPjPeHrvFfm+77v6PJ/l4krOTHNvdxyW5f5J/SnJ8kpdW1e3211FVPaaqLqmqS6644oot/noAAAAAgMPbnkUPIHtPjzG39eF5Jfmu7v7LtR90999U1bcleWeSOyT5vuy9mOBNdPeFSS5MkjPPPLN3aLwAAADALldPmz8m6aeIMoCdswxHMK+/+N5Rm7Q7eoP2W+33XevD5TXd/e4krx8ffuMW+wUAAAAAIMsRMK8/7/Ipm7RbW3f5Fvv9eJIbxvsf2KTd2ro7brFfAAAAAACyHAHz+5OsfVfjnhs1qKojktxtfPjerXTa3Z9N8qG1h1vZZCv9AgAAAAAwWHjA3N2fSnLJ+PDs/TS7X4aL8SXJG7bR/Vrbu2/SZm3dZdvoFwAAAABg11t4wDy6aLw9r6pO3mD9k8fbt3f3Zqe72Nfvj7f3rqoH7ruyqr4iyYPGh6/eRr8AAAAAALvesgTMF2Q4gvi4JK+sqtOSpKqOq6pnJXnE2O78fTesqh6Xp+67rrv/NsmfjQ9/v6oeVFU1bvc1SV6aoQaXJXnBvE8JAAAAAODwtmfRA0iS7r62qs7NcEqLM5JcWlVXJzk2QwDcSc7v7tdO6P57MlzA78wkr0/ymaq6IUOYnQwXDTy3uz9zcM8CAAAAAGB3WZYjmNPd70pyepLnJ/lwklsl+USSVyU5u7ufObHfq5P85yRPSvL2JDdkCNbfm+SZSe49/m4AAAAAALZhKY5gXtPdH0vy+HHZ6ja1hTafT/LccQEAAAAAYAZLcwQzAAAAAACrZamOYAYADk/1tAN+4Wjb+ik9e58AAABsjyOYAQAAAACYRMAMAAAAAMAkAmYAAAAAACYRMAMAAAAAMImAGQAAAACASQTMAAAAAABMImAGAAAAAGASATMAAAAAAJMImAEAAAAAmETADAAAAADAJAJmAAAAAAAmETADAAAAADCJgBkAAAAAgEkEzAAAAAAATCJgBgAAAABgEgEzAMAOqpp/AQAAWBYCZgAAAAAAJhEwAwAAAAAwiYAZAAAAAIBJBMwAAAAAAEwiYAYAAAAAYBIBMwAAAAAAkwiYAQAAAACYRMAMAAAAAMAkAmYAAAAAACYRMAMAAAAAMImAGQAAAACASQTMAAAAAABMImAGAAAAAGASATMAAAAAAJMImAEAAAAAmETADAAAAADAJAJmAAAAAAAmETADAAAAADCJgBkAAABggqr5F4BVI2AGAAAAAGASATMAAAAAAJMImAEAAAAAmETADAAAAADAJAJmAAAAAAAmETADAAAAADCJgBkAAAAAgEkEzAAAAAAATCJgBgAAAABgEgEzAAAAAACTCJgBAAAAAJhEwAwAAAAAwCQCZgAAAAAAJhEwAwdUNf8CAAAAwOoTMAMAAAAAMImAGQAAAACASQTMAAAAAABMImAGAAAAAGASATMAAAAAAJMImAEAAAAAmETADAAAAADAJAJmAAAAAAAmETADAAAAADCJgBkAAAAAgEn2LHoAsArqaTV7n/2Unr1PAAAAADiUHMEMAAAAAMAkAmYAAAAAACYRMAMAAAAAMImAGQAAAACASQTMAAAAAABMImAGAAAAAGASATMAAAAAAJMImAEAAAAAmETADAAAAADAJAJmAAAAAAAmETADAAAAADDJUgXMVXVSVT2vqj5UVddV1cer6s+r6kEz/o4jq+qSqupxeepcfQMAAAAA7CZLEzBX1b2SvCfJjyb5kiSfTXJCkocmeV1V/cRMv+pHktxnpr4AAAAAAHatpQiYq+qoJK9Icvsk70hyencfn+R2SZ6TpJI8o6rOOcjf80VJnp7ksiQfP6hBAwAAAADscksRMCd5bJI7J7kmycO6+9Ik6e6ru/vJSV4+tnvGQf6eX0lybIajpK87yL4AAAAAAHa1ZQmYzxtvL+ruj2yw/tnj7RlVdfcpv6CqviXJw5O8srtfMaUPAAAAAAD2WnjAXFXHZe85kV+zn2ZvTXLVeP+sCb/jmCS/muTaDEcvAwAAAABwkBYeMCe5R4ZzLCfJpRs16O4bk3xgfHjahN/x9CR3TPIL3f1/JmwPAAAAAMA+liFgPnnd/Y9u0m5t3cmbtLmZqvqqDEct/1OSZ21vaAAAAAAA7M8yBMzHrLt/7SbtPjPeHrvVjqvqiCQXJDkyyeO6+3PbH15SVY+pqkuq6pIrrrhiShcAAAAAAIedZQiY68BNJvvhJPdN8ifd/bqpnXT3hd19ZnefeeKJJ843OgAAAACAFbYMAfM16+4ftUm7ozdov19VdUqSnxvbP3Ha0AAAAAAA2J9lCJjXn3f5lE3ara27fIv9PiPJbZL8YpKrqurY9Uv2Hjl9y3U/AwAAAABgi5YhYH5/kh7v33OjBuO5lO82PnzvFvu983j79CSf2mC507j+J9f9DAAAYLKq+RcAgGW28IC5uz+V5JLx4dn7aXa/JMeP99+w44MCAAAAAOCAFh4wjy4ab8+rqpM3WP/k8fbt3f2BrXTY3Q/s7trfkuSysenT1v0MAAAAAIAtWpaA+YIMge9xSV5ZVaclSVUdV1XPSvKIsd35+25YVT0uTz1UgwUAAAAAINmz6AEkSXdfW1XnZjj9xRlJLq2qq5McmyEE7yTnd/drFzhMAAAAAADWWZYjmNPd70pyepLnJ/lwklsl+USSVyU5u7ufucDhAQAAAACwj6U4gnlNd38syePHZavbTDp3cnffZcp2AAAAAAAMluYIZgAAAAAAVouAGQAAAACASQTMAAAAAABMImAGAAAAAGASATMAAAAAAJMImAEAAAAAmETADAAAAADAJAJmAAAAAAAmETADAAAAADCJgBkAAAAAgEkEzAAAAAAATCJgBgBuomr+BQAAgMOTgBkAAAAAgEkEzAAAAAAATCJgBgAAAABgEgEzAAAAAACTCJgBAAAAAJhEwAwAAAAAwCQCZgAAAAAAJhEwAwAAAAAwiYAZAAAAAIBJ9ix6AACwbOppNXuf/ZSevU8AAABYNEcwAwAAAAAwiYAZAAAAAIBJBMwAAAAAAEwiYAYAAAAAYBIBMwAAAAAAkwiYAQAAAACYRMAMAAC7WNX8CwAAu4eAGQAAAACASQTMAAAAAABMImAGAAAAAGASATMAAAAAAJMImAEAAAAAlUxY7gAAIABJREFUmETADAAAAADAJAJmDjtV8y8AAAAAwM0JmAEAAAAAmETADAAAAADAJAJmAAAAAAAmETADAAAAADCJgBkAAAAAgEkEzAAAAAAATCJgBgAAAABgEgEzAAAAAACTCJgBAAAAAJhEwAwAAAAAwCQCZgAAAAAAJhEwAwAAAAAwiYAZAAAAAIBJBMwArLSq+RcAAABgawTMAAAAAABMImAGAAAAAGASATMAAAAAAJMImAEAAAAAmGTPogfAzqinzX+Vqn5Kz94nAAAAALC6HMEMAAAAAMAkAmYAAAAAACYRMAMAAAAAMImAGQAAAACASQTMAAAAAABMImAGAAAAAGASATMAAAAAAJMImAEAAAAAmETADAAAAADAJAJmAAAAAAAmETAvgar5FwAAAACAnSZgBgAAAABgEgEzAAAAAACTCJgBAAAAAJhEwAwAAAAAwCQCZgAAAAAAJhEwAwAAAAAwiYAZAAAAAIBJlipgrqqTqup5VfWhqrquqj5eVX9eVQ+a2N+JVfXYqnrJuj4/XVXvq6pfraovnfs5AAAAAADsFnsWPYA1VXWvJG9McvvxR1cnOSHJQ5M8pKrO7+5nbrPbj+amz/GaJLdMcvdxeXRVfW93/+FBDR4AAAAAYBdaiiOYq+qoJK/IEC6/I8np3X18ktsleU6SSvKMqjpnm13vSfLmJP8tycndfVySo5N8bZJ3Jrl1kt8bw20AAAAAALZhKQLmJI9NcucMRxg/rLsvTZLuvrq7n5zk5WO7Z2yz36/v7q/v7t/r7o+Nfd7Q3W9Jck6Sf80QQv/YHE8CAAAAAGA3WZaA+bzx9qLu/sgG65893p5RVXffaqfd/eZN1l2R5NXjw/tstU8AAAAAAAYLD5ir6rjsDXhfs59mb01y1Xj/rBl//SfG2yNn7BMAAAAAYFdYeMCc5B4ZzrGcJJdu1KC7b0zygfHhaTP+7q8fb98zY58AAAAAALvCMgTMJ6+7/9FN2q2tO3mTNltWVecmOXN8+Ltz9AkAAAAAsJssQ8B8zLr7127S7jPj7bEH+wur6tQkF44PX9Hdf3GA9o+pqkuq6pIrrrjiYH89AAAAAMBhYRkC5jpwkxl/WdWxSV6e5A5JLkvy6ANt090XdveZ3X3miSeeuNNDBAAAAABYCcsQMF+z7v5Rm7Q7eoP221JVt05ycYZTY1yR5Ju6+8qp/QEAAAAA7GbLEDCvP+/yKZu0W1t3+ZRfUlW3TPLSJGcl+fck53T3BzbfCgAAAACA/VmGgPn9SXq8f8+NGlTVEUnuNj5873Z/QVXtSfKHSR6S4QjoB3f3O7c/VAAAAAAA1iw8YO7uTyW5ZHx49n6a3S/J8eP9N2yn/zGcflGSR2S4iOC3dPffThgqAAAAAADrLDxgHl003p5XVSdvsP7J4+3bt3Nai6qqJBcm+c4kn0vyiO5+00GNFAAAAACAJMsTMF+Q5LIkxyV5ZVWdliRVdVxVPSvD0cdJcv6+G1ZVj8tTN+j3uUkeneT6JP+lu/9iJwYPAAAAALAb7Vn0AJKku6+tqnMznP7ijCSXVtXVSY7NEIJ3kvO7+7Vb7bOq7pTkCWu/IskFVXXBJmM4aer4AQAAAAB2o6UImJOku99VVacn+ckkD01yapJPJHlbkl/q7m2dezk3PTr7Fkm+cJaBAgAAAACQZIkC5iTp7o8lefy4bHWb2s/P/znJhusAAAAAADh4y3IOZgAAAAAAVoyAGQAAAACASQTMAAAAAABMImAGAAAAAGASATMAAAAAAJMImAEAAAAAmETADAAAAADAJAJmAAAAAAAmETADAAAAADCJgBkAAAAAgEkEzAAAAAAATCJgBgAAAABgEgEzAAAAAACTCJgBAAAAAJhEwAwAAAAAwCQCZgAAAAAAJhEwAwAAAAAwiYAZAAAAAIBJBMwAAAAAAEwiYAYAAAAAYBIBMwAAAAAAkwiYAQAAAACYRMAMAAAAAMAkAmYAAAAAACYRMAMAAAAAMImAGQAAAACASQTMAAAAAABMImAGAAAAAGASATMAAAAAAJMImAEAAAAAmETADAAAAADAJAJmAAAAAAAmETADAAAAADCJgBkAAAAAgEkEzAAAAAAATCJgBgAAAABgEgEzAAAAAACTCJgBAAAAAJhEwAwAAAAAwCQCZgAAAAAAJhEwAwAAAAAwiYAZAAAAAIBJBMwAAAAAAEwiYAYAAAAAYBIBMwAAAAAAkwiYAQAAAACYRMAMAAAAAMAkAmYAAAAAACYRMAMAAAAAMImAGQAAAACASQTMAAAAAABMImAGAAAAAGASATMAAAAAAJMImAEAAAAAmETADAAAAADAJAJmAAAAAAAmETADAAAAADCJgBkAAAAAgEkEzAAAAAAATCJgBgAAAABgEgEzAAAAAACTCJgBAAAAAJhEwAwAAAAAwCQCZgAAAAAAJhEwAwAAAAAwiYAZAAAAAIBJBMwAAAAAAEwiYAYAAAAAYBIBMwAAAAAAkwiYAQAAAACYRMAMAAAAAMAkAmYAAAAAACYRMAMAAAAAMMlSBcxVdVJVPa+qPlRV11XVx6vqz6vqQQfZ722q6ueq6n1V9Zmq+kRVvaGqvm2usQMAAAAA7DZLEzBX1b2SvCfJjyb5kiSfTXJCkocmeV1V/cTEfr8oyTuT/FSSuye5IcltkpyV5CVV9RsHP3oAAAAAgN1nKQLmqjoqySuS3D7JO5Kc3t3HJ7ldkuckqSTPqKpzttlvJXlpki9O8s9J7t/dxyU5Lsl/T3Jjkh+oqu+f6akAAAAAAOwaSxEwJ3lskjsnuSbJw7r70iTp7qu7+8lJXj62e8Y2+z03yf0yBMnf2t1/M/Z7XXc/O8nzx3Y/W1W3PMjnAAAAAACwqyxLwHzeeHtRd39kg/XPHm/PqKq7T+j39d39zg3W/68kneSkDKfMAAAAAABgixYeMFfVcUnuMz58zX6avTXJVeP97QTBD9ys3zHMvnRCvwAAAAAAu97CA+Yk98hwjuVkb9h7E919Y5IPjA9P20qnVXWHDBcJ3G+/o/dup18AAAAAAAbV3YsdQNW52XuO5dt096f20+7Pkjw8ycu6+5Fb6PfeSdZOi3Gv7n73ftr9UpInJPmH7r7Pfto8Jsljxod3y96we5mdkOTKRQ/iMKKe81PTeannvNRzfmo6L/Wcl3rOT03npZ7zU9N5qee81HN+ajov9ZzXKtXzzt194r4/3LOIkezjmHX3r92k3WfG22MPdb/dfWGSC7f4e5dCVV3S3WcuehyHC/Wcn5rOSz3npZ7zU9N5qee81HN+ajov9Zyfms5LPeelnvNT03mp57wOh3ouwyky6sBNDrrfxR6mDQAAAABwGFqGgPmadfeP2qTd0Ru032q/R++31fb7BQAAAAAgyxEwf3Td/VM2abe27vIF97sqVuqUHitAPeenpvNSz3mp5/zUdF7qOS/1nJ+azks956em81LPeann/NR0Xuo5r5Wv5zJc5O+4JFdlOKXFI7v7ZRu0OSLJJ5Mcn+SHu/vXt9j3FRlOlP2k7n7uftq8O8npSZ7d3f992rMAAAAAANh9Fn4Ec3d/Kskl48Oz99PsfhnC5SR5wza6f9Nm/VbVqUnuOaFfAAAAAIBdb+EB8+ii8fa8qjp5g/VPHm/f3t0fmNDvOVV17w3WPzHDkdOXZ28YDQAAAADAFixLwHxBksuSHJfklVV1WjKcPqOqnpXkEWO78/fdsKp6XJ66Qb8XJ/m7DM/zz6rqq8dtblVVT0ryhLHdU7r7c3M+IQAAAACAw92eRQ8gSbr72qo6N8NpKs5IcmlVXZ3k2AzhcCc5v7tfu81+u6q+Lcmbk3xxkr+tqmuS3Dp7n/tvdvdvzfRUAAAAAAB2jWU5gjnd/a4MF9t7fpIPJ7lVkk8keVWSs7v7mRP7/f+SfGWSX0jy/gzB8qcynBLjv3T3Dx786AEAdlZVHVlVd6iqO1fVsYsez6pTT5adfRRgOnMoHFrV3YseAwepqo5J8oVJTkhyVIZg/sru/thCB7aCquoOSb42w4UlT84+Nc3wIcVfJXlHd9+4qHGuiqo6IsMHPJvV86+7+8qFDXKFqOf8vOZ3hveleYzXjzgnyQMy7KMn7NPkc0k+kGEf/askr+ruTx/SQa4Q9Zyf96V52UfnVVUnJPmG7K3n2j56yyT/npu+z/9Vd79tQUNdCeq5M8yj8zGHzs/rfl6Hez0FzCuoqo5M8tDsnTxPy3Cxwn1dleQtGXbOl3T3/zlkg1whVfUFSb4ryf+d5F7rV+3TdP2L5dNJXprkBd391zs7wtUy/pH00Az1PCvDqW5u0iQ3rWUyvNH/UZIXdve/7PggV4h6zs9rfn7el+YzHmHz35I8OsnaBYo3quV6a/vqp5P8cZLf7u6/25kRrhb1nJ/3pXnZR+dVVZXkwRnq+ZDsPS3iZjVdq+dlSX4nw376kR0b5ApRz51hHp2POXR+Xvfz2k31FDCvkKq6c5LHJfmvSU7MgSfONT0ub0ryW939JzszwtVSVackeUqS787widFaPf8lybsyfHr0b0muS3K7cblLhk+Ybz227QyfMD21u19yqMa+jKrqqAwXzvyRDEcurtVz7ZPi/dXzpLHd2n762gwX3vz7QzX2ZaSe8/Oan5/3pflU1S0z1PInk3xBhlp+IsPFii/J5vvofcflK7L3P6V/keQnu/sfD+XzWBbqOT/vS/Oyj86vqh6R5OeS3C1DXW7M8J69lXreJ8ltM9Ty+iS/neTpu/mbN+o5P/PofMyhO8Prfl67rp7dbVnyJcN/2p+fYae7cVzekeRXk3xPkq9KcscMn3zuGdt/eYYjyX4qycszHDV2Y5Ibkvxjkoct+nktuKa/mOETyxsznJP7xUnOTXLCFrY9MkPg9D+TvGddXf8+yX0W/dwWVM8fTPLRdfvnW5I8PsmZSW5xgG1PTfLwDBPmlevq+adJ7rro56aeh8fiNT97Pb0vzV/Ty8ZaXJXkBUnOTnLENvs4NcmTk7x9rO31SR696Oemnqu/eF/akZraR+et51vHel6f5I1Jvj/JF2yzj/uP72OXj/W8JskjF/3c1PPwWMyjs9fTHDp/Tb3u1fPgnvOiB2DZwj/SEIbcmORDSX4myZdN6ONWSR45vgl9ftzRn7jo57bAmt6Y5J+SfG+Sow6yr9OTvCjDJ88/s+jntsB6fjLJzya500H0syfJw5L85dineqrnnDX1mp+vnt6X5q/pJ8ZaHj9Tf9+Q5PVJfnrRz009