{ "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": 3, "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_RCP45_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]))" ] }, { "cell_type": "code", "execution_count": 4, "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": 5, "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": 6, "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": 7, "metadata": {}, "outputs": [], "source": [ "# ISSM_JPL = ISSM_JPL\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_ISSM_JPL_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_ISSM_JPL_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_ISSM_JPL_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_ISSM_JPL_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_ISSM_JPL_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_ISSM_JPL_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_ISSM_JPL_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_ISSM_JPL_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_ISSM_JPL_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_ISSM_JPL_BM08_R5 = np.array([float(row) for row in f])\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.5226 0.5263157894736842\n", "0.8926 0.8947368421052632\n", "0.5318 0.5263157894736842\n", "0.47785 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_ISSM_JPL_R1_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_ISSM_JPL_R2_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_ISSM_JPL_R3_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_ISSM_JPL_R4_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_ISSM_JPL_R5_RCP45 = [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_ISSM_JPL_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_ISSM_JPL_R1_RCP45=np.vstack([SL_wTd_nos_base_ISSM_JPL_R1_RCP45, 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_ISSM_JPL_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_ISSM_JPL_R2_RCP45=np.vstack([SL_wTd_nos_base_ISSM_JPL_R2_RCP45, 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_ISSM_JPL_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_ISSM_JPL_R3_RCP45=np.vstack([SL_wTd_nos_base_ISSM_JPL_R3_RCP45, 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_ISSM_JPL_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_ISSM_JPL_R4_RCP45=np.vstack([SL_wTd_nos_base_ISSM_JPL_R4_RCP45, 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_ISSM_JPL_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_ISSM_JPL_R5_RCP45=np.vstack([SL_wTd_nos_base_ISSM_JPL_R5_RCP45, SL])\n", "\n", "SL_wTd_nos_base_ISSM_JPL_SU_RCP45 = SL_wTd_nos_base_ISSM_JPL_R1_RCP45+SL_wTd_nos_base_ISSM_JPL_R2_RCP45+SL_wTd_nos_base_ISSM_JPL_R3_RCP45+SL_wTd_nos_base_ISSM_JPL_R4_RCP45+SL_wTd_nos_base_ISSM_JPL_R5_RCP45\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": 9, "metadata": {}, "outputs": [], "source": [ "Time = range(1900,2100)\n", "ncfile = nc.Dataset('EnsembleSingleModelProjections/SL_wTd_nos_base_ISSM_JPL_RCP45.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_ISSM_JPL_R1_RCP45\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_ISSM_JPL_R2_RCP45\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_ISSM_JPL_R3_RCP45\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_ISSM_JPL_R4_RCP45\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_ISSM_JPL_R5_RCP45\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_ISSM_JPL_SU_RCP45\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": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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RiUz6QjbmmQbpJofr6rcStttIK12DQMMwhQiU7aGnq5ChYC56jh2ZYefepn/Dgk5fXyr3LLwAnyWNn06Oo5nt5JgeZufgKqQQuFIyk76e49m5rwA6pJRdUsoY8ARw4QeM+3fgViByAutTFEX5SDzQ+ADBeJCxvg0stY+QQKPQFKSgt5eiyRG6Zs2nwvWnDyVGCl7BbIvxh5EvgYTErFQWzLSwfuoQ/mEb2wO1HPAsZrExSY2nGov5HfbqMSJBP0idPQSSvp7jCfdCoP99xwPvnfsvQojFQLGU8qUTWJuiKMpHYiI8wWPNj7Eo4zRGR2y0R9KoNo0xKj18pvENfKmpFBStAGEgRYKpsm20dFcyGshGL3YiXRbua/oBAN1Dadw771KsMs5/jE4jTZIM88McGViCgQDAM/rJuM5dfMA5+V8vCqEBvwa+/aFvJMSXhRAHhRAHx8fHj79KRVGUJLqv/j7iRpyJvg2cYutgBhfl2jQ1LQ1kBHx0zV5IiXURSBgr2obFEeK+wWvALEhUpPLFgWcpiozh7XNwT8r5jNlyuCLQTWreQlymLbwe9ZCI+NCQCCQF1dNJX9PxhPsAUPy+4yJg6H3HKcA84B0hRA+wCtj8QV+qSinvllIuk1Iuy87O/uurVhRFOUFGgiM82fokq7LPYrI/QF2skCKTl0jcwkUtOxjOy6W6cCmGFkMKyVj5Fg52LsAbSidR4cZq0flh111IHQ5PlfJq0QbyEuN8MezEECEcpmdoH6h+b0csyaiZJlcrSPq6jifcDwDVQohyIYQVuBL4r8eISCl9UsosKWWZlLIM2AtsklIeTErFiqIoJ9Ddx+5GIpkcXM+nLPWMyXRmaxOsPLYPayJO/+wF5FMLUmO04m0sthAP9V+F4TChl6bwm5af4ZAxRnvd3FJ0DRL4bm8Dltx5ZJifYnOoEiP+px8tCU1iXWrwtv38pK/rQ8NdSpkAbgJeA5qBp6SUjUKIm4UQm5JdoKIoSrL0+/t5vv15TsvfRKSjk92J2WRpAez+EBu6j9BRWcHi7MUYWhQpDMaLXuDNlg0EYy4S1akURke4eHwbiYjG48YpdLrLWBZqYXHGQgwxTtD0CoODf/qUQiDJmjtFY+83ydu/NelrO67r3KWUW6WUs6SUlVLKn7x37odSyv/xIEAp5alq164oyt+Cu+ruwqSZmBpcx0bLYXpkPrXaCJ86vIuE2cx0RQ0ZiSqEYWZo9jZiQvDs0CYMjxUj185Tx76DALoHM7i34FKyYhN8r7MLU3oZmeaHeWFyBRgRDE2gWQzCRbUUNmZT5Dwr6WtTv1BVFOXvUud0Jy91vcTZJZfiaN3BK4nlpGphikcHmTPWw7G5tazwLEY3hZFanKmcF3ns2KXEDTPxOWmcN7GdivAA0Rkz/+b+PGHNziUj+0mvOgshemg3HST43nUjmiHJnD+F5fAVWIVkoKQp6etT4a4oyt+l3xz+DU6zk9DQSk7TjtIky5gnhtl4ZBf+FDeipISUWAmabqN3/tv0h3PZ712GXuLC5Bbc2fIThICdvlnsTlvKwkAzn54GzZVDhvkB3ulfDRhIK5gdCULO8wjHzNhT+oh1Jf+ukCrcFUX5u3No9BDv9L/DZVXXkN/wJE/pp+IUMZZ01JMTmOLQgkWscqxANweRpjjTaZt5sP4qpE0jUZXKT9p/g82I4x+z8c+er5Aa93F9yy5sNedh0erYxiB6IIQhBCIG6YsDWJrX4DTpWN1xKtPKkr5GFe6KovxdkVLyq4O/IseZg95XwSpRzwFZwwJjgNOb9jOSm0tuXhb2aC5awkHngtfZPbqc4XA+8dlp5CUm+PzISwgBd4uNeC0ZbJzYR01qLZo1BaflQVq6FwJgsunYPFGM6euJGAJr6hHqUo7RmelM+jpVuCuK8nfljd43ODZxjC/MuYHaY7dxj34+NpFgY/1ObIkYBxcuYolYT8LiwzBHGXW8w3PtF6Bn2jBybTxT9y2khMHhdO50X86cmVaubj6GtfpMHNo7PBU2QzyEYTEhIxr2hTaiw8W4LEGmPNNsydjOgXhr0tepwl1RlL8bcSPO7Udup8pThWiIUUE/O4yFLI72sKqrnvaqKhZmejAlnJjjqbTOf4Xnu84jipXEHA/nTO6iKjKANOA7zuuxGjEu63uX1KqzQYOI5VGm+/IAMIsYrvwQKe1fQUeS6tnFw9lvUhrL4nNt6Ulfqwp3RVH+bjzb9iy9M71cV/MPrGi+hV/oV2BG5/KDW4nabDTNmUNNdD0Jqx/dGqbB6GTfyDIS5anYbHH+0PRjAHaPz2GPbRGnj+1m3fgMluJVpJg28/RYGcgEWhoYMROULWAibMfp7OOt7CGiWpTPlvQzvfwTcMtfRVGUk0EwHuT3db9nWe4ybLv3YJUxtumLWBbsYM5YH3Xz53OqJxMkWGJpHKnZwhNtl2ByGiRK3dzS/issUicSNvOV1G9SHOrn8pZ9WBZcgRABWswvo3vNIEwYfgN3ZRB3+yZMQsecsYcdaUf5lF0jvOtbtHsXJH29KtwVRfm78GDjg3gjXm6suJoVPX/g+8Z1mJB8cd+LeD0eBsuLKZlZjbTFSTj8vOYzMRbOJlybQ224gyvHXkcIuC12EUHh4NL+bRTYCrBmzibN/Dg7u/70mAtbTggBWK1X4NMhJXUHf8hvIkd3MbdnE+HxOWSPfDLu564oivI3bSQ4woMND3JW2Vk43niAMZnGu4laVk81UzIzxoElSznfUYJuCmGKONla9hKv952ONR+ES/BA0/eREnqns/m97WJOHdvFaR3tWBZdjkkM8WKkES1mYLJZiY5asdaYSIzOwmTxU5ffhM80w6ctaYR61hM1hbC72pO+ZhXuiqKc9G47fBuGNLgp4xTmj2/hH+XX0aTB9fufo7ekhEReKpkzczE57URSJ3luuAaT2WBmdi5fHHue0ugoUsKNtm9SEBni0o592AtXYnHmoWkPMtJfBIAlzYfJruP2f5mYlDgzn+eV9DbWiRQSe78BwJa5j/FSTWrS16zCXVGUk1rdeB0vdb3EF+dcjXPrj9lvzKI+XsKGsWOkx4IcWLiEC5hHwuIHv8Zd6bvpmK5EznKSPzPGD3ruRkrYHFxNG0Vc2fUK5d4ZbPM3YRWNPDYWQSBxZENkzEGsupxgwInZ0c6DRZ2kJ1wsbL8KodtpzNnHGQePsrbel/R1q3BXFOWkJaXk1v23kuXI4rOTAXLCnXyHP+3av3RkC821c/BkaLhCxVhTMujz9LN1cDmZ6VMEs9P5yfAdWIwEoYSN71uu5bSxnazr6caYdzFmUwrd+qMYMy4QZvRoFIsnTsbYpQihM1j0EuOWaS6KV5GYqMVv9WL1PcYluyVnH34l6WtX4a4oyklra/dWjk0c43tVV+DeezsP6WfQH0vnjIFDWEwGR2fP5bzwKhL2IIY3zq3aEDHdylRtIef07uDcqd1oAm42riE96uWizn3YTS7cZeuw8DY7BlMASK3wEZux4S3YSDSmYUp/kxcy+1ieyMJa93kMdHaWPMAXtumYKmN0ZpqSvnYV7oqinJTCiTC/PvRr5qTXcMrhLUQME7eJKzAZOlc3vsqRpUuoTgNrzIPNlcmrnk6OeudQWjGCLZzgh957MCQ0xkrYbKzkyo6XqBibJLHyGkxC8OrMm2gJDbPDSmDAhsgX5I4uQpp9PFu+C7fuYEnjDQhpYnfpa1z1dhe2fJ1XUmYRys1L+vpVuCuKclJ6sPFBRkOj/NyzBMfALv5Vv5bJqJNNXe8SyPbQUlDKGVNrMTIS+CdD3Bm2kOMap7Oslq8NP0FpdAQB/LO8gdNGd7K8f5Bo4UoyPLUEE48xPvmnL0XdheMYURM+99UYhmC86FGGrX4uHD8VEcphKKWTdO/rLB/Qqc/PQQBuZ2XS16/CXVGUk85IcIQHGh7g4vz1FO++m8NGFbvkAhx6jAu7dnB48WJOc1lBmjAbTn7h7mEq6iGndoYFLU18afoFpIRHE58iFtE5t+sQ7mgM++KLgEFeGutDAK4Cia8rhUBFNjkTWSRcTTyf18byUDmuno3ETGH2Fj3Hta/HmFlkYSDiISd1Ge4e9YWqoijKX+z2w7ejGzr/4gsgIj6+m7gBr+7msva36a2txpviZunkSizlqTRNB3gjkMGyoiN0Omr4P6GHMBsGXpnCXbHzuKjjNSrHvPiXfBa3OZO6mYcgagVhQjMHQANz4tNIofNm1ZOkJFwsbrgBgNdnv8DV23pJK4izP1aEx2ZjbfqZiPKNSe+BCndFUU4q9eP1bOnawv/JXYer8QXuSpzHtO4iLRpg5XQLzRXVXCaLiZnDREcj/MA+QarVT7A6h6sPv8yqcANmYfCjxOdZMnmQpQMjRNLKyC1agT/2Bs2TAoCMmin8fSkMlC/H43cwnvMKPc5pLui+Ck130Ji7m8Kh/azrT9CUn0XMMDMv5zN0pZjYZR9Oeh9UuCuKctKQUvLzAz8n35bBxS07GDHl82TiNMaEh8vbttG2ZB4mh4niUAXuOQU8GvYzHHFzas0eEuNWbpTPEpcmdujzGYzYOav9KGnhMNFVV4GM8cb4PkDD7DATnrAgnYIi3xrilkleKNvGqsllpEzOY9o+RmvmXr6yNUpkEbQHsylOX0SOJYfbC3yki+6k90KFu6IoJ41Xul+hbryO280laN4uvh28hljcQnZoiiK7j4HUbD4bWMhMWoCupgkeIMbC7HqOZq/kHzuewJ0IYSC4LXwhZ7ftomJ8muGaCylylFM//QDxmAWBJGPOCOFxB/3552KLW9hd+TBp8RQWtH0GXSR4rWYrX97aRXpJlF2RMlIsdlakncnd5YJN8a14coJJ74UKd0VRTgrhRJhfH/41ZziKmd30CtuspxGIuxixZHBRz056a6spdFhx6ymk5ufxQ3MQk0gwf04HC461cw57sAqd38YvpHqqicX9IwQdueTNXsdYeD9tvhAAKWUxplrTCKenUumtZtJzmOb0bs5vuRENE3vLtrCsuY7l3jgNuTlEdAsrc65kd7YVzfYOFS2XoXcuSno/VLgrinJSeKDhAUYDw9zs9RE3u/h379mMGJkU+0dJKTAzbErhsumljJUEebZ9irYoXFT1MrvkqfyT/zH80k6nkU9LMIvTmhtxRuP0r7sKq2Fm1/g2DKEhzGYcnhniQQsR57lILcGWqmfYMHAWrlA+vZ5GfKZuvvhWlNAijbZANlXpy9FT8niytJcNx1ZRbjNhdutJ74cKd0VR/ub1z/RzX/19/LulmJSRBv6Tz1EanmDUls7p3jpGc3NZq2USF3EiMQd3iBBlqb3IEhuf3fsq6XKGFBHhTv/5rG47SInXT9usM1nirOLgxKNEddCkQc6SYcbrMxjPm0V+qIBjxc/jiaZQ038OYXOAg2VH+NqLnaTPC7EjUE6aNYV5aafx0xq44piJWouN54os1NkCSe+JCndFUf6mSSn56f6fUqpLNvUcYTjnFPYNp9FgKafSN4i11MmEyc2poVmMVEf4P9NxpKHzuXlP0jlUwzXaK9iJ80J8DZlTPSwaGMfnzCVrzil0+Q8zGJpCYMaeZRAadmAIMxn6acw4RjiQu4ezW24CYFflM5y5Zz9zzBEOugqIGRbWZF/OA5U2lvqOsCSRT2OqRtvom2zsfDfpfVHhrijK37Rtfdt4d2AnvwuZQDNzw9ilFIdmmLB7WJ7oZsyaxpXRanrsA2wesdIbifPp6hc57FjFD5oeZII0gtip8+ayqqUXW8Lg8LorSI9rHPVuJ2Y2IYRBRvUY/n43I3mn4Uw4eb3qYTZ2fQZ7PIVjBW+R7h3h4qMxZuZa6AxkUZt+Cu25uTRnNrKxdyExM9yb0sdpxwRvWKqS3pfjCnchxNlCiFYhRIcQ4rsf8Po/CCHqhRBHhRC7hBC1J75URVGU/y4UD3HLgVv4qu4ib6yN7eXfpHqwjl2pc5nl68eWbwVTGpV6Kh1FZp4OGJSl9zG/pJncg0GyLdMUikmemNpAZXcPBdMBjtSczkZ7CfvGniam6VgTOlnzvIwfyyTszqQsMJ+WvB2kJOyUTSxnwjlIf+oYX365D8/yINsnK0i3ZVKYsYpfzgpz/eFSsqXgp+U6V796hJH81ZQ7ypIh2ipbAAAgAElEQVTemw8NdyGECbgTOAeoBa76gPB+TEo5X0q5CLgV+NUJr1RRFOXP3FV3F2J6gOtGeomXbuCW/XYSYQc+WwrznGP0yXSuC9fyTvYRfjOUgiYS3DjvPl4PnMtNwedwiQhHEpXEp7zMG5jAm1JA2qwNNE7tYTruR+DA4pYIdOJBC5pjIwFHmL1Fr3B66w3oIsGh0le4cttuygqD7DZKiUsLq7Mv4eb5Dq7qnKJWt3FvqYnLnrqPgeKN1Ni3sTTztaT35nh27iuADilll5QyBjwBXPj+AVLKmfcdugB54kpUFEX5n5omm3io8Y/8PmzGhMZttus5Z2Q/b+QuZU5wALsbSmU+UpthsyhjOqRzRc1zjNoL+OLe1xnQcnAS4fBAPvNbJzDr8Mbay8gNB+iYOYrfCSY9Rt6yIcYbMvFlzic1kceblfdyXssNWAwreyufYEF7N+tGY4yXO+gOZrAw/XRerMolI9HOOZO57Mo0kfvOHUwVX0iqaZQ86wB7fGckvT/HE+6FQP/7jgfeO/ffCCG+KoTo5E8796+fmPIURVH+p4SR4Ee7f8TVEYPKyT6m132f/rfeolWWEDbbqPF4aTVyuTqRz0MFXRycsFCcNcKpBbvo6ZpNptnHAq2b1yaW4h6eIXcmxM4FG7jMnMX+8S1E7XHcIUFGjQ9vazrSZCcnsY6GgjqKZ0rJCZTTkrMHa9Dg0zu9pKwI8c5YJdn2AhKFy3gpf5qvNJcw6BDsH3qaLOsa4iY3y5yv4da+wOKc5PfoeMJdfMC5/7Ezl1LeKaWsBP4F+P4HvpEQXxZCHBRCHBwfH//LKlUURXnPI02PEBw9xrfGR6F8Pd/b52bR2BCvFa9gXmKIuDBxZryMQ456XpiajWaSfKP2t+xKbOAfereQq03THi9gajJKbf8kY+lFpJetpW5iO1EjgjBSMNsNHJkhgsNO4u4N+FxmOjNeY2n/Jnz2cUYymvjMW/spnjvD24FKJFYW5GziR/OtfKfZhVXAnZZ6zuyACddcVrgfJyE+A6YYodhg0nt0POE+ABS/77gIGPpfxj8BXPRBL0gp75ZSLpNSLsvOzj7+KhVFUd7T7+/nriO/5U5fHM1sZ3/t95l/cAtvehZjwmCOa5w+vZAzMPHr1HwSQZ1LZr+C1a4z++AEAyKHDPw0dWZS3TqDrll4as3lZM8MMxLuYTTDwBqLULByiJEDucSdRaQwl+3lT3J2800YwuBQ1WOcuf8YteYIXZnpDITSWJy5kV8uzmXDaIQlPvhpto/rXn2drsLzmGV/B79+Numai2l9K4HQSNL7dDzhfgCoFkKUCyGswJXA5vcPEEJUv+/wPKD9xJWoKIryJ1JKbt5zM9dOT1Hmn0Ce+2ue/cNDpEwkeLdwAXPNI/QYGVynF/CLrHaGx2yUZvo5p+BVDk6sxBI2ON1Ux6GhSkwjCTzhKM+tPoPPG3DMu52gJ0yO10JauR//QAp63IzTfAZ1JSMsH1iJXXexp/Ix5ndH2dAUxbo4ys7xcgqcFdTNXsyoLcxXujTuztO5ZPMtdM35ApnmPpAplDmyCVs7Sdc3UR4uTXqvPjTcpZQJ4CbgNaAZeEpK2SiEuFkIsem9YTcJIRqFEEeBfwQ+n7SKFUX5u7Wlawuh7u1c6/XCgiu4baeP9T3NPFp1Jm4jQrnZiyNRiNsyxavBMiwWwXXz72DQKGJpQw9rTY0MR9MZGDdTMeKjvXwWJRnzOTq+E2EyELEsTBadtHIf052pxFNW4k/JICEbKJippjFvB3l+Oxe82UP+ymleG5+FWdjJKzqPuysFvzoi2JYh8Oz9PpGKz2EIEwWWRvLSixBAIpjOj0vvZb/bnvRemY9nkJRyK7D1z8798H3/f+ME16UoivLfTIYnuWPfz3jEG0CkFtBRcT2eh35Et8inJaOUteZujiaKuIU0bkgLwoTBRXPfpcA6QmfTYuxGlHyTl12tpVR0BPA7U3hwweV8brKBrriX3vw4pcNBCtYPM7IvH2lJx62tYEdhPevbz2DU3UMspYWLX66jqDxIvTmX8aibFbnn8L1lmfxrU4wxm2D76J18WltHl6mahY4XCbtqSI8UEYqOcVfZVvan1BO0ZCa9X+oXqoqi/E249cCt3DjcT040ROK8O3jul79hVv8498zfRLbwYyPOOiOPh9MnmJqAJZ44ZxY8Q2NwPqNDOVxu3kF3TxbGoBmLbvCbDZfwlWAvXf56fLkhSkZspJYGiEzaiQVNWO1nUV9ssKZzNhFzgPbKpzi7vp6qcAy92mDfRAml7rk8umwBC6d15s7o3Ga8yeebDbpSN1Jt306/NovZRhVRfZrXcxvY5tmP4biIvN4VSe+XCndFUT7xdg7sJNLwDJf4/Yh13+SR5/exrLuHpytOw2dxsdTST2uilPUmyZagixSriTMX3UsUG8GjWXzZ8jLeoJuW4XRyfSHeXrKUUzQnjRMHcTk0TIEszJYEWbVeJhozMJzzmU4rpNAbwiRN7Kn9A+d0+Zl/OEHuGh9bh+dgN7nxzz6LIx7Bt1pj/MzZyVd3baO15Bqyze2MymyWZriQukana5K7854jbltCKOVCMovbkt4zFe6Konyi+WN+7tjxr/z7pA8jbz5NljU43nmLWNTC5op1zDKP06VncS1pfDtFQNzg4rImZlmb6e6vYX6sn0xmaG7JpLQ/yFB2Jm8WnINt7DCaMDicE8MdDFG4dpiRPUWgObFZ1+N1BcgMpbGr6hHOHDOzaOskhaun2OUvxRe3U154AT9fkMavj0a5L9XHmXtuY7z6WkwijtAizK4awDlTiV+L8MOSPxC35GI4r8ekJWiKeJLeNxXuiqJ8ov1i/618s78NN4LAhlvYdscd1PaNcPuyyzELgyJtGk8ij8OpCXzTcc63m1hQ+ijDsQKG24o537SXgeY0LP0QN5v47vIb+Pz0fqZjE/SWTTOrz0pa+Qxxv43QtIbFcQadBQ6qx1zU521nhTHN0q1t5M/xM+BMpd6XT1XaSn60oZavtcdoNicQnT8mN/9ipo1Csi1tuGYdIX/wbBIyzvfK7iRg1hGOrxFwOTl1Z4hMb/J/xaTCXVGUT6wdAzvwHHqINeEwbPwpT9/3BIv7RtlZuJCmtFIWmQc4Gi/hfIuVlwI6pRYTBcufIw0fh+qX8z3L40x7nXQPZuCOxLljzcXcFG+je6aD9EwrrrF8zNY4OQsnGT2UA9YKvJmzqBzRGXF3k56zg7VvtJLjjmKq1nlteDaZtkKe2HAm5X6dqqkEb8zcygWR2XSLdRRb65gqGKB69HykFPw2/yl6bAMI5/V404tZuX+SbfEQR7WBpPdOhbuiKJ9IvqiPp9/8J74+5UOfs4kdrYKcQ3U4g1HuXngxGSKIz3BwKan81JLALCTnFk2xzP42jd55XDKzD4cRpb41h7zJMLvn1iDceUyPN5JhS+HtdElqwE/RKcOMvVuBjgnN+SmcUYOoOcDwnAe4YPcwWV6dvBU+XhiYjyYcjCy8hIYMK19pi/Bb41G+0xykznU1WeZOutIM5pKNI57L7pQ6Xkt/F8OxifHM5SxuGaF1KobHOsPp9uT/FEiFu6Ion0i3776Z7w20o6fm0V9xHT1PPk7V8AS/X3kZPpODWaYxEkYee1wQCSf4tslBWtVDRAwHwWO5nGJqoLcxnfS+OKNZbu6suJJVo++iCcGuWRMs7EzgqZhBRm34xiUWx3oC7jTscZ1D8+7k6novGUckRWu9bJusZCpmI7vkAu5cmM1P6sLcK3fxjQN7aSr8B2xakF5bgtrsA+RNn8qEeYqfFN+HYVuEN+NiqofGmOo0SJgS/Mvs37LS+0F3dTmxVLgrivKJs73vHVYfeJRcXRI/605eu/23LBiYpD6/ijdzFlFpGqchUcAys5WGYIwLTILOFe9SLdrY07yG72jPMD3swNfpIG7R+Mdl3+DrE6/hj89gL5QUd+disSXIXTzO4Lv5YM4nmLaQjKDBnsqH+fzIEDmv6uStnKZLpNM0k0uZZxU/PH0B32qNcijQzurWPxKu+CwBI5OIZYySimeoHrieBDpfqfgFhjkXv+cfyA6EcB1JMGKWfLX4AS5tb8JsPpL0HqpwVxTlE8UX9XF0602cEQpjnPZ9Nj/+CrM7+jHHE9y2/Eos6OiGxjpcPGVEybNEKCyVrHc+RXeglKvHdyPiktbGbBxRnd+u2cSV0cN4Q2PUZOSx1+IhJTBN8YYhxnbUkjAkImUjaRGoy9/DxabDFD+bIK02iJEreGNkDpm2Iu4460xWTSRIHRxhOvAbFmadSl98KSmWTmTRO9R0XIdZs/HToofxmSPEU76ORVop2ztBi1lyQc42rh3eRSBu5VB6UdL7qMJdUZRPlIfe+g43jvTjL13Nuz2pWHbvI3fKx/3rLmNQ8zDLNEZMZvKu2UDTovyTNRtR+TBWGUcezmKu1ktHfRapEwkOLiglYs3E5G2j1F3MPeUT1HaNkVXrRc54mBqPYnGcgllk0p8+xaLix6l5JoEjJ0r23CDP9S/CJOwcWH0ZYYuZ81pG2WK7lS9HCjgav5Q0cz/tnn4quxeSZStiW9phdqccQHNdS9BZSM2BFo5JK4vcHXx//HEsGER0M8tGp5PeRxXuiqJ8Yuzo3Mr5R54jZk9lvPobtD/9FDUjXhrLqtmasZg8zUebnoNDM+HXE/xE03hlySGWiQMcaVnGl+TrTPY5kR0mxnIc3Fb4GVaO78RjzeJQ2TCr6k3Y06NkzfXRuy8dYS7EcC7G75DYan7Gmmfj2OM6pcuneHVwDr64mfisTbxclck36ke4x3k7P++Ms1/7EnbNzw7HDLNGxqlNWceoeYpf5d+P1XYu4xkrmVN/hLZgBsVmH/8ZuIMsa4jxmIMeWwlPZC1Mei9VuCuK8okwGZog8uJXKIknSJz5G177w30sHpwiarNx15KL0NHQpKQMG52GzsWufvZXOjnX8TBDvgKuG91OIqwxeDSdiNXEj5Zcy7XjLyGwkJMNkYk8LHqYktMGGXptMYYEzb0RIQRtcx/mgm0zOHqheK2XozMFtAczyMpay60b5vHNlnEeMN/Hz+tGaEi7lqjhZrd1ikUTb7Eu/Sp0YfCdsl+iWeYxlHM5VR0NDA/n4hRxbo39kmr7OD1xN1a7iW/EbyTXZ016P1W4K4rysZNS8tazV7JxZoqpFV9my1PbmdM9giMc4vH1m+gwcinTpghLD60YFLnbWeBYiLv4KdzGDDXHgmQyQ+f+bLQwPL1+NWfOHCYaD7M8q4p7c7MoGhmgYOUoenstMzPTmB2nYBHpvDO/hxva3sW1TyN7rY9Jk5MdE5VkOiv58YUbuajfz/bIQ3zraCsTeZcxGp9Np3WIub7nOT3ji5g1C7cWPorXbmEk9yYKR4aItKcSF5Ifxn/HalcHTbqbMkuAb8S/SmV6DwmfKek9VeGuKMrH7q13f8bFnQcYKJjH0cES7IfqyZ2Y5NCCRbzqnE+aCNNteAgAFvso/2aq4Nn5zWzgbSINBSyXnQw2ehAjGkeX5jISz8MWHGVhxlJ+Xu1n/dFWUooDeLLNdDTE0cwlmK2L2F6r8WX/r/G8aMK9NIA1S+fFwUU4zR7uOe/TlAV14qN/5Py2w2Tkn0JHZB1+Sw/Z05tZmXIBLks6b6YdYWfaMaYzv026P469IcikMPHV6ONcmrKPBumk1hTgl/plFBZ1c+2yh1he+r897+jEUOGuKMrHqndoPwvf/gVTdjf+qu/StWUztUMTDOfl8UzNOgLSjiHBgQndFOFnqWP8Z7WTq033EB1P4bypY/iG7QQaHfQVuflj5gXMnzpCiauGw/mDLKubwmyLU7xulMbXyxGYsDg3UlduY4nr95Q+EsdaHSG/MsCzPUuISzM7113BlMvBee1/oGBkD6s8czgUvBzDNIQ+tZVa5wKKnXMYs/j4bd4jxFK/illmkl3fwqDh5pLIW9yU9jJt2JkjQryqLyVaEeKcmjcYOpJNS3NG0vuqwl1RlI9NNBYg+NjlpBgGoTPuYNt997NiKEDUauHVdadQHy0hmyBRHASRXF6wjV2p61iR8TQ5iXHWNQ+jRzSG9nrwuu3cu/R8zh3bjseaQ266RlcoFXtgmtLTBxnesQrCfszOM+jLTWO4sp7z7qvDkhanYtEUr/XNYSJhwVuzie3Vhfy46R6aI3v5vLmQnf7rMWsTTE9vodyezULPGRhI/qPw98TcFxN0zSO/+W16Q0WsDtfzk9QHGRYWckjQIfNpr3Gwpmwf7e9UMLo/i5gtlvTeqnBXFOVjc/TRTdQGpmhf+zXeeewNFg8EsQZ9vLt6JW/q87CRYBwnMWBW7jvM1s+nuaKV0+QblNdJ3DJC394MwoaVV8+ax7KhRsxYWJW1gFuKMqjqbiVv6QR4q/EOjqNZ5xLwVPHKMsmP/3AnlqhO2Zopjk4U0BLOxJ6zmrs3LOZHrc/zqLGdHwQyeNt/E5oIM+bbQondzbqsyxEIHsx5kb60UsayzqGwcyuDY3OpigzyO9evCZkEYbNACgv75mVTm99C4yuzCbbaiLvS6Cn1J723KtwVRflY1G/7ASt7D3GwbDmdR6xkNPWRPtpP49xatufMxStd6AhAkJrSwHcsxfyy2sb1xu/I7BaUh7yMNKQSGbNStyGT6LADSyzM6pwz+HFVlE/tfxd3QZDMcmg9AJrmQaacwqPrU7nt3n/FORwnb4OPsbiT7d4qUpxl/PTCs7m67wjvRp/k9jEPO4I3okszYzMvkW8VnJpzJaBx0NXEluxuBnOvo6BvG1M9C8mK+7jbdgt2S4xGu5liEeedhQXke4Y4/MJ84n0acbeHQzUm5mT1J72/KtwVRfnIjbVtZdbOO2hKycAnLsH77j4q+/sZzclh38IaDkdLSSFOHDN26xi3FDVxc/YirrA8SPHMJAsGJvAN2fE1umiZ72F/Yj65wWGWZH6Kt/OHWXK4DrMtTsmGUfa/Xo45kcDkOocnTnXxg8dvIb9jCvcGP2aHwebBJVjNqdxx0ZUsmp6gZPRm/m0onf2RawjomUwGXibTHOWM/CvRMTNmmeKXRZsZLPgnsiYa0FtLsOgx7tD+k1K7l5dTHCwlxvZFebgdPvY/txRtNEE8NYPWagfLOw+QOaiullEU5SQTn+rD8vQXmDCbGZ/9PVq2bGVFn5eYxcy+tUt4O7QAExI/Fiwixk1znmdz5Coyshs5NbGTRQ0zxAJmRvZ46Cj28G7xPKqmuqlJW4Hw+BmYCmMJ+Cg9fZDDe5fgmglgcqxl11rBla8+waK6VrSVIQqyQzzXtYwoGi+ffhWa2cz3Wm/knJF8muObGI3Pwh98A6c2zdnFlxCXNkDy06JH6Sr8R9JnRnE3QjBh4eb471jl7ubRVDdnEGHvokxMIsbOZ1Zh94aIebIZKk2jomMXj602sVebSXqfVbgrivLRiYeZeHAj1kSMusVf58ijL7NhVEdEZth5yhr2m6uZlC7iaAgkG6ufw5i4nG3Vdq5N3MW8+gDmuMHQrnT63SnUrSmiaGCIYlc1sz15/DInnZKe/4+9+4ySo7r3vf+t6py7J+ecNZJG0kgaZZSRkECIHHUIBmMbfDA22Dhjg7HBGIyxTTBgg0CAJJRRzjnMaKTJOefQ09O5urruC3Oex+tcn+dwz+O5y6E/r6arqrv32v89v67Va/feNSTMGGLImYOudRxRnU7nZC35l6+w6vRxhEle8jLG2N08mcGwmpbJa6hOS+Kt6kexDqXQzyxaAmX4fSeAHtZmrkWSrRgULa8lbOFC2gbMvhDxNT30+6N43P8eN9kv8pHJzjXqIOVT7CgBOLxlPjbXGD5HIs6kGNKaj7BlvgqTYuOalMiPmCIiIv5ZKAr9H9xM/Fgv2wpX0bK5mkWjRlSDbZyfWUpHfDQVUioawoBAfvxJlogp/Dy/gIek3zC1fQCHJ0jvBRu9QQtNq63o6wPE6OKYFTOHx/INXHNuH+ZkD6ZMkZHzJgRBizc1k/Cok7v2bUeV4SO/2MnJ9iwaJRu+5Hl8MreUV+qeQ+i1oqhTqPRejxSowB9u4Mac5RCIw6jo+Mx+is3ZC9DJZrIay2kdz2CDbztfjTrADn0M+ZYgTZPMKC6R/dsWEO0exh2dTiA+jqTWw3ywCDRhOz2N/84Jd9mEd3ck3CMiIv6vGDn4PeJbT/JhYi7uU0amOy3o2y5Tl59PT0EMRz1FCICESIyxja9nXuY79rUsYR8rnWfJ6PYx3Giir8dK43IjY7VWrCoj8+PX8UK2n6UndqDSB0ldNEjlgSmoJCfYZ+O29fHQ5o+RU8Jkz3RS3RvPeV8yalsBv1mzkq+0bSatdYwoncjJ8fuRg42Mhiq5KW8Bal8GsqjQoG/nl/nRiOo0prbup3ZoCqu9R/lR1Ccc0iRiTfAznGeEQTX7di4kzjuIKyaHcFwMMZ1Hee+aAGLYwlDzo5QZOxgaK5zw/o6Ee0RExITzV35A1KnfsN9qx903h7Q+FY7as/QlJFJdmsd5dx5DihkZEYPo5Uez3+FZ1xPYLAM84n2Lorpxxke09FTaqZ5lp6cvHpMCCxLu4kBSH0lXTiEEPGSs6OL42XmYx7oR9JNRF5Rz53t7cafoyS0bpGfEysGxAnT6RF68+VZWDJSz/soFkvSj7HN+k3Conz7pPDfnT8PkmUS/zo0kSPwwbwiveSrzOjdzqXses30VvGJ/i3NiEu5sH3KaHjo07P1sHnH+AZwxkxBi7TgGzrBx0RjhsJnhlkeZbexkjuEyCbqOCe9z9YS/Q0RExL80pf0Mqm1fo1yvo0G7jthaDxm1lbjNJk7PK6VDiqdSTgJAJMyTc97kk6Zv0F5o5RX300yrcSL51fQcd3A5L4Z+lR2DW2J+wl0MRHXTMDxA8lA3KYv6qOguIbZ1BEEVTfz0Sore6saZbGbazGY8Xg07Bqeh0ph4df095Hv6eOr8VpKM7WwZ+Tmy7KEreJJb84pwuEspN/dQ4k7g6exztMQvYVn7Ro41LSU/0Mzb1l/TqIqlvThAtF2N3GDk8KkZxAaHcMZMRx0rYnZf4aOyHqSQEWfrV5lnameauo0kSxVaT9+E93vkzj0iImLiDDcT2HgT3SqBvXFrMZ8eY2prByElyPEF83AaLBwPZvHnTecUrs8/SEfnAvYVFvKg+/csbm5BG1DoPWrnclw8I4lGVM4wc2NvQGcd5w2DmuTGC8QUjzBgjMNSYQIlRGLxOHl/7MMdbWLSjHYEOczWzplIoooPV92NXq3wy9N/IMNUzc7RH+KX1XQFT3J9dh7xnlkccrQz3Z3E2wkVHM5cypLOjzhev4T0YBcfWF6kX2vh6nSFaLuA/6qNoydLiAqO4IyZhSoWtFIL26Y24JP1ONu+wmJLK5PDvcTJV+g4lIyvIXPCu/4LhbsgCNcKglAvCEKTIAjf/ivnvyEIQo0gCFcEQTgkCEL6376pERER/1A8w/j+uAZvyMfvU+cSfSjInD4/4bFOTs+ZzWCClVOeLPxoUBCYHNNEsbqP17NWUeo/ywO9e3CMheg5a+eyLp6hAh2hITWzYpYSbzHw7bQYZlzYjjnJg6ZAIXC0FEI9WFNjyd7ehqTTkFXWjVnlZ3NTKS5R5NCCW+mLjeGN478j33CB3aPfxSVH0yOdYmlGJum+UrbGtbB0NJ2DtlpeL57Lkq6POV27gKTgAB+ZfonfABUz1MQaZMYuJnDmfCG20DhjsXNQxfgRGWBfQTnjIR2uti+z3NpMtt9FbOAy/RXRxCRoiE/0TXj3/7fhLgiCCngNWAUUAXcIglD0ny6rAEoVRZkCbAZ+8bduaERExD8QyU/gg5sRx3t5NrmApEMmFo9ZCPdUUj69hMaCNJrH4uhR7KAIxOpHuStzEz8xfh2baowXBn9KSq+fnkYrVe54+ibr8PfrKXGUkmnN5hsF0Sw9/kc0piDx85x0HbwVfJfRWJOZdv4sogRRSzwkaJ1srZ3GkFrD5WnrKM/P5a1jbzJVe5zPRp9iUMpmUDrPzORUCgMlbExsZvVgMrWGbn4wfTLLuj7lbM0coqVRPjK+iGzzUT7DQJQ6SM+ZbCovp2KWfYzFlqGKGgGVh6PZpxgNaRhv/xLX2ppJHZeIcZczUm8jJi2HEt2jWJ1/H7NlZgFNiqK0KIoSBDYBN/zlBYqiHFEUxfv5w7PAxG8QGBER8fdJDiFvvg9ddzk/iksk5lwyK/1ZyE1HqC/I5VzpNAKjGi7IaQDoxCCPT3mVH3h+RtCh463+R8lvczMwZqS+KY6WyWa8AyYKbTnk2+fzTL6OZUf/iBwOkLqkn5rjX0I1dhJBY6es7QSaEYXADSby1Z3sqppMt8ZIW/4K9s+axpuH32Oeeif7nN+gR5rMaPA8eQlxzAwV815SG8sHoxhXBXh8ZhoLevdwtmYWNtnNR4ZfIiW6qSoxYAoHuXBsLi21dvRKCFf8bDS2bsJqgdOZRxiWVXg67mONrZn4EYgarmC800JMZgll4jp0gopmj3HCy/BFwj0Z+MuFELo+P/ZfeQD47P9PoyIiIv5BhcMoOx9FVb+H56PsKE25rPPPJHxlG53pKeyfvwjDWJC9UiEgAGEenfo6L3Z/H1eqg5/1/oiZbT24ZQ3Np+OonhSFf9RAljmOyY71vJsRoPTsVnxeF2lL+qgqfxj94DkAZoxewdQF7XekM0+5wqHqXJo0doZS57Bl0QJeP7iJZeoPOOD8d7qk6biCF0mIi2NRuJCNiZ3MGFWwyiYen25lSt8+LlaXYAwH+ED/K3y5TlqKtOCV2XloLXKzBxVqxhJmYDTWE9JYOZ9+kH4ZvJ33cL21lehBEXtvJT6XifjMBSxUluEFfuDrJt15ZsJL8UXCXfgrx5S/eqEg3A2UAi/8F+cfEgThoiAIFwcHB794KyMiIv7+KQrs/y7C5Q/4rd1Ki7OQe1zL4dIHDMbFsH3JtZj8QYHI7YkAACAASURBVPYGC5H489oqd+V9ysdN99JTnMId/R9xc/cpkKHpcCwXcxIIeHSkGK3MiNnAsQQnibWnGR/tJrFskIb6L2Hqq0MJj1Dk7SK2ReLyXZO5XjrBmbo0KlUJ+GKKeW/FKn5zaCvXqf/AAedjtAdn4QlWYo+N4lohjy1xfcR6+yjyZfLjSSriR49RUVuMBpn3Tb/CXTJEb5qGkUEtmw7cRlJnK2GVA2fyVGJ1lwjok7mUeoBeJYS/+3bWmdtw9IlYOquQVDZSU1eySJ5FO2F+5G7jutYLdNnTJrwcX2QqZBeQ+hePU4D/bRsRQRCWAd8FFimKEvhrL6QoyhvAGwClpaV/9QMiIiLiH9TxF+Dsb3nfauaolM3TPetQnX+HMZOeT1esRhAFznjTcCl6ABYmXqCycSYNSwspdl7lBz1vYPTJXL2YyKnEdIIhFXF6HWVxX6LBPoS/t4Oxvkqii5z0DGzA0DeMLLWQGvSS3uDi9F2zeDC0jSstCZwOpxO2ZfHW2pt5+cgOrtf8loPOr9IWnIsvWI0l1shaIZ89ccOMCOXcNLaaNzMlvN6L1DXkgijyru0FXFMH8Bo0XG2L5fK5a5jqvEpQk85AWgJFHKJbO4eLyYfpFn0Ee2/mFnMbxg4d+p565Lh4CkxLmSancZ4gf3C1saH5DMUdp+gNTPwG2V8k3C8AuYIgZALdwO3AnX95gSAI04DXgWsVRRn4m7cyIiLi79u51+HIs+wym3lfncYzbXeiv7QJP0G2rbgel8VCz4CerrAdgBxzF942MxevnU2Uf5iNHU/gcIW40hnHSXU6QVGFQ6tmQfwjDBsHafT78XYcxJLqZSx4C9peBcl/jqiQwqTaHspvKuH+0DYaumI45M9FNCby+7V38OLR3azT/prDYw/THLyGQLCWuGiBJUIRB6KdVGgP8N2u2zkYJ3FZuUJLQyqSSsvbCb/AXTyAHy27a4rxX05jqvsqsm4SLdkSi9V7qQ9dw6WEE3Rpxgj1r+VOUyvqJj26wTaUtCxKxYXkhuPZpQTYPdrA12oOkj5US33WIpzxuRNekv823BVFCQmC8DVgH6AC3lYUpVoQhGeAi4qi7ODPX8OYgU8EQQDoUBTl+glsd0RExN+Lyk3w2ZMcN1l4wRzPT+o3YLu0E8kzwK7Vq2hJSUfuClArJ4ACdo0bR5+Tk6uWYlAC7G/ZQPxogDp/FCeHM/Hr1ZhVItckfpmgbpRzahX+mo/RO4KE9GsReu1I3i2YZJGZtQ3Ur8rjTs1eOrvsfOYqRNRF8Yc19/LzU/tZp3+Zw877aQwsRwrWkRcrUSLMYE/cGIdMm/lZ2wYqbRKb9fW0N8bjV+l5M+c5AtnD+P0G3rxyLel1Hgr8jQj6mTQWNrHGUsvlkeWUx16kQz9EeHA5GwxtKNU61O5BlKwiFslzSVIcvIWfmsEqvlOxBUvQzclZDyEZp6KVzk54Wb7QL1QVRdkD7PlPx37wF38v+xu3KyIi4h9B3W6UbV/hstHC0/Zovtf4byRcOog00szeFYs5XTwDR9Mg50KZoIBKkMkZaufkqsWotAqHqu8ixemmXWvmSEUOPoMGvQjLk+5D1AQ4ZDIgVb+OSi+hdiwj2J+K5NmENixQVtdI14IUbrAdp6vHxg5nMWjNbFp5Lz89f4A1hl9zxHkv9YHrCEn1zIx1