classifier-left-right-random-forest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import mne\n",
    "import pyxdf\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "\n",
    "from scipy import signal\n",
    "\n",
    "import util.preprocess_util as preprocess\n",
    "import util.analysis_util as analysis\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "DATA_DIR = Path('./data')\n",
    "DATA_FILE_NAME = 'Sandra_MI_1h.xdf'\n",
    "DATA_FILE = DATA_DIR / DATA_FILE_NAME"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read in xdf file(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read data\n",
    "streams, header = pyxdf.load_xdf(DATA_FILE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EEG data shape: (1023780, 17)\n",
      "Markers shape: (1213, 1)\n",
      "Sampling Frequency: 250\n"
     ]
    }
   ],
   "source": [
    "# Extract streams from xdf data\n",
    "\n",
    "def find_stream(name, streams):\n",
    "    for stream in streams:\n",
    "        if stream['info']['name'][0] == name:\n",
    "            return stream\n",
    "    \n",
    "    print(f'Error: stream \"{name}\" not found')\n",
    "\n",
    "unicorn_stream = find_stream('Unicorn', streams)\n",
    "fs = int(unicorn_stream['info']['nominal_srate'][0]) # 250 Hz\n",
    "\n",
    "openvibe_stream = find_stream('openvibeMarkers', streams)\n",
    "openvibe_stream['time_series'] = np.array(openvibe_stream['time_series'])\n",
    "\n",
    "print(f\"EEG data shape: {unicorn_stream['time_series'].shape}\")\n",
    "print(f\"Markers shape: {openvibe_stream['time_series'].shape}\")\n",
    "print(f'Sampling Frequency: {fs}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extract Event Data\n",
    "\n",
    "From the Open Vibe Marker Stream\n",
    "\n",
    "- `event_codes`: array of codes sent to the marker stream at the start of each event \n",
    "- `event_time_stamps`: array of timestamps signifying the start of each event\n",
    "\n",
    "The mapping for this particular paradigm was:\n",
    "- 'beep': 0, \n",
    "- 'cue_no_blink': 1, \n",
    "- 'imagery_no_blink': 2, \n",
    "- 'break': 3, \n",
    "- 'cue_handR': 4, \n",
    "- 'imagery_handR': 5, \n",
    "- 'cue_handL': 6, \n",
    "- 'imagery_handL': 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def unique(input_list):\n",
    "    unique_items = []\n",
    "    for x in input_list:\n",
    "        if x not in unique_items:\n",
    "            unique_items.append(x)\n",
    "    \n",
    "    return unique_items\n",
    "\n",
    "def build_unique_mapping(unique_labels):\n",
    "    unique_mapping = {}\n",
    "    for i, label in enumerate(unique_labels):\n",
    "        unique_mapping[label] = i\n",
    "    \n",
    "    return unique_mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of events: 1213\n",
      "Event code for left MI: 7\n",
      "Event code for left MI: 5\n",
      "Number of left MI events: 200\n",
      "Number of right MI events: 200\n"
     ]
    }
   ],
   "source": [
    "# Get event codes\n",
    "event_labels = [x[0] for x in openvibe_stream['time_series']]\n",
    "unique_labels = unique(event_labels)\n",
    "label2code = build_unique_mapping(unique_labels)\n",
    "event_codes = np.array([label2code[x] for x in event_labels])\n",
    "\n",
    "# Get event time stamps\n",
    "event_time_stamps = np.array(openvibe_stream['time_stamps'])\n",
    "\n",
    "assert event_time_stamps.shape == event_codes.shape\n",
    "\n",
    "print(f'Total number of events: {event_codes.shape[0]}')\n",
    "print(f'Event code for left MI: {label2code[\"imagery_handL\"]}')\n",
    "print(f'Event code for left MI: {label2code[\"imagery_handR\"]}')\n",
    "print(f'Number of left MI events: {np.sum(event_codes == label2code[\"imagery_handL\"])}')\n",
    "print(f'Number of right MI events: {np.sum(event_codes == label2code[\"imagery_handR\"])}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocess EEG data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 1023780)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract the relevent EEG channels\n",
    "eeg_data = unicorn_stream['time_series'][:,0:8].T\n",
    "eeg_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rereference the EEG data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rereference according to the mean\n",
    "eeg_data = eeg_data - np.mean(eeg_data, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filter EEG data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "lf = 8\n",
    "hf = 16\n",
    "nf = 50\n",
    "\n",
    "# Design notch filter\n",
    "b, a = signal.iirnotch(w0=nf, Q=7, fs=fs)\n",
    "freq, h = signal.freqz(b, a, fs = fs)\n",
    "\n",
    "# Apply notch and bandpass filters\n",
    "eeg_notched = signal.filtfilt(b, a, eeg_data, axis=1)\n",
    "eeg_notched_bp = preprocess.bandpass(eeg_notched, lf=lf, hf=hf, fs=fs, ftype='butter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FZJGIrBSRtSLykLv8ZBFZJiIrRGSuiHRtybhKEgbQsWY75aXFLXlbpZRSSvnQvsIKfvvqfBZuy+Mfv+nPDeO7ItL0Q4FhwVb+NK4LP91xEn8Y24WvVmdz0j9mM2Pd/haKWnmLXybPQCVwkjGmPzAAOE1ERgCvAJcaYwYA7wH3tWRQYZ2GYxMn21fPa8nbKqWUUspHNu0v5oKX57Env5wpVw/lwsEZR3V9dHgQd53egzl/HU9qdBhv/rzdS5GqluKXybNxKXHvBrk3496i3OXRwN6WjKtd3zEAFG3+uSVvq5RSSikfWLDtIBe+8jM1TsMHfxjBmG6Jx1xXSnQoZ/VLZcG2PPJLq5oxStXS/DJ5BhARq4isAA4AM4wxC4HrgK9EZA9wOfBES8YUn5zBNktHOm99m9x9u1vy1koppZRqQdNX7uWK1xeRHBXKx38eRe+06OOu8/Q+qTichhnrdehGIBN/n4dQRGKAT4CbgIeBJ40xC0Xkr0CmMea6Bq6ZBEwCSExMHDx16tRmi6d4/zZOWXcnm6xdODj6ESxWXWemOZWUlGC3230dhvKQtlfg0TYLLNpevrG3xMm9c8vpFmvhlkGhRAR5tujJkdrLGMPtc8rJiLTwl8GhzRWuOg7jx49faowZcjTX+H3mZ4wpEJEfgNOB/u4eaIAPgG8aueY14DWAzMxMM27cuGaMaBxLPq9kyLK7WLDpI0b86dVmrFvNnj2b5m0v5U3aXoFH2yywaHv5xks/bMGwkf/+cTwp0Z4nuZ6013ml63h7/k4GjxhNZGjQcUaqfMEvh22ISKK7xxkRCQMmAOuBaBHp7j6ttqzFDTnnTyxMuJAR+99n6Zf/8UUISimllPKSmev30y8j+qgSZ0+d1ieFKoeTHzbmNHvdqmX4ZfIMpAI/iMgqYDGuMc9fANcD00RkJa4xz3/1VYADr3+ZDUG96LnoHnasX+KrMJRSSinVjHKKK1mxu4BTeiZ7pf7B7WNJjAzhmzXZXqlfeV+TwzZEJAO4GBgDpAHlwBrgS+BrY4zTG0EZY1YBAxso/wTX+GefCw4JJf7q9yl77USsUy+n6Ja5RMXE+zospZRSSh2HHzYcwBi8ljxbLMLEXsl8vCyLimoHoUFWr9xHeU+jPc8iMgV4A6gCngR+D/wZmAmcBswVkbEtEaS/SkzryIHT/k2qcx9bX7scp8Ph65CUUkopdRxmrN9PWnQoPVMjvXaP0/ukUl7tYM4mHboRiJoatvEPY8xEY8zzxpifjTFbjDFrjDEfG2NuAsbRwvMs+6NeI05jSeb/MbBsHgvf+Zuvw1FKKaXUMaqodvDT5hxO6ZV8xBUEj8fwznFEhwXx7Zp9XruH8p5Gk2djzBoAEblcRA759UtEzjLGVBljtng7wEAw/OJ7WRp5EsO2vczqH/1iVIlSSimljtK8LblUVDu9NmSjVpDVwik9k5mxfj9VNV4ZAau8yJMHBl8AfhKRnvXKHvZSPAFJLBZ6/uFNdlnbk/H9TWTv3OjrkJRSSil1lGauP4A9xMbwznFev9fpfVIorqhh/raDXr+Xal6eJM/bgWuAj0TkN+4y7/0tI0CF26OxXfIuNlNDyX8voaK81NchKaWUUspDTqdh1vr9jO2eQIjN+w/xndAtgYhgq866EYA8SZ6NMWYZcCIwSUSeAfTR0Aa069qXrSf8k26OLax67Xpfh6OUUkopD63OKuRAcaXXh2zUCg2yMr5HEt+t3Y/D6d+rPatDeZI8ZwMYY3KBUwED9PFmUIFswIRLmJ9+NcPyv2TRtH/5OhyllFJKeWDm+v1YBMZnJrXYPU/rk8LB0ioW78hrsXuq43fE5NkYc2a9905jzF+NMf66uIpfGHb1M6wKHcyAVY8wf/KtFBXoeCallFLKn81cf4AhHeOIjQhusXuOz0wi2GbhG511I6A0Nc/zdBH5vLGtJYMMNFabjQ7Xv8/qqLGMzJqC89l+LHjnASrKSnwdmlJKKaUOsye/jPXZRZzSs+V6nQEiQmyM7ZbIt2v34dShGwGjqRUGn3G/CjAZuM774bQe0fHJDL7tE7asnEfZ1/czYsuz7H/qbVb1vZlB5/wZW1DL/WarlFJKqcbNWn8A8N6qgk05vU8KM9fvZ1VWIQPaxbT4/dXRazR5NsbMqX0vIiX195XnuvYfDf1nsXbeF9h+eJhhqx9g19rXyB12BwMnXoFYdASMUkop5Usz1++nc2IEnRPtLX7vU3omY7MIX6/J1uQ5QHiauenfEo5T79Fn0f2eBSwf9SIGC4MW3MLmx4azZq6OgFFKKaV8pbiimgXbDjLBB73OANHhQYzsEs+3a/ZhjKZbgaCpMc9xtRtgFZHYw8rUURKLhYETLyfjnuUs6v93omry6DPzclY/Pp7NK37ydXhKKaVUm/PjplyqHYaTfZQ8g2vWjR0Hy9iwr9hnMSjPNdXzvLTeFgUsq7e/xPuhtV5WWxDDzr+ZmDtXsaDb/9GuchPdPj2Lpc+cy+7NK30dnlJKKdVmzFy/n9jwIAa1j/FZDBN7pSCCzroRIJpKnjONMZ0a2Tq3WIStWGhYBCMufQDLratYkHENPYvnk/rOOBa+cAU5e3f4OjyllFKqVatxOPl+wwHG90jCZvXdM0iJkSEM7RCnyXOAaOpfys8i8qmI/FFEOrZUQG1RVEw8I677F2V/WsrSxPMZmPsF9n8PZf6/b6IwL8fX4SmllFKt0tKd+RSWV/tsvHN9p/VJYeP+Yrbl6LS2/q7R5NkYMwS41b37rIgsFpF/ichEEQnxZlAi8oaIHBCRNfXKBojIAhFZISJLRGSYN2PwhYSUdgy/8Q1yrpzH2ugTGb73beT5/sz/798oL9VxUEoppVRzmrl+P8FWC2O6J/o6FE7tkwLAt2v3+zgSdSRN/o3CGLPDGPOqMeY8YBQwHTgF+ElEvvRiXG8Cpx1W9hTwkDFmAHC/e79VSu/ckyH/9xE7fvMN28N6M3Lb85Q83ZeFHz5DdVWlr8NTSimlAp4xhhnr9jOiSzz2kKaWvWgZ6TFh9M+I5ps12b4ORR2BxwN8jDHVxpjvjTF3GGOGAZO8FZQx5kfg8IXeDa4HFwGigb3eur+/6NxnBP3vnMH6U//HwaBUhq/9O/sf78/8KXeyZeVcjNPp6xCVUkqpgLQ1p5QdB8uY0MKrCjbl1D4prNxTSFZBua9DUU1oaqq6biLypoj8U0QyRORrESkRkZUiMsQYk9WSgeIaQvK0iOzGtfrh3S18f5/pOfJ0Mu+ex8oTXqXUGs3wHf+m6ydnkvtwZxY9+3uWf/sWJUX5vg5TKaWUChiz1ruGR/hyirrDndbbPXRDHxz0a9LYhNwiMhf4L67e3r/gSl6nA2OAR4wxw70amOshxS+MMX3c+88Dc4wx00Tkt8AkY8wpjVw7CXfPeGJi4uCpU6d6M9QWV1GST9XuJSTmLaFP1UoipZwqY2WttSdZ0YMhfSgR8RmIiK9DPWolJSXY7S2/wpM6NtpegUfbLLBoe3nPYwvLqaiBh0eHNVudzdFe980tIyJIuHt488WlGjd+/Pil7uf8PNZU8rzCPb4YEdlijOna0DFvaSB5LgRijDFGXFlhoTEmqqk6ADIzM83GjRu9GapPVVdVsmnJLIpXf0HK/h/p6NwNwB5JZXfKyXQ9+68kpnX0bZBHYfbs2YwbN87XYSgPaXsFHm2zwKLt5R0HSyoZ+uhMbjypG/83oXuz1dsc7fWvGZt4/vvNLLrnFBIjvTo/gwJE5KiT56bGPNcfUFvUxLGWshc40f3+JGCzD2LwO0HBIfQedQYj/vAyHe9fw96rFrGw1z3khWQwdO97RP97MAtfuJK9O1rvLxBKKaXU0fhhYw5Og19MUXe40/qkYAzMWKezbvirph4v7SEiqwABurjf49736iIpIvI+MA5IEJE9wAPA9cBzImIDKvDiA4uBLK1jJmkd7wTuZO/29eyZ/hgDc6cjU6azKGYiKWfdTftu/X0dplJKKeUzs9bvJzkqhD7pR/wDdovrkRJJx/hwvl6TzSXD2/s6HNWAppLnni0WxWGMMb9v5NDgFg0kwKV16knazW+zf89Wdnz+OP33f0rQO9+wJGo8cafeRec+Xh22rpRSSvmdimoHczblcP7AdL98NkhEOLVPCq//tJ2tOSV0SdQx7/6mqUVSdja1tWSQ6vgkZ3Rh+J//Q+mfl7M4/XJ6Fv1M548msvyp09m4bI6vw1NKKaVazIJtBymrcnCKHw7ZqHXN6E5EhQVxw7vLqKh2+DocdZimpqorFpGixraWDFI1j/jkdoyY9AI1N69iQbvr6VK2kszPz2HVEyez5IvJ5B1o9VNnK6WUauNmrt9PWJCVkV3ifR1Ko5KjQvnHb/uzYV8xf/9ina/DUYdpdNiGMSYSQET+DmQDb+Ma73wpkNoi0SmviI5PZsS1z1BSdA8LPv0X3ba9RfyS23Eu/itbbJ05kDSaqN4T6Dr4FELDwn0drlJKKdUsjDHMWn+Asd0TCA2y+jqcJo3PTOIPJ3bm33O2MbJLPGf1S/N1SMrNk/UozzHG1H/C7BURWYlriWwVwOxRcYy44u84ah5g08qfyFv1DVHZcxm6912Csv9L+YxgVob1oyxjLMkDT6dTzyGIxeNFKZVSSim/snZvEdmFFc06PZ033T4xk8Xb87hr2mr6pkfTIT7C1yEpPEueS0XkUuB/uJbI/j1Q6tWoVIuy2mx0HzweBo8HoLQon3WLv6ViwwxSDi6g/5Z/wpZ/kkMsO6KG4EgfRnzmaDr2HkpQULCPo1dKKaU8M3P9fkRgfA//WZK7KUFWC8//fiBnPj+XG95bxrQ/jSLE5t895m2BJ8nzJcBz7s0A89xlqpWKiIql/8kXw8kXA7B/zxZ2LfoSy/bZdC5aTHzRDFgPZZ+EsDm4G4UJAwjpMJyMvieSlN7Bx9ErpZRSv7Z2byFvzN3O0I5xJNgDZ/GRjNhwnr6oH5PeXsrjX23gwXN6+zqkNu+IybMxZgdwrvdDUf4qOaMryRm3ALdgnE6yd29m75ofqd65iNi8lQze+z7B2e/AAsgmgb32PlSlDsHevj8JHXqSnN4Zi1V/U1ZKKeUbm/YXc/nri7CH2PjHbwJvrYOJvVO4ZnQn3pi3nZFd4jm1d4qvQ2rTGk2eReQ+4GVjTF4jx08Cwo0xX3grOOV/xGIhtUMmqR0yca1bA5UVpWxcM5+CjT8TtG8ZGSWrSd48u24NyHITTLY1lfzQ9lRGd8QS3xV7WncSO/QiMaU9FquOo1ZKKeUd23JKuGTyQmwW4b3rR9AuLjAfhL/r9B4s2ZnHXz9cSa/UqID9HK1BUz3Pq4HpIlIBLANygFCgGzAAmAk85u0Alf8LCY0gc8gpMOQUABxOB5+u/pDte1ZhKywgrDiXmLIcUsq30Wn/AuL31RCyxiBAqQllry2DnKSRRPc/G4dDe6iVUko1j10Hy7hk8kKMMbw3aQQdEwL3gbtgm4UXfz+IM5//iZveX86HfxxJkHY++URTU9V9BnwmIt2A0bimpysC3gEmGWPKWyZEFSiqHdV8se0LXl/zOjuL6q2jE+resFI7y6ENK+EEEeaE6Jpqhpd9wag5H9Kz3MailaOQzFPpPup8omP9dx5OpZRS/iuroJxL/rOAihoH718/gq5Jkb4O6bi1jw/niQv7ccN7y3j6243cc4bPFoNu0zwZ87yZuj/AK/Vr5TXlfLz5Y95c+yb7SvfRM64n/xz3TwYkDqC4upjiqkO3oqqiQ/YPlB3gf7mredtZjdVAn8rVjNq8EOeq+wg23SnvcArpw86nfdc+frmUqlJKKf+yv6iCSycvoLC8mveuG0HP1Chfh9RszuyXyvxt7Xntx22M6BzHST38d6XE1sqT2TaUalBxVTEfbPyAt9e9TV5FHoOSBvHAyAcYnTa6LslNJNGjuspryll+YDkfLvyQvba9vJq3HhNrCHXmM7jov4yY/hodKqMICe6OQyzUCDgAh4DDAjXIL/siOEWIDs8gPr4HUWmZJHXoQVxCsibfSinVyuWWVHLJ5AXkFFfy9nXD6ZsR7euQmt19Z/Zi6c4Cbpu6kq9uGUNqdJivQ2pTNHlWRy2/Ip+3173N/zb8j+LqYkanj+b6vtczOHnwMdcZZgtjVNooqmKrGDduHIWVhSzZt4QF2QuYt/sn5pVluc/cehS1biD04Hdk7K+h/aIakquFKGcEkZJETFgHEmJ7YE9xPbiYktFZH1xUSqkAl19axWX/WUhWQTlvXT2MQe1jfR2SV4QGWXnpkoGc/cJcbn5/Oe9fPwKb/h/WYo6YPItIvDHmYEsEo/xbUVURr6x4hWmbp1FRU8EpHU7hur7X0Su+V7PfKzokmpM7nMzJHU6GEbC/dD+L9i1if9l+bGLDZrFhtVixWWx1+3Wb2BAR9uTvZFP2KnYWbGNDxQF+MsVUixPYB+zDVraAjI01dFxTTbsqiHXGExfaleSEQcR3GkRGj8FERra+HgullGqNCsurueKNRWzLLeWNK4cyvHPrfmamc6Kdxy7oyy3/W8Gd01bz0Lm9sYdon2hL8OSrvEBEVgBTgK+NMca7ISl/VO2o5ubvb2bFgRWc2flMru1zLZ1jOrfY/ZMjkjm7y9lHd1E7oN8vu07j5EDZAXYX72ZnwXY2Zq9mx8EtbC7bzU/OQhxSDqwmuHQVXZZPptvCapKqQ4mRNJIie5OYPpSo1C5EJ6UTm5BGcJD+kFJKKX9QUlnDVVMWsWFfEa9dPoQTuiX4OqQWce6AdLYcKOHFH7awYNtBnriwL2O6eTZcUh07T/737w6cAlwDPC8iU4E3jTGbvBqZ8hvGGB5Z+AhL9y/liTFPcGbnM30d0jGxiIWUiBRSIlIYmjIUevy27liVo4rthdvZmLeBFbsWsuHAauZUZFMolcB+YD/xWTPotb2KfpWV9KmoJq0yFIfEURocT0VIAo6IJLAnExSdQmhMKmHRCdhjk4mKTSQ8LFTHWyullBccKK7gxveWs2pPIS9fOihglt5uLrdNzOTE7oncMW0Vl7++iN8OyeDeM3sRHRbk69BaLU9m2zDADGCGiIzHNVXdn0VkJXCXMWa+l2MEQESeBs4GqnANfL3aGFPQEvdu695e9zYfb/6Y6/teH7CJ85EEW4PJjMskMy6Tc7r+sqBmXkUem/M3s3b/KlbuWsim4i3MrTmIAcRAWo2hR+UBBlTsYFhOIZl7K2lopuoSE0ahRFJmiaTMFk1lUDQ1ITE4Q2MhNAoJtmMJicASEkFQWCS2UDvB4VEEh0cSGh5JaEQUERF2QrS3WymlAFiTVcgb87YzfeVenAae/d2ANrvy3pCOcXx18xiem7WZf8/ZypxNOTx6Xl9O6aUzcXiDR2OegcuAy3F1wd0EfI5roZQPgU5ejK++GcDdxpgaEXkSuBu4s4Xu3Wb9tOcn/rH0H5zc/mRuHHijr8NpcXGhcQxPHc7w1OEwwLWiYnFVMWty17AqZxWrclexNGcVsyqdQCjh1jC6hnegqzWZ/pJG+woLzvICpDwPa0UBQVUFhFcXEl++j8jSIiJNKRbxbCRUtbGy2dqenMheOFMGENNtOJ16DSUiXFeZUkq1DQ6nYca6/bwxbzuLtucREWzl0uEduHJURzoF8AIozSE0yMqdp/Xg9D4