Fig6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "19399412",
   "metadata": {},
   "source": [
    "# Script to produce Fig. 6"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d12c71a9",
   "metadata": {},
   "source": [
    "## Load packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "40fae486",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import itertools\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from cartopy.mpl.geoaxes import GeoAxes\n",
    "from mpl_toolkits.axes_grid1 import AxesGrid\n",
    "import cartopy as crt\n",
    "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "757e0b2c",
   "metadata": {},
   "source": [
    "## Settings and folder paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0fa4c962",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use(\"seaborn-v0_8\")\n",
    "sns.set_style(\"darkgrid\")\n",
    "sns.set_context(\"notebook\", font_scale=3.5)\n",
    "\n",
    "idir = r'C:\\Users\\muis\\paper_gtsmip\\data'\n",
    "\n",
    "models = ['EC-Earth3P-HR','HadGEM3-GC31-HM-SST','GFDL-CM4C192-SST','CMCC-CM2-VHR4','HadGEM3-GC31-HM']\n",
    "rp = 10\n",
    "periods = ['1951-1980','1985-2014','2021-2050']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5773ecda",
   "metadata": {},
   "source": [
    "## Load data and store in single DataFrame "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "127f7063",
   "metadata": {},
   "outputs": [],
   "source": [
    "# bedlevel\n",
    "ifile = 'data/bedlevel.nc'\n",
    "ds_bedlevel = xr.open_dataset(ifile)\n",
    "pxyn = ds_bedlevel.to_dataframe()\n",
    "\n",
    "# HighResMIP\n",
    "df_models = []\n",
    "for model, period in itertools.product(models, periods):   \n",
    "    sim = 'future' if period == '2021-2050' else 'historical'\n",
    "    nc_file = os.path.join(idir,'%s_%s_surge_actual-value_%s_rp%s_best-fit_v1.nc' % (str(model),str(sim),str(period),str(rp)))    \n",
    "    ds_model = xr.open_dataset(nc_file)\n",
    "    df_model = ds_model['return_mean_surge_level'].to_dataframe()\n",
    "    df_model.rename(columns={df_model.columns[2]: '%s_%s' % (model,period)}, inplace = True)\n",
    "    df_model.drop(columns=['station_x_coordinate','station_y_coordinate'],inplace=True)\n",
    "    df_models.append(df_model)\n",
    "df_data = pd.concat(df_models, axis=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96c5eb6f",
   "metadata": {},
   "source": [
    "## Compute changes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8499ff98-bd1f-47bd-aa34-f60e4e496982",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ensemble stats\n",
    "df_changes=[]\n",
    "for model in models:\n",
    "    df_changes.append(df_data['%s_1985-2014' % model].subtract(df_data['%s_1951-1980' % model],axis=0).rename('abs_change_current_%s' % model))\n",
    "    df_changes.append(df_data['%s_2021-2050' % model].subtract(df_data['%s_1951-1980' % model],axis=0).rename('abs_change_future_%s' % model))\n",
    "    df_changes.append(df_data['%s_1985-2014' % model].subtract(df_data['%s_1951-1980' % model],axis=0).div(df_data['%s_1951-1980' % model],axis=0).mul(100).rename('rel_change_current_%s' % model))   \n",
    "    df_changes.append(df_data['%s_2021-2050' % model].subtract(df_data['%s_1951-1980' % model],axis=0).div(df_data['%s_1951-1980' % model],axis=0).mul(100).rename('rel_change_future_%s' % model))   \n",
    "df_changes = pd.DataFrame(df_changes).transpose()\n",
    "\n",
    "df_median_changes=[]\n",
    "df_median_changes.append(df_changes.filter(regex='abs_change_current').median(axis=1).rename('abs_change_current'))\n",
    "df_median_changes.append(df_changes.filter(regex='abs_change_future').median(axis=1).rename('abs_change_future'))\n",
    "df_median_changes.append(df_changes.filter(regex='rel_change_current').median(axis=1).rename('rel_change_current'))\n",
    "df_median_changes.append(df_changes.filter(regex='rel_change_future').median(axis=1).rename('rel_change_future'))\n",
    "df_median_changes = pd.DataFrame(df_median_changes).transpose()\n",
    "\n",
    "changes_current_stats = df_changes.filter(regex='abs_change_future').apply(pd.DataFrame.describe, axis=1)\n",
    "changes_future_stats = df_changes.filter(regex='abs_change_future').apply(pd.DataFrame.describe, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c9729fd5-7754-4ae0-96ae-fdf902e6a4fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# no of postite/negative models\n",
    "pos= (df_changes.filter(regex='abs_change_current') >0.0).sum(axis=1).rename('pos')\n",
    "neg = (df_changes.filter(regex='abs_change_current') <-0.0).sum(axis=1).rename('neg')\n",
    "no = pd.DataFrame([max(l1, l2) for l1, l2 in zip(pos, neg)])\n",
    "\n",
    "no_models = pxyn.copy()\n",
    "no_models['current'] = (pos>neg).fillna(0).astype(int).replace(0,-1)* no[0]\n",
    "no_models['current_bool'] = 0\n",
    "no_models['current_bool'].loc[no_models['current']>2] = 1 \n",
    "no_models['current_bool'].loc[no_models['current']<-1] = -1 \n",
    "\n",
    "pos = (df_changes.filter(regex='abs_change_future') >0.0).sum(axis=1).rename('pos')\n",
    "neg = (df_changes.filter(regex='abs_change_future') <0.0).sum(axis=1).rename('neg')\n",
    "no = pd.DataFrame([max(l1, l2) for l1, l2 in zip(pos, neg)])\n",
    "no_models['future'] = (pos>neg).fillna(0).astype(int).replace(0,-1)* no[0]\n",
    "no_models['future_bool'] = 0\n",
    "no_models['future_bool'].loc[no_models['future']>2] = 1 \n",
    "no_models['future_bool'].loc[no_models['future']<-1] = -1 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "877e9131",
   "metadata": {},
   "source": [
    "## Plot global maps "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4b2bd8f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to plot global maps\n",
    "def global_map(ax,pxyn,var,cm,clim,clabel):\n",
    "    # Projections\n",
    "    crg = crt.crs.PlateCarree() # the one we have defined the data\n",
    "    crgp = crt.crs.Robinson() # the one to plot the data\n",
    "    # Identify european coastal points\n",
    "    lonmin = -35\n",
    "    lonmax = 50\n",
    "    latmin = 20\n",
    "    latmax = 80\n",
    "    depththresh = -20\n",
    "    ioceaneu = pxyn[(pxyn['bedlevel']<depththresh)\n",
    "                    &(pxyn['lat']<=latmax)&(pxyn['lat']>=latmin)\n",
    "                    &(pxyn['lon']>=lonmin)&(pxyn['lon']<=lonmax)].index\n",
    "    ioceanNeu = pxyn[(pxyn['bedlevel']<depththresh)\n",
    "                    &((pxyn['lat']>latmax)|(pxyn['lat']<latmin)\n",
    "                    |(pxyn['lon']<lonmin)|(pxyn['lon']>lonmax))].index\n",
    "    # Identify coastal points    \n",
    "    icoast = pxyn[pxyn['bedlevel']>=depththresh].index\n",
    "    alfaoceaneu = 0.1\n",
    "    alfaoceanNeu = 0.4\n",
    "    alfacoast = 1\n",
    "    psoceaneu = 4\n",
    "    psoceanNeu = 20\n",
    "    pscoast = 6\n",
    "    # format\n",
    "    mpl.rcParams.update({'font.size': 20})\n",
    "    # plot map \n",
    "    ax.set_global()\n",
    "    ax.set_extent([-180, 180, -60, 70], crg)            \n",
    "    ax.coastlines(resolution='10m', color='gray', linewidth=0.5, alpha=0.8,zorder=6)\n",
    "    ax.add_feature(crt.feature.LAND.with_scale('10m'),facecolor='gray',zorder=4,alpha=0.20)\n",
    "    ax.add_feature(crt.feature.LAKES.with_scale('10m'), facecolor='gray',zorder=5,alpha=0.05)        \n",
    "    ax.add_feature(crt.feature.OCEAN.with_scale('10m'), edgecolor='face', facecolor='white')\n",
    "    # plot data\n",
    "    bs=ax.scatter(x=pxyn.loc[ioceaneu,'lon'].values,y=pxyn.loc[ioceaneu,'lat'].values,alpha=alfaoceaneu,s=psoceaneu,c=var.loc[ioceaneu].values,cmap=cm,vmin=clim[0], vmax=clim[1],transform=crg)\n",
    "    bs=ax.scatter(x=pxyn.loc[ioceanNeu,'lon'].values,y=pxyn.loc[ioceanNeu,'lat'].values,alpha=alfaoceanNeu,s=psoceanNeu,c=var.loc[ioceanNeu].values,cmap=cm,vmin=clim[0], vmax=clim[1],transform=crg)\n",
    "    bs=ax.scatter(x=pxyn.loc[icoast,'lon'].values,y=pxyn.loc[icoast,'lat'].values,alpha=alfacoast,s=pscoast,c=var.loc[icoast].values,cmap=cm,vmin=clim[0], vmax=clim[1],transform=crg)  \n",
    "    # Format lat lon grid    \n",
    "    gl = ax.gridlines(crs=crg, draw_labels=True, linewidth=0.5, color='gray', alpha=0.5, linestyle='--')\n",
    "    gl.yline = gl.xlines = True\n",
    "    gl.xformatter = LONGITUDE_FORMATTER\n",
    "    gl.yformatter = LATITUDE_FORMATTER\n",
    "    gl.ylabel_style = {'size': 12, 'color': 'gray'}\n",
