{ "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": [ "# IMAU_VUB = IMAU_VUB\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_IMAU_VUB_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_IMAU_VUB_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_IMAU_VUB_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_IMAU_VUB_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_IMAU_VUB_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_IMAU_VUB_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_IMAU_VUB_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_IMAU_VUB_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_IMAU_VUB_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_IMAU_VUB_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": [ "0.52415 0.5263157894736842\n", "0.8959 0.8947368421052632\n", "0.5218 0.5263157894736842\n", "0.47755 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_IMAU_VUB_R1_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_IMAU_VUB_R2_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_IMAU_VUB_R3_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_IMAU_VUB_R4_RCP45 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_IMAU_VUB_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_IMAU_VUB_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_IMAU_VUB_R1_RCP45=np.vstack([SL_wTd_nos_base_IMAU_VUB_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_IMAU_VUB_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_IMAU_VUB_R2_RCP45=np.vstack([SL_wTd_nos_base_IMAU_VUB_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_IMAU_VUB_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_IMAU_VUB_R3_RCP45=np.vstack([SL_wTd_nos_base_IMAU_VUB_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_IMAU_VUB_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_IMAU_VUB_R4_RCP45=np.vstack([SL_wTd_nos_base_IMAU_VUB_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_IMAU_VUB_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_IMAU_VUB_R5_RCP45=np.vstack([SL_wTd_nos_base_IMAU_VUB_R5_RCP45, SL])\n", "\n", "SL_wTd_nos_base_IMAU_VUB_SU_RCP45 = SL_wTd_nos_base_IMAU_VUB_R1_RCP45+SL_wTd_nos_base_IMAU_VUB_R2_RCP45+SL_wTd_nos_base_IMAU_VUB_R3_RCP45+SL_wTd_nos_base_IMAU_VUB_R4_RCP45+SL_wTd_nos_base_IMAU_VUB_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_IMAU_VUB_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_IMAU_VUB_R1_RCP45\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_IMAU_VUB_R2_RCP45\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_IMAU_VUB_R3_RCP45\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_IMAU_VUB_R4_RCP45\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_IMAU_VUB_R5_RCP45\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_IMAU_VUB_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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jpBsvcEy7CRk147H20WFt5qZaH8KRwvDSEAZjgOBEFvrECdq6ZuKP5JDkycNnHCfNk0dYa0ULt7M4tZ03dCl4S2+N+XpUuCuK8ok36BrDGvgjI8FSpgwuwByWtBn0CKIsTFxPe1wWo31LCVqG6Xd0UHqxgwwnmCruwJX9JoGxXJKyj8KAHbN7HpFwBEMkDpd1CGQY6T5AvEki9FGcpDPZlRjzNalwVxTlE2/z0Z+TaB5nSuG/4j3cxwEZRjcaodxykETRQbW2Dhmx4rF30mlu4NYTUXQpJbTP7cXk8BAcy0WX4KWtfyaNWEnwFDBi6yFrophg8ChIH9dknGWDMZ0H37xA0cE3Yr4mFe6KonyiNfW0k2N4nZHIIrKacpCaRp9Oh14GWZz8CueS8hnrXEHQPMy5lHqW1Dmx+yTGaTcTSd5F2JuII62e8FA8CcOrSPRbEVKPNHuJRAcR/noKHZKWiIPltRrb5xl5Zan6gWxFUZSYkVKyv+YnGHURFhX/C76aQbbIEHaXxvS4HZjlEKdD16FFzYzFX8CltXBDrcSQM5ezBccwZLnfedfu8OPumcMxvRubL5vOlAZSRouJ+Pag01m5KvUU+6J5eExjPL9SkpIdifnaVLgrivKJtbP+KOWO/fiMN2M5qiOAxB3VY2aCuQkbqUsrZax9BUHLMLWZR7mxOohB0xGZPoe4xFqiQRv2lBa8g6kMja4g351JRBciP2oiEqpDREaYkRLkrUgG150Y4PEbDWSPSdYeyYj52lS4K4ryiRQIR2lv+xkhzcLynK8RbBrltWiQRI9kftImJD4aPWuRmpHB5AasY90sbYhiLF7FhfCbUO4m5MpEWMMkdFxFl3RiCiUxlHka6cxA8x3DZMylzF6Huy+Hl5eGcdk0vrY5So0pPubrU+GuKMon0gsHN1CeeBZHyoMEd7twCYkuqMehH2KKZTs1mWVMtC8jaBniSOY+7t8PwhTHRJnAUTiGFjFiSerEN5DB5lAuORMleM2jJIfyiPj2ADoWpQ/zPGWY/e0cqtJxyxHJ+aLl5Iyr+9wVRVEuu+7Rcaz+3+CJZDLbdCfhzgk2hYPEBWFx8nqCJh3nhtchpZ7W7GqmXpigtDeCsXINvpZDRKc7CY+ngiGKteNGEj1W9FEL8Wm1GHqdyEg3cfYZRHUNTKkL8dRaHcVDcPVpCw1FpSwbrI35GlW4K4ryifPqwcfItA8yqfQ7TLzVS79OEufTk2poo0S/nyOpZXi7FxC09nMy8QD3HRDo4nNxGusIrkgBJKbEAfQDOTwnJ0j2FOOO76AzMIeo/210hjzmJ9Vz0D+T9QtHiRjgmxsjvLT2Vv6j7Tf0XWeP+RpVuCuK8olypOUCZbYNuOVssnunER3xs8cXwqTBVckvMB5v52L37SAFZ0r2se5ElER3BCorMJ/rRZvWTdTzzkNINUOLqBqZggTCWS7S2w+A1EhyzKTF2AeeJs4W6bhnn8AVX8wK13FyCsboFOq7ZRRFUS6bcFSj+sx/YtaHWDLlB7j3dnFOL3EEdOSZ6sjWnWZ/YhWRwakEbD20c4KbqyW6rOl4Oo4wdNdkdMYQ+rgxzL2lnPdFsfuz0KWeJdBlQ4u0Y7AuocK+B9mWwstLJdO74epTEXYuXsIN8YcZMNhpiXhjvlYV7oqifGK8cnQv05LfRsTdhuGEBS0Y5ex4GCE0liQ9T09aAl3nbwfg6OTt3LsPjJqOcLoXnWaHyiaiXhtC0/GYL44pA0uI6P3szS0muX8PekMqybZ0Lmg6np/bgTUCX90Y5a2Fy/lW+7OYzVFeMcxGF5kU87WqcFcU5RNhxB3AN/IoIS2Oxflfx1PdxymhYYoIJlv2k6hrY59hATpXMV5HJ4w0s6Qpiq5gBqKhlYGby9CbfejtPvqGiynqn4kx4qC/tJ85J94GGUJvu54sy24aoxfoShfcv9OAEHYMjiil+f3UGnIZC8zGI9RDTIqiKJfFc/ufpiThItl538C3a5SITtAzFsFkCrAw/kXOZmcxcv4GQLK7dD0P7hZgchAKnMUzfx66ySfRAmZESM+LE5A/MpuQbYy2iBGz5zxW61SSTX4GvV62zwqyvBGWNAV4fcVa7tO9QVjo2S6X8lblDOzZppivV4W7oihXvJr2PnL1f8SrFTFZrCNwbpRaf4QIgpmm1zAaXRxxr8LkzWEsuZXyi30U9UfQcpLQTQhGyk0YLF50liC7JuJZ2HYbQhrZNsPCvFPbMejj0czLcZiq+UPVeTLGBXfv1dNUWMqNg3tJSgiwzTiFs+nz6UgrZJ95TczXfEnhLoRYK4RoEUJcFEL86wdczxdC7BdCnBZCnBFCXHf5S1UURfnzRaIaO449Sop1jDlVP2R8Ryc+o44hr0Za/Cgz7VvYlTGNQOdSBBH2Z7/AffsEODIQXV24lq3DMPUEWtiAP2TkYl8JKe4SerM9TKs7jNACOBwLSTT0sSOuBpdd8LmdZuL9Yc5OKmN+YTM9Ip6juus4WlqJXbrJabXGfN0fGu5CCD3wOHAtUAncLYSofN+w7wIbpJQzgbuA317uQhVFUf4SLxw9wZy0HWjmlcR3FBEZ9FHvCuKywFz+hMtqoLFvFeZgGr3pDVx9cpR4bxRNP0ZgTjEjKYMYrG50xgivuPQsaPsUUX2UY5l+cnvPkmzPwC+mMBi/hyOlfm45BjPbvGxbvJKHQq9i0mts1K1gd8UsdEIy+bxk+sVXY77uS3nnPg+4KKVsk1KGgPXATe8bI4H/78sSEoC+y1eioijKX2ZgPMBo3yPohY5FU77LxO5OnDroCUumJbRSZj3CG/blGJ1TQPppiFvPDSclWlIqBCP4dFdhnn4SGdXRHTAQd+42rKEkDlbCNYfeQK9PwGBZQtTayPriOiZ3S1afttCbmk6Vv4Ws9AkOGYrYn7WGoYQMEifg+jo3k82TY772Swn3HKD7Pcc97557r38HPiOE6AF2AP/wQS8khHhICFEjhKgZHh7+C8pVFEW5dE/ufoHpafVkZH+J8NEoUW+Ys+NhnOk65kZ+R31cEQN98zCFkmnNPMln9noRwoRwjeBet5aB0n6MtnGEXuPN4UQqhhYxkhAhqe8gppCX8iQbYzKLnQUvo49KPrPfTrLHw7FpM1mZewq3MLPNeDvHiyqwaV4e2N1HMBpgu84S87VfSriLDzgn33d8N/CMlDIXuA54Xgjx315bSvmklHKOlHJOWlran1+toijKJTrY0k2Z5UkCMo+K5PvwHO6jK6xxlihr9YdJMbaxK7KMOG8BIjqBN7CZqZ0STBLPVWl4m5OIm34IKaHep6P8zFfQaUZO5g5RebGeFHsuTlZQn/cqXUluHnxLUNzv4eDMeXzavwWbJcwW/WK2Vi7CICLcfNCLPqQRDL7AFF9rzNd/KeHeA+S95ziX/77t8gCwAUBKeQywAKmXo0BFUZQ/VyAcZe/Jn5NuczJ76o+Y2N5FRErO+CMY83XMjPyRTY5lhF0lGEJJNGcd5LN7A0TNFqQphN54PcOz3Bht40jg1IUlZHhzqSuIsODkFoQ+mSlJbhrjXJzIqWb1KY2SwQTctjjS5Aj5eWM069J5Me9ORuOSKe2UFA2EcYdep+rWFqKrXTHvwaWE+0mgTAhRJIQw8c4HplveN6YLWAUghKjgnXBX+y6KovxdPH3wMIsy3kRY15AwXEHwgotmb5Q9pjB3+F5lwgSN7mnE+3Ih6iS7azfJHtCF/DhvX0P/QIDEaTuREo5OmJnedgs+s8Dg2oc16Gd6iqAmvIL9pc+ROyy5+lQSmWNOjkybzeq0GqQOXrLeQ11+OYkBPzdXO/H6tpE7pQ1rfJBQmzHmPfjQcJdSRoCvATuBZt65K6ZRCPFDIcSN7w77J+ALQoh64GXgfinl+7duFEVRYq5jxENo9FEQBhZN/R5jW1pxa5KjWoQF+QEqeZ1XjNdjCaYiQolcSN3FDcejaAY94+uSSDiRgnuhF6PVTUjCUM1XsIXjOJPZQVlHI3ZbJUmWCFuLdxLW+fjqVkHmuJvTZRXc6N5FQkqA3fq5vFy1GpMMct+OYcK+Q9gtrWTMcjLeUUlWY+y/FdJwKYOklDt454PS9577/nv+3QQsvrylKYqi/HmklDy151lWpDeRkfswweoI2niIOn+UXfYIG1y/46i1Cpc/nXR3PlGtn/mnj6ITgkhmGJO8m/N6PylV79yquLe3kvKxSXQmB5jSsA3NGM+a9JP8wjaL7qSdfH5nFGHIQuLEZApSOGmMXpHAo8UP4THbufGEC9tEA5FQI/abwoiwjsSXEsjJ6Y15L9QTqoqiXDE2n25lmuNpQqKQ8tRPM7Gvm96QxiZDiIezWknQNfB2eBGJ0oaMJDBo3cGMDg2h03DecA3hhlaiCwfQmwKMRfSY6+8HTUfEtwtjJER5Qj6njekczt/DjDYL5QPpFPf2caxqBquSajEaIjwZfx9N2aWUDAepamkiEjiKa34qBcldGJ8tJ6unlhHdRMx7ocJdUZQrwqg3xImzvyTFOsa86f/J2OZOolHJCSHpjg+zevTXrDfegNT0WIYmEZYdrD1yCk3AyJ3J5NTmcz5rCqkV72xS7Kr/NDm+JC4knyen/yI2+zRmJ23hsdxB7EEzX9geIGXCS2tOPqvGjpGU62e/fi5PT7keWzjAbbsbCfl20ZOdx8yKMxj3JpJZd5ETFdP5P2vvjHk/VLgrinJF+OVbu1iWsxtrwg3YnGWEW8a4EIrytM7HY/E7OWfIYCCcRaaII0ocwreRFA8EKjRM/s/RM9