Pattern_correlation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dd877883-b26d-4ae3-b746-dc8b2fb3f089",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ac6d6197-1823-4bd3-a17c-2069773dcf40",
   "metadata": {},
   "outputs": [],
   "source": [
    "import iris.analysis.stats as stats\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from pandas import read_csv\n",
    "from pandas.plotting import scatter_matrix\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import warnings\n",
    "from glob import glob\n",
    "import warnings\n",
    "from iris import load, Constraint \n",
    "import iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cc9b74fd-baf0-472b-8e98-466050c04978",
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.filterwarnings(\"ignore\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c0f1daf",
   "metadata": {},
   "source": [
    "## Data PATH\n",
    "Please specify the directory path for the 'CMIP6_Historical_1900-2014.cvdp_data' folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e74dfdca",
   "metadata": {},
   "outputs": [],
   "source": [
    "path_to_data='/your/path/to/data/CMIP6_Historical_1900-2014.cvdp_data/'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2586e3bf-4c87-4fa1-8ff1-89cae6ac9ece",
   "metadata": {},
   "source": [
    "# Calculate Pearson r Coefficients (Spatial correlation)\n",
    "###  Load Reanalysis spatial patterns for PDV (MON), AMV (MON), PNA (DJF), PSA (ANN)\n",
    "#### - Use as reference for correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f89e2b27",
   "metadata": {},
   "outputs": [],
   "source": [
    "cvdpfile_obs_1=path_to_data+'ERSST_v5.cvdp_data.1900-2014.nc'\n",
    "cvdpfile_obs_2=path_to_data+'ERA20C_ERA5.cvdp_data.1900-2014.nc'  \n",
    "\n",
    "\n",
    "## CVDP_LE diagnostic results include spatial patterns for the modes\n",
    "var_name_list=['pna_pattern_djf','psa1_pattern_ann',\\\n",
    "                'pdv_pattern_mon','amv_pattern_mon']#,'amv_pattern_lowpass_mon']\n",
    "\n",
    "### load spatial patterns of PNA, PSA1, PDV, AMV\n",
    "obs_cube_dict={}\n",
    "\n",
    "for i, cube_name in enumerate(var_name_list[:]):\n",
    "    constraint=Constraint(cube_func=(lambda c: c.var_name == cube_name))\n",
    "    if i < 2:\n",
    "        obs_cube_dict[cube_name]=iris.load(cvdpfile_obs_2,constraint)[0]\n",
    "    else:   \n",
    "        obs_cube_dict[cube_name]=iris.load(cvdpfile_obs_1,constraint)[0]\n",
    "    #print(obs_cube_dict[cube_name].shape)    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd3fc316",
   "metadata": {},
   "source": [
    "### Load CMIP6 LE spatial patterns\n",
    "#### - Regrid to same grid (nearest)\n",
    "#### - Calculate Pearson R coefficients \n",
    "#### - Store in dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "98ca1173-9a2d-47a5-bfb6-d2c027e0f4b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ACCESS-ESM1-5 r5i1p1f1\n",
      "ACCESS-ESM1-5 r1i1p1f1\n",
      "ACCESS-ESM1-5 r9i1p1f1\n",
      "ACCESS-ESM1-5 r2i1p1f1\n",
      "ACCESS-ESM1-5 r8i1p1f1\n",
      "ACCESS-ESM1-5 r7i1p1f1\n",
      "ACCESS-ESM1-5 r10i1p1f1\n",
      "ACCESS-ESM1-5 r4i1p1f1\n",
      "ACCESS-ESM1-5 r3i1p1f1\n",
      "ACCESS-ESM1-5 r6i1p1f1\n",
      "CESM2 r1i1p1f1\n",
      "CESM2 r8i1p1f1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "KeyboardInterrupt\n",
      "\n"
     ]
    }
   ],
   "source": [
    "r_dict={} \n",
    "model_list=['ACCESS-ESM1-5_r','CESM2_r','CNRM-ESM2-1_r','CanESM5_r', 'EC-Earth3_r','GISS-E2-1-H_r','INM-CM5-0_r','IPSL-CM6A-LR_r','MIROC6_r','MPI-ESM1-2-LR_r','NorCPM1_r','UKESM1-0-LL_r']\n",
    "for model in model_list[:]:\n",
    "    cvdp_file_list=glob(path_to_data+model+'*.nc')\n",
    "    r_dict[model]={}\n",
    "    for cvdpfile in cvdp_file_list[:]:\n",
    "\n",
    "        modelname=cvdpfile.split('/')[-1].split('.')[0]\n",
    "        #print(modelname) \n",
    "        LE=modelname.split('_')[0]\n",
    "        ensm=modelname.split('_')[1]\n",
    "        print(LE, ensm)\n",
    "        r_dict[model][ensm]={}\n",
    "        \n",
    "        cube_dict={}\n",
    "\n",
    "        for cube_name in var_name_list:\n",
    "            if ensm == 'EM':\n",
    "                constraint=Constraint(cube_func=(lambda c: c.var_name == cube_name+'_em'))\n",
    "            else:\n",
    "                constraint=Constraint(cube_func=(lambda c: c.var_name == cube_name))\n",
    "            cube_dict[cube_name]=iris.load(cvdpfile,constraint)[0]\n",
    "            #print(cube_dict[cube_name].shape)        \n",
    "        \n",
    "        for index in cube_dict.keys():\n",
    "            regridded_obs=obs_cube_dict[index].regrid(cube_dict[index], iris.analysis.Nearest()) #regrid smaller grid (OBS)\n",
    "            pc=stats.pearsonr(regridded_obs, cube_dict[index]) # iris anaylsis pearson r\n",
    "            r_dict[model][ensm][index]=float(pc.data)\n",
    "            #print(pc.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dddaf9ee-a786-4503-aa18-27b2f13b7367",
   "metadata": {},
   "source": [
    "# Calculate spatial correlation Mean Score\n",
    "#### Tranform to dataframe (pandas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f5306ce5-3b68-4211-8c9e-114749bc8891",
   "metadata": {},
   "outputs": [],
   "source": [
    "            \n",
    "daf=pd.DataFrame.from_dict({(i,j): r_dict[i][j] \n",
    "                           for i in r_dict.keys()\n",
    "                           for j in r_dict[i].keys()},\n",
    "                       orient='index')\n",
    "\n",
    "daf.sort_index(inplace=True)\n",
    "\n",
    "daf.index.set_names(['CMIP6_LE', 'ensemble_member'], inplace=True)\n",
    "daf\n",
    "\n",
    "daf.to_csv('Results_DIR/pattern_corr_cmip6_ersstv5_era20c_era5.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "651596bd-6289-4405-83ed-6515efc60087",
   "metadata": {},
   "outputs": [],
   "source": [
    "daf.reset_index( inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "681f3566-d3e5-4c3e-985e-81e32479202c",
   "metadata": {},
   "source": [
    "#### Remove Ensemble Mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ecaec11f-27e5-4141-b144-f9418fb9dc5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfnoEM = daf[daf.ensemble_member != 'EM']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eef8835e-8651-465a-9c3d-0c71de2292b3",
