{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import netCDF4 as nc\n", "import matplotlib.pylab as plt\n", "import imp\n", "import csv\n", "import pandas as pd\n", "import random as rnd" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "651\n" ] } ], "source": [ "# Read atmospheric forcing\n", "\n", "NumTensemble = 600\n", "Tlen = 651\n", "\n", "fname = \"../MAGICC/CMIP5_RCP2500/Temp_CMIP5_RCP85_HistConstraint.dat\" # File to read\n", "df = pd.read_csv(fname,sep='\\s+',index_col=0,header=None)\n", "df.columns.name = \"ensemble member\"\n", "df.index.name = \"Time\"\n", "SAT = np.array(df.values)\n", "\n", "print(len(SAT[:,1]))\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Normalize and crop temperature series\n", "Temp = []\n", "Tavebeg = 0\n", "Taveend = 80\n", "tbeg = 51\n", "tend = 251\n", "for i in range(len(SAT[1,:])):\n", " SATave = np.mean(SAT[Tavebeg:Taveend,i])\n", " SAT[:,i] = SAT[:,i]-SATave\n", "SAT = SAT[tbeg:tend,:]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Read ocean scaling\n", "\n", "NumOmodel = 19\n", "\n", "fname = \"../ScalingCoefficients/OceanScaling/OS_R1.dat\" # File to read\n", "with open(fname) as f:\n", " OS_NoDelay_R1 = np.array([float(row.split(\"\\t\")[0]) for row in f])\n", "with open(fname) as f:\n", " OS_WiDelay_R1 = np.array([float(row.split(\"\\t\")[3]) for row in f])\n", "with open(fname) as f:\n", " OS_Delay_R1 = np.array([float(row.split(\"\\t\")[2]) for row in f])\n", "\n", "fname = \"../ScalingCoefficients/OceanScaling/OS_R2.dat\" # File to read\n", "with open(fname) as f:\n", " OS_NoDelay_R2 = np.array([float(row.split(\"\\t\")[0]) for row in f])\n", "with open(fname) as f:\n", " OS_WiDelay_R2 = np.array([float(row.split(\"\\t\")[3]) for row in f])\n", "with open(fname) as f:\n", " OS_Delay_R2 = np.array([float(row.split(\"\\t\")[2]) for row in f])\n", "\n", "fname = \"../ScalingCoefficients/OceanScaling/OS_R3.dat\" # File to read\n", "with open(fname) as f:\n", " OS_NoDelay_R3 = np.array([float(row.split(\"\\t\")[0]) for row in f])\n", "with open(fname) as f:\n", " OS_WiDelay_R3 = np.array([float(row.split(\"\\t\")[3]) for row in f])\n", "with open(fname) as f:\n", " OS_Delay_R3 = np.array([float(row.split(\"\\t\")[2]) for row in f])\n", "\n", "fname = \"../ScalingCoefficients/OceanScaling/OS_R4.dat\" # File to read\n", "with open(fname) as f:\n", " OS_NoDelay_R4 = np.array([float(row.split(\"\\t\")[0]) for row in f])\n", "with open(fname) as f:\n", " OS_WiDelay_R4 = np.array([float(row.split(\"\\t\")[3]) for row in f])\n", "with open(fname) as f:\n", " OS_Delay_R4 = np.array([float(row.split(\"\\t\")[2]) for row in f])\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Read melting sensitivity\n", "fname = \"../ScalingCoefficients/MeltSensitivity/MeltSensitivity.dat\" # File to read\n", "with open(fname) as f:\n", " MeltSensitivity = np.array([float(row) for row in f])\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# AISM_VUB = AISM_VUB\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_AISM_VUB_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_AISM_VUB_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_AISM_VUB_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_AISM_VUB_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_AISM_VUB_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_AISM_VUB_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_AISM_VUB_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_AISM_VUB_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_AISM_VUB_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_AISM_VUB_BM08_R5 = np.array([float(row) for row in f])\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.52695 0.5263157894736842\n", "0.89255 0.8947368421052632\n", "0.53235 0.5263157894736842\n", "0.4736 0.47368421052631576\n" ] } ], "source": [ "EnsembleSize = 20000\n", "scaled_forcing = False\n", "\n", "countR1 = 0\n", "countR2 = 0\n", "countR3 = 0\n", "countR4 = 0\n", "\n", "SL_wTd_nos_base_AISM_VUB_R1_RCP85 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_AISM_VUB_R2_RCP85 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_AISM_VUB_R3_RCP85 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_AISM_VUB_R4_RCP85 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_AISM_VUB_R5_RCP85 = [0] * (tend-tbeg)\n", "\n", "for i in range(EnsembleSize):\n", "\n", " # Select forcing randomly\n", "\n", " # select global warming path\n", " iTens = rnd.randint(0,NumTensemble-1)\n", " Temp = np.array(SAT[:,iTens])\n", "\n", " # select ocean model\n", " iOmod = rnd.randint(0,NumOmodel-1)\n", " OS_R1 = OS_WiDelay_R1[iOmod]\n", " OS_R2 = OS_WiDelay_R2[iOmod]\n", " OS_R3 = OS_WiDelay_R3[iOmod]\n", " OS_R4 = OS_WiDelay_R4[iOmod]\n", " OS_R5 = OS_WiDelay_R4[iOmod]\n", "\n", " tau_R1 = int(OS_Delay_R1[iOmod])\n", " tau_R2 = int(OS_Delay_R2[iOmod])\n", " tau_R3 = int(OS_Delay_R3[iOmod])\n", " tau_R4 = int(OS_Delay_R4[iOmod])\n", " tau_R5 = int(OS_Delay_R4[iOmod])\n", "\n", " if tau_R1>0:\n", " countR1 = countR1+1\n", " if tau_R2>0:\n", " countR2 = countR2+1\n", " if tau_R3>0:\n", " countR3 = countR3+1\n", " if tau_R4>0:\n", " countR4 = countR4+1\n", " \n", " Temp_R1 = np.append(np.zeros(tau_R1),Temp[:tend-tbeg-tau_R1])\n", " Temp_R2 = np.append(np.zeros(tau_R2),Temp[:tend-tbeg-tau_R2])\n", " Temp_R3 = np.append(np.zeros(tau_R3),Temp[:tend-tbeg-tau_R3])\n", " Temp_R4 = np.append(np.zeros(tau_R4),Temp[:tend-tbeg-tau_R4])\n", " Temp_R5 = np.append(np.zeros(tau_R5),Temp[:tend-tbeg-tau_R5])\n", " \n", " # select melting sensitivity\n", " MS_R1 = rnd.uniform(MeltSensitivity[0],MeltSensitivity[1])\n", " MS_R2 = rnd.uniform(MeltSensitivity[0],MeltSensitivity[1])\n", " MS_R3 = rnd.uniform(MeltSensitivity[0],MeltSensitivity[1])\n", " MS_R4 = rnd.uniform(MeltSensitivity[0],MeltSensitivity[1])\n", " MS_R5 = rnd.uniform(MeltSensitivity[0],MeltSensitivity[1])\n", "\n", " # Compose forcing time series\n", " M_R1 = MS_R1*OS_R1*Temp_R1\n", " M_R2 = MS_R2*OS_R2*Temp_R2\n", " M_R3 = MS_R3*OS_R3*Temp_R3\n", " M_R4 = MS_R4*OS_R4*Temp_R4\n", " M_R5 = MS_R5*OS_R5*Temp_R5\n", "\n", " M_R1[M_R1 < 0.0] = 0.0\n", " M_R2[M_R2 < 0.0] = 0.0\n", " M_R3[M_R3 < 0.0] = 0.0\n", " M_R4[M_R4 < 0.0] = 0.0\n", " M_R5[M_R5 < 0.0] = 0.0\n", " \n", " # Linear response\n", " SL = []\n", " SL.append(0)\n", " for t in range(1,tend-tbeg):\n", " dSL = 0\n", " for tp in range(0,t):\n", " dSL = dSL + M_R1[tp]*RF_AISM_VUB_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_AISM_VUB_R1_RCP85=np.vstack([SL_wTd_nos_base_AISM_VUB_R1_RCP85, SL])\n", "\n", " SL = []\n", " SL.append(0)\n", " for t in range(1,tend-tbeg):\n", " dSL = 0\n", " for tp in range(0,t):\n", " dSL = dSL + M_R2[tp]*RF_AISM_VUB_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_AISM_VUB_R2_RCP85=np.vstack([SL_wTd_nos_base_AISM_VUB_R2_RCP85, SL])\n", "\n", " SL = []\n", " SL.append(0)\n", " for t in range(1,tend-tbeg):\n", " dSL = 0\n", " for tp in range(0,t):\n", " dSL = dSL + M_R3[tp]*RF_AISM_VUB_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_AISM_VUB_R3_RCP85=np.vstack([SL_wTd_nos_base_AISM_VUB_R3_RCP85, SL])\n", "\n", " SL = []\n", " SL.append(0)\n", " for t in range(1,tend-tbeg):\n", " dSL = 0\n", " for tp in range(0,t):\n", " dSL = dSL + M_R4[tp]*RF_AISM_VUB_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_AISM_VUB_R4_RCP85=np.vstack([SL_wTd_nos_base_AISM_VUB_R4_RCP85, SL])\n", "\n", " SL = []\n", " SL.append(0)\n", " for t in range(1,tend-tbeg):\n", " dSL = 0\n", " for tp in range(0,t):\n", " dSL = dSL + M_R5[tp]*RF_AISM_VUB_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_AISM_VUB_R5_RCP85=np.vstack([SL_wTd_nos_base_AISM_VUB_R5_RCP85, SL])\n", "\n", "SL_wTd_nos_base_AISM_VUB_SU_RCP85 = SL_wTd_nos_base_AISM_VUB_R1_RCP85+SL_wTd_nos_base_AISM_VUB_R2_RCP85+SL_wTd_nos_base_AISM_VUB_R3_RCP85+SL_wTd_nos_base_AISM_VUB_R4_RCP85+SL_wTd_nos_base_AISM_VUB_R5_RCP85\n", "\n", "print(countR1/EnsembleSize,10/19)\n", "print(countR2/EnsembleSize,17/19)\n", "print(countR3/EnsembleSize,10/19)\n", "print(countR4/EnsembleSize,9/19)\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'nc' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mTime\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1900\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mncfile\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'EnsembleSingleModelProjections/SL_wTd_nos_base_AISM_VUB_RCP85.nc'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'w'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'NETCDF4'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mncfile\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreateDimension\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Time'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mncfile\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreateDimension\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Emember'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'nc' is not defined" ] } ], "source": [ "Time = range(1900,2100)\n", "ncfile = nc.Dataset('EnsembleSingleModelProjections/SL_wTd_nos_base_AISM_VUB_RCP85.nc','w', format='NETCDF4')\n", "ncfile.createDimension('Time', None)\n", "ncfile.createDimension('Emember', None)\n", "\n", "SL_wTd_nos_base_R1 = ncfile.createVariable('EAIS', 'f4', ('Emember', 'Time'))\n", "SL_wTd_nos_base_R2 = ncfile.createVariable('Ross', 'f4', ('Emember', 'Time'))\n", "SL_wTd_nos_base_R3 = ncfile.createVariable('Amundsen', 'f4', ('Emember', 'Time'))\n", "SL_wTd_nos_base_R4 = ncfile.createVariable('Weddell', 'f4', ('Emember', 'Time'))\n", "SL_wTd_nos_base_R5 = ncfile.createVariable('Peninsula', 'f4', ('Emember', 'Time'))\n", "SL_wTd_nos_base_SU = ncfile.createVariable('Antarctica', 'f4', ('Emember', 'Time'))\n", "t = ncfile.createVariable('Time', 'i4', 'Time')\n", "\n", "SL_wTd_nos_base_R1[:,:] = SL_wTd_nos_base_AISM_VUB_R1_RCP85\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_AISM_VUB_R2_RCP85\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_AISM_VUB_R3_RCP85\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_AISM_VUB_R4_RCP85\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_AISM_VUB_R5_RCP85\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_AISM_VUB_SU_RCP85\n", "t[:] = Time\n", "\n", "SL_wTd_nos_base_R1.units = 'meter'\n", "SL_wTd_nos_base_R2.units = 'meter'\n", "SL_wTd_nos_base_R3.units = 'meter'\n", "SL_wTd_nos_base_R4.units = 'meter'\n", "SL_wTd_nos_base_R5.units = 'meter'\n", "SL_wTd_nos_base_SU.units = 'meter'\n", "\n", "t.units = 'years'\n", "\n", "ncfile.close()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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jWo7N0Fk/GuA/qkpp8p/kxaJLqPenSJgEMzr34RzupnL6g/GG6iBQ/o7jMmD497T5nZQyKaXsAU5zNuz/f6SUD0gpF0spF+fnp/9usaIoyh8rfGCQDNvvSDobiWlzeLrYBEIwEEuwIT+bQ3sOUGt08bxxCUtNZyiq7GIfl7FuWMebeZyBrGyumdhJH1ewt8DCzKlpPIM9gImIvTHt9Z9LuB8CaoUQ1UIIK3ALsOVdbX4LXA4ghMjj7DJN9/ksVFEU5f2SGAxiHn0VszGCP3gdPVYvRyqrmeN2EDUk63KcnDp5jG7dQwwbVS4vR3MbSAgrNw6G+HleKWYjRdRiYsmUYNyuMaezBUtoEtCpHHgr7WN4z3CXUqaAe4FXgDZgs5TypBDiO0KIjW83ewWYFEKcAt4AvialnExX0YqiKOkUPjBChuVZdEcVseRStmYECZqtODRBnsWMqb2dStnFJv0y6rV+ZlcdY7u8lqapFLnWNzlWVMaVU2/RYbqW3QVmciIRKjo7EIBdS1BT9u7Fj/PvnJ5zl1Juk1LOklLWSCn/+e1z35ZSbnn7byml/LKUskFK2Sil3JTOohVFUdLFiKVIHd2BVXQSTF3PlD3GrtoKSmwWjgcjXJOfxaF9BwilTIyRS51tlP7CfCa0XG4dCPOU003A6qYxdoql3hyO5ZiZ330MW2AEAK0igwdrrk77ONQbqoqiKO8QOTyOWz6NYcklFLyUPaZBet05LMp0EZPQFAtiifXzO2MxJdoEC6qaecX4KGURgwZtO6dzi8hN+ggnLmJPvhlLKkXDsVMIJCCZTNRRGvCnfRwq3BVFUd4mDUlszx4cphai9o8SM2u8VJOHWUBQT1FsszCx6y0ceoB2Wck88yCREjhjruLmgRD7xSRH8mZzRWAvi8dmsr3YzLze07gCfQDo2fk4ho9yaWck7WNR4a4oivK2eJcPR/AppObA572CVtHHqeIKVnsy2TcdZrXbxtRkD3v0WWSJEE2Vh3nV+BjupOQSuY0Rax4JzUpBKI99+Rq6prHk0DHg7HYD9qiOy5JDrPIDsreMoijKh0FkVytO05vEsjaS0lxsK7MQRjDTaSchJZ620+ToXnbLRuaZ+3GXjbLfMp/rhsP0iC4OFzVQE+tlTU89T5ebqevvJcPfcfZtIbebZHwaS24d49Ppj14V7oqiKEBqKoal5xEAfGNX0yaGOFo7m0a3g+PBCGU2C7G2Vk7pxVhJsLj0GDuNmxAS1hhbsQV1Dmc2MC8Q4i2PJGCzsmrfESAJEkwJDbcll59aZzGgmdM+HhXuiqIoQHj3KVyml0jkrCVl5LMjL8qwMHNrcS57fCEWhIK45CSvGguYZxqgovIkO8wrWOWNEpDHaM+fgSZ1bjldziPVZmYNeMnwn0BqAmG3oicCJDxz+fjgkwh/Z9rHo8JdUZQPPSOhI1ofQIgE09Mb6TNNcGRuA/lWMwJIScg4cZTRpJMkJpqKTtKsf4Sw2cyG1HZmjHp5tnA180LjnHJFGXW7WL2nGWQMYUhEykymrYC+oB9MGqWp4rSPSYW7oigfetHmbtxyC8nsK0jFStnnHOe42cEnSzxs9fopFZDj7+M1o5FZ2ghzqw7ykmkNc/1xXPqbhFwuhu2FXN+TwSNVFub2+HEGWtFNJoRZw0hFiHvmURA/TbhmLv0Fas1dURQlraSUGLt+gSbC+KI34jUFOLSwDqsQrMvLYp8vROXAAKGUwEcGi/LaOW2sY9huY2P8depGxthUvA5XKokeG6c/O5er3moGGcGk6yDN5NhLmJ4aIVFQihAajtho2selwl1RlA+1ROcozujTJDKWk/CX02IepNmZw3WF2eyaDmEA5UNt7DTqqBRels18gy3atVREEtToWyhO+tiWdxlXjKTYXGHlorYgllAzKbMFBEg9QTx/IVbRRyorhwzdhsnywfixDkVRlAtW6pVfYhIBgqZbCWkxDi6oIColny3L5+nRKcpCISyhIOMylyWedoZSV3HG7eD62C7mDI/xRMk64pqNoskBhjOLuOTwETCCmFNJEGbyHVWEvZ3Eiyqx6zauTi2gLpCX9nGpcFcU5UMrNe7H7n2UpLOJ6Gg1x7R+mvNKWZblwiQEbeEYlUNdHNBnUCh8XFq7nRfE9eTFUywwNlEVmuSp0uuoDiY56BFccTQC0f0kLNazHRgpEkULSdonMewOlqVqyMJFnu3dv3d0/qlwVxTlQyvx4v2YxBQh5+3ERYqjcz0MpwzuKstn8+gUJsOgcGSIAVnA0pw2gtGrOJzt4tr4mzQOj7DD00SHrYKasX4CljLmnDkGRgBLMgFCo9g5k9DYKeIFZWQmc5lhFJLyD3LISP+muSrcFUX5UDJCUax9vyJpbSA8OJMTWj8HK2oos1u4ypPJsyNTlE+McjqZTw4hVte+ygviOtypFCt4nLLpEJsrbsaiG/hFgjWtMZKxPcSttrObhEmDZPFiwplRpGbmUr0aIzBMeOc/YR6eSvv4VLgrivKhFH/hQcxilHDWJ0lhcGK2k+Nxnc+XF7DPF2IipVPaP0iXUcKSnHZk6ErezHeyNrGbpoERjmeXs8OxhBneUWyxMnK9x8EIYU3EAY1KdyOTk0dIZueTk6yk0Mgg3voIYYuTVeXqDVVFUZTzTsbjWE7/jKR5FsHBObSbhmmZVU+uxcStxR6eGhzHlkwQ9um4iHFNzQ5eFNdgkZLV8gnyJ+LsKPo4IYuJrHCEi9tiJOO7idnsCCSaMBEtbiKcJUkZGlcZZSS6XsXw9TG2JJNus1qWURRFOe8S2x7EzAgRz11IKWmv1tgXTXFXWT66lLw8EaC8d5B2vYIlWadx+a7kpWIHV6T2clF/P0cKZvKK5XIyomEqR/LRoicQehRbPAYIarOWMOTbj+7MID/ZQEZwmkTbFkbKCmhLeojG4mkfowp3RVE+VGQqgfn4f5DQZhMYrKdTG6O1cS5Ok8anS/N4pm+ApKZhGfRhJ8FHqt/kBfMaDCG5Rn8Sh9fEhGUjR3IdNPYHmD2YIBnfS8TmQCCxag68xfXEs21EkjbWGrnEDj9MwmzldI2bSHU93Zlq+wFFUZTzKvXqg5iMUaJ5dyF0wZmKFK9FUtxe7CHHYuah9j4yxnz0JApZknWanOmreL7Mzkp9P8v7u2gubWKLaQVaymBBp50QrZhSMRzxKCCoz13JUGAf0mylTJ+Huet1jOkeepfnMpFVB1JyUqtO+zhVuCuK8uGRSqA1/5gEdfhH6+jTvBxrmocmBJ8rz+fEUBtnnFlknvFiI8ktlXv5rf1Skhp8NPU4ycksPNNXs73EyZXH/eQGUxA5SNjmRCBxmbPpLColleFiIp7DBn+QRNsWRstr6bTlYdizyZtYziVRI+1DVeGuKMqHhr7rIUz6GJG8u9ASgq6SOFujOjcU5lBss/CjvccR/gSTYScXZZ8kd2wjz5ZbWWHs5eL+Lk4Vr+R3ljIsSQtNXRKvdT+WRAxXPAII6vMuZyx8CClhYWo2qdZHMMw2xlYFCWXMwB2qpLr6OcJxX9rHqsJdUZQPh1Qc9v2QuKzD753NiJim7eL5xAzJFyoK6O99gb3uUjLavVhJckdJG89lzSepSW5MPcXYdBXF/VfyYpmLDYdCGCSw+44SsTkASa6tmJY8C9JuZzxRxWVduzB8vXSvXEDvVC1CupmZ14p90Q5Wlj6V9uGqcFcU5UNB3/0QptQYEc9dmOKC3rIov43orM3LZIYtycNvdOJPWkj4YHnOCbKGr+PpCjMXy70sGeghlH0pL1vDZAWzmTGuM27bhTURw/n2WvvMgisJJM5gxA1u8FtJtr9ArGQ+k9VdxNzF5McKyFi4hcdHrXxFfCbt41XhrijKhS8ZRez9AXGjAf/kbEaFj9Mr5jOd0rmvopCOtp/wpnsm1jN+rCT5bO4YmwsqiGuCjyV/Q2dwAWJgEXuKPKw5EmEoO0L2+GmiVjsgqXLPZXfWJGgasdR8KlofQ5jtjF5nYWBiIZZ4JvWL7ue1mJnmpBl3hnoUUlEU5U+m7/wZWspLOO8ezHHBYGWcJ0NJLs/NoFbrY+/uSU5bChATCS7POYlrZD2byy0sYw/z+4fI5kr2WPsoG83HnpAkUm9gTcZxJGJowkxG4SISuhdLxMbtnYcwfH1MLF5PXzSIbsukIa+DUc8UL/mtJMxN3NWpbqgqiqL8aaLTiP0/JmosJjBZzZjw0bZ8HlNJnS9XFnD86D+zxz0LrTuMlSR3ueI8VZpD3AQ3J37Dyfhq+qey6XdW0Nif4kjlNHkj3STMFgDmZq9kn60LLZmgNFCEuf0FTKWLCK7ey1hoDnmGlYzG13h63EpS2Lmsv5HBiHpDVVEU5U9ivP5DRCpE2HP2qn24OsXjgQSrcjIoiWyj75iL/dZaTN4YG3LasY5fzm8qLCxjHzW905RPXsIJbYjKkUImXZAzsQdzKok1lcRmdjJeWAAyRn6ogGUtjyFsmYxeUc