mathing_and_comparing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e35293c5-2e68-4b7f-ba7f-d1ef7dab529e",
   "metadata": {},
   "source": [
    "Demo for comparison betweeen IMERG_lateV6 (0.1s* 0.1) and MRMS (0.01 * 0.01) around 16:30 May 16, 2015 -- The Severe Weather Outbreak (EF3 Tornado)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e46450d7-d42f-4d23-9e5f-0bde0844d753",
   "metadata": {},
   "source": [
    "1. Access and read data in cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aa92daad-5404-4b86-a78c-c280f621ad43",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Suppress warnings\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# # add path to environment in case of plot error\n",
    "# import os\n",
    "# os.environ['PROJ_LIB']=r'/home/user/.conda/envs/venvname/share/proj' # path to the proj4 folder.\n",
    "# os.environ['GDAL_DATA']=r'/home/user/.conda/envs/venvname/share' # path to the proj4 folder.\n",
    "\n",
    "# Direct access\n",
    "import s3fs\n",
    "import earthaccess as ea\n",
    "import fsspec\n",
    "\n",
    "# datetime\n",
    "import datetime as dt\n",
    "\n",
    "# api to read files: possible packages to use\n",
    "# import xarray as xr # for netcdf\n",
    "import h5py  # for hdf5\n",
    "import gzip  # for .gz to decompress the file\n",
    "\n",
    "# data process\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# plotting using self-defined functions for better visualization\n",
    "from plot_function import ndarry_mask  # mask the array\n",
    "from plot_function import map_plot  # plot the map\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns  # package for statistic data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ac13e148-b4fe-496b-a817-6be415a79178",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You're now authenticated with NASA Earthdata Login\n",
      "Using token with expiration date: 11/09/2023\n",
      "Using .netrc file for EDL\n"
     ]
    }
   ],
   "source": [
    "auth = ea.login(strategy=\"netrc\", persist=True) # use 'interactive' strategy the first time to log in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "52a85bc3-fe72-4710-942f-65a31b0f3c60",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Granules found: 49\n"
     ]
    }
   ],
   "source": [
    "#### dataset1 in ASCII format with compression ####\n",
    "\n",
    "# step1: provdie the daac name and short name of dataset\n",
    "    # define the name of daac and dataset\n",
    "DAAC1 = 'GES_DISC'\n",
    "ssh_short_name1 = \"GPM_3IMERGHHL\" # short name of dataset. \n",
    "\n",
    "# step2: get links #\n",
    "results1 = ea.search_data(\n",
    "    short_name=ssh_short_name1,\n",
    "    cloud_hosted=True,\n",
    "    temporal=(\"2015-05-16\", \"2015-05-17\"),\n",
    ")\n",
    "\n",
    "    # data_link list\n",
    "https_links1 = []  # external link, todo: find ways to directly access data using external link\n",
    "s3_links1 = []  # s3 link to directly accessing  data in AWS\n",
    "\n",
    "for granule in results1:\n",
    "    https_links1.extend(granule.data_links(access=\"on_prem\"))\n",
    "    s3_links1.extend(granule.data_links(access=\"direct\"))\n",
    "    \n",
    "# step3: select proper links for use (similar part to what we did when we worked in local enviroment)\n",
    "\n",
    "# step4: get credentials and open files with credentials #\n",
    "s3_fs1 = ea.get_s3fs_session(DAAC1) # valid in one hour, need to refresh automatically for longer access\n",
    "\n",
    "#### read hdf5 data ####\n",
    "# open files with s3 links, works only in AWS #\n",
    "with s3_fs1.open(s3_links1[33], mode='rb') as f1: # open files with https links : 4:30-5:00pm\n",
    "    with h5py.File(f1) as hdf5:\n",
    "        lon_gpm = hdf5['Grid']['lon'][:]\n",
    "        lat_gpm = hdf5['Grid']['lat'][:]\n",
    "        date_time = hdf5['Grid']['time_bnds'][:]\n",
    "        precip_gpm = hdf5['Grid']['precipitationCal'][:]\n",
    "        \n",
    "precip_gpm = precip_gpm[0,:,:]\n",
    "precip_gpm[precip_gpm == -9999.9] = np.nan\n",
    "precip_gpm[precip_gpm == 0] = np.nan\n",
    "precip_gpm = np.transpose(precip_gpm)\n",
    "\n",
    "LON_gpm, LAT_gpm = np.meshgrid(lon_gpm,lat_gpm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bc01c68c-b79d-46ff-a413-7a092265c521",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Granules found: 76\n"
     ]
    }
   ],
   "source": [
    "#### dataset2 in ASCII format with compression ####\n",
    "\n",
    "# step1: provdie the daac name and short name of dataset\n",
    "    # define the name of daac and dataset\n",
    "ssh_short_name2 = \"gpmmrms\" # short name of dataset. \n",
    "\n",
    "# step2: get links #\n",
    "results2 = ea.search_data(\n",
    "    short_name=ssh_short_name2,\n",
    "    cloud_hosted=True,\n",
    "    temporal=(\"2015-05-16\", \"2015-05-17\"),\n",
    ")\n",
    "\n",
    "    # data_link list\n",
    "https_links2 = []  # external link, todo: find ways to directly access data using external link\n",
    "s3_links2 = []  # s3 link to directly accessing  data in AWS\n",
    "\n",
    "for granule in results2:\n",
    "    https_links2.extend(granule.data_links(access=\"on_prem\"))\n",
    "    s3_links2.extend(granule.data_links(access=\"direct\"))\n",
    "    \n",
    "# step3: select proper links for use (similar part to what we did when we worked in local enviroment)\n",
    "\n",
    "# step4: get credentials and open files with credentials #\n",
    "s3_fs2 = ea.get_fsspec_https_session() \n",
    "\n",
    "#### read data under zip file (.gz)####\n",
    "# open files with https links, works both in and out AWS #\n",
    "with s3_fs2.open(https_links2[58], mode='rb') as f2: # open files with https links: 4:44pm\n",
    "    with gzip.open(f2, 'rb') as data_ori:  # decompress .gz files\n",
    "        precip_mrms = np.loadtxt(data_ori,skiprows = 6)  # read interested dataset\n",
    "        \n",
    "with s3_fs2.open(https_links2[58], mode='rb') as f2: # open files with https links\n",
    "    with gzip.open(f2, 'rb') as data_ori:  # decompress .gz files\n",
    "        datainfo = pd.read_csv(data_ori, header=None)[:6]  # read coordinate information\n",
    "        coor_info = datainfo.iloc[0:6, 0].str.split(' ', expand=True) # split coordinate information\n",
    "\n",
    "precip_mrms[precip_mrms == -999.] = np.nan\n",
    "precip_mrms[precip_mrms == 0.] = np.nan\n",
    "        \n",
    "# get the coordinate information\n",
    "x0 = float(coor_info.iloc[2, 1])\n",
    "xn = x0 + (float(precip_mrms.shape[1])-1) * float(coor_info.iloc[4, 1])\n",
    "x_coor = np.round(np.linspace(np.round(x0,2),np.round(xn,2),precip_mrms.shape[1]),2)\n",
    "\n",
    "y0 = float(coor_info.iloc[3, 1])+ (float(precip_mrms.shape[0])-1) * float(coor_info.iloc[4, 1])\n",
    "yn = y0 - (float(precip_mrms.shape[0])-1) * float(coor_info.iloc[4, 1])\n",
    "y_coor = np.round(np.linspace(np.round(y0,2),np.round(yn,2),precip_mrms.shape[0]),2)\n",
    "\n",
    "LON_mrms, LAT_mrms = np.meshgrid(x_coor,y_coor)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a379062d-e26b-4d15-a71b-963ef5e2c5c8",
   "metadata": {},
   "source": [
    "2. Matching grids: Match grids in MRMS to GPM grids. Treat the GPM grids as the center and match all grids in MRMRS that are within 0.1 degree to the center. \n",
    "zoom_region = [-130, -60, 20, 55]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "97c95869-3bf4-452a-b79a-846465ce81a5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "index_region = list(zip(*np.where((LON_gpm >=-105) & (LON_gpm <= -95) & (LAT_gpm >=30) & (LAT_gpm <=40))))  # grids for seleceted region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ea686e23-b0ab-4276-9f3e-9267ee930491",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "index_nan_gpm = list(zip(*np.where(~np.isnan(precip_gpm))))\n",
    "index_nan_mrms = list(zip(*np.where(~np.isnan(precip_mrms))))\n",
    "\n",
    "for_index = list(set(index_nan_gpm) & set(index_region))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2deafcdc-f44f-4fa7-bb69-875eee4910cd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#### main thread for matching ####\n",
    "\n",
    "pair_precip = pd.DataFrame()\n",
    "\n",
    "for i in range(len(for_index)):\n",
    "    precip_gpm_i = precip_gpm[for_index[i]]  # get precip grid for GPM\n",
    "    center_grid = [LON_gpm[for_index[i]],LAT_gpm[for_index[i]]]  # create the coordinate for the center\n",
    "\n",
    "    distance_matrix = np.sqrt((LON_mrms-center_grid[0])**2 + (LAT_mrms-center_grid[1]) ** 2)  # calcualte distance matrix for MRMS\n",
    "    index_search = list(zip(*np.where(distance_matrix<=0.1)))  # locate the matached grid in MRMS\n",
    "    index_search = list(set(index_search) & set(index_nan_mrms))  # delete the nan girids\n",
    "    index_list = [list(i) for i in index_search]  # convert the coordinate format to a proper format for index searching\n",
    "    index_list = np.transpose(index_list)\n",
    "    \n",
    "    if len(index_list)==0:\n",
    "        continue\n",
    "    \n",
    "    precip_mrms_i = precip_mrms[index_list[0],index_list[1]]  # find the matched grid in MRMS\n",
    "    distance = distance_matrix[index_list[0],index_list[1]]\n",
    "\n",
    "    pair_precip_i = pd.DataFrame({'GPM' : precip_gpm_i, 'MRMS' : precip_mrms_i, 'Dist':distance})\n",
    "    pair_precip = pd.concat([pair_precip,pair_precip_i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bc685bb4-0777-4715-8396-d29828b77464",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GPM</th>\n",
       "      <th>MRMS</th>\n",