ky7MYWuiiz2mP/JS6wP06gVet7bS3mxH0Ym8PvlHyPFuvE4LL126k3ntV7BLY2hM82mbdIIb4zsp71jJhegK2k09MLyQ+3U9hCpVCOEAQtZUVgZKsWLip4KPYHcF3734Pj6TgyPzn0QlxFOkF8E+8atCRpYfiIiI+J+p34vy8QYa9UYejXLwjdZ7yb50ntBANUeWzmd72Uqyq1s5GsoGBBCgaKyZ00sXglnNniv3kzk2TL9Jz95TBXgNWjQCrEy6A7Vaz2c2NeGa3xIWQhhjryE4VITk/hBVOMTsxmZcZVGsSLxIT6+NHSPFhDUmdi3ewPeuHGW18Tccdd5BXWAdIameJbFjOJjHh8njbLe8zsvN9xMSDfzK1kprqwGjxcfPpr2AYg7g6o3l1Yu3srzvFHohiN6+iPGiT1meOcbl6mWctl+l09SNOLKA+4VR/JdkZKMZc3Q+q/xTEdDyhOgjpfU0j1V+QkP6JDoy70OHjpkmNc3GSoLhygkvTyTcIyIi/s81HkT5+B7a9Ebuj7by5fY7mXKxFqn7IscXzuKdJTczubKOY/8R7ECmu5tLC2YTjtGz8eoTFLtacZo17CovxK3XowJWJ69Fr45nX7QANW8QkIKYEhcQGJ2G5N4M8jjTWzsIzbawMPEqHb0OdgwVIWsNHJ67gafrDrLY8hbHRm+mJnALIamR62Jd6JnH22nj7DC+ys/b7sYuR/Gso5X6Li3psV08XvIGKmTaG3PYemUhq4aPYtJKKLaFmCf9iaysEJfLl3LMVk+3qRvN6Hzuk934KgNIsckkmfJYEizCpSg8rvKxsHYft9Tv51jpKmTzddjEMLNNavZZjvLyVD2LKsLcNcElioR7RETE/5nmwyib7qRbb+buaAN3d6+n7HwrUvspTs2fzmurNzCtoopjwWwUBEAh2u+kYVYR4UQjr9Q+y6LRS3gMKnbWF+CUTagEWJOyCr06j0MxAkL927i9HkwJcwiOzyTk2Y0S6mNSdy+q2WbmxlTR1BPD3uE8ZI2Bs9Pv5jvtOymzfMjRkXVUB+8iHGrmxlgXInN5JcvFId0rPNN+Gzn+VF60tHN+QMe8rAvclrcNoy/M6bq5VNUnsWD8LAnmcVyxs0grfhdNnEjFpcUcsNcyYBzA4prFvwXcOGsD+JJzKFblMiuYR4vs55tigLsrPqVsoILtyx7GGppMkibMZKOK30d/zJaiUvym6ahVf22G+d9WJNwjIiK+uJZj8OEdDOgt3B6tY83AKpaf7kNqO8GxhTP51fUPMO1CNccDWYCAiIJO8tNXnIacauaZxl+zfvAAklZgZ28+gy4bKkHhhtQVaFSTOBqjoGr8iCHXKIbYGYT8c5B9JwhLTWQODmMoMzHPUkltVwIHRrNQNEaqiu/iqYHNlJh3cmR4LTXBexHCrdwY4wHKeDZ3hLPqX/Hdztso8ebxsqGD426Be0o+YWbiZaKGZD6oWofc7qHYX8uU6B7aUwrIKd6KolJzsWIBe6OqGdU5SfKUcse4i95WA1JqAYukXHLkDE7Lbn4qBHji3PuY9UMcWfAklmAshQZI1Mp8J+1NKlKvx2+axJLLQ8R5J36bvUi4R0REfDFtJ+GD2xjW27glSs3c0fncfMKN1HqCQ9fM4aUbHmTK2SrOBtJRA2FFQVBkXFnRyNlWvtX6Fvf1bEXRwC5fDr09UagFhXVpS1CJ0zgRE0Lb9Bmdzi509mLC8gLkwFXkwCXiXC5M8y0sUp3jSkcyR1wZCBojbTl38Q3Xnyg0Hebg0BrqpQ1olHauiw4QZjrfKxzgCi/xje5bKXMX85qug4viKE/OfZ8E4yCpLSFeqLuXxN4mrPI4yxLrqc+NIzP/HAyKnOqey96oasbVXoqCxVzbO0K3KxkxKYkbvPlEqxLYFHLxrhLkR6ffoKXQxojuSaySijKzGkHt5OG8P9AT8yBeUyrXnx2joMeN0WCY8HJFwj0iIuK/134aNt6KU29lfZSagvESvnRUJNR8jH2L5/Py9fdRcKaOy8Fk9CgEFFBQkBPNyEUOHun4gK+3v09YA7vIoqM+DrWgcGPaMkRxBheiZNRNh+gYrUNrzkMRFqME2gh5D2Hx+XDMN7FSOs2ltnROuFMQ1EacKbfyWOg3ZBovsmfgNlrlWzEI7axwKMgU883iXupDL/G1vltZ7JrOG+o2uqNreapwC/qQTO7lEE+3b6Bw4Co6VZCb0ytpLDATnd6HqlbkYHAu+x3V+AWZ+Uo20xrddIr5WG2xrPEUohXNPC+7OC37eLrid1yYM5M0zwq0gp9rzAY6tW18M3cTrujH8esc3HHCQ4a7g9QZH9HZYZ3wkkXCPSIi4v9b00HYdDcuvZV1djUZ3iK+edhGqPEou5cs4jdrN5B1upE6KR4rCuMKKIKA7NAhlUSzoftTvtvyBmE17FZn0lqRiFpQWJ++AkGYRpVNJtR6jI7RK2iM2aBdDsFBJM8udCGJ9DkiKwOnOdOazVlvAoLGhBC7li/rniNa1cb2/vvpUtbiUNew0GJFFjJ4rLiDNull7h+8iVXOWbylbkSdv58NyRcwjAqkVQn8uOcWpjjL0euD3JNSQUOBAeIl1JdEtuvmcMxWhayoWKeOJ7pCRY+5kJxQLAt9xQQVmUfxMhAc557ut6kpvZk0dz4OrZ95BjPHzRd5Nek4I3HfRRB03HPYTY5wmdg5f8Rod0H3xG+zFwn3iIiI/1r1NtjyIGOWBNZZFBIDOTx9OA65/gg7lyzmt2vuIeVUCy2haOIJ068IIAjIFg3SzBhu7tvLsw2vEFbBdiGHjooENEKY9RnXAlNpNss420/TPXoJtSETQb8CpHEk91bUYYni6T6uCVRytDGfimAsqE04ohaz3vYjtIyxfeBRellMirac6cZMgkIsj0xppi/wKreNrOOm4fm8a71I1qTNJJr7iWuVEZttvNQzmwJfHbH2UW6Lr6G+wIRHo0FTK7JRO5dzlkpE2cgGnYFAZRpj1kQWuVPI1eTRGxzmEYOWaNcgc9W7kBK/TLzHQpYxwGSthfeid3FE00VP2vcw++GWk4Pk6PYQV7YflRCme18eBvXKCS9dJNwjIiL+uvI/wc6vM2LP4gZzgLhgOj88kky45gjblyzh96vuIu5UO12ynSzCtCCCAGGLBmlOLNcPHOHl2p8TFmFLqICemhj0Kpn16WuQlWI69TKdXafoHz2HSpeBaFyOEAwijW8BgswqHmZWsIF9tZOoxgEaC5lRJayO+hEBWWTX0BP0CWXk68+Sr5uBS2Pky8U1jPl+x/Vj13P3wCI+TtvGjNzdBENaCq54ae7J4GhPGomhXgqSO7jW0k5NoQXXkJ6QSeTd8FyuOMoxSDY2CBqGamegN9q5aSwHmz6RE55evm8yMWO4Hkd8MxnDD4MYZLZJIVat57nEN3H547k6+QninWFuON9EhuU9kmbXI7nV9B+eiS3mHjyq7gkvXyTcIyIi/nenX4X936M/qpD1Rh9RUhI/PZqJUnWYbYuX8uaK23Cc6WYwbKIYmarPV3lULGqCc2K5uX8fL9c8T0gQ+NhTzGCHHZtGYU3KekJKHr1ahbre44w4LyJqs1HZFiL4IDT+CWE8LMrtYmq4m91Xi2nU2FDU0ZTExLHE/nMGAlZOjHydAbGEEtMZUtVz6TRpeLToIpL7bZaNr+WBwTIOl7zE1Lgq2obSua2hkl0DM2gfMGAUQszLraVMPUh1vgVPnZ6hIj1/6p5Bo+MSdimKW9xRDIxOJlU2sjQwFVGt4TVfPx8ajcx3nSY21k7O0HpE7ThL9RYCaj8/iP4Vsng9h2fNIbNPYm35KdIyNpJYOIKr04Rydjm9eWvp6uqnz2aKzHOPiIj4vygchkM/hlMv026fxC0mL7GhWH5xKhcqD7Nt8TLeWbIe47kBXIqOUsJc+I8YMYkE5sSxoXsbzzW8gh8Vm5zFOPstxBvULE24mRDJ9KsVKvoOMj52GVGTjzqqFMGrQx7/EFkYZ2l6G4XCMNsqJ9GhMxPSJbE01k+p5Q1avUmcHX2cEVUWs03lJGgWcsEh8kzGfkKerSx1r+cBbzJX5n6fRO04lxpLeazrAO/0zcPrUhA1DtYWHiBPclOVasFTradxSgyberLojConQYpmSX8+I1Iic512Cg3TCASHeVwbpE6nY5bqCEWaWVhHorEYR7lGE0OLvptt/JHeuC9TlZJJUYeX1RUbySo7ii3Oz2BlNOamm9mXW0aSZw+5piF01gHgjgktZSTcIyIi/iwUhB1fgysfUWOdyr3WMVL88Tx/MgWl/DBblqzkg/nXoSp3ElTUzCfMMTR/fq5ZxD83ga91bOTp5rcYD2vZNFyM22kgy2qgLPo2QjgYQOTMwG4CYzWI2gK0MZNQ3BZk14dIgovVqQ1kaP1suVJEr95A0JzJbTFXyTJUUOMu4LzrcQIqG/PNDUSrZ7M1WeR9x0ZC/hMs8d7JHbp+egs34vbGUH+pjAdG9/Pb7gWEgzIaWwa352wi1itxxWRl3KPlXE4Rn/VpGHBcJTMQx/TuUpCNrHemEmXOpXu0hYds0YSVEPP05cwYuBZFlMiwuChRxXHCfIn+wUpOzn+KrpgoShuHWF3/EpkrmlCJCt2HstAEbuRAbAqFvo9QJDVVYgq5Y391VfS/qUi4R0REgN8FH98DLUc5bZrJV+yD5HkSeeaoHaX6DK+vu4Oj+aWErnhRA0tR+Ow/gt0i4p+TwNOtb/C1tg/oDVr4dKCIYEDNlBgHk8y3EUJPjyRyxr0VeawZUVuEMSYbyZuA7PoASRjjhuQa4jVGPr6SxrBehzeqgEccu4jS9FHpmsl5z2OIqjDzLUPYxMn8Mk/mgvISPrmJ1aE7uDbuKG5HE6e6Z5PQIrNq9Czv9M1EQU1UTjy32D7A5JO5INvxpqrZPLCCSl8LY7ZWirxpFPSXkj0eZkF4GiqjlUMjzfwwKpY02c0CxUPSwAJC2mHmGgwkCrHsMOzG77Hwh8UPM2LRseTyFVb5fkfiqgECY1oGj0ylI3op42onJfJhOpUo9AQo8YvkKukTXtJIuEdE/Ksb74ONN6P01/CZbh7fie2mZDiJpw6pCbVd5YV7v0yDPZXxehmTILFC0bD5P6LDKuKfncALDS9yV/cu6j0x7O3PQ1ArzE+KJ1l9GyFBpM2n4qL3fZSxflS6Ysz2VPzedEJjG5GFUdYlV2MTk/mo2ohLp2EsaTLfMb8DQojykaVckB7Cqh6h1GRAo8rg34vH6Pb9DI96nC9pl1CUuBGPAu9fuYd7Bk/C4Dj7x/IQ1HHkzO1ixfhhNH6F42EH0mQjrzTfylDMQXwaF6UjxWSM5rFsSEuaeR6yb5TnfIN8FhXLTGWIuZ4YNGE7Kksny8UUEMOc9G+nzpHDnpkzCaoF7jq3iXlJn2Gd6mG0yYr//GxOZxWSGmwmTgnTrrKTSyfy6FK2znQzq2mASRNc1ki4R0T8K+u7Ch/egeIZZqN6Hr9IbGdefwqP7A0QGO7hRw8/wbDPSG+HlhjBy2LFwCef786p2NUEZsbxh5rvs2rgBKdG0jg/nIbOHmR5dBYm+RaCKFS7od73DoyPojFPw6qNw+3PIOh+D0Vxsia5DnVwEh93hQhoVQzklvKs+tcMK2Zq+9ZTLd5MgqaDqYY0vHoNj+XWMOj/PRa9im/ZErGZdlE3nMfHNev5hfd3VPVG4QzEorPlMnn5XmY19qGEFA6Y7QTMKTxfvwYpaQsIMvP75pE/7GC1Jx2DNZeRgRoeiUpiRNRwozRCjieVkGqMBMcIs5RMetV9DHha2JM8kyNT89AHgzx18Vmyp9Sh0obpOpGIv20ydelxFPj6car0WHCyCDdn3WvxrNjHvfr9VIVmTXhpI+EeEfGvqnYnbH0IWW3iNbGMN5ObWNOVze07+/FJEt//2rdwdavpC1jIFoeZEY7m48+fGnZoCE93sPPyV5jqrOPTnkm0uaOwprpZYS5F5b8BrxLkokui1/8eeF3oo0uwSIm45FQCvvcQw+OsTa7D65rBXqcbRa2iadZCXvU8T5OQQkPnbXSo55Kpa2eSPptau4qXorfRL+9lvsPAOquLsOzj3arbaenJ5UXfrzjTn4yMHkd+ApOnb2FylQtfWOBksoPqviV8EEhGSNmEQdYzt28BSzpUTDHOQzDqOddzmW8nZpEhwTqfjCGUiN/UwSydhUwlg06liQuMcTJzNseL7aSO9fHv3T8hoawf34iOjj2Z9CqxKCnxRCsy4yqRMqEStXsRx+KC5M95gThNH0M1dqKrZPi3iS1vJNwjIv7VKMqf9zs98ixeUw7Pk8unCdVsaJ/E8q0NjBqNPPPIowy3aBkL65ktdJESTvl/gz1WizjJyJFLG4h2DfOnjumMSXoSpg2zILgS0buUIVnijMuF1/cRBLxYUorQOjNxiVEE/O+ilrxcl9xBW+9MKkPjqAWRq0uX8Nv+57gkTqKl626G1XlMMvSTrc3m00SZvbxA2NTCozEaMjXDdA4W80rNLRR6O3h8+E1Oe1IR1QmkLxoix76DospxBkUV55ITeK/5YVpiq1CiPiXaF8PCrqnc3OXAkXAN8lgXvxZG2Z6YxXI/TAoYUBgnHNXACiEDa9hId6iNHbogZ/LmUpeqZXnfIW5RvYupyMtQtZ3h89m0Jydg10QRUEE6nUzTjHCx+0acCw4wL+4CIY+K5v3pZPvUiLH1E17mSLhHRPwrCXph+1eheiv92hn8WBvHCXs132qewbQtl2hPSOSlW+6hu8lIGIEbaEGl5LKFMABygg5rLhy9cCfuMYE/dU8nrIbsFb2U9N2O6J1DWyDEWX8ngnsPhAI4cjOQe2fgEcKE/O+gDQW5Nmmc8+1F9KrcqAQdV1fP5aX2FzgbLqNt4C58YhTTTT4StSm8EN9KjfhbpqQ6WWmVIaRm85UN7Osp4WHfx5iGhuhQotCYy8i+bi+pI+1MqhmnQavhpH46v26+F03Kh4RNHWSNZXNTbSILlVLUCemMdF/gibgc3IKa+8dFrLIGn6GTFJNEmVxISJE4rW7mshjHrlmFjJgFHmt7mdKkUyiyQOu+FEZ6U/CkF2BQhdAyzkrxLKHAbMqDVnRr/8BkXS8j9TZ6z2WxJqYGgzOJS9ETOw0SIuEeEfGvY6wbNt2J0ltJfXg5P7QHqddX8dz5aeQcOsfF/GLeWXQdrW0OrIKfW5VuWpQ8DgmfB3uygax0J7vOf4Xq4RhODaajswfJW9ZPXsPDCK4iLvtkroYuox09CUDspAS8nStQ1J2E3TvRKjLz4ywcatPhUXuRdVE0XjuZp9repso9lwb3BkQEZlu0iDo9PzFsZ8h+gHsSvKRowwz3l/KLmhsRfGGeHnmNMY8GNFEYoxeRvvQt0mt6KPK7OWkwsdm9gR2BLOxZv0VWeygdmMaXKxNJSlyJEvKz31nOywn5TA2qmR1QIyhePFENzFLFkxPKwq24+VRspUs/mR2zTcTI/bww+AqJ6Y24e4x0HEljwFGEJtOEConZQgWFhj7Ku1YSmn6a+NQulIBI674UdAPx3GpooeFcDL36IKbhPcC3J7TckXCPiPhX0HocNj9AOODmgnQX38uoJhga48Xd2SRXX2TzkhXsS5pB10A0KaKTO8Mu9ii5VH0e7KF0Ewuia3nz3A842JdNszsGW7qbzEWDpF/+JgFnBuc9YXqUE2hHyhE1RuLy7Di7b0KvO4ln+Dx6MUSeJZMj3d0ghhmJy2NsbiI39e6jt3c+1dLNmFU+ZhptNOhH2Ca+RVxRM7dZJbxhC+cq7uSNgSLmeSooGz6FW1FhMk5HmxpD3KQ3yTnXRa7Gx3vGdP4w/CTd1g4sSa+hCmtY2Tab+3tnY0jJwzN4leeirNSZc1nv0RAbVhHQdqO3jrA6nIdVMlKr7uOSW8W5ghLOFhi5zrWbW7UfoIr203s+lq6WSfiT0lGpwmQo7SzWnMYZLqbdb0FZtQ+HfoTRJhtdp1OYoXWjaR9kv9aCQ5GY0jnM4aJpE17ySLhHRPwzC4fh5C9RjjxHUJPA0dD9/DD7AOmjOh7dZsEy2sord97J2WA+TreJKaoe1sk63lTS6RUUFCCUb+WrbONLlz7g457JjAYNJM1wEj9lhNRL36Z/KJVzgXHC/lNoPdVorSai4tIZ61lGrP59evoHsWlCaIV0Ljl7MEsSZ0pXYM2WWNx/CX/bchqkWcRrgkwz2NgpnKch5iOWFTiJ1yhUueZy/OI66vwy941swuxxYtSJBHT3YM6pJ87zNpnV3eSog3xTs5odw7ejifsMfcwpHP5obq8rYZXqOkRzmCO+Q7zjmEZKUM/dATWi4mPM3kKu1sBsaRoyIXYILYx7M9g8X8t4dJAfe35FjqUc76CeluOTGLVMgyQBq+JiNYewmf20DmcRKGwhlDKO3qelZW8K4e4EZgz20awSUWugsHsUQ0jio/zFXCiI56sTXPpIuEdE/LPyDMPWL0HzIYaFUrYL8/h1xnZWdGRy5+Y2XAYjz224h0vD+agIs0zdwDw5jRcVPR5BQREgNNXBm4PPkt1Sy4e9JciI5C0fxpzqIuHCk1T1pVAjdmBwnUWQOrGkalAHlxAYjSVL+zzVfXpidCLuQAwj9JHg8vL2TQ8xVd/IdQ1X6B68kX45lnyDgEP08qfQ+xjnlnOjI8CIYmRX3Zc41J5N1ngVD4yeQFTCJEY5GAxvwJZ1gKKzn5I0fQQlZGJl+Ds0epNxpL+JZGon05XJg80LmK4vY3y8hhdjhugan80SrwZHWMSv60S29rFIySZdiqdDHOG8C1risti5WMtM1VnuD72DSuel62w8Xf0LkGKMqJBZxClKNFV0qNMYx41zYRc6vZeR2ih6z8aTNuxnXBmg2qAjp3ecZJeTensqry6aR2/uXnIHEya8/JFwj4j4Z9R+Bjbfj+IZoj14J6/GhThg28qDF6ew/GAFl7PzeWvOKpqHU4gXXKwQ20gOTeanCMgCKBoB7RQ9B+ofpK3DwK6xInQagYJ13WjMEtbzT3KyO5kBdQ2GwTMoiouYYjXenvuI0jRhU97mSn8sdq2BYX8AMTxGmjvIM1/6Dov9F7i1ookK94NoRSgzqqgLnuFy+namlwxjFBUOBmbQfuZO6j1u1gxtJjowSIJxHCzXMhyeSrzqY3JbD5BbMsYeaSY/CH6ZkKEbR8pLyKKfmQPTeHBoLSkqK3tV29llKiFhJJPrJTVhxhl1tBCnhWWBmahRcTTcw8hYLAenSzRnh/hG6DcUieW4Bww0XbgGpcYxRAAAIABJREFUtzkdxSFTqDRxrXAYt12N02VkNMVLMCmMMg5NB9KgVY894KXDpCejf5zpQwP4VVpem76Gw3NbmOdv5FtV36FJdWHCh0Ak3CMi/pnIEhz7OcqJXxIUYqgPfIvvpx1hOOzimS0Z5DdV8OE1y9kWPZ9xt5Fp6i6Wyi5GQ9N4XpBRAMWooih/iN9d/D5HejIZDRqIc9hJXluBKOiQT32bw04jonwF/egJRG2YqPx4fN3rmG7+E13DXVzxxKJTWXAGx7H4A6j1Np5+7Dvc372T6VUmLgU2EK2BTKGfU+HN2JdUM80u0RrUs7H/EbiaToyznLucF9GLEsWpLuo9T6EgUdz4SyyL60n0iXxFepRjoVnYY/ajjjmKTjKxuHsZd/iWE5KaeCr2BOH+VcwNaDEq4DG0ELL0s0DOITuQxDBeLrqDdOmi2LlcIttexUvhd1Hjp/FUMYO+UmSbTFTYyQ18hsM8wIDahKIRaJ6pR631M3g5hr4LUZg9IVx6EbM3TFlDP7aglwNpM/jjwnjSja38sOEOXJKDZkXAoEqa8KEQCfeIiH8Ww81//hqm+xKjobmc1K7kueyN5Pbn8O0tw6hCvfzk1ns4E5yMUZa4VlvH3JCDneFizgsyAOFoLY84PmPx8QNsGypELcL0nEJYtBshaKX/6DdoDEqYxyqQA+XooxUMtvnYBnQstDzJoZ5ExqQoRAwE5HHShsa4NH0+W5ev5pcX38HZtZymcBxZ2hDO0Gc0TD5GQd4YvjC8557Clav3Yx0cYP7QxzikUfKsg1iTU6nq+xaGUBPFod+gWzFO1ehM7grdiV8VIi7t9/hMHaS4U7iufzHLPfl8EPcJdaOzSOleS1ZIRVAcYzSqiXi1mhWBuYiouCyN0eExcjm7h4opVu5Vv8skKhnsSKKlaRFBrRq9OshqDjNZXUN3tIGgD1rTrIgOL6FBNa0nEpF6tYRVIooCJS0jJLmdtFoTeXbxtQjJvXy1s4yAtJohBHrUQU7qFJaLqgkfDpFwj4j4R6coUPEeymffRg7BYOBx3ox2sTnmI267NIX1B05zMauA12deR1cgkSxxiHmqDgqlQn6pGBn+fEZMOFXPu2M/YeiUzGlvOlE6IzOKsxma9imCN4baI4/hDHkxjZ5FDnVhywqD91ZmyzvRa8rZ0VlISNGgKDIa2U3hwDCv3PlVetIS+PXhHTQ770QvCqQJ9QwmfEDsjD5UaoWTHj1bnPdhq8pm8sgpJo9XYVRLLMpqoZb7qOorI3n4M+wLd+MbMfP80P2UK3nEWM9jiN9JUJSZNjSN60YXYAuP8ETyJtLa7mFOUIeIgsvSSMjQx4rQZJIDsYwqfirdCu3aAKfnjpKWUstPla1IHgMVFatxh6NRqWG+cpFF4mlGHWpcikif0U5TfhBFCtB9LI7BOgcgopMlSjpGiHe7Cag0vF6yks5CI9cNzERusRJEZlg3xhatHr0KHh+rwTTinvBh8YXCXRCEa4FXABXwlqIoz/+n8wuBl4EpwO2Komz+Wzc0IiLir/COwM7HoHYn7nABTcqD/DhjK65QLD/daCO78wxvLF3DbutclIDIQnUTUwQPBmk63wdCKKCCuJwgL1/5ARV9sciKSHHUVJKmDDCQ9wnSSCZXztyHxj2C1n0QRfTjyIsm1lXEIsuPuTJi5dzwJP4cJyHiXW5SpQBfffxnFPoGeWJvL83SKhyiB9n2DsLsGpKtErU+kc3OfDrq76V4oJfpI5swyx5KHD2k5fg42vo8IclIlvl3DBWrqOpdyLvyKrTiOOmJrzNiayXK72DW4CyWuQv5JGkfct8CypoewBEWcesGCVhbyMDKouA1CAjUBgLU+xWaUstpm27jDu0fiQ44aaiey/B4GgICReFW1qj2oBhC9FvVjEtGurL0iKYA7moLHRcTCPnVqEMykzoHiPIG0IYk9mbPp2ZSMTPc2eT2GtAJQUKGTt7UxDAs6rjT38u12t/iv95JTUXBhA+N/zbcBUFQAa8By4Eu4IIgCDsURan5i8s6+PNKCd+ciEZGRET8J4oCNdtQ9nwLPCOMBO9lmzGJ19K3sPhqHvfuOUp9dCpfufnrtIdSSMTFPF0rhaEozkt57Pv8bl0xqbnXfoApx89x1ptIlDZMWfx6PFMOM5ByjqHGRTTXLMM42oAcuIjWFsbimE1ZsJxUywts75xKr98AgFoOMKlriNbiPO669d+5/2oViY0puAQRu/44xhlbsCR76ZdE/jho4tLwzSQ1prB++ACJgT5MOolVmTU0GZZzoOkuDKFedJN3UTWUzLauErqJJdN8Cn/8HpzqMEUjRcwenYakH+SVuAOUNNxPckiLTwzgdDSg1rq5KVCKDSNjYYlKj0CLroG6WRoK0ytZGSynrW4azf3ZKAikyGPcqN6CTeuiN16H36Onw2KDpCDyADQfSMPTb0KUFSZ39WP1KVgDHs6nLaAmfyFpUjwzXCpiVF6s5lreFjO4IMYwOTTOT8U3UC3txG8Ko74i4pFcEz5Evsid+yygSVGUFgBBEDYBNwD/T7gritL2+bnwBLQxIiLiL7l6Yc83oW4X/nAGffK3eDbpODV6eOojPXmtx3h1wXoO22egDcnMV7eQL4yQFZzEK4qRgc//TcV4gVd6X6SzRkWvYqY4KpmiqFLap36KP6qBquMPI/Sko3fvR5b7saZDUmg612jfZsgHb3XMIRRWAEgadVEwOsKv7nqIioxZ/GBvJ5I3Ha1qGOuk14gq6MQrC2wZ1XDUVYjQcB3LBmrJd59HVqlZlNBCYoqX/Z0/xjucgdV8nnqbg/q+yVxQ8klVdVJie4nm+AGiAnZm9U4nKxDL7oTDTG+6l+Xe1agAp6kNydTF/FAOOYESBAVq/TKXlX6GklqRSn3cxC4Gm3M5330TiiISK4W5QbuJFHUf/TFaWiQjvX4boclBwr4QPcfjGapzIIQFJnUOYPUqWEISdamLac5YhAUrWZJCutZFrK6ZjWIRO4QMjEqQ76s+JH12JYIthKZWINAnUaJ2U2nKnvBh8kXCPRno/IvHXcDsiWlORETEf0lRoPxPKPu+hxL0MSZt4KQmj2cLjjGlPpPfbNvNhbgCHlj7LUZwkKsaZIa6kyTZgCTN4vuEkVFAhLnRday5spsmr5E4XZCZsbehtgo0lPwBp19P885n0I31EPJvRFSHiElPpCzcR67lVQ71T6Fy1AYo6EISUzoGUcdq+bfvvMLShjCP7PUQwoIlcSeJc3ciA/vGtBxyGfB2X0dJG0x17UJUwiQ4AtwQf54KVrCj4TYUlQZ/XBOX/FEc98SjJ8hS1WaakytoMYYpHC2gaLSQLlsHnVIU1155Eh0iY7oRZEsTieiZH5yDGT2DUpgLATcD+hN4yuKYbNlLoCuWq13XEQ6rcQS1XKv+lHxtAy6Tinq1heFBE/6yEGFVkIErUfRXxBAOqMjrHSbWGUClddCSuYT++FJUgoYEUSZfP0CCtpFPlFL+JBTiVWTuV+1mVslJVNE+NG0CoZMK6VY36So/lw25ZAeDEz5cvki4C3/lmPI/eTNBEB4CHgJIS0v7n7xERMS/pr4qlN1PIHSexS9Poif0MC+m/y/23jzKkuwuzPxubC/i7VvulVWZWVn7vvWqbi3daqEFITYBEsaAj8HMGMYztjEYg31sYGBgji3w6JhNgIRACCS0IKml3rurl6qu6tq3rKrMrNxf5su3vxf7vfNHFmrhOWNwGWwM+f2XJyPeu/Hid7/7i19E3Psap1NV/vFnJJO3n+bnHv57nM3upkCXd5vXGNQ6TIbb+WNZ5tzdib90W/Ivmp+g93qHmrA4VupjR/YJlnIN1vd/jJtX3gW3j2D2niSKlkn2KXYmhnnY/DKRjPm12++gG4agFNvqDXat1Hj+ibfw1X3/gH/0TBcZ61iZKww//LuY6QZnuw6fbylare3sunKQ4/UrJGSATGf5vv4XCFNlPnPnp+joBxDJBheMmDNBlnUtwzvjl0k4z/PC9jaFIMdbl4+QC9OspyvsvfndJJRFy+zSy0xhGT5PhAfoV1lCCWfdgOuchAMlilumGFiaZvrqo0hpkPGTvE08ybHEWdyExlQ6TeN2ku4jCnkoojGdYelUP0HTYqzaZLga0Cvs4cb+R+jkxpFIhoyI/c48BX2Zp9VR/qk6xpKI+Vb7eR7b9SzOQBNjBcRnBemiz9FMi2WZY+pmDuNmm8UHrL/2kPnLyH0BGP2Gv7cAS/fyZUqpXwd+HeD48eP3NEBsssnfKbwWPP9/ok79GrFM0op+jM86Nv/x0HUeOj/IR778Jb6y9X5+8V0fJELnqD7PPqNCOU4T+Sf4N0B4V+z3Ja7w2K1n6YQG29MuB4sfRGpjXNjyFOupO6x+7Z9jdJaI3d9F6BEjW/I8nrhE2TrJa7XDvFzJAyF2HHJkukI6GfPLP/STHJ8d5d2newizwfB9nyCz5RK3Olk+tZJk3U8wfvUB3r1cIRWfo+YM8ljfOQ6nzvJc/duZX3uUwCizmOjxiuMyS569apHvq/8OXz7UYCndZXdjJ7sau/GsLuW14wyFBTq6TzdzBWnWeGu8ly1BiYQymAsUr4eXcLZ4ZCZnEWs3WT2zH6UESS/HQ+olHnaeJdIFt8tJ1m+m6Q6DfL+kt2az8IUBektJhuo9Bjs2jb53cmHiOLFh4wmPHYbLkeQiluZxSe7nJ1Q/F4XPY9lX+Wc7vkauVEevgvFZDT8jeaS0TqeW4NatIsF8AhEJzC0hk+m/GTX314EdQohxYBH4buBDf62t2mSTv+soBZc/g/zKTyJ6a3Sjd3EtfoJ/d3iabqPAv/61kyxS5Mfe/k+omTnG5RrH7CWyImQinOTjssjUXak7eo8fWf8jwnoPwwx5fGgbeeedzMoEle2fZmH6BOb6Ixi9LxFFK6SKiofTAQdSX2LNH+Cjt96DG7aBiLF6g93z69x4aA+3hn6UB6/GxEQUd32V0p4vMd8p8NGlAiuxz5ZbB/jArCIfzlBJDDAypPG/p/+Ym959fG7mfVSdR6gnJC+makxpDjll8KOVP2BltMpvPbRENkrzyPIjlHtDlLqjGN2tuFpEK3sL6SxxKBxjZ7iLHA7VSPJscAc9v0B+xzythkft8lYEkPLy3B+/xoOpr6EJyULZZnkxTdsy4H0RQdtk+dkB6jdzlFswIieoDZzg2vYRlIyo6y3eYkv2JW4jSXCT3fxm3ONFrcvDpdP84uRXKOXq6Ktgf0qjnRYcyTWIpg1uTZcRTQOZUFh7fUaGWiQLEWHvr79yIZT6ixNoIcR72HjUUQc+ppT6OSHEvwXOKKW+IIQ4AfwJUAA8YEUp9V9cIvD48ePqzJkz/80HsMkmf+uoXEF+6cfR5k7iy+2sBf+Qj0w2+Uq+zPc9eZ7R67P8+qH3M5MZYThY51CywoDRpRTlWIt28XsoJGDIgPeFLzG2fB2AwwWX8cIHWPUPcd2+RSOxAovHUe5rRMEZhC7ZVVY8kTuNRONzS48x3+4CimQccORWhVwy5KmH/wUZt0QvSuKUpxg48knqXoJPRDErssq22REOTBcoBm2qZpGgNMSP5z6FLfO8snCMeec9NCjwcrLLJVNgqZDvXnuWAXuZ3z+6SNPosaM1yb7VYxS8YazONgIR004uItNzjMZlDkSjDJOnGyvOhBU6ietkJ+os91xazQE0EZN2y9wXvsL96acwRMxKKcF8NU19xMaY9AldnZWzfdSu5ci5wzjGHhrF/SjNJParrNo+35LKMG7dJIz6mdfG+Fjc5hk94qHBU7x34muU0g30ZUg9pdNKCg5oLcKbFs15ByEF4WhMerfL1lwb3VBck0l+qTTAhF/gp3/8uXsKDyHEWaXU8b9wu7+M3P862JT7Jpv8Z7SWUM/8LFz4A6RyaEV/n0/kBviNfTmOn2/w3q9+jU9PvJ3Tg/so+k0OJ5YZtVokMClGu/gNmWIdhaZijgSXebTyClEMOzI19pcP0os/wGVXZ7V0EVHZjQqWUd5XiGKfvkLINxVu0Gc1OFl7hNfXLJTy0ZDsWl5nvNpkev8jtEceY90roZs9yvs/Q2RW+QQJFuMZds7l2DddIh0GVM0iy4VJfij3Fe7T5zi39gDX1SMscYDTts+FRIxU8IHayzzWWeaTD9e4lJllwOvj8Mr9DDX3kugNEwtJw1mB9CwFmeVouI2tFJHApaDJgnmJ0miLWc+j181j6CGZbj/H/Vc5kXkKU4uolBPMNDPUykkSkz1koFG52Ef18gSpaBfYewkTBYywS9dbYKlg8/2pLP1ijjAeY00v8jtBg6cSIQ+Nvsq7x54iZ3cx5gXJpzXaCY1dnR7etEXQMgktRXgkon97ly2yhwJeMbN8pNjPI61v5j2Nt/BU7go/8hP/2z2FyabcN9nkfxa8FurkR+CVX4U4phO/j6eNE/ziiTT5OY0PfeFzvJbdw5Nj92PHIceiWcbTLTRdsC3axtPxIC8jQSnGvVneW38W4QeMOE2ODeRQ4sNc7w4wYy6jeTYiMhCdz+PFqzhOxFtLy+zLzHK9e4CnlsYJojoAfW6Hw7dWcfuGWDr2ASrRMHFQILvtJE7+Ch/XkyyqS+yZc9gzU8COJYv2EFP5vXxv+mW+Q3+GJfcBznZ2c413c9qWXLIiFIp3rZ/he9bXeO7+mD8ZfA1TmhxaPcHuysPY7iASRT25BulpbJnkvnCcCUoATEVtZqxLOH115j0IAoeE6ZHuDnKkd4oT2adIaCGVosUNt0it5JAebyMjweqlrdSu3I+l9hLbIwgVk6vfYJ4mzYFhfigFjuwRsZslzeD3eys8n5E8svMkTww+S9IMMKc2pB7qguH1gN5iApRgdVAiT4TsH2zR1w6JgC+kyzxpT/Ce2ndwtLcbDQ0lIy52TvHej97bYh2bct9kk7/phC7q9G+hnv9ltLBOL36U89G7+dkHkqx183z4i19kNizzpfEHUUJwrHOLnbkWekJQjIosRNv5DBADA16Fd7eeJdNtULR6PNDfwU58D1fbh7kduSAaaHEJvf0srrqMQHG4VOWR4hSL/hhPrRym6VUBsFTEwekKeSmYe/ztVNiH3xrDyixR6HuNT6VM5tTrHJjT2DGfw5SK6eQYl/P7eV/yCv+L8YdE0Q7eaO7lZfUBTls6l60IgeI9lbN8X7PL2aMWnxx+jobRZn/1CEcXvgnHHUAiaTjrkJ5Gw+S4v53doowG3IxbTCXPI9JtKm4CpTQyTpt0u5+j3Vc5mH6JhB5RyVpccgdoDZrkxluErsXy2YfpzB1D0ydB6KQ7C+Sq5zmX0smMHuBDTguiDFKf4FbU4/fcVW5s6/GO8ed5MH8GU5dYFwSppzX0SJBcVUSujkpIzu+VGAcC3mW2yXVjmrrJp9NlKvHDfFPj/QxGRQBU5FNbfJlfmUyybJt8+af+yT2FzabcN9nkbypRgHrj48infwE9WMOLD3NZfgs/f6SPq1Y/3/7kM/iVmC+Ov4VQN7ivdp3thQ5mUuDENl60m08pE5cNqT/Sfo2hzhJJPeD+vgoD6cc533oPtyIPoTpoqoTWfZ1AvkIcK7ZlWryz/zrNeIinKodouDVAgYLReovJWo2ldx9gTXuA3upB9ESDXP4Kn8/CvHiBQ/MR40tpFIKp9A4u5g/yHvsaP2p8kpQsc7H1AH8q38MbhskVS6Ih+dbFs/w9XzK1P8dvD32F+USFXesHOTH3XtLeILGIaDirqNQdUAkO+xMc0MqYwFRc43rmEj3DpRtaGIZP3umRro5wLPgKe1Ln0TXJYjLJud4Q4daYzKhHe2E/1asPE7R2I4SNGbQYrJyG9i2eHdnBfSM7ecyoEcaTKD3HxV6VjxlNOLDCO8tPsiO1gAogdUrDflEn0VaIlgZCwXDAVw9pFLb5fFunSyaMmbaSPG9N4Lgf4MHuxlTCALHf5gX/Iv/pxG6mt20DIRhfmubVD3/bPYXPptw32eRvGjJGnfsU8dd+FsNfwpd7OCe/jZ8/uo1rZh9PPP8y8ZLPcyPHCHSDB9auMZlvomdNTGmgRTv5tExTRzHkLfNQ8xTDvWVsLeRoaZlt+cNcbH4PU2GEJj0QRczuBQL5AkEc0+f0eEf/TaTI88zaIWrdBhuvrAiyns/OuELnkX5q8lFacw8hNJ9kZo4/yvTwxHMcWO4xupok0AyuZvZwMXuAJ6yb/DPzYxSVxdXeu/jN6K1cMFLMGQpTxnx46SIflCbLk3385uAXuZKcZs/qCY4uPU7a6yfWAlpOhTg1jxZlORqOs1fLYQrBJbXMrfQ16jJCKp10ep2crpFZLnJUfpHJ1E2UENw2spwLhrEmPDRzG83Z+2gvHgbpoEU9+tYuMLh6ljk94szkQ3zvYI6hyCTWdxAKwZe6s/zRpODg8Cnenn2OnOmh1iH3nE7idYHZFoAgkQ+JJ3w+t99geyLgPZ0etpKcS+SY5VEmOt9FKc6jlEQIjUVZ47f7O3xl/3b8hA1KYnVuYLtf4aGpAX7np3/pnsJoU+6bbPI3hThEvvEp4qf/L0x/jkBu51XtO/iFw7u5aZR47LmT9Nbg5cEDaCgeXrnEtnwHrWAjlIYZTfKnMs+Skgx7yzzYPM2wu4yjhxwvLTCWG+Ni+we45mpoCpSWw+q8QShO4oUxWdPjkf4ZbD3D87W9rHda/JnULUImyxXkMYtW823Ub74DpTQ0e50vZO+QC15k10pMtmfiGhYXswe5mN7HO42b/Lj5MYZFxBX//fyH8D7O6HlamqDod/jh5Ru8I9lPc2SA3+37HK8lb7B/5S0cWHmURJQm0l06zgphchHHH+JYPMoOPYkk5rQ2zXxyjk4s0LSIcmmBpF+kf9nniP5Vhuw1AqVxOSxzzeojMTSI1zhMe+EIMkwhpEth/RKjy6/jtKZ5fsshEhP7eX/GQpPjoOdZ9hv8SqlBdXuTtyaf5GjqOppQmFcF2ed0rGugKQ0zHZHd6lKfDHlpyOK453HYD/CExkVzHBV8ByPeIyi1cc+jY+k8mWnyhxMpZssFAIzeAones9jeK+ieSbm9l4PdSf7DT//zewqnTblvssn/aIIe8cu/hTr5EYx4jUCO8bT5nfzyoQPcEQUeff5lmnWLN/p340Qeb1s8x0jZIyqmEUqgogm+JossK8lEb4bjzTfo96sk9YATpQVGM+Oc63yI6Z4GwkFqGezWaXzjFbxAkTZ87ivNE4oRzrcGafea/NnL5U7eZ2RiDWMiQXPucRq3H0FJi8DocDL5KsPdi2xdF2hKsJjp42pyPzftSd6uT/ET5scYFy4v+d/Dr8r9XBA5IiE4Vl/gh5pr7CptZ7WY4NOlL3LOqLBv5VEm14+iK4PAqtNzlgitBkV3kiOqj22GyZpoc8aaYk1vECkNx2nSV1gh0Rhl2+pNDtuvkDY8GnGC19tbWMrvRVg76a4cRgZpwCPVvMrWhdcZqF5lMVXglfH7eHx0C+OigDIn6IiYT6eX+eo2jQPOK7wt9Rx9VhfVg/QrGsmXNMw1Dd2OyW1zSY96XBlJcceSPN5tU5CSipZikUcpuh/CUAXCuEds2LxaMvhCn8fLWwpITUML6tjdF7B7L6L7HcrtvaTVDhp9e5nrH+G9C0/z0R/8l/cUVpty32ST/1G4daKnfxXxxm+gqxbdeB+fyH8nv7VvP/XA5ugLr1Nxs9zJDpHzO7xj+Sx9A5IgnwalEUbbeUbmWZcBe9rXOdq6QCbqkDM9jhUXGUpt59XmB6m4MdIcQgoTp3ESP3Ea1xdkDI+jxWVWwx3c6iQIwy66iMGE/PYWfTvraKk81WvfROvOA6A06to6s/rTjDYXSfsarglXS3u4ahyiZeZ4Qlzhx8xPsp0enw1/kF+Tu5gVDmYc8X1r03yLMsj372HJafCHhc8zH1nsqTzIYGcCKSJ8Z5WevYgmTcaCnRzQMthGyE19mWvWDF0l0bSIUmmeXMLHqIywr/Eye5zrGJpiqjvImfgt9BJ7Cdu7kZGD0D0S4XWGbp1h68ollJKcHtxDZmI3J/KjmPokjYTF17INPjuqSFuzvEP/AodTM+gamFOC1EkN57yGbkpyW12yoy6ypPFidoRCWOF+rwvAgtiN5n8Q5HE86aGigHPDJb7aL3h20MQ1DUTUI9F9Fdt9Ad1dotTaRUEdoNq/h/n+YQLDJBGGjK3WeWD+Mr/4k//HPYXXptw32eS/M3LhEsGXP4K19EU0PFZ4mF8Z+Fb+aMcenNUuk6eucFsboZlIM9Zc4sHGVVJbbKJUgkharEcTvCgzaGGL/e0rHGxfwZIhw06TY8Ul0tY+Xqy+n1ZkEtqjiNjFaj9JYN7CDXSypseeXJ15dwfLroeSEYYRkxzyyO9okB9vE7RHWb3wXnqrR5AqYpFrRPJlBls9AOYLaS5n72dGTGIKxXdpJ/lh8zMYMsfHo+/nk3KMhjAYd9v848YKRzJbsNIDTJsz/EnyNHF3CxO1Q5gyQah38ZPLePYqaW+IPdE2tpqwbK5xw1igKjqAIJOp0leaw+6VyaxGHPVfYiBRYz0c4LXWW1nSDxMEkyBNNKtFInGVwsxlJq5dxopD5tJ9LG7by/0j28kl9lJ1cjxd9vn8oEdkNXk0/hwPJy+QSkSIDiRf1Ui9rGO2FLnxHoWtPRL5iCv2EAuWxkPdZbJS0iGLFz9BEL6PLkl8v8GN/gGeHknybL9OM6EjYh+r9zq2+zJG9zbp5igD8SHqg8eYGRwg0g3sMGB8dZX82hqddcFcVGC/3uEzP/fhe4qzTblvssl/D+KQ6OU/Ijr5UezgElKZvGJ/gI9ufZwXB8YZnlqkfHWBqeQ2YqFxYuUa+6M5xLYSsSXw4hx3oq2ckSajvTn2t6+w1Z1HoNiZqXK0uEYtfCdvrB/BtYaJjTSmu4hQX8OPq/iRTjnRoWjrLLmDdIImCEV6NKQwViM31kJPKFpzh6heeZywu5MwXmdFvU5g4pFIAAAgAElEQVTGvYYdKTq25Gp5O1esB+moHP10+H79y3yX/jRn5GP8QfQuXlI5YiH4jsYKH4wUg8XtCE3nZOINzso25eYeMkGRSPPx7FXCxDpS+Ay5O9klsgT2OjN6hSW9joKNskv/LBkjRlsdYHz9Bvv0a1TjHVztPsBseIxADgFgptawC5dJr1xm6yu3SPUiPN3k6sheJkfHGMod5mZplJPlmGf6XNZFyKPB53i7/RqljAsR2JcFzikd+7oiO+bRN9khkYm5aSa5mOznSG+FidAjwqAjHyCM3k1DTlKJq1zu6+flLQVeLet0TA0tDjHdsyTcVzDb09iNEfrCSTojDzEzNEis6ziBx/jaMn1rVTrrgjtRmQ4mhlKMojOR6/Fb//Lb7ynkNuW+ySZ/jcjlG/hf/n8w5z+PQYOqNs7HSh/mDyaOsarSjL8xRVBVrNpFUqHLW5YuMpLtIgdzSCTNeIgr0SCVsMe+9jX2dq6TinukjJCD+SUm0hEXq9/GXLQD39kKMiLZfRGsCzTcGKk0RpItYkpUXB2lXFIjLvkJj/xYFTMZ47UKrJ1/jM7qW1BhhBfewJWXSfkNpFDcKRlcyj3EXLwHJTQe5Db/0PgsCUK+IL+Vr8W7qAudHb7LD3ZqnEgOYDtFWrLLk8Y1Wn6Ovu7YxvGkFpBmA2m1yARFJv0JLLvNsrHGgl5FAnaiQ1//DIXcGqq6hUzVZU9niq7awbR3nDn/MDFJEDHJvimSA5ex2zfoO1mhcEsSC8FM/zh9Q2OkRh7mzJZxTvbHvFwSaH6TR7qf463WawwUN0op5m1B8rSGc06Q7PcZ3NPCzsTcME2u2AXGQo8jXgsNcOUu3PgJ6vExLhsdLhSLvLq1n4sFg1gT2KGH7p5F91/HaM/h1IdJh+N0t9zP4vAwStNI+S7ja0sMrq7SWzeZkSXqWAgFo1JjixXwPs6z33qdV8r7+Uf/6y/8F2Ps/49NuW+yyV8xyuvhP/0J1PmP40SXcUWCL+W+i98bfpTT+TGK02uUppZYNPqINIPd67Mcb05hjWWRSZ22tFmNtnI+TtHfm2Nf++rdLB3GU3UO5JbxvCNcaT9Mw95BbKSwvAUSPIMfL9MKLCwtImsJOlEWL+6QGnDJ73DJb6tjpkLiQGPt8jHqc4+h3BJxOIUb3sCK1gBYy0bcym/lcuJteFGOND7fLZ7lqHGdU/ERviwfYA2bASn5+60qb9HTFDODSKW4GC9zQdVJ98YxlEnDrtBJLWDpHhow5o6R1QxqVpVFvYZEYZkuff0zlErzaO1+zIpN31pIGG9hIThINZoAQLNapIcvkhm+SELdInU2oHRSovUEy4VBUsPbqe95glNbt3KyX3Ilk6BQX+T967/P8exlcv0uQoCxIHDe0HDOClJJn4F9HUQ24pxtsWjajAVwzG+howjlEK58lEV5Hy85FpeLJV7d2s982gSgv9tG917HDV9EbzdxWmPYageNbceo9g0AUOi2GKsuU1yt06sluaNy1MTGfIyDsWCH3uGbxSs8ZD1DP0tcz4zzQv44VqXLD//UJ+4pDjflvskmfwUopQjOvYr/7EdJtZ8B4fFC7h18ou+beba8C7Ua0j+1QMt3CHSLotvk/tVrjNgt4tESoVBU4n6mwwF6fo1dnSl2dG9jS5+UEbE/u0hJFJlqPU7F2kdo5RGxRzp8EUO7xGpPEiudtBkSqRQ+HtnRDrmJLtktHQw7QkaC+vQAazfejtfeiR7OEYW3EfFdoecCposZrttvp+WNAYIDzPKY/jpNleZJ+TDLZBlQ8L29Dm+JFeXMEBLBdNjiololCsvYURrX6LCcv05k1chHCfriIhmVoqO3qepNFHxd6OW+O+hulujOTuy1FF4wSiXcicREEGFlp0mOXCe35QKmuYh9RiP/ssJc1GhminQmD3P9yLs4NdrPqbJOS2icuP0S7+z8KdvLC9gjIWhgLG4I3T4nSNs+5b0dqkV43UnQ0EwOeTrH/DomMZHqoxc/wkvJ+3kpVeBGscyFgQI9U8OUiu3NOgn3HEvR1xDdFLa3lSizj7WtBwgtG6EUA611xtZWyKx2qLdyzJKhIwQoGJawT6zzAf0ZHre+xExqhJfyR3km9wC3rHFK7SajtVUyHcm//