LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install PIL \n",
    "# !pip install pytesseract\n",
    "# !pip install "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ADD\n",
    "\n",
    "    -> TRY EXCEPT \n",
    "    -> CHOOSE THE ENVIRONNEMENT OF THE CRISIS IF TOO LONG\n",
    "    -> CHECK THE INDIVIDUALS WITH 23 CHANNELS "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    }
   ],
   "source": [
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import torchvision "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import torch\n",
    "import mne\n",
    "import matplotlib.pyplot as plt \n",
    "import os \n",
    "import mne\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.autograd import Variable\n",
    "import torch.autograd as autograd\n",
    "\n",
    "from torch import nn\n",
    "from torch import Tensor\n",
    "from PIL import Image\n",
    "from torchvision.transforms import Compose, Resize, ToTensor\n",
    "from einops import rearrange, reduce, repeat\n",
    "from einops.layers.torch import Rearrange, Reduce"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importation & Pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sujet chb-mit-scalp-eeg-database-1.0.0 : 0 fichiers .edf\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Chemin vers le dossier parent\n",
    "dossier_parent = \"C:/Users/rayan/Desktop/B_Laval/S2/Cours/Apprentissage_NeuronesProfonds/Projet_2/EEG_Seizures_Transformers-/data\"\n",
    "\n",
    "# Dictionnaire pour stocker les données MNE par sous-dossier\n",
    "donnees_par_sous_dossier = {}\n",
    "\n",
    "# Parcours des sous-dossiers\n",
    "for nom_sous_dossier in os.listdir(dossier_parent):\n",
    "    \n",
    "    chemin_sous_dossier = os.path.join(dossier_parent, nom_sous_dossier)\n",
    "    \n",
    "    if os.path.isdir(chemin_sous_dossier):\n",
    "        \n",
    "        donnees_par_sous_dossier[nom_sous_dossier] = []\n",
    "        \n",
    "        for nom_fichier in os.listdir(chemin_sous_dossier):\n",
    "            \n",
    "            if nom_fichier.endswith('.edf'):\n",
    "                \n",
    "                chemin_fichier = os.path.join(chemin_sous_dossier, nom_fichier)\n",
    "                donnees_mne = mne.io.read_raw_edf(chemin_fichier)\n",
    "                donnees_par_sous_dossier[nom_sous_dossier].append(donnees_mne)\n",
    "\n",
    "# Affichage des informations sur les données MNE\n",
    "for nom_sous_dossier, donnees_mne in donnees_par_sous_dossier.items():\n",
    "    print(f\"Sujet {nom_sous_dossier} : {len(donnees_mne)} fichiers .edf\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X = list(donnees_par_sous_dossier.keys())\n",
    "# Y = list(len(elem) for elem in donnees_par_sous_dossier.values())\n",
    "\n",
    "# # Création de l'histogramme\n",
    "# plt.figure(figsize=(10, 6))\n",
    "# plt.bar(X,Y, color='skyblue')\n",
    "# plt.xlabel('Sujets')\n",
    "# plt.ylabel('Nombre d\\'enregistrements')\n",
    "# plt.title('Nombre d\\'enregistrements par sujet')\n",
    "# plt.xticks(rotation=45, ha='right')  # Rotation des étiquettes sur l'axe des x pour une meilleure lisibilité\n",
    "# plt.tight_layout()  # Ajustement automatique du tracé pour éviter les chevauchements\n",
    "# plt.show()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Temps d'échantillonnage - Nombre d'enregistrements n'ayant pas une durée de 1 heure par sujet "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sous-dossier chb-mit-scalp-eeg-database-1.0.0 : 0 fichiers\n"
     ]
    }
   ],
   "source": [
    "# Dictionnaire pour stocker le nombre de fichiers par sous-dossier\n",
    "nombre_par_sous_dossier = {}\n",
    "\n",
    "# Parcours des sous-dossiers\n",
    "for nom_sous_dossier, donnees_mne_liste in donnees_par_sous_dossier.items():\n",
    "    # Initialisation du compteur pour ce sous-dossier\n",
    "    cpt = 0\n",
    "    # Parcours des données MNE dans ce sous-dossier\n",
    "    for donnees_mne in donnees_mne_liste:\n",
    "        # Vérification si la durée de la donnée MNE est inférieure à la valeur donnée\n",
    "        if donnees_mne.times[-1] < 3599.99609375:\n",
    "            # Incrémentation du compteur\n",
    "            cpt += 1\n",
    "    # Stockage du nombre dans le dictionnaire\n",
    "    nombre_par_sous_dossier[nom_sous_dossier] = cpt\n",
    "\n",
    "# Affichage du nombre de fichiers qui satisfont la condition pour chaque sous-dossier\n",
    "for nom_sous_dossier, nombre in nombre_par_sous_dossier.items():\n",
    "    print(f\"Sous-dossier {nom_sous_dossier} : {nombre} fichiers\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Chemin d'accès au fichier\n",
    "file_path = \"C:/Users/rayan/Desktop/B_Laval/S2/Cours/Apprentissage_NeuronesProfonds/Projet_2/EEG_Seizures_Transformers-/data/chb-mit-scalp-eeg-database-1.0.0\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_summary(file_path):\n",
    "    # Initialisation des listes pour stocker les informations\n",
    "    seizure_presence = {}\n",
    "    \n",
    "    # Chemin d'accès au fichier\n",
    "    #file_path = \"chb04-summary.txt\"\n",
    "    \n",
    "    # Ouvrir et lire le fichier\n",
    "    is_seizure = False\n",
    "    all_files_str = []\n",
    "    seizure_start = 0\n",
    "    with open(file_path, \"r\") as file:\n",
    "        # Lire chaque ligne du fichier\n",
    "        for line in file:\n",
    "            # Traiter la ligne actuelle\n",
    "            if line.strip():\n",
    "                if \"File Name:\" in line:\n",
    "                  start = len(\"File Name: \")\n",
    "                  all_files_str.append(str(line[start:len(line)-1]))\n",
    "                elif (\"Seizure\" in line) and (\"Start Time: \" in line):\n",
    "                  if line[len(\"Seizure S\")-1] == \"S\":\n",
    "                    start = len(\"Seizure Start Time: \")\n",
    "                  else:\n",
    "                    start = len(\"Seizure 1 Start Time: \")\n",
    "                  end = len(\" seconds\")+1\n",
    "                  seizure_start = int(line[start:len(line)-end])\n",
    "                elif (\"Seizure\"in line) and (\"End Time: \" in line):\n",
    "                  if line[len(\"Seizure E\")-1] == \"E\":\n",
    "                    start = len(\"Seizure End Time: \")\n",
    "                  else:\n",
    "                    start = len(\"Seizure 1 End Time: \")\n",
    "                  end = len(\" seconds\")+1\n",
    "                  seizure_end = int(line[start:len(line)-end])\n",
    "                  if not all_files_str[len(all_files_str)-1] in seizure_presence.keys():\n",
    "                    seizure_presence[all_files_str[len(all_files_str)-1]] = []\n",
    "                  seizure_presence[all_files_str[len(all_files_str)-1]].append((seizure_start, seizure_end))\n",
    "    print(\"Seizure presence init\" ,seizure_presence)\n",
    "    return seizure_presence, all_files_str\n",
    "\n",
    "\n",
    "def separate_data_intervals(file_str, seizure_presence,path):\n",
    "  # Durée de chaque intervalle en secondes (2 minutes)\n",
    "  interval_duration = 10\n",
    "\n",
    "  raw = mne.io.read_raw_edf(path + file_str)\n",
    "  #data, times = raw[:, :]\n",
    "  total_duration = raw.times[-1] # en secondes\n",
    "\n",
    "  # Nombre total d'intervalle de 10 minutes\n",
    "  num_intervals = int(total_duration / interval_duration)\n",
    "  labels = []\n",
    "  data = []\n",
    "  \n",
    "  # print(\"Seizure presence: \", seizure_presence) \n",
    "  \n",
    "  # Diviser les données en intervalles de 10 minutes\n",
    "  for i in range(num_intervals):\n",
    "      # Calculer le temps de début et de fin de chaque intervalle\n",
    "      start_time = i * interval_duration\n",
    "      end_time = (i + 1) * interval_duration\n",
    "            \n",
    "\n",
    "      # Convertir le temps en indice\n",
    "      start_idx = raw.time_as_index(start_time)\n",
    "      end_idx = raw.time_as_index(end_time)\n",
    "\n",
    "      # Extraire les données de l'intervalle\n",
    "      interval_data, interval_times = raw[:, start_idx:end_idx]\n",
    "      data.append(interval_data)\n",
    "\n",
    "      \n",
    "      if file_str in seizure_presence.keys():\n",
    "        is_seizure = False\n",
    "        for start_seizure, end_seizure in seizure_presence[file_str]:\n",
    "\n",
    "\n",
    "          if (start_seizure >= start_time and start_seizure <= end_time) or (end_seizure >= start_time and end_seizure <= end_time) or (start_seizure <= start_time and end_seizure >= end_time):\n",
    "                \n",
    "            is_seizure = True\n",
    "  \n",
    "            break\n",
    "        if is_seizure:\n",
    "          labels.append(1)\n",
    "        else:\n",
    "          labels.append(0)\n",
    "      else:\n",
    "        labels.append(0)\n",
    "  return data, labels\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data_for_patient(file_path, patient):\n",
    "      \n",
    "    path = file_path +\"/\"+ patient + \"/\"\n",
    "    seizure_presence, all_files_str = read_summary(path+patient+\"-summary.txt\")\n",
    "    data = []\n",
    "    labels = []\n",
    "    for file in all_files_str:\n",
    "      try :\n",
    "        interval_data, label = separate_data_intervals(file, seizure_presence,path)\n",
    "        for i in range(len(interval_data)):\n",
    "          data.append(interval_data[i])\n",
    "          labels.append(label[i])\n",
    "      except Exception as e:\n",
    "        print(\"Error while reading file : \", file)\n",
    "        print(e)\n",
    "    return data, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot the data for a patient \n",
    "import mne\n",
    "\n",
    "def plot_patient_record(file_path,patient_record,duration,channel_names=None, duration_min=None,duration_max=None):\n",
    "    raw = mne.io.read_raw_edf(file_path + \"/\" + patient_record, preload=True)\n",
    "\n",
    "    original_channel_names = raw.info['ch_names'].copy()\n",
    "    # shape \n",
    "    print(raw.get_data().shape)\n",
    "    # print the time of the record\n",
    "    print(raw.n_times)\n",
    "    \n",
    "        \n",
    "    if channel_names is not None:\n",
    "        raw.rename_channels(dict(zip(original_channel_names, channel_names)))\n",
    "    else:\n",
    "        channel_names = original_channel_names   \n",
    "    \n",
    "    if duration_min is not None and duration_max is None:\n",
    "        \n",
    "\n",
    "        raw.crop(tmin=duration_min)\n",
    "        raw.plot(duration=duration, n_channels=23, scalings={\"eeg\": 75e-5}, title=\"Patient Record\", show=True, block=True)\n",
    "        \n",
    "    elif duration_min is None and duration_max is not None:\n",
    "        # Create an event array with a single event at 700 seconds\n",
    "        raw.crop(tmin = duration_min,tmax=duration_max)\n",
    "        raw.plot(duration=duration, n_channels=23, scalings={\"eeg\": 75e-5}, title=\"Patient Record\",xlim=(duration_min, duration_max), show=True, block=True)\n",
    "\n",
    "        \n",
    "    elif duration_min is not None and duration_max is not None:\n",
    "        \n",
    "        raw.crop(tmin=duration_min , tmax=duration_max)\n",
    "        raw.plot(duration=duration, n_channels=23, scalings={\"eeg\": 75e-5}, title=\"Patient Record\",  show=True, block=True)\n",
    "\n",
    "        \n",
    "    else :\n",
    "        raw.plot(duration=duration, n_channels=23, scalings={\"eeg\": 100e-5}, title=\"Patient Record\", show=True, block=True)\n",
    "\n",
    "    return raw.get_data()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting EDF parameters from C:\\Users\\rayan\\Desktop\\B_Laval\\S2\\Cours\\Apprentissage_NeuronesProfonds\\Projet_2\\EEG_Seizures_Transformers-\\data\\chb-mit-scalp-eeg-database-1.0.0\\chb01\\chb01_02.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "Reading 0 ... 921599  =      0.000 ...  3599.996 secs...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rayan\\AppData\\Local\\Temp\\ipykernel_16872\\2380645811.py:5: RuntimeWarning: Channel names are not unique, found duplicates for: {'T8-P8'}. Applying running numbers for duplicates.\n",
      "  raw = mne.io.read_raw_edf(file_path + \"/\" + patient_record, preload=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(23, 921600)\n",
      "921600\n",
      "Using matplotlib as 2D backend.\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[1;32mIn [11]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m data_1 \u001b[38;5;241m=\u001b[39m \u001b[43mplot_patient_record\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile_path\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m./chb01/chb01_02.edf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mduration\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m3599\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchannel_names\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43midx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43midx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m24\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      2\u001b[0m data_2 \u001b[38;5;241m=\u001b[39m plot_patient_record(file_path,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m./chb01/chb01_03.edf\u001b[39m\u001b[38;5;124m\"\u001b[39m,duration \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m3599\u001b[39m, channel_names \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mstr\u001b[39m(idx) \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m24\u001b[39m)])\n",
      "Input \u001b[1;32mIn [10]\u001b[0m, in \u001b[0;36mplot_patient_record\u001b[1;34m(file_path, patient_record, duration, channel_names, duration_min, duration_max)\u001b[0m\n\u001b[0;32m     34\u001b[0m     raw\u001b[38;5;241m.\u001b[39mplot(duration\u001b[38;5;241m=\u001b[39mduration, n_channels\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m23\u001b[39m, scalings\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124meeg\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m75e-5\u001b[39m}, title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPatient Record\u001b[39m\u001b[38;5;124m\"\u001b[39m,  show\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, block\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m     37\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m :\n\u001b[1;32m---> 38\u001b[0m     \u001b[43mraw\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mduration\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mduration\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_channels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m23\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscalings\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43meeg\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m100e-5\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtitle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mPatient Record\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshow\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mblock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m     40\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m raw\u001b[38;5;241m.\u001b[39mget_data()\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\mne\\io\\base.py:1808\u001b[0m, in \u001b[0;36mBaseRaw.plot\u001b[1;34m(self, events, duration, start, n_channels, bgcolor, color, bad_color, event_color, scalings, remove_dc, order, show_options, title, show, block, highpass, lowpass, filtorder, clipping, show_first_samp, proj, group_by, butterfly, decim, noise_cov, event_id, show_scrollbars, show_scalebars, time_format, precompute, use_opengl, theme, overview_mode, splash, verbose)\u001b[0m\n\u001b[0;32m   1768\u001b[0m \u001b[38;5;129m@copy_function_doc_to_method_doc\u001b[39m(plot_raw)\n\u001b[0;32m   1769\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot\u001b[39m(\n\u001b[0;32m   1770\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1806\u001b[0m     verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   1807\u001b[0m ):\n\u001b[1;32m-> 1808\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mplot_raw\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1809\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1810\u001b[0m \u001b[43m        \u001b[49m\u001b[43mevents\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1811\u001b[0m \u001b[43m        \u001b[49m\u001b[43mduration\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1812\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1813\u001b[0m \u001b[43m        \u001b[49m\u001b[43mn_channels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1814\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbgcolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1815\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1816\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbad_color\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1817\u001b[0m \u001b[43m        \u001b[49m\u001b[43mevent_color\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1818\u001b[0m \u001b[43m        \u001b[49m\u001b[43mscalings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1819\u001b[0m \u001b[43m        \u001b[49m\u001b[43mremove_dc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1820\u001b[0m \u001b[43m        \u001b[49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1821\u001b[0m \u001b[43m        \u001b[49m\u001b[43mshow_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1822\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtitle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1823\u001b[0m \u001b[43m        \u001b[49m\u001b[43mshow\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1824\u001b[0m \u001b[43m        \u001b[49m\u001b[43mblock\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1825\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhighpass\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1826\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlowpass\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1827\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfiltorder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1828\u001b[0m \u001b[43m        \u001b[49m\u001b[43mclipping\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1829\u001b[0m \u001b[43m        \u001b[49m\u001b[43mshow_first_samp\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1830\u001b[0m \u001b[43m        \u001b[49m\u001b[43mproj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1831\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgroup_by\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1832\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbutterfly\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1833\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdecim\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1834\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnoise_cov\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnoise_cov\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1835\u001b[0m \u001b[43m        \u001b[49m\u001b[43mevent_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mevent_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1836\u001b[0m \u001b[43m        \u001b[49m\u001b[43mshow_scrollbars\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshow_scrollbars\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1837\u001b[0m \u001b[43m        \u001b[49m\u001b[43mshow_scalebars\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshow_scalebars\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1838\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtime_format\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtime_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1839\u001b[0m \u001b[43m        \u001b[49m\u001b[43mprecompute\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprecompute\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1840\u001b[0m \u001b[43m        \u001b[49m\u001b[43muse_opengl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_opengl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1841\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtheme\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtheme\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1842\u001b[0m \u001b[43m        \u001b[49m\u001b[43moverview_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moverview_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1843\u001b[0m \u001b[43m        \u001b[49m\u001b[43msplash\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msplash\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1844\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1845\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m<decorator-gen-157>:12\u001b[0m, in \u001b[0;36mplot_raw\u001b[1;34m(raw, events, duration, start, n_channels, bgcolor, color, bad_color, event_color, scalings, remove_dc, order, show_options, title, show, block, highpass, lowpass, filtorder, clipping, show_first_samp, proj, group_by, butterfly, decim, noise_cov, event_id, show_scrollbars, show_scalebars, time_format, precompute, use_opengl, theme, overview_mode, splash, verbose)\u001b[0m\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\mne\\viz\\raw.py:405\u001b[0m, in \u001b[0;36mplot_raw\u001b[1;34m(raw, events, duration, start, n_channels, bgcolor, color, bad_color, event_color, scalings, remove_dc, order, show_options, title, show, block, highpass, lowpass, filtorder, clipping, show_first_samp, proj, group_by, butterfly, decim, noise_cov, event_id, show_scrollbars, show_scalebars, time_format, precompute, use_opengl, theme, overview_mode, splash, verbose)\u001b[0m\n\u001b[0;32m    350\u001b[0m precompute \u001b[38;5;241m=\u001b[39m _handle_precompute(precompute)\n\u001b[0;32m    351\u001b[0m params \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(\n\u001b[0;32m    352\u001b[0m     inst\u001b[38;5;241m=\u001b[39mraw,\n\u001b[0;32m    353\u001b[0m     info\u001b[38;5;241m=\u001b[39minfo,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    402\u001b[0m     splash\u001b[38;5;241m=\u001b[39msplash,\n\u001b[0;32m    403\u001b[0m )\n\u001b[1;32m--> 405\u001b[0m fig \u001b[38;5;241m=\u001b[39m _get_browser(show\u001b[38;5;241m=\u001b[39mshow, block\u001b[38;5;241m=\u001b[39mblock, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mparams)\n\u001b[0;32m    407\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fig\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\mne\\viz\\_figure.py:698\u001b[0m, in \u001b[0;36m_get_browser\u001b[1;34m(show, block, **kwargs)\u001b[0m\n\u001b[0;32m    695\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m fig\n\u001b[0;32m    697\u001b[0m \u001b[38;5;66;03m# Initialize Browser\u001b[39;00m\n\u001b[1;32m--> 698\u001b[0m fig \u001b[38;5;241m=\u001b[39m backend\u001b[38;5;241m.\u001b[39m_init_browser(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    699\u001b[0m _show_browser(show\u001b[38;5;241m=\u001b[39mshow, block\u001b[38;5;241m=\u001b[39mblock, fig\u001b[38;5;241m=\u001b[39mfig)\n\u001b[0;32m    701\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fig\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\mne\\viz\\_mpl_figure.py:2514\u001b[0m, in \u001b[0;36m_init_browser\u001b[1;34m(**kwargs)\u001b[0m\n\u001b[0;32m   2512\u001b[0m \u001b[38;5;66;03m# update data, and plot\u001b[39;00m\n\u001b[0;32m   2513\u001b[0m fig\u001b[38;5;241m.\u001b[39m_update_trace_offsets()\n\u001b[1;32m-> 2514\u001b[0m \u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_redraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mupdate_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mannotations\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m   2516\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(fig\u001b[38;5;241m.\u001b[39mmne\u001b[38;5;241m.\u001b[39minst, BaseRaw):\n\u001b[0;32m   2517\u001b[0m     fig\u001b[38;5;241m.\u001b[39m_setup_annotation_colors()\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\mne\\viz\\_mpl_figure.py:2160\u001b[0m, in \u001b[0;36mMNEBrowseFigure._redraw\u001b[1;34m(self, update_data, annotations)\u001b[0m\n\u001b[0;32m   2157\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmne\u001b[38;5;241m.\u001b[39mvline_visible \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmne\u001b[38;5;241m.\u001b[39mis_epochs:\n\u001b[0;32m   2158\u001b[0m     \u001b[38;5;66;03m# prevent flickering\u001b[39;00m\n\u001b[0;32m   2159\u001b[0m     _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_recompute_epochs_vlines(\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m-> 2160\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw_idle\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\backend_bases.py:2060\u001b[0m, in \u001b[0;36mFigureCanvasBase.draw_idle\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2058\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_idle_drawing:\n\u001b[0;32m   2059\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_idle_draw_cntx():\n\u001b[1;32m-> 2060\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdraw(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:436\u001b[0m, in \u001b[0;36mFigureCanvasAgg.draw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    432\u001b[0m \u001b[38;5;66;03m# Acquire a lock on the shared font cache.\u001b[39;00m\n\u001b[0;32m    433\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m RendererAgg\u001b[38;5;241m.\u001b[39mlock, \\\n\u001b[0;32m    434\u001b[0m      (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtoolbar\u001b[38;5;241m.