cassie_sensitivity_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2c0b3db1-290b-4b54-a2df-5819110e47b2",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Sensitivity analysis for CASSIE post-processing\n",
    "\n",
    "**Datasets this notebook expects to find** under the below given datapaths:\n",
    "- 4 folders, each containing a file `coastlines.shp` (output of Cassie webinterface application):\n",
    "    - `12_cassie_landsat_1984-04-30_1992-09-04_ext1000_spac5000_base2_multi-thresh/`\n",
    "    - `13_cassie_landsat_1992-09-20_2003-08-02_ext1000_spac5000_base2_multi-thresh/`\n",
    "    - `18_cassie_landsat_2003-09-19_2015-03-12_ext1300_spac5000_base1_multi-thresh/`\n",
    "    - `17_cassie_landsat_2015-04-13_2023-02-22_ext1300_spac5000_base1_multi-thresh/`\n",
    "- File `jarkus_transects_EPSG32631_reduced.geojson`  (output of jarkus_shoreline.ipynb, but manually removed overlapping transects in the curvatures with QGIS)\n",
    "- File `TG_rijkswaterstaat_1992_01_01-2021_12_31.csv` (combined and cleaned individual .csv files from rijkswaterstaat using function `prepare_TG_rijkswaterstaat_data` in the coastal_data module)\n",
    "- File `transect_red_with_derivatives.nc` (output of jarkus_shoreline.ipynb)\n",
    "- File `tidal_correction_10minutes.csv` (output of `tidal_correction_EOT_FES.ipynb`)\n",
    "\n",
    "Files that are **created** in this notebook (in case they don't exist):\n",
    "- 2 files `cassie_sens_along_dist.pkl` and `cassie_sens_along_dist_qc.pkl`(intersection computation for several along_dist values)\n",
    "- 2 files `cassie_sens_noqc.pkl` and `cassie_sens_qc.pkl` (using the intersection function with or without quality control)\n",
    "- 2 files `cassie_sens_beach_slope_median_grad_buffer.pkl`, `cassie_sens_beach_slope_mean_grad_buffer` (beach slopes compute with variable median or variable mean and different values for the buffer zone)\n",
    "\n",
    "**Required python modules**\n",
    "- Adjusted [CoastSat](https://github.com/3enedix/CoastSat)\n",
    "- [coastal_data](https://github.com/3enedix/coastal-sea-level/tree/main/coastal_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6a4c7cd9-9a1b-4f33-be61-a61d1310257d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Fill in your datapaths\n",
    "main_datapath_input = '/home/bene/PhD-docs/80_papers/1_dataset_combination/data/input/'\n",
    "main_datapath_output = '/home/bene/PhD-docs/80_papers/1_dataset_combination/data/output/'\n",
    "coastsat_git = '/home/bene/PhD-git/70_CoastSat'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c17c7fc-11b1-43a6-884d-8b2c15561e88",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e53b17d6-071a-496c-9c51-5fd0ba5081f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.1\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import shapefile\n",
    "import pandas as pd\n",
    "import statistics\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import os\n",
    "import pickle\n",
    "\n",
    "# plotting\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "\n",
    "# toolboxes\n",
    "import sys\n",
    "sys.path.append(coastsat_git)\n",
    "from coastsat import SDS_tools, SDS_transects\n",
    "\n",
    "from coastal_data import CD_statistics, CD_helper_functions\n",
    "\n",
    "# projection\n",
    "from pyproj import CRS, Transformer\n",
    "\n",
    "import pdb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "94a81b98-9db6-49f6-b0d7-8683141531c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# matplotlib fontsizes\n",
    "SMALL_SIZE = 8\n",
    "MEDIUM_SIZE = 20\n",
    "BIGGER_SIZE = 25\n",
    "\n",
    "plt.rc('font', size=BIGGER_SIZE)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=BIGGER_SIZE)    # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=MEDIUM_SIZE)   # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=MEDIUM_SIZE)   # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=MEDIUM_SIZE)   # legend fontsize\n",
    "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1fcda2f0-7655-4fca-8ee6-f03e1a95f036",
   "metadata": {},
   "outputs": [],
   "source": [
    "# png image resolution\n",
    "dpi = 300"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3ebf7d3-d920-432a-8a41-37f004b8c75d",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c7c7f990-51e3-4e2e-baf4-85950600b014",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Foldernames to the CASSIE shape files\n",
    "folders = ['12_cassie_landsat_1984-04-30_1992-09-04_ext1000_spac5000_base2_multi-thresh/',\\\n",
    "          '13_cassie_landsat_1992-09-20_2003-08-02_ext1000_spac5000_base2_multi-thresh/',\\\n",
    "          '18_cassie_landsat_2003-09-19_2015-03-12_ext1300_spac5000_base1_multi-thresh/',\\\n",
    "          '17_cassie_landsat_2015-04-13_2023-02-22_ext1300_spac5000_base1_multi-thresh/']\n",
    "fn = 'coastlines'\n",
    "\n",
    "# Transformation from EPSG 4326 to EPSG 32631\n",
    "crs_4326 = CRS.from_epsg(4326)\n",
    "crs_32631 = CRS.from_epsg(32631)\n",
    "transformer = Transformer.from_crs(crs_4326, crs_32631)\n",
    "\n",
    "datelist = []\n",
    "sllist = []\n",
    "\n",
    "for folder in folders:\n",
    "    sf = shapefile.Reader(main_datapath_input + folder + fn)    \n",
    "    shorelines = sf.shapeRecords().__geo_interface__ # shapefile to geojson\n",
    "    for sl in shorelines['features']:\n",
    "        date = pd.to_datetime(sl['properties']['date'], utc=True)\n",
    "        date = date.to_pydatetime()\n",
    "        datelist.append(date)\n",
    "\n",
    "        sl_4326 = sl['geometry']['coordinates']\n",
    "        sl_23631 = [transformer.transform(_[1], _[0]) for _ in sl_4326]\n",
    "        sllist_temp = [list(_) for _ in sl_23631]\n",
    "\n",
    "        sllist.append(np.array(sllist_temp))\n",
    "        \n",
    "# put the dates and shoreline coordinates in a dictionary (CoastSat-output-like)\n",
    "sl_cassie = {'dates' : datelist, 'shorelines' : sllist}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68f488a5-f102-4294-8aa8-271a46dd056b",
   "metadata": {},
   "source": [
    "## Load transects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a8b336f9-4c8d-4dff-b295-83d7fc5d00db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "152 transects have been loaded coordinates are in epsg:32631\n"
     ]
    }
   ],
   "source": [
    "# JARKUS-transects\n",
    "fn = 'jarkus_transects_EPSG32631_reduced.geojson'\n",
    "geojson_file = main_datapath_output + fn\n",
    "transects = SDS_tools.transects_from_geojson(geojson_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae9b3348-83f9-4b74-a6c4-949fa0375708",
   "metadata": {},
   "source": [
    "## Initialise settings for timeseries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "da423e55-c41b-4284-aa48-eb2bf719840e",
   "metadata": {},
   "outputs": [],
   "source": [
    "settings_ts = { # parameters for analysis of cross-shore time series\n",
    "    'along_dist': 1200,    # along-shore distance to use for computing the intersection\n",
    "    'sm_window_size' : 5, # window size for smoothing\n",
    "    # tidal correction\n",
    "    'beach_slope' : 'variable_median', #  number (= uniform beach slope), 'variable_median' or 'variable_mean'\n",
    "    'grad_buffer' : 45, # [m], 1/2 length of the zone where the gradients in each point are considered for mean or median\n",
    "    'water_levels_source' : 'tg', #'tg' or 'eot'\n",
    "    'tide_corr_thresh' : 100, # [m], threshold for maximum tidal correction     \n",
    "                    }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4a2018d-f470-4c9e-9350-9aa0a5d1ef35",
   "metadata": {},
   "source": [
    "## Intersection computation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "024c04fc-b42e-40ab-aad6-c6a1ad2ab3f8",
   "metadata": {},
   "source": [
    "### Along-shore distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "33751923-5a29-49c2-bf6a-26695af9316a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute timeseries for all transects and for several along_dist values\n",
    "def along_dist_experiments(settings_transects, qc):\n",
    "    transect_nr = list(transects.keys())\n",
    "    cd_along = pd.DataFrame(columns=transect_nr, index=along_dist_values)\n",
    "    cd_along.index.name = 'along_dist'\n",
    "    for i, along_dist in enumerate(along_dist_values):\n",
    "        settings_transects['along_dist'] = along_dist\n",
    "        if qc == 'noqc':\n",
    "            cd_cassie = SDS_transects.compute_intersection(sl_cassie, transects, settings_transects)\n",
    "        elif qc == 'qc':   \n",
    "            cd_cassie = SDS_transects.compute_intersection_QC(sl_cassie, transects, settings_transects)\n",
    "        \n",
    "        cd_cassie_sm = {}\n",
    "        for key in cd_cassie.keys():\n",
    "            # Interpolate over the nans\n",
    "            cd_cassie[key] = np.array(pd.Series(cd_cassie[key]).interpolate())\n",
    "            # Smoothing\n",
    "            cd_cassie_sm[key] = CD_statistics.moving_average(cd_cassie[key], settings_ts['sm_window_size'])\n",
    "            cd_along.loc[along_dist, key] = cd_cassie_sm[key]\n",
    "    return cd_along\n",
    "\n",
    "along_dist_values = range(50,2500,200)\n",
    "\n",
    "# not quality controlled\n",
    "if not os.path.isfile(main_datapath_output + 'cassie_sens_along_dist.pkl'):\n",
    "    # initialise settings dict, but along_dist will change\n",
    "    settings_transects = {\n",
    "        'along_dist': settings_ts['along_dist'],     # along-shore distance to use for computing the intersection\n",
    "        'max_dist_origin': 10000,                    # maximum distance to the origin of a transect in [m]\n",
    "                     }\n",
    "    cd_along = along_dist_experiments(settings_transects, 'noqc')\n",
    "    cd_along.to_pickle(main_datapath_output + 'cassie_sens_along_dist.pkl')\n",
    "else:\n",
    "    cd_along = pd.read_pickle(main_datapath_output + 'cassie_sens_along_dist.pkl')\n",
    "\n",
    "# Remove the first 6 transects (outliers)\n",
    "cd_along.drop(cd_along.columns[0:5], axis=1, inplace=True)\n",
    "\n",
    "# quality controlled\n",
    "if not os.path.isfile(main_datapath_output + 'cassie_sens_along_dist_qc.pkl'):\n",
    "    settings_transects_qc = { # parameters for computing intersections\n",
    "                  'along_dist': settings_ts['along_dist'],    # along-shore distance to use for computing the intersection\n",
    "                  'min_points':          3,         # minimum number of shoreline points to calculate an intersection\n",
    "                  'max_std':             15,        # max std for points around transect\n",
    "                  'max_range':           30,        # max range for points around transect\n",
    "                  'min_chainage':        -100,      # largest negative value along transect (landwards of transect origin)\n",
    "                  'multiple_inter':      'max',     # mode for removing outliers ('auto', 'nan', 'max')\n",
    "                  'auto_prc':             0.1,      # percentage of the time that multiple intersects are present to use the max\n",
    "                  'max_dist_origin':      10000,    # maximum distance to the origin of a transect in [m]\n",
    "                }\n",
    "    cd_along_qc = along_dist_experiments(settings_transects_qc, 'qc')\n",
    "    cd_along_qc.to_pickle(main_datapath_output + 'cassie_sens_along_dist_qc.pkl')\n",
    "else:\n",
    "    cd_along_qc = pd.read_pickle(main_datapath_output + 'cassie_sens_along_dist_qc.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc5687e3-4672-465f-ba72-e9929bcf9b2c",
   "metadata": {},
   "source": [
    "#### Statistics for the non-quality controlled timeseries"
   ]
  },
  {
   "cell_type": "raw",
   "id": "41b58ca3-fb25-4157-8bc0-0ae326893813",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "source": [
    "cmap = cm.get_cmap('viridis')\n",
    "color_list = cmap(np.linspace(0,1,len(cd_along.index)))\n",
    "                  \n",
    "fig, ax = plt.subplots(len(cd_along.columns),1, figsize=(15,350))\n",
    "fig.tight_layout()\n",
    "for i, transect in enumerate(cd_along.columns):\n",
    "    for k, along_dist in enumerate(cd_along.index):\n",
    "        ax[i].plot(sl_cassie['dates'], cd_along.loc[along_dist, str(transect)], '.-', label=along_dist, color=color_list[k,:])\n",
    "        ax[i].text(0.5,0.95, transect, bbox=dict(boxstyle=\"square\", ec='k',fc='w'), ha='center',\n",
    "                va='top', transform=ax[i].transAxes, fontsize=14);\n",
    "        ax[i].grid()\n",
    "ax[-1].legend(bbox_to_anchor=(1.2,206))\n",
    "ax[-1].set_xlabel('Time')\n",
    "ax[-1].set_ylabel('Distance [m]');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "292cc218-7edd-4b79-a155-a604b1c8fcea",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_transect_medians(data_df):\n",
    "    '''\n",
    "    Compute the median per transect of all solutions in data_df\n",
    "    data_df: DataFrame, rows: different solutions, e.g. along_dist values, columns: transects, per cell one timeseries\n",
    "    '''\n",
    "    transect_medians = {}\n",
    "    for transect in data_df.columns:\n",
    "        combined_single_transect = np.empty(len(data_df.iloc[0,0])) # length of one timeseries\n",
    "        for solution in data_df.index:\n",
    "            combined_single_transect = np.vstack([combined_single_transect,\n",
    "                                                           data_df.loc[solution,transect]])\n",
    "        combined_single_transect = np.delete(combined_single_transect, 0, 0)\n",
    "\n",
    "        transect_medians[transect] = np.median(combined_single_transect, axis=0)\n",
    "    return transect_medians"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "de2aa3ba-31bd-42ab-8b25-be0b211a8427",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_statistics(data_df, transect_medians, sl):\n",
    "    '''\n",
    "    Compute standard deviations, trends and difference to median (median, mean, min and max of all solutions)\n",
    "    (for the differences, take the median per transect)\n",
    "    '''\n",
    "    # timevector for trend\n",
    "    x = CD_helper_functions.datetime_to_decimal_numbers(pd.to_datetime(sl['dates']))\n",
    "    \n",
    "    std_av = pd.DataFrame(columns=['median', 'mean', 'min', 'max'], index=data_df.index)\n",
    "    trend_av = std_av.copy()\n",
    "    diff_to_median = std_av.copy()\n",
    "    \n",
    "    for solution in data_df.index:\n",
    "        std_list = []\n",
    "        trend_list = []\n",
    "        diff = []\n",
    "        for transect in data_df.columns:\n",
    "            ts = data_df.loc[solution, transect]\n",
    "            if np.all(np.isnan(ts)):\n",
    "                continue\n",
    "            ts_nonan = ts[~np.isnan(ts)]            \n",
    "\n",
    "            std_list.append(statistics.stdev(ts_nonan)) # when first value is nan, then std is nan\n",
    "            trend_list.append(CD_statistics.compute_trend(x, ts))\n",
    "            diff.append(np.nanmedian(transect_medians[transect] - ts))\n",
    "\n",
    "        std_av.loc[solution, 'median'] = round(np.median(std_list),2)\n",
    "        std_av.loc[solution, 'mean'] = round(np.mean(std_list),2)\n",
    "        std_av.loc[solution, 'min'] = round(np.min(std_list),2)\n",
    "        std_av.loc[solution, 'max'] = round(np.max(std_list),2)\n",
    "\n",
    "        trend_av.loc[solution, 'median'] = round(np.median(trend_list),2)\n",
    "        trend_av.loc[solution, 'mean'] = round(np.mean(trend_list),2)\n",
    "        trend_av.loc[solution, 'min'] = round(np.min(trend_list),2)\n",
    "        trend_av.loc[solution, 'max'] = round(np.max(trend_list),2) \n",
    "\n",
    "        diff_to_median.loc[solution, 'median'] = round(np.nanmedian(diff),2)\n",
    "        diff_to_median.loc[solution, 'mean'] = round(np.nanmean(diff),2)\n",
    "        diff_to_median.loc[solution, 'min'] = round(np.nanmin(diff),2)\n",
    "        diff_to_median.loc[solution, 'max'] = round(np.nanmax(diff),2)\n",
    "\n",
    "    print('std [m]')\n",
    "    display(std_av)\n",
    "    print('trend [m/year]')\n",
    "    display(trend_av)\n",
    "    print('Difference to median of all solutions [m]:')\n",
    "    display(diff_to_median)\n",
    "    \n",
    "    return std_av, trend_av, diff_to_median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c862fe63-3164-4833-b80c-4b60d206609d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "std [m]\n"
     ]
    },
    {
     "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>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>along_dist</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>87.09</td>\n",
       "      <td>167.73</td>\n",
       "      <td>28.26</td>\n",
       "      <td>5794.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>88.3</td>\n",
       "      <td>160.78</td>\n",
       "      <td>28.14</td>\n",
       "      <td>4814.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>87.72</td>\n",
       "      <td>154.13</td>\n",
       "      <td>30.31</td>\n",
       "      <td>3920.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>85.03</td>\n",
       "      <td>151.39</td>\n",
       "      <td>31.4</td>\n",
       "      <td>3533.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>850</th>\n",
       "      <td>84.17</td>\n",
       "      <td>144.83</td>\n",
       "      <td>31.03</td>\n",
       "      <td>2457.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1050</th>\n",
       "      <td>84.44</td>\n",
       "      <td>143.74</td>\n",
       "      <td>31.92</td>\n",
       "      <td>2178.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1250</th>\n",
       "      <td>83.22</td>\n",
       "      <td>142.91</td>\n",
       "      <td>32.67</td>\n",
       "      <td>2000.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1450</th>\n",
       "      <td>81.65</td>\n",
       "      <td>142.47</td>\n",
       "      <td>31.22</td>\n",
       "      <td>1881.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1650</th>\n",
       "      <td>80.39</td>\n",
       "      <td>140.2</td>\n",
       "      <td>31.08</td>\n",
       "      <td>1543.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1850</th>\n",
       "      <td>78.85</td>\n",
       "      <td>136.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>1098.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050</th>\n",
       "      <td>80.48</td>\n",
       "      <td>134.42</td>\n",
       "      <td>32.06</td>\n",
       "      <td>879.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2250</th>\n",
       "      <td>77.76</td>\n",
       "      <td>132.77</td>\n",
       "      <td>34.79</td>\n",
       "      <td>785.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2450</th>\n",
       "      <td>78.3</td>\n",
       "      <td>131.69</td>\n",
       "      <td>34.67</td>\n",
       "      <td>760.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           median    mean    min      max\n",
       "along_dist                               \n",
       "50          87.09  167.73  28.26  5794.75\n",
       "250          88.3  160.78  28.14  4814.33\n",
       "450         87.72  154.13  30.31  3920.62\n",
       "650         85.03  151.39   31.4  3533.83\n",
       "850         84.17  144.83  31.03  2457.09\n",
       "1050        84.44  143.74  31.92  2178.67\n",
       "1250        83.22  142.91  32.67  2000.59\n",
       "1450        81.65  142.47  31.22  1881.79\n",
       "1650        80.39   140.2  31.08  1543.21\n",
       "1850        78.85   136.6   30.7  1098.09\n",
       "2050        80.48  134.42  32.06   879.79\n",
       "2250        77.76  132.77  34.79   785.64\n",
       "2450         78.3  131.69  34.67   760.08"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trend [m/year]\n"
     ]
    },
    {
     "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>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>along_dist</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>-1.86</td>\n",
       "      <td>-2.88</td>\n",
       "      <td>-42.91</td>\n",
       "      <td>410.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>-1.89</td>\n",
       "      <td>-3.49</td>\n",
       "      <td>-42.48</td>\n",
       "      <td>319.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>-2.01</td>\n",
       "      <td>-4.06</td>\n",
       "      <td>-43.95</td>\n",
       "      <td>231.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>-1.94</td>\n",
       "      <td>-4.36</td>\n",
       "      <td>-47.06</td>\n",
       "      <td>186.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>850</th>\n",
       "      <td>-1.69</td>\n",
       "      <td>-5.57</td>\n",
       "      <td>-54.28</td>\n",
       "      <td>16.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1050</th>\n",
       "      <td>-2.04</td>\n",
       "      <td>-6.08</td>\n",
       "      <td>-56.67</td>\n",
       "      <td>7.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1250</th>\n",
       "      <td>-1.95</td>\n",
       "      <td>-6.14</td>\n",
       "      <td>-64.04</td>\n",
       "      <td>7.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1450</th>\n",
       "      <td>-1.81</td>\n",
       "      <td>-6.13</td>\n",
       "      <td>-65.34</td>\n",
       "      <td>7.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1650</th>\n",
       "      <td>-1.87</td>\n",
       "      <td>-6.08</td>\n",
       "      <td>-65.23</td>\n",
       "      <td>7.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1850</th>\n",
       "      <td>-1.82</td>\n",
       "      <td>-5.84</td>\n",
       "      <td>-48.97</td>\n",
       "      <td>7.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050</th>\n",
       "      <td>-1.69</td>\n",
       "      <td>-5.52</td>\n",
       "      <td>-45.68</td>\n",
       "      <td>7.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2250</th>\n",
       "      <td>-1.75</td>\n",
       "      <td>-5.43</td>\n",
       "      <td>-43.76</td>\n",
       "      <td>10.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2450</th>\n",
       "      <td>-2.02</td>\n",
       "      <td>-5.37</td>\n",
       "      <td>-43.49</td>\n",
       "      <td>9.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           median  mean    min     max\n",
       "along_dist                            \n",
       "50          -1.86 -2.88 -42.91  410.55\n",
       "250         -1.89 -3.49 -42.48  319.62\n",
       "450         -2.01 -4.06 -43.95  231.52\n",
       "650         -1.94 -4.36 -47.06  186.45\n",
       "850         -1.69 -5.57 -54.28   16.14\n",
       "1050        -2.04 -6.08 -56.67    7.06\n",
       "1250        -1.95 -6.14 -64.04    7.02\n",
       "1450        -1.81 -6.13 -65.34    7.11\n",
       "1650        -1.87 -6.08 -65.23    7.07\n",
       "1850        -1.82 -5.84 -48.97    7.09\n",
       "2050        -1.69 -5.52 -45.68    7.04\n",
       "2250        -1.75 -5.43 -43.76    10.3\n",
       "2450        -2.02 -5.37 -43.49    9.57"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference to median of all solutions [m]:\n"
     ]
    },
    {
     "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>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>along_dist</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>-1.09</td>\n",
       "      <td>13.1</td>\n",
       "      <td>-254.5</td>\n",
       "      <td>3075.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>-0.41</td>\n",
       "      <td>18.98</td>\n",
       "      <td>-230.89</td>\n",
       "      <td>3895.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>0.0</td>\n",
       "      <td>8.53</td>\n",
       "      <td>-213.32</td>\n",
       "      <td>2463.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-11.05</td>\n",
       "      <td>-433.47</td>\n",
       "      <td>34.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>850</th>\n",
       "      <td>-0.1</td>\n",
       "      <td>-11.25</td>\n",
       "      <td>-760.52</td>\n",
       "      <td>16.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1050</th>\n",
       "      <td>-0.38</td>\n",
       "      <td>-8.94</td>\n",
       "      <td>-731.72</td>\n",
       "      <td>13.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1250</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.46</td>\n",
       "      <td>-439.3</td>\n",
       "      <td>6.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1450</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.59</td>\n",
       "      <td>-22.46</td>\n",
       "      <td>129.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1650</th>\n",
       "      <td>0.0</td>\n",
       "      <td>8.54</td>\n",
       "      <td>-23.2</td>\n",
       "      <td>216.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1850</th>\n",
       "      <td>0.0</td>\n",
       "      <td>15.59</td>\n",
       "      <td>-16.58</td>\n",
       "      <td>304.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050</th>\n",
       "      <td>0.0</td>\n",
       "      <td>19.56</td>\n",
       "      <td>-18.49</td>\n",
       "      <td>383.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2250</th>\n",
       "      <td>0.48</td>\n",
       "      <td>25.2</td>\n",
       "      <td>-16.94</td>\n",
       "      <td>462.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2450</th>\n",
       "      <td>0.31</td>\n",
       "      <td>30.46</td>\n",
       "      <td>-30.1</td>\n",
       "      <td>566.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           median   mean     min      max\n",
       "along_dist                               \n",
       "50          -1.09   13.1  -254.5  3075.31\n",
       "250         -0.41  18.98 -230.89  3895.17\n",
       "450           0.0   8.53 -213.32  2463.92\n",
       "650           0.0 -11.05 -433.47    34.31\n",
       "850          -0.1 -11.25 -760.52     16.6\n",
       "1050        -0.38  -8.94 -731.72    13.62\n",
       "1250          0.0  -3.46  -439.3     6.13\n",
       "1450          0.0   4.59  -22.46   129.98\n",
       "1650          0.0   8.54   -23.2   216.39\n",
       "1850          0.0  15.59  -16.58   304.71\n",
       "2050          0.0  19.56  -18.49    383.5\n",
       "2250         0.48   25.2  -16.94   462.05\n",
       "2450         0.31  30.46   -30.1   566.11"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cd_along_transect_medians = compute_transect_medians(cd_along)\n",
    "along_std, along_trend, along_diff = compute_statistics(cd_along, cd_along_transect_medians, sl_cassie)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b7b856c-5194-462a-8c7f-ea1bfc899bc4",
   "metadata": {},
   "source": [
    "#### Statistics for the quality controlled timeseries"
   ]
  },
  {
   "cell_type": "raw",
   "id": "01e075cb-89dd-4a58-af8f-c2dd971d0d42",
   "metadata": {
    "tags": []
   },
   "source": [
    "cmap = cm.get_cmap('viridis')\n",
    "color_list = cmap(np.linspace(0,1,len(cd_along_qc.index)))\n",
    "                  \n",
    "fig, ax = plt.subplots(len(cd_along_qc.columns),1, figsize=(15,350))\n",
    "fig.tight_layout()\n",
    "for i, transect in enumerate(cd_along_qc.columns):\n",
    "    for k, along_dist in enumerate(cd_along_qc.index):\n",
    "        ax[i].plot(sl_cassie['dates'], cd_along_qc.loc[along_dist, str(transect)], '.-', label=along_dist, color=color_list[k,:])\n",
    "        ax[i].text(0.5,0.95, transect, bbox=dict(boxstyle=\"square\", ec='k',fc='w'), ha='center',\n",
    "                va='top', transform=ax[i].transAxes, fontsize=14);\n",
    "        ax[i].grid()\n",
    "ax[-1].legend(bbox_to_anchor=(1.2,206))\n",
    "ax[-1].set_xlabel('Time')\n",
    "ax[-1].set_ylabel('Distance [m]');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "39b6f5fd-0291-48f3-9cb1-e130c57a2cda",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "std [m]\n"
     ]
    },
    {
     "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>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>along_dist</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>120.5</td>\n",
       "      <td>154.67</td>\n",
       "      <td>41.47</td>\n",
       "      <td>553.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>98.73</td>\n",
       "      <td>136.89</td>\n",
       "      <td>39.51</td>\n",
       "      <td>550.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>102.57</td>\n",
       "      <td>139.02</td>\n",
       "      <td>41.35</td>\n",
       "      <td>543.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>105.57</td>\n",
       "      <td>141.19</td>\n",
       "      <td>43.0</td>\n",
       "      <td>544.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>850</th>\n",
       "      <td>108.42</td>\n",
       "      <td>143.35</td>\n",
       "      <td>44.39</td>\n",
       "      <td>546.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1050</th>\n",
       "      <td>111.78</td>\n",
       "      <td>145.42</td>\n",
       "      <td>45.52</td>\n",
       "      <td>545.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1250</th>\n",
       "      <td>113.77</td>\n",
       "      <td>147.79</td>\n",
       "      <td>47.31</td>\n",
       "      <td>544.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1450</th>\n",
       "      <td>116.0</td>\n",
       "      <td>150.42</td>\n",
       "      <td>54.71</td>\n",
       "      <td>548.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1650</th>\n",
       "      <td>117.5</td>\n",
       "      <td>153.04</td>\n",
       "      <td>56.64</td>\n",
       "      <td>547.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1850</th>\n",
       "      <td>119.06</td>\n",
       "      <td>155.58</td>\n",
       "      <td>62.42</td>\n",
       "      <td>546.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050</th>\n",
       "      <td>121.13</td>\n",
       "      <td>157.65</td>\n",
       "      <td>63.39</td>\n",
       "      <td>546.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2250</th>\n",
       "      <td>121.18</td>\n",
       "      <td>159.41</td>\n",
       "      <td>64.35</td>\n",
       "      <td>546.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2450</th>\n",
       "      <td>122.21</td>\n",
       "      <td>160.82</td>\n",
       "      <td>64.52</td>\n",
       "      <td>538.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            median    mean    min     max\n",
       "along_dist                               \n",
       "50           120.5  154.67  41.47  553.06\n",
       "250          98.73  136.89  39.51  550.86\n",
       "450         102.57  139.02  41.35  543.63\n",
       "650         105.57  141.19   43.0  544.23\n",
       "850         108.42  143.35  44.39  546.33\n",
       "1050        111.78  145.42  45.52  545.91\n",
       "1250        113.77  147.79  47.31  544.81\n",
       "1450         116.0  150.42  54.71  548.19\n",
       "1650         117.5  153.04  56.64  547.98\n",
       "1850        119.06  155.58  62.42  546.53\n",
       "2050        121.13  157.65  63.39  546.81\n",
       "2250        121.18  159.41  64.35  546.13\n",
       "2450        122.21  160.82  64.52  538.25"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trend [m/year]\n"
     ]
    },
    {
     "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>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>along_dist</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>-1.74</td>\n",
       "      <td>-5.25</td>\n",
       "      <td>-42.48</td>\n",
       "      <td>16.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>-1.64</td>\n",
       "      <td>-5.22</td>\n",
       "      <td>-42.34</td>\n",
       "      <td>14.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>-1.51</td>\n",
       "      <td>-5.18</td>\n",
       "      <td>-42.07</td>\n",
       "      <td>11.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>-1.62</td>\n",
       "      <td>-5.15</td>\n",
       "      <td>-42.04</td>\n",
       "      <td>10.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>850</th>\n",
       "      <td>-1.64</td>\n",
       "      <td>-5.18</td>\n",
       "      <td>-42.36</td>\n",
       "      <td>10.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1050</th>\n",
       "      <td>-1.58</td>\n",
       "      <td>-5.22</td>\n",
       "      <td>-42.35</td>\n",
       "      <td>10.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1250</th>\n",
       "      <td>-1.74</td>\n",
       "      <td>-5.3</td>\n",
       "      <td>-42.3</td>\n",
       "      <td>10.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1450</th>\n",
       "      <td>-2.0</td>\n",
       "      <td>-5.39</td>\n",
       "      <td>-42.54</td>\n",
       "      <td>10.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1650</th>\n",
       "      <td>-2.47</td>\n",
       "      <td>-5.52</td>\n",
       "      <td>-42.52</td>\n",
       "      <td>10.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1850</th>\n",
       "      <td>-2.87</td>\n",
       "      <td>-5.65</td>\n",
       "      <td>-42.36</td>\n",
       "      <td>9.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050</th>\n",
       "      <td>-3.08</td>\n",
       "      <td>-5.81</td>\n",
       "      <td>-42.16</td>\n",
       "      <td>9.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2250</th>\n",
       "      <td>-3.42</td>\n",
       "      <td>-5.99</td>\n",
       "      <td>-41.7</td>\n",
       "      <td>9.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2450</th>\n",
       "      <td>-3.6</td>\n",
       "      <td>-6.19</td>\n",
       "      <td>-40.83</td>\n",
       "      <td>9.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           median  mean    min    max\n",
       "along_dist                           \n",
       "50          -1.74 -5.25 -42.48  16.74\n",
       "250         -1.64 -5.22 -42.34  14.48\n",
       "450         -1.51 -5.18 -42.07  11.33\n",
       "650         -1.62 -5.15 -42.04  10.62\n",
       "850         -1.64 -5.18 -42.36  10.38\n",
       "1050        -1.58 -5.22 -42.35  10.38\n",
       "1250        -1.74  -5.3  -42.3  10.38\n",
       "1450         -2.0 -5.39 -42.54  10.21\n",
       "1650        -2.47 -5.52 -42.52  10.21\n",
       "1850        -2.87 -5.65 -42.36   9.65\n",
       "2050        -3.08 -5.81 -42.16   9.54\n",
       "2250        -3.42 -5.99  -41.7   9.54\n",
       "2450         -3.6 -6.19 -40.83   9.54"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference to median of all solutions [m]:\n"
     ]
    },
    {
     "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>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>along_dist</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>84.34</td>\n",
       "      <td>94.8</td>\n",
       "      <td>11.87</td>\n",
       "      <td>296.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>78.63</td>\n",
       "      <td>78.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>257.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>54.56</td>\n",
       "      <td>55.43</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>37.86</td>\n",
       "      <td>37.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>850</th>\n",
       "      <td>20.67</td>\n",
       "      <td>22.72</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1050</th>\n",
       "      <td>8.78</td>\n",
       "      <td>10.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1250</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1450</th>\n",
       "      <td>-5.98</td>\n",
       "      <td>-8.75</td>\n",
       "      <td>-32.75</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1650</th>\n",
       "      <td>-17.38</td>\n",
       "      <td>-19.29</td>\n",
       "      <td>-67.06</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1850</th>\n",
       "      <td>-29.56</td>\n",
       "      <td>-29.9</td>\n",
       "      <td>-98.14</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2050</th>\n",
       "      <td>-45.87</td>\n",
       "      <td>-40.35</td>\n",
       "      <td>-135.51</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2250</th>\n",
       "      <td>-58.47</td>\n",
       "      <td>-50.34</td>\n",
       "      <td>-184.88</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2450</th>\n",
       "      <td>-67.48</td>\n",
       "      <td>-59.72</td>\n",
       "      <td>-214.63</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           median   mean     min     max\n",
       "along_dist                              \n",
       "50          84.34   94.8   11.87  296.84\n",
       "250         78.63   78.6     0.0   257.8\n",
       "450         54.56  55.43     0.0  187.51\n",
       "650         37.86  37.28     0.0  133.44\n",
       "850         20.67  22.72     0.0   84.81\n",
       "1050         8.78   10.4     0.0   36.48\n",
       "1250          0.0    0.0     0.0     0.0\n",
       "1450        -5.98  -8.75  -32.75     0.0\n",
       "1650       -17.38 -19.29  -67.06     0.0\n",
       "1850       -29.56  -29.9  -98.14     0.0\n",
       "2050       -45.87 -40.35 -135.51     0.0\n",
       "2250       -58.47 -50.34 -184.88     0.0\n",
       "2450       -67.48 -59.72 -214.63     0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cd_along_qc_transect_medians = compute_transect_medians(cd_along_qc)\n",
    "along_qc_std, along_qc_trend, along_qc_diff = compute_statistics(cd_along_qc, cd_along_qc_transect_medians, sl_cassie)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80c145a4-7f40-4b2b-841b-db197f63d9fc",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Combined plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dae27169-a9cf-4ba4-9f2f-c28911e1957d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_statistics(ax, data, ylabel, label=\"\"):\n",
    "    ax.plot(data, '.', markersize=15, label=label)\n",
    "    ax.set_ylabel(ylabel)\n",
    "\n",
    "fig, axs = plt.subplots(3,1,figsize=(15,10))\n",
    "plot_statistics(axs[0], along_std['median'], 'Std [m]', 'no quality control')\n",
    "plot_statistics(axs[0], along_qc_std['median'], 'Std [m]', 'with quality control')\n",
    "axs[0].grid()\n",
    "axs[0].set_ylim([70, 130])\n",
    "\n",
    "plot_statistics(axs[1], along_trend['median'], 'Trend [m/year]')\n",
    "plot_statistics(axs[1], along_qc_trend['median'], 'Trend [m/year]')\n",
    "axs[1].grid()\n",
    "\n",
    "plot_statistics(axs[2], along_diff['median'], 'Diff to median [m]')\n",
    "plot_statistics(axs[2], along_qc_diff['median'], 'Diff to median [m]')\n",
    "axs[2].grid()\n",
    "axs[2].set_xlabel('Along shore zone length')\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.legend(bbox_to_anchor=(1.25,1))\n",
    "plt.savefig('../99_plot_dump/cassie_sens_along_dist.png', dpi=dpi, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fc12f3c-04cd-4af3-a28b-63f309cc7b3d",
   "metadata": {},
   "source": [
    "### Type of intersection computation (with or without \"quality control\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4a978ce1-d79e-4714-a1b7-522e35249961",
   "metadata": {},
   "outputs": [],
   "source": [
    "diff = cd_along - cd_along_qc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "958eefec-33f6-45dc-b48c-77521fe124a9",
   "metadata": {},
   "source": [
    "####  (Median) Differences per transect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d7d36e9e-12c9-4d31-8f38-c6b0dc4d1ee9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_20768/3329583374.py:5: RuntimeWarning: All-NaN slice encountered\n",
      "  ts_med.append(np.nanmedian(diff.loc[along_dist, transect]))\n",
      "/tmp/ipykernel_20768/3329583374.py:6: RuntimeWarning: All-NaN slice encountered\n",
      "  transect_med.append(np.nanmedian(ts_med))\n"
     ]
    }
   ],
   "source": [
    "transect_med = []\n",
    "for transect in diff.columns:\n",
    "    ts_med = []\n",
    "    for along_dist in diff.index:\n",
    "        ts_med.append(np.nanmedian(diff.loc[along_dist, transect]))\n",
    "    transect_med.append(np.nanmedian(ts_med))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7b411731-dba6-4edf-bd78-dda7b03bcbea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-115.00806389792115"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nanmedian(transect_med)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9af8671e-2d4f-4571-a126-08110e1ec8e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,5))\n",
    "# ax.plot(diff.columns, transect_med)\n",
    "ax.plot(diff.columns[6:], transect_med[6:], '.-')\n",
    "xticks = cd_along.columns[0::25]\n",
    "ax.set_xticks(xticks)\n",
    "ax.set_xlabel('Transects')\n",
    "ax.set_ylabel('Difference [m]')\n",