1c/ek8xuSn3TTa5R5RShNcv4D3729irz2CKec6l9/PJ8gf5QvkIvZpGbraK39WIhU7Ba3HfylW2ajUYLeLaJktxjrV4gEoQMd65ya7OFLmojS5gMr1GHylqvRMsWUfw7H6EjMhGp7DEWWpej25koQuJrhlIKyA/0SI31iU91EXoisjTacwUqN08Rqc1hhE1kcFtUB0UsJr3mS043Eg9QNPfD5FOng5vF+dICo+X5GHm6GNEwYcjeCiKKNkFYmAmaHEzbtGL8pjSJtA95vJXaCeXScWKrdEAjnRoa11cbWNFoZTdotg/S6GwBPVthHO7kfU+2sEYoUoCEttaxCpPkRy7THZwCqFCzCsa2VcF1hVBpX+MO4ffwoU9D3JqJM1MKsHI4hzvm/9jDliXKI62UeUNX31d6BcgV3RJT7hcKhpctC1smeARV7AzXENHEqs8N8z38sXcCV7PDnG5r0jD2VgsY7QbM9quIIOz3PFPo/whDDFJbfgYvcLGXDbZXoct9VX6azVEVbLmlVgQNp4AXcFYLDnBHN9pfJFCbp6TxSM8nX+QS/YOsj2Xkfoao/U1nNAHwIwhDl1+5hd+8Z7ic1Pum2zyX4GSCu/CGbwXPk6y/gKGmOdMZh9/WnwPn8/dx3rLJjVXJ+yCEhqD3XUeXLrM9nCFeEuRSrnIgsqzEpbpeT1Ge3eY6M1SDDcm4dritOkXWbreQVbsI/iJIsiIbPwGSXGapt+kHjgIFLouMAsuhR1Nslt72PkNKXh1i8ZMltrUbny3DxE3IV4BFFJorBRDbpYNphMn6LgHITJJEHBAzGALn2tyGzVy7EPjW6XBfbGkYCbpxopbYYM70iUKC+jKwDO6zBQusZy5QSG2GPdHsWWCrvCQQqELSS5XoVieIykMvOUDBJUx/M4QkUoBkNGWsZK3Yfg2ye1XSGXqqBASVzVSZwSdOwXO73uYG3tPcHHbONNpk7HFee6feonjvMLg4DpMxqgEEIA1JXAuazizG9ML3NkmuZAxWDVsDrlpHvLaDMmNq5UVfSdP57+Zp3J7OVcYZjWdBKDsSY7UfIq9WdrBaS6ySsgYfvoAjS27QBMkwoAttVVG1qskai6tToZFlaOqaQAkJeyQPR4TFziafYPpviIv5o5xLrUH240YaayxtbZG2t94AkmLFbrbRmvXMDpNtCjEHc7zr/79791TrG7KfZNN/gJiN6Dx3FOoi18i03uF0Fzh+cIJvlh4F08nDuA1NezFFvFGH2WsucyDy5fZ6S0RDea5MzTEtN7HSphH9OqM9e4w3pvFkR4AQwmPPCX8aC9r9mFCK4OQAXl1Cps3qLk9mmECUBiWwhnoUt5bJ7u1i9BARoLWYpL6rQKdhVGi0ELEdSAEBH4iw3xfl2t5i0X9GFFvFyqyMImYEEtoSG6qLVgYPIzG+1SCfUpgCZ21SHIralCJNfQoDUArUWWmcIlqZppC6LDFH8KQJvHdBbUd0ydfWCRjCOgM4q7swW8PodRGjTqvL5A3b+Lnl4nGZsgMLmBZEUSQuCoIbuS4qI5zceeDXJqYZM022DNzi6PX3+BocIr+chW5MyYa2XCSqIFzWcO+Ao4fUtkXcblP50Yiwbhf5m09ya5ohSR1lqw+TmUe46XMCU7mtzOX23g7NB0qjtcidjYa4F9mSt7hquXgpXbSGtoPlokexww21hitVMjUegQdh4rMsahZhAI0BVtiyWG1zL7Ubbxhj5eL+7lhj5PtuQw21xmtVcj67ka7pUTvttG7TfReCxEGSDuFclKYiQLCzhDZffz4v/6Re4rbTblvssl/hooVjYvX8V/5HPbaKVLqIrPJFC/kj/G51ONciLZCLcSsuki5sXTwrtocDy1fYqe7hDtQ5PzoJLN6H23fINlbY7x3h63eArqK0YXGkOlgyhFcdtBMTaA0E122yKmTmPIqa25EL7YAdffV9jZ9B2vYuRCloLvqUJvJ0Z7NE7VzKBWA2hgs0HJ00xlu97c5n3doB0eQnVFQOhYhJVq0SBLgsB+dx4D7sRmQgq4SVKKA27JFL0yhSRMpIqqpGWaKl/Hsdcp+gbJfQmMjQ7W1mEzCJ2nG6EEOvzZO0BoGBIKYPnOaIfMaWmKW2vA68WCLVKGD0EB0IZjNcqc1wavpxzg7fhAVB+y/dYMjN85xwD1Psb9BuEsSTKiNNeFCMKcF9lWBvihZ2hpzewvccBKUowHe1jM55K9ja6tcyOzgbOYgr2eOczY7Ts3OAmDGiiONmGPrEaXuHSrBbV5zQqazW+gO7AcngRmFDNUqDFWWSVUD/K7Dql5gRZiEYuO8F2LYTYMJexUx6HFqcJwVo0x/q8nW9RUGWzUSMt44L3GM7nbQe230bhtQSCeFsNOIRA4/YSK1Dc9u6FaxLhT/8d/823uK4025b/J3HhlLapdn6L72PM7yabLxJVadOi/lD/MV5yFOsxu3baBXPQg2stP+Xo2jq1McWrvFSFRnfmQbp0Z2s6hlMbsthrxlRt0FctHGGpgJPU9JlJCM03EmiYwkSiky6jJJ9QbdoELN01EIhKZwyi6FySaFHW0MO8Zdd6hMFWjPp4mbDkjFRmYOCAdlDbGaF1wrCqYSBQJ3FyrIAODgoaFwcdiFxttUzMMiwRZl0ZWC9VCyIHvUIoGQCQBCrcly7hrV3DxCiyh7JWxpgxLYsUNaFySEBm6JsD2EkhtZuSF6DJi3GDavkjWnqOUb1MoaesnFym3U3d1Gipn2Tl6zHuaKuZdty0tMzs2yd/4yE8YtkkM9gklFsE2BBUjQFwT2dYFbV8zkFDcHYSaZZpc3yNt6GruDBrUknM+O80Z2L6czB7mV2oISGwPQYC/gcAP2NyXbWz0a4Syn7Q4niwWq/TtQqQRO4DFUXaZ/ZZHkmkfgZliy+qjoCeK7Mi/HsBWPvlSH2pDJrf4iuW6P0fVl+tsNUmGAdndbEQYbInc7iDBA6QbSTqKcFJGVBH2jbWpjok008fWIZETNszN5g2k5yA/85GfvKa435b7J3zkiP6R6foreG69gr5wjE99iId3imfQBntOPcDUeo9s1Ea0QEW/EfSrscXT1JodXp9hbn2FlYAtvDO/kdmYAFQQUvQqj7iJ9wcZc55pWJquXMdUgfmKcwCqglMKR06Q5QxAs0AhiArnxOJyZDslta5Mba5Mc6NFeylObzdFadlBtA/Fn2R8gtALCGKKXdJjPwuW0zVK0BRluzAeus7FtAp096DyqYk6g06cc2jE0YsVqHFCLFEqZKBSxWKednKeRWSc2fDJRCk3pGFGKRJzEURZ6lES6ZbjbZk1zyVt32KJdZ9C8TcGcZi3pspxLExYkep+P0CGWOrO9Ca63DtJZKjKwUGfnnVtsb97GHnIJJiXBdkW4RYEGxBsyV3OCmgvTKclUzqBlDnLCLXLCBdswWEgbXE2Pcja7h/OZ3Xi6A4AdBexsedxXszjQlOxs+SyKVc46HqeLBc4PDyIdg75mjYHVBdKr64h6TFOWWLWKtPWNm6hCQb8UDOgRZiamPpDA1FoM1yuUui2ScYh2d/BAxuheD81zIY4QSqLMBLHtIBMOaBvT+koFCoEuFKAwDJ+BxB3GUzPk0uuQDAgcgWdrIAS5mZjj/2D2nuJ8U+6b/K2ntbjK2uvXiKfOk25OYeuznM3qfDVxkNfZzbzfR9yWCH8jK9dUzNbOKvuqM+xdn2Wgu04l18fl4R1UUnli6VPyVhn0VyiFdRA2mj6ArfVjixJBYiuRnkapEDO+iRWfJ4xX6EUxcbzRyXU7IrOlS2qgh2HH9NYzNCsZvLqO8BRfT+Kw0IwhhDFEkMgzl9GYtg3uyDzduzckN3QhKCM4hMa7VMQuZSNjk0asqEcR9TDCFRvSijUfKVbpOWt0ki2EHmPFNnqUxIiSOMrBiJLgFUBttFczXJL2ImXjNtu5xKBxG8tcY95Os5pJ0Ska6EUP3ZDIQFBd3EJjaQBt3qDvTo2x9Xm0oZBwVOFvU/gTkrsLJkEA4o6g2RDMS7jsCCp2Hzv8EY74aUwzw1oqyc10jivpMW6kxgm0jSsMTcYM9zocqsP9dYv9zZihXsSNRJMz6ZjTpRwXh/Ok/Q7F2irOehWj7uG3NepGPzUzg7qbaediQRlIWSGO08Q2m6TDFpnAIwGIu4JGSjTfRQs8iGMQIHUL5TgowwIhkAoiNAQKU8RYlkfSqTOYmqOcWsFMdZCOQppvnmk9MEnU+5HtYYLuMMIbYSm8yYd+5j/dU9xvyn2TvzUopWgsLrF+4TbBres46zdIxUtMpeC5xBBviEluR0M0XRvhyq/vVwxaTDYWOLRyk363Tiw0pvu2sZQt4RmKVNigL1ijHKxjKhB6P5oxiKOV0YwBAqMPpRRKNkhEZ0CbIoh6+L4GaqPzJnI+dtnDdCJkbNBrpPDqJurPidxA6H1oxjCa3k9o5Zh3dBZ0g1nNoaoJ/mzjDDCKxv1EvE1qZOIkrUjR8D0aErqavZGR6y5QJTJbdJ0ukRGRiFPoUQo9cjCljRElEVHy663QzC5OcomceYet4ioT6ho5fYW2YzDn5FjJZwiKCkd0MSoCraLTreSRKzbOsk+x3USOSsKtCnebItgmEeU3z5NsQacuWPYE14TOIkP0x5MMi340I0fTTjObSnEjNcRqov/r+9lhj8Feh90tONFIsrclGOtKmprLFcfnctbhcl+GmtHFadSwquvQ6uG5JlW9RMvMfv2zUhKGIp8hbZ202cLRuiTwsFAIw4RvyMi1wEdE4cbVkxDEhoVK2KDpKLWxwpUQCtvs4ThdnGSDdHKNXLKKY3fQ7PjNmosCp5MhXe9HtIeJesMIfwQ7GCYdvfkjxSiWUXyBaX7+F37gnvrDptw3+Z8Sr9uhenOW1tQi8dw0dmOOOKpxPuVwyipwVYywGJXpeBYEb8ZuRvbY1l5hR3WeottCCkEllaeeThMaklzYpBSsUw5qGFoGoZfQ9SJJI4emlfC0QWIFKprH0i+g6XeIIo/AE8Th3cxOlzgFDzMVI6WB30sQtHXEN7QDkUDoZTR9BM0YRNP78fUEy0bAtGkyZ0BVUyA2RD6CxgkpuS/WyAeCtu/RkZI2Fq7hIHWfyOgitSZSaxOYMQgdI0pixKm7WbmDdremDmzMlphZIWGvkdWX6BczTKgblKIVvKTOQjLLcjaDJ3Xstoe9GmNUBGLFQKtoWG5ENKiIhhXBsKI3CnJAohXePMywDa22YDES3CbHqjpMWk5iGwW6VprFZJbpZIme8ebgYsQ+Ob/FSC9gb0vjRD3FvpZGOVD4IuaG7XIpY3A9n2TZcvHadfRGl6jl0/UTNIw8kWYilCQddRkJ6oyqNlmjR8IIMPQYTRdgWm9KHDYEHgUQSxACpesoMwFCQwgQWohhujh2h2SqQTJTJZ2ukrJ76Po3nFsFiV6WVKMPp92P2e1HeoMQDGBGgxjqzWP1iVlQIZW4x7ps0Axr+F6VUNYJE5J+Z5h/+n//zD31kb+s3I17+vRNNvlvwO/1aK1VaC5W6M1ViZdWMWt36EVNbicSXE5kuC7yzMVl6mILERp0gS5oSIpRm/HuIn2dOkm/S6hr9BImgSWo50BzIkphh0kRYPg+jsqi61sgeRAv2UcsQ5S8g2bOEOlXgDYyiAm7BirWNm5nComZlOgphYgVcaAjfQ23msSt3l3qQqTQ9TLCHkUzBtD0fmLNoCZ85g2DZVNjVZfUNUkBk90Svtv3GfcCrEjSVTpt3cGzTG7rLrHuEad7IAKUACV0dJnYyMTDfvR4Kyn1ZpcVhksiu0Qis4JjrFHQFhlhitFgET2WdG2d5ShP009yrZfAbg5jVSV6VdC3HgLhhsSHBO4ouEdD6JeIPNytaCBj6HQFi0GSW7VRVuQOonAMXZToOhkWnRwrdv7rNzgBUkGTvNdmX3WF7V3B/pbN4WaKAV8gsGlrBlNOzK00nB6RLMsuy36bsBvirQlay0lMJNmoQyluslU2KOLixDGmIRG6hnIsVMZ6M6iUiYhAxBF4PZTQULoBuokwTHRLYJgudrJBOrNKJlslk13HMqJv+AzQ3TSpVoH00iSJXgnTKyH8MjIchHgADfvrm0sR4VoNGnqbdTnDUhBRwWPVdFm3Be1UhnYqSyeVpZMapp3KElobg/D737j4V92t/j9sZu6b/JUTeh6t6irNygqtpTXcpQbRWo1up0kv9lkzdO4kkszpSRZVmrU4QzdIoNSbhQwNSS52KXstMl4XM3QRIkLpErSYbNwmH3YpxIq0ZuOYGQwjB1qRQBXxZYRu3UI35xGiCqqNDGOCnkbUM+AbiiZCkwhdoZRAReLP/U+hIUQKTc9vlG30foReQOgbUwusi5BFU2PR0FgXksHIY7fvsy0MKcQCQ+n4mkXHtgjNiFhzkVoIIkahEGhoMoEe22ixgx4nELwpSkSEkVzHyqySSK1iGzVy5gKD4jb97ipGE3zPoBMmaPlJoq6GUZeYVYXeAGVA3KeI+iAcVLhDirBPoQoKLfemxD1pcccrMSv7WVKjNORWAgZp60XWE1laVopvxIo9sn6XftdjWzdmZ0fncDPFZFfHuXuPuKfFzCciFhI6SwnBnAi4Fbis9TyMrkfK75CLu+TpksUljY9tKDRDA9NCmgnQ9Te/VCmI440yirqbhQsdDB1NCEw9wjQ9LLtNMrNGqTRPJtNAiLvPzUsDvZfD6uRIt4ukuhvy1vw+RFRGxn3wDfIGiDUP12nSSnaoWh6LmmJO05jWEswmkrSTabrJND0njdK0P7evFkvSXkS2J8m4gownyfRC0l5ItlPjl37uO//rOtafhcRm5r7JXxdRENCqrtKqrNBaXKO73KC62qLZadEOQ6q6wZKZYkVzWMWhHjv04nFAgM5G2uuBoWIcGZKMfQpRCzP2saRHQrokVZdCFFKQkpKKyeoGhp5G07NEKkVgRaj0Gpq+DFSQTOPHPnGw8Wp+5BmE3zBYgA5Cv7sQ0TfKG5RyEHESoWXQrPxdgefQ9CKINCEhbRHQISKUMVbkkg/uUIglOzCY0C08x8a3NGI9BCfG/3/bO9cYzZLzID9vnet36etc1uPZ3dldxSFex1l8kYMDCQbJ4BgZKzKRbVlgcISxSKQAEsJWSEAgIA5/CASJWCYgI0J+hJtBjgwCzA9CHHudXWdtsvbsrr07l93p7pnu/m7nVvXyo+rr+foys9M93T2z7fN8Kp2qOlXnvOc9dd5T56063+kKL6OIOkQtkXXBF76IMCsXEE+IOzdI+y+Sz18ly9fo2mssD69yavMa0UDhu4amiCiKBDuMcBuwMjgNEdglsMuKLlvs+YbJW5T6NOiiYnpQE7POEmuc5mV7iivuAVb0ATars2xyivV4gXHcgxn7LeroV0OWi4LHr2/wyHjA9w8zvm+Ucn7imK/9R/rGYria1FxLlBdkwpezgpVqwGqxgSvWmXcT+lKRS8MZYzkXCRLHuDhFO0mYbSJA1wd1qLXgLNLU/ruzJkIiIY4bsu6EvLNJb26VufkV5udXiYwlqueIiyWi8SLpaJF8eIHOyhMk1SKmXoZmGdWFXW25MmMG2YSVpYaXuxtc6Qx5KU94Ie/xXGeOG2kf5My2OpG1dIuSXtEwXyjnBoa5ccF8ocyNHXMTy8KkIS8UI9GufVqBpy5c38cVdzBa496yi7osGKytMnh5hbUXr3Hl8gZra5tsjEdcV+VqnPFKlLOqGdddzsTOoYRBrdCiTO1InSV2NamWdHRIZksW7IRlW7DcFJy2FUvG0hdDbFIkTnGpYuMSa0pgHWQD1RG2qagqxVYGWxpsFW0Nat4khZ2GE0BykD7GzCPRMhKdQswcYhYQ0wcMVkuc1qhrcNoQNw1xvQlmA8lS+klMzyhgQRLQlDHCRCPEJRiXktQpab19/4rDZANMZ4M0XyNPrtGNVsir6/TGa/SHq2QbJfKSQYeCDg0yEmhAezCa7+LmwS4o9qzSnFHq0zXFYsT6/AKb+SLrLLLJIhsssM4ia26JdZbY1CUGOk9hbvqCp+cnbUrmq4JTZc33FWMenBQ8Ok55cBJxbuJ4XaHEKtSkrJmGVQrW9QbP2xFfc0NGzYDGbmJcQWIcsQHiGBMnnI0SznQT6JtwToL7RBXnLOIc4HvhqIIRosgRJyVZNiTPR/TyMQtZyXzs6GhGXM6TTOZJij7p5DTJxhNE9TxiF8AuAMmu016ZEetJyVruuNIVLnbhcjfjWmZ4uSNczQ1F7N8ZyO2Ebj0hLyuyCfRulLzxasV8kdAvIroT6BVKr1DyWjFAKn6Gp8NSWQta0lAySmpWuo6VeWFiGlRLYq1ItaYjFX2pSG5Ud3Qt3g2tcf8eo65KBqurDF68xtVL17ny8jo3rq+zWhZc1YhrJuYqOWvaY+xiGjX4Ll3o1ok33L2yoecaHnQlC82AJVdwyhUsUbEgJXk8xiQVGhU4U+BMidMKKEErnGtwtcNWMKoNrhLcyOAaw24DLUDGbsQPYJo+Yha9u0T6wXD3EdNHyREcaANqUVOjBtQoNnLAJoh6H7oKgkE0x6mh0hjjEgRDVu+xezPBmCGJuU4arZNxndxdp1PfIB+tkw82yDc2MSNFRkAiuB64vm4t6x6sX+ixfqbPxtIcG3N9NvM5BukcQ+kzZG5bGIRQSHcPgSC1Ff2qYqGueahynCrHvK6oeGiScq6ABwrlgcLRtUpBzQ02WdchGzpm5Ca8ohNe1Am1ljTU1BFoFKNxgsYxZN71EAHRtF2o0jiHqJ/j7V/CdOAgMUIngl7kmIsdvVjpxo6ugdzFpC4hrXOiqktcdYnGc0izANrZ8+F1QHcAABDgSURBVPiUmiqaMIwrBqnjRqKsZAO+2+vyXK/PlU7KZlpSZCNyM6bLiK6d0K0L8sqSFsLrBl0uXF6gc2OR+WFCt1BiN21z/kZkgI6BTMCYCkeJs0O0WKUerrKhBS9mOd/tLvJC7wxVljBnSubEh3kpOGsmdKTZas6NwsTCxFoe6O/VoA6XO/K5i8h7gF/Gn9PPquov7lifAZ8D3gasAR9U1e/cbputz/3wcM5Rrg8YXFll9ep1Xrm8zsrqKpfHIy5VFS9FKSvSZ6QphYsprVBZIUbIVcgVcueXfWeZ05oFW7IoGyyYG+Rmg4QRJpogUuAoQEuca9DG4qxDa8E1gm0EbQxqzasLvicCkiLSAekGQz0HpodEXdR00ChHJEGNn+2w1YEXZtwugqgJxtogGiEuQtj9mLy1Zy0wOiHSEYkbkroBabNJVg1Iy03SZoPUDsiaDSQeU85FTPo5437GpJ8x6WaMeznDXpdhr8eo02GUdRklPcZRl7F0GdNjTJcJXUb0mNDxfuNb0LEl/aZmvnYslIblKuJUZZivHAtVw1zVMFfVzFUVnarCaM1YCwpKSi0pqSm1pKKmEktlHNaAM+amw30vnAOnGPX/rRIREUtEIjG5SeiamK4IfYnpS0yHlFRjEpuRuIzY5hiXbx8/2IXFyQRrSipTUUSWUdKwkSjXU1jNDau5YaUvDPOaJt0kjTbpyZA+Q7o6JGsa0krIioR0nJIOO5jBEnZ4mnp8imayuDWnf9oyMvH3qEyETCA1io0LRsk6E3mFSXWJSbHCxsiyXi0wSM9xo7/AqNslyoSeVPSlZE4KelRkuG1HVUrNMB4zTgs0c8x3upxfeD1vPPtGHj3zKMvLy5w6dYo0TTkIhzYVUkQi4FvAu4FLwFeAD6vqN2fK/FXgh1T1EyLyIeAnVPWDt9tua9x3o9bRjCqK1Q1uXFnjhUsrfGt1hRc2r/OyWjZNyoic2iZoJZhakMogKkCEYMipWJAh87JBjyE5I3IZk7ohsUyIXYG4CtUGrEWtw1lBreCsQS2oFdRJMJRTZg3BzjYTg8QoEWIS77YwGZgMlQ4mylHJEJOBpGBShBiIETEI0YwhFggG2fefIr994j2MkYKxENXYxGLjBheVEBeoKVBTolKhpsaJxcW+jI0UGzuaGGwMTWyoY0MVJ9RxQpFkFHFOEWWUUUZhckrJKclCyKlkryeJ3Yg6OlrStRVdW9O1lm7t6DSOvFLyCrIa8rohrys6TU1Wl6R1SdQUqNbUWBosVhwWh90+5ntrnCKqiPMGOlFDgiHRmFRiMsnoSEZPcrqk9DSlqzldzcn0zo4PClQmW0a6NhVVVFFEDUVsmSQNo8QyTi3DvGHUqRh1alw+JI4GdGVMhxEdJnRcQVpBXCVo2cOWfWzVv7msfF5TLNBMFmiKeWIiUoFEIBdDIpCKkAgkwYDbeMIkGbKRrLESrzBym4ybMePGMnGGgg42WsQlc7g0IpWajivJbUXu3K4jtmKZRBNG8YhJWqPdmN7CHA+dPccPnn8DP/DAD3Bh/gJpdDDj/Woc5oDqO4CLqvp82PBvAO8HvjlT5v3A3w3x3wR+RURE79VUnLtAVXHOYVVprKOyNXVVM9gYcWN9k5cuX+KFl17i2o1XKMrSD9q7GNM40kYxTUVsKxKtQfxgmtGGpGkwaomcw6hDcOH69LM0/Nw3b8xE/UsyKgbFcE6FczJ1VwhqQsD3XJ34skiEmsjP+jDGz+c1Cc4sUkSnwhxfgxrj6xgB4+s6E+oY/16kiyRMx/PrnDG4CJwBFxmsEZ8fCU7ARcHVEQ7DCaGel88ZCUtw3ExbMb4sihMX0uoDihOLFbA4nERY/H+oW2JqEmoS3G16vneKqCOhJlZLqhVZ8JGmtiarG/puRGI3SZwlbsIAaW0xjSKNw9RK1EDSNGRNTWob0qYmsc2r22FVREGcogql+in8EUIUDHJPI1ISEonJNCEjISUll4wOOblkJESkxCQakxAR7dFrttRYaXA0OGpUa5xWOFPhzAAXlWyawo97xBV1XNHEJXVSYtOSOp3QZGOafAydAVk0JrE1phG0SdEmxdkc5zK0ybB1B1d3SOou85sdetc72LqPs6exTQdtOkiTo3WXus4xCIgSiZKgpEYRcf5aEgeiVKag7K9TzL9MYSqGJjyhUFHT0KhiFRwGFf+0FiHECqYB6ILpkqWzzr6GRlYobUkVVYyTCdfzgioVXJ6Q9DoszC1x4cyDvOnsI7xh8QLn++fpp/27bntHxZ0Y9/PASzPpS8AP36qMqjYisoF/CXn1MISc5a//60/zpYfevJXWLRMpu/J2l5ly6zpbZeQWdfIE3vAYymO7trN7+7v3t7cMui3tMKFsiMtBXRz3jkgbDJYIS4TbihsckYYlzt/w8Dc8gw+x1qGMIuFmGKn36Rr1vdFILbFzRM5iwjJy6m+e1s3E1Qfnl9L4J56oMRgrxE5JrBKpHyQT/H+P7FrOBKa++S3XjxBhSFSIxZCRkdElISEmIhEfM8GtEYnxtSW0HPGDcg4XbnAO9Sn/U59WsahYoAGp/F8vSkktFbWZYEyBRGMkHiPJBJIhkgzRaEKdOEoctcTUxFiNcSQ4F+E0wWmMJcW6BOdinI1xLsaGpZvEuNEc1i7hXIzaGOtinI32cL14v7u/PYfUtkvSAWMfdoy57pswxq0ojYFGoI4aGqlpTENjGmqpqU1DY5Q6EqrIUEURdZTSRBnW5DjTJdYlMjtPxy6QugUi+v48O2gGsDaAtSvwNQCuh3AwHn/9PH/nfW86cP074U6M+14dj5098jspg4h8HPg4wMMPP3wHu95NXlScK1fCTr1R3G4yt79Rttuc3iznB9Fupm+a2e2iz5bZlj/dyey2dXt6zzo7yrDjOEQ1mHfnjRm6tbwZD7eAmfXotK5femMY4lN9hGCmvcWpAXNBB8HHKs5P2DOq4PC9HjcbBGOFyPmyxgrG+TRu+vThgyDhXISfytQrHn5gNPgalK2yDrd1Zhyhxy/i9Sd+OxhvXIz6fqoRtm4SZsa8IDZoWjEmuOcFjLhwPryVEBqUBqGBadAmbMPhX0p36NT8ijdiFsGiWKAQYSTQiFBH/smnEUMdR1gRrES4KEZFUJOAxLjg2hIVPz6AhPMj/uakstW7F/D2UR3iFJwgdQc0B1kMR6mh0PZJRTcfpsUfhUyPBqaeOCdT4+w1Ns234QHTiWKlxkU1Lvb1rFEa42iMf+rS0LA0zObX6akNF5OKhLSg4s+vj2soK1hR7HS9RF5vEqMShRD7pzaJQQ0RGZFmxC71f7/gUmKXEZMhJMiWM9BPvAyN6sRyJ8b9EvDQTPpB4MotylwSkRhYYI/bmqp+BvgMeJ/7QQT+R5/4+YNUa2lpafme4k6e978CvEFEHhWRFPgQ8PkdZT4PfDTE/xzwP1+L/vaWlpaWk8Kr9tyDD/1ngC/ipy78mqp+Q0T+HvBVVf088C+BfyMiF/E99g8dpdAtLS0tLbfnjl5iUtUvAF/YkfcLM/ECONgfJbS0tLS0HDqvvWkYLS0tLS2vSmvcW1paWk4grXFvaWlpOYG0xr2lpaXlBNIa95aWlpYTyD37EpOIrADfPWD10xzBXxscEverbK1c+6OVa//cr7KdNLkuqOqZVyt0z4z73SAiX72Tf0W7F9yvsrVy7Y9Wrv1zv8r2vSpX65ZpaWlpOYG0xr2lpaXlBPJaNe6fudcC3Ib7VbZWrv3RyrV/7lfZviflek363FtaWlpabs9rtefe0tLS0nIb7hvjLiK/JiLXROSZmbwnROT/isjvi8h/EZH5mXWfEpGLIvKsiPzpmfz3hLyLIvLJ45RLRN4tIk+G/CdF5E/O1PlSkOupEM4eo1yPiMhkZt//YqbO20L5iyLyT0Vu99XkQ5frIzMyPSUiTkT+cFh32Pp6SET+l4j8PxH5hoj8bMhfFpH/LiLfDsulkC9BHxdF5Osi8taZbX00lP+2iHz0Vvs8Qtk+EmT6uoj8tog8MbOt7wQ9PyUid/WR4gPI9S4R2Zg5Z78ws61Duy4PINffnJHpGRGxIrIc1h2Hvn4ypJ2IvH1HnaOzY6p6XwTgx4C3As/M5H0F+OMh/jHg74f448DT+E8gPgo8h/874ijEH8N/uOtp4PFjlOstwOtD/AeByzN1vgS8/R7p65HZcju287vAO/EfJ/ot4MePS64d9d4MPH+E+joHvDXE5/AffX8c+CXgkyH/k8CnQ/y9QR8C/BHgyyF/GXg+LJdCfOmYZfuR6T6BH5/KFtLfAU7fI529C/ive2znUK/L/cq1o+778N+bOE59vRH4QzvbNEdsxw7lwjmswA4jBGxyc1zgIeCbIf4p4FMz5b6IN1DvBL44k7+t3FHLtaOOAGtAFtLbTuwx62tbuR2N8Q9m0h8GfvUe6esfAv9gJn3o+tqxv/8MvBt4Fjg3o49nQ/xXgQ/PlH82rN+mo53ljkO2HWWX2N6J+A6HZKwOoLN3sbdxP5Lr8oD6+nXgLx+nvmbS29r0Tj1wyHbsvnHL3IJngD8b4j/Jzc/97fXR7vO3yT8uuWb5APB7qlrO5P2r8Pj383fr/jiAXI+KyO+JyP8WkR8NeefxOppyL/X1QeDf7cg7En2JyCP4p6wvAw+o6lWAsJy6f+5JG7tD2Wb5KfwTxhQF/pt4t+DH74Fc7xSRp0Xkt0Rk+gXoI9PZfvQlIl3gPcC/n8k+Dn3diiNtY/e7cf8Y8NMi8iT+MacK+bf6IPcdfaj7COUCIDTqTwN/ZSb7I6r6ZuBHQ/jzxyjXVeBhVX0L8DeAXxfv975f9PXDwFhVn5nJPhJ9iUgff3H/NVXdvF3RPfKOtI3tQ7Zp+T+BN+5/ayb7j6rqW/Hump8WkR87Rrm+hn81/gngnwH/abqJPcretc72qy+8S+b/qOrs953vpb6OtI3d18ZdVf9AVf+Uqr4N36t7Lqy61Ue77+Rj3kcpFyLyIPAfgb+gqs/N1LkclgP8o+E7jksuVS1VdS3Enwz534/X14Mzmzh2fQU+xI5e+1HoS0QS/EX3b1X1P4TsV0TkXFh/DrgW8o+1je1TNkTkh4DPAu+fnlsAVb0Sltfw7fCu9LYfuVR1U1WHIf4FIBGR0xyBzvarr8Be7ew49HUrjraNHYWv6S58VI+w3Vd7NiwN8DngYyH9JrYPRDyPH4SIQ/xRbg5EvOkY5VoM+/zAjvoxwa8HJMBvAp84RrnOAFGIPwZcBpZD+iv4AcPpgOp7j0uumbxLwGNHqa9wfJ8D/smO/H/M9kG4XwrxP8P2AdXfDfnLwAt4X/dSiC8fs2wPAxeBH9lRvgfMzcR/G3jPMcr1Om6OrbwDeDFs41Cvy/3KFdIL+O87945bXzPrv8R2n/uR2rG7uogPM+DvqFeBOlzsPwX8LH7E+VvAL04bTij/c/ge4LPMzPDAz3L4Vlj3c8cpF/C3gRHw1Ew4GxrOk8DXgW8Av0wwtsck1wfCfp/GPzq/b2Y7b8f7xJ8DfmVWx8d0Ht8F/M6ObRyFvv4Y/tH26zPn5r3AKeB/AN8Oy+lNT4B/HvTy+zsuyo/hjetF4C8dQhvbr2yfBW7MlP1qyH8snOOng97uqv0fQK6fmWlnv8PMzYdDvC73K1eo8xeB39ixnePS10+Ea6EEXmH7YOmR2bH2DdWWlpaWE8h97XNvaWlpaTkYrXFvaWlpOYG0xr2lpaXlBNIa95aWlpYTSGvcW1paWk4grXFvaWlpOYG0xr2lpaXlBNIa95aWlpYTyP8HqNy2kknMOd8AAAAASUVORK5CYII=\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_ISSM_JPL_SU_RCP45[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_ISSM_JPL_SU_RCP45_alllines.pdf\", bbox_inches='tight')\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "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 }