ECG_Mortara_Extended.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fdf64122",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "# **ECG HEARTBEAT CLASSIFICATION WITH ML - Mortara Extended**\n",
    "\n",
    "by Erica Brisigotti (2097202), Ekaterina Chueva (2072050), Sofia Pacheco Garcia (2070771), Nadillia Sahputra (2070770)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6fdecb94-b3ec-485b-a4bc-b55dfb42a796",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import wfdb\n",
    "import wfdb.processing\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy import fftpack, stats, signal\n",
    "import pywt\n",
    "from scipy import stats\n",
    "import time\n",
    "import copy\n",
    "import pandas.errors as pe\n",
    "\n",
    "import sklearn\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.utils.class_weight import compute_sample_weight\n",
    "\n",
    "import tensorflow as tf\n",
    "import xgboost\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "from scikeras.wrappers import KerasClassifier\n",
    "import joblib\n",
    "from joblib import Parallel, delayed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b3ea9f17-2084-446d-8575-28196d4bbcbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# firstly, we set\n",
    "# - a saving option\n",
    "FILE_SAVE = False\n",
    "# - a second saving option for redudant yet maybe worth doing for small datasets\n",
    "EXTRA_SAVE_FOR_SMALL_DATA = False\n",
    "# - an option to show the structure of the datasets anayzed (both in general and for the specific chosen example)\n",
    "SHOW_STRUCTURE = False\n",
    "# - the color of the records from a healthier patient in plots\n",
    "ch = 'forestgreen'\n",
    "# - the color of the annotations from a healthier patient in plots\n",
    "cha = 'navy'\n",
    "# - an additional color for a healthier patient in plots\n",
    "che = 'deepskyblue'\n",
    "# - the color of the records from a more sick patient in plots\n",
    "cs = 'darkorange'\n",
    "# -  the color of the annotaitons from a more sick patient in plots\n",
    "csa = 'red' \n",
    "# - an additional color for a more sick patient in plots\n",
    "cse = 'maroon'\n",
    "# - an option for the classification algorithm (between xgboost and ??)\n",
    "ML_ALGORITHM = 'xgboost'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6a9c438e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we also define a function that allows to quickly display the distribution of its input\n",
    "def distribution(arg, a1, a2, title='arg'):\n",
    "    '''\n",
    "    function that returns the distribution of arg (which name/meaning is encoded in title)\n",
    "    '''\n",
    "    average = np.median(arg)\n",
    "    std = np.std(arg)\n",
    "    fig, ax = plt.subplots(1,1, figsize=(12,4))\n",
    "    info = ax.hist(arg, bins=np.arange(np.min(arg), np.max(arg)+1, 1), align='left', density=True, edgecolor='none', \n",
    "                   color='black', alpha=0.8, label = 'distribution of '+title)\n",
    "    ax.vlines(average, 0, 1, color='magenta', label='average')\n",
    "    ax.vlines(average+std, 0, 1, color='gold', label='1 $\\sigma$')\n",
    "    ax.vlines(average-std, 0, 1, color='gold')\n",
    "    ax.set_xlabel(title)\n",
    "    ax.set_ylabel('density')\n",
    "    ax.set_xlim(a1, a2)\n",
    "    ax.set_ylim(0, 1.1*np.max(info[0]))\n",
    "    ax.set_title('distribution of '+title)\n",
    "    ax.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fc85ecf",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "# 1A. Data Preprocessing Example"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2241d43c",
   "metadata": {},
   "source": [
    "We set the parameters for the preprocessing part by we choosing two random datasets, *one from the first half and one from second half of the set of patients. Such a split is justified by the knowledge that the second half of patients is generally more sick (since it was purposefully introduced to insert more sick patients).*\n",
    "\n",
    "To reach such a goal, we upload the two WFDB records using the <code>rdrecord</code> function. They are uploaded as <code>wfdb.Record</code> objects, with many different attributes. Of such attributes, we are interested in the sampling frequency <code>.fs</code> and in the time series, which is stored under as <code>.p_signal</code>.\n",
    "\n",
    "Similarly, the annotations for the two patients are uploaded using the <code>rdann</code> function, that retuns <code>wfdb.Annotation</code> objects with many attributes. Of such attributes, we are interested in the annotation indexes (saved as <code>.sample</code> ) and symbols (under <code>.symbol</code>)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "fcf48f82",
   "metadata": {},
   "outputs": [],
   "source": [
    "t_start_all = time.time()\n",
    "\n",
    "address_1 = \"Hour10RawData\"\n",
    "address_2 = \"Hour13RawData\"\n",
    "record_1 = wfdb.rdrecord('extended_input/'+address_1)\n",
    "record_2 = wfdb.rdrecord('extended_input/'+address_2)\n",
    "leads = record_1.sig_name\n",
    "\n",
    "if SHOW_STRUCTURE:\n",
    "    print('the structure of the wfdb.Record object is:')\n",
    "    display(record_1.__dict__)\n",
    "    print('\\nabout patient '+address_1)\n",
    "    with open(\"extended_input/\"+address_1+\".hea\", 'r') as file:\n",
    "        t = file.read().replace('#',\"\")\n",
    "        hea = t.split('\\n')\n",
    "        print('\\n',hea[3],\": the first two data are info about the age and gender\")\n",
    "        temp = hea[3].split(\" \")\n",
    "        print(temp[1],\" is the age of the patient and \",temp[2],\"is the gender of the patient\")\n",
    "        print(hea[4],\": are the meds the patient is on\")\n",
    "        print(hea[5],\": are the notes for the patient\")\n",
    "    print('\\nabout patient '+address_2)\n",
    "    with open(\"extended_input/\"+address_1+\".hea\", 'r') as file:\n",
    "        t = file.read().replace('#',\"\")\n",
    "        hea = t.split('\\n')\n",
    "        print('\\n',hea[3],\": the first two data are info about the age and gender\")\n",
    "        temp = hea[3].split(\" \")\n",
    "        print(temp[1],\" is the age of the patient and \",temp[2],\"is the gender of the patient\")\n",
    "        print(hea[4],\": are the meds the patient is on\")\n",
    "        print(hea[5],\": are the notes for the patient\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a4865385",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# we choose to plot just 2 out of the 12 leads\n",
    "s0_1 = record_1.p_signal[:,0]\n",
    "s1_1 = record_1.p_signal[:,7]\n",
    "s0_2 = record_2.p_signal[:,0]\n",
    "s1_2 = record_2.p_signal[:,7]\n",
    "freq = record_1.fs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63d6019d",
   "metadata": {},
   "source": [
    "We plot the records and annotations in both leads for both patients:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "637df436",
   "metadata": {},
   "outputs": [
    {
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NJCUlVRgbLy0trdpCJbqioiL+85//sHjxYg4fPkxOTo6lezHIcZ649EhwTwgnERERUeV0PUOrqKiIkSNHsmvXLvr06cPkyZNp3rw57u7uxMfH8+mnnzpcvKAyGRkZAKxYsYIVK1ZUOl/5g83KMqvc3d0xm811alNdZWVlYTQaLVfFy4qIiMBgMJCVlVXn7VS2H2ujWbNmdjPzAEtb7b3n48aNY/Xq1YA6KPv000956qmnuOmmm9ixYwf+/v6WeY8fP87AgQPJyspi1KhR3HjjjQQGBloGiN64caPN5ykwMJC+ffuyZcsWTCYTsbGxGAwGRo0aRV5eHqCu8LZp04YdO3YwYsSIGgfdwHrAVXZ/ffPNN0ycOBF/f3/GjRtHTEwMvr6+lgGxHR3MGRrneySEEELo5DivYbnicV55np6edOzYkddff528vDzeffddrrvuOq677jqb+Tp16mQJwGZmZrJs2TIeeughbr31Vn7//Xeb7LeMjAwGDBjA6dOnGTp0KKNHjyYoKAg3Nzf27NnD8uXLbT5PetGQb775hvPnz7NlyxY0TePqq6+mR48eeHt7Exsby3XXXUdcXBzt27enVatWNX6t9o7ztm7dytVXXw2o8aY7dOiAv78/BoOBZcuWsXfv3hp99m+//XZ+/PFHOnbsyMSJEwkPD8fDw4PMzEzefvttOc4TlxwJ7gnhJJKTk6ucrh9QLV++nF27dnH//ffz8ccf28y7ePFiS7eEutC7fL777rvMmDGjzutzBoGBgZjNZlJTUyt0dU1JSUHTNJsBkfWr62Uzw3SVBdwAu1XPaqtDhw5s27aNpKQkIiMjbZ47duyYZZ6qhIWF8eSTT3Lx4kX++c9/8uyzzzJv3jzL8//+97+5cOEC//vf/yp0/3jwwQcrXBkHdYX2t99+Y8eOHcTFxVmu8oK6oh0bG0uHDh0wmUy1vpqrFxPRB04G1dXG29ubnTt3VnjdixcvrtH6G+N7JIQQQujkOK9hueJxXlXGjh3L+++/T1xcXIXgXllBQUHce++9lJSU8MADD/DII4+wbNkyy/OffPIJp0+f5qWXXuLZZ5+1WfaVV15h+fLlFdY5atQovvnmG2JjY9m6dSvNmjWjT58+uLm5MWTIEGJjYzl27Bjnzp2r0NXcUfaO8/71r39RWFjIpk2bGDZsmM3827dvZ+/evQ6v/7fffuPHH39k3LhxrFixwqYb9fbt2+uluIoQzsZY/SxCiMawZcuWClc9zWYzW7duxWAw0KtXL0B1uQQsFUzL2rRpk911G41GSkpKHG6L3o2yvrtYlKX/k61Ju+qiT58+gPVgoix9WtlKW8HBwQCcO3euwvx614+Gpo9/UnYcO92aNWts5qnOM888Q8uWLXn//feJj4+3TK/s86RpGlu2bLG7Lj1g9/PPP7Np0ybLVVaAq6++mri4ODZs2ADUbhyWo0eP8vXXX+Pl5cWtt95q09YuXbpUCOwlJiZy8uTJCuup6jNWm++REEIIUVtynNewXPE4ryp6Zpuj48vdd9999O3bl+XLl7N161bL9Np8nsqOuxcbG8uIESMs+/Pqq69m9+7dfP/990DtjvNSU1P54IMPALjzzjtt2hoSElIhsJeXl8euXbsqrMeR47zrr7++wviIcpwnLlUS3BPCSRw9epSPPvrIZtpHH33E0aNHuf766y2ZUfrYZ5s3b7aZd+PGjRWW14WEhHD27FmH2zJw4EAGDRrEV199xZIlSyo8bzab7WZ01URISAgAZ86cqdN6HKUPPD1nzhybbhkXL160jFtSdnDqfv36YTAYWLx4MQUFBZbpx44da7SrfdOmTcPd3Z1//etfNleR9+zZw1dffUWXLl0qHABVxsfHh6eeegqTycRLL71kmV7Z5+mVV17hjz/+sLuuYcOG4e7uzn//+1+ys7NtgnujRo0iLS2NTz75BD8/P5srso7YsmUL48aNo7CwkKefftqma0mbNm04fvy4TfZDQUEBDz30UIUxWaDqz1htvkdCCCFEbclxXsNyxeO8nTt32owBp0tISGDu3LkAVWbtlWUwGJg9ezYAzz33nGV6ZZ+nL7/8kpUrV9pdV+fOnYmMjOTHH3/k0KFDFY7zSkpKeOONNyyPa+LAgQOMHTuWlJQUpk6dSv/+/W3aeuHCBQ4cOGCZVlJSwpNPPklqamqFddXmOO/AgQOW91aIS410yxXCSYwbN47HHnuMlStX0q1bNw4cOMCPP/5IaGiozUHGjTfeSExMDK+99pqlitmRI0f46aefuPXWW1m6dGmFdV999dV8/fXX3HLLLZa0+ptuuomePXtW2p6vvvqKUaNGceeddzJv3jz69u2Lj48Pp0+fZtu2baSmptocDNXU1VdfzRtvvMGf//xnJkyYgJ+fH23atGHy5Mm1XmdVrrrqKh599FHeffddunfvzoQJE9A0jW+//ZazZ8/y2GOPcdVVV1nmb9myJZMmTeLLL7+kX79+XHvttaSkpPD9999z7bXX8u2339a4DYcPH+aVV14BsAxmfPjwYe69917LPIsWLbLc79ixIy+88ALPPvssvXr1YsKECWRnZ1u6oH700UcVBueuyp///GdeffVVPvvsM5555hmuuOIKHnzwQRYuXMiECRO44447aN68Odu3b2fXrl1cf/31dsfi8ff3Z8CAAWzbtg2j0WiTPagf5KWmpjJu3LhKrziXrehbVFRESkoKO3bsYP/+/bi5ufHss89aDlJ1jz76KI8++ih9+vTh9ttvp7i4mLVr16JpGr169arQXWPUqFEYDAaeeeYZDhw4QLNmzQgKCmLGjBm1+h4JIYQQtSXHeXKcB7bHeX/72984fvw4gwYNonXr1hiNRk6cOMGqVasoKiriySefrFE115tuuol+/fqxYcMGNm7cyIgRI5g8eTKvvvoqjz76qGVM5L1797J+/Xpuu+02vvvuO7vrGjVqFF999ZXlvm7gwIH4+fmRmppKp06dKi1uUbaib3FxMenp6ezatYsdO3YA8MADD/Dee+/ZLPPoo4/y888/M2zYMO644w68vb2Ji4vj3LlzjBw5skJW5pAhQ/Dx8WHevHlcuHDBEiB/9tlnGThwIAMHDuTrr78mMTGRwYMHc/r0aX744Qeuv/56Oc4TlyZNCNGkYmNjNUCbPXu2tmnTJm3EiBGan5+fFhgYqN16663asWPHKixz8uRJbcKECVpYWJjm6+urDRgwQFu8eLHNuspKTEzU7rjjDi00NFQzGo0aoC1cuLDC9svLyMjQnn32Wa179+6aj4+P5u/vr3Xo0EG76667tO+++85m3jZt2mht2rSx+xpHjBih2fu5ee2117QOHTpoHh4eGqCNGDGi2vdr6tSpGqBt27at0nn07SUmJlZ4bsGCBdqAAQM0X19fy3u3YMECu+vJy8vTHnvsMS0iIkLz8vLSevbsqX3xxReVvmfVvQZ9uar+7Pn888+1/v37az4+PlqzZs208ePHazt37qww36lTpzRAGzduXKVtePfddzVAmzx5sk27hg4dqgUEBGhBQUGW9c+ePVsDtNjY2ArreeaZZzRA69evX4XnOnbsqAHa3LlzK21j2T8fHx+tRYsW2qhRo7TnnntOO378uN22m81mbf78+Vq3bt00b29vLTIyUrv//vu1lJSUSj9jixYt0nr06KF5eXlpgM1ntKbfIyGEEKKm5DhPjvMqO8775ptvtD/96U9au3btND8/P83Dw0OLiorSbrvtNm316tV2twFonTp1qrQNP/74owZow4cPt0zbs2ePNnbsWC04OFgLCAjQRowYoa1bt05buHChzWelrA8//FADtNDQUM1sNts8N3bsWA3Q/vKXv1TaxrJ/Xl5eWnh4uDZ06FDtySef1Pbu3Vtp+5cuXar17dtX8/X11UJDQ7U77rhDO3HihOVzcerUKZv5V6xYoQ0YMEDz8fGp8B6npKRo9913n9ayZUvN29tb69Gjh/bee+9pJ0+e1ABt6tSplbZDCFdk0DQ7ucBCiEYTFxfHqFGjmD17tuUKlxBCCCGEcH1ynCeEEKIxyJh7QgghhBBCCCGEEEK4KAnuCSGEEEIIIYQQQgjhoiS4J4QQQgghhBBCCCGEi5Ix94QQQgghhBBCCCGEcFGSuSeEEEIIIYQQQgghhIuS4J4QQgghhBBCCCGEEC7Kvakb4GrMZjPnz58nICAAg8HQ1M0RQgghhAvQNI3s7GxatmyJ0SjXVp2VHOcJIYQQoqac4ThPgns1dP78eaKjo5u6GUIIIYRwQWfOnKFVq1ZN3QxRCTnOE0IIIURtNeVxngT3aiggIACAU6dOERIS0sStEZUxmUz8/PPPjB07Fg8Pj6ZujrBD9pFrkP3kGmQ/Ob+MjAzatm1rOY4QzkmO81yD/OY5P9lHrkH2k2uQ/eT8nOE4T4J7NaR30QgICCAwMLCJWyMqYzKZ8PX1JTAwUH4AnZTsI9cg+8k1yH5yfiaTCUC6ejo5Oc5zDfKb5/xkH7kG2U+uQfaT83OG4zwZ9EUIIYQQQjil9957j5iYGLy9vRk0aBA7duyocv558+bRqVMnfHx8iI6O5oknnqCgoKCRWiuEEEII0TQkuCeEEEIIIZzOkiVLmDlzJrNnz2bXrl306tWLcePGkZKSYnf+L7/8kqeffprZs2dz6NAhPvnkE5YsWcIzzzzTyC0XQgghhGhcEtwTQgghhBBO56233mL69OlMmzaNrl27Mn/+fHx9fVmwYIHd+bdu3crQoUO56667iImJYezYsUyaNKnabD8hhBBCCFcnY+4JIYQQoklomkZxcTElJSVN3ZR64eHhgZubW1M345JQVFTEzp07mTVrlmWa0Whk9OjRbNu2ze4yV155JZ9//jk7duxg4MCBnDx5kpUrVzJ58uRKt1NYWEhhYaHlcVZWFqDGztHHzxHOR983so+cl+wj1yD7yTW46n7SNI2SkhJKSkrQNK2pm1MnBoMBd3f3So/znGHfSHBPCCGEEI2uqKiIxMRE8vLymrop9cZgMNCqVSv8/f2buikuLy0tjZKSEiIiImymR0REcPjwYbvL3HXXXaSlpTFs2DBL4PjBBx+sslvu3LlzmTNnToXpsbGx+Pr61u1FiAa3du3apm6CqIbsI9cg+8k1uNJ+MhqNBAUF4ePjc8kUEysuLiYjI4OioqIKzznD8awE94QQQgjRqMxmM6dOncLNzY2WLVvi6enp8gd+mqaRmprK2bNn6dChg2TwNYG4uDhefvll3n//fQYNGsTx48d5/PHHeemll3juuefsLjNr1ixmzpxpeZyVlUV0dDSjRo2iefPmjdV0UUMmk4m1a9cyZswYqRzppGQfuQbZT67B1fZT2eO8sLAwPDw8LonjvPT0dAIDA2nbtm2F47z09PQmapmVBPeEEEII0aiKioowm81ER0dfUtlRYWFhxMfHYzKZJLhXR6Ghobi5uZGcnGwzPTk5mcjISLvLPPfcc0yePJkHHngAgB49epCbm8uf//xn/vGPf2A0Vhxq2svLCy8vrwrTPTw8XOIE6nIn+8n5yT5yDbKfXIOr7KeCggI0TSMqKuqSOs4zGo3k5uYCVNgPzrBfpKCGEEIIIZqEvWCLK3P1q9LOxNPTk379+rF+/XrLNLPZzPr16xkyZIjdZfLy8ip8pvQgq6uP9SOEEEK4GjnOa1ySuSeEEEIIIZzOzJkzmTp1Kv3792fgwIHMmzeP3Nxcpk2bBsCUKVOIiopi7ty5ANx444289dZb9OnTx9It97nnnuPGG2+UTEohhBBCXNIkuCeEEEIIIZzOxIkTSU1N5fnnnycpKYnevXuzevVqS5GN06dP22QFPPvssxgMBp599lnOnTtHWFgYN954I//617+a6iUIIYQQQjQKCe4JIYQQQginNGPGDGbMmGH3ubi4OJvH7u7uzJ49m9mzZzdCy4QQQgghnMel1QlaCCGEEKIB3Xvvvdxyyy1N3QwhhBBCCFHPXPk4T4J7QgghhBBCCCGEEEK4KOmWK4QQQogmp2ka+ab8Jtm2j4eP01dAE0IIIYRwWZoGxXlNs213X7gMjvMkuCeEEEKIJpdvyqfHOz2aZNv7H9uPr6dvk2xbCCGEEOKSV5wH7/g3zbYfywEPv6bZdiOSbrlCCCGEEEIIIYQQQrgoydwTQgghRJPz8fBh/2P7m2zbQgghhBCigbj7qgy6ptr2ZUCCe0IIIYRocgaDQbrGCiGEEEJcigyGy6JrbFOSbrlCCCGEEEIIIYQQQrgoCe4JIYQQQgghhBBCCOGiJLgnhBBCCCGEEEIIIYSLcvng3nvvvUdMTAze3t4MGjSIHTt2VDl/ZmYmjzzyCC1atMDLy4uOHTuycuXKRmqtEEIIIVzZokWLWLZsWVM3QwghhBBC1DNXPs5z6YIaS5YsYebMmcyfP59BgwYxb948xo0bx5EjRwgPD68wf1FREWPGjCE8PJylS5cSFRVFQkICQUFBjd94IYQQQgghhBBCCCHqyKWDe2+99RbTp09n2rRpAMyfP58VK1awYMECnn766QrzL1iwgIyMDLZu3YqHhwcAMTExjdlkIYQQQgghhBBCCCHqjcsG94qKiti5cyezZs2yTDMajYwePZpt27bZXeaHH35gyJAhPPLIIyxfvpywsDDuuusunnrqKdzc3OwuU1hYSGFhoeVxVlYWACaTCZPJVI+vSNQnfd/IPnJeso9cg+wn1+Bq+8lkMqFpGmazGbPZ3NTNqTdmsxlN0zCZTBWOK1xl3wghhBBCCNfjssG9tLQ0SkpKiIiIsJkeERHB4cOH7S5z8uRJNmzYwN13383KlSs5fvw4Dz/8MCaTidmzZ9tdZu7cucyZM6fC9NjYWHx9fev+QkSDWrt2bVM3QVRD9pFrkP3kGlxlP7m7uxMZGUlOTg5FRUVN3Zx6U1RURH5+Pr/88gvFxcU2z+Xl5TVRq4QQQgghxKXOZYN7tWE2mwkPD+fDDz/Ezc2Nfv36ce7cOV5//fVKg3uzZs1i5syZlsdZWVlER0czatQomjdv3lhNFzVkMplYu3YtY8aMsXTBFs5F9pFrkP3kGlxtPxUWFnL69Gn8/Pzw8fFp6ubUm/z8fHx8fBgxYgReXl42z6WnpzdRq4QQQgghGp+maU3dhHrl7K/HZYN7oaGhuLm5kZycbDM9OTmZyMhIu8u0aNECDw8Pm64yXbp0ISkpiaKiIjw9PSss4+XlVeEAHcDDw8MlTqAud7KfnJ/sI9cg+8k1uMp+MhqNGAwGCgoK8PPza+rm1Jvi4mIMBgNeXl4V9oMr7BchhBBCiLrSj3ny8vIuqYu4em+TyoZ0a2ouG9zz9PSkX79+rF+/nltuuQVQmXnr169nxowZdpcZOnQoX375JWazGaPRCMDRo0dp0aKF3cCeEEIIIeqfm5sbQUFBpKSkAODr64vBYGjiVtWN2WwmNTUVX19f3N1d9vBKCCGEEKJO5DivaThnqxw0c+ZMpk6dSv/+/Rk4cCDz5s0jNzfXUj13ypQpREVFMXfuXAAeeugh/vOf//D444/z6KOPcuzYMV5++WUee+yxpnwZQgghxGVHz7LXD/wuBUajkdatW7v8AawQQgghRF3IcV7jc+ng3sSJE0lNTeX5558nKSmJ3r17s3r1akuRjdOnT1sy9ACio6NZs2YNTzzxBD179iQqKorHH3+cp556qqleghBCCHFZMhgMtGjRgvDw8Eumkqynp6fNcYcQQgghxOVIjvMan0sH9wBmzJhRaTfcuLi4CtOGDBnC9u3bG7hVQgghhHCEm5ub045dIoQQQgghak+O8xqP84YdhRBCCCGEEEIIIYQQVZLgnhBCCCGEEEIIIYQQLkqCe0IIIYQQwim99957xMTE4O3tzaBBg9ixY0eV82dmZvLII4/QokULvLy86NixIytXrmyk1gohhBBCNA2XH3NPCCGEEEJcepYsWcLMmTOZP38+gwYNYt68eYwbN44jR44QHh5eYf6ioiLGjBlDeHg4S5cuJSoqioSEBIKCghq/8UIIIYQQjUiCe0IIIYQQwum89dZbTJ8+nWnTpgEwf/58VqxYwYIFC3j66acrzL9gwQIyMjLYunUrHh4eAMTExDRmk4UQQgghmoQE94QQQgghhFMpKipi586dzJo1yzLNaDQyevRotm3bZneZH374gSFDhvDII4+wfPlywsLCuOuuu3jqqacqrdRXWFhIYWGh5XFWVhYAJpMJk8lUj69I1Cd938g+cl6yj1yD7CfXIPvJ+TnDvpHgnhBCCCGEcCppaWmUlJQQERFhMz0iIoLDhw/bXebkyZNs2LCBu+++m5UrV3L8+HEefvhhTCYTs2fPtrvM3LlzmTNnToXpsbGx+Pr61v2FiAa1du3apm6CqIbsI9cg+8k1yH5yXnl5eU3dBAnuCSGEEEII12c2mwkPD+fDDz/Ezc2Nfv36ce7cOV5//fVKg3uzZs1i5syZlsdZWVlER0czatQomjdv3lhNFzVkMplYu3YtY8aMsXTBFs5F9pFrkP3kGmQ/Ob/09PSmboIE94QQQgghhHMJDQ3Fzc2N5ORkm+nJyclERkbaXaZFixZ4eHjYdMHt0qULSUlJFBUV4enpWWEZLy8vvLy8Kkz38PCQEygXIPvJ+ck+cg2yn1yD7Cfn5Qz7xdjUDRBCCCGEEKIsT09P+vXrx/r16y3TzGYz69evZ8iQIXaXGTp0KMePH8dsNlumHT16lBYtWtgN7AkhhBBCXCokuCeEEEIIIZzOzJkz+eijj/j00085dOgQDz30ELm5uZbquVOmTLEpuPHQQw+RkZHB448/ztGjR1mxYgUvv/wyjzzySFO9BCGEEEKIRiHdcoUQQgghhNOZOHEiqampPP/88yQlJdG7d29Wr15tKbJx+vRpjEbrdero6GjWrFnDE088Qc+ePYmKiuLxxx/nqaeeaqqXIIQQQgjRKCS4J4QQQgghnNKMGTOYMWOG3efi4uIqTBsyZAjbt29v4FYJIYQQQjgX6ZYrhBBCCCGEEEIIIYSLkuCeEEIIIYQQQgghhBAuSoJ7QgghhBBCCCGEEEK4KAnuCSGEEEIIIYQQQgjhoiS4J4QQQgghhBBCCCGEi5LgnhBCCCGEEEIIIYQQLkqCe0IIIYQQQgghhBBCuCgJ7gkhhBBCCCGEEEII4aIkuCeEEEIIIYQQQgghhIuS4J4QQgghhBBCCCGEEC5KgntCCCGEEEIIIYQQQrgoCe4JIYQQQgghhBBCCOGiJLgnhBBCCCGEEEIIIYSLcvng3nvvvUdMTAze3t4MGjSIHTt2OLTc4sWLMRgM3HLLLQ3bQCGEEEIIIYQQQgghGohLB/eWLFnCzJkzmT17Nrt27aJXr16MGzeOlJSUKpeLj4/nySefZPjw4Y3UUiGEEEIIIYQQQggh6p9LB/feeustpk+fzrRp0+jatSvz58/H19eXBQsWVLpMSUkJd999N3PmzKFdu3aN2FohhBBCCCGEEEIIIeqXe1M3oLaKiorYuXMns2bNskwzGo2MHj2abdu2Vbrciy++SHh4OPfffz+bNm2qdjuFhYUUFhZaHmdlZQFgMpkwmUx1eAWiIen7RvaR85J95BpkP7kG2U/OT/aNEEIIIYRoKC4b3EtLS6OkpISIiAib6RERERw+fNjuMps3b+aTTz5hz549Dm9n7ty5zJkzp8L02NhYfH19a9Rm0fjWrl3b1E0Q1ZB95BpkP7kG2U/OKy8vr6mbIIQQQgghLlEuG9yrqezsbCZPnsxHH31EaGiow8vNmjWLmTNnWh5nZWURHR3NqFGjaN68eUM0VdQDk8nE2rVrGTNmDB4eHk3dHGGH7CPXIPvJNch+cn7p6elN3QQhhBBCCHGJctngXmhoKG5ubiQnJ9tMT05OJjIyssL8J06cID4+nhtvvNEyzWw2A+Du7s6RI0e44oorKizn5eWFl5dXhekeHh5yAuUCZD85P9lHrkH2k2uQ/eS8ZL8IIYQQQoiG4rIFNTw9PenXrx/r16+3TDObzaxfv54hQ4ZUmL9z587s37+fPXv2WP5uuukmRo0axZ49e4iOjm7M5gshhBBCCCGEEEIIUWcum7kHMHPmTKZOnUr//v0ZOHAg8+bNIzc3l2nTpgEwZcoUoqKimDt3Lt7e3nTv3t1m+aCgIIAK04UQQgghhBBCCCGEcAUuHdybOHEiqampPP/88yQlJdG7d29Wr15tKbJx+vRpjEaXTU4UQgghhBBCCCGEEKJKLh/5mjFjBgkJCRQWFvLrr78yaNAgy3NxcXEsWrSo0mUXLVrEsmXLGr6RQgghhBCixt577z1iYmLw9vZm0KBB7Nixw6HlFi9ejMFg4JZbbmnYBgohhBBCOAGXD+4JIYQQQohLz5IlS5g5cyazZ89m165d9OrVi3HjxpGSklLlcvHx8Tz55JMMHz68kVoqhBBCCNG0JLgnhBBCCCGczltvvcX06dOZNm0aXbt2Zf78+fj6+rJgwYJKlykpKeHuu+9mzpw5tGvXrhFbK4QQQgjRdFx6zD0hhBBCCHHpKSoqYufOncyaNcsyzWg0Mnr0aLZt21bpci+++CLh4eHcf//9bNq0qdrtFBYWUlhYaHmclZUFgMlkwmQy1eEViIak7xvZR85L9pFrkP3kGmQ/OT9n2DcS3BNCCCGEEE4lLS2NkpISS5E0XUREBIcPH7a7zObNm/nkk0/Ys2ePw9uZO3cuc+bMqTA9NjYWX1/fGrVZNL61a9c2dRNENWQfuQbZT65B9pPzysvLa+omSHBPCCGEEEK4tuzsbCZPnsxHH31EaGiow8vNmjWLmTNnWh5nZWURHR3NqFGjaN68eUM0VdQDk8nE2rVrGTNmDB4eHk3dHGGH7CPXIPvJNch+cn7p6elN3QQJ7gkhhBBCCOcSGhqKm5sbycnJNtOTk5OJjIysMP+JEyeIj4/nxhtvtEwzm80AuLu7c+TIEa644ooKy3l5eeHl5VVhuoeHh5xAuQDZT85P9pFrkP3kGmQ/OS9n2C9SUEMIIYQQQjgVT09P+vXrx/r16y3TzGYz69evZ8iQIRXm79y5M/v372fPnj2Wv5tuuolRo0axZ88eoqOjG7P5QgghhBCNSjL3hBBCCCGE05k5cyZTp06lf//+DBw4kHnz5pGbm8u0adMAmDJlClFRUcydOxdvb2+6d+9us3xQUBBAhelCCCGEEJcaCe4JIYQQQginM3HiRFJTU3n++edJSkqid+/erF692lJk4/Tp0xiN0glFCCGEEEKCe0IIIYQQwinNmDGDGTNm2H0uLi6uymUXLVpU/w0SQgghhHBCcrlTCCGEEEIIIYQQQggXJcE9IYQQQgghnFHiDrhwrKlbIYQQQggnJ8E9IYQQQgghnIm5BFZPgy8HwVdXgimvqVskhBBCCCcmwT0hhBBCCCGcwcVTUFwIRxbDgUVqWn4anPihSZslhBBCCOcmBTWEEEIIIYRoaoe+gJX3QKsRkJtUOtEAaHDwf9D5zqZsnRBCCCGcmGTuCSGEEEII0ZQunoK1D6r7ZzfChSPg1Qzu3qGmxa+B3OSma58QQgghnJoE94QQQgghhGhKu98FUw74t1KPvUNg/JcQ2R9aDAKtBA5/1bRtFEIIIYTTkm65QgghhBBCNBVzMRz6Ut0f/T4EtlFBPp8QNa3rFEj8FQ5+Bv3+2mTNFEIIIYTzksw9IYQQQgghmkrCOshLBp9QiLkWwnpaA3sAnSaCwQgpu6VrrhBCCCHskuCeEEIIIS4bF/IvMG3pNJYdXNbUTRFCOb1B3ba/Fdw8Kj7v0xz8Wqj72acbr11CCCGEcBkS3BNCCCHEZWPelnn8Ev8Lf1v5t6ZuihBK6l51G9m/8nkCSsfiyz7b8O0RQgghhMuR4J4QQgghLhsHkg9Y7qflpjVhS4QolbZP3Yb2rHyegGh1m32m4dsjhBBCCJcjwT0hhBBCXBYKiws5nHrY8nhP4p6ma4wQAHkpkJsEGCCsR+XzSXBPCCGEEFWQ4J4QQgghLgu7z+8mvzjf5rEQTSqltEtucHvw8Kt8Pn/pliuEEEKIyklwTwghhBCXhUOph2weS3BPNDlHuuSCZO4JIYQQokoS3BNCCCHEZeFc1jkARrYdCcDepL2YSkxN1yAh0krHgAytoksulCmoIcE9IYQQQlTk8sG99957j5iYGLy9vRk0aBA7duyodN6PPvqI4cOHExwcTHBwMKNHj65yfiGEEEJcOvTg3vC2wwn0CqSguMBmDD4hGl3GQXXbvGvV8+mZeznnwFzSsG0SQgghhMtx6eDekiVLmDlzJrNnz2bXrl306tWLcePGkZKSYnf+uLg4Jk2aRGxsLNu2bSM6OpqxY8dy7ty5Rm65EEIIIRqbHtyLbhZNrxa9AOmaK5qQpkF6aVfx5l2qntevBRjcQCspLcAhhBBCCGHl0sG9t956i+nTpzNt2jS6du3K/Pnz8fX1ZcGCBXbn/+KLL3j44Yfp3bs3nTt35uOPP8ZsNrN+/fpGbrkQQgghGtv5rPMARAVG0bdlXwB2J0pwTzSR3EQoygKDEYI6VD2v0Q18w9X9vOSGb5sQQgghXIp7UzegtoqKiti5cyezZs2yTDMajYwePZpt27Y5tI68vDxMJhMhISGVzlNYWEhhYaHlcVZWFgAmkwmTScbpcVb6vpF95LxkH7kG2U+uQfZT9fJMeVzIvwBAuE84PcNVAYNtCdvIK8jDw82jQbcv+0ZUkF7aJTeoPbh7VT+/b7gKCObZ76EihBCAyu498jVoZug6GXyaN3WLhBCNwGWDe2lpaZSUlBAREWEzPSIigsOHHRs/56mnnqJly5aMHj260nnmzp3LnDlzKkyPjY3F19e3Zo0WjW7t2rVN3QRRDdlHrkH2k2uQ/VS5pELVldHb6M2m9ZsoNhcT4BZAal4qr3zzCv0C+zXo9vPy8hp0/cIF6V1yQ6rpkquzZO5VEdwryobf3wJTLnS/D5p3rlsbhRCu5ewmWH4rFKSrx8eXwZ/Wq+zf2so6DZufgQF/h7BqKnsLIZqMywb36uqVV15h8eLFxMXF4e3tXel8s2bNYubMmZbHWVlZREdHM2rUKJo3l6sgzspkMrF27VrGjBmDh0fDZmOI2pF95BpkP7kG2U/V23hqI5yGNiFtGD9+PADndpzj7W1vs499PDf+uQbdfnp6eoOuX7iY4kI4sFDdD6umUq7OkeDe7ndh2wvqfvJvcEdsrZsohHAxmhnW/kUF9vyjVAGesxth9zvQ74nar3fXPDj0hbogcc/vYDDUW5OFEPXHZYN7oaGhuLm5kZxsO+5IcnIykZGRVS77xhtv8Morr7Bu3Tp69qz66oOXlxdeXhW7Snh4eMgJlAuQ/eT8ZB+5BtlPrkH2U+WSS8cpa9WsleU9ur3H7by97W0OphzE4GbA3dhwh0WyX4SNnW9Cym7wbg69HnJsGUeCeyd+tN4/uwkKMsE7qLatFEK4kmPfQ8Yh8GoG9x6Ag5/Dhhlw4LO6BfcSd6jblF1wahW0G18/7RVC1CuXLajh6elJv379bIph6MUxhgwZUulyr732Gi+99BKrV6+mf//+jdFUIYQQQjSxhAsJALQOam2ZFhkQiYfRgxKthORsKVLgjN577z1iYmLw9vZm0KBB7Nixo9J5P/roI4YPH05wcDDBwcGMHj26yvmb1Nlf1O2VL4B/S8eW8SkN7uVXEtzLS4PEX9V9zwBVWTfh5zo1UwjhQg59rm57z1ABvk53AAZI3QPZ52q3TnOxCurptr+kKn0LIZyOywb3AGbOnMlHH33Ep59+yqFDh3jooYfIzc1l2rRpAEyZMsWm4Marr77Kc889x4IFC4iJiSEpKYmkpCRycnKa6iUIIYQQohGcunAKgHbB7SzTjAYjLQJbAHAuq5YnPqLBLFmyhJkzZzJ79mx27dpFr169GDduHCkp9oNbcXFxTJo0idjYWLZt20Z0dDRjx47l3Dkn3LeZJ9RtqINdcqH6zL341YAGYb2gx5/VtJMrat1EIYSLuRivblteqW59w6DFQHX/1KrarTPtABTng4cfuHtD4nY4I939hXBGLh3cmzhxIm+88QbPP/88vXv3Zs+ePaxevdpSZOP06dMkJiZa5v/vf/9LUVERt99+Oy1atLD8vfHGG031EoQQQgjRCPTgXkxIjM30qMAoQIJ7zuitt95i+vTpTJs2ja5duzJ//nx8fX1ZsGCB3fm/+OILHn74YXr37k3nzp35+OOPLb06nEqJCbLi1f2gKxxfrrrg3rlN6rbNGGh9tbqfsrtWTRRCuKDsM+o2INo6rW1pF9q970NJUc3XmfSbuo0cCD2mq/ubn4HYv8KPE+HIN7VurhCifrnsmHu6GTNmMGPGDLvPxcXF2TyOj49v+AYJIYQQwqmYSkycuahOetoGt7V5Tg/unc062+jtEpUrKipi586dNj0wjEYjo0ePZtu2bQ6tIy8vD5PJREhISKXzFBYWUlhYaHmclZUFqCI1JpOplq2vRuYJPMzFaG7eFHuFgYPbMXiG4A5ouSkU21nGPXEHBqA4vD+afxs8AO3iSYqLii65AfD1fdNg+0jUmeyjRmbKw6O0Qq7JO8L6u9J5Ku673saQspuSLbMxD3nRdrFq9pMxZQ9uQElYH8y9HsF973wMib9ahgDQTv5IceQQ8I1omNclAPk+uQJn2DcuH9wTQgghhKjK2YtnKTYX4+PuQ4S/7QlIq8BWAJzPOt8UTROVSEtLo6SkxNIbQxcREcHhw4cdWsdTTz1Fy5YtGT16dKXzzJ07lzlz5lSYHhsbi6+vb80a7aCwvN1cCWS7hRG7arXDy/mYkhkLmHOTWLlihU3Azmgu5PrUfRiADQeyKXQ/xA0YMJhyWffjVxS5B9X3y3AKa9eubeomiGrIPmocfkXnGA0UG7xZuX6rze9Dy2b3MaDgDUp2vseajH6YDRULPFW2nwYm/kYL4I8zOcRn7ae3/wjaZK2zPG8ozidh6QwOhE6r75ck7JDvk/PKy8tr6iZIcE8IIYQQlzZLl9zgGIwG2xFJWgaqYgbSLffS8sorr7B48WLi4uLw9vaudL5Zs2Yxc+ZMy+OsrCyio6MZNWoUzZs3b5C2GfedhvPgH9WL8eNrUHXSlAv//Qtumonxo4eDV6DlKUPidownzWi+EYy6aYo6sV/QCnLOMGbAFWgtBjXAK2k6JpOJtWvXMmbMGKlE7aRkHzUuw5lYOA1uQW0Yf/31tk+ax6Et/BLP3PNc11lDa3ctFBeAuw+m4uIq95P7Vy9ALnQbfB1d246HnF6YV09Ga38bWrO2uP94K+0zl9O23RWYh73SOC/2MiTfJ+eXnp7e1E2Q4J4QQgghLm0nM04CFbvkgoy556xCQ0Nxc3MjOdm2inFycjKRkZFVLvvGG2/wyiuvsG7dOnr27FnlvF5eXnh5eVWY7uHh0XAnUNnxABhDOmCsyTY8gtSg9qZcPEwXwL9M8DFNja1niByAh6enmhbUDnLO4J57GjyG1U/bnUyD7idRL2QfNZJ8Nc68ISDazvvtAV3uht9fx337HIh7DHKToPVouFkV3al0P+WoISvcg9qChwcEx8Ck0vE9NQ0GPAW/vYrbrrdw6/0QBLdvoBfoJM5shLzk0krEjU++T87LGfaLSxfUEEIIIYSozqHUQwB0CO1Q4Tk9uHc+6zyapjVqu0TlPD096devn00xDL04xpAhQypd7rXXXuOll15i9erV9O/fvzGaWnOZx9VtUC1OgvVxrfJsg56k7FK3EWVec7PSytAXT9Z8O0II12KvmEZZvf4CHv6QfkAF9gBOr4MLRypfpykP8tPU/cDWFZ83GOCqVyBmnHp86PPatd1VbJkNX4+EnyZCumPDQwjRmCS4J4QQQohL2oHkAwB0j+he4bmIABUsKSop4kL+hUZtl6jazJkz+eijj/j00085dOgQDz30ELm5uUybpsZ2mjJlik3BjVdffZXnnnuOBQsWEBMTQ1JSEklJSeTk5DTVS7Av84S6rUmlXJ1fadZibqLt9JS96jasl3VaUGlwL1OCe0K4PFOeqrRdmezSolCVBfeCroDJu6HTnXDlHFX9FjDGVzHup75ODz/wCqp8vq6T1e1vr8HHV8A3YyDtj8rnd0UlRfDrv6yPU/c2XVuEqIR0yxVCCCHEJSuvKI8TGSqYYi+45+nmSYhPCBn5GaTkphDiW3llVdG4Jk6cSGpqKs8//zxJSUn07t2b1atXW4psnD59GqPRep36v//9L0VFRdx+++0265k9ezYvvPBCYza9cpoZLurBvVpk7vm1ULc5ZYJ7JSbIOKjuh5cJ7knmnhCXhpxEWNQFQjrDHXHgbmcc0ezT6jagVeXrCW4PN3yl7ns1g6QdGOJXgXdH+/NbsgFbV11xu/0tKqs4L1n93lw8CV8Mgil7wDsEzv4CbceDe8UhEFxGVgJoJdbH6Qebri1CVEKCe0IIIYS4ZB1KPYRZMxPuF064f7jdeSL8I8jIzyApO4nOYZ0buYWiKjNmzGDGjBl2n4uLi7N5HB8f3/ANqqucRDWQvdEdAtvUfHk9uFc2c+/CEZVV4hkAgTHW6RLcE+LScOw7KLwIib/C+kdg9HxwKze+V+o+dRvi4P+wttdD7F8xnN+ER5tKKt1aAoaVZAPqPPzg3gNw4RiYi+GX/4PE7bDlOdUNOO0PCO0BE9aAfwvH2udsyv+OZhxqmnYIUQXpliuEEEKIS9a+JHXC0y2iW6Xz6EG/lNyURmmTuIzp4+0FtlEBvprytxPc07uHhfa0za7Ru+Vmn4XiwppvSwhhy5QL2U1QfOnUSuv9PxbAl4NtM8dyzkPOOTAYIaKvY+sMbg9hvTCYi4nK2WJ/Hj1zz954e+X5NIeWg6HVMBj1tpp2ZIm1e27aftjhwtV09eEN9N9tydwTTsiho4rvvvuuThu56qqrCA0NrdM6hBBCCCFqIrswm49//xiAAVEDKp0vwl9180zOSa50nkuZHOc1osw6dMkF+5l79sbbA/AJs1TXJfs0BFcsKCOEcJCmwdKxcH4r9H0cRr6lgmm1dW4rxK8Cn3Do/TAY3ezPZ8qDMxvU/cHPw553VQGdr0fBA6fAwxeSflPPN++qvvOO6joZNu4lOjvO/vNZDmbulddiIHSYAMe+VcGw3jNg1zw4/BWMeKNi1qEr0DP32t0Ax5fBhaMqS7E2F2mEaCAOfRpvv/12DAZDrarIGQwG1q5dy9VXX13jZYUQQgghauvj3z4mKTuJ1kGtmdxncqXzXe7BPTnOa0R65l6zWhTTgDLBvSTrtAtH1W3zrrbzGgyqa27afnViKsE9IWrvxI8qsAew621odRV0uK3m6zHlw+ZnVLBL5xsGne+0P/+5Taorf0A0XPmCqnr7v75qfLvEX6H1KGtwr7RIhsM634X2y98JKTiCKWU3RJVbvuyYezV149cqo9DDHzz94fCXkJcCCT9Du+trvr6mpgf3okdC/M9QnKcu1oR0atJmCVGWw6HmZ555htGjR9do5ZmZmdx2Wy1+9IQQQggh6uhw6mEApvWdhq+nb6XzWbrl5ly+3XLlOK+R6MG94HrM3NNPOvVuuGXpwT2pmCtE3ZTvUnrih9oF95bfogJc5ddVWXDvzEZ12/pqFbD3bwmtr1HBsrMbywX3Ks9Qt8u/BVqHP2E4ugS331+FqG9tn3d0zD17DEbb4h6d7oTd78DRb10zuKf/hja7Qo1rmLIL0g9JcE84FYeDe126dGHEiBE1Wnl6enqtrgILIYQQQtRVYmlF0VbNqqgeCET6RwKXb+YeyHFeo9G75dY2c08fcy8vVXUJM7jBxVOl67QT3AuSohqXnBM/Qm4y9HygqVviujJPwP6Pod8T4Gu/0JKN7HOQuA0wwPjPYeXdcGqVqn5dk665+enWwN6tP4FnM1gyHOJXg7nEftfcs7+o21Zlfp9bXVUa3Ct9Th//rXzXfAeUDHgK49ElGI5/rwr+6L8xmlYmc68Wwb3yrrhRBffiq3jfSopgz3vgE6q6DDsLTbO9iNK8iwruZRwCbmnKlglhw6Hg3u7du2nTpuYVvYKDg9m9ezft29fy6qQQQgghRC0lZqngXouAqqvz6Zl7l2twT47zGomm1T1zzydUBfS0EhXgcfMEUw5gsF99N7CtupXg3qWhKBt+/BOUFEJkfwjv3dQtcj1FOfDdeOuYaSNer36ZU6vUbYuB0PF2WPeg6mKa9Bu0GOT4tpN/V7dB7VX2mrkYvIOh4IKqLhs11HZ+Uz4k7VD3W11lna7fT9wGBZmQc1Y9Du7oeFt0zbuT7dGKANNZVfxCD+4VXFDjdUL9BPdaXaW66OYmQcpuiOhnfS4/HTDA0jEqaGYwQrsbwTuo7tutD4UXoShL3Q9sYx0CQYpqCCfj0KWGXr16ERgYWPOVG4306tULP78aDOwphBBCCFFHhcWFZORnABAZEFnlvHrmXlpuGqYSU4O3zdnIcV4jyU8vc4LYtnbrMBjBT40RSW6iNWjnHwXu3hXn1zP39IxB4doS1qnAHsDJnxpmG/npcOgLFXi6FG141DpO5Zk4x5Y5tULdtr1eBdTb3aAe7363ZttOLA3U6WPjGd2tGXnJuyrOn7QDzCbVFbdsZm5IZ1Uwp7gAjixW07xDVMXaWsjxjFJ3Mg5bJ+pZez6h4OFTq/XacPOENmPU/QOfqltNg20vwX8j4P3mKrAHKrMveWfdt1lfcs+rW68gVbAkpIt6nHGoyZokhD0O5xFHRUXxxBNP8NtvvzVke4QQQggh6iwpRxUc8Hb3Jqiaq/8hviEEeAWgoXEk7UgjtM75yHFeI9Cz9vxb1e1kWc+iyT5tHQfK3nh7AIEx1nmF6zu5osz9lfW33qNLYc0DkHEUvrkGVt6jikZcag4vhgOLAIN6nLJLZUNWRdPgdGm12nbj1W3/J0vX9xVcOO749u2NjdesNNCflVBx/tR96jZigBpvT2cwWLP39quK8HUpmJPjURrcu1Dm/59lvL1aFNOoTI/SruS731WB6vg1sPV5lYkM6uKFf0t1P8mJ/hdln1O3/qXvkyVz75AKRArhJBwO7rVr14533nmHwYMH07FjR1588UWOH6/Bj5kQQgghRCPRu+RGBkRiKHtSZIfRYKR3i94A7D6/u6Gb5pTkOK8RXCzNnqttl1ydnvWXedKauWdvvD2wBgLLdrETrslcDKfKBPQSt0NeWt3Xq5lh/SPwxyewsBOk7lXTd/9HBbYuFSVF8Mvf1f3Bz6qgmma2VsCtTF5KacatAUJ7qGkRfSFmnFpez5yrjma2drEtW9VW706fbSe4l1Ha7bN8JWywBvf0DLegOgT3LJl7ZYN79Tjenq7deOj5F3V/49+s4w92mghXzoEbl0Lfv6ppyfUU3DMXw7JbYGFXFVCsjZxywb2gK8DooSrm6u+TEE7A4eDeli1bOHnyJP/85z/x9vbmhRdeoFOnTgwaNIh3332XlJTLt8KcEEIIIZxLYrZj4+3p+rbsC1y+wT05zmsEeoZPbYtp6MoWybAU6KgkuOcVCJ6lXa6z5CTUpR35WnXF9m6uxmxDKy3yUEdJv6sAVnlZ8ZCyp+7rdxYH/6cCMX6RMOgZa3Ds7Kaql9MD6AHRqmuprt2Npcv/4tj2j30Hecng1QzC+1inB5QG9+xl7qWXdvts3qXic2XH4IM6Ze5le1TRLTewHjP3AIa9rMbeS90HO/+tpl1xEwx5Hjrcag181lfm3rY5cGK56kL73XXW97Qm9G65elah0d36fpd9z4RoYjUo7wNt2rRh1qxZ7Nu3j3379vH3v/+d1NRUHn/8cVq1asV1113H559/Tm6uXBkUQgghRNPRu+U6Gtzr01KdbF2uwT2Q47wGp2fuBdUxc08P5F08Zc2ysnfyrwsorRatD7ovXI+mwa//Uvf7PWEtvFAfARC9q2+HCfBQKjyaDR3/pKYd/67u629Kmlll7Gka7JqnpvV/Uo1PGdFfPU7bX/U6LlbS9V0Prp3fClWN1WougVVTVSEUgD6P23bLD6wquFdF5l5oDxXo1dWmmEYpS+Zezllrhm+W3i23HjP3AHxCoPcjttPKBioj+gIGFVy0F3SuiZIi+K1MwRRzMfyxsObrKd8tF8pkXMrvqnAeNQruldW9e3fmzp3LyZMn2bRpE9OnT2fXrl1MnTqViIgI7rrrrvpspxBCCCGEw8p2y3WE3i339MXTXMi/0FDNchlynNcA9My9oDpm7unBvfSDkFY6JlfZMbzKs4zRJ5l7jeb8tvrNlEzZpfa3hx/0mVG/2U16YY5214NvKHj6Q/QoNU0vAOGKEn+FhV1UsYatz6tKsG5e0P1+9byjRREyK+n6HtpNVbo15arqr+Wd366CQqfXwcHP1DTvEOj7uO18epAoL0VVx9XlpUF+amlbO1dcv9ENxn+u2mAwVv0bUA2TWwCad6h6kFFabMTSLbeeM/cABs2yfaxfgADwDLAOXZCyt27buXhKFaDx8FNdfgEOf6ECrjVRvlsugF9pFl/O+bq1UYh6VOvgXllDhw7lvffeY//+/dx0003k5eWxZMmS+li1EEIIIUSN7U1SJwVtgto4NH+AVwDNfVUWRHJOcoO1yxXJcV49qa/MPT2DKPu0ykTxCav6BFyCe40reSd8NRS+6A+5SfWzTr14RpuxqlunHshJ+q1u4+JlHFGBQ4ObCu7pWpQGD5N/d81x9zKOqMIgF45CYSZs/6eafsWNoBdY0rPhMk+oqrOVqWxcS4MRooar+2dibZ878SN8NQQWdISN/6emhfWGKftU5lpZ3sGqmyrYFr7Rg46BbVRwyp6218L9J2HakTpfNND0zD+9m2l2A2XugfoM3/WrGvdwxJsVnw/tqW71ixe1deGYug3qoCocewerYFx14yyWV75bLlgDfXrgTwgnUOfgXn5+PosXL+amm26idevWLF++nFatWvHkk0/WR/uEEEIIIWrkdOZp/kj+A6PByMi2Ix1eLswvDIDU3NSGaZgLkuO8elKUbe1iVtfMPf9WaswnXYuBtpU0y5PgXuPavwDQ1P7+eXr9rPNUadfZtqXVWkN7qgH9C9JVdlJtHfyfuo0ZB77h1umhPdT4cgUZ1uCWq9DMsOIulVHX6ioY+pJ6rwC632edzy9SBZk0szUIZE9VRWvajFG3ZQudaJoa5w1UwQW92++Y+RAQRQUGQ5muufHW6Xo2oL0uuWV5B9W9SA9AcCd1e+GIymzTg1b1PeaersVAeOAk9J9Z8bmwXuo2tY6ZexdKsxCDO4C7V5mM1O01W4+9zL0AJw7u5WeoCwJ6JV/NDPFrpaiSPSUm2DoHPuvj2pnKpWoV3CspKWHlypXcc889lq4ZmzZtYvLkyWzYsIGEhAReffXV+m6rEEIIIUS1Vh1ZBcDg6MGE+oU6vJwe3EvJubyLR8hxXgPQC1/4hKkiF3VhdIPAGOvjiGq64/mXdnmTsaEaXkmRbfXUkz/VPXsvP9160tmuNLjn7gVhpdlN9rqEOqIgEw6Ujj/WdYrtc26eKtMMVMGJfR87R/fDgguw6234/a3KKwWf3aSyET0D4PqvVGXcqX/AhJ+h7XXW+QwGa9dcfWw7eyzdcttWfE4Ptp7bot7PohxY96DK3nT3haH/VOP7RQ23rZBbXrMY220BnCst9NFyaOXL1SNr5t4R9Zk1F6uMTj/Hxq2tV44G9woyYd9H9scrBMgsDdrqxS8s3dlrEMQxl1i/w3Yz95zge1FWcSEsHQPfXw9bnlfTtr0E346FLc81ThsOfg573m+cbdXVludg2wuQugdiH3PNTOUy3KufxWrz5s18+eWXLF26lPT0dDw9PRk/fjx33303N9xwA56entWvRAghhBCigVzIv8CCnQsAGN9pfI2WDfVVgcC03EpOGi9xcpzXgDLrabw93RU3w843wd0H2t9c9bySudd4zm9V2W6+ESookroHTq2C7tNqv87EXwFNZVaVDS6E9VJBpNS90HFCzde7/mEVmGjWTlUrLa/FIBUEWf8wFOdDaHe4a4dtMYjaKsqBDTOgzTjoMsnx5ba9aC2McX6LGketfNaqno3Y8Q7r+xXSUf2V17yryuKqbNy9rARrZpa9725QOzUeXsZh2P4iHF9uzfQb9i/o91dVwAOqzq4N76cKm5zZAL0fUgEGvQpvqxGVL1ePLMG9C0esvxX+UepiQmMLLw3upR9SAXM3O/970g/C0nGqCIiHP9z4jeqmXJaekam/trLd2R2Vm6Qy3wxu6nuts4y552SZe1tnq+A2wK8vqwD0thfU453/hpFvVVwmL01ljQa2Ad+wum0/6wysmgJo6jckol/d1tfQjn1rvZ/4K5zeAG2uabr21JHDmXsxMTGMGDGCDz74gG7duvHBBx+QmJjIt99+y2233SYHfEIIIYRocq9sfIW0vDTah7Tntm631WhZS7fcvMuvW64c5zUwSzGNeug+BzDyDZiRCY9kQHjvqufVu9VlJbh8VoLT07vIhvWyBszKdtmsDT3LqEW5zC9LdlMtxiXLS4PDX6n7139pP2DXe4bKPisuLfKQ9gf89CfIS4XU/XXLSNz/MRz4FFbeVRq8dICmwYnl1sfHvoO3jPDbG9ZpxYVw9Bt1v+vk6tepd3lN+8P+8zteBTRofU3lQQ+9svDOf6vAXkBr+NN6FdgDlWXp7lV1O/SMzPifVTfBjCOqW7ebV50KZdSEpnfLzThizYRriPH2HBHQGryCwGyCtAPW6aY89XnRzBD7hArsufuAKQfiZlb8fdO75QaVZu5F9AMM6vU5+n9e/04HRNsGOvVuuXkpVVdLbkw5idbgd1hvQIPVU23nMeXZPs5Ph4Wd4YsBaozIstmjtXHoC7VdsAbanVXO+dILbwZ1MQDg5I9N2qS6cji4FxgYyNy5c0lISCA2NpYHHniAoKCgBmyaEEIIIYTjzmed5/sD3wMw99q5eFV3QlXO5TzmnhznNbD6KqZRllcz1eWvOs3aqjH6TDnOl2VyqbFUGI22FqiIX1N1wYbq6FlG5btf691yazMumd7ls3k3lV1jT0hHGPuhGq+uy93q9uQK+KQ9fNYTPukAR7+1v2x1Dn1uvb/uoernzzoDPz+gAi1unhBTJkNr/8fW+xeOQlGWCgy1Gl79eqsq3FCUDX+oLHAGV9GdcdAz0OdR1a5u98LUfdD66uq3XVbkANVlvyhLBWPWlGZ6thhUfWCwvgSW/k4U50HittJpDTTeXnUMBojor+4nl8my2/AYfDkY3guBhJ9VNt2kbep3MOOQNWMNVJdd/fuod8v1agYhpUFMR4PK+jiI5btl+4SWjuWoQW5iDV5cA/r9TVUduOWV6rsL1qxxXcoe28e75qmxO0EVn1l5V82rCes0DQ6VCegd+lIFHJ3V2dLfwfDeEDVM3XfxDHeHg3v79u3j73//O61atap+ZiGEEEKIRvbZ7s8o0UoYHD2Yvi371nj5MP/LN7gnx3kNrL675daEm6c1qFjV2GKi7vRxDQOiIbK/ykAqylbVU2tD06zBvfIZXHrmXlY8FF6s2XrPblS3ra6qer4ud8NfC2D853D3DtU1tyhLPWfKgbV/rnk2aPoh1Z1Yl7Ibsk5XPj/AmnutgbZWI1RxCj34c/GkNXPKUkCho6pmWx29++eF4xWLDST9rgIlAa2rfp/cveHqd+CxXLh2oQog1ZTBqKq5gsqoTNyuKuRWFVSsb24e1u6rR75Wt02VuQfWz7s+3qS52Dqepf557zpZ7cMrSocm2L9Afd8uxlszU5t3s826bHmluj232bF26Jl7Zcc5BbXP9PEInWHcvbxU2PtfdX/ws+r74RlgfV7/H1A2WGrKhV3vqPsj/63mT/y15gVHdCm71P8YNy/VpTs/FT7r5Rzvjz2Wru9XXTLDV9RozL3yfvnlF06ePMmFCxfQyv2wGwwGnnjiiTo1zhHvvfcer7/+OklJSfTq1Yt3332XgQMrH7D0m2++4bnnniM+Pp4OHTrw6quvMn58zcbkEUIIIYRz0TSN5QdVl61p/Wo3vlVY6QnA5TrmXnnOcJx3SdA0a7e/YDvjfjWG5l3VuGAZhyBmbNO04XJgydxrpU7+u9wNO+aq7mmd/lTz9WUlqBNko3vF7tfeweqENPuM6prrSKaaruxJbXX0IFl4b7j7dziwSAUKlo5W4wsWZqq2OOr0enXbZqwKLpzfojICe1eSwZd1Wo2Dpet0pxob7O4d8J8gFWy8cEQFHi1jrHVwrC2+4Woctbxk9R0tm8WoB1Wrq0atM9bptBqGz1XB/5ICFRzpfJca068xXXGTCs7omWjOENzTg1GJv1oDsENeUBctev5FPe5+PxxZAnvfV39llS8W0+oqFSjWvwPVqSxzD1QAK/u0mqflYMfWB2psxsKL0G1K9fM6atc8lXUZ0U9lthoMMOodNWbmmA9VkHLr8yporUvdp74/fpHQ9zE1BMDhr9T3MaqGhVz2vA+//kvdv+JmuPIF+OE29X/nt9dh1L/r65XWH70YUYvBEKgH91y78FStfoX27NnDxIkTOX78eIWDPV1jHPQtWbKEmTNnMn/+fAYNGsS8efMYN24cR44cITw8vML8W7duZdKkScydO5cbbriBL7/8kltuuYVdu3bRvXv3Bm2rEEIIIRrO0bSjpOSm4O3uzfCYGpzklnE5d8sty1mO85xK+kHVhaf7NPuDu1cl4zDkp6kMn+rGx2sojlQFFXVXtlsuqMyiHXMhfpUqIuHpX7P16QGm0J72u2CH9ax5cK8o29o1z5HgXlnuXtCrNKDiE6o+19lnKg3uBRaewm31ZDVW2ZUvqKCbPkZgZH9VCOH8FjUuYWXBvUNfqNuoYTD2Y2uA3GBQr//cZtU1ObS7beaeo8J6qS6eqXttg3vJlXSHbih+ETD4H42zrcp0nQw7XrE+rmn34vqkV7ZN+0MF9U6uUI87T4IrZ9vOGzNGtb3CGG8G6HKX7ST9M5/8m1qvh1/V7bAE92LstHGA6sKcsA4632mdfuE4rJikAt8GI7S7EYa+CB6+Kqj34+0qE9Hopi4A1FVxIeydr+4P+oc1GN39Xug2VT3Wu9CX7aabXlpIJrSHamfb8Sq4t/8jGPB/jgft0w7A+kesj7tNgeZdYNTb8O042PeBaldp0TKnoRe/Ce5g/c3OTaq8iIsLcLhbblkPPPAAKSkpzJ8/nz179nDq1KkKfydP1nEwRge89dZbTJ8+nWnTptG1a1fmz5+Pr68vCxYssDv/22+/zbXXXsv//d//0aVLF1566SX69u3Lf/7znwZvqxBCCCEaztbTWwEY0GpAjcfa0+nBvazCLAqLC+utba7GWY7znMYfC2FRN1j3oDXQUBN6hkiLIU13wqAXDricg3tnN8OyW+B0bMNtI6dMt1xQJ7h+kepEPm1/zddXWZdcnaWoRg3G3UvdD2iqkmzZ6rs15V/ahb+KTJeu6f/DeHQJHP5SZfGY8q1tDeulggmgMvMqK0qgFyTpOkWNl1Y2i04fMy+ldJ2ZpZl7QQ5m7untKLsOnd4dtHwhk0tZ864qiwlg+KvW342mEBClvkeaWWV+7Sk9X293o/35r3lfVSa+dhHMuAhXzlH3A8oNNREYo9ZrLobz26pvh6Vbrp3MPX1czVMrbbun73wLkn9XgbQLR1Vl8x9LM3fPb1XbBlj7oLXYUl2cWqmyaP1bVqx8rX9fmpVmgV4s879b/3+gX/yJuRYwqKD9+6HW70B1yhb1Ce6ksnIB2oxR6y7OV0F8Z2LKUxm7oN4bn7DS/8+a83YjdkCtMvcOHDjAiy++yPTp0+u7PQ4rKipi586dzJo1yzLNaDQyevRotm2z/0Xdtm0bM2fOtJk2btw4li1bVul2CgsLKSy0HuBnZalxJkwmEyaTk1TGERXo+0b2kfOSfeQaZD+5BtlPsOmUGhh5cKvBtX4fvI3eeLp5UlRSxPmL52kVWH/jz7nSvnGG4zynkfQ7rP2L9fHpDSp7ryYcHd+sIUnmHvz2Kpz8SVVcvWEJdLqjftdflG0dC6xsd8awXiobJHUftBxSs3XqlXIjKwkw1Sa4pxeP0JetrYBoSN1T+RhVpjxC88tUoU37A+KesHZRD+ulMma8Q1RgImVXxeIemmZ9bS3sdHsML/f6a9otF1TGH8CFw9Zp2edUd0sMpRVWLyM3LlWB6JhxTd0S6PUQbH4Gts1Rj6OGVd693dMfRrxufTzkefvzGQxqPfrYhm1GV759c7H1820vc6/VVaqidG6iKlLT6iqVRXdkiXp+9H/BK1gVqTi1EjJPwJmN1uVNOfDTRJiwpm5ZbXrGYue7bSv6lqV38c5LsWYRZ5Rm7ulBXN9Q9Z7vfV8FVVdMgil7q8841tfT8XaVXevmoR4bDKVDQhyyVmB2FnrQ1ivImqHo30oFP7PP2N/fLqBWwb0OHTpgcGTsgQaUlpZGSUkJERERNtMjIiI4fPiw3WWSkpLszp+UVHkp97lz5zJnzpwK02NjY/H19a1Fy0VjWrt2bVM3QVRD9pFrkP3kGi7X/aRpGjtOq5PgkoQSViavrPW6goxBpJSk8M2ab+jk18nmuUxTJrtzdtM/oD8B7gGVrMG+vLy8WrepsTnDcZ6uycdWXvcgmE1qkPzCi46P01TWeZVV2qTBPf3EriBDZSx4XGbHsJpm3Q+gAgYdbqv7OGll6UEAryDbk+GwXqpibk2r2ppLrIUnKsvcs1R7/UPNX9mJfVl6O/Rla6uaAegNZ+Nw04rQAtpgGPuhtXsegLuPGrfPYISo4SrgevaXisG9rHgVNHXzhJDOFTcSXlo4Kfl39f3UM3FqEtzT5804ap2mB2daDLYtSnA5CIhSf86g9yPw++tQcEFl3I3/on6+s5EDVHBPz4ytTM650u6zHvazXN29Vfbe0W9gyQiV/RU11JpF12O6+k4eWKh+A35/01qsYuhLsPPfKqj9vz6qynJNxq4s60zpmJSdJ1Y+j1czayD94ikI62G92NO8i3W+0e/BsH+pitgXT6rXVt0FLX09La+sWFAmsI26dbrgXmkGo57RCOo37eJJlx53r1bfjhdeeIG//e1vTJo0iagoJ/nyN5BZs2bZZPtlZWURHR3NqFGjaN68eRO2TFTFZDKxdu1axowZg4eHR1M3R9gh+8g1yH5yDZf7fkrOSSb/eD5uBjfuvelePN1r3/Vx1U+rWHtiLSHtQxjfxxoUOphykLu+uYuC4gK8I7x5afRLNVpvenp6rdvU2JzlOK/Jx1ZO3a+CK0YPmLwHFnQoHTw9wXrCUp2ymR8hXaqetyF5BqoASUmRKtDg4WD7LxXZZ9RJLahx3jJPqABOfYx3ZdmG3iW3XMZvWGkQrabBvQtHVGaPu6/tyXdZwR1UgKE4T72mEAfGmkutr8y90teZY/9E2BC/GgBzzHW4xYyFAX+H315TTwa1twYiW11lDe4N+D/blehdZUO6WrOBygrrqT7XBRnWisR+kTWrWKuPz5dz1hr4PviZmtZ1suPrEfXPKxBuXakCYj0eqPmYlZXRM2GrC+5djFe3gW0qr758zX9UUYr4Neq39fgyNb33I9bPeNfJ6nm9mi2o3552N8B349Vn7/QG6Dih5q+luMCaMdysmmrszdqVBvdOqgs+esAtpFz3a+8g6DYNtr+oxjqsLrinZ+7Z+x9Xl+Be/Bo4V9qdt3lX23EN60oP7gWVC+6BS1fMrVVw77bbbqOgoIBOnTpxzTXX0KpVK9zcbK8UGQwG3n777XpppD2hoaG4ubmRnJxsMz05OZnIyEi7y0RGRtZofgAvLy+8vCqO3ePh4XFZnkC5GtlPzk/2kWuQ/eQaLtf9FF96AN4muA1+PtUMjl2NjmEdWXtiLScunLB5Lz/Z9QkFxQUArD6+mpfGvoSHvZPNSrjSfnGG4zywHVsZYP78+axYsYIFCxbw9NNPV5i/7NjKAC+99BJr167lP//5D/Pnz695A/SuTu1uUF10Ivqpio1nf3H8pD83WXVvMripypxNxWBQWSU55yAv1fHg5KVCP4kP76PGpNo2B459V7/BPX3g/fLvrR5ES9tf+llwcMhzvZJjeO/Ks5WMbtC8u8pcS95ZfXBPM1uDe+H10C0XKj0RNiaqTElNL8ow7GXVRXHHXNtgQfQIdXtuU8X3x9KFuJIsQzdPCOutui/vKa2SGtG/Zq/Dp7nKmCq4oMZIc/NWgVijR/133RY113