    "ax.set_title(\"No qc minus qc, median over all along_dist solutions\")\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7932e953-68ca-4e9b-be83-1879eb25a3c5",
   "metadata": {},
   "source": [
    "#### (Median) Differences per along_dist value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8f186545-9d7e-4eb2-81b4-079721223c4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_20768/1664098702.py:5: RuntimeWarning: All-NaN slice encountered\n",
      "  ts_med.append(np.nanmedian(diff.loc[along_dist, transect]))\n"
     ]
    }
   ],
   "source": [
    "solution_med = []\n",
    "for along_dist in diff.index:\n",
    "    ts_med = []\n",
    "    for transect in diff.columns:\n",
    "        ts_med.append(np.nanmedian(diff.loc[along_dist, transect]))\n",
    "    solution_med.append(np.nanmedian(ts_med))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "18be1a4d-92f4-4c51-b3e6-9f320eca5a6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,5))\n",
    "ax.plot(diff.index, solution_med, '.-')\n",
    "ax.set_xlabel('along_dist')\n",
    "ax.set_ylabel('Difference [m]')\n",
    "ax.set_title(\"No qc minus qc, median over all transects\")\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "602f474b-5999-4c03-b2a9-f406eb24ba97",
   "metadata": {},
   "source": [
    "#### Std and trend per transect for along_dist = 1200m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "954b10b1-9165-4433-85e8-f1858414ccb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# timeseries without quality control\n",
    "if not os.path.isfile(main_datapath_output + 'cassie_sens_noqc.pkl'):\n",
    "    settings_transects = {\n",
    "        'along_dist': settings_ts['along_dist'],     # along-shore distance to use for computing the intersection\n",
    "        'max_dist_origin': 10000,     # maximum distance to the origin of a transect in [m]\n",
    "                     }\n",
    "    cd_cassie_noqc = SDS_transects.compute_intersection(sl_cassie, transects, settings_transects)    \n",
    "    pickle.dump(cd_cassie_noqc, open(main_datapath_output + 'cassie_sens_noqc.pkl', 'wb'))\n",
    "else:\n",
    "    cd_cassie_noqc = pickle.load(open(main_datapath_output + 'cassie_sens_noqc.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e87bf89a-5cc1-414b-8887-69a36d581512",
   "metadata": {},
   "outputs": [],
   "source": [
    "# timeseries with quality control\n",
    "if not os.path.isfile(main_datapath_output + 'cassie_sens_qc.pkl'):\n",
    "    settings_transects = { # parameters for computing intersections\n",
    "                  'along_dist': settings_ts['along_dist'],    # along-shore distance to use for computing the intersection\n",
    "                  'min_points':          3,         # minimum number of shoreline points to calculate an intersection\n",
    "                  'max_std':             15,        # max std for points around transect\n",
    "                  'max_range':           30,        # max range for points around transect\n",
    "                  'min_chainage':        -100,      # largest negative value along transect (landwards of transect origin)\n",
    "                  'multiple_inter':      'auto',    # mode for removing outliers ('auto', 'nan', 'max')\n",
    "                  'auto_prc':             0.1,      # percentage of the time that multiple intersects are present to use the max\n",
    "                  'max_dist_origin':      10000,     # maximum distance to the origin of a transect in [m]\n",
    "                }\n",
    "    cd_cassie_qc = SDS_transects.compute_intersection_QC(sl_cassie, transects, settings_transects)\n",
    "    pickle.dump(cd_cassie_qc, open(main_datapath_output + 'cassie_sens_qc.pkl', 'wb'))\n",
    "else:\n",
    "    cd_cassie_qc = pickle.load(open(main_datapath_output + 'cassie_sens_qc.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ac98fc8d-7f72-4c00-b998-7655f2b08b7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "median std without quality control:  111.0749617476627\n",
      "median std with quality control:  168.79424348272374\n"
     ]
    }
   ],
   "source": [
    "# standard deviation\n",
    "std_noqc = []; std_qc = []\n",
    "for transect in cd_cassie_noqc.keys():\n",
    "    std_noqc.append(statistics.stdev(cd_cassie_noqc[transect][~np.isnan(cd_cassie_noqc[transect])]))\n",
    "    std_qc.append(statistics.stdev(cd_cassie_qc[transect][~np.isnan(cd_cassie_qc[transect])]))\n",
    "print('median std without quality control: ', np.nanmedian(std_noqc))\n",
    "print('median std with quality control: ', np.nanmedian(std_qc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7087af70-b3ea-4dc3-9d6f-49b6e7b3a3c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,5))\n",
    "ax.plot(transects.keys(), std_noqc, '.-', label='no quality control')\n",
    "ax.plot(transects.keys(), std_qc, '.-', label='with quality control')\n",
    "ax.legend()\n",
    "xticks = cd_along.columns[0::25]\n",
    "ax.set_xticks(xticks)\n",
    "ax.set_xlabel('Transect')\n",
    "ax.set_ylabel('Std [m]')\n",
    "ax.set_title('Std')\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d62bd78b-2a17-4e34-b1e7-ee418feda198",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "median trend without quality control:  -1.4835500000000001\n",
      "median trend with quality control:  -1.5256500000000002\n"
     ]
    }
   ],
   "source": [
    "# trend\n",
    "trend_noqc = []; trend_qc = []\n",
    "x = CD_helper_functions.datetime_to_decimal_numbers(pd.to_datetime(sl_cassie['dates']))\n",
    "for transect in cd_cassie_noqc.keys():\n",
    "    trend_noqc.append(CD_statistics.compute_trend(x, cd_cassie_noqc[transect]))\n",
    "    trend_qc.append(CD_statistics.compute_trend(x, cd_cassie_qc[transect]))\n",
    "print('median trend without quality control: ', np.nanmedian(trend_noqc))\n",
    "print('median trend with quality control: ', np.nanmedian(trend_qc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b9393ee9-f713-4a21-8fad-8f6d424f5297",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,4))\n",
    "ax.plot(transects.keys(), trend_noqc, '.-', label='no quality control')\n",
    "ax.plot(transects.keys(), trend_qc, '.-', label='with quality control')\n",
    "ax.legend()\n",
    "xticks = cd_along.columns[0::25]\n",
    "ax.set_xticks(xticks)\n",
    "ax.set_xlabel('Transect')\n",
    "ax.set_ylabel('Trend [m/year]')\n",
    "# ax.set_title('Trends')\n",
    "ax.grid()\n",
    "plt.savefig('../99_plot_dump/cassie_sens_trend_per_transect.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "664c0b40-0720-4dc0-a601-7231c7bb1a07",
   "metadata": {},
   "source": [
    "## Tidal correction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec4a4e91-7684-44e0-9648-39db57f1ca35",
   "metadata": {},
   "source": [
    "### Water levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e1f10ccd-3621-4194-b021-5b06a905d87a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get tide gauge data (North Sea)\n",
    "tg_data = pd.read_csv(main_datapath_input + 'TG_rijkswaterstaat_1992_01_01-2021_12_31.csv', index_col='datetime[utc]', \\\n",
    "                  parse_dates=['datetime[utc]'])\n",
    "tides_ts = np.array(tg_data['ssh[cm]']) / 100 # [m]\n",
    "\n",
    "dates_ts = CD_helper_functions.datetime_to_decimal_numbers(tg_data.index)\n",
    "dates_cassie = CD_helper_functions.datetime_to_decimal_numbers(pd.to_datetime(sl_cassie['dates']))\n",
    "# reduce SDS-timeseries to the timespan where tide data is available\n",
    "idx_tides_cassie = np.where((np.array(dates_cassie) < dates_ts[-1]) & (np.array(dates_cassie) > dates_ts[0]))[0]\n",
    "dates_cassie = dates_cassie[idx_tides_cassie]\n",
    "\n",
    "tides_cassie = np.interp(dates_cassie, dates_ts, tides_ts)\n",
    "\n",
    "# reduce cross_distance and sl_dict to the timespan where water level data is available\n",
    "cd_cassie_red = {}\n",
    "for key in cd_cassie_qc.keys():\n",
    "    cd_cassie_red[key] = cd_cassie_qc[key][idx_tides_cassie]\n",
    "\n",
    "sl_cassie_red = {}\n",
    "sl_cassie_red['dates'] = np.array(sl_cassie['dates'], dtype=object)[idx_tides_cassie]\n",
    "sl_cassie_red['shorelines'] = np.array(sl_cassie['shorelines'], dtype=object)[idx_tides_cassie]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dfa1d5f-33c1-43bd-a82d-670ed69f68f4",
   "metadata": {},
   "source": [
    "### Variable beach slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4ece6ce8-ead9-4a9d-9ff1-b9654bb4e8db",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_variable_beach_slope(jarkus_data):\n",
    "    # 1. For each sl_dict-date, find the corresponding time-index in jarkus\n",
    "    sl_years = [_.year for _ in sl_cassie_red['dates']]\n",
    "    jarkus_years = [pd.to_datetime(_.values).year for _ in jarkus_data.time]\n",
    "    # idx: which jarkus-gradient belongs to which shoreline in sl_dict\n",
    "    idx_time = [jarkus_years.index(_) for _ in sl_years]\n",
    "\n",
    "    beach_slope = {}\n",
    "    for transect in cd_cassie_red.keys():\n",
    "        # 2. Relate cross_distance coordinate system to jarkus.cross_shore\n",
    "        sl_cross = cd_cassie_red[transect] - 3000\n",
    "\n",
    "        # 3. Buffer around the shoreline position for each point in time\n",
    "        bnd_sea = sl_cross + settings_ts['grad_buffer']\n",
    "        bnd_land = sl_cross - settings_ts['grad_buffer']\n",
    "        # for each point in time, there is one cross-shore distance\n",
    "        # bnd_sea/land define the maximum seaward/landward point along the profile to take into account for each point in time\n",
    "\n",
    "        # 4. Get the gradients in the buffer zone\n",
    "        # get the respective transect\n",
    "        idx_along = np.where(jarkus_data.id == int(transect))[0][0]\n",
    "\n",
    "        # filter the dataset for transect and time\n",
    "        grad = jarkus_data.altitude_red_1st_derivative[idx_time, idx_along, :]\n",
    "        if np.all(np.isnan(grad)):\n",
    "            beach_slope[key] = np.nan\n",
    "            continue\n",
    "\n",
    "        # compute mean/median \n",
    "        if settings_ts['beach_slope'] == 'variable_mean':\n",
    "            grad_mean = [] # mean gradient\n",
    "            for t in range(0, len(grad.time)):\n",
    "                idx_cross = np.where((grad.cross_shore <= bnd_sea[t]) & (grad.cross_shore >= bnd_land[t]))[0]\n",
    "                grad_mean_temp = grad[t, idx_cross].mean().values\n",
    "                grad_mean.append(round(float(grad_mean_temp), 3))\n",
    "            # Interpolate over the nans\n",
    "            grad_mean = np.array(pd.Series(grad_mean).interpolate())\n",
    "            beach_slope[transect] = grad_mean\n",
    "        elif settings_ts['beach_slope'] == 'variable_median':\n",
    "            grad_median = [] # median gradient        \n",
    "            for t in range(0, len(grad.time)):\n",
    "                idx_cross = np.where((grad.cross_shore <= bnd_sea[t]) & (grad.cross_shore >= bnd_land[t]))[0]\n",
    "                grad_median_temp = grad[t, idx_cross].median().values\n",
    "                grad_median.append(round(float(grad_median_temp), 3))\n",
    "            # Interpolate over the nans\n",
    "            grad_median = np.array(pd.Series(grad_median).interpolate())\n",
    "            beach_slope[transect] = grad_median\n",
    "        elif settings_ts['beach_slope'] != str: # uniform beach slope\n",
    "            beach_slope[transect] = settings_ts['beach_slope']\n",
    "            \n",
    "    return beach_slope    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1dc297e0-e412-45d1-a87a-0890ecad5d99",
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute beach slope with *variable median* and different values for grad_buffer\n",
    "if not os.path.isfile(main_datapath_output + 'cassie_sens_beach_slope_median_grad_buffer.pkl'):\n",
    "    settings_ts['beach_slope'] = 'variable_median'\n",
    "    grad_buffer_values = range(5,110,20)\n",
    "    beach_slope_median = pd.DataFrame(columns=cd_cassie_red.keys(), index=grad_buffer_values)\n",
    "    beach_slope_median.index.name = 'grad_buffer'\n",
    "\n",
    "    jarkus_data = xr.open_dataset(main_datapath_output + 'transect_red_with_derivatives.nc', engine='netcdf4')\n",
    "\n",
    "    for grad_buffer in grad_buffer_values:\n",
    "        settings_ts['grad_buffer'] = grad_buffer\n",
    "        beach_slope_median.loc[grad_buffer] = compute_variable_beach_slope(jarkus_data)\n",
    "        \n",
    "    beach_slope_median.to_pickle(main_datapath_output + 'cassie_sens_beach_slope_median_grad_buffer.pkl')\n",
    "else:\n",
    "    beach_slope_median = pd.read_pickle(main_datapath_output + 'cassie_sens_beach_slope_median_grad_buffer.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9af4ca08-2c7b-4024-a4a6-01b1dc3f7158",
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute beach slope with *variable mean* and different values for grad_buffer\n",
    "if not os.path.isfile(main_datapath_output + 'cassie_sens_beach_slope_mean_grad_buffer.pkl'):\n",
    "    settings_ts['beach_slope'] = 'variable_mean'\n",
    "    transect_nr = list(transects.keys())\n",
    "    grad_buffer_values = range(5,110,20)\n",
    "    beach_slope_mean = pd.DataFrame(columns=cd_cassie_red.keys(), index=grad_buffer_values)\n",
    "    beach_slope_mean.index.name = 'grad_buffer'\n",
    "    \n",
    "    jarkus_data = xr.open_dataset(main_datapath_output + 'transect_red_with_derivatives.nc', engine='netcdf4')\n",
    "\n",
    "    for grad_buffer in grad_buffer_values:\n",
    "        settings_ts['grad_buffer'] = grad_buffer\n",
    "        beach_slope_mean.loc[grad_buffer] = compute_variable_beach_slope(jarkus_data)\n",
    "        \n",
    "    beach_slope_mean.to_pickle(main_datapath_output + 'cassie_sens_beach_slope_mean_grad_buffer.pkl')\n",
    "else:\n",
    "    beach_slope_mean = pd.read_pickle(main_datapath_output + 'cassie_sens_beach_slope_mean_grad_buffer.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ba5ba97-e025-452b-9da7-d0693eec07dc",
   "metadata": {},
   "source": [
    "### Correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "589d575e-c1d3-4873-8998-625468ce3767",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tidal_correction(cd_dict, beach_slope, med_or_mean):\n",
    "    '''\n",
    "    compute and apply tidal correction to cd_dict\n",
    "    '''\n",
    "    cd_tidalcorr_df = pd.DataFrame(columns=beach_slope.columns, index=[med_or_mean + str(_) for _ in beach_slope.index])\n",
    "    \n",
    "    reference_elevation = 0\n",
    "     # threshold the corrections to eliminate outliers\n",
    "    thresh_pos = settings_ts['tide_corr_thresh']\n",
    "    thresh_neg = -thresh_pos\n",
    "\n",
    "    for grad_buffer in beach_slope.index:\n",
    "        for transect in cd_dict.keys():\n",
    "            corr = (tides_cassie - reference_elevation) / beach_slope.loc[grad_buffer, transect]\n",
    "            corr[corr > thresh_pos] = thresh_pos\n",
    "            corr[corr < thresh_neg] = thresh_neg\n",
    "            cd_tidalcorr = cd_dict[transect] - corr\n",
    "            cd_tidalcorr_sm = CD_statistics.moving_average(cd_tidalcorr, settings_ts['sm_window_size'])\n",
    "\n",
    "            cd_tidalcorr_df.loc[med_or_mean+str(grad_buffer), transect] = cd_tidalcorr_sm\n",
    "    return cd_tidalcorr_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5d8056d8-0bb0-4fb2-983e-9c7ca81aaed0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_20768/498903977.py:14: RuntimeWarning: divide by zero encountered in divide\n",
      "  corr = (tides_cassie - reference_elevation) / beach_slope.loc[grad_buffer, transect]\n",
      "/tmp/ipykernel_20768/498903977.py:14: RuntimeWarning: invalid value encountered in divide\n",
      "  corr = (tides_cassie - reference_elevation) / beach_slope.loc[grad_buffer, transect]\n",
      "/tmp/ipykernel_20768/498903977.py:14: RuntimeWarning: divide by zero encountered in divide\n",
      "  corr = (tides_cassie - reference_elevation) / beach_slope.loc[grad_buffer, transect]\n",
      "/tmp/ipykernel_20768/498903977.py:14: RuntimeWarning: invalid value encountered in divide\n",
      "  corr = (tides_cassie - reference_elevation) / beach_slope.loc[grad_buffer, transect]\n"
     ]
    }
   ],
   "source": [
    "cd_tidalcorr_bsmedian = tidal_correction(cd_cassie_red, beach_slope_median, 'median')\n",
    "cd_tidalcorr_bsmean = tidal_correction(cd_cassie_red, beach_slope_mean, 'mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ad078af0-806e-43e3-aa94-22ded460c0c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# tidal correction with uniform beach slope\n",
    "cd_tidalcorr_bs_uniform = pd.DataFrame(columns=beach_slope_mean.columns, index=['uniform'])\n",
    "beach_slope_uniform = -0.01\n",
    "\n",
    "reference_elevation = 0\n",
    "thresh_pos = settings_ts['tide_corr_thresh']\n",
    "thresh_neg = -thresh_pos\n",
    "\n",
    "for transect in cd_cassie_red.keys():\n",
    "    corr = (tides_cassie - reference_elevation) / beach_slope_uniform\n",
    "    corr[corr > thresh_pos] = thresh_pos\n",
    "    corr[corr < thresh_neg] = thresh_neg\n",
    "    cd_tidalcorr = cd_cassie_red[transect] - corr\n",
    "    cd_tidalcorr_sm = CD_statistics.moving_average(cd_tidalcorr, settings_ts['sm_window_size'])\n",
    "\n",
    "    cd_tidalcorr_bs_uniform.loc['uniform', transect] = cd_tidalcorr_sm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44086291-dd54-4549-90dd-989db5e4df62",
   "metadata": {},
   "source": [
    "### Compare uniform, mean and median for different buffer zone lengths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a21ce1bd-fca8-46a3-b5be-67b7123d1555",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "std [m]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_20768/1561225331.py:25: RuntimeWarning: All-NaN slice encountered\n",
      "  diff.append(np.nanmedian(transect_medians[transect] - ts))\n"
     ]
    },
    {
     "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>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>median5</th>\n",
       "      <td>87.52</td>\n",
       "      <td>99.52</td>\n",
       "      <td>44.05</td>\n",
       "      <td>301.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median25</th>\n",
       "      <td>88.15</td>\n",
       "      <td>101.64</td>\n",
       "      <td>37.01</td>\n",
       "      <td>302.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median45</th>\n",
       "      <td>86.96</td>\n",
       "      <td>101.97</td>\n",
       "      <td>44.41</td>\n",
       "      <td>303.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median65</th>\n",
       "      <td>87.67</td>\n",
       "      <td>102.37</td>\n",
       "      <td>45.18</td>\n",
       "      <td>303.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median85</th>\n",
       "      <td>87.75</td>\n",
       "      <td>102.34</td>\n",
       "      <td>50.77</td>\n",
       "      <td>302.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median105</th>\n",
       "      <td>88.09</td>\n",
       "      <td>102.39</td>\n",
       "      <td>47.43</td>\n",
       "      <td>303.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean5</th>\n",
       "      <td>87.52</td>\n",
       "      <td>99.52</td>\n",
       "      <td>44.05</td>\n",
       "      <td>301.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean25</th>\n",
       "      <td>87.67</td>\n",
       "      <td>101.22</td>\n",
       "      <td>42.67</td>\n",
       "      <td>303.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean45</th>\n",
       "      <td>87.75</td>\n",
       "      <td>101.61</td>\n",
       "      <td>45.39</td>\n",
       "      <td>303.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean65</th>\n",
       "      <td>87.26</td>\n",
       "      <td>101.99</td>\n",
       "      <td>46.1</td>\n",
       "      <td>303.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean85</th>\n",
       "      <td>87.41</td>\n",
       "      <td>101.95</td>\n",
       "      <td>48.7</td>\n",
       "      <td>302.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean105</th>\n",
       "      <td>87.61</td>\n",
       "      <td>102.12</td>\n",
       "      <td>44.26</td>\n",
       "      <td>303.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uniform</th>\n",
       "      <td>88.85</td>\n",
       "      <td>108.98</td>\n",
       "      <td>49.0</td>\n",
       "      <td>303.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uncorrected</th>\n",
       "      <td>152.46</td>\n",
       "      <td>174.17</td>\n",
       "      <td>95.4</td>\n",
       "      <td>516.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             median    mean    min     max\n",
       "median5       87.52   99.52  44.05  301.08\n",
       "median25      88.15  101.64  37.01  302.87\n",
       "median45      86.96  101.97  44.41  303.02\n",
       "median65      87.67  102.37  45.18  303.47\n",
       "median85      87.75  102.34  50.77  302.94\n",
       "median105     88.09  102.39  47.43  303.74\n",
       "mean5         87.52   99.52  44.05  301.08\n",
       "mean25        87.67  101.22  42.67  303.04\n",
       "mean45        87.75  101.61  45.39  303.83\n",
       "mean65        87.26  101.99   46.1  303.84\n",
       "mean85        87.41  101.95   48.7  302.95\n",
       "mean105       87.61  102.12  44.26  303.57\n",
       "uniform       88.85  108.98   49.0  303.81\n",
       "uncorrected  152.46  174.17   95.4  516.43"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trend [m/year]\n"
     ]
    },
    {
     "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>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>median5</th>\n",
       "      <td>-0.38</td>\n",
       "      <td>-2.24</td>\n",
       "      <td>-46.06</td>\n",
       "      <td>13.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median25</th>\n",
       "      <td>-1.15</td>\n",
       "      <td>-2.6</td>\n",
       "      <td>-46.22</td>\n",
       "      <td>15.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median45</th>\n",
       "      <td>-0.81</td>\n",
       "      <td>-2.7</td>\n",
       "      <td>-46.17</td>\n",
       "      <td>15.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median65</th>\n",
       "      <td>-1.2</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>-46.25</td>\n",
       "      <td>15.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median85</th>\n",
       "      <td>-1.22</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>-46.4</td>\n",
       "      <td>15.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median105</th>\n",
       "      <td>-1.12</td>\n",
       "      <td>-2.95</td>\n",
       "      <td>-46.57</td>\n",
       "      <td>15.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean5</th>\n",
       "      <td>-0.38</td>\n",
       "      <td>-2.24</td>\n",
       "      <td>-46.06</td>\n",
       "      <td>13.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean25</th>\n",
       "      <td>-0.89</td>\n",
       "      <td>-2.59</td>\n",
       "      <td>-46.22</td>\n",
       "      <td>14.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean45</th>\n",
       "      <td>-0.94</td>\n",
       "      <td>-2.71</td>\n",
       "      <td>-46.29</td>\n",
       "      <td>14.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean65</th>\n",
       "      <td>-1.09</td>\n",
       "      <td>-2.81</td>\n",
       "      <td>-46.3</td>\n",
       "      <td>15.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean85</th>\n",
       "      <td>-1.31</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>-46.35</td>\n",
       "      <td>15.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean105</th>\n",
       "      <td>-1.09</td>\n",
       "      <td>-2.93</td>\n",
       "      <td>-46.54</td>\n",
       "      <td>14.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uniform</th>\n",
       "      <td>-1.39</td>\n",
       "      <td>-4.4</td>\n",
       "      <td>-46.32</td>\n",
       "      <td>12.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uncorrected</th>\n",
       "      <td>-1.62</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-47.41</td>\n",
       "      <td>12.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            median  mean    min    max\n",
       "median5      -0.38 -2.24 -46.06  13.99\n",
       "median25     -1.15  -2.6 -46.22  15.73\n",
       "median45     -0.81  -2.7 -46.17  15.02\n",
       "median65      -1.2  -2.8 -46.25  15.22\n",
       "median85     -1.22  -2.9  -46.4  15.04\n",
       "median105    -1.12 -2.95 -46.57  15.15\n",
       "mean5        -0.38 -2.24 -46.06  13.99\n",
       "mean25       -0.89 -2.59 -46.22  14.35\n",
       "mean45       -0.94 -2.71 -46.29  14.77\n",
       "mean65       -1.09 -2.81  -46.3  15.42\n",
       "mean85       -1.31  -2.9 -46.35  15.07\n",
       "mean105      -1.09 -2.93 -46.54  14.18\n",
       "uniform      -1.39  -4.4 -46.32  12.58\n",
       "uncorrected  -1.62  -5.0 -47.41  12.59"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference to median of all solutions [m]:\n"
     ]
    },
    {
     "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>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>median5</th>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.82</td>\n",
       "      <td>-23.1</td>\n",
       "      <td>4.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median25</th>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>-16.67</td>\n",
       "      <td>2.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median45</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-3.77</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median65</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.13</td>\n",
       "      <td>-1.82</td>\n",
       "      <td>4.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median85</th>\n",
       "      <td>0.12</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-5.66</td>\n",
       "      <td>4.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median105</th>\n",
       "      <td>0.13</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-3.43</td>\n",
       "      <td>3.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean5</th>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.82</td>\n",
       "      <td>-23.1</td>\n",
       "      <td>4.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean25</th>\n",
       "      <td>-0.13</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>-12.24</td>\n",
       "      <td>2.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean45</th>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-3.71</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean65</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-2.01</td>\n",
       "      <td>2.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean85</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.04</td>\n",
       "      <td>-5.66</td>\n",
       "      <td>4.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean105</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-3.43</td>\n",
       "      <td>2.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uniform</th>\n",
       "      <td>1.7</td>\n",
       "      <td>1.99</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>13.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uncorrected</th>\n",
       "      <td>-2.36</td>\n",
       "      <td>-2.76</td>\n",
       "      <td>-47.31</td>\n",
       "      <td>24.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            median  mean    min    max\n",
       "median5      -0.17 -0.82  -23.1   4.67\n",
       "median25      -0.2 -0.57 -16.67   2.56\n",
       "median45     -0.01 -0.16  -3.77   2.43\n",
       "median65      0.02  0.13  -1.82   4.17\n",
       "median85      0.12  0.19  -5.66   4.79\n",
       "median105     0.13  0.19  -3.43   3.11\n",
       "mean5        -0.17 -0.82  -23.1   4.67\n",
       "mean25       -0.13 -0.57 -12.24   2.32\n",
       "mean45       -0.12 -0.12  -3.71   2.63\n",
       "mean65       -0.02  0.01  -2.01   2.87\n",
       "mean85        0.01  0.04  -5.66   4.17\n",
       "mean105        0.0  -0.0  -3.43   2.07\n",
       "uniform        1.7  1.99  -0.54  13.68\n",
       "uncorrected  -2.36 -2.76 -47.31   24.3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# move uncorrected shorelines from dict to dataframe\n",
    "cd_cassie_red_df = pd.DataFrame(columns=beach_slope_mean.columns, index=['uncorrected'])\n",
    "for transect in cd_cassie_red.keys():\n",
    "    cd_cassie_red_df.loc['uncorrected', transect] = cd_cassie_red[transect]\n",
    "\n",
    "# stack together corrected shorelines from mean, median and uniform beach slope, plus uncorrected shorelines\n",
    "tidal_corr_stack = pd.concat([cd_tidalcorr_bsmedian, cd_tidalcorr_bsmean, cd_tidalcorr_bs_uniform, cd_cassie_red_df])\n",
    "\n",
    "# compute statistics\n",
    "tidal_corr_transect_medians = compute_transect_medians(tidal_corr_stack)\n",
    "tcorr_std, tcorr_trend, tcorr_diff = compute_statistics(tidal_corr_stack, tidal_corr_transect_medians, sl_cassie_red)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "671cd57e-fec4-4c65-8561-004e96dc9b41",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove mean as results are almost the same as median\n",
    "tcorr_std_ = tcorr_std['median'].drop(['mean5', 'mean25', 'mean45', 'mean65', 'mean85', 'mean105'])\n",
    "tcorr_trend_ = tcorr_trend['median'].drop(['mean5', 'mean25', 'mean45', 'mean65', 'mean85', 'mean105'])\n",
    "tcorr_diff_ = tcorr_diff['median'].drop(['mean5', 'mean25', 'mean45', 'mean65', 'mean85', 'mean105'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b64060d2-7ef6-4e6c-8357-3dc079cf5763",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_20768/3985296555.py:6: RuntimeWarning: All-NaN slice encountered\n",
      "  med_per_transect.append(np.nanmedian(beach_slope_median.loc[buffer, transect]))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "median5       -0.012167\n",
       "median25      -0.012633\n",
       "median45      -0.012750\n",
       "median65      -0.012167\n",
       "median85      -0.013000\n",
       "median105     -0.013000\n",
       "uniform             NaN\n",
       "uncorrected         NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bs_per_solution = pd.Series(index=tcorr_std_.index, dtype='float64')\n",
    "\n",
    "for buffer in beach_slope_median.index:\n",
    "    med_per_transect = []\n",
    "    for transect in beach_slope_median.loc[buffer].index:\n",
    "        med_per_transect.append(np.nanmedian(beach_slope_median.loc[buffer, transect]))\n",
    "    bs_per_solution.loc['median'+str(buffer)] = np.nanmedian(med_per_transect)\n",
    "bs_per_solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bd0e79e1-da8f-4090-9fd3-09f226a9b929",
   "metadata": {},
   "outputs": [],
   "source": [
    "bs_per_solution_plot = bs_per_solution.copy()\n",
    "bs_per_solution_plot['uniform'] = 99\n",
    "bs_per_solution_plot['uncorrected'] = 99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "54f7a644-a693-4c8f-8c91-82440f0075a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1700x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plots\n",
    "fig, axs = plt.subplots(4,1,figsize=(17,10))\n",
    "fig.tight_layout()\n",
    "\n",
    "axs[0].plot(bs_per_solution, '.', markersize=15)\n",
    "axs[0].plot(bs_per_solution_plot, '.', markersize=0)\n",
    "axs[0].set_ylabel(r'Beach slope')\n",
    "axs[0].set_ylim(-0.014, -0.011)\n",
    "axs[0].grid()\n",
    "axs[0].tick_params(axis='x', colors='white')\n",
    "\n",
    "axs[1].plot(tcorr_std_, '.', markersize=15)\n",
    "axs[1].set_ylabel('Std [m]')\n",
    "axs[1].grid()\n",
    "axs[1].tick_params(axis='x', colors='white')\n",
    "\n",
    "axs[2].plot(tcorr_trend_, '.', markersize=15)\n",
    "axs[2].set_ylabel('Trend [m/year]')\n",
    "axs[2].grid()\n",
    "axs[2].tick_params(axis='x', colors='white')\n",
    "\n",
    "axs[3].plot(tcorr_diff_, '.', markersize=15)\n",
    "axs[3].set_ylabel('Diff to median [m]')\n",
    "axs[3].grid()\n",
    "\n",
    "plt.savefig('../99_plot_dump/cassie_sens_tidal_corr_buffer.png', dpi=dpi, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "384958f7-714b-4ccf-8715-072a3329fa24",
   "metadata": {},
   "source": [
    "### Compare TG vs EOT water levels\n",
    "for median, 45 m buffer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4765b7bf-2e1a-4d24-b5b2-ec2753d43cb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_20768/498903977.py:14: RuntimeWarning: divide by zero encountered in divide\n",
      "  corr = (tides_cassie - reference_elevation) / beach_slope.loc[grad_buffer, transect]\n"
     ]
    }
   ],
   "source": [
    " # get EOT20 data\n",
    "data = pd.read_csv(main_datapath_output + 'tidal_correction_10minutes.csv', index_col='date',parse_dates=['date'])\n",
    "tides_ts = np.array(data['corr_eot[cm]']) / 100 # [m]\n",
    "    \n",
    "dates_ts = CD_helper_functions.datetime_to_decimal_numbers(data.index)\n",
    "dates_cassie = CD_helper_functions.datetime_to_decimal_numbers(pd.to_datetime(sl_cassie['dates']))\n",
    "# reduce SDS-timeseries to the timespan where tide data is available\n",
    "idx_tides_cassie = np.where((np.array(dates_cassie) < dates_ts[-1]) & (np.array(dates_cassie) > dates_ts[0]))[0]\n",
    "dates_cassie = dates_cassie[idx_tides_cassie]\n",
    "\n",
    "tides_cassie = np.interp(dates_cassie, dates_ts, tides_ts)\n",
    "\n",
    "# reduce cross_distance and sl_dict to the timespan where water level data is available\n",
    "cd_cassie_red_eot = {}\n",
    "for key in cd_cassie_qc.keys():\n",
    "    cd_cassie_red_eot[key] = cd_cassie_qc[key][idx_tides_cassie]\n",
    "\n",
    "sl_cassie_red_eot = {}\n",
    "sl_cassie_red_eot['dates'] = np.array(sl_cassie['dates'], dtype=object)[idx_tides_cassie]\n",
    "sl_cassie_red_eot['shorelines'] = np.array(sl_cassie['shorelines'], dtype=object)[idx_tides_cassie]\n",
    "\n",
    "# apply tidal correction\n",
    "cd_tidalcorr_eot = tidal_correction(cd_cassie_red_eot, pd.DataFrame(beach_slope_median.loc[45]).transpose(), 'median')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c1ba4b8b-79a3-4b04-b46d-5d13a99184f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_20768/107482808.py:7: RuntimeWarning: All-NaN slice encountered\n",
      "  med_diff_tg_minus_eot.append(np.nanmedian(diff_tg_minus_eot[transect]))\n"
     ]
    }
   ],
   "source": [
    "# difference TG-EOT\n",
    "diff_tg_minus_eot = cd_tidalcorr_bsmedian.loc['median45'] - cd_tidalcorr_eot.loc['median45']\n",
    "\n",
    "# median difference per transect\n",
    "med_diff_tg_minus_eot = []\n",
    "for transect in diff_tg_minus_eot.index:\n",
    "    med_diff_tg_minus_eot.append(np.nanmedian(diff_tg_minus_eot[transect]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "78069694-c79d-47d4-8aa2-fdc2c4e7b625",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,5))\n",
    "ax.plot(cd_along_qc.columns, med_diff_tg_minus_eot, '.')\n",
    "xticks = cd_along_qc.columns[0::25]\n",
    "ax.set_xticks(xticks)\n",
    "ax.set_xlabel('Transects')\n",
    "ax.set_ylabel('Difference [m]')\n",
    "ax.set_title(\"TG - EOT\") # median beach slope 45 m buffer, median per transect\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d3b6f937-aa45-4c24-8f3f-6b7d12c0dca8",
   "metadata": {},
   "outputs": [],
   "source": [
    "trend_eot = []\n",
    "x = CD_helper_functions.datetime_to_decimal_numbers(pd.to_datetime(sl_cassie_red['dates']))\n",
    "for transect in cd_tidalcorr_eot.columns:\n",
    "    ts = cd_tidalcorr_eot.loc['median45', transect]\n",
    "    if np.all(np.isnan(ts)):\n",
    "        trend_eot.append(np.nan)\n",
    "        continue\n",
    "    trend_eot.append(CD_statistics.compute_trend(x, ts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "4da0cd9f-8874-4665-89e6-deb743d6d30d",
   "metadata": {},
   "outputs": [],
   "source": [
    "trend_tg = []\n",
    "for transect in cd_tidalcorr_bsmedian.loc['median45'].index:\n",
    "    ts = cd_tidalcorr_bsmedian.loc['median45', transect]\n",
    "    if np.all(np.isnan(ts)):\n",
    "        trend_tg.append(np.nan)\n",
    "        continue\n",
