{ "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 math\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import matplotlib.patches as mpatches\n", "import matplotlib.colors as colors\n", "from matplotlib.legend_handler import HandlerLine2D, HandlerTuple\n", "from matplotlib.colors import LinearSegmentedColormap\n", "from scipy import stats\n", "import sys" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "matrixMalEX=\"data_GG.csv\"\n", "matrixMal=\"data_GM.csv\"\n", "matrixIt=\"data_L.csv\"\n", "matrixIt_Total=\"data_L_Total.csv\"\n", "n_qty=2 #CAMBIAR SEGUN LA CANTIDAD DE NODOS USADOS\n", "n_groups= 2\n", "repet = 10 #CAMBIAR EL PRIMER NUMERO SEGUN NUMERO DE EJECUCIONES POR CONFIG\n", "\n", "p_value = 0.05\n", "values = [2, 10, 20, 40]\n", "# WORST BEST\n", "dist_names = ['null', 'BalancedFit', 'CompactFit']\n", "\n", "processes = [1,10,20,40,80,160]\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": 21, "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", "dfG['%Async'] = dfG['%Async'].fillna(0)\n", "\n", "if(n_qty == 1):\n", " group = dfG.groupby(['%Async', 'Cst', 'Css', 'Groups'])['TE']\n", " group2 = dfG.groupby(['%Async', 'Cst', 'Css', 'NP','NS'])['TE']\n", "else: \n", " group = dfG.groupby(['Dist', '%Async', 'Cst', 'Css', 'Groups'])['TE']\n", " group2 = dfG.groupby(['Dist', '%Async', 'Cst', 'Css', 'NP','NS'])['TE']\n", "\n", "grouped_aggG = group.agg(['median'])\n", "grouped_aggG.rename(columns={'median':'TE'}, inplace=True)\n", "\n", "grouped_aggG2 = group2.agg(['median'])\n", "grouped_aggG2.rename(columns={'median':'TE'}, inplace=True)" ] }, { "cell_type": "code", "execution_count": 22, "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[\"TH\"] + dfM[\"TS\"] + dfM[\"TA\"]\n", "dfM['%Async'] = (dfM['%Async'] / dfM['N']) * 100\n", "\n", "dfM['%Async'] = dfM['%Async'].fillna(0)\n", "\n", "#dfM = dfM.drop(dfM.loc[(dfM[\"Cst\"] == 3) & (dfM[\"Css\"] == 1) & (dfM[\"NP\"] > dfM[\"NS\"])].index)\n", "#dfM = dfM.drop(dfM.loc[(dfM[\"Cst\"] == 2) & (dfM[\"Css\"] == 1) & (dfM[\"NP\"] > dfM[\"NS\"])].index)\n", "\n", "if(n_qty == 1):\n", " groupM = dfM.groupby(['%Async', 'Cst', 'Css', 'NP', 'NS'])['TC', 'TH', 'TS', 'TA', 'TR']\n", "else:\n", " groupM = dfM.groupby(['Dist', '%Async', 'Cst', 'Css', 'NP', 'NS'])['TC', 'TH', 'TS', 'TA', 'TR']\n", "\n", "#group\n", "grouped_aggM = groupM.agg(['median'])\n", "grouped_aggM.columns = grouped_aggM.columns.get_level_values(0)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:17: 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" ] } ], "source": [ "dfL = pd.read_csv( matrixIt )\n", "dfL = dfL.drop(columns=dfL.columns[0])\n", "\n", "dfL['%Async'] = dfL['%Async'].fillna(0)\n", "\n", "#dfL = dfL.drop(dfL.loc[(dfL[\"Cst\"] == 3) & (dfL[\"Css\"] == 1) & (dfL[\"NP\"] > dfL[\"NS\"])].index)\n", "#dfL = dfL.drop(dfL.loc[(dfL[\"Cst\"] == 2) & (dfL[\"Css\"] == 1) & (dfL[\"NP\"] > dfL[\"NS\"])].index)\n", "\n", "if(n_qty == 1):\n", " groupL = dfL[dfL['NS'] != 0].groupby(['Tt', '%Async', 'Cst', 'Css', 'NP', 'NS'])['Ti', 'To']\n", "else:\n", " groupL = dfL[dfL['NS'] != 0].groupby(['Tt', 'Dist', '%Async', 'Cst', 'Css', 'NP', 'NS'])['Ti', 'To']\n", "\n", "#group\n", "grouped_aggL = groupL.agg(['median', '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": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:17: 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" ] } ], "source": [ "dfLT = pd.read_csv( matrixIt_Total )\n", "dfLT = dfLT.drop(columns=dfLT.columns[0])\n", "\n", "dfLT['%Async'] = dfLT['%Async'].fillna(0)\n", "\n", "#dfL = dfL.drop(dfL.loc[(dfL[\"Cst\"] == 3) & (dfL[\"Css\"] == 1) & (dfL[\"NP\"] > dfL[\"NS\"])].index)\n", "#dfL = dfL.drop(dfL.loc[(dfL[\"Cst\"] == 2) & (dfL[\"Css\"] == 1) & (dfL[\"NP\"] > dfL[\"NS\"])].index)\n", "\n", "if(n_qty == 1):\n", " groupLT = dfLT[dfLT['NS'] != 0].groupby(['%Async', 'Cst', 'Css', 'NP', 'NS'])['Sum']\n", "else:\n", " groupLT = dfLT[dfLT['NS'] != 0].groupby(['Dist', '%Async', 'Cst', 'Css', 'NP', 'NS'])['Sum']\n", "\n", "#group\n", "grouped_aggLT = groupLT.agg(['median'])\n", "grouped_aggLT.columns = grouped_aggLT.columns.get_level_values(0)\n", "grouped_aggLT.set_axis(['Sum'], axis='columns')" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "grouped_aggL.to_excel(\"resultL.xlsx\") \n", "grouped_aggLT.to_excel(\"resultLT.xlsx\")\n", "dfLT.to_excel(\"resultLT_all.xlsx\")\n", "grouped_aggM.to_excel(\"resultM.xlsx\") \n", "grouped_aggG2.to_excel(\"resultG.xlsx\") " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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N%AsyncGroupsNPNSDistMatrixCommTamCstCssTimeItersTES
000.020,8020802,21000000304.03,976.6342780
100.020,8020802,21000000304.03,976.6714520
200.020,8020802,21000000304.03,976.5284890
300.020,8020802,21000000304.03,976.5982940
400.020,8020802,21000000304.03,976.5804600
.............................................
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2400 rows × 14 columns

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" ], "text/plain": [ " N %Async Groups NP NS Dist Matrix CommTam Cst Css Time Iters \\\n", "0 0 0.0 20,80 20 80 2,2 100000 0 3 0 4.0 3,97 \n", "1 0 0.0 20,80 20 80 2,2 100000 0 3 0 4.0 3,97 \n", "2 0 0.0 20,80 20 80 2,2 100000 0 3 0 4.0 3,97 \n", "3 0 0.0 20,80 20 80 2,2 100000 0 3 0 4.0 3,97 \n", "4 0 0.0 20,80 20 80 2,2 100000 0 3 0 4.0 3,97 \n", "... .. ... ... .. .. ... ... ... ... ... ... ... \n", "2395 0 0.0 40,80 40 80 2,2 100000 0 0 1 4.0 3,97 \n", "2396 0 0.0 40,80 40 80 2,2 100000 0 0 1 4.0 3,97 \n", "2397 0 0.0 40,80 40 80 2,2 100000 0 0 1 4.0 3,97 \n", "2398 0 0.0 40,80 40 80 2,2 100000 0 0 1 4.0 3,97 \n", "2399 0 0.0 40,80 40 80 2,2 100000 0 0 1 4.0 3,97 \n", "\n", " TE S \n", "0 6.634278 0 \n", "1 6.671452 0 \n", "2 6.528489 0 \n", "3 6.598294 0 \n", "4 6.580460 0 \n", "... ... .. \n", "2395 7.632454 0 \n", "2396 7.279475 0 \n", "2397 7.590942 0 \n", "2398 7.298454 0 \n", "2399 7.356583 0 \n", "\n", "[2400 rows x 14 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfG" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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TE
Dist%AsyncCstCssGroups
2,20.0001,1051.150761
1,16015.706663
1,2032.814834
1,4022.719029
1,8017.936359
............
3180,1388.108210
80,1039.045234
80,1603.600383
80,2019.728576
80,4010.049131
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240 rows × 1 columns

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N%AsyncNPNSDistMatrixCommTamCstCssTimeItersTCTHTSTASTR
000.020802,21000000304.03,971.2131370.9756490.00.002.188786
100.020802,21000000304.03,971.3088361.0460990.00.002.354935
200.020802,21000000304.03,971.1353791.0602760.00.002.195655
300.020802,21000000304.03,971.2179850.9578560.00.002.175841
400.020802,21000000304.03,971.1926070.9336320.00.002.126239
......................................................
239500.040802,21000000014.03,972.3519270.0000000.00.002.351927
239600.040802,21000000014.03,972.0276800.0000000.00.002.027680
239700.040802,21000000014.03,972.3123050.0000000.00.002.312305
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TCTHTSTATR
Dist%AsyncCstCssNPNS
2,20.0001100.3308930.00.00.00.330893
201.2454510.00.00.01.245451
400.8908160.00.00.00.890816
800.9196440.00.00.00.919644
1601.1760740.00.00.01.176074
...........................
3116010.0405860.00.00.00.040586
100.0436470.00.00.00.043647
200.0406430.00.00.00.040643
400.1090380.00.00.00.109038
800.1312500.00.00.00.131250
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240 rows × 5 columns

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" ], "text/plain": [ " TC TH TS TA TR\n", "Dist %Async Cst Css NP NS \n", "2,2 0.0 0 0 1 10 0.330893 0.0 0.0 0.0 0.330893\n", " 20 1.245451 0.0 0.0 0.0 1.245451\n", " 40 0.890816 0.0 0.0 0.0 0.890816\n", " 80 0.919644 0.0 0.0 0.0 0.919644\n", " 160 1.176074 0.0 0.0 0.0 1.176074\n", "... ... ... ... ... ...\n", " 3 1 160 1 0.040586 0.0 0.0 0.0 0.040586\n", " 10 0.043647 0.0 0.0 0.0 0.043647\n", " 20 0.040643 0.0 0.0 0.0 0.040643\n", " 40 0.109038 0.0 0.0 0.0 0.109038\n", " 80 0.131250 0.0 0.0 0.0 0.131250\n", "\n", "[240 rows x 5 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grouped_aggM" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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N%AsyncNPN_parNSDistCompute_tamComm_tamCstCssTimeItersTiTtTo
000.02008021000000304.030.1998110.0224.0
100.02008021000000304.030.1998130.0224.0
200.02008021000000304.030.1998950.0224.0
300.02008021000000304.030.4240271.0224.0
400.02008021000000304.030.5320201.0224.0
................................................
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240000 rows × 15 columns

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" ], "text/plain": [ " N %Async NP N_par NS Dist Compute_tam Comm_tam Cst Css Time \\\n", "0 0 0.0 20 0 80 2 100000 0 3 0 4.0 \n", "1 0 0.0 20 0 80 2 100000 0 3 0 4.0 \n", "2 0 0.0 20 0 80 2 100000 0 3 0 4.0 \n", "3 0 0.0 20 0 80 2 100000 0 3 0 4.0 \n", "4 0 0.0 20 0 80 2 100000 0 3 0 4.0 \n", "... .. ... .. ... .. ... ... ... ... ... ... \n", "239995 0 0.0 40 0 80 2 100000 0 0 1 4.0 \n", "239996 0 0.0 40 0 80 2 100000 0 0 1 4.0 \n", "239997 0 0.0 40 0 80 2 100000 0 0 1 4.0 \n", "239998 0 0.0 40 0 80 2 100000 0 0 1 4.0 \n", "239999 0 0.0 40 0 80 2 100000 0 0 1 4.0 \n", "\n", " Iters Ti Tt To \n", "0 3 0.199811 0.0 224.0 \n", "1 3 0.199813 0.0 224.0 \n", "2 3 0.199895 0.0 224.0 \n", "3 3 0.424027 1.0 224.0 \n", "4 3 0.532020 1.0 224.0 \n", "... ... ... ... ... \n", "239995 3 0.099933 0.0 112.0 \n", "239996 3 0.099903 0.0 112.0 \n", "239997 3 0.099926 0.0 112.0 \n", "239998 3 0.100183 0.0 112.0 \n", "239999 3 0.099943 0.0 112.0 \n", "\n", "[240000 rows x 15 columns]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfL" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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TiItersToIters2
TtDist%AsyncCstCssNPNS
0.020.0001101.9991203.02242.03.0
201.9991833.02242.03.0
401.9991503.02242.03.0
801.9991313.02242.03.0
1601.9991153.02242.03.0
.................................
1.020.01320800.1059801.0112.01.0
1600.1056451.0112.01.0
40800.0683591.056.01.0
1600.0523861.056.01.0
801600.0266512.028.02.0
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330 rows × 4 columns

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" ], "text/plain": [ " Ti Iters To Iters2\n", "Tt Dist %Async Cst Css NP NS \n", "0.0 2 0.0 0 0 1 10 1.999120 3.0 2242.0 3.0\n", " 20 1.999183 3.0 2242.0 3.0\n", " 40 1.999150 3.0 2242.0 3.0\n", " 80 1.999131 3.0 2242.0 3.0\n", " 160 1.999115 3.0 2242.0 3.0\n", "... ... ... ... ...\n", "1.0 2 0.0 1 3 20 80 0.105980 1.0 112.0 1.0\n", " 160 0.105645 1.0 112.0 1.0\n", " 40 80 0.068359 1.0 56.0 1.0\n", " 160 0.052386 1.0 56.0 1.0\n", " 80 160 0.026651 2.0 28.0 2.0\n", "\n", "[330 rows x 4 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grouped_aggL" ] }, { "cell_type": "code", "execution_count": 202, "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": "markdown", "metadata": {}, "source": [ "A partir de aquí se muestran gráficos" ] }, { "cell_type": "code", "execution_count": 204, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[0.21241231578947362, 0.21241231578947362, 0.21241231578947362, 0.21241231578947362, 0.04632109565217393, 0.04632109565217393, 0.04632109565217393, 0.04632109565217393, 0.025296672413793103, 0.025296672413793103, 0.025296672413793103, 0.025296672413793103, 0.0355868547008547, 0.0355868547008547, 0.0355868547008547, 0.0355868547008547], [0.1981199732142857, 0.1981199732142857, 0.1981199732142857, 0.1981199732142857, 0.06233977876106192, 0.06233977876106192, 0.06233977876106192, 0.06233977876106192, 0.026912142857142853, 0.026912142857142853, 0.026912142857142853, 0.026912142857142853, 0.0343439649122807, 0.0343439649122807, 0.0343439649122807, 0.0343439649122807]]\n", "[[2.1241231578947364, 0.4632109565217393, 0.25296672413793103, 0.35586854700854703, 2.1241231578947364, 0.4632109565217393, 0.25296672413793103, 0.35586854700854703, 2.1241231578947364, 0.4632109565217393, 0.25296672413793103, 0.35586854700854703, 2.1241231578947364, 0.4632109565217393, 0.25296672413793103, 0.35586854700854703], [1.9811997321428572, 0.6233977876106191, 0.2691214285714285, 0.343439649122807, 1.9811997321428572, 0.6233977876106191, 0.2691214285714285, 0.343439649122807, 1.9811997321428572, 0.6233977876106191, 0.2691214285714285, 0.343439649122807, 1.9811997321428572, 0.6233977876106191, 0.2691214285714285, 0.343439649122807]]\n", "[[0.22657399999999997, 0.22961033333333333, 0.37444533333333335, 1.0861523333333334, 0.18071299999999998, 0.2686593333333333, 0.48245, 1.8810366666666667, 0.22639533333333337, 0.31453400000000004, 0.564293, 2.4626886666666667, 0.4612826666666667, 1.0638560000000001, 1.5319243333333334, 2.1236686666666666], [0.21594133333333332, 0.36930899999999994, 1.1269756666666668, 1.1670603333333334, 0.22462733333333332, 0.47068400000000005, 1.5951943333333334, 1.693723, 0.7059706666666666, 1.368441, 1.8698483333333336, 2.2059883333333334, 0.4813296666666667, 1.3010543333333333, 1.8387883333333335, 2.1851773333333333]]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:36: 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:53: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n" ] } ], "source": [ "#Reserva de memoria para las estructuras\n", "TP_data=[0]*2\n", "TH_data=[0]*2\n", "TM_data=[0]*2\n", "\n", "TP_A_data=[0]*2\n", "TH_A_data=[0]*2\n", "TM_A_data=[0]*2\n", "\n", "for dist in [1,2]:\n", " dist_index=dist-1\n", " \n", " TP_data[dist_index]=[0]*len(values)*(len(values))\n", " TH_data[dist_index]=[0]*len(values)*(len(values))\n", " TM_data[dist_index]=[0]*len(values)*(len(values))\n", "\n", " TP_A_data[dist_index]=[0]*len(values)*(len(values))\n", " TH_A_data[dist_index]=[0]*len(values)*(len(values))\n", " TM_A_data[dist_index]=[0]*len(values)*(len(values))\n", "\n", "# Obtencion de los grupos del dataframe necesarios\n", "\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", "#Calculo de los valores para las figuras\n", "#1=Best Fit\n", "#2=Worst Fit\n", "dist=1\n", "for dist in [1,2]:\n", " dist_index=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]: #TODO Poner a 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", " #Guardar datos\n", " if tipo == 0:\n", " TP_data[dist_index][i*len(values) + j] = p1\n", " TH_data[dist_index][i*len(values) + j] = p2\n", " TM_data[dist_index][i*len(values) + j] = p3\n", " else:\n", " TP_A_data[dist_index][i*len(values) + j] = p1\n", " TH_A_data[dist_index][i*len(values) + j] = p2\n", " TM_A_data[dist_index][i*len(values) + j] = p3\n", " j+=1\n", " i+=1\n", "print(TP_data)\n", "print(TH_data)\n", "print(TM_data)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#TP_A_data=[[0.1997793257575758, 0.1997793257575758, 0.1997793257575758, 0.1997793257575758, 0.040469166666666695, 0.040469166666666695, 0.040469166666666695, 0.040469166666666695, 0.019951386363636366, 0.019951386363636366, 0.019951386363636366, 0.019951386363636366, 0.010227022727272729, 0.010227022727272729, 0.010227022727272729, 0.010227022727272729], [0.20020575000000002, 0.20020575000000002, 0.20020575000000002, 0.20020575000000002, 0.039894712121212116, 0.039894712121212116, 0.039894712121212116, 0.039894712121212116, 0.020662818181818185, 0.020662818181818185, 0.020662818181818185, 0.020662818181818185, 0.010635333333333332, 0.010635333333333332, 0.010635333333333332, 0.010635333333333332]]\n", "#TH_A_data=[[1.9977932575757578, 0.40469166666666695, 0.19951386363636364, 0.10227022727272729, 1.9977932575757578, 0.40469166666666695, 0.19951386363636364, 0.10227022727272729, 1.9977932575757578, 0.40469166666666695, 0.19951386363636364, 0.10227022727272729, 1.9977932575757578, 0.40469166666666695, 0.19951386363636364, 0.10227022727272729], [2.0020575000000003, 0.39894712121212117, 0.20662818181818185, 0.10635333333333331, 2.0020575000000003, 0.39894712121212117, 0.20662818181818185, 0.10635333333333331, 2.0020575000000003, 0.39894712121212117, 0.20662818181818185, 0.10635333333333331, 2.0020575000000003, 0.39894712121212117, 0.20662818181818185, 0.10635333333333331]]\n", "#TM_A_data=[[0.2083043333333333, 0.2661843333333333, 0.41778833333333326, 0.9868953333333335, 0.242685, 0.3060793333333333, 0.4986676666666667, 1.2530743333333334, 0.305179, 0.373607, 0.7375183333333334, 1.5113886666666667, 0.501651, 0.8987069999999999, 1.138518666666667, 1.5091376666666665], [0.205789, 0.4116923333333334, 1.0607546666666667, 0.9947066666666666, 0.27494700000000005, 0.669121, 1.2705783333333334, 1.3951336666666665, 0.4765406666666667, 0.9758123333333333, 1.267633, 1.4479673333333334, 0.4905743333333333, 1.0088953333333333, 1.4447113333333332, 1.4516683333333333]]\n", "\n", "\n", "for dist in [1,2]:\n", " dist_index=dist-1\n", " 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[dist_index], TM_data[dist_index]).tolist()\n", " sumaTP_TM_A = np.add(TP_A_data[dist_index], TM_A_data[dist_index]).tolist()\n", "\n", " ax=f.add_subplot(111)\n", "\n", " ax.bar(x+width/2, TP_data[dist_index], width, color='blue')\n", " ax.bar(x+width/2, TM_data[dist_index], width, bottom=TP_data[dist_index],color='orange')\n", " ax.bar(x+width/2, TH_data[dist_index], width, bottom=sumaTP_TM, color='green')\n", "\n", " ax.bar(x-width/2, TP_A_data[dist_index], width, hatch=\"\\\\/...\", color='blue')\n", " ax.bar(x-width/2, TM_A_data[dist_index], width, bottom=TP_A_data[dist_index], hatch=\"\\\\/...\", color='orange')\n", " ax.bar(x-width/2, TH_A_data[dist_index], width, bottom=sumaTP_TM_A, hatch=\"\\\\/...\", color='green')\n", "\n", " ax.set_ylabel(\"Time(s)\", fontsize=20)\n", " ax.set_xlabel(\"NP, NC\", fontsize=20)\n", " plt.xticks(x, labelsP_J, rotation=90)\n", "\n", " blue_Spatch = mpatches.Patch(color='blue', label='Parents PR')\n", " orange_Spatch = mpatches.Patch(color='orange', label='Resize PR')\n", " green_Spatch = mpatches.Patch(color='green', label='Children PR')\n", " blue_Apatch = mpatches.Patch(hatch='\\\\/...', facecolor='blue', label='Parents NR')\n", " orange_Apatch = mpatches.Patch(hatch='\\\\/...', facecolor='orange', label='Resize NR')\n", " green_Apatch = mpatches.Patch(hatch='\\\\/...', facecolor='green', label='Children NR')\n", "\n", "\n", " handles=[blue_Spatch,orange_Spatch,green_Spatch,blue_Apatch,orange_Apatch,green_Apatch]\n", "\n", " plt.legend(handles=handles, loc='upper left', fontsize=21,ncol=2)\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", " plt.ylim((0, 25.5))\n", " #ax.axvline(4)\n", " \n", " f.tight_layout()\n", " f.savefig(\"Images/EX_Partitions_\"+dist_names[dist]+\".png\", format=\"png\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Reserva de memoria para las estructuras\n", "TC_data=[0]*2\n", "TS_data=[0]*2\n", "TA_data=[0]*2\n", "\n", "TC_A_data=[0]*2\n", "TS_A_data=[0]*2\n", "TA_A_data=[0]*2\n", "\n", "for dist in [1,2]:\n", " dist_index=dist-1\n", "\n", " TC_data[dist_index]=[0]*len(values)*(len(values))\n", " TS_data[dist_index]=[0]*len(values)*(len(values))\n", " TA_data[dist_index]=[0]*len(values)*(len(values))\n", "\n", " TC_A_data[dist_index]=[0]*len(values)*(len(values))\n", " TS_A_data[dist_index]=[0]*len(values)*(len(values))\n", " TA_A_data[dist_index]=[0]*len(values)*(len(values))\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", "for dist in [1,2]:\n", " dist_index=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", " \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[dist_index][i*len(values) + j] = test_tc_real\n", " TS_data[dist_index][i*len(values) + j] = test[0] \n", " TA_data[dist_index][i*len(values) + j] = 0\n", " else:\n", " TC_A_data[dist_index][i*len(values) + j] = test_tc_real\n", " TS_A_data[dist_index][i*len(values) + j] = test[0]\n", " TA_A_data[dist_index][i*len(values) + j] = test[1]\n", " j+=1\n", " i+=1\n", " \n", " \n", "##########################\n", "\n", "print(TC_data)\n", "#print(TA_A_data[1])\n", "#print(TS_data)\n", "#print(TA_data)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for dist in [1,2]:\n", " dist_index=dist-1\n", " 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[dist_index], TS_A_data[dist_index]).tolist()\n", "\n", " ax=f.add_subplot(111)\n", "\n", " ax.bar(x+width/2, TC_data[dist_index], width, color='chocolate')\n", " ax.bar(x+width/2, TS_data[dist_index], width, bottom=TC_data[dist_index], color='purple')\n", "\n", " ax.bar(x-width/2, TC_A_data[dist_index], width, hatch=\"\", color='chocolate')\n", " ax.bar(x-width/2, TS_A_data[dist_index], width, bottom=TC_A_data[dist_index], hatch=\"\\\\/...\", color='purple')\n", " ax.bar(x-width/2, TA_A_data[dist_index], width, bottom=sumaTC_TS_A, hatch=\"\", color='red')\n", "\n", " ax.set_ylabel(\"Time(s)\", fontsize=20)\n", " ax.set_xlabel(\"NP, NC\", fontsize=20)\n", " plt.xticks(x, labelsP_J, rotation=90)\n", "\n", " labels = ['Spawn', 'Synchronous', 'Asynchronous'] # Necesario para subdividir\n", " brown_Spatch = mpatches.Patch(color='chocolate', label='Spawn')\n", " purple_Spatch = mpatches.Patch(color='purple', label='Synchronous')\n", " red_Apatch = mpatches.Patch(facecolor='red', label='Asynchronous')\n", "\n", " #handles=[(brown_Spatch, brown_Apatch),purple_Spatch,red_Apatch] Dos colores para misma leyenda\n", " handles=[brown_Spatch,purple_Spatch,red_Apatch]\n", "\n", " plt.legend(handles=handles, labels=labels, loc='upper left', fontsize=24, ncol=1, handler_map={tuple: HandlerTuple(ndivide=None)})\n", "#bbox_to_anchor=(1, 0.5) --> Para sacar fuera de la grafica la leyenda\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", " plt.ylim((0, 8))\n", " f.tight_layout()\n", " f.savefig(\"Images/Malt_Partitions_\"+dist_names[dist]+\".png\", format=\"png\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "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,NC\", 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='upper 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": 33, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/usuario/anaconda3/lib/python3.7/site-packages/numpy/lib/stride_tricks.py:262: UserWarning: Warning: converting a masked element to nan.