{
 "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])"
   ]
  },
  {
   "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])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# FETI_VUB = FETI_VUB\n",
    "# Read ice scaling\n",
    "fname = \"../ScalingCoefficients/IceScaling/ES_FETI_VUB_scaledto08.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    FS_FETI_VUB_BM08 = np.array([float(row) for row in f])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# FETI_VUB = FETI_VUB\n",
    "# Read response functions\n",
    "\n",
    "fname = \"../RFunctions/RF_FETI_VUB_BM08_R1.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    RF_FETI_VUB_BM08_R1 = np.array([float(row) for row in f])\n",
    "\n",
    "fname = \"../RFunctions/RF_FETI_VUB_BM08_R2.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    RF_FETI_VUB_BM08_R2 = np.array([float(row) for row in f])\n",
    "\n",
    "fname = \"../RFunctions/RF_FETI_VUB_BM08_R3.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    RF_FETI_VUB_BM08_R3 = np.array([float(row) for row in f])\n",
    "\n",
    "fname = \"../RFunctions/RF_FETI_VUB_BM08_R4.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    RF_FETI_VUB_BM08_R4 = np.array([float(row) for row in f])\n",
    "\n",
    "fname = \"../RFunctions/RF_FETI_VUB_BM08_R5.dat\" # File to read\n",
    "with open(fname) as f:\n",
    "    RF_FETI_VUB_BM08_R5 = np.array([float(row) for row in f])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5202 0.5263157894736842\n",
      "0.89845 0.8947368421052632\n",
      "0.5301 0.5263157894736842\n",
      "0.4724 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_FETI_VUB_R1_RCP85 = [0] * (tend-tbeg)\n",
    "SL_wTd_nos_base_FETI_VUB_R2_RCP85 = [0] * (tend-tbeg)\n",
    "SL_wTd_nos_base_FETI_VUB_R3_RCP85 = [0] * (tend-tbeg)\n",
    "SL_wTd_nos_base_FETI_VUB_R4_RCP85 = [0] * (tend-tbeg)\n",
    "SL_wTd_nos_base_FETI_VUB_R5_RCP85 = [0] * (tend-tbeg)\n",
    "\n",
    "for i in range(EnsembleSize):\n",
    "\n",
    "    # Select forcing randomly\n",
    "\n",
    "    # select global warming path\n",
    "    iTens = rnd.randint(0,NumTensemble-1)\n",
    "    Temp = np.array(SAT[:,iTens])\n",
    "\n",
    "    # select ocean model\n",
    "    iOmod = rnd.randint(0,NumOmodel-1)\n",
    "    OS_R1 = OS_WiDelay_R1[iOmod]\n",
    "    OS_R2 = OS_WiDelay_R2[iOmod]\n",
    "    OS_R3 = OS_WiDelay_R3[iOmod]\n",
    "    OS_R4 = OS_WiDelay_R4[iOmod]\n",
    "    OS_R5 = OS_WiDelay_R4[iOmod]\n",
    "\n",
    "    tau_R1 = int(OS_Delay_R1[iOmod])\n",
    "    tau_R2 = int(OS_Delay_R2[iOmod])\n",
    "    tau_R3 = int(OS_Delay_R3[iOmod])\n",
    "    tau_R4 = int(OS_Delay_R4[iOmod])\n",
    "    tau_R5 = int(OS_Delay_R4[iOmod])\n",
    "\n",
    "    if tau_R1>0:\n",
    "        countR1 = countR1+1\n",
    "    if tau_R2>0:\n",
    "        countR2 = countR2+1\n",
    "    if tau_R3>0:\n",
    "        countR3 = countR3+1\n",
    "    if tau_R4>0:\n",
    "        countR4 = countR4+1\n",
    "    \n",
    "    Temp_R1 = np.append(np.zeros(tau_R1),Temp[:tend-tbeg-tau_R1])\n",
    "    Temp_R2 = np.append(np.zeros(tau_R2),Temp[:tend-tbeg-tau_R2])\n",
    "    Temp_R3 = np.append(np.zeros(tau_R3),Temp[:tend-tbeg-tau_R3])\n",
    "    Temp_R4 = np.append(np.zeros(tau_R4),Temp[:tend-tbeg-tau_R4])\n",
    "    Temp_R5 = np.append(np.zeros(tau_R5),Temp[:tend-tbeg-tau_R5])\n",
    "    \n",
    "    # select melting sensitivity\n",
    "    MS_R1 = rnd.uniform(MeltSensitivity[0],MeltSensitivity[1])\n",
    "    MS_R2 = rnd.uniform(MeltSensitivity[0],MeltSensitivity[1])\n",
    "    MS_R3 = rnd.uniform(MeltSensitivity[0],MeltSensitivity[1])\n",
    "    MS_R4 = rnd.uniform(MeltSensitivity[0],MeltSensitivity[1])\n",
    "    MS_R5 = rnd.uniform(MeltSensitivity[0],MeltSensitivity[1])\n",
    "\n",
    "    # Compose forcing time series\n",
    "    M_R1 = MS_R1*OS_R1*Temp_R1\n",
    "    M_R2 = MS_R2*OS_R2*Temp_R2\n",
    "    M_R3 = MS_R3*OS_R3*Temp_R3\n",
    "    M_R4 = MS_R4*OS_R4*Temp_R4\n",
    "    M_R5 = MS_R5*OS_R5*Temp_R5\n",
    "\n",
    "    M_R1[M_R1 < 0.0] = 0.0\n",
    "    M_R2[M_R2 < 0.0] = 0.0\n",
    "    M_R3[M_R3 < 0.0] = 0.0\n",
    "    M_R4[M_R4 < 0.0] = 0.0\n",
    "    M_R5[M_R5 < 0.0] = 0.0\n",
    "    \n",
    "    # Scaling of forcing\n",
    "    if (scaled_forcing == True):\n",
    "        for i in range(len(M_R1)):\n",
    "            if M_R1[i] > 0.0:\n",
    "                dump = np.log(M_R1[i]/8)/np.log(2.0)\n",
    "                M_R1[i] = M_R1[i] * FS_FETI_VUB_BM08[1]**dump\n",
    "            if M_R2[i] > 0.0:\n",
    "                dump = np.log(M_R2[i]/8)/np.log(2.0)\n",
    "                M_R2[i] = M_R2[i] * FS_FETI_VUB_BM08[2]**dump\n",
    "            if M_R3[i] > 0.0:\n",
    "                dump = np.log(M_R3[i]/8)/np.log(2.0)\n",
    "                M_R3[i] = M_R3[i] * FS_FETI_VUB_BM08[3]**dump\n",
    "            if M_R4[i] > 0.0:\n",
    "                dump = np.log(M_R4[i]/8)/np.log(2.0)\n",
    "                M_R4[i] = M_R4[i] * FS_FETI_VUB_BM08[4]**dump\n",
    "            if M_R5[i] > 0.0:\n",
    "                dump = np.log(M_R5[i]/8)/np.log(2.0)\n",
    "                M_R5[i] = M_R5[i] * FS_FETI_VUB_BM08[5]**dump\n",
    "\n",
    "    # Linear response\n",
    "    SL = []\n",
    "    SL.append(0)\n",
    "    for t in range(1,tend-tbeg):\n",
    "        #print(t)\n",
    "        dSL = 0\n",
    "        for tp in range(0,t):\n",
    "            #print(t,tp)\n",
    "            dSL = dSL + M_R1[tp]*RF_FETI_VUB_BM08_R1[t-tp]\n",
    "        SL.append(dSL)\n",
    "    SL_wTd_nos_base_FETI_VUB_R1_RCP85=np.vstack([SL_wTd_nos_base_FETI_VUB_R1_RCP85, SL])\n",
    "\n",
    "    SL = []\n",
    "    SL.append(0)\n",
    "    for t in range(1,tend-tbeg):\n",
    "        #print(t)\n",
    "        dSL = 0\n",
    "        for tp in range(0,t):\n",
    "            #print(t,tp)\n",
    "            dSL = dSL + M_R2[tp]*RF_FETI_VUB_BM08_R2[t-tp]\n",
    "        SL.append(dSL)\n",
    "    SL_wTd_nos_base_FETI_VUB_R2_RCP85=np.vstack([SL_wTd_nos_base_FETI_VUB_R2_RCP85, SL])\n",
    "\n",
    "    SL = []\n",
    "    SL.append(0)\n",
    "    for t in range(1,tend-tbeg):\n",
    "        #print(t)\n",
    "        dSL = 0\n",
    "        for tp in range(0,t):\n",
    "            #print(t,tp)\n",
    "            dSL = dSL + M_R3[tp]*RF_FETI_VUB_BM08_R3[t-tp]\n",
    "        SL.append(dSL)\n",
    "    SL_wTd_nos_base_FETI_VUB_R3_RCP85=np.vstack([SL_wTd_nos_base_FETI_VUB_R3_RCP85, SL])\n",
    "\n",
    "    SL = []\n",
    "    SL.append(0)\n",
    "    for t in range(1,tend-tbeg):\n",
    "        #print(t)\n",
    "        dSL = 0\n",
    "        for tp in range(0,t):\n",
    "            #print(t,tp)\n",
    "            dSL = dSL + M_R4[tp]*RF_FETI_VUB_BM08_R4[t-tp]\n",
    "        SL.append(dSL)\n",
    "    SL_wTd_nos_base_FETI_VUB_R4_RCP85=np.vstack([SL_wTd_nos_base_FETI_VUB_R4_RCP85, SL])\n",
    "\n",
    "    SL = []\n",
    "    SL.append(0)\n",
    "    for t in range(1,tend-tbeg):\n",
    "        #print(t)\n",
    "        dSL = 0\n",
    "        for tp in range(0,t):\n",
    "            #print(t,tp)\n",
    "            dSL = dSL + M_R5[tp]*RF_FETI_VUB_BM08_R5[t-tp]\n",
    "        SL.append(dSL)\n",
    "    SL_wTd_nos_base_FETI_VUB_R5_RCP85=np.vstack([SL_wTd_nos_base_FETI_VUB_R5_RCP85, SL])\n",
    "\n",
    "SL_wTd_nos_base_FETI_VUB_SU_RCP85 = SL_wTd_nos_base_FETI_VUB_R1_RCP85+SL_wTd_nos_base_FETI_VUB_R2_RCP85+SL_wTd_nos_base_FETI_VUB_R3_RCP85+SL_wTd_nos_base_FETI_VUB_R4_RCP85+SL_wTd_nos_base_FETI_VUB_R5_RCP85\n",
    "\n",
    "print(countR1/EnsembleSize,10/19)\n",
    "print(countR2/EnsembleSize,17/19)\n",
    "print(countR3/EnsembleSize,10/19)\n",