    "    trend_tg.append(CD_statistics.compute_trend(x, ts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "127af1a8-3672-40d7-85d8-382a7f2aa734",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABQQAAAHcCAYAAACAvKfKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAA9hAAAPYQGoP6dpAADpZElEQVR4nOzdd3hUZd7G8e+ZkknvCUmAQELvVZTeRJEiuooi9t7XXt+1rLqWtaGsZW3YBbsIFkSkI733lkJCeu+TmfP+MYq6AgIpk3J/rutcycyc85z7hGEy+c1TDNM0TURERERERERERKRZsHg7gIiIiIiIiIiIiNQfFQRFRERERERERESaERUERUREREREREREmhEVBEVERERERERERJoRFQRFRERERERERESaERUERUREREREREREmhEVBEVERERERERERJoRFQRFRERERERERESaEZu3A8iJc7vdpKenExQUhGEY3o4jIiIiIiIiIiJeYpomxcXFxMXFYbEcvQ+gCoKNWHp6Oq1bt/Z2DBERERERERERaSBSU1Np1arVUfdRQbARCwoKAjz/0MHBwV5O0zQ5nU7mzZvHaaedht1u93YckRrTc1qaGj2npanRc1qaGj2npanRc1oasqKiIlq3bn2oXnQ0Kgg2Yr8OEw4ODlZBsI44nU78/f0JDg7Wi700CXpOS1Oj57Q0NXpOS1Oj57Q0NXpOS2NwLNPKaVERERERERERERGRZkQFQRERERERERERkWZEBUEREREREREREZFmRAVBERERERERERGRZkQFQRERERERERERkWZEBUEREREREREREZFmRAVBERERERERERGRZkQFQRERERERERERkWZEBUEREREREREREZFmRAVBERERERERERGRZkQFQRERERERERERkWZEBUEREREREREREZFmRAVBERERERERERGRZsTm7QAi0jStTc5j2vzddI0L5vRuMfRuFYrFYng7loiIiIiIiEizp4KgiNSJN5fuZ8nuHJbszuG/i/YRHeRgTNcWnN4thlMSI/CxqYOyiIiIiIiIiDeoICgidWJvVikAJ7UNY8fBYrKKK/lgZQofrEwhyNfGqM7RnJwQQbe4YDrFBOFrt3o5sYiIiIiIiEjzoIKgiNS6apeb/TmeguBz5/UmOtjBir25fL81kx+2ZZJTUslXG9L5akM6AFaLQfuoQLrFBdP1l61vfJiKhCIiIiIiIiJ1QAVBEal1B/LLqXK58bVbaBnqh8ViMKJTNCM6RfPYWd3ZkJrPj9uz2JxWyNb0IvJKq9iZWczOzGI+X58GQKcWQXx7y1DNOygiIiIiIiJSy1QQFJFatyerBIDEyMA/FfSsFoN+bcLp1yYcANM0ySiqYFt6EVvTi9iaXsiiXdnszCxm6Z4chnWMqvf8IiIiIiIiIk2ZCoIiUuv2ZpfQ2Ujhn9XzYcMB6HEuWO2H3dcwDGJD/IgN8WN0lxYAPDx7K28vT+KjVSkqCIqIiIiIiIjUMi3zKSK1bk9WCTfYvuKk4vnw5XXwQm/4+RWoKj2m4y8YEA/AD9syySquqMOkIiIiIiIiIs2PCoIiUuv2ZpfQwTjguWHzg6ID8N298Hw3+OlxKM096vGdYoLo1yaMarfJJ2sO1ENiERERERERkeZDBUFpMEoqq/nvor2sT8n3dhSpAdM02Z9VSKJx0HPHtYtgwjQIT4TyfFj0lKcw+M3dsGc+5O4Fl/NP7fzaS3Dm6hTcbrMer0BERERERESkadMcgtJg/Pu7Hby7IplRnaN567KTvB1HTlBOSRUhlek4HNWYNj+MiA4Q1Qn6XgLbZ8PSaXBwA6z6r2cDMKwQ2hrCEiA8AcITmdBmJP/0tZGaV86yvTkM7aC5BEVERERERERqg3oISoNx+eAELAYs2JHF1vRCb8eRE7Qnq4QORhoARmQHsPzyMmOxQrez4ZqFcMlX0PUsiOoCNl8wXZCfBPt+gjVvwbx/4PveOC7sEQTAR6tSvHItIiIiIiIiIk2ReghKg5EQGcD4nnF8vTGdVxbu5T9T+3o1T2G5k/s/30RQqcE4ryZpXPZml9DeSPfciOr85x0MAxJHeDYAtxtKMiBvP+Tvh7x9sPlTKEjmastXvMow5m3NJLu4kqggR31dhoiIiIiIiEiTpR6C0qDcMKIdAHM3H2RfdolXszz75XLO3XEb4Qe+92qOxmZPVgntLb8sBBLV8a8PsFggOA7aDoY+F8HoB2Hc0wBEbH2bU1tWU+02+XStFhcRERERERERqQ0qCEqD0iU2mNGdozFN+O+ifV7L8dPOLPpue5KR1o1cbsymtLLaa1kaG88Kw54hw4ftIXgsOpwG8YOguoL7/b8EPMOGtbiIiIiIiIiISM2pICgNzg0j2wPw+foDHCwsr/fzF1U4mfPJDM6yLgcgyigiNSOz3nM0Vvsyi2j365DhyE4n1ohhwJh/ApBw4Et6OjJIyStj+d7cWkopIiIiIiIi0nypICgNS9Iy+sXYODkhHKfL5PXF++s9wrOz13CH89U/3JebuqveczRGpZXVUJRGgFGJabF7Vgw+Ua0HQKfxGKabJ0O/BLS4iIiIiIiIiEhtUEFQGo5FT8Pb42D+w9z4Sy/Bj1alkFtSWW8RFu/Kpv2mZ4gz8qgIjCfV0QGA8gwVBI/FvuxS2ls8vQONiPZgtdeswdEPgmGha+Fi+hi7+X5rBtnF9fd8EBEREREREWmKVBCUhqP1AM/X1W8w1LaNHi1DKHe6eHt5Uo2azSmu4JtV2yirOvo8gCWV1cz8dBYX2+YD4HvOSxQFewqCZl7991RsjDwrDB/HgiJ/Jboz9JoKwKOBn1LtdvPZOi0uIiIiIiIiIlITKghKw5E4HPpdDoAx+2b+PjQWgLeXJ1Fc4TyhJt1uk5X/uYxx3wxkzr8vZfH2tCPu+/ScDdxZMR0AZ+9LIGEYhHmGvPoWJ5/Q+ZubPVm1sKDI/xp5H1gddHduZoRlIzO1uIiIiIiIiIhIjaggKA3LmEcgpDUUJHNq+qu0iwqguKKaD1ae2Nxx8xcv4oyK7wA4r3oOQR+dycPvfUfO/wxDXr4nhxbrXyDRkkGVXzT20x8FwK+FZ+hyWKV6pR2Lvdklh4YME1kLPQQBQlrBgKsBuNc+i+TcEn7ep8VFRERERERERE6UCoLSsPgGw8QXADBWvcaDPQoAeGPJfiqcruNqqrzKhXXRk1gMk4zALpRbg+hj2cMte67k4Wef55M1qZimSWllNa9/8iXXWOcA4DNpGviFAhDe2tPLraX74F8OORbYk1lMh0NDhmuphyDA0DvAEUxnI5kzLcv5QIuLiIiIiIiIiJwwFQSl4Wk/GvpeAsCw7Q+TGGIhp6SST9akHlczX377HaPNFbgxCLvgdfxuWkpZZE/CjBL+Yz5B1pf3c/Hry/nH5+u5o3w6NsONs8tZ0Hn8oTYCYjxzCEYbBSQfzK61S2yKql1uSvLSCTHKMA0LRLSvvcb9w2HwLQDcYfuEn7YeIKOwovbaFxEREREREWlGVBCUhum0xyC4JUb+fl6K8fTce3XRPpwu9zEdnlVcQdS65wE42HIsjpY9IKwt/tfNx9X/KgButM3mpgN30GnrNLpbknD6hGAf//QfG/ILpYBAAHJTdtTSxTVNKXlltDV/6R0Y1hbsvrV7glOuh8AWxFuymcx8Hp27rXbbFxEREREREWkmVBCUhsk3BCa+CEDn5A8YHbCPtIJyZm9IP6bDZ331Nacaq3FhIW7Sw789YHNgnfAsnPsWbnsAp1i2c53NU3C0j3sKAqP/1FamJQaAkoxdNbumJm5vdumh4cJGbQ4X/pVPAAy/G4CbbV+wbNMuFu9Sr00REZGmJquoQlO1iIiI1DEVBKXh6nAq9L4IA5NnfV7DQRXPzNtJUk7pUQ/bmVFM150vAVDQ7kyM6MMUp7qfg+XaRZjRXQEw242GXlMO216+vYVnn5y9NbiYpm9PVgntjVpeUOR/9b0UIjoQaRTxX5/nefTL9cc9t6SIiIg0XGv3Z/Hu07fyyBOPMW9rhrfjiIiINFkqCErDdvq/ICiW0PIUHg36koOFFZz76gq2pRcd8ZCZX3zBaOt6XFiIGPfgkduO7IBx1Y9wwSyM898HwzjsbmUOT69Bn6KkmlxJk7c3u4QORprnRl30EASw2uH89zAdQZxs2cH1xS/wyk976uZcIiIizUC1y82iXdkUljm9HYWyqmq2fPh/3Gn9iCfN58n86Ebu+XgtxRXezyYiItLUqCAoDZtf6KFVhydXz+ZvUWnklFRy/msrWJOU96fdF+/KZnj6GwCUdzkXItodvX0ff+g01vP1CJx+nh6CoRUHTuwamok9WSW0t/xaEOxUdyeK7oJx3ru4DSt/sy7FsuRp9v9Fr1ERERH5sx0ZRVwz/Us2vXsnd7z4Dql5ZV7N88HHH3NR1SeHbl9sm8+kzTdx/rS5rNyX68VkIiIiTY8KgtLwdTwdel2AYbp5xnyGy2JTKK6o5qI3V/LTzqxDu7ncJl/M/pwR1o24sRI45v7aOX+ApyDY0p2u+WyOwDRNsrMziDIKPXfU1ZDhX7UbhTH+OQBusX7Ctx++gGmadXtOERGRJsLpcjN9/k5mvfQAL+Zfz822L3mh/H4ee/kNdmcWeyXT0q37GLvrQayGSVbCWXDBTFy2AAZZt/FK2V38441Pefyb7VRWa6oQERGR2qCCoDQOY5+AqM5YSjJ5KP8+pkfPodpZxdXvrOGrDZ5eaZ+sSeWcwncAcPa8AMITauXUrl8KgjFGPikZObXSZlOTXVxJTGUyAGZwK3AE1vk5jf6XUdjnegCuzH2WZQu+rvNzioiINHbbDxZx84szGbj4Ih6yvk2gUYHLJ5gAo5LnnI/x5KtvsulAQb1mKixzUvTZrbS2ZJPvE0v0+dOh0xlYr56PO6QNbSxZfG5/iD1LP+XM6cvYml5Yr/lERESaIpu3A4gcE78wuHoBfHcvxrp3mVj0Id1DN3Jx4TXcOmsD2cWVrF74Nf+1bsVl2HCMvLvWTu20BVJsBBJklpCTugPiY2qt7aZiT3YJHX4ZLmzU5XDh/xEy8XH2pOygfe5PdF9yA8VdOxEUW3/nFxERqU/VLjfpBRWk5peRmldGan4ZGTn5hGatpEPRSkLMQjKDe+GMH0h0uz70aBVG24gALBYDp8vNqz/uwLXkOV6wfIHDUo3T6o/ttH9i7XMhzg+nEpC0kBfdj3PD6ybXXXIJA9tF1Mt1ffH+i1zmXoQLC/7nvwm+wZ4HWnTFcs1P8PElBCUv5Q2fZ3kqJ40J04sY2akFlw1qy9AOkRhHmAdaREREjkwFQWk8fALgzOmQOBK+vpWEiq384Pd/3FlxBY/NNZnl86Gnz2ufiyGsTa2eOs+nFUGVOyhJ3w2MqNW2m4K9WfWwoMjhWCy0vuo9tj89ki7u3eS8fS5BtywC//D6yyAiIlIPtqQVcu17a0krKCPByGCEZQMjLBs52bIdX+N3i24ULYMtL1OwOYDV7s58anQlN2oAlRUVXFf8Ip2tqQBUJpyKY9I0CG0NgP3CmVR/OJWA/Qt4xXyCq2e4KbvwIkZ3aVGn1zX/57WcnfYMGJDV+yZi2w3+4w4BEXDxF/Dt3VjWzuA++0cMcO1g6e7uvLIrnmnhXTh7SE/O6dsSfx/9aSMiInKs9FtTGp/uf4OW/eCzq/A7sIqXfF7kPFdPTrbswG2xYx1+Z62fsiwoHip34M7ViraHsze7lJFGPSwochgOvyCKznqPA59NoFVlCiXvTiHwqq/B5qjXHCIiInXlx+2Z3P7RKq52f8KZjhXEG1l/eLzcL5ayNiOpDojDnbKC8Nx1hLpLGWNdyxjWQu57nh0tUOkThs+Ef+PoMRl+37PO7odt6ke4ProA/30LeN36FFe/b1Iy+QIm9W5ZJ9eVVVBK6Hc3EWKUcTCwG7ETHzz8jjYfmDgNWnSDb+9htHU9o63rPY+VwMFvw1n7XRusMd3p2Hc4kf3/BhZrnWQWERFpKlQQlMYprA1c/i0sehIWP8Nw6yYAjP6XQ0irWj+dGZYIOeBTmFzrbTcFe7JKuLo+Vhg+gpN7duGJdf/mxv03EpyxEvf6D7GcdHm95xAREalt765I4pXZS3jbPo0+tl8+mLTYoc0g6DAG2o/BL6oTfr8v7rmq4eBGXPuXUL57MY70ldirS6jocg6+E5729Lo7HLsv1gs+wj1zKv57f+QN21Nc8bGb/TlnMfXkeKKDfGvtukzTZNHbDzCZbZTjS8Ql74LVfvSDBlwNrU6Cnd9A5lbcGVuwFCQRa+QRSx5krIdv3iN10TRCp75JUMt6HLUgIiLSyKggKI2X1Qaj/gEJw+GLa8FdjTHk9jo5lW90O9gNoRWpddJ+Y5eemU1LI9dzo65XGD6CK88Zz3vPfMuNfELaxh9orYKgiIg0QAVlVXy3JYOTEyNIiAw44n4ut8mTc7axadm3zPZ5gSijENM3FGPcM9DpjKMv4GW1Qat+WFv1I3DoreB2QVUpvr/OzXc0dl8sUz7EnHURfnt+4C3709y4oJLpC/oypH0Uf+vbktO6xuDnU7MeeN/P/55J+W+DAYUjHiMmuv2xHRjX27Pxy+qIlcW4M7aya/PPpG1fzYCSH2lduoWK14eyrtud9P7bnVis6i0oIiLyv1QQlMYvYSjcugWqyz3zDNaB0FaeXm+x7oOUV7lq/Ca4KSmprCawZB84wB0QhcVL8/dFB/kS1mUY7PiEgMy1XskgIiJyNGVV1Tz3ysuMK5zJp64e7Ig5k5En9WRCz1hC/X0O7Vflgps/2kDM7g/40Oc97IYLs0U3jPM/gPCE4z+xxfrbQh3Hwu6Lcf77mB9fjN/uebzl8wxb3W14b+8Y7t01iPt9AhjbPZa/9W3JKYkRWC1/vaiHaZocyC9nV1IKuXvX02/LI/gYLvZFjSZx+FXHf02/cgRhaXMKnducQucJsHr9RqxzbqKvaxN9tz7Opl1zsf/tZbp06X7i5xAREWmCVBCUpsFiqbNiIEBQnKcg2NLIZWdWDp1a1e0E243J7xcUsdTngiKH0arbEFzbDcKdGVB0EIJjvZpHRETkV263yYtvf8D9RY/ha3FyimU7zpzP+GFuP26dcyr+nUZxdr94OkX7898tLq5zPsq59sWeg7ufg3Hm9Dp9r/MnvxQF+f5+WP8+3aqTedLyBv9n/5CPq4fz3vpT+WzdAQJ8rEQH+xIZ6ENkoMOzBfgQ51dJaGU6ZambsGZvJ6x0D+3NZEYbBZ72DcizRNDm0tf/OJdhDZ3UpxdV3X9i2Sf/pu/O5+np3EjxzFP5tPUtjJpyO+GBmmNYREQEVBAUOTZ+4ZQYAQSapWQn71RB8Hf2ZpfQ3uKFFYYPo1f71uww4+lmJFO4exkh/c71ah4REZFfvf7FPK5Jux9fw0lxiwE4rOCTvopx1lWMYxVJu99k5o6RTHP34Dn7G/S07sc0LBhjHoWBN9Zq0eyY2Rww/lkY+X+w4QNY/QZB+UlcafuWK23fsoxeLHD2IKKgiJjCPGLII8bII8bIx9+o/HN7v1xCnj2GkrAuRE14GGvgEeYzrAEfu43BU+8nJ/ksMmZeRUL5Vs498CSLn5mHzzmvcEoPzS0oIiKigqDIsTAM8hwtCazYRcnB3cAwbydqMPZkldDHSysM/68QPzuLfbvRrSqZvB1LVBAUEZEG4Ysl6zhj442EW0rID+1B2JVfenr7ZW6DtTNwrf+Its5M7rXM5F5mAuDyDcN63tuQOMKb0T38w2HQzXDKjbD3R1j1Ouyex2A2Mti+8YiHFVlCyPNPxBnZhYDWPYhq1xd7TFfCfYOpjwlGItt0JfKuJaTMfYqYtc8yjHWs++wyctosIDLYvx4SiIiINFwqCIoco7LANlCxC3fuHm9HaVD2ZpcwuYEUBAEqYvpDyjfY0ld7O4qIiAgrtifT/ocriLdkU+DbirCrvvht6G+LrjDuaaynPgxbPsdcMwMjfS35vm0IvOpLrJGJXs3+JxaLZ2XjDmMgbz+sfRvy9kFQLATH/XELiiXY7sdxzFxYR5mtxE+8n6q+4yl7/XT6sp3PZ/yDs//+LIY3el2KiIg0ECoIihyr8ETIAXthkreTNCgpmbnEG1meG5HeLwiGdR4KKRBTuhOc5WD383YkERFppvYczMM16xJ6WPZTYg0h5OrZEBj15x19AqDvxRh9L8aZs58lS9dzRkjr+g98PMITYMw/vZ3imPm07EX28EfxX3QnE/Pe5scfx3DqqWd4O5aIiIjXWLwdQKSx8G3RAYDQ8gNeTtJwOF1urPn7sBombkcoBEZ7OxLdunYnywzFTjVlyVptWEREvCO3uIKdb1zFEDZQiQP7xZ9gRLT76wNDWmEa1roP2Ay1HHEVeyNHYzdctFtyG2lZOd6OJCIi4jUqCIoco/BWngmoY93plFe5vJymYUjOLSPB9BRIjejO3pnw/H/Ehvqzzer5t8rcssjLaURExNtySir5aUcWybmluN1mvZyzstrFj6/eznjXj7iwUHn2mzjanlwv55ajMAzaXvY6uZYIEoyDbHv77/X2nBAREWloNGRY5BgFtfQMh40jlz1ZeXRsdZghP83M71cYNqI6ejnNbwoj+0LWz7hSVno7ioiIeFFqbilrX76M0dVL2OhO5CujGwfD+mO07Ef7uAg6xwTROTaY8ACf4287r4xdmcVkFVeSn59Pde5+LIXJ+JYcILpsD+eZCwDIHf440b0m1valyQmyBkbgnPgSfDWFMWVzmffVO5x29mXejiUiIlLvVBAUOUZGQBRlhh/+lJOZsksFQTwrDLc/tKBIZ++G+R3fxIGQ9TJRBRvANBtEz0UREalfaQXlzH7lPm50zQMDhli3MoStUPgxFQV21m/uwEqzM/9xdyY4ujV9OyYwuHs7uraO+vNiE65qKErjYPJOtm/bRHbKTnxLDxBvZNHLyCLSKDpshpTuNxI/8vp6uFo5HjF9zmDbxkvomvQu/TY8wL7ew0hMaGALuIiIiNQxFQRFjpVhkOvTCv/K3ZQe3AkM9nYir9ubXcKYXwuCDWBBkV+16zmIyhU2QtyFOHP2Yo9q7+1IIiJSjw4WlvPcK6/wb+e7YEDxwHvwD42ibNci7AdW4FuZw0DrNgayzXNAAbDKs1XiQ6UtCKt/KH5B4biKs7EUp2I1XcQCsb+e5H+m+auwBVPq3wpncDxGWFsC251CfK+z6umK5Xh1ufDfpPx7CfHO/ez78Gpa3zMPu01zN4qISPOhgqDIcSgLjIfK3VTn7PV2lHpjmiZZxZXszyklKaeU/b/bUnMKecqe4dkxquEUBBNjIthktKM3O0nfvIg2o1QQFBFpLjKLKrjz1S94ueJprIZJWfepBJ12HxgGQSdf7ek5nrMbkpdC0lJcB9biKs3D5izGgomDKhzVuVCUC0V7D024XWVaOUA0JX4t8YtuR8uELvi3SISwthDaBl+/UHy9eeFyXAy7H/5TZlD53hhOcq5hwYdPMOqSf3g7loiISL1RQVDkOJjh7SD3RxyFSd6OUi8Kyqq4+M1VbE4rPOzj7YwM7IYL0+6PEdKqntMdmcVikBHSCwp3UrZnGYy60tuRRESkHmQVVXDFfxfwfOljhFjKqIztj/9Z0/44dYRhQFRHz9b/Cqz80tnP7aaitIC1O/axdlcS2/alYpYXUmwE0qJNZwb17s6p3eJIPIH5BqVhimzXh8097qTH5icYtHcaOzeeSqdep3g7loiISL1QQVDkOPi2aA+7IaQ81dtR6pxpmtz96SY2pxVitRi0CvOjbUQACZGerW1kAN0KFsI3YER1anDz9BmtB0DhxwTlrPN2FBERqQfZxZVc+PoK7ix6lo7WNKoDYnBM/QBsjmNrwGLBNyicwSeFM/ik/rjdJnuzS4gO8iXE31634cVrepx9N9v3/kCXsjV0+uJ0cr5tS1CHwTgSB0HrUyCiXYN7jyMiIlIbVBAUOQ7hrT0LZ8S40qlwuvC1N925Zt5ZnsS8bZkEWN18fM1JdGsT8+edFid5vjagBUV+FdNtGGyBuKokzPICDL9Qb0cSEZE6kltSyYVv/My4vPc43b4G0+KD7YIPIOgwv7uOkcVi0KFFUC2mlAbJYiHushnseP08Oju3E1mRBJuTYPMHnsf9I6D1yRDdFfzCwC8UfEN/9zUMAiKPvfAsIiLSQKggKHIcgmI7AtDSyGFvdgEd4yK8nKhubEkr5PFvdhBLLvMDHyNgRib4R0Jo/C9bawhtA/sWeQ6I7OjdwIfRpUMHUsxo4o0s0rYuo2X/8d6OJCIidcDpcnPpjFW0zf6J23w+A8CYOA1a9fduMGk0QqLjCb5/BYs27GDh/LlEF26gn2UXvSz7cJTlws5vPNsRmFYfjC5nQv/Loc1g9SgUEZFGQQVBkeNgBMVQji9+RgWZKTvpGDfI25FqXXGFk5s+XIfhquCjkP8QUJnpeaAsx7OlH2YIbgPsIehjs5Dk35348gXk71iigqCISBP13opkKtO38bzjFc8dJ18HfS70bihpdAzDYHifLgzt1Zl52zJ44Ifd7M/Mo5uRxBDfvQwILcZeVYSPsxBfVzEBrmICzRKCKcXuqoItn8KWT3FHdMDS/3LodQH4h3v7skRERI5IBUGR42EY5Dpa0qpyLyXpu4CmVRA0TZP/+2ILSbmlvBTwLm0rd4JfOFw2B0w3FKT8z5bsGS6TMMzb0Q+rKrY/7FuA/eBqb0cREZE6kFtSySvzNzPL/hwBVEDboXDaY96OJY2YxWIwtnssp3WNYe7mg0ybH8r07A5QfqQjTHoY+7nA+iOTrMsJyN0N39+P+4eHoeskLCddAfED1WtQREQaHBUERY5TWUAbqNxLdc5eb0epdR+vSWX2xnQus/3AeNcCMCwweQa06ObZIaaHdwMep9BOQ2Hfv2lVuhXcLrA03TkfRUSao+d+2MUV1R+TaMvADG6JMfkdsGoBEKk5i8VgYq84xvWI5bstGSTllhLqbyfM34dQPzuh/j6HbmcVV/Dl+rFMXreL3oU/MtX6I91Jgi2fwJZPKA7phHnSVQQPuBB8Arx9aSIiIoAKgiLHzQxPgDxwFO73dpRatSuzmIdmb2WAsZ0H7O+DCYx5FBJHeDvaCevUYwAl3/gSaJSTu38DEe36eTuSiIjUku0Hi1i/egn/tM8FwBj/LAQ0zbl9xXusFoPxPWOPuk+biABuObUDfx/dng2pg/h43eU8sXEJE5zzmGRdTlDhTph/F8XzH2JF8Bkc7Hgx8R260bNlCBGBWoxERES8QwVBkePkaNEB9kBwWaq3o9Sa8ioXN36wjjBnNm/4T8fqroYek2Hgjd6OViNB/r6st3emT/UG0jYvUkFQRKSJME2TR2dv5nHbG9gMN3Q5Ezqd4e1Y0swZhkGf+DD6xIfhnNiNRTvP4YlNu2mZ9Dljy+fQ1sjktKLPcK/+nIUre3GH6zQORg5iZJdYRneJpm98GFaLhhaLiEj9UEFQ5DiFtfIsoBHjOkiF04WvvfEPQ3149lZSsvL4wm8awe4Cz9DgiS82ifluCiP7QsYGzJSV3o4iIiK15PutGbRPmUVv+17cPkFYzvi3tyOJ/IHdauHUri04tWsLYAhllU+ya/Uc/Na/QevcZYyybmCUdQO5hUEsXd6DWUt78H8+fejWuQujOkczvFMUwb4a/i4iInVHBUGR4xQc1xGAVkY2+7IL6RjXuFeQW52Ux6w1KTxtf4uu5l7wC4PzPwAff29HqxV+7QZCxltEFWz0dhQREakFFU4Xr81Zwru2WQBYTn0Igo8+pFPE2/wdPnQc8jcY8jfI3Qur38Rc/x4RlUVMsi5nknU5mLBzayuWbO7BrWZPfFv3YljX1ozo1pqYsBCwWP7yPAVlVezJKiEi0EFcqC8OW+P/4FpEROqGCoIix8kIiqUCB75GJVkpu+gYd4q3I9XIsj05XGKdx2TrYs8iIufOgLA23o5VaxJ6Dce91CDOfZDi3DSCIlp6O5KIiNTAm0v3c23pfwm0VuBq2R9r/yu9HUnk+ES0g7GPY4z5JxxYDXsXYO5ZAOnr6GQ5QCfLAa7iW8jAsy3wHOYy7Bh2Xyw2h6cIPugW0luPY3VyAauT8li9P5+dmcV/OFVUkIOWoX60CvOjZZgfrcL8OaN7DJGau1BEpNlTQVDkeFks5Pq0pGXVPooP7gIad0Ewe+9GHra977kx5hFoN9K7gWpZdHQL9llak2imkLxhId1HX+jtSCIicoIyiyrY8dOH3Ghdg9uwYT3zxWPqNSXSIFnt0GYQtBmEMeofUJYH+xfB3gVU716AtTgNA/O33U0nVDmhqhjKcuDzqyhwt+GL6vNZ6O4FeKZ6iQ3xpaDMSbnTRXZxJdnFlWxILTjUzis/7WHmNQOJj2gao0FEROTEqCAocgJKA+Mhbx/V2Xu9HaVGTNMkMmMhdsNFcewgggbe5O1IdSIzpBeJBSmU7V0OKgiKiDRIe7KKWbo7h/bRQZyUEHbYoY7T5q7lfmMGAMbgv0OLbvUdU6Tu+IdDt7Oh29meP9JME9zVZOcXsnDbAZZsP8DW5CxsZiWnWtZxre1rulqSedvn3yQF9uHgSffQod8oIgMdmKZJfpmTtPxy0grKOJBfzoH8chbsyCIlr4wpr61QUVBEpJlTQVDkBJhhCZC3EHtRkrej1EhqXjmdqneBFfy6jGkSi4gcjhF/MhR8TXD2Om9HERGR3ymtrGbu5oPMWp1KSvI+Rlg38o27BTtsnTmpXQuGd4piRMdo4iP82ZBaQKet04i15VEZ1AbH8Lu9HV+kbhkGWO1ERUYyeVgkk4f1prDMyY87MkkvOJWt0XfRL+VtfNa+QduS9bT9aQocnAAj7sVwBBFekk14aRY9KrLAnQO2LO5pW8BDZlc+zu/IBa//zMxrTqF1+NGLglnFFTw+dzuZRZW8fGFfwgJ86ukHICIidUkFQZET4GjRAfZCSFmKt6PUyIYDBfSzeHo52lqf5OU0dSe2+3DYBAlVu6msKMPhq0/DRUS8xTRNNh4oZNbqFGZvSCewKpvrbF9zgWMBvoYTgDLTwZq9HVm2uzs3uLtRHt6V9tV7eMX6AwCOs14Au583L0PEK0L87fytb6vf7uj+OAy6HhY9CRs+hB1zPNsR+AFPGVaCQ2/ljYJ+THntyEVB0zT5akM6D3+9ldKycoIoY8ayMG4/rVMdXJmIiNQ3FQRFTkBYq84AxFSnU+F04WtvnCu47dm7hzONXNwYWOJ6eztOnYlv3518ggkzitixeQWdTxrt7UgiIs3Syn25PDR7Kzsyiokll7tts7nAdyE+eAqBRHfDLM3CvzSbYdbNDLNuBqCgJAAnNiyGSUWXc/FtYvPditRIaGuY9BIM+jv8+AjsmAtWHwiMhoBICIiGwCjP19zdGNu/5v8qn8caejP/LTj5sEXBrKIK7v9iC/O3Z9Lb2MOrvi8SZBZzyfJ/ce3wdgQ49GekiEhjp1dykRMQHNcRgFZGNvtziugYG+blRCemKnk1AMVB7QlxBHk5Td0xLBaS/bsTVracvB1LQQVBEZF6V1zh5MYP1+EoSeMJn6+ZbFmIjWrPg/EDYfg9kDjCsyxC1nbP4gr7FmEmLyW00rNyqtMnFN8JT3ntGkQatKhOMOUDqK7yLFhyuKlg3G6YcyvGune4t+JFbCE38lLBoENFwVZhfnyxPo2HZ2+lqMLJpfb5PGh7D6tZDQZc7vqUWatP5YohCfV/fSIiUqtUEBQ5AUZwSyrxwWFUkZG8k46xjW+lYafLTUj+JrCA0aqft+PUuarY/rB3OUFJ31JR9X/4+ujlT0SkPv1nwR4mln/F/b4fYf+1ENh2KAy/2/P198WLFl092ynXY7iq4eAGSF2Jvc1gT48nETky21Hm+LNYYMI0sNoxVr/BXZX/wRZSzQsFw5jy2s90igliwY4sfKlkRvC7jKz6CUwgcQTsW8g4y0ouXLyEiwe2wW7VCt8iIo2ZXsVFToTFQq7DM39L0YHtXg5zYnZmFNPD3A1AYGLjK2ger86nXU0ldnq4tvP1Z+96O46ISLOyP6eUbcu/5gHb+55iYMJwuOwbuGwOJAw7+qJWVhu06g8Db4QmPL2FSL2xWGDcM3Dy9QDcVvkqdwT/RFqBZxXiDtZMlkb8y1MMNKxw2mNw8Ze4Oo7DYpicW/4xX29M9/JFiIhITakgKHKCSoM8QyVc2bu9nOTEbEjJo6dlHwCWZtBDMLhFPAc7XgxA1+3T2JSa5+VEIiLNx/SvlvKc9T9YDBP6XgKXzoa2g70dS6T5MgwY+4Rn3kHg5qrXeShyATfF7uC7gAeJLN3jmXPw0tkw6GYwDKzD7wRgkmUZX/20HNM0vXkFIiJSQyoIipyoiPYA+Bbu83KQE5O+dxPBRjlOiwOiu3o7Tr1oO+kfVFj86WZJZs6Hr1BV7fZ2JBGRJm/xjgzOTXqYKKOQyvAucMa/vR1JRMBTFBzzCAz1FPouL3mDO/MfwVpV7JnX89rF0HbIb/u37IczYRQ2w81p+TNZuDPbS8FFRKQ2qCAocoL8Y7sAEFGR7OUkJ+jAWgBKwrt7hmM1BwERuAfeDMCU0nd5ecEOLwcSEWnaql1ukj5/iEHWbVRZ/HBc8C7Y/bwdS0R+ZRgw+gEYcf9v951yI1z6NQTH/ml3+4i7ATjXuohZC36ur5QiIlIHVBAUOUERbbsBEG+mk1da5eU0x6e4wkls6VYAHG0GeDlN/fIfdjOVjnASLRlkLX6LbelF3o4kItKomKbJvK0Zx/T6+eM3n3BR5SwAqs94DqI61nU8ETkRI+6BKR/BJbNh7OOeVYoPp81AKluegsOoZkD6B6xLya/fnCIiUmtUEBQ5Qb4xnQCINgpISjvo5TTHZ/OBQnoZewHwT2heBUEcQfiM8AyNucn6Gfd/shqnS0OHRUSORYXTxS0zN3DNe2uZ+J+lvPTTHtzuw88jVpCZQv+1d2ExTHa3+hv+J02t57Qiclw6j4PE4X+5m2PUPQBcYF3Ahz+uqetUIiJSR1QQFDlRviEUWMIAyE3e6uUwx2dzcgZdjBTPjVb9vRvGC4z+V+IKakmckUe/rM94bXHjnAdSRKQm/rtoL8Of/om3l+3HdYSi3u9lFlVw/n9XsHDjbm6yfcm1xpd8Om8hl85YRXZx5R93drvIf+8SIihkv6UtiRe/VEdXISL1LnEkFdG98TOqSNz7LnuzS7ydSERETkAzmThMpG4U+LchtCSf8owdwDhvxzlm+XvXYjdclNnD8Q9p7e049c/ui3XkvTD7Zm60fcXo+aM5rWsLOrQIOuzupmlSWO4ko6iCjMIKMosqyCisJKPI873bNLlqSCJDOkTW84WIiJyYj1alsOT7T7jT+hPvzxnD5+tP4fGze9C9Zchh9998oJCr311Dm+L1fOf7CnHkAHA3s9ie3JpPnx/MSeMup3//UwDImfsICSXrKTUd5E94nQSHf71dm4jUMcPAd9Q9MPMCLrHO45kfN/DwlCF/fdz/cLrcfLE+jdZh/gxsF1EHQUVE5GhUEBSpgcqQRCjZgCV3r7ejHBdH5noAKlv0xt8wvJzGS3pNxVz2IuG5u7nUmMNdn8bw2fWDsFoMsooq2HSgkE0HCth4oJDNaYV/OU/kwp3ZnNE9hn9M6ErLUE2YLyL1Z09WMUG+dloE+x7T/gt2ZPLOV9/yuf05/I1KJlp/Zk7myVz3n6mcPngAt4/pSIDjt7eIczalc98na7nBnMW1jjlYMCGsLYS3w9y/iC6k0sU9E+bMJPvHdoR3H0342tcBmNXiDq7o28ymphBpDjqOpSysM4H5OwjfOoPMov7H/BoEng8Znvh4AcPyPmUjcbS45SESowLrMLCIiPwvFQRFasAW3RHSIKhkv7ejHLOMwgoSq3aCFQIST/Z2HO+x2jBG/R98chlXW7/h3dTTOOcVz88no6jiD7smGAfpacnE5vDHPyCIwKAQQoKDCQkJJTwslG1ZTt79OZlvt2SwcGc2N41qz1VDE3DYrF66OBFpLpbvzeGiN1bisFl54m89OKtPy6Puv+lAAXd/sIxZ1ufxNyoxQ9tAYSoTrCs51bKO//48kYmbzuXeSf04tUsLXvhxN3MWLGSm/T90syZ7GulzMYx9AhxBGGV5OLd+zb7FH5BYtIao8r2w2vMh2Sz3KE49/+a6/hGIiDdYLPiPvgc+vZxLLN/y5uIt3DHhr6ehqXC6eHneJowV/+EN69f42zzTDbzyYQjX/f3/MJrrB9UiIl6ggqBIDYS06grrIdqZSrXLjc3a8Kfl3JCaT29jDwA+8Sd5OY2XdZkEsb0IOLiRG2xf8VjqxQBYDDgp0snUgNUMKVtARNE2z/4mUPLL9vt1ZBzB3DD0Bm5JHsSK5BKe/n4nn649wEMTuzKiU/QRT19V7cZuNfTmV0ROSGG5kwdnreA528tkmBHcOWsya5LzeGBC18N+IJGSW8YVM1bxKC/TznIQM7glxtULoDgDvrsX36Ql3GL7nMmVC3nyg6k8ETqKIYWzmevzAb6GE9MvHOPMF6HLxN8a9Q/HftKldDrpUr5fs4MlX7/DCPcKKnCQNvAh4iM0VFikyeo6idKgBEKL92OsfovkgV2ID/c/4vuatUm5LPzsZS4tf49YWx4AlQEtcZSmcUXeNJb81JdhoxrPFDwiIo3dMRUEExMT6zoHhmGwd2/jGnZ5PFJSUnjxxReZO3cuKSkpOBwO2rdvz3nnnccNN9yAv7/eMDdG4fHdAGhLBgfySmkbdfg56BqSnfuSGWvJ8txo2de7YbzNYoHRD8L753C5z4+EDr6SPvYk2qTNxZa0CIp/WX3YYoOoLuCqhKoycP6yVf/Sk7CyiOhVT/JhVGeWjL6XO1YFsT+nlMtmrObULtEkRAaQV+okr7SSvDLP1/xSJ9WVpfjYfYiLCKZtRABtIv1JiAigTUQACZEBRAc5sFhULBSRw3v4yw08UP5vhls3AdDVSOL6n29l84FCXrqwL63CfntvkVdaxaUzVvG3is85w74a02LHOO9dCIj0bJd+Ddtn4/7+/4grTOVFn/+QWfo+LewFngbajcKY9DIExx4xz+n9O9M18QHu/2IzFU4XM0Z3r8vLFxFvs1jxG3UXfHUDF/M1U57pjcs/msRWcfRsHUqv1qH0ahWKxXSxddcOEtc+yL2WJDCg3L8lfuMew9F1Entf+hvtchfSdfH1FHVbTHCLNt6+MhGRZuGYCoJJSUkYhoFp/vUKdCeqKfeQmTt3LhdeeCGFhYWH7isrK2P16tWsXr2aN954g2+++aZeCq9SuyzhbXFiw8+oIj1lN22jGn6BrSJpFQBFAW0J9gvzcpoGoN1oaDMEa/JSzl157h8fazUAep4H3c72/MH8v9wucJbDjjnw/f9hZO9gWPZlLO9+PtOtl/DS6iLmb8/6wyF+VDDGso4zrcsY7tiEgUlKXjT7cmPZuzOOdWYcn7pj2WvGYQREMqpzNGO6tmBYhyj8fDQEWUQ8Zm9Io9/WJxhu24Tb6ovFYmEYm/nM+igXH7iLCdPLmHZ+b0Z0iqa8ysVV76ymRd5q7vGZBYBxxpN/XGXeMKDrJCwdToPl/8G95FlaVBfgtvpgOe0xOOlqz4cof6F1uD/vXdmMp6MQaWYsPc+j6sfHiSo5wI+Ou8AFVUlW8pKCyTWD2WIG47C4uN/YBhYotwTA0DvxG3ID2D1zDra68l32PjOUdu5k0t+eTPBtC8FHnSVEROracQ0ZnjRpEqGhobUaoKCggK+++qpW22xINm7cyHnnnUdZWRmBgYHcd999jBw5kvLycmbOnMnrr7/Ozp07GT9+PKtXryYwUJPpNipWG7n2OGKcKRSmbod+Dbsg6HKbBOZuBAPMlv28HadhMAw49SF48zTAhIj20PN86HEuhP9Fkd5iBUcg9JoCHU+HHx+BNTOwb5nF7b7fc+GYe3m1eAh2THo519I9dx6tMn/C6ir/QzOJRgaJZHAq6/9wf6ozik82DOfhtcPJs0UxtEMUp3Vtwagu0UQGOmr5ByEijUV6QTk7v3ySu2w/YmJgmfwWBMXCh+fRuTSJuf4PM7X8Li5/28nNozqw42ARaSn7+MZ3Olbc0OsC6H/l4Ru3+8Hwu7D0ngqbP8bSaRxEdarfCxSRxsNqx2fiszDv/zCLMzCqSvAxXMSQT4yRf2i3atNCRocptDr70T99yOrwD6HgzHfJ/WICceU7yf/oGsIuec/zHk1EROrMcRUE//Wvf9G1a9daDbB169YmXRC89dZbKSsrw2azMW/ePAYOHHjosVGjRtGhQwfuvvtuduzYwXPPPceDDz7oxbRyIooD2xKTn4Iza5e3o/ylvdkldHPvBisENecFRf5X6wFw9Y9gWCC294m9AfULgwnPQ+8LYc6tkLGZFovu5aHoblB8EMrzfts3LAF6TPYUHR3BkLsbcnZBzu7ftsIUWluyud3yKbfaP2ORqyczd47k/u19cRk2TkmI4D9T+xChwqBIs+J2m8x89xXuMN8DA9xjHsPaebznwSt/gA/OJTp3D1/7P8KlZbfx4o9gp5qZPi8SQSG06A7jn/vr17mQljDktrq/IBFp/DqNhU5jMQCcFVCWA6XZUJpLWUEGuTmZbM33Z/Tkq8BuP2wT/Xr3Zvr6f3Ft0m2E7f8a96KnsYy4u14vQ0SkudGiInVo9erVLFy4EIArr7zyD8XAX91xxx3MmDGD7du3M23aNO677z7sR/hFKQ2TO6I95C/GUdDw58DckJzPGIsnp6X1X68E16zUVo/JVv3h6oWw6jX46V+QtdVzf0A0dD/HUwhs2fePf4wHx0LCsD+2U1UKO76Bde9gSVrCSOtGRlo3km+EMss5hFn7RzJ3cwyXDGxbO7lFpFYs35OD24RB7SLqZA7Q2d/O4frcJ7AYJoXdLyFk0I2/PRie4CkKfjQFv9SVfOT3JHdU30Av9w76WXaBIwTOf09D8USk7th9IaSVZwP8AbvTybpvvvnLQ6ecO4XHn93Kw/wXy8J/QYsuf1zESEREatUxLYkaHx9PfHw8Pj4+tR7Ax8eH+Ph42rRpepPHfvnll4e+v/zyyw+7j8Vi4ZJLLgEgPz//UAFRGg+/2M4AhJYneznJX0vZt40wo4Rqw+7pJSJ1w2qDgTfATath7FNw8Rdw+3Y440lo1e/YeiD6BEDPyXDZHLh5HQy5HQJbEGYWcJ1tDj/43EXujmV1fy0icsy+2pDG1DdWctGbKxn93CJmLNtPcYWz1trfu3sHg1bd5Jm3NnIwIWc//+fXE/9wuOQr6DIRq9vJNMsLXG773vPY3/7711MhiIh4SVSQg/Zjb2RG9ekAmJ9dDRmbvZxKRKTpOuZFRepKhw4d6rR9b1qyZAkAAQEB9Ot35N5Hw4cPP/T90qVLGTNmTJ1nk9rz60rDrdxpFFc4CfJtuD083amrASgJ60qoTUNN61xwHJxyXc3biWjnmedw5P2w+wdKvnuYwIKdhKYtAi6sefsiUmMbUgu469ONjLf8TKy1gHW5iTz5dT7PfL+Tc/u14pJBbWkX9cd5gksqq9mSVsjmA4VsSiskvaCcHi1DOCUxnJMTIggL+O2D2MrSfCwfnUe0UUCqPYFWV33k+fDhcOx+MPkd+P5+WPmq576hd0CnM+rq8kVEasXUAfGcs+Ym2mWmM4zN8NEFcNV8CIrxdjQRkSZHQ4br0Pbt2wFo3749NtuRf9SdO3f+0zHSeATGdQGgpZHL5oPZ9EiI83KiwyuvchFVtAWsYIvXcOFGyWqHzuOw5qXCvLtpX7mV7OJKooJU3BXxpozCCm56ZzlPG9OZ5LPcc6cVnNjY6o5nw+r2TF/ZHnubk+jcoSO5qbsoObgL35Jk2pBJFyODMyyZRFFAUUYAOWuC2UowTkck/mEtiIhuiTV5EQnuZLIJw/+yzzF8Q44eymKFsU96pkMoSoNBf6/7H4SISA1ZLAaPnt2Li/5zM58bD5JYmArvnwOXzQW/UG/HExFpUmpUEFy8eDEAsbGxdOjQoVYCNRUVFRXk5OQA0KpVq6PuGxYWRkBAAKWlpaSmptZHPKlNAREUG0EEmcXkJG+DBloQ3JpeSC9jDwABCVpQpDHzSxwMQB/LHpYnZXNaj6O/xohI3SmvcnHn2/N5oeph+ll3Y1psGAnDIWMT9tJselv20duyD5gHB/FsvzrMu7AoCokyCj03nEDWLxtQZjrYO+ZNTml5jMN+DQN6nnfC1yYi4g3dW4Zw9qDuXLr8Hr70fYSIzC2enoIXf+7pAS0iIrWiRgXBESNGYBgGb775pgqC/6O4uPjQ94GBgUfZ0+PXgmBJSckR96msrKSysvLQ7aKiIgCcTidOZ+3NUSS/+fXn+lc/31zfeILKt1J8YBtO5/Cj7ustG/ZncrHhmeewOqYX6DnTeIW1x2UJINBdSvL21Tg7tzjmQ4/1OS3SWHjzOW2aJk+/N5sncu+ltSUblyMY89y3MdsOA9OEwhSMtLUY6WtxJq/GlrUZm+mkwhpIZXBbfKLa4xPdDjMsEcITMANjoKIQoyyH4rwMDhxIJScrjfL8DIzKIjI7TuXCk4fp/28Tp9dpaWpO5Dl984hEvtl8kIuK7+YTx6MEpizH/cnluM6ZARYNchPv0uu0NGTH87w0TNM0T/REwcHBlJaWsmrVqqPOkdccpaamEh8fD8DFF1/Mu+++e9T94+PjSU1NpV27duzZs+ew+zz88MP885///NP9H374If7+WjHQm2K2v87JFUv4yH4O/t0neTvOYS3ZnsQzFQ9SagQwv9fLx7awhTRYXbY9TcfKzUyzXEZCr1HejiPSLCXv3cxlhf8h2CinwBbN2g53UOIbe8T9LW4nVncVTqv/cb8GV7rAx6KXbhFpPjLLYcZOK60rdvCez5M4DCdJ4cPZGH+FXgxFRI6grKyMqVOnUlhYSHBw8FH3rdHHK/Hx8Wzfvp2ysrKaNNMk+fr6Hvq+qqrqL/f/teefn9+Ru8Hfd9993H777YduFxUV0bp1a0477bS//IeWE+N0Ovnhhx8YM2YMdvuRFwvZ51wPW5YQQw5Dxo2rx4THbs+W+wGobNGHcePHezmN1FShZT2s3Uxi9W5Gn/ZvfGzHtGj8MT+nRRoLbz2nt389jfGFz2Iz3GSG9SP8so8Y5h9eb+eXpkuv09LU1OQ5PaXKxUNzWnLzxhJesU+jbd4iotr1xOe0h+oorchf0+u0NGS/jiQ9FjUqCI4fP57t27czf/58hg4dWpOmmpygoKBD3x9tGPCvSktLgaMPL3Y4HDgcf148wG6364Wojv3VzzikdTfYAlFVqVitNiyWhvWpZW5JJW0rtoMVAtqdoudLExDRdTisfZ4+7GJ3Tjm9W4ce1/F63ZCmpjaf0263SVJuKXarhWA/O0GO372ul2SR882/6LntbTBgY8Q4el3/Nmjldqllep2WpuZEntN2u53nzuvNx4kRPDi7lH9ZXyNg9XQOWMJpdcbtf92ASB3S67Q0RMfznKxRQfC2227jrbfeYtq0aUyePJnu3bvXpLkmxdfXl8jISHJycjhw4MBR983Pzz9UEGzdunV9xJNaFtGmGwBtSSejsJy4sIY1hHvjgQJ6GXsBcLQZ4OU0UhuMlv1wY6G1JZulu3bSu7UWihGpDXuySrjns02sTc7/5R6TLpZUxvlsYJRlHV3cu4nEM9vKp6GXc/YNz4H12HroiojI8TMMg/NPiqd7y3/w+oxSrnZ+QKuV/2RxqQ9Dz7kRQ8OHRUROSI3ewcbExDBnzhyCgoIYPHgwjz/+OElJSbUUrfHr0qULAHv27KG6uvqI++3YseNPx0jjYo9shwsLgUYFB1L2ezvOH+zNLuGt+Rtob0n33NFS8302Cb7B5AW0B6B0z3IvhxFp/Kpdbl5euIdxLy5hc3IWI22becznHZY6buFbn3u5mZl0c+/CgslGdyIP+t3Padc9jVXFQBGRetEtLoTzb5/GgpC/ATBw84O89ezdrN2f7eVkIiKNU416CCYmJgKeOfKKi4t54IEHeOCBBwgMDCQ0NBSr1XrEYw3DYO/evTU5fYM3ZMgQlixZQmlpKWvXruXkkw/fg2fRokWHvh88eHB9xZPaZHOQa4shujqd/NSt0KubtxNR4XTx0k97+O+ifZxkbgQfqAxsjSMg0tvRpJY4Ww6AXbsIzFrj7Sgijdq29CIe+2QxMZlLeN66jlF+m/Ezyw89btp8KWs1lJyWo0iLHEq+NZJbEsMJ9tUwIRGR+hTs58PIW95gz2sVtM/4hitLXmPNjIU81OZ+Lpt0GgmRAd6OKCLSaNSoIPi/vQF/XbC4uLiY4uLiox7bHLp2n3XWWTzxxBMAzJgx47AFQbfbfWgF4tDQUEaOHFmvGaX2FAW0JbowHWfmLm9H4aedWTz01VZS8sqIIZd7QudDBTjanOTtaFKLwjoNgV3v09m5nfSCcuJCj7wokUhTlVdaRXY57MsuxbBaqXaZuNwm1W43LreJn4+VmGBfwgN8/vjewzSpytzOz99+gP/+H3jP2IXVx/zlMSAwBjqeDp3OwEgYToCPPwFAG29cpIiIHGJYrLS/5gOKlr2Oz08P059d9Ei5mhennUNZ/+u56dQuRARqXlcRkb9So4LgpZdeWls5mqQBAwYwdOhQlixZwptvvsmll17KwIED/7DPs88+y/bt2wG45ZZbNClpI1Yd1g4Kl2Mr8F7P14OF5Tzy9Ta+3ZKBHxU8EPAdlzEba0WFZ4deU7yWTWqfb+IgALoZSfyw/yBxfRK9nEik7lW73GxILeCnnVks2JHN9oNFgA02LDvs/naqiTcy6WTLoIdvFp1tGbQx04l1puDnKmYYHJpAxRnVHXuXcdBpLMT2AYuGA4uINEgWC8FDr4Ue4yj97CYCUhdyl20mm9at5Jr1NzB65CiuGJyAr/3II9ZERJq7GhUEZ8yYUVs5mqwXXniBwYMHU15ezmmnncb999/PyJEjKS8vZ+bMmbz22msAdOzYkTvuuMPLaaUmHDGdIAlCSpPqpH3TNLnjk42sS87HZrVgsxj42DxfbVYLdqvBhpQCyqqcnGdbygN+nxLkzPEcHD8QTv+X5g9sakLjKbJFEFydS9b2FaCCoDRBpmmSV1rF4t3Z/LQjm2U704mp3E9Pyz4uNvbTzSeJQKMCm2FixY0FEwturIYbK26CzSJsuD2NOX/ZflFp2lhj6U5IzzPpNnIy9tB4r1yjiIicoNDWBFzxJWz8iOpv7qFn1X5mmvfy8vxJXLHjct68YjB+PioKiogcTo0KgvLX+vTpw6xZs7jooosoKiri/vvv/9M+HTt2ZO7cuQQFBXkhodSWsPhu8DPEuQ5Q4XTV+ieSK/bm8vm6tKPuc7KxnccDP6Jd9R7PH72hbWDMI9B1EjSDYfrNjmFQHN2P4PR5WNJWARd6O5HICfl+awbv/5xMaWU1ZVUuKpwuyqpcOKsqiatOoRt76WHs5wrLPp42UnA4jrxQ1yHm7761B+AMa0dxYALZjnjSLK3YY8ZRHtiGS4Z3ITzAp+4uTkRE6pZhQO+p2NqNwpxzO/adc7nF9jk/pe3l+nef4NVLT1FPQRGRw1BBsB5MnDiRTZs28cILLzB37lwOHDiAj48P7du3Z/Lkydx00034+/t7O6bUUEgrzwrRrchmT1YenVpG1Wr7H6xM4QbrV0wM2UfLQANclViqKzBclVhclVhcFTiq8qEacATDsDthwLVg963VHNKw+LcbBOnzaFW8qU4K0SJ1rcLp4t7PNlFYVkl7I42eln10Nzw9ALsayfjanX86xvQNxYjrA3F9qI7uzs+b9nDKoEHYbD5gsYJh8WwWK/iFYwTH4WMYRAARQGdgdH1fqIiI1K2gGIwpH8DWz3F9cQMj2Uhe8hPc8P6DvHpxf3xsmgZCROT3VBCsJ23atOG5557jueee83YUqSNGUAxlhh/+lJOZtL1WC4JZRRXkbFvIS/ZZUIZnO2wIC/S7DEbcD4G1W5CUhim04xBYAn2MXWw+UMBJCRHejiRyXGZvSGdC5Vzu9/0IPyr/9LjbJwh3bG9sLT0FQOL6YIS1PdTr2XQ6yd3/DWbrU0Dz8IqING+GAd3PweoTiPnRBZxjXUL23hf5+0e38Z+pfbBZVRQUEflVrRYE8/Pz2bhxIzk5OZSXlx9adfhILrnkkto8vYh3GQY5jnjiK3ZSmrYDPFPV14qP16RyveVLz41O46DHZLD7gc0Btl++2v0gIAoCImvtvNLwGbG9qDJ8CKOEfdvXc1LCqd6OJHLMTNPk66WredP2Hj64wCcQYnsdKvwR1wdLWAIWLe4hIiLHo+PpGGdOh69u4Drb1zy6I4TbP76K58/vjdWiaXRERKCWCoILFy7koYceYunSpcd8jGEYKghKk1MenAgVO3Hn7Kq1Nl1uk/U//8RN1o24DSuW0x+H8IRaa18aOZsPucHdiC1cT8W+FYAKgtJ4rNqfx6m5H+Jjc1HdaiC2K+Z6hvmKiIjUVJ8LoSQTfvwnD9jf55bNwdxjs/Dvc3piOVxR0DQhbR2U5UCbQeDQ/O4i0rTVuCD4yiuvcPPNN2Oa5l/2CBRp8iI6QNa3+BXur7UmF+7MYnL5x2AFs/u5KgbKn8WfApvXE5K7HtM0MbSAjDQSXyxezT+tPwFgG32/ioEiIlK7htwGJVmw8hWesf+XK9YH84DNwmNndf/t/VJ1FWz7En5+GdLXe+6z2D1FwY5joePpENHOa5cgIlJXalQQ3L59O3//+98xTZMePXrwyCOPYLfbGT9+PIZhsGfPHvLz81mzZg2vvfYa69atY8iQIfz3v//VIhrSJAW07AzbIaIyudYKMz8tXshj1tWYGFiH3VELKaWpieg8FDa/QnfXdlLzyomP0OurNHxpBeV03vMWDls1ZbEn4992qLcjiYhIU2MYcPrjUJqFfctnvGp/ngtWBTBmfx6hZhGnV3zLWc5viSIPgArTTo4RTit3Juxf5Nm+vw/C23kKg50nQNvBXr4oEZHaUaOC4PTp03G5XERFRbFkyRKCgoLYunXroccTEhJISEigb9++XH311dx77708/fTT3HzzzcyfP7/G4UUamqi23QFoY6aTW1JJZFDNVvhNzSvjpANvgxXK2o0nIKpTLaSUpsan7SkAtLekM3fPfuIjunk5kchf+3zRWq6xLgDA/9T7Di0SIiIiUqssFjjrFSjLJWDfQmb4/Jsf8vpxlnUZvoZnJftMM5R3q0/jI9co8ggmwTjIKMt6zg7cSteqLVjy9np6EP78Mgy9E0Y/4OWLEhGpuRrN0r1o0SIMw+Dvf/87QUFHn2PBMAyeeuopRo0axU8//cRbb71Vk1OLNEiO6A4AhBqlpBxIrXF73y1aygTLCgACTr2nxu1JExUQQY4jHoC8Hcc+l6uIt5RXuQhb/zIOw0l+RB9IHOHtSCIi0pTZHHD++xDbiwijmCm2hfgaTkojepA64gUKr13HebdPY869Z/HS1L60at+DN13jmFB4Fz3LX+FOy51sjhjraWvJM7Doae9ej4hILahRQfDAgQMA9O3b99B9vx8i6XQ6/3TMNddcg2mavP/++zU5tUjD5ONPjjUagPyUrX+x89FVVbtpsellrIZJVuwIiO1ZCwGlqSqPOQkAn4OrvZxE5K99v3Ij55rzAAge+4B6B4qISN1zBMGFn0K7UdD1LLj8OwJuWkLrEZfRMS6CNhEBxIX6Mb5nLO9deTJL7h7JTSPb4x8UxqdlfZmYdgn/ck71tPXTY7DsRa9ejohITdVoyHBFRQUAcXFxh+4LCAg49H1+fj7R0dF/OKZ9+/YAbNu2rSanFmmwCv3bEFmcRUXGzhq1s3j1Ws5wLwYDwsfeX0vppKkK6jgYkj+jbdlmSiurCXDUyiLyIrXONE1cS1/A13CSGdyDFu1HeTuSiIg0F4HRcPEXx7Rr63B/7jy9E7ee2oGfdmbz0aoUXt8xAQdO7rR/Aj88ADZfOPmaOg4tIlI3atRDMDw8HIDS0tJD90VFRR3qJbhr164/HZOTkwNAQUFBTU4t0mBVhXpWIbPm7alRO64l07AbLlJCTsLW5uTaiCZNWGjHIQD0MvayKSXby2lEjmzN1l2Mq/gGgMDT/qHegSIi0qDZrBbGdG3BW5edxPtXnsyXwVOZXn2W58Fv76JsxZtezScicqJqVBDs3LkzALt37z50n7+/Px06eOZRmz179p+O+fW+qKiompxapMGyt+gIQFBp0gm3sX//bkaUfg9AwJh7ayOWNHURHSixBONrOEndttLbaaQRq3C6OJBXistt1kn7efOfwc+o4oBfFwK6nV4n5xAREakLQzpE8v2tw8gbcBevV48HwPf7O9g091UvJxMROX41GlM2ZMgQFi1axOLFi7n00ksP3f+3v/2NJ598khdffJHOnTtz/vnnU1ZWxjvvvMNrr72GYRiMGqUhQtI0hbbuBmsgxnkAp8uN3Xr8dfeD3z5DguFkj6M77buNroOU0uRYLOSF9yIwZwmupJ+Bs7ydSOpYYWEBm+a+gk9xGv7uYvxcxfhVF+FwFePrLMKnupjywNaUth2D0WkcYe364+vzx1/7ldUudhwsZtOBAvJ3/0xU2nz6VyynLRlsJoHdvj3JjewP8QNpExdLhxaBtIkIwG614HabuEwTl/uXrbIEo+gggdHxGD4Bh82cdiCFoflfgQHGyHvVO1BERBqdAIeNh87sztqe0/nyA5OznN/QbdW9vJ5SylkX3URUkMPbEUVEjkmNCoITJkzgX//6F1999RUvvfQSvr6+ANxxxx289tpr5Ofnc/XVV3P11VcfOsY0Tfz8/Lj3XvV6kqYpPL4rAPFkkppdSGJM2HEdX1aQSe8sz9wm5QNv0x/McsxsbQdCzhIi8jdgmuYfFnmSpqWkuJAD08cxtProixf5FGwjZMM22PAC6WY4i+nPer+BHAjtR0mVm5Cs1YxmFWOsa4kz8jwH/fK06c0eelfugbTPcR8w2GHGs8TdmZfMdgRSTksjm1aHthwijSJPNvxJaz2R+NNvxK9Vrz/kSZn7b1oaleyzdyTxpEm1/nMRERGpL/3aRtDtrnfZ+MaV9Mr6isszHuOZ59I46dw7Gd0t7q8bEBHxshoVBE8++WRmzJhBdXU1+fn5xMbGAhAREcH333/Peeedx/79+/9wTHR0NO+++y5dunSpyalFGixLSCsqcOBrVJKWtIPEmIHHdXzSnGfoSiU7jUS6Df1bHaWUpiiqyzBY8296mjvYl11Cu+ggb0eSOlBRVkzy9Il0q95KMX5sjZpIiTWYEiOQEksgxQRRSCAlpi/RxVvpVbaMk6o3EGfkMYV5TKmYR8lBX6qxEmr7bQ7gKosfeXHDcfQ4k+CE/uTv/pnKPUsIyFhFaHkyXY1kulqSj5qt0rQTaJTRKXUWvDGLtMDuBA66ipD+51NWVkyvgx8DUHTy7fqwQ0REGj1fHzu9rptB/odXEbbnc+4132TLrB/5b8d7uPi88/D30SJvItJw1fgV6vdDhX+vX79+7NixgwULFrB161aqq6vp0KEDp59+Ov7+/jU9rUjDZbGQ59uauIo9HNi5Hk45joJgeQFt9r4PwP5uN9DpBIYbS/Nlb92PaqzEGPnM2bGNdtFajKapcVaWsXf6WXSr2kip6UvmpA85pe/RpuA4FwDTWU7Zrp9wbvsGv33zCCzPBMDlG46l8ziMLhPxSRxBjN330JGR0Z1g8C+/44szIWU5ZtIynOmbMP3CcYfEQ0hrCI3HCGuDERZPpTWAhT98jmPjuwyuXknLki0w71bK5/8fBX5tiKOSXUYiPUaeX1c/IhERkfplsRI29Q2cPw/A9eNjdCeJ7nuu57t/f0L8eU/RtWNHbycUETmsOv3Iwm63c/rpp3P66Zo0XJoXV1w/2LcHR+oSTPP6Yx66eWDFJ7Qyy9hrxnHS6RfVcUppcnz8yQroRFzpNop3L4VhKgg2Je6qCna/eDbdytdQajrYP/Yduh+1GPgbw+6Hf7dx0G0cmCYc3AiuKqwt+4HF+tcNBLWAbmdjdDsbn6Ps5gBOP/MCXBOmsGT9FtIXvsmQwjnEk41f6TYAknvcREd92CEiIk2JxYp90PXYe55L5hf302Lvx4ytXkDxB8NY0u56Bl1wH1b70X6DiojUP70jF6kDUX0mANCvag37skuO+bjSLXMB2BF+KhFBfnWSTZo2Z9xJAPhnrPFyEqlNZnUVO186l66lP1Nu+rBj5Bt0Hzj2xBozDIjrDa0HHFsx8ARYLQYj+vVg6h3TKLpmNa+2fobZrkF8ZBnPgLEX18k5RUREvC4wihYXv07xRd+R7OhEkFHO0H3PkfZkfzLWzvF8KCci0kDUag/BiooK1q5dS0ZGBmVlZUyaNIng4ODaPIVIo+DbcRTV2GhjyeLT9Wtod/rIvz7I5aRV3s8ABPYcV8cJpakK7zwUdr9Du8ptFJRVEeqvT6MbPVc1u185ny6FS6g07awb9DKDR5zp7VTHrHurMLpfeTUFZZdiYBDib/d2JBERkToV1H4ggfesYP1XL9J2w7PEu5Lh6wtJ+b49FQNupMPIizGs+n0oIt5VKz0EU1NTufTSSwkNDWXYsGGcd955XHbZZRw4cOAP+7355psMGDCAMWPGYOrTEWnKHIFkhvUFoGL7d8d0SPqmBQRQTq4ZTO+Tj20YoMj/CuowGIAuRjIb96Z6OY3UmNvFntcupGPuAipNG0v7vcDg0yd7O9UJCfX3UTFQRESaDcNipc/Zt1F27Uq+Dz6XUtNBfNUeOi69jcx/dWXdJ09RUVbs7Zgi0ozVuIfgqlWrGDduHPn5+X8o8h1uzrQzzzyTG2+8EafTybx58zS3oDRpji6nw/JVtMlbTmllNQGOo/93y1r3NXHAtoABDPV31E9IaXqC48izxxDuzODgtuXQo523E8mxKs2BrO24M7dRmLKJqoPbCCzcTXt3MVWmlfndn2b8mRd6O6WIiIgch5ZxLWl5+5vsS0ll3zfT6HtwFjHuLGK2Pk7+1umsbTWFjhNvJ6pFnLejikgzU6MegoWFhUyaNIm8vDxiYmJ4+eWX2bx58xH3j4qK4owzzgBg7ty5NTm1SIMX0dszj+AAYxsrd/51T63I9IUAmB1UKJeaKY7qA4CRutLLSeSo3C7Y/QNVH11M1ROJ8HQ7eGcClu/uJmzb+7TIX0eAu5hi04/ZHf7FuHMv93ZiEREROUGJ8a059bpnsd6+laUd7iHdiCaMYgYfeB3bywNYuegbb0cUkWamRj0Ep0+fTmZmJpGRkaxYsYL4+Pi/PGbMmDF89dVXrFq1qianFmnwjKhO5NtjCHNmcGD999DzuiPum5Oyg1auVKpNC50GTarHlNIU+bcbDOnfEle0CafLjV0rutaO8nzM3fMxQltDdBfwDTmuw91uk5S8MpJ3b8ax5SM6Zswh3JVzaNVet2mQakaxy2zNfktrnBGdCGrdgw5d+3JOx7hjXq1cREREGq6QkBCGXHg/1c47Wf/Du0SsfYF4Vwq9FlzC7IOPM+G8q7FY9DtfROpejQqCX3/9NYZhcPvttx9TMRCgW7duAOzdu7cmpxZp+AyD0jYjCdvzEQEpP2Ga1x7xD/r9K74gEtjh043uLVrUb05pciI6D4Ul0MvYxda0AnrHh3s7UpOQNOMq2mbNP3Q73xZNfmAHqiI6YYnpRmDLbpQY/uSUVpNT6iK7tJrMEifZpdXkFFfQOmcpk1jAcMuOQ23kmYF84RrK2sARBLfpRfeEOPrGhzEqJgir/hgQERFpsmx2H/qMuwrnyPPZ+cr5dCpaxvjtd/PhS6lMuvpBgnw1766I1K0aFQR3794NwLBhw475mNDQUACKiopqcmqRRiGqz0TY8xEDnGvZk1lMh5jDr7rt2O8pMhS11mIiUnOWmO5UGH4EU87eravpHa9h6DVVnrmH+KwfAThohhNr5BFWnUVYQRYULIPffcbV8UiN/NJR04WFXYEDyGg3maCe4zmvZSRX6k2/iIhIs2T3C6LTLbPZ9861JKZ8ykW5L/Lhc2mcfNULtIsO8nY8EWnCalQQLC8vByAgIOCYjykpKQHA19e3JqcWaRQcHUbgxE5rSzaz1q+iwxmn/mmfoqICOpVvBANanXxW/YeUpsdqIzukB60LVlGxbwWggmBN7Z/7PF0xWWntg/3SL9iYmUFZ2hYs2TsILNxJVNk+WrlS8TWqsGJixY0FN1Zch9qoCm6Ltd9FWPtcSJfgOLp48XpERESkAbHaSLz8DQ5+HU/suueYWvUZX7+URfLkVxjVvbW304lIE1WjgmBUVBRpaWmkpqbSq1evYzpm7dq1AMTGxtbk1CKNg08AWeF9aZm3ksrt8+AwBcHty+dwsuHkoBFNfMc+XggpTZEl/mQoWEVozjpM09T8czVQXVZAm5TPACjufTWnxodBfBicdAwlPdME0w1uFz42n7/eX0RERJonwyD2zIcoimpDwPe3MdFYwtKPp/Bq2n+4ekxvTSUiIrWuRjPNDxgwAIBvv/32mPZ3uVy89tprGIbBkCFDanJqkUbDr6tnZe12BcsornD+6XHnds//n/ToYaCijdSSqK6eqRy6uXZwIL/cy2kat53fvUIA5eyjJYNPO//4DjYMsFhBxUARERE5BsEDL8M9ZSaVFj+GWLYwbNmlXDdtFquT8rwdTUSamBoVBC+44AJM0+Stt95i/fr1R93X7XZz3XXXsW3bNgAuuuiimpxapNEI7zUegP7GDn7ekfKHxyqd1bQrWAFAaK8J9Z5Nmi6ftifjxqCtJZPNO3d5O06jZbqqidgyA4C97S7Bz1GjjvUiIiIif8ne+XQcV35DhU84XS3JTC+8ifmv38/tH60ms6jC2/FEpImoUUHwnHPOYdCgQVRWVjJ69GheeuklsrKyDj1uGAaZmZm899579O/fn7feegvDMBg7diwjRoyoaXaRxiGyA/k+sTiMatLXf/+HhzauXUaskUsFPiT00zxvUot8Q8j2SwQgf+dSL4dpvHYt+ZgYdyb5ZiB9J1zr7TgiIiLSXLTsi+8Ni6lqOwJfw8l99o+4dPs1XPPMu7yycC+V1a6/bkNE5ChqVBAE+PLLL+ncuTMFBQX8/e9/JzY29tBcVX379iUuLo7LLruMjRs3Ypom3bt354MPPqhxcJFGwzAoa+NZPTgodSGmaR56KG/DHACSgk/C4vD3Rjppwipj+wPgOLjGy0kasRWvALCxxdlEhIV5OYyIiIg0K6Gt8bn0S5j0EtU+wfSy7OMT414q5z/GhOcX8NPOrL9sQkTkSGpcEIyMjGTNmjXceOONOBwOTNM8tFVWVh763mazcc0117B8+XJCQ0NrIbpI4xHVxzMceIBrLTsOFgHgcpvEZCwGwNpJvQOl9oV2GgpAQvkWCsv/PH+lHF3KluV0qtyE07SSOO5Wb8cRERGR5sgwoM9F2G5ejdlpPD6Gi1ttnzO9+Daef3smL8zf7e2EItJI1cpkSP7+/kyfPp2HH36Y77//njVr1pCVlYXL5SIiIoI+ffpwxhlnEBcXVxunE2l0fNoPx4mdVkYOM9evokvcGDbt2kdPcycY0OaUs7wdUZqg4A6D4Vvobuxn5f4MhnVt7e1IjUr2D9OIB9YFjeDktu29HUdERESas6AYjCkfwNYvcH9zF53LUvnC50Ee/Gk/37a4mzN6xHo7oYg0MrU6O3pERARTp05l6tSptdmsSOPnE0B2RH/icldQteN7GD+G5FVf08cwSXckEhfRxtsJpSkKS6DIGk6wK4/0bStABcFjlpOeTI+C+WBA8Ii/ezuOiIiIiKe3YPe/YUkYDt/ejXXLpzxqm8Gtn4SSEHUbnWOCvZ1QRBqRGg8ZFpFj49d1LADtCldQWO4kIPlHAMrbjPZmLGnKDIOCyD4AuFNWejlM47Jz7jR8DBfb7V3p0n+Et+OIiIiI/CYgAs55A3ffy7AYJk8b05k+413yS6u8nUxEGpEaFQTHjh3LJ598gtOpualE/kpYr/EAnGRs54OFm+hfvQ6AuAGTvBlLmjjfhIEAtCjcQLXL7eU0jUNJSTFd0j4BoKq/VhYWERGRBsgwsEx4Dmf7M3AYTh6v+BePv/O53u+JyDGrUUFw3rx5TJkyhdjYWG699VY2btxYW7lEmp6I9uQ7WuJjuLCteJ4wo4RSSxB+vxRsROpCZJdhAPRiF9vTi7ycpnFYN/c1wikmw4imx+gLvR1HRERE5PAsVuznz6A8pj8hRhm3Zd7H9C8XejuViDQSNSoIRkdHY5omeXl5TJ8+nb59+9KvXz9efvllCgoKaimiSBNhGFS0GQXAZcY3AOTEDAFrrU7lKfIHlpa9qcJOpFHEzh360OavOKtdtNzxNgDpnS7BYrN7N5CIiIjI0dj98LvkE0qCEokz8hi38Wa+WL7F26lEpBGoUUEwLS2Nr776irPOOgubzYZpmqxfv56bb76ZuLg4LrzwQubPn19bWUUavcg+nmHDPoYLgPDeE7wZR5oDm4Oc4K4AlO9Z5uUw3ldU4eTL9Wm8uXQ/M1el8NWGNH7YlsnyPTmsT8nnx7kf085MoQxfuo6/0dtxRURERP6afziBV35FiU8UnSwHaPndVazfd9DbqUSkgatR1ySr1crEiROZOHEi2dnZvPfee7z99tts2bKFiooKZs6cycyZM4mPj+fyyy/nsssuIz4+vrayizQ69nbDcRp27KYTNwZB3c7wdiRpBsxWA2DbRoKy13k7ildUVrv4aUc2szemsWv7Jq7lc3pZMrBTjR0XNqo93xsuOlACBuyOPZNeQeHeji4iIiJybELj8b/8C8pfP50BbOfH9y4j85bPaREa4O1kItJA1doqw1FRUdx+++1s2rSJ1atXc9111xEaGoppmiQnJ/PPf/6TxMRExowZw6xZs6iq0gpI0gz5+FMSewoARRG9PSuEidSxyK6eeQS7VG8nvaDcy2nqh8ttsnxvDvd8uon+j83nnvcX0W/703xjvYPJtsX0t+yil2UfXS3JdLSkkWDJpJWRQ6BRQQUOEibc6e1LEBERETkultgeMOUDqrAx2vyZ1W/e6u1IItKA1cnkZf369aNfv348//zzfPHFF7z11lssWLAAt9vNggULWLBgAaGhoUydOpUrrriCPn361EUMkQYpbNj1MGsZocNv8HYUaSYcbT0L13SyHODrvSleTlN3TNNka3oRX21I4+uNB8koqsAHJxdb53GL75cEU+rZr/0YjD4Xgd0PLDaw+oDV7tksdnyDW+KrYr2IiIg0Qn4dR5J92otEzbuB04s+Y8f2W+ncpZu3Y4lIA1Snqxk4HA6mTJnClClTWLhwIVOnTiUzMxOA/Px8Xn75ZV5++WX69+/Pfffdx1lnnVWXcUQahs7j4aF8b6eQ5iQwilxHKyIqD5C3czmhoS28nehPTNMkq7iS5NwyknNLSckrIzm3jAqni86xwXSL82wtQ/0wDOMPxybnljJ7Qzpfbkhjb3bpry1yju8a7veZRURVuueuFt3htEcx2o2q34sTERERqUdRgy5k57I36VS6ltzvHocuH3g7kog0QHVaECwrK+PTTz9lxowZLF68GPD80QfQtWtXkpKSKCsrY/Xq1ZxzzjlMmDCBWbNm4evrW5exRESanbIW/YlIOYA9fQ2EjvdqlspqF1vTi1iXnM/61AL2ZJaQkldGudP1yx4moZTQ2sjGBycztrWmGH8AQv3tdIsLpntcCOEBPny3NYP1KQUAWHDTz5bMRTEpjHSvIDRvE1QBgTEw6h/QeypYrF65ZhEREZH65DP6fph9DgMKviU9aSdxbTt5O5KINDB1UhBctmwZM2bM4JNPPqGkpORQETAkJIQLLriAq666ir59+1JSUsJHH33EtGnT2L59O3PmzOHJJ5/k4YcfrotYIiLNVnCHwZDyJW3Lt5Dqqt+CYFZRBWuT81mXks+65HyS09MJchUSThERRhEnG3lMNrJobc8m0ZZDS7LwN8v+0EaaJZaNzng2VyawZV9bPtnTlnwC6WykcqVtG+MDd9O9eis+1cWQ88tBdn8YfAsMvAkcgfV6zSIiIiLelND3VLZ834fules5+PWjxN38vrcjiUgDU2sFwbS0NN59913efvtt9uzZA/zWG3Dw4MFcffXVTJ48GT8/v0PHBAYGcvXVV3PllVdy4YUXMmvWLD788EMVBEVEallIp6HwI/Qy9rCsyF2n53K63KxNymPzxtWYu36gfela2hq59DOKCaMYu8115N8+5u++D2zhmeOvKI2W7oO0tB5knHXloYcrDQcOs9Jzo+KXOx3B0GYwJAyF7udAUExdXKKIiIhIg+cadi/8cD49c76hKH03wXEdvB1JRBqQGhUEq6qq+PLLL5kxYwbz58/H7XYfKgJGRUVxySWXcNVVV9Gp09G7J1ssFm677TZmzZpFcnJyTSKJiMjhRHaizBJIgLuEqoLUYz7M5TYpLHeSX1ZFWaULm9XAbjWwWy3YrBbsFs/3ZU4XP29PJmvjPCIOLmIQGzjF+KWr3mFG6Zo+QRgBEeAf6SnahbWF0Daer2FtIDTes+gHQFkeHNz425axCXL3eIqB9gBoMxDaDoWEYRDbS8OCRURERICeg05n3U996Fu9nl2zH6Pbde94O5KINCA1KgjGxsZSUFAAeHoDWiwWxowZw1VXXcWkSZOw2+3H3FZEhGdFx+rq6ppEEhGRw7FYyAvvjX/OUqqy99D70R8JcNgIdNgIcNgIcFgJdNhwuU3yy5wUlFWRX1pFWWUFdrMaO9UEUEG4UUSEUUw4RYQbRUQaRYRTTLyRxUTLTnwMF/yy5ofTsJMXOYDAbqcT0Ko7BER6CoD+ERj245gr1j8c2o30bL+qLIbCNIho51kdWERERET+wDAMigfeAUsuolPG11Rm78MRlejtWCLSQNSoIJif71kptXXr1lx++eVcccUVxMfHn1Bb4eHhPPTQQzWJIyIiRxHcYTDkLOV622wmmivwqXRir6zGB89mN6rxwYkdFz44cRjV4Di+cxT4tsSZMJrwXuOxJw6lhU9A3VyMIwiiO9dN2yIiIiJNxKAR41m5tDcnmxtImf0oiVfO8HYkEWkgalQQPPvss7nqqqsYO3YshmHUKEhYWJgKgiIidSi422mw4ilijHxijPzjb8Big4AoTy+/gIhD37v9I3AHRGNrO5jQiHZQw98HIiIiIlI77FYLmX1vgbWXE5/6Fe7c/VgiErwdS0QagGMuCGZnZxMVFfWH+z777LNaDyQiInWkVX+qL57N+iXf06f/ydh8/MDmA9b/2Q53n9UHrIf/lWH5ZRMRERGRhmfkmIksW9OLwcZG0uc8Rtylb3o7kog0AMdcEIyNjeXkk09mwoQJTJw4ke7du9dlLhERqQNm/CDSwwro3WkcHMc8ryIiIiLSOAX52tnb7SYGb7uaFvs/h7x/QLh6CYo0d8fcqcPtdrNixQr+8Y9/0KtXLxISErj55puZN28eTqezLjOKiIiIiIiIyAk67fQzWezuiRU3ud897u04ItIAHHNBcOnSpdx777107doV0zRJTk7m5Zdf5owzziAiIoJzzjmHt99+m+zs7LrMKyIiIiIiIiLHISbEl/WJ1wMQuutTyNvn5UQi4m3HXBAcNGgQjz/+OJs3b2bfvn288MILnHrqqdjtdkpKSvjiiy+48soriY2N/cO+IiIiIiIiIuJdY8dOZKGrF1bclMx7HEzT25FExItOaB74tm3bcvPNN/P999+Tk5PDJ598wiWXXEJkZCRut5uff/6ZBx54gN69ex/aV0OLRURERERERLyjU0wQS1peAUDgjk8wZ14AhWleTiUi3lLjhSEDAwMPDRfOyMhg2bJlfxhanJKSoqHFIiIiIiIiIl42+tQJPOmcQpVpxdj5LZUv9Cf522m4XC5vRxORelbjguDvGYbBwIEDDw0X3r9/Py+++OIRhxYPHDiQJ554guTk5NqMISIiIiIiIiL/Y2C7CMzBt3Ku+0nWudvjcJfRZuVDbHl0INM++orFu7Jxutzejiki9aBWC4L/q02bNtx0001/GFp86aWXHhpavHLlSv7xj3/wzjvv1GUMERERERERkWbPMAzuG9eFWQ9cSfZ5s/m8xS2U4ksvdnLDjstZ/+5djHziO7amF3o7qojUsTotCP7er0OLZ8yYcWho8T333EO3bt0wDKO+YoiIiIiIiIg0a34+Vk7v3pK/Xf8I9ptXk9tyFD6Gi1tsX/B21R3cN+MbDhaWezumiNSheisI/t6vQ4ufeOIJNm3axL333uuNGCIiIiIiIiLNmk9EPBFXfQ6T38EdEE17SzrPVD7KzW/+RHGFFgYVaaq8UhD8X3a73dsRRERERERERJonw4BuZ2G55idcATF0tKRxV8E/ueX9nzWnoEgTZauthnJzc1mxYgX79u2juLj4mFYpevDBB2vr9CIiIiIiIiJSEyGtsF78Ga43x3KycwfZyY/xwOdP8cS5vTTVl0gTU+OCYEZGBrfffjufffYZ1dXVx3WsCoIiIiIiIiIiDUhMd6wXfID7/XOYwM9kbHySlyIe5aZRHermfAWpkLcPEoZ5eiqKSL2o0ZDh7OxsBg0axKxZs3A6nZimeVybiIiIiIiIiDQwicOxnPUKAFfZviX/x2l8uT6t9s+zZz68MgjePRPeHg+ZW2v/HCJyWDUqCD700EMkJSVhmiaTJ09mwYIF5Obm4nK5cLvdf7mJiIiIiIiISAPUczKc+k8AHrC/z4LPXmXF3tzaa3/1G/DBeVBZ5LmdvAxeHQrf3Q8VRbV3HhE5rBoNGZ4zZw6GYXDxxRfz9ttv11IkEREREREREfG6wbdgFh7AWP06T1tf5rr3wvmy+2gCfW0EOGwEOWwE+toIdNgI9rMzoG04fj7Wo7fpdsG8f8DPL3tu95oKw+6E+Q/B9q/h55dgy6dw2mPQY7KGEYvUkRoVBLOzswG44ooraiWMiIiIiIiIiDQQhoFxxlO4ig7i2DmHF8x/s37j5/gaVTiowhcnvlTha1Rhp5pNto60G3MNkf3OArvvn9urLIbProJd33luj3qAzF43smJfHkXxjxFqO40hu/9NeEkqfH41O+dO5/2Imxg8aBind4vRwiYitahGBcG4uDiSkpIICAiorTwiIiIiIiIi0lBYrFjPfQPXO5MIPrCS4dZNR9w1wrUWvruW6p/uxtZzMvSeCnF9Pb38Cg/Ah1MgczPYfOGsV/iOQdz13GKKK39doDQCHx7jKus33Gz7gk6Vm3go7Trmz+rHP8PPZOyZF3BKu6j6uW6RJq5GBcFhw4aRlJTE5s2b6devX21lEhEREREREZGGwu6H9ZIvYec34KryFPTsfr99tfuRW1zOvM/eZHjFfOIq8zxzBK5+A6I6Q7ezYc0MKMmAgGiqz/+AJzcF8sbStQB0bBFIu6hAAh02gnzt2H278iWXMmTv87TO+IGx1tWMLVxN6jsv8ln4eHqMv5GOHTp692ci0sjVqCB45513MnPmTJ599lmmTJmCr+9hugSLiIiIiIiISOPm4w89zj3iwxGxMP62flz3ziqM5CWcb1vMeNsarNk7YOETnp2iu5I18V1umJPNmuT9AFwzLJG7Tu+E3fq/a552hDGDIWs7ZSvewNg0k9Zk07rgbarff5dNwYOJHXkdUb3HgaVG66WKNEs1+l/TrVs33nrrLXbu3Mnpp5/Orl27aiuXiIiIiIiIiDQiwb52Zlx5CqHdT+PvVTfSu+wlFnd6ANoMge7n8vOIDznj7STWJOcT5LDx6kX9uH9cl8MUA38nugv+k57F7949ZJ/6Inv8emAz3PQsXkLU7AvZ98pkz0IlInJcatRDEOCCCy6gQ4cOjB8/nq5du9KzZ086duyIv7//UY8zDIM333yzpqcXERERERERkQbCYbMyfUofWgT58tay/VyysQtXDhlHqJ+d597bhmlCl9hgXrmwL20jj2M9ArsfUUMuJWrIpezcvIp937/EqOI5JGbPZ/VrN9D/2le16IjIcahxQXDXrl3cfvvt5OTkALBx40Y2btx41GNM01RBUERERERERKQJslgMHpjQhZgQB49/s4M3l+4/9Nj5/Vvzz0nd8LVbT7j9Tj0G0KnHAOZ/8jKnbr2PkzJmMuf1Foy76p9YLCoKihyLGhUEU1JSGDZsGNnZ2ZimCUBwcDAhISFYNIZfREREREREpFkyDINrhrUjOsiXOz/ZiNVi8OhZ3Tmvf+taO8epk29gfXUWfXY+z7i0F3nzzSguu+LGow9BFhGghgXBRx55hKysLCwWC3feeSc33HADbdq0qa1sIiIiIiIiItKIndWnJX3jw/CxWYgJqf2FSPtMeYikd9JpmzSLiw48wlNvhnHn5RfUqAeiSHNQo7L5jz/+iGEY3HLLLTz11FMqBoqIiIiIiIjIH8RH+NdJMRAAw6DtxS+TEzsMP6OKa9Pu4543vqaksrpuzifSRNSoIJiZmQnAOeecUythRERERERERESOi9VG5GUfUhrWlSijiJsP3se1r80nu7jy0PRmIvJHNRoyHBsbS1JSEj4+PrWVR0RERERERETk+DiCCLj8c6r+O5L2pencnP0wg/9VgdOw42+34udjI8Bhxc9uxd/HSo+WIdw3rouGFkuzVaMegmPGjAFg9erVtRJGREREREREROSEBMfic8lnuOyBnGLZzgc+/+IKy1wSnLvJLykjObeMHRnFrEsp4J0Vydz/xWb1IJRmq0YFwTvvvJOAgACeeuop8vLyaiuTiIiIiIiIiMjxa9EN65T3MC02TrLs4gH7B8xx/INdQdezsd1/+enkdbw6vBqHUc3n69J4d0WytxOLeEWNCoLt27fniy++oLi4mMGDB/PDDz/UVi4RERERERERkePXbhTG9StgzCPQcSw4QrA6SwhJW0TCxmcYu/ISNgTewmmW1Tw6Zxurk9TBSZqfGs0hOGrUKAAiIyPZuXMnY8eOJTQ0lA4dOuDv73/UYw3D4Mcff6zJ6UVERERERERE/iyqo2cbfAu4XZC5FZKXQ/JSSF6OX1kur/k8z/vVo7ntfZPP/j6aFsF1tBKySANUo4LgwoULMQzj0G3TNMnPz2fVqlVHPMYwDEzT/MNxIiIiIiIiIiJ1wmKF2J6e7ZTroLoKFjwKy1/kItuPnFy1g6dm3MuTN16Ej61GAylFGo0aFQSHDRumwp6IiIiIiIiINB42HzjtUWg3iurPrqVDWRpP5N3G/Ld2Me6qR8BSz0VB04TyfPANrf9zS7NV4x6CIiIiIiIiIiKNTruR2G5cQfaHVxOV9iPj0qdz8JV1xF4yA4JaHFsbFUWQvh7S1kDGZghvBwNvBP/wYzs+ZSV8d4+nDasDwtpCeOIvW8IvWzvP/eqQJbWoRgVBEREREREREZFGKyCCqKs+Y8EHTzJo97PEZi+jenp/bDHdITDaswVEQ2AUBLbAsPnTNmcB1q+/hYPrIXsnYP6xzVWv4R54E/vbX8KmbBebDxSxJa2Qogon0cG+xAQ7aO9byGlpL9P24Le/HeeqhJydnu1/uMPbYekxGXpMhsj2dfszkWZBBUERERERERERab4MgxFT7+WhNztwQeojdK1KhpTlh93VBvQCSP3tvqrAVuSH9SDLvwNRKd8SU74by8LHCf1pOluqJ/GB61Qq8QEgKSOHa6xzudg2Gz+jCrdpMNM1gmnV5+IwqmhjZNHWyKCNkXloa2tk4MjbC4ue9GyxvaHnedDtbxAcW9c/HWmiVBAUERERERERkWbNYjG46+JJnDM9lJC8jbQwCog0CokyCoikkEijkFhrEeGWEpLdUWwwO7LamcBGd3tyKkIgx9OOwQDGW1Zyu+0TEi0ZPGB/n7/7z2NPlxvxCwymzbqnCKjIAGCXowfTHVexoqwleaVV2K0WCmwt2WGz4GO14GOzYLdaKC8ppF/FCs6yLmOYdTPWgxvg4Ab4/v8gYSiM/D+IP8VrPztpnFQQFBEREREREZFmL9jXzgfXDuGjVW1IzS9jT2E5iwsqSC8op9LpBuefj7FbDWIDHUQE+hAR4CAhMoAeLftQHXs77vSvsSx+ipCiNPpteui3g0Jaw5hH6NjtbKYfw7yAZVXV/GdBR65ZMpQgZyFn2ldxVegaWhVvgv2L4eBGuHUz+IbU4k9DmrpjKghaLBYsFgubNm2ia9eutRpg69at9OjRA4vFQnV1da22LSIiIiIiIiJyrKKDfbnl1A5/uM80TfLLnKQXlJOeX8qW9asZP3o4MWEBBPvaMI5U1Iu7FHqdD2vegiXPQFUZDL0dBt0Mdr9jzuTvY+PusZ05t18rHv56G2/vCubt7FPpG1zEu45/E1i8D1a+BsPvqsmlSzNzzD0ETdP8651qoK7bFxERERERERE5XoZhEB7gQ3iAD52i/SnfC4lRAdjt9r8+2O4LA2+Ak64EVxU4gk44R2JUIO9cfhLfb83k0Tnb+P/27jssiqttA/i9S+8goIgQC/YWjV2MvWKLxhhLbLGXFPNGjYkK+iYxxkSTaCwxisZYYsGK3VhQsYAm0RiNBRFErPS6y57vDz7mBdldFnZxYff+Xdde17hzzplnlscBHs7MuZwIfCoPwA/Wy4Hw5UCriYCtc4nHJ/MiN3YAREREREREREQmzdJGr2JgHplMhp4NvXDsow54v3NNHBCtcVvlDWQmAhdX6x8nmY1iPUNwzJgxcHBwMGgAaWlpBh2PiIiIiIiIiMiU2Vlb4KPudfAkNQs/RAzAD9Y/QoT/CFmrSQYpPJLpK1ZB8NKlS6UVBxERERERERERFcOHXWuj0xV/3FGFwC/jIXDxJ+D1/xg7LCoHdCoItm/fXvNDMk3U/fv3ERoaipMnT+KPP/5AbGwscnJy4OHhgWbNmmHIkCF46623YGmpW03177//xrJly3Ds2DE8ePAAjo6OqFevHoYPH46xY8fqPA4REREREREREQBUcrbFu+1qYtmpAfjOegXEueWQtZzAWYJUJJ2qUCdPnizlMMqWefPm4fPPP1e70MmDBw/w4MED7N27F0uWLMHOnTvxyiuvaB1v7dq1mDp1KrKysqT3MjMzERYWhrCwMKxfvx779++Hu7u7wc+FiIiIiIiIiEzXxA410PlCB9xR7vr/WYJrclczJtKCi4qoERcXByEEHBwc8M477yA4OBhnzpxBREQENm7ciBYtWgAAIiIi0LVrV6Smpmoc6/Dhw5gwYQKysrJQqVIl/PDDD7hw4QIOHjyIgQMHAgDOnz+PgQMHQqVSvZTzIyIiIiIiIiLT4GRrhSld6mC58g0AgDi3DMjSXKcgAlgQVMvd3R2LFi3Cw4cPsXHjRowePRr+/v5o1qwZ3nnnHYSHh2Pw4MEAgFu3bmHp0qVqx1EqlZg2bRpUKhWcnZ1x9uxZvPfee2jZsiV69uyJnTt3YsqUKQCA06dP49dff31p50hEREREREREpmF4q6r4w6UrolSVIMt4Dlz62dghURnHgqAaixYtwsyZM+HkpP6eewsLC6xYsQLW1tYAgB07dqhtt2vXLty+fRsAMHv2bPj5+RVqs3jxYri5uUnbRERERERERETFYW0px/Se9bFcOQAAoDq7DMhOM3JUVJaxIFhC7u7uaNy4MQDgzp07atvs3r1b2h49erTaNvb29tJsw2vXruHWrVsGjZOIiIiIiIiITF+fRpVx2ysA91SVIM94Clxaa+yQqAxjQVAPeYuEyOXqP8awsDAAQJ06deDl5aVxnA4dOkjbZ86cMWCERERERERERGQO5HIZZvVugOU5bwAAcs58x1mCpBELgiX0+PFj/PPPPwCAunXrFtqfmpqK2NhYjfvzy78/b0wiIiIiIiIiouJo6+eB534DEK2qCIuMZ0DEOmOHRGWUpbEDKK8WL14MpVIJANItv/nFxsZCCAEA8PHx0TqWr6+vtB0TE2PAKImIiIiIiIjInMzo1QA/Ln8DX8t/gjgyF7JTXwNW9oCVHWDtkLttbQ84eQP+HwAVtU9iItPEgmAJXLhwAd999x2A3GJf3krB+aWkpEjbjo6OWsdzcHCQtlNTNS8NnpWVJd2mDADJyckAAIVCAYVCoVPsVDx5nys/XzIVzGkyNcxpMjXMaTI1zGkyNeUhp2t62CGn0WD8ff0wGsijgazk3Jca4uo2qFqMh+r1mYCN+oVVqfwoTl6yIFhMjx49wqBBg6BUKiGTybBhwwbY29sXapeZmSlt561GrImNjY20nZGRobHdwoULMX/+/ELvHzlyRG0MZDhHjx41dghEBsWcJlPDnCZTw5wmU8OcJlNT1nO6kRwYqPgcLiIZ9rJM2CMLdsiCvSxL2u5lcRE9EAGLCyuRHbkZ16sMQaxbW0AmM3b4VELp6ek6ty3XBUGlUgkrKyu9xwkODta4CnB+KSkp6N27t/RswC+//BKdO3dW29bW1lbazs7O1jpu/ll/dnZ2GtvNnj0bH330kfTv5ORk+Pr6onv37nB2di4yfio+hUKBo0ePolu3bgbJNSJjY06TqWFOk6lhTpOpYU6TqSlPOV2/eSKu3E+ESgAqIaBSCWlbCGBtVF9svn8SgZYbUEMZj2bRq9FYcRnotQjwamzs8KkE8u4k1UW5Lgi+TJmZmejfvz8iIyMBAB999BE++eQTje2dnP431VbbbcAAkJb2v1V/tN1ebGNjU2A2YR4rK6syfyEq7/gZk6lhTpOpYU6TqWFOk6lhTpOpKQ853bKGJ1rW8NS4XwiBM7drYcahNmgRvxXvWe6CQ9wlqNZ2RU7TUbDqsQCw5eSj8qQ4OVmuC4KWlpYGWZW3cuXKWvcrlUoMHjwYJ06cAACMGzcO3377rdY++RcSyZtRqEn+hUTyLzBCRERERERERFQaZDIZXq/liXY1O+Lo9XqYeLgHBif8hH4W4ZBfCUZS7F9wmRCauxgJmRydCoILFiwolYPPmzdP7zHq1i3d1XBUKhVGjBiBffv2AQDefvttrF69ush+jo6O8PX1RUxMDG7cuKG1bf799erV0y9gIiIiIiIiIiIdyWQydG/gha71BmDfX60w/dBOBGUshMuTSCRtHAGXUVsBi3I9n4zU0OkrGhQUBFkpPFTSEAXB0jZx4kRs3boVANCnTx9s3LgRcrlcp77t2rXDli1bcPPmTcTHx8PLy0ttu1OnTknb/v7++gdNRERERERERFQMcrkM/ZtUQa+G0/D1Tw6Y8WgWXO4fRcrO9+D01gouNmJidKtsIffecm2vkrQp6z766CP8/PPPAIAuXbpgx44dxbof+4033pC2169fr7ZNeno6tm3bBgCoX78+ateuXeJ4iYiIiIiIiIj0YW0px4djR2Gx80zkCBmcrm9G+uHSuXOUjEengqBKpdL4unv3Llq0aAEhBHr16oXt27cjOjoamZmZyMzMRHR0NHbs2IFevXpBCIEWLVogKioKKpWqtM9NL0FBQVi6dCkAoG3bttizZ4/aBT20GTBgAPz8/AAACxcuxJ07dwq1mTFjBhISEqRtIiIiIiIiIiJjcrSxxIQJ7+Nb60kAAPvzS5AdvsbIUZEh6XUTeFJSErp3746oqCj88ssveOeddwq18fX1ha+vLwYOHIhNmzZh1KhR6Nq1KyIiIuDi4qLP4UvNsmXLMH/+fABAlSpV8PXXXyMqKkprnzp16hSaPWhlZYUffvgBffv2RXJyMvz9/TFnzhy0bNkSCQkJWLNmDXbu3Akg9/biESNGlM4JEREREREREREVQ0UnWwwcPwcrVj7DFLENlodnIMfRAxaNBhg7NDIAvQqCS5cuxe3btzFp0iS1xcAXDR8+HGfOnMHq1avx7bffltpiJfrKK9IBwIMHD9CuXbsi+0RFRaFatWqF3g8ICMCqVaswbdo0PHr0CO+9916hNi1btsSuXbtgYWGhV9xERERERERERIZSs6IjEkd+hS3BzzFUfgzKkPEQju6QVW9v7NBITzo/Q1CdnTt3QiaT4a233tK5z+DBgwEAISEh+hy6XBk/fjwiIyMxfvx41KhRA7a2tnB3d0e7du2wcuVKnD17Fh4eHsYOk4iIiIiIiIiogObV3eH21vc4mNMClkKB7F+HAPFXjR0W6UmvGYL37t0DgGLd+pvXNjo6Wp9Dl6qTJ08afMyGDRvip59+Mvi4RERERERERESlqWcjH2xMWI4Lx0ahFW4gafO7cJl+kSsPl2N6zRDMe2be1au6V4bz2hZntV4iIiIiIiIiIjKeEe3rIrzZ98gQ1nBJ/hdZUeHGDon0oFdB8NVXX4UQAosWLUJ6enqR7dPT07Fo0SLIZDI0btxYn0MTEREREREREdFLNK1PS/xumbvOwoNjPxo5GtKHXgXBcePGAQBu3ryJjh074o8//tDY9s8//0SnTp1w48YNAMCECRP0OTQREREREREREb1ElhZyqF4bAwDwiTsMVdpzI0dEJaXXMwSHDx+OXbt2ISQkBJGRkWjWrBkaNWqEFi1aoGLFipDJZHj06BEuXbpU4LbigQMHYtiwYXoHT0REREREREREL0+nLr1w41JV1EU0bh5dgzpvzDJ2SFQCehUEAeC3337Dhx9+iJUrV0KlUuGvv/5S+0xBIQRkMhmmTZuGJUuW6HtYIiIiIiIiIiJ6yRxtrXCv2mDUvbcYDlc3Av1ncnGRckivW4YBwMLCAsuWLcOVK1cwefJk1KpVC0BuATDvVbNmTUyePBlXrlzBDz/8AEtLveuQRERERERERERkBE0CJiBN2MAnJwZ3Io4YOxwqAYNV5ho1aoQff8x9oGRWVhYSExMhhICbmxtsbGwMdRgiIiIiIiIiIjIir4oVEe7WFW0SQ/H89E/wa9HD2CFRMek9Q1AdGxsbVKpUCV5eXiwGEhERERERERGZmIqdJgMAGiefxMOHsUaOhoqrVAqCRERERERERERkuvxefR13rWrBRqbE36ErjR0OFRMLgkREREREREREVGzZTUYBAGrG7ERyRraRo6HiMEhB8J9//sH06dPRvHlzVKhQAVZWVrCwsND64sIiRERERERERETlV+0uo5EGO1STPUTY4RBjh0PFoHdVbsmSJZg9ezaUSiWEEIaIiYiIiIiIiIiIyji5rRPiXumLWve3weavDVD0HQwrC96MWh7oVRA8dOgQPv74YwCATCZD69at0axZM1SoUAFyOROAiIiIiIiIiMiUvdJtKrB2G9rnXMCxiGvo1aqxsUMiHehVEPzuu+8AAG5ubti7dy/8/f0NERMREREREREREZUDNr5NEO/YAF6pf+PRqbUQLb+DTCYzdlhUBL2m8UVEREAmk2HevHksBhIRERERERERmSFH//EAgE6pB3Dh7lMjR0O60KsgmJ6eDgBo166dQYIhIiIiIiIiIqLyxbHZYGTIHVFV/hhnj+zgGhPlgF4FwSpVqgAAsrO5tDQRERERERERkVmydkB2g7cAAK/G/YbA3X9AmaMyclCkjV4Fwb59+wIAzp49a5BgiIiIiIiIiIio/HFpNwEA0NXiCt77oz+OfDceqTFXjRwVaaJXQfDjjz+Gm5sbvv32W8THxxsqJiIiIiIiIiIiKk8q1Qd6fY0sG3d4ypIQkLIDjmvbIWtlR+DSWiAj0dgRUj56FQS9vb2xZ88e5OTkoG3btjhw4ICh4iIiIiIiIiIiovKk1UTYzLyJ6O4/46SsJRTCAjaPrgChHwHf1gF2TWJhsIyw1Kdz586dAQAVKlTAv//+i759+8LV1RW1atWCvb291r4ymQzHjx/X5/BERERERERERFSWWFihatu3YN2gD0auO4b6Tw/ibctTqK2MBf7cAjy9BYzYBdg6GztSs6ZXQfDkyZOQyWTSv4UQSEhIwMWLFzX2kclkEEIU6EdERERERERERKajsosdfp7SCx9s9UL3fwLQUnYDvzj8ANsHEcCmQcA