plot_bkg_comparison.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b33e198d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.style.use(\"science.mplstyle\")\n",
    "matplotlib.rcParams['text.latex.preamble'] = r'\\usepackage{amsmath} \\usepackage{amssymb}'\n",
    "\n",
    "\n",
    "list_to_plot = [\"SALAD\", \"CATHODE\", \"CURTAINs\", \"FETA\", \"Truth\"]\n",
    "\n",
    "colors_dict = {\"Truth\":\"grey\",\n",
    "               \"SALAD\": \"#009E73\",\n",
    "               \"FETA\": \"#CC79A7\",\n",
    "               \"CATHODE\":\"#D55E00\",\n",
    "               \"CURTAINs\":\"#E69F00\",\n",
    "                \"combined\": \"#e41a1c\",\n",
    "               \"combined_scores\": \"#0072B2\",\n",
    "                \"ideal_ad\":\"#56B4E9\"}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "978c56ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data contains: mj1, delta mj, tau21_j1, tau21_d2, deltaR, m_jj\n",
      "shape:  (400000, 6)\n",
      "min:  [ 4.94362693e-03  1.90454969e-07  2.29636542e-02 -1.57495635e-03\n",
      "  2.00533226e-01  4.50000000e-01]\n",
      "mean:  [0.13518009 0.12043567 0.57921261 0.4824966  0.55880448 0.49511516]\n",
      "max:  [0.84141445 0.73663813 0.9893744  1.00536549 0.85372049 0.55      ]\n",
      "shape:  (1866816, 6)\n",
      "min:  [ 2.77189794e-03  6.36700002e-16  1.53287807e-02 -1.46005545e-02\n",
      "  1.96302757e-01  4.49882358e-01]\n",
      "mean:  [0.13403158 0.12060626 0.57994329 0.48253784 0.55669686 0.49518479]\n",
      "max:  [0.94141889 0.88695806 0.99113005 1.01191103 0.92550874 0.54990494]\n",
      "shape:  (731868, 6)\n",
      "min:  [-0.26088436 -0.33336361 -0.37281863 -0.4324499  -0.36947759  0.4498837 ]\n",
      "mean:  [0.13462199 0.12114861 0.58171537 0.48369635 0.5593914  0.49501906]\n",
      "max:  [1.3124152  1.35115918 1.32156165 1.26961946 1.31514621 0.5499076 ]\n",
      "shape:  (1044782, 6)\n",
      "min:  [ 0.00126073  0.          0.00679913 -0.00465461  0.16702601  0.44988187]\n",
      "mean:  [0.1285373  0.12729118 0.54484807 0.45374795 0.55956109 0.49514224]\n",
      "max:  [0.81743252 0.80235654 0.99286478 1.01752569 0.87719786 0.5499084 ]\n",
      "shape:  (121339, 6)\n",
      "min:  [ 2.19207910e-03  1.11894507e-06 -5.69377642e-04 -2.67070692e-04\n",
      "  2.48984680e-01  4.49882294e-01]\n",
      "mean:  [0.13496635 0.12055243 0.58018826 0.48272053 0.5595958  0.49502351]\n",
      "max:  [0.81570579 0.72605278 0.99362225 0.99692427 0.82035039 0.54990505]\n"
     ]
    }
   ],
   "source": [
    "# loading and understanding the data\n",
    "data_path = '/global/ml4hep/spss/rrmastandrea/synth_SM_AD/scaled_data_wide_seed_2/'\n",
    "\n",
    "cathode_data = np.load(f\"{data_path}nsig_injected_0/cathode.npy\")\n",
    "curtains_data = np.load(f\"{data_path}nsig_injected_0/curtains.npy\")\n",
    "feta_data = np.load(f\"{data_path}nsig_injected_0/feta_o6.npy\")\n",
    "salad_data = np.load(f\"{data_path}nsig_injected_0/salad.npy\")\n",
    "salad_weights = np.load(f\"{data_path}nsig_injected_0/salad_weights.npy\")\n",
    "\n",
    "truth_data = np.load(f\"{data_path}nsig_injected_0/data.npy\")\n",
    "\n",
    "\n",
    "print(\"data contains: mj1, delta mj, tau21_j1, tau21_d2, deltaR, m_jj\")\n",
    "for data in [cathode_data, curtains_data, feta_data, salad_data, truth_data]:\n",
    "    print(\"shape: \", data.shape)\n",
    "    print(\"min: \", data.min(axis=0))\n",
    "    print(\"mean: \", data.mean(axis=0))\n",
    "    print(\"max: \", data.max(axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc361059",
   "metadata": {},
   "source": [
    "Looking at these numbers, we see that we can train a **balanced** multiclass classifier of CATHODE vs CURTAINS vs FETA vs SALAD  using 400k events of each method. We evaluate on the 120k truth events in the end. \n",
    "\n",
    "For training, a train / test / val split of 60/20/20 would mean 240k train, 80k test, and 80k val events. \n",
    "\n",
    "Before training the classifier, let's have a look at a few histograms in physical space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4f030cb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from https://github.com/rmastand/FETA/blob/ee4942e668b94df7b504b1503b027bdc28827eb1/helpers/datasets.py#L227\n",
    "\n",
    "# standardization transformation and its inverse:\n",
    "def minmaxscale(data, col_minmax, lower = -3.0, upper = 3.0, forward = True):\n",
    "    if forward:    \n",
    "        minmaxscaled_data = np.zeros(data.shape)\n",
    "        for col in range(data.shape[1]):\n",
    "            X_std = (data[:, col] - col_minmax[col][0]) / (col_minmax[col][1] - col_minmax[col][0])\n",
    "            minmaxscaled_data[:, col] = X_std * (upper - lower) + lower      \n",
    "        return minmaxscaled_data\n",
    "\n",
    "    else:  \n",
    "        reversescaled_data = np.zeros(data.shape)\n",
    "        for col in range(data.shape[1]):\n",
    "            X_std = (data[:, col] - lower) / (upper - lower)\n",
    "            reversescaled_data[:, col] = X_std * (col_minmax[col][1] - col_minmax[col][0]) + col_minmax[col][0]\n",
    "        return reversescaled_data\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "01d394fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load minmax and transform data to physical space\n",
    "\n",
    "col_minmax = np.load(f\"{data_path}col_minmax.npy\")\n",
    "\n",
    "\n",
    "physical_data = {\"CATHODE\":minmaxscale(cathode_data, col_minmax, lower=0, upper=1, forward=False),\n",
    "                 \"CURTAINs\":minmaxscale(curtains_data, col_minmax, lower=0, upper=1, forward=False),\n",
    "                 \"FETA\":minmaxscale(feta_data, col_minmax, lower=0, upper=1, forward=False),\n",
    "                 \"SALAD\":minmaxscale(salad_data, col_minmax, lower=0, upper=1, forward=False),\n",
    "                 \"Truth\":minmaxscale(truth_data, col_minmax, lower=0, upper=1, forward=False)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "98b3270b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1728x576 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot them in comparison to see if transformation etc. worked correctly\n",
    "\n",
    "\n",
    "num_bins = 30\n",
    "sim_color = \"#990000\"\n",
    "dat_color = \"#1c4587\"\n",
    "linewidth = 2\n",
    "\n",
    "fig, ax = plt.subplots(2, 6, figsize = (24, 8), gridspec_kw={'height_ratios': [3, 2]})\n",
