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{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Create plots for panels used in figure S1"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
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    "import matplotlib.patches as patches\n",
    "import seaborn as sns\n",
    "from scipy.optimize import curve_fit\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "# folder to save all panels for figure S1\n",
    "savefolder = r\"plots\\SI\\fig_S1\"\n",
    "\n",
    "# file containing the data for the controls\n",
    "results_ctrl_file = r\"data\\shape_analysis\\histograms_HealthyControl_deformed_undeformed.txt\""
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## A"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "#define a color seed for each patient\n",
    "color_dict = {'VS': 'C0', 'VL': 'C1', 'RS': 'C2',\n",
    "              'KM': 'C3', 'LM': 'C4'}"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
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   "source": [
    "def asymptotic_exponential_growth(x, lambda_):\n",
    "    \"\"\"(Inverted) exponential growth function with maximum at 1 for x->infinity:\n",
    "    f(x) = 1 - exp(-lambda * x)\"\"\"\n",
    "    return 1 - np.exp(-lambda_ * x)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "def weighted_means_and_errors(counts, counts_abs):\n",
    "    \"\"\"Calculate weighted mean values and error from the input arrays\"\"\"\n",
    "\n",
    "    weights = np.zeros_like(counts)\n",
    "    weights_normed = np.zeros_like(counts)\n",
    "\n",
    "    # sum up number of cell in each velocity range, for every measurement\n",
    "    counts_sum = np.sum(counts_abs, axis = 2)\n",
    "    sum_counts = np.sum(counts_sum, axis = 1)\n",
    "\n",
    "    # calculate absolute and relative weights\n",
    "    for k in range(len(weights[0,:,0])):\n",
    "        weights[:,k,:] = np.sum(counts_abs, axis = 1)\n",
    "        weights_normed[:,k,:] = weights[:,k,:] / sum_counts[k]     #needed for calculation of the error\n",
    "\n",
    "    # calculate weighted averages, mask nans\n",
    "    means = np.ma.average(counts, axis = 2, weights = weights)\n",
    "    means = np.array(means)     #remove mask again\n",
    "\n",
    "    # calculate weighted variance and from that the error\n",
    "    var = np.zeros_like(means)\n",
    "\n",
    "    for l in range(len(counts[0,0,:])):\n",
    "        var += (counts[:,:,l]-means)**2 * weights_normed[:,:,l]\n",
    "\n",
    "    errs = np.sqrt(var)\n",
    "\n",
    "    # make sure that probability sums up to 1\n",
    "    for i in range(len(means)):\n",
    "        means[i,:] = means[i,:]/sum(means[i,:])\n",
    "\n",
    "    return means, errs\n",
    "\n",
    "def deformed_probability_curve(df, v_min=0, v_max=3, binsize=.25):\n",
    "    \"\"\"Compute the values for the shape probability diagram to find a cell\n",
    "    in a deformed state for velocities between v_min and v_max in the DataFrame df\n",
    "\n",
    "    returns: *tuple* (deformed_bins, deformed_hist_normal)\n",
    "        - deformed_bins: *array* limits for the bin ranges of the histogram\n",
    "        - normalized counts for each velocity range\n",
    "    \"\"\"\n",
    "\n",
    "    bins = int(v_max/binsize)   #number of Bins in histogram\n",
    "    #find index of cells in a deformed state. Class definitions are:\n",
    "    #1-parachute, 2-slipper, 3-asym. parachute, 5-multilobe, 7-undefined deformed\n",
    "    #4-discocyte/undeformed, 6-tumbler\n",
    "    deformed_index = ((df['shape'] == 1)\n",
    "                      | (df['shape'] == 2)\n",
    "                      | (df['shape'] == 3)\n",
    "                      | (df['shape'] == 5)\n",
    "                      | (df['shape'] == 7))\n",
    "\n",
    "    #create new column in df that is True for deformed state\n",
    "    df['deformed'] = False\n",
    "    df['deformed'][deformed_index] = True\n",
    "\n",
    "    df_deformed = df[deformed_index]\n",
    "\n",
    "    deformed_hist, deformed_bins = np.histogram(np.array(df_deformed['velocity']),\n",
    "                                                range = (v_min,v_max),\n",
    "                                                bins = bins)\n",
    "    #get the counts for all events to use for normalization\n",
    "    all_hist, all_bins = np.histogram(np.array(df['velocity']),\n",
    "                                      range = (v_min,v_max),\n",
    "                                      bins = bins)\n",
    "\n",
    "    #normalize the deformed histogram\n",
    "    deformed_hist_normal = deformed_hist/all_hist\n",
    "\n",
    "    return deformed_bins, deformed_hist_normal"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
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   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x360 with 1 Axes>",
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      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "patients = ['VS', 'VL', 'RS', 'LM', 'KM']\n",
    "labels = [\"P1\", \"P2\", \"P3\", \"P4\", \"P5\"]\n",
    "\n",
    "result_summary_folder = r\"data\\shape_analysis\\result_summaries\"\n",
    "\n",
    "lw = 2.5\n",
    "xlabel = \"Velocity [mm/s]\"\n",
    "ylabel = \"Probability for deformed shape\"\n",
    "fontsize = 18\n",
    "color_ctrl = 'darkgray'\n",
    "color_ctrl_fit = 'darkcyan'\n",
    "alpha_ctrl = .99\n",
    "alpha_ctrl_fit = 1\n",
    "lw_ctrl =2\n",
    "v_min = 0.\n",
    "v_max = 3.\n",
    "binsize = 0.25\n",
    "\n",
    "with sns.axes_style('darkgrid'):\n",
    "    plt.figure(0,(6,5))\n",
    "\n",
    "    #plot control curves\n",
    "    #load data from txt file\n",
    "    results_ctrl = np.loadtxt(results_ctrl_file)\n",
    "\n",
    "    v_ctrl = results_ctrl[:,0]\n",
    "    probs_ctrl = results_ctrl[:,3]\n",
    "    probs_ctrl_err = results_ctrl[:,4]\n",
    "\n",
    "    #fit the control data\n",
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    "    fit_bounds=(0, np.inf)\n",
    "\n",
    "    ind_vmax = v_ctrl <= v_max\n",
    "    v_ctrl = v_ctrl[ind_vmax]\n",
    "    probs_ctrl = probs_ctrl[ind_vmax]\n",
    "    probs_ctrl_err = probs_ctrl_err[ind_vmax]\n",
    "\n",
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    "    popt_ctrl, pcov_ctrl = curve_fit(asymptotic_exponential_growth, v_ctrl, probs_ctrl,\n",
    "                                     sigma = probs_ctrl_err, absolute_sigma=False,\n",
    "                                     bounds = fit_bounds)\n",
    "    ax = plt.subplot(111)\n",
    "    plt.errorbar(v_ctrl, probs_ctrl, probs_ctrl_err, color=color_ctrl, lw=lw, alpha=alpha_ctrl, label='CTRL', zorder=1)\n",
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    "    plt.plot(v_ctrl, asymptotic_exponential_growth(v_ctrl, *popt_ctrl), '--', color=color_ctrl_fit, lw=lw-.5,\n",
    "             alpha=alpha_ctrl_fit, label='CTRL fit')\n",
    "\n",
    "    for ii, patient in enumerate(patients):\n",
    "\n",
    "        result_file = os.path.join(result_summary_folder, patient + \"_results_MCFM.tsv\")\n",
    "        df_results = pd.read_csv(result_file, sep='\\t')\n",
    "\n",
    "        dates = np.unique(df_results['date'])\n",
    "        dates = np.sort(dates)\n",
    "\n",
    "        day0 = dates[0]\n",
    "\n",
    "        color = color_dict[patient]\n",
    "\n",
    "        df_date = df_results[df_results['date']==day0]\n",
    "        #create new Dataframe to work with, leave out skipped cells\n",
    "        df = df_date[df_date['shape'] != 0]\n",
    "\n",
    "        bins, deformed_curve = deformed_probability_curve(df, v_min=v_min, v_max=v_max, binsize=binsize)\n",
    "        bins_plot = bins[:-1]+binsize/2\n",
    "\n",
    "        ax.plot(bins_plot, deformed_curve, c=color, lw=lw, label=labels[ii])\n",
    "\n",
    "    ax.set_xlim(0,3)\n",
    "    ax.set_ylim(0,1.1)\n",
    "    ax.set_xlabel(xlabel, fontsize = fontsize)\n",
    "    ax.set_ylabel(ylabel, fontsize = fontsize)\n",
    "    ax.tick_params(axis='both', which='both', labelsize=fontsize)\n",
    "    ax.set_title('All RBCs', fontsize = fontsize+2)\n",
    "    ax.legend(ncol=2, fontsize=fontsize-6)\n",
    "\n",
    "    handles, labels = ax.get_legend_handles_labels()\n",
    "    handles = np.roll(handles,-1)\n",
    "    labels = np.roll(labels, -1)\n",
    "    ax.legend(handles=list(handles), labels=list(labels), fontsize=fontsize-4, ncol=2)\n",
    "\n",
    "    plt.tight_layout()\n",
    "\n",
    "    savename = \"fig_S1_shape_analysis_baseline_all_rbcs\"\n",
    "    savepath = os.path.join(savefolder,savename)\n",
    "\n",
    "    plt.savefig(savepath+\".pdf\", dpi=900, format='pdf')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
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    "## B"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
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   "execution_count": 7,
   "outputs": [],
   "source": [
    "#define dict to store fit values\n",
    "dict_fitvalues = {}\n",
    "\n",
    "def dict_fit_values_patient(patient, dict_fitvalues):\n",
    "    result_summary_folder = r\"data\\shape_analysis\\result_summaries\"\n",
    "\n",
    "    v_min = 0.\n",
    "    v_max = 3.\n",
    "    binsize = 0.25\n",
    "\n",
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    "    # bounds of the parameters in the exponential growth function\n",
    "    fit_bounds=(0, np.inf)\n",
    "\n",
    "    result_file = os.path.join(result_summary_folder, patient + \"_results_MCFM.tsv\")\n",
    "    df_results = pd.read_csv(result_file, sep='\\t')\n",
    "\n",
    "    dates = np.unique(df_results['date'])\n",
    "    dates = np.sort(dates)\n",
    "    day0 = pd.to_datetime(dates[0])\n",
    "\n",
    "    #create dataframes to save fit parameters\n",
    "    df_fit_all = pd.DataFrame()\n",
    "    df_fit_healthy = pd.DataFrame()\n",
    "    df_fit_unhealthy = pd.DataFrame()\n",
    "\n",
    "    for num, date in enumerate(dates):\n",
    "        df_date = df_results[df_results['date']==date]\n",
    "        #create new Dataframe to work with, leave out skipped cells\n",
    "        df = df_date[df_date['shape'] != 0]\n",
    "\n",
    "        healthy_index = df['health'] == 0\n",
    "        df_healthy = df[healthy_index]\n",
    "        unhealthy_index = df['health'] == 1\n",
    "        df_unhealthy = df[unhealthy_index]\n",
    "\n",
    "        #calculate percentage of healthy cells in sample\n",
    "        percentage_healthy = len(df_healthy)/len(df)\n",
    "\n",
    "        bins, deformed_curve = deformed_probability_curve(df, v_min=v_min, v_max=v_max, binsize=binsize)\n",
    "        bins_healthy, deformed_curve_healthy =  deformed_probability_curve(df_healthy,\n",
    "                                                                           v_min=v_min, v_max=v_max, binsize=binsize)\n",
    "        bins_unhealthy, deformed_curve_unhealthy =  deformed_probability_curve(df_unhealthy,\n",
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    "                                                                               v_min=v_min, v_max=v_max, binsize=binsize)\n",
    "\n",
    "        bins_plot = bins[:-1]+binsize/2\n",
    "\n",
    "        #exclude nan values before fitting\n",
    "        ind_nonnan_all = ~np.isnan(deformed_curve)\n",
    "        ind_nonnan_healthy = ~np.isnan(deformed_curve_healthy)\n",
    "        ind_nonnan_unhealthy = ~np.isnan(deformed_curve_unhealthy)\n",
    "\n",
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    "        x_all = bins_plot[ind_nonnan_all]\n",
    "        y_all = deformed_curve[ind_nonnan_all]\n",
    "        x_healthy = bins_plot[ind_nonnan_healthy]\n",
    "        y_healthy = deformed_curve_healthy[ind_nonnan_healthy]\n",
    "        x_unhealthy = bins_plot[ind_nonnan_unhealthy]\n",
    "        y_unhealthy = deformed_curve_unhealthy[ind_nonnan_unhealthy]\n",
    "\n",
    "        popt_all_exp, pcov_all_exp = curve_fit(asymptotic_exponential_growth,\n",
    "                                               x_all, y_all,\n",
    "                                               bounds=fit_bounds\n",
    "                                               )\n",
    "        popt_healthy_exp, pcov_healthy_exp = curve_fit(asymptotic_exponential_growth,\n",
    "                                                       x_healthy, y_healthy,\n",
    "                                                       bounds=fit_bounds\n",
    "                                                       )\n",
    "        popt_unhealthy_exp, pcov_unhealthy_exp = curve_fit(asymptotic_exponential_growth,\n",
    "                                                           x_unhealthy, y_unhealthy,\n",
    "                                                           bounds=fit_bounds\n",
    "                                                           )\n",
    "        #days since treatment start\n",
    "        treatment_days = (pd.to_datetime(date) - day0).days\n",
    "\n",
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    "        df_fit_all = df_fit_all.append({'lambda': popt_all_exp[0], 'lambda_err': np.sqrt(pcov_all_exp[0,0]),\n",
    "                                        'days': treatment_days,\n",
    "                                        'percent healthy': percentage_healthy\n",
    "                                        },\n",
    "                                       ignore_index=True)\n",
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    "        df_fit_healthy = df_fit_healthy.append({'lambda': popt_healthy_exp[0], 'lambda_err': np.sqrt(pcov_healthy_exp[0,0]),\n",
    "                                                'days': treatment_days\n",
    "                                                },\n",
    "                                               ignore_index=True)\n",
    "        df_fit_unhealthy = df_fit_unhealthy.append({'lambda': popt_unhealthy_exp[0], 'lambda_err': np.sqrt(pcov_unhealthy_exp[0,0]),\n",
    "                                                    'days': treatment_days\n",
    "                                                    },\n",
    "                                                   ignore_index=True)\n",
    "\n",
    "    dict_fitvalues[patient] = {'all': df_fit_all, 'healthy': df_fit_healthy, 'unhealthy': df_fit_unhealthy}\n",
    "\n",
    "    return dict_fitvalues"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Fill dictionary with patient data"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
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   "execution_count": 8,
   "outputs": [],
   "source": [
    "patients = ['VS', 'VL', 'RS', 'LM', 'KM']\n",
    "labels = [\"P1\", \"P2\", \"P3\", \"P4\", \"P5\"]\n",
    "\n",
    "for patient in patients:\n",
    "    dict_fitvalues = dict_fit_values_patient(patient, dict_fitvalues)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
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   "execution_count": 11,
   "outputs": [
    {
     "data": {
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      "text/plain": "<Figure size 504x432 with 1 Axes>",
      "image/png": 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SPM+jtLQUV69exalTp3D8+HGo1WqMHTsWCxYsQMeOHZvjvbgETUXqenRdhOiaWEbXhdyMbE1FaldN/M4770S3bt2wbNkyjBs3DgqFwub2VVVV2LVrF77//nvceeedSElJaVyJCSGEENIgu4L4p59+ittuu83ugyoUCkyZMgVTpkzBnj17HC4cIYQQQqyzq3d6YwJ4fWPGjHF4X0IIIYRY53Dv9Pr0ej327t2L0tJSjBo1CgEBAc46NCGEEEIscCiIv//++0hMTMRvv/0GwNhD/eGHH0ZSUhJ4nodSqcTGjRsRHh7u1MISQggh5AaHkr0cPHgQAwYMML3et28fjh07hkcffRT/+9//AABff/21c0pICCGEEIscqonn5uYiIiLC9Hr//v0ICwvD/PnzAQAXL17Etm3bnFNCQgghhFjkUE1cp9NBLL4R/xMTEzF06FDT644dO6KgoKDppSOEEEKIVQ4F8eDgYJw8eRKAsdZ97do1xMfHm9YXFRU1OJacEEIIIU3jUHP6hAkT8MUXX6C4uBgXL16Eh4cHRo4caVqfmppKndoIIYQQF3OoJv7kk09iypQpOHXqFBiGwXvvvQcvLy8AQHl5Ofbt24chQ4Y4taCEEEIIMWdX7vTG4DgOlZWVkMvlkEgkzjx0iyhWleGT777HgLieOH8lHRVVlRgZPwj/HEtEWHAHyKVSXLqaiUG9+iA5LRV6vQGDe/fBvyeSEBkaBgDIyM7C8H4D0CO6E77d9Dt6R8ci8fQpdAmPgFqrRVbuddMxPRTuiInqhKSzKYiJ6gxVeRlyCwtM65WeXogMDcOp8+fQo2s35BYWorCk2LTe38cXwf7+OHPxAvrEdEdGdhZU5WWm9cH+AVB6euH8lctOeU8JyacgFoua9J6efuB+HD5+ul29p6Z+TnNnzcSeQ4nt6j0543O6e/RIfPbDunb1ntrj50Tvybnv6e3n51qNUXYH8ZEjR2LMmDEYM2YMBg4cCJFI5EhMbHMqq9W4mpXvlGNFdgxCxrU8pxyrPaHrIkTXxDK6LuRmFNvV+uNpu5+J33bbbdizZw/WrVsHb29v3HLLLRg3bhyGDx8ONzc3pxSUEEIIIfazO4i/8cYbeOONN3D69Gns3r0be/bswbZt2yCXyzFkyBCMHTsWo0aNgo+PjyvLSwghhJAaje6d3qtXL/Tq1QsvvfQSLl++jD179mDPnj147bXXwLIs+vXrh7Fjx2LMmDEICQlxRZkJIYQQAid2bMvLyzPV0JOSkmAwGBATE4N58+bhlltuccYpWgQ9E3c9ui5CdE0so+tCbkZOeSbekKCgIMyaNQuzZs1CaWkp9u/fjz179uDixYttOogTQgghrZXTgnhd3t7emDx5MiZPnuyKwxNCCCEEDiZ7IYQQQkjLc7gmvm3bNqxbtw6ZmZlQqVSC9QzD4Ny5c00pGyGEEEJscCiIf/HFF/j888/h5+eHvn37wtvb29nlIoQQQkgDHAriP/30EwYOHIhvvvmmXaRWJYQQQtoih4J4ZWUl7rjjDgrg7VBxcaFgma+vfwuUhBBCSEMcCuKxsbG4fv26s8tCWoGS4iLBMgrihBDSOjnUO/2FF17Azz//TB3XCCGEkBbkUE184MCBWLJkCaZPn44+ffogNDQULGt+P8AwDJYuXeqUQhJCCCFEyKEgnpycjFdffRV6vR5JSUlISkoSbENBnBBCCHEth4L4kiVLIJFI8MUXX2DAgAHw8vJydrkIIYQQ0gCHgnhaWhrmzp2L0aNHO7s8Fu3cuRPbt2/HmTNnUFRUhA4dOmDcuHF48skn4eHhYXNfjUaDTz75BNu2bUNZWRliY2Mxf/58xMfHN0vZCSGEEFdxqGObn59fsw4vW7NmDViWxbx58/DNN99gxowZWL9+PR555BFwHGdz30WLFuGXX37Bc889h6+++goBAQF49NFHkZqa2kylJ4QQQlzDoZr41KlTsXXrVsyaNQtisUvmUDGzcuVK+Pr6ml4PHDgQSqUSr7zyChITEzFkyBCL+50/fx5//PEHli5dinvuuQcAEB8fjwkTJuDTTz/FypUrXV52QgghxFUcisD9+/fH33//jenTp2PmzJkICwuDSCQSbOesJuu6AbxWz549ARjnMbdm7969kEgkuPPOO03LxGIxJkyYgK+//hparRZSqdQpZSSEEEKam0NB/OGHHzb9f/HixWAYxmw9z/NgGMalTdZHjx4FAHTu3NnqNpcuXUJoaCjc3NzMlnfp0gU6nQ6ZmZno2rWry8pICCGEuJJDQXzZsmXOLkej5OXl4bPPPsPQoUNNNXJLSktLLU7OolQqTesbIpWIEdkxyOGy1iWTSpx2LFdRFQkz8bm6zG3hujQ3uiaW0XUhxJxDQXzKlCnOLofdKisr8X//938QiUTNcjOh1elxNSvfKceK7BiEjGvWm/9bg6KSMsEyV5e5LVyX5kbXxDK6LuRmFNs13Oo6h3qntxS1Wo2nnnoKWVlZWL16NYKDg21u7+XlZbG2XTv/OU2hSgghpC1zuGv5tm3bsG7dOmRmZpqCYl0Mwzg1t7pOp8Nzzz2HM2fOYO3atYiOjm5wny5dumDPnj2orq42ey5++fJlSCQSREREOK18hBBCSHNzKIh/8cUX+Pzzz+Hn54e+ffu6vEbLcRzmz5+PhIQEfPXVV+jTp49d+40ePRqff/45du7caXoEoNfrsWPHDgwfPpx6phNCCGnTHAriP/30EwYOHIhvvvmmWZK+vPXWW9i5cyeeeuopuLm54dSpU6Z1wcHBCA4ORnZ2NsaOHYunn34ac+fOBQB0794dd955J5YuXQq9Xo+wsDCsX78eWVlZ+PDDD11ebkIIIcSVHArilZWVuOOOO5ota9vBgwcBGJO+1E/QMnfuXDz77LPgeR4GgwE8z5utX7ZsGT7++GN88sknKCsrQ0xMDL755hvExcU1S9kJIYQQV3EoiMfGxuL6deFQJFfZt29fg9uEhYUhLS1NsFwul2PhwoVYuHChK4pGCCGEtBiHeqe/8MIL+Pnnn53acY0QQgghjeNQTXzgwIFYsmQJpk+fjj59+iA0NBQsa34/QPOJE0IIIa7lUBBPTk7Gq6++Cr1ej6SkJCQlJQm2oSDetpRcrULqrnx49wDkATeWqwuAw/szEHt7IHzCFS1XQEIIIQIOBfElS5ZAIpHgiy++wIABA+Dl5eXscpFmVHK1Csmbr4PT89AksggczEHubwzg+YkseIMeyZuvo/eUDhTICSGkFXHomXhaWhoeeeQRjB49mgJ4G1c3gAMAb2CQn8Ci9CJTE8CNk9tweh7Jm6+j5GpVSxaXEEJIHQ4FcT8/v2YbXkZcK3VXvimA1+INDFTnGFMAr8XpeaTuck4eeUIIIU3nUHP61KlTsXXrVsyaNQtiscOZW9sEjUaL9Tu2YkBcT5y/ko6KqkqMjB+Ef44lIiy4A+RSKS5dzcSgXn2QnJYKvd6Awb374N8TSYgMDQMAZGRnYXi/AfD2lmPz3l3oHR2LxNOn0CU8AmqtFlm5103H9FC4IyaqE5LOpiAmqjNU5WXILSwwrVd6eiEyNAynzp9Dj67dkFtYiMKSYtN6fx9fBPv748zFC+gT0x0Z2VlQlZeZ1gf7B0Dp6YXzVy5jQFxPlARnQ14eCIavfz/HCK4Fz3DgupTiSPIJ03tKSD4FsVjUpPf09AP3IzX9ktPeU1M/J2e8p6Z+TnNnzcTJ1LPt6j0543MK8BuJ9Tu2tqv31B4/J3pPzn1Pbz8/12qMYvj62VHscOTIEfzvf/8Dx3GYOXMmwsLCIBKJBNvFx8c39tCtjk5ngErlnCZkpVLhtGM5U356GQ58fwGcwfqvgkjCYsSDXREY5fzHJ631urQkuiaW0XUhN6OAAE+r6xyqRj/88MOm/y9evBgMY15r43keDMMgNTXVkcOTZuYZILdU8TbjHeQG/3CP5ikQIYQQuzgUxJtjHm/SfI5svCx4Ll5fcVYl9nx1DiMfioFM0b4foRBCSFvh0Ldx7YxgpO07uz8bhRkVdm2rul6NXSvOYNTDMfD0l7u4ZIQQQhriUO900j5o1Xqc3Z9jeaWV5nV1mQ57vz6HvPQy1xWMEEKIXewK4keOHHH4BIcPH3Z4X+JaKX9lARZa0RkWCOolshrItdUGHPjuAi4fo+FmhBDSkuwK4o899hhmz56N/fv3w2AwNLi9TqfD7t27MWvWLDzxxBNNLiRxvoKMclw+VmBxXfhIMfxjxYi8VQLGym8Iz/E4vjUTJ3dctdmrnRBCiOvY9Ux88+bNePfdd/F///d/8PX1xZAhQ9CrVy+Eh4fD29sbPM+jtLQUmZmZOHXqFBISElBWVoZhw4Zhy5YtLn4LpLEMOg5Jv2dYXBc6RAyPIONwQfcgFhEjJchNAsAD6gqdYPuLR/JQXqjG4OmdIJVThzdCCGlOjRonfvLkSfz000/Yu3cvqqqqLA4t8/DwwNixYzFjxgz06tXL6QVubu1xnHjKniyk/iOcD77vhHBoPfMEy+PiekFTqcPhny+jIKPc4jG9AuQY/mA3ePjIGl2e1nJdWhO6JpbRdSE3I6eNE+/bty/69u0Lg8GAs2fP4tKlSyguLgbDMPD19UXXrl3RvXt3wbSkpPVQ5Vbh/MFcwXK/ju7oPDAQqanCIA4AMncJbpnTDce3ZSLjRKFgfVmBGntXnsPQmV0QEGH9F44QQojzONT+KRKJ0KtXr3ZR076ZcByPpN8zwHPmjS+siMGASVFgWdsZX0RiFvGTI+EVIMdpC53iNFV6/LM2DQMmRSKyr7+zi08IIaQeeoh5E7mUmI/irErB8pgRHeAd5GbXMRiGQczwDvD0kyPx13TotZzZes7A4+imKygrqEbPMWFgGrgxcIX8fGFrQmBgULOXgxBCXM2udu8TJ06gpKSk0Qc3GAw4ceIEKirsSyZCXKdSpcGZPVmC5Z7+csSO7NDo44XG+mD047FQeEstrj9/MBeHf74Enabh0QzOVlCQJ/ghhJD2yK4g/sADD+Dff/9t9MHLysrwwAMPICUlpdH7EufheeNwsPq1ZgCInxwJkdixPgzKYAXGPNUdfh3dLa7PTlVh/zfnUVWqcej4hBBCbLOrOZ3neahUKuTkWMnuZUVpaSkcmCSNONnV08XIvVgqWN55YCD8m9gJTe4hwa0Px+DYliu4erpYsF6VW4U9K1Mx7IEu8AujCVQIIcSZ7H4mvnTpUixdurTRJ6g/DI00L02lDqd2XBUsd/OSoNfYMKecQyRhMejeTvAKcMOZvdmC9eoKHf5efR7xU6IQ3svPKeckhBBiZxCfO9f6hOT26NixY5P2J4479ec1aKr0guX9JkZAIhfOAe8ohmHQ/dYQePrLcXTTFRh05k33Bj2PhF/SUVaoRtyoELq5I4QQJ2iWIE5aRu7FUmQmFwmWh8X5IDTWxyXn7NjDF+4+MhxadxHV5cIMb+f256C8QI34qVEQSyifACGENAV9i7ZTOo0BSVszBMslchH6Tohw6bl9Q91x21Pd4ROisLj+2pli/L36PKrLtS4tByGEtHcUxNups/uyUaUSBsne4zvCzVPi8vMrvKQY9WgMQrtbrvEXZ1diz8pzKMkRjlsnxBH5+XmCH0LaO0r20g4VZVXg4hELCU86eSKqX/NlUhNLRRh6X2ec2ZdtMVd7dZkO+745j9vmMFBGWK6125KWdk6wLDq6u0NlJW2fpXwAlOSHtHcUxNsZzsAhaUsG6o/sE4kZ9L87stk7lDEsg55jwuDlL8exLRmCaUsNOg5/rT6LnmPCEDMiuFHlu3o1Q7CMgjgh5GZCzentzPl/c1GaVy1YHjc6FJ5+8hYokVFEH3/c+kgMZO4W7ht5IGV3Fo5tvgKDXpiQhhBCiGUUxNuR8kI1zv0tTMij7KBAt6HBLVAic/7hHhjzVHeredozThbhn7VpUFcKe7UTQggRanIQ12q1yMvLg1ZLPY1bEl8zQxmnN2+uZlhjalVW1DrGZbsrZRj9eCw6RHtbXF94tQJ7V56z2JpACCHEnMNB/OzZs5g9ezb69euHW2+9FcePHwcAFBUVYc6cOTh8+LDTCkkaln6iEAUZ5YLl3YYGwyfEcm7zliKRiTBsZld0G2a501GlSou9q87h+gVV8xaMEELaGIeCeGpqKh544AFcu3YNkyZNMlvn5+cHjUaDzZs3O6WApGHVZVqc3nVNsNzdR4a4USEtUKKGsSyDPuPDMWBypMXpSvUaDv/+eBEXjuRS/n1CCLHCoSD+6aefIjAwEH/88QdeeuklwZfs4MGDcfr0aacUkDTsxPar0KmFU34OmBQJsbRxqVUDAoIEP67UqX8AJsztBambsJw8D5zacQ3Ht2aCMzTc4S2Ty8CMfVNxQZ1mtvyCOg0z9k3FyaLjTis3IYS0Bg4F8ePHj2PatGlwd3e3OCQoJCQE+fn5TS4caVjW2WJknxPO9R7Z1x9Bnb0afbzAwCDBj6uFdFXitie7w9Pfcu/59KQCHPj+gsUc8LUyuQxsNPyMPHUuVuavwMWaQH5BnYaV+SuQp87FomMLKJATQtoVh4K4RqOBp6f1KSwrKiocLhCxn7ZajxPbhTOUydzF6D2+bU064+knx21PxFq98chPL8fer8+hvFAtWFcbwPUwBnkdr8WX+Suwp3QXVuavgI43drrUcGoK5ISQdsWhIB4eHo6zZ89aXZ+QkIAuXbo4XChin9N/ZUFtYZKRvhPCIVM4N49Pc6S0lLqJMeLBbugyKNDi+ooiDfZ+fQ556WVmy/8wbDMF8Fo6Xos/VFtNAbyWhlPj/eQlzi04IYS0EIeC+MSJE/H777+b9UCvbVZfs2YNDh48KOjwRpwr/0oZ0pMKBMs7RHujYw9fp5+voCBP8OMKrIhBv4kR6DsxHIyF305ttQH/rE3DxtePoTrLON58ouguiCFGz5yReOLIR+iZMxIAYICwn4CMleOV3otdUnZCCGluDlXXHnnkERw6dAiPPvooOnXqBIZhsGzZMhQXF6OwsBBDhw7FzJkznV1WUsOg45D0e4ZguVjKov9dEe1iru6ug4Lg6SfHkQ2XLXbaAwB1ujvAAxEdI/FA1vOQZ3YEAwZDM6cAAFJC/jHbXsbKsSz+Q/Tx6+fy8hNCSHNwqCYulUqxdu1avPLKK5DJZJDJZMjIyICPjw8WLFiAr776CixLyeBc5dzfOago0giW9xwXBoW3rAVK5BrBXbxx2+Ox8PC19p4YqK+4o/y0N9wyjAHcuNQYyGtr5LUmdLybAjghpF1heBqEa5NOZ4BKVeWUYymViiYfS5Vbhd1fngPPmX9sfh3dMfqxWItjrp3h7FnhkMG4uF5OOXZD10VTpcfh9ZcsJrMx4gEI3zcHA74ZtAAcwwGM8Xq9EDcfd0dMdUKpXcsZvyvtkaXrkp9ehqObryCwLw/3oBuVh8o8DvknGQycEoXATo0fqUFIaxEQYL0juUNBfOHChbj//vvRu3dvi+tPnz6N9evXY9myZY09dKvTmoI4x/HY93UqirPN5+BmRQzGPh0H70DLOcmdoSWDOAAY9Bz2rU5FSZZj14+DARxjgIE1QCaRQi6RgxUxYFnG+K+IgUgqwm2Px1o/hoHDiT+umrZn6uzLilgry2vOIWbBsgyYmmXuPjIovKRWz+XlKYdKVQ2GRbt4POIs9X9X8tPLcPDHizDoODAiIOIWCdyDWFTmccg8oANvAEQSFiNmdaVATtosW0HcoWfimzdvxtChQ60G8aysLGzZsqVdBPHW5FJCniCAA0DsLR1cGsBbA5GYxZgnuuPQuovISStt9P4sRGB5EcQGAAZArRb26hdJbD8CMuh5i50JHdFrXBhiRnSwuv7otis4vTcLAMxuCG7cILCmGwRGZOGmQcSCETEQS1kMuqeT1fNwBg4XDucZj1173Po3IWbnZuvdnNxYLpGxjU4u1BR1AzgA8AYg84AOgT1EyD9jAF/TlcKg43Dwx4sUyEm75JL5xKuqqiAW01TlzlRZokHKnmzBcq8AOWJusR4M2hOGYTB8Vjfs/uIsSq47v6m5oUli6j/CaAqmgXPVnXedM/CCedjtJZY1cGOi43H6ryyHjl1fr9vDEDPc+u/i2f3ZuHK80PzGwMqNg+BmpebHTSGFnuMQNyoERzdfMQXwWrwBZgH8xvvkcHTzFUx8yXLFg5C2yu5Im5OTg+zsG0EkPT0dx44dE2xXWlqK9evXIyIiwjklJOB5Hse3Zgi+sMAAAyZHQSS+eToRph3KdUkABxoO4o4G0pY8Fyuy/bthTzpbZ51LU6VHVWnTZzuUyEWIGxWCgVOizGriteoHcMDYyjJwalSTz01Ia2N3EN+0aROWL18OhmHAMAxWrlyJlStXCrbjeR4sy2Lp0qVOLejN7GpyEXIvlQmWdxkYCP9wjxYoUctIO5SL5J3CiV4s4cGDYYy1d97OONWsQbyBDoic3jnBtcHzOLF1ofluTIznCezkhRGzuloM5HWJJCxGPNgVgVHUlE7aH7uD+JgxYxAaGgqe57Fo0SJMnz4dffv2NduGYRgoFAr07NkTHTrcHE28rqau1OHkn8LA5eYlQc8xYS1QopZjaaY2oCZg1+udXvu6+3QZeJ5HaJdwLEycj4zSK2B54/NxEScGy4sgZeR4Jvp59PKNs5iJrjZ/vEQuQv+7I8AZeORkZ4PnYPoBD/j6+JuavjmOB6fnwHP8jWW1yw083Gx0ajO9BxZ234BY05paF3hnBfE6NyaBnbwQNyoEZ/fnWAzkIgmLuNEhFMBJu2V3EI+JiUFMTAwAY9P6uHHj0K1bN5cVjBid2nEVWgsTf/S/OxISefN1ImoNet3e0UJNnMdV5TmEq7qbBXIePIJ7G3+9GYaBj9wH7w/5GC8ffQEXys4Ljr3k2kK86b8E3gXCL3tTEJeJ0DnemBJWe1YY7OPiwh19awK3zIxGrzs7Gm8C6t0I8BxnfmNgWs6DM5ivayiwiqUiRA8LNu5X/zxmNx6c+Xn0dctlXCeWNtR079yaOGDs3GYtgAPGZ+Fn9+XAN9SdAjlpl2iceANacojZ9QsqHPzhomB5xx6+GHJfZ6eUyV4tPcSslnmTOo/8yLPY1GEVeuaMxNDMKWDAgAePwN4sAmNv1HZry1qhK8fLR+fhfOk5wbFFjAgP+z+G3grzFiZL79OV1wNon+PENVV66DWGOjcKN24c+HotGMIbF+O/MokYOs6AroODBL3TbaEmddKWOX2IWa2UlBScPn0apaWl4DjzPySGYfDMM8805fA3NZ3GgONbMwXLpW4i9J3gvBpfWxM9LBiAsWldFlmBv4M3AbiRYnVI5iQUdUtDj9i+Fvf3kHji/YGf4NVjL+Kc6ozZOgNvwJqCVXjI/1H0de/vwndxc5IpxE2emKfuzY2l3ukAwIiEndsMOg5HN1HvdNL+OPQXpVarMXfuXBw6dAg8zxs7D9VU6Gv/78wgnpubi1WrVuHMmTM4f/481Go19u7di7Cwhp8Jjx492qxXfa0VK1ZgzJgxTimfK5zZm22xJ2/v8eEoqypGWb1KWnPM+91aRA8LRvSwYKzZtRLFhiLT8pSQf5AS8g9eD3nL5v4eEg+8P/BjLDw2HyklyWbrOHD4tnA1DOAwwD3eJeUnzmGpdzojgmCcOFDTO30K9U4n7Y9DY5NWrFiBQ4cO4amnnsL3338Pnufx7rvvYtWqVRgwYAB69uyJ7du3O62QmZmZ+PPPP+Hl5YUBAwY0ev/hw4djw4YNZj/x8a33C7roWgUuJljoYNXJC5F9/ZptRjEAOFl0HDP2TcUFdZrZ8gvqNMzYN7VF5+Y+xZ0ULOvj2w+BkoZvaBRid7wb/z/08RXmUufA4fvCNThWkeiUchLXqO2dXpukpzZjm3+sGBG3SMDUdBmhjG2kPXMoiO/atQvjx4/H888/j65duwIAgoKCMGLECKxduxY6nQ6bN292WiHj4+Nx+PBhrFq1CuPHj2/0/j4+PujTp4/Zj7e3t9PK50wGPYekLRnGdOB1iCQsBkxq3hnKThYdx6JjC5CnzsXK/BW4WBPIL6jTsDJ/BfLUuVh0bEGLBPJyXRnO86mC5RM63m33MdzECiyN/xD9/IQ3hjx4/FD0LRIqDlvYk7QWtYFcoZSaUq4CgHsQi4hbJFAopRTASbvmUBC/fv26qSYrEhlvd3U6YxpLsViMCRMmOLUmfjPNiJb2by5K86sFy+NGh8DDV95s5agN4BpODQDQ8Vp8mb8Ce0p3YWX+Cuh4Y1O/hlO3SCDfk/0X9DDvtS+HG0YEj7Syh2VykRxLBnyAeP9BgnU8eKwr+h5/XP3dtCzpqgp3rUpEapF52tbUIh3uWpWIpKsq07K0tHOCH+J8gZ28MPGl3maTnwDGQD7xpd4UwEm75lB0dHd3h8FgMP2fZVnk5+eb1nt6eqKwsNA5JXSC/fv3o3fv3ujRowemT5+OPXv2tHSRLCorqMa5v3MEy31CFOg2JLhZy/J+8hJTAK+l47XYpvrdFMBraTg13k9e0mxl43ke269tFSzvwfaEVNT4qVhlIhne6f8uBgcMtbj+ozPv4ffM35B0VYV5m88gt0yDj49XmgJ5apEOHx+vRG6ZBvM2nzEF8qtXMwQ/hBDiTA51bAsPD0dGRgYAY028S5cu2LVrF+69917wPI/du3e3mmQvo0aNQs+ePREWFobCwkKsW7cOzzzzDN5//31MmjSpwf1FIgZKpaJJZbh+/ToAQKstg6FmrGz968NzPA58e0EwlpZhgVGzYuDr525aplAIE4U0tYz1vT3sHTz/97NQG8wDOQdhb2C5SI53hr3jcBlEIrZR+54pTEF6+SXB8njZACiVCgevjwKfjP4ET21/AqcqTgnWfnr2fzAUXIRabwz0Wg74+EQlpsW645fUSmhrLotaz2HeljNYNas/pFLhn5e977Ox1+RmYeu6NMffBSGtjUNBfMiQIfjtt9+waNEiiEQi3HfffXjnnXcwZswYMAyDrKwszJs3z9lldcjrr79u9nrs2LGYPn06PvroI7uCuMHAN3m8bnq6caiYQiFFVZWxFuvmZv5M/vKxfOReFs7OFT0sGGJPkVkZao9Rl7PHFHeVxWHJgA+w6Nh8aDiN1e1krBxLB3yILrI4h8vQ2DHRP6duFCwLZcKg1PtCpapq0vWZ4/sYeG41kquEneZEAVsh4QzQFY8AAGgNwMZzlag/ykmt47Dgt9N4LlKYpMfecrTHceLOYOu6NMffhTPYygpIiCW2xok71Jz+xBNPmHqlA8ADDzyAV155BZ6envDy8sK8efPw+OOPO1ZaFxOJRBg/fjxyc3PNHgG0pKoyLU7vEs4k5eErQ/dRoS1QIqO+fv3Ry7eP1fUyVoY53R5FHz9hD29XqdJXYl+O8HFIH7aPU44vZsR42P8x9FVYfk/yoO2Q+v1tem0pz4hczOI/46OdUp6G5OfnCX5I69aco0tI++dQTdzd3R2dOpnPUfzwww/j4Ycfdkqhmktz9vS2hud5nNiWCZ1GOPVS/0mREDcwx7UrHbj+N44VWh9mpeE0+O7CasR4xzZbIN+Xswdqg3nHPxlkiGG6O+0cIkaEOf6PQlQoQlKVcKY+WeBOAAZoi24TrJOLWXwytQf6d1RitzC7q9NZCgBUqyPk5tHoCKFWq7FlyxYkJyc3vHErpNfr8eeffyIkJAQBAQEtXRxknytBznmVYHlUP38EtWCv2pNFx/HOqdcb3E7DqbHw2HycKjrRDKWCxQ5t3dk4SJmGJxRpDBEjwoP+D2Og+2CL62WBuyH13426YwFlYhZPDI1A/45Kp5aFEEKsaXQQl0qlWLx4Mc6da97hMjt37sTOnTtx5owxVeaBAwewc+dOHD161LRN9+7dsWjRItPrP/74A/PmzcOWLVuQkJCA7du3Y/bs2Th79izmz5/frOW3RFutx4k/rgqWyz3E6HV7R8Hy5ky8svTUWzBYmpjZAg2nxnvJ/3Xaua25XHYRaaXCseF9WMspVpuKZVg84Dcbd4RNtLheFrAX0oC/UBvINXoOXx/OxPFrKpeUhxBC6mt0czrLsujQoQMqKipcUR6rnn/+ebPXb71lTK05cOBA/PDDDwAAg8FglsM9LCwMRUVFeP/991FaWgo3Nzf06NED33zzDUaMGNF8hbciedc1qCt0guWdhitRWlGEQMWNZtG647ZXalbg/wKfQVd5tCnxio7XYtGxBVga/wH6+jU973cHRQiKNPYNE5Sxcrzc+7Umn7Mhf1iohXeUhiPG+0ZTen5+HsRiMXx8/JxyTpZh8VLPV1FcaUBiyZ+C9TL//WAYAzT5dwBgoNZzeGHTGXwytYdTzk8IIbY49Ex88uTJ2Lp1K+bMmQOp1LnNmNakpaU1eps+ffrg+++/d1WRmiQ/vQxXjguDpGcoC3iXoaCg3PRs01rilTu9J2BH6XZB4pWmBvKU4mScKRHO0uUn8keRQVjmxX3ecsqNgy1qgxp7sncJlt/T9T4oKm4Mv6t9RuzM58IswyIlZQy0ihJIfRME66V+BwBw0ORPQG0g/8/ONDwX6bQiEEKIRQ71murXrx9EIhEmTZqEH374AQcOHMCxY8cEP8QyTs8j6fcMwXJWDHToLxZ0uLOWeGW76g+nJ17heR5fnV8uWO7OuuOlDq/AjXETrMuqqj/Ht/MduL4flXrz1h+5SI7bQsa5/NwAcFu3QGjyJkFbPMzieqnfv5AFbQPAQyZm8ebtzdM7nRByc3OoJl63F/qSJUsEQad2FrPUVOHzSwIUnDWgolj4vDmotxgShbDH/Mu9XzOridfSQ9gUL2PleKX3YofL9k/ufpxTnRUsn+xzDzxFnhjkMQR/l+8zW/d75m+4N+o+iGpnnHABSx3abu1wG9wl7ha2di5VtQ7bz+YBYKDJmwjwopratzmp72GAMaCXdA4GhCuxu+HGI0IIaRKHgviyZcucXY6bRnUJh8LzwgCu8Gfg08Vyw0hfv/5YGv8BFh57CVpOmNCiloyVY1n8hw4P99IatFiV9oVgeSfPzqZe2rd43op/yveDr9MrO7f6OhLyD2FY0C0OnbchmRUZgilDgcZNdtIUX/x7BaXq2sQtDDT5d4DnWcj8/xZsK/VJREoJhwsFru3od7LoON5PXoLp3jPQTX6j1n9BnYb/7vsPXu79mssfcRBCWp5DQXzKlCnOLsdNged45BzTC2YoY0UMQuKFzeh1xSrj4C52h1ZrOYjLWHmTE69svboZ16uEudufjHkGbL7xBiNAEojubj1wtjrFbJvNGb+6LIjvsFALj/SIQnel6zuPpav02HI6t95SBv0lExHm7Y6dpcKJfiQ+x/DU0dsxir0Ng