p3PHRKq777xLOHZDGA2f3Ji4i2NfhtSqedGPNB94GzjPG7KlXvkREXvVOWL9mjPmu3u4C4KKWundbtbVgK3f8eAfdY7vz2AmPYRF9khcgMjiSkWkjGZk2EnANa9ldvJuVOStZnbuaVTmr+CxvLh8bB+n2dCb2nsiEDpfSJ6GBeaqdDqrKi6koLaa8tIjKsmKqyoupLi+mpqIYR0UpzooSnJUlmPJ8wvPW0btwDtGFX8JGqJxuY6O1I7nRvTCpA4nrNoxOPYYQFhbqg6+MUkp5R1FFNVMX7+bNn3ewJ7+cjNgw7juzJ78d2o6oUB2eUF+/jBg+v/EEXp69hRe/38Lczbk8fG4fzuyX6uvQWg1Pkuf7jDFT6xeIyG+MMR8aY570UlxHcg3wgY/u3SbkV+Rz46wbCbGG8MJJLxAepL2bjRER2ke1p31U+7qHGgsqCvhh9w98t/M73l73NlPWTiE1IpUJHSYwseNE+iX0cyXSFivBETEER8Tg8RT+xpCbtYnsdfOp3LmUiIOr6Zc/k8j8z2EdVHwaxDpbFw7G9cfWfjhpfcbSvmNXHXOtlAo4O3JLefPnHXy4ZDelVQ6GdYrjvjN7MaFXMlaL/kxrTLDNwq2ndOfU3q5e6BveW8b0lSk8fF5vkiK1c+V4yZEmzxCRZcaYQUcqa5ZgRGYCDQ1Yute9XDgici8wBLigsZk/RGQSMAkgMTFx8NSpUxs6TTWixtTw0v6X2FG5g1tSbqFjSMcWu3dJSQl2u73F7tcSyhxlrC5fzfKy5Wwo34ADB7HWWPqH92dg+EA6hnQ87l5943RQXZhNdc5mwoq2kFy2ma6ObYRINQDZJp4tQd05aM+kJr4HkcldCAk+/j/jtcb2au20zQJLW2svpzHsLHKy/qCDtQcdrDvoxCIwItXGhA42OkY39FSJ//DH9nI4Dd/sqOaTLdUEW2B8uyDGt7eREKZ/TQYYP378UmPMkKO5ptHkWUROB84AfsuhvbxRQC9jzLBjDfRYichVwB+Ak40xZZ5ck5mZaTZu3OjVuFoTYwwPzX+IaZun+WRmjdmzZzNu3LgWvWdLKqoqYs7uOXy38zvmZc2j2llNr/hePD7mcTpHN+/Uf87qSvasX0ju+rlY9i4mtWg1ySYHgEoTxFZbF/Ji++OMSIbgMCzB4a4t1I4tJAJbiJ2gsHCCQu2EhEcSFBKO1WpFLBasVhsWi42f58/nxBPHYhGwiGC1SN2r8k+t/XustWnt7eV0GjYdKObnLQf5eetBFm4/SHFFDQDdkuyc3jeVy0a0D5jeUn9ury0HSnjqmw3MXL8fA5zcI4nLRnRgbLdELG34Z7aIHHXy3NSwjb3AEuAcYGm98mLgL0cf3vERkdOAO4ATPU2c1dF7Z/07TNs8rVXPrOFLUcFRnN3lbM7ucjYlVSV8t/M7/rn0n/xu+u/469C/8pvuv2m24RWWoBDa9xtL+35j68qKDuxi96o5lG+bT1TucobmfkxIbvUx3+NMwDFPcGDBYKEaCzVYyJdYckIyKLN3xBnfjfCUTOI79iY9oyOhwTpjiFJtlTGG7bml/Lz1IPO3HmT+toPklVYB0CE+nLP6pTKySwIjOscFTMIcKLom2XntiiHsLSjn/UW7eH/RbmauX0z7uHAuG9Ge3wxuR6w+WOgRT4Zt2IwxNS0UT1NxbAFCgNrVDhcYY/54pOu059lzP+35iRu/v5Hx7cbzz3H/9MkDgv78W7u3HCg7wH1z72N+9nxOzDiRh0Y9RHxYC62M5XRiqkupKCulsqyYyooSqspKqKkopbqihOrKUpwVpTirynBWlYNxYIwTcTowTgf5B3OJi4nGGAcYJ2Ic4KzBVppNVOlOkmv2EkpV3e1KTChZljQOhnWgMqoTlrgOiC0ULFbEYgOrDYtYEZsNEStitYHFisViIzg8ksiEdGLjk4mJCG3TPSXHoy1+jwWy1tBeFdUOFm7P4/v1+5m14QB78ssBSI0OZWSXeEZ1SWBkl3jSY8J8HOnxC6T2qqpx8u3afby9YCeLtucRbLNwdr80Lh/Zgf4Z0W3mOZlm7XkWkanGmN8Cy0UOmUtLcE3/3K+RS73CGNO1Je/X1mwr2MYdP95Bt5huOrNGC0sKT+LVCa/y3vr3+NfSf3HB5xfw99F/Z2zG2CNffLwsFiQkkrCQSMJij35+1NmzZzOwqf8onE5KcnaQs30txXvX48jZQmjhNrpUrCOxdDaWfUe/YKnDCAeJIl9iKLHFUh4cR3VIPI7wRLAnEhSV7HoI0x5LqD2WsMhY7JExRIYFEWKztJn/EJTypf1FFfyw4QCzNhxg7uZcyqsdhAZZOKFrAn88sQsndE2gQ3y4fj/6ULDNwtn90zi7fxob9hXxzoKdfLIsi2nL9tA3PZrfD2vPGX1TiAnX3ujDNfX301vcr2e1RCDKdwoqCrjxe51Zw5csYuGyXpcxLHUYd/10FzfMuoHfZf6O24bcRpgtgHtjLBbsyZ2xJ3fGtcbRL0x1OcX7d1JTU4XTUYPD4cDpqMY4HTgdNTgdNe731RhHDVVlRVQV7sdRfAApzSGoIpfoyoOkVawjuiyfsPzKRsOoMRZKCCOHcMokgnJLBJXWCKqtYRix4hQr5vDNYsOIBSw21354AtaYdoQnticqpTNJyakk2LUHXClwjV1es7eQWesP8P2GA6zOKgQgPSaMiwZncFLPJEZ2jic0yL8f+GureqRE8ch5fbnztB58ujyLtxfs5J5PVvPA52sY2y2RcwakcUrPZCJCdNgdNJE8G2Oy3W9zgXJjjFNEugM9gK9bIjjlfU7j5LY5t7G/dD9vnPYGqXadB9KXusd25/0z3+e5Zc/x9rq3WbRvEU+MeYJe8b18HVqzk6AwIjN6NF+FVaWU52dTlJtNRUkeVSUF1JQX4CgrxFQUYSqLsFQWYa0qxl5dTJzjIEHVFQhOLMaBBQdW48CC0/3eiRXXfhDukWu7frlduQlmB/HkWRMpDkmmMjwNE51OUGw7whM7EJvamdTEeKLDgrR3TbUqlTUOthwoYX12MRuyi9iwr5h12UXklVZhERjUPpY7TsvkpB5JZCZH6r//ABIZGsTlIzty2YgOrN1bxOcr9zJ95V5mbThAaJCFU3omc07/NE7MTCTE1nZ/EfLkV4gfgTEiEgt8BywGfgdc6s3AVMv4bMtnLNq3iIdGPUT/xP6+DkcBIdYQ7hh6B2PSx3Df3Pu49KtLuXHAjVzV+yqslrb7w+qIgiMIS+5KWLIXRng5nZjSHAr3bado/w7KcnfiyN+NpXgvCWV76Vy5jJjymVgOHjoMpcBEsJF48m3JlIYmUxWRBtEZhMS3w57UkdDIOKy2IKy2YKxBwdisVoKsFqwWwWYVbBYLNqsQbLVoj51qccYY9hdVsn5fERuyi9mwr4j12UVszSnF4XT9Ww+xWchMieSUnkmM7BLPid2TGl/NzumA/B1QlAU1VeCsBkcVOKrdW5Vrc9b88t4AIu7NArhfD9+3WCE0GsJiITTG9RoW43pv1d7SoyUi9EmPpk96NHed1oMlO/P5fGUWX63exxersokMtXF6nxTO6Z/OyC7xbW6GJU/+RYkxpkxErgVeNsY8JSIrvByXagFFVUU8u+xZBiYN5Pyu5/s6HHWYkWkjmXbONB5e8DDPLnuWuVlzefrEp0kIS/B1aG2PxYJEJhMTmUxMtxENn+OoxlGYReG+HRTt307FwV048/dgK8kio3wfMWXriSwtgQPA5oarqDEWarBSja3utRwrlSaIAomi2BZHeXA8laEJmIgkxJ5McEwKYbGpRManEx8ThT3UhkUEi4AgrrxCBMH1WuUwVNY4sIpgs+qzDd5ijOFgaRW78srYXbeVsyuvjD0FZcRFhNAnLYq+7gSle3IkwTbvt0dZVQ1Z+eXkllSRV1pFXlkVeSVV5JVWkldWTV5pJQdLqsgvqyK/tJoqh7Pu2vSYMHqkRDKhVzI9U6PokRJFx/jwX/87qi6H3M2Qu8m15Wx0vR7c4kqIW1pwpDuZjnYl07Edod1w15bQzZWIq0ZZLMKwTnEM6xTHA2f3Zt6WXD5fuZevVu9j6pI9JNhDmNAriVN6JjO6a0Kb+EXfo+RZREbi6mm+1l3W+r8ybcArK14hvyKfV095Vf+s5qdiQmP4x4n/4LOtn/HYwse45MtLePHkF+ke293XoanDWYOwxnUkLq4jcb3GNXxOZQnVBXsoyN5G8YGdOMqLwFGDcfe+GUe1uzful1dx1iA1FcRWHCS1ch/2irVElhdD/q+rLzLhFBOG01hwIu6tdiCKBYOQjrBxjqus0BJDcXAiFaFJOOypWKLTCInLICKhPfHxiaREh5FgD9YkuwnFFdVs2l/Mhn3FbD1Q6kqOD5ZQVrCPuOr9pEku6ZJLmhzkjKA82lkPkmRyKaiKYXFOZxYv6cJ/nV3YZmlP15SYumS6T1o0mSmRRx1PtcNJdkEFu/NdCfuuvDJ255ezO6+MPfll5JY0nLxGhdqIt4cQGx5ERmw4/TKiiYsIITU6lB4pkfRIiSI6PMjVe1x2EEr2Q9E62HsASg5A8T7I2+pKlAt24eoyxtUrHNMBEjOh6ymu15j2YAtz9Qhbg12bpfZ9kHsLBkuQK7E1BozTVadxNrzvrIGKQigvgPJ8qHC/lhcc+r48DzZ8AcvfdsUXFutOpIe5XtMGQbA+99OYIKuFcZlJjMtMoqLawQ8bDvDFqmymr8zm/UW73Q+FJnJKzyRO6pnUaqcb9GSqurHA7cA8Y8yTItIZuNUYc3NLBHi8dKq6hm0t2MqFn1/IBd0u4P6R9/s6nDqBNM1PS1t3cB03zbqJ0ppSnjnxGU5IP8HXIWl7+UpNFZTmUFO0j5KDeynN20tV4T4chfugqgSMcU0baJzuKQSdgBMxhrLSIiLCwhBnDcGVudgrc4h0Fv7qFmUmhH0mlv3EUWSJptoSQo0lGIeE4LCE4LQG47CGYKwhOC0hGJtrQ1wPWhr3n9ONWOveI66kHrFiLFYsIRFYQuxYQ+3YQiMJDoskLCSI8GArYcFWwoNthAe7hrLUqv9rfu3v/OIuFYGQIAthQVbCgqzNmvRX1TjZllvCxn3Fri27iIP7dmAv3kZXyaKrZNHFup/27uQ4yBw6f7oJiUSi20N0BkSlQVEWZs8SpDzPVb8llG22riyo6sTCqs6scHYlxxJPYpgQHWl3fz6p+/x1n73e1yC/rIrswoq6IRUANouQFhNG+9gQMqNr6BpeRvuQMmKDHUQFOYm01RBhdWBzVEJNJdRUHPpaWeRKjksOuBLmslx34nqYoAiI6wyJ3SHBvSVmQlwXCPKzBMoYV8/47oXubRHkuvMEiw1S+v7SM91pLER4/te+Zv2ZWFUGVaWu7+nqMtd+dekv5bXvGyyrvbb00Gsd1a7PE5UOkamuf4tRae736RCVCvZk1zCYowm1xsnC7QeZuW4/M9cfIKvANR1h/3YxnNIjiVN6JdMjxT/Hvx/LVHVHTJ4DnSbPv2aM4foZ17P+4Hq+OP8LYkNjfR1SHU3GmravdB83fX8Tm/I3cfewu7m4x8U+jUfbK/A02GbVFVCcjaNwLyW5uyjL2UNVwR4oysZWmk1wVT5WZzU2ZyU2U0WQqfpVcthcSk0IZYRSakJdr4RQZYJwIpi6nvRDe9Xr75fVXk8olRJCtTWcams4Dptrc9rCMUERmKBQnLhmVnFabCBWHOKeWcVixWDDWK04nIbKA9sJLthMJ5NFV0sWXWUvXS17iaC8Lm5HSAyWhK5IjDtBrn2Nbgcx7VzjcQ9nDORvhz1LIWsJ7FmC2bcKcQ9tKAlKYKNpR01ILNWWYCollGoJpkpCqJIQ1+dzv1ZJMNE2B+1CSkixFhNv8oly5BNSeRApOeBKep1HsWSDLRRsIa4hD/YkV0J1yGu99xFJEOJfS1IftbI82LP4l2Q6a6kr6UQgtT90PRm6nOzqobYGNVrNMf9MLMuDvcvrbSugaI/n11tsrl9ggsMhKByCI1xbULi7zH3MGuz6RahoLxTvhaJs11+56hOrq21jO7h/Cerh+kUoMdOVYB8hATbGsHF/cV0ivWJ3AeAa9jO2ewLdkiLpkmSna5Kd1Cjfz1jU3CsM1lbaHVfPc8f65xtjTjraAJV/mLVrFguzF3LP8Hv8KnFWR5YSkcJbp73FnT/eyaMLH2Vn0U5uH3K7Pkiojk9QKMR1whrXiehOo2kgzfs1p9M1fvWQnsoK15/1jRPq9XzX/ZndOH857qyGqjJMVQnV5SVUlxdRU16Co7IEqSwhwr0lVZWAs9qVOtfrSRdq3xtcvepOxFmD