subseasonal_mode.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find Teleconnections (precursor regions) via correlation maps "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/svijverber/.conda/envs/RGCPD/lib/python3.8/site-packages/tigramite/independence_tests/gpdc.py:27: UserWarning: No module named 'dcor'\n",
      "  warnings.warn(str(e))\n",
      "/home/svijverber/.conda/envs/RGCPD/lib/python3.8/site-packages/tigramite/independence_tests/gpdc_torch.py:33: UserWarning: No module named 'dcor'\n",
      "  warnings.warn(str(e))\n",
      "/home/svijverber/.conda/envs/RGCPD/lib/python3.8/site-packages/tigramite/plotting.py:26: UserWarning: No module named 'networkx'\n",
      "  warnings.warn(str(e))\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from RGCPD import RGCPD\n",
    "from RGCPD import BivariateMI\n",
    "from RGCPD import class_BivariateMI, functions_pp\n",
    "from IPython.display import Image\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RGCPD docstring\n",
    "Class to study teleconnections of a Response Variable* of interest.\n",
    "\n",
    "    Methods to extract teleconnections/precursors:\n",
    "        - BivariateMI (supporting (partial) correlation maps)\n",
    "        - EOF analysis\n",
    "\n",
    "    BivariateMI (MI = Mutual Information) is class which allows for a\n",
    "    statistical test in the form:\n",
    "    MI(lon,lat) = for gc in map: func(x(t), y(t)),\n",
    "    where map is a (time,lon,lat) map and gc stands for each gridcell/coordinate\n",
    "    in that map. The y timeseries is always the same 1-dimensional timeseries of\n",
    "    interest (i.e. the Response Variable). At this point, only supports the\n",
    "    correlation analysis. Once the significance is attributed, it is stored\n",
    "    in the MI map. Precursor regions are found by clustering the\n",
    "    significantly (correlating) gridcells (+ and - regions are separated)\n",
    "    and extract their spatial mean (or spatial covariance) timeseries.\n",
    "\n",
    "    *Sometimes Response Variable is also called Target Variable.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    list_of_name_path : list, optional\n",
    "        list of (name, path) tuples defining the input data.\n",
    "\n",
    "        Convention: first entry should be (name, path) of target variable (TV).\n",
    "        e.g. list_of_name_path = [('TVname', 'TVpath'), ('prec_name', 'prec_path')]\n",
    "\n",
    "        TVpath input data supports .nc/.h5 or .csv file format.\n",
    "        if using output of the clustering:\n",
    "            'TVname' should refer the name\n",
    "            you have given the timeseries on the dimesion 'cluster', i.e.\n",
    "            xrTV.sel(cluster=TVname)\n",
    "        elif using .h5 the index should contain a datetime axis.\n",
    "        elif using .csv the first columns should be [year, month, day, value]\n",
    "\n",
    "\n",
    "        prec_path input data supports only .nc\n",
    "        'prec_name' is a string/key that can be chosen freely, does not have\n",
    "        to refer to the variable in the .nc file.\n",
    "        Each prec_path .nc file should contain only a single variable\n",
    "        of format (time, lat, lon).\n",
    "    list_for_EOFS : list, optional\n",
    "        list of EOF classes, see docs EOF?\n",
    "    list_import_ts : list, optional\n",
    "        Load in precursor 1-d timeseries in format:\n",
    "        [(name1, path_to_h5_file1), [(name2, path_to_h5_file2)]]\n",
    "        precursor_ts can handle the RGCPD cross-validation format.\n",
    "    start_end_TVdate : tuple, optional\n",
    "        tuple of start- and enddate for target variable in\n",
    "        format ('mm-dd', 'mm-dd').\n",
    "    tfreq : int, optional\n",
    "        The default is 10, if using time_mean_periods, tfreq should be None.\n",
    "    start_end_date : tuple, optional\n",
    "        tuple of start- and enddate for data to load in\n",
    "        format ('mm-dd', 'mm-dd'). default is ('01-01' - '12-31')\n",
    "    start_end_year : tuple, optional\n",
    "        default is to load all years\n",
    "    path_outmain : [str, bool], optional\n",
    "        Root folder for output. If None, default is your\n",
    "        '/users/{username}/Download' path.\n",
    "    append_pathsub: str, optional\n",
    "        The first subfolder will be created below path_outmain, to store\n",
    "        output data & figures. The append_pathsub1 argument allows you to\n",
    "        manually add some hash or string refering to some experiment.\n",
    "    save : bool, optional\n",
    "        If you want to save figures, data and text output automatically.\n",
    "    verbosity : int, optional\n",
    "        Regulate the amount of feedback given by the code.\n",
    "        The default is 1.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    initialization of the RGCPD class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_test = './data' # path of test data\n",
    "# define input by filling list_of_name_path: \n",
    "# format list_of_name_path = [('TVname', 'TVpath'), ('prec_name', 'prec_path')]\n",
    "list_of_name_path = [(3, os.path.join(path_test, 'tf5_nc5_dendo_80d77.nc')),\n",
    "                    ('sst', os.path.join(path_test,'sst_daily_1979-2018_5deg_Pacific_175_240E_25_50N.nc'))]\n",
    "\n",
    "# define analysis:\n",
    "list_for_MI = [BivariateMI(name='sst', \n",
    "                           alpha=.01, FDR_control=True, lags=np.array([1]), \n",
    "                           distance_eps=700, min_area_in_degrees2=5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "\u001b[0;34m\u001b[0m    \u001b[0mdistance_eps\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m400\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
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       "\u001b[0;34m\u001b[0m    \u001b[0muse_sign_pattern\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0muse_coef_wghts\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mpath_hashfile\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mhash_str\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdailytomonths\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mn_cpu\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mn_jobs_clust\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mverbosity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m      <no docstring>\n",
       "\u001b[0;31mInit docstring:\u001b[0m\n",
       "Parameters\n",
       "----------\n",
       "name : str\n",
       "    Name that links to a filepath pointing to a Netcdf4 file.\n",
       "filepath : str\n",
       "    Optionally give manual filepath to Netcdf4. Normally this is\n",
       "    automatically done by the RGCPD framework.\n",
       "func : function to apply to calculate the bivariate\n",
       "    Mutual Informaiton (MI), optional\n",
       "    The default is applying a correlation map.\n",
       "kwrgs_func : dict, optional\n",
       "    Arguments for func. The default is {}.\n",
       "alpha : float, optional\n",
       "    significance threshold\n",
       "FDR_control: bool, optional\n",
       "    Control for multiple hypothesis testing\n",
       "lags : np.ndarray, optional\n",
       "    lag w.r.t. the the target variable at which to calculate the MI.\n",
       "    The default is np.array([1]).\n",
       "lag_as_gap : bool, optional\n",
       "    Interpret the lag as days in between last day of precursor\n",
       "    aggregation window and first day of target window.\n",
       "distance_eps : int, optional\n",
       "    The maximum distance between two gridcells for one to be considered\n",
       "    as in the neighborhood of the other, only gridcells with the same\n",
       "    sign are grouped together.\n",
       "    The default is 400.\n",
       "min_area_in_degrees2 : TYPE, optional\n",
       "    The number of samples gridcells in a neighborhood for a\n",
       "    region to be considered as a core point. The parameter is\n",
       "    propotional to the average size of 1 by 1 degree gridcell.\n",
       "    The default is 400.\n",
       "group_split : str, optional\n",
       "    If True, then region labels will be equal between different train test splits.\n",
       "    If False, splits will clustered separately.\n",
       "    The default is 'together'.\n",
       "calc_ts : str, optional\n",
       "    Choose 'region mean' or 'pattern cov'. If 'region mean', a\n",
       "    timeseries is calculated for each label. If 'pattern cov', the\n",
       "    spatial covariance of the whole pattern is calculated.\n",
       "    The default is 'region_mean'.\n",
       "selbox : tuple, optional\n",
       "    has format of (lon_min, lon_max, lat_min, lat_max)\n",
       "use_sign_pattern : bool, optional\n",
       "    When calculating spatial covariance, do not use original pattern\n",
       "    but focus on the sign of each region. Used for quantifying Rossby\n",
       "    waves.\n",
       "use_coef_wghts : bool, optional\n",
       "    When True, using (corr) coefficient values as weights when calculating\n",
       "    spatial mean. (will always be area weighted).\n",
       "dailytomonths : bool, optional\n",
       "    When True, the daily input data will be aggregated to monthly data,\n",
       "    subsequently, the pre-processing steps are performed (detrend/anomaly).\n",
       "n_cpu : int, optional\n",
       "    Calculate different train-test splits in parallel using Joblib.\n",
       "n_jobs_clust : int, optional\n",
       "    Perform DBSCAN clustering calculation in parallel. Beware that for\n",
       "    large memory precursor fields with many precursor regions, DBSCAN\n",
       "    can become memory demanding. If all cpu's are used, there may not be\n",
       "    sufficient working memory for each cpu left.\n",
       "verbosity : int, optional\n",
       "    Not used atm. The default is 1.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "Initialization of the BivariateMI class\n",
       "\u001b[0;31mFile:\u001b[0m           ~/RGCPD/RGCPD/class_BivariateMI.py\n",
       "\u001b[0;31mType:\u001b[0m           type\n",
       "\u001b[0;31mSubclasses:\u001b[0m     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "?BivariateMI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time-handling subseasonal mode (DJF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "rg = RGCPD(list_of_name_path=list_of_name_path,\n",
    "           list_for_MI=list_for_MI,\n",
    "           tfreq=10, # <- subseasonal forecasting mode, all data will be aggregated to 10-day means\n",
    "           start_end_TVdate=('02-28', '12-01'), # <- defining DJF target period \n",
    "           start_end_date=('02-28', '07-01'), # <- Loading subset of data for computational efficiency\n",
    "           path_outmain=path_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "image/png": {
       "height": 200,
       "width": 400
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename = \"./data/subseasonal_mode_DJF.png\", width = 400, height = 200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time-handling subseasonal mode (JJA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "rg = RGCPD(list_of_name_path=list_of_name_path,\n",
    "           list_for_MI=list_for_MI,\n",
    "           tfreq=10, # <- subseasonal forecasting mode, all data will be aggregated to 10-day means\n",
    "           start_end_TVdate=('06-01', '08-31'), # <- defining target period \n",
    "           path_outmain=path_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CIiACIiACIiACIiACPUZASn6PXVBVRwREQAREQAREQAREQASk5OseEAEREAEREAEREAEREIEeIzBuj9VH1REBERABERABERABEWgTga+//tpcfPHFplKpuDPOM888ZrXVVmvT2bv3NB9//LG54oorkgoMGzbMLLXUUsnvIlZaUvL32GMPc9111xVx/qo89t9/f3PwwQe7bWeeeaY54ogjqvbPMMMMZvTo0VXb9CNO4LTTTjO//e1vq3bOOuus5qGHHqraph/1CWyyySbmvvvuq0q40047meOOO65qG/fm2muvXbWNHxw799xzV23X/V2Fo6t/DBkyxPznP/+pqsPvfvc7s+uuu1Zt0w8R6AYCP/jBD8zNN99cVdTNNtvM8M6SlEOAb8mJJ55Ylfm8885r/v3vf1dt69QflN3rbpQRhb8Veffdd83CCy9cc+hPfvITc8ghh9Rsb3TDL37xC3PhhRfWJH/11VfN+OOPX7M9b8MHH3xgFl10UUPDJi1XXnmlWXnlldObM39PNtlk5thjjzXPPfecSzPzzDObp59+2rC9KGlJyf/oo4/MO++8U1QZknw+/fTTZP2zzz6rOcc444yT7NdKPgFYpq/RhBNOmH+Q9kYJfPjhhzUsP/nkk5q0PPRp5iT65ptvatLq/q5B0rUb3nvvPcOLP5TPP/88/Kl1EegaAjRY0+8xvvmS8gjEvtdTTz11eScsMOdXXnnFHHXUUUmO0047rdlyyy2T382sfPfddzX3Hsf/97//bSabmrR8r9P3dE2iBjf85S9/Ma+//no09VlnndWUkj/WWGOZvfbay/z0pz91+b3xxhvm17/+tTnppJOi+beyUT75rVDTMSIgAiIgAiIgAiLQ5wT2228/g9HKCz2YE0wwgf/Zc8vzzjsvs05XXXWVef/99zP3x3bsvPPOZqKJJkp24YXx2GOPJb8HuiIlf6AEdbwIiIAIiIAIiIAI9BmBW2+9tcp1e+yxxzZ77rlnz1J48MEHc13Gv/zyy6hbUB4Qemy23nrrJAk9/z/60Y+S3wNdacldZ8455zSLLbZY5rnpuqYLJy34mE066aTpzcnvmWaaKVnXigiIgAiIgAiIgAiIQGcSwJ88lHXXXbdm/Fm4v9vX86z4vm7nnHOOOfDAA/3PhpYo9RdccEGS9t577zV33nlnIYOXW1LyubDpi5uUzq78+c9/jg46o/Krr756mDRznYEW6dYM/ksSEegFArq/e+Eqqg4iIAIi0J8EHn30UYMlP5Ttt98+/NlT64ybuOyyy+rW6ZlnnjF33XWXWXXVVeum9QmWXnppM9988yUDcNl+6qmnFqLkd6y7zrjjjuv8lPBV8n8aOOpvCS27nYDu726/giq/CIiACPQvgXQ0IL5pG2ywQc8CIdQlIS9Doc4TTzxxuMmtn3322TXb6m3YeOONq5Jcf/315uWXX67a1sqPliz5rZyo2WPefPPNmgoS6miJJZaoyoqIJumwkEAnxJEXQhJdc8015sUXXzRclJVWWsmNgJ5tttl8kqol7kb33HOPufvuu13UDEJPcgzhEfE5a0YY1T1q1CjzyCOPGFq+00wzjSEWKu5OhN6jPIMlb731lhk+fLj7Y+AM8VlpUS600EKm2UhGL730kuteYnQ4f0QcIQwU/mbTTz+968FZfPHFTb3emAceeCCJtQsX0i+77LIJInheffXVbmAK5Sc05ZJLLun+uE7NCqPkH374YXcP4WJGGZdffnl3jcYbb7xms2s4faP3NxlSRkJ9hcK9O8sssySbqMMNN9zgnhmYUQ+4cK81OwiKFxn368iRI53/4eSTT24WWGABs9VWW5npppvOnZP7JR3ONv3cJYVrcYV7gdBiXBdedlhSCKM744wzGlz71ltvPUMkh3rCsfAOBbfBWKg2nwbe6QgKrdaPaCXUhVCq1IVrwnuMa5TnvujLEi55rnw+5PXaa6+5PGDCH3XiXVXvOQvz9Os8T1x37qVnn33WEOuasvI3p3XRbEWKei+E5y6TAechkgfP0vPPP++eO/xshw4d6r4pfFe4BxsR3lVPPPFEVdIpppjCLLjggsk27jMG640ZM8YpEFw/7g2e3SmnnDJJ18hKGawbOW8jafj2vv3221VJ559/fvd9YOMXX3xhUGpGjBjh0sGbUIR8k2LvL6Kw8P7hG833H1bcr0R1adXtl3uf76F/9smH680f779mQy36yhKZiGeKv8cff9zgtsw3Zplllmn6+fd5hkt0IO4zfw7Y8MyiYyyyyCJRJTQ8vpV1ItWkrdpYrpu9Z1s592AdE3PVwT0Jne6iiy6qKhY6CgNw2deofP/7368Ko/rtt9+aM844I9drpqG87eQFhcuf/vQnZkSo+bv99tsbPtcJJ5xQc7x96GqOtwplTTr7gnDp7Eewsv7669fs92WzD0HlqaeeSvIk/fe+972K/UBGj7EKZcV+rJP0eSv2w1D5+c9/XrGNgmhelMEqDRU7kjovm5b32TjdNeedffbZXX725qvYm7Nmv+diu40qdnR3Q+e+//77K/bmzK2nz9fGgK3YWLW5+aZ5WUXbpf/qq68qNrRUxfbqZJbbundVbOjC3Pz9TvsBruywww6ZeVmFqWI/IC75KqusUpPOhr3yWSVLqxTXpKPutvsuSeNXGr2/SX/kkUfW5Hv00Ue7rGwDsmIVgpr9njnX3Pr3+dPWXdouwor9qEbz41rYeMMV7m3bYK1JYxWUuvk3kuBf//pXxTbsavL3dfJL+9Gt2AFLFRjkiXWNqsmL/PPExmSuOSarfrYhW5P29NNPr9iPbcWGlst8Nnj+zz333LxiJPusYls56KCDKpNMMknNuTwPv+T55f6yA7iS4/NWrHJfsQ2m3Hy5j+644468bKr2Ff1eIPMyGZC/bdhUbGSQim145bLgPmjkO8D7w18Tv7SGIk5VsfHA3b2b9a3hPWetgS5tvX9Fs95www1ryr3tttvWK0bufju3Tk2e1jLqjrHKU2Wqqaaq2Q8z3kW/+c1v3LPkT8AzE3vmSG+NZpUf//jHFb4XjQjPKJzRBfw1ii159/3yl7+s2MZII9m6NOR9/PHHVzg2lifvrz/+8Y8u7eGHH16TxjYs6p7LxqOvWCNMzbH+fHxPd99994o1kNTNq5kEVqmtOSffjoEK7yJf9nBpY/APKGu+12F+fp1vWSOCnuiPCZe2oVO57bbbovt4BzcjvK+t0aoqL2tUq7r3m8nPp8VqWrh0gpJvR0FXbKuyClh4cfy6taxUrBWlgoKW9aLxaVlal6GKnaAilxlKnbXG1D23z3ejjTZyL/3cTJvcmaXkoxDNNddcdcuGMmEndsg9K402ePh6NLq0Ibcy840p+dayXLGWj4bOY60uFWtlyMyfHdbyU0ERqldePhgnn3xypVOV/EsvvTTzAxLWzfbKVFA684SGH4218LisdWsprlhrck3aLCU477zpfXZCvJp8s8rht9seo9yGzGAo+XYiugrPtS9j3pKGirX2p1Ekv3k/ZSk19fK1Vr4kn9jKP//5z4rtaWuonDybhx56aKVenmW8F8pkABeepXrKfciaZ4pGV57Sl6XkW2tuxfYGNMS8nnJdBut2KfmXX355Zccdd2yIw7777luxls2KDTfYUHrr+hC73au20dCq17gNrznrtpe7YnsbqvKJ/bC9Fg3nbScfq/zsZz+rqVeekm97ByrbbbddzTHp8vrftveorjEkVo+sbdb3vubctucrK3nD2ztVyY9dHxpXtkfbKeEYQTxrv7SeGg3X2yeMcbU9TH53S8ueVPJtF0lDCru/GLZLq6mPKD0FWR86egOa+Vj4MlhXkwoPblESU/KxDmE99Oest4QjN3FMnnzyyZbq6c9pZ1SMZVtj9eRjuuqqqzZcZvLPa/WjzDbS+PPlZEkZwt+sD7YlHyY0QtLlyvrNPUndswQLY9axse08A+ntA1XyUbTSeTb6m/q9+OKL0eoNhpIfu2fy6mJnFI2WnefPdr23zAWrZpbYLuWW8s1ToMp4L5TJADZYW/OuTd4+63KV2XsYU/JRtuiRzsszvY+erZiUwZrztEvJt24rTXFo9jvw17/+NYbNbaNXaI455mjq/P668GzbCZEy82aHdTVqKu/Y+yJLycfi24wR0ZebXhGMMwMVeiiwMPt8WWJoYftApROVfHqFYoYQev28YPwIefj1Zno/ycvOfFyTjx374E/T0rI5B3Nb8m4Qq8wYZilF8IHEl9i6PBj8p2LCxAN+xkrbtWbWWWcdYy1xZpttton6tuL/ZrtIY1kZ66ITnZ0NX3fb3edmM1tjjTVqjsW39g9/+EPN9iI3MAun/WC6LJk+eaeddjK2K9SsueaaUX9DOF5yySXRIjANd3oWOvyA8QO01ljDfsJI4TMfE8ZINCL4pTFS3Qt+zIcddphhAoks31imX0+XzR/PzHz4SKeFsRHW8uruEyb3sAprkoQypMU+belNbf0NE+LpItzj3KvMlLfiiitGx43AA/++mNx4443mlltuie1yEQKYTpy/MDJW2tc4enCTG2NhxxiMhO8nIcUYJ2Mbr25sTDpr6lfkLIHp/Jv97e8Z+/F2g9GOOOII9zzgbx0TfDipY1rw9WQsTyjc99Zdy9iGshs3YRtHbtbEmC8yEc3S4xHICx/oGG+eYf8cMAsj43PSgu+0VWDTm93vMt4LZTGgwPiAW6NAtC6258L5z6+22mruGYslwo+bd2ijYrv9k+vBt4ZxXnybuM8Z8xETmMakDNax85S1jTEPXvhu8I7Ze++9TdbYqvA7wJgkIpTxTSUqSUxiPtQ+HRM2ZQ1qxK+ca87YCe6BtPBs28azsQppepf7fd1117l3VWznCius4MpslUIT6gH+fREek/WNse5Kzv8+TMu6bbS4iIS8I/kecH+FwtgSGA9U8P23vSBV2fC95N3Ri/L3v/89Oluu7YFJqosuFRPev80IYyjSYnvr0pua+91S06DOQYPtrmMJuNaQfaCc/3BYXPsSqWkp+fRYeF944YUwecVOUxxN//vf/74qHT9ilhvypus+3cq94IILanz/cR2yjZOafFvZELPk+3riEpGWv/3tb9F6MqYhLXRFxvy2Y/72WB24Dv7cfokfeUzS7jo+Pe4KdgB11SFYpu3LviZvjrEfhKq0/KDLHz9In6df0nXOtQuFcjOmwqdJL+2EH2Fyt95On3xfHq5l2v80yzJplfSaMlNPuqB9fn7J9eU5Tgtd7Fl+4QOx5NtBgzVloIfNfvzSRXC/N91006r09FBRv1gP22BY8uGIf6VtPFWVn/cAfvoxX2w7EK/mPRFzT+BZjQluaOneHbqRYxbN2DgPekNsg68qa3xWudf9feGXuLClpaz3QlkMuLey3PborcCdwwvXjXs/5v8Mc9inJet7AEPeiemeWzvos+b6kRafbp7TUMpizTnaZcmnbjwHttEYVs29p/N6w3meQ+GbidXb35t+yTsh/d3luJg/Ocfw/WWcQHgMrp82YkxN3qTnHZQW3j+4afgy+CXX0Pvfh8fwbGb1rsfcPawxskIPu8/XLzfZZJMKbq2h4LIcs0Dj4jUQseHTa84f+x62co5OtOQzTtNz9kveq+F9Ql1jLsV8R8P3SD0m1thbcy56SdLPf718wv096a7DhcC/3rbUw7q6dRsxowaiv3CxjyE+lzGlxlrBavJebrnlavJea621atL5DbGPl7VS+90DWmYp+XR5xhQhThZ7maEApgUmDPZiwNI+++zjuiY5NktiDSUGMcckpuTzIbCt6Vhyt91fv3DJizwtBxxwQM314RhreUknTX7bXo7oMbGXWruVfF4qsWvJy8dGpagpd4w5XENufp0BtlliLebRYwai5NtoJjV58jFDmYkJH19rlaow8IlBUVmNAY4dLCXf9oLFiu622SggNfWFfXq8D4q/vyZ++atf/SozXxratkewYidSyTQYoCjEFAuMETFB0beRUGrKYWNkVyUv671QBgMKHrvnYIyfeJYQkACFzV8Lv8RtJy1ZSj4GC9w6Y2J7T2ry5hzp9GWxpkztVPKpb0wYLOrZhktc16h7Wmg4h+n8elrx5bjY/YTxB/enmPA+ZWyEzzNcpr8d/A73+3W+PVmSVfaYks84EJ+nX2JMyBrTgzHRp/NLxlQNRHAB9Hn55SmnnDKQLJNjO03J57mL6SQxl+CYngOfZgfgxgwJMV02gVZnpWeVfCKtZEksQgu+klnCYE5/M/vlD3/4w6rktLRiL/+0lSI8iJa2z88vsSwVIVlKfl4rnhvXl8MvbZjGAReHCAA+P79EgYtJ7IHyESli6fGt9HmGS+uyU5M8FmkJ38Y8YexAmK9f7wQlP60QhvWIvYjpsUhLzOrPYOq8wcv4SBN9yLPwy4Eo+bzEfD7hEisGA9NQ5puxiIT1HAwlnw90XsMjq0GY7j3ZZZddolxQKlHKeYc0a+Whlytk7NfTluWQYew+QREbiDT6XiiDAeXefPPNazhg0CFiW57Eni0YoqCEkqXk+8hYYVq/TrADfz3CZRgFzqdtZtkoa/Jsp5Kf9Z6JWYvhQW9KTLLGl2AZDYVGWsjVr8eMduFxNqxmNLJbemyWdaGqyR99I31vhHnT6I71XMSUfHorfZn9Mq/sNFBieWPsbFViBop6YxQaPVenKfmxHk+4xxqE9CjFApFgIGlGMMb5a+uXvOdblVqHM5trLwg+flliX+Q1u/D1zhLbsqrZZa0JVduIUWytqlXb+JHlL8g+28VY48eWjs2NrzH+/I382QeNbHPFWjEy9+OLmJZ0PdP7Y7/tg+piHuP/R7ze0HfNp28mX/LIEttYqPE9JG0sf+uKVZMNvpd5wngF/Ko7TfB/jPnv+XLG/LNjTKwLkz8kWdqPSxIPP9kYrNiPlsG3tEix3Z9RX1xijVsFxfmYEqMfH3EbacPYwYgGH9NOlayxEb68xEG3Fl3/M1mmrwf5xARfcMam8F4gH9ulbGw4XjcXSCx9uI25B9KCn3/sPefTxXzz0+8qnzZr2ep7oQwGlNH2dtQUdYsttqgbZ537LyaM7WpE8t5nseeWPGPPbt65WmWdl2fR+xgX5ufdSOcd+0aTJus7nXXvprnFrjn5Zl1T9iGME7AuMf/7EfxPX/PYNwbffp6vLLFeB+45ztofbo89u3k6Bt8Jzp+WZp/d8HjbMAt/uvUs/jUJu2iDVaqNNbrUlJi5G2JM0Z8YW5MW5hzJuu/SafkdY5keAxE7Lmvb4M3ElFWigraj/GWJbW3V7GIimSyxLeGsXcl2LmRMuOh5SiIPITeTFwbGMjDUK9zWGugG1/n9eUte7HnCizPr5clxMS7WSpiXpdvHC4PBKQwc5C9rQFOYkbXYhz9z1/kY5AnljjWwwmNg/OKLL4ab3DqDlfKEcnJvDOSlmJd/q/ushdtNNpZ1fOxaxtKmlUrSoHDXk0bS1MsjvZ+Bw9Y6nN5c9ZuBi/zZkKDuXmaQPAMnUXaLlPCZbCXfRvgwmZkf8O/Pkb4e1tpsbBjXmkmVfHqWTF520003uT8GjaOQ07C2PRhutvAwLeuxdxWBCmIfLn9sWllie71noqj3QhkMaDwSWCAteQqTT2tDELt3Ou/mUFD4rHtmuCm6nvc+a/S5TWdcFOt0vmX+bvYbTVmyvtONfKM5HmNcWqzfdEPvvNi9weRWocSU/HrfGI7Puyd8/ugGsWeOICHWXcYnq1kS1CMt5NPqO9O6UKaziyqmNYm6bIN1R4zeL0y4xsSMMSHIix0zVbOLGXDrGRT9QTElP9aw8unrLXtWyc9TZmNKd95LohGFNNbCBn7W9rwLwwPolfy8dM3ui908YR7WLzH8WXedh50oBSgYtluwbvowQSNMfXosx3nCh5GPdp4QYSRm+c36aIR5oYzFXq5hmnavN8KkkTLFGj7MUFxPsiyO9Y7L229d4FwUAyJPNCK8aInSdO211xrrM2vsZCwNzzBYT4lPK3CNlCdMk2e58+m4r9KWwHQDmfcAUXSwZscUFJ9XuLRdya7hYwf6uQYC1ulQYu8kO3jbMDN4M5L1TBT9XiiDQVbZs6KBhVysW6Yhukv6eqQVvvCYcD3v2W1WyS+adVjOsteb/UZTnqxjGv2exBRelPBGjo/dG9bFzc2Qy/2AxN6njX5jXAY5/4hGFHtvZd3LOVkN6HsWUziJ8NZrcv7550erREQn/poRoqdZ1+KGZmiP6Wkxg0Sj52/cnNpojh2SrtmXZbPp09WsZ0lOp8/73cpDm5ef38fHqShBUcBlg+nfsxR8XoqElsK6mJZGXqr+mLyPImkaySurEcdLup400ptRL4+i9xfBhDLFuMRe4unyD6T7MJ1X+BsXL5R2OyNtuDl3nQ8fYSTtgMnoRzB2cD0lHqV3IJK20MfyipUh5sKDlc/OsOos8818TFFoaPzYsRtVpy/qXcVHK91wLuu9UDSDLCNKuj5V4IIfhCNOS+zjnE7D77xnt5F3mc+zLNY+/7KXzX5zMUI1wydW/th1H8g15xz0qnqJvU+L+sYU9dxS1oHoGISbTkujZcNLIW3YCPPKuhZ5zwzfZ4wjsVDBPu9Yvhh7swybKNWNhvn258hb8j2xg6DzkiT7Yg252H2VHFBnpfZq1TlAu+ME6MKNCbG7Yz0HsbR+25xzzulXO3KJWxCWxbSiR7cnXVl2gKvrmvLW3n/84x/OyhpWphkmzaQNzxGu8wHG/zNd5vTv8Bi/bkfY+9WOWRbBhMpwr9lZkKvqZQeZVf2O/YhZrGLpWtlmZ941/OFWggWEOP42vGAyL0BWnjQ4bYQsY2eCzEqSbK+nxGfNs5BkUGcl1qWdPiTGeZ555kknc7/pXWEeDRu61zWs7Uy1rgfNDhKNpvcb+QDagatVLjqxdxXbmN+iWSF/nnuk7PdCkQzIi2co3dCKuVukmdCDFLu+eWNkwjyKeHbLZh2Wt5fWY24xNIZRUusZwWL3Bhb8sOFgJ/gyjJcJpahvTOy55Tw2yIhhHFUzQlz7VoVnJ20Iij0P5M8cH1i9edfxHfXvVZT9mMECt8GYhA0p9tsoQ27+HPJFucfQyPsL18aYxPLNU5wZB1bvGxE7T9425jdg7hFctPMENmmxEZTSmxr+LSW/YVT5CbMeQBta0tjR1fkH5+zlA2rDb+Wk+L9dDAhph+Bfln5x8aKz0SSig0Fj/rwDtci0Uk8UqHS58e/OE/ydsVj2qsQalCjXvBQZEBYTrCJYlssWnhte5vzxcbCzNboxH3YWQaf0pxU0yoPVOq3kx+61LJ9KX6f0R8xvb3SZbjilj+OjFHMdiLkEhMdi/bSz47o/tnOtmCSMP9x6YtYsLL7c936QY+xdxTMa63ELz11vvV3vhSIYcE9ghEhfg9GjR9erpslyy2lUya97ggYStIt1A0XpqiS4yKWF9wiT++UF6+CYmAU6fc1jjfRnnnnG9TDmKXexBkS6nPTyYazimxQKA7n32GOPcFOp6/49Ep4kS8nH6k+QhLSgnMeU/Kwe0LSSz9il9ISkMaOJP28s33SePi3LLFedME2z67yrGYAbTioZyyPW85M3fiWWR7hNSn5IYwDrsQ8n2dnQR5lKPjcqH4yhQ4e6mWFjyggftE6ayZM6oVCkhQEn6ReeT1PP2ujTlb3EypJ+MTA7IV3vWd2BfEx7Wbj30sIAL/zbGQgbE2YUTitHsXTNbsNywjPBR5E/Bob5WSGxujA7KH/MMIpVyM5LUaPU4o+eltjHJN3YC4+huzT2QQ/T1FvnGcGHlnsuJldddVVi1Qr3x5R8GhyeCdxtnPykd5CGEH+77babUyS4ZszqnBa42Dky3ObYOWgckHfWbKM0nnhOuF98D136HGW+F4pmQNmZAT39MSeAANbHvAg4DHRMC+/u2LOUTlfU7zJZF1XGTsyH90c62AXl5Jra8KWZRbYhb6PKavqbF3veeVfRE5kVJQodgVlkGxGe3bQBgeOzlHyeWdxOuDeJ5ud73Ro5V1YaLPlpyVLyafTEeGOQiPUmxGb95lzpnorYIGgU6FiPDAxifNN5+jphwIo15G0o9dzgBP54lryvMEalhRlw6yn5MUt+rGGVzjvzt/2gFS6dMOOtveCZ9bIDbSoWSNXfMccck5k+Fl99hx12qElvPxpVeXIOe5NHJ+/gYCZd8eWwSmaF2NfEnraWhZq8m90Qi5NvI37kZkNseV8ev7QKUs0xsXkDsmJmWwtsdAZAa/mvyZcN9mNZUwZiPOcJ8d99ef3SdtvVHGK7y2rSkd6GT6tJywb7oEZnDOSYwY6Tz32VJ41eS64PE8d5bn5prRxusqD0Oay/fHTWT45rNU4+97v1wa9YN4aqcnC/Wot7ugjJb2Z59OX1SzsQPNnvV2yDpSYd6WOzInMMk035/MJlVv2shS2a3jZQouW3VvOKVSJrjuEdYN0wfLHd3AA8