       "      <th>Dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.36</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.072800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.36</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.070711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.36</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.060827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.36</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.042427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.36</td>\n",
       "      <td>3.53</td>\n",
       "      <td>0.078103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.089441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.078101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.042425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.019997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.050989</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>214135 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     GPM  MRMS      Dist\n",
       "0   1.36  0.24  0.072800\n",
       "1   1.36  0.60  0.070711\n",
       "2   1.36  0.49  0.060827\n",
       "3   1.36  0.65  0.042427\n",
       "4   1.36  3.53  0.078103\n",
       "..   ...   ...       ...\n",
       "83  0.01  0.12  0.089441\n",
       "84  0.01  0.36  0.078101\n",
       "85  0.01  0.26  0.042425\n",
       "86  0.01  0.14  0.019997\n",
       "87  0.01  0.12  0.050989\n",
       "\n",
       "[214135 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pair_precip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "22594a72-938f-42c6-916b-54a1ece7b395",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pair_precip['error'] = pair_precip['GPM']-pair_precip['MRMS']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0383ffc-1b32-4112-b2bd-7ac6a74781d3",
   "metadata": {},
   "source": [
    "3. Hotmap plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "819cd53f-f479-4e27-af48-5b4a318172ac",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<Figure size 1200x1200 with 0 Axes>"
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     "data": {
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",
      "text/plain": [
       "<Figure size 600x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "varname = 'Precipitation Rate (mm/h)'\n",
    "df_plot = pair_precip\n",
    "var1 = 'MRMS'\n",
    "var2 = 'GPM'\n",
    "color_hist = 'blue'\n",
    "color_cmp = 'Blues'\n",
    "\n",
    "fig = plt.figure(figsize=(12,12))\n",
    "\n",
    "joint_kws = dict(bins=100)\n",
    "# fig = plt.figure(figsize=(16,16))\n",
    "\n",
    "x = np.linspace(0,150,100)\n",
    "y = np.linspace(0,150,100)\n",
    "\n",
    "mean_i = np.mean(df_plot['error'])\n",
    "MAE_i = np.mean(np.abs(df_plot[var2]-df_plot[var1]))\n",
    "\n",
    "ax1 = sns.jointplot(data = df_plot, x=var1, y=var2, kind='hist', edgecolor = None, color = color_hist, joint_kws = joint_kws)\n",
    "# ax1.ax_joint.axhline(y=0, color = 'r', linestyle = 'dashed')\n",
    "# ax1.ax_joint.plot(x[1:],y,color = 'b')\n",
    "ax1.ax_joint.cla()\n",
    "plt.sca(ax1.ax_joint)\n",
    "plt.hist2d(df_plot[var1],df_plot[var2],bins=100, norm='log',cmap=color_cmp)\n",
    "plt.title('Scatter plot for {}'.format(varname),y=1.2,x=0.6)\n",
    "plt.ylabel(var2,fontsize=12)\n",
    "plt.xlabel(var1,fontsize=12)\n",
    "plt.text(80,75,'mean error: {}'.format(round(mean_i,2)))\n",
    "plt.text(80,72,'mean absolute error: {}'.format(round(MAE_i,2)))\n",
    "# plt.axhline(y=0,color = 'black', linestyle = 'dashed')\n",
    "plt.plot(x,y,color = 'red', linestyle = 'dashed')\n",
    "\n",
    "cbar_ax = ax1.fig.add_axes([1, 0.1, .03, .7])\n",
    "cb = plt.colorbar(cax=cbar_ax)\n",
    "cb.set_label(r'$\\log_{10}$ density of points',fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0ac5611a-331b-4316-9e54-d0c6d90a401c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x1200 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pnDx5EmvWrMGIESNMy405orby9vaGk5MTvvrqq1zX51TY1sSYmJhclyk/9Iy8vLygUqlw+/Zts3W3bt2yWC9bGOuR2/lUKpVZK3dhr0PNmjVNX4Lyojyut7c3JEnC/v37TUFBTsZlOZ9L1rDmccrLrl27cOvWLezZs0cYPcOW8X6NdSmp5wUgPm6dOnWCk5MT3nvvPfzvf/8z5T5v2LAB9vb22LJlCxwdHU37/vzzzwU+T2Ffh5bW37t3r8DnKyl6vR6bN2/G66+/bvOx7t27ZzF4JqqMmOpAZYqlD2Yg++ddY+tUr169oFarsXz58lyPZQyElEHPihUrzLbNq7VEo9FYXP7444/jypUrqFq1qqmFOufN1g+aM2fO4OTJk8Ky7777Dm5ubqbOaEouLi5o3bo1Nm3aJNRZr9fjm2++QbVq1UydiwrbgmdJ3bp1ERQUhO+++07ohZ+SkmLqlFPUw4oV1OOPPw5ZlnHz5k2Lj0+jRo0AAO3atYOHhwc+//xz4T4UlDWPE5D79S+q561S9+7dcfbsWbP0i3Xr1kGSJHTt2jXfY9jirbfeQu3atfHRRx8hKSkJgOG+2tnZCSkVaWlp+Prrr832L67XYb169SyO8lHaDh48iJiYmEKnBilptVpERUWZOk4SVXZs8aUy5dFHH0W1atXQr18/1KtXD3q9HidOnMDixYvh6uqKCRMmADAMb/Tuu+9izpw5SEtLw7PPPgsPDw+cPXsWcXFxmDVrFurVq4datWrhnXfegSzLqFKlCn777Tfs2LHD7LzGIOjTTz/FiBEjYG9vj7p168LNzQ2NGjXChg0bsHHjRtSsWROOjo5o1KgRJk6ciJ9++gmdOnXCG2+8gcaNG0Ov1+PGjRvYvn07Jk+ejNatW1t9LQIDA/HEE09g5syZCAgIwDfffIMdO3Zg/vz5eQaT8+bNQ8+ePdG1a1dMmTIFDg4OWLZsGU6fPo3169ebAquGDRsCAFauXAk3Nzc4OjqiRo0ahWqlVKlUWLBgAZ577jk8/vjjGDNmDDIyMrBw4UI8ePAAH330kdX331bt27fHyy+/jBdeeAFHjhxBp06d4OLigtu3b+PAgQNo1KgRXnnlFbi6umLx4sUYNWoUevTogdGjR8PPzw+XL1/GyZMnsWTJkjzPY+3jlNtzrl27dvDy8sLYsWMxY8YM2Nvb49tvvzULrnMeY/78+ejTpw/UajUaN25s+tk8pzfeeAPr1q3DY489htmzZyMkJAS///47li1bhldeecVstIWiZm9vjw8//BBPP/00Pv30U7z33nt47LHH8PHHH2Po0KF4+eWXER8fj0WLFllsoS+u12GXLl3w1Vdf4eLFi8V+DQrjxx9/RMOGDW2u03///YfU1NRi/2JDVG6UYsc6IjMbN26Uhw4dKtepU0d2dXWV7e3t5erVq8vDhg2Tz549a7b9unXr5JYtW8qOjo6yq6ur/Mgjjwg958+ePSv37NlTdnNzk728vOTBgwfLN27ckAHIM2bMEI41depUOTAwUFapVMJoB9euXZN79eolu7m5yQDkkJAQ0z7Jycnye++9J9etW1d2cHCQPTw85EaNGslvvPGGHBMTY9oOgDx+/PgCX4eQkBD5sccek3/88Ue5QYMGsoODgxwaGmrWkzy3mdv2798vd+vWTXZxcZGdnJzkNm3ayL/99pvZeT755BO5Ro0aslqtzncGuLxmjPr555/l1q1by46OjrKLi4vcvXt3+e+//xa2MY6KcPfu3QJdg4LOUJXfcb/66iu5devWpmtRq1Ytefjw4fKRI0eE7bZu3Sp37txZdnFxkZ2dneXw8HB5/vz5ZufJydbHKbfn3MGDB+W2bdvKzs7Oso+Pjzxq1Cj52LFjZsfIyMiQR40aJfv4+MiSJAmjRChHdZBlWb5+/bo8dOhQuWrVqrK9vb1ct25deeHChcKoA8a6Lly40OxaWnrdKOX3uLVu3Vr28vKSHzx4IMuy4fGpW7eurNFo5Jo1a8rz5s2TV61aZTbiRVG8Di1JSEiQXV1d5QULFgjLjaM6KEeLyO35NmLECNnFxcVUzu065nZ9lOcLDg62eK1z2z+359j7778ve3t7y+np6blfBKJKRJJlK37bI6JiFRoaioYNG2LLli2lXRXKAx+niuG1117Dzp07cebMGatG9ihqhw8fRuvWrfHff/+ZWvWtodPpULt2bQwdOhRz584twhoSlV/M8SUiokrtvffew82bN/HTTz+VdlUAAK1atYIsyzYFvQDwzTffIDk52TRLIBEx8CUiokrOz88P3377bYUb8kuv1+Pbb781GzuZqDJjqgMRERERVQps8SUiIiKiSoGBLxERERFVCgx8iYiIiKhSYOBLRERERJUCA18iIiIiqhQY+NogKyurtKtARERERAXEwJeIiIiIKgUGvkRERERUKTDwJSIiIqJKgYEvEREREVUKDHyJiIiIqFJg4GsDjupAREREVH4w8CUiIiKiSoGBLxERERFVCgx8bZCVlQVZlku7GkRERERUAAx8bdC3rw5arba0q0FEREREBcDA1waSZF/aVSAiIiKiAmLgS0RERESVAgNfIiIiIqoUGPgSERERUaXAwNcGen0WJ7EgIiIiKicY+BIRERFRpcDAl4iIiIgqBQa+RERERFQpMPAlIiIiokqBgS8RERERVQoMfImIiIioUmDgS0RERESVAgNfIiIiIqoUGPgSERERUaXAwJeIiIiIKgUGvkRERERUKTDwJSIiIqJKgYEvEREREVUKdqVdgfJMr89CVpa6tKtBRERERAXAFl8iIiIiqhQY+BIRERFRpcDAl4iIiIgqBQa+RERERFQpMPC1gSzLyMrKgizLpV0VIiIiIsoHA18byLIW/fpJ0Gq1pV0VIiIiIsoHA18bSZJ9aVeBiIiIiAqAgS8RERERVQoMfImIiIioUmDgS0RERESVAgNfIiIiIqoUGPjaiEOaEREREZUPDHxtJMta9OqVwSHNiIiIiMo4Br420uuzANiVdjWIiIiIKB8MfImIiIioUmDgS0RERESVAgNfIiIiIqoUGPgSERERUaXAwJeIiIiIKgUGvkRERERUKTDwJSIiIqJKgYFvEdDrs5CVlVXa1SAiIiKiPDDwJSIiIqJKgYEvEREREVUKnGu3CBhSHdSlXQ0iIiIiygNbfIuALMtITU1FZmZmaVeFiIiIiHLBwLcIyLIW/fsDaWlpkGW5tKtDRERERBYw8C0yEvr21UGr1ZZ2RYiIiIjIAga+RUiS7Eu7CkRERESUCwa+RUiny0RqairTHYiIiIjKIAa+RUiWtUx3ICIiIiqjGPgWIb0+C7LMEeKIiIiIyiIGvkVMp8tESkoKMjMzmfJAREREVIYw8C1isqxFnz4Z6NYthSkPRERERGUIA99ioNNlQa/nTG5EREREZQkD32KSlZWCBw8eMOWBiIiIqIxgT6xiYhjhQQu1Woc//3SEk5MTAECSJNjb20OSpFKuIREREVHlwsC3GEmSPfR6LXr2TAJwHyqVE+zsHLB9uwZ2dnaQJAl2dnbQ6XSwszM8FFqt1rSOiIiIiIoOA98SYJjRTYIsA1lZqejSJQWAFpLkiF9+AQYOVOGPPzQAgMce0+P331Wws7ODvb35THDGYNnYcY6tx0REREQFI8lMQLVaq1bRMHx30Jr9r9fLUKnsLa7LuY0kGYJZw8OghSQ5QZbTLO6vUjkB0OKHH/QYMsQRkmRnaj22RNmizACZiIiIKjMGvlaSZRnNm5+CrYFvbtvktU6WZUiSZAqcAR0AtfC/LMtQq52wfr0dnntOj02bXODs7Fwi14aIiMoee3t7uLm5sRGEKjUGvlZKTEyEh4dHaVeDiIiowBISEuDu7l7a1SAqNQx8rSTLMpKSkkq7GiUuMTERwcHBiIqKqnRvnrzvvO+875VHRb3vbPGlyo6d26wkSVKFejMsLHd390p7/3nfed8rG973ynnfiSoiTmBBRERERJUCA18iIiIiqhQY+FKhaDQazJgxAxqNprSrUuJ433nfKxve98p534kqMnZuIyIiIqJKgS2+RERERFQpMPAlIiIiokqBgS8RERERVQoMfImIiIioUmDgS0RERESVAgNfIiIiIqoUGPgSERERUaXAwNdKsiwjMTERHAaZiIgqMn7eUUViV9oVKK+SkpLg4eGBWb8dh6OLGwCgZhVnYZunmgSURtWIiIiKjPHzLiEhAe7u7qVdHSKbsMWXiIiIiCoFBr5ERFRqNm3ahJ49e8LHxwfu7u5o27Yttm3bluc+8fHx6N27NwIDA6HRaBAcHIxXX30ViYmJwnayLGPRokUICwszbffhhx8W590hojKOgS8RESErK6tQy609ntK+ffvQs2dPbN26FUePHkXXrl3Rr18/HD9+PNd9VCoV+vfvj19//RUXL17EmjVr8Ndff2Hs2LHCdhMmTMCXX36JRYsW4fz58/jtt9/QqlUrq+4PEVUMDHyJiAqgS5cueO211zBx4kR4eXnBz88PK1euREpKCl544QW4ubmhVq1a+OOPP4T9zp49i759+8LV1RV+fn4YNmwY4uLiTOv//PNPdOjQAZ6enqhatSoef/xxXLlyxbT+2rVrkCQJmzZtQteuXeHs7IwmTZrg0KFDedY3ISEBL7/8Mnx9feHu7o5u3brh5MmTpvUzZ85E06ZN8dVXX6FmzZrQaDSQZRmSJOHzzz9H//794eLigg8++AAAsHz5ctSqVQsODg6oW7cuvv76a+F8ue2Xn08++QRvvfUWWrZsiTp16uDDDz9EnTp18Ntvv+W6j5eXF1555RW0aNECISEh6N69O8aNG4f9+/ebtjl37hyWL1+OX375BU888QRq1KiBpk2bokePHgWqFxFVTAx8bTSxU0281bUW3upaC/7OjsLtf39HCrdT0clmNyIqP9auXQtvb28cPnwYr732Gl555RUMHjwY7dq1w7Fjx/Doo49i2LBhSE1NBQDcvn0bnTt3RtOmTXHkyBH8+eefiI2NxdNPP206ZkpKCiZNmoSIiAjs3LkTKpUKAwYMgF6vF849bdo0TJkyBSdOnEBYWBieffZZaLVai/WUZRmPPfYYYmJiTC2pzZo1Q/fu3XHv3j3TdpcvX8b333+Pn376CSdOnDAtnzFjBvr3749Tp07hxRdfxObNmzFhwgRMnjwZp0+fxpgxY/DCCy9g9+7dwnmV+xmD9j179hT4Guv1eiQlJaFKlSoF3ufWrVvYtGkTOnfubFr222+/oWbNmtiyZQtq1KiB0NBQjBo1Srj/RFQJyWSVhIQEGYCckJBgWrb/4j3h9tmBq8Ltv6gksxsRlQ+dO3eWO3ToYCprtVrZxcVFHjZsmGnZ7du3ZQDyoUOHZFmW5ffff1/u1auXcJyoqCgZgHzhwgWL57lz544MQD516pQsy7IcGRkpA5C//PJL0zZnzpyRAcjnzp2zeIydO3fK7u7ucnp6urC8Vq1a8ooVK2RZluUZM2bI9vb28p07d4RtAMgTJ04UlrVr104ePXq0sGzw4MFy375989wvOjparlu3rvzvv/9arKclCxYskKtUqSLHxsbmu+2QIUNkJycnGYDcr18/OS0tzbRuzJgxskajkVu3bi3v27dP3r17t9y0aVO5a9euBa4LGVj6vCMqr9jiS0RUQI0bNzb9rVarUbVqVTRq1Mi0zM/PDwBw584dAMDRo0exe/duuLq6mm716tUDAFM6w5UrVzB06FDUrFkT7u7uqFGjBgDgxo0buZ47ICBAOI/S0aNHkZycjKpVqwrnjoyMFNIoQkJC4OPjY7Z/ixYthPK5c+fQvn17YVn79u1x7ty5PPcLCgrC+fPnC5xXu379esycORMbN26Er69vvtv/3//9H44dO4aff/4ZV65cwaRJk0zr9Ho9MjIysG7dOnTs2BFdunTBqlWrsHv3bly4cKFA9SGiiqfCjeMbGhqK69evmy0fN24cli