JwzSrROiK8j/pNzk1UXbAry1TUu27aq5Sr8w2HCavVOG0nfoQDn6oiFj3/bJ2n/W3q/8iFI+px62tUJqLBoO4f+tzapbym8koLgBk9qg9qN2unfisunizdnqYK49jrEtzuehXcS/hZjYdZ2fFOicnadntjNFYV3Kvq9zA/Hb6/UWXO68L7OnYBwxGZlWTuQWmXfNdUq+Dexo0beeihh8jLy+PHH3+0O09DH/R5enrSr18/1q9fzy233AKA2Wxm/fr1zJgxw+4yQ4YMYf369fz1r3+1TFu7di1DhtRw/AshhBBCOI1j6erAskPzGnTFqkSH0A4269TtPr/bcj+7MJutp7cyou2IOm/PGTnDcV6Tj61cWIB26AsMQHGnSWgmE8bQXrgl/kpJ2kHMDo6haMhMwB3Q/FpQXGKGErNDyzUEd+9QDDnnKM5ORGvuOmNA2lPTcUaN57fjBpSE90drdQ3uzEE78wvFRUW2BRrqwJhxTG0jIMb28+HfDnc3bwxF2ZiS90Hzbo6tL2mXWl/zHlV+3owth+OW/DvmU6soaX971StN2Y2HKQfN3Ydi/7ZQh7FADT4t1Gc76zTF5ddjysM9/YC6G9IHTX++79+gz0z1nuvTmnXB3d0HQ+FFTKkH1YD8pdySd2MESkK6V/oeGMP74Za0Q1UtBUrC+jr8/bRsp1kHjAU7KE47BCVFuAPm8H6UuAfW6T1yBZfnmL0euDfviiFtP8Vnt6AFtsFtw8OYB8xCu+Jmy1zGCydwA8z+rSmp7v3xCoOu96k/gOLiMk+6ww3f2c5f+rwxsJ3aRsbhKrdR6X7KPo8HoPmEU2yzzYqMAW3Ub0rGcTTflupz3uwK+9tt3gt3nzAM+akUJ8ShRY+0v9L0g3iYTWgefhR7R1b8vvi2VO3LSrD5nTDEr8ZtxZ8wD5mDua/t/20Aw4kVuJtNaH5RgBlDbiLFKfvRAqoItNaA24Xj6rfFv43l98IQ2E69J+lV74vKOMN3qFbBvUcffZTAwECWLl3KoEGDCAwMrO92OWTmzJlMnTqV/v37M3DgQObNm0dubq7lCu+UKVOIiopi7ty5ADz++OOMGDGCN998k+uvv57Fixfz+++/8+GHHzZJ+4UQQghRd0fT1FhJ7Zu3r/O6OjZXV4WPpR1D0zQMBgPJOcmczz6P0WDkpi43sezgMmJPxF6ywT1nOM5r6rGV9696j8jc8xQZ/Vlz2ID5yEo6XMinK3D+8DZ2XXBsXMfInO0MAjJNPvyysvZjQdaHIbkGwoF929dx5mDTBRnrk6PjjA4/+yMhwL4UL87kJzPe4Il7QRqbln9Itmd0tcs7on/SVqKAg+fyOVluXw/26kpE3i6Orp7H8eBbHVrf4POxRAD7k4wkVPHZCc0LYShQdPQH1hT9VGkmTHju71yR+SPhwDnvfuxc/bNjL6wSvqYkxgDmi6dZucJ2uyH5hxiulVDgFszP2w4AB6pc1zD3NjQvPsy+nz/hbMBIy/TRCVvxA7afzCctyf57EJ3lQd8yj3echpT0mn3X+ub5EA0c/XUFHiW5dAAS8oLY18Tf2cZ0uY3Z29PUirbs5/ymD2hWeJJmRfEYV/yJda3fI9dTZfL1Sd5Ca+BwYiHHGuizEJWdQ3/gwskdbHZgG+X3U3juToYAF4u92FjN8m0u5tIbyDi2iZRzuXQDzuV4sauS5fq4d6c1scTHvseBUPvjBnfI+IauQJp7O7auWlXheY+SHMYDhvxU1vz0PSVG1SNyxJmZBJUU4rb5abaeKiHDx/aiR9+kT4gGjnkMxq84iSgSOfTrSk4eqZ9xUkef/V39thzJIO2Mev3BBRlcBRSe383PtdjfzjC2cq3enePHj/PKK68wZsyY+m5PjUycOJHU1FSef/55kpKS6N27N6tXr7Yc2J0+fRqj0fqP5sorr+TLL7/k2Wef5ZlnnqFDhw4sW7asduOwCCGEEMIpWDL3QuueuRcTHIObwY2cohySc5KJDIi0ZO11Cu1E/6j+LDu4jPPZ5+u8LWflLMd5jaGysZX7+R+DTHDrdjfXjlKZHIYjWbDmf0QFlBDpYJEO494ESIJmrbrWrbBHPXBbswSO7KFXx5b06Nu0bamrGo0zmp+G+0fqAkD362fSPaAVxu+HwZkNjMp4leLb1kJQNWNVOcBt8YuQA10GX0/ndrbvr3HvKdi4iy7ep+jo4OfA/eMHVZtH3kW38oUmyioZjfbha3ibLnJ9/0g0e91SU/fg8dU/LQ8jRz3J+LZ1/AyYi9H++1fcSgoYf2VH69h1gHH3MTgHF7w7OLSPjL+shz2H6R1poueI0nblpeLxcQoAA298uPIuh3n94ON3LA/73/Cg6mpYA8Zfd8GvG+kcYYTcPMiE1v1uoFUP1/6eOOJyHbPXcL4ZLF1F6+wNNtOv9ttOyZhPAHBb+iZkQ8cBY+nQqYE+CymRsPgtQowZVf6PqGw/GQ6lQSIERlxR/f+YzI7w2fuEFhygeYsYSIeWXYYSOcT+coajubA6lisMR2hjb92ahvvnTwEQPPRxxne1P482/yEMpmzGXdlFFcbJPoP7wlOWWYZqqykZX2a8TXMJ7h+rDMh2o2dgOLUSdm6ha0tvOo+sh/1g89vyoPW3pXAofPAUPiUZjL9maM3G7sQ5xlauVXCvW7duXLx4sb7bUiszZsyotBtuXFxchWl/+tOf+NOfKimhLYQQQgiXomkax9OPA9asu7rwcveiXUg7jqUfY0/iHq4NuJZd53YB0LdlX8L8wgBIzU2t87aclTMc5zX12Mpu8T+BO7h1n4qbfiIVojJDjdmnMTp6EpyvsgaNAdGOL9NQ/NTFb7fCDOtrcjWnN8Bvr2Po9Sjg4Dijx9YDGoT1wiOktEtX66vhzAYMOWfx+HkK3PWr/e65muZ4t90sdbLq3rwjlG9T+5tg4xMYz2/BeGgRnIlVY+rdvNz+GFJ5KZCXBBhwj+xdcX1leXhAzBg49h3uZzdAKzvDDSVtt94PiMb9ivGVj6HlMA81dlniNjzSdkN4mcybVPWbmenVgVBH9lHLQbDnXdxSdlo/m+l71G1wJzz8qwjWNWsF1/0PVk2G0B54BLao+UsJU4kexpRdUKBO0N0i+rju96QWLrsxe1uPUOPB6WPB+UdBzjmMF45Yf6uz4wFwD+lQ9XewLkJVEQpDXjIe5nxVRKQKFfZToSoUZPSLqP5/TFgXaDEEQ+I2DCfVkBtuIR0q/5xfMR4MbhguHMYj72zFsQdT9qhxBN19cO9yR+XvUbM2kPYHHrlnIKIHnPgO0NR4dxdPYTy/CWPeOTW2LUD6CVX4w90X9+hhcFFdnHHLSaif72Rlvy0eoarKcc55PLKO13isR2f4/jg4oqutN954gw8++IAdO3bUd3uEEEIIIRyWnpdOdmE2BgzEBMfUyzqHtFYnx1sStqBpGutOrANgYPRAwv1UUYRLObjnDMd5ZcdW1uljK1c2VrI+tnJZtR1b2VBcAJEDoEWZg3t9YPCcc6oKriNySzM8/VvWuA31Ti/okeein92TK+CbayB+NcbfXnF8uVOl3avKZqr1mQF9VICQpN9gxytweLG14i2owd6/uRo+bgcFmVVvo+CC+gP7g+8HtYNWI0ArgbV/hsNfQcZh2PKc/fXpRS+C2jlWITR6lLo9t8n+80ml3+V2N8Cdm+ohsFeqRSVVR0srXF7wdvCCi169NGW3quhcdp2RA6pfvus9MGkr3PqTY9srr/XVqltx2n5VGAEgrEft1iVcg8EAg/6hitX0eABu/EZN16uolhSp33qwBp0agleg9bc5sxZFNfJUBprDBZvKF4MqW1CiPO8giBqq7iesr/h8RukQGZEDwDOg8vWEl3acPxOrbvXfqT4zoHXpb9ehz8ust7T6bvMuav/obdSLYNSV/tui/36VpVf81dvgYmqVuffmm28SEBDAkCFD6Nq1K61bt7ZbRW358uX10kghhBBCCHtOXVDZMlGBUXi5V8zAqo2hbYby2e7P2JKwhUOph0jITMDL3YtR7UZxsUBltKXlplnG5LvUOMtxXpOPrTz4OdusLb9IVZ1TP+lzpOJsdunJoX8l1Rgbk4/KOiU/pWnbUVv7P7HcNZzfjLHdo44tVxpoos1o6zSvZnD1O6pi5q55sPkZNd2/FUzZoyqoHvsezsSp6Ue/VtUv49eqwg39nwQPX+v69KqavhHgUUnF7lt/UsG80+tUFeX8VDj6DST8GdpcYztvRmlVzRA71SftaXWV9bWaiytW19VPZns/XL+VkvXAW9ngXlYCZJ9GM7qT4d3J/nLlBbVXXWnz0yDpd4i60hqQjLRzAm5PyzoUSPRprgL551WFX4I7VB2sEJeGntNV5WajOxSqYkrkp6n7eaWVzt291fe6IQW1V0G6zBOqKntN1DS412kixD5urUJbVXAPILSHqhB/8UTF53IT1a1fNRev2o6Hg5+pCzRXvWb73fYOURnZe+dD//8Ddy9IP6ie16vv6m3MOlWxwu6ZOFh5D1z9H+hwS9Xt0Onbj7Bz4aB5Vzi93toGF1Or4N6+ffswGAy0bt2anJwcDh6s+OIvxYNdIYQQQjgXPbjXNqR+KqgBDIoehJvBjYTMBO5dei8Ao9qOws/TD/fSk2aT2cTFgosE+QTV23adhbMc5zXl2MpaQIzKcirLYISAaHUCdjHesSBJjhMF93xLg3uumLlXXAgJ1oHkDWgEFZbLctHMsOY+yM+AEW+o7q55KZB9GjCAvbHoBj+rMrUK0iH9AOSchTX3w83fw6//ss538H+Qn24NApYUwrAyz18obUtVJ8qe/jDq39bHP/4Jji6FpWNg/BfQZZL1OT2DJ9jBzLfQ7uAVBIWZKvutbLZbQabqOgf2T2brQl9fyi4oMamMwDMbAdDC+1Fi9HZsPQaDClAe+04FEloOhsTSrsT2smsaQsy11uDeoGcbZ5ui6emBcK9Aa4D54imVXQvqd6Oh/98FlBb10f9f1ERNg3s+Iep/2/Hv1cWq6rLKA2PUrd59uawcPbhX+dAXAMSMUxdSMg7Buc2Qm6Qeh/dR7+/mf6jXfvBTdRFFz5rTs+gCotX8xQVq2bJtjvubWvaHW+GJYjC6Vdx+eal71K29QKo+7eg3MOzl+stybiS1Cu7Fx8fXczOEEEIIIWruVIYK7tVXl1yAAK8AhscMJ+5UHOl5avyl27vfDqgx+YK8g8gsyCQ1N/WSDO4503FeU42tbO74J/sndIFtVHDP3olOeZpWJrjnRN1y810wuHduE5hy1Elky6Fw7FtC88sFnY8vhwOfWuefdhiSf1ePQzrbH8vKpzncuETdT94NXw2GE8vh5+kqSGbZ/mb1p9v9rsre8w5Wj0/8oG5rknUzbgG4+6jA4do/q4BccGnFbz1YGOxgkSCDEaKGw8kfVQbM4cUqSy/oCut70Kwt+Nas0ES1gturDLeibBVADO2ugnOAFjUMajJ0px7cO/cLpN9gGXPL0qWvoXWbAvs/gva3qvvi8tOsnQruJe+EPe+paQP+3vDb9W+lbrMbIbgH0G2qCu6FdK4+GKZfxLL3P8+SuVfNOJfeQRA1DM5uhK9Lu+GGdrNmPw/4u8om/HUudJtWMXPPzUN1jc48ofZN2f+nZf+fHVkMXe6uui2FF61d75vbyYzueAf88pR6vYc+V5mdLqRWY+4JIYQQQjgDS+ZecP1l7gG8d9N7fHPXNyyZtITV965m1BWjLM9Zimq4YgaUcIi5YyUBQj2LwZGxkbISoCgLjB72x2FrbHq33DwX7Jarj5sXcx1EjwSgef4B6/OaBtut1WApzISdb0Gi3v3LgYy1iD6qyxjAH6VdgDvdCZ3LZNS1GKICWEXZKsAH6v7x79X9rvc4/po8A2DcQhXUMuXAiknW8eYuqAHkHQ7uld32HwvUa/95unp8tnR8q5ZDHV+XowxGCO2p7qfuBXMJxK8GQGs5vGbritK7Fm9W3eIAWl7ZeJkzgW3gz6fh6rcbZ3vC+eiZtwcWqu9kcMeKGdwNIaA0szvnbNXz2VOb4N4VN8G1i2DcourndSS45+9AEZuhL6rfC61EPS7b3b7HA6r9WfEqoKaP5adn7oF1Pxz6wjqt8CJkn7E+XjlZXSzRpe6DJSNg2S2QVTqfvm6/FiroWJ6HD/T9q7p/ZEn1r8vJSHBP1IlZM7M/aT/Fjg4sLYQQQtSj+AvxQP0H97w9vOnbsi/9o/rTIdT2BDvUT2W/pOZIcO+SVVn3Sj1IpI/jVhV9HLKwnmrcpqamd8s15YIpv2nbUlMnV6jbdtdbxpcLKTisuoKCGgQ/ZZfqZnbtIjVtz3sqkwMcH7etz2NqG7quk9VYTgGt1eMr51i7bO6aVxrYWwbF+SoQ4Oh2dEY3uO5zlQGY/LsKGJaYrGP4OdotF6DDBGsAF9Tg9ee2qkw4sI7LV9/CSoN7KXvVNnPOgXcwWvQ1VS9nbz2+Eeo9jf2rmtZQbRbCnqDS3309SzdyYMN3yYUymXs1DO5pmnUM1ZoE9wwGlb0X0af6eS2FpM5bLz7oHM3cA/VdHjZXdYP2DITOd1mf8/CFfn9T9zf+Tf2eunla9wdYC4EcX2btDpy8U936RpRehNFgV2lwPvssfHWlyiQ+sRy+GgJFORWzAu1pMUjd6r/DLsSh4F5gYCDffPNNjVeekZFBYGAgv/zyS42XFY4rMBVwPus857POU1T+S9fAVh1ZxS2f38L9396PpmmNuu2aOJRyiDu+uoOXY19u6qYIIcQly1RiYlP8JuJOxlFYXNjg2/t6/9ecyFCDPNd3cK8qlsy9S6Rirhzn1UCrEeo2cVvFE53ykmqQNdYYPAPVCRO4VtfcC8dVJpvRHdqMgdDuaF5BuGsFGPSxk1L3qtvm3aHrFNVF1ZSrlnP3tQ3YVcVgUNl0wR1UsClmrBqj6q7tqhprzBjoeDsEd1LVcbe9BCdLswor68pdncBoGPy8un96vcpe0UpUl92adOc2uqmx+0K7W0/I1/7ZWhSkwYJ7vdRt2j5r1kyniWpg/JowukH/v9lOK83SFKJRlL+o07yL/fnqW0BpcK+mY+7lp5X+HzI4FmCrDd/w0otTWsXgY02CewAD/w5PmODRi9Yqubo+j6jCInrV8e4P2BYGCu+rinuUFMJnveDYMutFtlbD4arX1f2U3Sqj7+Bn6n9AWE/1e5hzDvZ9AOl6Jd4qgnv672f2aRVAdSEOBfdycnIoLq55ZpamabVe1lVtP72dv638GwmZDozFUg+yC7O56qOrGP7hcPX3wXA2xW9qlG0DbD+jBrvdnLCZHw//2GjbrYnU3FRu+fwWdp7bySc7PyG7MLupmySEy9l6eiuv//I6S/cvdepAvmhaC3Yu4N6l93L/d/fz7rZ3G3Rb5y6e4x8//wOzZubmLjfTqlmrBt1eWWG+l1ZwT47zaiCksxp0vbhAVfWsip65V9NsroZiMLhm11y9S27UcDVunsGIVtrF1HC+9Jg3dZ+6DeulXueENTDgKdX97J7f1HhNjvINg3sPwuQ91pNL/xbWaqxGN7jqVXX/99dVVgioggy1FVXaZTZxh7VLblB726qQjogZA1P3w51bVBXK9NKuy77hNcsCrAk9uBe/xtqNrWstx6zr9aC1Mmmvh9Q4XUI0Fj1jS+doteq60gsu5ZxThYEcpXdJ9YuwXripbwaDNXO5bNfc4gJrIK4+AosefnDDYnUxJqy3KopUvh03LFHBuvxUVUBj+4vquTZjVNfmoCvU+3dui/VCQ9+/Wi+e/Pa6tZhGSBWB24BWgEG9Rlf6X0kNuuXec889uLm51egvPDz8sqqaW2IuYdbPs1h2cBlXf3w107+bzsmMkw26zZ3ndpKel44BAx5GD9Ly0pi2dBpvb3m7UU7AC4oLLPe/3v91g2+vNnad32XTbXjb6W1N2BohXI/JbOKRHx9h/o75PLXmKZYfWt7UTWpUqbmpzNsyj39v/jcpOfb/yR9LO8bZi7UYK+USs+PMDsv9X8/82qDb+uHwD5g1MwNaDeDN8W826vGGpVvuJRLcAznOc5he1RNUF0RQV/bj16hiDvq4PiUma5chZ8ncA2twz5Uy98p2yS2llY7PZjirKrNaMvf0LqIePnDVK3DL8qozNCpjdK86C6/9zdDzL+p+cb7KiiwfGKiJ0J5qbMaCdNXtDGrXbl1AFFz3mTU42XVKw3UvDO0OlK67pFCN7ddicO3W5RkAd/8GU/bB6Pcbp0ukELrm3dTFG8vjRsrc82sBGMBsUtl4jtKDe/4NfHHTUjE33jotL1ndunlaCwvVVUQ/+PMZuGub+g0vr3kXuGsHDHxaXfgwF6vs3u73q+f1zPpdb6ux9dy91XAFXe9RFzjykq1V16vat2WrCDtSPMuJOFQtd/bs2XXaSLt2VZSFv4SsO7GO05mnLY83nNxAoHcgb45/02a+3KJcpn83nU5hnZh9Td3e20OpKrX0+s7X8+q4V/ln3D/5au9XvLPtHQZED+DK1lfWaf3VKXticyzNgcGl69mnuz5l3pZ5+Hr68vGtH9MlvOIX9UzmGZvHWxO2MrbD2MZqohAu72zhWfKLreMzvbjhRUa1G0Uz72ZN2KrGM2/LPBbvU+M2xWfG8/YNtoNt703cyx1f3UGwTzC//PkXPBvq6qkLOJhirV55OPUwJeYS3KqrxFYLmqax/KAKMk/oNqHRA0zRzaIB63h/rk6O82ooZpyq6nn0axj8D9Xt8dvSrK3QHjBlryoqYMpRJxRVZQg0Nn1cJlcpBlOUA2fj1P224y2TzW3G4Lb5KQyn10F+RpngXq/Ga9vIf6uxudIPQOtr6lb4wd1LtT35d9j/sZpW14zPdtfDjEzVba++Tr7t8fSHAf8Hv5UWIxn8bN2CcoHRQHS9NE2IGjEYrBVzQWWCNQY3D1UJPDdRdX11dPw8vZtsQAN/X/TM58wT1mn6uHe+kfUbhPcJqfp5dy8YPldlZZ9cAX0ft1b8bTVCFRRK+Fk9jrnOWiW97Xg4sEjd9/Cr/qKb3pU3KwFaOEn2vQMaJbh3uVi6fykAU/tOxcPowce/f8y64+soLC7Eq8y4E2uOreHXs7/y69lf+fPAP9MioPaprAeT1YlU1/CueHt4888xqlLYV3u/YsHvCyzBPU3TyC7MJtA7sNbbsqdsFktaXhoX8i8Q7OPYAUT8hXh+OvwTE3tOtIxfVFNf7f2KrMIssgqzeGfbO/z35v9WmEcPuHZo3oFj6cf4Jf4XNE2r8clgblEus9fNZnDrwdze/fZatVcIV5RQoK5ajWo3iuPpxzlz8Qx7E/dyVdtLf6BrTdP45ZR1PLE1R9eQlptmydwylZh4ctWTFJuLSc1NZUv8FpuqqpeTtNw0UnKt/xPyTHkkZCbQLqT+Az/nss5xLP0YHkYPru1Yh65wtdS+eXsAjqcfr9X/E2cjx3k11PFPsOFR1RU0dR+c32p9Lm2/ytjTuwR1udt64uEMfJ28W+4vT8H5bXDzMnWSd3qDCk41a6u6ROuad+eiZwzNiuJh34fWgc/1zL3G4OGjMgN/nQv9/lr39UUOVME9y+N6yPj08FN/De2qV1XXuMJMaNv4v8lC1JuoodbxUo0OhUrqh3+UNbgX0df2OXMxxr3vE5KfB1gvclgy9xo6uKdfoMo4ZJ1Wk0q5DaHlEOtQCbp2N6gMaHNpoaWyY622u94a3Ot4e/W/i4Ft1P92F8vck2q59eh4xnEAru1wLU+NeIpI/0hyinIsY+AVlRSxcOdCnl/7vGWZ1UdX12mbB1LUWBrdwrtZpt3f/34MGIg9Gcsnv3+Cpmm8s/Ud+vynD0+vfpoCU0Flq6ux8l2Sjqcfd3jZOevn8O8t/+bqj6/maNrRGm/bVGKyyZpYe2yt3bEOT19Uwb2JPSfi7e5NQmaCZazAmvhs92d8f/B7nlr9FPuT9td4+cZ29uJZ9iXta+pmiEvAqXx10jQgagCdwjoB2GQpX8riM+M5n30eTzdPOod1xmQ22QxBsCdxj83wCyuPrmyKZjoF/f9Ru5B29G7R22ZafdOLaMQExxDgFdAg26hKTHAMHkYPck25nM863+jbF03MOxja3ajuH/rSmjWm2/o8nPhB3e8yuXHbVh1n7pabc15lf53bBDteUdNOlXbJbXt9heyQMwEj1Z3Ns9RtcCfwad44bdUFXQHjPi7tmlpH5YNi5U/wnV2b0eqkWQhXduUcdVHmpu8ad7tVFdXY9TZuG//K8HPPYNz1lnW6JbjXwN1y9S6s6WWCe3rWYEN3Ca4JnxDbjOe211nvtxljvd95UvXr0otqSHDv8lRsLrYc4LcOao3RYOS6TuoD9c0fqgLd21ve5p+x/7Tp3vbT4Z9qPTZedmG2JZjVNdw6Lkfb4Lbc0+ceAF6Oe5np30/n/V/ft7RlzoY5Nd5Wam4qm+M320wrKikiIz8DgF6RqhtE2a65JeYS7l16L1O/mWoz5h3AhfwL/BKvsmHyTHn8e8u/bZ7XNI2zF8/y5Mon+WLPF3bblJCZgMlsws/Dj+Exw9HQWHF4RYX5zlxUP3xdw7syodsEAD7Y8QHmGgxYWlRSxP92/8/yeM76mr+HlblYcJF8U371M9ZAXlEeE76YwK2f38rsdbOlAIKoNU3TLJl7fVr2oXUzNaiuHjS/1G2JV5W4+rTsw7S+0wD44dAPluf14JXeRXnVkVWsO76ukVvpHPQuuV3Du1r+J+nZ5fXt1AUVcG4b0ngVcsvycPMgJjgGgGPpjT8khXACV9ykbs9vsRZz6Pu4uj21So09Fj0Swns3Resq58zdcg99ab2//0MoyLRWom03vsLsZwKvRvMq01tkwP81bPsaWvmKvo2RcSeEsOUZAOM/hw63Nu529SBZTrnxm035qhBEKeOvL6kqsGXnbfDMvdI4Q+YxNZ4sNF5gsab6P6luw/vYVhv3agbXfgpDX7IN9FXGEtyLr/cmNiQJ7tWTxKxEis3FeLp5Eu6vDpzu7HknAOuPr2fVkVV8+NuHlvnbhbTD3ejOnsQ9/HTkJwpMBVzIv1Dtdnaf383076az5tgay4lUZEAkIb62/dNnXz2bOdfMwdPNk9iTsTbBta/3f81fvv+LwxV984ryuOOrO5i6dCobT220TE/LVeMRuBvdGdBKdR04mm7NwDuQfIBN8ZvYnLCZn4/9bLPOtcfVYJb62FTrjq+zDEb/w6EfGPj+QEZ8NILvD37PC+tfsJsZoQcS2zdvz9A2qspY2fGeQAUYz11UV0Cim0Uzrf80DBjYFL+Jx358zOGg15aELSTnJFse707cbXn91SksLmTVkVV2MyZTclIY/cloJn41sV4CcJn5mTy9+ml6vNODtDzVvs/3fM4fyX/Ued3i8pRdlE1WSRYA3SO6W8Ya04PmzsZUYmJ/0n5OpJ+ol++UnuU7tM1QxnYYi4fRg2Ppxyy/P3rw6p7e9zCk9RDyi/N57KfHyMzPrPO2XY2eSd0xtCMdQ1VVxpMXGqaoVHyG2la74KYb661DaAdAgnuXLb3L5LnNcOGIuj/waejzqLrvHQLXfuZ8BQEsmXtO2C330OfW+4UXYfs/1cmruw+0Gllh9iK3QErGLlAPmrWDrk6WJVlTBiPc+hMY3GDIC03dGiFEY9Ir5maXC+4dXwZ5yWgBrclzD8NgyoVj35fO20jdcgNagYe/KmCRWdpLr7ECizXV4RZVLf1mO8X/uk0pHRPUgRCYXtAo8deaVTBuYhLcqyd6Fkt0s2iMpR+Y9s3bM7LtSDQ0Zvw4A7Nm5rqO17Fy6kq+nvQ1MwbPAOAfa/7BsA+HMfqT0SRmJ1a6jbiTcdz+5e1sOLmBv634G8sOLgOgT4s+FeY1GAzc0+celt61lDZBKvL87o3vcn9/VU1m3Yl1TP9uOoXFhVW+rqKSIp5b95ylC953B6wpynqX3FC/UMuJ3JHUI5bnNydYM/307sG69cfXA/DokEcZ1mYYZs3M53s+Z/nB5Tyx4glLRiCAWTPz6a5PK7RNP6HqENrBmiVSLriXlJOEyWzC082TCP8I2ga35bXrXsPd6M6qo6ss2R9l2QsI6Cet4zuOt3SB3np6a4X57Hlj0xvM+HEGf13x1wrr/ui3j8jIz+BAygGbsapqo7C4kD999SdLpmhZDV21uSFomsaao2sqFERxdcXmYlYdWeVQMN8ZZOSp76Kfpx++nr60DlKZe866X55f9zy3fH4LYxeOZc2xNXVe3+7zuwHoH9WfQO9AhscMB2DlEZVNov/mdI/ozsIJC2kV2IrC4sIG647qzPTPdKhvKOF+6iJXem56g2zLkrkX3DSZewAdm6v/e01RTEo4gZBOKsND5xuuBkS/+h2Yul9V+wx0spMeKDPmnpNl7uUml3ZvNkCvh9S0naUF6VpfY79yIqC1vR4m74E7N6sKh66u3fUw4wIMeb76eYUQl47KuuWWDvtgjhnP6cBr1LSD/4P0g41XUMNgsI55ml56rt1YgcXaiBlb9/+/LYaoKuj5qZD0e/XzOwkJ7tUTPfilZ7XoZgyZYfP4oUEP0SmsE8E+wTw46EGubH0luaZcLuRfILMgk892fWZ3/Zn5mTy95mnL4/zifMu4T31aVgzu6bpFdGPVvatYd986xncaz6wRs/jglg8I9ArkRMYJPtjxQaXLlphLeOC7ByxBRFABRj0gqAejwv3C6RHZA4D9SfstWYJbErZYltuTuMemK9v+ZDVm3aDoQUzpOwVQgS79Nd7T+x5+fehXPrr1IwAW71tMTlGOTfv0E6oOzTvQJUyNBZCQmWAzn75fogKjLBUbb+t2G90j1NgoZU/Ac4ty+fuqv9P73d7sOr/LZlvnstQPbcvAllzZRhUpKd9N2Z6LBRdZsFNdVV57fC2rjq6yPJdvyufbA99aHte1+9q+pH2czDhJoFcgHUM70im0E+M7qW4sde1CmV2YzY4zO+yOs9ZQXX7jTsbx8A8Pc80n17D+xPoG2UZTeGXjK8z4cQZ/X/X3pm6KQ/RAe0hp9So9uHc683Sjdfc2lZgc6rqeXZjN8kPWK3V1HdP0fNZ5knKScDO40SNC/caN6zgOgNiTsRQWF1ouMnQL74aHm4flt/BA8uUX3NMDwcE+wTT3U+Nepec1bHAvJiSmQdbvCMncu8wZjBDRz/q47P3Q7hAQ1fhtcoTeLbc2Y+4V5TRcBsM5NT41YT0qZuCV765aXnivphvUvSF4BjhfxqcQomHpwb3ymXt6EYuQLpz1Ly1kl/AzfNpDFY4I7mjb/bSh6Jlsp0vPyfTgnjONuVef3DxUkBBUVd6yLp6CY8usQ3I4EQnu1RO9i5p+4qvr07IP9/dT2XJXtr6SbhHWwhcebh4svH0hz4x8xjJm3Vf7vqoQxNI0jafXPE1qbirtQtqxcupKDFj/6fdtWfWAu17uXpZxiQwGA6Pbj+YfI/8BqABKZT7d9SlbErbg5+HHB7d8QGSAKhCij5WXmqMODMP9wmnfvD0BXgHkF+dzOPUwv5751RIgu7WrGrNgzoY5ZOZnkpabRnJOMgYMdA7rzMi2Iy1B0aKSInpE9OC5q58j1C+Uke1G0i6kHTlFOXyz35qRZtbM7EpU6+8U1okQ3xAiAyIBOJx62DKfHkTsHFamwhrYHQ/qpQ0v8e2Bb8kpyuH7A9/bzK8H96ICoxjWZhgAW05voTpl2wxqPC7drvO7uFhw0fLYkUyfEnNJpc/p7/eQ1kNYde8qVkxdYXnddc2yunvJ3UxaMokxC8ZwKkOdVBcWF/LC+hfo914/NpzYUKf127PtzDYASrQSnv352Spfe31JzE5kzbE1mPTxJOrZ7vO7WbhzIQAbTm6oUQGaplI+uBcVGIUBA7mmXJsM24ZyMOUg/f7Tj+5vd+eZn5+pct6fj/1sk428JWFLjcbWLE/P2usc3hlfT18AS+beH8l/sCVhC8XmYpp5N6NloDqw6hLexdJu3dy4ufR6pxe/nf3N4W2bNXOd2t4Uyn5WQn1VNWF9eID6VGAqsAzV0JSZe2Ur5rravhL1pHmZIgqDnm26dtSETy0y9zQNfnsD3g+FnyY2TLvOlA77EnWV7YDonoHQdUrDbFMIIZyF3i0356z6zdWVZsppIZ3J9WyJFqQuLKKZVWDvjtjGqerbqfS3f+9/4dRqa4ahM2bu1Ze2pReWTpTp4nsxHv7XB364FT7rBRset91fTczh4N6uXbtq/Hc50TOaygf3AP4+4u/Mu2Ee/77+3xWecze6c3//+1l691JigmPILsxm6R9LbeZZvG8xa4+vxdPNkzfHv0mnsE42QcKyxTQc1S9KXWE+nHa4QrELUCeW7257F4BZI2cxuv1oxndUWWB6d7TEHNWFOMw/DKPBaKmO+OKGF7nn63soKimif1R/5o6bS8fQjlwsuMhX+76ynPS2DWmLn6cfbkY3Hh78MKC6GP/3lv/iXvojZTQYua/ffQAs2rnI0tY9iXtIyk7C39Ofga3UQWDXMPU+HEqxVvLZk7RHrbdcdmP5E3BN02yyw8pmHUKZ4F6zKPq27IubwY2k7KRqqyTqlZL1MQHLZtCV70Jc/nF5yw4uo/vb3fln7D8pKimq8Lwe3NNfq8FgsARN65K5dyH/giXwWGwuZt0JVSzgjU1v8L/d/+NiwUVe3fhqvWdx6YEVUFmiO8/tdGi52gYBzZqZaUun8fDyh7n767vJLcqt1XqqUr6b6Ee/fVTv26hveuaVHtzzcvciIiACaJyKuf/e/G9ySwcO/nrf1zYVssvTM2NnDJ6Br4cvGfkZNkMF1JT+nSp7ASXCP4IOzTugofHID48AMKrdKAylWRblhwjYFL+Jj3//mJyiHP5v1f859LkqMZdwy+e3cM0n11TIIm4KmqaxOX5ztWN36t1yg32CCfVTwb08Ux55RXn12p7jGcfR0Aj0CrR8LptCm6A2eBg9yDPlXRIVc+U4rxZ6/QWC2sM170PUlU3dGsfo3XJNOWqgdkec/Al++T9VJOTo0oY5kTmnLhzT6iowusFVr0HzbjBpC3j41v/2hBDCmejBPVMuFKmxrikugItqaCUtRJ27mmPKFBfq+ZfGydoDVdRIHzJhxZ1q/D2D8dLKmi7vihtV4DR1L6QdUAHVlXerMWF1u9+BsxsrX0cjczi4179/fwYMGODQnz7v5eJiwUVLRoY+vl1Z7kZ3bux8o+Vkx57yQawScwlpuWks2beENzerMUf+Nuxv9IzsCcBLY17C082TMe3H4OXuVeM2twlug5+HH4XFhXbHY4u/EE9WYRY+7j7c0eMOAEsXz/XH11NgKuBE+gnAmjmhnwDvPLcTs2ZmQrcJLJywEA83D6YPmA7AZ7s+Y2+iGjugbFDyjh538Mv0X1gyaQktAmx/JG7teivBPsGczTrL2mOqEIceYLzmimssr99ewG5P4h6gYnBPHzfvYMpBNE0jPjPeJgspITPBUuADsJy4RQVG4evpS+dwlRFXNgBVnlkzW7avZy+WLUKgt/PK1lfaPK7Mgp0LKCopYuHOhcxeN9vmOU3TLG0pG4jQP491ydw7lHrI5vGWhC0UlRTZdCk+nnHcktFZH4pKiiyBBD0QvfLoymqXW7hzIT3e6VGrsdbiTsZZutftPLeTJfuW1Hgd1dG7Et7c5WYAvv3jW/Yl1U9Kd7G5GE3T6j2DSA/YNPdtbpnWobm6alifXU81TWPhzoVMWjzJEpA7lHKIDSdVVmjLgJZoaJbMR3v0rvrDY4Zbgv762J/5pny+3PMl721/z+bv6/1fE3cyjo2nNrLx1Eab30P9O6lnVuv0YL0eZJ/Wb5rlOf235WTGSVJyUixDDRgwcObiGV775bVq34tj6cc4kHyA05mnmfrN1Abr2uqI7MJsHv/pcaYuncqdX91p6XpbnqnERFahOhgN9gnGz8MPb3dvoP6z97YmqPFO+0X1swRVm4KHm4clK/5S6Jorx3m1ENod7j8GvR9q6pY4zjMQSr+b5CU5tswfC2wflx8Tqq5MeZCqeloQpX5fGfB/cO8f6j0WQohLnYevKsQE1q65F46qgJJXEPiqC+tam7HWZTpPatw2jnhT/SbrwS2/lo2TNdhUfJpbs/cO/g+O/wDnt6riIg+cgk6qeCpn4pqqhRU4vDcWLqz8hOpylluUy/+t+j/S8tJoG9zWctJXG7d1u423Nr/FmYtnWLRrEd8f+N4SWAn2Cebu3ndb5u0Z2ZON0zcS6BVYq20ZDUY6h3dm57mdHEw5aCmIodMztTqHdbaMVde7RW9aBrTkfPZ54k5ZAyH6if7g6MG8zdv4uPswZ/QcJnSfYFnfDZ1v4NWNr5KSm8Jnu9W4guUzDqOa2R+fxtvDm7t73c1/tv+HD3Z8wMh2Iy3dW/WAY9n16Zl7aaY0MvIz8HTztJxw6zqFdsLN4EZGfgaJ2YnsPKuywvpH9UdDY+e5nWxO2MydPe8kryjPEuCIClRt7NuiLweSD7A7cTfXd76etNw0copyiAmOsWzjWNoxcopy8PXwZXT70YAKBF8suEgz72aWLsG3d7+dbae3cebiGc5nnbd07yvrTOYZm0DK1/u/ZnjMcMvrP3PxDOl56XgYPSzjCYJ1DMiknCQKiwsrDQTvT9rPFSFXWLoelqW3s11IO05mnOS3s7+x9thaLhZcJNwvnOs6Xcenuz5lwe8LGNF2hN3160rMJaTnpdPMuxnGKq4tHEw5SFFJESE+ITw06CEe+O4Blh9czqNDHrUJMpWlB4cKiwv5x8//oF/LfpUG1E9mnCT+Qjydwzpb3m/9c+np5klRSRE/H/+Z+/rfV+XrqSm9wudt3W7DYDCw7OAy3tr8FotuX1Sn9e5L2sfkryczpPUQ9iftp0t4Fz6+7eO6NxhIz1eBpWCfYMu0Pi36sCl+E7sTd3NPn3sqLFNsLubbP76lX1Q/S9fF6vwr7l+WwN0rG19h4e0LLQHdcR3GcU+fe5j89WS+2vsVo9qNYnjMcMtvE6jg0vlsFYRvHdSaYTHDiDsVx5aELdza9Vbu+foehwIwHkYPfr7vZ6KbRVvmL//7eF3H61i0axEAV8VcZfOdC/cPJ9I/kqScJO5cfCdJ2Um0DmrNP0b+4//Zu+/wqMr8/eP3THpII40QepMqXRBQpDdFsaJiR9RdUAFXf7K6q+iufF11LejKWrHAWhewIJKlo4hSpUaQDgmBVNInmfn9cZhJQhJInznJ+3VdXJOZnPKZPEk4uc9TdP/i+/Xxto/1v/3/0/397teVna4s8/u5eG+9bFu2vt/3vW7tcWtFvow16kzeGV234DpX4JlTkKMF2xfowQEPltrWOcWARRaF+ofKYrEoMjBSxzKO6XT26TJ7tVeVs2d1df6/rSkdIjrot9O/ad/pfRradqi7y6kWrvMaCIvFGEaVuk/KOCyFXmBoe05y6fmGkncXzQ9VE1LiJTkk/wipUT3uBQIA5xPUTMpNMW6gRHaVkovm23POw+loMUzqM8NYIbyue835BEhXfiItuEQqyKnZ/wc8VZfbjWG5u+YbvdglqdeDUmhrqcUQKf4T6VjNdXCprgqHe3feeWdt1mE6u5N2K9oerWlfT9OBlAPytnrrH2P/UaVedE4BPgGaPmi6nl7xtJ5b/VyJz93Z604FnLNSWHRQdJXPJRlh2Objm7X75G5N6DLB9fqK31fo7Z/fdm3jZLFYdHXnqzXv53l655d3dDj1sKSiScUvaX6J3rnuHbUPb68WYSXH3/t6+erSlpfqm73fuHrIOSeer4jbe92udza9ox0nd6jbq8Yf0kG+Qa75r6Sinnvxp+NlK7RpY8ZGScaiIue2i7+Pvy6KvEh7Tu3R9oTt2nTcWAWnT7M+8rZ6a/Pxzdp8fLNu7n6za0husF+wgv2MlfF6xfbSR9s+0pbjW2R32DXxk4k6ln5Mn9z8iauXoLPXXveY7gr2C1ZkYKROZ5/WkbQjahfeTgdSjT+YB7QcoF6xvbTlxBat+H2Fbu91zkTSKuq1NqDlAHWK6qT3N7+vB79+UNm2bN3Q7QbtSCyaW7D4e20c0FhBvkHKzM/U0fSjZQYt7256V8+tfk79W/TXxzd97Frt2cnZe+nqzldrwbYFOpV1Sn9b9TdJ0vhO43VH7zv00daPtP7wesWfilfHqI5ltmFeQZ5uXHijdiXtUph/mF4Z90qZ20lFw5l7x/bW4NaD1SW6i3Yn7daL617UnNFzytzn18RfXW2VmpOqv/zvL3rlylf02Y7PNLzdcMWGxMrhcOj1Da/r1R9flUMOhQeEa9196+Tv4+/qQTd3/Fzdv/h+bTq2SaezTp+3x2157A67TmaelMPhUCPfRgr1D1WhvVCH04yfmTaN2+iP/f+oxbsXa+PRjcqx5ZT6+a6IvII8fbDlAz2/9nlJxqItkhHmHkw56OpVVB3OnloRAUUhlPN7vLwho9/99p1rfrw3rn5DYy4ac95z/HD4hxI98tYeWqt9p/fph0NGiDOs3TANaDFAYzqM0bJ9yzT5v5PVrUk3Lbptkev79XjGcdkddvl5+ymqUZQr+Pnl2C+a9/M87Uvep8jASA1rN6zEuY+lH1P62TuQCRkJSslJ0bfx3+rGbjcqNSdVVotV7cLbldinb/O+WnXvKqXlpqljZOnv9wEtB2jR7kWu9n5u1HMa0HKAbu91uz7a+pESMxM1e+VszV45W/ddcp/+3xX/r8T+W44bX1dn0Lw0fqlbwr1v47/VgZQDimoUpQldJujtX97WR1s/0n2X3Ffqd6rz93rjgMau0DWy0dlwL6vmeu7lFeTpl+NGT3mPCPciO0jx9aPnHtd5DUhwq6Jw70IOfGtM2h7VQwprJ+37rzHBe+tRF963olLOjlyI6MxCEgAaruDm0ukdRYtVOBcaiu5ZtI3VSxryzzovzSWyqzRsrrR8ihRrkukoqqPdeOOG2JmjUvZJyTvQCFclYxoJSUrYIJUxZZY7sKBGFb38w8u65dNbdCDlgGKCYrTgpgUXXNiiIib1nORasEGS5oyeo/k3zNfUAVOrfexzdYs2QrL1h9e75kv74fAPum/Rfa6ee12alOxdd1uv2+Rt9dbWhK0qdBQqyDdIMUHGQhYWi0VD2w4tFew5FR8a6+ftpz6xfcrcriyRjSL11rVvlZhfaUT7ESX+wGwR2kJBvkHKL8zXW5ve0qrUVZKk+y65r8xjOttr64mt2njMCAL7NuvrqtM5zNUZGDUPaV5q311Ju7Tst2U6lHpIBfYCzVw6U2fyzsjhcLjm/3Iez9lz5ekVT+uhbx6S3WFXZGCkooOiNaqDcZH8t1V/K3OOMOcw5HEdx+mxwY/phm43SJI+2vqRqw5J6hZTcviKxWJxDc39PeX3Usf9eu/XriB549GNruMV5wz3ukV3cwUMSVlJ8vf216Rek9Q8tLkrvPl428el9peMXnUvrX/JVWdabpruW3KfjuUeK3N7Z8/MkR1GysvqpaeGG8OQv9z5pY6lHyuxaIJk9Np6Z5PRU617THd5W721fN9yTfp0kp5e8bTu+fIe5RXk6ctdX+qVH1+RQ8b3e0pOitYcXOPqUSkZwUzXJl3lkEOPLH1EGbkZZdZYHrvDrslfTtZl/75Ml791uS554xKtOrBKxzOOy2a3ydfLV01DmqpteFs1DW6q/ML8Ugst2AptOpZ+rMS/4ouvOC3YtsAV7J2rIsOYK8K1SEJg0c+ec37NI2lHygxunEPvJWOhmvKGCjscDn289WPd+997JUmTekxy/Sy88sMrrgVxLmt1mSwWi2aPmK224W0lGQtaOAN0qdiiRqEtZbFY1CGig6IbRSu3INcVHD41/CnNGT2nxL+PbvpIX93+lb66/Sv96fI/STJ+3n5L/k2S8XvF38e/VO0tw1qqe0z3Mm/oFA+d2oe316UtLpUk/XXYX7X0zqV6aMBD8vXylSS99ctb+vHIjyX235pg/O758xAjIN14dGONBmQV5fw5vKv3XXrkskcUExyj5Oxkfb3n61LbFp9vz6k2FtX46ehPyivIU3SjaFevcXe6KMLo1ekcEg6YQsjZKWQqEu4lGNdHajnc6D0iuSZ4rzHO3ikRXc6/HQDUZ8V/NzscRb2mW5//Jnmdu3iydN9RY27U+s7LV7qk2E34If8smrs2vJOxSFVBrpTws3vqOwfhXhVtTdyq9Nx0dYnuoq/v+Fp9m/etkeNaLVa9fd3bWnrnUq2dslY3XXyTLm99eaneVDVhRPsRCvAOUPzpeLV/qb16v95bk7+cXGKbc4fONg1uqqs6XeV63i68XYXnPOrdtCj87Nusb6V7OQ5qNUj/vLLoTkXxIbnS2aHGZ1eHnfvTXDnk0DWdrnGFBedyhm5L9izRkbQj8rJ4qV+LfurV1Hj9YOpBpWSnuHp0FZ9PsUVYC/Vp1kcF9gI9+HXRELUjaUd066e36sOtH2rdoXXy9fJ19Yp0hnvbErZp1YFVssiihwc+LEmuGgvsBZrw8YQSk7MfSTuinSd3ymqxanSH0fL18nXttydpj7Lzs13z0507/FgqWim4+EIjkvTVnq80/ZvpJV5bsG1BiedZ+VmuULBrk656oP8D6hxlXNw/OvhR19fkuq7XSSrqcVecw+HQX//3V7276V1J0mtXvaYr2lyh/MJ8vXbsNc37eZ5ybbmu7fcn71f86Xj5WH00sv1IScb3y8CWA1XoKNQVb1+hqz+8WjlnJwI/nn5cN39ysysA/dPlf9K0S6dJKgpJ9iXvU79/9dOfvzfCkumDpuvevkag9G38t65gKDIwUo18G2nmoJny9/bX+sPr9cTyJ0q9p3MV2gv11Z6v9N9d/9UHWz5wzT/obfVWoaNQs76f5QqiWjduLavFKovF4gqBnPPCSUY4eN2C63TF21eU+HfJG5eU6in3zV6je3h4QLhrBWwn59fjQo6nH9fMb2eWWGW6uHNXy5WkEP8QV7Di7PVaXPFVqBMzE8vt4ffsqmf11IqnlF+YrxHtRujxKx7X5L7G76Bl+5bJ7rCrfXh710rYkY0iFXdPnGvOwuKrT5+7qJHFYtHAVkV3FH2sPhrSZkj5XwgZP4deFi/tTtqt/+03Fo6pSoBUPNwb0X6E63ek1WJVx6iOenjQw9ozY49rPtPJX052tVdKdopr0ZDxncbr4iYXy+6wa/m+5ZWuozqSs5O14YixYvXYjmPl4+WjO3sZPbve3fxuqQV0nD08i4d7zkC4JucMdAX/7Ue6db49J2fPdVbMhalUJtw7efbmU0y/ovDt1K81u6iGMyx0hocA0BCFnB1xk35AStkrZRySvPykVsPdWlaZgpsZvQgbgosnSxfdJPX9k9S9WKchi8W48SVJh74re986RrhXRVaLVb5evnpx7IslerTUBF8vX3WM6ljuHHQ1JSwgrMS8eOm56bLZbWrTuI1ahrVUs5BmZQ45c/ZukVSpIYvORSikokU4Kuvy1pfryaFP6vZet5c5v5tzaK4kDWs8TH8b8bdyj+UM95y9SnrG9lSQb5DCAsJcvYO2JWwrWryjfclfrJP7lAxCZ142UxGBEdqdtFvPrHxGkvTQwIdcQ2GLhyPD2g7Tf27+j27tafSEaxXWSg8NeEiSMUm/s9efVPTH7ICWA1zzczUNbqomQU1U6CjUjpM7XOFe8bm/nJy9L4sv2GF32PXGhjckGb2lfvrDT5KM3n1pOWmu7bYnbpfdYVdscKyig6Ll6+Wrj276SB/d9JHrD31J6te8n3ysPjqaftQ1FNEp/nS8Fm5fKKvFqscGP6YrO12pF8a+oPCAcBU4CvTKhldK9D574yejrkGtBinUP7To69236Ou9P2W/Fu1epPWH1uuqD6/StoRtCvEL0ZvXvKlBrQbpgf4PlAo6M/MzVegoVN9mffXH/n/UlR2NCVJX/r5S+5P3S5Kr1+mQtkO0YOICeVm8tPS3pfp2r3Hn7NyVTjPzM3XjwhvV9ZWumvHtDD363aOuIctPDXtK2x/crnbh7XQq65RmfGt04S7+ve8MgT799VM9GfekVh1YpW0J27Q7abcssijAO0AB3gHysfqo0FGot35+S5LxPTJ/y3xtT9wuiyxaetdS3dP3Hn056Ut9d9d38rH6aO+pva650grsBTqQckC/J/+u2Stmq8/rfXT5vy/Xol2L9Jf//UVL9izRzZ/crLKUFe5JxlxzkrTst2UlXnc4HNp9yvhecwbLZQWNR9KOuHqK/nnInzVvwjwF+gaqT2wf18JBkkrcTHByBvtL45e6enE6wz3nPJOS0Ws3wNsY7jyyw8gy55QsrnFAY13S3Fgk4IMtH0gqCm8qIzooWpe3vlxh/mFlzkno9OTQJzWs7TDlF+brqf89pVxbrisEbhfeTmEBYa73+m38t+Uep6bYHXb9eORHZedn64fDP6jQUagu0V1cIf7N3W+Wv7e/fjv9W6kwuMyee2f/f0jOqplwz1Zo0/L9Rsh57s0dd2kZ1lK+Xr7KKcjR8fQaXmQAqC0VDfcK8oxVAiUp5hIp+uxN2oSfpMXjpcyEmqknhZ57AKAw4+9PpR2Qfj87SqLFEMmnkdtKgoxFqMZ/Kl3xQumpI9qeXXDjwLeSvbDuazsH4V4VfX7z51p659Jy5xczi/v73a/uMd01sftELbtrmZbfvVzf3PGNvrvzO8XdE1dm77qmwU315jVvqm14W9cquBXh7MUW5BtUIqiprLv73K2nhz8t7zJW57mj1x0a13Gc3r7mbV0VeZV8vHzKPU6rsFaKblQ0b2Hx4dDOYbcvrX9J+5L3ydfLVyPajSix/4j2I3RFmysU5Buk/s37a8olU/TG1W/IIuOHvntM9xJfn1EdRskii27vdbvevu5tV4jg9PCgh/XUMGP4afEw5NvfjD/qx1401vWaxWJx1fjVnq+UnpsuH6tPqYn/paLefM4hsZK09uBa7U/ZryDfID06+FFFNYpyLQZSfKijawXeZkW9LhsHNNbAlgNL9Jpp5NvIFZY650lz1X82lBjRfoTu73e/JGPl1X9f8291a2SEkR9u/VCf7fhMK39fqa/2fCWrxaqHBj5U4jiD2wzWgwMedC0i896m9/Sn7/6kjLwM9Yjpoa/v+NrVA9LHy0evjn9V4zqO05eTvtSGBzZo+d3LFXdPnD666SN5Wb10cczFCvUPVU5BjlYfWC3JGNLp1LNpT/2hv7EC4lMrntJXe75Sn9f7aOJ/JirxjLHC4PNrnteWE1tks9tK1HpPn3t0W6/b5O/jr3+MLdllvUfTopVXR3cYrT7N+igzP1P/2f4fPbD4Ab20zlgd++rOV2vn9J3aOX2nvr7D+A9+xe8rdCz9mN76+S09u/JZSUawGtUoylXzRZEXuXqsLY1fqm0J2zTy3ZEa+d5IjXp/lD7c+qHSctN04swJ/em7P2nNQWP59jN5ZzTlv1N0xVtX6PFljysrP0sOh6PM1XIlaVwnI1xZ+fvKEj0vj6Yf1Zm8M/L18nW14dd7vi6xjSTN3zJfdoddl7e+XJP7TnZ9P1ksFj0x9Al1iOigmZfN1B8v/aPOdXnryxURGKHEzERN/u9kbTmxxdX7svi0AB2jOmrFvSv06OWPatYVs0odpyyXtb6sxPOyfqYq4u1r39b6+9eXWv27uEa+jfTmhDcVGxyrlJwULdmzxNXL0fnz5Ayxfj72c4WG5ubYcvTXOKOnbOF5LjIOph7Ue5veKzHc+6V1L+n2z27X0yuedv3sF/89FeIfov4t+ksqWtTCqfice041PSx38e7FSs9NV2RgZKnfn+7ibfV2BfbOmwSAx3OFe4fOv93pX415hPwjjIU3IjpJQ18zhikd+NaYVD37VPVqsWUb8/9J9NwD0LCFng330g9Ie85OddT+WvfVgwtrPVqSRTq1XZZja9xdDeFeVbUKa1Ujk9W7W2xIrBbdtkjPjXpOHSI7qF1EO/n7+Mvfx/+8w2ZHdRiluHviKv0H1j/G/EM///HnGl05sbi24W01d/zcEgttlMdisegfY//hCouKT7Q/sftESXL1ThncerBC/EuuTOxl9dJ717+n7Q9t18KbF8rXy1eXNL9Ej1z+iC6KvEgvjn2xRAB5SfNLtP2h7Xp6+NPl1jTmojGyyKJtCdt0JO2IDqUe0q6Tu+Rl8So1vNj5x/8nv34iyei1V1abOXtPJZ5JdA2d+3zH55Kkmy6+qWiRkKYl5xqUiib2Lz5fYnmcvdBWHlipf/30Lz294mk9veJpfbHjC0klw0lJurjJxbon9h7d2t3ovTjr+1massgIQ+/uc3eJEEwyestOHzRd6+5fpyDfIB1MPahTWafk7+2vhRMXqnloyRWb2jRuo7nj56pn056KDopWu4h2ahve1jXXmXNeNskIqKSSvb4kaeqAqeoU1UmpOama8e0M2ew2bTq+Sfd8eY9+TfhVC7cvlGTMjbnz4Z1aM2WNlty+RE8MfcI1lL5n056aMWiGLLJoYveJuqv3Xa7j+3n7acFNC/TsiGcV3ShaBfYC/XTU6EVZvGdSh8gOuqzVZbI77Ppo60dasmeJJCnAO0CPDn60VFs49335h5d1w4IbdCT9iPy8/RTqH6p24e308pUvl+p56my7YxnH9PnOzzX1q6k6kn5EBfYCSVJj/8Yltu0R00OxwbHKsmVp2b6i3nvOHqIXRV5kLGRSLLhyysjNcH0PlhX0923WV8vuXqapl04tsSJu8a/b7OGzJUkbjmzQTQtvcg0hbRXaqsS2TYKa6IH+D5S5CnVZig+pDfAOKLUAR0X5ePlUaJEUb6u37upzlyRp4faFrp8/589c89Dm6hHTQ3aHXd/v+96135c7v9TgtwbrnV/ecS3gYnfY9emOT7Vg+wI9t/o5V2/Rc9kddj2w6AH9ffXfNe6DcTqeflzbE7Zr3s/zjGPv+tI1LPncn33n1+fccM8ZAhfv4dkkqIkkY2h2dfya8Kvu+uIuPf7945Kke/reU+b3hbs4e3c652kEPJ4z3DtzVDrfcPKDZ3+3x1xS1Fuh94PSbZulxh2MFR2X3VW9Ibq/fyU5Co2aGsLKiwBQHme4l33SWFjDy1fqeJN7a8L5BUZJTY25tb3WPebmYkwc7qWkpGjSpEkKCQlRWFiYJk+erMzMzPNu/+CDD6pjx44KCAhQy5Yt9dBDDyk9vfQk9ag9Xlavaq0oXNMub3254u6J06LbFpUY0to7trdrTraLIi/Sk0OfrPAx/9D/D/ruru/ULqJdqc818j1/t+rooGhXz6EPt37omri++JBcp3PnDxvbsWR45hTsF+waVvfjkR+VlZ+l1QdXS5Ku6XKNaztnT8Cfjv6k3Um7Nf7D8a7tis+XWJ7h7Yxhy6sOrNJL61/SR1s/0kdbP1JSVtJ5Q5InhzypqZeWXDCmvEVQJGOVZOdcfJIRmpa14EFFOP8oz7IZw23PDZ19vXz1wtgXSvUSjT8dr2sXGHfSxnUcp5suvkkBPgFqHtq8zKHR0wZM046Hd+i5Uc+V+v738fLRrT1v1dd3fO0KQ1qGtiwVUN/R+w5J0jub3tGBlAPy9fLVhj9sKDN4Hdl+pIJ8gyRJDjk0ocsE/fTAT9oybYuW37NcV3e+Wo8PeVw9YowAdXSH0Rp30TgNaTNE13S+RhZZtO7QOl33sTGXYruAdvL19i1xDovF4grBX1j3gmvI8s/HjAllnQub3NnbGL79z/X/1Nu/vK1dJ3fpP7/+R9m2bF0UeVGJHrOVMbbjWP3p8j+pkU8jOeRQRl6GmoU0c/Usq6riw7lHdij6OtamqztfLclYJMQZ7hZfoKn40NytJ7Zq+jfT9diyx3Q847jmrJmjWz+9VVe8e4U+OfmJPtz2oWu/b+O/1ec7Plfc/jil5aQpOTtZ9y++Xx1e6qD9KUYvs8QziXr9p9f12o+vlajpxBlj3s9zf/ad7fXzsZ9LLGxzKsvovVO8554zUC0+h+i5DqUe0ubjmyVJaTlpWnNwjfIK8vTS+pfU/dXuuuili3Ttgmtd83n2atqrWj2/a4PzJgGLasA0gppJFqvRKy+rnPA9P1Pa8qrxcZdzpheI7CaN/9KYC+rg0qLtnApyKz4v3+6zC3l1uZ2VcgE0bP5hUvGb6W3Hl3wOz9TLmOvdcuaQe+uQVHpco0lMmjRJCQkJiouLk81m091336377rtPCxcuLHP7EydO6MSJE3rxxRfVpUsXHT58WA888IBOnDihL774oo6rhyeJbBRZ5tyBj1/xuK7sdKU6Rnas00Dynj73aN2hda4VPiXpyk5XltquXUQ79Wza0zWM9tyeccWN6zhOb258U39f9XedzDyp3IJctQxrWSLIuKz1ZfK2emvT8U269uNrXT222oa3LTGXYXk6RXVSq7BWrjn3xl40Vu3CjYBzQMsB5YYkVotVMwbN0LaEbfrh8A+6sduNF5zLcVzHcVq0e5EkaWDLqi/D7pwP0encnnuSsajMtAHT9MoPr2hi94ka3Hqwpn5VFEZO6VuxoekX6sUV2ShSq+5dpeTsZEU2inT1MHQa0maIWoa21JF0Y265K9pc4ep1ea5Q/1AtuX2Jfjv9m1o3bl3m0FKrxar3b3hfX+35ShO6TCh1rCV7ligtN01+Xn66MfrGMs9zb9979cXOL3Q0/ahu+eQWvTr+VdewbGcPr5t73KzPd3yu/Sn79X9r/q/E/pP7TK7Wogh/6P8H3dbzNl31wVU6ceaE5oyec8F59S7Ey+qlJ4c+qeX7lrtWq61tUY2i1D68vStwC/ELKfG9OfaisZqzZo42Ht2oGxbeUGp/52rLm84Yi5uEB4Srb/O+Wr5vuau3W0RghEL8QnQw9aBrv15Ne2lrwlZ9tuMzSXIt8vPKj69IMnountvj8aLIixTdKFpJWUn68ciPGtp2qCTpt9NGrzXnz7wkNQsx5o09mXlSeQV5pX6PJmUm6foF1ys9N12f3PKJnlz+pPYllx2QjWg3Qtd1u04DWw4sc0oGd3LeJChvURpUjfOm7Ndffy2r1arrr79er776qoKCyv6/JCUlRU899ZSWL1+uI0eOKCoqShMmTNCzzz6r0NDQMvdpsLx8jB4iafuN3iFBZfRs3rtQyk2WwtpLHSeW/nzUxcaqgSumSuv+nxHOBURIp3ZIX19vDLVtM066cqHkV87XP+OodOhsj+TOt9fc+wMAs/IOkGSMhlCPP7i1FFRQx4nShqelBPff5DVlz709e/Zo2bJleuedd9S/f39ddtllmjt3rj755BOdOFF2D4Fu3brpyy+/1Pjx49WuXTsNGzZMf//73/X111+roKCgjt8BzMBisah7TPc672l4eevLS4RuN118k2s12nP9v8H/T1aLVYNaDTrvsMNpl05Tm8ZtlJSVpOdWPydJurLjlSWClZZhLfXH/sb8ZgX2Ag1rO0zf3/29sUDDeeYudLJYLCV6Yf3fmP/TjMtmaMZlM3Rpy0svuO/r41/X7OGz9cTQC69OW3zoZFkLq1RU8ZVQg3yD1LVJ6dWGJePrt+i2RXpq2FMac9EY/XPcPxXmH6YrO16p7k27l7lPVfh5+yk2JLZUsCcZodPjVzyulqEtKzTfZevGrTWqw6jzzhkX6h+q23vdXirYu/eSe+Xr5atQ/1DNvWquon2jy9zf38dfL417SeEB4dqVtEvXfHiN9qfsl0UWDWg5QJLxdf3yti9L1dsyrKXGdx5/3vdQEcF+wfrvbf/Vd3d9V+L7ojru7nO3/nPzf1xzGdaF4j8j13a9tsQK6c1Cm+nSFkWfH9JmiJ4d8ax2PbzL1esv0McINf29/fW3kX9z/Sw7JWcn62DqQUU3itbtvW7XoFaD9OaEN9WzaU/XNiPaj9C0AdP0xJAn5Oftp1t73loqfLVYLBrdYbSkosV+cmw5rlW1nQv4SEbI6O9t9KpNOFNy4n2Hw6En455UWm6aHHJo4n8mlgj2wvzD9PKVL2vDAxu0aeom/fvaf2t0h9HlBtru1Ce2jyyyaM+pPSyqUYMmTZqkXbt2KS4uTt98843Wrl2r++4rv1d38Zu4O3fu1Pz587Vs2TJNnuxZPT09RszZaVUSfyn780nbjMeLbpDKC9R7/MEI/wrzpZNGD1x9f0/RHHoHl0rrznOT5Jd/GENyWwyRwqs2vykA1CvexW5St6za1DCoY1YvafwXKuw5zd2VmLPn3oYNGxQWFqa+ffu6XhsxYoSsVqs2btyoa6+t2MST6enpCgkJkbd3+V+GvLw85eUVDT3KyMiQJNlsNtlstvJ2g5s528asbfTute9qx8kdigyMVOeoznIUOmQrLP1eesX00re3f6uowKjzvlcveelf4/+lh759SPuS92lom6Ga3HtyqX3u7XOvCgoL1DS4qW7oeoMsFku55y7LfX3vM4asdr5Wfha/89Z0bhsFeAVoYreJJV4rj1VWLbp1kU5nnVbbsLZVbuc2oUXzZl7d6Wr5WnzLPVbniM6Sw6htXIdxGtt+rBxy1On32LA2wzSsTdF/9LV17g6NO2jp7UsV6h8qf6u/4nbHlXuu7tHdtejWRXrku0e06YTRc6xbk24K9Ap07eNn8dMjAx/RNR2v0YSFE1RoL9RzI56T1WGtkfcQ4hOiEJ8Q0/68S8bP8scyJk++vfvtpd7L3CvnanvidoX5h6lrdFdX6DZ76Gz1btpbg1sM1rvL3tXNw29WhygjtH7/2vfl5+2nMP8wTVg4QfmF+Xp2+LMlAvGnhj6ltze9LW+rt6b2n6qCggLd3uN2Tew2Ub5eZf88jGo/Sh9t+0jL9y3XX4b8RfGn42V32BUREKHGvo1L7BMbHKsDqQd0JOWImgUVrQC/ZM8Srfh9hbyt3q5ewpL02OWPKdAnUGM7jC2xWrYnt22ob6guaX6Jfj72s77Z+43u6X1Pmdt58nvwNM6buL/88ovrWm/u3LkaN26cXnzxRcXGlr6Z5byJ69SuXTv9/e9/12233aaCgoLzXus1SDH9pL3/KT/cSz07h2Tj8ywcZ7FI0b2MHoCnthtz5p3cZISBo96Vlt0p7XxXuvSJ0r0Dc1KkHW8bH/ev+NQnAFCvDX/duCkych5TFZhJVHfZL31a0utuLcOUVzqJiYmKji7Zk8Tb21vh4eFKTKzYxN2nT5/Ws88+e967wJI0Z84czZ49u9Trq1atUmBg9YZ/ofbFxcW5u4RqOaMzOqiDF9xuj/ZU6HhTwqboZKOTivWK1dr/rS1zm/ZqL6VK3x35rlK1Ot0aeKt0WFp6eOmFN1b122jp7oqdpywOh0NNfJsovSBdbTPaaunSqh+rvrtQO00MnKiwxmFanbZaHR0dy/1aPhD7gAocBUranqSl2/l6O9nsNl3c6GK18G+hX3/4Vb/q1zK3y1CGjuhIiddCFKJtR7epT0gf7ftln/ap5LCARCXqD7F/UHZhtrL2ZGnpnpJf9xEaIRVKu37cpV3apQuxO+wK9Q5Ven66bnzvRnVpZPTWi7RE6rvvSv7e8Mkzev0u+2GZ1vy0Rim2FEX4ROiTk8ZCQKMaj1JSfpLis+N1bdS1ikmMkST9cLTkgh2erkVeC/2sn/XJL5+43sO5srOz67gq8+Imbu2zRPaStyRH4i8qKON9eqfuk0VSQXAbOc7zdbCGd5WXPpf95FY5sk7JS5K91RgVdrhZXtv/LWvCj3J8OkQFV30phXcqOv+xH+VdmCdHWHsVNL1cquTX2uw3cRsC2sgcaCcP02yYdLMx/3Lx34u0k+fzhLbxqHDv8ccf1/PPP3/ebfbsqViIcT4ZGRm68sor1aVLFz399NPn3XbWrFmaOXNmiX1btGihoUOHKiIi4jx7wp1sNpvi4uI0cuRI+fhceEgp6p6ntNFluZcprzBP0Y3KHnra0FWmncZrvArthR61kqmZTNCEKu9b1z9P0cei9cBXD2hfzj7tyzHCxMFdBmvcoHElttu4YqPid8ZrY/5GHUo7VOJzFze5WP+46R/yshjfL9WZf9Hdemb01Jfvf6njecc1fNTwMqdzSE5OdkNl5sRN3NrnZc/TOFllzUrQyq8+VK530Vy3VnuerjpzVJIUt/mw8rellXucJln5ulRSzu8r5e3IlZekTTldlPDddwrxvlGXesUrIG2f0r6cpB+bPevar33qf9VV0vGCGG3+rmo3EyXz38RtCGgjc6CdzIF28lyecBPXo8K9Rx55RHfdddd5t2nbtq1iYmKUlJRU4vWCggKlpKQoJqbsO+ZOZ86c0ZgxYxQcHKxFixZd8I8gPz8/+fmVvkj38fEhNDIB2snzubuNIn3Ov3gHDBVtJx/x8+ZOdfXzNKjNIL1y1Su6b1FRcHJF2ytKnbtFY2ORGmew5+vlq/zCfNdK1AF+519oxixahrdU44DGSs1J1YH0A+oeU3ouTv4v4iaup7Es6Cwl79Lw7lFytC62KFfyTlkOOOTwDdWIq24+/9CwM92k959To4KTkiRHSBv1uuFp9XLO05dxlRwfdlFUzg5d2TNMjlhjESyv7z+RkqWmF4/WuEvGlXf0cnnKDUKUjzYyB9rJHGgnz+cJN3E9KtyLiopSVNSFJzAfMGCA0tLStHnzZvXp00eStHLlStntdvXv37/c/TIyMjR69Gj5+fnpq6++kr+/f43VDgBAQzK83XDd2/deLdmzRP9v8P9zLaJSnHPFXMnoqff5rZ9re+J2NfJp5Fpltj6wWCzqEt1FPxz+QbuTdpcZ7oGbuB4noquUvEve6fskn6uLXj9zSJJkadxBPr6lF3gqoXFbyT/CWFlXkqX/LPkUD+0j2ktd75R2vCPvtTOkWzZI3n7GKr2SvGJ6yasaX+cG0U4mRxuZA+1kDrST5/KEdvGocK+iOnfurDFjxmjKlCmaN2+ebDabpk2bpptvvtk1yfLx48c1fPhwffjhh+rXr58yMjI0atQoZWdn6+OPP1ZGRoZrXpWoqCh5eTGMDACAypg1ZJYev+LxcofUXtHmCg1vN1wXRV6k+/vdLx8vH/Vt1rfMbc3OFe6d3O3uUjwWN3E9TMTZ1a2Tz/meda5227gCK9haLNLYD6TfvpCCW0rd7i69zcDZ0r5FUtJWaf0s6bLnpJS9xueielS9fgAA4GLKcE+SFixYoGnTpmn48OGyWq26/vrr9dprr7k+b7PZFB8f7xr7vGXLFm3cuFGS1L59+xLHOnjwoFq3bl1ntQMAUF+cb668UP9QvXXtW3VYjft0iTaCkt1JhHvVxU3cOhLR2XgsFe45V8qtYO/atlca/8oTFCuNeV9afLW0+WUp57TkKJT8w6Viq2gDAICqM224Fx4eroULF5b7+datW8vhcLieDxkypMRzAACAmtKtSTdJ0q6kXcrKz1Ij30ZursjcuIlbB5w991L2SA5H0dx6ac6eezU4dL7deKnXg9LWudLuj4zXut51/vn8AABAhZk23AMAAPAUbRq3UcuwljqSdkSrDqzSVZ2ucndJpsZN3DoQ1kGyWKW8dCkrwehhJ1VuWG5lDH5BKsyTfn1LatJHuuzvNXt8AAAaMKu7CwAAADA7i8WiKzsaQxNnfT9Lu07uksPh0N9X/V09XuuhCQsmuLdA4FzeflLY2V6OyWdXKc4/YwR9khH+1fT5Rv5buveAdPN6yZs5EQEAqCmEewAAADVg7EVjJUnZtmxdv+B6/em7P+m9ze8pMz9TxzKOubk6oAzh58y7l7rfeAyIkvzDauecoW0I9gAAqGGEewAAADWgS3QXPTjgQfl6+cpmt2nx7sWSpF5Ne7m3MKA8xefdk2pnvj0AAFDrCPcAAABqgMVi0fRB07XuvnWKCIyQRRY9PPBhfX7r5+oQQVgCD3TuirmVXSkXAAB4BBbUAAAAqEGRjSK19M6lyrZlq2VYS0nSpB6TFKc4N1cGnMM5LNfZc+/0TuOxcUf31AMAAKqEcA8AAKCGRTaKLPF8VPtRbqoEOI/wTsZjdpKUkywl/mI8j7nEfTUBAIBKY1guAABALfP18nV3CUBpvkFSsNG7VMfXS+kHjI+b9HVfTQAAoNI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\n",
      "text/plain": [
       "<Figure size 1500x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1, ax1 = plt.subplots(2,2,figsize = (15, 8), sharex = True)\n",
    "start_point = 25100\n",
    "end_point = start_point+500\n",
    "\n",
    "ax1[0][0].plot(s0_1, '-', label='I', color=ch)\n",
    "ax1[0][0].set_xlim(start_point, end_point)\n",
    "ax1[0][0].set_ylim(min(s0_1[start_point:end_point])-0.5, max(s0_1[start_point:end_point])+0.5)\n",
    "ax1[0][0].set_ylabel('I [mV]', fontsize=12)\n",
    "ax1[0][0].set_title('patient '+address_1, fontsize=14)\n",
    "ax1[0][0].legend(ncol=2, fontsize=10)\n",
    "ax1[0][0].grid()\n",
    "\n",
    "ax1[0][1].plot(s0_2, '-', label='I', color=cs)\n",
    "ax1[0][1].set_ylim(min(s0_2[start_point:end_point])-0.5, max(s0_2[start_point:end_point])+0.5)\n",
    "ax1[0][1].set_ylabel('I [mV]', fontsize=12)\n",
    "ax1[0][1].set_title('patient '+address_2, fontsize=14)\n",
    "ax1[0][1].legend(ncol=2, fontsize =10)\n",
    "ax1[0][1].grid()\n",
    "\n",
    "ax1[1][0].plot(s1_1, '-', label='V4', color=ch)\n",
    "ax1[1][0].set_ylim(min(s1_1[start_point:end_point])-0.5, max(s1_1[start_point:end_point])+0.5)\n",
    "ax1[1][0].set_xlabel('index', fontsize=12)\n",
    "ax1[1][0].set_ylabel('V4 [mV]', fontsize=12)\n",
    "ax1[1][0].legend(ncol=2, fontsize=10)\n",
    "ax1[1][0].grid()\n",
    "\n",
    "ax1[1][1].plot(s1_2, '-', label='V4', color=cs)\n",
    "ax1[1][1].set_ylim(min(s1_2[start_point:end_point])-0.5, max(s1_2[start_point:end_point])+0.5)\n",
    "ax1[1][1].set_xlabel('index', fontsize=12)\n",
    "ax1[1][1].set_ylabel('V4 [mv]', fontsize=12)\n",
    "ax1[1][1].legend(ncol=2, fontsize=10)\n",
    "ax1[1][1].grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4c5c929",
   "metadata": {},
   "source": [
    "We implement filtering since the signal is visibly noisy and includes a baseline wonder. We filter with both fast Fourier transform and Wavelet transform tecniques, which are applied serially.\n",
    "\n",
    "First of all, for the **Fast Fourier Transform**, we want to display and analyze the frequency spectra (which is extracted by the <code>power_fft</code> function)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "84685f81",
   "metadata": {},
   "outputs": [],
   "source": [
    "def power_fft(vect, timestamp):\n",
    "    '''\n",
    "    function that returns the fft, absolute value of fft and range of\n",
    "    frequencies (vect_all_freq) used in the fft\n",
    "    '''\n",
    "    vect_fft = fftpack.fft(vect)\n",
    "    vect_power = np.abs(vect_fft)\n",
    "    vect_all_freq = fftpack.fftfreq(vect.size, d = timestep)\n",
    "    return vect_fft, vect_power, vect_all_freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3a27c0b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "timestep = 1/freq\n",
    "norm_fft_1, norm_power_1, norm_all_freq_1 = power_fft(s0_1, timestep)\n",
    "norm_fft_2, norm_power_2, norm_all_freq_2 = power_fft(s0_2, timestep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5ba171e8",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2, ax2 = plt.subplots(1, 2, figsize = (15, 3), sharex = True)\n",
    "\n",
    "ax2[0].plot(norm_all_freq_1, norm_power_1, '-', label='I spectrum ', color=ch)\n",
    "ax2[0].set_xlim(0,max(norm_all_freq_1))\n",
    "ax2[0].set_ylim(10**(-2), 10**5)\n",