J2EZWLeRhZI06RWrANSk5xiraCRJuebWMlSTLvZO4yCujzViJOm5X5hQJ/2MlfFeYH6EMhhQL74Rabb8pm7WmkaSGqE8sWNiE2hlxcm3FseafP0G4uHH8rcuID6JW5bB2p+gXXHybbQRf8qa5Z///OcaDkxYlSUjRoyoSQ9H29CuOYTzxhgzR0FMbK9mJTbRpVWYa+KlMyt7+j3GuZi40AaHqMneKqUV5oGJlccqoTXpY9915ufhvolJqItRLmsQcDP1Mi9Jq/Kzn/2spry2FzAzO97j6fpZt6mKtWxXHWOt7RXbSKpJi07AOyuUsF5h3sxSnxYbWrgmT47JmvCROZDCPP163rw06XPy3eIb6o/1S+5ha2BKJ09+8661ht2q4/htDWBJmmZXenbgrYXadolFBcEySXSL0OWDrno7G6aL/+0LSWsTXz4sljGLvk/XCctYC9hOUmLSXY60qvEXxAqZlpgLTzpN0b/tDMPOmpHOl/jixEkOByPRmmdOAVrkvSxEq+AapYVoRfZj71xfsNzTm4SbCLGi093F6WOb/c39joXNvuCqDsUChtU+JtxTjPVIi/2YpjfVWIF8AgaFh71M1IuINDFLrT+mmaWdeM7grhdahYjIgiUHa3Fa8CkNo/JgaY4N+saSH+tJsS//aK8fLgo++gfn5JoTXjMtvJOIvR+ek/cSacMxGLhyYXFL9wiU8V4oiwF1X2WVVaLWd+pmJ8lzYx+wqvG+xq0Pt8ltttkmjc1dsyxf4JrEBW0og3VBRev4bGLvOwrNeDJ8prnWXHOuPe8Y7oV0DzDpeQ7TYWe5LoyBSQuuhoxVwy3IC8/weuutZ2655Ra/qe6SsuMaGwrfWd7LaQs/+oRVZJOkvF9xGWGQavg+SBI0uBLrkciy5JOlbVTV5Mx7d+WVV3Z+9PSmwRLO9H6mhXdoOqqSbYib2ABgO2mfY8r3it5oAg9kzYGA90FabGPC2IZ8erOhR6KZuWHsjOLRdwW91XkDcKl/Wjdiboh640VqChxuaLZV0Ej6rFZW3myr6XyZCtiWs+rPdhOnk7nZCdPp+D0YlnwKZ2/cqjL7smH9okfAKprR2UJJZ2/kin1YaurYyoZYi78oS34sb8qPRfOII46oWGWhwkxxWJt9/WNLWu5pKdOSz7mYgThWFr+NMjNNuP+dtewVSz5MsB5nWaOz6p+2NpAuy9LNOeoJZcCSHTsfVm8sNFiOrTtKxb6cK1hE0mltl2bFup3UnMoqqpl5267kip2YqmInPYvOWB2eI6t+jbCzXdxV1vAwX9a552LWY2azTKflN/yZCdg2SNy06VYBjfaYkdYq7jVMsE5a/95o3vR0br311m5GWPuxiqaxrlQ1eZb1XiiLARWwDbBKVh1hx/2Rt580V1xxRQ0LNpRpyS+LNeXudUs+dQx70bmG6T+uOdc+vd3/tv77FazwMbEKrPuW+7TpZb13gU9vGwyx7DN7Gnkn2oar8wawc2Vklt0O2o/m2+hGa7CsYUNPR5bYxkwuD1/f2JJrYA0i0axjM5nH8ohtswaraJ62wRHlxnenWbENw2he1tWoYhuR0exsjP2aY6zxJ5q20Y34cRYu/azk20FumUp87Gbz2+hyY2ruoiT2EShKyUdBsJaAmpvR1yW9RHGnazO9PdYQK1vJh2/sI5Yum/+NMmX9nWvK3ktKPky4FjHF2XMIl9a3u4JSGW5jPUsJJv9GxE4+kqmMp8+V/m2tOpU8I0Ksizmdh//NB95aghquX0zJ//GPf1xzvM8/tsSVLCZ2kHNT92s6b1wBrGU+lnXF+upW4JY+pt5v2zNQsT0pNXmW9V4okwGVsDOF123gxZiggNAAyZIylfyyWFOX2PvRWkSzqtnQduszXnOf4TaTJWW663BO3CloqMaua71tNlhBxY7ZySq62x4zUublu88++9SUJUvJt2OmnDKfl1/WPt6DRYgdi1BVXvSXtEtNeB7cobPKlLc9ZqDw+dqelgqGnbzjY/vQXazXgc+maolbVeyYmNtX1YEZP7IaW1nfKuqbPr+NGJeRe2Ob5a5jiRYpDLxhpjQGuTQqDIZhIN6mm27a6CGDmo7uwosuuig6Q266YPBgkFisbrFusfTxZfxmIJJ92dXNmtH3uCHRNZgW+4FPb+rq3wzK5L61DcHceuCuYHtDotd+oIO6rDXddY9nRfXJKhiDkqxSkDugicGosW7mdJ4MKrMNHrPRRhuldzX1m1CXWd3EYUYMCuZ+ZCKwmNiGl3s34ErQrNDNzTOWFbKRLn5CjjbDm/sDPgz2TUtZ74UyGVAHojHhTtHMOxt3BwYPnnjiiWkMbfldFuu2FL4DTmIb8i4SF5G7cBdsVJiRmwhM1qCRewhuP7bxWONmkj6I7wiuKrHvUdY3hiARuBHh/tOMMDM47ixFCK5uoeAylJ6PI9wPD9sbm/kuCtP69aOPPjoaRMDv590JB2b4blS8PhILlEJwAt4DacEFFHedVoTJQmOSFdAj7caJS5IPPhHLp6FtjbUFmkvVz5Z8Twrr03HHHVexPns1LTN7Ydw2ustxQ6A7q2gp05Lvy0preOONN47WD+smA1r9wMlYNxSD/9IDStphyfflp+cEawkWOX9NWGKV2HzzzSvWf9oljVmV9tprL59NsrQRGKry8XnGrJ4xS0/MHY3McX3yefklbkV50ujA23QeuIycccYZzlLE4Ch6YLBc4SJjlZokuf1g1JQJC38RgkXI+sZX6Nb09Y0tcYGzynt0QFusHNxrWEpig06xaFvFvmLH0LhD6ZFLnzOrpyJmyff3vY3dX7G+pjV5wdX65kcHBsbKzraHHnqowuBY7s902cLfWEibGSTGAHMsibEB7D5f7gEGMPt6ZZWR7WW8F/z5ymJA/lhIbfQL53KZfifAgW08d9YvOupa5cvol2Va8v05ymDdD5Z8z48lg5q5/7PcNBlAaQ0EFTtfR3hYQ+u4g3Fs7JnFxcUP/rdjXmqeadvorHsOaxx092tssC/3LNtxveO5KVLodfXvBr+0Daa6p6AcdqxbzTfX5wGn7bffvqnyomvhTmPHM9aUyefLgF4CFuT1NmS5BfI9bFVwv45de3rNee+GQo+rbbxX1YFncaAyFhlYEJISCTD4lsE8DE5hUAVWIPvRNISBolXe7cLELAyEZFAPVkHr42xiLeVOraft+nZx3+0H04UQpOU+oJBVnVrRAsvFIEQmZwoFy3FsMGyYppl1Xk2EdiT+MX/cZ0wKwrPDH4NUs6xdeeexHwVndWIAGs8j1mnmmAgHveYd38o+BtxhAWRwLwOpiMmNhboVsYq2CyHquTBQ1vrROyYsW32nwJvBegwOZsA5Fk4484clK6tHIKsOZb4XymLg68LAQN5nDP7mHsFyS7hEevc6Ucpk3Yn1LaNMWKMZJM81JwjDjDPO6EKpMsC8lfdMWEbuIcI4Yqkl5jnvgFgYyPCYZtaZoZUwnzy3fM8oO88tlmtrhGgmq4bS8q6gNy8cKEugikbnTyFMMzoDehFlJjgA72GYtFpeysRgZvLk2bWuti5P8qWszfTYNASh4EQM+Gcwcih2vI8L3BJua3ZdSn6zxJReBHqIAA0bXA5QDv0fH4d6CjQuDrh6hLL33nsba/UIN2ldBERABESgBwlYTwU3m62vGkYAJp1iwi5J8wQOOeQQYz0wkgNp7GDkatUY5DPSZFiehJYi0IcEsHbEFHOswVi10mESQUQ4Rdt1XUOLHhyJCIiACIhA7xMgVCjjCQgLiRCik/E61oW39ytfQg3T31TGvw1UwaeYjY84KaFSylIERGBwCVi/+2jMZOKj45ITxpGnO5spynmJp2flo7FArHuJCIiACIhA7xPApdWOXauqqJ24suq3fjRGwE6iVjXPAb0idpxIYwfXSSUlvw4g7RaBXifw/e9/P1pFoujgK0kkD/xIiZ7D5C1Y+NPCpCv4PkpEQAREQAT6gwCTFdqBpUll+WaEE38mO7SSS8AO+K/aTxSnZqIGVR2c+iGf/BQQ/RSBfiPAoDAG/DBwqxXBh5/wY60O+GzlnDpGBERABERg8AkQHpMZZr3YmPiGcJ2SxggwCJlgLIxnQBjTwIDmooJ/yJLf2HVQKhHoWQK42lx77bXGhutqKooEESfwG7zjjjuk4Pfs3aGKiYAIiEA2gcMPP9xFPfMpbPhjg2unpDECzPHiFXyOYABuUQo++cmSDwWJCIiAI0D4Mawyw4cPdyP7CceWFvz4CTOKiw5h0yQiIAIiIAL9S+Css84yRFfzcskllxgb797/1DKDwHfffWfsXD1JKFJ6xfHPH+jEkuHppOSHNLQuAiJQRQBXHgbffvLJJy5GOPMgNDNDalVm+iECIiACItBzBIiswxwbXhjD1U1z5fhyt3vJnC2PPfZYclpCV+O6U6RIyS+SpvISAREQAREQAREQAREQgQ4gIJ/8DrgIKoIIiIAIiIAIiIAIiIAIFElASn6RNJWXCIiACIiACIiACIiACHQAASn5HXARVAQREAEREAEREAEREAERKJKAlPwiaSovERABERABERABERABEegAAlLyO+AiqAgiIAIiIAIiIAIiIAIiUCQBKflF0lReIiACIiACIiACIiACItABBKTkd8BFUBFEQAREQAREQAREQAREoEgCUvKLpKm8REAEREAEREAEREAERKADCEjJ74CLoCKIgAiIgAiIgAiIgAiIQJEEpOQXSVN5iYAIiIAIiIAIiIAIiEAHEJCS3wEXQUUQAREQAREQAREQAREQgSIJSMkvkqbyEgEREAEREAEREAEREIEOICAlvwMugoogAiIgAiIgAiIgAiIgAkUSkJJfJE3lJQIiIAIiIAIiIAIiIAIdQEBKfgdcBBVBBERABERABERABERABIokICW/SJrKSwREQAREQAREQAREQAQ6gICU/A64CCqCCIiACIiACIiACIiACBRJQEp+kTSVlwiIgAiIgAiIgAiIgAh0AAEp+R1wEVQEERABERABERABERABESiSgJT8ImkqLxEQAREQAREQAREQARHoAAJS8jvgIqgIIiACIiACIiACIiACIlAkASn5RdJUXiIgAiIgAiIgAiIgAiLQAQSk5HfARVARREAEREAEREAEREAERKBIAlLyi6SpvERABERABERABERABESgAwhIye+Ai6AiiIAIiIAIiIAIiIAIiECRBKTkF0lTeYmACIiACIiACIiACIhABxCQkt8BF0FFEAEREAEREAEREAEREIEiCUjJL5Km8hIBERABERABERABERCBDiAgJb8DLoKKIAIiIAIiIAIiIAIiIAJFEpCSXyRN5SUCIiACIiACIiACIiACHUBASn4HXAQVQQREQAREQAREQAREQASKJCAlv0iayksEREAEREAEREAEREAEOoCAlPwOuAgqggiIgAiIgAiIgAiIgAgUSUBKfpE0lZcIiIAIiIAIiIAIiIAIdAABKfkdcBFUBBEQAREQAREQAREQAREokoCU/CJpKi8REAEREAEREAEREAER6AACUvI74CKoCCIgAiIgAiIgAiIgAiJQJAEp+UXSVF4iIAIiIAIiIAIiIAIi0AEEpOR3wEVQEURABERABERABERABESgSAJS8oukqbxEQAREQAREQAREQAREoAMISMnvgIugIoiACIiACIiACIiACIhAkQSk5BdJU3mJgAiIgAiIgAiIgAiIQAcQkJLfARdBRRABERABERABERABERCBIgmMW2RmyksEREAEREAEOpHA8ccfbz766COz9957m1lmmaWpIr700ktm1KhR5qmnnjLTTTedmW+++cwKK6xgxhtvvNx83n77bfPtt9/mpsnaOfbYY5sZZ5wxa7fb3mq5cjNtYOd7771nHnroIfPss8+61Msvv7xZbLHFzLjjDlyluOKKK8yjjz5q1ltvPbPSSis1UJrWkzz88MPm6aefNi+//LJZYIEF3DWdYYYZWs/w/x85cuRIc+2115r555/f7LjjjgPOTxmIQMsEKhIREAEREAER6GEC5557bsV+JN3fiBEjGq7p8OHDK1ahT471ebCcddZZKyeddFLlq6++ysxvttlmix4b5pO1Ps0002TmO9ByZWZcZ8fHH39cOfTQQysTTzxxTb0mmWSSyqabblr5z3/+UyeX7N2PP/54Zfzxx3d5H3fccdkJB7jnrrvuqiy77LI1deBazDPPPJXLLrus5TN89tlnlXnnndflvf7667ecjw4UgSIIyF3HPtUSERABERCB3iRgFWKz//77N125ww47zKy88srmueeeS44da6yxkvXXXnvNHHjggWbDDTc0//3vf5PtZa8MVrk+//xzs84665ijjz7aWEU2qaZn8umnn5prrrnGrL766ubdd99N9je68s4775gtttjC2EZTo4e0lO6qq65yZXzggQeS430d2DBmzBiz3XbbmXPOOSfZ3+gKvTY77bSTef755xs9ROlEoFQCUvJLxavMRUAEREAEBovAfffdZ773ve8ZFNBm5OKLL3bKrHe1WWqppcyNN95o3n//fafAXnfddc4VgzxvvvlmYy225rvvvqs5xRJLLGGWW265hv6sZbnqeBoYaSmqXOl8G/m9ww47mPvvv98lnXbaac0ll1xiXn/9dcfjyiuvNAsttJDb98gjjzg3m08++aSRbF2at956y6y11lrOdabhg1pI+OCDDxrq4a/VD37wA8M9QqMF96Nf/vKXBoWf/Xvuuac5++yzGz4LjRMUfNyNJCLQMQSK6A5QHiIgAiIgAiLQKQSs1bly0EEHVayPeI1LRj13HavIV6zffXKcVQorX375ZU3VcF1ZZZVVknTW8luTppkN1kKe5DVkyJCKHT9QdfhglYtC3H333UnZZppppor1Y68qGz+sn35lySWXTNKdfvrpNWliGy666KLK1FNPnRxnlSO3Xoa7zhprrJGcx/bCxIpTOf/88yt2PIRLR13z3LF8BrbxUxk6dGiSt6+D3HU8IS0Hi4AZrBPrvCIgAiIgAiJQNIF77723gpLsFS2W1jqb/K6n5B988MFJWjtAt2It0plFfPLJJ5OGhLVuN6QQxjK7/fbbK+OMM4477+STTx5VogejXL6stjckYWKt235zzdK6qST1sJb9mv3hhldeeaUS5pu+TkUr+dY9J6nDHHPMkXutrEU+SXv55ZeHxa5atz1EFRoLvlGQroOU/Cpc+jEIBOSuY59KiQiIgAiIQPcT+PWvf+386J955hlXmQknnNCcccYZBnebRoWIK17sIFMz6aST+p81ywUXXNBstNFGbjsRZ2wDoiZNvQ1E4Nl2222TKDzWAm5sI6XmsHaXyxeAiEQ33XST+wlPXFyyxA5aNeuuu67bbRtA5s4774wm/fvf/26s5du5QPkEe+21l9lnn338z8KXdjBtkufOO++cGxkpLAf3T0zw3V900UWNHXyduP8QDejMM8+MJdc2ERgUAlLyBwW7TioCIiACIlA0AZRRayxz2aJE4oNNyMxwYGW9c6Kcell66aX9aubS+6KT4NZbb81Ml7Xj5z//uUHRR6w7SaYS3e5y+fIyQNUzJWzoRBNN5HdFlwy89fK3v/3Nr1YtbW+L8T77U001lWEwLMqxjdpTla7IH348AXmuueaauVkzlsL2qLg0NhKPYVBwWggfiqKP2F4YQwOTRs3ss8+eTqrfIjBoBKTkDxp6nVgEREAERKBoAliTzzvvPMMA0IUXXrip7Ikg8+qrrybHxCzqyc7/v2JdP5JNoSKZbMxZQdllMC0ywQQTZFqB212usMhhFJphw4aFu6LriyyySLL9iSeeSNbTK1NMMYX51a9+5SLRbLbZZundhf7++uuv3f3gM61XD5T2sPGWVQ/SEQef/UcccYRT9v05tBSBTiAw8JkrOqEWKoMIiIAIiEDfE7B+3C6yC8pXK4I7io33nkTjwVVlsskmy83qzTffTPYTfadRIXJP6BZCZBcmT4pJO8uVPr+f8Irtc845Z3p3ze/Qks1EUzHZaqutzCGHHGJQ9Nsh1v/ffPHFF+5U9Bx4K33euamHb7RRj7CHguNw1cItjEalRAQ6lYAs+Z16ZVQuERABERCBpgisuuqqA7Km4tbDzKdeQhcZvy29DJXgDz/8ML0787eNKmNGjx7t9tuJrwxuO1nSznKly2CjCCWbbLSZZD1rJZwxNqvRY6PwtE3Bp5zN1oFj6tWDBo8UfEhJOpmAlPxOvjoqmwiIgAiIQFsJhG4ap512Wu6533jjDUPMfC92tle/mrvEx51eBy9M1pU3wJd07SiXL0+4pDfDSz1/fNKFaeitsOFH/eGDtmy2Dul6NDvPwqBVVCcWgRQBKfkpIPopAiIgAiLQvwR23333ZKDuP/7xD+ffH6OBn/d+++1XNdstvvONyPXXX59M/ITLyr777lv3sHaUK1aI0AoeKvCxtGxjbEEo4ey44fZ2rjdbB8qGi5SXTqiDL4uWItAMASn5zdBSWhEQAREQgZ4mwEyzO++8c1JHlOtddtnFjBo1yjCrKVFhiKKy4ooruqgwc801V5K2EV9vEh977LHJMSj4jfimt6NcSaGCFR9ZJ9jU1Gqr4yOaOkmdxK3UITymE+pQp4raLQJRAhp4G8WijSIgAiIgAv1K4PjjjzdElfE++RdccIHhb7zxxjPffPNNElKS6D1XXnll4sc/5ZRT1kX2+OOPm/vuu8+ls5MouRCfdQ/6/wmKKJedsMqMHDky95SMEVh77bVdmnDgsR+8mndwmIaxBPXckPLyytqHZf6GG27I2p1s33LLLd0YjWbrQAZhPRptvCUn1ooIdAgBKfkdciFUDBEQAREQgc4ggJL70EMPmV/84hcm9MvHRccLMeNxu3n99df9poYs8jQKvKy22mpm5pln9j/rLoso180331wV1Sd2UiYP80p+qKQ34o4UpkG5piFTtLz22mtuArF6+TJRGdGSmq0D+Yb1aKSnpV5ZtF8EBoOAlPzBoK5zioAIiIAIdDQBfLJPPfVUs8ceezjLO0o/s9oSVQXlkUg+yPDhw5N6TDfddMl61kqo5G+33XZZyTK3l1WurBOGUWbCcKFZ6cM0naIcN1sH6taJ9chiru0ikEVASn4WGW0XAREQARHoewK45PCHb35MXnzxxWTzsssum6zHVp566ik3cRL7xh9/fLP55pvHkjW0rdVyEfZxiy22yD1HGBoyjOpDvPl6EqYhlnwZgvtMvTpw3nHH/Z+KQ8x7P//BBx984OZB4HeetKMeeefXPhEogoCU/CIoKg8REAEREIG+JHDttdcm9V5ppZWS9djKv/71r2TzKqusYhrx4U8OaHIlq1zrrruu4a9RGTp0aJKUwcf1hJmGvdRr9Ph0zS5nnXVWc8UVVzR8GGMDaKw8+OCD7hjqwcDpLMEty89yyziMxRdfPCuptotARxMo3lmuo6urwomACIiACIhANgEmqcJXHiv0X//61+yEds+YMWNcpB0S4UazzDLL5KZnMK+XRRdd1K82tCyzXHkFIKqPDyeJa1I4IDV23G233ZZsXm655ZL1wV7xYwwoR1jGWLkYGO3DZi622GJJ/WNptU0EOpmAlPxOvjoqmwiIgAiIQFsJYF2/6667XBz7etZifPZ9qMU999yzaiKoWKHvv//+ZDPuNs1ImeXKKweDZ9dbbz2XBMX33HPPzUzODL6EF0UYULzWWmu59U74t9VWWyXFOPvss3MbK6ecckqSdqeddkrWtSIC3UZASn63XTGVVwREQAREoDQCa6yxhvOX5wS4vNx6663Rc51wwgluYC47J554YnPwwQdH0/mNb7/9tnnppZf8TxO6wSQbc1bKKlfOKZNdzMjrhXrGmODDzhgD3+g58MADE47+2MFcDhs2zPXQUAZmKt56662js/H+7ne/M1dffbUrKgOpd91118Ests4tAgMiICV/QPh0sAiIgAiIQC8RINzib3/7W1clFNZNN93UoPgR3x5F9pJLLnHhG3/+85+7NPh7n3766SaM4BLjgWuPF45pVskvq1y+THlLIgn5SECffvqp2XDDDc2RRx5pRowYYZ599llz1llnGdx6iMGPoFDvtddeeVkOyj6uEz72COFP11lnHXPZZZe568p4CRT6Qw45JCnbSSedVLd3JkmsFRHoRAL2JSYRAREQAREQgZ4lYH3lK/b76/6sYlq3nt99913FKvfJMf7Y9NIq6xXr+lE3PxLY0JlJfnaW3IaOSScqo1zpc2T9tr74FasEJ3VIs/C/bSSbip07ICub3O12XoIk/+OOOy43bas777nnnsqMM86YnMeXO7086qijWjqFnaQryXv99ddvKQ8dJAJFEZAl3z7ZEhEQAREQARHwBLC0X3XVVeaCCy4wM800k9+cLNmPhf/hhx92cfSTHTkrb731VrK3WX98f2AZ5fJ511tOMMEE5vzzzzdnnnmmIbpNWth/wAEHGKLrNDPBVzqfsn8TAYk5D4gwRJnTwoDoG2+80Rx22GHpXfotAl1HYCxaC11XahVYBERABERABNpA4JtvvjEvvPCCeeaZZ8yHH35oiCE///zzm0YmviqzeINdrnfeeceMHDnSMZlvvvnMkCFDGprxt0wmzebtQ2U+9thjZtpppzXUg+tLY0oiAr1AQEp+L1xF1UEEREAEREAEREAEREAEAgJy1wlgaFUEREAEREAEREAEREAEeoGAlPxeuIqqgwiIgAiIgAiIgAiIgAgEBKTkBzC0KgIiIAIiIAIiIAIiIAK9QEBKfi9cRdVBBERABERABERABERABAICUvIDGFoVAREQAREQAREQAREQgV4gICW/F66i6iACIiACIiACIiACIiACAQEp+QEMrYqACIiACIiACIiACIhALxCQkt8LV1F1EAEREAEREAEREAEREIGAgJT8AIZWRUAEREAEREAEREAERKAXCEjJ74WrqDqIgAiIgAiIgAiIgAiIQEBASn4AQ6siIAIiIAIiIAIiIAIi0AsEpOT3gk7TjgAAQABJREFUwlVUHURABERABERABERABEQgICAlP4ChVREQAREQAREQAREQARHoBQJS8nvhKqoOIiACIiACIiACIiACIhAQkJIfwNCqCIiACIiACIiACIiACPQCASn5vXAVVQcREAEREAEREAEREAERCAiMG6xrVQREQAREQAQSAt9884358ssvq/6++OKLqt/sr9n2hd325f/Skcd4441nxh9/fLdsZL2ZtElhtSICIiACIlBFQEp+FQ79EAEREIHeJfDBBx+Y999/36SXH330kVPc08o6Cnqny7TTTmvCv2mmmabq9wQTTNDpVVD5REAERKAUAmNVrJSSszIVAREQARFoG4G04h77nVcYlGEs6LFlbFtWWr/dHzPOOOOYr776yv1h9WfdL8P1RrfRIPn888/N2GOPbRpphEw22WRO6U8r/75hMPnkk+dh0T4REAER6FoCUvK79tKp4CIgAv1E4MUXXzTvvPNOjSXeK/N5LMYdd1wz9dRT1/yh+Prtecd30r577rnHXHbZZWarrbYyq666qvnPf/5jPvzwQ7fMWv/uu+8yq0CjJKsBMMccc5iJJ54481jtEAEREIFOJiB3nU6+OiqbCIhAXxJ4+eWXzQsvvGBQ7P0yr9MVq7lX1lmGyju/p5hiip7hONZYY7m6eB5TTjml4S9PsP7TAMhqBLz77rvmzTffjGYx3XTTGZT98I9tEhEQARHodAJS8jv9Cql8IiACPU3g1VdfTRR5r9B/++23NXWeYYYZzIwzzlilzHvFftJJJ61J36sb0kp+I/WkkcMfinqWfPLJJ1WNAMYuvPbqa+a1118zI0eOdH/+WKz7odLv133ZfDotRUAERGAwCUjJH0z6OrcIiEBfEXj99dedQu+VeZYxv/JZZ53VzDbbbO7Pr+NWIjHOFx8O3pJfFBN89/mDe1pwB3rtNavwB39PPfWU4S+U2WefvUb576cGWMhC6yIgAoNPQEr+4F8DlUAERKAHCbzxxhtV7jYo9Aw0DQVf+bnmmqtGoZdFOKRUve7ZFK3kV5+l+tdUU01l+FtkkUWSHQz+DZV+1l955RX3x7gBL7hOeUu/X9IrIxEBERCBsglIyS+bsPIXARHoCwL4z48ePdo89thjzlpPOMpQcPGYc845qxT6mWaaKUyi9QYIDIaSHyvWRBNNZOabbz73F+7H/Sqt/D/88MOGPy9Y92kw8Lfwwgu76D9+n5YiIAIiUBQBKflFkVQ+IiACfUWAQZwo9F6x//jjj5P64/89zzzzJAo9LiBYdCUDJ9ApSn5WTbybVbj/vffeSxR/GgHPP/+8ue+++9wf6WaZZZZE6R8yZIgi+oTwtC4CItAyASn5LaPTgSIgAv1G4PHHH3dK/RNPPOFccXz9mcUVi+xCCy3k/hR9xZMpftnpSn6sxj4m/2KLLZbsfumll8zTTz/t/p577jnDeI2bbrrJ7ec+wsq/wAILGJR+iQiIgAi0QkBKfivUdIwIiEBfEEDxwlLvB1l++umnSb2xvqKEoZCxlLSHQDcq+TEyuG7xt95667mxGlj3UfqfeeYZ8+STT7o/jptkkknMoosuahZccEGn8McGBsfy1zYREAERkJKve0AEREAE/j+Bzz77zGCt90o9Aym9MDPqEkssYRYYYhX7oQu5gZh+n5btI+CV/LwJrtpXmmLOROQk3wtEjkxwhtL/7LPPOqU/dO2ZeeaZXVos/PzRSyARAREQgRgBKfkxKtomAiLQNwTGjBmTKPVYUL/88suk7vPOO6/hD0s9gywlg0/AK/ntjK7T7loz/8Eyyyzj/jg3jU3uU9x6sPTfeuut7o99Xtn3S83QCxWJCIgABKTk6z4QARHoOwIoSiMfHGkeGfWIIdSlF0Ibhoo91ntJZxHoByU/TZz4+/ytvvrqiWsPSj/Wfu5l/hCi9tDbtPjii7s/za2QJqnfItBfBKTk99f1Vm1FoG8JvPvuu27W0gcffND5PgMCqycDHL1ij4+0pLMJ9KOSH16RLNcelH6U/bvvvtv9Ec3JK/ssJSIgAv1HQEp+/11z1VgE+obAt99+myj2I0eOTCajwv95sWGLmWGLDXPWz74B0gMVHXvssV0tetldp5nLFLr2ME6BMSWEdmXp3XqYjwFFHys/975EBESgPwhIye+P66xaikBfEcB3GYs9f2+//bar+4wzzmgIYThs2DDn+tBXQHqosv1uyc+7lDSAiMTDHzPyemWf5Q033OD+mHXXu/TQgyURARHoXQJS8nv32qpmItBXBN5///1EsSc6DoJrAwMYUezDGOV9BabHKislv7ELyoy8fvCun7gN6z5/L7/8srnmmmvM/PPPn7j04PMvEQER6C0CUvJ763qqNiLQVwRw2cBajysOSx8ZBwult9rjziDpHQJS8pu/llNOOaVZeeWV3R89W97CT4hO/i6//HI3mZt36WEAukQERKD7CUjJ7/5rqBqIQN8RIKqIV+zffPNNV3+U+RVXXNFZ7eWG0Lu3hJT8gV1bFHj+1lprLWfRD334Wb/kkksSdx6U/qmmmmpgJ9TRIiACg0ZASv6godeJRUAEmiHABEFesX/iiSeSQ73FnqVCBiZYenZFSn5xlxb/fP422GADZ9H3Cv9DDz1k+JtwwgkTd55ll13WjDfeeMWdXDmJgAiUTmAs291dKf0sOoEIiIAItEjAhwW8//773WBCssF/2Cv3DKjtd7n44ovdZF0MuOz1yZCefvppc9ppp7mY8VtssUW/X/pS6u/deVh+9NFH7hxE6KGnjD+585SCXZmKQOEEpOQXjlQZioAIFEHg4YcfdvG+R4wY4bJjoh+v2CsM4P8RZlDloYce6jYQG33BBRd00VWGDh36f4l6aI1G36mnnmpWW201s+WWW/ZQzTqvKl988YUbqPvII4+YUaNGuQJOMMEEibLPvSYRARHoXAJS8jv32qhkItCXBO655x5z1113mSeffNLVH6vhcsst5/40A238lvj0008NPR133HGH+fDDD12iueee2yy55JLub7LJJosf2IVbGSh6yimnmFVXXdVstdVWXViD7izyiy++mESv+uyzz1wlaHR763531kqlFoHeJiAlv7evr2onAl1BACXVz9RJeD9kzjnmNMst/z/lXr7AjV3Gb775xin79IJg8UZQ8FH2l1pqKTPXXHM1llEHp2IOhD/84Q9mlVVWMVtvvXUHl7Q3i0Yjkt41oln5Qe+4z3llX9GsevO6q1bdSUBKfndeN5VaBHqCwDvvvJMo9++9956r0wILLOCs9ksvvXRP1HGwKkFj6dFHH3V/b731livGwgsvnMx82q2DlMeMGWNOOukkFw5ym222GSy8fX9eZtf1yj7jJBBc6ryyrwhXfX+LCEAHEJCS3wEXQUUQgX4jQNe/t9wzMydCuD7cclBEJcUS8Mo+vtVfffWVwXffx0Qnuko3yQsvvGBOPPFEs9JKK5ltt922m4res2Vl8jks+yj9PpYHPUcrrbiSWWbZZXq23qqYCHQ6ASn5nX6FVD4R6CEChOhDub/33ntdrQiH6P3tZfkr/0LTW+LDI77++uvuhN1m3aeBeMIJJziL8XbbbVc+NJ2hYQJvvPFGouwzIBxhbIi37mtMTcMolVAECiEgJb8QjMpEBEQgj8ADDzzglHssyQhhHr1yP8sss+Qdqn0lEcBvH4XfR03pFuv+Sy+9ZI4//nizwgormO23374kOsp2IAQYY+Mt+36MDbPuemV/zjnnHEj2OlYERKBBAlLyGwSlZCIgAs0RYBCod8khIgqCIumVew3Qa45nWalfe+21xLr//vvvu9N46z4Ddjtt0DNK43HHHWeWX355s8MOO5SFRfkWRICGPQo/LmNeuHYo/EsssYTfpKUIiEAJBKTklwBVWYpAPxNg8hyv3HuXEKz1Xrnv9cmauvXaf/nll4my7wdSTjfddEkYzplnnrkjqvbKK6+YY4891jAD64477tgRZVIh6hNgLIW37hN/HxkyZIiLkrT66qvXz0ApREAEmiYgJb9pZDpABEQgi8BNN91k/vnPfxpvEcbP3iv3+N9LuoPA888/7xR+lDI/MNrH3B82bNigVuLVV181v//9780yyyxjdtppp0Eti07ePAHeDV7Zf/vtt10GTKq17rrrumvafI46QgREIIuAlPwsMtouAiLQMAF8u1HuvQUYdw+UeyK4SLqXADHRR44c6f5w60GIie4V/qmmmqrtlaMcv/vd7wwhVnfeeee2n18nLIaAn9OBXj/f40dEnnXWWUcRtopBrFxEwEjJ100gAiLQMgFcJ1DumaUWmXXWWc2aa64pi1zLRDv3QBpyKPyjR492hZxowonMkkv9b0bd+eefv20FRyE85phj3OReu+yyS9vOqxOVQ+D/xu7cY9599x13EiY6w7LfC5O3lUNNuYpAYwSk5DfGSalEQAQCArhwoNzzhy/3JJNM4pT7NdZYo+MGagbF1moBBIhu4637n3zyictxoYUWcj7yWGLLFmZZ/e1vf+t6E3bdddeyT6f820Tgs88+c8YCLPuE3xx77LGdoo+yP/3007epFDqNCPQWASn5vXU9VRsRKJ3AXXfd5ZR7777BpERY7/UhLh19R53g448/TpR9HyaRibVw02JQ7AQTTFBKeZm996ijjnKRWXbbbbdSzqFMB4+AH7hP7yChOCebbDKn7OPGw4y6EhEQgcYJSMlvnJVSikBfE2BWSyz3xFdHhg4darDcL7DAAn3NRZU3biAlcyFwjyBMesTf+OOPb8Ydd9xMRIRRXWyxxcwiiyySmSa9g8GaRx55pBvv8cMf/jC9W797hMC7776bWPa//vprM+OMMybKvgbx98hFVjVKJ5D99i391DqBCIhANxBgllSU+3/961+uuIRSRLkn1rVEBIiDPmbMGIO7xQTjT2C+/OpLg5Wfv0bk/vvvNyhtWGm5t5ghFaWfXoGYeAWvUqnEdmtbjxAgfOtmm23meoZw4cGyf+GFF7rZsrHqr7zyyj1SU1VDBMojIEt+eWyVswh0PYEbbrjBKfhEWZlwwgkTv3vWJf1JgPCauGwxwRnuFGUq2/PMM49hcG0YxQcL7xFHHGEI5bnHHnv050Xow1ozFgRFn0Yhsuiii7pIPJpQqw9vBlW5YQKy5DeMSglFoH8IEMca672fqRarPX73M800U/9AUE0TAkRRuuOOO1yI1EYt9MnBA1iZbbbZqhR8spIlfwBAu/jQOeec0/DHmA8s+7gNEumJ3wzOZWItiQiIQDUBKfnVPPRLBPqaANYyrPf33nuv44C/Pa45+N9L+osAA1xvu+0252dPT85gCPdjWryS/91336V36XcfEJhvvvkMfyj33rKPdR8jBMo+YXwlIiAC/yMgJV93ggiIgHO78K45X331lZlhhhmcck/kHEl/Ebj++utd1JwPPvigVFecRqjimpMWr+SX6SaUPqd+dx4BDA/8jRo1yln2aZBinEDRx2efQd0SEeh3AlLy+/0OUP37ngDWMBQ7Jhkab7zxzHrrrecUfGLfS/qHwKWX/tU88sjDbgBtp9SawbxpkZKfJtLfv4nOxB8uhv5dNnz4cPP973/fWff7m45q3+8EpOT3+x2g+vctASYy+tvf/uZcMoCwzDLLuI+iurv755bAv/6KK64wjz/+uKEHp9MkZq2Xkt9pV6kzyrP00ksb/v7973+bW265xZx//vnm0Ucfdco+A7glItCPBKTk9+NVV537ngCD1lDwGVDJYNqNNtrIRSvpezB9AuDVV1811157rXnuuefMt99+29G1xi+fAZdemAkViTUAfBot+5fAiiuu6Nx4brrpJmfZ94o+lv1xxhmnf8Go5n1JQEp+X152VbpfCTBYEcvtdddd5xCssMIKTsFn4iJJ7xPAYn/jjTcaZqjtFiX5scceq1LyZcnv/ft0oDWccsopzTbbbOMm6kPZv/LKKxOrvkJuDpSuju8mAlLyu+lqqawiMAACzzzzjLPeMyspU8VjvcfqJel9AiNGjDA333yzefPNN7uusi+88EJVmaXkV+HQjxwC+OoTIQxFHxeeE044way99trOhUcDc3PAaVfPEJCS3zOXUhURgWwC//jHP5yC/80337hJZFDwmV1U0tsEUO6ZqZhwmN0qb7/9dlXRpeRX4dCPOgSYuG+TTTapUvaJyMO21Vdfvc7R2i0C3U1ASn53Xz+VXgRyCRAx5/LLL3chEVGONt54YxdiLvcg7ex6AnfeeacLK5hWkLuxYgwQD0VKfkhD640SwKLvrfpY9s8991wXfhNlf6655mo0G6UTga4iICW/qy6XCisCjRNghlIG13700Udm7rnndu45888/f+MZKGXXEbj66qtdKMF2zkpbNqT0pFdS8ssm3tv5EyJ4wQUXdGNTCLsZDsz1g7p7m4Bq108EpOT309VWXfuCwH/+8x+n3GPNRZixFgs+MfAlvUngwgsuNKMfG22++OKLnqwgk2JNN910rm5eyU8r/z1ZcVWqFAJzzDGH2Wuvvcxdd93l/PUJRuCV/cUXX7yUcypTERgMAlLyB4O6zikCJRHAMoV7zhtvvGGmn356Z71XNImSYHdAtij3ox4d1ZEx7ovEgwK21lpruSy9kt8t0YGK5KC8iiWw6qqrJi48jF85/vjj3Wy5hNucaqqpij2ZchOBQSAgJX8QoPfbKc8666wkFvc+++zTMdXv1HK1Aujrr7921vt//vOf7vBll13WKfj6ULVCs/OP6Rfl3l8J4vlLyfc0tCySwAwzzGB22mmnRNknCpW36q+22mpFnkp5iUDbCYxlrSGVtp+1zgkZaPWb3/zGHHPMMWb88cevk7r9u1988UVz8cUXm8MPP7z9J+/CM04wwQSJpZEudm+JG+yqtLtcZd3XTz75pFPwn332WTPxxBM75X6VVVYZbLw6fwkE+k259winmWYac+SRR7qfNGj3339/M/vss5tf/vKXPomWIjBgAoxfYlDu3Xff7fLCWIJVP5yMbcAnUQYi0EYC/5s6sI0nrHcqBgoyAv7EE0/suMlamPadD81CCy3kBu3Uq4v2i4AnUNZ9zaRWRx99tEHBHzp0qNlvv/2MFHxPvTeWhD3985//bA444AAz4sERSYO5N2rXWC1Qvrz4wZEdaJ/yRdSySwlMMcUUZuuttzZ77LGHa0Q+8MAD7pvvJw/s0mqp2H1MoOPcdXbbbTfz3//+tyMvCTMv/vrXv+7IsqlQnU2g6Pv61Vdfdb73Dz/8sKv4BhtsYNZff/3OhqDSNUWAQbR/+ctfDO8drNf9LDR0vPieQCn5noiWRRMYNmxYEoEH9x3GOdFjut122xkG7UpEoFsIdJyS3y3gVE4RGCwC+Iuef/755r333nMfHCa2IiScpDcIfPrpp+bSSy81TzzxRN8r9+EV/fzzz81EE02UuPtJyQ/paL1oArgK46rDu/Xvf/+7a2z/7ne/c4q+ekuLpq38yiIgJb8ssspXBEogQOx7JnFBVlxxRbPZZpsZZnSUdD8BYtv/9a9/dRbD0HLd/TUrpgb0WnHPy5JfDE/l0hgB5hbZd999zTXXXON89QnY8PLLLztlf5xxxmksE6USgUEiUKqSj6Xls88+M5NMMknbqkeMcAYfDnTArrcata3gTZ7Id98XFfucAbEoFgPhxvHffvutYUBrJ0nR5WJsBq4Uk002WaJwlFlfniPOd8MNN5irrrrKnQoL0zrrrFPmaZV3mwh88MEHzh3gqaeeSqJQtenUpZ8G//lJJ53UzDjjjFXnoheKRk0zjZlnnnnGKflkhKIvS34VUv0okQDfRXz1Z511Vqfs33jjjYmiz0SDEhHoVAKFK/mPP/64Ofnkk819991nXnjhBfPll1+aWWaZxQ1WZcDqtttuaxixHgoxvX/0ox+5TSjXXrbcckvjB1nhE5dWHvlIMCDtkUceMXwAnn76aYOSjzCAZvnllzf4Qm+xxRY+y6olytpWW23ltmEh+vnPf+58YE8//XTDgJspp5zSTSJELF0G3vi8OYDzMR02Mu+885oTTjjBrZf579prr3XlwF2DrnwEposttpirx/e+973M03NdDjvsMLd/1113dfXi+lxwwQVm+PDhhhjBKJIM3iSuOgM48UusJ1y7P/zhD4aZNrFucL2WXnpps9JKK7kIGOmPe738itpfZLnuvfdeA3vuL6470ZVozGDFmW+++czaa6/t2BKXPpRW72vyGDVqlHPJYTl69GhDZB6UJaKMoOD7cILh+bTeXQSY4IkB2dxT3E+9IBh0eA7mmmsus8gii5h6MyzzDn/ooYfc++y1115z93kWB/Z7QcnXZFiehpbtIoCe4BV9fPS9+87qq6/eriLoPCLQFIFCQ2geddRRDYWVZOT6mWeemSjwRAYZMmRIbsEZjBv2CNB1hgL/4Ycf5h7HTmb7xId52mmnrUqLUouPJ4KyT7oddtihKg0/fvjDH5rzzjuvZrvfsNRSS7mp5P3vopdEliC+PIPw8mTPPfc0J510kuvJSKdj9lP/IiJyEfF/aRS888476aTuNy4gp5xyiosyEE1gN3INGIgEx5gQ4g7rM40GGlRIO0JoFlUuFOvtt9/e+WPG6hdum3rqqc0f//hHs8022ySbW7mv4cOELIRn9cySDIMVGrB0G88222zBVq12A4G33nrLXHnllS4iUi8o97wrsGauvPLKZtFFF235Ejw44kFzwYUXOMMFRpr333+/Jq+f/OQnbhtGCBq8CoRQg0gb2kCA55bvDO6TCL2qfAsH0hPehmLrFH1IoDBL/oUXXpgo+OOOO66zcqPcTT755Obf//63YYS6f2mfc845zsrz4x//2CEnDcoUgsXed+GiMHmfN/L0QgxbrPz+A0kM20033dS1sOnCxdLKpEAvvfSSO+T66683NEBQWrOEDy9+d2nhY0NPAD0MdKvTTYfQYFh33XXdepnddVj7aES88sor7ly8ROhBwHqPNQsLPGwZrHf22WcbrM4jR47M9dMmDfMQYEWbeeaZzQorrOB6W+6//353LFxR3Gk00FOAVT4tuI3QMPLWNCIOwAPLHT0N//rXv1yZsXz465nOo4zfRZZr8803N7fccosrJvcfA1zp3eDac7/QO8K9BQPujZ133tk1nnzvRbP3NSfiHDSMvHB9mNCKRjBluOeee8ybb77pesq4LnAm5Kyk8wm8/vrrzt2KiZ38c9P5pY6XkHuRBuZyyy0XfT/Ej8rfOvy+4S7BeuutZ+aZZ57cxHLXycWjnSUTQC9BL/BWfb7B3k+f3l2JCHQKgcIs+QsvvLDrckUJpXWL4hgKHzWs0VgfkcUXX9z48H9hOvycfQhNFM20iw5psbgz2h3Bmo9y6xsDbqP9hxX0F7/4RaLY0wtAdy8uOF5CS77fhoUbt50ll1zS3H777U55xpKP0K2Mwo3wccMlqWzZfffdk14EFEys+bjUhEK3IY0cLMcIsfx/9atfhUlMaMn3Ow499FDDn+/NYDuuA1w7lFYEP8TLLrvMrft/XB+UeZRNhEYHk4PhTuKFHgKUZBoUoXAf8IEuQ4osF/cm9wBCQ4+GZcxKiaK/5pprJj0i8Pztb39bU71G7muure9JooHA9eYjwv3uG5Q0zGh8odwjuJJhUZJ0LgHctq644grz/PPPd71yT48V7z5CthYpuB+eccYZrtePd3o9YTIsnkuMFRIRGEwChDP27pz0amHRlzvlYF4RnTskUIiSzyAq/DCxoqMgYrmPCVZpLDS4QdAYQEFKt3rrKUNjxoxxCibK4kwzzeRaz1mDT1H0UZI4L3LXXXdVTRSUVvKxmlImLKcxabeSj6KJfzt1hQvxsrNi9KJUMzYAtgw8Rlmn7l7SSj5RWfwgTp/GL2lAoVgiXCd6MfzYCLb9/ve/NwcffDCr7lowYDDc73bYf/Cll8M3BtheppJfZLmY5vyiiy5yVWGMBg3ULGFMAhMVIbhE0ThMS737mp4YrPVYexFcH7h+KDz0iIXCc4bST48NQjk33HDDMInWO4DA22+/ncxEzH3frUKjnHtxjTXWMMsss0wp1cAdkrFVe+21l+vlrXeSAw880BkV/Cy49dJrvwiUSYB3Mor+rbfe6k6D4QdlPzSglXl+5S0CWQTGztrRzHY+At46yyDBLD/v6aabzg1oxY2GqDtpBb+Rc6I44oe54447mkMOOcRkKfjkhYIaujJgBc0TLPhZCn7ecWXtO+aYYxLLH9O3Zyn4nJ9G1t577+2KAlvGPOQJCnGWoEB6rjSUvFXfpw9n/6NcMQWftFg1fvazn/nDSl8WWS7cjLAW0tjxrmRZFQgt/PXusaw8GEDuFXzcfVD4DzrooBoFn+N51uil8kIjRNI5BHBLZHwGMxEzWLtbFXzeAfQa8oxzL5al4ONeiYLPu5rBuo0IzwCKlUQEOoEA9yO96RiH6GG67bbb3KBcnn+JCAwmgf9zdB9AKRgAhRsL/uG4TKD0oIijIBFZJ5SBTtrDRyftrhLmH65jHfWND7b7sJNhmnA9bBCE2wdrnV4FL37QrP8dWzJBx3HHHed25b1cUL6x+mcJjSPcmnwPCBGSvDDQmevsBXeePCGSz09/+tO8JIXsK7pcDA5vVLjPvNS7x3y69BIlxwvjILBQcp2yBJcJr+jg4y0ZfAJE3yLOfbeHwsS1EXdKnu2sBnyRtL3bYzONCH/vF1kO5SUCAyXAPUyvF1Z9XNB89B3vbjnQ/HW8CDRLoBAln5MS9QDrOoM26aYmJCZ/+OozkIpILgwURIEsWjgnA1NRdnBT4Q+l6cEHH6xS7OtZflrpWSi6Lj4/6oSFywt+8fV8r0OLe57iR9SbsPHjzxEucfnxEg6cZXCRt0zS6xFGPPLpwyWNBSwbRAgqU9pRLuqAXzX3F+MfUOYYc4DPtZd695hPFy4pOwPFvdDFSy9OPSEdvTY0cPjrpF6oemXvpf0YNhhPwUed57ZbhXEg9GC10/WLbwVKPr2U6dDKeRyl5OfR0b7BJIDbL7oPPcsMyCUoiR+UG45bG8wy6tz9Q6AwJR//M/yOiYMfWjaxRvNHHHk+IgzGJPwZ0WEGIliXL7nkEjcoFYU+tDa3ki8Dd/PcYVrJcyDH8FIIQyiedtppTWWHMorCGVPmGctQT2LHcUzoihX6/OflRxSOspX8ssrFoGbCkt50002JO01eXZvdh4LDtWUshZdQ4ffb6i2Z88APFK6XVvuLIYDrIAPOeb+FDeFicm9fLoOh3Pvacf9jNGDsUTMiJb8ZWko7GASYz8Rb9e+8885E0W