    "print(countR4/EnsembleSize,9/19)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "Time = range(1900,2100)\n",
    "ncfile = nc.Dataset('EnsembleSingleModelProjections/SL_wTd_nos_base_FETI_VUB_RCP85.nc','w', format='NETCDF4')\n",
    "ncfile.createDimension('Time', None)\n",
    "ncfile.createDimension('Emember', None)\n",
    "\n",
    "SL_wTd_nos_base_R1 = ncfile.createVariable('EAIS', 'f4', ('Emember', 'Time'))\n",
    "SL_wTd_nos_base_R2 = ncfile.createVariable('Ross', 'f4', ('Emember', 'Time'))\n",
    "SL_wTd_nos_base_R3 = ncfile.createVariable('Amundsen', 'f4', ('Emember', 'Time'))\n",
    "SL_wTd_nos_base_R4 = ncfile.createVariable('Weddell', 'f4', ('Emember', 'Time'))\n",
    "SL_wTd_nos_base_R5 = ncfile.createVariable('Peninsula', 'f4', ('Emember', 'Time'))\n",
    "SL_wTd_nos_base_SU = ncfile.createVariable('Antarctica', 'f4', ('Emember', 'Time'))\n",
    "t = ncfile.createVariable('Time', 'i4', 'Time')\n",
    "\n",
    "SL_wTd_nos_base_R1[:,:] = SL_wTd_nos_base_FETI_VUB_R1_RCP85\n",
    "SL_wTd_nos_base_R2[:,:] = SL_wTd_nos_base_FETI_VUB_R2_RCP85\n",
    "SL_wTd_nos_base_R3[:,:] = SL_wTd_nos_base_FETI_VUB_R3_RCP85\n",
    "SL_wTd_nos_base_R4[:,:] = SL_wTd_nos_base_FETI_VUB_R4_RCP85\n",
    "SL_wTd_nos_base_R5[:,:] = SL_wTd_nos_base_FETI_VUB_R5_RCP85\n",
    "SL_wTd_nos_base_SU[:,:] = SL_wTd_nos_base_FETI_VUB_SU_RCP85\n",
    "t[:] = Time\n",
    "\n",
    "SL_wTd_nos_base_R1.units = 'meter'\n",
    "SL_wTd_nos_base_R2.units = 'meter'\n",
    "SL_wTd_nos_base_R3.units = 'meter'\n",
    "SL_wTd_nos_base_R4.units = 'meter'\n",
    "SL_wTd_nos_base_R5.units = 'meter'\n",
    "SL_wTd_nos_base_SU.units = 'meter'\n",
    "\n",
    "t.units = 'years'\n",
    "\n",
    "ncfile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "SL_wTd_nos_base_FETI_VUB_SU_RCP85_50pc = np.percentile(SL_wTd_nos_base_FETI_VUB_SU_RCP85, 50, axis=0, out=None, overwrite_input=False, interpolation='linear', keepdims=False)\n",
    "SL_wTd_nos_base_FETI_VUB_SU_RCP85_83pc = np.percentile(SL_wTd_nos_base_FETI_VUB_SU_RCP85, 83.33, axis=0, out=None, overwrite_input=False, interpolation='linear', keepdims=False)\n",
    "SL_wTd_nos_base_FETI_VUB_SU_RCP85_17pc = np.percentile(SL_wTd_nos_base_FETI_VUB_SU_RCP85, 16.66, axis=0, out=None, overwrite_input=False, interpolation='linear', keepdims=False)\n",
    "SL_wTd_nos_base_FETI_VUB_SU_RCP85_95pc = np.percentile(SL_wTd_nos_base_FETI_VUB_SU_RCP85, 5, axis=0, out=None, overwrite_input=False, interpolation='linear', keepdims=False)\n",
    "SL_wTd_nos_base_FETI_VUB_SU_RCP85_05pc = np.percentile(SL_wTd_nos_base_FETI_VUB_SU_RCP85, 95, axis=0, out=None, overwrite_input=False, interpolation='linear', keepdims=False)\n",
    "SL_wTd_nos_base_FETI_VUB_SU_RCP85_99pc = np.percentile(SL_wTd_nos_base_FETI_VUB_SU_RCP85, 99, axis=0, out=None, overwrite_input=False, interpolation='linear', keepdims=False)\n",
    "SL_wTd_nos_base_FETI_VUB_SU_RCP85_01pc = np.percentile(SL_wTd_nos_base_FETI_VUB_SU_RCP85, 1, axis=0, out=None, overwrite_input=False, interpolation='linear', keepdims=False)\n",
    "\n",
    "np.savetxt(\"PercentilesSingleModelProjections/SL_wTd_nos_base_FETI_VUB_SU_RCP85_50pc.dat\", SL_wTd_nos_base_FETI_VUB_SU_RCP85_50pc, delimiter=\",\")\n",
    "np.savetxt(\"PercentilesSingleModelProjections/SL_wTd_nos_base_FETI_VUB_SU_RCP85_83pc.dat\", SL_wTd_nos_base_FETI_VUB_SU_RCP85_83pc, delimiter=\",\")\n",
    "np.savetxt(\"PercentilesSingleModelProjections/SL_wTd_nos_base_FETI_VUB_SU_RCP85_17pc.dat\", SL_wTd_nos_base_FETI_VUB_SU_RCP85_17pc, delimiter=\",\")\n",
    "np.savetxt(\"PercentilesSingleModelProjections/SL_wTd_nos_base_FETI_VUB_SU_RCP85_05pc.dat\", SL_wTd_nos_base_FETI_VUB_SU_RCP85_05pc, delimiter=\",\")\n",
    "np.savetxt(\"PercentilesSingleModelProjections/SL_wTd_nos_base_FETI_VUB_SU_RCP85_95pc.dat\", SL_wTd_nos_base_FETI_VUB_SU_RCP85_95pc, delimiter=\",\")\n",
    "np.savetxt(\"PercentilesSingleModelProjections/SL_wTd_nos_base_FETI_VUB_SU_RCP85_01pc.dat\", SL_wTd_nos_base_FETI_VUB_SU_RCP85_01pc, delimiter=\",\")\n",
    "np.savetxt(\"PercentilesSingleModelProjections/SL_wTd_nos_base_FETI_VUB_SU_RCP85_99pc.dat\", SL_wTd_nos_base_FETI_VUB_SU_RCP85_99pc, delimiter=\",\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yU9dGlP9mDEoDF8ctqMJLWVQ5pw0aN+x5Fo/FREy6m3ptJctPdTIWZUIkLiFzuIfDWTMod9WTNzoQ8fyXU+7Knzkn3nesB4qB5cAm4PeKovyP+2sVRblXUZRqRVGqh4eH/9KskiRJH5qBliZGuzrpyRyjyrOEODx0pe/ngaFZaKTQI+oJe+tJs00nQTHQEmwkZWSEYyUFrIzycsRQT2kvnMxfScLwaxg1jcriUpafraMjtSTi+S+n3HuA7PccZwF9f2bMq0KIoBCiHWjknbL//xFCPCWEmC2EmJ2cnPzXZpYkSYq48/t3o+lhVpTGgfAsioSTTEsDWZ5bMSoNHJ0oRGh95MXPp1PRWHLgaSZsMaQkOenyXcf8qib6bGb08avI6r9ES2wmqTHdGHQGMo2OiOe/nHKvAooVRclXFMUI3AG89r4xO4EVAIqiJPHOxzRtVzKoJEnSh8XndnHp+GE60u047LMA6Eo9xsODiwiTiEvdDe4eksy5ZKsxNLsbiHM6OFaax2qjkxOh8+SMCI4Xrcbi2E2ix8P+glksPtvAybJ8mryWiK/hA8tdCBECHgT2AA3ANiFEnaIo31UU5YZ3h+0BRhVFqQfeBh4RQoxGKrQkSVIkNRx9Gy0YJD95hFdCy5nMMHExtRS5r8esnuY19wK0wCWyEpdhJ0z5sc0MJyaSGTfBiHMjM8/U0ZQahS16FTk9Z3AaLHgLQvRmpGBy2Dmf/P5Ptq+8y7rOXQixSwhRIoQoFEL8+7vnviOEeO3dn4UQ4u+FEJOFEBVCiK2RDC1JkhQpQghq9u/GHhckwT2JUWIZiavlHwdWIohC1b+ImAgRrY+h2JBGs7MZq8dBVXEO1wgXZ11VJLgFhyetQfEeIGvczt7cOcxvrcMdDqCL3USGWBPxdcg7VCVJkt6jr+kSY91dxKd3sz24lhzGsCZUUeq+Bot6iBf9iwj7a8lNWEoIyDu1mf60NNLixnA4rmfKxXpOFFvJVZaT3V0JKHRWxDJhScDKNAxqOqMxGRFfhyx3SZKk97iwfzeaAXJJ4pLIIWhr4NGB9YCKSf8C7pFCDAQojiqhw9WO0TXKpex01oRcNIxUogo4Ubwaf/AoeSNOTqSXM3OiEX8oiN4yn+yuvayqejLi65DlLkmS9C6vy8mlyqNYs7p5I3ANsbiJTzhDmWcRVt1u/hiei+o9T55tLiZFR+qZF+jKziY+aRzH6GryW5vZNSOKqe5lZPYewxIKcKEiC1cgmijdbMzeMZ68fgrnM2ZGfC2y3CVJkt5Vf/gg4WCAEovgYHgmxph6HhlYj4ofs+5FxocWgTZIfuwsBtx9YO+iJzmJVV4XPX0ncFngXN5yPOFKcobcNMdmkmfpBm8YnWkmXXEtnC2toCtdF/G1yHKXJEnina/2rdn3JvGpoxwOrERPiALbJYp9s4jW72CLfipmTx1ZMRXEqyasta/QVpCPLXUM+9ACUga72b7QzLzR5ST3HyfJ6+L4lDK0URWDeSFG3zA7ly2isKuVCvfuiK9HlrskSRLQWVvDRH8fpQkjvKwtISaqnkeGrkPHCBbdK/QM3IgItlMcvwS7d5TQcD2DcXEsdfoZ7z5ObwI0pS/Gr1WTMeJnzBRDVL4Dg9uM3jCZXlsjQwlJzD17kEbVGvH1yHKXJEkCava+SWqsg5rQfLyYWWoZIS2Yi82wmS3GSdiczeREl5Oss6K/sI3GkiISUkZw9JcR4xzj+eUmlvWtJHrkJPkToxwunoq134FqXYXeP8D2Vcsp7mzhEcsO8pJDEV+PLHdJkj717EODtJ45TVlqB8+G1pJgaue+8ZUYlUvo1MM0D34OEWikJH4pLvcQ7rEGxqOszB/X8HSfojZXYcg2B3/wHOkjQYKKDk+FQOfIx6DLpCG9h7HYeL5Qt4UMs5shY1bE1yTLXZKkT73z+3eTYnbQw2QGSeB2o5/osI1Yw+94NqqIpIkucqLLSNBHo7v4EucrppCa1I+jJx1DwMNzK3Qs712NyV5N4egIx3PKiR8dRWddDr42Xl22jHkXqvis+RBBFCr6uj8w099KlrskSZ9qoUCA2gN7mJbRwW9D15GlH+d25wyi1LfRdE00Dd6N8F+kNH4pPkcfXcE+nCYDM4dUQn3nOFShI2CYgeavJXkshFkL0j/Lit4+F50STWVpAE3V8cXWLVh0GgYEZ3JnR3xdstwlSfpUa6w8SnRwALcunXqRz1f0YXSEsRk289vofDJHesmNLiNOH4Oo38HJubMpsbbj7I4mqApeXApLu9agd9SQPzpBfWIuCQE3OsscNP85DsyZz5qTB9gQV4MPlWOx0zG2+SK+LlnukiR9qp1763XmpHXxpHYd09QAK3z5xOpfwquO0dF/L/gvUha3mNBEF0eSdagBF5O7jTByiVfn6TFoZajuOmIdgiSvk9aZiShjK1DCYV5fkInN7eKe4RdQ1TBmwuz1L+JOfXTE1yXLXZKkT63+lkaCPRfQmU0cCc/g64qOMGNE617h57Z8cof6ybWWYjPYcLa8xaEF81iknMfVozEWZeaN+RqLutdgcNWSPepiICoei9mK3liMU62mtriUW47sZH5iBw7VSLW6gNSOVRwPFEZ8bbLcJUn61Kp56w3mpXTzlLaBtShM0uJJMjyNXRdksO8eFP9FyuMWoY138Mz0Aor76shuikFx9rFlqQ6rrwCzvQmzB/LtQ9TOzEHY16AGx9i2Yha5/T3cHXoZl6ISrwWoHN2EanDjKzga8bXJcpck6VPJY59goHoPiVY/b2mL+apiIKS0YVEP80RcAXmDgxRFV2A1xtLVe4zTM6dym7MSb984TUkJHJ3mY0HXagyuBtLGAzgNFnRx+ejUBDrjmuhLSeOuw1spiJvApTNxQqxF9WeT3HeYzEPyahlJkqSIOL9/N3PiOnhGW8vtWEkSZtINv2ZEr+DsuRt9sJGKuIWEhhr47vplLGs5hHoxHRHy8tv1KjG+dGyjbej9KpOHe6iZWkrYtQy9p5ntK5cxu+48d8Xuo1+nIymocX7kNgwM8fT6azg1f2XE1yfLXZKkTx0tFKT1wHZybXbe1NbyWQwE1dOY1Ev8e2wxeYODzLJMRac387ropScnlXtqT6KNdrGzrIzurAnmdK7G6G4hwS7QVJVQ6mwUoaO60IvXZObu038kPspHSLWw33cXIpxAQ1qYs4VptFqmRXyNstwlSfrUaaw8xjTjRbaFl/J54tEBWfon6dHrEH1fItrfSk7ifCYGLvCDWzdwff0ufC1Gxi3xvLxyjGhfIimDnQhNZdpAB2cnzyPsmYbqrebNxUu59sRBNmRXccZkIsZro3ViPdFKDXtmFpPk8MLwgYivUZa7JEmfKkIImnb9kVLbEHu0m1mHEXRvYVCHeDx+Erl9fcyPeucmoycyolANGl/dU43wjvGzBTMIxgwzr30dRk8X0U4DxrCGJ30ZasjNy0sKiXG7uK/peYIGiA2bect5H6oS5HhuGmMxBnLbGliqNUd8nbLcJUn6VOlrbKDIe4Q3wvP4gkjDqQTI0W+m1aAnpucu0v1dJKTMpHn4InuWz+D+Y1vxD4xRlV7OxRkNxHlSSe