V3/xvrQjNbWPzlvPz2YImb50hr6OzHDqgvfv1n1UPXekpubReetpDp2/pl736nlQi1NkrICqel+SZyR5cXffMEN/X5rkJ5J8sLufebD9raKq+s4kf9QzXrSrqu6S5NTufstcfa6Kqjo/ya9096dm7PP+SW7b3a+aq89VoZ7z85qfl/el+VXVMb0DF5rZqX6XnXrOy/vS/Oyj86qqO/XM56Udz5t5Sq/AeS/npp7zM4/Oyxw6P6/7ee3GegqYV0BVVe/AP9RO9QvA4c37EgAAAGsEzAAAAAAATLJn0QMAAGD/quoLkpyc4aKJyXCBj8u7+5OLG9XqUk+WnX10flV1TNbVc7d+BX4u6skyM4fuDK/7eR2O9XQEMySpqj1JzhqX07LBG1KS9yZ5Q5I3dff1ixjnKqmqL88W6tnd/+9iRrha1HPxqurUJEfOfS6tw0lVHZ3k7tl4H31fd1+7qLGtmqp6SJLzMrzuT9xPsysyvC+9uLtffajGtorUk2VnH51XVZ2e5Dsz1PMe2fuetOaaJO/LUM8/7O73HNoRrhb1ZNmZQ+fndT+v3VBPAfMKqqoHJLlPhitJvidDqLRp4FlVv5TkNt396EMwxJVSVY/OcDXfk9Z+tJ+may+WyzNcufMFOz22VVRV35jh4l9nrP/xPs3WTzyXJDm/u9+w02NbReo5v6r65iQ/npvOoy9I8jubXQSwqi5PcmJ3+/bPOlV1iySPyfBH/X/K5nPo3yX5gyS/3d2fPzQjXC1VdVKSP0ly/7UfHWCTtdf/Xyf59u7+2E6NbRWp5/zG/yD9WPaZQ7v7dQfYzhy6AfvovMYPOi9I8h0ZarmVenaSi5L84OFwBNmc1HNnmEfnYw6dn9f9vHZTPQXMK6SqTk7yp0nut8+qy5L8WHdfvMm2lye5Q3cfuYNDXDlV9WtJfiDDi7yTXJrhSNCPJvnM2OzoJKdkOHL0nuva/kZ3P+5Qj3mZVdX/SPIL2TtpXpXkA9m4nndLcvz4s07yE9397EM32uWnnvOrqicmWavL+jf3TvL2JI/q7g/vZ1vz6D6q6rQkFyf5khz4j6U1neRDSc7t7vft1NhWUVUdm+SdSb54/NHrk7wmm78vnZPkG5MckeSDSb5qlf4Q3UnqOb+qelSSF2U4zd7aa37tPxMXJ/m+/X0F2Rx6c/bReY0feP5Vkvtm2D//Kclrs3k9z87wN1QneVuSB/im4kA9d4Z5dD7m0Pl53c9r19Wzuy0rsCS5VYZPNm9IcmOS6zJ8xePGcbkhyfMyfmiwwfaXJ7lh0c9jmZYkjxxrd32S5yY5ZQvbnJLkOeM2NyR5xKKfx7IsGb7qsbZ/vizJ1+xvfxzbV5KvzvChydq/wwMX/TyWZVHPHanpVyb5/FifS5P8TJIfznDUw9rPr0zyNfvZ3jx603rcIcnHx7pdneGT+UdkOEXGbTL8x+nI8f7dx3W/meGDkhuTfCzDUTgLfy7LsiR5+libDyf5ym1sd+9xmxuSPG3Rz2NZFvWcvZ53zfAfobXX7wsyfGD3tnV/i34oyZfuZ3tz6M1rYh+dt55PHut5RZJv2cZ2Dxu3uSHJkxb9PJZlUc8dqal5dN56mkPnr6nXvXpOf76LHoBli/9QyY+s+0/8dyfZM/78tCR/tu4N6SVr6/bZ3pvRzWvy+qkv2CRPGmv+ukU/j2VZkrxirOezJmz7rLGeFy/6eSzLop47UtMXjHV5fZJb77PujAznvLoxyaeSPGiD7c2jN63HL4/1enuSU7ex3SnjNjckee6in8cyLRmOZrghyVdP2PZrxn+PSxf9PJZlUc/Z6/kr617zJ+yz7twxLLlxnCu/YoPtzaE3r4l9dN56/sNYzwdP2PbBa/v3op/HsizquSM1NY/OW09z6Pw19bpXz8mLU2SsiKp6c4bzCv1Id//6Buu/P8nzk9wyw9dCHtHd161b7+s0+6iqK5Mcl+S47v7cNre9ZYaTsF/d3SfsxPhWTVV9LMntk9yuu6/Z5rbHJvn3JFd290kHar8bqOf8quqDGb5Cd+/e4KIJY93+MMlDknw2w3nZXrFuvXl0nXX1PL23eaqL8dQa70nyoe7+sp0Y3yqqqk8nub67jz9g4423vzrDhSiPmXdkq0k951VV783wlc2v7e6/3WD9qUlenuGcop/M8J+pt61bbw7dh310XmM9jujufS+ctNXtr0lyY3ffZt6RrSb1nJ95dF7m0Pl53c9rt9XziEUPgC2753j7oo1WdvdvJfnmDEfefVOS/11VJsrNHZXkuu2Gy0kybnPd2AeD45Ncs90wNEnGba7J8FV6Buo5v1OSXLtRuJz8R90enuGCCrdK8pKq+vZDOL5Vc2qGD9m2fR7l7n5vhm/knDr7qFbbtUluPZ6vbVvGDz5vNfbBQD3ndackn0vy1o1WdvdHkjwwyZuSfEGS11XV1x2y0a0m++i8bkhyZFVt9ZoA/6GqjshwWqcbZh/V6lLP+ZlH52UOnZ/X/bx2VT0FzKvjuCT/3pucgL67/zLDCev/LcnXJXl9VQmY9u+yJMdW1b4XTTygcZtjk/zz3INaYR9Jcpuquvt2N6yqe2QIQz8y+6hWl3rOb+2KvPtv0H1Dkv+a5LeS3CLJH1TV9+z80FbS1Rnm0G1/mDkeLX5Mhg9F2esfM5y7esoFZB+XYZ9956wjWm3qOa8jk3yuN/n64/h36oOTvDrD366vrqqzD9H4VpF9dF7vy/BtzvMmbPudGcKm9846otWmnvMzj87LHDo/r/t57ap6CphXx79lCJs2/XSuuy9J8g0ZLlT1n5K8qapufwjGt4peluHCaC+uqq/Y6kZj24syBFUv26GxraJXZajn71fVlk/LUFVfmOT3M9TzlTs0tlWknvP7lyRHV9UdN2vUg8dmOE/ekUl+u6p+8FAMcMW8LcPfET8zYdufzlDbv5t1RKvvNzK87p9VVc+uqpMPtEFVnVRVz0ryixle97+5w2NcJeo5r49k+FDpDps16u7PJvnWDNcIOTrJxVX1sEMwvlVkH53XCzPU84Kq+uHxCMVNVdUtq+qHMtSxxz4YvDDqOTfz6LzMofN7Ybzu5/TC7KJ6OgfziqiqN2U4Kvms8UjlA7W/e5I3JDkpwyced8xwrmHnaxpV1XFJ3pXkLhm+dvD6DOevvjTJRzN8XaYzvKmfkuE0JWcnOSdDMLJ2tdptn8LgcDQGm5cmuV2G0zO8OMlrc9N6JsNpRdbX87wMR9temeFcrv96aEe+nNRzflX1exnq80PdfcEWt3lGkv+RvUc/l3l0UFXfmGGf7Az/AXp2d28aGFfVfZP8eJJHjj86u7vfuKMDXTFVdWGS78vefe492fx96Z4Zgv5KcmF3/8AChr201HM+VfUnGV67393dL95C+yOT/F6S70jy+fHHe8yhN2UfnVdVvSrJ/5WhblcleXM2r+cDktw2Qz1f1d1CvHXUc17m0fmZQ+fndT+v3VRPAfOKqKqnZTji64Lu3tKRdFV11wwh8x0z7Jztzeimxgsp/GmGo72TA3x9PkMdk+Gou28bz5PFqKruleQVGc4vttXJpTKcruTc7v7HnRrbKlLPeVXVd2X4I/3t3X3fbWz300melvHfwDy6V1X9VJKnZ+/+eVWGr4Jt9AfTPTKcWzwZ9tOf7u6fP6QDXhHjEfNPSbJ2hNP+Xv9r70n/muSp3e0onA2o5zyq6jEZjqb5f7r7rC1uUxlOOfS944/8LboB++h8qmpPkp9P8qMZvlqcHLien03yvCT/s7uv39kRrhb1nJd5dGeYQ+fldT+v3VRPAfOKqKozklyS5NNJvqS7r9jidnfMcGTul8Wb0X5V1bkZjmx8YJIT9tPsygwXXLiouy8+RENbOVV16yQ/kOGcQffJ3klyX51hn74owwcn1x2aEa4W9ZzP+K2Ff8hwrrbv6u63bGPbJyR5bsyjN1NVD07yC0nutc+qtT8w9t1n35Xk/O7+3zs9tlU2foXuGzOc9uq0JCdnOG91ZfhWw0czfEPpTUneMOWCtbuJeh68qjohyeUZjvz6+u7+621s+0tJHh9z6H7ZR+c1fhPskbl5PZPh/1Pr6/my7v74Isa5KtRzHubRnWMOnZ/X/bx2Qz0FzCukqr4zw4no39LdH9zGdidmCKiO6O6n7dT4DhdjvW72htTdVy50YCtovJDXPbLxG/z7nV5ke9STZVZVp2ULf9R39/sWNkhgYcaDHo7o7ssWPRaAVWQeBZaZgBkAAAAAgEmOWPQAAABg0arqTlV1p0WPAwAAVo0jmAFW3HjOsd/McE62Ry96PMBijBeuPbK7/2XRY1k1VXVMkk8lubG79yx6PHAg40WD/nOSdPebFzwcdjlzKKvGHMqyORzmUQEzTFBVt03yjgwv/rsuejzLpqoekOGCdEcmeU+GCylsevXT8cIVtxGQbt+6NyMX/WAljB+KPCpJuvv3FjycpVNV35zkx3PTefQFSX6nu2/cZLvLk5y4qn+ULpJ5lFVTVbdPckVW+D+iO6WqTk/yY9lnDu3u1x1gO3PoROZQVo05dHPm0UPvcJhHBcwwwbo3pJV98e+Eqjo5yZ8mud8+qy5L8mPdffEm216e5A7qOdjm17SPznABtU5ylwwXVkuSOJKRZeSP+v2rqicmefbaw3WrOsnbkzyquz+8n23No+tU1Qu20XxPku/KUOcXrfu5b4awlPwturGqelSG1/Ce7J1D1/7De3GS7+vuT+5nW3PoOlX1M9tofssk52eo9U0uKt/dPzvnuGAO5tD9M4/OZ7fNowJmmMAb0s1V1a0yhB/3yPBG9LkMn8DdfmzSSX41yRN6g4nHm9FNVdWN2ftGPlUL71hG5tCNVdVXJvn7DEeKvC/JHyf5RJKvT/Kt488/meRh3f23G2xvHl1nwjy673+iKvZRlpR59Oaq6q5J3p3k1kn+Ncmrs3cOPTPDa/ufk3xTd39wg+3NoevMMIcOD9STJWQO3Zh5dF67bR4VPABzeUyS05Jck+RxSS7q7uur6rQkP5/k3PHnp1TVdxzolBn8hzpwE+Aw8aMZQuQ3Jnlod183/vzXquqMJC9Ocrckr62qh3f3GxY0zlXz/gz/SdrMkUm+NsMf9M7FyCFRVeccxOa3mW0gh48nZAhF3pEh/LhybUVVnZvkgiRfnOSvquqc7n73Yoa5cv41yXUHaFNJ7pRhDvXtOQ4Jc+iOMI/ujF0xjzqCmV2rqj56MJsn+cL4xPM/VNWbk9w/yY90969vsP77kzw/w1c/XpPkEevCE5927qOqrs+wn/1dkqcn+cwmzY9O8qoMb0ZnrV/R3X+5U2Nkd6uqxxzE5sckeU7MoTdRVR/M8Ef7vbv7PRusPzbJHyZ5SJLPJvn27n7FuvX/f3t3HixZWeZ5/PsUFDhVBbJLoaAw4wat7BiCNpRNOwQiij3TIouNIeASqIjQbo2oKAi0yiKtKOIy2gpDhIAz2q0CthaNsrkhy8imhqCsshQg2zN/vOdSya275n3zZp7M7yci41bdPG/68PPUe04+efI9zqMdIuI8YG/gXuBo4PSJvkHTbLsEuA/3Sc2jCt9W8ir7DhFxDeVDuJdN8i2PZwLnUdYUvRvYMzMv63jeObRDRFwBbEe5WvGdmfl/ptjWOVTzzjm0PufRukZtHrXBrJHVcUCayxWirf3HX1tE3AWsQ7lR34pJttmVsm7TWpQrxPYa29aD0VNLRXy5AAAeRUlEQVRFxPaUT4i3o3yCeXhmnjfJtq2/IYDax5P6+iLiQcq61Eum2GY1yrp4+wGPAm/MzLOb55xHx2mutjkVeBblapy3d74R6tjOeVTzznPRuiLiAcq3ERZN8WHSYuACYBnl3/yrM/NHzXPOoR0iYgHlmzUfpXww/G1Kg2SVK+ucQ9UPzqH1OY/WNWrzqA1mjayIWEH5+senKHdFnY0lwGm0+B9/bRHxCHB/Zq4/zXY7AP8GrAtcRvnqzX0ejFbVHJDeQTkgLQG+S7lC/OZx27X+YKT26Tip/xPlatrZWABsivvsUzTHpScyc61ptgvgc8AhwOPAIZn5ZefRiTVz5McoyzQF8EXgfZl5z7htnEc1ryLiD8DGwP/IzG/NcuwGlK/cus82IuIh4JHMfPo0261JuSn1npRviO2Tmd93Dp1Yc8XiZyjL3T1IWfrupM7l7pxD1Q/OofU5j/bGqMyjNpg1siJiOfBS4MjM/PQsx3pTgHEi4k/AepRPOx+dZtsXAT8ANgB+DryS0uT3YDSBcQekh4HjgRMy85Hm+dYfjAZdRPx188drOtciG2URcRPwbGC/sStoZzHWk/oJRMS1wPOA52Tm72ew/SmUD6GeaH5+COfRSTXrWJ9B+VrnncB7M/NLzXPOoz0UEZs1f7xtunOEUdIs4/Jq4PjM/KdZjvVcdJyOZYaWZuaU665HxELgm5QbqD4MvB74PM6hk4qIvSnno88ErgcOy8yLmuecQ3vMeXRVzqH1OY/21rDPowv6XYB6LyIuah6fiIgN+13PALmcciXTjv0uZEhcQ5lTdp5uwyw3A9gV+COwDfBD4L/0srg2y8w/ZOY+wGspJ0IfBn41xxtbaHZ+CFwM3BwRJziXAmUOhe7mUD/dnthYpnvOZOPMfBdwAmXu/QzgfjmFzLwK2IlyA5s1gTMjYnlEvLi/lY2Em5vHjRHxtohYo98FDYixc9Gd+l3IkLiq+fm3023YNOj+nrKu/dOAcykXSmgSWdb8fwFwCvBc4PsR8a8RsXF/KxsZzqOrcg6tz3m0h4Z9HrXBPBp2ozTzjqI0R06MiI36W9JAGHsjv0NfqxgeP6Ic4PedycaZeR3w18DvgS3xTr7Tag5IL6QckLYAvhsR51DWFlXvBWXtrLG59J/7XE+/XUHJZPt+FzJEvkfJ9OCZDsjM9wPHNOPmsgbhSMjiVMpceh7lQ9ErKDedVO+M7Z/PonwYcmNEHNbfkgbC2LloN/Po45R7NPy2Xjmt9wPKfvbmmWycmY8DBwBnAQubh6aQmQ9m5hGUht6VlPP+64B39bWw0eA8uirn0PqcR3tsmOdRl8gYARHxQ8rVYkspX70FeHCqmwiNgmbZgSMp2bxnskXsJxm7OrALQGb+R28qbJfmq8dXACuALTLzjhmO25RyIHsuLf46yHyLiG0oX0HaAXiIcgW4+fVIc4NKKPPorpQP7p43ynlHxG7ARcADmTmrD4giYm3KzUEyM5f1oLxWioi1KFeOrA4ckJmXzGLs4ZR7CjgPzEJE7AWcTlkTHMyvJyLiH5o/js2hOwNLRj3r5grEFwJk5i/6XE7rNcsv3Ua5iGrXzFw+i7Gfpry5dw6YoeZ+AIcBx1Ju4O3Ne3vIeXRVzqH1OY/Or2GbR20wj5iIeAalMfLyzBz1TzxVWUTsR/nU8pLMvGEW4zYE3gosyMyP9Kq+YTPugLQ2LT4YtVFEbDDK6zE3+9/aAJl5b5/LkboWEYuA9wKbAWTmm/pb0fBrbmK7XWZe0e9apDHNRQ8LMtMrGmchIpYCn2DlHOoHx/PAeVSDyHm0O8Myj9pglqSWa64GXRfAg7kkSZIkSZpPNpglSZIkSZIkSV1Zvd8FSJLUL81X419AWc9ubF36Byhrj12bmQ/1qzZJaoOIWI8J5tDMvLt/VUlSeziPShoGXsE8Ipob2q2Wmb/rdy2S1E8RsRA4FNifcvfemGTTBH4KfA04MzMfnZ8KJWmwRcSrKHPoK4ANJ9nsDuBC4OuZ+Z35qk2S2sB5VNKwscHcMhGxB3AUsD2wGnA1cBbwxcx8YopxtwEbZqZXrUsaWRGxJXA+sAWTN5bHS+BG4DWZeW2vapOkQRcRGwPnALuM/WqaIWNvNJYDr8/MP/aqNklqA+dRScPKBnOLRMQRwEljf+14KoErgX0z86ZJxt4GbJSZq/W2SkkaTBGxEfArylUiDwDfAP4duAa4FXiQMp8uBjYBtgReCbwBWAu4HXhRZt4x78VLUp9FxBLg58Dmza9+wKpzKMAinjqH7g4sAG4Ats3MFfNYtiQNDOdRScPMBnNLRMQ2wOWUq5avBc4G7gJ2BfZpfn838OrMvHSC8TaYJY20iDgZeCfwM2DvzPzDDMdtAnwb2AY4JTOP6F2VkjSYIuJY4IPALcDrMvPnMxy3NfAt4NnAxzLzmJ4VKUkDzHlU0jCzwdwSEXEWcBBwEbBXZj7c8dx2wNeB5wMrgNdm5oXjxttgljTSIuIGyhUjfzXbpS6apTWuBm7MzOf2oj5JGmQRcQ3lXHOXzPzJLMe+FLiEcvPUrXpRnyQNOudRScPMBnNLdDRGts7Mqyd4fgnl696vAv5CWZ/pgo7nbTBLGmkR8RDwcGau2+X4PwNrZOaiupVJ0uCLiBXAY5n59C7H30e54fTiupVJUjs4j0oaZgv6XYBmbBPgoYmaywCZ+QDwWuBfgTWB/x0Rr5/H+iRp0N0HLImIWZ+UNx/iLQbur16VJLXDQ8DTImLhbAdGxBqU89OHqlclSe3hPCppaNlgbo9k5R1kJ94g83HgQOALwELgaxFxUO9Lk6RWuIxy3PtQF2OPpqx1/9OqFUmVRMRfN48N+l3LMDDPCf0SWB04rIuxh1HOTWe03qimFxGbNY9ZN6q0KvOsyzwn5Tw6INxH6zPTutqYpw3m9vgdsCgiNp1qoyzeApxGaYacGRFvm48CR0lEXNQ8PhERG/a7nrYzz7rMc1KnAAEcGRHnRsRLphsQETtGxDnAkZQP+U7ucY0jweZdT/wQuBi4OSJO8N/+nP0Q8xzvs5Q59MSIOCkilk43ICI2jogTgRMoc+jnelzjKLm5edwYEW9rrm5U98yzLvOcmPPo4HAfrc9M62pdnq7B3BIR8VVgf+DtmXnGDMccD7yXlVc/h2sw1xERT7DyivKHgH8B/jkzb+9fVe1lnnWZ5+Qi4oPAsazM517gWuBWSlYJLKIsS/RCYGyNvACOzsyPz2vBQ6pjH32QlfvnHf2tqt2aTDs9CHwuM4/sRz1tZ54Ti4jPAwez8tzyauDXTDyHbtU8FlDm0M9n5lv7UPZQGrePJuX/gxMy8zN9KqnVzLMu85yc8+hgcB+tz0zramOeNphbIiIOAL4KXJmZO85i3NHAR2iaKTaY64iIH1IyXQo8r/n1g5m5pG9FtZh51mWeU4uIPYHjgBePe2rsgBjjfv8L4AOZ+d1e1zYqbN7VFxG7Nn9cCuwK7AY8z+N+d8xzcs03444BNmp+NdmbibG59Hbgw5npVXcVRcQ/NH8c20d3Bpa4j3bHPOsyz6k5j/af+2h9ZlpXG/O0wdwSEbEWcBVlzaYDMvOSWYw9HPgUZQWNgd0Z2yoinkF54/nyzOxmPS11MM+6zHNyEbElsAzYknLgXkw5kX+A8gnxNcDFmXlt34ocUjbv5kdEbJCZd/a7jmFhnis1X9PcnRnMocCFmflIn0odGRGxANguM6/ody3DwDzrMs9VOY8OFvfR+sy0rjbkaYNZkiT1nc07SZIkSWonG8ySJEmSJEmSpK6s3u8CJEmSNLGIWAS8gPLV2bF11B8AbgOuzcyH+lVbG5mn2iAi1mOCfTQz7+5fVe1lnnWZ5/yJiM0AMvN3/a6lTdxH6zPTuoY1T69gboGI2CIzb6r8mguAZ3mw6l5EPBNYzQzrMM+6zLOuZp28fQEy86t9Lmdg2byrIyIWAocC+wM7seqNJ8ck8FPga8CZmfno/FTYLuY5GDwuTS0iXkXZR18BbDjJZncAFwJfz8zvzFdtbWSedZnn/IuIxcD9wBOZ6YWB03Afrc9M6xqFPG0wt0BEPAJ8AzguM6+f42stBN4EvBf4SmZ+tEKJrRYRewBHAdsDqwFXA2cBX8zMJ6YYdxuwoQf8pzLPusxzMETE+pQDvif549i8q6u5+eT5wBZMnuV4CdwIvMabUj6VedbncamuiNgYOAfYZexX0wwZe/O2HHh9Zv6xV7W1kXnWZZ7909FgTm+CPDn30frMtK5RytMGcwtExHJgZ+AJ4MfAN4FzM/OuGY4PYDfK1XevA9YDVgAHZub5vai5LSLiCOCksb92PJXAlcC+k1093rxR2sgD/krmWZd5Do6OBrMn+R1s3tUVERsBv6Jc1fAA5cPlf6fcRf5W4EFKfouBTSh3nX8l8AZgLeB24EWZece8Fz+AzLM+j0t1RcQS4OfA5s2vfsCq+yjAIp66j+4OLABuALbNzBXzWPbAMs+6zLO+iDhrFpuvDhxAmV+/0vH7zMw3Vy2spdxH6zPTukYtTxvMLRERewPHUXa4bB6/oZzM/xK4E7gHeARYB1iXshPvAGxLefMUwKPAGcCxo/6GKSK2AS6nXH1zLXA2cBewK7BP8/u7gVdn5qUTjPeNUgfzrMs8B4sN5lXZvKsvIk4G3gn8DNg7M/8ww3GbAN8GtgFOycwjeldle5hnXR6X6ouIY4EPArcAr8vMn89w3NbAt4BnAx/LzGN6VmSLmGdd5llfRDzByqsTZzSk+Zkdf/dctOE+Wp+Z1jVqedpgbpHmSuQ9gIOBvYCFzVNT/Z84dlC6ifL1xS9l5m09K7JFmk+QDwIuAvbKzIc7ntsO+DrwfMrV3q/NzAvHjfeNUgfzrMs864uIQ+cwfDHwSTypf5LNu/oi4gbKh8N/Nduru5urya8GbszM5/aivrYxz7o8LtUXEddQMtslM38yy7EvBS6hrG+/VS/qaxvzrMs86+toMF9H+aB9KqsBL2u2/1HnE5m5rCcFtoz7aH1mWteo5WmDuaWau04uo6zjshPlhkobAGtSrh65E7ieskMuz8wr+lTqwOp447l1Zl49wfNLKFfkvQr4C2X9mws6nveNUgfzrMs86+viqpFVXgIbzE+yeVdfRDwEPJyZ63Y5/s/AGpm5qG5l7WSedXlcqi8iVgCPZebTuxx/H+XGiYvrVtZO5lmXedYXEecBewP3AkcDp+ckDZlmTr0Pzz0n5T5an5nWNWp52mDWyIqIByk37FoyxTarUda82o+yvMgbM/Ps5jnfKHUwz7rMs76OBvOfKM2P2VgAbIon+U+yeVdfRPyJcp+EdWa71lrzRvQe4O7MfEYv6msb86zL41J9EXEnZcmgJTnLG59GxBqUG4Ddn5kb9KK+tjHPusyzNyLiNcCpwLMo3wJ7e2ZeNsF23uRvGu6j9ZlpXaOW54J+FyD10dha1pNvkPk4cCDwBcqSJF+LiIN6X1ormWdd5lnfLc3Pd2fm5rN5ANv3se5BdR+wpHkDNCtN827sjZNWuoxybvahLsYeTfk67U+rVtRu5lmXx6X6fkm5kddhXYw9jJLxjNZzHBHmWZd59kBmnk+5L8WpwNbAf0bEGRHR1Qf2I859tD4zrWuk8rTBrFH2O2BRRGw61UZZvAU4jfJm88yIeNt8FNgy5lmXedZ3efNzxy7G+nWfVdm8q+8UylIsR0bEuRHxkukGRMSOEXEOcCRlPz25xzW2iXnW5XGpvs9S9tETI+KkiFg63YCI2DgiTgROoOyjn+txjW1innWZZ49k5orMfDfwEspVzIcA10fEm/pbWeu4j9ZnpnWNVJ4ukaGRFRFfBfanfC3pjBmOOR54Lyuv4gm/slSYZ13mWV9EHEU5UP/HbG+OEhHrA3fg1xSfFBG7A9+j7GvfAk7KzCkbxhGxI3AU8HfNr/42My/qaaEtExEfBI5l5Yca9wLXArcCDzW/XwRsArwQGFvTLYCjM/Pj81rwgDPPejwu9UZEfJ5yA++xjK4Gfs3E++hWzWMBZR/9fGa+tQ9lDyzzrMs8ey8iAngH5Vi1BLgUeDtwIy6RMS330frMtK5RytMGs0ZWRBwAfBW4MjNnfEVjRBwNfITmzaoH/MI86zLP+iJiN+Ai4IHMXHuWY9cGLqCc5Hvn7obNu96IiD2B44AXj3tqLOcY9/tfAB/IzO/2urY2Ms86PC71TnOF9zHARs2vJnuDNrav3g58ODNbc1XTfDLPusxzfkTEJpRvfuwDPAacBRyKDeZpuY/WZ6Z1jUqeNpg1siJiLeAqypo4B2TmJbMYezjwKTzgP8k86zLP+porRNYGyMx7+1zO0LB51zsRsSWwjLJW41LKutUBPEBp4l8DXJyZ1/atyBYxz7nxuNRbzc18dmcG+yhwYWY+0qdSW8E86zLP+RMRewGnU24uDc6bM+I+Wp+Z1jUKedpgliRJc2bzTpIkae4iYhFleaHNADLTtZklDTwbzJIkSZIkSZKkrizodwGSJEmSJElSr0TEZhGxWb/rGCZmWlfb87TBrJEUEVv04DUXtHkymAvzrMs86zNTDbuIWCMi3hgRb+x3LcPAPJ/KOXQwRcQzzbAe86zLPJ/KebS/ImIxcAtwU59LGRpmWtcw5GmDWaPquoj4SkQ8f64vFBELI+JQ4DfAQXOurJ3Msy7zrM9MB4jNu55YC/gy5a7zmjvzfCrn0B6KiD0i4sKI+HNE3B8Rl0bEIREx3Xu1K2jxG9FeMc+6zLMa59HBMP7m05o7M62rtXm6BrNGUkQsB3YGngB+DHwTODcz75rh+AB2A/YFXgesB6wADszM83tR8yAzz7rMsz4zHSwRsT5wB/BEZq7e73qGQUem3m2+AvN8KufQ3omII4CTxv7a8VQCVwL7ZuaETbqIuA3YyH10JfOsyzzrcR6tLyJm8yHw6sABlH33Kx2/z8x8c9XCWsxM6xq1PG0wa2RFxN7AccCWlH/ESfkU+Ergl8CdwD3AI8A6wLrA5sAOwLbAYsqJ1qPAGcCxmXnH/P5XDA7zrMs86zPTwWHzrj4zrcs8V+UcWl9EbANcDqwGXAucDdwF7Ars0/z+buDVmXnpBONt4HUwz7rMsz7n0boi4glKhjMe0vzMjr97nO9gpnWNWp42mDXSmk+C9wAOBvYCFjZPTfUPY+wf/U2Ur85+KTNv61mRLWKedZlnfWY6GGzeTaz5umu3FgOfxEyfZJ71OYfW1VzZdBBwEbBXZj7c8dx2wNeB51OuUnxtZl44brwNvA7mWZd59obzaD0dzbvrgNun2Xw14GXN9j/qfCIzl/WkwBYy07pGLU8bzFIjItYDlgG7ADsBS4ENgDUpn87fCVwPXAIsz8wr+lRqK5hnXeZZn5nOjc27+rq4ymGVl8BMn2SeveUcOncRcQPl6sStM/PqCZ5fAnwDeBXwF+D1mXlBx/M28DqYZ13m2XvOo3MTEecBewP3AkcDp+ckDa5mf70Pj+tTMtO6Ri1PG8ySJGnWbN7V15Hpnyhv1mdjAbApZvok89Sgi4gHKWvRL5lim9UoazHuR/la/Bsz8+zmORt4HcyzLvNUG0TEa4BTgWcBPwPenpmXTbDdYuB+PK5Py0zrGqU8bTBLkqRZs3lXX0TcBDwb2G/sDfosxm5A+eqdmTbMU4MuIlZQGnhrTbNdAJ8DDgEeBw7JzC/bwHsq86zLPNUWTWPuY8BhlAsYvgi8LzPvGbdNq5t388lM6xqVPBf0uwBJktRKtzQ/352Zm8/mAWzfx7oH2eXNzx27GOsVA6syTw263wGLImLTqTbK4i3AaZQ1Gs+MiLfNR4EtY551madaITNXZOa7gZdQrhA9BLg+It7U38ray0zrGpU8bTBLkqRu2Lyr7wrKVQ024OswTw26sXl0z5lsnJnvAk6gvIf7DLBhj+pqK/OsyzzVKpl5FWUt68Mp61ifGRHLI+LF/a2svcy0rmHP0wazJEnqhs27+sbezHeT6aOUO07/aLoNR4h5atB9jzKPHjzTAZn5fuCYZlz0qK62Ms+6zFOt01xRfyrwQuA8YGfKOesn+1pYi5lpXcOcp2swS5KkWYuI3YCLgAcyc+1Zjl0buIByjrWsB+W1UrOO5doAmXlvn8tpPfPUoIuItYCrgNWBAzLzklmMPRz4FC1eq7E286zLPDUMImIv4HTKvT/AfXLOzLSuYcrTBrMkSZo1m3eSJEkadBGxCHgvsBlAZg7Vurf9YKZ1DUueNpglSZIkSZIkSV1xDWZJkiRJkiRJUldsMEuSJPVZRGzRg9dcEBGb1X7dNjBPDTr30brMsy7z1KBzH63PTOsaxTxtMEuSpFkZxROmeXBdRHwlIp4/1xeKiIURcSjwG+CgOVfWTuapQec+Wpd51mWeGnTuo/WZaV0jl6cNZkmSNFsjd8I0Dy4DDgR+HREXR8RbImL9mQ6OYllEnAHcCnwW2BD4RW/KHXjmqUHnPlqXedZlnhp07qP1mWldI5enN/mTJEmzEhHLgZ2BJ4AfA98Ezs3Mu2Y4PoDdgH2B1wHrASuAAzPz/F7U3AYRsTdwHLAlkM3jN8CVwC+BO4F7gEeAdYB1gc2BHYBtgcVAAI8CZwDHZuYd8/tfMTjMU4POfbQu86zLPDXo3EfrM9O6Ri1PG8ySJGnWRu2Eab40zfc9gIOBvYCFzVNTnbBF8/Mm4CzgS5l5W8+KbBHz1KBzH63LPOsyTw0699H6zLSuUcrTBrMkSerKKJ0w9UNErAcsA3YBdgKWAhsAawJ3U5r41wOXAMsz84o+ldoK5qlB5z5al3nWZZ4adO6j9ZlpXcOepw1mSZI0Z8N+wiRJkiRJmpgNZkmSJEmSJElSVxb0uwBJkiRJkiRJUjvZYJYkSZIkSZIkdcUGsyRJkiRJkiSpKzaYJUmSJEmSJEldscEsSZIkSZIkSeqKDWZJkiRphiIim8dvI+Jpk2xzS7PN6pOMHXs8HhF3RsRFEbF/hdp263jtcybZ5jnN88sneG6diPhoRPw8Ih6IiL9ExB8i4icR8cmI2HauNUqSJGn4rD79JpIkSZLG2Qw4HPhEF2M/0vxcCDwfeC2wLCK2z8wjKtX3PyPipZl56Uw2johNgEuA5wA3AV8H7gaeCbyA8t/6EPCzSvVJkiRpSERm9rsGSZIkqRUiIoF7gKRcrPFfM/POcdvcAjwbWJiZj40bS2bGuO3/Bvh+89ctMvOWLmvbDbgYuAH4b8B/ZuYu47Z5DnAzcElmvqzj92cCbwbOAg7OcW8SImIpsDQzr+qmNkmSJA0vl8iQJEmSZudB4FhgbeCYub5YZl4IXAcEsONcXw/4KXA+sHNE/N0Mx+zc/DxtfHO5qfE2m8uSJEmaiA1mSZIkafZOB24E3hIRz6vwemNXNdf6euE/Ao8Bn4iIhTPY/q7mZ43/FkmSJI0QG8ySJEnSLGXmo8D7KOsod7MO85MiYnfKWswJXD736iAz/x9wBmWpjLfNYMjZzc8zI+LEiNg9ItavUYskSZKGmw1mSZIkqQuZeS5wKbBPRLxsuu3HRMSHm8fHI+Jc4N8oVzCfnJm/rVjiR4D7gA9FxNOn2fZ04HhKw/woyprQd0bEzRHxhYjYumJdkiRJGiI2mCVJkqTuvaf5+cmIiCm3XOmY5vF+4BXAj4EDM/OImoVl5h2Uq6vXBz44zbaZmR8AlgL7AicDP2r+fjBwZUQcUrM+SZIkDQcbzJIkSVKXMvNS4FxgJ+DvZzgmmseCzFwvM5dl5td6VOKngd8D74yIZ8+gtj9n5tmZ+e7M3JXSnP4YsBpwWkQ8o0d1SpIkqaVsMEuSJElz8z7gUeD4iFij38V0ysyHgX8C1gSO62L8isw8GljevMYudSuUJElS29lgliRJkuYgM28E/gXYHHhHn8uZyP8Cfga8Adihy9e4v/k502VAJEmSNCJsMEuSJElz91Hgz5S1jpfUeMGI+HJEZEQcNJfXycwEjqQ0h4+f5H/rqIjYapLnXgYsAx6j3NRQkiRJetLq/S5AkiRJarvMvDsijgNOrPiyYxeDPDbXF8rMiyLiO8Cek2yyP3BiRFwH/AS4DVgMbEW5EWEA78nMW+daiyRJkoaLDWZJkiSpjlOBtwPPqfR6L6IsTfF/K73eUcB/p9ywb7w3Aa+iNJN3AzamNJX/AHwD+GxmLq9UhyRJkoZIlG/MSZIkSRoUEbEOcBfwycz8x37XI0mSJE3GNZglSZKkwfNy4FHgU/0uRJIkSZqKVzBLkiRJkiRJkrriFcySJEmSJEmSpK7YYJYkSZIkSZIkdcUGsyRJkiRJkiSpKzaYJUmSJEmSJEldscEsSZIkSZIkSeqKDWZJkiRJkiRJUldsMEuSJEmSJEmSuvL/AQ6qhv/ZkuoaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if(n_qty == 1):\n",
    "    groupM_aux = dfM.groupby(['NP', 'NS'])['TC']\n",
    "else:\n",
    "    groupM_aux = dfM.groupby(['NP', 'NS', 'Dist'])['TC']\n",
    "\n",
    "grouped_aggM_aux = groupM_aux.agg(['mean'])\n",
    "grouped_aggM_aux.columns = grouped_aggM_aux.columns.get_level_values(0)\n",
    "\n",
    "j = 0\n",
    "f=plt.figure(figsize=(20, 12))\n",
    "    \n",
    "ax=f.add_subplot(111)\n",
    "grouped_aggM_aux.unstack().plot(kind='bar', color=['b', 'g'], ax=ax) \n",
    "ax.set_ylabel(\"Time (s)\", fontsize=20)\n",
    "ax.set_xlabel(\"NP, NS\", fontsize=20)\n",
    "ax.legend([\"Best Fit\", \"Worst Fit\"], fontsize=30);\n",
