{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Approach 4: Autoencoder - Boxplot\n",
    "This notebook reads eval results of sets of identical trainings and plots them into a boxplot. The names of the training results can be configured in the cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n",
      "Found 3 sessions\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import numpy as np\n",
    "from sklearn.neighbors import KernelDensity\n",
    "from glob import glob\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "\n",
    "from py.Dataset import Dataset\n",
    "from py.FileUtils import load\n",
    "from py.PlotUtils import get_percentiles\n",
    "\n",
    "DIR = '/home/kleinsteuber/vscode/ResizedSessions256_NoBackup' # dataset directory\n",
    "CONFIGS_BY_LATENT_FEATURES = [\n",
    "    {\n",
    "        \"label\": \"16\",\n",
    "        \"train_names\": [f\"ae2_beaver_01_16f_200e_{i}\" for i in range(10)]\n",
    "    },\n",
    "    {\n",
    "        \"label\": \"32\",\n",
    "        \"train_names\": [f\"ae2_beaver_01_32f_200e_{i}\" for i in range(10)]\n",
    "    },\n",
    "    {\n",
    "        \"label\": \"64\",\n",
    "        \"train_names\": [f\"ae2_beaver_01_64f_200e_{i}\" for i in range(10)]\n",
    "    },\n",
    "    {\n",
    "        \"label\": \"128\",\n",
    "        \"train_names\": [f\"ae2_beaver_01_128f_200e_{i}\" for i in range(10)]\n",
    "    },\n",
    "    {\n",
    "        \"label\": \"256\",\n",
    "        \"train_names\": [f\"ae2_beaver_01_256f_200e_{i}\" for i in range(10)]\n",
    "    },\n",
    "    {\n",
    "        \"label\": \"512\",\n",
    "        \"train_names\": [f\"ae2_beaver_01_200e_{i}\" for i in range(10)]\n",
    "    },\n",
    "    {\n",
    "        \"label\": \"1024\",\n",
    "        \"train_names\": [f\"ae2_beaver_01_200e_{i}\" for i in range(10)]\n",
    "    },\n",
    "]\n",
    "\n",
    "CONFIGS_DENOISING = [\n",
    "    {\n",
    "        \"label\": \"0\",\n",
    "        \"train_names\": [f\"ae2_beaver_01_200e_{i}\" for i in range(10)]\n",
    "    },\n",
    "    {\n",
    "        \"label\": \"0.100\",\n",
    "        \"train_names\": [f\"ae2_beaver_01_noise0.01_200e_{i}\" for i in range(10)]\n",
    "    },\n",
    "    {\n",
    "        \"label\": \"0.150\",\n",
    "        \"train_names\": [f\"ae2_beaver_01_noise0.0225_200e_{i}\" for i in range(10)]\n",
    "    },\n",
    "    {\n",
    "        \"label\": \"0.200\",\n",
    "        \"train_names\": [f\"ae2_beaver_01_noise0.04_200e_{i}\" for i in range(10)]\n",
    "    },\n",
    "    {\n",
    "        \"label\": \"0.300\",\n",
    "        \"train_names\": [f\"ae2_beaver_01_noise0.09_200e_{i}\" for i in range(10)]\n",
    "    },\n",
    "    {\n",
    "        \"label\": \"0.400\",\n",
    "        \"train_names\": [f\"ae2_beaver_01_noise0.16_200e_{i}\" for i in range(10)]\n",
    "    },\n",
    "]\n",
    "\n",
    "CONFIGS_SPARSE = [\n",
    "    {\n",
    "        \"label\": \"0\",\n",
    "        \"train_names\": [f\"ae2_beaver_01_200e_{i}\" for i in range(10)]\n",
    "    },\n",
    "    {\n",
    "        \"label\": \"1e-5\",\n",
    "        \"train_names\": [f\"ae2_beaver_01_sparse1e-5_200e_{i}\" for i in range(10)]\n",
    "    },\n",
    "    {\n",
    "        \"label\": \"1e-4\",\n",
    "        \"train_names\": [f\"ae2_beaver_01_200e_sparse_{i}\" for i in range(10)]\n",
    "    },\n",
    "    {\n",
    "        \"label\": \"1e-3\",\n",
    "        \"train_names\": [f\"ae2_beaver_01_sparse1e-3_200e_{i}\" for i in range(10)]\n",
    "    },\n",
    "    {\n",
    "        \"label\": \"1e-2\",\n",
    "        \"train_names\": [f\"ae2_beaver_01_sparse1e-2_200e_{i}\" for i in range(10)]\n",
    "    },\n",
    "]\n",
    "\n",
    "CONFIGS_DENOISING_AND_SPARSE = [\n",
    "    {\n",
    "        \"label\": \"Base\",\n",
    "        \"train_names\": [f\"ae2_beaver_01_200e_{i}\" for i in range(10)]\n",
    "    },\n",
    "    {\n",
    "        \"label\": \"$\\sigma = 0.1$\",\n",
    "        \"train_names\": [f\"ae2_beaver_01_noise0.01_200e_{i}\" for i in range(10)]\n",
    "    },\n",
    "    {\n",
    "        \"label\": \"$\\sigma = 0.15$\",\n",
    "        \"train_names\": [f\"ae2_beaver_01_noise0.0225_200e_{i}\" for i in range(10)]\n",
    "    },\n",
    "    {\n",
    "        \"label\": \"$\\sigma = 0.2$\",\n",
    "        \"train_names\": [f\"ae2_beaver_01_noise0.04_200e_{i}\" for i in range(10)]\n",
    "    },\n",
    "    {\n",
    "        \"label\": \"$\\sigma = 0.3$\",\n",
    "        \"train_names\": [f\"ae2_beaver_01_noise0.09_200e_{i}\" for i in range(10)]\n",
    "    },\n",
    "    {\n",
    "        \"label\": \"$\\sigma = 0.4$\",\n",
    "        \"train_names\": [f\"ae2_beaver_01_noise0.16_200e_{i}\" for i in range(10)]\n",
    "    },\n",
    "    {\n",
    "        \"label\": \"$\\lambda = 10^{-5}$\",\n",
    "        \"train_names\": [f\"ae2_beaver_01_sparse1e-5_200e_{i}\" for i in range(10)]\n",
    "    },\n",
    "    {\n",
    "        \"label\": \"$\\lambda = 10^{-4}$\",\n",
    "        \"train_names\": [f\"ae2_beaver_01_200e_sparse_{i}\" for i in range(10)]\n",
    "    },\n",
    "    {\n",
    "        \"label\": \"$\\lambda = 10^{-3}$\",\n",
    "        \"train_names\": [f\"ae2_beaver_01_sparse1e-3_200e_{i}\" for i in range(10)]\n",
    "    },\n",
    "    {\n",
    "        \"label\": \"$\\lambda = 10^{-2}$\",\n",
    "        \"train_names\": [f\"ae2_beaver_01_sparse1e-2_200e_{i}\" for i in range(10)]\n",
    "    },\n",
    "    #{\n",
    "    #    \"label\": \"$\\sigma = 0.1, \\lambda = 1e-4$\",\n",
    "    #    \"train_names\": [f\"ae2_beaver_01_noise0.01_sparse1e-4_200e_{i}\" for i in range(10)]\n",
    "    #},\n",
    "]\n",
    "\n",
    "CONFIGS_COMPARE_SESSIONS = [\n",
    "    {\n",
    "        \"session\": \"Beaver_01\",\n",
    "        \"label\": \"Beaver_01\",\n",
    "        \"train_names\": [f\"ae2_beaver_01_noise0.01_200e_{i}\" for i in range(10)]\n",
    "    },\n",
    "    {\n",
    "        \"session\": \"Marten_01\",\n",
    "        \"label\": \"Marten_01\",\n",
    "        \"train_names\": [f\"ae2_marten_01_noise0.01_200e_{i}\" for i in range(10)]\n",
    "    },\n",
    "    {\n",
    "        \"session\": \"GFox_03\",\n",
    "        \"label\": \"GFox_03\",\n",
    "        \"train_names\": [f\"ae2_gfox_03_noise0.01_200e_{i}\" for i in range(10)]\n",
    "    },\n",
    "]\n",
    "\n",
    "ds = Dataset(DIR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating the scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_scores(train_names, session = \"Beaver_01\", **kwargs):\n",
    "    kde_scores = {\n",
    "        \"auc\": [],\n",
    "        \"tnr90\": [],\n",
    "        \"tnr95\": [],\n",
    "        \"tnr99\": []\n",
    "    }\n",
    "    loss_scores = {\n",
    "        \"auc\": [],\n",
    "        \"tnr90\": [],\n",
    "        \"tnr95\": [],\n",
    "        \"tnr99\": []\n",
    "    }\n",
    "    # correct name\n",
    "    session = ds.create_session(session).name\n",
    "    for train_name in tqdm(train_names):\n",
    "        try:\n",
    "            lapse_losses, lapse_encodings, lapse_labels, lapse_images = load(f\"./ae_train_NoBackup/{train_name}/eval/{session}_lapse.pickle\")\n",
    "            motion_losses, motion_encodings, motion_labels, motion_images = load(f\"./ae_train_NoBackup/{train_name}/eval/{session}_motion.pickle\")\n",
    "        except:\n",
    "            print(f\"{train_name} is missing, skipping...\")\n",
    "            continue\n",
    "\n",
    "        # # # # # # # # # # # # # # # # # #\n",
    "        # 1. KDE AUC & elimination rates\n",
    "        # # # # # # # # # # # # # # # # # #\n",
    "\n",
    "        kde = KernelDensity(kernel=\"gaussian\", bandwidth=0.01).fit(lapse_encodings)\n",
    "        preds = kde.score_samples(motion_encodings)\n",
    "        y_anom = preds[motion_labels == 1]\n",
    "        y_norm = preds[motion_labels == 0]\n",
    "        # likelihoods need to be inverted (anomalies have lower likelihood)\n",
    "        X = -np.concatenate([y_norm, y_anom]).reshape((-1, 1))\n",
    "        y = np.concatenate([-np.ones_like(y_norm), np.ones_like(y_anom)])\n",
    "\n",
    "        fpr, tpr, thresholds = roc_curve(y, X[:,0])\n",
    "        kde_scores[\"auc\"].append(auc(fpr, tpr))\n",