5dipEjR2Lt2rXCutatW+Off/4pqSoSUTllb28vlCVJEpZJkgQApjQFvV6Pfv36Yf78+WbHMgav/fr1Q3BwML744gsEBgZCr9ejYcOGyMzMzPXcyvMo6fV6BAQEWEwx8PT0NP3t4uJicX9Ly43nNJIf5gPnt19Bbdy4ES+99BJ++OGHAufh+vv7w9/fH/Xq1UPVqlXRsWNHvP/++wgICEBAQADs7OwQFhZm2r5+/foADF8q6tata3Vdiaj8qnCBb0REBHQ6nal8+vRp9OzZE4MHDzYt6927N1avXm0qOzg4FMm5O9TxyrP83bFos33iU8QPty51C57XRkRlW7NmzfDTTz8hNDQUdnbmb7fx8fE4d+4cVqxYgY4dOwIADhw4UCTnjYmJgZ2dHUJDQ20+Xv369XHgwAEMHz7ctOzgwYOmQNJW69evx4svvoj169fjscces+oY8sNZxTIyMgAYWqS1Wi2uXLmCWrVqAQAuXrwIwNDSTUSVU4VLdfDx8TG1Avj7+2PLli2oVauW0OlBo9EI2xSmEwURUUGNHz8e9+7dw7PPPovDhw/j6tWr2L59O1588UXodDp4eXmhatWqWLlyJS5fvoxdu3YJP9dbq0ePHmjbti2efPJJbNu2DdeuXcPBgwfx3nvv4ciRI4U+3ptvvok1a9bg888/x6VLl/Dxxx9j06ZNmDJlSp773bx5E/Xq1cPhw4dz3Wb9+vUYPnw4Fi9ejDZt2iAmJgYxMTFISEgwbbN582ZTiggAbN26FatXr8bp06dx7do1bN26Fa+88grat29vCvR79OiBZs2a4cUXX8Tx48dx9OhRjBkzBj179hRagYmocqlwgW9OmZmZ+Oabb/Diiy8KP8nt2bMHvr6+CAsLw+jRo3PNk8spIyMDiYmJwo2IKC+BgYH4+++/odPp8Oijj6Jhw4aYMGECPDw8oFKpoFKpsGHDBhw9ehQNGzbEG2+8gYULF9p8XkmSsHXrVnTq1AkvvvgiwsLCMGTIEFy7ds2Uh1wYTz75JD799FMsXLgQDRo0wIoVK7B69Wp06dIlz/2ysrJw4cIF0ygXlqxYsQJarRbjx483pSgEBARgwoQJpm0SEhKEvFwnJyd88cUX6NChA+rXr4+JEyfi8ccfx5YtW0zbqFQq/Pbbb/D29kanTp3w2GOPoX79+tiwYUOh739lw887qsgk2fj7UAX0/fffY+jQobhx4wYCAwMBGPLIXF1dERISgsjISLz//vvQarU4evQoNBpNrseaOXMmZs2aZba8MHOXW0p1CHRxFspMdSAiotJUFJ93RGVVhQ58H330UTg4OOQ5EPrt27cREhKCDRs2YODAgblul5GRYcodA4DExEQEBwfb/Ebwz5UHQjkuLUMo96hn3jrjWOEys4mIqKwors87orKgwoZQ169fx19//YVNmzbluV1AQABCQkJw6dKlPLfTaDR5tggTERFVBPy8Kx3p6elmo7lYw8HBAY6OjkVQo4qpwga+q1evhq+vb749hOPj4xEVFWUaWoiIiIioJKWnp8PJrSqgzT0fvqD8/f0RGRnJ4DcXFTLw1ev1WL16NUaMGCEMIZScnIyZM2di0KBBCAgIwLVr1/Duu+/C29sbAwYMKMUaExERUWWVmZkJaFOhCR8BqG0YYlWXiZiza5GZmcnANxcVMvD966+/cOPGDbz44ovCcrVajVOnTmHdunV48OABAgIC0LVrV2zcuBFubm6lVFsiIiIiAHaOkGwIfGXJMFhXy5YtoVarMX78eIwfP76oalchVMjAt1evXrDUZ8/JyQnbtm0rhRrlrk0tzzzX779432yZu6J3W5PqDNqJiIjKPQmAYkbEQu8Pw2Re7IhoWYUex5eIiIiIyKhCtvgSERERlTuSynCzZX/KE68QEREREVUKbPEtZelasaycnKJjmJfZPtH3xXH+ztxMEcq1/FzyPCYRERGVQZJkY46vDftWEmzxJSKqhNasWQNPT89iPUdoaCg++eSTYj0HUYViTHWw5UZ54hUiIqIyYc+ePZAkCQ8ePCjtqpSKy5cvw83NLd8vJNeuXcNLL72EGjVqwMnJCbVq1cKMGTOEWb/WrFkDSZIs3u7cuVPM94So7OKP4EREVOnpdDpIkgSVSmwPyszMhIND4cdVLex+WVlZePbZZ9GxY0ccPHgwz23Pnz8PvV6PFStWoHbt2jh9+jRGjx6NlJQULFq0CADwzDPPoHfv3sJ+I0eORHp6Onx9fQt9f6iEMNWh2LHFt4yJvp8p3Cyp5uUg3BoEuQi3M9GJwu1SbJrZjYgKp0uXLnjttdcwceJEeHl5wc/PDytXrkRKSgpeeOEFuLm5oVatWvjjjz+E/c6ePYu+ffvC1dUVfn5+GDZsGOLi4kzr//zzT3To0AGenp6oWrUqHn/8cVy5csW0/tq1a5AkCZs2bULXrl3h7OyMJk2a4NChQ3nW9+OPP0ajRo3g4uKC4OBgjBs3DsnJyWbb/fzzzwgLC4OjoyN69uyJqKgo07qTJ0+ia9eucHNzg7u7O5o3b44jR46Y1v/0009o0KABNBoNQkNDsXjx4lzrY7wfJ06cMC178OABJEnCnj17cO3aNXTt2hUA4OXlBUmSMHLkSACALMtYsGABatasCScnJzRp0gQ//vhjnvc/MzMTb731FoKCguDi4oLWrVtjz549pvXGVI8tW7YgPDwcGo0G169fR2hoKD744AOMHDkSHh4eGD16dIHua277FdR7772HevXq4emnn8532969e2P16tXo1asXatasiSeeeAJTpkzBpk2bTNs4OTnB39/fdFOr1di1axdeeumlQtWLSpqtaQ7ZE1iEh4dj6dKlpXt3yiAGvkREBbR27Vp4e3vj8OHDeO211/DKK69g8ODBaNeuHY4dO4ZHH30Uw4YNQ2pqKgDg9u3b6Ny5M5o2bYojR47gzz//RGxsrBDcpKSkYNKkSYiIiMDOnTuhUqkwYMAA6PV64dzTpk3DlClTcOLECYSFheHZZ5+FVqvoHZuDSqXCZ599htOnT2Pt2rXYtWsX3nrrLWGb1NRUzJ07F2vXrsXff/+NxMREDBkyxLT+ueeeQ7Vq1RAREYGjR4/inXfegb29PQDg6NGjePrppzFkyBCcOnUKM2fOxPvvv481a9ZYdW2Dg4Px008/AQAuXLiA27dv49NPPwVgCApXr16N5cuX48yZM3jjjTfw/PPPY+/evbke74UXXsDff/+NDRs24L///sPgwYPRu3dvXLp0Sbj/8+bNw5dffokzZ86YWkIXLlyIhg0b4ujRo3j//fcLfF+V+wGAJEn5XpNdu3bhhx9+sClISUhIQJUqVXJdv27dOjg7O+Opp56y+hxUfkRERODs2bOctc0SmaySkJAgA5ATEhJsOk5alniLupch3KxxJDJBuF2MSTW7EVHhdO7cWe7QoYOprNVqZRcXF3nYsGGmZbdv35YByIcOHZJlWZbff/99uVevXsJxoqKiZADyhQsXLJ7nzp07MgD51KlTsizLcmRkpAxA/vLLL03bnDlzRgYgnzt3rsD1//777+WqVauayqtXr5YByP/8849p2blz52QA8r///ivLsiy7ubnJa9assXi8oUOHyj179hSWvfnmm3J4eLipHBISIv/f//2fcD+OHz9uWn///n0ZgLx7925ZlmV59+7dMgD5/v37pm2Sk5NlR0dH+eDBg8K5XnrpJfnZZ5+1WLfLly/LkiTJN2/eFJZ3795dnjp1qnD/T5w4IWwTEhIiP/nkk1bdV+V+sizLdevWlTdt2mSxnrIsy3FxcXJwcLC8d+9eU708PDxy3d6Sy5cvy+7u7vIXX3yR6zbh4eHyK6+8UqjjGhXV5x3lzniNNS0myo5t3rb6pmkxkY9VPtjiS0RUQI0bNzb9rVarUbVqVTRq1Mi0zM/PDwBMnYeOHj2K3bt3w9XV1XSrV68eAJjSGa5cuYKhQ4eiZs2acHd3R40aNQAAN27cyPXcAQEBwnks2b17N3r27ImgoCC4ublh+PDhiI+PR0pK9vCHdnZ2aNGihalcr149eHp64ty5cwCASZMmYdSoUejRowc++ugjIQXj3LlzaN++vXDO9u3b49KlS9DpdLnWq7DOnj2L9PR09OzZU7iO69atE+qT07FjxyDLMsLCwoR99u7dK+zj4OAgXFejnNcEKPh9Ve4HGPJxBwwYkOv9Gz16NIYOHYpOnTrluk1ebt26hd69e2Pw4MEYNWqUxW0OHTqEs2fPMs2BCOzcRkRUYMaf+Y0kSRKWSQ87lhjTFPR6Pfr164f58+ebHcsYvPbr1w/BwcH44osvEBgYCL1ej4YNGwo99JXnVp5H6fr16+jbty/Gjh2LOXPmoEqVKjhw4ABeeuklZGVlmd0HJeOymTNnYujQofj999/xxx9/YMaMGdiwYQMGDBgAWZbN9pVl2WJ9AJg6jeXcRlkXS4z38ffff0dQUJCwTqPR5LqPWq3G0aNHoVarhXWurq6mv52cnCzefxcXcSz0gt5X5X4FsWvXLvz666+mTmmyLEOv18POzg4rV67Eiy++mOu+t27dQteuXdG2bVusXLky1+2+/PJLNG3aFM2bNy90/aiEcea2YsfAt5QpJ5eo5iX2Aj55I8lsnybV3fI8ZvNQd6FsqZPcudvipBf1Awr/hk1EeWvWrBl++uknhIaGws7O/O02Pj4e586dw4oVK9CxY0cAwIEDB2w+75EjR6DVarF48WJTwPn999+bbafVanHkyBG0atUKgCG39sGDB6ZWaQAICwtDWFgY3njjDTz77LNYvXo1BgwYgPDwcLO6Hjx4EGFhYWbBJgD4+PgAMOQ9P/LIIwAgdHQDYBoFIWcrqrHj2Y0bN9C5c+cC3f9HHnkEOp0Od+7cMV1XWxT2vhbGoUOHhPv7yy+/YP78+Th48KBZoJ/TzZs30bVrVzRv3hyrV682G43CKDk5Gd9//z3mzZtnUz2phHBUh2LHrwZERMVk/PjxuHfvHp599lkcPnwYV69exfbt2/Hiiy9Cp9PBy8sLVatWxcqVK3H58mXs2rULkyZNsvm8tWrVglarxf/+9z9cvXoVX3/9NT7//HOz7ezt7fHaa6/h33//xbFjx/DCCy+gTZs2aNWqFdLS0vDqq69iz549uH79Ov7++29ERESgfv36AIDJkydj586dmDNnDi5evIi1a9diyZIlmDJlisU6OTk5oU2bNvjoo49w9uxZ7Nu3D++9956wTUhICCRJwpYtW3D37l0kJyfDzc0NU6ZMwRtvvIG1a9fiypUrOH78OJYuXYq1a9daPFdYWBiee+45DB8+HJs2bUJkZCQiIiIwf/58bN26tdDXs7D3Nad69eph8+bNua6vX78+GjZsaLoFBQVBpVKhYcOG8PIyzNx5+PBh1KtXDzdv3gRgaOnt0qULgoODsWjRIty9excxMTGIiYkxO/7GjRuh1Wrx3HPPFfp+E1VEDHyJiIpJYGAg/v77b+h0Ojz66KNo2LAhJkyYAA8PD6hUKqhUKmzYsAFHjx5Fw4YN8cYbb2DhwoU2n7dp06b4+OOPMX/+fDRs2BDffvutxRY/Z2dnvP322xg6dCjatm0LJycnbNiwAYAhhzk+Ph7Dhw9HWFgYnn76afTp0wezZs0CYGjN/v7777FhwwY0bNgQ06dPx+zZs01DkFny1VdfISsrCy1atMCECRPwwQcfCOuDgoIwa9YsvPPOO/Dz88Orr74KAJgzZw6mT5+OefPmoX79+nj00Ufx22+/mfKhLVm9ejWGDx+OyZMno27dunjiiSfw77//Ijg4uLCX06r7anThwgUkJCQU+pw5paam4sKFC6bUkO3bt5u+KFWrVg0BAQGmm9KqVaswcOBAUxBNZRxnbit2kpxXUhblKjExER4eHkhISIC7u3v+O1jJmlQHJUupDknpYm4dUx2IiMiSkvq8q8yM11jT+k1IdpZz1wtC1mYg49+FfKzywBxfG12LT4dbpiEvrYa3o7Du70v3hXL7OoX/xm0pyD0VLQ5C76pIFFbWQ5k3bCAuU+b81vAxD4SV+chERERUhJjjW+wYyhARERGVBRzVodjxChERERFVIJyyOHds8SUiIiIqCyTJxhZfQ6pDREQEc3xzwRZfIiIiIqoU2OJro5WHo6BxNswEpFLklA+q5yeU10SIU5ACQCMfT6GsnHzCkkbVXPNc/8/lB0K5aain2TbKjmrKUR3StebHVS5jZzciIqIipJLMg4nC7k95YuhCREREVBawc1ux4xUiIiIiokqBLb5EREREZQHH8S12DHxt1Kd2Vbi4GvJy/7wSJ6xb8s91oTysWaDZ/nuui/vEJKcL5cca+ha6Tm1qewpl5YQXlijzhguSv6uc9IKzvxEREVFZxsCXiIiIqCxgjm+xY+BLREREVBYw1aHY8asBEREREVUKbPG1Ucc6VUyzo3SpW0VYt+t8vFBW5gADQPvqHkJZhiyUvzkabbbPU02qCeX88nEtjfsbfT9TKJ+8kSSU6wa6me2T39i/l2LTzPbxdLEXyj6ufMoRERFZxFSHYscohIiIiKgsYKpDseNXAyIiIiKqFNjiS0RERFQWMNWh2PEKEREREVGlwBbfYtStXlWh3Kiah9k2x6PuC+W4tAyh3D7E22yfbedihLKjWi2UHw33ybdu1bwc8ixbmvTCyyXvfer4OZntczdZK5TTxWKBJsogIiKqFIoox7dly5ZQq9UYP348xo8fX0SVqxgYdhARERGVCTamOjz8IT8iIsI04hSJmOpARERERJUCW3yJiIiIygIOZ1bsGPjaKC5ZiwyV1uI65WQNliZv6FVfzMdVTiRx6a5YBgBXe/E4asXPIltOxwrlpkFeZsdQ5ucqWZr0Qunc7RShXMPHxWwbTlhBREREZQWjEiIiIqKyQJJsHM6MLb75YeBLREREVBZwHN9ixytERERERJUCW3xt5O1qB/eHeaxbz9wR1l1LSBXK49qF5nu8JtXdFEuUZeDMTTG39kFqplBuHSqOH3wi6oHZMaLviQ99m1qe+dZNqX6AmNOrzPkFzPN+rRm3VzkWMPOGiYioQmLntmJX4Vp8Z86cCUmShJu/v79pvSzLmDlzJgIDA+Hk5IQuXbrgzJkzpVhjIiIiIioJFS7wBYAGDRrg9u3bptupU6dM6xYsWICPP/4YS5YsQUREBPz9/dGzZ08kJZmPnkBERERUYow5vrbcKE8V8jdjOzs7oZXXSJZlfPLJJ5g2bRoGDhwIAFi7di38/Pzw3XffYcyYMSVdVSIiIiIDpjoUuwr51eDSpUsIDAxEjRo1MGTIEFy9ehUAEBkZiZiYGPTq1cu0rUajQefOnXHw4ME8j5mRkYHExEThRkREVNHw844qsgrX4tu6dWusW7cOYWFhiI2NxQcffIB27drhzJkziImJAQD4+fkJ+/j5+eH69et5HnfevHmYNWtWntv0beArlNdE3BDKs3dcNtuna4jYEa1jmPlkE0oNgsQOY5FxaqGckaUXyoHuTmbHSErLEsoHLt0Xyh3q5F8PJWVnN8C8I56S8r5YouzMxs5uRETFpyCfd1RMOJxZsatwV6hPnz4YNGgQGjVqhB49euD3338HYEhpMJIUPwXIsmy2TGnq1KlISEgw3aKiooq+8kRERKWMn3dUkVX4pjIXFxc0atQIly5dwpNPPgkAiImJQUBAgGmbO3fumLUCK2k0Gmg0muKsKhERUanj510pYo5vsatwLb5KGRkZOHfuHAICAlCjRg34+/tjx44dpvWZmZnYu3cv2rVrV4q1JCIiospOORyrNTfKW4Vr8Z0yZQr69euH6tWr486dO/jggw+QmJiIESNGQJIkTJw4ER9++CHq1KmDOnXq4MMPP4SzszOGDh1q1fk2Hr8JJ1dD4n9tT1dh3ciW1fPd/5uj0UI5+rg46cWzjwTle4wa3o55rr/9IM1sWVKWmCfbNNhTKO+/KOb8AkDLmmLeb0Emo8gvh/dSrFg3Txd7s22UObwFyemNjEsXyvldIyIiIqr4KlzgGx0djWeffRZxcXHw8fFBmzZt8M8//yAkJAQA8NZbbyEtLQ3jxo3D/fv30bp1a2zfvh1ubuYzpBERERGVFJtbbdnim68KF/hu2LAhz/WSJGHmzJmYOXNmyVSIiIiIqCCkhzdb9qc8VfgcXyIiIiIioAK2+JY0D409nDWGvNT9UfeEdf/dTRDKL7cJMdv/+ebVhPKeC+IxvlXkAANAQx8Podyket5pGi1reJgt++fyA6F84bY4ZbMyn9eSU9HJQrlRNddctsxdHT9xjGHlGL2A+VjABRn7V5nTq8z5tVebf+er5uWQ73GJiIiKS3lOdUhNTUX9+vUxePBgLFq0qNTqkR+2+BIRERGRTebOnYvWrVuXdjXyxcCXiIiIqAwor8OZXbp0CefPn0ffvn1L5fyFwcCXiIiIqAwojcB337596NevHwIDAyFJEn7++WezbZYtW4YaNWrA0dERzZs3x/79+4X1U6ZMwbx586y92yWKgS8RERFRJZWSkoImTZpgyZIlFtdv3LgREydOxLRp03D8+HF07NgRffr0wY0bNwAAv/zyC8LCwhAWFlaS1baaJMuyXNqVKI8SExPh4eGBk1dj4ebmDgC4m5ghbPPLhVih7OKgNjtOz5reQtlSRzSlP8/cFco+LuLUks1D3fM9Rn7O3U4xW6bViU+VOv5iZ7akdPOOaW6KWS4KMulFfpSnseaY0fczzZZ5u4md24qirkRE5Z3x8y4hIQHu7rZ/vpA54zV2G7QCkr1T/jvkQs5KQ9JPYxAVFSU8VgWdhlqSJGzevBlPPvmkaVnr1q3RrFkzLF++3LSsfv36ePLJJzFv3jxMnToV33zzDdRqNZKTk5GVlYXJkydj+vTpVt+P4sQWXyIiIqIKJDg4GB4eHqabtWkImZmZOHr0KHr16iUs79WrFw4ePAgAmDdvHqKionDt2jUsWrQIo0ePLrNBL8DhzIiIiIjKhiKawMJSi6814uLioNPp4OfnJyz38/NDTEyM1dUsTQx8iYiIiMqAohrH193dvUjTUpR1kmXZYj1HjhxZZOcsLgx8bbTs0A04OBtyXV0dxMyRJ+r6CuVd1+LN9v/t4h2hfPmBOCnEs48Eme3Tu4GPUD55Q5x8Ytd58Tzd6lW1VPU81Q8wnyTiUmyaWI4R66rM+QWAK7FirnAtP/G41uTRKvexlK8bHZ8qlNvU9hTKBZmsQnldA6uIeVc+rnz5EBFRxeXt7Q21Wm3Wunvnzh2zVuDygjm+RERERGWAJNk6pJnhOC1btkR4eDiWLl1qU30cHBzQvHlz7NixQ1i+Y8cOtGvXzqZjlxY2WRERERFVIBEREQVOdUhOTsbly5dN5cjISJw4cQJVqlRB9erVMWnSJAwbNgwtWrRA27ZtsXLlSty4cQNjx44truoXKwa+RERERGWABFtnXyv8vkeOHEHXrl1N5UmTJgEARowYgTVr1uCZZ55BfHw8Zs+ejdu3b6Nhw4bYunUrQkJCbKhn6WHgS0RERFQGFFXntsLo0qUL8pvSYdy4cRg3bpy1tSpTGPgWoYQMnVDed0PsZNbI10LnrwdiJ6zIe+lCedXhG2b7PNesulBuUt0tz3rdTTafWCIuSZxsw1JnNqU6fnkPqn3seqLZMnu1mEYelyR2RLufIpYtdZDLrwOcpY5qymXKunm5mO9Tw9tRKCuvq7Kz25UY8fEGgKahnkKZk2AQEVFJa9myJdRqNcaPH4/x48eXdnXKFH4sExEREZUFRTSOb2FyfCsbBr5EREREZYGNqQ6yTfnBlQOHMyMiIiKiSoEtvjZa8HiY6eeEH07cEtadjxPzd+PTxbxaAEjJFPNEPRVJofEpWWb