    "\n",
    "ax.axvline((3.5), color='black')\n",
    "ax.axvline((7.5), color='black')\n",
    "ax.axvline((11.5), color='black')\n",
    "ax.axvline((15.5), color='black')\n",
    "    \n",
    "ax.tick_params(axis='both', which='major', labelsize=24)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=22)\n",
    "    \n",
    "f.tight_layout()\n",
    "f.savefig(\"Images/TCR_Tiempo_Barras.png\", format=\"png\")\n",
    "j = (j+1)%5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Para Tipo = 1\n",
      "Para Tipo = 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Gt8+YfYoOoeFNenpS0SE0tKt+eVXRITQ852D39aV5WOM8WZIkSWoInRaYV8vMFuDW8ldPGQK0VVwGWN6qT8UFZkrbbewFnJGZj3XUMSImA5MBRg0aVcVT9D6Le91ncU+SJNVCjfJkSZIkqSFEZnEfQ4mIh4AtM3NEG9duoHTwyaBKVzBHxFeAs4AZmXlCNbGMGzcu586dW82QXuVHrrrPj1x1j3Ow+5yD3ec87B7nYPc5B7uv3udhRNybmeOKjqOe1HueLNUDXx+6r95fH+qdc7D7nIPd5zzsnkaYg+3lyp0d8ldrC4FhETGojWujKG2fUWlx+VxKxeUrgRN7LEJJkiRJkiRJUpuKLjDfU45hj9aNETEY2AWoaKlERJwDnANcDRyXRS7LliRJkiRJkqQmUXSB+XpKp22fuk778ZT2Xr52dUNEbB8RO6x7g4iYCpwLfBf4THkPPEmSJEmSJElSjVV8yF8tZOZDEXEZcFJEzARmAWOBKcAc4LpW3WcD2wFrNnSJiM8D5wHPArcBR0estd/Losz8RU1/CEmSJEmSJElqUoUWmMtOBZ4GJgMTgCXApcDUClYj717+Phr4ThvX5wAWmCVJkiRJkiSpBgovMGfmKmBa+aujfmPaaJsETKpFXJIkSZIkSZKkjhW9B7MkSZIkSZIkqUF1aQVzRLwXOJrSfskbZeb4cvsYYA/gF5n5Ug/FKEmSJDUE82RJkiQ1m6oLzBFxPnAGb61+zlaX+wHfo7Sv8qXdjk6SJElqEObJkiRJakZVbZEREUcCZ1E6OG8X4Gutr2fmk8Bc4NCeClCSJEmqd+bJkiRJalbV7sE8Bfgj8NHMfBB4o40+84C/6m5gkiRJUgMxT5YkSVJTqnaLjPcAV2VmWwnzaguBEV0PSZIkSWo4NcmTI6IfcApwAjAGWAzcAEzNzKWdjH0XMBE4ENgeGAw8AXwfmN7Z+EYS50XRITS0PCc77yRJktSOalcwB9DSSZ8RwPKuhSNJkiQ1pFrlyRcD3wAeAU6mVByeAvykXHzuyGeB0ygVlc8H/gl4DPgX4K6I2LDKWCRJkqT1VLuC+Q/A3u1djIgNgA8CD3cnKEmSJKnB9HieHBE7USoqz8zMw1q1PwVcAhwJXNfBLX4AfC0zX2nV9h8R8QfgTOBY4FuVxiNJkiS1pdoVzDcA74+I09u5/mXgnXSc6EqSJEl9TS3y5KMorYyevk77FcAySttftCsz565TXF7t+vL3nauIRZIkSWpTtSuYpwOfAC6IiCOABIiIi4C/BsYBvwVm9GSQkiRJUp2rRZ68O6VtN+5u3ZiZyyPigfL1rtim/H1RF8dLkiRJa1RVYM7M1yNiP+CbwKeADcqXvkAp+b0GOCkz3+zRKCVJkqQ6VqM8eSSwJDNXtHFtAbB3RAzs5GDBtZS36pgKvEmFq6lffOxFrtr3qkqfohCTnp5UdAgN7apfXlV0CA3POdh9zsPucQ52n3Ow+5yH3dPIc7DaFcyUP2Y3KSK+QGnVxBbAK8Ddmbm4h+OTJEmSGkIN8uQhQFvFZXjrsMAhQMUFZkorrfcCzsjMx9rrFBGTgckAowaNquL2kiRJajaRmUXHUBfGjRuXc+fOLTqMdsV5UXQIDS/Pca53h3Ow+5yD3ec87B7nYPc5B7uv3udhRNybmeOKjgMgIh4CtszMEW1cu4HSlhyDKl3BHBFfAc4CZmTmCZXGUe95Mvhvs7vq/d9lI3AOdp/zsHucg93nHOw+52H3NMIcbC9XrvaQP0mSJEm9YyEwLCIGtXFtFKXtMyotLp9Lqbh8JXBij0UoSZKkplf1FhkRMQQ4FtiF0gEhA9rolpn54W7GJkmSJDWMGuTJ9wAHAnsAd7Z6nsHl5/hVhXGdA5wDXA0cl36EUZIkST2oqgJzRLwX+DkwHOho3btJqyRJkppGjfLk64EzgFNpVWAGjqe09/K1rZ5/e2BAZj66TlxTgXOB7wKfycyWKp5fkiRJ6lS1K5inU0qaV6+AWJCZq3o8KkmSJKmx9HienJkPRcRlwEkRMROYBYwFpgBzgOtadZ8NbEer4nZEfB44D3gWuA04OmKt2veizPxFd2KUJEmSqi0w7wX8MDP/pRbBSJIkSQ2qVnnyqcDTwGRgArAEuBSYWsFq5N3L30cD32nj+hzAArMkSZK6pdoC82vAM7UIRJIkSWpgNcmTy6ugp5W/Ouo3po22ScCkno5JkiRJaq1flf1vB/asRSCSJElSAzNPliRJUlOqtsB8BjA2Ir4U62zgJkmSJDUx82RJkiQ1paq2yMjMJyPig8BdwPER8QDwSttd89ieCFCSJEmqd+bJkiRJalZVFZgjYhvgR8Bm5a+3t9M1ARNnSZIkNQXzZEmSJDWrag/5mw68C/hvSidRLwTe7OmgJEmSpAZjnixJkqSmVG2BeX/g1sw8rhbBSJIkSQ3KPFmSJElNqdpD/voBD9UiEEmSJKmBmSdLkiSpKVVbYP4tsHMtApEkSZIamHmyJEmSmlK1BeYzgX0j4shaBCNJkiQ1KPNkSZIkNaVq92CeANwOXBsRJwL3Aq+00S8z8yvdDU6SJElqEObJkiRJakrVFpjPbfXnD5W/2pKAibMkSZKaxbmt/myeLEmSpKZRbYF5v5pEIUmSJDU282RJkiQ1paoKzJk5p1aBSJIkSY3KPFmSJEnNqtpD/iRJkiRJkiRJAiwwS5IkSZIkSZK6qMMtMiKiBWgBdszMx8uPs4L7ZmZWu7+zJEmS1BDMkyVJkqSSzpLbX1FKlJet81iSJElqZubJkiRJEp0UmDNz344eS5IkSc3IPFmSJEkq6XQP5oj4dES8tzeCkSRJkhqFebIkSZJU2SF/VwEfq3EckiRJUqO5CvNkSZIkNblKCsySJEmSJEmSJK3HArMkSZIkSZIkqUssMEuSJEmSJEmSuqR/hf2GRsToam6cmc92IR5JkiSpkZgnS5IkqalVWmA+pfxVqazi3pIkSVKjMk+WJElSU6s0uf0L8HItA5EkSZIakHmyJEmSmlqlBeaLM/P8mkYiSZIkNR7zZEmSJDW1wg/5i4h+EXFaRDwaEcsj4rmImBYRG1U4/ssR8f2IeDIiMiKernHIkiRJkiRJkiTqoMAMXAx8A3gEOBn4PjAF+ElEVBLfV4H9gSeAl2oVpCRJkiRJkiRpbYUeMBIRO1EqKs/MzMNatT8FXAIcCVzXyW22z8wny+N+D2xco3AlSZIkSZIkSa0UvYL5KCCA6eu0XwEsAyZ2doPVxWVJkiRJkiRJUu/qdAVzZtayCL070ALcvc5zLo+IB8rXJUmSpLpT4zxZkiRJaghFJ8UjgSWZuaKNawuAYRExsJdjkiRJkiRJkiRVoOgC8xCgreIywPJWfWoiIiZHxNyImLt48eJaPY0kSZIkSZIk9UlFF5iXAYPauTa4VZ+ayMwZmTkuM8cNHz68Vk8jSZIkSZIkSX1S0QXmhZS2wWiryDyK0vYZb/RyTJIkSZIkSZKkChRdYL6nHMMerRsjYjCwCzC3iKAkSZIkSZIkSZ0rusB8PZDAqeu0H09p7+VrVzdExPYRsUMvxiZJkiQVKiL6RcRpEfFoRCyPiOciYlpEbFTh+C9HxPcj4smIyIh4usYhS5Ikqcn0L/LJM/OhiLgMOCkiZgKzgLHAFGAOcF2r7rOB7YBofY+IOKbcDjAcGBgRZ5UfP5OZ363hjyBJkiTV0sWUcuMbgWm8lSvvGhHjM7Olk/FfBf4M3AcMrWWgkiRJak6FFpjLTgWeBiYDE4AlwKXA1AoSZoBjgX3WaftK+fscwAKzJEmSGk5E7AScDMzMzMNatT8FXAIcydoLMtqyfWY+WR73e2DjGoUrSZKkJlX0Fhlk5qrMnJaZ787MQZk5KjO/kJmvrdNvTGZGG+P3zcxo52vfXvtBJEmSpJ51FKVP701fp/0KYBkwsbMbrC4uS5IkSbVSeIFZkiRJUpt2B1qAu1s3ZuZy4IHydUmSJKlQFpglSZKk+jQSWJKZK9q4tgAYFhEDezkmSZIkaS0WmCVJkqT6NARoq7gMsLxVnx4XEZMjYm5EzF28eHEtnkKSJEl9hAVmSZIkqT4tAwa1c21wqz49LjNnZOa4zBw3fPjwWjyFJEmS+ggLzJIkSVJ9WkhpG4y2isyjKG2f8UYvxyRJkiStxQKzJEmSVJ/uoZSv79G6MSIGA7sAc4sISpIkSWrNArMkSZJUn64HEjh1nfbjKe29fO3qhojYPiJ26MXYJEmSJAD6Fx2AJEmSpPVl5kMRcRlwUkTMBGYBY4EpwBzgulbdZwPbAdH6HhFxTLkdYDgwMCLOKj9+JjO/W8MfQZIkSU3AArMkSZJUv04FngYmAxOAJcClwNTMbKlg/LHAPuu0faX8fQ5ggVmSJEndYoFZkiRJqlOZuQqYVv7qqN+Ydtr37fmoJEmSpLe4B7MkSZIkSZIkqUssMEuSJEmSJEmSusQCsyRJkiRJkiSpSywwS5IkSZIkSZK6xAKzJEmSJEmSJKlLLDBLkiRJkiRJkrrEArMkSZIkSZIkqUssMEuSJEmSJEmSusQCsyRJkiRJkiSpSywwS5IkSZIkSZK6xAKzJEmSJEmSJKlLLDBLkiRJkiRJkrrEArMkSZIkSZIkqUssMEuSJEmSJEmSuqR/0QHUu5UrVzJ//nyWL19eaBw3H3hzoc/fF8ybN6/oENq1wQYbMHToUIYNG0a/fr7vI0mS6l9LSwtLlizh5ZdfZtWqVYXGYq7cPfWcJwMMHjyYbbbZhgEDBhQdiiRJaoMF5k7Mnz+fTTbZhDFjxhARhcWxdOHSwp67rxg7cmzRIbQpM1m5ciWLFi1i/vz5jB49uuiQJEmSOjV//nwigjFjxjBgwABz5QZWr3kylHLlF198kfnz5/P2t7+96HAkSVIbXCrZieXLl7PFFlsUmjCrb4sIBg4cyKhRo1i61F+OJElSY1i6dCmjRo1i4MCB5sqqmYhgiy22KPwTpZIkqX0WmCtgwqze4NYYkiSp0Zi/qDf4+5gkSfXNjFCSJEmSJEmS1CUWmJvcwucWsvuo3XnzzTcBOOHwE7jpupu6fd8Z02Zw9slnt3v90D0P5Xe/+l3V973/d/dz2F8f1p3Quu2rX/0qxx13XKExSJIkqfbMlatnrixJUvPxkL8u2OqirVi0dFHN7j9ioxG88I8vdNrv0D0PZfGixdx8380M3XzomvajDziaPzzyB3702x8xctuRNYuzCLvuuSs/vPOHax4fuuehnHnhmez5oT1r8nx33HEHEydOZP78+WvazjjjjJo8lyRJUqOrlzwZzJXBXFmSJPUOVzB3QS2T5mrvP3Lbkdx6061rHv9x3h9ZsXxFLcLqczKTlpaWosOQJEnqM+opTwZz5e4wV5YkSZWywNzgDj7sYH72g5+tefzT7/+UCYdPWKvP/972v3zqwE+x77v3ZcK4CcyYNqPi+//4f37MJ/b5BPvvuD8nH30yz89/fs21i6ZexIRxE9j33ftyzN8cw/2/u3+tsW8sf4Mvn/hl9nnXPkz8yEQef/jxNp+jpaWFq751FR/b+2OM32k8Xz7hy7zy0itt9r33rnuZsFvp55t68lReWPACp3/mdD70Vx/i6suvBuChex/is4d+lv3G7sfR44/m3rvuXTN+33335cwzz+QDH/gAQ4YM4cknn+TKK69k7NixbLLJJrzjHe/g29/+NlA6Gf2ggw5i4cKFbLzxxmy88cYsXLiQc889l4kTJ771d/TjH7PTTjsxdOhQ9t13X+bNm7fm2pgxY7jooot473vfy6abbsonP/lJT8CWJEnqJebK5sqSJKn2LDA3uJ3fvzNLX13KU394ilWrVvGLH/+Cgw47aK0+Gw7ZkPO+eR63z7ud6VdP54dX/5A7brmj03vfccsdXHnplVzwnxfw8wd/zi577MKZnztzzfUd37cj1/78Wm57+DY+8rGP8KUTvrTWipA5P5/D+L8dz+yHZ/ORv/sI/3TsP/HmyjfXe57/+a//Yc4tc/j2D77NrPtmscmmm3DBmRd0Gt/5l57PVqO2YtqV0/jVH37Fpz/3af70/J847dOncewpxzL74dmcMvUUvnj8F3npxZfWjPvud7/LjBkzePXVV9luu+3Ycsst+YiVE6kAACAASURBVOlPf8pf/vIXrrzySk477TTuu+8+NtpoI26++WZGjhzJa6+9xmuvvcbIkWt/jPLxxx/nqKOOYvr06SxevJiDDz6YQw45hDfeeGNNnxtuuIFbbrmFp556igcffJCrrrqq059NkiRJ3WeuXFmuvHjx4jXjzJUlSVK1LDD3AatXZvzuV79jzDvHMHyr4Wtd323v3Xjn2HfSr18//mrHv+LAjx7Ifb+5r9P7zrxmJpNOmsTb/+rt9O/fn89M+QyPP/z4mpUZBx92MEM3H0r//v2ZeOJEVr6xkmeeeGbN+LHvGcuH//bD9B/Qn09N/hQrVqzgofseWu95brzmRv7hi//AiJEjGDhoIJNPn8zsn81ec5hKNW6eeTN77783H/jwB+jXrx97fmhPxr5vLL+e/es1fSZNmsROO+1E//79GTBgABMmTGD77bcnIthnn3048MADufPOOyt6vuuvv54JEyZwwAEHMGDAAP7xH/+R119/nbvuumtNnylTpjBy5Eg233xzDjnkEB544IGqfy5JkiR1jbnyW9rLlWfNmrWmj7myJEmqlof89QEHH34wkz8+mYXPLlzvI38Av7/v93zrq9/iiceeYOXKlax8YyUfnvDhTu/7wvwXmDZ1GtPPn75W+5+e/xNbb7M11/zHNfzoez9i8aLFRARLX13Ky39+eU2/ESNHrPlzv3792HLrLVn8wmLW9fz85/nn4/6Z6Bdr2jbYYAP+vPjPbLn1lhX9HbS+1+yfzebO295Ket9c+Sa77b3bmsfbbrvtWmNuvvlmzjvvPB5//HFaWlpYtmwZ73nPeyp6voULF7LddtutedyvXz+23XZbFixYsKZtq622WvPnIUOGsHDhwqp+JkmSJHWdufLa92orV37+b97a2sNcWZIkVcsCcx+w9TZbM3L0SO66/S7Onnb2etfPOuksjph0BN+85psMGjyIaVOn8fJLL7dxp7WNGDmCz0z5DAd9/KD1rt3/u/u5+vKrufz6y3nHu99Bv3792H/H/cnMNX0WLXzrEJaWlhb+9Pyf1lsxsvp5pn5jKu/b/X2V/shrRMRaj0eMHMFBhx3EWReeVdGYFStWcNhhh3H11Vfz0Y9+lAEDBvCxj31szc+x7v3XNXLkSB566K2VJpnJc889x6hRo6r+WSRJktTzzJXXvldbufK4kePaHGOuLEmSKuEWGX3E2RedzeU3XM6GQzZc79qy15bxtqFvY9DgQTx8/8NrnaTdkY8f83Gu+tZVPPHYEwC89pfXuO0nt6255wb9N2DoFkNZ9eYqrrj4Cpa+unSt8fMemsfts27nzTff5HtXfI+BAwfynvevv9rhsGMO4/J/u3zNxwlfevEl5tw6p6IYNx+2OQuefWsFxEEfP4g7f3Env7njN6xatYoVy1dw7133rpXAt/bGG2+wYsUKhg8fTv/+/bn55pv5+c9/vub6iBEjePHFF3nllbYPUjniiCP42c9+xuzZs1m5ciXTpk1j0KBB7L333hXFL0mSpNozVy5pL1eeP39+m+PNlSVJUiVcwdxHbDNmm3avffGrX2T6+dO58KwLef9e72f8IeN59S+vdnrP/Q7aj9eXvs6ZnzuTF+a/wEabbMSeH9qT8YeMZ69992Lv/fbm8L8+nMFDBnP08Uev9TE/gH0O3Idf/PgXnHfqeWwzZhsu+M8L6D9g/Sl35HFHkpmcdNRJLFm0hM2GbcYBhxzAPh/Zp9MYJ508iQvPupBL//VSPnvKZznmxGOY9t/TuORfL+Gsz59Fv3792GmXnfjS17/U5vhNNtmESy65hCOOOIIVK1ZwyCGHcOihh665vsMOO3DUUUfxjne8g1WrVvHII4+sNf7d734311xzDSeffDILFixgl1124Sc/+QkDBw7sNHZJkiT1DnPljnPlg/ZYfxU2mCtLkqTKROuPaTWzcePG5dy5c9drnzdvHmPHjl2rbauLtmLR0rZXxPaEERuN4IV/fGGttrkL149N1Wn90b961dZ8qxdxXscfgVTn8hz/v+0u52H3OAe7zznYffU+DyPi3sys/6ShF9V7ngzmyt1lntx9vj50X72/PtQ752D3OQe7z3nYPY0wB9vLlV3B3AVtJbWSJElSszNPliRJaj7uwSxJkiRJkiRJ6hILzJIkSZIkSZKkLrHALEmSJEmSJEnqEgvMFfAgRPUG55kkSWo05i/qDc4zSZLqmwXmTmywwQasXLmy6DDUBF5//XUGDBhQdBiSJEkVGTBgAK+//nrRYagJrFy5kv79PZ9ekqR6ZYG5E0OHDmXRokW0tLQUHYr6qMxk2bJlLFiwgC233LLocCRJkiqy5ZZbsmDBApYtW+YKU9VMS0sLixYtYtNNNy06FEmS1I66eBs4IvoBpwAnAGOAxcANwNTMXFrr8R0ZNmwY8+fP57HHHuvObbptyctLCn3+vmDeK/OKDqFdAwYMYMSIEbztbW8rOhRJklRH6jlPXp23LFy4sPBP/Jkrd08958kAG220EcOGDSs6DEmS1I66KDADFwNTgBuBacDY8uNdI2J8Zna2fLi749vVr18/Ro8e3dXhPWbH83YsOoSGl+e4skaSJDWcus2ToVRkroc3yM2Vu8c8WZIkdUfhBeaI2Ak4GZiZmYe1an8KuAQ4EriuVuMlSZKkemSeLEmSpEZQD3swHwUEMH2d9iuAZcDEGo+XJEmS6pF5siRJkupePRSYdwdagLtbN2bmcuCB8vVajpckSZLqkXmyJEmS6l49FJhHAksyc0Ub1xYAwyJiYA3HS5IkSfXIPFmSJEl1r/A9mIEhQFtJL8DyVn3e6OnxETEZmFx++FpEPNZptGrPMKCuj++Oc6PoEFR7dT0PnYNNwTmootX1HISGmIfbFR1AK+bJfUdd/9tsgH+X6r66noPgPGwSdT0PnYNNwTnYfW3myvVQYF4GbNnOtcGt+vT4+MycAczoLEB1LiLmZua4ouNQc3MeqmjOQRXNOdjnmCf3Ef7bVNGcg6oHzkMVzTlYO/WwRcZCSh/PG9TGtVGUPtbX3qqMnhgvSZIk1SPzZEmSJNW9eigw30Mpjj1aN0bEYGAXYG6Nx0uSJEn1yDxZkiRJda8eCszXAwmcuk778ZT2hLt2dUNEbB8RO3R1vGrKj1CqHjgPVTTnoIrmHOxbzJP7Dv9tqmjOQdUD56GK5hyskcjMomMgIi4FTgJuBGYBY4EpwK+B/TOzpdzvaWC7zIyujJckSZIaiXmyJEmS6l29FJg3oLSyYjIwhtKJjtcDUzPztVb9nqbtxLmi8ZIkSVIjMU+WJElSvauLArMkSZIkSZIkqfHUwx7MkiRJkiRJkqQGZIFZkiRJkiRJktQl/YsOQI0tIjYDtqR0QvnizHyp4JAkSZIkSZIk9RL3YFZVIqIf8FHgE8A+wFbrdHkBuAP4PvBjTyZXLUXEEODdtHqTA3gsM5cVGpiaSkS8C9iJtefh7zPzD4UGpqbhHJTqjzmK6oGvDypS+f/BD9LGHAR+7f+HqjXnYO+ywKyKlE8g/wfgy8DWwFLgHuAJ4EUggM2BdwLjgI2A54GvAv+RmasKCFt9UHnV/CRKb3LsxvqfxHgTuBe4AfiOq+pVCxExFjiR0jwcsbq5/H31C+siSvPw25k5r3cjVF/nHJTqjzmK6oGvDypaRBxEaQ7+DaX/B2OdLknp/8ObKdUKbundCNXXOQeLYYFZFYmIR4Ftgf8Bvgv8qr3VyeVVzvsBE4EjgGczc2xvxaq+KSI2Bc4GPgcMBh4DfkPbb3LsRWnV0HLgMuBfMvOVAsJWHxMR2wP/Bvwd8DpwJx3Pw78GNgRmAl/MzCcLCFt9iHNQqj/mKKoHvj6oaBHx18A0SgvOngZupf05+P8oFf9GU3rj7bTM/N/ej1p9iXOwWBaYVZGIuBj4emYuqnLcVpQSltNqE5maRUQsofTL2LeBazLzqU76vwM4BpgMDMrMYbWPUn1dRKwAHgK+CczMzKWd9N8IOByYAuyUmYNrH6X6MuegVH/MUVQPfH1Q0SJiFXATMD0z76xwzD7AqcAhmekZYeoW52CxLDBLaggRcQqlj6+sqHLcIODEzPxmbSJTM4mIj2bmj3p7rLSac1CqP+Yoqge+PqhoEfG+zPy/3h4rreYcLJYFZkmSJEmSJElSl/QrOgBJkiRJkiRJUmNyfxFVJSJ2AM4HdqZ0+vBVmfmdNvp9Crg6Mzfo5RDVBCJiCHAwsDswChgCLAMWAHOBWZ3tOyd1V0RsC3yStufhPcD3M/OZ4iJUX+cclBpTRGwIDM/MZ4uORX2Trw8qWkTsBUyk/Tl4bWb+prgI1dc5B3ufW2SoYhGxDaWDIzYF/gxsQulNiluAT2bma636WmBWTUTE3wMXUTr9Ndrp9hJwemZe1VtxqblExFTgDGAg0ELpVOLlwGBgC0qfEFoJfC0zzy0oTPVhzkGp/pQPCrqQVgsxKP0bfGOdfubJqhlfH1SkiOgPzAD+ntLvagspFfRWz8FRwEggge8Cx2bmqmKiVV/kHCyOW2SoGmdTSlQOKJ92PRz4BvA3wG0RsWmRwanvi4jDgSuBJym9YLwL2AjYoPz9XeX2J4D/KveXelREnAicC/wU+BAwJDNHZOZ2mTmC0rvj+5Svnx0R/1BYsOqTnINS/Sl/yu9W4L3AI5Ry5nOA30bEVkXGpubh64PqwBeBScDFwOjM3CYz98zMfcrftwFGA9OBT5f7Sz3JOVgQVzCrYhHxKPCzzDx9nfbDgWsorW4+IDNfdmWGaiEi7gFeBcZnZksH/foBs4FNMnNcb8Wn5hARvwcezcxO38CIiJnAuzJz59pHpmbhHJTqT0RcB3wE+GBmzouIAD5H6VNX84H9MnN+ua95smrC1wcVLSL+CPwyM4+voO9/Avtm5jtrH5mahXOwOK5gVjVGA79ftzEzfwB8nNLHAWdHxGa9HZiaxk7AdR0VlwHK168DduyVqNRstgdurrDvz8r9pZ7kHJTqz57A5Zk5DyBLLgM+TGlbgjkRMbrIANUUfH1Q0bYBKt3X9i5K2xVIPck5WBALzKrGEmBYWxcycxbwd8BYSitHt+jFuNQ8XqPyF4BR5f5ST3sR2KHCvmPL/aWe5ByU6s/WlLbwWktm3gUcAGwG3BERY3o3LDUZXx9UtIXAXhX2/X/A8zWMRc3JOVgQC8yqxjxg//YuZuYtlIrMOwBf762g1FRuAU6PiIM76hQRE4DTqHwFh1SNG4EpEfG5iBjYVoeIGBARnwdOBmb2anRqBs5Bqf4sonRo0Hoy815gPPA24JfAO3oxLjUXXx9UtGuAYyPigohoc2FQRIyKiAuBz5b7Sz3JOVgQ92BWxSLiFEr7yL0rM5/qoN+BwI+Age4tp54UEVsDvwa2o3SQ328pnQi7AhhEadXyXpQ+7vcssHdm+o6kelREvA24DRgHLAXuZ/15uCulgyfvBT6cmX8pJlr1Rc5Bqf5ExE3AZpm5Twd9dgV+AQyl9HuYebJ6lK8PKlr5jY0bgEOBpDT/1p2Do4CgdNjk4Zn5RjHRqi9yDhbHArMqFhFDKe0vNy8zn+2k727Azpn5nV4JTk2jvMf3l4GjaHu7jAXA94CvZ+afezM2NY+IGAAcDxxN6Re1DVtdfh24j9I8vCIzV/Z+hOrrnINSfYmI44FvA7tk5oMd9FtdZN7MArNqwdcH1YPyJ06PpvRmx0hgCLCM0vYF9wDfK2+zKdWEc7D3WWCW1LAiYivWebHIzBeKjUrNJiKC0t6aq+fhS+mLq3qRc1AqXkQMAsYAizt7g7t82N/bM3NOb8Sm5uXrgySpt1hgliRJkiRJkiR1iYf8qSYiYmpEfDYi/OifJEm9KCK2iogfR8T/KzoWSVJ9iohNyqvppUJExIbOQfW2iNgmIh6MiA8XHUtfY4FZtXIucAXwSER8rOBY1IQi4syIeLPoONQ3RcQOEXFDRDwSEb+MiL9vp9+nImJVb8enprcR8LfAVkUHIml9EfHfEXF++XwTqShTgHYPbpe6KiL2iYi7I2JZRDwVEeeUD15b18dxDqr3DQJ2Bt5WdCB9Tf+iA1Cf9RlgE2B/4L+Bm4oNR00qig5AfU9EbAP8BtgU+DOwPfChiDgC+GRmvlZkfOr7IqLdA8TKVv8Sd3FEnAdkZr6vxmFJqtyk8vfPR8RXMnN6kcFIUk+JiB2AW8sPfw9sDZwDfDQiDva8HNVaRPylsy7l79eUFwJlZm5a47CaggVm1URmfqf8x2+5TYZ6SpUfoXJVkGrlbEoFvAMyc3ZEvK3c9gXgtoj4SGa+UmiE6ut2pnRY0+J2rq/O7zYEPGxDqj/78dZCjGMBC8zqERHxoSq6v71mgaiZTQWWAh/MzHnlgyY/B1wE3BkR+2Xm/EIjVF+3MfAqcF871zcE9qC0er69XFpd4CF/khpGRLRQZbEkM32DQz0qIh4FfpaZp6/TfjhwDfAQpeLzyxHxKeBq56F6UkTcQOljpd8Czlp31XxEvBN4HDg8M2cWEKIkqQBV5spBaeWeOYp6TEQ8AVyXmWev07438FPgJWC/zHzWPFm1EBEXAKcCs4CTM/O5da6bJ9eIK5glNZI3gT8CsyvoO47SO5NSTxtN6SN/a8nMH0TEMuCHwOyIGN/rkakpZOYREfG3wGXA4RFxSmb+sHWXgkKTJBVrBfAIcF0FfT8MfKS24agJbQ08uW5jZt4VEQcAvwDuiIj9ez0yNYXM/OeIuA6YQelMsHOBizOzZXWXwoLr4ywwqyoRMYjSR/l2BhZRenfyD230Gw+ckZm+cKgnPQIsy8yTO+sYEWdigVm1sQQY1taFzJwVEX8HzKT0RshVvRiXmkhm/jQifgl8Fbg+Im4BPp+ZzxQcmtT0ImJLYDdgFDCE0pY2C4B7M/NPRcamPu8hoF9mTuusY0QMxgKzet4iYGRbFzLz3nKd4OfALymd1ST1uMx8ICL2Ak4Bzgc+HRGTM/N3BYfWp/UrOgA1jogYAvwWuBQ4kdJm/b+PiH9uo/sIYJ9eDE/N4T7gfRHh/10q0jxK+2a2KTNvAf4O2AH4em8FpeaTmUsz8xRgb2Bb4OGI+BJvHfInqRdFxE7lN3sWUvoo+AxK+yvPKD9eGBG3RsTOBYapvu0+YOeIGFB0IGpa/wcc2N7FzLwPOIDSPvTn9FZQaj6Z2ZKZF1NaHLkA+HVE/AewWbGR9V0WaVSNU4H3UVot9V5gAqUk5msRcXmRgalp3AzcTTvviq/jV5TerZR62ixgfMT/Z+/O462uysWPfx7gIKEmKYiBKU4paWaKaTaIinaVNMu0NOri3C3Ha7dBFAXvtX4qRpp200oyrTSj0pzFq3nTVBxuaphDWgGK4CwIMjy/P/YGD3CGvfcZvmezP+/X67w2+/tda+3n2DrxnIf1XStaPRwnM28GDuTtU4qlLpOZ9wE7Av9F6XCdW/DxP6lbRcQHgHuAD1N6euVYSrnyXuXXY4GfArsCd5fbS53tGuAXtPKk1SquBY7o2nDUgK4HPhYR27fWIDMfolRkfqXbolLDysy/Z+Z+wBcp/X52O+bJXcJD/lSxiHgIeDwzD212rRfwPeCrwI8z8+jydTfsl7RGiogBwC7AjMz8RzttdwK2y8yfdktwanjlg0smUdor/JTMvL3gkKSGEBE3AlsCu2fm7DbaDaH0j+BPlH/hlaQ1RnlLzWHA3Mx8qZ22mwCbZead3RGbFBHvAs6klCd/xy0zOpcFZlUsIl4DvpaZl7Rw7yxgHDAlM4+wwCxJkqRGERGvAuMz83sVtD0JmJCZ63V9ZJIkSV3PQ/5UjYVAi/t5ZebpEbEUGB8RQemxA0mSJKkRBJU/cpu4hZIkSVqDuAezqvE0pX3jWpSZZwITgH/FvW9VsIgYHxFHRISr6FUY56GK5hyUus19wPERsVFbjcr3jwd8LFeFioifRMTE8tZfUrdzDqpozsHO5RYZqlhETAROBDbOzNfbaDee0r426RYZKkpELKO0Qugp4BuZ+duCQ1IDch6qaM5BqXtExC7AHcAi4GrgT5ROrV8ErAUMpXQA4MHl9yPd+1FFKv/9APAycFZmTi4yHjUe56CK5hzsXBaYVbGI2IrSScO/yswH22n7VWBEZh7eLcFJq4iIfwXWBfak9Evc+gWHpAbkPFTRnINS94mIXYELgBHlS81/0Vq+JcZ04MTMvKc7Y5NWFRG78/bfD3tn5vsLDkkNxjmoojkHO5cFZklrvIjonZlLi45Djc15qKI5B6XuERFbADsDQ4D+wAJgNnB/Zj5dZGySJEldwQKzKhIRe2XmtBr7jsrM2zo7JkmSJEmSJEnFssCsikTEYuAu4HzgxvZWQEVEE/BJ4CTgw5nZt+ujlCRJkiRFxIbATpT2/16+kn4W8EBmvlBkbGoMzkEVzTnYvfoUHYDqxgcpFZevBeZFxK2UTst+GniJ0r5y6wNbAbtS2sPmXcAtwA5FBKw1U0SsBRwJbAfMAX6emU+20G4UcGpm7tnNIaoBOA9VNOegVL8iYgxwhD+X6goRsS0wCRhF6Xe0aHY7gYyIacApmfloASFqDeccVNGcg8VwBbOqEhEfBr4CfApYh5UPL4HSD+5rwFTgB5l5f/dGqDVZRPQH/ghsz9t/SSwGTs/Mc1Zp+wXg8szs3b1Rak3nPFTRnINSfYuIccBEfy7V2SLiA5SeOk3gV8CfKK3WWwj0o7SK78PAZyn9/fGxzPy/YqLVmsg5qKI5B4vjCmZVpXzi9T0R0ZvSowbvAwZR+uGdCzwKPJSZy4qLUmuwk4APAP8FXAW8BxgPfDsihmXmV4oMTg3DeaiiOQclSS35DqWnWnbPzNmttPlRRJwO/AH4NrBfdwWnhuAcVNGcgwWxwKyalPdgvq/8JXWXg4GrMvP08vtHI+Jm4HvAVyOiKTOPLi48NQjnoYrmHJR6mIj4WxXN1+uyQNTodgPGt1FUASAzZ0fE94EJ3ROWGohzUEVzDhbEArOkerIF8IPmF8qr5Y+PiFeAcRHROzOPKCQ6NQrnoYrmHJR6nmHAy0Cbv9CW9e/aUNTAgtW3MGxNsvK+pFJncA6qaM7BglhgllRPFgJNLd3IzNMjYikwPiICuL1bI1MjcR6qaM5Bqed5BngqMz/RXsOIOA1XTKlr3EfpHxuvzsznW2sUERsBxwP3dltkahTOQRXNOVgQC8yS6snTwK7ARS3dzMwzIyKBM4CR3RiXGovzUEVzDko9zwPAHhW29ZR1dZVxwB3A4xFxNW8fbrUIWIu3D7c6uPz+C8WEqTWYc1BFcw4WJDLNbyTVh4iYCJwIbJyZr7fRbjxwJpCe0K7O5jxU0ZyDUs8TEd+idPDm5pn5bDttxwBHZmalBWmpYhGxK3ABMKJ8qfkv/MsfBZ8OnFg+wF3qVM5BFc05WAwLzJLqRkRsBRwB/CozH2yn7VeBEZl5eLcEp4bhPFTRnIOSpPZExBbAzsAQSvt+L6C0R/j9mfl0kbGpMTgHVTTnYPeywCxJkiRJkiRJqkmvogOQpEpExF4d6DuqM2NR43IeqmjOQann8edSkiQ1OgvMkurFTRFxe0R8MiLa3Us0Ipoi4tMRcSdwQzfEp8bgPFTRnINSz+PPpepORIyJiNuLjkONyzmoojkHO1efogOQpAp9EDgfuBaYFxG3AvcBTwMvUdqsf31gK2BXYE/gXcAtwA5FBKw1kvNQRXMOSj2PP5eqR5sCuxcdhBqac1BFcw52IgvMkupCZj4K7BMRHwa+AnwKOJSVT4SF0i9xrwFTgR9k5v3dGqjWaM5DFc05KPU8/lxKkqRG5yF/kupS+RHUnYD3AYMo/RI3F3gUeCgzlxUYnhqE81BFcw5KPY8/lypKRPytiubrAQMys91tXaRKOQdVNOdgcSwwS5IkSZJU5yJiGfAyMLuC5oOAQRZW1Jmcgyqac7A4bpEhSZIkSVL9ewZ4KjM/0V7DiDgNmND1IanBOAdVNOdgQXoVHYAkSZIkSeqwB4AdK2zro8zqCs5BFc05WBALzJIkSZIk1b+HgA0iYlgFbf8O/KFLo1Ejcg6qaM7BgrgHsyT1UBGx/P+g/wFsnZkLW2jzLLAp0JSZS1rou9zyvaj+DPw4M6+sMIYpwL+W347OzBtaaHMmcAZwdGb+aJV7OwMnAR8FNgIWUTro6C/AXcBFmTm/klgkSZIkSVLP4x7MktTzbUKpSPudGvou31OqCdgaOBDYIyJ2ysx/r3KscyLi5sxcWknjiBgD/BQI4HbgN8BSYDNgBPBJYCrwVJVxSJIkSZKkHsItMiSpZ3sZeAn4VkQMrLZzZp5Z/hqXmZ8FPkFpr6mTKnxsaLmngG2BIyppHBH9gYvKn7V3Zo7KzJMy85TM/ExmbgJ8BJhXRQySJElqQUTs1YG+ozozFjUm56CK5hwslgVmSerZFgBnAe+ktA1Fh2TmNOBxSquKd66i61nlWCZGxNoVtN+OUsyPlj+zpVjuzsxXqohBkiRJLbspIm6PiE9GRO/2GkdEU0R8OiLuBFbbAk2qgXNQRXMOFsgtMiSp57sIOA44NiIuzMwnOjhelF+r2YR/NjAJOB34Ou0Xu18svw6JiLXdZ1mSJKlLfRA4H7gWmBcRtwL3AU9TehougPWBrYBdgT2BdwG3ADsUEbDWOM5BFc05WCAP+ZOkHqp8UN+szNw4Ij4L/Ar4TWZ+plmbZ2njkL/MjFXGHEXpL1CAzTLz7+3EMIXSIX97A3+itFXGOsBWmflcuc2ZrHLIX0QEcC+lVdL/B1wK3A08lplvVfvfQpIkSe2LiA8DXwE+RSlnW/UX/gBeo3QOxg8y8/7ujVBrOuegiuYcLIYrmCWpDmTmNRFxD/DpiPhoZv5vJf3KxV9Y+ZC/AL7bXnG5hRjeiIgzgP+mtGXGUW20zXJR/KfASOD75VuLI+Ih3v7L/LVqYpAkSVLrMvMe4J7y4+E7Ae8DBlEqsMwFHgUeysxlxUWpNZlztv0yuQAAIABJREFUUEVzDhbDFcyS1EM1X8Fcfv9hSquA7wN2LRdxn6WNFczNJPAKpdXEP87MKyqMYQrlFcyZeVv5L+k/A9sAO2TmIy2tYF5ljOGUVkCPAD5EqdAN8A9gZGY+U0kskiRJkiSp5/GQP0mqE+V/ib2GUpH2kAr7RPmrV2aun5l7VFpcbmW8pZT2YO4FnFNhnxmZeUFmfikztwGGA/cAmwDfrTUWSZIkSZJUPAvMklRfvgksBr4dEX2LCCAzrwduB/4lIvauof/jwBfLb/fszNgkSZIkSVL3ssAsSXUkM58GLgY2A44vMJSvUdp241xq+7vk9fJrtNlKkiRJkiT1aBaYJan+TKS0n/I4SqfidlhETImIjIixlbTPzIeAK4APAIe2MN5mEXFCRKzXwr2gFDvAH2qPWpIkSY2onLdmRPw9Ivq10ubZcps+rfRd/rU0IuZFxO0R8YUqYpjSbIz9WmlzZvn+aodjR8TOEXFl+XtYFBGvRcTTEXFdRHw9ItauNBZJKlqf9ptIknqSzHwpIs6mwj2QK7T8HxyXtNlqZeOAg4EtW7i3HvA94NyI+COlk3pfBzaktC3G5sALwCm1BixJkqSGtwlwEvCdGvpOKL82UTqE+kBgj4jYKTP/vcqxzomIm8vnlbQrIsYAP6X0NN/twG+ApZSeUhwBfBKYCjxVZRySVAgLzJJUny4AvgIM66Tx3k+pAHx9pR0y858RMZnSvtCrmgF8GtgH2BX4HLA+sIBSonw2MDkz53YwbkmSJDWmlylt2fatiPhRZs6rpnNmntn8fUTsBdwKnBQRF2TmsxUO9RSwLXAEcGl7jSOiP3ARpdj3ycxpLbTZDajq+5GkIrlFhiT1UJkZmblxK/cWZeZm5TaRmUta6FvR/sYRMQDYHvjvzHx5lXHGlse6rZU4vtUshh+tEt9vM/MrmbljZg7OzKbMXC8zd8rMcRaXJUmS1AELgLOAdwJndHSwcqH3cUqrineuoutZ5VgmVritxXaUYn60peJyOZa7M/OVKmKQpEJZYJYkfQxYDJxfdCCSJElSFS4CngaOjYj3dsJ4yxdoZBV9ZgOTgI2Ar1fQ/sXy6xD3WZa0prDALEkNLjOvy8x+mfl80bFIkiRJlcrMxZS2a2uitn2YV4iIUZT2Yk7g/iq7nwPMAU6JiHe30/Zv5fEHAX+MiK9GxAcjom+1MUtST2GBWZIkSZIk1aXMvAa4B/h0RHy00n4RcWb5678i4hrgJkormCdn5t+rjOENStt0rE1py4y22ibwWeAO4APA94EHgTci4t6I+EZEvLOaz5ekonnInyRJkiRJqmenAHcDkyJi13IRtz3L921O4BXgLuDHmXlFjTH8CDgBODwivpeZj7TWMDP/AewREcOBvYERwIeafX0lIkZm5jM1xiJJ3coVzJIkSZIkqW5l5j3ANZSKs4dU2Gf5QdW9MnP9zNyjA8VlMnMppT2Ye1HaMqOSPjMy84LM/FJmbgMMp7QaexPgu7XGIkndzQKzJEmSJEmqd9+kdHD1t4vazzgzrwduB/4lIvauof/jwBfLb/fszNgkqStZYJYkSZIkSXUtM58GLgY2A44vMJSvUdp241xqq7m8Xn6NTotIkrqYBWZJkiRJkrQmmEhpP+VxwDqdMWBETImIjIixlbTPzIeAKygd4HdoC+NtFhEnRMR6LdwLSrED/KH2qCWpe3nInyRJkiRJqnuZ+VJEnE2FeyBXaPnCvCVV9BkHHAxs2cK99YDvAedGxB+BRymtWt6Q0rYYmwMvUDq4UJLqgiuYJUmSJEnSmuIC4NlOHO/9lArA11faITP/CUxu5fYM4NPAj4EBwOcoHQ54CKXV12cD25X3Y5akuhCZWXQMkiRJkiRJPUpEDABeBCZl5teLjkeSeipXMEuSJEmSJK3uY8Bi4PyiA5GknswVzJIkSZIkSZKkmriCWZIkSZIkSZJUEwvMkiRJkiRJkqSaWGCWJEmSJEmSJNXEArMkSZIkSZIkqSYWmCVJkiRJkiRJNbHALEmSJEmSJEmqiQVmSZIkSZIkSVJNLDBLkiRJkiRJkmpigVmSJEmSJEmSVBMLzJIkSZIkSZKkmlhgliRJkiRJkiTVpPACc0T0ioiTI+LxiFgYEf+MiEkRsXYVY6wfEedFxFPlMeZGxP9ExMe6MnZJkiRJkiRJamR9ig4A+C5wAvAbYBIwvPz+gxExKjOXtdU5IjYF7gDWAX4MPAGsB2wPDK00iIEDB+awYcNqCF+SJElrigceeGBeZg4qOo6exDxZkiRJ0HquXGiBOSK2BY4HpmbmQc2uPwNcAHwe+Hk7w1xB6fvYPjOfqzWWYcOGMX369Fq7S5IkaQ0QEX8vOoaexjxZkiRJ0HquXPQWGYcCAUxe5fqlwAJgTFudI+LjwEeBczLzuYhoioj+XRKpJEmSJEmSJGklRReYdwaWAfc1v5iZC4GHy/fbsl/59R8RcR3wJjA/Ip6IiDaL05IkSZIkSZKkjim6wDwEmJeZi1q4NwsYGBF92+i/dfn1UmB94F+BI4G3gJ9FxOGdGawkSZIkSZIk6W1FH/LXH2ipuAywsFmbt1pps2759XVgj8x8CyAifgP8DTg7In7a2kGBEXEMcAzAJptsUn30kiRJkiRJktTAil7BvABYq5V7/Zq1ac2b5ddfLC8uA2Tmy8C1wEa8vcp5NZl5SWaOyMwRgwZ5WLgkSZIkSZIkVaPoAvNsSttgtFRkHkpp+4zWVi8DzCy/Pt/CvefKr+/qQHySJEmSJEmSpFYUXWC+vxzDh5pfjIh+wA7A9Hb6Lz8ccOMW7i2/9kJHApQkSZIkSZIktazoAvNVQAInrXL9aEp7L1+5/EJEbBER26zS7reU9l8eExHrNGv7buBA4MnMfKorApckSZIkSZKkRlfoIX+Z+UhEXAQcFxFTgRuA4cAJwJ3Az5s1nwZsCkSz/i9HxNeAHwJ/ioifAH2Bfyu/Htct34gkSWoIMSHab6Q25RlZdAiSJK1xzFE6zhyl45yHHVPPc7DQAnPZScCzwDHAaGAecCEwPjOXtdc5My+JiHnA14GzgGXAPcBhmfnHrgpakiRJkiRJkhpd4QXmzFwKTCp/tdVuWBv3pgJTOzcySZIkSZIkSVJbit6DWZIkSZIkSZJUpywwS5IkSZIkSZJqYoFZkiRJkiRJklQTC8ySJEmSJEmSpJpYYJYkSZIkSZIk1cQCsyRJkiRJkiSpJhaYJUmSJEmSJEk1scAsSZIkSZIkSaqJBWZJkiRJkiRJUk0sMEuSJEmSJEmSamKBWZIkSZIkSZJUEwvMkiRJkiRJkqSaWGCWJEmSJEmSJNXEArMkSZIkSZIkqSYWmCVJkiRJkiRJNbHALEmSJEmSJEmqiQVmSZIkSZIkSVJNLDBLkiRJkiRJkmpigVmSJEmSJEmSVJM+RQcgSVKlYkIUHUJdyzOy6BAkSZIkSWsYVzBLkiRJkiRJkmpigVmSJEmSJEmSVBMLzJIkSZIkSZKkmlhgliRJkiRJkiTVxAKzJEmSJEmSJKkmFpglSZIkSZIkSTWxwCxJkiRJkiRJqokFZkmSJEmSJElSTfoUHYAkSZIkSfUsJkTRIdS9PCOLDkGSVCNXMEuSJEmSJEmSamKBWZIkSZIkSZJUEwvMkiRJkiRJkqSaWGCWJEmSJEmSJNXEArMkSZIkSZIkqSYWmCVJkiRJkiRJNbHALEmSJEmSJEmqiQVmSZIkSZIkSVJNLDBLkiRJkiRJkmpigVmSJEmSJEmSVBMLzJIkSVIPFRG9IuLkiHg8IhZGxD8jYlJErF3DWP0j4pmIyIj4flfEK0mSpMZjgVmSJEnqub4LnA/8BTge+BVwAnBdRFSby08EBnZueJIkSWp0fYoOQJIkSdLqImJbSkXlqZl5ULPrzwAXAJ8Hfl7hWDsCJwFfByZ1frSSJElqVK5gliRJknqmQ4EAJq9y/VJgATCmkkEione5z03A1M4MUJIkSXIFsyRJktQz7QwsA+5rfjEzF0bEw+X7lTgZ2AY4qL2GkiRJUrVcwSxJkiT1TEOAeZm5qIV7s4CBEdG3rQEiYjNgAjAxM5/t/BAlSZLU6CwwS5IkST1Tf6Cl4jLAwmZt2vID4BlKBwVWLCKOiYjpETF97ty51XSVJElSg7HALEmSJPVMC4C1WrnXr1mbFkXEGGAf4MuZubiaD87MSzJzRGaOGDRoUDVdJUmS1GAsMEuSJEk902xK22C0VGQeSmn7jLda6ljucz5wA/B8RGwZEVsCm5abrFe+NqArApckSVLjsMAsSZIk9Uz3U8rXP9T8YkT0A3YAprfR9x3AIGA08GSzrzvK98eU3x/VqRFLkiSp4fQpOgBJkiRJLboKOBU4Cbir2fWjKe29fOXyCxGxBdCUmY+XL80HDm5hzEHAxcBNwI+BP3d+2JIkSWokFpglSZKkHigzH4mIi4DjImIqpe0uhgMnAHcCP2/WfBql7S+i3HcxcM2qY0bEsPIfn87M1e5LkiRJ1bLALEmSJPVcJwHPAsdQ2u5iHnAhMD4zlxUYlyRJkgRYYJYkSZJ6rMxcCkwqf7XVbliF4z1LeZWzJEmS1BmqLjBHxLrAR4BNgIHAm8ALwMOZ+VjnhidJkiTVB/NkSZIkNaKKCswR8Q7gUOBISqdY91p+q/ya5XbzgF8DP8jMRyocuxdwInAsMAyYC1xN6bG/+RV9F2+P1R94rDzORZl5XDX9JUmSpGp0ZZ4sSZIk1YM2C8wR0YfSISLjgHcBC4E/AfcDzwMvAe8ANgC2AXYFvgwcGxG3Aadk5qPtxPDd8mf8htKjf8sPLvlgRIyqcm+5iZRWi0iSJEldppvyZEmSJKnHa28F8+PAZsBNwE+B32XmorY6RMR7gbHAl4CHIuLIzLy8lbbbAscDUzPzoGbXnwEuAD7Pyqdjt/W5O1I6BOXrtLNHnSRJktRBXZonS5IkSfWiVzv3/wLsmJmjM/Pq9pJmgMx8IjNPBTYH/oPSyo3WHErp8cHJq1y/FFgAjGnv8wAione5z03A1Er6SJIkSR3Q1XmyJEmSVBfaXMGcmQfUOnBmvsXqheNV7QwsA+5bpe/CiHi4fL8SJ1N69PCg9hpKkiRJHdUNebIkSZJUF9pbwdzVhgDzWlnxMQsYGBF92xogIjYDJgATM/PZzg9RkiRJkiRJktSS9vZgrkhEbAPsS2lbi19m5qsVdu0PtPY44cJmbd5qY4wfAM8A51f4mStExDHAMQCbbLJJtd0lSZKkNnUgT5YkSZLqQlUrmCNifEQ8FxHrN7s2CngIOA+4GHgwIjaocMgFwFqt3OvXrE1r8YwB9gG+nJmLK/zMFTLzkswckZkjBg0aVG13SZIkCeiSPFmSJEmqC9VukbEv8HhmvtTs2reBBM6gtJp4M+DECsebTWkbjJaKzEMpbZ/R4urlcp/zgRuA5yNiy4jYEti03GS98rUBFcYiSZIk1aqz82RJkiSpLlRbYB4GzFj+JiKGAjsBF2fmf2bmccDtwIEVjnd/OYYPNb8YEf2AHYDpbfR9BzAIGA082ezrjvL9MeX3R1UYiyRJklSrYXRunixJkiTVhWr3YH4X0HxVxkcorcr4fbNrDwDHVjjeVcCpwEnAXc2uH01p7+Url1+IiC2Apsx8vHxpPnBwC2MOovQI4k3Aj4E/VxiLJEmSVKvOzpMlSZKkulBtgXkupa0rltsDWAzc2+xaXypcGZ2Zj0TERcBxETGV0nYXw4ETgDuBnzdrPo3S9hdR7rsYuGbVMSNiWPmPT2fmavclSZKkLtCpebIkSZJUL6otMD8MHBAR2wELgc8B/5uZbzZrMwx4rooxTwKeBY6htN3FPOBCYHxmLqsyPkmSJKkIXZEnS5IkST1etQXmc4D/Af6v2bVJy/9Q3jt5JKWVyBXJzKXlMSa1025YheM9S3mVsyRJktRNOj1PliRJkupBVQXmzLwrIj5JaY/kBK7MzBubNdmN0mrk33RahJIkSVIPZ54sSZKkRlXtCmYy8yZKB+i1dO924IMdDUqSJEmqN+bJxYkJPsDYEXlGFh2CJEmqYx4yIkmSJEmSJEmqSZsF5ogY0ZHBI6JfRAzvyBiSJElST2OeLEmSJJW0t4L5voj4TUTsUs2gEbFeRJwI/A04uOboJEmSpJ7JPFmSJEmi/T2YjwLOAu6OiCeBXwJ/BKZn5svLG0VEb2BrYFfgE8D+QD/gV8BlXRC3JEmSVCTzZEmSJIl2CsyZ+ZOIuAo4ETgWGE/pVGwiYjHwMqUE+Z3lLgEsBa4Dzs3Me7oobkmSJKkw5smSJElSSXsrmMnM+cDZEfEdYG9gFPBRYBNgA+BN4Cngz8AdwG8zc1ZXBSypGJ7O3nGe0C5JaxbzZEmSJKmCAvNymbkMuLn8pW5mca/jLO5JkqSuYJ4sSZKkRtbeIX+SJEmSJEmSJLXIArMkSZIkSZIkqSYWmCVJkiRJkiRJNbHALEmSJEmSJEmqiQVmSZIkSZIkSVJNLDBLkiRJkiRJkmpigVmSJEmSJEmSVBMLzJIkSZIkSZKkmvSppVNEbA8cBgwH1s7MUeXrw4APAbdm5sudFKMkSZJUF8yTJUmS1GiqLjBHxETgVN5e/ZzNbvcCfgGcBFzY4egkSZKkOmGeLEmSpEZU1RYZEfF54DTgVmAH4NvN72fm34DpwAGdFaAkSZLU05knS5IkqVFVuwfzCcBTwKcy88/AWy20mQFs1dHAJEmSpDpinixJkqSGVG2B+f3AzZnZUsK83GxgcO0hSZIkSXXHPFmSJEkNqdoCcwDL2mkzGFhYWziSJElSXTJPliRJUkOqtsD8JLBbazcjojfwUeCxjgQlSZIk1RnzZEmSJDWkagvMVwM7RsQprdz/FrAl8PMORSVJkiTVF/NkSZIkNaQ+VbafDBwMnBMRhwAJEBHnAR8DRgB/Ai7pzCAlSZKkHs48WZIkSQ2pqgJzZr4ZEXsA3wO+APQu3/p3SnvOXQEcl5lLOjVKSZIkqQczT5YkSVKjqnYFM5n5KjA2Iv4d2BnYAHgVuC8z53ZyfJIkSVJdME+WJElSI6q6wLxcZr4E3NyJsUiSJEl1zzxZkiRJjaTaQ/4kSZIkSZIkSQJqWMEcEf2BI4EdgI2BphaaZWbu1cHYJEmSpLphnixJkqRGVFWBOSK2B24BBgHRRtPsSFCSJElSPTFPliRJUqOqdouMyZSS5jOAYUBTZvZq4at3m6NIkiRJaxbzZEmSJDWkarfI2BX4dWb+Z1cEI0mSJNUp82RJkiQ1pGpXML8B/L0rApEkSZLqmHmyJEmSGlK1BebbgV26IhBJkiSpjpknS5IkqSFVW2A+FRgeEd+MiLYOL5EkSZIaiXmyJEmSGlJVezBn5t8i4qPA3cDREfEw8GrLTfPIzghQkiRJ6unMkyVJktSoqiowR8TGwO+Ad5W/NmulaQImzpIkSWoI5smSJElqVFUVmIHJwHuBnwA/BWYDSzo7KEmSJKnOmCdLkiSpIVVbYN4TuDkzj+qKYCRJkqQ6ZZ4sSZKkhlTtIX+9gEe6IhBJkiSpjpknS5IkqSFVW2D+E7BdVwQiSZIk1THzZEmSJDWkagvM44CREfH5rghGkiRJqlPmyZIkSWpI1e7BPBq4HbgyIr4MPAC82kK7zMyzOhqcJEmSVCfMkyVJktSQqi0wn9nszx8vf7UkARNnSZIkNYozm/3ZPFmSJEkNo9oC8x5dEoUkSZJU38yTJUmS1JCqKjBn5p1dFYgkSZJUr7oqT46IXsCJwLHAMGAucDUwPjPnt9P3vcAYYB9gC6Af8DTwK2Bye/0lSZKkSlR7yJ8kSZKk7vNd4HzgL8DxlIrDJwDXlYvPbTkCOJlSUXki8B/AX4H/BO6OiHd0VdCSJElqHNVukSFJkiSpG0TEtpSKylMz86Bm158BLgA+D/y8jSGuAb6dmc0PG/zviHgSGAccCXy/0wOXJElSQ2lz1UNELIuIJeXH65a/X1rB15LuCV+SJEnqft2UJx8KBDB5leuXAgsobX/Rqsycvkpxebmryq/bVRGLJEmS1KL2VjD/gdJJ1wtWeS9JkiQ1su7Ik3cGlgH3Nb+YmQsj4uHy/VpsXH6d04HYJEmSJKCdAnNmjmzrvSRJktSIuilPHgLMy8xFLdybBewWEX0z861KB4yI3sB4YAltb6+xwot/fZEpI6dU+hGFGPvs2KJDqGtT/mdK0SHUPedgxzkPO8Y52HHOwY5zHnZMPc/Bdg/5i4gvRcT23RGMJEmSVC+6IU/uD7RUXAZY2KxNNSYDuwLjM/OvrTWKiGMiYnpETF+8eHGVHyFJkqRGEpltP8kXEcuAMzNzYveEVIwRI0bk9OnTiw6jVTEhig6h7uUZ7u7SEc7BjnMOdpzzsGOcgx3nHOy4nj4PI+KBzBxRYdsuzZMj4hFgw8wc3MK9q4GDgbUqXcEcEWcBpwGXZOaxlcbR0/Nk8Gezo3r6z2U9cA52nPOwY5yDHecc7DjnYcfUwxxsLVdudwVzV4uIXhFxckQ8HhELI+KfETEpItauoO97I2JiRPwpIuZGxOsR8XBEjKukvyRJktSDzQYGRsRaLdwbSmn7jEqLy2dSKi5fBny50yKUJElSwyu8wAx8Fzgf+AtwPPAr4ATguohoL74jgJOBp4GJwH8AfwX+E7g7It7RVUFLkiRJXex+Svn6h5pfjIh+wA5ARcuKI+IM4AzgcuCobO8RRkmSJKkKbR7y19UiYltKReWpmXlQs+vPABcAn6ftw0euAb6dma82u/bfEfEkMA44Evh+pwcuSZIkdb2rgFOBk4C7ml0/mtLey1cuvxARWwBNmfl48wEiYjxwJvAz4PDMXNbFMUuSJKnBVFpgHhARm1QzcGb+o4JmhwJB6bCR5i4FvgOMoY0Cc2a2tmrjKkoF5u0qiEGSJEmqVVflyWTmIxFxEXBcREwFbgCGU3ra705WzpOnAZtSyq0BiIivAhOAfwC3AYdFrLQ34pzMvLWa2CVJkqRVVVpgPrH8VamscOydgWXAfSt1zlwYEQ+X79di4/LrnBr7S5IkSZXoqjx5uZOAZ4FjgNHAPOBCYHwFq5GX59KbAD9t4f6dgAVmSZIkdUilye1rwCtd8PlDKB1OsqiFe7OA3SKib6WHlwBERG9gPLCEtrfXICKOoZSsM3StoUwZOaXSj+l2Y58dW3QIdW/K/0wpOoS65hzsOOdgxzkPO8Y52HHOwY5bA+dhV+XJAGTmUmBS+autdsNauDYWGNsVcUmSJEnLVVpg/m5mTuyCz+8PtFRcBljYrE3FBWZK223sCpyamX9tq2FmXgJcArDZupt52IkkSZKq1VV5siRJklQXCj3kD1gAbNjKvX7N2lQkIs4CjgMuycxvVxPIBltvwNg7xlbTpVsdPuHwokOoe5edcVnRIdQ152DHOQc7znnYMc7BjnMOdlxPn4eHh/8bS5IkSdXoVfDnzwYGRsRaLdwbSmn7jIpWL0fEmcBpwGXAlzstQkmSJEmSJElSi4ouMN9fjuFDzS9GRD9gB2B6JYNExBnAGcDlwFGZ6XYXkiRJkiRJktTFii4wX0XpJO2TVrl+NKW9l69cfiEitoiIbVYdICLGA2cCPwMOr+A0bUmSJEmSJElSJ2h3D+bM7LIidGY+EhEXAcdFxFTgBmA4cAJwJ/DzZs2nAZsCsfxCRHwVmAD8A7gNOCwimnVhTmbe2lXxS5IkqXF1ZZ4sSZIk1YuiD/mD0urlZ4FjgNHAPOBCYHwFq5F3Lr9uAvy0hft3AhaYJUmSJEmSJKkLFF5gzsylwKTyV1vthrVwbSwwtivikiRJkiRJkiS1zcf6JEmSJEmSJEk1scAsSZIkSZIkSaqJBWZJkiRJkiRJUk0sMEuSJEmSJEmSamKBWZIkSZIkSZJUEwvMkiRJkiRJkqSaWGCWJEmSJEmSJNXEArMkSZIkSZIkqSYWmCVJkiRJkiRJNbHALEmSJEmSJEmqiQVmSZIkSZIkSVJNLDBLkiRJkiRJkmpigVmSJEmSJEmSVBMLzJIkSZIkSZKkmlhgliRJkiRJkiTVxAKzJEmSJEmSJKkmFpglSZIkSZIkSTWxwCxJkiRJkiRJqokFZkmSJEmSJElSTSwwS5IkSZIkSZJqYoFZkiRJkiRJklQTC8ySJEmSJEmSpJpYYJYkSZIkSZIk1cQCsyRJkiRJkiSpJhaYJUmSJEmSJEk1scAsSZIkSZIkSaqJBWZJkiRJkiRJUk0sMEuSJEmSJEmSamKBWZIkSZIkSZJUEwvMkiRJkiRJkqSaWGCWJEmSJEmSJNWkT9EB9HSLFy9m5syZLFy4sNA4btznxkI/f00wY8aMokNoVe/evRkwYAADBw6kVy//3UeSJPV8y5YtY968ebzyyissXbq00FjMlTumJ+fJAP369WPjjTemqamp6FAkSVILLDC3Y+bMmay77roMGzaMiCgsjvmz5xf22WuK4UOGFx1CizKTxYsXM2fOHGbOnMkmm2xSdEiSJEntmjlzJhHBsGHDaGpqMleuYz01T4ZSrvziiy8yc+ZMNttss6LDkSRJLXCpZDsWLlzIBhtsUGjCrDVbRNC3b1+GDh3K/Pn+ciRJkurD/PnzGTp0KH379jVXVpeJCDbYYIPCnyiVJEmts8BcARNmdQe3xpAkSfXG/EXdwd/HJEnq2cwIJUmSJEmSJEk1scDc4Gb/czY7D92ZJUuWAHDsZ4/ltz//bYfHvWTSJZx+/Omt3j9glwO49w/3Vj3uQ/c+xEEfO6gjoXXY2WefzVFHHVVoDJIkSep65srVM1eWJKnxeMhfDTY6byPmzJ/TZeMPXnswz3/t+XbbHbDLAcydM5cbH7yRAesPWHH9sL0P48m/PMnv/vQ7hrxnSJfFWYQP7vJBfn3Xr1e8P2CXAxh37jh2+fguXfJ5d9xxB2PGjGHmzJkrrp166qld8lmSJEn1rqfkyWCuDObKkiTW+eyIAAAgAElEQVSpe7iCuQZdmTRXO/6Q9wzh5t/evOL9UzOeYtHCRV0R1honM1m2bFnRYUiSJK0xelKeDObKHWGuLEmSKmWBuc7td9B+XH/N9Sve//5Xv2f0Z0ev1OZ/b/tfvrDPFxi59UhGjxjNJZMuqXj8a395LQfvfjB7vm9Pjj/seJ6b+dyKe+eNP4/RI0YzcuuRfPFfvshD9z60Ut+3Fr7Ft778LXZ/7+6M+cQYnnjsiRY/Y9myZUz5/hQO3O1ARm07im8d+y1effnVFts+cPcDjN6p9P2NP348z896nlMOP4WPb/VxLr/4cgAeeeARjjjgCPYYvgeHjTqMB+5+YEX/kSNHMm7cOD7ykY/Qv39//va3v3HZZZcxfPhw1l13XTbffHN++MMfAqWT0ffdd19mz57NOuuswzrrrMPs2bM588wzGTNmzNv/ja69lm233ZYBAwYwcuRIZsyYseLesGHDOO+889h+++1Zb731+NznPucJ2JIkSd3EXNlcWZIkdT0LzHVuux23Y/7r83nmyWdYunQpt157K/setO9Kbd7R/x1M+N4Ebp9xO5Mvn8yvL/81d9x0R7tj33HTHVx24WWc86NzuOXPt7DDh3Zg3FfGrbj/vg+8jytvuZLbHruNTxz4Cb557DdXWhFy5y13MuqTo5j22DQ+8elP8B9H/gdLFi9Z7XN++eNfcudNd/LDa37IDQ/ewLrrrcs5485pN76JF05ko6EbMemySfzhyT/wpa98iReee4GTv3QyR554JNMem8aJ40/kG0d/g5dffHlFv5/97GdccsklvP7662y66aZsuOGG/P73v+e1117jsssu4+STT+bBBx9k7bXX5sYbb2TIkCG88cYbvPHGGwwZsvJjlE888QSHHnookydPZu7cuey3337sv//+vPXWWyvaXH311dx0000888wz/PnPf2bKlCntfm+SJEnqOHPlynLluXPnruhnrixJkqplgXkNsHxlxr1/uJdhWw5j0EaDVrq/0247seXwLenVqxdbvW8r9vnUPjx4z4Ptjjv1iqmMPW4sm221GX369OHwEw7niceeWLEyY7+D9mPA+gPo06cPY748hsVvLebvT/99Rf/h7x/OXp/ciz5NffjCMV9g0aJFPPLgI6t9zm+u+A3/9o1/Y/CQwfRdqy/HnHIM066ftuIwlWrcOPVGdttzNz6y10fo1asXu3x8F4Z/YDh/nPbHFW3Gjh3LtttuS58+fWhqamL06NFsscUWRAS77747++yzD3fddVdFn3fVVVcxevRo9t57b5qamvja177Gm2++yd13372izQknnMCQIUNYf/312X///Xn44Yer/r4kSZJUG3Plt7WWK99www0r2pgrS5KkannI3xpgv8/uxzGfOYbZ/5i92iN/AI8++CjfP/v7PP3Xp1m8eDGL31rMXqP3anfc52c+z6Txk5g8cfJK11947gXevfG7ueK/r+B3v/gdc+fMJSKY//p8XnnplRXtBg8ZvOLPvXr1YsN3b8jc5+eyqudmPsfXj/o60StWXOvduzcvzX2JDd+9YUX/DZqPNe36adx129tJ75LFS9hpt51WvH/Pe96zUp8bb7yRCRMm8MQTT7Bs2TIWLFjA+9///oo+b/bs2Wy66aYr3vfq1Yv3vOc9zJo1a8W1jTbaaMWf+/fvz+zZs6v6niRJklQ7c+WVx2opV37uX97e2sNcWZIkVcsC8xrg3Ru/myGbDOHu2+/m9Emnr3b/tONO45Cxh/C9K77HWv3WYtL4Sbzy8istjLSywUMGc/gJh7PvZ/Zd7d5D9z7E5RdfzsVXXczmW29Or1692PN9e5KZK9rMmf32ISzLli3jhedeWG3FyPLPGX/+eD6w8wcq/ZZXiIiV3g8eMph9D9qX0849raI+ixYt4qCDDuLyyy/nU5/6FE1NTRx44IErvo9Vx1/VkCFDeOSRt1eaZCb//Oc/GTp0aNXfiyRJkjqfufLKY7WUK48YMqLFPubKkiSpEm6RsYY4/bzTufjqi3lH/3esdm/BGwt454B3sla/tXjsocdWOkm7LZ/54meY8v0pPP3XpwF447U3uO2621aM2btPbwZsMIClS5Zy6XcvZf7r81fqP+ORGdx+w+0sWbKEX1z6C/r27cv7d1x9tcNBXzyIi//fxSseJ3z5xZe58+Y7K4px/YHrM+sfb6+A2Pcz+3LXrXdxzx33sHTpUhYtXMQDdz+wUgLf3FtvvcWiRYsYNGgQffr04cYbb+SWW25ZcX/w4MG8+OKLvPpqywepHHLIIVx//fVMmzaNxYsXM2nSJNZaay122223iuKXJElS1zNXLmktV545c2aL/c2VJUlSJVzBvIbYeNjGrd77xtnfYPLEyZx72rnsuOuOjNp/FK+/9nq7Y+6x7x68Of9Nxn1lHM/PfJ61112bXT6+C6P2H8WuI3dltz1247Mf+yz9+vfjsKMPW+kxP4Dd99mdW6+9lQknTWDjYRtzzo/OoU/T6lPu80d9nszkuEOPY96cebxr4LvYe/+92f0Tu7cb49jjx3Luaedy4X9dyBEnHsEXv/xFJv1kEhf81wWc9tXT6NWrF9vusC3f/M43W+y/7rrrcsEFF3DIIYewaNEi9t9/fw444IAV97fZZhsOPfRQNt98c5YuXcpf/vKXlfpvvfXWXHHFFRx//PHMmjWLHXbYgeuuu46+ffu2G7skSZK6h7ly27nyvh9afRU2mCtLkqTKRPPHtBrZiBEjcvr06atdnzFjBsOHD1/p2kbnbcSc+S2viO0Mg9cezPNfe36la9Nnrx6bqtP80b+eqqX51lPEhLYfgVT78gz//7ajnIcd4xzsOOdgx/X0eRgRD2Rmz08aulFPz5PBXLmjzJM7zr8fOq6n//3Q0zkHO8452HHOw46phznYWq7sCuYatJTUSpIkSY3OPFmSJKnxuAezJEmSJEmSJKkmFpglSZIkSZIkSTWxwCxJkiRJkiRJqokF5gp4EKK6g/NMkiTVG/MXdQfnmSRJPZsF5nb07t2bxYsXFx2GGsCbb75JU1NT0WFIkiRVpKmpiTfffLPoMNQAFi9eTJ8+nk8vSVJPZYG5HQMGDGDOnDksW7as6FC0hspMFixYwKxZs9hwww2LDkeSJKkiG264IbNmzWLBggWuMFWXWbZsGXPmzGG99dYrOhRJktSKHvHPwBHRCzgROBYYBswFrgbGZ+b8ru7floEDBzJz5kz++te/dmSYDpv3yrxCP39NMOPVGUWH0KqmpiYGDx7MO9/5zqJDkSRJPUhPzpOX5y2zZ88u/Ik/c+WO6cl5MsDaa6/NwIH/v717j7KsLO88/v1xBxGMAiaAEAQZAUkgAoEYBVp01HgJ8Rbv9xsKgmE04lIQL+PAoEiCKJho0MSBODCaiBgJqAwyCoMmsjSihCYGHASJcpNG6Gf+2KdDpanuPlVd57z7nPP9rFWrus5+d/E061e9n3rP3u+7TesyJEnSGvRighn4EHAUcD5wCrDH4Ot9kxxWVeu6fXh9z1+jDTbYgJ122mmxpy+ZPd+9Z+sSJl4d7501kiRp4vS2T4ZukrkPb5DbK68f+2RJkrQ+mk8wJ9kLOBI4r6qePef164DTgD8E/mpU50uSJEl9ZJ8sSZKkSdCHNZhfAAQ4dbXXzwLuAl484vMlSZKkPrJPliRJUu/1YYJ5f2Al8M25L1bV3cC3B8dHeb4kSZLUR/bJkiRJ6r0+TDBvD9xSVSvmOXYDsE2STUZ4viRJktRH9smSJEnqveZrMANbAPM1vQB3zxlzz1Kfn+S1wGsHX96R5PvrrFZrsg3Q6+27c0Jal6DR63UOzeBMMINqrdcZhInI4c6tC5jDPnl69PpncwJ+LrX+ep1BMIczotc5NIMzwQyuv3l75T5MMN8FbLeGY5vNGbPk51fVmcCZ6ypQ65bkyqrar3Udmm3mUK2ZQbVmBqeOffKU8GdTrZlB9YE5VGtmcHT6sETGjXSP5206z7Ed6B7rW9NdGUtxviRJktRH9smSJEnqvT5MMF9BV8cBc19MshmwD3DliM+XJEmS+sg+WZIkSb3Xhwnmc4ACjl7t9dfQrQn3l6teSLJrkkcv9nyNlI9Qqg/MoVozg2rNDE4X++Tp4c+mWjOD6gNzqNbM4IikqlrXQJI/Ad4EnA9cAOwBHAVcBiyrqpWDccuBnasqizlfkiRJmiT2yZIkSeq7vkwwb0h3Z8VrgV+n29HxHOBdVXXHnHHLmb9xHup8SZIkaZLYJ0uSJKnvejHBLEmSJEmSJEmaPH1Yg1mSJEmSJEmSNIGcYJYkSZIkSZIkLYoTzJIkSZIkSZKkRdmodQGaXEl2B/YCtgMKuBm4uqp+0LQwzYQkWwC/yzwZBC6rqrsalqcZMfh38BDmz+FXq+qadtVpFphBqZ/sk9WavbJas0dRa2ZwvNzkTwuSZA/g9cBzgYevennweVWYbgLOBT5WVd8bb4WadkmeSpfBp9C9SZbVhhRwL/BF4KNVdeF4K9S0S7IZ8ArgdcDePDCDqxTwHeCjwCer6u7xVKhpZwalfrJPVh/YK6slexS1ZgbbcYJZQ0myK/DfgMOBXwCXApcD1wI/pfuhfSiwG3Ag8Hhgc+A84G1V9c8NytYUSfJ44BRgP2A58CXWnMGD6JrqnYD/CxxTVf97/FVr2iR5CfA+YAe6fwe/yNpz+DTgccANwHFV9ekGZWuKmEGpf+yT1Qf2ymrNHkWtmcG2nGDWUJKsoHt358PAeVV15zrGPwh4DnAUsFdVbTb6KjXNktwH/C/g1Kq6dMhzDgaOBp5RVS4JpPWW5E66d7lPq6rrhzxnZ7ocvqaqthxlfZp+ZlDqH/tk9YG9slqzR1FrZrAtJ5g1lCTPqqrPjftcaZUkv1lV/zDuc6W5kvxqVf2/RZ778Kq6aalr0mwxg1L/2CerD+yV1Zo9ilozg205wSxJkiRJkiRJWpQNWhcgSdKkS7JRkq2S+HipmjCDkiSpj+xR1JoZHA8nmLUoSbZLsslajm+b5AnjrEmzI8mBSf40yTeS/GuSWwefvzF4/aDWNWr6JfnDJH+b5CZgBfBvwIokNw1ef0HjEjXlzKDUT/bJas1eWa3Zo6g1Mzh+LpGhBUnyQrrdibcD7gH+B3BsVf10tXEvAs6uqg3HX6Wm1eAdxzOBl9HtAHsj3Y6vdwOb0e0Wuz1QwKeAV1XVfW2q1bRKsgXweWAZcBfwbR6Yw32AzYGv0G2cc1eTYjWVzKDUT/bJas1eWa3Zo6g1M9iOt4draEkOoGtEbgXOB3aka16WJXlqVX23ZX2aCW8DXg58kG6H7H9dfUCSHYFjBh/XAO8fZ4GaCScCTwCOAs6qqhWrD0iyKfBauomGdwP/ZawVatqZQaln7JPVE/bKas0eRa2ZwUa8g1lDS/I5YF9gv6r6yeC1p9E10/cCT6qqfxy87p0ZWnJJfghcUlWvGWLsx4FDqmq30VemWZLkX4Bzq+rYIcaeAjyvqh4x+so0K8yg1D/2yeoDe2W1Zo+i1sxgO67BrIXYB/joqqYZoKouAA6kW9Pm4iT7tCpOM2FH4PIhx36d7vEXaaltC3xvyLHfBbYZYS2aTWZQ6h/7ZPWBvbJas0dRa2awESeYtRDbAD9e/cWq+gFwMHAHcFGS3xp3YZoZN9L9ojaMg5gnr9ISWA48ZcixTxuMl5bScsyg1Df2yeoDe2W1thx7FLW1HDPYhBPMWogbgV3mO1BV1wGHALcDFwH7j68szZBPA69KclKSee+4SLJDkpOBVw7GS0vtTODZSc5N8jtJNp57MMnGSR6X5K+B3x+Ml5aSGZT6xz5ZfWCvrNbsUdSaGWzENZg1tCSfAXavqseuZczOdDtx7gTg2nJaSkk2Ac4Fnkm3+/UNg48VwKZ0j/ntQLdr9t8Cz6mqe9pUq2mVJMCpwJsGL60EbuH+HG7D/W/gng68ubzYagmZQal/7JPVB/bKas0eRa2ZwXacYNbQkrwYOBs4uKouXcu4nYFLgJ1tnDUKg01zXgjsB2wPbAHcRXf30BXAZwbrHkojk2RP1p7Dc6rq6nYVatqZQak/7JPVJ/bKas0eRa2ZwfFzglkLkmRT4N6qum8d47YEHlZV14+nMkmSJKkd+2RJkjSrnGCWJEmSJEmSJC2Km/xJmkpJHpHkCa3r0HRLst1gvcM1Hd/WHGrckuyS5LYkz2xdiySpn+yVNWr2yeoj++TRcYJZI5Hk4iRnJ9m1dS2aWS+lW+NQWnJJXpjkx8CPgZ8n+USSh80z9MmYQy2xJA9d2wfwMGBL4CFzXpPUE/bJ6gl7ZY2EfbJask9uZ6PWBWhqHTL4/PwkZ1TV0S2LkaSlkuQA4FPArcD5wI7Ay4BlSZ5aVd9tWZ9mws1DjCngE3P+bM8n9cchg8/2yZKmin2yesA+uRH/J2pUdgEeDCyje2dSWm9JXrqA4fuOrBDNuncANwD7VdVP4N93a/8UcEmSJ1XVP7YsUFMvwO3A54D5NhN7MPAHwFcANxGT+sc+WSNhr6wesE9Wa/bJjbjJn6SJkWQl3TuMGfKUqqoNR1iSZlCS64GPVdX7V3v9UcDfA1sAh1XVt5O8CDjbHGopJTkCeD9wLfC6qrpyteO7AdcAz6mq8xqUKElqwF5ZrdknqzX75Ha8g1nSJLkT+DZw6hBjnw08f7TlaEZtQ7em3H9QVT9IcjDdWnIXJfGuNI1EVX0kyfnAR4DLk3wUOK6qbl81pF11kqSG7JXVmn2ymrJPbscJZi1Kkv2A/YEd6N6FvIvuUZgrq+qKlrVpqv0D8JCq+p/rGpjk0WOoR7PpRrrHmx+gqq5LcgiD5hk4e4x1aYZU1Y+Bw5McDvwJ8AdJjqmqcxuXJs08+2Q1ZK+s1uyT1Zx9chtOMGtBkiwDTgd2Z/5HryrJNcCRVXXRWIvTLLgKeEOSzavqF0OMH/bxQGkhrgR+D3jXfAeravmgef4KcOT4ytIsqqrzk1wEnAR8JskrgA81LkuaSfbJ6gF7ZbVmn6zesE8erw1aF6DJkeRQ4EJgc+B4uk1J9gJ2HXx+MnAC3Z0aFwzGS0vpU8B7gS2HHGsGNQpfAPZN8vg1Daiq64FDcOMIjUFV3V5VbwAeDzyCLqM+/ieNkX2yesJeWa3ZJ6tX7JPHx03+NLQkXwMeBDyhqu5cy7gtgUuB26rq4HHVJ0njkmRT4N6qmm9n4rnjtgQeNmikpZFLsjHwJmAn4M+r6juNS5Jmgn2yJHXsk9VX9smj5QSzhpbkDuDYqvroEGPfAJxcVcO8ey5JkiRNLPtkSZI0y1wiQwtxD7DVkGO3GoyXJEmSpp19siRJmllOMGshvgYck2TvtQ0aHD8G+OpYqpLmkeTiJGcn2bV1LZpd5lCtmUFpbOyTNVG8Pqg1M6jWzODScokMDS3J7sDldHddXAL8H+AGYAWwKbADcBDdgv0/Bx5XVd9vUqxmXpKVgz/+Ejijqo5uWY9mkzlUa2ZQGg/7ZE0arw9qzQyqNTO4tDZqXYAmR1Vdk2R/4APAM4DD5hm2AjgfeHtVXTvO+qTV7AI8GFhGt3O71II5VGtmUBoD+2RNIK8Pas0MqjUzuIS8g1mLkmQL4DeA7YEtgLuAG4HvrG3nbEmSJGma2SdLkqRZ4wSzJEmSJEmSJGlRXCJD0kRKsh+wP92ahqvuDroBuLKqrmhZm2aHOVRrZlCaDEm2Bl4FfL6qfti6Hk0/rw9qzQyqNTM4Xk4wa0GSbErXHD8GuAn4q6r6wTzjDgOOq6plYy5RUy7JMuB0YHcg8wypJNcAR1bVRWMtTjPDHKo1MyhNnG2Ak4HrACeYNTJeH9SaGVRrZrANl8jQ0AbryV1Gt6bcqh/SXwLvrKqTVhv7IuDsqtpwvFVqmiU5FPgS3TqGH+f+HdrvBjbj/h3aXw38GvCfq+qSNtVqWplDtWYGpf5Jcto6hmwNvAT4InAtUFX15pEXppni9UGtmUG1ZgbbcYJZQ0tyHPBe4H3AOcAjgHcBBwAfq6oj5ox1gllLLsnXgAcBT1jbJjlJtgQuBW6rqoPHVZ9mgzlUa2ZQ6p8kK4Fi/julVpl7vOyTtdS8Pqg1M6jWzGA7G7QuQBPlucA5VfXOqrq6qr4IPA74CPD6JGe1LU8z4LeAs9a1A3tV3QGcCTx2LFVp1phDtWYGpf65HrgDeAuwyzwfh9JNLr9h8PUj25SpKef1Qa2ZQbVmBhtxDWYtxK7AGXNfqKqVwJFJfga8I8mGVfXKJtVpFtwDbDXk2K0G46WlZg7VmhmU+mdP4D106ywfCrypqn606mCSVb933VxV1zeoT7PB64NaM4NqzQw24h3MWoi7gY3nO1BV7wROBF6e5BOYLY3G14Bjkuy9tkGD48cAXx1LVZo15lCtmUGpZ6rqF1V1LPDbwI7Ad5Mcm8RlMDROXh/UmhlUa2awEddg1tCSXA78sKpespYxxwPH0z0muJNry2kpJdkduJzuncZLuH/B/hXApty/YP8hwM+Bx1XV95sUq6llDtWaGZT6LckGdEtlnEC3od/rgZ8APwCeU1XntatO08zrg1ozg2rNDLbjBLOGluRE4M3AjlV1+1rGvYuuoXbzEi25JI8EPgA8g+4CsboVwN8Ab6+qa8dZm2aHOVRrZlDqvyS70C0vdxhwAfB7wHOdYNYoeX1Qa2ZQrZnBNpxg1tCSPAp4JfDXVXXVOsa+Edivql4xluI0c5JsAfwGsD2wBXAXcCPwnXUt6C8tFXOo1syg1H9JXgycAmyLdzBrTLw+qDUzqNbM4Hg5wSxJkiRJI5RkE2Bz4M6qurd1PZIkSUvJjdg0lCRPXI9zD1vKWiRJkqS+GKZPrqp7qurnq08u2ydrnJJsneQtSXZrXYtmkxlUa2ZwdJxg1rAuTHJxkqcPsxt2ko2THJ7kq3RrzklLIsmmSY5I8pEkxw+Wbplv3GFJLh53fZoN5lCtmUGpV+yTNSm2AU4G9m5diGaWGVRrZnBENmpdgCbGvsAHgc8DtyT5MvBNup2xbwUCPBR4FHAgsAz4FeDvgH1aFKzpM1hD6TK6dZQyePm4JO+sqpNWG/5w4OBx1qfZYA7VmhmUesc+Wb2Q5LR1DNmaLo+vTnIo3absbx59ZZoVZlCtmcF2XINZC5LkIOAI4FnAlsDqAQpwG3AecEZVXTHeCjXNkhwHvBd4H3AO8AjgXcABwMeq6og5Y18EnF1V67yTSFoIc6jWzKDUT/bJai3JSrrcZS3D5h4vrw9aSmZQrZnBdryDWQtSVZcDlw8e/3sssCfdjtgF3AxcDXyrqla2q1JT7LnAOVX1zsHXVyf5EvBh4I1JNq6q17QrTzPCHKo1Myj1kH2yeuB6urvljwfOn+f4rwOXAG8ALhxfWZohZlCtmcFGnGDWolTVfXSP/n2zdS2aKbsCZ8x9YfBL2pFJfga8I8mGVfXKJtVpVphDtWYGpR6zT1ZDewLvoVtf9FDgTVX1o1UHk6z6/f/mqrq+QX2afmZQrZnBRtzkT9IkuRvYeL4Dgzv5TgRenuQT+O+bRsccqjUzKEl6gKr6RVUdC/w2sCPw3STHDrP5pLQUzKBaM4Pt+EuHpElyLd3mOPOqqhOAdwMvo5tgkUbBHKo1MyhJWqOqugrYn+5acAJw1WCNcGkszKBaM4Pj5wSzpEnyZeCZSR68pgFVteoCsvO4itLMMYdqzQxKktaqqlZW1X8H9gZ+DFwKfIgHbj4pjYQZVGtmcLxcg1nSJPkU3WPhjwKuWtOgqjoxyU+B/cZVmGaKOVRrZlCSNJSqug54SpIXA6cAaVySZowZVGtmcDxS5cS9JEmSJEnTLMkmwObAnVV1b+t6NHvMoFozg6PjEhmSJkKSJ67HuYctZS2aXeZQrZlBSdJ8hrk+VNU9VfXz1SdVvD5oKZhBtWYG23KCWdKkuDDJxUmePswOsEk2TnJ4kq8CF4yhPs0Gc6jWzKAkaT5eH9SaGVRrZrAh12CWNCn2BT4IfB64JcmXgW8C1wK30q2j9FC6NUkPBJYBvwL8HbBPi4I1lcyhWjODkqT5eH1Qa2ZQrZnBhlyDWdJESXIQcATwLGBLHrgDbIDbgPOAM6rqivFWqFlgDtWaGZQkzcfrg1ozg2rNDLbhBLOkiTR45OWxwJ7AtnQXjZuBq4FvVdXKhuVpRphDtWYGJUnz8fqg1sygWjOD4+UEsyRJkiRJkiRpUdzkT5IkSZIkSZK0KE4wS5IkSZIkSZIWxQlmSZIkSZIkSdKiOMEsST2VpAYf1yfZbA1jlg/GbLSGc1d93JfkliQXJ3nRAmr45Jzv8bQ1jDlhcPzV8xzbP8lfDv4OK5LcluTaJH+T5K1JHjRsLZIkSRLYJ0tS32y07iGSpMZ2Ao4GPrCIc989+Lwx8J+A3wcOTfLYqnrLAr/XSUm+VFX3DTM4yYuBvwACXAycD9wH7ALsBzwdOA/44QLrkCRJksA+WZJ6wQlmSeq3fwMKeHuSj1fVLQs5uapOmPt1kicCXwaOTnJaVS0f8lv9ENgLeCVw1roGJ9kCOJ2u9idX1d/PM+Z3gAX9fSRJkqQB+2RJ6gmXyJCkfrsLeA+wFXD8+n6zQQP7T3R3S+y/gFPfM6jlxCEf13sMXc1Xz9c0D2r5elX9bAE1SJIkSavYJ0tSTzjBLEn9dzpwLfC6JLsvwffL4HMt4JwbgVOAXwXeOsT4nw4+b+/6cZIkSRoR+2RJ6gEnmCWp56rql8Af060Pt5j15f5dksPo1pgr4IoFnn4ScBPwR0l+bR1j/3nw/bcFLkvyxiT7JtlkoTVLkiRJ87FPlqR+cIJZkiZAVX0WuBw4PMnvDnveYOfqE5K8L8lngQvp7sw4taquX2ANd9A9fvggukcB1za2gOcAXwF+E/hT4CrgjiTfSPK2JFst5L8vSZIkrc4+WZLac5M/SZocfwR8HTglyYGD5nRdVq1HV8DPgEuBP6uqTy+yho8DRwGvSPLhqkRjr0oAAAIqSURBVPrOmgZW1b/Q7cS9B/Akuh2xD5jzcUSSQ6rqukXWIkmSJIF9siQ15R3MkjQhqupy4LN0Tefzhjwng48NquqhVXXoejTNVNV9dGvLbUD3KOAw53yvqk6rqpdW1aOBPejuMtkJ+NBia5EkSZLAPlmSWnOCWZImyx8DvwT+a6t12qrqC8DFwFOSPGkR5/8T8JLBl8uWsjZJkiTNLPtkSWrECWZJmiBVdS3wEWAX4MiGpRxL9zjhySzuWnL74HPWOkqSJEkagn2yJLXjBLMkTZ4T6daJewew5VJ8wySfTFJJXj7M+Kr6FvBpuo1JXjDP99slyVFJtp7nWOhqB/ja4quWJEmS/gP7ZElqwE3+JGnCVNWtSd7PkGu7DWnVG473LuCcdwDPBXab59jWwIeBk5NcBlxNdzfGdnSP+z0S+AndhiySJEnSerNPlqQ2vINZkibTacDyJfx+e9M1tl8Y9oSq+hFw6hoOfw84HPgz4CHA8+k2PXke3V0l7wceM1hnTpIkSVoq9smSNGapqtY1SJIaSvIQ4KfAKVX11tb1SJIkSX1gnyxJw/EOZknS4+l23P5g60IkSZKkHrFPlqQheAezJEmSJEmSJGlRvINZkiRJkiRJkrQoTjBLkiRJkiRJkhbFCWZJkiRJkiRJ0qI4wSxJkiRJkiRJWhQnmCVJkiRJkiRJi+IEsyRJkiRJkiRpUZxgliRJkiRJkiQtihPMkiRJkiRJkqRF+f8EQpL8Xaww7QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(1,3):\n",
    "    print(\"Para Tipo = \" + str(i))\n",
    "    \n",
    "    j = 0\n",
    "    f=plt.figure(figsize=(20, 12))\n",
    "    numC =2 \n",
    "    for numP in values:\n",
    "\n",
    "        ax=f.add_subplot(positions[j])\n",
    "        \n",
    "        t_par = grouped_aggL['Ti'].loc[(0,i,100,numP,slice(None))].mean()\n",
    "        grouped_aggL['Ti'].loc[(1,i,100,numP,slice(None))].plot(kind='bar',color='green', ax=ax) \n",
    "        \n",
    "        ax.axhline(y=t_par, xmin=0, xmax=1, color='purple')\n",
    "        ax.set_ylabel(\"Time (s)\", fontsize=20)\n",
    "        ax.set_xlabel(\"NP,NS\", fontsize=20)\n",
    "        ax.tick_params(axis='both', which='major', labelsize=18)\n",
    "        ax.tick_params(axis='both', which='minor', labelsize=22)\n",
    "        \n",
    "        locs, labels_aux = plt.xticks()\n",
    "        plt.xticks(locs, labels=labelsP[j], rotation=90)\n",
    "        \n",
    "        \n",
    "        blue_patch = mpatches.Patch(color='green', label='Malleable iteration')\n",
    "        handles=[blue_patch]\n",
    "        plt.legend(handles=handles, loc='lower left', fontsize=12)\n",
    "        \n",
    "        f.tight_layout()\n",
    "        f.savefig(\"Images/Iter_type=\"+dist_names[i]+\"_Perc_type=\"+str(100)+\".png\", format=\"png\")\n",
    "        j = (j+1)%5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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