\n", " args = [np.array(_m, copy=False, subok=subok) for _m in args]\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f=plt.figure(figsize=(18, 10))\n", "#for numP in values:\n", "\n", "x = np.arange(len(labelsP_J))\n", "\n", "width = 0.4\n", "middle = 0\n", "ax=f.add_subplot(111)\n", "\n", "for dist in [1,2]:\n", " dist_index=dist-1\n", " sumaTP_TM = np.add(TP_data[dist_index], TM_data[dist_index])\n", " bar_res = np.add(sumaTP_TM, TH_data[dist_index])\n", "\n", " sumaTP_TM_A = np.add(TP_A_data[dist_index], TM_A_data[dist_index])\n", " sumaTP_TM_A = np.add(sumaTP_TM_A, TH_A_data[dist_index])\n", " \n", " bar_res = np.divide(bar_res, sumaTP_TM_A)\n", " bar_res = (bar_res-1)*100\n", "\n", " supper = np.ma.masked_where(bar_res < middle, bar_res)\n", " slower = np.ma.masked_where(bar_res > middle, bar_res)\n", "\n", " plt.ylim(min(bar_res)-20, max(bar_res)+150) #FIXME Error cuando el max o min de dist=1 es mayor que el de dist=2\n", "\n", " offset = -width/2 # Best Fit\n", " patch = \"\"\n", " if dist == 2:\n", " offset = (width/2) # Worst Fit\n", " patch = \"\\\\/...\"\n", " \n", " ax.bar(x+offset, supper, width, color=\"orange\", hatch=patch)\n", " ax.bar(x+offset, slower, width, color=\"cyan\", hatch=patch)\n", "\n", "\n", "ax.set_ylabel(\"Porcentual difference(%)\", fontsize=20)\n", "ax.set_xlabel(\"NP, NC\", fontsize=20)\n", "plt.xticks(x, labelsP_J, rotation=90)\n", "\n", "\n", "orange_Bf_patch = mpatches.Patch(facecolor='orange', label='Best Fit AC Improvement')\n", "blue_Bf_patch = mpatches.Patch(facecolor='cyan', label='Best Fit SC Improvement')\n", "orange_Wf_patch = mpatches.Patch(hatch='\\\\/...', facecolor='orange', label='Worst Fit AC Improvement')\n", "blue_Wf_patch = mpatches.Patch(hatch='\\\\/...', facecolor='cyan', label='Worst Fit SC Improvement')\n", "handles=[orange_Bf_patch, blue_Bf_patch, orange_Wf_patch, blue_Wf_patch]\n", "plt.legend(handles=handles, loc='upper right', fontsize=24)\n", "\n", "ax.axhline((middle), color='black')\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_Difference.png\", format=\"png\")\n", "j = (j+1)%5" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f=plt.figure(figsize=(20, 12))\n", "#for numP in values:\n", "\n", "#mpict=[73.51697466666666, 65.51325966666666, 52.19395466666668, 34.024326, \n", "# 60.883046666666665, 6.740908999999999, 4.857252666666667, 8.431013333333334, \n", "# 8.392006666666667, 9.646620333333333, 8.497321000000001, 12.982642333333333, \n", "# 8.007896666666667, 9.762626333333332, 6.662559000000001, 5.316057333333333]\n", "#mpivar=[75.53585966666667, 69.56025666666666, 18.082787666666665, 154.39648000000003,\n", "# 11.998184, 1.27766, 2.194493, 2.2691966666666668,\n", "# 1.8346349999999998, 1.7718876666666665, 1.6892053333333334, 1.5759526666666668,\n", "# 1.9237193333333333, 1.1625573333333332, 1.2879283333333333, 0.850577]\n", "#threadct=[2.0040453333333335, 2.00311, 2.6234766666666665, 2.699171666666667,\n", "# 2.0707343333333337, 2.2307106666666665, 4.222584666666666, 4.246569666666667,\n", "# 4.020083, 4.503111666666666, 5.467942333333333, 5.048009,\n", "# 3.8683916666666662, 4.705335000000001, 5.640752666666667, 5.811503999999999]\n", "#threadvar=[1.9998449999999999, 2.000283666666667, 2.957152666666667, 2.401677,\n", "# 0.8674843333333334, 0.6187566666666666, 1.3398349999999999, 1.735828,\n", "# 1.4827233333333334, 1.245435, 1.7185836666666665, 1.7706360000000003,\n", "# 1.645278, 1.414596666666667, 1.6812523333333333, 1.5372543333333333]\n", "\n", "threadbf = [0.3616343333333334, 0.4390483333333333, 0.6118543333333334, 1.116497, \n", " 0.34279300000000007, 0.39713933333333334, 0.6350386666666668, 1.4056896666666667, \n", " 0.37821333333333335, 0.488335, 0.7908086666666666, 1.4691523333333334, \n", " 0.5579729999999999, 1.1923566666666667, 1.3307016666666667, 1.7391976666666666]\n", "\n", "threadcf = [0.42423133333333335, 0.6090126666666666, 1.3089426666666668, 1.3461553333333331,\n", " 0.427392, 0.7682310000000001, 1.3941153333333334, 1.3357656666666664, \n", " 0.8323596666666666, 1.318649, 1.5996213333333333, 1.6247436666666666, \n", " 0.7896679999999999, 1.2355183333333333, 1.4249120000000002, 1.6693683333333331]\n", "\n", "normalbf = [0.2083043333333333, 0.2661843333333333, 0.41778833333333326, 0.9868953333333335,\n", " 0.242685, 0.3060793333333333, 0.4986676666666667, 1.2530743333333334, \n", " 0.305179, 0.373607, 0.7375183333333334, 1.5113886666666667, \n", " 0.501651, 0.8987069999999999, 1.138518666666667, 1.5091376666666665]\n", "\n", "normalcf = [0.205789, 0.4116923333333334, 1.0607546666666667, 0.9947066666666666, \n", " 0.27494700000000005, 0.669121, 1.2705783333333334, 1.3951336666666665, \n", " 0.4765406666666667, 0.9758123333333333, 1.267633, 1.4479673333333334, \n", " 0.4905743333333333, 1.0088953333333333, 1.4447113333333332, 1.4516683333333333]\n", "\n", "x = np.arange(len(labelsP_J))\n", "\n", "width = 0.45/2\n", "\n", "ax=f.add_subplot(111)\n", "\n", "ax.bar(x-width/2, normalbf, width, hatch=\"\", color='blue')\n", "\n", "ax.bar(x-width*1.5, normalcf, width, hatch=\"\",color='green')\n", "\n", "ax.bar(x+width/2, threadbf, width, hatch=\"\", color='orange')\n", "\n", "ax.bar(x+width*1.5, threadcf, width, hatch=\"\", color='red')\n", "\n", "ax.set_ylabel(\"Time(s)\", fontsize=20)\n", "ax.set_xlabel(\"NP, NC\", fontsize=20)\n", "plt.xticks(x, labelsP_J, rotation=90)\n", "\n", "normalbf_patch = mpatches.Patch(color='blue', label='Normal - Bf')\n", "normalcf_patch = mpatches.Patch(color='green', label='Normal - Cf')\n", "threadbf_patch = mpatches.Patch(color='orange', label='Pthreads - Bf')\n", "threadcf_patch = mpatches.Patch(hatch='', facecolor='red', label='Pthreads - Cf')\n", "\n", "\n", "handles=[normalcf_patch,normalbf_patch,threadbf_patch,threadcf_patch]\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", "#plt.ylim((0, 15))\n", " \n", "f.tight_layout()\n", "f.savefig(\"Images/Mall_AR.png\", format=\"png\")\n", "j = (j+1)%5" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "labels = ['(1,10)', '(1,20)', '(1,40)','(1,80)','(1,160)',\n", " '(10,1)', '(10,20)', '(10,40)','(10,80)','(10,160)',\n", " '(20,1)', '(20,10)','(20,40)','(20,80)','(20,160)',\n", " '(40,1)', '(40,10)', '(40,20)','(40,80)','(40,160)',\n", " '(80,1)', '(80,10)', '(80,20)', '(80,40)','(80,160)',\n", " '(160,1)', '(160,10)', '(160,20)','(160,40)','(160,80)']\n", "\n", "labelsExpand = ['(1,10)', '(1,20)', '(1,40)','(1,80)','(1,160)',\n", " '(10,20)', '(10,40)','(10,80)','(10,160)',\n", " '(20,40)','(20,80)','(20,160)',\n", " '(40,80)','(40,160)',\n", " '(80,160)']\n", "labelsShrink = ['(10,1)', \n", " '(20,1)', '(20,10)', \n", " '(40,1)', '(40,10)', '(40,20)',\n", " '(80,1)', '(80,10)', '(80,20)', '(80,40)',\n", " '(160,1)', '(160,10)', '(160,20)','(160,40)','(160,80)']\n", "\n", "labelsExpandIntra = ['(1,10)', '(1,20)','(10,20)']\n", "labelsShrinkIntra = ['(10,1)', '(20,1)', '(20,10)']\n", "labelsExpandInter = ['(1,40)','(1,80)', '(1,160)',\n", " '(10,40)','(10,80)', '(10,160)',\n", " '(20,40)','(20,80)', '(20,160)',\n", " '(40,80)', '(40,160)',\n", " '(80,160)']\n", "labelsShrinkInter = ['(40,1)', '(40,10)', '(40,20)',\n", " '(80,1)', '(80,10)', '(80,20)','(80,40)',\n", " '(160,1)', '(160,10)', '(160,20)','(160,40)', '(160,80)']\n", "\n", " #0 #1 #2 #3\n", "labelsMethods = ['Baseline', 'Baseline single','Baseline - Pthreads','Baseline single - Pthreads',\n", " 'Merge','Merge single','Merge - Pthreads','Merge single - Pthreads']\n", " #4 #5 #6 #7\n", "\n", "OrMult_patch = mpatches.Patch(hatch='', facecolor='green', label='Baseline')\n", "OrSing_patch = mpatches.Patch(hatch='', facecolor='springgreen', label='Baseline single')\n", "OrPthMult_patch = mpatches.Patch(hatch='//', facecolor='blue', label='Baseline - Pthreads')\n", "OrPthSing_patch = mpatches.Patch(hatch='\\\\', facecolor='darkblue', label='Baseline single - Pthreads')\n", "MergeMult_patch = mpatches.Patch(hatch='||', facecolor='red', label='Merge')\n", "MergeSing_patch = mpatches.Patch(hatch='...', facecolor='darkred', label='Merge single')\n", "MergePthMult_patch = mpatches.Patch(hatch='xx', facecolor='yellow', label='Merge - Pthreads')\n", "MergePthSing_patch = mpatches.Patch(hatch='++', facecolor='olive', label='Merge single - Pthreads')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "def get_types_iker(checked_type='tc', used_direction='e', node_type=\"All\", normality='m'):\n", " if checked_type=='te':\n", " var_aux='TE'\n", " tipo_fig=\"TE\"\n", " grouped_aux=grouped_aggG2\n", " elif checked_type=='tc':\n", " var_aux='TC'\n", " tipo_fig=\"Mall\"\n", " grouped_aux=grouped_aggM\n", " \n", " if node_type=='Intra':\n", " grouped_aux=grouped_aux.query('NP < 21 and NS < 21')\n", " elif node_type=='Inter':\n", " grouped_aux=grouped_aux.query('NP > 21 or NS > 21')\n", " \n", " if used_direction=='s':\n", " grouped_aux=grouped_aux.query('NP > NS')\n", " if node_type=='Intra':\n", " used_labels=labelsShrinkIntra\n", " elif node_type=='Inter':\n", " used_labels=labelsShrinkInter\n", " elif node_type=='All':\n", " used_labels=labelsShrink\n", " name_fig=\"Shrink\"\n", " \n", " if normality=='r':\n", " handles=[OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergePthMult_patch]\n", " else:\n", " handles=[OrMult_patch,OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergePthMult_patch]\n", " elif used_direction=='e':\n", " grouped_aux=grouped_aux.query('NP < NS')\n", " if node_type=='Intra':\n", " used_labels=labelsExpandIntra\n", " elif node_type=='Inter':\n", " used_labels=labelsExpandInter\n", " elif node_type=='All':\n", " used_labels=labelsExpand\n", " name_fig=\"Expand\"\n", " if normality=='r':\n", " handles=[OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergeSing_patch,MergePthMult_patch,MergePthSing_patch]\n", " else:\n", " handles=[OrMult_patch,OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergeSing_patch,MergePthMult_patch,MergePthSing_patch]\n", "\n", " title=tipo_fig+\"_Spawn_\"+node_type+\"_\"+name_fig+\"_\"+normality\n", " return var_aux, grouped_aux, handles, used_labels, title" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "def obtain_arrays_iker(grouped_aux, var_aux, used_direction='e', normality='m'):\n", " vOrMult = list(grouped_aux.query('Cst == \"0\" and Css == \"0\"')[var_aux])\n", " vOrSingle = list(grouped_aux.query('Cst == \"0\" and Css == \"1\"')[var_aux])\n", " vMergeMult = list(grouped_aux.query('Cst == \"2\" and Css == \"0\"')[var_aux])\n", " \n", " if var_aux == 'TC': # Para el tiempo TC se utiliza el que no tiene en cuenta la app para los hilos\n", " vOrPthMult = list(grouped_aux.query('Cst == \"1\" and Css == \"0\"')['TH'])\n", " vOrPthSingle = list(grouped_aux.query('Cst == \"1\" and Css == \"1\"')['TH'])\n", " vMergePthMult = list(grouped_aux.query('Cst == \"3\" and Css == \"0\"')['TH'])\n", " else:\n", " vOrPthMult = list(grouped_aux.query('Cst == \"1\" and Css == \"0\"')[var_aux])\n", " vOrPthSingle = list(grouped_aux.query('Cst == \"1\" and Css == \"1\"')[var_aux])\n", " vMergePthMult = list(grouped_aux.query('Cst == \"3\" and Css == \"0\"')[var_aux])\n", "\n", " \n", " if used_direction=='e':\n", " vMergeSingle = list(grouped_aux.query('Cst == \"2\" and Css == \"1\"')[var_aux])\n", " if var_aux == 'TC': # Para el tiempo TC se utiliza el que no tiene en cuenta la app para los hilos\n", " vMergePthSingle = list(grouped_aux.query('Cst == \"3\" and Css == \"1\"')['TH'])\n", " else:\n", " vMergePthSingle = list(grouped_aux.query('Cst == \"3\" and Css == \"1\"')[var_aux])\n", " else:\n", " #FIXME Que tenga en cuenta TH al realizar shrink merge\n", " vMergePthMult = list(grouped_aux.query('Cst == \"3\" and Css == \"0\"')[var_aux])\n", " vMergeSingle = None\n", " vMergePthSingle = None\n", " title_y = \"Total time(s)\"\n", " \n", " if normality == 'r':\n", " vOrSingle = np.subtract(vOrMult, vOrSingle)\n", " vOrPthMult = np.subtract(vOrMult, vOrPthMult)\n", " vOrPthSingle = np.subtract(vOrMult, vOrPthSingle)\n", " vMergeMult = np.subtract(vOrMult, vMergeMult)\n", " vMergePthMult = np.subtract(vOrMult, vMergePthMult)\n", " if used_direction=='e':\n", " vMergeSingle = np.subtract(vOrMult, vMergeSingle)\n", " vMergePthSingle = np.subtract(vOrMult, vMergePthSingle)\n", " vOrMult = None\n", " title_y = \"Saved time(s)\"\n", " elif normality == 'n':\n", " vOrSingle = np.divide(vOrSingle, vOrMult)\n", " vOrPthMult = np.divide(vOrPthMult, vOrMult)\n", " vOrPthSingle = np.divide(vOrPthSingle, vOrMult)\n", " vMergeMult = np.divide(vMergeMult, vOrMult)\n", " vMergePthMult = np.divide(vMergePthMult, vOrMult)\n", " if used_direction=='e':\n", " vMergeSingle = np.divide(vMergeSingle, vOrMult)\n", " vMergePthSingle = np.divide(vMergePthSingle, vOrMult)\n", " vOrMult = np.divide(vOrMult, vOrMult)\n", " title_y = \"Relation Config time / Baseline Time\"\n", " \n", " data_array=[vOrMult,vOrSingle,vOrPthMult,vOrPthSingle,vMergeMult,vMergeSingle,vMergePthMult,vMergePthSingle]\n", " return data_array, title_y\n", "\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "def legend_loc_iker(data_array, len_x):\n", " max_value = np.nanmax([x for x in data_array if x is not None]) # Los valores None es necesario evitarlos\n", " min_value = np.nanmin([x for x in data_array if x is not None]) # Los valores None es necesario evitarlos\n", " if(min_value < 0):\n", " min_value = 0\n", " middle_value = (max_value + min_value) / 2\n", " offset = (max_value - min_value) * 0.1\n", " \n", " def array_check_loc(ini, end):\n", " up = True\n", " lower = True\n", " for i in range(ini, end):\n", " for j in range(len(data_array)):\n", " if not (data_array[j] is None):\n", " if data_array[j][i] > (middle_value + offset):\n", " up = False\n", " elif data_array[j][i] < (middle_value - offset):\n", " lower = False\n", " if not up and not lower:\n", " break\n", " else:\n", " continue # Only executed if inner loop did NOT break\n", " break # Only executed if inner loop did break\n", " return up,lower\n", " \n", " up_left, lower_left = array_check_loc(0, math.floor(len_x/2))\n", " up_right, lower_right = array_check_loc(0, math.floor(len_x/2))\n", "\n", " legend_loc = 'best'\n", " if up_left:\n", " legend_loc = 'upper left'\n", " elif up_right:\n", " legend_loc = 'upper right'\n", " elif lower_left:\n", " legend_loc = 'lower left'\n", " elif lower_right:\n", " lower_right = 'lower right'\n", "\n", " return legend_loc\n", " " ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "def graphic_iker(data_array, title=\"None\", title_y=\"None\", title_x=\"None\", handles=None, used_labels=None, ylim_zero=True):\n", " f=plt.figure(figsize=(30, 12))\n", " ax=f.add_subplot(111)\n", " x = np.arange(len(used_labels))\n", " width = 0.45/4\n", " \n", " legend_loc = legend_loc_iker(data_array, len(used_labels))\n", "\n", " if not (data_array[0] is None):\n", " ax.bar(x-width*3.5, data_array[0], width, color='green')\n", " ax.bar(x-width*2.5, data_array[1], width, hatch=\"\", color='springgreen')\n", " ax.bar(x-width*1.5, data_array[2], width, hatch=\"//\", color='blue')\n", " ax.bar(x-width*0.5, data_array[3], width, hatch=\"\\\\\",color='darkblue')\n", "\n", " ax.bar(x+width*0.5, data_array[4], width, hatch=\"||\", color='red')\n", " if not (data_array[5] is None):\n", " ax.bar(x+width*1.5, data_array[5], width, hatch=\"...\", color='darkred')\n", " ax.bar(x+width*2.5, data_array[6], width, hatch=\"xx\", color='yellow')\n", " else:\n", " ax.bar(x+width*1.5, data_array[6], width, hatch=\"xx\", color='yellow')\n", " if not (data_array[7] is None):\n", " ax.bar(x+width*3.5, data_array[7], width, hatch=\"++\",color='olive')\n", "\n", " ax.axhline((0), color='black', linestyle='dashed')\n", " ax.set_ylabel(title_y, fontsize=20)\n", " ax.set_xlabel(title_x, fontsize=20)\n", " plt.xticks(x, used_labels, rotation=90)\n", " plt.legend(handles=handles, loc=legend_loc, fontsize=21,ncol=2)\n", " \n", " if not ylim_zero: # Modifica los limites del eje y. No es buena practica que no aparezca el 0\n", " max_value = np.nanmax([x for x in data_array if x is not None]) # Los valores None es necesario evitarlos\n", " max_value += max_value * 0.1\n", " min_value = np.nanmin([x for x in data_array if x is not None]) # Los valores None es necesario evitarlos\n", " min_value -= min_value * 0.1\n", " if min_value < 0.1:\n", " min_value = 0\n", " plt.ylim((min_value, max_value))\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/Spawn/\"+title+\".png\", format=\"png\")" ] }, { "cell_type": "code", "execution_count": 189, "metadata": {}, "outputs": [ { "data": { "image/png": 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grXCQDtBO6n+z6V8AALoZHzI1AQ0AAIC1xkE6QDup/82mfwEA6GZ8yOQENAAAANYqB+kA7aT+N5v+BQCgm/EhExPQAAAAWOu6D9IBaA/1v9mchAcAoJvxIasT0AAAAJgR4wfpALSL+t9sTsIDANDN+JBVCWgAAADMmEUz3QAAZoT632xOwgMA0M1KejxOQAMAAAAAYKiENACg2Xy+0y8r6bGCgAYAAAAAwNAJaQBAc/l8ZxBW0kNAAwAAAABgRIQ0AKCZfL4DgxHQAAAAAAAYGSENAGgen+/AYAQ0AAAAAABGyiQOADSPz3egfwIaAAAAAAAjZxIHAJrH5zvQHwENAACAaXMSBgDoRfckDgDQDEIaQO8ENAAAAKbNSRiA9lL/6df4JA4A0BxCGkBv5s90AwAAAOa+8ZMwx2TFSRkA2kP9ZxDeLwDQPN0hDePDtaXklIGfWwd+7s3RvwzKChoAAADT5i9lANpL/QcAYJzzA+2gfxmcgAYAAMBQOAkD0E7qPwAA3YwPm0//MjgBDQAAgKFxEgagndR/AAC6GR82m/5lcAIaAAAAQ+UgHaCd1H8AALoZHzab/mUwAhoAAABD5yAdoJ3UfwAAuhkfNpv+pX8CGgAAACPhIB2gndR/AAC6dY8PaR7jf/ojoAEAADAyDtIB2kn9bw/9CwD0Ynx8SDMZ/9M7AQ0AAICRcpAO0E7qfzvoXwCgV4tmugGMlPE/vRHQAAAAGDkH6QDtpP43n/4FAGCc8T9rJqABAACwVjhIB2gn9b/Z9C8AAN2MD5magAYAAMBa4yAdoJ3U/2bTvwAAdDM+ZHICGgAAAGuVg3SAdlL/m03/AgDQzfiQiQloAAAArHXdB+kAtIf632xOwgMA0M34kNUJaAAAAMyI8YN0ANpF/W82J+EBAOhmfMiqBDQAAABmzKKZbgAAM0L9bzYn4QEA6GYlPR4noAEAAAAAMFRCGgDQbD7f6ZeV9FhBQAMAAAAAYOiENACguXy+Mwgr6SGgAQAAAAAwIkIaANBMPt+BwQhoAAAAAACMjJAGADSPz3dgMAIaAAAAAAAjZRIHAJrH5zvQPwENAAAAAICRM4kDAM3j8x3oj4AGAADAtDkJAwD0onsSBwBoBiENoHcCGgAAANPmJAxAe6n/9Gt8EgcAaA4hDaA3AhoAAADT5iQMQHup/wxi0Uw3AAAYOiGN9tC/DG7+TDcAAABg7us+CXNMTLoAtIn6DwBtVMrpAz2v1iVDbgmzi/MD7aB/GZwVNAAAAIbCX8oAtJP6DwBAN+PD5tO/DE5AAwAAYGichAFoJ/UfAIBuxofNpn8ZnIAGAADAUDlIB2gn9R8AgG7Gh82mfxmMgAYAAMDQOUgHaCf1HwCAbsaHzaZ/6Z+ABgAAwEg4SAdoJ/UfAIBu3eNDmsf4n/4IaAAAAIyMg3SAdlL/20P/AgC9GB8f0kzG//ROQAMAAGCkHKQDtJP63w76F6C91H/6tWimG8BIGf/TGwENAACAkXOQDtBO6n/z6V+A9lL/gScy/mfNBDQAAADWCgfpAO2k/jeb/gVoL/UfmIjxIVMT0AAAAFhrHKQDtJP632z6F6Cd1H9gMuoDkxPQAAAAWKscpAO0k/rfbPoXoJ3Uf2Ay6gMTE9AAAABY67oP0gFoD/W/2ZyEB2gn9R+YjPrA6gQ0AAAAZsT4QToA7aL+N5uT8ADtpP4Dk1EfWJWABgAAwIxZNNMNAGBGqP/N5iQ8QDup/8BkrKTH4wQ0AAAAAACGyiQdQDup/+2hf+mXlfRYQUADAAAAAGDoTNIBtJP63w76l0FYSQ8BDQAAAACAETFJB9BO6n/z6V9gMAIaAAAAAAAjY5IOoJ3U/2bTv8BgBDQAAAAAAEbKJA5AO6n/zaZ/gf4JaAAAAAAAjJxJHIB2Uv+bTf8C/RHQAAAAmDYnYQCAXnRP4gDQHup/swlpAL0T0AAAAJg2J2EA2kv9p1/jkzgAtIv632xCGkBvBDQAAACmzUkYgPZS/xnEopluAAAzQv1vNiGN9tC/DG7+TDcAAABg7us+CXNMnHQDaBP1HwBgJpWcMvBz6zSeOzHnB9pB/zI4K2gAAAAMhb+UAWgn9R8AgG7Gh82nfxmcgAYAAMDQOAkD0E7qPwAA3YwPm03/MjgBDQAAgKFykA7QTuo/AADdjA+bTf8yGAENAACAoXOQDtBO6j8AAN2MD5tN/9I/AQ0AAICRcJAO0E7qPwAA3brHhzSP8T/9EdAAAAAYGQfpAO2k/reH/gUAejE+PqSZjP/pnYAGAADASDlIB2gn9b8d9C9Ae6n/9GvRTDeAkTL+pzcCGgAAACPnIB2gndT/5tO/AO2l/gNPZPzPms2KgEYpZbtSyltKKReVUm4vpTxUSrmvlHJ9KeXUUsrCae5/61LKh0spN5dSHiyl3Dn2WgcM62cAAACYmoN0gHZS/5tN/wK0l/oPTMT4kKnNeECjlPL0JLcm+WCSlyd5epIHkzw5yR5JTk7yg1LKSwfc/x5Jvp/kz5M8I8lDSbYYe61LSilvm+aPAAAA0CMH6QDtpP43m/4FaCf1H5iM+sDkZjygkWTe2L8XJzkiyWa11k2SbJDkvyW5JclTklxQStm6nx2XUp6c5MIkmyf5bpLdx/b9lCRnJClJ3l9KOXgYPwgAAMCaOUgHaCf1v9n0L0A7qf/AZNQHJjYbAhp3J3lurfXltdZ/qbXenSS11odrrf87K0IaDybZOMlxfe77uCTbJ7k/yWG11h+M7fveWuuSJBeMbff+IfwcAAAAPeo+SAegPdT/ZnMSHqCd1H9gMuoDq5s/0w2otf4qyfVTPH5DKeWaJIuT7N3n7o8a+/fcWut/TvD4aUn+IMlepZRdaq039Ll/AACAAY0fpAPQLur/qJScMvBz6zSeu6ruk/DHjH0PQPOp/8BknlgfaLvZsIJGL34x9u+8KbfqUkpZkMcDHV+bZLNrkvxq7Ov9B2saAADAoJy0A2gn9b/Z/KUkQDup/8AVBVnEAAAgAElEQVRkrKTH42Z9QKOUMj/JvmPffr+Pp+6apIx9/YOJNqi1PpbkR2Pf7jZQAwEAAAAAVmGSDqCd1P/20L/0y0p6rDDrAxpJ/jTJ1kkeS/JPfTxvYdfXd0yx3fhjC6fYBgAAAACgDybpANpJ/W8H/csgrKTHLA9olFL2SPK/xr79SK11wpUwJrFh19e/mWK7B8b+3WiKdryplLKslLLsrrvu6qMJAAAAAEB7maQDaCf1v/n0LzCYWRvQKKUsTHJBkg2SfCfJyf3uYlhtqbV+sta6T611ny233HJYuwUAAAAAGs8kHUA7qf/Npn+BwczKgEYpZbMkX0+yY5Ibkxxaa32wz93c3/X1k6fYboMJtgcAAAAAGBKTOADtpP43m/4F+jfrAhqllE2SfC3J7kluT3JgrfXOAXZ1R9fX20yx3fhjPxvgNQAAAAAAemASB6Cd1P9m079Af2ZVQKOUsmGSryTZJ8nPsyKccfuAu7shSR37+tmTvN46SXYe+/aHA74OAADQek7CAAC96J7EAaA91P9mE9IAejdrAhqllCcnuSjJC5P8IivCGTcOur9a631Jlo19e9Akmz0/ySZjX1866GsBAABt5yQMQHup//RrfBIHgHZR/5tNSAPozawIaJRSnpTkS0lemuSeJAfXWn8whF2fO/bvUaWUhRM8vmTs3+/UWn80hNcDAABayUkYgPZS/xnEopluAAAzQv1vNiGN9tC/DG7GAxqllHlZEaQ4JMl9SX6/1nptj8/doZRSx27HTrDJJ5LclmRBkn8tpew29rwFpZQPJPl/xrZ7+zR/DAAAoNWchAFoL/UfAIBxzg+0g/5lcDMe0Eiyb5JXj329bpILSik/n+T2//Wz41rrb5K8MisumbJXkh+UUn6VFat0vDVJTfKXtdavD+2nAQAAWspJGIB2Uv8BAOhmfNh8+pfBzYaARncb1k+y1RS3Lfvdea31+iS7J/m7JD9Jsl5WBDYuTnJQrfXU6TQeAADgcU7CALST+g8AQDfjw2bTvwxuxgMatdZOrbX0eNvhCc+9teuxz07xGj+vtb651rqo1rp+rfWptdaX11ovHfXPBwAAtI2DdIB2Uv8BAOhmfNhs+pfBzHhAAwAAoHkcpAO0k/oPAEA348Nm07/0T0ADAABgJBykA7ST+g8AQLfu8SHNY/xPfwQ0AAAARsZBOkA7qf/toX8BgF6Mjw9pJuN/eiegAQAAMFIO0gHaSf1vB/0L0F7qP/1aNNMNYKSM/+mNgAYAAMDIOUgHaCf1v/n0L0B7qf/AExn/s2YCGgAAAGuFg3SAdlL/m03/ArSX+g9MxPiQqQloAAAArDUO0gHaSf1vNv0L0E7qPzAZ9YHJCWgAAACsVQ7SAdpJ/W82/QvQTuo/MBn1gYkJaAAAAKx13QfpALSH+t9sTsIDtJP6D0xGfWB1AhoAAAAzYvwgHYB2Uf+bzUl4gHZS/4HJqA+sSkADAABgxiya6QYAMCPU/2ZzEh6gndR/YDJW0uNxAhoAAAAAAENlkg6gndT/9tC/9MtKeqwgoAEAAAAAMHQm6QDaSf1vB/3LIKykh4AGAAAAAMCImKQDaCf1v/n0LzAYAQ0AAAAAgJExSQfQTup/s+lfYDACGgAAAAAAI2USB6Cd1P9m079A/wQ0AAAAAABGziQOQDup/82mf4H+CGgAAABMm5MwAEAvuidxAGgP9b/ZhDSA3gloAAAATJuTMADtpf7Tr/FJHADaRf1vNiENoDcCGgAAANPmJAxAe6n/DGLRTDcAgBmh/jebkEZ76F8GJ6ABAAAwbU7CALSX+g8AwDjnB9pB/zI4AQ0AAIChcBIGoJ3UfwAAuhkfNp/+ZXACGgAAAEPjJAxAO6n/AAB0Mz5sNv3L4AQ0AAAAhspBOkA7qf8AAHQzPmw2/ctgBDQAAACGzkE6QDup/wAAdDM+bDb9S/8ENAAAAEbCQTpAO6n/AAB06x4f0jzG//RHQAMAAGBkHKQDtJP63x76FwDoxfj4kGYy/qd3AhoAAAAj5SAdoJ3U/3bQvwDtpf7Tr0Uz3QBGyvif3ghoAAAAjJyDdIB2Uv+bT/8CtJf6DzyR8T9rJqABAACwVjhIB2gn9b/Z9C9Ae6n/wESMD5magAYAAMBa4yAdoJ3U/2bTvwDtpP4Dk1EfmJyABgAAwFrlIB2gndT/ZtO/AO2k/gOTUR+YmIAGAADAWtd9kA5Ae6j/zeYkPEA7qf/AZNQHViegAQAAMCPGD9IBaBf1v9mchAdoJ/UfmIz6wKoENAAAAGbMopluAAAzQv1vNifhAdpJ/QcmYyU9HiegAQAAAAAwVCbpANpJ/W8P/Uu/rKTHCgIaAAAAAABDZ5IOoJ3U/3bQvwzCSnoIaAAAAAAAjIhJOoB2Uv+bT/8CgxHQAAAAAAAYGZN0AO2k/jeb/m2CUjopJT3cOilly5Xbw3QIaAAAAAAAjJRJHIB2Uv+bTf/OfYt72KaT5Igk5/W4PUxt/kw3AAAAAACGoeSUgZ5XB3we9Kd7EueYGW4LAGuP+t9sT+zfRTPbHIasE+EMhs0KGgAAANPmL2UAgF50T+IA0B7qf7NZSaOZOhHOYBSsoAEAADBt/lIGoL1ujvpPf8YncQAYlrmxipb632xW0miWToQzGBUraAAAAEybv5QBaC/1n0GYtAFoJ/W/2ayk0QydrDmcoX8Z3EABjVLKtqWUF5ZSXlFKOaiUsmcp5UnDbhwAAMDc4CQMQHup/wAAjHN+YG7rpLeVM/Qvg+s5oFFKeV4p5ROllJ8kuS3JN5Ocn+SrSa5Nck8p5dJSyp+UUhaMprkAAACzlZMwAO2k/gMA0M34cG7qpPfLmuhfBrfGgEYp5eBSyrIkVyf54ySbJLk0yTlJzkzyqawIavwkyUuSfCTJHaWUM0opm46q4QAAALOPkzAA7aT+AwDQzfhwbumk93BGon+ZjikDGqWUi5P87ySbJ3lfkt1rrZvXWg+utb6u1vqWWutxtdbDa627J3lKVrx7L0vyZ0luKqUcMuKfAQAAYBZxkA7QTuo/AADdjA/nhk76C2d0xv7VvwxmTStoPDMr3lnPqLW+u9b6w6k2rrXeV2tdWmt9xdhzL0jy3OE0FQAAYK5wkA7QTuo/AADdjA9nt076D2cc0fW9/qV/awpo7FZrPbfWWvvdca319lrrG5P87WBNAwAAmMscpAO0k/oPAEC37vEhs8sg4YzznnC/8T/9mTKgUWv97XRfoNb62HT3AQAAMDc5SAdoJ/W/PfQvANCL8fEhs8sg4YyJtjf+p3drWkGjJ6WUBaWUg0op+w5jfwAAAM3hIB2gndT/dtC/AO2l/tOvRTPdAFazuIdtOultpQ3jf3ozv5+NSylvSHJUkiNqrb8cu+93knw1ydZj338zySG11geH3FYAmPVKOb2HrW7OikHaMRkflNe6ZIStAmiXklMGfm6dxnOn1n2Q/nj9B6Dp1P/m078A7aX+Q/N10t9lUIz/WbN+V9A4Kskm4+GMMadnRTjjvCSXJ3lRkjcNp3kA0ESStADtpP4DtJP632z6F6C91H9otk76C2eMMz5kav0GNHZO8t3xb0opmyU5IMlZtdbX1lr3T3JdkqOH10QAaCKDNIB2Uv8B2kn9bzb9C9BO6j80VyeDhTPGqQ9Mrt+AxhZJ7uz6/oVJSpKlXfddnmTHabYLAFqge5AGQHs4SAdoJ/W/2fQvQDup/9A8nUwvnDFOfWBi/QY07kmyWdf3L0lSk1zVdd+jSTaYZrsAoCXGB2kAtIuQHkA7qf/N5iQ8QDup/3NZKZ2UsuXYv+nhtmJ7mqqT4YQzxqkPrK7fgMYNSV5eStm4lLJhktck+U6t9Z6ubbZP8vNhNRAAmm/RTDcAgBkhpAfQTup/szkJD9BO6v/c1c9kfKdre5qnk+GGM8apD6yq34DGR5Jsm+T2JLcmeVqST44/WEpZJ8m+Sb43pPYBAAA0mJAeQDup/83mJDxAO6n/c9Mg4Yxetmdu6WS0/WslPR7XV0Cj1vovSZZkxQoZv0hySq31012bHJxkqyT/NrQWAgAAAADMKSbpANpJ/Z97FvewTSerT97r3+bopP+VVAZhJT1W6HcFjdRa/99a6y5jt795wmNfrbWuW2v9yPCaCAAAAAAw15ikA2gn9b9ZOpl48l7/NkMng13mZlBW0mOAgAYAAAAAAL0wSQfQTup/M3Qy+eS9/m2GQcIZ542wPbTBlAGNUsrm032BYewDAAAAAGBuMkkH0E7q/9zWydST9/q3GQYJZ/SyPUxuTSto3FpK+ZtSymb97riUckAp5aokfzpY0wAAAAAAmsAkDkA7qf9zUye9Tcbr37lvcQ/bdCKcwTCtKaDxD0lOSnJHKeW8UsoRpZRtJtqwlLJuKeV5pZR3lVJ+nOTrSTZK8q/DbTIAAAAAwFxjEgegndT/uaWT/ibj9W+zdSKcwbBNGdCotf5Fkt2TfCnJYUm+kOSnpZT/LKUsK6VcUkr5Zinlh0nuTXJ1kvckeSzJcUmeW2u9dqQ/AQDMKgbhAO2k/gMAveiexAGgPdT/uaGTwSbjhTSaqRPhDEZhTStopNZ6Y631D5M8PcnJSS5JsiDJXkkOSLJvkl2S3Jrkk0kOrrXuUmv9VK31sVE1HABmJ4NwgHZS/wHaS/2nX+OTOAC0i/o/u3Uyvcl4IY1m6UQ4g1FZY0BjXK31rlrr6bXWQ2qtGyfZPMmzsiK4sX6tddda6/+stf7bqBoLALOfQThAO6n/AO2l/jOIRTPdAABmhPo/O3XS32R8Z5L7hTSaoZM1vx/0L4PrOaDxRLXWu2utN9Va/7PW+vAwGwUAc5dBOEA7qf8A7aX+AwDMXZ30H844YorHnR+Y2zrp7f2gfxncwAENAGAyBuEA7aT+A7ST+g8AMHcNEs44bw3bGR/OTZ30/n7QvwxuoIBGKeX1pZR/K6X8Zynlnq779yilfKCU8ozhNREA5iKDcIB2Uv8B2kn9BwCYmwYJZ/SyvfHh3NKJ/mVt6SugUUqZX0q5KMmnkrwgybpJFnRt8p9J3pzk6KG1EADmLIM0gHZS/wHaSf0HAJh7FvewTSf9Td6PMz6cGzrpfyWVRP8yqH5X0PiLJIcmOT3JU5J8rPvBWusvklyZ5JChtA4A5jyDNIB2Uv8B2kn9BwBolk4GC2eMMz6c3ToZ7DI34/Qv/es3oHFMkm/XWk+utT6SpE6wzc1Jtp92ywCgMQzSANpJ/QdoJ/UfAKAZOpleOGNc9/iQ2WWQcMZ5T7jf+J/+9BvQeGZWrJAxleVJNh+sOQDQVAZpAO2k/gO0k/rfHvoXAJqpk+GEM8aNjw+ZXQYJZ0y0vfE/ves3oPFgkgVr2Ga7JPcO1hwAaDKDNIB2Uv8B2kn9bwf9C9Be6n9zdTLccMa4RUPcF8OxuIdtOunt/WD8T2/6DWj8nyQHllLWnejBUspGSQ5Ksmy6DQOAZjJIA2gn9R+gndT/5tO/AO2l/jdTJ6MJZzA3ddLf+8H4nzXrN6Dxj0l2TPKpUsr63Q+UUjZI8skkW4z9CwBMyCANoJ3Uf4B2Uv+bTf8CtJf63zydCGfwuE4Gez8YHzK1vgIatdbPJvnnrHhX3ZXkfyRJKaWT5GdJXpvkrFrrBUNtJQA0jkEaQDup/wDtpP43m/4FaCf1v1k6Ec7gcZ1M7/2gPjC5flfQSK31tUn+PMnPk2ybpCR5cZJfJHlzrfUNQ20hADRW9yANgPZwkA7QTup/s+lfgHZS/5uhk/4m4zsjbAszr5PhhHXUBybWd0AjSWqtH6m17pRkyyS7JFlYa31GrfXMobYOABpvfJAGQLsI6QG0k/rfbE7CA7ST+j+3ddJ/OOOI0TWHGdbJcFdSUR9Y3fzpPLnW+ousWDkDABjYopluADAEpfzPDPL/udYlw28Mc4SQHkA7qf/N1n0S/pg43gNoC/V/7hoknHFekmWjaxIzpJPRXObmifWBthtoBQ0AAOCJJOEZhJN2AO2k/jebv5QEaCf1f24aJJzRy/bMLZ2Mtn+tpMfj+l5Bo5Sye5K3JnlOkm2TrDvBZrXWusk02wYAAHOIv5QBaKtSNsog9d8qStBk/pIaoJ3U/7lncQ/bdLL65P3N0b9N0Un/K6kMwkp6rNDXChqllJcnuTYr3j1bJ7k9yY8muP14uM0EAIDZzl/KALSX+g9MxPgQoJ3U/2bpZOLJe/3bDJ0MdpmbQQn10P8lTt6X5OEk/63WulWt9bm11t+d6DaCtgIAwCznJAxAO6n/wGTUB4B2Uv+boZPJJ+/1bzMMEs44b4TtoQ36DWjsnOTztdavjqIxAAAw9zkJA9BO6j8wGfUBoJ3U/7mtk6kn7/VvMwwSzuhle5hcvwGN/0rywCgaAgAAzeEgHaCd1H9gMuoDQDup/3NTJ71NxuvfuW9xD9t0IpzBMPUb0Dg/yQGllHmjaAwAADSHg3SAdlL/gcmoDwDtpP7PLZ30Nxmvf5utE+EMhq3fgMY7kzya5OxSypYjaA8AzHEG4UA3B+ntoX+Bbuo/MJnu+gBAe6j/c0Mng03GG/83UyfCGYxCXwGNWut9SY5M8rIkPyul3FZK+T8T3K4fSWsBYNYzCAeeyEF6O+hf4InU//bQv01QypYppZNS0sOts3L7wYzXBwDaRf2f3TqZ3mS88X+zdCKcwaj0FdAopTwvyTVJnjL23I2TLJzgts1wmwkAc4VBODARB+nNp3+Biaj/7aB/m6HXk++dDOdk/aJpPBeAuUv9n5066e/zvTPJ/cb/zdDJmt8P+pfB9XuJkw8k2SDJnyTZsNb6lFrrlhPdht9UAJgLDMKByagPzaZ/gcmoD82nf5thcQ/bdOIvKQGgaTrpP5xxxBSPG//PbZ309n7Qvwyu34DG3km+WGv9+1rrb0bRIACY+wzCgcmoD82mf4HJqA/Npn/boRPhDABookHCGeetYTvjw7mpk97fD/qXwfUb0PhNkjtH0RAAaBaDcGAy6kOz6V9gMupDs+nfZutEOAMAmmpUlzkzPpxbOtG/rC39BjS+nmTfUTQEAJrHIA2YTHd9oHnUf2Ay6kOz6d9m6kQ4AwCabHEP23Qy2HjA+HBu6KT/lVQS/cug+g1onJRkYSnltFLKk0bRIABoFoM0YDLj9YFmUv+ByQjpNZv63yydCGcAQNt1Mr3xgPHh7NbJYJe5Gad/6V+/AY2/T/KzJH+R5I5SyuWllAsnuH15+E0FgLnKIA2YzKKZbgAjpf4DkxHSazb1vxk6Ec4AgLbrZDjjASHt2WuQcMZ5T7jf+J/+zO9z+5d3fb1ZkhdNsl3tZ6ellAVJXprkd5PsM/bv5mMP71prvaHPdo7vd3GSy3rYdMta6/JBXgMAetM9SDsmJmUB2kL9ByajHjSb+j+3ddL7yfqbo38BoIk6GW5YU0h7dhoknLE4ybInPG78T+/6XUFjQY+3jfvc7wFJvpzknUkOyePhjGF5LMmdU9weG/LrAcAEJGkB2kn9B2gn9X9u6qS/yRj9C9Be6n9zdTKalbRM2s8+i3vYppPe3g/G//Smr4BGrfXXvd4GaMt/JflKkvckedMAz5/KT2utW09x++WQXw8AJmGQBtBO6j9AO6n/c0sn/S9zrX8B2kv9b6ZOXOaMx3XS3/vB+J8163cFjVG5qNa6Va310FrrKUkumekGAcDoGKQBtJP6D9BO6v/c0Mlg1yDXv/D/s3f/sbq16V3Qv6sdqNAWStsXKOBgeW2AglhKCUYwPpSAoICAxERxJGJa9A9QcdrBILwzBa3YAQJiBgsKWkOCQ+SXhSCUXkCkEN6WBpiiJRMr0CIOBfqD/iBpl3+c/XTtOWfvfdaznrWeve57fT7Jk3P22ffeZ73vvfb3XOte17NuOC7535+K5gwmlWXng/qQpz3ZoDEMw2ffvb7/Sx+/9nXJQYzj+D3X/EcAQHsUaQDHJP8Bjkn+79+S5owP3n1sfgGOSf73paI5g0nluvNBPvC4d7zm828nGZP8+CRff+/jOT72iuMCgAO4X6R94JmPBYDbuZ//73rmYwHgduT/vi1pzjjlxXJp8ur82mMe4Bjkfx8qlzdrsrbhfcOyL3zrrXUPZLVmHfnAw17XoPFb86Ih45tf+rg1bwzD8DVJfuzdx9+YFz9d/804jn/t2Y4KAL6vSAPgWDTpARyT/N+v04wxlacX6y3CAxyT/G9bZdmTtN6z2RHxnCrrPklFPvCqJxs0xnF891MfN+QHJvnJSf5hko9P8hl3r18xDMOvHcfx/c95cAAcnaIM+vDh+HnmMpr0AI5J/repMm+x3iI8wDHJ/3Yt3ebs7aeH0qDKNtvceJIeH+11T9D4KMMwfHKS7xjH8bueGPNxST5+HMd/cO3BreAfJfmSJH8gyYfGcfyuYRg+NslPT/LFSf7FJF8yDMM3jeP4+x/7JsMwfH6Sz0+Sd77zndsfNQAADbIIwxLOF+iDJj0u5XxpS+WyxXo36eAohmHJez8/nHH0FKU+yf82XbvNGX2obNOcceZJekw+5sLxH0nyBa8Z8+67cc9uHMevHcfxC8dx/OpzU8k4jt8zjuOfS/Izk/wfd0N/8zAMj/6/GMfxS8dx/JxxHD/njTfeuMGRAwDQnvNF1oef+0AAuDn5D/2qLFusv78ILx+A+9y075v8b89pxpjKq/WA+e1H5fInqSzhSXq8cGmDxnD3at44jv8kya+/+/BH5cUWKAAAsJBFGIDjkv/Qp8p176RUHwIck/zvS+XhesD89qGybJubpTTpcXmDxhxvJPmODb7vFv7Svd//mGc7CgAAOmERBuCY5D/0p7LOY67lA8Axyf8+VB6vB8xvH5Y0Z3xww+PhCN7xugHDMPySl/7oMx/4syT52CTvTPLvJPnQCsd2a+NzHwAAAD2w5yzAMcl/6Edl3T3I5QPAMcn/tlWergfMbx+WNGeckry92RHRv9c2aCT5g5maF8a8OPsee3bLkOSfJPkvrj+0m/hp937/Dc91EAAA9MZFOsAxyX9oX2Xd5owz+QBwTPK/TZV59YD5bd9pxpjKNvUhRzWnQeNX50VjxpDkdyT540n+xAPjvifJNyf58+M4/r+rHeEVhmEYxnF88MkYwzB8vyRfdPfh303yNTc7MAAADsBFOsAxyf8WDUPF4izL9iC/xMv5AMAxyP+2VC6rB9T/fauo/1nbx7xuwDiOv3Mcx/92HMffmeSrk/zhu49ffv2ucRw/uLQ5YxiGTz2/kvyQe5/6pPufG4bhY176uvHu9d4Hvu1fH4bhVw3D8BnDMAx34z92GIafkeQrkvyMu3H/2TiO37vkuAHgo9lzELjPnrOtGYY5r8owvHH364s/M7/AR5P/7TnNGFOxONuzyrI9yC91Px8AOA7534bKsnpP/d+nivqfLby2QeO+cRx/6jiOv2ejY/nIvdf9p1l81Uufe+cF3/Mz8+KpH1+f5DuHYfhIku9I8ueT/Et58dSPXzuO4/949dEDQBJFOPAqF+l9qTx8cW5+gZfJ/75UHl+cNb99WNKc8cGFf9c5HwA4Fvm/b5Xrbsar//tS0ZzBVi5q0GjQr0zyPyX5UJJvTfJJSb47yV9L8juT/KRxHH/z8x0eAP1RhAMPcZHeh8rjF+fmF3iI/O9D5enFWfPbhyXNGXPGP8bjz6EP8p9Lyf99qqyzzZn6vw+V158P5pfldtOgMY7jMPP1DY983Xsf+J5fOo7jLx/H8SeO4/hDx3H8fuM4/qBxHH/SOI6/ahzHr7vVfx8AR6EIBx4jH9pWefri3PwCj5EPbau8fnHW/PbhNGNMxTspgY8m/6F9lXW3OVP/t60y73wwvyy3mwYNAOiHIhx4jHxoU2Xexbn5BR4jH9pUkf9MKpozgFfJf2jfFtucqQ/bVJl/PphfltOgAQCbUIQDj5EPbalctlhjfoHHyIe2VOQ/k4rmDOBh8r91w/DUqzIMb9z9+urn6cVW25zJh7ZUzC+3okEDADajSAMecz8f2K/Kspsx8h94jHxoQ0X+M6lozgCeJv/7VJH/R3GaMaaiPuxZ5fInqSTml6U0aADAphRpwGPO+cA+Va5bjJP/wGM06e1bZdni7Jn870vFzTlgHvnfl4r8Z1KxPtCzyrJtbs7ML5fToAEAm1OkAY9587kPgAdVrrs5dyb/gcdo0tunynWLs2fyvw8VN+eAy8j/PlTkP5PKOueDJu39WlL/f/ClP5f/XOYdT31yGIZfs/Qbj+P4W5d+LQD0536R9q64KQuwV5VlF+fveeTz8h94jDzYl8ryxdm3H/i8/G9bZf758OGYX2Ai/9tW0ZzxvIb3Dcu+8K231j2QJOufD5q092lJ/X/Kq9cA8p/5nmzQSPL+JGOSSxNxTKJBAwA+iiINYP/WvDl3Jv8B9m9p/p/y+L8B8r9NlcvOB/MLvEz+t6lyeT2gSa9flW2adZwv+3OaMaYy73yQ/8zzugaNX3CTowCAw1CkAezb2jfnzuQ/wL4tzf/Xkf9tqVx+c878Ag+R/22pLGvW/M6Y3x5VPEmFSUX9z9qebNAYx/HLb3UgAHAcijSA/TrNGFNZtlgj/wH26zRjTEX+96yyfJsz8ws8RP63obL8SVp/IOa3NxXNGUwq6n+28DHPfQAAcEz3i7QPP/OxADBf5brFGvkP0KaK/O/d0ptzifkFHicf9u+abc7Mb18qmjOYVNT/bEWDBgA8m/tFGgD7V1lnscZFOkBbKvL/CK7d5sb8Ao+RD/sm/0mWPUmFflXU/2zp4gaNYRg+ZRiG3zwMw9cOw/CRYRi+9YHXt2xxsADQn3ORBsC+VdZ9J40mPYA2VOT/UZxmjKk8fT5YhAceIx/26zRjTEX+96yy7Ekq9KmyXf0vH3jhogaNYRh+WJK3k3xBkk9M8ilJvi3Jtyb5hLvX30ny9eseJgD0zB500AcXWf2qbPOYU016APtWkf9MKvPOB4vwwGM06bWpIv97d802Z0qeAEkAACAASURBVPSlsm39Lx944dInaPyGJO9M8ovGcTzfTfrvxnH8UUl+bJI/m+S7k3zueocIAAAtcJHVp8q2e9Bq0oM+yP/+VOQ/k8pl54NFeOAxmvTaUpH/R3DtNjf0obJ9/a9JjxcubdD4eUn+9DiOf/TlT4zj+DeT/KIkbyR57/WHBgAALbEI05+KxRdgHvnfl4r8Z1JZdj64SQc8RpNeGyry/yhOM8ZUXj0fzG8/Kpc/SWUJTXq8cGmDxo9I8lfvffw9Sf6p8wfjOH5Lkj+Z5Jdcf2gAANASizB9qbg5B8wn//tRuc3iLG2oXFcPqA8B2lSR/0wqD58P5rcPlWXb3CylSY/LGzS+LcnH3vv4H+VF08Z9/yDJD7vmoAAAoE0WYfpQcXMOuIz870Pltouz7FtlnWZN+QDQlor8Z1J5/Hwwv31YUv9/cMPj4QjeceH4v5Xkn7738V9L8jOHYfi4cRy/++7PPjfJN65xcAAA0J77izDvis741lSWXZy/Z7MjAloh/9tWWb44+/ZWB3VYw/uGZV/41lsrHUFl3SdpyQeANlTkP5PK0+eD+e3Dkvr/FNcAXOPSJ2j8mSSnYRjOjR3/c140bNQwDG8Nw/AVST4ryf+64jECAEBjvFOmXd45AVxD/rdraf7PGU9bKtvMr3wA2LfKNk9SlP9tqsw7H8xv+04zxlTU/6zp0gaN/yHJ78q0hcnvTfLfJ/lpSd5K8jOTfHmSL1rrAAEAoE0u0tvk5hxwLfnfJvlPsv02Z/IBYJ8q225zJv/bUrnsfDC/fauo/1nbRQ0a4zj+jXEcf/04jt949/E4juPnJfn0JD87yWeM4/gLx3H8xxscKwA0QBEO3OcivT2nGWMqr16cm1/gPvnfntOMMRWLsz2rbHtz7ux+PgDw/Cq3eZKi/G9DZVm9p/7vU0X9zxYufYLGg8Zx/H/GcfyKcRylDgAHpwgHXuYivS+Vhy/OzS/wMvnfl8rji7Pmtw+33ObsnA8APL9bbnMm//etss78qv/7UNGcwVYuatAYhuFbh2F4z2vGfMEwDN9y3WEBQKsU4cBDXKT3ofL4xbn5BR4i//tQeXpx1vz24dbb3Lx5xdcC+yH/2yf/Sdbb5kz934fK688H88tylz5B4xOSfNxrxnz/u3EAcECKcOAx8qFtlacvzs0v8Bj50LbK6xdnzW8fTjPGVLyTEvho8r99pxljKvK/Z5V1tzlT/7etMu98ML8st8oWJy/5wUm+e4PvCwCNUIQDj5EPbarMuzg3v8Bj5EObKvKfScXNOeBV8r9/Ffnfuy22OVMftqky/3wwvyz32gaNYRg++/y6+6Mfcf/P7r1+6jAM/0aSfyvJ39z0qAFg9xThwGPkQ1sqly3WmF/gMfKhLRX5z6Ti5hzwMPnft4r8P4KttrmRD22pmF9uZc4TNN5O8pfvXmOSz7v38f3XX0zyvyT5kUl++xYHCwBtUaQBj7mfD+xXZdlinPwHHiMf2lCR/0wqbs4BT5P/farI/6M4zRhTUR/2rHL5k1QS88tS75gx5rfmRWPGkOTXJPmqJH/hgXHfk+Sbk/yZcRy/erUjBICm3S/S3nX3MUAy5QP7VLluMU7+A4+5nw8feOZj4VWVZYuzZ/K/LxU354B55H9fKvKfSWXd9QH2pbJsm5v33H0s/7ncaxs0xnF89/n3wzD88iR/aBzH9296VADQFUUa8Bh5sE+V627Oncl/4DGa9Papct3i7Jn870PFzTngMvK/DxX5z6SyzvmgSXu/ltT/H8yLDSjO5D+XmbPFyfcZx/ENzRkAsITHnQG0obLs4vwx8h94jEW7faksX5x9iPxvW2X++WB+gfvkf9sqmjOYVNY9HzRp79OS+v+h8fKf+S5q0LhvGIbPGobh3xuG4T8ZhuFXDMPwWWseGAD0R5EGsH9r3pw7k/8A+7c0/58aL//bVLnsfDC/wMvkf5sql9cD5rdflW2adTRp789pxpiK+p81XdygMQzDTxiG4S8n+eokvyfJ+5P87iRfPQzD28Mw/MSVjxEAOqJIA9i3tW/Oncl/gH2T/yTLbs6ZX+Ah8r8tlWXNmua3TxVPUmFSUf+ztosaNIZh+NFJ/mySn5Lka5P8tiRfePfrX0ny2Um+chiGf2bVowSArijSAPbrNGNMZdlijfwH2K/TjDEV+d+zyrKbc+YXeIx8aENl+ZO0zG9/KpozmFTU/2zh0ido/IYkn5zk3x/H8aeM4/jucRx/y92vn5PkV9x9/tevfaAA0BdFGkCbKtct1sh/gDZV5H/vrtnmzPwCj5EP+3fNNmfmty8VzRlMKup/tnJpg8bPSfJHx3H8vQ99chzH35fkf7sbBwA86X6RBsD+VdZZrHGRDtCWivw/gmu3uTG/wGPkw77Jf5JlT1KhXxX1P1u6tEHjhyb50GvG/PUkbyw7HAA4mnORBsC+VdZ9J40mPYA2VOT/UZxmjKk8fT5YhAceIx/26zRjTEX+96yy7Ekq9KmyXf0vH3jh0gaNb07yGa8Z888m+YfLDgcAjujN5z4AYBUusvpV2eYxp5r0APatIv+ZVOadDxbhgcdo0mtTRf737pptzuhLZdv6Xz7wwqUNGpXkFw/D8PMf+uQwDD83yS9J8pVXHhcAADTGRVafKtvuQatJD/og//tTkf9MKpedDxbhgcdo0mtLRf4fwbXb3NCHyvb1vyY9Xri0QeM3JvnuJH9kGIY/MQzDFw7D8K5hGL5gGIYvT/Lld5//TWsfKAAA7JtFmP5ULL4A88j/vlTkP5PKsvPBTTrgMZr02lCR/0dxmjGm8ur5YH77Ubn8SSpLaNLjhYsaNMZx/BtJfl6Sv53kX0nyxUl+X5L/6t6f/6vjOH7duocJAAB7ZxGmLxU354D55H8/KrdZnKUNlevqAfUhQJsq8p9J5eHzwfz2obJsm5ulNOlx+RM0Mo7jn8+Ls+fnJvl1Sf7ru19/XpI3x3H8c6seIQAANMMiTB8qbs4Bl5H/fajcdnGWfaus06wpHwDaUpH/TCqPnw/mtw9L6v8Pbng8HME7XjdgGIZ/N8nXjuP4V89/No7j9yT53+9eAADA97m/CPOu6IxvTWXZxfl7NjsioBXyv22V5Yuzb291UDybyrpP0pIPcAtD3rvo68aFX0ePKvKfSeXp88H89mFJ/X+KawCuMecJGr8vyS/a+DgAAKAj3inTLu+cAK4h/9u1NP/njKctlW3mVz4A7Ftlmycpyv82VeadD+a3facZYyrqf9b02idoAAAAS3gnxbWG9w3LvvCtt674W71zAriW/G+T5gyS7bc5ezkfANiHyrZPUpT/balcdj6o//tWUf+ztjlP0AAAZtMpDdznnRTtOc0YU3n14tz8AvfJ//acZoypWJztWWXZzblL3c8HAJ5f5TZPUpT/bagsq/fU/32qqP/ZgidoAMCqdEoDL/NOir5UHr44N7/Ay+R/XyqPL85+OOa3B0tvzi15itY5HwB42e2fpHjNNmeX/hsg//etct3NePV/XyqaM9jK3CdofNIwDO+85LXpUQPAbumUBh7inRR9qDx+cW5+gYfI/z5Unl6cNb99uPU2N27aQB/kf/vkP8l625yp//tQef35YH5Zbu4TNP6ju9dc4wXfGwA6olMa9mDIexd/7XjF1z5NPrSt8vTFufkFHiMf2lZ5/eKs+e3DacaYindSAh9N/rfvNGNMRf73rLLsSSrveeTz6v+2VeadD+aX5eY+QeNbk/ytC15/e/UjBYBm6JQGHiMf2lSZd3FufoHHyIc2VeQ/k4qbc8Cr5H//KvK/d0u3uXmK+rBNlfnng/llubkNGr9tHMdPv+S16VEDwO4pwoHHyIe2VC5brDG/wGPkQ1sq8p9Jxc054GHyv28V+X8EW21zIx/aUjG/3MrcBg0A4GKKNOAx9/OB/aosW4yT/8Bj5EMbKvKfScXNOeBp8r9PFfl/FKcZYyrqw55VLn+SSmJ+WUqDBgBsSpEGPOacD+xT5brFOPkPPEaT3r5Vli3Onsn/vlTcnAPmkf99qch/JhXrAz2rLNvm5sz8crl3PPcBAMBWhrx30deNC7/ucfeLtHfdfQyQyIO9qlx3c+5M/gOP0aT3OsP7hmVf+NZbV/ytlWWLs+956c/lfx8qbs4Bl5H/fajIfyaVdc6H+/nwgauPijUtqf8/mOTte38u/7mMJ2gAwE3opAVoQ+W6d068TP4Dj7Foty+V5YuzD5H/bavMPx/ML3Cf/G9bRXMGk8q654Mm7X1aUv8/NF7+M99rGzTGcfyYcRy/6BYHAwB9U6QB7N+aN+fO5D/A/i3N/6fGy/82VS47H8wv8DL536bK5fWA+e1XZZtmHU3a+3OaMaai/mdNtjgBgJvyuDOAfVt6c+7tpwZH/sPt7GebO9qydnPGmfxvS+Xym3PmF3iI/G9LZVmz5nfG/Pao4kkqTCrqf9ZmixMAuDmdtAD7dZoxprJssUb+A+zXacaYivzvWWXZzTnzCzxGPrShsvxJWua3PxXNGUwq6n+2oEEDAJ6FIg2gTZXrFmvkP0CbKvK/d9dsc2Z+gcfIh/27Zpsz89uXiuYMJhX1P1vRoAEAz+Z+kQbA/lXWWaxxkQ7Qlor8P4Jrt7kxv8Bj5MO+yX+SZU9SoV8V9T9b0qABAM/qXKQBsG+Vdd9Jo0kPoA0V+X8UpxljKk+fDxbhgcfIh/06zRhTkf89qyx7kgp9qmxX/8sHXtCgAQDP7s3nPgBgFS6y+lXZ5jGnmvQA9q0i/5lU5p0PFuGBx2jSa1NF/vfumm3O6Etl2/pfPvCCBg0AAFiFi6w+Vbbdg1aTHvRB/venIv+ZVC47HyzCA4/RpNeWivw/gmu3uaEPle3rf016vKBBAwAAVmERpj8Viy/APPK/LxX5z6Sy7Hxwkw54jCa9NlTk/1GcZoypvHo+mN9+VC5/ksoSmvR4QYMGAACswiJMXypuzgHzyf9+VG6zOEsbKtfVA+pDgDZV5D+TysPng/ntQ2XZNjdLadJDgwYAAKzIIkwfKm7OAZeR/32o3HZxln2rrNOsKR8A2lKR/0wqj58P5rcPS+r/D254PByBBg0AAFiVRZi2VdycA5aR/22rWJxlUln3SVryAaANFfnPpPL0+WB++7Ck/p8zHh6nQQMAAFbnIr1dbs4B15D/7Vqa/3PG05bKNvMrHwD2rbLNkxTlf5sq884H89u+04wxFfU/a9KgAQAAm3CR3iY354Bryf82yX+S7bc5kw8A+1TZ9kmK8r8tlcvOB/Pbt4r6n7Vp0ACAVSnCgftcpLfnNGNM5dWLc/ML3Cf/23OaMaZicbZnldtsc3Y/HwB4fpXbPElR/rehsqzeU//3qaL+ZwsaNABgVYpw4GUu0vtSefji3PwCL5P/fak8vjhrfvtwy23OzvkAwPO75TZn8n/fKuvMr/q/DxXNGWxFgwYArEoRDjzERXofKo9fnJtf4CHyvw+VpxdnzW8fbr3NzZtXfC2wH/K/ffKfZL1tztT/fai8/nwwvyynQQMAVqUIBx4jH9pWefri3PwCj5EPbau8fnHW/PbhNGNMxTspgY8m/9t3mjGmIv97Vll3mzP1f9sq884H88tyGjQAYHWKcOAx8qFNlXkX5+YXeIx8aFNF/jOpuDkHvEr+968i/3u3xTZn6sM2VeafD+aX5TRoAMAmFOHAY+RDWyqXLdaYX+Ax8qEtFfnPpOLmHPAw+d+3ivw/gq22uZEPbamYX25FgwYAbEaRBi0ahjcyDJVhyIxXfd/4y9zPB/arsmwxTv4Dj5EPbajIfyYVN+eAp8n/PlXk/1GcZoypqA97Vrn8SSqJ+WUpDRoAsClFGrRnq3dOvOycD+xTZZ35lf/AyzTp7Vtl2eLsmfzvS8XNOWAe+d+XivxnUrE+0LPKsm1uzswvl9OgAQCbU6RBW04zxlTWWax584qvZTuV627Oncl/4DGa9Papct3i7Jn870PFzTngMvK/DxX5z6Sy3vqPJu19WlL/f/ClP5f/XEaDBgDchCIN+lGxWNOzyjo3587kP/AYTXr7UllncfZM/retMv98ML/AffK/bRXX+0wq654PmrT3aa0n6cp/5tOgAQA3o0iD9lUs1vRuzZtzZ/IfYP+W5v9T4+V/myqXnQ/mF3iZ/G9T5fJ6wPz2q7LN+o8m7f05zRhTUf+zJg0aAHBTijRoV0VzxhGsfXPuTP4D7Jv8J1l2c878Ag+R/22pLGvWNL99qlj/YVJR/7M2DRoAcHOKNGhPxcX5UZxmjKksOx/kP8B+nWaMqcj/nlWW3Zwzv8Bj5EMbKsufpGV++1Ox/sOkov5nCxo0AOBZKNKgHRUX50wq150P8h+gTRX537trtjkzv8Bj5MP+XbPNmfntS8X6D5OK+p+taNAAgGdzv0gD9qni4pxJZZ3zwUU67NEw3H9VhuGNu19f/tyrL3pXkf9HcO02N+YXeIx82Df5T7LsSSr0q6L+Z0saNADgWZ2LNGB/KpozmFTWPR806cF+VSzOMqnI/6M4zRhTefp8sAgPPEY+7NdpxpiK/O9ZZdmTVOhTZbv6Xz7wggYNAHh2bz73AQCvqFx+ce4iq1+VbZp1NOnB/lQszjKpyH8mlXnng0V44DGa9NpUkf+9u2abM/pS2bb+lw+8oEEDAAA+SmXZxbmLrD5Vtn2SiiY92I/K8sVZ+d+fivxnUrnsfLAIDzxGk15bKvL/CK7d5oY+VLav/zXp8YIGDQAA+ChLb85ZhOlPxeILHMnS/D9F/vemIv+ZVJadD27SQSuG4fyqDMMbd79mxqsW/o2a9NpQkf9HcZoxpvLq+WB++1G5zTaXmvR4QYMGALzk5YvtSy7OgR4svTlnEaYvFTfn4Giueeec/O9H5TaLs7Shcl09oD6EdlRsc8akIv+ZVB4+H8xvHyq3zX9NemjQAIAnVCzOwhGdZoypPJwPFmH6UJH/cESnGWMq8r9nFTfnmFTWadaUD7B/leVP0qI/FfnPpPL4+WB++yD/uT0NGgDwoIrFWeBhlafzwSJM2yryH3hYRf73rGJxlkll3SdpyQfYt2u2OaMvFfnPpKL+PwL5z+1p0ACAV1QszgIPq8zLBxfp7ZL/wEMq8r93bs5xVtlmfuUD7Jf8J9nuSYryv00V9f9RnGaMqch/1qRBAwBeYXEWeEjlsp93F+ltkv/Ayyry/wjkP8n225zJB9in04wxFfnfs8q2T1KU/22pqP+ZVOQ/a9OgAQCvuGZxVhEOfaosuxhzkd6e04wxFfkPR1GR/0dxmjGmYnG2Z5XbbHN2Px+ANlTkf88qt3mSovxvQ0X9z6Qi/9mCBg0AeMVpxpjKw8WZIhz6U7nuYsxFel8q8h+OoiL/mVQePx/Mbx9uuc3ZOR+A/au4Ode7Wz5JV/7vW0X9z6Qi/9mKBg0AuFjl8eJMEQ59qaxzMeYivQ8V+Q9HUZH/TCpPnw/mtw+33ubmzSu+FriNyut/3uV/++Q/yXrbnKn/+1CR/2xJgwYAXKTydHGmCId+VNbtlJcPbavIfziKyjqLs2fyoW2V158P5rcPpxljKt5JCUdRmffzLv/bd5oxpiL/e1ZZd5sz9X/bKvKfrWnQAIDZKvOKM0U4tK+y7s25M/nQpor8h6OorLs4eyYf2lSR/0wqbs7BUVTm/7zL//5V5H/vttjmTH3Ypor85xY0aADALJXLinVFOLSrss3NuTP50JaK/IejqGyzOHsmH9pSkf9MKm7OwVFU5D+Tivw/gq22uZEPbamYX25lFw0awzB84jAMv3AYht84DMOfGIbh7w/DMN69ftwK3/8HDcPwm4Zh+BvDMHzHMAzfPAzDVwzD8EvXOH4AeldZdjGmSIM2bXlz7ux+PrBfFfkPR7I0/+eMP5MPbaiYXyYVN+fgKCrLnqQo//tUkf9HcZoxpqI+7FlF/nNLu2jQSPKzkvyRJP95kp+b5FPW+sbDMPyoJF+b5Ncl+XFJvifJD0ryuUk+OAzDB9b6uwDoUeW6izFFGrRn65tzZ+d8YJ8q8h+O5tb5r0lvnyrXbXMm//tScXMOjqJy3ZMU5X9fKvKfScX6QM8q8p9b20uDRpL8f0n+eJL3Jfn8Nb7hMAxDkj+Y5NOTfEOSnz6O4ycm+cQkX5jke5P8B8MwfN4afx8Avalctzh7pkiDtpxmjKmss1jz5hVfy3Yq8h+O6DRjTGW9/Nektz+VdbY5k/99qLg5B0dRWedJivK/DxX5z6Sybv2vSXt/5D+3t5cGjT82juMPG8fxXxvH8b1J/tRK3/dfT/LT8qIR4xeP4/gXkmQcx+8ax/FLkvyOu3FfNAzD91/p7wSgC5V1FmfPFGnQj4rFmp5V5D/wsMq6+a9Jb18q625zJv/bVpl/PphfaN+a25zJ/7ZVXO8zqaxf/2vS3h/5z+3tokFjHMfv2ehb/7K7X//0OI5f+8Dn359kTPLD82LLEwDI+ouzZ4o0aF/FYk3v5D/wkIr8792aN+fO5H+bKpedD+YX2if/SZatB5rfflW2qf81ae/PacaYivxnTbto0NjQ6e7XP/nQJ8dx/MYkH7r7UIMGAHe2WJw9U6RBuypuzh2B/AdeVpH/RyD/SZbdnDO/0L7TjDEV+d+zyrL1QPPbp4r6n0lF/rO2bhs0hmH4oUk+9e7DDz0x9Ovufv3MbY8IgHZstTh7pkiD9lRcnB/FacaYivyHo6jI/6M4zRhTkf89qyy7OWd+oX8V+d+zyvI3a5nf/lTU/0wq8p8tdNugkeTT7v3+m54Yd/7cpz0xBoBDOc0YU7muWFekQTsqLs6ZVOQ/HEVF/jOpyP/eXbPNmfmFflXkf++ueZKu+e1LRf3PpCL/2UrPDRoff+/33/nEuO+4+/UTHhswDMPnD8Pw9jAMb3/kIx9Z5eAAaFllnWL9fpEG7FPFxTmTyvr57yId9qki/5lU5P8RXPskRfML/anI/yOQ/yTLnqRCvyryny313KAx3Pv9eM03GsfxS8dx/JxxHD/njTfeuPKwAGhbZd3F+nORBuxPxc05JpVt8l+THuxPxeIsk4r8P4rTjDGVp88Hi/DQj8p2+S8f9uU0Y0xF/vessuxJKvSpIv/Z2jue+wA29O33fv8Dnxh3/ty3PzEGALLdzdo3V/xe0JfhfcPrBz3krbeu/Jsrl1+cfzh+nntV2S7/NenBvlSWLc6+Z7Mj4jlV5D+Tyrzz4f4i/LuiPoQWVbbN/y9L8oEVvy/bqsj/3i3d5ubt7Q6JZ1LZPv9dB9D3EzS+6d7vf8QT486f+7sbHgsAzat4Jz0cRWXZxblO+D5Vts1/i3awH5Xli7Pyvz8V+c+kctn54J2S0K7K9vnv5lw7KvL/CK7d5oY+VG6T/56kR8cNGuM4fiTJ37/78Cc8MfQz7379um2PCIB2VRTfcCRLb85ZhOlPRf7DkSzN/1Pkf28q8p9JZdn54CYdtKdym23ONOm1oSL/j+I0Y0zl1fPB/Pajcrv816RHxw0ad77y7tef/dAnh2H4kZmaN77iJkcEQGMqFmfhaJbenLMI05eK/Iejueadc/K/H5XbLM7Shsp19YD6ENpRWdasSZ8q8p9J5eHzwfz2oXLb/NekR/8NGr//7tefMwzDP//A539NkiEvtjf5ygc+D8ChVSzOwhGdZoypPJwPFmH6UJH/cESnGWMq8r9nFTfnmFTWadaUD7B/leVP0qI/FfnPpPL4+WB++yD/ub3dNGgMw/Cp51eSH3LvU590/3PDMHzMS1833r3e+8C3/SNJ/lJe/Hf+oWEY/oW7r/m4YRj+0yT/8d24t8Zx/Cer/0cB0LCKxVngYZWn88EiTNsq8h94WEX+96xicZZJZd0nackH2LdrtjmjLxX5z6Si/j8C+c/t7aZBI8lH7r2+5t6ff9VLn3vn3G84juOY5Jcm+b+TfHqSrxqG4duSfHuS9+fFf//vGsfxd6/xHwBALyoWZ4GHVeblg4v0dsl/4CEV+d87N+c4q2wzv/IB9kv+k2z3JEX536aK+v8oTjPGVOQ/a9pTg8YmxnH8O0k+K8l/meT/TPKOJN+WF1ua/JvjOP6Hz3h4AOySxVngIZXLft5dpLdJ/gMvq8j/I5D/JNtvcyYfYJ9OM8ZU5H/PKts+SVH+t6Wi/mdSkf+sbTcNGuM4DjNf3/DI1733ie/9reM4/rpxHH/8OI4/YBzHTx7H8XPHcfRWNwAecM3irCIc+lRZdjHmIr09pxljKvIfjqIi/4/iNGNMxeJszyq32ebsfj4AbajI/55VbvMkRfnfhor6n0lF/rOF3TRoAMB+nGaMqTxcnCnCoT+V6y7GXKT3pSL/4Sgq8p9J5fHzwfz24ZbbnJ3zAdi/iptzvbvlk3Tl/75V1P9MKvKfrWjQAICLVR4vzhTh0JfKOhdjLtL7UJH/cBQV+c+k8vT5YH77cOttbt684muB26i8/udd/rdP/pOst82Z+r8PFfnPljRoAMBFKk8XZ4pw6Edl3U55+dC2ivyHo6isszh7Jh/aVnn9+WB++3CaMabinZRwFJV5P+/yv32nGWMq8r9nlXW3OVP/t60i/9maBg0AmK0yrzhThEP7KuvenDuTD22qyH84isq6i7Nn8qFNFfnPpOLmHBxFZf7Pu/zvX0X+926Lbc7Uh22qyH9uQYMGAMxSuaxYV4RDuyrb3Jw7kw9tqch/OIrKNouzZ/KhLRX5z6Ti5hwcRUX+M6nI/yPYapsb+dCWivnlVt7x3AcAAPtXWXYxdr9Ie1fsMQmtWHpz7u0L/o77+fCByw6PG6rIfziSpfl/yvx/A17OB/apIv+ZVNycg9sZ3jcs/+K33rryb68se5Ki/O9TRf4fxWnGmIr6sGcV+c8teYIGADypct3FmE5aaM9W75x42Tkf2KeK/IejuXX+f9mCr2V7leu2OZP/fam4OQdHUbnuSYryvy8V+c+kYn2gZxX5z615ggYAu9bmOydeppMW2nKaMaayzmKNHUs7gAAAIABJREFUPNinivyH5/U8NeBpxpjKevmvSW9/KssWZ9/z0p/L/z5U3JyDo6is8yRF+d+HivxnUlm3/vck1f2R/9yeJ2gAwIMq13XOvkwnLfSjYrGmZxX5Dzyssm7+W7Tbl8ryxdmHyP+2VeafD+YX2nfNNmcvk/9tq7jeZ1JZv/7XpL0/8p/b06ABAK+orLs4e6ZIg/ZVLNb0Tv4DD6nI/96teXPuTP63qXLZ+WB+oX3yn2TZeqD57Vdlm/pfk/b+nGaMqch/1qRBAwBescXi7JkiDdpVcXPuCOQ/8LKK/D8C+U+y7Oac+YX2nWaMqcj/nlWWrQea3z5V1P9MKvKftWnQAIBXbLU4e6ZIg/ZUXJwfxWnGmIr8h6OoyP+jOM0YU5H/PassuzlnfqF/Ffnfs8ryN2uZ3/5U1P9MKvKfLWjQAIBXnGaMqVxXrCvSoB0VF+dMKvIfjqIi/5lU5H/vrtnmzPxCvyryv3fXPEnX/Palov5nUpH/bEWDBgBcrLJOsX6/SAP2qeLinEll/fx3kQ77VJH/TCry/wiufZKi+YX+VOT/Ech/kmVPUqFfFfnPljRoAMBFKusu1p+LNGB/Km7OMalsk/+a9GB/KhZnmVTk/1GcZoypPH0+WISHflS2y3/5sC+nGWMq8r9nlWVPUqFPFfnP1jRoAMBslW1u1r654vcC1lG5/OLcRVa/KtvlvyY92JeKxVkmFfnPpDLvfLAID+2rbJv/mvTaUpH/vbtmmzP6Utk+/+UDGjQAYKaKd9LDUVSWXZy7yOpTZdv816QH+1FZvjgr//tTkf9MKpedDxbhoV2V7fNfk147KvL/CK7d5oY+VG6T/5r00KABADNUFN9wJEtvzlmE6U9F/sORLM3/U+R/byryn0ll2fngJh20p3Kbbc406bWhIv+P4jRjTOXV88H89qNyu/zXpEfyjuc+AICeDMP773734bwowt+VuRdd4/jujY6K61QszsLRLL0593amRZj5+c9eVeQ/HM0175yT//2o3GZxljZUrqsH7t+kkw+wb5VlzZrv2eyIeE4V+c+k8vD5YH77ULlt/jtf8AQNgI3olO5DxeIsHNFpxpjKw/kg//tQkf9wRKcZYyryv2eVZYuz9KmyTrOmfID9qyx/khb9qch/JpXHzwfz2wf5z+1p0ADYjCK8bRWLs8DDKk/ng/xvW0X+Aw+ryP+eVSzOMqms+yQt+QD7ds02Z/SlIv+ZVNT/RyD/uT0NGgCbUqS1qWJxFnhYZV4+yP92yX/gIRX53zs35zirbDO/8gH2S/6TbPckRfnfpor6/yhOM8ZU5D9r0qABsDlFWnsszgIPqVz28y7/2yT/gZdV5P8RyH+S7bc5kw+wT6cZYyryv2eVbZ+kKP/bUlH/M6nIf9amQQPgJhRpbblmcdb8Qp8qyy7G5H97TjPGVOQ/HEVF/h/FacaYisXZnlVus83Z/XwA2lCR/z2r3OZJivK/DRX1P5OK/GcLGjQAbkaR1o7TjDGVh4sz8wv9qVx3MSb/+1KR/3AUFfnPpPL4+WB++3DLbc7O+QDsX8XNud7d8km68n/fKup/JhX5z1Y0aADclCKtD5XHizPzC32prHMxJv/7UJH/cBQV+c+k8vT5YH77cOttbt684muB26i8/udd/rdP/pOst82Z+r8PFfnPljRoANycIq1tlaeLM/ML/ais2ykvH9pWkf9wFJV1FmfP5EPbKq8/H8xvH04zxlS8kxKOojLv513+t+80Y0xF/vessu42Z+r/tlXkP1vToAHwLBRpbarMK87ML7Svsu7NuTP50KaK/IejqKy7OHsmH9pUkf9MKm7OwVFU5v+8y//+VeR/77bY5kx92KaK/OcWNGgAPBtFWlsqlxXr5hfaVdnm5tyZfGhLRf7DUVS2WZw9kw9tqch/JhU35+AoKvKfSUX+H8FW29zIh7ZUzC+3okED4Fnd/0ec/aosuxhTpEGbtrw5dyb/21CR/3AkS/N/zvgz+dCGivllUnFzDo6isuxJivK/TxX5fxSnGWMq6sOeVeQ/t6RBA+DZnf8RZ58q112MKdKgPVvfnDuT//tWkf9wNLfOf016+1S5bpsz+d+XiptzcBSV656kKP/7UpH/TCrWB3pWkf/cmgYNgF1487kPgAdVrlucPVOkQVtOM8ZU1lmskf/7VJH/cESnGWMq6+W/Jr39qayzzZn870PFzTk4iso6T1KU/32oyH8mlXXrf03a+yP/uT0NGgDwoMo6i7NnijToR8ViTc8q8h94WGXd/Nekty+Vdbc5k/9tq8w/H8wvtG/Nbc7kf9sqrveZVNav/zVp74/85/Y0aADAKyrrLs6eKdKgfRWLNb2T/8BDKvK/d2venDuT/22qXHY+mF9on/wnWbYeaH77Vdmm/tekvT+nGWMq8p81adAAgFdssTh7pkiDdlXcnDsC+Q+8rCL/j0D+kyy7OWd+oX2nGWMq8r9nlWXrgea3TxX1P5OK/GdtGjQA4BVbLc6eKdKgPRUX50dxmjGmIv/hKCry/yhOM8ZU5H/PKstuzplf6F9F/vessvzNWua3PxX1P5OK/GcLGjQA4BWnGWMq1xXrijRoR8XFOZOK/IejqMh/JhX537trtjkzv9Cvivzv3TVP0jW/famo/5lU5D9b0aABABerrFOs3y/SgH2quDhnUlk//12kwz5V5D+Tivw/gmufpGh+oT8V+X8E8p9k2ZNU6FdF/rMlDRoAq/KPbP8q6y7Wn4s0YH8qbs4xqWyT/5r0YH8qFmeZVOT/UZxmjKk8fT5YhId+VLbLf/mwL6cZYyryv2eVZU9SoU8V+c/WNGgArMo/sn2rbHOz9s0VvxewjsrlF+fyv1+V7fJfkx7sS8XiLJOK/GdSmXc+WISH9lW2zX9Nem2pyP/eXbPNGX2pbJ//8gENGgAr849svyreSQ9HUVl2cS7/+1TZNv816cF+VJYvzsr//lTkP5PKZeeDRXhoV2X7/Nek146K/D+Ca7e5oQ+V2+S/Jj00aACsTBHep4riG45k6c05+d+fivyHI1ma/6fI/95U5D+TyrLzwfoAtKdym23ONOm1oSL/j+I0Y0zl1fPB/Pajcrv816SHBg2ADSjC+1KxOAtHs/TmnPzvS0X+w9Fc8845+d+Pym0WZ2lD5bp6QH0I7ajY5oxJRf4zqTx8PpjfPlRum/+a9NCgAbARRXgfKhZn4YhOM8ZUHs4H+d+HivyHIzrNGFOR/z2ruDnHpLJOs6Z8gP2rLH+SFv2pyH8mlcfPB/PbB/nP7WnQANiMIrxtFYuzwMMqT+eD/G9bRf4DD6vI/55VLM4yqaz7JC35APt2zTZn9KUi/5lU1P9HIP+5PQ0aAJtSpLWpYnEWeFhlXj7I/3bJf+AhFfnfOzfnOKtsM7/yAfZL/pNs9yRF+d+mivr/KE4zxlTkP2vSoAGwOUVaeyzOAg+pXPbzLv/bJP+Bl1Xk/xHIf5LttzmTD7BPpxljKvK/Z5Vtn6Qo/9tSUf8zqch/1qZBA+AmFGltuWZx1vxCnyrLLsbkf3tOM8ZU5D8cRUX+H8VpxpiKxdmeVW6zzdn9fADaUJH/Pavc5kmK8r8NFfU/k4r8ZwsaNABuRpHWjtOMMZWHizPzC/2pXHcxJv/7UpH/cBQV+c+k8vj5YH77cMttzs75AOxfxc253t3ySbryf98q6n8mFfnPVt7x3AcAcCz3i7R33X1MeyqPF2fm98iG4f2Lvm4c373ykbCeyjoXY/K/DxX5D0dRkf9MKk+fD+a3D0tvzr298O9zvsD+VV5fD3w4fp5bd+ttzpwv+1RZZ5sz9X8fKvKfLXmCBsDN6aRtW+Xp4sz8Qj8q63bKy4e2VeQ/HEVlncXZM/nQtsrrzwfz24fTjDEV76SEo6jM+3mX/+07zRhTkf89q6y7zZn6v20V+c/WNGgAPAtFWpsq84oz8wvtq6x7c+5MPrSpIv/hKCrrLs6eyYc2VeQ/k4qbc3AUlfk/7/K/fxX537sttjlTH7apIv+5BQ0aAM9GkdaWymXFuvmFdlW2uTl3Jh/aUpH/cBSVbRZnz+RDWyryn0nFzTk4ior8Z1KR/0ew1TY38qEtFfPLrWjQAHhW9/8RZ78qyy7GFGnQpi1vzp3J/zZU5D8cydL8nzP+TD60oWJ+mVTcnIOjqCx7kqL871NF/h/FacaYivqwZxX5zy1p0AB4dud/xNmnynUXY4o0aM/WN+fO5P++VeQ/HM2t81+T3j5VrtvmTP73peLmHBxF5bonKcr/vlTkP5OK9YGeVeQ/t6ZBA2AX3nzuA+BBlesWZ88UacdhfvtwmjGmss5ijfzfp4r8hyM6zRhTWS//NentT2Wdbc7kfx8qbs7BUVTWeZKi/O9DRf4zqaxb/2vS3h/5z+1p0ACAB1XWWZw9U6Qdg/k9horFmp5V5D/wsMq6+a9Jb18q625zJv/bVpl/PphfaN+a25zJ/7ZVXO8zqaxf/2vS3h/5z+1p0ACAV1TWXZw9U6T1z/z2r2KxpnfyH3hIRf73bs2bc2fyv02Vy84H8wvtk/8ky9YDzW+/KtvU/5q09+c0Y0xF/rMmDRoA8IotFmfPFGl9M799q7g5dwTyH3hZRf4fgfwnWXZzzvxC+04zxlTkf88qy9YDzW+fKup/JhX5z9o0aADAK7ZanD1TpPXN/Pap4uL8KE4zxlTkPxxFRf4fxWnGmIr871ll2c058wv9q8j/nlWWv1nL/Panov5nUpH/bEGDBgC84jRjTOW6Yl2R1jfz25eKi3MmFfkPR1GR/0wq8r9312xzZn6hXxX537trnqRrfvtSUf8zqch/tvKO5z4AAGhPZZ1i/X6R9oGrj4q9uT+/74o9JltVcXHOpLJ+/r/r6qPi+Q3D+1/6kw9nbv6P47s3OiquU5H/TCry/wiW3px7++7P1f/Qn8o2+S8f9uXaJ+ma3z5ULm/WoV8V+c+WPEEDYFU6IftXWXex/lyk0Sed0m2ruDnHpLJN/n/ZCt+L/ZH/batYnGVSkf9HcZoxpvL0+SD/oR+V7fJfPuzLacaYivzvWWXZk1ToU0X+szUNGgCr8o9s3yrb3KzVOds3RXibKpdfnJvfflW2y39Nev2S/22qWJxlUpH/TCrzzgf5D+2rbJv/mvTaUpH/vbtmmzP6Utk+/+UDGjQAVuYf2X5VvJOe5RThbaksuzg3v32qbJv/mvT6Jv/bUlm+OGt++1OR/0wql50P8h/aVdk+/zXptaMi/4/g2m1u6EPlNvmvSQ8NGgArU4T3qaL45nryoR1Lb86Z3/5U5D/Xk//tWJr/p5jf3lTkP5PKsvNB/kN7KrfZ5kyTXhsq8v8oTjPGVF49H8xvPyq3y39NemjQANiAIrwvFYuzrEc+tGHpzTnz25eK/Gc98qEN17xzzvz2o3KbxVnaULmuHpD/0I6Kbc6YVOQ/k8rD54P57UPltvmvSQ8NGgAbUYT3oWJxlvXJh/07zRhTeTgfzG8fKvKf9cmH/TvNGFOR/z2ruDnHpLJOs6Z8gP2rLH+SFv2pyH8mlcfPB/PbB/nP7WnQANiMIrxtFYuzbEc+tK3ydD6Y37ZV5D/bkQ9tq8j/nlUszjKprPskLfkA+3bNNmf0pSL/mVTU/0cg/7k9DRoAm1KktalicZbt3c8H2lGZlw/yv13yn63JhzZV5H/v3JzjrLLN/MoH2C/5T7LdkxTlf5sq6v+jOM0YU5H/rEmDBsDmFGntsTjLrZzzgTZULvt5l/9tkv/cgia9tlTk/xHIf5LttzmTD7BPpxljKvK/Z5Vtn6Qo/9tSUf8zqch/1qZBA+AmFGltuWZx1vxyqTef+wCYpbLsYkz+t+c0Y0xF/nM9TXptqMj/ozjNGFOxONuzym22OdOkB+2pyP+eVW7zJEX534aK+p9JRf6zBQ0aADejSGvHacaYysPFmfmF/lSuuxiT/32pyH/Wo0lv3yryn0nl8fPB/PbhltucadKDdlTcnOvdLZ+kK//3raL+Z1KR/2xFgwbATSnS+lB5vDgzv9CXyjoXY/K/DxX5D0dRkf9MKk+fD+a3D7fe5kaTHuxf5fU/7/K/ffKfZL1tztT/fajIf7akQQPg5hRpbas8XZyZX+hHZd1OefnQtor8h6OorLM4eyYf2lZ5/flgfvtwmjGm4p2UcBSVeT/v8r99pxljKvK/Z5V1tzlT/7etIv/ZmgYNgGehSGtTZV5xZn6hfZV1b86dyYc2VeQ/HEVl3cXZM/nQpor8Z1Jxcw6OojL/513+968i/3u3xTZn6sM2VeQ/t6BBA+DZKNLaUrmsWDe/0K7KNjfnzuRDWyryH46iss3i7Jl8aEtF/jOpuDkHR1GR/0wq8v8IttrmRj60pWJ+uRUNGgDP6v4/4uxXZdnFmCIN2rTlzbkz+d+GivyHI1ma/3PGn8mHNlTML5OKm3NwFJVlT1KU/32qyP+jOM0YU1Ef9qwi/7klDRoAz+78jzj7VLnuYkyRBu3Z+ubcmfzft4r8h6O5df5r0tunynXbnMn/vlTcnIOjqFz3JEX535eK/GdSsT7Qs4r859Y0aADswpvPfQA8qHLd4uyZIu04zG8fTjPGVNZZrJH/+1SR/1zG/PbhNGNMZb3816S3P5V1tjmT/32ouDkHR1FZ50mK8r8PFfnPpLJu/a9Je3/kP7enQQMAHlRZZ3H2TJF2DOb3GCoWa3pWkf9czvweQ2Xd/Nekty+Vdbc5k/9tq8w/H8wvtG/Nbc7kf9sqrveZVNav/zVp74/85/Y0aADAKyrrLs6eKdL6Z377V7FY0zv5zxLmt38V+d+7NW/Oncn/NlUuOx/ML7RP/pMsWw80v/2qbFP/a9Len9OMMRX5z5o0aADAK7ZYnD1TpPXN/Pat4ubcEch/ljC/favI/yOQ/yTLbs6ZX2jfacaYivzvWWXZeqD57VNF/c+kIv9ZmwYNAHjFVouzZ4q0vpnfPlVcnB/FacaYivznVea3TxX5fxSnGWMq8r9nlWU358wv9K8i/3tWWf5mLfPbn4r6n0lF/rMFDRoA8IrTjDGV64p1RVrfzG9fKi7OmVTkP48zv32pyH8mFfnfu2u2OTO/0K+K/O/dNU/SNb99qaj/mVTkP1t5x3MfAAC0p7JOsX6/SPvA1UfFOoahct3i7Nn9+X1X7DHZqoqLcyaV9fP/XVcfFXsj//tQkf9MKvL/CJbenHv77s/l/xENw/sXf+04vnvFI2EblW3yXz7sy7VP0jW/fahcvh5Ivyryny15ggbAqnRC9q+y7mL9uUhjP65558TLdEq3reLmHJPKNvn/ZSt8L/ZH/retYnGWSUX+H8VpxpiK+h+OorJd/suHfTnNGFOR/z2rLFsPpE8V+c/WNGgArMo/sn2rbHOzVufsvqzVnHGmCG9T5fKLc/Pbr8p2+a9Jr1/yv00Vi7NMKvKfSUX9D0dR2Tb/Nem1pSL/e7fGk3TpQ2X7/JcPaNAAWJl/ZPtV8U76ozjNGFO57HxQhLelsuzi3Pz2qbJt/mvS65v8b0tl+eKs+e1PRf4zqaj/eZj57U9l+/zXpNeOivw/grXfrEWbKrfJf016aNAAWJkivE8VxTeTyrLzQT60Y+nNOfPbn4r853ryvx3XbHNmfvtSkf9MKup/Hmd++1K5zTZnmvTaUJH/R3GaMaby6vlgfvtRuV3+a9JDgwbABhThfalYnGVSue58kA9tWHpzzvz2pSL/WY98aMM175wzv/2o3GZxljZU1P88zfz2o2KbMyYV+c+k8vD5YH77ULlt/mvSQ4MGwEYU4X2oWJxlUlnnZq182L/TjDGVh88H89uHivxnffJh/04zxlTkf88qbs4xqaj/eT3z24fK8idp0Z+K/GdSefx8ML99kP/cngYNgM0owttWsTjLpLLuO+nlQ9sqT58P5rdtFfnPduRD2yryv2cVi7NMKup/5jO/7btmmzP6UpH/TCrq/yOQ/9yeBg2ATSnS2lSxOMuksk3xfT8faEdl3vkg/9sl/9mafGhTRf73zs05zirb1//yoT/mt23yn2S7JynKhzZV1P9HcZoxpiL/WZMGDYDNKdLaY3GWs8q283vOB9pQuex8kP9tkv/cgia9tlTk/xHIf5LttzmTD30zv+06zRhTkf89q2z7JEX50JaK+p9JRf6zNg0aADehSGvLNYuz5rcflW0XZ8/eXPh13FZl2cWY/G/PacaYivznepr02lCR/0dxmjGmYnG2Z5XbbHOmSa9v8r9PFfnfs8ptnqQo/9tQUf8zqch/tqBBA+BmFGntOM0YU3m4ODO/fajcZnGWNlSuuxiT/32pyH/Wo0lv3yryn0nl8fPB/PbhltucadLrm/zvS8XNud7d8km68n/fKup/JhX5z1Z21aAxDMMPH4bhtw/D8OFhGL5rGIa/NwzDHxuG4Wct/H6nYRjGGa9PXfu/BeBhirQ+VB4vzsxv+yq3XZxl3yrrXIzJ/z5U5D8cRUX+M6k8fT6Y3z7cepsbTXp9k/99qLz+5938tk/+k6z3JF3534eK/GdLu2nQGIbhJyX560l+dZIfk+S7k3xqkp+f5E8Nw/Brr/j235vk7z3x+t4rvjfAhRRpbas8XZyZ3/bd8p0T7Ftl3fmVD22ryH84isq625zJh7ZVXn8+mN8+nGaMqaj/mU/+t60y7+fd/LbvNGNMRf73rLLuk3Tlf9sq8p+t7aJBYxiGH5Dkjyb5lCR/JclPHMfxByf5IUl+S5IhyRcPw/BzFv4Vf3scxx/+xOsfrPIfAjCbIq1NlXnFmfltm+YMkvVvzp3JhzZV5D8cRWWbbc7kQ5sq8p9JRf3P5eRDmyrzf97Nb/8q8r93WzxJV/63qSL/uYVdNGgk+ZVJfnSSb0/yC8Zx/FCSjOP4reM4vjvJH74b98XPdHwAG1CktaVyWbFuftt1mjGm4uK8Z5Vtbs6dyYe2VOQ/HEVl223O5ENbKvKfSUX9z3LyoS0V+c+kIv+PYKs3a8mHtlTML7eylwaNX3b36+8fx/EbH/j8l9z9+tnDMPy4Gx0TwA3c/0ec/aosuxhTpPWp4uK8d1venDuT/22oyH84kltscyYf2lAxv0wq6n+uJx/aUFn2JEXz26eK/D+K04wxFfVhzyryn1t69gaNYRg+MclPufvwTz4y7C8m+Za733/u5gcFcFPnf8TZp8p1F2OKtL5UXJwfwa22uZH/+1aR/3A0t85/TXr7VLlumzP535eK+p/1yId9q1z3JEXz25eK/GdSsT7Qs4r859aevUEjyY9PMtz9/kMPDRjH8XuT/F93H37mgr/jjWEYvmYYhn989/r6YRi+dBiGf27B9wLYwJvPfQA8qHLd4uyZIq0PldefD+a3D6cZYyrrLNbI/32qyH8uY377cJoxprJe/mvS25/KOtucyf8+VNycO45heN2rMgxv3P06/fnlNOntU2WdJynK/z5U5D+Tyrr1v/zfH/nP7e2hQePT7v3+m54Yd/7cpz0x5jE/MMlPTvLdSd6R5DOSfF6SvzIMw7sXfD8AuldZZ3H2TJHWtsq888H8HkPFYk3PKvKfy5nfY6ism/+a9Palsu42Z/K/bZX554P57V9l/fzXpLcva25zJv/bVnG9z6Qi/49A/nN7e2jQ+Ph7v//OJ8Z9x92vn3DB9/5HSb4kyeck+QHjOH5yXjRr/MtJ/kKSj03yJcMw/NtPfZNhGD5/GIa3h2F4+yMf+cgFfz0Abaqsuzh7pkhrU2X++WB++1exWNM7+c8S5rd/FfnfuzVvzp3J/zZVLjsfzG/fKtvkvya9fZH//P/s3Xm4LVdd5//3gjAkkIAmYZ4EGxBoSDOI0A47HUIbDNoyyqTY/AxC2y0aVIIiQUYVfHAAZLB/QSYZoiiTYvjlCwiIBglqwKBiMEBAEplCggxZvz9qb+rcc8/Zu/beVXWqVr1fz3Oee+7Za+/UzXedT61ataoKNpsPtL7lCsz/qZg1aBOY/2rTEBZobHQzuCZyzufnnH8+5/yBnPNX5j/7Rs75XcCJwHvmTX81pbTv/4uc80tyznfPOd/9+OOP72pzJUmD0cXk7IKDtHEJrK9qgSfnpsD81yasb9kC838KzH/BZifnrG+5AvN/KmYN2gTmf8mCzeYDrW+ZAvNftcD8V9uGsEDj8h3fH7mk3VF7tN9YzvmrwFPmf70Z1SNQJEmiu8nZBQdp4xCsf3AO1rdUgQfnUzFr0CYw/3U461umwPyfilmDNoH5X7Jgs5Nz1rdMgfmvWmD+lyzY/GIt61uewPxXLTD/1YUhLND41I7vb7Kk3eK1S1r8b79/x/e3bvFzJY1cSnt9BSkdP/9z7zYqxaxBm2C7wbqDtGELNjs4X7C+ZQk8OFctMP+1P+tblsD8Vy0w/0u3zWPOrG9ZAvNftcD8L902d9K1vmUJzH/VAvNfXRnCAo1/APL8+zvu1WD++JHbzf/64Y62I69uImm6AgdnqgXt9IedgzQNR7Dd5OyCg/AyBOa/akH7+W8+lMf6liEw/1ULzP8p2PZOita3DIH5r1pg/k+B+S/Y/E66KlNg/qtLB75AI+f8JeC8+V9P3qfZPYHrzb9/R4v/+Xvu+P6iFj9XUlGC5jtjd7LlC9qdrFkM0jQc21w5sZuD8HELnJxVLegm/12kNyTL7pS27M5qhzP/xy1wcla1wPyfilmDNoHj/5IF5r9qQXf5bz4My6xBm8D8L1mw3Z10VZbA/FfXDnyBxtyr538+IqV04z1ef+L8zw/knC9s+qEp7f/AgZTSNYBfmf/1EuBvmn6upCkJ1tsZu5MtW9DNydrbtPhZ2l5bizMWHISPU7D+wbn1LVfQXf67SG9YZg3aBOZ/yQInZ1ULzH/VAvO/ZIH5r1rQbf67SG9cAvO/dG3cSVdlCLrPf/NBw1mg8WLg48DRwJtTSncASCkdnVL6NeAB83ZP3v3GlFKef525x+f+fUrpf6eU/tNisUZK6eoppe+muhNMJyc2AAAgAElEQVTHd8/bnZFzvqrdf5KmKqXnrvh6HCldd/5n/XMNUbD+4MydbLkCr6SfilmDNsF6/cFB+LgEmx2cW98yBd3mv4v0xiUw/0sWbD45a33LE5j/qgXmf+nMfy0E3ee/i/TGIzD/p6Dti7U0TkE/+e8iPQ1kgUbO+Urgh4DLgLsCF6SUvgB8Hvg5IFMtonj7mh99B+C3gI8CV6aUPgtcAbwb+B7gG8CTcs4vb+UfIjXiIG0cgs0Ozq1vmQIH36oFm/UH82E8Np2ctb7lCcx/1QLzv3TbPObM+pYlMP9VC8z/KTD/Bf095sZFeuMQmP9TMWvQJji8P1jfcgT95b+L9DSQBRoAOecPAXeiWlDxMeBaVAs23gKcnHN+zgYf+1jg94ELgC8C1wf+A/g74HeAO+ecf3X7rZfW5SBt+La5rZn1LUvg5KxqwXb9wXwYh00nZ61vWQLzX7XA/J+Cba6cs77lCPqZnNU4BOb/VMwatAnM/5IFPuZGtcD8Vy3Yuz9Y3zIE/ea/i/Q0oAUaADnnT+ecfzrnfJuc87VzzjfIOZ+ac37Hkvek+deZe7z2kpzzj+Wc7zT/rGvknI/JOd855/y/c84f7vQfJC3lIG3Ytr2tmfUtQ+DkrGpBOydrzYfhmzVoE5j/JQvMf9UC838qZg3aBOZ/yQJPzqkWmP+qBeZ/yYLtLtZSWQLzX7Vg//5gfctg/qt/g1qgIU2Pz5warlmDNsHynbeD8HELnJxVLWj3SnrzYdwC879kgfmvWmD+qxaY/yULnJxVLTD/VQvM/9Jt85gzlSUw/1ULzP8pMP/VPxdoSAfOZ06NU9BsZ+wgbZwCJ2dVC7oZfLtIb5wC87905r8Wgu7z33wYj8D8L50n57QQmP+qBeb/FJj/gu7upGg+jFNg/k/FrEGbwPxXm1ygIQ2Cz5wal2C9nbGDtPFxclYLQbf1dZHeuATm/xSY/4L+8t9FeuMQmP9TYP4Lun/MmfkwLoH5PxWzBm0C879kQbd3UjQfxiUw/1ULzH+1zQUakrSWYLOdsYO0cdlmctb6liPodnJ2wUV64xCY/1Mxa9AmMP9LFvSX/y7SG77A/J+KWYM2gZOzJQv6ecyZi/TGITD/VQvM/5IF/dxJ0fwfh8D8Vy0w/9UFF2hIUmPBdjtjB2njMWvQJti7P1jfMgT9TM5qHALzX7XA/C9Z0G/+u0hv2ALzX7Vg//5gfcvQ52POXKQ3bIH5r1rgybnS9XknXfN/2ALzX7XA/FdXXKAhSY0E7eyMHaSVIdi/P1jf8Qv6nZzVsAXmv2qB+V86818LgfmvWrC8P1jfMvT9mBsX6Q1TYP6rFqzuD9Z3/Mx/QXt3UjT/yxCY/+qSCzQkaaWg3ZWSDtLGLVjeH6zv+PV55YSGLTD/VQvM/ykw/wXtP+bGfBi3YHV/sL5lmDVoE5j/JQvMf9WCZv3B+o7frEGbwPwvWdDunRTN/3ELzH91zQUakrRU0M3g20HaOAXN+oP1HTdPzgnan5xdMB/GKTD/p2LWoE1g/pcs6OYxN+bDOAXmv2qB+V+ywPxXLWjeH6xv+QLzv3Rd3EnR/B+nwPxXH4446A2QpOEKujk5t7BzkOazB4cvWK8/7K6vty8cj1mDNoEH5yULNjs4/4WGn2/+j0tg/o9P4swN37nqfYH5X7Jg88nZ8xq0Nx/GJTD/VQvM/9KZ/1oIzH/VAvN/Cja9WGvVPsB8GJfA/FdfvIOGJO0p6ObKid127sQ1XMFmB2OulC5T4MF56bq4cmI3838cAvNftcD8L10fjzkzH8YhsL6qBeb/FJj/gs0v1rK+ZQrM/6mYNWgTmP8lC8x/9ck7aEit+2dcKTcMm185CfACurtyYrfFTlzDFGx3MOZK2oPSzdXTgQfnU9DVlRO7mf/DFpj/qgXm/xT09Ziznfnwog3er24F291J0fwvS2D+j8d280BN3hu0Oz7UsATb3UnR/C9LYP6rFpj/JQvMf/XNO2hIrXOlXBn6mpxdcKc9TEE7j7lxJW0ZgtX9wfqWYdagTWD+lyww/1ULzP+pmDVoE7SX/07ODk/Qzp0Uzf8yBJ6cUy1oN/+9k96wBO3cSdH8L0Ng/qsWmP+lM//VPxdoSK0zhMswa9AmcLBesqDdx9w4SBu3oFl/sL7TEJj/JQvMf9UC81+1oN38d5HesATtPubM/B+3oHl/sL7lC9rPfxfpDUubjzkz/8ct8HhftcD8nwLzX/1zgYbUOkN4GgIH6yUL2p2cXTAfxilo3h+sb/kC87905r8WAvNftcD8L12bJ+cWzP9xCtbrD9a3bEE3+e8ivWEx/wWbzQda33IF5v9UzBq0Ccx/tckFGlInDOGyBU7Olq6LydkF82FcAuurWmD+T4H5L7C+OlRg/k+B+S/Y7OSc9S1XYP5PxaxBm8D8L1mw2Xyg9S1TYP6rFpj/atsRB70BUrl2hvCjcGXk5tLT0mZvfOpT290QwMHZVHQ1ObtgPoxDsP7BOVjfUgXm/1TMGrQJzP+SBea/aoH5PxWzBm0C879kwWYn534B61uiwPxXLTD/SxZsfrHWa7G+pQnMf9UC819dcIGG1ClDuCyBg7OpmDVoE2zXH3bng/ay8QIt2HKRVrD55CyY/6UJzH/Vgnbz33wYlsD8Vy0w/1ULzP/SbXpy7jysb2kC81+1wPwv3TZ30j0P61uSwPxXLTD/1RUfcSJ1ztsZlSFwcKZa0E5/2JkPGo5g84Pzncz/MgTmv2pB+/lvPgyL+a+FwPxXLTD/p2DbOyla3zIE5r9qgfk/Bea/YPM7KapMgfmvLnkHDakXrpQbt6D5zvifsb6lC9qdrFnkg4Zj2ysndjL/xy1wcla1oJv8fwXwohY+rzwHcxcl81/g5KwOFZj/UzFr0CZY3h/M/zYc3KNuA/NftaC7/HceaFhmDdoE5n/Jgu3upKiyBN3mv/kg76Ah9ciVcuMUrLcztr5lC7o5WeugbFi2vXJiN/N/nIL1D86tb7mC7vLfydlhmTVoE5j/JQs2m5xVmQLzX7XA/C9ZYP6rFnSb/95JdVwC8790bdxJUWUIus9/80Eu0JB6ZgiPS7D+4Mz6livwSvqpmDVoE6zXH8z/cQk2Ozi3vmUKus1/F+mNS2D+lyzYfHLW+pYnMP9VC8z/0pn/Wgi6z38X6Y1HYP5PQdsXa2mcgn7y30V6coGGdAAcpI1DsNnBufUtU+DgW7Vgs/5gPozHppOz1rc8gfmvWmD+l26bx5xZ37IE5r9qgfk/Bea/oL/H3LhIbxwC838qZg3aBIf3B+tbjqC//HeRnlygIR0QB2nDt81tzaxvWQInZ1ULtusP5sM4bDo5a33LEpj/qgXm/xRsc+Wc9S1H0M/krMYhMP+nYtagTWD+lyzwMTeqBea/asHe/cH6liHoN/9dpCcXaEgHyEHasG17WzPrW4bAyVnVgnZO1poPwzdr0CYw/0sWmP+qBeb/VMwatAnM/5IFnpxTLTD/VQvM/5IF212spbIE5r9qwf79wfqWwfxX/1ygIe0jpb2+gpSOn/+5d5v1+Myp4Zo1aBMs33k7CB+3wMlZ1YJ2r6Q3H8YtMP9LFpj/qgXmv2qB+V+ywMlZ1QLzX7XA/C/dNo85U1kC81+1wPyfAvNf/XOBhtRY0E34+sypcQqa9QcHaeMUODmrWtBt/rtIb1wC87905r8Wgu7z33wYj8D8L50n57QQmP+qBeb/FJj/gu7upGg+jFNg/k/FrEGbwPxXm1ygITUSdBu+PnNqXIL1+oODtPFxclYLQff57yK98QjM/ykw/wX95b+L9MYhMP+nwPwXdP+YM/NhXALzfypmDdoE5n/Jgm7vpGg+jEtg/qsWmP9qmws0pJUCw1e1YLP+4CBtXLaZnLW+5Qi6nZxdcJHeOATm/1TMGrQJzP+SBf3lv4v0hi8w/6di1qBN4PxAyYJ+HnPmIr1xCMx/1QLzv2RBP3dSNP/HITD/VQvMf3XBBRrSUkE/k7Mah2C7nbGDtPGYNWgT7N0frG8Zgn4mZzUOgfmvWmD+lyzoN/9dpDdsgfmvWrB/f7C+ZejzMWcu0hu2wPxXLfDkXOn6vJOu+T9sgfmvWmD+qysu0JD2FXhyTrWgnZ2xg7QyBPv3B+s7fkG/k7MatsD8Vy0w/0tn/mshMP9VC5b3B+tbhr4fc+MivWEKzH/VgtX9wfqOn/kvaO9iXfO/DIH5ry65QEPal5OzWgjaXSnpIG3cguX9wfqOX59XTmjYAvNftcD8nwLzX9D+nRTNh3ELVvcH61uGWYM2gflfssD8Vy1o1h+s7/jNGrQJzP+SBe1erGv+j1tg/qtrLtCQ9uXkrKC7+jpIG6egWX+wvuNm/gu6e8yZ+TBOgfk/FbMGbQLzv2RBN3dSNB/GKTD/VQvM/5IF5r9qQfP+YH3LF5j/peviYl3zf5wC8199cIGGtK9ZgzaBg7OSBd2cnFtwkDYuwXr9wfqO16xBm8D8L1nQ7WPOzIdxCcx/1QLzv2RBt3dSNB/GJTD/VQvM/9KZ/1oIzH/VAvN/Crq6WMt8GJfA+qovLtCQNhY4OCtZ0O3JuYWdO3ENV7DZ77uDtDIF5n/p+njMmfk/DoH5r1pg/peuj8ecmQ/jEFhf1QLzfwrMf8HmF2tZ3zIF5v9UzBq0Ccz/kgXmv/rkAg1pI8H+YW0Il6GPk3MLi524hinY7mDMQVpZAg/Op6Cvx9yY/8MWmP+qBeb/FPSd/y7SG6Zguzspmv9lCcz/qZg1aBM4PixZsN3FWta3LIH5r1pg/pcsMP/VNxdoSGsLloe1IVyGviZnF26zxXvVnaCdx9w4SCtDsLo/WN8yzBq0Ccz/kgXmv2qB+T8VswZtgvby30V6wxO0cydF878MgSfnVAvazX8X6Q1L0M7FWuZ/GQLzX7XA/C+d+a/+uUBDWkuwOqwN4TLMGrQJHKyXLGj3MTcO0sYtaNYfrO80BOZ/yQLzX7XA/FctaDf/XaQ3LEG7d1I0/8ctaN4frG/5gvbz30V6w9LmY87M/3ELPN5XLTD/p8D8V/9coCE1FjTbGRvC0xA4WC9Z0M1jbsyHcQqa9wfrW77A/C+d+a+FwPxXLTD/S9fmybkF83+cgvX6g/UtW9BN/rtIb1jMf8Fm84HWt1yB+T8VswZtAvNfbXKBhtRIsN7O2BAuW+DkbOm6mJxdMB/GJbC+qgXm/xSY/wLrq0MF5v8UmP+CzU7OWd9yBeb/VMwatAnM/5IFm80HWt8yBea/aoH5r7a5QENaKdhsZ2wIlylwcDYFXU3OLpgP4xCsf3AO1rdUgfk/FbMGbQLzv2SB+a9aYP5PxaxBm8D8L1mw2ck561umwPxXLTD/SxZsfrGW9S1PYP6rFpj/6oILNKSlgs0mZxcM4bIEDs6mYtagTbBdfzAfhi3Y7OB8wfqWJTD/VQvM/5IF5r9qgfmvWmD+l26bx5xZ37IE5r9qgflfum3upGt9yxKY/6oF5r+64gINaV/BdpOzC4ZwGQIHZ6oF7fSHnfmg4Qi2m5xdMP/LEJj/qgXt57/5MCzmvxYC81+1wPyfgm3vpGh9yxCY/6oF5v8UmP+C7S/WVVkC819dcoGGtK82JmcXDOFxC5r3B+tbvqDdyZpFPmg4trlyYjfzf9wCJ2dVC7rJfxfpDYv5L3ByVocKzP+pmDVoE5j/JQvMf9WC7vLffBiWWYM2gflfsqCdi3VVhsD8V9dcoCHtq63J2QVDeJyC9XbG1rdsQTcna2/T4mdpe+a/YLODc+tbrqC7/HeR3rDMGrQJzP+SBU7OqhaY/6oF5n/JAvNftaDb/HeR3rgE5n/p2rxYV+MWdJ//5oNcoCEtMWvQJlgvrA3hcQnWH5xZ33IFXkk/FbMGbQLzv2TBZgfn1rdMQbf57yK9cQnM/5IFm0/OWt/yBOa/aoH5XzrzXwtB9/nvIr3xCMz/KWj7Yi2NU9BP/rtITy7QkLYQbBbWDtLGIdjs4Nz6lilw8K1aYP6XbtPJWetbnsD8Vy0w/0u3zWPOrG9ZAvNftcD8nwLzX9DfY25cpDcOgfk/FbMGbYLD+4P1LUfQX/67SE8u0JA2FGw3WeMgbfi2ua2Z9S1L4OSsaoH5PwWbTs5a37IE5r9qgfk/BdtcOWd9yxH0MzmrcQjM/6mYNWgTmP8lC3zMjWqB+a9asHd/sL5lCPrNfxfpyQUa0gaCdibrHaQN27a3NbO+ZQicnFUtMP+nYtagTWD+lyww/1ULzP+pmDVoE5j/JQs8OadaYP6rFpj/JQu2u1hLZQnMf9WC/fuD9S2D+a/+uUBDWkvQ7pWUPnNquGYN2gTL+4OD8HELnJxVLegu/82H8QnM/5IF5r9qgfmvWmD+lyxwcla1wPxXLTD/S7fNY85UlsD8Vy0w/6fA/Ff/XKAhNRZ0E74+c2qcgmb9wUHaOAVOzqoWdJv/LtIbl8D8L535r4Wg+/w3H8YjMP9L58k5LQTmv2qB+T8F5r+guzspmg/jFJj/UzFr0CYw/9UmF2hIjQTdhq/PnBqXYL3+4CBtfJyc1ULQff67SG88AvN/Csx/QX/57yK9cQjM/ykw/wXdP+bMfBiXwPyfilmDNoH5X7Kg2zspmg/jEpj/qgXmv9rmAg1ppcDwVS3YrD84SBuXbSZnrW85gm4nZxdcpDcOgfk/FbMGbQLzv2RBf/nvIr3hC8z/qZg1aBM4P1CyoJ/HnLlIbxwC81+1wPwvWdDPnRTN/3EIzH/VAvNfXXCBhrRU0M/krMYh2G5n7CBtPGYN2gR79wfrW4agn8lZjUNg/qsWmP8lC/rNfxfpDVtg/qsW7N8frG8Z+nzMmYv0hi0w/1ULPDlXuj7vpGv+D1tg/qsWmP/qigs0pH0FnpxTLWhnZ+wgrQzB/v3B+o5f0O/krIYtMP9VC8z/0pn/WgjMf9WC5f3B+pah78fcuEhvmALzX7VgdX+wvuNn/gvau1jX/C9DYP6rSy7QkPbl5KwWgnZXSjpIG7dgeX+wvuPX55UTGrbA/FctMP+nwPwXtH8nRfNh3ILV/cH6lmHWoE1g/pcsMP9VC5r1B+s7frMGbQLzv2RBuxfrmv/jFpj/6toRB70BUpcSZ27x7k0nZ8/b4r+p4Qm6GXzvHKQ9CldOj0XQrD9Y33Hz5Jygu8ecmQ/jFJj/UzFr0CYw/0sWbDY5+wsr2pkP4xSY/6oF5n/JAvNftaB5f7C+5QvM/9JterHWsnNB5v84Bea/+uAdNKR9zRq0CRyclSzo5uTcgitpxyVYrz9Y3/GaNWgTmP8lC7p9zJn5MC6B+a9aYP6XLOj2Tormw7gE5r9qgflfOvNfC4H5r1pg/k9BVxdrmQ/jElhf9cU7aGgU0tPSZm986lPb3ZBDBA7OShZ0c+XEbjt34i9a873qT7DZ77srpcsUmP+l6+LKid3M/3EIzH/VAvO/dNs85qzpPmB3PmiYAvNftcD8n4K+8998GKZgs4u1rG+ZAvN/KmYN2gSOD0sWmP/qk3fQkDYS7B/WrpQrQ5dXTuy22IlrmILtDsZcSVuWwIPzKejrMTfm/7AF5r9qgfk/BX3n/ys2eK+6F2x3J0XzvyyB+T8VswZtAseHJQu2u5Oi9S1LYP6rFpj/JQvMf/XNBRrS2oLlYW0Il6GvydkFV1YOU9DOY24cpJUhWN0frG8ZZg3aBOZ/yQLzX7XA/J+KWYM2QXv57yK94QnaecyZ+V+GwJNzqgXt5r+L9IYlaOdiLfO/DIH5r1pg/pfO/Ff/XKAhrSVYHdaGcBlmDdoEDtZLFrQzObvgIG3cgmb9wfpOQ2D+lyww/1ULzH/Vgnbz30V6wxK0eydF83/cgub9wfqWL2g//12kNyzbPOZsN/N/3AKP91ULzP8pMP/VPxdoSI0FzXbGhvA0BA7WSxZ085gb82Gcgub9wfqWLzD/S2f+ayEw/1ULzP/StXlybsH8H6dgvf5gfcsWdJP/LtIbFvNfsNl8oPUtV2D+T8WsQZvA/FebXKAhNRKstzM2hMsWODlbui4mZxfMh3EJrK9qgfk/Bea/wPrqUIH5PwXmv2Czk3PWt1yB+T8VswZtAvO/ZMFm84HWt0yB+a9aYP6rbS7QkFYKNtsZG8JlChycTUFXk7ML5sM4BOsfnIP1LVVg/k/FrEGbwPwvWWD+qxaY/1Mxa9AmMP9LFmx2cs76likw/1ULzP+SBZtfrGV9yxOY/6oF5r+64AINaalgs8nZBUO4LIGDs6mYNWgTbNcfzIdhCzY7OF+wvmUJzH/VAvO/ZIH5r1pg/qsWmP+l2+YxZ9a3LIH5r1pg/pdumzvpWt+yBOa/aoH5r664QEPaV7Dd5OyCIVyGwMGZakE7/WFnPmg4gu0mZxfM/zIE5r9qQfv5bz4Mi/mvhcD8Vy0w/6dg2zspWt8yBOa/aoH5PwXmv2D7i3VVlsD8V5dcoCHtq43J2QVDeNyC5v3B+pYvaHeyZpEPGo5trpzYzfwft8DJWdWCbvLfRXrDYv4LnJzVoQLzfypmDdoE5n/JAvNftaC7/DcfhmXWoE1g/pcsaOdiXZUhMP/VNRdoSPtqa3J2wRAep2C9nbH1LVvQzcna27T4Wdqe+S/Y7ODc+pYr6C7/XaQ3LLMGbQLzv2SBk7OqBea/aoH5X7LA/Fct6Db/XaQ3LoH5X7o2L9bVuAXd57/5IBdoSEvMGrQJ1gtrQ3hcgvUHZ9a3XIFX0k/FrEGbwPwvWbDZwbn1LVPQbf67SG9cAvO/ZMHmk7PWtzyB+a9aYP6XzvzXQtB9/rtIbzwC838K2r5YS+MU9JP/LtKTCzSkLQSbhbWDtHEINjs4t75lChx8qxaY/6XbdHLW+pYnMP9VC8z/0m3zmDPrW5bA/FctMP+nwPwX9PeYGxfpjUNg/k/FrEGb4PD+YH3LEfSX/y7Skws0pA0F203WOEgbvm1ua2Z9yxI4OataYP5PwaaTs9a3LIH5r1pg/k/BNlfOWd9yBP1MzmocAvN/KmYN2gTmf8kCH3OjWmD+qxbs3R+sbxmCfvPfRXpygYa0gaCdyXoHacO27W3NrG8ZAidnVQvM/6mYNWgTmP8lC8x/1QLzfypmDdoE5n/JAk/OqRaY/6oF5n/Jgu0u1lJZAvNftWD//mB9y2D+q38u0JDWErR7JaXPnBquWYM2wfL+4CB83AInZ1ULust/82F8AvO/ZIH5r1pg/qsWmP8lC5ycVS0w/1ULzP/SbfOYM5UlMP9VC8z/KTD/1T8XaEiNBd2Er8+cGqegWX9wkDZOgZOzqgXd5r+L9MYlMP9LZ/5rIeg+/82H8QjM/9J5ck4LgfmvWmD+T4H5L+juTormwzgF5v9UzBq0Ccx/tckFGlIjQbfh6zOnxiVYrz84SBsfJ2e1EHSf/y7SG4/A/J8C81/QX/67SG8cAvN/Csx/QfePOTMfxiUw/6di1qBNYP6XLOj2Tormw7gE5r9qgfmvtrlAQ1opMHxVCzbrDw7SxmWbyVnrW46g28nZBRfpjUNg/k/FrEGbwPwvWdBf/rtIb/gC838qZg3aBM4PlCzo5zFnLtIbh8D8Vy0w/0sW9HMnRfN/HALzX7XA/FcXXKAhLRX0MzmrcQi22xk7SBuPWYM2wd79wfqWIehnclbjEJj/qgXmf8mCfvPfRXrDFpj/qgX79wfrW4Y+H3PmIr1hC8x/1QJPzpWuzzvpmv/DFpj/qgXmv7riAg1pX4En51QL2tkZO0grQ7B/f7C+4xf0OzmrYQvMf9UC87905r8WAvNftWB5f7C+Zej7MTcu0humwPxXLVjdH6zv+Jn/gvYu1jX/yxB0mv8pceb8i11fy36ucrhAQ9qXk7NaCNpdKekgbdyC5f3B+o5fn1dOaNgC81+1wPyfAvNf0P6dFM2HcQtW9wfrW4ZZgzaB+V+ywPxXLWjWH6zv+M0atAnM/5IF7V6sa/6PWzDc/E97fkUkjj+++nPn94e205C4QEPal5Ozgu7q6yBtnIJm/cH6jpv5L+juMWfmwzgF5v9UzBq0Ccz/kgXd3EnRfBinYLj5v/fk7M5J2pSqr0MnZ7W5wPwvWWD+qxY07w/Wt3yB+V+6Li7W7Sr/m4z39vpSM8EY8382g9e/Hk48sfp6/eurn2m4XKAh7WvWoE3g4KxkQTcn5xY8SB+XYL3+YH3Ha9agTWD+lyzo9jFn5sO4BOa/aoH5X7Kg2zspmg/jEpj/qgXmf+nMfy0E5r9qgfk/BV1drGU+jEtgfdUXF2hIGwscnJUs6Pbk3MLOnbiGK9js991BWpkC8790fTzmzPwfh8D8Vy0w/0vXx2POzIdxCIZe34jlrz34wXDuudXXgx+8vL1WCcz/KTD/BZtfrGV9yxSY/1Mxa9AmGFL+O95rWzDm/Lc/jI8LNKSNBPuHtYPwMvRxcm5hsRPXMAXbHYwNY5CmtgQenE9BX4+5Mf+HLTD/VQvM/ynoO/9dpDdMwXZ3Uuwn//ebdF1Mzi5ua7y43bGTtJsKhpj/O29jvv9zxvd6TfubNWgTOD4sWbDdxVrWtyzBEPNfByUYUv6vO95zDLhKMJb8d/xfDhdoSGsLloe1g/Ay9DU5u3CbLd6r7gTtPObGg/QyBKv7g/Utw6xBm8D8L1lg/qsWmP9TMWvQJmgv/12kNzxBO3dS7D7/95p03T05u7BzklbrCIZ6cm6/+jbpD9pU0G7+u0hvWIJ2LtZy/F+GYKj5r4MQDCn/m4z39hoPaJnx5P8m438XaQyTCzSktQSrw9pBeBlmDdoEDtZLFrT7mBsP0sctaNYfrO80BOZ/yQLzX7XA/FctaDf/XaQ3LEG7d1LsNv93T7quOhm/aK+mgub94eDzf93+oHUF7ee/i/SGpc3HnDn+H7fA433VgqHlf5Px3l7jAS0znvzfZPzvIo1hcoGG1FjQbGfsIHwaAgfrJQu6ecyN+TBOQfP+YH3LF5j/pTP/tRCY/6oF5n/p2jw5t7Ai/1P65p2FJoAAACAASURBVNeZ8y92fe33c6gnXU88sfpadTLeE/VNBev1h2Hk/7r9QU0F3eS/i/SGpef810AF648HrG+5giHmf5PxnuOBdc0atAmGkP+bjP8X7TUsLtCQGgnW2xk7CC9b4ORs6bqYnF0wH8YlsL6qBeb/FJj/AuurQwXm/xSY/4LNTs5tXt91F+Z88+fqSWD+T8WsQZvA/C9ZsNl8oPUtU2D+qxaY/2qbCzSklYLNdsaGcJkCB2dT0NXk7IL5MA7B+gfnYH1LFZj/UzFr0CYw/0sWmP+qBeb/VMwatAmGlP+L2xqfe271ter2xd7aeJVgs5Nzw8j/dfuDVgnMf9WCIeW/2hZsfrGW9S1PMOT8bzLeczzQpmBI+b/J+H/RXsPiAg1pqWCzydkFB+FlCYY8OFObZg3aBNv1B/Nh2ILNDs4XrG9ZAvNftcD8L1lg/qsWmP+qBUPK/93PnF71jOlFey2zzWPODjb/1+0PWiUw/1ULhpT/6sI2d9K1vmUJesn/de+cteOryXhvr/GANhEMLf83Gf/7mJthcoGGtK9gu8nZBQdpZQg8OFctaKc/7MwHDUew3eTsgvlfhsD8Vy1oP//Nh2Ex/7UQmP+qBUPL/70mW/ebpN05Oatltr2TYn/5v199m/QHrRKY/6oFQ8t/dWE8+a8uBdtdrNuPJuO9vcYDWlcwxPzfZPzv4oxhcoGGtK82JmcXHKSNW9C8P1jf8gXtTtYs8kHDsc2VE7uZ/+MWODmrWtBN/rtIb1jMf8FYJmfVl2CI+b/fZOvuSVonZ9cxa9AmGEL+N62vizTWFZj/qgXd5b/jw2GZNWgTDCH/1ZWgnYt1+7HueM8x4LqCoea/4/9yuEBD2ldbk7MLDtLGKVhvZ2x9yxZ0c7L2Ni1+lrZn/gs2Ozi3vuUKusv/R218a9OIBCz/ikikVH3V7bW/WYM2gflfsmBMk7PqWtBp/m9h2WTrYpL2xBOrLydn2xIMJf/Xqe/O/qBlAvNftaDb/HeR9rgEQ8l/daXNi3X74XivK0H3+d9+PtgfxscFGtK+Zg3aBOuFtYO0cQnWH5xZ33IFXkk/FbMGbQLzv2TBZgfn1rdMQbf57yK9cQnM/5IFm0/OWt/yBOa/aoH5XzrzXwtB9/nvnVTHIzD/p6Dti7U0TkE/+e8iPblAQ9pCsFlYO0gbh2Czg3PrW6bAwbdqgflfuk0nZ61veYIh5/+q25Uvbmt57rnVl7c331bQSf6veeeUxc/VhW0ec7ZZ/q9b90N+vutuOccfX/258/u9766j1YIh5z+Y//0Khjb+X6e+O/uDluk3/zVUQT+PuXGR3jgEQ8t/dWXWoE1weH84uPo63mtb0F/+t79Iz/4wPi7QkDYSbDdZ4yBt+La5rZn1LUsw9MlZ9Skw/6dg08lZ61uWYOj5v+yZ8rufObrzmaTaRDC0/N99An7ZSfnDX9Petrly7uDyf9nvu5Nymwj6mZzdzib5b3/YRDC0/Ifm9fUZ5OuYNWgTDCn/1bbAx9yoFgwx/yubjP89BthOsHd/GMf43zHgKkG/+b/5Ij3H/+VwgYa0tqCdyXpP4gzbtrc1s75lCMYwOau+BOb/VMwatAnM/5IFY8j//Q669zsZs2ivdQVDzP/96tu0P2gvswZtgiHl/6rf9736g5YJxnJybpP8d5HeuoIh5j+Y/wcjGE7+r39idvEz7SfY7mKtDax756xv3kFL3QuGmv87mf99CfbvD+MY/zsGXKXn/N/CJuN/jwGHyQUa0lqCdq+k9JlTwzVr0CZY3h88STduwVgmZ9WHoLv8Nx/GJzD/SxaMKf93H3Svmoxzgm5dwVDzf7/6rtMftK5gSPnf5Pd9r/6g/QRjmpzdJP9dpLeOYKj5vxfzv2tBCflvBiyzzWPO+rbuo81coLOewPxXLRhS/oP5342x5P9m438XaQzTEQe9AdJ4BN2EbzfPnFLXgmb9Yecg7VH4jMmxCDY/OD+vq43SgQm6zf9XQPpdAM6cv3LmrpbLfn5mzi1uk1