7rPr3mlDuVlOc0EKZr2tpogU3xXmUk0AUZIIHpWYh5j+rKI97MVbMkw32chbKkXfFCS8AQKsTk+4bVTPPAy5s3SxNehEZJ+ZX55fzayl3OildXBalePzyy5MmIqLKy9bObbaNCFE5sMWXiIiIqAIpqgksKiK2+BIRERGVAUXV4svObblj4EtERERUFhTRqA6UOwa+RWhw08A81687EmW2zEExzm2GTi+UHe3Ns1F0ioGmj966L5RjE8Vc4i51q5gdQzm+rDKnV5nvCgABnmLOa0HGqFUe19utqlA+cf2BUHawM7+/4UHit1ZFGq1VY+VaymFW5uz+c1msW7WqYj6yNXnQlq6rMpeYiIiIigcDXyIiIqIygJ3bih87txERERFRpcDAl4iIiKgMMLb42nIDOKpDXpjqQERERFQGcFSH4sfA10Zv/HIeDs6GCRWWDwrPc9vhLYLNlm06eVsoKye98Hc3f4jUiheFWiWWo5PFY/xz2bxhv2mop1BWdhArSIcrZWcwSxNlKCnP06aWWA9LE0soO8DV8hMnsIjLEjsEAkCaYmKQgkwcoay/T22xbidviJNvJKWbTy6S3+Qhlq6R8riBVcS6FuS6EhERUf74iUpERERUBrBzW/Fjji8RERERVQps8SUiIiIqCziBRbFj4GujNtXd4OTqBgB4d+tFYd0TYb7itoqcUQAY2CRAKP99SZyMYue1eLN9XDViQ31NT3FiBXvFpBe3UtLMjnH3nDjJRZsa4sQSBckrVW6jnFgCKPzkEpYmhVAuU+YBe7uZ7xN5N0Uon7kplmv5ibm4QP51bVLdTShfijW/rsprkJSefx608rj5HROwbtIOIiIq25jqUPyY6kBERERUgXA4s9yx3YiIiIioDOBwZsWPLb5EREREVCmwxddGzzwSZPpWpczF3Hn+Tr77K8dwzdSLY9K+2KK62T7bLsUI5aO3xGO0DfYQys525g9zUpY4Bu0/kWIucZC7+bi3zULy/vZYkLzTyLh0oVyQ8YKVlDm/yvGEASAuScwDruHjkud6wDxXOL/7U5CxgZVjDFvKC87vOJbqkV+eM3OAiYjKH+b4Fj9+PBIRERGVBRzVodgx1YGIiIiIKgW2+BIRERGVAUx1KH4MfImIiIjKAAa+xY+BbxFSdigK9RI7VG0/d9dsn051fITylVhxooW9V807yD3i5ymUqzqmCuWL98RjuDiYZ7TU9nQVylq9LJTvp5l3/lJOAtEgyHwSiPwoO7MpO6YVZOIMJUv7uNX0EsrK62qp7sr7p+Trocn3vErKjniWJqM4dj1RKIcHiZ0ILXVUUx5X2WkwPUsnlOsHFP6xIiIiqmiY40tERERUBkiQTK2+Vt0e9m7jBBa5Y4svERERUQXCCSxyV+FafOfNm4eWLVvCzc0Nvr6+ePLJJ3HhwgVhm5EjR5p9S2rTpk0p1ZiIiIgItrX22pgfXFlUuBbfvXv3Yvz48WjZsiW0Wi2mTZuGXr164ezZs3Bxyc5z7N27N1avXm0qOzg4WDqcTZR5pLX8zPMs/zofK5QlxSB8g5pUM9vnp5PRQtlTI9a9U/WqQvnCPXGCC0vLQt3Fujnamz81lHmjysk3AquYT8SQXx6scr1yYgYA0NiL388KklurzItVXntL+bxanTjZhLuzvVB+kCJO+mFJfnWzlK+rnBhEmQdsKS9YeRxl7rRyn+9P3DI7xhMNA/OtGxERlSCO41vsKtxH3Z9//imUV69eDV9fXxw9ehSdOnUyLddoNPD39y/p6hERERFRKalwga9SQkICAKBKlSrC8j179sDX1xeenp7o3Lkz5s6dC19f31yPk5GRgYyMDFM5MTEx122JiIjKK37elR4OZ1b8KlyOb06yLGPSpEno0KEDGjZsaFrep08ffPvtt9i1axcWL16MiIgIdOvWTXihK82bNw8eHh6mW3BwcEncBSIiohLFzzuqyCRZluX8Nyufxo8fj99//x0HDhxAtWrmubJGt2/fRkhICDZs2ICBAwda3MbSN+Dg4GAkJCSYek6euy3mjdqpxO8VdfzMc2CVuZhfH70ulO+nmid4Dm4UJJTjksSA/dJ9Mfe2ZZCY8wsAiWlivupFxT5+TmLOKAD4uYnLYpPEsWMDPMz3cXMU82SV489a41JsmlC2dF2tocwvvpMo3j8nB7VQLs2xcZXPm6T0wo+HvPWMOEZ040BPoVwUjxURlT8F+byjopWYmAgPDw+Ejv8RKo2z1cfRZ6Ti2tKn+FjlocKmOrz22mv49ddfsW/fvjyDXgAICAhASEgILl26lOs2Go0GGo0m1/VEREQVAT/vSo8kGW627E95q3CBryzLeO2117B582bs2bMHNWrUyHef+Ph4REVFISAgoARqSERERESlocIFvuPHj8d3332HX375BW5uboiJiQEAeHh4wMnJCcnJyZg5cyYGDRqEgIAAXLt2De+++y68vb0xYMCAUq49ERERVVaGFl9bOrcVYWXKgIyMDGzYsAH37t3DwIEDERISYvMxK1zntuXLlyMhIQFdunRBQECA6bZx40YAgFqtxqlTp9C/f3+EhYVhxIgRCAsLw6FDh+Dm5lbKtSciIiKyTXmcsnjChAlo2rSpqSzLMrp27YoXX3wRkydPRpMmTcwmJLNGhe7cVpyMieh5JZBHRCYI5VNxCWbbhLiJSeypikkiHO3EDlUAsP/GfaHcppqHUO7bQByWbfu5u2bHCHATO4R5uYgdmc7FmA9fk5IldqDydxE7s3koJnwAzDu3KRVFBypLk14kpYud94qiI5pywo6qbuY5cKXVISwyTuyIp9WJL2trOgDeTTbvWFmQTnNEVLEU5POObGO8xjVf/xFqjfWfV7qMFFz9rHx2bqtfvz4ef/xxLFy4EADw888/Y+DAgZg5cyYeeeQRjB49Gn369BEmH7MGP8WIiIiIyoDKPI7vrVu3UKtWLVN5y5YtqF27NqZPnw4AGDNmDNatW2fzeSpcqgMRERERlS9arRaqHMPA7t69G926dTOVQ0NDTf22bMHAl4iIiKgMMA5nZsutvKpevToOHToEADh9+jQiIyPRpUsX0/rY2Fi4urrafB6mOhSjljXE3NubSWlm2+y8ek8o1/IW82ZjkszzV5sHip3wIh+kCuVvj0YL5ab+XmbH0CtSu6PjxWM0DfY02yfmgZhHmqKYNCH6gfn983cTz6PMJVbm53q7mefIOubzLLWUV3s3WfxOp8yBreFtPtlGfppUF6+7pdziHefihHJtH/FF6prfnYF1ebTK+6PMz/3x5G2zfZ5qkvfwfcznJSIqWSqVBJXK+uhVtmHf0vbMM89gzpw5iIuLw+nTp+Hh4YE+ffqY1p84cQK1a9e2+Txs8SUiIiKiUjV16lSMHDkSBw8ehEqlwtdffw0PD0MDYkJCAn799Vch9cFabNIhIiIiKgMq88xtGo0Gq1atwqpVq8zWubm54fbt23B2tn46ZyMGvkRERERUZqlUKlPrr83HKpKjEBEREZFNjMOZ2XIrr1588UX8+++/ua4/fPgwXnzxRZvPwxZfG+27dA8urobJEuLSMoR1g5sGCuUnG/ub7Z+mEyes+ONcvFD2dDbvuHUvVZwIw8NRnOSigbc4aPWZu+YTZ9RXbFOtqvjzQXK6+eQFjvbieer4ix23TkWZn+dusnhNUjLE4/p5iJ2y4ix05lN2eCtA/zCzjlkF6ailvMvWdKrzdvMWyjvP3xHKqVrz66p8nhQF5f211JFt53mxI17jap55HoOIiIpXZU51WLNmDXr06IHWrVtbXB8ZGYm1a9fiq6++suk8bPElIiIiojItMTERDg62z47KJh0iIiLKl6wYBpOKXmWbue2///7DiRMnTOX9+/dDa+GX0fv372PZsmWoV6+ezedk4EtERET5YuBLRW3z5s2YNWsWAEPQvmLFCqxYscLitq6urli/fr3N55RkPpOtkpiYCA8PDyQkJMDd3ZAv+/mha8I2p2LECR2WDqif73HXH78plH/5704uW2YLVubnZoh5wwPDffM9hrOdmL9rKbfYw9leKGfp9ELZ0qQQl2LFa3D+TqJQrllFzBN2sDPPvvF0Ec+rzD+2ZjIKS5STPrjlk+RbkFxjpZM3ksyW/XtLnMSkfhUx/7pjmPkEJEREJcX4effgwYMi61lPIuM1bvD2L1BrXKw+ji4jBWfm9xdik7Ls+vXruHbtGmRZRrdu3TBt2jT06NFD2EaSJLi6uiI8PByOjrZ/3rPFl4iIiKgMqGyd20JCQhASEgIAmDFjBgYNGoSGDRsW6znZuY2IiIjypdfr89+IyoSWLVsiPDwcS5cuLe2qFNiMGTOKPegF2OJLREREBcDMyOInwcbObTDsGxERUS5SHZQOHTqEJUuW4NKlS4iPjzd7zkmShCtXrth0Dga+Nlp3JApOrm4AgCBXJ2HdFXtxDNvXNp8z2z/YU8ylfatrLaH87CNBZvu8/vN5oXwtLkUoB3iKOb87I8UcUgCoWUUjlHvW9hPKialZZvukZYq5w8rc2+j75mPwBlcVr4lyn6j4VKF8Kd48B7auj5tQdnO0N9tGqbBj8gL5j1urzAF2tGKc2ybV3fJd9s+VB0I5Mi7dbB9Xx8KPU0xEZIvyNmIAlS/r1q3DCy+8AHt7e4SFhaF69erFch5+WhIREVG+VCpmRxa3ypbjm9PcuXNRt25d/PXXXwgMLPqJnYz4LCYiIiIqAyrzlMXXr1/H2LFjizXoBRj4EhERUQGwcxsVp6CgIGRmmqdMFjUGvkRERJQvdm4rfsZUB1tu5dXLL7+Mb7/9FjqdLv+NbcAcXxs1D/CCq5uh5+RvF2KFdelZ4rfjpHTzDmPX7otvJB/uFHsrvttd7OwGAJ89KU7Zt1Ex6cVvZ+KEcpaTeWewhHTxof/l3G2h/FiYv9k+MQnmnaxySs8yf7JW81JMhKHolNUsROx16nRbnEgDAO4li98AExQd7zT25t/flJNcFEVnMOU+yg50gHWTWii1qeWZ73mi4sWJQZSTbRRFPYiIiEpKq1atsHnzZrRq1Qrjx49HjRo1oFabxwSdOnWy6Tz8eCQiIiIqA2zN0y3POb7du3c3/T1q1Ciz+yLLMiRJsrlFmIEvERER5as8B1XlRWUe1WH16tUlch4GvkRERJQvBr5UnEaMGFEi52Hga6P3tp6HnZMLAMDbTZwUwsNFzG+toigDwANFvmpMklieu/Oy2T6NfcUJD55RTHLRv5FY/t+Bq2bHOBcjTnrRQjGJwt837prt08DbQyhrFT18lRNcAMClWDEXtY6fk9k2OdUPcLGwVFx2+GqCUI55YJ57rFK8QSvzjy3l+BZ20ouC5NEqj5lkIWE3v3xjS+dRXkfl5BrKnUor57e48qCJqOSxc1vxq8ypDiWFH0FERESULwa+JcDWkRnKUdy7bt06AMCwYcMgSZKpnJ/hw4fbdF4GvgQASElUQW0nw9GZb2xERERUvEaOHAlJkjBkyBA4ODiYynl9wZIkiYEv2UaWgQt/VcG6dYHw8tPina9uwcmVwS8REVFJq0ypDrt37wYAODg4COXixsDXRq1qekHj4goAOBL5QFin04sBpDLn15L7KRlC+UGq+SwmSRlibm2WItd2YJMApKcDhw8D+/cDN2/WxODBQJcu2T+hnLmZgrQ0YPY7Djj0k2Gc37ib9liz2AO9Xo21+I0rxF3MtVWOU9wxzMtsnz0X7uV5fwI8xVxVs3F/LWhVU8w1jowzz/E9EyPmAQe7OwtlZe4xAARXzTv/2BrKfFZHK8YPLghrxiUuCZbyeS3l/ea3DxERVSydO3fOs1xc+BFTQcgycO2CHQ795Yj/O28IenPO/Ld8OfDII8DkycDTTwPXr0qY+LIjLp1XQa2W0aBnAv770xOnt3sirF0SQh5Jyf1kREREVOQq83BmSsnJyQAAV1fXIj0uA99y7kGMHX7c6or9fzgi+qo4Q5u/P9CxI+DuDnz3HXD8OPD888A77wAPEpyQnCShqo8ei5Zl4JQmFmp7Gcd/88L2Jf4Y/lkkNC6cl52IiKikVKZUB0vu3r2L999/H5s3b0ZcnGEWWm9vbwwaNAizZs2Cj4+Pzedg4FsOyTJw7YQzDm2ogqjT2T/h2zvIaNYxHaOfdUKnTkDt2tnf/j76CFixAvjf/4DoaACQ0KyVDouWZcDXX8apY0CHYXdxNcIFCTEO2LfaFz1fjSmV+0dERESVS0xMDNq0aYMbN26gdu3aaNeuHWRZxtmzZ/H5559j69at+Oeff+Dv72/TeRj4ljOx552xZ3NgdsAryWjUKhOd+qShdfd0uLjJGNjEPFfV2xuYNg2YMgXYuBE4dy0DzwzXwj5HI7G9o4xHX4/B9+9Wx+kdnqjTLgmhzZjyQEREVBIqc6rD+++/j5s3b+Kbb77B0KFDhXUbNmzA8OHDMWPGDKxYscKm8zDwtdHec3dh55gKAPDxdBTWpWaIvXjSMnVIu2cPnVYFjVsW7Bz18HQVO3NlarPTC2QZSLovIe2eA1LjHHBtnw/unjV07FLZ6VGnezzC+96FT6CMLAAHYgDEmE/e0MBP7AzW9lFguJ8TgOwJNxYH1TX80Q/wjwE++wzY+3kA+m6MhbOboaPb/hv3heO0DhKP+/clcT0A2KvEujioVUI5PknszJelM0+vcFX0dnJTlGt4i9fd0jJl3dKyzCfbUD5eTRSTeihxcgbr8BoRlU96PdPfqPhs3boVY8eONQt6AWDIkCE4cOAANm/ebPN5+BFUgmL/88DhZbUh6w3BoMpOD0d3LTRuOkiSDECCXidDhgRdpiHg1WWJgaKk1qN253to2P8OnKsYZ3kr2ofxww+BjZu0iI22w/zXvDH8zQeo1SAr/x2JiKjCSk1NLe0qVHjlMcc3KSkJ3bp1Q1ZWFnQ6HV5//XWMHj260MeJi4tDw4YNc13fqFEjfPnll7ZUFQAD3xKTckeDo6tqQNZLUNnpodeqoNeqkHrPAan38t5X45EJpyqZ8AxJRe1eMfALLt5xdl1cgDGz7uGjcd64fEqD6cP90K53Klo/dweefvmMRUVERBVSamoqZFku9x2oyrLyGPg6Oztj7969cHZ2RmpqKho2bIiBAweiatWqhTqOv78/IiIiMGbMGIvrIyIi4OfnZ3N9GfiWAG2GChHLa0ObZgevWsloP/kC9DoJmUl2sNc6ISPp4cMgydA+/ClJba+Hk1cWVK7pUNsrA117FLe6TTOxaFMsfljmjv2/u+Dgn844vLM62gxMQLtB9+HiyZ+8iIgqk6FD7RARoYdarS7tqlAZolar4exs6HeUnp4OnU5n1fTW/fv3x7Jly9C0aVO88sorpueZTqfDihUrsGbNGowfP97m+koyJ9+2SmJiIjw8POD1zJeQHAwPeFiDIGEbN1cHyDJwak1txB71hoN7JppPPgmNR3bagJ9iAgc3JzGoTUzLP8XAWSN+f7FT5NU2DzYfA+/xeoFCOa+JI44dM4z/u2ePoezgADz7LFCt622E1MseLFiZzwsAdTzFPFlXRV2VebTR980n7LiTKE5Q4eQgvunWDxAn1igISxNYxCSI53HRiOcJD3LP97gllb+qzC9m3iwRFRfj512zZpexb58/nJ2d2epbxIzXuN2H22DnWPjPNCNtegoOvvsoEhIS4O6e/2cWAOzbtw8LFy7E0aNHcfv2bWzevBlPPvmksM2yZcuwcOFC3L59Gw0aNMAnn3yCjh07mtY/ePAAnTt3xqVLl7Bw4UKrAtR79+6hffv2uHjxIqpUqYKwsDAAwMWLF3Hv3j2EhYXh4MGD8PIynyyrMFT5b0K2uLHbH7FHvSGp9AgfeUEIesuLZs2AXbuAX38FWrY0TIyxdi0wd2QAPhrth6O7nFHYr0/bttihYyNXfPdd8dSZiIiKXvfumcjIyLCqRY9KTmJionDLyMjIdduUlBQ0adIES5Yssbh+48aNmDhxIqZNm4bjx4+jY8eO6NOnD27cuGHaxtPTEydPnkRkZCS+++47xMbGFrrOVapUweHDhzF16lT4+fnh+PHjOH78OPz8/PDuu+/i8OHDNge9AAPfYnXvojsu/RwCAAgbdB2eNZNKuUbWkySgXz/DjHD//AM89xygtpNx9ZQjVrzrgzVzqiIzvWAtAPfvSfhwmiOSEiW88gpw82YxV56IiIqEXp+Frl0zkJaWBq1WywC4iBlzfG25AUBwcDA8PDxMt3nz5uV6zj59+uCDDz7AwIEDLa7/+OOP8dJLL2HUqFGoX78+PvnkEwQHB2P58uVm2/r5+aFx48bYt2+fVfffzc0NH3zwAU6fPo3U1FSkpqbi9OnTmDNnDtzc8h5pqaD4A2kxkPXAnQh/RP1RE7JeQkCruwjuFAut+Qha5VLr1oZb06HR2PW9O7Z9445DW11xO9Iery6IQ1W/vO/oZ/MdkPDA8OJMTATGjQN+/rl8jz9IRFTRSZIKkqSCXp+F7t0BlUqPnTvtYGdnB5VKZXPHLCq6cXyjoqKEVAeNRpPLHnnLzMzE0aNH8c477wjLe/XqhYMHDwIAYmNj4eTkBHd3dyQmJmLfvn145ZVXrLsDANLS0rB7925cuXIFkiShVq1a6NKlC5yczOcosAYD3yKWGuuMyE11kHTdMMatZ81E1B8SWSGDOo+qegx45QHqtUjHyve8ce2cBrNH+GP8R3EIa2r5Z5X/jqmweYMhn/i9eemYP90Rv/4K/PAD8PTTJVl7UkpLA4rofYWIKiBZ1kOWDR2bdboM6PUqdOumgUqlhyRJ2LZNhkqlgp2dHdRqNYPgUuTu7l7gHN+8xMXFQafTmY2m4Ofnh5gYw+yu0dHReOmllyDLMmRZxquvvorGjRtbdb7Vq1dj8uTJSEhIMP2aIEkSPD09sXjxYowcOdKm+wNU8sA3v2Ttgkg9+y8ktQNkvT1OnhyFtNP1AL0asMtCQLerqNoyGnfvGx48FxfzDmTRd5OFsn9VMald2dkNMO/claUVR1ioqpgU41SMeUeue6lRQrlfXfFJ3aiaeYc4pdGtDWkcaA289hjQvXcWIi/aY8E4X7z8TjIeeyYNZ+ITTdvrtMBn71UDAPQfnIWnn9fiQVwWliy2xyvjZIQ2SodXFfPzNAsRX7ynosVrFhkndkoDgDjFxBgta4iTbdTxM4/wlMt2X4gXykcixUkw6gaY/+zi6Jr3S6qoJr1Q7qPsFJhXZ0VLvvoKGDsWmDsXePPN3LezVP+86sVJPogqLlnWQ6s1fL6oVHbo3t0Q7KpUOvz5pwwHBweoVCoGwYVQVoczUx4357B2zZs3x4kTJ2w+x/r16/HSSy+hevXqeOuttxAeHi5MWfzSSy/ByckJzzzzjE3nqbQ5vgVJ1i4oWeeAzAtTkfZfA0CvhkPwTXgN+APeraMgqSpH/lONGsCib+6j46Pp0GklLP/ADR9M8EDS/eyn2N7Nbjh/Rg03DxkT3zUEamNf16J2mB734iXMm1H8w7SRuT17gDFjgKws4MCB0q4NEZVVxlQHSzdZ1kOvz4JOlwmtNgM9emSha9dMdO6cjpSUFKSnp5vdMjMzodVqhZter4der2fusI1atmyJ8PBwLF261KbjeHt7Q61Wm1p3je7cuVMkY+rmNG/ePNSrVw///fcf3nnnHTzxxBPo378/pk6div/++w916tTB3LlzbT5PpW17yZmsDQCffPIJtm3bhuXLl+eZBK7krMuCpE6B7HwJOpULvFoehlO1G9CrVACcTds5ZabB0d4891WtlqCXVMi0z86/0WRkt9A6SOIoELJKBThktwrbZ6RBUky/a29nbGaTkOXoJG778M3E3l5sipPSUiE7ZdcXaWlAXtNTuriYbeuCFLw7OwWb6rlj9dIq