1VGB1VFGUE0ZFtw9pA731vhshkfm/t+vOjwZa1IPLEknH5JYWCMSj37srIirtzauM/T7jauophL2rYGsJdj3LCFz22LsIYWuX3Cqy11bTUXT9VqDXQmtPdHVq5ja/5ck157k6nkMjgBryC9Jcv1Xa1DbGwccHgfdT3Vt4OqhzV4FW7+HrbNg7rPw0z9cv0x0GgtdT3Il03Gdjv5e5QWQvfLQZLlg5y/H47pA+xGQ1NP1S1dTyXBwhOu97RjnYnY6XcNwahPpoiwoznYl1/k7YP10WPbWL+cHR7r/uuBOpmsT65gOYHH9pUdE6JHiGhN/40ndOFDsmvN7xroDfLV6H4Xlu+uqCw+20jkxgq6JdrokuhLqLkl2OsZHtMgzAMfKk2EbK4FXcS3R7agtN8YsbfQiP6I9z4eqqKng3E/PJSI4gqlnTcVm8a+nkLUn0zMOp4NnljzDO+vfYVzGOJ4c+6RP5ufW9go8babNjHEl9bV/9q7783Xt+3J3El9Tb3Mctl/jSi6iM1wJQkK3hnuQvajB9nI6f/llpbrMnViXuRJfe6Lrobi2lvx6W0UhbP8RtsxyJdMF7nkr4zq7kuiUvlBdxraNq+mclvjLv7PKYtf7ytr9IleCWiu2I6QNhNQB7tf+rllC/ElpLuRscG+b3K8boWTfL+cE2yGpl+vrkNIHkvtCci9Xcl9P7YO0Ww+UsCWnhC0HStiaU8rWAyV1Qz0ArBYhwR5Mgj2kbkuMDCHBHux+rd2CiQ0PPq7ea6/0PAM1xphXjjEm5WemrJnC3tK9vHHCG36XOCvPWS1W7hx2Jx2iOvD4ose58psreeGkF0iJOPpVApVqlURcPepBoRDRQsvdtxSLxd3rGA7E+TqatiE0Gnqe7dqMgYNbXUn0llmw4l33EA/oDLAr2JVMBttdw1mC7RAS6RpPHBwJCV1/SZjDA6D9IhIg4gToeNhzNuX57mR6Pexf6/qLyeoPYcnr7hPcf1mpl1BLYncSIhJJ6BTH8M6Hfl+WVdWwLaeUrTklbD1Qwr6iCnKKK8ktqWLT/mJySyqpdvy6w1cEwoKsvzwrEWRzPzNRW2YjPMh1zGoRHE5DjdPpem2gPk94kj1NF5E/A59Q749expi8Y7qj8pm9JXt5fc3rnNbxNIamDPV1OKoZXNzjYjIiM7h9zu1c+uWlvHjyi/SM7+nrsJRSqvUScSXACV1h+B9cf+EoOQDBEcxZsJQTT5rg6whbRlgstB/u2moZ4xqCsm8N7F8D+1ZD9gpY9+mh11qDITwewhNcv0CExxMekUCf8Hj6hMdDWjx0jHCdZwsBWwzGGkxJtZWDlZBbLuRWwIFyQ265oazKSVmVg/IqB6VVTsqrHZRVOykoq3a9r6qhrMqBGCcRUkWEpZoISxVhcmzPbniSPF/pfv1rvTKD+xcsFTieWfIMgnDbkNt8HYpqRiekn8B/T/8vN866kSu/uZKnxj7FuHbjfB2WUkq1DbYQ10OhgLE0/jBhmyDiGooS2xF6nvVLeUWRq3c6b5trjHVZrvs1zzUsJHula7+ioPGqgUj31vHYggNx9zQ73RvwwTHUdMTk2RhzDKPhlb9ZkL2AGTtncOOAG/VP+61Q99juvHfme9w06yZu/v5m7h5+N7/v8Xtfh6WUUkpBaBR0GOnamuKodg0HKc11PZfgqH0Yucr9vvbVvTkqXdfUPb9nmn5vDXYP5wqHoDDXcwIP/eaoP44ns22EA/8HtDfGTBKRbkCmMeaLo76b8olqZzVPLnqSdHs6V/W5ytfhKC9JCEvgjdPe4I4f7+DxhY/TJboLw1KH+TospZRSyjPWoF+mQvRjnswDMgWoAka597OAR7wWkWp2UzdOZUvBFu4Yegch1hBfh6O8KMwWxpNjnqRDVAfu/ulu8isaWIZOKaWUUsfMk+S5izHmKaAawBjjnjVcBYKD5Qd5aflLjEobxfh2430djmoB4UHhPH3i0+RX5nP/vPtp7QshKaWUUi3Jk+S5SkTCcC9ULyJdOL6p5pskIk+LyAYRWSUin4hIjLt8gogsFZHV7lddpMUDLyx/gfKacu4cdqdfLoupvKNHXA9uG3Ibs/fM5r0N7/k6HKWUUqrV8CR5fgD4BmgnIu8Cs4A7vBjTDKCPMaYfsAm4212eC5xtjOmLawaQt70YQ6uwNnctH2/+mEt7XkrnaJ0cpa25pMclnJhxIv9Y8g825G3wdThKKaVUq3DE5NkYMwO4ALgKeB8YYoyZ7a2AjDHfGWNq3LsLgAx3+XJjzF53+VogTER0AG8jnMbJY4seIy40jj/2/6Ovw1E+ICL8ffTfiQmJ4a9z/kqZexJ/pZRSSh27JpNnEbGJyNnANUAvd3GBt4Oq5xrg6wbKLwSWGWO8Nnwk0E3fOp1VOav4y+C/YA+2+zoc5SOxobE8MeYJdhbt5IlFT/g6HKWUUirgSWMPE4lIOvA9kA0sx/WQ4EAgBRhfrxf46G8qMtNdz+HuNcZ85j7nXmAIcIGpF6SI9AY+ByYaY7Y2Uv8kYBJAYmLi4KlTpx5rqAGp2FHMo3sfJSkoiVuTb8UinozO8Q8lJSXY7ZrsN7cv8r/g26JvuSrhKgZHDG62erW9Ao+2WWDR9gos2l6BZ/z48UuNMUOO5pqmkuc3gRXGmGcPK78ZGGyMubKh65qDiFwF/AE42T27R215Bq6E/mpjzDxP6srMzDQbN270Spz+6u6f7uabHd/w4Vkf0jW2q6/DOSqzZ89m3Lhxvg6j1al2VnP1N1eztWArU8+eSrvIds1Sr7ZX4NE2CyzaXoFF2yvwiMhRJ89NdUmOODxxBjDGPA+MOMrYPCYip+F6IPGcwxLnGOBL4C5PE+e2aG7WXL7Y9gXX9b0u4BJn5T1BliCeHPskgnDXj3dR7az2dUhKKaVUQGoqeS5v4pg3nzx6EdfS5TNEZIWIvOouvxHoCtzvLl8hIv69BE0LK6su4+/z/06n6E5c3/d6X4ej/Ey6PZ0HRj3AqtxVvLT8JV+Ho5RSSgWkppbnjhaRCxooFyDKS/FgjGmwu9QY8wi6smGTXlzxIntL9/LWaW8RbA32dTjKD53a8VTm753PG2veYHjqcEamjfR1SEoppVRAaSp5ngOc3cixH70QizoOq3NW8+76d/ld5u8YlDzI1+EoP3bnsDtZfmA598y9h4/O/oj4sHhfh6SUUkoFjEaTZ2PM1S0ZiDp21c5qHpj/AAlhCdwy6BZfh6P8XJgtjKfGPsUlX17CffPu46WTXwqoGVmUUkopX9L/MVuBN9e8yeb8zdw7/F4igyN9HY4KAJlxmdw+9HbmZs3lX0v/RWOz7iillFLqUE0N21ABYEfhDl5d+SoTOkzgpPYn+TocFUAuzryYrQVbeXPtm5TXlHPP8Hu0B1oppZQ6Ak2eA5jTOHlo/kOE2EK4Z/g9vg5HBRgR4d7h9xIeFM6UNVMoqy7j4dEPY7PojwWllFKqMY3+L9nITBt1jDEfN3846mh8vPljluxfwkOjHiIhLMHX4agAJCL8ZdBfsAfZeWH5C5TVlPHU2Kd0thallFKqEU11MTU20waAATR59qGcshz+ueSfDE0Zyvldz/d1OCqAiQiT+k0iIiiCJxY9wU3f38Sz458lzBbm69CUUkopv6OzbQSoxxc9TqWjkgdGPoCI+Doc1Qpc2vNSwm3hPDj/Qf4444+8ePKL+gCqUkopdRiPBjeKyJlAbyC0tswY87C3glJNm7VzFjN2zuCWQbfQIaqDr8NRrcj53c4nLCiMu3+8m+u+u45XT3mV2NBYX4ellFJK+Y0jPlrvXh77d8BNuFYX/A2gGZuPFFUV8ejCR8mMzeTK3lf6OhzVCp3W8TSeO+k5thZs5epvrianLMfXISmllFJ+w5N5qUYZY64A8o0xDwEjge7eDUs15tmlz3Kw4iAPjXqIIEuQr8NRrdTYjLG8csorZJdmc+U3V5JVkuXrkJRSSim/4EnyXOF+LRORNKAaSPVeSKox07dO58NNH3JZz8vondDb1+GoVm5oylAmT5xMQWUBV3x9BdsKt/k6JKWUUsrnPEmep4tIDPA0sAzYAbznxZhUA77a9hX3zbuP4anDuWngTb4OR7UR/RL7MeXUKdQ4a7jq66tYcWCFr0NSSimlfKrJ5FlELMAsY0yBMWYarrHOPYwx97dIdAqA73Z8xz1z72FQ0iBeOOkFQm2hR75IqWaSGZfJW6e9hT3YzrXfXss3O77xdUhKKaWUzzSZPBtjnMBL9fYrjTGFXo9K1fl+1/fc+eOd9Evsx0snv6Rz7yqf6BjdkXfPeJfeCb3565y/MnnVZIwxvg5LKaWUanGeDNuYJSIXik4m3OJ+3PMjt825jV7xvXj55JcJDwr3dUiqDYsNjWXyxMmc0ekMnl/+PPf/fD81psbXYSmllFItypPk+Q/Ah0CliBSJSLGIFHk5LkTkNhExIpLg3r9URFaJyGoR+VlE+ns7Bl/6Oetn/vLDX+ge251XJryCPdju65CUIsQawhNjnuCP/f/Ip1s+5eX9L1NYqX+MUkop1XYcMXk2xkQaYyzGmGBjTJR7P8qbQYlIO2AisKte8XbgRGNMX+DvwGvejMGXFmYv5OYfbqZTdCdem/AaUcFe/XIrdVREhBsG3MCjJzzKtsptXP715ewu3u3rsJRSSqkW4ckiKbM8KWtm/wLuAOoGVRpjfjbG5Lt3FwAZXo7BJ5buX8pN399Eu8h2TJ44meiQaF+HpFSDzulyDjck38DB8oNc+uWlOhOHUkqpNqHR5FlEQkUkDkgQkVgRiXNvHYF0bwUkIucCWcaYlU2cdi3wtbdi8JUVB1bw55l/JiUihckTJ+uyyMrvdQvtxrtnvEtkcCTXfnstX29vdd+WSiml1CGksSfmReQW4FYgDcjCtTQ3QBEw2Rjz4jHfVGQmkNLAoXuBe4CJxphCEdkBDDHG5Na7djzwMnCCMeZgI/VPAiYBJCYmDp46deqxhtpidlTu4KX9LxFpjeSW5FuItrXNHueSkhLsdh3fHShq26vEUcLknMlsq9zGWTFnMTFqIvqMsX/S77HAou0VWLS9As/48eOXGmOGHM01jSbPdSeI3GSMeeG4IvM0GJG+wCygzF2UAewFhhlj9olIP+AT4HRjzCZP6szMzDQbN270SrzNZe3BtVz/3fVEB0cz5bQppEQ09HtF2zB79mzGjRvn6zCUh+q3V5Wjivt/vp8vt33J6R1P5/6R9+uDrn5Iv8cCi7ZXYNH2CjwictTJsyezbTjdKwzW3iRWRP58tMF5whiz2hiTZIzpaIzpCOwBBrkT5/bAx8DlnibO/s7hdPDW2re44qsrsAfZef3U19t04qwCW7A1mMdPeJxbBt3Ctzu/5bdf/Ja1uWt9HZZSSinVrDxJnq83xhTU7rgf2rveaxE17n4gHnhZRFaIyBIfxNBsdhXt4upvr+aZJc8wOn007535Hmn2NF+HpdRxERGu63sdU06dQpWjisu+voy3172tC6oopZRqNTxJnq31F0gRESsQ7L2QfuHugc51v7/OGBNrjBng3o6qi91fOI2T99a/x0XTL2JL/hYeO+Exnhv/HAlhCb4OTalmMyh5ENPOmcaY9DE8tfgpbvr+JvIr8o98oVJKKeXnPEmevwE+EJGTReRk4H13mTpKWSVZXP/d9Ty+6HEGJQ/ik3M/4ewuZ+uDVapVig6J5rnxz3HXsLv4ee/PXPT5RSzet9jXYSmllFLHxZPk+U7gB+BP7m0WrjmYlYeMMXy06SMu+OwC1uSu4cGRD/LKya+QHJHs69CU8ioR4dKel/LOGe8QFhTGdd9dxysrXsHhdPg6NKWUUuqY2I50gjHGKSJvAt8bY/x72go/tK90Hw/Of5B5WfMYljKMh0c/TLrda9NkK+WXesX34oOzPuCRBY/w8sqXWbRvEU+MeUJ/gVRKKRVwPFlh8BxgBe6hGiIyQEQ+93JcAc8Yw/St07ngswtYtn8Z9wy/h8kTJ2virNqsiKAIHh/zOI+MfoS1B9dy0fSL+HHPj74OSymllDoqngzbeAAYBhQAGGNWAJ28F1JgcxonM3fO5OIvL+aeuffQNbYrH539Eb/v8Xss4smXW6nW7dyu5/LBWR+QFJ7EDbNu4MlFT1LpqPR1WEoppZRHjjhsA6h2r/ZXv0znnTpMtbOab7Z/w39W/4dthdtoH9meh0Y9xLldzsVqsfo6PKX8SqfoTrx35nv8c8k/eWf9Oyzet5gnxz5Jl5guvg5NKaWUapInyfNaEbkE15R13YCbgZ+9G1bgqHRU8tmWz3hjzRtklWTRLbYbT419igkdJmCzePLlVaptCrGGcPfwuxmdPpr75t7HxV9czF+H/pXfdP+NzkCjlFLKb3kyjuAmoDdQiWuauiLgVi/GFBDKqst4a+1bnD7tdP6+4O/Eh8bzwkkv8NHZH3F6p9M1cVbKQ2MzxjLtnGkMSh7E3xf8nVt/uJWCigJfh6WUUko1yJPZNsqAe0XkSdeuKfZ+WP6rsLKQ9za8x7vr36WwspDhKcN5fMzjDEsZpr1lSh2jxPBEXjnlFd5e9zbPLnuWCz+/kMfGPMbw1OG+Dk0ppZQ6xBGTZxEZCrwBRLr3C4FrjDFLvRyb36h0VPLjnh/5evvXzNk9hypnFeMyxnFdv+von9jf1+Ep1SpYxMKVva9kWMow7vjxDq7/7nqu7nM1Nw64kSBrkK/DU0oppQDPxjy/DvzZGPMTgIicAEwB+nkzMF+rcdawKHsRX27/ku93fU9JdQnxofFc1P0iLuh2AZlxmb4OUalWqWd8Tz446wOeXvI0b6x5g4XZC3ly7JN0iOrg69CUUkopj5JnR23iDGCMmSsiNV6MyWeMMazMWclX27/i2x3fkleRR2RQJKd0OIUzOp3B0JShOpZZqRYQHhTOAyMfYFTaKB78+UF+M/03/GXwX7iw24UEW4N9HZ5SSqk2zJNMcI6I/BvXw4IG+B0wW0QGARhjlnkxvhaxp3gPH236iG92fENWSRYh1hBOzDiRMzqdwQkZJxBiDfF1iEq1SRM6TKBvQl/um3sfjy18jP+s/g9X976aC7tfSJgtzNfhKaWUaoM8SZ5rB/U+cFj5QFzJ9EnNGlEL2l20m8mrJzN963QMhpFpI7lhwA2Mbzcee7Dd1+EppYCUiBQmT5zMwn0LeW3Vazy5+Ekmr57M5b0u5+LMi/V7VSmlVIvyZLaN8S0RSEvaUbiDyasn8+W2L7FZbPyux++4uvfVJEck+zo0pVQDRIQRqSMYkTqCZfuX8drq13hu2XO8seYNLut5GZf2vJTokGhfh6mUUqoNaDR5FpGzgVXGmJ3u/fuBC4GdwC3GmO0tE2Lz2Va4jddWvcbX278m2BLMJT0v4ereV5MYnujr0JRSHhqUPIhXk19lbe5aXlv1Gq+sfIW31r7FxT0u5opeVxAfFu/rEJVSSrViTfU8PwqMABCRs4DLgN/jGq7xKnCq16NrJlvyt/Daqtf4Zsc3hNpCubLXlVzR+woSwhJ8HZpS6hj1TujNcyc9x6b8Tfxn1X+YsmYK761/j4u6X8SVva8kJSLF1yEqpZRqhZpaYdC4F0gBuAB43Riz1BjzH8CrXbUicpOIbBCRtSLylLtsgogsFZHV7lePxlrn1uRywecXMGfPHK7pcw3fXPgN/zfk/zRxVqqV6B7bnadOfIrPzvuMUzueyvsb3ueMj8/gofkPsbt4t6/DU0op1co01fMsImIHyoCTgZfrHQv1VkAiMh44F+hvjKkUkST3oVzgbGPMXhHpA3wLpB+pvgpnBdf1vY4rel1BTGiMt8JWSvlYp+hOPHLCI/xpwJ94Y/UbfLLlEz7Z/Alndj6Ta/teS+fozr4OUSmlVCvQVM/zs8AKYAmw3hizBEBEBgLZXozpT8ATxphKAGPMAffrcmPMXvc5a4EwETniHHJpwWncPOhmTZyVaiPS7en8beTf+PqCr7mk5yV8t+M7zvv0PG6fczsb8zb6OjyllFIBTowxjR8USQeSgJXGGKe7LBUIMsbs8kpAIiuAz4DTgArgdmPM4sPOuQj4ozHmlEbqmARMAkhMTBw8depUb4SqvKCkpAS7XaceCxSB0F7FjmJ+KPqBn4p/osJU0CesD6dGn0rHkI6+Ds0nAqHN1C+0vQKLtlfgGT9+/FJjzJCjuabJ5NlbRGQm0NDTPPfielDxB+BmYCjwAdDZuAMVkd7A58BEY8zWI90rMzPTbNyovU2BYvbs2YwbN87XYSgPBVJ7FVYW8t6G93hn3TsUVRUxMnUkV/W5imEpw9rUyqGB1GZK2yvQaHsFHhE56uTZJ/9jNNZjDCAifwI+difLi0TECSQAOSKSAXwCXOFJ4qyUUrWiQ6L5U/8/cUWvK/hg4we8tfYt/jDjD8SGxHJS+5OY2GEiQ1OHEmQJ8nWoSiml/Jg/drd8CowHfhCR7kAwkCsiMcCXwF3GmHm+C08pFcgigiK4ps81XNLjEn7K+okZO2bw9favmbZ5GlHBUYxvN56JHScyInUEwdZgX4erlFLKzzSZPIuIFVhrjOnRQvEAvAG8ISJrgCrgSmOMEZEbga7A/e4FW8A1dONAC8amlGolQm2hTOgwgQkdJlDpqOTnrJ+ZsXMGs3bN4rOtn2EPsjOu3TgmdJjAqLRRhNq8NsmQUkqpANJk8myMcYjIRhFp760HBBu4ZxWuBVkOL38EeKQlYlBKtS0h1hDGtx/P+PbjqXJUsSB7ATN2zuCH3T/wxbYvCLOFcUL6CYzNGMvYjLHEhcb5OmSllFI+4smwjVhgrYgsAkprC40x53gtKqWU8pFga3BdklztrGbxvsXM3DmTObvnMGPnDAShX2I/xrUbx9iMsXSL6YaI+DpspZRSLcST5PlvXo9CKaX8UJAliFFpoxiVNgozwrA+bz1zds9hzp45PLfsOZ5b9hzp9nTGZozlxIwTGZoyVMdJK6VUK3fE5NkYM0dEOgDdjDEzRSQcsHo/NKWU8h8iQq/4XvSK78WfBvyJA2UH+HHPj8zZM4dPNn/C+xveJ9wWzsi0kYxOH83otNGk2dN8HbZSSqlmdsTkWUSux7XgSBzQBdeS2K/iWrJbKaXapKTwJC7qfhEXdb+IipoKFu1bxJzdc/gx60dm7ZoFuJYMH502mtHpoxmSPEQfOlRKqVbAk2EbNwDDgIUAxpjNIpLk1aiUUiqAhNpC68ZJG2PYXriduVlz+Xnvz0zdOJV31r9DsCWYISlDGJU2ihPST6BzdGcdK62UUgHIk+S50hhTVftDXkRsQMsvS6iUUgFAROgc05nOMZ25ovcVlNeUs3T/UuZlzePnvT/zzJJneGbJMySHJzMoeRA943rSI64HPeN6EhMa4+vwlVJKHYEnyfMcEbkHCBORCcCfgeneDUsppVqH2mnuTkg/AYDskmzm7Z3HvKx5LNu/jK+3f113bkpESl0iXfuaEpGiPdRKKeVHPEme7wKuBVYDfwC+Av7jzaCUUqq1SrWn1o2VBsivyGd93no25G1gw8ENbMjfwJzdczDuP/BFh0TTI7YHPeJ60CPelVB3jOqI1aLPbSullC94kjyPB94xxkz2djBKKdXWxIbG1k2HV6usuoxN+ZtcCXXeBtbnree9De9R7awGINQaSvfY7ock1N1iuxFiDfHVx1BKqTbDk+T5CuAVEckDfgJ+BOYaY/K9GplSSrVR4UHhDEgawICkAXVl1c5qthVsOySh/mr7V0zdNBUAq1jpFN3plyEf8T3JjMskKjjKR59CKaVaJ0/meb4SQETSgIuAl4A0T65VSinVPIIsQWTGZZIZl8m5nAuAMYY9JXtcyfRB19CPBdkLmL7tl8dS0u3prh5q9xjqgpoCjDE6jloppY6RJ/M8XwaMAfoCucCLuHqglVJK+ZCI0C6yHe0i2zGhw4S68tzy3F96qN1Jde3c0wD/mvovesT1oFtMN7rEdKFrTFe6xHQhPCjcFx9DKaUCiie9x88CW3EtjPKDMWaHNwNSSil1fBLCEg6Z4QOgtLqUjXkbmb5wOjXxNWzM28j7G96nyllVd05aRBpdY7seklB3ju5MmC3MFx9DKaX8kifDNhJEpDcwFnhURLoBG40xl3s9OqWUUs0iIiiCQcmDKIoqYtzocQA4nA72lOxhS8EWthZsZUv+FrYUbmH+3vl1DycKQpo9jQ5RHWgf2Z6O0R1pH9meDlEdSLOnYbPoCD6lVNviybCNKKA90AHoCEQDTu+GpZRSytusFisdojrQIaoDJ7c/ua68xlnDruJdroS6YAvbC7azs3gnq3JWUVJdUneeTWykR6bXJdPto9rTIdL1mhqRqtPpKaVaJU+6DObW2140xuzxbkhKKaV8yWax0Tm6M52jOx8yltoYQ15FHruKd7GzaCe7ityvxbtYsn8J5TXldecGWYLIiMxwJefuhLo2UU8KT8IiFl98NKWUOm6eDNvoByAidu+HAyIyANf46lCgBvizMWaRiFwK3AkIUAz8yRizsiViUkop5XpAMT4snviweAYmDTzkmDGGnPKcX5Lq4l+S6/l751PpqKw7N8QaQrvIdr/qre4Q1YHEsESdCUQp5dc8GbbRB3gbiHPtSg5wpTFmjZdiegp4yBjztYic4d4fB2wHTjTG5IvI6cBrwHAvxaCUUuooiAhJ4UkkhScxNGXoIcecxsmBsgPsLNp5SHK9rXAbP+75sW58NbiWM28f2Z72Ue3pGNWR9lHtaRfZjnR7OolhiToURCnlc54M23gN+D9jzA8AIjLOXTaqiWuOhwFqZ/WPBvYCGGN+rnfOAiDDS/dXSinVjCxiISUihZSIFIanHtrn4XA6yC7N/lVv9ab8Tfyw6wdqTE3duTaLjdSIVNLsaWTYM0izpx3yPiEsQYeDKKW8TowxTZ8gstIY0/9IZc0WkEhP4FtcwzMswChjzM7Dzrkd6GGMua6ROiYBkwASExMHT5061RuhKi8oKSnBbm+REUKqGWh7BZ5AajOHcZBXk0duTS55NXkcrDnIwZqDde+LncWHnG/DRpwtjjhbHPG2+F+9RloiA25ISCC1l9L2CkTjx49faowZcjTXeJI8fwIswzV0A+AyYLAx5vxjitJV50wgpYFD9wInA3OMMdNE5LfAJGPMKfWuHQ+8DJxgjDl4pHtlZmaajRs3HmuoqoXNnj2bcePG+ToM5SFtr8DTmtqsvKac7JJsskqy2Fuyl6ySrLptb8le8ivzDzk/1BpKqj2VdHs6aRFppNpTSY34ZUsMT/S7qfdaU3u1BdpegUdEjjp59uSnxDXAQ8DHuIZU/OQuO2b1k+HDich/gVvcux8C/6l3rJ97/3RPEmellFKtV5gtjM4xnekc07nB42XVZXWJ9J6SPewt2VuXZK/OXU1hZeEh51vEQlJ4EqkRqaREpBySWKdEpJBqTyUyKPB6r5VSzavR5FlEQoE/Al2B1cBtxpjqxs5vRnuBE4HZwEnAZnc87XEl8JcbYza1QBxKKaUCWHhQON1iu9EttluDx8uqy9hXuo/s0uy6rXZ/dc5qZuycQY2z5pBrIoIifpVc175PiUghKTyJYGtwS3w8pZSPNNXz/BZQjaun+XSgJ3BrC8R0PfCciNiACtxjl4H7gXjgZfdv/TVH282ulFJK1QoPCm+y59ppnBwsP/irxDq7xLW/Nnftr4aGAMSGxNbNPJIckex6DU/+pSw8majgKO3BVipANZU89zLG9AUQkdeBRS0RkDFmLjC4gfLrgAYfEFRKKaWam0UsJIYnkhieSL/Efg2eU15TXpdU7y/dz/4y13ag7AAHyg6w9uBa8iryfnVdqDWUxPDEQxLqw18TwhO8/RGVUsegqeS5boiGMaZGf0NWSimlDhVmC6NTdCc6RXdq9JwqRxU55TkcKDvgSqxLD9Ql1/vL9rMqZxU5ZTlUOasOuU4Q7BY7GdMzXEl8mCvZTgxPJCksqS75jg2J1fmvlWpBTSXP/UWkyP1egDD3vgDGGBPV+KVKKaWUAgi2BpNuTyfdnt7oOcYYCioLfkmw3cn1iq0rCAoPIqcsh7W5rl5sw6GzZFnFSnxY/CEJdf1Eu/Z9TEiMDhVRqhk0mjwbY/TXWKWUUqoFiAixobHEhsaSGZdZVz674NCpz6qd1RwsP0hOWQ4Hyg+4XssOkFOeQ05ZDntK9rD8wHIKKgt+dQ+bxUZimGsYSkJoQt1S6/Gh8SSEJRzyPjwovAU+tVKByb8mtFRKKaVUo4IsQXWrNTal0lFJbnnur5LrnPIc9pftZ1fxLlbkrCC/Iv9XPdngGo4SFxpHfFh8k4l2fFg84bZw7dFWbYomz0oppVQrE2INOeJQEYAaZw35FfkcrDjIwfKD5Jbn1r0/WOHaP5pEu6FebE20VWujybNSSinVRtkstroZRY7Ek0R7d/FuVuasPGKiXbvFhsY2uh8bGkuINcQbH1up46LJs1JKKaWOqDkT7fyKfPaX7Wd93nryKvJ+tRhNrYigiIaT7JBY4sLiiAuJIy7MtR8bGqsL1KgWocmzUkoppZrV0STaxhhKqkvIq8irS7jzK/J/tb+3ZK9rYZqKfGpMw8l2uC2c2NBYYkJiiAmNITbE/T4kpq68/mt0SDRBlqDm/viqldPkWSmllFI+IyJEBkcSGRxJh6gORzzfGENRVVFdcp1XkUdeRR4FlQXkV+RTUFng2ioK2FG4g4LKAkqrSxutzx5kbzK5rk3Aa8ujQ6KxWTR9asu09ZVSSikVMESE6JBookOim1ycpr4qR1Vdcl1YWUh+ZT4FFQWu13pJ98GKg2wt2Ep+ZT7lNeWN1hcVHPWr3u3Y0FjyCvPI25z3S9LtPh4VHKUL2bQimjwrpZRSqlULtgbXLYXuqYqairpe7AaTbvfrgbIDbMzfSH5FPpWOSj7/+fNf1SW4Ev7anuvokGiig12vUSGuRLx2v/ZYVEgUkcGRWMTSnF8K1Qw0eVZKKaWUOkyoLZQU25Hn1K7vu++/o++wvnXJdUHlr3u3CysLySnLYWvBVgorCympLmm0PkGICok6NLGul3hHh0QTFRz1q/LI4EgdWuJF+pVVSimllGoGwZZgUu2ppNpTPb6m2llNUWURhVWFrtfKQgqrCl2vtZv7WO047sKqQoqripusNzIo8lcJd1RIFFHB7s3ds127HxkcSVRIFPYgu/Z2H4Emz0oppZRSPhJkCapbwfFoOJwOiquKD0203e9rk/HCykIKKgsoqiwiqySLwkpX0u0wjkbrtYgFe5D9l8S6fsIdHHVIeUPJd1uYvUSTZ6WUUkqpAGO1WIkJdT20eDSMMZTVlFFUWURRVb3NvV9cVVxXVlxVTFFlEVvLttaVVzoqm6w/zBb2qyT7V8l3yGGJuPu8UGtoQKxAqcmzUkoppVQbISJEBEUQERRBKp4PL6lV6aisS6qbTL4riyiuLia7JJuNVRspripucnw3uOYHr59kRwZH1vWCN/k+2F5X1hJDTnySPIvIb4AHgZ7AMGPMknrH7gauBRzAzcaYb0WkHfBfIBkwwGvGmOeO9f5VVVVs3bqVsrKy4/gUyhsiIyNZunSpr8PwS+Hh4XTp0oXgYF1BSymllG+EWEMICQshISzhqK+tcdZQUlVSl2DXjt2uS7br9XrXnpddmu1KvKtKqHBUHPEe9iA79mA79iA7UcFRde8bSrojgyOP5Uvgs57nNcAFwL/rF4pIL+BioDeQBswUke5ADXCbMWaZiEQCS0VkhjFm3bHcfOvWrcTExJCZmYnFooPilf9zOp3s27ePVatWkZSURPv27X0dklJKKXVUbBbbMQ01qVXtqKa42pVIF1cX1yXVxVXFdT3b9d+XVJWQU5bD9urtdec1tjrlUX2O467hGBhj1gMNjWs5F/ifMaYS2C4iW3D1TM8Hst3XFovIeiAdOKbkuaysTBNnFVAsFgspKSns3buXadOmceGFF2oCrZRSqk0JsgYRZ40jLjTumK43xlDhqPgl4a4uZgADjroefxvznA4sqLe/x11WR0Q6AgOBhY1VIiKTgEkAiYmJzJ49+5DjkZGRmjirgGOxWBARcnNz+fzzz+nTp4+vQ6KkpORX31/Kv2mbBRZtr8Ci7dU2eC15FpGZQEMzi99rjPnsGOu0A9OAW40xRY2dZ4x5DXgNIDMz04wbN+6Q4zqmVgWydu3akZGRweH/rn1h9uzZfhGH8py2WWDR9gos2l5tg9eSZ2PMKcdwWRbQrt5+hrsMEQnClTi/a4z5+Pgj9C2r1Urfvn2pqamhU6dOvP3228TExHjlXg8++CB2u53bb7+90XM+/fRTunfvTq9evbwSg1JKKaVUa+BvYxc+By4WkRAR6QR0AxaJa3D068B6Y8w/fRphMwkLC2PFihWsWbOGuLg4XnrpJZ/G8+mnn7Ju3TENIVdKKaWUajN8NVXd+cALQCLwpYisMMacaoxZKyJTcT0IWAPcYIxxiMgJwOXAahFZ4a7mHmPMV8cby0PT17Jub6MjQI5Jr7QoHji7t8fnjxw5klWrVgGwaNEibrnlFioqKggLC2PKlClkZmZy5pln8vjjj9OvXz8GDhzI+eefz/3338/9999Pu3btuP766w+p89FHH+Wtt94iKSmJdu3aMXjwYAAmT57Ma6+9RlVVFV27duXtt99mxYoVfP7558yZM4dHHnmEadOm8f333//qvPDw8Ob7IimllFJKBSCf9DwbYz4xxmQYY0KMMcnGmFPrHXvUGNPFGJNpjPnaXTbXGCPGmH7GmAHu7bgTZ3/gcDiYNWsW55xzDgA9evTgp59+Yvny5Tz88MPcc889AIwZM4affvqJwsJCbDYb8+bNA+Cnn35i7Nixh9S5dOlS/ve//7FixQq++uorFi9eXHfsggsuYPHixaxcuZKePXvy+uuvM2rUKM455xyefvppVqxYQZcuXRo8TymllFKqrfO32TZa3NH0EDen8vJyBgwYQFZWFj179mTChAkAFBYWcuWVV7J582ZEhOrqasCVPD///PN06tSJM888kxkzZlBWVsb27dvJzMw8pO6ffvqJ888/v66nuDYxB1izZg333XcfBQUFlJSUcOqpp9IQT89TSimllGpL/G3Mc5tRO+Z5586dGGPqxjz/7W9/Y/z48axZs4bp06dTUeFaTWfo0KEsWbKkrqd54MCBTJ48uW44hqeuuuoqXnzxRVavXs0DDzxQV/+xnqeUUkop1ZZo8uxj4eHhPP/88/zjH/+gpqaGwsJC0tNdU1u/+eabdecFBwfTrl07PvzwQ0aOHMmYMWN45plnfjVkA2Ds2LF8+umnlJeXU1xczPTp0+uOFRcXk5qaSnV1Ne+++25deWRkJMXFxUc8TymllFKqLdPk2Q8MHDiQfv368f7773PHHXdw9913M3DgQGpqDl1CcsyYMSQlJREWFsaYMWPYs2cPY8aM+VV9gwYN4ne/+x39+/fn9NNPZ+jQoXXH/v73vzN8+HBGjx5Njx496sovvvhinn76aQYOHMjWrVsbPU8ppZRSqi0TY4yvY/CqzMxMs3HjxkPKli5detTDHZTyB0uXLmXx4sVkZGRw1lln+TocXRAgAGmbBRZtr8Ci7RV4RGSpMWbI0VyjPc9KKaWUUkp5SJNnpZRSSimlPKTJs1JKKaWUUh7S5FkppZRSSikPafKslFJKKaWUhzR5VkoppZRSykOaPPuIiHDbbbfV7T/zzDM8+OCDTV7z6aefsm7duibPmT17tkdTmG3YsIEBAwbUzes8atQoAHbs2EGfPn0AWLFiBV999dUR6zpW48aNY8mSJV6rXymllFKquWny7CMhISF8/PHH5ObmenyNJ8nz0dR10UUXsXz5crp06cLPP//8q3OOJXk+fGEXpZRSSqnWxObrAHzu67tg3+rmrTOlL5z+RJOn2Gw2Jk2axL/+9S8effTRQ47t2LGDa665htzcXBITE5kyZQp79uzh888/Z86cOTzyyCNMmzYNYwx//OMfycnJwWq18uGHHwJQUlLCRRddxJo1axg8eDDvvPMOIlJX/1dffcWzzz6L1Wpl1qxZ/PDDD9jtdkpKSurOqaqq4v7776e8vJy5c+dy9913c9ZZZ3HTTTexZs0aqqurefDBBzn33HN58803+fjjjykpKcHhcPDVV181eF55eTlXX301K1eupEePHpSXlzfjF10ppZRSyvs0efahG264gX79+nHHHXccUn7TTTdx5ZVXcuWVV/LGG29w88038+mnn3LOOedw1llncdFFFwEwfPhw7rrrLs4//3wqKipwOp3s3r2b5cuXs3btWtLS0hg9ejTz5s3jhBNOqKv/jDPO4I9//CN2u53bb7+9wdiCg4N5+OGHWbJkCS+++CIA99xzDyeddBJvvPEGBQUFDBs2jFNOOQWAZcuWsWrVKuLi4ho979///jfh4eGsX7+eVatWMWjQIG98WZVSSimlvEaT5yP0EHtTVFQUV1xxBc8//zxhYWF15fPnz+fjjz8G4PLLL/9Vcg1QXFxMVlYW559/PgChoaF1x4YNG0ZGRgYAAwYMYMeOHYckz8fqu+++4/PPP+eZZ54BoKKigl27dgEwYcIE4uLimjzvxx9/5OabbwagX79+9OvX77hjUkoppZRqST5JnkXkN8CDQE9gmDFmibt8AvAEEAxUAX81xnwvIuHAh0AXwAFMN8bc5YvYm9utt97KoEGDuPrqq5utzpCQkLr3Vqu12cYhG2OYNm0amZmZh5QvXLiQiIiII56nlFJKKRXofPXA4BrgAuDHw8pzgbONMX2BK4G36x17xhjTAxgIjBaR01skUi+Li4vjt7/9La+//npd2ahRo/jf//4HwLvvvsuYMWMAiIyMpLi4uO59RkYGn376KQCVlZWUlZU1a2z17wdw6qmn8sILL2CMAWD58uUNXtfYeWPHjuW9994DYM2aNaxatapZ41VKKaWU8jafJM/GmPXGmI0NlC83xux1764FwkQkxBhTZoz5wX1OFbAMyGi5iL3rtttuO2TWjRdeeIEpU6bQr18/3n77bZ577jkALr74Yp5++um66eXefvttnn/+efr168eoUaPYt29fs8Y1fvx41q1bx4ABA/jggw/429/+RnV1Nf369aN379787W9/a/C6xs7705/+RElJCT179uT+++9n8ODBzRqvUkoppZS3SW3voE9uLjIbuL122MZhxy4C/miMOeWw8hhcyfMpxphtjdQ7CZgEkJiYOHjq1KmHHI+MjNTETQWkpUuX8umnnxIfH8+AAQN8HQ4lJSXY7XZfh6GOgrZZYNH2CizaXoFn/PjxS40xQ47mGq+NeRaRmUBKA4fuNcZ8doRrewNPAhMPK7cB7wPPN5Y4AxhjXgNeA8jMzDTjxo075PjSpUs9+ARK+af09HQyMjI4/N+1L8yePdsv4lCe0zYLLNpegUXbq23wWvJ8eI+xp0QkA/gEuMIYs/Www68Bm40xzx5neEoppZRSSh01v5qqzj0k40vgLmPMvMOOPQJEA9f5IDSllFJKKaV888CgiJwvInuAkcCXIvKt+9CNQFfgfhFZ4d6S3L3R9wK9gGXuck2ilVJKKaVUi/JJz7Mx5hNcQzMOL38EeKSRy6SRcqWUUkoppVqEr+Z5VkoppZRSKuBo8uwjVquVAQMG0L9/fwYNGsTPP//cLPXu2LGDPn36NEtdR+P3v/89/fr141//+hf3338/M2fOBGDcuHEsWeKaifCxxx7z2v3ffPNNbrzxxl+VP/jgg6Snp3P//fcDUFhYyNlnn03//v3p3bs3U6ZMOWLd9957L+3atWtw+qGpU6fSq1cvevfuzSWXXALA1q1bGTBgQIPnN9Q+Dz74YN1S5koppZTyb371wGBbEhYWxooVKwD49ttvufvuu5kzZ45vgzpG+/btY/HixWzZsqXJ8x577DHuueeeo6rb4XBgtVqPJzz+8pe/cPvttwPw0ksv0atXL6ZPn05OTg6ZmZlceumlBAcHN3r92WefzY033ki3bt0OKd+8eTOPP/448+bNIzY2lgMHDgDQpUsXVqxYoXN9KqWUUq1Qm0+en1z0JBvyNjRrnT3ienDnsDs9Pr+oqIjY2FjANcH6ueeeS35+PtXV1TzyyCOce+657Nixg9NPP50TTjiBn3/+mfT0dD777DPCwsJYunQp11xzDQATJ/4yNfabb77JJ598QmFhIVlZWVx22WU88MADAJx33nns3r2biooKbrnlFiZNmoTD4eDaa69lyZIliAjXXHMNf/nLX3j++ed59dVXsdls9OrVq27p8FoTJ04kKyuLAQMG8MILL/D6669z1llncdFFF9Wdc9ddd1FeXs6AAQPo3bs37777Lu+88w7PP/88VVVVDB8+nJdffhmr1YrdbucPf/gDM2fO5KWXXmLHjh0NnjdlyhQef/xxYmJi6N+/PyEhIUf8WosIxcXFGGMoKSkhLi4Om63pb4MRI0Y0WD558mRuuOGGurZLSko64v2bsnfvXs4444y6/dWrV7Nt2zY6dOhwXPUqpZRSqvnosA0fqU0ke/TowXXXXVe3hHVoaCiffPIJy5Yt44cffuC2226jdhXIzZs3c8MNN7B27VpiYmKYNm0aAFdffTUvvPACK1eu/NV9Fi1axLRp01i1ahUffvhh3RCKN954g6VLl7JkyRKef/55Dh48yIoVK8jKymLNmjWsXr2aq6++GoAnnniC5cuXs2rVKl599dVf3ePzzz+v620dM2ZMg5/3iSeeqOttf/fdd1m/fj0ffPAB8+bNY8WKFVitVt59910ASktLGT58OCtXriQ+Pr7B87Kzs3nggQeYN28ec+fOZd26dR593W+88UbWr19PWloaffv25bnnnsNiObZvg02bNrFp0yZGjx7NiBEj+Oabbzy6rnZYR+1W+zVNS0tjxYoVrFixguuvv54LL7xQE2ellFLKz7T5nuej6SFuTvWHbcyfP58rrriCNWvWYIzhnnvu4ccff