1vIYSeGf0dnJ28BhHMYsR8kbqiEr64ayszCjrZH21FOMoZ9ZcTozvCwamTSJkY53tBP2UTyyO+TlnukiR9qjS+sZky2xBHwn/HVPSYdX9CVbw8Fj+Z7J4e5tgWowVcfHdSOqpOY+PuGoKqnl8uLkQ1DjO3fTUG3wA6bzRWLYwjbQWqv5utqxdS2t7C5/2v0WYwUOSN5bDzbsz6Ll6esgyXRcd1Pd2Mz34C+6TOiK9TlrskSZ8ajuEh0np3cig8k03hLHpVL7m6HTQaDaR33sZU9zDmxEnsdfdyaVYhP3/u54SdwzxTtgZ3XiWJrkyS+i/gU4zM7O2gtuwmRDiamtwJxmNsPLjrGdJynOyNiWZg7Cb8WgwhYz2nS9LJ6utjXfbTBIYm0dq7MOJrleUuSdKnRt0rv6HUNkhN+B4yUEnR/QJFCfMv8ZMpam+iOG01Xu8Yvy5LJcE1zpRz9QzE5bBrhgFVP8bs9tkYAg4CoQRiwzrGk+ajBWp5ffFirj1+kHVpp9gTFcWcsUlc8q4iybaf52fdgCIEd7qrcCsBWg8/xHBcW8TXKstdkqRPBffEOKktz1Il5nNjOIvzejv5uiPUGo0UtN3IPH8YnS2TJ1U//VMyeO6nP0CEfHxv5vUY0g+Q5sgnpfciPtXEtL5u6ktvB0K8MT8Nq9fDPadeQJeoUW+Mo2H085jCg5yPyaAlI5rszk6m5b3I8Jk7EXov2qShiK9XlrskSZ8Kbdv/i/yoUcZCD6AhKFe/gwC+kzCZmfU1JOWto887yoF8C9dXHsU61MXegiX0FregqC7mNOahC4cY02eSothwxpYyaqzjQnEpX359GzMnt/NUXAwVvRtwaGkkZLzCjumLsLh9PBz1Ip6xXIbHcnhh5nfR1bZEfL2y3CVJuur5HBOkdTxHTfhWKsLxvGHsI1dt5azJxNT6lcwwpqFa4viRRWGsKJmHXnkBlyWBJyvmY4w/TOFwBQnDbXhUMzO6emgs/gwYBtm+bCZF3R3cPvAmA1Yj+Itpdq0jkaO8HP9ZxmP0zOivJS61ioGzd3ImbycuxU+RJr8VUpIk6W/Wt/VbJOh1mIJ3cpEgt/HPCOC7caUsqz1LVOEazgTsNOaq/OLXP0HxOXhi2k2I/KOoQjD9kooCDBrzSLDk4zcn0Rg/wHBCIg+8/Cw55WP8INFGUs9dGFQ7WlYzR8ryiB4e576sX+NtWUVfSHAx6TRCb2Nf2mjE1yzLXZKkq1pgYpCUnu2c1x7Ggp69potkq6NUmk0sPzWD/JQ5CJ2BJ41BcgJDlLTUcj57NtX5cRhNp5nSO5UY5wRuo4U5feN05qyDmBpeXbSQ5dWVrLac5mhUElMG1zIRyiVV/yJ/yL4XTYUvaVtRdF66L13DiaKtqIqCK/7fcYc3RHzdstwlSbqqDb3wNVRRQUpoFlsUL//CfxIG/k/MFJY1N2PIXczrYS/9+Tr+45mnCJptfL/iBsyZb2IKWShvsAPQZShDpK1Ap7o4nGFFoPCVt/5ITKGXzdEZiJHriDee5Hz6NOpzbGQONTMvdT+eM1+kxdJFr7WLoOk6HnwrwM2NmRFftyx3SZKuWqHBJlIGDtMV+iYdaDh0B4hX3LxpjWLTnlQSijYQFBrb9G4e3f0iOp+DX07diCetD9XQwozWHEwhjSFbMvPtVtzWDPxphzgyfR6b9r7GrNwWnrHlsKDjTgyKj2jrHraVrsXgC/Ct+CdQhifR3l/CyYLtKKqVDXXLcMWfxjJ6KuJrl+UuSdJVa2Drl3GE7iRaJPBj1cHjumcIAM+Gy5gx5kOfPo3NSpD4qCGmN9bQmjWbvRlTsCXuIMZro6TNC4AzNAV/0iyshlM8W7aW1NEhPle1A0+GjouhOYR8xSRFb2FL1tcZjjOw1vMqsYZxhmpv5FzqMZwGNxmue0kItABBzJqI+NpluUuSdFUKtB4iZdSLR7uRnQRYqW7DogR4MjaW+14TWKbcgkPzclA3yndfeRbNGM23K65Hn3MWTT/K/NoYVOBS8mSmilyMgQmqyoL0pmTw8J9+T0H5ED+IncyUno1kGGvojYNjJQUkTvSzKX4r+vrP0O5MoCZzP3omc80lG37LMDETyQSTciO+flnukiRdfcJhRl76CkPBrzGGxh+VIe7V7WJIVelpKyLHnI0+Po9fqmE+37QXg9fB5oqNOGOM2Ey7SJ6IJ2NER9BgIMtdimZORMvdx86SDaypPMwK12nqEpKIHb4eHQrY/shzhV8nTJh/MP8XBnsWLT1lnCzYjqbAtXU347I1YvIKOmOcaANnIr4FstwlSbrqDJ/6JdHORSAK+DFBvql7AaOi8TNjAp87bMdQcSs9modRVx2L66voz5zFS1lTsWbtJqj3sLjGgoJCb/Rckoy5JIye4PmKNUR73Dyw4zkSpzn5nWUxSY4KpsW8wL6UO+hMtbLYtZ8cQy+eSzfQqI7TFtdA8egGLOowKAFcxHGwZAvNUycivgey3CVJuqqE/U7Y93vsoc9yDBctSi83645yxmhk1r5koorXoTcgw9H8AAAgAElEQVTH838I8EjlSwhjDI9OvRaR6EBvOE1Rdyw2j4GJmGQKtQrMvhGqr3HSmlDIN7c+Q1FhPy9EzWVy/3WkGJpojznPW8WrsLnHuSf290S3rqd5KJtjhVsxhZKZ3z2FoHmE+JEojuS9TqzTyFnj+ojvgyx3SZKuKvWv3Qe+e/Gj8ASCb+u3olfC7O9JYoojCmPhag6HnCxvegWrZ4Id5TcwbLaRGvtHBGHm1tsAUJWlGFQLhvA+tuXfwoIL1axuOY4jx0htaCXGkIXp8b/h2ezv4TWqPKj/ESZHCp3D2ZxOPo7T6OSapltw21qJsfs5nzrBRNQwExM3k2+Xd6hKkiRdtv6u42Rd8OIPT+M3ioMEpYN16mne9MWw4ZAO3ewv4ifMdncPa5tOM5E5i99lT0eXU4/X1MXc+liMmorTVk6KLpfMnrd59qbl6EMaD215mtSZdn5mvYXisVnMsr7C29HZ1OTmMtNdyWR9I2rbOhrHUjmf8TZFw/MwmnzotSC+cD5nsndjHslnnjEXQ8lgxPfisspdUZR1iqI0KorSoijKP/0/xt2qKIpQFGX2lYsoSZL0wUJakPEXH8IdvJv28AivCCP/qN+GJwBxB23oi1Zjjs3jFyEn/3B6M4rByrfK1yOiIFl9jSivjkldNgL6aGLVpZg9Q7y9RE9dfDkPvPQchXG97LXOInN4CTH6LlJjd/KLSd/G4nfzQPQviO9YwaXhDI4UvohBs1AxOhtFdZI0aONg8U50mkqafyPZnhZi2q0R348PLHdFUXTAr4D1wGRgk6Iok//MuBjg60Dkr86XJEl6n2Nv3E/S2JcICj2PqRpzlXpWKuc4dyaFRC2JqNIbaAqOUVT/ConucQ5MvYUOSxxxqS/hNk+w7FwyCqA3r8aIkXDgCNvm3MiSc5Vce+ogoRLBWfVmLJqFa2N/xB8TNjFsi+LLym8wO2LpdCRTYxhgILqLed3rwDRA0pCbquxRhmK60ew3MM3XS9LgEHktRyO+H5fzzn0u0CKEaBNCBICtwI1/Ztz3gB8AviuYT5Ik6QM1dB5mcpWBgJjC5lAbnZj4luGPtDfYSOnSEZ73ZTQELw3WcU1HFZ7CVfwoawoidQKjWkv6sJmUCRPCUkGUoYCM3j38YtNGkiZG+NazvyWp3MWzpnvJdBYyN/pPDEY5+E3pXZR4LjJPdxpzzwo6uiuozN1Jur0Qm2rG4vUxasyjOns3hvFiNrqyifE5WXHoEMV6XcT35HLKPRPofs9xz7vn/puiKDOAbCHEG1cwmyRJ0gfyBD04tv4ngdBttPnqeN6Qyi26o5SM9eK7aMU55VribAW8YW/j7pptKPF5fHvSEsIGlRz98/iMfpaeT0VR4zCZlqN4+tg3M4EhWwrffvY3JMY5OJk0g1j3HIyWi8yJepX/yHkMVWh80/Ij4jtn0jQez9GsXYTUIEWu+ZjDfuKH49hX+jKGoJml/jsIi24WnKhEQfBqRlzE9+Vyyl35M+f++95ZRVFU4CfAwx/4Qopyr6Io1YqiVA8PD19+SkmSpP/Fy28+RM743QTEKP+p92FE42tso+1UAs74VJIL1tLn7Sfn/HZMKLw19VYajdEYM47itPYztSkOS0DFYF1PGAWrax87l69l/YkDzGyvxz9FpVb9PJrOx+3WH7Mreib7s2azKbwZq8NMsxZFvdtCS9I5Jo0uISXgI6PbztuTOrBbhklx30q0p5XkwUGSR0ZIKnPyBduFiO/L5ZR7D5D9nuMsoO89xzFAOXBIUZQOYD7w2p/7p6oQ4ikhxGwhxOzk5OS/PrUkSRJwsGMvi8/EopHEq6OnqNcXc7fuDXTVAsWpYprxZRRFR1XzPiZNdGOfuomfx6cikvzkBI6haoJprfHoLAvQ6dOxDu7jibv+jqTxEb627XlsFW5e1n2d2EAis22bUQxOHiv7Z3J8bazS7ScwXMBo0wqOFmwjzpNKoTuRGLuT9vgM6tOOEzdWzi2jCRiEl6UnjqOawjhm2ng8cVPE9+Zyyr0KKFYUJV9RFCNwB/Da//dLIYRdCJEkhMgTQuQBJ4EbhBDVEUksSZIEDHuG6dr5EwzBDQz4T7IjsYA0Rvls2wGcnVF0zrmO5Jh8zncfYmXbMcL5S/lmZhGaUSXH+BRDcRMsr8lCr8tAb5rLcHCAgwvzGLXF8W9P/4yEGAd7LeuwBctxxR9lqfEgv0v+IoPmOB4y/RC1azITziyOJ1XiMtop8MzHHApjcMZzYNIOrJ5YHhm7nS5dL2U1tRgDQVJmOnio7FHyGlsjvj8fWO5CiBDwILAHaAC2CSHqFEX5rqIoN0Q6oCRJ0vuFRZhf7n2UNYNfRCh2XvL00qVk862JZ5moiaKuOJaStDWM2JtJrduF15bJLwsXM6QzYc48SsgwRPKYmYxRC0breryKD9SzHJyziDsPvsLk9la6ijIYN97CiLWLuw2/p12fzo8n3cGtYgvWcQM9+jBNfUVcTDtGuncO+U49cUNj7JtSj1/v5qbRL3NadxGj20tFSwPWVD+/XXoH1XEV7J/zPy44vOL0lzNICLEL2PW+c9/5X8Yu/9tjSZIk/e9eqN/CpjMlhEUKR0f+xMGkJSzw1VJW2c1IrEpa4YPoFT3DNVtJ0QLUV9zK7ugERJaT8uETNGUH2XAyD0PUalCsdITO89ZNN1Hc1cznd+5EnRzmjPkbhNUQpQnPkhh0cG/Zv1EcqmcNexl0ZOCvu41DhZsxh2KZPZSOxTFCS3ISHQmHKR9ahC1kx0uItSf2oQCtK/P4VeYmEp1hbjppgM9Gdo/kHaqSJH2iNI83o3/jENHBRfTojnDaFsO4Fsu3Tz9PUFM5uWQ9eeZcemqfJ2u8G3v5bXwnOYOwDeYO/47WjHGmtWQREy5Bb5xEvTJM05wMNFXl8ad/QVSij322+zEQT0Pec9wUrOHN+OVciCnkIcMPcbdPQT82g1PxNUxEDTLJMQtLIIw3nM7xgjdIcqRw//hK+oWdtI5Oosc8mMo07pv3HVSh4+7jLUxEyWeoSpIk/beAFuCPr32PReN3EDY2cnqgml3GpfzHxd+gHwuzZV0c17OOib5TxLefoid7Lv+aVUxQpyPf8DzjsXYMQR3TW1PQR63EoYwwmjxMbUkZD77xHFmjg5zJWwXGqZzNeot/9pzEo7Py7ZKv8aj++7i7yrHbRmnrK+Nsxj5iQ1MocsQxLhwcKzwFIszjA/ey39yAEgqw8vxxDNEh7r3jcezGWL5y4hIPWB5gmbgU8b2S5S5J0ifGr0/+gi80XoeqeNg2Ucmp5AUs7LtAeXsntTME14uH0flcBM9tYSg6hUMFS2g3RBOdeo6C0X4GE/2sOTsJk/U6BAH2R/s5sWQB8+uq2XDgEKOlaQxGfYbu2AYWm/eSLxw8WvIQq3SvE2v30x51icCp+3i7aAsqVpb05OPW+hm3RDFoa+eWng0ctV1ECYe5+dwbaF4dby+fT23SFFbXuPiC/3v40HNUS4j4XslylyTpE2F/x35m7tNjCCdzOO4kNt04F/35PHR+O+EEDW/B3WSqiUyc/DlhEaZl2u38KT4NJdnJmkuHqSkep6wrj5TAQhQ1gWPqCI5pCUT5vPzjc0+hZKicjf8mHqOdi3nPscndy56EhbTZ4lkl9nLGDRn1X+BUynHGovqZPjoDU1AjpOVwNmsPhSM5JJriCXp0TBs8i7Hdj5ap57FrH2ZKp4evdP6SDHM/HeJL5CvxEd8vWe6SJH3sNY410vDyPgp8MwlEv4G//QxbjBt4vPppTMEgh1bOYEVoDiNnf4vFOcTb0+/gp0lpCIvCus5nuFA0jjFkYn5TOXpTOeM00lVspTszk394+Xck+u0cz7oXobOyu/QP/IN9HBWFH+Z+jm+afsTx3gIqJipoc9moydxPfLCMPGcSXRYf1TkHMQfNfMl5KyM+HxlaD1PPNKAzhHngrsdIGffxudMHmRe3ly4xi5jgGnyiK+J7JstdkqSPtXHfOL/f8RM2Dm/ArDvK74IBhhKyWH+hktLhbjqXWFgd+DKutn2Yey/wZtEKDscl41aNTA++SNDsZTDRz7VVczBGrUSjnS3pmXROLWB11VEWn6qmoWQp/qgKTua+wpzwIMt9g/w66w4+F/MUZ7sKKNYrDNdfy9tFf0RRbCzpLWbA2INb72fCMsT93TfRbGrHxjDX1u8n4DBwbPJMOtPzuePtJlbF/h4/VhT/33P/VDuqNSfi+ybLXZKkj61gOMj33vpX7m+5AaPSyRNZAda6T9LVlsoN7SfQTw4QTngQ43gP4uIrnE0upiu1lNroNBJt55nWO8CZUgdzGytIUK8FMcF+1U9oWjwJjgm+sfUZ7DnJ9CZuoiumivbU4zxkH6UpKgdnjovmAYHV4MJT+XWO5b/MhHmYBX1TESEXIpBFfdoJFvVNpTtllIAvwMbxN3A2mAmZVP7jzge4/VAXC9Rfkm0axxf8Gv84Bc6m59EfY4743slylyTpY+uJyh9wx9nZGIXC0awqpjSc5JBzLveefw1dmsbuWcuZ4knGfeqXDFviOTT5Ol5LLkAf4+Smi0c5Ps2JzWVj+vBGAMYdp6hdUo43OorHn/4pVsXPuexv4FH72VP2Il+zW0gN2dlceA0+ey2aCGGufJjGxLM0ppym0DGZdHcsvWY4m72XRFc8qTElKCOxbAzvJnT6ndy/ve6zLLnopGzsD6yIb8ShreFn2WUcykzngYkn6Q7ZI753stwlSfpY2t60ndQjYTIC+QRi/sRbfXFk6l1cc7KKoFVPzap01o3dhKvy52haiJ/N+TyH41LAIri5/U805nkYjnWzsfZzKGoCJvcb/G7ltXjTbHxt2zNMaW2mquSLhPQW9pZuZnrIxO32JralX0OC8QAjXiN51Q8xpndwpHA7McF0po0WUx/TwbC1n6Dq5TrXerJGLKxiH8mNI/jHDQzEJ9OWPZcpDc9yY9oFfOFstsXdyR9Ksvi8eyuFvlZuQV4KKUnSp1D1QDWH97zJGvtyovQ7edgyj3/xP4f/hJ4ozUf0cg+5zgeg5kVw9PJfs+6iwxpLwGBg1fifUFQf54smuPnsHRh1hej8+3hy8nICxfFce+wg1x89SEvRIpy2GZxL3cZY9CCPjdrpMyUzmtVD1aiF2Rfux+ePYu+kzQhFx5L+GYxpHUQFs+iOb2BD/0JycJFOJdPtFxm6+M6zV393w98x/9jzLMvpxKb4qTT9Pf82NYvFnjNMnrhIU+Mixvsj/8WJstwlSfpY6XP18Z97vs/DfXdiVC7x82SVR3v+QPfxNLLswwTnGek1P0hS63m03mpeK1lNU2IO4yYrM0KvUzzk4MhMF3PbF5McnI/mP8fJqCh65xZT2tHM1198GmdSEl0Zmxg0HqGq8Az3egop9PeyrzCP3cMaqxq/TNCZzvHcVxmP6mfB4EwsdhchtZBzmfspHMsgM07D7RlinfY2zafSIQzHKmZR2FRNUmaYOfoGOpS7+fKsMkqDrWx0b6WtdS7JI32IkZaI76Msd0mSPjY8QQ9/v/8hHm7/DEYRojl2F0ld3Zgrw8Ta3ZyaPxld9lzyOzX8da/QllzClqJljJhiyDWeYXF7D0dnhIn1pTJ98Ea0YBeh8Wr2btiAzefiu0/+GL0KFwq/hl+0s798F8WijC+NHuZwSjkveMa5vu1zBMfzaUmsoSHtBMUTJWSPxdFrMVKfdgJDSE9+dAH6gTRu43Xa6tPQj2v4jEa6bbEMxiXzVfMuHOHFXD97LanKEF90PUVz40IMAQ87yxczXiqvlpEk6VNCCMFjxx5jff0ssn05GMy/ZftECeuqz6CbCPHM3PVck9tDfFcF3uo/MB6TyMNzv4DTYCLaNMT1l6q4UKzDHhNkQ8M9iLCTmP4d/PSO+8Gi4/tP/pgk+wTnyu7DZ9DYN+k5nFE2Hh9ux6mP4sdRLq7vvI3Q0BTspiHeLtxKnD+R6cOTGQ4OMBw3ylhUP1O0uSzqKOI63QsExhSC9e/U6OlJ5Zy1FvCDmC1oJHLjtHvRmQM8OPFLWi4twOT1UJ2Uz/ojLxJwuiK+n7LcJUn6WHjqwlNEnQuxyrGAGN0Wfqrl8dD5bQQH9fxsxq18IfttPL134D/9WzwmE48ueICwTkGnD3F78+v0pai0ZDnZWHsfqhAYR1/kJ9d9iUBqDN/Y/gwVrY1cKrmZCVsGVSmb6U52c6d/ClPdTfw6KZEFPTej9s8GZZQ9k55HUQQLB+fAUC/2+GRak84xY7ycr3SnkaffQbo2yMXqXAD64xLZkTKT78e9SSzjPJL/dXrjY3l05Ke0Ns5D7w1wNrmAFXW7uaegiinxH49nqEqSJEXUwa6DnDh2gHuHbsasnmCnYYTPNe3B025mS8lqyvN7MY1tQjm9hYCq8PzMzzNothBSdWzs2YHf4qW2xMDaxr/DHIpBc77Cq+WLGSrLZf2Zg9xwaD+9GbPpS1tIr+4Faor7STV+hm/0baXSloJzdB0xPUswG3o4lL+XMWsPc4ZmYev2MppcyJnsPeRNpPIPY0n06y8wW6llb/1MksbtIAQvFi3k9oQ2pqtn2JZ4G6/mzOTbwz+hrWkG+EPUJOdQ3nicO1LOIIxw1mSL+J7Kcpck6SPVPN7ML/b/mMf67kGvdtNt2kl6zyBcEFRmlFI9uZQbPLlw8jVCmpdjpSt5LTmfoKpn5dheYpQhLpRPYkbvMhI8WQRdb9IUZ+PsksUUD7Xxjc2bccVk0Fi4CbdvB4enteCPWsZjg/sRCLb5riGnay1RMU2ci+/mUupJJk1MImMgDXd0NscKXsXqt/Dd0XgOI7iRfZwcLiW7fhABnMoswZybyhfUF7lkmsYjU77APw3/lO6mcrSQwoWUXIqaz3BXbCUJJg93T/l34sfKIr6vstwlSfrIDLgH+Ke3HuGxjnswiyAmwxOcGcom9+QgPXHxPDHzs/wo3EGgshLNO0pV0TR+nrscoajMdxwj39PCyZmzyR5LJHeinJDnIA4xwo4NnyU65OHff/IEqmKgpvx+At59HC6/wFhcHuu0dJaPV/NrZR1FXbcSlVhPR9jE0fztpHhTKO2fjeozcmjSbsJKiO/3W3ktXMa9uq10hNJxnDOjqSpuvYnXp1/PD/W/xaWL4daZ/8ojo79iqKkYv6ZyISWH/OZz3GU5RoF1jEdLHqbOkUCjqSPieyvLXZKkj8SYb4z73voq9zffQnLQRrL+cf40UcHSYzW4jQa+NfdBHtdfRH+yHW2ik3N5+fymYCN+nUq5+zzT7Bc4sKCMjIkgk4cXEfKdJuS/wObrvwJRev7jqR+SaLdzvvx+PFoNZ3Or6MiIJin6Tr7f+nP2aUuJ6v08UckNjDsSeav0aUxhI7P6V2Ab8nFkyilcxnHu67PwVnA9j+iexCGi2dm0kOyxIQyaxubpN/G0dSt6ZYTPVTzO39m3Ym/KxisMnE8tILflPHeox5hsG+L/5H6RQ+EpfPPIm5gDsRHfX1nukiR96JwBJ/ft/Sq3Na1gkiePRP0P+ZM7lzXHThNC5dGF9zLH7GNGdQfaUB112em8VHALQwYTaf4+5o8dY/fiPArGdMzsW0vI34DmPsIfrn0Qb1osj7z5Wyoammgs2cSoaZQO23HOloQIJT7ML5r+iz7fJJpGHsSc0EZwwsie4u34DA7mDC4huUfP8fIWBqM7WDpqotHzGf5V/yOCGP4ve/cdHVd17/3/faZXaWY06tXqki1LLljuFTdsY2xjTDUG00IoIdwLISGhhYRAILQEQm8B444xxr3IlptkSbYlWZbVe5+i6eWc54/7/O7Kuuuu35MnT3xvkjuvv2bm7DVnr/3d67PO2mufc/jp0F0suXiKsCBQFZfHY2N6MMsqeDFjIxNC5wg0WvFKKqoSsklrvsCawHEmWbp5P+l6PrAs4+EDOxhNNjF5KHKHakRExD8Zb8jLQ4ceovRyHrMcEzEqPqYs5KP0ZC2ST+BXs5bi1mfw49oKQl1naU6M53DGcmr0VmKCw1w7vIfvZ1mZMGiluHsxoWAbIfd+/rToHmxpCfzo1Ecs2VNGV9Js2mIt2IRDnCgeJRD3CA/1fIt5SM/BkadQGftR+4c4kVhHT/QVxg2XkNidzvnMblpizhPnVREcXs8zypcRBRkPeB5lXfUhwjKBoCAns7SATPkW9lumMWqQobisw4eSM4kFZDRfYOVoGbPi2tlmncnzqffx2K7N2FNiCMhVKDXtV32cI+EeERHxXyYYDvL40ceJbdRw0/AiVIoDdFKJ5ZQDmV3ii7mZnDNcy/uNBwg1H6MnLp6zqdP53pJDXGCQZUPbOTItyNyRHLJ6ZxGUugmNfsM3c9bQnZ3JDy5+wfWfHcAWnU39mFL8nu85WmLDnnQv470+ll5p5zvbU6ijezGrz1KpUVCdfIj00QySekvpsQxwPuUo6qCKpO51/Eb1awQBHg88xLyGGhJHBlGKIq6SGUzUb6FbbWVfwgSiLgv4UVKWVMyY5hqWDh/n2oQmDpuK+FHuz3h45xakWA1upZ7CkTMMCJGbmCIiIv5JBMUgT5Q9QajByaN9tyEpaoHP6K4wY+zzcGG6ku3GjXzQfBTZpYMMWxOpSMnji4RSzEEbi4a/4eA0N4s9E4nvLMVPH5L9G06PnUZj4Xhuv7KTG9/dg1uXyIWCNQTcuzlePEx35i0Y5Rk8WbGP/fbH0VpaiLfspsY7gSPZXxLjs5LSvRCZMMqJrG9RhjRk9c3kTfkbyAWJX4TuQ9fjYtaVavxyFW5LAiXZtUiCg/fTVmC+IhFAwYHUa8hpqWJh3wmuS26g2pDOPYUvcfeunRgMIeyaaEqdxzkq5LEl/5qrPt6RcI+IiLjqgmKQJ8ueZPBSB0/33EdQ3kUsL3DhdCpp7QO4Sry8HfsAz7VfxHJxN66YVE6nxPN54rXoRC+LR77h0HQni4IzMDXNxCMfRGb7hpb4FE7MWETpQA0b3tqCX2Xhwrg7cPv2ci5vkKb8pQR0M3jx+HYqbPcTFVNPUtpXXOhex76C99GE1aR3LybRK+dA/haUYRXpzjj+ENyGQhB5WdxAoyOWe8/sxK7So5LCpM+2YJRd5IPU61G1KgkJCnanzyC/uZIFnce5Pq2eVm0MtxW/zerv95EktzNosDLHe4wjnjFsX7oeSa+66mMeCfeIiIirKiSGeOr4U7Q3NPLLnkfwyWykSf9K5YlUkruGCE9083z2KtZ0uxhXvZlATDrHkzTsiF9IWCaweOQbjk4bZGVoMjH1M3GqelAMbacrxsrOZXeQ4BvkqTfeISQYqC1cj8O/j8bkfqqKp+M0LOPZY7toHb6JGEsl8YUfUVf7MPsK3iMk85PTs5g8ezx7CzchAMmBEB/Yz6MSwrzF7ez1juPnZR/iVmgxBdxEzy4hVnOQ3daZ2HviCMvk7MyYzdjmsyxoK+OGjDr61XrWlvyReYfLyA/00GtOYl7wGEecaWxatpGHTzayvqziqo97JNwjIiKumpAY4qcnfsqly+d5pfdf8OEjQ3yM82WxxPQ5UExx8lx+CSXtCSyu/AzRnMaRJDllsXMYUJtZaNtFeWkXtwSLMdQuYlDfjqbvG/rNFrasuBtL2MHvXn8RnTNEXf56nP4D9FiGKSstwmm6mUdPnMA7MJ9k83Fiiz/k0sl/4Uj2Zmyafsb1zaFwOJMDeZvxKlwkh1182N+PSgjzHjezyTeFn578GF0ggDHoQT02i9jEfdTqs7hgG0tILrB9zFwKm88yv+koq8bUYVcqWVPyRyaerqHE1UFXbCrzgmUcc6Tw6bL7+WF5B5rBNIToiVd97CPhHhERcVWExTBPlz/NuctneL33KUKhEOmhH3H5iA7dkA/9dCe/zs2gsKGIWys/QzClcixFTV3UeC4as5lv/46zU5q53T8BRe0N9BivoOv9liGziU3X30OU5ObNt17A2uOgPu8OAt59DEaNcmhGFraYO9lQXktU7ziyovcTVfwZF48/wqm0MtrNlxg7MpG8wXGUj9nDsL6btJCfD3ttaASRj1nLZn8pt9TuJ3+4C5lCiTLBSExJFSPKKL73zCOolPNN1gLyW8+x4PIRVmfW4VXAmgnvkFfdxOThFnpjElkcOkT5aAIfLnuQH52pR21PQilAWlT4qo9/JNwjIiL+5sJimF+c/AXljWX8vv/nCH6BhOATtB4WUDhCmGfZeTM9nnHnJ7CuageCZQxl6WbatfEcipnKLMc+aibXcadnMkL9DXRE1WHo2YNfrePr5fdgwMvbf3ieuLZh6vNuQzG6l96oEAdm5zIYv5HbTnST2p3MuKidGPI2c/H4Q9QkNVKXcIpsRx55vVM5H3+GDvMlsjwyPugbQIvAZ9JavglMIru7lRuaThDSRqFS+IhaMAjAlsByAjoZO7MXk9VaxYK6g6zOrEOUh1lb/AZpdT2UDlxhyGJlhbiPE+4U3l36ME+erkA+mIsciWkGL93D6qteg0i4R0RE/E2JksgzJ5/hUOMB3hl4FpVbiSXwDN2HPAgekfi5I3ycEENJxViWXDyM3JrDmYwU+mWwM34xpa5j1E88xwbXNKSGlXREncfUvY+wQsuXy+8FtYyX3/0N8U2DNOTeQvTIflrNMg7PKaIncT23Hx0ms9dAqfFTdJm7qKx4gPr4fs4lHyZ9dAz5XXPoMrbRkHCaPIead4c70CHnU2ktB0Lj8DklHq3eSlhjRBW0o7s+iEkcZVN4JU6Tms3ZN5DRVs2C8wdYlVWPQuHn5qKXsDY5mdbTiN1kYrW4hzJPOm8ufpRnTx9FHBiPDIkZBhGzPIok79WP3ki4R0RE/M2Ikshzp55j75U9vDv8HAaHBp3/NwwcGkAIQuK8Yb4yWphYnsmkpnPI4vI5m5FOf7iPnQlLGeutpKnkOHc55iA2LqfTUIWp6yAerZlPVz+EJ9rA8x+9QXpzP7WFG4jvP0xjjIJDCybTkRd8tgwAACAASURBVLiWuw4OkzYsY2H0q+jSD3Pi/D00x3k4mfYdye5kxnXNw6PycDZ9DwU2NW86WtBLCj4T13JazOGyN4Znzn6KQQojC7owLAmSKAyzRVpOT2wMX2WvIa/xNAur9rI6qx693Mvthc9gaQsxresyrmgjN0nfUuYfw2uLHuM3J3fj7Z+CgMRMg4BZoaHRH+KUynzVa6G46meIiIj4H0GURF44/QK7Gr/hj/ZfYhnWg+9t7IebQQbJC4bYFrZwzbEEjPYmSCiiIjmOvmAre+KXEBdqo3P8fu4aXoLYvoBuXQXm7jKGLMlsWbYBpVzk9fd+RV5tC/XjbiO7cRvVKXoOXzuR3pjlbDxoIzoQYLn5JURrM1su/ZBuq4JjYzYT54tjUvd8gsg4mvUVeTY1r7laiJLkfCLeRB0ZnPSl8cuqL0hx9hPU6okrtZNosLGDRVxMzGVb2iLmnt5DSeNJVmZfwqRwcUf+jzF2qijubsJr0HErOzkYzuOla/+FN45/Te/gMgRglkFGtFzOIV+Al3QBilSGq16PSLhHRET8P5MkiRdPv8j2y9v4/ehzJPVH4/d+RvDweQQVpMwdZP+ohUmVVhSefqTUaVTGaukPtbM3biFymYv+wm+4Z3AFUuds+tSnMfeU0xuXxpbldxETdPCb114mubOfy+NuJLdhO41xao4umEif6XruPOIkSnKwyvIcQcMAH/Y9jt2k5HDmp5gDJmZ3LWZUVHIg72PynXJ+O9qKWZTxoWwdbSRz2JvNw1cOUNJbh8cYQ8K4bhITbBxiGodT57MrdTKrDm4ht6ualTkNxMkcbMy6F12XmYK+VoI6Fetl2zks5fPC7H/l3bKPaBu6CQGYoZejkQls9wR5O8pPUAY9ccGrXpPIskxERMT/E1ESefHMi2y+vJnXvE+T0W3F7d1B8FAZMo1E2oIBzvSZyTsbg8JjR8xZwul4LQPhdg7EzkdQDjM8djMb+1Yjdc5mUFFOdF85fdYUti7fQJJ3gNdffJ743lHqS9eR1fAd/QaJratm0h+9iluOuzEJPdxseQJJ3c873hcZVWo5nPUF+rCO6zpW4gwrOJD7KRNtMt50tBEtyfmj6g56wvEc8OdyU18Viy8dZjTainlsH8npI1QK4/go+x52pkzk9m8/o6C7itW59cTInWzM2Yi6N4X8gS4kjZw7Zdsol+Xw7LR/4YOjn/x7sE/XKxAR+NwT5C2jn6BcQEzVs+zsiatel8iVe0RExF/NHXTzk7KfcLTrKC8FnySvPRGn9xCyg3uQ6SXS5g1S12gmscUKfifhcWs5oR/B7WvnUMxcQsZORrJOcG/XOuibjI0yjIOV9FqT2LriLuK9w7z8q1+jCetpXDiLvAObcCvkvL5hGfrgcm4468Wgvsy66BfwKMK8rnyLoG+Qg9mfoJAUrGm/kS5R4lDupywelnjG1Y5HruV1zQaCbjWH/Tkstjdza8VOnFEW9CV2MhMHuSjP4amxz9OiUvGDLe8R72tnRW49Ojysz7mXmM5EUu39CEqJO+VbOaIcx8+LH+OPhzfR5FyLgMAMgwxbSGJnOMBOYxBRI0eWrOOm1rPozIlXvTaRcI+IiPirdI128fDhh2l1tPJb+S8Y25yA3XsWxYFNyI0iafMGablgQtcdCwE3gUkbOazsQvS2ccwyA3f8JTxJzdzffjvi4DhGQ0fQjlbTE5fE9uV3EeNz8MpLv0Ihi6PjuhRyd+wghIIXNy4jp38Jed1eTLozrDG/SliEV/XvEbC3czj7CxSSgvWtt9Mk+jmU+zkb+v086umlRRXPe9o70TqDlPkzme4fYuPJbbg0GsTSEGNjOmhQZnBnyRvgcvLDHR+QKO/jupxaJCHEbdkPkN5mId41hFIeYoNiCwe1E3gu7wHeOvYtTaM3IENglkGgxSeyQx7kiD6EaFRiilFxQ/tuBhL2o27OvOr1iYR7RETE/7WagRoeOfwIYTHMH4K/Ju2KAZuvDuWBD1BEh0mfO0jLOQv0WUD045n6Q/YIV9C5mzhlmsJQxjkUOom7L91H2B2HJ7gPpbuO5pQMdi+5gzi3jZdf+RUyeTKDN