+GeXBudOBGDk9HgE1c7ELys8AQAT30iCt3MakApo7NT4cG4GnnnaAz//YId+A3Xo2NVwXik9HRmpOuid9VDl+IompaYYroWTc3ZCU0YGJG32fVKlKlItZHdhW2jzaLp0coLxhFJmJiSt4TFQqRTfE1NUwrbIzARScu85C0dHAOrsbbMePraWXoWOjoBxrMysLMP2ucmZu5WVBSkzE3DIZfQQjQawe3hCrRZXzmVi0CAnaLWGa5NwTwekPGxBd3AA7O1N2xquWy51yLmtTgekPzyGpe2d7A3bK7e1xD7Htnq94blWFNva2WVfN1kG8pqRqjDbqtUPH+eHUlKKZluVSsxDKcy2qanIddgVSQKcna3b1or3iAJtm55ueF4UxbbOzgV/3RdmW+XrPiuP0XoKs23O131hti3Ie4Sd7R/3OVMd8tsOMHSEA4AePeyhUpnvZwyalcsAGdu2ybB7WGd7e/tK02IswcYc34f/R0REFEmqg4ODA5o3b44dO3ZgwIABpuU7duxA//79bT5+ThcvXsScOXMs1tvDwwMvvPACZs6cafN5KmXgW5BkbaWMjAxhOJDERMNP+NfOroE7gAfYCBX0cN9nGLlhR7VHMLnGAtP2ez5+Gk5Zlj/gj9dsitde+cxUXvbm4/BIfmBx28iaDbBw4fem8vQJT6Dq3VsWt42tXhv/t/x3U/ntt55GQNQVi9tmVquOC4fOZC/o1Ak4csTitvD2Bu7ezS736QPs3YtHHxZ7AxiHJhiK73DuXjg+negH32pZSE9R4RG3C5gxMxzqmdlvgrUBHMAn+AwT8PbrDqhVR4+7dyXER+qRoHVBLVzGCTSFKwwf9sYJDU9fiIHsbPgQ9J78Ktw2fmM6ZqiyznfuAD4+hr8nTQKWLbN83wAgMhIINRyh5idzUf2r3L8x3zt8Arr6DQAAzos+AubNyf24hw8Dj7QEAKj/9yns33kr1033/N9x6Bo1RffuAFauBF59NffjbtkCtOtpqMMP61Hl1Tymivz+e2DwYABAwre/od/IuriHcHjiPh7ACwkH/gNcmxm2Xb0aMOZTbdsGPP44HC0fFVmfLoFu3MNxG/fvB7p2BQDL2y9YkJ1PcewY0KpV7vWdMQMwvtGdOwfkMZ0lpkwBFi40/H3jhuFniNyMGwcYW0Li4gBf39y3HTECWLPG8HdqKuCaRxrQU08ZEtaN8tq2b1/g9+zXJ3x9cw+qO3fOHkgbMDw/4+Isb9uiBRARkV0ODweuX7e8bXg4cCbH675lS+DsWcvbhoQA165ll614j7DI2VkM5AcNArZutbwtIAbmw4YBP/6Y+7bJydmB8pgxhnEYc2PlewSmTQMWLcp929OngQaG9wh8+CEwa1bu2x4+bHgMAODTT4G3cn+PwO7dQJcuhr8L8h7x2GO5r1fI7fPOWnp9likILghj2oRKZQi0//wz3bzhwbStSvhfudwWkiQVaLKOojiX6ViSBJUNka81+yYnJ+Py5cumcmRkJE6cOIEqVaqgevXqmDRpEoYNG4YWLVqgbdu2WLlyJW7cuIGxY8daXU9L/P3982zpV6lUnLnNWgVJ1laaN28eZuXxhuWJBKGszcxEwoPsFqe8HkxZlqF9mKd7535anmPi6vQydPrsDfL6MchOJSHQ3R6SZGiJc1Dn/uJMzdLhtwvZ4+5VS8uCLaPlNcVJHEELvGn3MZZpx+JOtD0kScaHtT6F+oT5N/+5mIZf0B/X74Yi7q7xjcbwgXUFtbEaL+A1iGMM1vZzBVxccPZmIty1ebdCnL2VDF2GoRUsMDkLBZlIsWvdqkCVvHt73YxPQ9odw4d2UEomrB92PNtt+KPnlMawszd8FhdkABdTTq9LwV7SOh3w7KdtcA4BCEI0PsPrGIRNSIBH/jtbYK8G7Av4bpKcoUdasqE1zS5Va9PzzFpafSV98yMqgPw+74qeJLRyyrIOsqyDXm9oAe7VS1IEl9kbS5Is/K9crjxPwRlGrKgMjhw5gq4PGysAYNKkSQCAESNGYM2aNXjmmWcQHx+P2bNn4/bt22jYsCG2bt2KkJCQIq3HyJEjsWbNGowbNw6uikaDxMREfPXVV3jhhRdsPk+lnLnt1q1bCAoKwsGDB9G2bVvT8rlz5+Lrr7/G+fPnzfax9A04ODgYXuEjIakfBh1B9UzrdZIKHrVrmspOWelwdzcfTsTZxQGypELGw1QHBwc7OOZIdfDxEgMvWZLgVjU7OLHPSEMtL/G4GvuHbxCSBK2jk+kNxS49zdRakpAm/jyoUqmgzZEW0dBNBUkvPjX6NcrxRaGAP2PevJ+Jv/52xcK5ajwxUI82j0VD0mVvW809+5w3rqtx8mQVeFUFfH1l+HplYPcOFeZ84Izq1XXYtfsB1GrA3/NhO+LDnybP3kyElJEBSZf906Ra+a3XxcX0+5GUkYGG/rm1XSLXnyZPRYnjMGdo9dA7imkRoYpOZT45O7s5OiJdNv8ZU9nZ6/+W2GHSO4bH9Nw5oF6tQvyM+fAnz1xndnu47aRJwP/9H+DkJGP/9nQ4Ocpo0NIZVarIiL/xsNXRUqpDbnJLdbDgboaUZ6qDcM2KMNVBuCZ2dnB0YaoDUx2s3LaCpzrk9nnXvPlVqNUFHUs1O5g1T1OQcvyvx7Ztdrm23BrlXG5nZdpGYVtmS3J4NuPMbV0X/gU7JxtmbktLwe43exRq5rayYufOnXj77bcRHx+PcePGoV69epAkCWfPnsXy5cvh7e2N+fPnmz3+nTp1KtR5KsfXGQVrkrU1Go3FcfBS1faQ1A8/8O1yHycvzd4R9g7mwZbKwcFsWbom+0MrQ5N3i2OWxglZTuJx1faWX9w5A9ssiB8WykBR72jhvC65vBjzGANLzrRH914yuvcyfIgciRLrqs/xQVqtPtC8Tc4PRwdUqwUsWSbjxg01tu11Q59+esDF/DrKGg1kZF9/5ZtVzp9/ZI0m9/ui5OBgCqb0zuIHt17Ryiw7OJgfV9kCa/wszXFc5avw2xy/lN+6BdSrZ58dVObH/uG2ys/sHOe4etUQ9ALA2rUSmndwMk0ikpAgQXZ2Mc8xs7Mr+AenWp339ZW1eW+bW6u1SlXwx83StrnFMZJU8OMWZlugbGybM1gtym0LM/ZdYbZ1zONLqS3bajRiPnxRbZvztVxa29oX4j2iAHL7vMsrx1cZJG7fbm8KUJSBijLI5fi/2YpqVIeWLVtCrVZj/PjxVk0fXBp69uxp+vvtt9823Rdj++z169eFbYwjS+jy+vJrQaUMfEsyWZts4+QMPDdShyUf22HVcjv06ZdHq0YJSEoEft9sj00b7HH/noSZC1PRol3RzUxy/jxw9Gh2+fbtIju0iXHgkrAwU7ovPB7+iKDTGRr9ChNXEVFlJkGlArZv18Dx4RcROzs7BrOlrKg6t5Wk1atXl8h5KmXgC6DIkrXvHFhkenK1+GC3sC7urvhzpJ2deZK8Tif+rPjggfjzrKX3Db0iBcFd8Vu5v5v4zT/AzbzVoLq7IoVC8evmHeWoCAB+OSW2kPdv5G9eOQXleLLVvMR9DlwSx8Y9cVMsA8CYcVWxcomMYxEqnD1hBzQVfxoPrlqIVqqHjl0376zh6y62HuWse0QEsGKFG9avF3/pfn2kKz7+GHjtNcNjpRxTOCMr/x7QOc/z7bfiuluW+y3mK6+xchMepqN7emYvc3ExNL7qdIb1xRn4+uQz1nFx4fjBRLbJOQqDIaiVsX27A5ydnQvcEYzyppIMN1v2L69GjBhRIueptB8FJZWsTbbz8wOeekbGd19LWP4/Ff63Kv99dvwpYcUyCb5+QGgNICRURmgNGeENALdCfglessQQ2BqFhxs6iB85Anz9NTBhAnD8OGBh2vJCkWXgm4cDU4SGGjrQF0eLrzHw9cjRj02SAHd34P59w/rAwKI/LxGVb8ZUB1nW46+/nODk5FSphhqjiqHSBr4AMG7cOIwbN660q0EFMPY1Pb77WoWtv0mYEikhpEbufTK1WuCtN1S4GW3+ZuzpJePHX3XwCi7YeWU5e4SsAQMMoxy1b28IFGUZeOQRwyhaa9YYRoH65EvAP8CKOwjg4EFDsOvqCowaBbz3nvUtvnmxFPgay/fvAzaOXEREFZwkqeDs7Az7IswrpockG2df43eQfFXamduofKlXH+jeSw9ZlrB6Rd4/p23bKuFmtIQqVWS8N1OH50fo0bGzHj6+Mh7clzD0KTVuRRXs3eGffww5sa6uhjSEDh2Qo6cy8MYbhiFuq1QxDMH5/ECHPDuv58WY5jBwIFC7tuHvkmrxBQwtvjnXExHlZEh1kLB7t4vVIytQ3iTJ9htQdDO3VUR85lK58cprMnZuB35Yr8bEt7XwzGUA2FUrDK/850fKeH2yDONox4kJwBO91Th7WsKrI5yxelMKPDzzPueGDYb/n3wy947pPXoYcoCbNgWuXlbhzCkJjZoUbpTAzExg40bD388/n92hvCQDX2OZgS8RWSLLeqjVdnB0dGR6QxlXHju3lRQGvkXoyHtdhfIn+yOF8o6zd6GUliGOCOCtmDAhIcm8k1mmYhitK7HJQtnLyVMoRyeYHyNI8Xqo7ib2Zgp2N+8wlpop1vXn/8TObnV9zMd3rB+Qdy+pDnXyn77gzE1DJ8G6TYG64Y64cFaNL1cCL443H+HhwjkJB/bZQaWSMXhYJpIf9jUL8HSEmyPw6xagcwfg+lU1przsgrXfZ0LzsE/b5Tvidbx1zw7frncFoEKLrqnIOQW1Us2ahgB482bgv0MaPNHdsPxusjiG1h0LjwVgyEm+d88evn4yunWTEPnwqVOSqQ7G3Oe4e4Yxb0urM5hyDGKAHdOIygJZ1mPvXtcinamMRNLDf7bsT3njs5fKDUkCRo4xDOj+9UoN7sSYv8C//sqQBtGzjx5B1cyPERRkCH7d3GUcO6zGlPG5pyYcP6xG/F0V3D1ktO6Qx2D2D/Xubfj/zz8Ldn9y2vS94aX4xEA91Gog4GGecEoKkJSUx45WyDfVgTm+RGSBSmWYaIKtvVSeMfClcqVPfx3CG+uQlChhzttOwjBsiQnA5u8Nge/wUbmPrRveAFi+JhP2DjK2/a7G/FmWmxO3/WbouNGtdxbsCzCOvDHwPXTI0EmsoJISgR1/Gl6KAwYbonAXl+xAtKhbffNLdUhkqgMRWfDLLw5s7S1mxuHMbLmVFwMHDsT+/ftN5X379uHuXfNfxosan8FUrtjZAbMWp0GjkXFonx1++i67V/GP69VIS5UQVl+PNu3z7mHWqp0eCz4ztB6vXmGPwwfFl4I2C9j1hyEg7tUvj2lDc6he3TDUmV4P/PVXwe/TH1tUyEiXULuOLOQGG1t9izrPN9cWX+b4ElEeNBoNW3uLmXHiD1tu5cXPP/+MqKgoU7lr167YsWNHsZ+XmXPFaGLHGnmWAWDaHxeFctR9cQKEOgGK6ATAg1Qxt9XDSRxS5rriGH4WJrBI1Yotog8yxGPqZPNhamr7ugplzxRxm7gk85zbv5PFZe0LkNOr1CBIzBN+kHofY6ak4bO5zvhkriOatUlCULAea7405Bg/+Ww6avqI+bjR98V6ODmo8dTTwNF/dPhmjRoz3nbA6f80phlQ//gDSHgAVPXRo2NnQK1WY/eFeOEYXetWNatr796GYc3++MMwK5pysgYfVzvo9cCJE8D27cCOHcD+A4Zgt3f/TDxIzUJyhiHQ9vR2BC6ocS3KPO/VlpzX3ALfqg8fmpSk3I+vzFl2U2zIXFyiioutveVHeZiy2NfXFzEx2X2FZOVMWsWEH1NULj01LAMHdtrj2D/2mP2mM4aPSUf0dTVc3WQ8+kQm8uqIltM7M3TY/ocKVy5JmDcPmDXLsNw4msOjj+tQmMmI+vQBPv7YkOcry+Yz7+3dCzzzDBAbm3OphNBaegwYIgaV3r6GN4EYsQ+hzTiqAxFR2ZRzSDJr9wfKx6gO7du3x9y5cxEdHQ3Ph1OJbtq0CZcvX851H0mS8P7779t0Xga+VC6pVMC781Ix/HF3nD5uh9lvGlqFH38qA86FmG7XwwOY/ZEWY1+wx7x5hqC0Zk3D6AwA0Ld//p3acurYEXB2NqQnnDoFNG6cvU6nM8z4FhtrGBe4WzegVy8gpHEKqofKZm92vn6GdI3bJZTja3yP5AQWRERU3D799FMMHz4cn376KWRZhiRJ2LRpEzZt2pTrPgx8qVLzD9Jj4vup+PAdFyQnSZAkGQOfM0+3yE/fJ2T06K3HX3+q8PLLhkkpkpIA/0A9mrYo3GwUGo0hoN2yxZDukDPw3bgRuHDBMNnF1avZgeepaMs/7/j4FX2Lr1ZrGCkCYIsvERVOecofLa9UkgSVDdfZln1LWrVq1bBr1y5kZmYiJiYGoaGh+OSTT9C/f/9iPS8D31I2t0+YUN5xLk4oH771wGwfe7X4xPZxUeRZ2ot5WPEp5q2WOr0YbLnYi8e4mybmCQNAxm0xL9hVsU/HsPzzdyMixaiqUbAYfRUkRzRnnnC7t4DDe3T46081OnfXo0UTQz7zyRviGGBV3TRCOU0xJvH7c/X454Aj/v4buHTJsGzIEBVq+WU3H9sp8tuU9wUwpDfUa+GALVuc8P1mLd5+23CHdDpg9mzDNpMni0Fno2quZscBgKZ1Df/fiNIjLkmRBmEhbzun3K5jztZcawJfZc5ycWCeMFHZxBzf4ldUqQ7liYODA6pXr44RI0agdevWCAkJKdbz8SOGyjVJAhYtzcL33+jx+IDchzDLT0CQjJmzgSmTgDt3DMsGP23dsdp2MgSpJ4+qkZhoSCEwtvZ6eQGvvlqw4wQGGv6/E1t072TGoNbREXBQxM5s8SWivLDFl4rT6tWrhXJysmFSKVdXy41D1uLXNyr33N2BUeN08A+w7ThjxwHNWxj+rlUbeKSZdcepFqJHcKgOOq2EXbsMrb1z5hjWTZ6cnUubH+NwZrFFmOqQW35vzmUMfImISkdlGs7Mkrt372Ls2LHw8/ODh4cHPDw84Ofnh3HjxhXZGL8MfIkeUquBL78C2ncAZs2x7ScjY6vvH38A338PnD9vaO197bWCH8M0e1uyhOQimr2tIIFvZiaQbp7pQkRExcyY6mDLDTAMZxYeHo6lS5eW7h0qhJiYGLRs2RIrV66Eh4cHnnjiCfTr1w8eHh74/PPP0bJlS2H4M2sx1YEoh3r1gb92236ctp21+H6dBn/8ARgnppk0qeCtvQDg5mYY/SE5GbgTC7i62V6vvAJfNzfDm6YsG7YzjmlMRETlS3kYzkzp/fffx82bN/HNN99g6NChwroNGzZg+PDhmDFjBlasWGHTeRj4ljE963vnWQaAnefFDnB/Rz0QyvZqseNaaBWxYxdg3pp58b7YpFjHyzynxl0jTliRouggdviq+W/kjauLEVbLGmJZOSHCbeVMDQBqeOcdgVleLy67FJsmlDO15qM1KDtVKatSx89JKEffNx/gNzo+FQDQtGUWHBxkREUZLrSbu4yO/ZNw7rb5PvUDch9/LTAQuHgRkFMdUC1H30HldYtLyhDKyo54xrrnFfiqVIbgNzHRsJ2fX67VIiKiYlCZRnVQ2rp1K8aOHWsW9ALAkCFDcODAAWw2jjVqA6Y6EBUDRyfgkdbZwelzozLgZsWXb2O6w60iGss3r8A353Lm+RIRUUmKi4tDw4YNc13fqFEjxMfH57q+oBj4EhWTNh0Nga+bu4whIws/vjCQPbLD7dtFU6f8Al9OYkFEVHqkIriVV/7+/oiIiMh1fUREBPyK4KfIQge+6enpWLlyJc6dO2fzyYkqssefysCjT2RhxsI0q1p7Abb4EhFVJpV5VIf+/ftjzZo1WLJkCXS67FRKnU6HZcuWYc2aNXjyySdtPk+hc3wdHR3x+uuvY9u2bahfv77NFaDC615PzPsN9BBzT4/fvi+UEzPM82b9XcQc2GSVuE1Minm3/tQsMafX31U8hk5nPgNZxFWxLlVcxMFjGwSJ+a2WJkiIvi+2lmoUE3QUZFIFZX6uMkcWACLjxPvsqkj6VeYfB3ia5xZX8xLvX8Cyh9OkwZDbm5CaZbaPMje6Vc3sqDS3Fl/lfc7IEnOWlefZesYwOPGpa24AnHAnIxmAeR43A18iIioNM2fOxI4dOzBhwgTMmjULYWGGCb4uXryIe/fuISwsDDNnzrT5PFalOtSsWbNIhpQgorwZW3yLKtUhNcnQGuDsZnmaZAa+RESlRyXZfiuvqlSpgsOHD2Pq1Knw8/PD8ePHcfz4cfj5+eHdd9/F4cOH4eWV/wyx+bFqVIcJEybgo48+Qp8+fcrdcBlE5YmxxbeoUh1Skg3vii6uDHyJiKhscXNzwwcffIAPPvig2M5hVeB75swZxMXFITQ0FN26dUNAQICQVyJJEj799NMiqyRRZVXULb4pSYYfeZwZ+BIRlTm25uka923ZsiXUajXGjx+P8ePHF1X1KgSrAt8lS5aY/t60aZPZega+JUs5DqyyrBz3FwCuJ6UK5SoaMTfVxd78qWGvGBtWOY5vm1pVzPa5EpsilB+kivm60ffFsYGVObK5LcvJUr5ufnm/ltYrl525KdZdqxPzaC2NH6zMR1bmMCvHEwbMr8nRa9lDKqRkAYA7EhOBlBTAJZchf5XXyNtNLF+9Z5jz3Nji6+Six6noZGGbRtVcGfgSEZWyouifVh4nsCgpVuX46vX6PG85e+MRkfVcXLOD3aJo9c3O8TWfwANgiy8REVVsHMeXqIwryiHNUvPJ8TU2EDDwJSIqeZV5OLOSYtOUxTt37sTOnTsRHx8Pb29vdO/eHd26dSuquhERDB3cLl+2vcVXpwPSUo05vnm3+HICCyIiqoisCnwzMzMxaNAgbN26FbIsw87ODlqtFh999BEee+wx/PTTT7C3t8//QESUr6Jq8U1LyW4JcOFwZkREZY6tQ5KV5+HMSopVge/s2bOxbds2fPTRRxg5ciR8fHxw9+5drF27FtOmTcPs2bMxZ86coq4rWUk54QUAKOZiwJYzYnPinVTzCSxC3cWeVemKXO4jkeJkFQDgphGfYp7OYqcr5UQLkXfFDmUAUMNHPK+yI1dBJrCwhrJjmrIT3bnb+ddVuY9yIg3DNuKXRDfFxBluVXQA1LgYqUP0fcM1z28SD8Uh0K+RH65dM/yt0QADm/vh5I0ks7rq7AHADvcfyCjfk18SEZU/RTWqQ3kUGhqKF198ES+88AKCg4OL7TxW5fiuX78e7777Lt588034+PgAAHx8fDBlyhRMnToV3377bZFWkqgy8/MztM7G2jhnTH7TFQPZOb5JTHUgIqIS5OHhgZkzZ6JmzZro27cvNm3aBK3WfNQmW1kV+EZHR6Njx44W13Xs2BE3b960qVJElM3vYarDnVjbvskXJvBNS5OQZT67MhERFSOpCG7l1cmTJ3H48GGMGjUKhw4dwuDBgxEUFIS33noLFy5cKLLzWBX4+vj44NSpUxbXnTp1ytQKTES28zW2+N7O/y1txQqgZUvL+cAFCXzdcgz7yDxfIiIqSS1atMDy5ctx+/ZtrFmzBvXr18fixYsRHh6Ojh07Yt26dUhLMx8PvzCsSo584oknMH36dFSvXh0DBw40Lf/ll18wc+ZMPPfcczZVioqfMgf0qSYBQnnHOfNJL2IVeb8O6vy/Nznp1ULZ3VksKyeBOGdh5II7ieJ5lXnBylxcS5QTRwRXNc+1VV4TJWUeraXcYuWkEF4ueU++AZjn9Crr4VFVB8AesbFA2sNJQ5QTVADAmTPAa68BWVnAb78BY8aI65WBb5PqbsL6u8la2NkBzi4yUlMkJCQA3ubp4UREVExUkgSVDXm6tuxbljg6OmLYsGEYNmwYrly5ghkzZuC7777DwYMH8frrr2P48OGYPHkyQkJCCn1sq1p8586dixo1amDw4MFwd3dHWFgYPDw8MHDgQISGhmLu3LnWHJaILDC2+CYlSkhLtbyNXg+MHQtTeoKlbKOCtPgCHMuXiKi0SJLtN8AwZXF4eDiWLl1aunfIBlqtFj/++CNeffVVbNiwAZIkoWfPnujYsSOWLVuG8PBw/PLLL4U+rlUtvl5eXjh8+DDWrFmD3bt3Iz4+Hs2aNUP37t0xfPhwaDQaaw5LRBa4ugFOTjLS0iTcvSOheqj5UGSrVgEHDmSXbQl83dyAmNsMfImIyqvyPGXx2bNnsWrVKnz99deIi4uDv78/3n77bYwePRqhoaEAgMuXL+Ppp5/GW2+9hf79+xfq+IUOfNPS0tCjRw/MmjULY8aMwRjl76lEVKQkCfDxk3HjmoTYGPPANzYWeOstw98tWgBHjhRNiy8nsSAiKlmVeTizVatWYdWqVfj3339Nrbsvv/wynnjiCajVYppk7dq18frrr2PUqFGFPk+hA18nJyecOnUKdnbFM3YqEZnz9Zdx4xpw18LIDm9PAR48AJo1A2bPBvr2BaKjzY9R4BZfD8MYvmzxJSIqWTnTFazdv7waPXo0AgICMHXqVIwePTrf/N3w8HAMGzas0OexKnpt27YtDh8+jC5dulizO5UDPevnP+nFn2fFgWXj0zPN9knTipNcxKVmCOVERUc1ZYcrS+e9cEuceMHSRBKO9uK3Q+XEEcpjAuYd4CxNNpGfRtVchXJknNgxz9J5lZTb1PFzQs3qwJF/ADnVAXX8stdt2wZs3ACoVDLmLNLCQQMA9mYtvula4N4Dw9/OroayshOdsbOeTxVD2VLgq7w/ro75d/iryJQTlACV7xoQERWFTZs2oV+/fmatu7lp1aoVWrVqVejzWNW5bfHixVixYgXWrVuH5OTk/HcgIpsEBhr+v51j1IukJGDcOMPfL7ysR+OmMgKCDGkQDx4AqYqOcIkFbPHltMVERKXDOKqDLbfy6tdff8WRI0dyXX/48GG8+OKLNp/HqsC3bdu2iI6OxgsvvAAPDw+4ubnB3d3ddPPI75OViAol4OFoc1FRwPbtwIgRhmD46lWgWjVgylRDy7qbm2E4MsA8z9cYyLoz8CUiojJmzZo1uHLlSq7rIyMjsXbtWpvPY9VvcoMGDSrXCdRE5Y0x8F2/3nAzqlEDWLfOMPIDYMjv8g8Arl42BL516mRva+ysxhZfIqKyqTLn+OYnMTERDg75j42fn0IHvjqdDu+++y58fHzg5eVlcwWo/FDmhD7Z2F8oW8p3PHztnlC2U4mvyuhExQwsN8zPG1hFzLVV5gFH3zfPLVZOcnHyhpgXbCmXOL+cXuX9K0gup3KCDkuUk174e4r7OLraoW7d7HLVqsCgp4AhQ4E2bQ1vdI522W8GIcGGwDfyBtAmR5VNLb75jHCTV+BbkPtTmTCfl4iKUmUb1eG///7DiRMnTOX9+/dDqzWPJe7fv49ly5ahXr16Np+z0O/asiwjPDwcv/32G/r06WNzBYgof61aAT/+CGg0QK9egD6PJKWgIMP/ylSHgub4cgILIiIqCZs3b8asWbMAGIL2FStWYMWKFRa3dXV1xfqcP3laqdCBr52dHfz9/aHX620+OREV3KBB2X/nNTpEtWqG/2/lCHx1OkNnOKDgOb4cx5eIqGSpYGXnqxz7lycjR45Ely5dIMsyunXrhmnTpqFHjx7CNpIkwdXVFeHh4XB0tP1XR6t+pxsyZAjWrVuHxx57zOYKFKVr165hzpw52LVrF2JiYhAYGIjnn38e06ZNE/JCLP0UsHz5cowdO7Ykq0tULIwtvrduZS9LypHpwRxfIiIqC0JCQkzj9c6YMQODBg1Cw4YNi/WcVgW+TZs2xcaNG9GtWzcMHDgQAQEBZsHkwIEDi6SChXH+/Hno9XqsWLECtWvXxunTpzF69GikpKRg0aJFwrarV69G7969TWWORGE7S/mOrUKrCOUrMWI+q07OUpTNp+ONeSCOHfsgJe8xegGgmpeYAH/4qhjFnblpPvavr4c41bby/ijLynF/ASC4qlgXZV60Jcqxf5UsjVNcP8BFKOdsAfZ5mHodcyv7/BkPD+HgAHg+PF1uOctFGfgWZNziglwjIqLKoLLl+OY0Y8aMEjmPVR85w4cPBwDcvHkTe/bsMVsvSRJ0Op3Z8uLWu3dvIZitWbMmLly4gOXLl5sFvp6envD391cegqjcM475mzPHt6CztuXchi2+REQlS5IAVSUZ1WHdunUAgGHDhkGSJFM5P8YY1FpWBb67d++26aQlKSEhAVWqVDFb/uqrr2LUqFGoUaMGXnrpJbz88stQqXLPjsnIyEBGRvasY4lMgKQyKuhhju/t24bcXrXausA3OTl7fyKqPPh5RyVh5MiRkCQJQ4YMgYODg6ksW/jl10iSpNIJfDt37gzAEFT+888/iIuLQ9++fcvc8GZXrlzB//73PyxevFhYPmfOHHTv3h1OTk7YuXMnJk+ejLi4OLz33nu5HmvevHmmnodEZZmfnyFY1emA2FhDC7A1gS9g6OBWxl7WRFTM+HlXelQ2tvjasm9JMzaiGvtglVSjqtXZdXPmzMFHH32EtLQ0SJKEiIgIeHl5oXv37ujZsyfeeeedIqvkzJkz830RRkREoEWLFqbyrVu30Lt3bwwePBijRo0Sts0Z4DZt2hQAMHv27DwD36lTp2LSpEmmcmJiIoKDgwtzN4hKhFoN+PsbUh1u3ix84OvgADg6Aunphv0Y+BJVLvy8K/9atmwJtVqN8ePHY/z48aVdHYuMjai5lYuLVYHvsmXLMGvWLIwbNw59+vQRRnd4/PHHsWnTpiINfF999VUMGTIkz21CQ0NNf9+6dQtdu3ZF27ZtsXLlynyP36ZNGyQmJiI2NhZ+fn4Wt9FoNNBoNBbXUe6UHcLcQj2F8iVFZ7eohFSzY2jUYgqKXvEriKeLfb7nbVVTjPgsTXpx656ys5rYUU15TEud6pTHzdKJw/5ZMwGEsiMbAETGpVvYMltQkKMp8G3Z0nLgm9fkCx4ehsA38rYWLt4F28eSgnRcU3aAY2c3c7xGVJL4eVd6iqpzW0REBNzzm62okrLq7XPJkiWYNGkSFixYYNaJrU6dOrh06VKRVM7I29sb3t7e+W8IQ4e7rl27onnz5li9enWeebtGx48fh6OjIzw9PW2sKVHZoJzEojAtvoBhEovYWCCJHdyIiEpMZUp1KIiMjAxs2LAB9+7dw8CBA01Dn9nCqsD36tWrePTRRy2uc3Nzw4MHD2ypk9Vu3bqFLl26oHr16li0aBHu3r1rWmccweG3335DTEwM2rZtCycnJ+zevRvTpk3Dyy+/zG+4VGEYJ7GIjjb8X9jA17hdUpIEIPeOBkREREVhwoQJ2Lt3r2kKY1mW0bVrV/z777+QZRmzZs3Cv//+i7p169p0HqsCXw8PD8TGxlpcd+3aNfj6+tpUKWtt374dly9fxuXLl1HN+Mn/kLGXoL29PZYtW4ZJkyZBr9ejZs2amD17dpnNgSGyhq0tvpy9jYio5EmSbUOSlafhzJS2b9+Oxx9/3FT+5Zdf8M8//2DmzJl45JFHMHr0aHz00UdYvXq1TeexKvDt3r07FixYgP79+5umj5MkCVqtFsuXL8+1Nbi4jRw5EiNHjsxzG+VYv1SylLmJyskb/D3Nc2CPR90Xylk6sQXywu0kKJ1RJAK3qSUOaaec4AIA0jLFtJ0HKeLkGsmKREtL+bqWjpuTpdxca/J+lfsoJ6Nw8tQCsMPV63pE39ciIcFQr8IGvnfj9UhOz74ubooHsChyTZmvSkRkoJIkqGyIXm3Zt7TdunULtWrVMpW3bNmC2rVrY/r06QCAMWPGFHis37xY9ZEze/ZstGzZEuHh4RgwYAAkScKSJUtw/Phx3LhxA99//73NFSMi6/kHGAL/mNuGN0GrUx3Y4ktERCVAq9UK/bJ2796Nnj17msqhoaGIiYmx+Tz59/yyoHbt2vj7779Rv359LFu2DLIsY926dfD29sb+/ftRvXp1mytGRNYzBr63bwGybEvgW35bD4iIyhtVEdzKq+rVq+PQoUMAgNOnTyMyMhJdunQxrY+NjYWrq2suexec1T8yhoeH488//0RGRgbi4+Ph5eUFJyfz4Z2IqOT5Bxj+T02RkJRkS+e2oq8bERGR0jPPPIM5c+YgLi4Op0+fhoeHB/r06WNaf+LECdSuXdvm89icXafRaBAYGGhzRYgAy+PE9qrvI5RPRYtj/6YoBzkFkKoXl+27dFcoezma5+K2rJF3VHjsuvi7v73a/Lt1fjm+lvJ5i2KMVuV183EFPD2BBw8ApDgg/r4MQEKKnIHIODnXuhgZA9/0FBVc86iQhUsvSLKwQWHHAibmQRNVFpW5c9vUqVMRFRWFn3/+GZ6envj666/h8fDDKCEhAb/++iveeOMNm8/Dt1OiCiooyBD43ryZnavrVsDxzI3jnidyHF8iohKjgo2d21B+I1+NRoNVq1Zh1apVZuvc3Nxw+/ZtODs723ye8pwOQkR5MA5pFhUFpDxsJHdzK9iYvNnDmZXfN1EiIqoYVCoVPDw8YG9vPlNrYbHFl6iCMga+588DsmwIYAva4sscXyKikleZUx0A4NChQ1iyZAkuXbqE+Ph40xwMRpIk4cqVKzadg4EvUQVlnMPl7FnD//b2MjQFHDKYw5kREVFJWrduHV544QXY29sjLCys2EYIY+BL5Y5y0gvl5A0AcO1uilBOzBAno9DpzH/yj4gUE1pDfVyEcrMQsbnU0nmVy5QduSztk19nr+j7mWbL8utEB2S3+BoDXw8PCTV9co98c9ZNZwcAdkhKlIT65Xf/lBwtrC/sMYiIKguVZLjZsn95NXfuXNStWxd//fVXsQ6awE8cogrKGPheu2b4v6BDmQGAW44pi/V6QMXeAERExU6SbJt9rTynOly/fh0LFiwo9pHC+HFGVEEZA19jipSnZ8H3NY7qIMtAcnLe20ZGArGxha4eERGRSVBQEDIzzX/hLGoMfIkqKGPga1SYFl9HR8DYeTYhjyHNdu4E6tYF2rfPDrCJiMg6xs5tttzKq5dffhnffvstdDpdsZ6HqQ5U7lnKEfVxFaO8yLh0oRz7QCwDQFKGmHt6616aUE5OF4dRyWsCCKNLseIx7NTm70r55bgWJJ/XEm9vwMEBMH6Bzi/wVdbD1U3G/XsSjl5MRaLKENX6emhM68+dBQYOBLKygCtXgOgYwEeca8Si5PS8c3ytzWnOT1FMFEJERMWjVatW2Lx5M1q1aoXx48ejRo0aUKvVZtt16tTJpvPwrZ+oglKpgMBA63J8AcDlYeCblCQBEJtzY2OA555SIzHHqA+XLxUs8CUiIssqc+e27t27m/4eNWoUJEXztSzLkCTJ5hZhBr5EFVhQkPWBr3Gyi2TFJBYpKcDzT6sRHSWhThjg5gYcO2oIfNu2K4JKExFVUtLDf7bsX9KioqIwbNgw3LlzB3Z2dnj//fcxePDgQh9n9erVxVA7cwx8iSqwnHm+hQ18Xd0M/9+LlyDLhtwxnQ4Y+4IKJ49LqFpVxs+/Svj0/wyB76VLRVdvIiIqH+zs7PDJJ5+gadOmuHPnDpo1a4a+ffvCxcUl/51zGDFiRDHVUMTAlyoFZT6upfzcY9fF2RrSssSfU2LuiHnBWgtjAdfxc8qzrMz5BcxzWjX2Yp9TW8a5tSXwDfQ15Fa9N0mDmW9pUKUKoHEEom4AGg3w42YJPkFaBNeQAKhx/oIejnZi3ZV5tYD5tVeO61sU+byWMKeXiMq68pjqEBAQgICAAACAr68vqlSpgnv37hU68M0pIyMDcXFx8PHxgYND0X4mcFQHogrMOHsbUPjA9+mnDWkMAKDVAnfuGIJeSQJWrQHatDWsq1nL8P+Vy+U4uYyIqJLat28f+vXrh8DAQEiShJ9//tlsm2XLlqFGjRpwdHRE8+bNsX//fovHOnLkCPR6PYKDg62qy7Fjx9CtWze4ubmhevXqOHDgAADgzp076N69O/766y+rjpsTA1+iCsyWFt+hQw0TWKSmAlFRwMmTwJ87gDPngUFPZW9Xq7ah5TvyimGyCyIiso6xxdeWW2GlpKSgSZMmWLJkicX1GzduxMSJEzFt2jQcP34cHTt2RJ8+fXDjxg1hu/j4eAwfPhwrV6605q7jxIkT6NixI65cuYLhw4cL63x9fZGWloa1a9dadeyc+OMfUQVmS+Br5ORkaDmuVg0Is5C6EBwC2NnJSEuTcOuW2MpMREQFJ0mS2WgGhd0fABITxdQ9jUYDjUZjaRf06dMHffr0yfWYH3/8MV566SWMGjUKAPDJJ59g27ZtWL58OebNmwfAkJowYMAATJ06Fe3aWdfLefr06QgKCsKxY8eQnp6Or776SljfvXt3fP/991YdOye2+BJVYEUR+ObH3h4ICTX8ffFi8ZyDiIgKLjg4GB4eHqabMUAtrMzMTBw9ehS9evUSlvfq1QsHDx4EYBhmbOTIkejWrRuGDRtmdZ3379+PUaNGwdXV1WLwX716ddy6dcvq4xuxxZfooWYh7kJZ2enKTdE76mjkfbNjpCs6xNXxdxXLis5ugHkHsCTFgpM3ksz2qRvoJpRz67iVc8pzZeBrzYQOym2MNatZS8aVyxIOHMlEcIPsa2Dp/uZ3XS3h5BNEVBkUVee2qKgouLtnf6bl1tqbn7i4OOh0Ovj5+QnL/fz8EBMTAwD4+++/sXHjRjRu3NiUH/z111+jUaNGhTpXeno6PPJooVG2YluLHx9EFZhGA/TrB1y+DNSoUXznqVkbwDbg2lV2cCMispat0w4b93V3dxcCX1vlNpkEAHTo0AH6IujgUatWLRw9ejTX9Tt37kR4eLjN52GqA1EF9+uvwOnThumLi0utOoYObteu8i2FiKii8Pb2hlqtNrXuGt25c8esFdhWQ4cOxddff40dO3aYlhmD6wULFmDbtm02pVIY8VOKqBJQFfMr3TikGVt8iYisp5Ikm28A0LJlS4SHh2Pp0qU21cfBwQHNmzcXglEA2LFjh9Wd2HIzZcoUtGnTBr1790b79u0hSRJef/11+Pv7Y+rUqejZsyfGjRtn83mY6kCUi/wmjvB1N58EIyZBnOTi78txQrmmt5jzCwCuioRV5XndHMV8XgC4/UA8j71ajGwLMglEUeTJGuvaqomhHHVdhRpVnWD38NiRcelm+wR4itfNmtxi5vwSEeUuIiKiwKkOycnJuHz5sqkcGRmJEydOoEqVKqhevTomTZqEYcOGoUWLFmjbti1WrlyJGzduYOzYsUVaZwcHB+zYsQOfffYZvvvuOzg6OuLKlSsICwvDm2++iQkTJkBVBK04/LggIpsFBRmGPUtLA65dA2rXLu0aERGVP6Uxc9uRI0fQtWtXU3nSpEkADFMIr1mzBs888wzi4+Mxe/Zs3L59Gw0bNsTWrVsREhJifUVzYWdnh0mTJpnqUBwY+BKRzVQqQ7B76pRhSDMGvkREVrCxcxus2LdLly6QZTnPbcaNG1ckaQZlAXN8iahIhIUZ/udYvkREVFaxxZeIioQx8L10qXTrQURUXqkgQWVNs22O/QFD5za1Wo3x48dj/PjxRVW9IlWzZs1C7yNJEq5cuWLTeRn4ElnJ0uQMwVXFZWdvigNuX7prPhmFt7M4sLiPq9ghwVLHLWWHuIwscQxF5aQXTaqbd5DLT/T9TLNleXWaq1PH8H/OFt8a3uYdAI1mzQJ+/hlYsS4TgTmmOS5IxzwlZWc3S+KSxPtjzXmIiMqDwnRuKy3Vq1c3Gx84OjoaV65cgZubmykwvnr1KpKSklCrVi1Uq1bN0qEKhYEvERWJwqQ6yDLw6afA/fvAR3PU+GyFLv+diIgquKKawKI82LNnj1A+fvw4unfvjkWLFuHVV1+Fw8PB5zMzM/HZZ59h3rx5+OGHH2w+L3N8iahIGAPfqCjD6A55iYw0BL0AsPkHNf47UY7erYmIiolxVAdbbuXVm2++iYEDB2LSpEmmoBcwDHM2ZcoUPPnkk3jzzTdtPg8DXyIqEt7egIeHoTU3vxSsiAixPHeGGvl0KiYiogrs33//RcuWLXNd37JlS/z77782n4epDkRFSJmPGx4k5lhF3k0x38deLZTP3Ra3qeHjYrZPfpNraOzF77TKnF8g/7zfwubASpKh1TciwpDu0LBh7tseOWL4v29fYOdO4OB+FU7944C+fYG7yfkn7Crvv6UcX+Vj4e3GnF4iKttyzr5m7f5A+ejcpmRnZ4eTJ0/muv7kyZOws7M9bGWLLxEVmYKO7GAMfAcNAl5/3fD3W28B2gJ0UiMiorxFRETg7Nmz5SboBYDevXvjiy++wBdffAG9PrvDtl6vx8qVK/Hll1+iT58+Np+HgS8RFZmCdHDT64GjRw1/t2gBvPsuUKUKcOYMsGZNsVeRiKjMMnZus+VWXi1evBjBwcEYO3Ys/P390b59e3To0AH+/v545ZVXUL16dSxatMjm8zDwJaIiY2lIM6VLl4CkJMMUx+HhgKcn8P77hnXTpwMp5tkgRESVggqSKd3BqpsNYwCXtsDAQBw/fhxvv/02fH19cezYMRw9ehS+vr6YOnUqjh07hoCAAJvPwxxfomJknmeqMdvGTbGRcrzZqPh8hkiA+fjByhxYH1fzfF5lLq1yLGBrxrktSKqDMc3hkUcAY7rWK68An31mGO1h3Qo7TJ+ed12VLI11rMz7vf0gXSgrxxi2lCfMsX+JiEqOh4cHPvzwQ3z44YfFdg62+BJRkTG2+MbGAgkJlrcxjujQokX2Mo0GmDfP8PeCBcCdO8VXRyKisqoypzqUFAa+RFRk3N0BPz/D37m1+hpbfHMGvgDw9NOGZSkpwEcfFV8diYgqupYtWyI8PBxLly4t7aqUOQx8iahI5ZXuoNUCx48b/lYGvpIEzJ1r+HvZMiA6uvjqSERUFqmK4AaUz1EdSgoDXyIqUnmN7HD+PJCaCri6Zm+XU8+eQKdOQEYGMGdO8daTiKiskSTJ5hvlrcJ1bgsNDcX169eFZW+//TY+yvHb6Y0bNzB+/Hjs2rULTk5OGDp0KBYtWiRMkUdUHPKbeAIw70B15qb5MAcqxVfWSzHJQtlV0dtL2ZHLUl2Unbui74sduyzVzRJjnu+FC+bHPPRw0p1HmgFZMpClFTumGVt9O3YEVq0C3nwTqF07/+tWkAksAjzNr0Fe2wPmE4EQEVH5VuECXwCYPXs2Ro8ebSq7urqa/tbpdHjsscfg4+ODAwcOID4+HiNGjIAsy/jf//5XGtUlqlCaNzf8//vvhg5uHh7Z64zj9zZrnvv+HToAffoAf/wBzJwJfPNNsVWViKhMkR7ebNmf8lYhmzPc3Nzg7+9vuuUMfLdv346zZ8/im2++wSOPPIIePXpg8eLF+OKLL5CYmFiKtSaqGLp1M4zPm5gIrPpSXHf0Yce2vAJfAPjgA8P/330HnDpV9HUkIiqLbBrD18bpjiuLChn4zp8/H1WrVkXTpk0xd+5cZGZm/2R76NAhNGzYEIGBgaZljz76KDIyMnDU2BxlQUZGBhITE4UbEZlTqQwpCgCw5DNDvi4AZGYCp/4z/N08n8C3WTPgqacAWc6e3IKISgY/76giq3CpDhMmTECzZs3g5eWFw4cPY+rUqYiMjMSXXxqanmJiYuBnHG/pIS8vLzg4/H97dx4XVbn/AfwzbAMqDALK4gpdzcitgK64hLZgVpp6c8kUl8zccqHVzLVMy/S2uKBl6i9NvfemXtcUy/W6gApuda9aKC4gqASCynp+fzzOwCwMsw8z83n3Oq+ZOefMOc8z3st8efg+38cL2dnZ1V537ty