CBZW4NOkOE9ol0SnoWMG5yk9/+FjpzAstSzvHDlGxcFidf3mFiID+fOJ+PYJyeghlOhoSD71R+g6hBz2feake6dzDqSMGwUk9a+wQpGwNcyCumoK+Lz255Nub9UOGuKMrH3sGWQbL5NQgrsyu/w9ArLXiiki0OKE+aoHBiO7vlUhLto0R7pxHWzrD6ZCuaTTK8/DoS6s7QXrqApLL9SAmNp7+APaAnENiGXmrk20toTDvDseRBrjo/BYvRytSmNk5WTGW5ow6rOcwvUx+gIzmPNaeGSOjbjM9kYv9N13Ld6C6SnjAzmpiKhsaX/+3HvHj9t2Lek0v6QFVRFOWjyheKsOnI71hX2Epp2U+YOOhF7wlzzqZna8DNAfMfeEO/GoRGiruMCXSUXngNkPTen0bxmUk063pImrsdnU6jpns+RUOF9JlPkDLYT7p9LiVZL/H51Diyxwu5/dAgppCPnrRMFjhPk7Tcy0HjbJ6puInCoQmmnnqNiAjy6g0P8p2RR0n9g56wsHCmqIhDs+Yz7ohnxpgv5n1R79wVRflYe3zPcVblvIbOMpts+434DvYwFJU8bgzyUHIjfWGNPi2b/IweJgYrkOGjlPQ7cS8F29ADTLTsY6B0KgkFJwhreoLNdxFkgOTBw6TZiihOPsf3Mg0YolY+fXgy/fnxJE5MELTqyZ0/xjh2vjHt21giIW58ayMyOsIbq+5kWaiayRv60I3reHH1tUQNRg7MXkip28f08bqY90WFu6IoH1sNveNo47/AbIgwf8ZP6XqmCTRJfUkcXZ5x7vKvZw9LSE7pxnB+JUILsLB2G+EsjaGKm8k800BLzioyFzyFEHD8wh0kj5mIeLZiMdixmOzsKDxOp0nHmpZ70LIvMqu2hTNlk5mXeA6HNch/5XyRQXsat721H7PvIrvnXI8rO4X7tm3E2KHjqRtuZUXtcX559+dJCkxg7WmmlZSY90aFu6IoH0uRqMYTu55lTkYd+flfxVtnxjzkoy/ezM96h/h/UzayW8xDZ4iQl2DE5cknwbkLq/TQ/uliJp+vpNs1hL9Ijz2tjfFAIo7GFbi0/ZhCbrJsUwgUv8kWh51ZPWuZOtJDSXUffanpzBpuJLnSwxHLTJ4tvYklpxvJ6j1CbfEi6ucs4Hev/BB7vcbBpXNY0HCGX97zACGTgZzONq4+tIX4wf6Y90eFu6IoH0t/OtTA0oxn0PQlFGc/wPjWNjwSNucZyZE9GD1D9MtMiktO4K5ehS4yyvRz++n7lIm09vsJ1b/GhdKbyVr0e4SAxsYvIgIXsU40UuqYRSTlNL9Jj1AwlsOCjkWEIm0kjbuQaKQuGyeAhS9P/w45Q8PCxIj8AAAgAElEQVTMO7mB7pRK9q25lp+sf4yM4yP0zUyhy5ZHY/EkGkonU9TTydUHNzGQUUj8bH/M+6PCXVGUj522YQ/dnb8kyTzB3Ok/pfXp81ikxDk7jfVNfTxieYG3xTxS0zpICazFraVQenELgRkhJvSfJrOlifbEKVjKmrDYx7jomknShQwi3l0kmTIZMnbwYkkz5oiJZa1fwpC4n7k1DbQUFFGR0U6qxc9/FHyRcb2DdTufwWvJ49Wb7uD2/btZcKgaX7mO3yy+m/kNp3nq5jvJcHaxdu8GwtYEdi50cNx/NuY9UuGuKMrHSlST/GL7BpbnvU1qxj34LmZj757AFW/i0REn/5iwhepIJXpDiKLCTrqPlGH2dZERPsmFpfOY3l+Es+MIXXnLyZz9AhGpx119D77AmxikRijeTFPFaTqNBtY03EGCnKD8cC0DyalMG20hfaqHo7YZPFNwM9fufQNb0MzTN95DVfsFvrTpOcI5Gr//1Dq+/Mqr/OCLX8ccGOeGXRsxaZLtyyezbu8p5p2L/XfLqHBXFOVj5ZnDTSxKfQJNl01ZwT/hfO08USFoW5JOT08rkwMDDJJO2aRqzM33EzTEM/niRlrvTaKk8zZC9S/TUnorabNeQW8IU995F4ahc+iDPaSlTsaZWceeBLimtYTU4ALiBnaTPD6GUQsRf40bv7Dy0PR/Z3pTLWVdXTyz6m5Swh4e+d2jyPgIJ+7LZ/HGDp6/7kaGE1K5cfdGEt1jNM5fxNJDB4n3mijoif3vWahwVxTlY6Nt2ENb+y/IsI0wa9ojNP7hIkmAbnE2Pzncxn/aXqJazCA9vZXs5KlcPJdGsrOB6PJWGPgMaZ3nGdIsuNNySSp9m6FwDtajxUT8x0h2FHLecpLX8saZM26iZPBeHIFjzGyooSM7h+yKXjL0AR4u/2fwhFlxbC+bZ32KaKqZ3/7sR+h1QQa+LOg8Nx2P1cye+StYe3AT+f0dULaCjMbdJHktGDLLMOtGY94rFe6KonwsvLMd8yrLcw+Skn4PI/W5pA54CcWbed0UZnVkCw2RCswmH0XFZxnZvBBNZyRf9xptqVczYzyXiabXaZ50GzlX/RqBpO/Y3QS9b2Iy2qlPaefQ5EEyw5I1J1YjIgYmnd3MQHIq5YFWCgo8bEtaxo6Uq7hp5yucybuBzqpMfv2LH+Pwuxj9SpA9rGH5/hP85/1fZOnxN5ly4Qz2nCWMjhwkZcKAyC7HEFrBuGF+zPulwl1RlI+FZw43syjlCdBnkpf8DSZ2tGPQCcRtxbx8pI4p2ijjOJhUeYj0wFfoDWaQ49xP29VxTBtZSeDsejpyVmHMdGJLaeecczm6i00Izc+FQj89ud149JLP1M3EFb+Ugo4XsQT9JARcxF/jZVSfyLcqH2btgU2ETPM5eFUZ//Hkr8gb6Gb0fo2mnBJu+E013/zW15nWfIr5dUeJZs/CJtpIHIsSLJiMITQPXdTGicrMmPdLhbuiKB95bcMeWtt+QYZ9mJlTHqHuqYtk6QXWpTn84HAr/2R8gxZKyM1tJDkhjeZNDoxhL2LVIRLaP0Vydytu1zA9ecvJWPJ7/DKO6M4itHA7Y3l23NZ+WuIlD/WmoiVPIbO3hvy+BkaTE9BdM06mFuBrld+hqKWJ4sE0Xlo1nW9ufIY5zfUMXZuKe6Yk41n4xZ3XkjwWYNWRnbiyJrPK6qCvr4+xihlYQ5WYQylEfXsp738q5j1T4a4oykdaVJP8fPurrMg7QHLa3VzYn0qBJ4SWaKYmx0JO3wbaI0UkWEfIL2xAf+Buxq0ZpKduYWR0LdO9qUycfY7GKXeTVLUVq3mCrupriHhOEIlPoNM2TG2un9tdIVLcswl1FlDSupH+1FQSMnqZZh7n+Yx1NGu5rD7l5MmVC7jr6A5uOLKf/qpKout66a2fRGO8nZ6MaVy/91VG0vL4lH0xx3sO0jt9MQneDGzefCKheoxxJ7Bl5Ma8byrcFUX5SPv/t2MySNZ9Ce1YP2adIP6uSTy+fT8FUT+aEJRVHSIt+GnOdVmxaV0M5BiZ55vFeMNzDKTMJZiURGLFXnpHJxNs7EEaTNTlD3GuYIAZgSBz2tYxRBUVLc8R0etIDg6SN9VHrymDHxc8yC0HTvPirMUs7TzJg1s3MJg9g+CDTQx4sknc3cOG1Xdy886XGY9P5trkW3BNbODU9JVkuI3ETUxCi/agN7yFrjSPk+PqbhlFUT7BLg65udj2SzLtw0wp+Ql1z7ZTYNZhvyqHxxu6uC2yhwHSKSw5hc2YQv+LSQTNicgpRykZvgprdzPaYBdtZTdiW/EUQmqMbctH09zUT/IylDyAHY0vdlbgKtHIr2sgabyTsB0Ct4VI0YJ8tfK7LD96hpqsBeTSwcMvPMlIUjmD90+gM2qk/9HFj+67n1vfeoOw0URFwR2UBI/yx6J55LuCOMaqIOpGRjdhqEilb6KQBJvac1cU5RMqFNH42db1rMzbT3LandS+6mAyEpFgZnh6CtT/ka5oHpmODtIzW0k+cAMdcWXYkuvROauY5E3CffZZzk29jXDpOXIc5+jYv4CIv5vhLAcTFidOu+T/6Y/SZZtJ8GQ5BZ1vMZiRxPDVHhYExvhV/r1Ee/TowxX40kf40ZO/wG1P58I100nNPUfkYBzfX7ea6w+ewhgJE5x2G6smAjyZGqXQ5SZurAJ9VBINvoZxqo4eVyV6JCIqY94/Fe6KonwkPb6vjlWZTyAMOUS6P4OjawK7TpByZzmPv74BY9hEvH6CoqnHSBm+jpYLBqReMm7ysjhcwVDjk4w7yhhIm0LWzPX0d+XjuzhKxJZKZ+oAbRl+vuUcZ2Tky4x47Mw4+yJeexzhwiFW4aLGUclL9muZ15LCqVIP//X7RwgarTTMeoDUhW/gc8bxRHwWC88ESBwf5dSiW/jyQDot1v3oPDrixkoxheOIBLZimu5haGQuQhfBEOfFkdYd8/6pcFcU5SOntnMUz9DPSLKMU5j8H1zYMUiJRY99fiav9fRQPNZIEBOFldUYwhl4X5CMpFQRSW1mdrSMSN8pzL0dtFTdiX/lZuy+CYZ2pYDeRlPxCE25Q1zr8VLedy2jGYNUHa3HHBwlkjpBUUmUqN7At4of5sZqHdsrI/z0qZ8hpEZj1bcwLl+PVe9n33kDuYMlZA/2sGfZzfy4Nwe9/m0OywQs41lYw5lEg/sxT+1hyLmYkCmE3hwiy5PFrDFvzHt4SeEuhFgrhGgRQlwUQvzr/zDmU0KIJiFEoxDipctbpqIonxTeYIQnd/6JRdknyMx4iCPPw2yHAX28ieDiLHoPPo2LRMrT63AkjpC2qZyW7KsQ1hES9HoKPA5C9S/QOvVamqf7mW3dS9uOCmQ0QmeJiabMTiqCYb7WH88Z3ULEWRNZQ7U48xIZXaajVBvl26XfYl5NHG8Wavz4pUexBzw0TflHvFOPk5fRzIUmHRPeJZR0tbJ7yQ18eyiB+IiLvYY+NG8ajkAZ0WAdpoqztHqmETYH0euj5EUtBAZmsIWPwAeqQgg98DhwLVAJ3C2EqHzfmDLg34DFUsopwDdjUKuiKJ8Aj+w4zHX5z6IzVdK2ZzmFUQ27hOTby3nipccJRO0UmVpJmdRMYvMc2sdSCRpS8MR1s8w/nYHGXxMwZVJdtoppZU/TeyKH4GgEd0YBZ9LrsRHl54OjHBx7mJ7oKAvqNuFMyaZplZtrQ328krEWT+8s6uM1/m3rr0kbG+FM1ZdwZvkomr6LiWEdx5wrqWxt5dDcVazymZnsS2PEuJUmrZgUdyXRcAfGsrc5LlIxCxMGGaXQ6iPQvZjG7L00GJJi3sdLeec+D7gopWyTUoaA9cBN7xvzBeBxKeUYgJRy6PKWqSjKJ8GexgGSwo9iNUaxTjyM74KHYqMO+/xMNrefxORykcAE6dNOo5/IJLJhlJ70Vfjt3Vwty3H178He3c2xJffjWXKAhM5RRs840OzF1OYeYcIe5bGRYc6PPcC5eB/XHdpOxGCmZYmTTwXG6LRk87zpfkLuMA+8/XuK+rqomXY3roQc7Mu2YJZ+3rgwlxnnuqmZuhCTJYV7xoow6nfzgqmM3OHJyKgLY8EO9lgFub4CDFKjIGkI/4Vr6Uqp5QvB+Xxp3BbzXl5KuOcA793973n33HuVA+VCiCNCiGohxNoPeiEhxENCiBohRM3w8PBfVrGiKFekEU+Qzcd+w9TUZtITvkH91gjzk00YkswEFiTSVr2XMAbKio9jNIdIfjJK0+w70ESE0rgI8e4AxrqNtMxey5tLHKzxbqZzbx7CkMbZ/Ho6MoJ8d3SUeH8Fx3RLqWw+QYK7n47pKcwxS+Lw8W95D1NwQc+ahueY2tbC0Zlr8SfMxTnvIGW2c+ysL2Ha2Qkay6bTXjCFH3cnYBADvG4dJ6+vHDSJIfsNqq1+Kl3TMCDJTe3B33wLA/Et3KwrQkMDrzHm/byUcBcfcO799/EYgDJgOXA38JQQIvG//Scpn5RSzpFSzklLi/2ek6IoHw9SSn66dSvXF7yG0bKI0xsqmZFkxhTWSLytjKefeYygtDLLcQJL7hCJu9PpLp2MP1BCNKmd2YFS3GcexWPP4MmVt3GT/ff07M5Ei5rpynVzumiUu0Y9XO2Ht0b/hTH9WWadO0x30UzI9DFd18GjeZ8jtyGfSd0bWNR4mgOzZhNx3EBPdj1LcrbRdC6FrBojrfnlHJ2zih81j2AVCXSbNhPpm4vQ4tCnb6bH6CTHOx29EGSkdhNqvo1Raz/LzQkYpZ5vTfp3Ds/8Xcx7einh3gPkvec4F+j7gDGbpZRhKWU70MI7Ya8oivKhXqpuYXrczxH6eJwnPo8jFCVH04hbnM0LR14iHNRTrruIaWo7pq5kIu0BOrU78FuGuTlSwUjnnzD2u/njHf9CVsFuzAfdBEbNuDOSOVh+jtnjIR6eGGXn8LepzvVw7471uOIL6Swa59q4Jg4kzaW/61pS+zexpvYIe2flg+PzjJhbqZi5hfEeC/5DmfRm5rN95e18+UwjFdokjGIjx0aWootmoUvaAcYegnIKJkwkJ/cTPb8Oj97L7LgoWdFkvlv6n7iJMDGY9+FN+StdSrifBMqEEEVCCBNwF7DlfWPeAFYACCFSeWebpu1yFqooypXp3MAEba0/ItM2jMn1MEPnJPOSzRhSrVxI76O/fYBkxkivrEcf1RG3cYKz5fejaSYWWWx4PQ2Yzx5nx7r7qZkeZVXdm7haEwglFrF7UjWpQY1fuYY4E17J7owq7tr1HALB2YoSrs9uwWlM5KXo18ns2MrN1XvZNz0ZGf9PBHVD+FccJ9kzSseeIkYT09i49jPc09TETb5yDFxgozsLfbQcneMgiXHtNBtKsEfjcMSPoO9YTVATTEocpjJUxHdKf0K/8GG030Kia1nM+/qh4S6ljABfA3YCzcAGKWWjEOKHQogb3x22E3AKIZqA/cDDUkpnrIpWFOXK4A9F+d2bv2VRdjUm/b007UplaYkDnT+C/poM3nxzF3o05mQeRCT7iN8ILSsXEXJWkuAYJStqQtQ9QWtRFb9dtYKH2h9n8EQK0prNgUkHiRgkvx8bxG9IZHPkG5S2bqSor53GSeuYUXaK9MgojyT/M/n1h7i1eif7p9kJpXwHnQhweHkvi0JHadlRjMcaz6vX38+1nYPc6oxDL/XsnGhBi8wHy2niUy5wjFSSwynY7BNY+ucTDtrJS+pmdmgK3yl+hIv6MZLNcyk9M4ouOh7z3houZZCUcgew433nvv+ef0vgW+/+KYqiXJKf7djD1TnPoemm0rxpCZPz43A4A9iX5vC7bX9EQ89a824mSr1Y68w4ZzoYarwdzOMsF1l423+A32Pke//8L1w38Tr+g6Cz2qgurWUwUeOxESc5aDzf9T1GMk5xz9F9dOatxlTZzLxII49lfo70493ccnwbB6tMeDK+gyOiY8MCD1/QnuHim4V4DQ42rPsclRMRlvYMkhmt5LhrM26uRZhaIbuFM25Bij4Hk8WHY6ySiCedxJQ2FoWn86O8X3PG3E2uLp+Ms4KzRYcp6o+PeW/VE6qKovxdbK3rIIefYNAbGT78IHFmE5MjUQwZNjb1bScUhPnU4q4aR+/RoUWiNHU+iNBMrDQl4fe8itYwyPe//lPM5m4m7TkF6GjO7qcpL8Q3nB6W+nwcv3ADe8rj+dLrzzCWWEbPlATWGfZyIGEenppkbjm2kcMVeoby/5W4cBxbq8a5UfyC4bdy8AoHr6z7PHadjaXnTrI4XMIp1y4GWIMwDOMquMjE8Cg2fSkGU4gkfx7