    "        kde_scores[\"tnr90\"].append(get_percentiles(fpr, tpr, thresholds, percentiles=[0.9], verbose = False)[0])\n",
    "        kde_scores[\"tnr95\"].append(get_percentiles(fpr, tpr, thresholds, percentiles=[0.95], verbose = False)[0])\n",
    "        kde_scores[\"tnr99\"].append(get_percentiles(fpr, tpr, thresholds, percentiles=[0.99], verbose = False)[0])\n",
    "\n",
    "        # # # # # # # # # # # # # # # # # # # # # # # # #\n",
    "        # 2. Reconstruction Loss AUC & elimination rates\n",
    "        # # # # # # # # # # # # # # # # # # # # # # # # #\n",
    "\n",
    "        y_anom = motion_losses[motion_labels == 1]\n",
    "        y_norm = motion_losses[motion_labels == 0]\n",
    "        # likelihoods do not need to be inverted (anomalies have higher reconstruction loss)\n",
    "        X = np.concatenate([y_norm, y_anom]).reshape((-1, 1))\n",
    "        y = np.concatenate([-np.ones_like(y_norm), np.ones_like(y_anom)])\n",
    "\n",
    "        fpr, tpr, thresholds = roc_curve(y, X[:,0])\n",
    "        loss_scores[\"auc\"].append(auc(fpr, tpr))\n",
    "        loss_scores[\"tnr90\"].append(get_percentiles(fpr, tpr, thresholds, percentiles=[0.9], verbose = False)[0])\n",
    "        loss_scores[\"tnr95\"].append(get_percentiles(fpr, tpr, thresholds, percentiles=[0.95], verbose = False)[0])\n",
    "        loss_scores[\"tnr99\"].append(get_percentiles(fpr, tpr, thresholds, percentiles=[0.99], verbose = False)[0])\n",
    "    return kde_scores, loss_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_boxes(values, color=\"darkgoldenrod\", label_below=[], colors=None, label_dist_overwrites={}):\n",
    "    bp_dict = plt.boxplot(values, medianprops={\"linewidth\": 1.5, \"color\": color}, widths=0.72)\n",
    "\n",
    "    # Add labels to medians\n",
    "    for i, line in enumerate(bp_dict['medians']):\n",
    "        # get position data for median line\n",
    "        x, y = line.get_xydata()[1] # top of median line\n",
    "        # custom color\n",
    "        if colors is not None:\n",
    "            color = colors[i]\n",
    "        line.set_color(color)\n",
    "        # overlay median value\n",
    "        if i in label_below:\n",
    "            label_dist = label_dist_overwrites[i] if i in label_dist_overwrites else 0.001\n",
    "            plt.text(x - 0.355, y - label_dist, f\"{y:.3f}\", verticalalignment=\"top\", horizontalalignment=\"center\", color=color) # draw below, centered\n",
    "        else:\n",
    "            label_dist = label_dist_overwrites[i] if i in label_dist_overwrites else 0.0005\n",
    "            plt.text(x - 0.355, y + label_dist, f\"{y:.3f}\", verticalalignment=\"bottom\", horizontalalignment=\"center\", color=color) # draw above, centered\n",
    "\n",
    "def plot_scores(configs, scores, metric=\"auc\", savefile=None, figsize=(15, 10), xlabel=None, **kwargs):\n",
    "    plt.rcParams['text.usetex'] = True\n",
    "    plt.rcParams.update({\"font.size\": 18})\n",
    "    plt.figure(figsize=figsize)\n",
    "\n",
    "    plot_boxes([kde_scores[metric] for kde_scores, loss_scores in scores], **kwargs)\n",
    "    # plot_boxes([loss_scores[metric] for kde_scores, loss_scores in scores], color=\"blue\")\n",
    "\n",
    "    # Add labels\n",
    "    plt.xticks(np.arange(1, len(configs) + 1), [config[\"label\"] for config in configs])\n",
    "    if xlabel is not None:\n",
    "        plt.xlabel(xlabel)\n",
    "    plt.grid(True, ls=\"dotted\", lw=0.5)\n",
    "    # plt.ylim((0.7, 0.9))\n",
    "    # plt.legend()\n",
    "    if savefile is not None:\n",
    "        plt.savefig(savefile, bbox_inches=\"tight\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot result\n",
    "## By latent features\n",
    "Choose a metric (\"auc\", \"tnr90\", \"tnr95\", \"tnr99\") and optionally an output file for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions256_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:00<00:00, 20.47it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions256_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:00<00:00, 19.51it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions256_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:00<00:00, 15.15it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions256_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:01<00:00,  9.58it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions256_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:01<00:00,  5.59it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions256_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:02<00:00,  3.64it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions256_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:02<00:00,  3.62it/s]\n"
     ]
    }
   ],
   "source": [
    "scores = [get_scores(**config) for config in CONFIGS_BY_LATENT_FEATURES]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores(CONFIGS_BY_LATENT_FEATURES, scores, metric=\"auc\", savefile=\"plots/approach4/boxplot_kde_latentfeatures.pdf\", figsize=(7, 8), label_below=[0,3,4], xlabel=\"Latent features\", label_dist_overwrites={0: 0.005})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7ibmZ8VW/QMmN41fHnM6bb60s27r98VXrlv/YKOd30+D2VZ9yx53h+572wch/JJ57fiLJL9JXYvF4POwQAkWiX/mXU/neWJlsZgZbt3/b1++KJ/pQyKXxuws/BwCc+fFXVi2feHUvYolU5QqANMfPjjnl4zOeSOEPxq7/AyO2xoPqpXufT+LemnuekpB47vmJJL9IFzEpl8NBIdVv0+B2zF78xar7VSu5DAaG6j94XMhlMHvhF7h1x7UiNzdd+qt/YGgM87OTyGamK81b5W2WMtMYvu9pDN7xDdxaVSBX8ldx9vj9lftrpHX87JjzX469AKB+E3I2Pb3mMTF19gVsHtwhumlY6rnnF5L8It2cmM1mww4hUKT63b77OXwyeRyzF09hfnaydG9jaKzSRJhNT2P6vRcr6xeW0vjNW0/ho/EjpabB6XGcf+sp9I38C2wa3I7u1BAujX8P5998EnPT45ifncSHpx5Fd2oYt+74NuKJ1KpmrO7UEAAgmRpueA+HrE0hl7luXisdc7Zu/zZ2PXYGK7mr+ODk/7jmumv1JC3kMph570UM3/dvbiTstiH13PMLSX6RLmLFYjHsEAJFqt/A8BjufPAlTJ99Ab8+8XVsGtyxqofZ3Mw4Lo0fqUwn+4bxh//6H7GUmcavT3wd5996CrdsexgjX/3fAJSuCnY9fgYAcP6tp/DrE19HLLG5Mo/4T21zMNC8Y87oN3+JW3d8G8m+Ydz54EvIXv6PlSvqaqbfexHzsx/i7n0/vW7ZJ5PHEUukxA8XJvXc8wtJfpFuTtT+KgjJfrfu+Paq5sFmy5J9w9i+72er5pV7twGreyY2I55IVbp2k/XRqGPOpsEd9TYBgOuKTnXHnOplc9PjmDr7AnY9dqbu4M1z03/nxIgrks89P5DkF+krsUQiEXYIgUI/4jeNOuY0KmKtkE1PY/L1R7HrsTNrNvPOzYxjYEhmt/pqtB+bkvzklNMQyGazopLhN5L8Lpw+jOf/LIG//+sHfdvnSmEFsbh/h/ChP5I3wolE/O6YU15n4tU/xr0H3lizgJV7Rm7+4k6/VAJD0rkXBJL8Il3EJLXrBoF2Pws5z6pEidt3P4eJV/diYPiByogdtR1zPv3o9cozXOWOOdnMNL54x5+gsJTG+beeQmr4X1Su7CZOfB23bHsYwLViBQCxrs2V/Zav/sodcySj/dyT5GckPbQGAKOjo3ZiYqIt33XlyhX09/e35bvCQJqf3y959NtP2ksoJcfz2w9+gk8mf4JsZua6cQ5/+8FP8Ju3nlp13zGbnsald45gbuYdxBN9uGXbw/jC3U+jv78f2fT0dc/wlRm67+nKM2T19rtWfBtx84Mgzj3Nfs3iM8acs9aO1l0m6SQB2lvEcrmcmEviIJDm5/eJ5Lef5KIhAc35k+4G6PbbSBGLdMeOXC4XdgiBQj8iGc350+wGyPKLdBFbWVkJO4RAoR+RjOb8aXYDZPlFumNHR4fuGk4/4hdB9S6dUtq7VPuxKclPTiQhkEwmww4hUOhHJNNxk95fP9qPTUl+ke7YcfXqVWzevPZQOa4jzc/vm8t++2nvSLFRNOevels/3lpd6+bHPjX7baRjR6SbEwuFQtghBAr9iGQk5i+It1Y32mc73lpdjUa/SBexjbwS2wXoRyQjMX9+v7W62T7b/dZqjX6RLmK9vb1hhxAo9COSkZi/uZnxVa95qX5rdaNf8uW3Vpe5KZlveZ+1r5uZvXgKQDBvrdbop/fOagvk8/nmKzkM/YhkpOXPz7dWl93Wu8/yW6vveuiv1vV9raDVL9JXYsvLy2GHECj0I5KRlj8/31o9+M//LTZt2rTufQb51mqtfpG+EiOEkFr8eGv1//N//E/r3me73lqtzS/SRUxim7yf0I9IRmr+gnhrdSv7bNdbq7X5RbqISWvO8Bv6EclIy1+jt1bHE31rbjcwPLbqF3P1W6vXs8+g31qt1Y9FTDH0I5KRlr8g3lq9nn0G/dZqrX6RLmKEEFJN+a3VZVp5a/VvP/jJqnm1b61uZZ/temu1Rr9I907s6ekJO4RAoR+RjMT8+fXW6i3/1d7KVUqzfZb3AwT/1mqNfpEuYpJeJxAE9COSkZi/geEx3PngS5g++0Ldt1bPzYzj0viRyi/5ZN8w/vBf/yMuvXMEvz7x9cpbq//z0cMt7xMAsplpANd3VadfcyI9APDly5exZcuWtnxXGEjz83sAWb/9tA+4u1E050+6G6Dbj292JoQQEkkiXcQktsn7Cf2IZDTnT7MbIMsvkHtixpgRACPW2reD2L9fFIvFsEMIFPoRyUjJXxBvrf788yJu8vmlnzf65moX/DbyVu6WojDGHDTG7Pc+h5tvgZ0AThpjrDEmbYw5bYwJtu/oDbC0tBR2CIFCPyIZzfkrFj8PO4RAkeTXtGOHMeYgAFhrX/amRwA8Y6091GCb/dba14wxKWttZj0BsWOHf0jzc/3mcrvRHo+k/El3A3T7Bf1m50PW2nvKE9baKWPMnlYCW28BazfJZDLsEAJFop/EFyGW6etbe+gd4j8Sj0+/0OwGyPJrWMSMMSmUmgZryRhj9ki/50Vk4fdVhbQrFUJI+2l2T2wEQKbO/CuoX9wqGGP2VH1auY/WdrLZbNghBIp2P+I2mo9PzW6ALL9mzYn9KBWsWjIABhps9z5QanoEAGPMlDHmtLX2a/VW9u67HQSArVu3IpfLYXl5uTJAaE9PD1ZWVipvE+3p6UGxWKzcGC5f2pZ/sN3d3ejo6MDi4iIAoKurC7FYrDLd2dmJzs5OXL16tTLd1dWFhYUFWGsRj8fR1dWFbDaLYrGIWCyGRCJRme7o6EAymUQul8PKykplOp/Po1AowBiD3t5e5PP5ikNvb29bnBYWFirTy8vLmJubU+VUnScAyOVyqpyq8wQAmUxGjBNQuheyEafqPH322WcA4JtTOb4byRNQ6miyUafy9GeffYbe3l5f8wQAi4uLN3zszc/Pb8ipOk9Xr1719dgr567RsbcWDTt2ePe+jllrb6uZfxLAlLX2mYZ7X73NxwAesda+32i9dnbsWFxcFPW8g99o99PenCjNz+94/D4+JXV8COLc0+wX9Igd/XXmpQDMtRTdNaYA1A0iLJpVeNfR7kfcRvPxqdkNkOXXLJIJlApWLf3wmgxrMcaMGGPWKqn1miZDo7opQSPa/YjbaD4+NbsBsvwaFjGvi/yU10uxmlSDnolXANR7hmwUaxQ+Qggh5EZo5ZrwKIBnyxPeyBtvV02PGGNOlgtdvWfDvI4bf1Pu6CGFrq6usEMIFO1+xG00H5+a3QBZfk0fdrbWvuwNO7UHpabFkZrROlIA9qDUxJip2uawN53y5q05wkdYxGK6X6em3Y+4jebjU7MbIMuvpUjKQ06tsex9ANcNdWCtfX4DcbWFxcVFdHd3hx1GYGj3I26j+fjU7AbI8pPTxYQQQghZJ5EuYp2dNz78vwto9yNuo/n41OwGyPJjEVOMdj/iNpqPT81ugCy/SBex8vArWtHuR9xG8/Gp2Q2Q5SeniwkhRDx8lQ6RRqSLmKRL4iDQ7kfaC1+l0zrazz1JfpFuTpT0wF4QaPcjRCrazz1JfpEuYpLadYNAu580+vv7YYzx7QPA1/3199cby5sEgfZzT5JfpJsTtTZllNHuJ410Ou3rz/zy5cvYsmWLb/uTfD9LG9rPPUl+kb4Si8fjYYcQKNr9tMP8uYv23Enyi3QRk9SuGwTa/bTD/LmL9txJ8ot0ESu/Alsr2v20w/y5i/bcSfKLdBErFothhxAo2v20w/y5i/bcSfKLdBGT9DqBINDupx3mz120506SX6SLWCKRCDuEQNHupx3mz120506SX6SLmKR23SDQ7qcd5s9dtOdOkp+ca8IQkNSuGwTa/bTD/LUX6c/RbXRsSMl+G3GLdBHr6NB9IardTzvMX/vQPi6kZr9InyXJZDLsEAJFu592mD9CmhPpIpbL5cIOIVC0+2mH+SOkOZEuYisrK2GHECja/bTD/BHSnEgXMe33HLT7aYf5I6Q5ke7Yof2eg3Y/ycxePIVL499DIZfG4LZ9uOuhH617HzeSv+n3XsTshZ8jm5nB5sEdGD3wy3XvgxCXiPSfevl8PuwQAkW7n1Tmpsfx4alHcfvYX+LeA29gfvYDTJzY2/L2H40fwX/4QS/+0/97uuHyuenxVfPPv/kkps6+gOFdf45dj53BLdv2bciDEBeI9JVYoVAIO4RA0e4nlen3foih+57GoFdE7n74pzjz468gm55Gsm+44baFXAafTB4HAKx8/nnD5dXMTY/jk8nj2PX4u9g0uB0Amn4XIRqI9JWY5If//EC7n1TmZsaxZeiBynSybxjdqWHMzYw32KrEpfEjGBjaDQAwuD5/1curmX7vhxgYGqsUMEKiQqSLWG9vb9ghBIp2P4nMz04CADZ9cceq+cnUEOZnP2i4bTY9jU8mj2Nk118AABLdiYbLq5mbGWfzIYkkkW5OzOfzol7u5jeu+q3nCrLVdds1ukBhKQ0AiCdSq+bHEptRyKUbbnvpnSOrrqZqm4Nrl5fJpqdL39ndh/NvPonZi6cQT/ThrgdfwsDw2I3LEOIAkb4SW15eDjuEQHHVz1rb0uf3v/99y+u2m0Iuc928ldzVNdefn53Epxdfx+1jz11bv+o5sXrLyyxlZgAAl8a/h4HhB7DrsTMYGNqNiVf31o2DEE1EuogREhTlK7JqYonNa65/afwIbtn28Jr3tBotj3eXBk8d9jqTJPuGcddDP0IskarbCYQQTUS6iGm/Z0S/9lMuKCv51VddS5kZxBP1R+qen53E3Mw4bt+9+iqrO9HdcHllvdQQAGDzF3eumr95cAeu/u7cuh0IcYlI3xNbXl4W9XI3v6Ff+6ncz6q5EstmZrB1+7frbvO7Cz8HAJz58VdWzf+HX/wJfpNIYev2x+sun3h1L2KJFL769Cd1vxPAmoWTEC1Evohphn7hsGlwO2Yv/qLSqWJ+dhIruQwGhup3shjZ9ee4tarAreSv4uzx+/Gl+/8X/LMv70W8u6/u8jsffKmyz1u2PYzLM3+3qiPH1dkP2GORqCfSRYyQILh993OYeHUvBoYfQDI1jPNvfgcDQ2OVh4+z6Wl8+tHrGL7vaQClnozVvRnLnTG6Nn2psk295cnUcGX5yK6/wNnj9yOZGsbmL+7E1Nl/h3iiD7fuqH/1R4gWIn1PrKenJ+wQAoV+4TAwPIY7H3wJ02dfwK9PfB2basYwnJsZx6XxI033s56m0k2D2zH6zV9i+r0Xcfb4/QCAXY+fWX/whDiGkfJ2zjKjo6N2YmKiLd/12Wef4eabb27Ld4UB/dqL32+79dtP0tt4AXnx+IlmN6D9fsaYc9ba0XrLIn0lpn2AXPq5jXY/Qvwg0kWMEEKI20S6Y4fUeyp+Qb/2ceH0YTz/Zwn8/V8/6Ns+i58XMXWTf39nHvqjTt/2RYgUIn0lViwWww4hUOjnNnrvqBDiH5Hu2HH58mVs2bKlLd8VBvRrL37f7PbbT1pnA2nx+IlmN4AdOwghhBBfiHQRSyaTYYcQKPRzG+1+hPhBpIsYIYQQt4l0Ectms2GHECj0cxvtfoT4QaSLGCGEELeJdBHr7u4OO4RAoZ/baPcjxA8iXcQ6OnTr089ttPsR4geRPksWFxfDDiFQ6Oc22v0I8YNIFzFCCCFuE+mxE7u6usIOIVDo136MMWGHsCZ9fX1hh0CI70S6iMViuvXp1178HoZH+9BFhPhBpJsTtd9zoB8hRDuRLmKEEELcJtJFrLNT9/uV6EcI0Q6LmGLoRwjRTqSL2MLCQtghBAr9CCHaiXQRI4QQ4jaRLmLam6PoRwjRTqSLmMSHZf2EfoQQ7US6iGm/p0I/Qoh2Il3EtI+GQD9CiHZkjdvTZuLxeNgh3BBBjM/nYkFwNX+EEP+IdBFz9Z5KqwVH+9h7ruaPEOIfkW5OzGazYYdANgDzRwiJdBErFothh0A2APNHCIl0EZP2Kg+yPpg/Qkiki1gikQg7BLIBmD9CSKSLGO+puA3zRwiJdBHjPRW3Yf4IIZEuYh0dkdZ3HuaPEBLp3wLJZDLsEMgGYP4IIZEuYrlcLuwQyAZg/gghLfVRNsYcBHDFmxyx1j6/ni8xxpy01j6y3uCCZmVlJewQyAZg/gghTYuYV8BgrX3Nmx4xxhyz1h5q5QuMMTsB7N9QlAEh7Z5Kf38/0um0r/v0c5zFvr4+XLlypfmKbUJa/lplPTlpdV1Jw4tp9yOyaOVK7JC19p7yhLV2yhizZx3f0b/+sNqDtHsq6XTa15M1l8v5+ixVEAMPbwRp+WuVVnPsd/7ahXY/IouGf8oaY1IAdtZZlGmlkBlj9ltr377B2AInn8+HHUKg0M9t6EdIc5q1x4wAyNSZfwX1i1sFrxnx/RsLqz0UCoWwQwgU+rkN/QhpTrPmxH5c69BRTQbAQJNtR8r30Zrh3Xc7CABbt25FLpfD8vIylpeXAQA9PT1YWVmp/OXW09ODYrGIpaUlANealcojOHR3d6OjowOLi4sASq/siMVilenOzk50dnbi6tWrlemuri4sLCzAWot4PI6uri5ks1kUi0XEYjEkEonKdEdHB5LJJHK5HFZWVirT+XwehUIBxhj09vYin89XHHp7e5s6AcDly5c35FR+23FnZycKhQLm5uZ8cwKA+fn5dTn5kadqp+o8LS4uore3t+15CtKpOk9Xr15V51Sdp/L6mpzK00CpuVSTU3WeAODKlSttc2qEadR+7TUZHrPW3lYz/ySAKWvtM2tst7+6gBljrLW2pRsqo6OjdmJiopVVN0w+nxf1Tiq/3//lt5+095NJy5/f0M9dpJ0rftNuP2PMOWvtaL1lrXTvqtcxIwVgbo0vGwEw1XJ0IaK9TZ5+bkM/QprTrDlxAqWCVUs/1r7ftQdAqrbjhzHmMICMtfbl9QYZFOVLXK3Qz23oR0hzGhYxa23GGDNljElZazNVi1Jr9TqsV6SMMUfX+4A0IYQQ0oxWmhOPAni2POH1Ony7anrEGHPS647vFL29vWGHECj0cxv6EdKcpg87W2tfNsYc9JoHUyj1OqwerSOFUhNiP2q643vbHPL+fxKlTiJinhtbXl4W87DlhdOH8fyfJfD3f/2gb/v8fOVz3BS7ybf9HfqjTt/25QeS8hcE9COkOS2NndjoPpa19n0AfWssextVV23S0N4mX7RF3AT/ipg0tOePfvLQPqSWi34tFTESPHd87Xnc+d//r7D//i0AwOzFU7g0/j0UcmkMbtuHux76UdN9fDR+BJ9MHgcADAztxpf++b/F4H9x7emI2YunMH32BWQzM3X3ef7NJzF78RQA1F3+33zL4McbsiTEbVr9hXz58mVs2bIl4Gj8x0U/N0dQ9YnyA8bSmJsex4enHsXtY3+Jew+8gfnZDzBxYm/DbSZO7MVns5O498Ab2PXYGazkruLjtx6tLJ+9eAofnnoUw7v+vLLP828+WVk+eepbKOTSuPfAG7jroR9h9uKppt8ZNlLz5xf0cxfNboAsv0gXMamv8ph+74cYuu9pDG7bh02D23H3wz/F3Mw4sunpNbeJJTbj7n0/xabB7Uj2DeP2seew8J/+obLNpfHv4fax5yr7vOuhv8Ink8dRyGVQyGUwN/MOtu/7GTYNbsfgtn2498AbmJsZx9z0eLu0143U/PkF/dxFsxsgyy/SRUzqw5ZzM+PYMvRAZTrZN4zu1DDmZtYuKNv3/QzxRKoync1cK3jZ9DSWMtMYGBqrzNs0uB0AMHvhFwCArdsfX7W/8vL5Tydv0CJ4pObPL+jnLprdAFl+kS5iEpmfnQQAbPrijlXzk6khzM9+0PJ+ps++gOSWLyPZN4ylzAwAoDs1tGqd7tQwVvJXEU+k8Adjz123j1hVUSSEEIlEumOHpHbdMoWl0ksx4zUFJJbYjEKu8QszC7kMzr/5HczNvINkagjb979e2ld3X2XftfvNpuuPEJZNT2Mll1l19SYNifnzE/q5i2Y3QJZfpK/EyqNNS6SQy1w3byV3tel28UQfNg/uQCF3FfOzpZHByldg1c2RhVwGS5npNQvjpXeOYGBorNKsKBHJ+fMD+rmLZjdAll+ki1h5+H+JlK/IqoklNjfcJp5I4a6HfoTRA7/E7WN/if/4+p9ifnYS8UQKt2x7GJfe+R6y6enKFRtQalKsZfq9FzE/+yHu3vdTf2QCQnL+/IB+7qLZDZDlF+kiJpFy099KfvVV11JmBvFE3WfK6zK4bR9u6tyE337wCoByx48+nPnxV3D2+B9i8I4/QSyRQrKmiM1Nj2Pq7AsY/ebfXtf0SAgh0oj0PbHyC9gkUW6+q70Sy2ZmsHX7t9e1r3h336rmwv/uf/5HFHIZxBMpFHKZ6+55ZdPTmHz9Uex67AySfddfoUlDYv78hH7uotkNkOXHKzGBbBrcjtmLv6hMz89ONuxkMT87WRlpo0whl0Hu6j9hoKqrPnCtw8jU2RcwMDRWKVaFXAYTr/4x7j3whhMFjBBCAKVXYusZ/6tV2jm+2e27n8PEq3sxMPwAkqlhnH/zO6sKTjY9jU8/eh3D9z0NoNRxY/LUo5ib/jsMbvsG4t19uDR+BJ03fwm37ihdvZWL3KZbtuO3kz/BJ5PHseuxM5XvnDjxddyy7WEA17r5A0Csa7PYopbNZkX9Reg39HMXzW6ALD+VRazVgiP1FeIDw2O488GX1hzncG5mHJfGj1SKWDyRwq7Hz+DS+BGcf+spFHJpDAztxh37flnZJt6VwqV3jmB+dhIDQ2Ormgyz6WnMz05ifnYSM++9uCqWofuervsMGSGESMBI+yU+OjpqJyYm2vJd0oqY3/EsLi76+jyHtJ+X337SoJ+7aHYD2u9njDlnrR2tt4z3xBTT0aE7vfRzG81+mt0AWX5yIiG+s7i4GHYIgUI/t9Hsp9kNkOXHIkYIIcRZWMQU09XVFXYIgUI/t9Hsp9kNkOXHIqaYWExl59MK9HMbzX6a3QBZfixiipHUbh0E9HMbzX6a3QBZfixihBBCnIVFTDGdnZ1hhxAo9HMbzX6a3QBZfixiipF0oAUB/dxGs59mN0CWH4uYYhYWFsIOIVDo5zaa/TS7AbL8WMQIIYQ4C4uYYiRd8gcB/dxGs59mN0CWH4uYYiQ9kBgE9HMbzX6a3QBZfixiipHUbh0E9HMbzX6a3QBZfixiipH02pQgoJ/baPbT7AbI8mMRU0w8Hg87hEChn9to9tPsBsjyYxFTjKR26yCgn9to9tPsBsjyYxFTTDabDTuEQKGf22j20+wGyPJjEVNMsVgMO4RAoZ/baPbT7AbI8mMRU4yk1yUEAf3cRrOfZjdAlh+LmGISiUTYIQQK/dxGs59mN0CWH4uYYiS1WwcB/dxGs59mN0CWH4uYYiS1WwcB/dxGs59mN0CWH4uYYjo6dKeXfm6j2U+zGyDLT04kxHeSyWTYIQQK/dxGs59mN0CWH4uYYnK5XNghBAr93Eazn2Y3QJYfi5hiVlZWwg4hUOjnNpr9NLsBsvxYxBQjqd06COjnNpr9NLsBsvzkREJ8R1K7dRDQz200+2l2A2T5sYgpJp/Phx1CIJw4cQJf/vKX0dPTgy9/+cs4ceJE2CEFgtb8ldHsp9kNkOUnZ+wQ4juFQiHsEHznxIkT+O53v4tXXnkF27Ztw8WLF/HEE08AAA4cOBBydP6iMX/VaPbT7AbI8uOVmGKMMWGH4Dvf//738corr2BsbAydnZ0YGxvDK6+8gu9///thh+Y7GvNXjWY/zW6ALD9eiQlD0sFRS19fX9gh4MKFC7j//vsBAL29vQCA+++/HxcuXAgzrEAo+2lFs59mN0CWH6/EBGGt9fXj9z6vXLkS8k8IuOOOO/Duu+8CuNYu/+677+KOO+4IM6xAkHTfIQg0+2l2A2T5sYgRp/jud7+LJ554AuPj41hcXMT4+DieeOIJfPe73w07NN9ZXl4OO4RA0eyn2Q2Q5edUc2J/fz/S6bSv+/S7+a6vr0/EFYtWyp03nnzySVy4cAF33HEHvv/976vr1EEIaQ1TbnaSwujoqJ2YmKi7zBgDP+PN5XK+vxfH7xg3gqRYgiCI/EmCfu6i2Q1ov58x5py1drTeskg3J0q6JCbrR3v+6Ocumt0AWX4sYsRZtOePfu6i2Q2Q5RfpIkYIIcRtIl3Eenp6wg6BbADt+aOfu2h2A2T5RbqISXqdAFk/2vNHP3fR7AbI8nOmi/2F04fx/J8l8Pd//aBv+ywUCojH477tDwAO/VGnr/sja5PP53HzzTeHHUZg0M9dNLsBsvwifSVGCCHEbZx9Tmz24ilcGv8eCrk0Brftw10P/ajpvj8aP4JPJo8DAAaGduO23f8ON/cNVpaff/NJzF48BQAt77NRjGEjKZYgWFpaQnd3d9hhBAb93EWzG9B+P3XPic1Nj+PDU4/i9rG/xL0H3sD87AeYOLG34TYTJ/bis9lJ3HvgDex67AxWclfx4c+/UVk+eepbKOTSuPfAG7jroR9h9uKpNff50fgR/Icf9GJuetxXL7I+isVi2CEECv3cRbMbIMvPySI2/d4PMXTf0xjctg+bBrfj7od/irmZcWTT02tuE0tsxt37fopNg9uR7BvG7WPPYfH3/4BsehqFXAZzM+9g+76fYdPgdgxu24d7D7yBuZnx6wpVIZepXM2RcFlaWgo7hEChn7todgNk+TlZxOZmxrFl6IHKdLJvGN2pYczNrH1ltH3fzxBPpCrT2czqgrd1++OrpjcNbgcAzH86uWr+pfEjGBjafUNxE0II8Rfnitj87CQAYNMXd6yan0wNYX72g5b3M332BfT+Z/81kn3DiCdS+IOx565bJ1ZV9AAgm57GJ5PHMbLrL9YdN/GfZDIZdgiBQj930ewGyPJzpot9mcJSaRT7eE2BiSU2o5BrPMJ9IZfB+Te/g7mZd5BMDeEr3/j5mutm09NYyWUwMDRWmXfpnSMYGBqrXKURQggJF+euxMoUcpnr5q3krjbdLp7ow+bBHSjkruLy//d/r7lebcGan53Epxdfx+11rthIOGSz2bBDCBT6uYtmN0CWn3NXYmUKS+m6V2ONiCdSlW7zsxdP4cNT38KWW9697spq+r0XMT/7IXY9fqYy79L4Edyy7WFehRFCiCCcuxKLd/cBAFbyq6+6ljIziCf6Wt7P4LZ9uKlrM377wSur5s9Nj2Pq7AsY/ebfVork/Owk5mbGcftuXoVJQvNzOAD9XEazGyDLz7krsfKVUPneWJlsZgZbt397XfuKJ/pW3UfLpqcx+fqj2PXYGST7hivzf3ehdO/szI+/smr7iVf3IpZI4atPf7Ku7yX+0NHh3N9g64J+7qLZDZDl51wRA0qFbPbiLzAwXOp0MT87eV0njGrmZyeRzUxjcNu+yrxCLoPc1RmM7Po3lemJV/8Y9x54Y1UBA4CRXX+OW6sK5Er+Ks4evx93PvjSmt9JgmdxcVHUX4R+Qz930ewGyPJzsojdvvs5TLy6FwPDDyCZGsb5N7+DgaGxSvHJpqfx6UevY/i+pwEA3akhTJ56FHPTf4fBbd9AvLsPl8aPoPPmL+HWHaXiNHHi67hl28MArnXjB4BY1+ZKN/wy5U4lydTwdQWPEEJI+3CyiA0Mj+HOB1/C9NkXkM3MXDfO4dzMOC6NH6kUsXgihV2Pn8Gl8SM4/9ZTKOTSGBjaje3f/D8BlIre/Owk5mcnMfPei6u+a+i+p+s+Q0bCp6urK+wQAoV+7qLZDZDl5+wAwH4QxCCWkgbdlRRLEHCQVbfR7KfZDeAAwGJYXFwMOwSyAbTnj37uotkNkOXXUnOiMeYggCve5Ii19vkWt0l5k7cBOGqtnbqRIAkhhJB6NC1iXjGCtfY1b3rEGHPMWnuowTaHqwudMWY/gNMoFTMxdHbyLcwuoz1/9HMXzW6ALL9WmhMPWWtfLk94V1N7mm3jFa4y7wMYMcak1h9icEhKBFk/2vNHP3fR7AbI8mtYxLyis7POoowxplEh+1r5ys1jBEDGWptZd4QBsrCwEHYIZANozx/93EWzGyDLr9mV2AiATJ35V1C/uAGoXK1V8wyAR9YVGSGEENKEZvfE+nGtQ0c1GQADzXbuNSl+DaVOHW83WO8ggIMAsHXrVuRyOSwvL2N5eRkA0NPTg5WVFQDA5cuX0dPTg2KxWHm7aPndNuWRlbu7u9HR0VHpQdPV1YVYLFaZ7uzsRGdn56rprq4uLCwswFqLeDyOrq4uZLNZFItFxGIxJBKJynRHRweSySRyuRxWVlYq0/l8HgAwNzeH3t5e5PP5ikNvb29dp/I2fjmV/0IqX+7Pzc1t2KlQKMAYI8KpOk/5fB65XE6VU3WeFhcX1TlV52lpaQmXL19W5VSeXlpaQi6XU+VUnafFxcW2OjWi4XNiXpPhMWvtbTXzTwKYstY+03Dv19Y/DGCglfXb+ZxYPp/3/aE9Sc9mSYolCILInyTo5y6a3YD2+230ObH+OvNSAOZaDcDrqXiwyX20tiOpXZesH+35o5+7aHYDZPk1K2ITuPasVzX9KPU4vA5jzE5jTL1XLE+h1LQoBlevUowxLX3Wu65ruJq/VqGfu2h2A2T5NSxiXm/CqTpd41MN7nH1A3i5zvwRAB+vN8AgicfjYYdwQ1hrW/pkMpmW13URV/PXKvRzF81ugCy/VpoTjwJ4tjxhjNkJ4O2q6RFjzMlyofOK26qmRm8bAPibjQbsJ5rbrAH6uQ793EWzGyDLr+mIHdbal40x5ftZKZSGnaoerSOF0sPP/bjWHf9lrzNHmdsA3CPtObFsNotEIhF2GIFBP7ehn7todgNk+bU0dmL1iB11lr0PoK9mXgZA0/EVw6ZYLIYdQqDQz23o5y6a3QBZfpEexT4Wc/J1ai1DP7ehn7todgNk+UW6iEm5HA4K+rkN/dxFsxsgyy/SRaz8VLhW6Oc29HMXzW6ALL9IFzFJ7bpBQD+3oZ+7aHYDZPlFuog1G5PLdejnNvRzF81ugCw/OZGEQHmwSa3Qz23o5y6a3QBZfpEuYrlcLuwQAoV+bkM/d9HsBsjyi3QRK7/eRSv0cxv6uYtmN0CWX6SLmKR23SCgn9vQz100uwGy/OREEgKS2nWDgH5uQz930ewGyPKLdBErv11UK/RzG/q5i2Y3QJZfpItYoVAIO4RAoZ/b0M9dNLsBsvwiXcRcfRlkq9DPbejnLprdAFl+kS5ivb29YYcQKPRzG/q5i2Y3QJZfpIuYpHbdIKCf29DPXTS7AbL8Il3ElpeXww4hUOjnNvRzF81ugCy/SBcxQgghbhPpIiapXTcI6Oc29HMXzW6ALL9IFzFJl8RBQD+3oZ+7aHYDZPmxiCmGfm5DP3fR7AbI8ot0ESOEEOI2kS5iPT09YYcQKPRzG/q5i2Y3QJZfpIuYpNcJBAH93IZ+7qLZDZDlF+kiJumBvSCgn9vQz100uwGy/CJdxAghhLhNpIuYpHbdIKCf29DPXTS7AbL8Il3EisVi2CEECv3chn7uotkNkOUX6SK2tLQUdgiBQj+3oZ+7aHYDZPlFuogRQghxm0gXsWQyGXYIgUI/t6Gfu2h2A2T5RbqIEUIIcZtIF7FsNht2CIFCP7ehn7todgNk+UW6iBFCCHGbSBex7u7usEMIFPq5Df3cRbMbIMsv0kWso0O3Pv3chn7uotkNkOUnJ5IWMcb49kkmk77uzxiDvr6+sH9EFRYXF8MOIVDo5zaa/TS7AbL8YmEHsB6stb7uzxjj+z4JIYS0D+euxEjrdHV1hR1CoNDPbTT7aXYDZPmxiCkmFnPqQnvd0M9tNPtpdgNk+bGIKUZSu3UQ0M9tNPtpdgNk+bGIEUIIcRYWMcV0dnaGHUKg0M9tNPtpdgNk+bGIKUbSgRYE9HMbzX6a3QBZfixiillYWAg7hEChn9to9tPsBsjyYxEjhBDiLCxiipF0yR8E9HMbzX6a3QBZfixiipH0QGIQ0M9tNPtpdgNk+bGIKUZSu3UQ0M9tNPtpdgNk+bGIKUb7uJD0cxvNfprdAFl+LGKKicfjYYcQKPRzG81+mt0AWX4sYoqR1G4dBPRzG81+mt0AWX4sYorJZrNhhxAo9HMbzX6a3QBZfixiiikWi2GHECj0cxvNfprdAFl+LGKKkfS6hCCgn9to9tPsBsjyYxFTTCKRCDuEQKGf22j20+wGyPJjEVOMpHbrIKCf22j20+wGyPJjEVOMpHbrIKCf22j20+wGyPJjEVNMR4fu9NLPbTT7aXYDZPnJiYT4TjKZDDuEQKGf22j20+wGyPJjEVNMLpcLO4RAoZ/baPbT7AbI8mMRU8zKykrYIQQK/dxGs59mN0CWH4uYYiS1WwcB/dxGs59mN0CWn5xIiO9IarcOAvq5jWY/zW6ALD8WMcXk8/mwQwgU+rmNZj/NboAsPxYxxRQKhbBDCBT6uY1mP81ugCw/FjHFGGPCDiFQ6Oc2mv00uwGy/FjEFNPb2xt2CIFCP7fR7KfZDZDlxyKmGEnt1kFAP7fR7KfZDZDlxyKmmOXl5bBDCBT6uY1mP81ugCw/FjFCCCHOwiKmGEnt1kFAP7fR7KfZDZDlxyKmGEmX/EFAP7fR7KfZDZDlxyKmGEkHWhDQz200+2l2A2T5xVpZyRhzEMAVb3LEWvt8i9sAwD3ev89YazPrjpAQQghZg6ZFrFyMrLWvedMjxphj1tpDjbax1r5cs49f4VpBI22gp6cn7BAChX5uo9lPsxsgy6+V5sRD1QXJWjsFYM9aKxtjUrXzvO1HjDFrbkf8R9LrEoKAfm6j2U+zGyDLr2ER8wrSzjqLMg0K0giAY3WK2ZS3LHCMMS191ruua0h6IDEI6Oc2mv00uwGy/JpdiY0AyNSZfwX1ixuste8DuKfO/a8RABPrjO+GsNa29Pn973/f8rqEEELk0eyeWD+udeioJgNgYK2NvEJWwRizH8BU7fyq5QcBHASArVu3IpfLYXl5udIDpqenBysrK5Xq39PTg2KxiKWlJQDX3m2TzWYBAN3d3ejo6MDi4iIAoKurC7FYrDLd2dmJzs5O5PN5XL58GZ2dnejq6sLCwgKstYjH4+jq6kI2m0WxWEQsFkMikahMd3R0IJlMIpfLYWVlpTKdz+dRKBRgjEFvby/y+XzFobe3ty1OCwsLlelYLIa5uTlVTtV5+vzzz5HL5VQ5Vecpn88jk8mocqrOU6FQwOXLl1U5lacLhQJyuZwqp+o85fN5XLlypW1OjTCNrjK8JsNj1trbauafRKkoPdNw76g0Sf4KwFdb6Z04OjpqJybacsGGxcVFUTco/YZ+bkM/d9HsBrTfzxhzzlo7Wm9ZKx07+uvMSwGYa/H7jwJ4RGL3+nL11wr93IZ+7qLZDZDl16yITaBUsGrpB1C3abAaY8xhAEe9Ho2EEEKIrzQsYt7V01SdnoYpa+3bjbb17nO9Vl3ApHWxL7e/aoV+bkM/d9HsBsjya6U58SiAZ8sTxpidAN6umh4xxpysLnResZooFzBjTEpaASOEEOI+TYuY96Dyx8aYPV4vwz01o3WkUHr4uR8oFTUApwGcM8ZYY4wFkPbmtafHRouUe8BohX5uQz930ewGyPJraezE6hE76ix7H0Bf1fQUADefDiaEEOIUkR7Fvru7O+wQAoV+bkM/d9HsBsjyi3QRa/YQnevQz23o5y6a3QBZfnIiCYHyk+xaoZ/b0M9dNLsBsvwiXcQIIYS4TaSLWFdXV9ghBAr93IZ+7qLZDZDlF+kiFou11DnTWejnNvRzF81ugCy/SBcxSe26QUA/t6Gfu2h2A2T5RbqIEUIIcZtIF7HOzs6wQwgU+rkN/dxFsxsgy49FTDH0cxv6uYtmN0CWX8OXYoaBMeb3AP6pTV+3BcDlNn1XGNDPbejnLprdgPb7/TNr7RfqLRBXxNqJMWZirbeFaoB+bkM/d9HsBsjyi3RzIiGEELdhESOEEOIsUS9ia75iRgn0cxv6uYtmN0CQX6TviRFCCHGbqF+JEUIIcZjIFDFjzDFjTGqNZYeNMeeMMWljzOk2h7ZhjDFHvdjTxpiTtZ6ee3n5sZDC3BCu56iatY7FZnkyxhw0xny8Vp4l4MexpiHXN5rjdtMgzv1Vx1q9Y1GOh7VW9QfACICjACyAVJ3lxwCkAez31j0Ydszr9DvtfXZ68Z8GcK5q+Unvs9NzTAM4HXbc63RsKUdVed4TdsxrxLfmsdgsTwAOevP2ePs5CeDjsJ3W6XDSc1/1uZFch+jXLP4bzrGgY3GPN3+/F+u5OnlsyaMd52ToB0bAiTrs/QA/bpKsnWHHugHHk9Ve3oFlvYM0BSBds/7OoA8qn/1aylHZVapbo2OxlTx52x2sWccC2B+22zocyr/8Rqo/6811iI7N4t9QjiUci97y0wCOVk2P3MjvlHadk6qbE621z1trDYBDa6zyDIC3rbXvtzEsX7HWPmKtzVTNGqlZZVUvoirXnUHG5SOt5ugogLfbEM8N0cKx2CxPIwAmaraZAtDvW5Abp5Vj7Yq1dqr6U7XMhfNxzfh9yHFbaCHOPSgVsvL6Uygda3u8Wa16tOWcVF3EWmAPSn9ZaeJZAO97J1jGWvtMnXUybY5pIzTNkTFmBKXmth+0JSKfaTFPGVz/B0o/hBRun441jecjAN9+PoFjjCkXonp/MN3Tqkc7z8nIFjHvhwwAV6puUn5sjNnTcEOBGGNS3o3+tDfrqw3WLTcJiPjl14h15Ogo5P8Fvy7q5OkZAEfLPxNjzGEA/6rmSkYUaxxro1WdNiqdUxw6H+vGfyMIPRf7gVLRrZmfwRpX/Wt4tO2cjGwRw7W/ao+idOl8D0pJOC2x11cLXEHpr6cUgEZjmrn0C79pjry/HPej9EteE6vyZK19GaVODx8bYyxKV9yZ8MJriXrH2k6UPO5B6Vj9lTfflfNxrfhvBLHn4ho/83rzgBqPtp+T7byhGNYH124Yp6rmlW9G1t4sTwM4HHbMG3DdjzVujqN0Q/dj1OmlKfHTSo5Q+oV3smqZyI4dVfFddyy2kieUmmY+xrUeY+WeclI7QdRz2FOdG1zrMLDHhfOxUfx+5FjCsVg1b6Rm3ZPV51mTPLf1nIw1rHC6KTfD1Lb9TgC4t82x+Ia19jVjTAalm7aVG7des8yz8Nq1w4lu3TTMkfcX3x4At7U1qgCplyfvr+JjAB6x1r7mrfqI13y8Ks8SWOtYs9auajaz1k4ZY4BSASt3FhB7PjaJv+UmQeHn4hXv31TN/Os6Fq1xrLb9nIxsc2LVwVOvnfdKnXkucQVVXl6b9UmUDjax91BqaSFH/9L7/8fGGOs1swGlJqh0nW1E0yBP5ebh2manCcjqnXjDx5ry87GC9HPRXmvarM3DCErPiwFo6NH2czKyRczjNQBfq5k3iqpkScYYs9MYs79mXgrXHnouT58G8FWJJ00LNMrRD1D6i6/8ucdbfqjq/07QJE/lv4BreyeOoqordNg0cvA6Hx2smVfutFG+ihF7PrYYf9N9wI1z8X0Aj5QnvKurFDzPJh5tPyej0pyY8v7tx+qb4T8AcM4Y8zFKvyieRek5EDEjNDdhCsBJY8zXUPqr6ApKN1mnqhx+hdIvh+ruswCQEX4ilWmWo0x5xaqb0bXPH0ki5f1beyw2zJMx5mUAx4wxh1DK+1HIO1bXdPD+PWaMuQ3A/46S/zEAr1X99S/5fOxH8/jLpKq2yVTNl3Yuprx/a+N8BqUrp9MoHWv/HqWOG+UYm3lU9tWWczKsm4rt+KB0M/y6YWJQdTMcpfbb8pPrq0a/cOGD0oF4zHNIVzvg2o3nep+jYca9TseWcuT9LER27Gh0LLaaJ1y7iZ5u9HMIya+pA64Nl5X2PK47BiWfj83i9yPHYR+LNeuc81yPrSfPNd8V+DnJV7EQQghxlqjfEyOEEOIwLGKEEEKchUWMEEKIs7CIEUIIcRYWMUIIIc7CIkZUYIw56I0QkAo7Fj8wxowYY057TmIeaCZEGlF52JkQ1ziN0gOlh7D26OGERB4WMUKE4Y2CMGLrv3yQEFIFmxMJ8TDG7KkasDTM/fo6qG9QXoRIgEWMEEKIs7CIkcjgXZGc8zpLpI0xh6uWnca1kf+t99lftfyot03aGHO0av5J73PYGPPxevdbJ8ajNet/3EIMN+RVjr3m+2u9Txpjjnnfcbpm32vFc9ibZ72fyWEQEhC8J0aixAhKI3RPef8/bYyZsta+Zq39mvfL+6S11lRv5P2i34lrr6c4Zoz52F4bXX0/gF+j9BqRQwCOGmNes9ZONdpvPay1zxhjfl27fpMYbshrHewB8Kfed/xNo3hwbYT9e1AazXwE7JhCgiTskaH54cePD66NzJ1axzarXrkO79XsNeuUR+2uHuF7P4BzVftIVy1LoWbU7nr7bRLXqvWbxXAjXvXW8+ZZAPtr1ln1uvpG8Xj/pteTB3742ciHV2IkUnhNW/8SpWIzguYvNCy/+PCcMWteyFRe226tzXjrpTYS53pjuAGv9fC+Xf0uqEbxvI3Se+3Sxpi3AZy21j7vYyyErIL3xEhk8Jq7vgbgX1lrbwPQ6ssWM9ZaU/upXu53rOuJYQNerVLvZYZ147HWZrwYHkHp53K09r4bIX7CIkYiQfnZKwCH7LU38bbSlf1tAKmaN9i2mzVj2IBXmVTVvlJrr9ZaPGVs6X7cIygV1zU7shCyUVjEiDZGvCGbqj+pql/wz3jz9uP6X65TAGCM2e/1xtvjNaO9DOCkN2/EW76eoaCu2+96hBrFcKNe3vwrqPp5ofQa+g3F4/17zBiz0ytyh1D/So4QX2ARI9o4h9Kr46s/f+ote8b7/zmUrhBW3Tfyfjm/j1JnhqNV8w8BOOZ9PgbwrPf/llhrv+uhSQw35OVt3+/t7xiAH/gQz/vePn/lxTOCaz0YCfEdYy0f5CeEEOImvBIjhBDiLCxihBBCnIVFjBBCiLOwiBFCCHEWFjFCCCHOwiJGCCHEWVjECCGEOAuLGCGEEGdhESOEEOIsLGKEEEKc5f8HYsk3jAb9c+MAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 504x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores(CONFIGS_BY_LATENT_FEATURES, scores, metric=\"tnr95\", savefile=\"plots/approach4/boxplot_kde_latentfeatures_tnr95.pdf\", figsize=(7, 8), label_below=[0, 1], xlabel=\"Latent features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Denoising autoencoders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions256_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:02<00:00,  3.57it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions256_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:02<00:00,  3.43it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions256_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:03<00:00,  2.79it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions256_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:03<00:00,  2.98it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions256_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:03<00:00,  2.81it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions256_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:03<00:00,  2.72it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions256_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:03<00:00,  2.52it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions256_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:03<00:00,  2.63it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions256_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:04<00:00,  2.16it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions256_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:06<00:00,  1.65it/s]\n"