5mDVrllXbTqTLo43qau3TXNDh+m31RS72ZqgXwq3nHQZNmnmzmjmu5uS3DhoETJ0KXL8ObPwHMHw48NsZEQT7+wMRD9V8jdmzgY0bgX//G0hJAZ54ovpzdeXnan5GhRqJwIYsYFGT37K0868fCdX+9yJyJPy+sy9LjNnGxMTA3d0d48aNY2UHDQ4x4jtz5swaZzEef1AcdPLkyYiLi0Pbtm0xcuRIJCUlYcWKFbh165bqerpmPUqSpHc25JQpU5Cfn6/arly5YvmOEjkJLy9g8mTxfP58oKKisn7v41GGFVl/5BFgyBDxnBUeiGyH33eOz5nKmUmShIyMDJw+fRoZGRmQJMms6znEiO/48eMxcOBAvec0b95c5/4OHToAAC5evIjAwECEhITg2LFjaufk5eWhtLRUayS4KrlcDrlce7lZItJt1CgRsP72m5jodtzA/N6q3nsPWL0a2L0buHMH8NVeeZmILIzfd/Zj7uprzpTiW1JSgnfffRerV69GfpWlQP38/DB8+HDMmzfPpP+dOsSIb1BQEFq1aqV38/bWXaoo7UG1/NDQUABAbGwszp49i6ysLNU5u3fvhlwuR1RNiYdEZDA/P2DMGPH8s89MC3xbtQIeekjkB+/ZY/k2EhHVJqzjW2ncuHFISUnBhg0bkJOTg5KSEuTk5OAf//gHUlJS8Oabb5p0XZlk7phxLXLkyBEcPXoU3bp1g0KhQGpqKiZPnozo6Gj8+9//BiDKmbVv3x7BwcGYP38+bt++jWHDhqF3795GlTMrKCiAQqFAfn4+/Pz8rNUlIoNo5qem/pGn9rpxgI/WezRr/RpSY1iTZh7tn0Wlaq/di33wSAsRuMpkYrLapUtAs2aG32PSJODLL4Fhw4Gly8U+XTm9xtJsu6+Oi5pyn5qua4m2E9kSv++sT/kZf3vgN9SpZ/qftu4W3sHIJx9xin+rgIAAXLhwAYGBgVrHcnNz0bJlS+Tl5el4p34OMeJrKLlcjg0bNqBr166IjIzE9OnT8frrr2PdunWqc9zd3bF9+3Z4e3ujU6dO6N+/P3r37o3PP//cji0nck5hYcCgV8VzSQKCgoCmTY27xgsviMefdopcYVe1dSswcSKwbZu9W0JE1mKpJYudgUwmQ2lpqc5jZWVlJo9uO9XYw+OPP46jR4/WeF7Tpk2xjd8eRDYxKRFYvUoEvoZObKvqySfFEsfZ2UB6mnGpEs5k717gq6/ExMEXX7R3a4iIrGvw4MF47rnnMHXqVLRr1w7+/v7Iz89Heno6PvnkEyQkJJh0XWf65YCIaqGHWwEv9hLPO8Qa/365HHjqGfF85w7LtcvRXLsmHhs1sm87iMh6mONbaeHChejbty/efvtttGrVCqGhoXj44Yfx9ttvo2/fvliwYIFJ12XgS0RWl7QcWPgF8OZE097//PPi0dzAd/t2UWXCETHwJXJ+MgtszsLd3R3Tp0/H5cuXkZeXp/Y4bdo0uLu7m3Rdp0p1IHJVmhOmWoWpT47QXLxBF83FGMIbaC/EoHkfzQlxmq+rXvO5/kDuPSDI3/gFHl7qCYyGWPL4z5uAf5D+duly5IhIEQgPB37/3bDJfJqfiSGLU+iaJFfV1bwSrX2N63vVeF0GvkRkKGdbwEKhUEChUFjkWhzxJaJaLyQEiI4Wz3fuNO0aW7aIx4wM4PRpy7TLViRJrIIHMPAlcmaWSnVwpgUsdCkpKUFERIRJ72XgS0QOQVndQde81G3bgNdfF4tcVKdqwGzs3NaL52U49h/7/bi8eVOUhAOAByXJiYhcliRJuHTpkknvZaoDETmEF18EZs0CkpNFEOj1IDvg0CGgb1+gtFQsc5yYqP3e69eBU6cqX2/bBkydath9790Fhvb1Qf6fQNxFkSpha8o0h4YNK/tNRM7H3JJkzjSaqW9EV5IkljMjoko15d7qUlyqXiT3jo684Jsa59SUm6orJ1Yzx9WQ/FYAePxxIDgYuHEDSD0CPP20SFt4pZ8IegFg/Xrdge9PP4nHiAjgjz+AY8eAnBwRSNbU/m+/BfJui9cHjpajXoPKNX90fa6aKb6aH6Ou/moueqF53YxM8RgWJq7HRTCInJO5lRmcqapDbm4uPvroIzRp0kTrWElJCQYPHmzSdfnjk4gcgpubSHf47jsxYhsTA/TqJdIA2rQBzp0DUlNFYKs5UKBMcxg8WLz35Emxb+hQ/feUJGDRosrXv1+0bJ8MdV05sa2xfe5PRGRr7du3R6NGjfC3v/1N61hxcTFMXXjYmUbFicjJVc3zffVV4OxZkfO6cyfQrZs49o9/qL+nrEykRwBAjx6Viz8Ykuf7n/+op0hk/G6f0RRlqkNYmF1uT0Q2wnJmlSZMmICAgACdxzw9PbFy5UqTrsvAl4gcxrPPAp6ewMWLInD19gb+/W9R6WDAAHHO+vXq7zlyBMjPBwIDxSixMvDdtatywlh1Fi8Wj8qUiN8v2udrRTniG8aKDkROTSYzf3MW/fr1w9NPP63zmJubG4bW9Ce7ajDVgYgAGJZrm3Hzvt7jNeWq6rqPZg5sRq567VygMlfY1xeIiwP27BH7V64UwSwgJriNHStGaP/3P+Dhh8V+ZX5v567lyCooR3AE0KChJ3JzZDh4UOQK65KVBfzrX+L5xx8Do0aJEV9D8qWrqinnF9D+nDQ/x+vXxXGO+BIRmYcjvkTkUEaOBNzdRTA6cGDl/sBAMSIMABs2VO5X5vd2e0bkg7m5AU89Kybp6Ut3WL5cpEl06gT07y/2ZWcDBQWW6onhOOJL5BrcIDN7c1QRERF6t4ceegitW7fG888/jy+//BJ379416T4MfInIoQwYABQV6S5HVjXdQZJEoJqWJvbFPVVZkeLp7uL51q3iPE0lJUBSkng+fjygUIiKEgBw4YKlemI45eIVHPElImfVtGlTeHh44NKlS7h9+zb8/f2hUChw+/ZtXLp0CR4eHvD29sbRo0cxefJkREdH49atW0bfh4EvETkcuVz3/t69RZ3b334TE9+UaQ7R0UBQg8rzusRJ8PISSxefP699nU2bRNAcEiJSKACgZUvxqOt8a7p3D8jLE8854kvk3CyV4xsTE4PIyEgsVk5UcAB///vfcfPmTXz11VfIzc3FyZMnkZaWhtzcXHzxxRe4ffs2Vq5cqXr93//+F7NmzTL6Pgx8ichpKBSicgMg0h2UaQ7KfUr1fIGuXcXz7du1r6MsYfbGG5ULRtgr8M16MNpbp47oHxE5L5kF/gMcc8nid955B/369cP48ePh6emp2u/p6YkJEyagT58+eOedd+Du7o4JEyagf//+2Lp1q9H34eQ2IjJYqL+32uuTl9UTXhv6qR/XNRkuPEj9HM3JX7oWvTh3TX3C26ONtM9RGjBAVHpYt65ypPS557Qn1b34IrB7t8jzrbroRXq6WA3Ow0NMaFOyZeBbdbLbr3+Kx0aNAB9P3ecD2pPmuMgFETmSY8eOoV+/ftUej4qKwvoqZXs6d+6MzZs3G30fjvgSkVPp2RPw8RELWeTlAfXrA3/9q/Z5yprABw8Cf/4pUiPef79y/9/+pp5Ta0jgm5Ii8o8tSVnDtxHTHIicniuXM/Pw8MDZs2erPX769Gm4u7urXkuShDp16hh9Hwa+RORU6tWrrNULAPHxogqEpogIIDJSVG5o00Zsn34qJpI1bAhMm6Z+ftXAV9eEuC1bRIBddZTYEhj4EpEriI+Px7Jly7B69Wq1VdkkScKqVavwzTffoHv37qr9J0+eRPPmzY2+DwNfInI6yuoOgHZ+b1XKAPnqVbEwRu/eonbv5cvAo4+qn/vQQ2I0paAAyMnRvpayNNqPP1q25BkDXyLXITOzlJkyx9cRLViwAKGhoRgxYgRCQ0PRuXNndO7cGaGhoXjttdcQGhqK+fPnAwDu37+Py5cvY8iQIUbfh1lgRGQwzbzRx5v56T1f12INmnm/9TQuqmuBCH05vQBwNU99Cba2sUCDBl64exfo+oxoh66c13ffBQoLgdatRa3ewMDq7yGXA82aAZcuiZJmyvJmSgcPisfiYpFjXN3PY13t0LfwhzLwbRhS+XnquobmPkMWEyGi2sXcdAVHTnVo3LgxTp06hXnz5mHLli04ceIEACA8PBzDhg3Du+++q1rC2NvbG7/88otJ9+FPQiJyOj4+wIH/APfvaweoVQUGVi5LbIiWLUXge/480Llz5f7cXOC//618vWFD9YGvsa5x8QoichH+/v6YN28e5s2bZ7V7MNWBiJxS83Cg1SOWvWZ1E9wOHRKPyhHj3bsrK0qYSxX4cvEKIqfnypPbNBUWFqKwsNDi12XgS0RkoOoCX2Waw8svi0lypaWACVV2tFRUVFm1jSO+ROTkcnNzMXr0aAQHB0OhUEChUCA4OBhjx45Fbm6uRe7BwJeIyEA1Bb5dulROrNuwwfz75eaKqhMymVhFjoicm6UWsHBE2dnZiImJwfLly6FQKNCrVy/07NkTCoUCSUlJiImJQXZ2ttn3YY4vEVmNrklYeUXqE9E0J7dduHFP6z0tgn303ifI10trnzUmeykD34sXgfJyUSatsBBISxP7u3QBnngC+PBDYM8e4OZNICio5utW1xZlmkNwMOCr/yPQmkioeU3N/hvTDiKyDTeZ2Mx5v6OaNm0arl27hjVr1mDQoEFqx9avX4+EhATMmDEDy5YtM+s+HPElIjJQ06ZiCePiYuDKFbHv6FERBDdtKrYWLYDHHhP7Nm0y734sZUZErmLHjh0YPXq0VtALAAMHDsSoUaOwTVk30gwMfImIDOTuDvzlL+K5Mt2hapqDkqXSHRj4ErkWV051uHnzJlq3bl3t8TZt2uDWrVtm34eBLxGRETTzfJWBb9XyZv37i8e9e4EbN0y/FwNfInIVISEhSE1NrfZ4amoqgvXVpzQQE7qIyKY0F73QXHzCx0t7fWHNRS/Cg7zVXuvKJdZUU86rofmtVQPfP26U4MhRTwAytGhbiqt5EoJ8vRDaBIiKBk4cFyu5jR1r0KW1GBP41vQZMH+XqPaz1AIWMTExcHd3x7hx4zBu3DjLNM7KXnrpJSxZsgTt27fHmDFj4P5grfny8nIsW7YMq1atskhf+JOQiMgIVQPfc6dluH9PBv/6Elo8LKmd93J/Efhu2GCbwJeIHJ8MMCtdQfnO1NRU+PnpX1mztpk5cyaSk5MxceJEzJo1Cy0f/LA9f/48bt++jZYtW2LmzJlm34epDkRERlAGvhcuAMeOiK+ZmA4S3DR+mv7tZfF48GBlLV5jMfAlIlcREBCAlJQUTJkyBcHBwUhLS0NaWhqCg4PxwQcfICUlBfXr1zf7PhzxJSIygjLwvXQJ+M8BEe0+0aFC67wmTYAOscDRI8DIkcDatYCxP7MtFfiWlIhqFERUu7lyOTMA8PX1xccff4yPP/7Yavdg4EtEdtW4fs0RWU01aHUdrymn1de75jq3hRrFccODvNGwIeDnBxQUAAf2isC3Z3cPNNYR1M6aCfTqBezcKXJ+120A/hqtt1kqd+8Cf/4pnhsS+FaXs3z6NPDUU0CrVsDGjUDDhobdX9c1q16XiCzP3MoMjlzVwVb4E4yIyAgymajVe+KEWFLYx0fU7dUlPh7YewB4ZQCQ8QfQtQuwdCkwbFjN91GO9tatKwJtU1RUAG+8Ady6BfznP0CnTsCuXUBEhGnXIyKylP/7v/8z6X0JCQlm3ZeBLxGRkVq2FIEvAHTooD+N4LHHgcPHgNeGAT/tBIYPB44dA5Ys0T97u2qag6mzvL/9ViywUa8eEBgoVpzr2FGMQFcXrBOR/ViqqoMjGDZsGGQyGSRJqvnkB2QyGQNfIiJbU+b5AuoLV1QnIAD4cTPw6Vzgo1lAUhLQp48YEa6Oufm9N24A770nnn/8MdCvH/D888CpU0BcnFhV7umnTbs2EZG59u7da5f7MvAlIjKSsYEvALi5AVOmAteuACtWADt2WDfwffttkSP82GPAuHGAhwewfz/Quzewbx/Qowfw008i/5eIagcZYFaWrgMN+CIuLs4u92U5MyKq9RrU81DbLMHbQ33TvEeDeh7wdHdT285dK8K5a0Vw978HQCxh3KGDcffp1K0cALB9h4TcwjLkFpbhfhnUNsD4wLdqu8+keGDNGvFnz2XLRNALAAqFCHZ79QJKS4HFi4391IjImtwgg5vMjM2hQl/7YOBLRGSkyDYV6D+kFJ9+KvJnjfFkVwnu7hIuXpDh8qXqzzN1xLe4GBgzRjwfOxaIiVE/LpcDb74pnp8+bdy1iYgcHQNfIiIjubkB0+eW4K23jH+vnwKI+at4/sue6kdnTA18P/tMrCoXEgLMmaP7nLZtxePvvwOFhcZdn4isR2aBjfRj4EtEZGNPPysWvNibbNnA9+LFymD3738XqQ26NGwoAmNJAs6eNfz6RESOjpPbiMgpnbtWpPb60UZ11V5Xt+BDVZqLaxiy2MaFG/fUXrcI9tG6z99eAubMAg7ud4PCyw2a677dLQGyssTzwOAab6kSGirSGH77DRgwQP+57doB2dmiykN1ecpcrILIxlxpdpudcMSXiMjG2rUDgoOBoiLg0CHt4zk5QFmZSKkICTH8unXrAvPnA1u21FzPU5nuwDxfotpDZoH/SD8GvkRENubmBnTvLp7/9JP28esP0hwaBldWZDD2+jVp1048njpl/PWJiByVUwW++/btg0wm07mlpqaqztN1PCkpyY4tJyJX06OHeNQV+B49Ih5NreFriKojvhWauRZEZB+yytXbTNk44Fszp0rg6tixI7KUiXEPTJs2DXv27EF0dLTa/pUrV+K5555TvVZUNwuEiGo9Xbmomvuu5pWovTYkX9cUmjm91Xn2WfFFdeYMcDMbaNxY7L9xQ6zuBgBDE0TtX2to1UostXznDnD5MhAern1Oxs37WvvCg7yt0yAiIhtwqhFfLy8vhISEqLbAwEBs2bIFI0aMgEwj4c3f31/tXB8fw76siIgsITAQeOIJ8XzXrsr9b70lVlyLihJ1eK3F0xOIjBTPme5AVDuwnJn1OVXgq2nLli24efMmhg0bpnVs/PjxCAoKQkxMDJKSklBRw9/6iouLUVBQoLYREZlDM93h55+BtWvFSHBSklgZzpqU6Q4MfKkqft/ZESNfq3PqwHfFihXo3r07mjRporb/o48+wj//+U/s2bMHAwcOxFtvvYVPPvlE77Xmzp0LhUKh2jSvSURkLGW2VXKyWEhCueLauHGARnaWVSgnuLGyA1XF7ztyZg4R+M6cObPaSWvK7fjx42rvuXr1Knbt2oXXXntN63offvghYmNj0b59e7z11luYPXs25s+fr7cNU6ZMQX5+vmq7cuWKRftIRK4nOhoICADy84F+/YALF0Qt3o8/ts39WdmBdOH3nf04ajmzPn36oH79+nj55Zftcn9jOMTktvHjx2PgwIF6z2nevLna65UrVyIwMBC9evWq8fodOnRAQUEBbty4geBg3dXi5XI55HK5wW0motrFWpPZaqJvoQx3dyA+Hli/vjLdYfa8cigUVs5xeEBz6eJ69dSPcyKba+L3nf2oqjOY8X57mDBhAkaMGIHVq1fbpwFGcIjANygoCEFBQQafL0kSVq5ciYSEBHh6etZ4flpaGry9veHv729GK4mIjNejhwh8AaDbMxXo1Uey2b0bNBAjzFlZorpEbKz68TfeEBUftm4Vk+GIiHTp1q0b9u3bZ+9mGMQhUh2M9csvvyAjI0NnmsPWrVvxzTff4OzZs/j999/x7bffYurUqRg1ahR/wyUim+veHZDLJXh7S5i3oMLmIzbVTXBLTQWWLxcVJ5gKQWQb9pjbduDAAfTs2RNhYWGQyWTYvHmz1jlLlixBeHg4vL29ERUVhYMHD5pwp9rBKQPfFStWoGPHjnjkkUe0jnl6emLJkiWIjY1F27Zt8eWXX2L27NlYsGCBHVpKRK4uOBjYllyOn/aVIzzC9vevboLb4sWVzzMybNceIrKtoqIitGvXDosWLdJ5fMOGDZg0aRKmTp2KtLQ0dOnSBT169EBmZqaNW2oZDpHqYKwffvih2mPPPfec2sIVRESG+i2rqMZzHgmtq/Za1+Iamp7pon6OvrxgQ91Xv0S1C2HomuCWm1uZfgEAf/xh9O3Notl2wHoLeRDVKuaWJHvwXs0SdPrytnv06IEeytqKOixcuBCvvfYaRo4cCQD44osvsGvXLixduhRz5841o7H24ZQjvkREZBhdSxevWAEUF1eeY+vAl8hVWaqqQ5MmTdRK0pkaoJaUlODEiROIj49X2x8fH4/Dhw+b3V974O/QREQu7OGHxdLFhYXApUtA06bA0qXiWNeuwL59THUgcjRXrlyBn5+f6rWpc5hu3ryJ8vJyrYpXwcHByM7OVr3u3r07Tp48iaKiIjRu3BibNm1CTEyMaY23Mga+REQuzNMTePRRIC1NpDucPg1kZoolladMEYEvR3yJbMNS5cz8/PzUAl9zyTQaJUmS2r5dVdddr+UY+BIRGUgzf1dXLqqmq3klaq8NqSdsSk6vJs2cWH1tfbS1CHxPnwaUk7Vffx1Qzg++fBkoKwM8+I1B5FKCgoLg7u6uNroLADk5OdWue1DbMceXiMjFtXmQ5/uvfwE//wy4uYnlk8PCRBpEWRlw9ap920jkCixVziwmJgaRkZFYXLU8iwm8vLwQFRWF5ORktf3Jycno2LGjWde2F/7+TkTk4pSB79mz4vGll0SuLwA0bw6cPy/yfDUWyCQiS7NQVYfU1FSDUx0KCwtx8eJF1euMjAykp6cjICAATZs2RWJiIoYMGYLo6GjExsZi+fLlyMzMxOjRo81oqP0w8CUicnHKwFdp/PjK5+HhIvD94w+gWzfbtouIrO/48ePoVuX/3ImJiQCAoUOHYtWqVRgwYABu3bqF2bNnIysrC61bt8aOHTvQrFkzezXZLAx8iYhcXFAQEBoGZF0Xeb1VA9yIB4tqcIIbkfVVLUlm6vuN1bVrV0iS/qXSx44di7Fjx5rarFqFgS8RkYl0LaqgOYlM7mmbqRQ1LXpR0wIQnTqKHN+JE9VnlSsDX1uWNONiFURkLZzcRkRE+PprYPNmYNQo9f0c8SWyHWU5M3M2wHKT25wRf68mIiKEhIhJbZrCw8UjA18i67PQ3DajJre5Go74EhFRtZQjvrm5YnU3IiJHxhFfIiIL0sxP9dbItT1xqUDrPVHN1UdmNPOEDcl5tcSiF7ooFEBAAHD7tsjzbdPGKrdRo5mvDFimf6Z8rkQ2ZakhX6oWR3yJiEgvpjsQkbNg4EtERHpxghuRbcgs8B/AyW368A89RESklz1KmhG5oqqVGUx9P8DJbfpwxJeIiPRiqgMROQuO+BIR2ZDmRDZ7qmnRCyVbpzpYa6Ke5mQ2Tnaj2oZz26yP/zcnIiK9qqY6SJJ5f4olIj0Y+VodUx2IiEivpk0BNzfg/n0gO9verSEiMh0DXyIi0svTE2jSRDxnni+R9bCqg/Ux1YGIqJaxVa6pMbm0ERHA5csi8O3UyYqNsiLm9JKrYFWH6nHEl4iIauQMJc2WLAKi2wPzP7N3S4h0U5YzM2cj/fj7LhER1cgZSprl5wPnzgF/XLR3S4h049w26+OILxER1cgZVm9r0EA85ubatx1EZD8c8SUiohopA9/f/5CQW1iu2m+tmrvW4Fu/HIA7srIl3LlfrpXky5xfsjsO+VodR3yJiKhGysA367ooa+aIGjSUAAA3axjxXbYMWLNGpEYQkXNh4EtERDUKCgLq1gUkSYarmfZujWmClKkOOWIhDl0kCZg0CRgyBLh922ZNIwLAcma2wD/sEBFRjWQyMep75gyQeVmGv7SsJnKsxRo0FI/37slQVAT4