aaBG65B5WR6r4VdYfOBp3jnyRQN6ZVJrz63D4onzusPqBbEVRrkDdoz5OnPkxhfE9RLq/ynifnWX5cchQlItlQ3T1jJBHL8klLWAPYW6UnBbXYx4vocQiMcvzRE7u5ck7v0JdQRL37voDoQkjvRkaxyaNc/NQkM95x2gbyuGJqffy0MbfENVbaZqyjrXpGxg2JVPdvpxbD7/M8XJB26RvkuJL52TBMCXmJ9AfSMErbaxf93lCtkSuqdvPtYFSzoydoFvOQ+gC9BR1kNrfRMBeic4QJSWSgjZYRTCpi5uj5TyV8SK7E0+RK0wU1RTSUNyEIRrm4U0OrJFIzHuswl1RlL+pcFTj51v/xPLcfYR96+ioLmXVvHTo8RBd5GBPTTVW/FyVvA9PThRzveBCbjmhzmWYDWEmGaOEzv2WXVOW88qyJfzD278h2G/AXaixq6qbuUNRfuAbZMRv5XdJP2BV9bNkjDpprHyAqkkvkB528qr7Dm7a9QKnSgSnZj5ArquY7uQhnMmvU1ZtJBgx8/IND+JKTOOG0/tZ5k6n3XWBDlmG0Bm5UNJLVf8hehJmodNJ0rGj9c3Gm9TBnbKMF1I3sSX5MKnoqagu4sykNsL6AA+/ZiRryMe2ojUx77MKd0VR/qZ+s+ttVmY+QTBaTNuOa5kxJx3bxTEMZfE8X7sRgeRW43a6y3UY+qA/J5Xm5muwRuKYb7EQdT7CWYp49NMPcuuprRhbRgkX+9lW0k3BGPw8OkAoauDN3nvQBRpZdPYUF4tvwTrjKAt8dbxgvIPVm97gTKFg7+LbmTQ4E59tjMOFO1lR4ycSMPDi9Q8ynJLFmoZqpo5D2DVAh5aNTp9Ic8kgi0e20pg0H72UpBv1RHsWMJHUzj2yjFeT3+L1tF3YEcw+VkhdhROPxc3Dr0Npn59ji+eSZo39Q/wq3BVF+ZvZd66bhOAPMOmhf+8XyCpKoWQiiM5i4JWxN4lEBddwgP7JEYSm4THHs+/8YvJcVUwy63FEX6Dzoo7vfeXbTG0/R0lNNdFcLztyB7GFBY/qhkgIRGk8MZntM6q4b8dr9KVXMTDPxu3+TbxlW8rcFw7SkivZuHoNs7uWIUw+NlTtZ13NGNIH66/7HAPp+cxrb6RyeJA05wTtkXR0hmyaC50sda3neMISTBGNDIuG1rmU8cR27pXlbEt8m1cyNmNAsOhoAXUVQZxxI3zzDY2qzgjHZ87gmWU30h6v9twVRblCdI/6OFr7XQrje3DWfwGDyGFJYTyRIR9HUpsY8wSYTiP2gm7CSRE0n4lN3VVM6l/F/2XvPqOkug507/9P5Vxd1Tl3QydS00CTMwJEEgiEEAJJVkLBKGfLsmzZo3GSZVuyZMuyZAkFUEAgBCKHbnKmoSOdc6yc0znvB99717yz7nvHM9f9zoxdv2+naq9ee+2n1rP2OtWndoIcCpSX6b/Yxgubn8fgdrG44lMkS5CDeYMElQI/UjkodoRouZLMq8uf4slPf49bb+XqrBVsFN6gVjOS1E/baEqHD5fPZUHTShSKKB+UHeOmc/0ofRH23HgLHekFjOzvZEZ7IyOGnLRFkpGrRtKaNcB0/0ecSJiPIRQlWRdCbF2A29zOnVIhh01n+TB9KzFJYM7pXK6MktFn7mbzNzEmN4pcKyjgrVV30JmRw+XS2cO+3vFyj4uLG3bBSIzf7fkt09NPYO9ZibetlBuX5hK62E9nnoeavnbS6Gea6QQDOTIEt8BXvaNI75uNOqZmos6Nt+4ML2+8B5dOyx1730VURqgcM8CQXuBJrYf5XR66eiz8vOwZ7t/5DqpolDPTN3GT6TUiKPDthrakKL9fM5NVDetAJvFhWSWLzvdicPu5sHAmNdmTSPQ4WVdXQ7azle5AGgr1WLzJLRTFtlORuAirL0CS3gcti/Eau7iDkVSaLvGHjA8ISgJzz+dxrVhNj6WVh/bEmFkr0ZKWwWsbNtGTmol+yMOqC/ZhX/N4ucfFxQ27X+3Zw7y0D/H4xjJwchkL1hUiVnTiS4yxv+8sWoLcptxBfZEeIQSHBorxeLNIcxcwViuhGDzFL2fOoHpkEffu+AAp6uP89B7a9HCfKsCaVhcOn45PZCuZXHeSvJ5uKsrXMHbEF+QFu2g6n0anJsBvb5nBhprbiSCxtfQUcy90YnU56VmUx5HcZWjCIZ68UI3Ze4ZBZy4K7VS0phrQnuFk0jwyHC4sei80L8Fn6GGDLI+Thip+m/E+fgTmXi6gfqSeTut1Nu2NMbtGoC/Bwiv3PUJneg46h5/oJTf7k5zDvubxco+LixtWX56vJV/xKqJkoO/QPUxcnE/CdTuhUJgv/RXIEPkOX1BXoEdUS1x2ZHM6HGVa+0pSFQKZ1PNnq5r90+Zw187PUXn7uDa/m1o1rJWFuKvdiRiWcax9Ii6tgdlVFzhdOgHlJDuLXMe51plPZ8DLa7fN4N5rtxEQBXaNPsf0K00kuu2wRMXHufcjF0V+cKYOwbcfR/9IlNp5pGiqqcvo5HLSFPIHh7DoXciblxLQ97NensNpQw2/zHqPgExiZnURbTkG2hNruPtgjHnXZAwY9Lz43afoyMpH5wkRu+BAqa/FKr8w7OseL/e4uLhhU9Ntp6ftOZI0DvoqHyC7eASjjUqCjQ52a84SkURWs4+hrBiBZOjyWNgW8HPz9QdRCTLG6wfYFerm/ZtuYc3BPSQN1tI8t4dLSolFUoQH+lzow1Eu1Bawo2wJd+7dQUNuPi1zp/Dw4J+57smipcPPz26fxUMXV+IRFRwsvkxZdR0WtxPzUj+/ynoJgGcvtiLYd+Huy0OlX0qKqp2KEjdNplGU9PSQoPcga15GUDvEOkUW5/T1/CzrHcKyGFMaiuhNM9CcdIUNR2MsvCKnx6jjpe8+TXt2AdpABM4NIFd2Uyw7xqaL9cO+9vFyj4uLGxYOX5gdFS8yJrGOnqqNGHUTmDs/E8/hDk6ZGrBF/UzhCkmWDvpHKHAHNfzOIbCs+U4U4QQm6IKccTfwizVrWXjyKCNbztA1aYAT+ihTYlEednlJ8wepvp7Nz+Y/xFNb/8SANYvDy9fxou2n9EUTqamJ8pON83jozDw8GDkxopqS+ipMXjdpNw7yk8x/RkTG/dVdmNu34xpKRW1cRaLSxpEJdtp1uYzvaMes8yBvXkJYa2etKp1zuuv8U/bbRGQxylpLsCcaaEq+xO3HRJZeVNBmNfKTB5+iNbcYVTiG+nQfEh5SzXuo8TzIS2MfG/b1j5d7XFzc31wkJvLrXb9metpBettvQLAtZOndo3B/1UiDtoe6cDdZQjdz1ZXUF+sJR+W83mtg3OBUUhxjGKkW6Q+18oOVi5ly9TwTrh1hsNDBkVQ/o2IiD0WCFDo8NHan8MLMJ3j+k3eJKvR8eOs6nvb/HDEmcPGKnB9uWMQDJ8YSlKVzIaeeEU0XMHo9ZM/p49XsnxIWNCxrGaDg2hc4nAbUxjUY5UGOTO6nU5XGpJZmjFovipYlRLVOblGlc0Hbyj/lvElMFmNUdwkeo5ampEvcViGx5JKS6nQrP73vCZrzSlBERawnewlGo+iTd9A7dCdqncCDdSeHPYO/6ofD4uLi4v493vh2OzOS32PIPprA1fWsfrqU4LetdAUGOC6vwyhzcxdfcr4ogahC4K1OC7pAElPabsYkB4PczqYFYxnR0cisc3txpbs5UOgiU5R4UB5hQruDLreFZ0Y/yuYdn2PyBXhzwz3cz8dkBAeouGble7fdxL3HUpCUo7mU2URWyyl0QR+5ZQO8Vvh9XEIC5b1DzD65DXtIhtZ0KxqZxKFpQwzJrUy+3oRB40PVuhhR62K1KpUT2hZ+mfc6oiBR1DeasFpNc9Jl1lUKLL0o51JOMn+47WG6Mkcgi0nknOinJwya9B14bStJNkkUyQPUzx/+M1Tj5R4XF/c3te30efIVrxIIJeI8/gDLHi5D1eCgo7mbferLKIiwSdxK7QgjQYuMbb0JDMQkNtc8QkiQMVIf4buTTOg9DpYc+wqf2cu+iTYSJHhAKzC9xoYnquWllPu4pfIEeb1d/PnmB5hnOcuMviscb0nh8TW3cfcRFWrlZC5ltJLdehRlJEx+1iDvTHqIHiGbkUM2bt73CY5oFJ3pDpSCnGNTfThlEpOvN6LXBFG3LULQeFmlSuGAtpXf5f0KSYCRA6OJKpS0JF1m7XE5y87D6YJM/nTrQ/SlZiGLSZRUDtASFlGl7Cbkm8V4hR+lzsKk6zoGXNOHPYf4bZm4uLi/mTNNHQQGnkEjjzJwbDNz15eTrBDoP9TIHtVFJGLcJX7JUCrYspVU2g2cD0d45tIzeCU1o/Xw/Bg/fpWSW/Z+RFDjY9+MftTAA3oZM68NIQN+rryFcY09TK67xu6592Ao7ufOvt1U9adw37J7ueOoDL1yBhczO8htPoAiGmG0vI/PFq+jThhHmnOIu3ZtwR0NoTXejkJQcnWCQFDWw4TGdnTqIJq2BcjVflaok9ipaeXNvNeQBMizlYBcTktSFatPKbnpnMjx4mz+cPujfyl2CSZUDNESjqJMPEJEWcIKRx8kj2B2vY4OyYWl6/iwZxEv97i4uL+JDpuLS1WbyTT00HVqE+U3zKKg2EL/JzXsU10mQJjl4hH0ZhutxVqu++5WHj8AACAASURBVNTs9Ir86NL9DIpJ5KsFflHcT1tKKndu/z1RmZcDc3tAgE16FdMvukhUBHk3spCY38KyU8c4PulW2mYJ/LD1bdpcydwy77vcccSPWT6L89ndjLy+G0mQmGjrZNfGxZwW5pBi72fT9j/jj4XQGNYjl2lwFKjwqq4wqrkPnTqEtv0GlOoQyzRWPtU2817+awBkO4pRINCceI1VZ7SsPhmhomQEf9j4BEOJqchEmHHUTk0kjDLhLDFLEnfXddA+cg7zrwqcVDjZaVRRPeK/xgHZcXFxcf9HTl+YL488RrGlltaLGxkzdhll87MY3FLNodhlbHiZJFxjrLaGqnEmbBEF79llvHhtNQORsSTI4cPiTi7mFfDAp79BwsuRuT2EZHC/Qcmks0Fy1U6+9E3momEa9+7+kivF8ziyIovfNP2MIb+VpdOeYP1RO1ZhJqfzeiiu/ZqIQs7cxmb2bJ7PXsUq0ga6uHf7e0QRURrWIZMZsKapGDQdILvNjVYVRtd+Axp1iKWaBN7X1rE1/3UAMtwFqInRlFjHinMG1lX6qBhdwO/uehqnyYpchPkVDi5EAygMNcgywmw628PlsauZXhXlS52Xczo1WQl9iArDsGcSL/e4uLj/K6FojD98+xLjE0/QUreMgtR1TF8zEseX1zliu0CnzMYIeRvLZIc5P85MUCbjHZuSzdfn4PfPQyHAsYIOjheNYfNHr4Po4diMHtwqiftMCkafMDBG180xTxFbs9bx9Nb3qMsdw9Z103mr+SeEQlpWj3uWmyuGSGE6p0b0MPbaTgIaJYuv1PH1MwvYrl1PbmcTd+18H6VMg6Bfi1yWQIlFQUfS5+i6ZWgVMfQdCzCoQyzSmPid/hI7ct9CkARSvXmoCNBkaWLFOQMbjro4NrqI1+99Hp/OgEKUWHzMwamID7m2DV1WMxtPBblcup4RtUE+NPrpV0BKupOmqZOoTx497LnEv1CNi4v7DxNFiTd3v8HkxO20d0wlQ7if+XeU4K3s4ljdGRoVfaQoBtkY2cGVMSbCOjlbBpXc1DIZvWstNgmaczrYXVrKIx+9jizqonJKN4N6kfvNMOJoIdMTDnDVm8Ev8x7mlffepD0ljd/fdSvvdvwQbSjKpqwfMeOcjVSxnLM57Uy4vBe3QcuqMzV89vyNbDVvoLjpGjcd/gKdMoWAYQVqTEw0yKlN34J7MAu1XELfNRerOsIUjYFfGo9yKn0XggDJ3lxUgps2k501Jw3cetzFgbJx/PK+Z5FkclQxiSWVLg5HvQjqASzpp1hQlU9D4VJizR6+MsSwxlx4CxLpKMxjceMR7mz+GlgyrNnEyz0uLu4/7I+HtjHO8DY9gyVY7I9z4yNjCTc5qTx4jGplB0aZm/uDW7leoMeZpGKPS8m41nJGDd5FUxRsyT18PK2Uhz95A3nYzskJ3XRbRL5jiZF5ZD43mD+lLWDhpdyneXHLOzj0Ol679yF+3ftLMoND/EzzQ7LqbVjFsVzIvM7EqiPYEgysO3GNbZuX8HHmRiZUn+GGk3uwavKwm5aiiWqZqpNRn7GFdns2KkGGsXsGaZoYY9Qafmr+iurE88TkUVK8OcgUA3Rr/Gw8qmfFOSdfT5/Om3c8CoAqJrG0wsWRqAdR6SYl5RvG9M2k3zqVpl43/RqJzGg37WNLiOYY2FT5LvN6z+CWpQ57NvFyj4uL+w/5/PRRsvgJTm8amvbnWP7oJCRHkOOfHOCCsgWNEORh/0f05iroydFyzifHeL2cG/rv4WIYggYbb88fw6bP3kbjH+BEWQ/tKTHusETIPL6KpcY/YQ9reS7jWZ7Y+iExQeKfNz3By7Z3GO9p4OPIcwSHPOhUY6hPqGJC9VlsCQbWH7/Ktu8s48Pi25h5/jDTL1WQohtFt3kh+rCS6TqB2sytNLuyUaHE1DuNHI1IrkbBL4wf0GLqIaD2kuLNQlJ1M6iMct9+DTdcdrHlxqV8uOpOZBIoRJHllW6ORD1EFT5Skj8lJbYafyifS1EvgiyKFRftk8Zg0ATY/MWPuVF7laI0G+fCKcOeT7zc4+Li/t2OVFchczxNRNIiXf8eN22egTwmUvHHbzkp1KMixib3R3iyRBqKEmgJyojVzeTWwTs5EYCYysevbhzJ3dvfxeDu5sT4HtrTomy0hsm6uIKlqg+ISnKeS3mK73y9E13Qx0vffZ7HvZ+wwHmOfb6HafcpCCeUYBNPMrahBodZy4bKKj5bu5Qtk25lUeVuxtdfJN00iRbTHEwBgak6iSsZ2+l0p6MWtRgHyinQCJi1Ed5Qv0+fXsJu6CfVn05U3YVHJvHwLiUz67z8bs16dixahTImgSSy4riHoxEPIXmQlOQtqHR3Ebxu4ZoshDUawKVR4C3LZELXZTZe+5xl6fUYFBGGgndwnVKmDHNGf9UXqoIgLBEEoUEQhCZBEF74P4xbKwiCJAhC+d9uinFxcf+VXG1vpb/tQTTyML6a51n18ELUKhnH397N8WgNKuA79k9RJvu5MDaBoaiAr3oB6wbv4owPorIwb9yYzobdH2Kxt3FifC/t6X8p9vzrM1gc2opaHuVl80MsO3iCVNsgLz/wNHdEd3Pz0FFOeu6iyptCb1oJ4eAhCttq8OmVrK+sZttNS/h45s2s2reN8fUXSUyeQ2PCPIwBgTJDkEsZO+h0J6OJmDAMTGKsVobKaOMj/oBLZ6IroZG0UDJBdQ8+JB7boWBmXYDXNtzHjkWrUMckZGKMmys8VEQ8BGRhElM+BO0DeK+baRRipIX92PRaskr83FyxlRea3mFd9lUUsgQGwj+nIydErvu/wM8PCIIgB94CFgFdwHlBEHZJklT7r8YZgceAs8Mx0bi4uP98TX19XK66hxStk97LL3Dbg6vQ6JWcfHs3Fb6rqJGzfvBjEjJsHJiQhAwJ77UlrBlay3GfSJAY7yxMZNXBT0geaPwfxR5hgzVMYV8h0wYOkaAO8GPNPZSdus7IzlZ+8NAzLFWe5u7OXVzyruaMZzTXCjLIbNtFor0fUSlxy+l6Pl1+I9tnL+PWXVtItvejGLGCVrEEq0dklGmIqymVDLmT0AYT0TtHMVmnpNt0nZOufbiSc2lIPUZqxIxL2Y88LOPpHTJK24P8/M4H2T9jHtqohCRGWV3p45DowUcUc9oWYuIDOJsVqBGxRGNgDjJH1cD0A8dZmV5Pls6JIzoXl3wj3WPfIJjeh1o+f9iz+mtuy0wBmiRJagEQBGEbsAqo/VfjfgL8AnjmbzrDuLi4/xK6HU6Onr6HXGM3bVce5/b716M3qzn9wX4OD15CJylZ1fcp1ux+dk1MwqKQsF9dxpLBWznjj+ERJT6dY2JJxcckDTRyoqyP9rQI6xMijPYkUnq9mjSdh9/IbiX38gBjmxv4/nefY4q+hidbPqLav5AK9w2cKlEz7to21OEAxkiA2VU9fLx0KfunzWX9jj+jDgfpn7ABnT2DRE+UEaZm6lKu4nAnofelo/OOYLpeycWEkzT3X2cwPYermYdIl7QMyl0YfQqe+UJixECYn37nYQ5Om4MuKiJFo6yt9HEQHx5i6FM/J+q6D59PQVYU7HKB2eaTGHoGmR6tZVF+CwpBYDD8JP0JCQyOehlJKdLbtBFXdz+zhjmvv6bcM4HOf3HdBUz9lwMEQZgAZEuStFsQhHi5x8X9nbF7/Xx95D4KExppvrqJ279zL0arhvNfHeNg6xmMkpolXZ9gye9h+4RkMtUioZrlzO5bS1UgxmBUYne5jtmnP8Y61MyJsl7a0yLcZo5SJqnIqxogT+/kfWkp+rowZddreWHz84wytvLK9bdpCE3niOsOTha5KL+4jbBSRrbTzvjWIT5avpST4yax9usPiajUXJp1H2Ob9SR4I2SZL9KQ2IPXnYjBnYshmMUMvYq9pl0EeuR0ZZupyjxEhkxOTyxAilPJ81+KpDtivHrvY1ROnIo2KiJEIqyr8LNf5scliagTjxAa2IAYk1EaEhhUDHFf7FuU9U4WZLQx1tCLN1aEI/ok7Xn78Y04gXJQwz7743xsyyXR5GfzMGf215S78L95TfpfbwqCDPg1cPe/+YcE4QHgAYCcnJy/boZxcXH/qbzBCB/ve5gx1is01mxg3fpHMCfruLC3kr1VFRglDQvaP8VY2M3npUmU6ESEuhWM7lnL9VCM9rDEyRI1ZVc+xmxv5dikXrqTI9yqF5msFkk+GWS0doitzCPUqKG8/irPPfo98ozd/LruF7SGx3PI8QjnsluZeuEADpOeqU2d5Pc5+eCmZdTkFLBq71ZslhTOzNjIossyDMEIKYmHaTRECPvNmJwlmMLJlBtk7FJuRdFdQt2IemrTTpIuh55YjOwBNc9uF7H6YrzywJNcHj0BhSihCgZZWxlij8KPU4ohN9QTsi3GGpNRFJaTHd3FXEcXObIhFhW2YiKILbwBu2o+vaW/IpI4hO6Shdc1L3BuUI88EmG2c/gP6/hryr0LyP4X11lAz7+4NgJjgWOCIACkAbsEQVgpSdL/6ywpSZL+CPwRoLy8XCIuLu6/tGAkyru7n6LUeoLG6zexdvVzWNP1XDhygj1njmKRdMzt2Iq6uJtPRydRboihvb6MnM5baAuJ1AVFqrMUFNV9gMrbxdHyXvoSo6xTCkxLCGE+I2eCYoCdTMdx3Up5/TWeefxF8vU9vFP7Y3ojBey3P0Ot+SITrp2nJ8XC8vP1JHr8/HHNTQzpTCyq3E1b1kjOTFnLraeiqCJhLGk7aZEbEUN6jENlJIpmRiWI7PV9jtYzixOFh2hKvkiKINEbEyjq0PHoHglTIML3H36OthFjCMkhwe1jbWWEHZq/7NgFpZOYdwylITnGqIN53g/xBwQmZ/czQ9+MP5bFYPRpWtPb8Bf9AMQo5u0ZPDHmBTp7BFRikOmqywQ13cOe3V9T7ueBQkEQ8oFuYD2w4X++KUmSC0j6n9eCIBwDnvnXxR4XF/ffSzgq8tbXL1Bm/ZaWtvmsWfpjkrKMXDh+kj0Vh0kU9czu/5zoqE6+KLQy3xTD2LqQjLbVdIdiXAmINKUpyWv8I1HRxuEpvdgSotwaUTI9242uSsnkaC/fyKZiu2ZlYksNTz3+ffK1Pfyp9kcMRvLZY/seHcIRRrQ10ZKVwl2HL6GMxXj7lpuRBwJMbjzFldHlNIxayIbjEYRYCHPOVtojmQgRNabBSWQKOqxWP8f7D6OWL+Fw8VY6LLVYRBiQCYxrMvPggSj6UITnHv0e7vRCbGqBdJubNcdjfK7z4ZYkEESUkQQWehUkhveR7KlHowmydFQHWZINR2Qlbm6jfdyviaS1oWwT0O8s4J5lT+FqjzIi5uOGsIjFPg+PoWXY8/s3y12SpKggCI8A+wE58L4kSTWCIPwYuCBJ0q7hnmRcXNz/vyIxkd/t/B5l1h20ds5k1Q2/IiXXxIVTp9hz6CBJoolZzh3YCzrYM9LK0oQoxq5ZZDSuoj8s43wgRmeijPz6N3CrvByZ2ovTGGONQ8v0UjuqZhXTXT18K5+M95yBkv4WnnjyJUaou3m/5mVskRx2DT6JLfQNlpCNhvxMNu8+RVgu8Pbam0nu7cbqHOTgrBV4UsZyZ0WYGEGMxR/T4RyJIqbBODSRAoUWr7WPq+2toLuBb0a/xZChC11EwCmXKG/IYtMBG4pYjKcffxEseXTo5eT1Oll5KsY2gxc3MkBOqhTiRkeEVP87hMMSo/OczNPWExatDEb+iXaDnEDp00S0UQz75UjnSlh/22OoWoKsDEcpCSYSlQdwJ1zFoDgD3D+sGf5VDzFJkvQt8O2/eu3l/4+x8/7vpxUXF/efJRITeXPH9xlv/ZLWrumsnP8mKTlmzp89ybf7D5IimpkZ2EtLXjOHcxO42RLB2DuNzNoVDEb1nApEGTRK5DT8mn5TiCNTegloJW7uNjBj6iCKHjWzurvZJ5uIdFRGctjGd599hVG08UHND7BFs/lq4H583q+RyaBhZB5PfHUYj0bF+yuXkddcj4DE5yvuIUVMZMOxMCG5F33J53QOFaKK6jHaShmnUXPdUEuwU0/IWMxX436GX+lCFRWIAJOay9i8p5qgWsNjT7yEUZfGNbOC4g4nC89G2GL0E0QOQpSJ4hDzBi8T8zVh1PiZPnaAwlg3ztg8POFNNOd/hFhwASGgJfENAZtnDJtufogJ9VFmhdQoJDU+fTtRXRO1NiWpiknDnmP8CdW4uLj/JRoTeXPnDxhv/Zy27mmsmv87krPNnD17gr17D5EmJTBdrOBMdj1VGRbWWIMY+svJrr6BgVgqFYEIXnWE9OY3aU8Kc3RKH4JS4OZGM9Pm9qKwaZnV3MkhJqL9NozboGbzc69QHqzl/dqXsUkZbO9fQ9C9n4gmga70JJ7evo8Bg5ZtixdQ1FCNM8HC9iV3MaNXYFJVjJDaiapgN71DBahDFsyOUYzXC1yVLhDrH4XDZGPnuH9ClEWQiaCMyJnYPp0HvjnBoCWRFzd/D6PaxLUEFRMaXZRWhfnAGCImKJCp+ljp6aVo4AyBqMSU7AEmG9uRRA1DoRfoUefhnfwcMWsIbVsmprcHqE8ez2/n3sNdDQLJooqAehCPsRVPJIDQ2cOEwCBSqm7Ys4yXe1xcHPCXHfsbX71MWeI22nqmsHLe2yRnm6moOMjRoydJjyUwTV7FztQrOBKTWZPoRD84npzLU+kWCjgRiBLGg7XtHRozQxwrH0QnyVhelcCkxT0ovBpmNnRxNFxGwh4XzVm5vLD5ORY5TvP29X+iR5bN1+1ziXjPEdTn4NfKeHTnPjqsJvZOm0xBcz0dOfnsuGEDG655yG5WEDJ0Q8Zphmw56LxZWHz5jErwcM3ZRkyaSFviFQ4Uf4hcBBFICOqYd6WM245X0JyVw2v3P49Sq6TWoGbuVReqliCfGSIgCOgNp/hOWxtyj4tEnZMpxTbyxR4cYjmB0GM0ZuyH4neJyRQkHp6EavtVKkoX05CxnPW9SgJyP66EFmLKPjpsMoocV1HKo6TOHsTTbR32POPlHhcXRyAc5fc7nqUseRftfZO5ZfE7JKSY2Lfva86cuUx2LJFyVSN/TD6OyZDDTcnd6IZGkXNuPM2qcs57ooiRPnSDH1Od5+dEmR1rSMmSKgvjl3ehjCqYUddDpW08KQdtnB1TxiubHuOW3sP8quUXNCnz2dc4llighYCplER3B7ecukh1RjIXSwrJ7m7j6vhJHJyykkdPDmHqVRJMqiNqbCLgTsHoKiI9nE6qpZVrgwpEeSmXsr7hfPYhlDGBiEIi2ZfK7IoS1lcd5lLxGD5d/wReg8CgSs2S0w4aHUG6tRIQo1T4nHk1TuSSyKKsFkaabEiSGlv4SQaESbhKf0g0zYXamUHKV8VErlzm25lPolIWkBuUGDA1IGgHCAcDRNsHGBUawJjtxTzRS1qbnCyhbdgzjZd7XNw/OG8wzB+/foTxyYdp75nDuhVvoTdr2bFjG1VV9YyIpTJK28jPkvYzQTGGSWm1aB15ZJ+cRI1xPtecUWSBRuSunZwf5eJSkYcMh5pFtRbGLO9GLZeYWtVHZeN4Mk73s3/aHH5554Pc276DH3e8SY1qJEdqc4lFPHiS5lHWcJjC7nZOFOXSazWT4LRxYv4NnCuYxzNHBlDblUSzTxGQfIh+C2b7WAoECxHDReqGikGmYn/R72izNqL4H8We7RzDtCMWbm04TMWEKZxc9iCNVoFwTGL6MQcno2EiCglTbICbbNux+gTGmnuZmGYnSebAKU7FF/oubZYLSOMeI6SSYW5cgn5HiK6wjNpZr6JBRYfGhdZYg0wI0T8YJs9ej1IhkjW/G0GnINa8gEzfEvrUjYwc5lzj5R4X9w/M6Qvw4e5NjE8+TXv3UjaseR2VVsG2bR/Q0NBOcTSdHH0jP046wDpxFhk5x9G6U8k6MovzKTfQOhRB4blIMHSUM5OGaMwIUtClZ06LiZKVvWg0ESZetnH++Ggym/vZsnQ1H6xcx+ONH/O9nne5IhtBxdVURMGIM2Mui079CaPfy/7SIqIyiYhCReWyG2kyl/Hs/kGUHiUU7MXp0yKLmkiwj6VEo6IvUovHNZGI3MZXY3+G0+BGHgNJJpHatYQVx3uY33aM3bNuoHH2RirSFWjtfjIv+DkvE5ERY6ajgonOBnTKECtzG0jV+YkKOmyhZ+lkMtHiHxLMGUDh05F56QmGzjZRnTwdnz6DPkUEn6kGq8pOyB+E3l5Ghm2Ys0NkzOnE05NPcv3dFIrpALSHspk5zNnGyz0u7h9Un8PFlwfvZWzyFTp71nLnuleRBJH33nuL7m4bYyNZ6Ey1vGo9zuPhxSiKv0HnTiDtwBKOZs/D1h1E6a7ALVygYkYfveYoE+vNTOzTUbRqAI0mQOlZFw3f5mHxuPjp3Q9zcMpsflz7Jg8MfsnVWDbH6tORFKNwJ09l9eFXCSgVfFtWjCYcoD2zkCvzp2EXC3hirw15SEAs3InNY0UZNpPkHE2WyUOrM0SUUQxpr7K3eAs+bQRBBA1yIo138ODpY4ztbebT5etpKl/M0VQN1vpBIu0RegSJzEAPywa+RSVFmJLWwgSjE4PCj1uchSPyIE2ma2jHPkRAL0PVOg5L/QNc7OnHmXkjXiFAtWGIHG0tVimCo9dLlrMFhVJO9oJe9JkhBmtXM8O+EA0q3Ii8ELWhEGvZwLJhzTde7nFx/4DqOzs5fu5+RiU10T94D3dt+D5+v48//vFN3K4QUyN59FrOsM3YxsvBJfhGb0Vv15N6aD1fF0xDavGgcO1lQFPP0al9eJUi8y4lUejWUbiqB43OQ+GhAN3fpiCpZDz9+ItUF5TwVtWr3OI6yGVfFkc78pBr5xM05rB23w/pTLRyYWQmuqCPUxPn0jmxAOyZPHTcDoKfcH4FHo8VrS+TTP8IlKZu2hzpgJ9rqTu5nFGJXxMDIEnS46v5Dj8/8znJriE+Wv8oxydMpEkuJ+FEH36/iCEWYungAXKDXXgS3dyb0ESqMkBUMGELP84l7WSMeS9Dbi8hvxLz+QcY6CjlalRApk3joqYPmWGAkQonEbcHob+bnKiXpGwVaQuq8ftTMZ17gnHhPACOSWF+ITrwyNXMV4vDnnG83OPi/sGcqLlGW/ND5JgH8Xqf5fZ1DzIwMMB77/2BSEhkfrSIitSvcWHh5dAsBsd+gGFAhbXyQd4ZVUpG7RCSeyft1g4qygcQojKWnEkjM2KkaFU7Kp2brO0xPBVmBhMTefax72GzWvnwyossdp/mnD2LiqGxaIw3IQkCKw/+kEv5WXQkGpGJMXbduA5fbgKJ7UmsOO9G1PfiS6gj7DNjdBUyUkrGqRzE5cxCLatjd14lXdZ6wgoRBEgJZ6Osvol/PvFnQGLHxu/zZdkI/F0BNM1eRFFinLeOubYKPCoF+tGNfDfqQCsP4Yktplu6hwup9eSP3IRPJyA0FyM0PMJFr5qoBBp7FTtGmhin7kMV8RPosJHi60GuNlIw340mv5tw2yzKGu9GIckJIfEz0cNBOeSILn50+nPGJrQB/9tHhf5m4uUeF/cPZOfxI0Q9z5CgDqFR/JIbV66isbGBrVs/RRaVWCiO4uOMDyl3z2eNIUL/6D+j71aiu/gcvynMpaS6g4hnB3W5/Zwd7cDsVXLDhWSSVAkUrW5AqXGT9IGc6CUVl4tG8/KDT6KQi3xa9SwzvVVU9udx3jMDrWE56tAg086/xjfl41BEfDjNVk7PWYAv0URJtYmZdUFiKddwyh0IISOJjjFkaSUGvFEiohmleS+7zdW0Jnchyv7y+LzJNZOS2lzuP/kHnCYzB257gY/TE5Cft6PwxxgRsDNraA+6WIDmHLjffI5JUR9BWTYDoc3sTS4mLflnpGc0EfAp4PSDdHVPwCuC0VXPQLieS6PzKJd1EbU70Ax2YkAkNy8d8/xKJElN0oWnSHKUAtAgRHlFdNEpk7Ou8yjrrhyiPj+Rbfqpw1zt8XKPi/uH8d6ebaQofoKIhpzU9xg3fiqVlUc4cqQCXSzCBPlI3k37mLv778SSfpX+oi/RtaiINb/KO6lKiquvE/Tv4dy4AeqzfWT1a1lw1YLOaKVoxTWUMj+W3yqQWpV8OX8xb6+9kxGeLt6/+n0Kgx3s6y2kPrQKlX4aRm87OU3vsX3aeIw+NxfHTiNWkoDLnMS8swqKOkNEsk7gjIIilECGezRqjYceTyJq2mnOr6EtcprmLDdIYBaA7o2sauhm2dkttGaP5P3bn+WsJ4b8ogNjLMLcoWPk+5toMuSRXXia14OtqCMyXNGN1Ctv5VBxPbOSHySqFQk0leGqvg9nWEsMByV1X7BnTAGG5CSyAr2IfX1YQg5UugzGzrAhjjyCbKiY/KrHEKI6RCQ+IcB7QpSMqI1XL2xH0vs4OG4E1UkTSfX3D3ve8XKPi/s7F46KvPHFPzMu+UMc/lRmlP6ZjKx8tm3bQkNDK0mSB5MmlYP6M7zY+Rjuoi8Zyj2CvtpAn/sXnBUHKGzoxi1WUDF9gL6EMOOaTMxu1kJiIoXLL6KIRLC+rsTvNPLWnXdxcNocpg1d5v3al9BHA3zeP5HB2D0otJmYXS14ooc4U5iOMhJm5+Lbmaap44BmPGuPBkhw+fFlniEQlaPxpZMfysWPiMNjRm2o4LjRTrfyNP3pIQDyZBq01etZ3nyJKTWXqB4/mx8uvANfqxd5RGSSu4Ep9kpsKivVOaN5LOFjJgR9BGKldMc280F+IiOtrzPNUkvQp8VR8SC2/jHIZVHc0nbG1l5n75x5WPER62nH5O4nItcyLrcc5Zw9iBoniXXrsXQsRhAE+gSJn0puqomxuuMYRa4aetJUtBpLCaYUsEYw0a0yDXvu8XKPi/s71md388m+x5mQVkm3o5RVC/6EXKnm7bd/g93uJU/TT4dChSWs4pH+2+kZ+1t86dUYzqVwQfkqwY56UgIt9GuqOFI+QSAYwQAAIABJREFUSFApMvdyEmV2GeGcHIrnVSL3S1h+q6JPSufHjz1GU04+t3Tt5ddNvyQQUfDR4BICwkYEhQazo54661US7T6GLNkcnHczs/xnOSlfxj0HPSC34Uq5jhhRYXYXkiUYsIVVxMRB6vObkfdcpi69g4A6hoDEmEg+WVcnMrfmAPk9nexaei9vpYxH1uwhKebkxt59JIheziVPYXXiUW7nK8SwEXv0CQ4kLOJs0SmWaV9GkMVwNsynv3oNclFNt/UcWY2foddO4dy8GVgcXaiHehEkkQRzKWWlYZwlnyALWsk+/QqSLwO5IGOvFOB1IUKuv427+06gEbx0JKTQmTGBhSoLPiGbf5ZLTBHlw559vNzj4v5Onau9TlXdI5SlNdPnuJmNN/+c3t4+tmx5i0goSmFyBzURLWttC7AIAh3lzxOy2NGeGs0XxgcpPH8JQaymOaWdE6U2NGEZy06lkBqR4RtXyLjSfcgcYPijjvPJ4/jpPZsJqlT8oPq3bLZ9RVfQxDf2BwjKp6GSZCh812gwXSbRbuNs2WyaR5WT42uk3zuf26/6CFsa8CjtyCN6Mj0laCQlfREZMkUTF81taPpPUTnRhiSATiYxpXMyssAobjn5BVGUPHbbyzSFjChdAeYMnWSct5ZecxYuq5U/qd/ARAhf9CZaZBt5a0KYWbpXWKFpIWDLou/CvURc2UimTranv8sdx5U0Fq5AHXWjba1FHQkQ1GWwJH0qwYnbcVobMPZOxlB7F/KYAT8Sr0pezoteZg+dpsjXQFiuojZ5GtOMGViU2bwmSDiDESSFnF5TZNjzj5d7XNzfoU/3f4si+jJZJi8R8UU2rLmXkycrOXToCCohSGZWB057MQ855uMzXKNz/JtEVFFip1azIzyCojOXcUqXuFgyQG2+mxS7mhWXjHgVZpRTjBQX7kPeL8BHVj6aehOfLb4Ja8DBZycfpZxmrvnSOO78MUGlCVVMhi92Gkm8gDqs4otld6FSmtEH+yluzyevy4cv+TJBWQiNP42cUDbesBaHNEjA3Id/4ASOtE6ainwAFMjllF6cjluTxT1fb+GbUQv4U8lipKBAka+ROUMnydc7OJczkQc0+xkrayMgjqY38hAf5OYzNGIPt8q+Qoop6b2wEVfLbMzaIHtGvYEQ6GZZwyTaCg1o+5tRB9z4lEbGpC6hcISfvlG/BkEi8er9hIYmYorpuCAF+IkQJclbz3rbKbRSkHZzCUVJY8jRZfFbScLrjyBpZJQkhnjBn0imK37PPS4u7t/BFwjzuy9fZXz6VvxRE5kZ71FcUM6WLe/R2tqFRdtL2BRkRPdyioK5OJI/Zmj0QQjL6Kh6Ck9jM7mRNvpUV6mYYGPAEmRUm5EbGuRUp5WxIP0ypuIrKDsEHDtH8Ma6+6kZWcSU3ov8ueYlEhQB9nmm0+R7GgEXqmgYd+Qb1P4OWrMK2DdvNabuAAa1mxuvGJAHXDiSGxCRsLiKSI4kMxSLYZEaGFA1EeuvYd/0Pnzav9yGWRhLwt6/juTBC5Q1V/LQjc/To7aSFBxi/kAFZfIOlFkK5GoNbyjeISxZsIWfpVI7l09nNHOr/HmmyQdwtU9m4Mp6EiQd7fnfsi/1MAsai7FEFxCI9KBv6yIg1yBLKmV9wlQcoz+jN/0sGvtIIk0b0DnyMACvCQGORgeZP1hJdqgbuyoVbcYSlNY8fhcWCfkioJYxzuDjpVAiVrvADsMbXDKLPD3Mn4V4ucfF/Z2out5G5aWnKM+sosdZxk0L3iYUgt/85pcEAhFS06oIeAtZ3bEOSQjjynmRgcJuRJuRM62PMOrESZxmBW2WVirLbMRkIvMuJpLrUXJ61ELuEz8gPCmMskFGw4lyfvHAZvwaLc+cfZPH/V8RlCn43PEEg+FZaAJdBOVegqGjyASRg7NWUFMwCVmdg9EJbqae1OHXdeKz9COP6MnxFhELGemSNVMUHeS6/yK9yTaOz7MhycAsE5k/OIXzxmXcc2AL+1PKeW3uLWjEAIsGDzNGamV56lXqNUXcIL+Ehgju6FqaxDX8thRGJf2eh2RnCXuT6bj4JGp7EcGUK/wq8zNMQTO31C1E5bSjdNYSFeT0Woq51TidhGQbvWN/QlTlRtG2hOD1ZRRj4gIRXpO85NrOstFzjRgaorkr6EstZFsgRswRQqYSmKa081Iog1hUw2e6N+mjiQuZMG/4N+4IkvSfc5RpeXm5dOFC/CS+uLi/hS3f7EQtvIpF48QXuZdVNz7P8eMVHDt6DIUshDmnhoL21YwNFOJSXySS9xa2XJHB9nF0nS0go8tBt8FOVYGNqgIHCV4ly04b6U4rwSozsCThawLTJWRXNHwzuJZPF68mwevgveNPM1PXTGskmaP2n+IVlcQkO/ivIEZa6Lem8fWSDfhiekytvazBS3KrCU9CHVFFAL0vkzR/Pv2CB41YjdbThj3Sx5HyfrqS//LfMOOVSnA8CM4QmRfb2DFyDlGZjAmuKkZHrrPcepUsjQ+1ECNZZicQm0ZXbD1fJObQXXqIlcJOZKKErW45/pY5CAndfJS1Fa8sxJSeyWT0hlDaB0CCRlMhs0zFzFbnYS/8EmfuYWT+JHqu3Ml0zxgiyHhLCNHovsY051n0kSCB/CVUZZVwwRMFTwSFEuaF+niOfHq1PXyu/ohBeS91aRIaJGYaoxQMpvHIg8f+Q1kLgnBRkqTyf2tcfOceF/ffmN0V5A87fsKkrM/xhhJIy3iPnIxS/vCHNxkYcGA29aCRq1nY8BgCEkHjb3CNvIQvRc61K+so3HsZXYKDhuQujpc5GUzwU9CpZ261idNli7nz9G4SFnQRGCcROm3l9ZSnqJ4witmNx3mz5Wek6bycCc7hsvNRwuE2PGoPWvtJYjKRMxPmcbp8PrI2H/P8l5liMxEOijgSLyMTlaQ6xyAELdTozzPKZWfIU0efMcKe+T1E5aBAYpY0jlOq+xlXc47L6nxOFI2k0NtImf8qOZlBHmAfTpJIlQ8SEkfSHX6S4/J8vp3SxSrNS0yVD+DunMRgzTIMSpE9o96lWd3FuMFSSjo0qGxdyMQYjfqRYC7hScVoZMl1dI75ARGNDWfTNDIbVjNXmcpZIcp7oQ5G2SpY4ulhoGQFFVmjaLaHEXoCqBQSywKdPCIWckUf4OeKH9OvsdOeJGEUJFaYIpS75TSdm4bbN2HYPxvxnXtc3H9TB05f4XrnDxiVVEufu5yVC9+ivq6Vvd/uBmIkpTcxsmcRY4JF9KqukWn4LddLIthkFjr2z2XM5atUZ1pozBzizBg7MkliziUzIeNEdFEdd5zbiWtTmGgGdJ0v4ZXxLxFWKnjh+K+4XzpATCbjkOtJ2oPltFg6yei+gDzUhduQzmfLb8OptZBy1cZG00X09UW4dJ1E1E7UQSsp7mJ6FAM41VXkdXcQjgU4WTZEY7oXBMhSylDFHqfDm4yq3kG/NpH0YC9T3efxjUzhWb4iORjGIh8gIiXhitzJYfkITqdpKM3eRq6hipArnd6rq1AFEjmReYRTxioK7SMoa01Da+tHFovSqc2ixjSeRxXFlGrDDBV/jiurgrDLjHB6IyXCZPwC/CnmxGmvpMxWS9uEWzmenM/AQBAhGEOnlLjV1sbtxkIOG09xVjpEr8lDX4JEokxkgTnKRDfU1c0kykhCyghJETOPvPrkfyj3v3bnHi/3uLj/ZgKhKG9se5vi1D+hkYcRFA8xo/w+PvvsY7q6+jEaBknDwsyhhYRkQWLqD5AnnqCzWM31tnKyv3DgVJnpNHs5Pc5DW7qLVJua2dcyuV4wmzsO7SJD08//w957R0ty3fednwpd1Tm/nMO8NBkzgzwYgAAIgpkiaYoiTXFlHYraVVjZ3pW08mq1PE46u7aCZXl9TEtWYIQYQAoEEQkCMwgzmPTm5Zz7dc6h4t0/HkRrdWSaO4eyZOl9znl/9Ovb1dV1vv2p27+6dW/xpy0cj8y3Nz/AH41/jJ7MJv/2yq9yd3CDTaeH7+Z/lV1VYzuwzMjaqyCpXJu4wAsPnEfNtvjE4g36ZJN6PkE9tA5ArDaEaMZZDr9AV6GMt7JPM+zjK/csYisCCcEx7SRvKD9N/OoWWSlCxCpxV+lNSsMdnI+v8sm95wjIFQQaNftDvCjdw02vTGLkFSbankfYGvuLj9DaH2e17SbPxF+jvZrknuURgrkMsmOz7+vklcjdnJW6+AU9iJW8xd7UZ3H1Kq03H6Sv8KMkZC9PiyZvlF5nojzD/LkPcdHfQWuvgWS5RD0uP57b5Xyskz9KPE2ucZONZJ2KH7oVl4cjFqfLEjcWzmMxgKk6aK6Ki0TS9fPpf3oo90MOOeQtXr2+wquzv8rp7jfI1Qa498xvk02bfOupJ3Fcl65Ynjszj5G046z4XuG4+u9Z6pPZa4+R+dNTHH1jhutDA2wnilw8VaKhGZxZiNIKPc7JjS3ee/G7tO6zKH3Exap7+b/4ZWbDR/nAla/wT8q/T7de53LzXbxZ/gS32vfp2XoRX7OA7R3j84+/h3Qiwon5fT5uP0tt+16K3l0svYxmRIlWxlgN3MC0Vhjcy+JRdJ4/XWC9LQsSxFQPLemXKW94MSoyXqfJ2fJVzP4I7miM31j5TbqtLDIudeftvG7/CN/xlOgaWGOs90+R1RbZ3TvJrB9jL7TJ8/ErBFo+7l+cJJbJIzsWeW8bL8buQ5Ii/KonzoinwNboZ5EGVrD2u9Gm/yfG6GFNODxZn8FnzjF95j3ccH1IOw0kR9CpWHyqWCDS7fL50NOo1S1WOluYHpjUHB6MWEzmZa4vP0BT9GIpAs3RcYWErbbQ6w3681k+9nu/f1sZOJT7IYf8LaJl2vzml/4Tw/H/h6hepu78KA+c/Tm+/OXPk04XCfpLTLZGOdk4yZa2g1/7Xdo9yywcDTC/cQdHv7TDvi/CRlLh6kSNhYEC4ZrK+O4ZhG+Cn/zGF4g2alh/3yB/p0K+0MGvxP8FcsXmMxf/Je8LvImDxkulX+CmmGTf8zoDO7eQ5Cip7kf5wjtOoJWq/MLCV4mp7aSyPTSCm4BEtDZMybXY1Z9heMfAY1vYnQk+f+oariyQgKjyMDv7jyGyDqqwOFmextsBM6fP8Rsbv8H50jQKBk33AW7aH+XLWIy2bzF85Ek0f4Fs7hjbq5Pse1K8Ep/FYyqcX5gikSkhOxZlb5zXA2dYCo3woVaDD0sWmc6voZ+8iSTLmNf+PuOl8wgknrR22PSscXnqfvZKLsp+EwQM0eSTjSZLozvccF5AMwqsdFmoruBcwOGBsElfVmN6+X5K9OLIAs3247oytlbD0zIYKisc956iUn6N+37vX91WFn6ocpck6R3Ab3Ew8dpnhRD/8i88/w+BnwRsIAv8hBBi8/tt81Duhxzyg/Hdq0u8Pv8Zzna/RqnRyYnJX2dny+SVl19Ckhx6vYIHig9jSw7L/q/xIF9iORFioaMX8dV2ehe2uDHUz1ayyqUTFWq+JqN7bQj9/fzoi9/m5Oo6zoBF7RM29S6F15v38bu+n+WuuVf5P7d/i+O+PBv2FC/l/2duhjK0776EZlso3ju5dPoBLk75eWj5NX7EnCa99QgF/yaOp4HeiuOpd7MUeorO/TqRegOvr4M/PbfCTrAAgO52U8l9GiunIguXk5VbtAWrXDz/CD+X/hKf3H0aXarRdO5kxv4Yf2QnGYouMTbxJN7YDsXaAHMbpyi29nktuQKmw4W5KZLZCrLrUPElWPGMcCl+mv5WkZ9s7SG8V4jds0CgvYW5cp74+sfpETrfkSs8F8vzWscwxl4TuWSiSjBpl3jMY/HC0A3kwhVqWp29uEPQEdwXsbg/ZBFL+Zldu0BWtCNkgceIADKWVkS1bYYLEse8J4jrnRTtOtfd63ziX//ybeXhhyZ3SZIUYAl4FNgBrgAfFULM/bk2DwFvCCEakiT9NPCgEOIj32+7h3I/5JDvT6lm8NtPfJapjv9IWKvSMD/ImeOf5stf+iOqNYe4v85dlbvosTt4M3CZ4/wuXr3O9EiCndemOPnCEvNdnaSiClcnGiwM5Ak2NJKNh7l3ocp7L76EowuCb6uz8ZCG5VX5D8qnuVk7zc+8+rt8Qn+BkOTyeuXjXDTvpWa+SKycQlL7IPgwXz/fT1nL8Aubv49WfDebTgvDl0Z2dILVQRb9LxPN79FecjDVIMVRD08P3QAJhB1E5D5Bo9CLhOB4ZZZR9nj6sffwkfKL/KP1LxKkiOEeY878OL9nDdMfWGV84mkCnXNUjTYuZ+4mn17nenIPxXB4YG6ctmwdSbiUA23sBAaYZgSvVOex4jRhsUXHuX2SR4tQ6kdM/yxjZhtPx12eTBrcEj6U7TpSyyEkC05YeyQ6qrwZe4X+1B67sSalIHQ4Lg8mLc5qNmKjh8W985QkDQDNSKACTT2P7DhMlDxMaFPEtA6KToPrlVlyxZcQnhH+0ed+47Zy8cOU+z3ArwkhHnvr8S8DCCH+xX+h/Wngd4QQ33eJwEO5H3LIX44Qgs8/f5m9zP/NHV3XKNR7OTX561y/tsjc3CqaanPc7udUa4oVfYuW9odccC8z0xFjuniCo1/bJu/RWexKsNbd5M2pCg2tRaI2xnj6JD/x1FcJNg2kcQNxr03qjId9t4vfUP4XRqZX+Nnsv+O8J0vW7eGF/M8yQ5po7iqSpKP6HqSUOMpX7vHz+P7nuFDpYCHbTzW4iZAc/PUettVl1NpNerMqTdlLtbOH7xy7SFM1ce0A5N5Bs3AHAomp6gKnWis8/fh7eaz1Bv/b2h8SEkVMd4wV86N81jxKj3eH0ak/Jdg9TdMOcalyP1vb6yxEUvjrEvfNj5Es1EAIKqEOdtpGMLIaLbXJycosAbdK+2SVjrP7qIpO6+anaWuc4Mlula/FBOWsibrbAFfQq9iMOCukRnYxjcskKy1WOwxsFcZxuJC0mHRlsktnWS9NYCoOkush0GrDr9hktQyy63Ks5GNcmyLsiVO0KsyX32SnehXVdugtlNECXbz7T5+4rXz8MMe59wDbf+7xDnDX92n/D4Cnf4DtHnLIIX+Bhe08f/zsv+buvq+RaHewxCcY6nqUJ774DUxbo0fx8rba3ZTVKt+O/D7vtZ6k6dH5cvhuer5SY7S4w9XebtJRizeOVUgliviNKEPFx/mpb3yX4dQXKHW7JO+rMHMuCr0Sl3iAr5ffx0/e+Lf8mOcqYY/L1dr7+U7lBE7teaKOgeQ9jq6fZ2YwzH7/NL++Pc/e5gWu+Teww6toRoSyXaNkPMHAtgdL8jEbO8L+8atsBxdxzShW6nHs0jlcoTBeX+H+4jTPPfYOZKq8NP8zhN0ChjvBnPlz/HHzGB3efc6f/Czhvms0nQBP1R9jY