7OwEbJ2OHabb0Kgi2b9+ehT0iIiIiIiIiIirEwcYSq0c0xxeh/2DdWRneTJuJ7XYLYR9zAdj0FjB8B2DjaOwwzZLeMwSJiIiIiIiIiIjUsZDLMK9vfVT3dEDQXhmGZMzCFtuFcLgfDmx+Gxi+DbB2MHaYZkevRUWIiIiIiIiIiIiKMqJ1VWx8tyXu29XFO5mzkAY7IPoMsGUIkJ1u7PDMDguCRERERERERERU6trW9MCeqf5I8WyKEVmzkCZsgajTwNZhgCLT2OGZFYMXBB88eIDIyEiEhYUhIyPD0MMTEREREREREVE5VdXdAbumtIVrnXYYnT0T6cIGuHsCYutwQJll7PDMhkEKgikpKQgMDISvry9eeeUVtGzZEh07dkRUVFSBdlu3bsXgwYMxfvx4QxyWiIiIiIiIiIjKGSdbK6wZ2RyvtQ/Au4oZyBDWkN05BuUm3j78sui1qAgA3L59G7169cLdu3chhJDeV7f6cJs2bTBixAioVCqMGjUK7dq10/fwRERERERERERUzljIZZjdqx5CKjlh4i5glXwx7KN+R+aGgbAduR2wcTJ2iCZNrxmCWVlZ6N27N+7cuQN7e3vMnDkT+/fv19i+atWq6NSpEwBg7969+hyaiIiIiIiIiIjKuYGv+WD6+HF433IukoUdbB+EI2VNHyAjwdihmTS9CoKrVq3CrVu34ODggLCwMHz11VcICAjQ2qdXr14QQiA8PFyfQxMRERERERERkQlo+oob/vv+OAS6LkSCcITT0z+QsKIHkPrE2KGZLL0KgiEhIZDJZPjggw/QpEkTnfo0btwYAHDr1i19Dk1ERERERERERCaisosdvpw6Eiur/4AnwgVuKTfxZFkXKBMfGDs0k6RXQfD69esAgO7du+vcx93dHQCQmJioz6GJiIiIiIiIiMiE2FlbYPaogTjcYh3iRAV4ZkXj2bIuSH5429ihmRy9CoIpKSkAABcXF537ZGZmAgCsrKz0OTQREREREREREZkYmUyGd/p0xa3e23FfVEKlnIfI+KkH7t/6y9ihmRS9CoJ5s/0ePXqkc5+rV68CACpVqqTPoYmIiIiIiIiIyER1aNkcWSP24Z6sCiqJp3gWMtPYIZkUvQqCec8NPH78uM591q1bB5lMhlatWulzaCIiIiIiIiIiMmG1atZBapevAAAVsmKMHI1p0asgOHDgQAghsHr1akRHRxfZfv78+bhw4QIA4O2339bn0EREREREREREZOIc3asAAFxUicYNxMToVRAcPXo06tWrh5SUFHTo0AGhoaEQQkj7ZTIZVCoVwsLC0LdvXyxYsAAymQwtWrRAv3799A6eiIiIiIiIiIhMl7N7ZQCAK1KhyM4ycjSmw1KfzhYWFti7dy/8/f1x//599OvXD/b29tL+vn374tGjR0hPTwcACCHg7e2N7du36xc1ERERERERERGZPBf3SlAKOSxlKiQ9i4dH5arGDskk6DVDEAD8/Pzwxx9/oHfv3hBCIC0tDUBu8e/u3btIS0uDEAJCCHTv3h2XLl2Cr6+v3oETEREREREREZFps7CwQKLMGQCQ/DTOyNGYDr1mCObx8vLCvn378Pfff2PPnj2IiIjA48ePkZOTA3d3dzRt2hT9+/dH8+bNDXE4IiIiIiIiIiIyE0lyV3ioEpGeEG/sUEyGXgXB+/fvAwAcHR1RoUIFNGjQAA0aNDBIYEREREREREREROmWbkD2PWQlsSBoKHrdMlytWjVUr14dW7duNVQ8REREREREREREkkybCgAAZfITI0diOvQqCNrZ2QEAWrRoYZBgiIiIiIiIiIiI8lPaeeRupD02biAmRK+CYJUqVQAAOTk5BgmGiIiIiIiIiIgoP5W9JwDAIuOpkSMxHXoVBLt37w4AOHPmjEGCISIiIiIiIiIiyk/umFsQtM58buRITIdeBcEPPvgAdnZ2+Oabb/DgwQNDxURERERERERERAQAsHbxAgDYK54ZORLToVdBsFatWti8eTPS09PRunVrbN68GdnZ2YaKjYiIiIiIiIiIzJydWyUAgFNOonEDMSGWujaUy+WQy+X466+/UL9+fQBA586dAQCenp6IiorCiBEjMHbsWNSqVQtubm6wsLDQOJ5MJsPx48f1DJ8MQQgBhUIBlUpl7FDKHIVCAUtLS2RmZvJZmWQSmNNkajTltFwuh6WlJeRyvf72SURERERlgKNbZQCAqyoJEAKQyYwcUfmnc0EQyC0c5Xfy5EnI8n0RhBDIysrCtWvXNI4hk8kghCjQj4wjJycHT58+RUpKChQKhbHDKZOEEPDy8kJMTAxzlkwCc5pMjbaclsvlsLe3h7OzM1xcXIwUIRERERHpy8XTGwBgI1MgMy0Rto5uRo6o/CtWQfBF7du35y+U5VROTg5iYmKQlZUFFxcXODo6wsLCgl/PF6hUKqSmpsLR0ZGzTMgkMKfJ1KjLaSEEVCoVMjMzkZqairi4OGRkZKBSpUr8PkdERERUDjk7OSFV2MFRloHEJ3HwYkFQb3oVBE+ePGmgMOhle/r0KbKysvDKK6/Azs7O2OGUWSqVCtnZ2bC1tWXxhEwCc5pMjbacdnBwgLu7OxISEhAfHw9ra2tUqFDBSJESERERUUnJZDIkyFzgiAykPXsIVG9g7JDKPf42aIaEEEhJSYGLiwuLgUREZPLc3Nzg5OSExMTEQo8/ISIiIqLyIdXSFQCQnhhv3EBMBAuCZkihUEChUMDR0dHYoRAREb0ULi4uyMrKglKpNHYoRERERFQC6Va5twlnJz02ciSmodgFQT57p/zLW01Y2yrQREREpsTSMvcpKVxdm4iIiKh8yrLxAACoUh4ZORLTUOxnCHbv3h1WVlZ6H1gmk+HOnTt6j0Mlx+IuERGZC37PIyIiIirfcuzcgURAnv7U2KGYhGIXBB88eGCQA/MHcyIiIiIiIiIi0olDRQCAReYzIwdiGopdEPT29jbIDEEiIiIiIiIiIiJdWDp5AgBsszhD0BCKXRA8cuQI6tevXxqxEBERERERERERFWLt6gUAcFAmGDkS08BVhomIiIiIiIiIqEyzd8stCDrnJBo3EBPBgiARkRoymQwymQxBQUGF9q1fv17af+/evZceGxERERERkblxdq8MAHBBKoQy28jRlH8sCBKVgnv37kkFI31e6mRlZWHz5s0YM2YMGjRoAE9PT1hZWcHV1RW1a9fGW2+9he+//x6xsbEv+ayJXp6tW7eiR48eqFy5MmxtbVGtWjWMGDEC58+fN9gxsrOzsXbtWvTs2ROVK1eGjY0NHB0dUadOHbz77rsGPZYxfPHFF5DJZGjQoIGxQyEiIiIiKpKbRyUoRW4ZKz3xkZGjKf+K/QxBIjKeX375BZ999pnaYl9SUhKSkpJw69Yt7NixA9OnT0e/fv3wxRdf8Bd+MhmZmZl46623sH///gLvR0dHIzo6Gps3b0ZQUBDmzp2r13FiYmLQu3dvXL16tcD72dnZ+Pfff/Hvv/8iODgY06dPx7fffquxgF+W5X2Gffv2NXIkRERERERFs7exxhM4wxOJSHoaBwcPX2OHVK6xIEhUCqpUqVKokJBfjx49EBcXB29vbxw+fLjI8YQQeP/997F8+XLpvZ49e6JXr16oX78+KlSogLS0NMTFxeHUqVPYv38/YmJisGfPHnh5eWHVqlUGOS/KNXr0aIwePdrYYZilsWPHSoWsTp064YMPPoC3tzeuXr2KL7/8Enfu3MG8efNQuXJljBs3rkTHUCqVBYqBjRs3xkcffYQ6deogJSUFZ86cwbfffou0tDQsXboUlStXxowZMwx2ji/D48ePcfHiRQAsCBIRERFR+ZEkd4WnSETq83hjh1LusSBIVAqsrKzQsGFDrft1aZdnwYIFUjGwRo0a2LZtG5o1a6a27dtvv40ffvgBmzdvxpw5c0oQPVHZdOrUKWzevBlAbhFr165dsLCwAAC0aNEC/fr1Q7NmzXD//n3MnDkTgwYNgqura7GPs2fPHqkY2KZNG4SFhUnHAYBu3bqhX79+aNOmDRQKBRYuXIjp06fD0rL8fEsNDQ2FSqWCh4cH2rRpY+xwiIiIiIh0kmrpCiiArCTeMqwvnZ8hGBUVhbt376J27dqlGQ8RvSAyMhILFiwAAHh7e+PcuXMai4F5LC0tMXLkSPz111/o3bv3ywiTqNR9/fXXAAALCwusWLGiQJEOADw8PLBo0SIAQEJCAtauXVui45w9e1banj17dqHjAECzZs3Qp08f6Vg3btwo0bGMZd++fQCAXr16QS7n44SJiIiIqHzIsHYHAChZENSbzr8FVK1aFVWrVi1XMyCITMGXX34JlUoFAPjxxx9RqVIlnfu6urqW6u2AL662m52djSVLlqB58+ZwcXFBhQoV0LFjR4SGhhbol5KSgq+//hpNmzaFs7MzXF1d0a1bNxw/flyn48bGxmL27Nl47bXX4ObmBltbW7zyyit4++23ceLECZ3G2LRpEzp27Ag3Nzc4OjqiYcOGCAwMRGJiYrHP+0UqlQq///47Pv74Y/j7+8PDw0Na+KVJkyb4+OOPcf/+fa3H6NixI2QyGTp27AgAePDgAT766CPUrFkTdnZ2cHd3R48ePXDw4EGdzre8S01NlfKjW7du8PHxUdtu4MCBcHZ2BgCEhISU6FjZ2f9bsaxGjRoa2/n5+UnbWVlZJTrWi6tZnzhxAm+88Qa8vb1hZ2eHevXq4b///S/S0tIK9Dtw4AACAgKkdvXr18fChQsLxK5JdnY2jh49CuB/twsrFAp4eXlBJpOhV69eRY5x7do1KfYvv/xS6zn9/vvveOutt+Dr6wsrKytUq1atyPGJiIiIiNRR2OYWBEXaEyNHYgIElVtJSUkCgEhKSipWv4yMDHH9+nWRkZFRSpGZjpycHJGQkCBycnIMOm7VqlUFAFG1alWt7RITE4VcLhcARPXq1YVKpTJoHPoKDg4WAAQA8eeff4pWrVpJ/37x9e233wohhIiOjhYNGjRQ20Ymk4mNGzdqPebPP/8s7OzsNB4HgBg7dqxQKBRq+ysUCvHmm29q7Ovn5yfu3r0r/TswMFDreUdFRRXaHxgYqDU+AMLe3l6EhIRoPM8OHToIAKJDhw4iLCxMuLu7axxr8eLFWj+z/Eorp0vb8ePHpfNduHCh1rbdu3cXAISlpaXIzs4u9rG+//576Vh79+7V2G7AgAFS3hb3Opwnf54tXLhQyGQytV/jtm3bipSUFKFSqcQHH3ygMRd69uwplEql1mMeOnRIABBWVlYF4p4xY4YAIORyuYiNjdU6xvTp0wUAYWFhUaht/nP69NNPC8VY1HWvuHTNaX7vo/IiOztb7N69u0TXL6KyiDlNpoY5bVwnfv5EiEBncfmHIcYOpUwqTp2I9wkRlWFhYWHS7MCePXuW6ZVMJ0yYgMjISEyZMgVHjx5FREQEfv75Z1SuXBkAMGPGDFy7dg0DBw7E3bt38cknn+DkyZO4dOkSvvvuO7i4uEAIgSlTpuDx48dqj7Fu3TqMGzcOGRkZaNiwIZYtW4YzZ87g8uXL2LlzJwICAgAAa9euxaxZs9SOMX36dOzcuRMAUKdOHaxduxaXLl3CsWPHMHHiRERFReHtt9/W67NQKpWoXLkypkyZgo0bN+Ls2bOIjIzE7t27MXPmTDg6OiI9PR3Dhg3DP//8o3Wshw8fYsCAAbCwsMBXX32FM2fO4OLFi1iyZIn0fLzZs2fj77//1ivmsi7/51S3bl2tbfP2K5VK3Lp1q9jHGjp0qDTLcNGiRcjJySnU5sqVK9LM1yFDhkjtS+rgwYOYPXs2Wrdujc2bNyMiIgKHDh2SZuudO3cOX331FZYuXYrvv/8evXr1ws6dOxEZGYk9e/agdevWAIBDhw5hzZo1Wo+Vd7twhw4dCsSdtwiLSqXCL7/8orG/QqHAr7/+CgDo3r07qlSporbdrl278OWXX6JRo0ZYt24dLl68iFOnTuGjjz7S8VMhIiIiIipI7ugJALDJfGbkSExA6dcnqbRwhmDpM/YMwS+++EKaVbNmzRqDxmAI+WfKyWQysWvXrkJt/vrrL2FhYSEACE9PT2FjYyPOnz9fqF1oaKg01pIlSwrtv3//vrC3txcAxKhRozTOAMybkSSXy8XNmzcL7Pvzzz+lGZevvfaaSElJKdR/w4YNBWYzlWSGYFRUlNa/GMbExIgqVaoIAOKdd95R2yZvhmBenqibsRUWFibNKHv//fc1Hi8/dTmd/1glfY0aNUqn45fUrFmzpGNdunRJa9vFixdLbQ8dOlSi44WEhEgzUZs2bSo2bNggwsPDxdGjR0VQUJBwcnISAESTJk3Ew4cPS3QMIUSBz/DNN98sNLtPqVSK1q1bCwDCyclJ2Nraig8//LDQOGlpadJ1pXHjxlqPmdfuu+++K7Svffv2AoCoVauWxv4hISFSzDt27NB6Tl26dBGZmZla49EXZwiSqeHMEzI1zGkyNcxp4zp74FchAp3Fnc+bGTuUMokzBMkghBBIz1aa/EsIYeyPWqOnT59K256enhrbKZVKXLt2TeNLoVCUeqyDBw/GG2+8Uej9Ro0aoV27dgCAJ0+eYPr06WjVqlWhdgEBAahatSqA3JmRL/r++++Rnp4Ob29vrFq1SuPzTOfPn48qVaqoneW0atUqacblTz/9BEdHx0L9R44cqdMz1LSpVq2atJK0Oj4+PpgxYwYAYO/evUXm4LJly9TOwmrXrp30War7zExJSkqKtK3u65afg4ODtJ2amlqi4w0YMAAREREYO3Ys/vjjD4waNQpt2rRBt27dEBQUBHt7eyxZsgRnzpyBl5dXiY6Rn729PX766adCC5hYWFhg4sSJAHI/A09PT2lxlRf7jxo1CgDw119/ISkpSe1xrl69iujoaABQ+3zRvFmCt27dKrC4Sn7BwcEAchdx0faMUrlcjp9//hk2NjYa2xARERERFYeta+7P3o7KBCNHUv5xhRDSKEORg/rzDhs7jFJ3fUEP2FuXzf8KuhZB4uPj0ahRI437o6KiSv1B/kOGDNG4r3Hjxjh16hQAaL0dt3HjxoiOjsbdu3cL7duzZw+A3CKGra2txjEsLS3Rpk0b7NixA+Hh4QX2HTt2DEBukVLbSs3vvvuuQRfrSE5OxrNnz5Ceni4V/+zt7aV9UVFRGhevcHV11bpSdLNmzXD+/Hm1n5mugoODCy1aUVxubm569S9KZmamtG1tba21bf4CVEZGRomOp1AosHnzZuzbt09twfbRo0fYsmULateubZCVvLt164YKFSqo3de4cWNpe+DAgRqLza+++qq0HRUVhSZNmhRqk3e7cP369dXm3KBBg/D+++8jMTERwcHB8Pf3L7D/0aNH0v+Nd955R+vXwt/fnwuIEBEREZFBObjlFgRdRBIgBFCGH6tV1pXNKggRAQCcnJykbX0LNqWtdu3aGvflPetO13b5C6EAkJSUhNu3bwMAVq9ejdWrV+sUU3x8vLSdmZkpjdGiRQut/Vq2bKnT+NpER0fjm2++wb59+6QZWZo8ffpUY0GwVq1akMs1T+bOKyK9+JkVR/Xq1UvcVx8KhQI3b97UuL9OnTpS8St/EbiolXTzr/hrZ2dX7LjS0tIQEBCA06dPw8LCAjNnzsSYMWNQo0YNZGZm4sKFC1iwYAHOnDmDvn37YunSpfjggw+KfZz8DPn/B9CcD3kFQU0z++zs7DBs2DCsWLEC27Ztw/fff19gxuXGjRuhVCoB5BbOtclfyCQiIiIiMgQXT28AgA0UUGUkQW7vatyAyjEWBEkjOysLXF/Qw9hhlDo7K4uiGxmJu7u7tP3kieZl1X18fArNYho9ejQ2bNhQarG9KG/Gmzr5C1q6tHtxEQdNi4wUJT09XdpOTEyUPqOKFStq7VepUqUSHS/PwYMHMWjQoALH10bbLDZtnxfwv88s71bo8uTBgwc6z2zNXxwv6jbg/MXzom4vVicwMBCnT58GkLtATd6tuEDu7MRu3bqhU6dO6N69O06cOIGPPvoInTp10qsAZsj/P0Dh/0NA7jXk4sWLAIA+ffpoHGf8+PFYsWIFUlJSsHPnTowcOVLal3e7cIsWLbR+7YDSnzVKREREROangosLUoQdnGQZSH3+EM4sCJYYC4KkkUwmK7O30pqL/LcAXr58GWPHjjViNMaTv7jx4Ycf6vw55L+dMX/BtDRXa3727BmGDRuG9PR0ODo64uOPP0aPHj3g5+cHFxcXKabff/8dXbp0KRSbMURFRRnklmFNq80ago+Pj7QdGxuL5s2ba2wbExMjbfv6+hbrOEIIqehVu3btAsXA/CwtLfHf//4X7dq1g0qlQnBwMJYuXVqsY71soaGhUKlUcHd3R5s2bTS2a9KkCZo1a4bIyEgEBwdLBcELFy7g+vXrAIqeHQig0PMQiYiIiIj0ZW0pR7zMGU7IQPKzeDj71DN2SOUWqz1EZVi7du0gl8uhUqlw+PBhCCFKtZhVVuWfKZmeno6GDRsWe4z8s5UePXqktW1R+7XZvn07EhMTAQAhISHo1q2b2nYJCWXnIbhjxoyRnvFYUqNGjcL69euL1adatWo6F0Pr168vbd+4cUNr27z9lpaWqFmzZrFievToEZ4/fw4AaNq0qda2+Z9DWVRMZUHe7cIBAQFFFuvGjRuHyMhInDp1Cnfv3kWNGjWkQqmdnR2GDh1a6vESEREREamTLHcFVI+QnvDQ2KGUa1xlmKgMc3V1Rf/+/QEAd+7cwYEDB4wckXF4enpKs8+OHTtWohl1tra2qFWrFgDg0qVLWtsWtV+bv//+G0Dus/00FQMBICIiosTHMEctWrSQZldqK15mZ2fj/PnzhfroKv/q1XnPytMk/+rdmla9Liuys7Nx5MgRAJqfH5jfsGHDYG9vDyEENmzYgIyMDGzduhVA7sImLi4upRovEREREZEm6Va5z1HPTir5RA5iQZCozPv000+lWYGTJ0/Gs2fPjByRcfTr1w8AcPfuXezYsaNEY3Tt2hUAcPXqVVy5ckVju3Xr1pVofOB/RaSsrCyNz/VLT0/HL7/8UuJjGNrJkychhNDrVdzZgcXl5OQk3WJ97NgxxMbGqm0XEhKC5ORkAMCAAQOKfZwKFSrA2dkZABAeHq61KJi/MGmshVl0deLECaSmpsLKygo9ehT9bFhnZ2cMHjwYALBhwwbs2LEDSUlJAGC2jy4gIiIiorIh0ya3IKhMKdmz5ikXC4JEZVzz5s0xd+5cALnPRmvbti3+/PNPrX2EENJtq5qsX78eMpkMMpkMQUFBBoq29MyYMQM2NjYAgEmTJhU5w+7AgQP466+/Crw3ceJEqbg6YcIEtc/N27Rpk14zMfNmIaalpaktXObk5GDcuHGIi4sr8THM1ccffwwgt+g6derUQgtnPH36FLNmzQKQO7t23LhxasepVq2alPsvksvl6N27NwAgLi4OX3zxhdoxEhISpGMB2hfpKAvybhdu3769VPAsSt7nFx0djZkzZwLILXx27NixVGIkIiIiItKF0tYjdyNN88KbVDQWBInKgcDAQEycOBEA8O+//6Jp06bo06cPfvzxR/z++++4cuUKLl26hH379iEoKAgNGzbEnj17AOQ+2L+4t02WRdWrV8eqVasAAM+fP4e/vz/GjRuH3bt34/Lly7h48SJCQkLwySefoGbNmujduzfu379fYIxXX30VU6dOBZB7y27z5s2xfv16REZG4vfff8fkyZMxcuRIrQtWFGXw4MFS4XL06NH49NNP8fvvvyMiIgIbNmxAq1atsGXLFvj7+5f4GOaqc+fOGDJkCABg79696NatG/bu3YuIiAgEBwejdevW0tf8q6++KvEqt/PmzZNW8w0KCkK/fv2wc+dOXLlyBeHh4Vi6dCmaNGkiLbDRpUsXdO/e3QBnWHpCQ0MB6Ha7cB5/f3/Uq5f7kOb4+HgAuc+bNMfnmBIRERFRGeKQWxC0zDDPu+cMpWw/9IiIAOTOWlq1ahVatWqFOXPmIC4uDqGhodIv+erIZDL06tULX3/9Nby9vQvtz8jIkLbzL9pRlo0ePRp2dnaYMGECkpOTsXbtWqxdu1ZtW7lcDgcHh0LvL1myBHFxcQgJCcGNGzcwZsyYAvurV6+Obdu2oUaNGiWK0cfHBytXrsS4ceOQkZGBhQsXYuHChQXavP322xg/frx0CzPpbt26dUhOTsaBAwdw4sQJnDhxosB+uVyOuXPnSgX0kqhbty727NmDoUOH4unTp9i3b580w+5FnTt3xvbt20t8rJfh6tWruHfvHoDiFQSB3NuD82ZmyuVyjB492sDREREREREVj9yxIgDAJuupkSMp3zhDkKgcGTNmDO7evYuNGzdi5MiRqFu3Ltzd3WFpaQkXFxf4+fnhjTfewKJFixAVFYXQ0FA0aNBA7Vjh4eEAclffLU+/5L/99tu4d+8evvrqK3Ts2BEVK1aElZUV7O3tUaNGDfTt2xdLlizBvXv30KlTp0L9rayssHPnTmzcuBGvv/46XFxcYG9vj3r16uHTTz9FZGSk3s+DGzNmDMLCwvDGG2/A09MTVlZWqFy5Mnr27InffvsNW7duLXKVV1LPzs4OoaGh2LRpE7p164aKFSvC2toavr6+GDZsGM6cOWOQW+C7du2KGzduYNGiRejYsaP0dbSzs0P16tUxePBg7N69G8eOHSvxTMSXJa+YWb9+/WIXukeMGCFtd+vWDb6+vgaNjYiIiIiouKxdvAAA9tkJRo6kfJOJkizXSWVCcnIyXFxckJSUpPMzoQAgMzMTUVFRqF69OmxtbUsxwvJPpVIhOTkZzs7OkMtNq35erVo1REdHY/78+Zg3b56xw6GXxJRzmtRr06YNzp8/j5kzZ2LRokXF6nv8+HFpJutvv/0mLTRSluia0/zeR+WFQqHAgQMHEBAQACsrK2OHQ6Q35jSZGua08V2JDEfTfT2RLHOCc6D6xQbNVXHqRPxtkMgM3b9/H9HR0XBxccEHH3xg7HCIqJQ8efIEFy9eBFD824WB/6247e7ujv79+xs0NiIiIiKiknCqkPtILGeRAuQojBxN+cVnCBKZoVOnTgEA3n//fbi4uBg5GiIqLQkJCZg7dy7kcjnatGlTrL737t2Tno84ZswYabEcIiIiIiJjcvWoCKWQw1KmgjLlMSxdqxg7pHKJBUEiMzRixIgCzwYjItNUu3btYj1T8cGDB0hPT0dUVBQ++eQTKBQK2Nra4sMPPyy1GImIiIiIisPNwRbP4IyKSETys4eowIJgibAgSERERACA4cOHSzOI8yxYsABVqvCHLCIiIiIqGyzkMiTKXFARiUh9/hAV/IwdUfnEgiAREREVYG9vj9q1a+PDDz/EqFGjjB0OEREREVEBqZaugDIamQnxxg6l3GJBkIiIiAAAJ0+eNHYIRERERERFSrOqACiB7KRHxg6l3OIqw0REREREREREVG5k27gDAFSpT4wcSfnFgiAREREREREREZUbKjsPAIA8/amRIym/WBAkIiIiIiIiIqJyQ+boCQCwymRBsKRYECymgwcPQiaTSa+goCCd+v3999+YNGkSatasCTs7O3h6eqJ9+/ZYvXo1lEpl6QZNRERERERERGQiLJwqAQBssp4bOZLyi4uKFENaWhomT55c7H5r167F1KlTkZWVJb2XmZmJsLAwhIWFYf369di/fz/c3d0NGS4RERERERERkcmxcc0tCDooE4wcSfnFGYLFMHfuXERHR6NixYo69zl8+DAmTJiArKwsVKpUCT/88AMuXLiAgwcPYuDAgQCA8+fPY+DAgVCpVKUVOhERERERERGRSXCo4AUAcFElAEIYOZryiQVBHV2+fBk//PADbGxs8Pnnn+vUR6lUYtq0aVCpVHB2dsbZs2fx3nvvoWXLlujZsyd27tyJKVOmAABOnz6NX3/9tTRPgYiIiIiIiIio3HN2rwwAsIYSyEo2cjTlEwuCOsjJycH48eORk5ODTz/9FLVq1dKp365du3D79m0AwOzZs+Hn51eozeLFi+Hm5iZtExERERERERGRZhVcXJEi7AAAWUmPjBxN+cSCoA6WLl2Ky5cvo3bt2pg1a5bO/Xbv3i1tjx49Wm0be3t7DB48GABw7do13Lp1S59QiYiIiIiIiIhMmrOdJZ7DGQCQ8uyhkaMpn1gQLMK9e/cQGBgIAFixYgVsbGx07hsWFgYAqFOnDry8vDS269Chg7R95syZEkZKRERERERERGT6ZDIZkuSuAIA0FgRLhAXBIkyePBnp6ekYPnw4unTponO/1NRUxMbGAgDq1q2rtW3+/f/880/JAiUiIiIiIiIiMhOplrmPX8tKijdyJOUTC4JabN68GYcOHYKrqyu+/fbbYvWNjY2F+P+Vbnx8fLS29fX1lbZjYmKKHygRERERERERkRnJtK4AAFCkPDZyJOWTpbEDKKueP3+O6dOnAwAWLlyISpUqFat/SkqKtO3o6Ki1rYODg7SdmpqqsV1WVhaysrKkfycn566ko1AooFAodI5NoVBACAGVSgWVSqVzP3OUV9TN+7yIyjvmNJkaXXNapVJBCAGFQgELC4uXFR5RseX9TFecn+2IyjLmNJka5nTZkW3jDqQBqpTH/Hr8v+J8DiwIavDxxx/j8ePHaNWqFSZMmFDs/pmZmdK2tbW11rb5n0uYkZGhsd3ChQsxf/78Qu8fOXIE9vb2OsdmaWkJLy8vpKamIjs7W+d+5ix/gZeoOO7fv49XX30VAPDjjz9i2LBhBfZ/9dVXWLRoEQAgISHhpcXFnCZTU1ROZ2dnIyMjA6dPn4ZSqXxJURGV3NGjR40dApFBMafJ1DCnjS8lQwYAyHwSjQMHDhg5mrIhPT1d57bluiCoVCphZWWl9zjBwcEFVgE+efIkgoODYWFhgVWrVkEuL/6d1ba2ttJ2UUW3/LP+7OzsNLabPXs2PvroI+nfycnJ8PX1Rffu3eHs7KxzbJmZmYiJiYGjo2OBOKkwIQRSUlLg5OQEmUxWojFOnjxZrOdPAsD777+PpUuXam3z559/4rfffsPx48cRExOD58+fw83NDT4+PujSpQuGDBmCJk2aqO3buXNnnDp1qlgxvWjt2rUaV8+m/8k/Q9jW1rbQ/9X8fxAozv/jkjJETpuCrKwsXLlyBREREbh48SIuXbqEW7duSbPNcnJyDH7Mv//+G8uXL8fx48fx4MEDODo6ol69ehg6dCjGjh0LS8vy+y15woQJWLt2LXr16oX9+/e/1GPrmtOZmZmws7ND+/bt+b2PyjSFQoGjR4+iW7duBvk5l8jYmNNkapjTZcfxjFjgBlDBKgtNAgKMHU6ZkHcnqS7K728fpSQrKwsTJ04EkFuU0VRQKYqTk5O0re02YABIS0uTtrXdXmxjY6N2lWMrK6tiXYhycnIgk8kgl8tLVOw0J3m3n+V9XiVRkn7ajpeYmIj3338fv/76q1S8yPP48WM8fvwYly9fxjfffIPhw4dj2bJlcHV1LUnoWjF/dJP/M1L3meUvYLyMz9MQOW0KpkyZgvXr12vcb+jPZu3atZg6dWqBPwBlZmYiLCwMYWFh+OWXX7B//364u7sb9LgvgxBC+otsv379Xnpe6ZrTcrkcMpms2N8ziYyFuUqmhjlNpoY5bXw2rl4AALvs5/xa/L/ifA7luiBoaWlpkFV5K1euLG2HhITg33//haWlJerXr4+tW7cWan/9+nVp+9q1a1KbVq1aoXr16gAKLiSSt9qwJvkXEsm/wAiZnsmTJ2PKlClFtvPw8FD7flxcHHr27ImrV68CyM2zMWPGoF27dvDw8MCzZ89w9uxZrFu3DjExMfj111/x559/4vDhwwXyPDg4uEAhOr8VK1Zg5cqVAIB169ahRYsWatsVtVgO6SYoKAhBQUHGDsPs5C+mOzk54bXXXsPNmzcRH2/4FcoOHz6MCRMmQKVSoVKlSvjss8/QqlUrPH/+HGvWrEFISAjOnz+PgQMH4sSJE+WuUBsZGYmHDx8CAPr06WPkaIiIiIjIXNi65BYEnXJe3qOXTEm5LggCQN26dQ06Xt7sDaVSifHjxxfZfufOndi5cyeA3CJLXkHQ0dERvr6+iImJwY0bN7SOkX9/vXr1Sho6lQMVK1ZEw4YNS9RXqVRi0KBBUjFw2LBhWL16daFZpd26dcOMGTMwYcIEbN68GVevXsWgQYNw+vRp6UH6eXmqKcY81atXL3G8RGVZr1690LFjR7Ro0QL16tWDXC5Hx44dDV4QVCqVmDZtGlQqFZydnXH27Fn4+flJ+3v27ImpU6dixYoVOH36NH799VeMHDnSoDGUtn379gEAmjZtyj8UEBEREdFL4+D+/wVBkQooswFL7es3UEHlaxpCOdOuXTsAKHLWSf5nufn7+5d6XFQ+LVmyBOHh4QCAgIAAbNy4UeMt5g4ODti4cSMC/v85CufOncOSJUteWqxEZd3bb7+N0aNHo0GDBqU6I2/Xrl24ffs2gNznwOYvBuZZvHgx3NzcpO3yJq8gyNmBRERERPQyublXglLk/iwv0p8aOZryhwXBF4wePRpCCK2vEydOSO0DAwOl919cYOGNN96QtjU9qyo9PR3btm0DANSvXx+1a9c29CmRCcjOzpYWGbG1tcXq1auLLGLI5XKsWrVKenj+0qVLy/Sq0kFBQZDJZNIz9ZKTkxEUFIRGjRrB0dERlSpVQkBAAM6dO1eg3+PHjzFnzhw0aNAADg4OcHd3R//+/XHlyhWdjnvz5k28//77aNCgAVxcXGBnZ4caNWpgzJgxuHz5cpH9c3Jy8OOPP6JVq1ZwdnaGi4sLXnvtNXzzzTcFnhen63m/KDs7G/v27cO0adPQokULuLm5wcrKCu7u7mjVqhWCgoLw9Kn2b37VqlWDTCaTrlH//vsvJkyYgGrVqsHGxgaVKlXCgAEDcP78+SLjpeLZvXu3tK1pER57e3sMHjwYQO5jKG7dulXs49y7d0/Ko7zvNyEhIejevTsqVqwIBwcHvPrqq1i2bBkUCoXUTwiBzZs3o2PHjqhYsSLs7e3x2muvYdWqVYWeUarOgwcPpP9rffv2BQA8ffoUNjY2kMlkmDx5cpFj7Nu3T4p98+bNRZ5TQEAAvL29YWlpiY4dO+r4CRERERGRqXF3ssVz5C7MmJn4yMjRlEOCiu3EiRMCgAAgAgMDNbbLzs4Wfn5+AoBwdnYWt2/fLtRmypQp0ljBwcHFiiMpKUkAEElJScXql5GRIa5fvy4yMjKK1c8c5eTkiISEBJGTk1PiMXTNF2327NkjjTFs2LBi9R06dKjUd8+ePUW2DwwMlNqfOHGiRPGWRP7j3r9/X9SuXVv6d/6XhYWF2LZtmxBCiD///FNUqVJFbTsbGxtx/PhxrcdcsGCBsLS0VNsfgJDJZGLevHka+ycnJwt/f3+N/Zs1ayYuX76s9f94/vNWZ9SoURrHz3u5u7uLM2fOaIyzatWqAoAYNWqU2L59u7C3t9f42W7dulXrZ2bKOnTooPVrURK+vr4CgKhTp47Wdps3b5aOvW7dumIfJyoqqkCeTZ48WWO+DBw4UCiVSpGZmSkGDRqksd348eOLPO6qVasEAOHl5SVUKpX0/ltvvSUACBcXlyK/1wwYMEAAEK6urgXa5j+ndevWiREjRhSKsUOHDjpfp/m9j8qL7OxssXv3bpGdnW3sUIgMgjlNpoY5Xbb8M6+hEIHO4lHkfmOHUiYUp07EGYKlyMrKCj/88APkcjmSk5Ph7++P5cuX4+LFizh8+DAGDRqEFStWAMi9vXjEiBFGjpjKqtOnT0vbebNwdNWvXz9pOywszGAxlaa33noLsbGxmD17Nk6dOoVLly5h6dKlcHZ2Rk5ODsaOHYuoqCj06dMHGRkZ+OKLL3DmzBlcuHAB8+fPh7W1NbKysjBmzBiNsyLnzZuHefPmQalUom3btvj5558RHh6OiIgIbNq0CW3atIEQAgsWLMCyZcvUjjF8+HCcPXsWANCyZUts2bIFERERCA0NxVtvvYXIyEhp1fKSUiqVqFGjBv7zn//gt99+Q3h4OC5duoQdO3Zg0qRJsLa2xrNnzzBgwAA8fvxY61h//fUXRowYAU9PTyxbtgznz59HeHg4goKCYGtri5ycHEyYMAFPnjzRK2bKlZqaKi0qVdTzbvPv13exrFWrVmHlypUICAhASEgIIiMjsXv3brRq1QpA7iy74OBgzJgxAzt27MCwYcOwf/9+REZGYuvWrVIsa9aswaFDh7QeK//twvlnuY4bNw4AkJSUhF27dmns//TpU+zfvx8AMHToUGlG84u+++47bNy4Ea+//jo2b96MiIgIHDt2jN83iYiIiMxcikXuo3fSEw2/OKDJK/36pOkp7oyvn376SVhbW2uchdGyZUvx5MmTYsfBGYKlz9AzBCdPniyuXr1a5OvFvzZ17dpVGuPmzZvFOv7Nmzelvt26dSuyfVmYIWhjYyPOnz9fqE1oaKjUxtPTU3h4eKidefvjjz9K7UJCQgrtv3jxopDL5QKAmDNnjtp4cnJyxDvvvCMACCcnJ5GQkFBg/969e6VjBAQECIVCUWiM+fPnF/i/XpIZgrdv3y4w8+pFf/31l3B0dNR6LnkzBPH/sxbv3btXKKd//fVXqc2SJUs0Hq8o+Y9V0ldJZ9Lqy9AzBP/55x9pvKlTp2pt++TJE6ntkCFDin2s/LPpAIgPP/ywUJu0tDRRrVo1AUB4eHgImUwmvvvuu0LtHj58KJycnAQA0a9fP43HTE9PF3Z2dgKA2L17d4F9KpVKOpa2686SJUukmCMiIrSe08iRI9X+X+AMQTI1nHlCpoY5TaaGOV22nPiynxCBzuJmyBfGDqVM4AzBMmb8+PGIjIzE+PHjUaNGDdja2sLd3R3t2rXDypUrcfbsWXh4eBg7zMKEALLTTP+lw3OyDGXlypVo1KhRka8HDx4U6Jf/GXFeXl7FOmalSpWk7WfPnul3Ai/Jhx9+KM1myi8gIABVq1YFADx58gSff/652kUaxowZI800UjcrctGiRVCpVGjWrBkWLFigNga5XI5ly5bBxsYGKSkp2LFjR4H9K1euBADY2NhgzZo1sLQsvGj7nDlz9F6l2c/PT+PzBQGgUaNG0mys/M+r0+Tnn3+Gi4tLofeHDRsGb29vAOVnJmlZl5KSIm1rWgAoj4ODg7Sdmpqq13F9fX3x9ddfF3rf3t4eo0aNApB7TWnVqhU++OCDQu28vLwwYMAAANpz4dixY8jIyICtrS26du1aYJ9MJsO7774LADh+/DhiYmLUjhEcHAwAaNy4MZo1a6bxWK6urli+fLnW/wtEREREZH6ybNwBADkp2u+WosIK/wZLRerYsaNOD1vPr2HDhvjpp59KKaJSokgHvvQ2dhSl79M4wNqh6HZGlL+wkL9woIv87ZOTkw0WU2kaMmSIxn2NGzdGdHQ0ZDKZtBDDi+zs7FCrVi1cvXoVd+/eLbBPoVDg4MGDAIBBgwZpLTC4urqiUaNGiIiIQHh4uFR4UyqV0urg3bt3lwppL5LL5Rg1ahRmzJih+WSLKSEhAc+fP0dmZqZ0HXJ1dQUAXL9+HQqFAlZWVmr7NmrUCI0bN1abBzKZDE2bNkVcXFyhz6w4jhw5ovfiNRUrVtSrf1mRmZkpbVtbW2tta2NjI21nZGToddyBAwdqzIHGjRtL22+//bbGMV599VUAufmWmJgo5Vh+ebcLd+7cWe116d1338X8+fORk5ODDRs2YM6cOQX2R0ZG4urVq1Jbbfr27QsnJyetbYiIiIjI/OTYugPJANL42KPiYkGQ6CUKDAxEUFBQsfvl/0U4NTVV7QwvTfLPNnJ2di72sY1B22rbeYUJDw8PuLm5FdkufzEVyC2apaenAwBmz56N2bNn6xRTfPz/nklx584daYwWLVpo7deyZUudxtfm6tWrWLp0KQ4ePFggjhepVCokJCRoLKgV9Ry7ChUqACj8mRWHsVZKT0tLQ1RUlMb9+s7ULIn8z8Mrqkiaf0VqOzs7vY6ry/+f4rRLSUkpVBAUQiA0NBSA5ueaVqlSBT169MCBAwewfv16fPbZZwUK8HmzA62trfHOO+9ojAUoWMgkIiIiIsojHDwBAJYZ5eNuuLKEBUHSzMo+d/acqbOyN3YERXJ3d5e24+Pji1UQfPTof8uv5x+nLLO31/w1kcvlRbbJ3y4nJ6fA+0UtvKFJXgEQyJ01laeo2Wz5b9kuibVr12LSpElQKpU6tdc2u6ykn1l5cOnSJXTq1Enj/uLO6jaEFwv52qSlpUnbRd1eXBRd/v8Up526fIiMjERcXO73hz59+mgcZ/z48Thw4ADu3LmDsLAwtG/fHkBuAXTz5s0AgP79+xd5bdJW/CciIiIi82XhlPv7lk02C4LFxYIgaSaTlflbac3Fq6++iuPHjwMArly5gjp16ujc9/LlywXGMXf5ixuLFy9Gz549deqX/5bI/MWlop5ppk8h6saNG1IxsGLFipgxYwY6d+6MatWqwcnJSbotdN26dRg7dqzexzOEf//91yC3DJvCbcM+Pj7Sdt5qw5rkf8aer69vqcVkKHkrAzdp0qTAeb6oT58+8PLyQnx8PIKDg6WC4O7du6XCelG3CwOAhYWFAaImIiIiIlNj45L7e4O9IqGIlvQiFgSJyoH27dtjyZIlAIC9e/dqfcbei/bu3Sttv/766waPrbzJPxNJoVCU6FbSvFtrgYIzMNUp6YxEAFi/fj2USiUsLCxw8uRJ1KtXT227/DMWja179+6Ijo7Wa4yS3Fpfkme7ljZHR0f4+voiJiYGN27c0No2/35NX+eyJO/5gZpuF85jaWmJUaNGYdGiRdi+fTuWLVsGR0dH6XZhHx8fdO/evdTjJSIiIiLTZFehMgDAJScBUGYDltqf3U3/w1WGicqBnj17Sree7tq1q8jZRnliYmKklWe9vLx0ng1nyho0aCAt8HDkyJESjeHn5yfdbnnp0iWtbYvar83ff/8NIHdmp7YiUURERImPQaWrXbt2AICbN29qff5j3iI1AODv71/qcenjwYMH0szjogqCADBu3DjIZDKkpaVh+/btiI2NxdGjRwEAo0aNKnB7MhERERFRcTh6vILHwhVWUAK3Svb7nbniT+FE5YCNjQ0++OADALkrl06cOBEqlUprH5VKhUmTJkkrnX744YdFrnRqDuzt7dGlSxcAwMmTJ3Hx4sVij2FpaYkOHToAyC0qPnz4UG07lUqFDRs2lDjWvOcG5n9+4Yvi4+OxZ8+eEh/D0O7duwchhF6vkiy8U1a98cYb0vb69evVtklPT8e2bdsAAPXr1zfawiy6yrtd2MvLC82bNy+yfc2aNaX/L8HBwdiwYQNUKhVkMhnGjBlTqrESERERkWlzd7ZDSE7uH+GVlzcaOZryhQVBonJixowZaNWqFQDgwIEDGDlypMaFCtLS0jBy5EgcOHAAANCqVSv85z//KbXYgoKCIJPJIJPJNBY9ypL8q50OGTIEd+7c0dg2JycHmzdvLjQrc/LkyQByF0eYOHGi2oUXFi5ciKtXr5Y4zlq1agHIfS7f+fPnC+1PT0/HsGHDtC4kQqUrL++rVaumdv+AAQPg5+cHIDcf1OXajBkzpNu+Z8yYUWqxGkre7cK9e/cu8hmaecaNGwcACAsLw7JlywAAHTp0kD4bIiIiIqKS8HK2xXnnHgAA+e2jQGrJH9lkbvgMQaKX6PHjx7h27VqR7ezs7Ar9omxpaYmdO3eie/fuuH79OjZt2oRTp05h7NixaNeuHdzd3fHs2TOcPXsWa9eulRYpqF+/Pnbu3AlLS/53z+Pv74958+Zh/vz5iIqKQpMmTTB27Fh0794dlStXRlZWFu7du4fw8HDs2LEDcXFxuHr1aoHFE/r27Yu+ffti37592LdvH/z9/TF9+nTUqlULjx8/xvr16/Hbb7+hRYsWJb5teMSIEVi2bBlUKhUCAgIwc+ZMtG3bFra2toiMjMTSpUtx69Yt+Pv74+zZs4b6eMxCfHw8Dh06VOi9PC8Wttu1a4eaNWsW+zhWVlb44Ycf0LdvXyQnJ8Pf3x9z5sxBy5YtkZCQgDVr1mDnzp3SMUaMGFH8k3mJMjIy8PvvvwPQ7XbhPG+++Sbee+89JCQkSM/d1GUxESIiIiIibWQyGdq1aYc/jvqhifwOxF+/Qdb2PWOHVS6wQkD0Eq1cuRIrV64sst2rr76KP/74o9D7VapUwZkzZzBt2jRs2bIFsbGxmD9/vtoxZDIZhgwZgh9//BFubm76hq5V/hlq+RftKMuCgoLg6uqKTz75BKmpqfj+++/x/fffq21rbW0NW1vbQu9v2rQJvXr1wtmzZ3HhwoVCi7289tprWL16NV577bUSxdiiRQvMnz8fgYGBSEhIwOzZswu1+c9//oOGDRuyIFhMN27c0Hq76ov7goODS1QQBICAgACsWrUK06ZNw6NHj/Dee4V/QGnZsiV27dpV5lfTPXbsGDIyMmBra4uuXbvq3M/W1hbDhw/H8uXLAQAuLi4YNGhQaYVJRERERGbkrWa+WHK0I5rgDjIvboRdm2mAjneymDPeMkxUzri5uWHTpk24fPkyZs6ciddeew2enp6wsrKCp6cnmjZtio8//hiRkZHYvHlzqRcDASA8PBwAULt2bfTu3bvUj2coH374Ie7cuYO5c+eidevW8PDwgKWlJRwcHFC7dm28+eabWLVqFR48eKC2GOTk5ISTJ09i2bJlaNGiBRwdHeHk5IQmTZpg4cKFOHv2rN6f/7x58xAaGoru3bvDzc0N1tbW8PHxwcCBA3HkyBF88803eo1PL8f48eMRGRmJ8ePHo0aNGrC1tYW7uzvatWuHlStX4uzZs/Dw8DB2mEXKu124c+fOcHBwKFbf/LMfhwwZAjs7O4PGRkRERETmycXeCmgwEFnCCnaJN4GHfxg7pHJBJoQQxg6CSiY5ORkuLi5ISkqCs7Ozzv0yMzMRFRWF6tWrq531RP+jUqmQnJwMZ2dnroSpQVZWFlxcXJCVlYUNGzZg5MiRxg6JtGBOU0kJIeDj44O4uDisWLFCeo6mrtauXSs9S/DChQto2bKlQeLSNaf5vY/KC4VCgQMHDiAgIABWVlbGDodIb8xpMjXM6bLpamwSola/jX4W4choOhZ2/ZcYOySjKE6diL8NEpFezp8/j6ysLPj5+WH48OHGDoeISsnly5cRFxcHAOjTp0+x+69btw4A0LBhQ4MVA4mIiIiIAKCRjwsuVwgAAMiu7gCUWUaOqOxjQZCI9HL69GkAwKefflrmn39GRCWXk5ODwMBALFmyBL6+vsXqe+7cOZw7dw4AMGnSpNIIj4iIiIjMXMPX++OhqABbZRJybhwwdjhlHhcVISK9zJ07F3PnzjV2GERUylq2bFmsmX3R0dHIysrC9evX8dFHHwEAKlasqHUxFyIiIiKikurzqg827e+AsdiFhLPB8Gg4wNghlWksCBIREZHBdejQAdHR0QXeW7ZsGezt7Y0UERERERGZMlsrCygaDQWu7kKFh2FA8kPAubKxwyqzeMswERERlRonJye0adMG+/fvx+DBg40dDhERERGZsF4d2+GSqjbkUCHh/EZjh1OmcYYgERERGdy9e/eMHQIRERERmZmq7g447tEbLZ7/i5zLvwLdZgAymbHDKpM4Q5CIiIiIiIiIiExC9Q4jkCGs4ZEZjazoi8YOp8xiQZCIiIiIiIiIiExC+0Y1cMqiDQAg9sTPRo6m7GJBkIiIiIiIiIiITIKFXIbMhkMAAJXuhwKKDCNHVDaxIEhERERERERERCajXbcBiBUecBRpuH9um9o2Qgg8SclCUobiJUdXNnBRETMmhDB2CERERC8Fv+cRERERmQ8PJzsc8ugNn2cbkHVxA5LrdkRsbDTiH8Yg8XEc0hPjkZPyBA7KBLi1fBtd+r5j7JBfOhYEzZBcnjsxVKVSGTkSIiKilyMnJwfA/74HEhEREZFp8+74LrBzA2qlRQIrG6M+gPovNrIAzj6uZ4TojI8FQTNkaWkJuVyOzMxMODg4GDscIiKiUpeeng4LCwtYWVkZOxQiIiIiegkaNXwVZ/e1g3/2GSiEBRJlzsiwcoPSzhMWThXhUMELLh7e8K/xurFDNQoWBM2QXC6Hvb09UlNT4e7ubuxwiIiISpUQAsnJyXBycoJMJjN2OERERET0EshkMjSavgtX4+JR1bsSPO1sjB1SmcL7ZsyUs7Mz0tPTkZCQYOxQiIiISo0QAnFxcVAoFHBxcTF2OERERET0EjnbWaOR3ytwZjGwEM4QNFMuLi7IyMhAfHw80tLS4OLiAktLS86ceIFKpUJ2djYyMzP53CkyCcxpMjXqcloIgZycHKSnpyM5ORkKhQI+Pj6wt7c3crRERERERGUDC4JmrFKlSrC2tkZiYiJiY2ONHU6ZJIRARkYG7OzsWCwlk8CcJlOjLactLCzg5OQEFxcXFgOJiIiIiPJhQdCMyWQyVKhQAW5ublAqldIKjPQ/CoUCp0+fRvv27fkgejIJzGkyNZpyWi6Xw8rKioVvIiIiIiI1WBAkyGQyWFlZsTighoWFBZRKJWxtbfn5kElgTpOpYU4TERERERUfHyBFRERERERERERkRlgQJCIiIiIiIiIiMiMsCBIREREREREREZkRFgSJiIiIiIiIiIjMCAuCREREREREREREZoQFQSIiIiIiIiIiIjPCgiAREREREREREZEZYUGQiIiIiIiIiIjIjLAgSEREREREREREZEZYECQiIiIiIiIiIjIjlsYOgEpOCAEASE5ONnIkpkuhUCA9PR3JycmwsrIydjhEemNOk6lhTpOpYU6TqWFOk6lhTlNZllcfyqsXacOCYDmWkpICAPD19TVyJEREREREREREVBakpKTAxcVFaxuZ0KVsSGWSSqVCXFwcnJycIJPJjB2OSUpOToavry9iYmLg7Oxs7HCI9MacJlPDnCZTw5wmU8OcJlPDnKayTAiBlJQUeHt7Qy7X/pRAzhAsx+RyOXx8fIwdhllwdnbmxZ5MCnOaTA1zmkwNc5pMDXOaTA1zmsqqomYG5uGiIkRERERERERERGaEBUEiIiIiIiIiIiIzwoIgkRY2NjYIDAyEjY2NsUMhMgjmNJka5jSZGuY0mRrmNJka5jSZCi4qQkREREREREREZEY4Q5CIiIiIiIiIiMiMsCBIRERERERERERkRlgQJCIiIiIiIiIiMiMsCBIREREREREREZkRFgTJoGbOnAmZTCa9Tp48WWSfQ4cOYeDAgfDx8YGNjQ18fHwwcOBAHDp0SOfjpqenY/HixWjZsiUqVKgAR0dH1KtXDx9//DHu37+v8zh///03Jk2ahJo1a8LOzg6enp5o3749Vq9eDaVSqbWvUqnElStXsHr1aowbNw6NGzeGpaWl9Fncu3dP5zgA4NmzZwgMDMSrr74KFxcXODs749VXX0VgYCCePXum8zjh4eEYMWIEqlWrBltbW1SuXBk9e/bE1q1bixWPuTLnnA4KCipw7tpeunwuhsrp/FQqFdq0aVMgFtKuvOZ0TEwMdu7ciU8++QSdO3eGs7OzdA5BQUE6x5Fn69at6NGjBypXrgxbW1tUq1YNI0aMwPnz53Ueg9fpssGcc7qsXqeZ0/opjzkthMCZM2cwb948dOnSBZUrV4a1tTWcnZ3RoEEDTJkyBX/++afOsQC8TpsSc85pXqepTBNEBvLHH38IS0tLAUB6nThxQmN7lUolJkyYUKD9i68JEyYIlUql9bi3b98WderU0TiGi4uLCA0NLTL+n3/+WdjY2Ggcp3Xr1uLp06ca+wcFBWk9l6ioqCJjyHPx4kVRuXJljWN5e3uLS5cuFTnO/PnzhVwu1zhO3759RUZGhs5xmRtzz+nAwECt56Lr5yKE4XL6RcuWLSs0FmlWXnP63r17WmMIDAzU+TPIyMgQffr00TiWXC4XCxYsKHIcXqfLBnPP6bJ4nWZO66e85vQrr7xSZA7KZDIxc+bMImPhddq0mHtO8zpNZRl/cyKDyMnJES1atBAARMWKFXW6qH366adSu6ZNm4otW7aIixcvii1btoimTZtK+z777DONY6SkpIi6detKbcePHy+OHz8uzp07J7744gvh6OgoAAh7e3vx559/ahzn0KFD0kWxUqVK4ocffhAXLlwQBw8eFAMHDpTGb9++vcjJyVE7Rv6Lva2trWjdurXw8/OT3tO1IBgbGysqVaokAAhLS0sxc+ZMcfr0aXH69Gkxc+ZM6RtqpUqVRGxsrMZx1qxZIx3bz89PrF27Vly8eFHs3r1bdOrUSdo3fPhwneIyN8zpgjl99epVra/U1FSNsRgqp9WN6+zsLGQymfDw8JBiJfXKc05HRUUV+OG7Zs2aon379tJ7xSkIDhs2TOrXqVMnsXv3bnHx4kWxdu3aAtfsNWvWaByD1+mygTld9q7TzGn9lOectrCwEABEzZo1xaxZs8TevXtFRESECAsLEwsWLBBubm7S+LNnz9b6OfA6bTqY07xOU9nG35zIIJYuXSoAiLp164rZs2cXebG/deuWdNFq3ry5SE9PL7A/LS1NNG/eXLrg3b59W+04+S+wX3/9daH9586dk47TqVMntWMoFApRs2ZNAUA4OzurPdaUKVOk42zYsEHtOIcOHRKrVq0SkZGRQqFQCCGEGDVqVLELgvn7bNu2rdD+bdu2SfvHjBmjdoyEhATh6uoqAIhXXnlFPHnypMB+pVIp+vbtK41z6tQpnWIzJ8zpgrHowxA5rc4bb7whAIh3331XdOjQwSCxmrLynNNPnz4Vn3/+uTh8+LB4/vy5EEKIEydOFLt4cvLkSalP3759hVKpLLD/yZMn0owANzc3kZCQoHYcXqfLBuZ02bpOM6f1V55zuk2bNuLQoUMaZ0rdvn1beHp6SrHcuXNHbTtep00Lc5rXaSrb+JsT6e3+/fvSX1lOnDhR4KKn6WKfvxgRHh6utk14eLjUZtq0aYX2Z2dnSxe0evXqaZzlNHHiRGmciIiIQvvzXzwXLlyodoy0tDTpr0ANGzbU8EkUVtyCYHx8vPTXqB49emhs16NHDwFAWFhYiPj4+EL7v/76a+m4W7ZsUTtGTEyMdKw+ffrofE7mgDmdyxA/wBgqp1+0c+dOAUB4eHiIp0+fsiBYhPKe0+qUpHgSEBAg5VlMTIzaNlu2bJHG/eabbwrt53W6bGBO5ypL12nmtH5MMadflP8xH0uWLFHbhtdp08GczsXrNJVlXFSE9DZlyhSkpqZi1KhR6NixY5HthRDYs2cPAKBu3bpo3bq12natW7dGnTp1AAC7d++GEKLA/pMnTyIxMREAMGrUKMjl6tN59OjR0nZISEih/bt371bbNj97e3sMHjwYAHDt2jXcunVLbTt97d27Fzk5OQCAMWPGaGyXF2dOTg727t1baH/eOTk7O2PgwIFqx/Dx8UHXrl0BAEePHkVqaqoekZsW5rThGCqn80tOTsb7778PAFi8eDHc3d0NE6wJK+85bQipqak4fvw4AKBbt27w8fFR227gwIFwdnbWGAuv02UDc9pwmNNlgznkdKdOnaTtO3fuFNrP67RpYU4bDnOaSgsLgqSXbdu2Yf/+/ahQoQIWL16sU5+oqCg8ePAAANChQwetbfP2x8bGFlqlNywsrFA7dZo3bw4HBwcAwJkzZwrtzxunTp068PLyKjIWTeMYgq7npC2W7OxsXLx4EQDQpk0bWFtbFzlOVlYWLl26VKKYTQ1z2rAMkdMv+uSTT/DgwQO0b99eY8GT/scUctoQLl68iKysrCJjsba2ln4JuXjxIhQKRYH9vE4bH3PasJjTxmcuOZ13DQagtkjD67TpYE4bFnOaSgsLglRiiYmJ+OCDDwAAixYtgqenp079/vnnH2m7bt26Wtvm35+/X3HGsbS0hJ+fn9oxUlNTERsbq3cshpI3rouLi9ZCTuXKlaW/jL4Yy61bt6BUKgGUjXMqT5jTmnXr1g3u7u6wtrZGxYoV0bFjR3z11VdISEjQ2s8QOZ1feHg4Vq9eDSsrK6xcuVLrsck0ctpQSnJOSqWy0OxZXqeNizmtmTGv08zpkjOnnD516pTWY/E6bRqY05rxOk1lDQuCVGIzZ85EfHw82rZti7Fjx+rcLyYmRtrWdCtAHl9fX7X98v/bwcEBrq6uOo3z5MmTAn/JiY2NlaaZ6xOLoeSNW1Qs+ePR9LnoMs7LOKfyhDmt2bFjx/D8+XMoFAo8efIEp06dwuzZs1GjRg3p9g51DJHTeRQKBSZMmACVSoWPP/4Y9evXL3JMc2cKOW0ohj4nXqeNgzmtmTGv08zpkjOXnE5PT8d3330HIHeGX//+/Qu14XXaNDCnNeN1msoaS2MHQOXTmTNn8PPPP8PS0hKrVq2CTCbTuW9KSoq07ejoqLVt3jRuAIWeX5A3TlFjqBvHxsbGoLEYSknOSdPnoss4L+OcygvmtPqvf6NGjfDGG2+gZcuW8Pb2hkKhwM2bN7Fp0yYcOXIEiYmJePPNN7Fv3z706tWrUH9D5HSeRYsW4dq1a6hevTrmzp1b5HjmzlRy2lCMeU68ThsGc1q9snCdZk6XjDnl9KxZs3D//n0AwNSpU1GlSpVCbXidLv+Y0+rxOk1lFQuCVGzZ2dmYMGEChBCYPn06GjVqVKz+mZmZ0ra2ZxcAKHBRzsjIUDtOUWNoG8dQsRhKSc5J0+eiyzgv45zKA+a0+q//hx9+iKCgoELvt2rVCiNHjsTq1asxadIk5OTkYNy4cbh9+zbs7OwKtDVETgO5tzl88cUXAIDly5cXOg4VZEo5bSjGPCdep/XHnFavrFynmdPFZ045vWnTJixfvhwAUK9ePen7+Yt4nS7fmNPq8TpNZRlvGaZi+/LLL/HPP//glVdeQWBgYLH729raStvZ2dla2+afuv3ihTFvnKLG0DaOoWIxlJKck6bPRZdxXsY5lQfMafVf/6Jus5g4cSLGjRsHAIiLi1O7QpshcjrvWJmZmXjzzTcREBBQ5FjmzpRy2lCMeU68TuuPOa1eWblOM6eLz1xy+uTJk9Jto25ubtixY4fGvrxOl2/MafV4naayjAVBKpYbN25g4cKFAIBly5YVmE6sKycnJ2m7qCnIaWlp0vaLU5vzxtFlGrOmcQwVi6GU5Jw0fS66jPMyzqmsY06rj0VXEydOlLbzP1j5xXj0yel169bhxIkTcHJywvfff1+iOM2JqeW0oRjznHid1g9zWj8v4zrNnC4ec8npiIgI9OvXD1lZWXBwcMCBAwe0Pv+X1+nyizmtH16nyVh4yzAVy9KlS5GdnY0aNWogPT0dW7duLdTm2rVr0vbvv/+O+Ph4AEDfvn3h4OBQ4CGmeauhapL/Iab5H24K5D4M9cKFC0hLS0NiYqLWv77kjePp6VlgCrShYjEUHx8fPHr0qMhY8sej7nPJUxbOqaxjTquPRVf5fwh68OBBof2GyOlFixYBADp06ICwsDC1fR8/fixt530NHRwc0Ldv3yKPa2pMLacN5cVzat68eZGxAOrPidfpl4s5rZ+XcZ1mThePOeT033//jZ49eyIlJQU2NjbYvXs3WrdurbUPr9PlF3NaP7xOk7GwIEjFkjd9+O7duxg6dGiR7f/73/9K21FRUXBwcChwwbtx44bW/vn316tXr8C++vXrY+fOnVI7TRdkpVKJO3fuqB3D0dERvr6+iImJ0SsWQ6lfvz4iIyORlJSE+Ph4jcvKP3z4EMnJyWpjqV27NiwsLJCTk1MmzqmsY06rj0VXeSsaa2KInM77Gu3fvx/79+8vMqa8r2PVqlXNsiBoajltKCU5J0tLS9SsWbPQOLxOv1zMaf28jOs0c7p4TD2n79y5g27duuHZs2ewtLTEb7/9hq5du2rtkxeLupjV4XW6bGFO64fXaTIW3jJML1316tXh7e0NQP2U6PxOnz4NAKhSpQqqVatWYF+7du2kbW3jRERESNOe/f39C+3PG+fmzZvSX6rUyX8MdeMYgq7npC0Wa2trtGzZEgAQHh6u9RkReePY2Nho/SssaWfOOX39+nVpO+8zUBfLi8crjVjIcMpaThtCixYtpIdoa4slOzsb58+fL9QnD6/T5ZMp5rSuXsZ1mjn98pXVnI6NjUWXLl3w8OFDyOVybNiwAf3799caXx5ep82bKea0rnidJqMRRAYWGBgoAAgA4sSJE2rbTJ48WWoTHh6utk14eLjUZsqUKYX2Z2VlCRcXFwFA1KtXT6hUKrXjTJw4URrn4sWLhfb/9ttv0v6FCxeqHSMtLU24ubkJAKJ+/foazrywUaNGSWNHRUUV2f7hw4dCLpcLAKJHjx4a2/Xo0UMAEHK5XDx8+LDQ/kWLFknH3bJli9oxYmJihIWFhQAgAgICdD4nc8Sc1mzs2LHSsTZu3Fhov6FyuigdOnSQ4qCilbecVufEiRNSn8DAQJ369OrVSwAQlpaWIiYmRm2bLVu2SON+/fXXhfbzOl02mWtO6+JlXaeZ04ZVHnP60aNHok6dOlK7n376SbeTzYfXadNlrjmtC16nyVj4mxMZnC4X+5s3bwpLS0sBQDRv3lykp6cX2J+eni6aN28u/UDw77//qh1n7ty5Wn8gOHfunHScDh06qB0jOztb+Pn5CQDC2dlZ3L59u1CbKVOmSMcJDg7Wev75FbcgKIQQI0aMkPps37690P5t27ZJ+0eNGqV2jGfPnknfCKtWrSqePn1aYL9SqRR9+/aVxvn99991PidzZI45/ddff4lbt26pHT/PqlWrpDG8vLxEamqq2naGyOmisCBYPOUtp9UpSfHk+PHjUp9+/foJpVJZYP+TJ0/EK6+8IgAIV1dX8fz5c7Xj8Dpd9phjTpe16zRz2rDKW04nJCSIJk2aSOMsXbq0OKcr4XXadJljTvM6TWUdf3Mig9PlYi+EEJ988onUrmnTpmLr1q3i0qVLYuvWraJp06bSvtmzZ2scIzk5WdSuXVtqO2HCBPH777+L8PBw8eWXXwpHR0cBQNjZ2YkrV65oHCc0NFT6q0ulSpXEsmXLxIULF8ShQ4fEm2++KY3frl27Qj+Y5ElJSRHBwcEFXv7+/lLfxYsXF9inKZ779+8LT09P6RvdrFmzRFhYmAgLCxOzZs2Svnl5enpq/MupEAW/ufj5+Yl169aJS5cuiT179ohOnTpJ+4YOHapxDMpljjkdHBwsLCwsRNeuXcW3334rjhw5IiIjI8WFCxfEhg0bRLdu3aQxLCwsxJ49ezTGYqic1oYFweIpjzl98ODBAtfQWbNmSWP279+/wD51PyjnGTJkiNSvU6dOYs+ePeLSpUti3bp1UiEdgFi1apXGMXidLnvMMafL4nWaOW045SmnMzMzRdu2baX+w4cPF1evXtX6unv3rsZ4eJ02TeaY07xOU1nH35zI4HS92Ofk5Ih3331XaqvuNXbsWJGTk6P1eLdu3RK1atXSOIazs7PYt29fkXH/9NNPwtraWuM4LVu2FE+ePNHYPyoqSuu5vPjS9tf/8+fPCy8vL419vby8xPnz54s8p3nz5gmZTKZxnICAAJGRkVHkOObOHHM6ODhYpzx2d3cXu3fvLjIWQ+W0JiwIFk95zOn8X+OiXlWrVtU4Tnp6uggICNDYVy6X6zQ7i9fpssUcc7qsXqeZ04ZRnnK6uD8DA9pnz/I6bZrMMad5naayjr85kcHperHPExoaKvr37y+8vb2FtbW18Pb2Fv379xcHDhzQ+Zipqali0aJFonnz5sLV1VXY29uLOnXqiOnTp4t79+7pPM7Vq1fF+PHjRY0aNYStra1wd3cX7dq1EytXrhQKhUJrX0MWBIXIvSVizpw5omHDhsLR0VE4OjqKRo0aiTlz5hSa3q3N2bNnxbBhw4Svr6+wtrYWFStWFN26dRObN2/WeQxzZ445/ejRI7F27Voxbtw40axZM+Hj4yPs7OyEra2t8Pb2Fr169RLff/+9SEpK0jkWQ+W0OiwIFk95zGlDFQTzbNq0SXTr1k1UrFhRWFtbC19fXzFs2DBx7tw5nc+J1+mywxxzuixfp5nT+itPOW3ogmAeXqdNiznmNK/TVNbJhChijWsiIiIiIiIiIiIyGXJjB0BEREREREREREQvDwuCREREREREREREZoQFQSIiIiIiIiIiIjPCgiAREREREREREZEZYUGQiIiIiIiIiIjIjLAgSEREREREREREZEZYECQiIiIiIiIiIjIjLAgSERERERERERGZERYEiYiIiIiIiIiIzAgLgkRERERERERERGaEBUEiIiIiIiIiIiIzwoIgERERkQm7d+8eZDKZ3i8iIiIiMh0sCBIRERERlRGjR4+GTCZDtWrVjB0KERERmTBLYwdARERERKWnSpUquHr1qsb9PXr0QFxcHLy9vXH48OGXGBkRERERGQsLgkREREQmzMrKCg0bNtS6X5d2RERERGQ6eMswERERERERERGRGWFBkIiIiIjU6tixI2QyGTp27AgAuHXrFqZNm4ZatWrB3t4eMpkM9+7dk9o/fPgQK1aswKBBg1CrVi04ODjAxsYGVapUQf/+/fHbb79BpVJpPN7JkyelRUxOnjwJANi2bRu6dOkCT09P2NnZoU6dOpg5cyaeP3+uNfZ///0X7733Hho2bAhHR0dYW1vD29sbTZo0wbvvvovffvsNWVlZGvsnJCTg888/R5s2beDh4QEbGxt4e3ujf//+CAkJ0enzS0lJwbfffovOnTvDy8tLGqNVq1aYNWsWLl++LLUNCgqCTCbDhg0bAADR0dFc3IWIiIhKjUwIIYwdBBEREREZR7Vq1RAdHY2qVasWKO4BuQXBU6dOoUOHDpg+fTqGDx+OtLS0Am2ioqJQrVo15OTkwNraWmvBDwC6deuGkJAQODo6Ftp38uRJdOrUCQBw7NgxrFu3Dps3b1Y7Ts2aNREWFgYvL69C+7Zv34533nkH2dnZWmO5evWq2tukDxw4gOHDhyMxMVFj3969e2Pr1q1qzyMv/qFDh+Lp06daY8j7UTwoKAjz58/X2jZ/eyIiIiJ98BmCRERERKTV/fv38c4778De3h5z587F66+/DgsLC1y6dEkqiOUVqjp37oxevXqhUaNG8PT0REpKCu7evYs1a9YgPDwcR48exdSpU6WZcJrMmzcP586dwxtvvIGRI0eiatWqePToEX788UeEhobi9u3bmD59OrZs2VKg36NHjzBmzBhkZ2ejYsWKmDZtGlq3bg0PDw9kZmbi7t27OH36tMZZfkePHkW/fv2Qk5ODatWqYfLkyWjVqhWcnZ3x4MED/Pbbb/j1118RGhqKUaNGYefOnYXGOHHiBHr16gWlUgkLCwuMGDEC/fv3xyuvvILMzExcv34dBw8exL59+6Q+U6ZMwaBBgzBnzhzs2bOHi7wQERFRqeIMQSIiIiIzpssMQQDw9vZGeHg4XnnlFbXjCCFw584d1KxZU+OxAgMDsWDBAshkMty8eRO1atUqsD//DEEA+Pzzz/HZZ58VOk7Pnj1x5MgRWFpaIi4uDp6entL+devWYezYsQA0zwAEgMzMTAghYGdnJ72XlpYGPz8/PHr0CN27d8euXbtgb29fqO+aNWswYcIEALkzAbt06SLty8jIgJ+fHx4+fAh7e3uEhoZKt1y/KCYmBr6+vgXeGz16NDZs2KD260FERERkKHyGIBEREREV6auvvtJYDAQAmUymtRgI5M768/DwgBACe/fu1dq2WbNm+PTTT9Ue56OPPgIAKJVKhIeHF9gfHx8PAHBzc9O6arKtrW2BYiAABAcH49GjR7C1tcXGjRvVFgMBYPz48WjZsqXUJ79ffvkFDx8+BAB88cUXGouBAAoVA4mIiIheFhYEiYiIiEgra2trvPXWW8Xqo1KpEBcXh5s3b+LatWu4du0a/vnnH/j4+AAA/vzzT639hw0bpnERjWbNmknbd+/eLbCvcuXKAHIXBdmzZ0+xYs5r36FDB1SsWFFr2/bt2wNAoYJkaGgoAMDe3l6aRUhERERU1vAZgkRERESkVa1atWBra1tkOyEENm3ahLVr1+LChQvIyMjQ2LaoxTbq1q2rcV+FChWk7ZSUlAL7+vXrB1dXVyQmJmLAgAHo2LEj+vbti/bt26NJkyawsLDQOG5ERAQA4PDhwzqv6Js3IzHPlStXAADNmzfXOMOQiIiIyNhYECQiIiIirdzc3Ipsk5mZiYEDB+LgwYM6jamtWAhAazFNLv/fTS45OTkF9rm7u2Pv3r0YOnQoHjx4gBMnTuDEiRMAAGdnZ3Tt2hVjxoxBnz59CvRTKBRaVxXWJD09vcC/8wqdeTMViYiIiMoiFgSJiIiISCtts+ryfPHFF1IxsEOHDpg6dSpee+01eHl5wc7OTiritW/fHmFhYSjNde1ef/113L59Gzt37sSBAwdw+vRpxMbGIjk5GSEhIQgJCUGPHj0QEhIiFR7zFxYHDx6MuXPn6hWDrjMMiYiIiIyBBUEiIiIi0osQAj///DMAoF27dvj9998LzOLLLyEh4aXEZGtri+HDh2P48OEAcp81GBoaiuXLl+Pff//F4cOH8dlnn2Hp0qVSe3t7e6SnpyMxMVHrgiTaeHh4IDY2FnFxcQY7FyIiIiJD46IiRERERKSX58+fS8/SGzx4sMZiYGpqKm7evPkyQ5PUqFED7733Hi5duiQtbLJt27YCbZo2bQoAOHv2bKFbgXX12muvAch9HmFJxuDMQiIiInoZWBAkIiIiIr0olUppW1sRbO3atVAoFC8jJI2cnZ3RokULAIUXNunXrx8AIC0tDT/++GOJxu/bty+A3M/hp59+Knb/vMVbsrKySnR8IiIiIl2wIEhEREREevH09ISrqysAYOvWrcjOzi7U5tKlS5gzZ06px3L48GE8fPhQ4/6kpCRcvHgRAFC9evUC+yZNmgQPDw8AwNy5c4tcIOXs2bM4ffp0gffeeecdVKlSBQDw2Wef4dSpUxr7x8bGFnovbzGSx48fF1pBmYiIiMhQ+AxBIiIiItKLXC7H8OHD8eOPP+KPP/7A66+/junTp6NmzZpISkrCgQMHsGLFCjg6OsLb2xv//vtvqcWyZcsW9O3bF926dUP37t3RsGFDVKhQASkpKbh27RqWL1+OBw8eAAAmT55coK+zszO2bNmCXr16ISsrC3369MGbb76JN998E35+fgCAhw8fIjIyErt27cJff/2FZcuWoX379tIYtra22LhxI7p374709HR06dIFI0aMwIABA+Dj44OsrCzcvHkTBw4cwJ49ewrNBGzbti0AQKVSYdKkSXjvvffg7u4u3Upcs2bNUvvsiIiIyHywIEhEREREevviiy9w9uxZ/PHHH7h48SKGDh1aYH+FChWwc+dOzJs3r1QLggCgUChw4MABHDhwQGObqVOn4r333iv0fteuXXH48GEMHz4c8fHx2L59O7Zv365xHGdn50LvderUCfv378fQoUORkJCA9evXY/369TrF3rlzZ7Ru3Rrnz5/H5s2bsXnz5gL7S3N1ZiIiIjIfLAgSERERkd5cXFxw9uxZLFmyBNu2bcOtW7dgaWkJX19f9O7dGx988IG0mEdp+u6779CvXz8cPXoUERERePjwIZ48eQILCwv4+vqibdu2GDduHPz9/TWO0blzZ9y5cwfBwcHYv38//vzzTzx79gxyuRyenp6oV68eOnTogDfffBN16tRRO0aPHj1w9+5drFy5Evv378fNmzeRnJyMihUrwsfHB126dClUNAVyZ1seOXIEX3/9Nfbt24c7d+4gLS2NhUAiIiIyKJngTxdERERERERERERmg4uKEBERERERERERmREWBImIiIiIiIiIiMwIC4JERERERERERERmhAVBIiIiIiIiIiIiM8KCIBERERERERERkRlhQZCIiIiIiIiIiMiMsCBIRERERERERERkRlgQJCIiIiIiIiIiMiMsCBIREREREREREZkRFgSJiIiIiIiIiIjMCAuCREREREREREREZoQFQSIiIiIiIiIiIjPCgiAREREREREREZEZYUGQiIiIiIiIiIjIjLAgSEREREREREREZEb+DzSzqw/hzWM6AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,5))\n",
    "ax.plot(transects.keys(), trend_tg, label='TG, median = ' + str(round(np.nanmedian(trend_tg),1)) + ' m/yr')\n",
    "ax.plot(transects.keys(), trend_eot, label='EOT, median = ' + str(round(np.nanmedian(trend_eot),1)) + ' m/yr')\n",
    "ax.legend(loc='lower left')\n",
    "xticks = cd_along.columns[0::25]\n",
    "ax.set_xticks(xticks)\n",
    "ax.set_xlabel('Transect')\n",
    "ax.set_ylabel('Trend [m/year]')\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6fe4796e-7bc0-4ce0-8089-76f9d4826bad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2,1,figsize=(15,8))\n",
    "axs[0].plot(cd_along_qc.columns, med_diff_tg_minus_eot, '.')\n",
    "xticks = cd_along_qc.columns[0::25]\n",
    "axs[0].set_xticks(xticks)\n",
    "axs[0].set_ylabel('Difference [m]')\n",
    "axs[0].grid()\n",
    "\n",
    "axs[1].plot(transects.keys(), trend_tg, label='TG, median = ' + str(round(np.nanmedian(trend_tg),1)) + ' m/yr')\n",
    "axs[1].plot(transects.keys(), trend_eot, label='EOT, median = ' + str(round(np.nanmedian(trend_eot),1)) + ' m/yr')\n",
    "axs[1].legend(loc='lower left')\n",
    "axs[1].set_xticks(xticks)\n",
    "axs[1].set_xlabel('Transect')\n",
    "axs[1].set_ylabel('Trend [m/year]')\n",
    "axs[1].grid()\n",
    "plt.savefig('../99_plot_dump/cassie_sens_eot_vs_tg.png', dpi=dpi, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0ffd8c0-ee85-4dea-9c3d-40a3655096ce",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Plot to illustrate tidal correction\n",