   "metadata": {},
   "source": [
    "## Calculate Mean Score\n",
    "#### Apply Fisher Z transform\n",
    "#### Average \n",
    "#### Back transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d0eb19fa-c181-4c01-b7fb-e685f7c13177",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfnoEM.set_index(['CMIP6_LE', 'ensemble_member'], inplace=True)\n",
    "\n",
    "## Mean score of Pearson correlation values\n",
    "## Fisher z-transform, average z, back-transform \n",
    "mind=np.tanh(np.arctanh(dfnoEM).mean(axis=1))\n",
    "\n",
    "mind=mind.to_frame()\n",
    "mind.columns=['mean_score']\n",
    "withmean = dfnoEM.copy()\n",
    "## Arrange single pattern correlation coffecients and the mean_score \n",
    "## in one dataframe\n",
    "withmean['mean_score'] = mind[['mean_score']].copy()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf8a4417-9285-493f-b0da-f0d417a46bdc",
   "metadata": {},
   "source": [
    "#### melt dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0a8fd160-ca15-4bff-95a7-721950a44543",
   "metadata": {},
   "outputs": [],
   "source": [
    "withmean=withmean.reset_index()\n",
    "# melt the dataframe for whisker plot\n",
    "melted=pd.melt(withmean, id_vars=[\"CMIP6_LE\",\"ensemble_member\"],\n",
    "        var_name=\"CV_pattern\", \n",
    "        value_name=\"r_coeff\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ced26bb1-7e1c-4bfd-ada4-fcc05ec92153",
   "metadata": {},
   "source": [
    "## Create Boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "193585ca-43a7-4efd-824d-80932f0f2abe",
   "metadata": {},
   "outputs": [
    {
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+ppdfXqUBA1yHMp5xxnD9859/1z/+8a7i4+OVljZE8fEWDR+ernffLde3vvVt9evXT2ecMVxnnJGuSZOu1m9/+2t97we5GvaNi9TSckx7qj5T/a7PdeM872dF7Kkbbpiun//8Hv361w8rKytHH374vv7xj3f9/r4AAAAAAIQSEle9ZBkUp21aYcj79lRKSpqmTZuuZ555SvX1tfrqV8fqgQd+oT59+nj9GjfdNEs2m03PP/+MJCk7O0d33/1j3XvvXJ/EEhvbHsuoUV/RT3+6UCtWPK+ysi36yldGa9GiR3T//T91eZ0bb5yp+vp6LVw4XwcPHtTPfna//t//+4Fmz75Ljz32iH7yk7t15MgRPfnkszr//As1b969io+P1982rlfDqy+rb7/+GnLGCGV0I3nXG9nZOZo79x699NIf9MYbr+u88y7Q/PkL9KMf3eHh2b4frggAAMKH1dqspUsf05w582SxuPcPBQAglJkcDofD6CBCwQcf/J+GDRtldBi99otfPKDPPtuhF15YZXQoncZy5MgRSVLfvn399v579uzWoTaHWgaldmu7mKY69YsyaciQoX6KrN2hQwf13e9m6/77F+vyy6/w63tJHe/zurpqpaYO9/v7R6IdO7ZLkkaNGm1wJJGDfR54gdjnixYt0AeHWrR72j1dP/kkQ1cu0fn9YrRgwSI/RWYMvueBZ/Q+X778d9q4cb0uu2yCZsy4xZAYAs3ofR6J2OeBF+h9Xla2WaWlm5yPq6vbexgPHz7CuSw7e5yysnICEo8R+J4bIylpYKfr6XEFBKHq6iqtWvWiTCaTxoz5qtHhAACAIGW1Nqu0dLMcDofKyjbJZnOf+AYAeiI+PkHx8VRxwngMFYRftLW1qa2trcP10dHB8dUztRxTTFOd+/K29ubvjiiz+7qWY1Inwyp98dmfffYpbdv2sWbPvktnnJHe5fMBAEBkKikpksPRft7R1tam4uKiiKm6AuBbWVk5YV1NhdAVHNkDBMzPf/5AQN7n4Ycf0htvvN7h+qKidQGLpSOxsbEdrjtmb09cxXpKMvXp0+m23nz2tLQhncb28MO/7nQ9AACAJFVUlMlut0uS7Ha7KipKSVwBAMIKiSv4xYwZt+jqqyd3uH7w4KQARtP9GPbs2S1JPepjFQqfHQAAhIeMjCxt2bJRdrtd0dHRysjINjokAAB8isQV/CItbUiXVUXhKpI/OwAACKzc3HyVlm6WJEVFRSkvL9/giAAA8C2aswMAAAAhKiEhUdnZOTKZTMrKGieLhUbKAIDwQsUVAAAAEMJyc/O1a1cN1VYAgLBE4goAAAAIYQkJiVq4cLHRYQAA4BcMFQQAAAAAAEBQInEFAAAAAACAoMRQQQAAEPJi62o0dOUSt+XmL/ZLkloHxHncRiNH+j02AAAA9ByJq15auORhNR44EPD3TRo4UA/d89OAv6+33nvv73r99XX6+OOPVFdXq+nTZ2nmzB8aEktLS4tWrlyurKzvaPToMc7lVmuziouL9P/+3w+UljbEkNgAAL03fHh6h+uqG9sTV8OTB7mvHDmy020BAABgPBJXvdR44IDev25ewN/3gpd/E/D37I6///1d7djxqS644CJt3Lje0FhaWlq0YsXzSksb4pa4WrHieZ133gUkrgAghE2bNrPDdYsWLZAkLViwKFDhAAAAwIdIXMEvbr/9Lt1551xJUnl5qcHRBMbRo0fUp09fo8MAAAAAACBskLiKML/4xQP67LMduvHGmXr22aWqq6vVmDFj9ZOf/FwjRrT3+Xj99T/plVcKtWfPbp12Wl+NGDFKP/rRvRo5cpQk6Zlnlurdd8tVW7tHAwYM1De+cb7uuONuDRo02Pk+UVG97/s/deo1yskZrwEDBqi4uEiHDx9SZma25s2brwEDBkiSDh8+rGeeeVLvvfcPNTTUKzFxkC6+OEO33nq7+vdvf853v5slSfrlLx/UL3/5oCSpqGidpk2bIkmaM+dW53uWl/9LkvTFFwdUWLhK77//ng4e/EJnnvlV3Xnnj3T22V9zPjcz80Ldccfdqq+v19/+9ob69x+gNWv+pMzMCzVnzjxZrc167bUSSSbl5IzXnXfOVWxsrFef/Y03Xte6dSWqqtoph8Oh0aPP1O2336WvfvUs53OO/7e89dbb9dRTv9Xu3bs0evQY3XPPz5z/rY7H2dt4AAAAAAAwAomrCFRfX6ulSx/XrFm3qk+fPnrhhd9p3rw79fLLxdq27WMtWfKwbr75Vp199jk6dOig/vvfj3Tw4BfO7a3WZt1ww3QNHpwkm82qV15Zrbvuuk1/+MMrMpvNPo11w4a3dPrpw/STn/xcTU179cwzT+pXv1qkxYsfkSQdOXJEbW1tuuWW2bJYEtTQUK+VK5drwYL5euyxpyRJTz75rObMuVU33jhT3/52piRp0KDBWrhwsR566D796Ef36vTTh+nQoUPas2e3Wlpa9OCDC3To0EEVFNyg+HiL1q9/Q3fffZteeaXEJUH38sur9PWvn6/77ntIDkebc/krr7ykCy64SAsWLNKOHdv1u989rdTUVE2deqNXn7uurlZXXPF9DR16ulpaWrRhw5u6/fZZWrlyjYYOPd35vPr6Oj399JOaNm2G+vTpo6eeekILF/5Uq1atkclk8lk8AAAAAAAYgcRVBLLZbHr44d/onHO+LkkaM2asrr32Kr3xxms6dOiwRo0arRtumO58fmZmtsv2P/vZ/c5/t7a2/v/27jsqaqwNA/hDlypNsKHYAEFBFEQEUYpdLNgLlrX3hiu6dsW22LH37rqI2F1dRewFxbqWta2iAgoqKALC5PvDj6xDnQEcWH1+53COk9wkN2+uSeadmxvUqGGLdu1a4NatG6hVq3ah1jUlJQXz5y+GlpYWAEBTUxMzZ07B06dPYG5eCQYGBvDz+3eQ+rS0NJQpUxZDhvRDdHQ0SpcuLfZSKleuPGrUqCmWrVq1KgDA3LwSqlWzQOL/B9k/c+YUoqKe49dfF6N8eTMAQM2adhg9eih27dqOoUNHiuswNDTCjBlzstS7TJmy+OWXaQAAJydn3Lp1A+HhYTInivr06S/+WyKRwNHRCXfv/oVjx45IzUtMTMDKlethZlbh/2UFTJzoh2fP/pEacLig9SEiIiIiIiIqCkxc/YAMDAzFpBUAlC5dBhYWVvjrrzto3LgZVq5ciqVLF8DNzR02NjWhpqYmtfyFC+ewefN6PHnyCB8/fhSnP3/+rNATV46OTmLSCgAaNnTHjBkC7t69A3PzSgCAo0cP4bfftiMq6jk+ffr0VX3+QenSpWXajq6uHnR1v7wq/e+/H8DKqjpq13aQKlO7tgPu3ftLalpGD67s6v01c/PKuHfvrkx1AYCnT59g9erluH37Jt6+jRenP3/+TKpc6dJlxKTVl+18iUlsbIxU4qqg9SEiIiIiIiIqCkxc/YAMDAyymWaIuLg4ODo6YeLEqfj99134/fdd0NTUQpMmzTF06Ehoamri7t078PcfAzc3d/To0Qv6+oZQUlLCwIG9kZqa8s3rqqFRApqaWoiLewMACA8Pw6xZU9G2bQcMGDAUenolERf3BhMn+iE1NTVf23z//h3u3LmFRo3qZZn39WN6AGBoaJjtOnR1daU+q6qqyhyfpKSPGDNmGAwMDDF8+GiYmpaBhoY65s6dlWUdOjrS28lIMmbe94LUh4iIiIiIiKioMHH1A3r79m020+LFwdmbN2+F5s1b4e3btwgPP4llyxZCW1sbgwcPx+nTp6Cvb4AZM+aIYyhFR79SWF1TUpLx6VOSOM5UWNifsLauAT8/f7FMZOTVAm1TT68krKyspdaZQU1NejDzr8eRKiy3b99CbGwMFi1aLtVr6utxxoiIiIiIiIh+BExc/YDevo3HrVs3xMcFo6Oj8eDBPbRs6S1VzsDAAG3btsfp02F4+vQxgC+JI1VVVamEzbFjR75ZXa9cuYSkpCTxccHw8DAoKSmJ41alpKRkeTPesWNHpT7/2wtJuoeRqmr2vZPq1HHE5csXYWpaGgYG2feo+pZSUpIBQOoRzVu3buDVq5ewtLRSeH2IiIiIiIiIigoTVz8gfX19zJw5Bf36Df7/WwVXwcDAEM2be2P9+tVISHgPe/s6KFlSH3//fR/Xr1/DoEHDAHwZK2n37p1YsmQBXFwa4Pbtm/jjj8NZthEd/Qp3794BAHz+nIanT58gLOxPlCihCWdnF5nrqqGhgZ9/HoWuXX0RF/cGK1YsgZtbI7F3mKOjExYunIfNm9fD2roGLl48h6tXL0utQ01NDWXKlMPJk3+iUqWq0NBQR5Uq1WBqWhoaGho4cuQgdHR0oKqqCisrazRr1hL79u3B8OED0aVLD5QtWw4JCe/x1193YGRkhM6du+c39DKxsakJTU0tzJsXgO7deyI2NgYbNqxBqVIm33S7RERERERERMUNE1cFVEpXF3V2LiiS7eaXqWkZ9OzZBytXBiEm5hWsrKpj2rQAaGhowMrKGrt378CJE8eQlJQEU9PS6NNnADp27Argy2DkgwcPx549u3HgwF7Y2Nhi/