    "ax2[0].set_yscale('log')\n",
    "ax2[0].set_xlabel('frequency [Hz]', fontsize=12)\n",
    "ax2[0].set_ylabel('power', fontsize=12)\n",
    "ax2[0].set_title('patient '+address_1, fontsize=14)\n",
    "ax2[0].legend(loc='upper right', ncol=2, fontsize=10)\n",
    "ax2[0].grid()\n",
    "\n",
    "ax2[1].plot(norm_all_freq_2, norm_power_2, '-', label='I spectrum ', color=cs)\n",
    "ax2[1].set_ylim(10**(-2),10**5)\n",
    "ax2[1].set_yscale('log')\n",
    "ax2[1].set_xlabel('frequency [Hz]', fontsize=12)\n",
    "ax2[1].set_ylabel('power', fontsize=12)\n",
    "ax2[1].set_title('patient '+address_2, fontsize=14)\n",
    "ax2[1].legend(loc='upper right', ncol=2, fontsize=10)\n",
    "ax2[1].grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "249372b0",
   "metadata": {},
   "source": [
    "We notice a peak at around 60 Hz, which is due to the power supply of the recording device and must be discarded. We also see high values of the spectrum for low frequency, which should be discarded as well.\n",
    "\n",
    "Therefore, we decide to implement a band-pass filter: the precise cut frequencies are chosen in the interval $[0.1-55]$ Hz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "51e35baa",
   "metadata": {},
   "outputs": [],
   "source": [
    "down_freq_lim = np.array([0.1, 1, 2])\n",
    "up_freq_lim = np.array([25, 35, 45])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ea8ab8c",
   "metadata": {},
   "source": [
    "We notice a peak at around 60 Hz, which is due to the power supply of the recording device and must be discarded. We also see high values of the spectrum for low frequency, which should be discarded as well.\n",
    "\n",
    "Therefore, we decide to implement a band-pass filter: the precise cut frequencies are chosen in the interval $[0.1-50]$ Hz.\n",
    "\n",
    "The cut frequency are chosen visually based on the results of their filtering (which is done by the <code>filtered_power</code>) that are shown in the plot below.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f5670b2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def filtered_power(vect_fft, vect_all_freq, down_freq_lim, up_freq_lim):\n",
    "    '''\n",
    "    function that filtered the signla starting from its fft, by limiting\n",
    "    the range of frequencies from vect_all_freq to the range\n",
    "    [down_freq_lim, up_freq_lim]\n",
    "    '''\n",
    "    vect_filtered_fft = vect_fft.copy()\n",
    "    vect_filtered_fft[np.abs(vect_all_freq) >= up_freq_lim] = 0\n",
    "    vect_filtered_fft[np.abs(vect_all_freq) <= down_freq_lim] = 0\n",
    "    vect_filtered = (fftpack.ifft(vect_filtered_fft)).real\n",
    "    return vect_filtered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4983833f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+ycnJhISE+Kyd8SW1M1WD6sOd6sSV6sNdRdZJaGgoUVFRhSbvd7Y1R48e5eTJk2pn1M5UWqoTV6oPV6oPd5WtnUlOTi5xUqxq22PR4XCwe/du/P39adSoEUFBQRX6j5yWlkaTJk1cgoansyyLkJAQGjVqlL/s+PHjALRs2RKHw5EfQDz1OIGBgdjtdpo3b+5RWex2O1lZWbRo0QJ/f/9SvqIz53A4SE1NpUaNGj79QFBZqD5cqT7cqU5cVVR9WJZFYmIi8fHxtG7d2u1909kwW5bl03amMlE7UzmpPtypTlypPtxVRJ1YlkV2djaJiYns37+f1q1bu50rMDCQgIAA4uPjCQoKUjujdqbSUp24Un24Un24q0ztjKeqbWAxOzsbh8NB06ZNi+01WJ6CgoIICQkpcn1ERER+jxenjIwMatSokb8sPDycrKys/N8tyyItLY3IyMhij30q57CJkJAQnzfE2dnZhISE6E0F1cfpVB/uVCeuKrI+GjRowJ49e8jJySnyfTMnJ8fn7UxloXamclJ9uFOduFJ9uKuoOgkNDSUwMJC9e/fmn+900dHRWJZFo0aN1M6onam0VCeuVB+uVB/uKlM746lq/5eryH9eu91Oenp6/lCF7Oxs0tPT8xMPHzhwgN27d+dv36BBA7Kysjhw4ED+EOhjx44RFRWVv01UVBSJiYkkJSWRkZHBvn37cDgc1K9fv8Jel4hIdeNNjxB9SBIRkdIoqf1wrlc7IyIipVFW7Ue17bHoC+np6WzdujX/9/379wNQr149WrRoQU5ODtnZ2fnrg4ODad26Nfv37ychIYGgoCBiYmJchj3XrVuX3NxcDh06RE5ODmFhYbRu3dqrbqsiIiIiIiIiIiLeUmCxAtWsWZNevXoVub5FixaF7tOhQ4dijxsZGUlkZOQZl09ERERERERERMRT6jcvIiIiIiIiIiIiXlNgsYoZP348o0ePdlseFxeHzWbjxIkT5V6GzMxMxo8fT+fOnQkICCi0PM4y9ejRg+DgYGJjY5k+fbrL+vHjx2Oz2bDZbAQGBtKqVSuefPJJMjMzy63sznqy2Wz4+flRq1YtunfvzqRJkzh8+LDXx7PZbHz77bdnXK7p06fnl8v55e/vz4wZM4pcb7PZeP/99wtdfurX5MmTz7h8Z5Oqcg0tWrSIvn37Uq9ePUJDQ2nXrh2vv/66yzY33XQTderUwd/fn8DAQFq0aMGkSZOq7TXk7++fXx+nXiPO9bqGPFdVrhNnmTxta4KDg+natSsPP/xwtb1OTm9n6tSpo+ukHFSVa8iTtubuu+/Of19VW6O2pqxUlWvEWSa1M57RPU3FqSrXkNoZ71TVdkZDocUrOTk52O12QkNDue+++/jqq68K3W737t2MHDmSO++8k08++YT58+dz6623Eh0dzfDhw/O3GzFiBB9++CE5OTmsWLGC8ePHExISwksvvVSur2Pr1q1ERESQkpLC6tWreemll/jggw+Ii4ujc+fO5XruokRERLjk4HQ4HC4TRJy+HqBWrVpccskl+b/PmjWLJ5980mW7GjVqlGOpxVueXkPh4eHcc889dOnShfDwcBYtWsQdd9xBeHg4t99+e/52gwcPZsaMGdjtdlatWsWNN96IzWbjxRdfLNfXUVmvoeXLl1OzZs38RMSn5qTVNVR1lFdbk5WVxaJFi7j77rvx8/OrtteJ8//b4XBw8uRJmjRpUuh6J10nVU9ZtzXDhw9n+vTp5OTkqK1RW3NWUDtTfnRPUz2onSk/VbKdsaqpjIwMa9OmTVZGRoZlWZblcDistKw0n3w5HA6Py33jjTdao0aNclu+YMECC7COHz+ev+zLL7+0OnToYAUFBVnNmze3XnnlFZd9AOubb76xcnNzrRUrVli5ublWrVq1rA8//NCyLMvavXu3BVgzZ860+vXrZwUHB+evK6k8kyZNsjp27OiybMyYMdbw4cOL3Ndut1uXXnqp1b179/xlSUlJ1tixY61GjRpZoaGhVqdOnaxPP/00f/0PP/xg1apVy8rNzbUsy7LWrFljAdbDDz+cv80tt9xiXXvttUXWk2VZVnp6utW2bVurb9+++cuWL19uDRkyxKpXr54VERFh9evXz1q1alX++ubNm1tA/lfz5s0ty7KsHTt2WJdddpkVGRlphYeHW7169bLmzZvnVken+vDDD61atWq5LLPb7dbx48ctu91e6HpPj1OhHA7Lyk4tly97Zop1/MgBy56Z4r7ex9fQqcrqGirM5Zdfbl133XX5v99www3WxRdfbNnt9vxlf/vb36r1NeS8ZopaXxJPtzu9DTlVcnKyBViJiYlu2/iqrfGmnbGssr9Ovvrqq/x2xrLK7jrxtq1xvq9efvnl1fo6cTq1nSlsvafHqVDl2M6orTFObWvsdrs1btw467LLLnPZRm1N5Whrmjdvbm3YsKFStDO+vqdRO2NUlmvkVFXunkbtjGVZamd8fQ1VlnYGsJKTk0s8jnos5snIyaDzW76JSK+/bz1hQWFlesxVq1Zx9dVXM3nyZMaMGcOSJUu4++67qVevHuPHj/fqWI888givvvoq3bt3JyQkxKN9li5dypAhQ1yWDR8+nAkTJhS5z4YNG1i+fDkxMTH5yzIzM+nZsycPP/wwERERzJ49m+uvv55WrVpx7rnncuGFF3Ly5EnWrFlDr169WLhwIfXr1ycuLi7/GAsXLuThhx8utryhoaHceeedPPDAAxw5coTIyEhOnjzJjTfeyL///W8sy+LVV1/l4osvZvv27dSsWZMVK1YQGRnJhx9+yIgRI/D39wcgNTWViy++mH/+858EBwczY8YMLr30UrZu3UqzZs08qr8qKzcd3iqfJyF+QO2iVt6XCoHhZXo+X19Dp1uzZg1LlizhueeeK3KbDRs2sGTJEpo3b56/TNdQ5eKrtqY82hnw/XVSmrZm06ZNLF26VNdJVVWO7QyorVFbU/WvId3TFE3tjK4Rj6idKZLaGV1DRVFgsQr68ccf3bqx2u12l99fe+01Bg8ezBNPPAFAmzZt2LRpEy+//LLXbyATJkzgb3/7m1f7xMfHExUV5bIsKiqKlJQUMjIyCA0NdXktubm5ZGVl4efnx7///e/8fRo3bsyDDz6Y//u9997L3Llz+fzzzzn33HOpVasW3bp1Iy4ujl69ehEXF8cDDzzA008/TWpqKsnJyezYsYP+/fuXWOZ27doBsGfPHiIjIxk0aJDL+nfffZfatWuzcOFCLrnkEho0aABA7dq1adiwYf52Xbt2pWvXrvm/P/vss3zzzTd8//333HPPPUWePzk52eXvWqNGDbZs2VLs+vj4+BJfl7irCteQU5MmTUhMTCQ3N5fJkydz6623uqyfO3cuERERLtfQlClT8tdXt2vo1CGdp18juoa8UxWukzNpa6rzdXLq3zU8PNwll5Cuk7JTFa4hp5LamtmzZ+sayqO2puxUhWtE7YzuaSqzqnANOamdObvbGQUW84QGhrL+vvU+O7c3Bg4cyH/+8x+XZcuWLeO6667L/33z5s2MGjXKZZu+ffvyxhtvYLfb86PonujVq5dX5fOG87WkpaXx2muvYVkWV1xxRf56u93O888/z+eff87BgwfJzs4mKyuLsLCCp6H9+/cnLi6Of/zjH/zxxx+88MILfP755yxatIhjx47RqFEjWrduXWJZLMsCyM8BkpCQwOOPP05cXBxHjhzBbreTnp7Ovn37ij1OamoqkydPZvbs2Rw+fJjc3FwyMjJK3K9mzZqsXr3a4/XOfAuVSkCYedJWDhwOBykpKURERLi/9gDvno5XpWvojz/+IDU1lT///JNHHnmE2NhYxo0bl7/+wgsv5L///S8ZGRm8/vrrBAQEVOtrKC4ujho1auDn5+f2f1IZriFftTXetjNQta6Tkjhfy8mTJ3n55ZcJDQ2t1teJ8zpwOBykp6cXuR4qYVtTju0MqK0pqq0ZMGAAU6dOJS0tTW1NJW9rdE9TNLUzuqfxiNqZIqmdUTtTFAUW89hstnIZJlYewsPDiY2NdVl24MABr49js9nyLxqnnJycQs/nrYYNG5KQkOCyLCEhgYiIiPwne85jO1/LBx98QJcuXfjggw+47bbbAHj55Zd58803eeONN+jcuTPh4eFMmDCB7Ozs/GMMGDCAadOm8ddffxEYGEi7du0YMGAAcXFxHD9+3KOnEmDedIH8odg33ngjR48e5c0336R58+YEBwfTp08fl3MX5sEHH2TevHm88sorxMbGEhoaypVXXlnifn5+fi5/V2ejU9T6SslmK/Pu+/kcDgi0m+Of4ZtnVbiGnFq0aAFA586dSUhIYPLkyS6NcFhYGLGxsfj5+TFt2jS6du3KBx98wC233AJUv2uoZcuWhX9Qo3JcQ2prDF+1NQ6HgylTptC/f/9qfZ04/66ntzOnr6+UyrOdAbU1RbQ1p74WtTWVu61RO2Oonakc7QxUwXsatTOA2hlQO+MN34c2pVy0b9+exYsXuyxbvHgxbdq0yX8q0aBBA5fhT9u3b3fruVBaffr0Yf78+S7L5s2bR58+fYrcx8/Pj4kTJ/Lkk0+SkZGRX+ZRo0Zx3XXX0bVrV1q2bMm2bdtc9nPmU3j99dfz3yycbyBxcXEMGDCgxPJmZGTw7rvv0q9fv/yuzIsXL+a+++7j4osvpmPHjgQHB5OUlOSyX2BgoFt388WLFzN+/Hguv/xyOnfuTMOGDdmzZ0+JZZDKxdfXUGEcDgdZWVlFrvfz8+Oxxx7j8ccf1zUkFcLX10lp25pHHnlE14lUCr6+hgqjtkYqE19fI2pndI1Udb6+hgqjdubso8DiWeof//gH8+fP59lnn2Xbtm189NFHTJkyxSUvwaBBg5gyZQpr1qxh06ZN3H333QQGBnp0/E2bNrF27VqOHTtGcnIya9euZe3atfnr77zzTnbt2sWkSZPYsmUL77zzDp9//jkPPPBAsccdPXo0/v7+vP322wC0bt2aefPmsWTJEjZv3swdd9zh9tSwTp06dOnShU8++ST/zaJfv36sXr2abdu2Ffpk4siRI8THx7N9+3ZmzpxJ3759SUpKculK3rp1az7++GM2b97MsmXLuPbaa12eTIJ5ijF//nzi4+M5fvx4/n5ff/01a9eu5a+//uKaa67B4XB4VK9SeXh7Da1cuZI777yzzK6ht99+mx9++IHt27ezfft2PvjgA1555RWXoQ2Fueqqq3QNSYXx9Dp555132Lp1a5lfJ6Vta3SdSGWhtkbXkBRP7YyuETkzamd0DVWIEueNPksVN612ZVaaaeUDAwOtZs2aWS+//LLLPgcPHrSGDRtmhYeHW82aNcufov30aeXXrFnjdr7Tp1R3fp1epm7dullBQUFWy5YtS5yS3m63W8ePH7eef/55q0GDBlZqaqp19OhRa9SoUVaNGjWsyMhI6/HHH7duuOEGtzq4//77LcDavHlz/rKuXbtaDRs2LLSeAMtms1k1a9a0unbtaj300EPW4cOHXbZdvXq11atXLyskJMRq3bq19cUXX1jNmze3Xn/99fxtvv/+eys2NtYKCAjIn1Z+9+7d1sCBA63Q0FCradOm1pQpU6z+/ftb999/v1s9OhU2HbyzPux2e5lPK18VnVofZ6K8rqHWrVtbP/30U5ldQ2+99ZbVsWNHKywszIqIiLC6d+9uvfPOOy6v/4YbbrAuvvhitzp54YUXqu01VNT/SFlfQ8W1IcnJyRZgJSYmVsl2xrLK/joZOnSoFRoaWubXibNMnrY1p76PVOfrxOn091W1NWprTm9r7Ha7NW7cOOuyyy5zO3Z1voYqS1vTvHlza8OGDWpn1M5UumvkVLqncaV2Ru1MVWpnACs5ObnE49gs67TB9NVEZmYmu3fvpkWLFqWeKv1sYbfbWbNmDd27d/cqeWtZKzaRbTWk+nCl+nCnOnFVkfVRXBuSkpJCrVq1SExMJDExUe0MamcqK9WHO9WJK9WHu8rU1nTp0oXZs2fTqlUrtTNqZyot1Ykr1Ycr1Ye7ytTO1KpVi+TkZCIiIoo9jv5yIiIiIiIiIiIi4jUFFkVERERERERERMRrCiyKiIiIiIiIiIiI1xRYFBEREREREREREa9V+8BiNZ27RkREzoA3bYfaGRERKY2S2g/nerUzIiJSGmXVflTbwGJgYCAA6enpPi6JiIhUNdnZ2QDFzjzpXKd2RkRESsPZfjjvW0539OhRl+1ERES8UVI746mAsihMVeTv70/t2rU5cuQIAGFhYdhsNh+Xyjfsdjtgphov7ia5vDkcDrKzs8nMzNRU86g+Tqf6cKc6cVVR9eFwOEhMTCQsLIyAgKKbUbUzBdTOVE6qD3eqE1eqD3cVUSeWZZGens6RI0eoXbt2ke+baWlphIeHq51B7UxlpjpxpfpwpfpwV5naGU9V28AiQMOGDQHyG+PqyuFwkJSUxJ49e3x6MVuWRUZGBqGhodX2Q9GpVB+uVB/uVCeuKrI+/Pz8aNasWYnnUTtjqJ2pnFQf7lQnrlQf7iqyTmrXrp3fjhSlTp06BAcHq51RO1NpqU5cqT5cqT7cVbZ2xhM2S0k5sNvt5OTk+LoYPpOamkqvXr1YuXIlNWrU8Fk5cnJy+P333+nXr98Zd8U9G6g+XKk+3KlOXFVkfQQFBRV545KSkkKtWrVITk4mIiICUDujdqZyUn24U524Un24q6g6CQwMLLYHyeltjdoZtTOVlerElerDlerDXWVtZ4pTrXssOvn7+/u0y7yvZWdns3fvXoKCgggJCfFZOfz9/cnNzSUkJERvKqg+Tqf6cKc6cVWZ60PtjNqZykj14U514kr14a6y1onaGbUzlZXqxJXqw5Xqw11VrBMNYhcRERERERERERGvKbAoIiIiIiIiIiIiXlNgUURERERERERERLymwKKIiIiIiIiIiIh4TYFFERERERERERER8ZoCiyIiIiIiIiIiIuI1BRZFRERERERERETEawosioiIiIiIiIiIiNcUWBQRERERERERERGvKbAoIiIiIiIiIiIiXlNgsYK9/fbbxMTEEBISQu/evVm+fHmR2w4YMACbzeb2NXLkyPxtxo8f77Z+xIgRFfFSRERERERERESkGgvwdQGqk1mzZjFx4kSmTp1K7969eeONNxg+fDhbt24lMjLSbfuvv/6a7Ozs/N+PHj1K165dueqqq1y2GzFiBB9++GH+78HBweX3IkRERERERERERFCPxQr12muvcdttt3HTTTfRoUMHpk6dSlhYGNOmTSt0+7p169KwYcP8r3nz5hEWFuYWWAwODnbZrk6dOhXxckREREREREREpBpTj8UKkp2dzapVq3j00Ufzl/n5+TFkyBCWLl3q0TE++OADxo4dS3h4uMvyuLg4IiMjqVOnDoMGDeK5556jXr16RR4nKyuLrKys/N9TUlIAyMnJIScnx5uXVaac5/ZlGSoT1Ycr1Yc71YmrylIfvj5/ZaB2pmpQfbhTnbhSfbirLHXi6/P7mtqZqkN14kr14Ur14a6y1Ik357dZlmWVY1kkz6FDh2jcuDFLliyhT58++csnTZrEwoULWbZsWbH7L1++nN69e7Ns2TLOPffc/OUzZ84kLCyMFi1asHPnTh577DFq1KjB0qVL8ff3L/RYkydP5umnn3Zb/umnnxIWFlbKVygiIgDp6elcc801JCcnExER4evi+ITaGRGR8lXd2xq1MyIi5cubdkaBxQpypoHFO+64g6VLl7Ju3bpit9u1axetWrXi119/ZfDgwYVuU9gTvqZNm5KUlOTTDyY5OTnMmzePoUOHEhgY6LNyVBaqD1eqD3eqE1eVpT5SUlKoX79+tb3ZA7UzVYXqw53qxJXqw11lqZPq3taonak6VCeuVB+uVB/uKkudeNPOaCh0Balfvz7+/v4kJCS4LE9ISKBhw4bF7puWlsbMmTN55plnSjxPy5YtqV+/Pjt27CgysBgcHFzoBC+BgYGV4mKuLOWoLFQfrlQf7lQnrnxdH/pbqJ2palQf7lQnrlQf7nxdJ9X976F2pupRnbhSfbhSfbjzdZ14c25N3lJBgoKC6NmzJ/Pnz89f5nA4mD9/vksPxsJ88cUXZGVlcd1115V4ngMHDnD06FGio6PPuMwiIiIiIiIiIiJFUWCxAk2cOJH33nuPjz76iM2bN3PXXXeRlpbGTTfdBMANN9zgMrmL0wcffMDo0aPdJmRJTU3loYce4s8//2TPnj3Mnz+fUaNGERsby/DhwyvkNYmIiIiIiIiISPWkodAVaMyYMSQmJvLkk08SHx9Pt27dmDNnDlFRUQDs27cPPz/XWO/WrVtZtGgRv/zyi9vx/P39WbduHR999BEnTpygUaNGDBs2jGeffbbQoQEiIiIiIiIiIiJlRYHFCnbPPfdwzz33FLouLi7ObVnbtm0pan6d0NBQ5s6dW5bFExERERERERER8YiGQouIiIiIiIiIiIjXFFgUERERERERERERrymwKCIiIiIiIiIiIl5TYFFERERERERERES8psCiiIiIiIiIiIiIeE2BRREREREREREREfGaAosiIiIiIiIiIiLiNQUWRURERERERERExGsKLIqIiIiIiIiIiIjXFFgUERERERERERERrymwKCIiIiIiIiIiIl5TYFFERERERERERES8psCiiIiIiIiIiIiIeE2BRREREREREREREfGaAosiIiIiIiIiIiLiNQUWRURERERERERExGsKLIqIiIiIiIiIiIjXFFgUERERERERERERrymwKCIiIiIiIiIiIl5TYFFERERERERERES8psCiiIiIiIiIiIiIeE2BRREREREREREREfGaAosiIiIiIiIiIiLiNQUWRURERERERERExGsKLIqIiIiIiIiIiIjXFFgUERERERERERERrymwKCIiIiIiIiIiIl5TYFFERERERERERES8psCiiIiIiIiIiIiIeE2BRREREREREREREfGaAosiIiIiIiIiIiLiNQUWRURERERERERExGsKLIqIiIiIiIiIiIjXFFisYG+//TYxMTGEhITQu3dvli9fXuS206dPx2azuXyFhIS4bGNZFk8++STR0dGEhoYyZMgQtm/fXt4vQ0REREREREREqjkFFivQrFmzmDhxIk899RSrV6+ma9euDB8+nCNHjhS5T0REBIcPH87/2rt3r8v6l156ibfeeoupU6eybNkywsPDGT58OJmZmeX9ckREREREREREpBoL8HUBqpPXXnuN2267jZtuugmAqVOnMnv2bKZNm8YjjzxS6D42m42GDRsWus6yLN544w0ef/xxRo0aBcCMGTOIiori22+/ZezYsYXul5WVRVZWVv7vKSkpAOTk5JCTk1Pq13emnOf2ZRkqE9WHK9WHO9WJq8pSH74+f2WgdqZqUH24U524Un24qyx14uvz+5ramapDdeJK9eFK9eGustSJN+e3WZZllWNZJE92djZhYWF8+eWXjB49On/5jTfeyIkTJ/juu+/c9pk+fTq33norjRs3xuFw0KNHD55//nk6duwIwK5du2jVqhVr1qyhW7du+fv179+fbt268eabbxZalsmTJ/P000+7Lf/0008JCws7sxcqIlLNpaenc80115CcnExERISvi+MTamdERMpXdW9r1M6IiJQvb9oZBRYryKFDh2jcuDFLliyhT58++csnTZrEwoULWbZsmds+S5cuZfv27XTp0oXk5GReeeUVfv/9dzZu3EiTJk1YsmQJffv25dChQ0RHR+fvd/XVV2Oz2Zg1a1ahZSnsCV/Tpk1JSkry6QeTnJwc5s2bx9ChQwkMDPRZOSoL1Ycr1Yc71YmrylIfKSkp1K9fv9re7IHamapC9eFOdeJK9eGustRJdW9r1M5UHaoTV6oPV6oPd5WlTrxpZzQUuhLr06ePSxDy/PPPp3379vz3v//l2WefLfVxg4ODCQ4OdlseGBhYKS7mylKOykL14Ur14U514srX9aG/hdqZqkb14U514kr14c7XdVLd/x5qZ6oe1Ykr1Ycr1Yc7X9eJN+fW5C0VpH79+vj7+5OQkOCyPCEhocgciqcLDAyke/fu7NixAyB/vzM5poiIiIiIiIiISGkosFhBgoKC6NmzJ/Pnz89f5nA4mD9/vkuvxOLY7XbWr1+fP+y5RYsWNGzY0OWYKSkpLFu2zONjioiIiIiIiIiIlIaGQlegiRMncuONN9KrVy/OPfdc3njjDdLS0vJnib7hhhto3LgxL7zwAgDPPPMM5513HrGxsZw4cYKXX36ZvXv3cuuttwJmxugJEybw3HPP0bp1a1q0aMETTzxBo0aNXCaIERERERERERERKWsKLFagMWPGkJiYyJNPPkl8fDzdunVjzpw5REVFAbBv3z78/Ao6kR4/fpzbbruN+Ph46tSpQ8+ePVmyZAkdOnTI32bSpEmkpaVx++23c+LECS644ALmzJlDSEhIhb8+ERERERERERGpPhRYrGD33HMP99xzT6Hr4uLiXH5//fXXef3114s9ns1m45lnnuGZZ54pqyKKiIiIiIiIiIiUSDkWRURERERERERExGsKLIqIiIiIiIiIiIjXFFgUERERERERERERrymwKCIiIiIiIiIiIl5TYFFERERERERERES8psCiiIiIiIiIiIiIeE2BRREREREREREREfGaAosiIiIiIiIiIiLiNQUWRURERERERERExGsKLIqIiIiIiIiIiIjXFFgUERERERERERERrymwKCIiIiIiIiIiIl5TYFFERERERERERES8psCiiIiIiIiIiIiIeE2BRREREREREREREfGaAosiIiIiIiIiIiLiNQUWRURERERERERExGsKLIqIiIiIiIiIiIjXFFgUERERERERERERrymwKCIiIiIiIiIiIl5TYFFERERERERERES8psCiiIiIiIiIiIiIeE2BRREREREREREREfGaAosiIiIiIiIiIiLiNQUWRURERERERERExGsKLIqIiIiIiIiIiIjXFFgUERERERERERERrymwKCIiIiIiIiIiIl5TYFFERERERERERES85pPAYk5ODpMnT6Z169YEBwdjs9n49ttvfVEUERERERERERERKYUAX5z01Vdf5emnn6Zfv35cffXVBAYG0q5dO18URURERERERERERErBJ4HFH3/8kRo1ajBv3jyCgoJ8UQQRERERERERERE5Az4ZCn3o0CHq1atXLYOKb7/9NjExMYSEhNC7d2+WL19e5LbvvfceF154IXXq1KFOnToMGTLEbfvx48djs9lcvkaMGFHeL0NERERERERERKq5Cg0sTp48GZvNxu7du9m7d29+ICwmJoa4uDhsNhuTJ09myZIlDBs2jNq1a2Oz2fL3T0tL46mnnqJdu3aEhIRQt25dRo4cyeLFi4s8V1xcHB9++CGdO3cmNDSUFi1a8NZbbwFgWRavvvoqbdu2JSQkhNatWzNjxoxye/2zZs1i4sSJPPXUU6xevZquXbsyfPhwjhw5Uuj2cXFxjBs3jgULFrB06VKaNm3KsGHDOHjwoMt2I0aM4PDhw/lfn332Wbm9BhEREREREREREajgodADBgwA4I033gBgwoQJANSuXTt/myVLlvD8888zcOBAbr/9dvbt2wdAZmYmgwYNYvny5fTo0YMJEyaQkJDArFmzmDt3Lp999hlXXXWV2znfeOMN4uLiGDVqFIMGDeKrr77i/vvvJywsjDVr1vDVV19xySWXMHjwYGbOnMmNN95ITEwM/fr1K/PX/9prr3Hbbbdx0003ATB16lRmz57NtGnTeOSRR9y2/+STT1x+f//99/nqq6+YP38+N9xwQ/7y4OBgGjZsWOblFRERERERERERKUqFBxYHDBjA9OnTAdOr0CkuLg6AefPmMW3atPzgm9NLL73E8uXLufbaa/n444/zezLed999nHfeedx+++2MGDGCmjVruuz3xx9/sHr1alq2bAnAgw8+SGxsLA8++CBRUVGsX7+eBg0aAHDjjTdy3nnn8corr5R5YDE7O5tVq1bx6KOP5i/z8/NjyJAhLF261KNjpKenk5OTQ926dV2Wx8XFERkZSZ06dRg0aBDPPfcc9erVK/I4WVlZZGVl5f+ekpICmNm6c3JyvHlZZcp5bl+WoTJRfbhSfbhTnbiqLPXh6/NXBmpnqgbVhzvViSvVh7vKUie+Pr+vqZ2pOlQnrlQfrlQf7ipLnXhzfptlWVY5lqVQMTExAOzZsyd/WVxcHAMHDqRHjx6sWrXKbZ9WrVqxf/9+du3aRZMmTVzW3X777bz33nvMmDGD66+/HjBBy6effpqnn36aJ5980mX7wYMH89tvv/HRRx+59Pxznic3N5e9e/eWwSstcOjQIRo3bsySJUvo06dP/vJJkyaxcOFCli1bVuIx7r77bubOncvGjRsJCQkBYObMmYSFhdGiRQt27tzJY489Ro0aNVi6dCn+/v6FHsdZN6f79NNPCQsLK+UrFBERMA+BrrnmGpKTk4mIiPB1cXxC7YyISPmq7m2N2hkRkfLlTTvjk1mhi3POOee4LUtJSWHXrl20b9/eLagIMHDgQN577z3Wrl2bH1h06tatm9v20dHRxa7zJMhX0f71r38xc+ZM4uLi8oOKAGPHjs3/uXPnznTp0oVWrVoRFxfH4MGDCz3Wo48+ysSJE/N/T0lJyc/f6MsPJjk5OcybN4+hQ4cSGBjos3JUFqoPV6oPd6oTV5WlPpy9JqoztTNVg+rDnerElerDXWWpk+re1qidqTpUJ65UH65UH+4qS514085UusBiVFSU2zLnCypsHRQECgt74YU1LAEBAcWuy83N9bzAHqpfvz7+/v4kJCS4LE9ISCgxP+Irr7zCv/71L3799Ve6dOlS7LYtW7akfv367Nixo8jAYnBwMMHBwW7LAwMDK8XFXFnKUVmoPlypPtypTlz5uj70t1A7U9WoPtypTlypPtz5uk6q+99D7UzVozpxpfpwpfpw5+s68ebcFTortCdOnQXayRkAPD0o5xQfH++yXWUUFBREz549mT9/fv4yh8PB/PnzXYZGn+6ll17i2WefZc6cOfTq1avE8xw4cICjR4/mB1tFRERERERERETKQ6ULLBYmIiKCli1bsmPHDg4ePOi23jnxS2FDmyuTiRMn8t577/HRRx+xefNm7rrrLtLS0vInqrnhhhtcJnd58cUXeeKJJ5g2bRoxMTHEx8cTHx9PamoqAKmpqTz00EP8+eef7Nmzh/nz5zNq1ChiY2MZPny4T16jiIiIiIiIiIhUD1UisAhmxuacnBweffRRTp1vZt26dUyfPp1atWoxevRo3xXQA2PGjOGVV17hySefpFu3bqxdu5Y5c+bkD/Het28fhw8fzt/+P//5D9nZ2Vx55ZVER0fnf73yyisA+Pv7s27dOi677DLatGnDLbfcQs+ePfnjjz8KHRogIiIiIiIiIiJSVipdjsWiTJo0idmzZ/Pxxx+zefNmBg8ezJEjR5g1axa5ubm899571KxZ09fFLNE999zDPffcU+g6Z89Lp1NnzS5MaGgoc+fOLaOSiYiIiIiIiIiIeK7K9FgMCQnht99+44knniAlJYXXX3+db775hv79+xMXF8dVV13l6yKKiIiIiIiIiIhUGz7psVhYT7wBAwa4DHEuTHh4OM888wzPPPNMieeYPHkykydPLnTd9OnTmT59eqHrTu81KCIiIiIiIiJS4TZ9DAsfAnsWnPcE9Jro6xKJuKkyPRZFRERERERERKqFHd/DzzdAegJknYCF/zCBRqk0cuw5/HPBP7ny0yv5YcsPvi6Oz1SZHIsiIiIiIiIiIpXe8e1wcBH4B0PzIRAW6d3+2anw6x3m5063QHAErHrd9F5s/TcIDC/7MovXXvr9JaatmgbA2kNraRDWgPOanefjUlU8BRZFREREpHpw5MK2r2DvPLDs0KQftB0LgaGlP6YzlY/NVjZllDKXnJlMeFA4AX669RGRcpaVAr/d49qzMCAM+r8M3e72/DirXoe0eKgdC4OngM0PdnwLybth0/+g6x1lXnTxTlJaEv9b+z8AQgJCyMzN5K0lb1XLwKKGQouIiIjI2S8tAWb2g9ljYcMHsHE6zL0ZPu4O8Su8P96JnfD9lfBWOLxVwwxXS0so82JL6WXlZnH/j/fTY0oP+r/bn3WH1/m6SCJyNjuxC/7XIy+oaIMm/aFBF8hNh/l/h9VveXacnHRY/ab5ue+zEBAC/kHQ/V6zbN1/y6X44p3vNn9Htj2bzlGd+fWWXwnwC2DZgWVsT9ru66JVOAUWRaTyykmHpI1waCm2A79TJ3MbHN9qbtwsh69LJyIiVcXJg/Bpbzi8FIJrwTkPmyT44dGmXZl5gcll5amEVfBpH9j+FeRmmJvGTR/DZ33MuaRS+M+y//Djlh8BiE+N567v7iItO83HpRKRs9KJXfD5APPQKaI5jFsMY+Lg+rXQ5ymzTdxEbPvjSj7Wpo8h8yjUagFtripY3v5603PxyBrTc1F8av6O+QCM7jia6JrRXND8AgDmbJ/jy2L5hAKLIlJ52LNh+9cw9xZ4t7npBfJRJ/jsfAK+HkK/A5MI/LgzTG0Ib4bCB63hiyEw91ZY8QrsmQuphwqGpYmIiORkwHejIWUv1GkN166Afv+Cvs/AjRug1WWm/fnhCmzbvyz5ePt+g1kDICMRInvAdSth7CKo1dLc6P04xgy5Fp86ln6M/y43vXr+OeyfNK3VlPjUeD5eo4kPRKSMZR6Hry+Gk/uhbjsYtxQa9THrbDYTWGx/HVh2/OfdjL8jo+hjWQ5Y9Zr5ucf94OdfsC6svukFCbD9m/J5LeKRlMwUVh5cCcDgloMBuKjNRQDM2zHPZ+XyFQUWRcT3slNh2b/g/Rbw/RWwYRqc3GfWBdeGWi2x6rQhPaABVnBtwGZuAk/sgH3zzZC23x+Cr0bAfxvDfxvB7GtN/pEc9UwQEanWFj4ICSshpB5cMdcEF51C68JlX0G7a8CRi/+c62ia8lvRx9r2JXx9EeSkQrNBcPUCiOoJjfvClb9AUE04tBg2fFj+r0uK9eWGL8m2Z9MxqiNjOo/hnj73ADBz3UwcGvUgImXFcsCPY03v95pN4ar5UCPadRubDYb+F2q1wJZ6gHbHZhZ9vF0/wfFtpnd9p5vd18debr5v/7rsXoN4bfWh1dgtO81qN6Np7aYA9GvRD4BNCZs4kXHCh6WreKUKLK5YsYKLL76Y2rVrEx4eznnnncfnn3/u1TF27tzJ5MmTueyyy2jcuDE2m42YmJjSFAebzUa7du2KXL9nzx5sNhsjRowo1fFFpJxYlkmi/2F7WPSo6W0YHg09Jpibv7uPwj3H4dad5F6/gXkx75F7xxF4IBtu2wNjFsKIj8xTwDZXmSeENj+T6HjLp/Dz9TA1Gn79O6Ts9/WrFRGRirZ/Ifz1jvl55GdmWNnp/ALgohnQ6RZsloMeR97Cb+0U197vlmXyXf1wtXmw1eZKuPwnM0unU+1WJhcWwJKn9GDLx77fbIa2j+syDpvNxsi2I6kZXJP9yftZfXC1j0snImeNla/B3l/MBC2jf4AajQrfLjAMBr8NQMsTP0DiX+7bWBaseMn83Pl287DqdLGjzfdDS8w9j/jEqkOrAOjVuFf+ssgakcTWjcXCYtn+Zb4qmk94HVhcsGABffv2ZdGiRVx99dXceeedxMfHM2bMGF599VWPj/PHH3/w9NNP89NPP1G3bl38/NR5UqRayUo2T/d+uBJSD0BEjAkS3rYHBr4OMcNMT5LC+AWY3CVN+kHHG+D8yXDp53DTZrg31fQg6f2YGZaWfdLcVE5rDUueNjeEIiJy9stJh19uMT93uR1ihha9rZ8/DHsXe7f7APD/fSJ8OwoOLoYDi+C7y2HBBMCCLnfAyJkQEOx+nC53muBl2mHPk/RLmdt/Yj+bEzfjb/NnRBvTsSA0MJRBLQcB8OvOX31ZPBE5WxxZC4seMz8PfAMiuxa/fYuLcMRegR8O/H+7yz1txr7f4OAf4B8MPe4r/BgRTU1PeSyTBkp8Ys2hNQD0aNTDZXnvpr0BWHVwVYWXyZe8iubl5uZy22234efnx++//867777Lq6++yl9//UWbNm147LHH2Lt3r0fH6tevH0uXLuXkyZOsX7+ewMDAUr0AEamCjvwFH/eAbZ+bIOF5T8D4TSZI6B90ZscODIWmA+CCf8It281whKYDwJ4FSyfDZ+dDyr4yeBFypjYf2cwVn1xB+9fbc+vXt5KQqtlURaQMLX7SJNGv0Rj6vVTy9jY/HBe+zMZ6N2D5BcKuH8ykLrMuhJ3fgV+guXEc8h/XnFenCgiG8582P695Sw+zfGTBrgUA9GrSizqhdfKXD4kdYtbvXOCTconIWcSeDT9dB44caDUKOt/q2W79XiXHLwy/hJWw6o2CFZYFS540P3e5A2o2KfogMcPNdwUWfcKyLDYf2QxAl4ZdXNZ1iTa/r0tYV+Hl8iWvAou//fYbO3fu5JprrqFbt275y2vVqsVjjz1GdnY2H330kUfHatmyJeeddx6hoaFeFbisxMXFYbPZiv3as2ePT8omclbbM9fcqCXvMr0Ox/xhEugHlsN7gc3P5MC66jfTuySknpnJ85NzIEHDoHzpYPJBbvjiBtYeXku2PZsFuxYw/svxZOQUk8xaRMRTh5fB6tfNz0P/a3JVecJmY0edv5E7bjm0HWPSc9RoBG3HmklfetxvcmUVx7lfWjxs++LMXoeUypJ9SwDoF9PPZXmfZmYyhR3HdnA0/WiFl0tEziKr34KjGyG0AQx7v+S2walGIzbUz8uduOQJiDcTgLBhmhneHBAC5z5S/DGcgcW980yOR6lQR9KOcCLzBP42f2LrxbqscwYaNyZsxO6w+6J4PhHgzcZxcXEADBs2zG3d8OHmn3vhwoVnXqoKEBMTw1NPPeW2PDU1lddfNx9EQ0JCKrpYIme39R/AvDvAskPTgSZhfkidkvc7UzYbtBsDjc4zQ9sS/4IvBsEVv0D0ueV/fnHz9G9PcyzjGB2jOvLQhQ/x0M8PsS1pG/9Z9h8mXjDR18UTkaosNwvm3mxuttpfBy1Hen+Meh3hkmKS6xfHPwi63Q2LnzB5GdtfW7rjSKnYHfb83FbOQKJTndA6tK7Xmu1Ht7P60GqGxhYzPF5EpCiph2BpXu/0fi+a2Zq9sK/mYLqG7cJvz0/w5VBo/TfYNMOs7DPZffKX00X3MfkXM5JMZ4mGvYrfXsrUlsQtALSo04Lg09KitKrbirDAMNJz0tlxdAdtG7T1RRErnFeBxe3btwPQunVrt3UNGzakRo0a+dtUtKSkJCZPnlzouhMnTrgti4mJcdve4XAwatQoHA4Hr776Kg0bNiz7gopUR5ZlEtn/mZfUvv11MPyDMx/27K2I5jDmd/hmJBxcZL6PWwp1YkveV8rM2sNrmb9zPgF+Abx28WvE1otl8uDJ/P37v/Phqg+5uefN1A6t7etiikhVtew5OLoJwiLN0GVf6HKHafPiV5jeKLrpqzA7ju4gJSuFsMAwOkZ1dFvfs3FPth/dzqqDqxRYFJHSWfgg5KRC9HnQ8Ubv97fZsA+fgd8Pl5leihummeVtx0KvB0ve3z8Qmg4yaTr2/qI2poI5A4uFBQ39/fzpFNWJ5QeWsz5+vQKLhUlOTgbM0OfCRERE5G9T0Y4ePcrTTz99Rsd44IEH+PHHH7njjjuYOFE9ZkTKhD0bfrkVNn1sfu/9f2bWTE+HC5S14Ai4Yg7MGgAJK+Gbi01wMbSeb8pTDb2/4n0ARrUflT98YHjr4bRr0I4tiVuYtW4Wd/S+w5dFFJGqKn4FLHvB/Dz4Hd+9t4c1gDZXweZPYN1/ddNXgf6KNzOtdm7YmQA/91udXo17MXPdTFYeXFnRRRORyiQrxQwlDoowqZOKyp17uoOLYctngM3M8mwr5SS0wREmXdOmGWY0VeMLoO3Vnh8vZrgJLO6ZayatlAqzLWkbUHhgEUz7s/zActbFr+PKzldWZNF85qyZirlt27ZYllXo1+7du0vc/5133uGtt95i6NChTJkypQJKLFINZB6Hry8yQUWbPwx9Fy54zndBRafAcLj8B9OD8fh2+PFqqEY5MHzpaPpRft1hZuO8qedN+cttNhvje4wH4MuNX2JZli+KJyJVWcZR+P5Kk26jzVXQ5grflqdL3gOSzZ9Clm8evFdHfx02gcWuDQufnbVXYxPk3RC/gcyczAorl4hUIgmrYVob+OFK+GoYfNYHUg+XvJ/lgLi8Dkidb4GoHsVvX5KAYOhyGwyeAu3GehekdOZZPLTEBEmlwjh7LLZr0K7Q9c72Z1189ZnAxavAorOnYlG9ElNSUorszViZzZkzh/vuu4/27dvzxRdfEBDgVUdOESnMsW3w6Xmw77eCQF6X23xdqgLhDeFvP5my7fsN/nzO1yWqFn7e+jM5jhw6RXWifWR7l3Uj2owgJCCEXcd2sT5hvY9KKCJVUk4afHc5nNwHtWNh2Hu+LpHpfVKvA+Smw6b/+bo01YbzRq5rdOGBxSa1mhAZHkmOI4cNCRsqsmgiUhmkJ8G3l0J6gpmcKyjC9Hb/+iIoaRLBLTMhfjkE1jAjsHypdkvT3jlyYb9muq8oOfYcdh7dCUC7+oUHFjtEdQBg29Ft1WYCF68Ci87cioXlUYyPjyc1NbXQ/IuV2YYNGxgzZgx169Zl9uzZVTIwKlLp7PkFPu0Nx7dBzaYwdjG0uMjXpXJXrwMMmWp+Xvq0CTBKuZq3Yx4AI9u6T6ZQM7gmA1oMAGDBTn1AEhEPpSXA1yPh4B/mBvGyrzyfBbo82WzQ5U7z87qpJt+wlKuMnAy2Jm4Fig4s2mw2OkV1AmDTkU0VVjaRs8re+WZW5NRDvi6J9xY/bspdtx2M3wzXrzYzOyf+Bb/dU/R+ORnwR95szb0fNZ0UfM3Za3HPXN+WoxrZc3wPOY4cagTVoFFEo0K3aVarGSEBIWTlZrHvxL4KLqFveBVY7N+/PwC//PKL27q5c+e6bFMVJCQkcMkll5CVlcW3335LixYtfF0kkaotNwvi/gFfDYesEyah8bXLIbLwD/eVQofroNPNgAU/X2+G0km5SMlM4c/9fwIUmTB/YMuBACzYpcCiSLVnWZB9Ek4ehJS9JoCYlQL2HLMuZR+sfA1mdIEDC00PkivmQoMuvi55gQ7XQ0AoJG0ww9WkXG1M2IjdshMZHknDGkXf9Dt7zG9O3FxRRRM5e6z+N3w5BBbcDzO6Qsr+ii/DnnnwSW/4Xy/Y/q3n+yXvhvV5PdqHvmvyHNZuBSM/M8OQN0wruof5qlfh5H6o2Qx6PHDGL6FMKLBY4fae2AtATJ0YbEWk9/L388/PI781aWuFlc2XvAosDh48mJYtW/Lpp5+ydu3a/OXJyck8//zzBAUFccMNN7jsc/jwYbZs2eKzSV2KkpGRwWWXXcbevXuZNm0a559/vq+LJFJ1WRbs/NF8uFj1mlnW9U64ekHleJpXkkH/Nk8tUw/BvNvVq6ScxO2OI9eRS2zdWFrULfxBTv+W5uHU+oT1JKYlVmTxRKQySE/Ku2kdBm/XhX9HwLtN4L0YmNoQptSCN4Lg9QB4rzks/AekH4H6neHaZdDoPF+/AlchtaHdOPPzX1N9WpTqwJlGo0vDLkXe8AF0iDTD1NRjUcRLJw/A76fMWpyRBHETKrYMB5eYYcvxyyFhFXz/N9jxvWf7rn3H5ElsNgSaXFiwvPlg6POU+fnXu+D4Dtf9EtfDn3lDny/8FwSGnvnrKAtNB4BfACTvci+zlAtnYLF57ebFbte2vpnYxdmL/mznVTLBgIAA3n//fYYPH06/fv0YO3YsNWvW5KuvvmLv3r288sorxMTEuOzz6KOP8tFHH/Hhhx8yfvz4/OVJSUk8+GDBm1JOTg5JSUku27zyyivUr1+/VC+sJP/+979Zvnw5sbGxbNu2jcmTJ7ttM2HCBGrXrl0u5xc5K+Skw/avzM2SsydGWCQMex9aXerbsnkjMAwu/sTkhNz+NWz4EDrf7OtSnXXm75wPwNDWhfdWBGgQ3oDOUZ1Zn7CehbsWVpuZ1ESqvfQkWPkKrJ1i8iWeyuZvbpzsWQXLLIdZHtndTJLS4ToICKnYMnuq652mF8y2L2DA6xBWPp9tpeAG7vQcvqdzrt+WtI1cR26hs0eLSCFWvgr2bGjSDwZNMT3Gt38DJ3aZnH/lzWGHX24xk3S1HGnSX2z5DObdZgKFIXWK3jcnHTZ8YH7ucZ/7+t7/B/vmw4HfYfY4GLPQ3CNkp8LP15nX3fJSM8lKZRFUExr1Nb3298yFOrG+LtFZb+/xvMBinRICi3kzRleXHotet6IDBw5k0aJFPPXUU8yaNYucnBw6d+7Miy++yJgxYzw+TmpqKh999JHLsrS0NJdlkydPLrfAYnp6OgA7duzg6aefLnSb8ePHK7Ao1Ud6opkh+eQ+yM0AR45JBowtbxZnmxkikJVshgEcWQ2Hl5ntAPyDzLCA3o+ZYQVVTVQPk4T5j0dgwX3mA5Ma5zLjsBws2WuCz848ikUZ2HIg6xPW8/ue3xVYFDnbZRw1AcU1/y4IKEZ2N0OImw6EOq0hIMy0Q5Zlgou5GeYruE7l6TVSnKheENnDtJubPoJe/6jY8588YB4CBtY0N8SBYRV7/grkvIFz9hQpStNaTakRVIPU7FR2HdtFm/ptKqJ4IlWbPRs2fWx+PudhaNAZmg+Dvb/Axg8rZjKTrbPg2BYIqQsX/c+8nx1ZC8c2m6DnBcVMxrj5U8g8DrVaQIuL3df7+ZuOBjO6QsJK03O+299hzVuQuM7kYRz2Xt59USUSM8IEFnd+B93/Xvg2qYfNqLJjW0yqqh73Q1CNii3nWWLPiT2A5z0WtyVtK+8iVQqlejx37rnn8vPPP3u07fTp05k+fbrb8piYGKwyGm5Y0nEKO9fkyZML7aUoUq3Er4RNM8wTruOlfNOLiIHOt5g8hTUKT2BbZfR6EHb/bBrnn6+DMX+Af6CvS3VW2J60nWMZxwgNCKVLdPH5z3o37Q1LYeXBlViWVexwNhGpojKOmpuc1W9BTqpZFtkDzp8MLS8p/MbNZjO9EgNCgGJ6pVQ2NpvptTjvdtPDv+cD5kFdRdgfB9+OguwU8/uaN+HquOJ79ZSWI9e8rop6baef3nKwPclMMFlSoNDP5kfbBm1ZdXAVm49sVmBRxBN7foHMoybNUcwws6zD9SawuPOHigksrnnLfO8xwaSaALjgn2Y49Jp/m8/yzuWnsixY+2/zc7e/myBiYWo2gdHfm6HWhxabLzATgl3+A4RHleGLKSNtr4JFj5relmnx7mmoEtfBF4PNsHWAXT+aHvRX/QahdSu+vKVhOeCv/5oHdB3HQ+O+PiuKs8diTJ2YYrdz9ljce2IvmTmZhARW0lEVZcQ3Lb+I+NaeufDp+fDJOaYRPr4NsJlkxE36mad4saOh9RXmK/Zy83urUdDuGvOUctgHcPN2uHUXnPd41Q8qgvmQcdHHEFzb9Mb8s5innuKVpfuXAtCrSS+C/IOK3bZbdDcC/QJJSE1gf7IPEoKLSPk5ugXm3wvvNoNlz5ugYmR3GPUdXLfSpNE4Gx8mtBtnbkxP7Ch6YoCylrIXvhttgor1OkJofXOD+evdZXsey4JF/wdvhcN/omDr52V7fA8dSD5ARm4GQf5BJQ5RA+jQQHkWRbyy60fzvfUVJj0FmN5yNj8zo3J5T+KSuN58PvcLgC63FyyPHQX1O5n3Omfg8XQHF5n3v4BQ6HhT8edp3BeuX2MCWFE9zfbXr4Ho3mX2UspU7VamF6LlgPUfuK5L3m0m1cxIggZdod9LEBZl/l4/XVt18sr/8SjMvxvWvw+fD4T9C31SjKzcLA6dNDOhx9SOKXbb+mH1qRNaxzz0Orq9AkrnW0ooIlKdpOwzM7jt+Nb87hcIbcdA679Bs0Hmpqe6i2gKQ6bC7LGw7Dkz21rjMpjcKXmPGaoBUL+jGd5XjSzdZwKLfZr2KXHbkMAQOjXsxJpDa1hxYAXNajcr7+KJnB1y0sxQsF2zzZDhJv3gnEmmB0ZFcuSa4WaZx8z3EzvM+9/euWZ2ZKcG3Uyy/NhRZ2cw8VRBNeDcx+CPh00Qrs1VRQ/jPrrZ3MBnHDVDDVtfUbr8kQsfMulLos8zvRST1plcwltnwjkPmhvmsrD2HRMkBnPz+tO1UKNJ2bSdXnAOg46tF+tRzsQ2DUwvxR1HNeGBSIksy4zqAddhxGH1IbqP6dm3e7bpnV1etnxqvre8xLXnoM3P5EecPQ5Wvwk9J7oP812T11ux/XWe9dKr3QpGfFg25a4I3f4Oh/80r9M5zDk90QQV0+LNBGdXx5nenDHD4dPesGcObJkJ7ceVTRnseWm0yjpFyfHtBZODhkWaCdt+vRNuXF8Q4K4gB1MO4rAchAeGUy+sXrHb2mw22tRvw7L9y9iatJXODTtXUCl9Qz0WRaqLPfPg4+4mqGjzN43O7fvg4o+h9eUKKp6q3RgztMNymCHRWSmlO45ztuz/nQPvt4DvLzdf09rA54Oqzextdoed5fuXA3BeM89mbD2n8TmAGQ4tIh5IWGPyQi15ysyUmfiXucGY3tEkoi8vuZmw9QuYewvM6A7vNIDXA+E/kfBhO/isD/x8Pax61QQV/QJMwv0rf4XrV0Pr0Wd/UNGpx31mZEDqAfN3Ol3qIZh9LUzvAL9PghUvwk/XmTbj4BLvznXgDzPUzeYHQ/8LAcHQ8Bwz6gBgxStn/nrABC4XPWZ+7vusCZg6cuG3e0wbWoGceaw8Hdbcup55wKfAoogHjm4yedj9g81MxKdqeYn5vvOH8ju/ZZkgGJge4KdrcxXUjjUPtNa/57ru+HaTZxag+73lV0Zfans11GoJ6QkQ94DJrfvVcPPaI5rDFXMKhog36GIedAEsnFj6+5ysZNMD/+cb4d3m8EYwvBVmPot4Oku3J/6aatqVFhfBTVshpJ7JFensKFOB9hzfA5iJWzxJ1ZSfZzHx7M+zqMCiyNnOsmDFy/D1CNPYRvWCG/6CgW+45+CQAoOmmPyRybtN13tvhwqkJ8EPV8K3l5oE0DZ/0zuk4Tnmxnr/AtPwlueHsEpi+9HtpGSlEB4YTseojh7tc04TE1hccWBFeRZN5OyQvMe8x5/YaXqKDf8QLvsaGp5rhoZ9NaLshw057LD2P2ZI849Xm1mPE9cW5HACM1tnRAw0vsD0prjoY7jrCFz+IzQfXH0Cik4BITDwTfPzypdNfknLMr06l70A09rm9cixmeBrt79DzaZmwrSvhplgoScsh7mxBOh8m7mJdOqZt3zH16ZH5Jla/37eUOsOZvK2we+Yv/uRNbB7zpkf3wveBhZj65kJ2g6kHCA9O73cyiVyVnD2Vmw60H0CqJYjzff9CyA3q3zOH78cUvZAYHjB+U7l529SNYGZECwno2Dd8n+Z98WWl5he4Gcj/yAYPMX8vP59eLepeR8ObQBXzHVPWXXOJDN6Ki0elr/g3bmO/AU/joWpDc2Dw00zTNCZvHulxHXw3SjTm/1MOeyw+RPzc9e7THC0213m97Vvn/nxvbT3RN6M0CVM3OJUnWaGVmBRpLxknjBBo+UvQtxEMyRp1Ruwrxwb3dPlpJlhAb9PMg1qx/Ew9g8zFFeKFxwBF//P9PbY/EnBMC9P7PkFZnSG7V+bIGKvh+DOwyZ/2LXL4ZYd5oNZbroJPu75pfxeRyWw5tAaALpGd/VoeBpAj0Y9ANh9fDcnMk6UV9FEqr7ME/DNSDM0qEE3MzSo03jTE/3qODNkLTcDfrjK9IgrC2kJ8OUQ89AlIxFqNDbvc6O+NQ+u7kqAB3Lg3mS4bbdpdwZPgQ7Xlc+kIVVJ69Hmhg5MapL/NjJfix4z+Saj+8B1K0zwdfAUGL/JzLqak2b+zsc8uDnZ9DEkrDIBvr7PuK6L6mHybNmzYWcZ9ChxzhDb437TXobVNxO6Aax798yP74WtiaZuPA0s1gmtQ928IZE7j+0st3KJnBV2/2S+t7jIfV39TmaIam4GxC8rn/Nv+cx8b3WZCS4WpsP15mFM6iHTSx7M6KBNM8zPvR8rn7JVFi0uMjnwgyLM79F9YNxiqNvWfduAYOifV0erXjfpskqSm2ly9H7c3czOnZsJddvDuY+aUQh3JZgvZ6/Q3+4zE4WeiSNrTC/M4FomnyeYB2ZgHpiePHhmx/eSpxO3ODl7LCqwKCLesSyT2+q7y+E/DeDby+CPR8wb9spXTA+CLwbBO/Xgl9vME53ycmIXfNrHvPH7BcDgt2H4tNLlaaquGveFQXk5WRY/DitfK357ezYsnFSQz6ReB7hmGfR/CcIaFGwX0Ryu/MXkzbJnmxv+5N3l9zp8bO3htQB0a9TN431qh9bOz624IWFDCVuLVFP2HPP+cXSTCe5d/qPrbJiBoXDpl6bHWkZiXqL2MxyeemKXyc20Pw4Ca5j3yFt3m/e52FHmXGGRFZ73qEq58F/Q/xVTf2nx5uasfme4aAaMW+Sa+zCohgnYNukH2SfNw6icYnrXZaeaJPdgJlYLi3TfJvZy8/1Me8wn7zZD7m3+pj1zct707fqxwm76su3Z7D5u2lHnjZwnnMOhFViUauXoJhMg+vXv5ueSZKWYyU/ANb+ik80GTQeZn/f95nk5slJMGg97dvHbOewFk0IVNgzaKSAYLnzR/Lz0afNwY/Y4M4w2Zjg0KjnPd5XX+ea8AN8RuGZJ8TndW15ihrXbswpSWhQlLQE+Ox/++g9gQduxcN0qGL8RLnzejEIIizRfA9+ENleDZYcFE85sghjn/1OT/uAfaH6OaAaN+ppybPui9McuhT0n9gCe91hsXd/Uf2JaIsfSj5VXsSoFBRZFykr8SvisL3xzicn54MiFOm2g/bWmJ0fPiWaSlPCGpufB+vfNUNhvR8GxMs67sOcX+KQXJK03b/BX/Qbd7q5+w87KQre7TUJogIX/MB/CCrupi19h/v4rXza/d70brl1peocUxi8ARn4Kjc43w8h+HGuCBBUhZa8ZdvfLbbD4iYJJZcqJs8di9+juXu3XpaEZvrc+YX2Zl0mkyrMs02Nw36+m98blP0LNxu7bBYbCJV+YbfbHwZozGDqUshdm9Tffa8eaHtjd7yn4sC+esdmg1z9MT/Zr/jS9Em/4y/S2sRXy0TwwFC6ZZT4/JG2AX+8q+kZt+QuQdtjk2up+X+HbtLrUfN/7iwlqltb2b8z3Jv0g9JQk9vXaQ+MLzU3lxumlP74Xdh/bTa4jlxpBNYiuGe3xfs7h0MqzeBbJPOHrElRuBxeb3N9//Qf+egc+6Q2H/ix+n33zwZFj3vfrxBa+TTMvA4vbv4H/Nob/9YD3mptyFeXAQvO+Flzb9OAuTruxJqjlyIV5d5h0RCF1zMSM1UVAiGuHhqLYbAW9Fjd/UnTvwrR4MxNz/tDqX+CSz8w9TmH3ljYbDHjNlOPQYjNEvrSc+zYd6Lq83Vjzfeus0h+7FJw9Fj0NLNYIqkHTWk2BgnQdZysFFkXOVPoRmHsrfHIuHF4KAWHQYwLcuAFu3mqG0/Z/CQa8Cpd9BXccgjG/m0bP5m+GIn3UEX673+RZOhOWBctfgq8vMsdqeK55mtTkwjJ5qdVW32fhgryh0H+9Y/JgLX4CtsyCNVNM/rJPzi348HLZNzDk7ZJnRfMPMsHF4Nomd8yq18v3dVgOM6T7w3bmyeT69+HP58yQhp9vNEmYy1hyZnJ+T5Bu0d282rdzlMmDsy6+HHv2ilRVK14217DND0bOhMhuRW9btw30y3vo8ccjJhejt7JPwjeXmolH6rYz7Vi99qUquuQJqgHRvU09lvTgL7whjPzM/L03zYB1/3XfJnE9rHjJ/Nz/FdN7pzCR3U2+rZw0E2wurR15gUVnD8hTdbrJfHcOXyyNlL0mfUz2yRI3dQ4za1O/jUcJ9Z2cgcXtR7eXroxS+aScvSNAzlj2SZMTNzfd9PiK7mNSMMwea94PirJrtvleWG9Fp2aDzffDfxZ/LDAPtGePNecGE7j6+uKiR+9syJudue3VRb+vOdlspvd3jwkm53CTfiYtSK2Y4verrqJ6mIdaYFJ3nT6qIfUQzBoAxzab+hy3BGKGlnzcmo2hY147UNpci/YcOJiXW9gZuHZqc6VpDw//afJMV4BsezYHUg4AZvIWTznTc5ztw6EVWKxgb7/9NjExMYSEhNC7d2+WL19e7PZffPEF7dq1IyQkhM6dO/PTTz+5rLcsiyeffJLo6GhCQ0MZMmQI27eX8sNRSd3QxZU9xwSCprWBDR8Alplt8eatMPD1ovMY2mwm0HfpLJMLq+VI81RtzVvmWOveM13+vZVxzPR+/ONh0yh0uhnGLISaTc7oZQrmb9b70bzkx03MjfWfz5kPRb/dC3vmmu06XG8Cyq1He37siOYwIC+guHRy+TWODrsJgC/6P9NDpfEFcP7TphctmBvVmReYWeTK0F+H/wLMk726YXW92rdzQxNYXB+vHosiLjZ/Zt7rAQa8Aa0uKXmfrncU5Hade7N3Q6Iddph9jekFH97Q9Fao4XmvMCkjTQcUPOT67T4TdHOyZ8Mvt5jPE61GQezooo9jsxXM4rrrx9KVJeNYQQ+jws4Ve7l5eHZ0o+ll6Q3Lgt8fgfdbmvQx/21S4gyjzp4gzkT5nmpVrxUAO49qKPRZ48QuX5eg8lr5mgkU1W5lZgm+Yo6ZYCtlL6x+s/B9LAfszgsstiymranVwnymdeQUDJsu9HgWzP+7ec9qeSnck2wCnNkppp05ffROVjJs/9L83Olmz15nQLC5F7tjv7kXOnUCK3HX958QEGqCeCteLlh+bCvMvBCOb4WazUxdFtVjtTBd8yZZ2fl96TovxK8wQeqQeiaP56nCG5qgMZic9hXgYMpBHJaD0IBQIsMLSTNShDINLFqWmUhp6TNwoJjrzAcUWKxAs2bNYuLEiTz11FOsXr2arl27Mnz4cI4cOVLo9kuWLGHcuHHccsstrFmzhtGjRzN69Gg2bCj4gPbSSy/x1ltvMXXqVJYtW0Z4eDjDhw8nM7MUQ1uOn93dc8vUnl9gRhfzZCcrGSJ7wNhFMPIT7wJ59dqb4WtX/GKS32Ykwbzb4dPe2A6XMCzBybLMB+6Pu8GuH8wH+SH/gWHvK59iWYsZBrdsNzObtrvGBOdaXQZ9njITslw0w33WNU90vNHkDsnNgN8fKvtyAyx8EDZ+aJ7uDX3P9Dbq86TpRTtuScEQuy8GmRmty4gzv2L3Rt4NgwboGNURGzYOnzxMUlrZlUmkUsnJMA8UTuzy7AHftq/MLIxghrr2uNez89j8YPgHZkj0gd+960Hw+8MmABUQAqO+g4imnu8rZeucSdDmKnPz/s3FsGWm6e0ze5y5CQuuBUPeKbkHZMxw8720Q9QOLgIs03u1sP+HkNoQkzfJw5aZ3h172T9hxYsmoBFS1wQcfriy2OGa+TNC1/Ns4hYnZ47FvSf2klVRE+tJ+VKPxcKlJxVMaNL3n6bHdHAEXPBPs2z5i4XPFH9kjXmPCaxREMgpjKd5FvfOg0NLTCBr6FRThks+M+9dh/8sSCnktGWmeSBer4MZiSVlL6JpwZDoPx6B+ffCn/+ET8+D5F0mtcaYhVC7pXfHbdDZ3N86cgp6vXrD2Vuxaf/C04S0vtJ83/al98cuhT3H9wCmt6I3PeOdD7y2JZZBrGXRY6Z375KnYNaFsPLVMz9mGVFgsQK99tpr3Hbbbdx000106NCBqVOnEhYWxrRp0wrd/s0332TEiBE89NBDtG/fnmeffZYePXowZYqZSt6yLN544w0ef/xxRo0aRZcuXZgxYwaHDh3i22+/9b6ARzeewaurJhLXm6FgXw2HY1sgtD4MfdfkmWrct/THjRlq8isNeN3M5JWwioAv+nHeoWew7V9QdC6lQ3+anI7fjYKT+02C3muWQdc7lU+xvASEmJlNR35iZjod/R2cP9k8/S0tmy1vkhibaRwPl/GMeltmweo3zM8XfwJdbnX9/2jUx+T5imgOx7fDt5eYYEcZcOZX9HYYNJi8JK3qmnpVnkU5q+RmmqFds/rDlFrwfgv4oBW8GWYm3frzOUhY7dqrMPuk6XH8w5Umd12HG0yPDG/UagH98obK/v6wuWEoyfoPCm5Gh0+HaN3Y+ZTNBiM+Mj19cjNNQHFqtOmx4RdocjF68oCrcV6A4OgmSE/0vhzOG77GxaRaaTvGfN86y/Pk/UfWmhsmgMHvwJ3xpvejI8f0tHXkFrpbfmDRwxmhnRqENyAiOAKH5ci/aZQq7ng16bGYvMe8j8+9FbZ/W/I1tvwF045Edoe2VxUsbzfWzBSfnVJ4kGJnXq/mmGElD0P2JM/imrxJETvfVvBeFdEcBpn7W5Y+A0e3mJ8ddliVN3Fip1t0b1Oeut4J5+SNhFg7xUxamXXC9Ca95s/SDyVvnZcqw5k6wxsJq833hr2LP/bhpRUyUdjeE97lV3RyTii2LWkbjjOZQG/fAlj+L/NzdF6dLHzIdfSCD2nKvgqSnZ3NqlWrePTRR/OX+fn5MWTIEJYuXVroPkuXLmXixIkuy4YPH54fNNy9ezfx8fEMGTIkf32tWrXo3bs3S5cuZezYsYUeNysri6ysgqeyKSkpANiPrCMnp4ImjyiE89y+LEORjm3Gf8WL2LZ+hg0Ly+aPo+vfcfR+3OTHszvM15nq8neIvRL/JY9j2zSDqPTV8M1wrPDGWE36Y9VuBTZ/bCf3Yzu4ENsJk2zc8gvE0X0CjnMeNU8gK2MdnqFK/f9RFmq3w7/99fhtnoFj4STsf5tX4gcoj+ok7TAB827HBth7TcLR6orC/z9CG8FlPxDw5QBsh5fhmHsL9mHTz+hDnMNy5PdY7NSgU6n+dh0jO7Lj2A7WHlzLBU0vKHbbyvI/4uvzVwZFtTM5OTnVu52xLGw7vsJ/0aPYTu4tWOxvepfb7Jmmx8bhP2HxE1hhUVj1OgMWtoSV2LLNUCJ7l7tw9HsVcu2Al6kzOtyC/5bP8Tu4EL95t0HoA0XWh+3gIvx/vcu8f5z7fzha/e2sbF9O5fP/EY8EwMWz8Fv2DH5/vYMtOwWrflfsA97AatTXs79RYC0C6nbAdmwTuXsXYBWWJ5Gi68N//0L8gNzo87GKOl+zEQQEhGI7sYOcQ8vN6I4S+C94AD/LgSP2b9g73goOYPB/CTiwENuxzeT+9R5Wp1td9knLTmN/8n4AWtZu6fXfrmXdlqw9vJYtR7bQ0oMeOZXlf8TX5/e1otoZx4ldZ307Yzu4CP8fRmPLNq+ZDR/gaHcd9iHvgZ+/+w4n9xOw9m1sQO55z2Cd1nbYzn2CgNlXYq2ZQm63CSZfeJ6A7V+b/ZqNKPpad4q+kEDASlhF7skj+cdx1kXu0e0E7JqNDcjpdLvre1Xs1fg3/wS/vXOwZo8j98oF+G38EP/j27BC6pLbfvxZ0/5UlvcQN32exRbdF78t/wN7Fo6Yi7HaX28mnCxlWW0xlxKw7Hms3T+Tm5Fieqqepqj6CEhYbf736nUp/H8vuAH+0X3wO7wU+9YvcHT9e6nK6KndR01v6KYRTT37253Yjv+822iduJZXbAH8X46DvUf30qRWyaMb3erEsvD/fRJ+gL3znTgGvoX//Lvw2/gB1oIJ5I5dVvi1f4a8+R9VYLGCJCUlYbfbiYqKclkeFRXFli1bCt0nPj6+0O3j4+Pz1zuXFbVNYV544QWefvppt+VHt/7B77k/FbJHxZo3b56viwBASO4xotJW0uTkQupnFvTmPFjjfLbUvZbU1MYwf0k5nf1ywpudR6vk72masoCAtIPYtn7qtpXdFsiBGv3YUedvpCY3hl9/L6fyVB6V5f+jPITmXMhg22f4H/ydZV8+x5Hwnh7tV1yd9Ix/lSbZKRwPbs3vx3vDT8Vf4/XrPkCfQ0/ht/UzNicFsaNO4TebnkjITiAlK4VAWyC7V+5mn22f18ewnTCBzd82/EbLY54NwfD1/0h6eiGzhlczRbUzv/zyC2FhYT4okStf/I/UytpFp8QP8tuTDP967Ko9kkM1+pAe0BCA0NxEItPXEJW+igbp6whIT8CWnpB/jNTARmyuew2H0i+AOb+UuixhAeMYaFtGwKE/aFm/NfPmuQ9gqZm1j74HHyfAkcPB8PNZebRnie8fZxNfv4945jxoeg7+VjZ2v1BYmwxrPf8bdbE3pwWb2Lv4YzZsK74n0qn14e/I4OK8niS/bc0hY1fR5+wV0oPGqYvZM+dfbKo/vthz1MnYQr+DcTgI4Neci8g45f+tZY2/0TnzfXIXPsa8PfVw+BWUd2+mCdLX9K/J0gWFP6wvTnCaOdacZXPAi8mhi/ofqZF9kOi0Pwmwp3MipDXx4edg2cr+hq+6tzVFtTNZiVtYWAneq8rrPSQk9xgD991LgCONY8FtOBESS0zyHPy2/I/dh0+wocGtbvt0OzKF5vYskkI6snhjLmw6rX4sPwYENadW9l52fDmBbXVNb+OIrD0MTFqHnQB+2RVKzt6S63VQYBNq5hxg9fevE1/jPJd1B2b/H62xSAjrzp9Ld3D6BRdiu5IBfksITlyL/d0YAuxm4qZ1Na9kz69/eFFLVUPlbWeuARuwF9hb+s8aAFgWQwPqE5aTxMpvXuZIeK8iNz21PgLsaYxMNv8fv/yVSM6Gwv/3Wua0pzNLOb7sfRYfbOFxsfwdWdhtgYUPsS7CyoNm1uyU/Slu816cLsCexoD9EwnPNZ/hLgeysfhs7md0rFHEPAyFcNZJnYwt9EtYid0WyC+pfcj+6ScC7f0Z6vcZgUnrWP/5I+yPGFjC0bznTTujwGI19Oijj7r0hExJSaFp06Y08Evk4ouLme2rnOXk5DBv3jyGDh1KYGBg2R3YsiDrOGQexZZxDLKTTSLYnDRsuenmZ+f3nHRsaYewJa3HllyQyNuy+WG1uAT7uY8RGdkDz9O1lp6pj2iirv4fJCzBlrgaTu7H5rBj1YjGqt8Vq+lgGgVHUIqsflVOuf1/VDaLNsPq1zgv+xtyr3is2KdPJdWJbe88Anb8gWXzo8bl/+PiSE/yHF6M9VcELLyfDkdn0Lbv37CcebK89PWmr2EvXFy/BZe1SjE5JOu2x4o6x+QC9UD04Wi+/fxbEq1ELrroomJzmlSW/xFnr4nqrKh2ZtiwYURERPisXD75H0nehf/yf2Lb8T/T490/BEfPBwno+SBtAsNwH7g5HgDLnk1u/DJI2WOGRNdtT3BkT7r5+dOtDIpl+ysLFt5Pp6QPadP1fPw63lCwLn45/j/chs2RgiOyJ5FX/MTFgb4PCFeEyvI+UhFs21Jhzs+0DNxHsyI+/xVWH7Z98/HbZceq2YyBo8YXf46d2TB7MbG5q4i5aGaxN3H+379rfuh4AwMH3+i60j4Ea8Y8Qk7u5aKY41inTOLw5YYvYT90btS52M+xtvjl+G380PTEaXM1VswIABJWJ7Dsj2XY6to8+hxc5P+IZeG39En8Vr6EjYJhqVa9juRe9KnJNVaGqntbU1Q7E2o/zsVDB4CP3rPK+z3E/6ex+DnScET2oOaVC6gZEIpj2xf4zbmWVsk/EtNtKA7npBmALWEl/rPmA1D70ne4OLpPoce1bUuDOdfTLvUnYq94FULr4/fb3bAfbK0uYejIMR6Vz2/BJbB+Kr0ik3EMMNdTTk4Ov839kdiMhQDUG/QEF7cs/FqzxbfD+n40IZkmt7ajw410GPxvOpxFw6CrUzsD4D//Mtg4jXOj03Fc4P53L7SdOfA77AarZjOGXlr4KEwAUjrC9GnUy9zExf17mJzxRTm6Cf9lz2A78Du2zCTzwCc8GiuiOQTVMqk2so5jyzoOfoFY0X2wd7vP5PcE3pxuJjgaeeFIejcpYnh2Hr8/HsZ/dwJWRAyO7vfjv/ABxthOMrPBCS4e6H0747dgDhwEW7uxDBk6ruA8K/fDkv+je+b3dL76BdO7tAx5084osFhB6tevj7+/PwkJCS7LExISaNiw8AugYcOGxW7v/J6QkEB0dLTLNt26dSuyLMHBwQQHuz+ZtqUdItCe6tL93RcCAwPP7E02eTfsjzOJxY9uNLkQSzMTFTaTS6rlpdg63IAtoqlPkpIGhtYkoPUl0NqDWT+rgTP+/6jszvs/2DgN29ENBO6YZSZ2KUGhdZKbCQvvB8DW/V4CG3uRF63nvXBsA7b17xEw53qTt7NeO29eBVgWudu/5EcO0D5pF/wyp2BdWJTJS9n5thK77XeO7oyfzY/E9ESOZR2jYc1iPjDk8fX/yFn9/+mhotqZwNyTBAbW80GJTitHef2PZKXAyX2Qsg9ObIddP5lE9c4gQ7tx2C78F/4RzSix/1JgIMQMKvsyOvW8F/uxTfiv/y/B82+FhKUmh9ahpSYHlmWHyB74XfkLfqG1yq8clZSv30cqRMxgAGxJ6wm0p5kJV4rgUh/xZrSGrUm/kuso9lIIqoktdT+BiSuLzkeduB72/ATY8Ov9CH6nHzcwEHrcBwv/QcC6KdDt9vxUHTtPmAfB7SLbFV2e1W/Bgvvzf/Xb8j/o9SD0e4k2DUx4f9fxXV79zV3qxLIg7oGCmXVbXGTyxm39AtvRjQR+NRjGLoa63uWALOn81VlR7QxAYPp+91lkK1iJ7yGOXNjxHSSsghqNod04CK1b/EF3zYYdX4PNH7/h0/ALzXtQ1/EaSNsPfzyC/+8T8W/QEZoPNudYeD9gQfvrCGhWzOQr7cfBqlexJa4l8I9/wHlPwOYZAPj1+of7NVmUViNh/VT8d32P/5Ap+Q8TmpyMMwGbiBgCWl9W9Oe/pn3hlm1mosyaTfBrdD5+Z1FQ8VTVop0BaD4INk7D/+BC/It5vS71cWwdALaoHsXXUb1YiD4P2+E/Cdz6MfR+rPDt1r0PC+4190d5bJYdUg9gSz1Q6C62Y5vx2/wxDJlKTofrOZiXx7FV/VbFlynjKKwzE+TZhryDf4uL2L7pU1onrqDl3h8IDHyp6H1PExgYSGCAP+z6DgC/9uNcr8VeE2DN69hSdhO461tzHZchb/4/FVisIEFBQfTs2ZP58+czevRoABwOB/Pnz+eee+4pdJ8+ffowf/58JkyYkL9s3rx59OljnjS1aNGChg0bMn/+/PxAYkpKCsuWLeOuu+4q5IgeSFpf/IxflZFlmRnLtn4O27+EEzsL3y6oppldMLi2mRWzqK+QulC/C0R2g1Df3wBLNRNa1zSKv0+CxU9Am6sh0D0fSYmW/wtO7DCJsc9/xrt9bTYYPAWObTYB+u9GmeBiMTedLtKPwNxbuXbvD2ADh18gfo3ON9dX/ApIT4Bf7zKzzF78qZkRsAihgaG0qtuK7Ue3s+nIJo8Ci4XKTjUfJkLrKfm3LyXvgqgYX5fizFkOOLgEDiyExLXmAVbKPpP8vjAxw811WJkmPrHZcAx4kz37DtEq+QfY8IH5cmo7Fob8x/PrXqqe8IZm4rETO03y+xYe9k53TtzSpJiJW5wCQiB2NGz62MzuWlRg0ZmQvs2VZiK6wnS6GZY8CUkbzOQQzU1gdGviVrNrURO3bPuqIKjYdqx5gP7Xf2DlK1A7ltiYkYCZ8TPXkUtAaXp8/DW1IKg49D0zSRqY6/7ri0zw6KvhcO0KCKvv/fELcyaTAJztju/weWCxWGnxZvLFhFUFy5Y8BZd9ZWbALUxOGszPyyHX8wGI7Oq6/pxJpkPFpo/N57YL/2U+w8WvMBND9n/Z/Zin8vM3M8rPvBC2fGa+AGJGQJPic1y7aD7U3HOlHjTtZJMLwGEn9oQJjNDj/pJzwYXUgXae9ZCUKqBp3hDdI2sg84RnnyucE7d4kJuXbneb3NRr/2Oug9Pfw9e+U3DtOD+P1W1r7g1SD5hOSTlpZr/gOub/L/sk/PWOCeb/cgvH7TnkOnIJDggmqkaUexlOtf4DsGeZsuf1jE/uMB4WrqBbyjZz3qAaJb8up0N/Qtphcx03Pe2Bc2AYdL/PtI0rXjSTMfnoPkeBxQo0ceJEbrzxRnr16sW5557LG2+8QVpaGjfddBMAN9xwA40bN+aFF14A4P7776d///68+uqrjBw5kpkzZ7Jy5UrefdcMFbHZbEyYMIHnnnuO1q1b06JFC5544gkaNWqUH7z0WmIVCiymHoIN02DjRyaA4uQXAFHnQNMBZuazeu2hdqz5cCtSFXS/F9ZMMT2fVr8JvR/xbv9j28zsfwAD3yw2cFck/yDzAfd/veD4NjPz6OU/lvxhcPfPMGc8pB8hG3jPqs1VN/xJZD0zIxr2bHMD9sfDprH+fCBcNb/YDxkdozqy/eh2Nh7ZyKBWXvTgyk6Fde/Cuv+a1wAQXAtaX2k+lNf3PMeJlJHknUA59sLzVsYx07PQnm0CLOHRRX8gcwYTt31hHmKlHip8u5C6ULMZ1Gxqgiixl5dpL6UyZfNjQ4NbaD7kAQI2TzcBpppNocP1JsikIPzZr/EF5u9+cJFngUV7trmBg4KZpUvSdqwJdmz7Aga+4d6OnNgFW2ean8991G33fCG1oeNNZsbS1W8UBBaTigksZh43D7LAvO/3f9X8X9dsBosehYX/oFHLSwgNCCUjN4P9J/bToq7neboAOL4dFj5ofu73ckFQESCsAfztJ/isr/msOv8uuOTzsrm2tsw682OcrU54kSyzoqUehi8GmQdSIXXMZ5JDi80M7V+PgEu/gsKGCS99BlL2mv/d8ye7r7fZYOi7kJZg8uL9dm/ecj8zk3xxQ0SdGvWBYe/BvDvMTOz1OsDwad69voBg85o2fmgC+E0uwLbre2rkHMIKroOts3sOSDnL1WgEddqYz+IH/4BWl5a8z5G8wGKUB4HFNldD3D9MkHDHt+YBldPGjwqCiuc8DBc+X5CSI7gW1GxcMMPy6VpcZK6jtW9T5/eHiKIOEbVa41dcXkaH3fzfA3S/J/+9vlHrUeyNu5/mtlxydv1IYLtihnefbvtX5nurSwufmb3b301QMfEv2DMXWozw/NhlSIHFCjRmzBgSExN58skniY+Pp1u3bsyZMyd/8pV9+/bh51fwj3r++efz6aef8vjjj/PYY4/RunVrvv32Wzp1KngCN2nSJNLS0rj99ts5ceIEF1xwAXPmzCEkpJRBtKT1Z/Qay53DboaVrfsv7PzBDNUCM8NUy5HQdox5MuDNUwCRyiYgBC54Dn6+wQQIO9/qeQ8Hy4L5d5ubvxYXQesrSl+OsEgY9R3M7At75sDcm2DY+4XnR8xJMzdWf00FID2iBVcmZ3OyZgv+7gwqgtm3x33Q6Hz4+mLzweGbkXDlr0X2zOwY2ZFvN33LpiObPC/74eXw0zXuPZizkk2vrE0fwbmPQJ+nyjwfiRTjxC5fl8BIT4BFD5tgxqm9fsIioWFv8yGzQRfwD4aTB8wH4T1zTC8Tp+BaEHMRRPU0QeqIGBOUq4Ltj9X4Aogp+6TfUgU0usDceB1c7Nn2Cavyen/XNz0+PNF8iAm4pyeYXr7NTnu4sPwFcx3GDIeoEnIBd7/XBBZ3/QjHtpEUXJej6UexYSs8sLjocchINPkNL/xXQUDv3Emw8zs4/Cd+S56iZb2WbEzYyI5jO7wPLP56t8nV3WwQ9Jrovj4sEi6ZBZ/2hm1fwtZZplfJmchONT3cpHDJO0vexhdOHjRBxePbTHtx9QLzUCsnA34cA7t+gO//BlfMMR0knA4uNj1swYwoCQwv/PgBIeYh8KrXTTAipI4J1hfVC7IwnW4y12LKXtO+eZgT20X3e0xgcess6HI7/ov/DwBHlzvwr4JtpJSBpgPM//3+BSUHFnPSTOAdPOuxGBAMXe+AP5+DRf8HLUaae4qNM2CO6cBFj/vhwhe8e6hjs8GA1+HwnwQmrOKf5DKrTgkP4Hb/bHJjh9Q1D9XyREc0YoZ/HW50JJK+6RNqedMG7PrRfI8tYkLN0LrQ5XZz3a940WeBRV+kjKvW7rnnHvbu3UtWVhbLli2jd++CCHlcXBzTp0932f6qq65i69atZGVlsWHDBrek0jabjWeeeYb4+HgyMzP59ddfadPmDHpGJK4r/b7lKeMYLHsBPmhlhpTs+NYEFRtfABfNgLuOwKVfmCcUarDkbND+WmjQzQyt/PNZz/fb+BHsm28+XA6acua9IqK6m2vM5m96nMwa4Po+4bDD1i9gRtf8oCLd7+V/He9nK8F0a9St8OM27AVXzjMfeg8tgXm3m6BoITpEmqTJGxM2Frr+dLYd38CsCwt6Xw17H+5OgvszYMxCaHWZyTv053Pw1UWmR4tUjGTfBxbDsw8TMPN82PKpCWbUbAq1Wpgn2OlHzI3d4sfh28vM0MVfboGN001QMSjC9OYb/QPcmQCXfAbnPGiC+PXaq/2Rqqdx3hDH+GXmgVRJnAHIRn09b1/8g6D138zPm2a4rju2DTZ8aH4+74mSj1W3DbTMyzm9+s383orN6zQn9PSHU/ErC3qODH7bNUBi84MBr5mfN0zjgvDaAOw46mVPt30LYN+v4Bdo2pqierJE9YDej5ufFz5obpzPxPr3TKBWCldUWiRfStkLnw8wwZWI5ubzSO1WZl1gqBkl0mqUGUL5zaVweJlZl54EP19v2qsON5QclPEPNIHza5flBSi9CCo61Whkei+WJqgI5v+99RXmXu3zAdiSd5ARUA9Hj0IC71I9NBlgvh/4veRtE9eZ//fwhlAjuuTtweTMDW9orq9vRsKvfzcjqLCgyx0mQFiaeyL/QLjof9ht/gy0ZdDfL6v47de+bb53utmlw4TNZmN7XdM5LGz/b561t2Aebh/fZtqWZoOL3q7nRNMO7Y8reO+oYAosiqujG4q8ufeJ5N3w233wblNY9JhplEPqmKcO4zfC2D/MTZ5u5uRsY/MryIfz1zsmTUFJ0uJN8ngw+UNqtyybsrS50jwBD6xh8nDN6AofdYHPB8F/ouDHq82H+BpNTLBw0FusSjC9C7tHF9P7JLKrGfJj84fN/4OVrxa6mTOweDDlICcyThRb1EYnF+H/8zjTYLe6DG5YB51vMbkVA0JMqofR38HImeaJ/75fzQf99MTS1Ix4y9c3eznp9D78T2yp+6FOW7huJdy+D27dBfelwbgl5sNn27HmKXn9ztB8GPT8h+lVe9cRE2hvdUnhw1FEqpq6bSGknumF6MxpVZyDi8x3Z0DSU51uMd83f2o+yzktedIEH1qOLDr/4ul65gUnNk5n16GVALSrf9oEYw573hBoC9pdA80K6ZHbqE9er36Ly1JNgNKrwKJlmd4xYG5ca5XQ0/Hch03P5tSDRbZ3HrHnwKo3Sr9/deDrtsayQ0YSJO8xn9/WT4MZ3cwQ7VotTFDx9P8X/0C4ZKbp+ZqTaj6bzL3F9HRN3m2CkYPe8sGLKaUh70DDcwCwwqNZ3vARk+deqidnTt4ja0z+wuJ4k1/RKbiW+WzvH2x6Rf71DmCZXu5D3jmzjhb12vFrDdNxa8Th30z7Upjj283oFmxwyszsTn7R53PE8icwN90EAD1gO5C3XVTP4nNT1mxiOqUALH/Ro2OXNQUWpYBfgLnQT/3A5yuph+CX2+CDWDM7ZW46NOhqcoTcftDk6Mmb+l3krNV8iEl678iFuTeb70WxLJNDJOuEaXx6PlC2ZWkxwgTz21wF2EzahP0LIPOoCfb3ecqsbz4Ey7JYfch8KCiyx6JTs4HmegaTd3HPL26bRIRE0KxWMwA2Him616Jt33x6JryBzXKYJ4WXfV10I9xujJmhMyzKPBmd1d/kPZLy5eOh0H5/PEjNnANYYQ1hTJy5VpwCQkygoecE0xPx+lVw4zq4ci4MeMXkc1MwUc42NltBQM8ZNCyKZZlccOB5ENCp0Xmmt4UjB+bfa3qjbP/WDJXEBn2f8/xYTQeYz4S56URt/xyAdg1OCyyufw8SVuZNWvFK0cc61+QwbpO0imhy2XnU84CUbc9P5mFbQCic938l7xAQYoZjg7nxKypPa0m2fWlyMIeW0SQwZ6OUvSYAW9EyT+A//y5G7rqWwPcawfstYEYX0/M96wQ0PBeuXmiChIUJCDEpaFqONMH+DdNMT/8aTeBvP5vgSVURFgnX/Am37CT3xm2cCCliUiapHmo2Mf/3lsNMRlIcb/Irnqppf/PAuMvtpuPR334ywfjiciJ6aIpVmxTLjzqp+8wIrsKsNTNB0+KiQjt3tIvsQBxh5pdC7ncK4+cMLJ4+aUthzplkvu/4Fo5u8ej4JUopfMbswiiwKAWcuXJ8mWfRsuO36hUTUFz/vnnzaT7M9IK6fg10vKF0M+SKVFWD3zYfJBNWwtKni95uzRTY/rV5QDDs/fLJGxjRDC793PTauuxrGPkZjPnD/H7+5PxJYvYn7+dYxjGC/IPoGOnBBCnd/m56s1gOmD3WPJk/jbPXYpF5FhNW4z/7KvzIxdH6SpPAvKSJZiK7wpjfoUZjMwP2rH5mZl8pP1nHfTf0/PBy/De8j4UN+/DpniWyF6kOnL0PSwosHt9memEFhHjXk8Rp4Bumbdr1A3x6nnm/h7wZbrt5fhybDXo/BsAFScuoRy5tG5yS7/HkQfgjbxKYvs8VP5SuYS9oNgg/y85NJLPz2E4cnsy2bDnwX5qX47D7vZ6/n7S9GqLPMw/MFz/p2T4u57UKcu11ud37/auDgGDTY/BkBbfn2aaXod/GDwiwMvPKEmoCbHXbwwXPw9hFENG0+OME1TDpNkb/AL0eMpPwjd9g0m1UNTY/E2DRQzkBaJzXa/HgH8VvV5oei071O8HQ/5rRJZ5MSOaBzJxMNp88wjvUNgsW/5+53k+VcdQ80ALTJhSiU8NOLMbEMay980o+sWUV9Fg8PTdxYeq1N+kUsGBlCbPAe8Ky4Ld7PN5cgUUpUC8vAOCrPIsndnLhgcfwX/wY5GaYyR3GLja9RZoP0eyUUj3VaGSCi2ByAm7+zG0T2+6fYGHe0LAL/+XdDVpphNWH1peb5PNNLnALYq46uAowwcBgTz5M2mwmGXnDc0zQ6bvLISfdZZOOUeb9qdDA4vEd8PVF2HJSSQztjH3ohyUHFZ3qtjHBxYgYM0Rp5gVmKENp5GaaWTpnX1O6/auL0tbvmbAs+MP0TNpfcyCWJ09+RaoLZ2Dx0OLi0+E48ytGnVO6QEH9TmaGWZsfxK8wueRajTIJ9b3V5iqsqF6EWXYmcrwgsGg5zERjzt773dyHo7nJ6+Ux1pZCYM5J4k/Gl7ADNEpdgi1pnekR6ewl4gmbzcxMDSZ367Gtnu8LZvKbI6tNwKqTZtctVESM+V7Rw6HjHoDEv7DColjU6Fly7kmH+9PhrgS4aRP0ftQMd/aEzWZSbvR/yUx4V5V6KooUxTkcuriHWLlZJjUbeN9jsZzsPbEXC4uvgxpj1WphepsvP63dWjPF5M5t0M1MflSItvXbstxWE8C0H2nF58kNy03AdnKfyZ3o6SiBvF74bPrY5Gc8E+v+a0aneUiBRSlQNy+w6Iseiwf+IODzvtTN2ooVFAHDPjBP9RqfX/FlEals2l9bkFPqp2vNECpHLjhyiUn+Cf8frzC/t7umYDsfWnXIBBZ7NupZwpanCAgx+RbDIiHxL5NX6JReI0VO4JKyD74cCulHsOp3ZXn0o97f8NZuaYKLddrAyf0w80Izq7SnslNhxStmyNPssbBrtnfnr26OejG7d1nZNx/2L8DyC2JL3TOcjVXkbBPZw7wHZySZXolFKe0w6FN1uN7kvu3/ihnyOerr0k0QYbNxsLtJ+THWdpKmh/4wQdHFT8Deeeb1XPQ/z3rvNx8GDboSjsX1pJScZ9GRS/tjn5qfe/3D5PD1RuPzoeWlpledt70Wnb0VO94EYV6et7qIyMtdWJGBxcR1sP4DAOwXfcrRsM7lM3JEpCpzPsQ6/GfRk5ckrTP3NCF1oWaziitbMXYdM2l8mtSNxdY/b9Kvla8UDDdOT4Q1b5qfz32kyM5QwQHBREZ2ZIOV1+bt+7XY89ZPz4vJRJ9X9Ezwp2t0nskn78iBpc94tk9hju8wE415QYFFKVDfR4HFrV/Al0OwZR7jeHBrcq9dA51vVg9FkVP1eylv2FNez6t36hPwXiO6Jr6LzbJD++tgxPRKcd04eyz2bOxFYBHM8KBLvzAfxrfOhLiJ+b1nOkWZmdR2HdtFenZeb8bUQ/DFIEjZA7VjyR31A7l+YaUrdERTMxlUg25mps1Z/WDN2y7BTTfpSbDkaXivOfz+kJk8p0YTc6MpRUvaUPHnzJsowdHpVjICIyv+/CKVWUCw6YUIxfckKe3ELaer39G8T8Zedka5r9b6R/C+ZXpy+f18PUyNhmXPm5UD/w312hWz9ylstvxeHuNJZm8xuXwBbFs+oUbOIayQetBjQukKf8FzgA22fQ4Jazzb5+jmvAdXtrLPo3w2qeWDwOLqNwHL9KR1DvcUEVd12+dNFpZR9GRhzhmNG55bKe5pAHYdN4HFFnVbQOwo0yPRng3f/81MzjRnvBlxVb+TmfCyGJ2iCoZDl5RnsUFG3ihST4ZBn+r8vIDi+vdg98/e7Qum5+X3fzPfG3n+IFGBRSngHAp9bCvkZFTMOXd8B7PNDK6OVpezuPFzULOE3CMi1ZGfPwyZavKGhEVCVjK2rBNk+dXEfuHLcNFHng+xKUcpmSlsTzJDXb0OLIJ5yjZ8mvl59ZtmVk9HLvXD6xMZHomFxebEzaYhn3mhuXGo1QKu+u3Mc+aFRZqZGluNMkP0frvH5AHbMhMyjpltsk/C3vnwy+3wXjNYOhkyj0Gd1qan9a07oU8p8mZVJ0crOLB4dEv+LH2OboXnvRGp9vLzLBaR+yr9SEFvxug+FVOmEmxN2sqL1GVxvXMAm3koFBBq2skuXg4TbnMVx0PqU8fmoN6OL4veLicD/z/NTZuj16T83MJea9AF2o0zPy/2YOIXKJhJOnY01Ikt3XmrA2dgMbmCAovZJ/MmIQK631cx5xSpimy2knP6OgOL0b0rpkwecPZYbFm3pXkNwz80qaqObTaTM+3+yfSSH15yKqbODTuzyBlY3Duv6PQjlkX9jLzOXk0Helfgpv2hx/3m559v8O6Bvj3bpHRKWm8muBz+gce7KrAoBWpEmxtry26GIpa3w8vhxzHmfB2ux37xZ9j9lNxXpEg2m+m1eMdBuOEvcsatYG6LaTi6318mM56VhdWHVmNh0ax2M+qHl3LGyg7Xw+B3AJvJ7/HJubDrJ7o1aEtTcgj88xn4tLeZKdEZVCwpGbqngiPM0LyBb0FgDZMHbPY4eKcevBEM/46AL4eYp4C5GSaH18iZMH6z6WldmiF91U1S8b2BytzaKeZ7q0uhdquKPbdIVdG0v/m+99dCb3RsziFbDbpCaN0KLFjRtiZuxYGNXV3vhVt3wVXzTftYmklN/PzZ09oE+vocWWJy5hZmzVvYUveTHlAfR5c7z6D0wPlPmx76u3+GAyVMZpCyHzbNMD+f89CZnfdsVytvNtaK6rG49XPTs6dOmzNLEyBSHZT0ECu+8gUWdx8zk0q2rJP33lIj2qRQcr6WWi3NZEsNe5V4rM5RnVlJCJnYIO1w0emBjm0mxH4Cyz/EDIX21gUvmHuUjCT4fBDsmVvyPpnH4fsrYOf34B8Ml35Z/ORnp6kcd6JSOdhsZvIEMDPQlqfUw/DdaNMrqOWlBcm8RaRkfgGmt0ODrlg23/dSPNXqQ2Zog1f5FQvT7S4Y9Y1JWH5kDXwzkv/s/Yg423667PneBPWaD4NrV0CtmDMv+KlsftDjXrhlO5z3JNTO6xnizAdTo5GZxfrqOHP+dmM8nyxGIPUAZJ6omHPlpJsE1lDkLH0iZwPLssix52AVN/lKcRr3M739Ug8WOomf3968m5IymmWzLGw+shkwCfGpFWOGi4XUKfXxgjvdzCHLnzqOTKxVr7lvkHoof6j15nrXmfo6E3ViTVsCsOix4ifOWfWqyZnVdAA0qhw9RistZ47F5F1F1qndYedk1snSXy+n2jjdfO+kNE4iJTp1ApfT0w1lHiuY4K/huRVbriJYlsXOY+YhRcu6LQtW1G5lUijdnwG37DATzXqgdf3W4B/CcivELChidmi/vNmgrUZ9SzdZWmAoXPGLyaGckQhfjYAvhsKG6WZ0qvPhmWXByYNm8pmPOsOuH8n1C+TtqGHcteoT3lz8psenVFZZcRXVy+RvKYfAYkZOBvtO7GPP8d10WjyJxmmHSQptyOyGwwjf9C0xtWLIcmSV+XlFpOKUOr9iYWJHwc3bYOmzsONrSD1EtgU7AuvQ4eIPzHCw8vwQH94Q+j5tvjJPmOFOgeGVprdOlRTeCOyH4OjGEnt2JKQmMHfbXFYcXMGhlENk5mZSO6Q2beq3oVt0N85pcg6NIhoVf74d30J2ipkltNkgyLWX2UsR8bXMnEy+2vgVs7fMZs3hNWTbs6kTWofeTXpzY48bObepFzdmgaHQbDDs+tEM64rsWrDOcmBz3vxUksBiUloSh04ewoaN9pHty+SYLRq05Qnq8QpHTLvT5uqCIceWw+TRyk7BEdWLAzX60aUsTnreE7DpI3OTveUzaH+N+zYnD8C6dwFY0XgoC35/iQPJBzh58mRZlODsU6uZeUCYk2aGx5+SJiX+ZDwv//Eyc7fNJSM3g8jwSPq16Mc1Xa+ha3TXIg+578Q+ftv1GxsTNpKVm0W9sHr0btqbAdFdCTm0xGzkHNouchbKyMng203fMmfbHDYkbCAzN5OWdVsyrss4xnYdi5+nHYQie5iHMpnHTN5Y5xwPgC1+hfmhdqz3k2KVkwPJB0jNTiXIP8jkWDxdQIhXxwvyD6J9g/Ysjj9CPzJMYLHnBLftbHtN/kWrqZf5FU8VWtekeFr8hEkvte9X1wljgmqaiXJyC1Lg7SGIifb6/HVwI7ARR2YxueZPo8Ci5Ju7bS6t7H70ANL2/876fX8S6B9IoF+g+e4fSFhgGDWCahAeFI5/IT10LMvieMZx4lPj2XF0B9uStuV/7U/eD8BYUhhuSyLLsnFNegA7F7+ev78NG7M+n8XAlgO5tP2lNK/dvKJevkiFyMzJZOm+pWw7uo207DTqhtWlc1Rnujfq7nmjXEnl2HP467BJo1AmgUUw6RkG/xsGvcWhI+sZ/PEoLHsw61qOJKgiewaE1DZfHjiRcaI8S1K11esARw6ZfC9FBBZ3HN3Bm0veZO62udgt90Dgn/v/ZMYaMySwWa1m9G7am/OanUfLui2pH1Yfy7I4mX2S+JPxtP3zBaKBnwMief/TqziYfJC0zDTe/vht2ke2p0+zPgyLHUbdMAWLpWqZu20uT81/isS0RJflxzOOM2f7HOZsn8PQ2KH8c9g/qefp7MEtLi4ILPZ+NH9x7awd2DKTICii0uRXXBdvelXG1oulZnDNMjlmaGAoKyPasSjlJBfYM+Cna8zw6sAaZqKuvfMgIBT70Gnw564yOSc1G0Pvx2Hx47BggpkU4LQb6qz/Z+++w6Oqtj6O/ya9J6QRIKH3DiJFFOkgiGBDRKRcBctFRXyt10JRvKJXbFiuIjaQYrsqKCAaagSk19BrOoH0Msmc948xo+MkIQNpmO/nefJA5rR9VnJmZdbZZ+/VD8izIEfb5a1RMR9IsuY+Zz7w1SiuHtbx2tNPWB+H/r2wuP74ek1ZNkXncs7ZVk3KStIXe77QF3u+UPuI9rqz050a2mKoPN08lZ6brh8P/aiv9n6lLae3OBzmk+2f6GYPabZhkTmomdwD7GewNReatenMJq08tFK/nflN6XnpCvMN0/Utr9etbW9VgNdFjs8JVKKUrBQt2LFAn+34TKlF443/bl/SPj3z0zP6/sD3emPYG2UbAsnV3fpo76lfpFPRfyksVr/HoPclWx9VbhbSTB7lNNxRu4h2Wp+w2frN6TXWJ6L+vG9zjkynf5EkWRoO1iU9E+XhJ/WZI3V+0Npb8cQqKXm7tcdi/u83p0yuSvKpo7czc7RU/mpV9wr9q/kQubq4avux7XpdZeu1SGERNo/9+Jhqe1n0q0nySjumiUtuV3YpT8v7uvvKz9NPnm6eKigskNliVlpumvJLmj5eUksPTz1dcE4ypFV1+qh7eE+1M2cpOStZB5MPKjk7WTvid2hH/A7N2TBHV0ZeqVva3KIhLYbIx+MiZ3sFqlhyVrJ+OfqLVh9erfUn1iu3mLGb6vjX0QM9HtAtbW8ptmh/OdiftF85BTkK8AxQ05ByHljeZFKd8Hby9qqltNw0HUo5pDa121x4u0qUV5Cnz3Z8ptd/LvtjAzVOSCsp6SfroNB/cfL8Sb0Z86a+2feNLL8/HtO5bmf1adxHTUOaysvNSynZKdqTuEc74nZoT+IenUw7qZNpJ7V0z1KH/dVRgdbqpGSS/n02XqeVYlt2JPWIjqQe0fcHvtezq57V1Q2v1m3tblOfJn3K7Q9HoCKkZKVo+s/TtTx2uSSpXkA9je88Xr0b91awd7COnTumr/Z+paW7l2rV4VXaGb9T826ep9bhrS+888ZDpNWS4jZK2cmST5gkKSLr96JKg/7VYpIwSdoRv0OS1D6iXPoN2jQJaaqn049phds5eSZskT5qay1MFX0I7PO6FNxSUjkVFiXrmIkHPrf25F52u3TjMlucd8fMVrsj36jQkJ5RsOoHNdDVDa5Ww1oNpTzpbjk5SU1NEdTkj8JivZ6KORmjiV9PVH5hvtrUbqMZ/WeoaUhT7UrYpa/2fKVlscu0K2GXHv3hUf1r5b8U4BmglOw/coZJJnWv313dIrspwCtAx88d10+Hf1LnjJ2SSfrsXJJWLR6tTnU6yUUu2hC/QdPen6b0vHS7ZsVnxGtXwi69v+V9zR48W70a9arsyAAXlFeQp40nNmpZ7DIti11m+2wfFRil2zvcrmsaXiMfdx/9cvQXvbr+VW06vUljlozRZyM/K1txscFAa2Hx+I9Sp3/aXi4qpqkazaxeNORGefWMl6yFxQXy0HkXTwWZs6S4mD/GOZak09EyFeQo2y1U7iFty+eggY3+eArLMKS889Y87+Kmb05u0iMrn5bkoYeuekiTe0y2dXa5semNFBbhvC71ukieUkpikkItuRroF6btrn4yF5pltphlLjQr25xte3PJMmcpy5xV7L6CvYPVKLiRmoc2V4vQFmoe2lzNQpopeMV46eh+KfJaXT9yla7/Uw8ts9msBf9bILfGbvrp6E9af3y9tpzeoi2nt2jmLzN1fcvrdVv729SudjuZGMMElcAwDB0+e1hHUo/odNpp5Rbkys3FTb4evgrxCVGIT4iCvYMV4hOiIO8g25twRl6Gdifu1m+nf1P00WjtTLCfDKmOfx11jewqf09/JWYmKuZkjOIz4vXUyqf0v/3/0+vXv64w37CqOOVLEnMyRpJ0ZeSVFdL70mQyqXV4a8WcjNG+pH3VprBYaCnU8oPL9eq6V3Uy7aQs+fQiKcmCM/t0nyQjYYtMsl5j+5L26ZPtn+jrvV/beigOaDpAU3pOUcuwlg77uKnNTZKs19nWM1u16dQmbTmzRfHp8UrJTpGLyUX+nv6615Qml2zpmF8jjb3iX4oMiFS4T7g2b9ysZp2baW/SXq06vEp7k/ZqzbE1WnNsjUJ8QnRj6xs1st1INQlhohdUH4Zh6LsD32nmzzOVmpMqV5OrJnWdpAd6PCDPP42/1Mm7kzrV7aTRHUZryvdTdDj1sG5fdLveGfGOrqp/VekHCWhgHew9cau0f4H18SzDoqiMaOvyZjdX1Ok5rajHYsc6Hct1vy3DWir6WLTmNRyp+xNWShknrV8u7tK1r0jtJ0pmc7keU64e0nWfSouutvYm+XGc1P8d/bJxtq7Y+m/JJH3rVV+PDv1EvRr2sv0NnJ6eTmGxJIFNJP0snT+s7XHbNenrScovzFf/pv315rA3bTeQrqp/la6qf5We7P2kluxeogU7Fig+I95WVGwa3FQ3trlRw1sPVx1/+0kMnu79L+W/FynlZGi9vLXp1CZtOrXJbp0QnxD1b9pffRr3UbhvuPYm7dW83+bp+LnjmvDlBD3W6zFNunLSBT/XnMs5pxWHVmhv4l75e/qrb+O+6hJ54Yki/sowDJ1JP6P8wnw1CGpw2d7IRvk7m31W0Uej9fORn7X2+Fplm7NtyzpEdNA/uvxDg5sPlpvLH+WjCVdMUO9GvXXHkjt06Owh/eOrf2jRbYsu3Bmo8RBp/ZPSydWSOUeSm9ws2X/0WGw4sPxP8CJVRGHxysgrZciktRYP3aA86cRK+8Li0WWSpESfKxRZETUPk8k6HrFXLZ1OO62nf35BknRv13v14FUXP7M9hUXYzLt5ngICAqRvRkhH/qf/dLlduuJhh/XyCvKUmZ+pjLwMZeZnKrcgVx4uHvJw85C/h7/C/MKK7/FxfIV09DvrxBMD3it2spZa7rU0pN0Qjek8RvEZ8fpm3zdaunupTpw/oUW7FmnRrkVqFdZKN7a5UX0b9y1+rAPgEp1OO615v83TqsOrFJ8RX6ZtXEwuquVdS7nm3GIL7u0j2qtvk77q16SfWoW1svsjMq8gTwt2LNBrG17TplObNOKzEfps5GdqVKtifr+TMpO0LHaZTqWdUphvmAY1G2Q/IPFF2nBygySpZ4OKmxWxqLC4N3Gvbm13a4UdpyxyzDn6cs+XmvfbPJ1MOylJCvMN070979WEZyZUaduqqwWJB3Sft2RO2KLbP7lepzOT7XqFXNPwGj3c8+FSx7oq4u/pr96Ne6t349621wzDsF5bhkWa10yS1OjqabqrzVhJ1htYJz1P6poG16hv07564KoHdCz1mL7Y84W+3PulkrOS9cFvH+iD3z7QFfWu0HXNr1Pb2m3VIKiB/D395eXmxY0tXBLDMHQ+97xyC3Ll5+FXpsd4Y5NjNePnGfr11K+SrMWvlwa/pLa1S+7J0Cq8lZaOXqp7/3evNp3apH988Q+9fv3rGtR8UOkHa/sPa2Fxx1yp02SZjn4vn4JkGR6BMjW90alzrSiGYdiG3Whfp3x7LLYMt97M+DkzTff/44B05Hvr42INB0p/edS1XNXuJF2/yDqx4YHPpQOfq48kmaRjPpG6bvwueXkHVtzx/25qWd//z8dv0T+2fadsc7Z6NuipN65/o9jPKCE+Ibqv232adOUkxaXHKT0vXfUC6inIO6jEQ7imHZV3TqLk6qHn7vhZfU5v1eGzh5VXkKfs+GyN7jtaXaK62BXvOtTpoBtb36gXol/Q5zs/1+y1s3X83HHN6D9D7sX0Bs4ryNP7W97XO5vesXva5b3N72lgs4Ga3m+6wv3CyxSS5bHL9Z/1/9Hxc8ftznlc53GX/VA8l6OMggx9s/8b5RTkqF1EO3Ws0/Gi/77IyMvQL0d/UVx6nIK8g9Q1smuZ/q43DEPrT6zXR1s/0ppja2Toj8mMIvwiNKDpAA1vPVyd6nYqcR+NghtpwcgFGvn5SO1N3Kupy6fq7eFvl/47FdrOeiMr/YR1+I3GI1Q7a6tMlgLrtRtYfT7f70uyPgrdOqwMvf7LqH5gfYX7hmttVoZuMGVIh/8n9XzeWvAzLNKR7yRJib5XKLLcjurIMAw999NzyinIUdfIrnrkmkcuaX8UFuEooot05H9SQvETuHi6ecrTzbPsY/ZIUqFZ+uX3ImWnB6TgFhfcpI5/Hd3X7T7d29X6R/GS3Uv0w8EftD95v/ZH79es6FmK8I9Qm/A2alSrkYJ9guXj7qP8wnzlF+YrMy9TaXlpSstNU3puutJy05SWl6bMvEy5u7orwDNAgV6BaliroZoEN1Gz0GZqV7td2bpwV0P5hfnKNeeqwFIgf0//Yv9AQeni0uM099e5+mLPFyqwFEiy/r63CmulyMBI+Xr4qqCwQJn5mTqbfVapOak6m31WablpshgWnc0+a9tXHf866lKvi7rX766+jfuW+oefp5un/tHlH+rduLfu+eYeHU09qtsX3a5FoxZZH3cqJ4WWQr23+T29/evbyvnTQL3/Wfcf3X3l3Xr0mkcv+u51rjnXNgZRRRYW24RbeynuTdpbYce4kISMBC3YuUCLdi6yjTdTy7uWxnUap390+YcKcws1QRQWi9Oi8UClxn+iYBXKSNquFHnJ081T/Rr3011X3nXJvY9sf5SfWW+dEdTDX2peei+rRsGN9GivRzWl5xStObZGS3Yv0S9Hf9HWM1ttkxGVeDyZZDKZ5OXmpfpB9dW4VmO1CGuh1uGt1Sq8lSL8Isr8QSEjL0MZedbxbvw8/Bh/628kKz9Ly2KX6YfYH7T1zFa7m0/B3sFqEtJELUNbqmV4SzUNaapAz0Blm7O1P3m/Vh5aqbXH1sqQIQ9XD93f7X7d0+2eMj2yH+AVoPk3z9cjyx/RDwd/0OTvJuulwS/Zev0Wq/Wd0sbnpPOHpZ8fkuvvk7ZY2t8jV/dLnAW5nBw/d1zpeenycPWwzghdjlqFWXulxKbEyuLmLZeWt5Xr/kvVZJh0yypl/jBBfpknlW9Ie8O7qeOo1TJ5+FZeOy5zPxz8QbcFW3+OiSeilW7UVee6nfXu8HftevcWx9XFVVFBUWU70PHfZ0qvd7Xqh7XSmN9/d8xms5YvX67OdTsX+zeVt7u3nh/wvJqGNNULv7ygJbuX6Ez6Gb057E0Fev1RPN5yeoueWvmUjqZaH7tvGdZSfRr3UWJmor7d/61WHlqpPQl7NP+W+aUOP1NoKdRLa17SvK3zJFknj3A1ueps9lk9/8vz+vXUr/rPkP/Iz8OvTKddYCnQ1jNbdeL8CQV6Beqq+leV2zinlS0tN00pWSnycvNS3YC6lXLjsNBSqDdi3tB7x99T4bE/xpHuFtVNc4bOUW2/2mXel2EYWrhzoWavna3M/Ey7ZW1qt9HYTmM1rOUwh9/7zPxMfbvvW32y/RMdOnvoj23C26hvk77q27iv2kWU/QnBRsGN9O6IdzVmyRitOrxKs9fO1hPXPlHyBiaT1HK0tPlF6+RVjUcoMmONdVnzkWU7+UpwPue84jLiJJVvj0WTyaQrI6/UT7HxKjC5ye3sXil5pxTeUTq9Vso4KcMjQMneF77JfimWxy5X9LFoebh66PkBz1/yDQYKi3BU+/eu9eU5M/Su/0qp+yXvUKn7s05tajJZxzXpXr+7nu37rL7d/61WHV6lLae3KCEjQQkZCRfVpMTMREnSb2fsz7OOfx21j2ivdhHt1D6ivdrWbmuX6EtiMSxKzbYWmlKyU3Q2+6zMhdbHZfw9/RXsE6z6gfUV5ht2yYnLYlh0IPmA1h9fr02nNunw2cM6k37GdqfJxeSiCL8ItQpvpW5R3dSrYS/r9PblICEjQZtPb9bBlIM6cf6EsvOz5evhqzDfMDULbaaukV3VqFajckvO2fnZ+unIT9pyyvpY/DufvSMXk4tCfUPVPLS5roy8Ut2jupfpZ1SSuPQ4vbPpHS3dvVRmi/Vn1rNBT43vPF496veQ9wU+TOUX5utczjmlZqfKy81L4X7h8r2IDwGNgxvr89s+19ilYxWbEqu7vrpLX4z+QrW8a13Uef1ZrjlXU5dP1YpD1j+E20e0V4/6PXQg+YDWHFuj97e8r0Mph/TOiHcuaoy5rXFblV+Yr9p+tdUkuOIeIS16/PlA8gEVWgor9TGeXfG79P5v79tNKhIVGKV/XPEP3dL2FtujH+m56aXtpkabc/1r8lkZJ51YoRfbjVBe+3vUNKRp+Y+hu9v6AUrNR1pn8i4Dd1d39W/aX/2b9ldiZqK+2fuNtsZt1d6kvUrKTLKN+/hnhgwZhqFsc7YOJB/QgeQDWn5wuW15Le9aahXWSq3CW6lVWCsFeAWooLBAKdkpOp12WqfTTutU2imdSjul87nn7fYd6BWoJsFNrPkvqruuqHuFvNydm32wvBQ9PrcncY/2JO7R8XPHVWApkIvJRXX866h+UH11qtNJrWu3tntMqjzkmHN0Pve8MvMy5eriKm93b4X4hJTrWJgpWSn68eCP2nJmi46lHlPSuSTNX2T9sN49qrsGNR90UR+cs/Oz9cn2T/TfLf9VWm6a3TJ3F3eZLWal5qQq9XRqsZND/Nl1za/TE9c+ochA5/oveLp56vXrX5ffSj8t3bNUj/7wqLLys3RnpzuL38DD39pz4qd7pZ1vyyQpxzVYbp2nXtoA8pKtILLp1CblF+arbe22Gt1htNPntPm0dbzDdhHtyv0masNaDeXp5qlsc7ZOnD9RYU8OlORkQFMNyw2Up1Ffg9uO1PRBs+kl7aQnfnxCnwf66RtJDY089Yjsqndu/G/555nfZ21Vgwv0Ai7B+M7jVT+wvh76/iFtOLFBfT/oq3GdxyncL1yrD6/Wz0d/liSF+oTqX33+pWEth9l+F+7ucrcmfzdZR1OPauTnI/XfG/9rHdLqL7Lys/Twsoe1+shqSdJ93e7Tfd3uk4erhxbvWqwXol/QT4d/0p1L7tSHN39Y6t+bRcMxvLbhNZ04f8L2uqebp25uc7Me7vlwmSZCO5Z6TEv3LNWaY2sUnxEvbzdvNQ1pqoHNBmpQs0EldvAwDEMHUw7q56M/67fTv8lsMateQD1d0/AaDWg6wKn3gu1x2/Wf9f/R5lObbX/P1Quopxtb36gJV0wotafqpcjKz9LUZVP10xHrzLxtw9sqwj9C606s06ZTm3TTgpv04U0fqkXYhW+YFFgK9PTKp21jTDcJbqJ2Ee2UlJmkzac3a2/iXj3+4+N6ac1Luq75dWoV3kqFlkLtTNipFQdX2G5w+br76tZ2t+rOTndeUmeGK+pdoZcGv6SHlz2s97e8r4a1GmpU+1Elb9BmvLT539LRZTLt/Ui1s3+/kdvqjotuQ3krmrilfmD9ci+ed43sqmWxy7TVu566ZZ+wzto8eL60/U1JktHsVlnySr8RcinSctM04+cZkqzvC+Ux/A+FRTgqKiyeOyjlpFqnKr8UBbnS5lnW/181vcwzqxYnyDtIYzuP1djOY5WZn6n9Sfu1N2mv4tLilJqTan0s29VD7q7u8vXwVaBnoAK8rD0TA70CFegZKH9Pf+UX5isjP0Op2ak6lnpMh84e0oHkAzqaelTxGfGKz4i3FWAkqUFQA7UOb60QnxBbr7X8wnyl5aUpISNB8RnxSsxMLHXiGts5eAWpWWgztQ5vrba126pt7bZqHNy41A9jhmHo+Pnj+u30b9pwYoM2nNjgMDPXn1kMi+Iy4hSXEafVR1ZrlmapRWgLDW05VENbDHUqcRiGob1Je7Xy0Er9cvQXW5fw0kQGRGpYq2Ea1nJYmZLjX5kLzVp/Yr21iHxolV3vOv3+39iUWG04sUHzt86Xu4u7rm18rYa3Gq6+jfuW+cN3QkaC3t30rhbvXmz72XWP6q4pPafoysgry9xeD1cP1far7dRdxpKE+obq41s/1s0Lbtbxc8d1zzf36NNbP73gXfbSFFgKNPm7yfrl6C/ycPXQzP4zdXPbm21/oC47sEyP/fiYoo9F618r/6XZg53/ILPhxB+PQVfkh6BGtRrJ281b2eZsHT93vFLGwTt+7rheWfeKfjj4g+21rpFdNa7zOPVv2r/cCyl/d271ekonVqhFwTmpnB9jlCTlnpMOLrH+v93FjT9W26+27ul2j+17wzCUZc5SXkGeDOOPR4WKbuZk5GXoxPkTOnz2sA4kH9C+pH06cvaIzuWc08aTG7Xx5MYyHdfdxV0mk8maX3LTtC1um7bFbdPbv74tTzdPXVnvSvVs0FPd63dXk+Amdjcwcsw5OpN+RsfPHbd+nT9u+3+O2frG6evhq3C/cNXxr6OowCjV9auruKw4tT3fVpG1rMWdvII8nUo7pWPnjmlf0j7tSdyjvYl7HQqfxfHz8FOXel3Us0FP9WzQU81Dm5f5/aDAUqDj544rNjlWsSnWr4PJB23DDPyZi8lFkYGRah3W2jqhQlQ3NQtp5tR7T4GlQKsPr9bnuz7XhhMbHArHyYnJ2p24W1/v+1rPrX5O1zW/TmM6jSlTr1pzoVmLdy3WGzFv2Hqy1w+qr5HtRuraRteqSXATa/EqP1vHzln/BtmftF8Hkg/oxPkTSs9Ll4+7j+oH1lfXqK4a0XrEJX3gc3Vx1axBs+Tr4auPtn2kaaunKSMvQ/d1u6/4mLWfJGUnStvekOFXT796j9fVXhf/t2B+Yb7e/vVtvb/lfbvHOYty+PMDntfNbcs+fuP6E+slST3rl3/veDcXNzULaaY9iXsUmxxbqYVFc6FZU5dNVaY5S60ie+jZgS9SVLwIbi5u2puXpTyTSZ4mQ/P6PyPP8u5RV5hvnXxCuqTx4Po26atFoxZp6rKpOpx6WK9vtJ8kYWS7kXri2iccbp63CGuhxaMWa+LXE7UjfofuXHKnZl83W8NaDrOtc+L8Cf3zf//U/uT98nD1cFg+ptMYtYtop7u+uku7EnZp9OLR+viWj4t9wiYtN03/Wvkv299BQV5B6lCng06eP6lj545p4c6FWha7TE9c+4RuaXtLsT2fMgoyNP3n6VqyZ4mtmCdJaUpTQmaC1p9Yr2mrp6lH/R66rvl1alO7jTxdPXX8/HH9evJX/XzkZ51OP+2w3yW7lygyIFJTr56qG1rdUOo1k2PO0Zz1c/Th1g9t+TvQK1DZ+dk6k35Gb/36lj7a9pHGdh6rCZ0nlFgozSvIU2xKrDLyMhTqE6qmIU0veKM7PiNek76epH1J++Th6qFbQ2/VM6Oekbu7u46fO65JX0/SkdQjGrNkjBbctkDNQ5uXuK9CS6H+b/n/6bsD38nF5KLHej2mu7rcZYv7uZxzWrJriT7Z8YntKZu/ahzcWKM7jNYtbW8pt6LZDa1u0PFzx/X6xtf13E/PKSowquSnmIKbSy1HSQc+l9vqSZIkS9Ob5BJS9p6BBZYCWwcbTzdPNQtppo51OpZbp4Oip1baRbQrl/39WdHnzNfzXLVQkvZ9KnmFSIe+kmRSYcfJ0qYTpe3iksxeO1sp2SlqEtxE93S958IblIHJ+PNfyKiR0tPTFRgYqLS0NOsYi5L0YQtrYXHEt9ZHMy7F9reknx+Q/KOkfxySSiiQFD06MGTIELm7V81jvBl5GdqXtE+7EnZpd8Ju7U7YXeyHmpKYZFIt71q2iT08XD1kyFBGXobOZp/VmfQzxfZ68XLzUqvwVmoa3FShvqHycfdRrjlXOw/slEuQi/an7FdyVrLdNj7uPuoa2VVXN7xabWu3VaNajeTv6S9XF1elZqfqZNpJbY/bro0nNirmZIytJ55kfdRnULNBurbxtWoZ1tKh50dWfpZ2xO/Q2mNrteLQCp1KO2V3ju0j2qt17dZqXKux/D39lZmfqcTMRO2K36Xt8dvtCqwtQlvYioyl9UootBRqe9x2fXfgOy2PXW5XOK0fVF/9GvdTzpkcDbxqoFxdXZWQmaBd8busCSX1sG1dPw8/DW4+WDe0ukFd6nVxKMhZDIu2xW3Tgh0LtDx2ue2R526R3fRQz4fULapbiW2sTIdSDunWz29VRl6GRrQeoVeue8Xhj6WyXDOGYeiplU9pye4l8nTz1Ic3faju9bs7rLfm2BpN/GqiCo1CPdv3WY3rPM6p9g78cKCOpB7RnKFzdEOrG5za1lm3LLxF2+O2OxyrvN9DDMPQZzs+07/X/Fu5BbkyyaThrYfr7i53l/pIRLHvqTWcXUxSN0lfDpQCG0t3Hyn/gxXlnNC20thd1kdufleZeSavIE8HUw7aCo2xybHKLciVi4uLQnxCFBkQqXoB9RQVFKWowChFBkbaHkXLzs/WqbRT2p24WzEnYxRzMsbWy/7PgryC5ObiJrPF7NAjrry5u7ireWhztandRs1Dm8vLzUvmQrPOpJ/RkdQj+u3Mb7ZHuYuE+oSqe/3uahHaQo1qNVKAV4A8XT2VZc7S+ZzzOnH+hI6dO6YjZ4/o0NlDJd6cc3Nxk5+HnyyGRdnmbNv79p/V9qutqxtcrV6Neqlng54l9rw5du6Yvt33rRbvXmwX0051OunaxteqeXBz7d2+V606ttK+lH1aeXClXY5pH9FeYzuN1ZAWQxzyS15BnpbHLtebMW/aevTUD6yvh3o+pGEth1X5RAmGYej1ja/rzRhrj4hhLYdp5oCZpX6ovNRr5vi545ry/RTtTrTOBN+pTicNbTlU/h7++mrfV7bJLp689kndfeWFbwRYDIu6vt1V53LOafHti4vtpXWpnvjxCS3ds1STu0/Ww1c7jjVeUe8jc9bP0Vu/viV/T38tH7dcdQPqlro+ucZeUTxOJp7UucJzarNitFzP7pVGfCc1ub58D3YqWlrSR/KpLd0bZzdu/MX8fhRYCvTt/m/1y9FflJmXqVbhrXRL21suOE5ejjlHU76fYusB179pfw1tMVSHzx7WvN/mKbcgVyE+IXpvxHsljpN3KOWQxi4dq6SsJNUPqq9PbvnE7nHwLae36OFlDys+I15uLm6a3H2y/tHlH/L18JVhGNp0apOe/+V57U+2TnJxZeSVerrP07YxYM/lnNP83+brgy0fKM+SJ0m6ttG1uqnNTWoe2lzZ5mxtOb1Fy2OX2yZlKomHq4euqn+VejXqpUCvQO1L2qdv9n1ju4HTpV4XPdfvObUOdxwPb9OpTXpixRM6ed76uW5E6xGactUURQVFKTs/W78c/UVvb3pbB5IPSLJ+zhrWapi6RXaTv6e/EjITtD9pv3Yl7FJscqzd56ogryANaDpAozqMUoeIDg5/r289s1UPfPeAEjMTFewdrLnD5ip+e7zd70habprGLR2n3Ym7FeoTqoW3LSz25rnFsOiJH5/Ql3u/lJuLm9664S0NaDqg2HgVWAq05tgabTyx0fZZrnloc13T8Bp1jexaITcuDMPQ1OVT9e3+b+Xv6a/PRn5W8njAOWelpX2l5F3KcI+U15i1ci/j/Alrj63V9J+n28YMLRLsHaxhrYbppjY3qU14m0s6x3FLx1kL3v2mldzL/yJZDIu6zO2itNw0bW3QVEEnV/6xsO1dMvd9p8L+Xv3t9G+6bZF1mI/Pb/tcXaO6lriuM3mGwiKK/4VZOVHa/YHU5VHp2tkXv/OCXGleEykzTur3ttTxvhJXrQ6FxeKczzmvPYl7dOjsIZ3POa8sc5bcXNzk6eopXw9f1Qmoozr+dVTXv67CfMNK7YqfV5CnI6lHFJscq71Je7UncY/2Je4rcXbtP/Nw9VD7iPbqFtVNVze4Wh3rdizzo2BpuWlaeWilvj/wvWJOxtjdKfRw9VD9wPq28bySs5IVlx5nt46Xm5eubXSt+jftr2sbXVvq+Jo55hz9cvQX/W///7Tm6Bq7xNumdht1rttZrcJaqZZ3LRVYCnQq7ZRik2O17vg6u2JiiE+Irm95vYa3Gq72Ee1VUFBQ4u/HwZSD+nb/t/rfvv/ZxsIoOrd2Ee0UGRApF5OLUrJTtDdxr91xukZ21YNXPage9XuUKZaVacOJDZrwxQQVGoV69JpHdW+3e+2Wl+WaeTPmTb224TW5mFz09vC3S/zjQ5I+2vaRZv48U55unvruzu/K3BvwUMohDf5osDxcPbT5/s0VPtbOcz89p892fKa7u9ytJ3s/aXu9PN9DzIVmPb3qaX2x5wtJ1pkjn+7zdJl64PJhz5FdTDwNaW6wdYDqiSelgDKOZ1UWhiF92lFK3iX1eUPq/IDd4uqaZy6kaIb69SfWa8OJDdoZv7PYXut+Hn5qENRADWo1UKNajdSwVkM1rNVQgZ6BtptciZmJikuP08m0kzp57qQOxB1QmiXNridZsHewGgQ1UMvwlmoT3kZta7dV89DmpfacLrQUan/yfv168letP7FeW05vsdtnWfi4+6h5aHM1D22uFmEt1CK0hZqHNlewd7Dtw4FhGErOStaR1CPaFrdNm05t0tYzW+2OZZJJbWu3VZOQJrae5AkZCdoev932oVKy5pmR7Ubqlra32HoE/vV3xDAM7YjfoQU7FmhZ7DJb8bOWdy31iOqhhsENZZJJx84d08YTG209O0N8QvRgjwd1W/vbqt2Yxx9v+1gv/PKCCo1ChfmGaUrPKRrRakSxvf0v9poxDENf7v1SM1bPUJY5S4FegZrZf6aGtBhi+1laDIteWfeK3tv8niTp7eFva1Cz0h8r3ZO4R8M/HS4/Dz/99s/fKiS2RbmwX5N++u+N/3VYXhHvI1tOb9HoxaNlMSx6/frXdX3LCxfCyDX2HOLx/SgpdrHUa7Z05aPle7B1T1nHh2s1Rhryqd2iys4zhZZCzdkwR+9ved/hpkv3qO6aPXi26gXWK3UfJ8+f1NilY3Uq7ZR83X014YoJahbaTCsPrdQPB3+QxbCoQVADvTb0tWInTCqwFOjjbR9rzvo5tqeMIgMi5e3urSOpR2ydKtqEt9G/+vyrxJv4J8+f1PcHvtf6E+t15OwRGTIU5humTnU7qVfDXrq6wdUOj7TnmHP00baPNDdmrnIKcuRictHt7W/X3VfercjASB1IPqB5v83TN/u+kWSdlOT5Ac+rT5M+Dse3GBatOrxKc2PmXnAs71retRTqE6r4jHi78Q1bhrXUbe1uU88GPZVfmK+le5bqs+2fqdAoVLOQZnr/xvcV4RtR7O/I+ZzzunPpndqXtE/hvuFaOGqhXa9pwzD0zE/P6POdn8vV5KrXr39d17W4rtR2VoW8gjyNWTJG2+K2yd3FXWM7j1X/Jv3l5+mn87nnlZKVYhurvqF/pHoE1NaWnakaMnTYBa+Zv94gq+VdS10juyqvIE/b4rYpPe+P4YiiAqPUJryN/D395e3uLW93bwV4BqhLZBd1qtOp1Jt9+YX56vJWF2WZs7Rs3DK1DGtZPsH5k0lfT9LqI6v1bM8pGpeyVjr5i3XG7AH/ldlwrZD3kfzCfN3wyQ06dPaQRrYbqRcHvVjq+hQW4ZRif2H2fSr9MFaq000a/evF73z7XOnnyZJfpHTX4RJ7K0qX7we+S2UxLDqWekx7EvfoVNoppWSnKK8gT65yVeLpRPXu3FvNw5qrfUT7S3octsi5nHNafWS1Vh5aqd/O/FZiL5c6/nXUNbKrBjQdoGsbXXtRY9Ok5aZpxaEV+nb/t/r15K92s40VJ8AzQH2b9NXwVsN1VYOr7B4xLcvvh8WwaOuZrfpm3zdadXiV3WQqf+bn4acBTQdoXOdxFdK9vTwt2LFAz/70rEwy6Z0R79gVBi8Uk+8OfKcp30+RJM3sP1OjO44u9ViGYWj8F+O1/sR6tY9or6Wjl5bpMd+3Yt7SnA1z1LtRb827eZ5T53cxluxeoidXPKke9Xvos5Gf2V4vr/eQAkuBJn87WasOr5KLyUVP9X5K4zuPL/NdTz7sOXKIycIeUvyv0sB5Urt/lN+B4jdLC7tJbl7SPXGSl32vtb9TnknLtQ7FYciwjavr7+nv1N35onhcd911yi7MlovJRR6uHuUylmNeQZ62x23XtrhtOpJ6RCfPn1RmfqbdbMj1A+urUXAjNarVSC3CWigqMOqiBg/PK8jTb2d+05pja7Tu+DodTDlY4rruLu7qGtVVt7a7VYOaDXK4QVfa70hKVooW716shTsWKiGz+PGdI/widEfHOzS289gyT4ZQFbae2apHf3jU1rPS39NfPer3ULOQZorwj1CgZ6B8PHzk6eKp7Zu2q3+f/gr3Dy/TmL9n0s7omZ+e0Zpj1sH4u0Z21X+G/KfEHngzfp6hj7d9LB93H3095utSJ6J4b9N7mr1udolFv/JQ1Jsj3DdcG+/deFFPCzgjPTddQz8eqriMON3U5ia9fN3LZduOXGPHIR4bp0sx06Q2E6TBH5bvwT69QkraJl33qdR6jN2iqsozB1MO6pPtn+jw2cMK9g7WkBZDNLTF0DLnhISMBP3z239qR/wOh2U3t7lZz/Z79oLvaXHpcXpp7UtaeWilXQ/01uGt1cWli54Y+YQ8PSpm3Li49Dj9e82/tSx2me01Nxc3u2LryHYj9VTvpy54A9wwDG08uVE/Hf5JscmxyjRnKtw3XE2Cm6h9RHu1j2ivyMBImUwm22Q2S3cv1fKDy5VXkFfsPoe3Gq7p/afL39O/1N+RcznndMfiOxSbEqsIvwh9cusnahLSROm56Xp61dNaFrtMJpn06tBXK/wJoUtxPue8nljxhFYdXnXBdV1Nrurk10kv3fKSGoY0LHG9v970H9tprKZePdX28yywFGj98fX6cu+X+unwT6UOUdYspJlm9J9RYm+9DSc2aOzSsQr1CVXMfTEVMnP6+1ve17/X/Ft9G/fV+ze9b7esot5H3tj4hl7f+LpCfEK0csLKC44n6kyeYWAoFK/eNdZ/E7dK5qwyD35vpyDPejdPkro9WWpRsSZzMbmoSUgTh95htjeUtuX7hlLLu5ZuaXuLbml7iwzD0Mm0kzqddlqZeZkyZCjUN1SRAZGK8I+45GMFegVqZLuRGtlupJIyk7Tp9CbtiNuhY+eOKT03XW6ubgr3DVeLsBbqVLeTrqx35SX1PnAxuejKyCt1ZeSVen7A8zp+7rh2JexSUpZ14oWi8S3b1m5brgP/V6Q7Ot6hQymH9OmOTzV12VQtuX1JmWYm2x63XY/98Jgk6a4ud12wqChZJ0r69+B/a8hHQ7QrYZfe3fSuJveYfMHtisYjHdx88AXXLQ9Fj1TsTtgti2Ep12RvGIaeWvGUVh1eJQ9XD829Ya76NulbbvvH7xoMtBYWjy0v38Lijres/za7xaGo+HdTNHZweTCZTOU+WL2nm6dt4rWK5unmaRvXUbKOZbXtzDadTjutxKxE64RfPqFqFtpM3aK6XXTBL9Q3VP/s/k9NunKStsdt19a4rUrMSJTFsCjCP0JdI7uqY92Ol8W4q1fUu0I/jP9BC3Ys0MfbPtbp9NNaeWilVh5aWez6r37yqiRrT8yiXqXNQ5urWUgzhfiEKL8wX0dTj+qnwz/p+wPfy2wxy8PVQw9d9ZAmXjmx1J4hT/V+SgdTDirmZIwe+O4BfX3H1yUWt4se+by6wdWXGIGStandRq4mVyVlJSk+I/6CjyRfiqIeSHEZcaofVF/P9Xuuwo5V4xSN1Za6v3z3m51sLSpKUoP+5bvvS9A8tLmeH/D8RW8f4R+hpaOXalnsMn1/4HudzzmvZqHNNKbjmDL31qobUFevX/+60nPTtT95v3ILctU8tLlCvUK1fPnyCinO/PnYbwx7Q6M7jNbrG1/XtrhtKrAUyMfdRz0b9NQ/u/+zzJ0JTCaTXU4pjZuLm7pFdVO3qG56pu8z+t++/+nLvV/qxPkTMgxDV0ZeqXGdx+mahteU6di1vGvpk1s/0ZglY3To7CEN/Xio2kW009HUozqfe16uJuuYudW5qChZ50V4Z/g7+unIT/pm7zfak7hH+YX5CvAMUIivdciwQK9AHUg6oO3x2/Vbxm+6/tPr9UCPBzShywSHz2npuel68PsHte74OrmYXDSj/wzd3uF2u3XcXNzUu3Fv9W7cWxl5GdoZv1OHUw8rOz9b2eZs5RbkKjEjUetOrNOhs4c0evFoPdfvuWIfc/75iHXypD6N+1TY7+1V9a+SJP166lflFeSVSwei0uyM36m3Yqx/Jz/d5+ly/7uv+v/lg6oR0MA6JmLGKSku5uIS5555UuYZya+e1Pau8m8jLpnJZLI+NhfUoMKPFe4XrmEth9kNHF2RTCaTtSdMGcfqqM7+1edfOpJ6RBtPbtTEryfq6zFfK8w3rMT1T5w/oXu+uUf5hfnq16SfHu/1eJmPVce/jqb1m6apy6fqrRjruC2lPf67N3Gv9iXtk5uLm/o16efUeV2s5qHN5ePuo8z8TB1KOXRREwSV5OPtH+vLvV/K1eSqN4e9SVGxojS9Qfp1hnTsB8mcLbmXw2ydacek/Qut/+/80KXvD5etOv51NLTl0Arbv7urtddjaeMSXQ483Tz1jy7/0LjO1nG9Np/arNNpp5WQmaCMvAxlm7OVlZ+l1IxUWVwsysi3jhddNO5naXrU76Hp/aaXaUgNNxc3zRk6R0M/HqqDKQc185eZemHgCw7rnUk7o21x22SSqUJvZHm7e6tlWEvtTdqrnfE7K7Sw+PW+r/X9ge/lanLVnKFzqnUv18tOyO/j7J3dZx1640LFgcJ8yeQqXWgs1OM/Wv8N6yD5XvpN+OrExeRSLn+rB3gF2D3ubDabS1m7fBXd0Mox5yg1O1XhfuGVNhxFoFegbZLRSxHqG6rPRn6m//vh/7Tu+Dpti7MWshvWaqhXrnulxPEyqxuTyaQBTQeUOgyTJG0/vV2P/+9xHck5otnrZuvrfV/b9SbccnqLnlr5lI6mHpW3m7feGPbGBf8+9/f019UNr9bVDR1vQp3POa8Xol/QV3u/0rTV05SZn6n7uv0xXFt+Yb6+3f+tJGlAs9Lbfilah7dWmG+YkrOS9duZ38pUyL5YqdmpmrJsigqNQg1tMbRCPo9TWETxTCYp8lpp/2fSiZ+cLywW5Embfu+t2JXeisClcHd111s3vKWbFtyk4+eO6/ZFt+vTkZ8q1CvUYd1jqcc0ZskYnc0+q1ZhrTRn6BynJwy4odUNWha7TKuPrNbjPz6uL+74osQeOJ/tsD6KPLj54BJnzytvbi5uah/RXr+e+lXb47eXW2FxZ/xOvRhtfd96sveT6t+0+vRE+NsJ7ywFNrIWA498a50Z8FJteVkyCq29ISPKf0IH4O/K1cVVHet0LHbG6z8/jmU2zDp89rBiU2J1KOWQDp49qCNnjyg9L12uLq6q619XXaO6aljLYU5/8A3zDdOcoXM0buk4Ldq1SD3q93AYZ3Dx7sWSpG5R3YqdubY8dajTQXuT9mpH/I4KG8PsxPkTmvbTNEnSQz0fKtOM43BCrRaSq4eUn27NNUElFLnNOdLqf0r7P5U8AqRrXrTOjF6SI9aCg5pU7x5jNZ23u/cFx5aszkJ9Q/XRLR/pYMpBHUo5pHC/cHWu27nKJwGrCG1rt9X99e6XubFZs9fN1qGzh3T74tvVKqyVDBm2CXXq+NfReyPeU5vabS7peEHeQZo9eLYiAyL1RswbemXdK/Jw9dBdXawdoYomEQ3zDdO1ja695PMriclk0jUNr9FXe7/SmmNrKqywmJWfpUlfT9LJ8ydVL6CeZg6YWSET91Rcf2Rc/hr9/ofUseXOb7vnQynztORXV2pHb0XgUgV6BerDmz9UXf+6OnbumEZ8OkK/HP1FRcPkWgyLvjvwnW5ccKMSMhPULKSZPrrlI/l6OD+Mgclk0swBMxXgGaDdibs1b0vx4yaezzmv/+3/nySV+2xpF1L0oXV73PZy2V9+Yb6eWPGECiwFGtJ8iMZ3Hl8u+0UJTCap1e+/MzvfufT9peyRdv8+Pk23J0tfF8BF8fHwUfs67XVru1v1VJ+n9NEtH2ndPeu088Gd2jZ5m74f972e7fvsRfem6dmgp+7rbu018q+V/7KN/yhJmfmZ+nzn55KkMR3HFLt9eepQp4MkaWfCzgrZf15Bnh787kFlmbPUNbKr7u1674U3gnNc3aXQ3ycaSdxW/DqGIf04Xto7X7IUSLmp0qp7pF0ljN9ZkCcd+73HIoVFVILmoc01tOVQXRl55d+yqFjEZDJpRKsRWvWPVbq9w+0yyaT9yft1IPmAXE2uur3D7frfnf+75KLin4/3UM+HNOWqKZKkWdGz9FbMWzp5/qReWfeKJOsYjhU9tElR4XLtsbUVsv9j547p1oW3anv8dgV5BWn+zfPLbRidv6KwiJI1HGR9bCBlt5R+8sLrFynIkzbNsv6/65PWQfQBXLIGQQ20+PbFahbSTCnZKbrvu/s0++RsPfLDIxo0f5CmfD9FGXkZ6lS3kz4b+ZlCfR17NJZVbb/aerrP05Kk1za+pt0Jux3Wmbd1nvIK8tQqrJWuqHvFRR/rYnSu21mSbI+HXKr3Nr+ngykHFewdrBkDZlTInTz8RfuJ1sfOTq+VTq+/+P0YFusHQUuB1GS4FNW73JoIoHI9dNVD6lKvizLzM/Xgdw8qx2ydYfaNjW8oNSdVDYIaVOijaUU6RFgLi3sS9jjMtFseZq+drT2JexTkFaRXh776ty4YVKna1r8VlLi1+OWHvpQOLpFc3KQbv7d+bpGknx+UzhYzNuPpaMmcae04UbRvAOUmyDtIzw94Xhvv3aj3Rryn90a8pw33btDzA55XiE9IuR9vco/Jur/7/ZKkORvmqM8HfRSfEa/6QfU14YoJ5X68v7q6wdVyMbno0NlDikuPK7f9ns0+qxk/z9B1869TbEqswnzDNP/m+WUanuRiUVhEybxDpDq/D7p+7Ieyb7dn3p96K95dMW0Daqi6AXX1zZhvdFeXu+Tl5qXE/EQtO7hMR1OPys/DT5O7T9bnt31+SUXFIje1uUn9m/RXfmG+7v3mXiVmJtqWHUo5pHm/WXsyPnjVg5VeiCt6ZOxo6lGdyzl3Sfs6cvaI3v71bUnSM32fKdOspygH/pF/9Ghffb/1cbTiGJbS97PxOSluo3WSsb5vlm8bAVSqovEWg7yCtCdxj25ecLP+b/n/2fLNU72fqpTJcZqENFGAZ4ByCnK0J3FPue77h9gf9NG2jyRJs6+brTr+dcp1//iT2lda/43/1XFZoVla+/sY1F2fkhoPla5+QWo4WCrMk1ZMkCyF9tsc+tr6b+NhFx6zEcBFC/cLV/+m/dW/af9Sx5W/VCaTSY9c/Yj+PejfCvWxfnZqU7uN5t88X97u3hV23CJB3kG2zzRrjq255P3lF+Zr7q9z1feDvvp428cyW8y6puE1+nrM12pfp/0l7780jLGI0jUeav3AdvQ7qcM9F17fnC39+vuMZF2forciUAG83L30VO+ndE+Xe/TGN2+oXrN6qhtYV9c2ulb+nv7ldhyTyaRXhryiWxbcosOphzXq81F6YdAL8nT11CPLH1FeQZ56Nex1wUGZK0KwT7AaBzfW0dSj+u3Mb5fUhpfWvqT8wnz1btS70iYXwu+umiEd/sbaM35pP6nzg9aeh2f3SUnbrV/ZiZK7nxTaTqrbQ6p7lXW2z4Jcafub0t6PrPvq+5YUEFWVZwOgHNQNqKv3bnxP935zr2JTYhWbEitJur/7/ZU29q2LyUXd63fXykMrteHEhnIb/3Bn/E498sMjkqS7utxVaZOe1Vj1fp+4IWGT9YmqP4/5fuBzKe2o5BMudX3M+prJJA18X/qojRS/Sdr6qnTlo9Zl5mzrNpLU/NbKOwcAFe7WdrfqpjY3KTM/UwGeAZXaYaJXo17aFrdN0ceiHWa6dsax1GOa/N1k25iUbWu31WO9HqvQSWH+jMIiStd0hLT+X9LxFVJ2suRzgTsGO96WsuKts0q3n1gpTQRqqgDPAHX276whnYfI3b1iZrzz9/TXBzd9oDuX3qmTaSd155I/xlKsH1RfL1/3cpU9NtwjqoeOph7VxhMbL7qw+Nvp37T6yGq5mlz1rz7/4hHoyuZbWxr2pfT1ECk+RlpWwkyz5kzr8vgY6we9v+r1stR2fIU2FUDl6VKvi1ZMWKElu5coOStZvRv1Vq9GvSq1DVc3uNpaWDy+Qf/s/s9L3t+p86c06etJyivIU5/GffR4r8fLoZUoVXALyTtMykmWEn+T6v3+AduwSJt/n2TyiqnWHu9F/COl3nOklXdJG56RGl9vvZm19yPrRDCBjaT6fSr9VABULFcX1wobf7A0fRv31WsbXtP64+uVnZ8tHw8fp/cRmxyrO5feqbPZZ1XLu5ae6fOMhrUaJpdK7FlNH26ULqS1FHGltQfJgYWlr5uXLm3+t/X/PZ6zzsQG4LIXFRSlr8d8rVHtR8nf019ebl4a1nKYlty+pFweub5YPer3kCTFnCyhGHUBhmHopbUvSZJuaXeLGgc3Lre2wQmRV0tjd0kd7pfq9JDq97POyNnvben2GOneeGn8Pum6T6QO90ph7SWvEMmrlrVX/W3rpCv/r6rPAkA5C/EJ0X3d7tOzfZ+t9KKiJFsvj21x25SVn3VJ+zp5/qRGLx6tlOwUtQxrqdeuf41xFSuDySTV72v9/9Hv/3g9domUekDyDJI63Oe4XdsJ1kksix6JTj8l/TrTuuyKR3gMGkC5aR3eWvUD6yu3IFfRx6Kd3j45K1njvxivs9ln1Tq8tX4Y/4OGtx5eqUVFiR6LKIs246WELdKej6TOD5W83qYXpNyzUq0WUuvKnSEWQMWq5V1LLwx8QS8MfEGGYVSLnn3dorpJkg6dPaSUrBQFejh3l3H1kdXaFrdNXm5eerDHgxXRRJRVYEOp/9ySl/tGWHuMkFsAVJIGQQ1UL6CezqSf0ebTm9Wn8cX1Ujty9ojGfjFWCRkJalSrkebdNE9+Hn7l3FqUqOkIKXaxdOgr6epZ1s4SG56xLuvyiOQZ4LiNySQN+K/0cVvrI9Hv17e+HtyS8eMBlCuTyaTrWlyn9za/px8O/qAhLYaUedsCS4Ee+v4hJWUlqVlIM3028rMq6XUp0WMRZdFilLX3YfIO6czG4tc5u++Px9Oufdk6uxqAv6XqUFSUrOMstg5vLUmKOeVcr8VCS6FeWfeKJGl85/GK8I8o9/YBAC5fJpNJ1zS8RpK06vCqi9rHL0d/0U0LblJCRoKaBjfVwtsWkm8qW6MhkpuPdO6gdHK1tPsD6fxh69iKnaeUvJ1/pDRkobVXoyQFNZGGf2M/TiMAlIPBzQdLkn458otySprMsBifbv9Um05tko+7j+beMLfKiooShUWUhXew1Or3XiIx0yTDsF9emC+tuMt6B7DxMKkJkx8AqBxFj0OvOercTGpf7/1ah84eUqBXoO7pWoaJqQAANc71La+XJC2PXa68grwyb2cYht7d9K4mfjVRmfmZ6lKvixbctkDhfuEV1VSUxDNAaneX9f//GyH98vvTV92eli7Uc7TxEGnSKWncHuuQHMEtKrSpAGqmdrXbqX5gfeUU5GjFoRVl2iYpM0mvbXhNkvTktU+qSUiTCmzhhVFYrCSpqam64447FBAQoKCgIN11113KzMwsdf0HHnhALVq0kLe3t+rXr68HH3xQaWlpduuZTCaHr0WLFpX/CXR70tpr8cQq6dCX9svWPibF/yp5Bkp93yj/YwNACYpm1Pz56M8yF5rLtE1eQZ5e2/iaJOm+bvcpwKuYx6AAADVet6h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\n",
      "text/plain": [
       "<Figure size 1500x1500 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig3, ax3 = plt.subplots(len(down_freq_lim), len(up_freq_lim), figsize = (15, 15), sharex=True, sharey=True)\n",
    "\n",
    "start_point = 25150\n",
    "end_point = start_point+500\n",
    "\n",
    "for i in range(len(down_freq_lim)):\n",
    "    for j in range(len(up_freq_lim)):\n",
    "        norm_filtered_1 = filtered_power(norm_fft_1, norm_all_freq_1, down_freq_lim[i], up_freq_lim[j])\n",
    "        x_1 = np.arange(0, len(norm_filtered_1), 1)\n",
    "        norm_filtered_2 = filtered_power(norm_fft_2, norm_all_freq_2, down_freq_lim[i], up_freq_lim[j])\n",
    "        temp = np.concatenate([norm_filtered_1[start_point:end_point],norm_filtered_2[start_point:end_point]])\n",
    "        ax3[i][j].plot(norm_filtered_1, '-', label=address_1+' FFT', color=ch)\n",
    "        ax3[i][j].plot(norm_filtered_2, '-', label=address_2+' FFT', color=cs)\n",
    "        ax3[i][j].grid()\n",
    "        ax3[i][j].legend(fontsize=10, ncol=2)\n",
    "        ax3[i][j].set_xlim(start_point, end_point)\n",
    "        ax3[i][j].set_ylim(1.3*(min(temp)-0.5), 1.3*(max(temp)+0.5))\n",
    "        if i==0:\n",
    "            ax3[i][j].set_title(\"to \"+str(up_freq_lim[j])+\" Hz\", fontsize=14)\n",
    "        if j==0:\n",
    "            ax3[i][j].set_ylabel('from\\n'+str(down_freq_lim[i])+\" Hz\", fontsize=14, rotation=0, ha=\"right\")\n",
    "        if i==(len(up_freq_lim)-1):\n",
    "            ax3[i][j].set_xlabel('index', fontsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2692aed0",
   "metadata": {},
   "source": [
    "From the plots we can see that:\n",
    "- for small upper limits (e.g. 10 Hz), the filtered signal is too smooth and there is loss of important information\n",
    "- for intermediate upper limits (e.g. 30 Hz), the filtered signal is still quite noisy\n",
    "- for nigher upper limits (e.g. 50 Hz), the filtered signal is smoother without major loss of information\n",
    "- there is no visible variation coming from the different lower limits\n",
    "\n",
    "Therefore, we decide to set:\n",
    "- the lower cut frequency as the lowest value, 0.1 Hz\n",
    "- the upper cut frequency as the highest value available, 50 Hz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "74d932ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "down_freq_lim = 1\n",
    "up_freq_lim = 50"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de67fabe",
   "metadata": {},
   "source": [
    "We filter the results shown above with **disctere wavelet transform**. We use a sym4 wavelet and a maximum possible decomposition level.\n",