    "for one date (28.05.2020, water level = 0.67 o.NAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "07418d19-1cc8-4361-b5bf-36c7484fc0f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABdkAAAMdCAYAAABneH8LAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOz9d5wkd33g/78qd5juyTOb8yqDEZJAJMkyBxbCxsYYDh8+JLB/JIMN/howGOOzvwYM9z3gEMdhovCRMckHmGAkEBIIrXLavBN28nTPdK5c9fujentntbuzaeLu+/lQa3q6q6s+3bNTU/Wu9+f9VuI4jhFCCCGEEEIIIYQQQgghxBlTl3sAQgghhBBCCCGEEEIIIcRqJUF2IYQQQgghhBBCCCGEEOIsSZBdCCGEEEIIIYQQQgghhDhLEmQXQgghhBBCCCGEEEIIIc6SBNmFEEIIIYQQQgghhBBCiLMkQXYhhBBCCCGEEEIIIYQQ4ixJkF0IIYQQQgghhBBCCCGEOEsSZBdCCCGEEEIIIYQQQgghzpIE2YUQQgghhBBCCCGEEEKIsyRBdiGEEEIIIYQQQgghhBDiLEmQXQghhBBCnJHBwUEURTnn25YtW5b7rYgl8o1vfOOYn/0tt9xyWq+L45gvfelLvOhFL2L9+vVYlsX69et50YtexBe/+EXiOD7tMRw6dIi/+qu/4vLLLyefz5PP57n88sv5q7/6Kw4ePHja61nIMQGUSiX+5//8nzz/+c9nw4YNWJZFT08Pl19+Of/1v/5XPvvZz+K67pKNaaE+JyGEEEKIC4kSn+lRoBBCCCGEuKANDg6ydevWc17P5s2bGRwcPPcBiTN222238ZrXvAaAO+64g9/8zd9ctG1NT09z2WWXUSgUWo/dfPPN3HbbbfO+bmZmhpe+9KXceeedJ13muuuu4zvf+Q6dnZ3zrutf/uVfeOMb30ij0Tjh85lMhv/9v/83r371q5dsTABf+9rXeMtb3sL09PS8yw0MDJz0otRK/JyEEEIIIS40+nIPQAghhBBCrC7r16/n0UcfPenz73nPe/jud78LwOc+9zmuueaaEy5nmuaijE+sLG9605soFAr09/czOTl5Wq/xfZ+XvOQl3H333QBcf/31vPnNb2bLli0MDg7y8Y9/nJ///Ofceeed/O7v/i4/+9nP0PUTn9p873vf4zWveQ1RFNHW1sbb3/52brjhBhRF4fbbb+dDH/oQ9Xqd1772tXR1dfE7v/M7iz4mgI9//OP8+Z//OXEc097ezp/+6Z9yww03sGbNGsrlMocPH+auu+7i3/7t31bV5ySEEEIIcUGKhRBCCCGEWEA333xzDMRAfMcddyz3cMQJfP7zn1+Sn9FXv/rVGIiz2ewx27z55pvnfd3HPvax1rIvf/nL4zAMj3k+CIL45S9/eWuZj3/84ydcT6PRiDds2BADsWVZ8a5du45bZteuXbFlWTEQb9y4MbZte1HHFMdxfOedd8aqqsZAfM0118STk5MnXTYMwzgIgkUd00J+TkIIIYQQFyKpyS6EEEIIIYRYcFNTU7z5zW8G4AMf+MBp1+CPooh/+qd/AiCbzfLJT34SVT32tEXTND75yU+SyWQAeP/733/CuuNf+MIXGBkZAeBtb3sbV1999XHLXH311bztbW8D4PDhw/zLv/zLoo4piiL+5E/+hCiK6O7u5v/+3/9LX1/fST8PVVXRNG1Rx7RQn5MQQgghxIVKguxCCCGEEGJJbdmyBUVRWnXADx8+zDvf+U6uuOIKOjo6UBSFj370o63lwzDkP/7jP3jHO97Bddddx5o1azBNk7a2Nnbs2MGrXvUq/uM//uOU231y082hoSH+8i//kosvvphMJkNHRwfPfvaz+cQnPkEQBPOua3Z2lg984AM873nPo6enB8Mw6OjoYMeOHVx33XW85z3v4a677pp3HTMzM7z//e/nec97Hv39/ZimSU9PD8997nP54Ac/SKVSOeV7AhgfH+fv//7vj1nPmjVruOqqq3jjG9/IT37yk9b7+dnPfoaiKK167ECrJMjc20LUaD9SJuY5z3kOf/Znf3bar7v77rsZGxsD4A//8A/p6uo64XJdXV384R/+IQBjY2OtkilzfeMb32jdf93rXnfSbc597utf//qijun73/8++/fvB5KAdn9//0nHNZ+V+DkJIYQQQlyopCa7EEIIIYRYNj/60Y/4z//5P1Mul0+6zP/z//w//M//+T+Pe9z3fQ4ePMjBgwf58pe/zCtf+Upuu+02LMs65XZ/8IMf8F/+y385Zru2bfOrX/2KX/3qV3z3u9/le9/7HoZhHPfa++67j5tuuum4ZpXlcplyuczBgwf5xS9+wRe/+MWTNnb92te+xute97rjAunFYpG7776bu+++m4985CN885vf5DnPec5J38dHP/pR3vWud+E4zjGPT05OMjk5yQMPPMAnP/nJRW9u+mRf/epX+eY3v4llWXz2s589LsN6Pj/72c9a93/rt35r3mWf//zntzKqf/azn/Hc5z639VwQBK0LHVu3bp23We+R5wcGBrjrrrsIw/CY7PGFGhPAl7/85db9P/qjP2rdr1arTExMkMvl6OvrO+VnthI/JyGEEEKIC5UE2YUQQgghxLI4fPgwr3jFKwD4m7/5G57//OeTz+c5ePAgvb29reWCIGDdunW85CUv4dprr2X79u1ks1kmJyfZvXs3H//4xzl06BBf/epX6enp4dZbb513u48++ijf+MY3aG9v593vfjfPfvazSafTPPzww/zjP/4jAwMD/PjHP+ZDH/oQf/M3f3PMa33f52UvexnT09Ooqsott9zC7/zO77B27VpM02R6eppHH32Un/zkJxw8ePCE2/8//+f/cPPNNxPHMd3d3fzZn/0ZV155JRs3bqRcLvPTn/6Uj3/840xOTvKiF72IX//611x66aXHrec973kP73vf+wBIpVK85jWv4cYbb2TDhg14nse+ffu44447+Pa3v916zTXXXMOjjz7Kd7/7Xd7znvcAJ25Om81m5/0M5zO3TMzf/d3fcfHFF5/R6x9//PHW/csvv3zeZS+77LLW/SeeeOKY5w4cOIDneae1niPrGhgYwHVdDh48yEUXXbTgYwL45S9/CcCaNWvYtm0bX//61/kf/+N/cO+997aW6ezs5KabbuLd7373MeubayV+TkIIIYQQF6zlLQkvhBBCCCHON6dqfLp58+bW893d3fHu3bvnXd+BAwdi3/dP+nwQBPErX/nKGIg1TYsHBwdPuNyRbQLxFVdcEU9NTR23zPj4eNzV1RUD8Zo1a45rOHn77be31vHhD3943nEXCoXjHhsYGIjT6XQMxC960YviWq12wtfu27cv7u3tjYH4P/2n/3Tc83fccUdrHBs2bIgff/zxk46jUqnEMzMzxzy2mI1PX/rSl8ZAfOWVVx7zc5s75vkan1577bWt5U70M5prcnKyteyzn/3sY5774Q9/2HruTW960ynH/cY3vrG1/I9//ONFGVOlUjnmude//vXH/Lt88s00zfhf/uVfTridlfg5CSGEEEJcqKQmuxBCCCGEWDbvf//7ueSSS+ZdZvv27ej6ySdgaprGrbfeiqZphGHId77znVNu97bbbjsmW/6INWvW8NrXvhaAiYkJ9uzZc8zz4+PjrfvPf/7z591Gd3f3cY/9f//f/4dt2+Tzeb70pS+dNGN8586dvPe97wXgP/7jPxgYGDjm+X/4h39o3f/Sl7500mxngFwuR2dn57xjXShf+cpX+Pa3v42u63zuc5+b9+d2MtVqtXU/l8vNu+zc5+e+7kzXs5Drmm89MzMzrfsPPPAA//zP/0wul+MjH/kIIyMjuK7L/v37eec734mqqniex2te8xruvPPO47azEj8nIYQQQogLlQTZhRBCCCHEstB1nVe96lVn/Lp6vc7Q0BBPPPEEjz32GI899hgTExOtxo8PPPDAvK+//PLLueqqq076/DOe8YzW/QMHDhzz3Pr161v3P/OZzxDH8RmN/Vvf+hYAN9544ykD33NrqM9toloqlfj5z38OwLXXXst11113RmNYLJOTk7zlLW8B4B3veAdPe9rTzmo9tm237pumOe+yc+vvNxqNs17PQq5rvvXMDUo7joOmafz7v/87b33rW1m/fj2mabJjxw7+6Z/+iU984hNA0vj3L//yL4/bzkr8nIQQQgghLlQSZBdCCCGEEMvioosuOu3a3wMDA/zFX/wF27dvJ5fLsWXLFi6//HKe8pSntG5HGpEWCoV51zVf1jccm4H+5Makz3nOc1r10W+99VYuuugi3vnOd/K9733vlNsdHh5uZcJ//etfR1GUeW9PecpTWq+dm0H/4IMPEkURADfccMO821xKb3jDGygWi1xyySWtLPyzkU6nW/eP1Ao/Gdd1W/czmcxZr2ch13W66wF45StfedLGtq9//et56lOfCsD9999/XC31lfg5CSGEEEJcqCTILoQQQgghlsWRzPNT+frXv86ll17Kxz72MQ4dOnTK7PFTZdeeKjCoqkcPkcMwPOY5Xdf53ve+12oUeuDAAT70oQ/xu7/7u/T29nLZZZfxzne+k0OHDh233qmpqXm3O5+57+nIxQSAdevWnfU6F9KXv/xlvvOd76CqKp/97GePyXY+U2dSjqRWq53wdWe6noVc1+muB+B3fud35l3Xi1/84tb9X//614s+pnP9nIQQQgghLlRnXiRRCCGEEEKIBaBp2imXOXDgAK9+9atxXZdMJsNf/MVfcOONN7Jjxw66u7sxTRNFUQDYtGkThw8fPuMSLmdq27Zt3Hvvvdxxxx3827/9G7/4xS94+OGHCYKA3bt3s3v3bj784Q/z/ve/n7e//e2t1wVB0Lr/2te+lre97W2nvc2+vr7W/bnv78h7X25///d/D8BVV13F8PAww8PDxy0zNxN7YGCAr371qwBs3bqVZz7zma3nNm3axD333AMk2f8nqp1/xNDQ0DGvm2vu9ycaz5muayHG1NvbSyaTaV00efLzT7Z58+bW/SdfpFmJn5MQQgghxIVKguxCCCGEEGLF+tznPtcqT/HNb36TG2+88aTLzm0quRRuuOGGVrmWRqPB3XffzTe+8Q2+8IUv4Hke73jHO7jiiit40YteBHBMELRcLnPFFVec1XbnBtxHR0fP4R0snCM/o127dvFHf/RHp1z+zjvvbDXzvPnmm48Jsl9xxRV8/etfB+Dxxx+ft37+3MD95ZdffsxzO3fuxDRNPM/j8ccfP+WYjqzLsix27NhxzHMLNSZFUbj88svZtWsXcPxMiSeb+/yTL0qtxM9JCCGEEOJCJeVihBBCCCHEivXoo48CSWmZ+QLsjz/+OPV6famGdZxMJsMLXvACPvWpT3Hbbbe1Hv/yl7/cur9169ZWiZzbb7/9mNrWZ+LpT396K+B6xx13nNU6VkoG/Ilcf/31rfu33377vMv+9Kc/PeHrICntc6Te+cDAAIODgyddz+DgIAMDA0BSd//JAe2FGhMcW0d///79865r7vMbNmxYlDEt5OckhBBCCHGhkiC7EEIIIYRYsY6UWLFte96s34997GNLNaRT+u3f/u3W/bn101VV5fd///cBmJ2d5dZbbz2r9be3t/Obv/mbANxzzz2tjPAzkUqlWvfPNtg/1+DgIHEcz3ube0Hg5ptvbj0+96IEwHOf+1zWrFkDwL/+67+edIbCzMwM//qv/wrA2rVrT9hA9OUvf3nr/j//8z+fdPxzn5v7msUY0ytf+crW/SOZ6CcSBAHf+ta3gOSiyJOD4yvxcxJCCCGEuFBJkF0IIYQQQqxYF198MZAE2b/4xS+ecJkvfvGLfPrTn16S8dx5553s3r173mV+8IMftO5v3779mOfe/e53t5qC/s3f/A3f/va3511XsVjk4x//+HGP/+3f/m0rG/1Vr3rVvGOq1WrMzs4e89j69etb9/ft2zfvGJaaqqr89V//NQD1ep03vvGNRFF0zDJRFPGGN7yhVdv8r//6r49pWHvEzTff3GoO+5GPfIT77rvvuGXuu+8+PvKRjwDJ5/LqV796Ucd05ZVXthqe/uhHP+L//J//c8LP4a//+q9bNdJf9rKXsXbt2kUb00J9TkIIIYQQFyqpyS6EEEIIIVasW265hY997GPEcczrXvc6HnjgAW666SZ6e3sZGhriK1/5Cv/6r//KRRddxMzMzDGZ44vh9ttv5x/+4R+45ppruOmmm7jyyitZs2YNqqoyMTHBv//7v/OZz3wGAMMweP3rX3/M67dv387nPvc5/viP/xjP8/iDP/gDXvziF/Pyl7+ciy++GMuymJmZ4fHHH+f222/nhz/8IT09Pbz5zW8+Zj3XX3897373u3nf+97HyMgIT3/603nta1/LjTfeyPr16/F9n4MHD3L77bfzzW9+k29/+9ut7HdISs60tbVRq9X40Ic+RH9/P1dccQWmaQKQTqePCcQvtTe96U189atf5Z577uHrX/86k5OT/Pmf/zmbN29maGiIj33sY/z85z8H4FnPehZvfOMbT7ieTCbDJz7xCf7gD/4A13W54YYbeMc73sFv/dZvAcnP80Mf+hCu66KqKv/rf/0vMpnMoo4JkpkXv/71r5menuaWW27h5z//OS9/+cvp6+tjaGiIz372s3zve98DoL+/vxXcXg2fkxBCCCHEBSkWQgghhBBiAd18880xEAPxHXfccdzzmzdvjoH4+uuvP631ffjDH44VRWmt88m3nTt3xvv27Tvleo8sf/PNN8+7vTvuuKO17Oc///ljnvu7v/u7k45j7i2Xy8Xf/OY3T7qN73//+3FfX99preuyyy476Xo+9KEPxaZpnnIdJ/o5vO997zvp8qf7szkTcz/XU/0M4jiOC4VC/NznPnfe9/Xc5z43LhQKp1zX5z//+TidTp90Pel0+rif9WKP6YEHHoi3bt0677p27twZP/7446vucxJCCCGEuNBIJrsQQgghhFjR3va2t3H11Vfz0Y9+lLvvvptisUhHRwc7duzgD/7gD3jTm95ENptdkrG84x3v4NnPfjZ33HEHv/zlLxkdHWVqagrbtuno6ODSSy/lhS98Ia973evo6+s76XpuuukmBgcH+cIXvsAPfvADHnzwQQqFAmEY0tHRwbZt27jmmmv47d/+7WNqvD/Z29/+dl75ylfyyU9+kp/85CccPHiQarVKT08P69at49prr+UP//APue6664577bvf/W4uvvhiPve5z/Hggw9SLBbxPG9BPqeF0N3dzc9//nO+9KUv8aUvfYmHH36YYrFId3c3T33qU3nVq17FH//xH5+w/MmT3XLLLTzvec/jf/2v/8UPf/hDDh8+DMDGjRu58cYbedOb3sSOHTuWdExXXnkljz32GP/8z//Mt771Lfbu3UupVKKzs5OnPvWpvOxlL+M1r3lNq7zQavqchBBCCCEuNEocx/FyD0IIIYQQQgghhBBCCCGEWI2k8akQQgghhBBCCCGEEEIIcZYkyC6EEEIIIYQQQgghhBBCnCUJsgshhBBCCCGEEEIIIYQQZ0mC7EIIIYQQQgghhBBCCCHEWZIguxBCCCGEEEIIIYQQQghxlvTlHoA4e1EUMTY2Ri6XQ1GU5R6OEEIIIYQQQgghhBBCLKo4jqlWq6xbtw5VXRk55BJkX8XGxsbYuHHjcg9DCCGEEEIIIYQQQgghltThw4fZsGHDcg8DkCD7qpbL5QAYGBigq6trmUcjhFgNfN/nxz/+MS984QsxDGO5hyOEWOFknyGEOFOy3xBCnCnZb4hlF7jwv/uOfv/GKdCt5RsPELouP37GM1rfv/Dee9Gs5R3TSjIzM8PWrVtbsdGVQILsq9iREjG5XI58Pr/MoxFCrAa+75PJZMjn83IAK4Q4JdlnCCHOlOw3hBBnSvYbYtkFLqTmfJ/Pr4gge2ZOGZR8Pi9B9jl83wdYUeWzV0bRGiGEEEIIIYQQQgghhBBiFZIguxBCCCGEEEIIIYQQQghxliTILoQQQgghhBBCCCGEEEKcJQmyCyGEEEIIIYQQQgghhBBnSRqfCiGEEEIIIYQQQohT8mtF7KlDaFaWzNqLUVRtuYckhBArggTZhRBCCCGEEEIIIcRJxXFEY3wv9uQBotBH1Qyi0Ce36TeWe2hCCLEiSJBdCCGEmMOvFakdfpQ4ClAUDUXTUdQjX3UUTcfq2oiR7VzuoQohhBBCCLHoAqdKdfBBQruMX5vBLY1htvcDoKfzpHu3LvMIhRBi+UmQXQghhGiKfJfKwH2ETpXQbYCioqgqiqIdva9qODMjdFz8PPRUbnnGGbiEbgM904GiKMsyBiGEEEIIcf6zpwepjz1B5Lu4s6OEjTKoKt7sOKqeoj7yOFoqh5nrWe6hCiHEspIguxBCCNFUHX6IyHewC0PEnnPCZRTdIL3mImpDD9F+0XNQlKXtIe7OjlIdfhiiEKOtm9zWq1F1c0nHIIQQQgghzm+R71Idfgi/MkXg1HCLw8RRgNHeh9nWQ2PqIE5xiMyanVQH76fjouehWZnlHrYQQiwbCbILIYQQJFk6fmUKrzxB7DlYnetRzRRxFEEcEccRcejjlSbwZkdRVY3GxD6yay9ZkvHFcUxjfA/25AFCt0Fgl4jjmNK+u2jf9gy0VNuSjEMIIYQQQpw/4iggdOqEbo3AqRI6teTm1oijEK88gV8pJIkmfdvRrCwA6Z4tNCb3Y08Pku7fQWVgFx0XPQdFlTCTEOLCJHs/IYQQF7zAqVIffZzAqeFXCujZToxc9wmXjaMQvzKNn8rBxAHMXB9GW9eiji8KfaqDD+BXppI6mLOjEMdEno3VvZnSvl+Q23o1Zq53UcchhBBCCCHOD3EcUxt6EHd2DIhbj8WhT+S7RIGDX58l9hz0bCdW5zoUVWu9XjUsUt2bcKYHcYvDKIpKdegh8luvXqZ3JIQQy0uC7EIIIS5ocRxRHXyQKPCSEwTdwOpcd9LlzfY1hE4Nd3YUzcpSHXqQzkuuR9EW509q4FSpHNqVbLM0TlAtoFoZ9HQ7Xnkce/IAqd4tVA78muzGp5Du2bwo4xBCCCGEEOcPd3YUd3YUv1YkdOuEvkscOBDFrWUUzSDVswk903HCdejpPGbHmmSmZ2USRVFoTO4n079zid6FuCD98LULsx5Fgd/+7MKsSwgkyC6EEOIC1xjbQ2iXcWdHiaOAdN/2Y7J0nkxRFFLdG2lM7MedOYyiGdRGHiO3+WkLPjavPEl16AFCz8EtDhM6NfS2riSTSFFRzTROYSgJtHdvpn74EUKnSnb9ZUteK14IIYQQQqwOcRxjT+wn9B3cmVHQNFQ9hZbpRDVSqLqFalin1ffHzPcR+Q5+eQrVSNMY24OeymO29y/BOxEXpMdvSwLkcXz8c4pyeuuIYwmyiwUnQXYhhBAXLK9awJ46iF+bIWyUMdr7WnUm56MaKayOdc3snwKKomC292N1rF2wsdlTh6iPPk7o2TiFIeLQw+pcf0wZGz3VRqZ/B3ZhAGd6ALNzPQChWye3+UppiCqEEEIIIY7jzo4SujX8yhQoCpk1F6Fqxlmvz+rcQOS7uDPDqPoOqkMP0HHxdad1XC3EGbv81cBJgukHvwtOCfQU9F8FbRuSx2ujMHk/BDakOmH7S5ZqtOICIkH2k5iamuLee+/l3nvvZdeuXezatYtisQjAzTffzG233XZG6/vhD3/Ipz71Ke69916mp6fp7e3lGc94Bq973eu48cYbF+EdCCGEiKOA0LVRDRNVt455Lgo8akMPEgUubmkM1Upj5k8/48bIdRM4FbzSOJrVRm34YYxskv1zrrzKVFIj3q7gFIdAUUn3bjthc1PVsMj07cApDuPNjhL5TtIQdc/Padt8JWau55zHI4QQQgghzg9zs9iDegkj131OAXYARVVJ9WymMbkfpzBEes1OKod20XHxc6URqlh4N9524se//1/ALcMz3wXXvBOs/LHPe1W494Nw7wcg9ODFX1r0oYoLi+ztTqK/f2GmNsVxzBve8AY+9alPHfP46Ogo3/72t/n2t7/N6173Oj75yU+inO60FiGEEPOKAg9negB7+hBxGCQPqhqamUE102hmmtCpJVnixcMApLo2nfF+2OraQGNiH05xmMyanUmzp+3PPKf9eeg1qA49SOg7OMUhVM0i1btl3qx0RdNJ9W7FnR0lqBWJvAapnk1UDvyKdP8OMmsvlvIxQgghhBACrzR2TBa7ke9bkPWqukmqezPO9KFWI9Ta4UfJbb5yQdYvxLwe+TTs/Ro867/Bs/72xMuYOXjuP4Jmwa/+G2y8AZ76p0s5SnGekzPu07Bx40Ze+MIXntVr3/Oe97QC7FdeeSVf+cpXuPfee/nKV77ClVcmf2w+9alP8bd/e5KdgBBCiNMWBS71sT3MPv5TGhP78GszOMVh3NI4XnkKd3YUpzCMPXUIv1bEr0wRuQ2sjnWohnXqDTyJqhmkOjcQ+y5uaQK/Oo09dfCsx580YX2AyE9KxIB6ygD7EYqikOragNW9gShwaUzsx6/PYk8eoLzvbkK3ftbjEkIIIYQQq18cxzQm9hP5bpLF3tZ1zlnsc+mpNsz2NYR2Fb86jTszgl0YWrD1C3FSj30WFBWueuupl73qrcmyj31msUclLjCSyX4S733ve7nmmmu45ppr6O/vZ3BwkK1bt57ROg4cOMCHPvQhAK6++mruvPNO0uk0ANdccw0veclLuP7667nvvvv44Ac/yGte8xq2b9++4O/lQhb5Lo2JfQCk+7ZJTTghzlOR72JPHcQuDEIU4jfKeJUpYs9uNr+J4cl9cTQNwhAtk8do6zrrbeuZdvS2LoJqgSDVRmNsN6pukereeMbraoztJqjP4s6MEvvuaQfY5zKyXWhmNrm4UDxM6FQxo5DSnjvJbrjirMYlhBBCCCHOXBxHK2o2oVcaJ3SqeJVJUMDILUwW+1xmvo/QbeCVJlDNDPWRR9HT7RjZjgXflhAtM3vAbE+y1U/FzIGZT14jxAKSIPtJ/P3f//05r+MjH/kIQZCUKbj11ltbAfYjMpkMt956K8961rMIgoCPfvSj3Hrrree8XZEI7AqVQ/c2szdjnOIQqe5NSVOXBaiZLIRYfqHXwJ46hFMcJg4DAruMV54k9l0UzcDsXNcMoCvEoU8UeMShRxwk9xVNx1yAKbJWx1oit57UoOzbSm34YRRVw+pcd9rrcEvjzQz7mSSzKN+Lns6f+oUnoBoW6f4deOVJ/MoUodsg1b2R2vBDeJUpcpufhqJqZ7VuIYQQQghxau7sKLXDj6BoBpm1l5Dq2rCs40my2PcRBUkWu97WhaovXBb7XKnujTQm9uMUh8j076Q6eB8dFz/vuB5JQiyYOAKvBPYMpE+RQGXPJLXbjcySDE1cOFbOJdXzTBzHfPe73wXgkksu4dprrz3hctdeey0XX3wxAN/5zneI4yenWp6f4jgijsJFW79XnqS8724Cu4o9dYjG2F78ahF7epCZJ26nPr6HKPQXbftCiMUVOFWqQw8y+8TtSWC6WqAxsQ+3MAxxhNW1nsy6izFzPSiKiqIoqLqJnmrDyHZhtveT6t6I1bF2QYLNiqqR6tuGohnY04OEbp3q0AN4lanTen3oNqgNP0To2bizo6hWBrN9zbmNSVGwOtaQ7tsGcYw9eRCvPIlXGqNy6N6jtepXgCj0CZwqXrWAX5tZ7uEIIYQQQpwTrzJFdehBgkYFv1akNvQgpX13EzTKyzemI1ns5SSLfSESTU5GUTVSPZshinCKw4Rug+rggxdMvEMsg56nAjHc8w+nXvae/zcJyvc8ZdGHJS4sksm+SAYGBhgdHQXg+uuvn3fZ66+/nr179zIyMnJWZWlWkzgMaEwdwJkaAFUlt+lpmO0L02T2iMbUQRqjTyQNDQuDxKGPqlu4MyN41ekkcBWFOIXkqnqqZ7NkdAqxShypMe6VJ4jjCL82i1+dJg48FN3E6t6AnulclkbSqmaQ7tuGPXkQe3qAdN82Kod20b7jWoy27pO+Lo5CqoP3E/ouTnEIVJVU9+YFew9aqo3Mmp04syN45Umi0CeOY8oH7yG//ZkLWofzVOIowJ0ZxatOE3k2UeAS+W5ykDtHum8b2fWXL9m4hBBCCCEWStAoUx24n9C1sacPQRSht3VhhgGlvb8g1bOJzNpLzrgk4Lk4cRb74m5fM9NYXRtwi4dxS2MoioI9sY/M2osXdbviAvW0N8LYL+HBW5Ms9Wv/Fjq2HbtMeQB+9f/CE19ISoo+7U3LM1Zx3pIg+yLZvXt36/4ll1wy77Jzn9+9e/d5GWSP4xh3ZoT6+G5i38VvlFF1k8rALnJbrsLqWLsA24ioHX4UtziMb5dxi4dBUUj3bUc1MwSNEl55ArcwhG9lsDrWUB99HHvqIOm+7aS6N6Fo8ishxEoTxzFeZRKn2aw0ikKCWhGvWoAwQDVTmN2b0DPtyxJcn0vVTdJ9W7GnDmFPD5Dp207l4L3kd1yLke084Wvqo08QNEq4MyPEvkeqd+uCT91VNJ10zxbc2TH8aqEV1C7v/xXtO5656FN3A6eKUxjCnTlMHAatsj1RGBCHPvGcr3qmAwCzY73U7hRCCCHEqhJ6DcqHfk3o2ziFAVAU9GwnQW2GwC5jtq8hjiPc0jjZtZdgdW9akuNXrzzRzGKfWvQs9rmMbCehWyeoFvGtLI2JfejZziXbvriAXPoqGPopPH4bPPEvyS23EdrWAwrURqB6OFk2juHyVyevEWIBSURxkRw+fLh1f8OG+Wuvbdx4tAnd3Nc9meu6uK7b+r5SqQDg+z6+v3JLn/j1GeqjewidMqFn45UniNwGKBqp3s0EB+9LMto7zr40QhR41IYexK/P4NeKSZMV3cLq2UysG4QxKOkOzFQ7QX0GrzKNPzmIlmrDyPXiuTaVsT2kujeT6t6CaixdVoEQS+nIvmIl7zOOiAIPd3YUpzhM5DWIwoCgVkzKicQRqpnB6OhBTyfNbcKY5IBpuWkWevdWnOkBalODpHq3MrP/HvLbnoGi6YROLbm5dUKnRmCXkv1Wo4qR7werjSBanPehta8lVDTcyjRBOIzVHRPs+SX5bdegGgsbaI/jCL8yjVMYxK/PEMcxoV0hqM8Quo3jX6BqEIPnTpK28pSHHiG/49plv3ByoVtN+wwhxMog+w1xoYoCj8qBX+M7DZzpQeIgItW7Fc1Ko2S68cpj2DPjuNVZzI51+N7DaBOHSPduwVygEoYnEscxldG9+J6HWy+jZ7uJVINokY43n0xrX4fvOjSKo6RUi9mD95Hb9gyMTHtrGdlviAXx/H9G7X4K6r3vA2cGKsPJba5UJ9E17yZ62ltg7r+3wGdumpPv+xAvb4XtKDi2vGcQBESqVP0+YiXuLyTIvkiq1Wrrfltb27zLZrPZ1v1arXbS5T7wgQ+csCHrHXfcQSazWho2pIAtyd0YmAKIYOSBBdxGV3ILgIkjGzrB8wBO80YM+DB0ADiwgGMRYmX6yU9+stxDOAsa0Ne8AR5QhON/x1cCC7gEQpr7IQ+G7ppn+eZ+qQJUFvv99CY3BxgFqMDBny7yNo/IN28nMLdizDjALOz/98Ufkjgtq3OfIYRYTrLfEBcuBdie3J2G5Fg1BTRLV/hzH6/AwUeAR5ZgXDpwGdSB+lIfPzff+xSAD8N3n3Ap2W+Ic7cNdf0n6W08SIdzECtM+iC4Wjul1Ham008jGjdh/NjzDDX2+d053//wRz8kUpaurOYJBcExjTR/+MMfgi5h3CMajRMkbi0z+eksEsdxWvdNc/6saMs6mj1o2/ZJl3vXu97FX/7lX7a+r1QqbNy4kRtuuIHu7pPX+10qcRQmmeTVAl5lishrEMcRQbWIV52GOMZo68LI9aFoGpHvJTXTo5BUzyY0K0vb+iuwujeeemNNUehTOfBrAqeMWxgidBsYbd0Y7f2nlf0YRxFBfQa/WiSOAhTdwsx1o2U6UBQFI9dLqnM9Rr4XRV3dvy5O8TD10ccInDqalUHVDTJ9O7F6NqEocjX0QuH7Pj/5yU94wQtegGEs80HDk/iNMvb4nqNZz40Sfm2GyHcABT3bgZ7tQjNTyz3U0xY4ddzCEIpuYrR1Evnu0Trkc5o/q2YKq3sz6hKWrApqM7ilcVQzjdWzBT2VJb/1GjQre+oXz8MrTVA9/BCha+NXpwntZNaVZmWSn186P+/+2SuN49dmSPVuQc900HHxdUtas1QcayXvM4QQK5PsN8RKEEchcRwtSe+ZOI6pDT+UlCadGSFolLE61qK3dZ1kbBF+ZbLV7D2ZXd2THIOpGqmujaR6tqCZ6QUYW0R53y/x6zPYzVItVue6c17v2QhdG2d6AEU3SfVuRU+3kd92LZqZkv2GWAS/d2aLBy584ui3N/72jbDIJTVPJfI8fvyOd7S+v/HGG1FPEV+8kBSLxeUewnFWd9RwBUuljgaBPM+bd9m5JWDS6ZP/IbUs65iA/BGGYSzbH6I4CnBmRvErk0l95ObBTOTUCZxK0j09DDDTOayOtajGnOCYZaH3J7WL/eIAWs9WnPHH0TSVdO+W09h2SHnwIfCqBDPD4NbIdK2ft8HgcVQNo72XVL6boD6LVy0QzI4QViYwcj2EcYBdL+BoOmbHOlJdG9CzXauufEHoNvAm96L4dYLCIULDwupcjzu5h6A8SnbDFZi5nuUeplhCy7nfeLLId6iP7cadGSEKA8LqNH59BsIQVTexOvsxMp2rsmeCnmlD792IUxgiLI2BoqAZKYx0FlW3UI0UqmGh6NaS71f0fDeapiQng4VD6L1bqQ/eS8fO56JZZzc7ypkZwR55GMVr4BcOAZDKdWK0dR+7/5+H1tFPbJcISqOYqSze1H5ym592VuMRC2cl7TOEEKuD7DfEcvHKk1SHHyIOffJbr8Fs71/U7dVGHieqThJWJokbs6TyvVj5ec5JVQ2jax1Rey9+tYBfK+JPVwjNNEa+Fz8OCWaGyW644rTOi+djTx1C8WtE1Qk0JSKd70FVl+dcVk9n0Ho34RQGCIoDGH3bsIfvp33ns6G5r5D9hlg2SnTMt4ZhwAL3yTpTYXTsmHRdR5Pfj5aVuK9YfRGLVSKXy7Xuz1cCBqBer7fun6q0zEpTPngvQa1IFHiETpXArhK6VYhiUECz2jC6els1k58saRK4HXv6IM70AKmeLdRHHiWOfNJ9O04adIrjmOrQgwS1Iu7MYUK7itm59swC7HMoiorR1o2e7SK0K3jVabzSBF5pAi2dQ890EAUubnEY1cxgda0n3bttVWRXHsmsiEIXpziSBCqjCGfqEFqmHatjLZUDv8LsWEd2/WULkjEhxOmIoxB76hCNyf3EYYBfK+CVJyGK0NI5jLZutFRu1V3UejI9nSe7/jLiKETRjBX1foxsF4qi4hSHsacPke7bTvnAr+i46DmnHRQ/wikOUxt+mNCtYU8Poiga6b5tZ1zrXVE1zI61uMXD+LUCiqKQ6t541vt3IYQQQlw4GlMHaYw+QejZKKpKY/LAogbZG1MHcaYP4dWK+JUp9GwHVsfa03qtqhlYHWsx83349Vn86jRuYRhPN7A6N1AffSw5Rz3JufSphF6D+vgeAqdKUC9h5HsXvAfPmdLTOVLdm3AKwziFIVK9KpVD95LZfNWyjkucZ6YfgcEfQWUI/Abc+Lmjz4U+2NOAAm2n97sqxOmSIPsimdvsdGRkZN5l5zY7ndsEdaWL44iglpSC8WbHAVA0HT3dgZ7Oo6XaTqt5i6obpPu240wdwikMYPVspjG2B680QXbDFRjZzuNeUz/8KF5pHLc03jpgMHO95/yeFEVBz7SjZ9oJPZugPkvQKOHaVdxZFT3djt7skO7OjNC26WkrPgPcKQzh14q4pQniwGs238ngVabwq9M0nApmvp84jvAqk+S3XLXo2R5CuLNj1Md2E3kNAruCWxon9l1UK4vVuRbNXC19Jk6PomqL1szqXOmZDlIoOMUhnOkB0n1bKR+4h/adzz7tC4l2YYj64UcInBpOYQBF1Un3bT/rC5FGtjNpUl2eRM90UDv8KB2XXCelrYQQQghxQnEUUjv8CO7MCL5dxi0exsz3oeoposBFXYSyD07xMI3RJ/AbZbyZUbRUG1bXmZ/PK6qGmevBaOsmtMu4pQmcmcNkzIuoHX6E9p3PPqskjfrI48ShhzsziqIbmPmVcY6nZzqwOkPc2VGcYtKUsjr00PIOSpwf3DL86LVw4DvJ93EMinJskD3y4V9+A5xZeO0+6Ni2LEMV5yc5W10kl112Wev+nj175l127vOXXnrpoo1p0TTrCqf7d5BZdymp7o3omfYzCiipWhJoV40U7vQQzswIXrVAed9dVIcebNZkTjTG9+IUh/Cq0/iV6aSu3GlmC5wJzUxjda5L3lPvVvR0nqBRwpk6hD11kKBRonLgV9RHnyCOo1OvcBmEbp362BMETo2gWkRv60JP51BUDatjLZk1F6NZbXilCRrj+widGtXBBwic6qlXLsRZ8GtFSvvupjp4f1IbcnoAZ3oQ4ohUzyYy/dvPuwD7aqBn2rG6NhC5DZzCMIFdoXLoXuIoOOVr7alDSYDdrjYD7MY5BdiPsDrXQxzhlcYJnWry70QIIcSC82tF/NrKq2sqxOmKfJfygV/hzozgVaZwp4cgigjsChDjlacWfJtueaI1gy+Z7Zwi1bP5nGYsJglfHaS6N0EY4JUnCOozuM1A9BmNrzSOV57Aq0wTBx5W53oUdeWEf4xcN2Z7P2GjjDs7hl+dBpJZ2EKclSiAb92UBNj1DGx9MegnmJlrZOCK10IcHQ3GC7FAVs5e9jyzdetW1q1LGor8/Oc/n3fZO++8E4D169ezZcuWxR7aolHN1LkdVGhJ5qPe1kVQn6ExvgevOo1TPMzsE3fQmDyQlJaY2Idfn8WbHUdL57C6Npx65edAUZTWtLbs+sswO9cReTaNif34tRnsqYOU9t614gLTcRxTHX6YOPBwZ0ZQdOO4ixGqYZHu3UqqdwtxHCZ1o32H6sD9xOGpg2tCnC6/XqJ84B7K+3+JV5nCmR3Fnkgu7Jjt/WTWXoye6VjuYV7QjGwXZuc6QruKUxzGr81QOXQf8ZwGrXPFcURjcj/10ccJ7ApOYRBFM0n3n3uAHUA1Uhi5XoJ6icCp0Rjfe8wFVyGEEOfOnh6kvP+XlPf/End2bLmHI8QZC+wKpX2/wK8VcYrDzXKfeYxcN5HbIAoDvMrkgm7TrxWpDtxP6DWSEnmaQap364LNWtSsTHJOXJtpJU1FvnvqFzbFYUB95DFCz0lK2GTa0dP5BRnbQjLb+5vvs4hfSYLslQP3EDRKyzswsTo9+lkY+xW0b4PX7oWX/htY7SdedufLkq8D/7504xMXBAmyLxJFUfi930u6Ge/Zs4d77rnnhMvdc889rUz23/u931tRtXqXg6JqpLo2kO7fiWqk8WbHW0H1xtjuZjCnijtzGNVKk+o+t2yBsxmfmeshs+YiVMPCnRnBbpZjKe25E3sFZVo60wNJzfrSeJK90LXhpAd+ejpPuntzczrhYQK7QnX44SUesTgfJRnRuyjv+wVueQK3NEFjfHcysyLTSWbdJZjt/VIGZIUwcz1zsopG8avTVIcebGUVxVGYNBMbeoiZx35CY2wPfqOMUxhE1a2kBru2cA1ozHwfim7gzo4ShT710ScWbN1CCHGhOzrjsUroOTQm9i33kIQ4I255gtK+uwjsCvbkwWYZ0T5SPZvRM0nJ0dCu4FWmTpo0cKaOzPaLfBt7egBFUUn3bl3Q4x8Aq2MtiqbjzIwQBT710cdP+7X18T2Eno07OwKKitm5bkHHtpCszvXomXa8SjLbwKsVKe29i9rIY0Shv8yjE6vKni8npWF+8yPQdop/831XgqLCjJxbiIUlNdkX0Vvf+lY+/elPEwQBb3nLW7jzzjtJp482lbRtm7e85S1A0iX4rW996zKNdOXRzDSZ/u1JPfTSOM70AFo6j57txJ0ZRtEt0j1bl23Km2pYpPt24FUm8StT2G4dq3sT9ZFH8SqT5DY9bVmbyoRu/WiTm9pMUiYmNX/DHC3VhtmxFm92HL8yiaIo2FOdpPukRpk4c5HvUB99And2lDgK8atFvOpUs6lpHrO9X5rsrlBmez9xFOBXi7hqcphQHbwfUPArU8RRQByFSVDGrhDUS6hmmnTv1qSx8gJKSlutS3pLVKdRFAWre+OC9OAQQogL2ZNnPGqpHJqZwq8VpdG0WBWCRonqwH2Ebh1nepA4CrG6N7b6ealmGkXTCewKRlsXfq2Ime87p22GboPywXsIPRt7aiApd9i3fVHO+xRVw+pch1MYPnoM1LXhlO/Br5dwpgcJ6jNEbgOzc92CXwBYSMmx3SYidRxqYE/sR+nsI44j3NIYbesvT0oICnEqhUcBBba88NTLakaS5W5LqTSxsCTIfhJ33XUXBw4caH1fKBRa9w8cOMBtt912zPK33HLLceu46KKL+Ku/+iv+6Z/+ifvuu4/nPOc5vPOd72T79u0cPHiQD37wgzz44IMAvP3tb2fnzp2L8l5WMz3TgZbK41Wn8CvThHYFRTcWJZhzphRFwWpfg57K4RSHcaYOYeR7iOOI2T0/I7fpacvSQDSOY6pDDxHNUybmZMxcL5HbwCtPoZoZ6mNPoGfa5WRLnJE4jigfuIfALuHXZpLMlDBMLuS0r0GzpOb6Smd2rEsujlSmULRkBkwUBklQ3a4QOtWkkZCqord1JdlWi9TYVc+0o6VzeJVmE9ThR+i89HoUVQ5hVqvQbRDHIapuoWjGBT+LT4jl4BQG58x49Akas0Qda7GnB+S4T6x4cRQ2z3f8JJschXTf9mOOMRVFQUvnCRqzxHGIV544pyD7kbrvodvAnh4gDn3SfdsWNWlEz3SgpWebx0Dt1A4/Ou8xUBxH1A4/QhT6uKUJVCu9Kn6fFUXB7FgLtRjVMHBnRvHrs1id66kOPoBTHKZtw1PQUm3LPVSxkvl1MHOgnWbZytADOZ8QC0z+RZ3EZz7zGb7whS+c8Lm7776bu++++5jHThRkB3jf+97H1NQUn/vc53jwwQd55Stfedwyf/Inf8I//uM/nvOYz1eKqmK1r8HIdhN5dbRUbtGCOWdDs7Jk1lyUNGypFAicGqmuTVQO3UuqZzPZ9ZctaTDInj5EUJ/Ba540pfu2ndHnZXVtIPQdnOIwGWMnlcH76bz4OlTjBE1DhDgBd2YkaVRZHCFslFGtDFb3GjkwXkWSbKmNxFGENztO0CgTeQ2Ik/4ZerYTPd2OlmpbkgCp1bGOxsQ+3JkRVN2kMb6X7PrLF327ZyKOYyK3jqLpKLq1aJ9LHAZEgUcch2jW0nz+CyXyHapDD+JXjyYuoCgouomqmaiGhWqksDrWLctFaiEuFEmZmN0ETo2gNoNqZYjcRnL8qOqEni2zzcSKVh/bQ+gkJUSJQlL9O9DM45M49HQ+qWvu1HHLE7RtfOpZbS+OIyqH7iV0qziFQWLfIdWzBc3KnutbOSWrcz2N8b24M6OoukVj4gDZdZeccFlnepDQLuOVxiAOsTo3rKrjBIBU7zZozOKWx7En9idJbFFIqV6i/aLnrMja8mKFSPdAfSIJthun+N2c3Z8s17F9acYmLhgSZF9kqqry2c9+lpe97GV86lOfYteuXRQKBXp6erjmmmt4/etfz4te9KLlHuaqoOoGqt6x3MM4IUXVSHVvJEjncGZGsSf3J1fjAb9aJLflyiVp6uhVCzRGdxPYFYLaDEau+4wDm4qqke7ZnDSaLQyR6d9OZeB+2nc+S+pmi1OK44jGxD5CzyZslDFy3TLFc5VSFIVUzybc2TEir4HR1oOeaUc1M0t+wqYaFmb7GrzSOH5tBlAwO9a1poQvN68yRe3wo8mFCABFQTXSqEYKzUyjmsl91UyjNb+q+vFTy6PQJ3LrhG6d0G0QunWiwCUOPKLAJQo8mFNT1sj3kd92zarYN3uVKapDDxL5Nl55ijj0kyx2TUdRtOZ9DUUzW6Ur0n3bsbrWr4r3J8RqEccxtTllYo7MELWnDjZLxfTgFIfIrj1xEE+I5eZVCzjTh/BrRUK7itmx5oQBdgDNagNFIbArScC9UTqrczKvMpWUMZ0ZI3LqWN0blizYq+omZns/XmkCv1GCqQNJHfP00VKgoVtPepiN703KhdZLGLmeVXmxTFEU9Fw3WiafHPdVCgSNMun+nVQHH6Dj4uetqIQ7sYL0XwOH/i8c+j5c/Ir5l73/I8nX9c9Z/HGJC4oE2U/itttuO64kzLm46aabuOmmmxZsfWJl0jMdZMws7sww3uwYoVPF6tpAad9dZNZeTLpvx6IFp0K3TnXgPqJmFrpiWJjtp1cm5slUI0WqayNOYQh3ZhRF0aiP7qZtw8rKHBUrj1MYJvJsvPIEqCpmXrJRVzNFUUl1bVjuYQBg5HoI7KRPh5bOURt+mI5LrlvWAGzo2dRHH8crjRMFLl5lGhQVVTdQNANVM5Mg8olKoqhaEoQ3UsRxROjWiQPvmEWiMCAOfeIoTGrhH7kfBq36qrWhh2jbfOWKzVSL44jG2G7sqUPJLKnCELHvgqYlFwziJ71AAT3bhZHrIXSqNMb3kOrbRrp787KXiRPifOAUhvBrRdzSBHHgkWrOeDTaunFnRgmdGk5hmEz/TglkiRUnCn1qww8RBS5uaRzVymDM06dFUVW0VI7ArhDHMW554qyC7G7xMFEYEDRm0du6MLJd5/AuzpyR6yWol3Bnx9BSOWrDD2G0dRE0ygR2mTgMAIjjMDl30w3M9jVLOsaFpmoGqe5NBJlOnMIA7uwoqqbTGN+z4mYzihXi8lvg4L/B3e+FDddB9iS/A7v+Ozz8yaRJ6hWvXdIhivOfnK0IscBU3SDVuw2/WsArj9OY2IfVtYHG2B78apH89mcseFAoDgMqh3YR+g52YRCAdM+Wc2oMq2faMfK9+JVpvOZUSCPXjbXKD9jE4omjgMbkviQD165itPdJUEwsGEVRsDo3YE/ux50dQ9UM7MkDZNZctORjieMIZ3qQxvheotBL9pOVKVoR4xMEjpNM7WbwXT8SfDebwfKIyPeIQo/ITzLW48CFKDrxABQlqYnfpBopsusvW4y3ek5Ct0518AGCRtKfwS2NAgqpns3omXbiOIbmBYTkgkJA6FQJ6jMEtZmkSXK+l8h3sCf2kerZQrp327I2FhdiNQvdBvWxJ5plYorobV3ozRmPeqYTtzSBXyugp3O4pfEVc5FVnLv62G7c2TH0TDupni2YuZ7lHtJZqR9+jNBt4BQPA5Dq2njKi8x6OkdoV5pJIJNnPEsj8l28yiRBYxZiljzADkfK+K3HnjyIVxpHVTX8+iyR7xB5NqHXIPJsosCBKCbVe27ngSuJns5h5vvxypPN2Yxg5PtX7b9hsYh2/j5se3GSyf7Fq+HSP4bASZ7b+42kMeq+b8DsvuSxi1+ZBOOFWEAS/RBiESiKgpnvRUu14RaHcaeHCJsHAvbUQTL9C9fkNml0+iCBXcEtDhMHLqmerQsShDDb1xB5DbzZUTQzS+3wIxht3Su6Q71YPk5hmNh3j2axz5NZJMTZ0Mw0Rr4PvzyF3yjTmNiH2b72mCnTi82vzVAbeTRpAutUcWdHiX0PLZ3D6lyPohlJ1nngE4c+UegTB17rfuQ2CO3y8YH4JkUzUHQTLdPRDMYbKKqelFJRjWYjWgW3OIxXnmxdyFKNFOm+bUv2OcwnjmO80hi1w48Q+i7e7ChBvYRqZUh1b0LVk4ZUiqKApqNoOmrzz4qR7SDqWINfLeDXitiTlSRTMd+bNLibOkSqeyPpvh3SRFmIM1Qbfog4PFomxuo4OuNRUVWMbCd+tUAUeDjTAxJkP0941QL25AFCt0bgVJMgrZUl1bOZVNeGE5YvW4nc2THc2RH86jSR28DqWn9a5ztaOg+MEtgVNCtD6DVOWl7mRJzZEYhj/NpMMgNtmf72aFYWva2LoDZDw7OJfOfoBXdFQTVT6JlO9HT+vKtbbuT7CJwKbmkMLZWlNvQgHZdc3zqeEKLld74KP/hjOPBduO+/H338+83eiEd+Z3a+FH77s0s/PnHekyC7EItIM9Ok+3fizo4SVAsEqTYa4/uwOtYtWKOcxsRevPIEXnmiWZdw4QJOiqJgdW9Kmu3MjqAaFo2x3WfdNEgczytPYk8PYOR6SPdtX7ElH04lDgMak/sJ3RqhU8Ns75dp5mJRmPk+wkYZd3YUzcpSO/ww7Tufs6i/O3Ec41encQqDeOVJotDHK40T1EsoutHKzD5C0QyY52JkHMdJ6ZfmDZrNP3XrtDPPrO6NxFGAOzMKmk599HEU3Vy2oFgcR80ZXBO45Qli321mGw4TBx5Gvi/ZL5zGz0nVkuCfme/DrxXxqwXc6SE8w8LM9RJHIU5hGKtzHen+HeddMEGIxWA/uUxM79bj/k4bbd3NC1wFVN3Er5cwsh3LM2CxYOzJA0RhgD09ACSzFoy2LiK3TmNsD1bHOlI9mzHalj5D+3RFvpNcuG2WJNTSeYy27tN6raoZqFaGwC5jdazBK0+S7t162tt2i4cJvQax72J0nl0pzoVidayFOCYKPIy2rqT/S7Pvy2o9hzgdiqKQ6tpEY2IfTvEw6T6T2uFHyW+9armHJlYaIwu/9+0km/3Rz8Do3WAXkues9qQG+1NeBztesrzjFOctCbILscgUVcXqWk/o1pOgUCpL7fCjtO+49pzX7ZaSrut+fRa/Mo2e7cTML2z28JFghzszil+baZZsWH/aB7bi5Jzi4aSuZOjjV6fxa0VyW56+KmcK2IWBpIlaaQI0DUOmcIpFoigqVteGZMp0eQJV03GmBxYlizsKfdziYZzCEKFbS+qx1meS0jBxhJHvxcz3n/GUbEVJguqcQwaWoqikerZgTx7ELQyj9m2jNvxQ0iR2iWaRxGGAV5lMSkxUp4jDgDgOCZwaYaNC0JhFUXVSfVvRU2d+8VdRNcx8X1KPv17Cq07jzozgVSYxcr3EcYA7O4qR7yPTv3NFB4jE+cEtjUMcY3WuW+6hnBF3dpT66ONJJnO1WSbmBAkZqmGhpXP4tVnM9n6cwgBG9splGLFYKEGj1DzGLEAUo6XzrZJcqpnGaOsijnzc2RHS/TvJrluZDW+rww8TBS5OcRhUDatr/Rm9Xk/n8UoTRIF3RkF2vz5L6FTxa7OggJFZ3obriqqR6t64rGNYLqphYXWux50Zwa9MoSgKzky/zLgRJ7btxckNIAqSPkCrZNaOWN0kyC7EEkiCQutxpgaS6f2Khjs7itV5ZgeIcwWNMtWhBwndBu7MCKqVPuMDztOlZ7vw67N45XH0dI7a8CNJw0HJVD5rdmGI+uFHCJwaTmEAPdtFHMeU995Ffts1aM0aqatBFPrYkwcI7CqR28DsWCv/NsSi0qwsRq4bv1okyLRTH9+D2b5mwaZwB04VZ3oQd2aEOAoIPRu/WiBolCCO0VJZzI51aGZ6QbZ3thRVI9W3FXvyAPb0IOn+7VQP3Uf7zmedVWO3M+FVp6kOPkAceERhkJTPsSuEbhWiGBQFPdOB2bH2nC8cKoqK0daFnu0ktCt4lSm82TG8yiRmWw9RGOBXpjDyfWTXXnLMrAIhFkpt5DGcZiawX5+hbcMVyzyiU4vDgNrIo7gzI6061k8uE/NkRlsPoT2QNFlUDbLrL1s15UTE8RqTB4ijEL9aREvnSPduIQ6DJEGnXsSdGcUtjWN1rAP2o6jqsvQ6mU9jYj9+ZQqvOUsq1bP5jP+uHAmyB3YZ1bCIQv+01uEWDxPHEUGjhJbKS6+hZWa0dRHYFbzyJFoqR33kUYy2rjMq/yMuQKqe3IRYAvIvTYgloqdy6NkO/EoBPdNJbeRxjFzvWdWSC50alYFdRL6DUxhMAi09Wxa8oeoRyRS9DTQm9iWd3XWTxuT+M24cJBL21CHqo48T2FWcwiAoCkG1SOy7WN2bKO37BbktV2Hm+5Z7qKfFnjqYZLSWJ1A0XWY5iCVhtq9JelHMjKJZGapDD5Lb/LRzKsUVhwH18T040wPEcUxgl5P6xG4DlCNT7LtXVC1wVTNI926lMXWwmdG/nfLBe+nY+exFu1iXXCR8lNC38WZHCZ168oSmoac70DPtaFbbgjddUxQFPdOOnmknsKv41Sm88iRedRoj10MUhfiVKazO9WTWXrxgZdmEqI/txpkewKsWW4/pqRypns3LOKr5+fVZqoMPELp1/Op08jda1ZPjxXkuhGupNhTDxKsVMdq6cYrDC9pLSCyd0KnhlSbw6zMQha1ZToqmY+Z7MXI9hG4drzyOOzMCqkpjfC+oGpm+7cs8egi9BrXhh5OL3HYlOYfKdp7VhVTVSKHoJqFdhVwvfmX6lDNS4ijALSW13IlCmS21Qhw5J3VmhsmYO6kOPUT7jmed1+VyxGn6zDbI9MF/uef0lv/q86A2Bn96cHHHJS4oEmQXYglZHesI7GqSeW6kaIztoW3T6dc3j3yXxsTepMFkHOAUhoijkHTf9kUvMaIaqaOd3RslmDyA1bFOauGeocbkARpjuwnsSnKBRLdI920jbFRwS6PYkwdI9W6hcvBesusvWzGNDE8mClycqQECu0Lk2Zid6xY8sCbEiSiqhtW5AWd6AHd2HFCZfeJ2zPY1pPu2nfHFHrc0Tn3ksSRrvT6DV56EMEDRDMyONRjZrhWbwaYaKdI9W7GnDmI3A+2lfXeR23o15gKWborjiProEzjTA0m2f2EIUDByPeiZdlQzs2QnuXo6h57OEboNvMpk0gy3WsRs7yOOQ9zSGKmuTWTWXoRqpJZkTOL81Bjfiz15AL82gzc7CiRlC2ojj6Kl2lbcheU4jrEnD9AY30sUuLgzhwmdOlo6R6pr4yn3Y4qiYLT14M2OEbo1nMJQs2fM6f1tj+fU/o58D4zVVwLvfGFPHSKOQ/zKNKqVOe7Cq6Io6Kk2NHMb9vQh3MIw9Cg0Rp9AUdQzqlu+0JzCEPXRJ4hCF7c0QVAtohjWOZVq0tN5/FqBKArxyhOnXJdbmiAOA4LaDIqmo51F2TOx8BRNx+ra2Dr+UxQNe+qAXAwUUB6EwDn95asjUB1esM3/9Dd/c2FWpCg8/447FmZdYsmtzLNFIc5TiqY365uPHK1v3rXhlJkRyQnLIezJg0ShT1CfwW0GgKzujUuWVWnk+wgaZdzZMTSrjdrww7Rf9FzJHDhNjYl9NMb34jfKuMUhVD1Fqm9r0pAp141iWDiFoSTQ3r2pme1epm3jU1ds+ZUj/ybd8gSKbkiWj1hSejrXKhsTOtVWNrNXnkDPdJDq3YrVuW7e4FDo2dRHHkuaR/sO7swIkdtANdOYnevR0vlVsY/TrAypns04hUHsqYOke7ZQOXgP2Q1PIb0A2bZR6FMZfiCZsl8t4s2Oohgm6Z6tqMbylZLQrAzp3q1J35PSON7sOH5lGrO9nziKcGYOk+nfQXrNRavi5yhWlsbkfhoT+/Drs83SfFni0McpDJFZs4PKwH10XPTcFTNrInQbVIcfIqgVk2ONmRGII8zOdWd0wc3IduKVxvGqRTSrDa88OW+JGUguwjmF4eTz8pIgR2VgFz2XXbdoMy3FyUW+gzMzTFAvEYcB1jx1qxVVI927DXvqEG5xCEXZQn3ksWbvj6WdrZFkrz+CX51OLvIUR4gDD72tC+scyxFq6Tx+tUDoVPEqk8RxNO+/Tbc4TBR4hE4NI98rf0NWkLnHf346R2N8H6mujXJRXZyZKIAF/PvUGBmZfwFFgTg+9XOyr1nVJMguxBIz2ubUN8/kqR1u1jc/wQ4+jiOc4mEaE3uJfTcpjVAaJ/bdJADUvQl9CWt3H7koYE8eONpwsDC4rJkuq0V9bA/25H78RilpUmimSfduPSajTE+1kenfgVMYwpkexOxYA0AUeOS3PWPFHdxHvoNdGCSwy8Seg9W1Xk6kxZKzOtejWVm86jTe7DheeTJpAh24BI0SjbHd6NlONDONamZQzTRa86s7M0pjbDdR6CXB48oUKCpW53r0tq4V9zt3Kno6T6pnK05xiMbkflI9m6kffoTQrpDdcPk5/X5WDvwavCru7BhBrYiWaiPVvWnFZPdrVpZM/45mrdYJ3JlRvOo0ZvtaGhP7COwyuc1PXzHjFSufPXWIxtie5oXxw6hWplnP2qfR6oOwg8qhXbRf9JwFnVEY2NWkUaWiYrWvOeWFrNCzsSf34xSHkxIXs+NJY0sjRapn0xkHnhRVQ892EtRnkr4rU4eSMlAnqXvslsapj+0mcuuEbgN7dhzYSuhUsKcOSobpMrCnDhFHEV51Omloe4os7CTQvhV76hBOYYhU7xZqhx8BVVvwxpJxFBBHIXEYJvfDIOl/4tSSGRihi1uaJKgWUDSDVO+WBZk5q1lZUDVCu0Kc6cCvzZz04lPoNvBrRYL6LJBceBIri9m+lsCp4pUm0FN57KlDZNdfttzDEquFb4M9BebCzVB52gc/eOJNVSrsu/VW/EqFziuvpOdZzyK1JjnPdyYnKfzqV8w+8ABGezsXvfnNGHmpFLCayZmGEMsg1bW+Wd98DFUzjjkBiQIXv1rEq07jV6aIfCdpbloeJ3LqScOq7o3omY5lCQBpVmZOw8EOGmN7MNv7peHMPOpju1tTzY80qU33bjthNo5qWKT7t+MUD+OVJoijAAB7cv+Ka0RVH32iWYt9EkU30bOSxS6Wh57pQM90NGsPFwhqRYJqES2dx2jrJrArKJpxwn1m6NZwZkaJfRctk8fqWI+qr97yBno6R6Z/J3ZhAGdqALM5HT50a+S2XHVafUDiKCB0bSKvgVuvAODbZcKZIUKnlmQUdq5fkRch9HQeLZUjaJTxKhO4hSGifA9xHBO6d5Hf9owVVVNfrEx2Yag5m6ySzDw7cmFc1ZI+ON2bcQoDuMVhFEWlOvjAOV0Mj3w3Oe6rTuNVp4l9t/Vc/fCjmO19WF0bMdv7j7lYFvkOjckDrfKBQX0WrzJFHHgYue6kEflZXlwzcz0EtRmCWhFVM5h9/KcohoWR6UTPdqBnkqBjY3wPQX2WyHdxyxOEjTIRyTYDu0pjfB9Wx7oVk+1/IYgCD6cwRGBXiH0Xs3vDaf3bVDQ9aaY9daiVRFMbeogocFE1gyjwiEM/+dq8r6VymO39GG3d82aZ+/VZvNIYbmmcyLNPulzyN3mE2PfQs53JbLQFms2pKAp6Opd8LnGMV548aZDdmRkmjmP8+iyqlZEM6RVIUVXMfB9ucSQpw1kcIr1m56KXUBUrSGUYKoPHPhZ6MPIL4CQZ43EMbgl2fwlCH9Y+ZcGGs/FlLzvusaDR4BcvfSkoCs/8/Ofpe97zjn/h297G9N13c/+f/znDX/saz/3WtxZsTGLpSZBdiGWgGimMfB9+eYrArtCY2E8c+EnGRKMEkJwsuTWCeomwUQZVw+xYi5HrXvZs4aMNB0fQrDTVwQfIrL0kOcBegUGX5dSY2PekAHumdaJ+Mkkj281JWaFKAdXM0Bjfi57pWDHNUBtTB3FnR/Er082GrRvlZy+WnWZl0awsUeDhVwv49RlCu9J6XtENFM1A1U0U3SQOPIJ6KcmU69l8Vs3UViLVsMj07cApDuPNjhH5DnEcJ3XaN/0GcRwTBx5R4BIFbnLfdwk9m8i3iQOvta4gSk5SnKlDqKF9xmUnloOiKBjZpAHrkf1oNKexdH7r1SuujrZYGeIowCkMJwH2Zt8BVU8d93dbT+cwO9bhzY7hlcdRFIXG2O7jsiijwCV06oReo5Wtm2TuJhm8RCGBUyO0y8nyUUjo1AidKqFTA0XBaOsiCv3WBW2rcz1Wx1q88gR2YSipGd0o4ZUniQMP1Uhh9W5FT59bdp5qpNBSbXiVKQKnhmpm0KwMoV1FLU8cfY/Ni+1BvQgoGPlezGwPjINXGsdKZ5PygjuffU7jWUqhZyeNMlfpcU1y0SXAr0yh6EbrgsjpSJppb2v2+Bgk3buNxugTrefjOCaOQohC4ihENVNJjyFVT372+X7M9j5U3SJolHBL47izY0ReI7nY6dQIvTpxFEGc3OIobH6NiDx7QbPXn0xP55NzK7eGV56ADZcft0wcx7gzI4RunTjwMPO9Cz4OsTD0TCdeaRK/MoWRaccpDMrMmQvJY5+He/7h2MfcWfj6b576tXGclGV56usXZWhHHPjf/5vaoUNc9dGPnjjA3tT7nOfw1H/4B+7/i7/gwCc/ySV/+ZeLOi6xeCTILsQyMXN9BPUS7uwoWipLY/IAkWcTOjUCt0rk1pMLsErzhCXXu2KmuScNB9fjTA/iziYnWpUDv0I108nJX+d6aYhKs8np+N5jarkmJ+qnvkiSlOZZT9SsEa0aKaqDD9BxyXXLPmvAK0/SGH0C3y7jlSfQ0nn0TMeyjkmIuVTdxOpch9ne3zxJ9onCJPMuCpP6qnEYJPvXXDdm+5oV2/fgbCmaTqp3K15pDL9aJA5crO7NlPf/8rhloyiEMCAK/dZnlHz1CfwQ2EEc+qR6zj1wt5QURSHVvRHPTOGVxrEnD5Du3UL5wK9o2/CUJa81LFamIwFsrzyRlIyKwmbDz0EU3STVt/WEx19mrofId5KL4XqS5XqkDEbo1JJ9T+ifcJutQGUcEYd+cuznVIncRrKAoqBZ2WS22Ow4XmkcLZVHz3YS+S7O9ABxHBPY5SS47rtJU8juTeiZ9gULDlvdG/Er04RunaBWIKg2n9B0NDODohtJOY0oSsp0tfej6mbz4lxMfKRni6rhFIdJdW9akHEtlsCpUh18kNAuo1ltpPt3rLpSeHGU9HEKnOrRhvRn+O9B1Q3SfUcC7YdQjVTr3zZh+KSFFTQrh57OE/oOXmkcAMWwiH23OYuoTtAoEdjlo69XAEVLjokVFUVRUVS1GajvW7S/yVoqBwoEdgU9lcOeHiTVs/mYz8ivFYg8m6A+A4oix7grmKIoGPkevNlxQreGPTVw0tnC4jw1t8b5fDXP5y5j5qHnCviNN8Cl/2VRhzf2wx+iGgZrb7zxlMuuvfFGVNNk/Ic/lCD7KrYyInZCXIAUVcXqWo8zNUBj8iBx4EOUHHiqhoXR1oOWakOz2k4rKLvU9HQeva0rKcvQmE2+z3QmtTgnD6ClclhdG5pThC+8qflJLdfdc2q5pkn3bjmjn+WRhlONif1Jk7X+HVQH7qd957OX7eAxcKpUhx4g9OzkfRkpUpLFLlYoRdVOesEvjiJQlPP6366iKFid61GNFO7saLJvtjLEYdAMrPtJNu2Jzkc0DVUzUU0LbEj3bUe3lq/B6bkwc72ouoVTHKYxcYBUz2Zqhx8hcGpk11+6qgJoYmHEUYg7M4JbnsCvTkMcE4UBoV0msCuEThVFM0n3bpu39IDVuT4pkzI7gmqYOIUhojAgDhwivzlbxE9mjBzJ1CUOT/g7pxopjFwPWiqHZmVbxwuh1yCoz+LXS4R2BVfT0NMdhG6tGVw3F62MoKoZWM2SU3EUEfk2odsg8hpJdr5TRUu1YbavQTPTx71ez3YSVIuEmU7qo09g5vtWbNkNpzBEbfRxIt/FrxWaJchqNCb2ku7dRqpnE4q68k+dneJh4sDDr0yDpmGcZSk/VU/+/bulceIoQNVTSbkkTW+VTUJRmxeIKq1ZY6qVQU/nUY0UgV1tBtaTi9paKpeUd0vnlu04VlE1NKuNoFEmzHZRH3k0yX5edwlWe1Ij2S0eJopCAruc/F5JwHZFM7JdeOUpvMo0mtWGMzOyIA3fxSrw7L9Lbkf8DxWya+ANY8s3piexx8bQUikU7dT7EUXT0CwLe2zljF+cuZV/pCDEeUxP5TA71xLUZ9HSuWZQPbdq6gGnujYQpPNJdkqjTFAvgaY3p+l3EDpVGmO7k3qNHWuw2tdcENkgzglruZ5dVoWqm6S6N+JMD+LMjKCoGvXRx2nb+NRFGPn8osCjcvBeQs/BKQyBopDq3SInH2JVWokXLxeL0daNqltJvWSnhqLqqJqOYljNYImeBE6OlNLRzNbnE0QxjMSoxqlrua9kejpPpn8H9vQgzvQhzM71wJFa9U+XGq4XkDgKKe//JUGjRBR4BM3AeuTUAVA0A6OtGyPfd8p/F4qikO7ZTGNyP/b0IKC0EiaSBUDRzObvld7K2EVtZu4qGoqmoVrZk25LMzNoZgazYy2hXcGvzxLUiyiaidW9AT3TuSQXCxVVbZXkOl1mvh/PKePMHCZjpqmNPEZ+69WLOMozFwUetcOP4JXGmzMYhonDAL88hZbOY+b7iDybxuR+0r1bSfVsOa3eFsshjiPsqYOEboPQqSU1/M/hb51qWKR7t8y7jJHtJI5jIq+RXKCyk0aUACjMCaznV8zxotmxNsnSn9iPnu3AbF9D9dAu7GwXmf4duKVxwkYJolganq4Ciqol/cLKU80m0AeaCUAXznGeaLr81WB1LPcojqFnMnilErWBAdq2bp132drAAH61itkp+53VTILsQiwzM5eUglmt9HQePZ0n7gwJ7ApBo5TUQq4UUAyr+Xxy4G1P7Ec100m9xo61K76u79lwZkaaGZInr+V6pvR0HrO9P5nOXp1OHst0kureuFDDPqU4jqgO3k/o1nGLQ8ShR7pv+4o90RRCHEtLtZFJ7VjuYSwr1UiR6d+BUxzCmxlt1aov77ub/LZrpDHjBaI++jhBo4RTHE6SAwBFNzHyvejpdlQzfUZBa0XTSfduS0rNkAQmVd1CNSwU3VywQI+iqK0mz3EcrYoAkqJpWJ3rcArDSX1wRcEtT7QyhpebXytSHXqQ0G3gVSbxy0kN81TvlqSRdq2IPVlBS2Ux8n3EgYc9eZDMuktPGXxealHgJjMpPDs5VlSUJes9oTTLG2lWFjrWEgUeUeChGakVU+pyLs1Mk117CV5lCr9WIGiU0Nu6McMgKRED+LUZFN1ES7Ut82jF6TDbevAr03iVKTQznfSDaF5MFxeQG29b7hEcp/PpT2fypz/l0fe+l2d85jNoJ5kVGnkej773vaAodD796Us8SrGQVt5fPSHEqqSoGka2EyPbmdTxbZSTgHtlujllVW8G3NsJ3TpOYRA920XbhstXbXZ73KylGocBcejjN8rUDz96WrVcz5SR7yN060ldVjNNbeRR9Ez7ktW+r48+gV8t4M6OEjp1rK71EpASQqw6Sa36bbizowTVYtK4uWczpX13SUPUC4BbGscpDOFVCwT1EnpbF0Zb9wlLnZwJ1bCW9ML3agiwH6FnOtDSJbzKJFqmnfrhR5PZNcs4eySOY+zJ/TTG9yXB6eIwkdtAz7RjdW1olRozc734tSJerUA4NZAkinSspT7yKH51irZNv4GqL08ZrTiOm8fZU0lz2kYZiAk9h7BRxsj1LFuAW9XNFZ+EoWg6Vuc6jFwPXnmCoFokqM9i5nrRUrmknn17/3IPU5wmRdMx2rrwq0WiwKUxeVCC7Bei0IOZPaCa0H3J/MsW90DkQdelsIh/j3a+4Q1M3XEHhXvu4c7f/V22v+519Fx7Lan+ZP/iTE5SuOceDn7mM9QOHEBRVXa+8Y2LNh6x+CTILoRYcKpmYOZ6kqZgoU9oV5Ms9/osQW0GVDVpkBUGlPbOYHVvIrv2ElRjZdf7Dd0GjYl9eNWppIZ+HJ1wGXt6EEU1TlnL9UwkDfw2JVPSC8Nk1lxE5dAu2nc+a9EboTqFIZzpAbxqkaA2g5HrlkCUEGLVUhSFVNcGPCOFVxo7tiHqxt9Y0mCpWDqh26A2/BChZ+OVxtBSbVid68/rvgwrhdW5nsZELWnkrls0xvbQtvEpyzKWOI6pDt6PVxrHb5RwZ0YgjrG6NmC0HVu/XNF0zPZ+jFwvfn0GvzKNM3UII99LHMf4jRK5TU/DzPct2fhDr0FjfC9eZYo48JJSLb5NaCe10SO3AaqKsYpnyS6lpCzjJsJcb9L8uDwJ5cmk4elZ1rMXy8M4clGsMo2qW3iVqSX93RQrwN6vwQ9vgaf8/+AFn5x/2V+/D/Z8GV70Rbj0jxZtSJ1XXslT3/c+Hvnbv6V26BAPv+tdJ14wjlE0jaf8/d/T+bSnLdp4xOKTILsQYlGpmoHa1oXR1kUcRYRONanhXi0S1EuY7f3EcYw3O0Z6zc5zLq2yGKLAbTYfHSSOkrI4cehDFBHHIXEUEUchRCGh10BRNdJ92xa8tr6i6aS6N2NPHcAtDqH0bmP28duxOteR7tu24DMCAruabGtmlMCp4c2OJg3OOtYt6HaEEGI5mLkeVN1MGqJOHiDdszkJwjpVMusuleDreeRIybPId3GKQyiqjiVNu5eMqhtY7WtxZ0fx6zMoikIchcmxyxLNyDuiPvo4Xmk8aXpbnkI1U6S6N83bkFVRVcxcD0a2M3kPlWkCp0a6exOVg78m3beNzNpLFv341S4M0Rh9gih0CRpJg97AqSWNRUlmVBi5HvRs56rp77RSaGaadO/WpJGrXUFLrZ4eWSKh6iZ6poOgPkvUvgZ78oAE2S80+76ZfL3sv5562af8Cez+Euz/10UNsgNsevnLyV96KXs//GGm77oraYI+h6Kq9D7veVz8trfR8ZTluQAtFo4E2YUQS0ZR1aTESaad0Gvgzo7hzY7h14pYHeuIx3bjFIbIrL0YM9e77JntcRRgTx3CnjzYLIFTwitPJFnsRyiAoqGoSQOzpH76mkWbJqtZGazO9bgzozTG92LkeojjAHd2FKOtm3T/jnM+oPRrRRqTB/ArU8RxhF+fxStNJOVvujdJUEIIcd6Y2xDVnjqE1bkBaDZE3fz0FVlPWJy5xvhegkYJd3aU2PdI9W6VZrdLTG/rwm/M4pXGUVCI4wh35jBGWzep3q2Y7WsW/fiiMXmgOTOvgF+eQs92JOVhTrP8jqJqpLo34adyuLOjNCb3YTUTD7xqgdymp6Gl8wv+PkKvQW34EfzqdFKSsDhCHHigqmhWG3o61wwKr+wSLauBlmqTOuyrmJHrJagn/cFUTcevz0rz2gtJ8bHka+9vnHrZ/quSr4VHF288c3RccQXP/Nzn8KtVyo89hlssAmB1d9N+xRUYudySjEMsPjlzEEIsC83MkOnfgV8v4ZXHcaYH0NI5rI511IYeBEC1shjZJAveyHYt2UFvHEc4hWEaE/uIA5fAruCWxol9F9VIYfWsQ7UyKM3g+lIz2rpRVB2vMoU3O4ZXnsRo6yYKffxaES2VI9W9CT3bhZ7Jn9bJYxzHeOUJ7MkDBI0SURgQ1GbwagUIg2am12YJOAkhzjtHGqLahUHcmRGiwE3qHTt3kt969ZJn2oqF5VWmsCcP4NdmCOqlZoNTOZldakfK3jnTye+ZWx7HyB49dlHNDKneLUlW+SJcAHFmRmiM7cZvlPFmm+WCus5uNoOR7USzsjjF4dZsP6tzPaW9d4KiopkZVCtzzNcjjUHPNNvdKQxRb2avu6WkdriiGaR6Ni9KQF+I1Uwz02jpHH6tgJHvxZ46iLH16uUellgqtTGw2sE8jZiBmYNUB9TGF31Ycxm5HD3PetaSblMsLYmWCCGWlZHtQE/n8atJR/jGxF5UK4tmJicjgV3BnTkMgKJbpLo2kFl7EYq68LuvyHewC4M4hWHiwCV0G7jlcSKnjqIbWN0b0DOdK+KE5siMgMCp4Ven8StT+NUp9EwnRq6X0KkmCypHZg90YmQ7iI2kWak7M4oX2oRundCpEbp1iCOiwGs2hCtCFKOlshhdGyTIJIQ4rymaTrpvG+5MUgoi8mys7k2U9v6Ctg1XkOrZvNxDFGch8h2qQw8R+g5uaRTVSmO2r1nuYV2wVN0kvWYnoVPFrxbmHLt0YLT1EHkN7KmDtG+/dkGPO7zKVFIKyq3hFodRzTSpns3ndDyn6ibpvu3NxqOT2G4dLdOOqpsouomqWaiGgaIcG1RXjwTcU21oVhualXlS4F0BRYE4ojGx/7jsdb2tC6tj7YorrSjESmHm+7AnDxLUiniqRuBU0VNyYfWCoJkQ2Ke3bBwny6oyq00sLAmyCyGWnaKqmO396Nmu5GTCqSUnXkeeN1NJ0D3VRhy4eJUpcluevmAnYH6tiD09iFcehzgmsKv4tQKhXQVVw+xYi5HrPu3pxEtJT7Whp9qIfAevMk3QmCWoz6LoJpqVQTXTyedZK+IoKkEUA1AbeQRNgTj0iXyXKHCIPJugUYI4CeIbuV40a3GbqgohxEqhKCqp7o14ZhqvNEZjYh/pnk3UDj+CX5uhbdNTFuUCr1gccRxTHXqQyLdxisOAkszIWgEXyi9kiqKgp/Po6Xxy7FItENRnCeolVCtDqmcz5f2/JL/9WoxsxzlvL2iUqA7cT+ja2NNDSX+b3i0LEqRWFAWzvR8t1YZbGiOoFSF+0kKajqqbzVsKxbCS+4Z5XAD+yeI4xC1NElQLSfZ67xZJehDiFDQri2pl8KoFjFwP1YH7yW64HFOaAZ//8luh8AiM/QrWnSJbfOyXELjQs2NJhhbYNsNf+xrTv/gF9ugooevy/DvuaD3vV6tM3nEHCrD+JS9ZkjGJxSFnCkKIFUPVDazOpLZlHAaEXiPJtHbrBPUiQa1IkM5hdW2ktPcXZNddSrpv2xlvJ46jZuB5Bqc4RGhXiKMQvz6LXysS+y6oGka+DzPfuyqyhVQjRap7I1HHGoL6bPKZOVWol5IFFFCNNLGRAdbSmDiAGjrJVfzWSlT0bNeKqIcvhBDLxcz1oJkZnOIQ9uQhzI41SfmYRonc1qul1MgKFgUefrWAV53GryYzEtzZcWLPIdWzWWpWrzCqkSLVtYG4fQ1+fQavPIk9dZB07zYqB35FfvszMNq6z3r9odugfOheQq+BUxgABdK92xa8HI1mZcn07ySOY+IwIA5cotAjCjziIPkaOFUIS8e8TtGN5r/JZvb60WdAUYg8O8lez3Zida5bFcejQqwEZr4PZ3oQp3gYqyOgcuAejHwf2XWXyoWq89nmF8D0w/CLv4aX/xROlhgRBfCLdyX73c0vXPRhlZ94gl2vfz32xMTRc+8nXfDX29rY/4lPUDt4kMymTXQ+7WmLPi6xOCTILoRYkRRNb2U6QRIY96sFvPIEjYl9pLo3Uh99HK86TW7T004aFI5Cn9CuEDTKBHaFwK4QOpXWH7jQd/CrRYLGLEQRqpHC7FqflIVZhnrr50rVjGMan0a+S+g1iLwGodsgqJeAtaiajpHuQtEtVMNC1S0UzZAMPyGEIGkynenfiTMzgleaIHTryQXefckFXqtjnVyMXCZJINMjDnyiwCMKPYJGCb8ynczGAuIoTC422xWC2gx6Wxd6pn15By5OStF0zHwfqpnBmR5IAu192ygf/DX5rVefVUP3yHeoHPw1odvAnh4gDgPSfdsX9fdWURQU3QDd4ETh8DgMiAI3ufnJ1zjwgbiZAR83E+GT7xVNx+pcJ0FBIc6Qns5jtPfhV6ZoOBWMtl6iKMSvTGF1bSCz9mI0U2brnnee/ufw0Mdh9C74xn+C3/wI9F957DKTD8DP3pYso6fg6X+xqEPyZme590//FGdqivYrrmD97/4u+269laBeP2Y5RVHY9PKX88QHPsDET34iQfZVTILsQohVQVFUzHwfmtWGUxzGmRrAyPcQxxGze35O26anouoWoVMldKoEdvI18p3WOqIwIPIdIt8m8mxCz06y1hXQ0+0YuR40K7uM73LhqUYSRCfbCZCUixmJkynHqgTUhRDiZBRNJ927Ba8yjVcex57YR6pnE/WRx6iPPIaWymHkejFzPUlDamkMvSgCu4o9fYigNkPUDK4/uSZHHMfJ33WnRuBWidx6sohCs4b1umUZuzgzeqqNdN927OlDNCabGe2H7iW35SqsjrWnvR6vWqA69ACR18AtDBIHLqmeLcteAk/RdDRNP++ONYVYiaz2NRiZDtzyRFKGtF7EzPcRxzHu7Bjpvm1k1lwkM0TOJ7kN8IJ/hh/eAqO/gC9dDdk1kNucZI6XB6AxmSTbKQq84FOQ37SoQzr0+c/jTE3R8+xnc+1tt6GoKgc//enjguwA/TfcwBMf+ACFe+5Z1DGJxSVnA0KIVUWzMmTW7MSdHcOvFAicOqnujVQP7WotE8fx0TrjvkPkJV/jwDu6IlVDM1NomQ70bBeqLk1PhBBCHM/M96JZ2Vb5GNXKoKXa0N0agV3BmT4EitJsPN2N0daDke2UE/dz5FUL2FMH8StTrTJvcegTR2FyCwPiKCCOQqLAhTAEkovLRltPq7HkapyVdiHTrEwr0G5PHyTdu5XqwP3Em59GqmvDvK+N4xh76gCNsb1EgYNdGCL2HKyu9ZINLsQFSDVSpHu2ELp13NI43uw4frWI2bEGJiP8+iz5bdcseAkpsYwu+6+Q6obb3wzlQaiNJ7e5OrbBb30ctt646MOZvP12UBQue+c7T3k80rZtG6phUB8aWvRxicUjQXYhxKqjqBqp7o0E6RzOzAj25H70bHeziadDHLjHJLkpuoFqpNGynahmGtVISV1WIYQQp+1I+RivOk1oV/HLU/jlKVAVNKsNLdVG6Dbwa0UUZT8oKka2E6OZ5a5nO1dk8+ylFocBgV0GQNFNVM1E0Y3WZxPHEe7sGPbUIUK7TBQGBLUiXq0IYXB0RQqgaiiqjqJq6Klc8+eQk4vm5wHNTJPp2449dQh76hDp3i3Uhh4kaJSxOtefsCFqFHhUhx5MMlYbZdyZwwCkejZLqSAhLnBJ34QdBI0ybnkctzBMmK1ixTHl/b+kffszUY3Ucg9TLJRtN8GW/XD4jqTBaX0ieTy7FtY9GzbdAEt0TFYfHkY1DPKXXXZay+ttbfjV6iKPSiwmCbILIVYtPdNBxszgzhwmqBZQNCMpj5LKJYF0I4VqWJJNKIQQ4pwpmp6UrOhYm/T7cOqEbi0pU2Y3T4g0LQn2WtlW0B1Immm3dWPmejFyvRdE89Q4jpPyLfUZ/EYpacrt1HhyqRdIPltFM4njkLhZq9qrTDf7pcRoqSxG53pUI5X8TVc16SFynlON1JyM9gGsrk3EUwdxpg+hmhmsjjWYHeswsp349Vmqg/cTug288jh+pZA0Ve3ZLL0ThBAteqYdLZ3HK43jVwvEUUCqZxOlfXfTvv2ZaKm25R6iWCiqBpv/U3JbTlGEouundcwSRxFBrYaeTi/BwMRikSC7EGJVU3WTdN924jiWE24hhBBLQtUM1GxHK6M28t1mwL1G6NYJG0m2Ns36y1qqjciz8StTyeuNVFLPPd+LketB1c+PQGDo1vHKk3iVKYL6LHGUZJ/HUUjo2a0m3BA3g+U6qqonQXNNA0UhqJeSz09JLqYbuR5pUHeBUg2LdN8O7OlDuIUhXE1DT7ejZ9oJ3Tr21CFUM93st+PiFIeJ3EarDr+UChJCPJmiKFid61B0A292HHtqgFTPFkr77ya/7ZknnCkjxNmyentpjIzgFgpYPT3zLjv74INEvk/bjh1LNDqxGCTILoQ4L0iAXQghxHI50mTaaOsGIPKdVqZ74NYIG2U8khIpWiopaxK49WZJCwUj30uqawNmez+KunoOz+M4wq8V8cpTeJXJpOEoSemO0KkSejahW0+ajDcdKQ8TRWEShH9yYruqJmV2cj1S2k2g6gaZ/p0EdpmgUSaozxLUZuBIwD2dlIJxZoYhirC6N2Bku5Z51EKIlc7M9aKoOu7MYeyppP9D+cAvyW+9GjPft9zDE+dq+hEY/BFUhsBvwI2fO/pc6IM9DSjQdvpNtc9G9zOeQWNkhMPf+hY7Xve6ky4XRxF7P/IRUBR6nvWsRR2TWFyr5yheCCGEEEKIVeBIyTIj191sxu0kQWendjRICKhWBj3TThR6+JUpFE3H7FhHqmsDerbrrC4gx3G0oPXf4yhoZqHbra+R10gC6HYlaT4ax0kGv1MhsKtHg+qKgmZlUNN5NDODamWOazA3t5EpcYRqpqXMmziGojZ7HGQ7iaOQwK4kQfc5v0uKYZLq3YZmyjR7IcTpSZqU6ziFQezJA6R6t1E5dC9tG5+K1bVRkrhWI7cMP3otHPhO8n0cg6IcG2SPfPiX3wBnFl67L2mEuki23nwzh7/1LfZ/4hN0PPWp9Fx77XHL2GNjPPrf/huFe+5B0XW2/PEfL9p4xOKTILsQQgghhBCLRFEUNDOdBP/yfcRR1AxIVwmdKt7sON7sOFo6h57pIApc3OIwqpnByHYmJ4fQatJ15KQ/DgOi0CcOvKP3Qy85oUQ5Wru8eUNRm+UzFFAUlGRlre+ZE+yO4+bXKGiu76g4jpPAeuAR+S5B830QRaCQjLtjTdKE1EidMkjRGp9krYvToKjaMQH30KkSRyF6pkMuzgghzpieziX9HwoD2FMHkkbLww9jTx4k3bcNq2uD7FtWiyiAb90E4/eAnoGNN8Dwf0DoHruckYErXgu7/nsSjL/6LxdtSO2XXcZFf/Zn7Pv4x7nn1a+m/SlPIajVAHj07/6Oyt69zD70EHEYAnDpX/0V2U2bFm08YvFJkF0IIYQQQogloqgqejrXan4aenaSkdso4dpV3Fk1KYOR7SRolI4GwlsraN5vBsWJQ6IwTL6Pw2awW0mC6ooKqpoE1RV1TsB7zjqV5v/iOAmuRxHEEXHc/BqFxIFPHPpEgdcM5M95Q5qGns6jpfLoqTYUTU4vxNJQVA0907HcwxBCrHKalSHT7P9gTx1Cz3Ri5HoJ3Rr18b2ke7ckjZTPk/4p561HPwtjv4KO7fCffw5t6+CTa6ExdfyyO1+WBNkH/n1Rg+wAF7/1rRjt7ez58IcpPfxw6/HBL3+5lcigpVJc+va3s/Xmmxd1LGLxyVGwEEIIIYQQy+RIlrvZsZbQqRI0SsmtPrvcQztK1VB1IymDk86h6CaqZiZfTyNbXQghhFjJVMMi3b8DrzxJUJ8hqM+ipXMYuR7iwKUxeYBU10asrg3JzBn5u7fy7PlykmTwmx9JAuzz6bsymSE488SSDG3ba17Dht//fUa++11m7r8fZ3Iy6SPS00PXVVex4aUvPWVjVLE6SJBdCCGEEEKIZaYoCno6j57OE3eGSW3zKGg+Gzezx+ekkCtqq9RKq+SKqiXZ63F8NBM9judkpjdf3yoBEze/jZPXHSkpc2Tdcx8TQgghzmOqZpDq2kDU3o9fLeLXioT2AIqZwsz1EocBTmEQRTMwcj2YuV6MfC+amVnuoQuAwqOAAlteeOplNQOsdrCLiz6sI8zOTrbdcgvbbrllybYplp4E2YUQQgghhFhBkrrTHee2joUZihBCCHFBUTUDq2MNZr4Pvz6DXy3gFg/jlsbRU21oqRyhZ+OVxgHQrDaMXA96piMpn5bOLWgDcnGa/DqYOdBOs8dL6IG6uCHRxtgYiqqSXrPmtJZ3JieJwpDMulNk4osVS4LsQgghhBBCCCGEEEI0KaqKmevBaOsmtCv49VkCu0JQLyXPm+lW0D1wKkcD64qS9Clpzk7TM+3o6XbpWbLY0j1Qn0iC7UZ2/mVn9yfLdWxf1CH99LrrSPX18YJf/vK0lr/rFa/AGR/nd/btW9RxicUjv+VCCCGEEEIIIYQQQjyJoihJoDzTThzHRF6DwKkSOlX86jR+ZRoUUHQLzUyjmmlUu4pfK6LOCaxrVht6ph2tGXTXM+2omrGM7+w8038NHPq/cOj7cPEr5l/2/o8kX9c/Z9GHFcfxqRc6h+XFyiJBdiGEEEIIIYQQQggh5qEoCpqVRbOy0L6GOAwI3Rqh2yDybQK7Cs1Md6DVIDwJvlfxG6VjAu+qmWmVmNHT7eipHKqVlcaqZ+PyW+Dgv8Hd74UN10H2JCVadv13ePiTSZPUK167pEM8lch1UXUJ065m8tMTQgghhBBCCCGEEOIMKJqe1GLPdLQeiwKPyHeIPJvQs4l8m9CuHPMa1UyjmWkUI03QmEXRzKOBdVVDT+XQrDa0VFsS1E+1oVkZlEWuIb6q7fx92PbiJJP9i1fDpX8MgZM8t/cbSWPUfd+A2WYplotfmQTjVwhnchJvZgazu3u5hyLOgfyGCiGEEEIIIYQQQghxjlTdRNVNSOdbj8VhcDTg7jWSALxdnfMiFdVItW6hXUbRU8dkvQNJcN7KoplZVCuDZmbQrAyqmUm2eaH7na/CD/4YDnwX7vvvRx///iuTr0dKsex8Kfz2Zxd888V776X4618f81jYaLDv1ltP+po4jvErFaZ+9jPiOKbzN35jwccllo4E2ZfI8PAwH/vYx/j+97/P8PAwlmWxY8cOXvGKV/CmN72JTCaz3EMUQgghhBBCCCGEEAtI0XT0dA7SudZjcRgQ+Q6h7ySZ775NUJ89GgiGJPiuW6iGhapbKEe+6iaqqh23DdXMojWD72ozAK+ZGVQrc7Qx6/nMyMLvfTvJZn/0MzB6N9iF5DmrPanB/pTXwY6XLMrmC/fcw76PfSwpRdMUNBrs/djHTv3iOEa1LHa88Y2LMjaxNCTIvgS+//3v86pXvYpyudx6rNFosGvXLnbt2sVnPvMZfvCDH7Bt27ZlHKUQQgghhBBCCCGEWGyKpqNpSUmYI+I4Jj5SbiZwiXyXKHAJnCqEpWNXoGmompkE3Ju35L6FohlPquuuJIF6I9UqVaMazSatrQx66/wJxG97cXIDiAKIQtCtRd9sZv16up/xjNb3xXvvRdV1Oq+88qSvUVQVva2N3EUXsfEP/oDsli2LPk6xeCTIvsgefvhhXvGKV9BoNGhra+Nd73oXN9xwA7Zt89WvfpVPf/rT7N27lxe/+MXs2rWLtra2U69UCCGEEEIIIYQQQpw3FEVJstWN4wPCcRgk9d4DNwnEH7m5DcJG+fh1HRN4N1E0A1U3kvrvxwXhARQUw0TVU2jm0dI1im61gvitLPo5pWniOIY4TL5GEXEcNr9GxFHUfC5KHiNG1ZIxJGMzFi6w/+mtoKjwsh9B546jj6t6clsCG1/2Mja+7GWt7//vjh0YHR08+8tfXpLti+UnQfZF9ta3vpVGo4Gu6/z4xz/mWc96Vuu53/qt32Lnzp284x3vYM+ePXz4wx/mve997xlvo+FD2l/IUZ+eOAY71PBCHS8yUEPt/LnyKcR5Kohi3CjGDhX0WLrWCyHmJ/sMIcSZkv2GEOJMyX7jdGhJNrZ+tOSM0rzFcUQc+En2e+AThx5R6CXBeKcOUfUEq9NRNR1FN5NSM6qRBN81PfleM1CeVJLm6IYVFEVLAupzy9uchWR7BoqeQk+3JY1kU3m0dO6M4kvp+jioJnbbDjib+FgAMKeMsw+c21vjsvd/EDWVonGW8brQB1dPt75v+KBJyK3lbD/XxaTE8Tn+RoiT2rVrF89oThV5/etfzyc/+cnjlomiiCuuuILdu3fT2dnJ5OQkhmGc1vorlQrt7e1s+Kcyaip/6hcIIYQQQgghhBBCCHEe+UWwjS6muVw/wQUFcV6KnAojf91OuVwmn18ZMVG5BrKIvvOd77Tuv+Y1rznhMqqq8upXvxqA2dlZfvazny3ByIQQQgghhBBCCCGEWP3uUv4TaRpcHj+43ENpaWsUuf6h27hq77+dctmr93yX6x+6jaw9swQjE4tFysUsol/84hcAZLNZrrrqqpMud/3117fu33XXXbzgBS84o+388maf7u6zG+O5iOOImUd+iFeexKtMk91wmZSLEWKFC6KYXaMx16xX0FWZiimEmJ/sM4QQZ0r2G0KIMyX7jdUjjmPiKIA4TuI/itIsH3PmsaA4CpNbGBAFPlFgE3k2oWdDFLWWU3QDRTeO1nPXmt/rR+rLq2j2M1Hu/yLfzr6KwtP+CSXVgWKm0YwUqplKGr0aqdb3ypPrtAcufKLr6PdvmjnnZqnDn/8O++/6J7a9+c/Z+qb5l93/33cz/PnP8jfPhY2vvgWA0HX50ZxY4m/ffz+atfgNXFeLYtFn018v9yiOJUH2RbR7924AduzYga6f/KO+5JJLjnvNmcgYyW2pxTHYWoimBWiqT1oLURSpPiTEShYoMZYak9bkAFYIcWqyzxBCnCnZbwghzpTsN1abJwfUYyA889VoR/6nARbQ1noqCjwir0Ho2a1Gr7FXJw6DY7YaQ1JfPqozu+4VdI5+jf5f/ymNNb+N33EJcaoHVUsCZlHzBqCoOophoeQ3J01eNZUsjaNjMzjniOnMz34KwKabbjxlzG7ry17K8Oc+w8wd/8HFf3ILAGEEVmC3lskYoJ1F7C+Omj+bZrPb8yU51l6GOOipSJB9kTiOQ6FQAGDDhg3zLtvZ2Uk2m6Ver3P48OGTLue6Lq7rtr6vVCoA+L6P7y99xf84jgiimDBWCFEJIiTILsQKF0TxMV+FEGI+ss8QQpwp2W8IIc6U7DfEcVQDUu1oqfZjHo6jOGnsGvgQec1Grz7rHnlXaxnNdckNfQmGTrURhbFnfwVFM1CViOycZ6YfvwM914ue7kDPdqCl2s44OF0fGkJRFMz1608Zs7M2bABFoTY01Fo2CoJjlnEbNeLKJJHvEkd+M/vfIw6T+3HkQxwRxxFE0Wk0plWS/9Sk2a2i6Mc2vtX05gwCC1W34P/P3n3HR1HmfwD/TNmaTU8IEEInFIETaSoooqcHNuzlREE9UTjPdva7E/V3iqd3eicWRJRiAVGwnAX1JNIRBAtSTUiAJKSHZDdbZ+f5/bHJkpBeNpuQz/v1WnZ255lnvrthJpvvPvN9VANk1VTtCoLwfiEWjjxoY5hkDxG7/fhkCzabrYGWAVVJdofDUW+befPm4Yknnqj1fFpaGqxWax1btJfEwC0HaPX0y0TULnbkAjxeiaipeM4goubieYOImovnDWoaY+XteFq8d4v6Efi+IFB+RRZ+XFJtzdZMB3TJAyC7xVFKRUWA2Yw1X3/dtPZmM1wFBfj8888DT2hajWsGvl77LdBAlYzmqzrWvJW3zsXpdDbeqJ0xyR4ibrc7uGw0Ghttb6qsq+Ryuept88gjj+C+++4LPi4vL0dKSgomT56M+DAUZRdCR8muL+ErL4C3vBDW5GFh/yaLiBqm6QI7coHRPcFLMYmoUTxnEFFz8bxBRM3F8wa1VmHCwqY31gWEX4PQfRitlgXqwvtqJmyHYS8AFZJqgmI0QzZaIZsioBgCuTtJUWGwJcAQEQ81Mg6qqfbg2m/+7/+g2e347XnnNVpL3e/x4H8PPgg1IgLnTp0KX0Ux3HmHsLlam1Mji6FrFdD9GqD70V5fSEmyGijJIyvBke6SrEKSFUCuHPGuGCDJcr05QUk1Vo6INwTK8xhMkNTACHnJYAyMkFdMgfr7TcwrFhcXt+XLbBNMsoeI2WwOLnu9jX8jVFUGxmKx1NvGZDIFk/HVGQwGGAztX4xICB2qLEGXBBToUGUwyU7UKQioMusdElFT8ZxBRM3F8wYRNRfPG9QK1qQWbRZMiuo1S49EJKTAr/mge5zwu8vgd5bCD0BTVChmGxRzJDTNDd2eD08eICkGqNZoqJbowL01BlGpqSjevh0lGzag59SpDcaR9/knEH4/LL26w37gWwifB/5q5aIBwG/Pg2q1wWCxQpKVykS3CsgKJEWBJCnHJ6GtPiGtJAGQAIjKvHxlcl4ICIjK0jI6oPshhB6o4a7rELpWeQtMTiv8PgjNBV3X6s/vVybcayfkVQhFhV5VmqaBcjOSGki6B+4rE/BGM2S1ahLbwC0cedDGMMkeIpGRkcHlhkrAVKmoqADQtNIyREREREREREREBMSvnQZIEkrHvwLd2rPV/amWKKhyIIkrhIDQPNDcDvjdDmiucmgVxwAAksEExRQBxWiF5iqHbCgMJo8jh/ZE8TaBX/7vcRiTzLAkJQUTx5JigN/rhOYogePwQex5+jlAAqJPHQhveQH8zjJ47SU1YrJ2HwTV2pqcYaAG+wnPVE5A23RCCED3Q/f7Akl4v3Y8CV/tse5zQzSYkA98UVBV/x2yClmpHCGvqJCDo+Qrv0A4QanDXUen4cUke4iYzWYkJCSgqKgI2dkN13AqLS0NJtlTUlLaIzwiIiIiIiIiIqLOTzZCyGqbJNhPJEkSJIMZRoMZiEyAEAK61xlIuLsd0CpKoTlKqhoHRlobLYg9YzBy/xsFT0Extt96N5Iv/y1ixwyHKSEWAOApKkXJtp+Q+9FaaA4nDDE2RA2Pg6fwECBJUE+Y9FU2mE8MLSwkSQIUFYrSeEq5KiEfTL7rGvQ6kvKNJuRlOTBCvmpCVsUIn4sTn3YpQ4cOxYYNG5Ceng5N06DWM0HBvn37amxDREREREREREREjfObE6C4i9plX5IkBUavmyJgjE4KJN19buheF/xeJ3SfC1pFKSAEes+YhMyFX0GzV+DQsk9waNkndfQooESY0e/2i2HqlgzFGOhb+PV2eT2hVJWQlxQVaEJ1l0Ai3l9zhLyuBUbNV978Phfg98Pr7HiTtTLJHkITJ07Ehg0bUFFRgR07dmD8+PF1tlu3bl1wecKECe0VHhERERERERERUafmix8D5ch/oZb+Ai12eLvuW5IkKEYLFKMFBsQBQDDxbk7oA+uT/ZH9/v9Qun0vhN9fc1tFQfwZv0HvGy4JjnCvcjIk2Zurqo470PBEsULo8Bwrb5+gmoFJ9hC67LLLMG/ePADA4sWL60yy67qOZcuWAQBiYmIwefLkdo2RiIiIiIiIiIios3L2uQqm/HWw7X8VZaP+D8IUF9Z4qhLvMFqg9o7G4D//AX6PFxUZR+CtTA4bY6IQMSAFiskY1lg7I0mSISuc+LRLGTduHM466yxs2LABb7zxBmbMmIEzzjijRpt//etf2Lt3LwDg7rvv7pCz4xIREREREREREXVESsURVPSfjohf30Dsd3+Ep/tk+KKHQhijISDXu117jnpXTEZEDRvQbvuj9scke4j95z//wYQJE+ByuXDBBRfg0UcfxeTJk+FyubBixQosXLgQAJCamoo///nPYY6WiIiIiIiIiIio84j+4S8ApOBjc/bnMGd/3vBGElA8+aOQxkVdC5PsITZq1Ci89957mD59OsrLy/Hoo4/WapOamorPPvsMkZGRYYiQiIiIiIiIiIioMxMhbd5Sfo8XBf/bgmM/7YensAS614fTXv5bcL1W4ULpzj2QJCBh4uj2CYpCgkn2dnDJJZfg559/xn/+8x989tlnyM7OhtFoxMCBA3H11VfjzjvvhNVqDXeYREREREREREREnUrxuR+HO4Q6VWRmY98/3oC35NjxpL5Us41iNSNn1ddw5ebD1C0ekal92zlKaitMsreTPn364Pnnn8fzzz8f7lCIiIiIiIiIiIgoRHz2Cuyd9zp8peWI6N8LCRNPQ/b7X8Lv9tRoJ0kSup03HoeWfYKS7buYZO/E6q/+T0RERERERERERETNcvTTdfCVliN6xCCMmHcvel4yGbLRWGfb2NOGAQDKf0lvzxCpjXEkOxEREREREREREZ0UJM0JxX4Qsu8YAAm6IRr+yP4QavuVai7d8QsgAX2mXwpJbniMsyU5CZIiw51X1E7RUSgwyU5ERERERERERESdmuLIgvXg2zAW7wDECTObShK88WPg7H8D/La+IY/FnVcMSVVh7ZfcpPaK1Qy/0x3iqCiUmGQnIiIiIiIiIiKiTstYsBmRe54HdA3HZxmtRggYi7bDWPID7Kf8Gd7EM0IbkBCQZBmSJDXeVNfhd3kgm+ouJ0OdA5PsRERERERERERE1CnJrrzKBLsPurkbXH2ugDf2VOjmhMB6dxGMpT/CcuhDyO58RO7+F0rHvwTd0j1kMRlio+ApKIavzA5DdGSDbR0HDkFofpiSk0IWD4UeJz4lIiIiIiIiIiKiTsly+ENA90GLHozScS/CnTwVurUHIBsA2QDd2gPu5KkoHf8itOjBgK7BcvijkMYUNbQ/AKDg2+0NthO6jsMrPgckIHpEakhjotBikp2IiIiIiIiIiIg6JWPJTwAkOAbPAVRL/Q0Vc6ANBIwlP4Y0ph4Xng0AyFn1Fcp++bXONp6iUux/9g2U706HJMvoPmVCSGOi0GK5GCIiIiIiIiIiIuqUZE8RhGpp0oSmfltfCNUK2VMU0pgi+vVCrysvQPYHX2HPk6/CNiAFfldgYtODr38A5+FcOH49BKHrAIDev78Y5qSEkMZEocUkOxEREREREREREXVKQlIh6VoTGwtIug9CCn1KNOXaqVAjLDi84nM40g8Hn8//elNwblbZaEDvGy4OjnynzotJdiIiIiIiIiIiIuqUdGsPKPZMGIp3whd/WoNtDSU7AxOkRqa0S2w9Lj4HCZPGomj997Dvy4S3tAxC12GIiULU4H5ImDQWxpiGJ0alzoFJdiIiIiIiIiIiIuqUPAnjYbUfhG3fSyg/9Qn4I+pOoCsVh2Hb9zIACZ6E8aGNqbAUAGCItsEQGYEeF01Cj4smhXSfFF5MshMREREREREREVGn5E65FObcryB7ihGz/R54EifAF/cb6KY4ABJkdxEMpT/BVLgZ0DXopni4Uy4NaUw75zwJSBJGL5gLY1x0SPdFHQOT7ERERERERERERNQpCdWK8t88jqif/w+yuwCm/PUw5a+vqyV0cxLKR/4VQrWGNCbFbISkKEywdyFMshMREREREREREVGn5bf1Rum4F2HJ+Rymgo1QHFmA0AMrJRl+W194up0FV/JUQLWEPB5TYhxceUUQfh2SIod8fxR+TLITERERERERERFR56Za4OpzJVx9rgR0DZJmBwAINRKQ2zcFGjtuBJyrv0bpzj2IGzu8XfdN4cGvUoiIiIiIiIiIiOjkIasQxlgIY2y7J9gBIPmy82DunoCDr7+Piqzcdt8/tT+OZCciIiIiIiIiIiJqIyVbf0LS+Wcie+Ua7Hr4X4g5dQgiB/eDIToSkizVu13iOePaMUpqS0yyExERERERERERUadkKPkBkb88B2/8aXCccn+DbSN3PQ1D6S+wj3gYvtiRIYsp/eXlQFUuXQClO/egdOeeRraSmGTvxJhkJyIiIiIiIiIiok7JlL8BkuaEN+nsRtt6up0FY+F3MOZvCGmS3ZQQA0j1j1inkw+T7ERERERERERERNQpqeX7AQC+qCGNtvXF/gYAYCjbF9KYTnt1bkj7p46HSXYiIiIiIiIiIiLqlGR3MYRigjBGNdpWGKMAxQTZUxLSmMr3ZAAArH16QI2whnRf1DHI4Q6AiIiIiIiIiIiIqCUk6M1qLyBB0j0hiiZg99yXsPvxlwER0t1QB8IkOxEREREREREREXVKujEWkt8D2ZnbaFvZmQvJ74ZujAlpTIrVDDXCAtXGUexdBZPsRERERERERERE1Cn5oocBAKyHVzfa1npoNQAJvphTQhqTuXsC/C43dJ8W0v1Qx8EkOxEREREREREREXVK7l5TAQiYcv8Ha8YyQPfVbqT7YM1YCtPRrwPbJE8NaUwJE06D8Oso3vxDSPdDHQcnPiUiIiIiIiIiIqJOSYtKhbvXxTBnfwrLodUw534Nb9yp0M2JACTI7gIYS36E5LMDANy9LoQWPSSkMfW46GyUfPczMt9YBTUyArGnDQvp/ij8mGQnIiIiIiIiIiKiTqti0K0QsgGWIx9B8pXDlL/hhBYCkGS4el8BZ//pIY8ne/XXiBzWH87Dudj3zOuw9uqOyCH9YIiOBGSp3u1Srp4S8tgoNJhkJyIiIiIiIiIios5LkuEcOBPunhfAfHQt1LK9kL3HAAC6KRZa9BC4u58H3dqjXcLJXvklIAEQgcfOI3lwZuc1uh2T7J0Xk+xERERERERERETU6enWnnAOCP1I9cZEDe0PSPWPWKeTD5PsRERERERERERERG3klCf/FO4QqJ3J4Q6AiIiIiIiIiIiIiKiz4kh2IiIiIiIiIiIi6tQkzQlD0XaoFVmQfA5IQmuoNRxD72q32OjkxyQ7ERERERERERERdVqmo98g4sDrkPzuas+KOlpWzUbafkl23aeh7Of9cGQcga/MAUiAIcoG28DeiB6RCtnA9OzJgD9FIiIiIiIiIiIi6pQMxTth2zsfgABkA3zRQ6Ab4wBJCXdoyP96M46s+Bw+e0Wd6w2REUi57iIknX9GO0dGbY1JdiIiIiIiIiIiIuqULIdXAxDQogejfMRfIYxR4Q4JAHDorU+Q+9+04IB6Y1w0jPHRAABvcRm8JWXwlVfg4Osr4c4vQp/pl4QxWmotJtmJiIiIiIiIiIioU1LtGQAk2Ife02ES7GW705H7SRoAIG78SPS+/kJYkpNqtHHl5OPIii9QvPUn5H6yFrGnDUPUsAHhCJfagBzuAIiIiIiIiIiIiIhaQhJ+CMUM3doz3KEE5a/ZCADodu54DL7/5loJdgCwJCch9c8z0e3c8YAA8tZsaO8wqQ0xyU5ERERERERERESdkt/SHZLwAcIf7lCC7PszAUlC799f1GjblOsvAiTAvi+zHSKjUGGSnYiIiIiIiIiIiDolT9I5gO6HoXhnuEMJ8tkroFrNMERHNtrWGBMJ1Wqpd3JU6hyYZCciIiIiIiIiIqJOyZVyKbSogbDtfxWyMzfc4QAAFIsJfrcHutfXaFu/xwu/2wPFbGqHyChUOPEpERERERERERERdUqm/PXwdJ8M68F3EbvtLni6TYAWlQqhWBrcztPj3JDFZO3dE+V70lGw9jt0nzKxwbaFa7+D8Ouw9uk4NeWp+ZhkJyIiIiIiIiIiok7Jtvc/AKTKRwKmvHUw5a1reCMptEn2xLNGo3x3OrKWfQzJoCLpvNPrbJf/vy3IWvYxIAGJk8aELB4KPSbZ6+FwOLBz505s27YN27Ztw/bt25GVlQUA6NOnT3C5qXbv3o358+fjf//7H3JycmCz2TB06FDccMMNuPXWW6Gq/FEQERERERERERE1h25ODHcItSROHofC9d+jfE8GDr72HrLf/xLRwwfCGBcDSIC36BjKdv8Kb0kZIICoUwYi8Zxx4Q6bWoGZ3Xpccskl+Pbbb9ukrzfeeAN//OMf4fF4gs+53W5s2LABGzZswJIlS/Dpp58iPj6+TfZHRERERERERETUFZSeuSjcIdQiyTIGP/QHZLyyHCXf/Qxv8TEUrv++ZiMRuIsbPxID5lwPSZJqd0SdBpPs9RBCBJdjY2MxZswYbNmyBQ6Ho1n9fPnll5g1axZ0XUdSUhL+8pe/YPz48SgpKcHrr7+O1atXY+vWrbjiiiuQlpYGWeZctERERERERERERJ2ZajVj8P03w5F+GEWbdsKRcQRamT2wLjoStgEpSJhwGmwDe4c5UmoLTLLX4/e//z1mzZqFcePGYeDAgQCAvn37NivJrmka7rzzTui6jqioKGzatAkDBgwIrp8yZQr++Mc/4pVXXsH69evx9ttv46abbmrz10JERERERERERETtzzawNxPpXQCT7PWYNWtWq/v48MMPkZ6eDgB45JFHaiTYqzz33HNYvnw5SktL8dxzzzHJTkRERERERERE1ESBiU+bS4Jj6F1tHksV3afBlVMA2aDAkpzUYFtXTj50nx+WXkmQVSVkMVFoMckeQh999FFweebMmXW2sVqtuOaaa/Daa6/hl19+wa+//opBgwa1T4BERERERERERESdmOnoWgASgkXOazmx1rlAqJPsxZt/QPrL7yLpvDPQ//ZrGmybveprFG3cgUF3TUfCxNEhi4lCi0n2ENqwYQMAYPDgwejevXu97SZNmoTXXnsNALBx40Ym2YmIiIiIiIiIiJrA030yaifSj5P8FVDL0yF7iiEMkfDGjw15TMVbfwIAJEwa02jbbueOR9GGHSje8hOT7J0Yk+wh4nA4kJ2dDQAYMmRIg22rr9+7d29I4yIiIiIiIiIiIjpZOIbd03gjIWA6+g1sB16FUK2oSL0tpDE5j+QBACL6JDfa1jYgJbDN4aMhjYlCi0n2EMnOzoYQgctUevXq1WDblJSU4PKRI0fqbefxeODxeIKPy8vLAQA+nw8+n6814baIEDo0XcAvJPghQ9MBSarv0hwi6gg0XdS4JyJqCM8ZRNRcPG8QUXPxvEHtRet+HoTPgciMJXBHD4Mn8czAihP+7wX+L7bu/6OvpAyKxQJhMjb+f9tkgmK1wFtaHmyr1xGTzGMkqCOeL5hkDxG73R5cttlsDbaNiIgILjscjnrbzZs3D0888USt59PS0mC1WlsQZVtJDNxygNaehIiofezIBXi8ElFT8ZxBRM3F8wYRNRfPG9QeVHE+pmIJtIOf4TvPGQAAWQhcUq3N9hwBvZWDSCVZATxefJfdhH6EgOTxAopyvL0mIJ8QE1QeH1WcznBHUBuT7CHidruDy0ajscG2JpMpuOxyuept98gjj+C+++4LPi4vL0dKSgomT56M+Pj4VkTbMkLoKNn1JXzlBfCWF8KaPAySVH8NLCIKP00X2JELjO4JqDKPVyJqGM8ZRNRcPG8QUXPxvEHtywoctiLel4nxvSr/v+kSkHG8xdhkCWjl/8Xd3ePhPJSLYc5DsKX2bbCtfV8m9vn9sPRIxPDKmHSfhB3V2oxNliAbeHxUKS0PdwS1deoku6ZpMBgMre5n8eLFmDlzZusDqsZsNgeXvV5vg22rl4CxWCz1tjOZTDUS8lUMBkObvA/NJYQOVZagSwIKdKgymGQn6hQEVFniB1giaiKeM4iouXjeIKLm4nmD2ofks0PSKgDFVO3/W83/d6rc+iR7zG8Gw3koF9nvfopT5s6BpCh1thN+P3KWfwZIQMypg4Mx6XLtmGQeH0Ed8VwhN96EWiIyMjK43FAJGACoqKgILjdWWoaIiIiIiIiIiIiaLyJjKQDAb218QtLW6H7h2ZANKuz7DmLPE6+g4mB2rTaOg0ew+4lXYN+XCVlV0ePCSSGNiUKrU49kV1UVe/fubXU/PXr0aINoaqo+2Wl2du0Dqbrqk51WnwSViIiIiIiIiIiI6mc6urbhBroXiqcIxsKtUCqyAUhw9zgvtDHFx6D/7dcg/eV3Ub7vIH5++F8wxETBlBALSZLgLiiGr8wemIZAAvrfcS1MibEhjYlCq1Mn2QFgyJAh4Q6hTjabDSkpKThy5Aj27dvXYNvq64cOHRrq0IiIiIiIiIiIiE4Ktr3/wYklX+oWmDjU030y3MkXhTQmAEicNBZqZAQyF62Cp7AEvtJy+E4oJm5Kike/W69E7CjmAzu7Tp9k78gmTpyI5cuXY//+/cjLy0P37t3rbLdu3brg8oQJE9orPCIiIiIiIiIiok5NNyc23EBSoKs2+G394Ek6C76437RPYABiTxuGmPlDULb7V9j3Z8J3zA4AMMZEIXJwP0QNHwhJZjXvkwGT7CF02WWXYfny5QCAJUuW4OGHH67Vxul0YuXKlQCAYcOGITU1tV1jJCIiIiIiIiIi6qxKz1wU7hAaJCkyYkYORszIweEOhUKIX5WE0OWXX44BAwYAAObNm4eMjIxabR544AGUlpYGl4mIiIiIiIiIiIio8+BI9nqkp6dj48aNNZ5zOBzB+yVLltRYN2XKlFrlYAwGA1588UVccsklKC8vx4QJE/DXv/4V48aNQ2lpKV5//XWsWrUKQKC0zI033hi6F0REREREREREREREbY5J9nps3LgRN998c53riouLa61LS0urs+b6hRdeiAULFuDOO+9Efn4+/vSnP9VqM27cOHz44YdQFKVtgiciIiIiIiIiIiKidsFyMe3gtttuw44dO3Dbbbehf//+MJvNiI+Px8SJE/Hqq69i06ZNSEhICHeYRERERERERERERNRMHMlej5kzZ2LmzJlt1t/w4cOxcOHCNuuPiIiIiIiIiIiIiMKPI9mJiIiIiIiIiIiIiFqISXYiIiIiIiIiIiIiohZikp2IiIiIiIiIiIiIqIWYZCciIiIiIiIiIiIiaiEm2YmIiIiIiIiIiIiIWohJdiIiIiIiIiIiIiKiFmKSnYiIiIiIiIiIiIiohZhkJyIiIiIiIiIiIiJqISbZiYiIiIiIiIiIiIhaiEl2IiIiIiIiIiIiIqIWYpKdiIiIiIiIiIiIiKiF1HAHQC0nhAAA2O12GAyGMOxfh93hhLfCBa/TDd3hhCTxexuijkzTBZxOAbtDgipL4Q6HiDo4njOIqLl43iCi5uJ5g8JO98HoPv7Q7nACcvvn2arTfRqcuh58bHc4IRuYxq3iqHACOJ4b7Qgk0ZGioWY5ePAgBgwYEO4wiIiIiIiIiIiIiNpVRkYG+vfvH+4wAHAke6cWFxcHADh8+DCio6PDHA0RdQbl5eVISUnBkSNHEBUVFe5wiKiD4zmDiJqL5w0iai6eN4ioucrKytC7d+9gbrQjYJK9E5PlQGmW6Oho/iIiomaJiorieYOImoznDCJqLp43iKi5eN4gouaqyo12BE1KsrfHsHtJkpCRkRHy/RARERERERERERERtZUmJdmzsrIgSVJIi8lLEie3ICIiIiIiIiIiIqLOpVnlYqZNm4aYmJg2DeDYsWP4+OOP27TPrsJkMmHu3LkwmUzhDoWIOgmeN4ioOXjOIKLm4nmDiJqL5w0iaq6OeN6QRBOGp8uyDEmSsGvXLgwbNqxNA9i9ezdGjBgBSZLg9/vbtG8iIiIiIiIiIiIiolDqONXhiYiIiIiIiIiIiIg6mSaVi+nduzckSYLRaGzzAIxGI3r37t2hZoMlIiIiIiIiIiIiImqKJpWLISIiIiIiIiIiIiKi2jh8nIiIiIiIiIiIiIiohZhkJyIiIiIiIiIiIiJqISbZO4kvvvgCkiQFb48//niTttu9ezfuuOMODBw4EBaLBYmJiTj77LPx2muvQdO0Ju9/xYoV+N3vfocePXrAbDajb9++uPHGG7F169YWviIiaiuHDx/Gq6++imuvvRaDBw9GREQEzGYzevXqhWnTpmH58uXNOt553iCiw4cP4/7778fQoUMRERGBuLg4jBs3Dv/85z/hdDrDHR4RtYGdO3fi6aefxtSpU5GSkgKTyQSbzYbU1FTMnDkTGzZsaFZ/a9aswRVXXIFevXrBZDKhV69euOKKK7BmzZom9+F0OvHcc89h3LhxiIuLg81mw9ChQ3H//ffj8OHDzX2JRNROHnzwwRr5im+//bbRbXjOIOpaioqK8Oyzz2LChAno3r07TCYTevbsifHjx+OBBx7Ali1bGu2jw583BHV4DodD9OnTRwAI3ubOndvodosWLRImk6nGdtVvp59+uigqKmqwD5fLJS6++OJ6+5BlWTz55JNt9EqJqLn+9re/CUmS6j1Gq25jxowRhw4darQ/njeI6NNPPxXR0dH1HsODBw8WGRkZ4Q6TiFrh7LPPbvSzAwBx4403Co/H02Bfuq6LWbNmNdjPrFmzhK7rDfaTnp4uBg8eXG8f0dHR4rPPPmvLt4GI2sCPP/4oVFWtcbympaXV257nDKKuZ+XKlSI+Pr7B437atGn1bt9ZzhttkmTfs2ePuOeee8To0aNFbGysUFVVyLLc4E1RlLbYdZdw7733CgCiW7duTU6yr1mzRsiyLACIpKQk8eKLL4rvvvtOfPHFF+KKK64I9nP22WcLv99fbz+///3vg20nT54sPvroI7Ft2zbxxhtviAEDBgTXvf766238qomoKW699VYBQERERIjp06eLxYsXi40bN4rvv/9evPXWW2Ls2LHB43TQoEHCbrfX2xfPG0T0448/CqvVKgAIm80mnnrqKbF582bxzTffiNtuuy14/A4ZMqTB8wkRdWxVv4979uwp7r77bvHBBx+Ibdu2iS1btojnn39eJCcnB4/366+/vsG+Hn300WDbUaNGieXLl4tt27aJ5cuXi1GjRgXX/eUvf6m3D7vdLoYMGRJse9ttt4lvvvlGbN68WTz11FPCZrMJAMJqtYqffvqprd8OImohv98f/Hujer6ioSQ7zxlEXcvSpUuDeYZu3bqJuXPniq+//lrs2LFDfPbZZ+LFF18U559/vrjqqqvq7aOznDdanWSfN2+eMBgMQpZlIUlSk2+yLLd2113Cjh07hKIowmQyiYULFzYpye7z+cTAgQMFABEVFSXS09NrtZkzZ06wr6VLl9bZz7fffhtsc8kllwhN02qsLywsFL179xYARGxsrCgtLW3NSyWiFnjwwQfFP/7xD1FeXl7nek3TxDXXXBM8lusbQc7zBhEJIcQ555wjAAhVVcXmzZtrrX/22WeDx/gTTzwRhgiJqC1cdNFF4r333qv1e7pKYWGhSE1NDR7v69evr7Pdr7/+GhzBOmbMGOF0Omusr6ioEGPGjAmeV+r6fCGEEHPnzg3u69lnn621fvPmzcH9TJ48uZmvlohC5YUXXgh++f7II480mmTnOYOoa9mzZ0/wSvmzzjpLHDt2rN629V0515nOG61Ksq9cuTKYNFcURUyaNEncdddd4rHHHhOPP/54ozdqmKZp4rTTTgv+IZuWltakJPvKlSuD7ebNm1dnm4qKChEbGysAiOHDh9fZ5sILLxQAhKIo4siRI3W2Wb58eXBf//znP5v9Goko9IqKioTRaBQAxMiRI+tsw/MGEW3bti14bN5+++11tvH7/WLo0KHBL8q8Xm87R0lE7eW///1v8Jxw11131dmm+hfwW7ZsqbPNli1bgm3uvPPOWuu9Xq+IiYkRAMTQoUPrvVru9ttvD/bz/ffft/yFEVGbOHz4cHDkZ1paWo0EVn1Jdp4ziLqW8847TwAQCQkJorCwsEV9dKbzRquS7BMmTBCSJIlevXqJn3/+uTVdUR2ee+45AUCkpqYKt9vd5CR79VINR48erbdd9f88Bw4cqLHObrcHv22aMmVKvX14PB4RFRUlAIgzzzyz2a+RiNpH1Te7ERERda7neYOIql+GuXXr1nrbzZs3L9juq6++ascIiag92e324LF+0UUX1Vqv63qwrMyQIUMa7Kuq/mmvXr1q1Uv96quvgvt55pln6u2j+h/Qjz76aMteFBG1mao5mGbMmCGEEI0m2XnOIOpa9u7dGzwGWzrQurOdN2S0ws8//wxJkvB///d/GDFiRGu6ohNkZWVh7ty5AIBXXnkFJpOpydtu2LABADB48GB079693naTJk0KLm/cuLHGum3btsHj8dRqdyKj0YjTTz89uI3P52tynETUfqqOZ1mu+7TP8wYRVZ0HIiIiMHr06HrbNXQeIKKTh9frDS7X9fkhMzMTOTk5ABr+vV99fXZ2NrKysmqsqzr3NNbPmDFjEBERAYDnHqJwW7lyJT799FPExcXhueeea9I2PGcQdS3vv/9+cPnqq68OLpeWluLXX39FcXFxo310tvNGq5LsBoMBAHDqqae2phuqw+zZs+F0OnHDDTfgvPPOa/J2DocD2dnZAIAhQ4Y02Lb6+r1799ZYV/1xU/vRNA2//vprk2MlovZRUFAQPKbrOp553iAi4PgxPHDgQKiqWm+7hs4DRHTyWLduXXC5rt/rLfm9f+J2zelHVVUMGDCgzj6IqP0cO3YMd999NwDgH//4BxITE5u0Hc8ZRF3L1q1bAQDR0dEYOnQo3nnnHfzmN79BXFwcUlNTkZCQgP79++OJJ56Aw+Gos4/Odt5oVZI9NTUVAJr07QM13bvvvos1a9YgJiYG//rXv5q1bXZ2NoQQAIBevXo12DYlJSW4fOTIkRrrqj9uTT9EFH7PPfccNE0DAFxzzTW11vO8QURutxtFRUUAGj9+Y2NjgyM8ePwSnZx0XcczzzwTfFzX54e2+r1f9TgiIgIxMTFN6qewsDB49RwRta8HH3wQeXl5OPPMM3Hrrbc2eTueM4i6lj179gAA+vbtiz/96U+YPn06fv755xptMjMz8fjjj+OMM85Abm5urT4623mjVUn2GTNmQAiBjz76qDXdUDUlJSW49957AQDz5s1DUlJSs7a32+3BZZvN1mDbqj+QAdT61qit+iGi8Pruu+/w73//G0Dgl9KcOXNqteF5g4iac/wCx49hHr9EJ6cXXngB27ZtAwBcfvnlGDNmTK02bf35oTnnnrr6IaLQ27hxIxYtWgRVVbFgwQJIktTkbXnOIOpaSkpKAAD79u3Dyy+/jJiYGCxYsAAFBQVwu93Yvn07pk6dCgD45ZdfcPXVV0PX9Rp9dLbzRquS7LfeeivOOussLFy4EP/9739b0xVVuv/++1FQUIDx48dj1qxZzd7e7XYHl41GY4Ntq9d5d7lcIemHiMInPz8fV111FTRNgyRJWLp0KaxWa612PG8QUXOOX+D4Mczjl+jks27dOjz88MMAgG7duuHVV1+ts11bf35ozrmnrn6IKLS8Xi9mzZoFIQTuvffeZs/Lx3MGUddSUVEBIDA/nKIo+OKLL3D77bcjMTERJpMJY8aMwaeffhpMtG/evBmrV6+u0UdnO2+0uib7xx9/jClTpuDyyy/H9OnT8cknn2Dfvn04fPhwo7fOqiph1drbkiVLavT77bffYvHixVAUBQsWLKh3gsKGmM3m4HL1yYrqUv2yB4vFEpJ+iCggVOeN+tjtdlx00UXBWutPP/00zj333Drb8rxBRM05foHjxzCPX6KTy+7du3H55ZdD0zSYTCasXLmy3itr2/rzQ3POPXX1Q0Sh9fTTT2Pv3r3o3bs35s6d2+ztec4g6lqqH/NXX301Tj/99FptZFmuMXny8uXL6+2jM5w36p/VqoliYmJw1113YevWrVi+fHmtN6Q+kiQFawRT4Id4++23AwDuuuuuFk8mGxkZGVxu7LKGqm+VgNqXTLRVP0TU/txuN6ZNm4YdO3YAAO67777giLS68LxBRM05foHjxzCPX6KTR2ZmJi644AKUlpZCURQsX74ckyZNqrd9W39+aM65p65+iCh09u3bh3nz5gEA5s+fX6OcQlPxnEHUtURGRgaPwarR6nU55ZRTkJycjJycHGzfvr1WH1U6w3mj1Un2e+65B/PnzweA4MR5JztVVdtkduoePXoEl1evXo0DBw5AVVUMGzYMK1asqNW+atIAIFCvqKrN+PHj0a9fPwA1JwKoGsFan+oTAVSfIKCufuqqw9iUfogoIBTnjbpomoZrrrkGaWlpAIA//OEPjU6gzPMGEZnNZiQkJKCoqKjR80BpaWnwwyePX6KTQ25uLn77298iNzcXkiThzTffxOWXX97gNm35+eG7775DRUUFjh071uCEZFX9VF1qTkTt44UXXoDX60X//v3hdDrrzFf88ssvweW1a9ciLy8PAHDJJZcgIiKC5wyiLiYlJSV4HmjKpKU5OTkoKCio8XxnO2+0Ksn+9ttv48UXXwQAREVF4fLLL8fIkSMRExPTolInncmQIUPatL+qyxE0TcNtt93WaPtVq1Zh1apVAIDFixcHk+w2mw0pKSk4cuQI9u3b12Af1dcPHTq0xrphw4bV2a6hflRVxcCBAxuNnairauvzxol0XceNN94YnCPj2muvxWuvvdbodjxvEBEQOKY3bNiA9PR0aJoGVa37Y2JD5wEi6nyKiopw/vnn4+DBgwACo1RvuummRrdrye99oO7PD1V/1+zbt6/Oy8mBwN9JGRkZdfZBRKFVla84ePAgrr/++kbb/9///V9wOTMzExERETxnEHUxp5xySnBkut/vb7Bt1foT//7obOeNViXZq0awDxkyBGlpafXW66P2NXHiRCxfvhz79+9HXl4eunfvXme7devWBZcnTJhQY93YsWNhNBrh9XprTH50Iq/Xi61bt9bYhojC4/bbbw+OKrn44ovx1ltvNfkLT543iGjixInYsGEDKioqsGPHDowfP77Odg2dB4iocykrK8Pvfve74BWzzzzzDP74xz82adt+/fqhZ8+eyM3NrXFeqMv69esBAMnJyejbt2+NdRMnTgwur1u3rt4/fL///vvgVTQ89xB1PjxnEHUtZ599dnBOuYyMDJx//vn1tq36oj85ObnG853tvNGq4eb79u2DJEl4/PHHmWBvpZkzZ0II0eCtqvwDAMydOzf4/MyZM2v0ddlllwWX65sk0el0YuXKlQAC3+ikpqbWWB8ZGYnzzjsPAPC///2v3ssyVq9ejfLycgBo9JJSIgqd++67D4sWLQIAnHfeefjggw9gMBiavD3PG0RU/TywePHiOtvouo5ly5YBCMzLM3ny5PYIjYhCwOl04qKLLsLOnTsBAH/5y1/w0EMPNXl7SZIwbdo0AIG/C6u+QD/R1q1bg6PLpk2bBkmSaqw/55xzEB0dDQBYunRpvSVIq38+4ecHova1ZMmSRvMV1SdDTUtLCz5fleziOYOoa7n00kuDOYnVq1fX227dunUoLi4GAJx11lk11nW684Zohfj4eCHLsvjhhx9a0w01UVpamgAgAIi5c+fW287r9YoBAwYIACIqKkqkp6fXajNnzpxgX4sXL66zn2+++SbY5tJLLxWaptVYX1hYKHr37i0AiJiYGFFSUtKal0dELTR37tzgsXrmmWcKh8PR7D543iAiIYQ466yzBAChqqrYvHlzrfXPPvtskz6LEFHH5vF4xAUXXBA8nu++++4W9bN//36hqqoAIMaMGSOcTmeN9U6nU4wZMyZ4Xjlw4ECd/fztb38LxvLss8/WWr958+bgfiZNmtSiWIkotKr/TZKWllZnG54ziLqW2bNnB4/V5cuX11pfXl4uTj311GCbbdu21WrTmc4brUqyT5gwQciyLL744ovWdENN1NQkuxBCfPbZZ0KWZQFAJCUlifnz54vvvvtOrFmzRlx55ZXBfiZOnFgrCVbdddddF2w7efJk8fHHH4vt27eLN998M5iQAyAWLFjQxq+WiJrixRdfDB6HycnJYuPGjWLXrl0N3rxeb5198bxBRDt37hQWi0UAEDabTTz99NNiy5YtYu3atWLWrFnB4zc1NVWUl5eHO1wiaqErrrgieDyfe+654ueff27ws8P+/fvr7evhhx8O9jVq1CixYsUKsX37drFixQoxatSo4LpHHnmk3j7Ky8tFampqsO2sWbPE2rVrxZYtW8TTTz8tbDabACAsFgsHeBF1UE1JsgvBcwZRV1JQUBAcZKeqqrjzzjvF2rVrxffffy8WL14shgwZEjyOZ8+eXW8/neW80aok+6JFi4QkSeKmm25qTTfURM1JsgshxMKFC4XRaAxuc+Jt3LhxorCwsME+nE6nuPDCC+vtQ5ZljmQjCqNJkybVe3zWd8vMzKy3P543iOiTTz4RUVFR9R7Dqamp4tdffw13mETUCs397NCnT596+/L7/eKWW25pcPtbb71V+P3+BmP69ddfxaBBg+rtIyoqSvz3v/9t43eCiNpKU5PsPGcQdS179uwRAwcObPCYv+WWW+odDChE5zlvtCrJLoQQl1xyiZBlWcyfP7+1XVEjmptkF0KIXbt2idtuu030799fmM1mER8fLyZOnCheffVV4fP5mrzvd955R5x//vmiW7duwmg0ipSUFPH73/++zkvJiaj9tHWSXQieN4hIiKysLHHvvfeK1NRUYbVaRUxMjBgzZoz4xz/+ISoqKsIdHhG1Ulsm2at89tlnYtq0aaJnz57CaDSKnj17imnTponPP/+8yXE5HA7xj3/8Q4wZM0bExMQIq9UqBg8eLO69916RlZXVildMRKHW1CR7FZ4ziLoOh8MhnnvuOTF+/HgRFxcnjEaj6NWrl7j22mvF2rVrm9xPRz9vSELUU+29CdavXw+v14u//e1v2LZtG8aPH4/rrrsOqampsFqtjW5/9tlnt3TXRERERERERERERERh16okuyzLtWZsbfKOJQmaprV010REREREREREREREYae2toNW5OiJiIiIiIiIiIiIiDq1ViXZ09LS2ioOIiIiIiIiIiIiIqJOp1XlYoiIiIiIiIiIiIiIujI53AEQEREREREREREREXVWTLITEREREREREREREbUQk+xERERERERERERERC3UpIlPn3zySQDAnDlzkJCQ0KYBFBYW4tVXXwUAPPbYY23aNxERERERERERERFRKDVp4lNZliFJEnbt2oVhw4a1aQC7d+/GiBEjIEkS/H5/m/ZNRERERERERERERBRKLBdDRERERM2SlZUFSZJafevbt2+4Xwq1k/fff7/Gz37mzJlN2k4IgXfeeQdTp05FcnIyTCYTkpOTMXXqVLz99ttownihoIMHD+L+++/HKaecgqioKERFReGUU07B/fffj4yMjEa393q92LlzJxYuXIhZs2Zh9OjRMBqNwde0ZMmSJsXRt2/fZh8r55xzToN97tu3D/fffz/GjBmD2NhYGAwGREdHY+TIkZg9eza+//77JsUGtP59IiIiIuqKmlQupsr27dtRVFTUpgFkZma2aX9ERERERNSwJUuW4OabbwYApKWlNZrEbY3CwkLMmTOn2duVlJTg8ssvx/r162s8n5ubi9zcXKxZswavv/46PvroI8TGxjbY17JlyzB79mw4nc4az+/Zswd79uzBq6++ildffRU33XRTvX089NBD+Pe//93s19EWhg4dWu+6p556Co8//jg0TavxfHl5OXbt2oVdu3bhtddew+zZs/Hiiy9CUZR6+2qL94mIiIioK2pWkv2WW24JVRxERERE1EkkJydj165d9a7/61//io8//hgA8Oabb2Ls2LF1tjMajSGJjzqWOXPmoKioCElJScjPz2/SNj6fD5deeik2bdoEAJg0aRLuvPNO9O3bF1lZWXjppZewbt06rF+/Hpdccgm+/fZbqGrdf9p8+umnuPnmm6HrOmw2Gx544AFMnjwZkiRh7dq1ePbZZ1FRUYFbbrkFcXFxuPjii+vsp/qoeaPRiBEjRsDr9TZ4LNTlq6++gtfrbbTdhRdeiCNHjgAAbr311jrbvPzyy/jrX/8KAFAUBTNnzsRFF12EXr16IS8vD2vXrsWrr74Kj8eDV155BVarFc8991ydfbXV+0RERETUFTW5JnvIA2FNdiIiIqKTwsyZM7F06VIAoR8lTS3TXiPZ33vvPVx33XWIiIjASy+9FNznjBkzGiyvMn/+fNx1110AgKuvvhorVqyo8TeJ3+/H9ddfj/fffx8A8NJLL+GPf/xjrX5cLhdSU1ORnZ0Nk8mEjRs3YsyYMTXafP/995g4cSI8Hg9SUlJw4MABmM3mWn19/PHHyM/Px5gxYzBixAgYDAY8/vjjeOKJJwAAixcvbnIZnMZ89913OP300wEAI0eOxE8//VSrjaZp6NGjR/BK488//xxTp06t1W7nzp0444wz4PV6oaoq8vLyEB8fX6NNW75PRERERF1Rk0ayp6WlhToOIiIiIiI6iRQUFODOO+8EAMybN6/JNfh1XcczzzwDAIiIiMCCBQtqDfpRFAULFizAZ599BqfTiaeffhpz5syBJEk12i1duhTZ2dkAgHvvvbdW4hgAxowZg3vvvRfPPPMMjhw5gmXLlmHWrFm12k2bNq1J8beFN998M7hc3yj2ffv2BRPsZ555Zp0JdgA47bTTcMUVV2DFihXQNA1btmypNQq9Ld8nIiIioq6oSUn2SZMmhToOIiIiIuoi+vbti0OHDmHSpEn49ttvceTIEbz00kv47LPPkJ2djbKyMrzwwgu45557AARGLaelpeGrr77C1q1bceDAAZSUlMBoNKJ79+4YP348br75Zvz2t79tcL9VCdiqUdSHDh3Cf/7zH3z22Wc4cuQIjEYjhg0bhunTp2PWrFn1lh8BgNLSUixYsACff/459u7di7KyMkRERCAhIQE9e/bE2WefjSlTpmDixIn19lFSUoIFCxbgiy++wIEDB1BaWoqoqCgMGTIEl1xyCWbPno2oqKhG38+jR49i4cKF+N///hfsJy4uDsnJyRg3bhyuuOIKTJ48Gaqq4ttvv8XkyZNrbH/iYwDBn01rVJWJmTBhAv74xz/Wqq1en02bNiE3NxcAcNVVVyEuLq7OdnFxcbjqqquwbNky5ObmYtOmTbXe76qR7gAaTAjPmjUrmNhfuXJlWJPHTqcTK1asAACYTCbccMMNdbYrLy8PLjf2BUa/fv2Cy3a7vdb6zvg+EREREXUkzarJTkRERETUlr788ktce+21KCsrq7fNn//8Z/znP/+p9bzP50NGRgYyMjLw7rvv4rrrrsOSJUtgMpka3e/nn3+O3//+9zX263K5sGXLFmzZsgUff/wxPv30UxgMhlrbfv/997jwwgtRWFhY4/mysjKUlZUhIyMDGzZswNtvv42srKw69//ee+9h1qxZNRKlAFBcXIxNmzZh06ZNeOGFF7Bq1SpMmDCh3tfx73//G4888gjcbneN5/Pz85Gfn4+dO3diwYIF7V62Z8WKFVi1ahVMJhPeeOONZpWfrJ7cP/fccxtse95552HZsmXB7aon2TVNw8aNGwEEkszVE80nqlqfmZmJjRs3wu/3NzhBaCh98MEHwf8X06ZNq1XapcrAgQMhSRKEEPX+P6tSfX1qamqNdZ31fSIiIiLqSJhkJyIiIqKwOHLkCK655hoAwF/+8hecd955iIqKQkZGBhITE4PtNE1Dz549cemll+L000/HgAEDEBERgfz8fOzduxcvvfQSDh48iBUrViAhIQHz589vcL+7du3C+++/j+joaDz66KM488wzYbFY8NNPP+Hvf/87MjMz8dVXX+HZZ5/FX/7ylxrb+nw+XHnllSgsLIQsy5g5cyYuvvhi9OjRA0ajEYWFhdi1axe+/vprZGRk1Ln/t956CzNmzIAQAvHx8fjjH/+IUaNGISUlBWVlZfjmm2/w0ksvIT8/H1OnTsV3332HoUOH1urnr3/9K5566ikAgNlsxs0334wpU6agV69e8Hq9OHDgANLS0vDhhx8Gtxk7dix27dqFjz/+ODhhZl2T00ZERDT4HjakepmYuXPnYvDgwc3afvfu3cHlU045pcG2w4YNCy7v2bOnxrr09PTgBKON9VPVV2ZmJjweDzIyMmolo9tLU0rFAEC3bt1w+eWXY/Xq1di8eTO+/PJL/O53v6vV7scff8Tq1asBAOPHj8fo0aNrrO+s7xMRERFRhyKIiIiIiNrQjBkzBAABQKSlpdVa36dPn+D6+Ph4sXfv3gb7S09PFz6fr971mqaJ6667TgAQiqKIrKysOttV7ROAGD58uCgoKKjV5ujRoyIuLk4AEN27dxeaptVYv3bt2mAfzz//fINxFxUV1XouMzNTWCwWAUBMnTpVOByOOrc9cOCASExMFADEb3/721rr09LSgnH06tVL7N69u944ysvLRUlJSY3nFi9e3ODPqDUuv/xyAUCMGjWqxs+teswzZsyod/vTTz892K6un1F1+fn5wbZnnnlmjXVr1qwJrpszZ06jcc+ePTvY/quvvmq0vRBCzJ07N7jN4sWLm7RNQ9LT04UkSQKA6N27t/D7/Q22LywsFBMmTBAAhKqq4rbbbhMfffSR2L59u/j000/FfffdF/z/NnLkSHHo0KFafbTH+0RERER0smv6dZtERERERG3s6aefxpAhQxpsM2DAgAbroyuKgvnz50NRFPj9fnz00UeN7nfJkiU1RstX6d69O2655RYAQF5eHvbt21dj/dGjR4PL5513XoP7qKvMxz//+U+4XC5ERUXhnXfeqXfE+KBBg/DYY48BAP73v/8hMzOzxvonn3wyuPzOO+/UGNF9osjISMTGxjYYa1tZvnw5PvzwQ6iqijfffLPBn1t9qtcMj4yMbLBt9fUn1hpvTj+N9dVe3nzzTQghAAA333xzo2V2EhISsHbtWrz22msYOHAgXn/9dVx22WUYO3YsLr74Yjz//POIj4/Hf/7zH2zevBm9e/eu1UdnfJ+IiIiIOhom2YmIiIgoLFRVrXdSx4ZUVFTg0KFD2LNnD3755Rf88ssvyMvLC06QuXPnzga3P+WUU2qVzKhu3LhxweX09PQa65KTk4PLixYtCiZEm6qqbMeUKVMaTXxXr6FeVTMbAI4dO4Z169YBAE4//XScffbZzYohVPLz8/GnP/0JAPDggw/i1FNPbVE/LpcruGw0GhtsW73+vtPpbHE/jfXVHvx+P5YuXQogMEnvzTff3KTttm3bhg8++AAHDhyoc312djbefvttrFmzps71ne19IiIiIuqImGQnIiIiorBITU1tcu3vzMxM3H333RgwYAAiIyPRt29fnHLKKRgxYkTwVjURaVFRUYN9NTTqG6g5Av3EiUknTJgQrI8+f/58pKam4qGHHsKnn37a6H4PHz4cHAm/cuVKSJLU4G3EiBHBbauPoP/hhx+g6zoAYPLkyQ3usz3dcccdKC4uxpAhQ4Kj8FvCYrEEl6tqhdfH4/EEl61Wa4v7aayv9vDll18iJycHAPDb3/4Wffr0aXSbN954A+eeey6+/vpr9O/fH2+99RZyc3Ph8/lQWFiI1atXY9SoUdi+fTuuuuoq/O1vf6vVR2d7n4iIiIg6IibZiYiIiCgsqkaeN2blypUYOnQoXnzxRRw8eLDR0eONja5tLDFYvUSH3++vsU5VVXz66afBiULT09Px7LPP4pJLLkFiYiKGDRuGhx56CAcPHqzVb0FBQYP7bUj111T1ZQIA9OzZs8V9tqV3330XH330EWRZxhtvvFFjtHNzNaccicPhqHO75vbTWF/toakTnlbZs2cP7rjjDvh8PqSmpmL79u2YPn06evToAVVVkZCQgMsvvxxbtmzBGWecAQD4+9//jq+++qpGP53tfSIiIiLqiJpfJJGIiIiIqA0oitJom/T0dNx0003weDywWq24++67MWXKFAwcOBDx8fEwGo2QJAkA0Lt3bxw5cqTZJVyaq3///ti2bRvS0tLwySefYMOGDfjpp5+gaRr27t2LvXv34vnnn8fTTz+NBx54ILidpmnB5VtuuQX33ntvk/fZrVu34HL111f12sPtiSeeAACMHj0ahw8fxuHDh2u12bNnT3A5MzMTK1asAAD069cP48ePD67r3bs3tm7dCiAw+r+u2vlVDh06VGO76qo/riue5vQVakVFRfjvf/8LIPDl02WXXdboNm+88Ubw/9Rf//pXxMTE1NnOZDLhmWeewaRJkwAAr732Gi644ILg+s70PhERERF1VEyyExEREVGH9eabbwbLU6xatQpTpkypt21JSUl7hQUgUKqlqlyL0+nEpk2b8P7772Pp0qXwer148MEHMXz4cEydOhUAaiSLy8rKMHz48Bbtt3rCvaq8SLhV/Yy2b9+O66+/vtH269evx/r16wEAM2bMqJFkHz58OFauXAkA2L17d4P186sn7k855ZQa6wYNGgSj0Qiv14vdu3c3GlNVXyaTCQMHDmy0fVt66623gqVabrjhhiZdCVD9NVVdWVGf6uurv2dA53qfiIiIiDoqloshIiIiog5r165dAAKjextKsO/evRsVFRXtFVYtVqsV559/PhYuXIglS5YEn3/33XeDy/369QuWyFm7dm2N2tbNcdpppwWvAkhLS2tRHx1lBHxdqkZcA4H3qSHffPNNndsBgdI+EyZMABAYOZ+VlVVvP1lZWcjMzAQQqLvflKss2tLixYuDy00pFQMABoMhuOzz+RpsW3199e2AzvU+EREREXVULUqyl5aWYtWqVXj++efx3HPP4Z133kF2dnZbx0ZEREREXVxVOQyXy1WrPnp1L774YnuF1Kjf/e53weXq9dNlWQ6WASktLcX8+fNb1H90dDTOOeccAMDWrVuDI8Kbw2w2B5dbmuyvLisrC0KIBm/VvxCYMWNG8PnqX0oAwMSJE9G9e3cAwAcffFDvFQolJSX44IMPAAA9evQIJoqru/rqq4PLr732Wr3xV19XfZv2sG3btuCXSaNHj8ZvfvObJm03YMCA4PK3337bYNvqX1ZU365KZ3ifiIiIiDqyZiXZhRB49NFHkZycjGuuuQYPPPAAHn74Ydx0003o27cvpk+fXmMiHCIiIiKi1hg8eDCAQJL97bffrrPN22+/jddff71d4lm/fj327t3bYJvPP/88uHxiQvPRRx8NlgL5y1/+gg8//LDBvoqLi/HSSy/Vev5vf/tbcDT6DTfc0GBMDocDpaWlNZ5LTk4OLh84cKDBGNqbLMt4+OGHAQAVFRWYPXs2dF2v0UbXddxxxx3BCWEffvjhGhPWVpkxY0ZwctgXXngB33//fa0233//PV544QUAgfflpptuatPX05jmTnhapXrd9qeeegoZGRl1tsvLy6sxN0Bd9d47w/tERERE1JE1qyb7rFmz8Oabb9Y5mZQQAsuXL0dWVhbWr19f54dcIiIiIqLmmDlzJl588UUIITBr1izs3LkTF154IRITE3Ho0CEsX74cH3zwAVJTU1FSUlJj5HgorF27Fk8++STGjh2LCy+8EKNGjUL37t0hyzLy8vLwxRdfYNGiRQACZTluv/32GtsPGDAAb775JqZPnw6v14srrrgCF110Ea6++moMHjwYJpMJJSUl2L17N9auXYs1a9YgISEBd955Z41+Jk2ahEcffRRPPfUUsrOzcdppp+GWW27BlClTkJycDJ/Ph4yMDKxduxarVq3Chx9+GBz9DgRKzthsNjgcDjz77LNISkrC8OHDYTQaAQAWi6VGIr69zZkzBytWrMDWrVuxcuVK5Ofn46677kKfPn1w6NAhvPjii1i3bh0A4IwzzsDs2bPr7MdqteKVV17BFVdcAY/Hg8mTJ+PBBx/EueeeCyDw83z22Wfh8XggyzJefvllWK3WOvtyOBzBkfNVfvzxx+Dyxo0ba21z1VVXwWaz1fs6XS5XcAJYi8XSpHr2Vc455xxMmzYNH3/8MfLz8zF69GjMmTMH5557LhISElBWVob169fjpZdeQkFBAQBgzJgxmD59eq2+2vJ9IiIiIuqSRBNt2rRJSJIkZFkWRqNRTJ8+Xbz00ktiwYIFYs6cOSI6Ojq4fsGCBU3tloiIiIhOMjNmzBAABACRlpZWa32fPn0EADFp0qQm9ff8888LSZKCfZ54GzRokDhw4ECj/Va1nzFjRoP7S0tLC7ZdvHhxjXVz586tN47qt8jISLFq1ap69/HZZ5+Jbt26NamvYcOG1dvPs88+K4xGY6N91PVzeOqpp+pt39SfTXNUf18b+xkIIURRUZGYOHFig69r4sSJoqioqNG+Fi9eLCwWS739WCyWWj/rE2VmZjbp51X9lpmZ2WCfy5YtC7adPn16o6/jRBUVFeKqq65qUiznnHOOKCgoaLC/tnifiIiIiLqiJo9kr5qMx2Kx4JtvvsH48eNrrH/wwQdx1llnIScnB4sXL641aoeIiIiIqCXuvfdejBkzBv/+97+xadMmFBcXIyYmBgMHDsQVV1yBOXPmICIiol1iefDBB3HmmWciLS0NmzdvRk5ODgoKCuByuRATE4OhQ4figgsuwKxZs9CtW7d6+7nwwguRlZWFpUuX4vPPP8cPP/yAoqIi+P1+xMTEoH///hg7dix+97vf1ajxfqIHHngA1113HRYsWICvv/4aGRkZsNvtSEhIQM+ePXH66afjqquuwtlnn11r20cffRSDBw/Gm2++iR9++AHFxcXwer1t8j61hfj4eKxbtw7vvPMO3nnnHfz0008oLi5GfHw8Ro4ciRtuuAHTp09v0hW0M2fOxFlnnYWXX34Za9aswZEjRwAAKSkpmDJlCubMmYOBAweG+iXVUr1UzC233NLs7a1WK95//31s2LABb731FrZu3YrDhw/D4XDAarWiZ8+eGDt2LK6//npMnTq10QlvO+r7RERERNTRSULUUfulDsOHD8fevXvx8MMP46mnnqqzzTvvvIMbb7wRRqMRdru91sz1REREREREREREREQnkyYXTs/OzgYAnH/++fW2qRpl4/P5kJ+f38rQiIiIiIiIiIiIiIg6tiYn2e12OwAgMTGx3jYJCQnBZYfD0YqwiIiIiIiIiIiIiIg6viYn2auqyjRWx6+Krusti4iIiIiIiIiIiIiIqJNocpKdiIiIiIiIiIiIiIhqUpu7wdGjR2Gz2dqkXe/evZu7eyIiIiIiIiIiIiKiDkMSVXVgGiHLcpNLxTRpx5IETdParD8iIiIiIiIiIiIiovbWrJHsTczHUzvRdR25ubmIjIxs0y9AiIiIiIiIiIiIiDoiIQTsdjt69uwJWe4Y1dCbnGSfMWNGKOOgFsjNzUVKSkq4wyAiIiIiIiIiIiJqV0eOHEGvXr3CHQaAZpSLoY6nrKwMMTExyMzMRFxcXLjDIeo0fD4fvvrqK1xwwQUwGAzhDoeoU+HxQ9QyPHaIWo7HD1HL8fihDknzAK92O/54dgGgmsIXTx064rHj93jw1bhxwccXbNsGxdSx3rf2Ul5ejpSUFBw7dgzR0dHhDgdACyY+pY6jqkRMZGQkoqKiwhwNUefh8/lgtVoRFRXVYX5ZEnUWTTl+hK7BU5IDv9cJc3wfKCZrO0dJ1PHwdw9Ry/H4IWo5Hj/UIWkewFztcVRUh0yyd7Rjx+/xwFqtNEpUVFSXTbJX6Ujls8OWZN+xYwdGjx4drt0TERFRG9M1L9xFWXAVZkJoXggh4C7MQkTKCJjjOsYlfERERERERERtrV2T7Dk5OXj77bfx1ltvYd++fdA0rT13T0RERCHg97rgLjwIV9EhQPdDc9vhKy+Ernlhju8Nx6Ef4C3Lh633SMhKxxgFQkRERERERNRWQp5kdzqdWLVqFZYtW4Zvv/0Wuq5DCNGhhvMTERFR82luO1z56fCU5kDoOjRXGbzlhRBeF6AokCDBlZ8OY3QShBDQnKWI7DMKBlt8uEMnIiIiIiIiajMhS7J/8803WLZsGT788ENUVFQAAKrmWI2Li8O0adNCtWsiIiIKMXvWDuiOQgihw1dRCl95IYTmhaQaYIztCUNEHCB0uEuy4S3Lh+a2wxzfG2W/boYlaRCsPVIhSXLjOyIiIiIiIiLq4No0yb5nzx689dZbeOedd5CTkwPgeGI9KSkJl112Ga688kpMnjwZiqK05a6JiIgoxHSfG+UZ3wMA3MfyAGcxvPYiwK9BNphhjE+Bao2pdrWaDEtiX/gcxfCU5sKZdwCm2GQg/1f47IWI7DMKitkWvhdERERERERE1AZanWQvKirCu+++i2XLluGHH34AcDyxDgRmeZ03bx4eeOABloghIiLqxMqzdsJjLwIgwXV0PxShQTZFwBjXC6olqt7tDLZ4KCYb3MWH4Sk+Ar/bDqPuR+n+9bAlnwJzQp/2exHUabiLD8NddAiSokI2mOu5mXhFBBERERERhV2LkuxerxeffPIJli1bhi+//BKapgUT6waDAVOmTMH06dNx7bXXAgD69u3LBDsREVEn5i4+As1RDF9ZPoDuUEwRsEQnQjFZm7S9bDDBkjQQ3rJ8+MoL4PdUwByfAseRn+EtLwhMiqqaQvsi2oEQOvzuCsgG40nxesJBCAFn7h64Cg7C73UBEJAUAyRZrfPzpGQwQVbNMNjiENFzCCQ55FMOERERERER1dCsv0I2b96MZcuWYeXKlSgrKwNwfNT6GWecEUysx8XFAUAwyU5ERESdl655UZG7B36PE1pFCYDuMCf0hiI37wt0SZJgiukO1WyDu+QIXPkHYYjqBiEEfBWlsPUaDlNsz9C8iBATugZ30WG4CjKg+9wAAslf1RIN1RIFxRwJ1RINxRzBkdcNELoG+6Ef4T12NFBmqCTn+EoJgUS7YoCkGiHJKmS1MvmuGqE5jwFChy1lZNjiJyIiIiKirqnJSfZBgwbh4MGDAI4n1lNTU3HDDTdg+vTp6NevX2giJCIiorBy5u6D7vPAXZoDyAqgt64/xWyDtXsqPCXZgVHtbjtMcb1gz9oBT2k2InoNh2Js2gj5cBN+Da6iLLgKDkJoHvg9TvgcRZAUA2SDGVpFWWVJk8ovJCS5MuEeFUi+WyKhGCMgG81dPvmu+zwoP7gNvopSeMvy4CsvhGKOgBoRD+H3BW+63wfd64Lw+4BqJQqNsT0gSRKMUUkwRieF8ZUQEREREVFX0+Qke0ZGBgDAZrPh1ltvxe9//3uMHTs2ZIERERFR+PkcJXAXH4LPUQzhdcEU2wsobX2/kqzAnNAHvopSeEpz4cr/FYbIRAjhh89eDGvPITAndNxyc7rmhbsque73we9xwFtWAL/bAUgSAAFU5X8lCZLBDMVogWwwQXGXw1dRAlmuNgm8JEE2WKCYIqCYrJCN1sBy5b2ktLwEihA6IPTAIAmhA0JACAFJkiGphg6R3NfcdpRnbIPfY4e7OBt+ZxnUiFiY4no1+H9A+DXofh+8x3LhPZYHxRQJ++GfEDtkEmQDy/UQEREREVH7aNZfbJIkoaKiAuvWrUNKSgp69eqFHj16hCo2IiIiCiMhdDiydwWSmGV5kE1WqBGxQKlofOMmMkTEQjHb4C3Nha+8EJqrDKbYXqjI/gWekmzYev+mwUlV25uueeAqOAh3YRaErkFz2eEtz4fucQKyAmN0EgyRCQAk6D43dJ8Lus8Nv9cVKGeiH78MQFINkFUTJNUYGO2uGCGrgZtUPQEPVCbZm/KFQyC5LyqT6ccz/fWrKr8S3LdSGY9qhFy5rEbEQlYMzXmrmsxrL4I9czv8XjfcRVnQPU4Yo5s2Gl1SVCiKClNcbzjz9sNdchhWwyA4jvyEqP7jQhIvERERUae15pa26UeSgN+90TZ9EZ0kmpxkv+WWW7Bq1SqUlZXhxx9/xE8//YSHHnoI55xzDm688UZcccUVsNlsoYyViIiI2pG7MAt+Vzm8x44CQoc5rldrK8XUSVYMMCf0geYqh6c0B+6Cg1BtcTD6NRzbvx6WpIGwJg2qlXhuL1XlSdzFR+AuPgTh16C5yuErL4DudUFSVBhjesBgi6sRo2Ky1poYVte80L3HE+9C88LvqahR9iSwsRJMcAfqjxuanGMP/CMCfVY+FlWPISpHtAtAkiHLKqAogfrmigLIaqDGuaycMIJcghoRC2NUIgyR3aBao9vkKgN38RE4jvwEv9cV+OLC74UpPgWGiNhm9SOrBphie8FTfBjesjxIkgR30SGYE/q0OkYiIiKik8buJYEE+YmfPYHKqzGbQAgm2Ynq0OQk+6JFi/Dyyy/jww8/xFtvvYWvvvoKfr8fa9euxdq1azF79mxMmzYNN9xwA6ZMmQJFCc8fwkRERNR6fq8LzqP7obnt0CqOwRCVCNlghq633Sj2E6mWKCimiEA9bnsxNFd5YCLUvF/hLT0KW++RMNji22RfQggIzQtd81SOOPdA1yrvfe7jz/ncgRIrldtorjJ4y/IhfB5IqgHG2J4wRMRBkptWcqVqtDgQXeN53e+rjMcbvNc1D/xuB4Rfa5PXDKAyUS9V/nGlNzzQPZh8N0Ax2+D3Vk58e3Q/JNUIY2QiDFHdYIxKhKw2vzRLxdF9cOX9Cr/HAVfhIQCAJbE/FHPLBm0YImLgd5fDV14I1RIJR85uGCIToJgiWtQfERER0UnnlJtQ78iNjI8B9zFANQNJowFbr8DzjhwgfweguQBzLDDg0vaKlqhTaVa5GJPJhOuuuw7XXXcd8vPz8fbbb+Ott97Czz//DJfLhffeew/vvfce4uPjce2114Yq5nZRUFCAbdu2Ydu2bdi+fTu2b9+O4uJiAMCMGTOwZMmSZvW3Zs0aLFy4ENu2bUNhYSESExMxbtw4zJo1C1OmTAnBKyAiImq5ipzd0P1eeEpzAsnkqPaZSFKSFZhik6FaY+EpyYan6DA0SylMscko+3UzjNHdIRstgXrisgLISs3lyhuAYKJa+DyVyfTKe80D4fOirgyzEH4Iv1Y5waZWOdlm4N7vdUL4vJBUI0xxyVAj4tqsZrysGADFUGdCWOg6hN7MRLskQapKplfe1xWr0Ctfr+6H0LXAzV+5XPm8rrnhP5YHIA9QVKjmSCiWSPg9TnhKcwAAqjUGhqhEqObIwEh4pXI0fNWyEhghX7VPx+Gf4SnNDtTkLzkCSTHCktgXssHc3LeuBlNsMvyeCriLj8DS3QJ71k5Ep07oEHXniYiIiMJuypK6n//s94CnDBj/CDD2IcB0QrlGrx3Y9g9g2zzA7wUueifkoRJ1Ni2eRSspKQl//vOf8ec//xk///wzlixZghUrViAvLw9FRUV45ZVXgn/Mffzxxxg0aBBGjRrVZoGHWlJS2yQThBC44447sHDhwhrP5+Tk4MMPP8SHH36IWbNmYcGCBR12cjciIupavOUF8B47Cl95IYTPC3Ni3yaP1G4riskKS/dB8JUXwFteAGfeARijk6D7NUiyBEBu8u9NIcTxpHFV4lzXoPs1QK+ZUK9eM71mQApk1QRjfBJUa0y7/s6WZBmSbAxR30qTyvDomhd+lz1wZYPzGLSKwOy3sskK1RxIuPsqSht9XyRZBaTAhKXesnx4y/IhmyywJPRr1eSu1V+PKS4F7oKD8JbmQJYVuPJ+hbXH4Fb3TURERHRS+vl1YP97wBmPA2f8re42xkhg4t8BxQRseRxImQyM/EN7RknU4bX+rxkAI0eOxPPPP49//vOf+PLLL7F06VJ88skncLvdAIAVK1ZgxYoV6Nu3L6688kpcddVVGDeu80xGlZKSgqFDh+Krr75q9rZ//etfgwn2UaNG4cEHH8SAAQOQkZGBZ599Fj/88AMWLlyIxMRE/P3vf2/r0ImIiJpF6BocR3ZB1zzwlhdAsUSFbeJRSZJgjA4ktT2l2fCWHoW39Gj1BpU3OTBSWZIC97IMCQgkznU/UF+5FQmVI60NgQk/TRGQFBWybICkGoLrJEXt8l+Ey6oRcmQ8DJHxEELA76mA322H32WHtywfKMsPlJdRDMevLKj6ucgyJEkJfFFT+Zzf44BWcQyqNRqmuJQ2/RJHNdtgiEqEr7wQPksUnHm/whDVrdl13omIiIi6hF/eACQZGH1P421H3wNsfRL4ZRGT7EQnaJMkexVZljF16lRMnToV5eXleO+997Bs2TJs2rQJAJCZmYl//etf+Ne//oVevXrh0KFDbbn7NvXYY49h7NixGDt2LJKSkpCVlYV+/fo1q4/09HQ8++yzAIAxY8Zg/fr1sFgsAICxY8fi0ksvxaRJk/D999/jH//4B26++WYMGDCgzV8LERFRUznz06F7nfCUBMqAmGJ7hjkiQDaYYOk2AJqrHLrPAwgdQuiAEJX3xx9XLQsRKMEiGa01ypVIigFyVemSWpN7UlNIkgTVbINqtgExPaBrvkDC3ROoHy+EDqH5AqV3hB64OuDEybUkwBCVCGN095D8DIzRSfC77fCU5EAxWmHP2onYIZPaZLQ8ERER0UmlZB9gjA6MVm+MMRIwRgW2IaIaQvaXRlRUFG677TbcdtttyMzMxNKlS/H222/j4MGDAIDs7OxQ7bpNPPHEE63u44UXXoCmBUbPzZ8/P5hgr2K1WjF//nycccYZ0DQN//73vzF//vxW75eIiKglNLcdrvx0+JzH4Hc7YIzpXjlJZ8egWqIAS+PtqH3JqgGyLQ4GW1y9bUTVFyC6HxB6sFZ7qEiSDFNcClz56fCUZENSDKjI2QNb75Eh2ycRERFRpyR0wHsMcJUAlvo/zwEItPGUAQZru4RG1Jm0S4HVfv364fHHH0d6ejrWr1+PW2+9FVFR4bn0vL0IIfDxxx8DAIYMGYLTTz+9znann346Bg8O1An96KOPAn+EEhERhUHFkV3Q/Ro8pbmQDSYYIhPDHRKdJCRJgiQrgbIzBnO7jChXjBYYY7rD73bA5yiCu/gQPGV5Id8vERERUaeSMBKACJSBaczW/wsk5RNGhDwsos6mfWcxAzBx4kS8/vrryMs7uf/IyczMRE5O4FL7SZMmNdi2an12djaysrJCHRoREVEt7pJs+BzFgfrafg2m2GSWUqFOz2BLgGK2wXssD36vG47DPwVKDhERERFRwKmzA6X9fpgPrLkZOHawdpuyTGDNLcAPLwbmRDp1TvvHSdTBha0wpclkCteu28XevXuDy0OGDGmwbfX1e/fubXbtdyIiotbQ/T5U5OyB3+uEZi+CGhELxWwLd1hErSZJEkxxKXDmHYC75DCshoGwH/4R0QPGhzs0IiIioo5h6A3AoW+A3UuAPcsCt8gUwJYMQAIc2YD9SKCtEMApNwW2IaIaOPtTiBw5ciS43KtXrwbbpqSk1LndiTweDzye46OvysvLAQA+nw8+n6+loRJ1OVXHC48booCKnN3welxwF+dClwwwRfWAptddvqzq+frWE3U4sgo1Jhmekmy4jhXALyTIeQdhjk9pfNs2xN89RC3H44eo5Xj8UJOc9xrk+BGQtz0FuEuA8sOBW3XmWOhjH4V+6p+A1v5/0nwwVHvo8/kA0e7FNhrUEY8dvXLexyqapkGXO9b71l460s+lSpOT7P3792/THUuShIyMjDbtsyOx2+3BZZut4dGAERERwWWHw1Fvu3nz5tU5IWtaWhqsVk46QdRcX3/9dbhDIOpgKn/XHwWAhpPoO3Ibb0PUcUQBGAbYAdgFkL0LwK6wRMLfPUQtx+OHqOV4/FDj+kNOXoBE5w+IcWfA5C8DAHiUaBwzD0Ch5VToR43A0S9avSdZ+HBJtcdrvlwDXTLU2z6cOtSxo2k16n6vWbMGULvm+Gmn0xnuEGpp8k+irWuFn+x1Xt1ud3DZaDQ22LZ66RyXy1Vvu0ceeQT33Xdf8HF5eTlSUlIwefJkxMfHtyJaoq7F5/Ph66+/xvnnnw+DoWP+IidqD15HEeyZ30NzOeApyoJstMKc2LfB39GaLrAjFxjdE1Dlk/t3OZ1chK7DlfcrJEWFJWkALEkDYE1Kbbf983cPUcvx+CFqOR4/1HzTQr8LzQO8cvzhlN9NAdSOVVa6Ix47uteLrx58MPh4ypQpkBvJOZ6sqqp7dCRNTrKfffbZrU6M7969G8XFxRDi5B/5Zjabg8ter7fBttVLwFgslnrbmUymOmvZGwyGDnPAE3UmPHaoK9NcdriP/AhJc0MryYKqqrAm9oakNOVyQwFVlphkp85FViCiE+AtzYVwl8NXfAhK0kDIhvb9g46/e4hajscPUcvx+KEORdJrPDQYDIDaMf9/dqRjx6/XfN9UVYXSQWJrbx3lZ1Jdk5Ps3377bYt38sMPP+Cxxx5DcXFx8LnqJVJORpGRkcHlhkrAAEBFRUVwubHSMkRERK2l+9woP/gd/F433IVZgCTBktgPktI1LzWkrsNgi4fPXghvWR5USxSc+b/C1mt4uMMiIiIi6hgKfwayvgTKDwE+JzDlzePr/D7AVQhAAmw9whYiUUcV0ur4v/zyC6688kqMGTMGn3/+OYQQMJvN+POf/4yDBw+GctdhV32y0+zs7AbbVp/stPokqERERG1N6BrKMrbB73HAXZgJ4ffCktC33UfzEoWDJEkwRidB+DzQnMfgLsqC39vx6jkSERERtStPGfDJlcBbo4ANDwM/vgLsWVqzje4Dlv0GWJgCHDu5c3pELRGSJPv+/ftx/fXX49RTT8VHH30EIQSMRiP+9Kc/4eDBg3juueeQkJAQil13GMOGDQsu79u3r8G21dcPHTo0ZDEREVHXJoSAPWtnILlYnA3d64IprjcU08l9dRlRdao1FrLBBG9ZPoSuw3l0f7hDIiIiIgofXQNWXwikfwSoVqDfRYBqrt3OYAWG3wIIPdCWiGpo0yR7RkYGbrrpJgwfPhwrV66ErutQVRV33HEH0tPT8Z///AdJSUltucsOq1+/fujZsycAYN26dQ22Xb9+PQAgOTkZffv2DXVoRETURVXk7Ia3LB/esqPwO8tgjE6CISIm3GERtavAaPbuEJoXPkcxPCU50Fz2cIdFREREFB673gBytwDR/YFb9gOXfwKYoutuO+jKwH3mF+0XH1En0SZJ9kOHDuEPf/gDhg4dinfeeQd+vx+KouCWW27BgQMH8MorryA5ObktdtVpSJKEadMCMzLv27cPW7durbPd1q1bgyPZp02b1urJZYmIiOriKjgId2EmvPZi+MqLoNriYIzuGl98E51ItUZDNlngLS+AEBqceRzNTkRERF3UvncBSQLOeQGw9Wy4bbdRgCQDJXvaJzaiTqRVSfacnBzMnj0bgwcPxuLFi6FpGmRZxo033oh9+/Zh0aJF6NOnT1vF2uncc889UNXAJHJ/+tOf4HK5aqx3uVz405/+BCAwI/A999zT3iESEVEX4CnLQ0XOHmiucnhLc6CYbTDFdq0vv4lOZIzuAfg1+MqL4T12FL6K0nCHRERERNT+inYBkIC+FzTeVjEERrm7ikMeFlFno7Zko/z8fDz99NNYuHAhvF4vhBCQZRnXXHMNHn/8cQwePLit42x3GzduRHp6evBxUVFRcDk9PR1Lliyp0X7mzJm1+khNTcX999+PZ555Bt9//z0mTJiAhx56CAMGDEBGRgb+8Y9/4IcffgAAPPDAAxg0aFBIXgsREXVdmvMY7Fk74fc64S4+DNlggjmhD6+coi5PNdugmG3w2gug2uLgPLoP0QPPCHdYRERERO3LVwEYIwHF2LT2fi8gtyidSHRSa9ZRUVRUhGeeeQavvvoq3G43hBCQJAlXXHEFnnjiCZxyyimhirPdLVq0CEuXLq1z3aZNm7Bp06Yaz9WVZAeAp556CgUFBXjzzTfxww8/4LrrrqvV5tZbb8Xf//73VsdMRERUnd/rRNnBbdC9LrgKMyFJMsyJ/SDJSrhDI+oQjNHd4cpPh89eCFlR4bUXwhiZGO6wiIiIiNqPJQGoyAsk2w0RDbct/TXQLmZA+8RG1Ik0uVzMo48+iv79++OFF16Ay+WCEAKXXHIJdu7ciQ8++OCkSrC3JVmW8cYbb+Czzz7DtGnT0LNnTxiNRvTs2RPTpk3D559/jkWLFkGW23QOWiIi6uJ0nwflGduge5xwFWUBuh/mhL6Q1SaOUCHqAhSTFao1Gj57EXTNC2fuvnCHRERERNS+ksYG7g9+1njbHS8E7pMnhC4eok6qySPZn3nmGUiSBCEEEhMT8eCDD2Ls2LEoKyvD+vXrW7Tzs88+u0XbtYclS5bUKgnTGhdeeCEuvPDCNuuPiIioPpqrHOUHt8PvccBdfBjC64Y5oQ8UkzXcoRF1OMboJGiuMnjLCyCrRniOHYUppke4wyIiIiJqH6fMBDI+ATY9BvQ6G4joXne77c8BPy0ITJI6/JZ2DZGoM2h2ESVJklBUVIQHH3ywVTuWJAmaprWqDyIiIqrJW14Ae+YO+H1uuIuyoHucMMb2hGqNDndoRB2SbDBDjYiDVlECPSoRztx9MEZ357wFRERE1DUMugzof1FgJPvbY4Ch0wHNHVi3//3AxKgH3gdKDwSeG3xdIBlPRDU0K8kuhAhVHERERNRKrsJMVGTvDkxyWnQIwu+FKT4FhojYcIdG1KEZo7pBqyiFtywfsmqCpyQb5viUcIdFRERE1D4uXgF8Ph1I/xj4/rnjz39WOa9gVT5w0OXA795o//iIOoEmJ9kXL14cyjiIiIiohYTQUZG9G+6iLGguO9zFhwBJgqXbACimRiYvIiLIqhEGWzx89iL4IxPhzNsPU2xPThJMREREXYMhApj2YWA0+65FQM4mwFUUWGeKDtRgHzELGHhpeOMk6sCanGSfMWNGKOMgIiKiFtD9Ptgzd8BnL4TXXgRvaS5kgykwyanBFO7wiDoNY1Q3+CpK4C3Lg2K0wF10CJZu/cMdFhEREVH76X9R4AYAugbofkDl3xRETSGHOwAiIiJqGb/HibIDm+AtL4C7JAfe0lwoZhssSQOZYCdqJklRYYxMhN9lh9/jgDP/Vwg/5w8iIiKiLkpWmWAnagYm2YmIiDohn6MExw5sgM95DO7CTGiOYqi2OJgT+7HEBVELGSITAEWB51gehOaFq/BguEMiIiIiCq1F/YF3T296+xVnAYsGhC4eok6qWROfEhERUfi5S7LhOPwTdJ8brsJMCM0DY2xPGCMTwh0aUacmyQqMUd3gLT0KzVUOV34GTHG9oBit4Q6NiIiIKDTKsgDN3fT29mzAfjhk4XQ06377W0iS1PqOJAnnpaW1vh/qsJhkJyIi6iSE0OHM3QdXQQb8ngq4irIAIWBO6AvVEhXu8IhOClUToHqOHYViioA9cweiUydAkngBKBERERF0DehCn4tcOTkNN5AkQIjG17VFop46NCbZiYiIOgG/xwl71g5ozmPwOUrgKc2GpBhgTuwLxWgJd3hEJw1JkmGKTYa7MAvukmxIsoKKnD2w9Roe7tCIiIiIwsvnAlwFgDEy3JG0mxFPPw1FqV2O01dejgPz58NXXo7YUaOQcMYZMHfvDgBw5+ejaMsWlO7cCUN0NFLvvBOGKA6KOtkxyU5ERNTBeUpz4TjyE3SfB57SHGgVxyCbrDAn9IGsGMIdHtFJR7VEwRDdDb6yAnjtxQAAQ0QsTLHJYY6MiIiIqJXKDwPlWTWf83uB7A0A6hmRLQTgOQbsfQfw+4AeI0IcZMeRfPnlMBhq/s2lOZ3YcPnlgCRh/OLF6HbWWbU3vPdeFG7ahB133YXD772HiatXt1PEFC5MshMREXVQQtdQkb0H7uJD8HudcBcdhtC8MER1gzE6qW1qAxJRnYxRSdA9TniP5UAxWmA//BMUSxRUc9cZuUVEREQnoV8WA1ufrPmcpxRYeU7j2woRKHsy8vaQhNZZpL/6KhwHD2L0v/9dd4K9UuKECRj55JPYcffdSF+wAEPuu68do6T21nWKKBEREXUimsuOY/s3wl18CF57IVz56YDQYe7WD6aY7kywE4WYJEkwxadAklW4iw9B93lgz9wBoWvhDo2IiIiodYQ4fquqG97QDQCMUUDyBGDqMmDo78Mbf5jlrlkD2WBAjylTGm3bY8oUyEYjjq5Z0w6RUThxJDsREVEH4y46BEfO7kB5mJIj8LvsUMw2mOJTWB6GqB3JigHmhD5wFWTAU3IEkqzAcfhnRPY9LdyhEREREbXMmXMDtyr/koGI7sAdueGLqZNx5eZCMZsh1VGr/USSokAxmeDK5ft7smOS/STgLj4CLcLIy5eJiDo53e+D4/DP8B7Lhd/jgKvoMKBrMMZ0hyEykaPXicJAMUXAGN0D3mNH4SsvhCRJUG3xsCT0CXdoRERERK13yk2AKSbcUXQqqtUK77FjcGRmwtavX4NtHZmZ8NntMMbGtlN0FC6tSrL3798fAHDffffhzjvvbJOAqPmcR/fhWMURSKoJBlt84BYZf1Im3XXNA19FKYTmg6SokGQFkqxWLquQlMrHcuPfJhIRdSQ+Rwnsh36A31MBX3k+vGUFkFQDzN0GQjFZwx0eUZdmjEqE31sBb1keZJMFFdm7oFqiYYiICXdoRERERK0zZUm4I+h0Yk87DfnffINdjz2GcYsWQTGZ6myne73Y9dhjgCQh9jReCXmya1WSPTs7G36/H6eeemobhUMt4Sw4CIsWC8Vsg99TAe+xwCUoksEEQ0Q8DJEJUM026H4fhOaFrnkr7z3Vlr0Qfi+EX4NstEAxR0K1RAXuzZFQzLawJK79Xid8jhJojhL4Kkrgd9ubuKUUSLgrRhgiE2CK6QFDZAIkidMQEFHHornK4Ty6H96yPOiaF+6SI9DdFVCt0TDF9eKXhkQdhDkuBU7vr3AXH4Y1aRDsWd8jZvDZkFVjuEMjIiIiajm/FyjZB8hGIH5Iw22L9wG6F4gbCnThMpaD7rgDBWlpKNq6FesvuQQDZs1Cwumnw5yUBABw5+ejaOtWZCxaBEd6OiRZxqDZs8McNYVaq5Ls3bt3R05ODiwWS1vFQy0gywq0ilJojhIAgeS6YrJBMUfA7z6edK9OCAGh+yH8GiA06P6qZT8k1QTFcAxe1VitNIEExRQBxVKVdI+EYomEYrK1afkCzW2vllQvhu51Vcbrh9/jhN9TAd1TAd3vAyQFkiwHEueSHEhESXK15xRIigq/xw5P8WFIqhGm6B4wxvaAwRbPhDsRhZXfUwHn0f3wlOZA6H747MXw2gsAIWCKS4bBFh/uEImoGklWAvXZ89PhLj4MizIA9kM/IKr/OJZyIiIios5r/3vAmpnAiNuA8xc03Pa7p4B97wJT3waGXt8u4XVEsaNGYeRTT+Hnv/0NjoMH8dMjj9TdUAhIioIRTzyBWA5QPum1Ksk+fvx4rF69Grt378bo0aPbKiZqJnNCH0REWqBXJqH9Hge0ihJojmIAgaS7rBoDSXVdg/D7Ad3feMeSFEjYG8yQq24V5pojtmQlkHS3REGtvCmWqAYn5hNCD8aqeRzwuytvHgeE5gUA6H4tkFD3VsDvroDucwGVE1oHXo8ZEDqECHw5IIQOoeuA8AfbVfHIEhRzFFRrDHSfC+7iQ5BUE0wx3WGM6VmZcOcfx0TUPvxeF1x5gdGwQvjhcxTDW14A+P1QzDYYY3pAMfLLa6KOSDFaYIpNhqckG96yo5AkCa6CdFiTBoU7NCIiIqKWObAqcD/sxsbbjrgV2PsO8OsHXTrJDgC9r74aUUOHYv/zz6Nw48ZATqoaSZaReNZZGHzvvYgZMSJMUVJ7alWSffbs2Vi1ahVeeOEFXH/99TAYuu6lIuEmSTIUsw2K2QYgCULo8Huc0D0V0NwO6JoXkqxAVs2QTNVrmVfVMA/UNYckBcrH+NzBm+a2AxXHju9MliGrJshGC2SDGX5XGSSHBXK1kgay0RpMussGM/xe5/FkurcCEMcz4bruh/B5oPvcgXaeCgifp/KFAbLBAoMtITCS3hQRiLMBQuiArgeS+T43NGcZNFcZ/M4yeGQJqiUaiiU6kHAvOgTJYIIpugdMsT2hRsQx4U5EIaH7PHDmp8NdlBUYuV5RAl9ZfqBMl8kKU3z3ynM4EXVkBltc5dwJRZCNEXDm7odqjYUxMiHcoRERERE1X/EvgfvE3zTeNqlygG3RrtDF04nEDB+O8W++CZ/djrJffoGnODDY1RQfj+jhw2GIPPnmSqT6tSrJfu655+KRRx7BvHnzcPHFF2PRokVISUlpq9ioFSRJhmq2AWYbjNFJzdtYMUAxRdR4Svf7KpPunsp7F7SK0hrJckk1QjaYoRjNkAwWaM5jwVHvQggIvzewveapcQ+/Vj1wKCYrZGt0IKlutDa7HrEkyYAiQwIgq0aoligI0Qt+tz2YcNcqjsEjy4GR99Zo6F4X3EVZgYR7TE+YYnpCjYhlwp2IWk3XvHAVHISr8CCEX4PmPAZvWT6E5oVstMAU1wuqJSrcYRJRM5hik6F7XfCUHIFiMMOetRMRPYfCGJ3EGu1ERETUuThyAVM0YGzCgB9jJGCOARxHQx5WZ2KIjETCGWeEOwwKs1Yl2Z988kmYTCaMHDkSX3/9Nfr3748JEyZg5MiRiI2NhaI0nBx97LHHWrN7akeyYgiUgDEf/xZOCBEc9e73uaB7A8l3v6u82oYyJMUAoXlqlnGpHA2vmm2BUfEGEyTVBNlgDkliW5Kk4Mh6IZLhdzugOY9Bc5XXSLir1hjoXjfchZmQDWYYY3rCFNsThojYNo+JiE5uus8DV2Em3EWZEH4NPmcZvGV5ED4PJIMJ5oTeUCzR/DKPqBOSZBnmhD5w5v8KV1EWLN36w3H4R0CSYLAFJlw3xnSHrJrCHSoRERFRwxQjoLma1laIQFuZlSyITtSqJPvjjz8eTA5IkgS/348NGzZgw4YNTdqeSfbOTaqs2S4bTFARHXxe6H7oXhf8leVmhF+DbI6ErJqC7Ruq2R76uOVqCXcdfpc9kHB3Bka4Q5ahWqKhWmPg97rgLjwYGG0a0wPGmGQYImLCFjsRdXx+TwVcBRlwFx+B0P3Q3OXwlRVA97ogqQaY4ntBtfJKGaLOTjaYYI5Lgbv4EJw5eyGbrFCt0dB9HvjshcCRXTDY4mCM6QFTTA8Azbsyj4iIiKhdRPUDin4GcrcAPRsZjZ27GdA8QMLA9omtg9NcLhx+7z0UbtgAV04O/B4PzktLC6732e3IT0uDBCD50kvDFyi1i1Yl2YHAaOaGHlPXI8lKtfrwHZskyVCt0VCt0RC6XllS5ljgVlEamNjVGg3VEg2/xwlXwUHIRivMcb1gikuBYrKG+yUQUQehOY/BmZ8O77E8CKFDqyiF114YGLmuqDDFJkO1cd4HopOJao2G1ZAKX0UpNFcZvKVH4S09CtloCSTcNTd8jmJUZP8CmAMDEvxeN+cxIiIioo6jz/lA4U/AhoeBq78B5HpShboGbHgEkCSgzwXtG2MHVLZnD7bffjtceXnHSymf8LeearPh11degSMjA9bevRF76qntHyi1m1Yl2fUTZs4l6swk+cSEezl8lcl2zVECKApUSzQMETHQvU448w7AYIuHKb43TDHdIdX3i4iITmpeeyFc+enw2YuOT2hqL4LQfIF5HuKSK+d4kMMdKhGFgGwww1Q5Wt3vDZTN05xl8B7Lg/dYHiSDCQZrDITbDcCIY/u+hSU6Eeb4FBijk/j5gYiIiMLrtLuAH18CcjYC7/8WOOcFIGlUzTb5O4Fv7w20Uc3AaXeHJ9YOwltaim1/+APcBQWIHj4cyZdcggPz50OrqKjRTpIk9L76auyZNw95X3/NJPtJjp/qieoQSLjHQLXGBMo9VJWUqUy4S6oRhohY6Jq3coSaCmNMT5jjUmCwxYU7fCIKMaFr8JYVwJmfDr+rDLpfg89RBJ+9GND9kE0WmGJ6QrFEceQ6UReiGC1QjBYYo5Og+zyBydadZfCW5cOPQgDD4LMXQhY++OyFgatcYpJhiuvFzw9EREQUHpG9gPNfA9bMBHI2AO+MASK6A5F9AiOzyzIBZ35gtLYkAecvBKJ6hzvqsDq4eDHcBQVIOPNMnL5kCSRZRsbrr9dKsgNA0uTJ2DNvHoq2bg1DpNSemGQnaoQkKzBExMAQUZlwdx6Dr6IU3rJ8eMvyoZhtUCNioWseeIoPQzHZYIrvBXNcCmSDudn7030e+L0u6F5nYEJZjxN+rxO61wWh+2GITIApujsMkQmQZNZ3JQo1IUTlZMml0CqOwecshd9lByCga154ywuhOUsAXUCxRMIQmQi1E5TLIqLQkg0mGA3dYIzqBl3zwlNRDpQhkHAvOwrFEln5+cELd/EhyKYImONSYIpLhmJkOToiIiJqR8NuBMzxwNo7gbIswHE0cKsupj9w7ktAvylhCbEjyV+7FpAkDHvoIUhyw1cs2/r3h2wwoOLQoXaKjsKFSXaiZpBkBQZbPAy2eOg+d6AGa0UpPMVH4CkNjH43RMTB73HAmbsfkqICsgxJUgKlImQFkiwfX5ZkQJIh/L7KyWJdgO6vsU8h/NA1H4Tmg4AOv8cOT/FhSLIKQ1S3wISsUd0C+yKiVgse21VXrzjLIHQNQOXEzj4X/B4n/J6KQLJdQuDYj0yEYrSEOXoi6ohk1QhDZDxQJmDuNgBwBc4xHpcdHvn45wfdUwHn0X2BL9TjUmCK6cEv1ImIiKh99L8Q6PsrcCQtMMFpRV7g+YgeQM8zgd6TAZbABABUHD4M2WBA1LBhTWqv2mzw2e0hjorCrc2ycmlpaVi8eDG2bNmCvLw8uFwu/PzzzxhW7T/chg0bsGvXLkRFRWH69OlttWuisKiqwWqM7g6/2w5fRQm0ipJAOZnK+qtQVEiQAFkGIEGSZEiyBEAOXGYlBZ4Tuh+63wuheQMJd80HoXmh+72Av2bS3SNLUEyRUK3R8Pvc8B7LBSQZxqhEGKO7wxidBFk1Nek1CN0PIXRA6IAQlctV9wCEHpjMWOgIPFF9cmNxfHKPwIrKZ094vto6QNSaHFmSpMAvakkKvFeVj6UT7ptOqtmnVLvPWu9D9dcUjFcEnxLCD6EH3qfAfeCxEDpQ+R6KE74cOV4iRKp6ovrKOt+7ut6fevs8oQTJ8S9xVEiyAklRAveVz9Un8PP1B/4vVN6gV70eEdxXYN/S8f+3wRgqH8sKJEXtNHXHhdADV4l4KqB5HPC7HYGkudsBoXkq2wjoPnfgqhKvC35PBYTPE+xDUgwwRMbDEJkIWTWG66UQUSejGM1QzT1hjOkRmHC9orTa5wdjINmueeGzF6EiZw/M8b1hTujN0e1EREQUerIC9Plt4Eb103VIqtqk0qBC16E5HFAtHJB1smt1kt3pdGLGjBlYvXo1gOPJqrr+oymKgjvvvBOSJGH8+PEYNGhQa3dPFHaSJEG1REG1REH4tcrJUkvgLctvYYeB5J2kGKGaoyCpBsiKEZJqAISA5iqH5iqD31UOSIBiskGxREPXPJX7lGCwxUE2mCuTplrg3l95L/yBxLC/Kona9Wh64HUX//wFVLkr1MsOJMFRmXRH5ZcCQvdXfoHSlruqvi81mOQPJvwVA2SDCZJqhKwYIavGwHLVvWJos1CEXwuUXPIGbn6vM5BMdzvg91bU+KJD1/0QPg90zVMjsY6qCb4lCYrJCtkSBcVohWy0QlbbLlYi6npO/PygOcvgc5YEJ0xVLJEw2BKg+9xw5afDGJ0Ec0IfGCITOdcDERERURiZEhPhzM6Gp6gIpoSEBtuW/vADdJ8PtoED2yk6CpdWJ9mvvfZafP755xBCYNy4cTj77LPxz3/+s862Z555JkaMGIFffvkFq1atwsMPP9za3RN1KJKiwhiZAGNkQiCpXTlK/PgIcVFt1LgAoAO6AGQ5kGRUDIFbA388q5YoCJEM3VNxPOFemgNvaQ5kk7VyhLsrMEI+OEpdh9BFZYJdD45eDo5UrxpBXZV0rEy8Hn+u+gjvKuL4v/WOXK+exq9jhHvwjZMD471PHDFdYxR1M1WNqD6hTwkS/JABxMNnL4IuiaoXUcfrqvpHQOiV70P10f0njv6vFUPlfoP/1q1J71FjfVZeFQFJDtSEq34vVbuX5WpxB/7/CVQfpV/96oVg58GR8zV+FsFR61K1MkgygJr7luSqKwmOJ/rr/ZlKUjABHzge1ECSXlEhK2rgucrHgWNFDiTFfW74fe5AQt3nhu5zQfi1mu+zEBB+L/RgMv34Paq3lQBZNUO1xkAxWgIJdYOZSS0iChlJUSuvjKksR+coga+iFH5XZmCy9ch46H4fvGV5kE0RsCT0hSk+pU2/mCQiIiJC4c9A1pdA+SHA5wSmvHl8nd8HuAoBSICtR9hC7Ajix42DMzsbR1avxsBZs+ptJ3Qd+194AZAkJJxxRjtGSOHQqiT7hx9+iM8++wySJGHhwoX4wx/+AAD1JtkB4IorrsCuXbuwbt06JtnppCYpaoOJ1Vb1LUlQzDYoZhtMsT3h9zqhOcvhd5XBW3oUwNFG+6jZYeU/lQnc40np489L1R/X2O6E54DKhGrVmhPKpNTY+HjyPpjs1ytL04jqpWeaMeK+qmxN1XbB++NNqpLs3rJ8KGjCSO6q1y3LkFC9DE3lsixDktSar6laLNWeCD4tVXvvji+e+D+m6nF9fYpqT4nAVQtCr/wipeoLlsZfXnBXwYS8FPziI/iVgzjhHtV+PtW/oGkq5YSR7kpVmRu12vMyIFWOvpfkhpPzAHS/BuH3BUou+SvnMfB7IfxasBxTjfdDkiAbzFDNNsiqCZJqgmwwBZYbmbyGiChUZIMZptieMEZ3D0y27iiGt/QovMfyKud/iIfuqUDF0X0wxSbDHN8HqjWaXwQSERFRy3nKgC9vAdI/Cjyu+qO1epJd9wHLfgO4S4FbDgQmQu2i+s2YgSOrV+PXV15BzMiRSDj99FptXLm52PX44yjauhWSqqIvy2af9FqVZF+6dCkAYPr06cEEe2NGjx4NANi7d29rdk1E1ShGa6BWa0z3wIhedwUEROUoYrkyGSzXeFxj1HMXEKx1LkSgXEwuYO0xBKpyYuIftb5M6MyJCyFEZd34ytrrQg/+7GuMfm+D13h8JPzxkfHVl4O17HXtePkiXYPuc0N4tOOlWRoiS5WJ98oEvCRV9ucLXBVyIkWpLEtjhmyOhKyaIVUm0lnuhYg6MkmWYbDFwWCLg9/jhM9RHJyQWTZaYLDFQ/h98BQfBiQZqiUKSmX5mapljnQnIiKiRukasPpC4OhWQLUCKZOBw/8D/J6a7QxWYPgtwPbnAsn4MfeFJdyOIHrYMKT+8Y848NJL2HrTTYgeMQKawwEA2DV3Lsr370fpjz9WlukFht5/PyJ69w5nyNQOWpVk3759OyRJwrXXXtvkbXr0CFxSUlhY2JpdE1E9ZIMZssEc7jA6nGASWZIgVZaACUwO2nkT6E0hSVLlBLwAENpki1RVKqaF2wshggn4GhPK6nq1yWerEvX+YEK/KoFeVW5Jriq7pBo6zUSsREQNUUxWKCYrREwP+CpK4HMUw1OSDU/ZUaiWaMgGM/zuckgOC2RZCW4nG62VCffIQDvVGPiC8oTJsaVq2xAREVEXs+sNIHcLEDMAuHYdYOsJLOgBOAtqtx10ZSDJnvlFl06yA8Dge+6BIToa+55/Hsd++in4fNa77wav9FbMZgx94AH0mzEjXGFSO2pVkr24uBgAkJyc3Oxt9aaMWCQioi5DkiRIigHgyEsiojpJigpjVDcYIhPhd9uDo9urXwkkqUbIBjMUoxmSwQLNWQpJMTZyxVLlpNWKAkk6noCvKtVVNal1YG6NalfGVU1oXfUlZ7X5ZWQ1MIcGERERdXD73g2UhjnnhUCCvSHdRgWuii7Z0z6xdXD9b74ZvS67DNkff4ySHTvgzs8HdB2mhATEjR6NXpdf3ujEqHTyaNUn38jISJSUlKC8vLzJ22RkZAAA4uPjW7NrIiIiIqIuSZKkYFkYIf6fvfuOk6o6/zj+udO2N3bpvRfRiIqooGiMihh7rKhgYixobFF/MbEbNWossWtUMBGxIZqowQYiqAgqKlUFlg4L2/vU8/tjdmdn2F5ny/f9cl97Z+655z4ze4/sPnPucwzG56lYALqMgCe4+LO/LOz3c5stuN6E3VFVIqwiUV5VQqxqAWvL2mf9kYoFroPrtIQdX1+pMcvCsrsqkvCOYFK+co0NW/hi2Laqx/tsVyX1w9fnqHzeHrlfREREGi97FWDBoOPrb2t3QkwKlOW0elgdhSstjSEzZjBkxoxohyJR1qwk+/Dhw/nqq69Yvnw5Rx55ZIOOmTdvHgC/+MUvmnNqEREREZEuz7Ks4FoTzhgcpISeNwF/MPHuKSPgLQ+uf+HzVq2V0dgFsms8ORWJckcw0V2xiHVESRqrMpm+z+LaobVhLKBl1gYJrTVjVZ6vahZ+6EOBykW0w2MIJe2t0HofhK1hE7GWSeXM/vC1Tfb58CG4Zvy++yviC9uuWmxeREQkirwl4EoCu6th7f0e6OJ3q5Xu3IllsxHXq1eD2pdnZRHw+4nvU8+dAtKhNWtUTJ06lWXLlvHUU09x5ZVXEhtbdx3oBQsWMG/ePCzL4te//nVzTi0iIiIiIrWwbHbsMQnYYxLqbGdMoGLti7CFqjEVj01FQj78+z7PBfyhhayNz0sgUI4J+EK1SBseMJEz6i2rYh0Vq2oRd6zIGfYVie2qx2EJ+4hEuhXcRVi/lkVlcjx8f3SS3haNWtAklKzfJ7kPRH6QUbUeTdXi7lbVqfZ5PuIUte6zqt5fqn5GhD1nhRaQD14DVZdC1SL0Vdtgank+fMPrCy4cV7z1BxwOe/XXuc+HGw1W+V7a7ER8MGIL/xCmatH12soniYh0aHEZULI7mGx31v17A3k/B9ulDm2b2NqpT446itgePTjuiy8a1H7p2WdTvmsXv/7pp1aOTKKpWUn2q666iocffpjNmzdzxhln8O9//7vGMjDl5eU8+eST3HrrrQQCAXr37s3FF1/cnFOLiIiIiEgzWZYN7E1ftLo2VYtXVyThK5L2wSR+AAJmn1n1Jmx2vYlI7BsTAL8fQ2XiP/xDgOZMxa+FtW/SuCpxu+8HAFUJ3fDkPaGkd/XEsxWWUw87vtHJ/Yqk8j4J7uB/4efet+uw5DnhaFsCYwAA6h1JREFUm/uevyrxb4VtRybe90ncV36QQTBJbyJ+Nvsm1iuf3vfnZ2p83hcAcFCWvRmHveocVe9vNO8IsKqS7bawD0Bq/RBi3/2EfUgU9gFH2PUXLKG0T6ml8MS/rerOEctmr7qrJHSnie6YEJE69BwPm/4Lm96DkWfX3fabR4Lf+05s/bjaOdPI30Ea2146nmYl2VNTU3n55Zc59dRT+eCDDxgwYACTJ08O7b/77rvJz8/n888/p6SkBGMMTqeTOXPm1DvrXUREREREOqZg+Rhbqy9mbSpnP5uaZt+HbYfaGCAskY8JS9xXPg47nn33hX84QKiNCfVD8HF439VmZ0tj+bEBYyjb/RN2AjU3sohM/jdExAcpFZ1YVXdDVLtjoDIxbrOFHRfWptoHJ/skziuer8p5V304UhV3ZSI+7LmK0kqW1cS1ByrXM7A7CJVwalD5JFtYXPWdwhaZ2K9M/Ed8OarWZ1DyX6T92G8GbPwPfH4b9DsKEmopgbLiQfj+meD/38b+tk1D7OgCbjc2R9cusdMVNPsnPHXqVN5//30uvPBC9uzZw4IFC0L/WL7++utA1ac1GRkZzJ07l6OPPrq5pxURERERkS6uKvEJFu1/8dPQLLaI5HvDs+8RpVXC+qj2oQBVCf8agojoseqpGmaVVyv1Ult5F1MVX6ivfWbDh9unBE34t/CkbuVxfoA8cKX2wk7V6wy99rAPNBo9U9BU/9Cl6k