/UE2Ewyqxz9h6BwpR80BD+DzcRBgISChP//FDwV8b3+NJLL3U+47vssku4u+F1PqrEpQ0HdIYHz2lDamIVwiJ17rnnusgo4f7YOj0MYZjOWJp2bvPhRjknXeZEtWhWsmYbzlLgG8k/bHjkuZOEebVS9vD4RtbLKBchVfGtjlnn6cHAf5goCjRiiHjTrGD99Qq97x0hD3o/0tGi6uXd6liAevlqfy0BFHqscwyEb9U9qzbX9m8ZTOWe2nLPouQzVgv3s2ZESn4ztJR2sAhgeEHRpxee94V331l//fUHq0g6b58RKFTJhx03NANi+UMJJ9QfVlDijXt3Eqzu+GqjzDBYpRkhXBXuP17Bp/eAEI58JBgLgOKFe4gXHyGF3zFlzafrtGUYJxrXGbq12+EfW4/DDDPMkCTxsfuTDRkr/lpl7C5kc9HlYjKqcEArEW4IG0hYS+4zro+/HswY3KwQZpNeAu5fzsXcAvxGiHqET7Sk8whgpOBaSbkf+LVhVnFcnQgD22wUEpT8sGE88NIoBxEohwDfJqJG/eMf/3Dz7OCBQE8986o0e9+XU0Ll2ssEClfyQ1i4hXAj88cLmUl+mACLGxzBZ61ZJR/XCR+FBEWLPPOmTvcNC87XTf6yWL8Z0IxFnw8h7jd59aR+7ZBwIDUuMrgs5Fn0aVg12hgYSPmLLBf+7URG8YISfsQRR0Rdn0gT3mONuG1g9afngYHefgI27mWv5NMDJiXf0++MJcYC3ALDHqPOKFnjpRhsy31YUp4TGsf0iDXji+/zkJLvSWjZLQTwLPDuO0xqSOhxQiTjwy8RgbIIFBJCk5lSCcmIMs9Ak5hg9eQmxxLmxc8m6n83ssS3zQthBPMUX3oMwigz3aTkU8dwIDAx/usJljF6SIjMwuQ7ZQgvJB+FCAU+FhM+PC9Wz4GOlwjzy1ovslxMfOWthOSbp+BTHuropZF7jIYRbj5ewefYsOcma54Jfw6W+PATMpWZnHEdwTVLUjwBrG7EZOfZ6lYFH+WewAe4CrRzUG3e1YAnRgKikWDMaFbkrtMsMaXvBAKMReS9zfuewA2MVWTAvkQEyiJQmJLP5EgoG/zlSeifHfM7Dme4jXWJ+1ldOQexxPMEq384CLgRK2tefvXKlndsK/vCXg5Cktbzu2aWWxpRpG2lAdVoGZnO2wvnyhOU0HZJUeUK7zEakSgUWYIrEuM+vGTdY+G9w3iS8NpyLK5A/jxXX311dDZofw6WnJNxLyeffLIbN6Bu35DOwNcJhUnoV3zG29FIHXiJa3PoROXelxKu3O+tWPHJA6NRN7lf+nprKQLefQeF/6233nKKfnpmeFESgaIIFKLko1x5BYVQj/jhZ0kYJYZZPdMShm4MrfA+XTgy/frrr/eba5ZMkc64gFBChT/c3uh6WDZcjpgJtkxhMibfU4EySdcerjsxIT5+qNgTwagsYfZXxlMgzLqKoh/74GIFrRf2s8gyFlWu8B5jluMwRGZYXrpbsch79zP2xe4xLPfhvcLA8LQwQIvIVAi9CNQlDKEapsfSf/zxxyebiOnvn79ko1ZaIkDDiQnc+Ohy3bpROlm5hyduT9zbWPEZtN6KcL/H3jmt5KVjRKDdBNAldt99dxdWHCMC+gozzUtEoGgChSj5+JmhgCIoKEyChSsCfmcMlMWfnMG3dBWff/75Lh3xYrH+p4UY7l6I1kMEHuLv4yeNkLcXrJjkgc835yWMIJbsH/zgB873n3RhXH6UsoEIrhs+Ag95EfYN/+qyJnui7KeeempS5CuvvNIN/MSK+/DDDzu2zKxHI4tZKb3gxsT8BGUJM7KGSibuQYQLY4wFCi+NDRRPrgMf4tCKXVaZyLeoci2//PJm2mmndUXFLYaJTHBDo4GFsn7HHXc4Fx5iiuNHT4+UH4TLGIpQ+UDpx4UqnE8AZZ6xKXTbMoGSFxpLKGgIsfiZ3IzeKHyXaeTR4KC3Zsstt0ysyyussILj7PPQsjUCDHZmxmtctcIGWWu5Dc5RBBygR6iT3HJiJLDiI61a8TlWSj4UJN1OgG8BrnQIvYdhoJBur5vK3xkExrIKSSFzg+OvilUTxb6ezD333E4hjCmiZ511llN+0nmQL5Np4Q5BDwDjAEJB0Ur7Q6NE8QD5hsGaa65pbr311uQwLHXezYFlI37N22yzjXOTSDKxKyjjHBtzPwrTtbqOcs/kGn4G2rx8sA6cc845NUkYy+BnzV1ttdWcolqTKNgw11xzmZdeesltYRmbQ4BGFg2cvFuIHgas+f7DTmOsbKtzEeUiEgKNzLSk7zMskbycaVihjCP+XqWB4ENrEkUndl0YOB6GDyQ8LFZ8wq3VE7p7aViRt6Q1Av7ejPXAtJZj+4+iV413IlHHOl1ovGK4IAoaEUdaFRrEuC/yrEtEoNsJ8H324+ho/DK5qEQEiiBQiCWfgqDookCfffbZBgUxJlj8t99+ezcTbUzB5xhe/EQ2SXfjMkgFwZLOebBohu4zXsHHYsrHju52usDWXnvtRJHHApvleuEyb+AfYw5ofYezi9LAoRehLMFSz+AcJhwLw4OG58OqjLU5pkiG6YpcP+CAA9y1wOKcFq419wLW0XZLEeWi14l7CJeCUPx9hssNYTC5LrDHz94LbkpY6L2Cj+WdgemHHXaYCaMAkT70/+c3zwX3Nz0z9EzEhN6uU045xbnFScGPEaq/DVc/GMO6WxV83kGED+Z92Q0KPlfFG2cGYsUnH1nyoSDpFQIY3vBaQI9iUDrBHrLGd/VKnVWP9hAozJIfFhdr7WuvveZcN3DXwfWBkIBMetKMEH2BcJko/N59Ijyej/Nzzz1nxowZ46zoWIdoYHjXiTBtGeucl94AlK52KluwxeKLCxPKJiP1p59++jKq2HCeNHKw0uHSwgc8dLtqOJMSEhZRLtzBuNbci/RCoYh7t5pYkQmpiTsOQs9PeuwJPTK436Dw14ssQmOBBi7PESFpud4c1657PFa/bt524403GiJVhTMMd1t9CF5Ar9waa6zRVUXnOTr22GPNvPPOa2iID0R+85vfuNC1NHYlItArBHhGzjvvPBc6G33poIMOcu/9Xqmf6tF+AqUo+e2vhs4oAp1BAAXed7XS84KVXzL4BBiszLigcE6DwS9VcyXA0IFyj9WvGwV3BNwSinguGAfEsxYGcuhGJiqzCKQJ4Bnwhz/8wRlJ8VwgEAATMEpEoBUCpU6G1UqBdIwIdCsBZiX2FsoddtjBMIBXMrgE6PFi3MJA3fQGsxZY9BhPlO4RGswyNXtuFHLG5RC8oIjnQu46zV4Bpe8WArjsMMbrzDPPdD32uHoS2IR3gEQEmiUgJb9ZYkovAhECKJFYXBCiQQ3U5zhyCm1qggAubYTDJFRj3sDwJrJse1Jc8Ahm0Au9QQ8++KCLCMUYlyJczaTkt/121AnbTIDAIRdffLEbx0JUQhrKuH9KRKAZAlLym6GVkRYlIpzJNyNZw5vx9w4ndmr4wC5J2Gu8UCixvCAMnlpqqaW65Er0XjGJcnXBBRe4cQyMDepGYbIcBnKHUZe6sR6+zIydIvIUUYCKsOKTr4/QxbvEr/vzaSkCvUKAMNS46RHtjWABKPrhTOm9Uk/VozwCUvILYEvEFT9PQAHZmfnmm6+nlfxe4sW8AH6+B+6BJZZYoohbQHm0QIDIV0y0FJspu4Xs2n4I0ZSYkyEd0antBSn4hCNGjHBBAmi4MD9KEeJ7A6TkF0FTeXQyAcKA825gQC4ub+++86458qgjO7nIKlsHEZCSX8DFwJJUpNUtHT60gCJ2VBa9wovIPYTFRJifgLj1kvYTYCIrPn7dOolVryr33An0phA2Ez/joqz45Out9yj5EhHodQJEJzz00EPdRHfPj3neRW87/fTTe73aql8BBKTkFwCRCZL8ZE8FZNfzWfQCL2ZxPvzww921Ym4HwrdK2kuAmZVvv/12NylSe89czNl6Wbn3hPDFx52NiQyLDPMrJd8T1rJfCDBonZCxGJaIEsacQ0SXIqSuRASyCEjJzyKj7SKQQYD5AIjTjTAT8dChQzNSanMZBFAc//nPfzY0A3QZ5x9onv2g3HtGfvKrIq345C0l3xPWsp8I4KZ2zDHHmBNOOMEFFWA+FmZ/HjJkSD9hUF2bICAlvwlYSioCTEx11FFHORC8YBdccEFBaRMBJiS78sorDRPGdKP0k3LP9Rk9erSb0ZlxKkziVqRIyS+SpvLqNgJEcmNmdTwIMDhJ0e+2K9i+8krJbx9rnanLCbz11luJgr/ffvvJetKm68msv4SSowelG32w+02597cFA26Roq345CklHwqSfibAXCxTTTWVueGGG5yij7JP0A6JCIQEpOSHNLQuAhkEvvnmG3PggQe6vXvssYcU/AxORW8mNO2oUaMM/LtN+lW55zo999xzLtLRAgssYBZaaKHCL51X8rs1TGrhQJRhXxLYYIMN3OB2ZvPGmk8v8zzzzNOXLFTpOAEp+XEu2ioCVQT22Wcf93ujjTYyw4YNq9qnH8UTwC2Hrugvvvii+MxLzrFR5f7NN980M800U8mlGZzsGTeBlGHFJ1+v5Hdjzw7ll4hAUQT4JtHYvfnmm82vfvUr57NftHtcUWVVPu0nICW//cx1xi4j8Mtf/tJ88sknbpKr9dZbr8tK313FveuuuwxWqY8//ri7Cm5L26hyT8VQ8JnFcv/99y8sdnynAHv99dddA22OOeYobWI4KfmdcrVVjk4g8P3vf98p+rfeeqs55JBDzO9//3sz++yzd0LRVIZBJiAlf5AvgE7f2QSOPfZY8+qrr5pZZ53VzWbb2aXt3tIxmPbSSy91rLutFs0o975uWLpR9HFHYgC3V1r9/m5e4ouPZbHIuUPSPDwvWfLTZPS7Xwlsuumm7rkjrPBBBx1kjjvuOPfd6lceqvf/CEjJ150gAhkEsLQ++uijZooppkhmtc1Iqs0DIMBMjnDuNv/qVpR7j2njjTd2vUPDhw93UTKYvr4XhPjdhM2cbrrpSnPVgVM4420vcFMdRKAIAptvvrlhRnl6RH/xi1+YE088sWddAovg1Q95SMnvh6usOjZN4LrrrjO33Xabs7DS/SkpngCx7u+8807z2WefFZ95iTkORLkPi7Xttts6RR+leJKJJzGbbb5ZuLsr17Hi//e//zWrr766GW+88Uqrgyz5paFVxl1OYKuttnIGk3vuucf89Kc/NSeffLKZYYYZurxWKn6rBKTkt0pOx/UsgX//+9/m8ssvd/X7yU9+0nM+04N94R5//HFz9dVXm7fffnuwi9LU+YtS7v1JsUZvt9125uyzzza33X6bmWLKKcyaa67pd3fdkkHSDzzwgJlkkklKteIDRkp+190eKnAbCWyzzTZO0edbdsABB7iZculdk/QfASn5/XfNVeMcAsRi/+Mf/+hSYBFR3OEcWE3uQgnENefpp5/uqnj3RSv3IbbJJ5/cKfp//OMZ5tprrzWzzTabmX/++cMkXbOOFf+dd95xDRVc3MoUKfll0lXevUAAAwIukEQpw1h1+umnm6mnnroXqqY6NEFg7CbSKqkI9DSB999/3/kwUkncDVZdddWerm87K/e3v/3NjWtgxuBuGSyJcr/TTju5sHTLLLNMabhmmWUWs8UWm7sPMor+559/Xtq5yswYJR/lu6ywmWHZpeSHNLQuAnECTJi17LLLup0M8GdiQUl/EZAlv7+ut2qbQ4BoBPgTL7XUUlbp2iInpXY1SgArEr73H374YaOHDHo6ZpFcZ511zCqrrNK2siyxxBLOCv73v//dWfTx1+8mIVrQiy++6CLqtCP2v5T8bro7VNbBJLDjjjs6AwLP6I9+9CNz1llnGXoQJf1BQEp+f1xn1bIOAeIKEyoTV4lddtmlTmrtrkfg3XffNRdeeKFT/Oql7ZT9E088sVlppZUMMacHQ5iDgXEK9957r1l44YXNIossMhjFaOmcD4743+RXK6ywQkvHN3uQV/K7LSJTs/VUehEogsDOO+/sFP2HHnrI7LXXXubcc891Y2eKyFt5dDYBKfmdfX1UujYQwLIxevRoF2qMLk3JwAhcdtllzg/0m2++GVhGbTqaKDCLL764c81p0ykzT7Piiisa3F4YMNctSv4TTzxhnnjyCbPooouaeeaZJ7NuRe7wSn63uH4VWXflJQKtENh1111deM1Ro0aZ3Xff3U3GN9FEE7WSlY7pIgLyye+ii6WiFk+AKDp33323i6CDtWOcccYp/iR9kiOx7g8//HBD6LZuUPDHHmtss+CCC5qjjjqqIxR8bpN5553Xlemxxx4z/HWD0ChB2mXF51xS8qEgEYHmCKDc0xhHdtttN/PVV181l4FSdx0BKfldd8lU4KII3HzzzYZ4+AiRCJjVVtIagTPPPNN1ATN4uRuEKDYHHHiAm212sskm66gir7vuuoYyPfnkkx1VrlhhXnjhBTNy5EjXOGlnz4OU/NjV0DYRqE9gzz33dO6ApMSw1Q0Gmfq1UoosAnLXySKj7T1NAOvjBRdc4OrIIMdhw4b1dH3LqhwNpVtuuaVrJrSadtppzQYbbGDKjJYzUNaEbWWMSDfIYFjx4aIZb7vh7lAZO5XA3nvv7UJFY0hgYO4ll1ySPFOdWmaVqzUCUvJb46ajupjAc8895/wRqQIKH4MtJc0RePPNN93AWgYrd4NgGV9ttdUMg1slxRB466233PiBmWeeOQnTV0zO9XORJb8+I6UQgTwC++yzjznttNPcvCWE2rz00kvzkmtflxKQu06XXjgVuzUCn3zyifnTn/5kWBIicf31128toz4+6i9/+YuzNHeDgj/hhBM65R7LuBT8Ym9arPhffvllW33xfQ2k5HsSWopA6wT23XffZPK9/fffv/WMdGTHEpCS37GXRgUrgwCRX15++WXnnrP11luXcYqezZPwa4cddpgZPnx4x/txEjEHl5wTTzzRbLnllj17TQarYh999JGz4k855ZRtmfwqXU8p+Wki+i0CrRFgNtxpppnGzdPB+1LSWwSk5PfW9VRtcgjcdttt5o477nBTe2+yySY5KbUrTYAp0f/85z93/KRWREcixvwxxxzTMRFz0ix74TdWfCY4I6IOvSXtFin57Sau8/UygSOPPNJVD0POFVdc0ctV7bu6ySe/7y55f1aY2Tix4iNMdjT99NP3J4gma83ETEQg+uyzz5o8sr3JUfqI0b7NNtu4+Q7ae/b+OtvXX3/trPjjjz/+oFjxoe2VfE2G1V/3nmpbHoGjjz7aHHrooeaaa64xRB9bbrnlyjuZcm4bASn5bUOtEw0mART8Tz/91Ky11lpmqaWWGsyidM25mSSsG2K1E/p0s802M0OGDOkatt1cUKz4b7zxhhvrMPXUUw9KVbySr8mwBgW/TtqDBHC9O+CAA8zJJ59sTj31VGcIm3vuuXuwpv1VJbnr9Nf17svaMuEVyipKoNx06t8Cjz/+uLPodLqCj4JJnOeDDz5YCn79y1pYCh82c/nlly8sz2YzkpLfLDGlF4H6BJiMj0g7COOvPvjgg/oHKUVHE5Alv6Mvjwo3UAJM1IO7CdN346bjlYOB5turx1/8/9h7DzirqnP9/1V6R3obei8iIE1URFRiYkON5hpLjMYYNdFc4zW55qaZ3N/f5BoTlSIWFHtFpSgK0qv03plemGFmmGH6DPzXs4aNh2HKKXuds8uz/Ixn5py93/Wu79rMPPvd73rXm2/qVAwnp0E0atRIJkyYILfccotXp8Gx49q8ebMcPHhQRo8eHdPN46x/x4zkO/ZSoWMuJYCb96NHjwr2QPmf//kfXWbT2pfCpUPytdsU+b6efm8PHlEIKw8fEfwePXp4e8ARjC4lJUXvHZCRkRGBFbOnQtgNHDhQHnjgAUE+OFv0CezatUt3igW3sWyW6KDIj+UssG+vEkBALDMzU7Zs2SL/+Mc/5Mknn/TqUD0/Lop8z0+xfwcIgY/cYUR9ueFVzdcBFlotW7bM0WUxsVD6lptvkaHDhtY8EH5inMBdd90l2JG3f//+xvuqrQNG8mujw89IIHIC999/v65Stm3bNr1R1h133BG5UVqIOgGK/KgjZ4fRIPDll18KKsN0796defg1AEfFHOx4mJiYWMMRsX8b5RknTpwoN9xwQ+ydoQeagBOqblDk82IkAfMEsFnWH/7wB5k/f76upT9lyhTznbIHWwlQ5NuKk8acQGD//v1n0nTw2LFp06ZOcMtRPmDPgIULF0pxcbGj/LKcQTrGkCFD5MEHH7Te4isJnCFAkX8GBb8hAWMEWrRooSvuPPPMM/LGG29Iq1atWFrTGG0zhinyzXCl1RgRKC8v1wK/tLRUR/CRw812NoEZM2YIKug4tXXu3FmwGzHSQthIoDoCFPnVUeF7JGA/ATwNxzqoWbNm6dKaKLXJv6v2czZlkSU0TZGl3ZgQQLnMvXv3ysiRI+Xqq6+OiQ9O7RTrE/74xz86VuA3a9ZMp+WgdBsFvlOvImf4RZHvjHmgF/4gMHz4cPnhD3+oB/vPf/5T0tPT/TFwD4ySkXwPTCKHUElg7dq1smDBAmnfvj3z8KtcFCiHhvQc7FbqtAbBdtFFFwkWerGRQDAELJHv5FKvwYyDx5CAWwhcccUVkpOTI4sXL5ZXXnlFHn/8cV2a2i3++9VPiny/zrzHxo3Sj1a5TOTht23b1mMjDH84Tk7PadeunfzoRz+SQYMGhT9Anuk7ApbIZwlN3009BxxDAlOnTtVCf9OmTVroY2Eum7MJUOQ7e37oXZAEIPBR1xer/0eMGBHkWd4+DOk5L730kmRlZTluoPXr19elTW+77TbH+UaHnE+AIt/5c0QPvUkA66UQ0ceTc+Tno6wum3MJUOQ7d27oWZAE5s2bJ+vXr9fRYJZarITm5PQcLOT66U9/qtOqgpxiHkYCZxGgyD8LB38ggagRwNopCP1p06bJF198oYX+9ddfH7X+2VFoBLjwNjRePNphBLADJ6L4zZs3Zx7+6blBes5nn33muPz7Jk2a6IW12D0R6ybYSCBcAtzxNlxyPI8EIifQrVs3sTbHevfdd2XFihWRG6UFIwQYyTeClUajQaCoqEhQTQd5uTfddJPgF4+fW15enjz77LOOS89B1BUl1x555BE/Tw/HbiMBRvJthElTJBAGgWHDhumIPv4Gv/7669KlSxfp27dvGJZ4ikkCjOSbpEvbRgl8+umncvDgQbn88stl/PjxRvtyuvF9+/bJX/7yF8cJfORs3nPPPRT4Tr+AXOYfRb7LJozuepIA/vZOnjxZb6qIiD72qWFzFgFG8p01H/QmSAIHDhzQW2136tRJrrvuuiDP8uZhq1atkg8++EAqKiocM0CkU1x88cVa4DvGKTriGQIU+Z6ZSg7E5QTwFD0pKUn27NkjEPpciOusCWUk31nzQW+CJDB//nydpnPVVVcJFgL5tc2dO1f/YnWSwEdZzEcffZQC368XZRTGTZEfBcjsggSCIICADoQ+1lxhIe7KlSuDOIuHRIsARX60SLMf2wjgl8i3334r2IXPz2k6r732mt6YxDawERqC8EL0/s9//jNzMyNkydNrJ0CRXzsffkoC0STQo0cPwf40aIjmI7LP5gwCFPnOmAd6ESSBwsJCQclM/JG/+uqrgzzLe4c999xzgg1JnNJatmwp9913n9x7771OcYl+eJiAJfK5462HJ5lDcxWByy67TO99kpubq4W+q5z3sLMU+R6eXC8ODWk6ycnJWuD36tXLi0Osc0xPP/20XnBc54FROmDIkCHy//7f/+MmZFHizW5E3+SDA3e85dVAAs4hgLQd7IOydetWvU7MOZ751xOKfP/OvetGfvjwYb3YtnPnzr6N4v/ud7+T9PR0R8xd06ZNdQm1hx56yBH+0An/ELAi+RT5/plzjtT5BPA3wUrbQfU7pNWyxZYARX5s+bP3EAggio8SXUjTwS8Tv7XHH39cUAvfCa1Pnz7yzDPP6PKlTvCHPviLAEW+v+abo3UPAeyJgog+2rvvvCuZmZnucd6DnlLke3BSvTikNWvWyLp16+Siiy6SsWPHenGItY7p17/+ta5FXOtBUfiwYcOGumTpf/7nf4q162gUumUXJHAWAevaYyT/LCz8gQQcQQCBuFGjRkl6Rjrz82M8I6yTH+MJYPd1EygtLdWLbevVq+fLNB2Uo3TCJiPY0RCpORdccEHdk8YjSMAgAUbyDcKlaRKwgQDSdhITE3VwrmfPnnLDDTfYYJUmQiXASH6oxHh81AkgTSchIUELfPyy8FP71a9+FXOBD0GFUqVPPfUUBb6fLj4Hj5Ui38GTQ9dIQBFo27btmbSd9957T7Zv304uMSBAkR8D6OwyeAIQ9yiZiSiy30pm/vKXv4z5LrbNmzfXZTHvvPPO4CeNR5KAYQIU+YYB0zwJ2EAA6bVTpkzRllA/Pz8/3warNBEKAYr8UGjx2KgTQBS/pKREC/zGjRtHvf9YdfjII49IrGuA9+7dWy+uRW4lGwk4iQBFvpNmg76QQM0EkKYzePBg/TQeQp8tugQo8qPLm72FQGD9+vWyevVqGTlypIwZMyaEM9196MMPPxzT+t9Y+zB58mRBNZ+qDTmWbCQQawKWyI/1jXCsOcCnlxYAAEAASURBVLB/EnADAVTbadGihSxbtky++uorN7jsGR8p8j0zld4aSEVFhU7TqV+/vq/SdBDBj2XDolqsA7j55pvPcgN7FODm46233jrrff5AArEgYIl8VteJBX32SQKhEejateuZ+vmI5u/fvz80Azw6bAIU+WGj44kmCSBNB8ISefjYQc8P7YknnohpBB871/71r3+Vvn37noMbqTvIr0xJSdEVE845gG+QQBQJUORHETa7IgEbCKB4w8SJE3X6LYQ+quaxmSdAkW+eMXsIkQCEJBbbduvWzTdR/H/84x9SWFgYIil7Dodguvbaa3V5zNosQuSjIY2KjQRiSYAiP5b02TcJhEcAaTsIGO3bt4/188NDGPJZFPkhI+MJpglA4EPwIorfqFEj093F3P7HH38s8fHxMfGjQYMG8uMf/1hvcFWXA6NHjxY8dsXGZDk5OXUdzs9JwBgBboZlDC0Nk4AxAthMEfXzkYa7aNEiWb58ubG+aLiSAEU+rwRHEdi4caOsWLFC75Z38cUXO8o3E84gKv7NN9+YMF2nzWbNmsnf//53XQO/zoNPH4A5wWPWL7/8MthTeBwJ2E6AkXzbkdIgCUSFANJBEdFHQ9oOizmYxU6Rb5YvrYdIALn4uNv3Q038pKQkmTNnToiE7Dm8Xbt2WuCDdSht0qRJ0qdPH1m1apUsU5US2EggFgQo8mNBnX2SgD0E8Hdk7NixkpeXJ59++qk9RmmlWgIU+dVi4ZuxIICINlbdQ+DHxcXFwoWo9vnss89GtT+rs4EDB8qf//xn68eQXpHec/vttwueAiDNaMeOHSGdz4NJwA4CFPl2UKQNEogdAawDa9mypaxbt04/vY+dJ97umSLf2/PrqtFB5KOWLlbhe7396U9/krKysqgPE49JsZNuJA15+XfccYferOuzzz6TtLS0SMzxXBIImQBFfsjIeAIJOIpA+/btz+yGi78jx48fd5R/XnGGIt8rM+nycSD1AyUzIfBRq93L7fnnn5fMzMyoD/Fvf/ubbWlQqLSDG4b77rtPOnfuHPWxsEN/E6DI9/f8c/TeIHDFFVfo3XARKPr888+9MSiHjYIi32ET4ld3EMVv3LixXHLJJZ5G8Oabb+ryYdEe5LRp06R169a2dou0Kgp8W5HSWJAELJHPHW+DBMbDSMChBK655hrt2RdffMH0TwNzRJFvACpNhkYA1XQOHjyoBT4e4Xm14ZcY8g+j3SDw2UjASwQskc8db700qxyLHwn069fvzBNmpO2w2UuAIt9enrQWBoElS5bourlejuJv2bJFFi5YGAadyE6hwI+MH892JgGKfGfOC70igXAITJkyRe/Bsnv3bkGFPTb7CFDk28eSlsIgsHLlSjlw4ICO4ns19SMjI0OQpnPy1MkwCIV/CgV++Ox4prMJUOQ7e37oHQmEQqBJkyZipe0gmo/y0mz2EKDIt4cjrYRJwNoIyssVdbDQtqSkJExC4Z1GgR8eN57lDgLc8dYd80QvSSBYAthoccyYMVJQUMBFuMFCC+I4ivwgIPEQMwRWr16tF6FC4Hfv3t1MJzG2OnPmTMnNzY2qFxT4UcXNzmJAgJH8GEBnlyRgmADSdrAHC7QBvtgiJ0CRHzlDWgiTgBXF92ou/tq1a6NeLeAvf/lLmLPB00jAPQQo8t0zV/SUBIIl0KlTp7Nq5584cSLYU3lcDQQo8msAw7fNElizZo3s2bNHRo8eLb179zbbWYysz507N6o933PPPdK2bduo9snOSCAWBCjyY0GdfZKAeQKTJ0+WAQMGSHJyMtN2bMBNkW8DRJoInYDXo/jIw0duYbTapEmTdD5jtPpjPyQQSwIU+bGkz75JwCwBpO2godIOKu6whU+AIj98djwzTAKoFY9/uCNGjJD+/fuHacW5p+EGZt++fVFzcNCgQXLrrbdGrT92RAKxJmCJfG6GFeuZYP8kYD8BRPIR0Udj7fzI+FLkR8aPZ4dBwIrie7GiTmlpqSxYsCAMKuGd0rFjR3nkkUfCO5lnkYBLCVgin5thuXQC6TYJ1EEAJTWRo79jxw7BRpJs4RGgyA+PG88Kk8CGDRtk586dMnToUBkyZEiYVpx7GtJ0iouLo+Igagv/4Q9/iEpf7IQEnESAIt9Js0FfSMB+As2bNz+rdn5aWpr9nfjAIkW+DybZSUO0ovherKiDCP6RI0eight1wp988smo9MVOSMBpBCjynTYj9IcE7CcwduxYGTVqlOTl5TFtJ0y8FPlhguNpoRP49ttvZfv27TJw4EAZPnx46AYcfEZOTo4sXrw4ah6ikk779u2j1h87IgEnEeBmWE6aDfpCAuYIYBEunlqvWLFCsJ6PLTQCFPmh8eLRERDwchQfm14hHz8aDbmK2B2QjQT8SoCRfL/OPMftNwJdu3Y9K20nWumwXuFMke+VmXT4ODZt2iTbtm2Tvn376sdvDnc3JPc+/vhjXdM3pJPCPBhPQG688cYwz+ZpJOANAhT53phHjoIEgiGAwBa0Q0JCAtN2ggEWcAxFfgAMfmuOgBXF91pFHWzYgceI0WhxcXHywAMPRKMr9kECjiZAke/o6aFzJGA7Aat2PkpqHj582Hb7XjVIke/VmXXQuLZu3SpbtmyRnj16yrhx4xzkWeSuvPbaa1JeXh65oTostGrVSn7729/WcRQ/JgF/EKDI98c8c5QkYBEYPHiwXHbZZfrHaK5/s/p36ytFvltnzkV+Y8Et2thxY13kdd2uvvPOO5KRkVH3gREeAUFz8803R2iFp5OAdwhQ5HtnLjkSEgiWAER+/fr1ZdmyZVHdcDJY/5x4HEW+E2fFQz6h9BVEPmreYodbrzQs/kHN/2g07CnAhbbRIM0+3ELAEvnc8dYtM0Y/SSByAliEe/nll2tDjOYHx5MiPzhOPCpMAhD4J06c0AK/RYsWYVpx3mmvv/66lJWVGXcMzB588EHj/bADEnATAUvkc8dbN80afSWByAlA5Ddt2lRWr14tu3btitygxy1Q5Ht8gmM9PCtVx0tRfKTo7N69Oypop06dGpV+2AkJuIkARb6bZou+koB9BLA/DKP5wfOkyA+eFY8MkQBWwGPzqz59+siAAQNCPNu5h7/33ntSUVFh3MEhQ4YIdvxjIwESOJsARf7ZPPgTCfiJAHLzUYxi/fr1gsIebDUToMivmQ0/iZDAxo0btQUvRfEPHDgg+DLdsIbhoYceMt0N7ZOAKwlwx1tXThudJgFbCLRu3ZrR/CBJUuQHCYqHhU4AC1OxHbWXRP4nn3wi0cgDvuGGG0IHzjNIwCcEGMn3yURzmCRQAwGk7CB1Z/PmzWIFFGs41NdvU+T7evrNDR65+KmpqVrg467bCw21/hMTE40PZdCgQTJhwgTj/bADEnArAYp8t84c/SYBewhg8e3ll7HSTl00KfLrIsTPwyLgxQW38+bNC4tFKCc1a9ZMHnnkkVBO4bEk4DsCFPm+m3IOmATOIXDZ5ZdJly5d9Nq/tWvXnvM53xChyOdVYDuBY8eO6dr4PXv2FOxS54W2YsWKqGx8dd1113kBF8dAAkYJUOQbxUvjJOAKAg0aNGBufh0zRZFfByB+HDoBRPFLSko8lYv/9ddfhw4ixDNQgcgqDRbiqTycBHxFgCLfV9PNwZJAjQRQaQcBxT179sjKlStrPM6vH1Dk+3XmDY4bIr9hw4aeEfkLFy6U7Oxsg8REb+7xq1/9ymgfNE4CXiFgiXzueOuVGeU4SCB8AhD6aNwF91yGFPnnMuE7ERDYv3+/vqNGRZ22bdtGYMk5pyJVx3S78sorTXdB+yTgGQKWyI9GpSvPQONASMCjBMaNGyf9+vXT5a2/+eYbj44yvGFR5IfHjWfVQMBrC24/+ugjyc/Pr2G09rzdpk0bufbaa+0xRisk4AMCFPk+mGQOkQRCIGCluiKazyd834GjyP+OBb+LkEBZWZmgNn5cXJwMGzYsQmvOOD0aK/anTJnijMHSCxJwCQFuhuWSiaKbJBAlAiNHjhTsEh8fH8+0nQDmFPkBMPhtZAQQxc/MzPRMLv6cOXOkuLg4Mih1nN25c2e59NJL6ziKH5MACQQSYCQ/kAa/JwESAAErNx+FMlD8g40lNHkN2EgAIr9evXqeEfk7duywkc65piBUuLPtuVz4DgnURYAivy5C/JwE/EcAGQQXXXSRpKSkMJp/evoZyfffvwMjI05PT9dbS2PBbYcOHYz0EU2jy5Ytk8LCQqNdouzXhRdeaLQPGicBLxKgyPfirHJMJBA5gcDc/IKCgsgNutwCRb7LJ9Ap7iOKX1FR4Zko/po1a4yiRU7xbbfdZrQPGicBrxKgyPfqzHJcJBAZAew3M2bMGL15JUtqMl0nsquJZ58hAJHftWtX/ajszJsu/QbrCtLS0ox6P3DgQOnevbvRPmicBLxKgCLfqzPLcZFA5AQCc/Nzc3MjN+hiC4zku3jynOL6rl275ODBg54Q+GC6YMECoyW46tevL3fffbdTpo9+kIDrCFgin6XyXDd1dJgEjBPo3bu3TJgwQW9iGY19bowPKIIOKPIjgMdTKwls2rRJf4N8fC+03bt3Gx0GFga1aNHCaB80TgJeJmCJfG6G5eVZ5thIIHwCY8eO1SevXr06fCMeOJMi3wOTGOsh7Ny5U/r37y8oB+n2tnLlSjG5WKdx48Zy7733uh0T/SeBmBKgyI8pfnZOAo4n0KdPHxk6dKgkJSWJn4U+Rb7jL1VnO3jgwAFJTk4WLHbxQjP9y8CKLniBFcdAArEiQJEfK/LslwTcQ2D06NHaWdN/151MhCLfybPjAt+Qj4+GSL7bW05Ojq6va2ocSNFhRR1TdGnXTwS4462fZptjJYHwCFx88cW6IMjWrVtl79694Rlx+VkU+S6fwFi7j1Sdjh07Cha6uL3Nnz/f6IJblPViIwESiJwAI/mRM6QFEvADAb9H8yny/XCVGxpjRkaGYJGqF6L4QIQbFlOtQYMGcvPNN5syT7sk4CsCFPm+mm4OlgTCJgCR37RpU8HeNyiP7bdGke+3GbdxvJYo9oLIxy+AEydO2EjnbFNYBMRGAiRgDwGKfHs40goJeJ1A69atBUK/qKjIlwtwKfK9foUbHB9EPnJjvbDoFlV1TLarrrrKpHnaJgFfEaDI99V0c7AkEBEBK2UHwTy/7a1BkR/RpePfkwsLCwWLbocMGSLNmjVzNYi8vDxdIcjUINq1ayeDBg0yZZ52ScCXBCD0WSffl1PPQZNASAR69eolF154of4777dKOxT5IV0qPNgigCg+0lu8EMWfN2+e0bt7K4pgseMrCZBA5AQg8v0WlYucGi2QgD8JoNIOGqL5fmoU+X6abRvH6qXSmTt27LCRzNmmGjVqJNddd93Zb/InEiCBiAkwkh8xQhogAd8QGDVqlMTFxcm2bdt0wRC/DJwi3y8zbfM4Ecnv1KmTrkFrs+momktJSZH8/Hxjffbr18+YbRomAT8ToMj38+xz7CQQOgE/RvMp8kO/Tnx/BjaVSEtL0/n4bodhMj8PIuTaa691OyL6TwKOJECR78hpoVMk4FgCSJ1t3ry5rrKDEuB+aBT5fphlm8dolc4cOHCgzZajb27fvn3GOu3QoYP07NnTmH0aJgE/E0BlLy689fMVwLGTQGgEWrVqJYjml5SU+CY3nyI/tGuERysCEPn169f3xCZYJjfHGD9+PK8XEiABQwQYyTcElmZJwMMErEIYeIpfXl7u4ZFWDo0i3/NTbO8AkcO+f/9+GTx4sBb69lqPrrW1a9dKRUWFkU6bNGkiV199tRHbNEoCJCBCkc+rgARIIFQCeLo+fPhwSU1N9UU0nyI/1CvE58dbVXVQH9/tbePGjcaGwLr4xtDSMAloAhT5vBBIgATCIRAYzQ/nfDedQ5HvptlygK9WPr4XRGxSUpIRohAfP/jBD4zYplESIIFKAhT5vBJIgATCITBixAjp3r27oHy2FbgMx44bzqHId8MsOcRHlJqEyO/SpYu0bdvWIV6F5wZ2uS0oKAjv5DrOatGihS4vWsdh/JgESCACAhD53AwrAoA8lQR8TMAv0XyKfB9f5KEOHQK/uLjYE6UzFy9eHOrwgz6+R48eQR/LA0mABMIjwEh+eNx4FgmQgOgqOwjIYQFuenq6Z5FQ5Ht2au0fmPVYC4tu3d6stCMT48CjQDYSIAGzBCjyzfKldRLwMoGWLVsKovllZWVa6Ht1rBT5Xp1ZA+OCMG7QoIEnSmdmZWUZICS64tDYsWON2KZREiCB7whQ5H/Hgt+RAAmETmDUqFH6pK1bt4Z+skvOoMh3yUTF2s34+Hg5evSoeGEDLNysmCqd2a5du1hPFfsnAV8Q4GZYvphmDpIEjBFAOc0+ffrIoUOHdGlwYx3F0DBFfgzhu6lriHy0Xr166Vc3/w85eKaaF6oOmWJDuyRgJwFG8u2kSVsk4E8CVjlwr0bzKfL9eV2HPGpL5OPO1+3t8OHDxobADbCMoaVhEjiLAEX+WTj4AwmQQBgEhg4dqs+iyA8DHk/xDgEvifwTJ04YmZimTZtKq1atjNimURIggbMJUOSfzYM/kQAJhE6ga9euMmDAAIHG2bNnT+gGHH4GI/kOnyAnuFdYWKhz1jp37iyNGjVygkth+7Bp06awz63rRJbOrIsQPycB+whQ5NvHkpZIwM8EvJyyQ5Hv5ys7yLHjDhcLVb2QqrN3794gRx36YePHjw/9JJ5BAiQQFgGK/LCw8SQSIIEqBKyUnS1btlT5xP0/1nf/EDgC0wQg8tG8IPKTkpL0WOz+Hyp9WOW4nn32Wb1pGBYpI7rfqVMn/dWsWTO7u6U9EvAtAYh87njr2+nnwEnANgIdO3bUm3xiLyBU37NEv20dxNAQRX4M4bula0vke6Gyjqn6+O3btz8znRAe2dnZkpqaetYmG82bNxekPDVu3FinPeG1YcOG+uv6668/cz6/IQESqJsAI/l1M+IRJEACwRGAsIfIRzSfIj84ZjzKIwQOHjyoN8HCAhW3t+LiYiNDuPDCC8/YfeKJJ6SgoEBvlY3tsgO/Dhw4cOa4wG8o8gNp8HsSqJsARX7djHgECZBAcASQl1+/fn1BlZ277roruJNccBQj+S6YpFi6iMg3RGq/fv1i6YYtfWPDi1OnTtliq6qR4cOHn/UWUnOwyQa+AhvWNuTn55/zFXgMvycBEqibAEQ+Gv5NW9/XfRaPIAESIIFzCbRt21an7Gzbtk3wVfVv+rlnuOMdinx3zFPMvLRSdbyQj49HcSYaBEawqUz16tWT1q1b6y8TvtAmCfiFANbBoFHk+2XGOU4SMEsAaToQ+EjZ8YrIZ3Uds9eM660fOXJEj8ELIt8ai92T4vayonbzoD0SiAYBK3pv6ulcNMbAPkiABJxDACIfa+WQsuOVRf0U+c65vhzpCfLx0bwg8jMyMowwvuCCC4zYpVESIIGaCVDk18yGn5AACYROoGXLlnrR7dGjR3U0P3QLzjuDIt95c+IojyDyIWKRYuL2Zmqn2y5durgdDf0nAdcRoMh33ZTRYRJwPAGvbYxFke/4Sy52DiYmJkpRUZEnovgoaYlFryZa1cW1JvqgTRIggbMJUOSfzYM/kQAJRE4AKTsod42UndLS0sgNxtgCRX6MJ8DJ3R8+fFi754VUHSymMdW8VFPXFCPaJQG7CVgi3yu5s3bzoT0SIIHQCTRt2lSn7Bw7dkwL/dAtOOsMinxnzYejvPFSPn5N9ekjBY4KHyi9xUYCJBBdApbI58Lb6HJnbyTgdQJW4A7RfLc3iny3z6BB/70k8rH7rImGu342EiCB6BOgyI8+c/ZIAn4gAJGPtYgopYmUZTc3inw3z55B37EzLHLy4+Li9C5wBruKiunc3Fwj/bRr186IXRolARKonQBFfu18+CkJkEB4BBo0aKA3xjp+/LjrU3Yo8sO7Bjx/FnaHRfNCPj7GUVZWhhfbW1y3ONtt0iAJkEDdBCjy62bEI0iABMIj4JWUHYr88Obf82d5KVUHTyRMtX79+5kyTbskQAK1EAjc8baWw/gRCZAACYRMACK/ffv2rk/ZocgPeer9cYKXRL6pfHxcCV7Z+tofVzVH6SUCjOR7aTY5FhJwFgH8funXr59gf539+/c7y7kQvKHIDwGWnw6FMEZeWqdOnVw/bFM73davX98T6xVcP8EcgC8JUOT7cto5aBKIGoHevXvrvijyo4acHUWLALZ19kppSNS7NdFYWccEVdokgeAIUOQHx4lHkQAJhEeAIj88bjzL4QQgirE7bJs2bRzuaXDu5eTkBHdgiEdR5IcIjIeTgI0EKPJthElTJEAC5xDo2LGjdOvWTafrIG3HjY3pOm6cNcM+I4qP5pVIfn5+vhFiLVu2NGKXRkmABOomYIl87nhbNyseQQIkEB6BXr166ep8e/fuDc9AjM+iyI/xBDixeyuH3SuR/MLCQiOYW7dqbcQujZIACdRNwBL53PG2blY8ggRIIDwCbk/ZocgPb949fZbXIvklJSVG5qttu7ZG7NIoCZBA3QQo8utmxCNIgAQiI4AKO2huXXxLkR/Z/HvybEvkeyWSj/UFJlqHDh1MmKVNEiCBIAhQ5AcBiYeQAAlEROCCCy6QHj16aJGfl5cXka1YnEyRHwvqDu/TEvleyMlHPr6px/ldunRx+EzSPRLwLgFuhuXdueXISMBJBJCXj7Z7924nuRWULxT5QWHy10EQ+aiR74WFpcnJycYmD6vu2UiABGJDgJH82HBnryTgNwJ9+/bVQ963b5/rhk6R77opM+twcXGx4JGUV1J10tLSjACzoohGjNMoCZBAnQQo8utExANIgARsINC/f39thSLfBpg0EVsCXkrVAcnMzEwjQLHbLRsJkEDsCFDkx449eyYBPxFo1qyZoMpOfHy85ObmumrojOS7arrMO+s1kW9qt9vGjRubnwz2QAIkUCMBivwa0fADEiABmwm4NS+fIt/mC8Ht5rxWI//48eNGpoS73RrBSqMkEDQBivygUfFAEiCBCAkMGDBAW3Db4luK/Agn3muney2Sb2orai8sSvbatcvx+IuAJfK5462/5p2jJYFYEIDIr1evnuvq5VPkx+JqcXCflsj3ysJbUxthcbdbB1/EdM0XBCyRb6pEri8gcpAkQAJBEcA6POTlo2JfdnZ2UOc44SCKfCfMgoN8sES+F2rkA6upKB93u3XQRUtXfEmAIt+X085Bk0DMCFi737opZYciP2aXizM7hsj3So18EDYl8pmu48zrl175hwBFvn/mmiMlAScQcGNePkW+E64ch/iASjQVFRWeqZEPrKYe5bO6jkMuWrrhWwLWXhWm/o37FiwHTgIkUC0BbIrVsGFD2bNnT7WfO/FNinwnzkqMfPJaqg4wmhIAjRo1itEssVsSIAEQYCSf1wEJkEC0CUDoowphVlZWtLsOqz+K/LCwefMkq3ymV/LxMUumRD4j+d78N8BRuYcARb575oqekoBXCFgpO26J5lPke+XKs2EcViTfK5V1gIQi34YLgyZIwIEEKPIdOCl0iQQ8TsAS+W5ZfEuR7/ELMpThWSLfS5H8UMYfyrGM5IdCi8eSgP0EKPLtZ0qLJEACtROIi4sTpOsePHiw9gMd8ilFvkMmwgluWLVfW7du7QR3bPHBVCS/SZMmtvhHIyRAAuERsES+qQpa4XnFs0iABLxOoEuXLpKSkmKsep+d/Cjy7aTpclvWxlFeiVKb/OPftGlTl8823ScBdxOwRL6pG3l306H3JEACpghA5KMlJSWZ6sI2uxT5tqF0v6Hi4mI9CK+IfGs8JmYGu9+xkQAJxI4ARX7s2LNnEvAzAUvkJyYmOh4DRb7jpyh6Dnotkm9K5FviInozw55IgASqErD+HTKSX5UMfyYBEjBJwBL5jOSbpEzbthOwRL5X8s2t8dgOigZJgARiToCbYcV8CugACfiSQNeuXfW4KfJ9Of3uHTQi39jNzfrj6d6RVHrOSL7bZ5D+k0DNBBjJr5kNPyEBEjBHoFmzZtKqVSvm5JtDTMt2EygvL5eKigrxSj4++JiK5Fviwu45oD0SIIHgCVj/DpmuEzwzHkkCJGAPAaTsoCJhQUGBPQYNWWFOviGwbjNrCWKK/LpnzitPOuoeKY8gAecSoMh37tzQMxLwOgErL9/pi28p8r1+JQY5Piu1hSK/bmCWuKj7SB5BAiRgioD175CRfFOEaZcESKAmApbId3pePkV+TTPos/etSL5XFt1i+kpLS43MIiP5RrDSKAmERIAiPyRcPJgESMBGAm5ZfEuRb+Oku9mUJfK9FMk3uRmWm+eavpOAFwhYIp//zr0wmxwDCbiLACP57pov33vrxXSdFi1aGJlXLFBmIwESiC0BS+QzXSe288DeScCPBOrVqycdO3Z0fIUdRvL9eHVWM2YvRvJR4spEY+TQBFXaJIHQCFDkh8aLR5MACdhLANH8oqIiycrKstewjdYo8m2E6WZTFPnBzx5FfvCseCQJmCJAkW+KLO2SAAkEQ8BK2XFyhR2K/GBm0gfHWCLfSwtvTUXyfXA5cIgk4HgC1gJ4pus4fqroIAl4koAbFt9S5Hvy0gt9UF7Mya9fv75Y0b7QidR8BkQFo/k18+EnJBANAta/bYr8aNBmHyRAAlUJWJF8J5fRpMivOms+/dmK5Hupug6m0hICdk/riRMn7DZJeyRAAiEQsP5tU+SHAI2HkgAJ2Eagffv20qBBA0cvvqXIt2263W3IqyLfeqRv9+zk5+fbbZL2SIAEQiBAkR8CLB5KAiRghACi+YzkG0FLo3YS8GK6DvigzJWJxki+Caq0SQLBE6DID54VjyQBEjBDwErZceriW0byzcy766x6NZKPvHwTjSLfBFXaJIHgCVgin+tjgmfGI0mABOwl4PTFtxT59s63a61ZIt9L1XUwGciXM9EKCgpMmKVNEiCBIAlYIp85+UEC42EkQAK2E7Ai+U5N2aHIt33K3WnQEvleW3jbsGFDIxNSWFhoxC6NkgAJBEeAIj84TjyKBEjAHAGKfHNsadlGAl7NyW/UqJGNlL4zRZH/HQt+RwKxIECRHwvq7JMESCCQQIsWLQTB0czMzMC3HfM9I/mOmYrYOmLltZqqRhOr0Zl6MlFcVByrIbFfEiABRcD6XcV0HV4OJEACsSTQsmVLyc7OjqULNfZNkV8jGn99YC1Qraio8NTAjYn8Eop8T10oHIzrCDCS77opo8Mk4EkCrVq1EjzdtzIinDRIinwnzUYMffGqyG/WrJkRqk78x2xkoDRKAg4lQJHv0ImhWyTgMwIQ+WhOjOZT5PvsYqxpuFY9ea9F8ps2bVrTkCN6n9V1IsLHk0kgYgIU+REjpAESIAEbCCBdB40i3waYNGGGgBXJLy8vN9NBjKw2b97cSM+5ublG7NIoCZBAcAQo8oPjxKNIgATMEmAk3yxfWreBQP16lZtGeS2S36FDBxvonGuCkfxzmfAdEogmAYr8aNJmXyRAAjURoMiviQzfdwyBevXraV+8JvJ79+5thHFZWZkRuzRKAiQQHAFL5FuVwYI7i0eRAAmQgL0EKPLt5UlrBghY6TpeE/n4x2eJAbuxJSYm2m2S9kiABIIkYP27ZgnNIIHxMBIgASMEmJNvBCuN2knAqwtvwcjUrreHDh2ycwpoiwRIIAQCFPkhwOKhJEACxggwkm8MLQ3bRcCK5Htt4S34mCqjmZSUZBd+2iEBEgiRAEV+iMB4OAmQgBECTZo00cHE7GPO2xCLJTSNTLn7jFoi32vpOpiJ1q1bG5mQjIwMI3ZplARIoG4C3PG2bkY8ggRIIDoEEM3Py88Tp63Xo8iPzvw7vhcvp+u0b9/eCH+W0TSClUZJICgCjOQHhYkHkQAJRIGAU/PyKfKjMPlu6MLLkfwuXboYmQKW0TSClUZJICgCFPlBYeJBJEACUSDg1Lx8ivwoTL4burAi+V7Mye/Ro4eRKXDaYzkjg6RREnAoAYp8h04M3SIBHxKgyPfhpLtpyF6O5Pfr18/YVLCMpjG0NEwCtRKgyK8VDz8kARKIIgGK/CjCZlehE/CyyAcN60lF6GRqP4NlNGvnw09JwBQBinxTZGmXBEggVALMyQ+VGI+PKgFLBHuxug5ANmrUyAhPltE0gpVGSaBOApbI5463daLiASRAAoYJMJJvGDDNR0bAiuR7MScfZFq0aBEZoBrOZhnNGsDwbRIwTMAS+dzx1jBomicBEqiTAEV+nYh4QCwJeD2S37ZtWyN4WUbTCFYaJYE6CVDk14mIB5AACUSJANN1ogSa3YRHwIrkezVdp2PHjuGBqeMsltGsAxA/JgFDBLgZliGwNEsCJBAygWbNmgl0VE5Ojjjp6SJLaIY8ld48wesiv1u3bkYmjmU0jWClURKokwAj+XUi4gEkQAJRJODElB2K/CheAE7uykrX8WpOfq9evYzh37NnjzHbNEwCJFBJID09XRYuXCjWQluKfF4ZJEACTiLgRJFf30mA6EvsCHg9ko90HYgCE4/RduzYIYMGDYra5B05ckSPo3fv3lHrkx2RQCwJZGdny+zZsyU5OVnikxLkp/fcq/8933PPPfo1lr6xbxIgARIAAauKX0lJiWOAUOQ7Zipi6wjyydAKCwtj64jB3nEjYyK9BqI7Wm3z5s3y8ccfS0lxidx9z91y4YUXRqtr9kMCMSGAdS+vv/66FviJY4+LrN8p016eLvfffZ/wRjcmU8JOSYAEqiHQoEED/a6TMiKYrlPNRPnxrTZt2uhhe7lajLX63e75zczMtNvkOfaQovD111/Lq6++KpijCZdOoMA/hxLf8BoB/LFEBB+bzqVelC+rH02QIxNz5PDeQ/KiEvrR+LfnNaYcDwmQgBkClsh3UgETRvLNzLXrrEIAI9LtZZEfFxcnx44ds31uiouLbbdpGSwqKpK1a9fKunXrJCUlRdf7v+mmm2TcuHHWIXwlAc8SgMDHmpejgwq0wMdA1/08ScobnRT5SuSFl6fJA3ffL6YW1nsWLAdGAiRgOwFL5Dspkk+Rb/s0u9cgovleFvnIm9+6davtE4Q8fwgRO/PycTOyYcMGLe6zsrK0z+PHj5dJkyZJ165dbR8DDZKA0wjMmTNH/3vN7lUkq3+VUCnsTzu58Scp+udB80RF9KfJz5TQ79Onj9OGQH9IgAR8RMBa20iR76NJd9NQIfL37dvnJpdD8hXR73fffTekc4I92I7Ft7hZwE0IvrZt23Zm/cDIkSNl4sSJ0rdv32Dd4XEk4GoC7733nqxfv17yupToCH5xq/JzxrP1P9K00B/2kcj0l2fI/ffcZ+uN9jkd8g0SIAESqIWAFclnuk4tkPhR7AhA5ENoIprfunXr2DliqGfcZTds2FBKS0tt7yHcxbfgHR8fL9u3b9fCPiMjQ/vWpHETueiii2T06NEyZMgQ2/2lQRJwKoG5c+fKypUrpbBtmRb4JzrU/O91580ZWuiPeLuzzHx5ptx79736341Tx0a/SIAEvEvAEvmM5Ht3jl09ssDFt14U+ZgcjBH1tu1uwS4ARGktiPqEBLWAUFXlwVd+fr52B3sVDB8+XIYOHaq/TC0UtnvstEcCdhFAHfzFixdLabMKnaKTG1f3epe9P8iU8sYnZfSrXeXll18WlNUcM2aMXS7RDgmQAAkERcAS+YzkB4WLB0WbQKDIj3bf0eoPm2KZEPk1Lb5FVZy9e/fqr/3790tSUtJZQ23evLmMGDFC+vXrp4V927Ztz/qcP5CAXwhA3C9YsEBO1TulI/hZ/YIv53tw8jEd0R83M07eeOMNwc30ZZdd5hd0HCcJkIADCFgin5F8B0wGXTiXgCXyc3Jyzv3QI+8gSo5qNXa3wMW3q1etluSUZElLS5MDBw6c0xVy6wcPHqxrfGOx4Pnns5LtOZD4hq8IID0HaTpoqx5LkPShJ0Ief/ylOVKhqu6MfSlOkNMPoX/VVVeFbIcnkAAJkEA4BCjyw6HGc6JGwBL5Xq6wgzx3U81afLvoq0VnSnVim+vu3bvrL5Tw7N+//5ld8Uz5Qbsk4CYCqCIFUY629uFESR6VF7b7SaOP64g+hD5uGrD+5vvf/37Y9ngiCZAACQRLwBL5TNcJlhiPiyoBP4h8AG3SpImg/rzdzVp8e+WVV2pR37FjR7F2Era7L9ojAS8QQCUppNegfXt/ssRPyI14WGkXqk2zVMlNCH2k/yCiP3Xq1Ijt0gAJkAAJ1EbAiSU0mSdQ24z57DNL5Hs5XQdT2qFDByMzay2+veKKK3QqDgW+Ecw06hEC2Fvitdmv6dFsuStVDl6ZbdvIMgdUbp6V26NYL+R9//33bbNNQyRAAiRQHQEnRvIp8qubKR+/B6Hv5XQdTK2pTXNqWnzr48uJQyeBagkcPnxYC/yK8grZ8cMM2Xtt5YZv1R4c5ps5PYpk1aMJktW/UFasWCHYXIuNBEiABEwRsES+kxbeUuSbmm2X2vWDyDeVl28tvnXp1NNtEogKgZSUFJk9e7YUFhTK7hsyZefUyr0hTHSe36lEC/30C0/ozbVQYrOsrMxEV7RJAiTgcwIU+T6/ANwwfIh8LBrJywt/8ZvTx4lI/nnnnWfETSy+ZSMBEqieAFLaIPCzs7Nl/zXHZNuP0qo/0MZ3iy4ok1UqRz95dJ7eTXrGrJly4kTo1XtsdImmSIAEPEjAEvlOWnjLSL4HL7RIhmTl5Xs9ZQf16U00a/GtCdu0SQJuJoDAAQQ+Sssenpgjm36SErXhlDWt0EI//tJc2bd7r0x/efqZClhRc4IdkQAJeJqAJfKZruPpaXb34CyR7/XFt507dzYyUdbiWyPGaZQEXEoAFW4g8LHTc+K447L+52dvCheNYWGTrbUPJcrBydmScDBBpimhn5qaGo2u2QcJkIAPCFgin5F8H0y2W4doiXyvR/JRr95EQ2lOLsA1QZY23UoAa1Ug8LHjc+pFleUtYzmWb+9Llr3fz5KMpHSZ9sp0iY+Pj6U77JsESMAjBFhC0yMT6eVh+EXkjxo1ytg0LlmyxJhtGiYBtxGAwMdalaODKstaOsH/LXemys6bj0puRo6O6O/bt88JbtEHEiABFxOwIvlM13HxJHrddUvkez1dB7Xy69WrZ2Q6t2/fbsQujZKA2wi8/fbbsmnTJsnuXSSrVTnL8kYnHTOEHbemy7b/SJfC3AKZ8fJMfSPiGOfoCAmQgOsIWCKf6Tqumzr/OGyJfK+n62BGW7VqZWRi09PTjdilURJwE4GPPvpI1qxZI3ldSvQOtMUtyx3n/u7rj6oFwKlSVlQqL82aJRs3bnScj3SIBEjAHQQskc9Ivjvmy5deIqcM4tcPIr979+5G5hj/wA8dOmTENo2SgBsIfP7557J06VIpbFumI/gnOpQ61u3912SphcDJcurkSb12ADcmbCRAAiQQKgFL5DOSHyo5Hh9VAojmez1dB0DHjx9vjOu6deuM2aZhEnAygUWLFgm+ypuc1AI/N67Yye5q3w5PzFZPGxKlQqUTIcUINyhsJEACJBAKAWujPWsBbijnmjqWdfJNkXWx3Y4dOwqi0VlZ9m817yQsQ4cOlfPPN/NPYO/evU4aKn0hgagQWLZsmSCKj7by1/GS1bcwKv3a0UniuFxVSz9RSlRaEVKNvvzySzvM0gYJkIBPCFiV9Ro1auSYEZtROI4ZHh0Jh0BcXJw+zQ81pNu1axcOojrP8cOTkDoh8ABfEVi7dq18+OGHeswrH4+X9KHu21U2dUSerHo0UQral8q8efPO3LD4aiI5WBIggbAIoIQ2WuPGjcM638RJFPkmqLrcppWr7geRP2jQICOzhdrgq1evNmKbRknAaQRQQeett97Sbq15OFGSR+U5zcWg/Tk66IQW+se7Feu0I+vGJWgDPJAESMCXBBjJ9+W0u2/QforkX3LJJcYmaOvWrcZs0zAJOIXAzp075bXXXtPubLg/WRIm5DrFtbD9yO5dqNYTJMoxlW6EFCTk6bORAAmQQG0EKPJro8PPHEMANeSRU+aHSH63bt2kYcOGRthzJ00jWGnUQQQOHDggr7zyivZo812pcujKbAd5F5krx7sW68W4GUNO6FKg1o1MZFZ5NgmQgFcJMF3HqzPrwXEhmp+WliYnVVk5r7cuXboYGWJhYaFkZGQYsU2jJBBrAgkJCfLqq68KKkps/2G67LvWewv1C9qV6gpBKSPz9KZeM2bMECtaF2v+7J8ESMBZBKzfDVx466x5oTfVEPBTys6wYcOqIWDPW6y5bQ9HWnEWAWz4Nnv2bMnPz5fdN2TKrqlHneWgjd6UNK/QQj9xfK4gNWn6SzPk+PHjNvZAUyRAAl4gQJHvhVn0yRi6x1VuFOWHlB2TefkspemTfzA+GiYqR0HgZ2Zmyv4px2Tbj9I8P/qKBmoh/S8T5fAV2XJo/0GZNms6n9J5ftY5QBIIjQBFfmi8eHQMCcR1908ZzZYtW0qzZs2M0Ga6jhGsNBojAgUFBVrgJycny+GJObLpnpQYeRKbbtc/kCz7v5clKfHJMv3lGZKYmBgbR9grCZCA4wgwJ99xU0KHaiLgp3QdMOjZs2dNKCJ6H/nKW7ZsicgGTyYBJxDABnmI4B86dEgSxx2X9T9PcoJbUfdh092psvvGo5KVlqmF/sGDB6PuAzskARJwHgFG8p03J/SoBgItWrSQ1q1b+6LCDhBcOOzCGkhE/vbmzZsjN0ILJBBjAhD4e/bskdQR+arqTEKMvYlt99tuT5ftt6VLfnaezFAR/V27dsXWIfZOAiQQcwIU+TGfAjoQCgFE85F/iyoxXm+XXnapnHfeeUaGefjwYSN2aZQEokVgzpw5gn0fjg4q8L3At5jvuumooGxo8Ylieenll/jEzgLDVxLwKQFL5HPHW59eAG4btt9Sdtq0aWNkilCJww83Skbg0WjMCbz//vuyfv16ye5dpKvMlDfyflndYKGjbCg2AKsoq9D7Baxbty7YU3kcCZCAxwhYOfksoemxifXqcLp390+FHcyhqbz8U6dOyYoVK7x6mXBcHiYwd+5cfe3mdynRAr+4ZbmHRxve0LAB2JpHEuVk/VPy5ptvyvLly8MzxLNIgARcTcCK5FPku3oa/eO83yL5I0aMMDa5XHxrDC0NGyKwcOFCWbx4sZS0KpdVjybIifalhnpyv9mES3L1TVBpswr54IMP5Ouvv3b/oDgCEiCBkAhQ5IeEiwfHmoAfRX79+vWNYMfuwWwk4BYCS5YskQULFsipeqdk+ePxkhtX7BbXY+Zn8qg8LfQL25bJp59+KvPnz4+ZL+yYBEgg+gQskc+c/OizZ49hEIDg7dy5s6Sk+KcWdrt27cIgVfcpFRUVsmjRoroP5BEkEGMCK1eulE8++UR7sfTJI3Ksr/cX3tuFPH3oCb0wGelNX3zxxRmOdtmnHRIgAecSQE5+gwYN5Pzzz3eMk87xxDFI6EggAUTzcXeanZ0d+LZnv+/Xr5+xsTFlxxhaGraJwIYNG+S9997T1laoCH6GEq1soRHI6lcoq1SJ0ZxeRYInIu+++25oBng0CZCA6wiUlpbKyZMnxUn5+IBIke+6Sym6Dvtt8e3FF19sDDBTdoyhpWEbCGzbtk3eeOMNbQkLSVNU+glbeARyuxfrdQyZAwtk1apV8vrrr4dniGeRAAm4goATU3UAjiLfFZdP7Jz0W15+3759jd2JY8dQLGRkIwGnEcAmV7NmzdJuoSQkFpKyRUbgRIdSLfTThufLt99+q/kibY+NBEjAewScWD4TlCnyvXet2Toiv4l8wLPGbCvI08a4+60JqrQZCQFs1jbr5Ze1CWzuhJKQbPYQKD5dmShpzHHBk5Lp06dLQUGBPcZphQRIwDEErEg+03UcMyV0JBgCnTp10gtJ/LT4dvTo0cGgCeuY1NTUsM7jSSRgggD+Xb/yyitSWlIi229LF2zuxGYvgfLGJ2XVYwly5PIc2bt3r0ybOd03a5zsJUlrJOBcAtbNu5Mq64AWI/nOvWYc4xki234Sp5deeqm+sTExAWVlZfLNN9+YME2bJBASgczMTHn11VcFOzLvvuGo7LrpaEjn8+DQCKx7MEkOXH1MEg7Hy4szp/nqd2popHg0CbiPgFWcpEOHDo5yniLfUdPhTGcGDhyoHdu/f78zHTTgVdeuXQ1YrTS5adMmY7ZpmASCIZCXlyezZ8+WjIwM2T8lS7b9KD2Y03hMhAQ23psie67LlIyUdJk2a7ogVYqNBEjA/QSOHTumB0GR7/659N0IhgwZose8b98+34zd5O63fkp98s0F46KBlqjUHAj8hIQEOXxFjmy6hylk0Zy+rXekyc5bMiQ3M0dmvDxTp/BEs3/2RQIkYD8BRvLtZ0qLUSIwaNAg3ZOfIvlXXXWVmNr9Fik7y5cvj9LssRsSOJsABD7+LSeOOy7rH0g6+0P+FBUCO5TIh9gvzCuQmUroY1EuGwmQgHsJUOS7d+587zkWkmCTKDxatlaQ+wEKFh2bahs3bjRlmnZJoEYCEPg7duyQ1BH5emfWGg/kB8YJIG1n409TpKy4TF5W1Y1QZpONBEjAnQSYruPOeaPXpwkMHjxYf3fo0CHfMBk+fLixsSYnJxuzTcMkUB2Bt99+W3BzeXSw2qDp0YTqDuF7USZw4Kpjsu4XSXJK/YcNs7BxFhsJkIC7CGD/CxQwaNKkibRs2dJRznPhraOmw7nO+DEv/5prrpF69eoZmRRsgb1y5UojtmmUBKoS+Oijj2TNmjWS20PtxPqrBKloeLLqIfw5RgSOXJajb7rKm5yUd999V5YsWRIjT9gtCZBAOAScGsXHWCjyw5lRH57Tv39/PWo/5eUjJ799+/bGZpspO8bQ0nAAgXnz5snSpUuloF2ZrPjPeClpWR7wKb91AgFsloWbr+LW5fLJJ5/IF1984QS36AMJkEAQBJyajw/XKfKDmEAeItKwYUNByk5SUpJ+LOUXJkOHDjU21KRELno0BpeGNYFFixbJl19+KWUqSrz8iSNS0L6UZBxKIG14vhb6JzqWyvz58+XTTz91qKd0iwRIIJDAsWOVu4Q7rXwmfKTID5wpfl8rAStlx095+ZMnT5bzzzPzz6SktESnUNQKnR+SQJgEli1bJp9//rk+e+nvDsvxuOIwLfG0aBHIHFi5XiK3e7F8/fXX8sEHH0Sra/ZDAiQQJoHsbGfWyMdwzKiXMEHxNGcTGDBggHbwwIEDznbURu+wiKZN2zY2Wjzb1IYNG85+gz+RgA0E1q5dKx9++KG29M1Th+VY30IbrNJENAjk9CzSOfpZ/Qp1qd0333wzGt2yDxIggTAJMF0nTHA8zVkEUEazQYMGusa2szwz641VWchEL4mJiSbM0qaPCWBH5bfeeksTWPF4vGQMOeFjGu4cen7nEi3004edkHXr1skrr7zizoHQaxLwAQEuvPXBJPthiBD4ELzp6emSkZHhhyHrMU6aNEnOO+88I+PF7qPImWYjATsI7Ny5U1577TVtas0jiZIyKs8Os7QRAwJFbcq00E++OE+2bNki06dPF1TlYiMBEnAWAUbynTUf9CYCAtbut9gYyy8Ni2lat25tbLhM2TGG1leGkUY3Y8YMPeYNP0uWhEtyfTV+Lw62rGmFrFZ7GiRMyJVdu3bJtGnTJC+PN25enGuOyZ0ETp48KTk5OdKuXTtjJbcjIcOc/Ejo+fBcq5SmnxbfYpoHDhxobLaPHj0qKSkpxuzTsPcJIO1r5ksz9UA335UqhyZVVnvw/si9P8KT9U7JmocT5dCV2XLw4EF5ccY0yczM9P7AOUIScAEBJ0fxgY8i3wUXkZNcRF5+06ZNfZeXf/nllxubhlOnTglKHbKRQDgEkD43a9YsKS4qlu23pcu+a7PCMcNzHE5gw/3Jem5TEpOV0H9RlzN2uMt0jwQ8T4Ai3/NT7K8BYgdYlNLEQhM/LRrt3r270e2q9+zZ468LiaO1hQAeE2NRJl5333hUdt101Ba7NOJMAnhKs2vqUcnKyJJpL00XP1U6c+aM0Cu/E3DyolvMDSP5fr9Cwxg/ovlofkvZscYdBrI6TyksLJSVK1fWeRwPIAGLAK6ZV199VdLS0mT/lCzZdnu69RFfPUxg+w/TZduP0iU/J09mzJopWGzNRgIkEBsCjOTHhjt7NUjAErt+E/kTJkwwSFVk9erVRu3TuHcIlJeX6yo6R44ckcNXZMume1K9MziOpE4Cu284que8pLBYXpr1kqBsKhsJkED0CTCSH33m7NEwAYh8VJuByK+oqDDcm3PMYzOwCy64wJhDWHzLyhnG8HrK8OzZswUpXonjj8v6B5I9NTYOJjgCeHqDuT9ZcVLf8K1Zsya4E3kUCZCAbQQYybcNJQ05hcD5558vqLIDQeq3aP6IESOMTQNKcc2fP9+YfRr2BoE5c+bI1q1bJfWifFn9ywRvDIqjCIsAnuKs+WWinGx4St5++21ZtmxZWHZ4EgmQQHgEIPKbNGlidM1eeJ5VnsWc/Ejo+fhcq5Smn+rlY7qnTp0q9evXNzbzO3bsMGabht1P4P3335f169dLVr9CWfUYBb77ZzTyESSMz5VVv0qQ0hYV8uGHH7JSV+RIaYEEgiaAdB3spePURpHv1JlxuF9+zcvHU4yePXsamx08Hdm8ebMx+zTsXgJz586VFStWSF6XElnxeLxUNDzp3sHQc1sJpIzM07vjFrQrk88//1zmzZtnq30aIwESOJdAfHy8frNbt27nfuiQdyjyHTIRbnMDIr9z586ye/duwWZOfmqTJk0yOtzly5cbtU/j7iOwcOFCWbx4sRS3KpdlTx6Rkpbl7hsEPTZKIGPwCb07bl7XEvnyyy/lo48+MtofjZOA3wmg8AFa3759HYuCIt+xU+N8xy666CLtpN9KuGHcWHhsqlnRAVP2adddBJYsWSILFiyQk/VPyTdPHZaC9qXuGgC9jRqBY30q07iy+xTJ0qVLdZ5+1DpnRyTgMwLW3+o+ffo4duQU+Y6dGuc7Zi1C3bVrl/OdtdnD4cOH22zxO3Moj4jUDDYSWLVqlXzyyScaxOI/HJLj3YoJhQRqJXC8a7FO3Tk6uEBQcQeVmNhIgATsJwCRjxReRvLtZ0uLDiAwdOhQiYuLk7179+oNeRzgUtRcuPnmm40uwGVeftSm0rEdbdiwQd59913tHyL4x/oWOtZXOuYsAgXtSrXQTx2RLxs3bpSZM2fKqVOnnOUkvSEBFxPA+rmsrCzp1auXo0fBSL6jp8f5zlnRfL+l7KDCTo8ePYxNEMpy7du3z5h9GnY2gW3btskbb7yhnVzxm3jJGHLC2Q7TO8cRKGlRriowxUviuOOCql0vvPCCFBUVOc5POkQCbiRgpeo4OYoPrhT5bry6HOSzJfL9mLIzceJEozOBXGw2/xHAk7FZs2bpga95JFFQOYWNBMIhUNHglKxW5TUPT8zRQYMXX3xRcnJywjHFc0iABAIIWItunZyPD3cp8gMmjd+GTgC7wPbu3VsOHDggycn+2nlz1KhRRjfAAFM2fxHAvhPTpk3Tg97ws2RJuCTXXwA4WiME1v88SfZPOSaIPr4w/UVJT0830g+NkoBfCCQmJOqhUuT7ZcZ9PE4/R/OtCkMmpr+0tFTee+89E6Zp04EEUlJSZMaMGYKdjzffnSqHJmU70Eu65FYCm+5Jkd03ZEpGaroW+la6gVvHQ79JIJYEkpKTdJU9lBJ3cmMk38mz4xLfLJHvt7x8TM+NN94o9erVMzZTmzZtMmabhp1DAAu4sDiysLBQtt+WLvu+l+Uc5+iJZwhs+1Ga7PhhhuQey5FpM6Zx3Y9nZpYDiSYBBGQKCgocv+gWTCjyo3lleLQvpOsgbQepBgkJCR4dZfXDaty4sXTv3r36D214F6Jv/vz5NliiCacSQJWGl156SbDYeveNR2UqOnIlAABAAElEQVTXTf7aXM6p8+JVv3ZOzZAtd6ZJ4YlCmT5zumCRNxsJkEDwBKx8fKdX1sGIKPKDn1ceWQsBK5rPBbi1QArzo7Vr14Z5Jk9zOoGSkhJ5+eWXJTU1Vfar6P2225kr7fQ584J/e7+fKd/elyLlpeV6kff69eu9MCyOgQSiQiAx0R35+IBBkR+VS8L7nVgi348pO6NHjza6ADc3N1eWL1/u/YvIhyN85ZVX9BOww1dkyyaVh89GAtEicHDyMVn7cJKcUtmGc+bMkRUrVkSra/ZDAq4mkJSUpP13+qJbOEmR7+pLzTnOY1MsbI6FdB2k7fitDRs2zOiQly1bZtQ+jUefwKuvviq7d++WpDHHZf0D/qpMFX3a7LE6AvETcvSmWeVNT8r7778vixcvru4wvkcCJHCaAPaaQCVBpOm2bNnS8Vwo8h0/Re5x0Irm+zFlx/QC3KNHj8qWLVvcczHQ01oJvP3224JdjbHJ1arH/LWOpVYw/DDqBJIvPi4rH42X4jblMnfuXFmwYEHUfWCHJOAWAqhKhQpovXr2coXLFPmumCZ3OGmJfD+m7DRr1kzwNMNk++qrr0yap+0oEfjoo49kzZo1ktOzSJY/ER+lXtkNCdRMIH3YCS308zuXyMKFC7XYr/lofkIC/iVgper07NXTFRAo8l0xTe5wslOnTgKhj0dZftzIyfQOuPjlYq3qd8cVQS+rEpg3b54sXbpUCtqXydLfHZGKhierHsKfSSAmBLL6FerUHdx8Im2He3TEZBrYqcMJWCLfDZV1gJIi3+EXlNvcs6L5fkzZGTNmjLRv397YlJ06dUogEtncSQBPYr788ktB/vOS3x+Skhbl7hwIvfYsgdzuxVroZw4okJUrV8obb7zh2bFyYCQQDgGIfDy579u3bzinR/0civyoI/d2h9gBtkGDBuJHkY+ZNR3NP3jwoOTk5Hj7IvLg6LBw+rPPPtMj++pPB1Ukv9SDo+SQvEDgRMdSvU4k7cJ82bBhgy7x6oVxcQwkECkBrI3LzMzUm2Cdf7475LM7vIx0Znh+1Ai0a9dOp+yg7vfevXuj1q9TOpo0aZLe6tqUPxUVFYKcbjb3EMA+Bx9++KF2+KunD8rxbsXucZ6e+pJAcatyLfSTVeWnrVu3yosvvijl5Xzy5MuLgYM+Q8BK1enZs+eZ95z+DUW+02fIhf75OWUH0zVhwgSjs4ayi2zuIIAKOm+99ZZ29punDsuxPoXucJxe+p5AeeOTajFugsRfliN79uyR559/Xk6cOOF7LgTgXwJYb4jmlnx8+EqRDwpsthJAyk7Tpk19m7Lz/e9/X5o3b24r00BjpaWl8s477wS+xe8dSABVplALH23Fb+J1uUwHukmXSKBmAueJrP1Fkhy86pgcOnRInn/hBcnKyqr5eH5CAh4mYEXyKfI9PMkcWt0EWrVqpVN2MjIyxI/lNEEIi3BNNtbMN0k3ctuoLjVjxgxtaM0vEyVlZF7kRmmBBGJE4NufpsjeH2RKiopkvjDtBUlJSYmRJ+yWBGJDAGvh9u/fr6P4qCTolsZIvltmymV+jh49Wnu8ceNGl3luj7u33HKLNG7c2B5j1VgpLCyUzz//vJpP+FasCSQmJuocZvix4WfJkjA+N9YusX8SiJjAlh+nyc6bMyTraJYW+ojss5GAXwjs27dPsCZuyJAhrhoyRb6rpss9ziKS3bt3b/n22299G/Wx1iaYmrVVq1aZMl2tXebjVovlrDfx9GratGl6keLmu1Pl0KTssz7nDyTgZgI7bs2Qrf+RJvnH8+XF6dOE64PcPJv0PRQCEPloQ4cODeW0mB9LkR/zKfCuA5dccokenF+j+Xfeeac0bNjQ2AQXFBTI66+/bsy+ZRh/yOfMmSPbtm2z3uJrNQTwOHf69Ol6ceL229Nl3/eYu1wNJr7lcgJ7rs+UjfemSGlxiUyfMV2wuJyNBLxMoKysTCDy27Zty0i+lyeaYwuNAER+ixYtdDQf6SV+bKYf7eEPLKLHJhpKP76gFtohMr1+/XqK/Fog4/qeOXOmXpS4+8ajskt9sZGAVwkcuPqYrHswSU6dPKUXl69Zs8arQ+W4SEAL/OPHj+sofr169VxFhJF8V02Xu5xt3bq1QOgjwunXaP7tt98uJn8pIEfwzTfftPXCwI3DP//5T136EXsd9OjRQ+666y556KGHbO3HK8YwBy+99JKgvNr+Kcdkm4ris5GA1wkcuTxH745b0fiUvP3227J06VKvD5nj8ykBt6bqYLrq+3TOOOwoEYDIX7RokY7mX3755VHq1Tnd4EnGgAEDjOauHjlyRNatWyfjxo2LaOAQ9NiZdceOHdrOsGHDtE2URGWrmcCsWbMEOxHHX5orm+5xUdWRdLW50cenq/50UX8KpraseZD8hASqIZA09riUN46XS2Z215v0lZSUyPe+971qjuRbJOBeAhD5SL01/WTeBCGKfBNUafMMgX79+smoUaNk06ZNWjxCOPqt3XzzzXr335MnTxob+vz588MW+agGs3z5cn2jAAcxR9jQy49zFeoEzZ49W5eJTb0oX9Y+lBjq6bE9/kipyCNplT5c3pQiP7az4dre04bny4pH42W8Evrz5s0TCP0bb7zRteOh4yQQSCA+Pl4XD0EhDWQnuK1R5LttxlzoL6L5EPmotONH4di5c2ddW9dkyTmkRH388ceC0p3BNoj71atXi1Wlh+I+WHKVx7377rs6DS2rX6Es/68joZ3Mo0nAQwQyBxbIyseU0J8RJ1999ZUW+rfddpuHRsih+JWAm1N1MGfMyffrlRvFcY8fP166d++uhb61LXQUu3dEV9dff72cd57aPtJgg2AvL1cpGHU0iHsI1GeeeUYLfKQTPfjgg/rLjzdhdeCq9uO5c+dqdnldSuSbpw5XewzfJAE/EcjpWSSrHkuQY33VTa96MvjWW2/5afgcq0cJuF3kM5Lv0QvTacNC+gfEJaL53bp1c5p7xv1B2hLGbW2LbaJDPCbHAtCHH364WvPIG0fuPqrmoHXp0kUmTZqkF0dXewLfrJbAwoULZfHixVLcqlwW//GQVDQ0l4ZVrQN8kwQcSiCvc4kW+uNmxunfM/iddN999znUW7pFArUTyMrK0pV1+vbtK3FxcbUf7NBPGcl36MR4zS2k7DRt2lSnN6C+ux/blClTjA97z549Ov8/sKOdO3fKyy+/LM8995z+w3vBBRfIDTfcIE888QQFfiCoIL5fsmSJLFiwQE7WPyVfPX1QSlrU/eQkCLM8hAQ8Q6CwTZkW+imj8nQN/RkzZnhmbByIvwi4PYqP2WIk31/XbMxGi00kIPQRAUU0/4orroiZL7HqGAt3ED1PTU015sKpU6fkww8/1NEzcEbp0uzsyl1Xe/XqJaNHj9ZfuOFiC40A1i588skn+qQv//eAFLRTC1fDbUUq+t8kijEW9NdI9WdXl6WnRIrVVwtl0GwWWriEqz+vQvkM34NhX6iYNY0QGLg3tomR3cwjvQbLFEc1PGl07gVQ2rSiMqKvcvRlzU7517/+Jb/4xS+kUaNG1c8L3yUBBxKwRL4bq+pYOCnyLRJ8NU7AEvkQnn4U+QB866236g2mIMZNtfT0dF0KEzn6LVu2lIsvvlhwg8FSmOETxw0T1jGgIYJ/vFtx7cZ+rzbD2qmOgZh893R62lu5IkvUU6y1RSL7SkQ6ql+/IxuLXNlM5NdtReqdK5Zq76SOTzer/v9P7bq7XPWZpp44tFKbuFzSRPXXXORXbeo4ucrHq9Rmdp+qcpt71Y0NfD9SJgLBDJ/7qV2dr1Zj+H17kQ5V/qTkKxV4d7KIdblP6yzStUEV41V+zKoQuf90KVLYm9WlygF1/HhTYuUB49SN7G/bicDeG4r9MsVhpRrHcfVzO8XiZlUyFD7HBfjzjTrmEzXOpep1txrnBeq4QUqY/k7Zua5FHR2rjyF8p6ubavDapvgfVLyaq2tguJrni9TXw4r7wCCFbrjMLS9NXINfnBCZoca3Q40t6fRTrN6K32A1pgvV+H52wRmeJ+udkjWPJEp5I3UNLD0g//73v+XnP/+5tGrVyvKQryTgWALFxcU6VadDhw6uLJ1pgT1PiQ3r16/1Hl9JwBiBv//977J161Z54IEHZPjw4cb6cbLhF198UZBWY7I1a9ZMbrrpJhk8eLAry36ZZBOq7W3btglq4aN98/vDkjFYCZ262hXxleIake7cQSK/UE9vZuXUfNZlSpDiZqAuAVyzhbM/+a3aBfkZJfBrarix+I0Srt9PqDwCJTSX9zr3aIj0HyuRPi//3M+qvtNGCWKI+B9VEXGT4isFNo7/e0eRJ1S/tbUXlIj8VVrlEY+pm5/nOtV29Lmfnber8j3U/X9BnXuVGuNeJdira0OVOF3fuzJij/l5UM1TTX8R6/IFNz93qpuTjeomrqaGm77/UwwequUmyw7m6N/OaxA3LzckinxZx7WPqD6ugfuU2A9oI+d0kQFftpOOnTrKgw88KB06dgj4lN+SgPMIWL/3r7zySrn//vud52CQHtX7k2pBHsvDSCBiArinRFQUryNHjozYnhsNDB06VEfaTdbNLysrk8aNG4ddO9+NXE34jA3CrJziFb+Jl7QLgxC7cOR1FTlOUNHu+kr0IKL75vHKtBaIeUSE+6jod6aKKBcg30G1RHUsjrvbhjrMj6WLPHus0i7+f7GK3t+h7F6v+kVUGX4dUBHmhUqwIeUGrYeKxt57tjDT71+vhB2it2gYy41KOEPE36peRyq7EPb7lS2YKVL/m6/43K/soB+r4dtPT3PLUGP+RS0CF+egdn/q6SjxzC4inVQ0P5T258zKo+HDi+qGAdF0+D5BsYfwR3T+kHoP7ajyBzyS1Bf6VYcJbnjAChuEpanPS04zWq/E+/fV+9XdiOEpweXxlfMIu3gCAU6YTzytgfiNV31ALIM7ouA3KlvVNTuYw66d1+DP1M3PR3mV3uKJEK4VjA9PcM5XY0tRY8OUKVz6hhBPSfCk6nRDLf16FedJ043qMt++Tfr17ceIvgWHr44ksGLFCklISBBUxnNzsRBG8h15eXnXKQjbJ598Um8ugVeU1vRje+edd3SNepNjr1evnjz11FPSsaOKHLKFTAA7Cf/f//2fPm/NLxMlYbwS7sE2K4pqHY+Uj9ldRcYqYRzYICynKSFqtd19K9NDrJ9DfUUUeczhStENwfoXFTF9qv3ZefM45qakSmFm2a8uko90n1GHKo+AsFvRszIlwzrHet2pItiT45VgPi3M0d9fAyK1uJHpvE8EEWq07X3UjmtK+FbX9ihbgw9WfoIUl63q2FCbFcm3zsON1Tz1ewZjsNoH6qbr9mTrp8pXiPe31RxNVMLVaseUagXPw6dvCu5Uov1NdUxgK1fC/ULFCb6j/VAJXNyc4AYosOFGB6lLFocVvUTgW2Czizls2nUNZqp57aDmD623ujldp/xuX7/yZ+v/yO8ff6TyRhXvIf3sn+opSpU25LMOcuH7naRJsybywP0PSP/+/ascwR9JwBkEnn76aTl+/LheT4Kd693aAsItbh0C/XYTgfPPP/9MRRfk5vu13XHHHWL6F0dFRYW89tprfkUc0bhTUlJ0DjGMbHggOTSBX7Vn5K2/0+1cgY/j/q2E0JgA4Y988Ega8rBPB57lpyraipxziP3Ahsj+AiV662r/PvbdEX9Toh0519U1pLwgZ91qawqt7ypfm6k/Mz9UUV+rvaUEdk1tTsCN1E+UoI60dVPC/aO4swU+bN6m/BkVwB3vIc0kUODjvbZKqE9X71vtwGkhb/2MV9ykWQIfUXvMdVWBj+NuUkLhA+WL1R5VN3hKG5/V7GJ+llH1QyTX4IqA+UT0vqrAR19IQ3omIJjwmbqhqTo2ddiuG4/K5rtTpaigSF6c9qLeBR2ns5GAkwhg40qsbcNTd9N/p02PmyLfNGHaP4cAauajygLSdk6cOJ0KcM5R3n/jqquuMj5IbD726aefGu/HSx2gNjLWTSDlCYLk0BUBkfZwBnq7iuxi0WV1DeLre82/+wQLZMNtiBBj4Sga7D4ZILwr3/3u/4iSIyWltob0FuSh36CO+3GASK/unMAbgLxq1N29AYL9HSXyqzlEv2fdADRQ/tfVZ3V+VH3vD+omp+piYOuYiwPmBDcqNaXPQLhb7bBKS6nanj6dHoT3n1c3BEgNqqlhrq2bui3qScmSKr//7GQe6EMk1yCuJathETeeXFTXpqixfdtbJGOASofqV2Mlp33fy9I3zhXlFTJz5kz9d6A6c3yPBGJFwKqqA5Hv9kaR7/YZdKH/WK2OSjt5eXm+/gUPkd+p07mPtO2e0mXLlklmZoAQsbsDD9nLz8+XadOm6Wtz++3pAkEScaspNcUyHJC7fCb/2/oslFdUhEHON9oIJUxR9aa2hkh/be0B9TkWvX7WXaR1ldSTqudZawvwvuVD4DGXqhsGy59kJZQhFqs25LXjM7TvK8FYXcS48tPg/39JlXSYwDMDuSOdqqbWMmDs+Sp9J7Clq5sypPSgNVV/TqumY1V+cvb/Lw9IB0K1osBmJ/NAu5Fcg1ikbd24rFZRfaTlfKyeOFV3M4enRDXdVAX4gxtnpMCdUg9aXn/9dVm5cmXAp/yWBGJLwBL5bi6daRGsklhnvc1XEjBLACJ/6dKlWuRj11W/tltuuUWmT5+uFyKbYoCINDbD+u///m9TXXjCLnbnxFwcPXpU9lyXqVMLbBkYFrXW1iAOrVY1Soo87uLqwt7WCeoVKSad1a9yK28cHwWWhQw49Kxve9bh11kHn/4B5SexkHWf+tqvUleQpoJSj9ZCWRxWQ6BXkH7zlEonQkPEflKA2MV7gak69wRE/vFZuA1camqWcMXnPWu5IarFhF7AbNlHWtLvTo/Peq+6Vyywtlp16T/WZ9ZrJMwtG5Fcgy3VuLCYeubpJ1pY03FrUqXwR0nW76knPbgpw9OhEBrWuKC85iUzu8t7770n+PcXjaebIbjIQ31IICMjQ7A7/IABA/S+Nm5HUNuvL7ePjf47mADukIcNG6ZzMlFS06813FHiEovPrMiBqSlDjvnHH38suKlgq54AUgcSExPl0KRs2XqHype2q9UmNNFHQDbEOV0+kKqq8NSRwjNfRdp/oISWtfAVRroF8as9mBsB2EK9+H8eqyyfiCoq4TZUmvkfJYJxz4JIMHLgG58ePJ4E4D001LAPpiZ95dG1/x8CNZjWpLZJqMVAoEjHPGFfglAaqhxV1+xibtmO5BqEjRlqrjqoeflLpmWxMm0H+fr4+u8Mke7qpvE/VFrXf6oUryCi+TCUMjJPVjwWL5fM6C5z584V1Ca/7rrrvuuD35FAlAlgh3g0L6TqYBxB/gbEoWwkYC8BRPPR1q5da69hl1m79957pUGDMKKqIY4TJcHS0mwUryH27+TDX3rpJdm/f78kjTkuG36WbK+rYerHkJ3AjqhWwy6rdTWUkqzLt1+q62XoQZFXc86uxgPbiFyPU5FcLO59qUtdvakbD3WNX60ivmiITgfW3p+rBP6J008sUO4TOfl2tMBovR32qtqwUnXwPspk4gYllK/qUpvsZG75awfOP3cQ2dxH5JdtKufSsm29ogws9mYYdVjl5qtof5AN+05A6Od3KZUvvvhCByOCPJWHkYDtBBB0RMPu8F5oQYR7vDBMjsGJBCZOnCiLFy8W3Dlj4wm/bo6F1fvYldb0zU55ebm8+uqr8vvf/96Jl0PMfHrjjTdk+/btkjHkhKx6LCFmflTbMRbH5lXJA696YOfTN4iBOeYQXHU15JMH3Becc/gfVNQddeathgWoeGKAtAwstEWtf+teompFHeucqq9YgLvo9GLTd3Iry03iGOwjYDU7qupYtky/goHVJqv0owU9rJ/CezXBPDxPqj8Laz1GqKg+FhjjaQM2x8LXCrWewtpPAOsqLjuidkXuX5lGVr2ls9491qdQViqhP25mnHzzzTc6dQcVyNhIIJoEEOg5fPiw3iHeK+W9KfKjeQWxr3MIXH311YJyVatWrfKtyAeUO++8U6cuma42hEj+Bx98ILfddts5c+HHN95//33ZsGGD5PQskm+eUhFIp7VXg4iQWz4HbtKUUGq9W/NrbcfkqBuLvwWkZqBKzZ9UJLemiHB2wI1I1XUFgR7cpCoN4QkC7C9SwhBpOjgei4bRcPMAIemW1r/Rd56iWk4kzRTzSHyq7dzBauz4QnoO5hGbbz2eXin2IfhxMxfCDdvxbsX6JnvcjDi9h0hpaan85Cc/qc0DfkYCthLwWhQfcKw4jK2gaIwEgiVw+eWXy8CBA2X37t2yefPmYE/z5HGTJ0+OyrhWr1otSUlJUenLyZ189tlnghSmgval8vWfVUqK2xsWsloifLuKstaVy48dXGtqyLNWuk037Pxam8DHQYGLSSuUwKupIaUFedto2EAJQhA76lppK3YtuK3swfz/+6pIvsUc5U9ryrEP9GS2EsMPqTSov6vUFlSrsZop5pb9cF+xdwOeMNymfmfg6U91DalbD6s0nsCyrV+reQ2xFbQr1UI/9aJ8XZQBaXRsJBANAkVFRQKR37p1a8+k6oAbRX40rh72USsBq6ICovl+btdcc01UdqctryiX2bNn+xm1LFq0SL766ispb3pSFv31oFQ0qEWYuoUUFleizjpaoRLQyI+uqRWr8f6jls9ROcdqiFZbQtZ6L/AV4vZllbNvtRp0oPWxBNbMR/UgbJyEhvx5O2rjV1qLzv+xcDhwn4Nfq0h2bQ17GfxGHTNDpUE9qRarJgSkVZlkXptPdX2GqkfYC+BDJfYD11FUd17gJmCB9fWrO7aG90palGuhnzT2uE6je+GFFwQb+7GRgEkCEPjY4Ra5+M2aqYCJRxpFvkcm0s3DwAJcVNpBhRk/74KLOZw6daqcd15tisqemUaZMJSt82Nbvny5fP7553roC/+//QJR4ZmGRbBWm66E94LTAtp6D6+ItEOMBpa9DPwc3w9p/N07m1TEv6Zjs5T4uir+bLEaWDP/OyvffYda6th8Cg3+IZKPdm1zkcB1BZXvOv///+os0vD0v1mMBzdX1d0zIi3px8kiVmoTnpD8UKUvWc0kc6uPcF6xO7DVsKOytZeB9Z71ihvLV9UNgdUuO33Daf0cwmtFw5Oy6tEEOXJ5juzdu1eee+45KSwMeOoRgi0eSgLBEPBiqg7GTZEfzOzzGOMErGi+3zdFwc1Ov379jPNGB2vWrJH4+Pio9OWUTrC4GWsS0Bb+Y78gPcBTDbuOWtFwpMPckFiZJ42Npo6osaJM5eSEyprnSJ2pqY1XQhxVYtAQfZ4SL/KVEuOogAO7yKH/kxJ8Ew5XLsBE1Nb6a4KKM9WJXNiy2r2q7joaBC8q7aC5LVWn0muR/iplB3npVvutitBfHS/yvlpMvF8xx74CH6jvxx35LhKOpxYzu5xdRcg0c8u/UF+nqsXW1o7GKNM6Us05SoWigg6e4uxST33eVuMbq97fcXpdwgB1E4eSqRG2dQ8myYFrjsmRI0e00M/OVk9A2EjAZgLYGR4FQPr06eOZ0pkWIuvXsvUzX0kgJgTwiGzEiBF6E4r169fHxAendProo49K06bhR8GCHQcegaOyjF8a1ny89dZberhfPX1QjneNcKGkU8G92lXlT5+OECs9rmvcT4oX6X2gchMj7DbbXP3qx062NTUsjp2t7FhtpxJyUxJEWuxRX3tFrowX+bNK4YCIRb395T2ViD19zSJiHZhrbtkIfL1TRYchdK3WVvWHSkJubU+rRclYt2CV/lyiGP9IRe0HKOb91Nft6ns8EUHDX10sqK46XtPMdedh/K+Jcvhzda1Yte+x1uMJdSMzRon6LvsqS6zeqcaHawTtGnWjubZXZUnRynci+v/Gn6TInuszJTU1Vf7173+xDHBENHlydQS8GsXHWCnyq5txvhcTAozmf4c9WhvCYHfXt99++7uOPfrdrl27dPlQDO+b3x8WlOzzbEOE/r24ytr1gSUeMWDo6iEqyvp1z3N3nMXngQ0bUq1SYm2MiuoHNmthLXbM/asStrv6Vq4FgLiz2lsBaRvWe4GvEIw/CDgei3GtlJfA49zyPW5Y/theZENvVT5S3exU95QEf23xlAW8aopym2QeCUvsmAu//1uNsboNxjC2Qeq6+h/1+UJ1Q4AbFhvb1v9Ikx23ZsixrGNa6COyz0YCdhGAyEearFdq4wdyOe+UaoFv8HsSiCWBf/3rX7qk4Y9//GOxNsuKpT+x7Pv55583vhMuxodfbijhOW7cuFgO11jfBw4cEFxXaCueiJeUESplxS8NghzpFEgZwQZZSAkJR4Ch7v4hZQM73vZW6SlDVc5+dWIvFK6IdCOlBW1TH5UGErAOoPJd9/4f3MEc0W3cWIEZvkJhZoK5HURRHhMLhuFf+unrAXsnoMKO4TZwYXsZ8VZnadiooTzwswdk0KBB0mjZMqmvbuLRysaMkVKPbGJkGCXNnyaAPXpmzZolY9S189hjj3mOC0W+56bU3QNCXtz//u//So8ePeS//uu/3D0YG7wHg4KCAhss1W6icePGepOsCy64oPYDXfZpYmKiPPPMM9rrNb9MlITxdUSYXTY+17qbq/LwO6tUD1T5QV187KTKRgJBEOi7pK2MRkqaas+XlUm7qlXZlFgruuUWKb7++iCs8RC/E0DKKvZKefjhh2XChAmew2H+1ttzyDggkwSGDh2qI/gJCQni90W44HzDDTeYxH3GdnFxsfz73/8+87MXvkEFISuCv+GBZAp8J03qG+pmCwIf7X5v3VhWDor/N0Xg4ORjsvahJBmdnVUp8Ju3kIpbfyhFf/+7nLrsMpUytUGaPPmkNJs2zZQLtOsRAljIjVSddu3aeTJVB9NEke+Ri9VLw7By81E33+/1kS+99FL9SDoa85uZmSkvvvhiNLoy3kdOTo6+aSkpKZHNd6XKoStYlcM49GA7+EY9mUIpRjTUVXdrVZ3KEfD/MSAQf2mOXHbyoO75b/36yudXTpLi666TXLV5VsErr+j3GyqRT6Efg8lxUZcQ+NhZGak6DRuqdDoPNop8D06q24eEHXAnTpwoKGvl9w2yMJePPPKING8esEjR4ATv2bNHPvnkE4M9mDeNetrYQAcbm+y8OUP2XZtlvlP2UDuBwUqQocRit/2qhGd8ZSlOnPEXtXA3CrnctTvHT91IwNrfYkvPlvLhhx/qDe4wjlK178rxlSv1kCD0Gy9c6Mbh0ecoELCq6lx88cVR6C02XVDkx4Y7e62DgBXNR8oO7rT93m666aaoIVi6dKls2bIlav3Z2RGe/OBpBFJ19k85pity2GmftsIkgJKdG1QJSSzctdrPVZrOw22sn/hKAiERSGtfKV+ajNklhZ3K9QZ3n332mbZxsm1bOX46V7/Jb34j9VXwgo0EAgmgIMOhQ4cEQUV8ebVR5Ht1Zl0+LmxKMXnyZF0TmdF8VRRl/HgZMmRIVGb15MmTuqymG3eYnKYid1jPEX9prmy6JyUqvNhJEARGqao+2DALbaAqtfhf7URe7Fz5M/9PAmEQeO12dR2p9j+v58rRG/fL8R7F8tVXX53Z7O5kmzZScHofkBZqIe55at0RGwlYBKwovhfLZlpjxCtFfiANfu8oAojm169fXy/AxcJQv7eHHnpIWrSIzoZBRUVF8uyzz7oKOcqg7du3T1JH5KuFeYmu8t3zzs5Qgj5PRctSBojsUfXWn+l49mZYngfAAdpN4ItJDeTp3zSV/vvK5fU/HpOGY/dJVv9CWb58ucyZM0d3h3KaRc89p79vPXKk3S7QnksJQE+gdCby8CnyXTqJdNv9BFBGE0IfGzax0k7lfE6dOlXXtY/G7Kanp8vMmTOj0VXEfeCPOn5p6z/yTxyJ2B4NGCDQVMWUutQ3YJgm/UrgD483OSP05/4iW8a02i3pw04Idk1/5fQC3OIpU3TlHTC6YPBgv6LiuAMIIIqP4gwQ+Kis4+XGSL6XZ9cDY4PIRw