8VDTMuPGhLw3o6leDLmGUzrNMPhMjV3J0dCSOt180wP1gLy08vwx/+SUZln7DGfd04HdTEv2C52ZAhk3rY/irdOvIYSbYnV9RpOje8urHZ4vHaFrhw/XlorWN7fPx7xAeZjpUd63irY7kRREjLKrzh0blu0Bi12ZQl7hU/oeDek2Yfh+/0gKOvV0pUlyfD9fhAkWYeadG6O5/HDuer691xwl4pwX7QCE5R34U7vuyzvzAD7ub1I1Bs7wh01JGAvtwccOHx5/nP8kr7eoTK19tnlWlJAQJDgpzVpzAx4hNiLgriL5KeX4eCPF1Gl0iLzgA6qDAOyjwtzkCuUUsSMsD1D2eq0r5BYcKTBczYl8cq6y9+jTFdmtowBg6di5gq2jZbHIlAs/ILckvmr09OAHso7gDKdeU55CSvFnYwvvLc/BFF5Nnqm/AfKmt70ypJk9Ez5D0Tl2fDe/pDdgVySdUhwQ/DPNS2CK07jX9lzGMLe6Cfw1LBIKGXGP507lBNwp5eVHPwMsI/fgw36n7CXM08V29hAbuD00HE6q50c645SqNXcs8u1tMDAIMFPa9HYGfAIsRfD1yZAJxbpdIZGT6JQG8ANlhJr18OKGdzyYDcUVGcI1sXF9cK1iquYc2AGBG3wNgTJg7F+9Ca7t8+tvo45/8yArt7z9ttD7zR1kktI+Nds3WH1IfxR9bvZMrlIjrdClkLBms8cFRfXy+b5bU1qMT/xOZwoSjJbNlbZF/9L+QuM/kb6VQMrQ0bkTERm/ARRnQlbeLEbSid8C12Y5V7lgDGAe29/CIy+GgZWhpSeb+Gaew9sOpiAFez7UDBaVPFSPKpbgEK/gfh+Vj+kpZo/Tvj66p9I4X+3cgbLGDB4PPr/UKmvRJW+EpU1P1X6Smj4apRpKlBV81ptUOOhro+hp29vi50cLWlqH4nWqC1ODFMbwNX6G98HUhaY3EWOLZfUphnwAGPa3o+n9MCAcGXzF5S0WrYmQHHomTix7ejmK3YFcMA43Ozo5iuIGi98Hq41aGpSn9ofwB1JvLIm7StBAJeyUjzS7QnT6/Jy81p6N74bFGIFqvQ3vlDVBjUSKg5jtNeYRp3fmuzKLEEAB4DpmUlmARwARJwGUVd+BMubN1Uy+mp47nkBJTP2ghfLAFYK1Ol7UDeA1x6nZ8qbOC+fhhXsRigY43VRMFqslnyAcz1XQswKP6tHwsZjwYXL0Ludsfv98eDxtYWOhNZU6itNnRwbCuTtMYC3VW/tSjML4IBxKtv6ARwwTmX71q40bHt8UDOWkLRl1JzuAgOnREFk58QlIgmLgVOjLK778vxyXCq7KFjOwHIHOBkra3Rnpgul57En5y/B8nuj7keAW6DV/WSMDLeH3ilYfrD8H3C8fTcwDfkz6w/BsjBFR0QP/x94sXC8ev0AXktUeR3+33RHwMrOCPiiI/xXdobfqu7wW9MX3ltnWrwhuLfyJ1MAr6VgtOibYjmfvJhlMD/uv9CVxdn79hqtSl8JwDhaYU7XRyBj5Ra3c0YnR+IcVVoDpvTsAJGFP9n6ARxo3hnwSPtAQdwFAjt5YcSsrg0GcpGExYgHuyIwSjjRyanKE/g98zfhPowIj/s/BU9WuM+9Ufc3KoDzPI+VFhK7eEuVuL/TrAb3nxxxr2BZob4A56qF48wbS8/psTNL2PP7zrAJYHUV0Pt2dfjYjEEDVlsGtroAjJX88GJG+A3LieQov+1jq8cdEx2AjtqnYFC75llsbRA/WXQc311cY7Um3tROjsRxPM/jQn4Fvjt6Df+3MRm3rTiMLw9lwGBHY1rdGfAIsRc1p7tIYCcvxI0KwZm92eAs/AWLJCziRodYDOCFugL8VPSDYLmEleDzIV9Dl6XBOfVZ/FthnnAko6JxvcMTCw5b/KKf3eUReEg8Gty/o0c44v0HCaYr/ad8P3ooejaqLPUl5B9CsabIbJkIDO47/Dm8y6836diO0LFyVN71PXShQ61uwzIMeseewnUXzOnNgIGeMwh6p1tT28mRmtRdT1WlQ2JmCY5kFCMhU4WiSuu5HKyR0wx4xEEOB/GTJ0/ixx9/RGZmJlQqFer3j2MYBnv27LGyd/uXn16Gs/tzLAZwADDoOJzdl4Mij2wsL15qStqh5/VYW/gNqvlqwT5PxsyFUqNEevlFRDMx+BfmQfxoQQKq9JVQiBvOYmbg9PjqvPB5bJiiI+4Kn2zfmwQwJXKaIIifV59Dni4XQRLbve5tsZShbXRlJQLL7ZuUxZl4AJoeD9oM4ADwS/p67CpYDXunqY/1jkMP315wF7tDIXav+VcBhdgdQT5+4NXimuUKyEVuYBkWM/ZNtRjAJYzUYu/095L/26hOjqRhBo7HmetlOJxRgoSMEqTmljei14pl6poZ8LoHe1IgJ43iUBDfsmULFi5cCLFYjMjISHTo0MHZ5WrT7O2dbtBxyNishSjGw5S0I6UqBVe1mYJthweNxJSIe3HuXArKy8vgz/tDAQWqcKNjmY7T4kj+Ibvyif+ZtR2ZFmruj8c8DTFr/6/FwIDBCFWEIbsqy2z5gfK/Mc33fqv71R16Vl0tR3m52jQkqEB1AccKhBON3FPe+JnzeAC8xBMAB8agBcNZfm5uCwPA/fQ3MIQNgTbKeG0tjUH+Kulpy2XgYTGwp5WmYsWwVRb3sdYL21IKXgkjxZ3eE7Gj1DyXfnPNLnez4Xgez/6Wgmo7O6/aq+4MeBTIib0cCuJffvkloqKisHbtWgQFtZ6xmK2Ftd7prAjg6j2ClXBSjLr0ANb1fwsr8j6DAZaf0d4edqdZRjeWYRHNxuAkZ94c/s/1/Q0G8Wp9Fb69+I1geQ+fXhjeyKxrLMNiUsQ9+CL1U7PliRVHMFE5CW6ssAMacGO2MQCorJSiqlKDEC4Lbmd+wC/5+8ApzZvzQ3R6DKm23YRsYGVmw8sAYwDm5F4onl3TWsAZAIMWjEEDxqCBOPswvPbNB2MQjuE3Pw4Prz8fQ+nEddCFj7A4BvnJmGfw5fnPzZbxPKCviIbYI00QyJ+MecbmOS2p3ztdwkhNGdvCZRGmhC8yVk4Z21xEImIxoKMSB9OL7dqeZQDOQlVdygo7t9XOgEe904m9HOrYlpOTgxkzZlAAt8JS73RGBIT0k6H+HCE6Vov9XdYBgNUADgDLzwo7VMUwsYJlRwuOoFpvexzthvSfBM+bAeC+oJkoKMi3uI+np5fgp9b4sAmQi8yDtYbXILGi4QQ1In01Aq7+gQFJz8Lnt0mQpP2KzR7CXtdTKipMv6w8GPCM+fU1jhN/AAZWZracF7uhfPRHAIy1//zCQuSXlCGvTIPEc+mQHXzHYgC39KXL8By8dj5m9b1M6zQD/xfz7I1z84AmbwLUWQ9DkzcBdZ84/V/Msw6nXq0N5EHyYFMAB4Bu8mg8FfgMguTBFMAbied5pBdV4qfjWXj21xQkZlrPjggAQ6J8ra6TiBgMDFfi+ZGdsH5Ofyy/pyfkYvPfVykLTO4qh7TeN7CcZsAjjeRQTTw4ONhqDm9yo3d6bZM6IwIibpEgIEoKsQeHzAM68AZjAP8z5ivkeF+yeTxrM5OFMxFwgwLVdZrUtTVN6j3EwgQrgYFBKFIXYuOVnwTr+ir6QVmtREF1nsVaZt2gXZ+HxAO3h96B36+aP3s9UP43brGQohUA3CuuICRnB4Ly9kFsuPH8/7CbHLli819LlucxubwSBkUQ1N1nQN19JkSlVwQJWlQ+vVDu2dWYsY3TCBK91K39A0Bm5hX86z8Lt2QthxQ3fp+reCm+M4zDU+I/BIP5WF0l5OfWQ93dcgCuDcxfnV8Bz9LbkVUyAgCgq/lXHrQDj3Z7ukm50wFjIF8/ehPOnjWf972bPBrr+9MzcHuUq/U4erUERzJKcORKMfIrbvwORPopMCjCx+q+QyLN10X4uGFwpA+GRPmif5g35JI6d+v+7vh4Sg9TwhcpC8zr745YPwmivET4+HgltBwleiGOcSiI33///di2bRseeughiESum36yLasN5Ec3X0FgXx7uQcZbbvcgFhG3SJB/koHPbQyKcrMAG4/WZKwMy+I/hL6yE+7alIg50WJ416xjGRYdDDFIFwmb1BU6YTN2YGAQvr34DdQG82ZpEUS4S9m0SW3ulXdC/XxlBfp8ZBVux8jvn0T56I+gC+4P2eXtcDv7I+KvW55v/jdPYa/4YbwCbmO/QHHkOEAkAQBwniEonfAtPPe9iJROz0LlY7xpUfn0RkrP/6Bn+nLjOW1kagOAdGksftDPx5eiD02Z2R7RvYzrHj0xNjQAna+sFQRyj38WQe/TFfoOAywec1qnGZjWaQZ+3LYTn13nwdccQVcyArqSEaj2jQAcHyFHHGTgeJzPKzcG7YwSnL1eZnXo15ErxcCozlaPFaZ0w9ReHRAd6I7Bkb4I8bY8Zr/WgHAlPp7SA2/tSsOcaDFi/Yy/x7F+Eszr747v0vR48/ZoCuCk0exKu3rsmPkXLsdx+Oijj6DT6TBz5kyEhYVZDObx8fHOK2kLcSTtan21tSWFQoqqKuPdfm0q0p8v/4jVF76CwcJ4ZRkrw5xuj6GLeILZXfzsyGp08jDgcoUIP+ZlQRq+2mw/KSvFs+wLkDJSs+VdBsfg8YOzwdW7a7jVczTu8Z1uem0pTWr9Gl/d7Wqznj3h74EEN/Obh+FVGnyZlweeEYMXy8HqrHdOKxSxGNsxFPp6D4/f6f8ehgWNsLqfrbLZ2i419Qz2lAXheIkEQ9iz+EDyFebrnkIC1x2LB3mgi48YXS+sQGiOcLw65xaAkmnbwXmGWC3X7t078MtVGU6qJGbL3aUi/P7YQHi7SazsaX96UXvfe3vR2LSrhRUaHKnpRZ6YWVJnStmGbX18IDp42Q7OjrjZPjPSdE1Ou/rggw8Kpsmsjf2LFy+2uI5hGKSmpja2rDeV2qQdlgI4AGg4DdamfYPqa3qo9casbloO+O6KHLcFabE3Twod3wlivTtYcaVpPy2nxWXmEmKZ7mbHW3X+C0EAd2PcMN5bmHmtVtJVlaD2ABgnbnh1VSL+G52F0edeAWNQY2YZBEH8X4UMmWIxIvR6MDYCOABs8fAQBHA/mT8GBwyxskfTZKmlOF5ifE9HuDgM13wGABgRKkUXH+OfxsWuT8PPSwH5+V/M9mWrC+D152NQTf0NsJA9rtZtQVokq8Tg6tTnK7UGfH8sC8/eYjlTH3GcVs8hOacUCTW17YsFlQ3vZMXRzBJM6kkjb0jrZlcQX7ZsmavLcdOxN2mHjtdAFLIGomsPwVDVuWYZg735Muh4ABBBXx4Hqc9Rs/3Oc6mIZW8E8UwuAwkFhwXHH+c9Hu4iy4ld6k7c8PFxjek5Xu3MS1oOiD39DljG+B5uqapGqE6PbIn5r9XPXh54pVhl9T0aRHJcD7wVv3oXAJoCs3XjwyZA1Ighb/YycDx2FysFyxViBtOi69S+GAblI5dBVHIJkryTZttKCk7Dc98ClI/93PIYMgC+Mh7xfnokFpnXujeczMaMfiHw95BZ3I84ZtWRTHx79JpD+4pYBr1DvDA40gdDI33RNbDhfAuEtDS7vh2nTGna81IiZGlKSWsYVgd5h19QeflV07K6I9j05T0FQfwSfxFaXgspIwXP89hnECbeCZQHYaTXaIvnrD/zkpYDPj5eaTbzkhcq8aP+Nrwo/hUSxgARgBll5fjQz7zTzxZPDzxbUgpFvSc3Fe6RyAmZgLLIcThedhnZ+Z8IynFnRyvzdTfR/mta5OukguX3dJPDq36XYbEcZXd8A+Uvd0JUad45Tn5xC/T+3VHdz/IYcQAYFajF8WIx9PyNQK/Rc1iTeA0v39alaW+EmBkc6dOoIB7iLceQSB8MifRB/45KeMgoiSVpWxwaYrZw4UIkJydbXX/69GksXLjQ4ULdDF7u/ZrVCSzqkzAycPnTra43VHYCbzCf/lMPPS7zxl7vZ/kzyEWuYL/nC64jQHXObJmyJBm+3w/Ctj9/Fcy85M2pUHX5byxiv8UO6UKckj2BVyQbIGFuPA6YXFEBN858vwqWxVYPY62GZ6VQd5uKE30/QNKAFcgJnQBO4o7DFeZTnQJAf/94dFBYf+bsKGbnTtz/7lxMvviP2fI5GX9jxrvPwm2PcEIYzj0IZXd8A14krDm7H1kGacZeq+fzkvAY4idMMrP59HVklwoz8xFzPM8jrSYf+Qd/pdnctleIFxQS651t5WIWwzv5Yv6ozvjtkXhseTQer47pipFd/CmAkzbJod/azZs3Y+jQoejdu7fF9VlZWdiyZQs1w9eoncZTp5NArTZ+mQ+OG473I+fgpcsrBc+B4wwdcJnJgZplIOd4vNflIeyWD8f6E9kWjy8XSxDtPhgX1PvMlp/nUtGV6YZ/DPsF+8RqdZhYeB188X9Mw7OUJcmm4Vmfid6Fm+QxwKDHQPY84tnz6MQKbwTq8+Z4TKioxK9e5h0xfvL2xoS455DodQcW/51vfMZe877L9eU4VXVKcKwJHSc1eL7GctvzFzy3/AoGwBNnt4FngN+7jMTki//gvrPGIWWev24EAFSPMU+aow/qi/JR78Frzwtmyxnw8Nw9F6p7t8HgY7lmfUugFkeLJdBwNz5rPcdj1ZGrTZq1ylLmuPagpEqLxEwVEjKKcSSjBMVVxr8bmZjFg31DzIdw1SERsejf0dssEUsXf3cMifTB4Egf9An1hlRM8z6R9sMlt55VVVUQi+mutlZtENdoxNBqjb1jJVmHMOqf/6JboBfOycxrd9NKzqOjTo3F/n74b2ER+mW9g1XVLwGwPM2lWs/h3OVOEIeaB/HL/CUc4Q6jDGWCfV4qKjY2w9TMn50RMQORmT9BVDOvuNigxv9EywEHRhDOLKsQBPErEhF+0TJ4f0e24Bn75pyD4GDea1gpVdrskV5XQ4GstnPeawV/I/DP30xdzBgAT57Zhn4FFxGfd95subVArom+F1WFqVCc+spsOasth9eOR6C6dxt4mTfqcxcDwwO02Jtn/ln/eS4Ps+PD0MnPseevlsb0t0V6jseZnDIcqQna5/MqLOYj1+g5nMgqxVAbyVZu6xYAuUSEwZE+GBzhg0BP6ndA2i+7I21OTg6ys2/UBNPT0wVDzwCgtLQU69evR0REhHNK2E557nsR0FcjSyz8MuqprkI3nQ5/ZeWYln0g+crUe9qS6rIoeAQrwIjq5FKHDv9yBwTbjqiqxiD1jSxlIk6DTle+tTJLuWWVeVLkJCoRMkgF9yCt2XIkKnHPHRr81s38yzMpaxX6cE8gAd1Nz9gndZFhv/4A6iVaQy+v2yBhrQ/Bqqs2kPFaLfjyMnDl5dCdTgZfXobLGbnYe+Qixqgr0Sltt+A9MoBZAK+73PPXjYBYAk1eHhhvJVilD1hvJSoGLIC4+DykV82b48WqdHj99QxKJ3xnzLFbzzB/HZJK3c2GOXE88NWhTLx3d3fB9u3d9TK1afjX0cwSVGqtZyysKyGjxGYQnxAXhAlx7ePmhpCG2B3EN23ahOXLl4NhGDAMg5UrV2LlypWC7XieB8uyWLp0qVML2pZpNMYObAaDCHq98YuqfPRHKN/5CMrqja+Xcxw66cyfn1bxUszXPdXAWUTQV4dB4nGhwfKMqRY+n21sAL92wBe8gcW1A37oeEsR3IO0ZsunbeFx/j4OZyNuNF3+q5BhtdtXmFZpzLOu5YBN19KgiLyR6pXheCg0wPl/lNiQvgfu2iq4qavgpqmETF0JWXUlZNUVkFRXQlxZDlFVBUQV5WAqK8BohB0FAwA82sD7sfbeGQBeP69DuYV1xe7uEItCIJJoIZZxENX8BOj/hnvCMlQOFWbYk4uAhwaF49N/0s2W77tYiHO55egebH0saHug1hlwIqu0JnAXI6PYsf4ARzKKAVhPxELIzcTuID5mzBiEhoaC53ksWrQI06dPR9++fc22YRgGCoUCPXv2pJnN6lCrjcFFJGJhMBg7fenChuHooLnAtfVm28ZqtWYfioaR4RHtAiRwtmtqIsVliBXpNreptdTfD6E6LQZVNy6JDcczSM0LA3+Qg6gm1RVvYHDtgB8CepSj4IwHeIMxaLMGBgs3clgzFsjzAdzVgEc18FdxRzxUvAMeuip4aqvgxWbAc78eHmrjNgp1bW/Lr6yWozXgKyuhA6CDeQ/3wF5lUJxcCb1fLIB648d5Hnd+sRARpQYUihUok7qjVOaOUqk7Dq5OQ5dxvcB6K8F4K8G5Ob9DX0sqV+txx1cJ0OhtpCe0QSpi0C9MiVGxgegd1PBc961Ze+3HQFqG3UE8JiYGMTExAIxN6+PGjUO3bt1cVrD2jqkqxOUrWwWfQJzmRtO0gZXhI+3UBgM4ALiF/AKGtS8blYbX4XU/Jf7Ksh3EtbwIp/nOOMZFI5GLwQmuGz5L/ARBBvPJIXgDg/xkT9Sv00r1wFN/1v/Szq35aX9YKYfaeVk8978M39CXUCyPvLFepwV3MQ3CaWsApABlO2+8LAEANzew3kqwSmNgZ72VYJTGfyXxgyCJbTtN8J5yMcJ93BqVfCXS1w2DI30xJNIH/WrykTc2Y1tr1F76MZDWwaHeZzt27IBKpcLgwYMxcOBAeHlZnxzjZibJOgTPfS8iwm0KcuXdIJGIoNMZEF1+EH7fvorzQf6A2HyYWfc6QVzEafC86Dckc51tBnIRy+DJrq/gu6tv2TX2XM5xWFIonMWsLgMrxdO6F7FHZ54O8qN+9+GthNWQG+o3yTemQb59Estu3LAwBg1uyf4COyMWQS1WGtdXNTL4VFeDq64Gl3tdsMrdzc1mEK/e/Ct0xxLrPMv3Nt4AKH1qbgi8wSp9ADc3QcbFxqibj/z2mEB09LGevW5IpI/NIO4uFWFghLEX+ZBIH5ekPCWkvXEoiLu7u2P9+vX48ccfwbIsYmJiMGjQIAwePBjx8fFQKBQNH6Sdq80nzuirMa7ia/wd+ixUbnHoX/wzYkv+Ag/gnEyYbKRHvdnhFIwWayTv4xHdy0jguuPOKBl2XDGfOtPA8ZjQaRiigz7Aq0fnQcdbr5HLOQ5f5BUgXm17/mwRp8VK6SeYbZiPw3VuIM74d8LpHsMQn/x3qwvbBjCokLqhXKKo868C5RI3VEgUiCzNwZC8c3aVmwegjY6FISAASpYFpyoBX1oKTqUCX1YKWJhyQCQ1b3VQGFQYU7QWKf0/AM9KIbI0v6mDGG+lzfX61HPQHvzH5jYAAKnUrIbP1NT8TcsCAiEbbj7HvLV85G4SER4YEGb1VEMiffH9sSyzZbFBHjXJVnzRo4MnxCIa/kVIYzgUxH/55RdUVFTg6NGjSExMRGJiIr799lusXbsWYrEYPXr0wJAhQ/D88887u7xtQt0ADgBiXotbsz9HaVEY/KqNz62vSMSoZM2/sNw5DhE6YQBWMFp8KFmJBz2+wdSuEpzI0yG3yjxgnMktwy1iNf6vRIXPvN0tpgGVcxyeVpVaDOAGVgYRZ75cbFDjI9lXGFxt7IgWXZyJp09vRjdVlmB/Z6oSy2Bw94TB3QMauQfUcgWq5O6okilQJXU3BehyiRtKxcYflViOUoih4Rho9Rw0eoNghqo/tiywGMB5