    "fig.subplots_adjust(right=0.8, left = 0.2)\n",
    "\n",
    "title_dict = {0: f\"$m_{{J1}}$ [GeV]\", 1: f\"$\\Delta m_{{JJ}}$ [GeV]\", 2: f\"$\\\\tau_{{J1}}^{{21}}$\", \n",
    "              3: f\"$\\\\tau_{{J2}}^{{21}}$\", 4: f\"$\\Delta R_{{JJ}}$\", 5: f\"$m_{{JJ}}$ [GeV]\"}\n",
    "kwargs = {'density': True, 'histtype': 'step', 'lw': 2., 'alpha': 0.6}\n",
    "legend_loc = ['upper right', 'upper right', 'upper left', 'upper left', 'upper left', 'lower left']\n",
    "\n",
    "\n",
    "\n",
    "for ax_y in range(6):\n",
    "   \n",
    "    bins = np.linspace(min(physical_data[\"Truth\"][:,ax_y]), max(physical_data[\"Truth\"][:,ax_y]), num_bins)\n",
    "    for key in list_to_plot:\n",
    "        \n",
    "        # plot the distributions\n",
    "        if key == \"SALAD\":\n",
    "            ax[0, ax_y].hist(physical_data[key][:,ax_y], bins = bins, density = True, label = key, \n",
    "                        histtype = \"step\", linewidth = linewidth, weights = salad_weights,\n",
    "                       color=colors_dict[key])\n",
    "        elif key == \"Truth\":\n",
    "             ax[0, ax_y].hist(physical_data[key][:,ax_y], bins = bins, density = True, label = key, \n",
    "                        histtype = \"stepfilled\", linewidth = linewidth,\n",
    "                       color=colors_dict[key], alpha = 0.6)\n",
    "\n",
    "        else:\n",
    "            ax[0, ax_y].hist(physical_data[key][:,ax_y], bins =  bins, density = True, label = key, \n",
    "                                histtype = \"step\",linewidth = linewidth,\n",
    "                               color=colors_dict[key])\n",
    "    \n",
    "   \n",
    "\n",
    "        # plot the ratios\n",
    "        ax[1, ax_y].axhline(y = 1, color = \"black\")\n",
    "        loc_denom, _ = np.histogram(physical_data[\"Truth\"][:,ax_y], bins =  bins, density = True)\n",
    "        if key != \"Truth\":\n",
    "            if key == \"SALAD\":\n",
    "                loc_numer, _ = np.histogram(physical_data[key][:,ax_y], bins = bins, density = True,\n",
    "                                           weights = salad_weights)\n",
    "            else:\n",
    "                loc_numer, _ = np.histogram(physical_data[key][:,ax_y], bins =  bins, density = True)\n",
    "            \n",
    "\n",
    "            loc_ratio = np.divide(loc_numer, loc_denom)\n",
    "            ax[1, ax_y].step(bins[:-1], loc_ratio, where = \"mid\", linewidth = linewidth,color=colors_dict[key])\n",
    "    \n",
    "       \n",
    "\n",
    "    ax[0, ax_y].set_xlabel(title_dict[i], fontsize = 30)\n",
    "    ax[0, ax_y].tick_params(axis='both', which='major', labelsize=20)\n",
    "    ax[1, ax_y].tick_params(axis='both', which='major', labelsize=20)\n",
    "    ax[0, ax_y].set_yticks([])\n",
    "    ax[0, ax_y].set_xlim(bins[0], bins[-1])\n",
    "    \n",
    "    ax[1, ax_y].set_xlim(bins[0], bins[-1])\n",
    "    ax[1, ax_y].set_ylim(0.25, 1.75)\n",
    "    ax[1, ax_y].set_xticks([])\n",
    "    if ax_y > 0:\n",
    "        ax[1, ax_y].set_yticks([])\n",
    "\n",
    "ax[0,0].set_ylabel(\"Density\", fontsize = 35, labelpad = 40)  \n",
    "ax[1,0].set_ylabel(\"Ratio to Truth\", fontsize = 30) \n",
    "ax[0,0].legend(fontsize = 19, loc = \"upper right\")  \n",
    "#plt.legend(loc='lower center', ncol=5, bbox_to_anchor=(0.0, -0.45), fontsize = 15)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "\n",
    "dpi = 800 \n",
    "#plt.subplot(2,3,5)\n",
    "plt.savefig(f'plots/background_validation.pdf', facecolor='white', dpi = dpi)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c88080d",
   "metadata": {},
   "source": [
    "The distributions seem to agree with fig. 4 of FETA (2212.11285)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73502984",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f42a92be",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20,) (20,) (20,)\n",
      "[[0.004448946694624034], [0.004496465659544535]]\n",
      "[[0.004472663508986008], [0.0035253965751045335]]\n",
      "[[0.006251824512857329], [0.005741590207589242]]\n",
      "[[0.006107795010472428], [0.004331110455074949]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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mUbW1tYWtrS0AwN/+7d9+TVU/BnC6+G7HxgCcBnCs3XprOxmHqpa7nSuXy2kul/PaBaKhY9cfiLobRIEwjskUjGUyBWOZTCAix1U1B3gcyRaRCVhbWE3Cqt4dAxC3/9uAVX27DmuLrg1VPROgkwkPzeth71utqg27SvlpWBXRWy0CeDTMaxINu/V1ro6g0cc4JlMwlskUjGUyTdck206sn4K1x/XdsPbOrtm3cwAu2U3vgZVs3wPgMICCiCisat1FVf3LXjtlJ9j5XtsDWAPQddq2DxUAMRFJteyfHYM1VbzRh2sSERERERHRiOqYZNvJdQnWntDLADxv72Uny48CeEpETgBYUNUvdDvO3oZrYPtO2/3cAPBoh624Yi33U9i+tRfRWJiamuJ0Lhp5jGMyBWOZTMFYJtO0rS4uIs8BWIE1An2Pqj7lZ/9sVa2p6glVfQzWFPOfE5E1EflQsG6HLoabo/NuzpT11sT7cJu2RERERERENOZ2JNki8nUAF1X151T1xbAupKoNVX0GwFEAJ0Tkk2GdexfOevFtRCQmIpv2Nl2wR69Ptjk+C6Bgj6y7JWCtPSciIiIiIiJq2jZdXERegFWN+1y/LmgnrI+JyGdF5BFVfTnM89vrpRdxszBbXkRmAKy0VAKPt/SrICJpEZmEtc58EsCyqpbaXKYOJtk0hp599tmou0AUGOOYTMFYJlMwlsk0zS28ROR+WFW6rwzs4iKP+pmGPmq4hRcREREREZG53Ft4NaeLq+q5QSbY9jWNT7CJTHLvvfdG3QWiwBjHZArGMpmCsUymaVv4jIionQsXLkTdBaLAGMdkCsYymYKxTKZhkj0AW1tbyOVyWF1djborREREREREFCI7z5tw7rfdJ1tE7gbwHIAXVPUfBtExk01MTIBrsskEyWQy6i4QBcY4JlMwlskUjGUaddPT0wCw5dxvm2QDmAKQAZAC8HOtT4rIIwBmYFXhroZdIZyIhtPGxkbUXSAKjHFMpmAskykYy2SattPF7YJkzwPYsU+2iHwWQAX2HtIAVkTkuoj8jYg8LSIP9bG/RBShdDoddReIAmMckykYy2QKxjKZprmFV88HiKzDSrABay/qxwA8CmtPagBwTrgCKxmvqOq3gnZ0lHELLzKFiMDrewbRsGEckykYy2QKxjKZwL2FV6fp4rtZd229dRrACfuk98OaXu4k3Y/BmlIOEQGAZQBLXONNREREREREpvJVXVxE7mp9zN5n+4SqzqlqHMAhAM8AeBmAAJgHUBWRvwnSYSIiIiIiIqJh5SfJzgIoiMjEbo1U9ayqPq+qM6q6B1bS/XkAjzHRJhpNr732WtRdIAqMcUymYCyTKRjLZBrPSbaqXlHVpwA8IyJPezjurKpmABwEsF9EPuH12kQULVb/JBMwjskUjGUyBWOZTONrujgA2In2WRF50uNxNVhrt5/ye20iisYTTzwRdReIAmMckykYy2QKxjKZxk/hsyZXATSvxzVE5EqQaxMRERERERENG98j2X6IyN32ntovAbg0yGtHaWtrC7lcDqurq1F3hYiIiIiIiEJk53kTzv1AI9leqeoVsfbzmgGQH+S1ozQxMQHuk00mKBaLUXeBKDDGMZmCsUymYCzTqJuengaALee+DHrjdxG5G8CU36nmoyiXyymTbCIiIiIiIjOJyHFVzQEDni4ONKuTj02CTWQSayIK0WhjHJMpGMtkCsYymWbgSTYRERERERGRqZhkExEREREREYWESTYR9ezxxx+PugtEgTGOyRSMZTIFY5lMwySbiHr25S9/OeouEAXGOCZTMJbJFIxlMg2TbCLq2cc//vGou0AUGOOYTMFYJlMwlsk0TLKJqGdf+cpXou4CUWCMYzIFY5lMwVgm0zDJJiIiIiIiIgoJk+wB2NraQi6Xw+rqatRdISIiIiIiohDZed6Ec//WqDoyTiYmJpDL5aLuBlFgqhp1F4gCYxyTKRjLZArGMo266elpANhy7nseyRaRh1pud7mee1pEviMil0TkpIhMhNBnIhoSpVIp6i4QBcY4JlMwlskUjGUyjZ/p4jMAqgAWAUw5D4rIcwDyAE4DSAE4AaDIRJvIHJlMJuouEAXGOCZTMJbJFIxlMo2f6eINADOqetp5QEQeBrAAoKiqv+d6fA3AM7ASciIiIiIiIiKj+RnJvtudYNuOAlBYI9lNqnoFQN1n34iIiIiIiIhGip8k+0qbx1IAGqq61eY5VjIgMsSXvvSlqLtAFBjjmEzBWCZTMJbJNH6S7G1Js4jcDSAJYL1D+3t8XIOIhtChQ4ei7gJRYIxjMgVjmUzBWCbT+EmyYy33U/Z/l1sb2gk4p4sTGeK+++6LugtEgTGOyRSMZTIFY5lM4yfJPicinwQAe/uuPIDLqvp5dyMReQjAc6r6fOBeEhEREREREY0Az0m2qr4I4DERqcOqNJ6APZotIneLyGdFZB3WNl9pEXkyxP4SERERERERDS0/I9lQ1adgJdeHVHWPqp51PV0FkIW1n/ZjADYC93LEbW1tIZfLYXV1NequEAVy7NixqLtAFBjjmEzBWCZTMJZp1Nl53oRzX1T9F/+2p4RPAair6l+6Hv+k+/64y+Vymsvlou4GERERERER9YGIHFfVHOBzJFtEHhKRNVij1CUAmZYmZ0XkBTsJJyJDsPonmYBxTKZgLJMpGMtkGs9Jtl0x/GUAlwHMAzgEoOJuo6rn7CnlMyIyEUI/iWgIVKvVqLtAFBjjmEzBWCZTMJbJNLf6OOY5AMfsAmgAABF5tF1DVX1eRJYALPrsXyAiMgugoaqVro2t9ilYa8kvAZgEsKGqpTZtkvbdewBstrYhIiIiIiKi8eQnyb7iTrB7ID6uEZidDJ8AMOehPVQ163psQ0Riqlqw7yftNgVXm1kRSTPRpnFw4MCBqLtAFBjjmEzBWKZBOF95Zcdj96YeDPUajGUyjZ812Rc9tr/bxzV8E5GEiBRhVT+vezi0dV05YE2Ddz+eaR0VV9UyrNFvIuOdP38+6i4QBcY4JlMwlmkQLrz8jztuYWMsk2n8JNn39Ll9IKpaU9WMz5Hldslyw/XzlIgk2rSJ+bgW0chhlXwyAeOYTMFYJlMwlsk0fpJsEZEjrY91aPg5AN/0cY2BU9U591Rx2yyAk677FQArzrRxoLnue3kAXSSK3PHjx6PuAlFgjGMyBWOZTMFYJtN4TrJV9RkAJ0TkD0XkTudhdxt7i6+XAEyp6h+F0M+BE5E0gKp7/bWdhDcAbIjIgp1gg+uxiYiIiIiICPBX+AwAHgPwdQBZEWkAqIvIUfu5BKzp01UAqaAdHDQ7cZ4BrNHt1udV9ZCIrADIw/od21ZWd9va2mpOg5mensb09HSIPSYiIiIiIqJBW11dxerqqnN3wvnBV5KtqjUAB0VkAVZhsEnX0zUAz6jqCV89jZhdyKwsIjER2YC1XVlz8z57hDsLIA5rmviGiMzY/yZtTUxMhL7WJHf2pZ2PPfzRUK9B1Gp9fT3qLhAFxjgmUzCWyRSMZRpV7gHU48ePbzmP+x3JBtDcysrZ3up+AHVVvRLknI4OBcY6qatqI4zrOlS1YVcpPw1gn92nIoC8k1Dbv/MygA2nzaAc/9bKjseYZBMRkaneOJvb8didD+98jIiIKGqBkmw3VT3X7nERedrrumw7wc57OGQNdrIfsgqAmL2H9jrQHMWH/XMDwIy9n3aqdXsvItNMTU1BVbs3JBpijOPR9OY/7CyMNO5JNmOZTMFYJtOElmTvIgPAU5JtJ7I71kP3i53UbwB41D013CUGYArAZodTFMFtvIiIiIiIiMbejiRbRH4T4RYs8zLtOyoxWGvJW9dVO313Eu9OiX8M1qg3ERERERERDaHzlVeaP9+berBv12k3kp2ANfoclijnfsTRZoRZRGKwRq7zqlpS1aqInGxtB6vAWcG1Bhut08LtUfB7dit8RmSKZ599NuouEAXGOCZTMJbJFIxlGpQLL/9j8+dBJ9kNACuqGkoVLRG5FMZ5PFwvBmARN7cSy4vIDKzfqexqGncfp6oFEUmLyCSAS7Aqpi+798BW1YzdJm+3AYCGvX82kfHCrpIPbK+Uz+J9NAj9iGOiKDCWyRSMZTJNuyS7BuBsiNdoWxCtX+xiZLsmvXabHdXA3Qn1Lsd2bUNkqnvvvRfnz58P9ZzuSvlMsmkQ+hHHRFFgLJMpGMtkmh1JtqqehrVtVShUdSqscxFRtC5cuBB1F4gCYxyTKRjLZArGMpkmlOriInIXrOnZDVXdCuOcRETD6Ad/ldvx2Hs/sfMxGj9c+kBERERAwCRbRJYApOEqLiYiDQBFVf2DQD0zyNbWFnK5HKanpzE9PR11d8iDN87mmj+P+36sAJBMJqPuQuQu/vXOvXqHPclmHG/Xrzjm0gcaNL4nU7912rtaVSEioV2nH7HMLz5pkFZXVwFgwrnvK8kWkYdhbVnlrGt2tr9K2LdnRGQWwIyqftd/d80wMTHBgg4j6s1/uJlQMTkBNjY2ou4C+cA43o5xTKZgLFO/6PUb+OH6Jl7/xj+3ff5//9FX8L6P/ALeMzUJuWVP4Ov1I5b5xScNkj2QuuXc9/yqEJG7ASzDKmg2o6p7VPWgqj5m/3cPgHkAbwAo73YuIhot6XQ66i4QBcY4JlMwlqkfrr91Df/y+Zfx2te+hZ9dvtq2zc8uX8X3v/Yt/Mvnz+D6W9cCX5OxTKbx89XTIoCKqk7ZRdJ2UNWyqh4CcE5Eng7UQyIaGidOnIi6C0SBMY7JFIxlCptev4FXv/i3uPr9Om5cu75722vXcfX7l/DqF78BvX4j0HUZy2QaP9PFE6o630tDVZ0XkRd8XIOIiIiIiAboh+ub+PH5yz0nzXr9Bq6er+Pieg3v+ZWDfe6d+dzFVYe93gvtzk+SXffYvuHjGkRERERENCCqite/8c9dR7B3HHftOv71G9/G/g9PhloMbRy5i6syyR5tfpLsyx7bty9LSGOH386Nvtdeey3qLhAFxjgmUzCWKUxX/88lvH31p76OffvqT3H1/1zCHR/a7+t4xvJo4u4lnfkayRaRu1T1R722b/egiDypqp/3cf2xN6jtFMLGb+dG38bGBu69996ou0EUCOPYwi8+Rx9jmcJ09fuXoNf9jY3pdcXV7/tPshnLo4m7l3TmufCZqj4PIC8id3Vraxc9K3Z4Ouv12uPu2o3reOGf/w6T5aW2z0+Wl/DCP/8drt3wNs2HqFdPPPFE1F0gCoxxbLn418ebNxpNjGUK0423rvkuYKbXb+DGW2/7vjZjmUzjeSTbTpwFwIaIVNB5zfUsgCqAe9qMrsZg7adNPXrz2lv49a9/HtX69/Hjt9tvlXDuzTo+880v439unsXXHnsSd+y9bcC9JCIiIqJRtOe2vZBb9vhKtOWWPdhzm58JskT98a/f+DZuvy+OOyff5+m4NzZfx9XX6nj/Rx4IdH0/r4Y/AHA3rER7skvb3Z7nWu0eXbtxHb/+9c9j7eL38NaN3b8l/PH1a/jmxe/hN77+eZz+9aewd88tA+olkflGdakGERFRN7d/4B7ILQL1MSFSbhHc/oF7wu8UkU+33xdH7S/+Donf+rWeE+03Nl9vHhOUn32y6wCeAbBPVff4uQGIY4yqjm9tbSGXy2F1ddXX8V/4l/8P1fr3uybYjrduvI2NS9/H//iXb/q6HlEnxWKn1R/dFV45gzMXXvV83JkLr6Lwyhnf1w2Dvn0N9ZdfwKufbf+94aufnUT95RegHWaZkHfnK680b2ELEsdEw4SxTGG6/d/cg1tvf6evY2+94524/d/4T7IZyxS2Oyffh8Rv/Rpqf/F3eGPz9a7t3Qm219FvAE6eN+Hc95Nk1wAUVfWKj2MBAKraAHDO7/GjZmJiArlcDtPT056PVVUUXlntOEW8kx9fv4bCK2c6jryRufqZnKTTad/HHt7/Qcyf+XNPifaZC69i/syf4/D+D/q+blA3fvomtvKP4PW/+Ayu/bD929a1H57D63/xGWzlH8WNn7454B6a6cLL/9i8hS1IHBMNE8YyhUlE8L6P/AL27PU2C1L23oL3//sHAs3oYixTP/SaaAdNsAE4ed6Wc99P4bPHPFQW3+08U0HPMQ7+/gffxQ98fmh//adv4u9/8N2Qe0TDrp/JSZA/oEcOHMSpI7/dc6LtJNinjvw2jhw46Pu6Qejb1/Dd//br+GltDfqzH+/e9mc/xk9r38R3/9tvcER7yHFqP5mCsUxhe8/UJN597z7ILb2lCHLrHtx+Xxz7p4KVWmIsU790S7TDSLDb8TOS7YmITPT7Gib75sX/47ta+Ns3rmPt4vdC7hGRf70m2sOQYAPA5W98AT/dqkLffqun9vr2W/jp1gYu/7//o889Ixovu9VDGHb9nF1EFDa5ZQ8Ofvr/stZndxnRlr234PYP3IODn/pIz0l5P4zycjQaDHei7davBBsYQJINYHkA1zDWG9fe8p1k/+zGdbxxrbfkgGhQuiXaw5Jgqyoufa3QdQR7x3E/+zEufbUwEh/+yR9+oBscvXENV//5Bfzwxfb1EH744iSu/vML0BvDO3ukn7OLiPrhltv24uefPIIP/sbDeMe+29u2eUf8dnzwNx7Gz//uEdxy294B93C7UV2ORoPlJNpu/UqwgYBJtohMiMhDu93ArboCuXPvbb4rhL9jzy24k9t4UYgef/zxUM7jTrTdhiXBBoCfvPr3ePtHP/B17Ns/eh0/efXvQ+4RhSVoHPMD3WDcuPYmLv3NI3hj/TO4/mb7egjX3zyHH619Bpf+5lHcuDZ+9RDCek8maiW37MF7fuUgfunp9jH2S595HO/5lYOhjWAHieVRXI5G0WhNpvuVYAM+k2wRWRKR6wA2AWx0ucVC6emY+vD+f+M7yb51zy2RfKC7+NUCrn7b+2jN1W+fwcWvFvrQIwrLl7/85dDO5fxRdBumP3o/qX0Tet3f6Jhefxs/ObcWco8oLEHjmB/o+k9vXEN95ddx7eIa9O0us0mu/xjXLn4T9ZXfGOoR7X4I8z2ZqJ1Oa6XDXkM9qPdlvh/ToHhOskXkswAyAJ4H8FSX22JoPR1Tv/reD+G977zD17Hve9ed+NX3fijkHnX3rsRhfP9P5j0l2le/fQbf/5N5vCtxuI89o6A+/vGPh3q+1j9ww/RH78ZP3wD8FjC7/jPc+Mkb4XaIQhNGHPMDXX/9+F++gLfrVeBGj0uebryFa5c28OPvjFc9hLDfk4miMoj3Zb4fU2vhs1639/LjVh/HzAC4v9ctvESENfkDEBEsPDiNz6x92dM2Xu++ZS8Wfmk6kmqNtz9wBB/4/VP4/p/M4wO/fwq3P3Bk1/ZOgt1LW4rWV77ylai7MDB73nkncOte4O2feT/4lndgz7vuDL9TFIqw4tj9ga51VgbAD3R+qSqu/u9C9xHsVtd/jKuvFPDun0+PTaXicXpPJrP1433Zje/H5BQ5c3OKoQ1L4bOqxz2yMz6uQS6/+/O/gmT8A7htT2/fidy251Yc2v8B/Jef/3Cfe9aZO9HebUSbCfZ4a/2m2es61356V+LDkFv8FXORW27Fu+7nrIxxMAof6EZtCc+1H/49bvzUXz2EGz99Hdd+yHoIRONs2Jej0eC5q4i79bqPth9+kuyLXhqr6mkf1yCXvXtuwf967El8eP8H8e4uH/rffctefPg9H8TXZp70vZY7LN0SbSbY481JQty8rHPtt3cd/FXcetd7fR17613vw7sO/mrIPaJhNewf6EZtCc+1H37T99pqvfE2rl1kPQSicTfMy9FosLpt09WvRNtPkn1FRO7qtbGIPOLjGkbZ2tpCLpfD6uqq73Pcsfc2nP71p/DHH34CiTvibdsk7rwHf/zhJ3D6PzyFO4akqrg70XZjgj2awtqWyj3K5+a1oFQ/iQju+Y0FyDve7e24d7wb93xsYWymq46ifmyvNswf6HqdWeSI+v35xttvAH4LmN34GW5cG596CNwqkEzBWKZ+6HUf7DASbTvPm3Due06yVfUEgD8QkYlubW15r9cwzcTEBHK5HKanpwOdZ++eW5D5hV/Fq7Pt68m9+pvPIPMLvxr5CHYr5wOeGxPs0VQqlQKfo9s02mFKtPd95Hfxzokk5NbevrSSW2/DOycOYd+//y997hkFEUYcj5pRWsKz59Y7gT0+993d8w7s2Ts+9RDGMZbJTGHH8jAvRxtFb75SwFsXvC87euvCGbz5SjQ7B/WaYDuCJtp2nrfl3Pe1hZeqPgMgKyIv2dt5Pdnh9jSApJ9rUGeD2k4hTK0f1phgj6ZMJliJhV7XqQ5Loi237sWHPvO/8M7Eh7uOaMs73o13Jj6MD33ma5BbfSYINBBB47idUfhANypLePa+58MQn0m27LkVe/ePTz2EfsQyURTCjOVhX442ivbuP4zG6rynRPutC2fQWJ2P7D356mt1zwXNnET76mv1wNf3s4XX3SKyBqug2QyALIBShxs3PSbqk3/9xrd9fdP2xubr+NdvfLsPPdqd10JQw5Jo73nnHZjInsb7/vMfY+97Em3b7H1PAu/7z3+Miexp7PG55R6NrlH6QDcKS3j2vudXseed/uoh7HnX+7D3PayHQDSuRmE52ii67cARxKZP9ZxoOwl2bPoUbjsQzd+U93/kAV8Vw++cfB/e/5EHAl/fz