13pOxEo16805GNHTtWUGY0Wm3Hjh0yb968aHUXVj8ffPCB/qOe17VEvv5TZcWNsAzxJBIggf+fveuAj6rK3l967yHU0HtHOggoImIDe9dFV117d/+rrq66rrq6uvZeWVGxgiJSlCYI0qvU0AkQ0nvP/343vDgJKTOTKW9mzvn9JvNm3q3fnbx33rnnfMfjEKCiP/XVSGQqH/2nnsvDk2s2on3CQR1XRPc9Cpl3Sh59TB+Loq9h8Ok/NAhRvN2KzzmKkk8UREyLQJs2bbQ1Pz09Xaz5J1bplltuQXR0tMvWbMGCBdh6IqOkyzq1siM+gHB7viS2HHOfVswtIoKAIOBzCHx8SQjGfx6NGReF4NRfS/HsV7txbdYm5K1ejf8qdx2SNxReeQUqz6tOkEVFv7y83OdwkgkDqamp2LRpE1q3bi1KvvwgBAEzIEBrPn3Rac3Pzc01w5DcPoaLGUjmdyKQ0cmjIWMN3WEYkGsm4cPH3LlzURlUhR+e24EK9S4iCAgCvonAxt4BuOL1SFz6XhRWDwnC2Rsy8K8NazFEXSfeeuYZ5OXlIec5lf16wAANUIv+/ZGtgvRFfAsBy4BbV91D3YmwWPLdib70bRUCSUlJ2ppPHzph2qmGjLy+/dVNylWSn5+PZ5991lXdNdkPYzRmzpypy81+YQdKIiuarCMFBAFBwPsR+OrcYAz7PhoPPBmBNJWGYfK+fbhD7fitUcQFTPiX9dlnSDvh8tjp7LMR8vPP3g+KzFAjwPvYr7/+qo/p+uoLIkq+L6yyF8yR1nwGyMybN09TJHrBlJo9hZtvvhktWyqWEhfJ4cOHdZIpF3XXYDer1Rb8559/rs/PeX4nChIlj0KDYMkJQcBHEXjhplAMmR2NF24PQ0JpES5auxZRU6agWLFwBak4nundumlkwu+8E5H/eQF+ZRY5HHwUM2+fNhV8GgvHnz4enTp18vbp6vkFPK7EJ2Yqk/RoBMLCwjTd1Vp1oebT+ODBgz16Po4aPHFYvny5y/xLGRtx4MABt/kyMmDq/fff1/DNf2o3sjoUOQpKaUcQEAS8DIH8SD/MHxuE2WcGIXlXKAbtzkHUypUIUMai8PPPx8cqO3YX5Zsf/etyhK5ahcrkZFS0betlKMh0iABpMz9TuzjM/3LDn29AvMqj4AsiSr4vrLKXzLFz587Yp7ZemY01NjYW7du395KZ2T8N8vwmqOyOVH6rqlzjk05KUwYvufpBa/v27XjzzTc1WAv/vgfHuxfYD5zUFAQEAZ9B4EhLf0y/wh9pFfHovj0QSer6laiU/fCOHfGmUvY6q4ynCcrKG/zddwhQuVnKxIjkdb+NpUuXYs2aNTjttNNw5plnet38GpqQKPkNISPfmxKBFi1aaJYduo4MGjRI02uacqAuHBQZiGid4AOQq4Qc+vRvHThwoEu63Lt3L1566SXd15IH9+Fo/zyX9CudCAKCgPcgsObUcszuH4/YTYlopxL+dVbXzKHKfeOziAh0vfdeRC5ejACl7IcqNrFy5c5TqQwoIp6PAJmU6OLJ4OsbbvAdKz5XTpR8z//9+tQMaLUmy8u6deu0i4orOePNDHRvRQmXkpKCjIwMlw2T1vzs7GynBwCzn+eee07Pa/mdB3BoaI7L5igdCQKCgHchkN2hGOt6B2Nvag9UIgCDlLFitDJarPH3R8ILLyJkzx74KRfIEGX5rVKW/gqVlFHEsxGgS+uKFSswduxYTJw40bMnY+PoRcm3ETAp7n4EunbtqnluSYXFrLhk3xEByBawSvmV0qrvKjl48KB2E+revbtTumQMwPPPP48yFRS3+s+HsXdsllP6kUYFAUHAdxDIaVeCY12KkXOwI1KDItGroAA9lTtg7vF0lL3yMoKUS6K/CvAPmjMHforwobxPH98BxwtnSit+Tk4Orr/+eu3e6oVTbHBKouQ3CI2cMCsCAQEBiIyMxErlU5mZmYmRI0e6jDPerJgY4yKtJhkEyG3vKtm9ezfi4uKQrILWHCncWn3hhRf0FuvGK45ix9npjmxe2hIEBAEfRiCvVSnSuxagcn9rbAhNRMuSEnTZshmhy5aj8C9/gX9EJPw3bkCgcuEJVLvHpT5CuehtPwla8Em5PGbMGEyaNMnbptfkfETJbxIiKWBGBNoqBgQq+AzCpdJP674IEB4errPhbt682aVwsD9a8+lO5QhhhsoXX3xR+/1vO/84Nl9yzBHNuqyNIZvKkaqC/UQEAUHAvAgUJJYhrWc+IvfEYU1oawSoBIM9d+5E8DffoOT6qUCXLghQFv0AFbAZrHYVS1TQpohnIfCFokslbebUqVM1Dbdnjb75o5W7UPMxlBbchMDkyZO1BXn+/Pmg24hINQLc2Rg9erTL4Xj55Zf1lqgjOn7llVdw5MgRpIzPxIYrjziiSZe1ccV3pfjm5nxMWiy82y4DXToSBOxEILNTEZbcuxf01f9C+eC/diJRVpgKxC1X7qAljz6qWw6YMQOxd99tZy9SzR0IMKfKHhVjwfthT8Wg5IsiSr4vrrqXzJmJoKjol6htVir6In8gcOWVV7p8d4MUno888sgfg7Dz6LXXXgPZdA4Oz8GqGw/Z2Yp7qj3yahE++0sekvdXYODWcvcMQnoVBAQBmxDIbV2CJQ/uRX7LUvyqGNzePWEkCfu//4PyfUTBBx/o9vwWLECcsgiLeAYCdNWhjB8/3jMG7IRRipLvBFClSdchcNZZZ2kaR7Lt0Edf5A8E7lWWKPrKu1Ko6N+pMkjaK++99x62bduGY33zsezu/fY24/J6iVlV+OCBAjz1dCEqQv1w4UdReFZl2hQRBAQBz0CgML4M857ahbKwCixS+Uc+PMHCEvL00whS95cc5detRZEbiKJv/jUlMceOHTswauQo9OrVy/wDdtIIRcl3ErDSrOsQoDWfMk9lMWSwpsgfCNx22206U/Af3zj/iBSnd9uxrf3JJ5/oGIsstX2+8OE9zh+og3oYvr5cuefk4frpxdjWOxB9f4rBzLOCHdS6NCMICAKuQqA0ogIrb6t2/VygGL0+PnFvCVa7i2GKoSXr99+rhyKKvquWxO5+SEBBOX386Xa34Q0VRcn3hlX08TnQ146KPjOxUtEX+QMBJsq64IILXM4+xOQjDzzwwB8DaeLoq6++0jzGBS3KMPdfu5oobZ7TV80sxaw/52HMsjJ8MzkEvX+OwfYuAeYZoIxEEBAEbELg0OBc7Du1mqp3njIavXneebp+8OuvI0K9RNG3CU63FN6yZQu2qoRmI0aMQB8fpz8VJd8tP0Hp1NEITJkyRXPmL1q0SLt7OLp9T25v3LhxmkPf1XMoUhkl6TLUlMyePRtct/LwSvzwn+1NFTfN+UdfLsL0W/PQ8kglnr43HBe/HWmasclABAFBwH4EKoOqair/ojj0XzvhukNFP+TnhaLo16BjzgPDiu/LvvjGyoiSbyAh7x6NQFhYmLbmcxJizT95Ka+99lrFBtfl5BNO/oZUmHfddVeDvfz000/48ccf9fnvXtqGCouba4OV3HwiKb0S/7snH08+W+1/f+3rkXjkr+J/7+Zlke4FgWYjMPKNZFx6fV90XhRf01ZhQhl+Va47HyirMCX8zjsQqHy9xaJfA5GpDrarpGYbN27EsGHD0PcEU5KpBujiwYiS72LApTvnIUDqSKat3rVrFxYoFgSR2gjcd999YH4BVwsTc91xxx0ndbts2TJ8++23+vtZr2xDSaTrEnidNJh6vojfe7LiPnJtOWbfkI9rZpRgW69AjJgVjU8uCqmntnwlCAgCnoIAlfvLlHLfcVkcAktqq0VhWUHYcmEaflKGpHnduukpRV14IQKOHhVF34QLLIw6tRel9q+59jn5JAh4HAL0zY+OjtaUmqmpqR43fmcP+OGHH3ZLQhCy7tx+++2grz5ljUou89lnn+njH57fgUKVlMZsMvydZFx5VX8kbY/QQ7v2mxLMvS4PQ1eX4btzgjFsdjTW9A8027BlPIKAIGAlApbKfUAd5d5owq8SiEgPwrrrUvFxu3bYmJSkT0XfeBP8lEuiWPQNpNz/npKSou8tQ4cOBbO/iwCi5MuvwKsQYKApFf3CwkJx22lgZZ944glERUU1cNa5X5N1Z/ny5fjwww91R/Oe2o3ctiXO7dSO1lvsiEDs/lAc7Z+nMmIqasznizDt9nxEZ1biBUWNOeX9KOSH+9nRslQRBAQBdyNgjXJvOcbkNTHYMSkdq246hH+rQM6U2FhgTwqi//53XcxS0Y/5618tq8qxCxEwfPFPP923GXUsIRcl3xINOfYKBM455xz069dPP9GvUlRnIicj8OyzzyI0NPTkEy745tNPP9W9/Pz3PcjsXOiCHm3votPS6vwCJV0z8eWt+XjkxWr/+1v+E4kH/h5ue4NSQxAQBNyOgK3KvTHgwCJ/9Pk2CSmnZ+LXOw/gsSEDcSwiAv4qnij6ySd1sWy1O0nxV0QCkS+9pI/lj+sQ2L9/v86VM3jwYJ07x3U9m7snUfLNvT4yOjsRMLjzmQm3QLEjiJyMwDPPPIOgoKCTT7jom/Asc7q6dFgRiy4q8K6jXzrefvMgLplZ7X8/4dMovH21+N+76Och3QgCDkPAXuXecgBdFyXoj/tHZmPJvfvxyqBeKFRJswIUf37kyy+jKjwcuXPn6jJB77yDsC++sKwux05GgDvEFGHUqQ10wONKan8lnwQBz0cgSflNFhcXg5lwS0pKJMq+niUNCAjAoEGD8IvK5EifeVdL8uoYlIdWIr27eaz5kWnBGPFWMs7enYZ7Nm9H6yMV2v/+UkWPuaWHOR9KXL1u0p8g4CkIDPmwLUa/2gHxe8LhX9E897qgwgDktS5BTnKxft/Zpxx+O9vi1EPpCFBWfL82bVE6YjgqRo1C8DffIHDxYlQOGICK9u09BS6PHeemTZs0iQPJN0inLfIHAqLk/4GFHHkZAt27d8fvKkPh+vXrERcXh+TkZC+bYfOnExkZiW6KMeK3335rfmN2tNB6cxQCSv1xrF++HbUdX2XoB21xx9w0XLl7NwLKq7T//Q0vRCI/onkKguNHKi0KAoJAQwj0mZWEMS91QNK2SPiXO+5/NzwjGCnjM3W3BUml2DC4AtFbkjDkcCYCVqxQCv5IlCnFvkrde4KUVT/4++9RfvbZqFT3HxHnIEADFV1A8/PzcfPNN+t7vXN68sxWxV3HM9dNRm0FAvQ5v/LKKxEYGIhZs2bh0KFDVtTyvSJU8qdOneq2iff+vgXIZONu6fd1SzzySQam7NsHBPpB/O/dvSLSvyBgGwIdf4nD+ff2RP8ZrRCS6/idt4Q9YYjb/we1bkaXQvzt9ULM6ttWMe0UouTBB+GnSB9KzjwTJY8+qgcfpTLm+qldZRHnIEC6bLLqnH/++ejUqZNzOvHgVkXJ9+DFk6E3jUDPnj21os+n/JkzZzZdwUdLDBkyBJdcconbZt95cRzGvNjRbf1Twf/vs7k4+8AB5MQFYPKH4n/vtsWQjgUBGxEgG9akR7ph5JvJiDwWbGNtG4orr8a+6lphKTntinH1p5VY1SkOCer6cVRRa1IKlYGp7Nbb9HHs8OH6Xf44FoGjKlcBEyq2Vy5R56mHKZGTERAl/2RM5BsvQ+BstV06ZswYbNu2TVv0vWx6DpsOaccmTZrksPZsbajdmmic8ZTrs/L2/j4Jr/8rG2eonZ4D7YJw7rRIfD/BfQHJtuIm5QUBX0Ug5mAoxqtrxoR/dkFcPcnrnIFL602RgOLOt5SCFqW49sMApMaHoeeG9dh0W7Vyn6+y41ZcdhmgMubGqfuQiGMRoBWfxBpU8N3FFufYGTm+NfHJdzym0qIJEaBLCoNz6J9PLv3WrVubcJTuH1KPHj2Ql5eHA8oi5Q6JOB6MLovjseOcdJd03/63GPzvvjyMPnoEW7op/vtpYVjXz/Hb/C6ZjHQiCPgIAlFHQjD6tQ4Y+FlrMFgeLuQNYABvSH4gjgzMq4V2RiLwey8/XDOzFB337sdMdQ3tSred005DqDIw+W3YgLC161AsgaG1cLP3wwaF53fffYdhw4bhMj5IidSLgFjy64VFvvQ2BGJV8hL651PotpOe7hol0hNxvOKKK0CuYXdJeEYQLv9TPxUw59zLU6tdoZh1Yx5GHDuKX/tH4syvQ7GtW4C7pi39CgKCQBMIhKcH47TnOuHcv3ZHq82RYDZad0j7VTH1djt3fCDu+hfzaFRh/MKFeE9x6DPLd/ZrrwG9+wArVyDmoYfqrStfWo9AZWWldtPx9/cXN50mYBNLfhMAyWnvQaBVq1YgbeTq1auRk5ODU045xXsm5+CZkFpznwpAPX78uINbtq45v0o/9FXJZ0ivmd+y1LpKNpTqsC0Yv0wpQK+sTMwbGoNJs4KRE+U4Fg4bhiJFBQFBoAkEQnMCMeq19hgyrQ2iUkPgV+Xe/9XAYn+Uhyn6324n0/+uGhiIxFzF176yECFpaXhH+Y3zXlNxzdUImzYNfmpHObC0DKUjRzQxazndEALMf0NGOAbbjh07tqFi8r1CQJR8+Rn4FAIMxD148CDWrl2rE0F16eJ6H3BPAZzboMQqTd2o3CWdlsWhKqAKx3s6LqFZjw1B+O3CHLTJy8cX4+JxwdcBqBADvruWWPoVBBpEILjQH6NebY+hH7ZDzOFQZbl3r3JvOdAIRae5a2KG5Vc1xz+eHoRRGyowZksuCpX743RlMGEWdr877kDYG28gYN1a+CtazTL1nYhtCKSmpmL69Olo2bKlpswMVgnJRBpGQJT8hrGRM16KAGm2mCSLr86dO6NFixZeOtPmT4usO+700ecMWm6NRIyy3h0cntPsCfVbF4CNUzIQWVyK989MwnXTXejM2+zRSwOCgG8gEKhyZ9ByP+y9ZMQcMpdyb6wA/fKzOhXpxFjGd5bvvw0KxJSfyzB8XyZ2ww+ztm9Hr969EXrVVQhRSmrg0qWo6tETFeoeJGI9At+oRGP79+/HVQpH5sIRaRwB5zq9Nt63nBUE3IIALQCGfz7580mvKdIwAvTRv+CCCxou4IIz7VfEYtLD3ZrVU5+dFdh0bhoCy8rx2pntcOO0ima1J5UFAUHAwQgoH3sq9xfd3AfJKijev8w8lvuTZqrsAz1/aNhAtKtTAO5+nP75wFVHdiD6yFG88OIL2FVRgcK339bfh991J4I2b9bH8qdpBEicsWrVKh0zNm7cuKYrSAlx15HfgG8i0K5dO8VqVqb98wtV8pL+/fv7JhBWzppuTdzx2Lhxo5U1HF8sLDsIPeYnIq1PPoriym3qYPCmcmw4o3on4MXTu+CvbxehIlis+DaBKIVNj0DbddENWpbNPvgRbyVj1BvtEbcvDGSw8QThNWn/qGyURtZvMNjeVbkCBvvh3AUliEjIxorgVli5ciWSlYLaUlmhac0P/uorlF5yKaoiIzxhym4bIwOY6abDeDpmtk1MVHRGIk0iIO46TUIkBbwVgb59+2LXrl1Ys2YNoqKi0KFDB2+dqkPm1bZtWyQnJ+t4Boc0aEcjAWobv/PiBH1TZbZJa+S0lWX49TwVCafk3yP64JnnC8CU9CKCgDchMPrlDugzsyVST8lDcaxtD8HuxGHIh201HWZCSrjHKPcGXowRCFOZdRtzJVw6PAi99lbi7MVFyO2Xg5SyVli9ajUSJ05E+8hI+CvDSejHH6H49tuNZuW9HgTmzZunjXLnnnsumNNFxDoExF3HOpyklJciQLcdKvh02yGbjEjjCHDH495774Wfn/ssbaTNG/xRGwx/t13jg1VnL5tdikUXViv4/1QMF68/XISsjkVN1pMCgoCnIZDeowAByr2Fgaq0MJtdyHF/sXLL6bYgAYFFnqeK5CtDwfqrj2D5nQeahPoe5baztU8grv8+C237bkFVKPD+++9jvrLoV51QWOP6D2iyHV8tcPjwYU2ZSVdbyWxr26/A8/6zbJuflBYEGkWA1nsG8BQXF2v+/ArlLynSOALkJq6qcr+rS+dF8ZjwZMPsSHd8VIwZN1UnrPk/xRT09bWlODi0+cG7jaMjZwUB9yCwY1I6dqpXtApSH/52O0Uz6Z5xNNVr329a4sJbe6PX9y0QnO95tFa5bUuw4taD+P6l7dh+rnUUw0db+IOKPgL98PjHGcg9ZzvKY6rwySef4NtLLoHK6ASUq6y4w4VWs77fDzPb8h5NBT8mpv4cBfXVk++EQlN+A4IAOnbsqBlkyJ9fUlKC3ooBQaR+BEhf9u9//1ufXH1DKpJ2hCvroftsBREqOU7npXGar7oovqxm0E++UIRn/1ntztP5lxY4Fh6GbeenoTzUTdlzakYmB4KA8xA4MiAP8XvD0GZDNMIzg3B4cPUulvN6tL7lnnNa4FTlUtR2bTQCS9x3zbB+xLVLUrlfd20qVt94GNkdimuftOLTnvYByInxw/k/lqJrRgle+msZWu6Kxc7V21B68cXol5UF7N2DsC+/RPHUqVa06BtFSHf9ww8/YODAgbj22mt9Y9IOnKX45DsQTGnKcxHo1asXtqnU4/TPT0pKAv3PRWojwCzBTz/9NJhtcPWfD2P3hAzsPCsD5LIPKnKfRS64MEAr+iG5QTrV/INvF+Opp6sV/PC98TieBKQOyhMFv/ZyyicvRSBDJWhqtSUKrTdFIaDcD8f6upc9rMvCeIxRyj0Zstx5nbB3uXPbWCj37W1X7i37XXlKIJLTqrSin1BRijfvLUabndHYu3o3cpWv+SDFqY+dOxE2ezaKr7nGsqpPHpMcwwi2vfHGG4Xu2o5fgSj5doAmVbwPgcDAQLRu3RorVqxASkoK+vTpg0gVFCVSjQC58p999lm9ZbrxiqPYcXa6PlEZWIUd56RrRT+4wH2KPjNgMnDvlukleOSTY3psMbviURDuvtgB+e0IAu5AoDSiAlRM2/8Wi5bbInF4SK5bAnE7L4nXdJh8d+e1wd414M7g5kuPYcVtB5HdTOXecgzzxwZhzNpyTPmhFCVxFZh+cyGS9kQidfUBpJ1xBoYWqZih339H2E8/o/iKyy2r+twxg21peJs0aRImTJjgc/N3xIRFyXcEitKGVyBASq6wsDCdLjtLbZ0yEZQIUFpaql10srOzlcvLcWy+pFqJtsSGvsDtFa91qGKacJeMVpl5b1+3Q3d/0UNdsXFM86xu7pqH9CsINBcBskeRhjLp90hUqn/JIwOrY1Oa26419XvMbVGt3Cs3upA8910PrBlrfWVIh0kjxpK/7kNG1+odwfrK2ftdufLL/3VwIM5fXIYLlKK/S3mHzr66APEHwpGx+ggOnnoqRigLNjZtRNjy5ShWrjy+KMy2Tit+nMoMTMpM3ptFbEdAlHzbMZMaXoxA165dQbcUJtwgg0y3bs1LwOQNUD333HM4evQoUsZnYu2fUhuc0q4zM0Cebncwe5ySkYF7tmzRY3tW+W5mZLREW+WXfHBYruKpFj/8BhfNxCfoW25rPgQTT8flQ8trXYoOK+LQYmeE+j/IQUmUc0kFGFA76vX2aL8yxiMt9xUhldg3JgsLHk9xuotTRpw/fu8egKu/K8W5i8qweIw/ll2Yh+jDochbnY5dysB0KskNlD96mIoVK3ZzMkKX/3hVhwxK5n3nsssuA+muRexDQJR8+3CTWl6MAFNlb1ZZCBmI6+v8+f/97381tSh5oLlt3ZSknJGp/YEjMlxH4dcrOwcPbdygh/aOiq34TSXtooRlBaG7oueDfxWO93C8RU53In+chsCo1zuACZJIDUm6QhHbECgPq0RQsb/6f1TWfJX47Wg/5/jmD5reWiexarM+2iN97iuDqjTP/fynduGQcm1ylezpEIDUVv648PtSjNpQgVmTgrB5Yg4ij4WgZE0OtvTrh9MUk5my2wti4gAAQABJREFUOPmcov/9999r19nRo0bXZKd31bp4Wz+i5Hvbisp8mo1ASEiITvpEJX/Dhg1o06aN9tdvdsMe1sCbb76J7du3a6vWkgf3WT36PadlIu5AqKLyU2TQTpaO+fl4Ulm7KF937owfVSZjS/FXgYcMQmyp3Bb2jlPsFSIegUCgUk6HvdcOmZ2KsPHKox4xZjMO0rDmM17lwMgclCl/fUfJsPfbYeSbyWi5NdIj2XIYT3RYJQ776fHd2HdqNlRYj8tlXd9ABKgd44u+V6xuKmHW/y4KwSG168Ld0KrVhVijdpLHq3gxP3UvokW/RAXnIsB9sU+uAIj33C+++ELfd2+99VaEhyvqURG7ERAl327opKI3I0D/fL7otkPWnU6dOiEhQVmFfUQ+/PBD/YCTpZQsbl/bKlQo/FU2yBa7IpzG191SBag9u3adMtRXYpFiQ/qkS5cGh0mqzR7zElEWXonMzpIMq0GgTHIieVUM+OIOkiv9yU0yfYcNw7Dmt94YBR4f69M8az4fvka8nax3WBJ3uZc+116QqpSOfLR/HhY9vAfceaxQlnx3yqJRQeh8uAoXfVeCTkeqMPOsYJW1OFfvigStKcPy9u1xZnAw/FUAaui6dSgdMwZVXqr4ZmZmYtq0aSgoKMBNN90Eus+KNA8BUfKbh5/U9mIEkpOTEawuruvXr8eePXvQT22f+oJV4bPPPsPKlStRmFSGH56vDmS1Z5mpUGR3KEIbUvk5mEs/WgWmPaMs+KHqfWXLVnizZ88mhxhQ6o+2yqWAVs39o7ObLC8F3IdAP+XfHaP8kxnknd9KXHWasxKGNZ8xDtxlq1CuO7ZK5PFgjFBWe1rv4/aFgTtkniZVyvMlrXc+ljywTwfW8oHfLDJ7QjCGb67AxbNKEFECLFAMPHwQoaEkfA2wWDG/TVQ7zAG85q1bj/LBp6BSBaR6m/Des3v3blx44YU488wzvW16bpmPKPlugV069RQEevTooRNlbdq0Cfv378eoUaM8Zeh2jfPbb7/F4sWLUR5RiW/e+N2uNiwrkcpv9/gsJK+JRki+Y5g2ghRP/9PKohWvLPkbElvgxb59LLts8jjqaAi6/ZyggjrLkJMsDDxNAubiArHK1Wvwx23BXaQNVx1xce/e1x0t+PzNJ22LQHbHYpt+83wgpkvOoOltdCZdP6V0epyoIR/vWYBf7tmPbZOPoyS63HRTqFQPIKTWPOO3clwyqxTFEf5YPjRQ77wwniJmdSDmq53ksxTDTKBi3QlZtgxVyo20Qu0we4v89NNPWLhwIQYNGqSt+N4yL3fPQ5R8d6+A9G96BJhpjwr+1q1bcfjwYQwePNj0Y7ZngHPnzgVflC/f3wJavhwhZLdh0iyyfESmBTe7yX+qnZV2ird/e1w8nh7Q36726HZAd5DWm6NUUG4BSp3MPGLXIH20Uo+5iWixIwK71G+GyplI8xEILlK/99Ux2mXHmuDSNhuiMPKN9uCOCv9n/Ww3/jd/0M1tQSn36d0LsfyuA9hy0TEUx5lPubecInN6LB0ehIsXlKlg3BKktg3Aun6BJ65P5WixOgyzo6IwKVoFOO/YgaA5cxAQFIQyL7gf7VDzoZtObGws6IcfExNjCY0cNwMBB93GmzECqSoIeAAC9913n/bL37hxIz799FMPGLFtQ6T1nowGlK/f2aq4tR1/V1/00B7snJgBNMMY+MjGTeiUk4N90TF4cuAA2yZZT+nEneE45/+6a17vek7LVy5GgC5VzIzKB8wDKgBRxDEIkEKT/3etlOtcY9Lpl3j9/zDuORWDtFsFPDr+MtBY9445p+ZJfvuf/56i4ol2I72b5zwobu8agEveVEkYg/1wtqLWNITXzd9uOYiKigrcqNx0jp19tj4V/NJLiPnb34xiHvleWFiImTNnokpRhl5xxRWa9MIjJ2LSQYsl36QLI8MyHwLk6l3x6wrtM1ipXEboyuMNQv97+kJSZr26zanZMRlEWZhQhtaK1o/JemyRe9VOykCVw+BoRKRW8EsdxDJBF4TYg6GgBTmwLED77doyLinrOAQ6/hoLZkhlwO3uCeqBUMQhCDDYlNlv+Ts/2q/6f7CmYeWaPnhaW4x4Jxkdl8W6NaFdzZjsPCAb08q/HAKzche0+ENJtrM5t1Q72MYfm3oH4Il7ayd/yupQjNx2xUheH4t5JeXoe9rpSFT+636K7jlsyRKUKVfSKmXl9zT56quvNGU1s9pOnjzZ04Zv+vGKkm/6JZIBmgWByMhIJLdPxnKVhZDBQdxSbK+YDzxZSFdGJh3KnOd2uiTIMatjEQ4NzdWJs4KKrKOD+4vazh2lEqNkh4bhBeWDf9wJ2Q8ZHEy/5U7L4sCU9rltVQSciEsR6PdNK+37veWiNOQohUbEcQgwG3Ur5Z7GgFMGdZJaljSlDKbljhZd2DxVspUCvOqmQ1h/zRHkt/T8QG1a9OuTnHYlyOxYiORNcVialo02U6YgWWUkLx8wAMUnrPv11TPrd8tUbMEc5XZEgxnddAIcZLgx63zdMS4/tUXiiRty7sBK+hQENALz5s3Dxx9/rI/vvfdej6X5Igf+q6++qufx0z9StO+nq5f4zH90Ban4GpNrU1Jw9oEDKA4KxvP9+mKbK/w11SYDffVX3HpQWQQ9X2loDF+znItODcG5D6hA99YlitVpp3LZkVuTI9cmuCAAF9/UB6WKK780skIlXWp+fIwjx2dPWwycJwPTwaG+5dqVtD0CY9/ojKB0P1yhWGjGeGBG3APqms77T3l5OR566CGv2Rm353fszDpiyXcmutK2VyJA7t4ixeyya9curFMsL6eeeqqm2vSkye7btw/MZktZqijlnJUNsylM9pyeiSilbMQeqL01bdS7QN0ILlBjhX8A/qss+JtdSBtHbv1uPyVoZhLuPIg4F4EuCxPQSiVWIne5u36Pzp2h+1rnA9Qpn7RBjEpQx7gHKvyeLNxlW/unVKy54bBP7rgVJJbhaM88tN0Rix0b9uqlZKZ2TxHalhloe1Ttzl5zzTUYPny4pwzd48YpSr7HLZkM2AwI9O/fH4cOHQKtEeTRP/30080wLKvGwAvrM888o8uuVJbqAyPcawWjAl0RUqlcZSJhSdE3TPnf/1ntNlBe7dMHq1VyMlcLxxOnHkB6zE/UMQTC9uK8FSBNY3hWENZdm6roTc3NhOI8FBzbcpeF8crXvh36fd0KMYecn4HasaM/uTUq9/x9rL7xsE1UoCe35Pnf8H8ktW8uWu2OwYFVe1BcXIzevXt7xMRmzZqlE02OHTtWB9t6xKA9dJCi5Hvowsmw3Y/AiBEjQLYd0mqSXnP06NHuH1QTI8jOzsa//vUvzdKw7rpU7FZWUzMIqe6oQLfdEI3AEn+0KyjEXdt+R6jayp2hMtn+pDih3Sm0frZUVmYGhpaHVoFxBSKOQ6ClCsTu810SjgzIw7bzjzuuYR9sKTwjGKf8rw2GK3/7DoqpKDQnyDMpMC3WzlK5z24vsRoGNCWK+vfwwBy02BeJY6sOgdd3GqDMLGtVQq+vv/5as+jQDz801PMfPs2Mt/jkm3l1ZGwegcDtt9+OrKwsDFDBTzfffLNpx0wXoyeffBK5ubnYcvExbFYvM8rEx7vgpS93o5dKcb5EKfdvm5DFqCCpFDvPzMD2c0UhdcRvaMhHbdFtfoIOnkxRLlwitiNAdqjOi+O1xd7PPMlcbZ+IRQ0q91unHMO+UyVDtQUsJx3S/WqUymvQen2UzuNyww03nFTGDF8cP35c++FnZGTgr3/9K5iDRsS5CIiS71x8pXUfQCBPJWb6y1/+omd61llnmZIGjPzKTz31FNLS0rBzUjrWKiu+WeXNhwtwy4fF2J4Qh6f7DUC5n21Um66cF7fM95yWiU2XHnVlt17VV5BK1HTugz20q9YPz+/QgaFeNUEnTiZhTzh6z2qhdpmiEFTouew4dSES5b4uIk1/9i/309mJmWeij3JvvO2225qu5OIS7777Lsjodumll+LCCy90ce++2Z246/jmususHYhASEiI3iJlQqkUxQTTokULtG3b1oE9NL+p559/Hqmpqdg3JgurlD+rWeWe94vx9xeLcLylPy59R/nop8aYmrc7SNEOkumix7xEhGcGgXkARGxDoN3aGG2B3js2Czppk23VfbJ0/y9baerLXt+3QPRhFUxbZt4HYVsWSNxybEGrdlkmkGN+iYiMIJSvzsfOnTt1QKufSYwkZKVbovj8hwwZArPuNNRG1Ds+iZLvHesos3AzAgkJCUhKSsKaNWu0n36/fv1Mk5r75Zdfxp49e5B6Si6W3X3AzUg13P2kxWX46P4C+KuYy6tU1sdF4/yxS7nEMBgzbn+Yqf2K6bOfkBKOXnOU0nUkRGNd5R16V8ML5qAzXRWDEbHbcXa6zwdTNgZpm3VRGPphOwxVvPaMDwnO92yGHMu5inJviUbzjg8PzkWIct/xW6XoRVWirMGDByMoKKh5jTaz9u+//45PPvkEvE9yhyEqqvHMy83sTqpbICDuOhZgyKEg0FwEvvzyS3z77be6GVJUBgcHN7fJZtV/++23sWnTJqT3KMSCf+xuVlvOrNzpYCXm/CkPPbeV4/4nI/DiTbWDsVptisJwxRJCa7knSEVwlQoizcWaqYeFKaaJBTv7oe6I3R+K7/+73SsSGTUxXZtOd/05Acm/xSB+b5jH017WN3HmRNh6QRr2qh1GEcciMGBGK+XKlYRExUp2zz33IM6F9MOWM8lUsVVvvfWWJqi44447MEpl5hVxHQKi5LsOa+nJRxBggo8VK1aA26Svvfaa22bNhF2rVq1CnsqSOPu5HW4bhzUdz/pzHibPKcVb14fi1qcjGqwy9oWOaLs2usHzZjtRFVCFY30KsOGqVGQJK8hJyxNxPBiT7+6pE2DNfsHcv9GTBu+ELyLTVG6GBQlorR5qo9SOEP2svVGYlfb389OQMl6CrJ25vn1mJqH/F6205fzuu+9G69atndndSW1XVlbizTffBC35U1R23ssvv/ykMvKFcxEQJd+5+ErrPooAaSpJq0lLvpF0ypVQzJgxA0uXLkVJbAW+eWOrK7u2ua/n/1WIB14rwq8jgzD6m6YVeHJ/D/ystWdZNpWuphPY9M/DDhX4nNNWaAD5Q+m0NA4j3krG/lHZ+PUO87qS2fyjtqECH1o7LYtD4s4IhGUHAl6c6LegRRm2nZuGXRMzbEBIijYHAbIunTKtjb4X3XXXXejUqVNzmrOp7vTp0/Hrr79i3LhxNeQUNjUghZuNgCj5zYZQGhAE6keACafoExkeHg4GvrpKmGhk/vz5qFSeLTM+3uSqbu3q56ZPS/DO/fnIi/PHKXOisbuj9X7GE57oihY7wu3q162VlMJfGK8yVvZTwXET032ac58KPhV9ujX5iuKXsCcMHZVS30IFbEcdDUGgCt72duHvnTEXQjnrnpXusigew95tpzu/88470bNnT6cP5IcffsCcOXM0tfQDDzyAgADrr+1OH5wPdSBKvg8ttkzV9Qj8+9//1oG40dHRNVlmnTkKMhh89913uosZ0zajMtC8ZsFxK8vw7c35iDteibM/i8bc02z3t+/3VSv0mt0CAaWe69ZQFFemXHqo8Gcgo2uhM38epmubrjp02fnx2Z3w1iRHZL/pqnafWqmEX3THCVDJ3nxFSDG7Sz3Ibp2S5itTNu08O6hEfqPebA9UADfddJNTOeqXL1+OTz/9FB06dAAVfAbcirgHAVHy3YO79OpDCPznP//BunXr9IWOyaicJaQn++KLL3TzX7+71dR8462UYj/zxnwMX1WG+/4Zgf/eWDvQ1haMoo4FY4zy1Y85ZH8btvTnzLLFseVI65WPXRMy1HuBM7tye9u0YF96Q19UhFTiiw+3uH08jhoAKVVbq8zNbTZW+9V78gOovZgUqd8x/e03X3LU3iaknhMQaLsuGmPe6AS/wipcd911mmLT0d1s2bJF++GTQYcJr7qojOUi7kNAlHz3YS89+xACL774oqbXbNmyJR577DGHz/y3337DtGnTdLvfv6RYSlRGVjPLtHvyce2MEnx0VSiuf6HhQFtb5jDsvXbo9Esc/L2EM7wkuhzpyrJPZf/AiBwUJph7TW1ZK5alq8p59/VAZpcizPvnLluru708FSYq9LH7whB1LAShOQFqR8l3rPT1LUBeq1JtuWfciYg5ESD96tg3OiNQERpddtll2l/eUSM9dOgQaNQqKyvD/ffdj8FDBjuqaWnHTgREybcTOKkmCNiKwMsvvYzfVv2Gdu3a4aGHHrK1eoPlN27ciHfeeUefn/evXcjsVNRgWTOcePSVIjz5TCHWnhKIIT/EOHRI4SoRzEiV3p3Kl7cFMJaHVqKgRSkyOxaBXNienjiqxY4ITHiiC5gEa+UtBx36O3BUY8H5gcoiH6liPyIRczBUudsEISQv0GtZb+zCTXnKZanf5NbJaToZk11tSCWXIpCwOxzj3uiCkKN+OkM7M7U3V/Lz80Fj1rFjx3SyqwkTJjS3SanvAAREyXcAiNKEIGAtAga9ZseOHfHggw9aW63Bcjt27MArr7yizy96eA+O9s1vsKwZTlz+fSk+vzkPZeF+6Lo0FgfaOsfy2W51DAZ+3lrRELo3T4FTMVfQlURUILdNsabp3Dc6S1NROrVPBzbeXvG/j365AzZfegxbLjzmwJZtbypSuXy13hyFhF3h2u2LWUODCwLhp/yXRepHgPSw3GXaeNlRn4slqR8Rz/qW7o1U9CP2BYBK/uTJk5s1Ad7btm/fjosuugiXXHJJs9qSyo5DQJR8x2EpLQkCViHw+uuvg4FJXbt2xb333mtVnfoK7d+/H88995w+tfzu/TigUpp7giSnVmLAtgrMPsP2QFtb59f3m5YghZw3ZQdtDAMGWpPJJLNLoWbvyexcaFp+/u7zEjH44zZYeetBpyZDCs8IRuzBEESnKku8Uua52xOmsijTIh9c5I+gIsX6UdkYqnLOEoFyFUNxZGAe1vzpMBhDIuK5CDDofZxy3YnZod4VzSXdd+wRgypz/PjxuPHGG+1pQuo4CQFR8p0ErDQrCDSGADMAkse+V69eYBZAW4VbokYQ7+obD2P3eOGdbgzDka+3By3H3ppcqLG58xyV//KwShRHlSvf/jLktSpBTrti7Q+foR4E3CFJ2yJrumWwsa1ChT1WWSOjU0OUG02IVt5DswIRolxsqLgHlvgpS7znsi7Zioezy5MFav/obKy/6oizu5L2XYgAH3bPerI7Ig4HqkDcESog91qbev/xxx8xe/ZszdbDQFsRcyEgSr651kNG40MI0I9+8eLF6NevH2655RarZ56Tk4MnnngCJSUlus7R/vlYcesBFMeIVa0xEKkUkpedvuAitRGg60WZ8vkviarQOwH5LdVDQHIJyoMrwFgA/VIWXD4olIVVqLLqpY75vT1CZh1a05n8KSQnCKF5AdqyHpIfgKCCAAQXqncq6srSHqgoJwNVQCtZagLKqt9FebcHdTvqqGek7PZFKoFbBvaMk+y0diBo+iq9v0vCgM9b6QztVVVVWlknxaY1YhA+tG/fHn/7298QGxtrTTUp40IERMl3IdjSlSBQF4H33nsPCxcuxKBBg6za5iwtLcUTjz+B7JxsTD5/MoqKi7BgwQJlmS3Fr3fuN33Qbd35u+Nz+5WxGDCjlXbdcEf/XtknDebqxawMVf7qL4/Ve5WKG+B3/sqi7q/82/0qTxTyShC8Z1IVwZU4puJ7Nql4iawO5g7k9x7UXT+TPt+2RP8vW+qOP/roI3zwwQd6h5nJspg0qzHZtWsXXnrpJZAqk0QSjDMTMR8CAY8rMd+wZESCgG8gcMoppyAzMxMrVqxAWlqaVvYbm/nTTz+N9Ix00Pfxuj9dp8v7+fkhZdUOdFkSr1guij0q+LKxuTrrHN1UdiqKP3+lcMYpxhRah0UcgAB1e74Urnxpxb5cWd/Vi8d+VUrBFzE1AvSxTzk9Ez/9IwX7R2WLz72pV6t5gxs0vQ36zEzSjZB+OSgoCEOGDEFubi5WrVqFbdu2YejQofVmqj1+/DieffZZXfeee+5xSQbd5s3Wd2uLku+7ay8zNwkCgwcPRnp6ulb0efEcOHBgvSN7/vnnQR7i4cOH47bbbqspQ7/+uLg4rF+7HsxqWKLcdjI7i/WtBqAGDphl9vfJxxGjfLqjj4SIEtoATvK1lyOgnr1ykosVy9FRtRt4AEcG5Hn5hGV6E57sgg4rYhESEqLzqwQEqODzE8JdZbqCUtEnPTM/s5wh3E1+6qmnNBc+3XpGjhxpnJJ3EyIgSr4JF0WG5HsI0IJCBZ8W/YyMDAwYMKAWCKQn2717N/r27Vsv9WanTp30dum6DevRcnW4tpxSiRVpGoGDipWIzETRR0MRnhlU7VLSdDUpIQh4NAIVwVXaJWfFbQex6fKjYhjw6NW0bvDBKt5lyp29dbA6M9G+/PLL2he/bm3GiXGHmIr+2rVr0b9/f4SHh+tizz/3vN5Nvvjii3HOOefUrSqfTYaAKPkmWxAZju8iQEWfrDlU9LOysvSFlWi8++67YKpwKvKNede1adMG/dRDwJ49exD8Wzki04M1bz6ZVUQaR6AkugL7xmQh5YxMkCM9QmHnq0w8jSMlZz0ZAcZI5LUpwe4JmVj4yB7sOzUbRYpyVcT7EWi9KQrn/F93HcQ+duxYNMWEwx3i0NBQrehT2e/duzc+++wz0Bef7qLXXHON94PmBTOUwFsvWESZgnchYPDojx49GhUVFVi5ciWSkpJ0kJM1M+VOAB8MNm3ahMzuRVh3VSqOdy+wpqqUsUBg8Edt0VFtaQcr5hcRQcBjEVDuOAWJpTh8Si62XpAmLFweu5D2D5y+9/2/aKUbuOqqq3DeeedZ3dhPP/2kA3L9/f1RWVkpVJlWI2eOgqLkm2MdZBSCQC0EjMy4/DIiIkIr7bUKNPGhvLxc+1ryAl2l3CnXXnUYu84ULv0mYKv3dJ9vk9B1UQLC052fvKveAciXgoAdCDCI9ki/PGw/97iiwSy2owWp4g0IjH5V5QhRxgoKrfcNxXw1Ntdly5bhjTfeQIcOHfDwww9rRp3Gyss58yAgSr551kJGIgjUQuCVV17RVvxPP/201ve2fPj555/xyafTUVJUrF1RmMiGPOcitiPQeXE8es5JBNPBiwgCZkSgNKICab0KsGtihnLVkwBaM66RK8d05uNdkbgzHPHx8fjHP/6BFi1a2N09SR8SEhIQFhZmdxtS0fUIiJLvesylR0HAagSOHj2KVq2qt1mtrlSnIH0o+aCwY8cOZHQtxAal6Kf1FPedOjBZ/bGV8m3t901LJOwOV0G6VleTgoKAUxBgQjL+X6eMz8T+EdlO6UMa9SwE4veE49SXOqjYoiBNb/nYY4951gRktA5DQJR8h0EpDQkC5kWAtGdU9OfPn4/KoCqdmn7nWenmHbAHjCz6cCgGfdoarbZEwr9MOOA9YMm8ZojMNpzZpRB7VbA4XyKCgIFAl4XxGPpBO22AYICtLdnUjTbk3XsQECXfe9ZSZiIINInAokWLMG36/1BSqNx3VNIbWvW5xS9iPwL+pf4YPL01Wq+P1sw8OsWr/c1JTUGgXgTK1P9purLY7x2XJRb7ehGSL/t91RJ91S4j5ZJLLsFFF10koPg4AqLk+/gPQKbvewikpKQoP/1PsGPbDs2NTT/9tN7Cqe+IX0KMsu73UH77pKsj574o/I5A1XfbKI2s0MxYe07LwqEhOb4LhMy8SQSGv9MOjBsKDQvF9VOvx5gxY5qsIwW8HwFR8r1/jWWGgsBJCJB9h+47c+fORVUglPtOKnZMEvedk4Bqxhdx+8LQY24iWm2OQliWAllEEGgKAeX1VRJVjuM9CrSPfapkn20KMZ8/H5kWrN1zWm2K1Ow3N954I5joSkQQIAKi5MvvQBDwYQQWL16M/336PxTlF2GPcgOg+w6VDBHHIsAgXSr8LbdGIjRHFH7Houu5rTFRXVFcGbI6FOOoorukfz0DaUUEAWsQSNoegZEfdED4oUAMHToUt912G0JCFGeyiCBwAgFR8uWnIAj4OAJ79+7VVv2tW7ciq1ORDso91kfcd5z1s0jaFoHu8xPB95A8pfBLQmJnQW26dsvCK5HbugTHe+XjgGLCyehcZLoxyoA8A4FOv8Rh+Eft4VdUhXPPPRdXX321ZwxcRulSBETJdync0pkgYE4EmFmX7js//vgj4E/3nSPYfs5xcw7Wi0ZFq37nJXGKoScKsQdDEZIrSr83LG+V+h9iQHtBi1KViKpIcdbnY/8oobf0hrV19xyCCgMwYEYrdFuQoIcydepUTJw40d3Dkv5NioAo+SZdGBmWIOAOBJYuXarcdz5BQW6+dh3YcPURFEeL+44r14I377brohG3P0wr/cLF70r0bexL+dCXhlegMKEM2cnFSFe+9IcH5anPpTY2JMUFgaYRYHwPFfz4PWHo2rUrLr/8cvTp06fpilLCZxEQJd9nl14mLgjUj8D+/fsxffp0bNmyBdnKV5hWffoLi7gHgeTfYtBevRJ3RyBMMfaI0u+GdVDKPH3lC+PLkNNOKfPdCnFEBcXmtC12w2CkS19EoM+3Sej/ZXVixLPOOksr+KGhkn3bF38LtsxZlHxb0JKygoCPIFBVVaXdd3744Qc949+npGHrBWkoD5GgQHf/BOjL325tNGKVpT/yWAhClYtPQKkk43LEulQFABVBlSiOLUdOm2KdSfZo/3zlO1/oiOalDUHAZgToxkfrfRvu7iXG46rLr8To0aNtbkcq+CYCouT75rrLrAUBqxBYtmwZvvrmK6QdTdNBuVuVsn9wmPB1WwWeiwuRuafltkjE7Q1D1JEQhGUHIrBEOYdLYC/oI18RXIkyZY0vVexRRTHlKEwsQ35SCXLblGhXmzwVECsiCJgJgS6L4pWC31q57QVg+PDh2nrfqlW1Nd9M45SxmBcBUfLNuzYyMkHAFAhkZ2dj1qxZmDdvnh4PM+XSsp+fJH7HpligJgZBpb+VegAgjWd0agjCs4KU5d8ffhV+nu36ozYvKgOqQBpK7jAxcZRW3pU/fIH6bZLFhn7yOeolIgh4EgJk3aL1vsvCeAQEBuCKy6/QDDqeNAcZqzkQECXfHOsgoxAETI/A+vXr8e2sb7F7524UKd9kWvV3nZlh+nHLABtHgIm66PYTeTwY4RlBKnGXeinWn2DlBhRSEICgogAEFvtrlyD/cvVw4EiPLaWoV/lXK+oVQdXKenlohQpmrVbambOhRAV+U3kvUsGt9IkvaFGCwjgJBm98VeWspyLQZn20VvBjD4SiW49uuFK55/Ts2dNTpyPjdjMCouS7eQGke0HAkxCorKzUVv1vlLJfUVqOw6fk4nflq5/eVXyWPWkdZayCgCBgPgT6fdUSfb9pqQdG7nuy5wQGSvI8862U54xIlHzPWSsZqSBgGgT27NmjrfprV6/VLhNU9GnZp+uEiCAgCAgCgoD1CMQrl7r+yj2n9SaVL6NlHK67/FqMGDHC+gakpCDQAAKi5DcAjHwtCAgCTSOwcOFCfD7zC+Sn52omEir6hwfnNl1RSggCgoAgIAig208J2j0nSLnGjRo9SvvfJyYmCjKCgEMQECXfITBKI4KA7yKQkZGBb2d+i4U/L9Qg7J6Qoa36TBAkIggIAoKAIHAyAox9YXBtp6VxCAwJ0tSYkyZNOrmgfCMINAMBUfKbAZ5UFQQEgT8QWLNmDT6Z9SnSUo6q4MhSreinjM/8o4AcCQKCgCAgCKDdmhgMmtEGkYeDdMZa+t4zg62IIOBoBETJdzSi0p4g4MMIlJWV6cBcsvBUVVThkOLUpwtPZqciH0ZFpi4ICAKCABB9OAS9Zieh85I4DcfkyZN1cK2fn2clsystLcW3336Lc845B1FRUbK0JkZAlHwTL44MTRDwVAR27dqFr2Z9jc3rNqEipEop+se0sg/Pupd5KvwybkFAEDARAsxJ0Wt2C/Sd3RIBBX7o0aMHzjvvPAwePNhEo2x6KNu2bcM333yDr7/+GqRUfvvtt3HzzTc3XVFKuA0BUfLdBr10LAg0H4FVq1Zh2LBhzW/ISS3Mnz8fX8/6BnlZuTjeo0An0UodmOek3qRZQUAQEATMhUDyqhit4DMZXVRMFCafN9njElstX74cH3zwgX5ZojtmzBgsXbrU8is5NhkCouSbbEFkOIKAtQgcPHgQ7du3x3XXXYepU6fi9NNPt7aqS8sdO3ZMu/AsXrxY98vA3J0qiZZkInXpMkhngoAg4EIEmMyKrjkdl8XqXs844wxtvW/ZspoH34VDsburBQsWaMX+888/b7CNRYsW4bTTTmvwvJxwLwKi5LsXf+ldELAbgaeeegqPPvpoTf0LL7wQN9xwg76R1HxpooPffvsN3333Hfbu3YuqYGDHxONa2WeQroggIAgIAt6AQECpv7bc91YKfkCxnw6spWvOgAEDPGZ6M2fOxIcffqiv100N+o477sCrr77aVDE57yYERMl3E/DSrSDQXAT69u2LrVu3ntQMadj+/Oc/45JLLjnpnLu/YMbcefPmYe78eTh+LA2l0RVK2U/HrjPTURJV4e7hSf+CgCAgCNiNQIdfY7WCH7cvDNHxMbjgvCnwJFrMjz76CB9//DGMXVdrgCCn/+rVq9GxY0driksZFyMgSr6LAZfuBAFHIDBjxgxcccUVjTbFLVRa9q+99tpGy7njZH5+PuivP2f+jyjMLUB+Uil2TVRuPErhl6y57lgR6VMQEATsRSB+T5h2zWm/MkY3MXHiRL2j6glJrbKzs2Eo9xs2bLALgv/85z+4//777aorlZyLgCj5zsVXWhcEnILAZZddhi+//NKqtpkencr+TTfdZFV5VxZKS0vTyv48ZdmvKK9Adodi5cKTDuHXd+UqSF+CgCBgDwJBhQHo9UML0DXHT+X+69+/v1buuctqdjl06BDef/99reDv27fP7uEy1oBxYXyJmA8BUfLNtyYyIkGgUQTWrl0LshoUFdnGPU8WnltvvVUH6TbagRtO7t+/Xyv7DOKipPco1Mr+/lHZbhiNdCkICAKCQOMIdPolTrvmxBwMRUJSAqYo15wJEyY0XskEZ1NSUrRyTwWfRhZ7pG3btnonmcYmM7O72TM3b6sjSr63rajMx+sReOKJJ/Dee++Blhh75NRTT9XK/lVXXWVPdafWIQ8z3XgYpEs5MiBP+etn4PApuU7tVxoXBAQBQcAaBBJ3hWvXnHaro3VxJoRiYG1sbDWLjjVtuKMMr628b1C5z8nJsWsI3bp107vC3BlOSkqyqw2p5FoERMl3Ld7SmyDgEAToR2nwFtcXfGtNJ9xmpWX/4osvtqa4S8usW7cOdOHZvGmz7vfg8BzNxJPWO9+l45DOBAFBQBAgAmFZQegxN1Fb71EFDBo0SCv3vXr1MjVA9LOnYk8Fv7i42K6xkhmIij1fkZGRdrUhldyDgCj57sFdehUEHIJAWVlZjbLPxFj2CC1Rt9xyC84//3x7qju1zrJly3Rw7r7de3U/e8dlaTeezM62uSo5dZDSuCAgCHgtAuEZQeiyKB7dF7ZAcLY/WrZupRJanW/avCTGQtCt85133tHKPVnN7BEjnotsbf7+/vY0IXXcjIAo+W5eAOleEHAUAtOmTdMK/5IlS+xq8tJLL8Wdd96p/f3tasCJlZiUZfb8H3D8sPIh9YNi4clQbDzpyG1d4sRepWlBQBDwVQQi0oPRZWE8ui1MRHCuP6LjYnDm+Akgc05UVJRpYTGUeyr49oqZmdnsnZOv1hMl31dXXubttQiQdYeuPHPnzrVrjjfffDOY4KRfv3521XdWJQYa01//h/lzkJ+Vh/LwSm3Vp8JfFKeoLUQEAUFAEGgmApFpJ5T7RYkIyqNyH62U+zMxfvx4xMXFNbN151V3hHJv5hwrzkPOu1sWJd+711dm58MIfP/993jrrbcwZ84cm1EIDw/Xij4t++3atbO5vjMrZGZmamV/7ry5KC0pRUlMBfaemol9p2Yjq4O48TgTe2lbEPBWBKKOhmi3nK4LExBU4I8opdxP9BHlfsqUKbjxxht1jIG3rq+vzkuUfF9deZm3zyDw9ddf480338TPP/9s85zbtGlTo+ybLeCK7EK07C9avFhx7Jfrue0fna0U/izNymPzZKWCICAI+BwC0UeUcq/ccqjcBxZRuY9Syv1En7DckwKT/vZ0QRLxTgREyffOdZVZCQInIfDpp59qZZ/BrLZK7969tb8+A3TNJqmpqeCclixbiqz0TD28Y33ytWV/3+gsyaBrtgWT8QgCJkAg5nCoVu4ZVBtY7I9Ipdyf5SPKPbOgU7kfN26cCVZChuBMBETJdya60rYgYEIEmMKcln172HhGjx6tlf3LL7/cdDMrLCzUyv7i5Uuwb1c1G09u2xJQ0acrT0FiqenGLAMSBAQB1yIQq5JXacv9ogT4l/r5lHJPxZ5uOWTNEfENBETJ9411llkKAichQEX/5Zdfxo4dO04619QXpNu86667TJvhcfXq1Vi8bAnWr16np1IWXqEVfbryZHQtbGp6cl4QEAS8DIHY/WHoqqz2dMvxU959vuJzHxwcrBV7KvinnHKKl62qTKcpBETJbwohOS8IeDECWVlZeOWVV7Syz2NbZerUqdqyb9abx65du7B02S/KlWcJyouqGXgODsvRCv+hIfZlfbQVIykvCAgC7kMgbp9S7k/43EPRxcfExWLC+DO83ueeMVSG5b5v377uWwDp2a0IiJLvVvilc0HAHAjQmk+rPq37tkpQUJBW9MnE07FjR1uru6T88ePHtSvP/GU/IedI9cNMevdCpezTlScLZaH2JYtxyeClE0FAELAZgTYbotB+ZSw6/aJoL1WGWtJfkgbT26kw4+Pja5T77t2724ybVPAuBETJ9671lNkIAs1C4JdfftHKPhl5bJWWLVvWKPvR0dG2VndJeWYIZpDuwmWLkLJtt+6zIKlUMfJka9/9PEmu5ZJ1kE4EAWcgEHE8GO1/i1GvWMSnhOkuPEW5X79+Pd5++239sgcbXn/pb8+XWY0t9sxL6jQPAVHym4ef1BYEvBIBKvm07FPpt1V69eqllf1bb73V1qouLc+b6vLly/Hrr7/qfiuDqjT9Ji37ab0KXDoW6UwQEATsR8Cw2lO5DyhRKbGVkBFs+PDh+mVWowPHuWnTphrlvqKigl/ZJElJSSDr2V/+8heQ8lhEELBEQJR8SzTkWBAQBGohQPedV199Fdu2bav1vTUfxo4di3vuuQcXXnihNcXdVmbfvn01FJwFufl6HKkD83B4SC4ODc5BcUw1B7/bBigdCwKCwEkI1Ge1j4yOwsjhIzR7DI0NZpatW7fWKPelpbYzf3GHgso9X+3btzfzVGVsbkRAlHw3gi9dCwKegEBubq5W9KnsHzt2zOYhk26Tyr7Zaduys7O1sk93ngMHDuh5lkZU1Cj7h5TSLyIICALuReAPq32Mstr768F4itWeg92+fXuNcl9UZHuGbgbUGsp9ly5d3LsY0rvpERAl3/RLJAMkAlVVVfDzq96GdQQijm7PmjE5o8+SkhLddUhIiDVDaFYZWryp6PNF33ZbhYo+Xx06dLC1qsvLr127FnytWrsKhXnVlJt5bUqUZV9Z9xUrT3o3oeF0+aJIhz6LQH1W+/DoCIwePsojrPZcODJ90ef+rbfeQkGB7e6AvMYbyn3Pnj199rcgE7cNAVHybcNLSrsIASqRvBjSN5wXx8zMTM3xO2rUKIwZMwaTJ0+2aSQrVqzAnDlzsGbNGq28MXESmQf69euH//u//9P+mzY1aGXhH374QTPW0HpD6zD7ZEIpzuPiiy8GrTL2yjfffKPbCA8Pt+umYW+/69at04o+k2rZKq1bt9aKPpV98jebXfLy8vTvxVD6jfEe71mglP1qd578lrZvtRvtyLsgIAg0jEB9VvsevXti1PCRpve1N2a1Z88efS+jgs9dUVvF39+/Rrnn/UpEELAFAVHybUFLyroEAQYi0Y+bF8eGhPy/vGgGBAQ0VER/T+v5008/jcceewyVlfXTJAYGBurETv/5z38ctlvAiznnsHDhwgbHN2TIEMydOxcJCQkNlmnoxJEjRzBo0CDtPuNqJd8Y008//aQ59r///nvjK6vfyatPRZ/p1T1FUlNTaxT+nTt36mFXqZ8fLfu08B9W70LF6SmrKeM0KwL1We3DosNx6vDRHmO1J7bc+TTYcuzJQcKd65tuukkH1Jo1D4lZf0Myrj8QECX/DyzkyAQI0No9cuRIUKGikBbs9NNPR+fOnTULCplQjCClSy65BNOnT2/QIkxr/ZVXXonvvvuuZmadOnXCBRdcoN1/Fi9ejA0bNtSce+aZZ/C3v/2t5rO9Bxzf2WefXaPgUwkfN26c3olgsBX7pf83hcFhCxYsQNu2ba3uLi0tDaeddlpNMKy7lHxjwDNmzNCWfTLV2CrnnnuuVvYnTJhga1W3lqeSb7jzHEutjlMoii874c6Ti6P98tw6PulcEPAkBELyAtF6Y5R+Ja+JrvG179y7C8YNH+sxVntifvDgwRrlPj093eZloNGJyj1fNOSICALNQUCU/OagJ3UdjgAVeirBFLrl0EocExOjP/PPqlWrtAJN9x3Ks88+q91t9Ic6fx544AG88MIL+tsWLVpg5syZ2k3GKEbL/nPPPYeHHnpIf8VdAbrzDBw40Chi1/sTTzyBxx9/XNclpdm8efNgmXGQiZnOOusskMKRMmnSJPz444/6uKk/nP91110HJq8yxN1KvjGO5jDxkNv57rvvroWT0a7Z3w1XHkv//ayORdqd57Cy8Gd1sD24zuxzlvEJAs1FIDTnhGK/KQptlIIfVFC9KxsRHandcRiob3aGHEsMaJgyLPf2EBTQ595Q7vv372/ZtBwLAnYjIEq+3dBJRUcjQCs9/dUpDM6kH3toaOhJ3WzcuBF0dSkvL9dJP1JSUkC/RUv5/fffMWDAAF2GbVA5bsifkewvX3zxha5OhZ/uPfZKfn6+HjsfQjgmjtVSwTfazcnJ0XPYvXu3dhGiZbhr167G6ZPeycLw6KOP4qWXXkJdLmWzKPkcNN2UXnnlFW3Z546DLcL4BCM41x4XJlv6ckbZhvz3j/Q/QcepfPiLYm0PWHbGWKVNQcAdCIRlB9VY7KnYBxZVX7cjY6JwyoBBoHLLV3NilVw9r0OHDuHdd9/VL7pR2iq8fhvKfZ8+fWyt7vDyvG7T2MV7Eg1hjB+ju5A9MVR88OHuNSmYubPOgGE+uDE2zZFEGnVBYL90+2W/DHJu164dTj311EbvsXXbaOgzY9Fo0CIu//3vfxsqZprvRck3zVLIQC677DJ8+eWXGojXXnsNt99+e4Og0Jr9v//9T5+nO875559fq+z999+PF198UX/HwFpa/BuSRYsW6VTnPM8HA0sXnobqNPT9G2+8UTNuuhMZ86mv/AcffKDTj/PcfffdV7PrULfskiVLdBZDPhAYwtTshr+/mZR8Y3x79+7Vij4V/roPJUaZht67deumlf3bbrutoSKm/543GcPCX+O/H+aHgwNVoi0VtMvA3ezkYtPPQwYoCDQXgfDM2oq9QXsZkxCDU/qfopV6XnfrM+g0t29n1t+/f3+Ncm+rQYPjioqK0td1Kvhm2LGggYb3SRqS6lJ7cm3oUjlt2jSQn78pycjI0K6v77//vnaNrVuexjwqyg0Z3uqWt/Yzd8kZf8eHrvruO/QO+OSTT+zOK8A155hp0DnnnHNAYg2ziyj5Zl8hHxof/e+NiyX/mRpL8EHWHSrRFPp1z549uwYpWh+Sk5O1Xz9dcNhmfHx8zfm6B7wYcBeBLj2JiYn6VbeMtZ8tH1Q+/vhj7VrTUF36azJbIYODeeE8evToSdaS4uJiUIlnGQqtKYwdYHZDw9plRiXfmDMtQqTc5M3BVuGN4I477sAVV1xha1VTlTf896n0U/k3JKdDCY71zNMK/9G++SAnv4gg4A0IRKQH17LY+5dV0x/HJcVjyIDBNRb7oKAgj5suCSEMyz2VWVuF7qeG5Z4WbTMIrew0HP3222+1hsPdaEvCCiq48+fPR6tWrWqVs/zAHXgq05bxCLTaM9bAknqZn4nj1KlTLavbfcx1YYbjpvpl/BtdaG3dNaELFmPrDFdZUfLtXiqp6IsI8B/USOxB5Z5KfmNChZh0jBQq8JYXW15kDMvI0KFDtatOY2058hzHzsArCufEQN/GhNuXxkWD24t1LRu0qFCJp/DCSSsLt04tvzezkm/MnTcGWvXtsXycd955WtlnHIOnC7eP+eJv9Pdtv6OyoprxqTKoSlv403rl41iffOHh9/SF9sHxR6adUOy1j300/E4kik5q01JZ7KtdcWixd6abhjNhJ5WzodwbxAm29Mf7lKHcG/c6W+o7s+xFF12Eb7/9VndBYxev1YyPY5zA0qVL8fe//x2bN2/W5zl20ihHR0efNCQq8Yyl4HkK73+8Z7GtsLAwbNmyRVv4qWRTeO+i8aO5vP90yaH7DO+hFN7/uZNPFx3uQvB6+49//AOknabQqMadcGvj72ioIUZ0PTLEU5T8QGPA8i4IuBMB/qMbYs0FkJYE/vPS0k3/dz5lcyeAYlyMeMwnb0uhRZznaVElc4FRx7KMvcfcKjQUfFopGtuJMPrgRdBQ8hlHUFfJZzlaWLgFWXcuRhue8D5x4kTw9dlnn+kbyMqVK60eNndp+CLdJi37w4YNs7qu2Qry5mM8gFJRMPxVt2zfCv/Nx9Bqc3XehMLEMq30U+E/MiBPfPnNtpAyHo1A1LGQWhZ7nGAp5k4qfeup1NcXk+RJ8FFBNJR7umnYKlSaDeW+Y8eOtlZ3evlffvmlRsEnUQSJL+gyaQhz0tDARMY4WvoZA8dd6jvvvNMoUvNOnAwFn/doxsJxd9wQKtWkjZ4yZYpmveMOAgkXDKXfKGfrO33jDQWf+gP7NXa62RZ/g9z95z2E7jqkNGWMW1P0z9zlJ3kHHxCoa3iiiJLviavmhWOmgmyItRlReSOhdYVCy7+hsFs+bTPghvLzzz9r5XLZsmX6oUB/qf7wokbazPouWEYZa98t58B+m+LwZ7ucgyH17V7Q+sGxe4uQ0pQvxlzQjcfwV7dmfozB4AWaa0Vl3/JGZE19s5WJjY3VgeZGsDnjGGjpoqV/6/bfEb4sCB2XxephZ3Yu0hZ+KvzHeuebbSoyHh9BgP71ibvC1SsCLdQrfldYzcypwFKpp3JvPMjWnPTAA/4vvvfee1rBpzJqq1DJNZR7y+u8re04uzzdPw0hK1x911VavmmgoXsRCS8Ye8ZrcN1dGUvjzcMPP1xLwTf64Dut+wa1NV1l6RJUlzzDsnxTx59//nlNEd5bLBX8mhPqgGx6vIdQuKvMHYCIiAj9ue4fPjQwHw9dTg3hmtKLwJNElHxPWi0vHivZZgyxpMw0vqvvnYFLhlimCbekLyNLC5/C//nPf9b4tRt1+E6L/l133aUpLGmdoNXFXnHkHOwdg6fU4w3i6quv1g9eVPYt3a0amwN3YriVbKnsW1qKGqtr9nPc1eGLQeS8kXLHiTcaKv3YcwDxe8LQ6/sWKA+rUop+nrbwk7knP0ky7pp9bT11fJZKPZX7BPWyFCqE3H2kcl+fcmhZ1lOOyYhmKPclJSU2D5uGo5tvvlkH1dqS/8TmjhxQgbuJtKxTuDPeWHJCXpvoMknlmLsbdHc544wzao3CkrSisQRebIv3Zl73yUhHY12PHj1qtWXtB7Ib8UWhTlB3TJbt0MWX93ga5HgvoUttfbvnq1ev1gYYI4aADyCPPPII6P7LnQ1PElHyPWm1vHislum+ab22RnhRMsRSyedFwxBu0ZEfn8IbEf9BaWViNkJmbDUYashTf/3119cK4DXasPbdkXOwtk9PLkfrEB/AqOxT0afybq3QRYv5CCyVfWt2Tqxt393l6O5FdzIjGQ6DyajwU/HnTlXgWj+0XVvtE5vXuhRH+ucio2shMjsVIbeN7YqJu+cr/ZsDgRqlfmcEkrZHIG5v7Wsxr52k+jVe/B/2FqGbiaHc8yHbVjGUe1rveewJQrcWg9SBO4qW99T6xk/XUSOu6quvvjpJoba8dzdm8Sa+lvdpe+g5jfFx15wPK0ykyT4bC+ZmLJtl9mFLQ6HRHt95vTUUfLr/8HdxmkpAaW0+G8u23H0sSr67V0D61whYKunW/sNb/jNbWlws/SYNBZ9bh1QKqTwZQk78d955R1Ne8qLDixcvXAZrj1HO2ndHzsHaPr2hHBWGl19+GVdddZVW9D/99FOrp0X/UHLrG8o+qVW9UbhbwRssXxTSqVLp54suT1FHlN9rdSwbKkOqkNGxEEzIRaWf79ntPdOf1BvX0kxz0kr9CYU+aVsEYg7+YTjhOHv37q1dNAylvr5gSzPNx56xrFixAqQzbojusak2PVG5N+Zk6V5DN6umxDK+wtIt1qg3ePDgGqILBrlefPHFxqla71SWjXs2r23WxK/VaqCeD2yjqXbo+mo8wHHdGivPnSla72mEstQb6una1F/9ofGYepgyOG9HwGCQ4TxLS61zP7AsZ+mDZ0n5xfYYFf+vf/2LhycJt1V5sTKsyOTUt1fJd+QcThqoD3xB+rPp06fXKPtk5LFW6Df5pz/9qUbZr5s3wdp2PKWcoXTxt82bJX/D3Hrma++evfDf4YcWO/7wNa0KqEKmUvazlNJv+Q4/T5mxjNMRCIRnBCFpW6S20lOpjzoSUqtZKvW01lPB4W/M8ppWq6AXfKDCR+XeFqOC5bQ9Wbk35mHEtPGzNUHBlkoxXXbqyr333qut3rSCE1cG7N5yyy21inE30vK7Bx980Kr4tVqN2PGBbliWRiD221AcAFl56CbpDbvDouTb8WORKo5HwFJJr5uIo6HeLKPdLf34627BGUmxGmqHFn4m5uCFiUoSt/OMbWjSctE1hGJsaxrtGJ+5G8CtPEfOwejDF9+Z94Avxkjw4ctga7AGiwULFoAvsjcwlwAZIbxdSHNH/1dLH1huNzOQ11D8+e6X4oeElNo+1bTyW1r8+QBAOk8Rz0bAv9wPMYdDEX04BNGpoeo4BK32RCPoeO2nOkOpJ4UhlXr+lrxdyKhC5d7Y5bV1vt6g3BtztnQxNSipjXP1vTOviyHGfdH4zHc+HNJF9tJLL9XGh1tvvVUbXsaOHQveo7nzyASRhivMjTfeiAceeMCyCYcd04WHDyLc7WWQL8dlJMiiQYisPg1JXR2ioXKe8L0o+Z6wSj4wRsttYMsA1samzn9iQyzrW/6DknGnKbYespzQ786wTJDK0mA8oVuEJWuO0Z/lu+GmYzkGy4unZdm6xw3NoW45X/zMC7HhwkNlnz6X1sqsWbPAFy3dtBqdeeaZ1lb1inLcAueLgWKGMCDdUulP2ZOCuH2KM3pfGDovNkoBOe2K/7D4K6WfLj/loSe4Ef8oJkcmQCCoMEAr8FTkqdBrxT41BJHHgusdnaHUG2443mCprHei9Xw5Y8YMrdzbskNo2Yw3KffGvCzvtZb+9Mb5uu+WZagwcxex7oMhd1FJC81dcmK9fPly/bJsizkD6GLpTCMM2YDqZk2n5Z7MQDQA+YqIku8rK23yedKKZIjBNW98ru+dVvTDhw/XnCK1lSGWdGWM4rdGLJV8PvkbSr41dY0ynTt31pRiHBuj/a2hBbOcq+UcjDZ9/Z1xF/fff38tZd8WOjv6hfLFTMS8sBs+7b6IKx94+Ro5cmTN9Pk/RMXfsPqnqOOYQ8ryq14df/kjqLIorhwFLUoVk0+JZvMho0+BevG9ML6spj05cA4CodmBiKmjyEcrS31Y1sm38JDQELTt3BZkdqFiypdx7L40+KoAAEAASURBVJzRmbvVDz/8UCv3pE+2R7xRuTdwsDRGWSrwxvm673UDc2ngqqvk0x2H8W4ktmhIuAtACz537WmIcYaQXKOu8J5MNx2O8d///neD9Jl163ny55OvEJ48Gxm7xyJAy5IhVLKbElp1DZ98Kte0DBhiSYnFoEQq3XX5fI2yxrulRd2yLSYKYaBO3fqWn42HCrrr0GeRfPfcjqQC39QuAncKDLG0uhrfyXs1AtxKJp+zYdkn24Et8sUXX4Av1qeyz+1jEWjljwogfWcN4f+WpcWfx1Qm+UrcWdvdh3UqA6tqFP58/SBQrfwbDwFlYRVG0/LeCAIheQpjpczTb54KfIyyyPOdFvrggoCTakbFRCG5d/JJijypCX1dqDzSJYcvW9z9LHHzZuXemKfhcmp8buq9bvm6Aam8X9KSb+wQ8F7M4FUmwSIfPZVrumFyV4U75gzMZSIrkic4WjgOun2SfYf3Wbpxkp+fxByvv/46SJPJAGDL+72jx2CG9vzUookDphlWwsfHwJ8h/f3oS0yhgmwksqoPGm71GZy+VNwYsGkIrZOWdemG0xQHLy2caWlpugkG6FjDNGD0Z/nOi5aROpvJm6655hrL07WOae03HhB4Q7HcmahVsJ4PvIkZQXF8N1yG6inqlV8tWrRI++vb61fL3w7deBhzIdI0Avy/5Iu81sYx34+lH0NGegbKShq25pdEVZx4CDixC6AeBIwHAF/g+A/NofIeBFrjw7KCtCJf/V59HHriO/rR1yeJLROR3Ca5xhpvWOYbSuJTXxu+8h1/n4Zyb7hf2jp33jvoK+5JVJi2ztEoT2MHFXMKs4pTKW5MuItq/O5o6KLLjmHw4j2JBjeDOpPuloxXq48tj0o+kyIa6ueSJUtqDC/cXZgzZ07NOY7HKGd5fMUVV9gcGLt+/XodP2fsYDBHDpndrBU+FJxzzjm6ON8NOlFr67ujnFjy3YG69HkSArxQXHjhhTq7IE+SyrKxp3ueN2TChAnGoX6nZZLuNvQFpPCC0ZiSz3KGgk+l25ImTDdgwx9LJZ9jbEzJb2wONnTpk0VPP/108MUgLvrr27oVzwcwvpgbgZZ9MvuINIyA4ePfUAlax+o+APAhIC09DcczjiMkpQDxKbU51422ysMqQWt/WQPvjZ0vt6jHHQVXChV37m6EnnjXn2sUeaXUc/dDnfNrJJzBz98PkbFRiG8fh8S4RDA+iC9Dked7XWupK+foKX3R3WzatGlawbcldsdyfmQVmjp1qr4mNCcpomWbZj+2lSzC0lWSsW+Ggs95vvvuuzUKPpV9WsvrU/BZ9vLLL9fXbGanpTz55JM17j00fvEBoCm54IILah44miprnGfekRdeeEE/wPE7PoQ89thjOjGXUcbb3sWS720r6sHz4dM8WWoodM9gog5Li7w+of5w242Z9/h0z3K8wNf1C/zoo4/0xZp1eI50afX52dNHb8SIEXrrjmX/9re/abcQHtsjTPDBGzOVHl4AGXhU9yGE7XKnYtiwYTUXRVt3D3zdkl93bd5++23wRUuNPUKrHZV98jyLOB4Buq9Z7gDwmDsA6RnpehcqvyBfvxcX2c/nXxFcpR8WLB8KqGD7VfppRVu/q+eAWp95vsri/InyqFPOn21YfIdGFHeiFxAciIjYCK2wt4htgcTYBMTGVSvwhiJPBi/LYH3Ho+79LZI6l8o9XUAM66yts+Z1mA/7VPDr+pzb2panlee8/7+98wCXn6jaeBT8LKAoFrDiHxWwIogdBURRBBtiQWzYC4oVu4IgAoq9K9hQwYZiAQQEC80KCFhAAUGKBSuKis53fqNnnM1NssmWe3c373mee5NNpr4zSd45c+YM30oEZcnznve8eF73D3Mbn+VGIZYPqPCo44qrffbZJ9rl16XDdcxab3nLW8YgLIjlm8agADOe29/+9vF60z++tT6r0BSufI93EYMbN/c9/vjjiy222KIcrPK3NPmVsOiiEGiHAA8adnS4OLv44oujnTCLd1gU68KUIi8Tn75jEU2Z4BOWqULsttHS4wGAUT9mNBB9942L5pGNLrDNQyDn+MkfR3h57LHHHnGxKGWkPrz48mlQ7APx9uLTmuzC6y/OcfLuc1wIOlPsEP33ve990fazCx5oofjDIwR/Ivtd0BselgXUDMj5axIG3ZieoTHkmP9VXSuHXe2PZvLyx6Yc6u/xXlht9dWiCQDa89VXs7+r25+d44UmP3LuZJ0jhD3/PQr5qC+Z7pQROOqooyK5H9XHPemhfIHY8w3oq+Rr4XLCXodHHiaPS/h8LV2b7xnr1XhmWA/Hc4+yjhl3Br7sVeOzBH4kj/ycZ3AU4V1EPgxYEPJtS/JHyW/F4xgRkQiBmUHARvdhgw02MJ0ZurQi2Mc1mKYlGGkP5oM3XeeeTfkFeznUlt205cHMMAbimN1/sJd6sBd8sOnGdM80OMG07rVpdblhmoKw/fbbp7QpK2WnDtSFOnn9zD91sMFGl+RjWCM8KQ2zye8cf5Ej2KAumJ1lsI9Qwsjxbns0oh9MS7jIMC1k3XguzOVtMM8awTSCwVz5BSMf8beZAQQbWMf7thdGMM1vILxp9ILZFi8kHotWKVt7FWwWd+TnmuffzEKDudddNGhGqo/ZlCcst95666Fp7LXXXin86173uoHwtng/3bPB18C9uh+26DXFMbJdF6zxumnig62vCqY4C+acoTGs3zQHGSlfGzD65aFHM/1N8cwmf2j4WQiARlQiBGYKARvZB9OAp4epTMxM4xZMAxNsM6yh5eYDbpte1KZF2mYSFGx776FpdQnA4MNs/YJpHmrztlmFMOqLTSR/eGuYljeY/eXAoLHcl4b9FtkfjrNCCIFpImCeWoKZkoTNNtus9l067Dnmvu12GmzB/jSLOndpM9BFwQU+5kIzDnqbKmEa79QGZroyENRMfdI9m1kduFf1g0G3t5vNhFUFaXWNAZunY7P+Q+OgVPPwHFEKtBWR/LZIKZwQGIIAJNnMa4KZz0Stuy3kCebjPNgOeuHUU08dEnvp7VNOOSW8+c1vDragJ9hUXTCXXlGjY4svA5rfaclJJ50U9t5775iX7QUQzP4/fmwOO+ywsbJktsAWKwU0K/vuu+9YaS16ZEjC/vvvH+hD+cu9y7nI/qL3EtVv1hAw161hjz32CKtWrRr5uTWzqbDrrrtqVq6hcZnZ8HchM6B1Yq5IUzgUYyjQcrH9CNJ9s9cfOkNtprYpfJtZhDyv/JyBipnupLRszV5+e8l5PhhpMyjIExDJz9HQuRAQAkJghhBAg2MLwkI+Vesft7ZHkf0ZalAVZSERsDVSASJmttmJuLV9Pj2cbSwYbEOmaK61kCBNsFKYuzhumH5Wma8w45wrScy3/ZISMLuM+amntemmmwbM4qoE7Tsz8oTlaE43qoK1vmZutFO+DEBsd+/KuCj18tl1SHsXEcnvgpbCCgEhIARWAAFzlxpnQczVavow+Iep7VFkfwUaTlkuNAKHH354sJ2pR34meXaZLX3DG94QzHHDQmM16cqxTs3ffebhJpqamtOKcOaZZ0ZTKYiz34e8Q+irhAFarlU3t6RxNgYSz0DBHGcE26MkpUWau+++e1VSna4xW0vbexnN5W945zvfGWf9yZdBhS3mTfcJxwxPVxHJ74qYwgsBISAEVgiBiy66KH6AzKvSwMvfPxRtjubRZ+LrOVYIDmUrBJYdAdbNmEeskC/abPPclcNsvPHGwXYzjYupl70SC5Ah69t4l5VxLf82l5dDB1C2s3iwfQaGpoUGH6I9KXPZ008/fWC2oVx2/43jC9uDJ5jLzs4tJ5LfGTJFEAJCQAisLAK20/DYZJ+F4LYh18pWRLkLgTlB4Oyzz47PXK59dRLW5Wg7VgdzlSzvSBNqd9scKphryyUEncW5rI+rM78pZ49p5FOf+tSA+U+5PSHZeL1jndykBeK+5557BtZiVOXLmjhmG0YVyoxnPv5e9apXjZrMssbTZljWEyRCQAgIAdPsR1/57ILI+SiCz2389fumbqOkoThCYFER+Na3vlUcfPDB8c9MPkauJvuMPOUpTyke+9jHjpyGItYjwGZ1ttC2MFJfmPvnwtxaxw2k6mNU3zE2Gze9YoMrNohkV2F81FftbVOdwmhXzSVuYQu3ix//+MdxkzQ21yLvvm12Bnoi+aP1IcUSAkJgQRGYBNln23bIPhvuSIRA3xEwE45I7NnocBwxTzBxd1o2GZQIASEwHAGR/OEYKYQQEAI9RGASZH+HHXaIZH/bbbftIYKqcp8RMP/jhW1eFf9sY7mxoHj84x8fyb25UR4rHUUWAn1DQCS/by2u+goBIdAJgUmQfTSPu+yyS4EmUiIEFhkBzDwg97braWE7DI9cVduRvIDc23qXwnYKHzkdRRQCfUZAJL/Pra+6CwEh0BoBJ/sHHnhgccEFF7SOlwfcfPPNC9t5szA3cr20D82x0PliIWBuCiOxxzRnHMH+G3LPn22ENU5SiisEeo+ASH7vu4AAEAJCoAsCLEqD6B900EHFz372sy5RU1gWnznZt90h03WdCIF5QoCFmWjs0dzb7t5jFX2LLbZI5L6PCyTHAk+RhUANAiL5NcDoshAQAkKgCQHz8Z3I/mmnndYUtPbe2muvHck+hH+TTTapDacbQmCWEPjRj36UTHJGndXy+uAhB639dttt55d0FAJCYEIIiORPCEglIwSEQD8R+Pe//53I/sknnzwyCLjfhOxvs802I6ehiEJgWgjYhknF5z//+eJzn/tcPI6Tj22WlLT2toPqOEkprhAQAg0IiOQ3gKNbQkAICIEuCOADHDOe4447rku0gbBbbbVV8ehHPzr+oemXCIGVRICBqxN7fI+PI3e6050Sub/xjW88TlKKKwSEQAsERPJbgKQgQkAICIEuCECKIPu2DXqXaANhIUFO9m1nz4F7+iEEponApZdemrT2xx577NhZ4V1qp512in9jJ6YEhIAQaI2ASH5rqBRQCAgBIdANgaOPPjpuAsTCRHZhHFUe+MAHJsK/5pprjpqM4gmBRgSOPPLIRO4vu+yyxrDDbmKS48T+Hve4x7Dgui8EhMAUEBDJnwKoSlIICAEhkCNw5plnRrKPOc+FF16Y3+p0vt566xXspouG/y53uUunuAosBKoQwEOUm+OMu2kV6ePT3sn9OuusU5WlrgkBIbBMCIjkLxPQykYICAEhgHYUoo9m/zvf+c5YgDz0oQ9N2v2rXe1qY6WlyP1C4J///GfU2PtC2nFmmRw5Bp94ynn4wx/ul3QUAkJghREQyV/hBlD2QkAI9BOBz372s5Hws4nQOMLmQW67f8c73nGcpBR3wRE45ZRTisMOOywS/LPPPnvs2rJZFX0Pzf3GG288dnpKQAgIgckiIJI/WTyVmhAQAkKgEwInnHBC3FDokEMOKca1g95xxx2LRzziEcX2229fXOc61+lUDgVeTAS++c1vFkcddVTxta99rZiEOc5qq61W7LDDDrGf0de0cdVi9hvVajEQEMlfjHZULYSAEJhzBC666KICon/ooYeObcqz7rrrRqIP2ecPYibpDwLHHHNMJPaQezaumoRga+/kntkjiRAQArOPgEj+7LeRSigEhEDPEDj88MMT4WezrXHkVre6VSL897///cdJSnFnFIErr7wykXqIPYtpJyEMFp3Yq+9MAlGlIQSWFwGR/OXFW7kJASEgBFojcNZZZ0Wyj4Z/EjbUbEbk2v173vOercuhgLOHwOWXXz5A7M8///yJFXK77bZL5P5617vexNJVQkJACCwvAiL5y4u3chMCQkAIdEbgH//4RyL7RxxxROf4VREg+U74If+S2UeANRto6v3vkksumVihWbTtWnstop0YrEpICKwoAiL5Kwq/MhcCQkAIdEPgxBNPTIT/N7/5TbfINaExxYDwb7PNNsVtb3vbmlC6vBIInH766cXxxx9fHHfccfH4hz/8YWLFWGuttRKxZ1daiRAQAouFgEj+YrWnaiMEhEBPELj00kvjIl1MeU466aSJ1XqzzTYr7nvf+8Y/SP81r3nNiaWthIYj8Je//CWR+mOPPbY47bTThkfqGIIdlCH1eMe5yU1u0jG2ggsBITAvCIjkz0tLqZxCQAgIgRoEMOH58pe/HP9++ctf1oTqfhk3nPe5z30i4X/Qgx5UyKynO4ZtYrAjMpp6N8Nhs6pJy1ZbbVVga//gBz9YszWTBlfpCYEZRUAkf0YbRsUSAkJACHRF4IorrohE/ytf+Uo8/va3v+2aRGP429zmNsUWW2xRbLvttvFPWv5GuGpv/u1vfyuOPvroSOqPPPLI4he/+EVt2HFu3Ote94rEHnIvO/txkFRcITCfCIjkz2e7qdRCQAgIgUYEWKTp2n2OEMtJy5ZbbhnJ/gMe8IBik002mXTyC5Pen/70p4JNz/wPG/tpCeZWrrHHt71ECAiB/iIgkt/ftlfNhYAQ6AkCF154YSL8aPmnIex8iubY//Dec93rXncaWc18muedd14i9BB7Fs9OU/CMA7Hnb/PNN59mVkpbCAiBOUJAJH+OGktFFQJCQAiMi8BPf/rTZNKDHfg05Xa3u90A6d9oo42mmd2Kpf2DH/wgkfpvf/vbxa9+9aupl2XDDTdMGvutt9566vkpAyEgBOYPAZH8+WszlVgICAEhMBEEfvjDHybCf8opp0wkzaZE1l577QHSj43/TW9606YoM3UPj0Y///nPow09Rwg9mvppmEJVVRyvRyyE5g8PORIhIASEQBMCIvlN6OieEBACQqAnCJxxxhnF17/+9fT35z//eVlqzuJdyP6tb33r+OfnKzUAOPfccweIPGTe/5YLEwd+gw02KNDSQ+oxw7n5zW/ut3QUAkJACAxFQCR/KEQKIASEgBDoFwL4asct5zHHHBNJ/znnnLMiAJQHAMwErLHGGrV/a665Zrq3+uqrF7///e8LNo/iWHfu93E9Cpn/17/+tSJ1JVPWNaChZyEzxF4uS1esKZSxEFgIBETyF6IZVQkhIASEwPQQ+P73vx9JP+4eMU+RTA6BTTfdNBJ7Nh5DW8/gRCIEhIAQmAQCIvmTQFFpCAEhIAR6ggCa769+9auR9HPEVaekPQIsRsbzEO5H0drf8IY3bB9ZIYWAEBACHRAQye8AloIKASEgBITAIAJnnXVWwaLdk08+Of5N213kYO6z/QvTIgi9/+FedK211prtQqt0QkAILAwCIvkL05SqiBAQAkJg5RHA04wTfif/eKXpg7BYGCLvpF429X1oddVRCMwuAiL5s9s2KpkQEAJCYCEQYHOoMvGf94rd4ha3KNZff/1E6CH2N7jBDea9Wiq/EBACC4SASP4CNaaqIgSEgBCYFwTcLWXV8a9//euKV4PdeletWhWJPGS+fK4FsiveRCqAEBACQxAQyR8CkG4LASEgBITA8iJw8cUXJ9/0Pgi44IILissvv7xgAJAfr7jiilaFg5RD3Ov+0MLnZB53nRIhIASEwDwjIJI/z62nsgsBISAEeo4Afu2d+Ofk/8orrxwg9PjQlwgBISAE+oSASH6fWlt1FQJCQAgIASEgBISAEOgFAlftRS1VSSEgBISAEBACQkAICAEh0CMERPJ71NiqqhAQAkJACAgBISAEhEA/EBDJ70c7q5ZCQAgIASEgBISAEBACPUJAJL9Hja2qCgEhIASEgBAQAkJACPQDAZH8frSzaikEhIAQEAJCQAgIASHQIwRE8nvU2KqqEBACQkAICAEhIASEQD8QEMnvRzurlkJACAgBISAEhIAQEAI9QkAkv0eNraoKASEgBISAEBACQkAI9AMBkfx+tLNqKQSEgBAQAkJACAgBIdAjBETye9TYqqoQEAJCQAgIASEgBIRAPxAQye9HO6uWQkAICAEhIASEgBAQAj1CQCS/R42tqgoBISAEhIAQEAJCQAj0AwGR/H60s2opBISAEBACQkAICAEh0CMERPJ71NiqqhAQAkJACAgBISAEhEA/EBDJ70c7q5ZCQAgIASEgBISAEBACPUJAJL9Hja2qCgEhIASEgBAQAkJACPQDAZH8frSzaikEhIAQEAJCQAgIASHQIwRE8nvU2KqqEBACQkAICAEhIASEQD8QEMnvRzurlkJACAgBISAEhIAQEAI9QkAkv0eNraoKASEgBISAEBACQkAI9AMBkfx+tLNqKQSEgBAQAkJACAgBIdAjBETye9TYqqoQEAJCQAgIASEgBIRAPxAQye9HO6uWQkAICAEhIASEgBAQAj1CQCS/R42tqgoBISAEhIAQEAJCQAj0AwGR/H60s2opBISAEBACQkAICAEh0CMERPJ71NiqqhAQAkJACAgBISAEhEA/EBDJ70c7q5ZCQAgIASEgBISAEBACPUJAJL9Hja2qCgEhIASEgBAQAkJACPQDAZH8frSzaikEhIAQEAJCQAgIASHQIwRE8nvU2KqqEBACQkAICAEhIASEQD8QEMnvRzurlkJACAgBISAEhIAQEAI9QkAkv0eNraoKASEgBISAEBACQkAI9AMBkfx+tLNqKQSEgBAQAkJACAgBIdAjBETye9TYqqoQEAJCQAgIASEgBIRAPxAQye9HO6uWQkAICAEhIASEgBAQAj1CQCS/R42tqgoBISAEhIAQEAJCQAj0AwGR/H60s2opBISAEBACQkAICAEh0CMERPJ71NiqqhAQAkJACAgBISAEhEA/EBDJ70c7q5ZCQAgIASEgBISAEBACPUJg9R7VVVUVAkJgARH4wQ9+UJx00kmxZne9612Lu93tbhOv5amnnlqccMIJE0l3jTXWKJ7whCcUH/jAB4p///vfMc173vOexaabbjpS+n/84x+Lgw8+OMXdeeedi+te97rpd9uT973vfcW//vWvGPy5z31u22gK918E3vzmNxcf/vCHi+23377Yb7/9BnD505/+VLz61a8uXvjCFxarVq0auFf+8dvf/rb4/ve/X/zsZz+Lt+gbd77znYvVVx/tc03/4Bnhj7634YYbFhtvvHGx9tprl7Oe2O8rrrii+M53vlOcffbZxe9+97tis802i8/lmmuuOXYe3/ve94ovfOELxQYbbFA88YlPbExvl112Kf7yl7/EMOBPvbtK1zR+8YtfFKeffnqs+w1veMPi3ve+d3Gb29yma7Yx/DnnnFP85Cc/iX/XvOY1i9vd7nbxb5111hkpvbaR6Hs//vGPi5/+9KfF1a52tWK99dYrtt5662KttdZqm0RtuM985jPFaaedVjzoQQ8qNt9889pw559/fvGSl7wk3f/kJz8Zy+IXPvKRjxRvetObinvc4x7FgQce6Jd1LCMQJEKghwgcd9xxwT7IM1lzI37BCGAw4jqT5Zu1Qu2zzz7B3mvx77Wvfe1UimcELuXheY16XHfddWMZ73e/+6U0H/CAB4xcbiPnKZ31118/0H9Gkf/7v/9L6YyaRpd8jfiGF7/4xeHvf/97l2gzGfbMM88M4GeEKBhBGigj9bv73e8eVltttfDZz3524F7+Azxe9apXhWtd61qpHbyPGTkPj3jEI8If/vCHPErjuZHhcMc73jFc5SpXqUzv9a9/ffjrX//amEbXm/Sbgw46KNzsZjdbkif1t0F4OOOMM7omm8JT3lvf+tYx7Qc/+MHpet2JDXZTOY488si6YI3X26Zh5D7stNNOlXgb2Q8vf/nLWz+bZ511Vthyyy1T2b0fcKQ9n/GMZ4TLLrussdyj3KRtttlmm8p8r3GNa8R8x3leSd/fM/vvv39jEU2xMlAOGzgOhL/88svDTW960xjm05/+9MA9/fgfAsX/TnUmBBYfgUsuuSSYpjO+GHjpzpqYBi+YJjqWb9SP0qzVadrlmVeS/4lPfCJ9xCBAF1988UhQmaY3pbP33nuPlAaR/OMLkZg2yT/00EPDTW5yk1ju8sd75AqsUESb/QimTYx1ecELXrCkFO985zvjvY997GNL7vkFyKunUSZ0+e9NNtkk/PrXv/ZotccPfehD4epXv3rqF6RBH8vT4hzSPUn8d9tttyV5lAcZNoMQTj755Nqy19248sorw6Me9aiU/iyRfAZ2N7jBDVLZwLZcb6496UlPCtSjSQ455JA4WMzbqqrtGDiMM2Aql+Gwww5b0keq8r3//e8fGJB2lUsvvTRstNFGCaNxST75mzY/pmczG8FmjLoWqRfhRfJ70cyqpCOQa2RnkeTbVH96CYrke6s1H5eD5JvpQTATjNo/tKz+UeaD0xT2Xe96V6zQ3/72t5BrCd/ylrc0V7Tirk2np3z5IF944YUVodpdWk6Sb2YbqdyTJJntajrZUG9961tjXa597WtXEg3I6FWvetXw5z//uTbjHXbYIeEBWTTzq/CrX/0qmOlO1P6bmUa6b2YqjSSLvud9kT5h5ibhS1/6UqC/mQlEgFzlA4BnP/vZteXqcuMd73jHQL5veMMbAhppBjDHHntseNjDHpbu0/4oNNoK2mNXznjd2pD8+9znPuHmN795zJfZ21FkWBq///3vAzNoXi4zTQq8u81MKqDdf+973xuuc53rpPto++vk3HPPHQhL3szo0nakRzvmed3pTneayCCNdqL/eh3MlCZ897vfjWlD6I866qhw+9vfPt2/y13u0tgHy/VDgcGskqfPcRjJ591m5lXhete7Xnx+GEyXBWWEp2umW+Xb+m0IiOSrG/QKAZH8xWvu5SD5w1DLTWbucIc7DAue7pvte/rw8eHsKq985StTfAaI44hIfnf0mBl08xpMbaoE7fuNb3zj8LnPfS5ccMEFS4J885vfTG1IOLPBXhIGsk//cJLkA8VyQLPhDphVEA6Cz2ChSo4//vgBTTPkchyBCPqAlQENWuGyQNKe+tSnpjrsuOOO5SCVv9H65wTTMWhD8knwFa94RcwT849RpSkNzJ68TPe9730rB3OYTl3/+tdP4eo08I9+9KNTGLOBr5xRw0zHzVTId9999x21WikeeXkdKEOV2NqGZCpFWAa3bYQZLGZvPH0/DiP5njYzlfStOsFUx9P8xje+URest9dF8nvb9P2suEj+4rX7PJN8tJn+geJoi91aNxCkybWUxLXFiK3jVgUUya9CpfmaLeaM7QexrTO3utWtbpXa+FOf+tSSBLfddtt0//3vf/+S+34BAu/mE2j2q+TJT35ySqvKdCiPk88+vfvd785vdT7PZw+aNNWYqjgetpA4zlbUZYbN9Yte9KKoxfVnJDeBaUvyt9hii2CLVsdaf1CXBmV0Mx36ABrxOmENmNejbvYkf55tcWpdUgFzLE9ru+22qw3X5gZmNN6vwKnJ7IVBo+fLOpMm+eUvfxnyvk28vP3akHxm+Zh1Av86QZvva0Da9om6tBbxulxoWs+TzC8CNhWcPJQsRy3+8Y9/FKa1YnA8VnZG0ArSmmUB20mKTTmPnZx9VMdOY5YSwKOOaXpTkcxOP50POzHzg8I0wzGYaYAL+9gPixLvG9EqzPyhVdg2gewjW9iC0OSZp02cccLw7E2iL41TBuLyfLznPe+JyZgGt7AF1ZVJgk+d4PnGTDvibdPAR69LdWGNHBcPfOAD420jk4Vp4weCGiEqPv/5z8drZh5SvOY1rxm4X/6B1xg8pjzmMY8p8NwyjpgdeYpui0LTefnEyGTxzGc+M16mH+Jhqkp+/vOfF2aKUpgJW3q/44nFTF+qgtdeMyJZ2ExJsfvuu49cx6Y0jjnmmAJvSIiZ1hS3ve1ta8vy+Mc/vqBdkI9//OOFmW8NhDUNfXqebcBd2IzgwP38R+5BDK9J44hpvwvaBTEy3eh1KX9X4fmnTsysqLDZl+KII45IQZ71rGcVXbx20Z/BDDHT2pRO+cQGDoXNCsXL5MezIfkfAiL5/8NCZ3OAAA8+Hwb/qOISjj9ePo973OOKt7/97UUVEeRj8fCHPzy6uPNq2vR5vMZ13HFVybe//e3oxgu3eLhBs6n56EYMt2K80J///OcXthCuKmq8ZlOVKQ+zl45h+bjiAo20cI1nWpmCa5TD7CBTWnvuuWeKi9u4actvfvObwkwOCtO+FLYoMuIKeeS3ea2JRK6pDLioow6myUvBcO1I3WzBVawvRMi0LYXNqLQihRC6L37xi5GM4PIPF3y3uMUtYlvjim0RxEwYUjUg+W0HkB/96EdTPNPgNrpYvOiiiyLRMc8kkexANiBNfDzN5CSl0+bETDuKPfbYo7Bp/eiSkOfP7GajezszI4jt/8Mf/nBJUpSB/sFfTtJtMWW6Xjf4wIXp8573vEikbOo+9k2eR+JC/JqININyd0M4ytE0kkvqwgXTzBYQM4RyjCKnnHJKau973eteQ4noVlttlbIxM4V0zsnRRx8dFRCcm7emRrJGmIc85CEFJBWCzjM6qjDY+dGPfhSjM1jA5WeTNNXB4+HCEaKPQEBf97rXxUENz35bwXUm733eNfTXUWRYGmZKlJJlwNQkuEBlIICQbk6AucYgzwUFkNn6+88lR/q0y6iuVT0+fZdvppkQRbz8etXRZqvSZZ77OuG76YMY3g18a3lO+ea1Fdxs0j9tPUB0t9kUz0k+784DDjigKWj/7hkoEiEwFwhgz2r+etN0oT2tlee4KGTRUC5MIdeF57r5482Dx0VF9hFsjOPpYW9YNQ1Pgi972ctSGphm+CIhj8sRjwPY4ubXyudf/vKXB8o36R+kz4LRcr75b6aSTXtcmzVTqoRnARemJLh5y+OXz1lU1rRQFDtfPDmU4+W/aTc8yvi1abnQrK30f2+MapNPdGxs3Y6aetgHclh20e7XPrKx3kyBY8pRJ6bdHUjfsfKjEafopaONuQ5255hZeNy6I2Ho+/QDl3yRcF087H5zIT42x3nZquLSl84777w8ajo3/9pDy1uVpl8z8p3Syk/clSNmGtjm1wltgxkWfyyezCW35zYf+vmtynMWdHq5sP/OxZQC6d645jd5usPO8zUFrD8YJiwi9TrQT6pcMn71q1+NJiQspszXKHDd47YxzWhql2Hl9PtNaeRucE0Z4VFqjzh78PJXvaty7zOY5NTJS1/60pQOC5qXS2xGJOVrg4PabAlnPvWDzSYNmP/k8duY6zRhn2eOyY5768K8hzUskv8gMNruGtZLJUJgORFA84EGmA0yEPMwELVnaA7RQDPlbSQ6avTsxRC1VJgyuJYDraV9jOMGHz69yXQimnSkvBHRIx/5yKgZ4x5poPViIxWzv4yaT7Qehx9+eMwPbR6aVPNrXDtlTzp77bVX0njx24UNXcyTRtSUMwXPOYLGC406Qj2nJWZPOzAdCi5Grgu0vmyGwnQ3m7uAJ9oq84NdmCu42uJgioQJgPkEL5hKBXsb3EStpw0SCrMBjXG/9a1vxXTQ1pTln//8Z8zLZzZoO0wV0BIym4OmiHKhpUNTNM9C+elvbqrDkQ10mgRsfcaKfocpR5WgQUPj7ppuGyRHHNlICE0ZWjI01eSH+USTmLeUgj8X2oI+yuwMmkU0r5QLDR5p0a/Mbrcw2+8YhdkDNupCzIVmyu+xj31sMhfw5zUGsn88d0bs/GdhpCpufnOjG92oYLaAvsPzQl/CvOPEE0+MZgIpwpRO2OTJzRXQzjIzVyd1bUN4NNYut7zlLf209phrspmVyMW16Vzz9xkzI8wWYJLB88LzyD02EDJyGH/naYxy3rUOaKxpP2ZA6Sf0m7KZC7959zRh16asTe3SJj5hmtLoWvem9iMvGxinWRU2guK9mZvmEMYWNSeNO+1phJ/LUxc2o+J9i/A+zjeqKmfOO8ecAoy9eVYT9nme4MA71NzVRlNE8+YU33t5mN6ea7QjBOYBATPNSBoENBf2cVhSbDRmLByyhzn+VWlW7CWV7te50MwXQ6KNqFsAZR/VYB+rlF6Vd41ck0+50DKwSA3XanhcsGnkAW32crvQtA9scqdnL8qA2zsj2APYok1FI8N96sBCM7TPZXFNvuNvH+pKf9i5RxnCVi1Wy9uJNqiayWAhW+4KkLSqtGPlck7j9ziafMrz9a9/PfUjvHDYdH1jMY3Yp/A2KKgMi8vGfIbIzGSWeP5g0Z0NwlJa3nZoxnKxgVXAL7ffxz91leDDHQ2zh7PBQ1Ww0MaFZr7Ij9myqoXFbA6VPzN4CSmLEf/ofhEXjKP8oSEvS97e5VnActim3w996EMTVm029GFRpGPLYslcTGGR7jFrYgOfYAOHdM3j+ZEN2MyEKk9ipHNcv3qaz3nOc1qlkc9oUs620lWT3zbdUcPZwDXVvc3+BXhYcqyq+irlyLX9zBLh7YbZLFMSDSxk5V7TQu1R6+Tx6BvM0jCjwEyZl5vvAG5BR5GumvwueeTvC1v30SXqQofFHlAiBGYegV133TW9ZL72ta/Vljd/QZp2fUm4nDzWkXymiP2FVueqzhN2H9mEN62mX07HMskf5u4sJyxMzU9bIH5eV1tf0JhdXpeqsDnJh4CYprUyPQZovuEXeZc3cGI6H2Lv5bJFapXpcNHWYKRwhJ9Xkg+pzv1f2yxRbZ3NJj4NuCC/4FUlb3zjGxM2+FbPTWfy8MTPBwPgWCb57GLq7THM9SHt7mExx6jKdxjJx2wndxOIyVGdUNZ8I6mmsHVpdL0O8fI60gdHlfyZwQf6MDH795Qv+TP4csn7DzvrsvuulxE3n6YVH/BUwz0GlF08Onle+dHs5VM+7GDcRvLnv8t7bpZIPv3OFR9gyaB6mOTlp8/WCSZ7q1atSrh6O/oRsyibXa2LPpHrNhu7JH/6S5dBWbkg0yT5uM90fDClk/wHAS28tV4hmX0EfPU/Ja0y7/AasHAUcxy8VrAwbhTBdMHczxWmZUvmBXXpYCLgki+G8mv5kYWjTVOcedjlOMfkyLSjMStMKYwUNmZrg6LCNOcxDF5FjBzWhsdcx02hyoFoS9tsJV3OF3NxEdMC2g+xD12BOUedPP3pT49T/3X35+W6kYXiKU95SiouC5brhMXc9vqOt/E+kS/Yy+OwYNkFMwCm2KuE+MP6JWZjtD+L9FgA2ySYe/nzijnGKF6aeHbdbA3TMTf5qcoX7Gxwl265SUG6MIUTTGBcbK2Kn3Y+5u+MNh5u/PnzjHJsfaEj9572tKcVmLzxfrLNlKI5FeZF5MdCfxawIzYzUOD1xPtTvNjxX9c6kHzeZ/M6dMx6RYODd45bm/ZrU2+eWxw62GC+tn6YqvGt4x0+LanKn/6CqaZ7lZpW3qOkm5tC0debFi6Pkv68xql+689rbVTuhUXAXcdRQVyaQfywX+dDlgsfLzztQFpHFVzAmYY+enXBk0eTuF00YcplKcfDG4iTn/K9lfidfyCwnR7m+QAssBFFIG9uk1xVdg9XdY9rua1l2aMKduIu2FmW7bT9Hkc+rLk3n/zevJ3z8fT+gQu6nDzldYHku0DmqoQPnO3Sm24x6GqSfIBRFY7nj0EgHl3wbNUkrFHBBaDLsOfCw+XHvG/m3ljyMPk56z4g+0jZVj0PN6lzX1dCejm56Jp+ThK7xiW89xfOc7KMS1Mz6YreurC/93B4RMGbE8Tf24hBdZ13MdIdJqPUIY/jZRuWz6zdz+vQtmx5nKp6267BcUDr68ZQNLH2C8LNM8F6HVcs8Y6g3+ceftqWo004FAP0kzPPPDM+93iYQ8zUNLrCtNn1NsksWxjWreWKjPwZXbZCzGBGWng7g42iIi1FgA8W5B0NBsLCPf7Mk0tcoImbR/7G0aotzfV/V9AsQ2pZDMZiK5vijos/cQvokr/A/Vp+hOTPkrB40AUNTZsFXCyAdiF+HZlngWeT5AMKBgy58BFxadOeZnfswef6aBu6xEWxLDRllgSf5yzozoUFx+5akAV5dfizQN3MCWJUFvY2ubsjEAM4M5FKMyh5nnXnLICmrXgeeC74Q8Nd9lM97LmoSj/vmyy+btM36VMMuhlk8MfM2TSEZ4C6u7Tpox62fOT95ZKn6dfKxzwMgxrXyBOOAa8rHdD4m712IvLldPDBbt584uJo7pl3sLTgk/fcMJe9ZrYR3XQSt2sdiJPXYxyFDGmtlOTYUwbqNOw5a6o337bddtstVQdtuW2alX5zwiwZs2mEwyUl3yX2xzDPUqkdurbfQAbZjx122CH9ss3XYr62FiWVyTw4xfcVi+NnQXBrjRMA/ybjkAPXzX0Xkfy+94A5qT8fMLxDoJGEBLkwZYrJiZud2NbvcUMZWwAW/XZ7uFGOkBX8LOO5x00HRknH44zrKcLTmdQxJ1Jojlx71Db9PH45Dv71m8S1rlVh8n0HIL7DZBySNSzt5b6PltX7N1q7MsnPfePXafEpc1cMiQOObibF7yrhw8nHnQE25N4HElVhx7mW9y1/trukR3xmpxBb1FzYuo8u0QfCQqzw2uECBi6QaVuQ7D87H3Oi2GT+5gnnYSDXueaS374xEz738aDUJE94whMSyUdb62JrnoZuWrTZZpslkt+1DuST14PB5TwKmni+S14XjsNIvoelvuV6530U7zRlgu8YQWbZD+aEE06IXs8Y0NraseIVr3hFDNK1/TzdNkdMu5hB5/lHKPOskHzKwzssJ/lc67uI5Pe9B8xR/fmYMEUJ6cZkgc1EyiYNuNHkD9eBbJaUm4V0qSp2x5CZKi0kL3I0qNgK81LxHRyHpZ9rr4eFXY77aO9dwDa3F/XrTcfcPKAcronEl8OWf+c7Abcp07Q0tuVyLcdvPpjuXhByygfL3ahCEHwDJNqraa1CVwyp2zAcWSeAKVtOVBwTTKo23HDDaMpDHTCzqgrn4Ycd877JTESVaUNTGvlghWl7XLeOKmVzstz8iHYYp6/n76fy2pSq8uZhyiQxNy1kYDJMcJHLIIGBGv0ME588jWHx/X7XOhCvqR6e7jwcqTtadIQ64WK5SZrqjRLLJdfo+7X8CNFnJsY3MeNb6CQ/DzeNc4i+k3xmH3CZ3PX5nEa5SDOfVcqf02nlNw/piuTPQyupjAkBPkr4y+cPMw/sEbHh5iWHJtpJOX6zt9lmm+gLPEVuecIiPjQjLviVZkoUP/nYQ6KRdw0afrnnVfKZBercxiRiOeqak4a63UbzcuQfzvz6PJ7z8WbfBBaPQr4wozCPJbEqaLR9UMuMVv5BK9e1K4bEb8KRZwy7ff9wMrhlMSwzZzwT+DXPF4XmJlj+TJbL2PSbvul+38n7rne9a1PwZb2Xz1Kx9oF28vdB14JgBuHSpq/nYap8y7s5Y5uBB+2F4sFN8DD1geSDve8g6mUrH/N3R9c6oBzwQRz5j7OmoVyu5f5N3Z3k0zZ15nNerrr247n2WRjablg6pMf3yCVfH9W1/TyNtse8bLwP2D9lVkwmvV9Rl/w5bVu3RQwnkr+IrdqTOqE9ZOERf2w0hUkNiwPRwCNs4MQLuMsLiI92vuEP5Nd82ddq65gqdcmJjV+b5WM+ne/kYBbKm2/85ZufNZWrTZim+LN2DzLtHmJykm+uRFNRm0x1CJRjiOkOtsBNsyIQ8ZyApIz+e4JnHSf4DLAPOeSQ2kEGpNHDEh1NX1ehbzrJp2+OQ/KxYfZFg13LQfiylpLBFeQUsgrB552BjfookmvcTz311KFJ5M+pmyN5JAZbbFiEsGBymNAvnODzLsWeGWGRde7oYFg6DDYgpvQhTByZRfJFvVVx8zowSGxaWF8Vf5au0X5uXkf7oQxqkrzuefsx4AIHviHgSLs0DeLJA4WAS37etf3wZMUsAutr+N6x4WGT5F6cGNyOY67WlM8o93ygRFzvz6Oks0hxrrpIlVFdFhMBNEwHHnhgdPOHy8Q6gdiggWcHSpejjz7aT1sdednx4UYwk2gi+IRh11CXUciMx12JY74QmBmJNoMU83NcvOhFLyrwApEvkJ1k+fOPDDsXDpPcneGwsPNwH9KETTWC6RmDGIiku45l0SQeU5qEvuuLziANmP40Cf24bJbi4cmbAbMLnqeaCEj+TBBnlOci75t4fxkmaEKZAWGXzQ984ANpASrxIE+Ud9S/KjO7XEuYE4th5Szf513lgy+ewXxhZjksv/PnodwHMB90oQ2GmUvlbUqfKw9mPK1hR0yW2P0YYeAzzNtLUx2G5TVr921DsVSkvF7pYnZC2/rML+Q4H7hC8vOZmdx0J0ti4DRvP5xSjCqk85GPfCQSfQbvwyQfjPKOGbYOYVh6k7yfP4v5MzrJPOYtLZH8eWuxHpaXFyDuvA444IDo4zlflFcFR65VK3+4SMsl1zb6tXybcrSJTdPemDd88IMf9KitSHIKXHMyrHw10Ua6DJF0sw6I5Nve9rbGdPhAvelNb4ruRSH609LAoQ1z4oNnFdvttrZckBlczC2asADXBS871NH7a37Pw1Qdc5ML27CoKki6xkxYnWAK4ANfNIa26VJd0PgM7LPPPgP3qwaPw/q5bdKWnj2IR+4OdCDx//5gcM9MBzN5LJavIuZV8Ua9lpuYjDPYZeDhe0ZAkPP3SblskDEWPSIM4nJSzzW04k4U6Sv5Qk7ulyW//7jHPa58u9NvFoq6sCi0ThhI4vUH4d3K4t95Fty7uh0+bZMT73K9aFvaGMFzTXmgnBN1Ft0zOK8TnsfcVz0mpaMKZq0uvGvyheV+3Y8M2POZ7nw2wsOs1JG1Nz6wxeysjO9KlWvF87WOJBECM4+AafB548U/dom0j1hlmW26OJhmKYazj0gwn9kD4WxGIKVjZHLgHj++8pWvpPv2kghmArQkDBfsRRjMHjOFpWxGmJeEzXeJ3W+//ZbcL1+wj15K0wh1+fbEf5sGJ+UHbjbzUZmHLcwb2JG1atfTfPdO++BVpuMX83zNg4xfTkd2I/b2NhvTYIOvdM9PaAN2jfRwHG262W8v69Fcy6VymKZ97LxtSjz1YzNHC/RV6mfkOJi2qlX6NggN9rFL5QIbIwdL4rKjMM9KjmMejrLk9+t24zUCEGwx8EA6pFm1o6rZ9KdwNpBbUiYu0C+8TDxrNtioDEdfs0FhCmuEvzLcJC8a0Un52aZEYyVN+b2ephWtfAZtEB7YxdPDsXN3lZi5TgpDWJ6zKrGBUApnLiwDz/c4YrbQcfdcL58N9JYkZ5rsYH7fU77s7t1VzDQmxTezsa7Rl4S3dVzBzDvjn5HwJffbXLDZ3lQmm4EKtFVZeK/awDOFMxel5SCBb1fej/nm5c+hR+Aafc6xtoX6wQiu3+58tIHHwLuddrGB+ZJ0+ObaguCUL+8WM/FbEq7pwjR3vGWXZ8fETPSaitGre4wWJUJg5hHgBQjp9ocYImXePoLZ7cYPFC9NSLHZB6YwVeSRl62nwdFMQ4J5xwk2xR8xMBv7YJqZFIZ8bOFf3LKcl6GZPQTTigbT8scwNlOQtoo3zfaSl3JXkp+/vHnh87Iyze1YW4k3NS4fDJtqT/WFzIGHac/jx8o0U8E0c8E0hymM2ToG89W+JNlJknyw5oPpbcUHBWJi5ivxY2i7dgbTpsb7EF8PtygkH3Bpd+plU/vBbJzjOSS6i0BcHBuO5vkm2ALecN555wVztRfywXOOY5lc8Jx4OuaFJ5Augw2IPW1C22y55ZYpjJeXOLYIfkmRbVfpFJb+xLPKAJfnz+WSSy4J5kEmhWMQCrlm+/oLL7wwmFlIsEXJwdwYpjC2UVcwbZ4nMbUjz4XjQb8fV0yTntKjHfbcc89gZmjB9h4I5g899XXytAWXwWy2a7PkneFlox3A1bwyBVsgGd9l5YEYio9JCO9Qz5ejeX4J5gktvkdsNibQNn6fgYW57eyc7aRJ/r777pvKxPdlFKG/oYjwuvFeos1oO9rw9a9/fRyc+32e6zoxU7iUDuHNHCgwaAUrm8EO5jUuMOj3tDjaDrl1ybW+zjOcP//0MdPqh3PPPTfWw7zpBHObOpCvrRdqnb4HnCbJN29DqXzmfc+z7P1RJL/3XWB+AIB4QnjyF1zdOeQaAlIWtJL5C9njQ2JczE1nZR4Qeg/PEW2kbU4UzOwlXS8Tmq4kH81STlo8PzOn8OJN/IhGPCcGnmfVEYIHwamSSZJ80oeUMMiqKodfM7eNIZ+dWSSSb+ZRS+pudvlV0Ddeg4DnmnjHLj/aLtIDg70yyUdjZ64sl5Sn/EwwMGVWw2zjU9iqvgsJyvP38/LzY/a/4c53vnNlWI/jRzNXCeY6sxGLSd60Bf2xXDyvtth0rKTRctuC66H1hEDWzS56AXjvoYgY9q6kT0BAJyloxc2kq7EeDDzMfn2kbGeR5FMRBp1mutJYb/qpmWbVzkKTDs+ducIciiFpMei1/RuINhExE6E0e+jPVNWRwRDtPIpMk+TbGoeIP++ktrOdo9Rh3uKI5M9bi/W8vGg0dt5551AmF7yMIBg86MM0DGgn0CTmU6NoqnOBvNuOopUvbT7uZs8abLFfjJJP16IFz6UryScuJkOYJ+QfaTR90xa0fZDmPF9/yfNiZ5aBafk6mTTJJx/z3R0JY252QpkgCmaTHKepmWnxci4Syaf+uUmY2cIvmSkiTBuBVOWaVMfLNhsLZiMdk8hndMoknwBo/3faaafKAQOzX+bxJ2r+CJuboNjiPC4tEbTyudkOZfIZtTwwBBrSms8mefk58jwyszAu0c7zbHNue2mkfldn5tYmnTwMgx/aJK8f52hZ0VQ2PX95OpwzU2O22pWEkfefuR0uR5nIb96d5max8j3ysIc9LM7EjZrRrJJ86sNAzTZhHDCR83bEpAaNfNs+ygw179Oq7xyDKHNf29lUpg3mDCCZ6alSCpCvrSWIypc2aVWFmRbJZ9aPmXTw5j0n+R8CV+HUgJEIgblCAG8aRjqi2z/O8d3LorMui0FZEGj2wnGRHjur2od0CQa4FTTTlOiekwWHplkulmsbdtyoscgYF2V4CrAX/pLyTeMCi5fAxdYzRL/Z1HvVqlWV+Ewj/6o08bB0xhlnRI8+LHpkkaEN0qqC6loDAiwSNTOC6KKPRXNg2VXwYMFiXNKib/LsjeOuDleOuL/F974vYqwrE55+6AcsFMebFn2TeDYwrYsytes8I7wPbEAUNybD3emkBEzMBDF6VcLTEBuNlTe/apsXi11ZoE55aS8cCrAR1rSF9xcuI+kn6623XqzDrHo8sYFi/A7gAnQSwjcDz1i8s8Gbv6rvy7C8aDueV8plio3CBv2x7bp854blUXWfBcK8/z1f+jn9kIX3syg2Exmdc1A2NurKF4LPYnmXs0wi+cuJtvISAkJACAiBhUHAzNwKW/AXCRy7xg7bNXhhKr5AFcErC4NUdo896KCDFqhm/akKg2A84+HS02b7V2TQP6toL7/6Y1aRULmEgBAQAkJACHRA4NWvfnUMjcbVHAF0iKmgs4KA2bXHoriv/1kpl8rRDgF2t3fX1+yTsRKzeu1KujKhRPJXBnflKgSEgBAQAnOOgHkhKcwtZKwFZLFqT4A5r+JCF59N/fD7bmsFinH3ClhooGa4cmzOh5hDjcLWDM1wSVemaDLXWRnclasQ6IQA9p3lnUQ7JVAKbIu64kuxdHlhfgqvhWnKma8Ia4NYl4ANunkdKWzx5cyXWQX8DwKsBTFvUIW5LJUGeA47BRs02iLyuLGaLX4v8t3S57A6UymySP5UYFWiQmCyCGAWgMZpUsKOiousuRJek+opSqcNAuzi+qxnPaswLypxUbJ5o2oTTWGEgBAYAwHbM6A44YQT4rNnnqnGSGlxo66+uFVTzYTA4iCAFxTb3XViFRrmxWRiGa1QQsJrhYDvabbmOrewzYMKc1tZ2G60UTPcUyhUbSGwLAjYpn6R4OP5zTbCXJY85zETafLnsdVUZiEgBISAEJgpBGwjrujSF9euuBuUCAEhMD0EbLPEwvZRibNn66yzzvQymvOURfLnvAFVfCEgBISAEBACQkAICAEhUEZA3nXKiOi3EBACQkAICAEhIASEgBDa79TeAAAAlUlEQVSYcwRE8ue8AVV8ISAEhIAQEAJCQAgIASFQRkAkv4yIfgsBISAEhIAQEAJCQAgIgTlHQCR/zhtQxRcCQkAICAEhIASEgBAQAmUERPLLiOi3EBACQkAICAEhIASEgBCYcwRE8ue8AVV8ISAEhIAQEAJCQAgIASFQRkAkv4yIfgsBISAEhIAQEAJCQAgIgTlH4P8BLR7mMZI+p8kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "image/png": {
       "height": 200,
       "width": 400
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename = \"./data/subseasonal_mode_JJA.png\", width = 400, height = 200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note, if loading in monthly data, the exact same reasoning is followed. Where tfreq now stands for n-month means instead of n-day means."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sorting latitude\n",
      "Plotting Corr maps xrclustered, split 0, lag 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 656.098x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# if TVpath contains the xr.DataArray that is clustering beforehand, we can have a look at the spatial regions.\n",
    "rg.plot_df_clust()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded pre-processed data of sst\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rg.pp_precursors(detrend=True, anomaly=True, selbox=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Option for pp_precursors to vary pre-processing of datasets by given a list as an argument:\n",
    "An example: detrend=[True, {'sm1':False, 'sm2':False}]. This input means that default argument is True, except the variables sm1 and sm2 got a different argument. The precursor names sm1 and sm2 should refer to the names given in list_of_name_path."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Post-processing Target Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mrg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpp_TV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mname_ds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ts'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdetrend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0manomaly\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mkwrgs_core_pp_time\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mext_annual_to_mon\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mTVdates_aggr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Load and pre-process target variable/response variable.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "name_ds : str, optional\n",
       "    name of 1-d timeseries in .nc file. The default is 'ts'.\n",
       "detrend : bool or dict, optional\n",
       "    If True: linear scipy detrending, see sp.signal.detrend docs.\n",
       "    With dict, {'method':'loess'}, loess detrending can be called.\n",
       "    Extra loess argument can be passed as well, see core_pp.detrend_wrapper?\n",
       "    The default is True.\n",
       "anomaly : bool, optional\n",
       "    calculate anomaly verus climatology. The default is False.\n",
       "kwrgs_core_pp_time: dict, {}\n",
       "    see xr_core_pp_time? for optional arguments\n",
       "    - start_end_year selection is done prior to detrend & anomoly.\n",
       "ext_annual_to_mon : bool, optional\n",
       "    if tfreq is None and target variable contain one-value-per-year,\n",
       "    the target is extended to match the percursor time-axis.\n",
       "    If precursors are monthly means, then the the target is also extended\n",
       "    to monthly values, else daily values. Both are then aggregated to\n",
       "    {tfreq} day/monthly means.  The default is True.\n",
       "TVdates_aggr : bool, optional\n",
       "    If True, set rg.tfreq to None. Target Variable will be aggregated\n",
       "    to a single-value-per-year \"period mean\". start_end_TVdate defines\n",
       "    the period to aggregate over.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "fulltso: the original 1-d timeseries\n",
       "fullts: the pre-processed 1-d timeseries\n",
       "TV_ts: the pre-processed 1-d timeseries within target period\n",
       "dates_all: all dates of fullts\n",
       "dates_TV: all dates of TV_ts\n",
       "\u001b[0;31mFile:\u001b[0m      ~/RGCPD/RGCPD/class_RGCPD.py\n",
       "\u001b[0;31mType:\u001b[0m      method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rg.pp_TV?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sorting latitude\n",
      "Start linear detrending ...\n",
      "Done\n",
      "Visual test on 0th observationusing absolute anomalies w.r.t. climatology of smoothed concurrent day accross years\n",
      "original tfreq of imported response variable is converted to desired 10 (daily) means\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rg.pp_TV(detrend=True, anomaly=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>3ts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1979-01-09</th>\n",
       "      <td>-7.601616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-01-19</th>\n",
       "      <td>-2.785106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-01-29</th>\n",
       "      <td>-5.305454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-02-08</th>\n",
       "      <td>-8.257336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-02-18</th>\n",
       "      <td>-5.436037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-18</th>\n",
       "      <td>0.193873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-28</th>\n",
       "      <td>-2.411315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-07</th>\n",
       "      <td>0.672068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-17</th>\n",
       "      <td>-0.586045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-27</th>\n",
       "      <td>-0.029262</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1428 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 3ts\n",
       "1979-01-09 -7.601616\n",
       "1979-01-19 -2.785106\n",
       "1979-01-29 -5.305454\n",
       "1979-02-08 -8.257336\n",
       "1979-02-18 -5.436037\n",
       "...              ...\n",
       "2018-07-18  0.193873\n",
       "2018-07-28 -2.411315\n",
       "2018-08-07  0.672068\n",
       "2018-08-17 -0.586045\n",
       "2018-08-27 -0.029262\n",
       "\n",
       "[1428 rows x 1 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rg.df_fullts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mrg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraintest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmethod\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mseed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mgap_prior\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mgap_after\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mkwrgs_events\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0msubfoldername\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Splits the training and test dates. Only training data will be used\n",
       "for any analysis/model tuning including correlation maps, causal\n",
       "inference, transforming data, fitting sk-lean models. Only\n",
       "pre-processing (detrending/anomaly) is done on entire dataset.\n",
       "\n",
       "method : str or bool, optional\n",
       "    Referring to method to split train test, see options for method below.\n",
       "    default is False.\n",
       "seed : int, optional\n",
       "    The seed to draw random samples for train test split, default is 1.\n",
       "kwrgs_events : dict, optional\n",
       "    Kwrgs needed to create binary event timeseries, which was used to\n",
       "    create stratified folds. See func_fc.Ev_timeseries? for more info.\n",
       "gap_prior : int, optional\n",
       "    Possibility to exclude years (or train-test groups) prior to the\n",
       "    test datapoints to avoid train-test leakage. Note, not advisable\n",
       "    when using k-fold type of CV.\n",
       "gap_after : int, optional\n",
       "    Possibility to exclude years (or train-test groups) after to the\n",
       "    test datapoints to avoid train-test leakage. Note, not advisable\n",
       "    when using k-fold type of CV.\n",
       "\n",
       "Options for method:\n",
       "(1) random_{int:\n",
       "    Random k-fold CV, {int} determines the # of folds.\n",
       "(2) ranstrat_{int} :\n",
       "    Stratified k-fold, stratified based upon events, requires\n",
       "    kwrgs_events.\n",
       "(3) leave_{int}:\n",
       "    Leave_n_out CV. Chronologically split train and test years.\n",
       "(4) split_{int_or_float}:\n",
       "    splits dataset into single train and test set. if float: that % of\n",
       "    data is used for training. if int: that number of years are used.\n",
       "(5) timeseriessplit_{int}:\n",
       "    Also known as one-step-ahead CV. Always uses training data of the\n",
       "    past. The int determines the amount of one-step-aheads.\n",
       "(6) RepeatedKFold_{n_repeats}_{kfold}. Repeats K-Fold n times with\n",
       "    different randomization in each repetition (test set is different\n",
       "    each time).\n",
       "(6) False:\n",
       "    No train test split.\n",
       "\u001b[0;31mFile:\u001b[0m      ~/RGCPD/RGCPD/class_RGCPD.py\n",
       "\u001b[0;31mType:\u001b[0m      method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rg.traintest?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "rg.traintest(method='leave_10', gap_prior=1, gap_after=1, seed=2)\n",
    "# rg._get_testyrs()[0] # see test years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "sst - calculating correlation maps\n",
      "Progress traintest set 100%, trainsize=(270dp, 75%)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rg.calc_corr_maps() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting sst\n",
      "Plotting Corr maps sst, split 0, lag 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x250 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rg.plot_maps_corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "rg.cluster_list_MI()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting Corr maps sst_labels_init, split 0, lag 1\n",
      "Plotting Corr maps sst_labels_init, split 1, lag 1\n",
      "Plotting Corr maps sst_labels_init, split 2, lag 1\n",
      "Plotting Corr maps sst_labels_init, split 3, lag 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 880x1200 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rg.quick_view_labels(mean=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Getting MI timeseries\n",
      "There are [2, 2, 2, 2] regions in total (list of different splits)\n"
     ]
    }
   ],
   "source": [
    "rg.get_ts_prec()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the correlation (BivariateMI) map, the timeseries are named according the format:\n",
    "{lag}..{region label}..{precursor name}, where precursor name is defined in list_of_name_path. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>3ts</th>\n",
       "      <th>1..1..sst</th>\n",
       "      <th>1..2..sst</th>\n",
       "      <th>TrainIsTrue</th>\n",
       "      <th>RV_mask</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th>1979-01-09</th>\n",
       "      <td>-7.601616</td>\n",
       "      <td>-0.038873</td>\n",
       "      <td>0.509949</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-01-19</th>\n",
       "      <td>-2.785106</td>\n",
       "      <td>0.378001</td>\n",
       "      <td>0.100344</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-01-29</th>\n",
       "      <td>-5.305454</td>\n",
       "      <td>0.191989</td>\n",
       "      <td>0.129262</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-02-08</th>\n",
       "      <td>-8.257336</td>\n",
       "      <td>0.209246</td>\n",
       "      <td>-0.107803</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-02-18</th>\n",
       "      <td>-5.436037</td>\n",
       "      <td>0.909116</td>\n",
       "      <td>-0.113632</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">3</th>\n",
       "      <th>2018-07-18</th>\n",
       "      <td>0.193873</td>\n",
       "      <td>-1.140358</td>\n",
       "      <td>-0.071117</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-28</th>\n",
       "      <td>-2.411315</td>\n",
       "      <td>-1.285175</td>\n",
       "      <td>0.117341</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-07</th>\n",
       "      <td>0.672068</td>\n",
       "      <td>-1.214079</td>\n",
       "      <td>-0.041565</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-17</th>\n",
       "      <td>-0.586045</td>\n",
       "      <td>-0.958988</td>\n",
       "      <td>-0.214607</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-27</th>\n",
       "      <td>-0.029262</td>\n",
       "      <td>-0.957461</td>\n",
       "      <td>-0.187062</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5712 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   3ts  1..1..sst  1..2..sst  TrainIsTrue  RV_mask\n",
       "0 1979-01-09 -7.601616  -0.038873   0.509949            0    False\n",