\u001b[39m_wait_cursor_for_draw_cm() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtoolbar\n\u001b[0;32m    435\u001b[0m       \u001b[38;5;28;01melse\u001b[39;00m nullcontext()):\n\u001b[1;32m--> 436\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    437\u001b[0m     \u001b[38;5;66;03m# A GUI class may be need to update a window using this draw, so\u001b[39;00m\n\u001b[0;32m    438\u001b[0m     \u001b[38;5;66;03m# don't forget to call the superclass.\u001b[39;00m\n\u001b[0;32m    439\u001b[0m     \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mdraw()\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\artist.py:74\u001b[0m, in \u001b[0;36m_finalize_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     72\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[0;32m     73\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdraw_wrapper\u001b[39m(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m---> 74\u001b[0m     result \u001b[38;5;241m=\u001b[39m draw(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m     75\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m renderer\u001b[38;5;241m.\u001b[39m_rasterizing:\n\u001b[0;32m     76\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\artist.py:51\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer)\u001b[0m\n\u001b[0;32m     48\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m     49\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[1;32m---> 51\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     52\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     53\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\figure.py:2845\u001b[0m, in \u001b[0;36mFigure.draw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   2842\u001b[0m         \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[0;32m   2844\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[1;32m-> 2845\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   2846\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2848\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubfigs:\n\u001b[0;32m   2849\u001b[0m     sfig\u001b[38;5;241m.\u001b[39mdraw(renderer)\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\image.py:132\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[0;32m    131\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[1;32m--> 132\u001b[0m         \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    133\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    134\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[0;32m    135\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\artist.py:51\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer)\u001b[0m\n\u001b[0;32m     48\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m     49\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[1;32m---> 51\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     52\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     53\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\axes\\_base.py:3091\u001b[0m, in \u001b[0;36m_AxesBase.draw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   3088\u001b[0m         a\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[0;32m   3089\u001b[0m     renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n\u001b[1;32m-> 3091\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   3092\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3094\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maxes\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m   3095\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\image.py:132\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[0;32m    131\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[1;32m--> 132\u001b[0m         \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    133\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    134\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[0;32m    135\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\artist.py:51\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer)\u001b[0m\n\u001b[0;32m     48\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m     49\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[1;32m---> 51\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     52\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     53\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\lines.py:732\u001b[0m, in \u001b[0;36mLine2D.draw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    729\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m    731\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_invalidy \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_invalidx:\n\u001b[1;32m--> 732\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecache\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    733\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mind_offset \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m  \u001b[38;5;66;03m# Needed for contains() method.\u001b[39;00m\n\u001b[0;32m    734\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_subslice \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes:\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\lines.py:661\u001b[0m, in \u001b[0;36mLine2D.recache\u001b[1;34m(self, always)\u001b[0m\n\u001b[0;32m    658\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    659\u001b[0m     y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_y\n\u001b[1;32m--> 661\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_xy \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumn_stack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbroadcast_arrays\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mfloat\u001b[39m)\n\u001b[0;32m    662\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_x, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_xy\u001b[38;5;241m.\u001b[39mT  \u001b[38;5;66;03m# views\u001b[39;00m\n\u001b[0;32m    664\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_subslice \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "File \u001b[1;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36mcolumn_stack\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\numpy\\lib\\shape_base.py:656\u001b[0m, in \u001b[0;36mcolumn_stack\u001b[1;34m(tup)\u001b[0m\n\u001b[0;32m    654\u001b[0m         arr \u001b[38;5;241m=\u001b[39m array(arr, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, subok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, ndmin\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\u001b[38;5;241m.\u001b[39mT\n\u001b[0;32m    655\u001b[0m     arrays\u001b[38;5;241m.\u001b[39mappend(arr)\n\u001b[1;32m--> 656\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_nx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcatenate\u001b[49m\u001b[43m(\u001b[49m\u001b[43marrays\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36mconcatenate\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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d3Hn/rVy5MsfyQmZdli+f4ydTDL744guX827dumGxWNi9ezfNmzcnJiaGGjVqMHnyZD788ENCQkLIOmIdHh7Oxx9/TExMjFEXdrudChUqcO3atWx149yplZaWlut0d5kyZYiLi3Pxa9gwc8B5//792eL7+fkhIlit1mxhzj7t7zinzjdyuq67xFuxYgUxMTGICCaTCYfDQWJiIj/88IPxB45BQUHouo6u69hsNoKCgli7di0HDhzIUW9d1zl06BDBwcEkJiZmC7fb7WzdupUpU6aQkJDApEmTmDx5srGbMiIiwiX+9fUFsHv3bs6ePUvLli0JDAwkODgYm83mEu+7776jfv36Rr8ZHh7OM888Y7SJ0NBQ0tPTSUhIyCb/3LlziAgbN27k559/5uDBg8YutYSEBE6ePEmNGjWYO3cuvr6+dOnShUmTJnHmzBmuXbvG+++/T+XKldF13cjHarXyyy+/MHv2bHbt2sWpU6eMOOvWrWPZsmX07NmTadOmoes6Gzdu5PDhw+zatYszZ87Qpk0bFixYYFwLi8VCy5Yt2b17t0tbzxP+buO9n447WNNyPYGBgcbxrFmz5H//+5/ExcVli5cTkZGR2eZvnfPMTubOnZst3WuvvZajvxP+miv86aefjN0gIiIzZ86UTp06ycSJE8Vut0vlypWNOVuLxSI//PBDrjILFSqUbSeDiMi6deskPT3dJez6ed2c9MtKUFCQMQ++YMECERG5fPmyTJkyRQ4cOCCTJk2Szp07G/GLFCmS45qL1NRUY+cJIFOnTnXJKz093VgLERAQ4DKvumDBArHZbNKlSxepWLGiAEbcQoUKydWrVwWQL7/8UipWrChDhgwx5NavX98ln8WLF8uzzz4rLVq0kKSkJCPsk08+EUDeeustgcx1SHv37pVRo0ZlW9sgkrlDRyRzPr1Vq1Yu6xxEROLi4qRMmTIuawSef/55MZvNUrlyZYHMXUN//PGHiIixNmLhwoXSsWNH0XXdZdFuWFiYfPbZZ6Lrunz55Zfy4YcfiohInz59DPnOdSXjxo2To0ePytChQ0XTNPHy8jLiVKpUyTj28/OT//3vfxIeHu6iAyDvvPOO+Pn5SVJSkvj4+Mirr74qERERRvmcaydyWkCYnp6erR3lxqFDh2ThwoVis9mM+X7nLrXFixfLoEGDRCSzzTjlOq8PYCwonzBhgrFDEJDo6GgREVm+fLn069fP0Kdq1aqiaZoRb86cOQKI3W4XQMaNG5dNRz8/P0lJSXHxy3qtRTIXsgYFBRnnCxcuNHYx3i5vvfWWpKWlufjpui5vv/32Hcn7JxIaGnrPF58626iIGO3hRjh3ht2MFStWyOTJk3MMu/663SpZd6Zm5U7r6G7WLWoh7i0ZLCuASMAGhAODbhQ/N6MlK87FafeaPXv2iL+/f67hs2bNculUnTsOfHx85Oeff76jPKtWrSrJycl3lPZ6nFu//w7nzp2TESNGiIjkuCV2+PDhIiIuizedOG96XdeNnStZsdvt0qlTJxERGT16tGGElS5dWtLS0nKUeaPrvm3bNhH5/+siIuLp6WmEb9myRbp06ZLNaMmJs2fPyrJly0Qk02gpW7ZsjvGcO9Kuh78WeTr1NZvNEhMTIxMnTnTpDEeNGiUDBgzIVY+sJCcny759+4zz6+si6xZfkcwtyatXrzYM1TtB1/Uct/zfKgkJCVKqVCkXP2d91a1bVwoXLiwiYuzQy1o348aNk3Xr1hnnp0+floULF8rLL78sIiL+/v7SoUMHmTp1qsyaNUvWr19vxLXZbLfc/k+dOnXLhpni38c/ebfP3SYvjZZ8tRD3dnAuxO3jOzWvVbklfHx86N69O5BpSDr/0OtOsVqtLn849m9BRDhw4ADt27e/aVyTyZRtSux2WLduHWFhYQwfPhybzUbBggWBzG/WHDp0iNdff50LFy7QqFGjXP/oLys2m41t27bRo0ePbGFms5mpU6cyceJEF//FixfnOk2YlQsXLmA2m2nWrNmtFS6fkZGRwU8//cR7771n+GmaZnRkZrMZDw+PPNQwEw8PD9LT0/NaDYXinqJpWp4txFVGi0Jxh2iaxoULF6hatSrFixfPa3X+c7Ro0YITJ07ktRoumM1mihYtmtdqKBT3lLw0WgrkkOauscZrTA+gIVCpj+/U99d4jRlF5nB6PLAR+AywA3OAJkADoFAf36mT/krvBnwFpAOb+vhOzdZDrfEaUwt4to/v1K/XeI15CijWx3fqhntZLoUCICYmBk9PT9zc/lXr2fMN/zSDBVAGi0Jxj7mnvW0f36m/9/Gd+iVQ5C+vSn18p35LpnHSAVgLzACeA9r38Z06FbCt8Rrj/FxuE+AEMAF43il3jdeYr//6nUDmX/vX+CuoO+BilSkU94ry5csrg0WhUCjuI/e8x13jNWYM4NzXe6O5KLmFOE6cCz6cXwTct8ZrTB8gvo/v1Jt/alehUCgUCkW+415PD30M1AUy1niNOQ5ErfEaMwLwB/aQOT1kBeYCTdZ4jfkIKNjHd2riGq8xQ8g0dl4C/kfmqIyTYn9NNTUFHiZzqskf6Hwvy6NQKBQKhSLvuKdGSx/fqV9c5/XtdeejshyHkmVqp4/v1Pl/HX6Qg+i0Pr5TvwG+yeJX7071VCgUCoVCcVfQuYezOPlyQr6P79TRea2DQqFQKBSKbNxTu+KejrTkJdPYha+v+gSRQqFQKBT/FvLlSItCoVAoFIr/HspoUShyIcvfUysUCoXiH4AyWhSKXJg3bx5z5szJazUUCoVC8RfKaFHcc1JTUwHy3ahFQkICe/bsyWs1FAqFQvEX+cJo0TStuqZpezVNO69p2jlN097Pa50UORMbG5vtg4ClS5emfPnytGrVCoCffvopx7TLly+/qWGTmJj4t3VMT08nLCws13Dnxw/nz5/Phg25fxHil19+wWQy5Ttj7N/IggULXM5ff/31PNJEoVDcS/KF0ULm94lGicjDQCtgqKZpD98oQUJCAp988gkmk+mGgq9du3bDB9j9YMqUKS7n8+bNM47DwsLYvn07AEuWLLllmStXrsThcNwwTm5rNg4dOnTDdJs3b+bkyZPGeVxcHJs2bWLQoEHUqFEDHx8fBg4cCMDFixfRdZ24uDiOHTvGgQMHGDJkCA6HI9vXcAcMGJDj3+I//fTTJCQkoGkaZcuWNb7uCxAYGGjEu1l5nWVev349b7/9drawZs2aUbZsWQoXLgxAaGgoAHa7nWHDhrFq1SoAzp49y4IFC3j55ZcxmUyUL18+myxd143jQYMGcfnyZRISEpg4caJLWHx8PABr1qwhICAgm4zly5e7xE9MTOTTTz/lu+++Y9euXYa/2Ww24l24cCHHsvv4+GC1WrFYLC5tftu2bS7xDh06hM1mY9myZVgsFpYsWcLzzz+fra3ExsYC93YEzdmOIiMjef7557OFHT9+nK5duzJ48GDg/++dxYsXExAQQFpamhHf+XXuzZs38+mnnzJqVObfRHl4eHD69GlCQ0OZOnUqdrv6U22F4h+L88GVnxywCXjyJnEEkOXLl4uIiNlsFl3XZdy4cRIfHy8iIrGxsQJInz59xEmrVq3kemw2m/j7+4uIiK7r2cKz+iUnJ2cLt1gs8uyzz8qff/4pIiJ2u92Qe+3aNQGkdu3acv78eXE4HJJ5WTJp2LChAIauWfPKSReLxSLLly8XwEUXXdclPj5eLl26JKmpqaLrunz99deyYMECGTJkiEu8rPmLiHz66adis9lERMThcMgTTzwhrVq1kqFDh0rv3r1l9uzZ4qzvrK5Dhw45+gNSt25dAaRFixYCSPPmzV3C09LSxGq1yqVLl3JMX716dRERAWTbtm3SunVrAaRJkyZis9lE13UZM2aMca2dZduzZ48ho1q1apKQkCCLFy+Wffv2uch3ys7NtWnTRgApU6aM4ffQQw/JW2+9JfPmzXO5Zte74sWLS2xsrERFRRnX1Bk2dOhQ+f7772X8+PHZ6rVGjRo31Cmr3s7yVqhQQRo3bixPPfWUS7zmzZu7XO+JEydKWlqa6LouVapUES8vr2yyS5YsKcePHxe73S5DhgwRQI4dOyaAWCwWiY2NlcjISElKSsrWLtPT02XDhg1itVpzbLevvPKK6LouNptNTCaTnDx5UlasWCGAmM1mGTZsmADi7+8v33zzjQQGBmbTLykpKdu1a9eunSxcuFBERCZNmiSvv/66zJ49W0qVKiWAPP7449nk7NixQw4ePCg7d+6Uffv2GeVZunSpoe/vv/8uUVFRLmVYu3atvPfeey5+19dF1rI776ncyBr3zz//NPoNJ2azWdatWyd2u10cDscNZd1NrtfDarXet7xzw+FwiMlkuq00t6J3Tm31VomOjpbY2FhJSUnJNY7dbpeQkJCb6nb+/HnjGZSV+Ph4cTgcEhwcbOjrdE7MZrMRlpqaauRrMpmMe87ZZ4qIJCYmZsvH4XC4tGXAV7I/c3sAPby9vcXb21u6d+9+R86Z3inv+nw0yWdD25qm1QD2Aw1FJOUG8fJXwRQKhUKhyB+cEBGvrB6apvUA8Pb2/g2gYsWKdyQ4OjoagOPHj/cEEBGXjyDnqz+X0zStOLAOGJ6TwaJp2hBgyH1XTKFQKBQKxT0nv6xpQdO0gmQaLL+IyPqc4ojIfBHxymoBJiUloes6IsKGDRuYN28eIoLJZGL//v2MGzeOcePGkZycjIiQnp5uDEOFhISg6zpms5kff/wx2xSUruscPHiQVq1aGX6nT5/GZrO5xPPx8QGgcePGWCwW1qxZg67r7N69myFD/t/GOnHihDGf7ufnl+Mi0A8++AC73Y6IcOTIEUNvEcFisSAiTJw4EYDp06fjcDiw2+1GOS5cuMCkSZN45513mDx5Mt988w3h4eE4HA527tzJunXreOONN4w6s9lsJCYmupzPmjWLgIAAdu7cSevWrXnggQdu+To2aNAAgL59+wKwdOlSY61IVh5++GEcDgdpaWkMHTo0R1m6rjNq1CgGDRpk+J07dw5d13E4HPTp08e4FhaLBSCbriLCwIEDee+991z8r19vcz1eXl65hhUsWJAiRYrQvHlzF/9GjRoB8MknnwD/v6vqwIEDLvHc3Nx45plnjPOXX34ZgOeff/6Gby/FihUz1peICHa7nUGDBnH27Nls5a5fvz66rqPrOhUrVmT06NEsXLiQMmXKEBwcTMGCBXPMY9q0afj5+TF37lwAQkJCgMy1OQcOHMBsNhvt09kenffKwIEDuXTpEpcvX2br1q3Ex8cjIsTFxbF9+3ZEBLPZzNGjR+nXrx/NmjUDMq+Fc93Q5MmTqVevHo0bN86m25EjRyhRogRRUVGGn8VioW7dunTo0IFevXoxc+ZMrl69mmsdQmYb8vHxYeHChbRp04aIiAhq1apFQkICJpMJXdfx9/fn7NmzpKen43A4EBHef/99l/tJRAgNDUXXdb7//nv27dvHggULsNlsXLx4kQsXLhj1k5GRYdxroaGhOBwOEhMTEREcDgcpKSk4HA6XvmXSpEl4enqi6zrp6elcvXrVuE+joqKMeM77X9d1UlJSsFqtmM1mUlJSjHBnPtf3cTk5u91Oamqq0R+YTCYcDoeRt/N667rOhg0bjHgWi4X4+Hh0Xefw4cPYbDbWrs38Du7s2bNJSkrCx8eHyMhIdF0nLS2NwoULc/DgQc6cOYPZbHbRw2KxkJycTFBQEMuXL6dv377Gve/sy1NSUjCZTKSkpGA2m/H39+f333837u8rV6646Lx69WrsdjtJSUmICG+88QaBgYHExsby008/ce3aNY4ePYqu6/j5+WGxWPjmm2+IiYnBZDJhtVq5cuUKDoeD7777jnHjxgGZ6+KuXbuG1Wo16kZEeOqpp1iyZAk2m43g4GCSkpK4ePEiAMOHDyc1NZWAgAB+/fVXZs+ezbZt24iPj2fHjh388ccfRl+SkpLCtWvXuHDhAjt27GDp0qWsWbOGlJQUbDYbNpuNKlWqGH1E69at6dixI3v37sVqtfL777/j7+9PfHw8AQEBhISEsHLlSmM94sqVKwEIDw9n6dKlN7x/7jX5YnpI0zQNWAIkiMjwW0lTs2ZNqVWrFrt3776nut0qw4YNo06dOgwbNixbmMlkIiEhgerVqwOZD6d58+ZRrlw5o5MoWLAgV69epWjRorc07Hby5EkqVapElSpV7npZcsJsNlOkSBEsFgtFixZF13U0TePEiRMuD3hd13Fzc+P6dudwOBg9ejRxcXF88803VKhQwSVcRFwW6UZFRbnUQ1xcHFar9YbltdvtmM1mIiMjad26NZGRkRQqVMglD13XKVCgQObcqaYBGJ18yZIlcTgcuLu7A/D1119z6dIlNE3jwQcfpFGjRjz55JOICEWKFDHkXrhwgVq1arnktWHDBnr37m3kIyJYrVYKFy5sGM5Wq5XQ0FDq1q3LuXPnXOpRRFiwYAENGjTg3Xffxc3NjRkzZvDoo4/SoEEDzp07l63858+fp0aNGiQkJFCtWjWXuneWycnFixcpU6YM/v7+VKpUiQYNGmS7Zs4H17fffsuECRNyrfc7JT4+3rgHAHr27MmoUaP48ssvWbt2LYUKFSIpKQkPDw9+/vnnbPeWn58fjRo1Ij09naSkJCpXrgyApmkcPHiQRx55hIIFCxIYGMioUaPo3LkzvXv3pmrVqne9LIrccV5f5/2WFbvdToEC+WpCIBtms5miRYvmtRp/m4SEBMqWLQuApml5Nj2UX4yWR4EDgB+ZX5AE+FhEtuSWpkWLFrJv3z6KFy9+P1S8KZcuXaJ48eJGx/lvxt/fn4YNG7r4HThwAA8PD1q0aMHmzZt5+umns6VzGg3XP0CzcvbsWU6fPs0rr7xy1/V2sn//ftq3b0/Xrl3Ztm1btof1f5Ft27bRpUuX+55vWlqayz3sHJX4NzwEFIr8ijJa7gFeXl6iPpio+DssWbKEyMhIxowZk9eqKBQKxT+GvDRa8ve4m0JxD3n11VfVKItCoVD8g8g3C3EVirwgp3l2hUKhUOQNymhRKBQKhUKRL1BGi0KhUCgUinyBMloUCoVCoVDkC5TRolAoFAqFIl+gjBaFQqFQKBR3C/3mUe4cZbQoFAqFQqG4W9xTu0IZLQqFQqFQKPIFymhRKBQKhUKRL1BGi0KhUCgUinyBMloUCoVCoVDkC/KF0aJpWhFN045pmnZG07RzmqZNzGudFDfm008/BSAsLIwlS5ZgNpuZMGECdrudXr163ZFMTdMQEZKSku6eovmcuLi4vFbhX0HLli1xOBx5rYZCobgJ+cJoASxABxFpAjQFumia1upuCU9PT0fX7+kurX80Y8eOvSty4uLiePbZZ9E0jc8//xyA9957j9dee40ePXowceJEIiMj2bRpEw6HA7vd7pL+xIkTfPPNNznKNpvNALi5uVGmTJm7om9O3MsPJN4L2eXLlycqKuquy/2ncH0buRskJyfj5+dHenq64Xf27FkyMjKMdqZQKP6Z5AujRTJJ++u04F/urj0BnnvuOfbs2WOc67p+140Ym80GwPvvv09QUNAtp4uIiODs2bO3lVd6ejopKSm3HH/q1KnGsa7rbNiw4bbyc7J169ZsaTdt2gTA7t27AahZsyYAEyZMoGDBgsYDV0Tw8vJi9OjROcoeNmyYy/nixYuzxbn+TTkiIiJHWf7+/jle35MnT/L4448bsleuXMnkyZNzfAMXEaxWKwCpqak55gMwfvx447hs2bJYLJZc494Ik8lk6Dxnzhx8fHzw9PQEoF+/fphMJqONXc/EiZkDk7m16Zz8RYTk5GQjvEKFCjnGyUnW3TTOChYsCGTW8c8//5xjHIvFwvz58138lixZkquehw4dYtSoUUb9OWUUL16ct956626pno3w8PDb7ld0Xc/1ut4IZ9u8F/yXX/DuF/eqju/k3ryT9ndPEZF84QB34DSQBky7hfgCSO/evaVr167iPM/q6tat63L+4osvGscFChSQvn37yvz58+X9998XQHr16iU//vij1K1bV959913p3LmztGzZUp588kkpWLCgADJ+/HhDRr9+/QSQChUq5Jh/Tq506dLGcc2aNXOM89BDDxnHrVq1EkBatGgh7dq1k44dO7rEHTFihBH+xhtvCCCtW7d2KadT7zJlytxUv+LFi99yWf6ue/zxx0XTtFzDH3nkEQGkZcuWLv6LFi0ywjp27CjFihW7L/o6r8WturJly94wrEaNGretw+jRowWQDh063DTu888/L1OnTjXOvby8pF69ereUj7u7u8D/3zNTpkxxKc8777wjX3zxhaxYsUJatWolH330kQDy5ZdfyrRp0+Sbb76R9u3by2uvvSarV6+WDRs2yJIlS6RBgwbZ8vr0009vSafZs2cLIHXq1MkWNnToUOncubN88MEHf+saDx8+XH766Sf58MMP70ubuhO3bt26HP27d+8utWrVkrZt28rgwYON+6JHjx4yfvx4effdd6VZs2YyePBgGT58uEDm/b527VpZtmyZdOrUyaV/efzxx+WHH36Qnj17CiAvv/yyPPzwwzJ9+nRp2rSp1K5dW3r37i0vvfSSVKhQQYYMGSLe3t4CyHPPPeciq1atWsZxlSpV5LnnnjPyd/ZR8P/9WfXq1V3K9uCDDwogQ4YMkWnTphnxbsWtWLFCVq1aJXPnzpVOnToZz4vBgwfL8ePHpWjRogJIz549pV27dgLI999/L4CMGTNG+vbta8hq3LixNGzYULp16+ZyD9aqVcs4nzhxogwePFh++OEHI3zv3r0yYsQIGTx4sDzzzDMCSKVKlbLpeit99N1yRYoUydG/UaNGAvjm8MztAfTw9vYWb29v6d69+x05Z3qnvOvz0e7lcPi9QNO00sAG4D0R8b8ubAgw5K/TFvdZNYVCoVAo/gucEBGvrB6apvUA8Pb2/g2gYsWKdyQ4OjoagOPHj/cEEJHfs4bni+mhrIhIErAX6JJD2HwR8cpamX5+fuzfv5/169cb8UaOHEnlypU5evQokydPpmHDhnz11VdERETw+uuvc/r0aXbu3ElERARhYWEEBgayefNmYmNjCQ4OJiMjg9jYWIKCglizZg0FChTg1KlTbNmyhfDwcAoXLsz48eNJTk5m+PDhnDx5EoChQ4fSrFkzALZs2cJ7773non/z5s05fPgwAH/++Sfr16+ncuXKLnEqVqxIz5492bhxI2+//TaTJk3ixRdfZMGCBTz33HOcP3/emGLZtGkTFy5cYNGiRXzxxRdcuXKFyMhItmzZQtOmTZk2bZoxdbB7925DT4DJkyezZs0aPvnkE/r378+IESPYv38/27ZtY9++fbRp04bZs2cb8XMbur9Ttm/fzpEjRzhy5AibN2/OMc7GjRsZOXIkixYtMvxee+01IiMj8fX1pW/fvhw7doyvvvqKY8eOMX/+fDp27GhM2dSoUYM333wzR9lRUVE8/fTTxvkTTzxhHH/44Ye8//77AKxatQqATz75hOHDhzN48GA+++wzAPr27QvA008/bUyvvPvuu8ZU5C+//ALAkCFDGDhwIACPPfYYzz//PD/88AOjRo0CwMfHB4BXXnnFZUojJ06cOEGvXr2IiYnh5MmTLFu2jJdeeskIb9euHZDZNuLi4rBYLEydOpW1a9cyf/58oqOjuXz5cq7y27ZtS+PGjdm7dy+enp7ExMTQpUsXQkNDCQoKIigoiFOnTnHt2jUyMjIIDAwkLS0Nu93OxIkTOXjwILGxsezcuRN/f38uXrxIaGgokZGRpKWlMWzYMI4dO+YyzXj69Gnj+PDhw7zxxhvUq1cPgA4dOhhhv/76K99++y0mkwmAWrVqsWPHDiZMmMD8+fP5888/iYmJ4aOPPjLStGqVfWlcjx49jHvzueee4/Dhw7Ro0YJ33nmHsLAwTCYTJpOJ6dOnM2DAAEaMGAFkTic2aNCAt99+m+HDhxtt47PPPjPK88ADD9CkSRMmT57Mnj176NWrFz///DOrV6/mtddeo2/fvqxYsYKnn36aTp068dRTT/H+++/zzDPP0KVLZpf39ddf8/rrrwMwcOBAI58nnniCffv2ce7cOaMsH3/8MZ988gnLli3j8uXLbNu2jZ9++ok1a9bQr18/OnfuzPbt20lMTCQmJgaz2YzFYiE1NZWvv/4aPz8/oqOjCQ0NJSwsjMTERNzd3Rk5ciQbN27kwIED7Nu3jwULFrB06VIiIiJITEzEbDaTnJzMpUuXCA4OZvr06Rw8eJADBw7wxRdfsHHjRnbt2kXbtm2pWLEisbGxADRr1oz58+dz4cIFPv30U0qXLs2BAwcYN24cw4cPJzk5maioKE6cOIG/vz+nTp1i1KhRLFq0iO+++46wsDDCwsJISUkhLCyMUaNGMXjwYIYNG8bRo0fx8fHhrbfeMu7Rjz76CIvFwqFDh4iLiyMgIICTJ08SHR3NmTNnSEhIIDw8nIkTJ7Jp0yZCQkJo27Ytly5dYv369ZjNZvz8/KhduzaLFy/m008/Zd68eQQFBTFnzhxeffVVAH7//Xe+/vprLl++zLVr1zh48CBHjhzh2LFj+Pr6kpKSwr59+zh9+jShoaG0bt0aX19fzp07R79+/ejVqxeffPIJ69evZ8WKFVSsWJEmTZoAULduXR577DF+/PFH9u/fzwcffADAvHnzaNeuHX379mXs2LE8+eSTAJw7d45SpUrRtm1bIHPJQtbp1NWrVzNixAh27NjBokWLjPbt5Ntvv812z9xP8sVIi6Zp5QGbiCRpmlYU2EHmFJFPbmmaNm0qS5cupXHjxvdcPxFB07S/JSM0NJQHHnggm7+fnx+7du3K1nD+iaSlpVGiRAnjXNd13Nxc7eLu3bvj4+NDeno6cXFxVK9eHYvFQnx8PFWrVgUyF90WKVLEJV2hQoVc5lZjY2MpV66cSxyTyUSxYsVuqqeIcPr0acOAzIrdbmfcuHF8+eWXTJ06lcTERPr27Uvz5s1vXgE5kJKSQsmSJe8obW7s2LGD5ORkXnjhBU6cOEHlypVxd3enZMmS2erNSU7X4kaICF999ZXxgE9NTXW5tvcL546x64+zEhERQcWKFSlQoMAtyRQR0tPTCQkJ4eGHH8ZisZCWlkbZsmX/9n18s3zvpfys1KlT57bWzinuHbd77+UHNE3Ls5GW/GK0NAaWkLmuxQ1YLSKTbpTGy8tLfH1974d6iizEx8dTrlw57HY77u7uxMTE8Oabb9KhQweGDRvGoUOHaNu2bY4Pn+DgYJKTk2natGmOsrN2+Pej3U6ePBmr1crkyZPveV6Km3P58mVq166d12rkC+6ngaT475GXRsutvZrkMSJyFsj+Wqz4x+Hp6eliUFSoUMEYGn/33XfRNM0Yvr8e586i3HAuxIqJibl7Ct8Am81m7F5R5D3KYLl1lMGi+LeSL4wWxb8DZ0fq4eHxt2TcqQV/u/Tr1+9fN6yrUCgU+RlltCgUuVC/fv28VkGhUCgUWVCvkQqFQqFQKPIFymhRKBQKhUKRL1BGi0KhUCgUinyBMloUCoVCoVDkC5TRolAoFAqF4m5xT7+oqYwWhUKhUCgUd4t7alcoo0WhUCgUCkW+QBktCoVCoVAo8gXKaFEoFAqFQpEvUEaLQqFQKBSKfIEyWhQKhUKhUOQL8pXRommau6ZppzRN88lrXRQKhUKhUNxf8pXRArwPBOS1Eoobo+s6QUFBAFgsFv744w8cDge6rhMbG3vHX05+5JFHyMjIIDIy8m6qmw2Hw3FP5ec3jh49ypkzZ/JaDT777LN7Jvvs2bPs2LGDhISEe5bHxo0biYiIuGfyFf89RCSvVbjv5JuvPGuaVg14GvgcGHmz+CEhIWiahq+vL5UrV8ZsNhMYGEjFihVJT09n7ty5LF68mMDAQH755Re6du1K8+bN+f7772nVqhXVqlVD13WsVit2u52DBw/Sr18/TCYTSUlJlClTBrPZjLu7O56eniQmJhIYGEjNmjWJiIigfv366LqOyWSiaNGiREZGYrPZKFq0KImJiZQtW5aKFSty4sQJGjVqxJkzZ2jTpg2JiYlcvHiRxx9/HKvVSu/evenfvz9PP/007du358cff2TixIls3ryZs2fPUqFCBQoWLEh8fDz169cnMTGRYsWK8dtvv+Ht7U21atW4du0aiYmJWCwWSpQoQdGiRfHz88Nut3P+/Hm6d+9OamoqderUIS0tjatXr+Lu7k7t2rUpXbo0iYmJJCQkULlyZaxWKxkZGWRkZGCz2RgzZgwjR46kYMGC9OzZ0+UaREVFUalSJRc/b29vRITIyEjCw8N58MEHKVCgAMeOHeOZZ57BarWyaNEidF3n+eefJygoCF3XOXHiBMeOHaNo0aIAXL16lTJlyhAXF4enpydbt27l0UcfJT4+nooVK3Ly5EkWLlzIt99+S3JyMjVq1GDz5s2UKFGC8PBwDh8+zBdffEFoaCilSpXiyJEj2Gw2mjVrRuvWrUlJSWHAgAH89ttv9O/fn1dffZXt27fj7e1No0aNCA4O5umnn8bf358WLVowffp0GjRoYFzXqlWr8uabb9K6dWvKlClD8+bN+e677yhTpgwlS5bkscceA+Ddd99l0qRJuLm5ERcXR9GiRXFzc6NKlSrEx8dz5MgRnn/+eebOnUu9evWoVasWc+bMYdOmTYwePZq4uDg++OADlixZwqOPPkqHDh1ITU1F0zT69OnDt99+S5kyZYiJiaFNmzY88sgjLF68mIiICNq2bYubmxsWiwW73c7mzZt5+OGHqVSpElWrVmXWrFk0atSIJ554AoADBw5QrFgx9u/fz1NPPcW5c+do27YtBw8epEWLFogIIoLD4aBGjRrExcVRqlQpzGYzDoeDbdu20b9/f9LS0ihSpAihoaFUrFiRokWLYjKZsFgsnDt3DoDSpUtTu3ZtfvnlF5577jlSU1OZPHkyw4YNY9WqVVSpUoUnnniCEiVKkJqaSrFixTCZTFy4cAFvb28iIiIoXrw4Pj4+FC9enA4dOlC8eHHc3NxIS0vjzz//ZOfOnTRs2JB+/frRpEkTo40ePnyY8+fP88YbbxAYGEj58uUJCQlhxowZzJw5k5IlSwKwYcMGIiMjCQoKYuDAgVSvXp3g4GDq1q1Leno6hQsXplChQgQFBVGqVCl69+7NjBkz8Pb2Zvv27Xh5eZGamkqnTp3IyMigSJEifP311zRr1oySJUvSvHlz4uLiAChfvjy//PILo0ePZv78+TgcDt5++20sFgtJSUnMmTOHqlWr0q9fP8xmM4cOHaJXr17Y7XbsdjtpaWkULVqUIkWKoOs6BQoUwGQy4eHhgbu7OyJCYmIixYsXp2DBgmiaRmxsLGXLlsVmsxEeHk6pUqVwd3fHzc2NIkWKcPLkSRo1akTRokXx9fWlSZMmmM1mSpcujd1ux2KxcPnyZZo0aYLdbqdAgQKkpqbi5uaGzWbD4XAQEBBAyZIlqVevHhkZGYSHh1O/fn2io6M5cuQIAE899ZTRF5crV46LFy9St25dNmzYQIECBWjRogUpKSmULl2aKlWqYLfbOXXqFCVLlqR69epcuXKFiIgI/ve//1GpUiVSU1O5evUqGzduZMCAAdSoUYOAgACmTp3KggUL8PDwMNrCn3/+Sc2aNSldujQnTpygRo0apKSkUKNGDUQETdMoWLAgdrsdq9VKVFQUtWvXxmq1sn//fo4dO0bz5s0BaNu2LXv27EHTNOrVq0eJEiXYsWMHlStXplu3bkYfHRwcTHR0NI8//jiHDx825Pbq1YuoqChSU1MpWLAgNWrUoEqVKgwZMoQvvviCuLg4ihUrxty5cxk0aBDnzp0jJSWFypUrU716ddzc3Lhy5QrVqlXj6NGjFC9enIoVK1K8eHEGDhzImTNn6NWrF9OmTWPlypX07duXc+fOce3aNVatWkXjxo2ZOXPmPX9pvBlafrHUNE1bC3wJlABGi0j3m8TPHwVTKBQKhSJ/cUJEvLJ6aJrWA8Db2/s3gIoVK96R4OjoaACOHz/eE0BEfs8ani+mhzRN6w7EiMiJm8Qbommar6ZpvvdJNcU/kDudflIobgV3d/e8VuGeU6hQoTtO++qrr95FTRQKV/JL794W6Klp2lVgJdBB07Tl10cSkfki4iUiXi1atCAjI8MYshYRzGazy7nTrVq1iuDg4BzDnE7X9RuG36rbtm0b8fHxd0XW3XIPPfTQXZETEBBw/fXIdiFfe+01Iyw1NdWlXvft28ewYcNylO0cJs8qOzfncDjuuAzp6ekEBAT87bo4cOAAISEhWK1Ww8/Pz++utKVJkyYxd+5cRISPPvqImJiYW0r31FNPce3atdvOzzlFdz/a4vUuNTUVkcypi1GjRhn+iYmJHD58+JZkLFmyJJvMS5cuAZCcnIxIZjutX7/+TduWiGC3229Z/7tZb3a7HZPJZJzPmTPHpUxRUVFGfm3atPlbeVksljtOu3jx4jxpK/9kdztt5lbd7bYtZ/w7aZPX96l5Sb6ZHnKiadrj3ML0kJeXl/j6qgGXW0Ekc272bmC320lOTubQoUP07NkTHx8fFixYwMaNGwFIT09nzJgxzJo167bkWiwWihQpQkREBE2bNiUmJuau6KtQQObi8YyMDAoVKkSBAvlmqZ9CkSdompZn00Pq7lTcNYMFoECBAnh6ehqLcrt370737v9vX3p4eNy2wQJQuHBh7HY77u7uBAcH3zV9FQrInFLMugBToVD8M8l3RouI/AH8kcdqKPIA51qCYsWK5bEmCoVCocgL8suaFoVCoVAoFP9xlNGiUCgUCoUiX6CMFoVCoVAoFPkCZbQoFAqFQqHIFyijRaFQKBQKRb5AGS0KhUKhUCjuFvq9FK6MFoVCoVAoFHeLe2pXKKNFoVAoFApFvkAZLf9wTCZTXquQr9i/fz8Oh+OeyT9+/Pg9k61QKBSKG/OfN1rsdvt9yeedd97JNaxr1665hpUpU8Y4tlqt9/SB/G/gsccew2Kx3BVZH3zwQbZvHLVs2dI4/vXXX0lPT78red0OeZHnf4H89h02heK/yH/OaOnVqxfJyckAnDlzhqeeeuq+5Dt37txcw7Zt24bVas0xzGazGccffvghCxcuvOu63S4ZGRk3jTN8+PBsfqdOneL8+fPG+R9//MGUKVNuKmvz5s2Eh4ezaNEimjRpwpEjR7LFadiwIQcOHAAgNTUVgGvXruU6MnIrD6ivv/6aihUrkpSUlGP8/v37s3//ft566y0Al28s5cbly5dzrD9d1wkMDLxpeoCyZcveUrx/M3a7nUOHDhEaGsqlS5dYtmyZS/jZs2f5/fffady4seGXkpJifIwtK7qus2/fPtzc3Dhx4sQ9112hUNw5/2qjZd68eYSFhbn4HTp0yDAQLBaLy6fpAR599NFschITE3nllVf+tj43etgXLlyY8PBwBg8eTEREhEvYM888A2QaMG+++eZN84mKimLIkCEcPnw4x/CLFy/y448/3rLeH3zwASdPnjQMqKJFi3L+/HkXA0REXEYAZs6cSUBAABEREYgIL774IgMHDqRBgwaMHTsWgICAAMaNG2ek79ixo8u1CAsLo2fPnnTv3p3q1avzySefcPbsWdq0aYPZbGbYsGFs27aNQYMGce7cOdq3bw9ApUqVOHPmDFWrVqV79+7Y7XbOnDmDrmcuap84cSJubm6GnsuXL+e5555zKUtWevToQYUKFVzqzzniMmHCBH788UccDgebN2/Osf6co3lJSUnUqVOHU6dOMXjwYJc4QUFB1KtXzzhPSkoiKiqKQ4cOZZNnsViIi4szzh0Oh8v5nZCenm7Uz1dffYXZbDbCtm3bxsqVK/+W/L9Dnz59jGOr1UpiYiK+vr48+uijrFq1inHjxmW7P5s0acK3336Ln58fiYmJDB8+nJkzZzJp0iQAhg4dys8//wzA4sWLjdHOefPmAZmGjK7rxMbG3o8iKhSKW0VE8oUDrgJ+wGnA92bxq1evLg8++KDs2rVLRER8fX1FRKRcuXISHR0tIiJ//vmnADJ79mwJCgoSycxInCQlJYmIyLVr16RSpUoSFRUlV69eFRGRqKgoSUtLk127domu60Yah8MhOQHI6tWrXfx0XRebzSaAAPLGG2/IQw89JNu3b3dJ50z7+uuvG/r5+/tLr169suXTvHlz+eKLLwSQDh06GP6dOnWSyMhI2bRpk0ycOFEACQsLE6vVKseOHctR5+t1+P33313OAYmIiBCr1eri99lnnxnl+fHHH6VFixYu4YDoum4c//DDD1KnTh3jfO7cuRIeHi6nT5+WRo0aZUsLSEpKihQvXjzHsOtdVFSUeHp6SsOGDeWHH37IpufAgQMFkG+++UYGDBhg1PH1curXr59rHlWrVjXSZWRkSK9evSQ+Pt6Q0717d/nll1+y1UFCQoJYLBZ55plnBJBTp07J4cOHZdasWTJ06FABxGKxSEZGhtG2nOkTExMlNjZWjh49KoCcPXs227XTdV327NkjTZo0ERGRXr16yYoVK+TXX391iff0008b7c7T01NiYmJERCQhIUHatGkjgHz55ZeSkZEhycnJ0qtXL4mLi8uW36+//ipz587N5r9u3Trx8PCQJUuW3LCtZWX+/PlGWePj42Xq1Kmydu1a6d27txw+fFgAad++vdSrV8/lvnXWUYcOHbJdp+HDh8v777/vcs8B4uHhIYA8//zzYrfbpWrVqjJ9+vRscnv27CkOh0MsFouEh4eLiBi/eYG/v3+e5a3475LTMxjoAfTw9vYWb29v6d69+x05Z3qnvGz5XO/xT3V/GS3lbiO+0TH9VcnG7549eyQuLk6GDRsmucXLenz16lUB5LXXXhNAoqOjpVixYka6unXrSnR0tJQtW1aqVKkiIiJpaWkSGBgoVqtVVq9enaPR0qhRIxk9erSLDg899JAAcu3aNdm1a1eOD0gRkffeey9bhyoiommaS2edtSzXu2eeecYwYEREJk6cKFOnTpVq1apJenq6iIhs2rTplh/c17v+/fvLzz//nGNYRkbGDdNu2bJF/vzzT2fjzeZyq5vbcTcqS1xc3B3JbNSokcybN89oZ61atco17qhRowQQHx+fHB+uzmM3NzcBpFixYjJ48GDDv1u3bgLItm3bXK53sWLFjOteqVKlXPPftm2bbNiwQb744gvp3LmzbNmyRTZs2CClS5eWSZMmybp167Kladq0qYwYMcI4P3HihMyfP19EROx2e7Z25+Sbb74xwiZOnCgOh0OsVqvs3btXYmNjZevWrfL777/LCy+8IJ6entnabPv27QWQmjVrSvv27eXgwYPZdIuMjJSZM2dK586d/3bbAKRWrVoCyNKlS8Vut4uIiJubmyxcuFCOHj0qLVq0kEOHDhnlPXv2rMyZM8el3IsWLcpWF3fClStXxGw2Z/OHTMMrP5Gammocjxo1SgYNGiQWi0Xsdrvxoigikp6eLp07d76hLF3XZfPmzXLp0iWjz8pKcHCwTJo06W/pGxsbK1ar9aZ6ZH1hXbBggYhkvtwOHDjwpmlFMp8zISEhIiISHx8vwcHBRpwtW7bI0aNHXdLoui52u11MJpORt81mE4fDITabLdd8jh07JgkJCS4v2yIiu3btksOHDxvnGRkZRj4pKSkuMlBGy701WpwP+OsfdFnf7iHzbQ6QhIQEuXjxogCyZMmSW+rgunfvbhzv3LkzxzgvvPCCXL582ejkbiRv5syZNwyvWbOmAFKkSBEBpGHDhjnGu3z5srRr1+6m+s+YMSOb34QJE/52x+8cHbrederU6YbpVqxYYRh7yt26c7Yrh8Mhv/32203jd+3a9a7kGxcXJ/379zfOnaMRTr777juX+H5+fvLhhx9K2bJlxdPT86byCxcunOd1m5aWls3v888/d6l3Z9n79u0rsbGxAkhqaqr07t1bBg0aJHv37pXz58/LmDFjxNfXV86cOSOpqakyfvx444HtNOheeeUV0XVdLBaLtGrVSg4cOCAWi8V4cDhHCgGx2+3SrVs3SUxMFBGRr776ykWW8+Gj67pcvXpVwsLC5MUXX5QqVaq4jOw6H0wZGRnSuHFjw1hzPhR1XZfBgwfLyJEjZfbs2eJwOCQkJEQSEhKMB50TPz8/6datm/zxxx/i7u4uGzZsMIzxfv363bCunSNggKxatUqmT58u58+fFxGRc+fOyf79+11GTSHTyHQ+rJOSkrLJnDJlSraXoPT0dJeH91dffSUmk8kwEMuXL2+8SA4dOtR4CRERady4sTz88MOydOlSQ96WLVvkk08+ybVc27ZtE13XJTU1VUwmk/FCEhQUJCVLlpRixYrJm2++mWPahx56SNq2bSvlypXLMXz8+PHGccuWLV3Cli9fnqtOW7dulZdeesk4b9++vTRt2jTX+GPHjhXy0GjR/srsH4+macFAIpkV96OIzL9J/PxRMIVCoVAo8hcnRMQrq4emaT0AvL29fwOoWLHiHQl2LpY/fvx4TwAR+T1reIE7kpo3PCoiEZqmVQB2app2QUT2Z42gadoQYEjeqKdQKBQKheJekm92D4lIxF+/McAGoGUOceaLiNf1FuBrr70GwCOPPAJg7Mhw+o8cORKATz75BIC0tDR+++03APbs2XPTbZAFChRg5cqVbNq0Ccjc4XDx4kXWrVvH9u3bqVu3rhE3PT2d8+fPY7FYjK2yOVG4cOEb5pmV67dtP/7448ZxamoqY8aMyTWtc0voCy+8wGeffWb49+3blzVr1lCjRo1b1iMnrt+l9OSTTwIY9Quu/33iZOnSpSQnJ+Ph4ZGj3IIFC94w3/r16+foX7x4ceN4wIABuaavXLnyDeXfiNatWwOu/7GTE1m342ZlwIABLF682Djv3bs3AHXr1s1RprN9Vq5cmZSUFCBzR9GyZctu+LazY8cOQkJCAPD19aVv374u4T179syWplatWi7nTj3T0tLo0aOH4S8ixlZxEaFLly4AjB49mg8//JAXXniB+fPnc+rUKbZs2cLixYuz7fRz8tJLL7FgwQIAHnrooVzL49wVOGLEiFzjfPzxx7mGOalUqVI2v19//ZXAwEBatGjB6dOnAXj44Ydp1aoVmzdv5uuvvzbinjt3DrPZTFRUFJBZF86dSomJiVy8eJHt27fjcDhchr3T0tKw2+1kZGRgs9lITU01wvr06UNiYiLR0dFZh+OBzN1sX331FUeOHDHC5s+fj67r+Pr6YjKZ0