/JjirQLNQt/A1vRTQ6DBOFiWt4beEKdlU3pH3cKtwVRblwBUYQLQ8QMS4jOFXIuPBzankjk8kU95U5aDv+c9od2UQHDGwiwWdMDn49w07CJLglvglv7GMcNLyYEvVkRaCtvJfCoW4M7Wx0zstbQ4fowRby09TRjgyNIhfdzlj5DkKyjPriU0y5wzzvd6CbG5j2LCdpNlOd/3Lah67CXVGUC5ax419BTxL23IUlLhitMXjMF+OynAwyx/4d32AZW5Mr0Cbj3Jl7hnhoEc+UW1nJG5T0gDFRjT8F2T4PR6ol+UMnwDAwGQZZ9kIO50bA0Fk8kkNmz5tYalYzffEJTncuIssWJLe2hWdHrSSEldL4RxnOyefT049y9Z6RtI9dhbuiKBemyS7E0ccI62sJTWXi1QIcXz6XiWSKO3MGGejewzgG3gk7WSLGDdEqfjnTAGFwc2wzTH+SVm2AzEADQacgYjlFeX8HGhKA4oKL8ckpikfGKD3xIiKzlNiCSzgZH0M3WZnT+BLNfo1T0oRLu40TVTNZ7j1G1RurEPk1aR++CndFUS5IxqvfQUoLwZxPYU1oTNZbeWgyzJW5TpwD3yAZKeER/w2IQIqvZw8xIkrZVuJmNS+T11FJeyyGM1SBKWVjb0OEvMEzSKEhgLLMubS4RiEWZnnHJCIZxbHoTsYaNjE0PoeaqhYCljhPBx0kbPOZzFtKZizEzW8FSUo7Y5Fk2sevwl1RlAvPcCta+28J6dcRnbYyYQqyd3Et/pTOLdpvCU4n6Q/aGAu6qdIirAjU89PaGFbiXB/eQnzyWsb0JLZIKcfLJbm+DiqGujFJA4umESwsIYXOxScHsIydxNzwEYJVUxzuL0LLnqCo7AzPj9iIaRbc5tsJ2+x8q/U1JqL11KeeoDyWSvsUqHBXFOXCIiVy29+hy0z8ObdgTZmYasrg4XE/G3JSOL33gzDz0PBNEDX4B3eQNreVNws9rGcLBSeupUUMkO2vI2ZOMVA4TnHPKQwEICkruIxB8xS5I71UdLch8uux1qyirfA1JgwPFze8xrFJwTFhQrruZchTyO1dOwkOrKTctpe83a3sn1ZX7oqiKH+Y0y8hBvcS0D9OclJjzOLn5YYKklKyIfyvyEQpB/obmU46WWmKUBuo5Sczo2RIP9eMt9AdKcAUKkXoDvbNjmEPeCmcGEFDkm3L5HR2jDgmLj/cDZoZe9MdjJe/ztGeRprqXiVipNgUdhFzXoI/ax6zJvuZc6QEl2mC6l2b6cjPISc3lvZpUOGuKMqFQ08iX/4WSVmGP2sDVsNEYEUhT45Ns8HZR2aslZicYtPQekQSvmYxsb8gTEteEdfxHO6Tn6BHj+CIlBGmB93mZ86JA0hAIDGVLiEuUly6bxfW4Dhi4W3gtvJWYgJHeReebC/PDTkJm5zo7tuxpeLce+g0ET2XOX0PkpBmxOKNjBXWp30qVLgrinLhaP41wteF3/gMcspgxOHn0VIPFiFZHfoumizkyaOfICHN3GGJkh3L5ydVEo/0ct0JCwcZI8tXR1KLcWiuQASnyQr7EUBedg29tgDOcS9V/YOYq1fhLlnMybIttBkellXv5+QwtFgEwfxvErU5+MbxbXj9i6jTn8HVOczYyhvpHN7Dop70T4WfsOM9AAAgAElEQVQKd0VRLgxRH/KN7xEz5uF3LcEsNYYur+CliQAfES+QbxL0BGzs880l09C43cjkhfIxTucUc3PieYbGZyGCZQjDRsbkVrxZmSw9vBMJ2DQYKszHktRYu3s3WlY5WuP1BDLOsGWigo1zniMU1Hk0lUE46xYS9nJu6NmL3rWCIvMxCnfvpnflRgam2olZ4YQlM+3TcU7hLoRYK4Q4LYToFEL87f/Q7kYhhBRCLD5/JSqKopyD3T+AmA8/d2HyGYxmhfmx3UqRFmR1ahOxRJRfHP4sGvC31ig6Fn5Z5WGG7OTStxbTnUpgjxUhIofYvmoxxT3tWFPJs8sxJQtJoXPR7h1YDGDJZ7Bqdjabu7mobjsWU5xHx7OJ2KqJZ6xj5tQgFx3JxqpFqD3wCBO1S5Aig0HbOM9e1k9fUV/ap+M9w10IYQJ+BqwDGoBbhRD/bWMEIUQGcB9w4HwXqSiK8j+a7kPu/yUR/Qr8tko0BEevqKE9HOMW/ee4LR5e7LyKqVQGjZi4JFHAY5UDeJ053Dl0kmZ9mkx/LXHNjxRn6M7Oo+nkfgSQ5XQxlmGirHuQ0vFxbAs+gdNdTLNnH8HsSSo9/TT3mTltN+Mv+Cr2ZIx7m08RShayYOh+pDmfzJkbOBLZx47F4xiRGQRzLGmfknO5cl8KdEopu6WUCWATcN3vafdd4N+B9N8GVhRFeQe543+DIQiY7sAeFIwVpfh+IspcTrHCOkjvtM7WvisxIfgHM4y6p3msupal+iEc7UWYAlUgNWpPP8Tmqzdy6a6tCEATBqOl9ZR5I1zUvBdzxXJSlfMImkM8aWisq9lOeDTFkxY7/vyvIzUnXz35KpPTi5ljPIOzZxrLJV/gwMRL7FzoxS9L8Q/fSb93Udrn5FzCvRQYeMfx4Nvn/osQYiFQLqV88TzWpiiK8t569yJOPEsw9RF8uNCRPL+inEBK53YeJZ6Y4MHWuzFjcJswU5DK4IGScRImC7cdtjKQEFgTOVgCO9my9ir0UIjSqSEEYCmowZ5MMm//Gwh3Ica8G7AbTr5t6+P2xscxx3R+GsojmnEdSXstH+t9E3FmGaWWVgr27SN2xb0MBTt4bWYHwzkShm6hTEzwN5bfpX1aziXcxe85J//rQyE04EfAV97zi4S4WwjRLIRo9nq9516loijK76OnkNu+hi7y8dtvJiNmo2+WhYdCEdbJF6ixBtnefRV90TzysXAHdk4UnOSFqiY2+E/S75/GFZxBQoxg07t4vX4R63c+B4DDqjGZlU1TyzFcsRjOpfdgtZn4gWWIq+qex20Js73fTV9mFcGcG2ic6mDR4QIcWoBZhx4jftGnsZmy2ZyxjfaqIK6eG4gaWXza9SAHCtOff+cS7oNA+TuOy4DhdxxnAHOBnUKIXmAZsOX33VSVUj4gpVwspVycn5//x1etKIoC0PJrxPhJfPE7CcZ0olqSf6p1kYeXT9j30zUheKZzDQ4SfENYQEvws0I3LhlhyZEUFv8MpEgxu+1h/vOG27ho16vY9TggmSyfy9JTfRQPduGYewthj52XdBPmyp3UeroJdOv8NiObQN5XyEn4uPPgEJFUHgsGfokoXo0nbzGPJ5/gQMMEWYMLGUvO5xbLi/ykNEjUNzPtU3Mu4X4IqBVCVAshrMAtwJb/70MppV9KmSelrJJSVgH7gY1Syua0VKwoigIQnkDu+C4xOR+/ewWZKTsHFmRwIgWfFk+QiPTwQOvnMWOwEjcLpJ038w9wqGQuH+v24w9aMafcOMdf4JmN1+FNWmjqazn73Z5iKrx+qk4eQq9YSqp6MWeSGbya38z6Ga/hHo7zf7RiQnlfQRNWvtH6BpP++cyPP44zUkrG7I1sT77Ky42ncQY8+ILX0Cg6ea16H7qQ2BKl//PYzoP3DHcpZQq4F3gFaAM2SylPCiG+I4TYmO4CFUVRfq8d34F4CF/ycySDcfy2JN/KEyyR+7nI3MGWzo8yFMsmC8mXhAW/u5sfzryIiliAnK5BHJFSEkYb0Zwkr9ct4qaXH0UAJhMY9kyaDrxFKLeUrPmfxGuJ8gNHP59rfARXIMVPAiWMFNxAwl7NFzu3EuxbRY3pTXJPT2Bb/Cm6jT4emrkVDA33yEeRaCTKNhM1gRQQzpxI+/Sc03PuUsptUspZUsoaKeU/v33u21LKLb+n7Sp11a4oSloNHUYefpRQ6hqm3EW4pJ2HFtiABJ937eXYqJuXei8mnzCfJwM3giczhxhzFXD18SEc/mpSJj8zen7Ho2uuZ94bu8lJ+gFJqHQ2V+5rIaUJChf9FaOuCb6hw+cWPIBdJnir382B/PlEMtewbnQfuUeayDN3UdW6A9vF9xIhyXeLf0HInqS+6wo6mcHcnGcYdgdAQkNoNuWTH4xHIRVFUT44DAO57WsYIhu/9TasAUFfjs4TWU7utG7F8B/mV8fvxmUY1JPBGqz0eHbwyJw1rBiZxjPgQADZfU+y+bob8fkEK/t3A5DKzmf58U4cvnHMTXcQynDwzzE36+c8TpF7HKMzxX/mVhHK/SzV4X5W7XdjIsX8joexN30JabHxLc9PGc0NcFFHHXu1VTTZ3uJU4UmEFJQmCqibWsWJvKa0T5MKd0VR/rIcfRIx1Iw//ilGZBIbZr5dZ2e+OM6l+sts7vg0kzEHhaYIX8FB2DHED/JrsBow7/golmQmidgu+upnsLtkLjftevzsryuZNMpCOsW9HQwt3EBWwXx+JeIUVb3GkuJWinoj/L29lkDBl7HJJF94q5NwvJil07/EUfU5hMvD/8l6io7CXpq68jjCRorFOGcqtyEAl25n3cjNbE6Ww8AHZFlGURTlAyE8gXzlW8RlA1M5l1MYdfNGqUFPhs59jpfZO1TPrsF6ZhkRbpX5ZCNoce7kYMUCLjkzQt50IXFTL/mh0zy77CqWvLHj7eUYsDvzWXi4leHKucyuuJbt1knaszu5sXYL+eNx/i02g/6Su9HN2Xzr6Cv4JxewUDxJhn0DWk4lv858lT0lB6gZdqAnVuMjg1DlJtCSCDT+avhOnrLlskHfQ2iePe1TpcJdUZS/GPKVb0IsyLR+L35fkLgJvjcrk7sdO/F7u3ns1M2UpKDSYmUtVnzZL/G9hlspCcZYdMKKNIUp6H2OTWtvwNQ+ycXjZ3dLMdkzWXmolViGh+L5n6HPFOVBk5975z2EK6yzZySLXRU3kbTP5q97nyd+5lKqrXsp9JdjLlrIC869PF+ylcIpGxf7GtjDfNz5LyIdoyDgiyMfZ4fLwdrwq3zf8gAfP/1U2udKhbuiKH8Zut5AHPsNwdRHGc7Ko1DP4me1NmY52lkQfpxftd0HKRPVtjG+YGTjt3p5yupiwpnFFS1+TFLAxHPsu+wKThn5XH/yWQQgEFx8uhezLhm58j5smpN/JMrfLHwAB3HMHUl+VHENMfdlfMS7g9xDy/CY+6gbHcRasZr94iC/KnuOzLCJTwx5+E/xETyOEyQ8Z/9x3DxxNT5TJfZEB982bWIrObxpW5726VLhrijKB18yitzyJVKU4M/9BPYpQa9L8mp5jHu0R/ht9y10TWVxcTTJ5Xo5OQgmLU/x2PxraOz3UzVmJqTvJVDm4ZXyRazd9zucehSkpNEbJsPn49TqzzFfFvAfRPn4vIfJco0z81SAvypcRzDnZuYFT7F0dxkWEWPxxKtYZ9xGV/QQ35/xHOaUwd2ddn5hvh2nyUe87GmEgBX+JuaGV3Ewd4T/J/ZTdibzaGufQ+3oWNqnTIW7oigfeHLX9xH+Xqb1e+kMTZApHfxrvYOvZLxE25CTl7sX05TQyMwOsxY7Xvfr/H3DPdiTOmtaDOK2XnLiPWxZuIo5+95iZqQHAVQGE5QNj+BbsJHFtjm8ToLS2mepLDhBbVeI+1wrGSy5k4LYOLfvCpFIZnJR9ElsFfcw6TvKt+tfJKFFuKvNxAuOG5jGRar8MYQpTlWkkru8n+ChWaM8OPRd2vQMmntn43emODV3+L2G/CdT4a4oygfbeBvs+TFh/XIGSuuojObxaqGZmtITZEy8xiOnPkOZ2USJfZA74qVMWHy8bjNzOtfDmpYoTiZxjG/n9WWr0DumWTm+FwnkBaM09AyilS3GUrseLwZtZa+xqPp1Sodi/Doym/1Vn8OWSvHVA8cJhGayxPokGXlfIBbo5htzX8Rnm+TWk1ZGXUs5KOsxFT+HyT5GViqD7w3ey/fqJ7n/zDeZTFl5pbeRuMmg5+IJZk9elfZpU+GuKMoHl2Egn78Pw3Di93wB31AAQ2i80BBkdfj7PHjqyyR1Myt9SZbJCnIRRC2b+dmC9cwcjjFnOIDP9wrt8xdzOFHEhs4XEUjcsQRN/WOYsivoW/5J8pPwXM4Rrqp7mqxJne6hbB6cfS/SlMvfHX+d4Ngi5jmfp8B6M6nQOP8442kGMoZY15ZJrSOHX+nrsWbvw5J1BBsaP+j7Kj+cFeVfe7+KNamzabAJw4Djy8axRjPRXSNpnzoV7oqifGDJAw8gRg4SEHdxIDHMrGQej1ebuMXxY37beRunJ7JZHxRkF46xSjrpc+7hn+ruRkpY1xJmUt9PoqyAl/IWsrb1Rdx6GFtKZ3HfOGaTk7Fld9KQsPNb2yhXLrwfW0Ti6Yhx75wvk7TN5EtnXiLRuZIZ9j1Us4JkPMaPPA9xrHCQFWdKudUc4Jv6Z7HY+7EWbkUT8O3++/htuYMvjv4vSmMRfj54MaaYoGXJODqZHMqaYNgWTPvcqXBXFOWDaaobXv0HYvoiOmcuoypYQa9Lw1X3Cu39hbzWO491GTY8RS1cH55Fr3maU85CWvIzuOJYBAcncTiCvFhzMYsPvklVtB+TYbCodxRnIkV86ecothdxVItRv/TfMOtJGk9Mcd3srxNxL+LWvtfIOLKMEusxGk1FJKSD/7T9gjdqR1kwMJuvpc7wReOviZuSWMseR4gU94x8gr7sGawN/DVzIz5+OHIJjrDk4EIv2DLpcE1y8eglVPWp7QcURfkwMgyM33weqWtMz/g6Z/oi5CfNvLFglNyJN3m87UYWF7kp8XeyLLgEiURou/n3xgWUe5MsHT2DEW9nV+1ico91sdjfipSS+f3jZIdjyMV3EfXUEEWiLf4+FkuARScmuaniPsY9l3Pp8F7qDs0h1zzAImuKJGU8IX/GC43jNIws4H/7m/l7cTcDeLCVPYZmDvCxwEqyTRdTod/HyuAY3x9djjMgOTBvEnOWk9NOLxd5m/jG9E3MjqlHIRVF+RAy9t2PNrafgO3zPBM8w/JQHjvLDOos/8z9J/6aPLedOZ3DzM6GWYaDE9YDfK/+o8RNGhuODeELHaS9uo7RQYOrxl5HolE3MkWxP4yYcz1tJY2UoNE753GsWV3Ma5/mb3Ju5WjpR6idOMaat4qwE+QiVy8GjTyT/AWbF45Q613Ad7wHeMS6kX1yLrbi5zDbB1gji2gK3kjc9iU2+vr5ycRibD6N5roprAUmTtonWTxVzV2+jfy86nu8Zm1J+xyqcFcU5QNFTvUgdvwjMWMRuyqzqQvOJ2gGx8zv8ljbvfjidq6N+GicvZOLAg3sN01xJncxLUV2Vp2cxvDtYbq0nH3xMq7t3YoGlE/6qPH6oGo+L9VczjKsHK3cj6v0dWq6wjyhXcrzMz5FfqCTT+81gW7mksy9aMZlbJYP8eiiXqom5/KPI8dpcczhEf1qLDm7sWa1ssKqsWroK/Rm/i2fmurm14G5pCYcHKvxYapKccwaYKkvmzsmbuPl/F+SPbqY2Z5A2udRhbuiKB8choH+2N1IQ2N41ido9dVRH4Sehtd4pe8Kjo0VcneVnYr8p6gZvokxdKQlys/nZFM5HqdhYB/kOtnqmsf601tx6VEK/QHmDnpJFeTywLw7uEGzcyZnGNes