2ubDd8lkraHd746SrTaQEg1quFOMp1DdOwZhPa2mWztICPoHbSJ3rWJGnZoLX2QneY7ebpf51VhI+/UUdYNNGBMatCl3+Dm0C2KuSxytUk+4VDzC+7xOtyXsIiWkmzcOM8lI4SQXTyOnx6jDd1jsOHbpeXCmWKQI9okgUCYgpnlUvpJdhuLRBoOpzIZZMnhjx+ySA97efdfcZZ+ELlLf8n//tLuviRJHwfOAhf+C89/CvgUQH9//w+4i4cc8refluXwO1//CgnP7/DQ0C6F6glOjv8ST3/rm7xeeZag4uFh4xQxKcC3o09zj/MFPuI2eS5+EukVjfGlfWZ628kdCXNrtMnsYA5ZqCRrD/BT31rj5OoTFCMCz70mwaTE9Ycj2KrM55yfwn8tx783foY71Sr71ghfyn+QzfoSmvEdXLUDJfYIqtzBxeNNPuh8EXX6AjclaEVmkB0NUQ2Qbz1Ld07BlHzcDI9h9i8w1/c0jtGGufth7MopZAHj9RUeyVzl5QtvQwp08dTGLxJyCxjuFDeaP8/XW1N0+FOcPfX7hPuvYAgv3zQeYmZvnxwXGc6H+MDqMP6WgauYFBP91ONdDGVq6EvX0EULXdIY6FAInFvD210nt/cu3iz8PZ7p1Cnkmnjm83hMl6gkGJSKBEKXWEncIJwX6K0G623Qabt8MGJzNuBQ2TjN1tIJlnFASASMJINSG0ItM+9dQxUydxajjOoTeP0Bsq1druaeId3YpbvS4sLeFg3d4YkHZF46BkmjjbtyU3/lmfqhlWUkSXoE+DfABSFE5r/2xodlmUMOAdcVPHFpmpWNf8XdPZeotaIkI7/AytIOG5sNFEnitDXClN3Ld8JvEOFzvNvZ4aJ/jNytJMPXt1nsTJCKBVjsN7h5pExLa+G3jvKxF00evTZPOQD2KYUxK8/F8wNERrIsM8Zzm2/jA9t/yIfcXWSh8kblfbxe8CK31jEkH07sbUTcMSohQffQ6wzvHeGq0aTq3wJJoDQjVKsv0lFwMWSV+cAUbmSG2aObWM0+jNJ57OoUinCYbKzynvSrXD5zN2diKT6Z+gp+UaLlHONG4yO81JqkPbZGfOJpQt23aAkvL1onuZLJYTYyTKwnmNgO4XFsHN1HOdqNpAfpTKcwjAwOErt6NxOeHIkzOwTGKtyqfoCL1ru4joy83UDJtgA4IpkEpTRG/AWQttEti9UOEyTBMQnOtxn0O0E2Fx8iX4njyDayoxExOxlVk6yyTUHN4hcaZyoxhjyjaIqXVGOd+fJr1JoNBrJ7DKZ2KIYkvnmXzLOnIWF2crQ0xR3VUYxihk/9u396W5n5YdbcVQ4uqD4M7HJwQfXHhBCzf67NaeBPgHcIIZZ/kB08lPshf9d5fXmPJ1/5be7peQqv0qJWfy+afISbNzaxHI1Rp4Nz1hEuh6fJKl/lJ4w5VrUeFjYGGX91g41klI1khK1Og2sTDcqBKl67n8eu+vjoi7M0ddi6I8J5dZvLgUGMx5uEPFW+3Xyc0K0lPmXcpFs0WG2e5YXcGNXaNg4K6dhZov67iTdkfKMbHK94uVxVyAfXcRUDpRWhWXmdRL5OS1GY9x8lKC9w9cwmRvU4tdo9uK1eVNFi0tzgx1IvcuXoOR6MbPJ44Vt4aFG3z3Cl/gGutSaId84QnXyaUHKVqgjxvDnO1cwukYzB0bV2ugoHxQMnGKEWbsdv2ui5TQQuBU+MRf8IdzVmiZzLY015uey8k0vGOVoZA89OA2E4BBCMS1VMdZNM8mUGGnlSkSblgCDkwLmgy30RAzczye7maWqWjJBAM8JEpB5kSWNLrKN5KrTbAU7Xk/TqQ4DEbmOJ+fJNaFkM7k7TlS+Ri8h89R6Jl45Bp9HH3cU7OFcbpZ5dYsVYpiNh8BO/9c3bys0PeyjkO4Hf5GAo5O8JIf6ZJEmfAd4UQnxDkqTngeNA6q2XbAkh3vv9tnko90P+rrK0X+Zzz/0HJuNfos1XoFA+TlvkPVy9fItqK0rCDXCfNclKYINp/ev8D+Z1bDfJG/uTTL66xk40xHoySjpucnXKIB0t4rUSXLgV5pPPLuMosHwqwblgipViO4sfiTMeXWBPdHN9dYCPZC5yl10kb3fzUvYcG6UiLrAUO4HWcR9jOS8D8TRDisyNks62fw1bqyBbfqzyMtFsiqYqsRiYoLO+wetnstTqZym1ziKcELqc46i7w0/svcjN4Tt4OLjKnbWXkAQU7Ae4VH0vm1YP0b6rhCafJhTZI+fGea7Vz1xqm4EtjcmNOAHTxVU8WNEkhj9MoJhFqRVQZC/rWjc7Wjtna9OY93nIjPfwuvsQqVwEz24dqWAiAUewCElFssE5fPoVvG6V9XYTgDFZ4d54g1HhJ7V6D4VSEgMFWYDS6qCh9lB0KqCm6FDqDLXCHDd7adO7sFyDteo0a/UUip1jaO1NOks2+3GVJ+51eXVKZrA+zLsKFzhZ7yeVv8FG4wbtvQVOdFUomu3c/0sv3lZ+Dm9iOuSQv2Fs5Gr8p2c+T3/gcwyEdyhU+4ho72dhbpVCNYFXqJyzj1DTS7wc+AY/ZlwhYYe4tHOUscvr7MTCbCSjZGImN8dstttyaHaA++aj/ORTmzgqzN7RzZ3hPVIbQZ5/32nODr2BTos3c0cZX1/k/a0UrvDwRuEurmYEDoL52CStvrs5Uk1yLzl6VI1bNVjzrmN6C0iOByplAvtzNDVY8w9yJL3Ji6eC5K07KDrjgIJPX+GUnOUfbL7C3tAUD8nT9FkzuMLLlvF2LlXfRUXxEx5+mfCR7+D3ldh123i2GiG3WmBkK8pgxossBLY/hBltAyT03C4e0yTi72FW8uG2DEJaibULY6R6B1mojSPttdD2GriOoA2YlMqU5DzZyGX6zGV2Ei2qfoeQq3B3yOLOgI29P0V+b5iiGQRJQrM0duln2wniyHmmlH3ijsFEI8qkcoSgJ0rdKrNUnWbbreEaVzkzt0O07rLdpvDl+wXXRzVOlCf5SOlxhptJVkovI0efZTiZo0fUCLRsAFLBfrr+8a3bytGh3A855G8IW/k6n3vhj2n3fJHByBbVZhzFejc76yky5Q4UoXDCHkD12Dwb/hbvNF7liBHkja0xRq5tsRsNs94WIROz/pzUvdy5FORT39pHSDLXzg5zzrtDei/A1x85z/HJm4xJi2w1utAWq7y/miZAk/naUb6bilG3JVajI6yN38WI2ceHSgU68DHTtFn1rmN4s0hCQalKeFOXaeg2m/4ujuykee7oGPvuSepyEpQ6/uA0d2lFfnx1Drmri3POa/goYLidzDbfwbXaQ4hQFf/Y8yQH3kBVLObtLi5nXdw5mSPbUYIGOLKKE41jhZMojQpaIU2H2kE8JJgvNrDMFtePnmLv1DDb+iDOvo2+XcdtOHiAu3CR5TTr3h1i8uvY/hLbiTqSgAnVw73ROp31Topb4xTqbVh48LgOabuTm6KbAi6TSppJd4fOOky5/Qz4xlBlD7nWLsv1ReaDu3SmrnLnbAvdcpntV3jybsFyX5hHsmf4WPU96GKXpv5HRPRFOu0GqitwJah6g+wpgudUBb0jcIW0AAAgAElEQVQe4qd+Ze6/mp2/jEO5H3LIXzNze0X+5KXP0et/goHwDpVGAqd5P7ntJplKOwiZSacHWbN4IfwsDzYvM2VFmVnuY3AmxXY8zHY8TDZmcnPMYrstj27pXJjx8LEXSziyyo07xznLGhu5KE889HbGJxe5X36ZmuXDWlF5NJsjQpX1xhCv7CfJGl42owPcOnYXU+4gP5YtEzcDTJt11vQtDG8WkPFUPeipq9T0GrvBMMm0yqWB4+wpE9iShuzdRo9e5YJa4JNrVdojEkPuFSTJIWPdwdXaO1g3T+LtmsE/9gJt7UtYQuFyq4P1ZYP4YozuggcBWP4wTiyJo3nRSjnamyp9gSQ+ZZrZfcHV/qNcO3YXua5urLxAT9UgZyGASWROSw2W5D2qvhv4PVust2VwFJc2PNwVajAlojT3jlAodNMSPmThULJDXBN9bLhBeqUKd9obtLdSjNpJjngnSejd2K7JVn2RebbZ9L/BPXMthteKCAkuTR5cKDVCnXwsdTePCR+y/1kC7jxB62Ae+pYmk/ZGuCUrfE2Dm8EwQ16Y1BusmF18/sMv3VauDuV+yCF/DQgheG1lkxev/EdGw8/Q5s9TrrfRKp2ikvKSbkSRhMSo04mrt/hu+Bne3rpJr9nN1lycnqUMW8kIexE/e20mMyM2qUQer6nx6DXBBy81qQQCzN15lHtqN5mu9vKVC4/TN77Du6Un0RwLVjXOpqtEKbPb7OaVdDs7zRBryVEWjt3BvWY/H861kJo+rjsFdvXtg/KLq6DVdNT0TTLhLGWfD6M0xHz0KBmtGwkbJXwTPXaFd1l1Pp6KMqivEmYDWwRYaD3MjdpjNLwq3pGXSAy+jtdbpeR4uZ7xUpwJ0LMZQXcElqrjROLYsSSyYRCptpgggc+7Rim7ycuxo7w6dZ6dwRGsGmipGnLGwHUl2pB4EJk4RW6qi+BbYK19nabWwC8U7giYnFJ8qJlhCtl+6k4IhEvBDXDT7mZbRAkLk7vtbfpbS3SaGiOeEfoCE2iKl4qZZ7kxx+XwdcYzOUYXasRKNRoaPH9a4pnTOvfW2/iEHaTLs4zf2kYRAkeCXFBn2RPmFUXhm14Jrz/MlN5kyttiSHfZko5wkfPYNYMvvPczt5WxQ7kfcsh/QxqmzZOXX2dj8w840XYJr2qQLg7hlvoopzvJmjqqUBh220kFdrnpe4l3tdbRqxOImybhTJ3tRJhM2Md6d4PZEZNisEKoqfHYmxbvvmyx2dXD9skh7tl8k+cid/DMPQ8xOLDF+8WfkLCLBNZUptI1/DTYabbzRraN9XqchZ4p0uNneKTRzuMZQdGQuS7S5L0pLL2E5Kp4K16k/DQrbbvU1H7yjUk2fcO0FB8euYCceA1P5Cq/mIvytjp0yTeQpRZFe5AbjcdZbt2Dp3ea0MjLxNrWcAWs1IKkljU80+34DQlXkg966Yl2HK8fb7XOuBWiU9mjUFzi5dgkl8fuZm1wAqslo6bqqOk6ri0TBt6GhxOOw4q8yY7/BpttqxQC+8hC4qgmOKMECJd7KGX6qdsRhBBk3SCLTjtbbow20+WUtUufOU/YbDKsjTAYPEbIE8N2TXYaS8wyj8ESI+sufZu7eGyX1U6Ji6cE3i6ND7csRuUiutMAoOZTWPb5uaoGeMqjsudVGfV5mNRqTHptoqpghz6eFY9wRdxLRYmjOA4nl17jW//jz9xW1g7lfsgh/w1Yy5b4xqUnkMxvMpWYwxUyu+njqJkO0tUO6q7AJzTaCDATnaXMq7y3ZVDITNJ5PYPtSmwlwpT9Msv9LeaG6jT0Jl0FDx94tcXd83D52CnsPp3x1TmeHH2Ql+68wHh8iQ+5X6DXSpNYkxnJVtEwWa11cDnXwabRzrWpO+kOHeHhVoypgsyGaTMj71LxpXE8dWRHQ6/oWOVrLLZVKbon2WGMkieOLGz83kWctsuMKyl+JRvlmL2JT97GFl6WWvcx13iUgl8nMPoiib5reDSDkqmxvuXFuN6Gt6DhImF7AzixJHY4hmLZdLU8TFlZ8tUlXklM8OboWVYHJrEaoO438GTqOJaMV3I572qctVUM0kyHr7GanCUX3AFgWJY4TYx4tZdybgDH1RAC9t0Qm26MtBWl11AYN1J0WXOoRoZ+fZiB0DHavX0IIci0NllrzrMrL9NZtOnZ2iVZrGKosDbuoBwxucvfoNM9KLVYisRGSOOKJ8jzmpebmsSQV2fM6zCuN+jVXASw5I7wDI8yL05TUZNIrsvAfp6pHZmjOzLN8CL/5J99+rYydyj3Qw75K6JlObww/ToLK19gKHyRiF6l1gpR3jmOmRsgZXhwJEGbG8LSG7wafZ3BxhLH6v3ICzLJ7TJFv4/9aIBs1GJu2Gajo4ijOExuyXzgNYuuQpCLd97HqLlKq+zw1Km3cf3YWU76bvAj9hcZa+zSuSHoLtaQECxW2rmc72FVHWBz8h7uZpiHiiqGKbPklFmXszT9+wjFQrH8eCsqeeM6S4EoaXGUPb0HIcmExQ4ieZ1QZI4fL0d4X61GO9PIkk3aGmO28Qjr4ijq0BUifW8Siu7juBLbGS/lW0nEWgCBhK35cCMxzFg7SDKJpsRUM0OhvsXFjgluDJ9krW8cpyrQUg3UbAPblvBKNncImeOGD59bZj52g9XkddKhg0lmh4TCCbcdb2kEq9KGhIwlZHbdCDtOBMcI0m8ojLS2CdkLCGOfLm8//cEpevxHUCWViplnozbDmrOMzzDo2U/Tu5dGcQX1NofwUIOR3hqKJrBl2Alo3PB5eUnx84qu0qkrjPs8jOsNBrWDmSNNAa84Z7gkHmRDmcRQYkiuS296n/Edg2O7YYIG6F3zdBx5hnUlzs9/5A9uK3+Hcj/kkB8iQghubO7y8rXPE5CeYSiygePKZPaO4qaPsF+P0sBCEyoBWWExssy6503ur/iJp5J0zhZxhUQqGqTmldnsajI/aJMPl9AsiftnHN5xzSUbP8rOSBfjO7O81n2SV07fy07vEPeLl/ig/QWOFvbp3rKJNVu0HJWZUgfXi70stJ+mO3aSB5weog2JLctkQU5R9hQx9QJI4GnFUCoWc/I+W2ofO9oAtuzB51bwBa6jtS3y0abKu6sN+sUsHqlMyw2y2HyQeftejN4tQv1vEIpvI0tQqHgoL8SozUewWyqOquGGo5iJLoTqIWgIxsop9t0yF3uOMzd8lN22PqSihb7XRM41cRzwSjYnBYw1/OhSke3YDBuJ6+yFNkCCIaFzpDmAtzyM0gogA02hsu1Eadg+vM0Y/YZJV2sDyV7EtXK0efvoDx2l1z+OLusYTpOt2hzrjTksq0R3uUj/7h5aywWvS3ygQWSogRK32Q5oXPfpvOjx85rHQ0KTGPPqjOsGw3oTnwyugA07xCvOvUxLd5DxjOIqUSTXoX9vg8nNCpP5CMFmFEmrExt+mcjwKzQslUx2kJ3yUf75L/41T/n7V8Wh3A/574F0ucFTl79FMf81JhNX0RWLfG6QxvYJStV2ShhIAkJ42AuluBJ6k5MFicFikq65ClrDoRDwUfZr7McNlgYdtpNFbNWhswDvuOowuR3lyrG76DRTmIbD86ceYHriDiQvvN38Ju8zv8lENk9XysRrO+QNH9cL3Vw3p6DzLu5WJ+g1dfZMh2U5R1oqY/gyuIqJ7Gh4Ggn2jCZzisyW3ocla2huk5A+i5pc5MOWzLtrTYbcWXQ5hS08bBhnWbbPku1s4eu9QTS5gSwLqjWV8lKU6nKYVknHVT3YoRhmsgtUD37Dpq+wzVLAz9W+KZYHpyh5Isi5FoFUE7ti4grwSTZTjsRQQyegZdmK3WQrcYVMII8kJIbMJN31YYK1bgKODEDZ9ZJ3/ChGgHCzjZ5WhoC1immtIdwGSW8P3aEp+v3j+GU/lmux21hkszqH2dhgsJqlLV1GaYIkC4LdLQLDTXJHXG74dZ7XfFz1aHTqgjHdx4hu0q838csHjszaKm/a41wWd7Ipj2JoQyDryK5Fb2qV46tppkpevM0RQMaXWCE0dAkrsEup2M1m7QjTHeOUwr28c3WT//0ff/S2Mnko90MOuU2y1RYv3niJ3b1v0Bd6g7i3RLWSoLR1llKxh5KwAPALmVQgzeXINUYrPsZzfnoX63gaLlWvRtWnU/FbrPSZrHXXqfmaeA24d97l7gWVbOIOrJBCsJrn4thZbkzeQTbZRY+7xYdrf8A7a1foy9SJlW1cAeu1GNdKg2wHHuBo8BQTTpK05bIq5dkVdVreHLZWAQGyESdrBpl1Fba1CJbswSMMIuo8wfgKH3QVHqk36XcWCCgrCCGxYx5nVZxkv9NG65shHE0hSdCsq1RWwpRWwjRyXoSqY4djGIlOUFR81QqS22KurYf5/gl22gYQVQc108KbaWEaDgBJ4TBkehiwFDy+Lfajb7KWvEZBbxK0gvQ3+0nWBogaATySwBVQFl4sy4en0U5n3SJo7SLZGxj2Ph5ZpdM/Qldogm69H13ScYXDXmOVrdocVnmW0do+0WwTtykjyQJvt0H9iMWNMYnnAl4WdI1er2BM9zKsm/RqLTzSgRPTlsyi3cE19wyL8jh1zyCOpxsAr1FicG+do7vbTFYUlNYxXCuE4i0R7L+CE9mk0vCSK/WynOhnsWOAPtPHXRubhNKLLMZk/s3/8Wu3lc9DuR9yyP8P9kp1nr/2Anv7zzIYvkybP0+9HqG4dQfFQh8l5+B74nMldkJ7zPlmOVoLM7LpEt9sITlQ9us0NZVCyGSry2Gro0ExVEdy4cSGyz3zCpZ2imbYT7BR5GbfBDcnTrHdNYAEXKh8m482n+RseZ2OrInqCEq2zq1CNyvOOTr8dzHhGSHvSGxQYkdUaegFLK341gIYfnJmnFtukC01BIBX1PFrS7SHUnzMgQvNMgkxTUA5qGFnrGE2PKPsdYDUs0YwWAKgnteprIeobIRo5HVczYcZSWLF25BcF61aIRWJMNszylrvKIarI+cNAukmTsnEEaAKQb+tMGQp9LgmVniavcRrLEW3MSSZjmYHnbUhEq02guJg/pimULFtHbURJ1b34DP3UK1NDDuFwCbkidMZmqQ7MEK70oEsyRhuk73GCqnaMmrhFoPVFP6CjTBkkAWtPpu5cZdvjqnsRVQGdRj3eunXDDo9LWQJHAG7lsySFWdanGRZGqeh9mBrwyBrSK5DspqiL5NhqjTPRM1FtI5hVgZAsvF2LkBsnarTpFRpYy/cwUpbN+Gawf0z1/HWiiz1DPDG1Ck2u3t5+Pp3+dw//Pnbyuqh3A855PsghGAlXeC7156iXPkOQ5EbBNU65VIn5dRRSqUOau5BW68r2ApsU9E2GSno9C4ZaBUXS1Go+nRsBbLRFltdLlvtVar+g1LNxLbg1KqC6k7g8XgxVImbA1PMjh4j03bQAxyvzPD36k/wSO0GvcUqgaaDhcRStY3FxmmCngsM6uPkXYUNUSAlmrT0EqaeB0ngODpZK8G0G2VPDoAQhKQcId8yR31VPmy6HGtmicrX8StpXCGTFkPsBhPkehooHWlkWeA4EvWUj8pmiPJGELOuY/uCNOI9SAE/otWioHnZ6OhhtXeEoh5FKRjomRZKwcSyDw5WxJEYtmWGLYk27xbpxCvMxZbZ1xokzARtjS7a633EHS8SYAsJ29HwNOOEazqakUG2NrHtPVwsVEmjLXiE9uAw3XofYengpFW2cuzWl8iVFwnkFxiol9GLDrgSjiZYH3C5NCmzfkxiMORhQJPp1pqElIPb/w0XNk2FFTvOtDvFujyKoXTh6GO4SuDgszSKdJRLxMU+52pX6Kt4sUvHaeaPADJqOAWRHcpSkYbppegL0USmezfF1Noi5UCYa+PHePXEHaQTbciOQ8/uLvp+le7mLl/+tf/1trJ7KPdDDvkLNE2Hi/PL3Fh4Gl1c4khsFsv0USr0Us2MUagHcQBZgCQaFAKL+O0WbTsQ3LWRXGhoHkyPStVnsZ8w2Gl3SCUqmB4X1RYc3xCMpsJ43WECwmE71sGtgQmWhyaohGJIwuVIdpb3NL/FOxrXGKxnCdUdBLDvBlgqToHzdqK+E+SFwo5UJCOaGHoRSyuDJLBcDyk7zoyIkRFBVGETVVMM6nu8W25xb6tOUswSUWdRJAtbeEir3aTiGpX+CiJwUCZp5HRquwEqOwFqKT+upFMPt2NH2wCHvOZjO9nJWs8QBW8MuWiiFQw8WQPLPNiGz4UBW2HIdRmRyuSirzMbW2TfXyRsB0k2kyQbPcStEAoSQoDjepCbScIND1ojh2JtYtopXEwkJGL+YdpDI3R6e2mTE8jI2MImb26zW17EyMyQLO8QrzXRmgf+KkZcbp6VyB0Hf4+PLs2iTW0iv7UaRdaSDmTuJJjjOHvyCI7SgaON4KhBAAKtOu3VIiG1xKBnkTO1WaKlOM30cerpSRAqsq8AwRQFqYwl2zguRApFjs7NoRsGrx87xevHTnN9/BiWx4Ni2ejZGmbOYWB9m4fWr3Jh5wapaIJPvPD128rxodwP+TuP4wpmdnNcmfkuhcIr9PqnCWuFg955vo9yuQvDUQDwU8fV17FEjeCei3/XRbYlDI+C4VGxFJdstMluu81uW5NS8GDcc6IsOLYp0VGJk2xFSSV6WOocYnFgnP32XoQso5ktTmcv85HKt7nTXKC7VcTfOujpZiQfW5UxWq1HEZ4z5BSbPVGnLjcxtQK2VgOg4XhZd6OsuzHyrp8oNXr0XR5VMzxsVugQK0SUWTT54OaashIhG/ZS6jKoxhVcWcKoeKin/FR2AlR3A9iGTj2YoBFtx/b5yHv97CQ62OropSoFkUsmnryBUjRxrIP99Qjos2WGsWhX8hSDt1gLbVHXa4SFTNyIE2sliZkRZCQEAhwPajOOrwl6o4xq7tByUrjCRJFU4v5hEoEh2nydtMlJPKgIISg7WdL1NWq5edTMEsl6mVDNBklQ74Htk4L6mAe6ZOL+Fl75rZOWC1uGzLqls+T0syJN0FS6kZV+Wt4BHEUHINysEW+UiWh5BrUVjsvX6K00MVJHqe2dolUcPAiSp4qp56jrRWylSrBaYXh9g3g2y3pnH6+cPMflqZPkEgkA5LqFlDPwp8q8beYyF7auMV7aIh6q4YtbeBMm5Wo3A1+9dlu5PpT7IX/nqLYsbm3nmF56g3zuTTq1WRL+PRr1MOVyB+VCLy3LiyQ5RPQMkkjjtBpoewIlC7KQMFQFS1WwFJdcpEEmbrPbZpGNNBAyaJZgcguG8n4iooNs+AjzXaOs9QyTix+M6ZZcl8HsGu9MP8cFY4Zx1kk26ygCHAnSSpB0/Qil6ntpeMbIynXywqSllTC1EpLsIATk3CDbboRNN4bjqBxRMrxdXee8m6eDZaLKPB65CUBFCVEIq1TbbMoxlZam0Mzr1Pf91Pf91PZ9mE0v1UCCals3htfPfiTGdlsXqWg3bt1FqpjoeQPKFq574IWgC72ORAIDvydHPrBC2p9FV+skhCBhxIkZMXT3QJgCgWx70ZtB1IaBt55FWClMtwgINNlLwj9KMjBEm7eDhBxDRkYIQcUtUGhs0cwtIdJzhKs5QpaJ0yUw+gWlcQljQEaL2qjKwcnGFpCyJLZMhWUrxiLHySmDKHIvttZHU48fhEMIoo0aYbtKQttn3DPLceUGvVaKVu4ItdQxqjuncFoJQOB4ijT1Mqaex2Pl6UinieXz7IWTXDp2hlvDY2TakiAfjItUC006tzPcf/NNHl56lSPKHuFwA2/M+t6ffNCPIO8GeYq7+cRnvnJbOT+U+yF/qynUTWa3S9xYnKWYu0ZUWiCup1HkBs1miGo1SbXShiskAoECAZHDtWu4BQtpT0KpSriSRMuj4shQDlmUgi32E4JstEUx2ELIoDiCkRQM5DU0uZdS+ASL3RNsdA/R9B3UZnWjyfjeHO/Yf5m7xSLD0i5Ju4r6liBrmsK+6CBbfoC08QAFVaYobBqeKpZWRlYOfgXUXQ+7boRdN0LT9nNGSnFBWeU0W3TKK4SUTeS3RnJUPH6KEZlKUqIU8dBSFBoZH7V934HM0z4ahMgm+2jFkpQDIVKxNnbi3TQtD1LFQimZqGUT13zr4oKAuCsREy4BtY7tyZPT06hakaRSJeHKJIwYQTv4VnMB2HibEZSGgt4sozb2MJ08LhYyChG9i6h/gJivi6QnSUyOAOAIh6KTo1Jdx8gtoaQXCNoFtHYbq+9A5o0hCTnuIB2MhKThSuyZEruWzJodZlWaJCsNIcu9CE8fNb0dIR809htNIkaVqFykU9tiSr3FhDRPTBQxin1U9o9R2z6OWRkEoSJwMPWDewJceZ+23A6u5TLXM8Rs/yhb7d1UwiFQDmTuLTcY2djiwZlXec/2S3T5S3hjFnrUQg/b39vnlqMyI3dw3dPGtNzJDWeEXWuEqH+Ja7/067eV/UO5H/LfPS3TZm2/wK2VDLOb+5ilRULSHiGthE+pIzAxLJ1mI4xla3i9dbx6BV2UES0Dp2jj7ktQVJBcCVOVsRSZqt+mEDLJxRyyMZNCqIGlHnwPAk3BUFoi6MRBHSYdPc1m5yiZRAfira5Xf2qVR9df5l5jhiPqLu1ykbDdQuZgceGaTyUrukg3TrJTO8e+SFCSHVpaFclTQZUPhlI2hUraDZF2AiSdJneQ4qy8yai8SVxZQ5frAJiSSimgU40JKhGVSkil3tRpZHwHQk/7KFUiZOID1CMJyqEIqVgPu8F2WpaMXLOQqxZKxUL8mcg56JWHhMCjmBiSRUWtEvPukFALJDCJ2T5CVgiZA1M5koligbcWxtdsotRTWHYWV1gokoeo1knMN0jU10nMEyeihFHeeq0pTCqtFI3SKk5hDtVZRIs1sDsFdq/A6AcR+88uKtkSO5bEtqmy7PawKU9SkQeRlG4cTzctLfK9th7bImaUiUlFOtQdJtQZjkm3SJBHuBLNci+lvRO0Ukcwy0Pg+AGw1RqmVkSQxtvYZi8WY653mNWOPoqhCK721hLTrkuyUOSOrRke27jI/aVrJPxV9IiFx/+fj2fN0FkSbVzVerimDDBrT7JljXKwxhGAgy6ViLpFhpR9vvjP/z8rlf7AHMr9kL/RCCGo1uus7OZY3i2wvFsitV/EZ+4TVksE1Rqa3EJgIWQTgYssu+h6A01v4FHqYFq4NRsrJ2PkNERBBUM6mEZWUWjogkrAphA2yUVcimGTcrD5PZErjqA3pxA2Y7hqP+XQUfaSx8gkOxGyguLYHNld4KGtVzlXX2bEs0ubWiAqmm8pC0xVoaDEyBqDrDZOsmBNUFI0bE8NRa3hU1rf+8xVV6foasTcBgNuiSl2OaJs0qWuo0kH5RUHiapXpxaBSkSlHFapKh4aOR/1tI9qNsC2OUA6MEAp2kE20knaH6cqaVC3D0Res5Hq9vfeVwJ8QqBJDq7k0hIQVPMk9H1iSoWYZBK2NQJvSQ/Algwk20Rr6ITrBkozi2WUcYWDTwkS0dqJePuJejuJaXHCUhBJOrh6abgt6q19Wq0VXOaRPOuISBm708XuEDgJ+LMD6ArImxLrtocFp5tNMcC+NIAld+Oq3Rh6B658IFlJuISMOlG3RFLJ0qNsMi7PMCnN4eVgdSXHUcmnj9PcHsfMDWI3epHEQcnIkVtYWglbypANWix1d7De0U0+GMVVFP7s6qvPaHIitcA96RvcWZvhrD1D2Pv/tneusZokZ33/PVV9ee/nOmeuexnjG3bMGmMgQAAnEolx5KDIQRihhMROSCSQSCJFgCCQC0SGfAkJSIAcIoHi8IHcHGJEoiRESghgO9hm197ZnZ2Z3bme+znvpd++VT350H3mvOfM7O7M7pyzs5P+Sa2qrq6q/vfTVU9XV/f79v55LJxlNZvnIqf4on2cz8s7edq/gy0GezlomU36fouVfIfT011OjHY57Uvmwzn64QLZ8jYf/cQvvaa+0zj3hjecNM14/voaz17d4OLNba7cGjHdnbDIkMVol14wpN3awYZjxGaIKYniKVGUYIMUyXPyoTDaHpBuxMi2YCaKFkoaCuOWZ9pyTGNHGnmmsWPU8Qy7JeNOThq521raGZzYjQl1mTx+gu25r+bWifcybQ/oT4ac3rrOt9z8El+7/TxP+OucjNZZCof0KW7XMZWIKzzOF9OneK58OxuySGE9oU3o2ISW7DvUTA2By1jRIed0k/Os8ljwEguyfntqpRRh1AkZzxlGvYBRL2DXhCTbbXaHA9anJ7nmznPZvoP19gpb8RxjLEwdJqkcuMyMxAFCFIunQAjwDGTCIFxnIdxmySQMvNBxLYT6u6Q4CkkIipz2VOmMp8hkF1ekRKbNIFxkLj7DXOs0c9ESc8E8sUS395foBhPzZQp5Dg1ewrfXKBczypOKH+zrcg62p4YXdMCz8hjXOMOaeYKJnMUFpyjCRXRvLgPolAkLfodlWeO0XOOrzHO8W55mjt3beXaLAaP185Q3HqfYeowyOQv5ElKPlEs7IQ92WO/lPHu2z7NPnGHY6iBwe/omdAVfvfUCT+1e4L3TZ/n69BnemryEQSm85VqxzPP+HH9sz/MVnuA5Pcc1PYFHCc0ubbYZlEMW813OTae8NYOTBPTDAd1gjn67TyecIwsDNsIRq+EGq+EWC97z13/kE6+pXzXOveFYWNse8cyLqzx3Y5MXbg25eWuInWQs2QmL8Sbz3TVa8TYmGmOClCBKidpTVDKS3Zjheo9svUU5DMhKSE1BFhakYU4eFKRxyTT2TCNHGu87cmfvbLeiyiAx9KcdrC5QRKcYdytHnocnWN7d4l0bL/HerQu8NbnCKW6x3NpmMUzoSYFTYY0FruoJnveP84I7z6qeZCJd1HhikzEwKT2pRokWR19HdHzCgo44JVuctusss86AEYZKowJJyzLpWcYdy6QbsBuFbOR9dtJ5VvPTXCzexkV9BzftaRICNPNIUiKJQ1WhFfYAABgLSURBVIqDDlxm6m1T0JOMvpkwH26xGOzSp6TjLKGGt8t4PJlMkHJKJy1pJwV2PCXMPYNwgV64QC9YpBev0Avn6Qd9IolQcZTxNml0k9RepjAv4eIblP0dysUpvn/wPKSJ5UqxxHNyiheCU6xyih05Q2ZPUQbLeNu+nddqybzbYVk3OGlu8Lhc4W1c4Ixcp0N1J1NqwKiYIx31KNfOkG+cJR+fpcxPYfM5TH2MiiMLx+z2Mq6uWJ4/12V1bsA46KL1HUXkct4xucx7xxd4anSBp8YXeOfkEsOywxV/iufkLJf1NJf1NM/pOW74BVrsEpptOrrL6Tzhq1LP+TxghZheUDnwbtTHxQE7ccpquMlquMmtaINb4Tq34nXWgh0KcQfs9J5yjk99/H/db3erzn/j3BseBGnheOHmNl++usbFm9tcurHDeDOhWzhWoi1ODq7Ta68RxTuYaIyLS6bGsDmZY7TTI9kxaJagJJQyJTM5qS2YBmXtsPcdtzd37l889FPoT6CXWtp5hKFLGcwxjRbJ4gWyaIkimCeSHqdHI86PrvPE5Dqni5ss6ibzwQ7zrSnWCjsMuKWLXNdlXtQVrvqzbLFIKjEiStdkzEnKQDICcXSZsGw3WTBbLMiQRXZZ0BFzfkTPTevxb0VphaRtSdqGadsyikKuyyLPuid5KX+M68U5bvozrPslJmWrctypg8IfqAf2+2SLkp5k9CRlzg5ZjLbpmyltD7ELMLfnc6sHnAUp3meEWUGclnQzoVdYBtqhFwzoBAM6wRydYI5u0MdGDhftUrS2KFrrZOF1iugGZbxB2R3i+tPbUygKjBiwPlnmZr7IVZa5Fi6wFiwxkiWmdgUXLKMS3NZk1DHvd1hkkxVZ5TGu8IRc4TQ3WGYDg2fk+4yzOfKkQzlsUe4sUw5P49MT2GwOW/bQcsCeEMVTtiYMBxk3lywXV3p85cQSpa32a7XkLck13jm5zDsmlzk1XqM/GaNTx6pf4KYuVYtbZDPvIJrT1zEnpeS0eh5zhjNlwLxp0wq6FC1L0irZiRM2gm02gh02wh02wi3Wwy22guEdzjsGWsYTSGW3TCErLYOix3I24J2rJ/iZn/71++2OVZ9onHvDvVA6z42dhAvXNnj2WjX/vbY2wSQlJ8IRj/WvsdC9Tqu9QdHKWDVzrE96THY8fjqFMqGQhNykpDZnGpRMwmqEPY0deheHbTx0M6GTWdpZSFTEhK6F0Q5Ch8DHdAjol4YFp/RcRtdN6ZYTuj6hpSkxU8RaXBDjo4DShOQmYCoRQ+2yqvOs6QIbOs+QAZnEROLpS0bfZPQl4UywylK4zbzdZt4M6cuIvk/o+pSuy+gU+e03XvbIQ2ESh+wELVbtPFflBJc4y3PyBJeLc2wX80zyNnkWoGV1cbqTqs4IR1sK2pIxkIQFu8VysEvXpIRqsb6FUXugZKkF6gvCUmkXlePuOUtPW8xrl47t0wpaRF0I2yU+HlHGO5TxbuXEo03KYAsX71K2JkzDkBGD/UX77GZz7BRzbGufbTtgK+gzDBbJ7QIq4QE9op6ujlhgmxVZ5Qw3WOEWK6xyklsssUniuiR5D5kEBBNDODKYcRc/XcJli7hyjtLNkbolpm4ZnbnjyOOMnYHn5nzMpaUuq3Mhm32Lt0K3mHBqusFCskN/OqI9mRKNp9jdjDwLyLI2YRkzb2BelDkjzImlj6VlFQk9aexJYsckytgOhmwFQ3bskO1gyEa4zVYwxB06iRboihAZh62dd65QliGdrM9KOs+58Rxnx21OjVqcTGIWk4Bu6iEbodmQ3U6b9//WJ++1mx60+T069+DVMjS8uRmlBde3Ey7d3OLCi2tcvb7NeGNEyyUstTZZ6q3S7d5k1PbcynvIOOSESdDOmJSEZ7IpU5eRTHImUck4usa05dDFQztSaOeGdmZZyixLk4B5Z5lzwpwzLDjPwHnmXEHPZ1gczgi5hdRAKspUShLJGGMYaciQNqumw0XTJgk6pK2IVGNKCSkJiMQR4QilJMTTkpRTwQZnw3WeDDZ4r71E10zoSkJXE3o+peMy2mVBnDuMA2YGXB5hZGO2TI+rrHDNnuBFc5JL7gyX3WmulKcYpx0YCXfDUtIlY0V26TOlaxK6YULPTuiYjFDKapytEca3bs8NzyJlSOhCWs7QcYaeKL1I6QeGfmSIWyEmLnDhGB+OmYYpw7hkGCiXQ88oFCahIaFLQpdJHSY8ycR1SXynSjcdxqaPO+Ssq6evIK2SWMd0dcSAIeflK6ywxYJusew3OFFssFJsMkhTSAM0iTBTS5ApQWHQvAflKbx7K7Gfw7DCrj/J0C1SFAPw+/P2XpTdrrC+FLLVM2z2LWtzls2BIXRT+tOU3iSlP9nkPesTFpIxi9mYjqZIUOBtibeOMigobEl+osQGJWILEpNyxaQkJmW6F9avnt6NHoaOCJHxdERpicOUQnfSYrDb4dR2j3PDNsvTFnNToZdCO3VEaYHJEjQbodmL4Is76i4ATAAS4OZPvqyGB8U9OXcR+SDw81QXrU+q6icObY+BXwO+DtgEvkdVrzxYqQ2zOK9sTjKuru/y7MUbXL28ys7aNrYY0WuN6AS3QHYYF55JFuG9B50iNqEjKVmUcYWML08LJkXJOHFMWo50yWNU6XtP33sG3rNQKG9PhcVEWNyFQSG0XBujMRBSEFNIxJSYKREjaTPSDkPtcJ0eF7RDTgRWEOsJcXQkpyspXTK6MqVLSpeUNjknJeFJ2aJtMiKbEUlBKAWBFISUWHWElIRaEuIIvSPUkrYv62FUvcwwpsUac7ykZ7jlF7mhy9zSRVZ1gVu6yLrOM6SLFSXEEUtBj5QOU3qSclp2OW/WiG1BgCPAY7DVa4JiESMYqxjj7rqE4olQYgMtMyGyHhMKZRSQR4YssCShIbGGcWDYsZaxCZhKp3bOHaa0mbK/ntClmHm4eTdES4xPMJoQ+oSYCR3d4qRLeFs5YsnvcqIccbIYs5RN6GYp/WlKN0sJS4hKR1w6YieELsB6SyILbHGaHX2Km7rIxC+QuQGl66JFG1uGd+iYxMqw7Ri1SobtlN32Fkk8JA23KYMdYj8hKlNCnxL5lLPbGSs7+w5522RsdRW6r9wvjBraPqSFpYWhbSA2yglRAlEsBqtdbGkJcqEztQx2YXnDc+oqDMaWVqa0soIgy5A8RdQDSb1sHDKwQcWiYshFcMZQxh3yMCSPI6atmHGnxdrcApeXzrDZXcIQsRQo3/jKh/K6eVXnLiIW+EXgO4BrwGdF5NOq+uWZbB8HtlX1rSLyUeBnge85CsGPMoXzbI0zrt1Y5dlnLvLSlZtMt3ZpZWv0dJ3CTEkxpGoobUkWlhRhiZccr1NKzcltgXMFRnIkKAg6BdHAEYnSLiMGRZu4bBO6iLDs0cpDwjJACFA1eDF4NZQElBg8gsFXDlUKVEqmUqCmJJKCiIKeFMQyIaIkJicmp0VOTFGHORHuVY9ftfojKY+h8JbSWSZ5TKIxU43Z1TapRqREpBqSSUAmEbkJKAjITEhuQnIJcMZgjCACxkJkHaF1BMZhrSO0JU/Ym5w31xHrEHt3p6xGKY2lMCGFtZQmrOImJJd6fyYko0VGPBPGpHek7W9L6Nw5ar4LcZkSuSlROUV8gjIF3SHUhGUSWprQKxN6LqGfT+kXnn4O85kyKGCuMIQ+QjVGq5lgvMbktClYovRnKIgofcSmj1h3AeoCxFlsaQhKS+juMrdWk9uCJEyZRhOSeJ00HDGJthhHG4yjNcbRNpN4B2fKl63DeKHwETEhMQGRWCIMfRGWJCQkIKZLW5SWeFpSzWUHKlgiAhdgXAtTtPFlC5dH2LESDKG1k9PdSeiOE3rjMb3xmPb04LMSleoCjQh7j6sVpRQo4xZ5GDKNItJWiyIMKaMAFwR4I7TynCArKelQxIu49gpu8Bjl3FlsEBLUNS5gmTeG3Fa/gD71NSde9dy/Xu5l5P4NwEVVvQQgIr8BfBcw69y/C/gHdfw3gV8QEdE3akL/iHHOk03HbK2vsvrSS1y/fIW1G9fZ3dxA3BBPSSaGobFshR0SQjyeiJKey+nmBT3viBRCL6gVVAK8WDC2ek2rdk59PCZ0KI7YGLpSYMURmBIrJcZ7rCkRMViJEAkwxJhSEadIXj2sE9GqQVcrYD2EKUpWOXQREMGJ3F53pgpVBDWCN9WvOhMxTGwLL616O1UeAUwdNxxYB63+llao5uH34rKnqc4nWof1NgQvoNRaTOX8vRgcFo/FcTDuq8sRrl4KQqoxfxXmRAfWC6JDaVGdL8TJ/c1cWu+IfUHoCiJXEriCwDsC52h7T1eTaiTtt7GFEJdKVCpx4Q+EUaHEhWLUICr1YhDtIr6P8YLxhsBbrDME3hA6e/sVRyqLs3MPmgUPNsMFCZkdkgVT8mhK1knIgilZkJDbaR2fkgZj8iCltBlGlNhZYmdoOQiplp7AXKDYwBPSJ/RKpEoI1cjaR0RljNU2qi28i3AuwLnwQOh95R6Nc8RZRpTn1ZJVYZyltKZTWtOE1nSDdjIlzgtEhNIYCmPIIsskDlmNIy7NLzGpR9RiQ2KNaWdCP03pTyf0JiM66Zg4zYhKR1TmdFLoBS0yH5MN5sjaK2QLK6SnHiM9+wS6vEzYDYk6IXEnpNsL6bcjenHAoBXQb4W0QnP7dwDHxb203LPA1Zn1a3DHHcXtPKpaisgusMQd9zCvn5/4xX/If37n1wOgBxry3Q13t3Q91AH2eYV0uTP9dj2Pn6+We6zvoKZX1vKg6tvPszcyMShye/EIKnfOAb/Zsd4TqBJ6T+CU0CuBUwInWAfWQeAgLgXrlcCxH7qC0OVEJYROCWtHHJZK5CAsZ9Lq9Tvf0Ay4l27mxOGlxIvDmSruxOFNHdbxss7nQkdpckpT4ExBYQq8cTgpUePxpiqj4lA8iAMpEVw1VUOB0RJLTugLIjwxntCADYVBEBJGlkACLBG4AHUWXAcp5vFp7YR9gHcBzlV3fhzqb8Y5jCuxzmHLgqAs98OiSjeuxLgCFEpRcmPIAiENhCSExHrKUPHGoabEt3Nc12ERulNlcZhxwmas+BRblLTy+gK5JyI9bO2KwoakUYtJu8+k3WfYWeTm4nkmnQFJd45Jp8+4M8f23AmmrW49sj+EB9Z270x/Fd51ZsBPffjd913ufrgX5343r3m4Cd9LHkTkB4AfAHj88cfvYdd3EuYFZ7K127uVO3dTbXmZm4a7Cb1dh75M+uH96GyeOxPlwOqeW61Hr7fXdL/2w/VpvU+d2YNKNSrby1uP5qrycju/7pXbGxIrdTm5Xa/xda2+yiu69ze3e+v7+Y2vy+levXXcV2NEVa117tlQqyNTX8erW1xVUKm+DO9RVBRVxc8cvO6lC/UIntox1XZUX50H9Rj11T5VMfjazvV+UYxWdQl7h3K4PeieGfcOauYcVNqQ/Xak4slCKEJF6mM0eBSptVT1GKpnFlJrtOoR9uKK4LB13GqJEYfxjkCVSMGqVJ1SFeOrozJINb/vDUKAIQAVtLS1mRWnjqL0OF+i3iFeq/3p/uFKfSulGiO09i/wYqq4Sn0nZnBicCJMRSiN7N/BobdDL+DFo5JSeTmP1YLQpcRlQsdldPOUtiuJ1dNSiJwh0ACjUf2OeoAnoMRSSkBJQOEsrrQ4qaYFfdVU74o3FmcD8oWAF5cDLtmA0oY4G+BsQGkDyiAkjTpkcYssapNFbfKohbOP9vsk93J014DHZtbPATdeJs81EQmAOWDrcEWq+ivAr0D1KuRrEfxTf+enX0uxhoaGhv+vePknJft8FnibiJwXkQj4KPDpQ3k+DXx/Hf9LwH9/VOfbGxoaGt4MvOrIvZ5D/yHgd6hehfxVVX1GRP4R8DlV/TTwL4FfF5GLVCP2jx6l6IaGhoaGV+aeJp1U9TPAZw6l/eRMPAW++8FKa2hoaGh4rdzLtExDQ0NDw5uMxrk3NDQ0PII0zr2hoaHhEaRx7g0NDQ2PII1zb2hoaHgEecP+z11E1oEXX2PxZY7grw0eEA+rtkbX/fGw6oKHV1uj6/54rbqeUNVX/eexN8y5vx5E5HP38mf1bwQPq7ZG1/3xsOqCh1dbo+v+OGpdzbRMQ0NDwyNI49wbGhoaHkHerM79V95oAa/Aw6qt0XV/PKy64OHV1ui6P45U15tyzr2hoaGh4ZV5s47cGxoaGhpegYfGuYvIr4rImog8PZP2lIj8HxH5YxH5TyIymNn2YyJyUUQuiMifm0n/YJ12UUR+9Dh1ich3iMjn6/TPi8ifmSnzu7WuL9TLyjHqelJEpjP7/qWZMl9X578oIv9cHsC3wO5T2/fN6PqCiHgReW+97UHb7DER+R8i8hUReUZEfrhOXxSR/yoiz9fhQp0utU0uisiXROR9M3V9f53/eRH5/pfb5xHp+r5az5dE5PdE5KmZuq7UNv6CiHzumHV9QER2Z87XT87U9cD65WvQ9fdmND0tIk5EFuttx2Gv767XvYi8/1CZo/NjqvpQLMC3Ae8Dnp5J+yzw7XX8Y8A/ruPvAr4IxMB54AWqvyO2dfwtQFTnedcx6vpa4Ewd/xPA9Zkyvwu8/w2y15Oz+Q7V84fAN1F97+i3ge88Tm2Hyr0HuHSENjsNvK+O94Hn6rb0c8CP1uk/CvxsHf9QbRMB/iTwB3X6InCpDhfq+MIx6vrmvf0B37mnq16/Aiy/Qfb6APBbd6nngfbL+9V1qOyHqb43cZz2+mrgHYfbM0fsxx5Ip3lQC4ecEDBk/7nAY8CX6/iPAT82k+93qBzUNwG/M5N+IN9R6zpURoBNIK7XD5zYY7bXgXyHGuOzM+vfC/zycWo7VOafAD8zs/7AbXZof/8R+A7gAnB6xiYX6vgvA987k/9Cvf2AnQ7nO2pdh/IucHAQcYUH5Kxeg70+wN2d+5H0y9dor08Bf+M47TWzfqA9H7YDD9iPPTTTMi/D08BfqOPfzf7n/u720e6zr5B+XLpm+QjwR6qazaT9q/r27+8/iOmP+9R1XkT+SET+p4h8a512lspGexyVvV5N2x7fA/ybQ2lHYjMReZLqTusPgJOqehOgDvemf469nd2jrlk+TnV3sYcC/0WqacEfeBCa7lPXN4nIF0Xkt0Vk7wvQD4W9RKQDfBD4tzPJx2Gvl+NI29fD7tw/BvygiHye6jYnr9Nf7oPc9/Sh7iPUBUDdqH8W+Jszyd+nqu8BvrVe/vIx6roJPK6qXwv8XeBTUs15H5e9XkkbACLyjUCiqk/PJB+JzUSkR9XB/7aqDl8p613Sjqyd3Yeuvfx/msq5/8hM8reo6vuopmt+UES+7Rh1/V+qn8Y/BfwL4D/sVXGXvMduL6opmf+tqrPfd34j7XWk7euhdu6q+qyq/llV/TqqEd0L9aaX+2j3vXzM+yh1ISLngH8P/BVVfWGmzPU6HFHdGn7DcelS1UxVN+v45+v0t1PZ69xMFUdir1fSNsNHOTRqPwqbiUhI1fH+tar+uzp5VURO19tPA2t1+rG1s/vUhYh8DfBJ4Lv2zi2Aqt6owzWqdvi6bHY/ulR1qKrjOv4ZIBSRZR4Ce9XcrY0dh71ejqNtX0cx1/Q65qie5OA87UodGuDXgI/V6+/m4IOIS1QPIYI6fp79BxHvPkZd8/U+P3KofEA9rweEwG8Cf+sYdZ0AbB1/C3AdWKzXP0v1sHDvgeqHjvNczqRdA95ylDarj/HXgH92KP2fcvBB3M/V8T/PwQeqf1inLwKXqea7F+r44jHqehy4CHzzofxdoD8T/z3gg8eo6xT7z1W+AXipruOB9sv71VWvz1F937l73Paa2f67HJxzP1I/9ro78YNaqK6oN4Gi7ugfB36Y6onzc8An9hpOnf/HqUZ/F5h5w4PqDYfn6m0/fpy6gJ8AJsAXZpaVuuF8HvgS8Azw89TO9ph0faTe7xepbp0/PFPP+6nmw18AfmHWxsd4Lj8A/P6hOo7CZn+K6vb2SzPn50PAEvDfgOfrcO/CJ8Av1rb540Md82NUDvYi8NeOWdcnge2ZvJ+r099Sn+Mv1jZ7Xe3/Nej6oZl29vvMXHx4gP3yfnXVZf4q8BuH6jkue/3Fuh9kwCoHH5YemR9rfqHa0NDQ8AjyUM+5NzQ0NDS8Nhrn3tDQ0PAI0jj3hoaGhkeQxrk3NDQ0PII0zr2hoaHhEaRx7g0NDQ2PII1zb2hoaHgEaZx7Q0NDwyPI/wPZ1LW3Bz4nrAAAAABJRU5ErkJggg==\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_IMAU_VUB_SU_RCP45[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_IMAU_VUB_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 }