6jI/uoQUkjOrzeu4Jr2RcSMBkiN+ZpFui3IggIvRb+F336FI2M2o5QU3Nu0kYsyB0dyPudnvS7W+Yc5oSvkC83NJNhsHA+MYZrg5s4TO/FLfpwzzcy11NCkTuX6ie+Q1d7OoiObyTYOszDpIoNyGRuzHmZSgxytOIoWHxsUW9lrmMzLaet5pew0LZ7rkCMwywBV3jBfq0PUqsKEY9QU6WTM6f+QoykXKDkfRpZ49aM3Eu4RERH/V/a27eVnx39GqiaFJzseJHVER6+nBsPBd1GaQqTNGaTrTAxivxFBIcc+42F2hOqIczVyLnoCHYWniQ2M4bqG6xCD4PFtgUAX9Vl57Jt/KxnD3bz06ksEtFnYbh4l5ZOL4Iffrt/A5PZJqIMBCjNeY1boFASVvBDzJu7hNo6lb0UlKbn7yr1cUAxxKvNz3upxMDvo5EvLQvZJ8xljG+B0MI3liiCLyw4RHu2ld34KCyyVdKoTWDb5HeZUVpFfs4+S2EFmWy9xSaXigcx/ZUn1CCGthvjAILdrd/BN9Czetq7imfIuOvyzUABzoiSOuEU+0Yfol4mEE7VcT5C00d+wLXWAtUdlLD8T4EJuZFkmIiLi74QkSXxY+yFvVL3BdGMpd52/mSS/ir6+7zCc3onaGiJ9zhBdJ+LxDSgRDGYGpz7IZm81Ga56ao1FNBafIn9oHlN7SxHDI/jdO/HLR2jML+Hg7JsY29XMr17/DUOxkwnfdpnE9waQexR8ef1TTG+Jx611MaHgJWbY6pDCOp6MexPZQAPH0nagEpXceuVeLqgGqE3/nM+6R8gNe3k2/QG6hpIY4x6gKpDMnSoFOdVnEPpq6ZiRzrzYc/SrY1he8jZr9x/C0lHBjNQephpaOKbT8ZO051lVcRmP0UCeu5G1+r18HrOUr7RzefSsgp5gIWoBZhlDfO0T+NIYxIdEKE3PD1yDeJW/5ouEAHcfimJJhY0Lecu4lDvmqtfrLwp3QRCWAG8AcuADSZJe+g/HfwzcA4SAQeBuSZKu/qtGIiIi/kt4gh6eOfkMe9v2skK/lNXnlmIOyXDVvou+uRpDpp+kCTY6ypLwDUrIYrLomnIXm1yV5I9eoNFQQN2k08xsWU/OaDrhQBNB9/f0m0dpyZxD+TUrmHSllud//yrN2UvQ33yEmN+7wBPD3vlPkDGiw2u4xMyJv2NyyxBSOJoHEl/H1FfD0bTdqEUVq67cx0VdM0NJW9jUPUwUcG/+c6ibQyQFbNSFknlcbsHTUY/+8mG6xiczO60Gh9LITYW/Zf2ub1HZGpmT08oERQ9fR5n5MPoXrKy+jFevY7rrNAsNp3g16Q5q/AXcXpXMkKhBJ4NpBj8vB2Uc1gcRJRCzjTzZX0mN+WPOaSR+si2GCVcGqZp4O/aoaYyx/h3slhEEQQ78HlgIdAEVgiDskiSp/s+aVQOTJUnyCILwA+BlYN3V6HBERMR/rVZHK48deYxWZyt3a+5kwblr0IRChE69gtzZjHXmKKZ4D22HUwjYwihSptBUsoavnFUUO2toN+RyeXwDK+p+hDloIOg7Sdh3mguZPjpybuXSmInMOl/BUx+9Q/XEG0la9TX6N0T84SIqpt6NUpSjTNjJxMnfMKFmFEIGNib9FtPACY6mHkEraljUcD81lgqyor7jzW47DkU0a3OeI7Ohj2jRR58YxzNSGmX+y8w59RVDY6xMHteAX6bivoznuXnHTuT+XhbkN1DACL+zJFId/CEzW1tAgNXebxmrb+HJzEcR+1OY35LJqCQjWi5RpPfxsCijURMCmYCs0MSTrZ/zbfwpbKLAbz6NIWnIwelZj+CV53JRFcI7oOPOq1y3v+TKfQrQJElSC4AgCJuAlcC/h7skSUf+rP1p4Pa/ZScjIiL+exxsP8jT5U+jFJQ85HyM2V2ZEHQjHXsZlbqDuMV2hCA0fZeG6AuiyltOXe4MNjnOMclxjj59Np05blY2PIwyHCTo3k4o3M6ha2T0Zf+YAWM8G3dt5sZDBymbdyu5C99H8TsNI7ob6E6Zz4gxTGHab8lKqqXknIcgBh6M/SW6kSOUJZ/EGjQz5fI9NCbu5g7pKEuGPJRHFfNT64NMvNSKTgqiEsw8xDi+UFdz+873cCcZyJ3RiVwSeSr2CRZ8txudYGdRwXmSRA9Pxebgta8lMzhMzOgIa/TfEa3xcH/uz5lyJQWpLw6vBFaFRJTex+0SOBUiaOToC6K4v/U5Pknux2rX8NpXSpQhODnnSQJiLIc1fkThGCmq0FWv3V8S7slA55997wJK/3/abwS+/88OCIJwH3AfQFra1X+HYERExF8nJIZ4s/pNPq79mEJ9Aavq1jPTG43L2QEn38aY1EfshBHcHTr6qqwgU6Gd9gCVMQnsslcwyXmeEV0uo0kJXNs2GTHUh9/9LUGZk28XJNCbvhFVMMTLb71EdreL71bdSHHpu7jfTsQbvwFn1BhaEh3MK3yW9MAwY8976Fcm8hPT00jefVQnVJHqTWF843r60j7hJXcFiaEwv8y4l12jpcxpbkRFmPEko9EW8E7oNA9++RGeRB2p84fQiEFeU95H3pHDRGvcLMusRiOFeDxuOtF9pZhkXsZ217Ii8Qh2VRT3j3mJO2vTuTIiIwwkKSUadV7+iIAoA8miIjVNxuKeR/ggwc/EtgQe3e7EEZVM9eQ78KFin2aI8YHNTJvQzPHOv481d+E/+U36TxsKwu3AZGDOf3ZckqT3gPcAJk+e/J/+R0RExH+vIe8QT5Y9ydm+s8xXLmR51XIKRCXext1w+VssJS5ixjjor7LgaNEgi05CO+UBjisDHLKfpni0HptxLBiLKBpKIuQ/T8hzlFG9j73XzqQtcQV5nS08985rjEaVsOO+WCYmvcfIR5PwJd6GX6WlM62BJRNeI7MjRFbPKFXacXwo3I0ztJmm2CbSRvOY0LSa4Yy3+K2zHkFQc0PBq9guK5kfbkCJxMLQWL5Lt0LnPn6wYzveZA1pc4ZRiwE229di7KrEanGxKuE8bkHgKfNKYnvTUYQDzO49zoy0C1zU5/An0094qDqWSlcICYlklcCfdD7OIiAAoTQ910S1EDPyB7aY/SyrzuPmg600Zy2nL3EOg4So4xxLQwfJXdSF2hBitU9/1ev4l4R7F5D6Z99TgJ7/2EgQhGuBnwFzJEny/226FxER8V+pZqCGx489jt1nZ9HobdzeNRVzWMRb+QfU4mnMizxo1QHaDyfjH5FQZMxCNXYNXwU7abLXMdZ9BWf0FKIUk9F5FAQ83yMFL9GRGODknDvoNBWx/MQh7t+2hROTb8a2ZCcpUh+uL27CZ70Wr9yNN28fS8dup+CCgkSHjV3GhZS7r6ExdhN9uj5SBmdQ3DuJmLQXeMbeQ6/Kwl2m55BdFFkgv4JKUDCdUl4pCDGrZieL9h8mkKYka/oAilCYPZ2zcI42kZw2wBr9ZVoVSj5Q3oF1KIpop53F3gMUpHezxzILW+AxFtbJqPD/2zKKXAs/U3tw/+9gDxaaWCRtpdNbTos2yL0HJzKtrp2qSY/i1aZwQW5D493DkoRGUuf2Ig/pSat4kK1BmH2Va/mXhHsFkCMIwhigG7gZuPXPGwiCMAH4I7BEkqSBv3kvIyIiripJkth0eRMvV7yMWWFhfsfDPOAcg8znwH/6LawZ9egyPEguBc2HkxGDMtQTbsOfkMsLwTYMoxfJ97ThNy3GKhQgBZ0E3LtQSgOcHKfi/KQf4ZJH8eSn71Ba38U7N95BTsEfsPfGk1rxBL7oDEY0/cRM2sKkhBomnFVj8vfwlvEuOjw6TiV8i1PlJLljFaX+AMutTzPZ6WO/qYSfeh8gtcdJsaIXi6RHHXUN7xpbmXPuPHOPliFLh7GTO/H7VOzsKGLI7yK/sJFlUj/lGi2HPOvRBXWkd7axKOowyUkOPo2/jdy+dQwPhWgMS4BEpT7MEWUIAQFBIeAvMTLH+SvOyTowhi385NuxZPQPUjn5X/DKFdSJFWTbTpA2pZeE8TY0tmxiam7gtNJFWH31r3//j+EuSVJIEISHgH3821bIjyRJqhME4XmgUpKkXcArgAHYIggCQIckSddfxX5HRET8jXiCHl44/QK7W3aTKeQxtf4u7ghGERppwn/h9yRPakdtDDLSaGSkwYigs6Cb/gOuKNy8KA0z1XaSmIATyXwL0ST+2zZHzz7MQSfbF+VSk70Bq93B2+88g6jM5hcbs5iifZ/+6hXkDS5AVHjpsDRTPPUTUuij5JQCvdjLL/RPMOLv5UzSAUKCxKTGe8lVXeQ+1RaUQRk/TvoB33VMoVToJFNhI0NM4ER6BkbbGfLbXEwvP0XUGC9Z43uxeYxs6SzArlYxb3wFU4MetmitNNrXIilVjG+oYs6YCoyaAJ9anySveTpVHj+SqCAsiLxv8OP5/x6zGK1EkT/AmJHnqFV4GN81jbUH3RiQU1XyCHYGcbkOUiTrJfn6TixxPkwd8/DWzqR87EHO2frJ0UZf9br+RfvcJUnaA+z5D7/94s8+X/s37ldERMR/geqBap49+SytjlayXbO5qeMGZgkagu3lyHo/In3mEFJIoP1IAkGngCzlGtTj17HT38J2BBb1H0IpT0RhWYNC1BH0HiccqCB51MX7d6ykLn4e19Sd58nPPuJE8QrOTPyW9KCJ6NqfkRyMwTpUycVxNmbP2oVpWEbJ5QABUeLnyqfolNVyPukCyoCZ6y+v4RrzxywKNdMv17FB8wJ9LRYWaxqx4CNVlsW3qXLye84wqNGzdt9eUnKGScsbpmPUzM6efJypfu4yVhIXDPO5bCwtrgWoxQATG8uYnVuHX6Vhr/p5dA2ZVISCyCQFA/IwXxgCKIGwAGKmmtjoXYQcR/GEo1l9YTVTK44zmjCHhtgiRvwn0HqriU9yk7OwG41SIO7iLVzszaIjfzu7At144tRoW3RXvbaRO1QjIv4HcgacvHHuDTY3bsYsN5PecwePj5SQLlPhq91ClGYbpqluRtt0DFwwgdKAduoGvDFp/Crcw0hohBX2GtBfi0aVhxh2EXRvQy61owsHefX+R+jSZ3Dn7q0srGzl9VUrsMq2kdq7jLyB6ah9w6R0fEjrCg/zii8Q3RLP+O5GGoV0vhRu4GLUCdqi2ohy5PNgr4VlUc9gDIXZGl3IqyMPonTLWK25iFoSCEePpUregXlUQ31MCj/78G3SJwwRm+Kk3h7HroEcTEVNPBnoZxAFn/muo1OTR9xgL0WBs0zPb6Vdk0aT5+fU9esxBkGGnGplkEptCAkIKyE83kei9zW8vgFyB6cwua6YKVcO0pS7Hq8wgNf5MXrRhWx2H+PyHKj8MehO3c6OmB7Op37KZeUIiabr6I5aheg7e9VrHAn3iIj/QSRJYn/7fl46+xIj3hHSvSWoulfzos+IXhTxVb9NUvZR5FqR3pMWPANqZPHj0Ey4g7Ohbl6TDzNxpIYZAQGZaSNyNIR91QS95Vix05oax5+ufRjCAi+8+zoycnhraYh4sYbMrp+gC+pJ6jqEyVaO7cFRMmKdJFdayPZc4nvFXI5LeZyJP8SIZpjC7pk84T/LJPV+qlU6nlbeQ13/FK7RdDJWPoAePReSkom3XeFixliihh289OlvyJg5iMHsp3wwje+IY+H4aub5PJyRR1PuWYNLaSCzoY5JiecZm9BPpX4ml/rupzmgIDaoQURij9aHUylilwmozCJiZgUW9y4IalnYcjdRzQ4m2Bqoy1+H33cQWagHr1UkZUEbSSY/hr6JNNctpSxjJ6cUbSjU+ahMT3FRG09G5xW0l4aueq0j4R4R8T9Er6uXF8+8yLGuY1ileIzd97DElsVKuR7JO0T48hukT7iIu1dD/4loJEmNumQdjsQCnhOGuaLUsKTnKFblJBTGQsLhIQLuHUAf44e72XTDEvaMXUd2Zxv/+sUmyvOnM2LcSZ5jJem2IoKCgymVL+M1Owk9MYQQ0lN6JoAh3MJ78juol4uciTtMQAizpnkWD8m3ES34eDk6l3eHHkMh6lira0AvukEVS5vWS0Dys3XSfKZeqObFna+RMn8YQSGxo6uAmhQvD8nPE+cLsidUzFnmoQ+4KWw9yYzsJuIVDg5p17O5Zwk5fgVWUYFbEDmpD9Ijk3DKgNxh9PI/EfL0kDFcQk7rYvJb96PWjachIUDI9RV+uRLP7EHm5thRCAK62tV8qPZSl/kHbCodMdFPcMU4DqPLwfKD25CENspjI6/Zi4iI+H8kSRLbr2zn5YqXCYZCRA3PZUzvAp4QNegVWoJtZeiCn2MqGqSv0oS7VwOWTPST7qFGEeQJlZxieyu3eIIodWsQBA0h72mCvjMkyG2YhuHVex7hfPwEFp86xg1ljewdb0HUNzB28F+RoUTmrWDh2c8YmqLGcZOTmGY500Z66ZNieUt2F5f1jdRa6tD7o/lJr5ZVsj/RLldwr3I11f2rKDYPMdXbgl8MYTOYkCQvJ8cU0xsdy91HdvNA1Qckz7PjDKn5YjALc3YPz/hs9IsaPgutpFOWTFpzMxmaS8zMb2NUYeSD4M840DOO2X4VMgS65WGa9AFqZKDReFFln0YZ2o8qYGRW292EOmHuwAla4gpwBE4hBQI0p2mZOqOOlCg/GlsmRzsncsh4jAFlgGjtzbjM83EgMK3yCMWXqjk4oZFBYQK5roSrXvdIuEdE/BMb9Azy7KlnKesqw+hPQd1+C884teTpYwi7uhG7Xyc55yyuXg3tB+IIh5Woxq2CjJn8nhH2SSI391cSo5iETB+LLzyE4NqGIPRT3DfIzpVL2VW0Ank4zCObPifabuL7SaOkeBcR059Eb7SfydXvktNZh321SF1JmHUVDnTADtlSakikylpOv76PfFsKrzjryMDBnwwx/Nr1CLpAAg8YmvB5nHjkKrwaBVesidSkZqMVA/zu08+ZrdhM8mQnrS4TH2st3J7aRq7PTYWYwwHZIvSOUQo7T1JU0EW+upca9WSeHL6HyT4Lc0NKJCSa1SFOaXz0CnLUqS1odTsIhQfJH5iKoWs+BZ1lyDXJNJrdSP5y+rTxBGc1sjp1ELkg4GyezW+EdnrMe9DJ5yBab6FZrSe3pZm5p3ZgM9rYNsWPI/ohAgnjGOr/O3hwWERExD+esBhma+NW3qh6A0/Qi3xgEbc0j+N6QyIytYxg8zZirZsQksJ0n7DgdygRzWkYJ91Lm0rGkwofSY4ONvj0KDVL8OIn4NmPyl9LUshOV+I4Hr71SRwaI4tPl7H8ZAMXk020jFFQPLwBt8bHxaR+7t75Chr8NNwfApmHtecV2BUJvCPcQLu6j2rrEUJCkPt6o3jId5I2pYJbjaVUDN7NyugRkj3tOEJeXFo1HeYEatKycWqMXNs7wL1fvkNRwSksei+HRhO4EifwM28b/qCCTeJymsIZZNfWY4rqoXRCFzr8vMpd7B5cxEqPCgUCXkHkiiHAAZmITu0kaswJJFk5Op+V8W0P4O0XWeC8wKAOxNBp3AoT1QVW1hVVEBvtBnsan9h0VKsqUVGI3vxT2o1WYm0e1h74nJSeyxwvGuFS6iLcycvRBEe58cD7GCzxV30ORMI9IuKfiCRJlHWV8btzv6PZ0YzOm4Kx7Q7esBuIM2UQGqpD63+HqPROBmqiGe3SIGo0KCauQ5tSyueyIb4OBrlxsAmLvAhUKhqFAeLt27EKIyTaRH634WHqE/MYf+USt+0ro1+bSXW2mZjQDKx2DRVZEultR3n0y2/xJsOuNSGWtYfJ0MJ+xSzOy9K4YD5Pe1QrqV4trw31khH28U60iT/4b2aCPYOnVSO0egcYkslojk+lMr0Al0ZHjsfGvYe6mHX5BSZMuIKIwAdiInPNXhZ47TRIY/iWa7G2DzLOVkbmeAfjFe3UyzN5zP0Aee40bgyoARhWhTimc9AsKIlJOoVoOE5YcFPUMx9n3yzGD57EL4TplzcTCmuoSBjL+NJjPBRrAwTO9o5hU7APuSKJGO2vaIhNRe8Nc235WYprd2M3+Ng2M4au3MdQCEoWn/yE+/aeQpS0DI6NbIWMiIj4C9UO1fLy2ZepHqxGE4wmPLiOOa2xPKhLQm7UI7V/SnL6VmxXjLTsiUNETih7FuaCtQzg4yGZi7jRIe4JWJErJzOEgwuyVqbZvyfFGeDYvGt5cfw8VIEgj//pC/SueGoy4kgMZJLkn0RXjIPyXC2Pf/0KuS3ttJWKXMwPckOfEbQqfs8SujSDVFkP41d4uGskwMOODg7pdTxkKMTfv4bHZUacuGjBSbslnrNjChkxRJPlG+D+ilGs1ZVMNn9G0dg+en1aqqKsbAh04Quq2SJcR9+whbzLVRhy3JRmtiIDfindzkn7Uha6tWglgRAilw0O9irkGC2VmK3lBOQOEh1ZxHWvQDvczwzXfjxSL6KgoD66mNCkeu5J3Y1S46PXHs8H7lFsIT/ximeoT8xiQBKYWjvA+OpPiXY7qM0IcWTmLUi6bKZd2Mx9351FHdBQVTKdYWsMloDvqs+HSLhHRPyDcwac/PH8H/m8/nMUYS0MLyOhNYufe/0kxuUjeQYweF9ApmmndV8cYa+cYEIBpvHr8aq1fKC2cdAjZ4XDiVGWR0hwsl81gsFbxar+01RMmcfvrlmKPCwyv+IUc2uGqE63YjUkku1OwmYc4sCkELpRG7976znUAT/f3CCRGkxhXaCbg7KpNMtiqDPV0RJ9hTS/jJd7epFLMu5NiKfSM5/1fXnkhWKoVrbRFxXNmcyZ9JqsxIVGeKShHe3ZfhJkW1gy5gJmlY8L3ihidQLLAp1ckPI4FJhOan0LKcoO8mfZyBW7Oaos4RXHXRS7klkWlgNgV3n5RjeKXdeNJXEPftUAcc5M0npvExTLTgAAIABJREFUod0VzZThQwRDw/gQ6NYXcz7Pw7153xEbNYrHG8Un/XJqAxJJ0k+wpRQwIBMobPeQW7eFzO4r+FQi38wroSNnMdfUbebW/e8R5dZSV1TKiMWCLKwk2jXAkFB31eeFIEn/PQ9nnDx5slRZWfnfcu6IiH8G7qCbL+q/4OOLH+MOu8E2kaLacTzkCZGaOAnkKujfj0n/CcMXtXgHVYSMsRiLNxC0pLFF6WRXQMMcj5vUkAUI0iZ1sdcYxYrh7zGYDXx67U24NHqWnjzGgqpe6hNTkGsNxLkz8KjtVGbLKc9L4uHvPuL6fUcZiYNDJUamxRpRhBQco5R2fTe1losEFF7WO5zc5nDzntnAVkUuS/unsTJYzDF1O/3qEGfHFNISm4wx7OKm7jZU5R4EoZzrVGeZZOnB7VfQLjNRoBrCJenZLc3H3y5HPdSJaaLAQkU1TkHPr4K30zs6h2k+JQIQkIWoNPRwRhsgNm4PbkMLUT4r+Z3X0+xK4VrnYbTubgBGteM4mpjI9eO/ZnxsP2FJxl67ioPeKKz8kOaEfAJKGTmdXuLa91HYegqTW0l9ppVTM1ZScmU3aw9cRB+Ipn7cWOwmE7KQCsvoKEWnvyfO4WPUrGXKqeq/qu6CIJyTJGny/7FdJNwjIv6xeIIevrz0JR9e+BBX2IXSlYWydSa/bbORkTYLQaVHHKogWv0Z7mYbjjYdolKNpvBGSJ/KDqWbnQEl07we0oNmIIjPX8vO6Fj8MpEbXeXsWHgDbbEpTGio5YbyJlpjregkMzG+RLxKO82JAb4pzWNKWwVP/uldTD1ezowVCGTnMlPbzj5pPp0qiQvmWvr1vYzzBXl6eJgalZbfm8zkjMzgQce1VKuGaVIOUZWex6XEDJRSkFXDdSTsH8SjaCQ7dJkVsU1Y1F4Gh7XILQIWwUMlRdT056Lt7Mafq+Vaaz1pgX62yufwoWM9s90moiQZYSSu6HrYpwtith7CY76AJqgnt3cRtqE8Jo5WYHa3AiJBdQEnLUWMKdrOspTLyBR+KtxKdjoS0AgP0ZyQRViA7G4v2v4yUge/p6DdgEunpmzWPBJHTnPTgQZUxFI/tpDRqCjkIQ2xQx7GV+7G7PKAAIbsAOV5i7j5t+/+VfWPhHtExD8ZX8jHpkubeP/8+zjDTuTuTLQt07m1uY9l0ZmoEkoQ7XWY1B/hbetl5IoeSZIhy5yLPn8l+9Qh/hSUMdnr/vdQD/lqqFM6ORQzlQnBy4TzYzidM5HkgT5uPniakNyIoErHGLDgU/bTGe9ge+kkor12nv3mFQrLO7Eb4PD4VCbmynF5TVyQZXPZ2MRlcx16KcyPbHZiA0retKpxB3P4cf9aOmRhLugcXEjJoikuGQRY6Kxhzjc1dCrsyKV+rtN1MsHcQ8Anwy3XY1GN0k8MR92lBJrc2K0KinOGmO05T5M8mRdd92B2jyMrpEBColc5yndRwygtZ/BbziKX5OT0zyXQW0juaA3xnjZAwKfO55SpGEv2UW7KPItW56LJJ2OrMxO/+BBN8SkgQW6HB/nIUTShXZResqDzyWnIzyao6uDGsjZQJNFQmI9bb0AR1JLUZ2d85T70fi/IJKJzvcTmOekU4jkdnMatr378V82DvzTcI2vuERF/5wLhAFsbt/Ju9bvYgjZkrjQy6+dwW1Mvky0SypyVIIkobe8hdh2ju1WHJBqQkooxjl1Hh0HHsyFIc/q4MWRGkiDkO0uP2M7u+HmISgWzY5rZXzIfrd/H+j0HSLAr8OhzMfsS8Id7uZTUxo7pRUgIPHrqjyzdXo7MJ3GsMAb3lEKmBdo55Z1Ip3aE2pj9OFQeVoy6me+EP1oNtMtNbOxbiRROYpPVy8XkLLrNE1GKAeZ7T7N6xxFaAkEuGf3MCdiYk9CMTh7EEYzCpHXiBfb45jDUomBU5SJ+KtzkKyPklfPr0B002a5jkl+NADiEEPujO/BYq5FiThJAJGuwFHVHERmjl4j17QCUhNUlHLaMRxtfzYaC32GOsjEQFPh0qJDe8A9pjo9HHob8Fjc4DuLR7aS03UraQCwjZiO9yXZWnj6BX5/OxYmLCajVKAI6spr6KarZgToUQFBJWIpdmLPdXPFZOeq9G0dUEiZZ71WfN5Er94iIv1NBMci2hm28U/0OI6ERBE8qafXF/OhCA1lJpShSp4IURjayC6nze0Y7ZUjIkaeVos1dxqDRyOdBCZXHR3rYiCT5CfuqcQbr2WudyWiMlRzzCOdyiwkolSw6fZaJrQE8uiSMAQt+2TCdcS6+nlWEXAhzc9dm1nx6kOjuAI3xetpnFVGk6qEqPJ5uVZgGUx2dhh4yAkFuHRGp0gkc1GtZM7wQfbCQcquM2uRM7DojxuAo17mOsOqb/dR7ZdQnaykZCnJtQjOpeju2kAWrYgQPGs54i+m/omJQJWAqVrIydAp92MdWcQ57R29joi8amfRvjw44bujBbq3CFXuCsCzAmP5JWNvGkOZswhAaAUGLqB7PCVMRIetF1o3dRbJpBEdYxjbnXJrE22i3mlAFJXJbRhE9u7EZ91LYYaaoOQpBAHuUm+ltbpymLFqyMgkrFKi9UWS3NJNfdwyFGEamk4gd5yAqzUuNO4VKcR7O6Hg8shA6SYky5OGxF1/8q+ZFZFkmIuIf1IBngI/OfcS3rbtxSg7wJmNqnsbj5yopMo1HlbUAkJDZthFuPYi7W44kl6PImIM6ezHndEq+D8qxeD2khaP+PdT9/mpOxxQzkpaBJ91MR3wyGp+PmRfqmNDqRZQnowtH45H30hPjZuvsCUgyWOLezc1f7Sax2oNLpeDCtHFkJo1wPjyOPoWcxugGWqJa0Usia+wBuhVqDhqhxFVASmApVVYjDQnp+JUqkrx9rBn4jvmbj3I81khznJaCfhkzo7sosvRjE83EykYICzKqvYX0NOho1+mQT4pmnf8ocUEbe6Vr+Np9GxO88cjDGvxInDH0MpBwHFtMBWEhRFbvWFJbEkkebUMpBRDksYRU4ygz5eKPreDGgt1kmxwMiga2u1ZzQbEUu16FwSOS2e5ADG5iIPokWd1mJjaa0PlEQnIPeaNyBuPT6UlKQhIE9K5o8htqyWo5jSBJKC1hEorsqOODlI/mUSOfwqjeQliA+LCR/MAIBTITDeEzzH156181PyLhHhHxD+bSwCXeOvUW5bZyRERwZ5HSmMdjVZWMMeSgzlsBKj3C4FbCzfvxDsiRlCqUY+ajyV7EGaWCA74QmYEgBsnwv0O9iqC/itboVK4UTaIhN4+AUkVORxszajtIcqgJKZNRh3W45F30W1xsmT0JUS5jrniI1Xt2kXbEgc4rUjcuC+V4Na3hDAYUStqMDTRGN4MgscbhJTqo55OYMCm+TEzCauqtiXRY4hGQyBtt5tYrW0k/cJ79uRacWhUF3SpKTb1MiOnDLpmwykZQCGFqfTl0XYrikt6Cc1oKd7m/J8PXyymxgC+9d5DvS4CgmRAS542dtCbtY8hch0yUUdg6juQuLQmeXgQEZMpc/JpCjkXFISbt4bqsExRGeWgWM/jWczvndcWEFDJSBoJYujvw6D7Eoe0kaSiG0oZoop1hZHIJC1H0paTi1emQh8BkUzHu4mniB64gIGFI9WMtGGXUoOKYp4Q2TT5uTRRySUZWKI78oI9EWTxhUvDj4btQF4/8duNfNU8i4R4R8Q/AH/Kz/fx2vm7YTHOoCUQFpo5xLK4RWNp1mei4KaiyFyGojYR79iC2fId/RAS1HnX2UoSMmRyRCbR6fcQG1cgFJWKom7C/lnCgEZvGSMP46Zwqnow8LDK3uoYJLV6QJ6KSrIiEccuaGIgPsnX6FEJyJbM4yvXl35Cw387/Yu/Ng3ZL7vq+T/fZn3159+3e925zl1k0izTaQAIkQE4IMdiGGNuk+IMQV2I7qVAF/+BUKFeFuFKOqVCkCLgqEBLHplSAJCyBkCWNpNHVzNyZuXP3/b77+uzP2bs7fzzv3LkzaMToMpJG0vOp6jp9uvv0+Z0+fb6nu59++sztZuwuNNh5aoGr3lHaTspK5RI3yqsoYfhQHxbSCh+v95Fmhor7j7jVXKYXlAjSmMfbr/D3n/tD2nd3+NLpGpWux9Ftj8fq25yc6KGExazYRQjD9fgQt67XOVtfJn5qhn/U+zSnw1tc1If44+gfMh0vkWST5GiuVe7wytLHaZdXqffKnLp5jPndlGI+ABFgeY/R9x7iy/WE2WP/D++fu8FSAF/V7+PTyc9wuzCHkxsOr4YErRdoN/5vYitioj/N+y4HNPaBQhm7UKczMQnGUBw6zK11OXPpc3hpiOUr6kdDCodjXjRHeJ5H6JdmUNKhpH1OqXmOZYKinAQK9Njiy1aLdXuL9VLO//nLv/5AdWYs7mPGvIO5tH2J3/va7/GFvS+SyBiRVHjq5Xn+y+dvM+3M4x76APbMY2AU+d2Pk93+CirUiEID9/jH2F56is9lAhnHVChjjEanV8njs6R0aU8e4tJDT3DuoYcRBn743EWeuqkRcgGJxVDuYmW3OPdknS+ceBKF5P36S/wnz/0p859ts7QesTvVYPXdR7hRWqbjdlmvnudqaRcLeG8vwDUlPlvdp8wHMYWPsNZYIrdsprv7vGfvRX787L/jhULIzYkyR1fKLAwFjzV2aTQEBUIOyQ1SY3NtcIi7N6t8aeEUPFblF1qf4Hi0ynW9wDPxT0F0hiibIhM5VxoXOXfoj0jsHg+tzrO80mRy0EUA0l5CeKfZcBe4uvQKp4/9MU/XO8TWJJ/LfpzPix+l5xSo9hVTa9to/f/RK58DYHq4wPvPK0pxHVVukJfrKNfFzXKqez4nr77M7NaFUSt9LqF2dMhOtcSX8sfZ8A+R+BWEMRxSE5xUi8xoD1uUgJQ9cZ0v2212rQQwLFUMX7Eifvef/ssHqjtjcR8z5h1GO2rzRy//ER+//ses6RUwkurGEX7h2Q5Pr3Tx5t6Ls/whZHmGfPsl9OqnSDfXQBtk8yjO0R/l/MQpriQJxdxDCgut9lHJJVRyiX6lzKUzT/K1k48Tez61Xpf3XbzNmTWPQE+TiIQku0VvasAnP/AEu4VJiqbPe+Mv8d6vfZlDX9rhyPqAjYU57j52hLXSLC1vm63qi1wphgTa8AO9Mh0r4GxjGsf5CO3KGWLXx1Y5x3bWePfWSyze/QxfWwiRnSoP36lTF0POTMcUgowZa48ZsUekPW7tzHHndplPP/5+6g8JfmH7Tzgcb3BdL3I5+gh7ww8Q6SahFXNx6qu8tPAJJro2p+4sMLcrcHUKsoTlPkzqHuXlaop56E957+wFDgcuZ3k/X8g+xg3vMADTWxGF1jni4P8ityIc4XN8s8njlwW6uEBebaJdH6kV5QHMrIacvvJ53CzEDhS1o0OcpYyvmDNclicYlKYwwqKQwRmzzEN6EQ8LgcQSt9mQl/mibegKn9RKOFms8tDEVQazX4btd/Ej/+DfPVA9Gov7mDHvAPpJn4+f/zifvPlJrsbXMEJT7DT4qbMWH7myQ9Gawln+IZyl92KSAfnKJ8lXz6HCGJwCzuJ7iZZ+gLNuk2GqsHExOkalV1DpRbTaY3/mMC+eeQ8vHT9DZdDn3ZdvcXI9oxbPIYVPx9pHyVW+/P5pXp45gzCah3mZp7af5eiXr1K+AcfXO6wdXuLmmWO0ghLbxZusVC+x7ioqueBdg3leqDXolp8mKj5J7BaxVc5Sa5ujO+scbV2g477CZb/P+68fp7w/pOZHHJ801L0ux+3beGS00gqbd6ucGyzy8Y/9BD/snOcfbnyCmhpwV8+zMvhBrkU/RmyqdN0OLyx8ipb7MsfWqxzeLONn+WhGkHMU4Z1mrVDg6sJLHD3yGZ6upKw5j/JF9SO8KJ8glzaVfk55dwX4t6T2ZQAmszrvPg+z/Tl6k/PoQhmMoTIc0ti2OX7jZRrtOyAMpbmYypGIq5U5njeP0CrOkVs+Qiumk4AnxRlmqCMQQIQtn+GGdZezcoq28KkXFe+W05SrtwkblyhvP0Vl8/1cmfszfvyXfvuB6tRY3MeM+Q5yefMyv332t/li5xmUyJnaLfKh8wE/cnWfZuzhLH0Qa/5JZNAgX/8aav2L5PubgMCaOkW29EGeb5yklSlcU8AYg87vopILWPENdqeXuHTiMb526jEyx2Wi0+FD5zc4sTmBjc1AhgysdVR9n0+87yl2/SkmzTYfHHye09fPUXgppb5hMRmFrC0tcufYMh3PsF2+wLXKCn0LFuOAsjnDxYlH6ZefInHLWFqxeCDoh/fXyL2bXCxv8PjaceY2FMNok6ma5Eh1yHKwxrzYJjcWa+1Jbq1W+eTyD7L69HH+/t6n+U93v4CFZi+f5nb4A7wU/W1yE7AbrHJx8k8o9Hc5tl6nkGgMEukcxnZP0gkmuFLbIDv6Jzw1sYMsnOY5+T6eN+8jlAF+omhs72KrPyGyRuumV43P6bsBD99pEFWXiKo1EBI3DpnaTjlye5WZrUsIwKtlVA5F7M5WOWs/wpa3QOyMXgDFWHNKHOGMOYwjbMBgc4PYfoYXrJxL4jBpscPjfpkzg2XS8jrKGVLaexd+fwmN4rJ/lYveRf77X/3NB6pbY3EfM+bbiDGG83fP8/GLH+eZ3S+zK7aZbAv+3hc9nr45JKCCPfMI1uy7sOrLqK1XyNefId+9BcYgyrOEhz7MhcnHaBsLy/gIIdGqi