HUdEcFsNuNwOLDb7SQmJpKSkkJcXBwikk2fS5cuERQURHp6uhFms9mIjo7mzJkz/PTTT8Yuqzlz5pCYmEhsbCwigq7rhIWFUbduXc6ePcsjjzzCwoUL2bJlCw888ADr1q0zdkXmdI2GDPn/d8/JkyczYcIEli9fjt1u59SpUyxevJhOnToZcfr27UvXrl2JjIw0yvLAAw8Y4e7u7jzyyCN8/vnnABQqVAjI7I+zlrl169Y4HA7S09OxWq1Ur17dkPPMM88Yf0uQnp6OzWbDz8+PLVu2GPkMHjyYjz76iFGjRrmUqUiRInTr1o1OnTqh6zo2m83lP8LS0tKAzN2Izz77rOF//f97Oe+DAgWyjzcMHToUgGrVqvHtt98a9xz8/98k7N692yVN586d2blzp4tf1vbvfHbVqVPH8Lu+bPedW10jkpcOKAaUyHJ8GOhyozTVq1eXOXPmGPOVzrlO55yvyWSSzz//XD766CMRcZ2LdZKcnOxynpCQIEuWLHFZEFexYkWXOM5dGllx7laYNWtWtjCR/58LnzlzpmzZssWYM/3222+NMJPJZBw79R02bFg2WbGxsaLrujz44IMSERHhEhYZGSlNmzaVKVOmyMKFCw1/5+Kv3OCveXona9askVOnTomu6zJjxgyxWq2SkZEhP/zwg/zwww9y9uxZlzT9+/eXUaNGSUpKirFryOFwGPOwIiKFChUywpwLltPS0oyFp5GRkS7zqhaLRRITE8VisUjJkiVdwqpXr26sHXr55ZelV69eUrduXZk3b55LfYtkzn1fu3ZNnnnmGWPOPykpSWJjY+XBBx90kZvTwlSncy6oPnfunFitVlmxYoUEBAQIIElJSZKRkSG//vqr9O3b19BB13Vp3bq1hIWFGWuifvnlF6OenfUhIi4L4Xbv3u2yjkJEpHbt2tKxY8cbXsPAwEAJDg4Wi8ViyMsqW0QkICBAvL29xdfXVxISEow6eOSRR+TAgQPicDjk/PnzsmDBAjl16pQ4HA5jl9T27dvl22+/lcjIyGz5JyYmGrqKZN6P1y8EdJbP19dXvv32W2NnnUjmvVe6dGk5ePCgtG7d2mhjq1atkkOHDhm68Nc6BV9fXwFk48aNLtcp6/XnrzUIDRo0yDbHv3nzZrFYLLJlyxZjJ9XNcLZVJ3a73ViHISKydOnSHBfT3ik7duww1pL8Gzh37pxLm4iJiZHLly/fNJ3FYsl116aIiNlszrGt3U38/f1l//79Ln6xsbGSlJQkvXv3zjVdbnolJyfL8uXLb5jnihUrblqutLS0m+aVFeci6SlTptwwHmoh7k2NllrAmb/cOeCTm6Vp0aLFTS+Qc8vo38G5UO1GzJ8/X7y9vWXDhg05hjt32Di3kDoNqNWrV8vs2bONjiksLOyu6nWrOLeN3w432gbq7NifffZZw895c2Vdve5k586douu6xMXFSbNmzVweDCJi7ETSdV1efPFF45q6ubkZD4msndquXbukcuXKNy3DTz/9ZBiMzq3uzi3EzsVyzgeeiMjp06ezycjJsBQRqV69usu5c+HgzeI5uT7ujBkzZOzYsbmWxWms3wpZ68q5K6Jdu3Zy5MiRW5aRE2XLlr3tNMuWLRORzPZ84sQJSUpKMvQAshkBHh4exnGWnQ4CGG3s/Pnzkpqa6lKHL7zwgnz33Xfy22+/3baOWcnpGioU/zby0mjJNwtxbxcvLy/x9fXNazVumTNnztCkSZO8VuO+0LdvX1atWnVHaf/8809at27N9e1W07RsfitXruT555/PNpQqIiQnJ1O6dOkb5jVv3jz27NnD6tWrs4WJCG5ubogIzZo149SpU7dVDl3XjT+4g8w/oNuyZUuOUzI5cX15HQ4Huq7fdNrsTklPT6do0aJomnbHMmw2213V74knnmDv3r0ufhkZGRQpUsTFb8yYMcTFxRlD605KlSpl/Dv23SKndqhQ/NvQNE0txP2v818xWIA7Nlggc11ScHBwNv+c1u28+OKLOcrQNO2mBgtkGgLly5fPVYbz5rpdgwVwMVggc476Vg0WcJ1jhsw5e3d399vW41bJbW3R7XC3DarrDRYgm8ECMHXq1BzT/91/Ec6JG32uQ6FQ/H2U0aLIV2ialuPi4C+//PKu5/XQQw9RrFixXMOz/rX//eZWv1OkyJ17MSp1o8X1CoXi76OMFoUiF+7XxzTvhL8zTaNQKBT5lXyz5VmhUCgUCsV/G2W0KBQKhUKhyBcoo0WhUCgUCkW+QBktCoVCoVAo7hb6vRSujBaFQqFQKBR3i3tqVyijRaFQKBQKRb5AGS0KhUKhUCjyBcpoUSgUCoVCkS9QRotCoVAoFIp8Qb4xWjRNK61p2lpN0y5omhagaVrrvNZJoVAoFArF/SPfGC3ATGCbiPwPaAIE5LE+t8WlS5eIiorKazX+9djt9rsmKykpCV2/p7v37hir1ZrXKigU+RYRuat9RV4zbdq0vFbhvpEvjBZN00oB7YGFACJiFZGkG6Wx2+1cvnz5rnwmXkQwmUx/S0bdunX54YcfDHlZeeGFF/j6669JS0vLls5isbjoYTabb6prTnnkhN1ux+FwuOSRExs3bnQ5Dw8PJzExEch8eGbNS0RITU3N9gXe3bt3k5iYiK7rJCcnIyJcuXLFJc7FixeN47S0NESE9PR0jh8/bvgHBAS4fHcnKSkJEWHUqFFMmDCBEiVKkJ6eTlpamvEVZqdeuq4TFRVFWloa165dy1bO9PR0l/MyZcrg7u6OiBAbG2vIcZa7UqVKxMTEZEuXFV3XWbBggZE2OTnZJb5TrsPhICUlJVv6Xbt2ZfOLjIw0vj4tIkRHRzN9+vRcdXDq7HA4EBFExMXocTgcLvHDwsIMY82Z1maz5SgzJ+6mQbV48eIc/R0Oh3F9HQ4H586dcwlfuXIlERERZGRkkJycjNlsJiEhgdDQUEaPHs3u3buNMsXHxxMZGUlsbCwBAf//LuSsq9vhyJEjuYbt3r37pvfa3WLbtm2kpqbecXoRMdpLVnLT/1bqyeFwoOs6ERERxgtcdHQ0+/fvZ8uWLUb/Fh8fD0BGRgaTJ0820jrzSElJcWnTISEhBAQEGPeAs+2uXLmSqVOnEhgYSFxcnHG97XY7O3fupGDBgtn0jomJcTnv0aMHIkJYWBi7d+8mOjra+Mq8Mx9d15k5cybffPMNISEhREREAPDEE0/w+OOPEx0dzbFjx3Ksz7CwMLZu3UpqaipRUVGICGlpaXz//feEhISQkJBAYmIiJ0+e5Ny5c8a9m5CQwKVLl9i2bRvJycmMGTOGWbNmcfToUdauXWuUEzL70j179qBpGklJSVitVgICApg1axbh4eHous7Vq1dZtmwZIsLIkSPZunUr3bp1w2KxYDKZOHHiBKtWrcJqtZKUlHTTa30v0e7GQ/1eo2laU2A+cJ7MUZYTwPsikqsloWnaP79gCoVCoVDkP06IiFdWD03TegB4e3v/BlCxYsU7Eux8GTl+/HhPABH5PWt4vhhpIfNr1M2BuSLSDDABY66PpGnaEE3TfDVN873fCioUCoVCobi3FMhrBW6RcCBcRI7+db6WHIwWEZlP5oiMMdIyd+5cChYsSL169di5cyenTp2iW7duzJ49mzJlynDo0CGefvppRowYwf/+9z/27t1L3bp1KV26NCtWrKBSpUpcvnyZ6dOn8/rrr9O+fXsSExMpVaoU9erVY9OmTVSrVo2TJ0/Spk0bmjdvzpw5c3j00UepUaMG7u7uHD16lDFjxvDaa6/xxBNPkJycTHR0NE8++SQhISG8+uqrRhm6d+9OnTp1aN26Nf7+/syZM4eEhARGjBjBd999B8CgQYN45JFHmD9/PgMGDKBp06aUKlWKy5cvc/r0aWrUqMGwYcN44okneO+994iOjmbGjBkEBARQs2ZNihcvzvnz5/Hx8SE4ONgo15QpU3j99dfp27cvDz/8MAULFiQ1NZWiRYvi4eFBtWrV2L59O6VKlSI4OBhN0/j555959NFHcXNzIyoqisaNG3Pq1Cl8fX1ZunQp7dq1Y/r06Xh6ejJ58mSeeeYZNm3aREhICC+++CLjx4/HbrfTvXt3XnnlFZYuXUqXLl3o0KEDRYoUoUCBAly7do1y5cpRoEAB3n33XQBWr15NcHAwly9fpmzZskydOpUqVapQt25d5syZw8GDB3nzzTfp168fzz77LBMnTqRly5b4+/tjMpk4d+4c8+fPR9M0YmJiOH/+PM899xzp6em8/PLL7N69m44dOwLQsGFD6tSpQ8+ePRk4cCCPP/44Dz30ED/99FO2htqwYUMyMjLo0aMHXbt2pXv37rz00ktG/a1YsQI/Pz9q167N7Nmz6datG1u2bOGPP/7gq6++olu3btSsWZPz588TExPDtWvXmDx5Mj/++CMTJkxARFi+fDkbN27khRdeYPXq1cycOROTyYSfnx9jx45lzZo1VKtWjTZt2rB+/XrWrFnD1atX6devHy1btqRSpUo0aNAANzc3bDYbX3zxBV27dqVRo0a89dZbdO7cGbvdTps2bdi0aRNLly7F09OTRx99lH79+hEdHU16ejqrVq2ifv36BAYGMm7cOObMmcMXX3zBunXr6NKlC2azGbPZjK7rPPjgg9StW5cdO3ZQqlQpjh07hpubG927dyc6OpqqVatiNptZuHAhf/zxB927dyc1NZWpU6cycuRIKlSowJgxY5g8eTLHjh3j999/5/XXX+fjjz/GbrcTERFB+/btGTx4MN9++y3Hjx/n/PnzXL16FZvNxuLFi9m/fz+1atXC09PTuF4vv/wyy5cvd7mGV65cISMjg7Jly2I2m7Hb7URFReHn58ebb76JxWKhSJEiREdHk5aWRqVKldB1HQ8PD/bt20fdunWpWLEiuq4TGxtL6dKlKV68OG5ubixZsoQePXpQpEgR4uPjqVSpEu7u7i7rK9zd3UlLSyMsLIxatWpRoEAB4uLigMypwbp16+Lu7s4777zDtGnTSE9P58KFC4gI3t7elC5dmoIFCxIWFsaKFSuoX78+tWvXRtd13NzcWLFiBb169SIyMpLq1auzYcMGOnbsSK1atdA0jUKFCuHm5kZcXBwzZ86kZ8+eHD16lH79+hEVFcW0adPw8fEBICEhgWHDhhEcHMzy5cs5efIkDRo0oHjx4mzdupXBgwdz7NgxWrZseeu9/Q2YMGECNpuN0NBQdu7cmW2d4Jw5c6hatSrt2rVzuc650aBBA3bv3k3JkiV57rnn2Lp1Kz/99BN+fn7069eP1q1z3u+xf/9+2rdvz/Dhw5kxY8ZtlWHu3LlMmzaNXr163XZaRT6ZHgLQNO0A8IaIXNQ0bQJQTEQ+yC2+l5eX+PrmnwEXZ4dyPxARl3Uh95uAgADq16/v4rdt2za6dOlyX/LXdZ34+HhjXUhupKamYrPZKFu2bI7hGRkZFClShODgYGrWrHkvVP3P4+fnR6NGje6qTGefd7/vgV27dvHYY49RsGDBe56Xpml3ZT3fjbgb/Ujnzp3ZsWMHCxYsYNCgQS4yRYQHH3wQX19fChcuTMmSJXPMLyYmxljX8sorr7iEpaSk4O/vT5s2bYDMvicmJoZHH32U5cuXu7wwAsyePZv33nvPOJ81axa1a9emRYsWVKxYkeDgYDw8PKhUqZJLOovFQuHChV38fH19OX/+PJMnT+bSpUs5lj85OZm9e/fSq1cvwsLCKF26NEWKFOHcuXNMmTKFVatW4e7ubsR3ruVyc3OjUqVKXLp0iXnz5vHNN98YdXfixAkaNmyYTZ/ff/+dHj16ALBkyRJCQ0P5+OOP0TSNS5cu4enpiaenp1HHJpOJYsWKARAVFcVTTz3F2bNnsdvtFCxYMM+mh/KT0dIUWAAUAq4Ar4tIYm7x85vRolAoFHeL9evX8+yzz+a1Ggry/iUxN/6OXpqm5ZnRkl+mhxCR04DXzeIpFArFfx1lsPxz+CcaLPDP1etm5JeFuAqFQqFQKP7jKKNFoVAoFApFvkAZLQqFQqFQKPIFymhRKBQKhUKRL1BGi0KhUCgUinyBMloUCoVCoVDkC5TRolAoFAqF4m6h30vhymhRKBQKhUJxt7indoUyWhQKhUKhUOQLlNGiUCgUCoUiX6CMFoVCoVAoFPkCZbQoFAqFQqHIFyijRaFQKBQKRb4gXxgtmqbV0zTtdBaXomna8LzWS6FQKBQKxf0jXxgtInJRRJqKSFOgBZAObLiFdLmG9e/f/67pd7+5UbkUd4/w8HACAgLyWg3F32D69Ons3r37luLe6/vqtddeu2eyVZ+g+K9QIK8VuAM6ApdFJORGkUJCQnBzc+P8+fNUrFgRd3d3evTowdtvv83//vc/fv31V1555RVCQkIoX7481apVo3DhwsTFxVGwYEFKlChByZIlcXNzY/v27UybNo2VK1dSrlw5li9fzquvvkp6ejoJCQlYrVaSkpI4e/Ys3bt358qVK7Ro0YLk5GRq1qzJhQsXmDJlCrVr1+aRRx7hscceY8eOHbRp0wa73c6qVauYPHkyV69exWazcfToUWrWrInD4aB9+/ZUqlSJdevW8fbbbzNnzhzatWvHjh07CAwMpG/fvhw9epTq1atTpkwZihQpgpubG7t27aJEiRJ4enri6enJ9u3bKV68OFu3bmXQoEH4+PjQp08f9u3bR2JiIh9++CHx8fHExcWxY8cOunbtSvXq1QkMDKR48eIULVqUggULcvnyZcqXL4+bW6a927JlSwDeeecd0tPTefbZZ/H19SU2NpYBAwYAmQ//ypUrc+3aNdq3b0/lypU5f/48CQkJ1KpVi8TERObOnUupUqVo0aIFq1evZvDgwVSvXp2UlBQiIyNp0KABixcvZtKkSQBERkbicDgICQmhQoUKJCcn06lTJ06ePEnhwoVZtmwZEyZMYO/evVSuXJkdO3bgcDgoUaIE8fHxBAQE0K9fPyZOnMgTTzzBI488gr+/P4888ght27bljz/+YPz48ezbt49hw4ZRrlw5ypQpw5w5c5g5cyZxcXEsW7YMLy8vpkyZwlNPPcWiRYvYs2cPNWvWZOTIkcydO5fLly/z1ltv8fPPP+Ph4UH//v0ZMGAAHTt25MEHH+Trr7/mww8/5Pjx4xQrVgwAi8VCs2bNOHPmDOHh4bRu3ZqIiAgcDgdms5lz585RuHBhihUrhr+/P++88w7t27enRo0abN68mbi4OHRd54knnmD37t24u7vj5ubG/v37qVKlCuXKlcNkMtG4cWM8PDzQdZ0jR46wfv16vvrqK65du8avv/5K27ZtqVSpEk8++SQAq1evpkCBApQpUwabzcalS5eoXbs2DoeDqlWrkp6eTs2aNbHZbJQqVQqTyURGRgY2m41ixYpRrVo1oqKiSE9Pp0iRIkyfPp2RI0eiaRoeHh6kpqZiNpuJi4ujcOHCFChQgI8++ogffviBjRs3cuTIEaZNm8b+/fu5ePEiR48eZdasWezevZtffvmFdevWsXfvXnr27MlLL71EixYtOHHiBCtXrmTz5s14eHhgt9sxmUyEhIRQsGBB5syZw44dO4iOjjb6juPHjzNmzBh2797Niy++yMiRI/nhhx+oX78+AwcOpGTJkhQoUICnnnqK8ePHU7hwYdLS0oy6DQwMxMPDA19fX2rXrk3dunWJjo5myZIlPPnkk5QoUYLIyEjatm3L9u3bCQoKonfv3jzwwAOEhobSuHFjFi5cSIcOHQBYt24dZcqUoWnTpvTo0QM/Pz8AGjZsiK+vL8WKFePtt9/mySefpHLlyrzwwgts3ryZLl26YDKZSEhIwMPDg/DwcDw9PbHb7VSuXJnDhw9Ts2ZNHnjgAdzd3Tl27Bje3t6EhIQQFBTE4cOHeeqppyhcuDBlypTBbDZjMpn4+uuvKV68OEOHDqVhw4b4+/vj7+/PxYsXGTVqFGXLlkVESEhI4MqVK9SuXZt58+YxfPhwRIQpU6awfv16lixZgq+vL9u3b+fixYtERkbSsmVLPv74Y3RdZ/PmzSxcuJCLFy/Sp08fnnzyST777DOmTZvG2rVradq0KX369GHdunX06NGD4OBg3n77bTIyMqhQoQKrV682Xk5ffPFFunfvzvHjx1m8eDFvvvkmX331FZs3b6Zp06YUK1YMk8nE+vXreeihhzh9+jSdO3emZMmSrFu3Dk9PT9zc3KhVqxYDBw5k+/btxMTEoOs6JUqUoEmTJsyePZtnnnmGffv24ebm5vJi/Pbbb/PAAw/QtWtXypYty6JFiyhbtqzRf3Xp0gUfHx+2bt3KggUL6NatG+3atWPx4sWUKFGCoUOHkpSUxEsvvcSePXuYMGECTz75JDt37jTy2LZtG5s2bcLX15fHH3+catWq8csvv2C32zl58iQjRozgu+++Y/78+QwZMgSATp068eWXXzJ8+HAOHToEwL59+9i3bx8iwvjx42ndujVHjhwB4JVXXsnzF34tv1nomqb9DJwUke9vEi9/FUyhUCgUivzBCRHxyuqhaVoPAG9v798AKlaseEeCnS8Qx48f7wkgIr9nDc9XIy2aphUCegJjcwkfAgwBKFCgAHa7nU8//ZQCBQrQokULjh49StmyZalTpw4jR47k5ZdfJjIyEqvVysCBA7Farfz44480a9aMhg0bcvLkSZo1a0ZCQgLLli3j2WefpWrVqvj5+dGkSRPKli1LcnIyW7ZsoVevXuzcuZMOHTpw8eJFKleuzNWrV2nUqBG//fYbc+fOpUePHri5ufH4448THR3NY489xunTpxk7NrM4v/32GwUKFMDNzY3SpUsTFRXFm2++SYsWLXjiiSc4cuQIjRs3ZsGCBXz00UecPXuWd955h6lTp9KhQwfS0tKoVasWW7Zs4aeffmLRokXY7XaKFi2K1WrF09MTHx8fqlSpws6dO3nrrbeIjIzko48+Yvz48VSrVo20tDT27t3LCy+8gMPh4OLFi9SuXZu4uDi8vLxYvHgxvXv3Jjg4mKNHj/Lbb7+RkZHBc889h81m49q1a8TExODh4cGQIUPYuXMnNpuNXbt20bRpU2w2G+fOnePLL79kz549jBo1ilOnTjF27FiGDx+Oj48PQUFB9OvXDy8vLx5++GEiIyPx8fFh/fr1xrVeu3Ytdrudq1evAjBp0iRKlixJnz596NevH+fOneO7776ja9euOBwOKleuzDfffENMTAyPPvooAQEBTJ06lejoaEJCQjCZTAQGBlK2bFl27NjBgQMHaNeuHQBNmzbloYce4syZMwwbNgw3NzfKlSvHsmXLaN++PZMnT2bs2LGUKFGCKlWqULp0aTp06MCgQYNo1qwZFStWxGq18tVXX9GzZ082bdpEp06dqFKlCqNHj2bkyJFYLBZOnz7NqFGjWLt2LTabDR8fHz766CNsNhuff/45Dz/8MM2aNeOXX36hQoUKxMfH4+npyccff8ycOXOwWq288cYbmM1mypUrx/Tp02nbti0Wi4WhQ4fSpUsX3n33XRo0aEBUVBSNGjWidu3anD59GpPJRHR0NIULFzZG5jp16kRycjI+Pj4cOXIET09PGjVqRPXq1XniiScYNGgQs2bNYu3atYwbN45NmzYRGRnJo48+SrVq1XjwwQcpWrQoDoeD+Ph4xo4dy6uvvkqbNm0ACA4OplixYsZIXmxsLM8//zyffPIJQUFBPP300/Tq1Ytt27YxePBgwsLCOHbsGIcOHSI6Opr09HSefPJJZsyYgYgwatQoLly4QKtWrWjbti01a9akY8eOLFiwgMWLF9OuXTuuXLnClStXcDgcrF69moYNG/L9967vP++++y5btmzhypUrdO/enapVq7Jhwwbi4uJYsWIFNWrUMEYcLBYLpUqVokyZMnTt2pVdu3ZRr149zGYzUVFRHDt2jHbt2lG2bFnGjh1L9erVKVu2LMWKFaNjx478/vvvPP7446Snp7Nx40YKFixI3759+fbbb6lRowb169enbdu2uLm58e233xIXF0f58uVp0aIF27Zt44UXXuCJJ57g7bffBuCtt97C29ubQYMG8c033zBq1CgaN25MtWrV6NKlC2XKlGHAgAFs2LCBAwcOEBYWZrzpL1iwgB49elC+fHkWLFhAaGgon3/+OUWKFGHp0qU8/PDDLFmyxJgyHTlyJI8++ijBwcGMGjWK/v3707JlSzRN4+GHH+b8+fOcPHmS/fv3c+XKFV599VXsdju//PILABUqVCAmJibHvr5o0aKYzWYA2rVrx4EDB2jatCleXl7s3LmTkJDMgXZPT0/i4+MBcHNzY8mSJVy+fJkaNWowePBgbDabIdPb25vjx4+75FOmTBm+//57bDYbmzdvZs2aNXTu3JmgoCBKlixJxYoVCQoKIigoyEXGmDFjOHHiBIUKFeLixYtGOEDp0qV56qmnWL16teFXu3ZtLl++DMDSpUtZunQp6enpHD58GID33nuP2bNnU7ZsWQoVKkRUVFSO9XIjypUrR+HChYmIiACgSJEiZGRkZIvn4eFBenq6cd6tWze2bNlinJcoUYLU1NRc8+nTpw9r1qy5bf3uFvlqpEXTtGeAoSLy1M3ienl5ia+v733Q6tYQETRNy2s18jVr167l+eefv2/5TZw4EXd3dz799NP7lue9xOFw4O7untdq3BZWq5VChQrdl7yuXLlC6dKlKVu27H3JT6HIr2iapkZabpF+wIq8VuJOUAbL3+d+GiyAMQL2byG/GSzAfTNYAGrVqnXf8lIoFHdGvtg9BKBpWjHgSWD9zeIqFHeDQoUK3deHpkKhUChuTL4ZaRERE+CZ13ooFAqFQqHIG/LNSItCoVAoFIr/NspoUSgUCoVCkS9QRotCoVAoFIp8gTJaFAqFQqFQ5AuU0aJQKBQKhSJfoIwWhUKhUCgUdwv9XgpXRotCoVAoFIq7xT21K5TRolAoFAqFIl+gjBaFQqFQKBT5AmW0KBQKhUKhyBcoo0WhUCgUCkW+QBktCoVCoVAo8gX5xmjRNG2EpmnnNE3z1zRthaZpRfJaJ4VCoVAoFPePfGG0aJpWFRgGeIlIQ8AdeDFvtVLcb3Q9c/u/2Wy+L/ko/p/u3bvntQr5/rqYTKa8VkHxL2PlypV5rcJ9p0BeK3AbFACKappmAzyAazeKnJ6ezssvv8x7771HkSJF8PDwIDIykkKFChEZGUnZsmXx8vIiMjISs9lMcnIyDRo04OTJkzz88MMUKFCAuLg4EhISuHz5Mu7u7miaxqOPPkpERASRkZGUL1+eYsWKYbPZMJvNFC1alKNHj1KhQgWqVatGuXLlKFKkCAcPHmTFihV4e3vTq1cvihYtitVqpXTp0hQuXBgvLy/CwsI4cuQIaWlp1KlTB7PZTHp6OiKCp6cn4eHhDB06lKVLl7Jr1y5eeeUVfv/9d5o1a0aVKlUIDAykdu3aFClShNDQUKKionB3d+ehhx4iNDSUokWLcuXKFdq1a8e0adOoVq0aLVq0oGTJkuzevZv69evToEEDgoODKVu2LLNnz+azzz7Dw8ODhIQELBYL+/btw8/Pj/fffx9fX19mzJiBn58fS5cupWHDhuzbt4+QkBBmzJjBihUrKFasGA0bNqRz585MmTKFOnXqkJKSwhNPPEFQUBCxsbHUrVsXDw8PPvvsMy5evEiPHj3QNI3HH38cNzc3/Pz8cHd3p3nz5lSpUoWZM2fy/vvvc+rUKWrUqEF6ejqFChVi7ty5NG/enHr16lG1alUAtm3bRrFixXjiiSdISUlh5cqVaJrGxx9/TKVKlfjuu+944IEHKFGiBImJiRw5coSFCxfi6+tLREQECxYswMfHh+PHjzN27Fhq165N4cKFKVWqFAUKFOD777/n448/5quvvuKdd96hcOHCJCcn07JlS9LT07l27RqVK1cmOjqagwcPUqZMGQIDA6lRowbFihXDbrfj4+PDs88+y+XLlzl9+jQDBgwgIyODtm3bEhgYSEREBI0aNWLDhg0MGjSIdevWYbPZjHby008/MWrUKN544w1atWrF+vXriYuLw9fXF39/f9LT0+nRowcJCQkcPXqU2NhYhgwZQpEiRbh27RoVK1akYsWKbNu2DZPJRJkyZfDy8uLAgQM0b96cIkWK8OOPP7J582bq1q3LpEmT8PT0pESJEvj7+1OzZk3WrVvH559/TkhICNu3b6dt27ZUqVIFk8mEm5sbDzzwAH5+frRt25awsDA8PDwoVKgQuq4TEBDAQw89REZGBsWLF+fs2bMsX76ct956i+LFizNp0iRGjx5NcHAwvXr14quvviIiIoKtW7eyYsUK6tSpQ3h4OIUKFaJ69erExsZSqVIlYmNjCQgIoF27dkyfPp33338fDw8PdF0nJSWF8PBwgoKCeOyxxyhcuDAbN27k5ZdfBsBms5GRkcHu3bvp1q0bDocDESEqKoqaNWsafcy2bdvo1KkTf/zxB3Xr1qVSpUokJydTunRpAAIDAzGZTFStWpWKFStSvHhxzp8/T/ny5Vm0aBF9+vShXLly6LpO8eLFSUtLIyAggLp163LhwgWsViu7du2iV69ebNiwgeHDhxMeHs7q1atp3749o0eP5tSpU1itVqKioihRogSlS5fG3d2dS5cuUbt2bXRdR9M0dF2nUKFCWCwWEhMTqVy5MqdPn6ZZs2ZGnMTEREqXLo2maWiaRnh4OCVKlKBEiRKcPXuWWrVqkZKSwqpVqxg1ahRXrlzhwQcfxN3dHRHB4XCQlpZG0aJFcXNzw2Qy8dtvv9G/f39mz57N+++/D0BMTAwmk4m0tDQmTJjAJ598QpEiRTCZTBQqVIgqVaoQHh5OfHw81apVQ9M0Ro0axfjx4/H29mbTpk3s2rWLSpUqMXDgQNLT06lUqRIZGRl4enoSEhLCn3/+Sbly5XjwwQfp06cPPXr0oHv37litVsxmM40bN6Zy5crExcVRqFAhHA4HMTExOBwO/ve//3HmzBmaNm1KXFwc8fHxmM1mihUrRtWqVdmxYwfVqlXj9OnT9OjRgwoVKnD69GmaNm2KzWajYMGC2O12vvrqK+rUqYObmxu9evUiNDSUOXPmULBgQd5++20qVqzIihUrKFeuHO3ateP06dNcu3YNi8XCkSNHCAsL44svvmD9+vWsXLmSNm3aMGvWLMqUKUPdunVZtGgR/fr14/jx45QqVYr+/fsTFBRE5cqVqVWrFr6+vlSoUIHDhw9Tq1Ytdu3axeTJkzl69CjVqlVj3LhxvP322zRv3pzu3btToEABtm7dyjvvvMMPP/zAnj176NChAy+//DLLly8H4PPPP2fUqFF398l+m2gikqcK3Cqapr0PfA6YgR0i0v8m8fNHwRQKhUKhyF+cEBGvrB6apvUA8Pb2/g2gYsWKdyQ4OjoagOPHj/cEEJHfs4bnl+mhMsAzQE2gClBM07SXc4g3RNM0X03TfO+3jgqFQqFQKO4t+cJoAToBwSISKyI2YD3Q5vpIIjJfRLxExKt58+bGsG5Wp+s6IkJSUlK2sOtdTulzc7quk5ycnGu6iIgII++cnDNtTs5ut+fon5GRccv65eQiIiJumG9O9XZ9/axatSpbmMPhuGH9/vHHH7eso81mM44//fRTDh48yLFjx27ruuSk9/nz513iXLp0iWvXrpGcnIzVakVEmDdvHt988w3nzp1DRLh48aKRPjo6Gl9f3ztuPzmF3chP13WOHj3qUiaz2Wy0DV3XjePc2svhw4exWCy51lNOdWWz2dB1nX79+mULO3LkCCJCWlra32qHN3Jms9nQLzw83PDfvHmzMbVxuzJ1XefixYsubctisdCwYUOXPG5Vt9zaXG666brukrfTOdtdbu3FarUa19bhcHDixAmXPmDXrl2GjEWLFhEZGfm36/9GZbgdGWFhYcb5999/T0pKCrGxsXz99decOXOGmJgYo1w5tcus9ZWenk5gYKBL3WRt/1nTOhwO4uLiCAsLw2QyISLExcUZU1nXl8dZn9dfn4ULFxo6OvPJGicgIAA/Pz8jDxFh2bJl7N69G13XXfxzc5cuXeLixYs4HA7MZjMJCQnouk56ejozZ85kzJgxiAhhYWFER0eTmJjIli1bOHXqlNEWndffYrFgt9sNf2cdZWRkoOs6iYmJfPDBB0Z9OxwOHA4HFy5cwG63s27dOpd6uXDhApGRkURGRmKxWO67AZCVfDE9pGnaI8DPgDeZ00OLAV8RmZ1bGi8vL/H1VQMuCsW/kZCQELZs2cLbb7+d16ooFP85NE1T00M3QkSOAmuBk4AfmXrPz1OlFApFnvHggw8qg0Wh+A+Sb3YPich4YHxe66FQKBQKhSJvyBcjLQqFQqFQKBTKaFEoFAqFQpEvUEaLQqFQKBSKfIEyWhQKhUKhUOQLlNGiUCgUCoUiX6CMFoVCoVAoFHeLe/plU2W0KBQKhUKhuFvcU7tCGS0KhUKhUCjyBcpoUfyrWL58OXa7Pa/VUCgUCsU9QBkt/wBu9P2nESNG3EdN8j8DBgy4p0bLV199lc0vJSXlnuWnUCgUiv/nP2+03K8PRr700ku5hjVq1CjXsBkzZhjHu3fv5uzZs3dTrXxJenp6Nr9p06Yxc+ZMANzd3QHQ9dzXgyUnJ99SXvHx8cYHvAA++ugj49j5tdcyZcoAEBkZeUsyATIyMrL53WpbVEbS7VOrVi2X65gTJ0+evE/aKBSKO+U/Z7RkfVicP3+e7t273xfDZcWKFbl2iufOnePo0aNYrdYbyli/fj379++/pfwuXLhAYmLibeuZExMmTLjtNNfX6Z9//sny5cuJj4/n6tWrACQlJRESEnJTWUOGDDGO582bR7FixbLFGTNmDMOHDwdA0zQAvLy8uHr1KlarFR8fH0Mvi8VC6dKlXdIHBQVlk/n444/z008/UalSpRz1KliwIO+8845hHFWpUuWmZXn33Xe5ePEiRYsWzRbWoEED41jX9VxHjMqUKUNqaupN8/o343A4btq+Dx48yObNmwEIDg4mNDTUJfz6tteiRQvWrl17dxVVKBR3lXxjtGia9r6maf6app3TNG34raR5/vnns/k1bNiQa9euAWA2m9myZQs//fSTEf7111/nKOtGb+23Sk4PRietWrUiIiKCffv2ZRtJ+OWXXwAoUKAA77333i3l9emnn7Jr165s+VssFq5cucJnn312UyPJycSJE13OQ0JCOHjwYDYj7MsvvzRk1qhRg4SEBEwmExaLhdatW/P111/Trl07atasCcCaNWuoUaOGiwwRMepa13UiIyP56aefGDlyJC+++KIxXZaSkkKPHj3YsmULTz/9tIuM0aNHc/XqVU6dOkXNmjXZt28fPXr0YOHChbRq1YpXX30VyHzwff/99/z666889NBDAC6Gwr59+xg7diwAP/74o+G/fv16ypYtC2QaUcANjQibzWaU6fjx4zk+bBMSEggICDDOFy5cyNixY5kwYUK266TruovRFRMTw5w5c4zznEZ8vvvuOyCzfhMTE13ycsrMjY8//piDBw8SFxeXaxwnPXv2zPElwOFwYDabmT59+k1lONm5cycTJkwwDE6AixcvsnjxYnx9fencuTMiwrVr17KNnKWnpzNmzBi6d+/OqFGjgMypQ19fX7Zt28YLL7xgtD273W608T59+tyyfop7z4YNG/JaBcU/DRH5xzugIeAPeJD5ZepdQJ2bpBFAHnzwQRERsVqtIiJSqVIlCQkJERGR48ePCyATJkwQJ5lVkomu6yIiEhUVJd7e3nLmzBnZvHmz6LouDodDLly4IK+//rpk5aOPPpKcAGT16tUufseOHZOrV6+KU1dPT0958MEH5f333xeTySTp6elG2K+//ioVKlQw9Pv555+lWrVqOebl4+NjyHMSExMjDRo0EB8fHxk+fLgA4u/vLyIiJ0+eFBGRy5cvi91ul9TUVCOdyWQydJg/f77Y7XYBRNM0AWT9+vWyatUqI05W99Zbb8kPP/yQY5iISKlSpQSQqKiobOH79u2Tq1evygMPPJBj+jVr1kiZMmVyDAOkZcuWOfo79QZk4MCBLmHe3t7Svn17ASQ1NTVX2Tdyf/zxhyQnJ8sHH3wgISEhMmLECGnbtq0A8uqrr7rEvXz5sphMJklLS5M2bdoYOn3++ecya9Yso+xnzpyRbt26ySuvvGK0JUAKFiwoGzZskFOnTgkghw8fztaGjx07JidPnhRAdF2XTZs2uVwDEZGMjAx56aWXZO/evbJz507p2LGjmEwmERFxOBwuOjdt2lRWrlwpTZs2lYSEBLFYLBIbGysiIkFBQdlkO/n++++NMOd95UTXdSO/LVu2SEpKioiI9OzZ00jjTP/bb79J586d5ejRowLIm2++Ke7u7kaedrtdkpOTc70+Xbp0kc6dOxvn/fv3l6ZNm7rE2bBhg0yZMkXWrFmTrSyfffaZfPfddy5+Docjx/vwbnJ9nTnp3Lmz2O32e57/3SQsLMw4TkhIkK5du8qVK1dERCQuLs4lbk5tKSd0XReLxeLil5iYKH5+fjnGt1qtudbpnWCxWCQjI8PFLyAgQC5cuCDe3t63LS8+Pl5CQ0ON871798qzzz7r0tYcDoccPHhQdF2XJUuWiEhmP5+UlGTU58WLF8VkMhn3lzNdUFCQREZGuuQZGBiYqz6JiYku9QX4SvZnbg+gh7e3t3h7e0v37t3vyDnTO+Vly+d6j3+iA/oAC7OcjwM+vEkaoxM6f/68AEYHPG/ePLHb7fLJJ58YcaKjoyUjI8PlJqlbt64EBwfL3Llzsz1Qa9eu7eK3fv16l4dRaGioLF++XIKCgmTSpEkCyKpVq0TXddm6dauEhobe8OEXFxcnI0aMyDFs9erV8sILL7joajKZ5Nq1a9KuXTt5/vnnsz08csuna9euxoMkq/+iRYskIiLijh7cWV2vXr1y9F+6dOkN0/38889y7do1qVix4t/WITd3I6Pnueee+9vyH3roobuu8wcffHDDcOe1FhHDmMnNtWnTRj799FMpXry4tGrVyiVs6NChUq9evZvq07JlSylVqpQ4HA6ZNm2aix5ZmTBhgku6yMhICQ4Oltdff10GDRokbdu2lTp16uSaT7t27e5ZO7iZ69atm3Tr1k1ERIoWLepyfZ0vCCIibdu2lcaNG4tI5sNYRGTQoEEuxuKYMWMkOjpaDhw44FI/8fHx2eosK40aNZJTp065+Dn7qxo1akhAQIDLQ8hms+UqK+uDL+uDNuvLis1mM8pw/cN9wIABucqOiIhwObdYLMaLkM1mk4ULFwogr7zyivz6669GXVapUkXOnDkjgGzfvl02bNggHTt2FEAOHToksbGxxotnSkqKnD59Wg4fPixVq1aV+vXryzPPPCNeXl4u+vbt21cAWbFihcyaNUuCg4OzvURdb3BaLBbRdV3Wr18v6enpIvL/feeiRYvEbrcb8p0vryKZ16d58+ayYsUK6dOnj1y5csUln2+//VZefvlll7p09rmffvqppKamSu3ataV169Yyc+ZMI13JkiXl9ddfd5FVrVq1HNvpkCFDXM537twpgLzxxhuG3/Lly13ivPXWWwK4vDz36tXLeMEqVqyYVKlSxSVN4cKFhTw0WrS/MvtHo2lafWAT0BowA7vJrLRc50r+eqNWKBQKhUJxdzkhIl5ZPTRN6wHg7e39G0DFihXvSLBzwfzx48d7AojI71nDC9yR1PuMiARomjYN2AGYgNOA4/p4mqYNAYZc769QKBQKhSL/k28W4orIQhFpISLtgUQgMIc480XEK6sFOGrUKGPhpXPBpnPR3vjx4420K1euJCYmBshclGgymQDo378/ISEhhgwnTZo0cTm/cOECsbGxFC5cmJSUFNLS0rDb7VgsFt544w0Aevfujc1mA2Dx4sVMnz6dWbNmucipX78+kLnbpkuXLrnWh3Nh5RdffAHAZ599BkCnTp1Yt26dEc/X15e4uDgOHDiQbdErwPz58/noo49ISkpi/fr1LvL37Nlj5APQtm3bO9pJlBU3t8wmFx8fb/jlpBdAaGiosZ34ep588skb5vPYY4/dMH+A4cOHU7x48Rzj9e3b94byr+eFF14wjufPnw9k7hS6E2JjY/n99/9/uXBu5W7Xrh1Dhw7NFv/IkSMA1K5dG13X8ff3JzQ0lNDQULy8vPDy8sqWBuDSpUvExcXh7u7Oxo0bGTNmDAUKZL7HtGrVilWrVt1UV7PZ7PLrRNd1EhMTjSHd2bNnA/DAAw/g6+tLUFAQ165dIyUlhcDAQEaPHm0skM+JJUuWAFCiRIlc4zjv2RuxZ8+em8Z5++23Xc5btGhByZIlMZlMfPXVV0RHRzNjxgwiIyNp2bIlwcHB1KpVy4gvIqSlpRnlERHjrwrOnTtn7AwTyVx07qwjm81mHOu6boSFh4fTt29fDh06xNq1a41wgNatW2MymXjmmWdYtWoVqampxmLysLAw/P39ycjIwGq1YrVaSU5ONhZFnz9/npMnT6LrOjabzcjf4XBgt9u5du0aqampmM1mYmNj2b17N2FhYZw+fZqUlBRGjBiB2WzGZDLxxRdfEBsby6ZNmwzdMzIy2L9/P/7+/rz66qv88ssveHh43LT+IXMThfOaV6hQAYC1a9fmuKPOeQ83bNiQ6OhoUlJSsFgs2eJNnTqVjh07uviFhoYa9ey8Rnv37mXs2LGkpKRk2xTx3nvvUbp0adLT07FYLGzevBmr1UqBAgWoXr06w4YNY86cOdn+4sK5A7JmzZrY7XYcDgcRERFGeEREBKNHjwagbt26t1RHd0LWDQVZmTZt2j3L865z/XzRP9UBFf76fQC4AJS+UfzmzZsbc7JZyTofGRgYKCdPnsx1Id2tLHBzLnhykpaWli3Ohx9+KIDs378/Rxlly5Z1WQuQmJgoIiILFiwQQMLCwoz5XmcckRsvALxy5UqO4StWrJCLFy/KjBkzXOawnei6biyEdPLpp5+6nO/cuVMuXLhgxHcSFxdn5Am4zEFnleEsw4kTJ4z6evrpp8VqtcrcuXNdFoQ5FyPrui4bNmww6iBrvV+/dqRLly5GPkFBQeLp6SnvvPOOJCcnG/5OHUJDQyU0NFR0XXdZM3D27FkjXu/evaV169ZisVhk2rRp0rt372zzyU65ZrM5W704SUhIkPfff9+IGx0dbaStVauWUc6sOMMTEhKMMOd6KIfDIVOnThURkenTp8vIkSOz5emka9eucuzYMQkODpbU1FS5evWqEXZ9mw0KCjLWRPz4448yf/58OXXqlDRt2lREMtudv7+/sfAxKSlJRESWLFkiAwYMcFlo6SQlJcWl7d4Im80mAQEBIvL/CzPNZrMMHjxYfH195YUXXhCLxSKATJs2TX799VeJiYkRm81mtLu4uDjx8PCQCxcuZLtOAwYMkBUrVgggPj4+4uvraywKB8RkMmW7DjdbbGu32+Xw4cPZypi1D3n99dezLTS9WwQEBBj9hohI//7970k+d4O0tDSjfmNiYuT06dNitVrFbDbLmTNnXOLm1GYcDockJia6XBNd17Ot44mNjZXjx4+7+KWnp0tqamqO/bRTTk6cPn1aoqKici1TTExMtnZ/5MgROXr0qBw9ejTXdIMGDco1bOzYscbxgQMHxMvLS+x2u4SGhhrl+O233yQpKUlsNlu2hbUWi0WOHz8uqampEhMTY/TZdrtdLly4IOHh4dnyfPzxx0VE5NKlSyLy//Vx/cJl1JqWm6Np2gHAE7ABI0Vk943ie3l5ia+v733R7WbY7Xbc3Nxc3vKzYrPZKFSoEGfPnnX5oznntXH+74jdbkfXdQoVKnTvlf6baJpGbm3rRmE5ISIu/71y4sQJl/QigpubGyJC165dWbp0KeXLl0fTNGJjYyldurQxggCZ26UnTpzIN998c9O8Dx48yKOPPppNH7vd7nIdRASr1XrL18ZZB9u2baNLly4kJydTunTpbPWyb9++HEeNPvnkEz7//HPj/MKFC6Snp9O8efNbyv9e4OxUcmvnWa/j381H07Rs7ShrO4DMUZ2DBw/y4IMPAtCrVy+XLbRZ09erV4/p06dTqFChG45w3oxx48YxefLkO06vcGXZsmUMGDAgr9VQXIemaXm2piXfGC23yz/JaLkVNE3j0qVL1KlTJ69VuSv069ePFStW5Bh2u0ZLVg4cOED79u2zpc9J5p9//knLli1zfYj+XYoWLUqHDh3o3r17timFm/F36kCRSU51+H/s3Xd4FNX6wPHvhCoICIKIKKJe0asoKkRAxSu2q4KgWBEUyxVFRcVyLSgdBUFAUEC6SO8d6RAggZAAKRBISO+9bLbvzvv7A3d+2RQIEEjCPZ/n2SeZM2fOvNPfnTO7O2nSJONLBhcsWMDzzz9vdB9YrVbq1q1balt2u506depc1HgV5XJRmUlLtXgQ93/B5XYBKythgQv7Aq8uXbqU+QWAxXXq1Om851MexZ/jOBdlPcejXBhPwgLQt29fAPz8/Hj44YfLTFgAlbAoSjVRbR7EVS4fy5Ytu6DpPd9wWtRff/11QW1eajExMZUdwv+MLl260LZt28oOQ1GUCqCSFuWy8O9//7uyQzgnFfFsx/+6ol/vfzbqh0YV5fKgkhZFUaql4r85dSYqSVSUy4NKWhRFURRFqRZU0qIoiqIoSrWgkhZFURRFUSqKfjEbV0mLoiiKoigV5aLmFSppURRFURSlWlBJi6IoiqIo1YJKWhRF+Z+WmJjIqVOnKjsMRVHKQSUtiqL8T1u6dCnTpk0DICsrq5KjURTlTKpU0qJp2hxN0zI0TQsvUtZE07RtmqZF/f23cWXGqCjK5aVGjRq43W4cDgfNmjWr7HAURTmDKpW0APOA4r8L/zWwQ0RuBXb8PawoilIhPEmL+tHEqiclJeWitJuWlsa+ffsqrL24uDj8/f29yi63H8GtKqpU0iIifkBOseKewB9///8H8NyljKmi6HrZH11fsmQJx48fL3VcXl7eec0vPj6e/Pz8M9bJz88nOTn5rG39+OOPZY5zOp3GspW1jEXrAOTm5p51nmWx2Ww89NBDJco3btzIyZMn6dy581l/fXnnzp3ExcWddV67du1i+fLldO/enezsbABcLpcxvl+/fl7tmM1m4Mwnq/T0dJxOJ8HBwSXGjR07tkSZZ725XC6j3cOHD5eo53a7L9pJ8mzbt6IFBQUBkJmZSUhISKl1rFar149k2my2MtvTdZ3nnnvOiD88PJxTp06RlJSE3W7HZrMRGRl51rgWLlx4LotR5VXk/hIbG1uu9hwOB2vXrjW2hdvtZsSIEaxfvx6A7OxsLBaLEV/Lli1xuVyYTCajDbfbDfz/r6yLCLm5uQQGBpaYn81mY9++fRQWFhpldrudgwcP0qdPnxL1PcdW0XOniGCxWMjJyUFESl3Or776inHjxpGQkGCUvfzyywwcOJCoqCgmTJjAxo0bS10fnn1cRLDb7bjdbpxOJ6mpqRw7doy8vDxycnLo2bMneXl56LqOy+UiNzeX0aNH43K5vI6FlStXUlhYaKyzDz74gGPHjnHkyBFcLhdms5nQ0FDefvttr2W12+00adIEXdfZs2cPcLq7VESYO3cu48aNw2azGefCyqJVtWxQ07TWwAYRafv3cJ6IXPX3/xqQ6xk+SzvGgl1zzTVkZGQY45o1a4bZbKZJkyYkJSUB4Ovry/33389vv/1G8+bNeeaZZ5g7dy7vv/8+06dPN6atV68e1113XYkH95588km2bt3KP/7xD1q0aIHFYsHlctGwYUP27t1r1GvXrh0hISHcdNNNxMbGnvsKKsXtt9/O3XffzbJly2jWrBmZmZnGuFq1anHdddcRHx9/xjY++eQTfvnlF2O4TZs2tG3bllWrVnnV87wrLa969eoZJ6GyeNZJcS1atCA1NbXc8yqqe/fu5/SDesXVqVMHu91+0eqfr+HDhzN06NCzjr/++ut56qmn2LNnD1FRUaXWrVevHjVr1qRevXqkpaUZ5Y0bNyY3N5dvv/2WH374oVxx3XrrrYiIcVzMmjWL7du306FDByZNmsQTTzzB3LlzAfjHP/5h1HvnnXeYPXs2H374ITabjdmzZwOnj8dDhw6Va94ed9xxR5nJv4emaeW6uBbd92rVqkWdOnUoLCxkyJAhHD58GJfLRePGjYmOjva6WN5www2MHDmSHTt2YLFYWLlyJX369KFmzZr88cfp916tW7dG0zTq1q3LE088QVhYGK1bt6ZNmzb88MMP3HzzzXz99dckJCTgcrlIS0tj79695OXlERcXR5cuXbj++utZvHgxd911F/Xq1SMuLo769esTExNDv379jHmdq2effRan01niV9MbNWp01jdBl8Jdd91FWFhYhbb5/vvv06RJkxL7+tVXX13pF+gqLFhEOhQt0DTtWQBfX991AM2bNz+vhtPT0wE4dOhQDwARWe81n+qUtPw9nCsipT7Xomlaf6D/34PtL3KoiqIoivK/qNKSlprn1eqlla5pWgsRSdU0rQWQUVZFEZkBzAC48cYbJSEhgXXr1nH77bezdetW8vPz8fHxoWfPnvj5+fGvf/2Lbt268fLLL9O/f3+2bNnCqlWrGDNmDFdffTWzZ8+mT58+uN1uunbtys6dO3G73ZhMJmrVqkVGRgaLFy+mVatWvPDCCxw/fpxevXqxf/9+br/9dgoKCrBYLNSoUQNfX18++OADfH19adasGbfddhtr166lWbNm/Pjjj5w4cYLevXvTrVs3GjRowB133EFgYCCLFy8mMzOTL774gpdeeokJEybQtm1batasycKFC/n4449p0KABDocDp9PJ8ePHadKkCU888QTjx4+nVq1aNGnShJSUFCZMmMC3335Lfn4+Q4YMYeLEiYgIt9xyC76+vkRFRWG325k4cSJvvfUW99xzD02bNmXgwIF88sknpKSk0KRJE2bPno2vry+1atVi//79NG/enLy8PB5++GHS0tJo0KAB77//PlOmTKFp06b07t2bunXret3CP3XqFJs2beLaa6+lVatWdOrUiY8++ohff/0VgNdff51XXnmFyZMn88QTT/Dll196bevu3bszbtw4CgoKsNls/Otf/zLGDR48mAYNGvD111/zwAMP0KFDB2rWrMmkSZO8ujjmzJnDjh07WLVqFW3btuWzzz5jyJAhREVFsWjRIl577TWjbuvWralduzZxcXE4HA7uv/9+r3fYq1at4vfff+e6664z7ihMmTKFevXq8c477zBmzBiSkpJ45ZVXSEpKonfv3jz55JM0b96cYcOGccsttxjL/Y9//IOhQ4eyefNmnn76aZYuXcqbb77JunXrWLx4MVdccQXXX38933zzDQAHDhygU6dOPPnkk4wZM4aQkBDatWtHo0aNmDFjBv369eOOO+4AYN++fbz11lt8+umn3HrrrTz66KNs2bKFm2++mZUrV3LVVVdRu3ZtTCYT3bt3Z9q0aVxxxRVGF+HgwYMZPXo0kydPJi0tjdq1a1OrVi369u2Ln58fV199NS6Xi0cffZSMjAyuvvpqfHx8KCwspFevXkybNg2r1UqrVq3w9/fnwQcfpEmTJjgcDhwOB02bNmXUqFHcf//9xMbGsnHjRkaOHEl2djbffvstY8eOZfny5axfv77E3cODBw/St29f3nrrLSZNmsRVV11F165d8ff3JywsjDvvvJNvv/2Wn3/+2egGqF27Ng6Hw6udbdu28cQTT/Df//6XTp06sXfvXmrUqMHSpUtZuXIl1113HXXq1GHVqlVkZ2fTvXt3hg8fTq9evdiyZQsTJkygbt26HDt2jM6dO5OSksLGjRsZOHAgGzZsoF27dmzfvp1WrVrRqFEjateuTYsWLYiJiaFVq1bk5+dTs2ZNatWqRcOGDQkMDKR27doEBgYyc+ZMVq1ahcvl4vbbb+fUqVPExsZyxRVXcOzYMdasWcP8+fNp1qwZu3fv5pFHHuGxxx7jySefJCQkhKZNm3Lq1CleffVVtm3bxl9//cWXX35Jw4YN+f7775k6dSpOp5Pk5GTCwsLIyMgw4gkICDC6Rzdu3Ei3bt2YM2eO0c3gsWLFCv75z3/SpUsXcnJyuPnmm4mJiSnrtF2mf/7zn0RERJzTNJ9//jkpKSk0aNCAGTNmlGuakSNH0rNnT4YPH87KlSuN8s8++4wJEyaUOs3tt9/OiRMnAJg4cSKDBg0qd4z//e9/mT9/vtfdzXvuuYejR4+Wu43/ZdXhTss4IFtExmia9jXQRET+e7Z2OnToIJ6+8epARDjd+3V5SE9P55prril1mRwOB7Vr1/Yq03UdHx8fY3ytWrVKnTYtLY0WLVqUuMW/fft2Hn/8cWNYRCgoKKBRo0Ze9VJTU2nWrBk1a55/vu7ZVsW7Dj1iYmK4+eabz9hGQkIC1157bYn1cC4CAwO5+eabadq0abljPl8Wi4UrrriiSu2jJpOJK6+8krS0tLNuU5fLVWJ8eno6zZs3Jzk5meuvv94oL+ucWNWOUafTSa1atUodd6YL7oWy2WzUrVvXq+xs66awsJC6detSo0YNo4tO0zQyMzO55pprGDx4MKNGjSI+Pp5WrVohIvj4+KBpGpMmTeKaa67h5ZdfpkaNGsDp51o8/wcHB1O/fn2uuuoqrr32WmOeOTk57Nq1iwceeIBrrrmGGjVqGM8IhoaG8q9//avEtl6xYgUvvviiMazrOp9//jkPPfQQNWrUoFu3bqWu871795Kenm5Mm5+fz88//0zDhg2JiIhg5syZxMXFlTgvDB8+nI8//pjGjRsjIthsNmrWrMnDDz9MQUEBgYGB1KtXj8cff5ydO3eSnp5O3bp1mTRpEvfddx+bN29m5MiRhIWF0alTJ9atW0evXr3Iy8ujSZMmFBQUYDKZaNmyJXfddRfh4eHY7XbWrl1LVFQU77//PuvXr+fNN9/E5XLx+uuvs3jxYlJSUrjuuuvIzMzEx8eHwMBAUlNTueuuu2jfvj01atRQ3UMAmqYtBh4BmgLpwFBgDbAMaAXEAy+LSPGHdUuobkmLUn5FT1iKUhGOHDnCfffdB6hPfVxqnmfBSvv0VnmfQTofVS0JLUrXdXRdNxLtM72RK6+KXF5N01T3EICI9C5j1GOXNBClSlMJi1LR7r33XuNTGcqldaaPmhftrqloVTVhAfDx8THuPAMXdEfWoyov77moUh95VhRFqSyappXZ3aJUjl69elV2CEoVo5IWRVEURVGqBZW0KIqiKIpSLaikRVEURVGUakElLYqiKIqiVAsqaVEURVEUpVpQSYuiKIqiKBXlov6yqkpaFEVRFEWpKBc1r1BJi6IoiqIo1YJKWhRFURRFqRZU0qIoiqIoSrWgkhZFURRFUaoFlbQoiqIoilItVKmkRdO0OZqmZWiaFl6k7CVN045pmqZrmtbhTNMriqJcCLvdXtkhKIpyBlUqaQHmAU8VKwsHegF+F9p4VFSU8f8333xzoc0plUTXz/41ALGxsRw8ePCs9ZxOp7pQ/e233367qO1v3rz5orR7pv1h+vTp7N2794zTFxYW4nK5+OWXX6hbt25Fh2eYPXv2RWv74MGDZGZmXrT2K5KIXJS6lUlEcLvdlR3GBUtLS8PhcJQod7vdVWZbVKmkRUT8gJxiZREicvJc23K5XERHR3vaQNd12rRpY6z4MWPGoOs66enpZGZmGuWFhYWIiPHSdZ2jR4+yZs0ao2z+/PleG9Czw6anp+NyuYx6DocDEcFut5OYmEh2djZ+fn5GHV3XMZlMjBs3Dk3TEBFsNpux86enpzNgwAAjvqioKESE3r17o+s6p06dAk6ftD0vj507dxIWFobD4cBisRATEwNAQEAAuq4jIgQHB5OQkEB2drYRM4DVasXhcBg7alRUFDk5ObjdbhwOB7quG8uwe/du8vPzcTqdJCUlkZubS2RkJG+99RZmsxmn04nZbGbfvn1Mnz4dt9tN/fr1jXl51sOnn36KiOB0OrHZbFitVtLS0ozlKSwsZMCAAWiaRo0aNYwLoCdut9vNpk2bjJi3b9/O22+/bYwLCAhg3bp1nDp1ChFh27ZtiAhTp05l8ODB5OTkYDKZSExMJDg4GF3XOXz4MPPmzUPTNMLCwigoKCAwMJD169czdepUunbtSlZWFqGhoeTl5bFnzx4iIyOx2WzExcWRnZ3NwoULcblcOJ1