    "    gl.xlabel_style = {'rotation': 0, 'size': 12, 'color': 'gray'}\n",
    "    gl.top_labels = gl.right_labels = False\n",
    "    gl.ylocator = mpl.ticker.FixedLocator(np.arange(-60.,61.,20))\n",
    "    gl.xlocator = mpl.ticker.FixedLocator(np.arange(-180.,181.,40))\n",
    "    # Colorbar\n",
    "    cbar = ax.cax.colorbar(bs)\n",
    "    cbar.set_label(label=clabel,size=12)\n",
    "    return ax, cbar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d0ea5050",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ListedColormap' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[16], line 15\u001b[0m\n\u001b[0;32m     13\u001b[0m cm \u001b[38;5;241m=\u001b[39m sns\u001b[38;5;241m.\u001b[39mdiverging_palette(\u001b[38;5;241m150\u001b[39m, \u001b[38;5;241m275\u001b[39m, n\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m11\u001b[39m)\u001b[38;5;241m.\u001b[39mas_hex() \u001b[38;5;66;03m# 11\u001b[39;00m\n\u001b[0;32m     14\u001b[0m cm[\u001b[38;5;241m6\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m#D3D3D3\u001b[39m\u001b[38;5;124m'\u001b[39m; cm[\u001b[38;5;241m4\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m#D3D3D3\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m---> 15\u001b[0m cm \u001b[38;5;241m=\u001b[39m \u001b[43mListedColormap\u001b[49m(cm)\n\u001b[0;32m     16\u001b[0m clim\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m5.5\u001b[39m,\u001b[38;5;241m5.5\u001b[39m]\n\u001b[0;32m     17\u001b[0m label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNo. of models showing decrease/increase\u001b[39m\u001b[38;5;124m'\u001b[39m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'ListedColormap' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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96lPjT//0T+OEE07Y1nV3uctd4l3veldccMEFfdoZAGzPoe/hqtV6rKyU4sCBQhSLVUVvAAAMVb3ejLW1cuzfvx7r6+VoNG59fyqLZJrIIvvLiW/QI4dWqNfrzSiVqlGpGCEAAMBoap0Cl8kkMTc3E/PzM5Ekic5Lpsb/+l//K376p3863vrWt8ZHP/rRuP7664/4ueedd148+clPjsc//vGRy4lSABi+Q9+zbYy3r0Wj0RzyrgAAoNOhp8DNzGRjfj4f+bwxqEwXWWT/JKlSWtiR1g/jNE2jXK5HuVyNel3IBADA+JmdzcX8fD5mZgRPTJ9rr702/vu//ztuueWWWFtbi+OOOy5OPvnkuNvd7hannHLK0Pa1tFTo6d8x9+1bjGzWAACAcXR4421rnKmEHwCAcZPJJDE/n4+5uZnIZDTjMn1GMYsc1xxS4RvsUKPRjFKpFuVyVcgEAMBEyOUyMT+fj9nZnFPgYMjGNXACoHcObbytVhtRKlWjVmsMe1sAANATrWkUuZxmXBimcc0hnYkH23DoD9lqtR6lUjWqVSETAACTpV5vxtpaOdbXbx2Dms0qgAMAGKRbC95a40yr0WzqvAUAYLKUy7Uol2uRy2Vjfn4mZmc3ylhkkcBWKHyDLTj0h+pGyFSLRsM4UwAAJluapgd/yZrP52JhwRhUAIB+a73PajTSKJUqUS7Xhr0lAADou3q9EWtrjSgUkpifn4m5uXwkiRwSODqFb3AUh3ZVFouVKJVqYTowAADTqFqtR7Vaj5mZ7MExqBGCJwCAXjj0PVW93oxisRrVan3IuwIAgMFrNtMoFKpRLFZ/PI0ibxoFcEQK32ATrR+azWYaxaKuSgAAaKnVGlGrlSKbzfy483ImIhTAAQB049D3UJVKLYrFatTrJk0AAECaRpRKtSiVajE7m4v5edMogE4K3+DHDu+qLJWqUanoqgQAgM00Gs1YX69EoVCN+fmNzkujBwAAtubQ90yt0fKNhkkTAACwmUqlHpXKxjSKhYV85POmUQAbFL4x9Vo/DJMkiUqlHqVSNWq1xrC3BQAAYyFN0ygWbx09sLBg9AAAwJG03iO13kOVSrVIUwVvAACwFbVaI1ZWNqZRLCzkY3ZWARxMO4VvTK1Df/iVyxtjBBoNYwQAAKBb5XItyuWN0QMLC/nI5bJCJwCAuDWLbDbTKBYrUS7Xhr0lAAAYW41GM9bWylEoJDE/n4/5+ZmIUAAH00jhG1Ons+CtYowAAAD0UGv0gAI4AGDaKXgDAID+aTbTKBQqUSxWY2FBARxMI4VvTA0FbwAAMFitArh8PheLiwrgAIDpoeANAAAGJ00VwMG0UvjGxFPwBgAAw1Wt1qNaVQAHAEw+BW8AADA8CuBg+ih8Y2IpeAMAgNGiAA4AmFQK3gAAYHQogIPpofCNiabgDQAARo8COABgUih4AwCA0aUADiafwjcmSusHVLVaj/X1SjQazWFvCQAAOIJWAdzc3EwsLOQjm02ETgDAWGi9Z0nTiGJx4xdpAADAaGoVwJVK1VhcnI3ZWaUyMCm8mpkIraCp0WjG+nolarXGsLcEAABsUblci3K5FgsL+VhYyEeErksAYDQd+h6lVKpGoVCNNDVtAgAAxkGzmcbaWjlKpUwsLs5GPp+TQ8KYU/jGWDt0lEChUI5KpT7sLQEAAF0qFqtRKtVicTEfc3PGDgAAoydJkqhUalEoVE2bAACAMVWvN2NlpRT5fDYWF2cjl8vKIWFMKXxjLBklAAAAkylN01hfr/y4AM7YAQBg+FpZZK3WiELBtAkAAJgU1WojqtVizM3NxMJCPrLZRAEcjBm/QWBsGSUAAACTq9FoxupqKWZmNrouZ2Z0XQIAg2XaBAAATIdyuRblci0WFvIxP5+PJDGJAsaFwjfGRusHS7Vaj/X1cjQaCt4AAGDS1WqNWF4uxuxsLhYXZ3VdAgB9d+h7jY2TaE2bAACAaVAsVqNUqsWuXbMxNzcz7O0AW6DwjZF3aGfl+no5qlWdlQAAMG0qlXpUq/VYWJiN+XmhEwDQH60sslyuRaFQiWZT8y0AAEyTNE1jba0cpVI1du2aM4kCRpzCN0bWoT88CoVKFIs6KwEAYJql6cbfDcrlja7LfD4ndAIAeqL1nqLRaMb6eiVqtcawtwQAAAxRvd6M5eVizM3NxOLirPGnMKIUvjGSWj8wKpWNsaY6KwEAgJZGoxkrKyXjTwGAHWu9h9gosC9HqVQb9pYAAIARUi7XolKpx+Ji3vhTGEEK3xgp7WNNS1Gt6qwEAAA2V6nUD4ZO8/P5YW8HABgzh441XV+vRJpqvgUAADqlaRrr661JFMafwihR+MbIMdYUAADYjkKhejB0Mv4UADiWQ8earq1Vol7XfAsAABxb+/jT/I9Pj5ZFwjApfGPoWj8IqtV6rK0ZawoAAGxfo5EeHH+6a9es0AkAOKr1dWNNAQCA7rTGn+7aNRtzczNySBgihW8MTeubf5qmsbZWjkqlPuwtAQAAY65SqUe1Wo/FxdmYn88LnQCAiNB8CwAA9FarzqFcrsVxx81FNqsRF4ZB4RtD0fqGXy7XYn29EmkqaAIAAHojTSPW1ys/7rqci1xO6AQA00rzLQAA0E+1WiMOHCjE4mI+5ufzw94OTB2FbwxUK2hqNtNYWytFrdYY9pYAAIAJVas1YmmpEAsL+VhYEDoBwLRpb74th95bAACgXwqFapTL9TjuuLmYmclqxIUBUfjGwBWLlSgUqsPeBgAAMCWKxWpUKkInAJgWmm8BAIBhaDSasbxcjLm5mVhcnI0kCVkk9JnCN/qu9Y28VmvE2lo5Go3msLcEAABMmc1CJwBgcmm+BQAAhqVcrkW1Wo9du+Zidjan+A36SOEbA7G+XolSSdAEAAAMVyt0Ou64ucjnhU4AMClaP9Pr9Y3m23pd8y0AADA8zWYaq6ulmJ3Nxa5dc05/gz5R+EZfOOUNAAAYVc1mGisrJae/AcCEKRQqUSxqvgUAAEZHpVKPWq0Qu3bNxuzsjOI36DGFb/SNU94AAIBR5vQ3ABhvTnkDAADGwcbpb+WYna07/Q16TOEbPeOUNwAAYNw4/Q0AxptT3gAAgHHh9DfoPYVv9JRT3gAAgHHk9DcAGA9OeQMAAMaZ09+gtxS+sSNOeQMAACaF098AYLSlaRoRTnkDAADGX6VSj2q1EMcd5/Q32AmFb3RN0AQAAEyi1ulvu3fPxcyM098AYNhaP4sbjTTW1kpOeQMAACZCmnae/gZsj8I3tk3QBAAATLpmM43l5VLMz+djcTE/7O0AwNRqZZGlUjXW1yvD3g4AAEDPVSr1qNUKcdxxc5HPa8SF7VD4xrYImgAAgGlSKlUPnv6Wy2WFTgAwIK2fuc3mRvNtrdYY9pYAAAD6ptlMY2VFIy5sl8I3tkTQBAAATKtGoxlLS8VYXJyN+fkZxW8A0Getn7Xlci3W18uRpsPeEQAAwGBoxIXtyQx7A4y+Q4OmpaWCojcAAGAqFQqVWFkpRbO58dv31G/hAaAv0jRidbUUa2uK3gAAgOnTasQtFqsRIYeEo1H4xjEJmgAAADbUao1YWipEuVyLJEmETgDQI62fqdVqPZaWClGp1Ie8IwAAgOHSiAvHpvCNTbW+YdZqgiYAAIBDpWnE2lo5VldLmoMAoAdaWeT6erntlzoAAADTTiMuHF1u2Btg9LS+URYKlYNHZwIAANCuUqlHrVaI3bvnYmYmF2maRpIkw94WAIyN1s/ORiONtbVS1OvNYW8JAABg5LQacavVehx33NywtwMjReEbB7WCpmYzjdVVQRMAAMCxNJtpLC+XYmEhHwsLecVvALBFrZ+ZpVI11tcrw94OAADAyLu1EXc+ZmayskgIo075sdY3xHK5FktLBUVvAAAA21AsVmN5+dbRbEYOAMDRpWnE6mpJ0RsAAMA2bDTiFqNY3Pi7lBySaafwjYNWV0uxtlYO3xcBAAC2r15vxNJSIcrlWiRJInQCgMO0fjbWavVYWipEpVIf8o4AAADGU6FQjZUVjbig8G2K3Ro0NeLAAUETAADATqVpxNpaOVZXS8PeCgCMlFYWWShU2k5JBQAAoDu1WuPHTUUacZleuWFvgOFojTYtFqtRKBgnAAAA0EuVSj3q9ULs3j0fuVz24N/BAGBaNZtprK2Vo1ZrDHsrAAAAEyNNI1ZXyzE314hdu2blkEwdJ75NqTSNWFkpKnoDAADok0YjjaWlYpRKVR2XAEyl1s++arUeS0tFRW8AAAB9Ui7XYnm5aPQpU0fh2xQ5dLTp0lIhqlVBEwAAQL+tr1didbUUsiYApkkri1xfr8TKSskvXQAAAPqsXm8afcrUMep0ShhtCgAAMDyt0afHHTcfMzNGnwIw+ZrNNFZXy1Gva74FAAAYFKNPmTZOfJsSRpsCAAAMV6ORxvKy0acATK7Wz7ZKZWO0qaI3AACA4TD6lGmh8G2CGW0KAAAwem4d+TbsnQBA79w62rT84xHfftABAAAMU2v0abls9CmTS+HbhDp0tOmhVbwAAAAMX7Vaj6WlwsGTcIROAIy7ZjON5eVSlEq1YW8FAACAH0vTiLW1cqytlX/8WA7JZFH4NsFWVkpGmwIAAIyoZjONpaWijksAxlbrZ9dGQbfRpgAAAKPq8NGnMCkUvk2QVtBUrzdiaakY1Wp9yDsCAADgWHRcAjCODp04sTHC288wAACAUbYx+vTWWhJ/j2MSKHybEK2gqVWl22g0h70lAAAAtkjHJQDjyMQJAACA8ZKmaayslKJYrJhCwURQ+DYBWt+I1tc3TgnwfQkAAGD86LgEYNSZOAEAADAZCoXW6d3D3gnsjMK3CdCqyC2VasPeCgAAADug4xKAUXXoxImlJRMnAAAAxl21Wo/l5ULU642I0IjLeFL4NqZa33BqtY3uylqtMeQdAQAA0Cs6LgEYJYdPnAAAAGAyNBppLC0Vo1yuacRlLCl8G0Ot7spSqRrLy8VoNn3jAQAAmDQbHZfFqNedqAPAcKVpmDgBAAAwwdbWyrG+XokIJ78xXhS+jZnWN5hDv+kAAAAwmRqNZiwvF6JarUeE0AmAwWn9zKnXG7G0VDBxAgAAYMKVStVYXTWFgvGi8G3MpGkaKyulKJd1VwIAAEyD1ik7xWLVuAEABqI1caJSqZk4AQAAMEWq1UYsLxeiXtf8xHhQ+DYG2rsri7orAQAAplChUInV1VJEOPkNgP5pFb1t/Nwp6/QHAACYMo1GGsvLxahUTKFg9Cl8G3GtoKlcrsXSku5KAACAaVap1J28A0DftU4aBQAAYDqlacTqaimKxYopFIw0hW8j7NDuyrW18rC3AwAAwAio15ttp4ELnQDolUZj42dMtVof9lYAAAAYAYVC1RQKRprCtxHV+oahuxIAAIDDpenGuIFyuabjEoAdaf0MqdXqsbRUjEajOeQdAQAAMEpaUyhkkIwihW8jqtnc+CWG7koAAACOZG2tHIWCcQMAdKc1caJcrsXycsnPEgAAADbVmkJRrzeGvRVoo/BthLSCpXq9EcvLxajXdVcCAABwdMWicQMAbF+r6K1QqMTaWnnY2wEAAGDEtQ5wqlQ2DnCSRTIKFL6NiFbQ1Doistn0DQIAAICt2fi7pJN6ANia1s+L1dVSFIvVIe8GAACAcZGmt/5d0hQKRoHCtxHQKnprden7vgAAAMB21esN4wYA2JI0be/SBwAAgO049PRwxW8Mk8K3IWt9A1hbK0ehUBnybgAAABhnrXED1apxAwBs7tZC6eawtwIAAMAYK5drBw93kkMyLLlhb2CatV74KyulqNV05AMAALBzabrx98xdu2Zjfj5/8JTxwTx3Gp/73OfiX/7lX+KrX/1q/OhHP4rV1dXI5XKxb9++uNOd7hT3u9/94ud//udj3759A9nT0XzrW9+KT37yk/GlL30pvvvd78by8nKUy+XYs2dP7Nu3L84///y4973vHQ9/+MNjYWFh2NsF6FrrZ0G1WjdxAgAAgJ6pVhuxvFyMPXvmI5sdTAbZIoskIiJJlV0OTbPZjJWVku5KAAAA+mJhIR+Li7MDKX77/Oc/H3/wB38Q//3f/33Mz83n8/Erv/Ir8dznPjcWFxf7uq/NfPvb347/+3//b3z605/eUjfq3r1741d/9VfjWc96VuTz+QHs8FZLS4We5gb79i1GNmsAAEyT1s+Acrl2cAwNAAAA9FImk8SePfORy2UH8nyyyN4b1xxS4duQNBrNWF4uRrPpyw8AAED/zM3NxK5dsxERfSt++/M///N4wxvesO3r7nCHO8Sb3/zmOP300/uwq8195CMfiZe+9KVRLBa3fe35558ff/7nfx6nnXZaH3a2uXENnIDR0Cp6KxarUShUhr0dAAAAJliSROzZMx8zM7m+NuLKIvtjXHNIhW9DUKs1YmWlZMYxAAAAA5HP52L37rmI6H3x26te9ap461vfuunHFhcX4+STT46VlZU4cODApp9z29veNt797nfHKaec0tN9beYf//Ef47d+67c2/ft4JpOJU089Nebn52P//v2xvLy86T1OP/30ePe73x0nnnhin3e7YVwDJ2D4Wr9kWF8vR6lUG/Z2AAAAmBK7d8/F7OxMX4rfZJH9M645pMK3AWm9oKvVeqyulsJXHQAAgEHK5bKxZ898ZDK9C5v+6Z/+KZ73vOd1rD/0oQ+Niy66KO5+97sfXPvhD38Y7373u+Mv/uIvolZrL8C4xz3uEX/7t38bmUz/gpBvf/vb8djHPjaq1Wrb+kknnRTPec5z4lGPelTs3bv34Pq3vvWteOc73xnvfe97O8Kpn/zJn4y/+7u/6/v42IjxDZyA4Wp931pbK0elUh/ybgAAAJg2u3bNxvx8vqfFb7LI/maR45pDKnwbgNYLuVyuxdpaedjbAQAAYEpls5nYs2e+J4HD+vp6POxhD+vonnzRi14Uz3jGM4543VVXXRXPfvaz4+abb25b//3f//140pOetON9HcmznvWs+MxnPtO2dpe73CXe8pa3HLVj8lOf+lT8xm/8RkdA9id/8ifx+Mc/vi97PdS4Bk7A8LTi3pWVUtRqjSHvBgAAgGm1sJCPxcXZnhS/ySL7n0WOaw4p6eyz1gu4WKwqegMAAGCoGo1mLC8Xo17feSHEW97ylo6g6Vd/9VePGjRFbAQ8b3rTm2J+fr5t/fWvf31UKpUd72sz3//+9zuCpr179x4zaIqIePCDHxwve9nLOtb/+q//uqd7BOiVNE1jebmo6A0AAIChOrROZqdncskiZZFHovCtj1pFb+vrlSgU+vOCAQAAgO1oNndeEFGpVOI973lP29qJJ54YL3jBC7Z0/V3vetf49V//9ba1m2++OT70oQ91vaej+Zd/+ZeOtac+9anHDJpafvEXfzFuf/vbt61dffXV8cMf/rAn+wPolVsLnHvXoQ0AAADdKpdrsbq6s+I3WaQs8mgUvvXJrUVv5SiVqse+AAAAAAYkTSNWVopRq9W7uv7jH/94LC8vt61deOGFsbCwsOV7/PIv/3IsLi62rV166aVd7edYrrvuuo61+9///lu+PkmSeOADH9ix/p3vfGcn2wLoqVbRW6Oxsy56AAAA6KVqtR6rq6WI6K74TRa5QRa5OYVvfdB6oa6tlaNUqh3jswEAAGDw0jRiebkU1er2i98+/elPd6w96lGP2tY9FhcX40EPelDb2le+8pW45ZZbtr2fYzl8DEJExJ49e7Z1j5NOOqljbf/+/V3vCaCX6vVGLC8Xo9lU9AYAAMDoqVYbsbLSXfGbLHKDLHJzCt967NCit3JZ0RsAAACjbWWlFJXKRvHbVkOnf/u3f2t7fPLJJ8cd73jHbT/3/e53v7bHaZrG5Zdfvu37HMvMzEzH2s0337yte6ysrHSszc/Pd70ngF7ZKHorKXoDAABgpNVqG39/TdPtFb/JIjfIIjen8K2HWi/M1dXywV8aAAAAwKhbXS1FpVKLJEmOGTr96Ec/6uguvNvd7tbV855//vkda1/72te6utfRnHHGGR1rn/vc57Z1j89//vMda3e605263hNAL2z80qDY1agYAAAAGLR6vRErK8UtF7/JIm8li9ycwrceubXorbsxMQAAADBMq6sbJ5cfq/jtG9/4RsdaNx2WERFnnnlmJEnStnbNNdd0da+jue9979ux9q53vWvTsQOb+cQnPhH/9V//1bZ2zjnnbBpiAQxKrXbrLwsAAABgXNTrzS03cckiN8gij0zhWw+0XowrK6WoVhtD3g0AAAB0Z22tHKVS9ajFb9/97nc71k4//fSunm9ubi5OPPHEtrXrr7++q3sdzb3vfe8455xz2tZWVlbiOc95TiwvLx/12iuuuCJ+7/d+r2P9uc99bi+3CLAt1Wr9x78kGPZOAAAAYPsajWYsL5ei2Wwe9fNkkRtkkUem8G2HDi16q9UUvQEAADDe1tcrRz357ZZbbulYO/nkk7t+vsPDpptuuumYgdd2JUkSr3jFK2JmZqZt/Stf+Uo89rGPjfe9732xurra9rHrr78+/vRP/zSe/OQnx9raWtvHLrzwwnjEIx7R0z0CbFWtVo+VldKwtwEAAAA7cmjx25GacGWRsshjyQ17A+Ps0PGmit4AAACYFGtr5YiImJubiTRN20YA3HzzzR2fv2/fvq6fa8+ePW2P0zSN1dXV2Lt3b9f33Mzd7373+LM/+7P4nd/5nSgWiwfXf/jDH8ZLX/rSePnLXx6nnnpqLC4uxvLy8qb/ndlsNp71rGfF8573vJ7uDWCrNsabKnoDAABgMrSK3/buXYiItGMUqSxSFnksTnzr0q1Fb2XjTQEAAJg4a2vlqFTqHWHT4R2HERG7du3q+nkWFxc71g7veOyVhz70ofGBD3wgHvjAB3Z8rNlsxg033BDf/OY3O4KmXC4Xj3zkI+ODH/xgPP/5z49MRpwCDF693oiVFeNNAQAAmCyNRvPg33cPP/lNFimLPBZfnS60Xmhra+WoVutD3g0AAAD0x+pqqePvvdVqtePz5ubmun6Oza7d7Dl65eyzz45LLrkknvSkJ235mmazGcViMW666aa+7etIstlM5HK9++ewOkZgTNTrjVheLil6AwAAYCLV682DJ5wfWvwmixyccc0hjTrdptaIl1bnOwAAAEyylZVS7NkzH/n8RoSwWRCUzWa7vv9mHYv1en/+vr28vByvec1r4r3vfW/UarUtX9dsNuMzn/lMfOYzn4mf/umfjj/5kz+JE088sS97PNzu3fMDeR5gdG10vpc6ut4BAABgkmycdL6RRbZqc2SRg8sixzWHVPi2Da0X1vp6Ocrlrf9PCQAAAONsdbUUe/YsxMxMNhqNRsfHd3Lc/mZBVbPZ7Pp+R3LVVVfFc5/73Ljhhhva1k866aR48pOfHA984APjdre7XSwsLMSBAwfi61//enzsYx+Lj370o237+exnPxtPeMIT4u1vf3ucffbZPd/n4VZXS9Fo9O7rsWfPvPEIMEYajWYsLxej2VT0BgAAwOSr1RqxulqK3bs3it9kkYPLIsc1h1T4tkWtordCoRKlkqI3AAAApkeaRqysFGPv3oWYmZnp+PhmAdRWbdZRmc/nu77fZq677rp4+tOfHsvLy23rF154Ybz4xS+O+fn2bsZTTz01Tj311HjYwx4WF110Ufzmb/5mfOc73zn48R/96EfxrGc9K973vvfF8ccf39O9Hq7RaEa93rvAyYFRMB7SNI00TRW9AQAAMHWq1UasrpZj9+65yOU6y5pkkf0xrjmkFt8taBW9FYvVKBb7N9sXAAAARtVG8Vtp08K3nYwD6HfY1Gw240UvelFH0HTRRRfFH/7hH3YETYc777zz4r3vfW/c8Y53bFv//ve/H6961at6tk+Alo2it43vuYreAAAAmEbVaj3W1yub5oSySA6l8G0LkiSJcrkWhUJl2FsBAACAoWk208jn5zrWi8Vi1/csFAoda3Nznc/RrY997GPx9a9/vW3tnve8Z7zgBS/Y8j327NkTb3jDG2J2drZt/QMf+EB8//vf78k+ASI2it4iNsaL9LLLGgAAAMZNuVyLubmFjnVZJIdS+LYF1Wo91tbKw94GAAAADN1xx+3pWFtfX+/6fodfmyRJT4/sf//739+x9lu/9VuRyWwvErn97W8fT3rSk9rW0jSNyy67bCfbAzioVfS2tlaOWq37sS0AAAAwKRYXj+tYk0VyKIVvx1CrNWJ1tTTsbQAAAMBIOOGEEzrWbrnllq7vd/i1e/bs2XScajfq9Xr8x3/8R9vaySefHPe85z27ut+jH/3ojrUvf/nLXd0L4FBpmkaSJLG+XolKpfuRLQAAADBJZJHtZJGdFL4dRaPRjJWVUvy42RIAAACm3m1ve1rH2o033tjVvdI0jZtvvrlt7Ta3uU1X99rM97///SiX209wv+CCC7q+353vfOdYWGgfr3Ddddd1fT+AiFuL3gqFSpTLtWFvBwAAAEaGLFIWeSwK3zaRpmk0m81YXi4eHDEAAAAARJx++hkda9/97ne7+vvzTTfd1BEG3elOd+p6b4dbWVnpWNu3b1/X90uSpKPLdHl5uev7AURsfG8plapRLFaHvRUAAAAYKUfKIrshi5xMCt8O0wrqV1ZK0WwqegMAAIBDnX32HSOfz7etXX31f0WSJNsufrvmmms61s4555wd7e9Q2Wy2Y211dXVH92w0Gm2P5+bmdnQ/gGq1HuvrlWFvAwAAAEbOZlnkNddc01UTrixyMil8O0TrhbG6Wo56vTnk3QAAAMDoyeVyce6557WtXXXVlVEolCJJkm3d6z//8z871u51r3vtaH+HOvHEEzvWfvCDH3R9v3q9Hrfcckvb2uFdlwDbUa83YnW1NOxtAAAAwEjaLIv8+tevjFqtvu3iN1nkZFL49mNpmkaSJFEoVKJarQ97OwAAADCy7nOf+7U9LhQK8a//+vmo1RpHuGJzn/rUp9oe79mzJ84///wd76/lpJNOioWFhba1q6++Om666aau7vfVr341qtX2UYRnn3121/sDpleaptFoNGNlpRRdNKkDAADA1OjMItfjs5/9XETEtorfZJGTSeFb3Fr0VixWo1SqDXs7AAAAMNIe9KCHdKx96EOXxspKKRqNrZ2gfsUVV8S1117btvaIRzxi05EA3crlcnG/+7UHY41GIy677LKu7vee97ynY+3BD35wV/cCpleappGmESsrpWg2Vb0BAADA0WyWRV566ftjfb0SSZJsqfhNFjm5FL5FRJIkUanUolCoDHsrAAAAMPLOOuvsOP/8C9rWPvWpT8YVV3zlYCHH0QKner0er3zlKzvWL7zwwp7v9VGPelTH2hvf+Mb43ve+t637/Pu//3v8wz/8Q9va3NxcPOQhncEbwJG0vjeurm69UBgAAACm2ZGyyC996ctRLB67+E0WOdkUvkVErdaI1dXysLcBAAAAY+NXfuXX2h43m814yUteFN/73vdidbUUEUceNfCHf/iH8Z//+Z9taw960IPirne9a8/3+chHPjLOPffctrVisRjPf/7z4+abb97SPb75zW/G8573vGg224tUnva0p8WJJ57Ys70Ck601dWJ9vbLt0dAAAAAwzY6URX7jG9dFuVyLJEmOeK0scrJNfeFbo9GMlZXSsLcBAAAAY+X+939g3Pve/6NtbWnpQFx88TPic5/7XKytlTu6LVdXV+M3f/M3O47pz+fz8bu/+7vHfM5LL700zj333I5/jiaTycTLXvayyOVybetXXXVVPO5xj4tPf/rTR7y20WjE+973vrjwwgtj//79bR+7/e1vHxdddNEx9wwQcWvRW7FYiXK5NuztAAAAwFg5Whb5yU9+Omq1esc1ssjpkKRbGXY7gdI0jTSNWF4uGisAAAAAXbj55pvimc/81Y4gJiLirne9IB7ykIfE7W9/eqytrcXVV18dH/nIR6JQKHR87h/8wR/EL/3SLx3z+S699NJ48Ytf3LF+7bXXHvPa973vffHSl75004/d4Q53iJ/5mZ+JO93pTjE/Px/Ly8vxjW98Iz796U/HD37wg47P37t3b7z73e+Os84665jPu1NLS4Wo13uXW+zbtxjZ7NT3QcJQVCo1UycAAACgS8fKIh/+8IfGaaedFuvr63HVVVfJIrdpXHPI3LE/ZfK0av3W1kqK3gAAAKBLJ510crz2tW+O3/iN/x3799/S9rErr/xaXHnl1455j4svvnhLQdNO/cIv/ELUarX44z/+46jV2k9b+va3vx3f/va3t3Sf0047LV772tcOpOgNmBy1WkPRGwAAAOyALJLNTF2Lb2usQKFQiWq1MeztAAAAwFg744wz4w1veFvc/e732NZ1i4uL8Ud/9Efx/Oc/vz8b28Qv//Ivx7ve9a644x3vuO1rkySJRzziEfGBD3wg7nKXu/Rhd8AkStM0ms1mrK6Whr0VAAAAGHuySA43dSe+JUkSpVItSqXasT8ZAAAAOKbTTrtdvO51b4lPfOKf4rLLPhBf//oV0Whs3my2Z8+eeOxjHxu/9mu/Fre5zW0GvNOICy64IP7hH/4hPv7xj8e73vWu+M///M+oVCpH/Px9+/bFAx/4wHj6058e55133gB3Coy71tSJlZVSNJvpkHcDAAAAk0EWyaGStJXATIlarR7LyzosAQAAoF+Wl5fj6quvjAMHbomlpaXI5WZi377j4yd/8vw499xzI5cbnT68arUaV1xxRdxwww2xvLwcpVIpjjvuuDj++OPj7LPPjnPPPTeSJBna/paWClGvN3t2v337FiObnboBADBwrakTq6ulqFTqw94OAAAATKzNsshTTjkpzj//znHuuedGNpsd9hYPGuUsclxzyKkqfGs0mrG0VIwp+k8GAACAkTE7m4vdu+cPFoRwbOMaOME0a32PKxQqUSxWh70dAAAAmEp7987HzMzoNOCOunHNIaci6UzTNNI0jZWVkqI3AAAAGJJKpR7FYkXRGzDRkiSJSqWm6A0AAACGaGWlHI1GU53QhJv4wrdWh+Xa2sb/0AAAAMDwFArVqFaN/QMmV73ejLW18rC3AQAAAFMtTdNYXS0d/Hcm08QXviVJEsViNSoVoToAAACMgtXVkm5LYOK0pk6srpbCtzcAAAAYvnq9GevrGxMoZJGTaeIL32q1ehQKlWFvAwAAAPixNA3dlsBEMXUCAAAARlO5XItSqRpJkgx7K/TBxBa+pWkazWYzVleNFQAAAIBRo9sSmCSmTgAAAMDoWl+vRK3WGPY26IOJLHxrBearq+VoNoXnAAAAMIp0WwKTwtQJAAAAGG2rq6VoNlNNuBNm4grfWmMFCgXVmgAAADDqdFsC48zUCQAAABgPzWYaq6uliAjFbxNk4grfkiSJSqUWpVJt2FsBAAAAtkC3JTCOTJ0AAACA8VKrNaJYNIFikkxU4VuaptFoNGNtTYclAAAAjItmM4319XIkSaL4DRgbSZJEsVh1aiUAAACMkY2/y9eHvQ16ZGIK31rB+NpaOWTkAAAAMF4qlXqUyzXdlsDYaHWJAwAAAOOldXq7JtzxNzGFbzosAQAAYLytr5ej0WgKnICRlqZpNJtprK6Whr0VAAAAoAsmUEyOiSl802EJAAAA4y1N42AhicAJGEVpmkaSJLG+vtEZDgAAAIwnEygmw9gXvumwBAAAgMlRrzejWKwKnICRlCRJlMu1qFTqw94KAAAAsEMmUIy/sS5802EJAAAAk6dYrEa1qqgEGC1pmkaj0Yz19fKwtwIAAAD0gAkU42+sC9+SJIlKRYclAAAATJq1tY0mN4ETMApaDbhra+XwbQkAAAAmhwkU421sC982Rpw2Y22tMuytAAAAAD3WbKZRKFQETsBISJIkSqVq1GqNYW8FAAAA6LFisRr1ur/zj6OxLHy7dcRpRec3AAAATKhyuWbkKTB0t4441YALAAAAk2rjlHcTKMbNWBa+bYw4rRtxCgAAABNO4AQM06EjTgEAAIDJVa83o1Qy8nTcjF3h28aI0zTW14VNAAAAMOmMPAWGyYhTAAAAmB6FwsbIU02442PsCt82RpyWo9n0PxkAAABMg1KpFrWaU9+BwWqNOC0UjDgFAACAadE69V3x23gYu8K3atWIUwAAAJg2Rp4Cg9Qacbq+Xg7fdgAAAGB6bIw8rZlAMSbGpvCtFW63KisBAACA6dFopFEsVgVOwEAkSRLlci2qVSNOAQAAYNoUCpVoNJqacMfA2BS+JUkShULViFMAAACYUsViNer1hsAJ6Ks0TaPZTI04BQAAgCm2vl6OJElkkSNuLArf0jSNer0RpVJ12FsBAAAAhmh9vSJwAvpqowG3ogEXAAAApli12ohKpW4CxYgb+cK3NE0jSZJYX9dhCQAAANOuVmtEuVwTOAF90/o+AwAAAEy39fVypGmqCXeEjXzhW5IkUS7XolZrDHsrAAAAwAhoncQkcAJ6qRVkr6+Xh70VAAAAYAQ0m2kUi1VNuCNspAvf0jSNZjONQsFpbwAAAMCGjcCpInACeqrVgFuvN4e9FQAAAGBEFIvVqNcbmnBH1EgXviVJEsXiRhc3AAAAQEup5HR4oHc2GnCbGnABAACADuvrG024it9Gz8gWvqVpGvV6I0ql2rC3AgAAAIygVoGKwAnYqSRJolCohm8nAAAAwOFqtUZUKjUTKEbQyBa+JUkS6+s6LAEAAIDNCZyAXmg14JbLGnABAACAza2vVyJNU024I2ZkC9+q1bqRJQAAAMBRCZyAndKACwAAABxLs5lGqaQJd9SMXOFbK6wWNgEAAADHInACdkoDLgAAALAVxWIlms2mJtwRMnKFb0mSRLlci0ajOeytAAAAAGNA4AR0QwMuAAAAsB1pGlEoVDXhjpCRKnxL0zSazTQKheqwtwIAAACMCYET0A0NuAAAAMB2lcu1qNcbmnBHxEgVviVJEsVi1f8cAAAAwLYInIDt0IALAAAAdKtQqGjCHREjU/iWpmk0Gs0olYRNAAAAwPY59Q3YqiRJolTSgAsAAABsX7XaiGq1PuxtECNU+NY67Q0AAACgG9VqPWo1p74BR9c67U0DLgAAANAtNU6jYSQK31qnvZXLtWFvBQAAABhjxaIxA8DRtRpw1cgCAAAA3arVnPo2Ckai8M1pbwAAAEAvVKuNqNUaw94GMKI2TntrOu0NAAAA2LFCoRIRYQLFEA298