+ynp1+kIzODf6r6CO+LlS3v8JGIeVmZtw6xv5EnrJh5rbKPMN5u/Hx4l4rLxrdSnMblOYy94iUZ7ikv7vkF79j9w72QHm0KzmR7O4XR5kMSsGEe1KMuDJm4eu4dtOT/F45/FveJR5qHCXVGUD5Hk9p9inj6IP/sz/EtCcMuwg768KQ4mxtneM587FhaSHHuGgsBGcqSFVnr59zkz0Ay4/vRhdGuEV3Lnc/HJnRTHx8gJR2jqHSORYeMHi7/GJzUnflsUFvwT+aMCfdTNnfO+iymV4Kt7BokHK1iZ9Rx2/aM8lPU8T9UeoihYxTcHUuQ7h/lM/CtgncRR8hTVFoOV/ffQk/VDvjbRzjPh2QwNFNBTHGayMcwJEWd5KMk1Q/fysuOnlMfn8HH5AoZJ8IRHbfmrKMqHhN57AtP+fyKmNfHjghSX+uqwSIO2omd4sv0arqrLx3/kZZbmCupD1WwVU+wrm8lAnpUN7V0kI30c9MymsKODhlA7rliCxT0jxG1m/m3F17nBkkW2kAQX/ituf4rcXoNrF/wIAwtf3tOO7pvByszNuOUNPJD9O54v3YcnXMrX+7JpcDVzU/ybRM0SR9kjFJl0rvBezYj7Sb7pbee5UB19/QUM5EcYWOCnQ8ZZFomxpu8e3pT3M9M8h4/p27FpCT5X9y/YIzlpn08V7oqi/NnJeAz5+B1IaWPbzHJ65PVcPq7TU/4aP+q4mdlFGZg6DnHbrFeo6vsYx0gwaXbzar2bhuEpCoZb6MkqITQYYuXkPmxJydLuYQwh+MGKv2KlrYAmYWa84dcgJ5hxJsBVC39KzOzm8/uOYZ2YzfKMzWRoG/ll1lZeKNmLJ1LK3/RUsML1EjfFvoFXy8JR9gh5WpQNsTpG7Xv49ngbvwvV0z2Qx2B+lK7FU3STZEksxhVdt9AafZhZOXVsSO4hmwBfnPVdvnnCSuZ0NO1zqsJdUZQ/u/ivv4o51Ul7+UX8i/szfOlMioBrjG9PLMRpszMzPMA9M36OpfvrhIBDepzNi3JwxpMsbd/FlDObk75M1o68is2Ai7oGMBmSH6z4JCUZM7hOWBmveolA7hEaT01w1byf4rPlccdbzeSM1rPE/Qw5pjXcn7GDF0t34QmX8dXOGaxxbeKO2P+iS1RgL9lEnhbgBks2/bKb74618UqonjMDHobzYnQumaAXnYXxGJd2rabLt4UZZbVcnjhKiRznKzO/zd+0hWg07uHS6ZNpn1MV7oqi/FnFXnse++hjTDqX8rmST/DpPit5cYOfmA0m4g5WZMW5I+N76NOfJzuZycNGkL31uUxkalxx8iDSZGZXsoprB7ZiMyRLugZwJFL8aOnNJLPncR92/HlHGa58mWVHh7m24QeMOCu48eBblA/NYYFzC0XWZTzgPsSLpW+QH6rg612VbMh6lL+O3clBMR9bwVbyrcPc4DZxJjLFv4218WZoNqcGPIx4YrQvnaBXGiyIx7i4byn+4b1U1Fb9v+zdd3Rc5Z3/8fe906t678WSLVnFltxtjBvYpmOK6Z2EsEAIhCWEkEoChJIQqqmhY0wzYGNw70WWZVuybPVeZjS9l3vv74/s2ZOzvz2/zckvSrK78/pLM7pH85zn+5zPefTcme8wO9ZLmTTAwyUPcvfpIFOkn7N7qJhdUcukz2si3BMSEv5hol39aPbcS1TI4e7q88jxFXDhsMRW8wRfe02sLjOxJvQzZONCsh11vEUYZ1oSJ0r0zOnrINPv4GulkvP6vsQgRWjoGcQajPDszEvoz2zkMUFPxDxCT9W7LGnu55opv6EjuYrVzfuY1l9DtWELBfoKXjB18kX+12T4ivhRdz4rk97h58HL+FJYiiZlL5mmNi4yK3R73Tw93smBQAUtgxmMpUZom21nUJGpD8WYNTQD3elWUusKqI+PMD3eya+L7uW7nWHK+DWb+ytpDuThVVsnfW7Vk/4KCQkJCf8JyRNGfvd2NIKPJ6evpkm9mM/aHIxpYvzGr+f86gyW9f0IXYWZzGNXs58Y/YKKbTNNFLgc1PS385VQzbLeb0iJuZnZN0aGP8S6mtUcyZ/LelGHoA7SWfsqZ7d08t3Sn3E4vZFzmg7Q0D2dCv0uSgw5PG2ZYFvORjJ8Rfy4N4dlSe/xZPAC3hAvRW05QWbyXlboVQx4bDxtG+FAcCpHBtKwpUQ4MdvGqKAwNaCiylZGblMngeV51IZt1MY6eKzgXq7vjpAnPs9XvdPojKaj1i8gS5U26fOb2LknJCT83clRicDLv0SvHOTbglk8k3o7j7cNY4xqeDAGZ1Vm0Nj5OFnTRklqvY8J4LNYjANzrWjlGIvbD7NNqGBO/x7yQyPU99vI8QT4Y9U5bC5dxDtqLTpEumpeZ2HbCe4q+SnfZi9ieVMTc7orqdDvpNxg4lepTrblbCDTV8LDvTkss37AC8FzeU68EpWpg4z0TcwXLLgCozxlG2aPv5rD/X8K9qNzbIyKCrm+ZModOdTtG8Z/USa1USd1sTM8lXcPN3R7yFO9wMae6n8L9oU4M1REQtsmfY4TO/eEhIS/K0VW8P5xA0mBF+lNKuKm4l9xw3AvjbZ01hEmrSSZmd3PMrP6CGLXI2hiVp6WgtimW3BY1Jx/Yj/Hw1lMG2lmSqCbmsEJ8tw+3pq2gs/Kz+I9jUhS3EjftHeZ0bOXe0oe4eushSxvamJedymV+h0UGXU8lGGnNfVrcl1VPDRgZbH1A14PLecJ8RpEwyAZGZ9RF0tHFlv5rc3Gdm8dJ4etjKSHODbLzoSgYPIWMsUVZfF+H7brLNSMeJkRaecPOfdwbbedNO37fNJVzUA8BbXhbHoKXOwo28jMsbpJn+dEuCckJPxdeb88innoxwQ0Ji6c/ixV4VFuazdzColjOXoWDbzOqrKt+Fy3kuKu5DElhJypo7XYREPfaRzjApkT3VT72qkZdpDv8vLO1OV8XraQN7UhMuOZjBXuoGL0C35Y/DCbMxf8e7BP1W8jRy9yf9YYPcm7KJ6YwY+G1cy3ruft8DJ+KV6DqLGTmbmB0kgGVtUxfm1z8I1nJu0jJgayghyfacchgMozlUpnkGWtAo6boLI/SkP4FOuy7uKq3m6StJvY0FXDsJSE2riM08Wj7C3ajDVaRLV3zqTPcyLcExIS/m58B/vRHfkhosrJ9fV/QBHj/OKEG0Eq4o1kiUbbeq7L+5wh7bnkdJzLR0QYNYgcmJlEnsuG4Ywd3C5melqYPuKkwOHhvcplbCybzx80g+THZ+PIbCHH9wb/WvIQmzPms/JwM7P6SqnUbydVp/D9vCFGrU1Ujs/jYZuHeuvnvBdexiPiVQgqHxlZH5IeN1KiOsrP7U6+cs2ia1xPT06AkzMmcAG465k5GOMsl5rY1XZKevTMCR7nnfQ7ubz/KCbNIdZ31DKqWNAYz+H4lC6O5O0gI1DOw52XscesTPpcJ8I9ISHh7yJwcgz5y8fQq5t5tPJuThqLeOz0Hgo8C3nDIJHv28Sdme9zJrue/CM306zE+UIVp29uCqZIiMKjnUS8Qc52HqJq1E3hhJsPKpbyVelsfqluZgqr8CZ3kxZ9nh+X3s+WtPlcvO8ENcMlVBm2IGh03FvQgcPSTt3wUn7i7qTSvI83gqv5hfoSEKNkZXyAQZRZLJ3k/gk3G5xzGLBp6cj30Vrr/FNHGMd8lrRLlKRFsFw4QmGHhlmB43yRdDsXDe9Ape7k3TMzcAhGNKZzOTS1hZNZhyhy1fLz4YvI0KUSlSb/zD1xQzUhIWHSBbvGCXzwAVb1+2zKXsbrWau5Z2wDMwYWcFgl4Qx/zUMpb9BaVkhO871MyCKPCiHss41E1Soqmk4RcYc4e2I308bcFNudfDTlbHaVVPND1VZmqFcQNjqwKk/xo7J7+DZ1PpfvPkXNcAF1pk/wqfU8VNyCw9LOnP5VPOo9TqVxHy8HLuMX6osRhDi56e8h6rysDXfwA4eHd+0LGbBpOVXs5UStCy+gGl/KxYc15E7zUbygl9IOkZmBdraavsdS29dIdPP2mZk4BDNq8wXsnL6fk1mHqLLN4fHhK8k0jpNqvQqD5ptJn/PEzj0hIWFShQZtuP64jUzNb+k1FfJA+d3c6n2JhhM34hPgmPItv0h+hSNVmeQdvQ9iFh4ggFyjxp5kpfpoK8q4h+X2HVQNuyhyuPio/GyOFxdyi/5r5upvIU4Us/g49025i71Js7h6xxmKHTk0mN+hU1XA78v2ENE6WNpzEQ/GviFb18EzwWt5VrsIAZnipPcJmmz8wDvKxZ4Ar48txuuRaCl30zHFS1BR0I+cy5qWMNrzBshPcVLRLlER6uaw/nsscL2PVwrxUe9MwioTKuv5fDN9I4NJXcwdXsK/elZhNG8hU3iTuCLQri5l8STPeyLcExISJk1wbISJVw+Rrv41IRXcWvUzLg+9Tuah68hDxefCXn5kfYEDtRlkn7gLo7+AHwshAkUyg7k5FHX2ou0dY5l9B9VDLgqdLt6eeg6eQgOXWHcyT3sLQsSIzvAb7px2F8dMNVy/tZscTzpzrK9wUDWFV8u3IAtxLj2zlrvF90hWD/OLyA28qZmLoECl/gMcaUP8wmnjLE+EdSOLCQdiHJjupL8wQEwGy9BqzukLk3X1CQR01Ld7KIyMcUr1XRp8bzAWUvPJ8AxiKgtKykq+qn2PCeMYK/pWcndwORbz70mT9uEw6/iR7zqyRz2TPveJcE9ISJgU/rEu7C+fJEV4Di3D3D7t18yXvmT8wFpuUfQcFdq5zfJb9tVlkHrmepJcNTxPmJPpMcYq80gfs5NyvJsVtu1MH3JT4HLxWvV5WIpkFqQcZrZ4C+pAOor+Ga6vvZMBVSE3fttHWsDK/KTn+UpTyUdlX6KOG7mp4xJu0q3DIPi4V76BL1SzERSRGtUHjOf38Du7k2meOOuGFhKNxdjZYGcsK4wiieT2rGCaFKP+8h0M+aZxcVczqTEvA/JNVEfX0e1N4qvxqUiqVMKZi9g0/VWC6iBXdqzhRqmeZNN9mKVBOrOSeGTkAkYz91CWO/nRmwj3hKSzYsgAACAASURBVISEvzn38HGcr3djVr4gmSZ+WnYn+eoW9u6/mJcxM4adlaYH2Vubian3IjLGFrNRifJpUhR/fTYmr5+8A62cO76d6iEPBW4XL9dcQElRnNycw9RHb0XrzSVqfJGrG+4gGk7h1m2jGGMGFib9nncMZXxTtAlTOJe7OudzuekZorKOmzRXszcyD5Ui0yivZ7z8DK+OuUnxqHh9aA5RIcq3c8ZxJEcxxAyUt59NZr6fc2u+5bjtbG7r2YyMGmf0MsqFVzhmz2anqwxZnYUzt45vpq1DVNTc2X41l6iNJBnuQEWQvcV5PN03g77ivciCiv4zZZNeg0S4JyQk/E1NDO7B++YYgnSSTD7h3Zzz8JvcbDmwmuexoiFCtemH7K3LQj28hLz+82lWYjxlihCfmY4qEqd812FWD2+lesBDls/NS3UXUlcokTJ1D9UTt6HzlOA3v8nls+4gY0LN2v1uFDHOopRn+IOlhCM5O8jwVvFgfw7LzU9jixVys3kJp/yL0ChhFoQ34JzWzrsjbrweC+8PTSWki/LtrDFcJonUSDKVbWdR2NjFrPwWjg2t5J6+D3CpMtD668jVvsO2vnJOhHNQtCUMFGexu+w1rOE07u++jLN1bVg17xLUi3yWX8mbgykMlpwgJWLiZxtceGvGJ70OiXBPSEj4mxnt/oLge0GCiosK8UUOWGo5mpbO54eW8xOMlCKSbPgle+oNiONzKOpey7Ai80NdBKUhmbigon7nLlb1fUNtvxtrOMhLMy9kXqGEdcY2pgzejME1FYf1I9bMuo3avjBLj0YIGCZYYn6VX6Rm0ZV6hBLHAn426mGm+Q1ORxq5LbWCQefZ6BUfZ/s+JlJ9irdH3RxzFdI0moPbGmbbLBt+nUyeP5fsgXnMW7qdFKuXtr5lPDDwNkOaCpJ8Rgzar/m4YzoDcgqKro7WKWGOFn5InruEnw2tot74AXqxhdFMHW9Y6vja6ceVM0KNJ5n7Ntnw3SgRbTNPei0S4Z6QkPD/TVEUBk69jvSxhQlVnAp+zbgqlU250/isaQVrUbEcHQbDK+xtcCGMzKWs63r8gsIdmhDRBithvZ6GHTtZ1bmVGX1ONPE4r85Yxdklfoyzd1Ny+nbMzjoGk7dwReN1nHPCxcwODRPWdmYbPuHBLD0TxnZmjJ7Hz92HKDE1sy18LvdnJuGyr8SkOFnh+hxT1SkeHnfwhb2WQYeZgewAB2odhNUK5a5KUuLlXLpsPeNSLvauen4w8i6D6kZSfaMoql7eOT0Dp2BENi1kX3UbPektVI/X8ahjOkXGxxBED+3lZp4M1nIsMkrcqOLSfisXtY3jvltCiEHRUOKGakJCwj85WY5wpulRdJvr6NMLVMg/QZLhjZyzeefYBcwnyh1YUBm2cqixBalvCZX9lyEJCt9V+XHOSCZiMTJ7z06Wte5iVs84MVHNu3VLWFozganuMIXH78Tink5z5mG+V7OGtQfGKBnWMZS+mxLDXh7JChEX/azsv4J/jX5Eqm6Q12NX8NsMLSHbSlKkUZa5NzKl8gzX2V28PTYbn09NS7mbtikeYsB0WyNTMiUWF3zEfvdSakdsXDTxKSPiIjICx3DEBT7rnklANBBNO4ut07fgMI6wpH8hPw3LpBoew6/TcHRqCk+MFzNoGEIVyObBNidlehuu78io+wR6dkxjqHgRk92AIBHuCQkJf7Vo1EHb/gdJ3nEeLcl6psYfIiXq5snMy3mjfRVVuHiMZGRDO8caPyPYfhHV4ytQiwp3iV76a1OJpZmYt3cbi48eYHF3D069lW1T61iwcAhrWSt5R+7B7JvK5vxOflt8Nt/dNkKyW0dn3kdo9d08l2HDHMnk5q4F3KR9CVTwU+EKPkhKJmY/i/xIF4u9mzirrJuZEwHWjcwlFlPYXW9nIDeISlLT4JjL4imnyLOO8KH9Br439CW1vg4m5CVkxnfT6s1gu72csNqKJ6+ObVM/BGRuO7OC29RHMWhbGEw1sbXYysvjFgL6CbSORn535Aji7DD+OgXNPhVnOitYkXWC3fYo8MCk1iYR7gkJCX8Vv/8Mp/Y+TOaBG9icY6Yu9FPKg308lnwtb/SeSx19PC8Yieo9dDSsw918PbWeRgwqhR+LTlpqMpGyTMw7uJ2l+w6xvK+NjpQ8WovzqTm/n7T0HnIO3oc5OIV3Sif4LKmGO76xgwQny17ApXfTbrWT56nlvsEklhmfxR7P5z5rAwelIuLOOqYFWlgc3saawi4Eh4q3RhuIqCW+mWvDkRzFGrHSEKnm3NpdhCUTb9hu55meF0mOBfHF5pCs2snWoXLaQtn4dfkMl6ZwsOR9koOp/HRgKmfrNiAIfk6UJvGWIYVvHQoqBfKGlvFo22Z8F8aJpoP4oYm+SBZX5B/GpJGYFi2f9Pr8ReEuCMJK4PeACnhVUZTH/sPvfwDcCsQBO3Czoij9f+OxJiQk/JMYt22md986spq/yyvlVub6fsN8TwuPGa7htdFzWS0c5GeCAZ8hjc7a3zG+/3vURiqwqgWeVNnYMT0XOcfInEO7OP/bvcwfaedoVhkTpWYqLu0iXeshZ98DmCKlPFsRpiuaxXU7Pbj1TrrKn6dPB06dj7qx1fzE2Uql6UtaonO4P8tMr6cBKVjKHPc+lrOLy3K7OGbL5dhEHq6kCFsbbAT0MvmBXJZZdcwo38ZpRy3t0Xpe736CGMnEw9moxKO831mPTTbjNtdyvLqf3rR9lDtKedYhU6B/D7fGSvN0C7/zpdEbjKH2lnBev4ULwptw3yAhhiH0SiYxq8DagqPEsPCJ48fYKWHWJNfovwx3QRBUwPPACmAIOCIIwkZFUU792WXHgEZFUYKCINwBPAFcORkDTkhI+MeR5Rjd3b/F2XScjNa7+UW9kdUTT3LBxC5+r7qcl10ruVf9ETcqRmzmRjrLXmVs933MJJNUtcBrqlE2TC9AzjEye/8ervvqa6qdfewtqCBeJVJ6fjvGmJaC3Q+hlXL4ZZUK7bCG5f1RelNaGSh8gy6dClHWcEHfGn4Q+4hU/RAfxi7iiXwbrrHzkKPpLJ3YwqX6vSxKGuLzoWrsfhOdBT4OVTmJi1DvL+WigkGSjB7eHbuCupCb5wafxsMUtNFRnPEQn/c3EBB02LJnsn/aLnw6B+f3T+Vn8ZNo1WOcSs1hV2mcNybMROQ4sfFVPN65i+Qpp/DNUVCfFhj4toTS3HHmp/dji5Wx2f0jfHIqHal9k16rv2TnPhvoUhSlB0AQhA+Ai4B/D3dFUXb82fUHgWv/loNMSEj4x4tEbLS23g0nkzB0/wt3zzVw7fCTXD7+Dc9Il7EuvJKXNM+yEAsDaTWcSd2Mbdd9zDEYSFELvKka4ZWaIuQsAw2793P3xvVkhlzsLSlGc1aUotndhAKlTDlwKwpp/Hiqnso2L8kekUMFXzGeuY0RrUK2t4hbRspZo32OiMrAw6rL+TTTRmj4JpBVXGD/nJus+8jSBPljfwMRSWR/7QTd+QFUsorz5RyWVrYR9pr5med+HnFs5FzHfrxyDSa5lWOuXPY4SgiqTQyUFXKo9DMMMSO/7M7lQtU2YqKZbVPy+EinsN9hwBjQY+67gFdsbxNaESeUo6DZqOXMSBFLS89QZnFyOriYXd47iKEmlt5CTfoB4JZJrddfEu55wOCfPR6C/+eN3luAzf8/g0pISPjn4nIdprX1HpJOLcc2sZIHFum4r/dprhrbzO/il/K5NI8vtD8lV0mmI6eRM3I/rgN3MN8ikKwSeV07zMt1pSgpWpZ8sZUffPMuCHBkShaWS7xkFI7hcMxh3tErCausPFGgp7HJB0qYz6pfwW8aICAqNA4t4X5vH9WG1+iIVfNAah7tokhk8BY0KgfXjn3FzZmHsIXMfDBaS9AQZ+uccZzWGCkxE9emSJSldNA+XM2XpvN4r/MZ8sN2fNGpqMV2Puuvpj+awqg5n84qHz0ZWyh15vAH5ziF6jbGtCVsqg7xlk/EEQCtbSaXHJdYmvwm/rUySCA/Z6XfnMzl5S1YNBF2eW/jZHAlMa2TUHorOpODUDRv0mv2l4S78J889592mhcE4VqgEf7zhmeCINwO3A5QWFj4Fw4xISHhH0WW4/T2/YH+rtfIb7+bndTw+Bw1z3T8nAttu3k+fiFtchFfaR8mpORxvHg6naOpBIfOYb5VJlnU8rJ+lNdnliHoRW587W2uPfo1DrOFzmojaWscmEx+wt1LmNdzGS6NnndMKupbQnhNvXw87RVi6gjauJGrulZwh/gxSfpRPoleyBOFg0y4phP3NGLSnuYHw19xUfYp9tpL6PalMZQVZHftBFGNQp2czDVFY6jD8JLtMgpEE5+f/DGKYiEUScMjjfBFfwNBtHRmT6V1WjNe/QQXD2XwSPQ4KlHgQEYVG3KcfOvWYghpMA5cxDNtn6AsDOBrUFANCtg/zMWYF2ZtznGiipXPnI8wEi8jnNFMSOOhsvgk6fltuNobJ712f0m4DwEFf/Y4Hxj5jxcJgrAc+DGwWFGUyH/2hxRFWQesA2hsbJz8ryJJSEj4q4VCg7S13UtgfJCi1sd4LiuTTwoU3mm7j0XO4zwVuwyLGOYV7TMcF+pwFBfSc2o+sjefBUkxkgQDT5ocbJhZiikW4se/fZa5g230pacxsUyiYNEQQcFEdtMyklxrGdar2ROOUdQnczpnE7sLtyCLkO+ZwveG8lhleBE/Zh4R1vJ5yUkCw9cghwvJ0O3kqYktlKS62DBQi1/ScLTSSVupD5UistaoZm7aCBN9aTyafhsPubaydvxrgnIJKmmQE+5cDjimENIYOVNu5XjJt5ijRp4ZUFgmH8UnlvNJpcT7spdhnxarvZRzj6awWvMu/mslpBQQv9XSebqQhvIBZqaOMByp4mvPD3DpooTTDmJOGqG+4giC3oPYlIZsn5j0+v0l4X4EmCIIQgkwDKwFrv7zCwRBmAG8DKxUFMX2Nx9lQkLC39XY2EZOn/kJemcZ5tO/5a6pJjrNITa23EWdr4cnYpezXN3GTOEUX2gXQ0Y6Q02XYJB1zEqKY0bPD9MC7KktoGSon5+/9Duy/Q5OlWcgXu4ityDIULiE5Yemo0TWcEYvcMYewUSQL6vWMWTtAwQW9y/j/tBRio3bOKLU8YvUNLq0g4T77kSRddRpP+DZwE6GSWb9QC0ho8S2+jEmkqNkKlpuy/GRHlNYPzyPntTlfNz+FOWhQQKxIiRG2DhQxXA0icHkVFqn2xhPOs0sZxJPeTtIkkVOmBfxRkk3OwJq1HENqX3L+M32bbC8Hc8iCdEP0ReTGdWnckFFG9kGHy2BC9gTvAx/agdxnZuqkpMk555C8phIfcmEritE77SKSa/hfxnuiqLEBUH4F2ALf3or5OuKorQJgvALoElRlI3AbwEz8JEgCAADiqJcOInjTkhImATxuJ+Ojp8zOvoJ2c7rODF4Dj9r0JMRG2DbkfvJiTh5VVrFd9VfoyXCG8mr0Mem4Dy6hHR9kDqTgBoN1xVH6ZiSxcXfbuGOje8S06hpOyuF5IvHkDUiwaFSzuo6ByU6lzYVdI9FUUwtvFb1LjF1DHPEym29s7hasx5RG+MZ8RLezj2F35NPdHQFgsbFTbE3uUM+wbaJckZCSfTl+dlX5SCmgXlakTWZbuz9afxL+vVczih/OH4/KEaCEQs9oTA7RhuIoqIz38Dh6jZUisDDwzJXRk8SVGp5tVjDBl0vowE1GY4szj+UwgL/ViK3BIgVKwhnVAx/mUdqboBrc5qR0bHJdT8ntTmE04+TkTpCRcVB0AYQ96SSs8HHqdRanlp9Hgus2ye9ln/R+9wVRdkEbPoPzz3yZz8v/xuPKyEh4e/M6dxP++mHiATsFA0+yfNCHu/P0HKeaxtPnnwaQVbYrczgVvVm2uUCDmbORB5ZjNNZQo55ghp1Gl6VwHXVAjGrgV8++yQLzxxjNCMF9yUh0uvHmIhmMr8pgit8O5poNidiEr2eKP3F69icfQYEmOqcyiPjPmr0b9AqVPCoqZwTKacJj1yFFKhAZz7Oi84PSZdCvD9eT0QU2Fc3QU9eAAMiN6eHmILIeyPzaM66jOe6XmC56xBBqYhobIxtY+X0hVLxGrWcqAzQldPPVJ+F3zk7yZE0nDas4vclbewLKxjCGio6G/nhrmaiC2wElgQAiH9ppG+ogIVTuqhOHmckWsWXweuxWV2IxjM0lB7HkNGNPGEk/XUdsWGFBxfczYnkQqpR4ZJKJ72eiU+oJiT8LxeP++jseoyRkQ+wxGei6niU2/L1tCepuLf/Ze7tXY9dSUIWNCwTm1gvnIWcXkD4zCqQ1WSlDFKvFLLboPCTeiOV47088vSzpAY8dNRkoL3ahsUSJ2bLYH6XFW/kQcwxNU2BODZVFxvrX2ZEH0Etqbm2by7fUb7GoPPwrO4C3kvrxRMLEu69B0UyUGX+mBfc2zjqzuNYoAB7WohdNQ78RolpGrg2w8/EUA53pV/BAj3sOnYPyfEA3mgmQ6EQ20ZnElVU9OZIHJg+gKSKc6dN5vZAG2F5FuuKVKzXn8QeFilypHHTdjAZuojc5CNeJiMMizg2ZhExqrmi/ARJGj8HA2vYoa4hmjJKWW4XOcVNoMjoNltI3hTh1Zor+aJuBipBzQ8QuRgT22OWSa9rItwTEv4Xm5jYwekzDxOJ2CiOP8j2zhoeq9QhCgGePPYM13q3MkgGOYIDr2LmReOlmKNVeE81oDc6SdE7qZWL+WGWxIEqK9ds+YwbN39C0Kin60ojlrOGiYT1TD0Roje2Csm/FFFR2OuL4sh6gz8WHUcCcv1ZPDGkoU73HkNyAXeZFnMko4XoxHKijsWImgl+KL7GookBvhifRhiRw1UOThf5UQtwVUqE6bKBt8cW0Z51CY/1vsWl9q1E5Ew80Qi7bGl0B9LxmxWOVnrozfIwLWDgCccQBXELp3WX8JuSY7REZSxRDRcezmFxTxR5dgDtfDuSAtGDBnqPlVKTOcr89FME5RQ+CN1Ip1FLclIXDRUHUBk9CB0WMt4Os9PcwAurVxNS6alG4ZeCQKZiBhTyReuk1zYR7gkJ/wvFYm46On/F2NinmHVVpE48xcOyih1VGmb4W3mk+TnmKe14FDMFgp3t0kz6kmoQh8/CG05Cn9xNnmjBRyErq0SsuhC/f/rnTO/vYqw0iejNLszJcXSjemr6ghxSPc8MdxLjMZljche7q5/nmDmGoAhcNlrGA6EjaLUhXtSu5u20YdzCAOH+O5DD+eQYDrEusIEuRxLf+CtwpoTZUevAZ4pTppVZmxxjaLSSezJWslAPO5u/T2rcgzuaRY9fxV5bPVFFpLMkyKEKJxpF5CFbgCv8g/jllTxa6OYrfRPhqEDdeBLX7FbjKDKjveI4UomEMCAwsquImCSypryDLN0oZ6INfK5aSCwpQl3ZYSyZ3eDVkPSygd6hfB6YdQ0TeitqFH6OkyUUIigAMu7wQUyqXv7UsWXyJMI9IeF/EUWRGRv7lM6ux4nHPZSYHmBPawO/yRcJqBXW9m7gR/1vk4YXCQFFEXlZXINRV06oay5aoxPS2kiLV/CCRU9TtYkLj+7gux+/iyjIDF2kQzzHjtqnZmaLl2bm0R25nRlRgdPhGMfSn+X9vC5iooAppue5ARWN4nY6lAp+nFJOe0orMdccIrbzEMQYN6tf53zHKXbYi4goKpqmOTlV7EMjKFyZHKU4kMuLrrnYcpfweM9rXGHbQkROZzhgYZc9h/GIBWdKnMNTnYylBFjgU/MLVx9JsXJ2WJfwTM4RBuIK2XEtd+7UE9JXosxtI6exGVmC0CELfS2l1Gd6mJfaTEQxsCF2Oa3aXEpyu8kvOQzI6LcaCW638MDMW+kpyQJgJTbuEZKxKMUASHI3u0d3Y4uMk6HRMG+Sa50I94SE/yV8vlOc6fgpHk8zSZaZmB0P8siYzNZSNUXhMW478BHfj32ChIhakNkjzaTdMBvB3og3lIYh4zQOWWBEnsZjU4ykWkM8ue43zOg6jbvIQOgWL4IV8rrjJNtl3jK+xHnjGagU2CWe4uNpz9GpBxSBRa4knnG1AxoeV1/IRwVnCMlDhAdvQgpUkqY9zXPBt+mxJ7EjXIYtJcye2nF8pjiVOokLtCaOjJ7NKwWLWREY5uOmO0iLu5iIZHDCZaHFlUtME+donZf2PBfJMTVPjTtYFoBRruTu0lYOCofQSQLnDiRTOTAFuchHRdVu5EwZulT0HalDHVdxeVkbWdoB2uO1fKGehzHVw/zKT1DpQqi69Ygf6Xmq4HqaF5eBIFCIi2dEG5lyI4IiAH5anTtp87Sh04UZmDUO6qxJr3ci3BMS/oeLxbz09D7N0NC7aDTJVGQ9yZfHi3gmXSCUKTC/7yAP9L7JXLEdBfArJt5mJWphOrGBWrQmO5HM/XREq9lqMeOebuWC5p3c/ul7aOQ4tssE4ks8WEdlqps9vGu+nczoatb4BBxyjM8zfs+GjG4UBLSSisfGgqyInmS3NI/fZOkYtBwn5ppPxHYOAnANH7HM1sIBVx4xtcy+Oju9uQG0Iqw1C+jHZvG79AbUhWW82fEsS9wHCUqptHoy2TdRiE/W0F0SpqncSUwtcZ0rxPfcDpT4OfwhN8qHlr34ZaiMmFnaXEw4PYPi2t2I1X4UB9i2TsU+kMvsDC+zrN8SVvSsVy6gz5LBtMrdGK12BKcG1XsWXuVK9s6YjiKIGAnxpHiI6fJyRLkIkBkNNXNgbBcSYezlTjaVB0EyUufKnPS6J8I9IeF/KEWRGB39lK7ux4nF3OTnXot3ZC23dfhoyREp9duYd+xbfiW9iV6IIiuwQ5pNp3oBOGcQjhnR5zRxIJrMaWkmvZVWigU7jz73NJWDffjLVXivDyPoYMYJD6ekBh413s81QzosAhwxNPNk4et4RRkEgWkBkecd/YSieXzHfD4HslqJRzII992BHC6kQHWKnwc+otOexLF4Pp15Po5UuYhqFGbqZRr8FWxzLqa1tJ7rbVt4sP1R1LJChz+XpokMRsNW3ClBdtV6cZmCNAYVfjI+Sm64hl2WZTydc4gRWSJHUXFudymiMo2c/P0kzTiCoobgoVz62uvJM2m4rngzKeoRTipT2aqdQ15JJw3ZByAiwkYLbzkvZVfuTBRBQEOM7wvbuIgGkP/08R6/NMi+4c24JRf+DB+bajyEVSqCw9cQ91fjDA5Mev0T4Z6Q8D+Qw7GHru7H8fvbSUqaSa71YZ4/GuWPGUEMZoWZZ1p4aHgdC1VtIMCAlMcXrMIYn4o0UYYuaYD2tNM0haYxkGVCU6zj1m2fc/m2r5CMAq6b4oRropQP+NFMWPh1yvPMG8zmO4pAQBXhtzkvsSOpA5UiIALfc3q42hXjRe0y1hcNEFa1EZ1YSnTibNRimPvjr5E2ZuNYKBO/McquWaPYU6Kkq2TOJZ0+22qeL6mlJjrC1pbvUxLuYyScQ/OElTO+TCKGMIca3fRkesiICTwzbucsfyo9qhu5uWwfJ4X9WICFnkIyJmZSLJ6mqHI9cpaMdCaJnuNnI8ZSOCfnIJWGvTiVZN4VL0DKi1BTuBlBkFH2mVnfdz7fZsxFyRFQCTLXCtv4jpCHLF8CiEQVHwfHNjEa7iNqjLBjuht7Sozg6IVEfLMAkXzfOLW+/6uDy99cItwTEv4H8fna6Op6AqdrL3p9AdOm/I5vT5Vysy2CLUtDlX2UOa3f8ojwNnoxSkTW8pl8Dl5lFoKrirAYw5V/iK8C5QxTB/VWFg608L3fvE26141/vozvwjj5rhBZR+O8lvQQwfgMbuyFZLXIMVMLv85/B4kQACWxKL+yuTgRnccFOT4cxjPE/VOIjF+AHM1kgbyfK5zb6fKk0q+2cLjaQUeBD1GEczRqtPbz2ZA3G7FMz/Odz7PctQeflMxOeynH3dmE1DLt0xy0FAXQKPBdl5cb3HH88hr+tfAMuzRbUIDqeAbFw/MojI0xtWADSnmU+LCVgc+WE/LlU5fSzSzz86iFKLuYRV9eCrnFTYjqGPIJIx93rWazaRFkgIDCxZpdPCBLyPKFyIoWiRgnnDvp8BwmrpE5Uu2mNzdIyHYOwY5FgApL1M8qRw/nq0x0pJomfS0kwj0h4X+AUGiYnp6nGRv/HLU6iSlTHmbYsZQbj9k5YYlR4g8x92Arvws9RbE4jqJAs1zFPvlidL5K4lEr/uyTfB5LYzhUh1BupkSw8y9vvcD0nk4i+TBxS4wUc5ipbWE+NN7DCfUizuuBqTqBkDbIoznv0WxsRkBFSJS52e2l3jGVn6RU05nfgRxJJTJ4PXF/FenKKN/3/x6nU6RLSaW92MuxKR6iGoWpKoFp7tkcsK5gYGoedw1+wHdbP0SSRHY7yml1p+FT1AwWOzgwJUxMJXOpL8AdLh/62HKez/XzsWk3IQXKBDOFY3MpdkvUpn6JUB8g7rEw+vnFeH0lFJldLMx/nlTVOF0U0pxeRlJ5J/naXiIdFj7pvJSt6vlg+lOor1If4EfKKKr4ZchYUZDp8h/nmH0LkiDTVuqlqzCAx7kUX9ciUFRo5BhLPMNcq4hk9x1DiQUIZv1ztPxNSEj4JxUOj9DX/zIjI+sRBIGiou8gaa/lZ80DbLK6SNFB/al+7hl7lpViE4III3IWn8bXogtPRRXMZiJ5iK3Jbnoi5ajyjCRlSVy/+QNWHdiBZBJwXxNHPyVC3aCfb7w386J2NXO6BW4xSqTptey2NLEu80OMUoSgCgriEe4bs/CVupY3SluRZQdR2zlEnYvQKjHuCryNxelgPK5jLDPAvmlufKY42SLU+qro1p7P+rJiLpvYwieHHsAQ93LQNYVWRzJeWYst38H+8jA+g8RZgQg/cLrICi9ifYaGN1JbcctQIGrJtc9l2qhIjXk3qgUe4rKF8U3X4veWYtUqnJf9CqXaVhwkszlpLkwZJc14kkBvvfIjSwAAIABJREFUEn/svYL98UZQK4jIXKjex/10I0rXIJOOhMJwpIcDY58QV+J05wfozQvi8Cxlon8BKAIiCnNDPu4I+cjq2A6CiKZyNZrUMszSzklfG4Ki/GM67zY2NipNTU3/kNdOSPjvLhQapr//RUZGNwCQk3MpxvTv8tTRMT5Wa1ArUDvkYmn3u9wpfoZWiBNQDHwSv4xQeBbRQD4jJjf7jUF6Y6moM3QYCtUsb97N9Zs+QRePEFwioZobpmwswIHoVXytvoSyAYFp+ggVahNulZd1WR/ipoVBk4JLBSu94IxUciS9GwmFmKeRqH0FStzMmuDXVLrP4Ivq8CeF2VvpYSw9jBmoCxZhU13OyYIKVnj28tPuV0mPjnPEVcZxRyoeSYu9cIKDJSHcRoXqcIz7XE6qAw18k5zN85nNjMkyWSqRbFcjDR1apicfQpztISJl4dh9OQF/Pjq1TKP1Lap1+4gKGlos5binhNCYfXhHknmn91KOBuoBUCtxrhR2cLeqG1m6DokcFBQmYiPsH/+UUMxPT26Awawoo77lTEizQfnTDn92LMqdjlGyOrcgmjLQVKxEbckhEnWxIa0N2dvMjx7/6K+qvSAIRxVF+S8bwid27gkJ/42EQoP09b3A6NgngEhu7hUkZd/Kc8fsvDPkIK7WMG/UR13XRr6vvI1FFSauiHwTP5eu0EqkQCFd+hCH0u2Mxi3okq0kFeqY33qQ65/4mBSfl9B0mciKMKX+IAcGb+Q9ZSX5wwJzdH7qrTqMspEvU3bQJX5BUB3guElNcUSgyFvIN8kjxC0dxANVRG0rkSMZLAntZ477KIGIBpsZ9tfYGMwMoREUGr2Z+DTXsr24iobgcbYcv5PiUDfH3KVsnGjEJekYL3JwpDiAywjV4Ri/HnPR4J/KrqQ5XFlxnD55iBRgeqiaGceSaEw6iLI8QCBUhnfr3fhDKahVCo0pf6RevwsEhXZzAeMVEorZhmc0gw/ar+SYqwYQ0CkRbhK/4VbVKFHpemLSGgCcko0D45/jjzjpzQ4wnhynJ7Iah7vm30N9fjTC98a7Sevejjq9AvXs21Hrk/FGbLyVthvBeoJZFh8H7JWTvlYS4Z6Q8N+A13uSgcHXsNk2IQgq8vKuwpp9C6+0jvLWgJ2ASmS+PURd5xbukl4lRfAjI3A4Po8jwSuJBvM5pYvRlOrBKesxJqeQXGSkvrOZm55ZT5bTSbhcIXZFiEIhzB7HHXwgn0X+CJQYJpieplAWzeW0toej+veQNb3sTdEgo6bUl0G/0U1fygDxYCkx+wriwWJmh5pZ4llPMKzCaVQ4WGenLzeIKECNM5mQ/hoOltbTEDrGZyfvZYr/NM3uIr51zMEjaxgtctBcPI7TANPDUX495maGv5Y9SXNYW3mcXqkZC1AeqmRuUwqzkw8inRPB42kktHUN3qgBQZSoTnmHWfrtaIUYPeYsRipkYuYg/cNlvHfqfLpcpSAIpMtu7tR8yUXECUo3EJaSAHBLExwe34QjMsJgVgCnXsNx8Qp8kUJQFARgYSTAHUOtpA43oy2YjXrRDxHVOsZio7xR2ESB8RgLrE6Cw42MHriB9OTTk75mEscyCQn/pBRFZmJiOwODr+F2H0alMpOXeyW6rBt49kQ/H4d1hFQq5tjDzOvcyu2Rl0gVfSgKtEoN7AlciyOST4s+zkltjAhqrOkqNEVGKvrbuOWLD8kfHydSqKA+K0iqWsUW8Ye0h6eRPyrjS+qjyhhnVrAKl8rDccNnoN3O26k6RjRqUiJG3NowCjLxUBFx2wriwVIaAsdZ5DlIPApRQ4wjZR668v0IgkCFPY2o6WpOldaz0HeQH/W8RYG/lyOOYk64MvCJImMlE7TkB3EaBGrDUe5weZgRmMmO1DTWZZykV5IwC5AenMa8I0Zm5xxFngH+vouI9CzAHVchqIJMs7xDo34vJiHEkCmFwQqBgElF20gdHw2cy7AvF0UQqKGbe9Rf0yAX4pPXIKIH/hTqLbZtDEd7GMoI4BTSOaK7nKj4p46OakXinICLa3oOku6fQFe6BHV2DbIcp1U1ypdTD1OvO0mhxYGzeymOjnMR4xocRi+ns9y89683/lXr4i89lkmEe0LCPxlJCjE6+gkDg68TCvWh1+VSUHAj0ZQLeOLYGTbHTEiCwNljQc7u+4K1kT9iFYIoCpyILWBf8Co6pGyO6aL0qwFRJj1XQzzHyMz2o1z9zWfk2WzEshV084OoTDl8qb4fmy+DbEeU/vQTVBrjrPDORkahQ/81FvUG1qWrOabXo4triKhiKApI4SIU+1Ji/jJm+Y8xx3MUOa4QtERoKvXRm/OnUC+ZyEY2X8PJ0mrOce3moZ63MXnHOeQoptObgkcnMzzFRktOlJBaoDEU4Ta3l+nBRjZlaXgvpYveGJgEgfTANOY3aakva4fiJCJtNxBw5eKRRVSqcaZb36FBewSDEGHMYmawTIXTYObI0Dw+G1zERCgdEZnzVQf5jqqJ9PgKIsosRFQAOOLjHLdvY0jqY8ISoUc9nePG5SiIIAgYpCiXeUa5qHMvycY01MVnobZkE4kH2ZIxTlfRN8zTdWPVhhnouJhg93zUcRXDySGiciuHq6awsGsXv/7V7/+q9ZE4c09I+G8mFBpmZOR9hobfJx53Y7XUUlr1e7qEOu47eYZ94jAqxcyqYRfnD7zDqthnaAQJGZGm8LnsCl5Gi5BEiy6GT4yi0UjkFejwZuqY27Sfq975nFS3l1i+jPa8EPaU1WxXrUFrV6OJeRjJ24QxWeZW53JMHgMj2n2YNH/kizSJPSbzv/VJUQiLceK+aWgmzkIMZDLL10yNbwdICs6UME2lXoYyQ6iA4vFypPSrOV2Ty5Xjm3j10G/wuaPsdxYxEszHmRRioHGYE+kSAnBOIMj17gi5sUbW5wb5aUEfY3EBc1xFtqeGZd1xqkoHUdfOJ9x3Fa5BLSFFQKPpZEbyRzRoj6MjxliKgZPFyYwJOezoP4tvRhsJyCZS8fIvqo1cKXqIx9egkVcRRUFEwBYZ4qRjJ71iL16Nlr2pF2LT/enoBUEgNRbgGlsP546dwZRdi3rWbQiimjHJycbKwyRbvqXaMkB+MJOOU7ehHS5ClAXa8+M4TEM0V5QRMJ5LltuOrap40tdTItwTEv6BFEXC4djF0PB7OBw7AYGM9GVk5t/Kp3aRu0566VM7MMtprB0c4MrBl5gj70dRICinsC14KV/Hl3JSraLHIKEIcUwWiYICA16zloX7dnH5y19hCoSRSuKoFqo5nHYvp+LTyBqNEzV1cbxoP3VSCj9wLCdVSiKgPklM/xpvpfnZY9bCv/1zL8sqFE896okFmAMCc71NFAS+BhTGMkO0lHgZS42gUURKnMvw5qxmpFbmO4Mfs2bvZnrdFj51FeGW1Izme+gt7qPHLGCSFK73+LjCo0bR1PJWgY0tYhduSfw/7L15kCXHfef3yazz3a9f38f03IObuAGeIEgCvCSSK4mHJIassOSV17sb67DDGyGFLa3DXtvLXYePDXnXq1hLa8q2tFpxLUEKgTQkEuABAgMMjhnMDObq6em7+3W/+706M9N/vDeDngFIgSNcBN9noqKy8mVl/vpXWd/KzMqppJhmmNy6m4+lAXcWYtLcZ+mem2Q7NRg0Of8JPpD/S26zX0ai2Rr3uLhnhPOd2/nGy+/jydpNaGHzPnGS/8A6ys3mbpT+AkL72BiMMaz2znOy+SSL1iKrmYN8u/SfkEq3L+rGcHtnlS+tneU+Y+HOvx85+wEiFfKtyganDz3Bnd7zPJDd5uL6Rzj6wt+jsp1Du/DETYbV0ZDF6RmcpMxti+fY097GMhEdO/+m163hsMyQIW8DUbTF2tofsbr2h0TROq47wczMF4lLn+V/enmBbwUFOpbPwXbMl1a/zxe3fpsps4XWsJbcxh+FX+a77Oe0owgkOFZKftLGmvMp1Gt8/vG/4IHnjmIrDYdSGkfu4Nujv0rQ8sn1GpyafJrFiaM80LmNL+w8TFkV0fJFtPdv+a/GW7yY1Vds1WmObO1ukp37mepsc3/7WYpRC20rXp7rcHpvh3YuxdEF9sa/xLnZOzmUnOHvLH2NG1aO81J9kvOdMWrZlNUD25yajujaggNxyhfbLT7RHuNieZL/c2qdpyNDaAQlXSZbu4MvWh3mW3fR27mJaiyJDSi5zWTuUe7LP81+s4qSgvVJj4WxMY5VH+DP1u5jIZ6gSJfPyyf4WWHjqA+RYRyJBCDVCRfaL/JS9ylWnYTvFD/OurfnSis9o0I+WT3Hz9U2mRu7EWv8RkCwYG3xl7edY8p/giOFBWpqguPnfxnv0kEqLViYsHn+UMriRJbA85nfWOXOhZfJi4hIacq1KibrEgn4H//xP7uuujMccx8y5B2G1hHV7W+yvv41arVvY4yiMvJBRqd/kT/ZHuOPN6qck5MI4KNbW3x55Y/4ePuPkUBbj/Kt4Kf49+qjnLQ9ti2DQJMfSclN+zRGs9x3/EW++K0/4+DSCtoDc8Tl+UP/Eae99zC2HbORf5kTs9+jm1nmM/UP87n6A2R1Dkc+z9nsI/zj8TprXgz90Res3iwj1XsJG3s50j7Hzd2XsbWim4s5vq/NhdkOiQ1j6b1k/c+zWvH5ueo3+PmLj1Krak40pqgrj9WpNkv765wvgW0MD3d7fKEVckDv46+mNP8u1+TlyEYC+WgPt0R7+NnWYdLtW6lHLi0NxqRo9ziHi49zv/88FdUhdCUrMz4nnZv5ztoDPLJzBwrBA/I4PyfWmNd34Zu9ZPHRRiOFpJe2Od16mhPJi7yUuYWnCu8FIa+I+v7OBp/fWeMhp0h28jaE5dBULb558CLdye/yntxxUtvm6M5naS08wN4Vm8QSHD3icm5OUy0UcOOYD734DDesL9IsF3GjDl4c49gx0s4Q2XkqYot/8I/+xXXVo6G4DxnyDsAYTav1Ihsbf8rG5iOkaRPPnWRy8m+xYD/Ev760zrNqjEB6TAcxn19/jp/f+JccTBYJVJbvRR/na+lDPGuNUrUMYMhlE6wpD+Z8crUOP/XkN/nE0W+T7wSYimHtyP08tecXsDougbXIyxPPcnHsWUbTAr+w81EebL4XiY1tPc0fF5/iq2MXCe20P1dbeUzUbkHsvIdMM+HWzklG4zpaGi5NdTmzp8NGJcITY8zwSyyM3crN4Yv80tKfsefSOc41R7nUK1ItJ2zO1zg7FdK2JXNJyhfabT7d8WiVxvjj6SaPxRbbqcQ1DhPhIX6xeRvj1btoBFl2lMFg0HKdQvZx7hk5xo3pIo7RtPIWC2MTfK/zER7ZvJ8zyTg3iCV+VrzMnRzG0XsYp8JubVsPFjgZHuMpafhW4SPElo8wuv+p3iTgofoyn1WSG8ZuRjg+PdXlibmzbMwe57bsMaSvOBa/n0tLn2J8ZYTZnZTTcy7PH4blShYjJDddPMeHnz/KaNhic3wUJ2zjEWJ5WbreCAD7vXPsnzxJEk7xsb/79euqU0NxHzLkbcIYTbP5HFvVr7O19ShRtIGUHuPjH4fyz/B7S4q/6mo2rDFcpfnE1gJfXP9DPtZ+DK0lT8UP8rXkk3xPzrHVn8BBwYtgwiE7K6lbBT767FP8re9/nb1L6xgJzb2THD/webYytyDTTc6OH+Ps5FF6dp33t2/hyzsfYl94GxCz6T7Jb48/xbOFJRBgNIy256lU7yBsVNjXWWR/cAnLaOrFmJf3tLk40yW1Xab4GJ3iT+M7G/zs2mPcf/4oqzWfhW6Fpq9Ynm9xabbNpi/wtOEjvR6f6wTcwChHZxL+xBc8F9jERlBOR3i49l7u2fwg3bDAdmrQALKNco9x8/j3uEucohL3UBLWx7KcELfzpzsf55vtwxREm0+L03xQTJAzFWbNDA422iiksAjSDmd7z/OUWufPcvfTcEeQKDQWwmjuam/ymSjhgdI+XDdPoHs8PnOardmXuCX/LMqzeEa9lwubH6e0OsrhtYS1is2J/Tbnp21i22Gits0nnvo2d547SWukSLXgY5sIO5uj549ghGA2t8jBsZMw0cZkUgCObn+c3/jiv7yu+jUU9yFD3kKMUTQax9iqPkp16xtE8SZSulQqD1D3PsXXtrIcDVOWrGmMENza3OYLG1/nF7f+b/w04S/jh3k0/RBPiz1sWv1x4aITY6YtctOGzcw4d50+yU89803uO34cK9X0RvOc3f9xLo69l8jaYbFyggvjL9L0NiklOX515z4eaH4YT0+QiBrfzj/D7058i5rbAmMYC8pMbd9MuL2f0U6Vg92LuCYhcg0Xptucn+vQLMK4uRcr8zlaecNn1h/jA+e+T7MGF7sVap5hdbrDxkyLxWJfS+4NQj7T6fL+2Gdj3OGREc3jkctWKnGMzb31u/jw+sexehNsp/1zhOjR806zb+y73O2dZL63jWUMrZzN2cxB/qL7MP9v426MifmYWORek2GCHHvZh4eHMgpLWP0XpNEFjibL/L67l6o31hd005/GeCBo8HCc8sncDOO2T03U+fb0CYLJ0xwpnaDj5nlW38/p+kcpr1e4aSmmXrA4Oe9wds4msm1yQZcHnjvKQ0e/y0ivxYW5STpZG7uQp+dXQBrmSheYHzuDGGsjXIU2gvPiCC+ou1mvHeCGpS7/7B/+/euqa