    "\n",
    "Lastly, to be safe we want to **eliminate the baseline wander** of the signal to align the isoelectric line. We do this by substracting a low order polynomial function from the signal. </span> Just like before, we use the <code>stats.zscore</code> function to standardize the output. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "67117d09",
   "metadata": {},
   "outputs": [],
   "source": [
    "def isoelectric_signal(vect, freq, down_freq_lim, up_freq_lim):\n",
    "    '''\n",
    "    function that removes baseline wander, performs band-pass filetring using FFT and DWT filtering\n",
    "    input: raw signal, sampling frequency, lower/upper limits of band-pass filter\n",
    "    output: filtered signal with mean=0 and standard_deviation=1.\n",
    "    '''\n",
    "    timestep = 1/freq\n",
    "    vect_fft = fftpack.fft(vect)\n",
    "    vect_power = np.abs(vect_fft)\n",
    "    vect_all_freq = fftpack.fftfreq(vect.size, d = timestep)\n",
    "    \n",
    "    vect_filtered_fft = vect_fft.copy()\n",
    "    vect_filtered_fft[np.abs(vect_all_freq) >= up_freq_lim] = 0\n",
    "    vect_filtered_fft[np.abs(vect_all_freq) <= down_freq_lim] = 0\n",
    "    vect_filtered = (fftpack.ifft(vect_filtered_fft)).real\n",
    "    \n",
    "    \n",
    "    w = pywt.Wavelet('sym4')\n",
    "    maxlev = pywt.dwt_max_level(len(vect), w.dec_len)\n",
    "    threshold = 0.04 # Threshold for filtering\n",
    "    coeffs = pywt.wavedec(vect_filtered, 'sym4', level=maxlev)\n",
    "    for i in range(1, len(coeffs)):\n",
    "        coeffs[i] = pywt.threshold(coeffs[i], threshold*max(coeffs[i]))\n",
    "    datarec = pywt.waverec(coeffs, 'sym4')\n",
    "    \n",
    "    y1 = stats.zscore(datarec)\n",
    "    x=range(len(y1))\n",
    "\n",
    "    coefs_filt_1=np.polyfit(x, y1, 8, rcond=None, full=False, w=None, cov=False)\n",
    "\n",
    "    isoelectric_signal_1=y1-np.polyval(coefs_filt_1, x)\n",
    "    isoelectric_signal_1 = stats.zscore(isoelectric_signal_1)\n",
    "\n",
    "    return (isoelectric_signal_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6264c1a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig4, ax4 = plt.subplots(2,2,figsize = (15, 8), sharex = True)\n",
    "start_point = 25000\n",
    "end_point = start_point+500\n",
    "\n",
    "f_s0_1 = isoelectric_signal(s0_1,freq,down_freq_lim, up_freq_lim)\n",
    "f_s0_2 = isoelectric_signal(s0_2,freq,down_freq_lim, up_freq_lim)\n",
    "\n",
    "f_s1_1 = isoelectric_signal(s1_1,freq,down_freq_lim, up_freq_lim)\n",
    "f_s1_2 = isoelectric_signal(s1_2,freq,down_freq_lim, up_freq_lim)\n",
    "\n",
    "ax4[0][0].plot(s0_1, '-', label='I unfiltered', color=ch)\n",
    "ax4[0][0].plot(f_s0_1, '-', label='I filtered', color=che)\n",
    "ax4[0][0].set_xlim(start_point, end_point)\n",
    "temp = np.concatenate([s0_1[start_point:end_point], f_s0_1[start_point:end_point]])\n",
    "ax4[0][0].set_ylim(1.3*min(temp), 1.3*max(temp))\n",
    "ax4[0][0].set_ylabel('I [mV]', fontsize=12)\n",
    "ax4[0][0].set_title('patient '+address_1, fontsize=14)\n",
    "ax4[0][0].legend(ncol=3, fontsize=10)\n",
    "ax4[0][0].grid()\n",
    "\n",
    "ax4[0][1].plot(s0_2, '-', label='I unfiltered', color=cs)\n",
    "ax4[0][1].plot(f_s0_2, '-', label='I filtered', color=cse)\n",
    "temp = np.concatenate([s0_2[start_point:end_point], f_s0_2[start_point:end_point]])\n",
    "ax4[0][1].set_ylim(1.3*min(temp), 1.3*max(temp))\n",
    "ax4[0][1].set_ylabel('I [mV]', fontsize=12)\n",
    "ax4[0][1].set_title('patient '+address_2, fontsize=14)\n",
    "ax4[0][1].legend(ncol=3, fontsize=10)\n",
    "ax4[0][1].grid()\n",
    "\n",
    "ax4[1][0].plot(s1_1, '-', label='V4 unfiltered', color=ch)\n",
    "ax4[1][0].plot(f_s1_1, '-', label='V4 filtered', color=che)\n",
    "temp = np.concatenate([s1_1[start_point:end_point], f_s1_1[start_point:end_point]])\n",
    "ax4[1][0].set_ylim(1.3*min(temp), 1.3*max(temp))\n",
    "ax4[1][0].set_xlabel('index', fontsize=12)\n",
    "ax4[1][0].set_ylabel('V5 [mV]', fontsize=12)\n",
    "ax4[1][0].legend(ncol=3, fontsize=10)\n",
    "ax4[1][0].grid()\n",
    "\n",
    "ax4[1][1].plot(s1_2, '-', label='V4 unfiltered', color=cs)\n",
    "ax4[1][1].plot(f_s1_2, '-', label='V4 filtered', color=cse)\n",
    "temp = np.concatenate([s1_2[start_point:end_point], f_s1_2[start_point:end_point]])\n",
    "ax4[1][1].set_ylim(1.3*min(temp), 1.3*max(temp))\n",
    "ax4[1][1].set_xlabel('index', fontsize=12)\n",
    "ax4[1][1].set_ylabel('V5 [mv]', fontsize=12)\n",
    "ax4[1][1].legend(ncol=3, fontsize=10)\n",
    "ax4[1][1].grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d5b7003",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "# 1B) Data Preprocessing on the entire dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e84e67a2",
   "metadata": {},
   "source": [
    "We avoid saving the unfiltered signal in a .csv format, but the option is there if needed: in such a case, the data relative to each patient is saved in a dataframe, in which each columns correspond to one lead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1b53f0dc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "r  = np.genfromtxt(\"extended_input/RECORDS\", dtype='str')\n",
    "records = list(range(1,(len(r)+1)))\n",
    "\n",
    "if EXTRA_SAVE_FOR_SMALL_DATA:\n",
    "    t1 = time.time()\n",
    "    for path in records:\n",
    "        # Load the record\n",
    "        path = str(path)\n",
    "        record = wfdb.rdrecord(\"extended_input/Hour\"+path+\"RawData\")\n",
    "        signal = record.sig_name\n",
    "        df = pd.DataFrame()\n",
    "        for i in range(0,len(signal)):\n",
    "            df[signal[i]] = record.p_signal[:, i]\n",
    "\n",
    "        # Save the DataFrame to a CSV file\n",
    "        outfn = 'extended_output/'+'data'+path+'.csv'\n",
    "        df.to_csv(outfn, index=False)\n",
    "    t2 = time.time()\n",
    "    print('the metadata saving took',t2-t1,'seconds')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f967a87",
   "metadata": {},
   "source": [
    "Finally, we save the filtered signal in a .csv format: the data relative to each patient is saved in a dataframe, in which each columns correspond to one lead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0f8bb3ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the filtered data saving took 813.8283739089966 seconds\n"
     ]
    }
   ],
   "source": [
    "r  = np.genfromtxt(\"extended_input/RECORDS\", dtype='str')\n",
    "records = r.astype(int) #list(range(10,(len(r)+1)))\n",
    "FILE_SAVE = True\n",
    "if FILE_SAVE:\n",
    "    t1 = time.time()\n",
    "    # for every file provided\n",
    "    for path in records:\n",
    "        path = str(path)\n",
    "        # we load the record\n",
    "        record = wfdb.rdrecord(\"extended_input/Hour\"+path+\"RawData\")\n",
    "        # we add the names of the missing leads (which can be computed from the 8 that are already measured)\n",
    "        signal = record.sig_name \n",
    "        moreleads = ['III', 'aVR', 'aVL', 'aVF']\n",
    "        signal= np.append(signal,moreleads)\n",
    "        # we add the missing leads\n",
    "        psig = record.p_signal\n",
    "        III = record.p_signal[:,2]-record.p_signal[:,1]\n",
    "        aVR = (-record.p_signal[:,1]-record.p_signal[:,2])/2\n",
    "        aVL = (record.p_signal[:,1]-III)/2\n",
    "        aVF = (record.p_signal[:,2]+III)/2\n",
    "        # and fix their shape\n",
    "        III = np.reshape(III,(len(psig),1))\n",
    "        aVR = np.reshape(aVR,(len(psig),1))\n",
    "        aVL = np.reshape(aVL,(len(psig),1))\n",
    "        aVF = np.reshape(aVF,(len(psig),1))\n",
    "        psig = np.hstack((psig,III,aVR,aVL,aVF))\n",
    "        # we initialize a dataframe in which we're going to save the filtered signal\n",
    "        df = pd.DataFrame()\n",
    "        mask = np.arange(0, record.p_signal.shape[0], 1)\n",
    "        \n",
    "        # for every lead\n",
    "        for i in range(0,len(signal)):\n",
    "            # we save the relative signal\n",
    "            df[signal[i]] = isoelectric_signal(psig[mask, i],freq,down_freq_lim, up_freq_lim)            \n",
    "\n",
    "        # Save the DataFrame to a CSV file\n",
    "        outfn = \"extended_output/\"+\"data\"+path+'_2filtered.csv'\n",
    "        df.to_csv(outfn, index=False)\n",
    "    t2 = time.time()\n",
    "    print('the filtered data saving took',t2-t1,'seconds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3f2d41dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for future purposes, we add the new leads to the stored list of leads \n",
    "leads = record_1.sig_name\n",
    "if len(leads)<=8:\n",
    "    moreleads = ['III', 'aVR', 'aVL', 'aVF']\n",
    "    leads= np.append(leads,moreleads)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "690a328b",
   "metadata": {},
   "outputs": [],
   "source": [
    "main_lead = 'II'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28e621c4",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "# 2) R-peaks detection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3d91cc7-cbb5-4dc2-9023-8f56d38c0ba0",
   "metadata": {
    "tags": []
   },
   "source": [
    "Since we have the filtered signal $y$, we can implement an algorithm for the detection of the R peaks of the wave based on source [3].\n",
    " \n",
    "Our algorithm finds the peaks by minimizing the error $\\varepsilon$ between the parabola fit of consecutive signal windows and the actual signal. We use the quadratic mean parabola-fit error $\\varepsilon (i)$ : \n",
    " \n",
    "$$ \\varepsilon(i)=\\frac{1}{2 w+1} \\sum_{j=i-w}^{i+w}\\left(y(j)-y^{\\prime}(j)\\right)^2  \\; \\; \\text{for} \\; \\;\n",
    " i \\in \\{w, w+1, w+2, \\cdots, N-w-1\\} $$ \n",
    " \n",
    "where:  \n",
    "- $j \\in\\{i-w, i-w+1, \\cdots, i, i+1, i+2, \\cdots, i+w-1, i+w\\}$\n",
    " \n",
    "- $w$ is half of the window size, i.e. is a positive number such that $2w +1 = m< N $. Particularly, $m=2w+1$ is the length of both $y(j)$ and $y\\prime(j)$ and of the window that should contain the R-peak\n",
    " \n",
    "- $y(j)$ is the sequence defined by fragmenting the filtered signal $y$\n",
    " \n",
    "- $y\\prime(j)=a(i-j)^2+v$ is the sequence defined by the parabola fit, which has vertex at index $i$: \n",
    "\n",
    " \n",
    "The window size for R peak detection is defined by the average duration of the QRS complex (from 70 to 100 ms). \n",
    "Rounding down the QRS size (to 60 ms) and diving by 2 to get half of the window size, we get $ W = 0.060 \\text{s} \\: / \\: 2 = 0.030 \\text{s} $.\n",
    "This can be converted into indexes/AU by multiplying by the sampling frequency of the dataset $f_s$\n",
    "$$w = 0.030 \\: \\text{s} \\cdot f_s$$ \n",
    "     \n",
    "     \n",
    "So, our goal is to calculate the parameters that make $y^{\\prime}(j)$ to best fit $y(j)$. We simplify the task by changing variable to $x=(i-j)^2$. In this way, we can treat the problem as a linear regression $y^{\\prime}(j)=ax+v$, where the vertex of the parabola will be located at $(i,v)$. \n",
    " \n",
    "The two fitting parameters are computed such that:\n",
    " \n",
    "$$a=\\frac{m \\sum_{j=l_1}^{l_2} x(j) y(j)-\\sum_{j=l_1}^{l_2} x(j) \\sum_{j=l_1}^{l_2} y(j)}{m \\sum_{j=l_1}^{l_2} x^2(j)-\\left(\\sum_{j=l_1}^{l_2} x(j)\\right)^2}$$ \n",
    " \n",
    "$$v=\\frac{\\sum_{j=l_1}^{l_2} x^2(j) \\sum_{j=l_1}^{l_2} y(j)-\\sum_{j=l_1}^{l_2} x(j) y(j) \\sum_{j=l_1}^{l_2} x(j)}{m \\sum_{j=l_1}^{l_2} x^2(j)-\\left(\\sum_{j=l_1}^{l_2} x(j)\\right)^2}$$ \n",
    " \n",
    "where $l_1=i-w$, $l_2=i+w$ are the limits of the sums. \n",
    " \n",
    "In this way, we generate a parabola $y^{\\prime}$ that can adjust to the values in the time window (which will go through the entire signal). When the processed signal $y(i)$ reaches a peak, the fitting algorithm finds a parabola such that the vertex of the parabola will coincide with the maximum or minimum point of the wave. If the assumption for a parabola-shaped peak is correct, the fit is be very close to the actual signal. \n",
    " \n",
    "Nevertheless, such a fit can also resemble parts of the signal that are similar to straight lines since the corresponding values of $\\varepsilon(i)$ are low. To avoid finding \"false\" r-peaks, we place a lower bound to the $a$ coefficient of the parabola to differenciate a straight line ($a \\rightarrow 0$) from a wave."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c78ad23e",
   "metadata": {},
   "outputs": [],
   "source": [
    "peak_width=0.018\n",
    "freq=1000\n",
    "w = int(peak_width*freq) #number of samples in half of the window\n",
    "m = 2*w+1 #number of samples in a window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aa61ac04",
   "metadata": {},
   "outputs": [],
   "source": [
    "def yfit(yn, fs=freq, peak_width=peak_width): \n",
    "    '''    \n",
    "    This function receives as input parameters yn (ECG filtered signal), fs (frequency with which the signal was sampled) \n",
    "    and peak_width (approximate half-width of an R peak).     \n",
    "    The function performs a linear regression for each window of the signal, obtaining the coefficients of the linear fit.    \n",
    "    It also calculates the quadratic mean parabola-fit error 𝜀(𝑖) for each window.\n",
    "    Three arrays are obtained as an output: the first one, eps_i, containins the mean parabola fit error for each window.\n",
    "    The other two, a and v, correspond to the coefficients of the fit for each window.   \n",
    "    '''\n",
    "    \n",
    "    w = int(peak_width*fs) #number of samples in half of the window\n",
    "    m = 2*w+1 #number of samples in a window\n",
    "    \n",
    "    #i is a sequence that goes from w(half size of a window) to N-w being N the total number of data\n",
    "    i = np.arange(w,len(yn)-w)\n",
    "    #j is a matrix, each row is the array of the indexes of a window\n",
    "    j = np.zeros((len(i),m-1))\n",
    "    for k in range(len(i)):\n",
    "        j[k] = np.arange(k,k+m-1)\n",
    "\n",
    "    #x is the change of variable such that instead of a 2nd order fit, we can perform a linear fit\n",
    "    x = (j[0]-i[0])**2\n",
    "    \n",
    "    #yj is a matrix, each row is an array with the signal (data) values for each of the indexes of a window\n",
    "    yj = yn[j.reshape(j.shape[0]*j.shape[1])].values.reshape(j.shape[0], j.shape[1])\n",
    "\n",
    "    #a and v are the parameters of the fit such that yj_fit=ax+v. They both are arrays containing the parameters for each i.\n",
    "    a = ((m*np.sum((x*yj), axis=1))-(np.sum(x)*np.sum(yj, axis=1)))/(m*np.sum(x**2)-(np.sum(x))**2)\n",
    "    v = ((np.sum(x**2)*np.sum(yj, axis=1))-(np.sum((x*yj), axis=1)*np.sum(x)))/(m*np.sum(x**2)-(np.sum(x))**2)\n",
    "    \n",
    "    #yj_fit=ax+v is a matrix that contains in each row the fitted signal for each window \n",
    "    yj_fit = (np.tile(a.reshape(len(a)), (m-1,1)).T)*x + (np.tile(v.reshape(len(v)), (m-1,1)).T)\n",
    "\n",
    "    #eps_i is the Quadratic mean parabola-fit error  𝜀(𝑖) for each i\n",
    "    eps_i = (1/(2*w+1))*np.sum(((yj-yj_fit)**2), axis=1)\n",
    "    \n",
    "    return(eps_i, a, v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "658b72ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "def R_fit(yn, fs=freq, peak_width=peak_width): \n",
    "    '''    \n",
    "    This function receives as input parameters yn (ECG filtered signal), fs (frequency with which the signal was sampled) \n",
    "    and peak_width (approximate half-width of an R peak). \n",
    "    \n",
    "    The function returns an array R_i with the indexes of the R peaks, found by impossing the corresponding conditions     \n",
    "    on the output vectors of the yfit function on the signal (a and v fit coefficients and the error).\n",
    "    \n",
    "    Conditions:\n",
    "    1) The parabola fit error must be a local minimum    \n",
    "    2) We impose a lower bound in the 'a' coefficient of the linear regression to get rid of false Rpeaks on straight lines.\n",
    "    '''\n",
    "    \n",
    "    w = int(peak_width*fs) #number of samples in half of the window\n",
    "    m = 2*w+1 #number of samples in a window\n",
    "    \n",
    "    y_fit = yfit(yn, fs, peak_width)\n",
    "    i = np.arange(0,len(y_fit[1]),1, dtype=int)\n",
    "    a = y_fit[1]\n",
    "    v = y_fit[2]\n",
    "    \n",
    "    #CONDITIONS\n",
    "    #condition 1: it checks that the R points are minima of the Quadratic mean parabola-fit error  𝜀(𝑖)\n",
    "    c = np.arange(len(yn)-m+1)[2:]\n",
    "    cond_min_1 = y_fit[0][c-2]>y_fit[0][c-1]\n",
    "    cond_min_2 = y_fit[0][c-1]<y_fit[0][c]\n",
    "    cond_min_eps = cond_min_1 & cond_min_2\n",
    "    #condition 2: threshold for the a coefficient of the fit to avoid cases in which 𝜀(𝑖)->0 in straight lines\n",
    "    R_a_mask = abs(y_fit[1])>1.65e-2\n",
    "\n",
    "    # reshaping to fill in with zeros at the beginning and end (since during the first and last w there are no peaks)\n",
    "    zeros = np.zeros(w+1)!=0\n",
    "    zeros2 = np.zeros(w)!=0\n",
    "    cond_1 = np.concatenate((zeros, cond_min_eps, zeros))\n",
    "    cond_2 = np.concatenate((zeros2, R_a_mask, zeros2))\n",
    "    a_fixed=np.concatenate((zeros2, y_fit[1], zeros2))\n",
    "    v_fixed=np.concatenate((zeros2, y_fit[2], zeros2))\n",
    "\n",
    "    # using masks to find the desired results\n",
    "    R_i_mask=cond_1&cond_2\n",
    "    R_i=(np.where(R_i_mask))[0]\n",
    "    a=a_fixed[R_i]\n",
    "    v=v_fixed[R_i]\n",
    "    \n",
    "    return R_i, a, v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4ce062ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "def align_to_longest(r, test, level):\n",
    "    '''\n",
    "    function that returns a one-to-one correspondance between:\n",
    "    # - the vector of reference rpeaks\n",
    "    # - the peaks to test\n",
    "    # the third input (level) is a treshold to be set based on the RR distance\n",
    "    '''\n",
    "    \n",
    "    # we make sure that everything is a numpy array\n",
    "    r = np.array(r)\n",
    "    test = np.array(test)\n",
    "    \n",
    "    # we make deep copies that we can safely modify during the loop without loss of data\n",
    "    r_peaks = copy.copy(r)\n",
    "    test_peaks = copy.copy(test) \n",
    "    # and we initialize the vectors in which we are going to save the results\n",
    "    # we initialize to weird numbers so that we can make sure that all values have been changed by the end of the algorithm\n",
    "    bool_record = np.ones(len(r), dtype=int)*(-55)\n",
    "    idx_record = np.ones(len(r))*(-55)\n",
    "\n",
    "    # for every peak in the longest peak\n",
    "    for i in range(len(r)):\n",
    "        # we make sure that there are still peaks to be compared with it\n",
    "        if (len(test_peaks)>0):\n",
    "            # we get the current element to examine, which every time is the first element of the array (since we later\n",
    "            # delete the elements aftering examining them)\n",
    "            r_index = r_peaks[0]\n",
    "            # calculate the distance from all the remaining test peaks\n",
    "            distance = np.abs(test_peaks - r_index)\n",
    "            # and sort such distances to find the minimum distance and the index (of rpeaks) corresponding to it\n",
    "            sort_distance = pd.DataFrame({'distance': distance}).sort_values(by=['distance'])\n",
    "            # we set the corresponding index to be the \n",
    "            min_distance = sort_distance.distance.values[0]\n",
    "            # we get the index corresponding to the minimum distance\n",
    "            min_distance_idx = sort_distance.index.values[0]\n",
    "            # If the minimum distance is appropriate\n",
    "            if min_distance<level:\n",
    "                # we save the positive finding\n",
    "                bool_record[i] = 1\n",
    "                # and the index corresponding to that finding\n",
    "                idx_record[i] = test_peaks[min_distance_idx]\n",
    "                # and remove the information corresponding to this configuration,\n",
    "                # to avoid repetitions and grant a one-to-one correspondance\n",
    "                test_peaks = np.delete(test_peaks, min_distance_idx)\n",
    "                r_peaks = np.delete(r_peaks, 0 )\n",
    "            # otherwise (if the closest value is too far away to be considered close)\n",
    "            else:\n",
    "                # we save the negative finding\n",
    "                bool_record[i] = 0\n",
    "                # and signal the lack of a corresponding index with a non-numeric value\n",
    "                idx_record[i] = np.nan\n",
    "                # we forget about the data \n",
    "                #test_peaks = np.delete(test_peaks, min_distance_idx)\n",
    "                r_peaks = np.delete(r_peaks, 0 )\n",
    "        # if all peaks have already been compared, we for sure have a negative result\n",
    "        else:\n",
    "            # we save the negative finding\n",
    "                bool_record[i] = 0\n",
    "                # and signal the lack of a corresponding index with a non-numeric value\n",
    "                idx_record[i] = np.nan\n",
    "    return bool_record, idx_record"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "eb5c23b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def align(r, ann_idx, ann_symb, level):\n",
    "    '''\n",
    "    function that returns a one-to-one correspondance between:\n",
    "    - the vector of r-peaks r\n",
    "    - the annotations indexes ann_idx\n",
    "    while estabilishing this correspondance, the third input (ann_symbol) is altered identically to ann_idx\n",
    "    since the desired outcome of the code is to find the ann_symb corresponding to the r-peaks. \n",
    "    the last input, level, is a treshold which correspond to the maximum distance for compatibility\n",
    "    '''\n",
    "    if len(r) != len(np.unique(r)):\n",
    "        print('input has repetition!!! but we took care of it inside the function')\n",
    "        r = np.unique(r)\n",
    "                     \n",
    "    # we make deep copies that we can safely modify during the loop without loss of data\n",
    "    rpeaks = copy.copy(r)\n",
    "    ann_indexes = copy.copy(ann_idx) \n",
    "    ann_symbols = copy.copy(ann_symb)\n",
    "    # lastly, we initialize the vectors in which we are going to save the results\n",
    "    # - the r-peaks for which we find a corresponding annotation\n",
    "    real_r_peaks = list()\n",
    "    # - the annotation (symbols) for which we find a corresponding rpeak\n",
    "    real_ann_symbols = list()\n",
    "    ann_aligned = list()\n",
    "    # - a dummy initialization needed to enter the while-loop\n",
    "    sort_distance = np.zeros(100)\n",
    "    # - a register for the rpeaks (that will work, later on, as a boolean mask to be applied to the input r)\n",
    "    r_register = np.zeros(len(r),dtype=bool)\n",
    "    # - a list in which we save every peak once (if found in both rpeak and ann, the rpeak value is kept)\n",
    "    val = []\n",
    "    # - to lists where we keep record of where the values in val come from\n",
    "    ann_bool = []\n",
    "    rpeak_bool = []\n",
    "    \n",
    "    i=-1\n",
    "    while ((i<=len(ann_idx)+1) & (len(sort_distance)>1) & (len(ann_indexes)>=1)):\n",
    "        i +=1\n",
    "        # we get the current element to examin\n",
    "        ann_index = ann_indexes[0]\n",
    "        # calculate the distance of the many (remaining) rpeaks from it\n",
    "        distance = np.abs(rpeaks - ann_index)\n",
    "        # and sort such distances to find the minimum distance and the index (of rpeaks) corresponding to it\n",
    "        sort_distance = pd.DataFrame({'distance': distance}).sort_values(by=['distance'])\n",
    "        # we set the corresponding index to be the \n",
    "        min_distance = sort_distance.distance.values[0]\n",
    "        # If the minimum distance is appropriate\n",
    "        if min_distance<level:            \n",
    "            # we get the index corresponding to the minimum distance\n",
    "            min_distance_idx = sort_distance.index.values[0]\n",
    "            # we save the rpeak corresponding to minimum distance\n",
    "            real_r_peaks.append( rpeaks[ min_distance_idx ] )\n",
    "            # And the annotation index\n",
    "            ann_aligned.append(ann_indexes[0])\n",
    "            # and save the corresponding annotation\n",
    "            real_ann_symbols.append( ann_symbols[0] )\n",
    "            # we find where the peak was originally\n",
    "            asmr = np.where(r == rpeaks[ min_distance_idx ])\n",
    "            # to fill in the boolean mask \n",
    "            r_register[asmr] = True\n",
    "            # we save the results:\n",
    "            val.append(rpeaks[min_distance_idx])\n",
    "            ann_bool.append(1)\n",
    "            rpeak_bool.append(1)\n",
    "            # and remove the annotations (both index and symbol) and rpeak corresponding to this configuration,\n",
    "            # to avoid repetitions and grant a one-to-one correspondance\n",
    "            rpeaks = np.delete(rpeaks, min_distance_idx)\n",
    "            ann_indexes = np.delete(ann_indexes, 0 )\n",
    "            ann_symbols = np.delete(ann_symbols, 0 )\n",
    "            \n",
    "        # otherwise (if the closest value is too far away to be considered close)\n",
    "        else:\n",
    "            val.append(ann_indexes[0])\n",
    "            ann_bool.append(1)\n",
    "            rpeak_bool.append(0)\n",
    "            \n",
    "            # we forget about the data corresponding to this annotation since there's no correspondance in the rpeak\n",
    "            ann_indexes = np.delete(ann_indexes, 0 )\n",
    "            ann_symbols = np.delete(ann_symbols, 0 )\n",
    "    if len(rpeaks)>0:\n",
    "        val.extend(rpeaks) # I'm really not sure about this line\n",
    "        ann_bool.extend(np.zeros(len(rpeaks),dtype=int))\n",
    "        rpeak_bool.extend(np.ones(len(rpeaks),dtype=int))\n",
    "\n",
    "#     print(len(r_register),'is the length of the boolean mask r_register, just like the lenght of the input ',len(r))\n",
    "#     print(sum(r_register),' is number of Trues in len(r_register), just like the length of real_r_peaks', \n",
    "#           len(real_r_peaks))\n",
    "\n",
    "    return ann_aligned, real_r_peaks, real_ann_symbols, np.array(val), np.array(ann_bool), np.array(rpeak_bool), r_register"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8bd26ec7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def bool_mask_removing_repetitions(a2):\n",
    "    '''\n",
    "    the function produces a boolean mask that helps keep track of the changes that happen from a to np.unique(a)\n",
    "    '''\n",
    "    # we get the unique values and how many times each of them is present\n",
    "    a, counts = np.unique(a2, return_counts=True)\n",
    "    # we intialize the boolean mask in output, which dimensions must match the ones of the input and is initally all positives\n",
    "    output = np.ones(len(a2), dtype=bool)\n",
    "    # for the elements that are repeated\n",
    "    tocheck = a[counts >1]\n",
    "    for tc in tocheck:\n",
    "        # we get the indexes of the actual repetitions (i.e. all indexes beside the first occurrence)\n",
    "        idx = np.where(a2 == tc)[0][1:]\n",
    "        # and make sure to negate the boolean mask in such indexes\n",
    "        output[idx] = np.zeros(len(idx),dtype=bool)\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1defd9ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rpeaks_per_patient(x_d, data, lev1, n_leads, annotations=False, ann_idx=None, ann_symbol=None, lev2=None,):\n",
    "    '''\n",
    "    The following function aligns the r-peaks found in the 12 leads and decides which ones to keep based on how many times \n",
    "    (out of 12) they are found. The inputs are:\n",
    "    - x_d: the dictionary, in which is key is a lead name and corresponds to a list of r-peaks\n",
    "    - lev1: an input theshold (to be set based on the RR distance - see align_to_longest function)\n",
    "    - n_leads : minimum number of matching leads for a peak to be accepted\n",
    "    - annotations: an option tg=hat, if True, filters also to match annotations and will return two accuracy scores\n",
    "    - ann_idx: a numpy array of annotations indexes\n",
    "    - ann_symbol: a list of annotations symbols (its length must match the one of ann_idx\n",
    "    - l\n",
    "    '''\n",
    "    # we extract the names fl\n",
    "    names = list(x_d.keys())\n",
    "    # we find the longest one\n",
    "    l = [len(x_d[name]) for name in names]\n",
    "    # we find the corresponding index\n",
    "    best_idx = np.argmax(l)\n",
    "    # and we call the corresponding lead \"best_lead\"\n",
    "    best_lead = names[best_idx]\n",
    "    # we now create a dataframe in which I'm going to save everything\n",
    "    # it is going to have as many rows has the lead with most peaks\n",
    "    peaks = pd.DataFrame({ str(best_lead) : x_d[best_lead] })\n",
    "    # we now want to fill the dataframe with the remaining information\n",
    "    # for each one, we try to align the results to the best_lead\n",
    "    reference = x_d[best_lead]\n",
    "    \n",
    "    # for every lead (including the best one, so we make sure to save all the indexes in the same place)\n",
    "    for ol in names:\n",
    "        # we extract the test peaks\n",
    "        test = x_d[ol]\n",
    "        # to feed them to a function that returns the columns of the dataframe to be filled\n",
    "        # the first column contains booleans that signal if a correspondance for the peak was found in the dataset\n",
    "        # the second column contains the indexes that are potentially found in the correspondance (otherwise, they are NaN)\n",
    "        peaks[ol], peaks[ol+'_idx'] =  align_to_longest(reference, test, lev1)\n",
    "    # we calculate the number of times in which each peak is found\n",
    "    peaks['n'] = peaks[names].sum(axis=1)\n",
    "    # and decide if the peaks must be kept based on an input theshold\n",
    "    peaks['to_keep'] = peaks['n']>=n_leads   \n",
    "    # We delete the peaks that we don't keep\n",
    "    true_peaks = peaks[peaks['to_keep']==True]\n",
    "    true_peaks = true_peaks.reset_index(drop = True)\n",
    "    # we find the average location of each peak, selecting only the only ones to keep\n",
    "    name_idx = [name+'_idx' for name in names]\n",
    "    true_peaks['loc'] = (true_peaks[name_idx].mean(axis=1)).astype(int)\n",
    "    rpeaks = true_peaks['loc'].to_numpy()\n",
    "    where_repetitions = bool_mask_removing_repetitions(rpeaks)\n",
    "    rpeaks = np.unique(rpeaks)\n",
    "    \n",
    "    # if we can compare with annotations\n",
    "    if annotations:\n",
    "        # we compare with annotations\n",
    "        ann_aligned, r_aligned, ann_symbols_aligned, val, ann_bool, rpeak_bool, r_register = align(rpeaks, ann_idx, ann_symbol, lev2)\n",
    "\n",
    "        # we count the:\n",
    "        # - true positives\n",
    "        true_positive = np.sum( ( (np.array(ann_bool)==1) & (np.array(rpeak_bool)==1) ))\n",
    "        # - false positives\n",
    "        false_positive = np.sum( ( (np.array(ann_bool)==0) & (np.array(rpeak_bool)==1) ))\n",
    "        # - false negatives\n",
    "        false_negative = np.sum( ( (np.array(ann_bool)==1) & (np.array(rpeak_bool)==0) ))\n",
    "        # - true negatives\n",
    "        true_negative = len(data)-len(val)\n",
    "        # and concatenate the results in a (non-normalized) matrix\n",
    "        conmat_patient = np.array([[true_positive, false_negative],[false_positive,true_negative]]) \n",
    "        return r_aligned, peaks['to_keep'].to_numpy(), best_lead, where_repetitions, r_register, ann_symbols_aligned, conmat_patient\n",
    "    else:\n",
    "        return rpeaks, peaks['to_keep'].to_numpy(), best_lead, where_repetitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2262a482",
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_record_corrected(path, coef_parab):\n",
    "    '''\n",
    "    function that processes a single file by applying the previously defined functions\n",
    "    '''\n",
    "    # Load the record\n",
    "    path = str(path)\n",
    "    # we create a dictionary in which I'm going to save the r-peaks obtained in every lead\n",
    "    xd = {}\n",
    "    # and we do the same for saving the coefficients of the parabola fits\n",
    "    ecia = {}\n",
    "    lia = {}\n",
    "    # we initialize the estimates of the RR length\n",
    "    stat_RR = np.zeros(len(leads))\n",
    "    stat_RR2 = np.zeros(len(leads))\n",
    "    \n",
    "    # for every lead\n",
    "    for i,lead in enumerate(leads):\n",
    "        # we read the corresponiding data\n",
    "        data = pd.read_csv(\"extended_output/data\"+path+\"_2filtered.csv\")[lead]\n",
    "        # we find its R-peaks \n",
    "        rpeak, temp1, temp2 = R_fit(data)\n",
    "        # we save the corresponding coefficients for the parabola fits\n",
    "        ecia[lead] = temp1\n",
    "        lia[lead] = temp2\n",
    "        # we compute and estimator of th RR lenght for the peaks we just found\n",
    "        stat_RR[i] = np.median(rpeak[1:]-rpeak[0:-1], )\n",
    "        stat_RR2[i] = np.mean(rpeak[1:]-rpeak[0:-1])\n",
    "        # and save the peaks we just found in a dictionary\n",
    "        xd[lead] = rpeak\n",
    "        \n",
    "    # we calculate an estimator of all the RR lengths and use to deduce the maximum distance for peak compatibility\n",
    "    # (which should generally be smaller than the half of the average RR interval)\n",
    "    # in this precise case, we choose the median as the estimator since the distribution is quite asymmetric \n",
    "    # (so the difference between mean and median is huge)\n",
    "    # we need to get rid of the NaN that are returned by np.median if no peak is found in a lead\n",
    "    lev1 = np.nanmedian(stat_RR)/2\n",
    "    print('maximum distance for compatibility of same peak in different leads:',lev1)\n",
    "    \n",
    "\n",
    "\n",
    "    rpeaks, pp, best_lead, where_repetitions = rpeaks_per_patient(xd, data, lev1, 8)\n",
    "    # we select the data relative to the best lead (now that we know which is)\n",
    "    ii = xd[best_lead]\n",
    "    a = ecia[best_lead]\n",
    "    v = lia[best_lead]\n",
    "\n",