0ovIqLz7E1McenEozx36l2krkuj2+OJ65sc3xQ0wwkSkbLqtGFygwtnprlYO4URFif1Rd539wvMn72Jt19gsR/Srk+ytrTAoFCk7e6zUznP1VILheBEOM9W+T2sTfwQsV9HajVqoe+ucWh/B0t36XtDDrcmaLZiOsPbpHbG0QnNYmmPM9YNfBI6aYkbO5P8hfUEf/7hj/JD2Uv83PonOJqsk2qLrfQUL4Y/yWr6OAKLjeB59u1nmN7TVEKBQSCdRSznFGEww/XaJpvzf8mZ5jbl6jEueY/zPE8zFCW8TNHc7uLmnyeUn8CQU8LhkbUKZ27VUMUlevU6CIFMY6r7fY6stjhy5zxSa5xiTuVQxM58g+cLD7PpLpBYBQwGO81ZVrM8wQkqIgBAsodvfZZb1i2+Jo/SckqcLjqcyJuUOsfIijtYWRk3HH1xaTW4zSfLZ7k5dZZHnS6NzYBf+mfnH6iujcV9zJhvMUopnrn2DJ+69CmebZ+j6+zgZoZHbxT56edDju54OId/EGv+CSynRL71Cmr7HPnuddAKU5yie/ijXJ84TVcESEbrjOt8B5XexIqusjNZ5vLxR3nu9LtIXI96r8e7buxwdKfATNcnR7HiDTDNXc49Wudq9TgA83qFR9ovcfLyebwbEVOpjaMc1pYW6ZWqDOwOg9JFbpS22XYMrraomsfZrn2IVu1RjJDMtXc4uX2HQ3s7BJmiqqvMDD2swTbbw6sMHc10VXO8tMsJ9w4TtFFGstlt8Nn0DH/4yH/OkXqbn938D/xg93ksDJ18kvPhxzgffwRUicyssec8ix2uUwkNBpD2PJZ7iiSY42Ztk62JsxxtRDiNRW6XDnOOdzMQZbw8Y3prgBefZeD8EblIqCiPx7amWd6oY/wZwtLoW6UyDvG7HeY22zxy4zJurrADRXExYXt+gnOV02x6C+TSwwBaaRajKk/JM0zJ4sEdjwnkWXLrE7xgzXLZOsrxQokj6RzV7gmUEyEQWFkJIxR7lav8Ze1Z1hrnOeb1OeppXA9QsPe8x8/86qUHqndjcR8z5lvA2s4an3rlU3xh9Rmuquuk1pDD2/Bj5wRP3syphhZ2fRlr4Wms2iHU5ovkW+fQvW0AsuohWosf5k79BANZQggbY3J0vopKL9Mq59xZPMzVw8dZm5pFS0mt3+ddN7Y5uh0w0wtIjeJ6kJHPbXLrRMDV8gmUsJnV67xn90ssX76GWclR/hRL4YBeo8FudYKu22VQuMxqcYsNV4MBTx6jU/4Aw8oHMDKgPuxxeH+Vk+vrHO2VmDUTNFOIwlushJfp2Cnlss1DlV1O2reYZ3Rdu2GV2/1JfnPx79B/aJaf3vssP7X1WUomIlIlvpZ+mJfjjyKiCUy+woCXkckmTq4xgLBnsJ2TZP4SNxsbtJovMl0tEjcPcbWyyHXxEFpYeCqh2dpBRl8hNZ8BcmaSBqf3Z5jqTJAGTRACoRQy7OMMekzv7HF8ZZXGMMbxFcFSysbsNC81TrPlzqOFjRICjMtSWORxc4IZ61VBNzjiJgX737Mid3jZeoRKcYFD6QLV7gkEEiNzpHbRMmO1+RwXpp6hW77Fsp/ScEf6KtvgXZFw02KrO8PuDPxXv/X5B6qDY3EfM+ZtIAxDnr38LJ+++uc81z9Py1lntqN5+A48fc1wdENTtCex5x5Hzj6CyGLU7hXU9kX0YAtlefQWP8zW1GPsBdNkwgfA6CGEr9Aqtrk7O8HVIye4OX8IIyWFKOKh1S1mWxmz3QIz3QK50WxVBuwe7nB+ucmavwjAnFrlVOs8y1euM9x3aRR8FjptEq/IVnWSfb9Du3iD1cIWLScHI8A5Qb/8NGnwFJ4OmGtvM9db48R2xMneLPOmSVMrdqMrXM9v0XUyGoWcQ4UdTsjbHDZrCAHdsMjmoMa/Wvx73Dxxko/1vsJPb/45h9NNYuXx1fSHeCH5CNbQQ6SrpOo6IttDAEq6WM4hHOsomTfL3do6euoGTFfYq81wNVhmU8wDMJVsU+itEGafR2cXqGRFjoRTHGpP4alJtOWAMXhhD8Ihctij1mlzaLfDXHtAqZqQzzncnlriWuMo+/YECElq2fi6wLGoyZNmkaIYfU9VIBAMKFh/Ttv6Krft4wjvOLPpMqXhAgILg0JgEdtdrs//BavNc/jlbeY9Nbq/MfhXBf4VCTdt2nqaKydP0akdIugVsMwa/91v/cYD1cmxuI8Z8wAYY1jfXOeTL3yCv1h7ljVzi4V2h5NrhsduG46vG3ztYtUOweRJrMosDPZQO5dQrRsYnRMVZmkv/iCbjVP03AYIC6Mz7PASfXeblaki15aPcGn5OJnjYuc5J1Y3OLbeZ6ZXZXJQAKPZdlL2Z7q0llLOzy7SthoIoziWXeP07is0rm5wRx7naN5jLu3S9WtElsuuv0ureJM7xT1CKwMsUv9hksJT5N5jTA8y5tqbPLTX5ZFWjdl8kmkR4JkBa/FNrqlbDJ2YmUrEUWeVE+YWE6IDwDD22RpU+X8nPsqXHn0ff6v9Ff6znf/I4WSDji7yF9mPcD16FL9nIbINVHYHYeLRUIcziWsdxXKWyew6rdoKg0MtWtMVblemuSWXUcLGMSnz0RpeeJF28jnctM9MNMnR/jSNcAojR5+o86IBxV4LHUXkSYiTpcy1Byx1ujSqKVsz01ydOcJGeQEtLAyQu2UmsiKn4inOmAkEAoNGIIEcXz5HLM+zZVex7IepxcvI3McANhZGKDYbL7Iy9SxJ4xoTQR9bglFg3xYULkm8K4Jk12N/Yo5rJ07QqU0R9It4vRydrqCzOwTeEv/493/rgeroWNzHjHmLDAYDnr/4PJ++9FnODi9S7t/m0bsZT9zQHN8Ax29i1Y9gJo5i+RUYttGtm6i962Qqo1c5RG/yMVq1Y/T8SbR0sfIYlV7k6gJsN8psNSdZnZ4lDEbCdGhzmxPrXRb2bKb6FRwNQydke7rP7lLCjdkaK9YiSjh4JuZk/wKz6yt0dsscNV0eircY2BV6doVMZLSLG6xVNll198hFghEuqf8YSeHdlNQyS50eT24PeawVMJc3mBA+vtTsxqvcSm+ybrYICimzpS5H5QrHzB18kaK1IBq6XHfn+IOjP0FYqvDD7a/xI/tfpZr1OCsO8+zwgyT9JsEwwuTrGN0bFawIkM5hLGeZ3J2jVUppzyX0ZjSbdZ8b3iKxKCCMZi5bpxreop9+FTW8xWRcYymaZHowiW2qANhZQq29S6HbJUkjQgkYQz2MWUgHeIHP+sQ8t2cOkTkHv1/YRRqmyqm4wTHVwMMhRyFRSFxA4YqXiKwN+ladnEOIvExgfAraJ3c77DTOs958kbR6m0rQxbVGmik2oXBB4l2RmFWLdrHOzvQCd5YPk7k1ih0fexgjsj3KIsKtLFAoLVF3J3jOusAv//P/4YHq61jcx4x5A/1+n06nw8b2Bi/ePscL+1e4qVbJzDrHNwc8ftPwxHVDM69iTZ+BmUexLAvTWUPt3yBr32boNelWDtOvn6BTWSZyawij8KINWsE23SClV7S5eOQIF48+BMBku810e0CzlzHXtpnpFCmlgtQO2ZoasL2UcnW2yh1nESMkjklZCm9RHnQJ2w7LrV3endxCaJs9q0loG9ZqA/YLO+w5eyRmC9BoUSANHseyzvBwd4b37GrOdBzm8yp14WBQrOZr3MpXaKttil6fmUKbQ9Ymh1ijygCANLNIYpuX6sf55OGPMJ22+OjeV5iLb3OdCpf6j7DTn8cKDTLdH41BAEZ4WPZhpHOUOGhyd0qw1yjRmxJs1H123DoAwmhm9CaN5C46OUfeWqERBcwkNSaiBk5eGc1qyXPq3T0m9naRwwFdo+kXRqJdTRMKtkOvMcvO1Bzasg7G7itM6jIPJVUWVYMiHqGIsMlwTAGBjSDCllfYcAbcciSebjCXTjOhi8SVO3Tql9lrXMCU1/Ddg2tTYO0I/GsC74bAumHRchvsTM+xPTtDu1bHTRzqPQ/Hr+IHBWp2mWlTZip3kAgABjasBymfmOzwv/3iTz9QPR6L+5jve3q9Hndv3eFLLz3L9QufY2r3Fn4a42eK6hDqA8NED4o6wJ48jZw6A5aE3hbZ/nXCqEevtEivcphu/QSDYBo37ePFWwycDnvljDszda4vLXFj8RDKeu3zCMsbOzy0lvLQZoFKDAZN5g7Ym4rYWIAXp6fZ8hsA+DqkEe5it2OO7Wzw+OAGTT0gNwHbsslsIXXwAAAgAElEQVR6ucRKpU/X36YvN9FqHcFo7rexD9HMlznTX+SD+8sci0pMGB9PSBKTsaLWuZ5epScHFEopS/Ymh1jnsFmlLEIA0twmjWwGls9XFh5jzVvkRO8WjfQCl/Iaq/0jqF6VwjDHynv3rtHYs9jOcYS9QK8ywdqEy/aUYHPSsF4qkctReVRNmyPmBrX0Lia6Rb7fx+9XmUzqVJIagtH3TZ0kod7dZ3J/D7fTI80T9ksBQ98FwEOQlhsMJmfRno+2fQJZ51BW5mRcoWFKgGAo27hE+LqAYFTGqdhj291h247JsZjJm0w7irSywqB2nV7tCqI4WuMGQPTAvS3wrkncOwKxIWmVq+xNzrA9M0e7OUUlL1Gwpyk5dSZNgYXUpqhHIq6B9QBaXo/M3qQgbjCfv8DR6EV8Mv5Qfpif+7U/eaB6PRb3Md9XZFnG5uYmd67c5NmLX6V38yz17hqT3ZCTq1CzZ7GmzkB5Cvwa0rIxWQxxB9PfJmmv0MGhVzlMr3KYMJhE0aUbxLRKit2Kw1azxtrUDFsTU2gpAXDTlOXNfRb3Eqa7HrXQpxQbXJUxqETcnBdcmytxrdFAy9GDX0x7FNsd3rN1mYd7d6jnEX1Z5kZhka1ShZ1iQtdtkbFJrlex8rWDq7Qpm0MciZZ4orfMe3vHmMsP1hgXQ7ZNm9vZHfqmg3EVk36XBTaZZ4slvU5RxgdlZWEy6HpFXqmfZFc10OkOUbbPdjRJNJzAHTj48RBhFEJWENYU0l5EeTPs15rs1Avs1Q17zZzNis/AGc3/dkzCMrc4Ym7QSO9QGGySt2zi9gLlpI5lHACsPKPW6dBo79Pc28ceDAktQ6sU0C4FaDEqK+MXSSoNqEwQeNPM6ipH0goTuoyHQyhSctnGIsI2Hq6eQmChUOw4+7StPg6CKUdhKhsklbuElVskpTWElY+KNQV7Q+BdFXg3Jc4dQYzLbnOG1tLD9KaPIYpT1FRAXftM5g5TqbhX9wY27LgDcrGFlHcoWjeYV+eYyzbupemKAi1TwkkzSsTcGM7z1L9+4YHq+ljcx3xPEscxG6vr3HzxZW69/GVa/S3SbA8r69DohSzt5BxplbCbx7GnTiGqCwiVo3sbhP1twiwhVJrYKRF7dVK3jLIClIjYL8W0SpLbM1UuLy+zOjN/77xuljGz32Wym1AfQDXyqYc2Ux2FJSOiYsjdWcGd6QKrxSpb5QIIwUx7gyc3L3K0v8VEHCIE3CwucLM8S9c39JwBfadDyg5Wdhc7W0MwmnHhGJ+FdIlHBkd5un+C0/EyCTktBqyZbbbUNqGVYTzBpGgxxzbzepNFvUnTanPwLiFPJdpY7NozXJRz3NaKQRYRRSVUWMSNBK7ykLKOsOoI2cDYk3Rqk+zUSuzWJHtNxW7VYt8vYA6E1zEp86yxyF3m89vUww2KnZisPUvam8GYUYvczhKqnS71Tpd6q0293cKkMXvVAnvVIl3XvZendn10sUZQnGPeW2KaOpO6go3LthuiRB9PJ3jGo6KaOGbUqs/IGVhDhBNhB22S2jXiygpxaY28sAMHLXKTgtwW+LdHwytmK6DtnKB16Axx8xBOMEFFFJlJbSaTV/sUIzoOtOyIjF1ctUrVXGdSnmeGaxyYz1D6rLlTDPEJVMyhbIvIDnihfIqXKyd5ufwQL5cf4hcvf5L/9p/9qwd6BsbiPua7GqUUvV6PtRt3ufqXn6F74Rms4R6FaMhkRzGTTWJVl5DFJtJvIAsN8KuYLET3Noj623SzlMhIBn6JuzNN1qZKRK5N6ghSCyLPYVAo0C5XuDO3eK81Xgwjjm60WdzLaQwDaqFDNdQIkaG9IZ1azvXJMrcaNdbqHrEnmQp3+NHNr3C4vY+dCQaOx+1gltVgiravGNpdYtkiE3tY2QZ2toEww3vXW1RFjsSLnAyXWIrnmUiaSCXYUy3aqk0kEzLPomINmWKfabPLfL7FFHtU7QHyQLxULkhUgZZpcNE0uaYduplBxz5uWMNVpXsibqwa/fIMnXKVbsmmXZL0qhl7VclOoYCSI2kTRjPDJgussMhdJrJNqmEHvwNZb5pBv0meewidUxp0qbd71Pc71Lpdqt0uQqXsNGtsVyv0fJcEDRzojlckCCap+XPM+YepWU323Zx9t41hgK809bzKXDqNb0bj7QpFZg8xXo+suElUv0bYuEha3AKpATAaaEuyrQLD/QXSaAmTL4I9jRM0KVllmsplOgH7PgkcWoZ9JyYRbYTZoKBuUdE3meQaDXv3nogD3PHnuF5Yom8VKOiYQ9EGE0mHV8rHebF8klcOhHzTnzooR8Ncv8d8K+OxG5f49X/xTx7o2RiL+5h3PMPhkPZ+i/bKJnvnzrF++xLD7l28zgaFMCJIDAvpLOXGo1jNowi/DlJg4h7pcJ8o6hCnMWmekKmcdtFjs1llq1Fmu15kc6LByvQcu43m687rJwlemlFIcoI0p5BoptuG6Z5PYzCafSG8kG5ZcWmywpXpBsbvcGZ4jdOtNYIsQRlBJF02vQnWgybbnkvfGRJZPTT7WNk2froFagdDfO/clbzMkXCRhXiGZlInSDyMMkQmJZYK7Qiq9GnQoUmHpm4xqfZp0KFiD7BebYEa6KRFVrMaK7rIhvJpJU1EOo+jGliiiBAlIr9Ct1yjW67QKbt0StCpaNoli47n3XuhAVgmZ4JdZthkkbtMZls0wzbVXkYWlgiHVcKwikgNpcGASrdPtduj2u1S63bwhyGdapmdepVOscDQs8nQkKevlb1bpREsMOEvYSoTtIOQVAxwNVRViYmsRjOv4Rx83tmgyb0OaXGDqHaDYeM8ce0OSE2ETyedJ9k/jO7OIcMp7KyBbyoUdUBN20wlUFCvr3cDSzOwBij2cfQGgblLhRvU5U0Cuf86AR+agHVnhsvVZVaCaTJpU1Qxs/E208Md9q0qFyqnuFA9yYXycTYKE/eObfYyZlops3sdllp7vHtwh1Nel2lvh1u54Mnf+IMHem7G4j7mO4oxhjAMad9dpf3CebZffoH2xiVMfw8niSmagLJoUHJnCdxJrMIEIqiB5Y/WME+HxFGfZLDFpkxYqQUMfJdewWerUWFtaoKdxgSZZZNbFpHvE3v+vfMHccxMq89kN6XRFzSGHjNdSSExCDRS5GClaCcl8jWtcopT2qUqdhAio2WX2PKabHkN9twyQ0sTWSGpHGDnbQrZPm62B/k2udnBmARPefjKp5gXmEwmmEhrVJIiXu4ilEQJQ24JCiKhzIAKA2q6y1S+x4RpURddynaIFJpIOXTSIlvpIlvZAh1dpq8qJKqA0h7COCA8kmCSQaFCvxDQKwe0S9CrKNplSStwiW33dfelbLpMsc0UO0yxzYTZpp52KUcRTl+QhGWGYY2kF1DoJFR7fUr9AeXBaOvFMYnr0qmU6BZH9ySxJBqDzlPQ+b1zOdKn6k0TFBoEQZ1i0CCQRXztUTQOvgqQ9w185HZIt7jJbrVFJ4gJ3ZxUOJisih1WcJISXuZTyD3Kmc1UCqX8dZeHxjCwMxIxANPCVdt4