     ]
    }
   ],
   "source": [
    "denoising_and_sparse_scores = [get_scores(**config) for config in CONFIGS_DENOISING_AND_SPARSE]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/9hh++pP/DpH2PWhsac95brfk3OrxdPni69ni07TikqiSOjo6NtSjJulF83N7HR0dASdM6+Wc7hmN5t4fja4uK2RoyBpu5/zpcPIk8I1vLCAWs3qI3M/tduHC6v8nJlYndujrYwFF9aNcT1LRggeFC5lSErDOUSr3nJXcZkWMj4/n9BzFYjHEYjGMl+jjjsfj6M7/ygfA7Ows4vE44vE4elx93F12H/ezdh/36Ogooq5PQ5OKI8f0a9/A7k88gp033IetO/fhls98G5feOZv9Q77wOo/h+gNfxfY9BxFp34N9930HSx/8d1x86zkAwNtnv4LrD34t+5w33vObeO/cM9k/6EPN23DLfd/G1p37EGnfg+sPfg2XL54ruc1qVKhAWUkV7/X56K0P4KO3PoCXn7wZf3qiDfFXv47I1msBrPYguYfrLacSWExM5xRely+ew659D1RdcVnrf7Dyj9bq40zJ7NfPlStXfN2e39fkocopVKCUKlr6+61zjjo7rXORTpwAfuqnmgCs9iC5/5RJJKyCyl04TU1ZzzE5aS03cFBLRUQikaCbYCSTcytXJHVgdfIEtwSA7YVW0FpPAdleKLeY/VPuOde9TSmvf3wB1vA3933xeBwDAwMl19m/f/+a+wYGBtDZ2VlyeaHnc85p6uzsNOYPr4qdW7PN+7k1++77Tk4vTP4QNFMU6vUJNW8r+vh3X38a777+NG6579u4/QuvYPvuA/jhc324fPEcGpujuO6Gz+Dtl6yepuVUAm8+/0UA1pA7APjovgfR/Ut/iI/e+gAi7Xvw8bu/hcsXz+HSdPHXyi+1/gcr/2glIq8K9frk9y65jYxYM9WNjlpFTk8PcO+9zZiastbr7V3taUokrNntgNUCamDAmj2vv9+6b3jYKppKnQdFVCs262KyRwA8CmAQAJQ1TG7TppxT1kQP/QCwa9cupFIpLC0tYWnJmt64tbUVKysr2bnaW1tbkclkMDc3h/fffz9b5SaTSQBAS0sLGhoacOXKFfzFX/wFent7kUwmceWKNR1yU1MTPv/5z2NlZQVPP/00mpqaEA6HsbCwAK01Ghsb8Z3vfAe/8Ru/gdHRUfyDf/AP8Fu/9VuIRqO4//778cYbb+DOO+/E66+/jl27dqGhoQGRSAR79+7Fpz71KXzjG99AW1sbfvKTn+CLX/wivv/97yMWi+F73/tetq3F9skZ+xmJRNDa2oqZmZk1+wQA4XAYoVAoZ5+ampqwsLCQvZ2/T+FwOJtRIpFAc3MzkskkMplMdh9SqRRWVlaweOlN68Vp7cTMzAyUUmhra0NT2y78j3deRctH/wna2trWvE5bwtvwk//+X7EU2pXdp6XkLBq27cPcT96yXqer12BpZia7T03XfAzzs3+H6AcfrNmnC//53yOy4yYkr16DlcuXi+5TJpNBKBRCc7N1caGZmZk1++TcTqfTWF5ezu5TOp3O7kOhfXK/TouLi2teJ/exdxX29n/yY0SuXpN9nT6Y+W+I7LgZly9fXvM6NegUfvTCw4jd9R/Q8qE70RQO42Of/hbev/AiLvx/v429//hxfOzT38Lc396Bl5+8Gc3bdmPXz/1LzMT/HLrxp5BKpRDq6MLy8jIuXbqEtrY2rGyxvo/4+/h/RuuHbvf0fnJyK/V+khx7AJBKpda8TsWOvY2+TvPz8/jwhz9c8nWq5PvJyc69j5tx7BX6jKj0PgFWb7kfr9Nm7pNzLBf7jKj0PrnfN3ydqvd12sx9ulzm91NjYxjANfjxj+exdetydh/+5m9CuOWWZczOLq7Zp5WVNgwMhHHq1GUcOLCEtrY2nDq1hD/7s1Y8/vgSnnwSePrpFfzsz0bQ2bkFe/Zk8NWvLuHMmSZcd90VJJNb8PM/b+3TzIy1Tx/6UAOAFrz88hX83M9l6up1mp+fx7XXXuvp7whT9qnYZ0Ql92l+fh4f+chHqnKfyvFSJBXqjogCuFRsBa31mFIqrlbPIZqA1Ys05fE517VNbU3yMAJYU4A3NzcX3PlCV5h2z92e3yXY0tKCbdusb+/T6XTOY7ds2YIPPvgg5z7nwwAAfv3Xfx1XrlzBbbfdZjU+GsXv/M7vYMeOHbj1Vuuk/L/6q7/Cvn37AFhFxzvvvIPu7m5s3279gbp161Z86EMfwm233YZ4PI633347Z4heoX1qbW0tun/OPpW6nZ+be5/cy52hgPmPd25f+sD6w2/7T30sd3nbdgCL2Xblr/8zB7+G6df+PT7yDz+JlpYdmH7tMez+1BA+EutandQg0oBIe+5roZZ+ks0ipNJ48/kv4tI7LyES3Y1/9M/+JKd3qdg+ublzK7aP63k+p21O3vmvU/bYs7fbGtbY7mrD0sJ72N3dj61bt67ZxqVp66zdj/zDTyJiH6/Nzc1o/alboDJX4LwfDn7xR1hOJdDYHMVyKoH/9me/io/d+Iso9H5x9qm1tbXgcvc+uZV7P5W6XSrXQm1Y7+2Kvk5F9kH6fnL+v5n75NjsfcrvwTZxn4D1fQZsdJ8ikciaz2q+TtX3OgGbt0/OZ3uxNnz609btq1e3wX2o/PjHwD//5yF0dLSsabPT23PgwNbsOs3Nzbj11iUkky1wmjI9bfUiRaMNSCSa8cu/DHzmM9fA2ZVCw6VaW1vhRMHXyex98tqGjeyT178j/N6ncsoNt5uAVZzk68BqwVOQ1npKaz1u/yTsdSY8PKd4m5up0EUyoyX6uEdGRjA8PIzR0VFMTk6ip6cHvb29mJqaQjQaRW9vLwYHBxGPx5FIJHDE7uOOuaaZiUajGB4expkzZzA0NJSd8c4kFTm35hrrw9XruTWANZ31tp23Yjk1j8t/X3xWuGq0dec+XHxrdWruyxfPYSWVyBlmmPN4eyY8ZxY7R3LmDWx3DXcEVieEiL/6dWzfbZ33tZxK4N3Xn855nDPMrtg2a1m1Xs+BahePOfKiq8saNueYmrKKm7zvTrOc06LzT2n+wQ8akT9Br/PnzIkT1vM5M9yNjOQ+zim8im2zlvF9KmNybiWLJLu4iRc4vyiqtS46IlUp1Zt3uwfAuNY6Ue45pdvcLM63bfnXKIrH40XP+UkkEhgYGMiZvc6ZiGHYvoz16OgoOjo60NnZif379+P+++9HNBrNnq+Ur7e3N2d9U1Tk3JrvfdbzuTWAVQjceM8T6D78h7j+4Fcx8d17s71QJrj+wNfw3rlncPGt53D54jm8+fwXswUNYF1cdvq1x7KPb2yOYte+L+DNFx7GpWlrYoxzz30Ojc3t2YvLXnzrOVx86zkk56bx12e/jPfOPYOP3/0tANZr9KMXHsZfn/1y9jykN194GNfd8JnsOV/1pKHBy5URiCqHxxx5MTRkFS1jY1aBdOTIakEDWMXQyZOrj49GVyduGB+3lvf1Ae3tOntx2bEx6ycet85Ncs5hAqzznwYGrPud85AGBqzzmLpyTx2uC3yfypicm5eWD8E6vwgAoJTqAjDuuh1TSo3mFTWPKqXcM+8PwD4/yctzeljuG2fWufyepHg8jv379xdcZ2JiImddxy233JLzPBcuXMDc3BwuXLiAnp4eJBKJNcPp3Do6Ogr2aFUjZ9rp/OvwLCbeQWNze8F1llMJ/OiFh3H9wa9mZ6/bd993sKVpK959/RQAZ2KGdrz85M149ZlPYufef4pQcxSRaOELn+684T6EmqPZ9U2wfc9BfPzub2H61a/jv5z+RWzdeSu6D/9hdvmld87i7bNfzlnnxnuewEdvfQBvvvAwXv2PnwQA/Mz/8v9klzeGo5h+9et4+cmb8cHFc7j98y9ni65I+x588lffwGJiGv/l9C9mC6R9933Hh72tPs4YZyK/8JgjL3p6rALmxAlrhrnubmtSBcf4uFXQuA0PW4XNwIA1jTcAnDmz+uVlNGo9X2enVQhNTq4WXbGYNRV4PG5tzymQ3L1Z9YTvUxmTcyt7TpLWekQp1W/3BkUBxLTWA66HRGFd6LUDq1N3DwJwrnm0HcCgti4I6+k5PWzTV05PkFPATE1NlSxonKm/4/F4zvC5c+fO4aT7ax6sDtk7ceJE9ppKzvWQentXO+QSiQTi8TgG8z8Bq5TTA5Hfk5RMvINd+x4ouI4zLG7rdfty7o9c+7M5w+k+9Wtv5JxbU2ooGmANvSt1jaFq5Aw7XM+yPZ94BHs+8Uj2tjNpB2AVXrfveaXo9pzp1onq3fkzR3HyV5rxV797t2/bXF5eRtyeUMEPA7/Q5Nu2qLL6+5HtBfK67OhR68cxM7N68eaentIXo43F6rcoIvI0u509MUKxZVMA2vPuK9vrU+o5vSz3k3M+0KFDhxCLxXDkyJFsQQNYxdDY2BiO2p9C0WgU/f39GBgYwPDwMGKxGAYHB9He3o5++xPMuahsV1cXhoeHMTIygkn7kyoWi6Gvrw9nzpxBX18fOjo6MDg4iFgsll3fBM65Ndv3WAXMes6tcXo5AOvcmo/e/LWcxxY6t+byxXNIJqax84b7so9zzllyFw/1Iv+ERRMF8Qfr1ZWriIe2+LY9oDr+aL341nN4++xXsJyaw84b7sON9zxRdp13X38a0689huXUHLbvPoDW5tLLb7znN9dcKJmABmXucBQyTy38bggCc5MxObfNmgK8pvT09GB4eBgnTpxAPB7HZz/72Zxzg8bHxzE4OJgtkgBgeHgYJ0+exMDAAGZnZ9HT04O//Mu/zC6PRqMYHBzE1NQUenp6MDk5mS26otEoJicnMTg4mLP+ZKmve6rQ9Qe+honv3ovte+5EJLqn4Lk1P/nr72ULGPe5NTfe/S20RHfj7Ze+vObcGsDqbXr33NN479wzuP3zLwOwJnY499wv49L0n2PnDf8rGlva8fbZL6Mluqdor0wtC4X49pZwrllmuo6ODs/XX7q1swEnHmzBv/m9FC7OavyL+34H/+/o7+BfPZMqus49t4XwwN1N+Le/l8Lfz2o8ePcYnvj1lmx+hZa//p//AA98fTG7vUKm/uZqye26tbe3V3wI8t5DJ/Hx//nfQz/1gqfHV6K47DzwG7imfWfOc06/+nUkE+94fs71+LnPKTxZ0WckqS99CQDO4sAB/7aZyUTg72kij5V/iAH4O1XG5NyUcx2SWtHd3a2dc4LKUUrBz/2fmZlZM83rZtqs/VvP8777+tN479zTBX/Zv/v60/jRCw/jHz+6kLPO9GuP4d3Xn87+AbHzH/3f2PkRa0KLS9Nn8fZL1gQD23db5+64e52WUwm8ffbLuPTOS+Jvrzcjt/NnjuKPnn0Cnz7w6Yo+bynLy8vZa6L44enTZ/HkH6Ur/ry1/j4FNmcf1/OcE6fvxTU79+FnDlo9tsm5abz85M345K++kfP+cvv+b9+MPZ94JOcLiD890YZb7vs2dt5wX9nlhSZTefP5L2Ln3n/quec36M+4S9NnMfHde3HLfd/OfhHU2Nyecw5hvndffxpvv/QV7PvMt7NfBM397RQOfvFHAKwC6QfP/XLOc27deStuvOeJ7PYK2b77YMntSvav2tXCfnzpS8Djj7+ET3/6gG/b9Pt3w1/8xTeh9Zd8295mCeJ3Qy2o5tyUUpNa6+5iy80t78gIfp9b48xsR/Vro9/st33o57HtMyM5hbWXb/anX3sMF8//JyQT72Bb3mQb1e7SO2ex5xP/e/Z2pH0PWqJ7cOmds0WLpMXENLZ9KHdymqZrPpY9D7HQ8pbonuzy/JkTnV5ik4bGTr/2Dez+xCPZIb63fObbePnJm5Gcmy6a2/Rrj+H6A1/NDkPed9938Kcn2nDxreew84b78PbZr+D6g1/LPueN9/wmXn3mDlx/8GvW598X1n7+vfn8F7F9z51r7qfq981vAo8/fhAvveTnF0HzPn9h+wiAL/m2PaJKYZHko6am4M87MFEt5LbeITyVcPny5eyF7/xQDUN4Lk2fXfMt/MTpe9f1zf6bZx7Fq898Ep/6tTcAFP5m/83nH8oplN58/iFcfOs53HjPE9h63b6ca3lVu+xFmu1zAh2R6G5cvlj8GmOh5iiSiemcYufq0nz2nMNCy63e3bXnJFrT+T+E2w7/kXxHAlCp4jK89aexvDiH5Nw0FhPTORk5+V08/wf46K0P1ERxScGqhd+pQWBuMibnVrdF0pesgcA44ONA4EwmY/R88UAwJ9L7nVs1nERfCSZ/MElV4pv9m//Jf8RfPLbD0zf7jc1RXJo+a50b94VXsn/AFttWNXJ6dvKHpIaat5WcFfL6A1/F22e/gq3X7UOkfQ+mX3sMP9PzWHbfCy2/8Z4nCmYTf/Xr2LbzVqOuy1XR4jJtTWjjXBDauXC2oyW6Z83lFABzi0sKVj3+bqgE5iZjcm51WyQF4erVq8YXSUFgbjILCwtobm4u/8AaUolv9hcWFrLDwrx8sz/92jewffdBo/7AL8SZVt9tJbX2D3PHR299ACvpebz85M0ArD/+99z5myWX7/vMtwtu953XHkP3L5kzPBGobHH5sTtOINK+WggtL86ted7kXHzNc1VbcbmeyUIqxc+JVjZjopAg1OPvhkpgbjIm51a3RdI3v/lNPP7443jppZd822YQEzdUWhDDxvzOrRqGjdH6VfKbfWdYmJdv9i+9cxYfv/tbG25/0Ar9YR5q3lb08e++/jTeff3p7DDE+Ku/gb/5k89hx3VWj1qh5RPfvTenxw0A3jv3DELN0WxPnmkqWVw6x5m7qHcuY7CcumXNdqutuJybm6vpSVZqZeZLIvKGX8/7yOQuxyAxN5l6y22j3+wn56YBAO//8KnssLDGlvac53ZLzsWz6zS2tOPN5x/Ci4/twvd/+2ZcmjbnnCRnH/OHcy0m3kFjc3uhVbCcSuBHLzyM6w9+FTtvuA9bd+7Dvvu+gy3hbXj39VNFl4eao3j39VM5z3Vp+s+xffeBTdk3PxQ6NrwWl7d/4RVs330Af/Mnn8Pli+fQ2BzFdTd8Bm+/ZB2P1nC6LwKwCnM304vLSqi3z7hKYW4yzE3G5NxYJPnI5AtqBYm5ydRrbsupxJr7yn2z/9FbH8DLT96MPz3Rhr+b+k00hqMAcr/Zdz+/9c3+XLan6e2zX8H2PXfi9s+/jO27D2Diu/cWbEc1cnp18v/YTybewdadtxZYA7j891bP3Nbr9uU+13X7sJyaK7p8285b1xSsl945i+27zZuZrZLFZcguLgFrtrvG5na8/OTNePWZT2Ln3n+KUHMUkbwiyfTishLq9TNuo5ibDHOTMTk3Fkk+WlhYKP8gWoO5ydRrbhv9Zv+aD/9PmPjuvZ6+2Xf+UN5jTxYRad+DG+95AqHmKN4798zm7OAm2LpzHy6+9QfZ25cvnsNKKlFwJjpgdUijUySurvc6tu++s+jyeXu5ezsA1sz2ZoJKFpctO27OKR4/9Wtv4M5H3sOnfu0NbN99sOBrYWpxWUn1+hm3UcxNhrnJmJwbiyQfmX7Ru6AwN5l6y61S3+zHev5DzrCwUt/sOz1N+X/kb9t5K+b/frLCe7h5rj/wNbx37hlcfOs5XL54zrruzu6D2fNiknPTmH7tsezjG5uj2LXvC3jzhYdxafosknPTOPfc57AlHMVHb32g6PLG5vaca6M5BUb+OV+mqFRxmXz/v64peJxho/FXv57zWjjbAcwsLiup3j7jKoW5yTA3GZNzq9uJG4Lg5xWuawlzk6m33Ep9s79rX+ELGhf6Zr+xsXHNsLBP/dob2RP0l1OJ7B/Czh+yhXqvihVm1