0h2HsBlAHMADnW5PRa4h2SE1lETr0V4aKfb74t7ntLiTJ25/b726/r7ae3i9zyvU3X+KK5d/F4fe9ad3LoX8SMZHHy+/R/mg8+/iviRDEewx9AofqAb9iU8IoLbf2kBcqu3egi45d3WcUM8q4uI+meUlqONol4T7WFIsIeBnyQ7oaqPqeqLqnq2y60C4GzYnabR4oyQuHkpwkPteV074nVtSjtf+tKXfB0HAAsPHvFVCOrIgYNYeHA43qSHfanGKK6ZimJGRpA4dhvlD3TDvoTn3T//u7g1ngT29FjEc89t2Lv/EN79c+NVDyGsWCaKWtBYHrXlaKOqW6LNBPsmP0n2isf2cz6uQYZwT0F081rtltrrNdEOI8EGgEOHDvk+lvpvFNdMRTEjI4w45ge6/pI9exGf+V/Yu//DwC1dRrRveTf2vufDiKe+5nstdxii+MKI78lkiiCxPKrL0UaVO9F2Y4K9nZ8k21ONfVU95+MaZIBua/yGKdEexRFAR7dEO6wEGwDuu+++QMdTf43imqkoZmQEjWMTPtCNwhKePXvvwD3/4TTu+vAf45Y72tdDuOXOBO768B/jno+exp690dZDiOILI74nkymCxPIoL0cbVc7nDbeoP08MGz9J9jkReajXxnaFcRozvRbRGZZEexRHAN3ciYpbmAk2jYZRXDM16BkZQY36B7pRWsIje/bi9n+bwXt+s/2XE+/55Ku4/d9mIh3BdkTxhRERjeZytItfLfh6v7367TO4+NXhqCvd+tlhGD5PDBM/+2S/CGBGRD7Z4yHcX2LMeK1SOwyJ9iiOALZyPuC58QPceBrFNVODnJER1Ch+oHOM6hKeYa+H4Bi1L4yIKBrvShz2/H7rvH+/KxH94A5152cLryUAcQAZEblk75X9uQ63kwDaz/EiY/2ktua5iI7zAe8ntbU+9mx3ozgC2Kr1w1rYH+COHTsW2rmov0ZxzdSgZmSMaxyP0hKeUTbIL4zGNZbJPOMWy17fb4dpm0VH62dlrzNCTednungGwAKsPbL32f/NdLix6BmAra0t5HI5rK6uRt2Vgdj/sQVfbwC3P3AE+z+20Ice9W4URwAHqVQqRd0F8mAU10wNYkbGOMbxqC3hGXWD+sJoHGOZzDSOsdzr++2wJtitX+J7mRFqIjvPm3Du+0my6wAyqrqnlxuARhgdH2UTExPI5XKYnp6OuivUg1EcAXS0jpp4LcLTDSvZjp5RXDPV7xkZ4xbHo7iExwSD+MJo3GKZzDWusdzt/XaYE+zWL/G9Lr00jZ3nbTn3/STZNQAVD+1ZXZxGziiOADojJG5eivD0olqthnIeoiiNWxyP6hIeE/T7C6Nxi2Uy1zjHsjvRdhv2BLvd5+FxT7Td/BQ+e0xVtzy0n/J6DaJhMEojgO4piG5eq92SeUZxzVS/Z2SMm1FewkNENA6cRNtt1BJsBxNti5+R7F2JyKMi8pyILInIk2Gf32NfZkUk5aF9SkTyIrIgIkURSbdpk7SfW7DbJsPtNZE33db4hZloHzhwINDxNFijuGZqEDMyGMc0KP3+woixTKZgLGNHMj2KCbaDiXYfkmxVPa2qz6jqIoBlEflc2NfohZ1cn/DYHqqaVdWCqmZgVVBfaGmTV9WM3SYL4ISIsIK6gUZhBLDXIjphJdrnz5/3fSwN1iiumRrUjAzGMQ3CIL4wYiyTKRjLw+3axTXPMzqdzxvXLo7nsqPQk2w3Vb0CoOeR5DCISEJEirC2Dqt7OLTdft6VlseL9s1tqcOxffXsQzM7bhSeURgB9FqlNoxEJZfL+TqOBmsU10wNckYG45j6bVBfGDGWyRSMZewofDZMxSfveHDB15LJ2w4cwR0PjueyI99Jtog8KSIn7X2yXxKRtZbbd0TkOoCBVjJQ1Zo90uxnL4B2mWoDAEQkBitxb/19qrC2NBuo3MMf3XGjcIzKCODV1+qei+g4H/Cuvubl+6ebjh8/7us4GpxRXDM16BkZjGPqp0F+YcRYJlOMeyw7Rc7cuMvDaPOVZIvIKQAlWEnpJIDDsPbMdm6HANwDIKuqR8Ppan+p6pw9/dttFsBJ+2dnSnhrdlIHmkk4jbhRGgF8/0ce8FWl9s7J9+H9H3mgDz2iqI3imqkoZmQQ9cugvzAiGoQDj/y7HTcKj7uKuBu3Uxxtt3o9QER+E1bCOamq5+zHPquqz7e0exTAw6H0MgJ20bOqqhbsh2r2f+PYvvd3vMPjNGL8jAAOc8Vx6o/9//HZqLvQUdA1U1HEctAZGWFuh0QURJAvjMLe2osoTPemHoy6C8bqtk2XO9EepkJo1J3nJBvAfJttubS1kaqeFpGaiDypqp/3173BE5FZ2NPGVXXOeVxVGyJSAZDEzYQbuDnCnWh5nEZIkBHAcUq019fXo+5C5N77iVzUXejI77qn2w4ciSyG/c6quHPyfb6TEsYx9UMUXxgxlskU4xjLve6DzUR7NPlJss+1eUxEZKJ1/2xVPSci+3z1LCKqWgZQFpGYiGwAOKaqzjrsDKzCZ2VgxxTxjotct7a2mgUdpqenMT09HX7HKZBRHAEkIqLhEcUXRkTU2TAXBe41wXYw0R5eq6urWF1dde5OOD/4SbJfbfOYU4V7sc1zO0a5e+FxW6y6qjb8XKcTe+S6COA0rHXmUNWaiMzZo92ANT3cGb3uOIo9MTHBqolDbhRHAKMwNTUFVV8vaaKhwTgmUzCWyRT9iOVhLgr8k9qa52TZSbR/Ultjkh1A2DUF3AOox48f33Ie95NkH3TfEZG7VPWsiCyLyAuq+t2W9oe9XsBOsPMeDlkDUOjayrsKgJiIpFS1AljJN+yRbGDb/tqNPlyfyHjD/E0zERERUdj2f8zf4M7tDxxhgh3QoGoM+Emyl0TkcwCyALYAXATw87CqjVdFZAHW6G8d3hLlJlWtAZjr2jAkdlK/AeBR19Rwt9guhyfhSrqJyJth/qaZiIiIiMgrz0m2ql4RkWdwc+R4y368ICIzAE5g+xTxQ0E7OQAxWNO9W6d8O1PWqwAgIisAllv24M6g/f7aRMZ59tnhraxN1Kt+xTFnZdCg8T2ZTMFYJtP4GcmGql4B8JR9cz8+Y299NQtrvXJRVb8VsI9BxNFmFNouWLYBIK+qJVWtisjJ1nawRusL9sg6YP1OFdd5FmD9jqwqTmOBtQXIBP2KY87KoEHjezKZgrFMpvGVZO/GHuUtdW3YJ3YCvQhrFDoGIG+PsK/YlcMdcfdx9kh8WkQmAVwCMImdo9ZZALMico99f7PledrFMO8vTL259957cf78+ai7QRQI45hMwVgmUzCWyTShJ9lRswuQZXtos2NrsW4Jsz1i3Y8Ca2NhmPcXpt5cuHAh6i4QBcY4tvCLz9HHWCZTMJbJNJ6TbBF5qOWhmqr+yH7uaVhrlOOwplVnW/fOJiIioujxi08iIqL+2OPjmBlYhcAWAUw5D4rIc7CqiZ8GkIJVAK0oIhPBu0lEwyCZTEbdBaLAGMdkCsYymYKxTKbxM128AWBGVU87D4jIwwCcImC/53p8DcAzsBJyIhpxGxsbUXeBKDDGMZmCsUymYCyTafyMZN/tTrBtR2Ft27VtX2y7CnndZ9+MsbW1hVwuh9XV1ai7QhRIOp2OugtEgTGOyRSMZTIFY5lGnZ3nTTj3/STZV9o8lgLQ6LD+Wts8NlYmJiaQy+UwPT0ddVeIAjlx4kTUXSAKjHE8mu745Wd33MYdY5lMwVimUWfneVvOfT/TxbclzSJyN4AkgJUO7e/p8DgRERFRT+58OBd1F4iIiHriZyQ71nI/Zf93ubWhnYCP/XRxIiIiIiIiGg9+kuxzIvJJABCRu2Ctw76sqp93N7K3+npOVZ8P3EsiGgqvvfZa1F0gCoxxTKZgLJMpGMtkGs9Jtqq+COAxEanDqjSeAPAoYI1ci8hnRWQd1jZfaRF5MsT+ElGEWP2TTMA4JlMwlskUjGUyjZ812VDVp0QkCyChqmdbnq7aNwenixMZ4oknnoDq2NcypBHHOCZTMJbJFIxlMo2vJBtobs91ts1jrdt7EREREREREY0F30k2ERERERERjSdupdgZk2wi6lmxWIy6C0SBMY7JFIxlMgVjeTRxa8XOmkm2iLwA4NCAr7+uqr834GsO3NbWFnK5HKanp52Nyon65sAj/65v506n0307N9GgMI7JFIxlMgVjmUbd6uoqAEw4990j2SsANgfbHdQGfL1ITExMIJfLRd0NGhP3ph7s27lFhIVJaOQxjskUjGUyBWOZRp09kLrl3G8m2fbWXERERJHr54wMIiIion7immwiIsONYmGSfs7IICIiIuonJtlE1LPHH3886i6QDyxMsh3jmAapn7MyGMtkCsYymYZJNtEuRnEEsJ++/OUvR90FosAYxzRI/ZyVwVgmUzCWyTR7Oj0hInf5OaGIPCQiE757RDRE7nw417wR8PGPfzzqLhAFxjgmUzCWyRSMZcv+//hs80ajTdpV8hORhwGsAyiq6n/1dEKR+wFkANwPIKuqWyH0c6TlcjlldXEyAat/kgkYx2QKxjKZgrFMJhCR46qaAzqPZNcACICjbQ5+WES+IyLXReRvROQT7udV9ZyqPqOqRwE8IyIPhdt9IiIiIiIiouHUNslW1SsA9gFItHn6BIBz9u0xAGU74f7vrQm1qj4FYDHUHhMRERERERENqY5rslX1ip1st6qp6mOqehDAJIA/AvAjAE8B2BCRfxGRz4jIh/rTZSKKCqdykQkYx2QKxjKZgrFMpumYZO+iLiJ3As2p4VlV3QdrVPtlAAcBFADUROQ6gFhYnR1VW1tbyOVyWF1djborRIGUSqWou0AUGOOYTMFYJlMwlmnU2XnehHO/beGz3YjI3QDyAE6p6ssdns8AmIK1tnupw4j42GDhMzIFC5OQCRjHZArGMpkirFjOnX3p5s8PfzTw+Yi8cBc+87xPtp0wPyUinxWRLKwK5H/Z8nwhrM4SERERERF1c/xbK82fmWRTlPxMFwcAqOrzqvpRAKdD7A8RERERERGNuFqtNtDjhonvJNsx7lPBicbJl770pai7QBQY45hMwVgmU3iN5cIrZ3Dmwquer3PmwqsovHLG83HkXbVaRbVaHfixwyJwkk1E4+PQoUNRd4EoMMYxmYKxTKbwGsuH938Q82f+3FOifebCq5g/8+c4vP+DXrtHHjUaDRSLRczOzvo6fnZ2FsViEY1GI9yODRCTbCLq2X333Rd1F4gCYxyTKRjLZAqvsXzkwEGcOvLbPSfaToJ96shv48iBg367ST1aWlpCNpsNdI5sNoulpaWQejR4TLKJiIiIiGik9JpoM8EevGq1ikQiEegciURipKeMM8kmIiIiIqKR0y3RZoI9eOVyGXNzc6Gca25ubmT3UGeSTUQ9O3bsWNRdIAqMcUymYCyPrjfO5po3ChbL7kTbjQl2NE6ePIn5+flQzjU/P4/l5eVQzjVoEsbG77S7XC6nuVwu6m4QERER7Sp39qWbP3Of4b658EVp/nzg0/wsHoYzF17FI3/zQvP+/ttuH6oEu1KpoFarYWVlBcvLyyiXy6jX69jc3MThw4d9FwkbNpOTk9jc3Gz7XK1WQ6VSQaPRwObmJorFIgqFAmKxWPPfpdW+fftw+fLlfnc7FCJyXFVzAEeyB2Jrawu5XA6rq6tRd4UoEFayJRMwjskU/Yjl499aad6IBiWMWG5NpocpwQasdcrpdBq1Wg0zMzNIJpNIp9PI5/M4duyYEXtD76bRaKBSqSCdTmNhYaH575BOpxGPx1Eul0e6mrid500495lkD8DExARyuRymp6ej7gpRIKNcgILIwTgmUzCWyRSmx3KtVkMqlWr+nMlkthUGi8fjqFQqUXUvNI1GA7FYrO1zp06dQjqdbt6v1+tIJpOIxWKYnZ3F5uZm22MTicRIJN92nrfl3O97ki0iT/f7GkRERERENL5aC5953Ue7nxKJBJLJJGq1GhqNRjPhdjiPA9YXDoVCAaVSCaVSCZVKpWPxr0qlgkKhsONxJ5EXEWSz2Z4S+N3Olc1mISLNfhUKBWSzWZTL5R1t4/F42/O3rtOuVquYmZlp3u9UjTwej4/kKP8gRrIzA7gGEQ3AgQMHou4CUWCMYzIFY5lMETSWnSJnbl720R6USqWCRCKxbcTWSSCTySQajQaWlpawsLCAdDqNdDqNarXacSQ3Ho+jWCzueDyRSCCfzwMA8vn8jqTe67kWFxcBoNmvhYUF5PN5rK2tbUvMO41itz7nJP299KvbeYfVra0PiMhvAujtN+5NsE3SiGhonD9/PuouEAXGOCZTMJbJFEFi2V1F3F34zF11fFjWZ6+srOxILMvlMmKxGFKpFKrV6o5R23Q6jVOnTu04V6PRaI6QVyqVnhPWdvyeK5/PY9++fZidnUUikeh51HllZQXJZLKna+w2Oj7M2o1kJ2CNPod1i4yIzIqIr4gTkZiI7Pg6R0SSIpIWkZTz3+A9JRoNrJJPJmAckykYy2QKv7HcbZuubvtoD1qlUtk2RRoAisVic9TZGc2em5trjvbGYrFta5kdtVoNsVgMmUym7Qi0F0HOlUqlmtPGY7EY6vV6x2s4KpUKpqammvedomjt1Ov1kRzJbpdkNwCsqOqeMG72+QbOTn5PBDhFHsC2r01EJAEgr6olVa2oaglAxn6cyHjHjx+PugtEgTGOyRSMZcsP/irXvNFo8hPLve6DPSyJtrPu2p1sFgqFZpVxx8bGBhKJRHNN9dzc3K7nTaVSzW2xgvJzrng8jrW1teb9dmurK5UKJicnUavVmkXu3IlzqVTqOHreaa32sGuXZNcAnA3xGudCPFdXIpKwR6ATANp/ldLDOdCSYNsyAFq/3inCSsiJiIiIaMAu/vXx5o3GQ68JtmMYEm1nPXYymWwWEIvFYjv2ho7FYsjn89jc3GzuD53JbJ8cXK1Wsb6+3iyONjU11XZKeTut07mDnAuwRpoPHz7cvD81NbWjWnwikUA6nUalUsH6+jpOnz6NRqPRvGa7kXrA+jc7evRoz30ZJjvWZKvqaQCnw7qAqk51bxUeVa3BnqYuIlmfp0kBWAEw0/L4LHYm2et2WyIiIiIi6rO1i9/zvM7aSbTXLn4vkvXZznps59ZOtVpFLBZrjt7GYjGcOHECjz766LZ29Xp9W2I6NTWFubm5jslq6zXco8NBzgVYibBTGA1Ac8q5e9p5IpHYMQ29l2npy8vLyGb9pnPRClRdXEQeEpEnReSTLY9/stMxw86eZr7j6xsRiaHN6LiqNuznR3MuA5EH6+vrUXeBKDDGMZmCsUym8BrLCw8e8ZUoHzlwEAsPHvF8XBiq1SoOHTq0a5t6vb4jqXTvse1onc6dTCZRr9d72m98ZWX72GCQcxUKBaTT6W1FzJytysJQr9dHdrr4jpHsXojIQ7DWOycBCKyR3L90NTkrIi8AeEFVvxWwj4MWU9WGiLQ+HgduJtVtJGBNtSciIiIiogF79qHWSajDoVartU2WW8XjcRw9ehSlUgnxeBz1eh2NRqNZGK1arTaT8FQq1VzX7BQNO3bsGBYXF5FMJpvHFAoFxGIxbG5uolwuNxNiL+dyRp2dczUaDVy6dAmTk5NYWFjY8Xtks9ldp4H3olQq7ZgmP0pEVb0dIHI3rHXW67CmTtcApFT1+TZtPwtgWVW3gnfVOxHZBJBR1e47sFvtZ1W1bP+cBjCjqnP2/SSADVXdkX2LiNpt217n05/+tE5MTAAApqenMT097eO3IYqeiMDrewbRsGEckyn6Ecvyp083f9bf+aNQz90v//Spmx/NfvHPRuO1feGLN/t84NOj0ed+Mvl9uVwuY2lpCdVqFbOzs83E1XTZbBaLi4u+KoM767XbJfDDZnV1FaurqwCA48eP/5mqfhrwN5L9HIBjqvqi84CIPNquoao+LyJLABbbPT9M7OngjX6ce2JigttsEBERERGNmdnZWczOzkbdjYHL5/O+R7MrlcpIJNjA9gHU48ePbzmP+0myr7gT7B7sGPnt6SBva5zru0zj7tW8vSXXrkQkFsK1iIiIiIiIjOV3urgJX0r4SbIvemx/t9cLOPtRezhkDUDB63Vc10vCmv6+G2e9dRyuEW97BNz9PJGxnn322ai7QBQY45hMwVgmUzCWyTR+kux7+tze2YZr953XwzUFYFJE3BuxJQEkRCQPYE1VyyJSAxBrOTYOoGH3mchoXPZAJmAckykYy2QKxjKZxk+SLSJyRFXPuB/r0PBzAL7pq2cD1G6auIgsADisqu46+hVYCbm7pn3SfpzIePfeey/Onz8fdTeIAmEckykYy2QKxjKZxvM+2ar6DIATIvKHInKn87C7jb1/9ksAplQ1ytKUcewceYaIxERk064g3km7Efgsdo6wZ+zHiYx34cKFqLtAFBjjmEzBWCZTMJbJNL72yQbwGICvA8iKSANA3TXVOgErsa0C2H0zuD6w10gvuvqRF5EZACvO9ly2eIfjE7CS5nkAMREpAiiqatXePzvrTCG3r5HnVHEiIiIiIiICfCbZdlJ50J5SnQEw6Xq6BuAZVT0RQv/89K2BLiPLdpt9HZ6rwfqd2u5+rqpVbJ8uTjQ2xmFfRzIf45hMwVgmUzCWyTR+R7IBAKpagF3VW0Tuh7WV1pUwOkZEw2djYyPqLhAFxjgmUzCWyRRhxfIP/irX/Pm9n8h1bEfUb57XZHeiqufaJdgi8khY1yCiaPnd75BomDCOyRSMZTJFWLF88a+PN29EUQotyd7FygCuMdS2traQy+WwuroadVeIAjlxIpJVIEShYhyTKRjLZArGMnlVq/krieX3uG7sPG/Cub9juriITMAq6BUGLrAAMDExwf3/iIiIiIhCcPGrBbwrcRi3P3DE03FXv30GP6mtYf/HFvrUMxqEarWKWq2GRMJ7ylqtVtFoNEKvAzA9PQ0AW879dmuynwLw2V3OIWjZsiuktkRERERERLt6V+Iwvv8n8/jA75/qOdG++u0zzWNodDUaDRSLRRSLRV/Hz87OIpPJIJ/PIxaLhds5l3ZJ9iaAc7AqdDc6HDcDa3usTlPB7wEwC6sK91qgHhLR0Hjttdei7gJRYIxjMgVjmUzhNZZvf+AIPvD7p3pOtN0JttfRbxouS0tLyGZ33Uiqq2w2i6WlJeTz+ZB6tVO7JLsOoKyqL7Y7wK4ifklVn+ly7pKIPAeApS+JDLGxsYF777036m4QBcI4JlMwlskUfmK510SbCbZZqtWqr2nibolEAtVqf3dkblf4rAJgt/H331TV53s5uZ2It91vmohGzxNPPBF1F4gCYxyTKRjLZAq/sexOtK9++8yO55lgm6VcLmNubi6Uc83NzaFUKoVyrnZ2JNmqekVVz+1yjNd9sPtTwo2IiIiIaIi9+UoBb13Ymfx189aFM3jzlUIfemQed6LtxgTbPCdPnsT8/Hz3hj2Yn5/H8vJyKOdqp9108W7u7nN7IiIiIqJtRrGi9N79h9FYnUds+hRuO9Bbv9+6cKZ5DPXGSbS/+9wjzceGLcGuVCqo1WpYWVnB8vIyyuUy6vU6Njc3cfjwYczOzkbdxaFXrVY7Fiur1WqoVCpoNBrY3NxEsVhEoVBALBZr/pu7xWIxrK+v962vfvbJPuix/aSPaxDREPJbyZFomDCOyRTjFstORel204I7cUYz35U43MeedXbbgSOITZ9CY3W+pxFtd4Lda1JugjBiuTWZHqYEG7ASxHQ6jVqthpmZGSSTSaTTaeTzeRw7dqxv+zePg0ajgUqlgnQ6jYWFhea/cTqdRjweR7lcRqPRGGif/CTZKyLyuV4aisgSWPiMyBjpdDrqLhAFxjgmU4xbLHdbf9tqWKYL95poj2uCDZgfy7VaDalUqvlzJpPZVrwrHo+jUqlE1b2R0Gg0Oo5inzp1alsM1et1JJNJxGIxzM7OYnNzs+2xiUSib8m35yTbrjq+X0TWROQTInKX+3kRuUtEPikiLwFIqurnw+osEUVLRKLuAlFgjGMyxTjGcq+J9rAk2I5uifY4J9hAOLHcGg9eZz30UyKRQDKZRK1WQ6PRaCbcDudxt0KhgEKhgFKphHK5jGq1ikKh0DYprFQqKBS2r+Evl8s4dOhQ8xyTk5OYmZlBqVRCoVDA5OQkarUaarUastksRATZbLY5ol4qlSAimJub2/YFQKPRQKFQQKVSQaVSafav9fdxzulcv1AoIJvNbmvrfOHgblcqlZDNZrFv375tFcBrtRri8Xjbf9/WddrVahUzMzPb/v3bicfj/ZtBoKq+brAqkN8AcN2+XXL9fB3ASwDu9nt+k26f+tSn9Nlnn9UzZ84o0Siz3jKIRhvjmEzRj1jG//hM8zbM3vynl/Wff3+/vvlPL+s//t9o3lqfGzY/Pf+y/uv/3K/n/xTNm/PYT88PX38HJWgsO//P3bEwjHFQLBY1kUhse2xzc1MB6MrKiqqqXr58WROJhG5sbLRtd/ny5R3n3djY2HHeYrG4re3s7KwuLCxsO8a5xuXLl9v+P0gmk7q8vNy8f/nyZU0mkzv6UCwWNZ1Ob3us0zkXFhY0n893bbe5ubnt2pubm5pKpXa0a7WystJzPKVSKd3c3OypbTdnzpxRAF9UO//zM13cSc4zAB4D8DKsiuP7AJwD8CKAx1T1o6rqtRK5kSYmJpDL5TA9PR11V4iIiIhG3qhWlHZGtN3GeQQ7DO7/525elxcMwsrKyo5R7HK5jFgs1nz82LFjyGQySCaT29olEom20+objUZzpNw94hyPxztOrwaAZDKJer3uqf9zc3PIZDI7zptOp7G+vt7TlPd8Po+lpaWOI8jOSH3r6HOvo84rKys7/u062W103Cs7z9ty7vtOsgFAVSuqOqOqcVXdo6oHVXVeVU8H6yYRDaPHH3886i4QBcY4JlOMeyw7SZTbMCfYjtZkmgm2/1ju9qXKsCXalUpl2zRmwCr6ls/nm/fL5XLHSuPZbHZHglur1RCLxZDJZLYVkOulWnlrwt9NpVLB1NRU2+empqZ6LmCXSqV2TDF3X8NJtN2/QywW6/ilgDv5bu2jUxStnXq9vusXEUH42cKLiMbUl7/85ai7QBQY45hMwVge/orS1Bs/sdzrrAV3oh1lfDjrrt0JYaFQaFYZB9Bcg9xpdLXT2mLASlzn5uZ2LRDWTalU2tFnh9O3Tn2YnJzseUuseDyOtbW1HdduNBpYWlrCuXPn2h7X7trOFxebm5vN5Nz9+5dKJSwstN++b7d/z6CYZBNRzz7+8Y/zQx2NPMYxmYKxPJpaC59xurj3WPa6LGAYEu1KpdKc1u3s3xyLxbbt3+xMc+40wtqaQFerVayvrzeT26mpqR2Vtr1oPc49Mu30rVartZ2Ovbm52XPSWq/Xcfjw9m31nGu7z9H6+05NTaFarW67vjON3hmtPn36NLLZbPMLg07/FpVKBUePHu2pv34ESrLtyuJpAIcBJAHUYG3ZVVLVrcC9I6Kh8pWvfCXqLhAFxjgmUzCW21eUHubRbKeKuJtTdXycE22vsfyT2prn/89Oov2T2lok8eGsx3ZunThTqduNvlYqlW1TqOv1+rYkcmpqCnNzc33bEi2VSqFSqbRNsiuVyrZp77upVCpYXFxs+5z792v9fZ0p8e7kP5FI7Jim3su09eXlZWSz2Z7664fvNdki8jSAywAKAOYATAKYAfAMgE0R+cNQekhERERE1MIZzXQbpvW3rdzbdLn1uo823bT/Ywu+EuXbHziC/R9rP3W436rVKg4dOtS13fLyMorF4rbtqwC03bqr9TGnmFnrsWFx+tZuu7FUKtXTOvBCoYB0Ot1TcbLWKeXONmhhqNfrwzddXESeAzAL4CkAFQDuVegJAP8JwFMiElPV/xq4l0RERERENvd04e8+90jz8WGYFtxOt32w3Yn2OI9oB7X/Pz4bdRfacvaj7qXQWCwWw+bmJrLZLCqVCmKxWLNSuJPEVqvV5ihsKpVqTql2pkwfO3YMi4uLmJ2dRbVaRaVSQbVaRa1WQ6lUQiqVaiaYtVqtOfJbKBQwOzuLRCKBUqmEWq2GkydPNqe5x2IxbGxsYGlpqTnd20lW3SPvreeMxWJoNBq4dOkSJicnm21rtVpz9NtpB1hTz8vlctuRZmcqeJDR+lKphEwm4/v4Xohae173foDIwwDyqvpYD22/DuA5VX3ZZ/+MkMvlNJfLRd0NIiIiol3Jnz7d/Fl/548i7Elnretx/+lT0nzuF/9M27aJUrsE+8IXb/b5wKd117Y02srlMpaWllCtVjE7O4vFxcWet5ii9rLZLBYXF30VeGs0GrsWQwtCRI6rag7wN1083UuCDQB2uzkf1yCiIdRadZJoFDGOyRRBYrnwyhmcufCq5+POXHgVhVeim9Lsp6J0lFPHvSbN4zp13OT35dnZWWxsbEBVsby8zAQ7BPl8HqdOneresI1KpdKXBLuVnyRbujfZ5oqPaxDREOr31BqiQWAckymCxPLh/R/E/Jk/95Ron7nwKubP/DkO7/+g7+sGEaSidFSJ9rWLa55HpZ1E+9rFte6NDcH3ZfLK73TxXtaNh8FPku31a09v89ENtLW1hVwuh9XV1ai7QkRERIQjBw7i1JHf7jnRdhLsU0d+G0cOHBxAD3cKWlE6Cnc8uOBr2vdtB47gjgejKdBFRN7Zed6Ec99P4bPJkPoyNiYmJsA12URERDRM3In2bsnzMCTYAHxXhb79gSORr8smIrNNT08DwJZz389IdlVEnuyloYgsAbjk4xpENIS+9KUvRd0FosAYx2SKMGK524j2sCTYZDa+L5NpPI9kq+oJEfm6iCQAlFR1q7WNiDwEIANgSlUPB+4lEQ2FXvZ3JBp2jGMyRVix7E603Zhg06DwfZlM42ckG7Aqhj8GYFNEviMiayLykv3fSwA2AEwBmA+ro0QUvfvuuy/qLhAFxjgmU4QZy06i7cYEmwaF78tkGl9JtqpeUdUpAL8Hq3r4IQAz9n8vA3hGVQ+r6rnQekpEREREfdOaTDPBJiLyx0/hsyZVLQEoAYCI3M+kmoiIiIiIovDG2Vzz5zsfznVsR9RvfqeL79ApwRaRibCuQUTROnbsWNRdIAqMcUymCDuWWwufed1Hm8ivsGL5zX843rwRRSm0JHsXywO4BhENQKlUiroLRIExjskUYcayU+TMzcs+2kRB8H2ZvKjVagM9zo9ASbaITIjIQ7vdACTC6SoRRY3VP8kEjGMyRVix7K4i7tZtey+isHiN5TdfKeCtC2c8X+etC2fw5isFz8fR8KhWq6hWqwM/1itfSbaILInIdQCbsCqJ73aLhdJTIorcoN6YiPqJcUymCCOWu23TxUSbBsFrLO/dfxiN1XlPifZbF86gsTqPvfu5u/CoajQaKBaLmJ2d9XX87OwsisUiGo1GuB1rw3OSLSKfhbUH9vMAnupyWwytpyNsa2sLuVwOq6urUXeFiIiICEDv+2Az0aZhc9uBI4hNn+o50XYS7Nj0Kdx24MgAekj9sLS0hGw2G+gc2WwWS0tLIfXoJjvPm3Du+6kuPgPgflW90ktjEUn7uIZRJiYmkMvlou4GUWAHDhyIugtEgTGOyRRBYrnXBNvhTrS5tReFzU8suxPt3ZJnJtjmqFarSCSCrUROJBJ9mdE2PT0NAFvOfT/Txau9Jti2jI9rENEQOn/+fNRdIAqMcUymCBLLaxe/5zlZdhLttYvf831donb8xnK3EW0m2OYol8uYm5sL5Vxzc3N9L7bnJ8m+6KWxqp72cQ0iGkKckUEmYByTKYLE8sKDR3yNRh85cBALDzJZoXAFiWV3ou3GBNssJ0+exPz8fPeGPZifn8fycn83wBJV9XaAyDEAJ1X1Rz22f0RVX/bTuaBEZBZAQ1UrPo6NAciratuReC/nzuVyyg91ZAIRgdf3DKJhwzgmU/QjluVPn27+rL/zR6Geu1/+6VPS/PkX/2w0XtsXvnizzwc+PRp97qcwYvmtC2dQf+mR5v09t+0fqgS7UqmgVqthZWUFy8vLKJfLqNfr2NzcxOHDh30X8xoXk5OT2NzcbPtcrVZDpVJBo9HA5uYmisUiCoUCYrFY89+71b59+3D58uVQ+ygix1U1B/hYk62qJ0TkORF5QVW3ejgkD2DgZfxEJAXgBAC/8wryAOJ9OjcREREREYWkNZkepgQbsNYTLywsoFgsYmZmBsVisbm+eN++fUgmk4HXG4+jRqOBSqWCdNoqAzYzM4OZmRksLy+jUqmgXC6j0WggFosNtF9+Cp9BVZ8Rkc+JSAJAFdZWXu3EACR99s0Xu09ZWNuH1QOcY0eCHca5iYiIiIhofNRqNaRSqebPi4uL2xLqeDy+LVGk7XZLkk+dOrXt361eryOVSiEWi2F2dhabm5ttj00kEn1Nvv1s4XW3iKzBKmg2AyvpLHW4DXy3d1WtqWpGVYOsZk8BWOnTuYlG1vr6etRdIAqMcUymYCyTKcKI5dbCZ1730e6nRCKBZDKJWq2GRqPRTLgdzuNuhUIBhUIBpVIJ5XIZ1WoVhUIBjUYDtVoN2WwWIoJsNotardY8TyaTwaFDh5rT0512zrlKpRIymUzzmHK5jEOHDjWfn5ycxMzMDEqlEgqFAiYnJ5ttAWvae6GwM8WrVqs7+uS+frlc3vb7uvvZTa1WQzzedoLxjnXa1WoVMzMz2/7t24nH49t+r7D5GcnOA7gMa6p0t57dA+AlH9eIjD0V/BSAcFbWExERERFR3zhFztx62d5r0CqVChKJxLbRUyfRSyatyb+NRgOHDh3C8vJy8zGnXTabRTqdRiKRwOLiIgqFAvL5fLNNIpFAPp9HpVJpJvJOu4WFhWa7arWKyclJXL58GfV6HadPn272aWVlBYlEojk6nEqltn0BEI/HUSwWt53P6X8ikdjRp8XFRUxOTuLYsWPNNolEAsViEeVyeccXDu3sNtrsfs5J2Hs5Z7fzBuWnunhCVR9T1RdV9WyXWwXA2bA73WcxVW1E3QmiYTQ1NRV1F4gCYxyTKRjLZIogseyuIu7WbXuvKKysrOxIAMvlMmKxWPPxY8eOIZPJbEuwAWxLfHfTS+LonHt9fR3xeHzXY5LJJOp1a5Vso9Fojsr3MgLtmJqawuLiou8tuHoddV5ZWdnx79bJbqPjYfCTZO+YRt3FyBQHE5FZVS13b+nN1tYWcrkccrkcVldXwz49EREREdHY6bZN17Al2pVKZdtUZgAoFovbRn7L5XLHSuPZbDaU0ddyuYx0Oo1UKtVTVXP3evJYLIZMJoNisejpms7Id7up5o1GA6VSqVmorLVNLBZrJvqtWqeyu7+wcYqitVOv10P5t1xdXW3meQAmnMf9TBf3VF9fVc/5uMbA2Vt2Nfpx7omJCe7LSkREREQUkl73wXYn2lFOHXfWXbuTwkKhgGQy2RyhrlarANBxhDVI9XEn2VxeXsbk5KTnJNktlUphbm7Oc+Gw5eVlHDp0CLOzs9t+l6WlJRw9erQ5Cu1ev+1o97s7X1psbm42p7S7+1MqlXZMa9/tfH5MT09jenoaAHD8+PEt53E/SfY5EXlIVb/VS2MReVpVPW+0aFfy7lU9hCne8yxoRrS7Z599NuouEAXGOCZTMJbJFF5judcE2zEMibazHjuZTDb3cI7FYtv2cHaSzE6jrEGqYTuj0alUqjma3ikBbadarWJ9fb1ZpG5qampHZe9unDXjTmLsyGQymJmZQSKRwMzMTNt+TU1NoVqtbpsO7kyhd75AOH36NLLZLEolK6Xr1LdKpYKjR4/23G8//OyT/aKIfFZEEqr6lz0ckgHgKcm2E+x814Y3rSFAJXMRSQJgiU6iLjgjg0zAOCZTMJbJFF5j+drFNc/JspNoX7u4FkmS7azHdm6dpFIplMvltolmpVJpTu92ku1arbZtVLZarXYdpZ2ZmcHKyoqnJLter29LWqempjA3N+d527F0Oo3l5WVks1kcPnwYgDVyv7m5iWq1imKxiLm5uW1fPgBoTlF3j8A7BdTcehmhd67fT56TbBFZsn9MicgJWMlpp5XocQCex+JVtYbBruWeAjApIu6vNJIAEiKSB7DWj7XaRKPm3nvvxfnz56PuBlEgjGMyBWOZTOE1lu94sPfk0O22A0cimy7ubHHVjTOlOpVKbRu1bd3iC7BGolsT8kql0jV5jsVizRHpXkfHW6/vFERrHV1uVa/Xd6ynXl5exv33399MspeWlprF3pwku5WzBVoY6vV6aNPFO/EzXTwD4G4AYt+f2aUt4HENdxTaTRMXkQUAh1W1v19zEI2QCxcuRN0FosAYx2QKxjKZIqxYvuOXh3MJhbNndK/bVW1ubiKbzaJSqSAWizUrgLcWKcvn8839tJ1E2T2y7BQUA6z1yfPz84jFYs3R5FKphHg8jtnZWVSrVVQqFVSrVdRqNZRKpeb2Xc6XA6lUqnkdZ4r2sWPHsLi42EyQAWutudPXbDaLRqOBeDzeTMadafJO4n7PPfegUqk016JnMpm2/zbOVHCvo+duzj7h/Saq3nJgEXkVwHOq+vke219S1Xv8dC4oEbkM4FjrKLRd5GwDQL7TOmx7BDuhqm1H1Dudu51cLqec0kUmEBF4fc8gGjaMYzJFP2JZ/vTp5s/6O55L6kTinz4lzZ9/8c9G47V94Ys3+3zg06PR534y+X25XC5jaWkJ1WoVs7OzzYSU/Mlms1hcXPS1Nt350sHLNHkvROS4quYAf1t41QD0vjEaMNDq4iISE5G8iCwDiAHIi0hRRFrr07ct2yciCREpAkgDmLWPTXo8N5GR+EeBTMA4JlMwlskUJsfy7OwsNjY2oKpYXl42+ncdhHw+j1OnTnVv2EYvU+nD4qfw2WMe2/vfXd4Hu8r4rlO87Tb7OjxXgzUlfsc8gl7OTWSyjY2NqLtAFBjjmEzBWCZTMJbJC7/TxXvZEzwsfkayiWhMBVkDQzQsGMdkCsYymYKxTKZhkk1EPTtx4kTUXSAKjHFMpmAskykYy2SaZpItIveLyJODurCI3CUiT3dvOfq2traQy+WwuroadVeIiIiIiIgoRHaeN+Hcb67JVtVzInJORD4HYEFV3+hXJ0TkUQBpVT3atbEBJiYmwOriRERERAQM71ZTROTP9PQ0AGw597cVPlPV0/bWVGdFZElVvxDmxUXkLgBlAJvjkmATmeS1116LugtEgTGOyRSM5dF158O5qLswVBjLZJoda7JVtQpgCsBhEfmOiPyunRz7JiIPicgpWHtTF1X194Kcj4iiweqfZALGMZmCsUymYCyTadpu4WVvVfWUiCQAPAfghIhsAFiHlSivA2gAqKvqj5zj7GQ8DiAJIAHgMIBZAFUAJVWd79tvQkR998QTT0BVo+4GUSCMYzIFY5lMEVYsn6+80vz53tSDgc9H5Neu+2Tbe0bPA4CI/CaAGQDPwEqgAUBFpPUw54EKrOR6SlXPhtVhIiIiIiKiVhde/sfmz0yyKUq7JtluqvoigBed+yJyN6xRayfhrgNoqOq5UHtIREREREREQ6FWqyGRSHRvGNJxo6jnJLuVql4BcAUAk2qiMVEsFqPuAlFgjGMyBWOZTOE1lv/1G9/G7ffFcefk+zwd98bm67j6Wh3v/8gDno6jm6rVqu9kuVqtotFoIJlM9qFnw2VH4bN2ghY+IyIzpNPpqLtAFBjjmEzBWCZTeI3l2++Lo/YXf4c3Nl/v+Zg3Nl9H7S/+DrffF/faPbI1Gg0Ui0XMzs76On52dhbFYhGNRiPcjg2hXZNsEVkSkUsALovIdRH570y4icZXmxoMRCOHcUymYCyTKbzG8p2T70Pit36t50TbSbATv/Vrnke/6aalpSVks9lA58hms1haWgqpR8OrY5ItIi8BWACwD1YxMwGQAbAuIh8aTPeIiIiIiIi26zXRZoIdnmq1GnhNdSKRQLVaDalHw6ttki0ij8KqJH4WQBZAAVa1cAFwEMDKoDpogq2tLeRyOayurkbdFSIiIqKOnn1opnkjGnbdEm0m2OEpl8uYm5sL5Vxzc3MolUqhnGtY2HnehHO/U+GzDIByu32tRWQBwHMi8hlV/W996KNxJiYmkMvlou4GUWCPP/541F0gCoxxTKboRyznHv5o6Ock6iZILLsTbTcm2OE6efIkTpw4Ecq55ufnMTc3Z1RdienpaQDYcu5Lu43fReQ7AGZUdWvHk9bzKQAnAdyvqj/qQz+NksvllEk2ERERUfh+8Fe55s/v/USuYzsy2xubr+NfvnCmef/Wd982VAl2pVJBrVbDysoKlpeXUS6XUa/Xsbm5icOHD/suJjYok5OT2NzcbPtcrVZDpVJBo9HA5uYmisUiCoUCYrFY8/dttW/fPly+fLnf3R4oETmuqjmg85rseKcEGwBUtQJgGcCOkW4iMtfHP/7xqLtAFBjjmEzBWLa89xO55o1GUxix3JpMD1OCDVjrmdPpNGq1GmZmZpBMJpFOp5HP53Hs2DHUarWou+hLo9FApVJBOp3GwsJC8/dLp9OIx+Mol8tjUU28Vacku5evFYqw1m0T0Zj4yle+EnUXiAJjHJMpGMtkCtNjuVarIZVKNX/OZDLbCojF43FUKpWoutdVo9FALBZr+9ypU6e2Tfuu1+tIJpOIxWKYnZ3F5uZm22MTiYTRyXenJHvnHPLWBqpnAQQrL0dERERERBRQa+Ezr/to91MikUAymUStVkOj0Wgm3A7ncScBFxEUCgWUSiWUSiVks9nmNme7tdm3bx+q1SrK5TIOHTrUfH5ychIzMzMolUooFAqYnJzcMXJeqVRQKBTa9r9WqyEeb7+/+Pz89onN1WoVMzM3x2E7VSOPx+MjO3rfi06Fz3pl1kR6IiIiIiIaKU6RMzenGNowTRuvVCpIJBLbRnadRDOZTCKRSCCfz6NUKmFhYWHbsYcPH0atVtu1TSaTQbVaRb1ex+nTp5vXWVlZQSKRaI44p1KpHaPI8XgcxWJxxzkBdBzFbn3OGY1v/RKhl2NN03FNdo/Hdx3xJiJztCuUSDRqGMdkCsYymSJILLuriLv1uo/2IK2srOxIQMvlMmKxWMfE1EmGU6lUx5Ffp40zahyPx3dNYJPJJOr1+rbjndH2dtPWex11XllZQTKZ7NoO2H103ASdkux9IvLLA+0JEQ090/Y0pPHEOCZTMJbJFH5juds2XcOWaFcqlW1TqQGgWCwin893PMb5t9ktEXcqewPA7OxsT5XK3eeq1WqIxWLIZDIoFos72sZisW1JuZs7+a5UKpiammred4qitVOv18dyJBsAqiLyHRH5nIh8UkQm2rSRPvWLiIZQJpOJugtEgTGOyRSMZTKFn1judR/sYUm03euuHYVCoVllvJWzfnppaanjOZ02x44dC6WPqVRqW8Lu1m5tdaVSaa7vrlarALZPAS+VSh2/GOi0VtsUu63JFgCTsIqbpQFARBoAKgC+DuA0epguLiKPqOrLgXtKRERERERjr9cE2+FOtKNao+2sx04mk809pGOxWNs9pAG0Tbw7tXEnrLtVAm+nWq1ifX0d6+vrAICpqakdFcOdx6vV6rbp4M46b2e0+vTp08hms83R906/Q6VSwdGjR3vu4yiSdmsgROQGgEMApmBt05UCEHM1cR9UBrACoNJub20RuaSq94TX5dHz6U9/WicmJjA9PY3p6emou0Pkm4hwDSCNPMYxmYKxTKbwGsv/+o1v4/b74juS5Y0/+H+aPx/6w/+047g3Nl/H1dfqeP9HHvDfWZ/m5uaaxcV202g0sG/fvua/R7ukubWNW7lc3jFdfG5urlkwrVWlUtk22lytVjE3N4fNzc1t7arVKorFYtf+9yKTySCbzRo1mr26uoojR478map+Gug8XbyhqmdV9YSqzqtqHNaodgZWUn0F1ki3AJiFtWf2pohcEpGT9vTyu+xz7evnLzQKJiYmkMvlmGDTyPvSl74UdReIAmMckykYy2QKr7H8/o884Gs0+s7J90WSYANWknro0CHPx3ldt7y2tuapfevUcKcomjP92/14WFtu1et1oxJsAE6et+Xc75Rk75j8r6rnOiTdp3Ez6d4HK+leBnBZRK6DFciJjOHnjwPRsGEckykYy2SKsGL5wCP/rnkbJrVaDbVarevWVrVarbkGO5vNti0aVqvVkM1mAWDHPtmTk5OYnJxstq1WqygUCqhWq6hUKiiVSs1E2dnPulgsbku0nWseO3YM5XJ527XdU8H9KpVKY1FPou10cV8nEnkY1rRyZ3q5Q1X1llAuMqJyuZzmcrmou0EUGKcmkgkYx2QKxjKZwuRYLpfLWFpaQrVaxezsLBYXF3ve5moYZbNZLC4u+qoM3mg02u7vbQoROa6qOWD36uKe2NPLn1fVx1R1D6w13SfCOj8REREREdEomZ2dxcbGBlQVy8vLI51gA0A+n8epU6d8HVupVIxNsFuFlmS3spNu8+cCEBERERERjYleKp+308v+3aboW5LtcmUA1yCiAQhrH0aiKDGOyRSMZTIFY5lME9qa7I4XEDmmqmM9bZxrsomIiIiIiMzVlzXZnYx7gk1kElayJRMwjskUjGUyBWOZTDOI6eJEZIjWPROJRhHjmEzBWCZTMJbJNKEk2SLysIh8TkQeCeN8RERERERERKMolCRbVc8CeAbAYyKyJiJLIjIRxrlNsLW1hVwuh9XV1ai7QhTIgQMHou4CUWCMYzIFY5lMwVimUWfneRPO/dALn4lIDNb+2J8EsAGgqKpfCPUiI4aFz4iIiIiIiMzV18JnqtpQ1TkAfwRgCkAx7GsQUTT4ZRGZgHFMpmAskykYy2SatiPZIvKSqn408MlFNgA8pKq3BD3XKONINplCRNDvbf+I+o1xTKZgLJMpGMtkgl5GsqdCutZSSOfxRURmRSTl89iYiOwYhbfPuSAiRRFZEZHZ4D0lIiIiIiIiE9za4fFYGCdX1bKIhHEqz+zk+gSAOZ+nyAOIt5xzFkBNVcv2/RiADRGJq2opQHeJiIiIiIjIAJ1GskVEPhHSNa6EdJ6eiEjCHoFOAKj7PQdaEmxbQlWbG/mpagNWMs515zQW1tfXo+4CUWCMYzIFY5lMwVgm0+xW+KwsIpdE5KSIPBlgSy5fia5fqlpT1UzAkeUUgBX3A/ao9VH7v24V+/lEgOsRERERERGRAXZLsgXAPljTrYsANl1J9ydF5K4erzFSVQzsaeanWh+3R60T9o1oLE1NhVWugSg6jGMyBWOZTMFYJtN0WpMNADOwEsoZWCO7MVhJ96x9g4jUYI3krgCoqOqP+tnZAYmpaqPdWnJV3demfQpAQ1Vrfe8ZERERERERDbVOSXZDVU8DOA2reBhE5H5YCeWc/V8AmISViKftNu2S7nZrm4eSiMw6Rc08yCDiKupEREREREQ0HDrtk/2cqj6z64EiD8NKtp2Rbof7hDVYxcIi2SdbRDYBZFS10kPbGIApp62IpAHMqGrH6uR2mzlVnely7q8CuN2+u2XfiIiIiIiIaHRN2DcAuKqqHwM6jGR3S7DtNmcBnAXwPNAx6Z6EzzXZHguJ1e0100HMeymWZvcvo6qHurV1/rGJiIiIiIjIbLutyfakTdL9KICnAHzS67nsBDbv4ZA1AAWv13FdLwnA694BeQCP+r0mERERERERmaftdPFQLyByfdini9vTvidbHk7CWm9eBrDmXqtt78OdZ7EzIiIiIiIicgttJHsXVwZwjUDaTRMXkQUAh1U12/J4Gi0Jtr3tV41JNxERERER0XjbbZ/ssGS7N+mbOKytx7YRkZiIbNoJcyf3tDlu1v4xJiJJ+5aCVfyMCTYREREREdGY6/tItqqe6Pc13Owq4YuwpnrHAORFZAbASsv2XG23FrPXg2cBzMNKposAirAqpS93uCwTbCIiIiIiIur/mmwiIiIiIiKicTGI6eJEREREREREY6GZZIvI3SLyqoj8dxH55X5fWEQ+KSJrIrLU72sRERERERERDUIzyVbVKwCeAXAQwFkR+Y6I/K6I3BXWxUTkIRH5nIhcB3ACwGkAz4V1fiIiIiIiIqIotV2TLSJ3A8gASAO4H9Ze0S+o6hnPF7CS9DSAp+xzvQigqKqnA/SbiIiIiIiIaOi0XZOtqldUtaCqBwEcBiAATovIJRH5QxGZ6HZiEXlERF4CcBnAUQB5AHFVnWeCTURERERERCbyVF3c3lc6DeBhAFUAf6iqf+V6fgI3R8ABa8uroqqeDavDRERERERERMPK1xZe9l7SGQDHANwNazp5AsAhABVYifWLIfaTiIiIiIiIaOgF3idbRFKwRq7XAJTsAmpEREREREREYydwkk1ERERERERElraFz4iIiIiIiIjIOybZRERERERERCH5/wHEdoONKNevoAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot average of the log posterior of the runs above\n",
    "\n",
    "l = 3\n",
    "f_1 = 20\n",
    "f_2 = 30\n",
    "alpha = 0.4\n",
    "dpi = 800\n",
    "ms = 12\n",
    "\n",
    "#order = [\"cathode\", \"curtains\", \"feta\", \"salad\"]\n",
    "\"\"\"\n",
    "with open(f'log_posterior_mean.npy', 'rb') as f:\n",
    "    to_plot_central_old = np.load(f)\n",
    "with open(f'log_posterior_std.npy', 'rb') as f:\n",
    "    to_plot_err_old = np.load(f)\n",
    "\"\"\"\n",
    "with open(f'log_posterior_med.npy', 'rb') as f:\n",
    "    to_plot_median_old = np.load(f)\n",
    "with open(f'log_posterior_err_lower.npy', 'rb') as f:\n",
    "    to_plot_err_lower_old = np.load(f)\n",
    "with open(f'log_posterior_err_higher.npy', 'rb') as f:\n",
    "    to_plot_err_higher_old = np.load(f)\n",
    "\n",
    "# reordering hell!!!\n",
    "\"\"\"\n",
    "to_plot_central = np.zeros(to_plot_central_old.shape)\n",
    "to_plot_err = np.zeros(to_plot_err_old.shape)\n",
    "to_plot_central[0] = to_plot_central_old[15]\n",
    "to_plot_central[1] = to_plot_central_old[12]\n",
    "to_plot_central[2] = to_plot_central_old[13]\n",
    "to_plot_central[3] = to_plot_central_old[14]\n",
    "to_plot_central[4] = to_plot_central_old[3]\n",
    "to_plot_central[5] = to_plot_central_old[0]\n",
    "to_plot_central[6] = to_plot_central_old[1]\n",
    "to_plot_central[7] = to_plot_central_old[2]\n",
    "to_plot_central[8] = to_plot_central_old[7]\n",
    "to_plot_central[9] = to_plot_central_old[4]\n",
    "to_plot_central[10] = to_plot_central_old[5]\n",
    "to_plot_central[11] = to_plot_central_old[6]\n",
    "to_plot_central[12] = to_plot_central_old[11]\n",
    "to_plot_central[13] = to_plot_central_old[8]\n",
    "to_plot_central[14] = to_plot_central_old[9]\n",
    "to_plot_central[15] = to_plot_central_old[10]\n",
    "to_plot_central[16] = to_plot_central_old[16]\n",
    "to_plot_central[17] = to_plot_central_old[17]\n",
    "to_plot_central[18] = to_plot_central_old[18]\n",
    "to_plot_central[19] = to_plot_central_old[19]\n",
    "to_plot_err[0] = to_plot_err_old[15]\n",
    "to_plot_err[1] = to_plot_err_old[12]\n",
    "to_plot_err[2] = to_plot_err_old[13]\n",
    "to_plot_err[3] = to_plot_err_old[14]\n",
    "to_plot_err[4] = to_plot_err_old[3]\n",
    "to_plot_err[5] = to_plot_err_old[0]\n",
    "to_plot_err[6] = to_plot_err_old[1]\n",
    "to_plot_err[7] = to_plot_err_old[2]\n",
    "to_plot_err[8] = to_plot_err_old[7]\n",
    "to_plot_err[9] = to_plot_err_old[4]\n",
    "to_plot_err[10] = to_plot_err_old[5]\n",
    "to_plot_err[11] = to_plot_err_old[6]\n",
    "to_plot_err[12] = to_plot_err_old[11]\n",
    "to_plot_err[13] = to_plot_err_old[8]\n",
    "to_plot_err[14] = to_plot_err_old[9]\n",
    "to_plot_err[15] = to_plot_err_old[10]\n",
    "to_plot_err[16] = to_plot_err_old[16]\n",
    "to_plot_err[17] = to_plot_err_old[17]\n",
    "to_plot_err[18] = to_plot_err_old[18]\n",
    "to_plot_err[19] = to_plot_err_old[19]\n",
    "\"\"\"\n",
    "\n",
    "to_plot_median = np.zeros(to_plot_median_old.shape)\n",
    "to_plot_err_lower = np.zeros(to_plot_err_lower_old.shape)\n",
    "to_plot_err_higher = np.zeros(to_plot_err_higher_old.shape)\n",
    "\n",
    "print(to_plot_median.shape,to_plot_err_lower.shape, to_plot_err_higher.shape )\n",
    "\n",
    "to_plot_median[0] = to_plot_median_old[15]\n",
    "to_plot_median[1] = to_plot_median_old[12]\n",
    "to_plot_median[2] = to_plot_median_old[13]\n",
    "to_plot_median[3] = to_plot_median_old[14]\n",
    "to_plot_median[4] = to_plot_median_old[3]\n",