4KIj50qbqbopnrGTSHLSyhH7F2QGXZnLCyNmFJ82qJ9IiySVaoPRCaWW9FJP/r04Q7CsIT8nZHKAkfSsjb9k387/s6wkr97LtIcuVz+5T20SLJIjUYfhoMOSk4k/3lQ2D0BeArD+778Y3gwqg/vQF5FaVORp4bTMZLg5RnZeHJzcVVQ+UP6Vxa5GOU4447jk2bNjFr1izeeecdvv76a/Lz8wGIj49n3LhxnHLKKVx++eUkJSW1xClFREREREQ6lMg66U04vgVj6UisgIE8gzMxHYetfbwLEXdLELYdSvLv80GHqfh4osYPWEzYBx1hx1V+qLFPSaWqNRGqSi6ZQABjfFUlmAKB1imnVBdb9VnxoTUV9kmWV30QUJkAtwcXQq4og0PlwsWtURKorkWSw+vz2/at/2+LTPyHXk/VbP3wxL5ls1WV8qncX7m4skh78+tX4f0LYMM78PWDVc+/d27we+X/T4afDie80PbxRVnO8uXkfPVVxHP+0lJ+evzxWo8xxuAtLGTPp59ijCHtF79o7TAlylrsXoX4+HiuvPJKrrzySgB8Ph9+v5+YmJiWOkWHtnXrVh577DHee+89tm7dSkxMDMOGDePss89m5syZxMfHRztEERERERERaYCq2uotv6ZBS6makb/PAscRdz7UUFIp4u6Ahp4rUC3xX/XYF/wgoLnJf1vlzHtbZC37ehdJrpzVX/W49kWSrRr2h9fTb4EFkiuT9eF19CNq64fNyI/4MKAqqU+oXfjdC7bQ8VXJfxuBioWDg+tjqEyP1MKZAKfOD85mX/U87PgcyrKD+2JSgjXY978Uhp0S3TijJHvZMn567LGID8l9paX8+Nhj9R9sDLaYGIZdcUUrRijtQasVBHI4HDhUbwiA9957j2nTplFQUBB6rrS0lBUrVrBixQqef/553n//fYYMGRLFKEVERERERKSzCCaF7cFSSu2oSkrkTHx/xSz8yqS8v2rfvustUP1DgGqLJAf8oedadZFki4gEfmQyP3z2u1WVLKcyUR6WxI8og2PV0mdYKZ8m8AWCrz139YdVd4LsE19VuSArMmEflsQPxRmW+A/djRAWc+Rsfyus/E/VwrzhHyhIOzPkpOAXQMAHAT84NHk2vm9f0g89NPQ4Z/lybA4HaePG1XqMZbPhSEwkacQI+p9xBgmDBrVBpBJNzcqCv/fee0yZMgW7vR39i93OfP/995x99tmUlpaSmJjIzTffzDHHHENZWRmvvvoq//znP/nxxx856aSTWLFiBYmJidEOWURERERERKRVhJL/NjvQuosjhzM1JN6rLZi87+LHoTUOAmHbNST/I0r3+MNm81eV+Wl2Hf+ImfdhyfGK+vzVFuqt2B/ADvTAU7iHgBXWDxBM9kO12vyhUj22iOdaKvEf/pqqEvBhyXd7TYvnOiLr+IeX41F5nub55+Dgz/bMDyBtWNXzNkfwS+h/5pn0P/PM0OP/DhuGMzWVI155JYpRSXvTrNFy8sknk56ezjnnnMP555/PEUcc0VJxdRrXXnstpaWlOBwOPvzwQw4//PDQvl/+8pcMHz6cm266ifXr1/Pwww9z2223NfocZX47sX590CHSUL6AwR0wlPktHEa/dIk0hsaPSNNo7Ig0ncaPtIywv5krKs+0tBqW9A2JKN+zz3ZVkj48uR/YZzt8/z4fCvjDFvA13ohZ/n5j4aY3hfn52Am0/AuOSMZXldcJfgBA7XX52XdRXmufEjiRxzU5WR5KutuwrKpEPmHJe8tmq1p8tzLJbzmw7JVJf2fY85XlejrP/4viSnaBzUVZ4jDwttFJfQBhZZO9RG9B6Vp4veAO2Cn1Vv84cMy992OLjaW0rd6vCn4vuB1xocelXrB30RtC2vq9bwjLNHr59So2W/AnWfk/l4EDBzJt2jTOO+88xowZ0zIRdmArVqzg0IrbSS677DKeeeaZam0CgQBjx45l3bp1pKWlkZWVhdPZsE/zCwsLSUlJod/fCrDFJrdo7CIiIiIiIiIi0rkt8Q2hG3vZz1EU7VBEGixQXsj2P6VQUFBAcnL7yIk26/OO1157jVNPPRWn04kxhs2bN3Pvvfey//77c9BBB/Hwww+zc+fOloq1w3n77bdD2xdffHGNbWw2GxdddBEAeXl5fPrpp20QmYiIiIiIiIiIdHVLrV8RRyn7mZXRDqXDSCzNYfJ3szn4x//U2/aQ9e8w+bvZJJTltkFkEk3NmsleKT8/nzfffJM5c+awZMkSAoHgLUhWxe1CkydP5oILLuDMM89sN58utIWjjjqKJUuWkJCQQH5+fq0LwX755ZehUju33XYbd955Z4P6r5zJ/v0nr5OWnNBicYt0dr6AYcUOw/i+VtXiPyLSIBo/Ik2jsSPSdBo/Ik3XVcdPRHmeiMV1w0vvhO1nn4V3TQBj/BAwFbX2gwv1NqukSY0Lzlr7lMaxIrZDZXVCc2TNPt8jXnTYrkBFCxNWWqhyf6DqPQKc3mwG/HwPnti+7BhyLcYWQ7DkT2Xliqo4guVybNjs9qq1FSoWsa2vhI5ld1SU3nFiWYaUBVNC+0rPWoHlSsRmc2I5HNjsTiy7C8vhDJbxiUJ5Hq/XywcffMAJJ5xQreLE1lkv8PODf2PIVVczeOYf6uzn5wf/xtZZLzDiT3+h/0UzmhWT3+3mg4MPDj0+4ZtvsMd0zYVpCwsL6Z3Rvmayt8gKBqmpqVxyySVccskl7Nixg7lz5zJnzhy+//57jDF8+umnfPrpp8ycOZOTTjqJadOmcdJJJ+FyuVri9O3WunXrABg2bFitCXaAUaNGVTumMeLsfuLs/sYHKNJF+SxDjM0QZ+9av2iKtASNH5Gm0dgRaTqNH5Gm0/gJT1A3X8Nq6++7yG5Ykr+mY/CFkvnhbVusRvm+i9tWLkpQcTnYvSXk9jyN9Kz5DPzxNopSD6c8dgABewIGq9Y4/M7UyJS/zRZKuttsdghbpNayOYJJeXvFQrUEiKc01Ffp3vUYm5NaM1uWrWpBXCtYH3/fhW7Da+oT1q6mRXQtR0USv47kvReIsfmJd8K+VZ1zP/0EgAFTpxBfT8XnwWeeztYXnyd30ceM/N2MuhvXwx+AGF9Z6HG8E+xtt350u+Jrh6+7xZcJ7tu3LzfccAM33HAD69atY86cOcydO5fMzEzcbjfz589n/vz5pKSkkJvbeW+VKC8vJzs7G4B+/frV2TYtLY2EhARKSkrYtm1bW4QnIiIiIiIiIiKNEJxpbscKX0i3lUQudBu+aG1Vgjz4KPyBFfbdatAM8PSFp4W2HYEC0vYuqCuq0Fl3T5gNAT/G+DGB4BeBqm3jK6u4O6CGOwCML+Jh8fY1WI7YsAR9WFLcsoPNqlggN2zxXMse/NDAZgv+TMLvFGgQC8vpwmZ3YXPGYHPEYDlc2Bwx2Bwu/FbwZ2z8vmpZ9pItW7Asi4RBg+o9S+LgwWBZlGzd2sC4pKNq8SR7uNGjR/PXv/6Vv/71r3z55ZfMmTOHl19+mcLCQgoKClrz1FFXVFS1YERiYmK97SuT7MXFxbW2cbvduN3u0OPCwkIgePuXL9DOlmEWaccqx4vGjUjjafyINI3GjkjTafyINJ3GT0cXTOpjNTKpH5pi3pCfe1OuDYMVlwJE5PtrblnxYUEw+e4LJt595RFtrLhUwIYJ+PH5/RivN1S+p9FCyfawsjsRJXlsETPcsTmDM+xtjuDzFUn6yjGzZ9WHuGLjsbsSsMcmYncl4MnNxZGUhB/we731xuNMSsKdnY23vrb1CPgiP5zw+XwEbC13p0ZH0tz3sjW0apK9Um5uLj/88AM//PBDnUnkzqS8vOp/GA0pixNTUUOprKys1jb33XdfjfXav9sN8YX6B1Oksb7ZCS13D55I16LxI9I0GjsiTafxI9J0Gj9Sm7iBzzbpuLLtjbmeLIIpyGAa0mZchNd8+KG0NwGrhep/VJSgb14HVYJjp7Tiay8AltOJv7CQ9995p3otmX15vVhFRRAby/vvv9+cwMDniyh+tGDBAqijPHVnVlpaWn+jNtZqP4ny8nLeeecd5syZw4cffhj6hKFyYYXDDz+8tU7dLsTGxoa2PR5Pve0rZ6jHxcXV2ubmm2/m+uuvDz0uLCykf//+HNgL0pK7Ym01kabxBQzf7ISD+9BF6xKKNJ3Gj0jTaOyINJ3Gj0jTafxI/Xq2/SkDFmyseji+rwVteH0aY4IL3gZ8BPw+CPhCs+yN34fx+/D5/azxDmI067FTNaPesrvY3CeVkg2lDN6ygm6Hj8NyxAQXgw01srA7E7DHxpP75XesM4aU4UOZcPyvsDmavj5lwOPhw5tuCj0+4YQTsOwWAb+HgNcNvuD3gN9NwOsh4Kv47i/H+DwNvLOhCfa9e8BmI1iuqKKkT2WZn2r18SvvJnAEa+iHHtuq1dwHW0V9/+Djyuoe7UmLJtkDgQAfffQRc+bM4e2336akpASoSqyPGjWKadOmcf755zN48OCWPHW7k5SUFNpuyOz9yveqrtIyMTExoRnv4Ry2rrqAiUhzGI0dkSbT+BFpGo0dkabT+BFpOo0fqV36wlPBssib8BSB+D5tdNbIa9Fha9skO1hgt1FXWtQXMLDdkNRnJDa/m4CvPJjA9rpJHjuAkg072DF3AbHdHThT4sHuwOZwYXPGYnPGYBwFuHe42fjk82BBygGDKVq/MJh0tjsrytM4sewObHZHVbmain1YVkXC34vx+wj4Pfj3qX5RuPYjbM7qr8FUlufxe4MfHvirPkQIJtpNxbeKaf+h50xlBxXJ8coa/5Xz562w562KJQBsVT87yxZcI8AKX2jXVnVMZQI+vHRPg2voRyoqddffqI21SJJ92bJlvPLKK7z++uvs3Ru8daLyB9O7d2/OPfdcpk2bxkEHHdQSp+sQYmNjycjIIDs7m+3bt9fZNi8vL5Rk79+/f1uEJyIiIiIiIiIiXZ3NhbE52jDB3rFYNht2Rzz2mPjQc/1/04ecz9bhyS1gw8P/odfUw0nabwCOZBu+shy8OYUUrtnG3oU/4C8px5maSMrB/XHn76qo+14xU9uyVy3cWjl7e5/EszF+jN+PCQQIeCJr2XsK92DZrYpkup9ARUIef2Tt9uovKmwjlAy3QgvmYlFRNaeinn5lIj6UlG/++1oVS03Jd6varPiqNsF9ntLyertua81Kst9222288sorZGZmAlWJ9aSkJM444wymTZvGscce2+RPJTq60aNHs2TJEjZs2IDP58NRS52k9evXRxwjIiIiIiIiIiLS2vyxGdjLs6MdRodij3Ex8v9+x7q/PouvqITtbyyEN8IamKoNR2I8g684Bcvmx1u0t2EJ6srEcsAf8XTAF/nYU5CFzWGvWMi1Yka8MzZilnzVDHlHRdkVq0XytCZ8FnxEIj4Qsa9y4dvg84Gq74HwxwYC/oq2/oqZ+L6K/SbUPrzcTadLsv/1r3/FsiyMMTidTqZMmcIFF1zAySefHFGTvKuaNGkSS5YsoaSkhG+++YYJEybU2G7x4sWh7YkTJ7ZVeCIiIiIiIiIi0oV50w/Bvu2/OPJW40sbG+1wOozEIf054MEb2DrnXXK+WInxByL2Ww47GRPH0f+8k4hJTwXAmECwZEsgEJyhHghUJJcrnq/4biqS65Zlq6hRHqxDbvyRGfqEvmOwuVxRmdxshc96Z98CQK2nMuHuLyppozM2XLPLxUycOJFp06Zx9tlnk5aW1hIxdRqnnXYa9913HwCzZs2qMckeCAT417/+BUBqairHHHNMm8YoIiIiIiIiIiJdU+nA3xCTtZjEH5+mYNzdmJhu0Q6pw4hJT2X41Rcw5LKzKdm4DU9+cDFOV2oyCUP7Y4+JXOTUsmxYdhvYa+qtfgFvZBkYy2bvctVDLMsGdhs2hzPaoVTTrCR7ZmYmAwcObKlYOp1DDz2UI488kiVLlvDCCy8wffp0Dj/88Ig2Dz30EOvWrQPgmmuuwelsfxeJiIiIiIiIiIh0PvaSbZQMuYCEn18g7asrcfc6Bm/KaIwrBYOt1uM0672KPcZF8pih0Q5DoqxZSXYl2Ov3j3/8g4kTJ1JWVsbxxx/Pn//8Z4455hjKysp49dVXee655wAYMWIEf/zjH6McrYiIiIiIiIiIdBUpK/9CeLGP2O3vE7v9/boPsiDnmLdbNS6RjqbZ5WL2lZWVxerVq8nNzQWgW7dujB07lp49e7b0qTqEcePG8dprr3HBBRdQWFjIn//852ptRowYwXvvvUdSUlIUIhQRERERERERka6rIatxNr15Z+V3e9jz8Zfkf/8j7r25BDxeDnry1tB+X0kZed+uxbIgY9LBUYxU2kKLJNmNMTz33HM88cQTrF27tsY2Y8aM4Q9/+AO///3vu1y9oJNPPpkffviBf/zjH7z33nts374dl8vFsGHDOOuss7jqqquIj4+PdpgiIiIiIiIiItKF5PzynWiH0CGVZG5n/f0v4MnNr/rQYZ90pz0+lh3zPqJsZxYxPdJJGjGojaOUtlR7caUGysvL48gjj2TmzJmsXbsWY0yNX2vXruWKK67gqKOOIj8/vwVC71gGDhzIww8/zI8//khJSQl5eXmsWLGCm266SQl2ERERERERERGRDsBbVMK6+/6JJyefhMH9GHjRKdjjYqq1syyLHsdOAAO5K1ZFIVJpS82ayW6M4dRTT+WLL74AID09nbPPPpsJEybQq1cvjDFkZWWxfPlyXn/9dbKzs/niiy849dRTWbx4cYu8ABEREREREREREZG2sOvdxXjzCknZfzijb7kcy2Zj5zuL8Je7q7VNO2gMW/71HwpXb4hCpNKWmpVkf+WVV1i6dCmWZXH++efz1FNP1VhX/KKLLuJvf/sbV155Jf/+979ZunQpc+fO5bzzzmvO6UVERERERERERKSFWL5S7EWbsHnzAYuAMwV/0hCMQ1UYKuV9sxosGHjBKVi2uouExPXtiWW3Ub47u42ik2hpdpIdYPLkyfz73/+us21iYiIvvfQSW7duZfHixbz88stKsouIiIiIiIiIiESZvXgz8ZtexpXzDZh9Vja1LDzph1A6ZBr+xEFRia89Kd+dg+VwED+4b4Pa2+Nj8ZeWt3JUEm3Nqsn+7bffYlkWV111VYOP+cMf/gDAypUrm3NqERERERERERERaSbXni9I/foGXNlfgwkQXMkz7MsEcGWvCLbZ+2V0g20PjMGy2bAsq/6mgQD+Mje2GFcbBCbR1KyZ7Lm5uQAMHjy4wcdUtq08VkRERERERERERNqerWw3SWsfhoCXQGwPygaegSftQAKxGcH95dm48r4jbst8bOVZJK15iLwJTxCI6xXlyKPHmZaMe08O3oIinCnVy2aHK/5pC8bnJ6ZvzzaKTqKlWTPZU1JSANi5c2eDj6lsm5yc3JxTi4iIiIiIiIiISDPEbZ0PAS++lJHkHfoY5X1PJBDfG2xOsDkJxPemvO+J5E14DF/KSAj4iNv6drTDjqrk0UMA2PPpijrbmUCAra++Dxak7D+iLUKTKGpWkn3s2LEAzJo1q8HHvPjiixHHioiIiIiIiIiISNtz5X4PWBSPnAmOuNob2mODbTC4cr9ro+jap95TjwJgx7wPKVj9c41t3Nl5/PjACxSu2YBls9FrysS2DFGioFlJ9t/85jcYY5g/fz533HEHZt+FEcIYY7jjjjuYP38+lmVx1llnNefUIiIiIiIiIiIi0gw2dzbGEdegBU39iYMwjnhs7uzWD6wdSxjcj35nHo+/zM3au55m1c2P4C8LLmy66Z9vsvrWx1h51V/J+3YtAAPO/zWxPTOiGbK0gWbVZP/973/PE088wfr167n77ruZN28eM2bMYMKECfTs2RPLsti9ezdfffUVL730EmvWrAFg1KhR/P73v2+RFyAiIiIiIiIiIiKNZywHVsDXwMYGK+DFWM1KJ3YK/c85EUdCHFtffZ/iDVtDz2d99HlwvVjA5nIyYNqvQzPfpXNr1qhwOp3873//45e//CWZmZmsXbuWm266qdb2xhiGDBnC//73PxwODUgREREREREREZFoCcT3xl6UiTPnW7zpB9XZ1pn7bXCB1KT+bRRd+9b710eTMXk82Z99TdH6TDx5BZhAAGdqMskjB5MxeTyu1LoXRpXOo9mZ7oEDB/LDDz9wxx138MILL5Cfn19ju9TUVC655BJuu+02EhMTm3taERERERERERERaQZ3xgTiizaRuP4JCg+8E39CzQl0e8lWEtc/CVi4Mya0bZDtjHtvHgDOlEScSQn0PmkyvU+aHOWoJNpaZDp5QkICDz74IPfccw/ffPMNq1evJjc3F4Bu3boxduxYDj74YFwuV0ucTkRERERERERERJqpvP8pxO78EJs7h9QV1+LuPhFvt18QiOkGWNjKs3HmfU/M3i8g4CMQk055/1OiHXZUfTvzLrAsDn7mdlzdUqIdjrQTLVqzxeVycfjhh3P44Ye3ZLciIiIiIiIiIiLSwowjnsJf3EHyD3djK99DTNZnxGR9VlNLArE9KTzgFowjvs3jbE/ssS4su10JdomgwugiIiIiIiIiIiJdlD9xAHmHPkbcjveJ2bMUe/FmMIHgTsuGP3EQ7h5HUtb3RHDERTXW9iCmezfKdmdj/AEsuy3a4Ug7oSS7iIiIiIiIiIhIV+aIo2zgmZQNPBMCPixfEQDGkQQ2pQ/DpR26P6VvfUTet2vpNn5stMORdkIft4iIiIiIiIiIiEiQzYFxpWFcaUqw16DvaccS2yuDTf98g5LNO6MdjrQTGikiIiIiIiIiIiIiDZC77Ht6HncE219fwKo/PUTqgaNIGjkYZ0oSls2q9bjuRx/ahlFKW1OSXUREREREREREpAty5q4kafWDeNIPoni/G+psm7TqXpx5qyna/0940w5oowjbnw1PzoXKXLqBvG/Xkvft2nqOspRk7+SUZBcREREREREREemCYrKWYPlK8fQ8qt627h5H4tr7Fa6sJV06yR6TkQpW7TPWpWtSkl1ERERERERERKQLchT+CIA3eVS9bb1pvwDAWbC+VWNq7w56+vZohyDtkJLsIiIiIiIiIiIiXZCtPAdjj8G4kutta1zJYI/B5s5tg8jar8K1GwGIH9gbR0J8lKOR9sIW7QBERERERERERESk7VkEGtXeYGEF3K0UTcew5vYnWHPHk2CiHYm0J0qyi4iIiIiIiIiIdEEBVxqW342tdGe9bW2lO7H85QRcqa0fWDtmj4/FkRCHI1Gz2KWKkuwiIiIiIiIiIiJdkDdlDADxW9+qt238lrcAC2/qfq0cVfsW2ysDf1k5Aa8v2qFIO9IiNdl9Ph/vvfceS5YsYdOmTRQVFeH3++s8xrIsPvnkk5Y4vYiIiIiIiIiIiDRSeb8Tidm9kJidHxNwJlM6+DywOSMbBbzEZ75CzK6PAIvyvidGJdb2ImPiQWzZtJ2cL1bSffL4aIcj7USzk+xLly7lwgsvZOvWraHnjKm9KJFlWRhjsCyruacWERERERERERGRJvIlj6C836+J3f4ucVveInbnR3i6HUggtjtgYSvfgyv3OyxvEQDl/abiSxkV3aCjrPdJR5H71Q9kvjAPR1ICaQeNiXZI0g40K8m+fv16pkyZQllZGcYYXC4Xw4cPp1u3bthsqkQjIiIiIiIiIiLSnpUM/x3G5iRu29tY3kJispbs08KAZaNswBmUDrkgKjG2J9vf+oikMUMo3bqT9X/7J/H9epE0ajDOlCSw1T6puP9ZU9owSmlrzUqy33vvvZSWlmK327nzzju5+uqrSUxMbKnYREREREREREREpDVZNkqHzaC8z/HE7lqIo2AdNk8+AIGYNHwpoyjvdSyB+N7RjbOd2P76B2ABFYU8SrftpnT77nqPU5K9c2tWkn3hwoVYlsU111zDn//855aKSURERERERERERNpQIL4PpUM1U70+yaOHgMpgyz6alWTPzs4G4PTTT2+RYERERERERERERETaq/3u+kO0Q5B2qFmF07t37w5AXFxciwQjIiIiIiIiIiIiItKRNGsm+6RJk3j99ddZvXo1Bx10UEvFJCIiIiIiIiIiIm3E8pXizF6Bo2QzlrcYy/jqak3x6KvbLDaRjqBZSfbrr7+eefPm8Y9//IPzzz8fh6NZ3YmIiIiIiIiIiEgbitn1CQk//RPLXx72rKmhZeVqn0qyVwp4fRT88CPFG7fhLSgGC5zJiSQOG0DK/iOwOZUr7Sqa9ZMeP348Dz/8MNdccw1nnHEGL774IhkZGS0Vm4iIiIiIiIiIiLQSZ863JK57HDBgc+JNGUXA1Q0se7RDa/eyPvqCba++j7eopMb9zqQE+p97Ej2PO7yNI5NoaFaS/a677gJgwoQJvPvuuwwcOJDjjjuOUaNGER8fX+/xt912W3NOLyIiIiIiIiIiIk0Ut/UtwOBLGUnh/rdgXMnRDqlD2PLv/7Dzv4tCE/5d3VJwpacA4MkpwJNbgLewhE3/fJ3yrGwGXnByFKOVttCsJPsdd9yBZVkAWJZFWVkZ//3vf/nvf//boOOVZBcREREREREREYkOR9FGwKJo9LVKsDdQwZoN7PzPIgC6TTiAAedNJa5vz4g2ZTuy2Pbq/8hZ9j07/7OQtIPGkDxmaDTClTZia24HxpjQ176P6/sSERERERERERGR6LCMH2OPJRDfJ9qhdBhZC5YC0OOXExh5w8XVEuwAcX17MuKPM+jxywlgYPeCJW0dprSxZiXZA4FAs75EREREREREREQkOvxxvbCMF4w/2qF0GEU/ZoJlMeD8k+pt2/+8k8CCovWZbRCZRFOzZ7KLiIiIiIiIiIhIx+PueTQE/Dhzvo12KB2Gt6gER3wszpSketu6UpNwxMfVujiqdB5KsouIiIiIiIiIiHRBZf1PwZc8jMQfn8ZWujPa4XQI9rgY/OVuAh5vvW39bg/+cjf22Jg2iEyiqVkLn4qIiIiIiIiIiEjHFJP1Ge5exxC/6RXSll+Nu8dEfMkjMPa4Oo9z9/5lG0XY/sQP6EPh2g3sWfgVvaZMqrPt3oVfYfwB4geq5n1n12JJ9tzcXGbNmsXHH3/M6tWryc3NBaBbt26MHTuWX/3qV1x88cV069atpU4pIiIiIiIiIiIiTZS47h+AVfHIELN7MTG7F9d9kNW1k+zdjzyYwjUb2Pyvd7CcDnoee1iN7bI+/pLN/3oHLOg++ZA2jlLaWosk2Z999lluuOEGSktLATDGhPbt2LGDnTt38uGHH3LHHXfw0EMPcemll7bEaVtVcXEx3377LcuXL2f58uWsWLGCzZs3AzBw4MDQdkOtWbOGxx9/nI8//pgdO3aQmJjI6NGjmTZtGr/73e9wOHRTgYiIiIiIiIiItJ1AbPdoh9DhdD/mUPZ+9jWFazey6dnX2P7GB6SMHYarWypY4MnOp2DNz3hyC8BA8n7D6H70odEOW1pZszO7f/vb3/jLX/4SSqynpKQwbtw4evXqhTGGrKwsVq5cSUFBASUlJVxxxRXk5+dz0003NTv41nTyySfz6aeftkhfL7zwAldeeSVutzv0XHl5OUuWLGHJkiXMnj2bd999l/T09BY5n4iIiIiIiIiISH3yjng+2iF0OJbNxsj/u4SNT80l96sf8OTks/ezryMbVcw/7jbhAIbOPA/Lsqp3JJ1Ks5Lsq1ev5tZbb8UYQ+/evXnwwQc566yzcDqdEe18Ph9vvPEGN954Izt37uSWW27hpJNOYr/99mtW8K0pfDZ+WloahxxyCF9++SXFxcWN6ueDDz7g0ksvJRAI0LNnT/7yl78wYcIEcnNz+ec//8lbb73FsmXLOOOMM1i0aBE2m9aiFRERERERERERaa8c8bGMvOFiijdsJfvzbyneuA1fQVFwX0oSiUP7kzHxIBKHDYhypNJWmpVkf+KJJ/D7/XTv3p0vv/ySAQNqvnAcDgfnnXcekyZNYvz48ezdu5cnnniCp59+ujmnb1Xnn38+l156KYceeijDhg0DYNCgQY1Ksvt8Pq666ioCgQDJycl8/vnnDB06NLR/ypQpXHnllTz11FN89tlnvPzyy1x00UUt/lpERERERERERESkZSUOG6BEugDNTLIvXLgQy7K4+eaba02wh+vfvz//93//xx//+Ec++eST5py61bVE3fj58+ezYcMGAG6++eaIBHulBx98kLlz55KXl8eDDz6oJLuIiIiIiIiIiLSJ4MKnjWVRPPrqFo+lowh4fZTt2IPNaSeub88625btyCLg9RPXryc2h72NIpRoaFaSfceOHQAcccQRDT5m4sSJAOzcubM5p+4Q3n777dD2jBkzamwTHx/P2WefzbPPPsvq1av5+eefGT58eNsEKCIiIiIiIiIiXVbMroWARaiIeDX71hI3dPUke84XK9nw5Cv0PPZwhlx2dp1tt8/7iOyl3zD86gvImHRwG0Uo0dCsJLvdHvwExufzNfiYyrZdofb4kiVLABg5ciS9evWqtd3kyZN59tlnAVi6dKmS7CIiIiIiIiIi0urcvY6heiK9iuUvwVG4AZs7B+NMwpM+vu2Ca6dyln0PQMbkQ+pt2+OXE8he8g05X36vJHsn16wk+4ABA1i3bh2ffPJJg2ezV5aJaUh5mY6suLiY7du3AzBq1Kg624bvX7duXavGJSIiIiIiIiIiAlA85tr6GxlDzK5PSPzpaYwjnpIRv2/1uNqz0m27AUgY2LfetolD+weP2bqrVWOS6GtWkv24445j7dq1/P3vf+e0005j//33r7P9Dz/8wIMPPohlWRx//PHNOXW7t337dowJ3mrTr1+/Otv2798/tL1t27Za27ndbtxud+hxYWEhAL6AwReo7bYeEdlX5XjRuBFpPI0fkabR2BFpOo0fkabT+JGW4ut1LMZbTNLG2ZSnjMHdveGlo6vZ53oMXp/t6xqta+x4cwuwx8VhYlz1j62YGOzxcXjyCps9DgM1vG+2Ljq22+P/05qVZL/22mt55plnKC4uZtKkSdxyyy1cfPHFZGRkRLTLzs7mxRdf5N5776W4uJjY2Fiuvfba5py63SsqKgptJyYm1tk2ISEhtF1cXFxru/vuu48777yz2vPf7Yb4wvZ3cYm0d9/shPb2D7lIR6HxI9I0GjsiTafxI9J0Gj/SEhzmOE5kNr5N7/GV+/Am92MzhpPDHq/YYQhY7fP6rGnsWDY7uD18tb0BMRuD5faA3d6w9nXxGcKLb6/YYcDRPt+31lZa2v5ed7OS7AMHDuTZZ5/l4osvpri4mD/96U/cfPPNDBo0iJ49e2JZFrt372bz5s0YYzDGYFkWzz77bKcvF1NeXh7adrlcdbaNiYkJbZeVldXa7uabb+b6668PPS4sLKR///4c2AvSkmuvnyUikXwBwzc74eA+4LBp7Ig0hsaPSNNo7Ig0ncaPSNNp/EjLioet8aR7M5nQrxnXU8CCjVUPx/e1oJ1dn3WNnTW90indspMxpVtIHDGozn6K1mey3u8nrnd3xjbnPQMCXotvwh6P72thc7av962tFBW3v9fdrCQ7wEUXXUR6ejqXX345O3bswBjDpk2byMzMBAiVTAHo06cPzz33HFOnTm3uaYHgIqpOp7PZ/cyaNYsZM2Y0P6AwsbGxoW2Px1Nn2/ASMHFxcbW2i4mJiUjIV3LYLP1jKdJoRmNHpMk0fkSaRmNHpOk0fkSaTuNHWoblLcLylYA9ppnXU+SxDlv7S7IH1Tx2Un8xktItO9n+yrvsd/tMLLu95qP9fnbMfQ8sSD1wZLPHYMBW/X2ztcv3rfW1x/+fNTvJDnDSSSeRmZnJ/Pnz+fjjj1m9ejW5ubkAdOvWjbFjx/KrX/2K008/HYejRU7Z7iUlJYW26yoBA1BSUhLarq+0jIiIiIiIiIiISFtL2PgSAP74+hf87Mx6TT2K3f9bQtH6Tay98ykGzTidhCGR6zEWb9rG5tlvU7Q+E5vTQe+pk6MUrbSVFst4OxwOzjrrLM4666yW6rJB51y3bl2z++ndu3cLRBMpfLHT7du319k2fLHT8EVQRUREREREREREWkvMroV1Nwh4sLuzce1dhr1kO2BR3vvYNomtvYpJT2XIZWez4clXKFy/iR/+9BDO1GRiMtKwLIvyPTl4C4qCpdwtGHL5OcR0T4t22NLKOvy08lGjRkU7hBolJibSv39/tm3bxvr16+tsG75/9OjRrR2aiIiIiIiIiIgIiev+wb7lW2oWLAft7nUM5X1PatWYOoLuk8fjSEog8/l5uPfm4s0rxJtXGNEmpmc6g393JmnjlOvrCjp8kr09mzRpEnPnzuXHH39k9+7d9OrVq8Z2ixcvDm1PnDixrcITEREREREREZEuLBDbve4Glp2AIxF/4mDcPY/E2+0XbRNYB5B20BhSHx9FwZqfKfoxE29+EQCu1GSSRg4meewwLJstylFKW1GSvRWddtppzJ07F4DZs2fzpz/9qVqb0tJSXn/9dQDGjBnDiBEj2jRGERERERERERHpmvKOeD7aIXRolt1G6gEjST1gZLRDkShr0Mcpdrsdu91ebdHSyueb8tUVFkA9/fTTGTp0KAD33XcfGzdurNbmxhtvJC8vL7QtIiIiIiIiIiIiIh1HgzLdxphGPd8ZbNiwgaVLl0Y8V1xcHPo+e/bsiH1TpkypVg7G6XTy2GOPcfLJJ1NYWMjEiRO55ZZbOPTQQ8nLy+Of//wn8+bNA4KlZS688MLWe0EiIiIiIiIiIiIi0uIalGS//fbbG/V8Z7B06VIuvvjiGvfl5ORU27do0aIaa65PnTqVZ555hquuuoqsrCz+8Ic/VGtz6KGHMn/+fOx2e8sELyIiIiIiIiIiIiJtQkn2NvD73/+eww8/nMcee4xPPvmEnTt3kpCQwOjRo5k2bRqXXHJJlyifIyIiIiIiIiIiItLZKLNbixkzZjBjxowW62/s2LE899xzLdafiIiIiIiIiIiIiERfs5Lsn332GQDjx48nLi6uQceUl5ezfPlyAI466qjmnF5EREREREREREREJKqalWQ/+uijsdls/PDDD4wZM6ZBx+zYsSN0nM/na87pRURERERERERERESiytbcDowxbXqciIiIiIiIiIiIiEh70ewke2MFAgEA7HZ7W59aRERERERERERERKRFtXmSffPmzQCkpKS09alFRERERERERERERFpUo2qyb926tcbnd+3aRWJiYp3Hut1uNm7cyK233oplWey3336NObWIiIiIiIiIiIiISLvTqCT74MGDqz1njOH4449v9IkvuuiiRh8jIiIiIiIiIiIiItKeNCrJXttipY1ZxDQ2Nparr76a3/72t405tYiIiIiIiIiIiIhIu9OoJPusWbMiHl988cVYlsXdd99N3759az3OsixiY2Pp3bs348aNq7e0jIiIiIiIiIiIiIhIR9CoJPv06dMjHl988cUAnHbaaYwZM6blohIRERERERERERER6QAalWTf16JFi4Caa7WLiIiIiIiIiIiIiHR2zUqyT548uaXiEBERERERERERERHpcJqVZJfoqlxwtrikFIfNinI0Ih2HL2AoLTUUFVsaOyKNpPEj0jQaOyJNp/Ej0nQaP9IuBby4yqseFhWXgs0ZvXhq0B7HTsDrozQQCD0uKi7F5uyaqd2iklKgKjfaHlimhaIxxvDdd9/x/fffk52dTVlZWb0v9LbbbmuJU3dZmzZtYujQodEOQ0RERERERERERKRNbdu2jX79+kU7DKCFkuwvvfQSd955J1u2bGnUcX6/v7mn7tLy8/NJS0tj69atpKSkRDsckQ6jsLCQ/v37s23bNpKTk6MdjkiHovEj0jQaOyJNp/Ej0nQaPyJNo7HTvhljKCoqok+fPthstmiHA7RAuZi//OUv/O1vf2vQ9HzLstrVNP6OrvIiSklJ0YAXaYLk5GSNHZEm0vgRaRqNHZGm0/gRaTqNH5Gm0dhpv9rbhONmpfq/+uor7rvvPgCOO+44vvvuO7799lsgmFD3+/1kZ2ezYMECTj31VIwxTJo0iV27dhEIqyEkIiIiIiIiIiIiItIRNSvJ/vTTTwMwcOBA3nvvPQ444ACczqqFCizLolu3bhx//PHMnz+fJ598kqVLlzJlyhQ8Hk/zIhcRERERERERERERibJmJdm/+OILLMvi6quvxuGov/LMFVdcwZlnnskPP/zAU0891ZxTCxATE8Ptt99OTExMtEMR6VA0dkSaTuNHpGk0dkSaTuNHpOk0fkSaRmNHGqtZC58mJSVRWlrKggULOO644wBYt24d++23H5ZlUV5eHjGzHeA///kPp512GhMmTODLL79sXvQiIiIiIiIiIiIiIlHUrJnsXq8XgB49eoSeS0xMDG3v3bu32jH9+/cHYMOGDc05tYiIiIiIiIiIiIhI1DUryd69e3cACgsLQ8/17NkTu90OBGe172vXrl0AFBUVNefUIiIiIiIiIiIiIiJR16wk+3777QfA+vXrQ8+5XK7Q86+99lq1Y+bMmQNAnz59mnNqEREREREREREREZGoa1aS/cgjj8QYw6JFiyKeP+ecczDG8OKLL3LbbbexZs0aVqxYwVVXXcXcuXOxLIsTTzyxWYGLiIiIiIiIiIiIiERbs5Lsp512GgDvvvtuRMmYa665hkGDBhEIBLjnnns44IADOOyww3j66acBSEtL4+abb27OqTulO+64A8uyGvV1xx131NnnmjVruPzyyxk2bBhxcXF0796do446imeffRafz9fg2F599VVOOOEEevfuTWxsLIMGDeLCCy9k2bJlzXzVIi3P6/Uye/ZsTjrpJAYMGEBMTAwZGRnsv//+XHLJJbzxxhv19qGxI13J0Ucf3eB/dxpC40e6uv/973+N+n2tksaOdCXvvfced9xxByeddBKjR48mIyMDp9NJWloaBx98MH/84x/58ccfG9yfxo90FVu3buXpp5/mnHPOYeTIkSQkJBAbG0u/fv049dRTmTt3bqOueY0d6UqKi4v57LPP+Pvf/87ZZ5/N4MGDQ7+vDRo0qNH9afxIBNNMn376qfn4449NTk5OxPObN282kyZNMpZlRXztv//+5rvvvmvuaTul22+/3QCN+nrllVdq7e/55583MTExtR572GGHmezs7DpjKisrM7/+9a9r7cNms5m77rqrpd8KkSb7/vvvzdixY+scNykpKXX2obEjXc3kyZMb/O9OfTR+pKsrLi42AwcOjLhmb7/99nqP09iRrsTr9Tbo3xyn02nuu+++evvT+JGu4tZbbzWWZdU7dg455BCzZcuWevvT2JGu5uijj671Wh04cGCj+tL4kX01O8len/Xr15t58+aZ1157zXz77betfboOLSsry6xatarOr++++84kJycbwCQnJ5vS0tIa+1qwYIGx2WwGMD179jSPPfaY+eqrr8z//vc/c8YZZ4QG7FFHHWX8fn+tMZ1//vmhtsccc4x5++23zfLly80LL7xghg4dGtr3z3/+s7XeFpEG+/777023bt0MYFwul7nsssvM22+/bb7++mvz8ccfmxdeeMGcf/75pn///rX2obEjXVFlkv2QQw6p99+humj8iBhz3XXXGcD06NGjwUl2jR3parxer0lJSTGnnnqquffee82rr75qFi9ebFasWGHeeecdc91115mUlJTQNfv000/X2pfGj3Qlv/vd7wxgEhISzAUXXGBmzZplli5dar7++mvz73//24wfPz50rQ4fPtwUFRXV2pfGjnRF4ZOL0tLSzHHHHWcSExMbnWTX+JGatHqSXVrW+++/Hxpkv/vd72ps4/V6zbBhw0KJ+A0bNlRrM3PmzFA/L730Uo39fPrpp6E2J598svH5fBH79+7dawYMGBD6n1NeXl6zX59IU5WVlZkRI0YYwPTu3bvOZKDb7a7xeY0d6aoqf9mcPHlyk/vQ+BEx5ptvvjF2u93ExMSY5557rkFJdo0d6ar2vUb3tWnTJpOWlmYA07179xrba/xIV3PTTTeZ+++/3xQWFta43+fzmbPPPjt0Pdc2A1ZjR7qqZ5991syZM8f8/PPPoecq70BsaJJd40dq06wke12fxkjrOPfcc0ODcPHixTW2ef3110Ntaru9sqSkJPRL69ixY2tsM3XqVAMYu91utm3bVmObuXPnhs7197//vWkvSqQF3H333aFrcdGiRU3qQ2NHuqqWSLJr/EhX5/P5zEEHHWQAc+edd5pFixY1KMmusSNSu8svvzx0za5evbrafo0fkeqys7ONy+UygDnggANqbKOxI1KlsUl2jR+pTbOS7L169TLXXnutWb58eUvFI3UoKCgwcXFxBjCDBw82gUCgxnbht5vs2rWr1v4uu+yyULuffvopYl9RUVGottSUKVNq7cPtdofK1xxxxBFNe2EizeTz+Uzfvn0NYI4++ugm96OxI11VSyTZNX6kq3vwwQcNYEaMGGHKy8sbnGTX2BGp3R//+MfQdf/1119X26/xI1KzQw45xECwrExNNHZEqjQ2ya7xI7Wx0QxZWVk89thjHHbYYYwYMYK77rqLn3/+uTldSh3eeOMNysrKALjwwguxLKvGdkuWLAFg5MiR9OrVq9b+Jk+eHNpeunRpxL7ly5fjdrurtduXy+XisMMOCx3j9Xob8EpEWtYXX3zBjh07ADjrrLNCz5eWlrJhwwZ27dpFIBCotx+NHZGm0/iRrmzz5s3cfvvtADz11FPExMQ0+FiNHZGalZWV8c477wBgs9kYMWJEtTYaPyI1q7ymbbaaUz4aOyJNp/EjtWlWkn3KlCnY7XaMMWzYsIE777yTUaNGMWHCBB5//HGysrJaKk4B/vWvf4W2L7roohrbFBcXs337dgBGjRpVZ3/h+9etWxexL/xxQ/vx+Xz6kEWiYtmyZaHtww8/nOXLl3PCCSeQlJTE8OHD6dOnD927d+eSSy5hy5YtNfahsSMC69evZ/z48SQlJREbG0u/fv049dRT+de//lXnL3QaP9LVXXHFFZSWljJt2jSOPfbYBh+nsSMSyev1snXrVl599VWOOOIINmzYAMDFF19MUlJSRFuNH5Ga7dmzJ3Rd13RNa+yINJ3Gj9SlWUn2999/n127dvHEE09wxBFHYILlZ/j666+59tpr6devH1OmTOHf//43xcXFLRVzl7Rly5bQp2UTJ05k6NChNbbbvn07xhgA+vXrV2ef/fv3D21v27YtYl/44+b0I9IW1q5dG9petmwZEydO5MMPP4yYvZ6bm8sLL7zAgQceyGeffVatD40dkeAdal9//TXFxcW43W527NjBf/7zH6ZPn86BBx5Y7ZfDSho/0pW98sorLFiwgNTUVB566KFGHauxIxK8E8SyLCzLwuVyMXDgQM477zy+++47AH71q1/VOLY0fkRq9uCDD+Lz+QA4++yzq+3X2BFpOo0fqUuzkuwA6enpzJw5k6VLl5KZmck999zD6NGjMcbg9/v56KOPmDFjBj179uS8887j3XffDf0PXxruX//6V2ggT58+vdZ2RUVFoe3ExMQ6+0xISAht7/shSEv1I9IWcnNzQ9vXXXcdfr+fm266iY0bN+J2u9mwYQM33HADlmWRn5/PGWecESovU0ljR7oym83Gsccey0MPPcTHH3/MypUr+eyzz3j00UcZPXo0EPww65hjjmHr1q3Vjtf4ka4qNzeX6667DoD77ruPnj17Nup4jR2R2qWnpzN37lwWLFhASkpKtf0aPyLVffXVVzz66KNAMHE3c+bMam00dkSaTuNH6tLsJHu4gQMHcvPNN7N69WpWrlzJjTfeSN++fTHGUFZWxuuvv86pp55K7969a/yfvdTu5ZdfBiA2NrbGT6MrlZeXh7ZdLledfYbXC62s9d7S/Yi0hZKSktC22+3mgQce4P7772fIkCG4XC6GDh3Kgw8+yD333ANATk4O9913X0QfGjvSlb311lt8/PHHXH/99Rx77LEceOCBHHnkkVxzzTV8//33oQ93s7KyuPbaa6sdr/EjXdUNN9zAnj17mDBhApdeemmjj9fYEYG+ffuyatUqVq1axcqVK3n33Xe56qqrKC0tZebMmdx///2hyUbhNH5EImVlZfGb3/wGn8+HZVm89NJLxMfHV2unsSPSdBo/UpcWTbKH+8UvfsH999/P1q1bWbRoEZdccgmpqakYY8jJyeHZZ59trVO3usp/tJr7NXv27Aadb9myZfz0008AnHrqqTXO5KgUGxsb2vZ4PHX2W7nIAkBcXFyr9CMSrrXGTvj12q9fv9Cswn3deOON9O7dG4BXX3014g82jR1p71rz357U1NRaz+t0Onn++edDtQDnz59f7U4QjR9pz1pr7Hz66afMmjULu93OM888U+vicnXR2JH2ri3+7nE6nYwdO5axY8dy4IEHctJJJ/H444+zbNkyLMviL3/5C7/73e+qHafxI+1ZW+cMioqKOOmkk0K1ou+9915++ctf1thWY0fau7YeP42h8SN1abUke7jJkyfz97//nb/+9a91/iEvNWvIgqeVwhcEqu82kvDZv/ventJS/Yi0hfDr9bjjjsNut9fYzuFwhH7ZzMnJITMzs8Y+NHZEIjkcjogEx+LFiyP2a/xIV+N2u7nssssAuPrqqznwwAOb1I/GjkjtDjjgAP76178CMGvWLD788MOI/Ro/IkHl5eWceuqpfPPNNwBcf/31/OlPf6q1vcaOSNNp/EhdHK3Zucfj4d133+WVV17h/fffj/j0pSNzOBy1Lv7WGJUzauvi8Xh47bXXAOjZsycnnHBCne3DF0yo/BS7NuELJoQvpFBTP4ccckiT+hEJ11pjJ/y6a8yiIXv27GHIkCHVjtPYkfaoLf/tqcmYMWNC2/vOZNf4kfasNcbOW2+9xU8//YTD4WDMmDG8+uqr1dqHL8q9evXqUJsJEyYwePBgQGNH2r9o/9tz6qmnhsqMvvnmmxx//PGhfRo/0p611djx+XycffbZLFq0CIBLLrmk3kW4NXakvYv2vz110fiRurRKkn3hwoXMmTOHt956i8LCQoBQWYZhw4Zx/vnnM23atNY4dZupvG2+tb377ruhRR2nTZtW6wzdSomJifTv359t27axfv36OtuG769c2K5SeDKlof04HA6GDRtWZ1uR1hg7++23X2jb7/fX2TZ8v8NR9b9AjR3pCNrq356a1FQPt5LGj7R3LT12KieO+Hw+fv/739fbft68ecybNw8IzsitTLJr7EhHEM1/e7p37x7a3rJlS8Q+jR9p71p77AQCAS688EL++9//AnDOOec0qCyvxo50BNH8t6cuGj9SlxYrF/Ptt9/yxz/+kX79+nHccccxe/ZsCgoKMMaQkZHBVVddxZdffslPP/3EHXfcwfDhw1vq1J1aY0rFVJo0aRIAP/74I7t37661Xfjt/hMnTozYN378+NDiC/uWBQjn8XhYtmxZtWNE2tJRRx0V2t64cWOdbcP39+3bN2Kfxo5I7cJn5fbp06fafo0fkabR2BGpXfidUzXdIq/xI13ZZZddFrpL6te//jX//ve/G7w+iMaOSNNp/EhtmpVk37hxI3fffTejRo1i/PjxPProo+zcuRNjDPHx8Zx//vm8//777Ny5k8cee4wJEya0VNxdQk5ODu+//z4QXEj2F7/4RYOOO+2000LbtS30UFpayuuvvw4EP0EbMWJExP6kpCSOPfZYAD7++ONab4MJv1vh9NNPb1B8Ii1t8ODBjBs3DoAPPviA0tLSGtsVFRXx0UcfATB06NBqt49p7IjUzOfz8eKLL4Yeh3+wVUnjR7qSGTNmYIyp86vy1n2A22+/PfT8jBkzIvrS2BGp3RtvvBHa3n///avt1/iRrur666/n+eefB+DYY4/lzTffxOl0Nvh4jR2RptP4kVqZZrAsy9hsNmNZlrEsyzidTjN16lQzZ84cU1JS0pyuxRjz+OOPG8AA5qGHHmrwcR6PxwwdOtQAJjk52WzYsKFam5kzZ4b6njVrVo39fPLJJ6E2p5xyivH5fBH79+7dawYMGGAAk5qaanJzcxv1+kRa0muvvRa6Xi+//PIa2/zud78LtXnwwQer7dfYka5o4cKFJi8vr9b9Ho/HTJ8+PXRNn3zyybW20/gRqbJo0aLQtXz77bfX2k5jR7qi+fPnm507d9bZZvHixSYxMdEAxuFwmPXr11dro/EjXdHtt98eul6POOIIU1xc3Og+NHZEqgwcONAAZuDAgQ1qr/EjtWl2kt2yLHPYYYeZxx9/3OzZs6el4hJjzPjx4w1g7Ha72b17d6OOfe+994zNZjOA6dmzp3n88cfNV199ZRYsWGDOPPPM0ECeNGlStYEc7txzzw21PeaYY8w777xjVqxYYV588cXQ/1QA88wzzzT35Yo029SpU0PX5EknnWTeeecd8+2335q3337bTJkyJbRv3LhxpqysrMY+NHakq5k+fbpJTEw0559/vnnuuefM4sWLzcqVK82SJUvMo48+akaPHh26Xnv06GE2bdpUa18aPyJVGppkN0ZjR7qe6dOnG5fLZU4//XTzxBNPmEWLFpmVK1eaZcuWmTlz5phzzz03NCYAc+edd9bal8aPdCWPPfZY6Frs27evWbp0qVm1alWdXx6Pp8a+NHakK/r555/NrFmzIr7S09MNYNLT06vt27VrV439aPxITZqVZL/zzjvNxo0bWyoWCbN+/frQYJo6dWqT+njuueeMy+UK9bPv16GHHmr27t1bZx+lpaURict9v2w2W71/OIq0laKiInP88cfXer0CZvz48bX+Q1lJY0e6kvBZ6nV97b///mbNmjX19qfxIxLUmCS7MRo70rU09N+euLg48/e//73e/jR+pKuYPHlyg8ZO+FdmZmat/WnsSFcza9asRo2fRYsW1dqXxo/sq1lJdmk9f/7zn0OD6tVXX21yP6tWrTK///3vzZAhQ0xsbKxJT083kyZNMk8//bTxer0N7mfOnDnmuOOOMz169DAul8v079/fnH/++eaLL75ocmwirWXu3LlmypQpplevXsbpdJru3bub4447zrz44ot1foocTmNHuoq1a9eaRx55xJx99tlm7NixpmfPnsbpdJrExEQzdOhQc84555g33nijwWPHGI0fEWMan2Q3RmNHuo69e/ea1157zVx22WVm/Pjxpn///iYmJsbExcWZvn37muOPP9787W9/q7ekTDiNH+kKWjrJbozGjnQtLZlkN0bjRyJZxhiDiIiIiIiIiIiIiIg0mq2lOlq0aBEXXXQRw4cPJykpCYfDwdq1ayPaLFmyhKeeeoqXX365pU4rIiIiIiIiIiIiIhI1juZ2UFpayvTp03nrrbcAqJwYb1lWtbZ2u52rrroKy7KYMGECw4cPb+7pRURERERERERERESiptkz2c855xzeeustjDGMHz+eG264oda2RxxxBPvvvz8A8+bNa+6pRURERERERERERESiqllJ9vnz5/Pee+8B8Nxzz7Fs2TIeeOCBOo8544wzMMawePHi5pxaRERERERERERERCTqmpVkf+mllwC44IILuOSSSxp0zMEHHwzAunXrmnNqEREREREREREREZGoa1aSfcWKFViWxTnnnNPgY3r37g3A3r17m3NqEREREREREREREZGoa1aSPScnB4C+ffs2+thAINCcU4uIiIiIiIiIiIiIRF2zkuxJSUkAFBYWNviYjRs3ApCent6cU4uIiIiIiIiIiIiIRF2zkuzDhw8HYPny5Q0+Zt68eQD84he/aM6pRURERERERERERESirllJ9qlTp2KM4amnnqK8vLze9gsWLGDevHlYlsWvf/3r5pxaRERERKJk8+bNWJbV7K9BgwZF+6VIG3njjTcifvYzZsxo0HHGGObMmcOJJ55I3759iYmJoW/fvpx44om8/PLLGGMaHMOmTZu44YYb2G+//UhOTiY5OZn99tuPG264IXS3bV1yc3OZM2cOl156KYcccgjdunXD6XSSlpbGQQcdxLXXXsvq1avr7aex4+e7775r8Gus5PV6OfDAAyP62bx5c4OObe77JCIiItIVWaYxv5nuIz8/nyFDhlBQUMAJJ5zAv//9b9LT07HZbFiWxapVqxgzZgzl5eU8+eST3HrrrZSXl9O7d282btxIbGxsS74WEREREWkDmzdvZvDgwc3uZ+DAgQ1O/EnLmj17NhdffDEAixYt4uijj261c+3du5cxY8aQnZ0dem769OnMnj27zuNyc3M5/fTT+eyzz2ptc9RRR/H222+TlpZWZ1//+te/uOKKKygtLa1xf3x8PE8//TQXXXRRjfuff/55rrjiCnw+X53nsdlsXHvttTz44IPYbDXPZ2rs+Fm5ciUHHnhgg9sD3H777dx1110Rz2VmZtb7wVZz3ycRERGRrsrRnINTU1N5+eWXOfXUU/nggw8YMGAAkydPDu2/++67yc/P5/PPP6ekpARjDE6nkzlz5ijBLiIiItJB9e3bl1WrVtW6/5ZbbuGdd94B4MUXX2T8+PE1tnO5XK0Sn7QvM2fOJDs7m549e5KVldWgY7xeL6eccgqff/45AJMnT+aqq65i0KBBbN68mSeeeILFixfz2WefcfLJJ/Ppp5/icNT8p827777LxRdfTCAQIDExkRtvvJFjjjkGy7JYuHAhDzzwACUlJfz2t7+lW7duNd5xu3v3bnw+HzabjV/+8pccf/zxjBs3jvT0dPLy8vjwww958sknKS4u5uGHH6asrIynnnqq3td5xRVXMHPmzDrbDBs2rAHvWJWVK1dy3333YVkWGRkZ7N27t0HHtcT7JCIiItJlmRbw4Ycfmp49exrLsoxlWcZms0V8VT7fvXt38/HHH7fEKUVERESknZo+fboBDGAWLVoU7XCkBrNmzWqTn9Grr75qAJOQkBBxzunTp9d53GOPPRZqe9ZZZxm/3x+x3+fzmbPOOivU5oknnqixn9LSUtOvXz8DmJiYGLNixYpqbVasWGFiYmIMYPr372/KysqqtXnooYfMtddea7Zt21ZrzGvWrDHp6emhmJYtW1Zju8zMzFCb22+/vY53ofE8Ho854IADDGCuuuoqM3ny5NC5MjMzaz2upd4nERERka6qWTXZKx133HFs2rSJxx9/nF/96lekpKRgjMEYQ1xcHBMnTuT+++9n48aNHHvssS1xShERERERacf27NnDVVddBcB9993X4Br8gUCAv/3tbwAkJCTwzDPPVCu9YrfbeeaZZ4iPjwfg3nvvrbE++0svvcT27dsBuO666zjkkEOqtTnkkEO47rrrANi2bRv/+te/qrW5/vrreeSRR+jXr1+tcY8ZM4Zbb7019Pi1116r76W2uLvvvpsffviBgQMHct999zX4uJZ6n0RERES6qhZJskOwPt+VV17Jhx9+SG5uLh6Ph7KyMoqLi1myZAk33ngjSUlJLXU6EREREemgBg0ahGVZoTrg27Zt4//+7/8YO3YsqampWJbFo48+Gmrv9/v5+OOPuemmmzjqqKPo1asXLpeLxMREhg0bxrRp0/j444/rPe++i25u2bKF66+/npEjRxIfH09qaipHHHEETz31VL21t/Py8rjvvvs48sgjycjIwOl0kpqayrBhwzjqqKO45ZZbWLp0aZ195Obmcu+993LkkUfSs2dPXC4XGRkZTJo0ifvvv5/CwsJ6XxPArl27uPPOOyP66dWrFwcffDBXXHEFH330Uej1fPrpp1iWFarHDoRKgoR/tUSN9soyMRMnTuTKK69s8HGff/45O3fuBOA3v/kN3bp1q7Fdt27d+M1vfgPAzp07Q6Vlwr3xxhuh7UsvvbTWc4bve/311xsc677CJxT99NNPTe6nKSrLxAA8++yzJCYmNvjYtn6fRERERDqbZtVkr7Njh6PWuogiIiIiIgAffPAB55xzDgUFBbW2+eMf/8g//vGPas97vV42btzIxo0beeWVVzj33HOZPXs2MTEx9Z73/fff5/zzz484b1lZGV9++SVffvkl77zzDu+++y5Op7PasV9//TVTp06tVuu6oKCAgoICNm7cyJIlS3j55ZdrXdj1tdde49JLL62WSM/JyeHzzz/n888/55FHHmHevHlMnDix1tfx6KOPcvPNN1NeXh7xfFZWFllZWXz77bc888wzrb646b5effVV5s2bR0xMDC+88EKti4DW5NNPPw1t//KXv6yz7bHHHhuaUf3pp58yadKk0D6fzxf6oGPw4MF1LjZauT8zM5OlS5fi9/ux2+0NjrmSx+MJbTfl+KbyeDxMnz4dn8/H9OnTOeGEExp8bDTeJxEREZHORllwEREREYmKbdu2cfbZZwPwl7/8hWOPPZbk5GQ2btxI9+7dQ+18Ph99+vThlFNO4bDDDmPo0KEkJCSQlZXFunXreOKJJ9i0aROvvvoqGRkZPP7443Wed9WqVbzxxhukpKTw5z//mSOOOIK4uDi+//57/vrXv5KZmcmHH37IAw88wF/+8peIY71eL2eeeSZ79+7FZrMxY8YMfv3rX9O7d29cLhd79+5l1apVfPTRR2zcuLHG8//73/9m+vTpGGNIT0/nyiuvZNy4cfTv35+CggI++eQTnnjiCbKysjjxxBP56quvGD16dLV+brnlFu655x4AYmNjufjii5kyZQr9+vXD4/Hw008/sWjRIubPnx86Zvz48axatYp33nmHW265Bah5cdqEhIQ638O6hJeJuf322xk5cmSjjl+zZk1oe7/99quz7ZgxY0Lba9eujdi3YcOGUNK7vn4q+8rMzMTtdrNx40ZGjBjRmLABWLhwYWi7Ied84403ePPNN9m8eTOBQICMjAzGjRvHKaecwrRp04iNjW3Qee+++25WrVpFr169eOSRRxoVczTeJxEREZFOJ7ol4UVERESks6lv4dOBAweG9qenp5t169bV2d+GDRuM1+utdb/P5zPnnnuuAYzdbjebN2+usV3lOQEzduxYs2fPnmptdu3aZbp162YA06tXL+Pz+SL2L1y4MNTHww8/XGfc2dnZ1Z7LzMw0cXFxBjAnnniiKS4urvHYn376yXTv3t0A5le/+lW1/YsWLQrF0a9fP7NmzZpa4ygsLDS5ubkRz7Xmwqenn366Acy4ceMifm7hMde18Olhhx0WalfTzyhcVlZWqO0RRxwRsW/BggWhfTNnzqw37iuuuCLU/sMPP6y3/b4KCgpM3759DWAsyzI//PBDje3CFz6t62vAgAFm6dKl9Z73m2++MQ6HwwDmzTffjNjXkIVP2/p9EhEREemMGjSTvb7bNJvCsiw++eSTFu9XRERERDqOe++9l1GjRtXZZujQoXXut9vtPP7447zxxhv4/X7efvttrrnmmjqPmT17dsRs+Uq9evXit7/9LX//+9/ZvXs369evj5jdu2vXrtB2eP3tmqSnp1d77u9//ztlZWUkJyczZ86cWmeMDx8+nNtuu40//OEPfPzxx2RmZkaU8bjrrrtC23PmzImY0b2vtlwXae7cucyfPx+Hw8GLL77YpPKRRUVFoe36Yg/fH35cY/upr6+GmDlzJjt27ABg+vTp7L///rW2TU5O5pRTTmHy5MmMHDmSpKQkCgoK+Oabb3jhhRdYu3YtW7du5Ve/+hWffPIJRxxxRI39eDweZsyYgc/n48wzz+TMM89sdNxt/T6JiIiIdEYN+q23coEkY0yzT1jZj2VZze5LRERERDouh8PBtGnTGn1cSUkJ2dnZlJSUEAgEQs9369aNvXv38u2339Z5/H777cfBBx9c6/5DDz00tL1hw4aIJHvfvn1D288//zz/+Mc/GvV77VtvvQXAlClTSEtLq7NteA31pUuXhpLs+fn5LF68GIDDDjuMo446qsHnb01ZWVn84Q9/AOCmm27iwAMPbFI/ZWVloW2Xy1Vn2/D6+6WlpU3up76+6nPfffcxZ84cAIYNG1bjGgKV+vTpw86dO2v8gGXy5MlcffXVXHfddTzxxBOUl5dz4YUXsm7duhpfw1133cWqVatIS0vjySefbFTMldryfRIRERHprBqUZD/qqKOUFBcRERGRFjVixIgG1/7OzMzk0Ucf5d133yUzM7POyR/Z2dl19lXXrG+InIG+78KkEydOZPTo0axbt47HH3+c//3vf5xxxhkceeSRHHbYYWRkZNTa79atW0Mz4V9//XVef/31OuMIFz6DfuXKlaEPF4455pgG99HaLr/8cnJychg1ahS33XZbk/uJi4sLbXs8njrrkrvd7tB2fHx8nf3Up66+6vLCCy+Eavenp6fz9ttvk5ycXGt7l8tVZzLb4XDw2GOP8f3337NkyRI2bdrEO++8w1lnnRXR7ptvvuH+++8Hggvg9uzZs8Exh2ur90lERESkM2vwTHYRERERkZbUrVu3BrV7/fXXueiiiyKSe3Wpb3ZtfYlBm80W2vb7/RH7HA4H7777Lueeey4rVqxgw4YNPPDAAzzwwAMAjB49mpNPPpnLLruMIUOGRBy7Z8+eBsVfk/DXtHfv3tB2nz59mtxnS3rllVd4++23sdlsvPDCCxGznRtr33IkdSXZi4uLazyupn7qU1dftXn55Ze59NJLMcaQmprKBx980KDFQ+tjWRZXXnklS5YsAeCTTz6JSLKHl4k54YQTuOiii5p8rrZ4n0REREQ6u8YXSRQRERERaQF2u73eNhs2bAgl2OPj47nmmmuYMmUKw4YNIz09HZfLFbrjcsCAAWzbtq1FShzWZciQISxfvpxFixbxn//8hyVLlvD999/j8/lYt24d69at4+GHH+bee+/lxhtvDB3n8/lC27/97W+57rrrGnzOHj16hLbDX197udv0zjvvBODggw9m69atbN26tVqbtWvXhrYzMzN59dVXARg8eDATJkwI7RswYADLli0DgrP/a6qdX2nLli0Rx4ULf1xTPI3pqyZz585lxowZBAIBkpOT+eCDD+osQ9RYY8eODW1v3749Yt+8efNYvXo1EFw/q/K93Ff4Bzv//e9/Q+/lySefHLqLpLXfJxEREZGuQEl2EREREWm3XnzxxdAM9nnz5jFlypRa2+bm5rZVWECwVEtluZbS0lI+//xz3njjDV566SU8Hg833XQTY8eO5cQTTwSISBYXFBREJFEbIzzhXrnQZrRV/oxWrFjBeeedV2/7zz77jM8++wwILhIanmQfO3ZsqJTOmjVr6kxchyfu951BPnz4cFwuFx6PhzVr1tQbU2VfMTExDBs2rM62r732GhdeeCF+vz+UYA+v5d8S6voAJfyujv/7v/9rUH9XX311aDszMzOUZG/N90lERESkq7DV30REREREJDpWrVoFBEvL1JVgX7NmDSUlJW0VVjXx8fEcd9xxPPfcc8yePTv0/CuvvBLaHjx4cKhEzsKFCxtc/mZfBx10UOgugEWLFjWpj/YyA74mkydPDm0vXLiwzraffPJJjcdBsLTPxIkTgWBSefPmzbX2s3nzZjIzM4Fg3f267rJ4/fXXmTZtWkSC/bDDDqszzqaovPYhcsHdltZa75OIiIhIV9LgJPv111/P9ddfX2stSb/fX+utoeE2bdrEQQcd1KK3UoqIiIhI51RZYqWsrKxaffRwjz32WFuFVK8TTjghtB1eP91ms3HaaacBkJeXx+OPP96k/lNSUjj66KMBWLZsWWhGeGOE1zlvarI/3ObNmzHG1PkV/oHA9OnTQ8+HfygBMGnSJHr16gXAm2++WesdCrm5ubz55psA9O7dO5QoDhdex/zZZ5+tNf7wffsuMBouPMGelJTEggULWiXBbozhqaeeCj3ed4HbGTNm1Pt+G2MiPnioXDDYGMOgQYMi+mvp90lERESkq2lwkv3RRx/lH//4B9nZ2TXuX79+PYMGDaq2wNO+ysrK+O677/juu+8aFaiIiIiIdD0jR44Egr9DvvzyyzW2efnll/nnP//ZJvF89tlnrFu3rs4277//fmh76NChEfv+/Oc/hxYF/ctf/sL8+fPr7CsnJ4cnnnii2vO33npraDb6tGnT6oypuLiYvLy8iOfCZ0b/9NNPdcbQ1mw2G3/6058AKCkp4YorriAQCES0CQQCXH755aEFYf/0pz9FLFhbafr06aHFYR955BG+/vrram2+/vprHnnkESD4vtS2iOibb77JtGnT8Pl8JCUl8cEHH3D44Yc3+vXNmzev2usJ5/P5uOaaa0IfngwcODD04Uxracn3SURERKQravGa7K290JSIiIiIdB0zZszgsccewxjDpZdeyrfffsvUqVPp3r07W7ZsYe7cubz55puMGDGC3NzciJnjrWHhwoXcddddjB8/nqlTpzJu3Dh69eqFzWZj9+7d/O9//+P5558HwOl0ctlll0UcP3ToUF588UUuuOACPB4PZ5xxBieddBJnnXUWI0eOJCYmhtzcXNasWcPChQtZsGABGRkZXHXVVRH9TJ48mT//+c/cc889bN++nYMOOojf/va3TJkyhb59++L1etm4cSMLFy5k3rx5zJ8/PzT7HYIlZxITEykuLuaBBx6gZ8+ejB07FpfLBUBcXFyrliipz8yZM3n11VdZtmwZr7/+OllZWVx99dUMHDiQLVu28Nhjj7F48WIADj/8cK644ooa+4mPj+epp57ijDPOwO12c8wxx3DTTTfxy1/+Egj+PB944AHcbjc2m40nn3yS+Pj4av385z//4bzzzsPn82G323nooYdISkoKLT5aE5fLxYgRI6o9/5vf/IYBAwZw2mmnceihhzJ48GASEhIoKCjg66+/5oUXXoioez5r1qyIOw9aQ0u9TyIiIiJdlmkgy7KMzWYza9asqXH/6tWrQ23q0tB2IiIiItIxTZ8+3QAGMIsWLaq2f+DAgQYwkydPblB/Dz/8sLEsK9Tnvl/Dhw83P/30U739VrafPn16nedbtGhRqO2sWbMi9t1+++21xhH+lZSUZObNm1frOd577z3To0ePBvU1ZsyYWvt54IEHjMvlqrePmn4O99xzT63tG/qzaYzw97W+n4ExxmRnZ5tJkybV+bomTZpksrOz6+1r1qxZJi4urtZ+4uLiqv2sw4Vf0w39GjhwYI19Neb4xYsX1/va6jJ58uRQf5mZmfW2b+77JCIiItJVtfhMdhERERGRlnTddddxyCGH8Oijj/L555+Tk5NDamoqw4YN44wzzmDmzJkkJCS0SSw33XQTRxxxBIsWLeKLL75gx44d7Nmzh7KyMlJTUxk9ejTHH388l156KT169Ki1n6lTp7J582Zeeukl3n//fVauXEl2djZ+v5/U1FSGDBnC+PHjOeGEEyJqvO/rxhtv5Nxzz+WZZ57ho48+YuPGjRQVFZGRkUGfPn047LDD+M1vfsNRRx1V7dg///nPjBw5khdffJGVK1eSk5ODx+NpkfepJaSnp7N48WLmzJnDnDlz+P7778nJySE9PZ0DDjiAadOmccEFF9RYJmZfM2bM4Mgjj+TJJ59kwYIFbNu2DYD+/fszZcoUZs6cybBhw1r7JQHw7rvv8tVXX7F8+XI2b95MTk4O+fn5xMXF0bNnTw466CBOOeUUfvOb34RKC7WV9vQ+iYiIiHQkljENq+9is9mwLItVq1YxZsyYavvXrFnD/vvvj2VZdS5K1dB2IiIiIiIiIiIiIiLtXYMXPhURERERERERERERkUhKsouIiIiIiIiIiIiINJGS7CIiIiIiIiIiIiIiTaQku4iIiIiIiIiIiIhIEzkae8BTTz1Fjx49qj2/Z8+e0PZdd91V6/Hh7UREREREREREREREOjLLGGMa0tBms2FZVouc1BiDZVn4/f4W6U9EREREREREREREJBoaNZO9gfl4aSOBQICdO3eSlJTUYh+AiIiIiIiIiIiIiLRXxhiKioro06cPNlv7qIbe4CT7okWLWjMOaYKdO3fSv3//aIchIiIiIiIiIiIi0qa2bdtGv379oh0G0IhyMdL+FBQUkJqaSmZmJt26dYt2OCLN4vV6+fDDDzn++ONxOp3RDkekWXQ9S2eha1k6E13P0pnoepbORNeztAs+NzwdtgblFXvAEdPobjri9ex3u/nw0ENDj49fvhx7TONfe1sqLCykf//+5Ofnk5KSEu1wgCYsfCrtR2WJmKSkJJKTk6McjUjzeL1e4uPjSU5O7jD/EInURtezdBa6lqUz0fUsnYmuZ+lMdD1Lu+BzQ2zY4+TkJifZO9r17He7iQ8ruZKcnNzuk+yV2lP57PZRtEZEREREREREREREpANSkl1EREREREREREREpImUZBcRERERERERERERaSIl2UVEREREREREREREmkhJdhERERERERERERGRJlKSXURERERERERERESkiZRkFxERERERERERERFpIiXZRURERERERERERESaSEl2EREREREREREREZEmUpJdRERERERERERERKSJHNEOQERERERERERERASABb9tmX4sC054oWX6EqmHkuwiIiIiIiIiIiLSPqyZHUyQG1N9n2U1rA9jlGSXNqUku4iIiIiIiIiIiLQP+10E1JJM3/gOlOeDIxZ6HgyJ/YLPF++ArG/AVwaxaTD0lLaKVgRQkr1We/bsYfny5SxfvpwVK1awYsUKcnJyAJg+fTqzZ89uVH8LFizgueeeY/ny5ezdu5fu3btz6KGHcumllzJlypRWeAUiIiIiIiIiIiIdzJTZNT//3vngLoAJN8P4/4OY5Mj9niJYfj8svw/8HjhpTquHKlJJSfZa9OzZs0X6McZw+eWX89xzz0U8v2PHDubPn8/8+fO59NJLeeaZZ7AaesuLiIiIiIiIiIhIV/HDP+HH1+DwO+DwW2tu40qCSX8Fewx8eQf0PwYOuKQto5QuzBbtADqC/v37c/zxxzfp2FtuuSWUYB83bhxz585l+fLlzJ07l3HjxgHw3HPPceuttfwPQkREREREREREpCtb/QJYNjj42vrbHnxtsO3q51s7KpEQzWSvxW233cb48eMZP348PXv2ZPPmzQwePLhRfWzYsIEHHngAgEMOOYTPPvuMuLg4AMaPH88pp5zC5MmT+frrr7n//vu5+OKLGTp0aIu/FhERERERERERkQ4rdz24UoKz1evjSgJXcvAYkTaimey1uPPOO/n1r3/drLIxjzzyCD6fD4DHH388lGCvFB8fz+OPPw6Az+fj0UcfbfK5REREREREREREOiUTAHc+lOXW37YsN1i73QRaPSyRSkqytxJjDO+88w4Ao0aN4rDDDqux3WGHHcbIkSMBePvttzHGtFmMIiIiIiIiIiIi7V7GAYCBZXfV33bZ3cEEe8b+rR6WSCUl2VtJZmYmO3bsAGDy5Ml1tq3cv337djZv3tzaoYmIiIiIiIiIiHQcB14BxsDKx2HBxZC/qXqbgkxY8FtY+RhYFhw4s+3jlC5LNdlbybp160Lbo0aNqrNt+P5169Y1uva7iIiIiIiIiIhIpzV6Gmz5BNbMhrX/Cn4l9YfEvoAFxduhaFuwrTGw30XBY0TaiJLsrWTbtm2h7X79+tXZtn///jUety+3243b7Q49LiwsBMDr9eL1epsaqki7UHkN61qWzkDXs3QWupalM9H1LJ2JrmfpTHQ9S4Md+yy29P2xLb8HynOhcGvwK1xsGoHxfyZw4B+gMdeUz4sz7KHX6wXT+AIgHfF6DlSsJ1nJ5/MRsLXv4ift8f1Vkr2VFBUVhbYTExPrbJuQkBDaLi4urrXdfffdx5133lnt+UWLFhEfH9+EKEXan48++ijaIYi0GF3P0lnoWpbORNezdCa6nqUz0fUsDTMEW99n6F66ktTyjcT4CwBw21PIjx3K3rgDCexywa7/NapXm/FyctjjBR8sIGA5a21fnw51Pft8EfXEFyxYAI72nTIuLS2NdgjVtO93rAMrLy8PbbtcrjrbxsTEhLbLyspqbXfzzTdz/fXXhx4XFhbSv39/jjnmGNLT05sRrUj0eb1ePvroI4477jiczqb/QybSHuh6ls5C17J0JrqepTPR9Sydia5naZpTW7Y7nxueqno45YQp4IipvX0tOuL1HPB4+PCmm0KPp0yZgq2eXGa0VVb3aE+UZG8lsbGxoW2Px1Nn2/ASMHFxcbW2i4mJiUjIV3I6nR1m4IrUR9ezdCa6nqWz0LUsnYmuZ+lMdD1LZ6LrWaLKCkQ8dDqd4Gj69diRrmd/IPK1OxwO7O089vb43irJ3kqSkpJC23WVgAEoKSkJbddXWkZERERERERERKTL2vsDbP4ACreAtxSmvFi1z++Fsr2ABYm9oxaidD1KsreS8MVOt2/fXmfb8MVOwxdBFREREREREREREcBdAB/8Fja8HXxsDFhWZJI94IV//QLK8+C3P0HqkKiEKl1P+14qtgMbM2ZMaHv9+vV1tg3fP3r06FaLSUREREREREREpMMJ+OCtqcEEuyMeBp8Ejtjq7ZzxMPa3YAJVyXiRNqAkeysZPHgwffr0AWDx4sV1tv3ss88A6Nu3L4MGDWrt0ERERERERERERDqOVS/Azi8hZQj89kc4/T8Qk1Jz2+FnBr9n/q/t4pMuT0n2VmJZFqeeGlzpeP369SxbtqzGdsuWLQvNZD/11FOxLKvNYhQREREREREREWn31r8SLA1z9COQ2Kfutj3GgWWD3LVtE5sISrK3qmuvvRaHI1j2/g9/+ANlZWUR+8vKyvjDH/4ABFfuvfbaa9s6RBERERERERERkfYtexVgwaDj629rdwZnuZfltHpYIpW08Gktli5dyoYNG0KPs7OzQ9sbNmxg9uzZEe1nzJhRrY8RI0Zwww038Le//Y2vv/6aiRMn8n//938MHTqUjRs3cv/997Ny5UoAbrzxRoYPH94qr0VERERERERERKTD8paAKwnsroa193vAprSntB1dbbV4/vnneemll2rc9/nnn/P5559HPFdTkh3gnnvuYc+ePbz44ousXLmSc889t1qb3/3ud/z1r39tdswiIiJSxe8uwVdWiGXZwGbHCn05gt/tdrDsKtUmIiIiItLexWVAye5gst2ZUHfbvJ+D7VKHtk1sIijJ3upsNhsvvPACZ555Js899xwrVqwgOzubjIwMxo8fz2WXXcaJJ54Y7TBFREQ6vIC3HE9RNt6ibLzF2QQ8ZfUfBFg2B/bYRBzxKTjiUnDEp2KPSwom50VEREREJPp6jodN/4VN78HIs+tu+80jwe99J7Z+XCIVlGSvxezZs6uVhGmOqVOnMnXq1BbrT0REpKsL+L14i3OCSfWibPzlRcHnA34C7hJ85cUE3CVgWcGEuWXDsgW/V24Hn7djLyvAW5KDZdmDnVtWMOEel4I9PgVnfAr2uBTNehcRERERiYb9ZsDG/8Dnt0G/oyChV83tVjwI3z8TXCR17G/bNETp2pRkFxERkQ6nZOd6yrI2AAZjAvjdpfjLi/G7iwm4S4ONLLC54gAwAT/GeCBgMMYPJgCmer+WMwa7Kw6bKw5/eRHe4hwsWzDxbnPFE9djCLHd+mPZ9SuUiIiIiEibGX4aDDkpOJP95UNg9AXgKw/u+/GN4MKoP70BeT8Fnxt5bjAZL9JG9BeiiIiIdChl2Vsoy/oZb0kevpI8/O4SMMGMuc0VizMpA3tsIvaYhFCCvCbGGDABjN9HwFuG31NGwFOGr6wISvJD7SxnDPaYBJyJ3Qh4SindtZ7Y9AHEdh+M3RXf2i9XREREREQAfv0qvH8BbHgHvn6w6vn3KtY/rPibgOGnwwkvtH180qUpyS4iIiIdhq+0gJLtq/GVF+PO2YblcOJISMMRm4g9JrFRM8wtywoufGqzY3PG4IhPDe0L+DwEPGX4PaXBxHtJHr7iXGyxCTgTMwj4vJTtycSV2ou47oNxJqa3wqsVERERaT+MCRDwlhPwlBHwluP3lmNzxBCT1lcl9aRtOBPg1PnB2eyrnocdn0NZdnBfTEqwBvv+l8KwU6Ibp3RJSrKLiIhIh2D8Pgo3f0PA56E8ZyuWw0V8r+F1zlZvKpvDhc3hwhGfEjq3tzgHb3EO7uwteBwunEnpBAI+PPm7cMSnEtt9MI64FGwOJ5bd2SpxiYiIiLSmgM+Nr6wIf3kRfncpAW9ZKKke8Lqpqd6e8XmI6zGk7YOVrmvIScEvgIAPAn5wxEQ3JunylGQXERGRDqF42yr85cW4c7ZCwEdsj2Ftlsi27A5cKT1xJvfAV5qPtygbT94uPAVZOBLScPnc+ErzIw+y2bE5XFh2Jza7E8vhwuaMIy5jIPbYxDaJW0RERKQmAb8Xf1khvvLiYEK9Ytv43KE2xhiM34vxewj4vBi/t+K7h4Dfh/F7iMsYSFn2ZiXZJXpsjuCXSJTpKhQREZF2rzxnK+687XgL9+IvL8aV1gd7TNvXQ7csC2dCGs6ENPzuErxF2fiKc/AV5WCPTcCyO4PJdZsDbMFSNJbNEfxut2PZHbhzt5Iy7PCI8jQiIiIirc34fZTnbqM8ewv+8qKq542fgNdd8VUemrVufJ7qnVgWlsOJze7C7/XjLcnHHpOIrzRfv9tI63p+CMT3gPOXNaz9q0dC8U64ZGPrxiVSQUl2ERERadd8ZUUUb1+N312Mp2A39rhkXEkZ0Q4Le0wC9pgEAj4P3qJs/OXFwT9IA76a7qQGwHI4iesxlIKNX5Ey/AgcsUltG7SIiIh0OX5PKeV7MynP2Yrx+/B7SvGVFRLwlBPwlWO8kcl0y+nC5ozFFp+Cze7CqiiFZ7M7I9a/Kdu7GV9ZAcb0pTx3B4lKsktrKtgMvvKGty/aDkVbWy2c1vDJ0Ue3TEeWxbGLFrVMX9JgSrKLiIhIu2UCPoo2f0PA66Y8eyuWw0lst37RDiuCzeEiJq1PxHMmEMAE/JiADxPwQ8BPwO/Fk7+Lsr2ZxPUYSuGGZaQMnxiVGfkiIiLS+XmLcynbuwlP/m6MCeArK8RbnE2gvAQIfvhvc8Zii0sJfnfGYHPEYtlsDerfmZCKv6wwWM4vfwcJfcdoAVRpPwI+sBp2LbcXpdu3193AssDUNpsnbJ/GYVQoyS4iIiLtVvG21fjKCnHnbsMEfMT1GBYxg6q9smy2ij9QnRHP2xwxlGdnViTah1Cw4UtSR0zE5oyNTqAiIiLSqRgTwJ23k/K9mfhK8wkE/PhKcvEW5WB8nqp1ZhLTm/07lT02GWw2fKV5OOKS8BbntIu7DUXwlkHZHnB1rLtGD7z//hqf9xYW8tPjj+MtLCRt3DgyDj+c2F69ACjPyiL7yy/J+/ZbnCkpjLjqKpzJyW0ZtlRo/3+lioiISJdUnrsdd+42vMXZ+MuKcKX27vCzvh1xScSmD6Q8ZwvlezOJ6zGYgg3LSBl+BDaHK9rhiYiISAfmKciiePsqAp4yAj43nqIcfCW5EAhgc8XhSu+PIz61xWabWzYbjrjkipIxftx5O5Rkl5ZTuBUKN0c+5/fA9iXUWpvRGHDnw7o54PdC7/1bOciW1f/MM6s95ystZcnpp4NlMWHWLHoceWT1A6+7jr2ff843V1/N1tdeY9Jbb7VBtLIvJdlFRESk3fGVF1G87Qf87lI8+buwxyXh7CR/tDniU4gx/XDnbKc8eyuxGTYKN35FyrDDO8QsfREREWlfAn4vJdtX487djt9bjid/N/6yQrCoWMumO/aYhFY5tyM+FV9JPr6yIjz5OzH9xmLZ7K1yLuliVs+CZXdFPufOg9ePrv9YY4IlUw64rFVCa0sbnn6a4k2bOPjRR2tOsFfoPnEiB9x1F99ccw0bnnmGUddf34ZRCkDHKk4kIiIinZ4J+CjKrKjDnrMFy+Yktlv/TlXj05nQDVdaH/xlRZTnbMVbkkdh5opg/XYRERGRBvIUZJG3bhHlOdvwFO2lbPfP+N3FOJMyiO89iriMQa2WYAewxyaB3Y6vJA/j9+Ep3NNq55IuyJiqr8qa43V9AbiSoe9EOPFfMPr86MbfAnYuWIDN6aT3lCn1tu09ZQo2l4tdCxa0QWSyL02XEhERkXbDGEPxtlX4y4tw527H+L3E9RjaKWd4u5IyIODHU5CF27YDy7Io2vwtSYMPxupgizSJiIhI2wr4PJTsWBOave7O2UbAU4Y9NpGYbv3arAydZVk441PxFucQ8Ptw5+0gJrV3m5xbOrkjbg9+VXrIBgm94PKd0YspCsp27sQeG4tlr/8OEctuxx4TQ9nOrvUetRed7y9WERER6ZCM30fh5m/wFu7BU7gHf1khrtRerTr7KtpcKT0xAT/eomzcFbdWF2/9nsQBB3aqmfsiIiLScjwFWRRt+56Apxxv0V48BVlgWcR064szMb3N43HEp+ItysFfVoCnwEXA78Vmd9Z/oEhj7HcRxKRGO4o254iPx5OfT3FmJomDB9fZtjgzE29REa60tDaKTsJpmpSIiIhEnd9TSv7Pn+MpyKI8byee/N0Vddi7Rzu0VheT1gdHQhrewr14Cvfgzt1O8bYfMCYQ7dBERESkHQn4PBRtXknhpuX4Sgsoy9oQ/J0pNpH4XiOikmAHsMckYDmc+ErzwQTw5O+OShzSyU2ZDcc8Gu0o2lzaQQeBMay67Tb8bnet7QIeD6tuuw0sK3iMtDnNZBcREZGo8pbkU5i5nIC7lPKcrfjLinAkdiMmrW+Xmc0d061f1R+lFa/ZX15M8uCDsTljoxydiIiIRJOvNJ/y3O2483YQ8Lr3mb3eD2dit2iHiCM+DW/hHgI+D+68HcSm9492SNLZ+D2Qux5sLkgfVXfbnPUQ8EC30dDB76oYfvnl7Fm0iOxly/js5JMZeumlZBx2GLE9ewJQnpVF9rJlbHz+eYo3bMCy2Rh+xRVRjrprUpJdREREosadv4uiLSsJeMooy96M8ZTjSu2NK7nzz2APZ1kWMen9MSaAJ28XAU8ZMcZP/o9LSBp8CM4E3fIpIiLSlfg9ZbjztuPO3YG/vAhjDL7yQrwFe4K11+OSiEnrh83RPhKIzoRUvIV78JUVYHPEEPC6sTljoh2WdCY/vgYLZsD+v4fjnqm77Vf3wPpX4MSXYfR5bRJea0kbN44D7rmHH269leJNm/j+5ptrbmgMlt3O/nfeSdqBB7ZpjBKkcjEiIiISFaVZP1OU+TX+8iJKszZgvG5iMwZ2uQR7JcuyEZsxCGdyd3wl+ZRmbcJXVkjBz59Tnr0l2uGJiIhIKzN+H+W52ynY8CV5az6mdOf6ilJ6OyjZuRb33i0E/B5i0vsR131wu0mwA9icsdicsXhL8gGDO29HtEOSzuanecHvYy6sv+3+vwNj4Oc3WzemNjLgrLOY9Oab9DjqqOCdvsZEfFmWRY+jjmLSm28y8Nxzox1ul6WZ7CIiItKmjAlQvPV73Lnb8ZYW4M7dimXZie05FLsrPtrhRZVlWcSk9sbuiqM8ZxulWT8Tmz6Q4m0/4CsrIKHfWCxLcyREREQ6CxPw4Snciyd/N+6CXRDwE/B58JXm4y3Jw3jdYIE9NhlnQhr2uKR2+7uAIyEVT/5u/N5y3Hk7iOsxJNohSWeSszr4vfsv6m/b8+Dg9+xVrRdPG0sdO5YJL76It6iIgtWrcefkABCTnk7K2LE4k5KiHKEoyS4iItLFmYAfX1kR9pg4bI7Wva034HNTmPkNvuIcPIV78OTvxuaKIzZjULuajRVtjvhU4hwxlGdvoXzPJlxpvQHwlRWpTruIiEgHZ/y+4GLn+bvwFGYFE+sBP/7SAnyl+fjLiwGwueJwpvXBGZ+KZW//6RtHfDDJ7ivNx+6Mxe8uwR6TEO2wpLMo3gkxKeBKrL+tKwliU6F4V6uH1dacSUlkHH54tMOQGrT//0uLiIhIqzEmQMGGZfhKcgELR3wKruQeOJN74IhPbbGFR40JUL53M6VZPxPwunHnbsdXkoc9LpnY9AFYtvY5Iyua7K444nsOozxna0Sd9rwfPyN58HjVaRcREelAAn4vnoIsPPm78BTuARMg4PfhLyvEV1aAv7wIDFh2J87k7jgT0jrch+o2hwtbTDy+knxiUnrhzttJfK/h0Q5LOgu7C3xlDWtrTLCtTZN4pO0oyS4iItKFlWVtxFeSizt/F5Zlx+8pwVuSh7X7JyyHC1dSd5xJ3XEl92jS4lXGBGtylu76kYCnFL+7BHfeTgKeMpzJ3XGl9GqxRH5nZNkdxHYfjKdgN97Cvfi9buIyBlLw8+fE9xpRcQeAK9phioiISA38nlI8BXvwFGbhLdoLxhDw+4JJ9dIC/O7iisS6A0dCNxzxqdhjEjr070bO+DTceTvwu0tx525Xkl1aTvJgyP4Bdn4JfeqZyb3zC/C5IWNY28TWBnxlZWx97TX2LllC2Y4d+N1ujl20KLTfW1RE1qJFWEDfU06JXqBdmJLsIiIiXZSvvIjS3T/hLS3AW7g3+GQBYHfgiE3EHpcc/AOpYuEqe1wKrqQMnEkZOBO61XvbsqdwDyU71+EvK8TvKcdTsAt/WRHY7cSk98OZ0K2VX2HnUGOd9m79Kd31I2VZG4nNGEBs9yHYXXHRDlVERKRLM8bgK8nDU5iFpyArODsdgjXWywrwlRUSKC8BwHI4cSZm4IhPweaK79CJ9XCO+BTc+TvwluZhj4nHV1qAIz4l2mFJZzDwONj7PSz5E5z1Cdhq+Vsk4IMlN4NlwcDj2zbGVlKwdi0rLruMst27g7P0Ifj6wjgSE/n5qaco3riR+AEDSDvwwLYPtItTkl1ERKQLMsZQvPUHAn4v7rwdWM4Y4jIG4XcX4ysrwldWiK8kHwjWA3XEJWF3l+IrzcfasxEsC0d8Ks7EDJxJ6TgT0rAqftH1luRTunMt3uIcAj4PnoIsfCV5YFk4U3rgSuqOZbNH8dV3TBF12vduxnLF4UruTsDvpWxvJjFp/YjrORRHrBY9EhERaSsBvxdv4d5gKZiiPRifB2MMAU9p8PepssLg4qWA5XDhTO6OIy4Fe0znXOzdsjuwxybhKy3ApPbBnbdDSXZpGQddDd89ATuWwhu/gqMfgZ7jIttkfQufXhds44iFg66JTqwtyJOXx/JLLqF8zx5Sxo6l78kn89Pjj+MrKYloZ1kWA846i7X33cfujz5Skj0KlGQXERHpgsqzt+ArycWTvwv8PmIzBmJzxmBzxuBMTA/+ceguwVdehL+8CE/BHijYAzYb9pgE7LGJ+N0leItzsbKsiqR7GpbdgbdwDwG/D2/RHrxFOYDBkdgNV0pPbHbVRWwOuyuO+F4j8Jbk4i3aizt7K56KP9hNwI87dxuulF7E9Rymmu0iIiKtzFOUTVHm1xi/N7hwaXlRsMZ6eRH4/QDYYuJxpvTEHpfcZe46c8Sn4i8rwu8uxp23g/g+ozvNTH2JoqR+cNyzsGAG7FgCcw6BhF6QNDA4q7sgE0qzgjO9LQuOew6SB0Q76mbbNGsW5Xv2kHHEERw2ezaWzcbGf/6zWpIdoOcxx7D2vvvIXrYsCpGKkuwiIiJdjN9TRunOdcFZ68W5OBK7YY9JiGhjWRb22