O8vLySEhIYN26dRw4cICTJ09isVgoLCwkMDCQoKAgQkNDjfUZGhpKp06diI+P548//iAyMhKTyUR+fj4Wi4XVq1cb23j79u3GtkxOTqawsJAePXoYCVlmZiZWq5X8/HxWrlyJyWQytoHb7cbpdPLRRx9hs9nQdZ39+/djsVhwu924XC6uvfZawsPDSU9PJyIigoyMDOM4CwwMJCUlxVj3LpfLa9t49rdnnnkGl8tlDO/atcvrGAL4+eefycvLQ9d18vLyjMTT5XJRUFBgHE/h4eEsXboUXddp0qQJERERxv5pt9v57rvvSElJITQ0lP3792O1Wr3mExMTw6RJk0hPT6dBgwa0atWKTz/9FIDo6Gg+//xznnrqKSIjI3G5XLjdbsxms9c5xmKxYLfbiY+PL7HsDoeDU6dOceLECeO4/s9//kN4eDgJCQlER0djtVqJiYkhIiLCOO7y8vIICQnBYrEYx7TNZsNms3H8+HFWrlyJxWIhJyfHOGfous5HH32En5+fsW7z8vJITk4mJyfHOAdYLBZOnTqF1WpFRDh16hTJycnGdsrPz0dEiIuLIygoiGPHjuFyuQgJCTHGFxQUMGTIEAoKCrBYLOTl5eFyucjKyuLPP/8kLi4Oq9WKrus4nU4OHTpERkYGffv25cSJE2zZsgUfHx+uvvpqNmzYwJdffommaXz//fcEBAQwdOhQEhISMJvN/PXXX/j4+BAREcEnn3zCypUrcblcHDlyhM6dO3PkyBFyc3OZN28e48ePp3///sa+Yjab+eOPP9A0jXHjxhEbG0tBQQHx8fGMHj2ahg0bomka27dvN/aLzMxMwsPDSUtLY9u2bTidTkSEnj170r59ex555BHi4uL49NNPsVgsmM1mXC4XeXl53HzzzTz66KPYbDZmzpxJdnY2mqYxZ84cvv32W95++22GDx9OcnIydrudtLQ0IiMjee+99wgLC8NkMpGcnMyQIUPIyclh586dvPzyy4wbN4433niDzz//nIEDB/Ljjz/i5+dHp06dmDp1KpqmsWrVKq6++mpOnjxJVlYWq1atMuYdHh5Ox44dqVevHr/++iuzZ89mzJgxPPnkk2iaxi233EJubi6FhYVMmjSJFi1a8NVXX3HixAluvvlmNE2jsLCQmjVrct9997Fx40amTp1a1mX3ktCqSvbkoWlaa2CDiLQtVr4b+EJEgsrZTtVaMEVRFEW5PASLiNfjGpqmPQvg6+u7DqB58+bn1XB6ejoAhw4d6gEgIuuLjq9Sd1oulKZp/TVNC9I0rVyJjaIoyrmqVatWZYegKP+zLqukRURmiEgHEenQvn17r26e4i/P7dyq8vLc1r1cX3l5ecTFxZVre5hMJuPWbNEXnH5W5UzteF7Hjx9n0aJFFb7uCwsLjW66ol2Bxdsrq03PcnrGl7acxWPy3Gq/0H3GbrcbXZaltVPW8ogIqamplbLflLWMni6Is02fn59vdIcU3QYWi8UYHjNmDJ999lnR88gZX6Wtw/K8kpKSLtp6ysrKMrp3KmublLeup5vPM75onaJd1VarlWXLlpWoU1hYSExMTIltWny76LqO2WwuEUNeXt45x20ymbz2meKvLVu2sHv37jO2abPZzriNHA6HcZx7utw844p2qxaPS8T7POJ2u4356LpeZtyeRwBsNptRd+/evZhMphLrPD8/H13Xq8SzLZdt91CHDh0kKEjdcLmcePrwFaUijR8/nrS0NPLy8pg9e7baxxTlLDRNU91DAJqmLQYCgNs0TUvSNO0dTdOe1zQtCegMbNQ0bUvlRqlUljlz5lR2CMplyJOkDB8+nBYtWlRyNIqinEnNyg6gKBHpXcao1Zc0EKVKeuuttyo7BOUy1KZNG5o1a1bZYSiKUg5VKmlRFEW51Hr27Amc/qjszp07KzkaRVHOpEp1DymKolSWmjVrcvvtt1d2GIqinIFKWhRFURRFqRZU0qIoiqIoSrWgkhZFURRFUaoFlbQoiqIoilJRzv6LthdAJS2KoiiKolSUi5pXqKRFURRFUZRqQSUtinIGun5R73QqVYj6+n5FqfpU0qIoZ3DDDTdc9Hm43W5+/vnnc57ujz/+IDU19SJEdHlLTk4mKiqqRPmNN97I5s2bKyEiRVHKSyUt/0Muh3eSFosFk8l0yeaXkpJy0efhcrn45ptvznm63377jYSEhHOebv78+eTm5p7zdJeLlStXMnny5BLliYmJ5OXlXfqAFEUpN5W0/A/p168fhw8fruwwyM/PP6/pdu3aRf369bnqqqsqNqBKdr6/Xq3rOjVq1Ch3fYfDgcViYfz48SQmJp7z/C4XNWrUKLPbz+12X+JoFEU5F1UqadE0bY6maRmapoUXKRunadoJTdNCNU1brWnaVeVpy2q1snz58osWa3UUFxd3Se9SlKVx48bnNV1SUhJw6Z8z8ff3Jysrq8Las1gsXsOapuFyuYiMjPQqf/fdd8/Yjtvtxsen/IfwjBkz+Oqrr6hVqxYOh6P8AXP6bhDA4cOHiY6OPqdpqxofH58SyUlYWBgAr7/+emWEpChKOVWppAWYBzxVrGwb0FZE7gYigXLdR7darbz88ssV1iVSnbpW4uPjSy338fFBRIiOjmbu3LmXNKa8vDwWL14MnF6XZrO5zLplXVDfeOONCountO05YsSIUuuOGDGCoKAgAHJychg9evQFzbtVq1a89957Jcpvu+027Ha7MTxr1izj/8LCQvbv3w/8//rRdf2c7gzUrl0bm81GrVq1cDqdRvnZ7nzl5uZSq1YtAGbOnMmWLVvKPc9z9dxzz5U5zrP/eHgSqfOhaRoAnTp1Ak7fhbzYqtM5RLn8FH+zd7b90e12k5OTczFDOi9VKmkRET8gp1jZVhHxnJ0OANeXpy3PydzlcmE2mxERVqxYwYkTJ9i3bx+jR4/G5XJhtVpxOp3GhcBkMqHrOrquk5+fj9lsZt26dQwaNAiXy4WIsHbtWsxmM7quGxte13XMZjOFhYXk5OQQHR2NiJCVlYXD4cDhcGC1Whk2bBg5OTm43W6cTiciwrx58/jhhx/IycnB4XAgIsbLM8+srCxeeukldu3axR133IHb7eb48eO43W4jXk8srVu35ssvv8Tf3x8APz8/Zs+eja7r7Nmzh1mzZvH2228TGhpKcHAweXl5xrzcbjcOhwOTycTJkycREYYMGYLVasXtdmMymbDb7Rw4cIDs7GxOnjyJ0+nE5XJx4MABUlJS0DSN7du3k5aWhoiwYcMGfv/9d1577TVj+4SHh3u2LwATJ07k6NGjOBwO6tSpQ35+Prqu43Q6sVqtJZIcz50Ph8NBYWEhBw8eJD093Tgw4+Li+Ouvv7BarRQWFvLmm28yc+ZMVq1aRVxcHHXr1gXAbrcza9YsXn75ZYYOHcquXbu4++67EREOHToEwJYtW0hJSeHLL78kKSmJ7777jkWLFnHDDTeQnZ3N8OHDiYmJYffu3YSEhOByuYztISLY7XbcbjcRERGMGTOG7OxsZsyYQW5ubomuoSuuuILc3FzjAduNGzei6zoNGjTgoYceIjo6mjp16hAbG0thYSH3338/SUlJNG3alMzMTHRdx2q10rx5c9566y1j295+++2sWbOGlJQUjhw5Qn5+PiJCfn4+jRs3xmKx8O677+J0OtF1nT59+pCfn092djZNmjQBYO3atZw8eRJd1439suhyeo4Dh8OBy+WisLAQt9uNiJCTk2MkR57pAgMDjek8f9euXUtBQQF2u534+HimTp0KwPLly3nttdcwmUzExcXxxBNPcOWVVxIbG4umaVxxxRUsWLCAoUOHAhjbfc2aNRw8eNBrPkWP7YMHDzJq1CiOHDlibIP4+Hj++c9/8uGHHxIVFYW/vz+pqalkZWV5bavAwEDcbjeHDx/GZrORl5eH2+3G5XJhMplo27Yt7dq1IzMzk0OHDuHj48POnTvZtGkTJ06c4Pfff2fdunUsX74ct9tNQUEBoaGhHDhwgOTkZJYvX86wYcP46quvOHz4ML///js1a9Zky5Yt3HvvvcYdOj8/P1q0aEFUVJQR37fffmssq9PpJD09nZSUFBwOB7m5uYSEhCAi/Pnnn0aiPGbMGK9tKSK43W6ysrLIycnBZrNhNpuN847nguap53Q6uf7664mPj/ea94IFC1i1ahXR0dG4XC42bNiAw+EgOTmZkJAQBg8eTGZmJikpKQQHB1NYWIjVamXy5Mm8/vrrrFmzhvnz5xMZGYnL5SIhIYEuXbqQmZmJw+Fg1apVzJ8/3zjPRkZGYrfbWbBgAbNnzyY4OJi9e/fidDrZsWMHycnJDBgwAE3TWLNmjXEs2O12oqKiMJvNpKamGvvgyJEj+fDDDxk7diz79u1D0zQKCgoQESwWCyLCe++9x5o1a7DZbNjtdiwWCz179mTUqFFs2LCB0aNHEx4ezoEDB1iyZAlms5n8/Hxq1KjBqVOnANixYwcNGjRg/Pjx+Pn5sXr1aubNm0dCQgKpqaksXryYEydO8Ntvv7FmzRr69u2Lpmnk5eWxYsUKJk2ahIgQHh7ODz/8QHp6OqtWraJv37588sknNGnShP379zNr1iz69euHj48PderUYd++fUybNo2PPvqI4cOHYzKZuPXWW+nUqRNXX301BQUFNGrUiEOHDjF79mxWrlxZ/HJ7SWlVLfvXNK01sEFE2pYybj2wVEQWlKOdqrVgiqIoinJ5CBaRDkULNE17FsDX13cdQPPmzc+r4fT0dAAOHTrUA0BE1hcdX/O8Wq0EmqYNBlzAwjPU6Q/0v2RBKYqiKIpyyVSp7qGyaJr2JtAd6CNnuDUkIjNEpIOIdLj++tO9SEW7Pop2gXhu4RcUFJCSkuLVJVP8FRcXxy+//ILL5TJukXq6dnJzcxER47a651apiGAymYzbpAcOHMBmsxEeHo6IkJ2dbbT/1ltvsWrVKtxuN9HR0dhsNmOcw+HAZrOh6zpHjx7F7Xazfv16dF0nNjbWuEXpmZeIcOedd7J3715jmT0xAaxfv57k5GRefvllMjIy0HXdmJ+u6+Tm5mK1Wr2WX9d17Ha70Q3lWU6TyYTNZjO6qDzdAJ988gnvvvuuVzfbxx9/zNq1a42PmkZGRmKz2XA6nZjNZtasWcMjjzxCz549ja43i8XCyZMncblcDBgwwGtbX3HFFV7dL9nZ2V5dgQcPHuTJJ5/0iis6OhqTyUR6ejqrV682tmF8fLzxvExqair+/v6ICLt37wZO3zbPzc0lIyODyZMn061bN0wmEyNGjKCgoIDffvvN2J88f5OSkox5FxYW4nK52Llzp9endjzfFZKZmQnALbfcwqBBg1i8eDG6rrNr1y4KCwu9+qJXrFgBnH625P777/fs95jNZmPZLRYLaWlpHD58GKfTaWwfgO7du3P33Xdz6NAhY/3dddddxv7leU5m+fLlTJ48mdjYWHx9fYHTz9WEhoYSFBTk1XXn2c/8/PyYM2eOccu8+MtzrHhenn2naNmHH36Iw+Hg0KFDOJ1OnnzySUTE6L7x7I8euq4TEBAAnO4S8hzLnq6IX3/91difPdujd+/ejB49mkmTJgHw6KOPeu1bVquV8ePHM2XKFLKysoiNjcVkMmG1Wr26w8xmM0OHDiUqKoqCggLj/ODZ78PCwtixYwc5OTlG9+bgwYNZsmQJx48fx2q1snDhQjIzM41tZLVaMZlMRhdbdna20SW7YsUK/vGPf5Cfn298ZD0/P5+srCy6dOnC3LlzjW0xa9YsDh48iNVqJSgoyOiyKHo+sFgsxr4pImRmZhrnpqSkJKOr2hOb2Wzmr7/+YsKECUY3iGeZPd1DqampxnnL7XaTnp6OyWRi+vTpWCwWo+sxJyeH8ePHc8MNN/Df//6XmJgYDh06xGuvvUZubi4mk4nCwkKmTJmCruvMmjWLxMRE3G4348aNY9OmTeTl5ZGTk8P3339PRkYGsbGx2O12bDYbqampHD58mEceeYSff/6ZpKQkCgoKWLx4MdOnT2f69Ok88cQTnDhxAl3XOXLkCKmpqcTFxRnnJM+2jo2N5e2332b8+PGcOHECTdPQdd24tlgsFp544gnefPNN9u7di91uR0S4//77ee2113jwwQcZP348y5cvx2QyERAQwJIlS3A6nZw8eZKcnBxj/0xPT2fPnj2EhobStGlTAgMDOXHiBOvWraNr164sX74cf39/+vbtC8Bdd91F165dWbVqFQsXLmTy5MnExMTw/fffExkZSXZ2Nrt27WLJkiXs3r2bNWvW4OfnZ+zrjzzyCGlpaZw8eZKxY8fy+++/ExMTA0C7du3o06eP8QGIGjVqMHHiRE6cOFHq9fdSqfLdQ5qmPQVMAP4lIpnlbefGG2+Uf//738yYMePiBFoN3XnnnSxdupS2bUv0vF1S5/MR37y8PBo3bkxaWhrXXnst+/bt48EHH6zQuETEeEDTo3isM2bM4NChQ8ycOfO853PLLbcQEBDANddc4zVvETnjp4FEhKioKLZt28aHH37IRx99xPvvv1/u7fn7778THBzMm2++Sdu2bWnYsGG5Yz7fj2VXFLvdzokTJ2jXrh1w+rtm5s2bx86dOwGYOnUq77//frk+TdW7d2969OhB7969jbKi2/1iLWdp+9eFsFqtXHHFFRXWnlI2l8uFpmnn9BUDnk/3VeQ2ryo0TVPdQwCapi0GHgGaapqWBAzl9KeF6gDb/t74B0Tk/XK0pb6CvZjinxqpLJ4Lz7nwfDdLo0aNACo8YQHKdXKpXbv2OX9cuLjjx49Tp06dEvM+2/w1TaNNmza0adMGgF9//fWc5tupUyeaN2/OAw88cG4BU/mffKlTp47XftOrVy+v4Q8++KDcbdWtW5eaNS/9qa+iL14qYbl0zmd/OZcERym/KpW0iEjvUopnn297KmnxVrNmzQv6mGhFOXr06HlP6/nUz6Xi+aivR7NmzS74y+2KJyyXSrt27c4rYayKrrzyyvNelunTp5fYroqiVA9VKmmpaOrbLb29+eabXHvttZUdxnkbPnz4JZ9n8a+779atG88888wlj0OpOJWVNCqKcuGqxYO456NGjRo0aNCgssOoUj766KNL8gOAF8uQIUMu+Tzr169fouxy7KP+X1f0O4QURam6Ltuk5aqrruKXX36p7DAURakGFiw4/dVP48aNq+RIFEU5k8s2aTnXJ70VRfnf5bl7VvQTRYqiVD2XbdKiKIpyLmrWrEnLli0rOwxFUc5AJS2KoiiUfOhaUZSqRyUtSrXz008/VXYIymXoyiuvrOwQFOVycFG/a0QlLUq18+WXX1Z2CIqiKErpLmpeoZIWRVEURVGqBZW0KJeVS/l18wEBAdjt9ks2P0VRlP91/9NJi9VqJSwsrLLD4PDhw2WOa9++/SWMpPrz8fHBarVWSFubNm0yfoHZY82aNcb/r776KqmpqRUyL+X8ORyOEj/ZMWnSJNLT0y/4d6IURalaLtukRUTIyMgo8QOBv/zyi3FRCwkJ4T//+Y/XeJvNdlHi6dixY5nj2rdvz8yZMzGZTCXGnSmhqcqmT5/uNVxaIuFwOLyWuaCgoNS2ynM3o7Rf6f34449L/URIYWEh8fHxZbblmb5bt27GLzF/9913ADz//PNGvYSEBAYOHMg333wDwLRp084Yo8ViYdGiRRf8m1jdu3e/oOkvB8nJyfz4449YLBYGDhxY4juZBg0axOuvv87YsWMB2LNnDxs3bmTWrFkl2tJ1nT179hAcHHxJYlcU5fxVqaRF07Q5mqZlaJoWXqRspKZpoZqmHdU0baumadeVp63w8HB69erFgQMHvMpHjhxJYWEhcPqiWfx3SJo2bXrBy1GawMBATp48Web4r7/+mry8PEwmU4kuDrPZDMDSpUvZv39/ueb3448/lnkSFpFzuhuxevXqEtPv3r2bgwcPesXo5+fHrl27yMnJYcCAAV7T1KtXj969exMVFWUkhvPnz6dhw4ZGnUaNGhEVFeU1ndPppG7duowdO5Y1a9aQlJSE2WzGarUyatQoBg4cSHJyMvD/32bqSYSmTJnC1KlTycvL44UXXsDlcrF582YeffRRWrduDZy+m+Ln50dMTAw//vgj8fHx1K5d2yuGtWvXMnr0aOO3j9555x0j7tTUVMaMGQOU/UvDr7/+Oh9//DFpaWn06dOHZ555BrfbjcPhYNeuXQAcOnSo1LtqkydPNta5x8aNG732pcTERL755ptSu8Y8CV/R5C05OZl58+aVGivAgAEDSk2gL5Su6+d85yM0NNRrmpycHEJDQ0lISODbb7+lfv36ZbZZq1YthgwZQmRkJL169WL06NGMHTsWk8lESkoKAFOnTqVjx4507dqVDh06sHPnzvNfQEVRLj4RqTIv4GHgPiC8SFnDIv9/DEwvZ1sCyNChQ6WoJk2aSGZmpoiIbNu2TQD5z3/+Y4w/vUq8mUwm+fXXX8VsNkt+fr6IiLjdbhERmTx5slfdNWvWlJje0+6SJUu8ylwul5w6dUo8sX7wwQdyxx13yIMPPug1Xe3atSU3N9eoJyISHR0tnTt3LnVeu3fvFkDuuusur/KcnByJjIyUKVOmyNVXXy2TJk2SWbNmia7rXvUCAwNLxL5nzx6jHiDdu3cXQGbOnCmxsbHSokULIz7Pa9y4cbJ69Wrp1auXAHLPPfd4LcP06dMFkFOnTsm0adO81sPRo0clNjbWa5qir6ysLGnYsGGp4wCZM2eO8f/IkSMFkDFjxnjV+fDDDwWQxx9/vMT0uq6X2XZpL8/2CQwMlNDQUAEkOzvbWF/FX3Xq1JFRo0YZ68Kz/hYsWCB79uyRiIgI+eabbwSQJ598UgBJTk6WPn36GG0sWrRIDh8+bMzv9ttvFxGRAQMGeG27Z555RgBp1KiRuN1uWbdunTz44IOi67qxnD/88IOEhoaKiMi1114rKSkpkpmZKW+99Zb4+vpKRESEfPfdd+J0OuXw4cNy9913S0BAQIl976WXXhKbzVaifNeuXQJIgwYNSt1n7Xa78f/x48dFRCQ2NlYAueqqqyQlJUUeffRRWb16tfTo0UP2799fYp2KiFgsFmO5NU0rdd3Xr19fbr311jK3ZUREhGzevFnsdru89tprXnEGBASI0+ksdRkUpTrJyck572mBICl5zX0WeNbX11d8fX2le/fu5/XyTO9pr8R8ihdU9gtoXTRpKTbuG2BaOdsxTkIWi0U+/fRTcbvdxgVYRGTt2rVeJ7y/N4bx/8SJE0XkdCICyMCBAwUQs9ksbdq0kTfffFMA6devnxw9elSGDBliTB8TEyNHjx6VrKwsGTdunACycOFC0XVdjhw5IiIizZs3lxtvvNHrhNmmTRsBxGazyW+//VbqSVVE5L333is1wRowYIAMHz68xHL9vROUeHXt2lU0TRMRkbfeeku+/vprASQpKUl0XZenn37aq37Pnj3P6WL+yy+/lFqel5d3xuk2bNggUVFRcvPNN5c63rMtLtbLc5E9n9edd94pgISHh5ervifp8LxatmxZar25c+eWWv7www97be/i+3NZr4kTJ8rPP/9s7Ac9e/aU7777Tpo2bSp79uwpc7ovvvjC+P/bb7+VZ599VkT+P1kubb8cP36817Zzu92SnZ0tI0eOlI0bN0qdOnUkJCTEqPPnn396zfORRx4RQDp27CiA3H///SXi8iQyS5curZB9oHHjxgJIYmKifPTRR6LrutSsWVOaNGki6enpsnjxYomKipIWLVqIyOk3MuPGjfNa7oSEBBE5/Qbl4MGDRuJf/I1CaXRdF5fLJSIiR44cMd4oFR0PyK233lpi2ry8vLO2fzZWq/Wc6hdNPM9k06ZNXuvhnXfeEX9/f9F1XVJTU73q7t+//7xi8bTtWf+6rhvrT9d1sdvtEhcXV642pk6dKvHx8West2fPHvntt9/E6XQa2ywvL08SExPFz8+vXNu7OE87nje3p06dMtpxu91iNpvlrrvuEpvNJvv27fPaP9LT00XXdXE6nWK1WiUqKkqioqJERCQrK0uysrIEELfbLTabzVg/ubm5RhtFt2diYqLExMSI2WwWkcpNWrS/Z1ZlaJrWGtggIm2LlI0G3gDyga4iklnG5EXbqVoLpiiKoiiXh2AR6VC0QNO0ZwF8fX3XATRv3vy8Gk5PTwfg0KFDPQBEZH3R8VXqmZayiMhgEbkBWAh8VFY9TdP6a5oWpGla0KWLTlEURVGUS6FaJC1FLAReKGukiMwQkQ7FM8CJEyd61du0aRP33nsvr732mlF2ww03sHTpUgCio6OZP38+cPpTCH379gXgnnvuAeDRRx8tMe/NmzczcODA00EuXEhWVhbBwcFeD/a1a9eOY8eOAfDnn3/SunVrWrVq5dWO51Mwfn5+ZS0mAM8++ywAI0aMoE+fPixfvpxevXrRpk0bbrzxRqNeRkYGy5Yt46WXXiq1nVtuuYUBAwawYMECr09WTJ8+nfXr1xsPh9avX5/bbruN664r13PQZ/XXX3+dtY4n4z4fDzzwwHlPW9UU/Xr5zz777Ix1s7KygNMPI//6669nrLt8+XKOHj3Ke++951XevHlzrrrqqnLF5vnkVFpamrFPwukHs2NiYtB1nT/++MN4oBjgiiuuYOrUqWzfvp0jR46wYMECnnjiCQ4dOlTqPJ577jnq1q0LnPnbkDMyMsoV87l68MEHAViwYAEff/wx69atY+zYscybN4/mzZuze/duPvro/99LRUZGMnfuXBYsWACc/tSYR9u2bUlMTOT6669n2bJlrFu3DqfTSUhICNOmTSMvL4/g4GCsVitz584lIiKCkydPsnfvXhITExk1apTxQPOnn35Ku3btWLhwIQ0bNmTcuHE4nU4sFgs+Pj643W7++OMPBg0aRFpaGuHh4TidTtasWUNERARxcXGEhISQlpaG1WolKyuL3NxcQkJCCAoKIiwsDKvVitVqxWQyYTabyc/PZ+PGjRw9epTVq1cTFxfHhg0byMzMJCsrC39/f/Ly8nC73TidTjIzMwkKCmLHjh3MnDmzzHffHTp4nbKZOHEiixYt8ipbsmQJNpuNtLQ0vv766xIP+w8ZMoTExERyc3PJysry2iZw+tOExc8LM2bMIC4uDpfLhcPh4J///CdTp05l+PDhJCQk8OSTT3rVHz16NC1btuTEiRNYLBZiY2MxmUzGr4N37NiRDz74gDfeeKPU5ezRowcRERGsXr3a+EAInP7AwY4dO+jSpYtxHako7dq1M/7v3LlzqXUGDRpk/F/8k66eT1B6lPfccNEU7y+q7BfFnmkBbi3y/0BgRXnaufbaawUwHszz8PTJiYiMGTNGPv/88xJ9xR6llRfvmyzeL1r8YVsRkXfeeUdeeukl4wHg4vi7D3379u2i67rExMSIyP8/B7Bt2zbjeZzBgwcb05XVz2u322XMmDGyc+fOEuOCg4MlKChIOnbsKK+99poUFhaW2kZRH374oddwSkqKmM1mcTqdEhYWJsnJybJ//35jXXgecnQ4HKLruixatEhq1qwp/v7+xvJ6hISEiMj/P5TrebndbnG73caDyqmpqfL5558b43VdF4vFIm63W9q1a+c17erVq4357NmzR3755RcZOXKkPPbYY2K32416ZrNZEhIS5OjRozJ//nxJS0uTTZs2yfPPP++1XSjyfIPnddttt5X6HETLli2lsLBQIiMjZcuWLeJ2uyU1NVXmzZsnX375pcydO9d4IDg7O1tWr14tuq5LkyZNjDYsFotERUXJvn37BE4/SFxYWChvvfWWiJzuX/7xxx9l4MCBcvDgQeOh488++0xERDp06FBiGwJit9uNfvLix8WxY8fk6NGjEhkZKW63Wz799FNZtmyZPPPMM8ZzSb169ZLJkyfL9u3bZdCgQRIdHS0iYvSD5+bmSmRkpBQUFJSYf0pKSqnPupQW58mTJ+WHH34Qm81mTGOxWOS5556T/fv3S6dOnYxnrdq1ayd33323AOJyuYz198ILLwgg0dHRXttn5syZEhUVJT169BBA3n777RLbUKR8z5wUV1BQILGxsWWOHzVqlJhMpnNutzzS0tK8zgdFz3NVnecc5Nk3K4rdbjee4/BIT083zlMpKSkVNq8JEybIJ5984lUWGxsrycnJEhQUdF5t/vXXX8b/OTk5sm/fvhJ1dF2XjIyMc9pfPXU9D7wXFRAQIAUFBbJly5YztoF6puU0TdMWA48ATYF0YCjwDHAbp3+EKR54X0SSz9ZWhw4d5IknnuDHH38ss05OTg5Op/O8+97Ka9OmTTRv3rzML4rbunUr//73vym+LTZt2kR2djavv/46AHv37qVLly4XNdaKsnbtWnr27GkMu91u47s0NE0rsaxw+t168+bNWbp0Ka+++qpR7nQ6qVWrFgCtW7fGYrGUeFftaXPMmDEMGDCARo0a8d577zFx4kTq1avnVXfv3r18+OGHhIaGnnU5NE1j0aJFdOrUiRtvvNF411m/fn0aNmzIK6+8QmpqKgMHDuSll17C6XRSs2bNcrVbdB3k5+dz1VVXlVgvRZf9TDxfpHbDDTeUOr5169bExcWdtR0PXdfRNI2TJ09y3XXXceWVVxp3AYt+J865iImJ4eabbz6naYruNwD+/v4MHjyY559/nk8++QQRISwsjBtvvNG42+C5G/P666/z559/GvGeOnWKW265xWirsLCQK6+8EhHBx8eHa6+9ln79+hkfYVeU8srMzMThcNCyZcvKDuWS0DSt0p5pqVJJS0Xq0KGDBAVVn0dbROS8LwbVTUZGRolbjuW1ePFiHnvssRLTl5UIlUXXdXx8zt47OmnSJD799NNSx23atIlnnnnGGDaZTDRo0KBc8z/XeC9UeZe3qhM5/R1D9erVIykpieuvv/6s00ybNg273U7//v1LJLAeGzdu5MYbb+Smm26ifv36FR22olxWVNJyEVS3pEW5MHv27OFf//pXZYdRbpc6aVEURakolZm0VP+3XooC1SphAdi2bVtlh6AoilLtqKRFUSrB448/XtkhKIqiVDsqaVEURVEUpVpQSYuiKIqiKNWCSloURVEURakWVNKiKIqiKEpF0S9m4yppURRFURSlolzUvEIlLYqiKIqiVAsqaVEURVEUpVpQSYuiKIqiKNWCSloURVEURakWVNJymbLb7ej6RX2I+5y43W7sdvsFt3Opf68nNDSUAwcOlDouMjKSvLy8ClmuC5WXl1fZIVS4uLg4tm/ffsnmN2TIEJKSki7Z/BRFOXdVKmnRNG2OpmkZmqaFlzLuc03TRNO0puVpy263ExERcdZ6R48eJTy8xOwq3JkuKnv27PEadrvdpf6fmppa7otTv3792LBhwznFWFFcLpfX8OLFi/nwww/p378/AMHBwWecPj8/v9RyEaFRo0aMGDECp9NZYnxaWlq5Y/z888+9hou216pVK2N+Dz/8MKNGjcJms/H222/Tt29fo95tt93G+PHjady4MQAxMTFMmTKFJUuWlDnfI0eOUFhYSL9+/bzKy0p8ateuTVJSEj/88EOJcR9++CHx8fE4HA4jBoAff/yxzPl7lqu8Zs+ezU8//cS9995b7mkqir+/P3PmzPEqCwgIKPNXt8+FyWTi559/Noa/+eYbRo4cyciRIy+4bUVRLiIRqTIv4GHgPiC8WPkNwBYgHmhazrYEEF3XxcPlcsnKlSulqK+//lpeeOEFY/jYsWNSnK7rkpubW6L8XAASGRkpIiJ2u90rJkA6dOggNptNCgoK5J577pGUlBQREWnRooVR94YbbpBJkyZ5TVuWLl26yIIFC7zKoqOjxW63S0FBgRw+fFgAmTVrltc6EhFxOp1ew55YPFatWiVbt26VefPmyalTp+SRRx6R/v37C2D8BWTr1q3idruNYcAYDgwMFEBsNpvoui5Op1O++uormTBhggASFxcnJpNJFi9eLFFRUZKdne3VTqtWraSgoEBERKxWq1HeqVMnozwrK0u2bt0qq1evlh9++EEAeeutt2TlypUCyPvvvy9Op1O2b98uTZo0kfj4eDl06JCcPizEa35FXzfccEOJsjlz5ngNx8fHS1BQkIwbN06++OILWb58uURGRgogv/zyiwDy2muvidvtlpMnTwogQ4YMEUAmTpwoQ4cOlXHjxgkgbdq0EUAefvhhERG577775B//+IcA0q9fP2Oenu3kiT8hIUHmz58vIiJPPfWUfPrpp7Jw4UIB5LPPPvPapjt37pQff/xRwsLCJDc3V/r16yerV68WX19fo/1jx47JoEGDZNu2bRIYGCjx8fFy5MgR4zizWCwSFhYmDoejxP4YEREhU6dOlT59+khcXJwkJCQY47Kzs2XTpk1e+7TD4ZC5c+fKG2+8IVarVY4ePSoxMTGyfPly6dGjh/z555+yfft2GTp0qCQnJ8ukSZNk37598thjj0lcXJwkJydL165dRUQkODhYGjduLEePHpWIiAivfdLlcsn333/vte088eu6LrNnzy7t8CqV1Wo1/k9MTPQa52nTZrOJiEhOTo5kZWWVu+358+eXaPPAgQMlzmfVQWFhYbnrJicnX7Q43G53iTLPubDotiyPgoKCEttH1/US59YLcb5tlXadcDqdxnmyPA4fPixpaWnGOgOCpOQ191ngWV9fX/H19ZXu3buf18szvae94vPR5BLfbj8bTdNaAxtEpG2RshXASGAt0EFEssrRTpkLds0115CRkeFVdt111/H6668zduxYAF5++WWWLVtW6vRXXnklhYWFXmX/+Mc/OHXqlDH83nvv0aJFC4YNG3a2UEvVpEkTcnJyylX3xRdfJD8/n23btvHiiy+yYsUKY9zrr7/On3/+edY2nn76aTZv3nxesVZFV1xxBVartbLDuOQ8+2arVq3Qdf2M3R2apuHr60tgYKBXebdu3di4ceMFx/Lyyy/z3HPPsWDBAjZt2lRqnfr162M2m4Fz2+fPV4sWLUhNTT2vaVu1asWTTz7JG2+8wcMPP0yzZs146KGHOHHiBB07dmTevHkAbNiwge7du/PAAw/g7+/PzJkzeffdd412Ro4cyffffw/A2LFjycnJYfny5cTExPDUU0+xa9cufv75Zz766CMGDRrEHXfcwbvvvsuECRNISUnhvvvuIyAggClTpgDw73//my1bttC3b1/69u3L9ddfz4kTJ0hPTycwMJCcnByeeeYZ9u7dS7169ejQoQNt2rQhIiKCtWvX0rp1a+677z6ys7O5//77CQsL48orryQyMpK7776bjh07YjabSUhIoHXr1vTr149HHnmEevXqcffdd5OTk8OOHTuoX78+nTt35uGHH6Z+/fpERkai6zqxsbFs2LCBq6++2jgX3XbbbbjdbkSE6OhoAO6//35jXyy6jjyeeuopfvzxR/bu3UunTp0YM2YMQUFBJCcn07p1a6Kjo9m/fz8FBQWcPHmSgwcPsnjxYm6//XasVivx8fHUrVsXm83m1e7y5cvp1KkTADfccINRvmnTJvbt22fc5ezSpQs9e/bkiy++ICQkhGHDhtGuXTv279/v9avt33zzDWazmcmTJ5fYh15++WVefvllDh48yFVXXcXgwYMB2LJlC0lJSaxfv57rrruOqVOnnsuuWRmCRaRD0QJN054F8PX1XQfQvHnz82o4PT0dgEOHDvUAEJH1XvOp6kmLpmk9gUdF5BNN0+KogKRFURRFUZTzVmlJS5V6pqU4TdPqAd8CQ8pZv7+maUGapgUVLb/11lu96t13332llnueV3j33Xd5+umnady4MV9//TW33XYbAA899JBR96WXXvKatl+/ftx5553GsI+PDw0aNChP2BWibdu21KhRAzj9zsvjySefLNf0RWMv6uGHH77w4JRL5vHHHzf+b9GixVnrFr0DcD5q164NQMeOHUsd36NHD1544YUSx5pH/fr1jeOrujxPctVVV51T/QcffNBruLR11aZNG693+Y8++miZ7V199dVnnF/v3r29hq+77jrjf8/zQOXd7s2aNeOZZ57h008/5cUXX6RDh9PXKc851PP8V1EjRozg999/5/XXXy/XPM6Fj0/5LlnXXHPNObV777338vbbb1d4HKXx7O9F3XLLLefd3v+c4v1Flf0CWvP3My3AXUAGEPf3ywUkANeerZ1mzZoJIBEREWfsq1uzZs059S2XprQ+/OIsFkup9QoLC+XUqVPGcwgiIklJSaLruhQWFkpOTo5XeXl17NjR69mZ4txut5hMJikoKCjRf1v8mZbSnnnx9G06HA7Jzc0Vp9Np9NcDcvz4cWO6os+AeJbV6XTKrl27vJatQ4cOsn79epk3b56kpqYa42bNmiUiIu3atTPa8fTHep7zcLlckpmZKbGxsUbMBw8eFKfT6dWnGxQUJDt37pQhQ4YY8e3cuVNERPz8/OSJJ56QsLAwYzko8hxOVlaW5OXlSWBgoEycONHrOQiR088reNbdn3/+abTviTc3N9d4RkTXdbFarbJ8+XKjflRUlLz77rtit9ulffv2smrVKgHkjz/+EECCg4Nl8uTJsmnTJmnSpIksXLhQWrduLS6XS8aMGSNTpkwREfHany0Wi7GdQkJCZOrUqXLXXXfJddddZ9TJy8uT4vLz8431GBISIoCYTCZj+9tsNjl69KiEhITI3Xff7TVtWc8KhIeHlygvjWe9paamyq+//ioip58D27Ztm9jtdjGZTOJ0OgWQPXv2eG2Df//738Y6z8/PF13XjX1ixowZ4u/vLxs3bpSBAwfK8ePHJSAgQF555RWvbTlx4kTj+aA9e/bIpk2bJD8/X5YuXWrEmJaWVuJ4zs/Pl//85z9eZaNGjTLWSVRUVLmWvzRlHcue7VSdbd++XXbs2FHquL1798qyZcvKHO8RHx8vu3fv9ipzOp1y/PhxERHjPOpwOIzt5tkvilq2bFmJNuLi4sTpdEpaWlqZz5VYLBbj+CguOzu7zLj9/f1LLd+6davX+TEnJ0ceeughYz/Qdd3r3L1ixYoy5+F5XqxofIWFhaVe9+x2e6nLOGfOHBk2bFjRc5p6psWjtGdaioyLo5zdQx06dJCgoKCzVasyNE275B/nvZTCwsKYN28eP//8Mzk5OTRp0uS82lm0aBEvvfQStWrVquAIS/L19aVTp05MmTKFoKAg412mx8yZM3n66ae5/vrrz9iO2+027oLB6TcKmqadUyy6rl/Qu7vLWUhICEeOHOHNN9887zYcDgc333wz8+fPp0uXLpdk/1KU6krTtErrHqp5Xq1eJJqmLQYeAZpqmpYEDBWR2ZUb1aVR/MHey81dd91lfMT0fBMWgNdee62iQjqrd955h2PHjgGUSFig/LfYiyYswDknLHBht6Mvd+3ataNdu3YX1Ibno+WKolRtVSppEZHeZxnf+hKFcsnVr1+/skNQivnPf/5T6vfBKIqiKJWjSiUtilKV1KxZk5o11SGiKIpSVah7zoqiKIqiVAsqaVEURVEUpVpQSYuiKIqiKNWCSloURVEURakWVNKiKIqiKEpF0S9m4yppURRFURSlolzUvEIlLYqiKIqiVAsqaVEURVEUpVpQSYuiKIqiKNWCSloURVEURakWVNKiKIqiKEq1UKWSFk3T5mialqFpWniRsmGapiVrmnb079czlRmjUvlEpLJDUBRFUSpBlUpagHnAU6WUTxSRe/5+bbrEMSnnwO12k56ebgxbLBaOHj3KgQMHWLx4MQBJSUnn1faaNWvo2LEjPj5Vbbe9PLjd7soO4ZxU5C9w2+12Jk+efFET4oiIiIvWtq5f1K/GqLbUern8VKmzv4j4ATkV0VZKSgo//fQT8fHxnDhxAoARI0YY4+fPnw+A1WrFZrMZ5Xl5ecb/npOiiOBwOIrGWaJO0XKXy2X8X1hYiNvtxuFwICK43W5SU1NxOp2ICGazmQ8++ABN0xARdF0nOjoal8uF1WolNTWVtLQ0dF2nXbt2WK1Wo+2CgoJST7K6rrNw4ULjJFlYWIjdbsdqteJyuQCYPXs2MTExOJ1OTCaT1zKsWrXKa9jhcHhd0IrO88SJE+Tm5uJ0OomKiqJmzZpce+21aJrGF198Qf369bn33nvp3Lkzr732GvPmzeOGG25g586dPPjgg4gILpeLrKws+vfvj6Zp1KxZkzFjxuByuXC73eTk5DBs2DCef/55AgMDAfj666+NWESEcePGMW7cOAoKCgDYsmULw4cPN7aH2+1m9OjRhIWF8eGHHxIZGYnVaiU9PZ2IiAiWLVuG3W4nPj6exx9/HBHB39+fFi1acNNNN7Fr1y4GDRpEfHw8f/31F0FBQQwePJjAwEA++OAD/P39+fjjj1mwYAGhoaFERUWxa9cuvv32W7Kzs0lJSWHt2rWkpaVRUFBA69atiYyMJDw8nMTERHbu3Mn48eNJS0sjMzOTwMBADh48yC233MLOnTsZMGAAOTk55OXl8euvvzJy5EgefPBBJk2axLBhwxg7dqyxHy9duhSXy8XRo0eNbSUixMXF4XK5+Pjjj8nIyPDapg6Hg6ZNmxrDmzZt4tSpU/Tv358lS5agaRomk4no6Gji4uI4efIkJ0+exGw2s2XLFqxWKykpKbjdbmM/L7qfuFwuCgsLS91fi5ZlZ2eTkJAAwL59+/j111+B08el1WolJycHf39/Zs+eTe3atYmJieHf//4333//PUePHuWjjz4iMTERTdNo1aoV48aNY/ny5bjdbux2u3EcTps2jSlTppCamsrChQupW7cun3zyCT4+PixZsoQ9e/aQkpLC4sWLOXnyJPfffz8fffQRFoulxLFx8OBBwsLCcDqdWCwWRASbzcbQoUP58ssvOXXqFMuWLeOOO+6gbdu2HD9+nIyMDL777jv27dtHVFQUhw4dIi8vj+3bt5OUlERUVBRRUVGsWbOG0NBQ0tLSWLlyJUOHDiU8PJzt27ejaRq6riMivPbaa+zevRs4nXwWFhZy+PBhNm7ciM1mw9/fH5PJZCy/ruskJyezefNmADIyMkhJSfHadm632+tc5llmk8lEUlIS6enpmM1mdF3HYrHwxRdfGMdf8e3r2deSkpJ4/PHHGT9+PCdPniQ0NJTvv/+euLg4Tpw4QXBwMHa7HYDk5GQyMzNxuVwkJSUZ5ymbzYau68ydOxeTyYTZbCY3N5d169ZhMpmMRCUwMJDOnTvjcrmM/U/XdfLz8/n444959dVXWbJkCXl5ecY8Fy1aREZGhte5JT8/Hz8/PwoKCtB13Vhmz7lURMjOzkZEmDlzJjk5py9h+/fv59ixY0RGRhIQEIDdbictLc2Yxmq1snnzZk6dOmUcg5mZmei6TmpqKmazmbFjxxITE8P+/ftJSkpix44d7Nixg48++og2bdrQr18/ZsyYQWxsrLHvFRQUMHToUPz8/HA4HAQEBJCenk5QUBDJycns2rWLuXPnMmbMGPr168fHH3+M2WwmMzOTjh07smXLFjIzM/nss8+YOXMmFouF/v3707t3b7Zv384dd9xRYhtfSlpVu9WuaVprYIOItP17eBjwJlAABAGfi0huOdqpWgumKIqiKJeHYBHpULRA07RnAXx9fdcBNG/e/Lwa9typP3ToUA8AEVlfdHyVutNShmnALcA9QCrwc1kVNU3rr2lakKZpQZcoNkVRFEVRLpEqn7SISLqIuEVEB2YC95+h7gwR6SAiHdq0acO6devIy8szbvM9/fTTuN1uzGYzIoLT6UTXdTIzM43bphX98ty+LfoqOq9Dhw4Vjd+Iq7TpDxw44NWOp6vobPPVdR2bzVaijsViwWKxkJCQYJQdPnyYI0eOlHvZUlNTOX78uNHeN998YyzP119/zWOPPea1jerVq2csqydGXdex2+18++239OvXj+joaHbt2mXcdna5XNSsWdOrnaioKK9YUlNTjdujZa17z7DVauXkyZPGug4LC+ODDz5A13VCQ0ON9fznn3/y0EMPGdvDbrej6zomkwmXy0VcXBzh4eF8/fXXZc7vzTffZOrUqbjdbkwmE7t27cLtdhu34IvXL76dgoKCSEhIICQkxIi3oKCAzMxMXn31VYYPH86xY8ewWq3Y7XZExOiGKdrWrFmz6N27N7m5uaSmpp5x3xER45b74cOHSUhIIDw83Bg3ceJEvvzyS+MWeUBAgDEuISGBnJyccz42PP8XXS+LFy8mKysLkdNdiImJiei6jtvtRtd1IiIicDgcHDp0yKjneXm2b2nbJSUlhYMHDzJ9+nRExKtLuOhxOGHCBOLj43G73QwePLjM5Sga83//+1+v+XpeIqdv/NrtdlwuF/n5+cb2LLpMZe0XIsLatWv58MMPjfIWLVp4bS+3201ycjIi4lVP5HQXR3p6Orque50TS5vf2V6ede057j3b50xtJSYmlro9nE4nI0aMID8/3ygvehwnJycbbRc9L3riMJvNOBwOHA6HsU6Lr7vi03mWe+XKlVit1hJxn+1aUNbxW/xlt9uxWCxERUVhs9lYsmSJcYye7bjwdDcVbcuzfzgcDsxmM1lZWQQGBpKammoci5762dnZxnFY9FjwnBOSkpKwWCw4nU6vZbHZbF7teOqbzWbjvLNmzZrzuJJXnOrQPdRCRFL//n8Q0FFEXj1bOx06dJCgoOpxw6VHjx6sW7eussOoUAUFBTRs2BCAhQsX0qdPH2Nc8eHy0jSNl19+mWXLllHV9tv/JZ51r2laJUdScdxuN4sXL6Zv374XbR4iUqHrrKLbU5Ty0jRNdQ8BaJq2GAgAbtM0LUnTtHeAnzRNC9M0LRToCgyq1CAvgsstYQGMhAUokaCcT8IC8O2337J06VIOHDhwQbEpF0bTtMvuYlmjRo2LmrBAxSd5l9s2UJTyqHn2KpeOiPQupXj2JQ9EqZJGjx4NQMeOHSs5EkVRFKUyVKk7LYqiKIqiKGVRSYuiKIqiKNWCSloURVEURakWVNKiKIqiKEq1oJIWRVEURVGqBZW0KIqiKIpSLaikRVEURVGUinJRf1pbJS2KoijA1q1biY6OruwwFKW6u6h5hUpaFOUMPD9zr1z+Jk+ezMGDBys7DEVRzkAlLUq1cql/c6hVq1YXfR6eHzU7V2vXriUzM/MiRHR5W7p0KePHjy9RvnHjRpWkKkoVp5IWpdoICgrCx8eHl1566ZLNMzk5+aLPw+Vyceedd57zdD/88MN5dWfs2bMHs9l8TtO43e5znk9VlZ2dTUxMTKnjJk2adGmDURTlnKik5X/IiBEjiIyMrOww+PTTT89rupo1T/9U1ooVKyowmrNbu3YtSUlJF619Hx+fUt/hHz169IzT6bpOjRo1yj2fHTt2MGvWLN5//30SEhLOKUbPur8c1KpVC4fD4VVmt9sBCA4OroyQFEUppyqVtGiaNkfTtAxN08KLlQ/UNO2EpmnHNE37qTxt2e12tm7denECraa2b99OWlpapcxbRCgsLATgl19+Oa82Nm7cyE033XRBcSxatIhFixad0zRTp07l2LFjwOlEoSLuvlgsFiNx0DQNEWH9eq9fYOfee+89Yxtutxsfn/IfwqtWrTrvdQ+Qm5vLsWPHzjnhORfLly+/aG171KpVC6fTCYC/vz8pKSnUrVv3rNPt2LHjYoemKMpZVKmkBZgHPFW0QNO0rkBPoJ2I3AmU7Iwuhdls5t///neZ41NTU88/ymqq6Dv6i9l3P2LEiBJl+/fvp3PnzsDpi/SZuhtEpMSzK7qu8913311QXJ9//jl9+vQhJiaGH374wWucxWIx2ve86/aso61btxrxREZGcu+99xIdHc3VV1/tVa+8Ro4cSfv27enfvz+A8VxKjx49ypzG4XAQHu6Vy+N2u0lPTwcwEsKy5Obmctttt9G1a9dS121xRet4LvA//PADP/30Exs3biyz7oV6+eWXefHFF0sdV1ay+ccff1BQUFDuedSuXdtYpgcffJCWLVuWa7rHH3+83PO4FC71813K5aW6Pr9VpZIWEfEDcooVDwDGiIj97zoZ5WkrPz8f8O6LX7t2LQ6Hg8LCQq677jrg9Ibz1C3LsWPH2LRpk3GSOHToUHlCQERwu92ICGlpaWRnZ3Pw4MES80tMTGT06NG4XK4SJ6LCwkJjGVJTUxERrrrqKoASt7g9pk2bxqhRozhy5IgRh8vlQtM0cnJy2L17N/369WPnzp38+uuvwOkLk9vt5sCBA4SFhXm1HRwczO7du4mPj/dan263G6vVSl5ennEnZc+ePQwdOpRTp04BkJSUhKZpdOnShfDwcPbt24eIkJqaahw0LpcLm83GiRMnCAwMxMfHhzfeeMNrPXq2V2xsLADdunXzWuZXXnmFjz76yGu9eS7oIoK/vz8TJkwATt9xGjx4sLFeJkyYQP369Rk9ejTvv/++8a57zpw5RnsLFy6kb9++PProo2RmZrJx40Zyck7vqjVq1GDLli3ouo6IMGPGDL799lusVitLly7l8OHDnDp1ihYtWvDxxx8zZMgQTpw4QXZ2Npqmee0Pbdu29TqZaJoGQJ06dbjrrrsQETRNw2q1EhoaSrdu3dB1nQYNGnitDxFhypQpAERFRdGkSRNSU1ON9oq64YYb0HWdU6dOGfO+9tprKSgoYOnSpdSuXRuA8ePHM3/+fAoKCnA4HLRt25aYmBh8fX1LtFmaGTNmeA17EofiVq5caSQh06dPN5anT58+6LqOyWRC0zTee+89li9fztChQ9m1a5dX4rZs2TJSU1NZvXo1oaGhXu3Xrl0bu91e5kXf7XaTnZ3Npk2bcDgcOJ3OUuvabDYAo47L5cLlchnj+/bty5NPPgmc3gaaphEeHs6JEycoLCzE39+foKAg9u/fb5yHTCYTOTk5iAhOpxNd19F1HbfbzebNm5kxYwYJCQn8/vvvxl02Xdd5/PHHjXbg9Js2l8tFYWGh0ZbVajXWpec4Lrpcnv8956ziPGUFBQU4nU6ysrK8zgci4rXvltWGp6vVM21GRga6ruN0Or22iyd2OP2mwrNMRem6jtVq5eDBg8THx3u94cnIyMBut3stq2ddZmVlsW7dOuPNq4hgt9uNZ72KHpNOp5O4uDgA0tLS0DStxDNhnjc2IkJmZiaBgYEkJCRgsVhwOp1s2bIFt9uNyWTyulP78MMPEx4ebuxjUVFR7NmzB7fbbbwArFar0X5BQQEulwun08mcOXM4deoUFovFuItut9uNdSciZGRkEBgYaOxPcHrfrVGjBnv37jXO3S6Xi4yMDGN/CQ4OJjMzE7fbzZEjR9i8eTO6rpd53blUtKqWrWua1hrYICJt/x4+Cqzl9B0YG/CFiJw1a9A0rWotmKIoiqJcHoJFpEPRAk3TngXw9fVdB9C8efPzatjzZvPQoUM9AETEq9+8OjxdVxNoAnQCfIFlmqbdLKVkW5qm9Qf6X+L4FEVRFEW5BKpU91AZkoBVclogp78iuGlpFUVkhoh0KJoBDh48GH9/f0JCQnj66afp3Lkz8+fP57vvvqNDhw60bNmSxo0bs23bNg4cOMBjjz3Gvn372L9/P4cPH2b9+vV8/fXXANx///107dqV//73v7z88svs27cPgPnz55OWlkZISAgLFixg5cqVrFixgpkzZ7J582bj45VffvklAI888giDBw/m5MmT+Pn5AXDnnXcyffp0vv/+exYtWkRgYCAffPABn3/+Obfccgvz588H4LPPPuOtt95i165dTJ48mby8PHbs2MHy5ctJSUkhI+N079mrr77Kl19+idVqZffu3XzzzTfGevrss8+M/1u3bs2UKVPIysoiNDSUZcuWAae/n+SNN94gOzubn376iaeeeopJkyYRFRXF6NGjiYyMZOrUqVxxxRX4+fnRr18/1q1b59VFc+jQIQYPHlzqRn300UcZN24c06ZNY82aNdx///1e4318fNiwYQO7du0q89kki8VCeHg4Cxcu9Cpfu3at0e0FkJWV5TVcNL6yPom0c+dOr+Fp06aVWq9WrVoAvP/++4wfP54nnnjCGHe2hztvvvlm4P/3C4/i66J9+/bG/59//jkAbdq0McqGDh0KnP5U1d69e3njjTfo0aMH//znP+nWrRtTp04tMe/u3buzefNmfv75Z+D08q5Zs4atW7eyd+9eAPr06UOzZs1KjX3btm0AvP3228Dpbrs//viD3r17M3bsWLKysrDZbOTm5hIdHc3Jkyf573//y/bt2zl+/DinTp1i7dq1fPXVV2zdupUDBw4Y+67HHXfcAZy+zf/uu+8CJR/iXrJkifF/cHAwBw8epKCggPfff5/g4GDq1KnD4sWLCQ0NxWazeT370qhRo1KXregD0d27d2fnzp3GPnjq1Cmjm2HLli08/vjjLFmyhEGDBmGz2YiIiMBqtTJ16lQj/qioKOM4b9u2LaNGjWLgwIHMnj2bF198kf79+5OXl0dAQACbNm3i448/Zt++faxZs4Zt27bx66+/snr1aq699loAZs2aRf369YHTXcae89DNN9/MokWLjC4Uz340a9Ys/P39SUpKwt/fn/Hjx5OcnMzGjRv5+eefOXDgAElJSWRmZhIaGkpkZCTx8fFkZWWRn5+P2WzG4XAQHx9PZmYm7dq1Y/ny5WRlZaHrOoWFhWzevJmoqChSUlKw2WzY7XZsNhtms5mMjAxGjRrFiRMnGDFiBD///DMREREcOnSIUaNGGdtu7dq1fPjhh2zevBmbzcY//vEP4PTH0MeOHcu6detIT0/Hz8+Pd955h507d3Lq1Cn69OnDl19+Se/evTGbzZjNZrZs2WJMY7VaCQoKIiwsjICAAJo3b86YMWMYNGgQgwYNIiQkhOTkZD755BOmT5/OiRMnyM/Px2q1kpmZSXJyMm+88Qbbt283zo9ZWVls3LiRmTNnkpWVRdOmTVmzZg1BQUHk5ubicDiYOHFiiX3r8OHDbN68mVdeeYW5c+dy8OBB4uLisFgs/PHHHwQHB/P666/Tu3dvJkyYgNlsZvXq1QQEBNCzZ09eeOEFRowYwbBhw7yey5s3bx7Dhg1j7ty5DBgwAIBx48YxcuRIevbsycsvv8wDDzzAQw89xEsvvUTfvn1ZtGgR3377rbH/ALRs2ZKff/6Zzz77jHfeeYf27dsbH0LwmD17dqnHzSXj6feqKi+gNRBeZPh9YMTf/7cBEvm7W+tMrxtvvFFOL97Fk5eXJ/fff/8Ft5OcnFyuesuWLStXvZUrV5ZaDsiSJUvEbDbLsmXLxOFwlDvG8kpLS5M77rhD2rdvL06n02veX375pQQFBZW5XTZu3CgWi0X69+8vmzZtKhE7IIWFhQLIRx995DX+n//8p2RmZsquXbskKSlJ0tPT5ejRo151+vfvL4AsW7ZM1qxZY5RnZGTIL7/8YgwvWLDAa7733HOPMdy1a1cBxN/f31iO4cOHl4g1NzdXdF0XETH+tmjRQkRErr/+egEkICBAhgwZYkyzePFiyczMNNaFiMj+/ftFROTXX3+VYcOGycaNG+Wpp54SXdflySefNGLwzONMxo0bJ2+++aY0atRI1q9f7xVvWaZNmybr168XQCZOnCjDhg2TDh06SE5OjjgcDtF1XWw221nnXV633Xab8b/FYpHjx4+LiIjJZJIvvvjCK+ahQ4eKiMhvv/0mn3/+ubjd7nLNo0+fPjJ//nwREVm+fLmkp6cb+9eZ1kVsbGy553Ex2O12yc3NNYZnzJhh/L9hwwbx9/evhKj+91TmPlBVAEFS8tr9LPCsr6+v+Pr6Svfu3c/r5Zne017x+VSpZ1o0TVsMPMLpOynpwFDgT2AOcA/g4PQzLTvLaMLQunVrefTRR70eprwY5O8HI6uD9u3b8/vvv9OhQ4ezV74ALperxPd6ePYzTdOMj/iWRdf1Eh/l/f777xk1ahRms5n69evTv39/fv/9d6/2z7YdXC4Xy5cvp3fv3uVeluKxPv300/z1119njD8kJIR27dqVKHe73dSoUYO4uDjmzp3L22+/zY033ljuWOD0A3TZ2dm0bNkSTdOYM2cOb731Vrmm9Tzs6Ha7ueKKK85pv9U0DZvNRq1atYxtWJnOtg+dSZ8+fXjmmWfo06ePV3seVemcqChVkaZp6pkWABEp62rS93zauxQf6arsk/e5qFGjxiX5ZtPSvojsXNZTad89MnLkSEaNGmXc+i7eTVCe9mvWrHlOCQtAvXr1