epNAblGSK5Ttu0jxWo8p14JOVqStGuxYM9wpLnKnMcugJPHyBC+LkblGG0nbrbFSXGC9OM96MEnovla2tX7CzH6Xxf11TrWv8Z7+dY6IDRp2h8Bp43rhvZeGzgWfiY7ysf91POY+5h2EUYpkd5et8y+xdvYLhJdfQfdDUBphDJ72CESZgjtN4E9i+U2EXwVpYZI+KhnQzgfsE9O2FT1H0g1cBp5Hv+DTD3xC32ZQCOiUKqxNzdybG/4qhShmdr9Ho59gaYE0AkcJKpFNLfRo9DWOUuSlDlmlTVwaMgg0Xd8llD6J8Emky8AJ6NsWoZUTywRb9Sgn+xTSfZyshZ11sPIh2iS42sHLPYLMI8h9CplPURXwtDf6kVBYWEJTYkiFwWvinXeo6R4VBvjkGBwGqko7b9JNa/RUk65uMNRVUlNEm4DMLhAFJcJCmYEvGPiSoS8YFhVhQdMPBH3PZuB494ZOXsU2GZPsMMn2SMTNDvWsQyUZUApjROQQx0XyvovVkjgtM+olhRGFKKTYH2ArRey6DIpFuqUCQ98jc5zR7Jw8QaUDjHmtOezIgMCtUfCqlL0qda9Jw5qhTINMWgxsGNqCoS2InIzEzjDSIAzYWuLnklJmUU4ltez1wyT3k8qUXERo0ceii212cM02vtikILdw5A4WLYR4zbZUWfRyj37m0c+L9FWdfTHLlj/HRmmGVsXHctvYOiPHZuAU2S5NsVWcZrcwQXQwX35U+Q3VYUyz12VmsMXi8C4nB9d4b+8VDrND2U+wbX3fswLpwCbt2/T7LntpmZ18ki7TFKYNH/vdf/9Az+BbFXf7r0sw5vuDfDBgsLrC5uWLtM6fo3f7Ogwi/MymIOsUnSkcp47jlDDCom9rBt4cO9NzxFMpuUrIVcbAEfQ8h14wEup+IThwxdG0wkIJbVlvakchiihFCcU4pRhlPHltn0rUw88tHDvCOBHYCaFnCH1J7FiEtkPLcbljCfqOZugYnDymFsdU44RiEhIkQ7wwpqBjPBK0SZhQCkdZuMrBVS6udnHMyFnM4osmPgkBMT7Ja/58l0DnOLmFVBa5cclVQGYK9FSVgVmgq6vsmxKJKBF6RSI/IPIcQk8SehAGhihQRL4i8mHoWoSOQ2h796YQvpGK6VKhQ402y7Sp0aGiehSzIcUsohRG+MMcM3AxbQe7JXD3FcEwJohCbJUilEGJNrHrEvoesecxdF161TKmXgMNJk/I8iHa5EAMaYxIBb5bwy9O4zTPIAsT4E+gvQbadgGBMIZMG/rKYDLIMkMtg2YGzcwwGme3DtxIrJUIMaKPpItj2rhWC499fNFC0kGKLpboIekiRI42EGuXSDlEuUWoHPZzZ7SvSoRqhqFuEuopunKavcoEvWrAcEISFRSxa4gcl8jz6QZ19oImif2agAttaAx7zAx2OblzloVojeVwhRPJCieSVZrWENd+7eVhDAxtm07qsd5t0snq9LMZMjWNZSoEVkKgBwR2GydusTBsUc6GPHfqQ3/zh/av4S2JuxDix4F/zeiu/K4x5n9+Q7wH/D7wJLAP/Iwx5s7ba+qYbwaTZQy3t9m7dZ2tCy/Ru3aFfHcPJ9a4mUvBBHjGR9suA8en59t0PYuOa9N1i/Rm3k0vcOn7PgP/VZEu0C+W6BZLZI77Dc/vpSmlMKIYxxSSlPowY6q3h6O3sdAIKTC2QFgagUJLQyYkibRILI0UBhEYBm6KyiL8LMLLY5wsxklTgiinqBRSGyQgjEAYgYWFZRwsHByhR6JsYjwyfKPxjMLWGkfbSGVjFGRakhlBZiA3o656ZlwS6kRWia4VkFo+me2R2x65bZP6hjjISHxN7GtiVxC7gsixR85yiCyfTDpvWkYl06fMkBJ95uhRpk+ZPoU8pJDHFLMB5SikFIWU+hH2EERfYHUEVtdgBgajQAnQlkVubFJpkzmKzIbEdhn4BUShjI2FlWssZbCFhSUcHOlSkx6u9LClj+0UsZwitl3Alh6udHGFja9tfPMGqVDA8MAdzO7RIjoQ6h6W6ODQxpWt+wT6r4p1YmyGxmeoPWLl0MsskkwS55JQOUTKJ8rLhGqZnlWj484Qe00Sv05UKDIsBgwDj9B3iAKbyJfEnsPQLRA6BYyQvJFiGtKMOsyGu7xv70WOxGucSFY4md5lKd7ENa+N9SgDfeXQ0w57+NxQi7TVFJGZws2mKMYuxayLn+zhhW1q0QB0h5vlEldnJ1iZWeb27DxbM/PYgU/TdZjxHP7BbPOv2PV289cOywghLOAa8FFgDXgO+C+MMZfuS/OPgUeNMb8khPhZ4G8bY37mG+U7HpZ5c7TWJElCvLlF9/YNtq9fZXvlOtHuNqI7xI4VtrGxpY+UHplTIHZcQsdl6HkMXYe+5zJ0XYaeS+iNWmlDzyMMAkLfZ+AXGAYFUvf1Ii2MxtIj5ycJQZrgZSlOnuGoDFunSJVj6xQ3T3DyBGkUUissrbBVhq1SbKWwDvJyTY6DxsZgYw7abgIpwZaMlkkVozC0AcTo4xQajJZkxsIoG6MdtLIwxiHHI5ceSoy2mfRQlo2yJMYGZQu0DcoSKEegbENmQ25DbkFqSzJbkFqSzJaklkUq7QPnkEiXXLy5MN+PbyKKDCgyPNgOKDEg0CGBjilmIYU8ophEFOOIQhgTDFJkbEHsYFIPk3iIxEdmBSzhYgsXS3oI6WJLGylsLOFgCQsLG0tY2NhYwsZ+NU6O9kfpbOTXEbY3RyEYIsUQebC9f18QIsUASfi6uJyYnITYKBJjkWh75JRNoiWJtglxCEVAXwQMZEBfFuhaJdp2haFdJncK5E5A5gQkXkDs+sSeR+K6xJ5D7DokrnNvuuTXo5QNaaRdJtI2zazDVNZiKm/RTDtMZm1mkj1m0j2m0n0CnY5a3Mqlrzx62qdrAvo6INVlRF7DzgvYysbNNV4e4WQRVhqhs5QeguuVec4efpwrhw+z05wgrVapeS4znsu873I4cDlRCHikFHCo4OLKb+ZefGPezmGZ9wA3jDG3DjL+t8BPAvcvRvyTwP944P8j4H8XQgjznRrQfxvQWpPEMeGgy6DXZthrsbW9ze7qGvu3b6Hae4gsBQMODgIbtA24aMslP2jppbZN5EgSy2Lo2sS2TWxbpI4ksSSZZZMJiZYCJSVKitEyoghG/1g2WJSw5x/BnT2FnSd4eYqncjw1El3L5LjaIE0GJFgqQqBwjcSJBbUY6FoYIRBGjhayEhbGgBACIcVo1oQUox99pAApR+mlGLV+JCDBSAFCYCQgRgJqhIWRo/y1JTACtBAoKcglREKiBWgpyKUcXaew7vlzIVHi1XBJLq3RVlhkwiYTzj2XCocMBy3efGjnGyGMwiUlIMY7cD4x9QO/a1I8neJlKa7O8HSGp3JcrfByhZ8ZvBwKmaEcQymVeJmHnRdw8gJOVsRVRdx0Fg8PiYUQEikkAvn1BVcCwYF7E5RJ0SbDmAxDiiEFkyJEH0GKIEGKBEmMI2NsGSFkhhE5mhwlcrTIyVAHzpCJlJSMSCqGQhALl1C4RNJjKIORswKGdsBQNghtn8jyCa3RNjqo45ntkjkuue2Q2Q6ZbWMeQMx8lVDJB1TzPlP5NpV8QC3sU+kNqOX9g7gBtaxPRQ2oZANKSZ9SNMTkhljbJNol0T6J9oi1R6Y9cu3S1TX6usYdTuAogassCkZjK5CZoawthPbYEUXWy0Vu1UvcmZumPzuLNTlDfWqK+UKBWd9l0Xc56Tl8wLGZ8RzK9oPVxW81b0Xc54HV+/bXgKffLI0xJhdCdIEmsPd2GHk//+T3/yV/Of+ug73X3uTmwP+6t4m4P/51Ed/4ON7kOG8Sjk3CscffwnHf+Nzf3HFfP/5+zBvSaOTX7ZK+E7BMjk2OTXawvc+ZHAuFYzICk1A1GY7OcczIuVrhaoVjFK7WB/sGV2s8ZfAUeBo8pXEV+LnAz8FTEl8JnNzG0jZS21i5h5V72HmAlVcRegIBGAwYg7k3H9ugjWI0F0NjjDqI0xgU8Oq+AjJgn0zskopRHkaY+5waCa3UKCtHSUNuGzLLkDqQWoKhK4lsi55t0XFshrZNYrnE0rvXq0ikQyILJLJGKlxi6b4u7kFffgBCa2w16nXZSmErhaNHW9uM/I5WVMkIVEiQtQiGMQUTUTQRgY4o6iGBGlJQIa5KcfMUR6U4WYqbZ1gqw8lz7Fxh5wqZaYyR5MYmNzbKuCjjoIyNMg4GG60ttHZJzSQtNUfPWBSNoWgMBa0Bl8y4pMYhlhaRbRjYkq5r0/MEXQd6jkXPcUm8wms9Bq9C7lVAeNjaYOvXnrjSwdBTfKfFTVrc/BvU+/s5PVfhn//Embcpt6/PWxH3r6cmb2yRv5U0CCF+EfhFgKWlpbdw6r9KeRhxPFp5kxOb14e9wQLxdeXytXTidQe8Sdr78zPm68SbeyGv5mfekEZ83fO93s5R1uLr2izMm70aXouXGCQaaTQCgzQaaUZhwoA0ZpTOGKR+1T96sOU9/yj+1a3Uo3yENthqlLelDZYxWBqk1thaH+xrHK2RWmMBtjEIDZbSSAAtXveCfLXLbe6NzBiMGIW/Gv/qVWsEyNH6HUYItBj1HjQCLQRGCHJpEQkYIu6tAZgf9AZGPQqBcgxKCHIhyAElBEpYo14FEoU16kVgkQkLLSyUka+bGz6yevQtTWGsg3IFS+t75SWNPijj+8IOytnSGivTyFRjhaN9iT4Y0sookFIyCuvVe8dreQoM1qt+cXDPzeilI63RHHGEQAuNkQZjgRGjHhnmoBwRB79XgEEe1M2DnqM5WIxLCzASYyRoiVACaSRCC6xcjOqClggjEdrCMmCZClLWMdggbIy0iC3BUEIqILVASY2SGi0MyhUocfD7gbTQo1fsvXucCEkmIRUCI61R71PaaGmjLWfkXvVLh9z20Jb7ugbem+EAjnq1Jn7v8FbEfQ1YvG9/Adh4kzRrQggbqAKtN2ZkjPkd4HdgNOb+IAb/i//61x7ksDFjxoz5vuKt9NufA44LIZaFEC7ws8CfviHNnwI/f+D/O8DnvpvH28eMGTPmu52/tuV+MIb+3wCfYTQV8t8YYy4KIf4n4HljzJ8Cvwf8gRDiBqMW+89+K40eM2bMmDHfmLc0z90Y82fAn70h7Nfu88fA3317TRszZsyYMQ/KO3M6xZgxY8aM+RsxFvcxY8aM+R5kLO5jxowZ8z3IWNzHjBkz5nuQsbiPGTNmzPcg37H13IUQu8DdBzx8gm/B0gZvE+9U28Z2fXOM7frmeafa9r1m1yFjzORfl+g7Ju5/E4QQz7+VVdG+E7xTbRvb9c0xtuub551q2/erXeNhmTFjxoz5HmQs7mPGjBnzPch3q7j/znfagG/AO9W2sV3fHGO7vnneqbZ9X9r1XTnmPmbMmDFjvjHfrS33MWPGjBnzDXjHiLsQ4t8IIXaEEBfuC3tMCPGsEOIVIcQnhBCV++J+VQhxQwhxVQjxY/eF//hB2A0hxK98O+0SQnxUCPHCQfgLQogfvu+Yzx/Y9dKBm/o22nVYCBHdd+7/475jnjxIf0MI8ZtCvIWvG7x9dv3cfTa9JITQQoh3HcS93eW1KIT4j0KIy0KIi0KIf3oQ3hBC/IUQ4vrBtn4QLg7K44YQ4rwQ4on78vr5g/TXhRA//2bn/Bba9nMHNp0XQnxFCPHYfXndOSjnl4QQf6OPFD+AXR8WQnTvu2e/dl9eb9tz+QB2/fJ9Nl0QQighROMg7ttRXn/3YF8LIZ56wzHfOh0zxrwjHPCDwBPAhfvCngM+dOD/BeDXD/yngZcBD1gGbsLBd5dH/iOAe5Dm9LfRrseBuQP/w8D6fcd8HnjqO1Reh+9P94Z8vga8j9FHhf4D8LFvl11vOO4R4Na3sLxmgScO/GVGH30/DfwvwK8chP8K8BsH/r91UB4CeC9w9iC8Adw62NYP/PVvs23vf/WcwMdete3/b+9cQuwoojD8Hc0gGEUzxuiQGJJZKBJUfBCJGhkXSohEkUE0iKITfEAExY2E6FLxsVHURSAgBIwufIIoEQRdKPExJhOjZGJGgxiHBIIwPmB0cVzUaaa6uROm7+2qe7mcH5quOl1d95+/T53bdeoOZfWjwNIuaTYCfNiin0bHZV1elXs3EfabyKnXpcAlVZ8mcRxrZOA0dVAJQsAMc+sCFwE/WnkbsC1qt4cQoNYBeyJ7qV1qXpV7BDgJnGH10oPNrFepXcUZD0X1zcCOLun1LPBMVG9cr8rnfQDcDEwCQ5Eek1beAWyO2k/a9ZJG1XY5uFXaLqH8EnGUhoJVG5qN0Dq4JxmXbeq1G3gwp15RveTTVR1oOI71TFpmHhwEbrPyncxt99dq0+7lp7Dn4hVjFNinqrOR7XWb/j3dafqjDV6rRWSfiHwuIuvNtpygUYFu6nUX8GbFlkQvEVlFmGV9BVygqtMAdi7SP13xsQVyi7GFMMMooMAnEtKCD3WB1zoRmRCRj0Wk2AE6mWZ19BKRM4ENwDuROYde8yGpj/V6cB8DtorIOGGa86/Z59uQe0EbdSfkBYA59fPAw5H5HlW9DFhvx70ZeU0DK1X1SuAJYLeEvHev6HUt8I+qHozMSfQSkbMIg/txVZ05VdMWtqQ+VoNb0f4mQnB/MjJfr6pXEdI1W0Xkxoy8viP8a/wVwCvA+0UXLdp2rFldvQgpmS9UNd7fuZt6JfWxng7uqnpIVW9R1asJb3VTdmm+TbsXspl3Sl6IyArgPeA+VZ2K7jlm5z8JU8O1uXip6qyqnrTyuNkvJui1Iuoiu16Gu6m8tafQS0QGCIPuDVV918zHRWTIrg8BJ8ye1cdqckNELgd2ArcXzxZAVX+38wmCH3akWx1eqjqjqn9Z+SNgQESWkkCzunoZWvlZDr3mQ1ofS5Fr6iBHtYpyrnaZnU8DdgFjVl9DeSHiZ8IixCIrr2ZuIWJNRl7n2meOVu5fhOX1gAHgbeCRjLzOB0638jBwDBi0+jeEBcNiQXVjLl6R7TdgOKVe9vftAl6q2F+kvAj3gpVvpbyg+rXZB4FfCLnuJVYezMxtJXAEuK7SfjFwdlT+EtiQkdeFzK2trAV+tT4aHZd1eVn9HML+zotz6xVd/4xyzj1pHOtoEDd5EL5Rp4H/bLBvAR4jrDgfBp4rHMfabye8AU4S/cKD8CuHw3Zte05ewFPA38D+6FhmjjMOHAB+AF7Ggm0mXqP2uROEqfOmqJ9rCDnxKeDVWONMz3EE2FvpI4VeNxCmtgeiZ7MROA/4FPjJzsWXngCvmS7fVwblGCG4HgEeaMDH6nLbCfwRtf3W7MP2jCdMt478vw1ej0Z+tpfoy4cGx2VdXnbP/cBblX5y6XWHjYVZ4DjlxdJkccz/Q9XhcDj6ED2dc3c4HA5He/Dg7nA4HNJT1P4AAAA1SURBVH0ID+4Oh8PRh/Dg7nA4HH0ID+4Oh8PRh/Dg7nA4HH0ID+4Oh8PRh/Dg7nA4HH2I/wGjs4E9tnl9pwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "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_FETI_VUB_SU_RCP85[i,:])\n",
    "\n",
    "plt.show()\n",
    "fp3.savefig(\"Figures/SL_wTd_nos_base_FETI_VUB_SU_RCP85_alllines.pdf\", bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}