{ "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_RCP85_HistConstraint.dat\" # File to read\n", "df = pd.read_csv(fname,sep='\\s+',index_col=0,header=None)\n", "df.columns.name = \"ensemble member\"\n", "df.index.name = \"Time\"\n", "SAT = np.array(df.values)\n", "\n", "print(len(SAT[:,1]))" ] }, { "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": [ "# SICO_UHO = SICO_UHO\n", "# Read response functions\n", "\n", "fname = \"../RFunctions/RF_SICO_UHO_BM08_R1.dat\" # File to read\n", "with open(fname) as f:\n", " RF_SICO_UHO_BM08_R1 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_SICO_UHO_BM08_R2.dat\" # File to read\n", "with open(fname) as f:\n", " RF_SICO_UHO_BM08_R2 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_SICO_UHO_BM08_R3.dat\" # File to read\n", "with open(fname) as f:\n", " RF_SICO_UHO_BM08_R3 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_SICO_UHO_BM08_R4.dat\" # File to read\n", "with open(fname) as f:\n", " RF_SICO_UHO_BM08_R4 = np.array([float(row) for row in f])\n", "\n", "fname = \"../RFunctions/RF_SICO_UHO_BM08_R5.dat\" # File to read\n", "with open(fname) as f:\n", " RF_SICO_UHO_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": [ "0.52325 0.5263157894736842\n", "0.8949 0.8947368421052632\n", "0.5225 0.5263157894736842\n", "0.4715 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_SICO_UHO_R1_RCP85 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_SICO_UHO_R2_RCP85 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_SICO_UHO_R3_RCP85 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_SICO_UHO_R4_RCP85 = [0] * (tend-tbeg)\n", "SL_wTd_nos_base_SICO_UHO_R5_RCP85 = [0] * (tend-tbeg)\n", "\n", "for i in range(EnsembleSize):\n", "\n", " # Select forcing randomly\n", "\n", " # select global warming path\n", " iTens = rnd.randint(0,NumTensemble-1)\n", " Temp = np.array(SAT[:,iTens])\n", "\n", " # select ocean model\n", " iOmod = rnd.randint(0,NumOmodel-1)\n", " OS_R1 = OS_WiDelay_R1[iOmod]\n", " OS_R2 = OS_WiDelay_R2[iOmod]\n", " OS_R3 = OS_WiDelay_R3[iOmod]\n", " OS_R4 = OS_WiDelay_R4[iOmod]\n", " OS_R5 = OS_WiDelay_R4[iOmod]\n", "\n", " tau_R1 = int(OS_Delay_R1[iOmod])\n", " tau_R2 = int(OS_Delay_R2[iOmod])\n", " tau_R3 = int(OS_Delay_R3[iOmod])\n", " tau_R4 = int(OS_Delay_R4[iOmod])\n", " tau_R5 = int(OS_Delay_R4[iOmod])\n", "\n", " if tau_R1>0:\n", " countR1 = countR1+1\n", " if tau_R2>0:\n", " countR2 = countR2+1\n", " if tau_R3>0:\n", " countR3 = countR3+1\n", " if tau_R4>0:\n", " countR4 = countR4+1\n", " \n", " Temp_R1 = np.append(np.zeros(tau_R1),Temp[:tend-tbeg-tau_R1])\n", " Temp_R2 = np.append(np.zeros(tau_R2),Temp[:tend-tbeg-tau_R2])\n", " Temp_R3 = np.append(np.zeros(tau_R3),Temp[:tend-tbeg-tau_R3])\n", " Temp_R4 = np.append(np.zeros(tau_R4),Temp[:tend-tbeg-tau_R4])\n", " Temp_R5 = np.append(np.zeros(tau_R5),Temp[:tend-tbeg-tau_R5])\n", " \n", " # select melting sensitivity\n", " MS_R1 = rnd.uniform(MeltSensitivity[0],MeltSensitivity[1])\n", " MS_R2 = rnd.uniform(MeltSensitivity[0],MeltSensitivity[1])\n", " MS_R3 = rnd.uniform(MeltSensitivity[0],MeltSensitivity[1])\n", " MS_R4 = rnd.uniform(MeltSensitivity[0],MeltSensitivity[1])\n", " MS_R5 = rnd.uniform(MeltSensitivity[0],MeltSensitivity[1])\n", "\n", " # Compose forcing time series\n", " M_R1 = MS_R1*OS_R1*Temp_R1\n", " M_R2 = MS_R2*OS_R2*Temp_R2\n", " M_R3 = MS_R3*OS_R3*Temp_R3\n", " M_R4 = MS_R4*OS_R4*Temp_R4\n", " M_R5 = MS_R5*OS_R5*Temp_R5\n", "\n", " M_R1[M_R1 < 0.0] = 0.0\n", " M_R2[M_R2 < 0.0] = 0.0\n", " M_R3[M_R3 < 0.0] = 0.0\n", " M_R4[M_R4 < 0.0] = 0.0\n", " M_R5[M_R5 < 0.0] = 0.0\n", " \n", " # Linear response\n", " SL = []\n", " SL.append(0)\n", " for t in range(1,tend-tbeg):\n", " dSL = 0\n", " for tp in range(0,t):\n", " dSL = dSL + M_R1[tp]*RF_SICO_UHO_BM08_R1[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_SICO_UHO_R1_RCP85=np.vstack([SL_wTd_nos_base_SICO_UHO_R1_RCP85, SL])\n", "\n", " SL = []\n", " SL.append(0)\n", " for t in range(1,tend-tbeg):\n", " dSL = 0\n", " for tp in range(0,t):\n", " dSL = dSL + M_R2[tp]*RF_SICO_UHO_BM08_R2[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_SICO_UHO_R2_RCP85=np.vstack([SL_wTd_nos_base_SICO_UHO_R2_RCP85, SL])\n", "\n", " SL = []\n", " SL.append(0)\n", " for t in range(1,tend-tbeg):\n", " dSL = 0\n", " for tp in range(0,t):\n", " dSL = dSL + M_R3[tp]*RF_SICO_UHO_BM08_R3[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_SICO_UHO_R3_RCP85=np.vstack([SL_wTd_nos_base_SICO_UHO_R3_RCP85, SL])\n", "\n", " SL = []\n", " SL.append(0)\n", " for t in range(1,tend-tbeg):\n", " dSL = 0\n", " for tp in range(0,t):\n", " dSL = dSL + M_R4[tp]*RF_SICO_UHO_BM08_R4[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_SICO_UHO_R4_RCP85=np.vstack([SL_wTd_nos_base_SICO_UHO_R4_RCP85, SL])\n", "\n", " SL = []\n", " SL.append(0)\n", " for t in range(1,tend-tbeg):\n", " dSL = 0\n", " for tp in range(0,t):\n", " dSL = dSL + M_R5[tp]*RF_SICO_UHO_BM08_R5[t-tp]\n", " SL.append(dSL)\n", " SL_wTd_nos_base_SICO_UHO_R5_RCP85=np.vstack([SL_wTd_nos_base_SICO_UHO_R5_RCP85, SL])\n", "\n", "SL_wTd_nos_base_SICO_UHO_SU_RCP85 = SL_wTd_nos_base_SICO_UHO_R1_RCP85+SL_wTd_nos_base_SICO_UHO_R2_RCP85+SL_wTd_nos_base_SICO_UHO_R3_RCP85+SL_wTd_nos_base_SICO_UHO_R4_RCP85+SL_wTd_nos_base_SICO_UHO_R5_RCP85\n", "\n", "print(countR1/EnsembleSize,10/19)\n", "print(countR2/EnsembleSize,17/19)\n", "print(countR3/EnsembleSize,10/19)\n", "print(countR4/EnsembleSize,9/19)\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "Time = range(1900,2100)\n", "ncfile = nc.Dataset('EnsembleSingleModelProjections/SL_wTd_nos_base_SICO_UHO_RCP85.nc','w', format='NETCDF4')\n", "ncfile.createDimension('Time', None)\n", "ncfile.createDimension('Emember', None)\n", "\n", "SL_wTd_nos_base_R1 = ncfile.createVariable('EAIS', 'f4', ('Emember', 'Time'))\n", "SL_wTd_nos_base_R2 = ncfile.createVariable('Ross', 'f4', ('Emember', 'Time'))\n", "SL_wTd_nos_base_R3 = ncfile.createVariable('Amundsen', 'f4', ('Emember', 'Time'))\n", "SL_wTd_nos_base_R4 = ncfile.createVariable('Weddell', 'f4', ('Emember', 'Time'))\n", "SL_wTd_nos_base_R5 = ncfile.createVariable('Peninsula', 'f4', ('Emember', 'Time'))\n", "SL_wTd_nos_base_SU = ncfile.createVariable('Antarctica', 'f4', ('Emember', 'Time'))\n", "t = ncfile.createVariable('Time', 'i4', 'Time')\n", "\n", "SL_wTd_nos_base_R1[:,:] = SL_wTd_nos_base_SICO_UHO_R1_RCP85\n", "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_SICO_UHO_R2_RCP85\n", "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_SICO_UHO_R3_RCP85\n", "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_SICO_UHO_R4_RCP85\n", "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_SICO_UHO_R5_RCP85\n", "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_SICO_UHO_SU_RCP85\n", "t[:] = Time\n", "\n", "SL_wTd_nos_base_R1.units = 'meter'\n", "SL_wTd_nos_base_R2.units = 'meter'\n", "SL_wTd_nos_base_R3.units = 'meter'\n", "SL_wTd_nos_base_R4.units = 'meter'\n", "SL_wTd_nos_base_R5.units = 'meter'\n", "SL_wTd_nos_base_SU.units = 'meter'\n", "\n", "t.units = 'years'\n", "\n", "ncfile.close()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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LaWcfT5RcwurSl5mbdQWHNx2gxdrMmDFCGDWR+tXow0HujcnGGVLoDg9FfVwy3CVJ+tTq6uqipqaG+fPnYzab8fv7GbBvQaOxkJf7jXe/of0giqOBVpsR19BsQvhI7uimOSYfc4GLC7P9ZB7cyt4+FScLnAggtns5PfosbguFSRcadigtzJzgivrYZLhLkvSppCgK27dvx2g0Mn/+fACqT98FKBQUfP/dlz4CrL8TgP/VTiY+EE9MbzcTuuy8PHkZN018mTLVJJ45lsCJIgdhNUQ8OXSPLGH5UDtXGDM4jp2c5b+nTdkW9fHJcJck6VOpqamJtrY2Fi9ejF6vZ2TkFE7nYXS6RDLSV7/7De37URz1nIw3oxueQTA8Sm5rB/tTZnHZvA2UZX+G2lf206Z20JQ5hhLRE2lfTarHybdtpdjDAXwzH8DjSkTnnBj18clwlyTpUycSibB9+3bi4uKYNWsWiqJwuubbABQV/ui9j9pf+yaKgEcjczGGjVi7uskdcNO6MJ4F2Tps23aws9/A4YlDCBSUvlX4Ila+ExKYVRpOFD2KyeijqX4BrUPv8fnj7GwWyH5SCDEghKj+F9uFEOJBIUSTEKJKCBHdhQElSZI+oFOnTtHf38+yZcvQaDT0D2zA42lBr08jJeWyd7+heRfKUDO74pNIcE0k4Lczqbmd7YVzuWLGViZ7C3i5Mpa2VDf2+ACBkamMjczi+r565tpy2J+8i6yCY9SePJ+gWoM3U//ufYyzszlyfxpY+W+2XwwUvfHzFeBPH7wtSZKk6AgGg297OFg47KWh4acAFBXe+55H7cr6OwipBa/4zkelqEhs7cE6GiH96jqmZnyWIy/VMxh2c2TiMErQSrDnKkqdXXwxeQYdplYSpzxHQ/0MvKE4yotm0GaL/jjfN9wVRdkD/Lvrdq4AnlXOOATECiHSxqtBSZKk8XTo0CFcLhcXXXTRGw8He4Rg0InBkEly8nscx9ZtQjg7WGvLI3ksl8hoD7OaOzg1P5tpeXEofz9E5XCQ6vwRPLoIvp5r0ITU/CCkQRgCDE57gGF3PAMDE+mKTaTdPEB+W1/Uxzke59wzgM63vO5642/vIoT4ihCiQghRYbfbx2HXkiRJZ29sbIy9e/dSXFxMXl4eXm8X7e2PAlCQfzdCvCMSFYXwxjvxaFQcdC8giJ/Mxh7CIoaiVbUU2rPZcFrPmD5EVaGL4NACQp4ivt5ZRUFiIScmP4Q+Zoz2U/MIqTTsLchlbut6Gq2fjEsh3+ubAeW9ChVFeUxRlDJFUcqSkpLGYdeSJElnb/fu3QSDQS688EIAGpt+haKE0OszSUm59F31yskXUI8O8JR5GrGBRDSOLsraOhm70kdx6mfZ+Uo3voifo1OGCQZS8A+sZI69gSsyzqOh4BVik+uoPTYHjxLH0ZxSkgafY2atnqn92qiPdTzCvQvIesvrTKBnHD5XkiRp3NjtdioqKigrKyMpKYnBwb3Y7Vs5c137XQihfvsbwiFCW+/GodfTMToHr+KiuK6bsUQjqcszcfy1mnaPh4FYP62JAXw912MM+7k3rGMsrRYKNtLUXIgjUIzDbKPVdJLLj3lRXKOMWRKjPt7xCPd1wBfeuGpmHuBSFKV3HD5XkiRp3Gzfvh2tVsvixYsJh/3UN/wEIbTExOSQ+s6FOIDgwQfQet08qp1PTNiIqauXqT19iNUjJDels7c9iILCkVkD+O0riPhT+WHzMaz5yXRNeRz3gIW+vjJERGF/jpVrj1QQGQqyftEQzZpI1Mereb8CIcTfgCVAohCiC/gJoAVQFOURYBNwCdAEeIAvRqtZSZKk/0Rrayv19fUsX74cs9lMa+sf8HrbAcjPu/PdR+2BMSi/n0ZjLN6xyfjD/SytbcczQZA+YxWv/74ZBYW2HDd9oXyCQwtZYj/NgoLzaJr2OwiGaWpYhBLRUp2eybS6l4hr17J7VogxvYcpNdFfQ/V9w11RlOvfZ7sC3D5uHUmSJI2jSCTCtm3bsFqtzJs3D6+3g7b2P6FWm9HrU97zunb3tm9gDoZ4Wr0ElSKIb3aQM+zE+90c6p9qxhXyElRHOFTixdd6DWbFxfdDRvqmrwFTHzVH52JXZxLQaRkN7GBBjYa6XANtKfWUOktpy37PryXHlbxDVZKkc9qpU6fo7e1l+fLlaDQa6ht+BkA47CYv9xvvOmoPOpsxVL7KQWM2Wl8eXl8Xi+ua8J2noGrPoG7Qi0pA7XQ7zr6rUEJmft50kuC8HsbSjtJdk0+XagqGYJCT8S4uPuRkyBJDZVEnNn8ss4xhVMbofy0pw12SpHNWMBhkx44dpKWlMWXKFByO1xkc3I1GY8FoLHzPK2Rc6z6HCCu8FlyCT3jJrHdhC/rQX7KEvQcd6NRa3KYAR/QTCY1OZdFoLRNnZmAvegl7Ryp1o/Mx+fw0xZpZfugYIUXHsUlBQmo/i7y5bOldgds/Lepjl+EuSdI569ChQ4yMjHDRRRehKD7qG+5Dr08nEHCQl/fuo3Zny8vEt9ax0TAVQzCBsKuLpQ31BC+Mo2L9IIoQBMJBDs7y4+1fhUnTy72KQu/MJ3G5Y2lum41Xa8KvVpPRuAv9mIbqkly6Y5uZ7iqhITCBv4bvZ3nocNTHLsNdkqRzktvtZu/evZSUlJCXl0dr6x/x+3sRQoXJVERK8iVvqw+HvQS2fAsfWg4F5jOqGmZm3QCamAh9qhwGfV5U6hDDeSPU268F4MftdQxeuAO/KkBLzXTaEvKJ9YzhcZ0m2SHozJ1FVdYekrxJaLTZ/GL0eWxihP0Wf9THL8NdkqRz0ps3LF1wwQW4xxrp6HyC2Ng5+Hxd73muvevId0keGGWNZj66SAzqPjvTWrrwLSzmdOcoNqORQDjC+oQZhL25zIqcoGCZHZ+thdPVM2k0TiJ52MWIf5DcjlHcyYWczD6CUARTI0Xc2r+HfFUPXwncRXv7ezxSeJzJcJck6Zxjt9s5duwYZWVlJCYmUl//E9RqE37/ACZTEcnvOGofcVVhPbiGfpWN1uA0hkUPl9bWEUnRcbAXzForLo+H6hlaXIPLMZhOc4/ZgStnH7VNs+n2TySCGvXYCOmtLegtBppTVDjMXcwam8CK/ibmquq4O3Qz05RZrFS3RX0OZLhLknROURSFLVu2oNPpWLJkCf3963A6D5OUdBFebxt5eXe87RkykUiQrvIvEzcS5FmxFEUopPUMktzrpSUxjZCiIqQdJhgXYo/7KoRmlLvHTjEydydj/Xk4egpoS0sjc6AHY1cdJm2YzoRlVGZuJ8udxtJhNxeLCu6LXIktvIIbFBMZwUlRnwcZ7pIknVMaGxtpbm5myZIl6HRhGpt+hdUyFafzGCZTMclJb3/yY3vbI+TUNVGtymUsnMdwqIMVlbV4Uyw0qtUkJ6vxedSszVhCJJhAieV1is+vQ/jMnGicQU1OPsUdnei6GzAoQYYTrmN34d/Qh3Vc4bZxQ2Q3j7KYzsDnuE0x0hmIUO6W59wlSZLOWigUYsuWLSQkJDB79mxaWh8gEBgiMXE5Xm/Lu47a3e56vAfvR+9ReJlljKndnN/fhGFE4ZjVRmZ8Br12L835hQyMTUEft4/v57QS1o9QdWwRHbE5aD0RbB316Hw+9EkLOZpZjkfr5IaRTL7q3cZaZrLVdxv3YsAejPBU0ENbfPTnQoa7JEnnjCNHjjA0NMTKlSsZG6umq+s5MjNuoK//75jNpSQnrfhHbSQS5NSJO8hvc7NDzIKIjWCgnakHh+mNt4I1jSHRijcmji3ifFT6Hu5K3E8kuQvf4YV06lNpTMlg0unDaN0u0hMSaDQZaEo6xpUjSdzh3E25UsrDvm/zK2HEE4ZNHj83Hfk9d1ZtjfpcyHCXJOmc4Ha7KS8vp7CwkIKCXOrq7kWvT8FkKsLjaSUv9+1H7e3tj5Faf5JA0Mh+5mLX9nFlay2EBQ2pSVhyHYwNxbA+5VIiipq5OWsoyuvF3FjGYV8ax4vzWVSxB/1gHym2EB3qK9le9BxzxjT8cKiKaiWHH/q/zf3CgojAPneI2XXPMqqEiLMWRX0+ZLhLknRO2LlzJ8FgkBUrVtDR8QTusXqKi/4f7R2PYbFMJinpon/Ujrrr6Kp5gKwuHy+JJSioSAvVk1IRpjXRRmZxBr3tClVpq+jHSnL6em7J7MIwVEB9ZSoNhdlkt3Ri62xCZwrhVH+N1yY/TF7Qx+/svfRG4vl68E7uE8nEKnDUHSGuewcRdxffWnoHd+UkR30+ZLhLkvSJ19vby/Hjx5kzZw4m0xitbQ+SlLSSQMCBz9dFQf63/7E2aiQS5OiJb5HbOkq7kkGnUkyftoUVBwfxa1T4CqfTE2hgQD+PvYYU9JZTfL9gP5qwCc3GSRwozaVdm8Skyj1EtAKb4TPszttCjKabR/pHCEV0fCn0De5ScihQVBxzR/ANN5LV9jo/WvxN0vQjeIpLoz4nMtwlSfpEe/PSR6PRyOLFi6mr/38IoaWw4Hu0tv2RWNts4uMX/aO+rvGPWBw1pDsCvMxyxtRjlPnrMLcF6UzPYqy4lqH+YjYlzALNKLdMXEOsLkJK+So2p5rYXzCJy19/HpQI+bYJnLL66EnYwyN9LsyhEDeHvsk1kUnMQ8cxTxi7d5Tppx7nx8u+hcUwxmJVM19obIz6vMhwlyTpE62mpob29naWLVuG07mZ4eEDFBZ8D7t9K4GAnfyCu/9x1O5ynaar8yEyGoLsV2bhJZ4RbS1lG0K49To8S+MYrk1gf/JluFSwqOg5ZtlGSa65jsbWDsrnTOfSba+g9XpITImnhfPYVfwED9oHyQn6uC14OzPD01mFjoqAlx5/mLITD/I/59+K1wgLNa0kRmx0qIJRnxcZ7pIkfWIFg0G2bdtGSkoKkyfn0tj0K2y2maSkXEZb+6MkxC8iLnY2AJFIgL0VdxI7EMTqgV1iLgP6fpY2OTCOBRidPo0RVx9Npms4pVeRnrKDL2TVY+legGrHCC+vmEVmdR1JA91EUuJw+67n5ekP8GtHP2U+L3cHbsMUmcdXMXBCGaXbo6G04QVemXQhTbFWluqaiY+YGFDseBRP1OdGhrskSZ9Y+/fvx+VysXLlSppbfkMo5Ka05Jd0dj5NKOQkP//b/6jdc/y/MSkt5DdGWKcsIYwGnahhwsFR3LYEemY20mf/LK+bLegN3Xx/0gaUkTQS98zn5UkGugIxTK8+hD82HnPwMjaVPsO3Xc1c6PFyX/DzdEfO5x4M1KpHaXfqSR44RnVcLDsy8rlA30RcRM9opI87wk9zZfDVqM+NDHdJkj6RhoeH2bdvH5MmTcJm66Gv71Vycr6CTpdIR+cTJCWtxGqdAkBrbyUB55Po68y4FQsNooR2UyPLtvrQhiM0XWbAUbWErXEFBFUhbp/+BFpU5FXcQd1AOdsmzmRl+d8JGS1YTDM4nlLNxexn9aibR0Ir2RVewa8w0aPxUzukxuAbJOCp56mSeVyob8CmaBiNuLg5+BKxmjHU/XFRn5+zCnchxEohRL0QokkI8YP32J4thNglhKgUQlQJIS55r8+RJEkaL1u2bEEIwQUXLKKu7kfExOSSm3M77R2PEg57yc//FgD+oI8jlXcR9qqZZHfwinIhY5oxsvs7KegdYaA0nbGxDI6aF9OlibC8+AUmWBxknvwGwWM7eeDay1j5+lpQayA1n36VmYzE57nD6WQt0/hz6Hp+hwWfOsKhQR+asCCuewu/nXkJF+nrsSoCb8THNb5XyIpx4lGMpOo/BqdlxJnnYj4EXAxMBK4XQkx8R9mPgBcVRZkBrAYeHu9GJUmS3tTY2Eh9fT2LFy/G7ngcr6+DCaW/JhRy0tX1F1JTr8BsOnOj0As7f0qioRPDyTROMpFREUePoYoLd4+haLUMLFLTNHItBwwhsuOquC7nMO72+VhrNPx+SS4z9+9CH/Thyyog7J2EK+8BfuxwsEeTyk98d/FbbFiEiq0j/RgUEymdW/n53MtZrq3HqkRQwmEuGn2NieY+BsNWzGovVbGJUZ+jszlynwM0KYrSoihKAFgDXPGOGgWwvvG7DYj+AoGSJH0qhUIhNm/eTEJCAhMm6unsfJrMjM8TFzeH1raHUJQI+Xl3ArC18nUvfl8jAAAgAElEQVTSNC8z0FzClGAzu8U8uo3dlB0fJcnto+cCE20Nt7LBrGBWjfKd6c/RPGpj+unPUe47gjI8RPJgH/70AnSBUloKH+C/HL3UaE3c7v4lPyWWAtS8GmrDEkjA5qjidzPms1DdgI0gujCUuTZzXmwrA+FYEtQjPK0upKd7IOrzdDbhngF0vuV11xt/e6ufAjcKIbqATcA3x6U7SZKkdzhw4ABDQ0OsWHEBDQ0/xKBPo6DgO3i9HfT0vEB6+nXExGTR0DuAs+dHjHnN5HUOsollBASIcDVLTnsZS9DTHP4im00mvCLC12Y/gksJMKPye/Q3rWPblEKKW2vwpKSDPo3m9Jf43VAL/WoNN7t/zteIYy4aXjT0EDdkQ+sb5rn8NKZrWogXPowRNYVDO7kwvoaBSBzJaicbVRkM2e8klH1t1OfpbMJdvMfflHe8vh54WlGUTOAS4C/irQ9xePODhPiKEKJCCFFht9v/791KkvSp5nQ62bNnDxMmTECl3ojH00xp6a/QaMw0tzyAEBrycr/OqC/Ia3t/QILBQeOJudhUQVpELg3WGpbvU2Pw+6idv4pDIodWrcKq/NfItHQxWL8SW3s/Dy3IYebJgzgTrERi0xk2NPEz7zFCQuGm8G1cqmSzCh1rbSMkNPsIqY3sjg2Qqe8hWYxhjehIse9jVeIJhhQbicLJMRFPhetOYvxp5Iwaoj5XZxPuXUDWW15n8u7TLrcALwIoinIQMADvOqmkKMpjiqKUKYpSlpSU9J91LEnSp9a2bdsAWHB+Lh0dj5GWdjUJCQsZGTlFf/96srO+hE6XzO/WPc3MxHJONi/m6sBuNijLcGmdFA22MqndRUvZAmo9C9ljCDIz5iQrC3azrj+JSzov4qmUHiafOMRQrAF1UiEoEW5Tr8MaDvLlmKUUexfyFQxstwWwnKzDa8qmXt2LxjpIptqFTTFg6j/M1UlH8YgYrGKMTmFirffrJI4VEfBsxe7aE/W5OptwPwoUCSHyhBA6znxhuu4dNR3AcgAhxATOhLs8NJckadw0NzdTU1PD+efPp7v7PrTaBIoK70VRFJqaf4tWG0dOzld4ZFcl0ywP4RxNRdWh0KAqYkyYabdUsvJAMi5rLrWWa1lv9BKvGuaL8/7Kqy4tV7V8mb3OXRj72/EaNGiSilFHNFxmfIK8gI874/NQOW7lHmI4aQwRrNqH1zqF7kg/9oQR8tTDWMIG1H3HuDbpEKhUqFHwoOHJ0BdJd84i6NmBX9tDxHZp1OfrfcNdUZQQ8A1gK1DLmatiTgsh7hNCrHqj7G7gViHESeBvwM2Korzz1I0kSdJ/JBQKsWnTJuLi4sjMrMbtrqO05OdotTaGhvYyPHyA3Nzb2dfsxdX3SyxaDy9WreIzmoMcUmbSamnl4vYwpqExqqbeyuYYLx4VfG3Ok1T6Qhi6FqDvG6NJ60Qb8uNOT0Wl1rLI+DRlfjf3JCbQ0vPjM9eyayO0t+1BpSvFLnzUp7go1jgwhWNQ+k9yTcJhzBo/HiUGLQEejVxLsmMZQc9e3IZu9s2/luFM9fsP+gPSnE2RoiibOPNF6Vv/9uO3/F4DLBjf1iRJks44dOgQg4ODXHXVIjo6v05K8mUkJV2AokRoav4tBkMWSsxVPLP+j9xYeoIt9ZdzQ2AXr6sWEVAFUKlPMqnCxMlpX+OITk2zDlYXrEXRdfN6Xyz3965gra6clD47pyfEkqsksECzjsX+Qe6Pj2WP/bv8SYknIMIcdB8n22WmKTmeY4ltzFT3oQ8bCfef4rOxFaTqXPRGkklXDfDn8CrMjqsIeQ/hiWmlavpKZnadQDGfVfR+IPIOVUmSPtaGhobYvXs3JSUlBAJ/QKOxUFx85tiyr+813O5asnLu4rsv7uazBWvoHcqjvTODDPUoXSKdk/GnuLg5iY6ka2k2JVEeE2F6bBVzMvbxxJCOr/avZqN/P2m97VROsJIbLuY8sZ8LQi08abPxvO867veXYgVeNrQytbqVzuTpHEzoZKa6G3U4BmXgNBdZTlJkHKAjlE6GeoDNgcUEHF8k5DuO29BIw4TFTOyvY1hrwNkun+cuSdKnmKIobNy4EZVKxZQpA4yMVlFS/FN0ugTCYT8tLQ9gsUzmgb3pLEv+E1oR5o/Vn+M+4xq2KQsZ1A8wPdCFvnMW3UmTWWdyE6tzcdPkNTw2rGXCyGR6ht3E9zTTkJ9MQWASZapjrFCOss4SzwPqOfx4aCX5qHgqwcHF2zZRn3c5+2K7manuQAkbUNvrmG+sY6alndZgBrnaHk74ptE6fCchfzVuQy1dRXPIdbbTEaNld9oaKvPKoz53MtwlSfrYOn36NM3NzSxYMJEB+8OkJF9GSsqZLyO7u5/D5++hyfcFAs7nKIpv4vnaq7lcXUl1qAi/0FIfd5xpjTPoTL2QLfoB3Gott814io1OFY6Annl956N0H6M3JRmtPpVp2lNcwi52mtP5kSmX23tuYy5ank1w8dmXH+N00Wr2xw4yRddKSDGgdzQxWdfGElsdHYFUsjU9dPjzOej8EeFAA+6Y09hzp5Di7qfWGuZoyho0kTQudc+J+tzJcJck6WPJ6/WyefNm0tJS0ekfRauNp6TkZwAEgyO0tj2M2jCPJ/f3c1XRJqp6ZlDdV8wV4YNUU8rp2DqWO/U4ItdzXD1Ao8HK1cXrcHiCHA6HuLbnYhwd5YwZzbRmGpgX6eVKtnDUnMt3bGauav8Ol6Nng3mYS159mMbsyziYoFAY00hA0WIcbCZX3cslcSfoCySQrB3CFU5ky/DPCAY6GDFV48oowOZzcizBRU3C31GHSwmqBng2uSbq8yfDXZKkj6UdO3bg8XiYPn0Ij6eBCaW/QquNBaC9/RFCIRcPHjmf26c8i9tr4/G663gs9Vm2Kovxq51EDHXYmu+kh1F2mc3MTDpBgbWNV4NuSt05+NraEZEIB6eZuMyp51rVOuqMWdwRK5jb/h1uVaxUaIeYvvNZBszFHMjIIMVcR0DRYB7uIEUM8pnEI4yETcSo/UQULeuH7sMXHGLEUoUnOQNd2Mf+lC7arNsRyixCqkbUYStXjsyO+vzJcJck6WOns7OTiooKpk/PxT32JOnp15GYuBQAn6+Xzq6nqXOdx/zEvVgMDh6u/gJXpJ6iZ8jMKCYOJB+jrPXzeBQdr8ZGSDIOcnnRdjbaBUHCTKrPJGbUyYFZGVzTmcFq3V/p1idxW6KWnO5b+X4ojQbVCNYT64l4ApSXLiPGVkMYgcnVQ2zExbWJhwiFVfjRY1J52Tj0Q0YCEVy2KnzxSUREhPLUavpijhJWzSPCcVT+DJ5pu4FYon8ppAx3SZI+VsLhMOvXr8dqtRAX9ywGQyZFhff+Y3tzy38TDIc52Z3GjPTDbGq5CHvAyuqBDRwT02izNpHnTsbinM56Uz9+tY4vTX6OyuZsWo19rKibj6W/i+PTJ3BRVzrXGx/BpTVxS0YOpoFLuc87hT7hZ6x1N/Htp9g+82bCsacRKOhdDqxhN6uTDqKP+OkLJ5GmcbDD+U36fAk4Y08StNrwatXsSt2PU9dCUDcHdeQQeEp4sXMhpbofMV3zTNTnUYa7JEkfKwcPHmRgYIBp05wEQ+1MnPBbNBozACMjVfT1vcqezrlcXbyJ9uE81rUu5Q+Gx9mjOh+EmwZrI5Nbb+JEoJrWmFRumPAi3T2lHLM2MK2zkOS2ThqKSigetXCD+Y+E1So+n3cBQUcev3Eux0+YzsHD5J/YxO7Z38CV2IBWhBDuESyhUa5JPESs4qImWEChrpNDo9fR4JnMcPxJQmYTwzE6dqdsxqMZwaefjC54iLBrFhu6UynR/oZhn57j3blRn0cZ7pIkfWy8eU17fn4SiOfJzvoScXFnrixRFIWKqp8y4jczM7afsKLwcPUX+HJ2OR53LIPEszf5ODM6V+F31LA9pYSFGQeAWOrpQT8KU09H6E7NQhiNfE3zFDEiwI1FX8Q16OLXA6uJAWo8lUze8zwVU79Ee3ovMcKP3xPAGhzhysQK0pU+jgYnM0NfT51nIUdGVjCccIKIUU+vRcfe5LX4NQb8+mwMgWPgWMR2+xC52mep8eTSEVhKbqQg6nMpw12SpI8FRVHYsGEDKpUgPf1lTKYi8vPv/sf2mpa/owROMjQ8CZu1iadrryHB2s3C1kMcErNwGlpRIgYmdxt5LreQbEsnE5I6Ge7W0K5tYXllBqNmK115Vu71rSVeuPjChLvpdh7mZ91fJQ0VR0LVTN/+FC35y6ksArPKy4hHIS4wxMXxJymijX2BmczWnqbbX8oO5804k04SMWhpiRUcSlhDUJtJSGNC5z+NtXcZ5a7DpKvL2T02B7VIY65lLXFJm/7NTIwPGe6SJH0sHD9+nJaWFiZOHESl7mXixN+hVusBGPGOUdfwGwaG88hJOcLhnjJOONP5avffqY6ZjVr42ZNUzYqO8/hbrAU0Cpfnvo67LotTcZVcUFmEiChUT8vl146dZKi6+Wrp96gfWc8P2u9mElp20cDsLX/GGV/Ithn52NSjOHwakoMOlsTVM51a9vtnMEtXizucyKahO3AknSKiU3E6wUdl7MsEDNNB8aAJtlHcvZht3tewiU5e9VxGsdZFacwB6tOTOBRXEvX5lOEuSdJHzuVysW3bNtLSjJgtr1FQcDdWy2QAIhGFxzffj1U1QpzBxbA3nr/Ur+Lr4bWY9PH0ksKRxBOUDk7klFtFpzaNz+W9wkjrZNoS2plcG0+sy8fxOTP4Vc9eCjQN3F10F0c867m99bssUExsUHezYPPjBHRm1ixaQpzWRbffQkagj9mx7ZxPBRX+yRRr21ArKl4b+g49yY1EtIKKZAd11o34TIvQ+tsQoUEubJ/Ji8G/oUPHWt81LNUdJVnbQmVuBq/5LkHf/M7F7MafDHdJkj5SiqKwfv16IpEQWdmvkBC/gOysW/6x/Y87jlJkfBnfSA4qvZOHT93EpfoNFA65OaqdBeoeeg3DFNht7LNM59KsbfgdBYzq/Bi67eT0hakom88P+vYzQVfJz3K/yrbwIT7X+g0uC8ezTu1gyZY/EYmE+fOKa4jTDdPpjycn0M4E2wAr2EONv4gE9TDxYoTXXN+mNaGHiBr2p7bQZtqDx3IxMZ5jEAryjbYM/kd5hXCkhC3By7lcvxadysuR4iwOOS9hcc9Kpvq7oz6v0X80mSRJ0r9x8uRJmpqamDChE7M5zMSJ/8WbC7ltqe5loOeP5OhyMaXUs6buKsyRBorqB+jPL0UoYTamHWf5QB5/VV/E9MQqErxaAn4trnAFk1u1nJg8my+PHqVMe4A/ZKzmeV0/lzVexU3BDLaqnSza8ScUn5NHLruFBP0QHYEEcgPN5FpHuFLZSnsgi5ACeZpuXhu9nVqbm7AKytNPMmRox2O5HPPIZgxBC7/pDLJM7MYduoSTJHGJ9ilc4RROTLbS2bOMBY6F2ISBlsKqqM+rPHKXJOkjMzIywpYtW0hKEiQk7mZC6W/Q61MAqOsb4b83bmSOsQdjUhOVA1M42p/A8qY6MjINdIhMmmynSfFa2DB2NclGO7OVFoTbikepZWK9lsbcUi5R13Gh2MFfklfwYFws57dN4E5vKfvVI5Ttewq1q5vHV95EQsww7cFEsgOtpFp8XKesYyiQRHcojqmGRjZ7PkelWYdfBdsz9zEY04fftALTyAbSvbE839nHIlHHcPBr9AotCzTP0BMs5ejUBIa6LuE8x1JiRJDdof/C3+OI+tzKcJck6SPx5hMfg8EA2Tmvkpl5A0lJFwAwMOrjlqePsjp7E9oYJ06/jWdrVvKFgV2kmuGEfjox9HHM1sno4HWEhZpVxl0ExzLwqO2kNnixJ6QxOXWY6wObWBc/h5+kz2VGq8IPR+ZzWuUhv+JFdP21PHPBjcRaRukIxpMT7CDRHOTGyFp8IQunfLnMN1WxxX8lh2NScKsUXs96HZdBQdHPx+DewJzReF7orSVRNYYj8BNC6pMUqV/jtHcJp6ZrCbVdRtngInyqTl4feARHh4pEV/SjV4a7JEkfierqaurr68nLryEpKZWiwnsA8AXD3PrsMQoNh0nEj9owwsMnP89Vo+sQHiMjcbmolAhrMyrIG7icDl8GV8dtIOAuxKX2Ymnqwqc3kzBBy9edr7DTWsKd+TdS2lrLfYOX0iP8aJs2Ym4/wsuLrsGQ4KcrFEtWqJ/YmAA3RV6CiJYDIxNZZD3GxtClHNLnMagJsT17PW5jInp1CWrPVj43aOVR+wn61YkM+X+GRfNXklQHOOC+ntaZoxiar2Ta0AK6xH52tq4l4oYrM5soDZuiPr8y3CVJ+tC53e43ls3zkZ5+ismTHkStjiESUbj7xZM09PRzuakFc0odLzasIn/kAOZ+PZlpGnpUaQxYqknwTuekay4XJe5CjGYzrPGT0N6IJqxGlCXzXfvzHLJkcWvpD8hp28Av+2/EQ5jW4dfJqNrO5rmXEkpT0RO2kh5yYjH4uUl5GZ0CuwZnsjiukvWRizmqKaZL62VP1no8xolYgzaEfw/39Wm4Z6SanbrZBEJ3kan9LXrRwmbXtxia3UJC89UUOWdRGV7D4eaDxKp9XJ/byHbPpTyabYn6HMsvVCVJ+lC9eTrG7/cyYeI2Skt+itlcDMB/v17PxlO9/DK3BlveHioHptDRFeKiDg9xKbHU6bOJp5tyK7S3rmKK7TRpowZGtQqpXY1o3EGGzi/mF/1PUmlK5cYp/0Vy2x/4ZdfdGBC8ptrGFbvXs2/aUpy5ZuwRM8lhDya9hy8oL2HGzyb7fBYmnuJVsZIGVS4NxmFOJe/Ea15E0lgf+nAtD3YHmBq287+6azgvMJUJ4v8RUFS85vwJxnlbSW9cTdxINjvHHsbhcFNiHWJZyhD3O1fzjO08Jom6qM/zWR25CyFWCiHqhRBNQogf/Iuaa4UQNUKI00KI58e3TUmSzhVVVVXU1taSnVNJQcFS0tKuBuClik4e2tXMN7N9xGasY8hv47XTs1nR2UgopQC/xYyWEFvTmrB33kCywcFM3wB+tRarowrdsA9nWQH3OZ6mxpDA6ml/IL7z9/y07U5S0fBny2ZWrV1H5YQ5dJSmMKQYiY8EMWrd3Ki8QgJuNvSfx4z4Nl5VX0CDyOWErYtTKTsZs15OoruZbN9pXuxwUhRy8XX9t5kXTmO28jNGIzZeHPoV5tnl5DXdgH7YxibHH3A43CxJbmV5ioe7R2/iGfN5XK7xcovpY3BaRgihBh4CLgYmAtcLISa+o6YIuAdYoCjKJOBbUehVkqRPOKfTyaZNG4mNHaK42EtJ8X0IITjUMsi9r57ikhQzBXFPoja4ePrEVVzetR9/UgkZmn4GVMn4LFUMDVxPOKJhaaSGiDDh9R3C3BvGPTmTH7v/SovOyjUzHsbY+wDfb/kGExQDv4/dxo0vbqQhfxI1U/MZUfRYIxFiNKN8TnmVNDHEhoF5FMQ6eU27mE7SOJhYT1N8BW7bahJHDrDI3cwzvXaCioHP6H7BlyJdzA0/iD1UykuDvyBt1jaKmm7EOehkW+9ThL1hrs2uosRm4Uuem9kcM43V+kEacx5irWF31Of6bE7LzAGaFEVpARBCrAGuAN66lMitwEOKogwDKIoyMN6NSpL0yRaJRPj7318lFPJRXHKQqVOeQ6Mx0WJ389W/HKPUEsNS0xosKXU8c+pa5rUfIGjJI9kXoC0pn1TRxlplEf2eVK7U70WnmOgQB5nUoiecF8t3I6/SqzHy2ekPobb/ibuavsJ5ERu/t+3l8k3r6U/K5+jsyXgVHcYIGFRurmEdOaKfTfY5xFo0bI2Zi1cxsCvtGA6jnbHYG0kYfJmvDvVxy+gwFZFibtffxWNiDdMC5XQGlrJ++CuUzvw7eU3Xc3roIPVDlcRp4ZqcwwSVaVwT+Ay1umxuNfaxN+2vOHSDXKiIqM/32YR7BtD5ltddwNx31BQDCCH2A2rgp4ry/9m77yCp6kTv/+9zOsdJPTknJjDDMOQMgggqAoquiKJiQHfXNedd06ob1F3TGtacxYSAIJKRODAJmByZHHtmOkzn7nOeP7zPr/a3dZ9an71y77P39qtqqqa7v33qW+fT9ZlT3zmnj/zd329IEISNwEaAtLS0f2a+YWFh/6LKysro6Ogkd0IZJZPuwWjMw+b2c+P7FegQWK+vJjpnN/u75mFpbkYlJqDx5+CLa0ODn4MRsTQPFHOx8TDmoIFOZRVF9RoU8Rpu1O/FhprVpS/ht33CHS1Xcn7QwpvGSvKqvkAlJLB77lSCKFDLoBVdXCrsIE/oZs9wKQFdEkcMyQhyiJ0ph7HrwG3+GYnWj/jjwCAL/C4+Cy3iGfVaNgkvkeuvp813NTttqykp2UlCyyoOW79m2NVPtjHEiuTjDAYWcZV8MT2KeO4ydvNd7JdY1VburvZhaE7+tyY8d35Muf97f2Lkf2c7ucAiIAU4LAhCkSzLtv/fm2T5DeANgGnTpv39NsLCwv6bGhwcZN++vUTHdDNpUiFJSVfiD0rc8mEl/aMeHosbJyL/TVrGshg+JZAVMKLSziNKs5cuMYdRwxhlAxcw21yOxadlUGiipFFCYRa5xnIcr6Rg1dQ/4XDv4ZbmJazyJ/Oprgn/0AcU9Eay/fx5yAgoJAGd4OUiYSfFQjvfjxTSp51EhykSI04+TT3CuD4er2E++YNv81r/MOmhAI9J13FAWco3wm9J9A3T7LuHPfbZzCg8hK5pLnus7+MJuJhr8TArtpJG92quVi7BKRp50NzOlqjtDKut3Lvbx6EV60lOjOJcf7vMjyn3HiD1bx6nAH3/zpgyWZYDwFlBEJr4oezLf5JZhoWF/csKBoNs3vwFouihuLiXgvwvkWW4/8vTnGgf5YkkA6bMx3EGtZysLCDPYUWMWIrBU0ZXVDaCYGfH6GKyjM3k+gTcgR4Ke8ZQagWuSDmDLAmsKn0Ka6CGaxrzudqbwzfqbiqFV7jtpJ7tSxcjCIAsoBX8LBN3Mp0Wjo3lUKeZz5BRTzy9vJt6EqehGL82m/P63+KFgRGUsor10oP4FBq+ER7BGIAW75MccGQxb0IVzsYkjo58AAqJS5NHyTLVUza+nps080GE30Q08Zn5AIOqYe7d4WfrdT/ngGERM7Vl53y//5izZcqBXEEQMgVBUANrgW1/N2YLcB6AIAgWflimaf8pJxoWFvavae/ePQwOWsnLq2T6tBdRKo08u7uJLaf6uC81DlPsn1Hpx/i2egn5fQMQvQiVfQxnnBFB9vNFYBZmzSgz/aNIvlHS+9vRCBKXZTagCoW4dNJD9MojrG4wcIurhANKK59bnuPm3Sp2LF2GKPJDsRPifOE7ZtFE2VgWZZoLGTLqyREa+Sj1OHbTAoIKCxu73uetgSHG5Ugukn5HjMLJJ8LTaIMm2r3PcsydzNz0FjrqXZyw7kDWCVyX2k2msYmdjp9znWYxapWf38TUscm8n0HVEHftC7Hp5l9xwLCISFsN+q4d53y//8Mjd1mWg4Ig3Abs4of19HdkWa4TBOG3QIUsy9v+7bULBEGoB0LAfbIsj5zLiYeFhf2/r6WlhbKyEyQmNjF/wZ0YjXl8eLyD1w62cVNqHDG8T0RCHV+fuZD81rNIcYvQj0WiiDvJsJDKQTmVIALzFTVofQpiRs6gCYiszGtCFwqypvhuzqo0LK/v5y7HBZxUOHk5/Ske/EJk76KLEUWQZdDKMovE3cwTGiizZXFQtxKvVmC6cILnkjsZjbgCVaCdZ3r3sWrcRQX5XC/fxwbhO+7hC5yhQoa8v6EKJ1Nj+qmsr8Hq60UZpeFWyxlUCgcfj9/BY5rJxGuHuSemgbeUpxlWWtl4WubDDXdyWpyGZmwAW0QRHSr9Od/3P+oiJlmWvwW+/bvnHv2b32Xg7n/7CQsLC8PpdPLVV5vQG8ZYuHAy8XEXsbtugMe21bE6KZo0+0FiZ+3kaNd0Us/0E7LMw2jPBs1nDGqyqAzF0R+yMDF6B8n2SMwjlWg9IisntKCRQlxR+CsadEksqS/n4dHV1As+ns56ghv2yFRMvwSFQkAG1LKC+exjoVDDMXsuBwwrCCkllgp7eDrZwUjkTcS59/KXnnKK/X42Kc7nkeC1/IE3WSMeZkBaiMd3B03as2T5AhxqPohXdhMTb2J95FEQJP7ivovnVRPJNHdyh6WW1+QWRhSjXDam5OOVd9NEPopxD76oBDL6bfx2YB+w5pzu//AVqmFhYT85SZL4/PMP8Pt9LFzkJi/vXqq6xrh9UzWzYsxMHu4gbv67tI+loToWIBQzE5OjiEBoC574ZE4F4miU0oiP28J0RzT6wdPoPLAqpxk1Elfl30qdOY8F9Xt41LqWdiHIo1m/ZckpP/1ZKxGVAiCjCqmZLx9lsaqCw85CDhiWo1T4uFzYzmNJAsPmXzDJ9gEv9jVjCAk8rdvAJu88PpB/z2yxgQZpLQbfOrrNNWgHPBweOoykESiKjuaCiF0EZSNPBzbyvmIiE2MauCX2DH8J9GBT2Fii07Al8wG6SIMghHQaLuuqZk3SH5GCWec8g3C5h4WF/eS+P7Sb7u5hCgrbmDP7NTpGvNz0fgWpOg3LHS6iZr6IO6Rm+FAMREzB7CwlEDqBmChQGUrljJSGOXY3s70qzL316N1BLs1pRCXKXJN7E6cjS5nduJUnBtfTh8Tjqb8nu9+FxrACVCIgowjpmB+sZon2MPtdkzlsOA+d4GKd8CWPJkbRbb6RlUMv8aC1H1vIzO2Rt9HgjGeL/BtSFMMcDt1Bpn8pfRFVDLf30eGswxWlYJUulknmr/FKidynuI7tUiEzEiq4KrqBl30D2BVOlsQY+cb0MFY5DkQRvdfNA9LL5KSeZLQ9mpEqP0uvPbcZhMs9LCzsJ9XZeZZD3x8nNraHC5c/gt2n4/p3j6EIyVwnaNAVPYNKM8bpA8UIqkmYx6PAAJQAACAASURBVKcRkjsRo1o5IU+jKpSGMeok2couSlp9BD0+LstqQKWAa7NvpDJmDtObPuWp/g1Ykfhd4p/R+qzkuC9C0CiQkVEE9cwPNLJEv5MdntmU62dhFuzcwOc8kZBGS8SV3N/zey5zOmj2Z3JP4m2oRlxskX+DqIAdwceZ4Z/MoK6K2oYaxvwDdKZL3BuwkGr+EmdoAreor+S4O5+laQdYaOznFd8ADoWTZfExfKx9BLdsBFGgwNrCvdGPonFoaNqRgbtHx1h87DnPIVzuYWFhPxmXy8Vnn72PWu1ixYoVyKpcrn2jjCG7l0eiYhEjXyLS0sLR46UovAWYPTMIyQ48+sO0aidTFkgnwtCIPmofVzdqGXPKrMmoR6kSuCHzBk7GLaKk6V1+13sz48BfIl/Hpunm/M7lCBoVAGLQwEJ/KwsNX/G5byn1uiKiGOIWvuBZSz6NpoW8cvZpJvn8HB2fz0PZ6ykdPMMz8l8ZUMXwne83XBzIoIuTVDaWESJATWGA55zRWCK+Zjg4mWs1a2hwZfOzCV+TohR5x9+NS+FhWVwa72gfJiQoEYIS65wfcHHEbqwnLDTVmAmq9PiTF6CK+vuzyX964XIPCwv7SUiSxMef/AWPJ8SyZZHEJa7k2ndO0DLo5PHEBEKBz4jPPEpFfRFKaw5G3xwkQvRFfU9QncshXxZR6n78yZ9yc7sZ+/APxa5Sw00ZN3I08XwKm//KH3puIQB8qP2IxtgmLmxbiqDWIABywMASfxuzjF/ybuAyujXpRItn+aW0lXciS2gzZPNh+yvoJYGvbdfw7MTzuLp3G79kKxXqfPa6H+DqoIVa937qhyrQarQ05Am86NASYfyWjtBsrlFdRr87npuK3sftt7DZX4NP9DHdMpU39beAIGByjvGI5hFSrGpqj6bj96gYj4snpJlKgjMPb8h1zvMIl3tYWNhP4ttv36Ov10NpqZPSqY9yy0eVVHSO8dv0ZPyDB0id9xVNPVkEW3MxBeaDLHAq8wwZfjWbxidiFh14099lxaABRZfEqrQGFGqRn6dt5FDSUia0vcozXRsRgW1s53BaJcvPLkFU6REFCPn0XOhvpdj0Da+HrmJEacGoPs2v/PvZaSyhX6Pizc6vGBINbLX+iren5vJYx2sso4LN6gVUjt/CTUEdx0a+ome8DXOEkYFkHc+O92DQneQM87lOvBKPX8+tk9+l1WagXnmKkBgi2rKWL43nATDZVsb9wodYd5ipHlQQ0OloLTaSNTiTCGc2We1bsWo6znke4XIPCwv7D6uu3ktFRRcpKVaWX/gH7vmihoNNwzyam4K3uZqUJW8yYIvFfiofY3AhSCr2lQywyNXAO65ViIQQUt8nxy0xuSnEirRmQhoVG1N/zsHUJWS1v8IzHRvRIXLIe4ivJu1jadcilKIRUYCAV8clgVYyTXt5Vboat2hEZTzEfa4KKtUFuBWjPDrYTaUimYPWe9k5Q8vbbU8wQe7mOc0VjLiXc7XXy56hz3EGxoiKN6AxJnJf4ARaTR2H1Au51b0elSLA+skfcGLUR6+qH5WsxhH7EO2GFARJ5mbXayzscFJTFolfUNCXZaQjxsmSpjWIQhSpbR/z4eRCzsbrWH+OMwmXe1hY2H9If38T23ccwGTysHbtAzyx4yzbz/Rzf14KgZpmkpe+hMevou9ECXr/UsSglu0zvVzm+4zXrdfjkxVMiv2IboWV9ZVaLkxpxKvRsTHjTo4mzSWr43Webb+JSFmkavwU7xd/xeKeRWhkM6IAPo+ay0LNRJoqeFO+Cq+oRo7YwWP2JtqUOWiEbi63j7NFMY0zY7dSN8XKprbHUMtB7lDfSmwom4ttw+yz7kYhKohKVZMnTWC6YjsqsYOtpoXcY70Ri26EOfmfcXjMx6hqHKUinfaE+wiqtWj8Ll4cfh3HYS+nbB7sUToqCh1M6JRY1rARAbCPfc+9163FnhpLRHv3P9yv/1Hhcg8LC/uneTw2PvnkHQRE1q69ilePuPjkRBe3TUhCrO4iYekLIHrpODIbtfsixKCezXMFrpWf5a9dt2KXtazVf8G3Uc08VK7l4sQWnGoTN2TfT0X8FLK63ua51uuJkUVqnc28nfses4cXYghFIAjgdotcKTURMp7lY3k1XlHGH/0Fz450MygmES2dRRWSeVFxBUPOi/FNPM2H7S/RJ1u4W3Eji7VeUhs7OeE4RbQujlDcEHPc08iO3oRCsPJu9Hk82b+BzIgOkjI+pG5Ex7DWiaRbRlfsz0AUSPF08VTlLhpqbfgVcGoyjGhbWHeoCE/0tQghB/tj3By6/DJklYLlp4bJ7R4959mEyz0sLOyfIklBPv7k9zidWlaunMSW5ihePdjEDZnxGKuGiVv8F7TaYZqOzUC0r0QR0vPlfA0bFA/xXt0NDEpmblNt5qOkU9xRrWG1pZVRdQTX5j5CTexEMrre47nma7DIArXOs3yY+irFrgWYApEIAjhdElfTzKDRxQkWY1N5CERt5YXhfpxCBHFSH+0qDW/JdyF5CsjO+IZbu7/kmFTIQ4qr2BhVg78CWn39ZFpysOoaWeaaSYrlPWR8PBt9Ia/1r6XQUkMg4VPEDhMDSUGcUXfhNhUDsMhaz9LvdlA7bqMrXUllVj+Lq12s67yAroxL8ATG+Hh6BP0FOahcIR7bV0ZjwjBmrf2c5xMu97CwsH/Kt9/+np5uHVOnRlExPplndzVyVWossTV2Yua9SURkK03lU5CGr0ARMvDFfB0b9A/xRc2VdATiuUn5HXuSarmlTs01Ua0MqGK4puApmqIySe/ZxJ+a12KRBZodXWyxvEhmaOEPN6cWwD7u5xqhnQaDmhZK6dENIZv38trgMAFBQ6Rs51tjDJv9TxERVLI85jWWDpXxcXAxf9Us4g7j9/QdF5HkECVZxZz1VLPCN4UEyxsEBAUPmS5nc/9yJiUcxandwdwqHV9NT8Ye80skpQlkmXW1FaQc20anKcSx+U4ibWM89pESR+y1dGVMp1fp5qMlSfgtOjI6naxp3U9TjBrDuERLWvQ5zydc7mFhYf/Xjh9/mcrKAMnJIoORl/C7HQ38LNVCesM45mmbiI6ror1mIv7ua1CEjGyep2FDxEPsalhGnSuLVcqTOKNOsbItyE2mdrpV8ayb+DvORqSQ1ruN5xtXY5EFWh3d7NW+gEWzCFPAhCzD2LiT61XtnNAmMYiFenMLWl0lbw6OIABq2ccfYvKp9TxBMv1s0PyRHHsXjwfWUxUVzy9ce+goA7PKQkFRLG2ddazSlhAb/QYuhZGNqg0cG55GYcpOslr2Ey9G8MbCi/CYV4Aso5Alrtz/DZbOE5ycFKAjaoBr98OcJgPVk36O25hGebSf7xYmgiiw9mQLfnUXLqUMoozfEiRt+NxXb7jcw8LC/q/U13/I3r39mEwiqryreHxHA2tSLWQ1ejCWbCE+bT+9rdm4W25GDOnZPFfDhtgHOdI8mzLrZGYrWjhPs4vhQQ83GDo5q0jkyuJn6DXFkdK/hxcaLsAiC7Tbu6kKvIgq6Tz0AT2SLGAbH+FqdRd7NXm4BA0nYiqIEXt5a3AYJUFGRZF7Exbjc95JnljF7aE/o/AFuTFwN6bEfla1nKB3ENINhSSWuug91cMlMdlE6d7AqoniqsBdnHWkMTF+Ezd+Xc7eSybwSv71BDVZCKEQ2mCIVTvfxaVt4dCiEWY0+rnnCwFJkciRGbcTUur4ZoJIbWkCepuXVS3VKCQ7ZrsXryWAyeZDOWwgJpR9znMKl3tYWNiP1tm5nW3bTqFU6okuXcPj37azKiWGCU0e9AU7SczewWBnBmOnf4Us6/lyoZpbY+6jrG0au3sWUSj28YDyPepdfm7S9dKgSGPt5GcYMkSTPlDGC3XzscgCbY5u2mwv4yw8D31QS0gWGXP1skZrZbdqEn5FiP3x+8n1hXh7qBslQU5r1NyVsI74sSuYK25jo+NtekIWbgnezuLk/ZjK/Tg8CqbELEacVIWzTMeyJDMR6ndo08Vx5fgjuGQNKzxv8LPdHTxy80rqYy8DWUQMhTC5XSw48jqn8tpRe8f59WcK8rslWjLm0JGxFq8In8420p+ipaRjkOTxViLcw3h1CkxRA7is8SSFLLRGJaMfqDvnWYXLPSws7EcZGjrMV1/txO+PIW3mMh7f1cuKlBgmtvrQ5OwnKf9LrL2pWMvvJiRq+WKRhjui7uTE2Wl807GcHHGYZ5Sv0ed3sU47RKWQzbopz2DXGcgeqOPF2skYZYFWRxfDfa/SM+U8tCE1flmB09XG+boAhxSTcKqdHEg4wNKxCH7vPIWIxJcmM79NupOioalcxQtcYt/DwdAk7gvewPWRn+A7rEEhmpmftJLR/C1ojmUyNbUVo3I3FfpUrrE9jkY1zkMn/4Jxno8NSx7AYZyE0jNGSBNBlG2QtK6Xqczr4oojClackAgpRPbOuhFBW8qQDjYticSllFnY3EjKaCdGnweVZRS9Q4lvNJlccnizIIIetYGMqAnceY7zCpd7WFjYP2S3n+arze/gcKSTMnkWTxy0sTw5mpI2P8rUIyRN/JixwWSGyu7Dp9Ty5UIF90T8kpMdM/i67RKyxBEeV76PFBjhAo2NIxRy7fQ/4laL5PV18VJdNgqgzd6Bq+sNmmach1pS4pGU+Ly1TNFHcFrMpNfQx4nYMm7vi+dG/0lk4ClLMu8mPcLiHj13eO+jyNPMK4GVvCMt4cbgR3iqtCTqspgeeyFdme+RWD6FrLRdaBWVfGss4hfWh0gRO7lz4CuqbszhvYgNSAo9JnsfzogkokabUDifJ8ru5uFNCix2Px1pGVRMuJboYDz1cQq2zjcT7XAws6uF3KEegjqZNHM9PSMFxISiaY5K560J8WjP2gn2juMwh9fcw8LC/ou5XK1s2foHBgfyiMqewFNlIZYnRTHtbAAx/gTJJe/iHEmg/+gDuFQ6ti/wc7/pV5R1zubL1tVkiiP8UrWN7EAjiRo3uyjlpllPE1CGmNg3yov1iYSQ6R5pxtfzITUzF6GUFTgkFRpvNVm6VLoFM7VRdXSYWni9w8QcThIANiYXszfpEa5u7eRe54NoQ15u9d1BqxTNtdZNeMd1lEQvJDdiCm0J75JVN4/kpHdRiu28qV/M09abmO4+zeop3/GK5VoaDNNQ+LtJcozTF52EwXGcyOG/8oudKqY1BXAY9exfuBCb6nyig0a+z9dwaKKWiYPdlHY3YPB5iIjvQbCr6bMXky5k8lZBLD0qE0KVHa9LIlYcY5Zt/JznFi73sLCw/yOPp5vt2x/mbHsh+vgEnq8zszolhsJWH0JsNUlT38Rjt9B3+CFsGj0H5oxwr+lhjnTN58uWy8gQR1ivPMgy6SBGdYAvhLncNesRQqKf0h4/zzdG4kJmaKgGz8DXnJq5CAUiQ5KSRH8VKv0EHAIcjjuOVrCzrdNDknAWtyBwRcZiKhPu4dG6bdzsfJNuMZ617gdJ8XVw4eAelGIkCxNXE6W30Gn8ggnds4m3/AnEMR5VreOj0YtZrjpK7kWN/Fp4Gq/KgNa+mzh5Gl3R0ehtm7li/06u/D6EKuintrCA2gkz0TtKMUhKNs820BUjsaj1NPmDXaD3kxlXR+dwETHBGKzRGbyVm4jY4ULutqEVvMwdOUG+o55eY+45z+5HlbsgCMuBF/nhHqpvybL8h//DuMuBL4DpsixX/GSzDAsL+0/n8fSwa9ftNDRMQhUZwWudyaxNiyOz0Y0QX03SjNcJuCLo+f5hhnVGKme0crvpD+w9ewHb2y8mQ7ByufIk17AFlRjiXWEJv5l9P5LgZUa3wHPNGkaRGesrx23fz5kZ8xAR6JJECqQG3NoifAonOxOPs8Ap8ltbFxrBg10QWJO7nlbLOj449QwXuPdzQFnKvfabWOrYQ6xthAR9NjNjLkapgX5hF7nOCcQZn8SrDLIxdDcnHMWszd1KS1IBW9RXIAatRA5+gtq8nm6dmtyWN3n0o5OkDI3TnWCitnQuI6p8Yu05uDUi780zog8Ns+rMGQx+L3GJLUjjGjoHp5AsZPBmUQI9mFBU2BHcfqa5apliLUcjB5AFAZVZcc7z+4flLgiCAngFWAr0AOWCIGyTZbn+78aZgNuBE+diomFhYf95PJ5e9h+4lTNnShENBt4dyOb69ATi68Yh+QSp094h4Iqk+8BD9BojaC85xsaIt9jaspJ9XeeTwwCXqqq4RfgUZIkXFKt4dvYvkfEw/6yS37dLDCJh7zyCLVRNU+kMBKBZglIGcClzGdf1sD+2nAf6tfzMX4ckwIgo8rO8u3GaprK7/BfkBM7yuriS960LWDOyGU3Az6ToxeSZpxJSjdPjryJfaSJa8yRjKh1XuJ/CSgRXz/qKTzUbcGij0Dr2Y3E2YI/dyIjo58atL3PV7pN4NAr2zClkNKUIr3MiqQ4LnRYl26epKOk/Q95gN0q9i6TkJvq6C4nwxzJoSeXNnHSEVhfKvjEyvJ3MGz5KVNBOUKFECEEoOpdIXeY5z/DHHLnPAFplWW4HEARhE7AKqP+7cU8CzwD3/qQzDAsL+0/l9fZx6PBNVFeVIql0fDoygZvSkjCfcRDK+J6cqZ/gc1roO/AgZyOjcOZ/wTXRO/m0cQ1HexeSH+rmcl0l1wtfEpIEnlJfw5uzr0WQnVzUpOGRHokuWcLRvhurtpuOvGJkZJoIUCIGcWOhLbKWHkMDH3dBAY34BRgVlfys6Enigio2V2xEECTul26ibSCSNbatqFQaFiWsJ1qbyLjaSl+wl2mqISJUH9CqjmON40nMRjsFkzv4q+5OhKAX89CzJAbSaUv8OQb3KK++9Cy53V0cLTbSOWEhCjESyT6VVJ+G8hwNLck2Lqk7jTboIzm5Dn9AT0/7FGKUqbxRko7Vp0NZNkr0+AgLRo+S5ukmoPjhJiJOYyQlYg7FZbsYyPaf8xx/TLknA3/7FWY9wMy/HSAIQimQKsvydkEQwuUeFvYvyuvt48jRDVRVlhBAz1eOPH6ekoLqjAN/zk4KJ3+Nx5FE34H7qE2Iwpj1PKtjT/NG7VVUDcyiONDG1cYT/EzYgTeo4EHdjXw+83KEkINrazT8yirTJEuMN31Nf5ybgaRsQoToxM1EdEjA0fgjFASHea1nDI3gwQcMqnRcXvwiFw+W85vet2nVpfKU/TIi+4aY52nEbExhSfQaVAoN1eZqLJ4YZohHMCm/46gqjw2Oh8lNa6MjvYgW/UzUtkaM4y+QpFxPbfJcJnS28szLz+AwhHh5TToW1UyC/kj0tmIMIZHvJquICtaytKkfndFGUmYjPZ1F6D3x1KdmcygpFUWTA2P/ILPsJ5lor0MWRSRBQBYF6vMXcFvFWULtX/H1eVPoTY5k0TnO8seUu/DvPCf/fy8Kggg8D1z/DzckCBuBjQBpaWk/boZhYWH/KTyeLo6X3UhlRQkev5Htngn8IjEdudaBO/8rJhftwmVLp//7uziWYaYg5UGmxgzy/OkNNA5PZoq3no0Rh1gmHMIdVPFLw+18N+NCxJCLe8pVXDkONXIQT90ndGXpsEUn48ePQ/SQJUUgKYbYmVjOPVYXa7y9DCl0qCSJbk0E1xf/maca3+CC8RN8Fzmb93pnUdDbgEFykx2zgKmmWXhEH19YtrJqdA6pihcwKM7wibiAh50bySoZoip2EbIEMQMfIQT2E6P/DbUxWSwuP8odm95hyxyBgZTppHsTGPNnkGFPw6cSOFjkonioGrUUICP9FP6QjrNNM0GXzkszJ+C1gvroIEW2OmaNnUQr+QgoVaiDAZqzijDrk/nlt9vZmxeH6qoUZsQfpHyg+Jzn+WPKvQdI/ZvHKcDf3gDQBBQBBwVBAEgAtgmCsPLv/6kqy/IbwBsA06ZNkwkLC/t/gsvVxomTN1JZMQWX18ReXx63xWfhaXBgn/ghMwoPMz6STc+hOziYJzI35TbyDAGervwlXWO5zHFXcVf0bqZzBndQyXUxD3GkZBEqv5enj8Fiv0i17Cd4+k1ailNxG/R4RDciMhYpApu+hbOR1XzS6yZDHuO41sw0r4MmXTyPZ93DpuoHiZdGeSl6DWcaI5k2UkFQq2Vu5NUka5Oo0jdQY2xmg3UGFs3DqBjkCflqPhAvRDPbQL15KkqrjSTvU7gJoDH9gabIOG7c+hm5HYf59XVKiuznk+pWMxacwoRRA31RAtaYZkr6+zGZh0hNqaOzswTGU9lXUESzIQZVjZ3M/nYWjB4lxj+KV61B8IMtIoYDs5Zzz67v6ZIq2H2riakpx/GG1BwYiWakq/ecZ/pjyr0cyBUEIRPoBdYC6/73i7Is2wHL/34sCMJB4N7w2TJhYf8anM4GyituoKpqJk5XBEdD+dxiymS8yYFj6mvMzK7CMVRA57FfcjR/gKUZfyROoeHXJ+/H5ozlovGD3Gf5lkx68ISUXJfwG44ULMDgCfHCMT+lISUVkgdqXqd2agEhlQqH0o4ppEMpi9RGn2QSTTzTayMgqPjQHME6h50qYzbbo87nk7qHGVZH8UftpbgqAxR4m/FFJ3K58UoUCgUvxX9MiWcCG605RGtvxy+EuMH7ACczSnFnxSFLENdxAo3iFXyKXAIRd+BSqLj3w9eozKikIjuVGSOT8WMk6C9lgg1aE30Y5XKSXX4ysquQJCWN9QvpteSxo2QCcpeHuIpW5o0cI8vdgU+pRgYkUcF3C1ejDeq4bPf31C8aY3bmSQAO2SJJlx2cFzOKZiTmnOf6D8tdluWgIAi3Abv44VTId2RZrhME4bdAhSzL2871JMPCws4Nu/0UlVU3cerUXBzOSM6Qz3ohDWeXHc/c55iR3Iqtdwpt1TdTm7efy3K+JOSP4aET9xDwaNng3MxtsXswM45LUnFTysMczl1AjF3ilZNuMmUFZSE7QtM71EwvAlFkTDVCVCAKpeDiWHwZ99u6WeSz06BMZI/Jze02O0fNRYzIkTzR/VcORE7liC0e5ZkRTIKIOmkGV6gXMawa5Zmkd/nVwFXkSaeJ0b7GoBDBGuEpeqdl44vSIw55mO55lU7xFAiLGI1dT4zdxkVlb/Lh3FPkDy1l9oiZs4ZCJnZbUIVkuuM7iZA7iYzsJz21hrOdJYx48vl6ailjAQ3GY/1M7zvJZPtpZFEgqFCiDAWpLJ5DQ/YsZpw8SVROG0XXHkWj8HPSHk0o5GRRhA1kGcN3IvqOBLj13GYryPJ/zerItGnT5IqK8MF9WNh/ldHRo1RV30ZNzXxstmjaxQIWjSfhcjtQzPsD+XF9jLacR037OkayXmVZVjVdznSer74DpV/iPuc7rI89TgAlsgx3pN/Nt5mLKewJ8Kd6P0YEygMDyD1f05I/AVmAcZUdUyCSgLqbgcgy/jhsJUoK8IEpA1kc5AbHOIcjJpMyPkhaaJC3Ey5iuMGHdsDLkC6WuZYlTFGmc8JQw76IE9zVfxWREX8i1nuSk0IO6+J+jyvXgixBRHs708x/4HTAj0FYS2fqRRR2niU4/hJelcC0/vNQa7RY1dOZ1SLh1AUJGKoR1HayciqQgiraOqZyNHs6NbFJqFrsFNdXMnvsJPqQB69ag9bv42xqLhXF56Ec9lGqPMJ5ed8ToXHSbU2lVh7kfBOotH6UJ02MVyyiPfpCJFUvd7xywz+VmyAIlbIsT/tH48JXqIaF/Q/U3/8VZ2oe40zNBTidZqyKAuZZ47ELfUSf92dSo6wM16zm+NgyNMUPsypxmO8HpvJJ3bXog26e877A8tg6BohFg48nMm7i24wlLK9z8etecAEH3U3IrjL6CvIIISGJPkwBM13mamZRxZNDDkYlCxsTzVzqGmSFY5yj5hKm22txqEy8Yj4f93EXam+IZksRNxsXECcY+cCyjRRfAvc4FhARtxGL3c6L2st5Ju9mQpFaxCEP8xyfE9Tu5lTQiE51N52Jkyk5e4Ze8c8keGcxdySJyvRiijuimd0ZxGGw4jM2EhPTSXpqDW2dpVRq53Fwdj7SSJCsXdXMHzpCnN+KR62FEHi0Bg7PvJhuZSrTRw+yLH8PcfoRvIPJHA7qKIkYZbkhgO9MIaOnFjOmLYJYGUFWYIocOecZh8s9LOx/EFmWOdvxF5qaXufUmRW4XXokXQlFXWbGTE1kz36LKKONvvLrOCilk1VyJzPNPt5vv5TDbYtICw3wivQniqP7qJNziBIcvJy2ls/SVvCL42Nc71TThcSZ4SN4TVYcqakECaFAQCWHaLbs4VFbGxOCPo5RyG/SnDw1MswczzgNmgzmOk5z3DSRqjEj/jMe3EoTI6ml3K0qRRIkXon9lHW2RQipW4kd24XKIbIi9XkqMkohKGFsbOMG45/ZE7Jj1aSC6U76TXHk9m6nX9pBSc+liNGJHEwqZlW5F4M3gNPcDMZ2CvLLCfq17Dl7JbvyZ+FEQ2zZWeacPUKuqw2fUo0kCCgkiYpJ82mILiYnUM39ie+QYurHPxxBqO18HMkVzDKKOBqW0N46j4AYAbof9v/ohE72pmcxe0h1zrMOl3tY2P8QkhSgsekROjp2UHlqFT6vhijtVLQdekYSjjNp+lfoVR7Olv2CY7ouZhV8SIZa4Mmau+kcyGBhqIo/KV8jWu1mvzSLXLGDrUkLeSt5Lc8eHOO8oIZa/PR0bWY0I4KgOpIAQVQo8am7idYf4l3rMH7JyNOmIvZGD/Dq0DgTfC7GBAO5vm7ejliCs95NyKGk1lRAQVIyN/pL6VL306pr4xplAoGJj1LYPMQJ/WRumPJbXEYTYp+LGWNbmO/azicqBSHDbOzxGzGEBNL7/4JvxMfswKUcmzSdgm4N6w65kRRebFF1xGdUkpzYTGX3InbEXUhnSSzGpiGWVO9gkqMGBIGAQok66Kc1cyJ18aWYlH3clvAsmRFd+Ed0xBxaTHfWIL7IEXxnrqNtqBCQQVQgiH7GC+rZk1yASVDwUusDsrFRNgAAIABJREFUNPkLznne4XIPC/sfIBCwU1t7O719pymvXk0woCKJqdCpYyDvE+YUliFKCmpP3Eld/NdcktmCFDRyT8WvGbcZuE36ktt1WwkIKj5mFQvEcvYnTufV6Bt464CdiWg4zjjjPZ/Rk5uOjIBECBUCQ6ZKbvSdYKrNR32wmLtTJVTiAB8OOIkLugEJm8rEp97JeE74cCnMHEkt5CZzInPchVToa0nRBMjN3YfK3UhKk8BdmQ/xdfIF4AliON3I/b6/Um8f5N08DSr1egYSljDBNo539BmixiYxnjSNA6ZU1hx3kDLqxasdQo47SfHEI4yOJ/LK2ANU501AMeJm7pZvmTpcgUoO4Fdq0AZ9DMUk0Zg4Fb/Ry7rkd5gQ1YbfpsG8YwK+xEIaIwVsVcsJeaMQBR8golCN4yqqZndsCT51Bo+0/pkltpO4Azrc/d5znnm43MPC/psbd7Vw5swtDA56qDq9CllWkeCaRMAtMj71JRZk1hFwxnO85lqcea/yM4uTWnsufzlzGypvgDd5hqX6U3RL8XzJRawW9lAWX8Q26XrerwgQgZLdch++sf0MZv9Q7IIACoUDjId4znEWUdLwjnA+L2S3MC2g4oWBUXQhPypCbNXPoKtdwD8m0mjOoD8nxGOuAlLcCdRFVpCUUoEj4TgTmnzUSKVcOu0+xjQRKDoczOrexo3t3/LXfJGWQgui6X76zSnM6e+gY+RLkk0rqZ48mdyeID//bgylJDEe0URi0XdERfaxeexa9iXMxR8SKN17jBldRzEHnXhUWsRAAJ9WR23SHIZidKxM2kKRpZGAU41+swml8lIaIqJwthYCoBR/KGyFbhz/xANsi5rOkH42D7a/xeVDuwlKKnZ5p/O8fgXTEnqZfY5zD5d7WNh/Y8PWfdTV3UXvYDKN9QsQ0GKxTsSutmKZ/xXF8Q2M9xdxoHMKaVOeY74+xKaeVexpWEI6g7wn/p5M9RBlwWL2KBawTviGKssEakau59lxNQ5kdvhq8YltuJISkWQQBXDp67k0dJg5djcdgXwei0mgKqaZ1d4YHu8/Dci4BDXvSTNxVov4FSr2ZcdTFBvghaErCCFhTf8WVc42/P4gUTWxPJB6PftiZiM4/ZhP1/N46wdIQ1389iIFLuNEvLF3I4tqzms/whBunDm3Uq00sPrEKAXdAgHVOELqfiZOOshR5/lsFh/Enmwkp7yOOfX7iPcN41FqkQERmdr0mXTFWrgwdRelcTUE3Co0W4147FdQG1mA3xuFwudBKXoISgYUphGCE7/jS8Mcuo0XcWfnR2zo+xpRlvkquIinQ1dhF42ki710JP0/cJ57WFjYvx5ZlunofJX29udp6pzJYEcuasFE5NBEBhKOUDr5EFHGXvpblnEwMMiSqR8SISp4vO4hunsTuUJxiCcU76AkxBfBpdQrClgvfE25uQhP3w3cKuloIcAZ535sUQKy0oSMjCB60BsO8aCrESQ1XwVX8/vMFvzKTu51WLhupAqAE8ocyvti8I0o6IwyUz5xkJvH53DB4Gxs2l7GSl7HH9FNRFc2O4TJvD3pciRJQNk4yqrqLdxwah9bJ8PmNWok/VrGYpaT6g4RP3CI5vgceo3JpA65ufnYIAafGp+hneQZHzNsiOLx4B/oiUsisbmLpSc/I3O8A5+oIiSIqEN+WhJLaI9L5rz0A6xLqCYYUCLsjcfWcxnt+hJkrQJdcBiF4CUk69DHNiLlVfC59jxazeu4pecLbqv9FK3kZXNoPi8E16A2u7jA9D1KIZJGoR5DIOKcfwbC5R4W9t9MKOSmvuEBBge/paz+QoIjFjTBKPRjuQwXfcn87BOIMlRXX0Vv6jdcHj3OsNfCXTUPg13iZdUrXKI4yqDPyOfiSkZVFjbwBftVK0gZuph8lJThpGf8O2yWqB+WYQBJ3cml8l6mjDtp8U/meVMmh5Mq0YjxvNfvYYq7Cr8ssikwDWuTipBC4PtCkBOt/Ln7F6QEYrGmfcdI3meoHal0tK/kteRl9GgTiOobJupUK0+e3ITSO8RTVypoio/GH3k344YM5gy6GNb0UJ61AIUUZPWJHoo6tMiijDJrO5El5bzjv5nThkmYh0a5cNcmCq21SIKCgKhEIwVojSmgKzGF2ZnHuDzxM0IhBYGyLIY6rsSlyERhdBGh6MERTMITjMGUUoU75zSfKS6gPvIW1vd/w6b6h4kOOdgdmsomxQIsWT3cqXkNZ/dkdogy7frvCAlBQuPnelEmfBFTWNh/K273Wc7U3Ibd3sb+yjXofCo07niCAT2mGXspjj+GZyyNgz2ZZOUeJk8rsde6kE9r15AX7OZN5Z9JEwc56UznO/3FyEoVN4Q28Y30IIuCOSiBg3QxEjqNz2BAlmUQQsTojvJzTxU+WcNOz1W8mFmHXT9ImmIKm9p3Y5LcNAQS2Ducgd+uojcODk/sYb57Lr8auhyUPgZKXsFn7MHXeTkvx6VSETWJlPF+PHVubjmyhaWtFRyaCW8tUuLUTcUdcysiGibYndRF6ZEEBRP6raw46UHvMyLperHM+5DthvPZr1qCxuVl1uEDlHSVo0QiKCpRSwHaonLpSUplZnYZMxKqCEpKPLUTsbavJRCIwazpQi35sAayEcQQERnHcKTXs5UVnImZyKXD+3m47Q2Sg8OcliZwxJhLTGEL8f4xBupL2RKRS7f2e5xqB6ZgGpbQ5cTa9Lx9/5X/VMbhi5jCwv6HGRjYRkPjrxkbN3Cich06ScLgzKIvtoOZBceINbcw2DGb46p2lhUeApQ80fQgXZ3xbFDs4iHVxwRD8KltFnXRs1GJIS4P7GFf4EVWYqSHIGXBMmx6L4gGAAR1H5fJu5nksVHrm8HbmmL2Fe4npDBxjbuYB/q3EpAEPnNOpqfPSFAncah0lMG4APd03c58by6uqAYGJr6FenA+X7vXszk3E63kY17TMbTHh7mz8msEg48XfiFwNEKDx3wNrsglWLwS48ogZ6JNmDwuVlX0kDEQA2jQ5W3jdKGS5xRPEgrBrEPfM7X5KDrJh19UIUoheoxp9KamMiP7BJcmfENQUjFSPx9b6ypkj4oEXT0B1SjDvlxElZuYgp30WAb4nJU0x6xk8VgZ35/cQK6/i36SORxbgD9/iELHKGOVGXyiv5DuqHJ6jd+gCZmZNHQNWczG6apEL577z0O43MPC/sWFQl6aW56kr28TDX0lDLSUopdB68xmrKiOZck7UYh+qprmICeXs9oYoMZeyEt1txI9bucj9R+ZJ56hwxPBh8Fl+GJS0Yg+Sr1OBkK/43yUVAgO6qQj+I2af/vC7wBZ6sNc6z+NDQ2fjP+KdzNqGDDtQdaU8Hl7HRP9O2get7DLmoPfo6IlzcXJvBEynXN4v/0SzEEj1qyv8emGaT37JM/m6LBpzawYOIDvjIdLvz9Evr2L6ktC/LVAw7AyCU/0bXi1KeiCElatiNbvZ057B/PqRDT+OERjF8PzK3hZfzkO0URpZTkzzhzEFBjHJ/5w4VC3PpX+9BRmZJezOmEHQUmNtXEp9ublGF0jZBkrsGpy6PNNRakbJTJ/M98LEVSZF9GXkMw0Ry3fVvyCyd5GXETTmJhAb44X0eYjtDOaQ8qFNCb00BSxDRmY3LuEROFiAt4zXLD7QSxjY1TnFQIbzunnIrwsExb2L8zlauN0za9wu5rYV3sJ6tEoFCEN4yotufmVZMUew2VL4bALZsf1Y1DIvN55C9Vt+awRDvOE8n20+Ng/ns0X0ZeRGPCjEbzEekpZSCICcEBop1d1FlH84dYOWmUH14V2Eyu7qPAu4mNtPofS9xBUqJguTeTNjq24/Qq+s+bRY49g3BjgUNEIVk00d9hWstRZRFBtw5q5DevoRfwpLYHaGCMF421c0/Q1nkMyi5qrcZUq+OTiAPtCajzG5bgjr0QQfrgBhs7nJc4xxvLTvVhG0xCR8RSX81HOVAaU8RTWnWF25T6iPWP4BBVqOUCrMYvB9BRm5pxgavxpgiE1tpbFOBoWkeqvxWBw0uafhScUg8rchy2+kZ2hdAaTMxlLjKbY2cLjTa8zx11FAAMd8Tq6c2XEMQHFQT313umU50FNdD1ulYuMkSKS3GuIdDVxyYHtxNhsjERG8c7Fa+g2x7Lnf7H33mGS3eWd7+fEOpVDV+iuDtVxctCMNAozCigiCSMwIBFsWGyM8Rqv/Ther+3r9d27DmtgMcYX22BzwTIYYRAYCSGEhMJoNHl6Zrqnc65OVdWV4zl1wv7Rsi73rh+b1bX3saE///Rznvp19e/U+fa3fuf9ve97fuF9r+ua74RldtjhBxjHsUmvfp7ZuY9Qabo5ffEn8FgmUtvPVqrFvcnH0Dw5ZlcOUAvPcH+3wXK9l9+Y/EWChQp/rf4Bt4pjrJkBPm48yPjgLezNrSHYKruN27gZlUV0XpYu0FJa20/sEXSOS89xnznNvBDm05Vf5mtDL7DpewpcB/jM8iLH6l/jXL6Hc4Ve2gKM7i4y12myR7+bTxQOEqqlqMTPsy7LPCb9DE9cpxAwa/yfs5+gY2yTnhc28XT7Gf01k8+pEht0UU38HG3XEAA+o43WrHLbzAzDax249EGs4CaP3+zlWvBNDC7M8L4zXyFRzWAIMg6w4u5jayTOiaEzPBz7JkZbIzf5ANbkAYbFi+jKWaasO7GaGmZ4hTFPhhfEFEr3XRTjYfZV5/n0hT/g1sZ5LFwsRkMs7ZagIOD7ospy5QAvH+7gcmyKgjtPuN5FX+7H2b+5wj1nP0ZHqUQpEOBP3/ouHr/rQU5c/hbXlWr/4hrZWbnvsMO/MZrNVcau/SrVyjkur9xAZfEwAgYNMUxyYJEDyW9itAJcKLu5LpZFE23+Mv0Bzswf4p3O8/wn+VFU2jxl7ObTne8loKgMZ9ZQ2iM8ZHcTROCkmGFanUTBBqBDnuYD5nMYYptXKu/icX+I86mnMUUXN9l7+NPFJ1ipBXg+M0S1rbHYVefqUIlI+wD/zp3k6OrdgMNS5zme0e7h0QEXlmDzgbXHee/C18mc9hFthFl/a5VvDBR4rqrQ9N5JLfI+EGT8bRutVWP/2hzXLZj4av0gwOX9LZ7c20sqPc/x08/SU1ylLUjIjsWUd4T6niB3DJ1iJLxAS/dRmrkL/7yPIfUaafMw860TOIJD1r/Fc4KbjZALbchFIRZhd3WR3536c07Uz2ALMqsdGku7FOyCTOBbAtnMIK/cMMSFzhVWA0u4jSDdmds4vlLklrGrdBSLVL1e/va2+3j++qM8ePor3HthnI6KxbnDLt7/2OXXdf2/35X7jrnvsMO/ERzHYX3jy0xO/xdahs3Z0XcgNUVwZMpJhTfEn8YXWmY110vdk2G312C6PMIfzfwsnaUt/lD5NDeLk8wR5ZPqwzy/+05uXZzAVbbY1z7EA7jJ0OYJ7zUcK48AKGKJh5xn2eekuSj28UzlZ/ju0JPkPKu4ld386fIcg9UNnt8cYrEeoewzOL+nQG/by6HeBHfnbsafPUYhsMATkQRfSMUpaCLHMxf5+NJHMFbBHO2ieXsvZ06c5bGSQo4IlegvYLqGUC2HrlqLaHGZYwtrhIpDKGaATLzJYzd2EiguceL0c/TmVzCRELGZ9O/G2qfxhsGX6Aus0WiEqczewWA6S4dS5ErjfrLtfZiiyRVXm7MKOB0myoCHTCzGcHWZ353+DLfXT2EjshZzsTTswsrJBL8lUFuOc+b6A5xLVZgNT6DpCrvT13HLSoNDc4tEikVqHg9fP/YG1jtd3H/ueQ4tlmgLAlcGfZT9ARJajYc/d+Z16WDH3HfY4QeIen2BsYnfpF49x+zGfnJTt2JJDRqqi8HuFYa7n6VtuJlpKez2V7AdiU8t/gzTy/38rPgNPiz9HbYg8Jh8I58c+CCO28dd4+exW/28w07Si8Qr8hZj8jgiJiIWN4inud++xLKkcqrwczzRlWYyfgZHCvBIzc+vrJ/j3FYvo8UkpuhwaaRMr1AjNRDnRqmT2NSPgaXx131FvtabYs0rESnl+eTC73NbaZTlaz3YHW9i8v4XedIpcKau0PQ/SD30MCDSW7Pw1Na4aX6KWKkTd70bQ7V58vogZWmdE2eeI5VbwkREACZC+1D2Odw5+CIJb45KNYE1d5CjuSlqVpwz9Ydp21GqosV51WLK3SAaqVMe6mYzHGGgmub3p/6S2xsvAQ5rCY2lAQ0rrRD8lkBrNcjo4UOcHpKY7hgltiWwOzPCgYzJnsVlwqUSZa+Xk7uuRxY2uX18Fo/hkI5ILPT6McQAbVsh5GphxuN86KNffF1a2DH3HXb4AcC2dRaX/pzFpU/RasuMXX4rRl3DEk20ToMj3c+ievOkCwk0T5GYZnAud4zPzL6XmxrX+K/KZ+gTsrysDvGx6E9zZvAGjizNMLBU4AZzN/eisYXON3wzWGYGAYEecYZH7BdoSC0ute7kaeEGTg98g5ZcZ5h+PrVwgZVCmPNbPei2zHpXFTVSYHeXl1TSJjHxfvz5Q3ytu8IX+mMs+1TUSpMPLn6Z3yp9lkrZS6HwbpaP1ziXfJEvl9zUxBiV2K9gK3GCukW0lOHoyhRdBQVfdQDJ8nBp0MW1xBbHLj5Lf2Zx+ytIkJlOHKJjX5Zb+14h6KpSKvUQWoiyvzDHy423sK4fR3JUNiWbUXeTZiSNFgyy0D9A0eNhf3GO/2P2UY43TwI26wkXS/1u2nMakW86tLM+xvYf5OXdQVbdo/RmBFLlPnpqFnvn5gmWK5T8ARZiXSRKS3QXdFoyjA67aAd91FtBHCAQVFno309LDFDyS/zfH/7l16WJHXPfYYd/4xSKp7ky/pvY7WWmlm+kPHcMQ6kg+A0O9owSjM1QrQfJY9DvbZKrx/ij+Q/jbDr878oX+BHpDJtikD8OPsJf734HIg4PXDhLRz3Fu50IXgSecadZc+YQcPAIRR52niUirnOVHl4p/jzPDj3Jpn8Bt5jg9zYydK63OJVLUTM1xFCFbF+Jgx0QG2rgX7uT2MwjnI24+JMRhYWAG7lmcGjhKo9u/Q4hocZm4TayiTdy/sCjfFmX2NQb1MI/Rst3N7IDffkch9NTJAs6vuogqhEhGxC53FNgYPo79G6lsRBoSn6WU/tJ7Z3gpq4LKJJJITPE3pUyuUIPF+tvxWUmAZhXTArxeZrhCq3Afq4lEjRUleOZK/z2whc5bJzFEWA94WI56Uaf8RF90sQuuJjcfZjTu2KUnKv05RS8UheddZ19UzMEqlWqXj8NxSFariE5MNMlsNbnRiZAteVFEsHs7GG5a5Bwo4EImEGToLPFr/7Sn70uXeyY+w47/Bul2UwzNvm7VEvfoVjrYHn0rVRtG0FukupdpKfnPKalsKHLdHsamJbMo0s/zqX0fj7gPMXPy19DFGwe89/KHwz/PPlAlJumxuhZtXjA6uM6FK5JRU6r4+DoSBjcwRlu4DJjipcrWz/Hs52LTCXOgKDynqrEw0ubvJQdoKB7CblrFEbypBIWkX4dsd6N5+oHySlDfHy3wETIjdxo457P80frH+NN2jnKepwiv8BLu07z1UCOhVoG3XWAevSDWFKIvsIWh9NT9BRKeKt9uJrd6IrAZGeR6PJTxMsZLEQqrhRbu2Ls3XV2u0OjpaCv9RBfa3Ky8BB68zARW6UlOGyENmj3XqMU6iMj7eFaR4C2JPFA+hV+Y/lLjFhXsESR1aSLlYgbYzxI7Fs6QkViZvAQl/qjGOYc0boHKxgjWaqxb3IKf62Grig4mGhth7IHLuxRUPxujGoIw1KQ3F7yvYO0XV4006SpKsiRCj1VYPFmclT57U/9K0iFFAThfuATbD8g+y8cx/mD/8/rvwT8FGACOeAnHcdZ/p+e9Q47/BBjmnXmF/+UlZW/wLIElibeTCbfA3KFWOc6Q6kLyK4q2bqfsNagx2PwndV7+eryg9ypj/Jd5ZfpFvK86NnN7/X9IlcSe0nkMrzzuxcYbPfyFtxUMHjUM4pu5xEcm2OMcgfnmFfgyeo7eJY4Fw5+lbZkcJPh55dnV7icS/H1xgGCSpPUyCLOsE1/n0Fb91C5+kEc8wQf3+PifFRFbpnIEyUeWXiK33H9FT5Xi1z7YV7sTPKl/peZqixjNRUa8V9H1/YSq5S4fvk0/fkMrmYXWuNmZFNkOVLCs/lN9l3NYOEiF7gRfV+TI0MnudO3QaPloznby+zqbmYq9zNoBPA6ArbapNJ7iupQlkL7PhakB5iK+BFtm3cvPscvrf8t3c4MbUlkoc/NihagfTFE8pkKlm0yOXiYmX0epFYOf1PCDA7TW8qx++QZPK0W5quVpYrZ5tIg5Po0/LofvR6gVRExo/2UEx24TAcEgUpQQ1S36F1MoF5LMulkeDla52hj/V9cT/+kuQuCIAH/F3AvsAqcFwThG47jTHzPsFHgBsdxGoIg/HvgD4HX1zhhhx1+yLBtk9X1rzI1+zEkJ8/GyjFW50+gyzlinRMM9o7h8hWotNxIbZWEr8rZtZt4dOVhDtXm+YryOxxR51iUo3yo+zf5u/778NSrvPvFS/hbMR5mBA8WT2ozbJEGGw4wwb28wqLa5sX69bxk3MmLg9+k5jpDygryH6cKpDf9PNU4hEcyuL5nEeOgjtTnYBgq16YfQDLezNcHooyFJNSWiTxd5uDsOB+x/4wDwRWadh/PaI/wx/sqLDVGEUtpmqF30vLfja/R4taJ84zk1pCMMHLzFgJNhYK7jlR5kq7iGraUZD15N+49c9yU+ioBtUaxEmV2/BAvrD5EtNnNrrbEfkCPrCDvfplGcIBs+QSnFY1M2INXr/OrY4/x/tKTdDhpdEVktsfDkh7DecVP98t5yh6TM0OH2PKYiIKO4PZjR3dxYHyKgVMXkR2Hv49vrMTg2i6ZgEujXYhgFxSK3jCNoR4sTUOyHRqKh2xSJFZrcGisByXX4Jw7x0td+2kqcQ7VNvD7/nU8Q/VGYM5xnAUAQRC+BLwFeM3cHcd5/nvGnwF+/J9zkjvs8IOI49hsZp5ifOojiPYq1XwfCxPvoSTUSCRGOdh3DY9/i7quYbUVAlqT8c0DfG7pPXRXs3xa+Si3ucbJiX5+L/7v+NTwexFNi0dOXsTTiHAfgwzg8KKywLK0jAMMssgbeZk1V41TjV1cLL6DF1PfJeN/lKDt5T/PmuhrAmcae/HIBrd2LqDur1EYkjGbHk4uHkQ338bL3YMs+CV8dQN5okp0fp1fb3yRh+JnUQQ4J7+bXzkwTEa/jFq6guU+RrP7F3A3FW6ZvMJQbg3R9INxI5GKRkvSaTS+hae0TNu1n8z+ITqHz/DG+KcQBIfVrT6+vfQmMpu3cZ2ucJstYks6vsFTNEbS6LVbmbDey3MeL62gQl9pgz858+c82P4uHrtCUxOZ6gwwX+zC/XWFyFyF1Q6B7x7Ygy6ZmF6JajxGX67FdWcv46/Wtgu3gKIXTh4QEDtcBEtBpIqPiqxgxlK0OgKItkhblFiOx5DcFXrn/NxyvkmpkeGFmMrZXUcIO/D+do3jiKR8w6SFfx3m3g2kv+d4FbjpHxn/AeBb/9ALgiD8NPDTAH19fd/nFHfY4QcLx3HI5Z7jyuRHEK05muUuZqc/RNaEZHicG/vG8QVyNHQXzbaC19ViOrOHv1p6F95Kk99XPs39rguUBTf/Lf5O/nj4J2kLMg9eHCdeDHKYAY4hcEFa5mVlEZttU7+LM+RcBa42U1zO/xzP9b7CWugv0WwX/2ER/EsKC80h3LLOnYl5YgMFlve4yehhvp3upe48wpXO/Wx4JGIVA9fVMt6lLO+unuInEs8xGFhjQT7Kh/e8lTmmcZX/Ckntx+z6TVzNBDdPTjOQ38SxvVjmDcQLHkzBwGi8hGjXqAeHsPbArv6nOebNUjc8nFq+mbNL95Iqd7KvLXLQEZFDK3iGz1L1B6hs3cGzxj1c6PYBDjetjfNrC3/DMc6jOG3KfpmrwTizq310fK6O1HZYiHgp7wtjywrleJhEw+b6yUVipy8jOiAApgjnRgQWR2S6W17UfBhrXWYrlMDsjmJJLgQg64uQiXnx1KvccE5iaHWZWUnnM6ljlEMPco8NHwKGRS+CKwRUKZgmohj6F9fZP7mhKgjCw8AbHcf5qVeP3wvc6DjOf/gHxv448HPAHY7j6P/Y++5sqO7ww8b2Sv1pxiY/geTM0arFmJx7M5mWi4HwFXq6J/H6iujt7bpQTW5zZf06vrzyo0RrZX5W/joPiOdoiiqPxt7ER0c+SMtReNPoGImtAD1ShLsQGJNWGZcXsAQYYpHbOcO6VqTeTDLVeISnk2MsR8bRLIX3LdiEVwNkmgE0Red4JM1AV47FEQ9zQoLHmnEq8jtZCh+kooqkCjqVpRqe5Q2ur07zUOcoD6qnqEkhfmPwx3laq+Cuv4gtBRE73oPSHORIepbeYpY2Htr2XnqzHsDC1qdxsCl1K/hGTrEveRGX1Gax1MfzK3dQSx/liKEQa7tAMgj0ncXu3kStHGJC2s/XelS2PBqutsFb517gw/mvMGzPIDgOuYiLeTFFejqO92qRvMdFzu/BEUTq4TBRvcXuxRydmQyCYyMi4ACZEDx7VCLo0XBnO9B1BdMbxIomMTweBAQqmof5eBJLbZCcknjj5Rmk6jQvRUe4krqFY6qfux2BvYILAJEMZUtgrhVmpb2thVDyIj/227/6unT0z7mhugr0fs9xD/A/7AYIgnAP8Jt8H8a+ww4/TNi2ydrGE0xMfRJZWKZdjXNu9Seo1FRGOs6zb+8ULleDpqFgWiKKbHJ6+Ra+nn6Q4dYaH1H+jNtdY9QEF5+NvYWPD/0kVdz86NkxguUIUSnFXRLMS6t8SV7EFGCYRY5zlkV3ifFmkvnCe3g6eY304N/g0xU+MK6hrYeomy4qWou7OufYG8mw1O/mcXWYz1v9VFxvJ5McAeDIps5Wug6r6+xpZLk7PsmyoY39AAAgAElEQVRPRb6JjM5nux7gk+EO7OYTaE03UuS9hGrDHLq2QKx2Cl30UreP0pvzINpgW5u0XQ7Ng5t097/CwdAKLVPl9Poxxpdupaua5FjThWLJqIF1PPvOoSkaeu1OHpcDnN3txhZFksUM/2n887zN+g4JI4clwnpHkGvNPWy9IuOUmuT8DnZnBEnUCDomI+kc0StzIDiIznbgxZAELg0JrOx2ES5HiOTdWDWNaqSHdtCHI8gYksxcvJtc2Is/V+beZ2Y5mD7HouLhueG76DhwD29yZH5J2LZUSViibreYbSVZMiLYgFepsDvxPMPSd7iiqMDrM/fvl+9n5S4DM8DdwBpwHniP4zjXvmfMEeArwP2O48x+P394Z+W+ww86plllfulLLCx8FlXO0qgkOb3+ZrRmlcHO88Tji0iSScNQcSsGNd3Lt+ffyIvZm7jZmuTn5a9xnThPQfTx2eTb+HTqEYy2yCPnrqE0EvSIPm4SdCalFealDWxBYIQFrhfOsewuQS3FhPFGnuq+ymZgnkRF482zEmTDmI5EJFThRCDNsKfASsLN133D/IXnOgzPm6m4EwQNh7vSLTZWa2QKmwhWm1tCi/xvrseI2xmeCV/Px2I9bLSvYosamv/txIu97N9YxGvoVJQwrnovyYIfwRFwnBqtzhXkkVfY03kZVWqTriY5k76FZuZ69tT8BJoagmTg6x5Fi2wSau3llPsAj/co5DwuXG2DG1fG+PDml7hRuIynbaArImlPHxfWB6jPNKkqErYgEGqZeB2J1EaGYL2B/T2GDrARggsHNSQlBEUvtqhihuKY4TCW7MIWYCXSyVw8iWTXOHRhg4cuncRd3ODS8D20U7dwo+JjAAkAmwUsJ8tsq49FPYqJgEtskvKeZb/6FF3yLA6w1gzylH0DH/ro469LV/+see6CIDwI/BHbqZCfdRzndwVB+M/ABcdxviEIwrPAQWDj1V9ZcRznoX/sPXfMfYcfVFqtdS6P/znl4leRpSb54gjn1t9IjzBHX/ISwVAW2xbQLRm30ma+MMBT8/ezWOrlbeJJflJ+ml4hx4Yc4U/6fowvJn8Ef7nK28/PU6GXPbjYK1aZkOZYl+rItDnMJCnpKmmtjqs8wiXnBN9KXqXgXmHPho8Tiy7aVR+CaNPdmed2T5pOpc5SxMMfRY7whP82TPcdGIqH4arF/cs6y+tVZqsbZFQfx8Ib/Jr8JQ7o40x6evhotJtz0gYIHryeR+jbirArm0a2bba0LjpyYWK1MCBiebaoj5xiqO80UU+BRtvNuY2jrGRuJlnupbvoRnBEtMgC3s5p4oSZFm/h8U6N8YiGIwh0lbZ4+8K3eZf5TQb0NSQbyh4/s+Y+Lk1o1HUT2bKJV5oETJHkVg7VtLAFEL/H4kwBRkdUcl1BnJofB5V2oAM7EsVUNAByvgDTnSkKfo2+hTXe8eIp9s9fYbX7GJWhOxnyJekRJGwcqqzhY5y5Zj/zRoq2I6MKLQa0M+zWnqdbHadsq8xW40wwiJWU6T00wdrCfn7iZ7/yuvS1U8S0ww7/C3Eci7X157hy9XO41HPgCMzljrOQOcJu31m6usdwuZq0TRlBtGnbKqeWb+GFtdvw603eLz/Nj4qncAsGo94RPtP7Tp6I3sG+5TTHJyoU3D3cZDpEpS2uybOUBQcfNY5xGUubYUu28eWv5yXXEC8mLyKYFY4s+xhe9WCbCqpHZ6gnxwlpjSAGF0Kd/HryPmb8d2Jog8i2wx1Zk3tWdMa2Klxq5VkKRjiWyPJh8zHeUD5NTvbxqUiMxz0mohSkg3fQn1foKW1hiiJlsZvUegjNCtFUGzRTF4n1nWW4YxZRcLi2tYux3Alcpb3syrqRdA1JK+PvHqVLsyjYN/F3kRgvdioYsoy/WefQ+gzvL3yFE1wk0mhiiQIZVz9jm7uZWK3ibxnEyw0iLYuOSgUASxSQ7G1fM0WQbFiLqMz0B2iKARxbwQxEsMIxTJcGCJS8Hqbj/axGOujMrnLv6UvcffFlrMg+8sN30xVKERVkTMdhjTxerlDQAyzoe9AdDVnQGXSdYUR7mbhnjE3HxXipl3PNmwl3CwztO0MgkMezfhOB9F0sk+aB3/qt16W1HXPfYYf/BTSbG1y48Fmq1a/j0go0mhGupH8Eb7tCT+ISocgKogiGqSBLJhO5PXx36Q5mywPcI4zyPvnb3CDOogsyX4vdw2d73sakmuL+q1OolSA+28ftToOqtMSclMcURLrIcFAYZd2zimBpuIon+Lugm0vxi3TmBW5Y9BEoekBwCPVUOBDNcNTIgiPyuc7jfLLzfra8N2FLCgNlg7ev29y4YfBcs8KTdoNsd4QDsSI/U/kCb9l6noqo8mgoyOf9LmRhmE79Xka2KrjbBlXFjaecJFaOUFREjJ4rRPvOsTd2DUUyyTSijGZvRC9ez+BmCKUaAMHE1zlOLJTDFvbynGeYp7skih4XitlmKLvGWzee4UHxOwzUcyiWQ1P2sWQc5MKUilIsE602iNRbuNsmDmDLIpK53Z647gJNh4pbZabPT8ETwBJdmL7QtqFrbkCg6tGYivUzH0sSrmc5cf4K7zj5PEHfAKXBO4hEhvEKEi0cZpwyijMNus2SsR/DcaMITfpdFxjQzhGMTLEh2YyXexgtHccbhdTAGN2JBYKVDvzz92DWTnA+6uelsMne9XP8x1/f6S2zww7/qmi3W0xdfYKFpcfwhi6DIzC/ejflSpye4BgdXRPIsollSSA4ZJtRXly6jTOZ6xmyNnmX9F3eJJ/FT5O0K87nkj/KlzofILTV4IbZIlkxwfUNi13SJovyAkVBRMHgANO41RlyaoFQrY8l6xDfiG5i2msMrXkZ3nAjmTKKx6BzuMBR7wYD5RoX/Pv54+77eTl0B7oaxKO3uWvD5F3rNv5Km2+ZNf46rKCnvOz1rfOz63/Dm3PPowsiXwh4+St/GI9zO33VIborZWxHokkvsayPLdlNu2uSWM8lDiau4FFalHU/l3LX0ygdpncjgVqMAeCOzhINbeC4unnJvY9nEjI5nwvBsekubnHzxmXerX+Vg+Y8gWYbS5AoOCPMzUcoLBWJVpsEWgYAprT92cqmjS1AwQ+qAaaksRbxshH2Y7g8tH1h7FAHpssNQN2tMhXrZy7eg0/Pc8PlcR45+Qo9apJm6gT+yCCKIFHG5pLTRLLn8RpNMsY+2o6GS6jR7zpHr2cGT3yJtUCJ+XqEpcJemlIH/YkFhkLTRKsG1sqtrIr3cznRz8moxFhIwhYFfPUq91x4jj/7nd95XfrbMfcddvhnxDRNZseeZ3r2b3AHziHJBlsbh9koD5PwzhPuvIbqamHbAo4jUNDDnNs4yrn1G9CbGm+XTvKI/AKDwiZNUeXJ2Bv4UuIBLivDHJ9do9aKMNCAY3YBQ55jWWxjI5JkkwFxgpxnBdGW8JeP8C2vn0nPNL0ZieF1D+6WgiDZBPsr9CcKHLSyFM0kX07cy2Pxe9nSOpFNk/0bDR7OStydt1m02nzRZfLtfjdWj4frmxP84tLnuat0npog8pjfyze0vQTat9NbAZch4pgJ2o0OMoKC1HWNvuQoh+LjeJQmjbbG1dwhmuV9RDd6UfLd4Ei4gqtEO1bAHeOkex/PxFVy/m1DT5by7Mku8PbGk9zIZZLVCqIDdTtOJt1JbrqJt9hAchwsQUB3uVBNA9m0MSWHtQ4B0RJQ2242g142Qj7aHh+mP4IVDGMp26mIFZ/GVLSfxWgStV3mpitjPPLKBfqVJGbvTXiCKURBYBOb87aBZW3iN1o02ilsZNximZR6laQng9KZZq0nTVG3yRd7KJcGGHHP0ysvIOYd1ut3cC16D1e7+zkblSm5trNnOrPrDKxMM7gyTVd2Da+7i3//uU+/Li3umPsOO/z/pFkzmLl0krnFr+GJnEXWKpQ397NZGqLDu0q4awzF1cS2BSxbJNuKcXHzOs6tX0+j5eVe6SIPSac4Lk4gY3MucIAvdT7AN4PHGdqsYNQ8xMoid7Ty+KVZlqQWbWS81NnFDE1tDkOpEKkOM+30cdKTIVCu07/pIVhXQHDwd9eIDZQZ0XLUa12cDB7nG9E7mPENIdg2qY0Sb9hS+IkcuEyHU5LJFzplRkc8yKrDWzaf50Orf8XBZpqCKPJ5by+XxBN01rsJtFRsI0bW6CCvtnF3XmMkMcbh2DU0WadueBjPHcAuDxJeG4ZiLyCieLOEO9LUQ2Fedu/mVNRF0aeC49BVzjOSXeGh2tPcLF4iVcujWDam5aKwHqM4aWMXtj//uuZGFG20ho4ANDSH2U4BHAl/3UPB5yUX8NH2BzB9YaxAEFtScHDIh/xMRgdZ6ujE1ypw7OpV3nNhhpScgORRXP5XO0Y6FldsE9Ms4DFELCsCQFhao1tdJ+YyoHuTfO84NXETq+pHWg0TNbZQ63Uy1QBXvHcz03uMKz1drIS8AHgaNVKrc/SvztG/tkbA8NGQ48x4FEajcKyZ4y8/8rHXpcsdc99hh/9JzLbF2mye6dFnKDZewt95BVGwqGR3UTWChP2r+GIzSLKJbQsYlsxCZYAruQNczRzE1BXuk87zkHyaY8IUCjarrgRfjd/DY9F7kWoerJJMZ9HmtkaOuDRHWmrRQsVFi2HmMLUl6koWdyuB3hrkpNyCZpG+jJtAY9u43J0NYkMV+oIFNvQhLrlv5OmO20i7uxAcm3imyK68xE9lRfYbAnlsHg/BV/Z5yftlemsb/PzSX/Jg6UUilsFVKcY31ANs2UeIlgOU2gk2rACtYI5E5xgHY+MMhxaRRJua7mM2txen3I9vdR92uQcAV2ANXyRHNtjJ8/5+LkdVmi4Z0bbpKm8xkFvn3uqL3CqeZai+iWZZWJZEdc1LZVGmvuGiqbpoaRq+Vh3FsABYjTtMdYoopkqk4qGmucmHgtvxc38I0+MDQcQWYSMcYSrWTzocJ1Ze5fZLYzwykSXu60foPIyserEch0kspiwLw6jjbbvAURFp06Ws0KU0CLsU9J51qp0XaGlzeAoi/mUbipBuRTirHmQpcYSF3kHmEgksSUIy2/RsLtOfXqB/vUi0Crpbp6jlmQnXyXa0MNxbmHIdgJsLe/jML/7t69LpjrnvsMM/gWM75NIVps5MspJ+BTUwjjs+h1lOUK9HEBSDYHQWd3A7w9eyJIp6kGuFPVzJ7meysIseJ8/d4iXuUy5ylBkkHBa1bp6M3c43QycoGgnaFZXevM7dtTR+eZF1EdooqBj0s4TlWqTu2sDTitBq9jNqGcj1BsktDdUUcQQHV7JOYqiCHIY56wAz6nWcCl9PUQmiWAbJtU06il7eURC5W5eQBIGLmsOXh1280KWi6gYPp/+OB8uPc6KxwQadfEfey2L7KO1mkg0rQlYS0KLz7I1NcCg2Ttyz3f9kq9xNpjCIVOxDWjuA1YwCoIVWMBItZkNJXvEnWAkp2JKIahr0FrIM5Na4s3mKY/YoQ40MXkHHsaG2rlFaclPI+Sm7/bhNHW+tgQDoqsPVfoHFqIy/oRGpeil7fLQCQUxPAMsfxFa3UxYbbpn5jl6WIklyPh99m/O85fwk96/p+MO7kDp2IYgSNcdizLZZMx0cvY1qqwC4xQpJJU9CduNze2glL1FNnMd2TRHIWDCvsFhKcNpziIXYHpZ7R0jHO7ElCRyb+NYmg5vrdG/l0BrLtKQtyv48mUiNjKfF36fUq7ZCv56kX08y0EoyqHuw9Qo/8rv/5XXpdsfcd9jhH6CcazB9doHZa5exlXFc0SVES0CvRnEEAW9gA2/nNSRFx3Gg2AwzWx5gujjCZH4XpWaIm8RJ7pUu8Ab5Cn1ODoApzwBPddzKM8GbybR7aZUFbsgWOKHPIMg5MoKGjYSXOj3CIk0tjSVnkesJ6tUY85aJVjWJVrbjxJZm4umrEettshlKMm1ex5j/KLO+AQAizRKxTA6lEOGhAtxjyQQQycjwRK/CEz0qJbvOjdnvcl/1Se6qrmBYCS6ah7hgHmfTipNFRQmtMBCdYW/HNMOhJWTRxLIUtnK7qBT7EXMDmNm9OLaKILWQ4+tsxj1cCnZyJRykqW0X8HTUi3QX8vQX1rm7dpIj+jVSdhaX0saxob6pUlgMsl6LYdsOgVoFyXKwBVjucriYEqm73MTLbrSWj2oojOUNYHn8WG4vCAK2CJlQiLlIHyuRBLJd5+DUNR65us7hlhc1fgDJlwBgwzGYNgWKbQfRABCQ0EkoGWKym4gcQ3WXaHSOUotfwJJnsFdkxtO7uaodYTWWYq0zSaYjjiOKCLZFIr9BsrRBsJlGNmYoqets+iq0ZOs1fXUZMQZaSfr1bjr1LmItN31mnqZT4Cx+vk0XE04XxwyJL3/8wdel4R1z3+GHHsdxKGzUmD2/wOx4GsOZQo0uIkk6NL04go0nkMETm0LWtm+Xi40gs+VBpovDTBVGyDbi7BeWuFUa47hyjaPM4nV0mqLKy6GjPBc6xhnvDaSbMRK5Og8WZugS0pREmyrb8dcwJTqkRRquNJZdQyj0UGi4abQsglUJEQFbdDA7WoT6GrR6OpgT9rCo7OdaaB+65EK1DfblZ3Hn6ojFXu6rqdyOjB+RmggvJWSeSkjMOhv0NE9xS+NFbq+UsRp7eKl9G5POADlHQQxsMBCZY1/HDHvCs7iVFgC1Yi/VrWH0Ug9mbgSz1gWAFcyT72kzFwpxNRAl51dAEHCZOr3FDN2FPCOFJW5uXORAa5YeNYus2pi6QHEhSD7XQaWu4q5XkV9NVcxEHC72iyzFNYINjXjRh+4O0fYFsbx+LLcPRBEHh1LAw0Kol7VwjC2vj97MAm8aneG+dIuotw8xthtRcmE5Nou2yVpboGE4GLYA2ATlDF2SQ1RJEpJEjMACtc7LVCKjbOYsruRuZE4dYi3axXqii6Z7+5rJbZ2OyiqB1hpKe466M0pDKr2mLZ/lYaDVTb+eJKUn0Vqd1PVOMg4o4jKynWbTFnlJ3E+BAAHbYr/ToFeo4pMraHWVX/vE/9Ce6/tix9x3+KHDsmzWZ3JMnZ1nfiYPwipKxwaCoqPaOoJo4Qls4onNIGs1LFtkpdLDQjnFQqWf+dIAW80Iu4VVTkjjnFDHucGZIeA0AJj29PNy8DpOBm7gFQ4S3mrzYH6KXnuFhmiQJ4iDiIpBQljFVDawhCxmxYNZCdJoCGgNEQEBS3RodrRQOts4nRHS2h5m3QeYCe6iLakIjs3u2iJ9+VX0vJuOQi832yo3ouBDoCrBi3GZF3w1pqWruBjjjsoEA/k+Zo3bmHZSFAFfaJmh8AIjoQVGQou4lSYArVqURmYPtcIA7UI/7XIPDiI1v0G+p85qWGMiGCHj10AQkGyT7mqGeLFMTyHL0fIER+rjDDkrhH1VLEOkntUoboSpFDWkSgvx1UKiTIfD5V6RxYQGaPQUA9hy5NVVuQ/b7cERJXAc6m6V+Wgv6VCCnD9ItLrOsfEp3jpbZpgQSnwvoqsDgJLdZt0UqBqQMx1MbCSpRFJqkpSTRBUFUWpRDVxlxrXAFcHDvNPNui9OJhKjEIriiNtP3/A2MnhbKzjmNKZ1Dam9tv3oQUsjpXeR0pP06TH62hreloe0FeaU08E0MmWapJwcgg2TYoK2IBPAYJdTp1eoEZBqCHIdBBAckyTLuDJN3vdnOxWqO+zwD9KotFi4ssLY+TSZtQYuVwbCeSR0VFq4tDqeyBLujllERWerGWGhNMBytYelSh9LlT5clslhcZ4b5Gmuk+c55CwSdqoALGpJXg4e4Yz/Ok5JB/Dnbe7bmiTprFIXoEAIEBCxiJJFkjYwzCLtmo1d89FuSqj6tnmYok0lYmB2qejhJKuBvSwG97Dm7wZAdCwOVmfZU5xDLFrIhSSRdh/HRBd77O3QR06FVwI6p7Ulxjyj9DfSdG/tJ9/ez4YVRFdqdIaWGQktsCs0TyqYRha3QwbNchetrREa+QH0Ygqj3Etbgo2ISDbZZC2isBAIUNO249GSbdJT2SRRLNFZLrKrvMSh+iQj7UW61Cx2Q6CVV6lmfdSLCkJje1VuibCSgLFeiXSHhi246CmHEcXvMXNtO+ccx0GXRWbjKdbCMTaDEcKNLIempnnrRIaDtooS3YOg9SMIIoZjkzEd8m3ImTYl28aRy8TkFrvlDmKiG8OuMelaZtSvM+Nxs+qPsNkRo+oPv6Yb1Sght5fBnEfR55D1ebymRZ/RRUrvos/ooN+UGdFbdLa3WABOOjGesPewwHbYx+sYCA7UBBVRsIgJDQadOkmxjleqIkrb7R81sUaXsEjYk8UVqyF11hBkm/Lkcd724Udfl+53zH2HHyiaNZ3ZC/Ncu7zO5loLwWghB0s4Wh1VaOBWarh9OdwdC7hCK5TbPpYqfSyUUixXe1mu9NJsu9ktpDkiznJUneOwuMCwtQaAjcCMJ8VF317Ouw9w2RrhSG6Lfa15JKFKGR9lggDItImQwbQLmHoNu2Jh11UkXXptvlWPSSMkYvuDlML9zHQfYiEyjCkpAMT1PEerEwyXl7BL4JT6cJvD7JW87LdEVARMHCa9Tc6505xTp7DqXlyNIUpWEFuuEQmkSQXTpAJpBvxpgtp2+b1tSzQLqW0zzw3RLAzQskJkQjKbHRZbMYv1gMaGz4sjbO/6RRoFustZOio1IvUaA9VV9upz7GrOEdMLCHWbVlGlseVCL8uw7eWU/DCVhIWEi7xPw2sHCTc7QPFjaR5szYMjb5+zaJm0EViMdjPRO0jVoxGrrHNgco63LG2xR/AiBfchqd0IgoTtOJQsh6zpkG07LDltLLlGWGozIgZwiXUmvE2mPQYLfoW1kI98OI7u8m1PzrFR2lnE9iKysYzcXsbTWmeg6dteiRtR+iw3w0aNAWMVVUhjC5uMOnFOOjdx1tzNNacTQ5ARHAcBAUewCdIkKdTpFRpExDqq1EAQQJIMIuomUXUdX7CMEqsgeVuvTkWiVeyjuTVMM9PNetHDr39ip0J1hx8yGpU60xfmuHZlk8yagdMUEQN1BK2KItTxyFW8/hxaZBE5tEy2HWC1liRdTbL26s+G4WVQ2GCfuMQBdYmD0jIHrXk8r3aj3lJCXPTv46J7DytWJ8miTp++BrQp46NECIftVbfHqaFYJWyjhlNr4FRMxLb42nzrmk3Np6IHwpQifSx272U+PowlbReweM0Gh2vTHK5MESvnEEsBrOYIithPSvAxYgmojoCFw4SrzHPqOsu2QMUIUrc0VC1H2L9Jr2+dVCBNfyBNwLV9d+E4As1qnHahn1YpRaOYYt0YJON3sxWQyEcsMiGZLY/2mpH79BrJUoZ4uUSo3iDaKJMyNxguz9NXWMVfr+FUHfSSgl6R+fu0j7YMs10w06WSCWlYapBIO4JqB7E1L5bmgVfPGdtGazZoiirL8SSXB3ZjqW26tta4bWySu0omUa0X0T2ES+pAFMTXzDxvOixbFhOYtMUGmgYuj0XV02I2KLIa8pIPRtFV72vXQLAbSO015PYasrFMuLlFf1WnX4/QY8RJmn5SNvSZawSti2jiFDYm8+zhfPsgZ60hLjvdrAohbEEABwQc3EKbDrHOAGUSUgOP2EAQbFS1iV8rEpK38HtLaJEySrD82nz0SoJWIYW9EUZeUXEvNfGVN1H1DNmAj6ePHeWTH/3D1/X/sWPuO/yrx7JM1qZXuHJxhcX5Ks0CYEuIviaC2MQjV/BpJbTQKmpwhbpWYbMd/n8ZeaYRx+3o7BFWOCAtsk9ZYa+YZpe1gsb2rXFTVJnwDnFFGaauBwk3dWS7QQuFCkGKTghH2DZr1W6hmGUcvY5T0xGrOqJlAmCKDjWPRNPjoxGIsRXtZaZvP5uRrtfOqUvPsq82z/76PF3VTVwVF06tB53dBKUYuy2FkC1Roc0Zpch5qcWK7aJqy7hceYLeDZK+TZK+DZLeDAlv9rXQim2LNGtx2oUUjdIAGX2AZfrJ+LzkghJbQYG8T8YW/5+2th21Al3FHLFSCb/RItCqk6qukyqt0FPaIFCtItfaGGUJ63vuPFpeh6UETMRdFP0hBDWM1w4gE8B2ebbTEV+NVwu2hbdaRW22KGt+5hPdLPR34rbqHJhf5/bNJj1iCNXVgyrF8IguBEF4zczTls2obHPRZ/339s49xpLsru+f36nHrbrPvrd7errntTP2LsYmmNgmEEgAJxKJIXIs5CBsWQmJUQAFJJJIERAIkESJePyRkGApIEIkohD+gDwgATlREuePEMAYr9e7a+96dnZ259nd0/d9633OyR9V3XO7PTOemZ3uHU/q0zo6jzp16te/OvWtqlNV9zBvWfJ2StI1zFohk+YKhdvYt+u2iF/Fz26yES24MNc8HbU4l25wOltjs3DpeNv4zot09GdpmMss7AovZ0/zB8Xb+GN7npflJFtOi1z2fGVpUNCXmNMy5YIzou0kOE5GszWmE4zoBGNarSlBd4zTSPZtyqMVst1T2BsrmGsht4YdrnohV7ttbrZ6DP0mqRF0och1QCwB785e4Df/2Q8/1HFTi3vNY4M1huGNLV549jKfe2GX0ZZBZy7KV4i3IFBzOt6EoLON273OPJyw61h24lVuRCe5Pt/g6vw0TZ3ytFzjreoGz3jXuODc5Gmu8ZTe2t/W0O1y0TvLSPeQ3CWzDjMCpvTYtQMyuS0UbhGhsgjiFBXHOHGEFBlWIA48omaH+coJtk6c4aWzb2N7sAHVSWCQj3kmep1nFq/xluh1BtMYf7ZCVJxGnE1W6bGRa77gZTyrFJesx9CA649pBTustHZYb+6w3rzFyeYOg2CEkvJYtFaIoz7pbJNpfI7t/CxX5SyXvbNstwJGbYdZ8/adg1jLiekup3a3WBuP6UULeumCE9Mxp2c3WZ9t05+NaMxSmBns0l0HnmGxYrm0HnLxxIBRcwXf6eDbNqKamEZw+2occNOE9mxGdzKlEIftlWntrx8AABnbSURBVD7bJ05QhB3O7CY8M9EMVB/f3aTpdOk7Cl8JhcC1BnzWF14IDS+1NFfbGUnbIQ3baDdY7jAofQsnv4mXb9NP52wuIp6eGd4adzibneRMdpLVvIk4V1DNS/jyCn5xgyxbcD1d59PFW3mes7wkp7nq9Jgrhdk/51naNmVVFpxUc9b9CafCbTrNCWEwpe3PCMM5jdYML1zsm6XzgHS0zmy8ys5sjVfTTS5mp7kmXeZKUWiFzVzg9kkSwNUF6/GI9WhUxkXO270OH/m1v/9Qx1Mt7jXHTp6lXHvldZ799BVev7hgMbIU2kMci6cyAlPQaQ7xe9cp2jsMgwUjBbeSFa4vNrgebTKJumwy5LxslULuXuetzg2ettdYtdP9bWXisiWrjOyAiW0zplWOi9sVxvTIxd+vKzpHpTEqSVBphJPGSBqTeQ5J2GbaHbC9eoorJ8+xu3KCcbePVQ6eyTmX3OBccoO3Rq8xiG+hIgc/CujlLRwCxqrDTdvhqg0ZIihvhu9PaTaGtIMRq+GQQTBirYr3BBwgzZsskjVm2QbDYpMte5rX1RkueWfZbQZEgTrg3+5iwfp4yMnhDidHO5yYjhjMJ/SiGWvzEf35mNZ8gT9PkfzgvpHQMD4ZcnWty41ej1HQoXDbOKoFToh1/QP1nSwliBZ0pjNWxjMyr8uku0ncWkM5TTqZQ0c3CdQabcen4wh5SzFuO1wPheuh4lJgudy03AylfFArS/+PNSg9xCm2aGQ7rCRTNhYJ5+eGty/anMtPcio/wVreR9kcLTfAv474Wxg95Nai4LlslRc