    "    # we add the R peaks to the dataframe while also adapting them to the frequency reduction that we're about to implement\n",
    "    rpeaks = np.array(rpeaks)\n",
    "    final = (rpeaks/4).astype(int)\n",
    "    df = pd.DataFrame({'R': final}) \n",
    "    outfn = \"extended_output/\"+\"data\"+path+'_12Rpeak.csv'\n",
    "    df.to_csv(outfn, index=False)\n",
    "    \n",
    "    # we save together the parameters for the parabola\n",
    "    temp = pd.DataFrame({'i': ii,'v':v, 'a':a, 'path': [path]*len(a) })\n",
    "    # crop them to have a one-to-one correspondence with the R peaks\n",
    "    temp = (((temp[pp]).reset_index(drop=True))[where_repetitions].reset_index(drop=True))\n",
    "    \n",
    "    # and adjust the index column to match the future frequency reduction\n",
    "    temp['i'] = (temp['i']/4).astype(int)\n",
    "    coef_parab = (pd.concat([coef_parab, temp])).reset_index(drop=True)\n",
    "    \n",
    "    print('process_record_corrected: match in len of output files: ',len(temp)==len(final))\n",
    "    if len(temp) !=len(final):\n",
    "        print(\"current coef_parab and rpeaks lengths don't match\")\n",
    "        print('input lenghts :',len(ii),len(a),len(v) )\n",
    "        print('pp starts from',len(pp),'and end to ',sum(pp))\n",
    "        print('where_repetitions starts from',len(where_repetitions),'and end to ',sum(where_repetitions))\n",
    "        print('r_register starts from',len(r_register),'and end to ',sum(r_register))\n",
    "        print('output length :', len(coef_parab) )\n",
    "    \n",
    "    return final, coef_parab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a8744e3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "FILE_SAVE=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c3beeaf1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "if FILE_SAVE==True: \n",
    "    # we initialize the names of files to be processed\n",
    "    r  = np.genfromtxt(\"extended_input/RECORDS\", dtype='str')\n",
    "    records = records = r.astype(int) #list(range(1,(len(r)+1)))\n",
    "    # and a database in which we're going to save the parameters of the \n",
    "    coef_parab = pd.DataFrame({'i': [], 'a':[], 'v':[], 'path': []})\n",
    "\n",
    "    if __name__ == \"__main__\":\n",
    "        t1 = time.time()\n",
    "        # for every file\n",
    "        for path in records:\n",
    "            # we process the data\n",
    "            rpeaks, coef_parab = process_record_corrected(path, coef_parab)\n",
    "            # we reset the dataframe indexes (to avoid future bugs)\n",
    "            coef_parab=coef_parab.reset_index(drop = True)\n",
    "            \n",
    "            print(path)\n",
    "        \n",
    "        # we reset the dataframe indexes (to avoid future bugs)\n",
    "        coef_parab=coef_parab.reset_index(drop = True)\n",
    "        # we make sure that the peaks found are correct (the corresponding parabola fits intersect with the x axis)\n",
    "        coef_parab['Delta'] = -4*coef_parab['a']*coef_parab['v']\n",
    "        print('Delta>0 for all peaks :',np.all(coef_parab['Delta']>0))\n",
    "#         print(\"rpeaks - coef_parab['i'] :\", np.mean(rpeaks - coef_parab['i']), '±', np.std(rpeaks - coef_parab['i']) )\n",
    "        t2 = time.time()\n",
    "        print('The R peak data saving took', round((t2-t1)/60,2), 'minutes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "dae31bd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# outfn = \"extended_output/coef_parab.csv\"\n",
    "# coef_parab.to_csv(outfn, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8c802082",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>i</th>\n",
       "      <th>a</th>\n",
       "      <th>v</th>\n",
       "      <th>path</th>\n",
       "      <th>Delta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>713.0</td>\n",
       "      <td>0.022785</td>\n",
       "      <td>-5.829751</td>\n",
       "      <td>10</td>\n",
       "      <td>0.531315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>922.0</td>\n",
       "      <td>0.023006</td>\n",
       "      <td>-5.676080</td>\n",
       "      <td>10</td>\n",
       "      <td>0.522336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1728.0</td>\n",
       "      <td>0.021002</td>\n",
       "      <td>-5.469172</td>\n",
       "      <td>10</td>\n",
       "      <td>0.459446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3119.0</td>\n",
       "      <td>0.021595</td>\n",
       "      <td>-5.425946</td>\n",
       "      <td>10</td>\n",
       "      <td>0.468691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3724.0</td>\n",
       "      <td>0.023883</td>\n",
       "      <td>-6.104432</td>\n",
       "      <td>10</td>\n",
       "      <td>0.583167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>36813</th>\n",
       "      <td>898549.0</td>\n",
       "      <td>0.022743</td>\n",
       "      <td>-5.071217</td>\n",
       "      <td>22</td>\n",
       "      <td>0.461349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36814</th>\n",
       "      <td>898872.0</td>\n",
       "      <td>0.021104</td>\n",
       "      <td>-4.950542</td>\n",
       "      <td>22</td>\n",
       "      <td>0.417905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36815</th>\n",
       "      <td>899196.0</td>\n",
       "      <td>0.022610</td>\n",
       "      <td>-4.951609</td>\n",
       "      <td>22</td>\n",
       "      <td>0.447814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36816</th>\n",
       "      <td>899514.0</td>\n",
       "      <td>0.020665</td>\n",
       "      <td>-4.905580</td>\n",
       "      <td>22</td>\n",
       "      <td>0.405494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36817</th>\n",
       "      <td>899793.0</td>\n",
       "      <td>0.019793</td>\n",
       "      <td>-4.857970</td>\n",
       "      <td>22</td>\n",
       "      <td>0.384616</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36818 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              i         a         v  path     Delta\n",
       "0         713.0  0.022785 -5.829751    10  0.531315\n",
       "1         922.0  0.023006 -5.676080    10  0.522336\n",
       "2        1728.0  0.021002 -5.469172    10  0.459446\n",
       "3        3119.0  0.021595 -5.425946    10  0.468691\n",
       "4        3724.0  0.023883 -6.104432    10  0.583167\n",
       "...         ...       ...       ...   ...       ...\n",
       "36813  898549.0  0.022743 -5.071217    22  0.461349\n",
       "36814  898872.0  0.021104 -4.950542    22  0.417905\n",
       "36815  899196.0  0.022610 -4.951609    22  0.447814\n",
       "36816  899514.0  0.020665 -4.905580    22  0.405494\n",
       "36817  899793.0  0.019793 -4.857970    22  0.384616\n",
       "\n",
       "[36818 rows x 5 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef_parab = pd.read_csv(\"extended_output/coef_parab.csv\")\n",
    "coef_parab"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d2b838a",
   "metadata": {},
   "source": [
    "Now that we've analyzed in details the positions of the peaks, we can artificially reduce the frequency of the dataset to improve computation time. We take care only of the datasets since the R peaks were already adapted during saving."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "bd0d0380",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resaving filtered file with new frequency, 250Hz\n",
    "if FILE_SAVE==True: \n",
    "    mask = list(np.arange(0, record_1.p_signal.shape[0], 4))\n",
    "    r  = np.genfromtxt(\"extended_input/RECORDS\", dtype='str')\n",
    "    records = r.astype(int) #list(range(1,(len(r)+1)))\n",
    "\n",
    "    for path in records:\n",
    "        path = str(path)\n",
    "        datac = pd.DataFrame()\n",
    "        data = pd.read_csv(\"extended_output/\"+\"data\"+path+'_2filtered.csv')\n",
    "        for i in range(0,len(leads)):\n",
    "            mask = np.arange(0,len(data),4)\n",
    "            datac[leads[i]] = data.iloc[mask, i]\n",
    "            outfn = \"extended_output/\"+\"data\"+path+'_filtered.csv'\n",
    "            datac.to_csv(outfn, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d69329e-624f-4d08-ada2-57735db623cf",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 3) Segmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63e8ee8e-1d61-4a30-ad45-735ae34149b9",
   "metadata": {
    "tags": []
   },
   "source": [
    "The following segmentation algorithm is based on the known ratios that can be computed between:\n",
    "- the average length of the RR interval (~ 800 ms from 600-1000 ms) \n",
    "- the average length of the PR interval (~ 160 ms from 120-200 ms)\n",
    "- the average length of the QT interval (~ 410 ms from 360-460 ms)\n",
    "\n",
    "Such ratios are used to find the actual length of thePR and QT interval ($L_\\text{PR}^i$, $L_\\text{QT}^i$) based on the actual length of RR interval $L_\\text{RR}^i$ (which can be easily computed). \n",
    "\n",
    "$$ L_\\text{PR}^i = L_\\text{RR}^i \\cdot \\frac{410 \\; \\text{ms}}{800 \\; \\text{ms}} $$\n",
    "\n",
    "$$ L_\\text{QT}^i = L_\\text{RR}^i \\cdot \\frac{160 \\; \\text{ms}}{800 \\; \\text{ms}} $$\n",
    "\n",
    "Once $L_\\text{PR}^i$ and $L_\\text{QT}^i$ are computed, the hearbeat is identified as:\n",
    "\n",
    "$$ \\left[ x_\\text{R-peak}^i - L_\\text{PR}^i  , x_\\text{R-peak}^i + L_\\text{QT}^i \\right]  $$\n",
    "\n",
    "The length of such interval is $\\frac{(410+160) \\; \\text{ms}}{800 \\; \\text{ms}} = \\frac{57}{80} \\sim 71.25 \\% $."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "22f37275",
   "metadata": {},
   "outputs": [],
   "source": [
    "def segmentation_R(annotations, cp):\n",
    "    '''\n",
    "    function that performs a relative window segmentation based on the relative known proportions between RR, PR and QT \n",
    "    '''\n",
    "    ann = annotations\n",
    "    \n",
    "    # we initialize the variables we need for the while loop\n",
    "    patient_start = np.ones(2)*-3\n",
    "    j = -1\n",
    "    while np.any(patient_start<0):\n",
    "        j += 1\n",
    "        # we calculate the RR intervals\n",
    "        RR_intervals = (ann[(j+1):]-ann[j:-1])\n",
    "        # firstly, we crop the RR interval to match the relative proportion of RR:QT=0.8:0.41 \n",
    "        QT_size = (RR_intervals*0.41/0.8).astype(int)\n",
    "        PR_size = (RR_intervals*0.16/0.8).astype(int)\n",
    "        \n",
    "        patient_start = ann[j:-1] - PR_size\n",
    "        patient_end = ann[j:-1] + QT_size\n",
    "        \n",
    "    # j is the number of annotations ignored from the beginning\n",
    "    # i is the number of annotations ignored from the end\n",
    "    # once I've made sure that the annotations are aligned, we delete the first and last one just to be safe\n",
    "    RR_intervals = RR_intervals[1:-1]\n",
    "    QT_size = QT_size[1:-1]\n",
    "    PR_size = PR_size[1:-1]    \n",
    "    \n",
    "    patient_start = ann[j+1:-2] - PR_size\n",
    "    patient_end = ann[j+1:-2] + QT_size\n",
    "\n",
    "    cp = cp[j+1:-2]\n",
    "\n",
    "    patient_R_peak = ann[j+1:-2]\n",
    "    #patient_ann = list(annotations['symbol'][j+1:-2])\n",
    "        \n",
    "    return RR_intervals, QT_size, PR_size, patient_start, patient_end, patient_R_peak, cp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9344509e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the segmentation algorithm took 0.12 seconds\n",
      "overflow of i from end 0.0 %\n",
      "overflow of i from start 0.1142 %\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient</th>\n",
       "      <th>idx</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>ann_index</th>\n",
       "      <th>RR_length</th>\n",
       "      <th>i</th>\n",
       "      <th>a</th>\n",
       "      <th>v</th>\n",
       "      <th>Delta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>761</td>\n",
       "      <td>1335</td>\n",
       "      <td>922</td>\n",
       "      <td>806</td>\n",
       "      <td>922.0</td>\n",
       "      <td>0.023006</td>\n",
       "      <td>-5.676080</td>\n",
       "      <td>0.522336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1450</td>\n",
       "      <td>2440</td>\n",
       "      <td>1728</td>\n",
       "      <td>1391</td>\n",
       "      <td>1728.0</td>\n",
       "      <td>0.021002</td>\n",
       "      <td>-5.469172</td>\n",
       "      <td>0.459446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>2996</td>\n",
       "      <td>3434</td>\n",
       "      <td>3119</td>\n",
       "      <td>615</td>\n",
       "      <td>3119.0</td>\n",
       "      <td>0.021595</td>\n",
       "      <td>-5.425946</td>\n",
       "      <td>0.468691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3695</td>\n",
       "      <td>3834</td>\n",
       "      <td>3734</td>\n",
       "      <td>197</td>\n",
       "      <td>3724.0</td>\n",
       "      <td>0.023883</td>\n",
       "      <td>-6.104432</td>\n",
       "      <td>0.583167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>3851</td>\n",
       "      <td>4136</td>\n",
       "      <td>3931</td>\n",
       "      <td>400</td>\n",
       "      <td>3931.0</td>\n",
       "      <td>0.026308</td>\n",
       "      <td>-6.647490</td>\n",
       "      <td>0.699521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36772</th>\n",
       "      <td>22</td>\n",
       "      <td>2847</td>\n",
       "      <td>897817</td>\n",
       "      <td>898052</td>\n",
       "      <td>897883</td>\n",
       "      <td>331</td>\n",
       "      <td>897883.0</td>\n",
       "      <td>0.020734</td>\n",
       "      <td>-4.895165</td>\n",
       "      <td>0.405983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36773</th>\n",
       "      <td>22</td>\n",
       "      <td>2848</td>\n",
       "      <td>898147</td>\n",
       "      <td>898385</td>\n",
       "      <td>898214</td>\n",
       "      <td>335</td>\n",
       "      <td>898214.0</td>\n",
       "      <td>0.020911</td>\n",
       "      <td>-4.793734</td>\n",
       "      <td>0.400964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36774</th>\n",
       "      <td>22</td>\n",
       "      <td>2849</td>\n",
       "      <td>898485</td>\n",
       "      <td>898714</td>\n",
       "      <td>898549</td>\n",
       "      <td>322</td>\n",
       "      <td>898549.0</td>\n",
       "      <td>0.022743</td>\n",
       "      <td>-5.071217</td>\n",
       "      <td>0.461349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36775</th>\n",
       "      <td>22</td>\n",
       "      <td>2850</td>\n",
       "      <td>898806</td>\n",
       "      <td>899037</td>\n",
       "      <td>898871</td>\n",
       "      <td>325</td>\n",
       "      <td>898872.0</td>\n",
       "      <td>0.021104</td>\n",
       "      <td>-4.950542</td>\n",
       "      <td>0.417905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36776</th>\n",
       "      <td>22</td>\n",
       "      <td>2851</td>\n",
       "      <td>899133</td>\n",
       "      <td>899358</td>\n",
       "      <td>899196</td>\n",
       "      <td>318</td>\n",
       "      <td>899196.0</td>\n",
       "      <td>0.022610</td>\n",
       "      <td>-4.951609</td>\n",
       "      <td>0.447814</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36777 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       patient   idx   start     end  ann_index  RR_length         i  \\\n",
       "0           10     0     761    1335        922        806     922.0   \n",
       "1           10     1    1450    2440       1728       1391    1728.0   \n",
       "2           10     2    2996    3434       3119        615    3119.0   \n",
       "3           10     3    3695    3834       3734        197    3724.0   \n",
       "4           10     4    3851    4136       3931        400    3931.0   \n",
       "...        ...   ...     ...     ...        ...        ...       ...   \n",
       "36772       22  2847  897817  898052     897883        331  897883.0   \n",
       "36773       22  2848  898147  898385     898214        335  898214.0   \n",
       "36774       22  2849  898485  898714     898549        322  898549.0   \n",
       "36775       22  2850  898806  899037     898871        325  898872.0   \n",
       "36776       22  2851  899133  899358     899196        318  899196.0   \n",
       "\n",
       "              a         v     Delta  \n",
       "0      0.023006 -5.676080  0.522336  \n",
       "1      0.021002 -5.469172  0.459446  \n",
       "2      0.021595 -5.425946  0.468691  \n",
       "3      0.023883 -6.104432  0.583167  \n",
       "4      0.026308 -6.647490  0.699521  \n",
       "...         ...       ...       ...  \n",
       "36772  0.020734 -4.895165  0.405983  \n",
       "36773  0.020911 -4.793734  0.400964  \n",
       "36774  0.022743 -5.071217  0.461349  \n",
       "36775  0.021104 -4.950542  0.417905  \n",
       "36776  0.022610 -4.951609  0.447814  \n",
       "\n",
       "[36777 rows x 10 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FILE_SAVE=True\n",
    "r  = np.genfromtxt(\"extended_input/RECORDS\", dtype='str')\n",
    "records = r.astype(int)\n",
    "# records = list(range(1,(len(r)+1)))\n",
    "\n",
    "if FILE_SAVE:\n",
    "    t1 = time.time()\n",
    "    \n",
    "    # we want to save a dataframe of heartbeats: to do that, we initialize some temporary means of storage\n",
    "    # - the number that identifies the patient\n",
    "    number = np.array([0])\n",
    "    # - the number that identifies the heartbeat of each patientx\n",
    "    idx = np.array([0])\n",
    "    # - the index of the start of the heartbeat\n",
    "    start = np.array([0])\n",
    "    # - the index of the end of the heartbeat\n",
    "    end = np.array([0]) \n",
    "    # - the index of the R peak of the heartbeat\n",
    "    R_peak = np.array([0]) \n",
    "    # - the symbol for the annotation of the heartbeat\n",
    "    annot = [None] \n",
    "    # - the duration of the heartbeat\n",
    "    RR = np.array([0])\n",
    "    # - the parabola fit coefficients\n",
    "    ddff = pd.DataFrame({ 'i': [], 'a': [], 'v': [], 'path': [] })\n",
    "    \n",
    "    # we then fill such temporary means of storage by updating them after analyzing every file/patient\n",
    "    for path in records:\n",
    "        path = str(path)\n",
    "        # UPLOAD: since we don't use the signal at this step, it doesn't matter if it's filtered or not\n",
    "        # data = pd.read_csv(\"extended_output/data\"+path+\".csv\")\n",
    "#         annotations = pd.read_csv(\"extended_output/data\"+path+\"_Rpeak.csv\")\n",
    "        annotations = pd.read_csv(\"extended_output/data\"+path+'_12Rpeak.csv')\n",
    "        ann = annotations['R'].values\n",
    "        cp = coef_parab[ coef_parab['path']==int(path)]\n",
    "        cp = cp.reset_index(drop = True)\n",
    "        \n",
    "        # and use my custom function for SEGMENTATION\n",
    "        patient_RR, QT_size, PR_size, patient_start, patient_end, patient_R_peak, cp = segmentation_R(ann, cp)\n",
    "        cp = cp.reset_index(drop = True)\n",
    "\n",
    "        #print(path,patient_start[0])\n",
    "        # we add the remaining pieces of information\n",
    "        # - the number of the patient is the same for all its heartbeats\n",
    "        patient_number = np.ones_like(patient_start)*int(path)\n",
    "        # - the number that identifies the heartbeat of each patient is a simple increasing vector of integers\n",
    "        patient_idx = np.arange(0,len(patient_start),1,dtype = int)\n",
    "#         # - the location of the R peaks (by cropping the annotations accordingly)\n",
    "#         patient_R_peak = ann[j:-i]\n",
    "#         # - the symbols for the annotations (by cropping the annotations accordingly)\n",
    "#         patient_ann = list(annotations['symbol'][j:-i])\n",
    "        \n",
    "        # lastly, we check that all the annotations are in between the corresponding starts and stops\n",
    "        check = np.all([(patient_R_peak>patient_start),(patient_R_peak<patient_end)])\n",
    "#         if not check:\n",
    "#             print(check)\n",
    "           \n",
    "        # and update the temporary means of storage\n",
    "        number = np.concatenate((number, patient_number))\n",
    "        idx = np.concatenate((idx, patient_idx))\n",
    "        start = np.concatenate((start, patient_start))\n",
    "        end = np.concatenate((end, patient_end))\n",
    "        R_peak = np.concatenate((R_peak, patient_R_peak))\n",
    "        #annot.extend(patient_ann)\n",
    "        RR = np.concatenate((RR,patient_RR))\n",
    "        ddff = pd.concat([ddff,cp])\n",
    "        ddff = ddff.reset_index(drop = True)\n",
    "    \n",
    "    # finally, we put together the heartbeat dataframe  \n",
    "    hb = pd.DataFrame({\"patient\": number[1:].astype(int), \"idx\": idx[1:], \"start\": start[1:].astype(int), \n",
    "                       \"end\": end[1:].astype(int), \"ann_index\": R_peak[1:].astype(int), \"RR_length\": RR[1:].astype(int) })\n",
    "    hb['i'] = ddff['i']\n",
    "    hb['a'] = ddff['a']\n",
    "    hb['v'] = ddff['v']\n",
    "    hb['Delta'] = -4*hb['a']*hb['v']\n",
    "    t2 = time.time()\n",
    "    print(\"the segmentation algorithm took\",round(t2-t1,2),'seconds')\n",
    "    outfn = 'extended_output/heartbeats.csv'\n",
    "    hb.to_csv(outfn, index=False)\n",
    "    print('overflow of i from end',round(sum(hb['end']<hb['i'])*100/len(hb),5), '%')\n",
    "    print('overflow of i from start', round(sum(hb['start']>hb['i'])*100/len(hb),5), '%')\n",
    "else:\n",
    "    hb = pd.read_csv('extended_output/heartbeats.csv')\n",
    "    \n",
    "display(hb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0872b7d",
   "metadata": {},
   "source": [
    "To ensure that the modifications made on the dataframe are applied also to the parabola fit coefficient, we added the to the parabola fit coefficients to the hb dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af58f295",
   "metadata": {},
   "source": [
    "We discard the peaks that have been fit with parabolas that have the wrong concavities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b7917493",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the percentage of discarded peaks (for mis-fitting) is: 0.00272 %\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient</th>\n",
       "      <th>idx</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>ann_index</th>\n",
       "      <th>RR_length</th>\n",
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       "      <th>Delta</th>\n",
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       "      <th>0</th>\n",
       "      <td>10</td>\n",
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       "      <td>1335</td>\n",
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       "      <td>922.0</td>\n",
       "      <td>0.023006</td>\n",
       "      <td>-5.676080</td>\n",
       "      <td>0.522336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
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       "      <td>1450</td>\n",
       "      <td>2440</td>\n",
       "      <td>1728</td>\n",
       "      <td>1391</td>\n",
       "      <td>1728.0</td>\n",
       "      <td>0.021002</td>\n",
       "      <td>-5.469172</td>\n",
       "      <td>0.459446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>2996</td>\n",
       "      <td>3434</td>\n",
       "      <td>3119</td>\n",
       "      <td>615</td>\n",
       "      <td>3119.0</td>\n",
       "      <td>0.021595</td>\n",
       "      <td>-5.425946</td>\n",
       "      <td>0.468691</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
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       "      <td>3695</td>\n",
       "      <td>3834</td>\n",
       "      <td>3734</td>\n",
       "      <td>197</td>\n",
       "      <td>3724.0</td>\n",
       "      <td>0.023883</td>\n",
       "      <td>-6.104432</td>\n",
       "      <td>0.583167</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>3851</td>\n",
       "      <td>4136</td>\n",
       "      <td>3931</td>\n",
       "      <td>400</td>\n",
       "      <td>3931.0</td>\n",
       "      <td>0.026308</td>\n",
       "      <td>-6.647490</td>\n",
       "      <td>0.699521</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36771</th>\n",
       "      <td>22</td>\n",
       "      <td>2847</td>\n",
       "      <td>897817</td>\n",
       "      <td>898052</td>\n",
       "      <td>897883</td>\n",
       "      <td>331</td>\n",
       "      <td>897883.0</td>\n",
       "      <td>0.020734</td>\n",
       "      <td>-4.895165</td>\n",
       "      <td>0.405983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36772</th>\n",
       "      <td>22</td>\n",
       "      <td>2848</td>\n",
       "      <td>898147</td>\n",
       "      <td>898385</td>\n",
       "      <td>898214</td>\n",
       "      <td>335</td>\n",
       "      <td>898214.0</td>\n",
       "      <td>0.020911</td>\n",
       "      <td>-4.793734</td>\n",
       "      <td>0.400964</td>\n",
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       "    <tr>\n",
       "      <th>36773</th>\n",
       "      <td>22</td>\n",
       "      <td>2849</td>\n",
       "      <td>898485</td>\n",
       "      <td>898714</td>\n",
       "      <td>898549</td>\n",
       "      <td>322</td>\n",
       "      <td>898549.0</td>\n",
       "      <td>0.022743</td>\n",
       "      <td>-5.071217</td>\n",
       "      <td>0.461349</td>\n",
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       "    <tr>\n",
       "      <th>36774</th>\n",
       "      <td>22</td>\n",
       "      <td>2850</td>\n",
       "      <td>898806</td>\n",
       "      <td>899037</td>\n",
       "      <td>898871</td>\n",
       "      <td>325</td>\n",
       "      <td>898872.0</td>\n",
       "      <td>0.021104</td>\n",
       "      <td>-4.950542</td>\n",
       "      <td>0.417905</td>\n",
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       "    <tr>\n",
       "      <th>36775</th>\n",
       "      <td>22</td>\n",
       "      <td>2851</td>\n",
       "      <td>899133</td>\n",
       "      <td>899358</td>\n",
       "      <td>899196</td>\n",
       "      <td>318</td>\n",
       "      <td>899196.0</td>\n",
       "      <td>0.022610</td>\n",
       "      <td>-4.951609</td>\n",
       "      <td>0.447814</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36776 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       patient   idx   start     end  ann_index  RR_length         i  \\\n",
       "0           10     0     761    1335        922        806     922.0   \n",
       "1           10     1    1450    2440       1728       1391    1728.0   \n",
       "2           10     2    2996    3434       3119        615    3119.0   \n",
       "3           10     3    3695    3834       3734        197    3724.0   \n",
       "4           10     4    3851    4136       3931        400    3931.0   \n",
       "...        ...   ...     ...     ...        ...        ...       ...   \n",
       "36771       22  2847  897817  898052     897883        331  897883.0   \n",
       "36772       22  2848  898147  898385     898214        335  898214.0   \n",
       "36773       22  2849  898485  898714     898549        322  898549.0   \n",
       "36774       22  2850  898806  899037     898871        325  898872.0   \n",
       "36775       22  2851  899133  899358     899196        318  899196.0   \n",
       "\n",
       "              a         v     Delta  \n",
       "0      0.023006 -5.676080  0.522336  \n",
       "1      0.021002 -5.469172  0.459446  \n",
       "2      0.021595 -5.425946  0.468691  \n",
       "3      0.023883 -6.104432  0.583167  \n",
       "4      0.026308 -6.647490  0.699521  \n",
       "...         ...       ...       ...  \n",
       "36771  0.020734 -4.895165  0.405983  \n",
       "36772  0.020911 -4.793734  0.400964  \n",
       "36773  0.022743 -5.071217  0.461349  \n",
       "36774  0.021104 -4.950542  0.417905  \n",
       "36775  0.022610 -4.951609  0.447814  \n",
       "\n",
       "[36776 rows x 10 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hb = pd.read_csv('extended_output/heartbeats.csv')\n",
    "temptemp = len(hb)\n",
    "hb  = (hb[ (hb['Delta'])>0 ] ).reset_index(drop = True)\n",
    "hb.to_csv('extended_output/heartbeats_right.csv')\n",
    "print('the percentage of discarded peaks (for mis-fitting) is:', np.round((1-(len(hb)/temptemp))*100,5),'%') \n",
    "hb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94e207bd",
   "metadata": {},
   "source": [
    "To ensure that the modifications made on the dataframe are applied also to the parabola fit coefficient, we added the to the parabola fit coefficients to the hb dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea608908",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "# 4) Extraction of features\n",
    "In the previous step we extracted the RR length which is strictly related to the heartbeat frequency.\n",
    "\n",
    "We also noticed visually that parts of the data is extremely affected by noise, which appears either as extremely high peaks or local plateaus. These anomalous features can hinder the extraction of features, so we need to remove them beforehand.\n",
    "\n",
    "After removing the anomalous heartbeats, we will focus on proper feature extraction which will be executed in 3 rounds (DWT coefficient extraction + 2 rounds of visualizable features extraction)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e64a2f6-e2ca-446b-b1c7-b51cc4c485d6",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## 4A) Discarding anomalous heartbeats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d7d9a97",
   "metadata": {},
   "source": [
    "We now want to check that the distribution of the RR_length of the heartbeats found, *which was already averaged over the number of leads in which the peaks was identified*. Ideally, if the patient is healthy and the algorithm is identifying the R-peaks correctly, the data should be normally distributed around the average RR_lenght, with a relatively small width."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6b947009",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we also define a function that allows to quickly display the distribution of its input\n",
    "def distribution2(arg, a1, a2, title='arg', val = 0):\n",
    "    '''\n",
    "    function that returns the distribution of arg (which name/meaning is encoded in title)\n",
    "    '''\n",
    "    average = np.median(arg)\n",
    "    std = np.std(arg)\n",
    "    fig, ax = plt.subplots(1,1, figsize=(12,4))\n",
    "    info = ax.hist(arg, bins=np.arange(np.min(arg), np.max(arg)+1, 1), align='left', density=True, edgecolor='none', \n",
    "                   color='black', alpha=0.8, label = 'distribution of '+title)\n",
    "    most_common_length = val #(info[1])[np.argmax(info[0])]\n",
    "    diff_to_average = (average - most_common_length)*2/3\n",
    "    rr_down_lim = int(most_common_length - diff_to_average)\n",
    "    rr_up_lim = int(most_common_length + diff_to_average)\n",
    "\n",
    "    ax.vlines(most_common_length, 0,1, label = '')\n",
    "    ax.vlines(average, 0, 1, color='magenta', label='average')\n",
    "    ax.vlines(average+std, 0, 1, color='gold', label='1 $\\sigma$')\n",
    "    ax.vlines(average-std, 0, 1, color='gold')\n",
    "    ax.vlines(rr_down_lim, 0, 1, color='green', label = 'limits', linestyle='dashed')\n",
    "    ax.vlines(rr_up_lim, 0, 1, color='green', linestyle='dashed')\n",
    "    ax.set_xlabel(title)\n",
    "    ax.set_ylabel('density')\n",
    "    ax.set_xlim(a1, a2)\n",
    "    ax.set_ylim(0, 1.1*np.max(info[0]))\n",
    "    ax.set_title('distribution of '+title)\n",
    "    ax.legend()\n",
    "    plt.show()\n",
    "    return rr_down_lim, rr_up_lim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a699447e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hb = pd.read_csv('extended_output/heartbeats.csv')\n",
    "rr_down_lim, rr_up_lim = distribution2(hb['RR_length'], 0, 1200, title='RR_length', val = 160)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa9d8633",
   "metadata": {},
   "source": [
    "We observe that the distribution has long tails.\n",
    "Therefore, we decide to discard the heartbeats which values of RR_lenght fall into the tails of the RR_length distribution. Particularly, we decide to set the limits for this process as the 65% confidence intervals (i.e. average_RR_lenght $\\pm$ sigma_RR_lenght)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "07bcb5c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "hb = pd.read_csv('extended_output/heartbeats.csv')\n",
    "ol = len(hb)\n",
    "# - mean of the std distribution\n",
    "mean_of_rr= np.mean(hb['RR_length'])\n",
    "# - sigma of the std distribution\n",
    "sigma_of_rr=  np.std(hb['RR_length'])\n",
    "# we then compute the distances from the average in units of sigma \n",
    "hb['n_sigma_rr'] = np.abs(hb['RR_length'] - mean_of_rr)/sigma_of_rr # 2 sided\n",
    "\n",
    "# finally, we can select the rows by keeping only the rows in which\n",
    "# hb = hb[(hb[\"n_sigma_rr\"] <= 0.1)].reset_index(drop=True)\n",
    "# print('percentage of peaks kept: ', round(len(hb)/ol*100,5),'%')\n",
    "# hb.to_csv('extended_output/heartbeats_filtered0.csv', index=False)\n",
    "# hb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acc4a6da",
   "metadata": {},
   "source": [
    "We take only the left peak in the RR length distribution which corresponds to shorter RR length. This choice results in the best hearbeat shape."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1eb0a4b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percentage of peaks kept:  38.79327 %\n"
     ]
    },
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       "       patient   idx   start     end  ann_index  RR_length         i  \\\n",
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       "14266       22  2806  885154  885318     885200        232  885200.0   \n",
       "\n",
       "              a         v     Delta  n_sigma_rr  \n",
       "0      0.023883 -6.104432  0.583167    0.122306  \n",
       "1      0.024146 -6.465836  0.624489    0.109986  \n",
       "2      0.024110 -6.011916  0.579799    0.133599  \n",
       "3      0.021522 -5.379752  0.463136    0.121279  \n",
       "4      0.024083 -6.443208  0.620688    0.127439  \n",
       "...         ...       ...       ...         ...  \n",
       "14262  0.020842 -4.616683  0.384882    0.148999  \n",
       "14263  0.020474 -4.590373  0.375940    0.144892  \n",