vzF6NrVR2ob165FYPbs6eLnsLA/ERb2J0qXLoPg4AMy19XTswm0tLQwd+5MfPqUBBcXN6m3CLZp44OXL1/g9993ITU1FY6OdTF1agAGDuwttZ5x4yZg+fLFGD16CFJTU/H77/tRpkxZjB8/CRs2rMWwYQOQlpaGs2cjoKGhgaVLV2PdulXYsGEN4uPjYGBgiOrVbeDq6paPiMvH0NAIM2fOxfLli+HvPxZmZmYYN24Ctm/f8s23TURERERERFScKAmCIBR1Jf4Lrl27DjOzKkVdjQILCJiGx48fYf36rUVdlVwlJyeje/cOcHf3wrBho4q6Oj+E5OQvjyiWKFFCanp09D8oXbpiUVTpu/fo0d8AgCpVqhVxTX4cjLniFXXMZ86cDACYPHlmkWy/KBR1zH9EjLniMeaKx5grHmOueIx50ShVKveOOcoKqgcREREREREREZFc+KggFYn09HTk1NnvW7yprzjJa99VVFQUXCMiIiIiIiKi4omJqx/ML79MK+oqAAA6d26L6OhX2c4rXboMtm37Hdu3B2d5bO17kNe+yzMGGBEREREREdH3jIkrKhLz5i3C58+p2c5TU1NXcG0U60fedyIiIiIiIiJ5MHFFRaJKlaq5zs8YKPx7lNe+ExFR/p0+HYbw8JPi53/+eQLg30HaAaBhQw+4ubkrvG5EREREJD8mroiIiOi7VbKkQVFXgYiIiIgKgIkrIiIi+m64ubmzNxURERHRd0S5qCtARERERERERESUHSauiIiIiIiIiIioWGLiioiIiIiIiIiIiqVik7j69OkTRo0ahcaNG6NZs2YICwvLttyWLVvQpk0b8a927dqYM2cOAODSpUuws7MT53Xs2FGRu/BdCwpajA4dvIu6Gtl6+zYe69evxqtXL6WmP3v2D9avX43ExMQiqhkRERERERERFUSxGZx9/fr10NbWxvHjx/H06VN0794dx44dg7a2tlS5nj17omfPngCAz58/w83NDa1atRLnV6lSBSEhIQqrd+AUfyS+fq2w7WXQLVUKfjPmKny7xdHbt/HYuHEt7O3roEyZsuL058+fYePGtWjRwhu6urpFWEMiIiIiIiIiyo9ik7g6cuQI5s79kogxNzdHjRo1cPr0aTRv3jzHZcLCwmBsbIyaNWsqqppZJL5+jZ8uX1f4djfUraXwbf7oUlKSoaFRoqirQURERERERPTDKDaJq5cvX6JcuXLi5zJlyiA6OjrXZfbs2YP27dtLTXv69CnatWsHVVVVdOvWDe3atSuU+ikrK0FVNZsnK5UKZfXyU0L29cnDjBlT8fjxQ/Tp0w8rVizDq1cvYWVljQkTJqFSpcoAgMTERPz66xycPn0K2to66NChE5SVv+yoqqoyXr58AR8fbyxcuBT167uK605PT4e3dzO0adMOAwcOybMu9erVxogRYxAd/QpHjx6CRCJBs2YtMXLkGHHf3r2Lw6pVy3Ht2lXExb2BiYkpPD0bo2/fAVBTU8PLly/Rs2cXAMCIEYPEdS9fvgbjx48GAHTs2BoAULp0GYSGHgIAREe/QlDQEly+fBEpKamoVasWxoz5GRUrmgP40h59fFph2rRZuHTpAs6cCYeVlTUmTpwCH59WmDVrLiIiLuP48WPQ0tJC69Zt0LfvQCgry3ZM6tWrjZEjxyA2NhaHDh2AkpISevbsje7de+LQoQNYv34NEhLeo1EjD4wbNwEaGhrisg8e3MfSpQtx69YtqKurwdnZFSNHjoGRkZFU3eWtY0bMM7crZWVllCrFHmvfwtu3OgDA+CoQY654jLniMeaKx5grHmOueIy54jHmiseYF08KS1y1a9cOL1++zHbe+fPn5V5fbGwsLl68KI5vBQA2NjYIDw+Hrq4unj9/jj59+sDU1BT169fPd70zSCQC0tIkWWcIBV51/gjIvj55LSYIiI5+hcWLF6J//0HQ0NDA+vWrMXLkUOzcGQINDQ3MmDEVkZFXMWLEWBgaGmHXrm148SIKKioqSEuTwMSkDKpXt8GxY3+gbt1/Y3v1agTi4+Pg7u4lc9127NgKG5samDx5Jp48eYy1a1dATU0dffsOBADExb2Fjo4ehg0b/f/j+gwbNqxBfHw8fv75F+jrG2LKlFmYMWMSxowZDwsLKwBApUqVMHToKCxfvhgBAb/CyMgY6upqSEuTICHhPQYM+AklS5bE2LETUKJECWzbtgnDhw/Gzp17oKFRAunpX+q/bNkiuLl5YObMuVBWVhGnBwUtQcOGHv9PDl3B+vVrUaFCZXh6Npb5WOzYsQ3Ozi6YNi0A58+fwbJlixEXF4e7d//CyJF+iImJxrJlC1GuXAX4+vYGALx9+xZDhvRHxYqVMHXqLHz6lIRVq4IwYsRgrFu3FWpqavmuY8Yxy3zsJBIJXr/mOGHfQnz8BwBgfBWIMVc8xlzxGHPFY8wVjzFXPMZc8RhzxWPMi0ZeiUKFJa727t2b6/yyZcvixYsXMDQ0BAC8evUKTk5OOZYPDQ1Fw4YNxfIAoKOjI/7bzMwMXl5euHbtWqEkrr4n7969w5w5C1Czph0AwNKyOjp3bosjRw7A1tYeZ86cwvTps+Hp2QQAULu2A9q3byU13piXVxNs2LAGqampUFdXBwCcOHEM5uaVUblyVZnroqWlhZkz50FZWRnOzi74/DkVW7ZsQMeOXaGnp4cqVapi2LBRYvmaNe1QooQm5s6dgdGjf4a6ujqqVv2yPXPzSqhR49/HRitUqAgAsLCwlBr76rffdiA5ORmbNu2Anl5Jcb0dO3rj4MH9aN++k1jW2romxo4dL37OGADezs4ew4d/6dHl6FgPly5dwOnTJ+VKXJUvb4aff/4FAODgUBdhYX9i//5Q7NlzANraX9pyZORVnD4dJiaudu3aBgBYuHCZWMbMrAIGDOiNU6dOoHHjZuL6C6OOREREREREREWp2LxVsFmzZvjtt98AfHnc79atW2jQoEGO5UNCQrI8JhgbGwtB+NIF6t27dzh37hysrKy+XaX/owwMDMWkFfDlEToLCyv89dcd3Lv3FwDA1dVNnK+lpQVHR+kkoodHYyQlJeHSpS+95dLS0nD6dBi8vJrIVRdX14ZSj641bOiBlJQUPH36GMCXHmK7d+9Ajx4d4eHhgkaN6mHGjElITU1FTEzuj5LmJCLiMhwd60JLSxtpaWlIS0uDlpYWLC2r4/79u1Jlv34U8mt169aT+mxuXgmxsbFy1aNOHUfx38rKyihTphwsLauLCSngS3LrzZt/B/+/e/cOHB3rSZWxtq6BMmXK4ubNG4VeRyIiIiIiIqKiVGzGuOrbty/8/f3RuHFjKCsrY8aMGWIPqiVLlsDExARdu3YFAFy9ehUfP36Eq6t0UuHYsWPYuXMnVFVVkZ6ejjZt2sDLy0vh+1LcGRgYZDPNEHFxcYiLi4OWlnaWQcgzL1OqlAlsbWvhxInjaNCgEa5evYJ3796JvbRkr4thps9fthMXFwcA2L17B5YvX4Lu3XvB3r4OdHV1cffuX1i4cB5SU1Pk2laG9+/f4c6dWzhx4niWeXXq1M21fhl0dKS7MqqpqSE1NVWuemR+06Gqqip0dXWyTPt6P+Pi3ohjkWWuZ0LC+0KvIxEREREREVFRKjaJKy0tLSxdujTbeSNHjpT6XKdOHZw5cyZLuR49eqBHjx7fpH7fk7dv32YzLR6VKlWGkZERkpI+ZnmDXnbLeHg0xqpVQUhJScaJE8dgYWEJM7MKctYlPtu6ZQw0HhZ2Au7unhg4cKhY5unTJ3JtIzNdXT24urqhd+9+WeZpaWlJfVZSKqrR97NnZGSc4/GztKxeBDUiIiIiIiIi+naKzaOCpDhv38bj1q1/HyuLjo7Ggwf3YG1tAysrawDA2bOnxflJSUm4cuVSlvV4eHghNTUF4eGncPr0Kbl7W33ZTjgkkn8HAw8PPwkNDQ2Ym3/pVZSSkgw1NXWpZY4dOyL1WVVVDQCy9CZSU8t+uoNDXTx58hiVKlWGlZW11F+FCuZy74MiWVvXwOXLF5CU9FGcdvfuHbx69RK2tna5LElERERERET031NselyR4ujr62PmzCno12/w/98quAoGBoZo3twbGhoacHV1Q2DgXHz8+BFGRsbYuXMrSpQokWU9BgaGqFWrDpYvX4wPHxLh4SH/oN9JSUmYPHk8vL3b4cmTx9i0aR18fDpBT08PAODo6ITg4N9gbV0D5cqVx7FjRxAVFSW1DlPT0tDQ0MCRIweho6MDVVXV/yehvgzOvm/fHnh6NkWJEiVQpUpVdO7cHX/8cRgjRgxG+/adUapUKcTHx+P69WuwtbWTGuC8uOncuTtCQ4MxZsxwdO/eE58+fcKqVUGoUqUqGjXyLOrqERERERERERUqJq4KSLdUKWyoW6tItptfpqZl0LNnH6xcGYSYmFewsqqOadMCoKGhAQCYOHEaFiyYg6VLF0BTUws+Ph1hZWWNU6dOZFmXl1cTzJs3CzY2NaXe3CerLl264+XLF5g27RcIggStWrXFwIFDxV5YvXv3x7t377B27UoAQMOG7hg1yg/jx48W16GhoYHx4ydhw4a1GDZsANLS0nD2bARKly6DoUNHITh4F/bs2Y1SpUwQHHwA+vr6WLNmE9asWYFlyxbiw4dEGBkZw9a2FqpUqZafkCqMgYEBli5dhaCgxZg27ReoqamhXj0XjBgxRuxhRkRERERERPS9UBIyXsNHubp27TrMzKoUdTUKLCBgGh4/foT167cWdVXg6uqA0aPHoX37zlnmJScnA0C2Pb3o28gp5tHR/6B06YpFUaXv3qNHfwNAsU+Yfk8Yc8VjzBWPMVc8xlzxGHPFY8wVjzFXPMa8aJQqpZvrfI5xRURERERERERExRITV/RNpKWl5fiXnp5e1NX7pn7kfSciIiLFe/s2HjNmTMK7d1nfPExERPRfxzGufjC//DJNIdtp1KhejvNq1aqNoKA1OHs2QiF1UTRZ9p2IiIiosOzd+zvu37+LkJDf8dNPA4q6OkRERIWKiSv6Jtat25LjPC0tLQXWRPF+5H0nIiIixXr7Nh7h4WEQBAGnT5+Ej09H6OsbFHW1iIiICg0TV/RNWFlZF3UVisyPvO9ERESkWHv3/g5B+PI2ZolEwl5XRET03eEYV0RERERE/1Hnzp1GWloagC/jbJ47F17ENSIiIipcTFwREREREf1Hubi4QVX1y0MUqqqqcHFpWMQ1IiIiKlxMXBERERER/Ue1a9cRSkpfbumVlZXh49OxiGtERERUuJi4IiIiIiL6jzIwMETDhu5QUlKCm5sHB2YnIqLvDgdnJyIiIiL6D2vXriOiop6ztxUREX2XmLgiIiIiIvoPMzAwxJQps4q6GkRERN8EHxWk79Llyxexe/eOLNNPnDiOw4cPFEGNiIiIiIiIiEhe7HFVQNPm/4LXCW8Uvt1SesaY9nOAwrf7X3H58kWcOnUCnTp1k5oeFnYc7969Q4sW3kVUMyIiIiIiIiKSFRNXBfQ64Q3+9o5T/IbZaajICYKA1NRUaGhoFHVViIiIiIiIiL5LfFTwB3P79k2MHz8abdo0g5eXK3r37oZjx46I8w8fPgBXVwfcv38Pw4YNgKenC3r37ob79+/h06dPmD17Opo2bYiOHdvg+PGj4nLr169G69ZNIZFIpLZ37twZuLo6ICrqeZ51y9j23bt3MGrUELRo4YEuXXwQHh4mVe78+bMYNWoIWrVqjCZNGmLAgN64fPmiVF127dqG6OhXcHV1gKurAwICpiEgYBpOnTqJ69evidPXr18tLnfmzCn07esLD4/6aN26KVasWIK0tDSp9bZs6YkbN66jX7+e8PCoj7CwP8XpDx7cw4ABveHp6YI+fbrhxo1IWQ9LvuOeYc+e39ClSzu4uzujc+e2+O237VLzC6OORERERERERIrGHlc/mOjoV6hZ0w5t27aHuroGbt26gdmzp0NJSQmNGzcTywUETIWPTyd0794Lq1YFYdKk8bC2tkbZsuUxa9Y8HDy4H7NmTYWdnT1MTEzh6dkEGzeuxfXr11C7toO4npMnj8PSsjrKlzeTuY5TpkyEt3cbdOvmiz/+OIwpU/yxbt1WVKtmAQB49eoFXFzc0LWrL5SVlXDx4nn4+Y1AUNAa2NrWgrd3W0RFPce1a1cQEBAIADAw+PJq6JiYaHz4kIgxY/wBACYmJgC+jH01ffovaN3aBwMHDsWLF1FYvToIEomAYcNGiXVLTk5GQMBUdOvWExUqVISxsTFevIj6//Rp6NSpGwwNjbBx41pMnOiHPXsOoUSJEjLvu7xxB4D9+/di0aJf0blzdzg5OePatQgEBS1Gaupn+Pr2zlT3gteRiIiIiIiISFGYuPrBeHk1Ff8tCALs7OwRGxuDAwdCpRJXXbv6onnzVhklMW7cKNjb18bAgUMBANWr18CpUydw7twZtGvXAebmlVClSjWcOHFMTFylpqbi7Nlw9OrVT646enu3EcemcnVtiB49OmLbto2YPn0OAKB9+85iWYlEAnt7Bzx58hgHD+6DrW0tmJiYwsjIGGpq6qhRo6bUuvX09CCRSKSmC4KAFSuWoFmzlvDz8xenq6mpYeHC+fD17Y2SJfUBACkpKRg+fDQaNGgktd6UlBSMGDEWdeo4AgCMjY3Rp093XL9+DfXq1Zd53+WNu0QiwYYNa9CihTeGDx8NAKhbtx4+fPiAbds2olOnruKjjIVVRyIiIiIiIiJFYeLqB5OQkIANG1bjzJlwvHnzGunp6QCAUqVMpMplJDcAoFw5syzTdHR0oK9vgNevY8Vpnp6NsXv3Dowe/TNUVVVx8eJ5JCUlwcOjsVx1dHNzF/+trKwMV9eGCAs7IU6LjY3BmjUrEBFxGXFxbyAIAgCgZk07ubaT4fnzfxATEw13dy+pRwPr1HFEamoKHj9+BHv7OgAAJSUl1KvnkmUdqqqqYhkAMDevDABS8ZGFvHGPjY3Fmzev4e7uKbUeT8/GCA0NxuPHD1G9uk2h1pGIiIiIiIhIUZi4+sHMnj0Nd+7cRu/efWFuXhna2trYuzcYZ8+GS5XT0dEV/62mppZlWsb01NRU8bOnZxOsWbMC165FoG7dejhx4hhq1KiJ0qVLy1XHjMf6/v1siLi4L29ulEgk8Pcfg6SkJPTrNxDlyplBU1MT69atwtu3b+XaToZ3794BAMaNG5nt/NjYGPHfurp6Yjy+pq2tDWXlf4eMyyiTmpoiV13kjXtGXAwMjKTKGBp++ZyQkFDodSQiIiIiIiJSFCaufiApKSm4cOEcRo8eh7ZtO4jTM3osFVS5cuVhZWWNEyeOwda2Fs6fP4MBA4bKvZ63b9+idOkyX32Oh5GRMQAgKuo5Hjy4j8DApVKPt6Wk5D/5oqdXEgDw88+/wMLCMsv8MmXKiv9WUsr3Zr6JjLi8fRsvNT0+/subLvX09BReJyIiIiIiIqLCwrcK/kA+f/6M9PR0qKmpi9OSkj7i7NnThbYNT88mOH36FE6fDkNKSgrc3b3kXsfp0/++RVAikeDs2XBYW1sD+DdBpa7+7z5ER7/CrVs3pNaRuTdYBlXVrNMrVKiIUqVM8OrVS1hZWWf5yxjfqjgyMTGBsXEphIX9KTX95Mk/oa2tjcqVqxZRzYiIiIiIiIgKjj2ufiA6OjqoXt0amzatg7a2NpSUlLFt2yZoa+sgKelDoWzDw8MLK1YswfLlS2BnZw9jY2O513HgwD4AX8Zg+uOPQ3jxIgrTpgUAACpWNIeJiSmCghahX7/BSEr6iPXrV2cZo6tChYqIj4/D4cMHULlyFZQsqY8yZcqiYkVznD0bjtOnT4lJH2PjUhg2bBRmzpyCpKSPqFevPlRV1fDy5QucOXMKs2bNL7Zv3VNWVsZPPw3Ar7/ORsmS+nB0dEJk5FWEhgZjwICh4sDsRERERERERP9FTFwVUCk9Y+BAEW03H6ZODcD8+QGYNWsq9PRKon37TkhOTkZIyO5CqZepaWnUqGGLW7duoE+f/vlax/TpAViyZAE2blwHExMTTJ8+GxYWVgC+9LQKCJiPhQvnYdKk8TAxMUHPnj8hMvIqHj9+JK7Dw6MxIiOvYsWKpXj37i2aN2+FX36ZhnbtOuLBg/uYM2cGEhMT0KdPf/TtOxCenk2gpaWNrVs34tCh/VBWVkHZsuVQv74rVFWL93+T1q3b4fPnVOzevRO//74TpUqZYtiwUejcuXtRV42IiIiIiIioQJSEwhrg6Dt37dp1mJlVKepqfNcOHz6A2bOn49ix0+Ig4sW1p9P3KDk5GUDWmEdH/4PSpSsWRZW+e48e/Q0AqFKlWhHX5MfBmCseY654jLniMeaKx5grHmOueIy54jHmRaNUKd1c53OMKyIiIiIiIiIiKpaK9zNQ9N0QBAHp6ek5zldRUVFgbRRLln1XKm6vKyQiIiIiIiIqBpi4IoU4cuQgZs+enuP8iROnokULb7Ro4Q3g38fWvgey7jsRERERERERSWPiihTCxaUB1q3bkuP8MmXKKrA2ivUj7zsRERERERFRQTBxRQpRsqQ+SpbUL+pqFIkfed+JiIiIiIiICoKDsxMRERERERERUbHExBURERERERERERVLTFwREREREREREVGxxMQVEREREREREREVS0xcERERERERERFRscTEFRERERERERERFUuqRV2B/7qF8/2RmBCr8O3q6plgzM9zFb5dIiIiIiIiIiJFYeKqgBITYuHnHaHw7QYecFD4NomIiIiIiIiIFImPChLJKCUluairQERERERERPRDYY+rH0xAwDQ8fvwIffsOxIoVS/Dq1UvUru2AyZNnICEhAfPmzcLdu3dQsWIlTJgwBVWrVgMASCQSbN++BQcPhiI2NgampmXQq9dPaN68lbju8+fPYvfuHXj48G+kpqbC3LwS+vUbhLp164ll1q9fjZCQ3Vi0aDkCA+fi0aO/UaFCRYwaNQ52dvYy7UNiYiKWL1+MCxfOITExAfr6BnBycsb48ZPEMg8f/o01a1bg5s1IpKenw9y8EgYMGAJHxy91efnyBZYtW4irVyMgCALs7etgxIgxKF/eTFyHq6sDhg0bhZiYGBw/fgTa2jr47bdQpKSkYP36Vfjzz2N4+zYeFSqYY9CgoXB2di3QsSEiIiIiIiIiaUxc/YBiYqKxbt0q9O8/GMnJyVi06FfMnx+AV69ewdu7Lbp164nVq5dj2rSJ2Lp1N5SUlLBo0a84evQgevfuBwsLK1y5cglz5syAnl5JuLg0AAC8evUCLi5u6NrVF8rKSrh48Tz8/EYgKGgNbG1ridtPTk5GQMA0dOrUDYaGRti4cS0mTvTDnj2HUKJEiTzrv2zZQty+fRMjRoyBoaERYmNjcP16pDj/n3+eYvDgvqhQoSL8/CagZMmSuHfvLmJiYgAAqampGDlyCFRVVTB+/C9QUVHB+vWrMWzYAGzZsgt6eiXFde3cuRV2drUxadIMCIIEADB58nj89dcd9O07EOXKlcfJk8fh7z8W69ZtQbVqloVxiIiIiIiIiIgITFz9kBITE7B69UaUK1ceAPDo0d/YsWMrfvll2lc9qASMGzcK//zzFKqqqggNDcbEiVPF+Y6OToiLe4ONG9eKiav27TuL25BIJLC3d8CTJ49x8OA+qcRVSkoKRowYizp1HAEAxsbG6NOnO65fv4Z69ernWf+7d+/Ax6cjPD2biNOaNm0h/nvDhjXQ0dHBihVroaFR4v/1/bfX1+HD+xEbG40dO/aIMbC2roFOndpg374Q+Pr2EcsaGhphxow54ueIiMs4f/4sli1bDXv7OgCAunXr4fnzZ9i8eQNmzZqXZ/2JiIiIiIiISDZMXP2ASpcuIyZsAKBcuS+Px2Ukkr6e9vp1LF68iIKysjLc3BohLS1NLOPgUBd//vkH0tPToaKigtjYGKxZswIREZcRF/cGgiAAAGrWtJPavqqqqpj0AQBz88ritmRRrZolduzYCmVlFTg41EWFChWl5l+7FoEmTZqLSavM/vrrDiwsLKViYGJiipo17XDz5nWpspkf/4uIuAwjIyPUrGknFYs6dRxx5MhBmepPRERERERERLJh4uoHpKOjK/VZTU0ty/SMaampqXj//h3S09PRtGmjbNcXF/cGxsal4O8/BklJSejXbyDKlTODpqYm1q1bhbdv30qV19bWhrLyv+8F+HdbKTLVf/Ton7F+/Sps2rQOCxfOQ/nyZujXbxC8vJoCABIS3sPIyDjH5ePi4mBgYJRluqGhIaKjo7NM+9r79+8QFxeHRo3qITMVFRWZ6k9EREREREREsmHiivKkp1cSKioqWLlyvVTCKYOBgSGiop7jwYP7CAxcKvW4X0qKbMkoeejq6mLUqHEYNWocHj78Gzt2bMGMGZNRpUo1VKpUGXp6JREX9ybH5Y2MjPDkyeMs0+Pj46Gnpyc1TUlJKdO29VCqlAnmzAksnJ0hIiIiIiIiohxlzUIQZVKnjgMkEgk+fvwAKyvrLH9qampigkpdXV1cLjr6FW7duvFN61a1ajUMGTISEokEz549/X99HREW9meOSTNr6xp48OAeXr58IU57/ToWt2/flBqLKzsODnURHx8HTU2tbGNBRERERERERIWHPa4oTxUqmKNNm/aYOvUXdOvmCysra6SmpuLJk0d4/vwZ/P0no2JFc5iYmCIoaBH69RuMpKSPWL9+NUqVMin0+gwe3Bdubu6oXLkKlJSUsH//XmhqaqJ6dRsAwE8/9Ue/fr0wbFh/dOnSA3p6JfH33/ehp1cSrVq1QYsW3ti+fQv8/Eagb99BUFFRxoYNa1CypD7atPHJdduOjk6oW7ceRo8eiu7de6JSpSr4+PED/v77AVJTUzFo0LBC318iIiIiIiKiHxUTVwWkq2eCwAMORbJdRRo7djwqVKiA/ftDsX79amhra8PcvDJatmwN4EtPq4CA+Vi4cB4mTRoPExMT9Oz5EyIjr+Lx40eFWpcaNWxx5MgBvHr1CioqyqhWzRKBgUthYmIK4EuibcWKdVi1ahnmzp0FADA3r4SBA4eKdV28eDmCghZh7tyZAATY29dBQMCv0NMrmeu2lZSUEBDwK7Zu3Yjdu3ciJiYaenolUbWqBTp06JzrskREREREREQkHyUh49VvlKtr167DzKxKUVfjh5GcnAwAKFEi+zcDUuHLKebR0f+gdOmK2S1CBfTo0d8AgCpVqhVxTX4cjLniMeaKx5grHmOueIy54jHmiseYKx5jXjRKldLNdT7HuCIiIiIiIiIiomKJjwpSsSIIAtLT05GengYASEtLk5qvoqKS5U1/RERERERERPR9YuKKipUjRw5i9uzpOc6fOHEqWrTwVmCNiIiIiIiIiKioMHFFxYqLSwOsW7cFKSmpAAANDXWp+WXKlC2KahERERERERFREWDiioqVkiX1UbKkPgdnJyIiIiIiIiIOzi4PvoCRfjRs80RERERERFSUmLiSw+fPqUVdBSKF+vw5FSoq7JhJRERERERERYOJKxl9/pyOd+9eIzU1hb1Q6LsnCAJSU1Pw7t1r6OjoF3V1iIiIiIiI6AfFrhQyEgQl6Ooa4P37N0hPTyvq6nz3Pn/+EmM1NTZRRckccxUVVejqGkBTU7soq0VEREREREQ/MGYF5KCpqc0v8Qry6NHfAAAzs4pFXJMfB2NORERERERExY2SwOfeiIiIiIiIiIioGOIYV0REREREREREVCwxcUVERERERERERMUSE1dERERERERERFQsMXFFRERERERERETFEhNXRERERERERERULDFxRURERERERERExRITV0REREREREREVCwxcUVERERERERERMUSE1dERERERERERFQsMXFFRERERERERETFEhNXRERERERERERULDFxRURERERERERExRITV0REREREREREVCwxcUVERERERERERMUSE1dERERERERERFQsMXFFRERERERERETFEhNXRERE35GoqChYWloiISGhqKtCRERERFRgTFxlMmHCBFhaWuLRo0dS01++fAl/f3+4urrC3t4ejRs3xsyZMxEbGyuWiYiIQL9+/eDo6AgHBwe0bt0aa9euRWpqKgDA0tISdnZ2sLe3F/+GDh0qLh8aGgpvb2/Url0bTk5O6Nq1K27evCnz/Mz8/f1Ro0YNqe3Z29sjLi4OAPD48WMMGjQITk5OqF27Npo1a4Y1a9aIy/v6+sLS0hLnz5+XWu+6detgaWmJgIAAcdrkyZPRtGlTWFlZYdOmTXnGOSQkBNWrV5eq19q1a/NcrjDkdpwKcowyviy6u7tDIpFIbbNVq1awtLTE3bt3AQB79+5Fhw4dUKdOHbi6umLixIlF/iUzr7g0bNgQKSkpYvk///wTHh4e4uev25uDgwN8fHxw5swZqW14eHjA0tIST58+lZo+ffp0WFpa5tp2FNWenzx5gqFDh8LFxQUODg7o0qULrl69mmvsLl68CF9fX9SpUwcODg65li0seZ1v8iu7/5v29vb4448/AAAfPnzAtGnT0KBBA9jb26Nhw4YYPXq0uPyyZctgaWmJRYsWSa335s2bsLS0RJs2bcRp2R3TyMjIAtW/sPn6+mapo5OTkzj/5MmT6N69uzi9Q4cO2LlzZ47ry6sd58fX5xZZREdHo0uXLnByckKdOnXQpk0bHD9+PN/bL4iIiAj0798fdevWRe3atdG0aVPMnDkTUVFRAKT3LS0tDQsXLoSHhwfs7e3h6uqKgQMH4sOHDzLNz46HhwdsbW2ljoWPjw8AxZ0Lsjt+ly5d+ubnEl9fX2zatEm8dnXo0AGCIIjzN23aBF9fX6nyst4TZCev+yh51p/5uMkSq127dqFRo0aoVasWBgwYIHX/VhgyxzOjbq6urpgyZQo+ffoEAIiNjcXYsWPh4uICe3t7eHp6Yvbs2eJ6/P3984zl196+fYtZs2bB3d0d9vb28PDwgL+/P548eSKuz9LSEr///rvUckeOHIGlpSWGDBkiNf33339H06ZNUatWLXh4eODPP//Mss2ePXvC1tYW79+/z7VuuSXRM8epQYMGUnFSNFnb39f/XzNfLxs3bpztfczt27fRr18/1KlTB/b29ujWrVuW7QB5X9cFQcDq1avh4eGBWrVqoWnTprhx40YhRyIreWKTcQ9dv359jB49Gm/evAHw5d4gc1v7miLvPbZt2wYfHx/UqFEj1zplOHXqFLp37w5HR0c4OztjxIgRiI6OzjNmOd3Tfn1fUbduXfTo0QO3bt3Ksx754evri+rVq+PevXvitISEBFhaWorX2fx48OABRowYgXr16qF27dpo3rw5Fi1ahMTERADS52gnJyf0799fvPcPCQmBpaUl/Pz8pNb5+vVrWFtbS53PDx8+jC5dusDOzk7qGCpKdufAr89rmc/X8fHx8PHxwYgRI5Camoply5bB2to6S7vO+O4oy/VA1vP34sWL4e3tDWtra5mvIX/++SeaNGkCOzs7dO3aNUv+Q5Z4yDKvsDBx9ZWPHz/i6NGj0NfXR3BwsDj95cuX6NChA1RVVbFr1y5cu3YNO3fuRKlSpXDlyhUAQFhYGPr37w9XV1f88ccfiIiIwKJFi/Do0SO8fv1aXNeuXbsQGRkp/i1fvhzAl4tVQEAApk6diqtXr+LkyZMYNGgQ1NXVZZqfk65du0ptLzIyEkZGRgCAgQMHwtLSEqdOncKVK1ewbNkymJmZSS1fqVIl7NmzR2ra3r17UblyZalpVlZWmDZtGmxtbWWOt4WFhVS9+vfvL/OyGdLS0uQqL8txyu8xyqChoYELFy6In2/evIn09HSpMklJSRg3bhzOnz+PgwcP4vXr15g+ffo33/+cyBKX5ORkbN26Ndf1ZLS3S5cuoW3bthgxYoR4EctQqVIl7N27V/ycmpqKI0eOoGLFinnWUxHtOTExEW5ubjhw4AAuXboEHx8fDBgwAPHx8TnWS0tLC+3bt4e/v3+e+5AbWY+nrOeb/Mr8fzMyMhJNmzYFAMyZMwcvXrzA3r17ERkZid9++w1169aVWr5SpUrYt2+fVAI3JCQky3kDyHpM7e3t5a5vYf0/yImfn59UHS9dugQA2LFjB/z9/dGxY0ecPn0aFy9exNSpU3Hq1Klc15dbO5ZHfvdbT08Pc+bMwYULF3D16lVMnToV48aNw/Pnz+VaT3p6ulSiQ14nT55E//794eLigsOHD+PatWvYunUrzMzMxBh/bc2aNTh79iw2b96MyMhI7Nu3D40bN5Z5fk4WLlwodSxCQkIAFO25IL8K8n/h+fPnOHr0aK5lZL0nyEyW+yh51//1cYuIiMh1+xcuXEBgYCCWLFmC8+fPw8jIKMuXJlnIE9/w8HBERkZi165duHLlClauXAkA+Pnnn6Guro4jR47g6tWr2LhxI6pXry53XYAvbbRLly548eIF1q1bh6tXryI0NBS2trY4ffq0WC67uGZ3Tv7tt9+wceNGLFq0CJGRkdi9ezcsLS2lyjx//hyXL1+GpqYmDhw4kK96fy0jTrt378a1a9ekfnCSVWFdA/LTvr++Xs6bNw+LFi2Suge8desWfH19UbduXZw8eRJnz56Fj48Phg4dihMnTojlZLmuL1q0CKdOncLGjRsRGRmJDRs2oEyZMnLtoyAIWe5JZSFrbDLuoQ8cOIDXr19jzpw5Mm9DUfceJiYmGDJkCDp16iRTvRITE9G/f3+cOnUKJ06cgLa2NkaNGiXzfmUn477i7NmzsLW1xfDhw+Veh6zHUk9PDwsXLsxPNbPd3p07d9C5c2cx3teuXcO6deuQkpKC+/fvi+UzztEZMfv6uliuXDmcPn1a6jtCaGholu8C+vr66NmzJwYNGlSgun/r+0QAePXqFbp164bq1atj0aJF4vfDRo0aZWnXGd+XZbkeyHr+rlixIvz8/KQ6FeTmyZMn8PPzw4QJE3D58mXUq1cPQ4YMUUiscpLXtpm4+srhw4ehqakJPz8/hIaG4vPnzwC+ZPEtLCwwa9YslC9fHkpKSjA2NsagQYPQsmVLCIKAWbNmoX///ujduzcMDQ0BAFWqVMHcuXNRrly5PLd948YNMcuspKQEbW1tNGzYEFZWVjLNl1d8fDyePXuGLl26QFNTEyoqKqhWrRqaN28uVa5ly5Y4c+aMeGK5ceMGBEGAnZ2dVLnu3bvD2dkZGhoa+aqPrEJCQtCmTRssXboULi4uUr+25KWgx0nWY+Dj4yN+8QGAPXv2iL/gZ+jevTucnJygoaEBfX19mX7JB/79FX7Hjh1o1KgROnfuLOvu50jWuAwaNAhr1qyRqWeYiooKOnTogKSkJPFX3ww+Pj4IDQ0Vbyz+/PNP1KxZE6ampvneh8Jsz7a2tujcuTMMDQ2hoqKCTp06QUVFRepinJmtrS3atm2LChUqyFXv/LRnWY6Xn58fXF1dUbt2bfj4+ODixYtZtrl8+XI4Ozujfv36MvWSzHDjxg20bNkSxsbGAIDSpUuja9euUmUqV64MU1NT8dfZlJQUHD16tNB+LfP398fEiRMxcuRI1K5dO9ceTt/Khw8fEBgYiF9++QVt27aFrq4ulJSUULNmTaxevTrf6924cSOaNGkCe3t7eHl5Ydu2beK8jF/59uzZg8aNG8PNzQ0dOnQAAHTp0gX29vZYtWqVWP7kyZNo3LgxHBwc4O/vL17TtLS0UKlSJSgrK0MQBCgrKyM9PR0vXrzIs36WlpbYtm0bWrVqBTs7O3z8+DFf+5nRjgcNGoTevXuL7cnExAS9e/dG+/btsyxz48YNeHp6iglpIyMjdOjQATo6OjLNl5cizwX5tWzZMgwcOBBTp05F3bp1ERgYmO91DRo0CIsXL871xlHWe4Ls6pnbfVRB15+XkJAQtG7dGnZ2dtDS0sLYsWNx5cqVPJO1BbnnyFC+fHk0atQIDx48APBln9q3bw89PT0oKyujQoUKaNeuXb72a/PmzVBSUsKyZctQpUoVKCsrQ09PD926dUOvXr3Ecq6urnj16hUeP34MAIiJicGtW7fg6ekplklPT8fSpUsxceJEWFtbi8co8w9AwcHBqF69Onr06CH1I29BlSlTBg0aNMCdO3fyLPst7oWAgre/2rVro2rVqlL7MH/+fLRs2RIDBgxAyZIloa2tjQ4dOqB///6YPXs2BEGQ6br+7t07bNy4EbNnz0bFihWhpKSEcuXKwcTEJM96eXh4YPXq1ejUqRPs7Ozw8OHDbx4bIyMjNGvWTGz3BVWY9x5NmjSBl5cXDAwMZNq2t7c3GjVqBG1tbWhpaaFXr164ceNGoXzBV1dXR7t27fDq1atcfxTJkJ9j2a1bN0RGRkr9SJBBEARs2LABXl5eqFu3Lvr27St1Xsxue3PnzkWLFi0wevRo8d69XLly8Pf3z7b3q46ODtq0aSN17dTV1YWrqysOHz4sTtu7d2+W70v169dHixYt5P6OUJjXRlk8fvwYXbt2hbu7OwICAqCioiLTcrJcD2Q5fwNAu3bt0LBhQ5nvefbt2wcnJye4u7tDQ0MDQ4YMQXx8fJ4/BBUmX19fzJ8/Hz/99BNq1aol9YNLdpi4+kpwcDC8vb3RokULJCcnIywsDABw5swZqRurzJ4+fYqoqCi0atUq39u2t7fH1atXsWDBAly8eDHLow15zZeXgYEBKleujAkTJuDw4cM5fmHR1dVFgwYNcPDgQQDZJ2Hy68mTJ3B2doaHhwemTZsm86Nyf//9N1RUVBAWFob58+fLvL2CHidZj0HGxT0hIUHmL+xXrlzJ8qtmTj5+/Ih79+7hyJEjUl9q80vWuNSrVw81a9aU6ZHOz58/47fffoOamlqWhGDlypVRpkwZnD17FkDhtKlv2Z7v37+Pjx8/omrVqgWqY07kbc+yHC9nZ2ccOXIEly5dQosWLTBy5Eip9vrw4UNoaGjg9OnTWLRoEebPn49nz57JVN/atWtjxYoV+O2333Dv3r0ce9z4+PiIvxAdP34ctra22d5g79u3D3Xr1kXLli2xYcOGLI/Z5uTQoUPo0KEDIiIixOSNIl2/fh2fPn3KkhwtqLJly2Lz5s24du0aZs2ahfnz52dJap88eRJ79uzBiRMnxC+OGb9yf/2rZHh4OPbu3YtDhw7h/PnzWXpHeHt7o2bNmujcuTNq164t86NpBw8exPr163Ht2jVoaWnlaz+fPHmCFy9eoEWLFjIvU6dOHezYsQObNm3CrVu3snxpyGt+QX3rc0F+nTlzBnZ2djh//jxGjhyZ7/W0bdsWqqqquSYj8ntPkNd9VH7WP2XKFDg5OaFz584IDw/Pdb3379+X+hXb2NgYxsbGuSYhM+T3niPD8+fPERYWBmtrawBfzqGzZ89GaGholh925HX27Fk0a9YMqqqquZZTUlJCmzZtxB/VQkND0bx5c6kfG588eYI3b97gn3/+gYeHB9zc3DBlyhSpa0d6ejr27t2Ldu3aoW3btrh3755MiSZZREVFITw8HObm5jKVL+x7IaBg97yCIODKlSv4+++/xX349OkTIiIism37rVq1QlRUFJ4+fSrTdf3GjRtQV1dHeHg4GjRoAA8PDwQGBoo/SOQlJCQEc+fORWRkJCpVqiTTMl+TNzavX7/GkSNHxHZfUIV971EQV65cQZUqVfL8fyeL5ORkBAcHw8DAAHp6ejItI++xLFmyJPr164cFCxZkmbdv3z5s3LgRy5cvx5kzZ1CtWjUMHDhQ6vr59fbKlSuHq1evyvVdKiEhAaGhobCxsZGa/vWxioyMhJKSklxP7uSlsK6NeXnw4AG6d++OLl26YPz48XItK8v1QJbzd37cv39fqvOFmpoaqlSpItN1sTDt3bsXo0aNQmRkJOrXr59rWSau/u/hw4e4fv062rVrB21tbXh5eYk3b2/fvs0105uRIZclG9ytWzc4ODiIf0FBQQC+NNy1a9fin3/+wejRo1GvXj2MGDFCXHde83Oyc+dOqe1ldLlVUlLCli1bYGVlheXLl8PLywstWrTAuXPnsqwjowdRcnIy/vjjj0LpNeHo6IgDBw7g3Llz2Lx5M54+fSrzf3ZdXV0MHjwY6urq0NTUlHmbsh6n/B6jr+uXcXE/duwYatWqletFMzw8HL///jvGjBkj035IJBL4+flBU1NTrv3PiTzt18/PD9u2bUNMTEy28zPam62tLZYuXYrFixdn+whURpuKjo7GnTt3svxqkBNFt+f3799jzJgxGDhwIEqVKiVTHeUlb3uW5Xi1b98eurq6UFNTQ79+/SCRSKQuRPr6+ujXrx/U1NTg5OSE8uXLS42x8+DBA6k4Ozg4iGMTTJo0CV26dMHevXvRsWNH1K9fHxs3bsxSh5YtW+LcuXN4//49QkJCsr3B9fX1xdGjR3HhwgUEBARgy5Yt2LJlS54xAAAXFxc0aNAAysrKhfL/IDcLFy6UikWfPn0QHx8PQ0PDPB/Xzk5O7RgAmjZtijJlykBJSQn16tWDq6srLl++LLX8sGHDoKenl+d+Dxs2DDo6OjA1NYWbm1uWL5gHDhzAtWvXsGrVKri5ucn8C2G/fv1gamoKdXV1KCvn7zbi7du3ACB1bgwKCoKDgwPs7e2zvcns168fRo0ahbCwMPTs2RP16tVDYGCg+KhEXvNz4ufnJ3U8Jk6cmKXMtz4XZL7uyPNYRLVq1eDj4wNVVdUC/V9QUVHB6NGjERQUlOs4Q/m5J8jrPkre9c+fPx8nTpzAmTNn0KNHDwwfPjzXMT+TkpKgq6srNU1PT0+mHoP5vedwd3eHo6MjevfuDTc3N/GYLlmyBO7u7ti8eTNatWoFd3f3fD9yFx8fL1dc9+3bh7S0tGzPye/evQPwpRd0cHAwQkND8ezZM8ydO1csc/bsWcTHx6NVq1YwMzND7dq1C9zryt3dHbVq1YKnpyfKly+PESNGyLRcYd8LZZC3fWdcL2vWrIkePXrgp59+Eu9pEhISIJFIsr0HzJgWHx8v03X93bt3+PDhA27fvo2jR49i27ZtCA8Px7p162Tar65du6Jy5cpQUVHJ13ULkC023bp1g6OjIzp27IgKFSpgwoQJMq9fUfceBfHXX39hyZIlcu1XdjLuK2rVqoVDhw4hKChI5kRYfo5lr1698OLFiyxjEO3bt08cw0xDQwNjxoxBdHS01Pn06+19/PgR6enpMiUD/fz84OjoKD6dNG/ePKn59evXR2xsLB49evRNjlVhXRvzcuvWLUgkkhx/nAkPD8/SrjPGrZP1epDX+Ts/kpKSsiRLZb0uFqZWrVrB1tYWSkpKKFGiRK5lmbj6v+DgYFhZWYmZx3bt2uHs2bOIiYmBgYFBjl/UAYhdTXMrk2HHjh2IiIgQ/4YNGybOc3Z2xtKlS3HhwgUEBwfj2bNnUoOr5Ta/X79+4oBvXz8q0rVrV6ntZQxyCAClSpWCv78/Dh06hAsXLsDNzQ3Dhg0Tb16+3u6bN2+wYsUK2Nvby33TPmXKFLFuU6ZMAQCYmZmhYsWKUFZWhpmZGSZNmoRTp07JNCiniYlJvr4syXqcCnKMMrRv3x4hISF5nlwuXLiAn3/+GUFBQTL3uNLW1pb5VxlZyNN+q1evDg8PD3Hcr8wy2tuFCxfg4OCQ4+OPLVq0wPnz57Fp0ya0bNkyy4W3OLTnxMREcTDVr8ceyK49F4S87Tmv4yWRSLBo0SI0adJE7EWTmJgoJgoAiF3tM2hqakpdqCwsLKTiHBERIf6CrK6ujp9++gm7du3C1atX4e/vjwULFog96DLo6OjAzc0Na9euxd27d7N95t7GxkZ8DKtWrVro37+/VLfx3JQtW1amcoVhzJgxUrHYuHEjDAwM8Pbt21wHw89PO96/fz/atWsnDs57+vRpqWMHQOYxTb5u25mPcQZ1dXW4u7vj0qVL2L9/v0zrlXdMlexktOOvB8geNmwYIiIi8NNPP2Xbi0BZWRkdO3bE5s2bceXKFSxYsAC7du0SvzjnNf/rgVG/7gofGBgodTy+HhgVKLxzwcuXL6Xq8PLlS3Fe5uvO1+0lL4VxPDJ4eXmhfPnyuSaQ8zqHZheXvO6j5Fk/ADg4OEBTUxPq6urw9vaGu7s7jh07luP2tbS0soy3mJiYCG1t7Tzrk997jrCwMFy5cgUnTpzAxIkTxeucjo4Ohg8fjr179+Ly5cvw9fXF+PHj8xwUd9WqVeJ+9evXD4B8cTU3N0f58uWxdOlSqKuro2bNmlLzM2IxYMAAGBoawtDQEAMHDpQahyk4OBhubm7io2zt2rXDwYMHkZycjIiICKn2LauwsDBERkZi1apVuHv3bpZrdk4K+14og7z3vBnXy2vXrmHIkCG4ePGi2Fsl4/Gf7F4EkDHN0NBQpvuwjOMzYsQIaGtro2zZsujZs6fU8clNYVwzZYnNjh07cOXKFZw6dQpz5syBvr5+ljLZtWVAcfceudm/f79Yt8yJiPv376Nfv36YPHkyXFxcxOktW7YUl5H1OppxXxEeHg4TExO5ernk51iWKFECw4YNw8KFC6V+zImOjkb58uXFz+rq6jAxMZEafP7r7eXWpjMLDAzElStXcObMGSxdujTLExjKyspo06YNtm/fjmPHjhX64OuFcW1UVVXNcj+S8f87I9Ho4+ODpk2bwtfXN9unFxo2bJilXct7Pcjr/J2X7P7P5XVdzOmeqbDJ056ZuMKXx5r27duHp0+fwsXFBS4uLvDz80N6ejpCQkKyPIObWaVKlVCuXDkcOnSo0OpkZWWF9u3b5/hseOb569atEwd8y88Advr6+hg2bBiSkpKyvGUi48SyZs2afGV4Z8yYIdZtxowZ2ZZRUlICAJkG+s3vL/yFfZxyO0bOzs6Ij4/HvXv34O7unu3yFy9exMiRIxEYGAhnZ2eZt5vf/c+JvHEZNWoU9u/fn+sjDvr6+pg1axZ27dqFv/76K8t8HR0dNGzYEJs2bcq2TRV1e/7w4QP69u2LqlWrYvr06WL7BGRrz/KQ93jmdbwOHDiAAwcOYPXq1bh69SoiIiKy9DQoLOrq6mjTpg0sLCyy/X/Qvn17rFu3LtvkZHbkicXXx6Qo2Nvbo0SJErkOZi1vO85469q4ceNw4cIFREREwM3NLct5MXOcCiMWaWlp+Oeff2QqWxjnoIx2fOTIkXwtr6qqioYNG8LZ2Tnbtpfd/K8HRpX1scjCPBeULVtWqg6FlXwt7GuCn58f1q1bl+Mb4/I6h2YXl7zuo+RZf07L5LZ9S0tLqbdqxcXF4fXr1zL9YFTY8f2atrY2fvrpJ+jq6uY5Vs2gQYPE/croZePq6opjx47J/Fisj49PjnGtVKlSro+exMfHIywsDBcuXBDvlQMDA5GQkIBjx47BwcFBqn3LQ0lJCe7u7mjatKnMg3l/q+OS33tedXV1jBgxAsnJydixYweALz8Y1K5dO9vr9cGDB1GuXDmYm5vLdB+W0Vbze74vjHgV9PtAhuzasjwK+97ja61btxbr9vXxePDgAfr06YOxY8dmSbAcOnRIXKZ169Zybc/U1BQzZ85EYGCgzEno/B7LDh06QCKRIDQ0VJxWunRpqfvk1NRUxMbGonTp0tluT1NTEw4ODoX2Xap9+/bYuXMn7O3ts/yoWlCF0ebLli2b5XvEs2fPYGBgIA6VoKSkhOnTp6NRo0bo0aOHzPdSmeV1Pcjt/J2X7P7PZb4ufv78GY8ePYKFhQWA/N0z5Yc85zQmrvBlvJAPHz4gJCQEoaGhCA0Nxb59+zBkyBDs2bMHw4cPx7179zBlyhS8fPkSgiAgPj4ea9asweHDh6GkpITJkydjzZo12Lp1q/jr+JMnTzBx4kSZBrz9888/sW/fPrG78PPnz3HgwAHxV6u85svr/fv34ttK0tPT8enTJ2zatAn6+vrZvj2ld+/e2LBhQ45JmNTUVKSkpEAikSA9PR0pKSm53kiFh4eL2fro6GjMnj0bDRo0yPd4KbIo6HGS5xgoKSlh9erV2LhxY7YXzUuXLmH48OGYP38+GjRoUAh7l3/yxsXMzEy8KciNqakp2rVrhyVLlmQ738/PD5s2bcryzHt+FGZ7zviiam5ujoCAAJlOqBKJBCkpKeKvMikpKUhJSSnwfmUnr+P1/PlzqKmpwcDAAJ8/f0ZQUFCBx8T7WlBQEK5du4bk5GSkp6fjxIkTePjwIWrVqpWlbL169bBx40YMHjw423UdPnwYHz58gCAIuHXrFtauXYsmTZoUWl2/JR0dHfj5+SEgIAD79+8X9+Pu3bv5fvtNUlISBEGAoaEhlJWVER4enu3jrpkZGxvLPEYZAFy+fBmRkZFITU1FamoqQkJCcOnSpTzHFihMSkpKmDhxIlauXIktW7YgLi4OwJcvx3///Xe2y2zatAnnz5/Hx48fIQgCrl69isuXL4vn4Lzmy6u4nwu+FQcHB3G8sJzkdU+QWV73UfKs/+XLl7hy5QpSU1Px+fNnHD58GCdOnICXl1eO2/fx8cH+/ftx8+ZNfPr0CQsXLoSjo2OWgccVYd68ebh79674/+/3339HUlKS1LUwox19/Zed3r17Iz09HaNGjcKTJ08gkUiQmJiI3bt3Y/PmzVnKe3t7Y8OGDejYsWOWeSVKlEDr1q2xdu1avH//HgkJCVi7dq342FtoaChKliyJo0ePivfKBw8ehI+PT56PC2bcI2b85XR/2L9/f5w+fRq3bt3KdX3fmrztO4OSkhIGDRqE1atXi08Q/Pzzzzh06JD4cpuPHz8iODgYa9euxYQJE6CkpCTTfZiZmRnq168vPsobExODbdu2yTzUQmHJb2wKqjDvPdLS0sR2mPF/Lbfe03///Td69+6NkSNHZvvikJxkfB/K+MtpGzY2Nqhbt26BXuwii4zHwb/u0du6dWts374dDx8+RGpqKhYvXgxTU9Ncx5oaP348Dh8+jKVLl4pvvIyOjsavv/4qd8+cChUqYOvWrTn2Wv76O6UgCHkeq8LWunVr7NixA3/99RcEQcCLFy8QFBQEb29vqXJKSkqYNm0aGjdujB49esg8fqEs14MMuZ2/gS+Jp5SUFKSnp2e5F8lp3y5evIjw8HCkpqZi1apVMDAwgKOjY651zthOdu06t3mFoeCjyn0HgoOD0apVK1SpUkVquq+vL9avX49nz54hODgYy5YtQ6dOnfDx40cYGRmhUaNG4sj/7u7uWLt2LVauXCl+US9TpgzatGkj1ZW2S5cuUhngKlWqIDg4GCVLlsSOHTswZ84cpKSkQF9fHx4eHuK4R3nNz8nOnTuz3FBs374d5ubmiI2NFV/tra6uDhsbG6xduzbb5JG+vn6uX2r69u0rjsMSERGB+fPnY9iwYTm+3vXixYuYOHEiPnz4AH19fTRq1Chfb+uRlyzHKb/HKLNq1arlWI+MZELmfZb3V8rCImv7zTBkyBDs3bs3z/X269cPTZo0wY0bN7K8ecbU1FTut4Qooj0fP34c169fx/3793H8+HFx+vTp03P8Je3KlSvo2bOn+Dnjgv+tBjjM7Xh17txZ7Omno6ODXr16yd1d+sGDB1m+7I8ePRo9e/aEiooKpk6dihcvXoiP+gYEBKB27dpZ1qOkpJRrb8Lt27djypQp4ngJXbt2xU8//SRXXRUhMDAwSwL25MmT6NatG0xNTbF+/XpMnToV6urqMDMzy3Ow+JzasbW1NQYNGoRevXpBIpHAw8NDpsccRo4ciVmzZmHSpEno379/ngOef/r0CTNnzkRUVBRUVVVhbm4ujrehSF5eXlizZg1WrVqFpUuXimPBODs7Z9sONDU1sXDhQvGG0NTUFEOGDBEHic1rfk7GjBkjNb6XlpYWzp079584F3wrY8eOzbXnQF73BJmVK1cuz/soWdeflJSEWbNm4dmzZ1BRUYG5uTkWL16c7RfYDM7OzhgzZgyGDRuGhIQEODo6fvO3TOXk8+fPGD16NGJiYqCmpobKlStjxYoVUo/rbNu2LcuA49m1IV1dXezatQtBQUHo06cP3r17BwMDAzg5OWX7pb1EiRK5HreJEydi5syZ8PT0hLq6Ojw8PMRX2AcHB6Nr165Zrtt9+vRB69at8ezZsxzfpvn1Y1XAl8eCszvuGT94LV26VKYXwXwr8rbvrzVp0gRLlizBtm3b0L9/f9jZ2WHz5s1YsmQJVq1aBUEQYGVlhWXLlsHV1VVcTpb7sMDAQEyePBn169eHjo4OWrduLfWonSIUJDZ5UdS9x8qVK8Xxa4Ev5+m6deti69at2ZbfsGED4uPjMXfuXKkx3w4dOpRrz9n58+dLvdChXLlyOHnyZLZlBw0ahJ49e6J///6F+vh3Zk2bNsX69evFR3Lbtm2LN2/eYODAgUhISICtrS1WrVqV63hbNWrUwM6dOxEUFIRWrVrh8+fPMDExQdOmTfP1tvvc7j327dsnNZ6Yra1trnEsbO3atcPHjx/h5+eH6OhoGBgYoGnTpjl+v508eTKUlZXh6+srPnJ/6tSpLO36119/hZeXl0zXgwx5nb8nT54s9d1s27ZtaNeunVSb/VrlypXx66+/IiAgANHR0bC2tsaKFSvyHGtt1KhRUp+//r+T27zCoCTI8mwWERERERERERGRgvFRQSIiIiIiIiIiKpb4qCAVOy1btpR621IGb2/vQhkMu7jr169ftm/jq1OnTr4GsaSi9aO35/x4+fJljq8Vzu0xLSq4VatW5TjORlE9yvyjy2mMroEDB+Z7PDX6F8/RxRPvhWS3f/9+TJ06Ndt5eT3ORsULj6XseG0s/iIiItC/f/9s561du1buISr4qCARERERERERERVLfFSQiIiIiIiIiIiKJSauvhMTJkzA4sWLC7yelJQUWFlZITY2tuCV+s4x5orHmCseY654jLniMeaKx5grHmOueIy54jHmiseYK94PGXOB/vNiY2OFsmXLCklJSYIgCEJKSorQvn17oWLFigIAISwsTKq8RCIRfv75Z8HQ0FAwNDQUxo0bJ0gkEnH+vHnzhDFjxihyF/5z5I35yZMnhUaNGgl6enpCxYoVs6yPMc8b27niZY65IAjCx48fhcGDBwtGRkaCnp6e0KBBA3He1KlTBVVVVUFbW1v8e/TokTifMc9b5phfuHBB8PLyEgwMDARjY2OhQ4cOwsuXL8XybOcFJ2/MFy1aJFSqVEnQ1dUVypQpI4waNUr4/PmzOJ8xzxvP54qXOeZ37twR6tSpI+jr6wv6+vqCp6encOfOHbH8/PnzBRsbG0FHR0cwNzcX5s+fL7U+xjxvmWP+5MkTAYDUNXLGjBliebbzguO5RfHkPbcw5gUnbztPTk4WBg4cKJiYmAgGBgZCq1athKioKHH+fyXmTFx9B+bPny/069dP/JySkiIsWrRIOHPmjFC6dOksjXfVqlWChYWF8Pz5cyEqKkqoXr26sHLlSnH+8+fPBSMjIyE5OVlRu/CfI2/ML126JGzZskVYvXp1tokrxjxvbOeKlznmgiAI3bt3Fzp37izExsYKaWlpQkREhDhv6tSpQvfu3XNcH2Oet8wxP3z4sLB7927h/fv3wsePH4U+ffoITZs2FeeznRecvDF/+PCh8PbtW0EQBCEuLk5wd3cXFixYIM5nzPPG87niZY7527dvhSdPnggSiURIS0sTlixZItSsWVOcP2/ePOHq1avC58+fhXv37gkVKlQQdu7cKc5nzPOWOeYZiauvE91fYzsvOJ5bFE/ecwtjXnDytvN58+YJtra2QnR0tPDp0yehR48eQrt27cT5/5WYM3H1HXB3dxe2bt2a7bxy5cplabzOzs7C6tWrxc/r1q0TnJycpMpUrVpVOHXqVKHX9Xshb8wzHD9+PNvElSAw5nlhO1e8zDG/d++eoKurK7x//z7b8nklrgSBMc9Lbu1cEATh6tWrgo6OjviZ7bzg5I351968eSN4enoKgwcPlprOmOeO53PFyy3mnz9/FoKCggRNTc0clx8+fLgwbNgwqWmMee4yxzyvxBXbecHx3KJ48p5bGPOCk7edDxo0SBg3bpz4+eDBg4KFhYVUmf9CzDnG1Xfg1q1bsLS0lLn8nTt3YGdnJ362s7PDnTt3pMpUr14dN27cKLQ6fm/kjbksGPPcsZ0rXuaYX7p0CRUrVsTUqVNhbGyMmjVrYs+ePVLLHDhwAIaGhrCxscHKlSuzrJMxz11e7fz06dOwsbERP7OdF5y8MQeAHTt2QE9PD8bGxrhx4wYGDhwoNZ8xzx3P54qXU8z19fVRokQJDB8+HBMnTsx2WUEQcObMmSz/Dxjz3OUU84oVK6J8+fLo06cP3rx5I05nOy84nlsUT95zC2NecPK28759++LcuXN4+fIlkpKSsH37djRv3lyqzH8h5kxcfQfevXsHXV1dmct/+PABJUuWFD+XLFkSHz58gCAI4jRdXV28e/euMKv5XZE35rJgzHPHdq54mWMeFRWF27dvo2TJknj58iWCgoLQq1cv3L17FwDQqVMn3L17F69fv8batWsxY8YM7Ny5U2qdjHnucmvnN2/exIwZM/Drr7+K09jOC07emANAt27dkJCQgAcPHmDQoEEwNTWVms+Y547nc8XLKebv3r3D+/fvERQUBHt7+2yXnTZtGiQSCfr06SM1nTHPXeaYGxsb48qVK/jnn39w9epVJCYmonv37uJ8tvOC47lF8eQ9tzDmBSdvO7ewsECFChVQrlw56Onp4e7du5gyZYpUmf9CzJm4+g4YGBggMTFR5vI6OjpISEgQPyckJEBHRwdKSkritMTEROjr6xdmNb8r8sZcFox57tjOFS9zzDU1NaGmpoZJkyZBXV0dDRs2hLu7O44dOwYAsLa2RtmyZaGiooL69etj5MiRCA4OllonY567nNr5w4cP0bx5cyxZsgQNGjQQp7OdF5y8Mf9atWrVYGNjgyFDhkhNZ8xzx/O54uUWc21tbQwaNAg9e/bM8mapoKAgbNmyBYcOHYKGhobUPMY8d5ljrqOjAwcHB6iqqsLU1BRBQUE4duyY2LbZzguO5xbFk/fcwpgXnLztfPDgwUhOTkZcXBw+fvwIHx+fLD2u/gsxZ+LqO2Bra4sHDx7IXN7GxkaqK+CNGzeydP++e/euVDdOkiZvzGXBmOeO7VzxMsfc1tZWruWVlJSkfkEDGPO8ZNfO//nnH3h5eWHy5Mnw9fWVmsd2XnDyxjyztLQ0PHr0SGoaY547ns8VL6+YSyQSJCUl4cWLF+K0DRs2YO7cuThx4gTKly+fZRnGPHd5xTzji3rGdZLtvOB4blE8ec8tjHnBydvOb9y4gd69e8PQ0BAaGhoYPnw4Ll++LPWo8n8h5kxcfQdatGiB8PBwqWkpKSlITk4GAKSmpiI5OVm8MPbs2RMLFy7Eixcv8PLlSyxYsAC9e/cWl33x4gXi4+NRr149he3Df428MZdIJEhOTsbnz58hCAKSk5ORmpoqLsuY543tXPEyx9zNzQ0VKlTAnDlzkJaWhnPnzuHUqVNo2rQpAGDfvn14+/YtBEHA5cuXsXTpUrRp00ZcnjHPW+aYv3jxAh4eHhg6dCgGDRqUpTzbecHJG/N169aJvxz/9ddfmDNnDjw9PaWWZ8xzx/O54mWO+fHjxxEZGYn09HQkJCRgzJgxMDAwQPXq1QEA27dvx8SJE3H8+HFUrlw5y/oY87xljvmlS5dw//59SCQSxMXFYcSIEWjUqJH42BTbecHx3KJ48p5bGPOCk7edOzo6YsuWLXj//j0+f/6MFStWoGzZsjA2NgbwH4q5okeDp8L3+vVroVy5ckJSUpI4rWLFigIAqb8nT54IgiAIEolEGDdunGBgYCAYGBgI48aNEyQSibjs/PnzhdGjRyt6N/5T5I15WFhYlnkNGzYUl2XM88Z2rnjZxfz27dtCvXr1BC0tLaF69epCSEiIOK9Lly6CoaGhoK2tLVhaWgpLliyRWh9jnrfMMZ82bZoAQNDW1pb6y8B2XnDyxrx3796CiYmJoKWlJVSsWFHw8/MTPn36JM5nzPPG87niZY757t27BUtLS0FbW1swNjYWmjdvLty4cUMsb25uLqiqqkr9Hxg4cKA4nzHPW+aY79ixQzA3Nxe0tLSE0qVLC76+vsKrV6/E8mznBcdzi+LJe25hzAtO3nb+5s0boVu3bkKpUqWEkiVLCi4uLsKlS5fEZf8rMVcShEzPcdB/0sSJE2FiYoJRo0YVaD0pKSmws7PD6dOnYWJiUjiV+04x5orHmCseY654jLniMeaKx5grHmOueIy54jHmiseYK96PGHMmroiIiIiIiIiIqFjiGFdERERERERERFQsMXFFRERERERERETFEhNXRERERERERERULDFxRURERERERERExRITV0REREREREREVCwxcUVERERERERERMUSE1dERERERERERFQsMXFFRERERP9pvr6+mDFjRoHLEBERUfHDxBUREREVW2/evMGsWbPg5eWFGjVqoEGDBujXrx/Cw8PFMh4eHrC0tMS+ffuyLN+hQwdYWlpi/fr14rTMCQx/f39YWlrC0tISNjY28PT0xLx585CUlCS1rn379qFNmzaoWbMmnJyc8PPPP+da9/9aoiQjjpn/AgMDi7pqRERE9ANTLeoKEBEREWUnKioKXbt2hba2NsaMGQMrKysIgoALFy5g6tSpOHXqlFi2TJkyCA4ORps2bcRpDx48wMOHD6Gvr5/nturXr4/58+cjLS0NERERmDRpEpKSkjB9+nQAwJYtW7BmzRqMGzcOtWrVQnJyMp4+fVrIe5y7tLQ0qKioQElJ6ZttY+jQoejatavUNC0trW+2PSIiIqK8sMcVERERFUvTp0+HIAjYs2cPWrRogcqVK6NKlSro0aNHlt5VrVq1wvXr1/H8+XNxWnBwMJo2bQptbe08t6Wuro5SpUqhTJky8Pb2hre3N06cOAEASEhIwMKFCzFv3jy0adMGFStWhKWlJZo2bVqg/QsMDETTpk1ha2sLDw8PzJ8/HykpKeL8ZcuWoVWrVggJCYGXlxdq1qyJpKQkPHnyBD169EDNmjXRtGlThIeHw97eHiEhIeKyMTExGD16NBwdHeHo6IgBAwbIlGjT1tZGqVKlpP4y4nfp0iVYWlriwoUL6NixI+zs7ODj44M7d+6IyycmJmLcuHFwdnZGzZo14enpiU2bNknNnzx5MpydnWFvb48ePXrg1q1b4vyQkBDY29sjPDwczZo1g52dHQYNGoTExEQcPXoUTZo0QZ06dTBu3DgkJydL1T0tLQ2zZs0S93nevHmQSCQ57mtqaip+/fVXuLm5oVatWmjfvj3OnDmTZ4yIiIhIsZi4IiIiomLn3bt3OHPmDHr06JFt4qlkyZJSnw0MDODu7o49e/YA+JKU2L9/Pzp06JCv7ZcoUQKfP38GAJw9exbp6el48+YNWrRogQYNGmDo0KFSSbL80NTUxOzZs3H48GFMnToVhw8fxsqVK6XKREVF4eDBg1iyZAn27dsHNTU1DBs2DCoqKti9ezfmzp2LoKAgpKamist8+vQJPXv2hIaGBrZu3Ypdu3ahVKlS6NOnDz59+lSgOgPAggULMHbsWISEhMDAwAB+fn4QBAEAsHjxYjx48ACrV6/GkSNHMHv2bJiamgIABEHAgAEDEBMTg9WrVyM0NBQODg7o1asXYmNjxfWnpqZi48aNCAwMxKZNm3D79m2MGDECoaGhWLp0KZYvX45Tp05hx44dUvU6cOAABEHArl27MH36dOzevRubN2/OcT8mTJiAK1euYMGCBThw4ADatWuHwYMH4969ewWOERERERUeJq6IiIio2Hn27BkEQUCVKlVkXqZ9+/YIDQ2FRCLByZMnoaenB0dHR7m3ffPmTRw4cADOzs4AviSPBEHAypUr4e/vj+XLlyMtLQ09e/YsUCJo6NChqFOnDsqXL4+GDRti4MCBOHTokFSZz58/Y/78+bCxsYGFhQUuXbqEJ0+eYP78+ahevTrs7e0xYcIEpKWlicscOnQIgiBgzpw5sLKyQpUqVTBjxgwkJSUhLCws1zotWrQI9vb2Un+Zlxk5ciTq1auHKlWqYMiQIXj8+DFiYmIAAC9evIC1tTVsbW1Rvnx5ODk5oXnz5gCAixcv4t69e1i6dClsbW1RsWJFjBo1CmZmZlI96NLS0jBlyhTUqFED9vb28Pb2xsWLFzF79mxYWVmhXr168PT0xKVLl6TqZWJigkmTJqFKlSpo0aIF+vbti40bN2a7n8+ePcOhQ4ewePFiODo6wszMDD169ICbmxt27dqVx5EjIiIiReIYV0RERFTsZPTgkUeDBg0gCALOnTuH4OBgtG/fXuZlz5w5A3t7e6SlpSEtLQ2enp6YPHkyAEAikeDz58+YNGkSXF1dAXx5zM/FxQVhYWFo0aKF3HUFgKNHj2Lz5s149uwZkpKSkJ6enuXRNlNTUxgbG4ufHz9+DBMTE7EXEwDUrFkTysr//hZ5584dREVFoXbt2lLr+vTpU569xPr06ZOll1qpUqWkPltaWor/NjExAQDExcWhdOnS6Nq1K0aOHIk7d+7AxcUF7u7uqFu3rlivT58+iQnBDCkpKVL1UldXR+XKlcXPRkZGMDY2hqGhodS0hw8fSq3Hzs5Oavwve3t7LFmyBB8+fICOjo5U2Tt37kAQBLRs2VJqempqKurVq5dDdIiIiKgoMHFFRERExU7FihWhpKSER48eoXHjxjIto6ysjLZt22L16tW4fv06AgICZN6eg4MDZs6cCVVVVZiYmEBNTU2cl5G4qVq1qjhNV1cXJiYmePnypczb+Nr169cxZswYDB06FA0aNICenh5OnjyJefPmSZXLPDC6IAh5Ds4ukUhgZWWFRYsWZZmX+RHLzPT19VGxYsVcy6iq/nv7mFGXjIRbw4YNcfLkSZw+fRoXL17EwIED0axZM8yZMwcSiQTGxsbYvn17lnV+nVj6ev0Z2/j6eGRMy238qrxkxDE4ODjL9kqUKJHv9RIREVHhY+KKiIiIih19fX24urpi27Zt8PX1zTLOVUJCAvT09LIs16FDB6xevRoNGzaU6pWUF01NzRwTNhk9l548eYLSpUsDAD5+/IjXr1+jbNmyMm/ja9euXYOpqSmGDh0qTpMlCValShXExMQgJiZG3L/bt29LJXFsbGxw6NAhGBgYZBujb83Q0BBt27ZF27Zt4ebmhjFjxmD69OmwsbHBmzdvoKysDDMzs0Lf7o0bN6QSe9evX4eJiUmW3lYAUL16dQiCgNevX7OHFRERUTHHMa6IiIioWJo6dSqAL2NXHTlyBI8fP8ajR4+wY8cOtG7dOttlzMzMcPHiRSxZsqTQ6lGpUiV4enoiICAAV69excOHDzFhwgQYGRnB3d0912Xfvn2Lu3fvSv3FxMTA3NwcMTEx2L9/P54/f44dO3bg4MGDedbFxcUFlSpVgr+/P+7du4fr169j7ty5UFVVFRM23t7eMDIywpAhQ3D58mU8f/4cV65cwdy5c/N8s2BGQu7rv8TERJljtWTJEvz55594+vQpHj16hGPHjsHMzAzq6uqoX78+ateujSFDhiA8PBzPnz9HZGQkli5dioiICJm3kZPY2FgEBATg8ePHOHr0KNavX4/evXtnW7ZSpUrw9vbGhAkTcPToUTx//hy3bt3C+vXrcezYsQLXhYiIiAoPe1wRERFRsWRmZoaQkBCsXr0agYGBiImJgb6+PqysrDB9+vQcl9PX1y/0usyfPx9z5szB4MGDIQgC6tSpg02bNkFTUzPX5Q4fPozDhw9LTfvpp58wfvx49O3bF7Nnz0ZKSgpcXFwwYsSIXPcL+PI4ZFBQECZNmoQOHTqgXLly8Pf3x/Dhw6GhoQHgS++x7du3Y8GCBRg5ciQSExNhYmICJyenPHtgLV++HMuXL5ea5u3tjcDAwFyXy6Curo5FixYhKioKGhoasLOzw6pVqwB8ebxvzZo1WLx4MSZPnoz4+HgYGRmhdu3aaNu2rUzrz423tzckEgk6deoEJSUldOjQIcfEFQDMmTMHq1atwq+//oqYmBiULFkSNWvWhJOTU4HrQkRERIVHScjP6KdEREREVCzcu3cPbdq0wZ49e1CjRo2irg4RERFRoWKPKyIiIqL/kOPHj4tjcr148QJz586FlZUVbGxsirpqRERERIWOiSsiIiKi/5CPHz8iMDAQr169gp6eHpycnDBhwoQ83zZIRERE9F/ERwWJiIiIiIiIiKhY4lsFiYiIiIiIiIioWGLiioiIiIiIiIiIiiUmroiIiOibev/+PerXr49nz54VdVUAAMuWLUP9+vVhaWmJkJCQoq5OobO0tMTRo0cLXOa/JCoqCpaWlrh165bCt71t2zYMGjRI4dslIiL6UTBxRURERN/UqlWr0LBhQ1SoUAHAv0kGJycnJCYmSpX19fXFjBkzxM+fPn3C2LFj4eLigkGDBiE+Pr5AdXnw4AGCgoIwffp0nD17Fi1atCjQ+og6deqE27dvIyIioqirQkRE9F1i4oqIiIi+mU+fPiE4OBjt27fPdt6aNWtyXX7z5s1QV1fHhg0bUK1aNSxatKhA9cno9eXl5YVSpUqhRIkSBVoffd9SU1PzLKOurg5vb29s2bJFATUiIiL68TBxRURERN9MeHg4lJWVUadOnSzzfH19sWXLFsTExOS4/Pv371GpUiVYWlqiWrVqSEhIyHV79+/fR+/evWFra4u6devC399f7NW1bNkyDB06FABgZWUFS0vLHNcTExOD0aNHw9HREY6OjhgwYACePn0qzl+2bBlatWqFQ4cOwcvLC/b29hgyZIhUj7D79++jV69eqF27Nuzt7dG6dWtcvHhRnP/w4UMMGDAA9vb2cHZ2xpgxY/D69Wtxvr+/PwYOHIg1a9bAxcUFderUQWBgICQSCZYtWwZnZ2e4uLhkm/x78+YNBgwYADs7O7i7u2Pfvn25xi2v/c2OpaUlduzYgcGDB8POzg5NmzbFxYsXER0djb59+6JWrVpo06YN7ty5I7XctWvX0KNHD9jZ2aFBgwaYOnUqPnz4IM739fXF1KlTMXfuXNStWxf16tXD5s2bkZqaiunTp8PBwQGNGjVCaGholjo9ffoUXbt2Rc2aNdGsWTOcPXtWar48MXdzc0PDhg0BAMeOHYO3t7fYrnr06IE3b96Iy3l4eODkyZP49OlTrjEjIiIi+TFxRURERN9MREQEbGxsoKSklGVes2bNYGFhgSVLluS4fM+ePRESEgIbGxssWrRITDxl59OnT+jXrx+0tLTw+++/IygoCJGRkZg4cSIA4KeffsKsWbMAAGfPns2S1Ph6PT179oSGhga2bt2KXbt2oVSpUujTp49UYuLFixc4fPgwgoKCsGHDBty9exeLFy8W5/v5+aFUqVL4/fffERoaiuHDh0NDQwMAEBsbi+7du6NatWoIDg7Gxo0bkZSUhMGDB0MikYjruHLlCqKiorBlyxZMnz4d69atQ//+/ZGamoodO3Zg2LBhWLBgAW7fvi21D8uWLYOHhwdCQ0PRqVMnjB8/Psfxn2Td3+ysXLkSLVu2xL59+1CjRg2MHTsWv/zyC7p27Yq9e/fCxMQE/v7+Yvn79++jb9++8PDwwL59+xAUFIR79+6JxyjDgQMHoK2tjd27d2PAgAGYPXs2hgwZAnNzc+zZswdt27bFpEmTsiQ9f/31V/j6+iI0NBQuLi4YMmSIWEbWmF++fBn379/HunXrsGnTJrx+/RpjxoxBu3btcPjwYWzbtg1t2rSR2m6NGjWQnp6O69ev5xovIiIiygeBiIiI6BsZPHiw8PPPP0tNe/78uWBhYSHcvHlTuHTpklC9enXhwYMHgiAIQo8ePYTp06dLlZdIJEJMTIyQnp6e67Z+++03oXbt2kJiYqI47eLFi4KFhYXw9OlTQRAE4ciRI4KFhUWu6/n999+Fxo0bCxKJRJyWlpYm1K1bVzh06JAgCIKwdOlSoUaNGkJCQoJYZsWKFYKXl5f42d7eXggJCcl2G4sXLxZ69uwpNe3du3eChYWFcOPGDUEQBGH8+PGCm5ubkJaWJpZp166d0KpVK6nl3N3dhXXr1omfLSwshF9++UWqTK9evYSxY8dKlTly5IjM+5sdCwsLITAwUPx8//59wcLCQtiwYYM4LSP+cXFxgiAIwrhx44QJEyZIreevv/4SLCwshDdv3giC8KUNdOrUSZwvkUgEJycnYeDAgeK01NRUwcbGRtyHjDa1YsUKsUx6errQpEkTYeHChYIgyB5zJycnISUlRSxz+/ZtwcLCQoiKisoxFoIgCI6OjsLu3btzLUNERETyUy3qxBkRERF9v1JSUmBsbJzj/Lp168LV1RULFizAqlWrsi2jpKQEExOTPLf16NEjWFpaQkdHR5xmb28PZWVlPHz4EBUrVpSpznfu3EFUVBRq164tNf3Tp094/vy5+Lls2bLQ1dUVP5uYmCAuLk783KdPH0yaNAl79+6Fs7MzmjRpgipVqojbiIiIgL29fZbtP3v2DLa2tgCAqlWrQkVFRZxnbGwstU0AMDIyktouANSqVSvL5/Dw8ALtb3a+ftwy4zhbWFhkmRYfHw9DQ0PcuXMH//zzD44cOSKWEQQBwJf9NjIyyrJeJSUlGBkZSU1TU1ODnp5ervutrKwMW1tbPHr0SNxPWWJerVo1qKuri/OsrKxQv359tGrVCq6urnB2dkazZs1gaGgotQ4NDQ2kpKTkGCsiIiLKHyauiIiI6JvR19fH+/fvcy3j5+eHNm3aFPitbIIgZPtIIoAcp2dHIpHAysoq24HgS5YsKf5bTU0tyzYykjAAMHz4cHh7e+P06dM4e/Ysli9fjmnTpqFDhw6QSCRo2LAhxo8fn2UbGckbAFBVlb5VU1JSynO78pJ1f7OTuX45Tct4FE8ikaBjx47o3bt3ljKmpqY5rkNJSSnbafLst6wx19LSkpqnoqKCDRs24Pr16zh37hyCg4OxcOFCbNu2DVZWVmK59+/fZ0lmERERUcExcUVERETfjLW1Nfbu3ZtrGQsLC7Rt2xa//vqrVE8XeVWtWhV79uzBhw8fxF5XkZGRkEgkYk8nWdjY2ODQoUMwMDCAnp5evusDAObm5jA3N0fPnj0xdepUBAcHo0OHDrCxscGRI0dQtmzZLImownDjxg106NBB6nPlypWzLVuY+5sXa2truXq/yevGjRtwdnYG8CWRefPmTTRr1gwAChRzJSUl2Nvbw97eHkOHDkXLli1x+PBhMXH17NkzpKSkwNraunB3iIiIiDg4OxEREX07rq6uePToEd6+fZtruREjRuDu3bu4ceNGvrfl7e0NTU1NjB8/Hvfv38eVK1cwZcoUNGnSRK5Eibe3N4yMjDBkyBBcvnwZz58/x5UrVzB37tw837SXITk5GdOnT8elS5cQFRWFGzdu4Nq1a2ICrVu3bkhMTMTo0aNx48YNPH/+HOfPn8fkyZOl3rCXX8eOHcPu3bvx9OlTrF69GhcuXECvXr2+2f7Kqn///rh58yamTJmCv/76C//88w/CwsIwZcqUQln/zp07cfToUTx+/BgBAQF4+fIlunbtCiD/Mb9+/TpWrFiBmzdv4uXLlzhx4gRevXollQyNiIiAmZkZzM3NC2U/iIiI6F/scUVERETfjKWlJWxtbXH48GF07949x3JlypSBr68v1q1bl+9taWpqYv369Zg9ezY6duwIDQ0NeHp64pdffpF7Pdu3b8eCBQswcuRIJCYmwsTEBE5OTjL3SFJWVkZCQgL8/f3x+vVr6Ovrw93dXXxMzdTUFDt37sTChQvRr18/pKSkoEyZMnB1dS1Qr7MMw4cPxx9//IFZs2bB0NAQc+bMEcdw+hb7KysrKyts27YNixcvRo8ePSCRSGBmZgYvL69CWf/YsWOxadMm3LlzB2XLlkVQUBBKly4NIP8x19XVxbVr17Bt2zYkJCSgTJkyGDJkiNSbBQ8dOoROnToVyj4QERGRNCWhIIMiEBEREeXh9OnTCAgIwOHDh6UGGif6Hjx48AC9e/fGH3/8kWXgfCIiIio49rgiIiKib8rNzQ1Pnz5FdHQ0ypUrV9TVISpUsbGxmDdvHpNWRERE3wh7XBERERERERERUbHEwdmJiIiIiIiIiKhYYuKKiIiIiIiIiIiKJSauiIiIiIiIiIioWGLiioiIiIiIiIiIiiUmroiIiIiIiIiIqFhi4oqIiIiIiIiIiIql/wH1nCfpLu33AwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20,10))\n",
    "sns.set_theme(style=\"darkgrid\", palette=\"bright\")\n",
    "sns.color_palette(\"Set3\", 10)\n",
    "counts=melted.groupby(['CMIP6_LE']).count()/5\n",
    "\n",
    "\n",
    "g=sns.boxplot(x=\"CMIP6_LE\", y=\"r_coeff\",\n",
    "            hue=\"CV_pattern\", \n",
    "            data=melted, palette=[\"m\", \"c\",\"r\",\"g\",\"y\"], showmeans=True, meanprops={\"marker\":\"o\",\n",
    "                       \"markerfacecolor\":\"white\", \n",
    "                       \"markeredgecolor\":\"black\",\n",
    "                      \"markersize\":\"6\"})\n",
    "yticks=g.get_yticks()\n",
    "for xtick in g.get_xticks():\n",
    "    ax.axvline(x=xtick-0.5, color='black', linewidth=0.3)\n",
    "    g.text(xtick+0.04,-1.15, '('+str(int(counts['ensemble_member'][xtick]))+')', horizontalalignment='center',size='medium',color='black',weight='regular')\n",
    "\n",
    "#g.set(xlabel=\"Pearson r coefficient\", ylabel=\"CMIP6 LE (number of ensemble members)\")\n",
    "ax.set_xticklabels(ax.get_xticklabels(),rotation=90)\n",
    "ax.set_ylabel(\"Pearson r coefficient\",weight='normal',fontsize=14)\n",
    "ax.set_xlabel(r\"CMIP6 Large Ensemble\"\"\\n\"r\"(N° of ensemble members)\",weight='normal',fontsize=14,labelpad=22)\n",
    "sns.despine(offset=6, trim=True)\n",
    "ax.set_ylim([-1, 1])\n",
    "plt.xticks(fontsize=12.5, weight='regular')\n",
    "#plt.grid(True)\n",
    "plt.legend(bbox_to_anchor=(0., 0.23), loc=2, prop={'size': 15}, borderaxespad=0.)\n",
    "plt.savefig(\"Results_DIR/boxplot_cmip6_pattern_spatial_correlations.png\", dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e4b0fb8-9ba5-4e10-b034-0eebdf341f6a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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