8RisZCVlcX+/fsB6NSpU92SzIMHD2bHjh0UFBRQUFDA2LFjAbj88sv5+uuv6+4zYcIE4uPjAbjggguYO3cuQ4YM4fnnn+eTT1wTnuzevZvNmzeTmZnJtm3buOmmmzjzzDPrerH79evHpZdeynnnncd5553XLJ9/1qxZLF26lKFDhwKuXyZqe26tVisXXnhhk+ctXLiQcePGkZiYCMDvfvc7Nm3adMT7fvvttwwYMIDvv/+erVu3MmHCBMaMGUNUVNRRf4aamho2b97M7Nmz2bNnD2PHjmX16tXExMQ0eV3tLxq1HnzwwUOOz5s3j8mTJzN37tyjjkkppZRS3qU9z35g5MiR5ObmkpOTw7vvvktOTg5Lly5lxYoVJCcnU1FRAXDIsASr1UpNTc0R6xaRX+3Pnj2bmTNnMn/+fFauXMnAgQOpqKggNjaWlStXMm7cOF599VWuu841lfaXX37JDTfcwLJlyxg6dKhH9z0SYwxXXnllXU/rxo0b65LI0NDQunHOTZ13LKZMmcIFF1yAiNC1a1c6derEhg3HNmwnIyODc845h6CgIDp16kT37t3ZvHnzMccGkJ2dzbXXXsvUqVN1zLRSSinlhzR59gMbNmzA4XAQHx9PYWEhSUlJBAUF8cMPP7Bz584mr42JiSEmJqaul7J26EOtGTNmkJeXR3l5OZ9++imjR4+msLCQ2NhYwsPD2bBhAwsWLAAgNzcXp9PJhRdeyCOPPMKyZctwOp3s3r2b8ePH8+STT1JYWEhJSckxfc6goCCqq6sBOPnkk/noo4/qHrLLy8tr8LM2dt7w4cOZM2cOBw8epLq6mg8//NCjGNq3b8+sWbMA2L9/Pxs3bqRz584A9OjR46g+z3nnncfs2bMB19du06ZNdXUdi+rqan7zm9/w5JNP0r1792OuRymllFLe0+aHbfhK7ZhncPWuvvXWW1itVi699FLOPvts+vbty5AhQzxK6KZMmcI111yDiBzywCDAsGHDuPDCC9mzZw+XXXYZQ4YMoW/fvrz66qv07NmTzMzMugfisrKyuPrqq3E6nQA8/vjjOBwOLrvsMgoLCzHGcPPNNx9xWEJjJk2aRL9+/Rg0aBDvvvsujzzyCBMnTsTpdBIUFMRLL730qzG+vXr1avC8ESNG8OCDDzJy5EhiYmLqvpZH8re//Y2rrrqKvn37YozhySefJCEhgdzc3Lqx5Ye74447eO+99ygrKyMjI4PrrruOBx98kFNPPZXvvvuOXr16YbVaefrpp+uGyByLn3/+mSVLlvDAAw/UPdj51VdfkZaWdsx1KqWUUqp5SWMJQ2uRmZlpNm7ceEjZ0qVLGTx4sI8iajlvvvkmS5Ys4cUXX/R1KD7z4IMPYrfb66aqa8wXX3zBtm3buPnmm5vt3na7/Zh76RuzdOlSFi9eTEZGBmeddVaz1n0sZs+ezbhx43wdhjoK2maBRdsrsGh7BR4RWWqMGXI012jPs2rV7HY7r732GkVFRTz88MONnteciejWrVu58MILSU5ObrY6lVJKKeUfNHluxa666iquuuoqX4fhU7fffvsRe52b2+GzaSillFKq9WizDwzWjutVKlDov1mllFLK99pk8hweHs6+ffs0GVEBw+l0sm/fvrrZSg6fglAppZRSLaNNDtvo0qULK1euZO/evZqEqIBRXV3Nrl27qKioOOYZT5RSSil1fNpk8hwcHExmZiYfffRRs8+GoI7Pvn37SElJ8XUYfi0hIYGBAwf6OgyllFKqTfJJ8iwivwEeBHoCw4wxSw473h5YBzxojHlGRNoB/wWSAQO8Zox57nhiiIqK4tJLLyUvLw+Hw3E8ValmtGjRIoYNG+brMPyWzWYjLi6O4OBgX4eilFJKtUm+6nleA1wA/LuR4/8Evq63XwPcZoxZJiKRwFIRmWGMWXc8QYSEhJCamno8VahmtmXLFjIyMnwdhlJKKaVUg3ySPBtj1kPDDz2JyHnAdqC03vnZQLb7fbGIrAfScfVOK6WUUkop1SL8asyziNiBO4EJQIOT84pIR2AgsLCJeiYBk9y7lSKypnkjVV6UAOT6OgjlMW2vwKNtFli0vQKLtlfgyTzaC7yWPIvITKChJ7/uNcZ81shlDwL/MsaUNNIrbQemAbcaY4oau7cx5jXgNfc1S4522UXlO9pegUXbK/BomwUWba/Aou0VeERkyZHPOpTXkmdjzCnHcNlw4CIReQqIAZwiUmGMeVFEgnAlzu8aYz5uxlCVUkoppZTyiF8N2zDGjKl9LyIPAiXuxFmA14H1xph/+io+pZRSSinVtvlkhUEROV9E9gAjgS9F5NsjXDIauBw4SURWuLczPLzda8cTq2px2l6BRdsr8GibBRZtr8Ci7RV4jrrNxBjjjUCUUkoppZRqdXzS86yUUkoppVQg0uRZKaWUUkopD7Xa5FlEThORjSKyRUTu8nU86tdE5A0ROVB/Hm4RiRORGSKy2f0a68sY1S9EpJ2I/CAi60RkrYjc4i7XNvNDIhIqIotEZKW7vR5yl3cSkYXun40fiIiu9e5HRMQqIstF5Av3vraXHxORHSKy2v0s1hJ3mf5M9FMiEiMiH4nIBhFZLyIjj6W9WmXyLCJW4CXgdKAX8HsR6eXbqFQD3gROO6zsLmCWMaYbMMu9r/xDDXCbMaYXMAK4wf19pW3mnyqBk4wx/YEBwGkiMgJ4Etd8+l2BfOBa34WoGnALsL7evraX/xtvjBlQb35n/Znov54DvjHG9AD64/peO+r2apXJMzAM2GKM2WaMqQL+B5zr45jUYYwxPwJ5hxWfC7zlfv8WcF5LxqQaZ4zJNsYsc78vxvVDJx1tM79kXErcu0HuzQAnAR+5y7W9/IiIZABnAv9x7wvaXoFIfyb6IRGJBsbimvoYY0yVMaaAY2iv1po8pwO76+3vcZcp/5dsjMl2v98HJPsyGNUwEekIDAQWom3mt9xDAFYAB4AZwFagwBhT4z5Ffzb6l2eBOwCnez8ebS9/Z4DvRGSpiExyl+nPRP/UCcgBpriHRv1HRCI4hvZqrcmzagWMax5FnUvRz4iIHddqn7caY4rqH9M28y/GGIcxZgCQgesvcj18G5FqjIicBRwwxiz1dSzqqJxgjBmEa5joDSIytv5B/ZnoV2zAIOAVY8xAoJTDhmh42l6tNXnOAtrV289wlyn/t19EUgHcrwd8HI+qR0SCcCXO7xpjPnYXa5v5OfefJn/AtTBVjIjUri6rPxv9x2jgHBHZgWuo4Um4xmdqe/kxY0yW+/UA8AmuX1L1Z6J/2gPsMcYsdO9/hCuZPur2aq3J82Kgm/sp5WDgYuBzH8ekPPM5cKX7/ZXAZz6MRdXjHn/5OrDeGPPPeoe0zfyQiCSKSIz7fRgwAdc49R+Ai9ynaXv5CWPM3caYDGNMR1z/Z31vjLkUbS+/JSIRIhJZ+x6YCKxBfyb6JWPMPmC3iGS6i04G1nEM7dVqVxh0L9/9LGAF3jDGPOrbiNThROR9YByQAOwHHgA+BaYC7YGdwG+NMYc/VKh8QEROAH4CVvPLmMx7cI171jbzMyLSD9fDL1ZcHSVTjTEPi0hnXD2bccBy4DJjTKXvIlWHE5FxwO3GmLO0vfyXu20+ce/agPeMMY+KSDz6M9EvicgAXA/kBgPbgKtx/3zkKNqr1SbPSimllFJKNbfWOmxDKaWUUkqpZqfJs1JKKaWUUh7S5FkppZRSSikPafKslFJKKaWUhzR5VkoppZRSykO2I5+ilFKqJYiIA9dUgLXOM8bs8FE4SimlGqBT1SmllJ8QkRJjjL2RY4LrZ7azoeNKKaVahg7bUEopPyUiHUVko4j8F9fKZe1E5K8islhEVonIQ/XOvVdENonIXBF5X0Rud5fPFpEh7vcJ7uWfERGriDxdr64/uMvHua/5SEQ2iMi77sQdERkqIj+LyEoRWSQikSLyo3vhgdo45opI/5b6GimlVEvTYRtKKeU/wkRkhfv9duAvQDfgSmPMAhGZ6N4fBgjwuYiMBUpxLek8ANfP9WXA0iPc61qg0BgzVERCgHki8p372ECgN7AXmAeMFpFFwAfA74wxi0UkCijHtWT7VcCtItIdCDXGrDy+L4NSSvkvTZ6VUsp/lBtjBtTuiEhHYKcxZoG7aKJ7W+7et+NKpiOBT4wxZe7rPvfgXhOBfiJykXs/2l1XFbDIGLPHXdcKoCNQCGQbYxYDGGOK3Mc/BP4mIn8FrgHePMrPrJRSAUWTZ6WU8m+l9d4L8Lgx5t/1TxCRW5u4voZfhuiFHlbXTcaYbw+raxxQWa/IQRP/VxhjykRkBnAu8FtgcBOxKKVUwNMxz0opFTi+Ba4RETuAiKSLSBLwI3CeiISJSCRwdr1rdvBLQnvRYXX9SUSC3HV1F5GIJu69EUgVkaHu8yPl/9u5Y5QGgyAMw+9XeoWcQO/hAdLZSEovYeMBUpk2YB17m1R2IiEBr6GFIKnSTIo/gVRhUZEkvk+5sAzTfQyzm2xD9Ri4B2ZV9fmjDiXpwDl5lqQjUVXTJBfAy+YN3xK4rqpFkgnwBrwDs51rQ+AxyQ3wtHM+plvHWGweBH4A/T21V0mugFGSM7p950tgWVXzJF/Aw+90KkmHy6/qJOnEJLmjC7XDP6rXA56Bc7/Sk3TqXNuQJH1bkgHwCtwanCX9B06eJUmSpEZOniVJkqRGhmdJkiSpkeFZkiRJamR4liRJkhoZniVJkqRGa4J8x2fw9zeeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "channel_number = preprocess.name2channel('C3')\n",
    "\n",
    "# Show the effect of the filter\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.title(f'Filtering on channel {channel_number}')\n",
    "plt.psd(eeg_data[channel_number], Fs=fs, label='Raw data')\n",
    "plt.psd(eeg_notched[channel_number], Fs=fs, label='Notch filtered')\n",
    "plt.psd(eeg_notched_bp[channel_number], Fs=fs, label=f'Bandpass filtered [{lf}, {hf}] Hz')\n",
    "plt.tight_layout()\n",
    "plt.legend(loc='lower left', shadow=True)\n",
    "plt.xlim([0,60])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.title(f'Filtering on all channels')\n",
    "\n",
    "for i in range(eeg_data.shape[0]):\n",
    "    plt.psd(eeg_notched_bp[i], Fs=fs, label=f'{preprocess.channel2name(i)}')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.legend(loc='lower left', shadow=True)\n",
    "plt.xlim([0,60])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build an event time-series/stream\n",
    "\n",
    "Create a time series from the event codes which is the same length as the EEG data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "eeg_timestamps = unicorn_stream['time_stamps']\n",
    "eeg_start_time = eeg_timestamps[0]\n",
    "label_start_time = event_time_stamps[0]\n",
    "experiment_start_time = min(label_start_time, eeg_start_time)\n",
    "\n",
    "relative_eeg_timestamps = eeg_timestamps - experiment_start_time\n",
    "relative_event_time_stamps = event_time_stamps - experiment_start_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_closest_time(timestamp, eeg_timestamps):\n",