       "14264  0.019858 -4.851659  0.385368    0.124359  \n",
       "14265  0.020002 -4.761657  0.380965    0.104853  \n",
       "14266  0.018473 -4.759226  0.351675    0.086373  \n",
       "\n",
       "[14267 rows x 11 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# most_common_length = (info[1])[np.argmax(info[0])]\n",
    "# diff_to_average = average - most_common_length\n",
    "# rr_down_lim = int(most_common_length - diff_to_average)\n",
    "# rr_up_lim = int(most_common_length + diff_to_average)\n",
    "\n",
    "# finally, we can select the rows by keeping only the rows in which\n",
    "hb = hb[(hb[\"RR_length\"] >= rr_down_lim)&(hb[\"RR_length\"] <= rr_up_lim)].reset_index(drop=True)\n",
    "\n",
    "print('percentage of peaks kept: ', round(len(hb)/ol*100,5),'%')\n",
    "hb.to_csv('extended_output/heartbeats_filtered0.csv', index=False)\n",
    "hb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7489c32d-a20d-4638-8931-dcf37776ec76",
   "metadata": {
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    "tags": []
   },
   "source": [
    "We also noticed a correspondance between anomalous/noisy signal in a heartbeat and the corresponding average and standard deviation. \n",
    "\n",
    "Therefore, we examine the distributions of these quantities (heartbeat signal average, heartbeat signal standard deviation) by computing their estimators. From those, we decide to discard quantities that differ more than 1$\\sigma$ from the corresponding expectation value. This process is one-sided for the standard deviation (since it is non-negative), and two-sided for the average.\n",
    "\n",
    "The hearbeats are discarded by scrapping the corresponding lines in the hb dataframe. This step is essential to preserving the integrity of the heartbeats that are left. In fact, if this step was to be done before segmentation, the signal would be cropped based on the r-peaks: as a consequence, segmentation would lead to half-heartbeats what would be troublesome in feature extraction.\n",
    "\n",
    "This process was done for just for the main lead, from which most of the visualizable features were extracted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c4f3db87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percentage of peaks kept:  34.86 %\n"
     ]
    },
    {
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      "text/plain": [
       "       patient   idx   start     end  ann_index  RR_length         i  \\\n",
       "0           10     3    3695    3834       3734        197    3724.0   \n",
       "1           10     5    4290    4438       4331        209    4336.0   \n",
       "2           10     6    4503    4635       4540        186    4540.0   \n",
       "3           10    10    9377    9517       9416        198    9416.0   \n",
       "4           10    13   12902   13038      12940        193   12940.0   \n",
       "...        ...   ...     ...     ...        ...        ...       ...   \n",
       "12817       22  2802  884411  884532     884445        171  884446.0   \n",
       "12818       22  2803  884581  884705     884616        175  884616.0   \n",
       "12819       22  2804  884752  884890     884791        195  884791.0   \n",
       "12820       22  2805  884944  885095     884986        214  884987.0   \n",
       "12821       22  2806  885154  885318     885200        232  885200.0   \n",
       "\n",
       "              a         v     Delta  n_sigma_rr   average     sigma  \\\n",
       "0      0.023883 -6.104432  0.583167    0.122306  0.125140  1.282548   \n",
       "1      0.024146 -6.465836  0.624489    0.109986  0.070133  1.194577   \n",
       "2      0.024110 -6.011916  0.579799    0.133599  0.032837  1.153598   \n",
       "3      0.021522 -5.379752  0.463136    0.121279  0.106027  1.083039   \n",
       "4      0.021849 -5.409388  0.472762    0.126413  0.139068  1.067681   \n",
       "...         ...       ...       ...         ...       ...       ...   \n",
       "12817  0.020842 -4.616683  0.384882    0.148999  0.021810  1.304211   \n",
       "12818  0.020474 -4.590373  0.375940    0.144892 -0.008226  1.264192   \n",
       "12819  0.019858 -4.851659  0.385368    0.124359 -0.021391  1.252058   \n",
       "12820  0.020002 -4.761657  0.380965    0.104853  0.044036  1.207879   \n",
       "12821  0.018473 -4.759226  0.351675    0.086373  0.047126  1.146561   \n",
       "\n",
       "       n_sigma_average  n_sigma_std  \n",
       "0             0.482312     0.191843  \n",
       "1             0.005806    -0.144877  \n",
       "2             0.336763    -0.301730  \n",
       "3             0.312706    -0.571802  \n",
       "4             0.605909    -0.630588  \n",
       "...                ...          ...  \n",
       "12817         0.434617     0.274760  \n",
       "12818         0.701148     0.121580  \n",
       "12819         0.817974     0.075138  \n",
       "12820         0.237386    -0.093965  \n",
       "12821         0.209972    -0.328664  \n",
       "\n",
       "[12822 rows x 15 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hb = pd.read_csv('extended_output/heartbeats_filtered0.csv')\n",
    "# we initialize the values of average and standard deviation\n",
    "average = np.zeros(len(hb))\n",
    "sigma = np.zeros(len(hb))\n",
    "# we recover the information that we need from the hb dataframe as arrays\n",
    "start = np.array(hb[\"start\"]) \n",
    "end = np.array(hb[\"end\"]) \n",
    "# we extract the quantities we need\n",
    "path = \"blah\"\n",
    "for beat in range(len(hb)):\n",
    "    if path != hb[\"patient\"][beat]:\n",
    "        path = hb[\"patient\"][beat]\n",
    "        data = pd.read_csv(\"extended_output/data\"+str(path)+\"_filtered.csv\")\n",
    "        y = np.array(data[main_lead])\n",
    "    \n",
    "    average[beat] = np.mean(y[start[beat]:(end[beat]+1)])\n",
    "    sigma[beat] = np.std(y[start[beat]:(end[beat]+1)])\n",
    "    if np.isnan(average[beat]) or np.isnan(sigma[beat]):\n",
    "        print((end[beat]+1)- start[beat])\n",
    "        \n",
    "# and add them to the dataframe\n",
    "hb['average'] = average\n",
    "hb['sigma'] = sigma\n",
    "\n",
    "# we can now select the rows based on the distribution\n",
    "# to do that, we compute the\n",
    "# - mean of the average distribution\n",
    "mean_of_average = np.mean(average)\n",
    "# - sigma of the average distribution\n",
    "sigma_of_average = np.std(average)\n",
    "# - mean of the std distribution\n",
    "mean_of_sigma = np.mean(sigma)\n",
    "# - sigma of the std distribution\n",
    "sigma_of_sigma=  np.std(sigma)\n",
    "# we then compute the distances from the average in units of sigma \n",
    "hb['n_sigma_average'] = np.abs(average - mean_of_average)/sigma_of_average # 2 sided\n",
    "hb['n_sigma_std'] = (sigma - mean_of_sigma)/sigma_of_sigma # 1 sided \n",
    "# finally, we can select the rows by keeping only the rows in which\n",
    "hb = hb[(hb[\"n_sigma_average\"] <= 1) & (hb[\"n_sigma_std\"] <=1)].reset_index(drop=True)\n",
    "print('percentage of peaks kept: ', round(len(hb)/ol*100,2),'%')\n",
    "hb.to_csv('extended_output/heartbeats_filtered.csv', index=False)\n",
    "hb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1454176c-7079-44bd-9e16-04f12f40a3a5",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## 4B) Wavelet transform coefficients extraction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7d6304f-c3ff-4dc9-94c8-765dab85ad76",
   "metadata": {},
   "source": [
    "The feature extraction using discrete wavelet transform (DWT) is based on the idea presented in the reference [2]. DWT decomposes a signal into detail signal and approximation signal and as a result we obtain for each decomposition level a set of coefficients. Information about QRS complex is mainly concentrated at detail levels 3-5, while information about T and P waves is located at detail levels 6 and 7 [2].\n",
    "\n",
    "We tried to implement the algorithm with similar decomposition levels, but we receive Warnings that decompostion level was too high. In the end, we settled for 4 decompostion levels, therefore obtaining information about QRS complex. We take approximation signal and detail levels 3-4, which in the end matches proposals discussed in [1]. The idea is that we can extaract features of P and T waves separetly and add them later.\n",
    "\n",
    "The algorithm for feature extraction using DWT is the following:\n",
    "\n",
    "1) We firstly determine a fixed size segment near R peak so that the number of decompostion coefficients (thus features) is aslo fixed.\n",
    "\n",
    "2) We take each segment (beat), perform DWT and return a row of features corresponding to this beat."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d32eb97",
   "metadata": {},
   "source": [
    "We perform this procedure on every lead and save the features in a matrix, where each row corresponds to a particular beat."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "59445fad",
   "metadata": {},
   "outputs": [],
   "source": [
    "def leads_dwt_coeff(hb, lead):\n",
    "    '''\n",
    "    function that returns the DWT coefficients of the input lead for all heartbeats\n",
    "    '''\n",
    "    # we retrieved the columns of the dataframe that we need\n",
    "    start = np.array(hb[\"start\"])\n",
    "    end = np.array(hb[\"end\"])\n",
    "    R = np.array(hb[\"ann_index\"])\n",
    "    # we examine the cases that we arbitrarily consider degenerate (to allow for a not-too-small fixed window)\n",
    "    cond = (hb['idx']==0)\n",
    "    \n",
    "    # we find the locations of the beginnings\n",
    "    j = np.where(hb['idx']==0)[0]\n",
    "    # and of the ends\n",
    "    k = np.concatenate([(j -1)[1:],[len(hb)-1]])\n",
    "    # and find a treshold that can grant us a not-too-small window based based on the histogram of the values\n",
    "    # (neglecting the lower part of the plt.hist(R[j]-start[j]) and the upper part of the plt.hist(end[k]-R[k])\n",
    "    temp_start = (R[j]-start[j])>=27\n",
    "    temp_end =  (end[k]-R[k])>=163\n",
    "    # and find the indexes corresponding to the degenerate cases\n",
    "    where_start = np.where(temp_start)[0]\n",
    "    where_end = np.where(temp_end)[0]\n",
    "    # so that we can neglect them in finding the fixed window\n",
    "    fixed_start = R - 30 #np.min((R[j]-start[j])[temp_start]).astype(int)\n",
    "    fixed_end = R + 90 #np.min((end[k]-R[k])[temp_end]).astype(int)\n",
    "\n",
    "    path = 'blah'\n",
    "    # for each patient\n",
    "    for i in range(0,len(hb)):\n",
    "        # we make sure to change the file/patient if we have exhausted the previous one\n",
    "        if path != str(hb[\"patient\"][i]):\n",
    "            path = str(hb[\"patient\"][i])\n",
    "            data = pd.read_csv(\"extended_output/data\"+path+\"_filtered.csv\")\n",
    "            y = np.array(data[lead])\n",
    "            \n",
    "        # we select the signal in the fixed window\n",
    "        z = y[fixed_start[i]:fixed_end[i]]\n",
    "        # we extract the coefficients\n",
    "        coeffs = pywt.wavedec(z, 'db4', level=4, axis = 0)\n",
    "        # we initialize the output to zeros\n",
    "        if (i == 0):\n",
    "            l2 = len(coeffs[0])\n",
    "            l3 = len(coeffs[1])\n",
    "            l4 = len(coeffs[2])\n",
    "            # we extract the positions needed to make a single array\n",
    "            t1 = l2\n",
    "            t2 = t1 + l3\n",
    "            t3 = t2 + l4\n",
    "            features = np.zeros((len(hb), t3))\n",
    "        # if it's not a degenerate case, we save the result in a row\n",
    "        if i not in np.concatenate([where_start,where_end]):\n",
    "            features[i,0:t1]  = coeffs[0]\n",
    "            features[i,t1:t2] = coeffs[1]\n",
    "            features[i,t2:t3] = coeffs[2]\n",
    "        # otherwise we leave zeros\n",
    "\n",
    "    return features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "55376705",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the extraction of wavelet features took 0.67 minutes\n"
     ]
    }
   ],
   "source": [
    "parallelization = True\n",
    "if parallelization:\n",
    "    n_jobs = -1 #to use all available CPU cores \n",
    "    time1 = time.time()\n",
    "    features_list = Parallel(n_jobs=n_jobs)(delayed(leads_dwt_coeff)(hb, lead) for lead in leads)\n",
    "    features = np.hstack(features_list)\n",
    "    time2 = time.time()\n",
    "    print(\"the extraction of wavelet features took\",round((time2-time1)/60,2),'minutes')\n",
    "else:\n",
    "    # If want to do it without parallelization\n",
    "    for i, lead in enumerate(leads):\n",
    "        f = leads_dwt_coeff(hb, lead)\n",
    "        if (i == 0):\n",
    "            features = f\n",
    "        else:\n",
    "            features = np.hstack((features, f))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f56edbc-6b66-451a-bbaa-820689e94e9b",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 4C) Extraction of visualizable features:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d04b7284-7b84-4b08-b435-b81cde8ae3c7",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### First round of visualizable features\n",
    "\n",
    "We can now extract the features that were dependent on the ones we just extracted. They consist of:\n",
    "- the start and end of the QRS complex\n",
    "- the (real) position and height of the R peak\n",
    "- the difference (err) between the two estimates of the position of the R peak\n",
    "- the value and position of the maximum of the heartbeat\n",
    "- the value and position of the minimum of the heartbeat\n",
    "- the positive and negative integral of the heartbeat "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bc5d69e",
   "metadata": {},
   "source": [
    "We already fitted the peaks found with parabolas and saved their parameters: we just need to convert the saved parameters (which were for linear regression because of the change of variable) to the classic parameters of the parabola\n",
    "\n",
    "$$  \\begin{cases} \n",
    "    \\begin{matrix}\n",
    "    y'(j) = a \\cdot x + v \\\\\n",
    "    x = (i-j)^2\n",
    "    \\end{matrix}\n",
    "    \\end{cases} \n",
    "    \\;\\;\\; \\Rightarrow \\;\\;\\; \n",
    "    y(j) = A\\cdot j^2 + B \\cdot j + C\n",
    "$$\n",
    "\n",
    "\n",
    "As a result, we get that\n",
    "\n",
    "$$  \\begin{cases} \n",
    "    \\begin{matrix}\n",
    "    A = a \\\\\n",
    "    B = - 2 \\cdot a \\cdot i \\\\\n",
    "    C = v +  a \\cdot i^2\n",
    "    \\end{matrix}\n",
    "    \\end{cases} \n",
    "    \\;\\;\\; \\Rightarrow \\;\\;\\; \n",
    "    \\Delta = -4 \\cdot a \\cdot v\n",
    "$$\n",
    "\n",
    "so that we can calculate the intesections of the parabola with the x axis and their distance from each other as:\n",
    "\n",
    "$$ x_\\pm = \\frac{-B \\pm \\sqrt{\\Delta}}{2A} \\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\;\\; \\delta_x = \\frac{\\sqrt{\\Delta}}{A}  $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3b3838a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def x_int_points(i,a,v):\n",
    "    # we find the parabola coefficients from the fit coefficients\n",
    "    A = a\n",
    "    B = -2*a*i\n",
    "    C = v+a*i**2\n",
    "    Delta = np.sqrt(-4*a*v)\n",
    "    # we find the intersections\n",
    "    x_low = (-B-Delta)/(2*A)\n",
    "    x_up = (-B+Delta)/(2*A)\n",
    "    # and order them\n",
    "    temp = np.column_stack([x_low,x_up])\n",
    "    temp.sort(axis=1)\n",
    "    x_low = temp[:,0]\n",
    "    x_up = temp[:,1]\n",
    "    # and the distance from each other\n",
    "    delta_x = Delta/A\n",
    "    return x_low, x_up, delta_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e7060f66",
   "metadata": {},
   "outputs": [],
   "source": [
    "def integral(y_beat):\n",
    "    reg_int = np.trapz(y_beat)\n",
    "    abs_int = np.trapz(np.abs(y_beat))\n",
    "    pos_int = (reg_int + abs_int)/2\n",
    "    neg_int = (abs_int - reg_int)/2\n",
    "    return pos_int, neg_int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d5f96c8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start < start_fit & end_fit < end : True\n",
      "overflowing from the start: % 0.0\n",
      "overflowing from the end: % 0.0\n",
      "the extraction of visualizable features took 0.36 mins\n"
     ]
    }
   ],
   "source": [
    "hb = pd.read_csv('extended_output/heartbeats_filtered.csv')\n",
    "t1 = time.time()\n",
    "\n",
    "# we find initialize the variable that we want to retrieve and store into arrays based on their meaning\n",
    "# - the position of the R_peak found as the local maximum/minimum\n",
    "x_R_peak = np.zeros(len(hb))    #np.array(hb[\"ann_index\"], dtype = int)\n",
    "# - the height of the R peak\n",
    "y_R_peak = np.zeros(len(hb))\n",
    "# - the coordinates of a point of the peak\n",
    "x_point = np.zeros(len(hb))\n",
    "y_point = np.zeros(len(hb))\n",
    "# - the difference between the annotation index and the actual position of the peak\n",
    "err = np.zeros(len(hb))\n",
    "# - the value of the maxima for the entire heartbeat\n",
    "Ms = np.zeros(len(hb))\n",
    "# - the index of the maxima for the entire heartbeat\n",
    "argMs = np.zeros(len(hb))\n",
    "# - the value of the minima for the entire heartbeat\n",
    "ms = np.zeros(len(hb))\n",
    "# - the index of the minima for the entire heartbeat\n",
    "argms = np.zeros(len(hb))\n",
    "# - the value of the postive integral for the entire heartbeat\n",
    "pos_int = np.zeros(len(hb))\n",
    "# - the value of the negative integral for the entire heartbeat\n",
    "neg_int = np.zeros(len(hb))\n",
    "\n",
    "# we then extract the information needed from the dataframe as arrays\n",
    "# - the start of the heartbeat\n",
    "start = np.array(hb[\"start\"]) \n",
    "# - the end of the heartbeat\n",
    "end = np.array(hb[\"end\"]) \n",
    "# - the RR intervals\n",
    "RR = np.array(hb[\"RR_length\"])\n",
    "# - the coefficients of the parabola fits\n",
    "i = hb['ann_index'].to_numpy()\n",
    "a = hb['a'].to_numpy()\n",
    "v = hb['v'].to_numpy()\n",
    "# - the average\n",
    "average = np.array(hb['average'])\n",
    "\n",
    "# we can now do the some of the vectorizable processes:\n",
    "# we exploit the known proportion QRS:RR = 0.09:0.8 to calculate the expected QRS semi-duration\n",
    "delta = np.array(RR*0.09/(0.8*2), dtype=int) \n",
    "\n",
    "# we save the instances in which the expected QRS semi-duration is null (will be needed later)\n",
    "skip = np.where(delta ==0)[0]\n",
    "okay = np.where(delta !=0)[0]\n",
    "# the expected QRS semi-duration is then used to find the \n",
    "start_fit = (hb['ann_index']-delta).astype(int)\n",
    "end_fit = (hb['ann_index']+delta).astype(int)\n",
    "\n",
    "# we make sure that the \n",
    "print('start < start_fit & end_fit < end :',np.all([start<start_fit, end>end_fit]))\n",
    "\n",
    "path = \"blah\"\n",
    "for beat in range(len(hb)):\n",
    "    if path != str(hb[\"patient\"][beat]):\n",
    "        path = str(hb[\"patient\"][beat])\n",
    "        data = pd.read_csv(\"extended_output/data\"+path+\"_filtered.csv\")\n",
    "        y = np.array(data[main_lead])\n",
    "    \n",
    "    Ms[beat] = np.max(y[start[beat]:(end[beat]+1)])\n",
    "    ms[beat] = np.min(y[start[beat]:(end[beat]+1)])\n",
    "    argMs[beat] = np.argmax(y[start[beat]:(end[beat]+1)])\n",
    "    argms[beat] = np.argmin(y[start[beat]:(end[beat]+1)])\n",
    "    pos_int[beat], neg_int[beat] = integral(y[start[beat]:(end[beat]+1)])\n",
    "        \n",
    "    if (beat in okay):\n",
    "        # we select the beat\n",
    "        x0 = np.arange(start[beat],end[beat]+1,1)\n",
    "        y0 = np.array(y[start[beat]:(end[beat]+1)])\n",
    "        # we select the absolute of the net signal (obtained by subtracting the noise)\n",
    "        z = np.abs(y0-average[beat])\n",
    "        err[beat] = (np.argmax(z)+start[beat]) - x_R_peak[beat]\n",
    "        # we find the actual R peak (since the annotations aren't always accurate)\n",
    "        x_R_peak[beat] = np.argmax(z)+start[beat]\n",
    "        x_fit = np.arange(start_fit[beat],end_fit[beat]+1,1)\n",
    "        y_fit = np.array(y[start_fit[beat]:(end_fit[beat]+1)])\n",
    "        # we define\n",
    "        temp = np.abs(y_fit-average[beat])\n",
    "\n",
    "        if (y_fit[temp==np.max(temp)]>average[beat]):\n",
    "            cut_level = average[beat] #+ sigma[beat]\n",
    "            x_fit = x_fit[y_fit>=cut_level]\n",
    "            y_fit = y_fit[y_fit>=cut_level]\n",
    "            y_R_peak[beat] = np.max(y_fit)\n",
    "            x_R_peak[beat] = x_fit[np.argmax(y_fit)]\n",
    "            bool_mask = (y_fit>=average[beat])\n",
    "        else:\n",
    "            cut_level = average[beat] #- sigma[beat]\n",
    "            x_fit = x_fit[y_fit<=cut_level]\n",
    "            y_fit = y_fit[y_fit<=cut_level]\n",
    "            y_R_peak[beat] = np.min(y_fit)\n",
    "            x_R_peak[beat] = x_fit[np.argmin(y_fit)]\n",
    "            bool_mask = (y_fit<=average[beat])\n",
    "        x_point[beat] = (x_fit[bool_mask])[-1]\n",
    "        y_point[beat] = (y_fit[bool_mask])[-1]\n",
    "        \n",
    "# we finally take care the instances in which the expected QRS semi-duration is null\n",
    "# such instances are degenerate cases in which (x_point,y_point) will coincide with (x_R_peak,y_R_peak)\n",
    "x_point[skip] = x_R_peak[skip]\n",
    "y_point[skip] = y_R_peak[skip]\n",
    "\n",
    "#QRS_start, QRS_end, QRS = x_intersections_points(x_point, y_point, x_R_peak, y_R_peak, average)\n",
    "QRS_start, QRS_end, QRS = x_int_points(i,a,v)\n",
    "print('overflowing from the start: %',len(np.where(QRS_start<start)[0])/len(hb)*100)\n",
    "print('overflowing from the end: %',len(np.where(QRS_end>end)[0])/len(hb)*100)\n",
    "\n",
    "t2 = time.time()\n",
    "print(\"the extraction of visualizable features took\",round((t2-t1)/60,2),'mins')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bb850f73",
   "metadata": {},
   "outputs": [
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient</th>\n",
       "      <th>idx</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>ann_index</th>\n",
       "      <th>RR_length</th>\n",
       "      <th>i</th>\n",
       "      <th>a</th>\n",
       "      <th>v</th>\n",
       "      <th>Delta</th>\n",
       "      <th>...</th>\n",
       "      <th>QRS_end</th>\n",
       "      <th>QRS_length</th>\n",
       "      <th>x_R_peak</th>\n",
       "      <th>y_R_peak</th>\n",
       "      <th>pos_int</th>\n",
       "      <th>neg_int</th>\n",
       "      <th>arg_max</th>\n",
       "      <th>arg_min</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3695</td>\n",
       "      <td>3834</td>\n",
       "      <td>3734</td>\n",
       "      <td>197</td>\n",
       "      <td>3724.0</td>\n",
       "      <td>0.023883</td>\n",
       "      <td>-6.104432</td>\n",
       "      <td>0.583167</td>\n",
       "      <td>...</td>\n",
       "      <td>3749</td>\n",
       "      <td>31</td>\n",
       "      <td>3724</td>\n",
       "      <td>6.758225</td>\n",
       "      <td>61.548385</td>\n",
       "      <td>43.932010</td>\n",
       "      <td>29</td>\n",
       "      <td>34</td>\n",
       "      <td>6.758225</td>\n",
       "      <td>-2.843242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>4290</td>\n",
       "      <td>4438</td>\n",
       "      <td>4331</td>\n",
       "      <td>209</td>\n",
       "      <td>4336.0</td>\n",
       "      <td>0.024146</td>\n",
       "      <td>-6.465836</td>\n",
       "      <td>0.624489</td>\n",
       "      <td>...</td>\n",
       "      <td>4347</td>\n",
       "      <td>32</td>\n",
       "      <td>4336</td>\n",
       "      <td>7.046848</td>\n",
       "      <td>48.884109</td>\n",
       "      <td>38.297277</td>\n",
       "      <td>46</td>\n",
       "      <td>52</td>\n",
       "      <td>7.046848</td>\n",
       "      <td>-2.263854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>4503</td>\n",
       "      <td>4635</td>\n",
       "      <td>4540</td>\n",
       "      <td>186</td>\n",
       "      <td>4540.0</td>\n",
       "      <td>0.024110</td>\n",
       "      <td>-6.011916</td>\n",
       "      <td>0.579799</td>\n",
       "      <td>...</td>\n",
       "      <td>4555</td>\n",
       "      <td>31</td>\n",
       "      <td>4540</td>\n",
       "      <td>6.351008</td>\n",
       "      <td>40.824288</td>\n",
       "      <td>36.315191</td>\n",
       "      <td>37</td>\n",
       "      <td>43</td>\n",
       "      <td>6.351008</td>\n",
       "      <td>-2.469228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>9377</td>\n",
       "      <td>9517</td>\n",
       "      <td>9416</td>\n",
       "      <td>198</td>\n",
       "      <td>9416.0</td>\n",
       "      <td>0.021522</td>\n",
       "      <td>-5.379752</td>\n",
       "      <td>0.463136</td>\n",
       "      <td>...</td>\n",
       "      <td>9431</td>\n",
       "      <td>31</td>\n",
       "      <td>9416</td>\n",
       "      <td>5.977025</td>\n",
       "      <td>48.968368</td>\n",
       "      <td>33.862090</td>\n",
       "      <td>39</td>\n",
       "      <td>45</td>\n",
       "      <td>5.977025</td>\n",
       "      <td>-2.247441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>12902</td>\n",
       "      <td>13038</td>\n",
       "      <td>12940</td>\n",
       "      <td>193</td>\n",
       "      <td>12940.0</td>\n",
       "      <td>0.021849</td>\n",
       "      <td>-5.409388</td>\n",
       "      <td>0.472762</td>\n",
       "      <td>...</td>\n",
       "      <td>12955</td>\n",
       "      <td>31</td>\n",
       "      <td>12940</td>\n",
       "      <td>5.910465</td>\n",
       "      <td>46.781101</td>\n",
       "      <td>27.568527</td>\n",
       "      <td>38</td>\n",
       "      <td>44</td>\n",
       "      <td>5.910465</td>\n",
       "      <td>-2.256585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12817</th>\n",
       "      <td>22</td>\n",
       "      <td>2802</td>\n",
       "      <td>884411</td>\n",
       "      <td>884532</td>\n",
       "      <td>884445</td>\n",
       "      <td>171</td>\n",
       "      <td>884446.0</td>\n",
       "      <td>0.020842</td>\n",
       "      <td>-4.616683</td>\n",
       "      <td>0.384882</td>\n",
       "      <td>...</td>\n",
       "      <td>884459</td>\n",
       "      <td>29</td>\n",
       "      <td>884446</td>\n",
       "      <td>6.925881</td>\n",
       "      <td>39.872366</td>\n",
       "      <td>37.288740</td>\n",
       "      <td>35</td>\n",
       "      <td>43</td>\n",
       "      <td>6.925881</td>\n",
       "      <td>-2.474315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12818</th>\n",
       "      <td>22</td>\n",
       "      <td>2803</td>\n",
       "      <td>884581</td>\n",
       "      <td>884705</td>\n",
       "      <td>884616</td>\n",
       "      <td>175</td>\n",
       "      <td>884616.0</td>\n",
       "      <td>0.020474</td>\n",
       "      <td>-4.590373</td>\n",
       "      <td>0.375940</td>\n",
       "      <td>...</td>\n",
       "      <td>884630</td>\n",
       "      <td>29</td>\n",
       "      <td>884616</td>\n",
       "      <td>6.898319</td>\n",
       "      <td>37.835509</td>\n",
       "      <td>38.981487</td>\n",
       "      <td>35</td>\n",
       "      <td>43</td>\n",
       "      <td>6.898319</td>\n",
       "      <td>-1.884702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12819</th>\n",
       "      <td>22</td>\n",
       "      <td>2804</td>\n",
       "      <td>884752</td>\n",
       "      <td>884890</td>\n",
       "      <td>884791</td>\n",
       "      <td>195</td>\n",
       "      <td>884791.0</td>\n",
       "      <td>0.019858</td>\n",
       "      <td>-4.851659</td>\n",
       "      <td>0.385368</td>\n",
       "      <td>...</td>\n",
       "      <td>884806</td>\n",
       "      <td>31</td>\n",
       "      <td>884791</td>\n",
       "      <td>7.322608</td>\n",
       "      <td>35.924832</td>\n",
       "      <td>38.977481</td>\n",
       "      <td>39</td>\n",
       "      <td>47</td>\n",
       "      <td>7.322608</td>\n",
       "      <td>-1.924187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12820</th>\n",
       "      <td>22</td>\n",
       "      <td>2805</td>\n",
       "      <td>884944</td>\n",
       "      <td>885095</td>\n",
       "      <td>884986</td>\n",
       "      <td>214</td>\n",
       "      <td>884987.0</td>\n",
       "      <td>0.020002</td>\n",
       "      <td>-4.761657</td>\n",
       "      <td>0.380965</td>\n",
       "      <td>...</td>\n",
       "      <td>885001</td>\n",
       "      <td>30</td>\n",
       "      <td>884987</td>\n",
       "      <td>7.348022</td>\n",
       "      <td>39.979970</td>\n",
       "      <td>33.203798</td>\n",
       "      <td>43</td>\n",
       "      <td>51</td>\n",
       "      <td>7.348022</td>\n",
       "      <td>-2.298536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12821</th>\n",
       "      <td>22</td>\n",
       "      <td>2806</td>\n",
       "      <td>885154</td>\n",
       "      <td>885318</td>\n",
       "      <td>885200</td>\n",
       "      <td>232</td>\n",
       "      <td>885200.0</td>\n",
       "      <td>0.018473</td>\n",
       "      <td>-4.759226</td>\n",
       "      <td>0.351675</td>\n",
       "      <td>...</td>\n",
       "      <td>885216</td>\n",
       "      <td>32</td>\n",
       "      <td>885200</td>\n",
       "      <td>7.020000</td>\n",
       "      <td>45.599522</td>\n",
       "      <td>37.893063</td>\n",
       "      <td>46</td>\n",
       "      <td>54</td>\n",
       "      <td>7.020000</td>\n",
       "      <td>-1.870375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12822 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       patient   idx   start     end  ann_index  RR_length         i  \\\n",
       "0           10     3    3695    3834       3734        197    3724.0   \n",
       "1           10     5    4290    4438       4331        209    4336.0   \n",
       "2           10     6    4503    4635       4540        186    4540.0   \n",
       "3           10    10    9377    9517       9416        198    9416.0   \n",
       "4           10    13   12902   13038      12940        193   12940.0   \n",
       "...        ...   ...     ...     ...        ...        ...       ...   \n",
       "12817       22  2802  884411  884532     884445        171  884446.0   \n",
       "12818       22  2803  884581  884705     884616        175  884616.0   \n",
       "12819       22  2804  884752  884890     884791        195  884791.0   \n",
       "12820       22  2805  884944  885095     884986        214  884987.0   \n",
       "12821       22  2806  885154  885318     885200        232  885200.0   \n",
       "\n",
       "              a         v     Delta  ...  QRS_end  QRS_length  x_R_peak  \\\n",
       "0      0.023883 -6.104432  0.583167  ...     3749          31      3724   \n",
       "1      0.024146 -6.465836  0.624489  ...     4347          32      4336   \n",
       "2      0.024110 -6.011916  0.579799  ...     4555          31      4540   \n",
       "3      0.021522 -5.379752  0.463136  ...     9431          31      9416   \n",
       "4      0.021849 -5.409388  0.472762  ...    12955          31     12940   \n",
       "...         ...       ...       ...  ...      ...         ...       ...   \n",
       "12817  0.020842 -4.616683  0.384882  ...   884459          29    884446   \n",
       "12818  0.020474 -4.590373  0.375940  ...   884630          29    884616   \n",
       "12819  0.019858 -4.851659  0.385368  ...   884806          31    884791   \n",
       "12820  0.020002 -4.761657  0.380965  ...   885001          30    884987   \n",
       "12821  0.018473 -4.759226  0.351675  ...   885216          32    885200   \n",
       "\n",
       "       y_R_peak    pos_int    neg_int  arg_max  arg_min       max       min  \n",
       "0      6.758225  61.548385  43.932010       29       34  6.758225 -2.843242  \n",
       "1      7.046848  48.884109  38.297277       46       52  7.046848 -2.263854  \n",
       "2      6.351008  40.824288  36.315191       37       43  6.351008 -2.469228  \n",
       "3      5.977025  48.968368  33.862090       39       45  5.977025 -2.247441  \n",
       "4      5.910465  46.781101  27.568527       38       44  5.910465 -2.256585  \n",
       "...         ...        ...        ...      ...      ...       ...       ...  \n",
       "12817  6.925881  39.872366  37.288740       35       43  6.925881 -2.474315  \n",
       "12818  6.898319  37.835509  38.981487       35       43  6.898319 -1.884702  \n",
       "12819  7.322608  35.924832  38.977481       39       47  7.322608 -1.924187  \n",
       "12820  7.348022  39.979970  33.203798       43       51  7.348022 -2.298536  \n",
       "12821  7.020000  45.599522  37.893063       46       54  7.020000 -1.870375  \n",
       "\n",
       "[12822 rows x 26 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# before updating the dataframe with the first round of visualizable features, we need to make sure that the indexes \n",
    "# found for the start and end of the QRS complex don't exede the indexes for the start and end of the heartbeat\n",
    "# we find and fix the overflow from the start\n",
    "condition = (QRS_start < start)\n",
    "bad = np.where(condition)[0]\n",
    "QRS_start[bad] = start[bad]\n",
    "# and we find and fix the overflow from the end\n",
    "condition = (QRS_end > end)\n",
    "bad = np.where(condition)[0]\n",
    "QRS_end[bad] = end[bad]\n",
    "\n",
    "QRS_start = QRS_start.astype(int)\n",
    "QRS_end = QRS_end.astype(int)\n",
    "QRS = QRS.astype(int)\n",
    "x_R_peak = x_R_peak.astype(int)\n",
    "argMs = argMs.astype(int)\n",
    "argms = argms.astype(int)\n",
    "\n",
    "# so that we can update the dataframe\n",
    "hb['QRS_start'] = QRS_start\n",
    "hb[\"QRS_end\"] = QRS_end\n",
    "hb[\"QRS_length\"] = QRS\n",
    "hb['x_R_peak'] = x_R_peak\n",
    "hb[\"y_R_peak\"] = y_R_peak\n",
    "hb['pos_int'] = pos_int\n",
    "hb['neg_int'] = neg_int\n",
    "hb['arg_max'] = argMs\n",
    "hb['arg_min'] = argms\n",
    "hb['max'] = Ms\n",
    "hb['min'] = ms\n",
    "hb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf7874f0",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Second round of visualizable features extraction\n",
    "We can now also extract the features that were dependent on the ones we just extracted. They consist of:\n",
    "- the positive and negative integral of the QRS complex\n",
    "- the start and end of the P peak\n",
    "- the position and height of the P peak\n",
    "- the positive and negative integral of the P peak\n",
    "- the start and end of the T peak\n",
    "- the position and height of the T peak\n",
    "- the positive and negative integral of the T peak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c4a8a7a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we extract the index/position of the R peak\n",
    "x_R_peak = hb['x_R_peak']\n",
    "\n",
    "# we initialize the arrays needed for the second for-loop\n",
    "# - the positive integral of the QRS complex\n",
    "r_pos_int = np.zeros(len(hb))\n",
    "# - the negative integral of the QRS complex\n",
    "r_neg_int = np.zeros(len(hb))\n",
    "# - the index of the start of the P peak\n",
    "p_start = np.zeros(len(hb), dtype=int)\n",
    "# - the index of the end of the P peak\n",
    "p_end = np.zeros(len(hb), dtype=int)\n",
    "# - the index/position of the P peak\n",
    "x_P_peak = np.zeros(len(hb), dtype=int)\n",
    "# - the value of the signal in the P peak\n",
    "y_P_peak = np.zeros(len(hb))\n",
    "# - the positive integral of P peak\n",
    "p_pos_int = np.zeros(len(hb))\n",
    "# - the negative integral of P peak\n",
    "p_neg_int = np.zeros(len(hb))\n",
    "# - the index of the start of the T peak\n",
    "t_start = np.zeros(len(hb), dtype=int)\n",
    "# - the index of the end of the T peak\n",
    "t_end = np.zeros(len(hb), dtype=int)\n",
    "# - the index/position of the T peak\n",
    "x_T_peak = np.zeros(len(hb), dtype=int)\n",
    "# - the value of the signal in the T peak\n",
    "y_T_peak = np.zeros(len(hb))\n",
    "# - the positive integral of the T peak\n",
    "t_pos_int = np.zeros(len(hb))\n",
    "# - the negative integral of the T peak\n",
    "t_neg_int = np.zeros(len(hb))\n",
    "\n",
    "# we compute everything that we can do outside of the following for-loops\n",
    "# particularly, we focus on finding the non-degenerate cases, based on the following conditions\n",
    "start_condition = (QRS_start > start)\n",
    "end_condition = (QRS_end < end)\n",
    "# we find the indexes that correspond to the condition\n",
    "good_start = np.where(start_condition)[0]\n",
    "good_end = np.where(end_condition)[0]\n",
    "# and also the indexes that don't satisfy the condition (\"degenerate cases\")\n",
    "bad_start = np.where(np.bitwise_not(start_condition))[0]\n",
    "bad_end = np.where(np.bitwise_not(end_condition))[0]\n",
    "# based on the start condition,I set the info for the P peak\n",
    "# in the non-degenerate cases:\n",
    "# we set the beginning of P peak as the start of the heartbeat\n",
    "p_start[good_start] = start[good_start]\n",
    "# and the end of the P peak as the start of the QRS comples\n",
    "p_end[good_start] = QRS_start[good_start]\n",
    "# for the degenerate cases:\n",
    "# we set both the beginning, end and P peak itself as the start of the heartbeat\n",
    "p_start[bad_start] = start[bad_start]\n",
    "p_end[bad_start] = start[bad_start]\n",
    "x_P_peak[bad_start] = start[bad_start]\n",
    "# based on the end condition, we set the info for the T peak\n",
    "# in the non-degenerate cases:\n",
    "# we set the beginning of T peak as the end of the QRS comples\n",
    "t_start[good_end] = QRS_end[good_end]\n",
    "# and the end of the T peak as the end of the heartbeat\n",
    "t_end[good_end] = end[good_end]\n",
    "# for the degenerate cases:\n",
    "# we set both the beginning, end and T peak itself as the end of the heartbeat\n",
    "t_start[bad_end] = end[bad_end]\n",
    "t_end[bad_end] = end[bad_end]\n",
    "x_T_peak[bad_end] = end[bad_end]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "394c9bad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the algorithm took 0.36 minutes\n"
     ]
    }
   ],
   "source": [
    "######## another loop over beats <- for QRS integrals + P + T + FFT\n",
    "t1 = time.time()\n",
    "    \n",
    "path = \"blah\"\n",
    "for beat in range(len(hb)):\n",
    "    \n",
    "    if path != str(hb[\"patient\"][beat]):\n",
    "        path = str(hb[\"patient\"][beat])\n",
    "        data = pd.read_csv(\"extended_output/data\"+path+\"_filtered.csv\")\n",
    "        y = np.array(data[main_lead])\n",
    "        \n",
    "    ###integrals for QRS    \n",
    "    y_r = y[QRS_start[beat]:QRS_end[beat]]\n",
    "    r_pos_int[beat], r_neg_int[beat] = integral(y_r)\n",
    "    \n",
    "    ### for P\n",
    "    if (beat in good_start):\n",
    "        y_p = y[p_start[beat]:p_end[beat]]\n",
    "        y_P_peak[beat] = np.max(y_p)\n",
    "        x_P_peak[beat] = np.argmax(y_p) + p_start[beat]\n",
    "        p_pos_int[beat], p_neg_int[beat] = integral(y_p)\n",
    "    else:\n",
    "        y_P_peak[beat] = y[x_P_peak[beat]]\n",
    "        p_pos_int[beat], p_neg_int[beat] = 0,0\n",
    "        \n",
    "    ### for T  \n",
    "    if (beat in good_end):\n",
    "        y_t = y[t_start[beat]:t_end[beat]]\n",
    "        y_T_peak[beat] = np.max(y_t)\n",
    "        x_T_peak[beat] = np.argmax(y_t) + t_start[beat]\n",
    "        t_pos_int[beat], t_neg_int[beat] = integral(y_t)\n",
    "    else:\n",
    "        y_T_peak[beat] = y[x_T_peak[beat]]\n",
    "        t_pos_int[beat], t_neg_int[beat] = 0,0\n",
    "        \n",
    "    ### for FFT\n",
    "#     y_beat = y[fixed_start[beat]:fixed_end[beat]]\n",
    "#     vect_fft, vect_power, vect_all_freq = power_fft(y_beat, timestep)\n",
    "#     temp = np.where((vect_all_freq>down_freq_lim) & (vect_all_freq<up_freq_lim))[0]\n",
    "#     f_keep = vect_power[temp]\n",
    "#     if beat==0:\n",
    "#         f = np.zeros((len(hb),len(f_keep)))\n",
    "\n",
    "#     f[beat, :len(f_keep)] = f_keep\n",
    "    \n",
    "t2 = time.time()\n",
    "print(\"the algorithm took\",round((t2-t1)/60,2),'minutes')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a20d84b8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient</th>\n",
       "      <th>idx</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>ann_index</th>\n",
       "      <th>RR_length</th>\n",
       "      <th>i</th>\n",
       "      <th>a</th>\n",
       "      <th>v</th>\n",
       "      <th>Delta</th>\n",
       "      <th>...</th>\n",
       "      <th>P_pos_int</th>\n",
       "      <th>P_neg_int</th>\n",
       "      <th>T_start</th>\n",
       "      <th>T_end</th>\n",
       "      <th>T_length</th>\n",
       "      <th>x_T_peak</th>\n",
       "      <th>y_T_peak</th>\n",
       "      <th>T_pos_int</th>\n",
       "      <th>T_neg_int</th>\n",
       "      <th>err</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
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       "      <td>3734</td>\n",
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       "      <td>-6.104432</td>\n",
       "      <td>0.583167</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>11.797751</td>\n",
       "      <td>3749</td>\n",
       "      <td>3834</td>\n",
       "      <td>85</td>\n",
       "      <td>3792</td>\n",
       "      <td>1.182186</td>\n",
       "      <td>30.376032</td>\n",
       "      <td>8.034964</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>4290</td>\n",
       "      <td>4438</td>\n",
       "      <td>4331</td>\n",
       "      <td>209</td>\n",
       "      <td>4336.0</td>\n",
       "      <td>0.024146</td>\n",
       "      <td>-6.465836</td>\n",
       "      <td>0.624489</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.212262</td>\n",
       "      <td>4347</td>\n",
       "      <td>4438</td>\n",
       "      <td>91</td>\n",
       "      <td>4401</td>\n",
       "      <td>0.768660</td>\n",
       "      <td>16.687709</td>\n",
       "      <td>10.646080</td>\n",
       "      <td>-5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>4503</td>\n",
       "      <td>4635</td>\n",
       "      <td>4540</td>\n",
       "      <td>186</td>\n",
       "      <td>4540.0</td>\n",
       "      <td>0.024110</td>\n",
       "      <td>-6.011916</td>\n",
       "      <td>0.579799</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.430260</td>\n",
       "      <td>4555</td>\n",
       "      <td>4635</td>\n",
       "      <td>80</td>\n",
       "      <td>4608</td>\n",
       "      <td>0.425245</td>\n",
       "      <td>12.188186</td>\n",
       "      <td>7.535912</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>9377</td>\n",
       "      <td>9517</td>\n",
       "      <td>9416</td>\n",
       "      <td>198</td>\n",
       "      <td>9416.0</td>\n",
       "      <td>0.021522</td>\n",
       "      <td>-5.379752</td>\n",
       "      <td>0.463136</td>\n",
       "      <td>...</td>\n",
       "      <td>0.066896</td>\n",
       "      <td>2.398533</td>\n",
       "      <td>9431</td>\n",
       "      <td>9517</td>\n",
       "      <td>86</td>\n",
       "      <td>9480</td>\n",
       "      <td>0.833437</td>\n",
       "      <td>23.128275</td>\n",
       "      <td>6.352681</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>12902</td>\n",
       "      <td>13038</td>\n",
       "      <td>12940</td>\n",
       "      <td>193</td>\n",
       "      <td>12940.0</td>\n",
       "      <td>0.021849</td>\n",
       "      <td>-5.409388</td>\n",
       "      <td>0.472762</td>\n",
       "      <td>...</td>\n",
       "      <td>1.418005</td>\n",
       "      <td>0.698474</td>\n",
       "      <td>12955</td>\n",
       "      <td>13038</td>\n",
       "      <td>83</td>\n",
       "      <td>13009</td>\n",
       "      <td>0.701916</td>\n",
       "      <td>17.514196</td>\n",
       "      <td>5.187805</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>12817</th>\n",
       "      <td>22</td>\n",
       "      <td>2802</td>\n",
       "      <td>884411</td>\n",
       "      <td>884532</td>\n",
       "      <td>884445</td>\n",
       "      <td>171</td>\n",
       "      <td>884446.0</td>\n",
       "      <td>0.020842</td>\n",
       "      <td>-4.616683</td>\n",
       "      <td>0.384882</td>\n",
       "      <td>...</td>\n",
       "      <td>0.617346</td>\n",
       "      <td>1.576059</td>\n",
       "      <td>884459</td>\n",
       "      <td>884532</td>\n",
       "      <td>73</td>\n",
       "      <td>884483</td>\n",
       "      <td>0.295010</td>\n",
       "      <td>8.835401</td>\n",
       "      <td>9.625350</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12818</th>\n",
       "      <td>22</td>\n",
       "      <td>2803</td>\n",
       "      <td>884581</td>\n",
       "      <td>884705</td>\n",
       "      <td>884616</td>\n",
       "      <td>175</td>\n",
       "      <td>884616.0</td>\n",
       "      <td>0.020474</td>\n",
       "      <td>-4.590373</td>\n",
       "      <td>0.375940</td>\n",
       "      <td>...</td>\n",
       "      <td>1.215807</td>\n",
       "      <td>5.345422</td>\n",
       "      <td>884630</td>\n",
       "      <td>884705</td>\n",
       "      <td>75</td>\n",
       "      <td>884689</td>\n",
       "      <td>0.423744</td>\n",
       "      <td>6.404042</td>\n",
       "      <td>7.297645</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12819</th>\n",
       "      <td>22</td>\n",
       "      <td>2804</td>\n",
       "      <td>884752</td>\n",
       "      <td>884890</td>\n",
       "      <td>884791</td>\n",
       "      <td>195</td>\n",
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       "      <td>0.019858</td>\n",
       "      <td>-4.851659</td>\n",
       "      <td>0.385368</td>\n",
       "      <td>...</td>\n",
       "      <td>0.524454</td>\n",
       "      <td>8.666391</td>\n",
       "      <td>884806</td>\n",
       "      <td>884890</td>\n",
       "      <td>84</td>\n",
       "      <td>884842</td>\n",
       "      <td>0.071903</td>\n",
       "      <td>1.628707</td>\n",
       "      <td>7.669827</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>12820</th>\n",
       "      <td>22</td>\n",
       "      <td>2805</td>\n",
       "      <td>884944</td>\n",
       "      <td>885095</td>\n",
       "      <td>884986</td>\n",
       "      <td>214</td>\n",
       "      <td>884987.0</td>\n",
       "      <td>0.020002</td>\n",
       "      <td>-4.761657</td>\n",
       "      <td>0.380965</td>\n",
       "      <td>...</td>\n",
       "      <td>0.519430</td>\n",
       "      <td>2.148908</td>\n",
       "      <td>885001</td>\n",
       "      <td>885095</td>\n",
       "      <td>94</td>\n",
       "      <td>885028</td>\n",
       "      <td>0.239815</td>\n",
       "      <td>5.334271</td>\n",
       "      <td>5.575230</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12821</th>\n",
       "      <td>22</td>\n",
       "      <td>2806</td>\n",
       "      <td>885154</td>\n",
       "      <td>885318</td>\n",
       "      <td>885200</td>\n",
       "      <td>232</td>\n",
       "      <td>885200.0</td>\n",
       "      <td>0.018473</td>\n",
       "      <td>-4.759226</td>\n",
       "      <td>0.351675</td>\n",
       "      <td>...</td>\n",
       "      <td>0.054161</td>\n",
       "      <td>6.570225</td>\n",
       "      <td>885216</td>\n",
       "      <td>885318</td>\n",
       "      <td>102</td>\n",
       "      <td>885280</td>\n",
       "      <td>0.291819</td>\n",
       "      <td>12.746369</td>\n",
       "      <td>2.195963</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12822 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       patient   idx   start     end  ann_index  RR_length         i  \\\n",
       "0           10     3    3695    3834       3734        197    3724.0   \n",
       "1           10     5    4290    4438       4331        209    4336.0   \n",
       "2           10     6    4503    4635       4540        186    4540.0   \n",
       "3           10    10    9377    9517       9416        198    9416.0   \n",
       "4           10    13   12902   13038      12940        193   12940.0   \n",
       "...        ...   ...     ...     ...        ...        ...       ...   \n",
       "12817       22  2802  884411  884532     884445        171  884446.0   \n",
       "12818       22  2803  884581  884705     884616        175  884616.0   \n",
       "12819       22  2804  884752  884890     884791        195  884791.0   \n",
       "12820       22  2805  884944  885095     884986        214  884987.0   \n",
       "12821       22  2806  885154  885318     885200        232  885200.0   \n",
       "\n",
       "              a         v     Delta  ...  P_pos_int  P_neg_int  T_start  \\\n",
       "0      0.023883 -6.104432  0.583167  ...   0.000000  11.797751     3749   \n",
       "1      0.024146 -6.465836  0.624489  ...   0.000000   4.212262     4347   \n",
       "2      0.024110 -6.011916  0.579799  ...   0.000000   3.430260     4555   \n",
       "3      0.021522 -5.379752  0.463136  ...   0.066896   2.398533     9431   \n",
       "4      0.021849 -5.409388  0.472762  ...   1.418005   0.698474    12955   \n",
       "...         ...       ...       ...  ...        ...        ...      ...   \n",
       "12817  0.020842 -4.616683  0.384882  ...   0.617346   1.576059   884459   \n",
       "12818  0.020474 -4.590373  0.375940  ...   1.215807   5.345422   884630   \n",
       "12819  0.019858 -4.851659  0.385368  ...   0.524454   8.666391   884806   \n",
       "12820  0.020002 -4.761657  0.380965  ...   0.519430   2.148908   885001   \n",
       "12821  0.018473 -4.759226  0.351675  ...   0.054161   6.570225   885216   \n",
       "\n",
       "        T_end  T_length  x_T_peak  y_T_peak  T_pos_int  T_neg_int  err  \n",
       "0        3834        85      3792  1.182186  30.376032   8.034964   10  \n",
       "1        4438        91      4401  0.768660  16.687709  10.646080   -5  \n",
       "2        4635        80      4608  0.425245  12.188186   7.535912    0  \n",
       "3        9517        86      9480  0.833437  23.128275   6.352681    0  \n",
       "4       13038        83     13009  0.701916  17.514196   5.187805    0  \n",
       "...       ...       ...       ...       ...        ...        ...  ...  \n",
       "12817  884532        73    884483  0.295010   8.835401   9.625350   -1  \n",
       "12818  884705        75    884689  0.423744   6.404042   7.297645    0  \n",
       "12819  884890        84    884842  0.071903   1.628707   7.669827    0  \n",
       "12820  885095        94    885028  0.239815   5.334271   5.575230   -1  \n",
       "12821  885318       102    885280  0.291819  12.746369   2.195963    0  \n",
       "\n",
       "[12822 rows x 43 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we update the dataframe with the newly extracted visualizable features \n",
    "hb['r_pos_int'] = r_pos_int\n",
    "hb['r_neg_int'] = r_neg_int\n",
    "hb['P_start'] = p_start.astype(int)\n",
    "hb['P_end'] = p_end.astype(int)\n",
    "hb['P_length'] = (p_end - p_start).astype(int)\n",
    "hb['x_P_peak'] = x_P_peak.astype(int)\n",
    "hb['y_P_peak'] = y_P_peak\n",
    "hb['P_pos_int'] = p_pos_int\n",
    "hb['P_neg_int'] = p_neg_int\n",
    "hb['T_start'] = t_start.astype(int)\n",
    "hb['T_end'] = t_end.astype(int)\n",
    "hb['T_length'] = (t_end - t_start).astype(int)\n",
    "hb['x_T_peak'] = x_T_peak.astype(int)\n",
    "hb['y_T_peak'] = y_T_peak\n",
    "hb['T_pos_int'] = t_pos_int\n",
    "hb['T_neg_int'] = t_neg_int\n",
    "hb['err'] = hb['ann_index'] - hb['x_R_peak']\n",
    "\n",
    "hb.to_csv('extended_output/heartbeats_full_absolute.csv', index=False)\n",
    "hb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77218b7f",
   "metadata": {},
   "source": [
    "### Looking at some of the visualizable features\n",
    "\n",
    "To further check the behaviour of the two r-peak finding algorithm that we use (one in the beginning, one in feature extraction)\n",
    "we can take a look at the the difference (err) between the two estimates of the position of the R peak. \n",
    "\n",
    "If the two algorithms work well together, we expect the distirbution to be centered around 0 and be quite tight around it. Different behaviours could be caused by a bias (i.e. average shifted from 0) or by the algorithm not being very precise (i.e. wider distribution/standard deviation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5120e0dc",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribution(np.array(hb['err']), -5, 5,title='err')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aa50cca",
   "metadata": {},
   "source": [
    "The R-peak finding algorithm and local minima/maxima algorithm return very similar results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f044a26",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Preparing extracted features for ML\n",
    "Finally, we can adapt the features extracted to be relative to the heartbeat (i.e. not absolute to the entire dataset) but also absolute in terms of time (since future datasets might have different sampling frequencies)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8a6c343f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient</th>\n",
       "      <th>idx</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>ann_index</th>\n",
       "      <th>RR_length</th>\n",
       "      <th>i</th>\n",
       "      <th>a</th>\n",
       "      <th>v</th>\n",
       "      <th>Delta</th>\n",
       "      <th>...</th>\n",
       "      <th>P_pos_int</th>\n",
       "      <th>P_neg_int</th>\n",
       "      <th>T_start</th>\n",
       "      <th>T_end</th>\n",
       "      <th>T_length</th>\n",
       "      <th>x_T_peak</th>\n",
       "      <th>y_T_peak</th>\n",
       "      <th>T_pos_int</th>\n",
       "      <th>T_neg_int</th>\n",
       "      <th>err</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3695</td>\n",
       "      <td>3834</td>\n",
       "      <td>3734</td>\n",
       "      <td>0.197</td>\n",
       "      <td>3724.0</td>\n",
       "      <td>0.023883</td>\n",
       "      <td>-6.104432</td>\n",
       "      <td>0.583167</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011798</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.139</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.097</td>\n",
       "      <td>1.182186</td>\n",
       "      <td>0.030376</td>\n",
       "      <td>0.008035</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>4290</td>\n",
       "      <td>4438</td>\n",
       "      <td>4331</td>\n",
       "      <td>0.209</td>\n",
       "      <td>4336.0</td>\n",
       "      <td>0.024146</td>\n",
       "      <td>-6.465836</td>\n",
       "      <td>0.624489</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004212</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.148</td>\n",
       "      <td>0.091</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.768660</td>\n",
       "      <td>0.016688</td>\n",
       "      <td>0.010646</td>\n",
       "      <td>-5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>4503</td>\n",
       "      <td>4635</td>\n",
       "      <td>4540</td>\n",
       "      <td>0.186</td>\n",
       "      <td>4540.0</td>\n",
       "      <td>0.024110</td>\n",
       "      <td>-6.011916</td>\n",
       "      <td>0.579799</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003430</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.105</td>\n",
       "      <td>0.425245</td>\n",
       "      <td>0.012188</td>\n",
       "      <td>0.007536</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>9377</td>\n",
       "      <td>9517</td>\n",
       "      <td>9416</td>\n",
       "      <td>0.198</td>\n",
       "      <td>9416.0</td>\n",
       "      <td>0.021522</td>\n",
       "      <td>-5.379752</td>\n",
       "      <td>0.463136</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>0.002399</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.140</td>\n",
       "      <td>0.086</td>\n",
       "      <td>0.103</td>\n",
       "      <td>0.833437</td>\n",
       "      <td>0.023128</td>\n",
       "      <td>0.006353</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>12902</td>\n",
       "      <td>13038</td>\n",
       "      <td>12940</td>\n",
       "      <td>0.193</td>\n",
       "      <td>12940.0</td>\n",
       "      <td>0.021849</td>\n",
       "      <td>-5.409388</td>\n",
       "      <td>0.472762</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001418</td>\n",
       "      <td>0.000698</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.136</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.107</td>\n",
       "      <td>0.701916</td>\n",
       "      <td>0.017514</td>\n",
       "      <td>0.005188</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12817</th>\n",
       "      <td>22</td>\n",
       "      <td>2802</td>\n",
       "      <td>884411</td>\n",
       "      <td>884532</td>\n",
       "      <td>884445</td>\n",
       "      <td>0.171</td>\n",
       "      <td>884446.0</td>\n",
       "      <td>0.020842</td>\n",
       "      <td>-4.616683</td>\n",
       "      <td>0.384882</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000617</td>\n",
       "      <td>0.001576</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.121</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.072</td>\n",
       "      <td>0.295010</td>\n",
       "      <td>0.008835</td>\n",
       "      <td>0.009625</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12818</th>\n",
       "      <td>22</td>\n",
       "      <td>2803</td>\n",
       "      <td>884581</td>\n",
       "      <td>884705</td>\n",
       "      <td>884616</td>\n",
       "      <td>0.175</td>\n",
       "      <td>884616.0</td>\n",
       "      <td>0.020474</td>\n",
       "      <td>-4.590373</td>\n",
       "      <td>0.375940</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001216</td>\n",
       "      <td>0.005345</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.108</td>\n",
       "      <td>0.423744</td>\n",
       "      <td>0.006404</td>\n",
       "      <td>0.007298</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12819</th>\n",
       "      <td>22</td>\n",
       "      <td>2804</td>\n",
       "      <td>884752</td>\n",
       "      <td>884890</td>\n",
       "      <td>884791</td>\n",
       "      <td>0.195</td>\n",
       "      <td>884791.0</td>\n",
       "      <td>0.019858</td>\n",
       "      <td>-4.851659</td>\n",
       "      <td>0.385368</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000524</td>\n",
       "      <td>0.008666</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.138</td>\n",
       "      <td>0.084</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.071903</td>\n",
       "      <td>0.001629</td>\n",
       "      <td>0.007670</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12820</th>\n",
       "      <td>22</td>\n",
       "      <td>2805</td>\n",
       "      <td>884944</td>\n",
       "      <td>885095</td>\n",
       "      <td>884986</td>\n",
       "      <td>0.214</td>\n",
       "      <td>884987.0</td>\n",
       "      <td>0.020002</td>\n",
       "      <td>-4.761657</td>\n",
       "      <td>0.380965</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000519</td>\n",
       "      <td>0.002149</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.084</td>\n",
       "      <td>0.239815</td>\n",
       "      <td>0.005334</td>\n",
       "      <td>0.005575</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12821</th>\n",
       "      <td>22</td>\n",
       "      <td>2806</td>\n",
       "      <td>885154</td>\n",
       "      <td>885318</td>\n",
       "      <td>885200</td>\n",
       "      <td>0.232</td>\n",
       "      <td>885200.0</td>\n",
       "      <td>0.018473</td>\n",
       "      <td>-4.759226</td>\n",
       "      <td>0.351675</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.006570</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.164</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.291819</td>\n",
       "      <td>0.012746</td>\n",
       "      <td>0.002196</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12822 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       patient   idx   start     end  ann_index  RR_length         i  \\\n",
       "0           10     3    3695    3834       3734      0.197    3724.0   \n",
       "1           10     5    4290    4438       4331      0.209    4336.0   \n",
       "2           10     6    4503    4635       4540      0.186    4540.0   \n",
       "3           10    10    9377    9517       9416      0.198    9416.0   \n",
       "4           10    13   12902   13038      12940      0.193   12940.0   \n",
       "...        ...   ...     ...     ...        ...        ...       ...   \n",
       "12817       22  2802  884411  884532     884445      0.171  884446.0   \n",
       "12818       22  2803  884581  884705     884616      0.175  884616.0   \n",
       "12819       22  2804  884752  884890     884791      0.195  884791.0   \n",
       "12820       22  2805  884944  885095     884986      0.214  884987.0   \n",
       "12821       22  2806  885154  885318     885200      0.232  885200.0   \n",
       "\n",
       "              a         v     Delta  ...  P_pos_int  P_neg_int  T_start  \\\n",
       "0      0.023883 -6.104432  0.583167  ...   0.000000   0.011798    0.054   \n",
       "1      0.024146 -6.465836  0.624489  ...   0.000000   0.004212    0.057   \n",
       "2      0.024110 -6.011916  0.579799  ...   0.000000   0.003430    0.052   \n",
       "3      0.021522 -5.379752  0.463136  ...   0.000067   0.002399    0.054   \n",
       "4      0.021849 -5.409388  0.472762  ...   0.001418   0.000698    0.053   \n",
       "...         ...       ...       ...  ...        ...        ...      ...   \n",
       "12817  0.020842 -4.616683  0.384882  ...   0.000617   0.001576    0.048   \n",
       "12818  0.020474 -4.590373  0.375940  ...   0.001216   0.005345    0.049   \n",
       "12819  0.019858 -4.851659  0.385368  ...   0.000524   0.008666    0.054   \n",
       "12820  0.020002 -4.761657  0.380965  ...   0.000519   0.002149    0.057   \n",
       "12821  0.018473 -4.759226  0.351675  ...   0.000054   0.006570    0.062   \n",
       "\n",
       "       T_end  T_length  x_T_peak  y_T_peak  T_pos_int  T_neg_int  err  \n",
       "0      0.139     0.085     0.097  1.182186   0.030376   0.008035   10  \n",
       "1      0.148     0.091     0.111  0.768660   0.016688   0.010646   -5  \n",
       "2      0.132     0.080     0.105  0.425245   0.012188   0.007536    0  \n",
       "3      0.140     0.086     0.103  0.833437   0.023128   0.006353    0  \n",
       "4      0.136     0.083     0.107  0.701916   0.017514   0.005188    0  \n",
       "...      ...       ...       ...       ...        ...        ...  ...  \n",
       "12817  0.121     0.073     0.072  0.295010   0.008835   0.009625   -1  \n",
       "12818  0.124     0.075     0.108  0.423744   0.006404   0.007298    0  \n",
       "12819  0.138     0.084     0.090  0.071903   0.001629   0.007670    0  \n",
       "12820  0.151     0.094     0.084  0.239815   0.005334   0.005575   -1  \n",
       "12821  0.164     0.102     0.126  0.291819   0.012746   0.002196    0  \n",
       "\n",
       "[12822 rows x 43 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List of column needed substraction and calculation change to temporal domain\n",
    "first_change = ['QRS_start', 'QRS_end', 'x_R_peak','P_start', 'P_end', 'x_P_peak', 'T_start', 'T_end', 'x_T_peak']\n",
    "# List for column needed calculation change calculation to temporal domain\n",
    "second_change = ['RR_length', 'QRS_length', 'pos_int', 'neg_int', 'arg_max', 'arg_min',  'r_pos_int', 'r_neg_int', \n",
    "                 'P_length',  'P_pos_int', 'P_neg_int', 'T_length', 'T_pos_int', 'T_neg_int']\n",
    "\n",
    "for col in first_change:\n",
    "    hb[col] = (hb[col] - hb['start'])/freq\n",
    "\n",
    "for col in second_change:\n",
    "    hb[col] = hb[col] / freq\n",
    "    \n",
    "hb.to_csv('extended_output/heartbeats_full_relative.csv', index=False)\n",
    "hb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "64d01bf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['patient', 'idx', 'start', 'end', 'ann_index', 'RR_length', 'i',\n",
       "       'a', 'v', 'Delta', 'n_sigma_rr', 'average', 'sigma',\n",
       "       'n_sigma_average', 'n_sigma_std', 'QRS_start', 'QRS_end',\n",
       "       'QRS_length', 'x_R_peak', 'y_R_peak', 'pos_int', 'neg_int',\n",
       "       'arg_max', 'arg_min', 'max', 'min', 'r_pos_int', 'r_neg_int',\n",
       "       'P_start', 'P_end', 'P_length', 'x_P_peak', 'y_P_peak',\n",
       "       'P_pos_int', 'P_neg_int', 'T_start', 'T_end', 'T_length',\n",
       "       'x_T_peak', 'y_T_peak', 'T_pos_int', 'T_neg_int', 'err'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(hb.columns.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe15a4a3",
   "metadata": {},
   "source": [
    "We can also remove the quantities that won't be ML features since they cannot be relavive to the heartbeat in any way.\n",
    "Lastly, we convert the dataframe into a numpy matrix, which is put side by side to the matrix with the DWT coefficients. \\\n",
    "In the ML features matrix, each row correspond to an heartbeat."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "892196b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_names = list(hb.columns)\n",
    "no_features = ['patient', 'idx', 'start', 'end', 'ann_index']\n",
    "for nf in no_features:\n",
    "    features_names.remove(nf)\n",
    "\n",
    "hand_extracted_features = hb[features_names]\n",
    "hand_extracted_features = hand_extracted_features.to_numpy()\n",
    "ML_input = np.hstack((features, hand_extracted_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "55f008c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt('extended_output/ML_input.txt',ML_input)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3eecef3",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "# 5) Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "311edc61-51da-4ff3-b7e3-fa30fca97671",
   "metadata": {},
   "source": [
    "For this dataset, we just need to load the trained model (from ECG_PhysioNet) and have it predict based on the features obtained before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "007c54c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_xgb_2 = XGBClassifier()\n",
    "model_xgb_2.load_model(\"multi_maxdepth5_nwindow3.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e36e89d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "num = 2072053\n",
    "np.random.seed(num)\n",
    "X = ML_input\n",
    "Y_pred = model_xgb_2.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2638321b",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels, freqs = np.unique(Y_pred, return_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a1d055d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1] [12800    22]\n"
     ]
    }
   ],
   "source": [
    "print(labels, freqs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ea5eaf06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of sick beats to total beats classifed: 0.1716 %\n"
     ]
    }
   ],
   "source": [
    "print('Percentage of sick beats to total beats classifed:', np.round(freqs[1]/sum(freqs)*100,4), '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a25019c6",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "# 6) Saving output for plotly display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9936bb55",
   "metadata": {},
   "outputs": [],
   "source": [
    "hbb = copy.copy(hb)\n",
    "\n",
    "def change_values_vectorized(array):\n",
    "    array = np.where(array==0, 'N', array)\n",
    "    array = np.where(array=='1', 'V', array)\n",
    "    array = np.where(array=='2', 'R', array)\n",
    "    array = np.where(array=='3', 'A', array)\n",
    "    array = np.where(array=='4', 'other', array)\n",
    "    return array\n",
    "\n",
    "Y_label = change_values_vectorized(Y_pred)\n",
    "\n",
    "hbb['ann_symbol']=Y_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9a15b2bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "hbb.to_csv('extended_output/everything.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2040dfb",
   "metadata": {},
   "source": [
    "# References\n",
    "[0] St Petersburg INCART 12-lead Arrhythmia Database. https://physionet.org/content/incartdb/1.0.0/\n",
    "\n",
    "[1] A survey of heart anomaly detection using ambulatory electrocardiogram (ECG). Hongzu  Li and Pierre Boulanger. 2020\n",
    "\n",
    "[2] ECG signal classification using support vector machine based on wavelet multiresolution analysis. Ayman Rabee and Imad Barhumi. 2012.\n",
    "\n",
    "[3] Segmentation of the ECG Signal by Means of a Linear Regression Algorithm. Javier Aspuru et Al. 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "688d1309",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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