    "to_plot_median[5] = to_plot_median_old[0]\n",
    "to_plot_median[6] = to_plot_median_old[1]\n",
    "to_plot_median[7] = to_plot_median_old[2]\n",
    "to_plot_median[8] = to_plot_median_old[7]\n",
    "to_plot_median[9] = to_plot_median_old[4]\n",
    "to_plot_median[10] = to_plot_median_old[5]\n",
    "to_plot_median[11] = to_plot_median_old[6]\n",
    "to_plot_median[12] = to_plot_median_old[11]\n",
    "to_plot_median[13] = to_plot_median_old[8]\n",
    "to_plot_median[14] = to_plot_median_old[9]\n",
    "to_plot_median[15] = to_plot_median_old[10]\n",
    "to_plot_median[16] = to_plot_median_old[16]\n",
    "to_plot_median[17] = to_plot_median_old[17]\n",
    "to_plot_median[18] = to_plot_median_old[18]\n",
    "to_plot_median[19] = to_plot_median_old[19]\n",
    "to_plot_err_lower[0] = to_plot_err_lower_old[15]\n",
    "to_plot_err_lower[1] = to_plot_err_lower_old[12]\n",
    "to_plot_err_lower[2] = to_plot_err_lower_old[13]\n",
    "to_plot_err_lower[3] = to_plot_err_lower_old[14]\n",
    "to_plot_err_lower[4] = to_plot_err_lower_old[3]\n",
    "to_plot_err_lower[5] = to_plot_err_lower_old[0]\n",
    "to_plot_err_lower[6] = to_plot_err_lower_old[1]\n",
    "to_plot_err_lower[7] = to_plot_err_lower_old[2]\n",
    "to_plot_err_lower[8] = to_plot_err_lower_old[7]\n",
    "to_plot_err_lower[9] = to_plot_err_lower_old[4]\n",
    "to_plot_err_lower[10] = to_plot_err_lower_old[5]\n",
    "to_plot_err_lower[11] = to_plot_err_lower_old[6]\n",
    "to_plot_err_lower[12] = to_plot_err_lower_old[11]\n",
    "to_plot_err_lower[13] = to_plot_err_lower_old[8]\n",
    "to_plot_err_lower[14] = to_plot_err_lower_old[9]\n",
    "to_plot_err_lower[15] = to_plot_err_lower_old[10]\n",
    "to_plot_err_lower[16] = to_plot_err_lower_old[16]\n",
    "to_plot_err_lower[17] = to_plot_err_lower_old[17]\n",
    "to_plot_err_lower[18] = to_plot_err_lower_old[18]\n",
    "to_plot_err_lower[19] = to_plot_err_lower_old[19]\n",
    "to_plot_err_higher[0] = to_plot_err_higher_old[15]\n",
    "to_plot_err_higher[1] = to_plot_err_higher_old[12]\n",
    "to_plot_err_higher[2] = to_plot_err_higher_old[13]\n",
    "to_plot_err_higher[3] = to_plot_err_higher_old[14]\n",
    "to_plot_err_higher[4] = to_plot_err_higher_old[3]\n",
    "to_plot_err_higher[5] = to_plot_err_higher_old[0]\n",
    "to_plot_err_higher[6] = to_plot_err_higher_old[1]\n",
    "to_plot_err_higher[7] = to_plot_err_higher_old[2]\n",
    "to_plot_err_higher[8] = to_plot_err_higher_old[7]\n",
    "to_plot_err_higher[9] = to_plot_err_higher_old[4]\n",
    "to_plot_err_higher[10] = to_plot_err_higher_old[5]\n",
    "to_plot_err_higher[11] = to_plot_err_higher_old[6]\n",
    "to_plot_err_higher[12] = to_plot_err_higher_old[11]\n",
    "to_plot_err_higher[13] = to_plot_err_higher_old[8]\n",
    "to_plot_err_higher[14] = to_plot_err_higher_old[9]\n",
    "to_plot_err_higher[15] = to_plot_err_higher_old[10]\n",
    "to_plot_err_higher[16] = to_plot_err_higher_old[16]\n",
    "to_plot_err_higher[17] = to_plot_err_higher_old[17]\n",
    "to_plot_err_higher[18] = to_plot_err_higher_old[18]\n",
    "to_plot_err_higher[19] = to_plot_err_higher_old[19]\n",
    "\n",
    "\n",
    "ymin, ymax = -1.42, -1.37\n",
    "\n",
    "\n",
    "# reorder\n",
    "order = [  \"salad\", \"cathode\", \"curtains\", \"feta\"]\n",
    "\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize = (16, 5)) \n",
    "\n",
    "to_bold = [0, 5, 10, 15]\n",
    "legends = [ '$p_{\\mathrm{SALAD}}(x)$','$p_{\\mathrm{CATHODE}}(x)$', '$p_{\\mathrm{CURTAINs}}(x)$','$p_{\\mathrm{FETA}}(x)$',]\n",
    "for i in range(20):\n",
    "    \n",
    "    if i in to_bold:\n",
    "        \n",
    "        print([[to_plot_median[i]-to_plot_err_lower[i]],[to_plot_err_higher[i]-to_plot_median[i]]])\n",
    "\n",
    "        plt.errorbar(i, to_plot_median[i], [[to_plot_median[i]-to_plot_err_lower[i]],[to_plot_err_higher[i]-to_plot_median[i]]], fmt='o', color = colors_dict[order[i%4]],\n",
    "                     elinewidth=5, markersize = 15 )\n",
    "    elif i >= 16:\n",
    "        plt.errorbar(i, to_plot_median[i],yerr = [[to_plot_median[i]-to_plot_err_lower[i]],[to_plot_err_higher[i]-to_plot_median[i]]], fmt='x', color = colors_dict[order[i%4]], \n",
    "                     elinewidth=3, markersize = 15, label = legends[i-16] )\n",
    "\n",
    "    else:\n",
    "        plt.errorbar(i, to_plot_median[i], yerr = [[to_plot_median[i]-to_plot_err_lower[i]],[to_plot_err_higher[i]-to_plot_median[i]]], fmt='x', color = colors_dict[order[i%4]], \n",
    "                     elinewidth=3, markersize = 15)\n",
    "\n",
    "    \n",
    "plt.vlines([3.5, 7.5, 11.5, 15.5], ymin, ymax, ls='dashed', color='k')\n",
    "plt.ylim((ymin, ymax))\n",
    "plt.xlim((-0.5, 19.5))\n",
    "plt.xticks([])\n",
    "plt.yticks(fontsize = f_1)\n",
    "plt.ylim(-1.42, -1.37)\n",
    "\n",
    "plt.ylabel('$\\\\langle LP(\\\\text{model } i|\\\\text{samples } j) \\\\rangle$', fontsize = f_2)\n",
    "\n",
    "\n",
    "plt.text(1.5,ymax + .003, '$x \\in$ SALAD', ha='center', fontsize = f_1)\n",
    "plt.text(5.5, ymax + .003, '$x \\in$ CATHODE', ha='center', fontsize = f_1)\n",
    "plt.text(9.5, ymax + .003, '$x \\in$ CURTAINs', ha='center', fontsize = f_1)\n",
    "plt.text(13.5, ymax + .003, '$x \\in$ FETA', ha='center', fontsize = f_1)\n",
    "plt.text(17.5, ymax + .003, '$x \\in$ Truth', ha='center', fontsize = f_1)\n",
    "\n",
    "plt.legend(fontsize = f_1, loc = \"lower right\")\n",
    "\n",
    "\n",
    "fig.savefig(f\"plots/discrim_methods.pdf\", dpi = dpi)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a80166c5",
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "id": "9d76f312",
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "id": "723bcd15",
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "id": "8328801f",
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   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
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  {
   "cell_type": "code",
   "execution_count": null,
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   "metadata": {},
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   "cell_type": "code",
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   "cell_type": "code",
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   "id": "1e65b3a6",
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  {
   "cell_type": "code",
   "execution_count": null,
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