M1pbwAAAEAvtQIngMNpwAUAAAB6pV5vRqVSN4FiiIZe+JYkiUAaAAAA6JlarRG1mjEDQLtWA26ppAEXAAAA6I1i0alvwzTUwrc0TaNeb0SlIowGAAAAeqdQcKIT0M5pbwAAAECvbZz6VnPq25AMtfAtSZIolYRNAAAAQG9tnPrW0GkJRMRGA26z2Yxy2WlvAAAAQG9ptBueoRW+tUYLlMtOewMAAAB6r1is6rQEIqLVgKvoDQAAAOi9er0ZtZr6p2EYWuGbsAkAAADop2q1HvV606lvMOXSNI00TWWRAAAAQN849W04hlL41gqbymV/6AAAAED/lEpOfYNplyRJlMs1RbAAAABA31SrjajXG/KHARtK4VvrtDd/1gAAAEA/lcu1aDad+gbTqtWAq+saAAAA6LdSqaYJd8AGXvh262gBYRMAAADQfwInmF5JkkS1Wo9mU/ErAAAA0F+acAdv4IVvSZJEpSJsAgAAAAZj49T5VOAEU8ppbwAAAMCgaMIdrKGMOi2Xa8N4WgAAAGAKpWkalUpd4ARTJk3TqNUaUa83h70VAAAAYEpowh2sgRa+pWka9XojarXGIJ8WAAAAmHKlkiY8mDZJkmjABQAAAAYqTdOoVjXhDspAC9+ETQAAAMAw1OuNqNcbOi1hSrQ6qysVWSQAAAAwWJpwB2dghW+tsEnhGwAAADAM5XJNpyVMiVYDrlpXAAAAYNBqtUY0Gk1NuAMwsMK3JEmiUqkLmwAAAICh2CiCSQVOMCU04AIAAADDUippwh2EgY46FTYBAAAAw5KmEZVKXeAEEy5N06jVGlGvN4e9FQAAAGBKacIdjIEUvqVpGvV6M2q1xiCeDgAAAGBTmvJg8rXGnAIAAAAMS5qmUa1qwu23gRS+bYw5FTYBAAAAw1WrNaLRaOq0hAnV6qSuVOrD3goAAAAw5eQT/TewUaf+MAEAAIBRYNwpTK4kSaJWayhuBQAAAIauUqkbd9pnfS982xhzutFNDQAAADBsTqWHyeY1DgAAAIwKTbj91ffCt40xp057AwAAAEZDvd407hQmkDGnAAAAwKiRU/TXQEadlsu6LAEAAIDRUanUdFrChEmSJKrVRqhpBQAAAEZFtVqPZtO4037pa+FbmqZRqzWi2fSHBwAAAIyOclmnJUwiY04BAACAUVOtGnfaL30tfDPmFAAAABhFjUYz6nXjTmFStMacVquySAAAAGC0aNTrn76POhU2AQAAAKNIpyVMjiRJolYz5hQAAAAYPdVq42DTHr3Vt8K3NE2j0WhGo9Hs11MAAAAAdE2zHkwWr2kAAABgVFWrDU24fdC3wjdjTgEAAIBRVqs1otnUaQmTQhYJAAAAjCoNe/3R11Gn/tAAAACAUVarGXcK4y5N06jXNwpZAQAAAEaRGqr+6EvhW2suba3W6MftAQAAAHrCCVEw/pIkiWpVDgkAAACMrmZzo3HP9Ine6kvh20bYJDgGAAAARlu1KmyCSSCLBAAAAEZdpWL6RK/1bdSpLksAAABg1G2MSGwqfoMxlaZpNJsmTwAAAACjTy1V7/Wx8E2XJQAAADD6ajWdljCukiSJWk0OCQAAAIy+er0RzWaqCbeHcr2+YavLstn0hwQAAADTJE3T+PKXvxiXX/6ZuOqqK+Omm26MtbXVyOVycfzx++Kss86Oe97zPvGwh/1cHH/88cPebkREfO97343PfvZf4sorr4j//u//jgMHDkS9Xo/du3fHiSeeGHe/+93jfve7Xzz0oQ+NXK7nMQrQA7qlAQAAYPqMaxb57nd/Jr761f+URfZIkvahjLBUqsb6eqXXtwUAAABG1L//+5fi1a++JL73ve8e83Pz+Xw8/vG/GE9/+rNiYWFhALvrdN113443vvE18fnPf25Ln3+b29wmnv/858djH/vY/m7sMEtLhajXmz273759i5HN9m0AAAzFgQOFaDR69zoBAAAARpsssvfGNYfsS+HbykrJqFMAAACYEm9725viHe9427avO/PMs+KSS/5fnHba7fqwqyN7//vfHa9//Z9HrVbb9rWPetSj4pJLLol8Pt+HnXUa18AJBqE1eeLAgcKwtwIAAAAMiCyyP8Y1h+x54VuaprF//3oYRwsAAACT741vfG387d/+1aYfW1hYjBNPPDFWV1djeXlp08855ZRT401v+ss46aST+7nNg9785tfHX//12zf92OzsbNzmNreJiIgbb7wxSqXSpp/3sIc9LF7zmtdEJtP/4GZcAycYlHK5Fmtr5WFvAwAAABgAWWT/jGsO2dPCtzRNo15vxvJysVe3BAAAAEbUpz/9yXjpS3+3Y/2BD/yZePKTnxp3vev5B9duvPFH8fd/f2n83d/9dUd34/nn3y1e//q39j28ueyy98erXvXKjvW73vWCePrTL4qHP/zBMTMzExERtVotvvCFL8TrXve6+OpXv9pxzYtf/OJ42tOe1tf9Roxv4ASDsrpaikrF5AkAAACYdJOURV588cXxoAc9cKSyyHHNIXt+4luxWIlCodrLWwIAAAAjplBYjwsvfFxH9+RznvOb8cu//KtHvO7aa6+JF73oN2P//v1t67/9278Xj33sE/uy14iI73znv+PpT/+VqFYrbetPecrT46KLLo4kSeKEExYjSZJIkuTgx5vNZvzJn/xJvPOd72y7bmFhIT75yU/Gvn37+rbniPENnGBQbrllPXocbwIAAAAjZtKyyLm5mdi9e77jumFmkeOaQ/b8GarVRq9vCQAAAIyYv/mbv+oImp74xAuPGjRFRJx77nlxySV/FnNzc23rb3/7W6NSqRzhqp17/ev/rCNoetrTnhnPetZzDha6VauNtqK3iIhMJhMveclL4gEPeEDberFYjA9+8IN92y9wdBuTJxqK3gAAAGAKTFoWWattXlsli9y+nhW+pWkaaZoe8Q8HAAAAmAyVSiU+9KFL29b27Tshnv3s527p+vPO+4n4tV+7qG1t//798fGP/2PP9nio//qvq+Lzn/9c29o97nHPeMYznt22drRM44UvfGHH2kc/+tHebBDYtiRJNOACAADAFJjELLLZPHpDnyxy63p64puiNwAAAJh8n/3sp2JlZaVt7TGPeXzMz3cez38kj3vcL8TCwmLb2kc+8uGe7O9wH/zg+9seZzKZeOELf6/jdLdqtX7Ee9z5zneOc845p23t2muvjWq12ruNAttSqx35NQsAAABMhsnNIjunT7TIIrcu16sb6bIEAACA6XB4x2JExEMe8rBt3WNhYSHue9/7xSc/+fGDa1de+bU4cGB/7Nt3wo732FKv1+Mzn/mXtrUHPeghcfvbn9nxuRudls3IZpNNQ6eLLroorr766jj++OPj+OOPj71790ajIQuBQWt1Q8siAQAAYPJNaha50dCXP+K9ZJFb07PCtwhdlgAAADANvvzlL7Y9PvHEk+Kss87e9n3uec/7tIVNaZrGl770hfi5n/ufO95jy1e+8h9RKBTa1h7xiEce8fNrtXrkcpsHTo9+9KPj0Y9+dM/2BnTP5AkAAACYDpOaRdZqt4463awJVxa5NT0ZdZqmaTSbzajXm724HQAAADCibrrpxlhaOtC2duc737Wre5133p071q6++squ7nUkV1zxlbbH2Ww27nWv+xzx850gBaMvSRKFbwAAADAFJjmLTFONfb3Qk8I3YRMAAABMh+uu+3bHWjcdlhERZ5xxRkc347e+9c2u7nUkh4dXZ511h5idnTvi5zvNHsaDIlUAAACYfJOfRTY2Pe2NrevZqFNhEwAAAEy+66//XsfabW97Wlf3mp2di3379sX+/fsPrv3whzd0vbfNHB6OnXnmWR2fs7KyHP/2b5fH179+RVx//fejUinHnj274+STT4573OMe8eAHPzhOPvnknu4L6E5rBEi9LosEAACASTfpWeQPfnB9VCql2LNnT5x00kmyyC70rPDNiW8AAAAw+Q4NhlpOPPGkru+3b98Jbfe85Zabo9lsRiaz80PqK5Vy7N9/S9vaoaHRTTfdGG9725viE5/4p6hWq5ve47LLLoskSeLnf/7n44UvfGGceuqpO94XsDNySAAAAJgOskhZ5LHs+E8uTdNoNtNoNJq92A8AAAAwwg4c6Ayb9u49vuv77d69p+1xmqaxvr7W9f0OdeONNx48Hapl7959ERHxyU/+czz5yb8QH/3oh48YNB26pw996EPxcz/3c/Hxj3+8J3sDupMkicI3AAAAmBKySFnksey48G0jbKr3Yi8AAADAiNssCFpcXOz6fgsLCx1ra2u9CZuWl5c61mZnZ+ODH3x//P7vvyRKpeK27lcqleJ5z3te/O3f/m1P9gd0R+EbAAAATAdZpCzyWHoy6lTYBAAAANOhVqt1rM3OznZ9v3y+89pjdT1u1erqasfaV7/6n/GZz/xLW/flfe5zv3jUo/5XnH/+3eL44/fF+vp67N9/Q/zzP/9TvO9972v7b242m/FHf/RHceaZZ8b973//nuzzaLLZnY9ZOFSS9PR2MFCt160sEgAAAKbDNGSRlUoxbr75hvjYxz4W733ve4eWRY5rDqnwDQAAANiyzYKgbDbb9f02u7Ze783J8pVKpWPtU5/6xMF/X1xcjJe//I/i/vd/YNvnHH/88XH66afGfe/7P+KpT31qXHzxxXHdddcd/Hiz2YwXvvCF8dGPfjT27dvXk70eye7d8329P4ybel0OCQAAANNiGrLImZk9ceaZp8W9733veMpTnjK0LHJcc8gdFb61KhLr9WZPNgMAAACMtmazMwPIZLoPmzKZzk7CNO1NzlCvd3aEtszNzcVrX/vmOOec8zb9eK3WiPn5iDPPPDPe9a53xROf+MS4/vrrD358aWkp3vGOd8QLXvCCnuz1SFZXS9Fo9C532bNnftOvOYyDJEk04AIAAMAUmZYssl5vRi6XGWoWOa455I6fQdgEAAAA0yOX6+yhazS674rcrKNyZibf9f0OtVkw1vL85//2EYOmjX3dmnccf/zx8apXvarjc/7mb/4mCoXCzjZ5DI1GM+r13v1zyFQFGEu1mgZcAAAAmBbTlEUmP54NOqwsclxzyB0VviVJ4rQ3AAAAmCIzMzMdazsZB9BodDbU5fO9CZs2C8YiIk4//Yx41KMefYx9pdFspgdPu//Jn/zJuN/97tf2OYVCIb785S/3ZK/A1hh1CgAAANNjWrLIww8dk0VunRPfAAAAgC1bXNzVsVYqlbq+X7FY7FibnZ3r+n6HyudnN11/+MMfuaVj9g/ttIyI+Pmf//mOz/n85z/f/QaBLUvTNBqNZjSbji0EAACAaTEtWeRmh47JIrdmx4VvuiwBAABgeuzevadjbSdH7BeL622PkySJvXv3dn2/Q+3evXvT9bvd7Se3dP1mnZaHu+6667a/MWDbNiZPyCEBAABgmkxLFtlq9ksPmQ8qi9yargvfdFkCAADA9DnhhBM61paW9nd9v/3726/dvXv3EccCbNfxx+/bdP3kk0/Z0vWHF9nc9ra37ficAwcObH9jQFdqtc7uZwAAAGByySLbySI7dV34pssSAAAAps9tb3tax9rNN9/c1b3SNI39+29pW9tqELQVp556m03XFxYWtnT94UU2s7Ozkc/n29bW1ta62xywbbJIAAAAmC7TlUU2IkmSg49lkVuzo1Gnm82YBQAAACbX6aef0bF2/fXf6+pet9xyc1Qqlba1s8++Q1f32szc3FycdNLJHevFYnFL17dOu2+NGGg0GlGr1TqeAxgMhW8AAAAwXaYpizy8BksWuTUK3wAAAIAtO/vsO3Z0Gn7rW9/q6l7f+tY3Nr1/L51zzrkda9///tbDsXq9ebDTcnl5+WARXMtm4xaA3rq1CHXYOwEAAAAGaZqyyEajveFPFrk1Oyx802UJAAAA0ySXy8W5557XtnbNNVdFvV7f9r2+9rUrOtbudrd7dL23zZx//t02ed6vbvn6QwOnK6+8suPjd7xjb8MxoFOSJBpwAQAAYApNUxbZaKSRpunBYjdZ5NZ0VfiWpmk0mxv/AAAAANPlPve5X9vjQqEQV1zxlW3f59/+7fK2x8cdtzt+4ifuvKO9He5+93tAx9qnPvWJjm7JIzm02OYLX/hCx8fvda97db85YMs04AIAAMB0kkXeShbZqesT3xoNXZYAAAAwjR70oId0rH3oQ5du6x5XXXVlfPvb32xb+5mf+dnIZrM72tvhzj77jnHWWWe3rV1//ffj8ss/s6XrW2FTuVyOSy9t/2+cm5uL+9///r3ZKHBUskgAAACYTtOVRTYiSRJZ5DZ0Vfi2MV5AlyUAAABMo7POOjvOP/+CtrVPfeqTWz62v16vx+te9/861h/zmMf3YnsdnvCEX+xYe+1r/18Ui4VjXttoNCNN0/jzP//zWF5ebvvYYx7zmFhcXOzVNoGjMOoUAAAAptM0ZZGt/EMWuXVdn/gmbAIAAIDp9Su/8mttj5vNZrzkJS+KH/zg+mNe++pXXxJf//oVbWv3ve/947zzfqKne2z5n//zMXG7253etnbDDT+Il77096JSqRzz+g9/+B/iHe94R9taPp+PZz7zmb3cJnAEaZo68Q0AAACm2LRkkY1GMz7ykY/IIrfBqFMAAABg2+5//wfGve/9P9rWlpYOxMUXPyO+9KUvbHrN2tpavOxlvxcf+tAH29bz+Xz8+q8//5jP+dGPfjge8IB7dvxzLDMzM/GiF72kY3TBl770+bj44qfHN75xzabX1Wq1eMc73ha/+7svimazPQd55jOfGWecccYxnxvYGUVvAAAAwLRkkW9725vjt3/7t2WR25CkaZp2c+H+/evRbHZ1KQAAADABbr75pnjmM3819u/f3/Gxu971gnjAA346Tjnl1CgUCnHttdfEJz/5z5se6f/bv/3ieOxjn3DM5/voRz8cf/zHf9Cxfvnl/76l/b7//e+OP/uzV3WsJ0kS97jHPeM+97lvnHLKbaJcLsV11307PvWpT8RNN93Y8fn3vve94+1vf3vkcrktPe9OLC0Venrq/r59i5HNdt0HCUNRqdRidbU87G0AAAAAQySL7G8WOa45ZFdflTRNFb0BAADAlDvppJPjta99c/zGb/zv2L//lraPXXnl1+LKK792zHs89anP2FLQ1AtPfOIvRZJk4jWv+dNoNBoH19M0jf/4jy/Hf/zHl495j5/6qZ+KN77xjQMpegM2OPENAAAAkEWymW2X1hkvAAAAALScccaZ8YY3vC3ufvd7bOu6hYXF+N3ffWlcdNHFfdrZ5p7whF+M17zmTXG7221vNEAmk4mnPe1p8Y53vCN27drVp90Bm2k0NOACAAAAskg6bbskMEkShW8AAADAQaeddrt43eveEp/4xD/FZZd9IL7+9SvauhgPddxxu+ORj/yfceGFT45TTjl1wDvdcLe7/WT8zd+8Nz72sX+Iv//7D8Y111wdabp5Yc3CwmI86EEPjuc+9+K44x3vOOCdAhFOfAMAAABuNelZ5M/+7M/G//7fz5ZFblGSHumreRTFYiUKhWo/9gMAAACMueXl5bj66ivjwIFbYmlpKXK5mdi7d2/c4Q53jDvc4U6RzWaHvcU2S0sH4pprro4bbvhBrK+vRzabjT179sZZZ50d55xzXuTz+TjhhMVIkiSSJBnw3gpRr/eu6GffvsXIZrc9AACG6pZb1o8YCAMAAADTbdKyyOOOW4jdu+eHsK/xzCG7KnxbWytHuVzrx34AAAAARs7evQuRy2UUvsGApWkat9yyPuxtAAAAAAxELpeJ449fHPjzjmsO2dUzGC8AAAAATJNGoznwojeYdmmayiEBAACAqdJoOPV+O7oqfGs2BU4AAADA9Gg2BU4waEmSCHsBAACAqZKmaXQxvHNqdXnimy8wAAAAMD2cOgXDoQEXAAAAmDbNpuK3rdpW4VuapsImAAAAYOrIQ2A4NOACAAAA00YT7tZt+8Q3YRMAAAAwbeQhMByKTgEAAIBp02ymkSTJsLcxFrZV+JYkibAJAAAAmDryEBgORacAAADAtHHi29Y58Q0AAADgGNJ0o9MSGCxFpwAAAMC0kUNu3bYL33xxAQAAgGnUbKaRpnIRGCRZJAAAADBtNAJuXReFb764AAAAwPSRicBgKXoDAAAAppFMZOuc+AYAAACwBc1mGkmSDHsbMBXSNFVsCgAAAEwltVlbp/ANAAAAYAtkIjBYXnMAAADANGo200hTuchWKHwDAAAA2AKZCAxOkiRecwAAAMDUSlPFb1uxrcI3X1QAAABgWhm7CIOl8A0AAACYVnKRrdly4Vuapr6oAAAAwNTSDAiDJYsEAAAAplWzmUaSJMPexsjb9olvAAAAANNIEQ4MVpo6ZREAAACYTrLIrdlW4ZsvKgAAADCt5CIwWF5zAAAAwLSSi2zNlgvfkiTxRQUAAACmlpPwYbBkkQAAAMC0kkVujVGnAAAAAFuQprIRGCSvNwAAAGBaaQjcGqNOAQAAALao2UwV48CAyCIBAACAaSWD3BonvgEAAABskWwEBiNN0/ByAwAAAKaVhsCtceIbAAAAwBbJRmAwFL0BAAAA00wD7tZs88S3fm0DAAAAYPSlaUSSJMPeBky0jdPeBJEAAADA9NKAuzVGnQIAAABskWwEBsNrDQAAAJhmopGtUfgGAAAAsEWyERgMrzUAAABgmslGtkbhGwAAAMAWiUag/5IkiWZz2LsAAAAAGC51Wse2rcI3gRMAAAAwzYRNMBheawAAAMC0E48cmxPfAAAAALZINgKD4bUGAAAATLs0TWUkx7CtwjcAAACAaSZngsHwWgMAAACmnaK3Y9ty4ZsvJgAAADDt5CMAAAAAAKPBiW8AAAAAwIhRZAoAAABMNz24x7aNE9/6uQ0AAACA0ScfgcHwWgMAAADgWLZx4pu0CQAAAAAAAAAAoN9SnYHH5MQ3AAAAgC0TkMAgCHYBAAAAIpIkGfYWRto2TnwDAAAAmG5qcQAAAACAQZBFHpvCNwAAAAAAAAAAAMaKwjcAAACALTJZAAbFiw0AAACYbrLIY9ty4ZsvJgAAAICABAZBFgkAAAAQkZp3elTbOPFN2gQAAABMN8U4AAAAAMAgJMLIY3LiGwAAAAAwUmSRAAAAABzLNk58AwAAAAAYBJVvAAAAwHTTGHhs2zjxzVcTAAAAmG7yEQAAAACA0bCtE99kuwAAAMA0k43AYHitAQAAANNOE+6xbbPwzRcUAAAAmF6yERiMTMZrDQAAAJhuSZLII49B4RsAAADAFslGYDC81gAAAIBpJx45NqNOAQAAALZINgL9l6apwjcAAABg6slHjs2JbwAAAABbJBuBwfBSAwAAAKZZJiMc2QqFbwAAAABbJHCCwZBDAgAAANNMNLI1Rp0CAAAAbFGSJJGm6bC3ARMtSRKFbwAAAMBUk41szbYK33Q1AwAAANNM3gSDIYcEAAAAppnCt61R+AYAAACwRZmMk6hgULzWAAAAgGmlRmtrtjnq1BcVAAAAmF6yERgcAS8AAAAwreQiW+PENwAAAIAtSBKFbzBIskgAAABgWskht2bLhW9pmvqiAgAAAFNLEQ4MliwSAAAAmFayyK1x4hsAAADAFijCgcGSRQIAAADTSha5NVsufEuSRNgEAAAATC25CAyW1xwAAAAwreQiW7OtE99UEwIAAADTKpPZVowC7JAsEgAAAJhWmUwSaZoOexsjb9uFbyoKAQAAgGkkE4HB8poDAAAAplUmk9EUuAXbblUWOAEAAADTSCYCg5OmqdccAAAAMLXkIluj8A0AAABgC2QiMFjGCwMAAADTSA65dQrfAAAAALYgk8lEmqbD3gZMhSRJ5JAAAADAVJKJbF0XhW86LQEAAIDpI3CCwfKaAwAAAKaR2qytc+IbAAAAwBZkMkkkiVwEBkkWCQAAAEwbecjWOfENAAAA4BiSRNEbDEM2K4sEAAAApks2K4fcqm0lR2ma+uICAAAAU0ceAsOhwxkAAACYNg4l2zonvgEAAAAcgzwEhsOJbwAAAMC0yWaTSNN02NsYC7ntfPLGWI+IJInw9QUAAACmhRPfYDic+AYAAABMG024W9fVV8oXGAAAAJgmshAYvDRNnfgGAAAATJ1MJokk0Qy4FV0lR7qcAQAAgGkiC4HhUHQKAAAATBNFb9vjxDcAAACAY8hkMpGm6bC3AVMlSRJFpwAAAMBUcfr99nR54psvMgAAADA9crmMTksYgiRJIpPx2gMAAACmg5qs7VH4BgAAAHAUxgvAcMkiAQAAgGnh9Pvt2XZqlKapsAkAAACYGnIQGC6vQQAAAGBayEG2J7fdC5IkiWy2H1sBAAAAxlmapvHlL38xLr/8M3HVVVfGTTfdGGtrq5HL5eL44/fFWWedHfe8533iYQ/7uTj++OOHvd1juuSS/398+MMfPPj4cY97XLzyla8c4o5gOgl8AQAAgMPJIonoovAtolX8lkSjkfZ6PwAAAMAY+vd//1K8+tWXxPe+992Oj9Xr9fjhD2+IH/7whvi3f7s83vSm18bjH/+L8fSnPysWFhaGsNtju/zyz7YFTcDwKHwDAAAADiWLpKXr1EjgBAAAAEREvO1tb4rnP/85mwZNm6lWq/Hud/9NPOtZT40f/OD6Pu9u+5aWDsQll/zRsLcBxEb3thwSAAAAaJFFciiFbwAAAEDX3vjG18Y73vG2TT+2sLAYZ5xx+9i7d/NRAt/5zn/H8573v+Pmm2/q5xa37ZWvfEUsLR0Y9jaAuHXyBAAAAIAsksN1Neo0IiKXy0ZErYdbAQAAAMbJpz/9yfjbv/2rjvUHPvBn4slPfmrc9a7nH1y78cYfxd///aXxd3/311Gr1drWX/7yF8frX//WyGSG32R32WUfiM997l+HvQ3gEEmSRC6XiXq9OeytAAAAAEMii2QzXf0pGjEAAAAA061QWI9XveqVHevPec5vxp/8yavagqaIiFNOOTWe9aznxJve9PY44YQT2j729a9fER/60KV93e9WfP/734vXv/7Phr0NYBOySAAAAJheskiOpKvEqNVlCQAAAEynv/mbv4rl5aW2tSc+8cL45V/+1aNed+6558Ull/xZzM3Nta2//e1vjUql0vN9blW9Xo9XvOLlUSqVhrYH4Mg2pk8AAAAA00gWyZF0Xb2WJIlOSwAAAJhClUqloyty374T4tnPfu6Wrj/vvJ+IX/u1i9rW9u/fHx//+D/2bI/b9Vd/9Rdx9dVXHny8sLA4tL0A7dI01YQLAAAAU0oWydHsKDESOAEAAMD0+exnPxUrKytta495zONjfn5+y/d43ON+oSPQ+chHPtyT/W3XVVddGe98518efJwkSTzveb81lL0Am5NDAgAAwHSSRXI0Ct8AAACAbfn85z/XsfaQhzxsW/dYWFiI+973fm1rV175tThwYP+O9rZdpVIpXvGKl0ej0Ti49oQnXBg/9VP3Hug+gCNLkiQymUwkSTLsrQAAAAADJovkaHZY+Jbt1T4AAACAMfHlL3+x7fGJJ54UZ5119rbvc8973qftcZqm8aUvfWFHe9uu17zm1XH99d87+PiMM24fF1+8tTEJwGDNzGjCBQAAgGkji+Rouk6L0jRV+AYAAABT5qabboylpQNta3e+8127utd55925Y+3qq6/s6l7duPzyz8aHP/zBg4+z2Wy87GV/GLOzcwPbA7B1skgAAACYLrJIjqXrwreNEQNJZLM6LQEAAGBaXHfdtzvWuumwjIg444wzOkYXfutb3+zqXtu1tHQgLrnkj9rWnvrUZ8RP/MRdBvL8wPbNzCh8AwAAgGkii+RYdly1lsspfAMAAIBpcehR/C23ve1pXd1rdnYu9u3b17b2wx/e0NW9tuuVr3xFW7foeefdOZ7ylKcP5LmB7TN9AgAAAKbPtGSRDh3r3o6/cjotAQAAYHrs37+/Y+3EE0/q+n779p3Q9viWW26OZrPZ9f224rLLPhCf+9y/Hnw8OzsbL3vZH0Yul+vr8wLdu3X6RHLsTwYAAAAmwrRkkQ4d696OvnI6LQEAAGC6HDjQGTbt3Xt81/fbvXtP2+M0TWN9fa3r+x3L97//vXj96/+sbe3ii38jbn/7M9vWdFnCaJJFAgAAwPSYlixS3tG9HaW4SZKoOgQAAIApslkQtLi42PX9FhYWOtbW1voTNtXr9XjFK14epVLp4NpP/dS94wlPuLDjc+UdMJpMnwAAAIDpIYvkWHY8wyNJkpiZyUat1ujFfgAAAIARVqvVOtZmZ2e7vl8+33lttVrt+n5H81d/9Rdx9dVXHny8a9dx8ZKX/P8iSTpHJ45Kl2WvT57b5D8VxkaapgrfAAAAYIpMQxaZJMlITJ8Y1xxyx4VvEaHwDQAAAKbEZkFQNtt9Icpm19br9a7vdyRXXXVlvPOdf9m29oIXvChOPvmUTT9/VIprdu+eH/YWYGS0guAkiUjTYe8GAAAA6LdpyCJnZrJRLvd8C9s2rjnkjgvfdFoCAADA9Gg2mx1rmUz3uUAm09lJmKadz7ETpVIpXvGKl0ejcWvT3s/8zM/Gwx/+yCPsKYlMZjSORltdLUWj0buvx54985t+zWFctKZPVKuacAEAAGDSTUMWOSo1V+OaQ/bsxDcAAABg8uVynVFCo9F9V+RmHZUzM/mu77eZ17zm1XH99d87+PiEE06I3/mdFx/x82dmslEq9XQLXWs0mlGv9y5wckoWk2BmJqfwDQAAAKbANGSR+Xw20hEI7cY1h9xxaV2SJJEkSeRyit8AAABg0s3MzHSs7WQcwKGdjy35fO/Cpssv/2x8+MMfbFv7vd97WezZs/eI1+TzPekTBPrA9AkAAACYHpOeRSZJRDabiSQZjekT46hnZ8rl8wInAAAAmHSLi7s61ko7OB6tWCx2rM3OznV9v0MtLR2ISy75o7a1Rz/6cXHf+z7gqNfNzIxGlyXQaaMBNxPyYAAAAJh8k55FzsxkFb3tUM8K33RaAgAAwOTbvXtPx1qhUOj6fsXietvjJEli7969Xd/vUK985StiaenAwcennXa7eO5zf+uo12QyiS5LGHFJksgiAQAAYApMehY5M2PyxE71pPDNiAEAAACYDieccELH2tLS/q7vt39/+7W7d++OXG7ngc/f//2l8bnP/evBx5lMJl7ykt+PhYWFo14n34DxIBgGAACAyTfpWWQ+b/LETvUkIWp1Qc/MZKNW65yHCwAAAEyG2972tI61m2++uat7pWka+/ff0rZ28smndHWvw3384x9re9xsNuM5z3nmju75wQ9+MD74wQ+2rd373veOv/7rv97RfYHt0YQLAAAA00EWKYs8lp6NOo3QFQ0AAACT7vTTz+hYu/7673V1r1tuuTkqlUrb2tln36GrewHTozXq1EhiAAAAmGyySI6lZ4VvaZpGPm/EAAAAAEyys8++Y+Tz+ba1b33rW13d61vf+sam9wfYinxeEy4AAABMMlkkx9LTSrVcLhNJEmH8LAAAAEymXC4X5557Xnz96187uHbNNVdFvV6PXG57McPXvnZFx9rd7naPHe8xIuJ2tzs91tfXt3VNJpNEJrNxglStVotvf/vbbR/fs2dP3OY2t2lbO+OMzq5TYDBmZrJRqdSHvQ0AAACgTyY5i8xmN2qsImSRO9GzwrfWaIGZmVxUqwInAAAAmFT3uc/92sKmQqEQV1zxlfipn7rXtu7zb/92edvj447bHT/xE3fuyR5/7/detu1rdu+ej9nZjajk+uuvj5/92Z9t+/hDHvKQeOUrX9mT/QE7c+v0icoxPxcAAAAYX5OYRWazSezbt+vgY1lk93o26rTFiAEAAACYbA960EM61j70oUu3dY+rrroyvv3tb7at/czP/Gxks8PLFfL5bKSOsYexkCRJZLOZyGaTYW8FAAAA6KNJzCJnZno6oHOq9bTwLU1TfzgAAAAw4c466+w4//wL2tY+9alPxte+9tUtXV+v1+N1r/t/HeuPeczje7G9rszMZCNJkoMn2gPjQRYJAAAAk20Ss0iHivVOTwvfkiSJXC4TmYyQGAAAACbZr/zKr7U9bjab8ZKXvCh+8IPrj3ntq199SXz961e0rd33vveP8877iZ7ucTtmZoRNMI4ExQAAADD5Ji+LzJk80SM9H3UaISwGAACASXf/+z8w7n3v/9G2trR0IC6++BnxpS99YdNr1tbW4mUv+7340Ic+2Laez+fj13/9+cd8zo9+9MPxgAfcs+OfXsjnhU0wbkyfAAAAgOkwSVlk60Axkyd6oy/JUD6fi0ql3o9bAwAAACPixS9+eTzzmb8a+/fvP7h24MD+eMELnht3vesF8YAH/HSccsqpUSgU4tprr4lPfvKfo1gsdNznec97YZx55lmD3HqbJNkInIDxsjGeeOP1W683h70dAAAAoI8mJYvM5zXx9VLPv5ppmhoxAAAAAFPgpJNOjte+9s3xG7/xv2P//lvaPnbllV+LK6/82jHv8dSnPiMe+9gn9GuLWzIzk9NhCWMsn89FvV4d9jYAAACAPpqcLDIbaZrKI3uk5+3MSZJEJpOJbFanNAAAAEy6M844M97whrfF3e9+j21dt7CwGL/7uy+Niy66uE872zoNfDC+Nsadeg0DAADANJiELFKO0Vt9Oz8vn89GqWTEAAAAAEy60067XbzudW+JT3zin+Kyyz4QX//6FdFoNDb93OOO2x2PfOT/jAsvfHKccsqpA97p5nRZwngTGAMAAMD0GOcscmYmK4PssSRN07TXN03TNGq1RqyslHp9awAAAGDELS8vx9VXXxkHDtwSS0tLkcvNxN69e+MOd7hj3OEOd4psdnSKVDKZJE44Ydewt3FES0uFqNd711i4b9+iU/qZSCsrxahWNw+5AQAAgMk1Tlnkrl2zMT+fH/Y2NjWuOWTfCt8iIvbvX4/e3x0AAACgN+bnZ2LXrrlhb+OIxjVwgkErlaqxvl4Z9jYAAAAAjmjfvsXIZJKRPPVtXHPIvjxDkmz8Ic3M9G2SKgAAAMCO5fOyCxh3aZp6LQMAAAAjLZvNRDabGcmit3HW19K62VmBEwAAADCakiRiZiYbfTgMHxigJEkim81ELuc0QwAAAGA0adrrj76lQRudlqMzJxcAAADgUDMzuYOn1gPjT4AMAAAAjKp8XgNuP/St8C1JkshkdFoCAAAAo0mRDEwO404BAACAUdWaPKEBt/f6XpVm3CkAAAAwimZndVnCpEiSJHK5TGQyAmQAAABgtOTzOUVvfdLXwrc0TWN2dqafTwEAAACwbTMz2chkMgInmCBJkjj1DQAAABg5Dg3rn74WviVJEtmscacAAADAaBE2weRJ0zTm5ry2AQAAgNGRJBsnvpk80R8DqUhz6hsAAAAwSmZnhU0waZIkiZmZnHGnAAAAwMhojTk1eaI/+l74tjHuVKclAAAAMBqMOYXJJosEAAAARsXcnMPC+qnvhW+3jjvN9vupAAAAAI5J2ASTa6MJ12scAAAAGL6N0+mzJk/00UBGnUbotAQAAABGQz5vzClMqlagbNwpAAAAMGyzs8ac9ttACt/SNI25OYVvAAAAwHDl87nIZIRNMOmc7AgAAAAM2+ysBtx+G0jhW5IkkclkIp9X/AYAAAAMz/y8YhiYdBtNuF7rAAAAwPBksxt1Uhpw+2tgo04jhMsAAADA8GQyGyMQdVnCZEuS5MfhcnbYWwEAAACmlKa8wRhY4VuapgfHiQAAAAAM2tzcTCSJMacwLQTMAAAAwLDMzRlzOggDK3xrhcoCJwAAAGAY5uZmhE0wJTThAgAAAMMyO5uLTCajAXcABjrqNE1ThW8AAADAwOXzuchmhU0wLVqnO8oiAQAAgEHTgDs4Ay18S5IkstlM5PO5QT4tAAAAMOUUv8D00YQLAAAADFo2m0Q+n9OAOyADLXxrmZ8XOAEAAACDkckkkc9ndVnClLm1CTc77K0AAAAAU2JuLj/sLUyVoRS+5fO5yOWG8tQAAADAlFlYyB8cewhMn/l5gTMAAADQf0mycRiYBtzBGVr1mcAJAAAA6LckSWJuTtgE00wTLgAAADAI8/MacAdtKIlPmqYxO5uLTMYfNAAAANA/8/MzwiYgFhY04QIAAAD95bS3wRtK4VsrcHbqGwAAANBPwiYgTdPI5zXhAgAAAP0zNzcTmUxGA+6ADe2M/zRNf9x1PawdAAAAAJNM2ARE3NqE69Q3AAAAoF804A7H0ArfWoHT3JzACQAAAOi9hYW8sAmIiI0m3Lm5GYWwAAAAQM/l89nI5bJyhyEYWuFbxEbgtLAwM8wtAAAAABNodjYX2azT3oANrSbc+XlZJAAAANBbCwuzw97C1Bpq4VuSJJHJZGJ+3qlvAAAAQO8sLMw67Q1os9GEm1cQCwAAAPRMPp+NmZnssLcxtYZa+BZxaOA07J0AAAAAk2B2Nhe5nNPegHatU98WFjThAgAAAL2hAXe4hl74tnHqW+LUNwAAAKAnFheFTcDm0jSN+fkZhbEAAADAjuXzuZiZycoZhmjohW8RrcDJqW8AAADAzszNzUQ267Q3YHNOfQMAAAB6ZXExrwF3yEai8M2pbwAAAEAvLCwIm4Cja536lskokAUAAAC6Mzubi1zOaW/DNhKFbxEbgdPCQt7/EAAAAEBXnPYGbIVT3wAAAICdWliY1YA7Akam8E3gBAAAAHQrSYwWALYuTdODxbIAAAAA2zE3NxO5nAbcUTBSyU5rzIDACQAAANiOhYV8ZDLCJmBrWk24i4uzw94KAAAAMEY04I6WkaowEzgBAAAA25XJJDE/L2wCtidN05idzcXMTHbYWwEAAADGhAbc0TJShW8RAicAAABgexYXZw820wFsVZIkkaZp7NqlCRcAAAA4Ng24o2fkCt8ETgAAAMBW5XKZmJubETYBXUmSJHK5bMzNzQx7KwAAAMCI04A7ekau8C1C4AQAAABsza5dcxERwiaga2maxuJiPnwbAQAAAI5EA+5oGsnCtwiBEwAAAHB0s7O5mJnJDnsbwJhLkiQymUzMz+eHvRUAAABgRGnAHU0jW/jWCpwWFow8BQAAANolycZoAR2WQC+kaRoLC/nIZoXXAAAAQDsNuKNrZAvfIjYCp/n5mchmR3qbAAAAwIAtLMxGNpvRYQn0RJIkkSTJwe5tAAAAgIiNBtxduzTgjqqRrihrBU7HHefUNwAAAGBDNpuJ+fkZYRPQc/l8LvL53LC3AQAAAIyIxcXZyGQ04I6qkS58a5mZycXsrMAJAAAAiDjuuNmDzXIAvZSmaezaNRu+vQAAAAC5XCbm5jTgjrKxKHwTOAEAAAAREbOzuZiZ0RwH9EeSJJHNZmJhwQQKAAAAmHa7ds1pwB1xY1H4liRJZDKZWFwUOAEAAMC0SpKIXbtmdVgCfZWmaczPz0Q2OxbRKQAAANAHc3MzMTOTHfY2OIaxSW/SNI25uZnI5cZmywAAAEAPLS7ORiaT0WEJ9FWrk/u44zThAgAAwDTKZJJYXNSAOw7GpoqsFWofd9zckHcCAAAADNrMTDbm5/PCJmBgZmZyMTc3M+xtAAAAAAO2a9dsZDJGnI6DsSl8i9gofsvlsrGwkB/2VgAAAIABOu64uUjTVNgEDEyapgeDbgAAAGA6zM7mYnZ2RgPumBirwreIjcBpYSEf2ezYbR0AAADowuLibGSzRpwCg3XryFMTKAAAAGAaJEkSu3bNasAdI2NXPSZwAgAAgOmRy2Vifl6HJTA8+byRpwAAADANNk5+14A7Tsau8K1lZsbIUwAAAJh0rcY3YRMwLGmaxuKikacAAAAwyVqNbxpwx8vYFr4ZeQoAAACTbXExH7lcVtEbMFRJkkQmk8SuXSZQAAAAwCRKkjDidEyNbdWYkacAAAAwuTZGnOZ1WAIjIU3TmJ3NxexsbthbAQAAAHps1665yGaNOB1HY1v41mLkKQAAAEye446bjwgjToHRkCRJpGkau3bNGXkKAAAAE2R21ojTcTb2hW+tkaczM9lhbwUAAADogV275iKX02EJjJbWyNPdu+eHvRUAAACgBzKZJHbtmjPidIyNfeFb63+8446bC/8PAgAAwHibnc3F/LwOS2B0mUABAAAAk2H37o2T3RW9ja+xL3yL2Ch+y2YzsWvX3LC3AgAAAHTp/2PvzqNkv8s68T+1V3X1dpeEJV4IkMVzspAwwjmGIAcOiOKgGVATdh0MkX0THAT9ATqMcwSPMAeEIM6IPzABiRwUEWGE/EzEyBKBiSYqYMYIyV17rX35/dGpe7tu9b23u2531/Z6nZNz6E93fetJuF31vU+9P5/HDktgFHQmUKTTJlAAAADAqFqbLpkedBmcpbEIvkWsNZzy+Uzkcv5QAgAAwCiambHDEhh+ndeo2VkTKAAAAGAUpdPJmJrKmjoxBsYm+JZIJKLdbh9vkgMAAACjY2oqG9mszWzAaDCBAgAAAEZTIhExO1t48H/LF426sQm+Raz9gUwkEsf/gAIAAADDzw5LYBSZQAEAAACjZ3o6H6lUUuhtTIxV8K0jk0nF9HRu0GUAAAAAZ7B+A5tmEzBK1k+gSKfHss0KAAAAYyWfz0Q+n7EBd4yMZUem3W5HoZC12xIAAACG3OysHZbA6Oq8ds3OFsLLGAAAAAyvdDoZ09O5aLfbepFjZCyDb+t3W6ZSY/mvCAAAACOvWMxFNmvTGjDaEolEpFLJmJkpDLoUAAAAYAOmToyvsU2Fdf6gzs3ZbQkAAADDJptNx9RU1lgBYGzkcmuvawAAAMBwMXVifI1t8C3CbksAAAAYRqlUMmZn88YKAGOl3W7H1FQ2MpnUoEsBAAAAHmTqxHgb6+BbxFrDyW5LAAAAGA6JxNoOy0QiIfQGjJXOa9rsbCGSSa9vAAAAMGimToy/sQ++JRKJ47sts1m7LQEAAGCQZmbykU77+zkwnhKJRCSTiZidNYECAAAABsnUickw9sG3iBO7LWdmCpFKTcS/MgAAAAydqals5HIZOyyBsZfJpGJ2Nj/oMgAAAGAiJRKJmJsrmDoxASYmBdbZbdn5gw0AAADsnlwuHcVizg5LYCK02+3I5TIxNZUddCkAAAAwcWZn8w7GmhAT9/9yKpWMuTm7LQEAAGC3pNPJmJkxVgCYHIlEItrtdhSLucjl0oMuBwAAACbG9HQusll/F58UExd8i4jIZNIxPS38BgAAADutc/p6RAi9AROlE36bmclHOj2RbVgAAADYVYVCJgqFbLTb7UGXwi6ZyIhju92OQiETzWYzyuX6oMsBAACAsdBut+MrX7kjbrvt1rjrrv8TBw8+EMvLS5HJZGLv3r1x4YUXxlVXXRXPetazYu/evYMuN771rW/F7bffHl//+tfj3nvvjYWFhVhZWYlisRjz8/PxAz/wA/H4xz8+rr766rjssssGXS4wgjrht7m5Qhw7VopWS+MdAAAAtsPJvchDhx6I5eXlSKfTepETJNGe0Jhj5197aakctVpzwNUAAADAaPvqV/8ufvu3/3v83/977xl/NpvNxgte8IJ45StfGcVicReq63brrbfG+9///vj7v//7TT/mP/yH/xCvetWr4od/+Id3rrANHDu2Go1Ga9uut3dvMVIpJ0/BINTrzVhYKA26DAAAABh5epHbb1T7kBMbfIs4EX47dqwUzeb2/Z8HAAAAk+T3fu8D8b/+1+9t+XGPecxj4oMf/GAcOHBgB6rqValU4p3vfGfcfPPNfT0+kUjES17yknj9618fqVRqm6vb2Kg2nICNVav1WFqqDLoMAAAAGFl6kTtjVPuQEx18i1gLv7Va7VhYMGoAAAAAtup3f/d/xEc/+gcbfq9YLMa5554bi4uLcfTo0Q1/5uEPf3jcdNNN8ZCHPGQny4xGoxEvf/nL49Zbb93w++l0Ovbt2xczMzOxtLQUhw4dilO1TH7iJ34i3v3ud0cikdjJkiNidBtOQK92ux2JRCJKpVqsrlYHXQ4AAACMHL3InTOqfcj0jj/DkEskEpFKJWJurhALC6WY7BggAAAAbN6XvvS/N2w0Pe1pT4tf+IVfiCuvvPL42ve///246aab4sMf/nDU6/Xj69/73vfita99bXz0ox+NZHLnGiG/8Ru/sWGj6bGPfWz8wi/8Qlx11VUxPT19fH1hYSG+9KUvxY033hjf/va3ux7zmc98Jh75yEfGa17zmh2rFxg/iUQi2u12TE1lo9VqRblcP/ODAAAAgIg4fS/y+uuvjyuuuOL4ml7k5Jj4E9/Wq9UasbhYHnQZAAAAMPRWV1fi2mv/UywsHOtaf+Mb3xgveclLTrkD8a677oobbrghDh061LX+tre9LZ773OfuSK3f+ta34md+5me6dk0mEol4zWteEy972ctO+9harRa//uu/Hh//+Me71tPpdHz605+OxzzmMTtSc8eo7rQETq3zWrS0VIlarTHgagAAAGD4naoX+aY3vSn+83/+z3qR22BU+5A6netks+mYns4NugwAAAAYev/v//sHPY2m5z//BacNvUVEXHLJJfGBD3wgCoVC1/r73ve+qFZ3ZvTf7/zO7/SMCrjhhhvO2GiKiMhms/GOd7wjnvnMZ3atNxqNeO9737utdQKTofMaOTubj3RaexYAAADOZKNe5Atf+MLTht4i9CIngc7KOu12OwqFbBQK2UGXAgAAAEOrWq3Gpz99S9favn374pd+6Q2nbTR1XHrppfGKV7yia+3QoUPx6U9/elvrjIg4fPhwfPnLX+5aO3DgQLz61a/e9DUSiUT86q/+atf4gYiIL33pS1GpVLalTmCydF4r5+YKkUye+XUTAAAAJtVGvcj9+/fH6173Or1IBN/WSyQS0W63Y3o6F7lcetDlAAAAwFD6//6/L8bi4mLX2nXXXRdTU1Obvsbznve8KBaLXWu33HLLKX66f1/4whei2Wx2rT33uc+NVCq1pevs3bs3nv70p3etVSqV+OpXv3rWNQKTKZFIRDKZjLm5QmyiTw8AAAATaaNe5M/+7M/29BZPRy9yfAm+naQTfpuZyUcms7U/eAAAADAJvvzl23vWTj5+/0yKxWI8+clP7lq788474/Dhw2dV28n+4R/+oWfth3/4h/u61uWXX96z9v3vf7+vawF0pNOpmJ0tnPkHAQAAYAJt1Iv8iZ/4iS1dQy9yfAm+baBzFOLsbCHSaf+JAAAAYL2vfOWOrq/PPffcuOCCC7Z8nauuuqrr63a7HbfddttZ1Xayb3/72z1rj3jEI/q61snjBSIijh492te1ANbLZtMxO5sfdBkAAAAwdPQiT9CL7GWe5ymshd/aMTdXiGPHStFqtQddEgAAAAzcwYMPxLFj3Q2Wxz72sX1d67LLLutZ++Y3vxnXXHNNX9fbyO/+7u/G/fffHwcPHowHHnggDh06tGHTaDM22gE6MzNztiUCRLvdjlwuE9PT7VhZqQ66HAAAABgKepHd9CJ7Cb6dRiKRiEQiEXNzU7GwUIp2W/gNAACAyfad7/TuWuxnh2VExPnnnx+JRKLr79t3331337VtZHZ2NmZnZ+Oiiy4662v93d/9Xc/aueeee9bXBei8FhYK2Wi12lEq1QZdEgAAAAycXmQ3vche5nhuQjqdjLm5Qjw4ARUAAAAm1ve/f1/P2oEDB/q6Vj6fj/3793et3Xdf7/WHwcGDB+P222/vWkskEn3vMAU4Waf5XizmIp/PDLocAAAAGLjDh7/fs6YXyXqCb5uUyaRidrYw6DIAAABgYNLpZKysLPasn81Ow5ObTQcPHoxWq9X39XbKjTfeGLVa9wlMV1xxRZxzzjkDqggYR53w2/R0LrJZwzoAAACYXNPTuVhYONazrhfJeoJvW5DNpmNmJj/oMgAAAGDXpVKJmJsrxOHDh3u+t3fv3r6vOzc31/V1u92OpaWlvq+3E775zW/Gxz72sZ71a665ZveLAcZe4sGxE7Oz+chkUgOuBgAAAHbf1FQ2CoVsHDp0qOd7epGsJ/i2Be12O/L5TExP5wZdCgAAAOyaZDIRc3NTkUwmY3l5uef709PTfV+7WCz2rA1Ts+no0aPxute9LprNZtf6gQMH4tnPfvaAqgLGXSf8NjdXiHRa+A0AAIDJUShkoljMRbvd1ot8kF7kqTkvfws6owYKhWxERKysVAdcEQAAAOysZDIR8/NTkUqt7Z07+Yj9iIh8vv/T0Td67EbPMQiVSiVe+cpXxn333dfzvf/n//l/IpvN7ngNnf/u2+XBLA0wAjq9yLm5QiwulqLRGL7RKwAAALCd1g6jyke73Y5EIqEX+aDd6EWOah9S8G2L1off2u2I1VXhNwAAAMZTIrF20tv6psdGjaBUqv/TiJLJ3oZKo9Ho+3rbpVqtxite8Yr42te+1vO9n/u5n4snPelJu1LH7GxhV54HGE5rJ7+1Y25uKhYWStFsCr8BAAAwnvL5TMzMnAi9RehFRuxeL3JU+5CCb33ohN+mprLRbrejVBqO9CcAAABsl0QiEfPzhUinu5tBJx+zH7Fxw2izNmpUtVqDDXaUy+V45StfGbfddlvP96666qp44xvfuGu1LC2VtzXoMjdXOKv/v4Dd1wm/zc8XYmFhe18TAAAAYBjkcumYns51hd4i9CJ3sxc5qn1Iwbc+dcJvxWIuIkL4DQAAgLGRSKw1JtLp3kZQJpPpWduoAbVZG+2o3I0Roqdy7NixuOGGG+Ib3/hGz/cuu+yy+B//439EOr177ZRms7Wt4w3b7W27FLCLEonEg6dwFmJhoRStll9mAAAAxkM2m46ZmbURpImT5mPqRe5eL3JU+5C2+J6F9eG3QqH3lw0AAABGzVrobSoymY1HBmzUbDqbcQDD1Gy677774nnPe96GjaZLLrkkfu/3fi+mp6cHUBnAmlQqGfPzU5FMJs78wwAAADDkstlUzM5uHHqL0IvUizwzwbez1Am/TU/nhd8AAAAYaZ2T3k4VeouIDZstpVKp7+dcXV3tWcvn831fr19f+9rX4md+5mfiO9/5Ts/3rrzyyviDP/iDmJ+f3/W6AE4m/AYAAMA4WAu9FSJi49BbhF4kZyb4tg26w2+DOwIRAAAA+nXipLfTH52/UcNlZWWl7+c9+bGJRCL27NnT9/X68alPfSpe/OIXx9GjR3u+9+QnPzn+5//8nzEzM7OrNQGcTif8lkoJvwEAADB6stn0GUNvEXqRnNnuDIKdACfCb7lIJCJKpdqgSwIAAIBNSSQSMT9fiHT61Ce9dZxzzjk9a4cPH+77uU9+7Nzc3IYjDHZCu92O3/7t344bb7xxw+8/+9nPjl//9V+PdFr7BBg+nfDbwkI5ms3WoMsBAACATcnl0jEzc+rxpuvpRXIm/mtto074rVjMRSKRiNXV6qBLAgAAgNNKJhMxN7e50FtExIEDB3rWHnjggb6eu91ux6FDh7rWHvawh/V1ra2qVCrxS7/0S/H5z39+w++/6lWvile+8pW7UgtAv5LJZMzPF2JxsRyNhvAbAAAAwy2fz8T0dC4izhx6i9CL5MwE37ZZJ/w2NZWNRCJiZUX4DQAAgOGUTCYeHJWX3PRjzj///J61e++9t6/nP3jwYFQqla61Cy+8sK9rbcXCwkL84i/+Ytx5550938tms/Ff/+t/jZ/8yZ/c8ToAtkMikYi5ualYXCwJvwEAADC0CoVMTE/no91ubyr0FqEXyZkJvu2ATvitUMhGIpGI5eXKmR8EAAAAuyiVWgtKbCX0FrHWDMpms1Gr1Y6v3XPPPX3VcPfdd/esXXTRRX1da7OOHDkSP/dzPxf/9E//1PO9+fn5eN/73hc/9EM/tKM1AGyntQ8L2jE/PxWLi+Wo15uDLgkAAAC6FArZmJ7ObSn0FqEXyZltrbvNpnXCb/l85vhsYgAAABgGqVRyyye9dWQymbjkkku61r71rW9Fo9HY8rW+/vWv96w9/vGP3/J1Nuvo0aPxwhe+cMNG0yMf+cj4+Mc/rtEEjKTOhwZzc4XIZDY3uhoAAAB2w9RUf6G3CL1IzkzwbQetD7/NzRUGXQ4AAABEOp2K+fmpSCb7bwk86UlP6vp6ZWUlvvKVr2z5Ol/84he7vp6bm4vLLrus77pOZ3V1Na6//vr49re/3fO9K664Im666aZ45CMfuSPPDbAb1offcjmDPgAAABi86elcFIv9hd469CI5HcG3Hdb5xc1m0zE/P9X3LzIAAACcrbW/mxbibP9q+oxnPKNn7eMf//iWrvGNb3yjZyzBM57xjEilduakoje/+c3xf/7P/+lZv/rqq+N//a//FXv37t2R5wXYTZ3e4+xsIQqFzICrAQAAYJLNzuajUMieVegtQi+S0xN820WZTCr27JmKZFL4DQAAgN2Vz2didjYfEXHWm7IuuOCCuPLKK7vW/uIv/iK+9rWvberxjUYjfvM3f7Nn/dprrz2ruk7lYx/7WHzuc5/rWX/iE58Yv/u7vxuFglPagfHRmUIxPZ2PYjE36HIAAACYMIlExPx8IXK5zFmH3iL0Ijk9wbddlkolY8+eqUin/acHAABgd0xNZWNmZntCbx033HBD19etVite9apXxb/927+d8bHveMc74utf/3rX2pOf/OS49NJLt6W29Q4dOhS/9Vu/1bN+0UUXxXvf+97IZrPb/pwAg9YJv61//QcAAICdlkwmYn5+KjKZdEToRXboRe4c6asBSCQ6v+g7c2QiAAAAdExP56JYzG3L7sr1nvKUp8TVV1/dtXbkyJG47rrr4rbbbtvwMUtLS/Ga17wmbr755q71bDYbv/zLv3zG57zlllvi4osv7vnndN73vvdFqVTqeb53v/vdMT09fcbnBBhVnfBbPp+JubmzH3MNAAAAp5NKJWN+firS6e3PwuhFcirpQRcwiTpNp7m5QiwvV6JabQy6JAAAAMbQ7Gx+20YKbOSd73xnPOc5z4lDhw4dXzt8+HC85CUviSuvvDKe+tSnxsMf/vBYWVmJu+66Kz7zmc/E6upqz3Xe8pa3xGMe85htr29xcTH+5E/+pGe9VqvFs571rG15jv/0n/7ThqMSAIZB57U/m03H3NxULC6Wo91uD7gqAAAAxk06nYq5uUIkkzu360ovUi9yI4JvA9IJv83OFmJlpRrlcm3QJQEAADAmEolEzM0VIpNJ7VjoLSLiIQ95SHzkIx+JF73oRV0Np4iIO++8M+68884zXuNlL3tZXHfddTtS35e+9KWoVCo7cm2AUZPJpGLPnrXwW7PZGnQ5AAAAjIlsNh2zs/kdfx69SDZi1OkAdcJv09O5mJ7ODbocAAAAxkAqlYw9e6Yik1kbKbBTobeORz/60fGxj30snvCEJ2zpccViMX7jN34jXvva1+5MYRHx1a9+dceuDTCKOmNnOu8RAAAAcDYKhWzMzRUiYuf7kBF6kfRy4tuAdcJvhUI2UqlkLC2Vw7QBAAAA+pHJpGJ2dmdHCmzkEY94RPzhH/5hfOYzn4mbbropvva1r0Wz2dzwZ+fm5uKaa66Jn//5n4+HPexhO1rXwYMHd/T6AKMokYiYm1ubQlGp1AddDgAAACNqejoXhUJ2RydObEQvkvUS7baY1TBpNJqxuFiOVsv/LQAAAGxePp85fpr4bjaaNnL06NH45je/GYcOHYqjR49GJpOJPXv2xMUXXxwXX3xxpFJOGtqsY8dWo9HYvpGEe/cWI5UyAAAmXedDiVKpFqur1UGXAwAAwAhJJCJmZwuRzaZ3PfS2Eb3I7TGqfUjBtyHUarViaakS9frGiVQAAABYr1jMxdTU7u+uZOeNasMJGH6d94xqtR5LS5VBlwMAAMAISCYTMTc3Fem0/tK4GdU+pD+JQyiRSMTcXCFyOZNoAQAAOL3Z2bzQGwBblkgkot1uRy6Xifn5qV0fkw0AAMBoyWRSsWdPUeiNoeJP4xDqfFAxO1uIqansgKsBAABgGCWTiZifn4pcLiP0BkBfOu8dmUwq5uennAgJAADAhnK5dMzNFUILkmGjkzGkOjsui8VczM7mB10OAAAAQySdTsb8/FRkMqmICKE3AM5aMpmIPXumIps1hQIAAIAT1nIrhYjQh2T4CL4NsfXjBvbsMW4AAACAtd2VRtIBsN06H17MzZlCAQAAQEQiceLviCZOMKwE34Zc54UjnV6bldzZzQ8AAMDksbsSgJ108hQKbzUAAACTKZVKxvx88fip4HqRDCvBtxHSSdPm85lBlwIAAMAusrsSgN2yfgqFE0YBAAAmTzabivn5qUinRYoYfv6UjpDOBxszM/mYns4NuBoAAAB2QyqVjD177K4EYPeYQgEAADCZpqayMTtbcAI4I0PwbcR0dlwWClk7LgEAAMZcNpuOPXumIpXy13cABqNz6mihYAoFAADAOJuZyUexuHYIk823jAqd8xHUeYHJZBwvCQAAMK6mprIxN1cYdBkATLhOL3J6Oh8zM/kBVwMAAMB2SyYTsWfPVOTzmWi320JvjBSJqRGXTCZifn7tBQgAAIDR1zlZp1jMaTQBMBQ6Uyjy+Uzs2WMKBQAAwLjIZlOxZ08x0ulURDjpjdEj+DbiOi86MzN2XAIAAIy6dDoZe/YUI5tNR4RGEwDDo/OelE6nHnyvSg24IgAAAM7G2sSJqdCCZJQJvo2Bk3dcplJelQAAAEZNPp+J+Xmn6AAw/NZOJ52KqansoEsBAABgi0ycYJwIvo2J9Tsu5+dPnA4AAADA8Juezh0/xVujCYBh19mIWyzmYm6u4L0LAABgRJg4wbgRfBtDJ9K5dlwCAAAMs2QyEfPzU1EoZO2uBGCkdN6zstl07NkzFem0VjMAAMAwM3GCcaQbMYY6Oy6npuy4BAAAGFbZbCr27ClGJpOKCLsrARhdnSB3Pp8ZdCkAAABswMQJxpXg25g6ecdl54MUAAAABq9YzMbc3FToMQEwDjq9yJmZfMzM5L2/AQAADIlUKhl79pg4wfhKD7oAdl4ymYi5uUKUSrUolWqDLgcAAGBiJZOJmJ3NRyaT1mgCYKx0plDk85lIp1OxvFyORqM16LIAAAAmVi6XfnBz0loPUi+SceTEtwnQefEqFo0+BQAAGJQTo03X9qD5uxkA46bz3pZKGX0KAAAwSNPTuZidLQy6DNhxgm8TYv3o0717jT4FAADYTWsbkYw2BWAyGH0KAAAwGEabMmmMOp1AiYTRpwAAALthbbRpITKZlEYTABNl/ejTTCYVS0tGnwIAAOwko02ZRE58m0BGnwIAAOy8E6NN107c9ncvACZN570vmTT6FAAAYCfNzOSNNmUiCb5NqJNHn2azRp8CAABsF6NNAeCE9aNPZ2fzwuAAAADbJJ1eG22az2dMnGAiGXXKg6NPp6JUqsXqanXQ5QAAAIysVCoZs7P5SKeNNgWA9TqjT3O5TKTTqVherkS93hx0WQAAACOrUMhEsZgz2pSJJvjG8abT1FQ2stlULC1VotlsDbosAACAkZLPZ2J6WqMJAE5l/ejTublClMu1WF2tDbgqAACA0ZJIJGJ2Nh/ZbDra7fagy4GBMuqUiDjRdEqnU8ePwQQAAODMOo2mmZn8oEsBgJHQ6UVOTeVifn4qUilhcQAAgM3IZlOxd+9UZLNr51zZfMukE3xjQzMz+ZidzXuRBAAAOI1MZm3zUC6XMdoUALag85659l5ajFzOcBIAAIDTKRZzMTc3pQcJ6+gm0KMz+jSXy0Q6nYrl5UrU681BlwUAADBUisVsFArZ419rOAFA/2ZnC1Gp1GNlpRIm9QAAAJyQSiVjdjYf6XTK5ls4ieAbG+q8UCaTiZibK0S5XI/V1eqAqwIAABi8VCoZMzP5yGQ0mgBgO3Q24ubzmchkbMQFAADoKBQyUSzmjvcg9SKhm+Abp9VpOk1NZSObTcXSUiWazdagywIAABgIjSYA2Bk24gIAAJyQTCZiZiYf2Ww62o7FhlMSfOOMOk2ndDoVe/ZMxepqLcrl2oCrAgAA2D0aTQCwO07eiLu8XIlGw0ZcAABgcuRy6ZiezkcyafMtnIngG1s2PZ2LXG7t9LdWywc+AADAeNNoAoDdtX4j7vy8jbgAAMBkSCQipqfzkc9nbL6FTUoOugBGS6fplMmkY+/eYuTzmQFXBAAAsDMSiYiZmXzMzhZC1g0ABmd6Ohfz81PHQ+gAAADjJpNJxZ49xeOhN5tvYXME3zgrJz4E8qILAACMD40mABgOJzbipmzEBQAAxtLJm330ImHzjDqlb4lEItrtduRy6chkpmJ5uRq1WmPQZQEAAPQtkYgoFnNRKGSPjxPQaAKA4TEzk49sNh0rK5VotYz+AQAARlc6nYyZmUKk00mbb6FPgm+clc4LbyKRiLm5QlQq9VhZqZo3DQAAjJxMJhUzM/lIpTSaAGAYdW/ELcbKSiWqVRtxAQCA0bO2+fbEidZ6kdAfwTe2RafplM9nIptNxcpKVdMJAAAYCU55A4DRcWIjbsTsbCGq1YbT3wAAgJGRTq9tvnXKG2wPwTe2zfrT39aaTvVYXnb6GwAAMLyc8gYAo6nznu30NwAAYFQ45Q22n+Ab2+7EyIFMZDJOfwMAAIaPU94AYHw4/Q0AABhmTnmDnSP4xo5w+hsAADCsnPIGAOPF6W8AAMCwcsob7CzBN3aU098AAIBh4ZQ3ABh/ndPfarVGLC87/Q0AABgMm29hdwi+seNOPv1N0wkAANhtuVw6pqdzkUxqNAHAOOu8x2ez6di7txirq9Uol+sDrgoAAJgUiUTE9HQ+8vmMzbewCwTf2DW9TadalMu1AVcFAACMs2QyEdPT+cjl0hpNADCB1u4DMrGyUolGozXocgAAgDFm8y3sPsE3BmZ6Ohf5fDqWlzWdAACA7VcoZKJYzHWdQg0ATI7Oe38mk4r5+akol+uxulodcFUAAMC4SSYTMTOTj2zW5lvYbYJvDETnRT6dPtF0KpWq0Tb9FAAAOEvpdDKmp/ORyaSON5oAAKamspHLpWNlpRK1WnPQ5QAAAGPA5lsYLME3hoKmEwAAsB2KxVwUChmNJgCgS+eeIJlMxNzcVFQq9VhZqQrJAwAAfbH5FoaD4BsDd3LTqVpdazq1Wt4cAACAzclm0zE9nYtUKqnRBACcUiKRiHa7Hfl8JrLZdKyuVqNSqQ+6LAAAYEQkEmubb/N5m29hGAi+MTQ6Tadcbq3pVCrVolSqDbosAABgiKVSyZiezkU2mz4eeNNoAgBO58SHUxEzM/koFDKxslKNet0kCgAA4NTy+UwUi9lIJm2+hWEh+MZQWf8BVSclbfwpAABwskQiYmrKWFMAoH+de4dUKhnz82vjT1dXTaIAAAC6bTTWVC8ShoPgG0Opd/xpI1ZWKppOAABA5HLpKBaNNQUAtsfJ409LpWqUy8afAgDApEskElEsZo01hSEm+MZQOzH+NB3ZbNH4UwAAmGDGmgIAO2X9+NPp6fyDkyiMPwUAgEm1NtY0F8lkwuZbGGKCbwy9jcefVqNWawywKgAAYLcYawoA7BbjTwEAYLJlMqkoFnPGmsKIEHxjZHSPPy1Evd6MlZVKNBqtAVcGAADslEIhE1NTdlYCALtr/fjTXC4d5XI9SqVquB0BAIDxlEololjMRS6XOb4m8AbDT/CNkdN5c8lkUrFnTzGq1bVdl82mrhMAAIyLXC4dxWIuUqmknZUAwECsv/eYmspGPp+JUqka5XJ9gFUBAADbKZFIRLG4dr/f2QCjDwmjQ/CNkdZutyOXy0Q2m35w7EDNKRAAADDCjBIAAIbNiVHrEdPT+SgUsrG6Wo1qtTHgygAAgLMxNZWNQiHbNW1CLxJGi+AbI239m06hkI1cLhPlci1KpdoAqwIAALYqlUpGsZg1SgAAGFqde5NkMhGzs4Wo15uxslKNRqM54MoAAICtyOfTMTVl2gSMA8E3xsL6XZfFYi7y+YxdlwAAMAKMEgAARk3nXiWTScWePVNRrdZjdbUazaZJFAAAMMwymVRMT+cinTZtAsaF4Btj5eRdl41GM1ZXa1GrCcABAMAwSSQSMTWViUIhezzw1lkHABgV7XY7crlMZLPpqFTqUSrVotUSgAMAgGGSyaRiaiob2WxaHxLGjOAbY6nzJpVKJWNuTgAOAACGhcAbADBO1t/DFAprp9gKwAEAwHBYH3jr0IeE8SL4xlgTgAMAgOEg8AYAjDMBOAAAGB4bBd6A8eS3nImwUQCuVKpFtSoABwAAO0ngDQCYJBsH4BpRKlUF4AAAYIcJvMHk8dvORFkfgJudFYADAICdIvAGAEyy7gBcJvL5dFSrjVhdFYADAIDtJvAGk8tvPRNJAA4AAHZGMpmIQiEbhUJG4A0AmHjr74Hy+UzkcmsBuFKpFs1ma4CVAQDA6BN4A/z2M9FODsA1m60ol2tRqdSjbeMlAABsWiqVjKmpbORyaYE3AICTnByAy+czUa02olyuRb3eHGBlAAAwenK5dExNZSOdTg26FGDABN8gTjSekslETE/nY2oqF5VKLcrlutEDAABwGifvqhR4AwA4tc49UrvdjlwuHblcOur1ZpTLplEAAMDpJBIR+fzapIlUKnm8DwlMNsE3WKfTeEokIqamclEoZI0eAACADeRy6SgUspHJrO2qbLfbkUgkBN4AADZh/T1TOn1iGkWptDaNAgAAWJNMJqJQyEY+n4lk0qQJoJvgG2xgo9EDtdpaAM7oAQAAJtXarspMFArZnl2VGk0AAP1ZP41iZiYfxWIuyuW1aRROsQAAYFKlUsmYmspGLpeORELgDdiY4BucxvrRA9lsOrJZowcAAJg8a7sqM5HPZ+2qBADYIeunURSLuZiaykalUo9yuW4aBQAAEyOTScXUVDay2bU4i14kcDqCb7AJG40eaLVaUS7Xo1KpR6tl5yUAAOMnk0lFoZCJbNauSgCA3bL+XqtQyEahkI1arRHlcj1qNZtxAQAYP51JE/l8NtLp5Enf04sETk3wDbboxM7LxPGdl9VqIyqVujGoAACMhbVxpplIp1MRYVclAMAgrL/36kyjaDZPbMY1BhUAgFGXSiUfnDSR6dp4C7BZgm/Qp/WNp7X0eSYajebxxhMAAIySVCoR+Xw28vlM1zjTCIE3AIBhkUwmYno6F8Xi2mbccrkWjYYxqAAAjJZsNn180kSEjbdA/wTf4Cytf/NNpZIxM5OPYjEXlUo9yuWaMagAAAy1bDYVhUJWkwkAYARstBm3Xm9GuVyLatUYVAAAhlcikTh+ulsqZZwpsD0E32AbnRiDGjE1lY1CIRO1WjMqlVrUasagAgAwHBKJROTz6SgUssebTO12OxKJhCYTZ6Xdbsftt98ef/VXfxV///d/H/fff38sLS1FOp2OvXv3xoUXXhhXXXVVPOtZz4q9e/cOutxYXl6OP/3TP4077rgj/vEf/zGOHj0a5XI5CoVCnHfeeXHJJZfEU5/61HjKU54SqVRq0OUCwHHr79nS6WTMzhai1ToxBtVmXAAAhkU6nYxCIRu5XNo4U7aVXiQREYm2VxXYMZ0PDyMims1WVCoaTwAADE4mk4p8PtPVZBJ0Y7t8+ctfjre//e3x3e9+94w/m81m4wUveEG88pWvjGKxuAvVdavVavHBD34wfv/3fz9KpdIZf/68886LN7/5zfH0pz99F6rrduzY6raOsNu7t9izqxqA8dC5t2u32w+eAlePWs0pcAAA7L5EIiKXy0ShkIl0ei3AoxfJdtKL3H6j2ocUfINdsr7x5BQ4AAB2S+d0t3w+G+l09+lusF3e8573xPvf//4tP+4xj3lMfPCDH4wDBw7sQFUbO3r0aPzCL/xC3HXXXVt+7HOf+9z4tV/7tUgmdy84NqoNJwAGZ/29Xqu1thm3XLYZFwCAnZdOp6JQsPGWnaUXuTNGtQ8p+Aa7zClwAADsBqe7sVve9a53xYc+9KENv1csFuPcc8+NxcXFOHr06IY/8/CHPzxuuummeMhDHrKTZUZExNLSUlx77bXxne98p+d7iUQi9u/fH8ViMR544IEol8sbXuM5z3lOvPOd79zpUo8b1YYTAMNh/T1grdZwChwAANsukYjI5zORzzvdjZ2nF7lzRrUPKfgGA9R7CpzGEwAA/Vs73W1thEDnL5SaTOykz33uc/HqV7+6Z/1pT3taXH/99XHFFVccX/v+978fN910U3z4wx+Oer3e9fOPe9zj4qMf/eiO7178xV/8xfjiF7/YtZbP5+NlL3tZPOc5z4lzzjnn+Prf/u3fxoc+9KG47bbbeq7z9re/Pa677rodrbVjVBtOAAyX3lPgGlGp1KLZ9PEAAAD9sfGW3aYXubNGtQ8p+AZDYOPGUz2aze17UQEAYHxls+nI5zORzaY0mdg1Kysr8fSnP71n9+Sb3vSmeMlLXnLKx911111xww03xKFDh7rW3/a2t8Vzn/vcHak1IuLP/uzP4g1veEPX2r59++IjH/lIXHDBBRs+pt1ux4033hi//du/3bU+PT0dn/vc52L//v07Vm/HqDacABhe6+8V6/VGVCqNqFbr4ZMCAADOJJlMPBh2y0Q6beMtu0cvcud7kaPah9TphCGw/kYgkUjE1FQ29u4txvz8VBQKGTcKAAD0SKeTMT2di337pmNurhC5XPr499w/shtuvPHGnkbTC1/4wtM2miIiLrnkkvjABz4QhUKha/1973tfVKvVba8zIqJer8e73/3urrV0Oh0f+MAHTtloilj7Xbrhhhvi+uuv71pfWVmJD37wgztSKwDstPX3iul0KmZm8rFv33TMzOQjk0kNsDIAAIZVLpeOublC7Ns3HcViLlKp7s+3YafpRepFnorgGwyZ7sZTMqan87FvXzFmZ/ORzaZP80gAAMZdMpmIQiEbe/ZMxZ49xSgUsrG+r6TJxG6pVqtx8803d63t378/Xv/612/q8Zdeemm84hWv6Fo7dOhQfPrTn962Gtf7whe+EN/73ve61q677rq4/PLLN/X4173udXHRRRd1rX3iE5+IlZWVbasRAAZh/f1jPp+J+fmp2Lu3GMVituvDTAAAJk8ms7ZJYv/+6ZidLUQ2m47OQEF9SHaTXqRe5OkIvsEQW3/DkMtlHkzRF6NYzB0/OhYAgPHX2VG5d28xpqdzXceDazIxCJ///OdjYWGha+3aa6+NqampTV/jec97XhSLxa61W265ZTvK6/HHf/zHPWs/93M/t+nHp1Kpnt2j5XI5PvvZz55taQAwFNbfUyaTiZiaysXevdMxPz8V+Xwm3HICAEyGtXvBE9PJ8vlM1/f1IhkEvUi9yNORnIERsNEo1D17irFnz1RMTdl9CQAwjjo7KvftO7GjskODiUH70pe+1LP2zGc+c0vXKBaL8eQnP7lr7c4774zDhw+fTWk9SqVS3HHHHV1rl112WRw4cGBL13na054WmUx3s/cv//Ivz7o+ABg2J0+kOHFPmo9czkQKAIBxszZlYu30384o02TSKFOGh17kGr3IjQm+wYhZf2ORSiWjWDyx+7JQyHTdhAAAMFoymVRMT+di377ihqdraDIxLP7mb/6m6+tzzz03Lrjggi1f56qrrur6ut1ux2233XZWtZ3sK1/5StTr9a61Jz7xiVu+zvT0dFx22WVda3fccUfUarWzqg8AhtnJEylmZwuxf/90zMzkI5tNDbAyAADORiKxNur+xJSJfNfEMX1Ihole5Bq9yI0JvsEIO3n35fT02u7L+fnCgx+SuiEBABh26XRnM0Pxwc0M2Z4Tf2GY3H///XHkyJGutcc+9rF9Xevk5k1ExDe/+c2+rnUqd911V8/a5Zdf3te1Tq63Wq3GPffc09e1AGCUnHxPuvYh6dqJINPTuchkhOAAAIZdIhGRy6VjdrYQ+/atbWZYfx+nD8kw0os8QS9yY84lhzHRHYJLxcxMOqan21GvN6NabUSt1ohWqz3ACgEA6Eink5HLpSOXy0QqtbYfqd0+ca+mycQw+6d/+qeetX52WEZEnH/++ZFIJLr+/N99991917aRf/7nf+5Zu/DCC/u61qMe9aietbvvvnvDphkAjKvuTRoRhUI2CoVstFqtqFYbUa02ol5vDrBCAAA6EomIbDYduVw6stn08Xu5Ti9GH5JhpxfZTS+yl+AbjKH1NyjZ7NpNTER0heCazdagygMAmEiZTOp4g0nYjVF277339qwdOHCgr2vl8/nYv39/HDp06Pjafffd13dtGzm53mQyGeedd15f1/qBH/iBnrXtrhcARsnJJxWfCMG1o1brhOAa0bYfFwBg1ySTieNht0wm1RN2i9CLZHToRXbTi+wl+AZj7OQblnQ6GZlMLiJy0Wg0jzefGg0hOACA7dbZTdn5J5nUYGI8HD58uGft3HPP7ft6JzebDh48GK1WK5LJZN/XXG/9tSMi9u3bF6lUf+PYzjnnnJ61+++/v69rAcC4OfkkuHw+E/l8JtrtdtRqJ3qRbSk4AIBtl0olj2+6XT++VC+SUacX2U0vspfgG0yQ9TczqVQypqZyMTWVi2azFbVaM+r1RtRqTc0nAIA+pVLJyGZTxxtMdlMyjk5u3kRE7N27t+/rzc3NdX3dbrdjaWkp5ufn+77m+msdPXq0a23Pnj19X+/kWiMiFhcX+74eAIyrk+95c7m1E0emp9vRaLSiVlubSmFDLgBAfxKJtQkTmcxa2C2d7p0wsfZzepGMNr3IbnqRvQTfYEKtv8lJJhNRKGSiUMhExNpI1E4Irl5vDqpEAIChl0wmIpNJPXiqW6prV5iwG+NqeXm5Z216errv6xWLxZ617Wo2ra6uRqPR6FrbiVoBgFM7eRxqZypFsZiLVqt9vA9ZqzWi1bIhFwDgVNLp5PENtzbdMin0IrvpRfYSfANOORJ1amrtRqnZbEW9bvclAEBHIrF2z5RObzw2YO1nNJkYT7VarWctn8/3fb2NHrvRc/RjlGo9lbm5QmznodydscsAMCgnj0TN5TKRy61tyG02Ww/+IwAHANCRTEZkMunjf6fXh2SSjFJ/b5Rq3cio9iEF34AeJ98cpVLdH+oCAKDBxOTaqLmSSvX/94X1JyV2nLwzsl/1er1nbVhr3cpzAsC4OPkeOplMRDKZimzWvTUAwHpOdWNS6UV228le5Kj2IQXfgNNy4wQAsDH3SUyqZrPZs3Y2TZGNmj+t1vacOL1RrWfTbNrJWgEA99gAAKfiPolJpRfZTS+y12jG9QAAAICByGQyPWsbNXU2a6Nditlstu/rrZdO9+73O5tdkRvt2tyuWgEAAACAbnqR3fQiewm+AQAAAJu2UbPpbBo4O9ls2ug6Z1PrRk01zSYAAAAA2Bl6kWd+jkkn+AYAAABs2vT0dM9aqVTq+3qrq6s9a/l8vu/rrVcsFnvWtrvWQqHQ9/UAAAAAgFPTi+ymF9lL8A0AAADYtPn5+Z61lZWVvq938mMTiUTs2bOn7+utl8lkeppj21lrRMTevXv7vh4AAAAAcGp6kd30InsJvgEAAACbds455/SsHT58uO/rnfzYubm5DUcY9Gv//v1dXx85cqTva23073ny9QEAAACA7aEXefrrI/gGAAAAbMGBAwd61h544IG+rtVut+PQoUNdaw972MP6utapnFzv4uJiVCqVvq518ODBnrWHPvShfV0LAAAAADg9vchuepG9BN8AAACATTv//PN71u69996+rnXw4MGexs+FF17Y17VOZaN6//Vf/7Wva23073nRRRf1dS0AAAAA4PT0IrvpRfYSfAMAAAA27cILL4xsNtu1ds899/R1rbvvvrtnbbubN5deemnP2jDXCwAAAACs0YvsphfZS/ANAAAA2LRMJhOXXHJJ19q3vvWtaDQaW77W17/+9Z61xz/+8X3XtpErrriiZ+3OO+/s61onP+5Rj3pU7Nu3r69rAQAAAACnpxd5gl7kxgTfAAAAgC150pOe1PX1yspKfOUrX9nydb74xS92fT03NxeXXXbZWdV2svPPPz8e8YhHdK3deuut0W63t3Sdf/iHf4gHHniga+3qq68+6/oAAAAAgFPTi1yjF7kxwTcAAABgS57xjGf0rH384x/f0jW+8Y1v9Bzz/4xnPCNSqdRZ1baRpz/96V1ff+9734u//uu/3tI1br755p61Zz7zmWdVFwAAAABwenqRa/QiNyb4BgAAAGzJBRdcEFdeeWXX2l/8xV/E1772tU09vtFoxG/+5m/2rF977bXbUt/Jfvqnf7pn7V3velfUarVNPf7uu++OT37yk11rF110UTzucY/blvoAAAAAgI3pRepFno7gGwAAALBlN9xwQ9fXrVYrXvWqV8W//du/nfGx73jHO+LrX/9619qTn/zkuPTSS7e1xo5HP/rRPTtD77nnnnjTm950xjEDhw4dipe//OVRr9e71l/+8pdve50AAAAAQC+9SL3IUxF8AwAAALbsKU95Slx99dVda0eOHInrrrsubrvttg0fs7S0FK95zWt6jurPZrPxy7/8y2d8zltuuSUuvvjinn824w1veEMUCoWutc9+9rNx/fXXx5EjRzZ8zJ133hnXXntt/Pu//3vX+uMf//j48R//8U09LwAAAABwdvQi9SJPJdE+U5wQAAAAYAMPPPBAPOc5z4lDhw71fO/KK6+Mpz71qfHwhz88VlZW4q677orPfOYzsbq62vOzb3/72+O666474/Pdcsst8eY3v7ln/Z577tlUvZ/4xCfirW99a896Pp+PH/uxH4vLL7885ufn4/77749bb7017rjjjp6f3bNnT3zqU5+Khz70oZt6TgAAAADg7OlFshHBNwAAAKBv3/nOd+JFL3rRhg2nzXjZy14Wr33tazf1s2fbbIqI+IM/+IN45zvfuemfX29+fj4+9KEPxeWXX97X4wEAAACA/ulFcjKjTgEAAIC+PfrRj46Pfexj8YQnPGFLjysWi/Ebv/Ebm240bZcXv/jF8e53vzv27du3pcddcskl8bGPfUyjCQAAAAAGRC+SkznxDQAAANgWn/nMZ+Kmm26Kr33ta9FsNjf8mbm5ubjmmmvi53/+5+NhD3vYlq6/HbssO5aWluJDH/pQ/Pmf/3ncd999p/y5H/zBH4znP//58exnPzvS6fSWn2cz2u123H777fFXf/VX8fd///dx//33x9LSUqTT6di7d29ceOGFcdVVV8WznvWs2Lt377Y976c//el44xvfePzrfv47AgAAAMAg6EX2ZxC9yJ3sQwq+AQAAANvq6NGj8c1vfjMOHToUR48ejUwmE3v27ImLL744Lr744kilUoMuscs999wT3/3ud+Pw4cOxvLwcMzMzce6558ZjH/vYeMhDHrKjz/3lL3853v72t8d3v/vdM/5sNpuNF7zgBfHKV74yisXiWT3v0aNH4yd/8ie7xkIIvgEAAAAwavQiN28Qvcid7kMKvgEAAAAMwHve8554//vfv+XHPeYxj4kPfvCDceDAgb6et9FoxEtf+tK4/fbbu9YF3wAAAABgPA2iF7kbfcjktl0JAAAAgE1517vedcpGU7FYjEc96lGnHCXw7W9/O170ohfFAw88sOXnbbfb8Za3vKWn2QQAAAAAjKdB9CJ3qw/pxDcAAACAXfS5z30uXv3qV/esP+1pT4vrr78+rrjiiuNr3//+9+Omm26KD3/4w1Gv17t+/nGPe1x89KMfjWRyc/sam81m/Mqv/Ep86lOf2vD7TnwDAAAAgPEyiF7kbvYhBd8AAAAAdsnKyko8/elPj6NHj3atv+lNb4qXvOQlp3zcXXfdFTfccEMcOnSoa/1tb3tbPPe5zz3j8x45ciTe+MY3nnaHpeAbAAAAAIyPQfQid7sPadQpAAAAwC658cYbexpNL3zhC0/baIqIuOSSS+IDH/hAFAqFrvX3ve99Ua1WT/vYr371q3HNNdcYbwoAAAAAE2S3e5GD6EMKvgEAAADsgmq1GjfffHPX2v79++P1r3/9ph5/6aWXxite8YqutUOHDsWnP/3pDX++XC7He9/73njRi14UBw8e7K9oAAAAAGDk7GYvcpB9SME3AAAAgF3w+c9/PhYWFrrWrr322piamtr0NZ73vOdFsVjsWrvlllu6vm42m/GJT3wifvRHfzTe9773RbPZ7Pr+vn374rzzztta8QAAAADAyNiNXuQw9CEF3wAAAAB2wZe+9KWetWc+85lbukaxWIwnP/nJXWt33nlnHD58OCIiFhcX46d+6qfirW9964a7Ky+55JK4+eabBd8AAAAAYIztdC9yWPqQgm8AAAAAu+Bv/uZvur4+99xz44ILLtjyda666qqur9vtdtx2220REbG8vBz//M//3POYVCoV119/fdx0001x4MCBLT8nAAAAADA6droXOSx9SME3AAAAgB12//33x5EjR7rWHvvYx/Z1rcsuu6xn7Zvf/OYpf/6HfuiH4pOf/GT80i/9UmSz2b6eEwAAAAAYDYPqRQ6iD5nelWcBAAAAmGD/9E//1LPWzw7LiIjzzz8/EolEtNvt42t33313z89ddNFF8frXvz6e8pSn9PU8AAAAAMDo2e1e5CD7kIJvAAAAADvs3nvv7Vnr96j/fD4f+/fvj0OHDh1fu++++yIiIplMxtVXXx0vetGL4kd+5EcikUj0VzAAAAAAMJJ2oxc5LH1IwTcAAACAHXb48OGetXPPPbfv653cbDp48GC0Wq14+MMfHh/+8If7vi4AAAAAMNp2oxf50Ic+dCj6kMlBFwAAAAAw7tY3hjr27t3b9/Xm5ua6vm6327G0tNT39QAAAACA8TBJvUjBNwAAAIAdtry83LM2PT3d9/WKxWLP2rA0mwAAAACAwZmkXqTgGwAAAMAOq9VqPWv5fL7v62302I2eAwAAAACYLJPUixR8AwAAANhhGzWCUqlU39dLJntbOo1Go+/rAQAAAADjYZJ6kYJvAAAAADus2Wz2rG3UMNqsjRpVrVar7+sBAAAAAONhknqRgm8AAAAAOyyTyfSsbdSA2qyNdlRms9m+rwcAAAAAjIdJ6kUKvgEAAADssI2aTWczDmCYm00AAAAAwOBMUi9S8A0AAABgh01PT/eslUqlvq+3urras5bP5/u+HgAAAAAwHiapFyn4BgAAALDD5ufne9ZWVlb6vt7Jj00kErFnz56+rwcAAAAAjIdJ6kUKvgEAAADssHPOOadn7fDhw31f7+THzs3NbTjCAAAAAACYLJPUixR8AwAAANhhBw4c6Fl74IEH+rpWu92OQ4cOda097GEP6+taAAAAAMB4maRepOAbAAAAwA47//zze9buvffevq518ODBqFQqXWsXXnhhX9cCAAAAAMbLJPUiBd8AAAAAdtiFF14Y2Wy2a+2ee+7p61p33313z9pFF13U17UAAAAAgPEySb1IwTcAAACAHZbJZOKSSy7pWvvWt74VjUZjy9f6+te/3rP2+Mc/vu/aAAAAAIDxMUm9SME3AAAAgF3wpCc9qevrlZWV+MpXvrLl63zxi1/s+npubi4uu+yys6oNAAAAABgfk9KLFHwDAAAA2AXPeMYzetY+/vGPb+ka3/jGN3rGEjzjGc+IVCp1VrUBAAAAAONjUnqRgm8AAAAAu+CCCy6IK6+8smvtL/7iL+JrX/vaph7faDTiN3/zN3vWr7322m2pDwAAAAAYD5PSixR8AwAAANglN9xwQ9fXrVYrXvWqV8W//du/nfGx73jHO+LrX/9619qTn/zkuPTSS7e1RgAAAABg9E1CL1LwDQAAAGCXPOUpT4mrr766a+3IkSNx3XXXxW233bbhY5aWluI1r3lN3HzzzV3r2Ww2fvmXf3nHagUAAAAARtck9CLTgy4AAAAAYJK8853vjOc85zlx6NCh42uHDx+Ol7zkJXHllVfGU5/61Hj4wx8eKysrcdddd8VnPvOZWF1d7bnOW97ylnjMYx6zm6UDAAAAACNk3HuRgm8AAAAAu+ghD3lIfOQjH4kXvehFXQ2niIg777wz7rzzzjNe42Uve1lcd911O1UiAAAAADAGxr0XadQpAAAAwC579KMfHR/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      "text/plain": [
       "<Figure size 2600x2000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(26,20), tight_layout=True)\n",
    "axes_class = (GeoAxes, dict(projection=crt.crs.Robinson()))\n",
    "axs = AxesGrid(fig, 111, axes_class=axes_class,\n",
    "               nrows_ncols=(2, 2),\n",
    "               share_all=True,\n",
    "               axes_pad=1.7,\n",
    "               cbar_location='right',\n",
    "               cbar_mode='each',\n",
    "               cbar_size='2%',\n",
    "               cbar_pad=0.25,\n",
    "               label_mode='keep') \n",
    "\n",
    "cm = sns.diverging_palette(150, 275, n=11).as_hex() # 11\n",
    "cm[6] = '#D3D3D3'; cm[4] = '#D3D3D3'\n",
    "cm = ListedColormap(cm)\n",
    "clim=[-5.5,5.5]\n",
    "label='No. of models showing decrease/increase'\n",
    "\n",
    "axs[0].set_title(\"a) Intermodel agreement on sign of change for 1985-2014\", size=20, loc='left')\n",
    "xx, cb = global_map(axs[0], pxyn, no_models['current'], cm, clim, label)\n",
    "cb.ax.set_yticks(np.arange(5, 5+1 , 1))\n",
    "cb.ax.set_yticklabels(np.round(np.arange(np.arange(5, 5+1 , 1),0)))\n",
    "cb.ax.tick_params(labelsize=12)\n",
    "\n",
    "axs[1].set_title(\"b) Intermodel agreement on sign of change for 2021-2050\", size=20, loc='left')\n",
    "xx, cb = global_map(axs[1], pxyn, no_models['current'], cm, clim, label)\n",
    "cb.ax.set_yticks(np.arange(5, 5+1 , 1))\n",
    "cb.ax.set_yticklabels(np.round(np.arange(np.arange(5, 5+1 , 1),0)))\n",
    "cb.ax.tick_params(labelsize=12)\n",
    "\n",
    "clim=[0,0.25]\n",
    "label='Standard deviation (m)'\n",
    "cm = ListedColormap(sns.color_palette(\"rocket_r\", 10).as_hex())    \n",
    "axs[2].set_title('c) Model spread for 1985-2014', size=20,loc='left')\n",
    "xx, cb = global_map(axs[2], pxyn, changes_current_stats['std'], cm, clim, label)\n",
    "cb.ax.set_yticks(np.arange(clim[0],clim[1]+0.02,0.05))\n",
    "cb.ax.set_yticklabels(np.round(np.arange(np.arange(5, 5+1 , 1),0)))\n",
    "cb.ax.tick_params(labelsize=12)\n",
    "\n",
    "axs[2].set_title('c) Model spread for 2021-2050', size=20,loc='left')\n",
    "xx, cb = global_map(axs[2], pxyn, changes_future_stats['std'], cm, clim, label)\n",
    "cb.ax.set_yticks(np.arange(clim[0],clim[1]+0.02,0.05))\n",
    "cb.ax.set_yticklabels(np.round(np.arange(np.arange(5, 5+1 , 1),0)))\n",
    "cb.ax.tick_params(labelsize=12)\n",
    "                      \n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}