Wj7noP4+N3fKnrB3EvvnMXbZ7+ccy2eG+95AtOvPYY3X3jYnujiAH72sy+UXH77F17O2W4yYZ/TlXcemSmuP/A1THz3Xmzfc2f2Glr5xeVP/vp72dzcxeONd38LLdHdePulLyPkKh6d6x5tvW4f3j33tDW9/OdzczO9uKyUevuMqxTmJsPcZEzOjUWSj0welxkk5iZTj7k53+w7J7Ov55t95w/bcDiM+Yuv4/oDX815bLFv9q+74TOYeefPc06gn7/4Oq6zr6tkCufcrPUs2/OJR3IKp1QqVXL5erZpgkoVl93/7M+zyxvDUbz90pdx+eI5bN99ELd//uU109dXY3FpXXrwLHy89CAymWvg79UhHiv/EAPU4++GSmBuMibnxiLJR8lk0ti54oPE3GTqMbdKfLP/wz8bzBkWVu6b/djt/ydefeYORKJ7sO1DXYi/+htrhpXVi3o85ipRXLqvvbN9z0HcvucV8TbrSSbDa+hJ1OP7tBKYm4zJubFI8lEmkwm6CRVTy9eLaG83Z5hUKbV0vHlViW/22z708znDwsp9s7915z50/9If4s0XHsZiYhrX3fCZNcPK/Hb+zFGc/JVm/NXv3u3rdpeXl30dWjHwC+ZOLetWC+/Vb34TePzxg3jpJW/nH4yNAYODwOws8NnPAsPD5dcZGQGGhqx1enqAf/fvLqOzc3vR5U89BUSjuds8cQKIx71v002pRwB8aX0rVaFaON6CwNxkTM6t7osk/rG/ftKT8ILI2uQTBjcqFKrPt/dGv9lPJHIvDurlm/3tew7iU7/2hrzRNaKWP083U729V8fHgb4+YHQUiMWAI0eAQ4eAM2eKrzMyYhVVzjqDg0BPTxTT08WX798PXLhgLR8bW7vNgYH1F0q1oN6Ot0phbjIm56Zq7Y/I7u5uPTExEXQzClJK1fUf7VKpVMrYrlo3v19/v3PbrP2r9dyAzdnHID5veMzJ1Ftuhw4BXV1Wrw9g9ex0dloFTSxWeJ3OTqvw6e93b88qenp7vS0fGACOHrWWTU1ZRdTcXG5vUyX2b714vJmhVv4W8Vs156aUmtRadxdbzsG8VPWSyWTQTTASc5NhbnLMTqbechsftwolRyxm/YyPF18nHge68/6U2b37KpzTuQotj8WsoXfxuPXT07O6rMuePf3ZZ+X7Yap6O94qhbnJmJybuX1gVDdMHs8aJOYmw9zkaim7Wh46GOR5l1NT1r+FCprJEpcWi0atQqfLdWmouTmVLXwKLXfOTYrHV7eRv81EQrAThqul96mfmJuMybmxJ4mqHmcvkmFuMsxNrlay01r7+uP3Nt2z6fnN2XT+ELdodHVZIUND1nA6p+A5eRL45jevZAufQsufesoqhDo6crft5pyzVE9q5X3qN+YmY3Ju7EmiqheJRIJugpGYmwxzk2N25FUisbZQKtWr099vLe/stG5Ho8Dv/m645PLRUev/TiE1Pr56zlIiYRVUAdaLgeH7VIa5yZicm7nlHdWN/AtUmkwp5dtPS0uLr9urlanTa+l48xuzI68KFSelJlAYGbFmohsdtYbl9fQAv/ALTdnhe4WWHzpkDe+LRq3JG5yepkTCmt0OKD5RRC3j+1SGucmYnBuLJKp6KysrQTehIjiExwy1crwFgdlROc7Qt/xeo3h8dVm+RMKamW5oyCp2urqsYmjbtgyGh4svj0ZXp/geHbWev7PTmtXu/vut5U7PUz3h+1SGucmYnBuH21HVM3k8K5mHx5scs6NynIkV8r9TicetQqcQ56oe7kkZAGDfvquYnW0oury7O3c7Fy6sDvNLJKwf94x39YLvUxnmJmNybua2nOqGyeNZyTw83uSYHXnh9PQ4pqZKFyzOTHjOpAyOH/wghEOHii+fmMidahxYHdJ34oS1vXocbsf3qQxzkzE5N/YkUdVLp9NVeyEy8kctT8cMBDslcyXxvUpeDA1ZxcuhQ1aRcuRIbsESjwNjY6sXfo1GrQkXBgas4XOxmHV+UTSaQX//FgCFl3d0rE7UMDZm/dvVZT1mZKT0lOO1jO9TGeYmY3Ju7Emiqre8vBx0EyhAtX4uVy2dz8X3KnnR02MVKidOAHfdZfUEnTmzunx83Cpy3IaHrSJoYMA6p8h6XKLkcncRFI1a2+vstHquJifrsxcJ4PtUirnJmJybcv4oqBXd3d16whmgXGWUUqi1vP1w6dIlbN++PehmGIfHm0yt5BbEfvj9XuVrVV383o9aOd7qoae8Fr4I4t8iMtWcm1JqUmvdXWw5h9sJbOQDTbpuLfwClWprawu6CUTkQb2/V/m7wV+1crz5/RrWSlHut1o53vxmcm4cbicgHVIzPz+/oSFA9SqdTgfdBCLyoN7fq/zd4K96P97IXzzeZEzOrSp7kpRSMQAxrfV40G2ppKWlpaCbYCTmRmQGvldlaim3Wh46VisTrJBMLb1P/WRybp6KJKVUPwBnQGlMa33S4zqOTgAntNYJe9kZAD0AEnmrzWqtOwF0AXhKKRW1HzMBYFBrPeWlvUREVNt/sAL8o7Xa+N2zNTMzgx07dvi6TSKqH2WLJKfY0VqP2bdjSqlhrXWRy74BSqmjAEacosi+bxRAn31zCsAAVgsvAIjZP7C3166Uirqfw3Qmj8sMEnMjWr8ghmLxXAcZfsbJMDfyE483GZNz83JO0oDWesS5obWOw+oFKuW2AsVNXCkVtXuHfl9rHddaJ5wfAN1OIebaVv5zGM3kLscgMTeSUEqJfja6LtF68TNOhrmRn3i8yZicW8kiyS5ougosSiilShVKMaVU/npRV1GUM2xOKdUL4FkvDTaZyQdKkJgbSUhPhH///fd5Ej35ip9xMsyN/MTjTcbk3Mr1JMWw9rwhwBomV6h4chwB8KI97A52QTVc4vEd+b1GSqke18/RMu0kIqIKkPaisReOiIhqSblzkjqQe96QIwGg6JWhtNZTSqn9ACaVUo8C6Cs26YJ9zlN+L9KU/Txx+zFxpdQZrfWhEs/RDwC7du1CKpXC0tJStnptbW3FyspKdhrC1tZWZDIZLC4uAgAikQgAIJlMAgBaWlrQ0NCAK1euAADC4TBCoVD2dlNTE5qamrCwsJC9HQ6HsbCwAK01GhsbEQ6HkUwmkclkEAqF0NzcjOXlZczMzKChoQGRSASpVAorKyvZ2+l0GsvLy1BKoa2tDel0OrsPbW1tVblPzu3N3Kd0Oo2ZmZma2ic/XicASCQSNbVPfrxO6XQai4uLNbVP63md3n//ffE+aa2RyWRE+3Tp0qW6PfYAaxKCWtonP16npaUlzMzM1NQ++fE6OcdbLe2TH69TOp3G5cuXa2qf/Hid0ul01f5dXo4qNUTE6QGyZ5xz3z8KIK61HiyyXgxAL4ARAI8COIq8c5tcjy05CYTrcRdQothydHd364mJiXJPF4gPPvgA11xzTdDNMA5zk+FJ9DI83uSYnQxzk2FuMvzdIMPjTaaac1NKTWqtu4st9zJxQ0eB+6IALpVYZ1BrfdI+/2gQwH4AQ/nnMdnnIiU8tAEA4gCK7ogJTL6gVpCYG/mJx5scs5NhbjLMjfzE403G5NzKFUkTsAqifB2wh8TlswuhM+777N6fPgD5w+XuR16xZU8xXuwrjkJD/4iIiIiIiCqmZJFkT6YQt2e5c4tqrcfXua0JrO196oLVQ+Q2C+saSvm6UaQwM4Uz9pzWh7mRn3i8yTE7GeYmw9zITzzeZEzOzctwuyFY5xUBAOypvcddt2NKqVGnkLKLp/sLPE8/rHOU3NbMnlfo2kjO5A7ORA6mymQyQTfBSMyN/MTjTY7ZyTA3GeZGfuLxJmNybuVmt4PWekQp1W8Po4sCiOVNtBCFdXHZDqwWPEeUUkOweo4S9mPGCl1gFmt7kpxtHnWtCy+TO1S7xcVFoyvqoDA38hOPNzlmJ8PcZJgb+YnHm4zJuZUtkgCraCmxbApAe959CQAFZ77Le1xniWUnvbSNiIiIiIiokrwMt6MKceZ9p/VhbuQnHm9yzE6GuckwN/ITjzcZk3NjkUREREREROTCIslHzpWDaX2YG/mJx5scs5NhbjLMjfzE403G5Nw8nZNERERERGQ6pZTv29S62OU/qZqxSPJRS0tL0E0wEnMjP/F4k2N2MsxNhrmRhLRgUUqx2BEw+X3K4XY+amhg3BLMjfzE402O2ckwNxnmRlT9TH6fmttyA125ciXoJhiJuZGfeLzJMTsZ5ibD3Iiqn8nvUxZJRERERERELiySfBQOh4NugpGYG/mJx5scs5NhbjLMjaj6mfw+ZZHko1CI82RIMDfyE483OWYnw9xkmBtR9TP5fcoiyUcmj8sMEnMjP/F4k2N2MsxNhrkRVT+T36cskoiIiIiIiFzM7QMzUFNTU9BNMFK957aRC99J163na0HU+/G2EcxOhrnJMDei6mfy+5RFko9MPlCCVO+5SQuWVCqF5ubmCrem9tX78bYRzE6GuckwN6LqZ/L7lMPtfLSwsBB0E4zE3GSYmwxzk2N2MsxNhrkRVT+T36cskoiIiIiIiFxYJPnI5C7HIDE3GeYmw9zkmJ0Mc5NhbkTVz+T3KYskH5l8Qa0gMTcZ5ibD3OSYnQxzk2FuRNXP5PcpiyQfmTwuM0jMTYa5yTA3OWa3PqdPn8ZNN92ESCSCm266CadPnw66SUbh8UZU/Ux+n7JI8lE9T6u8EcxNhrnJMDc5Zufd6dOncezYMTzxxBN477338MQTT+DYsWMslNaBxxt5NTYGdHYC7e3AwIC3dUZGVtfp6wOAbZu+zVpk8vuURZKPGhsbg26CkZibDHOTYW5yzM6748eP49SpUzh48CAikQgOHjyIU6dO4fjx40E3zRg83siL8XGryBkaAl58EZiYAA4dKr3OyAgwOAgMDwOTk8692f+grw9Qau3PRrZZq0x+nyqTK7xCuru79cTERNDNyHH69GkcP34c58+fx969e3Hs2DEcPnw46GYZg9f7kWFuMsxNjtl5t2XLFqRSKTQ2NmZzW15eRnNzM65evRp084zA401GKWX0t/uOjo4OzM3NeXjknwGYAvAv7dt7AMQBxABMF1nnvwEYAvCU6z4NoBfAfwLwrH3fYN56zvNJtpmrvb0ds7Oznh5bzar5faqUmtRadxdbzp6kTeYeUvF3f/d3HFIhkEwmg26CkZibDHOTY3be7d27F6+88gqA1dxeeeUV7N27N8hmGYXHW32bm5uD1rrsD3AIZ84Muu6LIxYDhofjJdbpxOTkSM59u3dfxfDwGLTW6O3tQ39/H7SO5/3It5n/460ArH4mv09ZJG0y95CKLVu2cEiFQCaTCboJRmJuMsxNjtl5d+zYMTz44IM4e/Ys0uk0zp49iwcffBDHjh0LumnG4PFG5UxNWf925/UVxGLuYXRrRaNAPJ5739ycQk/P5m2zVpn8Pg0F3YBad/78edxxxx0AgFDIivuOO+7A+fPng2yWUZzcaH2Ymwxzk2N23jlDrh966KHsUOzjx49zKPY68HijcpzRatFo7v3R6OqyQoaGrHOSurqs4ubkSeDxxxcRi7VmHzMxAezfbxVTPT3AU0/lPu96t1mrTH6fsidpk7mHVDhjMjmkYn2qdSxrtWNuMsxNjtmtz+HDh/HDH/4QV65cwQ9/+EMWSOvE4428SiS83efo77dmpOvstCZkOHECuPba3AkIpqasx0xOWs91110b22atMvl9yiJpk7mHVMzPz3NIhYDJ41mDxNxkmJscs5NhbjLMjbwq1IOT39PjNjJizWw3OmoVQT09wC/8QlN2KN3AAHDmjFVMWecaWUXT+Lh8m7XK5PepuX1ghuCQio0zeTxrkJibDHOTY3YyzE2GuVE5HR3Wv/k9OPH42nOGHImEVQSNjgK9vdZ9o6NANJrB8HADhoex5tykWMz6d2pqddl6tlnLTH6fskjyweHDh3H48GHMzs6iw3nHkmcNDezwlGBuMsxNjtnJMDcZ5kbldHVZ/+b36sTjxS/w6lxFxlnXsW/fVczOlj/mJNusZSa/T81tuYEikUjQTTASc5NhbjLMTY7ZyTA3GeZGXnR1WT1Bjqkpq5en2Ex1Tm9P/ux2P/hBCIcOWeuOjOQuc4bZOc+53m3WMpPfp4EUSUqpmFKq7g6VVCoVdBOMxNxkmJsMc5NjdjLMTYa5kRdDQ1ZRMzZmFStHjljFijNELh63Zq9zRKOrEzeMj1vL+/qs4Xb9/VYP0cCANfudcx7SwIA1NM/pRSq3zXpi8vvUU5GklOpXSvXaP0fXsY7zM6SUiroWdwEYVUpppdScUuqMUqqrwPrr2ma1W1lZCboJRjl9+jRuuukmbN++HTfddBMvwLtOPN5kmJscs5NhbjLMjbzo6bEmVjhxwpqBrrvbmnTBMT5uFTxuw8NW4TMwYE3zbT0uAcAqdC5csIqnu+5aLZDcPUfltllPTH6fKuvKwCUeoFQ/AGitR+zbMQCDWuuiIyvtomZEa51w3Teqte6z/9+rtR5TSkXdj9nINh3d3d16whlQWmV4TpJ3p0+fxrFjx3Dq1Cl8/OMfx49+9CM8+OCDnPRiHXi8yTA3OWYnw9xkmJuMUgrl/vYzgd/74ffxViuvUzW/T5VSk1rrotNpeOlJGnCKFQDQWscBlBsqd1uB4iee15uEQgXSBrZZ9Uwel+m348eP49SpUzh48CC2bduGgwcP4tSpUzh+/HjQTTMGjzcZ5ibH7GSYmwxzIz/xeJMxObeSRZJd1HQVWJQoc05RLH/4HICCvUYV3GbVS6fTQTfBGOfPn8cdd9wBYDW3O+64A+fPnw+yWUbh8SbD3OSYnQxzk2Fu5CcebzIm51ZuCvAYgESB+2dhFTLjBZYBwBEALyqlTmitT9rFzbD7AXkFT5fW2jltTrrNqre8vBx0E4yxd+9evPLKKzh48GA2t1deeQV79+4NuGXm4PEmw9zkmJ0Mc5NhbvXrS18CgLM4cMC/bS4vR9DY6N/2gMf83NimMfl9Wq5I6oBVnORLANhebCWt9ZRSaj+ASaXUowD6tNZTrodM2Y+LA4BSKq6UOqO1PiTZpn0OUz8A7Nq1C6lUCktLS1haWgIAtLa2YmVlJVvNtra2IpPJYHFxEcBqV6BzVeCWlhY0NDTgypUrAIBwOIxQKJS93dTUhKamJiwsLGRvh8NhLCwsQGuNxsZGhMNhJJNJZDIZhEIhNDc3Zx/f0NCASCSCVCqFlZWV7O10Oo3l5WUopdDW1oZ0Op3dh7a2tqrcJ+d2pffp4YcfxgMPPIDf+q3fQiwWwyuvvIJHHnkE//pf/2vMzMwYuU9+v07z8/M1t09+vE7z8/M1t09+vU7Ly8uYnZ2tqX3y43VaXFzMfq7Vyj758TpdvnwZAGpqn/x4nQBgZmbG6H1aXLSqlUwmA60zyGSsc3e2bGmA1jrv9uoFTbdssQZQXb1q3W5oaIBS7tsKSqm82w24evWq3abV24B13lBDQwMymavQutBtoKFhi91Ovea21aYteftg3a6F12lpaQnz8/NV+34qp+TEDU4PkNa6M+/+UQBxrfVgkfViAHoBjAB4FMBR5J1nVGCdCwD6YBVJ696mo5onbkin0wiHw0E3wxinT5/G8ePHcf78eezduxfHjh3jpA3rwONNhrnJMTsZ5ibD3GRqZUIAv/fD7+OtVl6nan6flpu4oVxPEmAVLfmiAC6VWMc9E92gUur3YQ2/i2utiw2XiwPotv+VbLPqVfOBUo0OHz6Mw4cP4/Lly9i6dWvQzTEOjzcZ5ibH7GSYmwxzIz/xeJMxObdys9tNwCpO8nXAHjKXz+59ypkN3h5q1wfgkH0h2WKl8axkm6ZwuhlpfZibDHOTYW5yzE6GuckwN/ITjzcZk3MrWSTZs9Gtmbob1kx1651AYQJWT9AsgELXO+oGMFXhbRIREREREa2Ll+skDcE6rwgAYE/tPe66HVNKjTpFjV3I3F/gefqRd4FZ13P0A3jWmcih3DZN1dbWFnQTjMTcZJibDHOTY3YyzE2GuZGfeLzJmJxb2XOStNYjSql+exhdFEDMdb4R7Pt6YA2HS9j3HVFKDcHqOUrYjxlzCiT7OY+6lsH9nB62aaSlpSVPs2lQLuYmw9xkmJscs5NhbjLMjfzE403G5Ny8TNyAUrPS2ecbtefdlwBQchY613WR1r1NU5k8LjNIzE2GuckwNzlmJ8PcZJgb+YnHm4zJuXkqkoiIiIioeiilfF+3FqakJvKKRZKPWltbg26CkZibDHOTYW5yzE6GucnUe27SgmVxcREtLS0Vbk3tq/fjTcrk3LxM3EAVsrKyEnQTjMTcZJibDHNbv9OnT+Omm25CW1sbbrrpJpw+fTroJhmFx5wMc5NhbjLMTcbk3Fgk+SidTgfdBCMxNxnmJsPc1uf06dM4duwYnnjiCbz33nt44okncOzYMRZK68BjToa5yTA3GeYmY3JuLJKIiEjs+PHjOHXqFA4ePIjGxkYcPHgQp06dwvHjx4NuGhERkRjPSfKRyeMyg8TcZJibDHNbn/Pnz+OOO+4AsJrdHXfcgfPnzwfZLKPwmJNhbjK1lNtGJq+odu3t7eUfZACTjzcWST7KZDJBN8FIzE2Guckwt/XZu3cvXnnlFRw8eDCb3SuvvIK9e/cG3DJz8JiTYW4ytZKb3zPtKaU4u5+Ayccbh9v5aHFxMegmGIm5yTA3Gea2PseOHcODDz6Is2fP4vLlyzh79iwefPBBHDt2LOimGYPHnAxzk2Fu5CeTjzf2JBERkdjhw4cBAA899BDOnz+PvXv34vjx49n7iYiITMQiyUeRSCToJhiJuckwNxnmtn6HDx/G4cOHkUwmmZ8AM5NhbjLMjfxk8vHG4XZEREREREQuLJJ8lEwmg26CkZibDHOTYW5yzE6Gua2Pc/Hia665hhcvFuDxRn4y+XjjcDsiIiIygnPx4lOnTuGGG27AW2+9hQcffBAAeB4cEVUUe5J81NLSEnQTjMTcZJibDHOTY3YyzM0798WLt27dyosXC/B4Iz+ZfLyxSPJRQwPjlmBuMsxNhrnJMTsZ5uad++LFTm68ePH68HgjP5l8vJnbcgNduXIl6CYYibnJMDcZ5ibH7GSYm3fOxYuB1dx48eL14fFGfjL5eGORREREREZwX7x4eXmZFy8mok3DiRt8FA6Hg26CkZibDHOTYW5yzE6GuXl3+PBh/OVf/iXuuecepNNphMNhHDlyhJM2rAOPN/KTyccbe5J8FAqxJpVgbjLMTYa5yTE7Gebm3enTp/HHf/zHeP755zE/P4/nn38ef/zHf8xpwNeBxxv5yeTjjUWSj0welxkk5ibD3GSYmxyzk2Fu3rlnt1taWuLsdgI83shPJh9vLJKIiIjICO7Z7Ryc3Y6INgOLJB81NTUF3QQjMTcZ5ibD3OSYnQxz8849u52TG2e3Wx8eb+Qnk483Fkk+MvlACRJzk2FuMsxNjtnJMDfv3LPbKaU4u50Ajzfyk8nHm7lnUxloYWEBzc3NQTfDOMxNhrnJMDc5ZifD3LxzZrF76KGHcP78eezduxfHjx/n7HbrwOON/GTy8cYiiYiIiIxx+PBhHD58GDMzM9ixY0fQzSGiGsXhdj4yucsxSMxNhrnJMDc5ZifD3GSYmwxzIz+ZfLyxSPKRyRfUChJzk2FuMsxNjtnJMDcZ5ibD3MhPJh9vLJJ8tLCwEHQTjMTcZJibDHOTY3YyzE2GuckwN/KTyccbz0nykdY66CYYibnJMDcZ5ibH7GSYmwxzk6n33JRSvq9bz5mbvO8sknzU2NgYdBOMxNxkmJsMc5NjdjLMTYa5ydR7btI/2ufn57Ft27YKt6b2mXy8VeVwO6VUTCnVE3Q7Ks3kcZlBYm4yzE2GuckxOxnmJsPcZJibDHOTMTk3Tz1JSql+ALP2zZjW+qTHdRydAE5orRMFlu+3/x10Le8C8JRSKgogAWDCXj7lpb3VKplMGjtXfJCYmwxzk2FucsxOhrnJMDcZ5ibD3GRMzq1skeQUM1rrMft2TCk1rLUeKLHOUQAjeUXRKIA+5zm11iN523gRqwUTtNbtSqmo+zlMl8lkgm6CkZibDHOTYW5yzE6GuckwNxnmJsPcZEzOzctwuwF3QaO1jgMoNxTutgLFTVwpFbV7h3LYz79miF0tFUgAEArxFDAJ5ibD3GSYmxyzk2FuMsxNhrnJMDcZk3MrWSTZBU1XgUWJMucMxZRS+es5vUIxAMMFiqW4vaxmmdrdGDTmJsPcZJibHLOTYW4yzE2GuckwNxmTcyvXkxSDdU5QvlkULp4cRwC8aA+7g11QDQOAfV7R/gK9RDFY5x7BWcf1c7RMO42QTCaDboKRmJsMc5NhbnLMToa5yTA3GeYmw9xkTM6tXB9YB1YnbHBLANhebCWt9ZRSaj+ASaXUowD63JMu5E/AoJTqBRB33T9lPy5uL48rpc5orQ8V2p59TlM/AOzatQupVApLS0tYWloCALS2tmJlZQXpdDp7O5PJYHFxEQAQiUQArL6QLS0taGhowJUrVwBYM3OEQqHs7aamJjQ1NWUvkNXU1IRwOIyFhQVordHY2IhwOIxkMolMJoNQKITm5mbMz88jk8mgoaEBkUgEqVQKKysr2dvpdBrLy8tQSqGtrQ3pdDq7D21tbVW5T87tzdynubk5ZDKZmtonP16nubk5NDQ01NQ++fE6zc/Po6Wlpab2ya/XKZVKYXZ2tqb2yY/XaWFhITtuv1b2yY/XyfndUEv75Mfr5ORWS/vkx+s0Pz+PUChUU/vkx+s0Pz+PSCRSlftUjio1X7zTA6S17sy7fxRWUTNYZL0YgF4AIwAeBXAUeec2uR4bhTVpw12lzkFSSl1AXrFVSHd3t56YmCj1kMDMzs6io6Mj6GYYh7nJMDcZ5ibH7GSYmwxzk2FuMsxNpppzU0pNaq27iy33MnFDoT2LArhUYp1BrfVJrXXCLqT2Axgqch7TEKziJ1GmHXEARXfEBE5lTOvD3GSYmwxzk2N2MsxNhrnJMDcZ5iZjcm7liqQJWAVRvg7YQ+Ly2YXQGfd9du9PH4BDeY89CmDIGVZn3xdTShXr3io09M8YqVQq6CYYibnJMDcZ5ibH7GSYmwxzk2FuMsxNxuTcShZJdu9OvMBMdFGt9fg6tzUBV++TfR7RWF6B1AOrECp0DaZuFCnMTLGyshJ0E4zE3GSYmwxzk2N2MsxNhrnJMDcZ5iZjcm5ehtsNwTqvCABgT+097rodU0qNOoWUXTzdX+B5+mGdo+QUQxOuiRmizlC8QsPu7ILqWXdBZaKGBi9xUz7mJsPcZJibHLOTYW4yzE2GuckwNxmTcyt7hSet9YhSqt8uYqIAYlprd09PFNbFZTuwOl34EaXUEKyeo4T9mDGtdcKe1OEMACil8jfX7trmUde6yNumkUwelxkk5ibD3GSYmxyzk2FuMsxNhrnJMDcZk3PzdBncQrPSuZZNwS5uXPclABSc+c7uDVpTHRV43EkvbTNJOp02+qJaQWFuMsxNhrnJMTsZ5ibD3GSYmwxzkzE5N3P7wAy0vLwcdBOMxNxkmJsMc5NjdjLMTYa5yTA3GeYmY3JuLJJ8VGB4IXnA3GSYmwxzk2N2MsxNhrnJMDcZ5iZjcm6ehttRZbS1tQXdBCMxNxnmJsPc5JidDHOTYW4yzE2m3nMLotjRutgVgfzBniQfpdPpoJtgJOYmw9xkmJscs5NhbjLMTYa5ydR7blpr0c9G1w0SiyQfLS0tBd0EIzE3GeYmw9zkmJ0Mc5NhbjLMTYa51R8WSURERERERC4sknxU7+NZpZibDHOTYW5yzE6GuckwNxnmJsPc6g+LJB+xq1aGuckwNxnmJsfsZJibDHOTYW4yzK3+sEjyEd9gMsxNhrnJMDc5ZifD3GSYmwxzk2Fu9YdFEhERERERkQuLJB+1trYG3QQjMTcZ5ibD3OSYnQxzk2FuMsxNhrnVHxZJPlpZWQm6CUZibjLMTYa5yTE7GeYmw9xkmJsMc6s/LJJ8VO8XIpNibjLMTYa5yTE7GeYmw9xkmJsMc6s/LJKIiIiIiIhcWCT5iONZZZibDHOTYW5yzE6GuckwNxnmJsPc6g+LJB9lMpmgm2Ak5ibD3GSYmxyzk2FuMsxNhrnJMLf6wyLJR4uLi0E3wUjMTYa5yTA3OWYnw9xkmJsMc5NhbvWHRRIREREREZELiyQfRSKRoJtgJOYmw9xkmJscs5NhbjLMTYa5yTC3+sMiiYiIiIiIyIVFko+SyWTQTTASc5NhbjLMTY7ZyTA3GeYmw9xkmFv9YZFERERERETkwiLJRy0tLUE3wUjMTYa5yTA3OWYnw9xkmJsMc5NhbvWHRZKPGhoYtwRzk2FuMsxNjtnJMDcZ5ibD3GSYW/3hK+6jK1euBN0EIzE3GeYmw9zkmJ0Mc5NhbjLMTYa51R8WSURERERERC4sknwUDoeDboKRmJsMc5NhbnLMToa5yTA3GeYmw9zqD4skH4VCoaCbYCTmJsPcZJibHLOTYW4yzE2Guckwt/rDIslHHM8qw9xkmJsMc5NjdjLMTYa5yTA3GeZWf1gkERERERERubBI8lFTU1PQTTASc5NhbjLMTY7ZyTA3GeYmw9xkmFv98TTAUinVD2DWvhnTWp/0uI6jE8AJrXXC63NKtlnt+AaTYW4yzE2GuckxOxnmJsPcZJibDHOrP2WLJKfY0VqP2bdjSqlhrfVAiXWOAhjJK4pGAfR5eU7JNk2wsLCA5ubmoJthHOYmw9xkmJscs5NhbjLMTYa5yTC3+uOlJ2lAa73fuaG1jiulesqsc1uBnp+4UipqF07lnlOyTSIiIiIiog0reU6SUioKoKvAokSZoiWmlMpfL6q1TpR7zg1ss+qxq1aGuckwNxnmJsfsZJibDHOTYW4yzK3+lJu4IQYgUeD+WRQuZBxHALxoD7uDXdwMe3xO6TarHi9EJsPcZJibDHOTY3YyzE2GuckwNxnmVn/KDbfrwOrkCW4JANuLraS1nlJK7QcwqZR6FECf1nrK43Oue5v2OUz9ALBr1y6kUiksLS1haWkJANDa2oqVlRWk0+ns7Uwmg8XFRQBAJBIBACSTSQBAS0sLGhoasnPih8NhhEKh7O2mpiY0NTVhYWEhezscDmNhYQFaazQ2NiIcDiOZTCKTySAUCqG5uRkXL15EW1sbGhoaEIlEkEqlsLKykr2dTqexvLwMpRTa2tqQTqez+9DW1laV++Tc3sx9unjxIrZt21ZT++TH6zQ3N4ft27fX1D758TrNz8/jwx/+cE3tk1+vUzKZRDgcrql98uN1mpmZQUtLS03tkx+v09/+7d9i69atNbVPfrxO77//PrZt21ZT++TH6zQ/P49rr722pvbJr9epWv8uL0dprYsvtHuAtNadefePAohrrQeLrBcD0AtgBMCjAI7COs9opNxzAjgj2aaju7tbT0xMlHpIYGZmZrBjx46gm2Ec5ibD3GSYmxyzk2FuMsxNhrnJMDcZpRRK1RpBUkpNaq27iy33MnFDR4H7ogAulVhn0DUT3aBS6vdhDb+Le3xOyTarXmNjY9BNMBJzk2FuMsxNjtnJMDcZ5ibD3GSYW/0pd07SBKziJF8HgKkC9zu9T2fc99lD7foAHPLwnOvepik4nlWGuckwNxnmJsfsZJibDHOTYW4yzK3+lCyS7Om64/aMc25RrfX4Orc1AeBSuees8DarijO2ktaHuckwNxnmJsfsZJibDHOTYW4yzK3+lOtJAoAhWOcVAQDsqb3HXbdjSqlRp6ixC5n7CzxPP6xzlMo+p4flRspkMkE3wUjMTYa5yTA3OWYnw9xkmJsMc5Opldw6OjqglPLtB4Cv2+voKHTGjkzZc5LsyRb67WF0UQAx1/lGsO/rgTUcLmHfd0QpNQTrHKKE/Zgxu5eo7HN62KaRQiEvp4BRPuYmw9xkmJscs5NhbjLMTYa5ydRKbnNzc75OpJBIJBCNRn3bnlOYVYKnV1xrPVJi2RSA9rz7EgBKzkJX6jm9LDeRl+kGaS3mJsPcZJibHLOTYW4yzE2GuckwNxmTc/My3I4qhONZZZibDHOTYW5yzE6GuckwNxnmJsPcZEzOjUWSj2plPKvfmJsMc5NhbnLMToa5yTA3GeYmU4+5jY0BnZ1AezswIDzpJT+3kZHV5+zrAxKJjbdzs7BI8lFDA+OWYG4yzE2GuckxOxnmJsPcZJibTL3lNj5uFTFDQ8CLLwITE8ChQ6Ufr9Tan2uv3ZFdb2QEGBwEhoeByUnrvv37V5+jr6/wcwSlNs5CM0QkEgm6CUZibjLMTYa5yTE7GeYmw9xkmJtMveU2NAQcPQr09lq3R0etHqB4HIjF1j6+p2e18HH7lV/J4NChhuxzDg1Zj3WeUymrx8rZTm+v9ZhqUF9lccBSqVTQTTASc5NhbjLMTY7ZyTA3GeYmw9xk6i238fHcnqNYzPoZL3FBnq6u3J943Bpud/SotTweB7q7c9eJxYDZ2dXbHR2r23J+gsIiyUcrKytBN8FIzE2GuckwNzlmJ8PcZJibDHOTqafcpqasfwsVNIV6iwpJJIAjR4Cvf/2D7H3RqFUouc3OrvYsVRsWST6qt/GslcLcZJibDHOTY3YyzE2GuckwN5l6ys3p2cm/vFE0mtvrU8qJE1aRdeutq9dkGhqyzklyCqWTJ4GnnsrtLZqYsM5TqoaJHXhOko/qbTxrpTA3GeYmw9zkmJ0Mc5NhbjLMTaYec0sk1hZKXoqWRMIqgM6cyc2tv99a1tlp3Y5GrfOS3KamrIkdenqsGfXuust771Wl1U9ZXAXS6XTQTTASc5NhbjLMTY7ZyTA3GeYmw9xk6jG3Qr1G+UVTISMj1uN6enJzGxmxCqDRUavw6emxzntyhvcNDFiFVX+/1bs0PGwtK3Ue1GZikeSj5eXloJtgJOYmw9xkmJscs5NhbjLMTYa5ydRTbh0d1r/5vUbx+OqyUs6cWT3PyMktkbCKoKEhawa7ri6rWIpGrWIIsNZxn5/kDMNziii/sUjykQpysneDMTcZ5ibD3OSYnQxzk2FuMsxNpp5y6+qy/s3vSYrHc69rVIx7Zjwnt4mJ3Od2dHd7P8/JbyySfNTW1hZ0E4zE3GSYmwxzk2N2MsxNhrnJMDeZesvN6elxTE1ZvUHlZqLLnxnPyc25nT+7nXOR2kTCGo7n5gyzC2r2OxZJPqrH8ayVwNxkmJsMc5NjdjLMTYa5yTA3mXrLbWjIKlrGxqzC58gRq1hxhsDF49bkDPmcXiHncU5u0ah1rtHAgFX8xOPW7HUdHdb9s7PWssHB1fOQBgZWh+YFgUWSj5aWloJugpGYmwxzk2FucsxOhrnJMDcZ5iZTb7n19FjnCp04Yc0w191tnWvkGB+3Cpp8Tk+RM8GDO7fhYavwGRhYHbbnzFwXiwEXLljr33XXaoGUP/udn5TWuvyjDNLd3a0nnIGPVWZmZgY7duwIuhnGYW4yzE2GuckxOxnmJsPcZJibTK3kppSCn3/7+53bevZPKTWpte4utpw9ST6qt/GslcLcZJibDHOTY3YyzE2GuckwNxnmJmNybryYrI+WlpbQ3NwcdDOMw9xkmJsMc5NjdjLMTYa5yTA3mVrI7UtfAoCzOHDAv21evRrCli3+bQ94rGLPxJ4kH9XbeNZKYW4yzE2GuckxOxnmJsPcZJibDHOTyWTMPa2HPUlERERERDXum98EHn/8IF56yc9zkuZ9PifpEQBfqshzsSfJR62trUE3wUjMTYa5yTA3OWYnw9xkmJsMc5NhbjIm58YiyUcrKytBN8FIzE2GuckwNzlmJ8PcZJibDHOTYW4yJufGIslH9XYhskphbjLMTYa5yTE7GeYmw9xkmJsMc5MxOTcWSURERERERC4sknxk8rjMIDE3GeYmw9zkmJ0Mc5NhbjLMTYa5yZicG4skH2UymaCbYCTmJsPcZJibHLOTYW4yzE2GuckwNxmTc2OR5KPFxcWgm2Ak5ibD3GSYmxyzk2FuMsxNhrnJMDcZk3NjkUREREREROTCIslHkUgk6CYYibnJMDcZ5ibH7GSYmwxzk2FuMsxNxuTcWCQRERERERG5VGWRpJSKKaV6gm5HpSWTyaCbYCTmJsPcZJibHLOTYW4yzE2GuckwNxmTc/NUJCml+pVSvfbPUQ+PP6OU0kqpubyfC16WA+gCMOp6zBmlVJd8N4mIiIiIiLwJlXuAUqofALTWY/btmFJqWGs9UGK1KQADAGZd98XsHy/LobVuV0pFtdYJD/thhJaWlqCbYCTmJsPcZJibHLOTYW4yzE2GuckwNxmTcytbJAEY0Frvd25oreOlhsIppaIAfl9rHc+7v1trPVJuufu+WiqQAKChoSpHN1Y95ibD3GSYmxyzk2FuMsxNhrnJ1FJuSqmgm7Bp2tvbK/ZcJV9xu6ApNMwtUaxQ0lontNZTec/TC+BZL8tr2ZUrV4JugpGYmwxzk2FucsxOhrnJMDcZ5iZTK7lprX398Xubs7OzZRLwrlxPUgxAosD9s7CKp3GP2+ko0yu0ZnleEdaltT7pcVtERERERERi5YqkDuSeN+RIANjuZQP2OU1Fe4mKLJ8CrKF99mPiSqkzWutDJZ6jHwB27dqFVCqFpaUlLC0tAQBaW1uxsrKCdDqdvZ3JZLJXAXbmcHdm4GhpaUFDQ0P2W4NwOIxQKJS93dTUhKamJiwsLGRvh8NhLCwsQGuNxsZGhMNhJJNJZDIZhEIhNDc3I5VKYWZmBg0NDYhEIkilUlhZWcneTqfTWF5ehlIKbW1tSKfT2X1oa2uryn1ybm/mPiWTSczMzNTUPvnxOiWTSSQSiZraJz9ep2QyicXFxZraJ79eJ6UUZmdna2qf/HqdZmZmam6fNvt1WlxcxMzMTE3tkx+vk/M7tZb2yY/XKZlM4vLlyzW1T369TtX6d3k5yukKK7jQ6s