ETssYlAbwJ+b/APx/IS/O7iYNkXqEi6J2KPTcDvLsXmdOEtygku6BUIBBdSTenVpHruUjPLZguW7UlMx1eaH5w9l7sDT0EWrqQMAgE/noLdOBLTie85DFdyj2iHLCIi0um483dRtPkb/J4yPHk78LtLwAA2G47YJOxxSThik+str9cZOeKScVsWvpJ8HLFJ+EpycSamRzss6QzGXAix6bDwKijYDMW7gl/hUofAL5+AwVOiEmJLy1q4ECyLMf/3f1g2W51tE4cMweZ0UrJlSxtFJ+G63v/tRUREuriS7asI+D2U527HsjuJSe1d7zE2uxNbQrdQHfWAz4O/vDiYcC8vxl9WGNHeHpuIK7UXdlfnvBW6PbAsC2dCGs6ENHxlhXgK9+DJ342ncA/OxHQCfi++4hzscSnBZHtqb80iExERaQHl2Vso3rYKv7uEsuxMMAZnYkZwtnoHX7i0JVg2O474FHxlBRjTF3fuDiXZpeUMmQqDfoZti4ILnJbsDj6f0Bv6HAEDjgGr7mR0R1KydSs2p5PkMWMa1N6RmIi3qKiVo5KaKMkuIiLShbjzdgZrhhbswXg9xHYf1KT66DaHC1tiN5yJFUl3rxu/u5iA34s9JkF1wduYIy4ZR1wyfncJnsK9eAv34i3KxpGQhivZTVFZAbaYBOJ7DCUmvT9WJ/rDQ0REpC2VZv1M6c71+MqLKM/egmXZies5GJszNtqhtSuO+FR8Jfn4yopw5+8kof9Y/f4hLcdmh4G/Cn51doEAlsPRoA/vTCCAr7gYR1zXKE3V3ijJLiIi0g75yovwFu7FmZSBIy65RfoM+NwUb1+N31OGt2gPjviUFuu7sp67RJc9JoG47gkEvOV4CvfiK8kNlgRKSMWZ1J1idwmlu38irsdQYtMHdMlb2EVERJrCGEPpzrWU7dmEtzQfd85WLIeLuO5DsDlc0Q6v3bHHJoHNjq80H2d8Cp783cSk9Yl2WCIdTkz37pRu3447O5uYjIw62+atXEnA6yVx2LA2ik7C6S8rERGRdsQE/JRm/UxZ1obgoj1AbMZA4nuPavYfcCU71hLwllOeux0sOy79odNp2ZyxxKb3J5DSE2/RXrzFufhK8rHHJeFK7kHAWx5MtncfTEy3ftVq8ouIiEgVYwIUb/0ed+52PMU5eHJ3YHPFEdd9sD6wroVlWcGSMSW5BHweirZ+h2Wz40rpGe3QpDPY+wNs/gAKt4C3FKa8WLXP74WyvYAFifWXxWzv0g89lNLt29n21lsMu/TSWtuZQIAfH3kELIuMww9vwwilkv41EBERaSe8xTkUb/0Bv7sYb2k+noIsnEkZGGNw5+8ivveo4OzjJtT59BTuwZ27HW9xNsZTRkx6P2x2Zyu8CmlPbA4XMWl9cSb3wFuUjbc4h7KsjdhiE3Al9cD4vZTu/gmbKw5nYgbOpAxcSRm65V1ERKSCCfgozPwGb+EePAV78BTsxh6bQGxG00rudSWupO74SvMpzdpAXPfBFG5aQeLAA4nt1i/aoUlH5S6AD34LG94OPjYGLCsyyR7wwr9+AeV58NufgguhdmCDp09n21tv8fNTT5F6wAFkHHZYtTZlO3ey6o47yF62DMvhYNAFF0QhUlGSXUREJMoCfi8lO9biztlKwOfBnbcDf1kR2Gx4cnfgLc4lNq0PJdt+oDx7C4n998eZkNbg/k3AR/G2VQR87oo/DP+/vfuOc6LM/wD+mUnPdnZZlrJ0FhbBSlFRAREsyCF4h2ej6IH17my/s92Jenei4qlnFwvoeYINC6J4KF1EEEQp0kH6wvaSTZnM8/tjkkmym2QLu5ts+Lxfr4VJ5pknTybfJJPvPPM8yfoEpnRykH0T3JpTs+GpLIKnohDO43shmSwwWJNhsCZDcVbCVXwAgHaJtyklC5I1PbYNJyIiiiHV60H57rXwVBbBVXoESkUhDPZUX6cHji9eF9lkgS27O5zH96H62G7Y2nZF5a8/QlVcsGf3iHXzqLVRFWD+ZcCRNYDRDuQOB/Z/DXhdoeVMdqDfDcC6mVoyfsBdMWluU0nr2xd5t92GHS+8gDUTJyKtf38olZUAgE3Tp6N8+3aUbNwI4fUCAPLvuQdJnTvHssknLSbZiYiIYshVchiVhzZDdTvhqSyEu6wAEALmtHYwpWq9f9ylR1FdsBvGpHSY0zugbMcqWNrkIqlDfr3GQa86vB2q2wFX8SFAABb2HjppSbIB5tRsmJKzoFSVwOMogVJZBKWiCAAgm20wWFNgsFZCqS6DV2hXTZTvWYekrFyY03N4BQQRESU8r7MSrrKjcBbt15aLD0CpKoUxuQ0sGR0bdVXhycpgtsOW3QPVx/eg+theWLI6w3FoK4THhaSOfWPdPGpNNr0BHP4OSO8BXLUcSO4AvNIecByrXbbXlVqSfe+XrT7JDgC977gDprQ0bHv6aZT+9JN+/75339WHGDVYrcj/v/9Dt0mTYtXMkx6T7ERElPBUxQWluhyywQzZYo+LJKHX7UDVwc1wlxXA666Gq/ggVHc1DNYkWDI66clzU1IbGG1pcJcdhaeyCEp1Ocxp7SCEgLvsCOw5vWFKzgQgIIQAhAoA+rLqccF5fC88lcXwOithzmjPybkIkizDlJIJU0omhOqF11UFr7NSG6qo/Bg85ce0S2/NyQC6wFV6BMJRBByQYU5rB0tGR5hTs3mZPBERJQQhhNaxoewo3KVH4XVpvUS97mq4y47CW10BU2o2LOk5MW5p6ySbLLC16wnn8b1wHf8V8HX4UBU3kjufGuPWUaux7V3t+HTYM1qCPZrsMwBJBoq3tkzbWkD3KVPQ6YorcPDTT1G8fj2cBQWAqsKSlYU2Z52FTuPG1TkxKjUvJtmJiCjhCFWBp7IYnopCuCsK4a0uC1kvGc0wmO2QzXYYLEkwWLT/ZbMNksEESTY2Ww8lr9sBZ9F+OI/thep1w112DJ6KY4Akw9Kmoy9hHkqSDbBkdIQxqQ1cJYfhLjkCT2UxLBkdUXVoS52PqXo9cJUehmzRxt0mCibJBhhtqTDaUgEAwqvA66qE4qyEu9oBAKg+ugOK1QajPR2q4oa79AgkgxHm9A7amO/JmezVR0RUB6+7Gkp1GaCqMKVkQjbWfTUaNR//8aK79Chc5UchPC4IIaC6q6BUl0NxlEMobkACzBntYU5pG+smt2qyweTr0b4PruKDEKoCABBeN6wd+8e4ddQqFG4CIAFdR9Vd1mACLGlAdVGzN6slmTMy0H3yZHSfPDnWTaEwmGQnIqJWTwgBb3UZ3BXHfZM7FgNC9f1Qcvh66FZBMhghGc1aj3aTGZLBrCXVwyUHJRmSbPD9GQH/ssEIoz0N5pRsGJPS6zUepxAq3GUFcBbt13oIA1CqK+AqPQThcWv1ZXSos4e9wWyDvV0PeKpK4S49DOexPTBYkwF/b2IhAIiQZSEEhFcBhApLRicmQqlOWoyna3+qAA4KmFLaQlQXw118CG7pEAzWFBiTMqAqLriK9kM2WWFp0wnWzM4wWJJi/RSI6CQiVAWq4oZQ3FB9f0JxQ/UG7oNQtRPoBqP2vW8wQpJ9/xuMkA0mSAYTZLOtya5287odUBxl2l+19r9QgscNlmBKyYQ5rT0s6TnNMuG0UL0QQo2LK/hiRagKvC6HdsWWqwqqywGvqxJetwOqu1orI7xQnJXwOsqhOMsBrxeQoHXESMmC0ZbKqwCbiCQbYMvuBmfhfrhLj0KoinZ1pstV98ZEnirAnAIY6vl+9LoBufWnPR2HD0OSZdhy6ncljbOgAKrXC3uHOnr7U5Nr/dFGRERxRwgVXmclVI8z6Ievy7fsgurxLXvdejJbT2r7b/uT2pIMIVRACO1/3w9GoapaIl31QnjdWiIZgOpxQXFW6ENfQNWGT5FMZsDj1MvpJElLvBt9CXffY0KWtTZJgf8lWQZkA+QyC6qP7oRkMMKU0hamlLYwp7SFwWIPqdrrdsB9/DCcxQcgPC6oXgVKVTE8lcUQihuS0QRr2656D+L6MiWlw2hLgbv8uNYjTnEBkLTn4ntO2m0AkCAbzTClt4fBbGvMy0kEc1o2jBnt4HVVQXGUwuMohbe6Ai5Z1nrB2zPgdVejumAXjMmZsGZ2hiU9RztBRTEhVAVedzVUdzW8Lod2wtGXVFK9HhgsSTDa0vSrGGRLEk/CUauhVJfDVXIYrtLDUF1VEcsJ1ev7U/XvcEmSo8a6ZDBCNtm0OSrM9pD/ZYNJS+p7FQivB8L/v6r4lhUtuV5dpvWAhtYRQFVc2nvPo70nhRAw2tOgKi54KgpRdXATjElttAmq03NgMNsjtk9VXFA9brid2vN2Ht8HN7xQFVfgeMvjCjk2AiRIRhNkgxmS0XdCwbcsG83aZ7Xv2EGStOMJ+DoRaMdBkrbfjGbthISvjkgdDYQQEIor8BnkqYbqckD1VEOoXkgG7bhLNpoDx2C+ThCS0ayd/NCPY7T2h7QPvpMrHv/z9R1vepz68w/sc2fo/lO92rGo4obwuOF1O+B1VQCqACQJBluK9tloTYFk4HdYc5AkGdasLnCVHIKnvBDCq8CQrnUScRUdgCGzQ7OcdKIEYMsCqo5qyXZTHZ06SnZq5dJb/wS731xwAazZ2Ri5enW9yq+aMAHOI0dw+Y4dzdwyqonfGkREcU71aD9SZIMRstkad0krf0JdcZT6/rQeW/4JWALlhO+HrgL4f6CqXl8eODihLdX6PzDeuO8PKoQqIKAl36Gqvh5KlRCKB4CvN64t1TeJY7Lei0uoKoQ3uLebK7DsrKzV7rAMBhitKTBYkrWxOkuPoAqAbEmCOaUthO+gr3TbchgkbfIsT1WxNmyN0CaXNLfpCKM9Q/vR3wiSbIAlPYdjg1KL0oZXSoI5vQO8zorAe76qFJLRBFNSBlTFDaWyCFUHjbBkdIQ1szOM9vRYNz0haFemuPXEUuAEpivQi9fjhNftgPC4wmzr0T7rVC8MJivcxoJAslHWPteMtjQYbKkw2nyfnRzOguKEUl0Bd+lhuEoOa9/3vmE9vM5K/Zgi+PgCqqJf3FWLXOMEv361mjkkGS0bTZCk+s09oR/neBWonurASS2PUz/hDwla8lAIuEuOwF1yBJLZBpMtDV6PE0pVMaoObYHRng5TchuoiieQNPe91/3HKYqq/V915BcYZcn3vP3HWFo7oCoQQtWS1pLWgUE2GADJ4OvNb6jXFXmRaFcCaJ0UtJ7eQjup53Hqc8QE9o8XquIJXFXg2+dNTVW9gFeBUD1QFU9IRw9VcWm91Gs8B6M9w3fMmHxC+4PqT5IkWNt0gks2wlN+DIpXBdAZlYc2w3lkCwy2NJjTsmFOzYHRnsaTwKRpNxDYswDYsxDoPSF62fXPaP93HNL87WoBoj6/UU+gPDWN+MrUEBGdpLSePr5hTdyhl7T6xyv003tYmazan9kK2aj9H0hKA8G9ffy3IUkhPybhGwol0oGr9oMxuLeW4us55PJd+lwKpbocUL2+8l6tx5Kv55LwegI/dmv2IG9qkgSDNVm7rNeaErEHjCTLkGRrxPX+CUO1XvNqaC96oUJ4PdpJheoKKFWlWp0miy8hlQKvswJeoe1PT/lxuBxar3XIMkzJmTAmtWGPcmr1JEnSe0CLDBVKdak2rm3ZMbjLjsFgTYYxuQ1UxQ1n4a8w2NJgbdMJksGkD+UEoULAN1mv6rsPIjCkU60ejq1ruAOhKoFetF6v73PUA9X3v/aZ6g7tEat6a33+COENfBapKiJlDVVfUlFLpHv0pLt2UtGj96oNIUu+7w8bZJMN3upyeCqLQhJfksEI2eyfuyI5dB4L9jRMKIHJs/3DjkF7j2or9fu1k9XB8axAKG7fsC2++Fa9gR7R/uMPfVn23W0IGapFMpogyb7Etm9YF9XlgMufWHdW6MPAeXwn+PRjC9mfLDf6emYHhnvThnyTtCvg9KvhvIGr4VQvhOKBqlbXvtoNAAxa8l32Xe2mX1UXUpdX6wkdTAJkkzaXhcHXM14/VgOgepy+jgnlcJcdBcqOascT9jR43dXwVJX4Pj+CesrrPeY9UBQvgC6oOrwdBuGOfEKhLrIE+E4kSDWuhPPd6etVLgWS4wYDJMk3lJ7BEJIw12LAHYgF39A9NZPbet2yATAYIetD8xl9HRACPdmloH/99wuhBo5NfTEnVE/t18H3GspGi3Z8aLT4vlssvqsYmRKJJUt6DiSDEdUlRwEAjoLdsNqTYXA5oDhKtStHTRaYU9vBnJoNc0pbvmYns1MmA7s/A759COh0AZAUobPRupnAT69o30H9bmjRJsYD1eWCbOT7JBa414koLNXrgVA8+o8PCG/Qsv9HifbjX4IUOrxHzZ5Bvh8TWjJF+BKWvh9rvl4ubo/W+9hdVgDVIPnqDvwY8icdJN+Ps0AvZ98lv8H3+R8t+OytvuxrQ80e07IhaHtffSLox5Ma9ENMT3h4fc9H9f32VAOPqz8/odeh1RNuPyohvacDvQ21y4GF1wVV8fgu0/Vf4uv7QWowQW6KA03/62Uwas/d1xOrZoI/mBAqVLczcIKgZs9JgyGQKNN/lPnGQ5WN+n3+uoIT2SFJbSGCfqjLCL6M2f9jUJJlSEZLk/Ry0eo3QIIBiNDBypSc6fuhX6315nVWaGPBlxdqP+QNNgDd4S4/BpPFqg21YUtvdK91ongmyTJMSW1gSmoD1eOEp6oESlUJXIX74TIYYLRnwOR21pqAuBGPFEi2S4Hkj55+CX7/hwx7EDj5GDgRqa0T/p6W/s8cIOgzKGiOg2j0q3RCP+frs62/5yv83y1B3yf646tBJyP0E5/an9Zj0+P7HgnzALJBG5LBbIVsSw2ctJBk7SopjzZ8hVJVAojiwO4zmn0ncmsmo2rMYeEfRivkO7jGd6qvTM1jA0jBxwzaFUv6MULY44X6ZBCjJ4cVRXuNK379EUY5+KSq8L0GgeMSvd1SYIgR7TkZtISuJAWGLQtJ2gbuq1+b6/O0gvYJ/N+PCD3u0Pa87/vRP3xY8BAbvmMx/zZqze/besZ7nU0V+r7QGhV4j2r7D77bNTsCROd1OaBUl/rGNfcAErS5IexpMFpTmyzpJoSqJdy97pD/tStInNpxr2Twxb3Bl3iXQ2Jbkg2+E1eBhHo4sskKc5oV5rR2vs4L5doJy7Jj8JQdi9xICZBkI4SsnXg0WpNgNKQGjq9q/A9J8n0uKSGfVYGrC7VjegS9Z7R4A4Lfe0KoWk9w/di1jp0pwXesatZeI6PJ9/ltCGqPovf+F75hXLR5ZOoZi7KsP0+D2QbJkFJr7H3ZaGmWHvPUdMwpWYA5GSjQPh3cZceAsmOAwahdOWpL1U64Fe0HIGnDndl9V13Z03xj5vOqq5NCryuA7qO1nuzvDADyrwMU35BQ2z/QJkbd8QFQ4hsmpffvtWT8ScRZUAB3cTHMmZmxbspJiUn2BOA4ugNWZyoCY9UB/jP8EqTQHxD6QX/Ng2r/ZuF6wAaPh1cfgXaEH0uvHoIm7AsMDxH8A7ieB17BPS78CUTZoB0cG4yhB1xBSdiwyVn9B3rwfgn9QS8F/YgI+b8BPyRCx54O+pEW8hr6f8irei+a4B41UL2BJGTYH4baeNNazyPfDwhfUt3/Y6Ipfmg1hP+S14pfN8Aot67LAYMTANrvkRoJ/aD3WnDiGEEnD7RehoExy0N2v+9Hir8HVi0SfJfqGkPjEwjznpNqJUS0uAgkQ7QTDP5EQZiTAv6eQ8HtlGUYzDbItlTtf7M94SeIkiTJ15vTDnNaOwjVq/Vwd1bA49Z6jNra9YTZwp6edPKQTVZtTOG0HK1XdFUxlMpCKBWF2rwIWvdDhDshqX92GgxB391G/fs6eLgD/zFOyAFKUM/LwEefHLFHpkYEErL+x9c/w0OHOogkkBQP/t4O+j94/ojgRHy4nq/14UuwSbIRssEIyWQJ3Vf+E7C+3ryRGKzJ8F8boI2f7PYNceHUxo32OOF1ltf4PgrMYSGbLNqVCZADiVP9pAYg+fa95DtBqh2TSPr3jBRyYjzSvvUnfgXqc1iiF6l1nOhLsnsFACOcRfthQFDs1YhHAQEJvvZCDiStg3piQ5L047PQY2r/iRLfkGZNQQQtBL9XIHyL/v3k3/dAyHFqcKI73MmMkHr9xzI1H9yfaPX9I2omayP05q5L0JV1ckiHDRmSZIQQXl9i3a0l1i3JMKZmw2hLa5berJIkQzJZIJtaNmEnmywwm9rCnNpWG3KruhzC6/F99pmC3t9G/QpExTcxtaVNp7qPnX3bNqXgRH3gc034TsjVPC5tRP0h72MRJiYldl5IINp7TsDWrgdkocBbXa5dNeoo1U7c+97/BksyZLMNHkdJyBVustmmDfljS4XBklzjapYauYATHCaJYuzyecAX1wG7PgV+mBm4f+Hvtf/9nx29xgEXv9Hy7WsCRWvXouj770Pu8zoc2PH88xG3EULAU16OY8uWQQiBjNNOa+5mUhhMsieA6uP74HBp4/9GG/JBTwKG9AgKTSgHkuI16mnQAVLtbUOrrKuuGgfx/vuCfvwK1O8AXu+RGtLrNXEI/485NdyPPAT1Igvu6Q34ewPrPay9CiC8UL2BH/6hl60HJYRr9qgTKvQeUv7Hk2W9x7l+uWfQayaCXkv/a6sNr9EF1QW7IcOrrw/+0apNSARA/8Huj62gIVKCerKH/ZGtH5cHykv6vpIC2/sSzf7nH3qyw3eiqjHJkUgkQPL1FJStyb5liz5MQmCSp5qXaAeGHVC9nkDvtlrP2f+u8X0OBPVgqxdZDvTckmXIBhNka4o+GVhwG09WkmzQetPY02D0/fBt6R/pRPFCkiT9/aAqHihVxfC6q30rEXTVUPAJbP/nXCBxoypOCJc3/AnG1kCC7/gjMKFz8FAWgd7cgZ7e+ol5/Xs1kNiVJMnXWaBpP28lSdITi8ag+Rb15HvwxNW+215nRb0S33XyJ7KDk+lN1QO8Bi9kAH1RfXQnDE2VAD9ZSKE39CE9asa0vzNL8FWMYU5iCf9VGkL19ab2HZP65gyA8OrxZbAmwZjSFgZ7aqsbNqoxZKNZ69kb5wJDxDRPp4raQx42y8NQHJINJsjJmb4rR1V4nVXwOsuhVJfD66wMFDRoVzBow2faoDhKtRM89fiOlAwmrSe8r6e89n/KSfEZ0+qZkoCxH2u92Te9Dhz6Fqgu1NZZ0rQx2PtPA3r+JrbtPAGFa9Zgx3PPheThFIcD2597ru6NhYBssaDnLbc0YwspEibZW8j+/fvx3HPPYeHChdi/fz8sFgt69uyJCRMm4NZbb4XdHnkG+bo4jmyDuSzMwU1QhxXykSX9x67+v09wArJOwZenh9wXWA7cCr0/qqDLMQO9ooN774uQhPcJvbahee/w5Br7yvejX9Z7yQcS6DWT0MGJaX2YFyBoH0hBPQoBIbTXQpJlrSeTnmQIPkEiBfZP0D6pffIINfZ1zZ6O/uceuq12qaxXvx08NI0sGwPtqDUpZ9CVCmGfnxRUPkwdsoxo46KHvGyyDEm2ACZLpJFMGiTS+OOQpKAkUOKdoCKiliMbTTCntTuhOvThPPw9JgMrgk/h1u7pqH93+u8LTvYh0LFA7w0QNJxFAz739M/y4O9MOfQ4ozUKTr7X5J+zI6QHdI2rE4KHhgt7ZaVaY7iTGkOLhHy/1r/R/oWg/7RlrwBQClgy2sPgS+4HhiND4HsZge/HwPMIWvY/L0nSe+yHfL8HH8M0idDnEm4/aYdjNTox+J9HSCeDGnXoVx4EjleCj5ti+f3vf69zqA+ik5ckyTDaUmC0pcCS0RGq1wPVf8WVuxpeTzW81RWBDWTZN7RZ8HBmQd/Pvu9myWCC7CiDx2xB8OTGstnum2cp1Tf3iEVL4vvG8udvojjSfbT2B2gTXateIEGGDbJ37IjMQYP020Vr10I2GpFxxhkRt5FkGcbkZKTk5SF3/Hgkde3aAi2lmphkbwELFy7Etddei7KyMv0+h8OBdevWYd26dXj99dfxxRdfoHv37o2q35SSBVOyNZA09AsZBiYoSaaP0+j/UeC7v9YPU/9/Dc3kiuhtqa8al7iG9LKv73dbUI/dWpdt+xPV2oOET5bXeqDQXjj++0StfRc8lmGNdXUKJJa1ZLYRoT/WghO2wUN81LivxnA7Ampg2Z9I9Sd3w1xO1xw95aLxX/Jqbdut1Q0X05rVZ/xxIqJYkySpWYY7oMaTJMk3XEzroagCKBUwJrXhsUYrwOQ6EdUkG0yQbSbAlqLfJ1SvNsSZWxvqTJsI1/+7X/F1APOd2A2Tl5BMZsgmGwwmCySTDUpVSYSEevCQaVriXTZbfXOYWH1DZto4Pnxzeq2blvu48isgo2fgftmo/SWI3CuvRO6VV+q3F/TsCVN6Os59990YtorqI3GiME799NNPmDBhAhwOB5KTk3H//fdj+PDhqK6uxrx58/Daa69h+/btGD16NNatW4fk5OQGP4bXngM1OakZWk/11RI/08IMfNK8WvgqakUVcKkC1V4JRsEfvtS6MZ4pUTCWKZEwnimRMJ4pkZxYPBsAoxkwpur3ROo2579KSVXcUD0uqIoTqscJ1emCqHQAKNEKyhIkgxmyQZsXAbIRshw0qXCNORJCSLKvB7w/6W7WJ+KVZJM+PrwcNEGvJBmCOhQGrshiz/lQtqojgGxGdXJPwNMMD6AAQNAoE42cqs7jAVyqAQ4P0BTdEfo+9gRkqxWO5njOPl4P4DLa9NsOD2CI8wsym3N/NJYkRGO6GFN9DR8+HMuWLYPRaMSKFStwzjnnhKyfOXMm/vKXvwAAHnnkETz00EP1rru8vBxpaWno9HgZZGtq3RsQERERERERERG1MiuV7miD4zjFWFF3YUp4qrMcB+9LQ1lZGVJT4yMnGufnJVq3devWYdmyZQCAG2+8sVaCHQDuvvtu5OfnAwCeffZZeDxxeCqGiIiIiIiIiIgoRlZJF8EGB04RP8a6KS0q2VGEoRvn4Kztn9VZdsC2TzF04xwkVRe3QMuoJg4X04w++eQTfXnKlClhy8iyjIkTJ+L+++9HSUkJli1bhpEjRzbocRYO/AoZqRwuhlo3RRVYd0hgYEeJ46RSq8d4pkTBWKZEwnimRMJ4pkSSKPEshNDGhPd6oCqKtix8k7YLFUL1+saK9waNG+9t/rFg5ZoTv2pzsUEy1JifTZuzTZu7zeArX/f8GJI+9I05MG690eRbtkDW77dANpm1x2gEqew+yO/Ow4Ks2+G6YjFgste9UUMoLuClNoHbtxY3ajJVj8eDr776ChdffDFMphMfMGb/7E+wc9Xj6H77n9Dt1uhld878Bftnv4EHzwNyJ06u92N4XS58ddZZ+u2L16+HwRLf8wuUlwPt74t1K0Ixyd6MVq5cCQBISkrCWUHBWtPQoUP15VWrVjU4yW4zeGEzeBvXSKI4oUgCFlnAZmjdB1ZEAOOZEgdjmRIJ45kSCeOZEklCxbPRAKBhEzeHjuIsgpLuwl/Ad0toy0Jot2otq4Fkvm+yV+G7rd3vS/B7qyEUL4SqhJ0M1v/I+hpfwl1L1AcS8/6x6mEITtRrY85DNgKSBBVhpnuTDb5EvEVLvhtMgf+Dl4Pvkw2AyQBp5KswfH0T7O/2A874I9DhXMCeDUQ7GZDauX4vhAQAjsBtExqVNfUAsMhe2E1AE+TYUbzsGwBA58sugb2O+rpdOQ7733wdxUu/Ru8bJ9f7MbwqYFGq9dt2ExDv89srcdg+Jtmb0S+//AIA6NmzJ4zGyLu6T58+tbYhIiIiIiIiIqLEFjrBqRQ6W2vomianJ+FVr5ac9yoQvp72+n3Ct+xP4CtuqKo/SR+lclkOTBAbPGmsbPAlzo2BpL3BUGev+cwlVwQezl0FLLundqFaO0mCZ+pRyAaTb0Jaa/13Tpyo+vVXSJKEpK5d6yyb3K0bIEmo2r+/+RtGtTDJ3kycTicKCwsBAJ06dYpaNiMjA0lJSaiqqsKBAwcilnO5XHC5XPrt8vJyANqlVYrK+WupdfPHMGOZEgHjmRIFY5kSCeOZEgnjmRIJ4zmWJEAyAgaj1gHfpOWp65vQ14a98SXnVcWXjPff1v5XVQXC7YZQHVpv+2htqZF013rMGyBJMjLrM65OrSICxTtWB27KBhjMSTBY7NqfyQ7ZmgSDOQmypHVe9/N4PIBo+FSW/rkWm2rORXdxMYwpKfAC8NZVpyTBlJICV2Fhgx5fVZSQ24qiQJXjexrPeJzTkkn2ZlJREZjtODk5uc7y/iR7ZWVlxDIzZszAI488Uuv+jUcBezm/jCgxrD8MNP+gdEQtg/FMiYKxTImE8UyJhPFMiYTx3BpJ0FKLTZRe9I8vo9Reta3Lq42qsvpgcEwpAMp8f6Fk4cGYoNuLvloEVWr8mCSLFy9u9LbBJJMJ3vJyfPHpp3WPP+PxQKqoAKxWfPHFF/V/EEVBcEp90aJFQJQROeKBw+Gou1ALi+891oo5nU592Ww211ne4ptQoLq6OmKZ+++/H3fddZd+u7y8HLm5uTg9B8hIbeXjltFJT1EF1h8GzuqA1j8OH530GM+UKBjLlEgYz5RIGM+USBjPVBdtzPm2IWPNB8aZ940171/nH+ZG9fommd2t9bj31u75LBktgUlaa8TewI4SJKMJRlsqjEnpMNrSYbSnQzZFnxDU4/Fg8eLFGDlyZJNMfPr93LkoWb8eZ1gsyLnkkqhljy5ahI1CIL13b5x92WX1fgzV7cb//vIX/fYll1wCuR65zFjyj+4RT5hkbyZWa2CcJ7fbXWd5/zAwNpstYhmLxaIn44MZ5QSYHIQIACAYz5RAGM+UKBjLlEgYz5RIGM+USBjPFI2EzCVjAUlCyeCXoNo7NLgGIYQ2nrzihqq4fMsubdlVDq9aY8iU0kMwWFOhuCuhOorh8Y2dLxktMFqTYbAmw2BNgcGaAqM1udZ47yaTqUmS7O1HjkTJDz9g+8yZaDtoEKzt2oUtV330KLY/+SQgSWg/alTUxxa+MfX1kxHuGh1+PVWQJAWQfIMH+d+WAtrJDSF8Jzb8E+4K34kPUWMcf2/QmP+BSXgBSRsSyD9Wvxw0Vr9/3H7ZGLhtMEKSQoevaYp929SYZG8mKSkp+nK0IWD8qqqqANRvaBkiIiIiIiIiIqKThmyGkI2NSrAD2gSzksni64meErJOCAHhrgJ2B+5TqkqhOHxDQcsSZLMdBrMNsskGxaHVEzxZq2QwwWBNgTDZAQDOwv1QrTZIRhNkgwmSwawvN0SXa67BntmzUX3kCJZffjl63nILsocPg719DoQQcOzfj6NLvsbeN+bAXVoGS3YWsi88CxX7fvSdRHBDeD0Qwgt4fUnvGsMyqZ7QEwxlO76FbGq6lLE/Ga8n5SVJS5pLco2Jf6OQDZAMRsiylnivcLjq3qaFMcneTKxWK7KyslBYWIiDBw9GLVtSUqIn2XNzc1uieURERERERERERK2C15oFg7OwWer2J+CDJXU6BarXC9XtgNddDdXtgKeiMCQ/LRnNkH2Je9lohewog9dgASCh6vAWuMJemaENQyPJhjDrQvl7h+fdPRFbH30R7pISbJ3xGLbOeCxMYcCYkoTe90yCq/yQllj3T0orvCHD7GgJ78BwO1536FA61YW/QjYZwybAtUS50B7Q97//PuG/LzihLtToUy3IEiBpk9tqE9/KgQS8fyJcyQDIku9/7T5XtTNKpbHBJHszys/Px8qVK7Fr1y4oigJjhEkDtm3bFrINERERERERERERaTyZA2A4sADGks1QMvo1++NJkgSD2QaD2QZ/33OhqlAVJ1SPy/fnhKq44HVW6IlkL2QAfeE4sgMGgwxZDiSGJV9vbEky+IZiqYMviW3KNCN/+hQc+nApStb9AuH1hrbVYED6Wb2Qc/kACFMpqg4U1+MJwteTXIaq1OjZ7q4GhO8kgND/8e8YrSc6JH0ZkLTkOKRA73RJrt1j3fd4AsI3nExgbP3gEwH+nvf68DJhkvTVDibZTyrnnXceVq5ciaqqKqxfvx6DBw8OW2758uX68pAhQ1qqeURERERERERERHHP0eW3sBQsR/L2l1F2xt8hLG1avA2SLMNgtsNgtofcr433riXdPW43UA5taBnhG/dccfnGJldP4MGBjr8bhPZjz0T1gUJ4KhwAAFNaMuxdcmCwWUPHOPePbe5L6kuyrCe54e8t7lNzuJikDn2adLiYEyV8Y7prE9lqY7t76jE0d0uLnz2WgK644grMmDEDADB79uywSXZVVfH2228DANLT0zF8+PAWbSMREREREREREVE8M1QdQFX365C08w1kfH8bXDnD4UnLhzCnQUCOuF1L9XqXTFZt8lOrAMoFLJm5tSbyFcLXg1tVtOV61IugHuP+3uMp3X236zueeSvnvwoACIxnb/TWPdxOS2OSvRkNGjQI559/PlauXIk33ngDkyZNwjnnnBNS5l//+hd++eUXAMCf//znuJwdl4iIiIiIiIiIKFbSfnwQ2hgnGuvBL2A9+EX0jSSgaPgnzdquhpAkCTBovcwp8fBVbWb//ve/MWTIEFRXV2PUqFF44IEHMHz4cFRXV2PevHmYNWsWACAvLw933313jFtLREREREREREQUj+ru/X0ixeOZ1+XGsa+/Q+lP2+E6XgzV7cGZL/5NX69UVaNkw1ZIEpB13lkxbOnJi0n2ZnbGGWfgvffew3XXXYfy8nI88MADtcrk5eVh4cKFSElJiUELiYiIiIiIiIiI4lfRhZ/GugkxU7X3ILY98QbcxaWBEwc1Roox2K049NFiVB8ugCU7Eyl5XVu4lRR50CJqMmPGjMHPP/+MO++8E3l5ebDb7UhPT8eAAQPwxBNP4Mcff0TPnj1j3UwiIiIiIiIiIiKKE56KKvwy4zW4i0qR1K0Tukz8DQw2S61ykiQhe8RgQADF6zbFoKXEnuwtpEuXLnj66afx9NNPx7opREREREREREREFOeOfL4cnpJypPXvhfy/3gxJlnH406XwOl21ymac2Re/vv0ZyjfvikFLiUl2IiIiIiIiIiIiajUkxQFDxR7InlIAElRTGrwp3SGM9lg3rUmVrN8MSECX634DSY4+IImtYztIBhnOo4Ut1DoKxiQ7ERERERERERERxT1D5T7Y97wDc9F6QNSY2VSS4M4cAEf3a+FN7hqT9jU159EiSEYj7N061qu8wW6F1+Fs5lZROEyyExERERERERERUVwzH1uNlK1PA6qCwAygQYSAuXAdzMU/ouKUu+Fue06Lt7HJCQFJliFJUt1FVRXeahdki7kFGkY1MclOREREREREREREcUuuPupLsHugWrNR3WU83BmnQ7VmaeudhTCXbITt148hOwuQsuVfKBn8AlRbToxbfmJMGalwHSuCp6wCprSUqGUrd/wKoXhh6diuhVpHwaIP5kNEREREREREREQUQ7b9HwOqB0pab5QMeg7OjpdCtbcHZBMgm6Da28PZ8VKUDH4OSlpvQFVg2/9JrJt9wlLzuwMAji1bF7WcUFXsn/cFIAFp/fNaomlUA5PsREREREREREREFLfMxT8BkFDZ+1bAaItc0GDVykDAXLyxhVrXfNpfdgEA4NBH/0PZ5p1hy7gKS7D9yTdQvmUXJFlGziVDWrKJ5MPhYoiIiIiIiIiIiChuya5CCKOtXhOaepO7QhjtkF2Fzd+wZpbUrRM6XTkKBz/8H7Y++jKSe+TCW61NbLrntQ/h2H8YlTt/hVBVAEDnay6HtV1WLJt80mKSnYiIiIiIiIiIiOKWkIyQVKWehQUk1QMhJUbaM/eqS2FMsmH/vC9QuWu/fn/B4m/1+V9lswmdr71c7/lOLS8xoo2IiIiIiIiIiIgSkmpvD0PFXpiKNsCTeWbUsqbiDdoEqSm5LdS65tf+8mHIGjoQhSt+QMW2vXCXlEGoKkzpqUjt3Q1ZQwfCnB59YlRqXkyyExERERERERERUdxyZQ2GvWIPkre9gPLTH4E3KXwC3VC1H8nbXgQgwZU1uGUb2Qxcx0sAAKa0ZJhSktB+9FC0Hz00xq2icJhkJyIiIiIiIiIiorjlzP0NrIf/B9lVhPR1d8DVdgg8bU6DamkDQILsLISp5CdYjq8GVAWqJRPO3N/EutknbMOtjwKShLNemQ5zm7RYN4eiYJKdiIiIiIiIiIiI4pYw2lF+2sNI/fnvkJ3HYClYAUvBinAloVrbofzUv0IY7S3ezqZmsJohGQxMsLcCTLITERERERERERFRXPMmd0bJoOdgO/QFLMdWwVC5DxCqtlKS4U3uClf2+ajueClgtMW0rU3F0rYNqo8WQnhVSAY51s2hKJhkJyIiIiIiIiIiovhntKG6y5Wo7nIloCqQlAoAgDCmAHLipTkzBvWHY/5ilGzYijYD+8W6ORQFT4EQERERERERERFR6yIbIcwZEOaMhEywA0DHK0bAmpOFPa99gKp9h2PdHIoiMSOQiIiIiIiIiIiIqBUrXvMT2o08FwffX4RN9/0L6af3QUrvbjClpUCSpYjbtR02qAVbSQCT7ERERERERERERBTHTMU/ImXzTLgzz0TlKfdELZuy6TGYSjajov998GSc2kItbB67XpwL+HPpAijZsBUlG7bWsZXEJHsMMMlOREREREREREREcctSsBKS4oC73QV1lnVlnw/z8e9hLljZ6pPslqx0QIrcY53iB5PsREREREREREREFLeM5dsBAJ7UPnWW9WScBgAwlW1r1ja1hDNfnh7rJlA9MclOREREREREREREcUt2FkEYLBDm1DrLCnMqYLBAdhW3QMuaV/nW3QAAe5f2MCbZY9waikaOdQOIiIiIiIiIiIiIIpGgNqi8gARJdTVTa1rOlukvYMvDLwIi1i2hujDJTkRERERERERERHFLNWdA8rogOw7XWVZ2HIbkdUI1pzd/w5qZwW6FMckGYzJ7scc7JtmJiIiIiIiIiIgobnnS+gIA7Pvn11nW/ut8ABI86ac0c6uanzUnC95qJ1SPEuumUB2YZCciIiIiIiIiIqK45ex0KQABy+GvYd/9NqB6ahdSPbDvfguWI4u1bTpe2rKNbAZZQ86E8KooWv1jrJtCdeDEp0RERERERERERBS3lNQ8ODtdDuvBz2H7dT6shxfD3eZ0qNa2ACTIzmMwF2+E5KkAADg7XQYlrU9sG90E2o++AMXf/4y9b3wEY0oSMs7sG+smUQRMshMREREREREREVFcq+p1I4Rsgu3AJ5A85bAUrKxRQgCSjOrO4+Hofl1M2tjUDs5fjJS+3eHYfxjbHn8N9k45SOnTDaa0FECWIm6X+7tLWrCVBDDJTkRERERERERERPFOkuHoORnODqNgPbIExrJfILtLAQCqJQNKWh84c0ZAtbePbTub0MH3vwIkAEK77ThwFI6DR+vcjkn2lsckOxEREREREREREbUKqr0DHD0So6d6XVLzuwNS5B7rFD+YZCciIiIiIiIiIiKKM6c8+sdYN4HqSY51A4iIiIiIiIiIiIiIWiv2ZCciIiIiIiIiIqK4JykOmArXwVi1D5KnEpJQopVGZf6fWqxtdHJjkp2IiIiIiIiIiIjimuXIN0ja8RokrzPoXhGmpH+m0MRKsqseBWU/b0fl7gPwlFUCEmBKTUZyz85I658H2cQ0byxx7xMREREREREREVHcMhVtQPIvzwMQgGyCJ60PVHMbQDLEumktomDxahyY9wU8FVVh15tSkpD7+9FoN/KcFm4Z+THJTkRERERERERERHHLtn8+AAElrTfK+/8Vwpwa6ya1mF//8xkOL1iqd9o3t0mDOTMNAOAuKoO7uAye8irsee19OAsK0eW6MTFs7cmLSXYiIiIiIiIiIiKKW8aK3QAkVOTfcVIl2Mu27MLhz5YCANoMPhWdr74Mto7tQspUHyrAgXlfomjNTzj82RJknNkXqX17xKK5JzU51g0gIiIiIiIiIiIiikQSXgiDFaq9Q6yb0qIKFq0CAGRfOBi975lSK8EOALaO7ZB392RkXzgYEMDRRStbupkEJtmJiIiIiIiIiIgojnltOZCEBxDeWDelRVVs3wtIEjpfM7rOsrlXjwYkoGLb3hZoGdXEJDsRERERERERERHFLVe7YYDqhaloQ6yb0qI8FVUw2q0wpaXUWdacngKj3RZxclRqXkyyExERERERERERUdyqzv0NlNSeSN7+MmTH4Vg3p8UYbBZ4nS6obk+dZb0uN7xOFwxWSwu0jGrixKdEREREREREREQUtywFK+DKGQ77nneRsfZPcGUPgZKaB2GwRd3O1f7CFmph87B37oDyrbtwbMn3yLnkvKhljy/5HsKrwt7l5Bq3Pl4wyU5ERERERERERERxK/mXfwOQfLcELEeXw3J0efSNpNafZG97/lko37IL+97+FJLJiHYjzg5bruDr77Dv7U8BCWg7dEALt5IAJtkjqqysxIYNG7B27VqsXbsW69atw759+wAAXbp00Zfra8uWLXj++efx9ddf49ChQ0hOTkZ+fj6uvfZa3HjjjTAa+VIQERERERERERHVpFrbxroJMdF2+CAcX/EDyrfuxp5X38PBD75CWr+eMLdJByTAXViKsi074S4uAwSQekpPtB02KNbNPikxsxvBmDFjsGzZsiap64033sBtt90Gl8ul3+d0OrFy5UqsXLkSc+bMweeff47MzMwmeTwiIiIiIiIiIqJEUXLu67FuQkxIsoze9/4Bu1+ai+Lvf4a7qBTHV/wQWkho/7UZfCp63Ho1JEmqXRE1OybZIxBC6MsZGRkYMGAAvvvuO1RWVjaonq+++grTpk2Dqqpo164dHnzwQQwePBjFxcV47bXXMH/+fKxZswbjx4/H0qVLIcuci5aIiIiIiIiIiIgAo92K3vdMQeWu/Sj8dgMqdx+AUlahrUtLQXKPXGQNORPJPTvHuKUnNybZI7jmmmswbdo0DBo0CD179gQAdO3atUFJdkVRcPvtt0NVVaSmpuLbb79Fjx499PWXXHIJbrvtNrz00ktYsWIF3nnnHUycOLHJnwsRERERERERERG1Xsk9OzORHseYZI9g2rRpJ1zHxx9/jF27dgEA7r///pAEu9/MmTMxd+5clJSUYObMmUyyExERERERERERBdEmPm0oCZX5f2rytrQk1aOg+tAxyCYDbB3bRS1bfagAqscLW6d2kI2GFmoh+THJ3ow++eQTfXny5Mlhy9jtdkyYMAGvvvoqNm/ejJ07d6JXr14t00AiIiIiIiIiIqI4ZzmyBIAEfQDyWmqOQy6QCEn2otU/YteL76LdiHPQ/aYJUcse/GgxCletR68/XYes885qoRaSH5PszWjlypUAgN69eyMnJydiuaFDh+LVV18FAKxatYpJdiIiIiIiIiIiIh9XznDUTqQHSN4qGMt3QXYVQZhS4M4c2HKNa0ZFa34CAGQNHVBn2ewLB6Nw5XoUffcTk+wxwCR7M6msrMTBgwcBAH369IlaNnj9L7/80qztIiIiIiIiIiIiak0q+95RdyEhYDnyDZJ3vAxhtKMqb2qzt6u5OQ4cBQAkdelYZ9nkHrnaNvuPNGubKDwm2ZvJwYMHIYR2CUunTp2ils3NzdWXDxw4ELGcy+WCy+XSb5eXlwMAFFVAUSNdLkPUOvhjmLFMiYDxTImCsUyJhPFMiYTxTImE8UxNSckZAeGpRMruOXCm9YWr7bn127BG/Gnx2PCYbOp49hSXwWCzQVjMdddpscBgt8FdUt6gx1fDPHc5zt+P8fh5wSR7M6moqNCXk5OTo5ZNSkrSlysrKyOWmzFjBh555JFa9288CtjL4y+4iBpj/WGgMV9kRPGI8UyJgrFMiYTxTImE8UyJhPFMTcUoRuJSzIGyZyG+d51Tr21kITAm6Pa6QwKq1Ph4bKp4lmQD4HLj+4P1qEsISC43YDDUr7yfIiAH3Vx3SADG+H4vOhzx1z4m2ZuJ0+nUl81mc9SyFotFX66uro5Y7v7778ddd92l3y4vL0dubi5OzwEyUiOPS0XUGiiqwPrDwFkdAKPMeKbWjfFMiYKxTImE8UyJhPFMiYTxTE3PDuy3I9OzF4M71TOmVAnYHbg5sKMENCIemzqet+RkwvHrYfR1/IrkvK5Ry1Zs24ttXi9s7duiX32fNwDVI2F90O2BHSXIpvh+L1ZUxl/7WnWSXVEUmEymE65n9uzZmDx58ok3KIjVatWX3W531LLBQ8DYbLaI5SwWS0hC3s8oS/wiogQhGM+UQBjPlCgYy5RIGM+USBjPlEgYz9R0JE8FJKUKMFgaEFOh5Yxy45LsmqaL5/TTesPx62EcfPdznDL9VkgGQ/hH9HpxaO5CQALST+/doMdW5drPXY7z92I8flbIdRehxkhJSdGXow0BAwBVVVX6cl1DyxAREREREREREVF4SbvfAgB47XVPFhrvci67ALLJiIpte7D1kZdQtedgrTKVew5gyyMvoWLbXshGI9pfNjQGLaVW3ZPdaDTil19+OeF62rdv3wStCRU82enBg7XfAMGCJzsNngSViIiIiIiIiIjoZGc5siR6AdUNg6sQ5uNrYKg6CECCs/2IFmlbc7JkpqP7TROw68V3Ub5tD36+718wpafCkpUBSZLgPFYET1mFNvy7BHS/+SpY2mbEutknpVadZAeAPn36xLoJYSUnJyM3NxcHDhzAtm3bopYNXp+fn9/cTSMiIiIiIiIiImo1kn/5N2oO6RKeNiGmK2c4nB1HN2ubWkrboQNhTEnC3tc/gut4MTwl5fCUlIeUsbTLRLcbr0TGGcwrxkqrT7LHs/POOw9z587F9u3bcfToUeTk5IQtt3z5cn15yJAhLdU8IiIiIiIiIiKiuKda20YvIBmgGpPhTe4GV7vz4WlzWss0rIVknNkX6c/3QdmWnajYvhee0goAgDk9FSm9uyG1X09IMkcFjyUm2ZvRFVdcgblz5wIA5syZg/vuu69WGYfDgffffx8A0LdvX+Tl5bVoG4mIiIiIiIiIiOJZybmvx7oJMScZZKSf2hvpp/aOdVMoDJ7iaEbjxo1Djx49AAAzZszA7t27a5X5v//7P5SUlOjLRERERERERERERNR6sCd7BLt27cKqVatC7qusrNT/nzNnTsi6Sy65pNZwMCaTCc899xzGjBmD8vJyDBkyBH/9618xaNAglJSU4LXXXsNHH30EQBta5vrrr2++J0RERERERERERERETY5J9ghWrVqFKVOmhF1XVFRUa93SpUvDjrl+2WWX4ZVXXsHtt9+OgoIC/PGPf6xVZtCgQfj4449hMBiapvFERERERERERERE1CI4XEwLmDp1KtavX4+pU6eie/fusFqtyMzMxHnnnYeXX34Z3377LbKysmLdTCIiIiIiIiIiIiJqIPZkj2Dy5MmYPHlyk9XXr18/zJo1q8nqIyIiIiIiIiIiIqLYY092IiIiIiIiIiIiIqJGYpKdiIiIiIiIiIiIiKiRmGQnIiIiIiIiIiIiImokJtmJiIiIiIiIiIiIiBqJSXYiIiIiIiIiIiIiokZikp2IiIiIiIiIiIiIqJGYZCciIiIiIiIiIiIiaiQm2YmIiIiIiIiIiIiIGolJdiIiIiIiIiIiIiKiRmKSnYiIiIiIiIiIiIiokZhkJyIiIiIiIiIiIiJqJCbZiYiIiIiIiIiIiIgayRjrBlDjCSEAAJVVDhhlKcatIToxiirgcAhUVEqMZ2r1GM+UKBjLlEgYz5RIGM+USBjPFBdUD8zOwM2KSgcgmxpcTWuMZ9WjwKGq+u2KSgdkU3ynjCuqHAACudF4IIl4ag01yJ49e9CjR49YN4OIiIiIiIiIiIioRR04cACdOnWKdTMAsCd7q9amTRsAwP79+5GWlhbj1hCdmPLycuTm5uLAgQNITU2NdXOITgjjmRIFY5kSCeOZEgnjmRIJ45kSCeO5ZQghUFFRgQ4dOsS6KTom2VsxWdaG1E9LS+MblxJGamoq45kSBuOZEgVjmRIJ45kSCeOZEgnjmRIJ47n5xVuHY058SkRERERERERERETUSEyyExERERERERERERE1EpPsrZjFYsH06dNhsVhi3RSiE8Z4pkTCeKZEwVimRMJ4pkTCeKZEwnimRMJ4PnlJQggR60YQEREREREREREREbVG7MlORERERERERERERNRITLITERERERERERERETUSk+xERERERERERERERI3EJDsRERERERERERERUSMxyR5nhg0bBkmS6vVXH1u2bMHNN9+Mnj17wmazoW3btrjgggvw6quvQlGUerdr3rx5uPjii9G+fXtYrVZ07doV119/PdasWdPYp0onsS+//DIklh9++OF6bcd4plhZuHAhHn74YYwePRr5+fnIysqCyWRCRkYGzjrrLNx9993Yvn17vetjLFMs7d+/Hy+//DKuuuoq9O7dG0lJSbBarejUqRPGjh2LuXPnNigOGc8US5WVlVixYgWeeuopTJgwAd26ddOPL7p27drg+hjPFO/279+Pe+65B/n5+UhKSkKbNm0waNAgPPXUU3A4HLFuHiW4Y8eO4fPPP8dDDz2ESy+9FFlZWfpn7uTJkxtc36JFizB+/Hh06tQJFosFnTp1wvjx47Fo0aJ61+FwODBz5kwMGjQIbdq0QXJyMvLz83HPPfdg//79DW4TnRw2bNiAxx57DJdeeilyc3NhsViQnJyMvLw8TJ48GStXrmxQfYxlAgAIiitDhw4VAOr1V5fXX39dWCyWiNufffbZorCwMGod1dXV4vLLL49YhyzL4tFHH22qp08ngcrKStGlS5eQOJo+fXqd2zGeKVY8Hk+9PpNNJpOYMWNGnfUxlimW/va3vwlJkuqM5wEDBohff/21zvoYzxRrw4YNixg7Xbp0aVBdjGeKd59//rlIS0uLGF+9e/cWu3fvjnUzKYFFO3aYNGlSvetRVVVMmzYtan3Tpk0TqqpGrWfXrl2id+/eEetIS0sTCxcuPMFnTYnmggsuqNfvu+uvv164XK6odTGWKRiT7HHGn2QfMGCA2LRpU9S/aBYtWiRkWRYARLt27cRzzz0nvv/+e/Hll1+K8ePH62/UCy64QHi93oj1XHPNNXrZ4cOHi08++USsXbtWvPHGG6JHjx76utdee62pdwUlqDvvvFMAENnZ2Xr81JVkZzxTLHk8HpGWlibGjh0rHnvsMTFv3jyxfPlysW7dOvHpp5+KO++8M+QH78svvxyxLsYyxdqNN94oAIikpCRx3XXXidmzZ4tVq1aJH374QfznP/8RAwcO1OOnV69eoqKiImJdjGeKB8EdVDIyMsTIkSNFcnJyg5PsjGeKdxs3bhR2u10AEMnJyeKf//ynWL16tfjmm2/E1KlT9djq06dP1M9uohMRnPTLzc0Vo0aNalSS/YEHHtC3O+OMM8TcuXPF2rVrxdy5c8UZZ5yhr3vwwQcj1lFRUSH69Omjl506dar45ptvxOrVq8U///lP/bvAbreLn376qQmePSUK//dxhw4dxJ///Gfx4YcfirVr14rvvvtOPP3006Jjx456XF199dVR62IsUzAm2eOM/4fC0KFDG12Hx+MRPXv2FABEamqq2LVrV60yt956q/4Gfuutt8LWs2zZMr3MmDFjhKIoIeuPHz8uOnfurP+oKSkpaXSb6eSwfv16YTAYhMViEbNmzapXkp3xTPGgZrzUtGfPHpGRkSEAiLZt24Ytz1imePCXv/xFPPHEE6K8vDzsekVRxIQJE/QYi9TjlvFM8eLVV18V//3vf8XOnTv1+/xXzNU3yc54ptbAf9WG0WgUq1evrrX+ySef1OPvkUceiUEL6WTw0EMPiQULFoijR48KIYTYu3dvg5PsO3fuFEajUe9c6HA4QtZXVVWJAQMG6PEe7jNZCCGmT5+uP/aTTz5Za/3q1av1xxk+fHjDnigltNGjR4v33nsv4m+848ePi7y8PD2+VqxYEbYcY5lqYpI9zjRFkv3999/X36CRhi6oqqrSE0L9+vULW+ayyy4TAITBYBAHDhwIW2bu3Ln6Yz311FONbjMlPkVRxJlnnqkf+C9durReSXbGM7UWN998sx4/mzdvrrWesUytRWFhoTCbzQKAOPXUU8OWYTxTPGtokp3xTPFu7dq1etzcdNNNYct4vV6Rn5+vn8Rxu90t3Eo6GTUmyR580vK7774LW+a7777Ty9x+++211rvdbpGeni4AiPz8/IhXGN100016PT/88EO9nxfRggUL9Nj505/+FLYMY5lq4sSnCeiTTz7RlyNNPmK32zFhwgQAwObNm7Fz586Q9ZWVlfjmm28AACNHjkSnTp3C1jN+/HikpqYCAObPn3+CLadE9swzz2DDhg3Iy8vDvffeW+/tGM/UWiQlJenLTqez1nrGMrUWmZmZOPXUUwEAu3fvDluG8UyJhPFM8S44RqdMmRK2jCzLmDhxIgCgpKQEy5Yta4GWETWMEAKffvopAKBPnz44++yzw5Y7++yz0bt3bwBa/AshQtYvW7YMpaWlAIBJkyZBlsOntoI/0/mZSw0xbNgwfTnc8TBjmcJhkj0B+WdB7t27N3JyciKWGzp0qL68atWqkHVr166Fy+WqVa4ms9msf5isXbsWHo+n0e2mxLVv3z5Mnz4dAPDSSy/BYrHUe1vGM7UG1dXV+kGWLMvIy8urVYaxTK2JP84iHegznimRMJ4p3vljNCkpCWeddVbEctFilCge7N27F4cOHQIQ/bMyeP3Bgwexb9++kHX+90Rd9QwYMEDvCMP3BDWE2+3Wl8MdDzOWKRwm2ePUtm3bMHDgQKSkpMBqtaJTp04YO3Ys3n777agH45WVlTh48CAA7WxaNMHrf/nll5B1wbfrW4+iKLV69RABwC233AKHw4Frr70WI0aMqPd2jGeKZx6PB/v378e8efNw7rnnYteuXQC0HmYpKSkhZRnL1JocO3ZMj7VwccZ4pkTCeKbWwB9fPXv2hNFojFguWowSxYPGfFbW3K4h9RiNRvTo0SNsHUTRLF++XF8OF2OMZQqHSfY4VVBQgB9++AGVlZVwuVw4dOgQPvvsM0yaNAmnn356xDfVwYMH9ctPIl2m6pebm6svHzhwIGRd8O0TqYfo3XffxaJFi5Ceno5//etfDdqW8UzxZt++fZAkCZIkwWw2o0uXLrj66quxceNGAMBFF10UNs4Zy9SazJw5E4qiAIA+PEYwxjMlEsYzxTun04nCwkIAdcdWRkaG3tORsUXxqKk+K/23k5KSkJ6eXq96jh8/rl9xRBSNqqp4/PHH9dvhjocZyxRO5NPgFBOyLGPEiBG47LLLcNpppyEzMxMVFRXYsGEDXn31Vfzyyy/YunUrhg8fjrVr16Jz584h21dUVOjLycnJUR8rePzgysrKZqmHTm7FxcW48847AQAzZsxAu3btGrQ945lai8zMTLzwwgv43e9+B4PBUGs9Y5lai++//x7PPvssAO0Hw6233lqrDOOZEgnjmeJdQ2IL0OKrqqqKsUVxqak/c+v7ngiupyFDl9LJ6ZlnnsHatWsBAOPGjcOAAQNqlWEsUzhMsseZ+fPnhz17df755+PWW2/F1KlT8dZbb6GgoAB33HFHrQkPgifbM5vNUR8r+A1ZXV3dLPXQye2ee+7BsWPHMHjwYEybNq3B2zOeKd507NgRmzZtAqBd5n/o0CEsWrQIb7zxBm699Vbs2bMH999/PyRJCtmOsUytQUFBAX77299CURRIkoS33noLdru9VjnGMyUSxjPFu4bEFhCIL8YWxaOm/sxtyHsiXD1ENS1fvhz33XcfACA7Oxsvv/xy2HKMZQqHw8U0gv/H54n+zZkzp1bd0S4PMZlMeP311/Vxmj7++GN9ogU/q9WqLwdP1BBO8OUlNputWeqh+Ndc8bxs2TLMnj0bBoMBr7zySsTJ86JhPFNDNOdns5/JZEK/fv3Qr18/nH766Rg9ejSef/55rFmzBpIk4cEHH8SNN95YazvGMjVUS8RzsIqKCowePVofm/qxxx7DhRdeGLYs45kaqqXjuSEYzxTvGhJbQCC+GFsUj5r6M7ch74lw9RAF27JlC8aNGwdFUWCxWPD+++9HvBqfsUzhMMneyhiNxpAETvBkDABCJtur6xLBqqoqfbnmpSlNVQ+dnFwuF2666SYAwJ/+9CecfvrpjaqH8Uytxamnnop//OMfAIDZs2fjf//7X8h6xjLFM6fTibFjx2L9+vUAgLvuukvvwRMO45kSCeOZ4l1DYgsIxBdji+JRU3/mNuQ9Ea4eIr+9e/di1KhRKCkpgcFgwNy5czF06NCI5RnLFA6Hi2kEo9HYJLP5tm/fvlHb9e3bV1+u2ZM9eMIFf2+0SIInXAieiCFcPeHGoKpPPRT/miOe58+fjx07dsBoNKJv376YN29erfJbt27Vlzdv3qyXGTx4MLp16waA8UwNE+vP5rFjx+rjV3/44YcYNWqUvo6xTA3VUvGsKAomTJiApUuXAgD+8Ic/1DlJNeOZGirWn8/RMJ4p3lmtVmRlZaGwsLDOGC0pKdGTMIwtikdN+Zn7/fffo6qqCqWlpVFHBPDX07ZtW45hTWEdPnwYF110EQ4fPgxJkvDmm29i3LhxUbdhLFM4TLI3kn/IllgQQkRcl5ycjNzcXBw4cADbtm2LWk/w+vz8/JB1wYn8+tZjNBrRs2fPqGUpPjV1PPsvY1IUBVOnTq2z/EcffYSPPvoIgNYL2J9kZzxTQ8Xys7lt27b68q+//hqyjrFMjdHc8ayqKq6//nosWLAAAHDVVVfh1VdfrXM7xjM1Riw/n6NhPFNrkJ+fj5UrV2LXrl1QFAVGY/if8dFilCgeNOazEgj/mev//bht2zacffbZYetQFAW7d+8OWwcRABQWFmLkyJHYs2cPAOD555/HxIkT69yOsUzhcLiYVii4B3CHDh1qrT/vvPMAANu3b8fRo0cj1hM81MyQIUNC1g0cOFCfeKHmkDTB3G431qxZU2sboqbCeKbWIvjKonCX7zGWKd7cdNNN+lVEl19+Of7zn//Ue/4MxjMlEsYzxTt/jFZVVelDe4UTLUaJ4kG3bt30HEa0z0oAWLFiBQCgY8eO6Nq1a8g6/3uirnp++OEH/eoOvieoprKyMlx88cV6ju3xxx/HbbfdVq9tGcsUDpPsrYyiKHjzzTf12xdccEGtMldccYW+HGmCKIfDgffffx+AduYsLy8vZH1KSgpGjBgBAPj6668jXv4yf/58lJeXA0Cdl9PQyWPy5MkQQkT98w9NAADTp0/X7588eXJIXYxnai0++OADfbl///611jOWKZ7cddddeP311wEAI0aMwIcffgiTyVTv7RnPlEgYzxTvgmN09uzZYcuoqoq3334bAJCeno7hw4e3RNOIGkSSJIwdOxaA1mvXf9KxpjVr1ui9f8eOHQtJkkLWDxs2DGlpaQCAt956K+LV/sGf6fzMpWAOhwOjR4/Ghg0bAAAPPvgg7r333npvz1imsATFjSVLloiSkpKI691ut5g0aZIAIACIMWPGRCzXo0cPAUCkpqaKXbt21Spz66236vXMnj07bD3ffPONXuY3v/mNUBQlZP3x48dF586dBQCRnp4uiouL6/1ciZYuXarH1/Tp0yOWYzxTrH388cfi8OHDUcssX75cJCcnCwDCaDSKbdu21SrDWKZ4MX36dD2Gzj33XFFZWdngOhjPFM+6dOkiAIguXbrUqzzjmVqD888/Xz/OWL16da31Tz75ZL2OrYma0t69e/W4mzRpUr222b59uzAajQKAGDBggHA4HCHrHQ6HGDBggB7vO3bsCFvP3/72N/2xn3zyyVrrV69erT/O0KFDG/rUKIG5XC4xatQoPX7+/Oc/N6oexjLVxCR7HJk0aZJITk4W11xzjZg1a5ZYvny5+PHHH8XKlSvFs88+K/Lz8/U3XnZ2ttizZ0/EuhYuXChkWRYARLt27cTzzz8vvv/+e7Fo0SJx5ZVX6vWcd955tX4ABPv973+vlx0+fLj49NNPxbp168Sbb76p/xgBIF555ZXm2CWUwOqbZBeC8UyxNWnSJGE2m8W4cePECy+8IJYuXSp+/PFHsWbNGvHf//5X/P73v9fjE4B45JFHItbFWKZYe+655/T46Nixo1i1apXYtGlT1D+32x22LsYzxYOdO3eK2bNnh/xlZmYKACIzM7PWuiNHjoSth/FM8W7Dhg3CZrMJACI5OVk89thj4rvvvhNLliwR06ZN02MrLy9PlJeXx7q5lKBWrlwZ8pk6c+ZMPfaGDBlS6zM3kvvuu0/f7owzzhDz5s0T69atE/PmzRNnnHGGvu7++++PWEd5ebnIy8vTy06bNk0sWbJEfPfdd+Kxxx7TO8DYbDbx448/Nv3OoFZr/PjxetxceOGF4ueff456LLx9+/aIdTGWKRiT7HEkuJd6tL/+/fuLLVu21FnfrFmzhNlsjljPoEGDxPHjx6PW4XA4xGWXXRaxDlmW2VOCGqUhSXYhGM8UO/X9bLbZbOKpp56qsz7GMsXS0KFD6xXPwX979+6NWB/jmWJt9uzZDYrnpUuXRqyL8Uzx7rPPPhOpqakR4ysvL0/s3Lkz1s2kBFbf42L/XyRer1fccMMNUbe98cYbhdfrjdqenTt3il69ekWsIzU1VSxYsKCpdwO1cg09Fo52ZRxjmYIxyR5Htm7dKp555hkxYcIE0a9fP9GuXTthMplEcnKy6NGjh7jqqqvEBx98ELX3TE2bNm0SU6dOFd27dxdWq1VkZmaK8847T7z88svC4/HUu57//ve/YuTIkSI7O1uYzWaRm5srrrnmmrCXKhLVR0OT7EIwnik2jh8/Lt577z1x0003iYEDB4rc3FxhsViEzWYTHTt2FKNGjRKPP/54nUPKBGMsU6w0dZJdCMYzxVZTJtmFYDxT/Nu3b5+48847RV5enrDb7SI9PV0MGDBAPPHEE6KqqirWzaME11RJdr+FCxeKsWPHig4dOgiz2Sw6dOggxo4dK7744ot6t6myslI88cQTYsCAASI9PV3Y7XbRu3dvceedd4p9+/adyNOlBNWUSXY/xjIJIYQkRIRR9YmIiIiIiIiIiIiIKCo51g0gIiIiIiIiIiIiImqtmGQnIiIiIiIiIiIiImokJtmJiIiIiIiIiIiIiBqJSXYiIiIiIiIiIiIiokZikp2IiIiIiIiIiIiIqJGYZCciIiIiIiIiIiIiaiQm2YmIiIiIiIiIiIiIGolJdiIiIiIiIiIiIiKiRmKSnYiIiIiIiIiIiIiokZhkJyIiIiIiIiIiIiJqJCbZiYiIiIiIiIiIiIgaiUl2IiIiIqIounbtCkmSMHny5Fg3pdWaM2cOJEmCJEnYt29frfXDhg2DJEkYNmxYi7eNGsf/egb/xcPrN3ny5LBtCxd3RERERE2FSXYiIiKiOObxeDBv3jxMmjQJ+fn5yMzMhMlkQlZWFs466yzccsst+Prrr6GqaqybSkREREREdFJikp2IiIgoTn366afo06cPrr76arz99tvYtm0biouLoSgKioqKsGHDBrzyyisYOXIk8vPzsXDhwlg3mSiuPPzww3pPZmoet9xyCzZt2oRNmzZh9uzZsW4O/vnPf+rt+cc//hHr5hAREdFJwhjrBhARERFRbTNmzMCDDz4IIQQA4KKLLsLYsWPRt29fpKeno7i4GNu3b8eCBQuwePFi7NixAw8++CBGjx4d45YTNdyyZcti3QRqpOzsbPTr1y/WzdB17NgRHTt2BAD88MMPMW4NERERnSyYZCciIiKKM//5z3/wwAMPAADatm2L9957D8OHD69V7qKLLsJtt92GTZs24Y477kBRUVFLN5WIiIiIiOikxyQ7ERERURw5fPgwbrnlFgCA3W7HsmXL0Ldv36jb9O/fH4sXL8a7777bEk0kIiIiIiKiIByTnYiIiCiOPPPMM6iqqgIAPPLII3Um2P1kWcZ1110Xct++ffv08ajnzJkDAJg/fz4uu+wydOjQAUajEcOGDatV14IFC/Db3/4WnTp1gsViQWZmJs455xw8/vjjqKysjNqO0tJS/POf/8Q555yDjIwMmEwmtG3bFn379sW4cePw8ssv49ixY2G3XbJkCa6++mp069YNNpsNdrsdXbt2xdlnn4177rkHS5Ysqde+aI62Bdu2bRumTp2Krl27wmKxoF27dhg3bhzWrFlTr3Y0dv/OmTNHfz337dsHl8uFZ599FmeffTaysrIgSRIefvjhWtutXbsWU6dORV5eHpKTk5GUlIQ+ffrgtttuw86dO+vV5rqUlJTgvvvuQ58+fWCz2ZCdnY2LLroIH3zwQb22HzZsGCRJChuPAOD1ejFnzhxcfPHFyMnJgdlsRnp6Onr16oURI0bgsccew9atW/Xy/n31yCOP6Pf5913w3759+/T1qqpiyZIluOeeezBkyBBkZWXBZDIhPT0dp59+Ou655x7s37+/Qc/j0KFDuOuuu9CzZ0/YbDZkZmbi4osvxpdfflmv/XL8+HE8+uijGDJkCLKzs2GxWJCbm4shQ4bg0Ucfxfbt2yNuqygK3njjDf39brFYkJWVhQsuuADPPvssnE5nvdrQFPz72x+fS5cuxRVXXIEOHTrAZrMhPz8ff//73/XPPr8vvvhCb7/NZkPfvn0xY8YMuN3uFms7ERERUb0IIiIiIooLqqqKtm3bCgAiKSlJlJWVnVB9e/fuFQAEAPHmm2+K66+/Xr/t/xs6dKhevrq6WowbN65WmeC/Dh06iB9//DHs423dulV06NAh6vYAxPPPP19r2zvvvLPO7TIzMxu9L06kbV26dBEAxKRJk8RHH30k7HZ72G0NBoOYN29exDac6P6dPXu2Xm7dunXi9NNPr7X99OnT9fIej0fccsstUR/PZDKJWbNmNXq/CiHEli1bRPv27SM+xg033BDS9r1799aqY+jQobXi0a+iokKcf/75db52V155Zdh9Fe0vuC3Tp0+vs7zdbhfz58+PuC+Cn8fKlStFZmZmxLpmzpwZdb++8847IikpKWp7unTpEnbbXbt2ib59+0bdtlevXmLHjh1R2xBNuJirT9kZM2YISZLCtuncc88VFRUVQlVV8ec//zli2y+55BKhKEqdj1tX3BERERE1FQ4XQ0RERBQntm7diuPHjwMAzj//fKSmpjZZ3c8++yx+/vlnnH/++bjllluQl5eH0tLSkJ68kyZNwscffwwAOO2003D33XcjPz8fxcXFmDdvHubMmYPDhw9jxIgR+Pnnn/XJBf2uv/56HD58GCaTCVOnTsWll16KnJwcqKqKw4cPY+3atfjoo49qte3zzz/HM888AwA49dRTccsttyA/Px9paWkoKyvDtm3bsHjxYnz33XeNfv6NbVuwn3/+Ge+99x7at2+Pu+++GwMGDIAQAl999RUef/xxOJ1OTJs2DRdeeCHatm1ba/sT3b/BbrzxRmzatAkTJ07EVVddhZycHOzfvx8WiyWkzNtvvw0AuPTSS3HttdciLy8PkiRh48aNePbZZ7FlyxZMmzYNOTk5GDNmTIP3a1lZGS6++GIcOXIEAHDVVVdh0qRJyM7Oxo4dO/D000/jzTffxKZNmxpct9/DDz+MlStXAgAuv/xyXHvttejcuTOsViuOHz+On376CZ9//jkkSdK3ueKKKzBgwAC89NJLePnllwEgbBuC97GiKGjfvj3GjRuHc845B927d4fVasWBAwewevVqvPTSS6isrMQ111yDDRs2ID8/P2Kbjxw5gnHjxsFgMODxxx/HeeedB7PZjFWrVuHRRx9FaWkp7r//flx66aU45ZRTam3/9ttvY9KkSQAAq9UaErOVlZX4+eefsWDBgrBXIhw5cgRDhgxBQUEBUlJSMG3aNFx00UVo164dysrK8L///Q///ve/sXPnTlxyySXYsGED0tLS6vlqnJgvv/wSa9euxTnnnIM//vGPyMvLQ2FhIf7973/jyy+/xOrVq/H444+jTZs2+Pe//41LL70Uf/jDH9C1a1ccPHgQM2bMwJo1a7Bo0SK89tpruPnmm1uk3URERER1inWWn4iIiIg0//3vf/Velw888MAJ1xfckx2AmDhxolBVNWzZzz//XC83YsQI4XK5apWZNWuWXmbChAkh63bv3h21N7ifqqqiuLg45D5/D/suXbqIioqKiNsWFRVFe7oRnUjbhAj0ZAcgzjrrLFFaWlqrzDvvvKOXefrpp2utP9H9K0Tt3tlvvPFGxOfy4Ycf6uVee+21sGWqq6vFhRdeKACIrl27Co/HE7G+SO666y79cR577LFa691utxg1alTE3uN+0Xqy5+bmCgDit7/9bdS2hIuP4N7pddm7d69wu90R1x84cEB07NhRABDXXXdd2DL+5+GP54MHD9Yqs3LlSr0n95/+9Kda6w8dOqRfLZGdnS02bdoUtU01XX755QKAyM3NFbt37w673YYNG/Re8n/9618j1h+N/3k2pCc7fFcc1OyFriiKOPvsswUAkZKSIqxWq7jjjjtq1VNVVaW/H0899dQ6H5c92YmIiKilcEx2IiIiojhRWFioL7dr165J605PT8cLL7wQ0ts32IsvvggAMJlMmD17Nsxmc60yU6dOxUUXXQRAG9vd33sZAI4ePaovX3DBBRHbIUkSMjIyQu7zb3vmmWciOTk54rZt2rSJuC6aE2lbTW+++WbYXr/XXHMNOnToAAB6r+tgJ7p/a7rwwgtxww03RFw/Y8YMAMC4cePwhz/8IWwZq9WKF154AYA2fv+yZcsi1heOy+XC7NmzAWhXINx77721yphMJrzxxhswmUwNqjuY//U7//zzo5ZrbHz4de3aNWo7O3XqhP/7v/8DAHz22WcQQkSt7/nnnw97NcJ5552HwYMHAwgfK88//zwcDgcA4NVXX0W/fv2itinY5s2b8fnnnwMAXnjhBXTv3j3sdmeccQZuu+02AFpMtxS73Y5Zs2bBYDCE3G8wGHDTTTcBACoqKtC2bVs8+eSTYbf39/D/+eefUVZW1vyNJiIiIqoHJtmJiIiI4kRFRYW+nJSU1KR1jxkzBikpKWHXKYqC5cuXAwBGjhyJ3NzciPVMnTpV3yY4Kdu+fXt92T/Jan35t12xYgV2797doG0bUj/Q8LYF69+/P0499dSw6yRJwhlnnAEA2LNnT8i6pti/NV177bUR1x06dAjr168HAEyYMCFiOQDIz89HVlYWADR4OJ7169ejpKQEgDYUjiyH/2nRqVMnjBo1qkF1B/O/fu+9956efG4J5eXl2Lt3L7Zs2YLNmzdj8+bNsNvtIesiSU9Px+jRoyOuP+usswDUjhUAWLhwIQCgW7duGDt2bIPa/OmnnwLQktHRHh8InHA6fPgwDhw40KDHaayRI0dGPBkS/N4aP358xBMep512mr4c7TUgIiIiaklMshMRERHFieAkeFVVVZPWHSk5DGiJPn/y0t/DNpLg9Zs3b9aXu3Xrpvc0fuaZZ3DKKafgoYcewpIlS+pMjE6cOBEAUFRUhH79+uH3v/89Zs+ejV27dkXd7tixY3rys+ZfcPLtRNoWrE+fPlHX+5OHwSdLgKbZvzVFez1/+OEHffnqq6+GJElR//xXUAT3+K+P4DHOBw4cGLXsoEGDGlR3MH/P5dWrV6Nbt264/fbb8fHHH+vzFzSlX3/9FX/84x/RtWtXpKWloXv37ujXrx/69++P/v37Y9q0aXrZ4CtPaurVq1fEkw5A5FjxeDz6637++edHvPIkEv9r73A4YDQao77ul19+ub5dQ1/7xsrLy4u4Lj09vcHlau4/IiIiolhhkp2IiIgoTvh7FANAQUFBk9YdbRiU4uJifbmuYWpycnLCbgcAc+fOxTnnnANAm8T173//O0aMGIH09HQMHToUr7zyCpxOZ606R4wYgRdeeAE2mw1OpxPvvfcebrjhBvTq1QudOnXCzTffjJ9++qnWdi+99JKe/Kz5N2XKlCZpWzB/L+ZI/ElVr9cbcn9T7d9g0V7PY8eORX2MSBraS9zfix0AsrOzo5Y9keGP/va3v+GGG26AJEk4duwYXnzxRYwfPx7t2rVD//79MX369CZ5v3z55Zfo27cvXnjhBfz66691lq+uro64rr6xoqpqyP3FxcX6MDTBV2DUV0u99o0Vbb8En5Sob7ma7zUiIiKiWDHGugFEREREpAkeBmHDhg1NWnfNMZAjaWjP2WAdO3bE6tWr8c0332D+/PlYvnw5tm7dCo/HgxUrVmDFihV46qmn8MUXX9TqqXrbbbfhd7/7Hd59910sXrwY3377LcrKynDo0CG8+uqrmDVrFh544AH84x//aPG2NaUT2b/Bor2ewYnH//73v1F7vQerazz6moLHJK/redU1fnk0/nHd7777bsydOxdLlizBDz/8ALfbrV+58PTTT+Odd95p8PAqfkVFRbjmmmvgcDiQnJyMe+65BxdffDF69OiBtLQ0fQz9JUuWYMSIESf8nOqjMbHif+27deuGzz77rN7bdevWrcGPRUREREQBTLITERERxYm+ffsiKysLhYWFWLlyJcrLy5Gamtrsjxs8RnJdw0YEr480tvKIESP0RGRRURG+/vprzJo1C0uWLMHu3btx1VVX4ccff6y1XXZ2Nu644w7ccccdUFUVGzduxPz58/Hiiy+itLQU//znPzFw4EA9kfrwww/j4YcfbtBzbWzbTkRT79+6ZGZm6suSJEWdOPNEBLevoKAg6smJxvawDta3b1/8/e9/x9///ndUV1fj22+/xbvvvou3334blZWVuPrqq7F79+5G9QD/4IMPUFpaCkCbdHbkyJFhywX33m8Obdq0gSzLUFUVhw8fbvD2/te+oKAAffr0gdHIn3tERERELYHDxRARERHFCUmSMHnyZADamOyvv/56izxu9+7d9eEZvv/++6hl165dqy/XJ3mbmZmJq666Ct988w1+85vfAAA2btyInTt3Rt1OlmWceeaZ+Mc//oFvvvlGv//999+v8zHrq7Fta6jm3L/h+CdgBYD//e9/jaqjPvr3768vr1u3LmrZutY3lM1mw0UXXYQ333wTM2fOBKAN3/L555+HlKtvb/AtW7YA0JLckRLsQOh4983BZDLpr/vKlSsb3Fve/9o7HA58++23Td4+IiIiIgqPSXYiIiKiOHLHHXfoCdmHHnoI27Ztq9d2qqrinXfeadRjGo1GDB06FACwePFiHDhwIGJZf+LfYDBg2LBhDXocfw9yIPqkkTWdeeaZ+lAmDdmuIRrbtvpoqf3r17NnT/Tt2xcAMG/ePOzfv79R9dTlrLPO0l+X//znPxETwocOHWrWZH+0185qterLLpcrYh2Kouhlao6T7udwOPD222+fSFPrZcyYMQCAvXv34tNPP23QtsHD5Tz55JNN2i4iIiIiioxJdiIiIqI40rFjR7zwwgsAtN7sQ4cOxfLly6Nus3XrVlx88cV46qmnGv24t912GwDA4/HghhtugNvtrlXmzTff1JOlV155ZciwHBs3bsTGjRsj1i+EwNdffw1A613ctWtXfd17770XdRLJH374QR+mozFjR59I25rKie7fhvrrX/8KAHA6nRg/fjyOHz8esazL5cJLL71U58SvNVksFn2C2Y0bN+o9yoMpioKpU6eGfb71UVxcjM8++yxqj+7gBH7N+Ajeh7t3745YR69evQBo77kPP/yw1nqv14s//OEPjRrCpaFuv/12JCUlAQBuuukmbN68OWLZgwcPhtweOHAgRo0aBQD44osvMH369KiPtW/fPsydO/cEW0xEREREHKSPiIiIKM5MmTIFBw8exEMPPYRjx45h2LBhGDVqFMaOHYv8/Hykp6ejuLgYO3bswMKFC7Fo0SJ4vd6QiVMbavTo0fjd736HDz74AF9//TUGDx6Mu+++G/n5+SgpKcG8efPw5ptvAtCG1Hj66adDtt+4cSOmTJmCgQMHYsyYMTjzzDORk5MDj8eDvXv3Yvbs2Vi8eDEArbdtcPLz3nvvxc0334yxY8figgsuQF5eHpKSklBUVIRVq1bh+eefB6D17p46dWqDn9uJtK2pnOj+bairr74aX331Fd566y2sX78effv2xU033YShQ4eibdu2qKqqwu7du7Fy5UrMnz8fxcXFmDhxYoMf56GHHsL777+PgwcP4t5778XGjRsxceJEZGdnY8eOHXj66aexbt06DBw4sFFDxpSXl2Ps2LHo2rUrxo8fj8GDB6NLly4wGo04cuQIFixYoPf+79Spk94L3O/cc8/Vl++88048+OCDaN++vT6MTNeuXWE0GjFhwgQ88MADcLlcmDx5MjZu3IiLLroIqamp2LJlC55//nmsX78eQ4YMafZhWHJycvDyyy9j4sSJOHbsGAYNGoSpU6fi0ksvRU5ODiorK7F582Z89tln2L59e62TB7Nnz8aAAQNw5MgRPProo/jqq69www03oH///rBarSgqKsLPP/+MRYsWYcmSJbjiiitw9dVXN+tzIiIiIkp4goiIiIji0kcffSS6du0qANT5d8opp4ivvvoqZPu9e/fq62fPnl3n41VXV4tx48ZFfZwOHTqIH3/8sda2s2fPrlc7zzvvPFFUVBSybZcuXerczmq1irfeeqtR+/FE2hbcvkmTJkV9nEmTJgkAokuXLmHXn8j+rfk89u7dW+fzVhRF/OUvfxEGg6HO556UlCQcDkeddYazefNmkZOTE7HuKVOm1Nn2oUOHCgBi6NChIfcHx3C0v44dO4oNGzaEbd+ECRMibhfcljfffFPIshyx7FVXXSW+/vpr/fbSpUvr/Txqmj59ul5PJHPmzBE2my3q844Ua/v27RMDBw6s176bMmVK1LZG4t9++vTpTVK2vp9XS5cujfoaBGvoe4aIiIiosdiTnYiIiChOjR8/Hpdffjk+/PBDfPnll1i3bh2OHTuGiooKpKamomvXrjj77LNx5ZVXYvjw4fWe5DESq9WK+fPnY8GCBZgzZw7WrFmDwsJCJCUlIS8vD1dccQVuv/12JCcn19r2mmuuQdeuXbF48WKsXLkSBw8eREFBARRFQXZ2Ns4880z8/ve/x1VXXQVZDh2xcMWKFVi8eDEWL16MrVu34ujRoygpKYHdbkfPnj0xYsQI3HLLLY0aKuZE29aUTmT/NobBYMATTzyBG2+8EbNmzcKSJUuwb98+lJeXw263o3Pnzjj99NMxatQojBs3DjabrVGPc8opp2DLli144okn8PHHH2P//v1ISUlB//79MXXqVFx99dWYM2dOo+ru0qULNm7ciMWLF2PJkiXYs2cPCgoKUFlZifT0dJxyyikYM2YMpk2bhpSUlLB1vPPOOxgwYAA+/PBDbN++HRUVFWHHXZ8yZQp69+6NmTNn4ttvv0VpaSmysrJw2mmnYcqUKZgwYQKWLVvWqOfRGJMmTcKoUaPw4osvYtGiRdi9ezccDgdycnLQuXNnXHrppRF7oHfp0gXff/89Pv30U7z33nv4/vvvUVBQAI/Hg/T0dPTq1QvnnHMOfvOb3+D8889vsedERERElKgkIRo4ZT0RERERERGd1Pwn9aZPn46HH344to2JYM6cOfq8AXv37m2W+RaIiIiIAI7JTkRERERERI107NgxfXLWpKSkRl9x0lQOHTqkT5R86NChmLaFiIiITh7syU5EREREREQNEm54qqFDh7bokDrhTJ48GW+99Vat+9mTnYiIiJpT8w06SURERERERERERESU4NiTnYiIiIiIiIiIiIiokdiTnYiIiIiIiIiIiIiokZhkJyIiIiIiIiIiIiJqJCbZiYiIiIiIiIiIiIgaiUl2IiIiIiIiIiIiIqJGYpKdiIiIiIiIiIiIiKiRmGQnIiIiIiIiIiIiImokJtmJiIiIiIiIiIiIiBqJSXYiIiIiIiIiIiIiokZikp2IiIiIiIiIiIiIqJGYZCciIiIiIiIiIiIiaqT/BwmsNaV6OHRCAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1500x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "jarkus_data = xr.open_dataset(main_datapath_output + 'transect_red_with_derivatives.nc', engine='netcdf4')\n",
    "\n",
    "fig, axs = plt.subplots(3,1,figsize=(15,8))\n",
    "fig.tight_layout()\n",
    "plt.subplots_adjust(hspace=0.4)\n",
    "plt.rc('xtick', labelsize=15)   # fontsize of the tick labels\n",
    "\n",
    "def plot_profile(transect_id, ax, xlim=None):\n",
    "    time_idx = 128 # 28.05.2020\n",
    "    lw = 2.2\n",
    "    idx, = np.where(jarkus_data.id==transect_id)[0]\n",
    "    profile = jarkus_data.altitude_red[28, idx, :]\n",
    "    # ax.plot(jarkus_data.cross_shore, profile, c='peru', lw=lw)\n",
    "    ax.fill_between(jarkus_data.cross_shore, profile, -20, color='peru', alpha=0.4, lw=lw)\n",
    "    ax.set_xlim(xlim)\n",
    "    ax.set_ylim([-14,14])\n",
    "    \n",
    "    # uncorrectd shoreline position\n",
    "    sl_uncorr = cd_cassie_red[str(transect_id)][time_idx]-3000\n",
    "    ax.axvline(sl_uncorr, c='darkorange', lw=lw)\n",
    "    ax.text(sl_uncorr-18, -10, 'uncorrected', rotation='vertical', c='darkorange', fontsize=18)\n",
    "    \n",
    "    # corrected shoreline position\n",
    "    sl_uncorr = cd_tidalcorr_bsmedian.loc['median45', str(transect_id)][time_idx]-3000\n",
    "    ax.axvline(sl_uncorr, c='firebrick', lw=lw)\n",
    "    ax.text(sl_uncorr-18, -10, 'corrected', rotation='vertical', c='firebrick', fontsize=18)\n",
    "    \n",
    "    ax.axhline(tides_cassie[128], c='dodgerblue')\n",
    "    ax.set_title('Transect ' + str(transect_id), fontsize=21)\n",
    "    ax.grid()\n",
    "    \n",
    "plot_profile(4000600, axs[0], [-400, 600])\n",
    "plot_profile(4001780, axs[1], [-700, -50])\n",
    "plot_profile(4002540, axs[2], [-500, 250])\n",
    "\n",
    "axs[1].set_ylabel('Elevation over NAP [m]', fontsize=20)\n",
    "axs[2].set_xlabel('Cross-shore distance [m]', fontsize=20);\n",
    "\n",
    "plt.savefig('../99_plot_dump/profiles_tidal_correction.png', dpi=dpi, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc7a3b5c-8082-49f5-8dea-54529223ef05",
   "metadata": {},
   "source": [
    "## Wave run-up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5b4c0b91-f459-46f4-b27b-a5f080b71a84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4000000    [nan, nan, nan, nan, nan, nan, nan, nan, nan, ...\n",
       "4000020    [nan, nan, nan, nan, nan, nan, nan, nan, nan, ...\n",
       "4000040    [nan, nan, nan, nan, nan, nan, nan, nan, nan, ...\n",
       "4000060    [nan, nan, nan, nan, nan, nan, nan, nan, nan, ...\n",
       "4000080    [nan, nan, nan, nan, nan, nan, nan, nan, nan, ...\n",
       "                                 ...                        \n",
       "4002903    [nan, nan, nan, nan, nan, nan, nan, nan, nan, ...\n",
       "4002904    [nan, nan, nan, nan, nan, nan, nan, nan, nan, ...\n",
       "4002940    [nan, nan, nan, nan, nan, nan, nan, nan, nan, ...\n",
       "4002960    [nan, nan, nan, nan, nan, nan, nan, nan, nan, ...\n",
       "4003001    [nan, nan, nan, nan, nan, nan, nan, nan, nan, ...\n",
       "Name: median45, Length: 152, dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cd_tidalcorr_bsmedian.loc['median45']"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "coastsat",
   "language": "python",
   "name": "coastsat"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}