vIYfeuihs37pW2kJC2B8EVzr1q0ZPnz4OcXhUbduXeMjur169TJuoZeHj48PderUOa/5Ahc0bUXz3KY/H8888wxt27atwGgURblUqlTSUtHUOyZvffv2NT46XJmKf4naufAkXeX5MrCKkJ2d7TXcpk2bKvN9HUuXLr1k31Rb1X7j6EJ+yqHoHZbizuXL+hRFufQu26TlQm4fX64+/vjjyg4BOP1w4/kYNmzYJf8NnOLJ0QsvvMCzzz57SWMoy6X8av2mTUt99v2y89xzz1V2CIqinMFlm7TUqVOHTp06VXYYSgUaOnRopX+xkY+PzyW7y6Ncej/++GNlh6AoyhlctvdCr7zySj744IPKDkOpYJ5vZ1WUi6G6frW5ovyvuGyTFkVRlPLy/CyESloUpWpTSYuiKP/zNmzYAHDJn5lSFOXcqKRFURTlb+pOi6JcsIt6EKmkRal2iv5UgKJUJHWnRVEu2EXNK1TSolQ7U6ZMqewQlMuUutOiKFWbSloURVH+1qBBg8oOQVGUM7hsv6dFURTlXFitVvUdPIpSxak7LYqiKFy6n4ZQFOX8VamkRdO0OZqmZWiaFl6kbKmmaUf/fsVpmna0EkNUFEVRlLNav349wcHBlR3GZadKJS3APOCpogUi8oqI3CMi9wArgVWVEJdSTSUnJ1d2CEo1MXnyZD7//PPKDkMp4kJ+P66goICcnJxSxwUGBjJs2LDzbrs4t9uN1Wr1Ktu0aRMHDx6ssHmcyd69e8+pflZWFqtWlX0pTUlJ8Rp2uVxV5rf8qlTSIiJ+QKl7maZpGvAysPiSBqWcl/j4+Es6P8+nPhISEoz/HQ4H119/vVHnu+++Q0SM19kEBASwceNG0tPTSx1/5MiRUstF5KJ+CsUTu67r53Qisdls533iadWq1XlNV5GKx+7n54fb7cblcgGnf5H7XE7eS5YsMf739/fnk08+YcKECRUTbCVzOp3k5+eXuJAC5d7/Aa9jCU5fzE6cOFGi3vTp07FarV7t+vv7k5OTg9vt9vqV8IKCAlauXMn+/fvRNI169eoxceJEAHbv3k1ERAQFBQXMmzcPHx8fzGYzmqaxYsUKAGbMmIGIEBERQWBgIEOGDCE6OpqAgACjDYCnnnqKq6++Gk3TOHXqlLHs27dvZ+PGjQwfPpzY2Fiv5fAMJyYmAjBixAhSU1OJjY0lLy+P48ePs2DBAvLz873WZ82aNalXrx47duwgPz8fEWHdunV8+OGHhISE8O6773L8+HGveZlMJkwmE99//z0WiwV/f3/GjRtHTEwMgwcPRtM0XnjhBYKDgyksLGTXrl0kJSUBEBoaiqZpNG/enJEjR/Lwww9js9n48ssvWbRoEVlZWcTExJCVlcWQIUPo0KED//73v1m/fj0mk4nt27fz0ksvkZGRAcDOnTvZsmULDRo0IDMzk5YtW3L33Xezfft2dF3n1VdfpUWLFpw8eZJ58+aVa9+5WLSqkj15aJrWGtggIm2LlT8MTBCRDuVsRwBatGhB165dsVgsREVFcezYMQBuu+02rr/+enbs2GFM8+qrr7J//34SExNp0KABbrcbi8VijO/WrRshISHUqFGD+Ph4+vTpw8KFC7nqqqto06YNTZs25aabbmL69Oncc889BAcH89RTT/HXX3+d0zq49tprSUtL4z//+Q+zZs06Y91atWrRo0cP8vLysFqt+Pv7c+uttxIVFWXU8fX15dChQ2dsp0GDBnTu3Jm4uDgiIyO55ZZbiI6Opl+/fvzxxx+0aNGC1NRU7rvvPqKiojCZTFx//fXGQVSW8tQpy6effsqkSZPOa9ryePTRR9m5cycADRs2pKCg4ILbrFOnDna7vVx177nnHiIjI732sTPp3Lkzbdu2ZebMmWXWueaaa4wT0bBhw5g+fTppaWnlar88HnvsMfbv34/NZvMqf+utt5g7d64xfNddd3HrrbfSr18/fvvtN9LT0wkJCQHgtddeY9WqVV5tvPjii4SEhBj7bfFfaW/cuDG5ubk88MAD+Pv7nzHGAQMGMG3atBLlN954Y7mT6a5du7Jr1y6v9Qlw0003ERsbyy+//EJubi4mk4kjR46wc+dOGjVqRPv27enVqxcHDhzg1KlTBAYG8vXXXxMXF8eiRYvo0KEDd955JxERETRv3py8vDzMZjP33HMPLVu2xGKxUKNGDaxWKzVq1CAtLY2bb76Z6Oholi5dCsDzzz9PQEAAaWlp9OzZk/DwcKKjo8u1XErpOnXqxIEDByo7jOokuPi1WNO0ZwF8fX3XATRv3vy8Gva8STx06FAPABFZ7zWfapS0TANOicjPZ5i2P9D/78H2Fy1IRVEURfnfVWlJS7X4yLOmaTWBXpwlERGRGcAMgObNm0tGRgbh4eE0aNAAh8NBUlIS8fHx3HDDDWzbto2TJ0+yevVqvvvuO3r16kWDBg2oWbMmP/30E19//TUxMTE0a9aMI0eOsHLlSr777jvi4uK49dZbSUlJoVGjRgQGBvLSSy+Rk5PD9u3badGiBYcPH+aDDz7A4XCQlpbG5s2b+fHHH1mwYAHR0dEUFhbSpUsX4uPj6dixI99++y3bt29nwoQJNG7cmM6dO9OwYUPCwsJ48803mThxIldccQU9e/bkk08+oV+/fuzZs4f58+czduxYmjZtSrNmzcjJycFsNuNwODh48CB//PEHa9asITc3l82bNzNs2DB69+5Ny5YtadWqFddffz2NGzfm0UcfZevWreTl5dG8eXNiY2N54IEHcDgcXHXVVWRlZdGkSRPsdjs1atTg+PHjOJ1Ojh07xt69e7nvvvu48847ueuuu/j+++95+OGH2b17Ny+++CINGzbE39+f5s2bc+WVVxIbG8vYsWMZNmwYjRs3xsfHh/r16/P2228zefJkPv30U2bNmsWOHTsYNWoUS5cuxW63k5+f73XrPjQ0lL1791K3bl1uvvlmxo0bR1hYGA6HgxdeeIEaNWoYX0J30003kZycjMPhoHbt2sat7nXr1hEVFUWLFi248cYbKSgo4Pfff2fNmjX07t2btm3bMnjwYB555BHuvfdefv/9d1q0aEF0dLTxzuyRRx5h9+7djBkzhiFDhvDHH3+QmprKtGnTeP/997n//vvJzMzEarVSs2ZNXnnlFaZNm0arVq04duwYJ06c4IcffuDAgQM899xzjB07lsWLFzNy5EhjPjt27KB169Zs2bKFXr16MWvWLKKiovj2229ZtmwZw4cP56uvvmLHjh1MmDCBZ599lszMTAIDAwkKCqJ79+40a9aMxx57jCFDhhAfH88nn3zC3r176du3L3l5eaSkpGCz2fDz8+Pqq6/m008/5aeffgJgxYoVBAYGAvDKK6/w4IMPUrt2bd5//30WLVrEwoULefjhh0lOTsbHx4c777yTxx57DJfLRbNmzTCZTMTHx9OlSxfCwsJIS0vjhhtuwOFwkJ+fj8lkomXLljgcDkaPHk3z5s25/vrrjdvnnTt35tZbb6Vz584sXLgQEaFv375GPJ47ECdPnqRfv34sWrSI9u3b89JLLzFjxgweeeQRrr76aj7//HOysrL49ttvad68OTt27OCOO+4ocdt+2rRprF69GovFwhdffMGff/5J586d+eKLLzh16hQWi4Vbb72V2NhYtm3bRo8ePejSpQvz5s2jdevWNGnSBKvVSmFhIbNnz+b999/nyiuv5JprrmHQoEG8+uqrNGvWjIMHD9KmTRuuvvpqbDYbV155JTVq1KBGjRrYbDZyc3OpXbs2BQUF1K1bl48++oi9e/eyY8cOEhISeOuttzh+/DgFBQV06tTJiP+PP/6gX79+xh3W+vXrYzabgdN3tFasWEGjRo2MLo6ix0SvXr0IDQ01ulPOpujdyqqgefPmpKenc8UVV5TaNVaatm3bMnDgQN57772LHJ1SIYr28VeFF9AaCC9W9hSw51zaad++vSjVh9vtvijtnt7FL47Y2FhxOp3nNI3dbr9I0Vxc8+fPl/z8fHE4HJUWg67rXsNF95kmTZqcc3tFt11AQIAAZ9xfVq1adc7zKMpkMpVYhnPhdDrFZrOVOq54uxW93+fn50tISIh89dVX8sUXX5R7uoCAAMnOzpb+/ftLQUGB6LougNhsNjly5Ii0aNFCABk6dKjoui5Lly6VO+64Q7Zv327UnTt3rnzzzTeyYcMGETm9HsxmszidTtF1XXbv3i0dO3aUxYsXe807JibG2KbfffediIhMnDhRBgwYIH/88Uep62jQoEFewyNGjJAHHnhAOnXqJIMHD5aQkBCJjY0Vkf9f5y6XS5588kkZOHCg6Louuq7LsWPH5O677zbm7ykvOp2IyNdffy21atUSkdPnBrvdLsePH5d69eoJIN26dZNDhw5JamqqxMXFSXR0tLz77rvy448/SkpKitH+vn37JDMzU3x9fWX69OmSl5cnIiKdO3eWl19+WTIyMuSWW26RiIgIWb9+vYwaNUoAyc3NlfXr10teXp7ExMTIJ598Im63Wx577DEZNmyYLFiwQFJTU+WTTz4RQBYsWODZLkFS8tr9LPCsr6+v+Pr6Svfu3c/r5Zne017x+VSp7iFN0xYDjwBNgXRgqIjM1jRtHnBARKaXt60OHTpIUFDQRYlTqT42btxIt27dKjsM5SJzOp3UqlXrgtpwuVxs376dp5566uyVq7jiz7VVFM8dmdq1a593G263mxo1agCnP913/fXX89tvv/HBBx+UqFv82aZz5XQ6ycjIoGXLlgDk5eVht9tp3rz56Qugpp21jVGjRnHrrbdy6623ct9995VaR/5++N6zXHD6wXebzcZvv/3G4MGDzzn29evX8+yzz57zdOURHx9PbGwsjzzyyHlNr2maeqaloqmkRVGU/1XlvSBXBZ5rUGnxvvrqq16f8lKqhspMWqrFMy2KoihK+VWXhAXOHKtKWJTiqtT3tCiKoiiKopRFJS2KoiiKolQLKmlRFEVRFKVaUEmLoiiKoijVgkpaFEVRFEWpFlTSoiiKoihKRbl4vxaLSloURVEURak4FzWvUEmLoiiKoijVgkpaFEVRFEWpFlTSoiiKoihKtaCSFkVRFEVRqgWVtCiKoiiKUi1UqaRF07Q5mqZlaJoWXqTsHk3TDmiadlTTtCBN0+6viHn98MMPFdGMolQ7CQkJOJ3Oc5pm6dKlFymai+vo0aOkpqaWOX7t2rUEBATw3XffXfRYli1bdtHn8b9C1y/qp2rLVFhYSJ8+fcpVd/Xq1dhstosc0f+eKpW0APOAp4qV/QQMF5F7gCF/D5+XiIgI4//BgweXezoRqbSD5JdffilRNn78+EqIpHR2u/2Szs/zM/Znk5OTQ2xsLIcPHz5jPbfbjcvlKnP8xVi+8i7DhbZbVuy9evXi6NGjJcr37dtXZtuvvvrqBccdHh5+9krl5HK5yMzMPGu9iRMnsnXr1jLHP/fcc6SkpBixRUZGIiLk5+ezePHic07uzuSVV16psLbg9EXRY8iQIQQEBFRY22Utt9vtPq/2FixYYPwfHx9PTEwMAFar9YzTbdmypURZWFgYTzzxxFnnGRcXx9q1awkJCSl1fF5e3lnbAHA4HOi6jojgdDrZtGkTACkpKUYdXddxOBxe0/Xq1Ys9e/ac07Wj6DFWWFjInj17SExMPOt0xd9UeNpJTEwkNze3RH2bzUZERITX/ESEl156ydg2AGPHjkVEjFeVUDSgqvACWgPhRYa3AK/8/X9vYFF52rn77rtl9uzZYrVaxW63i8ViEUDS0tIkMTFRANF1XaKioiQzM1McDoc4nU7JyMiQlJQUcbvd4na7Zf/+/bJ48WJ58sknJTc3V/Lz82XixIlis9lE13Vxu92SmpoqJ06ckMcff1zMZrPk5+eL0+kUi8Uiuq6Lw+GQhIQEcbvdcs8998iOHTvEYrFIdna2rFy5UqKjowUQm80mDodD7Ha7mEwm+e677wSQ5cuXi8VikczMTMnNzRVAsrKyJDQ0VBwOh+Tk5EhSUpLk5uaKzWYTm80mAwcOlBUrVoiu65KYmCgBAQGSn58vv/32m9jtdomJiZGUlBTZs2ePbN++XUwmkzgcDrFarWIymSQ5OVmSkpJE13XJzMyUyMhIsVqtUlBQICaTSfLz8yUzM1MAefHFFyUoKEgWLlwoJ0+elNzcXDl06JC4XC7JyMiQtLQ0CQ0NlZSUFElPT5eBAweKyWQSs9ksqampYrVajW2TkZEhCQkJkpubK7qui8vlEl3X5fjx49KtWzcBBBC73S4FBQWSlZUlIiKPPPKI7N+/X3Jzc8Vut8u8efPkmWeeEUB27dolv/zyi4wcOVIiIyNlw4YNsmLFCnG5XPLTTz/J4MGDZdmyZZKamirJycnStWtXycjIkFmzZgkgU6ZMET8/P8nNzZWoqChJSEiQzZs3y6pVqyQjI0O2bNkiu3btkk2bNonT6RSXyyWTJk0Sp9MpJpNJgoKCxG63S3Z2trz99tty8803S+/evSUzM1NmzJghbrdbQkJCxOVySWFhocTFxUlYWJjExsZKQECAxMXFycyZMyUyMlJMJpNkZ2dLZmamhIaGyoIFCyQ2NlY+/vhjefTRR8VsNovZbJajR48KIPHx8WKz2SQsLEy2bdsmJ06ckFtuuUVGjBghmZmZkp+fLxaLRU6ePCmAJCUlyZ49e+TLL7+U5ORkiYyMlMDAQAFk0qRJEhYWJikpKVJYWChLliyRnJwcWblypeTk5EhkZKQUFBRIQECAuN1ucTgcYjabJTc31zj+dF03jpvx48dLXl6eWCwWcbvdEhERIf7+/rJgwQIpLCyUo0ePSlZWlrEfbNy4UdLS0uT48eOyY8cOueuuu+T48ePi5+cnd9xxh6Snp8sPP/wgDofDOB66du0q99xzj+Tk5IjNZhO32y2xsbESGhoqS5cuNfanoq8VK1ZIkyZNBJBx48bJnj17ZNKkScbx5HK5xO12y8KFCyUxMVFiYmIkMTFRrFarWK1WyczMFLPZLKNGjZK6deuK0+mUtLQ0AWTu3LmyYcMGCQgIkKSkJBk1apR8+umnEhsbK6mpqbJq1SrJysqSLVu2yIkTJ8Rut0tISIhkZ2cbyxwQECAxMTECiNPplNTUVLnhhhvk7rvvliFDhsjevXtl9OjREhERIWvWrJGxY8dKbGysTJ48WRo2bCifffaZ5OTkyFVXXSUvvfSSbN++XaKiomTu3LkSGhoqwcHBxrp47rnnpFevXjJ//nxp2rSp13oaM2aMZGVlydq1a42yhg0bylNPPSUnTpwwjtfXX39dABkxYoRX3WuvvbbU9X+mV9HY6tev7zXugQcekOPHj0twcLCMGjWqxLQ///xzibJhw4ZJcnKy2O12oywoKEiuvPJK+eCDD2Tu3Lle9Z999lkBZN26dUbZ/Pnzjf//+c9/CiBdunTxmu6DDz4QQAYNGiRhYWHSvHlzGT9+vDG+d+/e0rBhQwHk0UcfPef1Asg111xTYn2UZ7qlS5fKbbfdVub4Hj16eA3XqlVLgKBSrt3PAs/6+vqKr6+vdO/e/bxenuk97RWfj1Zlsqe/aZrWGtggIm3/Hv4npxMXjdN3hh4QkfhytFO1FkxRFEVRLg/BItKhaIGmac8C+Pr6rgNo3rz5eTWcnp4OwKFDh3oAiMj6ouOrWvdQaQYAg0TkBmAQMLusipqm9f/7uZegSxadoiiKoiiXRHVIWvoBq/7+fzlQ5oO4IjJDRDqISIf27dufsfvI5XJVSveX2+2+4DYSExMrvRvvbC9PH3DRl8PhQETIyMgoMa6goKA8XYekpKRgs9nOWjc1NZVDhw7xzTffeMVUEev/Yqyfs8VV2vqsyG1TdPuUte4vxXpJSUk55+Uoa3nsdjsFBQUUFhaW2cbJkyeZPn06AO+88w4zZ868aMv2wQcfVNj+p+s6OTk5xvC0adPYsmWLMbx27doS68ntdpd7P3I4HFitVq91KSIUFhYyd+5czGazV32LxVJqO2+++SZhYWHceuutxrw/++wzrFYruq6zdetWXn31VZKTkxERBgwYgMvlIjIykrvuugs4/RzNm2++SWxsLCLC7NmzmTRpEiLCwoULAfjzzz+ZMmWK17x37NhBixYtTl84li9HRLBardSsWZPPPvuM7Oxsr+0xZcoUsrOzETl9ffDMT0R49NFHeeuttzCbzSQmJuJwOCgoKCA1NRUR4fnnn+ejjz4iNDSUnJwcrwdw4+PjERFWr17N1q1bvWLs168fiYmJuN1uoqKiiI2NJSAggODgYO6++25j3x06dCgzZszA7XZjNpv57bffgNPPbFksFsaPH4+u62RmZhIaGkrjxo1xuVxs2LCBV199FbfbjZ+fH2lpaTgcDqZPn06rVq3o06cPBQUF2O12VqxYAUB0dDTff/89v/76K06nE6fTSbdu3Rg5ciRr1qwhIyOjYq7s56k6dA9FAANEZLemaY8BP4lI+7O106FDBwkKUjdcLie6ruPjUx3y7MvP888/7/Xg5+UmNzeXBg0aULNmzcoORfmb2+3GbrdTr169yg4Fh8OBj49PmfuH3W5H13WuuOIKo+yXX36hTZs2PP300xc8f891WtM0r7KiwxXhwIEDdOrU6az1NE2rtO6hKnWEapq2GHgEaKppWhIwFHgX+EXTtJqADehfeREqlUklLJXnck5YABo3blzZISjF1KhRo0okLAC1a9c+4/g6deqUKPvkk08qbP6lJScVnbAA5UpYKluVSlpEpHcZo856Z0VRFEVRlMubeuuqKIqiKEq1oJIWRVEURVGqBZW0KIqiKIpSLaikRVEURVGUakElLYqiKIqiVAsqaVEURVEUpaJc1F8XVkmLoiiKoigV5aLmFSppURRFURSlWlBJi6IoiqIo1YJKWhRFURRFqRZU0qIoZfD8EquiKIpSNaikRVHKMGHCBCZPnlzZYSiKoih/q1JJi6ZpczRNy9A0LbxIWTtN0wI0TQvTNG29pmkNKzNG5ewSExO9hgsKCoiMjGTVqlXk5+eX+eukIoKul/1puZ9//pmkpCSef/75Co23LIWFheTk5FySeSmKoihnV6WSFmAe8FSxslnA1yJyF7Aa+LK8jV0Ot/YLCwuJi4ur7DDKTdM0WrVqZSQmd955J40aNeK2227jhRdeYNasWWVO+/3339OxY8dSx61bt44vvviCG264gTVr1lyM0A0pKSkA1KxZE6fTeVHndb5EhE2bNl3U+AoKCi5a2+W1b98+4/9nnnmmEiOpXn755ZfKDkFRLooqlbSIiB9Q/K1tG8Dv7/+3AS+Up63MzEy6du3qVebn52c8p1D0VfTdfXR0NCKC2+0uV8wFBQX8+eefXmW6rhttehKnzZs3ExMTw6ZNm1i8eLEx7tChQ5jNZjRNIyIiAoDY2Fhj/ps3b+amm27iyJEjrFu3juzsbKPN4n89nE4nGRkZZS6Druvk5ORgt9uxWq2sWrXKGLd7927uu+8+r+c5Skv+PMu4du1a48KybNkyrzq33XYbx48f9yr74osvgNPJjaZpHD58mClTpqBpGqNHjyYoKKjEnRgRoWfPnl5lX331lddwWloaNWvWJC8vr9RlBnC73VgsFpxOJ5qmkZSUZGzriIgILBYLmqbRsmVLAGrXrs2OHTvQNA2n04mfnx/h4eE4nU7S0tKYMmUKIsK8efMQEVwul7HOCwsLsVgs3H///TidTqxWK3PmzDFiOXXqFCLCtGnTALDb7Rw9ehSHw4HD4TDq5efnAxAQEICIEB4ejqZp+Pj40K1bN2rXro2mafTr1w9/f38AEhISvLYTwNixY1mxYgX5+flkZWV5rRez2YzFYsFisRhljz32GI0aNeLAgQNERkbSsmVLdF3H5XJhtVqNmIvOoyIUFhYa/+fk5NClSxcAevTowebNm3E4HKSkpOB2u5k7dy55eXl8+OGHzJkzBxEx1p3D4WD58uWl7ruxsbE0atQITdMwm818+eWXaJrGvHnz+Ouvv9B1HRHx2l52ux2Xy1WiLU/7OTk5iAh2u91rfRQ9XseMGUNKSgq6rhv7iWf7AlitVqOuJwbPcWa323E4HF77d1ZWllE/IyPDK8n89NNPSU9PL/O5rNzcXGJjY3E6nWW+uSvr/GGz2c64Ls6m6PpxOp2EhYUZ5UXHud3uEvvqufIsv2c5i57vi98RPpc2iyt+vgdwuVzous6AAQO86pTH0aNHjf9jY2PJy8vzmt5sNlNYWMju3btLxOM5Tku7znnO9Z6yY8eOERISApzeFp43bMW3Q2xsLHa7ncTExFK3/aWkVbW7EZqmtQY2iEjbv4f9gZ9EZI2maZ8Bw0WkQTnaqVoLpiiKoiiXh2AR6VC0QNO0ZwF8fX3XATRv3vy8Gk5PTwfg0KFDPQBEZH3R8VXqTksZ3gY+0DQtGGgAOMqqqGlaf03TgjRNC7pk0SmKoiiKcklU+aRFRE6IyJMi0h5YDESfoe4MEekgIh1uvfVWgoODS+0OKvpKT0/npptuYtu2bYgIFouFY8eOceTIkTKnKe3Wm+dWYPHxnrLvvvsOp9NJ+/btCQwMREQICQnB6XSyZ88egoKCcLvdiAiJiYnG9EOGDKFFixYEBgZy6tQpLBaL0aVhsViM9ou+9u3bx8KFC432it4a9cTkdDpLdGN5bm936tTpjOvM6XTidrvRdR2r1UpOTg4xMTE8+eSTXtsjOrrMTWV49tlnSU1NZejQoUbZ1q1bSyxXcY899pjXshUWFrJkyRJjmXVd54svvih1O7366qvk5+eX2FbLly832o+JieGJJ54AYOHChdjtdhYuXEh2djZpaWkEBQUxY8YM8vPzeeihh7xu5x8/fhxd19m/fz9w+ta9p5vszz//xOl0kp+fj9vtZsuWLWRnZ+Pn58fx48fJysoylknXdYKDg0lKSmLw4MHMnz+f8PDwEusCYPbs2ezfv7/EdrZYLNSqVQun08moUaNwOp04HA4yMzONeosXLy6xj0RGRgIwZ84c/vvf/wIQERFBTk6O0RWyYsUKnE4nkZGRxnRBQUHEx8ef9bg708vhcKDrOjabzdg3R44cSb169XC73bjdbgoKCjh48CAOh4NNmzYZ9ex2u7G/FBYWsm/fPv766y+vdTJ9+nRjvTmdQN4JcgAAQLlJREFUTq/9tFmzZsbtdV3XjW45l8tFbm5uqXEWFhYa+93hw4eNYU+3jmffAAgPDy+xjYoOFxYWeo0LCgrC39/faM8zzmw2M2/ePFwul9ex7KkzfPhwYzsVfXniLP5/Wa/Szi+rVq3CbDaf83Sel6cL3mazYbPZSp3Ws++X1p7n/6LLV9b87Ha70bXn6ZbznEMdDke51kFZ69DhcJx1/mazudTrxdnW1dq1a43/Pdu36LnNc/7y/C1tuxatr+s6oaGhbN++3difbTab13UkJyfHqztSREhJSeGPP/7A4XDgcrnIysoq96MTF0t16B66RkQyNE3z4fSDurtFZM4ZmgCgQ4cOEhRUvhsuy5cvp2PHjrRq1er8Ay+nxMRErr32WmrVqnXR51VejRs3Jjc394LbiYyM5LbbbjOGRaTE8yk//vgj33zzDYcPH8ZqtfLAAw8Y47744gt+++0345mJovLy8mjcuLEx7HA4KnwdFhQUcPLkSXx9fYHTz0VZrdYL2i/cbjc1atQAYNu2bUYidCE8iaaPjw+apjF9+nTee++9Muu7XC5q1qx5QfMMDg6mffv2F9TG+Vi4cCF9+vQBSt+fzkdoaCjr16+nQYMGfPzxxwDUq1ePPXv2kJGRQbdu3S54HopyOdM0rdK6h6pU0qJp2mLgEaApkA4MBa4EPvy7yirgGylH0OeStPyvczgc1K5du0La8ryTv+6668jPz2fv3r0MHDiQJUuW8M9//pPCwkKuvPJKytqEZV2YdF2nRo0a/Pnnn/z000+EhoZWSLzV3dChQxk+fHhlh1Ht1axZE6vVWqXeTChKVVWZScuFvf2qYCLSu4xR6vN7F1FFJSwAtWrVolatWsZT/126dCE4OJgaNWoYd0fOlHOW9U7ax8eHw4cPc++999K3b98Ki7e6UwlLxfDz87vgu1GKolx86ihVLoqi71g93SMX+i723nvvvaDpFaUsRbspFUWpuqr8g7iKoiiKoiigkhZFURRFUaoJlbQoiqIoilItqKRFURRFUZRqQSUtiqIoiqJUCyppURRFURSlolTcL6iWQiUtiqIoiqJUlIuaV6ikRVEURVGUakElLYqiKIqiVAsqaVEURVEUpVpQSYuiKIqiKNVClUpaNE27QdO0XZqmHdc07ZimaZ/8Xd5E07RtmqZF/f23cWXHqpy/qvTL4mfi5+fH7t27KzsMRVEU5W9VKmkBXMDnInIH0An4UNO0O4CvgR0iciuw4+/haiUtLY28vLzKDuOis1gsjBw50qtM13X27NnD2rVrgdO/2FyagoICMjMzy2x7w4YNfPDBB1gslooLuBSxsbEA/Otf/6Jr165nrV9dkrALUVhYWNkhKIqiVK2kRURSRf6vvTuPj6o6Hz/+OSyCoCKKIkWo4FcRqtYFVJS61roiavm27rTV0sWlWP1ZRL+4i1qpYlEoFGURBBRkEVEQWUXWhCAhAQKBhOxkmWyz3+f3x9y5zmRjS5hM8rxfr3nlzrnnnnvOM+fOnNxzZ64k2MtlQArQFRgETLGzTQHuPFhZfr+fcePGVS2/Wj7Lso7Jh06XLl147bXXotIOHDgAwODBg6moqHDS/+///o+ysjIAtm/fTrdu3UhMTGTevHls2LDhkPY3ceJEEhMTD7ueRxILESE9PZ0VK1bQvn17Ro4cSfv27XG5XDz00EO0bNmSa6+9ljvvvNOpv4g4bSwrK2PHjh08+eSTnH766TXWYcOGDQwcOJBx48bRvn37auvHjx+Px+Ops56WFfr5gPT0dHJzc6PWjRkzhuLiYhYuXEjPnj2j1s2bN4/du3cjIowfP55AIIBlWfzf//0f6enpnH322Xg8HoqLi51tAoEAIkIgEHDSPB4Pe/bswePxMHToUCcOkUpLSykuLsbn8xEMBvnvf/9LWVkZq1atAsDr9TJu3DgWL15Mp06dmD59Og8//LBTL4Bdu3bV2P6VK1eSlZVFIBCgsLCwzlhVVlaydetWSktL+eqrrzjxxBOj1osI5eXlBINBUlJS6iwrMia7d+8+pLwAe/fu5Z///CcAjz32GJdddhl79+7ls88+c/IEg0EgFBfLspw+VbWufr8fEXHi7fF4+OKLL5ztJ0+ezPXXX89HH30UNUBbsmTJIde3IUX2o0jJycnHuCZKxZZprP8lGmPOAlYB5wMZInKynW6A4vDzOravl4adfvrp5Ofn10dRSil1WE455RSKiooOa5szzjij2qD8UHXr1o3MzMyotD59+rB9+3YuvfRSNm/eXOu27du3p6KigiuvvJLExERatGgR9c8YQMuWLZ2BYmPxs5/9jD59+rB48eJDPqN4/fXX8+2339KmTRu8Xu8R7fekk06itLT0iLaNJbtPbhaRvpHpxpiBAP369VsA0Llz5yMqPy8vD4CNGzfeASAiC6P20xgHLcaYE4CVwGsiMtcYUxI5SDHGFItItetajDFDgaH200uPSWWVUkqp5iVmg5ZGNT0EYIxpDcwBpovIXDs5zxjTxV7fBajx1IeITBCRvpHBXLVqFV9//TWDBw+mb9++jB49mn79+tG2bVtmzpzpbPvAAw+QmZnJCy+8wJo1a0hNTWX79u1s3ryZpKQkPvnkE7Zv305ycjIZGRns27ePf/3rX0yfPp0ZM2awdetWNmzYQE5ODg888AAffvgh2dnZDB06lLffftvZz89//nNuueUWVq9ezW233VatDQ888AAAo0aN4tNPP622/qGHHuKBBx4gISGBSy+9lNmzZ3P11VezevVqxo0bx4wZM3jvvfcAGDZsGBs3bmTBggVMnDiRF154gYULFzJnzhzuvPNO3nzzTZ5//nkA1qxZw6xZs5xpgEWLFvHdd9/x8ccf8/zzzzNt2jTuv/9+Jk6cyODBg3nggQe4/PLLD/VlrVO7du2qpb333nusWLGC/fv317pdamoqEJpeCzv33HNJSEggKyuLAQMGMGnSJCZPnsyyZcsYPXp0tTLC0w+RZQBMmzbtsNpw991306lTJ04++WR+//vf15n3wgsv5M0332Tx4sUAXHzxxVHrr7vuOi655JJq23Xo0OGg9dixYwfdu3fn008/ZcGCBU5s77rrrmp5XS4XaWlp3HDDDVxwwQW8/vrrTJgwgXXr1tVa/hNPPAHALbfcAsDq1asBSEhI4J133uGWW27hoosuYt26dezYsYPMzEwKCgooKirij3/8I5s3byYjI4P09HR2796N2+0mJyeHvXv3UlJSwnfffccdd9zB448/7uwzISEhav9nn30299xzDwCff/65sy4nJwf4ccrtjTfe4K233uLyyy+ntLSUxMREdu3axV//+ldnmyuuuKJaG19//XWmTp1KZWUln3zyCZ999hlbt24lLS3NmYL6+OOPWbt2LX6/n8rKSlwuF+PHj+fNN9+krKyMrKwsPB4P69atw+fz4XK56N69O9u3b6ewsJBdu3YRDAbx+XyUlZVRUVHhTHH5fD68Xi+lpaWkpqaSnp7OrFmzqKiowO/34/P5qKio4Be/+IXT7uzsbF566SUqKiqwLMuJp8vloqioiNzcXGdas7y8nEAggM/nw+fzYVkWwWAQj8eDx+PBsiwsy8Lr9Trrw49gMOi8ZoWFhYiIs1142jE8DRfeprKykqysLKdtaWlplJSUcODAATIyMpxpw8TERMrKypw6VFRUUFpaSiAQoLy8nJKSEkSEH374gQMHDuD1enG73ViWRWlpqRO3yspKRISUlBQSExMZP348eXl55OXl4fF4SE5OJjMzk23btjF+/HiCwaBT35ycHILBIKmpqU6swn89Hg/BYJCSkpKotgWDQYqKiiguLqaoqMh5XQsLCyksLCQ1NRW3243H46GsrCwqln6/32nD3r17KSoqIhAIsHfvXtasWUNpaSkVFRW43W4KCwspLS3F6/VSXl7OkiVLSElJITc3l7lz5+LxeMjKyiInJ4ddu3aRnZ3txCM87ZyZmYnX66WwsJBJkyaRkZGBiFBZWUlxcTHff/89e/fuJTc3F5/PR3FxMV6vlx07dlBSUkJ2djZ+v7/W94djoVGdabGnfqYARSIyLCL9n0ChiLxhjBkOnCIiz9RVVq9evWTYsGH85S9/qXOfwWCQQCBAmzZtjr4Bdfjqq6/o2LFjrR/0ixYtcgYxwWCQli1bArB+/Xq++OILXnnlFTweD23btj2k/VmWVesFr1VlZGTQvXt3IPSG/+tf/5q5c+ceZKvqSktLnQ/Wr7/+mr59+9KtW7eoC2cnTZrEww8/TFFREW3btuX444931hUWFjJv3jwefvjhamWPHDky6gLf1NRUevXqFZXncNpcmx07dtC5c2dOPvlkQt0R5405HPtAIEBlZSUJCQn079+fdevWcc0119Ra5u7duzn77LMREafMQ1W1TeE3rNatW/P4448zbtw4MjIyWLVqFf379+eCCy6otazI/R9KXfLz8zn99NNZv34927dvP+hgrCEYYxAR3G43b731Fi+88EK1PCJCZmam04cBfD4fxx13XI1lFhYWkp+fT+/evZ20u+++m6uvvpr77ruP008/vf4bolQTYozR6SEAY8wAYDXwAz/edGkEsB6YDXQH9gG/EZE6J3r79u0rmzZtasDaqpokJCRwwQUX0Lp1ayB0QWdeXh579+4lKSmJP/zhDwwdOjTqLNeh2rdvH2eccUaDDzDDjDG0bNmy1osgVdNx7733cv/993P77bfHuipKNXo6aGkAOmhRR8sYQ6tWrWJ+OlQ1PK/Xy3HHHXfYZ8KUao5iOWhpdUSlKtUM/Pa3vz1mZ3VUbOnrrFR80EGLUrU4kikspZRSDafRfXtIKaWUUqomOmhRSimlVFzQQYtSSiml4oIOWpRSSikVF3TQopRSSqn6Yh08y5HTQYtSSiml6kuDjit00KKUUkqpuKCDFqWUUkrFBR20KFWHv//977WuM8Y4d5pWsVX1/lBffvlljGqilGpITXbQ4vF4ePfdd8nIyIh1VWLCshr0WqjDNnbsWF566SUA1qxZU2OehISEg5YzZsyYeq1XpGXLlrF27VqmTJkCwD333MM777wDgNvtjsobjm9RURGHev8ur9fLqlWr6qWuHo/niLcVEcrLy+ulHo1BSkpK1N3CAeeO6UqppqXJDlqSk5N58sknycrKqjPfunXrKC4uPka1OjRLly496jJ69ep10A82n8+Hz+c77LJFpNoHdUlJCVOnTgUgGAxW2+bxxx/nxRdfBOAXv/hFjeVeeumlrFy5slq6y+VylocNGxa17vnnn3eWLcs66M0Ns7KyGDNmDOvWrau27pe//CVXXXUVv/vd7wCYNWuWs+6UU06hsLAQgMWLF3P//fcD0Lp1a1q0qPsw6tKlCyJCTk4ODz74IP/5z38IBoPk5+c7N+gbMWJEtcHc0qVLMcawYMECpk2bxpQpUxg1ahRpaWn07NkzKi6zZ892lmfMmOEse71e568xhrvvvpvNmzdzww03OHlGjBhRY5wgNDhKSUlhy5YtNb6uubm5jB8/vs72H43Isqvuf+7cuRQUFNCnTx/nTEtdg9pt27axZMkSPB4PRUU/3iQ+stykpCT27Nlz0HrVNlBdsWJFrdskJiYetFyl1EGEP4AawwPoBiwHtgPJwN/s9P+1n1tA30MsSwDZsGGD1AWQX/7yl3XmORYsy3KWQy9LdVdeeeUhl9e2bVupqKioM8/LL78sL7/8ssycOfOQyxURGTlypEyZMsV5Pn36dAHkJz/5iRQUFFSrf0lJiYRfD5FQ+9xut0ybNk1ERILBoJMeWW5YZHmR5USusyxLzjvvPPl//+//1Vn3VatWCSC9evWSQCAgIiJffvllVNmR9QTkwgsvdJa/+OILAeSyyy6Lyh8IBOT222+XZcuWSXZ2tgCSlJQkpaWlTp4ePXo4y263W/bu3Ru1rw4dOshbb70lqamp8tRTT8mUKVOi9lH1sWbNGtm9e7ckJCQIIIMGDaoWr5NOOklERO67775q20fGMBwLEZH9+/cLIB6PR44//ng57bTTBJAZM2ZExdK+k7r06NFDLMsSr9dbZ+zrcsUVV0Q9T0lJkddff92pZ7iN8+fPl/Hjxzv1vvjii6u9ZuG/Xbt2lR07dsjo0aNFROTss8+WIUOGyKRJk+R3v/udXHnllTJ69GgBZM+ePeJ2u+XGG2+Uf/3rX049li9fLosWLapW34ceekg2bdoUlbZ69epaj91wnQ52TNZm8uTJ1eKbnJxcrQ7xqLi4OOr9TzV+wCap/pk7EBjYr18/6devn9x+++1H9AhvHy6v2n6qJsTyAXQBLrGXTwR2An2A3kAvYMXhDlpWrVoVFeytW7eK3+8Xy7KkoqJCAOnfv7+z/pJLLjnsF9Dr9UowGBS3213jeo/HI48++qisWbNGRKTGA/Tiiy+W9PR0EYn+0AkGg1JcXByVPnv2bBGRGj/gwwB54403otJWrVol+/fvl4qKCklISJAXXnhBRo4c6ZTr8XhERGT9+vV1tvfxxx+X/v37S6dOneSuu+6q8QP1F7/4hXz++ecyc+bMqPRRo0YJIBkZGQLIiBEjpHv37pKWlubkuemmm5xYhdvi9XqjBgpvvfWWMxiKHBjceuutznYul0sqKytFRKS0tFT27dsXVZdWrVqJZVnOACByXTi9oR7r1q2Tp556SgB55plnasxz4403Hna5kf0ksg2DBg2qljcQCIjf7xdATj31VLn//vvluuuuq7P8gQMHyrx586R169YCyObNmwWQRYsWOX1u9erVUlRUFNVnLMty+ndN/R+QnJwcWbBgQdRAJNyWV199NSpt7ty51eoW7kPh8gB56aWXotLuvfdeGTNmjPzhD3+otY3PPvusXH/99fLuu+8KIA8//LC4XC655557RESksrJSrrzySvnmm2/E4/E47Qlv73a75aWXXhIRkTFjxoiIyIABA6Las2rVKvH5fM62lmU5g/ewnTt3SmpqqoiEBp/JycmSkZERFbPI1zoyrnfccYezXFFRIUlJSVGvicvlktzc3Gqvw+Go+jqWlZUd0naRgxTLsqRnz56yc+dOqaioqBaDhlRTzBuLsrKyqPq5XC4pLCyMyuNyuZzl8vJy8fv9Ue2JfH3qGhRmZmZWKzMyv9frlfz8fPF4PLJr1y75zW9+E9NBi7F31igZY+YDY0Vkqf18BfC0iGw6hG0bb8OUUg3m7rvvZu7cuTWu69KlCzk5OUdc9sUXX0x2djZ5eXlHXEYs3XfffVHTh4di/vz5bNu2jVdffbXatV1hvXv3JiUlBQhdC1ZeXs6mTZvIzc2lU6dOHDhw4KjrDvD000/zwQcfUFlZWeP6li1bctVVVx32tWOPPfYYnTp1cqawD+bGG29k6dKlXHjhhQSDQZKTkw9rf1X16dOH7du3H1UZx9hmEekbmWCMGQjQr1+/BQCdO3c+ooLDx9bGjRvvABCRhVH7aayDFmPMWcAq4HwRKbXTVqCDFqWUUiqWYjZoaZQX4hpjTgDmAMPCA5ZD3G6oMWaTMeagg5qDOdKAK6WUii+XX345HTt2jNn+L7vsspjtO940ukGLMaY1oQHLdBGp+RxvLURkgoj0jRwBbtq0yZkLC3+bJry8atUqzjnnHCdNRDj11FMREXJzc6Pm0fx+f63Xz/h8voNeY3PNNdewdevWWre/5pprEBEsy6Jz585kZ2c76zMyMpyv2L777rsEAgFEJCpP5MPlctG9e3cWL14clZ6VlUUwGKS0tJSKigogdLo4vD789d2DPfr161fra1D14Bs9enTU8/Ap5PA3c8aNG4eI8NFHH0Xlu+WWW5z9vfbaawSDQZ555pmqrzfffvtt1Dc2MjMzEREyMzOB0DeKIuZcq6ntG1b1cQby1FNPdfpXba6++mq6detWLf2+++5zlk888cRD3me43iLCvHnzALj22mtrzOt2u51vF0Ho21u333478+fPr3MfQ4cO5c477wRg5syZAKSmpvLss89iWRbffPNNjf0mOTmZ8847j+HDh/Piiy/idrsREb7//nsuu+wyp+6jR4/muuuuc/ZXWVnJk08+CeB8bb4mrVu3BqB79+5OWtVvHL344ouHND3ywQcfOMuTJ0/mvPPOIyEhgfLycizL4sILL+SFF15g6NChUf1r7NixzvTTgw8+yMSJE51vtP32t78FIC0tDYC77rrLee/4/PPPmTNnDhkZGRQWFhIMBikvL8fv97N582YgdKzs2LGjWn92uVy4XK6o96H7778fv9+Px+MhOTnZSQ8fD+G8kdusXLmyXq9RPNj7Zbguubm5eL3ewy7nwIEDB10/ZswYgsFgVHr4NYxMW7duHUVFRdXSww+v1+u879b0CH9z8fvvvz+imKxfv77ONJ/PF/UZZFkWZWVlLFq0KCqWkXEMr6vtEY5LeFvLskhPT6/2Wbd48WJGjhyJx+MhGAw6760xcyw66aE+AANMBd6tZf0KDvNC3KpX60+cODHq+bvvviv//e9/ned+v19irUuXLpKVlVUt/ac//amMHTv2kMq47bbbZOHChXXmAeSpp5467Pp9+OGHkpqaKpZlyahRo2TTpk1y5plnys6dO+W5555zLuxcsGCBsx+IvlA0IyNDnn/++Wr1GTNmjADy+9//vtY6A9KtW7eo9EceecQpPyx84WfktoMHD5bLL788qj5PPvmklJSUyB133CEnnniik37mmWfWerHmueeeW+cFq3v37pV169aJ1+t10mbMmCGA/PznPxfAuXCxsLAwatsbbrgh6oLYU045Jaq+wWBQTjjhBAHkggsucNbNmjXLaaeIyK5du+T1118Xn88nbdq0kQ4dOki3bt0EkHbt2jlxCQQC8vLLL8vzzz8vK1askJycHAHk17/+dVS92rVrJ71795b//Oc/kpSUJCeeeKLs379fOnfufAi9RmTPnj1y2WWXSVJSkmzfvt1J9/l8Tn/v0qWLiIQuBAxfCCsicuDAASkpKZG1a9fK5ZdfLjfddJNTrzZt2ggg6enpMnLkSElLS5P27ds7237yySfy61//WoYNG+Z8kyi8bfji7KeffloAefDBB8XtdjsXtlbtU2Hl5eXVLjydNGmSs1z1As9nn31WRH680DEvL0+ys7MPKW4iIueee261LxUsXLhQTjvtNP3mjTrm0G8POY0eYL+ZbAW22I9bgbuA/YAXyAO+PlhZPXv2lPPPP/+gwW+MB/z5558vOTk51dKXLFkiiYmJh1TGnXfeKXPmzDlovvpq/7///W8pLCx03ugjB39r166VJ5980vkA+Pe//11jGeE39H/84x+11mvkyJGSk5MjnTp1qrZu9erVddbR4/HIgQMHRCT0Vdaqsdy9e7fs3r3bqWf4K7eAPPLII3LSSSc5Xw8/cOBA1Ae6x+NxPviqftD95S9/kfz8fLEsSwKBgFiWJf/zP//jrA8EAjJ58mT5+OOPJRgMytatW2XlypVy8803CyB/+tOfBJBvv/3W2cayLGnfvr0kJiZW22dNH7QVFRXyzTffSF5enkyePDnqK84iIllZWVHfIigtLZVAICCjR4+WyZMnO2VmZ2dLSUmJHDhwQN58801xu90yd+7cOuMe6WD/FETWa/z48fLII49ErV+5cqUMGDBApkyZIiNGjJDp06dL7969a2zze++9JyIi+/btk2+//VaWLFnifPBHxiw8QAaivrUWWUZj1Rj+yVLNTywHLY32QtyjZf+GRKyrcUQCgQCtWrU6qjKeeeYZbrzxRm688cZ6qtWhExHnR9PCcnJyKC0tpVevXkddvs/n4/3333emDOqbMQaR0KnWtm3b8u9//5tHH32U4uJiTjnlFCffNddcwwUXXEBFRYUzvZWdnU27du04+eST69xHTTGqySuvvMIjjzzC7Nmz+dvf/ha17quvvqJ///6ceOKJtGzZMvzG4dS/Pj3xxBO899579VrmwUydOpVvvvnG+dFCCP1q8vjx45kwYYKTVlhYyJIlS7j33nsPuexnnnmGIUOG8LOf/axe66xUc2CM0W8P1bd4HrSo2Bo4cCALF4aOk+XLl0ddX9FYbd68mUsvvRQIXTfUu3fvGNfo6GVlZZGZmckVV1wR66oopSLEctBydP/OK9UEhQcsQFwMWABnwAI0iQELQNeuXenatWusq6GUakQa3beHlFJKKaVqooMWpZRSSsUFHbQopZRSKi7ooEUppZRS9cVqyMJ10KKUUkqp+tKg4wodtCillFIqLuigRSmllFJxQQctSimllIoLOmhRSimlVFzQQYtSSiml4kKjGrQYY7oZY5YbY7YbY5KNMX+z0/9pjEk1xmw1xnxujDk5xlVVTVhTvR+XqtuECRMoKyuLdTWUUnVoVIMWIAA8JSJ9gCuAR40xfYClwPkiciGwE3j2YAVZlkV6enqDVvZwZGdnU1paWi3d7Xazd+/eet9fLD94v/zyy6j9jx8/nldeeYUePXoAMGTIkBq3C2+Tm5tb4/r777+fe++9l5/85CcN2r7IO1EPGjQICPWnxMTEqHwpKSmsW7cOt9sNQDAYjLojMVDteZhlHf1PGZSUlGBZFoFAIKpvrV279qjL9nq9iAjr16/H5XKRnJx81GUeLsuy8Hg8UWki4qTt37/fyTNixIhq2wcCgUPaj8fjYdOmTfzpT3/ipJNOOvqKK6UaTKMatIhIjogk2MtlQArQVUSWiEj4HWgdcObBykpMTKRnz55UVlY6aW+99ZbzIVJRUUFOTg6rV6/mlVdeISMjA/jxDpOA82EEoTfxWupMMBiMSovMu3LlSkpKSnj88cf5/PPPKS8vZ/LkyUDozfK5556jR48eGGMoKirC5/PRr18/cnJyKC0tZf369aSkpABw2223UV5eTkFBAYFAgLy8PKeOkR/ilmXRr18/PvzwQ6c+fr+fyspKJ9/UqVMxxrBw4cKoD1AR4dZbb6WystJ50w9v4/P5AEhNTWXTpk288MILjBo1ihUrVrB69Wr27duHMYbbbruNFi1aMGDAAIwx/OUvf2HkyJHs3buXBx54gKlTp7Jy5UqMMSQmJlJRUcGMGTNo0aIFxhi6dOnCa6+9RiAQIBAIUFRUxKBBg5gxYwYzZ84kJyeHFi1aOK9Reno6PXr04P777yczM9OJwYoVK6LiYozBGEOHDh0wxvDUU08BMGnSJESEDz74gPvuu49du3bhcrl47LHHWLBgAcYYWrZsySWXXMKQIUMwxjBr1iz69OlD//79adeuHbfeeiutWrViyJAh9O3blz179vDII484+V9++WUefPBBBg8ezDnnnEPLli0555xzmDZtGtu2baNXr1488sgjPPXUUzz22GOMHTuW6dOnY4zhjTfeoG3btjz33HMUFhZy3XXXYYyhY8eOtGzZktatW9OhQwdmzJgBwFVXXeW0u6KiAr/fT8+ePXnuueeYOnUqXbt2Zf/+/WzZsgWXy4Xf7+ejjz7ijDPOoLS0lGAwSNu2bXnwwQe54oor+POf/8z555/PBRdcwOTJk3n//fcZOnQoc+fOJScnhx49elBSUuLEu6ZjItyPfD4f+/fvJzc318nr8XjYvXs3ABs2bCAQCCAijB07luOPPx4Rwev1UlBQwOrVq7npppswxtCtWzd+//vfc/zxxzNq1Ci2b99O586d2b9/v9OPCgoK2L59OxMnTmTBggWsXbuWZcuWYYyhbdu2LF26lOOPP55+/fo59Vy/fj1erxev18uaNWsQEZKSkkhPT8fv90e1J/JYLyoqcuJe9ZjKz89nz549VFRUAJCUlETkXegjtwvvo+oxXdV///tfZwCXkJDArl27otZbluXEuOp+DkVkW+vTjh07gNCg0uPx1Fony7JwuVzO87riISJR7/Xh8gsKCmos37IsioqKqqWH81btv5ZlOfus6bUoLy/H7/dHDbIrKirw+XzOa1BTPSI/Y6oK/+NQm4qKimrrI+vt8XgIBoPV4lKVZVm43W48Ho/Ttpo+08L9PfweGyumsZ4KN8acBawidIalNCJ9ITBLRD4+yPaNs2FKKaVUfNssIn0jE4wxAwH69eu3AKBz585HVHD4xMHGjRvvABCRhZHrWx1RqQ3MGHMCMAcYVmXA8hyhKaTptWw3FBh6TCqplFJKqWOqUU0PARhjWhMasEwXkbkR6b8Dbgful1pOD4nIBBHpKyJ9TzjhBADefffdqNNzmZmZBINBRITy8nJWrFgRdRo1vK6+H5mZmeTk5ESlBYNBgsEgqampzJ4925m+2bFjh5PH7XY7y2vXrj3k/e3bty+qXTU91q9fz/fff18tPXwqsLbHzp07ycnJYezYseTn5ztTT36/37m2IjxFUfWahA0bNgChU5J5eXlOHcNTUe+99x4VFRVOLMJlVD2NWlhYiIg4p3/D01kHi8v7779P27ZtWbx4cVS6ZVm8+eabAAwcODDqtOt7773HsmXLuPfeexERcnJySEpKcqYmwlMalmU5U3crV65k69atlJSUMHz4cHw+HyUlJc6UlN/vx+v1OvFfsmQJWVlZ5OXlEQwGSUlJobKykrvuuoupU6fy2muvMWHCBHbt2sWGDRv4+9//jt/vZ/78+QQCAZYuXUp+fj4iQnFxsdOujRs3UllZyZdffsmzzz7LwoULKS8vJy0trdZ+VVxcjGVZBINBNm/ezJQpU1i2bBl+vz8q3+7du/H7/ezfv/+oj4/w1Eq4/4X7xbhx4xARSktLmTdvHhUVFXi9XmeKocrxz6uvvuq8dikpKVF9OTMzk3Xr1rF582YmTpzI119/jWVZXHTRRVHlhKcsIh/XXnst06ZNc55XjUX4cSh9UB/1/whf2xXretRWt9rWhad44u0RS41qesgYY4ApQJGIDItIvxn4F3CNiBQcSll9+/aV6dOn89Of/pS2bds2SH3VocvNzSUrK4tLL70Ur9dLmzZtquWxLMu5VqUx8Hg8fPfdd9xwww215gkEArRq1ShPWDZ5Xq+XFi1aUFpayuTJk50BIUBlZSXt2rU7rPKef/559u3bx7Rp0+q7qko1KcaYmE0PNbZBywBgNfADP94pcgTwHtAGKLTT1onIn+sqq2/fvhJ5oZtSSimljl4sBy2N6l9EEVkDmBpWfXms66KUUkqpxqXxnItXSimllKqDDlqUUkopFRd00KKUUkqpuKCDFqWUUkrFBR20KKWUUiou6KBFKaWUUnFBBy1KKaWUqi9Hfwv7OuigRSmllFL1pUHHFTpoUUoppVRc0EGLUkoppeKCDlqUUkopFRd00KKUUkqpuNCoBi3GmG7GmOXGmO3GmGRjzN/s9FeMMVuNMVuMMUuMMT+JdV2VUkopdWw1qkELEACeEpE+wBXAo8aYPsA/ReRCEbkI+AIYGcM6qsNkWRZZWVkkJycfVTl5eXmsXbuWgQMH1lPNlPpRZWVlg5YvIg1avlLNQaMatIhIjogk2MtlQArQVURKI7K1Bw569FdWVuLxeAgGg/j9fgASEhKc9UVFRfVZ9aNSUFDAW2+95Tyv7c1t9OjRh1zm7t27cblcta7fs2cPPp+vxnW1pdcksq75+fn89a9/xRjDZZddhs/no3379rRs2ZIzzzyT888/H5/PhzGGgoICvv/++6iyMjMz2bRpE99//z1utztq3f79+znjjDO46qqr+OKLL9i1a1e1OpeVlTnPU1NTWb58eVQev9+PiJCZmenUW0SwLItAIBDVDoCSkhIWLVrEF198gd/vZ+PGjViWhdvtxu1243K5sCyL7OxsRITc3Fz8fj+WZWFZFl6vl5KSkqhYhfe7cuVKfD4flmUhIvj9fkpLS6PyWpbFzp078fl8fPHFF056eB/5+fn4/X78fj+ZmZnOtuFyLKvmn0tISUmp8TX+3e9+FxVPEWHx4sXk5OTQv39/p8zIdoiI82Ff3x/KmZmZ1fqIiOB2u51+VF5ezuTJk0lLSyMrK4t58+bh8XgYP348n376KV9//TU7duyoVnZxcTHp6ekAuFwujDG0b98eYwwHDhxw2h8MBoHQ8ZKdnV1jPWuLc3JyMunp6ViWRTAYpEWLFixbtsxZX15eHtWuyL9hka9TUVEReXl5QOj97dNPP3XWffDBB9X+KYh8ncLtqCqy3x+tFStWHDSPiDB//nw8Ho9znGRmZuLxeJz1h6q4uDjqedU25ubmMnJkzf/ffvfdd7hcLioqKqLSa3vvO9Q4hd87ANxuN4FAIOp1qKqudRD6bPB6vU6+YDBIQUEBCQkJTJo0ib1790a9b4SF+xz8GJdwPw0EAgQCAbxeLykpKcyfP9+py7p165z3Ub/f77x/5eTkkJ+fT1FREVOmTDmkWDQU01hH/8aYs4BVwPkiUmqMeQ14CHAB14lIwUG2b5wNU0oppeLbZhHpG5lgjBkI0K9fvwUAnTt3PqKCwwPzjRs33gEgIgsj1zeqMy1hxpgTgDnAsPBZFhF5TkS6AdOBx2rZbqgxZpMxZtOxq61SSimljoVGN2gxxrQmNGCZLiJza8gyHfh1TduKyAQR6SsifU8++WS6du1KWlqacxoeYMeOHXz33XeUlJSwdetWMjMzmT9/Pqmpqc6p72AwiM/nIxgMOqfp3W63c4rO7XZHpYXLD08JhE/NiQh79+5FRAgEAgSDQcrLy3n77bcJBoN4PB6GDRvm1L+yshK3282aNWvw+XyUlpayfft2CgsL8Xq93HjjjQQCAZKSkigrK2PKlCnOaexwPUQEr9fLmjVrnPbk5eWRmJhIIBCgpKQEn89HcXEx8+fP54MPPuDOO+90yhg5ciRPP/00RUVFWJZFWVlZ1JRAZGyys7OZM2cOEyZMIBgMsmfPnkN+nb/55htmz55NcXExO3fu5OWXX66WJyMjg3fffZdNm6qPQQcOHEgwGMSyLDweD506deLBBx+krKwMEaGgoID169c7r0NZWRm5ubnk5OSwZs0afvOb32BZFrt372batGlA6BQowDXXXIPb7aZfv34AzJo1i7y8PJ544gn27NnDxIkTWb58OVdffTWvvPIKrVq1wu12k5ycTEVFBWVlZWzbto1169Y59U1OTmbhwoU8/PDD7Ny5k6SkJLxerxPn/Px8kpKSeOedd6L62B//+Ec+/fRTHnzwQQoLC/nqq68AuPLKK6PiMW7cONauXYtlWcybN4/CwkKWLFmC1+vlwIED3HLLLbRr146MjAzmz5/P7t278Xg8iAgul4vKykpyc3OdvvzBBx/wxhtvcPPNNzN8+HCefPJJSkpKCAQC3H333QAkJiZSXFzMgAEDnCm2Dz/8kI0bN0adFg8/wsdAuI8uW7bMOb727NmDiLBgwQLKy8vx+Xzcc889DB8+HLfbzYEDB/joo4/Iy8vjq6++Yt68edx6660sWLCAn/zkJ8yZMwfLsjhw4ACWZfHoo4+Sn59PQUEBSUlJ5OXl4fP5cLlcvP766wwaNIhrrrkGl8tV7XT3nj17yM3NBeDaa69l27ZtAOzatSuqPRUVFaSnpzN//nyys7PJy8ujd+/euN1uvF4vW7ZsYc6cOaxatYpVq1YB8Pnnn5OYmMioUaNITU1l7NixfP/990yaNIm0tDTmzp1LWVkZLpeL1NRUXC4X69evx+VyMXToUAB++OEHvF4vEJraKiwsZMKECezbt4933nmHQCBA9+7d8fv9BAIBsrKyWLRoEcuWLaOwsJCZM2dSUlLC9u3b+fbbb6msrHTev8LHenjKprKyktLSUkpLS51++ec//5nCwkJGjBhBQUEBHo+HTZs2kZCQEPV+FD42g8EgY8eOZf/+/Xi9XpKSknC73aSnpzNnzhwn5klJSSxfvpwFCxZgWRZ5eXl0796d9PR0vv32WzZv3uyU6/V6+cMf/sCYMWOYPHkye/bsYdmyZfh8PgoLCwkEAvj9fpYsWcK6detwuVzOe1lxcTFr1qwB4NVXX2X58uXOa7Z7924+/vhjCgsLycvLc9rg8/nweDx4vV5efvll0tLSqKiocKZHCwsLKSoqwufzUVJSgmVZrF27llGjRnHdddexYMECrrnmGjZt2kQwGHSOpfDx5nK5cLvd7Ny5k+zsbN5//338fj/79u2jqKiIyZMnk5yczJdffsmCBQuYO3cuL730Eh06dOChhx5i9+7d7Nq1C5/PR35+Pu+88w6LFi2ioKCAxx57jKysLHbu3Mnnn3+O3+/nrrvu4o033uDjjz9m/vz5FBYWsnXrVt5++22WLl3KgQMHKCgo4J///CdlZWU89NBDDB8+nLfffptHH330EN7hG06jmh4yxhhgClAkIsMi0s8RkV328uPANSIyuK6y+vbtK1U/7LxeL23atKn3eh+t9u3bk5OTw0knnRTrqtSL8Ify//7v/wIwYMAAJk+ezFlnnQXACSec4MxhRwoEArRs2ZJQN4hWXl7OiSee6Dyvz34bHoC2b9/+iLYXkRrrDKH55JYtWx5N9Y5ovzXxeDy0bdv2qPdbUFCAiHD66acfdVmNRXJyMiNGjHDm949UXa/Jtm3bOP/884+47PB1COF+un37dvr06VNjXsuyaNGi0f1PqpoIY0zMpodaHVGpDecq4EHgB2PMFjttBPCwMaYXoRsx7QP+fCSFN8YBC0BhYWG9fJg0Fueeey7nnnuu8zz8X01YTQMWgFatau+OJ5xwAj//+c9JTEys9eK6I9WiRYsjHrAAdQ4cGmrAcrD91qS++thpp51WL+U0Jj/72c+OesACdb8mRzNggdDxEXmM1DZgAXTAopqsRjVoEZE1QE1H/ZfHui7HUlMasDSkLVu2APDKK6/EtiJKKaViQofjSimllIoLOmhRSimlVFzQQYtSSiml4oIOWpRSSikVFxrVhbhKKaWUin/hry7XNz3TopRSSqm40Kh+XK4+GWPKgOp3SmteOgEHYl2JRkDjoDEI0zhoDMI0DiFHEoefikjUDzaFf1yuvjX2H5erTzuq/mJfc2OM2dTcYwAaB9AYhGkcNAZhGoeQeItDUx60KKWUUuoYqHpGpKHoNS1KKaWUigtNedAyIdYVaAQ0BiEaB41BmMZBYxCmcQiJqzg02QtxlVJKKdW0NOUzLUoppZRqQprcoMUYc7MxZocxJs0YMzzW9Wloxpi9xpgfjDFbjDGb7LRTjDFLjTG77L8d7XRjjHnPjs1WY8wlsa39kTHGfGiMyTfGbItIO+w2G2OG2Pl3GWOGxKItR6OWOLxojMmy+8MWY8ytEeueteOwwxhzU0R63B4zxphuxpjlxpjtxphkY8zf7PRm1R/qiEOz6Q/GmLbGmA3GmCQ7Bi/Z6T2MMevt9swyxhxnp7exn6fZ68+KKKvG2MSDOuIw2RiTHtEXLrLT4+uYEJEm8wBaAruBnsBxQBLQJ9b1auA27wU6VUl7CxhuLw8H3rSXbwUWAwa4Algf6/ofYZuvBi4Bth1pm4FTgD323472csdYt60e4vAi8HQNefvYx0MboId9nLSM92MG6AJcYi+fCOy029qs+kMdcWg2/cF+TU+wl1sD6+3XeDZwj50+HviLvfxXYLy9fA8wq67YxLp99RCHycDgGvLH1THR1M60XAakicgeEfEBM4FBMa5TLAwCptjLU4A7I9KnSsg64GRjTJcY1O+oiMgqoKhK8uG2+SZgqYgUiUgxsBS4ucErX49qiUNtBgEzRcQrIulAGqHjJa6PGRHJEZEEe7kMSAG60sz6Qx1xqE2T6w/2a1puP21tPwS4HvjMTq/aF8J95DPgBmOMofbYxIU64lCbuDommtqgpSuQGfF8P3UfuE2BAEuMMZuNMUPttM4ikmMv5wKd7eWmHJ/DbXNTjsVj9mneD8PTIjSDONin9y8m9J9ls+0PVeIAzag/GGNaGmO2APmEPmR3AyUiErCzRLbHaau93gWcSpzHAKrHQUTCfeE1uy+8Y4xpY6fFVV9oaoOW5miAiFwC3AI8aoy5OnKlhM7zNauviDXHNkcYB5wNXATkAKNjWptjxBhzAjAHGCYipZHrmlN/qCEOzao/iEhQRC4CziR0duS82NYoNqrGwRhzPvAsoXj0IzTl84/Y1fDINbVBSxbQLeL5mXZakyUiWfbffOBzQgdqXnjax/6bb2dvyvE53DY3yViISJ79hmUBE/nxtHaTjYMxpjWhD+rpIjLXTm52/aGmODTH/gAgIiXAcqA/oemO8K+/R7bHaau9vgNQSBOJAUTF4WZ7ClFExAt8RJz2haY2aNkInGNfLX4coYurFsS4Tg3GGNPeGHNieBn4FbCNUJvDV3oPAebbywuAh+yrxa8AXBGn0OPd4bb5a+BXxpiO9inzX9lpca3KNUp3EeoPEIrDPfY3JnoA5wAbiPNjxr4GYRKQIiL/iljVrPpDbXFoTv3BGHOaMeZke/l44EZC1/YsBwbb2ar2hXAfGQx8a5+Vqy02caGWOKRGDOINoet6IvtC/BwTx/Kq32PxIHQl9E5Cc5nPxbo+DdzWnoSuck8CksPtJTQvuwzYBXwDnGKnG+B9OzY/AH1j3YYjbPcnhE51+wnNsz58JG0G/kDoIrs04Pexblc9xWGa3c6thN6MukTkf86Oww7gloj0uD1mgAGEpn62Alvsx63NrT/UEYdm0x+AC4FEu63bgJF2ek9Cg4404FOgjZ3e1n6eZq/vebDYxMOjjjh8a/eFbcDH/PgNo7g6JvQXcZVSSikVF5rcXZ6NMQNjXQellFKqKZNjdFfnqpraNS1KKaWUaqIO60xLx44dpWvXmH9N25GcnHxARE6raV27du0WdOzYsaZVSimllDoCxx9/PGlpaaWEvm11zB3WoKVr167MnTv34BmPkV69eu2rbV3Hjh154oknjmV1lFJKqSatd+/e3HHHHfkHz9kwmtw1LVUNGDAg1lVQSiml4tqaNWtiXQVAr2lRSimlVJyot0HLxRdfHPV87ty5vPzyy3Vucyh5lFJKKaVAz7QopZRSKk4ck2taioqKeOGFF8jOzgZgxIgRXHrppVF5hg8fznHHHce2bduoqKhg+PDhXHfddceiekoppZSKA/U2aPF4PAwaNMh57nK5uP766wF47bXXGDJkCH379iU7O5uHH36YxYsXVysjKyuLzz77jIyMDB566CGuvPJK2rRpUy2fUkoppZqfehu0tG3blvnz5zvP586dy7ZtofsxrV27lrS0NGddeXk5FRUV1cq45ZZbaNGiBWeddRbdunVjz5499O7du76qqJRSSqk4dkymhyzLYvbs2Qc9axK6+WTtz5VSSinVfB2TC3EHDBjAtGnTnOcpKSk15vvqq6+wLIuMjAwyMzPp0aPHsaieUkoppeLAMRm0PPfcc2zbto2BAwdy66238sknn9SYr0uXLgwePJg//vGPvPTSS3o9i1JKKaUc9TY9lJiYGPX87rvv5u677wbglFNO4d133622TWQegCuvvFJ/t0UppZRSNdLfaVFKKaVUXGg09x564403Yl0FpZRSSjVieqZFKaWUUnGh0ZxpaSiN5c6USimllDo6eqZFKaWUUnHBiMihZzamANjXcNU5bD8VkdMiE4wxA+3F2cD+Y1+lRqUD4Ip1JRoBjYPGIEzjoDEI0zgceQxOF5EO9V2ZQ3FY00NVBwiN3H7g77GuRIz9C40BaBxAYxCmcdAYhGkc4jAGh3WmJZ4YYzaJSN9Y1yOWNAYhGgeNQZjGQWMQpnGIzxjoNS1KKaWUigtNedAyIdYVaAQ0BiEaB41BmMZBYxCmcYjDGDTZ6SGllFJKNS1N+UyLUkoppZqQJjdoMcbcbIzZYYxJM8YMj3V9GpoxZq8x5gdjzBZjzCY77RRjzFJjzC77b0c73Rhj3rNjs9UYc0lsa39kjDEfGmPyjTHbItIOu83GmCF2/l3GmCGxaMvRqCUOLxpjsuz+sMUYc2vEumftOOwwxtwUkR63x4wxppsxZrkxZrsxJtkY8zc7vVn1hzri0Gz6gzGmrTFmgzEmyY7BS3Z6D2PMers9s4wxx9npbeznafb6syLKqjE28aCOOEw2xqRH9IWL7PT4OiZEpMk8gJbAbqAncByQBPSJdb0auM17gU5V0t4ChtvLw4E37eVbgcWAAa4A1se6/kfY5quBS4BtR9pm4BRgj/23o73cMdZtq4c4vAg8XUPePvbx0AboYR8nLeP9mAG6AJfYyycCO+22Nqv+UEccmk1/sF/TE+zl1sB6+zWeDdxjp48H/mIv/xUYby/fA8yqKzaxbl89xGEyMLiG/HF1TDS1My2XAWkiskdEfMBMYFCM6xQLg4Ap9vIU4M6I9KkSsg442RjTJQb1OyoisgooqpJ8uG2+CVgqIkUiUgwsBW5u8MrXo1riUJtBwEwR8YpIOpBG6HiJ62NGRHJEJMFeLgNSgK40s/5QRxxq0+T6g/2alttPW9sPAa4HPrPTq/aFcB/5DLjBGGOoPTZxoY441CaujommNmjpCmRGPN9P3QduUyDAEmPMZmPMUDuts4jk2Mu5QGd7uSnH53Db3JRj8Zh9mvfD8LQIzSAO9un9iwn9Z9ls+0OVOEAz6g/GmJbGmC1APqEP2d1AiYgE7CyR7XHaaq93AacS5zGA6nEQkXBfeM3uC+8YY9rYaXHVF5raoKU5GiAilwC3AI8aY66OXCmh83zN6itizbHNEcYBZwMXATnA6JjW5hgxxpwAzAGGiUhp5Lrm1B9qiEOz6g8iEhSRi4AzCZ0dOS+2NYqNqnEwxpwPPEsoHv0ITfn8I3Y1PHJNbdCSBXSLeH6mndZkiUiW/Tcf+JzQgZoXnvax/+bb2ZtyfA63zU0yFiKSZ79hWcBEfjyt3WTjYIxpTeiDerqIzLWTm11/qCkOzbE/AIhICbAc6E9ouiN8y5rI9jhttdd3AAppIjGAqDjcbE8hioh4gY+I077Q1AYtG4Fz7KvFjyN0cdWCGNepwRhj2htjTgwvA78CthFqc/hK7yHAfHt5AfCQfbX4FYAr4hR6vDvcNn8N/MoY09E+Zf4rOy2uVblG6S5C/QFCcbjH/sZED+AcYANxfszY1yBMAlJE5F8Rq5pVf6gtDs2pPxhjTjPGnGwvHw/cSOjanuXAYDtb1b4Q7iODgW/ts3K1xSYu1BKH1IhBvCF0XU9kX4ifY+JYXvV7LB6EroTeSWgu87lY16eB29qT0FXuSUByuL2E5mWXAbuAb4BT7HQDvG/H5gegb6zbcITt/oTQqW4/oXnWh4+kzcAfCF1klwb8Ptbtqqc4TLPbuZXQm1GXiPzP2XHYAdwSkR63xwwwgNDUz1Zgi/24tbn1hzri0Gz6A3AhkGi3dRsw0k7vSWjQkQZ8CrSx09vaz9Ps9T0PFpt4eNQRh2/tvrAN+Jgfv2EUV8eE/iKuUkoppeJCU5seUkoppVQTpYMWpZRSSsUFHbQopZRSKi7ooEUppZRScUEHLUoppZSKCzpoUUodFmPMqRF3is01P95FuNwY80ED7XOYMeahOtbfbox5uSH2rZRqPPQrz0qpI2aMeREoF5G3G3AfrYAEQncxDtSSx9h5rhKRyoaqi1IqtvRMi1KqXhhjrjXGfGEvv2iMmWKMWW2M2WeMudsY85Yx5gdjzFf2T85jjLnUGLPSvuHn16bmu45fDySEByzGmCeMMdvtG7/NBOf+QiuA249JY5VSMaGDFqVUQzmb0IDjDkK/wLlcRC4A3MBt9sDl38BgEbkU+BB4rYZyrgI2RzwfDlwsIhcCf45I3wT8ot5boZRqNFodPItSSh2RxSLiN8b8ALQEvrLTfwDOAnoB5wNLQ7M7tCR0W4KquhC6h0zYVmC6MWYeMC8iPR/4Sf1VXynV2OigRSnVULwAImIZY/zy4wV0FqH3HgMki0j/g5TjJnSfmLDbgKuBgcBzxpgL7KmjtnZepVQTpdNDSqlY2QGcZozpD2CMaW2M+VkN+VKA/7HztAC6ichy4B9AB+AEO9+5/HjnWqVUE6SDFqVUTIiIDxgMvGmMSSJ0Z+Ira8i6mNCZFQhNIX1sTzklAu+JSIm97jpgUUPWWSkVW/qVZ6VUo2eM+Rx4RkR21bK+MzBDRG44tjVTSh1LOmhRSjV6xpheQGcRWVXL+n6AX0S2HNOKKaWOKR20KKWUUiou6DUtSimllIoLOmhRSimlVFzQQYtSSiml4oIOWpRSSikVF3TQopRSSqm4oIMWpZRSSsWF/w+Xj4l3XS/nWQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<MNEBrowseFigure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_1 = plot_patient_record(file_path,\"./chb01/chb01_02.edf\",duration = 3599, channel_names = [str(idx) for idx in range(1, 24)])\n",
    "data_2 = plot_patient_record(file_path,\"./chb01/chb01_03.edf\",duration = 3599, channel_names = [str(idx) for idx in range(1, 24)])\n",
    "# plot_patient_record(file_path,\"./chb01/chb01_03.edf\",channel_names = [str(idx) for idx in range(1, 24)], duration_min=2900, duration_max=3100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exemple des crises des patients :\n",
    "\n",
    "    -> patient 2, enregistrement 16, entre 130s et 212s \n",
    "    -> patient 3, enregistrement 2, entre 731s et 796s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting EDF parameters from C:\\Users\\rayan\\Desktop\\B_Laval\\S2\\Cours\\Apprentissage_NeuronesProfonds\\Projet_2\\EEG_Seizures_Transformers-\\data\\chb-mit-scalp-eeg-database-1.0.0\\chb02\\chb02_16.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "Reading 0 ... 245503  =      0.000 ...   958.996 secs...\n",
      "(23, 245504)\n",
      "245504\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rayan\\AppData\\Local\\Temp\\ipykernel_16872\\2380645811.py:5: RuntimeWarning: Channel names are not unique, found duplicates for: {'T8-P8'}. Applying running numbers for duplicates.\n",
      "  raw = mne.io.read_raw_edf(file_path + \"/\" + patient_record, preload=True)\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[1;32mIn [12]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m data_2_16_all \u001b[38;5;241m=\u001b[39m \u001b[43mplot_patient_record\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile_path\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m./chb02/chb02_16.edf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mduration\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m3599\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchannel_names\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43midx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43midx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m24\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      2\u001b[0m data_2_crise_16 \u001b[38;5;241m=\u001b[39m plot_patient_record(file_path,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m./chb02/chb02_16.edf\u001b[39m\u001b[38;5;124m\"\u001b[39m,duration \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m3599\u001b[39m, channel_names \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mstr\u001b[39m(idx) \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m24\u001b[39m)],duration_min \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m80\u001b[39m, duration_max \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m262\u001b[39m)\n\u001b[0;32m      4\u001b[0m data_3_all \u001b[38;5;241m=\u001b[39m plot_patient_record(file_path,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m./chb03/chb03_02.edf\u001b[39m\u001b[38;5;124m\"\u001b[39m,duration \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m3599\u001b[39m, channel_names \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mstr\u001b[39m(idx) \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m24\u001b[39m)])\n",
      "Input \u001b[1;32mIn [10]\u001b[0m, in \u001b[0;36mplot_patient_record\u001b[1;34m(file_path, patient_record, duration, channel_names, duration_min, duration_max)\u001b[0m\n\u001b[0;32m     34\u001b[0m     raw\u001b[38;5;241m.\u001b[39mplot(duration\u001b[38;5;241m=\u001b[39mduration, n_channels\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m23\u001b[39m, scalings\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124meeg\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m75e-5\u001b[39m}, title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPatient Record\u001b[39m\u001b[38;5;124m\"\u001b[39m,  show\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, block\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m     37\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m :\n\u001b[1;32m---> 38\u001b[0m     \u001b[43mraw\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mduration\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mduration\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_channels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m23\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscalings\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43meeg\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m100e-5\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtitle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mPatient Record\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshow\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mblock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m     40\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m raw\u001b[38;5;241m.\u001b[39mget_data()\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\mne\\io\\base.py:1808\u001b[0m, in \u001b[0;36mBaseRaw.plot\u001b[1;34m(self, events, duration, start, n_channels, bgcolor, color, bad_color, event_color, scalings, remove_dc, order, show_options, title, show, block, highpass, lowpass, filtorder, clipping, show_first_samp, proj, group_by, butterfly, decim, noise_cov, event_id, show_scrollbars, show_scalebars, time_format, precompute, use_opengl, theme, overview_mode, splash, verbose)\u001b[0m\n\u001b[0;32m   1768\u001b[0m \u001b[38;5;129m@copy_function_doc_to_method_doc\u001b[39m(plot_raw)\n\u001b[0;32m   1769\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot\u001b[39m(\n\u001b[0;32m   1770\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1806\u001b[0m     verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   1807\u001b[0m ):\n\u001b[1;32m-> 1808\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mplot_raw\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1809\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1810\u001b[0m \u001b[43m        \u001b[49m\u001b[43mevents\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1811\u001b[0m \u001b[43m        \u001b[49m\u001b[43mduration\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1812\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstart\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1813\u001b[0m \u001b[43m        \u001b[49m\u001b[43mn_channels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1814\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbgcolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1815\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1816\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbad_color\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1817\u001b[0m \u001b[43m        \u001b[49m\u001b[43mevent_color\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1818\u001b[0m \u001b[43m        \u001b[49m\u001b[43mscalings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1819\u001b[0m \u001b[43m        \u001b[49m\u001b[43mremove_dc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1820\u001b[0m \u001b[43m        \u001b[49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1821\u001b[0m \u001b[43m        \u001b[49m\u001b[43mshow_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1822\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtitle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1823\u001b[0m \u001b[43m        \u001b[49m\u001b[43mshow\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1824\u001b[0m \u001b[43m        \u001b[49m\u001b[43mblock\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1825\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhighpass\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1826\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlowpass\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1827\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfiltorder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1828\u001b[0m \u001b[43m        \u001b[49m\u001b[43mclipping\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1829\u001b[0m \u001b[43m        \u001b[49m\u001b[43mshow_first_samp\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1830\u001b[0m \u001b[43m        \u001b[49m\u001b[43mproj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1831\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgroup_by\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1832\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbutterfly\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1833\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdecim\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1834\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnoise_cov\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnoise_cov\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1835\u001b[0m \u001b[43m        \u001b[49m\u001b[43mevent_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mevent_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1836\u001b[0m \u001b[43m        \u001b[49m\u001b[43mshow_scrollbars\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshow_scrollbars\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1837\u001b[0m \u001b[43m        \u001b[49m\u001b[43mshow_scalebars\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshow_scalebars\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1838\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtime_format\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtime_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1839\u001b[0m \u001b[43m        \u001b[49m\u001b[43mprecompute\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprecompute\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1840\u001b[0m \u001b[43m        \u001b[49m\u001b[43muse_opengl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_opengl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1841\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtheme\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtheme\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1842\u001b[0m \u001b[43m        \u001b[49m\u001b[43moverview_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moverview_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1843\u001b[0m \u001b[43m        \u001b[49m\u001b[43msplash\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msplash\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1844\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1845\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m<decorator-gen-157>:12\u001b[0m, in \u001b[0;36mplot_raw\u001b[1;34m(raw, events, duration, start, n_channels, bgcolor, color, bad_color, event_color, scalings, remove_dc, order, show_options, title, show, block, highpass, lowpass, filtorder, clipping, show_first_samp, proj, group_by, butterfly, decim, noise_cov, event_id, show_scrollbars, show_scalebars, time_format, precompute, use_opengl, theme, overview_mode, splash, verbose)\u001b[0m\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\mne\\viz\\raw.py:405\u001b[0m, in \u001b[0;36mplot_raw\u001b[1;34m(raw, events, duration, start, n_channels, bgcolor, color, bad_color, event_color, scalings, remove_dc, order, show_options, title, show, block, highpass, lowpass, filtorder, clipping, show_first_samp, proj, group_by, butterfly, decim, noise_cov, event_id, show_scrollbars, show_scalebars, time_format, precompute, use_opengl, theme, overview_mode, splash, verbose)\u001b[0m\n\u001b[0;32m    350\u001b[0m precompute \u001b[38;5;241m=\u001b[39m _handle_precompute(precompute)\n\u001b[0;32m    351\u001b[0m params \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(\n\u001b[0;32m    352\u001b[0m     inst\u001b[38;5;241m=\u001b[39mraw,\n\u001b[0;32m    353\u001b[0m     info\u001b[38;5;241m=\u001b[39minfo,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    402\u001b[0m     splash\u001b[38;5;241m=\u001b[39msplash,\n\u001b[0;32m    403\u001b[0m )\n\u001b[1;32m--> 405\u001b[0m fig \u001b[38;5;241m=\u001b[39m _get_browser(show\u001b[38;5;241m=\u001b[39mshow, block\u001b[38;5;241m=\u001b[39mblock, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mparams)\n\u001b[0;32m    407\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fig\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\mne\\viz\\_figure.py:699\u001b[0m, in \u001b[0;36m_get_browser\u001b[1;34m(show, block, **kwargs)\u001b[0m\n\u001b[0;32m    697\u001b[0m \u001b[38;5;66;03m# Initialize Browser\u001b[39;00m\n\u001b[0;32m    698\u001b[0m fig \u001b[38;5;241m=\u001b[39m backend\u001b[38;5;241m.\u001b[39m_init_browser(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m--> 699\u001b[0m \u001b[43m_show_browser\u001b[49m\u001b[43m(\u001b[49m\u001b[43mshow\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mblock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mblock\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfig\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    701\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fig\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\mne\\viz\\utils.py:190\u001b[0m, in \u001b[0;36m_show_browser\u001b[1;34m(show, block, fig, **kwargs)\u001b[0m\n\u001b[0;32m    188\u001b[0m     block \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m    189\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m backend \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmatplotlib\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m--> 190\u001b[0m     plt_show(show, block\u001b[38;5;241m=\u001b[39mblock, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    191\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    192\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mqtpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mQtCore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Qt\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\mne\\viz\\utils.py:165\u001b[0m, in \u001b[0;36mplt_show\u001b[1;34m(show, fig, **kwargs)\u001b[0m\n\u001b[0;32m    163\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m show \u001b[38;5;129;01mand\u001b[39;00m backend \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124magg\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m    164\u001b[0m     logger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mShowing plot for backend \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mrepr\u001b[39m(backend)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 165\u001b[0m     (fig \u001b[38;5;129;01mor\u001b[39;00m plt)\u001b[38;5;241m.\u001b[39mshow(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\pyplot.py:387\u001b[0m, in \u001b[0;36mshow\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    343\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    344\u001b[0m \u001b[38;5;124;03mDisplay all open figures.\u001b[39;00m\n\u001b[0;32m    345\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    384\u001b[0m \u001b[38;5;124;03mexplicitly there.\u001b[39;00m\n\u001b[0;32m    385\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    386\u001b[0m _warn_if_gui_out_of_main_thread()\n\u001b[1;32m--> 387\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _get_backend_mod()\u001b[38;5;241m.\u001b[39mshow(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib_inline\\backend_inline.py:41\u001b[0m, in \u001b[0;36mshow\u001b[1;34m(close, block)\u001b[0m\n\u001b[0;32m     39\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     40\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m figure_manager \u001b[38;5;129;01min\u001b[39;00m Gcf\u001b[38;5;241m.\u001b[39mget_all_fig_managers():\n\u001b[1;32m---> 41\u001b[0m         \u001b[43mdisplay\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m     42\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfigure_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     43\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_fetch_figure_metadata\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfigure_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     44\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     45\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     46\u001b[0m     show\u001b[38;5;241m.\u001b[39m_to_draw \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\IPython\\core\\display_functions.py:298\u001b[0m, in \u001b[0;36mdisplay\u001b[1;34m(include, exclude, metadata, transient, display_id, raw, clear, *objs, **kwargs)\u001b[0m\n\u001b[0;32m    296\u001b[0m     publish_display_data(data\u001b[38;5;241m=\u001b[39mobj, metadata\u001b[38;5;241m=\u001b[39mmetadata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    297\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 298\u001b[0m     format_dict, md_dict \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minclude\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minclude\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexclude\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexclude\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    299\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m format_dict:\n\u001b[0;32m    300\u001b[0m         \u001b[38;5;66;03m# nothing to display (e.g. _ipython_display_ took over)\u001b[39;00m\n\u001b[0;32m    301\u001b[0m         \u001b[38;5;28;01mcontinue\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\IPython\\core\\formatters.py:178\u001b[0m, in \u001b[0;36mDisplayFormatter.format\u001b[1;34m(self, obj, include, exclude)\u001b[0m\n\u001b[0;32m    176\u001b[0m md \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    177\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 178\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[43mformatter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    179\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[0;32m    180\u001b[0m     \u001b[38;5;66;03m# FIXME: log the exception\u001b[39;00m\n\u001b[0;32m    181\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\decorator.py:232\u001b[0m, in \u001b[0;36mdecorate.