5w0W1wQ3VY66EHEuubourWEOfBU81tzjb3GIz3GEtHNH054SNBUFzhh/OkNs3O6RJm9l8leF8wFa8xqXoDC8nZ7gZrYI9uE+U1fTyGSejKSfnu5yIxqwlY07EY9a1Zt1rsdJYwQm6KOVi8phkMeTVMOD9v/2x+z62DuzLWtxrjgJrLfPRkM+9eJkXn9th60pGlAoWIaAg0ND253jtbeLuiFG4YOalTAuXW8kq1+MN4kWTk8WEc7LNU7LFObXFOWeHc7LFaXsLt3paV+CwQ59bDBjTYWzbjEyXKV1m0iFV4bJhSJ6hsuR2SBM0hjgMGHfXuL52mpurm4x6q0y6A7TjspoN2UhvspbeZDXeYj2a0okNKmuT6QEj3WPXdkidAuOmeO4Cz5sR+hNawZRBMNoPHX9xwFfGKhZ5j3m+ykSfYMducl1O8Zp3ikvBJjPVPVC/kRWsTqac3b7Bme3rnLp1gxOTXVYWM1pJRCde0IoXNKJ0/+dtAYwIaeiT9RvMB03GnRbDVptp0CZxW2gnxDoNDigYIEWOl8SEi4QwLggThVd0SIOTZEEf43aAJoXTRLcUecshaSkWTcUsVAxDxU5DsR0It3w5MBxUivcYVezgFyN66ZxBHHNykXN+Ds/EXc4XG2zka7RNE6zB2BFa3UR7O+w4Ea8UPp+PV3jF9BiagBghcYTI8YlUAyuCKzm9xoxBY8RmY5eTwS6rwZhBY8RKMKEVzPEbC5QyB/73KG0zivpsL9a4Hq9zLT7JzWid7WiNuGiW/rGG0CwYZBM24hmn5hM2pjuciIaciEoBX8kT3NYaqrWOtE9igx6oAIVBrAaTkwd9dGsdG64gfpOG4/KHwZAP/9R3PNQxWIt7zRsimk25+PJrPPfpm1y7EjOMc8QaAizNQtH0UuKVCdP2jKQxJZKUYd7hRrpOvAjpJxEbdsQp2WVTdjkr2zyltnlKbjJgToFiTosZbUa2w67tMTIdprSZ0WEhbRIVHhQka0oBzzNUniJ5hkETOy7jVoft3kkmnT7TzgrTdg8Cl5P6FqcXVxikYxqxRpIAnYbk1kc7FqMKcDNcJ8Z3FwTenIY3p92Y02tM6Pkzuv4M55A4AKQ6ZKIHjM0au6yxpda54a6zrU6wyxojBhgUQZoymE44vbPL6Z1tNoY7rE1u0Z+N6EQzWvGCZpIQpil+miGAVorM90mCgKzhk7U9km5A3AxYhCFxo0nkhSRugL6DcO+f7IocJyvwU4uXO3hFE8euYpxTLFoDZqHPIhAWgSIJhTRwiEPLIlRMAsUwcJj56ov+96AoCPI5oncp9C5+PqUfx5xepJyfC08vmpzXG5wpNunYFtYadD5kyi633Cm3vJgbVni1aHI9Ddm1HrHjEzkNMg+CRkLHm9P2F3T8OR1vTt+fsBqM6TcmdP0Z7caM0I+/yDZjhWnSZTcesJMO2I0H3IoH7CZlehz3aKSadhHTzhespFM2owmn50PWZ7dYi0cMkhndbIHCgt9Chauo5iqyH05A2EcFXZQ4iBcg6s5TUo9dy27DYeQLw4Yw9GHLucnPfV8t7jVHQDyf8epr1/jc57a4eHnM9nSGzSHUgmcc8BS6nbBoToiCKUNcdvMeadKglaQM8ikbesim7HJetjgnW2zIkK7EaPFYEDI1LcamzcS2mdBmTptImsQSkjvBFxtlDFLk+8KNzimUEPk+iyAkCxvY0IOGg/V8MA3cXOMWBoUB0SAFSmW4ToLvRjTcBU1/RtOf0/JKoWj7CzxV3NEvhXVZmA5z22VMn5GsMFR9orxFkobkSUARN2CmCBcZ/cmMtemEldmU3nxKJ57RjhaEaUSYJHhFjnFcct8j83xyz9tPJ6FP0gpImw3Shk/qB6Reg9zx0dUrk3dCinw/qMKgCsESkLorpH6P1O+T+j2SoEXccIl9IWoI8xCiSsgX/sEHr3u4xrKaWvqZoZMl+PkcyafofIJNR4TRnLdG8JVph1WzQcOuE1vFLJ8ztRETlTB2CkauZuIaEs+QOQbrGJSbE3oxTS+m6ca0vcW+ePe8Ge1q3/hOfof/GrRRTNIu46xXxmmXcVqmJ2mPOAowMwd3Dp0spZdFrCZTTiyGrM93WUsmB0UbwGsijR4q6CJBDxUOkOYACQeo5hqqOUDcg33VGo1Np2Q6YiE5Q1+41m3yhbU+V3othg3F0C9fZW0WBf0sZSWd0kuGdNIxgyhm7dwJPvyR77nrPr4Xj3oO1fcBP0/5pOCXrbU/fWh5A/hV4D3ALvBd1trL92qzFvejwWjNcHeXV1/b4tmLV7h0Y5f5PEUycEShpYH2hCTIycKUmQPT3ENnijBL6eYzzuhtzpktTrHLqizoSoxHQWFc5jZkbpvMabKgSURIIgGZapCrQ+Oqe1iL6KIUpDzDGk2OoF1B+wrjKYznYlzBeg64FnENnpPiuzENN6bhRgRuTOhF+wLRcqP9N0nuRqIDkqJJrJskRUiaBRSph4k9iBSyEJwpeFNDY1zQmqa045ggTfB1hqs1AhSuS+G5FK67L9LZnkgHAWl1hZ17XlnXddGOs/8F5N13mEF0ji00uQi5UuRKkTkeuQrJvZDMbZK7TTIvJPVCkiAgCjyihkPcUCSeQ+E6d92Epw0ruWUthUFmWU0Nq5llJTW4eYzNF+TZgiJdkGc5sRVyPDxHUbgJhRuRezHGS8HNsW6O72YEbkrolkLd9KIqjmm6EU0vxvsS+yYtfGZ5m1lWhnneOpCO0iY6clFzcGeG5iynn87pJzP6yYyVdF6FGb10gWer7bkB4rcQr4UEXVSwgjR6yJ6AB739PMrDWo3RCTaLIB5DMsZEQ2w2pygidgOXG50mr610eHljjc+fOsXlU6dRyqObFYRFAnpMZq6SF1/AmEtQXCfUllPOKS40L/C2/tt4z+Z7+IrNr6DX66G+VL+4B49M3EXEAV4GvhW4CnwS+LC19sWlOn8LeKe19vtF5EPAd1hrv+te7dbi/qUp8pzd0ZjPX73C85evcGlrl+kkosgLrFUorwmuwviWyIGZ9dDG0khj+sUua8WYgZ4z0DMGdkabGNdWXxLikFuflAYxAQkBiTTIpBTpQvnV64EWEYPjFCilq7hAJEOkACkQpbHKYh0DyiCuQXkGzylwKyFoOAkNN8V3Mjw3xXXzAw8X74Yxgs48dOZhMgebOJAIxIITCRKBii1OBE4EKgGTehRFg0w3Sb2A3PPL4Lr74qvdUhAL18E4CuMotFJYJRil4NBVraUc2zaiKJSDVmX9QhSFSBn28qqskyuHQjkUjkvh+OSOR+765K5H7npknkdahcK7+5X6HsoYGrqgVRR0c8tKBquZYi1TrOTQyywruaVXBS/LKHRCTMLUSVi4EVFjSh6MsK0t3OZ1Gn5C4CYETrofe86d72oOkxuHOA+JiiZRERLlYRUv55skWQObKFS1j7yFpjHXtJKEbhbRySM6WRm62aLMa01DuSg3QLwQ2Yv9FuK3S+H2y0Al5HhNcAOsWKzOsEWKzSNsOitFe7GDjXax8QibzbHZnNSk7Kz0udUfsN1fZWdllVGvz7C/zu7qCSadFkKMNkNScx2tX8Po1xGzhbLl65AeLhuNTc62znKhd4F3bryTd516FxutDeTwcNkj4H7F/c6DRAf5OuCitfZS1fCvAx8AXlyq8wHgp6r0bwC/ICJi36wxnyMmzVJuTSa8euUKL7/6Gjeu7zAaTynSDMRilUsiGmNSKGa4KiEoYgLJ8UXTwCAiuFYholDWQZSDQDV2KlQZQLDWgoUzIiAWUOVD+0yQ3CKZwVEWRxmU0oijUa5GObrMS0HmGEbKQYlFROOoHKUWOErTUYZuJcriVLELuAZcC0qwKCyyHwx7ZSwtK/NGK3TuYgoXXbjk2idJW+jIpdAuWrto7VEYD208tKnKjUdhPQrroo1Lbhpoyitgo1QZHIUWB9tQmEAwqwpbiW4pvrKft1V+b5kVwVB+jbq3TO8JtZTtl8Lt7JcXTpkup3V78ANVrMXXGl8bGsYQaEugoactrRS6c+gUOSu5pldAq4B2YW+H3NKuyjxjySQndlMSNyL35hTeDNOYQDBEdXdxwlvYxpSiMSKrfkemAfS0S6h9MuORaZ+8ijPjcSsekBQBiW6U8X66QZaXJ1VJFZJYnNjiJOBHBUGW0ypSmkVOy2paumDTGNoYWkbTYk7XzmiJQhwfcXzYi72wHP7oBogbYh0P3AYot7yosAW2yECn2DyGPIIswiYTTDLBTq5hk3Ep3nkEeQzVUItWilmzxbjdZdZqM2+2mDfbRCst5s3zRM23M2o32en7TJopKddJuYa2l8B+CmHp5FZ+K0bH6XCmucHZ1bOc6X4Lp9qnuNC9wIXeBU62TqLudMf6JnM/4n4auLKUvwp8/d3qWGsLEZkAq8CtR2HkMj/+sX/If/3KP/VF5bZUQw6eTWRp+Z3Kbn/AUH5cvFy2/2Hevdvq96DfO7jePde5H7tYSssbXm6rk4Q5JNAW2f9455Hicn896whR1uJYcGw5jryfrmLHgm8sDQMNDZ61NArwjMU3VGEvbfCNxjtQVqYDDaG2hEtxoC2htviFQaSgkIJClbFWZdqoDK1yjMoxqkA7KbmbkKmU1M9ZNDQ7qiAWzUI0EZYYjUGhtYM2CqMdjFaQKeyiAfkZJHsKcoVkLpIpnMzFyR18wLPQsJYAQ2AhtNDGsmYhRAgEAhShKEJxCEThigPKwTpe+cBQuYjrYHsCpvwhLIpKgIsUq1MoEmyRQh5jTQY6w6az8mpaZ9g8xugEozO0zrG2KE+kbjXc5fpknk/meuSuQ+4qCiVox0H7iiJ0ydw2sbdC5AvThmUY5tzoztnpTIjCKcpNELUFbH1R3xAruMZD2QZO1sCzTULbRfEMij6KHi5dXNvBoYtn+yh8WMDNHbh5oLXLVXgw3nGqy0++/6sepmvfN/dzCN7pcuXwFfn91EFEvhf4XoBz587dx6a/GC/L2Ux37rjh298oHJS3vSVSmXTH6y97e/kBmw+kb7fFHW5Klte/04iDLKds2Za15UkFK0se20vL7TPM3jIrh5bvravAVkJu5UAZVhBT3QfY2/YpqH4z42BeVdtTlc3KWsTu3UeAsuz/1oaiXKassHdjUbYlZb3qby9d/tDW0vYO3AuU7VJtE8qHPI6xqMoux5SxshZlwanisv3q9GUtIhZrbXmLLmX75T9lsFhKh5Sx7OdNuV9EgwUrBlOtp61Gi8ZKgbYaY3T5pg6GWBswYE05AQhaQQFiFU7hoYxTBuugjMIxLg6Csi4YF4WDIw2UBCgRQhFCCyeq866Syo9iyzeGrEFM+aqdmAJlNZiiDFZjbYYlwdjSfgvVfyFYAWMtutRmjBW0QATMKX1WmHKtQsrLnrK7ly0oa8BqHFsgFBir0RRoseRSkDua3DcUgSZ3CnIXMteSu5B7htwtyF2DdS3agcL1KByP3FFoR0Cc8v+kADSIUz3HcbBSBhFB8MpfksRD4YE4eJzmFB4UHmgf8BFpAk0UTRQBigDBq/rik839iPtV4OxS/gxw/S51roqIC/SA4eGGrLW/BPwSlGPuD2PwT/6dh5u9pKampub/J+7nnvyTwDMickFEfOBDwG8dqvNbwHdX6b8C/M8ndby9pqam5suBL3nlXo2h/yDwccq75F+x1r4gIv8I+CNr7W8B/xr4tyJykfKK/UNHaXRNTU1Nzb25r8de1trfAX7nUNlPLKUT4DsfrWk1NTU1NQ/L4/f+Tk1NTU3NG6YW95qamponkFrca2pqap5AanGvqampeQKpxb2mpqbmCeRN+8lfEdkBXnvI1dc4gp82eEQ8rrbVdj0Yj6td8PjaVtv1YDysXU9Za098qUpvmri/EUTkj+7nV9HeDB5X22q7HozH1S54fG2r7Xowjtquelimpqam5gmkFveampqaJ5AvV3H/pTfbgHvwuNpW2/VgPK52weNrW23Xg3Gkdn1ZjrnX1NTU1NybL9cr95qampqae/DYiLuI/IqIbIvI80tlXyMi/1dEPisivy0i3aVlPyoiF0XkJRH5i0vl76vKLorIjxynXSLyrSLyqar8UyLy55fW+URl17NVWD9Gu86LSLy07X+1tM57qvoXReRfyCOY9PEBbfvIkl3PiogRkT9ZLXvUPjsrIv9LRD4nIi+IyA9V5QMR+e8i8oUq7lflUvnkoog8JyLvXmrru6v6XxCR777bNo/Iro9U9jwnIr8nIl+z1NblysfPisgbmqT4Iex6r4hMlvbXTyy19ciOy4ew6+8t2fS8iGgRGVTLjsNf31nljYh87aF1jk7HrLWPRQC+GXg38PxS2SeBb6nSHwX+cZV+B/AZyukhLwCvUE3aU6XfAvhVnXcco13vAk5V6T8BXFta5xPA175J/jq/XO9QO38IfAPlJEu/C3zbcdp2aL2vBi4doc82gXdX6Q7lxO/vAH4W+JGq/EeAn6nS3175RIA/DfxBVT4ALlVxv0r3j9Gub9zbHvBte3ZV+cvA2pvkr/cC/+UO7TzS4/JB7Tq07vsp55s4Tn+9HXjb4f7MEevYIzloHlXgkAgBU24/FzgLvFilfxT40aV6H6cUqG8APr5UfqDeUdt1aB0BdoFGlT+wY4/ZXwfqHeqMn1/Kfxj4xeO07dA6/xT4J0v5R+6zQ9v7z8C3Ai8Bm0s+ealK/yLw4aX6L1XLD/jpcL2jtutQ3T4HLyIu84jE6iH89V7uLO5Hclw+pL9+Dfibx+mvpfyB/nzYDzxiHXtshmXuwvPAX67S38nt6f7uNGn36XuUH5ddy3wQ+LS1Nl0q+zfV7d8/eBTDHw9o1wUR+bSI/G8R+aaq7DSlj/Y4Kn99Kdv2+C7g3x8qOxKfich5yjutPwBOWmtvAFTx3vDPsfez+7Rrme+hvLvYwwL/Tcphwe99FDY9oF3fICKfEZHfFZG9GaAfC39JOaHq+4DfXCo+Dn/djSPtX4+7uH8U+AER+RTlbU5Wld9tQu77mqj7CO0CoOrUPwN831LxR6y1Xw18UxX+6jHadQM4Z619F/B3gV+Tcsz7uPx1L9sAEJGvByJr7fNLxUfiMxFpUx7gf9taO71X1TuUHVk/ewC79ur/OUpx/+Gl4j9jrX035XDND4jINx+jXX9M+Wn81wD/EvhPe03coe6x+4tySOb/WGuX53d+M/11pP3rsRZ3a+3nrbV/wVr7HsoruleqRXebtPt+JvM+SrsQkTPAfwT+mrX2laV1rlXxjPLW8OuOyy5rbWqt3a3Sn6rKv4LSX2eWmjgSf93LtiU+xKGr9qPwmYh4lAfev7PW/oeqeEtENqvlm8B2VX5s/ewB7UJE3gn8MvCBvX0LYK29XsXblP3wDfnsQeyy1k6ttfMq/TuAJyJrPAb+qrhTHzsOf92No+1fRzHW9AbGqM5zcJx2vYoV8KvAR6v8V3HwQcQlyocQbpW+wO0HEV91jHatVNv84KH1XapxPcADfgP4/mO06wTgVOm3ANeAQZX/JOXDwr0Hqt9+nPtyqewq8Jaj9Fn1P/4q8M8Plf8cBx/E/WyV/kscfKD6h1X5AHiVcry7X6UHx2jXOeAi8I2H6reAzlL694D3HaNdG9x+rvJ1wOtVG4/0uHxQu6p8j3J+59Zx+2tp+Sc4OOZ+pDr2hg/iRxUoz6g3gLw60L8H+CHKJ84vAz+913Gq+j9GefX3EktveFC+4fBytezHjtMu4MeBBfDsUlivOs6ngOeAF4CfpxLbY7Lrg9V2P0N56/z+pXa+lnI8/BXgF5Z9fIz78r3A7x9q4yh89mcpb2+fW9o/3w6sAv8D+EIV7534BPhY5ZvPHjowP0opsBeBv3HMdv0yMFqq+0dV+VuqffyZymdvqP8/hF0/uNTPfp+lkw+P8Lh8ULuqdf468OuH2jkuf31HdRykwBYHH5YemY7VX6jW1NTUPIE81mPuNTU1NTUPRy3uNTU1NU8gtbjX1NTUPIHU4l5TU1PzBFKLe01NTc0TSC3uNTU1NU8gtbjX1NTUPIHU4l5TU1PzBPL/ALYBpJPdgo92AAAAAElFTkSuQmCC\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_SICO_UHO_SU_RCP85[i,:])\n", "\n", "plt.show()\n", "fp3.savefig(\"Figures/SL_wTd_nos_base_SICO_UHO_SU_RCP85_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 }