0NxHzLkTUbrlEbzGba2vk61+g3iuIqUHpXKhzmjPsGjNYcXlMW6MwXAgU6dT1e/wxe3/h3TvR3+ffw5/iq9l+fkBB0pEAayGY0zk5KZhnVnintOv8Qnjj3OfSeO40Ypse+yOP9elibez9poj4uVEyyMHidwmrja4ku1IzzcfC9j8Z0IbC665/mDscd5svgCCsV4kGfP9k3o7TnsbsSB7gJZHZJYcHGqy8XZDtWRlIq4A5n5adKs5OG1x7njwvP0dhTL3RK1nGJlpsvaVJu1Qv/F65Eo5lPdHh/vJaQVj++MSh41GU6HFtrAHfW7eP/mR8i399BSAgCLLrXsWfZOPsk9zgn296q4qSa2Bcu5af4q+ihfbX6UllE8YJa5X4VM2CMc4jAePsooJBIhBPVki+eTdb5qVTjvlXBMQoqFEZI9UZeHUsNDmXHmpc0Zd5kX5l4kN3qSudIi5+WNPJfex6XWvUxuZDi8GtPzJaf29AU9dGwyUcAHXjzGw09/lxsuneP83j1sTFbQIyV6bgnfbzM7coHpkYsw0kVYhsi4vCRu53R0K83taca3e+xbrzK/doGuk/Brv/dH11XvhuI+ZMibQJq22al9l53tb7G98y2SpIaUPtnSQzzefZDv9+AMRerOKAA3tDb49PYT/NzWn1LvVvjz5CGe1jdwXhZJBVjG4JehNNshmihQFePcf/IFPvXcN7n7xEncKCXyfJZn72azchun5xIWxl7iUuUkiRXiacPPNMf4ZON9TEQfQJgSbdniG+Wneaz8FKvuGvO9LHPVIyTbs8huxL7eJVyTkEjB8njE4myDzVFN2b4N/I+RcxQPXXqcw0tnqNVt1sICO6WYtekuy1NdqoOZNO8JIx7q9XgwiHFKNs+MODwic5wIHYzyOFK7g3u2PkC+O0uq+70RLbvUC6c5PPEkdzon2dvdJhNplBSsZCf4bvJ+/k3zUyRJiwfTU+x3MszbB9jPQWxslEkRSKSQdFWbo+k2f2AVOGU55HRA18oCMJdEfFi4PGTnycmI58vHaU6cZaZyijTvcIpbeSG5n1r7CIdXFXu3EnaKFmdnXBambBJb4scR9518gU9973FuP3eKxfk5VuenaI+Oo1xJqbTJ3Mh5ipVNRCYBoGrGOc4drHQPEG9U2LvRZe/qFvs2X6KQb7I5s5/lzDQHm+t89n/5veuqg0NxHzLkDcAYQ7d3fiDmj9NsHsOYFNsu0nR/ir/s3sGLicOCM0UiPSytuKdxjk/v/BW3b57kWO9OHld3cVJM0BN9gcvZgtxUj9x0zEppDr+Z8N6XjvHQie9y85kL2Ikm9LKsTt3J0tQcz+8PWapcYKN4Di0UJaX5mWaZB1t3Uorfh6/mSUn5fuE4j5We4lTmJfa3Rpnd3E+3OYLX6zITrmOhCWzJ0mTI0nSdRsWlYN+DyN7L3t469148ytjaOhudHHUpWRsP2ZzssToW0HEM0hjuCSMe6vb4cBBiRiyOlX0eNZM8HwtyvXEONG7lxp278IMx+tNuNC23Sjx+nDtHjnKrOc9sq4WXaLSAdW+c78Qf5PcbD7IneI7Dfo9Jf5qD3MQesweAVMdIYfcFXfd4UvX4c8vlrOqhhaBrZwA4lKY8KDO8R0pq+fNsjZ+kMHqObDngjLyJl8x7OJvexkjN5+B6zGhbsVm2OTfrsFqxQQiK3Q53nznBx576Dgc3VtmYnmBzbopOKU++VKNSXmWivIxT6ICAxNicFLdxIbmB7doeCus+B9ci5tZW2dN6jt78COuVGVYsjx2/zmohYq1c5tM1wW/9g69cV50civuQIddJkjSo15+mVv8eOztPEIYr/R+823km/gQvxmVOmTKb7gQIQTnq8LHa0zyw8RS97RJPpHdznGlq0gHAtSSl0YDyZJv6aIV1e5qbFi/wwImn+NBLzzC1ugNAOzfK6vg+Xtqf5ejhJivlBVIrBOBQlPKp1gTv6d5OKX4fBTWHRnMqs8B3is/ysvc8k/UK+a1pgo7LeFAlp3oA1DIOK+M9VmdqxKVpfPceRslx99pp9qxeJKxrNpMs1XLE1liPjYmQ9UKCETCiFO8PQj7YC7g3ieiVbY76E3w9nmMtdRlv72NP4wam2wextAtAzwpoFi5QmT7BPdnnOBitMtEIcVJDKiVLzgzf69zD9xs5xpxtZv1p9smbOKQO4QkPbTTKpDiyn9+ODnnCpHxHRyyrDlWvhBKSrNHcjcMRIShmV4lHT+JWFihUGiw6BznJbZw0t2OCAgfWE2Z3EmJbsDThsjhpE7r9h+3Mzhb3nXqRD7zwDMU4YHN6iu2ZETLlFsXSFhPlJTLFBkIatBEsmf2c4lZqzWnM6jTzG4KJnZjJ2hn8kRb1ygirnsOq32KhItgsjbCTnyT2DqDtMQBuW3yUx/7D37iu+jkU9yFDXidK9Wg0nqVWf5J6/Una7VOAoa0meE79LCfVHAuizJY3jpL9/9R9pLnI3SsvUNhMOBvNc4ZJ6tLBAHZGsme0jjcZUy2Os25Pc2B1ibvPnOCDLz/NDeeWcOMELSTVyh7O7ilz9HDM83tXiJ3+8nRzieL9XcNtwc2MRXcwGt1JQZfQaE5mL3Ase4ytZJl8tUTYyeMEARPxNgChZbMyIlmb3KY+Dl7mFib1JAd2ttm3voi106UaZaiWYjZGQ6oTIRuliESCNIZbo5gPBgEf7IXslYoLuSm+lx7hWDqOigtMdvYx0zqIq/ot5p6MaZYv4U+e4vb88xzRi0y2uxRbKRIIpc0zch/f6UzSjg1j/h72iiPcmB6hLPpTBBMdIwBbumhjOKtDjqUBJ1WH57wsHasv9PuM4JCASm6V0tiLZEeXMSMWl6wDnOMI58yNEOaZryZM11Kk0WyVXS5O2jRz/alHhSDk8OpF3nPuJDeuXCTJ+nTmPbzxNsVClXJxEz/XQoj+9PeqnuKEeA/V7hzJyl5m1rNM1D3Gwk2ymVU6ec0lL+VcOWC57FMtjtHMzpJ4e0H0/6frxPYit519kRsXFhhp1fHikL/9yGPXVV+H4j5kyGtgjCEMl2k2X6DZeo5m83k6nZdJlOF85win+AgXxTQrVoWdzFhfzI1h3/YKB9YXsXc0S9EYy5RQQmAElPMpM5M10jGb5fwe4tjlxsUL3LFwknvOn2D/xVX8KAKgmR9labLCi/s1371xm1qhC8BEAneFPQ5FU0xEtzMa38V8eAgbi7bs8YJ/kjNqgajdRnVsnF5EJWkAkArJVs5jbSykNtlBlPZxIMqyt9ZhqrqFrsdsph7b5YjtkYid0ZC1ckxs9e/9G6KYe8OQ+4OI2+OYjj3JcfbzfX2AWjLGeHsvo705LNMXx7bdoVVeJT/xMjcXjnNIXGSs26VST7BTw6pt87Q/wnNpibQ3zpQ5wpHkEAf0AYqy/xldpVM0Ckd6/Tx1ystpl1Mq5DHHYtHui/kIMGvFjOVWmRk7SXFih6BQ4II4zDmOsMxe8j2Yr6ZMNhKUFGyVbZbHHNrZfsvci1Pmtze5YfksN6xeJCcD1F5NrlyjUNymVKhiOf0xc6VsNtUM5+QNbIeziEuTjC/PMNt1yFnb2N4mTTfheDngQtlho1SgkR0n8GcxVhE7TRitrbF/+RQHly4wuV3DTmOMGNQ/wLgeYWGU3/wXv3NddXgo7kN+4jHGEEUbtNsv0WqfoN06QaN1ko0WLHVnOJ/exIZzgDVngrpXpusXQQhElDK3tcbM6jZR22ZJlegJGwP4vmFPqUluvEdzpMiCv5fx7Tq3LJzj3nPHufXCeSY3txAYDLBTKrM46XNyXnH0cItqWQEwksKNUcjBaJaJ+EZG4ps5HB6mpPrfHHlBnOdUfIpmUEcFCfmwS0b3h2hiYbORz7I5EtGZiCjmK+xrS6ZqHQq1Ft0ObPmC7dJAzCsh1Xx6RWAOxjH3BhH3hSF3hQmJmeaMOcwpdYDleB/l3hy5pC/CqUhollbQo0uMj1zgiP8ye9QG5VZEt6tZkg4XHIczjs92OMFU9wBHwv0cSOaZEXNkrP5C0ImOUShc4SOFIDaai2nAC6Q8ZkvOiL6/CmjGvDaTxUtMTq3hj6ds2HMssp9L7CNVGSbrKTP1hFyoCR1JdSDmsdP/A/NBzHSjyt7qEgfbi0z663iVDvlcnXy+hp/pDOoHRHGeFTPPBXmIem+Gwtkx9m/MMmYSpFun49V5YTTmTMlQzedoZSsE/gyCDJXGNqO1dWY2LzCzucpoo4GTJIjL9U8ItJcl9bN03SKRl8V1U4p0KOouf/effPW66vVQ3If8RGGMJggm6tCBAAAejElEQVSWaHdO02mfpNY8w7mNdS41PBaiA6wwR8OdpO0XCfwcPb+AQSCClFytzezmDrKZsp1maONgBBQyIXPFFtnRiGa5yCV/Dr+Tcmh5kZsXznPnxbMcuLRINuy3viPHYWkix5k5wwv7upybMQS+wNeC2SRlLBVMJHsYiw+zN7qRm3uHyKsMG6rN8+kCK/EKUVQjGzbJp/3xco2g7uWp5i2ahYRyTjGFYKSTUmiFpN2UNUeyU4zZKUU0RiK2CwmR3b+vc1pzWxhxexRzRxRxS6gI9SwLei/nkyPsdO/BSyb6ZYmUoHwRNXKJXGmL2dwF5sUSSRSwHQg2UslF2+ESHkG3wuHOPDcEe9mfzjMlZyk6lcG1MMQmAiTeYNw8NZolnfCM0HxXal5CYTBU3A6VXJWRsRqliR7N3BiX2M8y82jtMN5QjLcSCqEmtiS1gsXmiE3X77fKhTFUOl1mmhsc6p3jgL7AeHaTfL5OLlfDdaMrdUQFHi1VYUNMc0kcQm1PcnhhnIlOgcjrcqHU5eVyxGLBopYr0vHHcFOPkWaNkeYOI40txuurjO9UyXd7r4g4/da48rIo1yd0s0jXoiLaVDo1ZCPGaQUU2iHldpdCEHHstiP88r/9k+uq60NxH/KuxBhNGK7R6y2wWV/k4tYGF6tVlms9LoXjrLCHrl8kyuSIMz6RnyNNLUQvRXRSRuptMq2AOBK0tYMSgpwfM57vUCyHmBHJdm6MVWeasZ06Ry5d5D0XLnDz0gX2rC+TCwatPmC75HN2VvDSfMzZOVgZA1/bTKqYSmoxnsxTTvYxF+/jUDjPVDDCcrzDYrzJWrpGGG/jR018/YoANZ0cW1kPkYkZdSJGSMgEGrqKuhLU8ymNQkIzH9MuRmwX0itC7mrDDXHKzVHMLXHIzVHMbOyypvaznNzAdnAntfgIGgfhtohGz2HKy+QLG0z4S7jWFo1QUY0Eq6lkLcnQCrPM9iY51JtjPp1lVk9TsSfIOyNXbI51TGoMnnSwBjOCOkZzAsVzQnECxRlSPCegUGhTrPRwRw3NwijrcpY2RUo9zWhLUQhTMrEhsi0aOclm2aZ3Wci1YbTbYTrcYD5dZC8LHHDOMpKr4vvdV+qIEpimSxTl2MhMs2HvJelOM7NRxm+MsZRXnCnHLBYk29ksqfApdDUjzW1GGjuMNLepNDYZadZx0/SVfAHjeCgvg/YypJ6PBUxGDSZ722R7bex2hN1S5DoJXvTKuRro5PNsVSqsjJcI5lz+86/8X9d1DwzFfciPNUnSotm+wMLGIgtbGyzttFiqx1xo5dnQU3S9HCbjIHwJnkMkXJLUQgQpTjsl1+whegldbaNdi0wmYSTbI5eNcYuKKOtT98rsWBXKjRZ719c5vLLG4dVl9q4vMlVdxY/7oqskrFVsLkxrFqbg4pRgcQKkZVNSmplkjEoyx0w8x95witF2kTBUrCU16kmNMNlBJg0yg9kr0B9aabh5tJeSsWMKMianU2So2NGCWl7RyCc08gmdQkQ9nxI4r9yrOQX7E8PNUcitcY+b45g9EdTVPFvxYXbiI2wmh+h4GfTIAqJ4ESe3SZrZIrVqdNOYnVhSCy1aoU8c+Mx2x9gXTjMbTzJlJhhxJii541fGxbXRtE2CRpAXNs5AyHvGcEYoXqa/nSOlnu2QK4W4JU1QLFDNTxHaOYo9zWhbUQwS/MSQCkkra1MtWTTy1pW/z1YpE1GN6XSNvfoiB+Q5DrmnKXitK2mMEpiag6zbqNClnhllbeQgzeQQsptnTWRYzNrULYVJDV4EhW6XUrtOsd2g2K5RbtXw4/iVPAFt2xg3g3Z9HCReqsnFEZWkSTlu4gUBVlfhNVPcSF1Vb7sZn51ygY3RHCtjPktjDstjmpVKiBI1pG4DMKU+xGO/Mvxw2JB3GcYYmr0uy9vLrOxssF7fYa3RZqWRsNqCjSRLz8pBxkFmJcq10NiExiGJBW47xm+GiEARGYFyJUUvougH5HMRVt4QZX1afpEtd5QIn5ntGkeWV9m/usb81hKTO2uMNqqMtJrY+pXP20Y2LI33Bbwv4oKtik2RHNNphZFkguneKCOdPLLnkYaGdtomSJuQ1PHSFpJX7qlYOKS2jbRTPJHikSJ1StsybPuGVjalnU1pZxO6+YRWJiXetcpCQUn2RpKDSciNSZuDSczBJKWcWjTSPWzqfWw5k9S8LJv5Br3MBoHfJHDaBDqkE0jCrkvSc5GBw3hYYCYaZyoZY0KPU7ZHKTijFJwKjnRRGDoYIq2RGHLCIjMQcYA6motoLqA4K1IuZgI2CwlJwaWbLRHlcuSFpBQYMrEhk6YIYwgci1bGol6wrkxBBJBGMZ7sMKNWmWOJfdYF9jvnGaeK7H9pBhU4UHOwtsCta9LUZ9ub5mzlFs5NzFNNbXqJQMYGP0wodlpXBLzUruPH4VX1T0swlsE3Akd7eFrgpZpsElGIuhTCLm4vwu2mSH21Piopaec8akWPjRGP1VGHlTFYraSsl3skdu+q9AYLKUp4Kk+p5zJVU8xUG5SsO/gv/+d/fl33z1Dch7xlaG1ohymNIKbW6bLd2manXWer1WSj2WOjFbMZGhrKIcAmtR2EK8EFbVkoJMpI4lQiA43fipBBSiotItfG8RRFPySTiXGyCuXb9NwsDadEW+bwYsNEY5O5jUtM7awyubPJWHOb8XqDsWaH0WaAq3YJrgUbI7BREayPwHbZISwU0YUyljtCIRjB72VxAhsrBKIYlXYgbWGlbSyTXvX3KyFBSiypsIRCC0XH0TR8RTur6PopPV/Ry6T0MikdX6Fe0TdsLRhPLGZTzf4k4Eja5WCSsC9NsGzJgjvBklNizfGoOoamndCREXGSkISSpGejQhs/timGLjPhGFPxGON6lKIsk3PK5O3+XkmXBpoeBoXAMoYsgoqQeFdGkaGDYQHFRRSXHMViRnO24FDP+2Rti4zVT++lGttotBCEjqSdgWbOIravXgdoRNWYNBvMiBVm5ApTrDPNOmNsYWmNClxo2sgdibuh8ddS0shn0xrnUnGW5dIom/kiVqrId9vkuy3yvRa54JXhGIzB0gZbK2yhyaqEfJripTZ2InDjlEwUkYlC3Ch5VT1WQtDJOTTzLjtFh2rRoloSbJY0W8WI7WJAKwuIV/ykZR5kGUsXcJSPHyUUegG5oI0X10ishHYWWlnoZMSVl9p3Vg/y1f/izR1zH67ENOQ10drQCBKq7Yitdshmo8HSzg7L1TrbjQ6tIKarIRYW2pUIV4AnSCyLRDgkxkLFFgRZ7NhFKo3BIrVsEm2B1rgqJeN28fwEPEnsuXTtLAFlhBZkggYztRXGGxuMrFUpt2sUuw2K3Q65oEc+CCn0UiptTT662v7YgmoJqiMu5/cWCXIlgswYkT2KEgVEIhGJgiQm1wzI1Xo4qoun6khqV+VlBFgCtKVJPEXb1vQ8TcdPaedSupmUnqfpZjQ9P0VZ+qrzpaE/Bp9qblIJk92QilBkJNiWIbYlVddjy/e4ZDKcjnOkIajARkQ22bZFJrIoRVnGkjJ7VJmyKZGVBbJ2Hs8qoOw8oZUntnziLOgsCAyhMAgkrpEUkbgIMoO1QhMMa0KzIDXfcTSbtmTbFWz5kq5rYdkOllAIKyFxFVlfkGYMDc+hLq+WjozpMWY22CO2uJMtxkyVcapMscZotIPTA1o2oiZxtg1uVZHZTHDqNh2dpZ7J0/J8Op5DYEMqDAKDk2gynRVuXbrEXalCCLCNxtMpmSQhm6R4icaNDXassPTVvgdQUtDJ2LRzNsujFrWCTy3vs11Q7OQT6gVNPQ+NHBipgRBDjLZKaFnCWEUsU8ZRBbKxg5MoUlGn46yA2UKmKxggFhD70PLAKzi4ysejiCdspmxBUaQUREJWaLKu+ze9Rf9ahuL+E8Rlwd7p9AV7cavJ0uYGG/U6zVaXThShTAQiRVsSrUDHoIxFKjMkwiW2XGIrS+zkwQHLUdhegnQN2AJLWLhG4GmF8RLIxmTDLvleg3yvRb7TIhO0yIRtslEXL+rhxz38OMKPEzKRothTlHpQ6IG85m8wQOBKGjmPTiZPrZhhZTxHz80S2T6xZaOEQJoYSwVIHSN1gmiD027i0NyVl8FIQyoNsW3ouVBzDR1f08oqmoWUZj4myMSk9qtbegCeFhQ15IxhDEXWKBytEQi0ttCpRaoskthCxS5bUY7tyMJPJNnYoZwWKad5RlSeeZ0jK3LYVhZl5YmtLImVRckMyrWJMv0HjTaSrpEYLbGMRdZIxhH4u1reAKkxVDEsuYJTHmy4UPNDOn5Cz0sIHU3sSkLHpuN4tGyfnuVjxNVeF0ZRokmFGvupMj4Q71G9w0jcYDRskOlGiJaF3AavqvG3EjIbCU7NYJQgcSSBJYhtSKWkJ106woPB+qV+r0u+08ZVCjdVuInCUT94VCFyJJ2MRScjWS8KmllJMydoZBW1vKGRh0ZeUM9BJwMIAyRo6Q1Eu9zfWyUkBbw0TzbKk4tscqHCiTooVonFAqE8jxQGV7s42sM1Pp728awSWTlG0UnIuj1sLyArLEr4VChRTMbJhRMUe9P40ShS+Qj6LYXiTx94/TfudfK6xF0I8UngfwUs4F8bY/7JNb97wFeBu4Ed4EvGmMU31tQhr0WqNLVuzFqjy4W1LS5tbrK1tUWrvkU36aGjFK0sIidDYGUJrCyx42EccL0Y6SQIF/S4xEvBiQxO2CUTdfGSDu4VAe7ixwFeHOElEW4S4yYpXpLiJhov1biJwUsNXgJeDH4CbtIXaANoIUilQFmSVEqUFKSWJLBtel6G0CnScxwaozbJhCS1JFqCEmYwa9z0F5R4lRcU0EakbWxlMDaEjiHMQOhpAlcR+Am9TEzHTwg9ReApIke/OjMDGQO+AU9D2cB4IrAiCyuR2InEjizc0MIPPLJJhlzq46oMrilikUOYHEZmCZ0MoesRuy6p7WJsGyNscC1c18LNWHhGInVfqLUWCCPJGUERQQ6DJRN6do+m36XpxTTdhKarWHM0LVfTtgVdWxLYFj3LJrAcAsuja2XoyBxdkUML61UeA/BMQJEWBdpMsc6NpkZRtSnFLYpRh5Feg5Fmi1Ktg1dXOA2FV0/xdxKcpkZEAAIt+1M2zWC4QhqD1GbwYBYIwIs1HvFV5WsJqQuxK+h5grYvqPvQ9qHrQ9eX9Lz+kEZ/E7Sy0M5Aagu0yKKtAkbm0TKPsQpomUfLfpyrffw4QzbOkQ+zFANBLgjwwwZeWMNJdpBqB2OvguNgeUWKFBjRJSomS1YcQDrzxF5I5KUoz8EnTz7OM97JMd4sYCsXJIiMg5VzsYoudsFFjjrInIOV7+93h4VnIcSra/EbzV8r7kIIC/jfgIeBFeAZIcQjxphTu5L9KlA3xhwSQvw88BXgS2+Gwe9GtDZ045R6q8f65hYXL53n0vIi1a0qjUTSsjMEnoN2wHM0jlTYaCxtsJIedtLDTgPsJMCNQuw4oBKHTMchXhLjxjFekuLHCi9RZGLw474Au0pipwZLGYyUKCHQUqCkQAlBMhDi1BpsUhDbFpGdI3EkHUui3FfSaykwA8UUlzcDUl++zV+HP4QhtRSRExO5msjpb7FzOaz6Yffq+MTui7WlJZa2kNpGagfLOFjaR5oSlvbJxRnyoY8wWSyTwzZZpMlh6Sw2GWzjYGFhA64R2ELjYfCMxkfjkZJ1Ezw3wZMxnoyxrQSsBG1rUluT2B1Su00iDaENgS2ILEloSUIp6UiL0HKIpUUkHCLpEEuHSHgEwicgQyCy9CgQifEf6i9hNBkCsnTJmICMCZjWO+TiHoW0Qz7uvyQsdtoU223KjTYj9RbZZoTbSnFaCreZYoX6lWtkLl/BH3CNECQ2JI4g9SSJZ0g9Q+hper6m40PbFzR9wXZGsO1Lur7oi7Yn6fk+oetjZAYjPYzwMTKHltlBXBYjsmiZBeHjpQ5e6uInLrk4Szb2yMaCTBiTiUK8sEsm7uDFHfwkoJDukJcOeTtD1sqRswvkrTw+Pg4zJNYkvZyi6aZ0LEiMg61tMpEi3+1QCjpkhEYWinjZebJT0+T2zWIXM4iMjdy1Ceva/uU7g9fTcr8POG+MWQAQQvwh8Dlgt7h/DvivB+E/Bn5bCCHM2/W29jowxqB1ijYpcZqSpDG9JKDX7bG0ssqFC2e4uHCWTtTCUgKEj5SArbBIkcRACjrFkGK0RiuNUQonFVipxk7BNglS98cNJRopNAiBRKClBinQQoAQVASUJYM5A6Lfdh141CBAX1mgHWkkaIk0/dafNDbSFFCWIPYkqScItcAyFlILLCPolyoQQnD5H2IgzkIisDBS9Lvpor/YgZECZRuwBMoSGFtgLJCWAEsiLTASjJSYQVjJQatOAlL290KghURIwaBYzKAY6Kf1BPiDJ4QQAiMMyMGefssPYTDCYAbn60H48sPmyiYFmv7DSQmJFgKFJL0cFgmKNrHoEmChhNVPj0X/USpJcEhwiMlfCSe4xLgkOK8azng9OCbGMxGujvFMjKdifBVSSrfxk4hcHJKNeuSCgFyvS6HTodDpUmh1KLS6lBstCvUubpBi9cdsBpVCXBFoLfq9Jj3oNSkp+qLsCLoufXGet4l8TehB4Bm6HrR8i4bvUPM9dvwMzaxP4Pr0fL/f+5MuRvoYcVmc+3uBizSXH6o2tnZwlY2bOripg58IijG4PYOXKNxE93t/SYSbRLhJiJcE+ElCVjXJqjqeZeMLm5zlkZdZ8laerMyRkVmkHCEQebpWhW5BEViGSChiEnSaEkUhcVijEZzH6W5jqy6ZvI8/Pc7c1D5Khw9TPniIwuQU0nrtXs6PI69H3GeB5V3HK8D9PyiNMSYVQjSBUWD7jTByN//Zv/kKj++57VXxZlcrw7xGi+O14l593m6uyW/qYH8bxP/A88TrzO+HpNkdd7Xd15v3tXGi340edJz7k976on49AvXjiDQKC71rr7FM/4VcP9zfLoel1jg6JaNTiqqDkya4KsVJUtw0wYtj3Li/94IAP4zwgwA/jMl0A7JhhBfFuHF/7yUxbhIhDWjZF15tSZSUKFv0h60sQWL1W8exawg9QeAJOi7sZCVB2aJ7aJTAnSFwXTqOR8/1CFyf2HWJbYfUdhFCYhnR741oiYXE1hJHSywjcZTEVhZ2amGnDlLZWMoglEAqkEow2TXMtDRSK2ylcLXCVglumuCkKZ5KcVWMq0IcwBYCW0gcy8LGxhU2jnDwpIsjPWxpI7ER0kIIu//uQwhiYZFIj8S3SDIpKSnKxKg0JI3btMItmnGE1jFpGhCriEhAYmcI/SK97Cit0iSdwiSpXyJ1chj5QwQ7od9MPbUELL01lQ+4eabIP/rMLW9qGa9H3F9LFa9tkb+eNAghfg34NYD5+fnXUfSr8cOY6aj6mgWLVxf5Srx5jbjXyONqzJVfhdmd/tXlXJWHee28ry5nV95/jX1XxZvdaa7N7wfEm1f7SZgrcn4lLHeF+2X1j+WgyyDoj6ei+3ZctluaQRnm8jm7wtogNFdsELqfJ/ryMRgj+sPmg3To/uf4hJYYLTCmLzRCA1ojdP/RZIxGmhRJitQaQYI0CQKFrZP+y1QTI4RB6ATQ/VY+g+9+WBbKAiMkWg42x8IIM2jlG5SUaCFJLQeFhbKs/ri5sOgPtkpAIIREIlFOf/glzmXpkB30kvp+sEx/5ow0GmFSpBFYWoHRSKURWoFS2AqEuhxnkLrvY2EMjgRXSAqpwCgBoWTQJeq3KwxYGCwTYZkQkBgsjLHQWH1fD66wMP0e0cCxgEEI3X+/cbl6ao02/c1g0AN7hdGgFdoohDYYodFGkaBJRH8tVC3611XjkogMiZ0llhlS6aFsj8T2SR2X1MmRuhmUnSF1KqS2j7GcqxpKQ350Xo+4rwB7dh3PAWs/IM2KEMIGSnDNfDLAGPM7wO9Af5779Rj8P/yd37ye04YMGTLkJ4rX0wd/BjgshNgvhHCBnwceuSbNI8AvD8KfB7754zTePmTIkCHvNv7alvtgDP3vA9+gPxXyd40xJ4UQ/w3wrDHmEeD/AH5fCHGefov9599Mo4cMGTJkyA/ndc1zN8b8BfAX18T91q5wCHzhjTVtyJAhQ4ZcLz8ZUyOGDBky5CeMobgPGTJkyLuQobgPGTJkyLuQobgPGTJkyLuQobgPGTJkyLuQt22xDiFEFbh0naeP8SZ82uAN4p1q29CuH42hXT8671Tb3m127TXG/PCvyfE2ivvfBCHEs69nJZK3g3eqbUO7fjSGdv3ovFNt+0m1azgsM2TIkCHvQobiPmTIkCHvQn5cxf133m4DfgjvVNuGdv1oDO360Xmn2vYTadeP5Zj7kCFDhgz54fy4ttyHDBkyZMgP4R0j7kKI3xVCbAkhXtoVd7sQ4vtCiBNCiD8TQhR3/fYbQojzQogzQohP7Ir/5CDuvBDi199Ku4QQDwshjg3ijwkhPrrrnMcHdr0w2CbeQrv2CSGCXWX/77vOuXuQ/rwQ4p+Lv+HKvT+iXV/eZdMLQggthLhj8Nsb7a89QohvCSFOCyFOCiH+00F8RQjxmBDi3GA/MogXA3+cF0IcF0LctSuvXx6kPyeE+OUfVOabaNuXBzYdF0I8KYS4fVdeiwM/vyCEePYttutBIURz1zX7rV15vWH35XXY9Q932fSSEEIJISqD394Kf31hcKyFEPdcc86bp2PGmHfEBjwA3AW8tCvuGeDDg/CvAP/tIHwz8CLgAfuBC/Q/R2wNwgcAd5Dm5rfQrjuBmUH4VmB11zmPA/e8Tf7atzvdNfkcBd5Hf92dR4FPvVV2XXPebcDCm+ivaeCuQbgAnB3Uo38K/Pog/teBrwzCnx74QwDvBZ4exFeAhcF+ZBAeeYtte//lMoFPXbZtcLwIjL1NPnsQ+PPXyOcNvS9/VLuuOfcz9NebeCv9dRNww7V1mjdZx96QG+eN2rhGhIAWr7wX2AOcGoR/A/iNXem+QV+g3gd8Y1f8VenebLuuOUcAO4A3OL7qwr7F/roq3TWV8eVdx78A/Ku3yV//PfDf7Tp+w/11TXl/CjwMnAGmd/njzCD8r4Bf2JX+zOD3q3x0bbq3wrZr0o5wdSNikTdIrK7DZw/y2uL+ptyX1+mv/wf422+lv3YdX1Wnr/UDb7COvWOGZX4ALwGfHYS/wCvL/b3Wot2zPyT+rbJrNz8HPG+MiXbF/d6g+/ebf9Phj+uwa78Q4nkhxBNCiA8N4mbp++gyb6e/vgT8wTVxb4q/hBD76PeyngYmjTHrAIP95eGft6WOvU7bdvOr9HsYlzHA/yf6w4K/9jbY9T4hxItCiEeFEJdXgH7TfPaj+EsIkQU+CXxtV/Rb4a8fxJtax97p4v4rwN8TQhyj382JB/E/aEHu17VQ95toFwCDSv0V4D/eFf1lY8xt/P/tnb1rFFEQwH9Pc1UU9dRgIARNYRNQ/CCCX2gjEhGRNDaKxkZQUGwskj9AtFHULlVAsRC1shNslKBGTFTQmFMQY0hAhPgBwWIs3hx5t1zEzd0+j2V+sOzb2bfv5mbnze7O3LGwS5ejEfWaBNpFZBNwHrjlfN67Uey1DfglIq8DcSb2cs4twU/ucyIy87euVWSZ+lgK3cr99+KD+4VAvENENuPTNaedc7sj6vUC/9f4jcA14H55iCp9a7ZZWnvhUzKPRSR8v/P/tFemPtbQwV1E3orIPhHZgr+rK+mu+V7a/S8v885SL5xzbcA94JiIlIJjJnT9Hf9o2BVLLxGZFZGv2h5W+Xq8vdqCIaLbSzlC4q49C3s55wr4SXdTRO6qeMo516r7W4FplUf1sZS64ZzbAAwAh8rnFkBEvuh6Gu+HNdktjV4iMiMiP7T9ACg451aRgc3S2kup5mcx7DUf2fpYFrmmGnJUa6nM1bboehEwCPTqdieVhYgP+CJEk7bXMVeI6Iyo13L9zJ7E8U1oXg8oAHeAUxH1Wg0s1nYHMAEUdfsZvmBYLqh2x9IrkH0GOrK0l36/QeBKQn6ZyiLcJW0foLKg+lTlReAjPte9QtvFyLq1A+PA9kT/ZmBp0H4C7I+o1xrmaitdwCcdo67zMq1eur0M/37n5tj2CvY/ojLnnmkcq2kS13PBX1Engd862U8CZ/EV5zHgYtlxtH8f/g7wHcEvPPC/chjTfX0x9QL6gZ/Ay2BpUccZBkaBN8BVNNhG0qtHP3cE/+h8MBhnKz4nXgKuhzaOdB73AEOJMbKw1078o+1ocG66gZXAQ+C9rssXPQfcULu8SkzKXnxwHQdO1MHH0uo2AHwL+j5XeYee4xG1W03+vwC9zgR+NkRw8aGO8zKtXnrMceB2YpxY9jqsc2EWmKKyWJpZHLN/qBqGYeSQhs65G4ZhGAvDgrthGEYOseBuGIaRQyy4G4Zh5BAL7oZhGDnEgrthGEYOseBuGIaRQyy4G4Zh5JA/TxPWTVRNVGYAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fp3=plt.figure()\n", "for i in range(EnsembleSize):\n", " plt.plot(Time,SL_wTd_nos_base_AISM_VUB_SU_RCP85[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_AISM_VUB_SU_RCP85_alllines.pdf\", bbox_inches='tight')\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" } }, "nbformat": 4, "nbformat_minor": 2 }