<locals>.fun\u001b[1;34m(*args, **kw)\u001b[0m\n\u001b[0;32m    230\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m kwsyntax:\n\u001b[0;32m    231\u001b[0m     args, kw \u001b[38;5;241m=\u001b[39m fix(args, kw, sig)\n\u001b[1;32m--> 232\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m caller(func, \u001b[38;5;241m*\u001b[39m(extras \u001b[38;5;241m+\u001b[39m args), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw)\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\IPython\\core\\formatters.py:222\u001b[0m, in \u001b[0;36mcatch_format_error\u001b[1;34m(method, self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    220\u001b[0m \u001b[38;5;124;03m\"\"\"show traceback on failed format call\"\"\"\u001b[39;00m\n\u001b[0;32m    221\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 222\u001b[0m     r \u001b[38;5;241m=\u001b[39m method(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    223\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m:\n\u001b[0;32m    224\u001b[0m     \u001b[38;5;66;03m# don't warn on NotImplementedErrors\u001b[39;00m\n\u001b[0;32m    225\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_return(\u001b[38;5;28;01mNone\u001b[39;00m, args[\u001b[38;5;241m0\u001b[39m])\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\IPython\\core\\formatters.py:339\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    337\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m    338\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 339\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mprinter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    340\u001b[0m \u001b[38;5;66;03m# Finally look for special method names\u001b[39;00m\n\u001b[0;32m    341\u001b[0m method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\IPython\\core\\pylabtools.py:151\u001b[0m, in \u001b[0;36mprint_figure\u001b[1;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[0;32m    148\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_bases\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FigureCanvasBase\n\u001b[0;32m    149\u001b[0m     FigureCanvasBase(fig)\n\u001b[1;32m--> 151\u001b[0m fig\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mprint_figure(bytes_io, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw)\n\u001b[0;32m    152\u001b[0m data \u001b[38;5;241m=\u001b[39m bytes_io\u001b[38;5;241m.\u001b[39mgetvalue()\n\u001b[0;32m    153\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fmt \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msvg\u001b[39m\u001b[38;5;124m'\u001b[39m:\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\backend_bases.py:2319\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[1;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[0;32m   2315\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m   2316\u001b[0m     \u001b[38;5;66;03m# _get_renderer may change the figure dpi (as vector formats\u001b[39;00m\n\u001b[0;32m   2317\u001b[0m     \u001b[38;5;66;03m# force the figure dpi to 72), so we need to set it again here.\u001b[39;00m\n\u001b[0;32m   2318\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m cbook\u001b[38;5;241m.\u001b[39m_setattr_cm(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, dpi\u001b[38;5;241m=\u001b[39mdpi):\n\u001b[1;32m-> 2319\u001b[0m         result \u001b[38;5;241m=\u001b[39m print_method(\n\u001b[0;32m   2320\u001b[0m             filename,\n\u001b[0;32m   2321\u001b[0m             facecolor\u001b[38;5;241m=\u001b[39mfacecolor,\n\u001b[0;32m   2322\u001b[0m             edgecolor\u001b[38;5;241m=\u001b[39medgecolor,\n\u001b[0;32m   2323\u001b[0m             orientation\u001b[38;5;241m=\u001b[39morientation,\n\u001b[0;32m   2324\u001b[0m             bbox_inches_restore\u001b[38;5;241m=\u001b[39m_bbox_inches_restore,\n\u001b[0;32m   2325\u001b[0m             \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   2326\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m   2327\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;129;01mand\u001b[39;00m restore_bbox:\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\backend_bases.py:1648\u001b[0m, in \u001b[0;36m_check_savefig_extra_args.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   1640\u001b[0m     _api\u001b[38;5;241m.\u001b[39mwarn_deprecated(\n\u001b[0;32m   1641\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m3.3\u001b[39m\u001b[38;5;124m'\u001b[39m, name\u001b[38;5;241m=\u001b[39mname, removal\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m3.6\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m   1642\u001b[0m         message\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%(name)s\u001b[39;00m\u001b[38;5;124m() got unexpected keyword argument \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m   1643\u001b[0m                 \u001b[38;5;241m+\u001b[39m arg \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m which is no longer supported as of \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m   1644\u001b[0m                 \u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%(since)s\u001b[39;00m\u001b[38;5;124m and will become an error \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m   1645\u001b[0m                 \u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%(removal)s\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m   1646\u001b[0m     kwargs\u001b[38;5;241m.\u001b[39mpop(arg)\n\u001b[1;32m-> 1648\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\_api\\deprecation.py:415\u001b[0m, in \u001b[0;36mdelete_parameter.<locals>.wrapper\u001b[1;34m(*inner_args, **inner_kwargs)\u001b[0m\n\u001b[0;32m    405\u001b[0m     deprecation_addendum \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m    406\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIf any parameter follows \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m, they should be passed as \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    407\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkeyword, not positionally.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    408\u001b[0m     warn_deprecated(\n\u001b[0;32m    409\u001b[0m         since,\n\u001b[0;32m    410\u001b[0m         name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mrepr\u001b[39m(name),\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    413\u001b[0m                  \u001b[38;5;28;01melse\u001b[39;00m deprecation_addendum,\n\u001b[0;32m    414\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m--> 415\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39minner_args, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39minner_kwargs)\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:540\u001b[0m, in \u001b[0;36mFigureCanvasAgg.print_png\u001b[1;34m(self, filename_or_obj, metadata, pil_kwargs, *args)\u001b[0m\n\u001b[0;32m    490\u001b[0m \u001b[38;5;129m@_check_savefig_extra_args\u001b[39m\n\u001b[0;32m    491\u001b[0m \u001b[38;5;129m@_api\u001b[39m\u001b[38;5;241m.\u001b[39mdelete_parameter(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m3.5\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124margs\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    492\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mprint_png\u001b[39m(\u001b[38;5;28mself\u001b[39m, filename_or_obj, \u001b[38;5;241m*\u001b[39margs,\n\u001b[0;32m    493\u001b[0m               metadata\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, pil_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m    494\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    495\u001b[0m \u001b[38;5;124;03m    Write the figure to a PNG file.\u001b[39;00m\n\u001b[0;32m    496\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    538\u001b[0m \u001b[38;5;124;03m        *metadata*, including the default 'Software' key.\u001b[39;00m\n\u001b[0;32m    539\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 540\u001b[0m     \u001b[43mFigureCanvasAgg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m    541\u001b[0m     mpl\u001b[38;5;241m.\u001b[39mimage\u001b[38;5;241m.\u001b[39mimsave(\n\u001b[0;32m    542\u001b[0m         filename_or_obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuffer_rgba(), \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpng\u001b[39m\u001b[38;5;124m\"\u001b[39m, origin\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mupper\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    543\u001b[0m         dpi\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39mdpi, metadata\u001b[38;5;241m=\u001b[39mmetadata, pil_kwargs\u001b[38;5;241m=\u001b[39mpil_kwargs)\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:436\u001b[0m, in \u001b[0;36mFigureCanvasAgg.draw\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    432\u001b[0m \u001b[38;5;66;03m# Acquire a lock on the shared font cache.\u001b[39;00m\n\u001b[0;32m    433\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m RendererAgg\u001b[38;5;241m.\u001b[39mlock, \\\n\u001b[0;32m    434\u001b[0m      (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtoolbar\u001b[38;5;241m.\u001b[39m_wait_cursor_for_draw_cm() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtoolbar\n\u001b[0;32m    435\u001b[0m       \u001b[38;5;28;01melse\u001b[39;00m nullcontext()):\n\u001b[1;32m--> 436\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    437\u001b[0m     \u001b[38;5;66;03m# A GUI class may be need to update a window using this draw, so\u001b[39;00m\n\u001b[0;32m    438\u001b[0m     \u001b[38;5;66;03m# don't forget to call the superclass.\u001b[39;00m\n\u001b[0;32m    439\u001b[0m     \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mdraw()\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\artist.py:74\u001b[0m, in \u001b[0;36m_finalize_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[0;32m     72\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[0;32m     73\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdraw_wrapper\u001b[39m(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m---> 74\u001b[0m     result \u001b[38;5;241m=\u001b[39m draw(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m     75\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m renderer\u001b[38;5;241m.\u001b[39m_rasterizing:\n\u001b[0;32m     76\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\artist.py:51\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer)\u001b[0m\n\u001b[0;32m     48\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m     49\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[1;32m---> 51\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     52\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     53\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\figure.py:2845\u001b[0m, in \u001b[0;36mFigure.draw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   2842\u001b[0m         \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[0;32m   2844\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[1;32m-> 2845\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   2846\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2848\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubfigs:\n\u001b[0;32m   2849\u001b[0m     sfig\u001b[38;5;241m.\u001b[39mdraw(renderer)\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\image.py:132\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[0;32m    131\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[1;32m--> 132\u001b[0m         \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    133\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    134\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[0;32m    135\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\artist.py:51\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer)\u001b[0m\n\u001b[0;32m     48\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m     49\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[1;32m---> 51\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     52\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     53\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\axes\\_base.py:3091\u001b[0m, in \u001b[0;36m_AxesBase.draw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m   3088\u001b[0m         a\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[0;32m   3089\u001b[0m     renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n\u001b[1;32m-> 3091\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   3092\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3094\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maxes\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m   3095\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\image.py:132\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[1;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[0;32m    130\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[0;32m    131\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[1;32m--> 132\u001b[0m         \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    133\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    134\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[0;32m    135\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\artist.py:51\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[1;34m(artist, renderer)\u001b[0m\n\u001b[0;32m     48\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m     49\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[1;32m---> 51\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     52\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     53\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[1;32mc:\\Users\\rayan\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\lines.py:776\u001b[0m, in \u001b[0;36mLine2D.draw\u001b[1;34m(self, renderer)\u001b[0m\n\u001b[0;32m    773\u001b[0m             gc\u001b[38;5;241m.\u001b[39mset_sketch_params(\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_sketch_params())\n\u001b[0;32m    775\u001b[0m         gc\u001b[38;5;241m.\u001b[39mset_dashes(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dashOffset, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dashSeq)\n\u001b[1;32m--> 776\u001b[0m         \u001b[43mrenderer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw_path\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maffine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrozen\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    777\u001b[0m         gc\u001b[38;5;241m.\u001b[39mrestore()\n\u001b[0;32m    779\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_marker \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_markersize \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "data_2_16_all = plot_patient_record(file_path,\"./chb02/chb02_16.edf\",duration = 3599, channel_names = [str(idx) for idx in range(1, 24)])\n",
    "data_2_crise_16 = plot_patient_record(file_path,\"./chb02/chb02_16.edf\",duration = 3599, channel_names = [str(idx) for idx in range(1, 24)],duration_min = 80, duration_max = 262)\n",
    "\n",
    "data_3_all = plot_patient_record(file_path,\"./chb03/chb03_02.edf\",duration = 3599, channel_names = [str(idx) for idx in range(1, 24)])\n",
    "data_3_crise_2 = plot_patient_record(file_path,\"./chb03/chb03_02.edf\",duration = 3599, channel_names = [str(idx) for idx in range(1, 24)],duration_min = 681, duration_max = 846)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings \n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "mne.set_log_level(verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings \n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "mne.set_log_level(verbose=False)\n",
    "\n",
    "data , labels = load_data_for_patient(file_path,\"chb01\")\n",
    "print(\"Data length : \", len(data))\n",
    "print(\"Labels length : \", len(labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.array(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les dimensions sont bonnes étant donné que nous avons pris 10 secondes, nous aurons 10xpas de temps = 10x256 = 2560 valeurs par channel pour un segment. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "patients_1 = [\"chb01\",\"chb02\",\"chb03\"]\n",
    "patients_2 = [\"chb06\",\"chb05\"]\n",
    "patients_3 = [\"chb07\"]\n",
    "patients_4 = [\"chb23\",\"chb24\"]\n",
    "\n",
    "data_1 = []\n",
    "data_2 = []\n",
    "data_3 = []\n",
    "data_4 = []\n",
    "\n",
    "\n",
    "labels_1 = []\n",
    "labels_2 = []\n",
    "labels_3 = []\n",
    "labels_4 = []\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "import pickle\n",
    "\n",
    "# tous les patients non pris en compte sont ceux qui utilisent des channels différents \n",
    "\n",
    "\n",
    "\n",
    "patients = [\"chb01\", \"chb02\", \"chb03\", \"chb05\", \"chb07\", \"chb08\", \"chb09\", \"chb10\", \"chb11\", \"chb12\", \"chb13\", \"chb14\", \"chb15\", \"chb16\", \"chb17\", \"chb18\", \"chb19\", \"chb20\", \"chb21\", \"chb22\", \"chb23\", \"chb24\"]\n",
    "\n",
    "patients_1 = [\"chb01\",\"chb02\",\"chb03\"]\n",
    "patients_2 = [\"chb06\",\"chb05\"]\n",
    "patients_3 = [\"chb07\"]\n",
    "patients_4 = [\"chb23\",\"chb24\",\"chb08\"]\n",
    "\n",
    "\n",
    "\n",
    "def load_data(file_path, patients):\n",
    "    \n",
    "    train_data = []\n",
    "    train_labels = []\n",
    "    test_data = []\n",
    "    test_labels = []\n",
    "    \n",
    "    for patient in patients :\n",
    "        data = []\n",
    "        labels = []\n",
    "        for i in range(2) :\n",
    "            try :\n",
    "                data_, labels_ = load_data_for_patient(file_path, patient)\n",
    "                data.extend(data_)\n",
    "                labels.extend(labels_)\n",
    "            except Exception as e:\n",
    "                print(\"Error for patient\", patient)\n",
    "                print(e)\n",
    "        \n",
    "        data = np.array(data)\n",
    "        labels = np.array(labels)\n",
    "        print(\"Data shape : \", data.shape)\n",
    "        train_data_3, test_data_3, train_labels_3, test_labels_3 = train_test_split(data, labels, test_size=0.2, random_state=42)\n",
    "        \n",
    "        train_data.append(train_data_3)\n",
    "        train_labels.append(train_labels_3)\n",
    "        test_data.append(test_data_3)\n",
    "        test_labels.append(test_labels_3)\n",
    "        \n",
    "    return train_data, train_labels, test_data, test_labels\n",
    "\n",
    "\n",
    "for i in range(len(patients_1)) :\n",
    "            try :\n",
    "                print(\"Patient\", patients_1[i])\n",
    "                data_, labels_ = load_data_for_patient(file_path, patients_1[i])\n",
    "                data_1.extend(data_)\n",
    "                labels_1.extend(labels_)\n",
    "            except Exception as e:\n",
    "                print(\"Error for patient\", patients_1[i])\n",
    "                print(e)\n",
    "                \n",
    "all_data = { \"data_1\" : data_1, \"labels_1\" : labels_1}\n",
    "                \n",
    "\n",
    "for key, value in all_data.items():\n",
    "    file_path_ = \"./saved_data/\"+key+\".pkl\"\n",
    "    with open(file_path_, \"wb\") as f:\n",
    "        pickle.dump(value, f)\n",
    "    print(\"Data saved successfully for\", key)\n",
    "    \n",
    "            \n",
    "len(data_1), len(labels_1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(patients_2)) :\n",
    "            try :\n",
    "                print(\"Patient\", patients_2[i])\n",
    "                data_, labels_ = load_data_for_patient(file_path, patients_2[i])\n",
    "                data_2.extend(data_)\n",
    "                labels_2.extend(labels_)\n",
    "            except Exception as e:\n",
    "                print(\"Error for patient\", patients_2[i])\n",
    "                print(e)\n",
    "                \n",
    "all_data = { \"data_2\" : data_2, \"labels_2\" : labels_2}\n",
    "\n",
    "# for key, value in all_data.items():\n",
    "#     file_path_ = \"./saved_data/\"+key+\".pkl\"\n",
    "#     with open(file_path_, \"wb\") as f:\n",
    "#         pickle.dump(value, f)\n",
    "#     print(\"Data saved successfully for\", key)\n",
    "    \n",
    "\n",
    "len(data_2), len(labels_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "patients_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(patients_3)) :\n",
    "\n",
    "            try :\n",
    "                print(\"Patient\", patients_3[i])\n",
    "                data_, labels_ = load_data_for_patient(file_path, patients_3[i])\n",
    "                data_3.extend(data_)\n",
    "                labels_3.extend(labels_)\n",
    "            except Exception as e:\n",
    "                print(\"Error for patient\", patients_3[i])\n",
    "                print(e)\n",
    "                \n",
    "                \n",
    "all_data = { \"data_3\" : data_3, \"labels_3\" : labels_3}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, value in all_data.items():\n",
    "    file_path_ = \"./saved_data/\"+key+\".pkl\"\n",
    "    with open(file_path_, \"wb\") as f:\n",
    "        pickle.dump(value, f)\n",
    "    print(\"Data saved successfully for\", key)\n",
    "    \n",
    "len(data_3), len(labels_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Patient chb23\n",
      "Seizure presence init {'chb23_06.edf': [(3962, 4075)], 'chb23_08.edf': [(325, 345), (5104, 5151)], 'chb23_09.edf': [(2589, 2660), (6885, 6947), (8505, 8532), (9580, 9664)]}\n",
      "Patient chb24\n",
      "Seizure presence init {'chb24_01.edf': [(480, 505), (2451, 2476)], 'chb24_03.edf': [(231, 260), (2883, 2908)], 'chb24_04.edf': [(1088, 1120), (1411, 1438), (1745, 1764)], 'chb24_06.edf': [(1229, 1253)], 'chb24_07.edf': [(38, 60)], 'chb24_09.edf': [(1745, 1764)], 'chb24_11.edf': [(3527, 3597)], 'chb24_13.edf': [(3288, 3304)], 'chb24_14.edf': [(1939, 1966)], 'chb24_15.edf': [(3552, 3569)], 'chb24_17.edf': [(3515, 3581)], 'chb24_21.edf': [(2804, 2872)]}\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(patients_4)) :\n",
    "            try :\n",
    "                print(\"Patient\", patients_4[i])\n",
    "                data_, labels_ = load_data_for_patient(file_path, patients_4[i])\n",
    "                data_4.extend(data_)\n",
    "                labels_4.extend(labels_)\n",
    "            except Exception as e:\n",
    "                print(\"Error for patient\", patients_4[i])\n",
    "                print(e)\n",
    "                \n",
    "all_data = { \"data_4\" : data_4, \"labels_4\" : labels_4}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data saved successfully for data_4\n",
      "Data saved successfully for labels_4\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(13862, 13862)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for key, value in all_data.items():\n",
    "    file_path_ = \"./saved_data/\"+key+\".pkl\"\n",
    "    with open(file_path_, \"wb\") as f:\n",
    "        pickle.dump(value, f)\n",
    "    print(\"Data saved successfully for\", key)\n",
    "\n",
    "len(data_4), len(labels_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data loaded successfully!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(40861, 40861)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "#  Load the data from the file\n",
    "with open(\"./saved_data/data_1.pkl\", \"rb\") as f:\n",
    "    data_1 = pickle.load(f)\n",
    "with open(\"./saved_data/labels_1.pkl\", \"rb\") as f:\n",
    "    labels_1 = pickle.load(f)\n",
    "\n",
    "# Now, 'loaded_data' contains the data you previously saved\n",
    "print(\"Data loaded successfully!\")\n",
    "len(data_1),len(labels_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "ename": "MemoryError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
      "Input \u001b[1;32mIn [38]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m./saved_data/data_2.pkl\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrb\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m----> 2\u001b[0m     data_2 \u001b[38;5;241m=\u001b[39m \u001b[43mpickle\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m./saved_data/labels_2.pkl\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrb\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[0;32m      4\u001b[0m     labels_2 \u001b[38;5;241m=\u001b[39m pickle\u001b[38;5;241m.\u001b[39mload(f)\n",
      "\u001b[1;31mMemoryError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "with open(\"./saved_data/data_2.pkl\", \"rb\") as f:\n",
    "    data_2 = pickle.load(f)\n",
    "with open(\"./saved_data/labels_2.pkl\", \"rb\") as f:\n",
    "    labels_2 = pickle.load(f)\n",
    "\n",
    "# Now, 'loaded_data' contains the data you previously saved\n",
    "print(\"Data loaded successfully!\")\n",
    "len(data_2),len(labels_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data loaded successfully!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(24122, 24122)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(\"./saved_data/data_3.pkl\", \"rb\") as f:\n",
    "    data_3 = pickle.load(f)\n",
    "with open(\"./saved_data/labels_3.pkl\", \"rb\") as f:\n",
    "    labels_3 = pickle.load(f)\n",
    "\n",
    "# Now, 'loaded_data' contains the data you previously saved\n",
    "print(\"Data loaded successfully!\")\n",
    "len(data_3),len(labels_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "ename": "MemoryError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
      "Input \u001b[1;32mIn [40]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m./saved_data/data_4.pkl\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrb\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m----> 2\u001b[0m     data_4 \u001b[38;5;241m=\u001b[39m \u001b[43mpickle\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m./saved_data/labels_4.pkl\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrb\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[0;32m      4\u001b[0m     labels_4 \u001b[38;5;241m=\u001b[39m pickle\u001b[38;5;241m.\u001b[39mload(f)\n",
      "\u001b[1;31mMemoryError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "with open(\"./saved_data/data_4.pkl\", \"rb\") as f:\n",
    "    data_4 = pickle.load(f)\n",
    "with open(\"./saved_data/labels_4.pkl\", \"rb\") as f:\n",
    "    labels_4 = pickle.load(f)\n",
    "    \n",
    "# Now, 'loaded_data' contains the data you previously saved\n",
    "print(\"Data loaded successfully!\")\n",