CNsRGADSi2nIn/cSwutNTsEbDODLy8GVqsCrSoz/FhXA49xKAIVm23qVpqJfzg+oGP0htCUqC4P9HMMqlTbXc6Uq+w6k1YIryAcK8i3OZs+GdQQzeDiSc0px5EoJEjLN85F7a8rxwtk/USZTgC0MgDo/VlDrZ9zdwTAMeod6IUwpR68QLwyJ9MWgCCV8FMIbWULasvz8PMEyVz5CcXgwt4eHB0aPHo3Ro41pO1UqFQ4cOICvv/4ap06dQnJy8k0bxD33vWgK4LXEvNYUwAHgrFT45RWr0VpMoVfFS7FA9yReua0LxGVX0cVHjNwq8xp76fm/kZa7GF/7e1nN461mWXyh9EYPjdYskBtYGTIiH0BkxjqzQM6L3bBI93/w0lTioXM7cEdmoj1v37gvAM7dHby3N3JkZcgTV6JSDlTKgQo5gwo5UOF243WlHChlQlFw/RlwrAjBXjJse3yQ3eezJDGzBC9tOWvqTPVN3EQ8cXabWcDmARwNisHAesPMeADlU++1eFxGJKoJUkogItK0nBszHNwvd4KtNg/kbuc3wOAfC77XTChX/4CrZ5LBVFQgO6cEJy4VwUtbCW9NJby0xp8wqCGuKAPDWe8ExjZQE+dVKpvr7XXVIMVzXxxGtc5yWfyrS3H71ZqUvxcBC4n3ALHYVMtf6+0N9qoP2FTj6+o6tX5xt2iwXsIx9oS0JQUFbSSIA8YpSVNSUpCQkIAjR47g5MmT0Gg08Pf3x+DBg51VxjanfPRHZjVxSyw1pWvV4QDyzZbxPPCM7nnow4ahf0clzp69ii5KEf6tl7wtKGcp5gZ6Qc3abo5UsyyeDgrAirwCDFRrYGBlpoxt5Z5dTRnbeLEbMm/9Cm6fH8U359bBU9e44UAMAFanQ8l9M1E+xB8fnBQOuRK8/8LO4FgR5GIWb97ueBazWv/964JZb+gtXUcCgCmQ8wC+6nGXKWNb3eXl904XJHppCOcZgtI7VkG5ZToYzrzPgPuhd6D3jYau2whczrwMeHqADw7Gfi83ZFWbDzMM93HDM1HFQEUFxJVVEFVVQVxVhd5RnY3N+aUqsEHBtstib028Admc1GoABwBvrR0d1fR6cEWFQFGhzc28PvgE0sFDra5Xb9+K4upyaGXupmZ/pvZ5v6cXmAZ+9wlpjxwK4t999x0SEhJw7NgxVFRUwNvbG/Hx8Xj55ZcxePBgdO58c4/h1IUNQ+mEb+HxxxyIDZY7mp2RCbNInai8FaX8OXgzNzpAMQwQwKhwOKcMSVdVCA8IQrxYg2/PZpjtu9DPB1pW+IUq5zhBYFezLF4LjcLO/HKkdHoWKh9jc7HKpzdSev4HPdOXo6jjc1D99xvMzbhk13vmpVIw9Z7nM1otvL9bg86T/4C3RIlSncrmMSS+CWDVsfho/GQMCFfadV5b3rw92mwSFwDYET0SfQLFGLB/C1b1mIjfuxgD+/Zut2Cwnw69/v0L5VPvbXQAr6XvEI+KkUvguf9ls+UMb4DXrqdQcu8fN5YxwLhgLdZcMe8MdrWkGse9xejj5QaD3A3w8wMAyMdanzK2Ps9Fb4ArKjQGfZUKXKnKdANgfAygAqcqAXTCnAF1lcpsZ5Lz1jg+1Wd9TAOPCNR/bEXFGeFc3AAAlgXj5V1Tq/c2JeZhajrwmZ71K5Vg/QLA+vs7rdyEtCSHgviyZcsgEokwceJEzJ49G927d29SD9f26AgXhzPaqXiO3QgZY/6cWw8gVSrMRqZWR2ArPxQPis1nHJsu+gcbtaPw1q40bHt8EPz8ebhLr5lluFLlzIQy8lto+RtfyrXPwL9QepsFchkjwYL491Ds1x+qs+ZfiqXiTsjMmQjNqk9hb7JKXipFxcS74fHHVrNAzkulKJ3zCHJKklGptz2XOGCcrc2t43cQufcE0N/Os1s3IFyJj6f0MAVyKQvM6++OSL8JODhmHLYnlQM8IGF4zI5Sg+s5GPkzLTehN4a6+0yIC8/BLeVbs+WsphTeOx6F2G8u9DWT33TxNKCThx7pFeZ/in/liNHDQwNH03xLevVpcBue54Hqauw5dgHf7j4jaNb31lTinF+kxX1r85GP87gEHHesjPU19IiAU5XYWMmBV5XAoCoBMm2fRzJ4KLw/+MTqekNONrRHE2qG9/kYbwq8lWC8vMFQOmnSyjj0Gzls2DCcOHECv//+Ow4dOmTqmT548GB07NjR2WVsc5KuqvDzlg34kv1VEMABIF0igaZe7ZjXK8DrfLGRuVUQxAewFxArvo7nx98OwDikrEcHTyRmqkzbGKo6406/17Cz8B2oYTDrhR6n0eKZoACoWRZyiLBk4Efo61cvSHIc3A78A4+tm6GxMRRKI3ODfsgQKA7/C0arBS+VouSZ56CLjoEuIhLeyz+FSKczW/5+8n+gt9Fjvi4dr8H7yUuwfvQmu7ZvSG0gf2tXmnHSFT/jzVOsnwT3BhZhZ7Ef7umoQWcP+1J+2qti2JsQFV+ANNt8DndxyQWMrXgXf0a+gdpB5YsUf+BW3a9Ypp+B1YYJAACVlsHRYgmG+tuuKduiN3BIuV6O0modbu0qrHkyDAMoFOjbPxaLkmx3ufOWizEwwsc0kUhAzTzo+nAeumClsYZvpdYPtX1T7jb4nL+01K7jNHieBmr8+nNnUPm/9yyuYzy9TLV74/j9OjX9umP6lcbn/IzCnSo4xKUcCuKrV6+GTqdDcnIyjhw5gsTERLz99tvQ6/Xo0KEDBg8ejCFDhuCuu1wzF3Rrt+3PX/El+x4UjNbi+rMWnocb1KEAGKTwUUjlOiKWNZ8TeUXMWXh2vM/0umcHL7MgDgD+2cVYrirE676eWFJYZOq8NlCtwYq8Aiz298M7JSpEqzWoGxok6ZfhuX4dJNeu2nxff4UPAHvfvejZ2R+avv3g/9OPKJw5C7poYxIgXXQM0u+fiU7bt6F0ziOm5S/3fg2Lji2wawy7jJXjld4NPz9vjAHhSmx7fBDO1mt1iJBr8HKsixKHiCQou30lfH6ZAFG5+Wfpo8vBqKxPsb/jPEQX/4V+Zb+CYYDF4nUAYArk+/Ml6O+jg6wRk9DU5iM/cqUYx66qUKk1IMhThpFd/KwGE38PGboGuJv1OmcZoEcHLwyO9MHQSB/EBHlCxAr3F0d1hjjK9uMzXq02a8LnSktrgn5Jzb+l4CvKATfrY8x5vR58uXP69jc0PI+z0SmQLy8z5ufPsm/Ocp+fN0EUan3YnfbIIdMwP+PNgDcYiX1zBhACAAzPWxjw6gC1Wo19+/ZhxYoVSE839sJOTU11xqFblE5naHSGKI818XCrFiboqOKlUDBa/NfPBxvqzfKlKRwFbYGxpv2I6E+8IfnBbL1BEYjiOUcB1njfdfhKMZ7fdMa0fgh7FmulH0IO2+O/AWOv89KJ30GjiEH2u+/A7fAhm9tf9g7Bil5TkeoXiWXDPdHBwxhV4uJ6CQJjTk4WQkLMv7Ti4nrhZNFxLDz2ErSc5RsbwBjAl8V/iD5+/Rp8D46oX9bU1DPwrveFXr/sgLH8jhIVpcJnw51geGGNuljaET7aa+a94nngv/oHTIF8XLAGtwbqcLBAgl25Mjx3SyezsdhqnQHHs0qR0EA+8o0PDUCUn/X8DZ8fuIKdqXkYEmXMkBYfroSXvPUEE16jQfUvP0OqrkB1XqHg+T5fZf+zecWTz0Axa47V9ZXffIXq71Y7o9jw3bkPrLv1NLFFE8cKWhgYd/eaDns3avp1n/Wb1fqVSjDuHtSprxWp/z0DNO07BAACAqzPq9CkBzwajQZJSUlISEhAQkICzp07B4PBYEoAc7PSjPsEknqd2qp4KT7R34MXxL9ZrIlz6lDT/7cYhuFV8U+QMjeaeEVV+ZBe/RvayDEAjDXxuj6QfGVXAAcAaKuh/+wFlJz2hFuFpek9jCrEcnzffTy2Rw4Bx4ogYoBAhWNfFn39+uOhro9hVdqX4C1MMilhpJjT7VGXBfCWYvCLBXjLTfX1Azhg7Oy2ULzeFMQP5EvB88DuPOPvzCf/pKOoUgs/dymOZBTjZFYptPUHxFtwJKPYZhB/YmgE5o6IbLVNv4xMBsWsOVbTrvJaLbiyUuO4fZWqJjFPzRj+mv/XNvWLgm337Oed1LMfYjEYhfWOgbzBAL5M2LrAV1aCr6wEl21nPgaRCG6z5sD9MeuTJBny8sCrSow3B0pvMDLKhtdeOBTEly9fjoSEBCQnJ0Ov14PneXTp0gUzZsygVKww9k6vmPgd3LfNgYRTo4qXmjKuJfMRuChdJ9jHUH2jL0ExvLCH6487RUfNtqk+/gNENUHcUy5GlJ8CV4qMX2gLdE/iO8l7kNZ7Bs+BBceIIK6pCVYVSJF7QglNCQCL83MZVYy8HY8pBqNUduMOMNidtdikWpenp5fFCR5OFh3HdxfXWAzgAKDjtfjuwmrEeMc2WyD39fVDaKh5Hw4fHz+nn6dy6GtwP/yOxYQy9fE8cJDriVvZU0jholDEedcE8Btb/5DU+GQ7RzJKMLO/9WZdmaM96FoJRiqFyD8A8A9o8rHEPXtDptPdeM5f+28jm/NZpY/NmyK+rMxi5r9GMxjASG13Q9Xs2IqqNatuLJDLb6TjrdOUz9ZLzWtqEfDyAiNqxHMd0mwcDuIdO3bE5MmTTR3a/Pyc/+XXlh3h4vCzbgGWsF9ivu4pU+7zY1JfuNf7w+b0HuD1N256JCyw0TBSEMT9c/+GqqoQvMLYSalXBy9TED/CdUcer0RHxnws7hWvIbjiNQjDL61A8Sk3lGbYTokr6hoNjxcX4B+DP0r3mA8vC/Fo+I/Y09NLkNjgZNFxu56Jazg1Fh6b79Im9bqCgjogOtr1k4hU930SACwG8voYBhglSsYoUTIAIJv3QwrXCae5KKTwnZDCRUEF+6csrc1Hfktn6/NvE3PyceMhHzdesLz2ubyxVl9a04Gv5rl+6Y1WgNr/sw3cUDhrLD9gRwa/+s/51Wpwudct5uW3iGHAeHmB9fOH8tufrN6c8BwHLve6sZnfTdFqW3baE4eC+L59+xAS0r6mSnSmpKuqmmFNsRiOz8zWieTZgu256jDU1rSkLDC5qxzbLvZCLu+DYObGsBoxDJBf2ITqPk8AAHqGeOL3M8YZwYayZ9GRFSbTSPO6FeIzubi8PxisxnpPZ8bDE4on/g/yu6eAEYlwZZ9wfHiIu7C2Zs+0iu8nL7EYwMUQQw/zlgMNp8Z7yf91Wu/01qK675OQXPsH0msHGpVzPpQpQqioCONFx0zLrnIBOM13QgrXCSl8FE5yXVANY/MoAyCG8pG7BCMWg/HxBevjnBsiRiSC9JZb63T6s56Xv8FjNaGznl143vhYQiS23bpQXoaS+6YYX9Tm5TfrwV/zf6V5Tb82ax8N4Ws8h64YBXDb3tqVZpZgpJaE4cHKhU2hBrWxmbN2HHOsnwRRXiL8fmoEnhRtNdtWfu5nVPd+HGAY9Aq5kaLyYdEuwXGzyjoh+IcdcMsTpgGsSzbhLrg/OResj49pWXqR8LljbIgvAgLMH5PYk07QUu90McQYwY7EQe4fs0AuY+V4ufdrDR6zrXE7+VWjA7g14WwBwlGAiSJjGtxZzDJ4R8VjMOUjb1NEHcPhteR9s2Vmeflra/rWav2lKvCqUvDVVWC9baer5W2NsW+EhnP21+mk10BefksYDw/TED3FnEchHTLM6ra8RgNIpTd9bZ9ue1zAUqYwCcNjXKgef0uEQXxilwE4rJUJxjHnxI4HLpgHcXHJBYjzT0Ef1BcRvm7wlImh1GbjNvaEaRu9mkX+KS+UZ6jhButN2KJuMfB4cQEkcT0F665YCOK9O4Ug0N92Bi9L+vr1x9L4D0yBXMJIMcfjYXSSdEYnXWd8X74WOuggY+VYGv+BcAx7G+d28iu7mtIdwTES/O+JaWDF1jsqyVI3wqCMgt4/DpDQDIOtmVlefjvxGg3QwPNqNiQUIlOnPxVgcCwvQsM5+5t2s8BXVICvqACXnQW+gfwCFe++A83+veY9+C1k6DN75u+tBGMhW6YzaE8koWLp25DUGXYLAJK08yh+czE8Fr0Bab8BTj+vw0G8qqoK33zzDXbv3o2sLGNgCgsLw7hx4/Doo4/e1NOR1s8UJmF4zIlSo7OPHgcrcwVdu54cMBwLh/kKhiaEhERCVz4Qkuvmz8blqRtQEdQXLGNM+jI6azdYhgfPASUX3VFwxhOcznoTKuPpBcXjT5mazusrV+tRWGk+FEzEAOFK6+N4G1IbyN9PXoL7vGegq9yYG70P+sHXxxvf5/+Al3u/5tIAbk/Tvyu4H1lq9+xpAMDxQBofjq5MFsSM9bzlAGDwj7EZwBltBTz3vQQGPHiGhcGnK/SBvaAL6AV9YC/o/bsDYsc/V9Ly7AlKnq/caN3ied4YLEvrJOZRqerMyndjHL+x1q8yjuNHw6lxm9xsX0eD5yotBQwG8MXFMBQX21/bd1OYDdcTd+0G9yeeblJZtSeSUPbyi4BGDZ8Vn5kSXUnSzsNnxWfgtFqUvfwivN7/yOmB3KEgrlKp8MADD+Dy5cvw9fVFbGwsACAjIwMrVqzAzp07sW7dOigbcTfZ3tTNFDbRX4VOHgbkcbngGfMQHigPgq/M+jO26tj7BEFcdvF3VAx7E5C4oV+QGPdd/xtV+VLkHveGptT22F7ZxElwf/IZm3f6V4qFtfAwpRukTezB3NevP9aP3iS4WYlxj22WZ+CunEnIlsohiwQ1cR5AjqIHQqrOCMaJL6kZJy6HBt2ZTPRkr6AXm46eTDq6MDlg6/wO6QNsjz8VF54BU3PbyPAcxMVpEBenQX7+F+P5GBEMvtHQBfaCPrC3MbD7xQAi19RWSMtjGAaMpyfg6QlRmH0ZNnm9HnypCg2mFeF5sIGBxmCutZ4Twh4Nd9ZzrNbPV1eBr64Cd93Yqa+hGr/u7BmUvfKieQre2pq+txKGkhKoN/5kmoeA0Wrhs+IzVEy4Cx7bt91IRa1RuySQOxTEP/vsM6Snp+P111/H/fffD1FNbc5gMGDDhg3473//i+XLl2PxYudm3mprajOFJSQY52c8rz+L+q3b3bxtj6fXdJ4I/sDrYPQ3AiurLYfm1M8ojbgTI69vQnmCFGWZSpvHUYeFIWjx25DE9Wiw3FeKhIkzbI0xJrbV753OAzjhPx1pvmOMGdsKfzUur5foRQ0ZTvBdccLQDbXVDAXUiGMy0Et0BQu6V0DbcYTNc4vzrUwYUoPhDRAXnYO46ByQ+jMAgGcl0PvFQh/Q0xjUA3tD7xsNiFpP8hfSvBixGIxfw5PGyEaOgmzkKFNefq7+ML2a2v2NWn+dcfz1htw1nIZX1cR3Zd95uJJi8KUqGEpVDeblr8VotfDYugVM/ccWGjUqlr4N31+3Wt7RAQ73Tp82bRoeeOABs+UikQgzZ85Eamoq9uzZc9MH8VqensbOYHkqYQez6DpB3GJzr9Qd6q53wS11g9lixflfkHO4GF6//44ynfUAy7m7o2LSVFQPH4EwOwI4YLlTWycK4k1iCuRHluKE3z1I8zWO90/zNc6W1rdwE5bUyZ1uxCPa04C0chFqG96rIMcxPgbDht2J8gHWx33XEucnN7qsDKeDpOA0JAWngXPGnAa8SIaSe7bCEBDX6OORm09tXn6RQgFRB/s6Qps69dV04Guoxz2vaVpNvxarbKBToIM3C4IADgAyOTxee9Oh41njUBAvLCw0NaFb0r17d2zevNnhQrVXGdUZgmV1a+LWmnvVsfebBfGqfClUf16HZ+kGK6lTAJ5hUD1sBComTwHvYf+4YsByp7Yov8Z3aCPmqvs+ieq+TyJt9w6z5Wm+4xB+3yfwTMoC/kmvWcrjzg5aDA8wplz98/qNhC8vjDRPvWqLLmwYGE4HcX6KIId7o3A6GJRRNjdhSzPAeXYEWEoKQhqvsZ36/P74C7xGbcy9b3q+XyLI0GfWElBWKujU1/DwPOf07IdMDq8PPoa0r3P7/TgUxP39/W3mRU9NTYU/zddrRs2pcV0rTKwQ7W39ZqiWPngA9MpO4K9nIP+UF8oybdeKLyjDcPKO+3HrSMdS31oM4r5UE3e12sD82YF0TAjVY4iv8RnbiADjv5ZypzdE3X0m1N1nAgCY6mKIC05Dkn8a4vxkiAtOQ1RhX7IPg09X2z3b9dXwXTcSEMmgMzXDG5viDd6RphnbCHEmRiaHKEgOBNnX34XnOPCVFeBLbjTri0JDbe+jcsLseTI5FI887vQADjgYxEeNGoUNGzage/fumD59Otia5Pscx+GXX37Bb7/9hvvuu6+Bo9w8ysvLcEWXLkg52kERAi9pw+lpeYMBBdd7oGx7FTi99S9DkZTDrriB+Dh8OrrKJbjVgbJWavXILTfPwc4AiPB1Xg/m+o8NPD0pj3OtBwaE4YEBYfjnn7/M+gWNCNDh7Zljm3Rs3s0XuvBboQu/1bSMqcyHpCDFFNTF+achqsoX7KsP7G3z2OLCVDC8AdBXQXo9EdLriaZ1nNTzxvP1gN7QBfYE5xVhTE9HSDNiWBaMpxfg6QV724sUT/wf5PfNqNODX1VnHL/xx5B1FYaLF6wn6tGoUbVmFcSx3VtHTfy5557D4cOH8dZbb+Hzzz9HVJSxme3KlSsoLi5GeHg4nn32WacVMjc3F6tWrcKZM2dw/vx5qNVq7N27F2FhDddIOI7DqlWrsGHDBhQUFCAqKgrPPPMMbr/9dqeVryHl5WVINwgzoNlTC9eePI7Kjz+A4Uo6AGsBnIeycxX8e5YhgY0Dx7G4UmqAnuMhbiDXeX2WZsEK8ZZDLnFeE2n9xwbWJrVor8LDI1u6CCa8eyC07rdBG3mbaRlbmQtxbW093/hsXBcgzCVQl7jAegc6VlsOafZhs7nVOZk39DXD3HQ1wZ3zDKXATlodRiKxmZffNLysoZ77GjXKFsxzepO6Q0Hcx8cHv/32G1atWoU9e/YgJSUFANCxY0fce++9ePzxx+HhYX36vcbKzMzEn3/+ibi4OAwYMAD//vuv3ft++umnWL16NebNm4e4uDjs2LEDzz//PL766iuMHDnSaWW0Ra1WI4vJEsRgW0HcUFiAqhWfQrPnL5vHlvtqETygFG41Ta+r+P/hUd0CHOHikFVuQKR34z5i6pnues2Rr70pOPdgaKOCoY0ydroDz1udia2WpIFe8PWxmlJIsw5CmnXwxnnlvtAH9kL5yKXgvMIbXW5CWkLF0rcBjXCYGi+V3hheVkujRsWSt1q2d7parcbOnTsRFRWFefPmYd68eU4rjDXx8fE4fNh4F//LL7/YHcSLioqwevVqPPHEE3j00UcBAIMHD0ZmZiY+/PDDZgviDAPkMbmC5dEWhpfxej2qf92A6jWrwFdbr52KpAYE9C6HslOVWeVFwWjxgeQrDNd8hut6BeIDfKwewxJLz8OpZ/pNjmEAxvZXBc+KwUm9wGobN9NXXay6GNKrf4OX2e4tTEhr4rHoDVOil1q8VIqKiXfD44+t5oFcJofHojecev5G9zaRSqVYvHgxzp0759SC2MI6OOH9wYMHodPpcPfdd5stv/vuu3HhwgVcu9aE3rqNwEl5qBiV2TIGDLp6RZst055IgurhB1C14lMbAZyHskslOk/Ih0/nKkHrYxUvxXydcV7hjHIegYFBph97WBpeRjVx0pCKUe+j6LEzKH7gAMrGLkdV7yegDRkETtK4UQ167yjbQdyghe8Pw+C141Eokj6FNHMfmOqiJpaeEMdJ+w2A1/sfATVztPNSKUqeeQ5V48aj5JnnwEtr5jKQyVtHxjaWZdGhQwdUVFQ4tSCucOnSJUilUkRERJgt79q1KwDg8uXL6NjRvoxFjjhZdBzvJy9BHCccW8syLC6UnUdfv/4wFOSjcsVn0O5toOncX4/gfiWmpvP66s5bDgApOY2vFWVYyNZGw8uIXRgWBmUnGJSdoOk22biM5yBSpZs9XxcXnAGjF/a9AAB9oO0MdCg4D1FZJkRlmZBduTHpj8EjtOb5ek3WuYCe4OWNa4UixFG1gbxi6dsorJM7XRcdg5JnnoP/Tz+2rtzpkydPxtatWzFnzhxIpa13xqTS0lJ4eXkJZrnxrpnxR2VHnl+RiIFS2fia6LG8Y3gtaQHUBjUKIOzta+ANeCNxPlYUToD7uj/AV1lvOmd9fOD/wgvwDsqC6N93UW/2TgAAL3bDp+rJpgAOADllGmhFLALt7P2t1hmQXSp8ttM7yg8eMtfNlSMSsQ5d4/asa9cuMBjMO8q02Wvk0wuIuhGc9ZweKLwA5vqpmp+TYPLOgDFoIIkYYPN9ipItJ68RVWRDVJENWfqfpmW8MhJ8hz43foJ7A3LXNdVfvy4crtehQweXnY+0MqNvQeDoPSg9eQJmuQ379kKnR/a47LQOfTP369cPu3fvxqRJkzBz5kxERETAzU04BCk+Pr7JBWxpBgPf6J7TJ4uOY9GxBehypQpP/8Hhi4kszkbceCQQl8nh+S0cdGI9FGUbrSZsAcNAPvkeKB57EqKyMxBtfx+MXmN5U3015ok3IZnrbBbI/z2fj9Fd7Ruzn5ZfIehgGewpg75aC1W1c7IjWXKz9U63R/fuPQTXpF1dI2kkEBEJREw2vjboICq5CN7NF5yN9+mbfdLuUzCqDDCqDCB1i2mZ3jvKNH5dHTPNqbX19HRhTk43N3q+f7OpqhJ+Vzb1bzcgwHrCLoeC+MMPP2z6/5IlSwQ1XZ7nwTCMzYQwzcHLywtlZWWm8tQqrZnz1lUTtLyfvARdrlTh1Y0cZHrg1Y0clk0HzkWwGHyOw/NbOYgaGI0gjusBj3kvQxwdA0nWIXhvf8hqE2QtOTRYI3lf0KRubxC3nKmtjdb+SNsiksDg33CvfSavcb3g6xOXXoG49Apw8Xeoo+9p0rEIaQ0cCuLLli1zdjlcomvXrtBqtbh69arZc/FLl4xjtjt37uyS877O3gvvjZ9AVtPsLdMDCzdyOBvOo286b3NeacZbCff/exayOyaAqenQ57nvRYsB3MDKIOLMa+YKRosPJSsxXPMZgMY9F6fhZaS1Mzz4B0ovH68Zx34akoJkiIovgOFtT9kqOI5nGHg3P+sbcAZ4/PMq9P5xxvHs/t0BK1O+SrIOwXPfi1B2mguVz42kOMqSZPh+/zjKR38EXdiwRpWPEHs5FMSnTJni7HK4xIgRIyCRSLBt2zbMnTvXtHzr1q3o1q2byzq1BS//GVy959YyPdAv3Ub1m2VNTeesp3kWt/LRHwlq4gZWhozIBxCZsc4skFfxUizQPWl6nZpXDp2Bg0TUcA9/iz3TKd0qaU3EcuiD+kIf1PfGMl01xIVnIambda7kkmkKVksa6kAnUl2G27n1ptc8K4beN9qUda52ylbJ9WOmv82eKf9BSs+3oPLpBWVJMnqm/AciTgPv7Q+hdMK3FMiJS7iut5KT7dy5EwBw5swZAMCBAwfg6+sLX19fDBw4EIBx4pXJkydj6dKlAAA/Pz889NBD+Oqrr+Du7o7u3btjx44dSEhIwJdffumysloaN2gLGxkFr9ffhrhbtMX1urBhKJ3wrenLwsDKTF8W5Z5dTV8WvNgNLxhexhHuxnG0Bh5p+RXo0aHh9K7UnE7aJIkb9B0GQN/hRs9fRlsBceHZG5nnCk5DrEo3rdc1NA97veQ1DKeHpPAsJIVnARiDO8+IAd5gulkQcRr0THkTGZEzEZnxk+nmmtFXUyAnLuNQEJ89e7bN9QzDQC6Xo0OHDhg+fDhuu+02wXPzxnr++efNXr/11lsAgIEDB+KHH34AYJzPnOPMm9XmzZsHhUKB77//3pR29ZNPPsGoUaOaVB5bpP0GIH/xU/B+60aTuiU8gPIp4xD1wtumpnNragO5574XkdLpWah8jF9CKp/eSOn5H/RMX47y0R9Bk6QELpuPmz2dU9ZgENfqOWSphE32FMRJW8RLPaALGQRdyCDTMkZTBnFBCsT5p6ENt53oyZ4pXBle+Mct4jSIuvID2HrrGH01PPe9iOLZiYJ9CGkKh4J4VlYW1Go1iouLARg7kAFAWZnx+auvry84jsM///yDDRs2oF+/fli1ahUUCscDQlpamkPbiEQiPP3003j66acdPndjnSw6jkXqb3D7CBbTD3CQWshYqWeA34Yz+KP7YSwrOYU+fv0aPK4ubBiKZydCdda8lqDy6W36cuiZcw3/1AviKTnlQAOpeq+qqlFvRBP83aXwkkss70BIG8PLvKALG2ZXbVhiIxd8Q+oHcMA4BLT8to8dPiYh1jiUCu3777+HXC7Ho48+isOHD+Po0aM4evQoDh8+jEceeQRubm747bffkJCQgIceegjHjx/HihUrnF32Vqu2d/q0g5YDOACIeWDyER6dr1ThveT/Ou3cvUKENe7TOQ1PpUdN6YTcUDlwPioHLYAm6nYY3IObdCxe7IbSid9BFzrUSaUj5AaHe6f369cPCxYsMFvu6+uLl19+GXl5eVi2bBmWL1+OV155BVeuXMFff/0l2L69qt873ZraXuulb05z2rljgzwgYhkYuBvV6vwKLc5dyYK/u8Rq+lVLPdMpZzq5Wek6joCu4wjTa7Yyz9QULy44DUleMtjqggaPw4vdUBn/IgVw4jIO1cQTEhIwYID19HEDBgxAQkKC6fWQIUOQmyucAKS9Cl7+s8UAzkmETdMyPRC8fL1wYxsCAoIEP7XkEhFCvWWCfRLTc3HgfBbuWpWIpKsqwforRfQ8nBBrOPcgaCPHoGrgiyib8C2KHj6BojnHUDFwAXjWel2I0VfD/dhHkNSZhpUQZ3JsZhEA6enpNtfxdVJ/sSwLudy+1J/tgceiN0zJ8GsZJBLkjx4NQ/1A7sCsNnUnNak/uUnSVRWyVcJe8Uev6/Dx8Urklmkwb/MZQSC/UkxjxAmxG8NApEqH+4nlYOqPJ62/qb4a3n/MoUB+k7BVyXIFh4L40KFDsX79emzfvl2w7o8//sDPP/+MYcNudB45d+4cQkNDHS9lG1N/VhuDRIL0GTNRNGIE0u+feSOQO3lWm6SrKszbfEbQQQ0AjufpoK3puK/Wc2aBXM/xyCwW1sQ7+dLEJ4RYYysJU32Mvhqee10/bTNpebYqWa7gUBB/9dVX4evri/nz5+OWW27Bgw8+iAcffBC33HILFixYAB8fH7zyyisAAI1Gg+zsbEyePNmZ5W71agM5GxSM9BkzUREZBQCoiIpC+v0zwQYFO31aurd2pUGtt5y5qn5cV+s5vLXL2Js/S1UNPVdvsg03CZQK6plOiDXloz8CLzafM6I2CVP9QM6L3VA++qPmLB65STA8X3/KC/uoVCp8/fXX+Pvvv5GVlQUACA0NxahRo/D444/Dx6d9TAOo0xmanLx+9+4dAACpVAyt1tj0NnbsnU0uW321NXFrgbwuuZjFJ1N7oH9HJfZfLMTLW83nh+8b5o2v7+ttZW/noglQhOiaWNbarkvdeQ3qJmGqm7GNF7tRohfSJE6fAAUwTh7y8ssv4+WXX3b0EMTJBoQr8fGUHg0G8roBHLA8vIx6phPSMHuTMFEAJ67icMc20joNCFfi8SERELOWM+SJWAZPDI0wBXAASLc08QnlTCfELqYkTD7mqVxrkzBRACeu1GZyp7dlnjUTmsjlEqjVOpeeK+mqCquOZAqecdcycDy+PJSB7sGeNmvi1DOdkMZxdS9kQiyhIN4MOnXqCgDw9JSjvNy+SVEcYe8zcZ2Bx3O/ncFn9/RAn1BvZJZY6JlOQZyQRnF1L2RCLKEg3gxq/7iVSgXc3FzXKcdW7/T6tAYOb/6ZhpXTe0FTbx9PmRh+7lJXFJEQQogT0TPxduTN26MhF5t/pFIWCHG3/DEHe8pwudByU3pTZ50jhBDiehTE25Ha3um1gVzKAvP6u2PxYE/4yIRBOTmnDJ/svyhYTs/DCSGkbaAg3s7UBvJgLxnm9XdHrJ8ECgmD+fEekFj4tLPKtIJl+y4UWsyvTgghpHWxK9nLli1bHDp4e8jS5oxkL7WaO1FFfn6e2et/M8ux7ECOXfvKxSw+ntIDA8KVLiiZudaWwKM1oGtiGV0XcjOylezFriAeExMDhmHQmORuDMMgNTXV7u1bq7YcxC157Y9z+Cut0K5tmyuQt4br0trQNbGMrgu5GTU5Y9v333/vtMKQlnU6p9zubWvzq297fJALS0QIIcRRdgXxgQMHurocpJm8OT4aL2w+IxhWZolczOI/46OboVSEEEIcQR3bbjIDwpX4ZEoPSEW2h5DVz69OCCGk9WlSspeUlBScPn0apaWl4Djzmh3DMHjmmWeaVDjiGgPClXhyaCRWHsqAzkJ6VrmYFeRXJ4QQ0vo4FMTVajXmzp2LQ4cOged5s05vtf+nIN561eZXtxTAAeOz8K8PZ5rlVyeEENL6ONScvmLFChw6dAhPPfUUvv/+e/A8j3fffRerVq3CgAED0LNnT2zfvt3ZZSVOYG9+dbWewwubzuD4NVXzFIwQQkijORTEd+3ahfHjx+P5559H167GyT2CgoIwYsQIrF27FjqdDps3b3ZqQYlzWMuvLrXwm6DWc/jPzrRmKBUhhBBHOBTEr1+/jvj4eACASCQCAOh0xik2xWIxJkyYQDXxVspafvXJXeWCQC4Xs3jzduqdTgghrZVDQdzd3R0Gg8H0f5ZlkZ+fb1rv6emJwkL7EoqQ5mUtv/qdUXLM6+9uCuTNmbGNEEKIYxwK4uHh4cjIyABgrIl36dIFu3btAgDwPI/du3ejQ4cOTiskcS5L+dUBINZPgnn93RHsJaMATgghbYBDvdOHDBmC3377DYsWLYJIJMJ9992Hd955B2PGjAHDMMjKysK8efOcXVbiRAPCldj2+CBBfvWAAODeW4JaqFSEEEIaw67c6fVVVlYiLy8P4eHhEIuN9wFr167F1q1bwbIsbr/9djz++OPtYk7q9pY7vTWi6yJE18Qyui7kZtTkCVBuZhTEXY+uixBdE8voupCbka0gTmlXCSGEkDbKrmfitfOJT5o0CQzD2D2/eHuYT5wQQghprRo1n3hycjKkUqld84vTfOJC1BRoGV0XIbomltF1ITcjp80nLpVKzV4TQgghpOU4NJ84zS9OCCGEtDyHOrYtXLgQycnJVtefPn0aCxcudLhQhBBCCGmYQ0F88+bNuHr1qtX1WVlZdnd+I4QQQohjXDLErKqqypQEhhBCCCGuYXekzcnJQXZ2tul1eno6jh07JtiutLQU69evR0REhHNKSAghjVBdXYmKChUMBn1LF4UQm0QiMTw8lHBzc3f4GHYH8U2bNmH58uVgGAYMw2DlypVYuXKlYDue58GyLJYuXepwoQghxBHV1ZUoLy+BUhkAiUTaLlI/k/aJ53nodFqoVAUA4HAgtzuIjxkzBqGhoeB5HosWLcL06dPRt29fs20YhoFCoUDPnj1pFjNCSLOrqFBBqQyAVCpr6aIQYhPDMJBKZVAqA1BaWuj6IB4TE4OYmBgAxqb1cePGoVu3bg6dlBBCXMFg0EMikbZ0MQixm0QibdKjH4d6n82dO9fhExJCiCtREzppS5r6+0pdyAkhxIb8/DzBssDAoBYoyc2Jrr9tDgfxkydP4scff0RmZiZUKpUgjzrDMNizZ0+TC0gIIS2poICCSEui62+bQ0F8y5YtWLhwIcRiMSIjI6kTGyGk3Um6qsJbu9IwJ1qMWD+JaXlqkQ6vrkrEm7dHY0C40uXl2LFjG5Yufcv02s1NgZCQUNx992RMmnQPxGIx/v33APbs2YXz51ORnX0NvXv3xfLlX7u8bK6Un16Go5uvILAvD/egGylNKvM4/LEzGQOnRCGwk1ezlMWez6Cu7OwszJ59HzQaDX7+eTPCwjq6rGwOBfEvv/wSUVFRWLt2LYKC6I6IENK+JF1VYd7mM1DrOXx8XIN5/d0R6ydBapEOHx+vhJYD5m0+g4+n9GiWQA4A77zzLgICglBVVYn9+/fg448/QElJCR577CkcPPg3Ll26gLi4HtBqNc1SHlfKTy/DwR8vwqDjkHkAiLhFAvcgFpV5HDIP6MAbgIM/XsSIWV2bLZADtj+Duv73v/fg4eEBjcb1n4VDGdtycnIwY8YMCuCEkHanbgAHAC0HfHy8EjvS1aYADgBqPYd5m88g6aqqWcrVtWs0evToiYEDB+OVVxajb9/++OWX9QCAV15ZjB9//AWvv/42goKCm6U8rlI3gAMAbwAyD+hQmKo3BXAAMOg4HPzxIvLTy5qtbLY+g1p//bUTFy+m4YEH5jRLmRwK4sHBwdBqtc4uCyGEtLi3dqWZAngtLQdsuaQ2BfBaaj2Ht3alNWPpboiN7Y7KykqUlBSDZV2SQbtFHN18xRTAa/EGIP+MwRTAaxl0HI5uvtKMpTNX9zMAgLKyMixf/jGeeeZ5eHhYnwPcmRxqTr///vuxbds2PPTQQxCJRM4uEyGEOE1xlRZv7UxD0lUVtAa+4R2sqB/Aa+WWaRD/vwMN7i8VMRgQrsSb46Phq2j6WPacnByIRCK4uSmafCxXUlfocHTTFeRfKQOnd/z61w/gtapUWmx8XZgCvD5WzCAwygsDp0ZB7iFpcHt71P8MvvzyM4SHR2D8+AnYsWObU87REIeCeFxcHP766y9MmzYNM2fORFhYmMVgHh8f3+QCEkJIU7y1Mw2Hr5S0dDGgNfA4fKUEb+1Mw6dTezZ6f44zQK/Xo6qqCvv27caBA/sxbNgIyOVyF5TWeY5uuoLci6UtXQxweh65F0txdNMV3DLbsURltj6D5OST2LlzO9asWefkktvmUBB/6KGHTP9fvHixYLA6z/NgGAapqalNKlxd169fx7Jly3Do0CHwPI+hQ4di0aJFCAkJaXDf6Ohoi8u3bNmC2NhYp5WRENL6pOSUt3QRzDhanpkz7zX9n2VZjB07Hs8//5KziuUyRdcqWroIZppSHmufgU6nwwcfLMX06TMRFdXJGcW0m0NBfNmyZc4uh03V1dWYM2cOpFIp3nvvPQDAp59+itmzZ2Pr1q1QKBpuTpo6dSruu+8+s2WRkZGuKC4hpBXpGeLZKmritXqGOPasdOnSDxEYGAiFQoGgoA6QydpGfni/jh6toiZey6+jh8P7WvsM1q37DuXlZZg27X6Ulxtv0tRqNQCgqqoSVVWVUCgcn6nMFoeC+JQpU5xdDps2btyIa9euYefOnaYpTqOjo3H77bdjw4YNePjhhxs8RmBgIPr06ePikhJCWps3x0c75Zl4U9V9Ju6ITp06u3S8sasMnBrllGfiTVX3mbijrH0GGRlXUFRUhMmT7xCse+SRWejSpRu+/fYnh89rS5PTrmq1WpSUlMDHxwdSqWsmHti3bx969+5tNkd5x44d0a9fP+zdu9euIE4IuTn5KqSNegZ916pE5JYJx/dKWcud24K9ZNj2+KCmFLFdk3tIGvUM+o//JaNKJRz9xIgsd25TKKWY+FLvphSxyWbNegh33DHRbFli4hGsW/cd3njjHYSHR1jZs+kcHpdw9uxZzJ49G/369cOtt96K48ePAwCKioowZ84cHD582GmFvHTpksUZ07p06YJLly7ZdYyff/4ZPXr0QO/evTF79mwkJSU5rXyEkPbjzdujIRebfzVKWWByVzmk9b4x5WIWb97uWM3amXJzr2P//j3Yv38PyspKoVKpTK9zc6+3dPEaZeCUKIgk5heaEQGBPURg6vWfFklYDJzieM3aWSIiItGv3wCzn4iISABA9+49EBPT3WXndqgmnpqaigceeAA+Pj6YNGkSNm3aZFrn5+cHjUaDzZs3Y+jQoU4pZGlpKby8hFl5vL29UVbW8ED/u+++G6NGjUJgYCCys7OxevVqzJkzB2vWrMGgQbbvoEUiBkqlc4ZwiESs047VntB1EaJrYllD1yUvj4FI1LQx04OifPHpPT3x/G8pUOs5SFngpXgPxPpJ0EkpxkfHKqDljAH803t6Ij7Cp0nnawjLMqZ/rb23U6eO47///Y/ZstdffxUAsHjxfzBhwt0uLaMzdeiqxMjZ3fDP9xdg0HFgREDkrRJ4BImg8GeR8bcx4YtIwmLk7G4I6uzt8jLZ8xk0ZR+GcTzOMHz9mUvs8NRTTyE9PR2bN2+GRqPB0KFDsXbtWgwZMgQA8Mknn+DPP//Erl27HCpUfT169MBDDz2E+fPnmy3/+OOPsWrVKpw7d65Rx6uoqMBdd92F4OBgrF+/3ua2Op0BKlVVo8tsiVKpcNqx2hO6LkJ0TSxr6Lrk5mYiONg5TZe2cqd/l6ZvttzpNytbudPzTzLNmjvd1Rr6vQ0IsN4Z0qFb1uPHj2PatGlwd3e3OBdqSEgI8vPzHTm0RV5eXhZr3NZq6A3x8PDAyJEjkZKS4oziEULaoQHhSmx7fJBZAAeAWD8Jtj0+iAK4iwV28sLEl3qbBXAAcA9iMfGl3u0mgDeVQ83pGo0Gnp7W7wwqKpw7LrBLly64ePGiYPnly5fRpUsXh4/b1MnYCSHtX0AAzRHRkuj62+ZQEA8PD8fZs2etrk9ISGhScK1v9OjReP/993Ht2jV07Gjs3p+VlYUTJ07gpZcan+ygoqICf//9N3r16uW0MhJC2ieau7pl0fW3zaHm9IkTJ+L3338364FeW6tds2YNDh48iEmTJjmnhACmT5+O0NBQPP3009izZw/27t2Lp59+GsHBwWYJXLKzs9G9e3csX77ctGz16tVYvHgxtm3bhsTERGzevBkzZsxAYWEh5s2b57QyEkIIIc3NoZr4I488gkOHDuHRRx9Fp06dwDAMli1bhuLiYhQWFmLo0KGYOXOm0wqpUCjw3XffYdmyZXj55ZfB8zyGDBmCRYsWwd39RhYcnudhMBhQt69eVFQUdu/ejd27d6OiogIeHh7o27cvlixZQjVxQgghbZpDvdMBQK/X48cff8TWrVuRnp4OnucRERGByZMnY/bs2RCLm5xHplWg3umuR9dFiK6JZc3ZO52Q5tKU3ukOR1qxWIyHHnrIbDIUQgghhDSf9jOTPCGEEHKTcbgmfvLkSfz444/IzMyESqVC/VZ5hmGwZ8+eJheQEEJaUn5+nmAZ9ZhuPnT9bXMoiG/ZsgULFy6EWCxGZGQkOnTo4OxyEUJIq1BQQEGkJdH1t82hIP7ll18iKioKa9euRVAQXUxCSPsjyToEz30vQtlpLlQ+N2bJUpYkw/f7x1E++iPowoa5vBw7dmzD0qVvmV67uSkQEhKKu++ejEmT7oFGo8bGjeuRmHgEV69mguMMiIzshJkzZ+OWW251eflcRXsiCRVL34Zk5izoomNMyyVp51H85mJ4LHoD0n4DmqUsDX0GYrEYS5b8B3/++Ydg32nTZuD55xufz8ReDgXxnJwcvPzyyxTACSHtkiTrELy3PwRGX42eKf9BSs+3oPLpBWVJMnqm/AciTgPv7Q+hdMK3zRLIAeCdd95FQEAQqqoqsX//Hnz88QcoKSnB6NFjsHnzr7jzzrswZ86jYFkWe/bswqJF8zFv3su4557pzVI+Z9KeSELZyy8CGjV8VnyGkmeegy46BpK08/BZ8Rk4rRZlL78Ir/c/arZADlj/DB577CkAgFLpg3ff/chsH39/f5eWyaEgHhwcDK1WON8rIYS0dXUDOACIOA16pryJjMiZiMz4CSLOONc4o69u1kDetWs0wsKMGSsHDhyMrKxr+OWX9XjggTnYuPF3yOVy07aDBg1Bfn4e1q37rs0F8boBHAAYrRY+Kz5DxYS74LF9G5ja2KNRN3sgt/YZ1AZxiUSCHj3sn7veGRzqnX7//fdj27ZtMBgszNBOCCFtmOe+F00BvJaI05gF8FqMvhqe+15szuKZxMZ2R2VlJdTqarMAXis6OhZFRYUtULKmqVj6timA12K0WvMAXkujNm7fQmo/g5KS4hYrg0M18bi4OPz111+YNm0aZs6cibCwMIhEIsF28fHxTS4gIYQ0BVNVCM+98yDNPgzGoGl4ByvqB3DT8vJsBKwIa3B/XiSDNnQoym/7GLyi6U2sOTk5EIlEcHOzPA91cvJJhIe3fOIbrqQY5Uvfhu5EEtCEFlxBAK89fl4uCkcMbPgAUikk/QbAc9EbYH18HS5HXfU/g5KSYkyYcBsqKirQoUMoJk68GzNmPGgxPjqLQ0G8boKXxYsXC2YD43keDMMgNTW1SYUjhJCm8tw7D7Kr+1u6GGAMGmM59s5D2V0/NHp/jjNAr9ejqqoK+/btxoED+zFs2AiLtfDff9+Es2dT8MYb7zij6E1SvvRt6BION7yhq2m10CUcRvnSt+H9wScOHcLWZ9C1azdER8cgKqoztFoNDhz4G199tQJZWdfw6quvO/e91OFQEF+2bJmzy0EIIS4hyTvR0kUw42h5Zs681/R/lmUxdux4i72eT5xIwqeffojx4ydg3Lg7HC6ns+jPpLR0Ecw0pTy2PoPp083nCxkyZDjc3NywcaOx30LHjuEOn9cWh4L4lClTnF0OQghxCV1Qv1ZRE6+lC+rn0H5Ll36IwMBAKBQKBAV1gEwmE2yTmnoWr776Evr1G+DS2l9jiHv0bB018RriJnQ8s+czqGvMmNuxceN6nD9/zmVBnNKuEkLatfLbPoYmfBR4ke0vXFfjRTJowkeh/LaPHdq/U6fOiInpjvDwSIvB4/LlS3jxxWfRtWs3LFnyfquZhMpz0RuQDB4KSKUtWxCpFJLBQ+G56A2HD9HQZ2BN/UfOzuTwp1xVVYX/b+/Ow5o63j2AfwMGkE3AolQBVwIiEBABtdYKiFhcqFjAlbqX1pWquP28/aktuKCoaEXUFhVcEFyQKtWiVako1rqLGxXZChURFBASyLl/cHOuMQFCWEPfz/P4VCdzzrwZkr7MnHNm9uzZg3PnziE7OxsAYGxsjOHDh2PGjBnQ1JR9swUhhDQnRvODel2DNtjvBNU3OVLlVSrqMm9uq9LpikK/aw2KsaGysjIREDAHXbp0xYYNoVBXl75O3lJU9A3qdQ268PMxEOXnSZUzamoyb25T6WwEg9j4hoTYZM6eTQSHw0GfPn2brA2FknhRUREmTZqE9PR0GBgYoE+fPgCAjIwM7NixA4mJiYiOjoaenl5jxkoIIU3ujctmiefEgeoEntF9ErpnREskcqZde7xx2SzrNM3m1atCBATMgVAoxIwZX+LZs2cSr/N45lBr6VFwPWiv+B+J58SB6gReMmoMtBPiJRO5uga0GzCybix5eX9j7dr/gavrcBgbm0AgEODSpQs4cyYBnp5e6Nq17qcXFKVQEt+2bRv++usvrFq1CuPHj2dvn6+qqsKRI0fw3XffYfv27fjPf/7TqMESQkhTExp/hOKRkWwir1JRZ1dse6Njxq7YxrRr36wrttXk2bO/kJf3NwAgMHCh1OtHj8bjww+7NHNUilPr1x+6GzaziZxRU2NXbBN26w79HduqE7m6RrOv2FYTTU1N6OrqIjp6H169KgSHw0G3bt2xcOFijB3r3aRtc5j3tx+Tw9ChQzFkyBCsWSP7IftVq1bh8uXL+O233xoaX4sTCqtQVFTWKOfS09NstHO1JdQv0qhPZKurX/LynsPIqHGejRavnX635zwU6dv8fwyvbsP6r+3Ntnb6v5V47fQCGWunf3AwqlnXTm9qdX1uDQ11anxNoRvbCgoK2Cl0WSwtLVFQoHwrBRFCiJjQ+CMU+l2TSOAAUKTPR6HfNUrgTUytX38YxMZLJHAAEJpbwCA2vs0k8IZSaDr9gw8+qHUhl7S0tCZf9J0QQpqDoSFt9NSSqP9rp1ASd3Z2xpEjR2BpaQkfHx+oqFQP6EUiEY4ePYq4uDj4+vo2aqCEENISaO/qlkX9XzuFkvj8+fNx5coVrF69GmFhYejRowcA4NmzZygsLISpqSnmzZvXqIESQgghRJJC18T19fURFxeH2bNnQ09PD3fv3sXdu3ehr6+P2bNnIy4uDvr6+o0dKyGEEELeofBiL9ra2ggICEBAQEBjxkMIIYQQOTVoXT6BQIBr164hKysLAGBqagoHB4d6LUdHCCGEEMUonMRPnDiB4OBgvH79GuJHzTkcDnR1dbF06VJ4eXk1WpCEEEIIkaZQEj99+jSWLVuGLl26YMaMGejVqxcA4OnTpzh8+DBWrlwJDQ0NeHh4NGqwhBDS3P75J1+qjO6YJq2FQkk8PDwcPXv2RExMDLS1tdlyV1dXTJw4Ed7e3ggPD6ckTghRei9eUBInrZdCd6c/e/YMXl5eEglcTEdHB15eXsjIyGhobIQQ0mJuvryBCee98Lj8kUT54/JHmHDeCzdf3miWOE6fPoXBg/uzf9zchuCLLyYgLu4IKisrAQA7d4bhiy/GY8SIoXBx+QgTJ47DTz/tRnl5eR1nJ8pOoZG4oaFhra9zOBxasY0QorRuvryBFdeXoEJUjvCKHfiq0xyYaZjjcfkjhP+zA0JGgBXXlyDIYSPsOto3S0xr166DoWFnlJWV4sKFXxEauhGvXr3CzJn+KC0thYfHaJiadgOXq4Z79+5g//4f8ehRGtata9ld1kjTUiiJjx07FseOHcOECROgpaUl8VpJSQmOHTtGN7YRQpTSuwkcAISMADv/2QGPDiNxuvhnCJnqrTArROXNmsjNzMxhbGwCAHB0HIDs7CwcPXoIM2f6Y/HiZRJ1+/d3RHl5OaKiIlFUVETbQrdhCiXx/v3748KFCxg9ejQmTpyInj17AgDS09Nx6NAh6Ovrw97eHtevX5c4zsHBoeERE0JIE9pw+3s2gYsJGYFEAherEJVjw+3vccjlWHOGCADo08cSN2/ewKtXhdDXN5B6XVe3AwCwW0WTtkmhJD5t2jT27yEhIeBwOADAPmqWm5uL6dOns3UYhgGHw6l10xRCCGkKryoKsf7O97j58gaEIkHdB9Tg/QQull+eB5fTg+o8nquiBruO9lhqsxL66tJJt75yc3OhqqqK9u012bLKykoIBALcv38XR45EY+TIMdDRqXkbS6L8FEriwcHBjR0HIYQ0ifV3vkfqi5SWDgNCkQCpL1Kw/s73WOewqd7Hi0RVqKysRFlZGc6fP4dLly7go48+hoaGBgDgr7+ews9vPFt/xIiRCAxc2Wjxk9ZJ4WvihBCiDB68utfSIUhQNJ6JEz9n/66iogI3txFYsGARW9a1qwn27NmPt2/f4t69OzhwIBJVVVX49tvvGhwzab0atOwqIYS0dpb6Vq1iJC5mqW+l0HFBQSHo1KkTNDU10bnzh1LLW6urq8PCwhIAYGdnj44dP0BQ0GqMG+cLKyvrBsdNWieFnhMPCwtjr3/LUlRUhK+//lrhoAghpLEstVkJR8OB4KqotWgcXBU1OBoOxFIbxaa4e/bsBQsLS5iadpdrfwoLiz4AgJycLIXaI8pBoZH4jh07kJqaipCQEHTuLLlyUWpqKpYsWYKXL182SoCEENIQ+uoG9boGPeG8F/LL86TKuRw1mTe3ddYwapG70+ty8+afAICuXY1bOBLSlBQaia9evRp3797FmDFj8OuvvwIARCIRtmzZgmnTpkFVVRVRUVGNGighhDSHQP5KqKtoSJRxOWrw6DAKXI7kaF5dRQOB/Ja9eezp0ycICJiD+Pjj+OOPVKSkJGPnzjDs2LEVAwYMgpWVTYvGR5qWQiNxX19f9OvXDwEBAZg3bx68vb3x5MkT3Lx5E+7u7vjuu+/osQZCiFKy62iPIIeN7IIvXI4au2KbqXo3dsU2dRWNZl2xrSYGBgbo0EEPBw78hJcvX0JDQwNdunTFnDkLMHr0Zy0aG2l6HKa2i9t1qKiowNSpU3Hr1i0AQEBAAGbPnt1YsbUKQmEViorKGuVcenqajXautoT6RRr1iWx19Ute3nMYGXVrlLZuvryBDbe/h2+HCTDTMGfLH5c/QkzxIQTyV7Z4AidtQ12fW0PDmgfFCk2nA4BQKMTGjRtx8+ZNmJiYsFPoqampip6SEEJaDbuO9jjkckwigQMAT8Mch1yOUQInrYLCu5j5+PggKioKEyZMQEJCAqKjo8HlcjFt2jRs3boVIpGosWMlhJBmZ2jYWeoPIa2FQtfEvby8wOVyERYWBjc3NwAAn8/HyZMnsWrVKuzcuROpqamIjo5u1GAJIaS50d7hpDVTaCRuYWGBEydOsAlcTFtbG6GhoVi7di0ePHjQKAESQgghRDaFRuJRUVG17ozj7e0Ne3u6XkQIIYQ0JYVG4nVtbff27VtwuVyFAiKEkIZowAM3hDS7hn5e5U7iVlZW+Pnnn9l/l5SUwN/fH48ePZKqe+7cOQwfPrxBgRFCSH2pqraDUKj4dqOENDehUABVVcW3MZE7iVdWVkrccS4UCvHbb7+hsLBQ4cYJIaQxaWvroajoBQSCChqRk1aNYRgIBBUoKnoBbW09hc9Du5gRQtqM9u21AADFxQWoqqps4WgIqZ2qajvo6Oizn1tFUBInhLQp7dtrNeh/ioQoE4VXbCOEEEJIy1KaJP73339j/vz5sLe3R79+/TB37lzk5ubKdWxFRQXWr1+PwYMHw8bGBr6+vrh+/XoTR0wIIYQ0rXpNp1+8eBEFBQUAqh8j43A4SExMxMOHDyXq3bt3r/Ei/L+2vvjiC6ipqWH9+vUAgK1bt8LPzw/x8fHQ1NSs9fgVK1bg4sWLCAwMhImJCaKjozFjxgwcOXIEffr0adRYCSGEkOYi9y5mFhYW9Tsxh4O0tDSFgnrfvn37sG7dOiQmJqJbt+qdXrKysuDu7o4lS5Zg2rRpNR778OFDeHp6IigoCOPGjQNQfaf9yJEj0aNHD4SHh9faNu1i1vSoX6RRn8hG/UL+jWrbxUzukfj+/fsbJRhFnD9/Hnw+n03gAGBiYoJ+/fohKSmp1iSelJQELpcLDw8Ptqxdu3YYOXIkIiIiIBAIoKam1qTxE0IIIU1B7iTu6OjYlHHU6unTp3B1dZUq7927NxITE+s8tmvXrmjfvr3UsUKhEM+fP4eZmVmjxksIIYQ0B6V4xKy4uBi6urpS5R06dMDr16/rPLZDhw5S5Xp6euzrteFyVWudyqivxjxXW0L9Io36RDbqF0L+n9LcnU4IIYQQSUqRxHV1dWWOuGsaob9/rKzRdlFREQDIHKUTQgghykApknjv3r3x5MkTqfL09HT07t27zmNzcnLw9u1bqWO5XK7EzXKEEEKIMlGKJO7i4oLbt28jKyuLLcvOzsaff/4JFxeXOo8VCoUSN8BVVlbi9OnTGDx4MN2ZTgghRGnJ/Zx4SyorK4Onpyc0NDSwYMECcDgcbN26FaWlpYiPj4eWVvU6yTk5OXBzc8PXX3+NuXPnsscHBAQgOTkZgYGBMDY2xqFDh3DhwgUcPnwYffv2bam3RQghhDSIUtydrqmpiX379iE4OBiBgYFgGAYDBw7EihUr2AQOVG/tVlVVJbUFYXBwMEJDQ7Flyxa8fv0aFhYW2LNnDyVwQgghSk0pRuKEEEIIkaYU18SVWUM2blE2eXl5WLt2LXx9fcHn82Fubo7s7GypevJuSCMSibBr1y64uLjA2toaY8aMwS+//NIcb6XRJCYmYt68eXB2doaNjQ3c3d2xadMmlJSUSNQrLi7GypUr4eTkBFtbW0ydOhWPHj2SOl9b2czn8uXL8PPzw0cffQQrKysMGTIECxYswNOnTyXqyfv9kbf/CGlraCTehN6+fQtPT0+oqalh4cKFAKo3bnn79q1cG7com2vXriEgIAB9+/aFSCRCcnIykpKSYGxsLFFv0aJFUhvSXLp0SWpDmtDQUOzdu5c95+nTpxETE4Ndu3bhk08+ae63pxAfHx98+OGHcHV1hZGRER48eIDt27ejZ8+eOHz4MFRUVMAwDCZOnIicnBwEBgZCV1cXERERePLkCU6ePAkjIyP2fPL2XWuXkJCA+/fvg8/nw8DAALm5udi9ezf+/vtvnDp1Cl27dpX7+1Of/iOkzWFIk4mMjGQsLCyYjIwMtiwzM5Pp06cP8+OPP7ZgZE2jqqqK/XtMTAzD4/GYrKwsiTppaWkMj8djYmNj2TKhUMgMHz6c+fLLL9mygoICpm/fvszWrVsljvfz82NGjRrVRO+g8b18+VKq7Pjx4wyPx2OuXLnCMAzDnDt3juHxeExKSgpb5/Xr14yDgwOzdu1atkzevlNW6enpDI/HY/bu3cswjPzfH3n7j5C2iKbTm1BdG7e0NSoqdX+catuQJjk5GQKBAED1dKtQKMSYMWMkjh8zZgweP34s8bhha2ZgYCBVZm1tDQDIz88HUP056dSpEwYMGMDW0dHRgbOzs8TnRN6+U1bipZBVVVUByP/9kbf/CGmLKIk3oadPn4LH40mV9+7dW+ra37+FPBvSiOupqalJLcYj3qwmPT29eQJuAqmpqQCAXr16Aaj9c5Kbm4vS0lK2njx9p0yqqqogEAiQkZGBb7/9FoaGhhg1ahQA+b8/8vYfIW2RUjxipqwasnFLWyXvhjTivuNwOBL1xMeKl81VNvn5+di2bRsGDRrEjsiLi4vRtWtXqbriPnn9+jW0tLQavJlPa+Tt7Y379+8DALp164Z9+/ahY8eOAOT//sjbf4S0RTQSJ6SZlJaW4quvvoKqqiqCg4NbOpxWYePGjYiJicGmTZugra2NadOmyXyigRAiGyXxJtSQjVvaKnk3pBH3HfPewxPiY8WjLGVRXl4Of39/ZGdnY+/evRJ3TNf0ORH3ifiz0hY38+nVqxf4fD5GjRqFyMhIlJWVISIiAoD83x95+4+QtoiSeBNqyMYtbZW8G9KYmZlBIBAgMzNTop74Wqj4erIyEAqFmD9/Pu7du4eIiAiYm5tLvF7b56RLly7sVHBb38xHV1cXpqam7M9c3u+PvP1HSFtESbwJNWTjlrZK3g1pPv74Y3C5XJw6dUri+Pj4ePB4PJiYmDRr3IoSiURYvHgxrl69ih9++AG2trZSdVxdXZGfn8/e8AYAJSUluHDhgsTnpK1v5lNQUIBnz57B1NQUgPzfH3n7j5C2iG5sa0I+Pj6Ijo7G119/LbFxi5GREXx9fVs6vCYhTjD37t0DAFy6dAkGBgYwMDCAo6MjLC0t4eHhgaCgIFRWVrIb0mRnZyMkJIQ9T8eOHTF16lTs2rULWlpasLS0xOnTp3H16lXs3LmzRd6bIlavXo3ExET4+/ujffv2uHXrFvuakZERjIyM4OLiAjs7OyxZskRisRKGYTBz5ky2vrx9pwzmzJkDS0tLmJubQ1tbGxkZGYiMjISqqiqmTZsGQP7vj7z9R0hbRCu2NbHc3FwEBwfj999/l9i45f1VzNqK96eKxRwdHXHgwAEA1deHQ0NDkZCQwG5Is3jxYjg5OUkcU1VVhV27duHo0aN48eIFevTogTlz5mDEiBFN/j4ai4uLC3JycmS+NnfuXMybNw9A9fXb9evXIykpCRUVFbC1tcXy5cthYWEhcYy8fdfaRUREIDExEZmZmRAKhTAyMoKTkxNmz54t8d2Q9/sjb/8R0tZQEieEEEKUFF0TJ4QQQpQUJXFCCCFESVESJ4QQQpQUJXFCCCFESVESJ4QQQpQUJXFCCCFESVESJ6SJhYWFwdzcnDb2IIQ0OkripFW6du0azM3N2T99+vSBg4MDRo0ahaVLl+LSpUtSm6OQhktLS0NYWFir+oUjLCwMv/76a0uHoZCmij0yMhLHjh1r9PO+Kzs7G2FhYUhLS2vSdkjDUBInrdqoUaOwYcMGBAcHY+HChRgwYABSU1Mxa9YsTJ8+XSn2Zf/qq69w584dmXtetzZpaWnYvn17javMtYTt27crbRJvqtj379+P48ePN/p535WTk4Pt27dTEm/laO100qpZWlrC09NTomz58uXYuHEjfvrpJ3zzzTfYs2dPC0Unn3bt2qFdu7b5VauqqoJAIED79u1bOhTSSEpKSqCtrd3SYRA50UicKB1VVVUsW7YM9vb2uHz5Mv744w/2tfz8fKxbtw6enp5wcHCAtbU1PDw8EBERgaqqKrbeuXPnYG5ujpiYGJltjBw5Em5ubnVO2f/222+YPHkynJycYGNjg6FDh2Lu3Ll49uwZW0fWNXFx2V9//YXNmzdjyJAhsLKywpgxY3Dx4kWZbf3yyy+YMmUK+vfvDz6fD3d3d3z33XcQCARsHYZhcPDgQXh5eYHP58POzg5TpkzB1atXa+/U/4tp+fLlAAA/Pz/2UsayZcsAAMeOHYO5uTmuXLmCHTt2YNiwYbCxscGZM2fq3XZ0dDSmT5+Ojz/+GFZWVhg8eDAWL14s0UfZ2dnsWvzHjx+XuLwiJo4vJSUFvr6+4PP5GDJkCLsneXFxMVasWIGBAweCz+fjyy+/RH5+vlQ8b968wcaNG+Hm5gYrKysMGDAA33zzjcQOau/2QUpKCvbu3Ythw4bBysoK7u7uEiNjeWKXRSQSITIyEqNHj4adnR369esHd3d3rFixAkKhkH3POTk5SE1NlTivuO+Sk5OxcOFCuLq6wsbGBv3798f06dMldnkTmzJlClxcXJCVlYX58+fD0dER9vb2OHbsGPz8/ABU/9IsbmPKlCm1xk+aX9scHpB/hc8//xw3btzAxYsX0b9/fwDAo0ePcPbsWbi5ucHU1BRCoRCXL1/Gpk2bkJ2djTVr1gAAnJ2dYWhoiLi4OPj4+Eic99atW3j69CkCAgLA4XBqbD81NRVfffUVzMzM8OWXX0JHRwf//PMPUlJSkJmZiR49etT5HpYtW4Z27dph+vTpEAqF2LdvH+bMmYPExESJTT5CQ0MRHh6O3r17Y+rUqTA0NERmZibOnj2L+fPns9uQLlmyBD///DPc3d3h5eUFgUCAU6dOYfr06QgLC4Orq2uNsbi5ueHFixc4cuQI/P390bNnTwBgtwYVW79+PSorK+Hj4wMtLS32fdan7R9//BG2traYMmUK9PT08PjxY8TGxuLq1as4deoU9PX1YWBggA0bNiAwMBD9+/eX+jmJPXjwABcuXICPjw88PT1x5swZbNq0Cerq6jhx4gS6du2KuXPnIjMzEwcOHMDSpUsRGRnJHv/mzRuMHz8eubm5GDduHMzMzPDixQscPHgQ3t7eiIuLk7oUEhoaivLycvj6+kJNTQ2HDh3CsmXLYGpqCnt7e7ljf9/OnTuxbds2ODs7Y/z48VBVVUV2djbOnz8PgUAALpfLXl7S19eHv78/e6yBgQGA6l8aiouL8dlnn8HIyAj5+fk4evQopk6div3797PfFbHS0lJMnjwZ/fr1w8KFC1FYWAgHBwf4+/sjPDwcvr6+sLe3BwB88MEHcr0P0owYQlqhq1evMjwej9mzZ0+Nde7du8fweDxm7ty5bNnbt28ZkUgkVXfx4sWMhYUFk5+fz5Zt2rSJ4fF4zJMnTyTqrly5kunTpw+Tl5dXa4xBQUEMj8djCgoKaq23bds2hsfjMVlZWVJls2fPloj39u3bDI/HY0JCQqTKpkyZwpSXl0ucWyQSscefPXuW4fF4zOHDhyXqCIVCZuzYsYyzs7PMvnlXXFwcw+PxmKtXr9b42vDhw5mysjKJ1+rbdmlpqdT5r1y5wvB4PCYiIkKinMfjMUuXLpUZL4/HY8zNzZlbt26xZRUVFcxHH33EmJubM2vXrpWoL/6Zpaens2Vr165lrK2tmbS0NIm62dnZjJ2dnUTb4j7w9PRkKioq2PK8vDymb9++TEBAgNyxy/LZZ58xn376aZ31nJ2dmcmTJ8t8TVbfvnjxgnF0dGRmzpwpUT558mSGx+MxmzdvljpG/B2Mi4uTM3rSEmg6nSgt8XW7kpIStkxDQ4MdPQsEAhQVFaGwsBCDBw+GSCRi9zkHAG9vb3A4HMTGxrJlZWVlOH36NIYMGYLOnTvX2r6Ojg6A6mnuyspKhd6Dn5+fxGjfxsYGmpqaeP78OVsWHx8PAFi0aBHU1dUljudwOOzx8fHx0NLSwrBhw1BYWMj+ef36NbslakZGhkJxvmvChAlS18Dr27ampiaA6unjN2/eoLCwEObm5tDR0cGdO3fqFY+trS34fD77bzU1NVhbW4NhGKnpX/EoVNy/DMPg1KlTcHBwQKdOnSRib9++PWxtbZGcnCzV5sSJE9nZDwDo3LkzevTo0eD+1dbWRn5+vsQlovoS9y1QPcp+9eoVVFRUwOfza+zbGTNmKNweaVk0nU6Uljh5v3sTTmVlJSIiInDy5Ek8f/5c6pr2u3ezm5iYYNCgQTh58iQWLVoELpeLM2fOoLS0FJ9//nmd7U+aNAlJSUlYvXo1QkJCYG9vj48//hijRo1ipzbrYmJiIlWmr6+PV69esf9+/vw5OBxOnXtjp6eno7S0FIMGDaqxzsuXL+Wa5q+NrOPr23ZKSgp++OEH3L59GxUVFRL1iouL6xWPrD7s0KEDAEjtO66rqwugev9xACgsLERRURGSk5MxcOBAmedXUZEe68hqU09Pr8F39X/zzTeYM2cOJk2ahE6dOsHR0RFDhw6Fu7u7xC8NtcnMzERoaCiSk5Olnt6QdXnIwMCA7ReifCiJE6X16NEjAJJJZd26dThw4AA8PDzg7+8PAwMDcLlc3L9/HyEhIRCJRBLn8PHxwYIFC3D+/Hm4u7sjNjYWhoaGGDp0aJ3t6+vrIzY2Fn/88QeuXLmC69evIzg4GGFhYYiIiICdnV2d55CVIGR5d8RdE4ZhYGBggE2bNtVYx8zMTK72aqOhodGgtu/cuYMZM2bA1NQUixYtgrGxMTuDEhAQUO/n/1VVVev9mrgN8X8HDRqEWbNmyd2mvD+3+rKzs8O5c+eQnJyMa9eu4dq1a0hISMDOnTtx8OBB6Onp1Xp8aWkpJk2ahLdv3+KLL74Aj8eDlpYWVFRUsGvXLpk3GdKTBcqNkjhRWuJp8E8++YQtO3nyJBwcHBAaGipR993p6Xe5urqiY8eOiI2NhZmZGf7880/MmjVL7kfCVFVV4eTkBCcnJwDAw4cPMW7cOOzcuZO9Q7qhunfvjkuXLuHhw4ewsbGpsV63bt2QkZEBPp8PLS0thdqq6xeFxmg7ISEBVVVV2L17t8SItqysrNmf+xePQktKSmqdRWhOWlpacHd3h7u7O4DqO/nXrFmD2NhYzJw5s9ZjU1JS8M8//yAoKAjjxo2TeG3Lli31ikPRzwJpXnRNnCidqqoqrF+/Hjdu3MAnn3zC3jkLVI+Q3h/JlZWVSdyN/C4ul4uxY8ciOTkZO3bsAAC5ptKB6qnY9/Xs2RPq6ur1nhKuzejRowEAmzdvlnicTEz8fj/77DOIRCJs3rxZ5nkKCgrqbEt8PbW+8den7ZpGx7t27ZKaKRHHJJ7+bmwqKioYPXo07ty5g8TERJl1Xr58qfD56xu7rM9U3759AUj+TLS0tGSeV9y3738HkpOTcfv2bbnjABT/LJDmRSNx0qo9ePAAJ0+eBFA9Vfjs2TMkJSUhJycHgwcPlpq+dXd3x5EjR7Bw4UIMGjQIBQUFiIuLq3Ua0sfHB3v37kVCQgIcHR3RvXt3uWJbtWoV8vLyMHjwYHTp0gXl5eXsNfX3F6hpCBsbG8yaNQu7d++Gl5cXPv30UxgaGiI7Oxu//PILjh49Cl1dXYwYMQJeXl6IiorC/fv34ezsDH19feTl5eHWrVt4/vw5kpKSam3L2toaKioqCA8PR3FxMTQ1NWFsbCxx45gs9Wl72LBhiIyMxKxZs+Dr6wsul4vff/8djx49gr6+vtS5bW1tkZKSgoiICHTp0gUcDgcjR45UvEPfExAQgD///BMLFy7Ep59+Cj6fDy6Xi9zcXFy6dAl9+/bFunXrFDp3fWP38PCAra0tbGxs0KlTJ7x48QIxMTHgcrkSx/H5fMTGxmLLli3o1asXVFRU4OzsDHt7exgaGmL9+vXIycmBkZER0tLScPLkSfB4PDx+/Fju2Hv37g0tLS0cPHgQGhoa0NXVhYGBQY33DpCWQUmctGoJCQlISEiAiooKNDU1YWRkBAcHB/z3v//FkCFDpOovX74cWlpaSExMRFJSEj788EP4+vrC2toaU6dOldlGt27d4OTkhKtXr0pNQdbG09MTx44dw/Hjx1FYWAhtbW307t0b27ZtY6dCG8vixYthYWGBqKgo7NmzBwzDwMjICEOGDJG4Rh0cHAwnJyfExMRg165dEAqFMDQ0hKWlJRYtWlRnO126dEFQUBB2796N1atXQygUYuzYsXUm8fq0bW9vj7CwMPzwww/YunUr1NXVMWjQIERFRWHy5MlS5/3222+xZs0ahIeHo7S0FAAaNYnr6Ojg0KFD+PHHH9nPjaqqKoyMjGBvbw9vb2+Fz13f2KdPn46LFy/iwIEDePPmDTp27MguUvPujY0BAQEoLi7GwYMH8fr1azAMg6SkJBgbG2PPnj3YuHEjoqKiUFlZCSsrK+zevRuxsbH1SuIaGhoIDQ3Fli1bEBQUBIFAAEdHR0rirQyHqe9dJIS0QbNmzcKtW7dw+fJlmTduEUJIa0TXxMm/3vPnz5GcnIwxY8ZQAieEKBWaTif/Wrdv30Z6ejoOHDgALpeLadOmtXRIhBBSL5TEyb/WoUOHcOLECZiYmCAkJERqYRBCCGnt6Jo4IYQQoqTomjghhBCipCiJE0IIIUqKkjghhBCipCiJE0IIIUqKkjghhBCipP4X4l7VvRmddxYAAAAASUVORK5CYII=\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
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    "# plot variables\n",
    "fontsize = 18\n",
    "linewidth = 5\n",
    "markersize = 12\n",
    "errbar_width = 5\n",
    "xlabel = 'Day since treatment start'\n",
    "\n",
    "# color for the control interval\n",
    "ctrl_clr = 'darkslategray'\n",
    "\n",
    "# compute control fit values\n",
    "results_ctrl = np.loadtxt(results_ctrl_file)\n",
    "\n",
    "v_ctrl = results_ctrl[:,0]\n",
    "probs_ctrl = results_ctrl[:,3]\n",
    "probs_ctrl_err = results_ctrl[:,4]\n",
    "\n",
    "v_min = 0.\n",
    "v_max = 3.\n",
    "binsize = 0.25\n",
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    "bins = int(v_max / binsize)\n",
    "\n",
    "ind_vmax = v_ctrl <= v_max\n",
    "v_ctrl = v_ctrl[ind_vmax]\n",
    "probs_ctrl = probs_ctrl[ind_vmax]\n",
    "probs_ctrl_err = probs_ctrl_err[ind_vmax]\n",
    "\n",
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    "fit_bounds = [0, np.inf]\n",
    "popt_ctrl, pcov_ctrl = curve_fit(asymptotic_exponential_growth, v_ctrl, probs_ctrl,\n",
    "                                 sigma = probs_ctrl_err, absolute_sigma=False,\n",
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    "                                 bounds=fit_bounds\n",
    "                                 )\n",
    "perr_ctrl = np.sqrt(np.diag(pcov_ctrl))\n",
    "\n",
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    "# limits of the 95% confidence interval\n",
    "ci_lower = float(popt_ctrl - perr_ctrl)\n",
    "ci_upper = float(popt_ctrl + perr_ctrl)\n",
    "\n",
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    "with sns.axes_style('darkgrid'):\n",
    "\n",
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    "    fig = plt.figure(0,(7,6))\n",
    "\n",
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    "    plot_title = 'All RBCs'\n",
    "    para = 'lambda'\n",
    "    para_label = r'$\\lambda$'\n",
    "    ylim = [0, 2.7]\n",
    "    health = 'all'\n",
    "\n",
    "    for jj, patient in enumerate(patients):\n",
    "        data = dict_fitvalues[patient]\n",
    "        color = color_dict[patient]\n",
    "\n",
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    "        ax=plt.subplot(111)\n",
    "\n",
    "        df_plot = data[health]\n",
    "        xdata = df_plot['days']\n",
    "        ydata = df_plot[para]\n",
    "        yerr = df_plot[para + \"_err\"]\n",
    "\n",
    "        # plot data on treatment\n",
    "        plt.errorbar(xdata[:-1], ydata[:-1], yerr=yerr[:-1],\n",
    "                     c=color, label=labels[jj],\n",
    "                     ls='-', lw=linewidth, marker='X', markersize=markersize,\n",
    "                     ecolor='gray', elinewidth=errbar_width)\n",
    "\n",
    "        # plot data off treatment\n",
    "        plt.errorbar(xdata[-2:], ydata[-2:], yerr=yerr[-2:],\n",
    "                     c=color, ls='--', lw=linewidth, marker='X', markersize=markersize,\n",
    "                     ecolor='gray', elinewidth=errbar_width)\n",
    "\n",
    "        plt.ylim(ylim)\n",
    "        plt.xlabel(xlabel, fontsize=fontsize)\n",
    "        plt.tick_params(axis='both', which='both', labelsize=fontsize-2)\n",
    "        plt.xticks([0,100,200,300])\n",
    "        plt.title(r'{} - {}'.format(para_label, plot_title), fontsize=fontsize+2)\n",
    "\n",
    "        # plot control region at end only\n",
    "        if patient==patients[-1]:\n",
    "            if health=='unhealthy':\n",
    "                ax.axhline(ci_lower, ls='--', lw=.5, c=ctrl_clr, zorder=0)\n",
    "                ax.axhline(ci_upper, ls='--', lw=.5, c=ctrl_clr, zorder=0)\n",
    "                axis_limits = ax.get_xlim()\n",
    "                ax.add_patch(patches.Rectangle((axis_limits[0], ci_lower),\n",
    "                                               np.diff(axis_limits), ci_upper-ci_lower,\n",
    "                                               color=ctrl_clr, alpha=0.15, zorder=0,\n",
    "                                               label = 'CTRL'\n",
    "                                               )\n",
    "                             )\n",
    "                ax.get_yaxis().set_ticklabels([])\n",
    "\n",
    "            else:\n",
    "                ax.axhline(ci_lower, ls='--', lw=.5, c=ctrl_clr, zorder=0)\n",
    "                ax.axhline(ci_upper, ls='--', lw=.5, c=ctrl_clr, zorder=0)\n",
    "                axis_limits = ax.get_xlim()\n",
    "                ax.add_patch(patches.Rectangle((axis_limits[0], ci_lower),\n",
    "                                               np.diff(axis_limits), ci_upper-ci_lower,\n",
    "                                               color=ctrl_clr, alpha=0.1, zorder=0,\n",
    "                                               )\n",
    "                             )\n",
    "        # set alpha of errorbars\n",
    "        for collection in ax.collections:\n",
    "            collection.set_alpha(.4)\n",
    "\n",
    "    fig.supylabel(\"Exponential growth rate [(mm/s)$^{-1}$]\", fontsize=fontsize)\n",
    "    plt.legend(loc='lower right', ncol=2, fontsize=fontsize-2)\n",
    "    plt.tight_layout()\n",
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    "    savename = \"fig_S1_growth_rate_all_Cells\"\n",
    "    savepath = os.path.join(savefolder,savename)\n",
    "    plt.savefig(savepath+\".pdf\", dpi=900, format='pdf')"
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   "cell_type": "code",
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   "execution_count": 9,
   "outputs": [],
   "source": [],
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