YLhpT/aX4F1bnnrp58iYATT6y+z/abJcaX/4uD7kV9m/QHNRF0nv8dHAfYH7oSDCn/oarxqvru1R+0n21Ozh3MPmDd33ezoKmgn/xvNx/M/64EneX/rrshnLnrz/1+fiKb5b/9YZnxnJyDzfLfE7RNBN3cSfHwfDhz/vt/5q6WZ+76c8+f7zqc9/e9K8HQxv+wevGN/WETswZtgjHn/4knglOBwzKoO2iklG6UUvrNlNI/p5S+klL6TErpTSmlk7b83GNSSs9IKX0kpXRFSumylNI7UkoPamvbVbqg2/D1pP24BOv1B6+kHp8xXTmhbgXd5/+2J+eWXyWTUvV1+NU0Wl9g/k+B+S/oL/+9k944BOb/FJj/gu4fc2Y+jEtg/k/FrEGbwPwvWdDtnRTNh3EJzH/VAvNfbRvMAo2U0p2Bvwf+D3Br4D+A44BTgT9PKT1pw8+9GXA+8IvA7YFvAMcA/w14fUrpRdtvvcoWGL6qBZv1Bwdp47LN5Kz1LUfQ7eTsQjeL9Ba3uTv33OrL29ltK+gq/9d95vDOnzet7+7+oGVmDdoEQ8p/f9/bFvSX/+3fQcP+0LZgqOP/TfLfR5wsM2vQJhjS/MC6v+9mwSpBP48562aRnvnftqC0/Lc/bCMYe/47Blgm6OcxZ+b/OARDzX8w//sXlJD/zgUOzyAWaKSUjgT+BDgW+CBwp5zz9YBvAZ5HdZnns1NK913zcxPwBuDbgIuA/5pzPho4Gvh54CrgJ1NKP9HSP0XFCfqZnNU4BNvtjF2kMR6zBm2CvfuD9S1D0M/k7Pb2GoTvfgahzxzcVjDU/G9S3736g7YRDCn/1/19NwNWCfrN/80X6Zn/fQiGmv+wWf57e/NtBPv3h3HkvyfnVunj5NxCu4v0zP+2BSXmvxmwqWCIJ+fWzX/HAMv0eSdd83/YgiHnP5j//QqGlP+wef47Fzg8g1igATwWuCVwOXD/nPMFADnnL+acnwi8cd7u2Wt+7g8B96RaiPHDOef3zj/3KznnXwd+a97uV1JK19zy36DiBGM5Oac+BO3sjF2kUYZg//5gfccv6Hdydju7B+H7Db49SN9UMIb836++Hoy1LRhS/q/7++7JuSbMfy0EJea/+4JNBcv7Q//j/03y35Nzq/T9mJt2FumZ/20LSs1/M2ATwer+MI78dwywzHjyfyfzv21BOxfrmv9lCIaW/2D+l2QoCzQeMf/z1TnnT+7x+q/P/7xrSun2G3zuOTnn8/d4/blABm5E9cgTaYfxTM6qa0G7KyVdpDFuwfL+YH3Hr88rJ7a3cxC+avDtyvl1BWPK/90HZR6MtS0YWv43/X3f3R+0TPn57yRtE0G7d1I0/8ctWN0f+h//b5L/9odVZg3aBAed/2D+dycoOf/NgHUFzfqD+T9+swZtgiHkP2yW/+4DVgnavVjX/B+3YIj5D+Z/SY446A1IKR0N3G3+1z/bp9lfAl8Arke1kOIfGn78bNnn5pw/mVK6ALjT/HP/tOHnahI2nZw9r7Mt0kEIuhl87xykPYq2Vk6ra0Gz/mB9x208J+egHoSfeGL193PPXT74duV8U0G7k7ML3ebDuv1BTQVDzP9VB9v2h03MGrQJxpz/J54IOfewgaMVbDY5+wsr2pn/4xQMMf/B/D8YwRDyH8z/bgTmv2pB8/5g/pcvGEr+w2b5D+4Dltv0Yq1l54LM/3EKhpr/y9gfxmcId9D4DiDNv79grwY556uAC+d/vUOTD00p3QA4btnnzn14nc/VlMwatAmGNDhT24JuTs4teKeFcQnW6w/Wd7xmDdoE5n/Jgm4fc2Y+jEtg/qsWmP8lC7q9k6L5MC6B+a9aYP6XzvzXQmD+qxaY/1PQ1cVa5sO4BNZXfTnwO2gAN97x/aeWtFu8duMlbTr/3AsvvJDZrmVHD3nIQ3j84x/PFVdcwf3ud7/D3vPoRz+aRz/60Vx66aU86EEPOuz1xz3ucTz0oQ/l4osv5lGPetRhr59++unc//7358ILL+Sxj33sYa//0i/9Eve5z304//zzecITnnDY68961rO4973vzXvf+16e/OQnH/b685//fE444QTOOeccnvGMZxz2+otf/GJud7vb8aY3vYnnPe95h73+ile8gpvf/Oa89rWv5UUvetFhr7/hDW/guOOO46yzzuKss8467PW3vvWtHHXUUbzwhS/kda973WGvx+L+W+8BPrrrxWsAj5x//07gY7te/9vXwdkPqb4/4xx43ycOff1mx8ArH1B9/4Q/hfM/vfO/DNwWeMn876ft2oDPU60behtVWD8S+ARw8Y42twQWfeLlwBW7NvDbgZMBOOWUU7jyyisPefXUU0/liU98IsBh/Q6m0/e4EHjvYS/DA6juq/P3wF/v8fr/ugKOOwrOOr/62u2tj4CjrgEv/Gt43e51XG8EPkm9EvbMXa8fSVV7gKcDZ1OtBbvF/GdHAT+2+A8BH9/1/usBD6faid8aeAmz2Ue++eptb3tbXvKSqu+ddtppfPSjh3b+E044gec///kAPPKRj+QTnzi0b9/rXvfi2c9+NgAPfOADueyyyw55/aSTTuIpT3kKMPy+x/972MvwvVT/6y5h73sfnURVivdeDE9+x+GvP//74YQbwTkfg2e8a48PeBhwO+BNwPOoft8vAO5I1RdeAdwceC2wO/cuBn4UuA7w78AxVDlyQ6p+A/AY4JpUHftDAMxmb/7mJyxy77nPfS5vfnP9c4AjjzySt72t6ntPf/rTecc7Dv33HXvssZx99tkAnHHGGbzvfe875PWb3exmvPKVrwTgCU94Aueff+jvxuD63kUc6o7AdwJfBV7F4U4A/gvwZWB21uGvP+7u8NA7wcVfgEf90eGvn36v+TcXAofnHvwS1fDph6ky/sxdr98JuBXVhr+Nw/0gcFOq/Uldu9mOdx9H3fMWLpr/+RgO7XkXAW88AS64AO5yF3ja06pb2D3mMfCXf3n4f/2tb4W/+is49VS4+91nh70+yL63+MffCDhl/v3ZwBd3bfzNgfvMv38tcO5Zh75+0rfBU76v+v6UV8GVXzv09VNvC0+89/wvJ1P/vi88BHg81b58Z+4t8uH0+d+/DPw+h7sXVQf9PPCa+c+qfDhr/ioc2vMumv8ZVD3vPsD5wFk7fr7oPM96Ftz73vA7vwM/8zNVfwA4+WS44x3hrLPghBPgnHOg2uXODtm6oYz3Dut7F7F6vHcU8ND59+dQxfDO+i8d7wG3PRZecv/q+9PexKELLvcb7+10L+DZ8+93jvcW+X83qv4D8DJgV9/jO7753YxD6w51z/saVezt3Lr3nAynnw7PeQ5ceins3uV+/vPwsY9VV07827/V/eH61599s81Qxnt79r2LWD3eewjVLveDVL8gC4s+sHS8B8Sjqz+f+15480c59P/wfuO9RT4cO/8ZLB/vAfwxhx+WHg9URXsTcNmu//oJwPXn3+/ueRcB//gIePvbq/r+8i/vrG/V5qSTYL7L5ZRT4JJL6v3FbDYb1Hhvr77HLWg23vtXdu5SK+eetXq89+JT4XbHwZsuhOct9lnPpNl4D+BngP+Hau98JoceB8Je473aMSyutHovh0/9fZL6ea1P3/HPu2j+599R97wzzoD3va/6fV/U9/jj61vavvGNsGuXy21ve9qwxnu7LPpeo/He4bsUuP3frx7v3f92cOGl8Nid452Y/7lqvPcs4N5UtV3k3s76n0JV3xOprwna6YHADYAPH1L7i+Z/7jXe27l1b6AaLz7pSavHe7vHA9e/fn2L80GN9+Z29r1G473dUyy33vH9qvHeXscK3IDV470ZcCmL/K4s6r/XeG+n76O6PuzfWPwWL7YiWDLem3vWjv/aySc3Ge/Nt36eDy9/efX3wY33OLzvHTYPsNd4b6djqH61oNl476OH5hYn3Ij6ZMyq8d73Au+j3l9czM75vf3HezOqkzj1/MBs/uqy8R5U44ET2NHzZvVr+4/36jannw73vz9ceCE89rE73jw3uLnli3Y12G+8t/AIql3uX7H37/Zh470djrwGvG3nXnfZeO8MqtrvdA3gv7Nzfu/Q+Z96vFcl+Ge/+c4Zq8d7N6PeCz2Qarz4+ROajfcAfuAH4C1vqa6irna5s0O2/qDHe/v2vYvmfy4b7wF8P9VZnX8G3sXh8wB7jvd2eMUPw82vR/Px3m5vnf+513gP4HHzPwP4yCGvvIo1xnvUe5Wg6XgPHv7wqs0P/AB84hOHjruGMt7bs+9dxOrx3j2oJtG+APzh/Gc767/veG/ul74X7nPran/xhD/l8OTda7y3c3548Zt76PxerR7vVRMZh84PX0yz8d75869Dtm62zngP6l3uDBjWeG+vvsc3WD3em0/v8UrqXe6i/qvGew+5Izz+HnDF1+B+i4ONYPV4b+FxVIOS11Hnxs76fj+7x3uHOgm4baPx3mzXOy8C/sf5q8d7t7gF/OAPwq1vfeh4AGaDGu8Nre91nXu7pXzA9zVKKT2c+pD7Gjnnr+/T7lVUs1tvzzn/9wafe2+qU/oA/ynn/E/7tHsmVcJ+NOd8u33anEZ1dh6qM3YX7tVO2tJxVKmv6bIPTJv1nzbrP23WX/aBabP+02b9p836yz4wbdZ/2qz/tFn/abP+sg9Mxy1zzsfv/uEQ7qCRVjfZ+nO3WoWSc34J9W0UpE6klM7LOd/9oLdDB8c+MG3Wf9qs/7RZf9kHps36T5v1nzbrL/vAtFn/abP+02b9p836yz6gqx30BgCX7/j+yH1bVTez292+6ecetW+r9T9XkiRJkiRJkiRJkiRpLUNYoLHzQbw3WdJu8dolB/y5kiRJkiRJkiRJkiRJaxnCAo1/oH4EyR33apBSuhpwu/lfP9zkQ3POn6V+fs+enzt3h3U+V+qQj9GRfWDarP+0Wf9ps/6yD0yb9Z826z9t1l/2gWmz/tNm/afN+k+b9Zd9YOJSznl1q643IqW/Au4B/G7O+XF7vH4v4L3zv94+53xhw899HfBg4E9zzqfs8fpNgYuBBHx/zvnPNvwnSJIkSZIkSZIkSZIk7WsId9AAePX8z0eklG68x+tPnP/5gaaLM3Z97n1TSnfZ4/WfpVqccQlw7hqfK0mSJEmSJEmSJEmS1NhQFmi8GPg4cDTw5pTSHQBSSkenlH4NeMC83ZN3vzGllOdfZ+7xuX8MvJ/q3/lHKaXvmr/nWiml04EnzNs9Nef81Tb/QZIkSZIkSZIkSZIkSQtHHPQGAOScr0wp/RDwDuCuwAUppS8C16VaXJGBJ+ec377m5+aU0oOAdwHfBrwvpXQ5cG3qf/vv5pxf2tI/RZIkSZIkSZIkSZIk6TBDuYMGOecPAXcCfgv4GHAt4DLgLcDJOefnbPi5nwBOAJ4F/APVwowvUT3S5CE558dtv/WSJEmSJEmSJEmSJEn7Sznng94GSZIkSZIkSZIkSZKkog3mDhqSJEmSJEmSJEmSJEmlOuKgN0CaupTSdYAbAscBR1I92ufSnPOnD3TD1JuU0tWBY5nXP+d8+QFvknqQUjoW+E7gxuz6/ad6JNeHsre5mgQzYJrMAKWUvoO9639hzvlzB7lt6of5P13uA+Q+YNqs/3SZ/9M0H/PdFfge4J7sX/93A3+Rc/7MAW2qOpRSugHw3azuAx/MOV91UNupblj/abP+2ouPOJF6Nh+Unwrcl2pgfgcg7dH0C8B7qIL59Tnnf+ltI9WplNJdqOt/T6od8k5fBS6kqv27gbfknL/c60aqEymlOwM/TlX/269o/iXgvcAfUGXAlR1vnnpiBkyXGTBtKaXjgEdQ1f/ewDH7NM3AR6h+/1+bc35nP1uorpn/0+Y+YNrcB0yb9Z8283+6Ukp3BB4DPJJqUS7sPQcM1e//wjuB3wPOzjl/pbstVNdSSt9KVf8fB+6886VdTXfW/8vAG4D/m3P+i263UF2y/tNm/bWKCzSknqSUbgn8FPAo4Hj2H5Dvludf5wIvzTm/rpstVJdSStcFfozqwOwuix+veNsioL8MvBZ4Wc75/d1sobqUUnoY8LNUV0xAXfsvUq2U/RzwFeBb5l/HA1eft8nA5cCrgGfnnC/uabPVIjNg2syAaUsp3Rv4GeD+wDU49Hf/G1SLchf1P3LX2zPwMeClwAs8WT8+5r/cB0yb+4Bps/7TZv5PV0rprsCzgftQ1/1LwAeBD1FdLb27/rcC7gHcZt4+z9v8KvDbLtQYl5TSTYCnAj8KXJO6H/wry/vACcC1520z1VX1Z+acX9/Xtmt71n/arL+acoGG1LGU0vHAU4DTqAIZqiB+D3Aeqwfm9wBOBI6mCuYLgF/MOb+pt3+ENpZSuibVwpwzgG+l2iFfBryf5vX/z/P3ZeBPgTNyzn/b579Dm0kpnQI8i2qVbAI+SbUK9j3AeTnni/Z535FUkzj3oLrjzvdRTdR8BXgR8Kyc82Vdb7+2ZwZMmxkwbfMr5p4N/ABV/b8C/Bk7xoC7b2M+z4xbUf/+3w/4dqrf/88CzwR+N+f8tX7+FdqU+S/3AdPmPmDarP+0mf/TllJ6LfAgqtr/C9XdUF4H/G2Tx9eklK5PlR0PA06mekT9p4BH55zf0dV2qz0ppV+lOg44kmrB9Z9Q9YH35JwvXfHeq1MdA5wK/AjVnbcz8DfAT+acP9DhpqsF1n/arL/W4QINqWMppS8B16EalL8ceE3O+R/X/IxrUQXzw4EfBK4G/FzO+Tda3ly1LKX0ceBmVFc+nA28BnjHOs8SSyndlOrA7GHAfwGuAh6bc/699rdYbUopXUV1ZdRrgJcB797kebLzhV4/Avw08G3A03LOv9LmtqobZsC0mQHTllL6OtWY7d1Utyj+o5zzlzb4nLtRjQH/J9Ut0X855/zMNrdV7TP/5T5g2twHTJv1nzbzf9rm9Q/gGTnn/2/Lz/pWqvr/FPCb1n8c5n3gn4DnUJ0H2PhRRSmlOwE/R3U88Az7wPBZ/2mz/lqHCzSkjqWUPkJ15cSrcs7faOHzvh14EvBPOefnbPt56lZK6TLgN6kOpL7QwuedCPwi8M6c89O3/Tx1K6X0Eqrbkf5LS593NaoJupxzflUbn6lumQHTZgZMW0rp7VQH0e9q6fOOAf4P8Lmc8wva+Ex1x/yX+4Bpcx8wbdZ/2sz/aUsp3Svn/L6WP/O6wC1zzhe0+bnqRkrp4cAfrLMwu8Fn3gq4ac75PW19prph/afN+msdLtCQOpZSSpuslD+oz1W7UkrX6eJZsV19rqR2mQGSNE3mvyRJkiRJkvbiAg1JkiRJkiRJkiRJkqSOXe2gN0CSJEmSJEmSJEmSJKl0Rxz0BkiSNCXzZ4d+N3AH4MbAdecvXQ5cAnwY+Iuc8+UHs4WSumQGKKV0e5bUP+f8Dwe1bZK65T5A7gOmzfpL05NSOg54APDfWL7/fwfwRznnSw9iO9WdlNIRVPVv0gfOzTl//SC2U92w/tNm/bWMjziRBi6ldAzwRiDnnE866O1Rv1JK1wF+m6r+jzno7dHmUkrfAfwKcCpwzRXNvwr8CXBmzvkjXW+bhssMKIcZMG0ppWOBJwEPozogX+YS4FXAr+WcL+t62zRM5n9Z3AdMm/uAabP+Wte8z3wWuCrn7MWVI5VSuhrwNOBngCMXP96n+eIEzZXAb1CNAa7qdgvVh5TSY6jGgDda/Gifpos+cAnwyznn/9v1tql71n/arL9WcYGGNHA7DsxyzvnqB7096pf1L0NK6RHAy6gm5BeDsX8DPgVcMf/7UcBNgBvseOt/AP8z5/yanjZVA2MGlMEMmLaU0gw4G7g+hx6Qf55D63/9Ha9l4HPAA3PO7+xhMzUw5n853AdMm/uAabP+2oRjgDKklP6YamFmAr4AvJvqKum99v93AL4HuB5VBrwp5/w/+t5mtSul9ALgJ6n6QAYuYHkfuOOOti/KOf9U39us9lj/abP+asIFGtLAeWA2bdZ//FJKdwX+kuqxYh+guhri7ftdETWv+X2BJwD3AL4GfFfO+YP9bLGGxAwYPzNg2lJKtwHOB64DXAz8LvBnVLcx/8quttemOjC/L9WB/C2obnl5Qs75Y31utw6e+V8G9wHT5j5g2qy/NuUYYPxSSj8BvBj4CtUddF6y+/d+j/dcCzgNeA5wbeC0nPPvdb2t6kZK6YHA64GrgN8Cnptz/tSK99wEOB34aaoTtQ/OOf9h19uq9ln/abP+asoFGtLAeWA2bdZ//FJKrwEeCrwSeHTT21SmlBLwcuCRwB/knB/e3VZqqMyA8TMDpi2l9FLgMcDbqa6E/XLD9x0F/BFwMvCynPNp3W2lhsj8L4P7gGlzHzBt1n/aUkqrHme1zHHAJ3AMMFoppfcC96Ta979izff+KHAW8L6c83/tYPPUg5TSOcCJwM/nnJ+35ntPB34deEfO+eQutk/dsv7TZv3VlAs0pB7MB+abOgK4Ox6YjVZK6dVbvP2awAOw/qOVUvokcEPgRjnnS9d873HAZ4BP55xv2sX2qXtmwLSZAdOWUvo4cDPgVjnni9d87y2Ai4B/zTnfqv2tU9fMf7kPmDb3AdNm/actpfSNbT8CxwCjlVL6HNVdMI7Ka558SSldDfgy8JWc87d0sX3qXkrpUuBo4Oic81fXfO81qe6i9MWc83FdbJ+6Zf2nzfqrKRdoSD1IKV1F9fyotKrtEh6YjZT1n7aU0leAK3LO37rh+z8HXDvnfGS7W6a+mAHTZgZMW0rpSuBK6z9N5r/cB0yb+4Bps/7TNh8DbMsxwEillL4EkHM+esP3Xw5clXM+ptUNU29SSl8Gvp5zvt6G7/8icPWc83Xa3TL1wfpPm/VXU0cc9AZIE/FV4BrAK4B1nx96FPBzrW+R+vQN4GpUz5v99JrvvRbwI61vkfr0GeBmKaWb5Zw/sc4bU0o3B65H9cxijZcZMG1mwLT9O3DDlNKxfIX4+wAAIABJREFUOefL1nnj/BEXx7B+bmg4zH+5D5g29wHTZv2n7YtUV88+GfjLNd97PeCNrW+R+vRPwJ1TSqfknN+2zhtTSqdQzQWf38mWqS8fB26XUrpnzvn967wxpXRP4LrARzrZMvXB+k+b9VcjLtCQ+vG3wN2Av8o5v2CdN84PzF2gMW4fBu4EnJ1zftk6b5zX38n5cTsH+HHgpSmlB+acr2jyppTSkcBLqa68/fMOt0/dMwOmzQyYtncDDwZ+I6X06DVvcfwb8z/f1f5mqSfmv9wHTJv7gGmz/tP2AWBGdffqd67zxvkYQOP2GuAuwO/Pf//f0uRNKaX7AS+n2v+/psPtU/f+kGqB1qtSSj+cc/67Jm9KKf1n4NVUfeAPO9w+dcv6T5v1VyM+4kTqQUrpBcDjgJfnnH98zfceC3wWb204Wimll1FNzL4s5/zYNd9r/UcupfTtVFc+HEl1BeQLgbcDH979HLr5c+buAJwMPB64BXAFcELO+Z/73G61xwyYNjNg2lJKdwPeB1wd+GuqEy5/nnP+3D7tvwW4D/CzwHcCXwfulXP+m362WG0y/+U+YNrcB0yb9Z+2lNJzgJ8H/jDn/KA13+sYYORSSteg+v2/K9WJtgup9v8XAJ8Crpz//CjgJsAdqfb/t6d6NN55wL1zzl/vfePVipTS0cCHgFtR3VXvHKq76q3qA/el2m98jGoMeHnf267tWf9ps/5qygUaUg9SSj8O/B7VRNyd1nyvB2Yjl1J6LPAi4IM557ut+V7rX4CU0snA66luU7tzx/vvHDoo2/l84kR1W9QH5ZzP6WlT1QEzQGbAtKWUfgx4CdXj7hb1/zR7H5jfaPE24GvAT+Scf7/XDVZrzH+B+4Cpcx8wbdZ/ulJKDwJeB1ycc77lmu91DFCA+Qm6l1HdSQcOHQPs+Zb5n6+j+v3/Ulfbpn6klG4KnE216A6a94H3U40BP9nVtql71n/arL+acIGG1IP5lVPPpboC4sHr3NpyfiXVwwByzi/vZgvVpfntqV4B/AfwXWvW/0iqqy7IOT+tmy1UH1JKNwTOAB4K3HBF888AfwA8J+f8ma63Td0yAwRmwNTNc+DpwClUJ2mW+RrwVuCpOee/7Xrb1B3zXwvuA6bNfcC0Wf9pmi+yeCTVCZnfXnMMkKjuokTO+ePdbKH6klI6AXg4cCLVHTKus6vJl4GPAOcCr8k5n9/vFqprKaUfAh5B9dij4/ZpdilVH3h1zvmPe9o09cD6T5v11zIu0JAkqUfzyZY7zL9uTHVwnoDLqa6k+jDwkTWfUSxpJMyAaUspXQ/4bpbX/y9yzl88sI2U1Bn3AdPmPmDarL8kgJTSMez4/fd3flpSSsezxz4g53zpgW6YemH9p836azcXaEiSJEmSJEmSJEmSJHXsage9AZIkSZIkSZIkSZIkSaU74qA3QJIkSZIkSZKmKKV0darnk5Nz/v0D3hxJktSTlNK1gbcCOed80kFvj/qTUjoWOAm4FfAl4P0557850I1Sr3zEiTRwKaXvnX/7YZ9HNT0ppVvMv70k5/y1A90Y9S6ldB3gt6kG6Y856O1R/8yAaTMDypJSuhbwHcDVgY/mnL/U4D0PBo70ZM30mP9yH1AW9wFaZv77/iXgqpyzF9NNXErpl+ff/nXO+W0HujHq3fyE3WcxD4ow3/8/BLgb1Rjg74E35JwvW/G+s4Hre8K+fDvGADnnfPWD3h61I6X0LcCvAw8AjgQ+CPxCzvnd89dPm79+3V1vfQ/w8JzzJ3rcXB0QF2hIA5dSugrIwBXAC4Hn5pw/e7Bbpb6klL4x//aTwLOB38s5f/UAN0k92nFg7iB9osyAaTMDyjC/KvaZwP8Cjpr/+GvA2cAZOed/XfLeS4DjnZydHvNf7gPK4D5ATXhyRjvtmAcEOA94Ws75rQe4SeqR+/9ypJTuCLwJuOWuly4HnpJz/q0l770EuIF9YJx2XHDbxJHA26hyfwakxQs553e1u2XqQ0rpGsD7gbuwo57AfwAnAscDb9zx2meB4+Z/z8A/AnfNOV/R1zbrYPz/7d15mC1Vee/x7+8cQOAwDwKCKOYqikZFxTiQyFFUjIii3qCCERQHkHiVaNQkBBSVqNFrUFQUCSDGkTjkximgYsAJEDUMcnHACWSUeYb3/rGqL5um+5zuPr33Pt31/TxPP/t0Va3qt89bvXbtqrfWskBDWs11H8wG3Qh8uKpeP454NFqT8l/AxcA7q+oDYwpJI+QHc9kH9Jt9wOKQ5DPA87j7B3Nof9PXAvtX1UnTtPXCXE/Z/8v3gMXB94D+SvKL2WxOu4FXwK8GlldV/dG8BqbV3hTXAQv4YVXtNI54NFq+/y8OSTYE/hvYplt0EXAl8DDgXrS/638HXlhVN03R3nOABWxSod1clUW6C1OSg4AjgVtpD1ucBTwGeBNwKrA+sBPwFuB9VXV9knWAl3fbrw28oareO4bwNUIWaEiruSRP6v65FfAkWiXlgzxB64ckL+n+OZH/JwDrmf9+8IO57AP6zT5g4UvybODztIszxwPH0S7M7QK8kXbB7k7g4KmeoPLCXH/Z/8v3gIXP94B+G7g5M7k4Zzb8+++xJFty13XAP62qh403Is1UkrVWoflmwG/x739BS/K3wNuAy4C9qurUbvnGwD8Af0V7f/gO8MyqunZSe88BFjDPAfotyanAzsCBVXX0wPIDgIkHLv6pqt44Rdu/pk198p2q2nkU8Wp8LNCQFqAkm1XVFeOOQ6OXZAltiKszxx2LZibJv65C87Voc9V5Ui7APmAhsg/otyRfBHYHjqqq10xatww4GngR7eLNoVX1tknbeGFOgP3/QuV7QL/5HtBvAzdnvg98bSWbrwW8udv+rYMrquotQwlQ0tAMTFU3513g+/+CluT7tCfm966qT02x/mnAvwIbA2cDT6+qKwfWew6wgCW5jja13feBv6aNhjidZcA5tHOABwyuqKpfTdlCq7UkV9D+tpdV1c0Dy9cBbqDleoequmCKthsCfwD+UFWbjihkjYkFGpIkDZFV01K/2Qf0W5KLgS2Aravq99Ns8ybgHbTj5G5PUXhhTlrYfA/oN98D+i3Ja2nFFsuAfwNeV1W/nWbbZcB1+PcuLQpTTFMzF/YHC1iSq2n9/3pVdcs02+wAfJ02Yt75wK4T5wueAyxsSbYGjgL2oL2/HwocWVX36Bs8B1h8ktwKXF9Vm0yx7ipgQyYVb0za5hpgnapaldGYtABYoCFJ0hAluQ1YQvvQNeWF2RW4F/ACPEmXFiz7gH5LcgtwU1VttJLtXkm7gBPgQ1V1ULfcC3PSAuZ7QL/5HqDuBs0HgGcD19MKNv53Vd0xaTtvzkiLSHdzfn3gb4HvzbL5hsAXsD9Y0LpzgOtX9gR8ku2AU4D7Az8DnlxVv/UcYHFIsgftPGBr4L9pU158Z9I2ngMsMgNFGOsOFmglWZs2ggasfASNq6cq8NDissa4A5D6Ksm6wINpVbLrdYuvBy4Bzq+qm8YVm0YjySZMkf+qump8UWkIzgMeBpxUVcfMpmE39/gLhhKVxs4+oDfsA/rtJmDtlW1UVUcnuRE4FjggyTpV9bKhR6exsP/vFd8D+s33gJ6rqt8Bew7coHkn8JIkr66qb483Oo1SkvVo89HvwNTXAc8DTquq68cToebZWcAutIdjT51Nw+79XwvfFcAWSdatqhun26iqfpnkz2hFGg8E/ivJU0YVpIarqr6U5BTgbcBBtPweB/zN4JQ2WnTOAx4P7E07v5+wN90UVsB+wJumaDvxGeAexRtafCzQkEYoyZrAK2id8WOZfqjb6uaqOxE4pqpuG1GIGrIkz6Tl/8nA5tNsczntxPwTVfXlEYan4TiDdmF+J2BWF+a1+NgH9JJ9QL9dCDwqycOr6icr2rCqPp7kZtr5377d0xU+QbNI2P/3lu8B/eZ7gIC73aA5HPgr4JtJTgTeUFWXjTc6DVOSh9BGTtkdWNlQ5bcm+RJwWFWdP/TgNExnAMuBx4w7EI3NOcCWwJ8BX13Rht2IGU8C/hN4KPBt7iri0gJXVTcAr0tyAnA07cb8c5K8uao+Mt7oNCSfBZ4AvD/JlsCPgB2Bv6MVZ7wbOLibyuTIqrqhO/d/Ge1csYB/H0vkGimnOJFGpJtX7ovAA5j5HMQF/Bx4th/OFrbuzfgzwBMnFq2kyUTnfBqw13RzFmv11w1Z/CHg7Kp69CzbbgpcjsPcLXj2Af1lH9BvSY4EXg28tareMsM2z6L1F2vRPV1h/hcu+/9+8z2g33wP0FSS7Ei7QfMY4Grg74GPA9dgvheVJHvTivMm/p4BLgMuBiaeqF8XuA9w74GmtwAvrapPjihUzbMkz6f15b+pqvvNsq3v/4tAkjcDbwf+tar2mWGbTWhFGjt2izwGFpkkoRVqHk4rwjkDeCPwTcz3opFkLVpu/5i7Pt9DOxf4HPBi2pQ3f9StvwLYlDY1ZoDfAQ+rqmtGGLbGwAINaQSS3JvW6W5OG77wk8DXaMMdTXwwK2AZ7YPZDsDTgBfS5iy8DPjjqrp85MFrlXVDWf4I2K5bdDL3zD/c9cF8Iv+70t6Yfwbs2FXcaoFJ8se0C263AI+rWbzxJlkH+BuAmV7U1erHPqDf7AP6LcluwJdpH7AfMNNR0ZLsCnyedm7ohZoFyv5fvgf0m+8Bmk53g+Yg2g2a9YFzaaPtmO9FIsmjgO/RRq8+C3gv8PXphrTvbso/DXgtbdSl22jvG2ePJmLNpy6f+9Cu9b5/lu//AbYFqKpfDSdCDVuS7YHzgVuBB1fVRTNstwHt3OEJ+J6waCXZijb12Z7AnbTPfuZ7EUmyGfB+Wo7Xok19+DHgjVV1U5IHAV8AHjyp6fnA831Yux8s0JBGIMn7gNcAZwN7dPOQzqTdfWjDGT0S+OeqOnh4UWpYkhxOG8LqIuC5VfWjGbZ7BO3C3P2At1XVoUMLUtLQ2AdI/ZVkKfBh2sX5o6rqzFm0fSLtxk2qavmQQtQQ2f9L/eZ7gFZm0g0a8ObMopHkk8BedNMWVdWdM2wX4Hjazf1PVdWLhhelpGFK8gRgTeDCqrp4Fu3WBZ4HLKmq44cVn8Yvye7AUcB98RxgUepG09gYuGxysV6SJcDTgYfQCnV+DJw603MGLXwWaEgjkORntCfnHjbb6rduapRzgJ9X1QOHEZ+GK8l5wPbAE6vqe7Ns+3jgdOD8qnroMOKTNFz2AZLUT/b/kqSZSPIk4P4A3oxbHJL8DtgC2LKqrphl282AS4HfV9XWw4hPkiRJ42WBhjQCSW4Cbq6qjefY/mpgrapad34j0ygkuQG4vao2nGP7a4GlVbVsfiOTNAr2AZLUT/b/kiT1U5KbgRurapM5tv8DsHZVrTO/kUmSJGl1sGTcAUg9cS2wXpJZX1zt5q5eBlw371FpVG4C1k6y5mwbdsNg3avbh6SFyT5AkvrJ/l+SpH66FNgwyTazbZjkvsCGwGXzHpUkSZJWCxZoSKPxA9rf2z/Moe0hwFLg+/MakUbpJ7R5hw+aQ9uDaPMVzmjOci0uSbbtvmZ9Y0erFfsAzYl9QL+Z/0XB/l9zZh/Qb+a/38z/onAyEOCjSWY8Gm6SdYCPAgX855Bi02osyT90X88YdywaD4+BfjP//Wb++8UpTqQRSLIr8HXaB6zPA++uqhUWXCTZCXgD8Lxu0VOr6htDDVRDkeR/Ap8G7gDeB7y3qi5ZSZstgYOB19GKe/aqqs8NO1atXpLc0f3zd8ARwMeq6tYxhqQ5sA/QXNkH9Jv5X/js/7Uq7AP6zfz3m/lf+JL8D1qR5TrAb4AP0q4Lnjc5l92oWTsATwUOBLYFbgQeWVU/H2XcGr8kd9KuHwOcCbylqr48xpA0Yh4D/Wb++83894sFGtKIJPk74HDu6mCvAc4HLqYNXVzAusB9gIfQhjOEVnF/SFW9faQBa14l+QiwPy3PBZwDnMvU+X9o97WElv+PVNWrxhC2xqw7KZtQtOPlnVX1gTGFpDmyD9Bc2Af0m/lfHOz/NVf2Af1m/vvN/C8OSZ4KfBbYgLuuBQJcxd3PATYZbEabJvn5VXXyiELVamTS3z+04+SHVbXTOOLR6HkM9Jv57zfz3y8WaEgjlOTPgXcAD5+0auIPMZOW/xj426r6yrBj0/AlOQA4FLh3t2i6DnjiOLgMOKyqPjzs2LR6SvKS7p9bAU8CngCsV1VLxxeV5so+QLNlH9Bv5n/xsP/XXNgH9Jv57zfzv3gk2QJ4M7AXsMVKNr8U+BTwj1V16bBj0+qtG1XtScAuwJ9W1cPGG5FGzWOg38x/v5n/frBAQxqDJDsAy2lDGG4FLKNdkL2e9mTEecA3q+r8sQWpoeiGrtyVGeQfOMVhTDUoyRLgUVV15rhj0dzYB2hV2Af0m/lf2Oz/tarsA/rN/Peb+V/4koT2/r+ic4Dzywv1kiRJvWCBhiRJkiRJkiRJkiRJ0pAtGXcAkiRJkiRJkiRJkiRJi90a4w5AkqQ+SrIJbWjT9bpF1wOXVNVV44tKq4Mk2wJU1a/HHYuGxz6g38y/1G/2Af1m/vvN/GtCkjWARwP3BQr4FfDDqrpzrIFp3iVZD9iZu6a4udvfP22Km9Oq6vrxRKhh8xjoN/Pfb+Zf03GKE2k1181X/QKAqjphzOFoDJJsDSz1Zu3Cl+SZwN7Ak4HNp9nscuAU4BNV9eVRxabVQ5JlwHXAnVVlIe0iYx/Qb+a/v5KsDyynzTd/dlX9dGDdUmB/4NnA/WnvAd8HPji4nRY++4B+M//9Zv77J8mmwPbAlVV1waR1Ad4EvB7YaFLTS4G3VtWHRxKohirJQ4C3ArsDa61k81uBLwGHVdX5w45No+Ex0G/mv9/Mv1bGAg1pNdd9qLscb9YteEl2A95Ae0JiKXAOcCzwsRU9IZHkEmBz879wJdkS+AzwxIlFK2ky8eZ8GrBXVf1+WLFp9TJQoFFVtXTc8Wh+2Af0m/nvtyTPAE7k7jdgPlJVB3SF2F8H/pS7HxcF3A68rKpOHFmwGgr7gH4z//1m/vsryduANwOHVNU7Jq37NPB8pj8eCjiqql4z3Cg1TEn2Bo6h3ZSbyPVlwMXAjd336wL3Ae490PQW4KVV9ckRhaoh8RjoN/Pfb+ZfM2GBhrSaGyjQ8GbdApbkYODdE98OrCrgLOAFVfWLadpeAtzb/C9M3TBmPwK26xadDHyNNnzZVCdlOwBPA3YFlgA/A3asqhtGGLbmUZJjZ7H5GsA+tL7h+IHlVVUvm9fANBL2Af1m/vstyQNoBblrd4uuAjah9fGv7/79d8DNwH8AvwDuB+wGbEB7imZHn6BZuOwD+s3895v577ckpwOPAx46aeSsvYGPd99+DngvcAHt3OAhwMHA87rvn1lVXx1l3JofSR4FfI/2+f4sWp6/XlVXTrP9prS//9cCOwG3AY+rqrNHE7Hmm8dAv5n/fjP/mikLNKTVnAUaC1+SRwJn0EbNOB/4NHAl8CRgz275VcCzquq7U7S3QGMBS3I47ebLRcBzq+pHM2z3CODztBs1b6uqQ4cWpIYqyZ3c9TTcjJp0rzXwve8BC5R9QL+Z/35LciRwEPBjYM+quijJdrTcrke7KXcbsGtVXTjQbhvgK7SbdUdX1YEjD17zwj6g38x/v5n/fuuu42wGrF1VdwwsPwXYBTiiqv5+mrZvp42+8X+qao8RhKt5luSTwF60UdT2XdGouZPahfagxj7Ap6rqRcOLUsPkMdBv5r/fzL9mygINaQSSvGIVmi8D3oM35xas7un5fYFvALtX1c0D6x4FfII2N+kNwHOq6pRJ7S3QWMCSnEfL7xOr6nuzbPt44HTg/Kp66DDi0/ANFGj8lDac3YosBXbutv/24IqqWj6UADVU9gH9Zv77Lcm5wIOBJ1TV9weWT+S2gBdW1WemaLsL7dzx/1bVg0cTseabfUC/mf9+M//9luRm4Pqq2mzS8iuB9YFNq+q6adquT3uI56qq2mLowWreJfkdsAWwZVVdMcu2mwGXAr+vqq2HEZ+Gz2Og38x/v5l/zZQFGtIIzOHp6XvsAgs0FqwkP6MNa/qIqjpnivXrAZ8EnkmbZ2yvqvrSwHoLNBawJDcAt1fVhnNsfy2wtKqWzW9kGpUkXwD2AK4BDqHNJzzle0LXH1yLff6iYR/Qb+a/35JcD6xRVWtPWh7gJmBNYOuq+v0UbdegDX9/a1WtN4p4Nf/sA/rN/Peb+e+3JL8BtgI2qKobB5bfCNw4uXBjivZXAOtNPofQwtAV6NxYVZvMsf0faKOvrDO/kWlUPAb6zfz3m/nXTC0ZdwBSz1wK/HqWX78dS6SaT/cBbpqqOAOgqq4HngP8K3Av4LNJ9hphfBqum4C1k6w524ZJ1qIdEzfNe1Qamap6Dm06o2uBfwbOSPLY6TYfWWAaFfuAfjP//bYGcOvkhV2R3m3dtzdOXt9tc3u3zRpDi06jYB/Qb+a/38x/v51Be9jquZOW/xLYMMm0N226dRux8tEXtfq6lJbnbWbbMMl9gQ0x/wudx0C/mf9+M/+aEQs0pNG4qHt9XVVtN5sv4NFjjFvzo1jJTdduTtIXAx+lPU15YpJ9hx+aRuAntJsrB82h7UG042FG8xVr9VVVXwR2AI4EHgF8J8nRSTYeb2QaAfuAfjP//XYZsCzJtoMLu+8nnoh++FQNkzwIWAeY1ZCoWu3YB/Sb+e83899vx9EKNN6V5I8Glp9Am9byLSto+1baNftvDSs4Dd3JtPx/NMm6M22UZB3adcEC/nNIsWk0PAb6zfz3m/nXjFigIY3GGd3rTnNo69PUC9+vgXW7CshpVfNK4P20D+zHJDlgFAFqqD7EXRdm3p1kq5U1SLJlkncB76T1AR8ecowagaq6oapeB/wJcDbwcuCCJPuNNzINmX1Av5n/fvte9/r2blqTielN3tEtvwI4YvLT1d0276Ll/wy0kNkH9Jv57zfz32PdtLVfArYEzkryxiRbAu8BTgUOTHJykj2SbJ/kQd2/TwEOoI3A9U9j+wW0qo6gjZL2NOC8JH+T5JHd6Dh3k2Stbt0bgPOAp3ZtjxhpxJpvHgP9Zv77zfxrRjLNFOiS5lHXwb4TOLWqls+y7abA5bT790uHEZ+GK8kJwN7AgVV19AzbHAG8kbtG34j5X7iSfATYn7vyeQ5wLnAxbdjaAtalTYfz0O5rCe2C3keq6lVjCFtD1N18+yvgcGA94LvAgcDPgeuwz19U7AP6zfz3V5LlwCm0HF9Ie5r64cADgZtp0199lXZMfIg27Pm2wEuBx3btnldVXxh58Jo39gH9Zv77zfz3W5K1gZOAZ3DXw1e/BH4H7DxdM+B2YP+qOmHoQWpokjwV+CywAXd/+O4q7v73PzjdTWhToz6/qk4eUagaEo+BfjP//Wb+NRMWaEgjkGQX4BvA9VW1wSzbbkCruq/ZFndo9ZBkH9owlmdV1YxHUUlyCG3YywLwZu3C1o2Gcihw727RdG/A6V4vAw6rKp+aWsSS3Ic2as6etAtxxwKvwAKNRcc+oN/Mf38NFN1CV3Tbve5XVSckOYZWkDH5mAjw2araa2TBamjsA/rN/Peb+VeSlwNvArZbyaZ30oZFf3NVnT30wDR0SbYA3gzsBWyxks0vBT4F/GNVXTrs2DQaHgP9Zv77zfxrZSzQkEage1J6A4CqumbM4WjEkqwP/JA2/+w+VXX6LNq+Fngv3qxdFLqhzHYFlgM7AFvR5qAPcD3tSarzgG8Cp1TVrWMKVSOWZHfgKGBiKiT/5hch+4B+M//91T09szftosxFwDFVdVa3bg1aAcdrgM27Jr+mDWv/7qq6Y+QBayjsA/rN/Peb+RdAkscDj6eNpLURbbSUifyfC3yjqi4bX4Qalu668A6s+O///PJGzaLlMdBv5r/fzL+mY4GGJEnSaiDJurSbdNsCVNV+441IkjRKSTYB7qyqq8cdiyRJkiRJkobDAg1JkiRJkiRJkiRJkqQhW2PcAUiSJElqkkyMoPLrccei0TP/kiRJ0uLXTXP3aNo0pwX8CvhhVd051sA0Mh4D/Wb++838CxxBQxq6JA+oql/M8z6XANt48X71Z/61qpJsDSw13wuTfYBmI8ky4DraFAcWUveM+V9c7P81KMn6wHLafMNnV9VPB9YtBfYHng3cn9YPfB/44OB2WrjMf7+Zf81WkrWAFwBU1QljDkdzkGRTYHvgyqq6YNK6AG8CXg9sNKnppcBbq+rDIwlUQ+Mx0G/mv9/Mv2ZqybgDkHrgp0mOT7L9qu4oyZpJXgFcCOy7ypFpFMy/AEiyW5JTklyd5Lok303y8u5my4qcCczrDR6NlH2A5iLjDkBjZf4XB/t/AZDkGcBFwOeBE4Fzk3yoW7cWcArwQWA34MHATsCrgR8n2WccMWv+mP9+M/+ao/WB44BjxxyH5u51wH8Bz5ti3aeAtwEb0877B7+2BI5KcuSI4tTweAz0m/nvN/OvGXEEDWnIkpwGPAG4k9Yxfwr4XFVdOcP2AXahVc8/F9gEuAF4cVV9cRgxa/6YfwEkORh498S3A6sKOAt4wXRP2Sa5BLh3VS0dbpQaBvsAJZnNhdU1gH1ofcPxA8urql42r4FpJMx/f9n/C9pIKsA5wNrdoqtouSzaU1ObAH8H3Az8B60o9360m7UbALcCO1bV+aONXPPB/Peb+ddcdU/eXk47B/Q6wAKU5HTgccBDJ42aszfw8e7bzwHvBS6g9QsPAQ6m3dAr4JlV9dVRxq354zHQb+a/38y/ZsoCDWkEkuwBvAPYgdbBFu0JuLOAnwBXAH+gfQDfiFZBtx3wGGBH2lCYAW4DjgYOr6rLR/tbaK7Mf78leSRwBrAUOB/4NHAl8CRgz275VcCzquq7U7S3QGOBsw/otyR30nI+4ybdaw1878XZBcr895v9v7qnnw4CfgzsWVUXJdmO9jT9esC6tPyBB+K5AAASJUlEQVTuWlUXDrTbBvgK7dg5uqoOHHnwWmXmv9/Mv+bKAo2Fr7uOsxmwdlXdMbD8FFoB7hFV9ffTtH078Gbg/1TVHiMIV0PgMdBv5r/fzL9mygINaUS6p+B2o80vujuwZrdqRX+EExfpf0Eb2vBfquqSoQWpoTH//dU9Pb0v8A1g96q6eWDdo4BP0OaluwF4TlWdMqm9BRqLgH1Afw3coP8pcNlKNl8K7Nxt/+3BFVW1fCgBaqjMv+z/+y3JubRpC55QVd8fWP544HTacfDCqvrMFG13oZ0//t+qevBoItZ8Mv/9Zv77rZuabK6WAe/BAo0FK8nNwPVVtdmk5VfSprDZtKqum6bt+rSHeK6qqi2GHqyGwmOg38x/v5l/zZQFGtIYJNkEWA48EXgssBWtqu5etA74CtrwRqcDp1XVmWMKVUNg/vslyc9oT8M+oqrOmWL9esAngWcCtwB7VdWXBtZboLHI2Af0S5IvAHsA1wCHAEfVNCfgXX9wLV6MXTTMvwbZ//dPkuuBNapq7UnLA9xEK9jZuqp+P0XbNYAbgVurar1RxKv5Zf77zfz32xxGUbvHLvCccMFK8hvaed4GVXXjwPIbgRsn37Sbov0VwHqT+w8tHB4D/Wb++838a6Ys0JAkaYi6k687V3RhLclS4HjgRbRhbv+yqj7drbNAQ1rgkjwbOBLYBjgbOLCqfjDFdsuA6/Bi7KJi/qX+6p6eurWqNphi3XW0KQ42rqprp2l/A7DUi3MLk/nvN/PfbwMFGpfSHsSYjSXAffGccMFK8m/As4GXVNWJA8vPBR4EbFFVV03TdhPayHsXV9W2o4hX889joN/Mf7+Zf83UknEHIEnSIjcx5/z0G7T56F4MfJT2JNWJSfYdfmiSRqGqvkibR/xI4BHAd5IcnWTj8UamUTD/Uq9dBixLcreLa933y7pvHz5VwyQPAtahjayihcn895v577eLutfXVdV2s/kCHj3GuDU/jqONgvKuJH80sPwE2rSGb1lB27fS7tl8a1jBaSSOw2Ogz47D/PfZcZh/zYAFGpIkDdevgXWT3HdFG1XzSuD9tJO1Y5IcMIoAJQ1fVd1QVa8D/oQ2isLLgQuS7DfeyDQK5l/qre91r2/vpjWYmN7gHd3yK4Ajkqw52Kjb5l20It8zRhSr5p/57zfz328TudtpDm0d7nqB66at/RKwJXBWkjcm2RJ4D3AqcGCSk5PskWT7JA/q/n0KcABwK/BPY/sFtMo8BvrN/Peb+ddMOcWJJElDlOQEYG/akPZHz7DNEcAbuWv0jTi0qbR4dBfe/wo4HFgP+C5wIPBznOJi0TP/Un8kWQ6cQjufuxD4Ce2J+QcCNwN7Al8FzgE+BPwS2BZ4KfDYrt3zquoLIw9eq8z895v577ckbwDeCZxaVctn2XZT4HI8J1zQkqwNnAQ8g7uKbn4J/A7YebpmwO3A/lV1wtCD1FB5DPSb+e8386+ZsEBDkqQhSrIPbQizs6pqxk/PJDmENuRZAXhhRlp8ktyHNmrOnrQPYccCr8CLsb1g/qV+GCi8ha7wtnvdr6pOSHIM7Ybs5IszAT5bVXuNLFjNO/Pfb+a/v5LsAnwDuL6qNphl2w1oT97WbIs7tPpJ8nLgTcB2K9n0TuBk4M1VdfbQA9PIeAz0m/nvN/OvFbFAQ5KkIUqyPvBDYA1gn6o6fRZtXwu8F2/WSYtakt2Bo4CJqZD8m+8R8y8tfkmeShtRbQvgIuCYqjqrW7cG7Qbua4DNuya/Bj4MvLuq7hh5wJpX5r/fzH8/dSOmbQBQVdeMORytBpI8Hng8bRSdjWhTz18PXAycC3yjqi4bX4QaNo+BfjP//Wb+NRULNCRJkqQxS7Iu7QL9tgBVtd94I9IomX9JAEk2Ae6sqqvHHYtGz/z3m/mXJEmS+sMCDUmSJEmSJEmSJEmSpCFbMu4AJEmSJEmSJEmSJEmSFjsLNCRJGpIkDxjCPpck2Xa+9ytp/tkH9Jv5l/rNPqDfzH+/mf9+M/9aFUnWSvKXSf5y3LFoPDwG+s3895v57xcLNCRJGp6fJjk+yfaruqMkayZ5BXAhsO8qRyZpFOwD+s38S/1mH9Bv5r/fzH+/mX+tivWB44BjxxyHxsdjoN/Mf7+Z/x6xQEOSpOH5AfBi4Nwk30zyyiSbzrRxmuVJjgYuBj4EbA78eDjhSppn9gH9Zv6lfrMP6Dfz32/mv9/Mv+ZDxh2Axs5joN/Mf7+Z/x5IVY07BkmSFq0kewDvAHYAqvu6EDgL+AlwBfAH4FZgI2BjYDvgMcCOwDLaSdltwNHA4VV1+Wh/C0lzZR/Qb+Zf6jf7gH4z//1m/vvN/GuuumKey4GqqqXjjkej5zHQb+a/38x/v1igIUnSkCUJsBuwP7A7sGa3akVvwhOVsr+gDWv2L1V1ydCClDQ09gH9Zv6lfrMP6Dfz32/mv9/Mf39109LM1TLgPXhzbkHzGOg3899v5l8zZYGGJEkjlGQTYDnwROCxwFbAZsC9gKtoT9JcAJwOnFZVZ44pVElDYB/Qb+Zf6jf7gH4z//1m/vvN/PdLkjtZcSHOSneBN+cWNI+BfjP//Wb+NVMWaEiSJEmSJEmSJK2igZtzlwK3zLL5EuC+eHNuQfMY6Dfz32/mXzNlgYYkSZIkSZIkSdIqSvIL4H7Ai6rq07NsuxlwGd6cW9A8BvrN/Peb+ddMLRl3AJIkSZIkSZIkSYvAGd3rTnNo69O0i4PHQL+Z/34z/5oRCzQkSZIkSZIkSZJW3ZlAgEePOxCNjcdAv5n/fjP/mpE1xh2AJEmSJEmSJEnSIjDx9PRcbs7dBnwbn6Je6DwG+s3895v514ykyjxLkiRJkiRJkiStiiQBNgCoqmvGHI7GwGOg38x/v5l/zZQFGpIkSZIkSZIkSZIkSUO2ZNwBSJIkSZIkSZIkSZIkLXYWaEiSJEmSJEmSJEmSJA2ZBRqSJEmSJEmSJEmrIMkDhrDPJUm2ne/9ajg8BvrN/Peb+ddsWKAhSZIkSZIkSZK0an6a5Pgk26/qjpKsmeQVwIXAvqscmUbFY6DfzH+/mX/NmAUakiRJkiRJkiRJq+YHwIuBc5N8M8krk2w608Zplic5GrgY+BCwOfDj4YSrIfAY6Dfz32/mXzOWqhp3DJIkSZIkSZIkSQtakj2AdwA7ANV9XQicBfwEuAL4A3ArsBGwMbAd8BhgR2AZEOA24Gjg8Kq6fLS/hVaFx0C/mf9+M/+aKQs0JEmSJEmSJEmS5kGSALsB+wO7A2t2q1Z0Mybd6y+AY4F/qapLhhakhspjoN/Mf7+Zf82EBRqSJEmSJEmSJEnzLMkmwHLgicBjga2AzYB7AVfRnqa+ADgdOK2qzhxTqBoSj4F+M//9Zv41HQs0JEmSJEmSJEmSJEmShmzJuAOQJEmSJEmSJEmSJEla7CzQkCRJkiRJkiRJkiRJGjILNCRJkiRJksYsyS5JKslhs2hzWNdml0nLK8m3hvmzJUmSJEnS7FmgIUmSJElSj3Q34ge/7khyRZJvJNl73PHNh9Wl4CDJY5N8LMkFSa5LckuSXyX5XJK/SLJ0nPFJkiRJkqTRWmPcAUiSJEmSpLF4S/e6JrA98BxgeZJHV9XB4wtr4UuyJnAk8CrgDuBU4D+AW4BtgCcDzwNOAp6/Cj/qA8CngF+vSrySJEmSJGk0LNCQJEmSJKmHquqwwe+TPAX4T+C1SY6sqovGEdcicRTwcuC/gf9ZVRcMruxGzngh8OxV+SFVdQVwxarsQ5IkSZIkjY5TnEiSJEmSJKrqFOCnQICdJpYn2TfJSUl+keSmJNcmOT3JPlPtJ8m3uulF1kryD930HrckOW7Sdi9M8s0kf0hyc5Lzk/x9kntNsc/q9rtZko8kuaTb57lJ9pu07XHAN7tvD500ncsu3TZrJXlNkh92P//GJBcl+WKSXef+vwhJnkArzrgKePrk4gyAqrqjqk4Epvs/fGSS/0hydRfbqd1+J2932ODvNYPYtuimXLm0y+WPkrxkBduPNZeSJEmSJC02jqAhSZIkSZImpHutgWUfAs4Dvg1cAmwK/Dnw8STbV9Uh0+zrJFqhx1eALwCX/f8fknwMeCnwW+DfgKuBxwGHA09J8tSqun3S/jYCTgduBT4HrE2bHuTYJHdW1fHddl/oXl9Cm1rkWwP7uKh7PY42gsU5wAnATcB9gJ2B3YCTp/mdZuKV3etHquqSFW1YVbdMsfgxwN8A3wWOAbalTYdySpJHTlXwMRNJNgW+AzwAOK372gr4MPD1lTQfVy4lSZIkSVpULNCQJEmSJEl0I0dsTyvOOGNg1cOq6ueTtl2LdrP+TUk+XFW/m2KX9+vaXjGp7b60G/qfB/auqpsG1h0GHAq8GvjnSft7BPAx4JVVdUe3/f8GfgK8ETgeoKq+kORqWoHGt6aYymVD4AXAWcCfTOxrYP2mU/wus7Fz93rKHNs/E9ivqo4biOmVtEKK/wUcOMf9HkErznhfVb1uYN8foBWDrMhYcilJkiRJ0mLjFCeSJEmSJPVQNz3GYUnenuRzwFdpI2i8r6p+NbHd5OKMbtmtwFG0Bz+eMs2POGTyDf3O/wJuB146eEO/czhwJbD3FO1uBA4eLKioqvNoIzE8JMn608Rxj/Bpv+ctwJ33WFl15Qz3M52tutffzrH96YPFGZ1jaf9nj53LDpOsSfs/vQ44bHBdVZ0JfGIlu1hdcylJkiRJ0oLiCBqSJEmSJPXTod1r0aal+C/gY1V14uBGSbaljWrwFNp0G+tM2s/W0+z/B5MXJFmXNnrCFcBrk9yjEa1w4iFTLL+wqq6dYvlvuteNaAUIK1RV1yb5d+BZwI+SnET73b9fVTeurP0s1Mo3mdKZ99hR1W1JLgU2nuM+HwysC/xXVV0zxfpv0UYcmc5qmUtJkiRJkhYaCzQkSZIkSeqhqpryjvqgJA+g3ZzfmFbE8HXgGuAO4P60m/r3mqb576dYtjFt9IrNuatAZKaunmb57d3r0lnsay9a0cmLgLd0y27uRhJ5fVVdOsvYBl1Cm0pkG+CCObRf0e85m99x0Ibd63S/11S5Wtn61SWXkiRJkiQtGE5xIkmSJEmSpnMwsCnwsqrapapeU1WHVNVhwNdW1LCqphpBYmL0hrOrKiv6mt9f4x6x3VRVh1XVg2ijguwDnNa9fm4Vd39a9zrd1C/jMPH/vsU067dcUePVOZeSJEmSJC0kFmhIkiRJkqTp/I/u9aQp1j1ptjurquuBc4GHJtlkVQJbiTu615WOxFBVv6mqTwBPBy4Edk6y6Sr87I90r69IMl1BBABJpht9ZL79FLgReGSSDadYv8tsdzjCXEqSJEmStGhYoCFJkiRJkqZzUfe6y+DCJE8H9p/jPt8LrAUcm2SjySuTbJzkUXPc94Qru9dtp9j/5kn+ZIo2y4D1adNs3Dqw/WFJKslhM/nBVXU68FHayCNfTfLAKWJYkuSFwMdnss9VVVW3AZ+g/X6HTYrlMcDec9z1KHIpSZIkSdKisca4A5AkSZIkSautDwL7AZ9NchLwO+BhwG7AZ4C9ZrvDqjo2yaOBA4GfJ/ka8GtgE2A74M+AfwFetQpxX9DF+oIkt3b7L1pBxMbA95KcD/wQ+A2wAbA7baqPI6vquoF9TTzccvssfv6raaN4vAo4P8m3gB8DtwBbA08GtmHVp1OZjb+lTbvy2q4o4zRgK1oOvwzsMdsdjiiXkiRJkiQtGhZoSJIkSZKkKVXVT5IsB94G/DntOsKPgecCVzOHAo1uv69O8hXajftdgY2Aq2g3998NnLiKcd+RZE/gH4G/oI0cEVpRwo+AQ2mjgiwHNut+9gXAm4BPTdrdHwN30gpSZvrzbwMOSHIc8ArgT4HHAWsClwFnAn/NCAs0quqKJE8E3gE8C3gM7Xc+gDZSyqwLNLr9DjWXkiRJkiQtJqmqcccgSZIkSZK02kkS4HLgG1X1F+OOR5IkSZIkLWxLVr6JJEmSJElSLz0M2BQ4YtyBSJIkSZKkhc8RNCRJkiRJkiRJkiRJkobMETQkSZIkSZIkSZIkSZKGzAINSZIkSZIkSZIkSZKkIbNAQ5IkSZIkSZIkSZIkacgs0JAkSZIkSZIkSZIkSRoyCzQkSZIkSZIkSZIkSZKGzAINSZIkSZIkSZIkSZKkIbNAQ5IkSZIkSZIkSZIkacgs0JAkSZIkSZIkSZIkSRqy/wfVXyCAvxD45gAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "checked_type='tc' # Valores 'te' y 'tc'\n", "used_direction='s' # Valores 's' y 'e'\n", "node_type='All' # Valores 'Intra', 'Inter', 'All'\n", "normality='m'\n", "#Values 'n' (normalizar), 'l' (logaritmico), 'm' (sin modificaciones), 'r' (Comparar respecto al primero)\n", "\n", "ylim_zero = True\n", "\n", "var_aux, grouped_aux, handles, used_labels, title = get_types_iker(checked_type, used_direction, node_type, normality)\n", "array_aux, title_y = obtain_arrays_iker(grouped_aux, var_aux, used_direction, normality)\n", "graphic_iker(array_aux, title, title_y, \"Parents, Children\", handles, used_labels, ylim_zero)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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l6VWrXSSfn+kZy+dnqNUuDnzc5cufZ2urt2fA1lbM5cufH/scT6ODvu/TISIpcZ7buY0mORlpX+Uy1OsQhklbkzBM7pddT5SkA7BShiRJkiRJknTazeyz/X8HYqDTd1/SPrrVGqrV67RamxSLc9RqF/et4nDz5rsjjavXQd/3yYuACvcOt82d++ACjaZZuWwgQ5IkSZIkSZIkaRhBHJ++vMXi4mK8trY26WlIBxIEz2dui+OPH+NMdLxKJKGMfiHJVbSSJEmSJEmSJEmSToIgCN6I43ixf3xgi5OdB35vEASPHM20JOlsiaINSqU6udzzlEp1omhjZ0sr4xFZ49LkRBGUSpDLJbeR3XgkSZIkSZIkSZL2tV+LE4B/CjwL3DiqSQRBsAh8B/AHgf8OeD/wHuCLwBrwUhzH//KoXl+aJvPz70ltZzI//54JzEbj9BM/8aN84Qt/j+/7vi/Tbj/I6uqjVCpJS5NyuUh6BY3isc5R2k8UQaUCnZ1uPM1mch9sdSJJkiRJkiRJkjTIvhU0jslfAP468CeAryGZ1zbw1cD/BPyLIAg+EwTB7OSmKA0nu0LCcK5e/TbOn+/9ap4/n+Pq1W8b5zR1zNbXI37jN/4GhcKXCQJ48ME3WV7+DI8//s/55m/+H0nCGUHfo/JA7fgnK6XoVs14/PF74YyuTgeq1YlMS5IkSZIkSZIk6cSYloDGvweeAT4IfGUcx18Zx/F9JJeO//2dfb4L+GsTmp80lCjaoFK5RrO5SRxDs7lJpXJtpJBGuXyBF1/8k4ThHEEAYTjHiy/+ScrlC0c4cx2OQct0AAAgAElEQVS11dUqMzO3esbOn9/i9/2+f88HPvClnZGYeyGNEKgDliTQ5HWrZjTTirzsaNmNR5IkSZIkSZIkaaAgjuPBOwTBNvBsHMd/63imlDqHTwOPA78Wx/HX7Lf/4uJivLa2dvQTk/qUSnWazc0942E4R6NRmcCMNC2eey5HEsDY65Of7B8JgcbRTkgaQak0OJwBEIbQaBzHbCRJkiRJkiRJkqZbEARvxHG82D8+bAWNB4MgKI7yZ8zz/487t1895ueVxqrV2hvOGDSu8ThsW5njUCikHxYLhbRRSxFouuxXHSOfh5rdeCRJkiRJkiRJkgYaNqBxGfj1Ef782pjn+Ud3bn99zM8rjVWxODfSuA5vHG1ljsPSUo3Z2XzP2LlzsLSUtve4M27S4RQHfCTDEOp1KNuNR5IkSZIkSZIkaaBhAxqbJJd0D/vnvxx2YkEQPBAEwSNBEPxD4M/tDP/4YZ9XOkq12kXy+ZmesXx+hlrt4oRmNB5RlLQ4yOWS2yia9IzuqVav0+nc7hnrdG5TrV6f0IzSLSyUWV6uUyiEQMB9981z/vwDfPazcOUKrK/v3vstYIreZJ15tVpSJWO3fB5eeSVpa2I4Q5L216349cgj38MP/dA8zz2X48qVEuvr/psvSZIkSZIknRUz++8CwAtxHP+tI50JEATBB0gPd7wL/N04jj911HOQDqNcvgAkoYFWa5NicY5a7eLd8ZMoiqBSgU4nud9sJvdhOhZlT1JbmYWFMgsLZdbXI1ZWKmxtJW9quw0rK919AG4CO28yU/Am68zrfter1aTdSbGYhDam4RggSSdBt+LX13zNL7C8/BnOn98CoN1usrKS/Ju/sOBBVZIkSZIkSTrtgjiOB+8QBNvAs8cU0PidwC/t3H0vcB64Dfxt4B/EcfzlAY+tsLOiWSwWP9hsNo94ttLZUColoYx+YZhcOT9ppVKdZnNvGCMM52g0KimPmLwrV0q023vf1EIBnn5690gINI5pVpIk6ah0z1eefrrGgw++uWd7oRDy9NON45+YJEmSJEmSpCMRBMEbcRwv9o8P2+LkWMRx/JtxHH9VHMdfBdwH/F7gp4DngF8OguAPDHhsPY7jxTiOF9///vcf04yl06/VGm38uJ3EtjLtdvqb1273j0zJmyxJkg6lW9mrUNgbzoDscwNJkiRJkiRJp8tUBTR2i+N4O47j/yuO4+8H/megCLwSBMHUzlk6jYrF0caPW7l8gXr9EmE4RxAklTPq9UtT3VamUEh/8wqF/pEpeZMlSdKhFItzALTbD6Zuzzo3kCRJkiRJknS6nJSwwz/Yuf0G4BsnORHprKnVIJ/vHcvnk/FpUS5foNGosL39cRqNylSHM6Jog899bolbt2Z7xmdnYWlp90gemKI3WZIkHVi34tfq6qMp5wB5lpb8N1+SJEmSJEk6C2b22yGO42kIcfw/u37+GuCNSU1EOmvK5eS2Wk3amhSLSTijO67BomiDavU6rdYm73vfe9jc/G22tn4/N29+N9/7vf+Mt9+OKRSScMbCQvdR54A64JssSdJp0A2PVqt5VlbgT/yJf8MDD3yZQqHI0lKNhQX/zZckSZIkSZLOgiCO40nPYV9BEPwe4P/cufun4jh+bdD+i4uL8dra2tFPTJIGiKINKpVrdDq3U7f/+T//i/yjf/QZ7r9/a9doHsMZkiRJkiRJkiRJ0skVBMEbcRwv9o9PvDpGEATngiAI9tnth3ZubwP//oinJB2LKNqgVKqTyz1PqVQnijYmPSWNWbV6PTOcAfDqq9/ERz7y3TQaDwIBEGI4Q5IkSZIkSZIkSTqdJh7QAH4XsBYEwVNBEHygOxgEQS4Igm8IgiAC/sLO8D+I4/jLE5mlNEbdygrN5iZxDM3mJpXKtYmHNAyNjFertbnvPq+++k18y7f8CLANNDCcIUmSJEmSJEmSJJ1O0xDQAPgm4J8A/yUIgneCIPgtoAP8EvA9O/v8U+CvTmZ60nilVVbodG5TrV6f0IymNzRykhWLc/vuk8/PUKtdPIbZSJIkSZIkSZIkSZqkaQho/Abw50jq+v8y0AYeBLaADZLgxsU4jp+M4zi7V4A0IQepOpFVWWGYigtHZRpDIyddrXaRfH6mZ+z8+Rzz8+8hCCAM56jXL1EuX5jQDCVJkiRJkiRJkiQdl5n9dzlacRzfAv75zh/pROlWnegGG7pVJ4CBi+7F4hzN5t4wxjAVF47KNIZGTq4IqFIut/iO7/gqPvGJR/nxH//9FItz1GoXDWRIkiRJkiRJkiRJZ9A0VNCQTqyDVp1Iq6wwyVYXUQS5XHo4ZJKhkZMpAipAE4j59V//TX73736JT37yh3j66b/LI4/80oTnJ0mSJEmSJEmSJGkSDGhIh3DQqhPl8gXq9UuE4dzEW1187GMbfPjDde7c2Tvn2dlgYqGRk6sKdABYX4eVFWi3YwDa7SYrKxXW16MJzk+SJEmSJEmSJEnSJEy8xYl0ko3aqiSKNqhWr9NqbU5Fu4so2uAnf/IacXw7dXsQBMc8o9Ogdfen1VXY2urdurXVYXW1ysJC+ZjnJUmSJEmSJEmSJGmSrKAhHcIorUqiaINK5RrN5iZxDM3mJpXKNaJo47imu0e1ej0znAFw69b2vu1a1K9496d2O32PdruVvkEaUhRtUCrVyeWep1Sqj/U4EkVQKkEul9xGFnyRJEmSJEmSJEkaCwMa0iGM0qqkWr1Op9Mbhuh0bk80ALFfK5Zh99FuNSAPQKGQvkehUEzfIA3hKMNeUQSVCjSb7Dx3ct+QhiRJkiRJkiRJ0uEFcRxPeg5jt7i4GK+trU16GlKPXO550r5uQQDb2x8//gkBpVI9tUXLbmE4R6NROaYZnRYRUGV9vcnKSsDW1r2/+NnZPMvLdVuc6MCyvrfj+K6WSkkoY+9zQ6NxqKeWJEmSJEmSJEk6M4IgeCOO48X+cStoSMekWJwbafw4pLVo2S2rXYv2UwYaLCzELC9/mkIhBAIKhdBwhg4tq6rNOKrdtDK672SNS5IkSZIkSZIkaXjZK7OSxqpWu0ilcq2nzcmkAxDdViyXL3+emzffBSCXg+3t5Gr8Wu1iarsWDW9hoWwgQ2NVLM6lVtAYR9irWEyvoFG0K48kSZIkSZIkSdKhWUFDOibl8gXq9UvMz7/n7th9901HRuqdd+6FRra37wVHDGdI0yet8s04wl5RBG+9tXc8n4da7VBPLUmSJEmSJEmSJAxoSMdudxji5s13qVSuEUUbE5tPtXq9p6oHQKdzm2r1+oRmJGmQbtgrDOcIgqTaTb1+6VCBqiiCSgVu3uwdn5+Heh3KFoGRJEmSJEmSJEk6tCCO40nPYewWFxfjtbW1SU9D2qNUqqe2JgjDORqNygRmBEHwfOa2OP74Mc5E0qSUSumtTcIQGo3jno0kSZIkSZIkSdLJFgTBG3EcL/aPW0FDOkat1t5wxqDx43Du3NxI45JOn1ZrtHFJkiRJkiRJkiSNzoCGdIyKxfTQQ9b4cbhz5yIw0zc6szMu6SwoFkcblyRJkiRJkiRJ0ugMaEjHqFa7SD7fG4bI52eo1SYXhgjDC8AloBsSmQMu7YxLOgtqNcjne8fy+WRckiRJkiRJkiRJ49F/2bykI1QuJ6GHavU6rdYmxeIctdrFu+OTUKtBpXKBTufeHFyYPbj19YjV1SrtdotCocjSUo2FhfKkpyUNVN75iFarSVuTYjE5BpT96EqSJEmSJEmSJI1NEMfxpOcwdouLi/Ha2tqkpyGdGFHkwuw4rK9HrKxU2Nrq3B2bnc2zvFw3pKEDi6KNqQp1SZIkSZIkSZIkabAgCN6I43hxz7gBDUkajytXSrTbzT3jhULI0083jn9COvGiaINK5Rqdzu27Y/n8DPX6JUMakiRJkiRJkiRJUyoroJGbxGQk6TRqt1sjjUv7qVav94QzADqd21Sr1yc0I0mSJEmSJEmSJB2UAQ1JGpNCoTjSuLSfVmtzpHFJkiRJkiRJkiRNLwMakjQmS0s1ZmfzPWOzs3mWlmoTmpFOumJxbqRxSZIkSZIkSZIkTS8DGpI0JgsLZZaX6xQKIRBQKIQsL9dZWChPemo6oWq1i+TzMz1j+fwMtdrFCc1IkiRJkiRJkiRJBzWz/y6SpGEtLJQNZGhsyuULAFSr12m1NikW56jVLt4dlyRJkiRJkiRJ0skRxHE86TmM3eLiYry2tjbpaUiSJEmSJEmSJEmSpDMmCII34jhe7B+3xYk05aIISiXI5ZLbKJr0jCRJkjRpG1FEvVTi+VyOeqnEhieJkiRJkiRJ0tSzxYk0xaIIKhXodJL7zWZyH6BsFw1JkqQzaSOKuFapcHvnJHGz2eTazkniBU8SJUmSJEmSpKllixNpipVKSSijXxhCo3Hcs5EkSdI0qJdKbKacJM6FIRVPEiVJkiRJkqSJs8WJdEBRtEGpVCeXe55SqU4UbRzba7dao41LkiTp9NvMOBnMGpckSZIkSZI0HQxoSANE0QaVyjWazU3iGJrNTSqVa8cW0igWRxuXJEnS6TeXcTKYNS5JkiRJkiRpOhjQkAaoVq/T6dzuGet0blOtXj+W16/VIJ/vHcvnk3FJkiSdTRdrNWb6ThJn8nkuepIoSZIkSZIkTTUDGtIArdbmSOPjVi5DvQ5hCEGQ3NbrybgkSZLOpgvlMpfqdeZ2ThLnwpBL9ToXPEmUJEmSJEmSploQx/Gk5zB2i4uL8dra2qSnoVOgVKrTbO4NY4ThHI1GZQIzkiRJktJtRBHXq1U2Wy3mikUu1mqGNiRJkiRJkqQJCILgjTiOF/vHraAhDVCrXSSfn+kZy+dnqNUuTmhGkjS89fWIK1dKPPdcjitXSqyvR5OekiTpiGxEEdcqFTabTYhjNptNrlUqbEQe+yVJkiRJkqRpYUBDGqBcvkC9fokwnNtpMTJHvX6JcvnCpKcmSQOtr0esrFRot5tATLvdZGWlYkhDkk6pz1++zO1Op2fsdqfD9Wp1QjOSJEmSJEmS1M8WJ5IknUJXrpR2whm9CoWQp59uHP+EJOnMiIAq0AKKQA04mjYjd1uaNPce7+8KAj6+vX0kry9JkiRJkiQpnS1OpBMgiqBUglwuubUitaSDardbGeMDFvEkSYcUARUgqV6U3FZ2xsdrI4r4maeeGhzOAOaKxbG/tiRJkiRJkqSDMaAhTYkogkoFdtqG02wm9w1pSDqIQiF9Qa5QCDiKhUJJEiSVMzp9Y52d8fH6/OXLbN+6te9+F2u1sb+2JEmSJEmSpIMxoCFNiWoV+tqG0+kk45I0qqWlGrOzQc/Y7CwsLcUcxUKhJAmStiajjB/cuzdv7rvPe+bnuVA+mvYqkiRJkiRJkkZnQEOaEq2M39tnjWt0tpDRWbKwUGZ5OaZQSO4XCrC8DAsLcBQLhZIkgKx2IsffZmQmn+fbrl499teVJEmSJEmSlG1m0hOQlCgWk7YmaeM6vG4LmW6Vkm4LGQAvLNVptbAQsrCQcmCZwEKhJJ0NNaBCb5uT/M74eL1nfj6zisZcGHKxVrN6hiRJkiRJkjRlrKAhTYlaDfL53rF8PhnX4dlCRmdTjWRhcLejWSiUpLMtAkrAh4H7gHkgAEKgDow/KPFtV68SzM72jAWzszz2yitUGg3DGZIkSZIkSdIUMqAhTYlyGep1CEMIguS2Xre6w7jYQkZnU5lkYTDkqBcKJensikiqZjSBGLgJvAN8GmhwVMfcC+Uyj770EnM7J49zYcijL71kMEOSJEmSJEmaYkEcx5Oew9gtLi7Ga2trk56GdGSiKKn80GolLVBqNYMc+ymV0lvIhCE0Gsc9Gymb329JOmlKJOGMfiFJQEOSJEmSJEnSWRMEwRtxHC/2j1tBQzphoggqlSRsEMfJbaWSjCubLWR0EkQRPPVU7/f7qaf8fkvSdMsqx9XiXuuT3M7t6Af0jSiiXirxfC5HvVRiw38UJEmSJEmSpBPLChrSCWMliIOzMoGm3UMPwc2be8fn5+GLXzz++UiShlEivYLGPEmrk86usTyjtJraiCKuVSrc7tx7jpl8nkv1uq1MJEmSJEmSpCmWVUHDgIZ0wuRyyZX1/YIAtrePfz6SxicIsredwn+uJemUiIAKvUGMAMg6cA/f+qReKrGZksydC0MqJnMlSZIkSZKkqWWLE+mUKBZHGz+LoiipNJLLJbdWApckSUenTFIVI9y5PyicAdktUfbabKXvmzUuSZIkSZIkaboZ0JBOmFoN8vnesXw+GVcSxqhUkjYwcZzcViqGNHQyzM+PNj6aiKQMf27n1i+FJI1PmaQqRsjgcAbA3lTtRhRRL5V4PpejXiqxsXPiMpeRwM0alyRJkiRJkjTdDGhIJ0y5DPU6hGHSDiEMk/u2IU9Uq9Dp9I51OvDEE4Y0NP2uXoXZ2d6x2dlk/HC65febJAuHzZ37fikk6XD6w29725H0ygO9qdqNKOJapZK0MoljNptNrlUqbEQRF2s1ZvqSuTP5PBdN5kqSJEmSJEknUhCfwqb2i4uL8dra2qSnIWkCgiB7Wz5vmEXTL4qSoFGrlbQuqtXG8Zktkb5oGJJc8S1JGl03/LY7GTqovUlIEs7oPajXS6UknNFnLgypNBpsRBHXq1U2Wy3mikUu1mpc8GRGkiRJkiRJmmpBELwRx/HinnEDGtLxWF+PWF2t0m63KBSKLC3VWFjwl+vjdu4cbG9nbw9DaDSObTrSUI4mlLFbjvQFwwAY8IWRJA1QIj381h/SyAN1+oMZXc/ncklftj1PE/DxQSc1kiRJkiRJkqZWVkDDFifSMVhfj1hZqdBuJ+0F2u0mKysV1tdtLzBu+61jtFrHMw9pWFEElQrsVLan2Uzuj7clT3HEcUnSHlEEpRLkcsltnN7OJI5jGo2Q7e2At94KSQtnbEQR9VKJ53M5glz6/5LNFT1GS5IkSZIkSaeNAQ3pGKyuVtna6vSMbW11+Kmfqt79Hf94F2OVxbUOTZtqFTq9hwc6nWR8fGokV3Dvlt8ZlyTtKy1N91/S+6o1myEPP9zg3LltfsfvaBBFe8MZ1yqVpK1JHBPfubPnOWbyeS7WUo7R/SERTyAlSZIkSZKkE8WAhnQM2u30sg333986wivm1S+fT1pHSNMkq6rLeKu9lEmu4A5JSu+nX9EtScqQlqb7azF0ekMab7+d5xOfuHeykRa4u16tcrv/uYDg3DkIAubCkEv1Ohf6e10dT8klSZIkSZIkSUfIgIZ0DAqF9LIN7fa98fFfMX82BekXswJQr0P/WsfoIpKe87mdWxdFdDhZVV3GV+2l+5n98M79TwMNDGdI0gjSUnOvAh+J6YbfGo2Qj3ykzquvlgc+dDMjgRdvb/Px7W0qjcbecAYcV8klSZIkSZIkSUfIgIZ0DJaWaszO9rYXuHUrz+pqbzmH8V4xf/ZEUVLxO81HPzqucEYFaALxzm0FQxo6jFotqe6y2/iqvfiZlaSxyErN/buQJPS2zbd8S2NPOCPtoXMZz5U1ftfxlFySJEmSJEmSdIQMaEjHYGGhzPJynUIhJI4D3nwzZGWlzvp67y/xx3fF/NlUrUJKG3fuvx8+9amxvALQX5K8szMuHUy5nFR3CcOkAkwYjqvaC/iZlaQxGSJNN2zg7mKtxkzfjjP5PBf3S+YdfcklSZIkSZIkSUcsiON40nMYu8XFxXhtbW3S05BSzcykhwgAXnllXIuyZ1Mul7Rk7xcEsL09llcgqUKw5xWAsbyAdEARSeiiBRSB7iLf4xn7+5mVpJFFUZIGbbWSUESttufEbYhdANiIIq5Xq2y2WswVi1ys1dLbmvQ9+caTT3J9a4tNYA64ODvLhZde8gRSkiRJkiRJmjJBELwRx/HinnEDGtLxCoLsbafw63ikIm5QZZUWbYoUeKv0l7jZPL9nvzCERmMcr1giaRGx5xVIyptLk9BtY7K7UsYsSQjjVsZj/MxK0tQYMtWxEUVce+opbt+6d2yfOX+eSy++uH+4Q5IkSZIkSdKxygpo2OJEOmbnzo02rnQRN6iwQpM2MdCkzWbtX3M+31sVIK20+EFfEd5KGc9zr1qBNLwoglIpqfxSKiX3DyatjckW2eEMgMfS58QNSrxAjmcp8QIRNw46KUnSMKIIKhVoNpOkbrOZ3E/5R+F6tdoTzgC4fesW16u2rZIkSZIkSZJOCgMa0jGrVEYbV7oqq3TY6hnbKv8KX1n/GcIwqVQShlCvj6Pqd7dCwc2+8XmgDnjVqkYzwnrcEFoHeMxre+eUEnqqsGJIQ5KOUrUKnb6QXaeTjPfZbKUf77PGJUmSJEmSJE0fAxrSMYoieK1vXfTcOfjoR+FTn5rMnE6qFu3U8S+Vf4FGA7a3k7Ym46n4nVahAOABDGfoIEZYjxtC8QCP2buYlxZ66rBFldWDTEqSNIyscEXK+Fwx/XifNS5JkiRJkiRp+hjQkI5JFMGTTyZXynfNzsLLLxvOOIg8s6nj7+O+I3i1rCtTvWJVBzPCetwQaiStdu55991Zfvu3zw94zN7FvKzQU9a4JGkMssIVudyeskoXazVm8r3H+5l8novj6eUmSZIkSZIk6RgY0JCOyeXLsNV7cTpbW8m4RvMx/hVv913p37XJbx9BS4asK1O9YlUHk7Ued7CLoMtAnUYjZHs7oNEIeeqpl3jyyRf5rd+aJ47798+ThDr6XptC+pwyxiVJY1CrQV/oAoA7d/b0vrpQLnOpXmdup5fbXBhyqV7nwnjKhUmSJEmSJEk6BkG8d+XmxFtcXIzX1tYmPQ2pRxBkbzuFX8MjNcNz3CH7TQsp0OCZMb5iBFTobXOSB+rY4kQH8bGPwU/8xN7xw7Q7KpV6K/R0/eW/HPFjP1YlqfhSJAln7P3cRtygwkpPm5M8s9RZpswjB5uUJGl/UQRPPJGEMvqFYdKzTZIkSZIkSdKJEgTBG3EcL/aPW0FDOqCIG5R4gRzPUuKFI6jaoCyDwhkw7pYMEVAlCWec2xkLMZyhw3jttdHGh5F2EXY+D3/4D5eBBrC9c5v+uS3zCHWWCSkQkASdDGdI0jEol2F7O31bs5kk8PranUiSJEmSJEk6mWYmPQHpJOq/0rxJmworAHcXM9fXI1ZXq7TbLQqFIn/kj9T4+Z/fuzA6P3988z4tzhEMDGmMryVDf+WMO9xrD2E4QwfXao02Poxuhfv/8B8i/spfqVIstuh0ijzwwPCf1zKPGMiQpEkoFtPLIEEyXqkkP9vORJIkSZIkSTrRrKAhHUCV1Z42AAAdtqiyCiThjJWVCu12E4hpt5s8+miFb/iG3qsfz5+Hq1ePa9anR4UPZm7LM0uNpTG9Urdyxm6dnXHp4IrF0caHVS5H/NiPVSiVmuRyMQ880CQJGXnltSRNtbQySLt1OlD1/EOSJEmSJEk66QxoSAeQ1UKjO766WmVrq3dhP447fPd3VwlDCIKkpfiLL3oh5EF8im/noyxyjqBnfPwtGbLKGRyizIFEdjuSWu1wz/vWW4aKJOlEKpehXk9OELMcpsySJEmSJEmSpKkQxHF2m4CTanFxMV5bW5v0NHSKlXiBZkpI41vW/2++Y/X6TuWMNAGf/GRGj3FNoRKQ9ncZAo1jnYlOnyhKLoZutZLKGbXaKIGtiCR00QTOAXd4662Q++9vEgRp+weAxx5JOhFKpfR2J/Pz8MUvHvt0JEmSJEmSJI0uCII34jhe7B+3goZ0ADWWyDPbM/bB9f/Et6789IBwBhQKh+xfoGP2GNC/2p0HDlnmQCIJYzQasL2d3I4WzqhwLzx0B4AHHmgSx6npDMBjjySNUxQlOYpcLrmNxtlJqlZL+uD129wc8wtJkiRJkiRJOm4GNKQhRdygxAvkeJYqqzzB1xNSICBprfGdq/+OeOvdzMfPzuZZWnJh/+SIgJeB3VWGAuAJwL40mqS0NiaJXC5me7s3pPH224aKJGmcoggqlaTIRRwnt5XKGLMT5TJ85VfuHd/aSkovSZIkSZIkSTqxDGhIOwZdCRlxgworNGkTA03avMyvUGOJbZ6lwTPcbv+3zOcuFEKWl+ssLLiwf3KkLYLHwGsTmIu0W2uf7TGNRsj2dkCjEfIDP1DHUJEkjU+1Cp2+U4ROZ7TsxEYUUS+VeD6Xo14qsdE98eyekN68mf7A1n7/BkiSJEmSJEmaZkEcx/vvdcIsLi7Ga2trk56GTpDulZC7f9mez0O9nlzEWOIFmrT3PC6kQINnAPjRH32Id97Z+8v0QiHk6acbRzV1HZkcvdUzugJg+5jnIu1W4l57k70ajZCHH270jJ3Cf+olaWJyufTjahAkbav2sxFFXKtUuL3rxHMmn+fSE09w4eWX96Y/dgvDpC+WJEmSJEmSpKkWBMEbcRwv9o9bQUNi/yshWynhjN3j6+sR77775p7td+7M8uKLtfH3Jj+jdreZKfECETeO8NWKI45Lx6UG5FO3vP12nk98oredSRgew5Qk6QwpZpwKZI33u16t9oQzAG53Olyv1weHM/J5qNmySpIkSZIkSTrJDGhIZFeL7o6/j/sgWoDS05D7ZHIbLSTjwOuvXyaO7+x5/NbWedbXy+PvTX4GRdzgKT7X02bmKT53qJBGFMFDDyVXvAZB8vO9v6O0RfD8zrg0SWWgDnSTF+cAeOutkB/8wTqvvnqvnYlreZI0frVacnzdbZTj7WbGiefmnb3nkneF4b3SbpIkSZIkSZJOLAMaEvtfCflu9PuhsgzNByEOktvKcjIOqa1NAL7iK96++/OovcnV6zKvc4vehYtb3OEyrx/o+aIInnyyt8X7zZvw1FPdkMbuRfBg57a+My5NWhloAK8AHwACHngAvv/7kzW8IHAtT5KOQhTdq7x2LsnHjXy8ncs48ZzrPmG/blsTD+iSJEmSJEnSiWdAQ2L/KyHfrv4x6Jzv3aFzPhkfQValDu3vJu+MNL6fahW2tvaO37oFTzyxO6TRAPKGgz8AACAASURBVLZ3bl0Y0TSJgArQhJ26MhcvVmg0Ira3XcuTpHGLoqQiWrOZ3L9z59754tDH2yji4ltvMdM3PJPPc7FSOVxpDkmSJEmSJElTz4CGRPJL9Xo9/crzKAKahfQHtjLGMwzbm1xHb1BY5s4dW9LoJKgCnb6xzs54to0ool4q8XwuR71UYsMPuiQNpVs5Y7dOBx5/HEqlIc4bdhIeF27e5BIwtzM8Nz/PpXqdC5/6VPYJqSRJkiRJkqRTof/iLenMKpf3/v47iuCpyjZZWab54hZwnvvum09tc9LpzPfcf6zWosRP06JNkQI1lijzyJj+C06niBtUWc3cPs99B3reYvHeFbBpOp2kkga4LqJplZUyyk4fbUQR1yoVbu+sMG42m1yrVAC44AddkgYaFO5sNpNwJww4b9iV8Liw86dn24c/nJygjFSSQ5IkSZIkSdJJYgUNaYBqFW51Mr4m+VtQ+7c8sv5dfDmlzcbt2+f5mZ+5evf+A/O3ebn8aZq0d5oRtKmwQsSNI5r9dIoieOih5MLQIEh+zrriNOIGFVZo0k7dPkuOqzw6yqsDJSDHr/5qiccfH3ypq5U0NN2ySvL0jkfc4CF+hIBniap/6W44o+t2p8P16uCqG5Kk/SuhdTrJuWOmrITHzZtJwiOO7yU9PPmQJEmSJEmSTiUDGtIAzWacsSWG+gpf/cg/YXllhfe807vg2enM87nPvcj6enL1Yz4PX3H1Gh22evdja2B1iNMmiuCpp5J1iK6bN+HJJ9PXIaqs7nnPukIKvMSfHqECSQRUgCYQ88ADTf7xP67wPd8zeAFk38UWaUKuX6/x9tv5nrG3385z/Xrt7v2IGzzJv+TmTojswVZ62Glz0GXhknTGRVHSwmRQ5a2ugYfTYXvdefIhSZIkSZIknVoGNKQMUQQEGRvDNpTXWVpd5fzW3gDB3NwDbG6We9qHf6n8C6lP1cqoDnEaVatw69be8a2te+sQ6+sRV66UeO65HN955TkW1tf37B8ADZ4ZsT1MFegN0nzFV3So1fZfAHHtWtPo8cfLfOQjdRqNkO3tgEYj5CMfqfP44/fK4ldZZYvtu/ffLBZSn2uz+OCRz1eSTqIoSgpaDBPOgH0yGLVaktodhicfkiRJkiRJ0qlkQENnVvdqyFwuue2v4HD5MhCnJDSCGGpJ1YtCOz1ccft2i0YDtreh0UjaiBdJXxjNGj+NBi1utFpJOGNlpUK7nVS5eLDdZnllZU9I42DvWfpCR7G4/wLIsBe8Ssep1YJXXy3z8MMNzp3b5uGHG7z6arlnTa+/PdDGY1+757B2Kz/Lv6p96zHMWJJOnmo1KWgxjHw+yWBkKpeT1G4YcjfFOz+fvq8nH5IkSZIkSdKpZEBDZ9LuqyHT2n1HUW8bjh4xUE4CA+1CelCgUNj7S/UaS+SZ7RnLM0uNpaHnPChQMu2iKFmLyFIswupqla2t3lWQ81tbLK3eawOT9p5F3KDEC+R4lhIvEHEj7RVSX7fVGrwAsu9iizQhWWt3u8fP7SoD9I3ROn/w5V8h2NW5KQ7gPz7x9XypfPGIZilJJ9uwhSy6FdPK5X12LJfpSfFevbq3qoYnH5IkSZIkSdKpZUBDZ1La1ZC7230PbPsdJlek55lldWmJW7O9oYvZ2TxLS7U9oQGAOsuEFAiAkAJ1lodq07FfoOQkqFaTuaeZnU3WIdrt9FWQQrud+Z5F3KDCCk3axCQVAyqspIQ0akDvAsjbb+f5xCeyF0Dm54dcbJEmIK1Sfv+a3h2SL903Ruv8uSf+Bec7vS2Zghj+wGtfGDooJklnzX6FLPJ5eOWVexXTRpZWVcOTD0mSJEmSJOnUMqChMynrasjuePbVkkl7k25QYHPhIivLy7xVeC8QUCiELC/XubGwkBoaAGjwDNs8S4NnhgpnwP6BkpNgUHuTl15K1iHSKo8APFgIM9+zKqt06F107rBFlVV6lYE6EAIBb70V8oM/WOfVV/cugIRhstjyxS9mr4+c9IomOvn2W9OLuME5Ar4xWue7Kyucu5OekHqwtTn0sUiSzoqPfQxmZgafvwydpdjvpKG/qobhDEmSJEmSJOnUmpn0BKRJKBbTf+HevUoyazvzHfLl/0xtp4pDmUdg4RlY6N1tmRcyQwMHWQjNWhwYtuz2pHXbm6RV0AjDe+sQS0s1VlYqPW1Obs/O8hVLP5D53C3aI4yXd/7AAw/AffftnVe3AsGgtZFuRZNuaKZb0QRcU9HxKpfTP3PdyjJ3iHm0urqncsZuc/tdHi5JZ8zHPgY/8ROD9wnDJEuxL08aJEmSJEmSJO1iBQ2dSfu1BkjbTv4W81f/t6HakowWGhjsYx/L3nZS1lUvX04PZwRBbzuGhYUy713+67QLBWLgzUKBzy0v8/GF7ZSWJYkihZHGd3vttb3zGqYyyWmoaKLT7TKv3w2JPdjKPu7cys9ysZbd5keSzqJ6ff99hg7JetIgSZIkSZIkaRcraOhM6l6wWK0mv2AvFvuqJpRvcB//lU71j0KrwHxxi6u185TLjxFxgxIv0KJNkQI1lvYENooUaKaEMYYJDewWRfCTP5m+rT/cMK2iCG7eTN8Wx3svHv2RhffQXHi6b8/s6iM1lqiw0lOxJM8sNZb2ndt+rW7G/TjpOETc4Cbv3L3/ZrHAe5t7j0d3zgX8bP3P8gmv4JakHnfu7L/P0CFZTxokSZIkSZIk7WIFDZ1JUZQdzui2BrhZ/gVoXIHt53in8fehfOPutiZtYqBJmwore6o71FjiPOd6xs5zbqjQwG7VanrlCUgPN0yjy5ezt4Xh3rFRq4+UeYQ6y4QUCICQwlBVTiB7cWW/RZeDPk46DlVWe+6/XlviVn62Z+xWfpZ/8fJ38X3lv3qcU5OkE+HcucHbd1dd25cnDZIkSZIkSZJ2MaChM6fbCrzZTEIO3VbgUZRsr7LaU40BoLNTwWHQtn4x8cD7wxh0cWVauGHaDKqeAemLG6O0LOlWM/kwnwXg03yIBs8MFc7ovv6gVjfjfpzOniiCUglyueS2e5y5u33nM5zjWUq8kNnKZxT9YaZfKi/wmfoyXw4LxAF8OSzws/U/y18u/42hvyuSdJZUKtnbwjBpgTJ0SNaTBkmSJEmSJEm7BHHW5fnHOYkgKAIfApaArwd+B3AL+DXgdeBqHMe/OezzLS4uxmtra0cxVZ0CpVISyugXhsnvyh+vvgmtAhTbUFuF8vrdfQJIjVkEwDbP3r3/ED/S02Lg7mtQoMEzQ8/1oYfSAw5BAJ/+9PRX0MiaP8D998Nbb+0d71Yp6W9Z0l8VY9j99jOomspRPE4nx/p6xOpqlXa7RaFQZGmpxsLC8H/J3TBYp3NvLJ+/t7A3rs9wvxIvpLZY6op3HaskSen++B+H1b787e5j+Eg8aZAkSZIkSZLOnCAI3ojjeHHP+KQDGkEQ/C6gSbLG3bUJ3A93e0R8GfiuOI5/dpjnNKChQXK57LYh+XzvYir5W1Bf6QlppNkdvIi4weM7FR369Qc5BokieOopuHVr77aPfhQ+9amhnmaigiB72+wsvPRS+vpExA2qrNKiTZECNZb2LFhnLUKPGoKR0qyvR6ysVNjaundAmJ3Ns7xcHzqkMSgM1miM9hkeZW1v0DHoHAG3+eRQ85eks2y/Y7gkSZIkSZIkDTLNAY0SSaWM14B/CqzGcfzlIAjOk1TU+IfAwyShjd8bx/F/2+85DWhokKxfuJ87B3fupDwgfBMaVzKfr/+K90FXr89zH1/kh4eaZ1b1ifl5+OIXh3qKiRsU0IDeRY5hQhm75Xh2qGom0kFcuVKi3d57oCgUQp5+ujHUc2SFwYIAtreH/wzvV4kjzTdF38Wj1VUebLbZPheQuxPzZljg9doSv1j+6aHmL0ln2X7HcEmSJEmSJEkaJCugkZvEZPp8GfjGOI6/PY7jz8Rx/GWAOI5vxXH8OvAY8C4wB/zABOepUyKrFXhqOAOSdicZQgp72hG0BrQW+G2yXqRXFGW3BvnSl4Z6iqkwPz94e6uV3HZbPTRpEwNN2lRYIeJG5mOLpP+9ZI1Lo2i3WyONpykWB48P+xmuVvsq+5Dcr1bTn38jivgzlRXe22wTAOfuxATAe5tt/kxlhY0oGvq/QZLOqv2O4ZIkSZIkSZJ0EBMPaMRx3I7j+FcGbP/PwM/v3P3g8cxKp1m5nFx5HobJVZBheO9+qmJ64CIAGjyzp8rDoIDAW6T0K9klipIKH48/nr3PSVoYuHo1aWWS5Zu/OeLKlRJfeO7rqVz5+yys32sl02GLKquZj62xRJ7eJ88zS42lQ89bKhTSv2hZ42mywmC1WvLzY3wt/UVm0j7DrYxMSNb49WqV2c5W6rbZzhbXs5IdkqS79juGS5IkSZIkSdJBTDygMaRuLYFzE52FTo1yOWmtsb2d3JbL8NhjKS058rcIaukhgawgxmN87YHm1G1jkNZ+ZbeTsjAQRckV/ltbSZnwfh/8YMS3fmuFdrtJADzYbvOhz36WR//1v767T1arGIAyj1BnmZACAenVTKSDWlqqMTvbuzI3O5tnaWn4L2BWGKxcTqrGvMyv7Glx8t/zgb2hrxGv4t7MSm4MuV2SNPgYLkmSJEmSJEkHNfUBjSAIZoD/Yefur05yLjq9oghefrm313gQwEefOM+ny187dKWG7qJrlnnuy9x2+fLeNgZ7Hj9/MhYG+sMm29tw/nwy/+4ix3d+Z5U47v0PDoA/tLZ2t5JGAAPbnJR5hAbPsM2zqdVMpINaWCizvFynUAiBgEIhZHm5zsLCaF/AtDAYQJVVOuytcvF5fn3PZ37Uq7jn9imzs992SVIi6xguSZIkSZIkSQc19QEN4C8BXwVsAz814bnolKpW94Yj4hheey35+T5m7o7Pc19mpYasRVeAWXJc5dHUbVEEN2+mbrorn09ahpwEae/nrVvwwAP3Fjlu306/ij8AllaTqiUxDGxzIh2lhYUyTz/d4JOf3ObppxsjhzMGaWVUh4mBy7zeMzbqVdwPP/bYwNe+eFLK8EiSJEmSJEmSJJ0yUx3QCILgEeDv7tz98TiO/9OAfStBEKwFQbD2W7/1W8czQZ0aWW1Fmq2YD/NZbvLO3bF3uJ35PFmLrgAv8aczKzxUq4Pnd9LKamd1UNg9XihkX8VfaN97Hwe9p9JJldUiCeAm7+ypojHsVdwbUcR/evnl7BfO5bhwUg4kkiRJkiRJkiRJp8zUBjSCIPidwL8E8sAbwA8P2j+O43ocx4txHC++//3vP44p6gSIIiiVIJdLbqMofZ8gyHiCYpu4b6jDVmZVh6xF15DCwPYbWYEGgFdeOXlltbM6KOweX1qqkdTL2KtduPc+DlrIlk6qGksZn/5EfxWNYV2vVrk9qFfS9vaBnleSJEmSJEmSJEmHN5UBjSAI3gdcAx4GvgD8qTiO353srHTSRBFUKkl1jDhObj/84SSMsTusUa0m2/cIYqilBzGyqjrUWCLPbM9YnllqLA2ca1agYX7+ZAUzumq1pCXLbvl8Mt61sFBmcfEv0h/SuDU7y+pS8n4N895JJ1GZR/iLLGZuT6uiMYzNQWkvYC4MR35OSZIkSZIkSZIkjcfUBTSCICgA/wb4OqAF/PE4jv/fyc5KJ1G1Cv0XkneDGM1mEt6IoqSNSaoYKK+nbnof96WOl3mEOsuEFAhIKmfUWR5YPQOyAw1Xrw582NQql5OWLGGYBGJ2t2hZX4+4cqXEc8/l+MIXXmNx8S9SKIRAwEzhd/Jzy3+WX11YyHzvIm5Q4gVyPEuJFw60iC1Ng0/x7cxnHEuAzEo9g8xlpb2AmXyei7tTUpIkSZIkSZIkSTpWQZxaOmAygiC4n6Ryxh8F/hvwx+I4/sKoz7O4uBivra2Ne3o6YXK5jMoYu4Qh/Fc2udOcS9n4JjSupD5unvv44uCuOyOLoiRU0molFTVqtZNZPWOQ9fWIlZUKW1v3kjOzs3mWl+ssLOz/HxtxgwordNi6O5ZndqgQjHTUDvIdjrjB43w2dVsAbPPsSHPYiCKuVSo9bU5i4O374N99aJ7v++GrlIf4rkmSJEmSJEmSJOnggiB4I47jPeXUp6aCRhAE9wErJOGMmySVM0YOZ0hd73vf/vu0WnCn9r9C/lbvhvytzPYmAF/inUPObq9yGRoN2N5Obk9bOANgdbXaE84A2NrqsLpaHerxVVZ7whkAHbYyKw1EUdLOJpfrbWvTbyOKqJdKPJ/LUS+V2MjaUcqQ1lKpW6VnkDKPZFbRKFIYeR4XymUu1etJK5MA3nww4NUPwXM/DP/2a29SWakQrfv5liRJkiRJkiRJmoSpqKARBMF54HPAnwTeBJbiOP7Fgz6fFTQURfC935uEHQYJQ6DxAs2oCNUlaBWg2IbaKufKv8odku/Hwvo6S6urFNpt2oUCv7z07fzswitE3OAyr3NzJ7Axz31c5VGrOWR47rkckH7M+dCHXsmsohFxgyqrNGmnbk+rNNBdMN/d5iafv9dqpSut4sBMPs+lep0LpzEloyPx0ENw8+be8TBMAleDHFVlmNKVEs12c++cCiGNp/eZlCRJkiRJkiRJkg4sq4LGxAMaQRCcA/4X4LuA/w+4FMfxzx/mOQ1oqFRKrmAfpLtYTzl9cbR7f2F9neWVFc5v3dsezL6H9y//TZ5euM0t7vQ87yw5XuJPG9JI8aM/+hDvvJOyik12q5O0xet+IQUaPPP/s3e/QW7c953n340ZUBpImbY5ur3KXQqAq6LazSSgK+vZ2gfmg13D0UVMJrZ1e1uVatJca21E4l2K1JXiq3PvLUnvdq6c1Z2GlTtKQVLyMWKft7YSxspYlEtZuLJV9Ca3N0oiQpkkJV8ZQHLx3Z5GEcYiRuZw0PegB0P86R/+zADzB/i8qlhD/LrRaAINDNi/T3+/LWOmY6B5wnzV93nt7FmCra2O9WZTKXK9ZtZFCMNAp0+bl/fza7YRQqpQJYmNR3bgz5BV3+eW67JeqTCbTPLix8r8sWETFhZJO4mX9dTyREREREREREREREREZMhMAY3pg9iZNh8nDGcAxIGvW5ZlWvcvgyD4e/uyV3KkVSrdl8dizZUUwhnM9snRRsWGbKHQEs4ACDY/4DuFX+Fu5nzHtjep41JQQKNNsejzgx+sG5c3Wp20BzSi2po0SxDHI9sxbjoGGuONyhlR4QyA9V4Hkci2850fAzumpvrbhsOJPX1mtFeCWS+X+a/+2gKCyJBGQEC5Wia3nAsfXyENEZGRGEYAT0RERERERERERMbHYQhoxJr+/uD2H5MPRrwvMiaSye4VNH7zNwHnNukeJ8xzLGNXo9tq/FD1PeP2K4ZWHJOsUHCp181BC4BqtTMU0e25nMIytoEwHQPJZPjzluu2tDVpN9tYUaSHqNYmDYb8z9BFHc/xzYBT37L44xPmEh61zRpuwVVAQ0RkBNqrgJWpkmMZQCENERERERERERGRCRXrvcpoBUHw+0EQWH3+SR/0/srR4HkQj0cve/ppdtqalKkScP+Euc/tnfUcTpBnkTv2hyO3U7Vt4+MnMS+bVFHhi3a23RmKOM6Mcf06gXGCw/Pg78d9vkSaXyHGl0jz9+M+nhcu71YhYzqR4GRjRZE9SKVab/vcJs3zxLhEmudbPnP2wnQ8f6gakLJTWBgrU1Hp470pIiKDi6oCVmMTl8IB7ZGIiIiIiIiIiIgctAMPaIiMguPAV78Kc3P3x+bm4Pp1uHq1/xPmDif4bPZXuduW9rgbj1PIdrbVaDjFo3v/R4yZqPBFs3g8wQMPeKTTYQuadBrO+RXW+YHxPt2CMH/r2+f4R5tn+DBlLAI+TJl/ZOX4SXzAXCHDmprisXyeeUcVBaQ/zZ8z7ZpzPo0rqbsFw3bLdDzPJlOULpSoX6yTslOR6yR7vDdFRGR3TFXAVGlNRERERERERERkcimgIWPLceCddyAIwj/vvBOOgfnEeJlqx9XtmYzDv1/8x7xn2wTAe7bN8uIixUzGeE36Td4eyb/pKMtmPeLxRNto+AzadooPfzjPs886lMvh61Uuw4u5H2bT/3HjNk1BmFXf580XXwTaWjvcrXHLdQE46XlMJ1r3ZzqR4PFr1xTOkIFcuQLHjnWOP/10+JnTqJpxmhsju5LadDw3V4Lxsh6JtvdgIp7Ay6pajIjIKJiCpKq0JiIiIiIiIiIiMrmmD3oHREbF98F1oVKBZDK8kr0x757EptwlpNH42egT/vnMF8llfqxlcjVBvGOytUFXRnbKZMInv1BwqVYr2HaSbNbbGU+noVZrvU9Qi4ObBacYuU1TEOaW64YpjwiNVhCNEMYt12W9UmE2meSk5ymcIQNrHDJRnzeNqhmmzwrYw+dF04fcfDIJZ89y6+ZN4/HsbL/X3IJLpVohaSfxst7OuIiIDJdHtuN3QII4HuYqbCIiIiIiIiIiIjLerMAwiXmULSwsBCsrKwe9G3KAfB9yudYJ/0QC8vn+J00bUtiUeAaf27gUqFAliY1HFpdCZNCjcR/pXyxmyFRYAdQvR97HAupc6hh/zrgxmE2lyJVKu95PkUGked4YBmvY1edFrw85ERE5FKK+PzqcOOjdEhERERERERERkRGzLOuNIAgW2sdVQUPGkut2VmOo1cJxx2HnxHjjhHkMi632dhjbGle3O5yIPKGuKyOHI5kM25p0LjBPbptKhM8mk6xHbcyyOHnqVFiuI6q0isiQ9VMdw9Sqp6teH3IiInIomL4/ioiIiIiIiIiIyGSKHfQOiIzCdheLruMOJyjxDHUuUTeEM6B7n3CHE+RZJIWNRXglfJ5FnYjfBc8LCwA0SyTgAe/3I9e3wBiEOel5TLdvzLL46Cc+wfy1a2ESJAjCn7lcWI1AZAS6fX40/Bv+dPAN9/MhJyIiIiIiIiIiIiIiIoeKAhoylpLJAccNk6jdQgANzUGPEs9MfDjD98MCFbFY+LPf7IPjhN0ZUimwrPDn2XyFunM7cv2nWDA+1/OOw2P5PLPbG5tNpTj18sv853/yJ+RrNZ4D8sAq3K86IDICHlkSxLuus8YGPtHHufENZfowO3689e5Fn/RSmtjlGOmlNH5RYSQREREREREREREREZGDooCGjCVTNQbPC//ePud5yv8vOyZRLbqHAKST74cFKQYpUNH8Wrhu+BrV61AqwU3nt9mk3nGfOWa4ys923Zd5xyFXKvFsvU6uVALg5toa69vL14GbbIc0VHVARqS5yk43LoXOwW5vKM+DY8c677O+vvOG84s+ueUc5WqZgIBytUxuOaeQhoiIiIiIiIiIiIiIyAGxgsDc2uGoWlhYCFZWVg56N+SA+X444V+phBebe15YpaEx51mr3V83kQirNdx0fpsKVZLYeGQVzhhQOh3OIbdLpcLARTvTa5HPh69VjEuRzWcsoM6lgfZt6eGHuXfnTsf4NHDBtIMiQ+Rzm9PciFwWeUz3ekM98gisrRmXp5fSlKud90/ZKUoXSgPuvYiIiIiIiIiIiIiIiPTLsqw3giBY6BhXQEMmzaAhgn74RR+34FKpVkjaSbysh5Nx+r8/t3EpHPlwSCwWXujfkMn4ZLMutl3hQx9Kks16ZJqel16vRZrnKVPtXI5NiWcG2rfnLMu47Nnr18NEiMiIPcJXWGOjYzzymG5/QzVYFrz8Mpw+Hf0glgX1OrHLMYKIiJOFRf1iZ2UaERERERERERERERERGQ5TQEMtTmTimLpZ7LbLxV7bCPjcJscyZaoEQJkqOZbxub27HTpAyeT9v2cyPouLOT70oTKWFVCtlllezlFsel56vRYe2Y7WMwnieGSHu+MKZ8g+ucLj/R/TzW+oZsePh6VnDEqzAemlNMdnjkcuT9qG7YqIyMB8bpPmeWJcIs3znOMbLbeP4vc5ERERERERERERGR0FNGTimOY8O8d9IE34Nklv3+7kFlxqm7WWsdpmDbfg9rU/LgVqbLben01cCn3d/zDxvLBFCUA263LsWOvzsrlZo9D0vPR6LRxOkGeRFDYWYZWBPIsDVxf5vXPnzAtj+hicNMWiz9JSmsuXYywtpVtCQ6M20DHteRBvDXPs3K7VOtcH7sThS1koV8u898F7HcsT8QRe1tvjv0JERCA6ZPsCK2MRuhUREREREREREZHRmD7oHRDZb54XXnzePL+ZSITj9/lADmisVN6+DdBabaFSjS4DUa5G9O6IUIlo4dFt/DBrFKJwXbDt6Oel2vR89fNaOJzYc7uX2/m8cdlHf+EX9rRtOVqKRZ/l5Ryb26GqRmUXoKX9zigNdEy3tzgJAlhbi14V+MIifG1701vBVstyC4uzHz07UPslERExiwrZtmuEbo9i6zoREREREREREREZPl06LhPHcSCfh1QKLCv8mc+3d7lwuR/OaKhtj7cytQuwsPpqc5LEHmj8sHMcKJXgQx+Kfl7spuerv9di74KtLeOyn7p6dbgPJodaoeDuhDMa2iu7HAq+D5/9LNy71zp+756x6kvZvh/OiBIQcPPtm0PcSRGRydZvmPYohm5FRERERERERERkNBTQkInUCBHU6+HPzkBAdPWHqHEv62FhdYwHBH21OfHIkqC1jUGCOB7Znvc9zLJZj3g80TIWjyfItrVX6P1a7J01NRW9YEofgZOmaqh4Yxo/EL4flpap16OX1+v3ewltq8UtvtTHR4ap4o+IiAyu3zDtUQ3dioiIiIiIiIiIyPBpdlIkUnT1h6hxJ+MQEESs299kqMMJ8iySwsYCUtjkWTzypbAzGYfFxTy2nQIsbDvF4mJ+39pINDuRy3WMBcD/kVtQX/gJYxsq3pjGD4Trtvb9idJWeuaPvvwUr3ws0f0+mCv+iIjIYHxu8z53e643DqFbERERERERERERGZ7pg94BkcPJA3I0tzmpbVp84XfLfPsv03hZD6cpaJCyU5Sr5Y6t9DsZ6nDiyAcyomQyzoEEMlr4Pj912XUk+QAAIABJREFU8yYBUATqQH3K4g9zH+PrVx/n/1Rf+ImSzXosL+da2pxEVXY5UJXuwa7Vhx7im790nq3vrfGeDf/h5Pv8k5/5OHk+jltwqVQrHJ85zvfvfp+7W/cnDxPxBN5h+neKiBxRPrfJsUyNza7rWcBZPqrvGSIiIiIiIiIiIrJDFTRkIvg+PPJIeLG5ZYV/9/1u93C4VTnLX61PUQ+g9B58/ncD/ve3oFwtk1vO4Rfvb8DLeiTa2nloMnSf+T6k0xCLhT99n9Vz58ifOcNz5TIl4HHgXCLOm9c+w9ev/gygvvCT5jBVdjFKmoNdq7EY37z7A+rfW8MCPlyFf/hv1vjVf/E5AEoXStQv1nnni+/w0qdeImWnsLBI2Snyi/mWYJmIiOyOS6FnOAPCal03eXv0OyQiIiIiIiIiIiJHhhUE0a0ZjrKFhYVgZWXloHdDDlCx6FMouFSrFaank/zWb3n8yZ+0TkzG4/DVr4ITMV/pF31yyzlqm+Y2Ayk7RelCqeU+javXk3ayo8qGjJDvQy7X0hZiNR7n9c1N7jWtNg08BiRSNh8pXQDCljIlntnX3RXpKuJ4BuChh8g/+CDra2sdd/kbG77y305RD+r6/BERGbEYlwzN7TpZQJ1LI9wbEREREREREREROYwsy3ojCIKF9nG1OJGxUyz6LS0M7t0rc+pUjq0tKBbvT1hubsLZX/wrznwn2TGh6RbcruEMgEq1tQ2Bk3E0IXpQXLdjMvtWWzgD4B5wC/h8Jayaob7wcig5Dnz72/Dii9AcogyCyHAGwIeqsBVsAfer/AD6TBIRGYEkNuU+K3AlsUe8NyIiIiIiIiIiInKUqMWJjJ1Cwd0JZzQcO1Yjm3U71t36m/+MgKCjbUl7+CJK0ja3IZDB+NwmzfPEuESa5/G5PdgGKp2v17ph1XWgkrRJYZNnUX3h5XC6ebM1nAFQqzE7NRW5+ntt83+1zRpuofMzT0RE9s4jS4J4z/UUBBUREREREREREZF2CmjI2KkawhW2HTHeNNY8odkrfJGIJ/Cy3u53Unb43CbHMmWqBECZKjmWBwtpJDtfr1nDqrNA2vtfKfGMwhkTplj0WVpKc/lyjKWlNMXtQNahFBE6Aji5tQUPHmsZuxuH1yLm//oJmomIyOAcTpBnkSks4zoKgoqIiIiIiIiIiEgUBTRk7NiGcEW12jYe+wCyXyIDXAAuAp+plikWfbysRyKeaFnd2j4Jn7JT5Bfzah1gMOgkuEuBGpstYzU2cSn0/6CnToHVOklyks4eTtPb4zh67SZNo/VRtVoGAqrVMsvLucMb0ogIHQHMp1Kc+o2X+BsbAuBvbPitRfjjiPk/VfkRERkdhxPUCSKXWaAgqIiIiIiIiIiIiERqn78UOfKyWY/l5VxLmxPLSvCHf3i/4oWVWCP46V8kc+JrLAKN69E/BCwv51hczJNfzOMWXCrVCkk7iZf1dhXK8LmNS4EKVY4zA8C7bLT8PYmNR/bIn8hvTII3nvvGJDhAxvDcVQw93E3jHXwfrl1raQcRAD+2/fdbhG1NZgnDGfP9bbX1IZpew3F5rSZNVOujzc0ahYJrPDYPlOdBLge1pn1OJMDzmHccfu3/Ps/axprx7qryIyIyeklsyhHfV5LYEWuLiIiIiIiIiIiIqIKGjKFMxmFxMY9tpwAL207xmc/k+YM/cAiCcB7/5T/8JomPvUKW++GMhsakrZNxKF0oUb9Yp3ShtOtwRnP7jjU2WGOj4++7autxCHWbBDcxTWL0Pbnhuq2T2IRXrlqEYYwc8Oz2z92GM/bcgkUOnKn1kWl8X/k+PPJIWAXGssK/A+TzkEqFY6lUeLuP6i+q8iMisj88siSIt4wliOMR0XdKREREREREREREBFXQkDGVyThdr4pvTFy+feN05PJuk7Z+0e+7skZU+w6TRluPo1yZYTeT4B5Zciy3PE8DTW5UBpxgn5sbaPVuLViO8ms1aWw7ud3epHP8QPk+fO5zsNl0jK2twZNPwksvQakUebd3N96NHLewKF2Ivo+IiAxX43uAqmyJiIiIiIiIiIhIv1RBQyaWk3H4kJ2KXBY1aXvu1XNYly1O3zhNuVomIKBcLZNbzuEX/cjt9N2mY5frHzamyW7TuF/0cZd+jtpv/2um1t+HAFLY5Fnsf3Ij2f8E+1Z8Gq5c6Xt9GEILFjkUslmPeDzRMhaPJ8gOuQ2Iz23SPE+MS6R5vnelFddtDWc03L0bLjNIGt5TpnERERkNhxOUeIY6lyjxjMIZIiIiIiIiIiIi0pUCGnKkFYs+S0tpLl+OsbSUpmgISpj0O2l77tVzvLDyQuQ2aps1XEMLj+PMDLQ/R71n+SCT4OdePceZG2coV8vw1ltsPf8/kfgf/xVe8W8NNrnheZBI9FytbsHbn/9MXy0imu25BYscClGtjxYX810r7QxqV+1wulWA6bLMy3ok4p3H/ft33zcGxkRERERERERERERERORgWUEQHPQ+DN3CwkKwsrJy0LshI1Ys+iwv59jcrO2MxeOJgSddi0WfQsGlWq1g20myWa/j/tNfnmYr2DJuw8KifrHeMf4IX2GNjb72I0F8sMoRh1Q/z6df9Dlz4wwBnZ8/KTs1eIsG3+d995dIlL8HFsQMH2t/lfowP1KKbg1h3PT2pHt7C5ZxeK1kuNI8TzmiskoKmxLPGO6UhnJn65XwjiljixMI30fnXzvP2sZay3giniC/mDe2XhIREREREREREREREZHRsizrjSAIFjrGFdCQo2ppKU212jmxadspLgw6wd+DddnqutwUKohxKSKCEO06T0zMhH96KR1WzohgCrv03Ob25PivnnuV//qFFaJesboFsfrgn3k+t9VfXnoyvd8toM6l6Dv5Pnzuc51tTo4dg5de6lnxxfRe2lXQSURERERERERERERERIbCFNCYPoidERmGajW6/L9pfC+mrKmuFTS8iBYeELbBiLqivl0Ke6wn/P2ij1twqVQrJO2kMZwBkLSTg2+f25Sp8vN+kc9dezMynAHw18kP8yMDbz3sLz/Or48Mh+n93rUdTiOAcf48rG1XwpibgytX+mrHUzF83pnGRURERERERERERERE5ODEDnoHRHbLNkzkm8aj+UCa8K2Q3r7dKfexnHELD8UfMrYS8MiSIN51D44xhUe2n509kvyiT245R7laJiCgXC1jGSIUFpYx7GLc/nYLEoBfdgs8VNuMXO9OIk7J+6XBdl7GRrHos7SU5vLlGEtLaYrF6Pf6XkS93xPEe7+/HQfeeQeCIPzzzjt9hTPAHGjaTdBJRERERERERERERERERksBDTmyslmPeDzRMhaPJ8gaJvj9ok96KU3scoz0UpqX/viT1DbPAGUg2P6ZIyqkcfVnrpL9SOckazwW59cWf63rfs70KFTzT/nJsa7O4BZcapu1lrEgohGEhcVTC08Zwy7G7VOgRhjKSFaiq5UEwB/nL3PS+e8H2raMh2LRZ3k5t90SKaBaLbO8nBt6SMPhBHkWSWFjEVbGybM40ve3l/VItH0OJuKJgYNOIiIiIiIiIiIiIiIiMnpWEHROlB51CwsLwcrKykHvhuyDYtGnUHCpVivYdpJs1iMTMcHfqOLQHBT4j8/Cf/JQ1FZTQCny8dpbdXhZzxgoaFR2aIQHTFLYlHim6zpHWexyLDKQATA3M8e7G++StJOcevQUN9++SaVa4fjMcYCdZd2e5xiXdrb+3fQS6XJESCOVglJpCP8aOYqWltLb4YxWtp3iwoXS/u/QkA3yuSQiMjl8wAUqQBLwgOF9NvrcxqVAhSpJbDyyYx24FRERERERERERkcFYlvVGEAQL7ePdL+0XOaTagxlPPPFyZDADwsnLs79zlq1ga2fs538CHklErk54Ij+ak3H6nvg8z2s9wxnho0VXfRgXSTtJOWJyvKF+sd4RoFnbWNtZXq6WyS2HLWainvvjzLDGBgBf8rL8em65pc3JvcSDTHuqJjDJqtXo97Rp/KgZ5HNJRGQy+IRV0RrB3EaVNBhGSKM9hFumutNuTSENERERERERERER6UYtTuTIiWpXcOPGGV599VzHuudePceZG2dawhkAv5wFy4re/gf3jMmNvvnc3gkN9JLE3vPjHWbdWi2sbaztXP3f3galWW2zhltwez7W15wMX8gvUkrZ1C0opWzO5T8FjiavJ5ltJwcaFxGRo87lfjijobY9PoytFzpCuDU2cSkMZfsiIiIiIiIiIiIyvhTQkCOnUHDZ7JjMD1hZeZFi0d8Z8Ys+L668GNleI9klE/H+3Tv4TdvZjX5P0CeI45Hd02Mddk7G4eFjDxuXN1oz9GJa5922IMzXnAwfKV1gqn6Rj5Qu8OvO3xlsh2XsZLMe8Xhr8CoeT5DtEh4SEZGjzPS9YveVk3xuk+Z5LC5RNlQ/G/eqaCIiIiIiIiIiIrJ3CmjIkWNuSxBQaKqy4BbcyHAGwJq5WAPHZ+irWkM33U7QzzGDBaSwybM49qWw/aLPnbt3jMsr1QrJPioZmNYZ9woksneZjMPiYh7bTgEWtp1icTFvbIskIiJHnel7xe4qJzVampiCGfe3ru8kIiIiIiIiIiIi0t30Qe+AyKBsO7nd3qRTc3ijn6oMUSrV3d+3IYkdeRJ/jhne4b/b07aPEr/ok1vOGYMyEAYvvKxHbjlnbHMSj8WNrVI8si194EWiZDKOAhkiIhPDA3K0tjlJbI8PLqqlSbtJqIomIiIiIiIiIiIie6cKGnLkhG0JrMhldlOVhW5VGf76/4KlJbh8OfxZLIbjQQBfKnTe1y/6pJfSxC7HSC+le7ZA8ciSIN4yliDOFR7ver9x4xZcY+gCwMLCy3o4GYf8Yp4paypyvdkHZnEMk+sOJ8izSMxwTMwxM/iOi4iIyBHmAHkgrJwU/sxvj/ev0dakV+UMYCKqoomIiIiIiIiIiMjeKaAhR0qx6G+3MemsyBCPJ7bDGyEv65GIJzrWywDLy1DdPtderYa3i0V4pwav/EWipVpDowpEuVomIKBcLZNbznUNaTRCAynsQ9nOpFj0WVpKc/lyjKWlNMUegZPd8Is+ZUOlEwjDGU8tPLUTvHAyDvWgHrnuuxvvdn0shxP8Jp/hGK0Bj2NMTVwoRkREZPL4QJrwvzbp7dsOUALq2z8HD2f009YEwu95h+U7noiIiIiIiIiIiBxuVhCYWw8cVQsLC8HKyspB74YMWbHos7ycYzOiIsPMzByPP36lo4WBX/RxCy7lapkpa4p//ONb/N0y3Pl+5/ZnZ+HPfvQhbvzZg7y78e5O643G/dul7BSlC6Vh/fP2TdTzGI8nWFzMD60FRCPUYqqeMWVNce0z1zqqYqSX0nt6rn1u41KgQpUkNh5ZTZiIiIiMNZ/odiaDV8xo1m/ljATxQxXCFRERERERERERkcPBsqw3giBYaB9XBQ05MgoFNzKcAXDv3gbQ2YoEoHShRHAx4N4/v8Zv/JwVGc4AWF8HvxiwtrHWUinDVAWiUq3s+d90EKKex83N2nZlkuHo1tokEU9EhjMguupJIt5a0aQbhxOUeIY6lyjxjCZLRERExp5LaziD7dt7+15T6RLOmNpuq3bYKqSJiIiIiIiIiIjI4Td90Dsg0q9ql0DE5maN333tPL98b2MnGNAIWADbYQCXRDzAtu+3N2n20A9B7futJ/hrmzWmrCm2gq2O9ZN2cvf/mANkeh67Pb+D6hZeyS/mI8MZwM64W3CpVCs7VUxM64v00miLVK1WsO0k2aw3tEoxIiJyGJi+c5jbrPUjiR1ZQSOFTYln9rRtERERERERERERmVyqoCFHht0jELG5sdZRtaG2WeP0jdOkl9IEQXiiPpuFeLz1vvE4/NQno7e7FWztqarDYWN6Hns9v/3yiz4xK/qjJWWneoYtnIxD6UKJ+sU6pQslhTNk1xrtfKrVMhBQrZZZXs7x6qvnWFpKc/lyjKWlNMWif9C7KiIiu2b6/mIRtj/ZHY8sCVq/MCaI45Hd9TZFREREREREREREFNCQIyOb9Yi3BSWadesSXq6WWQu7oJDJwOIi2HZ427bD28dT5lBBfjFPyk5hYe3cPqrBgajnMR5PkB1C4MQv+uSWc5EVR0YRamlvaeNrol2amNr5rKy82BHaUEhDROSoOmUYD9hLmxOHE+RZJIWNhdqZiIjIYM7xDaa5jMUlprnMOb5x0LskIiIiIiIih4RanMiR0WhL8Npr59nYWGtZFo8n+JPpGWgbbxYEzdsK/zTUNuFfv/UPScT/oKUKRyNU4GScIxvIaGhu9TAzc5zp6Rk2Nt4datsHt+B2VDEBmLKmhh5qaYRBzC1tZNKZ2/YELbc2N2sUCq5an4iIHDk+cK3L8r21b3M4oUCGiIgM7Bzf4AVWdm5vEezcvsrPHtRuiYiIiIiIyCGhChpypGQyDl/84js88cR1bDsFWNh2isXFPJ9//EpHK5KGn/8JeMRQfCMI4PO/C7/42h9w9qNnx6ZSRrP2Vg8bG2vcu7fBE0+8zIULpaFNTFcME+L1oD705zEqDFLbrOEWdn+1rIyXQdr2mMMcIkfPqu+TT6d5LhYjn06z6qtCjIwrF+gMht43nPZtIiIig8jzxkDjIiIiIiIiMllUQUOOpEzG6QgVNApiuAWXcrW8M/7zPwFf/RRYVvS2ylX42lsANW6+fZPShdIodvlAmVo9DLtqQNJOtjz3zeNR/KKPW3CpVCsk7eROtZJ+mMIgpnGZPNmsx/Jyru3Yt2ivoAGDhTlEDrNV3+f1XI57tfC4Xy+XeT0XVhead45+4FCkVbff+QlguK3VRERE+rEV8f+NbuMiIiIiIiIyWVRBQ46cYtFnaSnN5csxlpbSFIvhlcF+0ef8a+c7AgJXfhoeMESR7tyFLxXu3x7XyX1TdYBhVw3wsl5HFRMLi1OPdvaH94s+T77yJOVqmYCAcrXMk688iV/s70pvU+jDNC6TJ5NxWFzMt1TbWVh4injbMRqPJ8hmNYkn4+GW6+6EMxru1WrcPH1a1TRkDJl+508BeUChJBER2V8+tw96F0REREREROSQUwUNOdSKRZ9CwaVarWDbSR599BRvvnlt54r4arXM8nKOW5Vvc/6PfoPN+mbHNrq1NvnCcqN6RmhcJ/dtO7nd3qRzfJicjMO3K9/mxZUXCbavDgoIuPbmNT6e/HhLdYzzr53n7tbdlvvf3brLU994qq8qGl7WI7eca2lzkogn8DTRLk2iqu0kkx9v+VzJZr09V5I59+o58m/k2Qq2mLKmyH0sx9WfubqnbYrsxnrFHLxTNQ0ZPx6Qo7XNSQKFM0RE5CD43OZzfP2gd0NEREREREQOOSsIxq/E4sLCQrCysnLQuyF7VCz6fbcneN+a4rlgK3I79X8e3d4kCCD25fu3E/EE+cV83y02jpJi0efrX/8c9aYASywW59Of/upQW5wApJfSkW1OUnaqpX2MddnQcwa4/sT1vl6HvbRIkfHUHuoaRviil3OvnuOFlRc6xrMfyfJvP/tvR/rYIu3y6TTr5c7P4GazqRS5Uml/dkhkZHzABcqEFTO2gBRhaEPfBUREZP89wldYY8O4fO7uNO8c+2f7uEciIiIiIiJykCzLeiMIgoX2cbU4kUOrUHDbwhkQFc4AeCjYImPYzjvtm2gatwhDAik7NdRwhl/0SS+liV2OkV5K9922Y9ga7WBu3DjdEs4AsKJSKwNut73NDJjbxDSP93o+3ILb1344GQcv65G0k1SqFdyCe2DPtRy8RqgrrBYT7FTYKY74mMi/kY8cL3y3gHXZOtDPAJk8Jz2P6YShdNS29iobq75PPp3muVhMbVDkiPAJK2c0wkhbhJUzFM4QEZGD0y2cwd27rN/8Hf2/QERERERERBTQkP3VbWK/XdUw0R/FAhYhMqThfushPrjXOnbvB/CHX4Dnfu04f/qj1yldKA01nPHZG5+lXC0TEFCuljl94/S+T9S2TlZ32tq6S6HPIIR5u52T4KY2MY1xv+iTW851fQxTyKNdY1vNz3VuOaeTXhMqKtS1uVnb1XE+iC1D9Z4GHZcyKlHBinnH4bF8ngfn5oz3m00mW7bxei4XVt0Igp02KAppyOHm0trWhO3bo/28PywBXBEROWKCAJaX2Xzzj/q+GEFERERERETGlwIasm9effUcN26c6fvqdtsw0Q/RlR+OAdmI8Rt/9iBPvgKl96Beh2oZvvk5+IuvQf17a0OZiGo+YX/mxhnq1CPX28+J2ugKJK0GCcF0227zJLiX9UjEW6/eTsQTeFkPCKtj1Hrslynk0S5qW7XNmk56TSjT8byb43wQU9ZUz3V0XMqw9QpW/OC99yLvZ01NcdLzdm7fcl3u1Vo/R+/VatxydbzKYWb6XB/d571CoSIisidvvQX0fzGCiIiIiIiIjC8FNGRfFIs+Kysv0t6ipNvV7dmsR7xtoj8eT7Cw8JTxceyIsXc33uVrb8FHrsD/cBx+PQ1//rX7y/c6EdV+wj4wtGFp2K+J2n4mpc0hmMG32xh3Mg75xTwpO4WF1dE+pmyo6NHQHObopZ92KjI5TMfzbo7zfjSCWb0qaDTouJRhMgUrbp4+zWtnzxJsRR+X7ePt7U56jYsctGLR51d+BS5fDv/8yq9AsdhYOprPe1AoVEREhqPfixFERERERERkfCmgIfsiDGFEBxdME/6ZjMPiYp6Zmftl2qenZ0gmP45tp6K31XY7ZadaToB8qH2FbXuZiOqnIkS7/Zio7T0pbZHtMwjRUCz6WFb0x0bz4zkZh9KFEvWL9Zb2MX7RxzJUQGloDnP00qudikwWU6hr0OO8H83BrH7puJR2PrdJ8zwxLpHmeXxu933fbr+3TOGMhuZKG83tTpqZxkUOUrHo8/Wvf46NjfvfKTc24MYNePXVGDD8z/sG0+f9IL8HRERksg1yMYKIiIiIiIiMLwU0ZF90q+YwM3O8633v3dvY+fvGxhrLyzkeffRUx0TsJlBout04+dFoufGTXea99jIRtZsT8/sxURs1Wd3s2LGHBtpeseizvJwjiKgW0O8kuFtwu1YYSdmpvsMZ0LudikyWRqgrDHBZ2HaKxcU8mQGOqX6df+38wMEsHZfSzOc2OZYpUyUAylTJsdxfSMP3mY3t/itcc+Wok57HdKL1c3Q6kWhpgyJyGBSLPr/zO2ep1zcjl6+s1JsqaQyfqZ1VP22uRERk/J3jG+aFtVpHZUkRERERERGZXNMHvQMyGWw7SXUXQYZCwWWzbRJ0c7PG22/fZHExT6HgUq1WsO0kDzx6ivW3b2JVKyTtJF7Wazn58RfPncUi4qpiy9r1RFSjIkSvtibN9itA0JiUDp+jMmDRXMXk7t33WV7OtazbTdRrAWBZU31PgnerHLKb56Xx+roFl4rhdZfJksk4IwlkNPOLPmsbawPdZ25mTseltHApUKN1ornGJi4FHE6Y7+j7kMtxcmuL14F7u3z8RgWOeSc8Lm+5LuuVCrPJJCc9b2dc5DDoFhJtVii4I/sdYGpn1W+bKxERGV/n+AYvsBK57BhTvPTQGZwL/2qf90pEREREREQOKwU0ZF9ksx43bpyOXLax8a7xfqbKG9VqJXIi9mnDdpyMw3PvnYleGAS7nojqVRGiXcpO7UuAoFj0W8IrTzxxvSmocd/mZq3vyQzTaxEE9b4nQ5J20lhx5OxHz+7qeXEyjia+ZV+5Bde4bG5mjo17Gy3VNRLxBFcev7IfuyZHSKWjKVf38R2uC7Ua89s3bwHru9mBICCfTu+EMeYb265Uwp8ACmnIIWEKibbrVrFtr1J2KvI7TMrQdk9ERCZHnjeiFwTwkvWp7uFbERERERERmThqcSIjVSz6LC2luXHjDJYVfbjZXdp9mJZ1u0+kLuXgZ1O7P7HerSJEQ8pOcf2J6wQXA0oXSvsSzlhezm2HMQKq1XLT7U79TmYM47Xwsh4WVuSym2/f7Hs7zfyiT3opTexyjPRSGr/o72o7Iv3q9r6/8vgV8ot5UnYKC0uljMUoiT3Q+I7K/eNvHsgBp+hM3E5vj5+am2P62LHITa2Xy7yey7F67hzkclAuQxCEP3O5sFqHyCGw1+8qw3Dq0VMDjYuIyOTYMl60ETDS/lsiIiIiIiJyJCmgISPTHhQIgnrHOvF4gmyXthbZrEc8nhjoPh18H558kpNbW50TWInErtubQFgRohsLa19CGc1MbWEsQ4/0ficzhvFaOBnHWHGkn7BLO7/ok1vOUa6WCQgoV8vklnMKachImd73jTYmTsahdKFE/WJ939//cnR4ZEkQbxmzgFM82v2Oyc7jbx54DJjdvj27fXsemF9b47EgYHZuLnJz92o1buXzUGurTlCr3a+kIXLA+vmuMjV1bLDvhwMyBUl3GzAVEZHxMWW4CIF6vWv1PREREREREZlMCmjIyPQuR23x0Y+e7doeI5NxWFzMY9vhxJJtw+LiDJnMADty/jzcvds5gRWL8Vg+v+v2JhBWhEi0hRaaHZ85vutt75a5FcnWngIWQ3ktMJcC7xV2ieIW3JZWEgC1zZpOgsnI+EWf9+++33W5SDc+t0nzPGe4wRatwcUAuMab+Nw2b8DzIKIiRqOaxrPbP+ebl21uknv4YbCiJw/Wt7aiH6syunYRIv0qFn3udvncbQiC/lvO9dJenevcq+eMLdp2EzAVEZHxkuNjYRWyZkEAKyv6PSEiIiIiIiId2gsKiAxN73LUAW/3cdVhJgOZzEbTyBrh9BNA73DF6toat4B1wnDGSbYnrup12EM4A9i5Mv78a+dZ21jrWL62sYZ1OWxz4GW9fbmS3raTke1MbDtFNutRKLhUqxVsO0k263UNyLTb62sBYaglt5xrCVYk4gm8XVz1ajrZpZNgMgqNii3toaCGtY01csvh+0FVMySKz22e5BXuEgYifkBnMKLGJi4FY6/yW1T4e8E9HohaODcHa52BhN8aAAAgAElEQVS/iwCoVJhNJlkvd/5+mI1YHYis1iGynxrV2DoDvxa0VeSq1zcpFNyBvtdEaf+sL1fLvLDygnH93QRMRURkvFzlZ/nNN3+TO5m/DbFYeK5hZQW++U2ShgsUREREREREZHKpgoaMTD/lqKOCBK184CzQfmK+BvSukrDq+7xOGM5g++frwGrPe/bPyTi888V3mJuJLh8P7GvrjWzWIxaLd4w3rj69cKHExYt1Llwo7WISw2W3r0WDk3HIL+ZJ2SkswvBKfjG/qwlt06SIJktkFKIqtrRTBRfp5jyv7YQzuqlQjRz3uU3S/QoPbHa2DANgYwMeeih6WTLJSc9jOtFaSWmaMLjYsArkgeeA/Pvvs+qrKowcHHM1tuhqGb3Dwb3181nfsNuAqYiIjJ9fm/oUia/8z/DlL8O//JfwzW/q94SIiIiIiIhEUkBDRiab9TpaarSzrKkuS33C6gymyazeJ+FvnT/Pvbaxe8Ctnvcc3Lsb73Zdvp8Tt1ZEGfuNjTWWl3MU9xQSMT3ng02IOBmH0oUS9Yt1ShdKu642cOrRU1ht/X51EkxGpd/KLKrgIiZrbPReCUhiR467FPiRSnR4A4BaDe7c6RyPx8HzmHccHsvnmU2lwLKYnZriMe63Q1mF1lDj2hqv53IKaciBGTRw0U84uBdTK5Mouw2YiojIeJqZntn5+9zMnH5PiIiIiIiISCQFNGRkMhmHj370bNcQRhB0u5I4qlpDs94n4dcNpd7XI0f3pp+qDfsxcVsouGxt3Y1ctrlZo7CnkIgpcHN8D9vcHb/oc+3NawRNV9FaWJz96FmdBBOKRZ+lpTSXL8dYWkrvMZgU6rcyy/GZ/X8/yPiwAI9s5LIKVSrJ6PBGV7OzOy295h2HXKnEs/U6uXp9J5wBYXixI9RYq3HLVVWYg7Tq++TTaZ6Lxcin0xMVmBkkcBGPJ8gOIaA51TU8fF/KTun7hoiIAOH/TZ985cmWtqffv/v9A9wjEREREREROcwU0JCRKRZ93nzzWtcQhmVNdZk47XYFYwLofhJ+9ZOfNC6bBZgztyTZDS/rkehRMWQ/Wm/0ahuz+/LfPhBxZfYBiSpBHhBw8+2bB7RHclgUiz7Ly7nt90JAtVoeQvWY/t7jEJ6M3Y92RjKGAniKBRxORC5OYvMlL8udRGcbq67eNVR4Srb+TjKFF9crqgpzUFZ9n9dzOdbLZQgC1svliapqYmrb1s62Uywu5nfRuq3TVtfwcEjVukREpNn5185zt+0iibtbdzn/2vkD2iMRERERERE5zBTQkJEx9w2/Lwi2DBOnPtDZpiM0BeSBLifhfZ9vFQrGxScBrlzpum+DcjIO+cU8KTsFcGCtN7q3jdlL+e9uV1B3b+8yTH7RJ72UNpYgV3sJifrs2Xv1mNb3uIVFyk7xUPyhjvXubt3dt3ZGcnT43O6+wtYWc9/8d1zlZ42reGR5xfm7fCG/yP83N9NUP2hbRHsroCOIcX+DHiTuh45mDY87a7q/jNwt1+VerfXzbJKqmmQyDg88YDoy77twoTSUcAaw8z3OZMqaUsl6ERFp0Vw5o59xERERERERmWwKaMjI9FupIXri1IXOqadtObqGMwB+4Rf4oMvi+aef3in3PkxOxqF0oURwMeDlJ15umcjdr5P53SqW7K38d7fXc3STd41AhnXZYurLU5y+cbprf/j9qFIih5vps2f31WPua7zH6xfrlC6UOqq4NCgoJO1czKFB7t2Dr3+dd//Dv+u6DYcT5Fnk3zsn+U/f+SLnrzu8n/rhMJiRSsFTT7UELoDwtmf43HccyOfD+1oWJ+fmmD52rGWV6USCk6b7y8iZqpdMUlWTjY3uIdCZmeFXRDO1OYnH4lz7zDWFM0RERERERERERGTXpg96B2R82XayZ7uNhs6J024TDz1aWPg+q3d6tOK4erWf3doTJ+McyAl8205FPu+WNbXH8t9JotvOWPRqN7NbftEnt5zbmQSvB/We9/nR4z86kn2Ro8P02WPbAXAOGN77P2knIwNDCgpJuzLV6AVBAK+8Am+9RbLHlfsQhjR2WqA4dOYVP/5xcF2oVMLKGZ7XPZDoODvL5wF8n1uuy3qlwmwyyUnPY34EgUbpz2wyGbY3iRgfZ8WiT6HgUq1WsKyYMXwai8V5/PHhVkQDc5uT2QdmFc6Qga3qc1VkrHVrbdheVVNEREREREQEVEFDRiib9YjHE71XJKrtxvEua/e4atR1+VaXxQ8+/HBf+3RURT3v8XiCz3zm2h7Lf3tA++tpAU/Rs6LJLrkF11ihwOT3S78/kn2RoyP6PQDZLMALhCGN4fCyHom2x9qvdkZytEyZTtDX6/DWW1hYwzluHAdKpXC7pdLA1aLmHYdcqcSz9Tq5UkmTiAfspOcx3VYVZdyrmhSLPsvLue2gXWAMZ8zMzPHpT391aK1NGrq1qHq3RzUPkXarvs/ruVwYtAoC1stlXs/lWPXNE7oicrR0+70RGKuCioiIiIiIyCRTQENGJpNxWFzMY29fEWztlItunaQKJ07fB/o9UdnjqtFKpWt7k0+8+GKfj3M0tT7vFrad2mPlDAhfm/NAc1hiDniZYVYjaLebNhGmq15lMhSL53jttc+y2RTsmZmBxUXIZBoj+aE9npNxyC/mD6SdkRwtW6YT9LHwq9gnPvIJHTfSYd5xeCyfZ3a7Dc1sKsVj+fxYB2cKBbflM7wh/B4Zfq954onrfPGL7ww9nAHdv3uoOpIM6pbrcq/Wejzfq9W45ZondEXkaFFrQxERERERERmUWpzIyDSXp7btFNmsRyYDxeJ5CoU1qlWw7fCq9kxmDcht39MBul2h2P2q0d9LJKBLi5NxntRoyGScIU5a+MCTwN228fUhbd/M1D6iG1PfeBl/xeI5XnnlBbbaMjo/+EH7msMN8RxUOyM5WlLY0W1OquHYd979zj7vkYzKsNsZzDvORHx3aehsexcKgjoXL/ZudbZXpu8eQ6tyIy3Gvf3HeiX6eDaNi8jR4hd9YlZMFwmIiIiIiIjIQFRBQ0aivTx1tVpmeflJisXPkcmsceECXLwIFy40X9VeA1zCQIDp0JyjWzuNVd/nzS7hjAd384858nwgTficpum/UkmDS2c4A2Bze9no7GYiJPexXO+VZCwVCvmOcAaEnR4KheYRhXhk/3lkSRBvHbx7d+fg1NWX40HtDPaus+1d9/FhO/XoqchxVbkZzKrvk0+neS4WI59OR74HJuH9MpuMPm5N4yJydPhFn9xyrms4Y25mbh/3SERERERERI4KBTRkJKLKU29u3qVQ2OxxzzJhJY2okxwJ4ErXe/cqF/yJuUk7QeITPp9hUOb+8zvIie9uFSxGO6HoZBzjSa25mTmeXnh6p2LGlDXF0wtPc/VnRtdyRQ63atV8crTaUrhAIR7Zfw4nyLPI1Pr7EATw3nuwvAxvvQWodcK4UDuDvctmPeLxRMtYPJ4gu0/VK26+fTNyXFVu+tdv8GIS3i8nPY/pROvxPJ1IcNJTNRaRo84tuNQiWnI1xGNxrjze/fyFiIiIiIiITCa1OJGRMJWnrkZUd281RVhJI2o8T7fqGdC9XPCDwPyVSTtB4tL5fDYqlfRzFWivIMfoJxSvPH6F3HKu5eRXIp7gyuNXcDKOAhmyw7anjCEN24bwcyQH6JiR/eUXfdyCS7laxsIiDMzdl4gn1DphTAyzncG4t34wabRou98mL7ndJm9//u2majaqctO/bsGL5mN4Etp/NP69k/heFhl33X4vpOwUXtZT5SURERERERGJpICGjIRtJ7fbm7SP97qn6Qr4LfoJFMwmk+HVehH+djYLE3cy1FT9ot8T372uYBz9hGLjpJZbcKlUKyTtpE52SaRsNscrr7zQ0eYkFpsim71Gf6EkkeFqlL9uhMyCtnDG3MzcTuBMjj7T95BB2xk0KhA0JrkbFQiAiZjYzWScfQtktEvaScoR32FV5aZ/xuBFucyq7+8cw8N6vxx2844zEe9bkUnRCN62f6drSNkpShdK+7tTIiIiIiIicqSoxYmMRHR56mmy2W73eqjLsqm+Hvek54FlRS777ncmrTS1D0Q/F3C8z210C3LMsV8T3k7GoXShRP1indKFkiYyJVImc5VPfeppZmbu/2qbmXmIT3/62oFN9In0Kn/98LGH9Zl2xKz6Pvl0mudiMfLpdEvbhmG1MzBWIDh/fvc7Ln3xsh6Jtu+wqnIzmG4Bi+ZWJ2r/ISJHTSN4GxXkA/2+EBERERERkf5YQRCd+j/KFhYWgpWVlYPejYlXLPpt5am/RyZz17B2ApgB1rpssb9j9TlDQAPL4tl6va9tjIc05goa08D/Ru+AhWkbFvByH/cXOSg+YQWYCmErHg8dr3IQYpdjxissASws6hcn6XfT0dZe2QLCCeXH8vmdK+TbW5N85NQpvnvz5kDtDZ6LxcDwHf3Z69cnsCLY/mpcHa3KXbsT9T5pNptKkSuVdtZV+w8ROSrSS2ljOENtTURERERERKSdZVlvBEGw0DGugIbsH1M1B4DrwOkuy1NAqa9HyafT0eWSm04GT4YY3UMtc8A7hmWNye0y4evWvB0LeAq4OoR9FBkFH8gBzRNDCSCPQhqy37qdyAeVwT5qBv2O0U+go7Fe8yT13fff54O16NDq7NQUJ69d0yS2HGqrvs/N04bv9hMXmhaRcWEK3ipwKyIiIiIiIlFMAQ21OJFDwiGodzsc+y8TqnLJDb36d5uqlTQmtxsTUM0noFKElTMUzpDD7Dyt4Qy2b7sHsC8yyfyiz/e+/z3jcgtLZbCPmPVKdOsv07ixVYl7//OoEeJYL5chCFgvl/nB+rrxS/r61lZLmwiRw2jecZhNpSKXPXg8bLXXrV2QiMhh9N+8fZzvPg9bl+C7z8PP3w7Hk3av/3uLiIiIiIiI3KeAhuyT7idc1/78k2B1u+Kk/6tE5x2Hx/L58KSwZTGbSnVcqToZPMKqAYNy6ZzchrByhtpEyGHkE7bjiQGPYA4fRU+gioxCo0f53bqptRc8tfCUymAfJb7PbCz6q/NsMnpixhjoKJd3JqOjQhzB5ibHLItZw660hzxEDqOTnkfs2LGO8R+