    "len(data_4),len(labels_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data_names = [\"data_1\", \"data_2\", \"data_3\", \"data_4\",\"labels_1\", \"labels_2\", \"labels_3\", \"labels_4\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40861, 0, 24122, 0, 40861, 0, 24122, 0)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data_1),len(data_2), len(data_3), len(data_4), len(labels_1), len(labels_2), len(labels_3), len(labels_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = [data_1, data_3 ]\n",
    "all_labels = [labels_1,  labels_3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pickle\n",
    "\n",
    "# file_path_ = \"./saved_data./data.pkl\"\n",
    "# file_path_label = \"saved_labels.pkl\"\n",
    "\n",
    "# # Load the data from the file\n",
    "# with open(file_path_, \"rb\") as f:\n",
    "#     data = pickle.load(f)\n",
    "# with open(file_path_label, \"rb\") as f:\n",
    "#     labels = pickle.load(f)\n",
    "\n",
    "# # Now, 'loaded_data' contains the data you previously saved\n",
    "# print(\"Data loaded successfully!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_1.__len__()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# si la data n'est pas trop grande on peut la stocker dans un array / Pour un 1 patient par exemple\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "data = np.array(data,dtype=np.float32)\n",
    "labels = np.array(labels)\n",
    "\n",
    "train_data, test_data, train_labels, test_labels = train_test_split(data, labels, test_size=0.2,shuffle = True, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(train_data.shape)\n",
    "print(train_labels.shape)\n",
    "print(test_data.shape)\n",
    "print(test_labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy_fn(y_pred, y_true):\n",
    "    y_pred = torch.argmax(y_pred, dim=1)\n",
    "    y_true = torch.argmax(y_true, dim=1)\n",
    "    return (y_pred == y_true).float().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "def train(loss_fn: nn.Module,\n",
    "          optimizer: torch.optim.Optimizer,\n",
    "          model: nn.Module,\n",
    "          train_loader: DataLoader,\n",
    "          valid_loader: DataLoader,\n",
    "          epochs: int,\n",
    "          early_stop_patience: int,\n",
    "          device=\"cpu\"):\n",
    "    \n",
    "    history = {\n",
    "        \"train_loss\": [],\n",
    "        \"train_accuracy\": [],\n",
    "        \"valid_loss\": [],\n",
    "        \"valid_accuracy\": [],\n",
    "        \"epochs\": [],\n",
    "        \"best_train_accuracy\": 0,\n",
    "        \"best_valid_accuracy\": 0,\n",
    "    }\n",
    "    \n",
    "    best_valid_loss = float('inf')\n",
    "    consecutive_no_improvement = 0\n",
    "    \n",
    "    for epoch in tqdm(range(epochs)):\n",
    "        \n",
    "        model.train()\n",
    "        train_loss = 0\n",
    "        train_accuracy = 0\n",
    "        \n",
    "        for batch in train_loader:\n",
    "            \n",
    "\n",
    "            X, y = batch\n",
    "            X, y = X.to(device), y.to(device)\n",
    "            # print(y.shape)\n",
    "            # print(X.shape)\n",
    "            # print(y.shape)\n",
    "            # print(X.shape)\n",
    "            y_hat = model(X)\n",
    "            # print(y_hat.shape)\n",
    "            \n",
    "            # one hot vector \n",
    "            y = torch.nn.functional.one_hot(y, num_classes=2).float()\n",
    "            \n",
    "            \n",
    "            \n",
    "            loss = loss_fn(y_hat, y)\n",
    "            \n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            \n",
    "            optimizer.zero_grad()\n",
    "\n",
    "            \n",
    "            train_loss += loss.item()\n",
    "            train_accuracy += accuracy_fn(y_hat, y)\n",
    "            \n",
    "            \n",
    "        train_loss /= len(train_loader)\n",
    "        train_accuracy /= len(train_loader)\n",
    "        \n",
    "        history[\"train_loss\"].append(train_loss)\n",
    "        history[\"train_accuracy\"].append(train_accuracy)\n",
    "        history[\"epochs\"].append(epoch)\n",
    "        \n",
    "        print(f'Epoch {epoch + 1}: Train Loss {train_loss} and Accuracy {train_accuracy}')\n",
    "        \n",
    "        with torch.no_grad():\n",
    "            \n",
    "            model.eval()\n",
    "            valid_loss = 0\n",
    "            valid_accuracy = 0\n",
    "            \n",
    "            for batch in valid_loader:\n",
    "                X, y = batch\n",
    "                X, y = X.to(device), y.to(device)\n",
    "                y_hat = model(X)\n",
    "                \n",
    "                y = torch.nn.functional.one_hot(y, num_classes=2).float()\n",
    "                \n",
    "                loss = loss_fn(y_hat, y)\n",
    "                valid_loss += loss.item()\n",
    "                valid_accuracy += accuracy_fn(y_hat, y)\n",
    "                \n",
    "            valid_loss /= len(valid_loader)\n",
    "            valid_accuracy /= len(valid_loader)\n",
    "            \n",
    "            history[\"valid_loss\"].append(valid_loss)\n",
    "            history[\"valid_accuracy\"].append(valid_accuracy)\n",
    "            \n",
    "        print(f'Epoch {epoch + 1}: Valid Loss {valid_loss} and Valid Accuracy {valid_accuracy}')\n",
    "        \n",
    "        # Check for early stopping\n",
    "        if valid_loss < best_valid_loss:\n",
    "            best_valid_loss = valid_loss\n",
    "            consecutive_no_improvement = 0\n",
    "        else:\n",
    "            consecutive_no_improvement += 1\n",
    "            if consecutive_no_improvement >= early_stop_patience:\n",
    "                print(f'Early stopping at epoch {epoch + 1}')\n",
    "                break\n",
    "    \n",
    "    best_main = max(history[\"train_accuracy\"])\n",
    "    best_valid = max(history[\"valid_accuracy\"])\n",
    "    history[\"best_train_accuracy\"] = best_main\n",
    "    history[\"best_valid_accuracy\"] = best_valid\n",
    "    history['train_accuracy'] = [i.item() for i in history['train_accuracy']]\n",
    "    history['valid_accuracy'] = [i.item() for i in history['valid_accuracy']]\n",
    "    print(f'Best training accuracy: {best_main}')\n",
    "    print(f'Best validation accuracy: {best_valid}')\n",
    "        \n",
    "    print('Training finished')\n",
    "    \n",
    "    return history\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "def data_into_arrays(data,labels,max_size=10000):\n",
    "    data_array = np.array(data[:max_size]) # capacité max : 8.77 GiB\n",
    "    labels_array = np.array(labels[:max_size]) # capacité max : 8.77 GiB\n",
    "    new_data = data[max_size:]\n",
    "    new_labels = labels[max_size:]\n",
    "    return data_array, labels_array,new_data,new_labels\n",
    "\n",
    "def array_to_loader(data,labels,batch_size):\n",
    "    data = torch.tensor(data, dtype=torch.float32)\n",
    "    labels = torch.tensor(labels, dtype=torch.long)\n",
    "    data = data.unsqueeze(1)\n",
    "    data = data.to(device)\n",
    "    labels = labels.to(device)\n",
    "    dataset = torch.utils.data.TensorDataset(data, labels)\n",
    "    loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True)\n",
    "    return loader\n",
    "\n",
    "def split_train(model,data,labels,max_size,loss_fn,optimizer,epochs=10):\n",
    "\n",
    "    all_histories = []\n",
    "    \n",
    "    nbr_arrays = np.ceil(len(data)/max_size)\n",
    "    \n",
    "    \n",
    "    for i in range(int(nbr_arrays)):\n",
    "        \n",
    "        data, labels, new_data, new_labels = data_into_arrays(data,labels,5000)\n",
    "        \n",
    "        train_data, test_data, train_labels, test_labels = train_test_split(data, labels, test_size=0.2,shuffle = True, random_state=42)\n",
    "        \n",
    "        train_loader = array_to_loader(train_data,train_labels,32)\n",
    "        \n",
    "        test_loader = array_to_loader(test_data,test_labels,32)\n",
    "        \n",
    "        model = model.to(device)\n",
    "        \n",
    "        history = train(loss_fn, optimizer, model, train_loader, test_loader, epochs, 5, device)\n",
    "        \n",
    "        all_histories.append(history)\n",
    "        \n",
    "        if len(new_data) == 0:\n",
    "            \n",
    "            break\n",
    "        \n",
    "                \n",
    "    return all_histories\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def plot(history,metric = \"loss\"):\n",
    "    plt.plot(history['epochs'], history['train_'+ metric], label='train_' + metric)\n",
    "    plt.plot(history['epochs'], history['valid_' + metric], label='valid_' + metric)\n",
    "    # # cadrillage\n",
    "    plt.xlabel(\"Epochs\")\n",
    "    plt.ylabel(metric)\n",
    "    plt.grid()\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "def plot_confusion_matrix(confusion_matrix, labels):\n",
    "\n",
    "\n",
    "    plt.figure(figsize=(10, 8))\n",
    "    sns.heatmap(confusion_matrix, xticklabels=labels, yticklabels=labels, annot=True, fmt='g', cmap='Blues')\n",
    "    plt.xlabel('Prediction')\n",
    "    plt.ylabel('True')\n",
    "    plt.show()\n",
    "\n",
    "def eval( loss_fn: nn.Module,\n",
    "          model: nn.Module,\n",
    "          test_loader: DataLoader,\n",
    "          device=\"cpu\"):\n",
    "    \n",
    "    history = {\n",
    "        \"test_loss\": [],\n",
    "        \"test_accuracy\": [],\n",
    "    }\n",
    "    \n",
    "    confusion_matrix = torch.zeros(2, 2)\n",
    "    \n",
    "    model.eval()\n",
    "    test_loss = 0\n",
    "    test_accuracy = 0\n",
    "    y_pred = []\n",
    "    y= []\n",
    "    \n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for batch in test_loader:\n",
    "            X, y_ = batch\n",
    "            X, y_ = X.to(device), y_.to(device)\n",
    "            y_hat = model(X)\n",
    "            \n",
    "            y_2 = torch.nn.functional.one_hot(y_, num_classes=2).float()\n",
    "\n",
    "            loss = loss_fn(y_hat, y_2)\n",
    "            test_loss += loss.item()\n",
    "            test_accuracy += accuracy_fn(y_hat, y_2).item()\n",
    "            \n",
    "            y.extend(y_)\n",
    "            y_pred.extend(torch.argmax(y_hat,dim=1))\n",
    "\n",
    "    test_loss /= len(test_loader)\n",
    "    test_accuracy /= len(test_loader)\n",
    "    \n",
    "    y_pred = [i.item() for i in y_pred]\n",
    "    y = [i.item() for i in y]\n",
    "\n",
    "\n",
    "    for i in range(len(y)):\n",
    "        confusion_matrix[y[i]][y_pred[i]] += 1\n",
    "    \n",
    "        \n",
    "    history[\"test_loss\"].append(test_loss)\n",
    "    history[\"test_accuracy\"].append(test_accuracy)\n",
    "            \n",
    "    print(f'Test Loss {test_loss} and Test Accuracy {test_accuracy}')\n",
    "    \n",
    "    return history, confusion_matrix,y_pred,y\n",
    "\n",
    "def metrics(confusion_matrix):\n",
    "    TP = confusion_matrix[0][0]\n",
    "    FP = confusion_matrix[0][1]\n",
    "    FN = confusion_matrix[1][0]\n",
    "    TN = confusion_matrix[1][1]\n",
    "    \n",
    "    precision = TP / (TP + FP)\n",
    "    recall = TP / (TP + FN)\n",
    "    f1 = 2 * (precision * recall) / (precision + recall)\n",
    "    \n",
    "    print(f'Precision: {precision}')\n",
    "    print(f'Recall: {recall}')\n",
    "    print(f'F1: {f1}')\n",
    "    \n",
    "# plot la matrice de confusion mais avec le nombre de 1 qu'il fala\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.random\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "from torch import nn\n",
    "from torch import Tensor\n",
    "from PIL import Image\n",
    "from torchvision.transforms import Compose, Resize, ToTensor\n",
    "from einops import rearrange, reduce, repeat\n",
    "from einops.layers.torch import Rearrange, Reduce"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Traitement des 8000 enregistrements : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gc \n",
    "\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Partie pour prendre seulement les 8000 premiers segments de signaux : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data_1\n",
    "labels = labels_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_size = 8000\n",
    "\n",
    "data_array,labels_array,new_data,new_labels = data_into_arrays(data,labels,max_size)\n",
    "\n",
    "train_data, test_data, train_labels, test_labels = train_test_split(data_array, labels_array, test_size=0.2)\n",
    "        \n",
    "train_data, valid_data, train_labels, valid_labels = train_test_split(train_data, train_labels, test_size=0.3)\n",
    "            \n",
    "train_loader = array_to_loader(train_data,train_labels,64)\n",
    "valid_loader = array_to_loader(valid_data,valid_labels,64)\n",
    "test_loader = array_to_loader(test_data,test_labels,64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape :  (5120, 23, 2560)\n",
      "Train labels shape :  (5120,)\n",
      "Valid data shape :  (1280, 23, 2560)\n",
      "Valid labels shape :  (1280,)\n",
      "Test data shape :  (1600, 23, 2560)\n",
      "Test labels shape :  (1600,)\n",
      "Proportion of 1 in train labels :  0.0048828125\n",
      "Proportion of 1 in valid labels :  0.00625\n",
      "Proportion of 1 in test labels :  0.005625\n",
      "Proportion of 1 in train and test labels :  0.00525\n"
     ]
    }
   ],
   "source": [
    "# info about the data\n",
    "print(\"Train data shape : \", train_data.shape)\n",
    "print(\"Train labels shape : \", train_labels.shape)\n",
    "print(\"Valid data shape : \", valid_data.shape)\n",
    "print(\"Valid labels shape : \", valid_labels.shape)\n",
    "print(\"Test data shape : \", test_data.shape)\n",
    "print(\"Test labels shape : \", test_labels.shape)\n",
    "\n",
    "# proportion of labels in each\n",
    "print(\"Proportion of 1 in train labels : \", np.sum(train_labels)/len(train_labels))\n",
    "print(\"Proportion of 1 in valid labels : \", np.sum(valid_labels)/len(valid_labels))\n",
    "print(\"Proportion of 1 in test labels : \", np.sum(test_labels)/len(test_labels))\n",
    "\n",
    "print(\"Proportion of 1 in train and test labels : \", (np.sum(train_labels) + np.sum(test_labels) + np.sum(valid_labels)) / ((len(train_labels) + len(test_labels) + len(valid_labels))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fin de cette partie."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"Pour {len(data)} données\")\n",
    "\n",
    "# info about the data\n",
    "print(\"Train data shape : \", train_data.shape)\n",
    "print(\"Train labels shape : \", train_labels.shape)\n",
    "# print(\"Valid data shape : \", valid_data.shape)\n",
    "# print(\"Valid labels shape : \", valid_labels.shape)\n",
    "print(\"Test data shape : \", test_data.shape)\n",
    "print(\"Test labels shape : \", test_labels.shape)\n",
    "\n",
    "# proportion of labels in each\n",
    "print(\"Proportion of 1 in train labels : \", np.sum(train_labels)/len(train_labels))\n",
    "# print(\"Proportion of 1 in valid labels : \", np.sum(valid_labels)/len(valid_labels))\n",
    "print(\"Proportion of 1 in test labels : \", np.sum(test_labels)/len(test_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([64, 40, 165])\n"
     ]
    }
   ],
   "source": [
    "# regularisarisation \n",
    "# dropout\n",
    "\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from einops.layers.torch import Rearrange\n",
    "\n",
    "class ConvolutionModule(nn.Module):\n",
    "    def __init__(self, emb_size=40, nb_channels=23):\n",
    "        super().__init__()\n",
    "\n",
    "        self.shallownet = nn.Sequential(\n",
    "            nn.Conv2d(1, 40, (1, 25), (1, 1)),\n",
    "            nn.Conv2d(40, 40, (nb_channels, 1), (1, 1)),\n",
    "            nn.BatchNorm2d(40),\n",
    "            nn.ELU(),\n",
    "            nn.AvgPool2d((1, 75), (1, 15)),\n",
    "            nn.Dropout(0.5),\n",
    "        )\n",
    "\n",
    "        self.projection = nn.Sequential(\n",
    "            nn.Conv2d(40, emb_size, (1, 1), stride=(1, 1)),\n",
    "            Rearrange('b e (h) (w) -> b e (h w)'),  # Rearrange operation to flatten the spatial dimensions\n",
    "        )\n",
    "\n",
    "    def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
    "        x = self.shallownet(x)\n",
    "        x = self.projection(x)\n",
    "        return x\n",
    "\n",
    "# Example usage\n",
    "model = ConvolutionModule()\n",
    "input_tensor = torch.randn(64,1, 23, 2560)  # Example input tensor\n",
    "output_tensor = model(input_tensor)\n",
    "print(output_tensor.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Un segment est composé de 40 tokens et chaque token possède 165 valeurs. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tranformer plus stable mais a besoin de plus de données."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "class FocalLoss(nn.Module):\n",
    "    def __init__(self, alpha=1, gamma=2, reduction='mean'):\n",
    "        super(FocalLoss, self).__init__()\n",
    "        self.alpha = alpha\n",
    "        self.gamma = gamma\n",
    "        self.reduction = reduction\n",
    "\n",
    "    def forward(self, inputs, targets):\n",
    "\n",
    "        BCE_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction='none')\n",
    "        pt = torch.exp(-BCE_loss)\n",
    "        focal_loss = self.alpha * (1-pt)**self.gamma * BCE_loss\n",
    "\n",
    "        if self.reduction == 'mean':\n",
    "            return torch.mean(focal_loss)\n",
    "        elif self.reduction == 'sum':\n",
    "            return torch.sum(focal_loss)\n",
    "        else:\n",
    "            return focal_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.cuda.empty_cache()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "class LSTM_Conv(nn.Module):\n",
    "    \n",
    "    def __init__(self, input_size=165, hidden_size=128, num_layers=2, num_classes=2, dropout=0.5, weight_decay=0.001):\n",
    "        super(LSTM_Conv, self).__init__()\n",
    "        self.conv = ConvolutionModule()\n",
    "        self.num_layers = num_layers\n",
    "        self.hidden_size = hidden_size\n",
    "        \n",
    "        # LSTM layer with dropout\n",
    "        self.lstm = nn.LSTM(input_size, hidden_size, num_layers=num_layers, batch_first=True, dropout=dropout)\n",
    "        \n",
    "        # Fully connected layer with L2 regularization\n",
    "        self.fc = nn.Linear(hidden_size, num_classes)\n",
    "        # self.fc.weight_decay = weight_decay\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.conv(x)\n",
    "        \n",
    "        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)\n",
    "        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(x.device)\n",
    "        \n",
    "        out, _ = self.lstm(x, (h0, c0))\n",
    "        \n",
    "        out = out[:, -1, :]\n",
    "        \n",
    "        out = self.fc(out)\n",
    "        return out\n",
    "\n",
    "\n",
    "class GRU(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(GRU, self).__init__()\n",
    "        self.conv = ConvolutionModule()\n",
    "        self.gru = nn.GRU(40, 128, 2, batch_first=True, dropout=0.5)\n",
    "        self.fc = nn.Linear(128, 2)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv(x)\n",
    "        x, _ = self.gru(x)\n",
    "        x = self.fc(x[:, -1, :])\n",
    "        return x\n",
    "    \n",
    "    \n",
    "model_lstm = LSTM_Conv().to(device)\n",
    "model_gru = GRU().to(device)\n",
    "\n",
    "\n",
    "loss_fn = FocalLoss()\n",
    "loss_fn_2 = nn.BCEWithLogitsLoss()\n",
    "\n",
    "optimizer_lstm = torch.optim.Adam(model_lstm.parameters(), lr=0.001)\n",
    "\n",
    "optimizer_gru = torch.optim.Adam(model_gru.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/20 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1: Train Loss 0.12563487539467003 and Accuracy 0.9801339507102966\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  5%|▌         | 1/20 [00:44<13:58, 44.15s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1: Valid Loss 0.0217123299681892 and Valid Accuracy 0.9968750476837158\n",
      "Epoch 2: Train Loss 0.036953066562169366 and Accuracy 0.9939731955528259\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|█         | 2/20 [01:00<08:20, 27.82s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2: Valid Loss 0.026445358991622925 and Valid Accuracy 0.9968750476837158\n",
      "Epoch 3: Train Loss 0.0368478650160666 and Accuracy 0.9939731955528259\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 15%|█▌        | 3/20 [01:16<06:22, 22.48s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3: Valid Loss 0.02384307744602362 and Valid Accuracy 0.9968750476837158\n",
      "Epoch 4: Train Loss 0.037040508911013605 and Accuracy 0.9939731955528259\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 20%|██        | 4/20 [01:32<05:20, 20.02s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4: Valid Loss 0.0243537824600935 and Valid Accuracy 0.9968750476837158\n",
      "Epoch 5: Train Loss 0.03708679165824184 and Accuracy 0.9939731955528259\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 25%|██▌       | 5/20 [01:49<04:40, 18.67s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5: Valid Loss 0.023232879924277465 and Valid Accuracy 0.9968750476837158\n",
      "Epoch 6: Train Loss 0.030650147356625114 and Accuracy 0.9939731955528259\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 25%|██▌       | 5/20 [02:05<06:16, 25.08s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6: Valid Loss 0.19964818954467772 and Valid Accuracy 0.9968750476837158\n",
      "Early stopping at epoch 6\n",
      "Best training accuracy: 0.9939731955528259\n",
      "Best validation accuracy: 0.9968750476837158\n",
      "Training finished\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "history_1 = train(loss_fn_2, optimizer_lstm, model_lstm, train_loader, valid_loader, epochs=20, early_stop_patience=5, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(history_1)\n",
    "plot(history_1, metric=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss 0.2035922920703888 and Test Accuracy 0.994375\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 1.0\n",
      "Recall: 0.9943749904632568\n",
      "F1: 0.9971795678138733\n"
     ]
    }
   ],
   "source": [
    "_,confusion_matrix_1,_,_ = eval(loss_fn_2, model_lstm, test_loader, device=device)\n",
    "plot_confusion_matrix(confusion_matrix_1, [\"Normal\", \"Seizure\"])\n",
    "metrics(confusion_matrix_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/20 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1: Train Loss 0.02749861322673886 and Accuracy 0.9928571581840515\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  5%|▌         | 1/20 [00:31<09:55, 31.34s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1: Valid Loss 0.011290231498423963 and Valid Accuracy 0.9942708611488342\n",
      "Epoch 2: Train Loss 0.010202226190761264 and Accuracy 0.9950892925262451\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|█         | 2/20 [01:01<09:09, 30.53s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2: Valid Loss 0.011426728024768332 and Valid Accuracy 0.9942708611488342\n",
      "Epoch 3: Train Loss 0.009558684368883925 and Accuracy 0.9950892925262451\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 15%|█▌        | 3/20 [01:31<08:33, 30.22s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3: Valid Loss 0.013262030916909378 and Valid Accuracy 0.9942708611488342\n",
      "Epoch 4: Train Loss 0.006692871487134003 and Accuracy 0.9946428537368774\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 20%|██        | 4/20 [02:01<08:01, 30.10s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4: Valid Loss 0.09817291175325711 and Valid Accuracy 0.9942708611488342\n",
      "Epoch 5: Train Loss 0.0026338608383541995 and Accuracy 0.9975446462631226\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 25%|██▌       | 5/20 [02:31<07:31, 30.11s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5: Valid Loss 0.45541020135084787 and Valid Accuracy 0.00572916679084301\n",
      "Epoch 6: Train Loss 0.0018795556447003038 and Accuracy 0.9988839626312256\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 25%|██▌       | 5/20 [03:01<09:04, 36.27s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6: Valid Loss 0.023045380165179572 and Valid Accuracy 0.9942708611488342\n",
      "Early stopping at epoch 6\n",
      "Best training accuracy: 0.9988839626312256\n",
      "Best validation accuracy: 0.9942708611488342\n",
      "Training finished\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "history = train(loss_fn, optimizer_lstm, model_lstm, train_loader, valid_loader, 20, 5, device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(6/9)/((6/9) + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(history)\n",
    "plot(history, metric=\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss 0.022814226746559144 and Test Accuracy 0.994375\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# eval \n",
    "history_eval, confusion_matrix,y_pred,y =  eval(loss_fn,model_lstm, test_loader,device=device)\n",
    "plot_confusion_matrix(confusion_matrix, [\"Normal\", \"Seizure\"])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the model\n",
    "from pathlib import Path\n",
    "\n",
    "MODEL_PATH = Path(\"models\")\n",
    "MODEL_PATH.mkdir(parents=True,\n",
    "                  exist_ok=True)\n",
    "\n",
    "MODEL_NAME = \"model_ch23.pth\"\n",
    "MODEL_SAVE_PATH = MODEL_PATH / MODEL_NAME\n",
    "print(f\"Saving model to: {MODEL_SAVE_PATH}\")\n",
    "torch.save(obj=model_t.state_dict(),\n",
    "           f=MODEL_SAVE_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_data)\n",
    "len(all_labels)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}