       "  1979-01-19 -2.785106   0.378001   0.100344            0    False\n",
       "  1979-01-29 -5.305454   0.191989   0.129262            0    False\n",
       "  1979-02-08 -8.257336   0.209246  -0.107803            0    False\n",
       "  1979-02-18 -5.436037   0.909116  -0.113632            0    False\n",
       "...                ...        ...        ...          ...      ...\n",
       "3 2018-07-18  0.193873  -1.140358  -0.071117            0     True\n",
       "  2018-07-28 -2.411315  -1.285175   0.117341            0     True\n",
       "  2018-08-07  0.672068  -1.214079  -0.041565            0     True\n",
       "  2018-08-17 -0.586045  -0.958988  -0.214607            0     True\n",
       "  2018-08-27 -0.029262  -0.957461  -0.187062            0     True\n",
       "\n",
       "[5712 rows x 5 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rg.df_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train-test cross-validation and Response Variable mask handling\n",
    "\n",
    "- The different train-test folds are organized in the first level index.\n",
    "- Each fold contains precursor timeseries that were extracted from only training data.\n",
    "- The Training data is specified by the TrainIsTrue mask.\n",
    "- The Response Variable mask defined the dates to predict \n",
    "- The RV_mask the anker for lag shifting the precursors.\n",
    "\n",
    "The last two columns should be always present. \n",
    "- The RV_mask is defined by the start_end_TVdate.\n",
    "- The TrainIsTrue is defined by the method for cross-validation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TrainIsTrue</th>\n",
       "      <th>x_fit</th>\n",
       "      <th>y_fit</th>\n",
       "      <th>x_pred</th>\n",
       "      <th>y_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1979-01-09</th>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-01-19</th>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-01-29</th>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-02-08</th>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-02-18</th>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-18</th>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-28</th>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-07</th>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-17</th>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-27</th>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1428 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            TrainIsTrue  x_fit  y_fit x_pred  y_pred\n",
       "1979-01-09            0  False  False  False   False\n",
       "1979-01-19            0  False  False  False   False\n",
       "1979-01-29            0  False  False  False   False\n",
       "1979-02-08            0  False  False  False   False\n",
       "1979-02-18            0  False  False  False   False\n",
       "...                 ...    ...    ...    ...     ...\n",
       "2018-07-18            1   True   True   True    True\n",
       "2018-07-28            1   True   True   True    True\n",
       "2018-08-07            1   True   True   True    True\n",
       "2018-08-17            1   True   True   True    True\n",
       "2018-08-27            1  False   True  False    True\n",
       "\n",
       "[1428 rows x 5 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example lag shifting\n",
    "from RGCPD.forecasting import func_models as fc_utils\n",
    "fc_utils.apply_shift_lag(rg.df_splits.loc[0].copy(), lag_i=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Causal Discovery using Tigramite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Progress causal inference - traintest set 100%"
     ]
    }
   ],
   "source": [
    "rg.PCMCI_df_data(tigr_function_call='run_pcmci',\n",
    "                 kwrgs_tigr={'tau_min': 0,\n",
    "                             'tau_max': 1,\n",
    "                             'pc_alpha': 0.05,\n",
    "                             'max_conds_dim': 2,\n",
    "                             'max_combinations': 2,\n",
    "                             'max_conds_py': 2,\n",
    "                             'max_conds_px': 2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mrg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPCMCI_get_links\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mvar\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0malpha_level\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.05\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mFDR_cv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'fdr_bh'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Parameters\n",
       "----------\n",
       "var : str, optional\n",
       "    Specify variable you want to retrieve links for. If None, returns\n",
       "    links toward target variable\n",
       "alpha_level : float, optional\n",
       "    significance threshold. The default is .05.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "DataFrame of MCI coefficients and alpha values _toward_ var.\n",
       "\u001b[0;31mFile:\u001b[0m      ~/RGCPD/RGCPD/class_RGCPD.py\n",
       "\u001b[0;31mType:\u001b[0m      method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rg.PCMCI_get_links?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>3ts</th>\n",
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       "    <tr>\n",
       "      <th>1..1..sst</th>\n",
       "      <td>True</td>\n",
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       "      <th>1..2..sst</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">3</th>\n",
       "      <th>3ts</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1..1..sst</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1..2..sst</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0      1\n",
       "0 3ts        False   True\n",
       "  1..1..sst   True  False\n",
       "  1..2..sst   True  False\n",
       "1 3ts        False   True\n",
       "  1..1..sst   True  False\n",
       "  1..2..sst   True  False\n",
       "2 3ts        False   True\n",
       "  1..1..sst   True  False\n",
       "  1..2..sst   True  False\n",
       "3 3ts        False   True\n",
       "  1..1..sst  False  False\n",
       "  1..2..sst   True  False"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rg.PCMCI_get_links(var=rg.TV.name, alpha_level=.05)\n",
    "rg.df_links"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Plotting all fields significant at alpha_level_tig, while conditioning on parents that were found in the PC step\n",
      "Plotting Corr maps sst_labels, split 0, lag sst_labels\n",
      "Plotting Corr maps sst_labels, split 0, lag sst_labels_tigr\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1660x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting Corr maps sst_labels, split 0, lag sst_labels\n",
      "Plotting Corr maps sst_labels, split 0, lag sst_labels_tigr\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1660x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Plotting all fields significant at alpha_level_tig, while conditioning on parents that were found in the PC step\n",
      "Plotting Corr maps sst_corr_and_tigr, split 0, lag sst_corr\n",
      "Plotting Corr maps sst_corr_and_tigr, split 0, lag sst_corr_tigr\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x250 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# on the left you see all the extraction precursor regions, on the right you see the regions which were found Conditionally Dependent (~Causal)\n",
    "rg.plot_maps_sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Deprecated: rg.df_ParCorr_sum "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functionality for Causal Inference\n",
    "(Define your own Conditional Independence tests)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [27]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mRGCPD\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m wrapper_PCMCI\n\u001b[0;32m----> 2\u001b[0m corr, pvals \u001b[38;5;241m=\u001b[39m \u001b[43mwrapper_PCMCI\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdf_data_Parcorr\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdf_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m                                            \u001b[49m\u001b[43mtarget\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m3ts\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m                                            \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m1..2..sst\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m                                            \u001b[49m\u001b[43mz_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m1..1..sst\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[43m                                            \u001b[49m\u001b[43mz_lag\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      7\u001b[0m pvals\n",
      "File \u001b[0;32m~/RGCPD/RGCPD/wrapper_PCMCI.py:779\u001b[0m, in \u001b[0;36mdf_data_Parcorr\u001b[0;34m(df_data, z_keys, keys, target, x_lag, z_lag)\u001b[0m\n\u001b[1;32m    776\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m df_data\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mlevels[\u001b[38;5;241m0\u001b[39m]:\n\u001b[1;32m    777\u001b[0m     pcmci \u001b[38;5;241m=\u001b[39m pcmci_dict[s]\n\u001b[0;32m--> 779\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43misnan\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpcmci\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataframe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39many(): \u001b[38;5;66;03m#NaNs in array\u001b[39;00m\n\u001b[1;32m    780\u001b[0m         \u001b[38;5;66;03m# break\u001b[39;00m\n\u001b[1;32m    781\u001b[0m         val, pval \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mnan, np\u001b[38;5;241m.\u001b[39mnan\n\u001b[1;32m    782\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m x_key \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m pcmci\u001b[38;5;241m.\u001b[39mvar_names \u001b[38;5;129;01mor\u001b[39;00m z \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m pcmci\u001b[38;5;241m.\u001b[39mvar_names:\n",
      "\u001b[0;31mTypeError\u001b[0m: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''"
     ]
    }
   ],
   "source": [
    "from RGCPD import wrapper_PCMCI\n",
    "corr, pvals = wrapper_PCMCI.df_data_Parcorr(rg.df_data, \n",
    "                                            target='3ts',\n",
    "                                            keys=['1..2..sst'],\n",
    "                                            z_keys=['1..1..sst'],\n",
    "                                            z_lag=1)\n",
    "pvals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_trans = rg.transform_df_data()\n",
    "\n",
    "df_z_removed = wrapper_PCMCI.df_data_remove_z(df_trans,\n",
    "                                              keys=['3ts'],\n",
    "                                              z_keys=['1..2..sst'],\n",
    "                                              plot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecasting pipeline 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from RGCPD.forecasting import func_models as fc_utils\n",
    "from RGCPD.forecasting.stat_models_cont import ScikitModel\n",
    "import numpy as np\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "\n",
    "\n",
    "# choose type prediciton (continuous or probabilistic) by making comment #\n",
    "prediction = 'continuous'   \n",
    "# prediction = 'events' ; q = .66 # quantile threshold for event definition\n",
    "\n",
    "if prediction == 'continuous':\n",
    "    model = ScikitModel(Ridge, verbosity=0)\n",
    "    # You can also tune parameters by passing a list of values. Then GridSearchCV from sklearn will \n",
    "    # find the set of parameters that give the best mean score on all kfold test sets. \n",
    "    # below we pass a list of alpha's to tune the regularization.\n",
    "    alphas = list(np.concatenate([[1E-20],np.logspace(-5,0, 6), np.logspace(.01, 2.5, num=25)]))                       \n",
    "    kwrgs_model = {'scoringCV':'neg_mean_absolute_error',\n",
    "                   'kfold':5,\n",
    "                   'alpha':alphas} # large a, strong regul.\n",
    "elif prediction == 'events':\n",
    "    model = ScikitModel(LogisticRegressionCV, verbosity=0)\n",
    "    kwrgs_model = {'kfold':5,\n",
    "                   'scoring':'neg_brier_score'}\n",
    "\n",
    "    \n",
    "\n",
    "target_ts = rg.TV.RV_ts ; \n",
    "target_ts = (target_ts - target_ts.mean()) / target_ts.std()\n",
    "if prediction == 'events':\n",
    "    if q >= 0.5:\n",
    "        target_ts = (target_ts > target_ts.quantile(q)).astype(int)\n",
    "    elif q < .5:\n",
    "        target_ts = (target_ts < target_ts.quantile(q)).astype(int)\n",
    "    BSS = fc_utils.ErrorSkillScore(constant_bench=float(target_ts.mean())).BSS\n",
    "    score_func_list = [BSS, fc_utils.metrics.roc_auc_score]\n",
    "    \n",
    "elif prediction == 'continuous':\n",
    "    RMSE_SS = fc_utils.ErrorSkillScore(constant_bench=float(target_ts.mean())).RMSE\n",
    "    MAE_SS = fc_utils.ErrorSkillScore(constant_bench=float(target_ts.mean())).MAE\n",
    "    score_func_list = [RMSE_SS, fc_utils.corrcoef, MAE_SS]\n",
    "        \n",
    "    \n",
    "out = rg.fit_df_data_ridge(target=target_ts,\n",
    "                           keys=None,\n",
    "                           fcmodel=model,\n",
    "                           kwrgs_model=kwrgs_model,\n",
    "                           transformer=False,\n",
    "                           tau_min=1, tau_max=3)\n",
    "predict, weights, model_lags = out\n",
    "\n",
    "df_train_m, df_test_s_m, df_test_m, df_boot = fc_utils.get_scores(predict,\n",
    "                                                                 rg.df_data.iloc[:,-2:],\n",
    "                                                                 score_func_list,\n",
    "                                                                 n_boot = 100,\n",
    "                                                                 score_per_test=False,\n",
    "                                                                 blocksize=1,\n",
    "                                                                 rng_seed=1)\n",
    "lag = 1\n",
    "if prediction == 'events':\n",
    "    print(model.scikitmodel.__name__, '\\n', f'Test score at lag {lag}\\n',\n",
    "          'BSS {:.2f}\\n'.format(df_test_m.loc[0].loc[lag].loc['BSS']),\n",
    "          'AUC {:.2f}'.format(df_test_m.loc[0].loc[lag].loc['roc_auc_score']),\n",
    "          '\\nTrain score\\n',\n",
    "          'BSS {:.2f}\\n'.format(df_train_m.mean(0).loc[lag]['BSS']),\n",
    "          'AUC {:.2f}'.format(df_train_m.mean(0).loc[lag]['roc_auc_score']))\n",
    "elif prediction == 'continuous':\n",
    "    print(model.scikitmodel.__name__, '\\n', 'Test score\\n',\n",
    "              'RMSE {:.2f}\\n'.format(df_test_m.loc[0][lag]['RMSE']),\n",
    "              'MAE {:.2f}\\n'.format(df_test_m.loc[0][lag]['MAE']),\n",
    "              'corrcoef {:.2f}'.format(df_test_m.loc[0][lag]['corrcoef']),\n",
    "              '\\nTrain score\\n',\n",
    "              'RMSE {:.2f}\\n'.format(df_train_m.mean(0).loc[lag]['RMSE']),\n",
    "              'MAE {:.2f}\\n'.format(df_train_m.mean(0).loc[lag]['MAE']),\n",
    "              'corrcoef {:.2f}'.format(df_train_m.mean(0).loc[lag]['corrcoef']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test_m.loc[0].plot.bar(rot=0, color=['blue', 'green', 'purple'], figsize=(10,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from RGCPD.forecasting.stat_models import plot_importances\n",
    "plot_importances(models_splits_lags=model_lags, lag=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Match the retrieved timeseries of BivariateMI to the lag of forecasting \n",
    "Quick summary of code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rg.list_for_MI[0].lags = np.array([0,1,2]) # update lags correlation map\n",
    "rg.calc_corr_maps()\n",
    "rg.cluster_list_MI()\n",
    "rg.get_ts_prec()\n",
    "rg.df_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above, we now have a timeseries of the correlation maps lag 0 up to lag 2. Below, we can match these timeseries to lag-of-forecast by setting 'match_lag_region_to_lag_fc' to True."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rg.fit_df_data_ridge(match_lag_region_to_lag_fc=True,\n",
    "                    tau_min=0, tau_max=2)\n",
    "# Predicitons for lag 0, 1 and 2 using precursors extraction from correlation maps at lag 0, 1, 2, respecitvely."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## goal of package \n",
    "- Reproducible\n",
    "- Flexible\n",
    "- Time efficient"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### An example of flexibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up your pipeline\n",
    "from RGCPD.forecasting import func_models as fc_utils\n",
    "def pipeline(tfreq, start_end_TVdate):\n",
    "    rg = RGCPD(tfreq=tfreq, start_end_TVdate=start_end_TVdate)\n",
    "    rg.list_for_MI = [BivariateMI(name='sst_test', distance_eps=700)]\n",
    "    rg.pp_precursors() ; rg.pp_TV()\n",
    "    rg.traintest('random_5')\n",
    "    rg.calc_corr_maps() ; rg.cluster_list_MI()\n",
    "    rg.get_ts_prec()\n",
    "    target_ts = rg.TV.RV_ts ; \n",
    "    target_ts = (target_ts - target_ts.mean()) / target_ts.std()\n",
    "    prediction = rg.fit_df_data_ridge(target=target_ts)[0]\n",
    "    out = fc_utils.get_scores(prediction,\n",
    "                              rg.df_data.iloc[:,-2:],\n",
    "                              None,\n",
    "                              n_boot = 0,\n",
    "                              score_per_test=False,\n",
    "                              blocksize=1)\n",
    "    df_train_m, df_test_s_m, df_test_m, df_boot = out\n",
    "    print(df_test_m.loc[0])  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets choose a common timescale and target period for seasonal forecasts. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline(7, ('06-01','08-31')) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the code handling all the anoying data-handling stuff, one can easily test different settings. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline(15, ('07-01','08-31'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(0.784698 - 0.732105) / 0.784698"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note a ~7% increase in skill (MSE). The commonly used target window and timescale had pourer predictability \n",
    "(likely has to due with dependence of temperature on end-of-summer soil moisture). \n",
    "\\\n",
    "\\\n",
    "When one codes a pipeline for 1 goal (7 day-mean forecast, JJA summer). He/she is restricted by the investment when testing other plausible hypothesis. With this Python package, users are freed from the coding time-investment and can spend more time on testing different hypthesis very efficiently."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A non-extensive list of options to test:\n",
    "- Different timescales\n",
    "- Different target window(s)\n",
    "- Different precursors\n",
    "- Different precursor extraction methods ((partial) correlation, EOF, ...)\n",
    "- Different statistical model feature selection methods\n",
    "- Different statistical models / tuning methods\n",
    "- Different train-test splits\n",
    "- Different definitions for the target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (old) Forecasting pipeline 2 \n",
    "Used for MWR paper https://doi.org/10.1175/MWR-D-19-0409.1\n",
    "\n",
    "There is some multiprocessing based on Python's standard 'concurrent futures' module. This only works when run script is run in one go. Will not work another time. Has to do with the running the code as the __main__ file or something.. (don't know the details). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rg.store_df_PCMCI() # storing timeseries in .h5 format\n",
    "path_df_data = rg.path_df_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we load in the data, including info on the causal links. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from RGCPD.forecasting.class_fc import fcev\n",
    "from RGCPD.forecasting import valid_plots as dfplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fc = fcev(path_data=path_df_data, n_cpu=1, causal=True)\n",
    "fc.df_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fc.get_TV(kwrgs_events=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define statmodel:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fc.fit_models?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fc.fit_models(lead_max=35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_experiments = {}       \n",
    "fc.perform_validation(n_boot=100, blocksize='auto', \n",
    "                              threshold_pred=(1.5, 'times_clim'))\n",
    "dict_experiments['test'] = fc.dict_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "working_folder, filename = fc._print_sett(list_of_fc=[fc])\n",
    "store=True\n",
    "dict_all = dfplots.merge_valid_info([fc], store=store)\n",
    "if store:\n",
    "    dict_merge_all = functions_pp.load_hdf5(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "kwrgs = {'wspace':0.25, 'col_wrap':3} #, 'threshold_bin':fc.threshold_pred}\n",
    "met = ['AUC-ROC', 'AUC-PR', 'BSS', 'Rel. Curve', 'Precision', 'Accuracy']\n",
    "expers = list(dict_experiments.keys())\n",
    "line_dim = 'model'\n",
    "\n",
    "\n",
    "fig = dfplots.valid_figures(dict_merge_all, #expers=expers, #models=models,\n",
    "                          line_dim=line_dim, \n",
    "                          group_line_by=None,  \n",
    "                          met=met, **kwrgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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