+Wifk59fOaIdEmK7thEBAMytzPDgvSxnVj2O+BIRkUGU6Q6XLtm1GSarWxfw8REBe26O7nOUK9MpFICPjsCYiBwbR3yJiFyUsQs6KEua5V5zR4N61mmTNTX09UBwsAjcywo9cEfjA/Cu54GsLPHcnNFeLpRBpuLcNuvjiC8RERnEGUqaBQeLx5xqRnyVgW9oqG3aQ0S2xd9DiYjIIM6welvDB3m+N27oPq5MdWDgS3bBIV+rY+BLREQGqbp6myQ55vKoho74cmIb2UPVygymvp/0Y+BLREQAgNxC9eRUzcUpIiKApUvFo6MGvlVHfHUtvmGJEV/NnF7m/BLVHszxJSIig3h7A6NHA/HxgJuDfntwxJdqM5nM/A1gHV99+HsnERG5DGXgW12OLye3kTNgHd/qMfAlIiKXwcltVJtxbpv1MfAlInJRmrmm3jpyXp2NvlSH4uLKZYpZx5fsgpGv1TlolhYREZHxlCO+eXlASYn6MeUosKcnEBBg23YRkW0w8CUiIpcREAC4u4vnubnqx6pObLNUxYp797TvQ1QdmQX+I/0Y+BIRkctwcwMaNBDPNfN8LT2xrbwcGJYAdOzo2Kvdke3IYGZVB3t3wAEw8CUiIpdSXZ6vpSe23bgBnEoHLl4UwW9ammWuS0Smc6jAd86cOejYsSPq1KkDf39/nedkZmaiZ8+eqFu3LoKCgjBhwgSUaCRynTlzBnFxcfDx8UGjRo0we/ZsSJJkgx4QETm2+2XqmyOqrqSZpWr4enuILaIpcOQw0LatuFdcHLBzt3nXJucms8BG+jlU4FtSUoJ+/fphzJgxOo+Xl5fjhRdeQFFREQ4dOoT169fjxx9/xFtvvaU6p6CgAM8++yzCwsKQmpqKr7/+Gp9//jkWLlxoq24QEZEdVVfSzBqlzEJDgd2/AE/GAXfuAC+9CKxfb7nrE5FxHKrIyqxZswAAq1at0nl89+7d+PXXX3HlyhWEhYUBABYsWIBhw4Zhzpw58PPzw9q1a3H//n2sWrUKcrkcrVu3xvnz57Fw4UIkJiZC5ohrcBIRkcGqS3Ww1qptCgWwZTswYhiw8V/AK6+IoHviRMvehxxf1dXXTH0/6edQI741OXLkCFq3bq0KegGge/fuKC4uxokTJ1TnxMXFQS6Xq51z/fp1XLp0qdprFxcXo6CgQG0jIiLHU92IrzVXbZPLge/XAmPGiddvvw1cuGD5+1gCv+/sickO1uZUgW92djaClb/KP1C/fn14eXkh+8HfsHSdo3ytPEeXuXPnQqFQqLYmTZpYuPVERLWfMn9VuTkia09uyy0sU9uUn1UdL2Dx18AnnwCrVgEtWph3H2vh953ji4mJQWRkJBYvXmzvptQ6dg98Z86cCZlMpnc7fvy4wdfTlaogSZLafs1zlBPb9KU5TJkyBfn5+artypUrBreJiIhqD12T2yoqKgNfS6c6VCWTAVOmAK++ar17mIvfd/ZjVimzKmkSqamp+PXXXzFu3Dj7dqgWsvvv6+PHj8fAgQP1ntO8eXODrhUSEoJjx46p7cvLy0NpaalqVDckJERrZDfnwa/9miPBVcnlcrX0CCIickzKVIeqI763bwNlD6pU6PkqcAn8viNnZvfANygoCEFBQRa5VmxsLObMmYOsrCyEPvhb1e7duyGXyxEVFaU654MPPkBJSQm8vLxU54SFhRkcYBMRkeOqmupQUSEWtVDm9wYFAQ++GohsztwsXWb41szuqQ7GyMzMRHp6OjIzM1FeXo709HSkp6ejsLAQABAfH4/IyEgMGTIEaWlp+Pnnn/H222/j9ddfh5+fHwBg0KBBkMvlGDZsGM6ePYtNmzbhk08+YUUHIiIXoVy5rbxcjPQClq3o0KCeh9qmmfNLVB1LpTpQ9ew+4muM6dOnY/Xq1arXjz32GABg79696Nq1K9zd3bF9+3aMHTsWnTp1go+PDwYNGoTPP/9c9R6FQoHk5GSMGzcO0dHRqF+/PhITE5GYmGjz/hARke15eQH16wN5eWLUNyjIOjV8iaj2cajAd9WqVdXW8FVq2rQptm3bpvecNm3a4MCBAxZsGREROZLgYBH43rgBREZar4YvkTFkD/4z5/2kn0OlOhAREVmC5gQ3jvgSuQaHGvElIiKyBM2SZtZcvILIYJzdZnUMfImIyOVort5mzVSHBvXs81WrOZHOXu0gwzHutT6mOhARkcvRXL2NqQ5EroG//hERkcupLtWBk9vInswtScZyZjVj4EtERC6n6uS2oiLgzh3xmiO+ZE+s6mB9DHyJiMjlVB3xVaY51KkD+Prar02WxpxeIm38fwUREbmcqiO+VdMc+KdisivObrM6Tm4jIiKXoxzxvXsXuHhRPGeaAzmLmJgYREZGYvHixfZuSq3DEV8iInI5desCPj7AvXvAqVNiHye2kb1ZasA3NTUVfn5+FmiR8+GILxERuRyZrHLUVxn4csSX7E1Z1cGcjfRj4EtERC6JgS+R62GqAxERuSTlBLfbt8UjUx3I/swrZ8bZbTXjiC8REbkk5YivEkd8iZwfR3yJiMglKUd8lTjiS/bGldusjyO+RETkkjjiS+R6GPgSEZFLqhr4urkBDRrYry1EZBtMdSAiIpdUNdWhYUPA3d1+bSECmOpgCxzxJSIil1R1xJdpDkSugSO+RETkEu6Xqb+uOuLLiW1UG8jMLGdmXik018DAl4iIXFJAgEhvKC/niC/VDkx1sD6mOhARkUtyc6sc9WXgS+QaGPgSEZHLUga+THWg2kBmgY30Y+BLREQuq00b8di2rX3bQUS2wcCXiIhcVlISkJ4OdOli75YQgUO+NsDJbURE5LLq1gXatbN3K4gEVnWwPo74EhEREZFL4IgvERERUS3AcmbWx8CXiIhcgje/8aiWMzdNl3FvzZjqQEREREQm27ZtGx5++GG0aNEC3377rb2boxd//yUiIiKqDRxwyLesrAyJiYnYu3cv/Pz88Pjjj6Nv374ICAiwfWMMwBFfIiIiIjJJSkoKHn30UTRq1Ai+vr54/vnnsWvXLns3q1oMfImIiJzQ/TL1jWo/mQX+M9aBAwfQs2dPhIWFQSaTYfPmzVrnLFmyBOHh4fD29kZUVBQOHjyoOnb9+nU0atRI9bpx48a4du2aSf23BQa+RERERLWAsqqDOZuxioqK0K5dOyxatEjn8Q0bNmDSpEmYOnUq0tLS0KVLF/To0QOZmZkAAEmSdPSj9k6zY44vERERUS1QUFBgkfdrXkcul0Mul+t8T48ePdCjR49qr7lw4UK89tprGDlyJADgiy++wK5du7B06VLMnTsXjRo1UhvhvXr1Kv7617+a1Q9rYuBLREREZEdeXl4ICQlBi/AmZl+rXr16aNJE/TozZszAzJkzjb5WSUkJTpw4gffff19tf3x8PA4fPgwAeOKJJ3D27Flcu3YNfn5+2LFjB6ZPn25y+62Nga+JlEP75v52RkREZA2aeb0lHoCvr2+t/jO0q/L29kZGRgZKSkrMvpYkSVr/xtWN9tbk5s2bKC8vR3BwsNr+4OBgZGdnAwA8PDywYMECdOvWDRUVFXj33XcRGBhoWuNtgIGvie7cuQMAWr9VERER1Vb5+fnw8/OzdzNIB29vb3h7e9u7GTppBtKawXWvXr3Qq1cvWzfLJAx8TRQWFoYrV6643G/PBQUFaNKkCa5cueJyPzzZd/adfXcdztp3X19fk96Tn59v0nvJsQUFBcHd3V01uquUk5OjNQrsKBj4msjNzQ2NGze2dzPsxs/Pz6m+DIzBvrPvroZ9d82+K8lkMpf/DFyVl5cXoqKikJycjD59+qj2Jycn46WXXrJjy0zHwJeIiIjIRRUWFuLixYuq1xkZGUhPT0dAQACaNm2KxMREDBkyBNHR0YiNjcXy5cuRmZmJ0aNH27HVpmPgS0REROSijh8/jm7duqleJyYmAgCGDh2KVatWYcCAAbh16xZmz56NrKwstG7dGjt27ECzZs3s1WSzMPAlo8jlcsyYMcPkGaKOjH1n310N++6afSfX0rVrV52LUFQ1duxYjB071kYtsi6ZVFNviYiIiIicAJcsJiIiIiKXwMCXiIiIiFwCA18iIiIicgkMfImIiIjIJTDwJZ3mzp2LmJgY+Pr6omHDhujduzf+97//qZ0jSRJmzpyJsLAw+Pj4oGvXrjh37pydWmwdc+fOhUwmw6RJk1T7nLnf165dw+DBgxEYGIg6deqgffv2OHHihOq4s/a9rKwMH374IcLDw+Hj44OIiAjMnj0bFRUVqnOcpe8HDhxAz549ERYWBplMhs2bN6sdN6SfxcXFePPNNxEUFIS6deuiV69euHr1qg17YRp9fS8tLcV7772HNm3aoG7duggLC0NCQgKuX7+udg1H7TsRCQx8Saf9+/dj3LhxOHr0KJKTk1FWVob4+HgUFRWpzvnss8+wcOFCLFq0CKmpqQgJCcGzzz6LO3fu2LHllpOamorly5ejbdu2avudtd95eXno1KkTPD09sXPnTvz6669YsGAB/P39Vec4a98//fRTJCUlYdGiRfjtt9/w2WefYf78+fj6669V5zhL34uKitCuXTssWrRI53FD+jlp0iRs2rQJ69evx6FDh1BYWIgXX3wR5eXltuqGSfT1/e7duzh58iSmTZuGkydPYuPGjTh//jx69eqldp6j9p2IHpCIDJCTkyMBkPbv3y9JkiRVVFRIISEh0rx581Tn3L9/X1IoFFJSUpK9mmkxd+7ckVq0aCElJydLcXFx0sSJEyVJcu5+v/fee1Lnzp2rPe7MfX/hhRekESNGqO3r27evNHjwYEmSnLfvAKRNmzapXhvSzz///FPy9PSU1q9frzrn2rVrkpubm/TTTz/ZrO3m0uy7LikpKRIA6fLly5IkOU/fiVwZR3zJIPn5+QCAgIAAAGJJw+zsbMTHx6vOkcvliIuLw+HDh+3SRksaN24cXnjhBTzzzDNq+52531u2bEF0dDT69euHhg0b4rHHHsM333yjOu7Mfe/cuTN+/vlnnD9/HgBw6tQpHDp0CM8//zwA5+57VYb088SJEygtLVU7JywsDK1bt3aqzwIQP/dkMpnqrx6u1HciZ8WV26hGkiQhMTERnTt3RuvWrQEA2dnZAIDg4GC1c4ODg3H58mWbt9GS1q9fj5MnTyI1NVXrmDP3+48//sDSpUuRmJiIDz74ACkpKZgwYQLkcjkSEhKcuu/vvfce8vPz0apVK7i7u6O8vBxz5szBK6+8AsC5/92rMqSf2dnZ8PLyQv369bXOUb7fGdy/fx/vv/8+Bg0aBD8/PwCu03ciZ8bAl2o0fvx4nD59GocOHdI6JpPJ1F5LkqS1z5FcuXIFEydOxO7du+Ht7V3tec7WbwCoqKhAdHQ0PvnkEwDAY489hnPnzmHp0qVISEhQneeMfd+wYQPWrFmDH374AY8++ijS09MxadIkhIWFYejQoarznLHvupjST2f6LEpLSzFw4EBUVFRgyZIlNZ7vTH0ncnZMdSC93nzzTWzZsgV79+5F48aNVftDQkIAQGuUIycnR2u0yJGcOHECOTk5iIqKgoeHBzw8PLB//3589dVX8PDwUPXN2foNAKGhoYiMjFTb98gjjyAzMxOA8/6bA8A777yD999/HwMHDkSbNm0wZMgQTJ48GXPnzgXg3H2vypB+hoSEoKSkBHl5edWe48hKS0vRv39/ZGRkIDk5WTXaCzh/34lcAQNf0kmSJIwfPx4bN27EL7/8gvDwcLXj4eHhCAkJQXJysmpfSUkJ9u/fj44dO9q6uRbz9NNP48yZM0hPT1dt0dHRePXVV5Geno6IiAin7DcAdOrUSatk3fnz59GsWTMAzvtvDogZ/W5u6j8O3d3dVeXMnLnvVRnSz6ioKHh6eqqdk5WVhbNnzzr8Z6EMei9cuIA9e/YgMDBQ7bgz953IZdhtWh3VamPGjJEUCoW0b98+KSsrS7XdvXtXdc68efMkhUIhbdy4UTpz5oz0yiuvSKGhoVJBQYEdW255Vas6SJLz9jslJUXy8PCQ5syZI124cEFau3atVKdOHWnNmjWqc5y170OHDpUaNWokbdu2TcrIyJA2btwoBQUFSe+++67qHGfp+507d6S0tDQpLS1NAiAtXLhQSktLU1UuMKSfo0ePlho3bizt2bNHOnnypPTUU09J7dq1k8rKyuzVLYPo63tpaanUq1cvqXHjxlJ6erraz73i4mLVNRy170QkMPAlnQDo3FauXKk6p6KiQpoxY4YUEhIiyeVy6cknn5TOnDljv0ZbiWbg68z93rp1q9S6dWtJLpdLrVq1kpYvX6523Fn7XlBQIE2cOFFq2rSp5O3tLUVEREhTp05VC3icpe979+7V+f/toUOHSpJkWD/v3bsnjR8/XgoICJB8fHykF198UcrMzLRDb4yjr+8ZGRnV/tzbu3ev6hqO2nciEmSSJEm2G18mIiIiIrIP5vgSERERkUtg4EtERERELoGBLxERERG5BAa+REREROQSGPgSERERkUtg4EtERERELoGBLxERERG5BAa+RGS0VatWQSaTQSaTYd++fVrHJUnCX/7yF8hkMnTt2lW1X/ke5ebn54eOHTti3bp1FrsHANy6dQtTpkxBZGQk6tatC4VCgVatWmHIkCE4ffq0mb0nIiJHxcCXiEzm6+uLFStWaO3fv38/fv/9d/j6+mode/nll3HkyBEcPnwYSUlJKCgowKBBg/DDDz9Y5B6FhYXo0KEDVq1ahZEjR2LLli1Yu3YtRo0ahYyMDKSnp5vWWSIicnge9m4AETmuAQMGYO3atVi8eDH8/PxU+1esWIHY2FgUFBRovSc4OBgdOnQAAMTGxqJTp05o3rw5li1bhkGDBpl9j3/+85+4ePEifvnlF3Tr1k3tWGJiIioqKszqMxEROS6O+BKRyV555RUAUEtVyM/Px48//ogRI0YYdI1mzZqhQYMGuHHjhkXucevWLQBAaGiozuu5ufHHHhGRq+I3ABGZzM/PDy+//DK+++471b5169bBzc0NAwYMMOga+fn5uH37Nlq2bGmRe8TGxgIAEhISsHnzZlUgTERExMCXiMwyYsQIpKSk4Ny5cwCA7777Dv369dOZ3wuISWllZWUoLS3FhQsXkJCQgDp16mDGjBkWuUenTp0we/ZsnDp1Cn369EFQUBAiIiIwZswYTmwjInJxDHyJyCxxcXF46KGH8N133+HMmTNITU3Vm+awZMkSeHp6wsvLCy1btsTOnTuxbt06REVFWewe06ZNQ2ZmJr777ju88cYbqFevHpKSkhAVFaWzggQREbkGBr5EZBaZTIbhw4djzZo1SEpKQsuWLdGlS5dqz+/fvz9SU1Nx+PBhLFu2DL6+vhg4cCAuXLhgsXsAYhLd8OHDkZSUhNOnT2P//v3w8vLCxIkTTe4rERE5Nga+RGS2YcOG4ebNm0hKSsLw4cP1ntugQQNER0cjNjYWo0aNwubNm1FUVITJkydb7B66PPnkk4iPj0dubi5ycnKMfj8RETk+Br5EZLZGjRrhnXfeQc+ePTF06FCj3tulSxckJCRg+/btOHLkiNn3uHHjhs6SZeXl5bhw4QLq1KkDf39/o9pIRETOgXV8icgi5s2bZ/J7P/roI2zYsAHTpk3Dnj17zLrH999/r6oJHBMTA4VCgatXr+Lbb7/FuXPnMH36dHh5eZncViIiclwMfInI7po0aYI333wT8+fPx4EDB/Dkk0+afK0XXngB2dnZ2LFjB5YuXYq8vDz4+vqibdu2+P777zF48GALtpyIiByJTJIkyd6NICIiIiKyNub4EhEREZFLYOBLRERERC6BgS8RERERuQQGvkRERETkEhj4EhEREZFLYOBLRERERC6BgS8RERERuQQGvkRERETkEhj4EhEREZFLYOBLRERERC6BgS8RERERuQQGvkRERETkEv4f7vMUqEzJZk4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "varname = 'Precipitation Rate (mm/h)'\n",
    "df_plot = pair_precip\n",
    "var1 = 'MRMS'\n",
    "var2 = 'error'\n",
    "color_hist = 'blue'\n",
    "color_cmp = 'Blues'\n",
    "\n",
    "fig = plt.figure(figsize=(12,12))\n",
    "\n",
    "joint_kws = dict(bins=100)\n",
    "# fig = plt.figure(figsize=(16,16))\n",
    "\n",
    "x = np.linspace(0,150,100)\n",
    "y = []\n",
    "for i in range(len(x)-1):\n",
    "    y.append(np.mean(df_plot.loc[(df_plot[var1] >= x[i]) & (df_plot[var1] < x[i+1]),\n",
    "                            var2]))\n",
    "\n",
    "mean_i = np.mean(df_plot['error'])\n",
    "MAE_i = np.mean(np.abs(df_plot['error']))\n",
    "\n",
    "ax1 = sns.jointplot(data = df_plot, x=var1, y=var2, kind='hist', edgecolor = None, color = color_hist, joint_kws = joint_kws)\n",
    "# ax1.ax_joint.axhline(y=0, color = 'r', linestyle = 'dashed')\n",
    "# ax1.ax_joint.plot(x[1:],y,color = 'b')\n",
    "ax1.ax_joint.cla()\n",
    "plt.sca(ax1.ax_joint)\n",
    "plt.hist2d(df_plot[var1],df_plot[var2],bins=100, norm='log',cmap=color_cmp)\n",
    "plt.title('Scatter plot for {}'.format(varname),y=1.2,x=0.6)\n",
    "plt.ylabel(var2,fontsize=12)\n",
    "plt.xlabel(var1,fontsize=12)\n",
    "plt.text(80,75,'mean error: {}'.format(round(mean_i,2)))\n",
    "plt.text(80,65,'mean absolute error: {}'.format(round(MAE_i,2)))\n",
    "plt.axhline(y=0,color = 'red', linestyle = 'dashed')\n",
    "plt.plot(x[1:],y,color = 'blue')\n",
    "\n",
    "cbar_ax = ax1.fig.add_axes([1, 0.1, .03, .7])\n",
    "cb = plt.colorbar(cax=cbar_ax)\n",
    "cb.set_label(r'$\\log_{10}$ density of points',fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae9af398-00f0-48f2-aaed-9f69c2c7611d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e49e57d6-21b3-4e6d-925e-787fa22fab79",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}