sr/N75851BJMUPBKRQ+3cOa74a6Sr4f860lX49WX4J38aV+BWREREREREBqIWJ7IPfOAssGVcIwjAMnZA6b+9ibT7JFAwLDO1OOnWGkWvhRw2Ue1MTHT8yv7p1doEILg4ft/BxpbvQy7Haq3G68C9pkVRLUsaTC1Rmu9388yZ8ItQhGcti+dM39Xb2kS0t0k56XkTGE6Vw+Z/eeSRyHY91tQUwVb0/w1mUykdvyJyuPg+GH5fv//Dczz816bWoSIiIiIiIjLJ1OJEDkhj8tQczgBzOGPzDvzVrVMqf7wr5zCHM+LAFcOybuVZVYFADhtTxZcourJN9k+lqs/L/eBzmzTPE+MSaZ7H5/ZoHuj8eajVmAceg53KFrNTU12rdEW1XWtoVMEwVd+YtSwIAmMVjeb7RbVJUTUCOUiN9iVR4QzAGM4AdPyKyKGzev48+SDgOSAPrDYte/j/efeA9kpERERERESOKgU0ZMQGmTxtFQTwrWce4rf+i2uacNiVfJdlX8XcqqTbJLZ668ph0+8k+BxqzyP7Sb3IR8/nNjmWKVMlAMpUybE8/JCG70PTJPM8YfT0WSBXrzMPkE6HadPp6fBnOg2+v9N2zWS9UokMcUxbFie3r9I9CUy33W86keCkd//3dVSblPFrg9LczipNr/Z5cnBaAkMG1tRU120cquPX98P3dCy2894Wkcmx6vu8vrbG+vbtdeB1mkIahqCliIiIiIiIiIkCGjJiu7+C+Pt/afH2jQcnYMJhVLpVLek1UR110jyOKhDI4dPvCVFTxRiR0fCyHol4dOUEgJSd2se9GU8uBWpstozV2OQ8rw35gbp85zh+HHI5aExEN6oClMvw5JPwyCPMnznDrGEyejaZ3AlxzM7NhWPAY0EQBj+gs2pHKtVRtWO9Ev19yzR+9DQqspVhO44T3tZE+WEUFRhqNp1IcCKXM1aXaRjp8dtv6GK7vRHbYXHK5fC2QhoiE2HV93nt7NmW1mYQtjq7BWEo09P/kUVERERERGQw7RfkiQyBT1g5o0KYAere3iTKZs1ivfIUH7z7YuTy8ZlwOIxcol+zWVSBQA4fj3CSrlulHlXPkP3nZMJj7vxr51nbaC3xn4gn8LI6mb9XZaqR42ts4HMbhxPDeaBe3zkME9Grd+9ya/uK2we3togB9ablzVUw5oH5jQ3jQ8xv/yGVglKpY/lsMhlZrcDUPuXoiarIVtse1+f7YdPte7pFGLb+bj7Pj/+Df8B3v/MdY6WN2ePHR7ODjdBF473bCF0AtLcrct3O93itFo4bWhuJyHhoVAMytWRaB3jqKX0WiIiIiIiIyMBUQUOGrP0Kx6iTGQngaSBFeJo21XH7//2jp7h5+mZ4pVqE8ZlwOIxMJ9XVW1cOI4ewnU+3X2eqniEHw8k4vPPFd7j+xHVSdgoLi5SdIr+Y3wlwyO70amPiUhjeg5m+c8zNwbvRvxtXCcufN8qhf0D4rejBWAwsq7MKRtQkcLtEwniVbmSblLY2KEeb6bvJeAV2fW6T5nliXCLN88Nv17NPun1Pb3yzX9/a4k8LBU6eOsWp69eZPnasZb1p4OT6+mgqVZhCF6dPd1bTMIVNFBYXGXu9qgHNxmJw9eo+7pGIiIiIiIiMCyswTIAfZQsLC8HKyspB78aEShOGM9pNEV43miS84t08MdW4UsV0MmQ6kego7S1R0kS/FimgNIL7iRykh4E7EeMPEE6Nisi48LlNjuWO9ibNLKDOpSE9YNvV9hCGJfL5cKI34ur/PPfDGc1mgVzUd+9YzBhKxbLCkIjndb1Kd9X3ueW6rFcqzCaTnPS8MfqulGbcv5tEHdcJ4uRZHF41mH3S67t8s9mpKXL37rH68MPcunOHdcL3yUm6V43Zk27vN7j//nacMLARVeFjFPslIofKc5ZlXDZN2H5sfgzPp4mIiIiIiMjwWJb1RhAEC+3jqqAhQ2a6mqy+/adESzijrf/z6rlzYY9XwwndqL7rYuIRVitpltgeH8X9RA5SVDgD4Af7uhciMnouha7hDIAk9vAe0HHCydpUKgxLpFL3J289LxxrExXO6DZurNKRSkG9Hk4E9/juM+845Eolnq3XyZVKY/Zd6RRh7KbZeH03iTqua2wOtxrMPpl3HB7L57Gmpnquu761Bb7P/J075IBnCWvxzTdWGEWlil6V+BotTCB8j7dVp+lWzUZExke3z7DHgPlUav92RkRERERERMbKoQhoWJb1Q5Zl/ZxlWf/CsqzXLMt6x7KsYPvP3zno/ZNBmE54Row3rkgtlyEIWC2XufnCC8Yer1jWGE44jFKj9UNzK5k8vXu17/Z+IiIio1em2nX5MabwyA73QR0nDEm0hyUcJ+w/3xbSmDVsZnZuLnqBJoG78IFr3G+OAeH3k7OM03eTiuG4No0fdvOOY/5O32R2aup+GCLKKNoaRr3f2pXLYYgczAEtERlr3T7D5vU7WkRERERERPbgUAQ0gCzwCvDPgJ8GDGev5fAboPrCdv/nVcLp/5s9ttytn7WYOIRVSyKql4zkfiIHxfRrQ79ORMbNVEclhVb/lJ/c35YQV6/Cyy+3TOCefPpppo8da1lt+tgxTl65Er2NblU6Jp4LtFdWC+j9zfHw87nNI3wFi0uYiuQPtRrMMLVVwcP3O5bPdmkPAGGLgJMPPhjdQqRhFBOgze+3bsrlMEwO0QEtERlfvm8OW4J+R4uIiIiIiMieHJaABsB/JDzTepmwsq0cSX1WX/B9KJdZBV6nS8nvbbFjxzipK1QOxKrvk0+neS4WI59Os9p+Al7kULgCxNvG4tvjIjJOtoxT2aGbvL1Pe9KkrcLG/NWrPPbSS8xuBy5mUykee+ml7lXATFU6Jp5p8n4ErS/2kc9tPssN1tgwrpMgPvxqMMPQVgVvJ8jQ/B3RdTkZBEwbNjHLdouAO3ci2wQBMDc3uvdB4/12/Xr3ahrN7U5EZHK4Lich8jPsI6Df0SIiIiIiIrInVhB0P8m9LzthWVNBEGw13U4D392++WNBEPz5INtbWFgIVlZWhreDMlyNk7q1Gnl6hzMAph96iAvvvz/qPRsDPuGVphXCtjIee6l+ser7vJ7Lca92/8rV6USCx/J5tZqRQ2i4x7+IHD4+tznL73QNaVhAnUsj3hE/nLStVMIWDJ6nyZqR8IEzEPl6pwirfB1Nj/CVruGMFDYe2f2tBtOvdDq66kUqFYYeIKysEQSsArcIv+/PAieB+ahtWlYY9mhIJPbvCvXG+9lUycOywuCUiEyOWIzfCwLejFhkAY9fv67/D4uIiIiIiEhPlmW9EQTBQvv4oaig0RzOkAnguqwOEM4AuHfnzij3aPd6lXfe350hLD5TJpzIKG/f7tynjqoY585F/ju+9dRTLeEMgHu1Grd0JaEcSmrNIzLOfG6TY7lnBY2Rt4Top3qADIlLdDjDIrJ93hHSLZxhASWeOZzhDAiDSb3Gt1sTzhN+G312+2dkOAPC99JBtfhpVNMwtTxRm0WRifN7iURkOAPC30rfOn9+P3dHRERERERExsyhCGjIZFktl/tqa3LoHboJmqge7bXt8fsaVTHWt/d7vVzm9RdeYLXt37H6yU/ygaFqybrpxLyIiMiIuBSosdl1nX1pCeG6YduDZmqDMCKm7xsBRzmE53O76/LjzOzTnuySKbDQPO55na1DEomwbUmURvWNg2zxY9pntVkUmTi3N8whOoAP1tb2aU9ERP7/9u4+yrKzrhP999cvSacTqjGdXAGxuhlnBAoRkMZ7R1tHiGQkEl9nxqUVENCpkFxnouviiNa4RtRSxrkj9OgEaGcAIYfxzvgSjaIGozjTVxAbeQm0MOol3Y4IJB3oJumEpJPn/nFOJSfVdaqruuu8VPfns9ZZ++xnP3vXr+jm6ZO9v+d5AAA4FwloMHIHkpw8kxMn7ZupE/eAZtBDjMe2H5ifP3VWjHT/XB5x4kQO3HbbwJ805ZuEAIzYkRwbeKzSXRJif64e/qwDq5k9gHUy6PPGgJkONoj5DP6MtSGsJsgwO9udBWPprBj79k1uCGJQzZYxgPNOs6wRAAAAQ7Rl3AVwnrn++jOfOWN+frJukE7cA5rpdJc1Wa79UYNmv1j657LSn9PeSbiJDsB5ZTo7cniZkMau7Mgd+aERFjLdnW1quXbW2UK6C2P0B0u3Z6Mvb7Lc3+N+d6+w/MlEWPw8Pj/f/dw7Pd0NWCz9nD47O/iz++nOHZeVagbOG5XlF9hatG3QbEAAAACwCufMDBpVNVdVB6vq4J133jnuchjg0BvfeOYnT9o3U1czvfNI/f1l2k59iDFo9oup0+wv2pZkxo1rAEZsIVdke7Y+pm0kS5qcUohlEEarf7mPnUn2ZyMvb7Ia09kx7hJOb3b2zJckOZtzAYat01lxLq5NF1yQF+zbN7JyAAAAOPecMwGN1tr+1tqe1tqeyy+/fNzlsIxDnU5+t630PZTBtiWT983UiXpAc32y7HTZ/zCPPMTodJLdu7P38OFsqXpMry1J9vY3XHBB9ubUKXa2JHnBxRevT8kAsAaz+crsz9XZlR2jXdLklEIsgzAcnSS70/3Pk93pfraZS3K0r8+EzyyxDsYSOgKgq9PJoZe8JJ8YcHhq8+Z805vf7AsLAAAAnJVqZ/jAfJiqanfyyH8TP7219rG1nL9nz5528ODB9S6Ls3Co08mtc3M5eeLE6TsvsSnJN23dmpm3vGXyHn50OhMyRfOWJA8t0745yckcuv76HHjDG3I83ZkxnpLu/8GOJ5natSt7r7oqM+98Z/f3uPTSvOvo0Xw4j53WdSrdEMfMTTdN3p8DALCBdXLqUiaDJpjfleSOEdQ0PJvzmjw8YPL8m/Idow8dAdANZ7z85bn1wQdzsq95S5Irk8wkif8WBgAAYA2q6v2ttT2ntAtoMAr7d+/O8eXWaz+NbUlesHNnZvbtcyNkRTXwyKHOTbn1mmtWd5Op08m7XvrSfOjhh0+5zpcm+a4kmcAxA4DzQ+f2TuZvm8+RY0cyvWM6C1csZPaZPh9sfLuTrPZzYiU59XPKRnJJFnJvHjyl/cKHkvs3/8ToCwIghy67LO88enTZY1NJ5nbuTO66a7RFAQAAsKENCmicM0ucMNmOHzmypv5TSa664or8QGuZuesu4YzT2jyw/cD8/GPCGUlyMsmBxZ35+UcPzM8vG85Ikr9JcuisagSAM3f971yfa379mhw+djgtLYePHc4rfvMV6dzeGXdpnLW1fE6csCXvzsCJZcIZSfKFTc3fZ4AxONTp5PcGhDOS7syT2bdvZPUAAABwbhPQYCSmple+mT6V5FV9r7lNmzLzB38wgsrOFXMD2wfNXHI8vcBFX3jm0GlmOTlQg2fqAIBh6dzeyRsOvuGU9gceeiAv/Y2Xeqi94Q36nLj0c8f2JAtDrmX4prNj+QPHjmX+tvnljwEwNAfm51ecm2lq505fGgEAAGDdCGgwEnsXFrJl+/Zlj21Jsndp47XXDrukc8yNSa7LozNpbO7ud752hcVPkluTHLr00kf2//A0P+W45U0AGIOVHlo/3B7O3C1zQhob2kK64Yt+25O8MsmudIMau5LsT7KxH5B18uHckwdOXTLugQeS227LkWNrm3UOgLN3uhk/95o9AwAAgHU0MQGNqrps8ZXki/oOPb7/WFVNTM2s3szsbK7cvz9Tu3YlSWpzN0gwtXNnrrzwwswsdqxKrrsuufHG8RS6od2Y7uIlrbe9MbnhhqwUqXjMUidJ7j/NT5g6uwJhtDqdZPfuZNOm7rbj4S1sVKd7aH3iwRNmHtjQZtMNXywNY9yY5I4kD/e2Gz+cMZdbcjT3dT/zJtbkBP0AACAASURBVN2gxr33JrfcknzkI7n0oktXvggA626lGT+37dyZGbNnAAAAsI62jLuAPncOaH/Pkv2npHuHlg1mpvdKVfLkJycLC6YJHbajR7MtKwcvjt99d5Luuruns3fnzvWpC4bt+uuTN77x0W8oHz6czPWWAjLuwIbSub2TTbUpD7WHVuxn5oGNbjYbPYBxOvO5LSfy4GMbq5IHH0w+8pHxFAVA9i4s5Pde8Yo8/MADj2mvrVvzArNnAAAAsM7MRsHwdTrJZZcl11zTfUja2qMPS32jfagOJfnCafosflvowPzK3zy+auvWzLg5xUbQ6Tw2nLHoxInkNH/PgcnSub2TuVvmThvOSJLpHYO//QqT4EiOLX9gx45H3t59390jqgaARTOzs/mmN7852/q+kLBt58686C1vMXsGAAAA625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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "used_direction='e'\n", " \n", "if used_direction=='s':\n", " dfM_aux=dfM.query('NP > NS')\n", " dfG_aux=dfG.query('NP > NS')\n", " used_labels=labelsShrink\n", " name_fig=\"Shrink\"\n", " handles=[OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergePthMult_patch]\n", "elif used_direction=='e':\n", " dfM_aux=dfM.query('NP < NS')\n", " dfG_aux=dfG.query('NP < NS')\n", " used_labels=labelsExpand\n", " name_fig=\"Expand\"\n", " handles=[OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergeSing_patch,MergePthMult_patch,MergePthSing_patch]\n", "# < Expand\n", "# > Shrink\n", "\n", "vOrMult = list(dfG_aux.query('Cst == \"0\" and Css == \"0\"')['TE'])\n", "vOrSingle = list(dfG_aux.query('Cst == \"0\" and Css == \"1\"')['TE'])\n", "vOrPthMult = list(dfG_aux.query('Cst == \"1\" and Css == \"0\"')['TE'])\n", "vOrPthSingle = list(dfG_aux.query('Cst == \"1\" and Css == \"1\"')['TE'])\n", "\n", "vMergeMult = list(dfG_aux.query('Cst == \"2\" and Css == \"0\"')['TE'])\n", "vMergeSingle = list(dfG_aux.query('Cst == \"2\" and Css == \"1\"')['TE'])\n", "vMergePthMult = list(dfG_aux.query('Cst == \"3\" and Css == \"0\"')['TE'])\n", "vMergePthSingle = list(dfG_aux.query('Cst == \"3\" and Css == \"1\"')['TE'])\n", "\n", "vOrMult2 = list(dfM_aux.query('Cst == \"0\" and Css == \"0\"')['TC'])\n", "vOrSingle2 = list(dfM_aux.query('Cst == \"0\" and Css == \"1\"')['TC'])\n", "vOrPthMult2 = list(dfM_aux.query('Cst == \"1\" and Css == \"0\"')['TC'])\n", "vOrPthSingle2 = list(dfM_aux.query('Cst == \"1\" and Css == \"1\"')['TC'])\n", "\n", "vMergeMult2 = list(dfM_aux.query('Cst == \"2\" and Css == \"0\"')['TC'])\n", "vMergeSingle2 = list(dfM_aux.query('Cst == \"2\" and Css == \"1\"')['TC'])\n", "vMergePthMult2 = list(dfM_aux.query('Cst == \"3\" and Css == \"0\"')['TC'])\n", "vMergePthSingle2 = list(dfM_aux.query('Cst == \"3\" and Css == \"1\"')['TC'])\n", "\n", "f=plt.figure(figsize=(30, 12))\n", "ax=f.add_subplot(111)\n", "\n", "ax.scatter(vOrMult,vOrMult2, color='green')\n", "ax.scatter(vOrSingle,vOrSingle2, color='springgreen')\n", "ax.scatter(vOrPthMult,vOrPthMult2, color='blue')\n", "ax.scatter(vOrPthSingle,vOrPthSingle2,color='darkblue')\n", "\n", "ax.scatter(vMergeMult,vMergeMult2, color='red')\n", "if used_direction=='e':\n", " ax.scatter(vMergeSingle,vMergeSingle2,color='darkred')\n", "ax.scatter(vMergePthMult,vMergePthMult2, color='yellow')\n", "if used_direction=='e':\n", " ax.scatter(vMergePthSingle,vMergePthSingle2,color='olive')\n", "\n", "ax.set_ylabel(\"Time TC(s)\", fontsize=20)\n", "ax.set_xlabel(\"Time EX (s)\", fontsize=20)\n", "#plt.xticks(x, used_labels, rotation=90)\n", "plt.legend(handles=handles, loc='upper right', fontsize=21,ncol=2)\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/Spawn/Dispersion_Spawn_\"+name_fig+\"_Diff.png\", format=\"png\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Se sigue una distribución guassiana: False\n", "Han fallado un total de: 46\n" ] } ], "source": [ "normality=True\n", "total=0\n", "#Comprobar para cada configuración si se sigue una distribución normal/gaussiana\n", "for np_aux in processes:\n", " for ns_aux in processes:\n", " if np_aux != ns_aux:\n", " for cst_aux in ['0','1','2','3']:\n", " for css_aux in ['0','1']:\n", " dataList = list(dfG.query('NP == @np_aux and NS == @ns_aux and Cst == @cst_aux and Css == @css_aux')['TE'])\n", " st,p = stats.shapiro(dataList) # Tendrían que ser al menos 20 datos\n", " if p < p_value:\n", " normality=False\n", " total+=1\n", " #print(\"Se renuncia a H0\") \n", " #print(np_aux, ns_aux, cst_aux, css_aux, st, p)\n", " #else:\n", " #print(\"Se acepta H0\") #H0 es aceptar una distribución Gaussiana\n", " #print(np_aux, ns_aux, cst_aux, css_aux, st, p)\n", " \n", " \n", "print(\"Se sigue una distribución guassiana: \" + str(normality)+ \"\\nHan fallado un total de: \" + str(total))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "#Función para comprobar medias de distribuciones no normales\n", "def check_means(dataLists, np_aux, ns_aux, shrink):\n", " st,p=stats.kruskal(dataLists[0],dataLists[1],dataLists[2],dataLists[3],dataLists[4],dataLists[5],dataLists[6],dataLists[7])\n", " if p > p_value: # Si son iguales, no hay que hacer nada más\n", " print(\"Configuración: \" + str(np_aux) + \"/\" + str(ns_aux) + \" tiene medias iguales\")\n", " return\n", " #else: # Si son diferentes, hay que comprobar cuales lo son\n", " #print(\"Configuración: \" + str(np_aux) + \"/\" + str(ns_aux) + \" tiene medias diferentes ---------------------------\")\n", " \n", " best = 0\n", " otherBest=[]\n", " for i in range(1,8):\n", " st,p=stats.mannwhitneyu(dataLists[best],dataLists[i])\n", " if p < p_value: # Medianas diferentes\n", " st,p=stats.mannwhitneyu(dataLists[best],dataLists[i], alternative='greater')\n", " if p < p_value: # Mediana i < Mediana best -- Modificar best\n", " best=i\n", " for j in otherBest:\n", " st,p=stats.mannwhitneyu(dataLists[best],dataLists[j], alternative='less')\n", " \n", " if p < p_value: # Media newBest < Media otherBest[j] -- Eliminar otherBest[j]\n", " otherBest.remove(j)\n", " else: #Medias iguales\n", " otherBest.append(i)\n", " \n", " if shrink: # Las opciones Merge single(7) y Merge single - Pthreads(8) no se utilizan al reducir\n", " if 5 in otherBest:\n", " otherBest.remove(5)\n", " if 7 in otherBest:\n", " otherBest.remove(7)\n", " \n", " stringV=\"\"\n", " for i in otherBest:\n", " stringV+=labelsMethods[i]+\", \"\n", " print(\"Redimensión \" + str(np_aux) + \"/\" + str(ns_aux) + \": tiene los siguientes mejores: \" + labelsMethods[best]+\", \" + stringV)\n", " otherBest.insert(0,best)\n", " return otherBest" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Redimensión 1/10: tiene los siguientes mejores: Baseline, Baseline single, Merge, Merge single, \n", "Redimensión 1/20: tiene los siguientes mejores: Merge, Merge single, \n", "Redimensión 1/40: tiene los siguientes mejores: Merge, Merge single, \n", "Redimensión 1/80: tiene los siguientes mejores: Merge, Merge single, \n", "Redimensión 1/160: tiene los siguientes mejores: Baseline, Merge, Merge single, \n", "Redimensión 10/1: tiene los siguientes mejores: Baseline single - Pthreads, \n", "Redimensión 10/20: tiene los siguientes mejores: Merge, \n", "Redimensión 10/40: tiene los siguientes mejores: Merge, Merge single, \n", "Redimensión 10/80: tiene los siguientes mejores: Merge, Merge single, \n", "Redimensión 10/160: tiene los siguientes mejores: Merge, Merge single, \n", "Redimensión 20/1: tiene los siguientes mejores: Baseline single - Pthreads, \n", "Redimensión 20/10: tiene los siguientes mejores: Baseline single - Pthreads, Merge, Merge - Pthreads, \n", "Redimensión 20/40: tiene los siguientes mejores: Merge, Merge single, Merge - Pthreads, Merge single - Pthreads, \n", "Redimensión 20/80: tiene los siguientes mejores: Merge, Merge single, \n", "Redimensión 20/160: tiene los siguientes mejores: Merge, Merge single, \n", "Redimensión 40/1: tiene los siguientes mejores: Baseline single - Pthreads, \n", "Redimensión 40/10: tiene los siguientes mejores: Baseline single - Pthreads, \n", "Redimensión 40/20: tiene los siguientes mejores: Merge, Merge - Pthreads, \n", "Redimensión 40/80: tiene los siguientes mejores: Merge, Merge single, Merge - Pthreads, \n", "Redimensión 40/160: tiene los siguientes mejores: Merge, Merge single, \n", "Redimensión 80/1: tiene los siguientes mejores: Baseline single - Pthreads, \n", "Redimensión 80/10: tiene los siguientes mejores: Baseline single - Pthreads, \n", "Redimensión 80/20: tiene los siguientes mejores: Baseline single - Pthreads, \n", "Redimensión 80/40: tiene los siguientes mejores: Merge, Merge - Pthreads, \n", "Redimensión 80/160: tiene los siguientes mejores: Merge - Pthreads, Merge single - Pthreads, \n", "Redimensión 160/1: tiene los siguientes mejores: Baseline single - Pthreads, \n", "Redimensión 160/10: tiene los siguientes mejores: Baseline single - Pthreads, \n", "Redimensión 160/20: tiene los siguientes mejores: Baseline single - Pthreads, \n", "Redimensión 160/40: tiene los siguientes mejores: Baseline single - Pthreads, \n", "Redimensión 160/80: tiene los siguientes mejores: Merge, Merge - Pthreads, \n" ] } ], "source": [ "checked_type='te'\n", "if checked_type=='te':\n", " tipo=\"TE\"\n", " data_aux=dfG\n", "elif checked_type=='tc':\n", " tipo=\"TC\"\n", " data_aux=dfM\n", " \n", "results = []\n", "shrink = False\n", "for np_aux in processes:\n", " for ns_aux in processes:\n", " if np_aux != ns_aux:\n", " dataSet = data_aux.query('NP == @np_aux and NS == @ns_aux')\n", " dataLists=[]\n", " if np_aux > ns_aux:\n", " shrink = True\n", " else:\n", " shrink = False\n", " \n", " for cst_aux in ['0','1','2','3']:\n", " for css_aux in ['0','1']:\n", " if (cst_aux == 1 or cst_aux == 3) and checked_type == 'tc': # Usar tiempo sin app para TC con hilos\n", " lista_aux = list(dataSet.query('Cst == @cst_aux and Css == @css_aux')['TH'])\n", " else:\n", " lista_aux = list(dataSet.query('Cst == @cst_aux and Css == @css_aux')[tipo])\n", " dataLists.append(lista_aux)\n", " aux_data = check_means(dataLists, np_aux, ns_aux, shrink)\n", " results.append(aux_data)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[-1 4 4 4 4 4]\n", " [ 3 -1 4 4 4 4]\n", " [ 3 4 -1 4 4 4]\n", " [ 3 3 4 -1 4 4]\n", " [ 3 3 3 4 -1 6]\n", " [ 3 3 3 3 4 8]]\n" ] } ], "source": [ "#Lista de indices de mayor a menor de los valores\n", "aux_array = []\n", "for data in results:\n", " aux_array+=data\n", "unique, counts = np.unique(aux_array, return_counts=True)\n", "aux_dict = dict(zip(unique, counts))\n", "aux_keys=list(aux_dict.keys())\n", "aux_values=list(aux_dict.values())\n", "aux_ordered_index=list(reversed(list(np.argsort(aux_values))))\n", "\n", "labels_aux = [1, 10, 20, 40, 80, 160]\n", "i=0\n", "j=0\n", "used_aux=0\n", "heatmap=np.zeros((len(labels_aux),len(labels_aux))).astype(int)\n", "for i in range(len(labels_aux)):\n", " for j in range(len(labels_aux)):\n", " if i==j:\n", " heatmap[i][j]=-1\n", " used_aux+=1\n", " else:\n", " results_index = i*len(labels_aux) +j-used_aux\n", " for index in aux_ordered_index:\n", " if aux_keys[index] in results[results_index]:\n", " heatmap[i][j]=aux_keys[index]\n", " break\n", "heatmap[len(labels_aux)-1][len(labels_aux)-1]=8\n", "print(heatmap)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Crea un heatmap teniendo en cuenta los colores anteriores\n", "f=plt.figure(figsize=(30, 12))\n", "ax=f.add_subplot(111)\n", "\n", "myColors = (colors.to_rgba(\"white\"),colors.to_rgba(\"green\"), colors.to_rgba(\"springgreen\"),colors.to_rgba(\"blue\"),colors.to_rgba(\"darkblue\"),\n", " colors.to_rgba(\"red\"),colors.to_rgba(\"darkred\"),colors.to_rgba(\"yellow\"),colors.to_rgba(\"olive\"),colors.to_rgba(\"white\"))\n", "cmap = LinearSegmentedColormap.from_list('Custom', myColors, len(myColors))\n", "\n", "im = ax.imshow(heatmap,cmap=cmap,interpolation='nearest')\n", "\n", "# Loop over data dimensions and create text annotations.\n", "for i in range(len(labels_aux)):\n", " for j in range(len(labels_aux)):\n", " if i!=j:\n", " if heatmap[i, j] == 2 or heatmap[i, j] == 3:\n", " text = ax.text(j, i, heatmap[i, j],\n", " ha=\"center\", va=\"center\", color=\"white\", fontsize=25)\n", " else:\n", " text = ax.text(j, i, heatmap[i, j],\n", " ha=\"center\", va=\"center\", color=\"black\", fontsize=25)\n", "\n", "ax.set_ylabel(\"Parents\", fontsize=30)\n", "ax.set_xlabel(\"Children\", fontsize=30)\n", "\n", "ax.set_xticklabels(['']+labels_aux, fontsize=30)\n", "ax.set_yticklabels(['']+labels_aux, fontsize=30)\n", "\n", "#\n", "labelsMethods_aux = ['Invalid (-1)','Baseline (0)', 'Baseline single (1)','Baseline - Pthreads (2)','Baseline single - Pthreads (3)',\n", " 'Merge (4)','Merge single (5)','Merge - Pthreads (6)','Merge single - Pthreads (7)','Invalid (8)']\n", "colorbar=f.colorbar(im, ax=ax)\n", "colorbar.set_ticks([-2.55, 0.35, 1.25, 2.15, 3.05, 3.95, 4.85, 5.75, 6.65]) #TE\n", "#colorbar.set_ticks([-2.55, 0.35, 1.25, 2.15, 3.05, 3.95, 4.85, 5.75, 6.65]) #TC\n", "colorbar.set_ticklabels(labelsMethods_aux)\n", "colorbar.ax.tick_params(labelsize=20)\n", "#\n", "\n", "f.tight_layout()\n", "f.savefig(\"Images/Spawn/Heatmap_\"+tipo+\".png\", format=\"png\")" ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Intercept: \n", " 1798.4039776258546\n", "Coefficients: \n", " [ 345.54008701 -250.14657137]\n", "Predicted Stock Index Price: \n", " [1422.86238865]\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: Stock_Index_Price R-squared: 0.898\n", "Model: OLS Adj. R-squared: 0.888\n", "Method: Least Squares F-statistic: 92.07\n", "Date: Tue, 15 Feb 2022 Prob (F-statistic): 4.04e-11\n", "Time: 16:10:06 Log-Likelihood: -134.61\n", "No. Observations: 24 AIC: 275.2\n", "Df Residuals: 21 BIC: 278.8\n", "Df Model: 2 \n", "Covariance Type: nonrobust \n", "=====================================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "-------------------------------------------------------------------------------------\n", "const 1798.4040 899.248 2.000 0.059 -71.685 3668.493\n", "Interest_Rate 345.5401 111.367 3.103 0.005 113.940 577.140\n", "Unemployment_Rate -250.1466 117.950 -2.121 0.046 -495.437 -4.856\n", "==============================================================================\n", "Omnibus: 2.691 Durbin-Watson: 0.530\n", "Prob(Omnibus): 0.260 Jarque-Bera (JB): 1.551\n", "Skew: -0.612 Prob(JB): 0.461\n", "Kurtosis: 3.226 Cond. No. 394.\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/usuario/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:2495: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n", " return ptp(axis=axis, out=out, **kwargs)\n" ] } ], "source": [ "from sklearn import linear_model\n", "import statsmodels.api as sm\n", "\n", "Stock_Market = {'Year': [2017,2017,2017,2017,2017,2017,2017,2017,2017,2017,2017,2017,2016,2016,2016,2016,2016,2016,2016,2016,2016,2016,2016,2016],\n", " 'Month': [12, 11,10,9,8,7,6,5,4,3,2,1,12,11,10,9,8,7,6,5,4,3,2,1],\n", " 'Interest_Rate': [2.75,2.5,2.5,2.5,2.5,2.5,2.5,2.25,2.25,2.25,2,2,2,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75],\n", " 'Unemployment_Rate': [5.3,5.3,5.3,5.3,5.4,5.6,5.5,5.5,5.5,5.6,5.7,5.9,6,5.9,5.8,6.1,6.2,6.1,6.1,6.1,5.9,6.2,6.2,6.1],\n", " 'Stock_Index_Price': [1464,1394,1357,1293,1256,1254,1234,1195,1159,1167,1130,1075,1047,965,943,958,971,949,884,866,876,822,704,719] \n", " }\n", "\n", "df = pd.DataFrame(Stock_Market,columns=['Year','Month','Interest_Rate','Unemployment_Rate','Stock_Index_Price'])\n", "\n", "X = df[['Interest_Rate','Unemployment_Rate']] # here we have 2 variables for multiple regression. If you just want to use one variable for simple linear regression, then use X = df['Interest_Rate'] for example.Alternatively, you may add additional variables within the brackets\n", "Y = df['Stock_Index_Price']\n", " \n", "# with sklearn\n", "regr = linear_model.LinearRegression()\n", "regr.fit(X, Y)\n", "\n", "print('Intercept: \\n', regr.intercept_)\n", "print('Coefficients: \\n', regr.coef_)\n", "\n", "# prediction with sklearn\n", "New_Interest_Rate = 2.75\n", "New_Unemployment_Rate = 5.3\n", "print ('Predicted Stock Index Price: \\n', regr.predict([[New_Interest_Rate ,New_Unemployment_Rate]]))\n", "\n", "# with statsmodels\n", "X = sm.add_constant(X) # adding a constant\n", " \n", "model = sm.OLS(Y, X).fit()\n", "predictions = model.predict(X) \n", " \n", "print_model = model.summary()\n", "print(print_model)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 4 }