0Z1lp35t0/CiCutR4su4Eykzx4mATCedwFAH35Q/XydXd364mJiXJPF4jFxUWjT2ALCnOTYW4yzE2O2ckwNxnmJsPcZJibjFIKpWqNICmlJrXW3cWWezkLraPAfVEAlzxsvBeFh+t5Wp4nDqDojpigVsaz+o25yTA3GeYmx+xkmJsMc5NhbjLMrf6UK5ImYBVE+TpgD4kr436ULqbWLLenGC9WclbubCwiIiIiIqICShZJ9mQKcXuWO7eo1trLpA1dsHqA1rN8FtY1lPJ1w1thVrWampqCboKRmJsMc5NhbnLMToa5yTA3GeYmw9zqj5fhdkMAHnVuKKVyZrWze35GCxRSQPHZ8YouLzQLnjO5Q/61lUzDN5gMc5NhbjLMTY7ZyTA3GeYmw9xkmFv9KXsxWfsCsP32JA5RALG8iRaiAHpgDcFL5K0eR+mepILL7W0etZ8vat9XdnKHarewsOBpNg3KxdxkmJsMc5NjdjLMTYa5yTA3GeZWf8oWSYBVtJRYNgWg4OVt82fFW89yXheJiIiIiIiC4GW4HVUIu2plmJsMc5NhbnLMToa5yTA3GeYmw9zqD4skH4XD4aCbYCTmJsPcZJibHLOTYW4yzE2Guckwt/rDIslHzpWAaX2Ymwxzk2FucsxOhrnJMDcZ5ibD3OoPiyQfVesVh6sdc5NhbjLMTY7ZyTA3GeYmw9xkmFv9YZHko8bGxqCbYCTmJsPcZJibHLOTYW4yzE2Guckwt/rDIslHHM8qw9xkmJsMc5NjdjLMTYa5yTA3GeZWf1gk+SiZTAbdBCMxNxnmJsPc5JidDHOTYW4yzE2GudUfFkk+ymQyQTfBSMxNhrnJMDc5ZifD3GSYmwxzk2Fu9YdFko9CIU/X7qU8zE2GuckwNzlmJ8PcZJibDHOTYW71h0WSj5qbm4NugpGYmwxzk2FucsxOhrnJMDcZ5ibD3OoPiyQfcTyrDHOTYW4yzE2O2ckwNxnmJsPcZJhb/WGR5COOZ5VhbjLMTYa5yTE7GeYmw9xkmJsMc6s/LJJ81NDAuCWYmwxzk2FucsxOhrnJMDcZ5ibD3OoPX3EfRSKRoJtgJOYmw9xkmJscs5NhbjLMTYa5yTC3+sMiyUepVCroJhiJuckwNxnmJsfsZJibDHOTYW4yzK3+sEjy0crKStBNMBJzk2FuMsxNjtnJMDcZ5ibD3GSYW/1hkeQjjmeVYW4yzE2GuckxOxnmJsPcZJibDHOrP3zFfcTxrDLMTYa5yTA3OWYnw9xkmJsMc5NhbvWHRZKP0ul00E0wEnOTYW4yzE2O2ckwNxnmJsPcZJhb/WGR5KPl5eWgm2Ak5ibD3GSYmxyzk2FuMsxNhrnJMLf6wyLJR0qpoJtgJOYmw9xkmJscs5NhbjLMTYa5yTC3+sMiyUdtbW1BN8FIzE2GuckwNzlmJ8PcZJibDHOTYW71h0WSjzieVYa5yTA3GeYmx+xkmJsMc5NhbjLMrf6wSPLR0tJS0E0wEnOTYW4yzE2O2ckwNxnmJsPcZJhb/WGRRERERERE5MIiyUcczyrD3GSYmwxzk2N2MsxNhrnJMDcZ5lZ/WCT5iF21MsxNhrnJMDc5ZifD3GSYmwxzk2Fu9YdFko/4BpNhbjLMTYa5yTE7GeYmw9xkmJsMc6s/LJKIiIiIiIhcWCT5qLW1NegmGIm5yTA3GeYmx+xkmJsMc5NhbjLMrf4EUiQppWJKqZ4gth2klZWVoJtgJOYmw9xkmJscs5NhbjLMTYa5yTC3+uOpSFJK9Suleu2fox4ef0YppZVSc3k/F+yHdAEYdT3mjFKqayPbNAEvRCbD3GSYmwxzk2N2MsxNhrnJMDcZ5lZ/QuUeoJTqBwCt9Zh9O6aUGtZaD5RYbQrAAIBZ130x+wf287UrpaJa60SFtklERERERLRhZYskAANa6/3ODa11vNRQOaVUFMDva63jefd3a61H3PcVKpAk2zQFx7PKMDcZ5ibD3OSYnQxzk2FuMsxNhrnVn5LD7eyCp6vAokSxokVrndBaT+U9Ty+AZ700SLJNU2QymaCbYCTmJsPcZJibHLOTYW4yzE2Guckwt/pT7pykGIBEgftnUbiQKaYjv9dIKdXj+nGfc1SpbVadxcXFoJtgJOYmw9xkmJscs5NhbjLMTYa5yTC3+lNuuF0Hcs8rciQAbPeyAfv8ovxepCnAGkZnPyaulDqjtT4k2aa9jX4A2LVrF1KpFJaWlrIX/mptbcXKykr2pLvW1lZkMpnsAR+JRAAAyWQSANDS0oKGhgZcuXIFABAOhxEKhbK3m5qa0NTUhIWFheztcDiMhYUFaK3R2NiIcDiMZDKJTCaDUCiE5uZmfPDBBwCAhoYGRCIRpFIprKysZG+n02ksLy9DKYW2tjak0+nsPrS1tVXlPjm3N3Of5ufna26f/Hid5ufna26f/Hid5ufna26f/HqdlpaWMDs7W1P75MfrlEwmMTMzU1P75Mfr5PxuqKV98uN1cnKrpX3y43Wan5+vuX1az+vU0tICKaWUaL33339/U/epbLu11qV2qgfAsNa6M+/+UQBxrfVg2Q14nHDBnvmuD1aRJN5md3e3npiYKLe5QCSTyezBTN4xNxnmJsPc5JidDHOTYW4yzE2GuclUc25KqUmtdXex5V6mAO8ocF8UwCUPG+9F4aFzhcQBOA0Vb5OIiIiIiGgjyhVJE7CKk3wdsIfMlXE/8gobezrvYt1XsxXYZtVyukJpfZibDHOTYW5yzE6GuckwNxnmJsPcZEzOrWSRZE+2ELdnnHOLaq3HPTx/F6weIrdZWNdQytcNYKoC2yQiIiIiIhLzMtxuCMCjzg2lVBeAcdftmFJqtEBRAxSYqa7ExWOfdV1bqeQ2TbWRk97qGXOTYW4yzE2O2ckwNxnmJsPcZJibjMm5lb2YrNZ6RCnVb0/iEAUQy5uIIQqgB9ZwuETe6nGs7UlynvOo/fiofd9A3vJS2zRSQ4OXmpTyMTcZ5ibD3OSYnQxzk2FuMsxNhrnJmJxb2SIJsIqWEsumALQXWdZZ6H572UnpNk115coVoyvqoDA3GeYmw9zkmJ0Mc5NhbjLMTYa5yZicm7nlHRERERER0SZgkeSjcDgcdBOMxNxkmJsMc5NjdjLMTYa5yTA3GeYmY3JuLJJ8FAp5Gt1IeZibDHOTYW5yzE6GuckwNxnmJsPcZEzOjUWSj65cuRJ0E4zE3GSYmwxzk2N2MsxNhrnJMDcZ5iZjcm4skoiIiIiIiFxYJPmoqakp6CYYibnJMDcZ5ibH7GSYmwxzk2FuMsxNxuTcWCT5yOQDJUjMTYa5yTA3OWYnw9xkmJsMc5NhbjIm56a01kG3oaKUUu8D+HHQ7ShiB4CZoBthIOYmw9xkmJscs5NhbjLMTYa5yTA3mWrO7ae11tcWW1hzRVI1U0pNaK27g26HaZibDHOTYW5yzE6GuckwNxnmJsPcZEzOjcPtiIiIiIiIXFgkERERERERubBI8tdI0A0wFHOTYW4yzE2O2ckwNxnmJsPcZJibjLG58ZwkIiIiIiIiF/YkERERERERubBI8kgp1a+U0nk/F5RSw0qpaNDtI/KbUiqmlOoJuh1EREHgZyBRbWORtE5aa6W1VgDaAQwA6AYwzUIpOHYB22v/HF3Hel1KqdHNbFs12YScugCM2l8YzCmlziiluirX4uokydFep9/+UqUuv1jZQG5H7Z9hpVRss9tZbaTvW9f6FfmMs7d/Jshjtwo/s8t+BjK38gq1jbkVVu4zkbkVJv0dHNrkdtUsrXUCwDiA/UqpOQCPAhgMtFF1SCnVDwBa6zH7dkwpNay1HiixTheA+wFcAlAXf3RtVk5a63alVNR+P9Q8YY79WuuRvOd4EcD+zW5vtRDmdlRrfdJ1uxfAGQCdm93eaiHJLW/9LgC9lWiL1npMKXU/gKcA9FXiOb2q5s/scp+BzK20Yscoc1vLy2cic1trQ7+Dtdb88fADoN+Kq+CyUQBngm5jPf4AmCxw3wWP63YVWr8WfzYjJwC9Qe9XtecIIAqgv8D9cwB6gt6fas3NWe4+xmD90tUAokHvTzXnlvfYnmK/t4TtqejzCbZfVZ/ZXj8DmZssG+a2pj2ePhOZW05bNvQ7mMPtKqMHwFTQjag3dndpoeFdCY4TX8WcKkOYYwxAoa79OKrom7bNtIHj75C2e1BsMQAJXT+9llFs4H2rlOrVWo9Xsk328yWcHi7yhrkVVu4YZW5rePpMZG45NvQ7mEXSBiilepRSkwAmtNaD+fe7xikfzVvvqH2/M/nDUdeyIXvZnFJqyMfdEbP354JaO7HFmU3edAxAosD9syj8x0WgajEn+1h3ftZ9voRwm8bkqLWeArC/wB/2MQATlWxcOSblBgBa63jeXYPwefgIYF5uQHbYy2Z9cTeCAF4HL1znHfT7+Hnk9TOQueVu0+sxytxs6/xMZG7Y+O9gFknr5P4lCWss6ITW+lDew2KwDt5OWAfpkD12FPY3gEMA7rKXD8CqaJ2TF3vtdfoA9Fb7NwF2m7fDGtvZCetDr09bE1zk51JpHbD+YMiXsNtUNWo0pykAca31uP3N1dhm/+FoYo72h3SW/VkQz79/M5mYm0NZJyIPAxiqdM+Ih22bmluswB9UlTIMoKfAN7OBsr+wjGutR7R1/kFcbf5EH+v5DGRuubweo8xt7ba9fCYyN9tGfgdz4ob1c5803AVgQCl1AdYvzikA0K4TxGAdAGOwTmYbgzU+MgHrBUpgtUCKwSqQ9jvPo5QahDUhRFVerdgu4KKuXrSE/cZ19rXc+sNet6U9nqhcjWo1p/xfcFrruLJOLO/ajAKgFnK0f2E9CutLEl+Ynps9vGTM7tE55O6130ym5qasIUxl27dBCVjn6Z4s9SC/MrBHXcw6fzDa77P8oUkVJ/gMTIC5SY7RBJhb1jo+ExNgbvltiGIdv4NZJK1T3odiHNaBOgxrpox2Z4HdhXg/rKIoBmsmPNj/zgKYU0qNw5rw4SSs85oAYFIptan7UEFDWDs7SBSFv/1co0J/GHQUuC8Ka3aValFPOcVhTYu/Gb0ktZDjEKwvVBIVaItXtZAbtNYnlT3Nsk89SsblZn/Ztlk9SM7zDwI4AWsURMk/vnz80qYfwIhr5MVsqW3b3yR7bdt6369rPgOZW85zeD5GmVtpxT4TmVtJ6/odzCKpMoYB9DvfHtk9S3EAR+zbw7BPELNfmE77xb8f1lC822AN3UtordsLb6K6ON2jBbrLD8Ga7c8PE7D+YMjXgSqZSKNWc7L364K2rhmWz9MfkYLtGZ2j/cXJ0CYOgyq0TSNzs89XeLHA52EcVts3tUgyNTdYX7ZFVd7EDvaxl8gb5bAudiZDWus++9vYoc3qNV5nu6Kwchr2+t5yvonf4HY9fQYytzU8HaPMbc12PX0mMreSbVj372AWSZXhvNnj9oEcg9WF6LwQa74NdHWX9sAqkAZhfXAEfjB7tObbIPtNEPP6i3ijXbxa64RSKq7WXqMi6ve5CyXUak6zKPwNT3eR+zfK6Bztb87G3B/OSqkeH45TU3PrQOFhxjFY0+BuNiNzK9Q2pdSQdl1bRcL+vfYU7CEqdtvGYb3XS30T7NuQw0J/+BTIrpLKfgYyt4LbK3uMMreCyn4mMrfixL+DdRXMY27CDwpcJwnWwTkEa576o677NazeJec8Iw37Okr27WFY5zN1wfpW8oK9bBjWwd7jWrdqr7+EvHnwYRV7vl77xX5dhly3u2B9U+F+jUZR4Noqds6bPpd/reaEvGsP2NsYrkR7aylHO78u1+2ofZ8vbTc4t6N5z9EF69oWa45R5lY8k/zfW4I2dNm/l2J59/cCmPM5j2KfRZPu95gru009Vkp9BjK3dbVPu/7P3Iq3p+hnInMr2x7R72Blr0Bl2FVofrWdgPVt46DOHQ96FNaJYQDwLFaH2h1yukJhdznDGjZxRK9O1nAUVsUfs5ed0D6e1LYedi9YF6wcOrGObtQKt6Mf1usQhfUBkf+N1IuwJsTInyTjfljtH4H1ht6UCTJqOSdniIT9nNAb/Ma6TPuNy9EZklPkqdq1D+cmmZibfV8U1i9QRyd8HK5oam6uZT2wfpf0whqyMqwFPZcqb2KivGWT9vNu6uRC5T6L7OUDcL3XNrtNrrYV/Axkbp7at+YYtX+YW+F2RVHkM5HHW8k2iX8Hs0giIiIiIiJy4XWSiIiIiIiIXFgkERERERERubBIIiIiIiIicmGRRERERERE5MIiiYiIiIiIyIVFEhERERERkQuLJCIiIiIiIhcWSURERERERC4skoiIiIiIiFz+f1rphKjV7TpbAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores(CONFIGS_DENOISING_AND_SPARSE, denoising_and_sparse_scores, metric=\"auc\", savefile=\"plots/approach4/boxplot_kde_denoising_and_sparse.pdf\", figsize=(14, 6), label_below=[3,5,6], colors = [\"black\"] + [\"darkgoldenrod\"] * 5 + [\"blue\"] * 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores(CONFIGS_DENOISING_AND_SPARSE, denoising_and_sparse_scores, metric=\"tnr95\", savefile=\"plots/approach4/boxplot_kde_denoising_and_sparse_tnr95.pdf\", figsize=(9, 8), label_below=[], colors = [\"black\"] + [\"darkgoldenrod\"] * 5 + [\"blue\"] * 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare sessions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions256_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:02<00:00,  3.67it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session 'Marten_01' at folder: /home/kleinsteuber/vscode/ResizedSessions256_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Marten_01\n",
      "Loaded scans.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:13<00:00,  1.35s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session 'GFox_03' at folder: /home/kleinsteuber/vscode/ResizedSessions256_NoBackup/VIELAAS_Spring_Session03-VIELAAS_GFox_03\n",
      "Loaded scans.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:08<00:00,  1.18it/s]\n"
     ]
    }
   ],
   "source": [
    "compare_sessions_scores = [get_scores(**config) for config in CONFIGS_COMPARE_SESSIONS]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores(CONFIGS_COMPARE_SESSIONS, compare_sessions_scores, metric=\"auc\", savefile=\"plots/approach4/boxplot_kde_sessions.pdf\", figsize=(5, 7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores(CONFIGS_COMPARE_SESSIONS, compare_sessions_scores, metric=\"tnr95\", savefile=\"plots/approach4/boxplot_kde_sessions_tnr95.pdf\", figsize=(5, 7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.4 ('pytorch-gpu')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "17cd5c528a3345b75540c61f907eece919c031d57a2ca1e5653325af249173c9"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}