    "    return np.where(eeg_timestamps > timestamp)[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get indices of events\n",
    "indices = [0]\n",
    "\n",
    "for timestamp in relative_event_time_stamps:\n",
    "    indices.append(find_closest_time(timestamp, relative_eeg_timestamps))\n",
    "\n",
    "# Build event time series (use -1 since 0 is actually a code for no blinking)\n",
    "event_time_series = np.zeros_like(eeg_timestamps) - 1\n",
    "\n",
    "for i, (_, code) in enumerate(zip(indices[1:], event_codes)):\n",
    "    idx = i+1\n",
    "    if idx < len(indices)-1:\n",
    "        event_time_series[indices[idx]:indices[idx+1]] = code\n",
    "    else:\n",
    "        event_time_series[indices[idx]:] = code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Pnz4MHjxYo8giEmsNczb1q0wiorAZQ7vvvju777571u188cUXHHrooTmoSEREJEZ0o8280gVCIiIiHZg+rnJnpsuD8k5hU0REpANz6k+jK2RJNBQ2RUREOjCNbErUOn3YNLPBZrY4pLZnmNnZwXLCzMrD6EdERCQdXY2+K43x5pcuEBIREQnJtupavt4a7QdEVG2rAXQ1ej3th/yLVdg0s4uBX5H8o+Q9oBZ4yt0fCx6vcvfiYPkq4FygB/C4u1/XQtNFZnY3cCywEhjv7lvN7HJgItAd+B/gInffYmYzgK+BcmBv4Gp3f8yS98j5PXAisALYkdMdICIisfLd21/lo883R10GXczpojmbEpHYhE0zGwFcCxzr7uvNrAS4Nc22JwEHAkeTPHPwhJkd5+6vpGn+QOD77n65mc0EzgIeAP7m7ncHbd4AXEYyTAIMBMYCBwNPAI8BZwLDgEOAUmAJcG8bXuNEkuGW0tJSEolEpk9tl6qqqtD76Gy0T3MvDvu0pq6Gkm0lUZeRkaKaIkrWhlvr/K/nU2RFofZRSFo6Rldt2MyIAV0oL4327XZAty28ueagSGtoi6rqHiRWDgulbXdY/sknJBKrQ2m/UEX5uzQ2YRM4AXjU3dcDuPuGFm62fVLw793g+2KSgTJd2PzY3RcEy/OBwcHyyCBk9gvaeDblObPdvQ5YYmalwbrjgIfcvRZYZWYvZvzqkq9pOjAdoLy83CsqKtry9DZLJBKE3Udno32ae3HYp+s2r2P20tlRl5GRkrUlbCjdEGof40aMo1/PfqH2UUhaOkaLXnyGYw/Zj8nfOyS/RTX16SzYuCzaGtogsXIYFYPCqdesnP3325+KinDCbKGK8ndpnMJmc2oILnIysy4kT3dDcjTzRnf/Y4btbE9ZrgV6BcszgDPcfaGZTQAq0jxHM0BERGQXOnFdePSGnX9xuhr9ReAcMxsAEJxGXw6MCh4/HegWLD8LXGpm9fM3B5nZXu3osy+w2sy6ARdksP0rwHlmVmRmA4Hj29GniIiISIcRm5FNd3/fzKYCL5tZLclT5NcAc8xsIfAMsDnY9jkzGw7MDU61VwEXAuva2O1/AfOAz4OvfVvZ/nGSp/uXAJ8Cc5s8/nczq78sca67n9PGekREJGZamPIl0inEJmwCuPv9wP1NVh+TsnxNyra3Abdl0OZyYGTK9zenLN8J3NnMcyY0+b44+OrAT9L0U9FaLSIi0rHoI7gLk2uCQ17F6TS6iIiISFY0zpx/sRrZzEYw1/OFZh76trt/ke96RESkc1C4kc6u04TNIFCWRV2HiIh0HjpdK6LT6CIiItLJaC5tfilsioiIhEnn0QuKbg6QfwqbIiIiIdEImojCpoiIiIiESGFTREQkRKbz6AVHA875pbApIiISEoWawqPon38KmyIiIiISGoVNERGREOnqZ+nsFDZFRETCovPoIgqbIiIi0omYbkmVbwqbIiIiIdJZdOnsFDZFRERCos9GF1HYFBEREZEQKWyKiIhIp2FoxDnfFDZFRERC4q5bH4kobIqIiIhIaBQ2RURERCQ0CpsiIiIhccB086OCYqCb7eeZwqaIiIiIhEZhU0RERERCo7ApIiISEnfX1ejS6SlsioiISKdhpimb+aawKSIiIiKhyVnYNLM3ctVWPplZVUjtTjGzXwXLM8zsYzNbYGYLzezbYfQpIiKFJXk1ukjnlrOw6e7H5qqtbJhZUdQ1pHGVu5cBPwfuirYUERERkfzomquGzKzK3YvNrAK4HtgIHArMBBYBk4BewBnuXmlmpwHXAt2BL4AL3H2tme0J/BXYB5gLnAiMcvf1ZnYh8LPgOfOAK929Nhid/CMwDphlZke6+xlBXScG253ZQu1TgVOBrcD4oI509U0B9gOGBF//291vD9qZDFwCrANWAPOb6W4uMCjjHSsiBe93z33KS//fnlGXkZGu1V2p6RZurZ+s+IipZxwZah8i7WUkL9yS/MlZ2GzicGA4sAH4CLjH3Y82s0nAT0mO7r0GHOPubmb/BlwN/BK4DnjR3W80s1OAywDMbDhwHjDG3avN7A7gAuDPQB9gnrv/0swM+MDM9nT3z4EfAve2UGsf4E13n2xmNwGXAze0UB/AwcDxQF9gmZndCRwGnA+Ukdyv79B82DwFmN1cIWY2EZgIUFpaSiKRaKHs7FVVVYXeR2ejfZp7cdinf393M12sKwP7FP4bmHUDD/EE0OrNxpPvrObEfonQ+ig0LR2j7vDJJ5+QSKzOb1FN7SiC2mHR1tAGVdU9SKwMp95ahxUrVpBIrAul/UIV5e/SsMLmW+6+GsDMKoHngvWLSIY0gH2BR8xsIMnRw4+D9WOBMwHc/Rkz+zJY/21gFPBWMk/Si+QIIkAtMCt4jpvZX4ALzew+YDRwcQu17gCeCpbnkxxJbak+gL+7+3Zgu5mtA0qBbwGPu/uW4HU/0aSf35rZ/x20O7q5Qtx9OjAdoLy83CsqKlooO3uJRIKw++hstE9zLw77tOurz7J/6Vd8Z9TGqEtpVcnaEjaUbgit/afn784na/sX/M8sl1o8Rp/5O/sPHkxFxUF5rWkXn86CjcuiraENEiuHUTEonHqLrJx/+Zd/oaLikFDaL1RR/i4N62r07SnLdSnf19EYcH8P/MHdDwX+HejZSpsG3O/uZcG/Ye4+JXhsm7vXpmx7H3Ah8H3gUXevaaHdam8cT6/NsL7U15f6nJZc5e4HAdfQ8kiriMRM8jdI4Y9qiohEIcpbH+0OrAyWL0lZ/zpwLoCZnQT0D9a/AJxtZnsFj5WY2f7NNezuq4BVJOdc3pfj+tJ5BTjDzHqZWV/gtDTb/QHoYmYnt7MuESlAunF3kll9+JZ6OjQKi47R/IsybE4BHjWz+cD6lPXXAyeZ2WLgHGANsMndl5AMj8+Z2XvA88DAFtp/EFjh7h/kuL5mufs7wCPAQuBp4K002znJOaFXt7MuESkwet8SEUkvZ3M23b04+JoAEinrK1KWGx5z9znAnGaa+go42d1rzGw0cFQwPxJ3f4RkoGu27ybGAndnWnew/BjwWEv1pZy6r/9+ZMryVGBqM8+Z0OT7WQRzTEUk/tw1erUzxW8RaRTWBULZ2A+YaWZdSF68c3lbGwhGIzfTePW4iEhoHKVN2VX95QCaYiGdXcGFTXf/EDgiyzZGNV1nZvOAHk1WX+Tui7LpS0REWbNR8h6GUVchkp4l/zyUPCq4sBkWd/9m1DWISMekNy4RkfSivEBIRKRDcNep0gam8F2vfoTXNO4tnZzCpohIlhSuRETSU9gUEcmWu8auApqzKYVOx2j+KWyKiGTJAUzvXrKz+iNCUyyks1PYFBHJku6zuTNd6ysiqRQ2RUSypGglIpKewqaISJZ0NfrONB8uqeGm7hHXIU3oB5J3CpsiIllTuqqn0C1xoKke+aWwKSKSJc3ZFBFJT2FTRCRLyavRo65CCo2uRhdJUtgUEcmSRjZ3pjmbUsj0fzX/FDZFRLKkbNVIb+QSB/qDKL8UNkVEsqV3rp3o4oskHRYiSQqbIiJZ0icIpdB+2IVp0qZ0cgqbIiJZ0pzNnWlETwqZsn/+KWyKiGRJ2aqR3scbaTqBSJLCpohIDmi0pJEiloikUtgUEcmC65yxiEiLFDZFRLJQnzU1sJlC+RvQ3NVCpf+r+aewKSKShYY8oXcwQNMJmqN9Unh0RiK/FDZFRLJQ/6alPNFIb+MikkphU0QkCwpWIiItU9gUEclCw5xN3cy8gU5R7sw07l1Q9NPIv04bNs1ssJktDqntGWZ2drCcMLNlZrbQzN4ys7Iw+hSRaOheijvTG7nEgf7X5lenDZt5doG7Hw7cAfw26mJEJHd0Nfqu9EaepAFekaSuUReQCTO7GPgVyd9h7wG1wFPu/ljweJW7FwfLVwHnAj2Ax939uhaaLjKzu4FjgZXAeHffamaXAxOB7sD/ABe5+xYzmwF8DZQDewNXu/tjlvzg298DJwIrgB1p+psLXNXO3SAiTazbtI1H/rmCmrro3tVr65Q2d6L9sAtdjS6dXcGHTTMbAVwLHOvu682sBLg1zbYnAQcCR5P8lfeEmR3n7q+kaf5A4PvufrmZzQTOAh4A/ubudwdt3gBcRjJMAgwExgIHA08AjwFnAsOAQ4BSYAlwbzP9nQLMbuG1TiQZciktLSWRSKTbNCeqqqpC76Oz0T7NvZb26bPLq3loabq/7fKni8H+tT0pWds96lJaVVRTRMnaktDa77W5K3V13qn+H6Q7RrfXJv8Q+aiykoSvyHNVTewogtph0dbQBlXVPUisDKfemjpj5cqVJBLrQ2m/UEX5/lTwYRM4AXjU3dcDuPsGS/9n4knBv3eD74tJBsp0YfNjd18QLM8HBgfLI4OQ2S9o49mU58x29zpgiZmVBuuOAx5y91pglZm92KSfB82se9BWWbri3X06MB2gvLzcKyoq0m2aE4lEgrD76Gy0T3OvpX267OVKWLqU968/mT49ovt1tm7zOmYvnc2GyCrIXMnaEjaUhlfptnW7Yda9U/0/SHeMbtlRA88/y5ChQ6n4X0PzX1iqT2fBxmXR1tAGiZXDqBgUTr3dikaxzz6DqKgYGUr7hSrK96c4hM3m1BDMNzWzLiRPd0NyNPNGd/9jhu1sT1muBXoFyzOAM9x9oZlNACrSPCfTkyMXkAyzvyU5QvqvGT5PRFpQf/JcpymlkOnwlM4uDhcIvQicY2YDAILT6MuBUcHjpwPdguVngUvNrH7+5iAz26sdffYFVptZN5JBsTWvAOeZWZGZDQSOb7qBJ+8F8l/AMWZ2cDtqEpEmGi/O0du5iEihKviRTXd/38ymAi+bWS3JU+TXAHPMbCHwDLA52PY5MxsOzA1OtVcBFwLr2tjtfwHzgM+Dr31b2f5xkqf7lwCfkrwQqLnXstXMbiF5kdBlbaxJRJqov+2QRjYLi+6zmaTdUJj06yL/Cj5sArj7/cD9TVYfk7J8Tcq2twG3ZdDmcmBkyvc3pyzfCdzZzHMmNPm+OPjqwE/S9FPR5PtbWqtNRDKjN/MCpJvb70J/DBUe3R83v+JwGl1EpEV6My8sehsXkVSxGNnMRjDX84VmHvq2u3+R73pEJHfqT9dqzmbh0E+ikUK3SFKHD5tBoCyLug4Ryb3GzyWPtg7ZmaY37Ex/DBUW/b7IP51GF5HYarj1UaRViIhISxQ2RSS2Gkc2FTcLiQY2k3RVfuHSjya/FDZFJLYabn0UcR3SSLl/V9on0tkpbIpIbGnOpohI4VPYFJHYavy4SqVNKTw6UyuSpLApIvGliVcFS/MVpZDp6MwvhU0RiS1Hp9BFRAqdwqaIxJa7Lg4qNPU/Dw1sah8UKv3OyD+FTRGJLcc1X1MKno5R6ewUNkUktjSyWYCCH4gG9aSQadQ5vxQ2RSS2NGdTCpoCjQigsCkiMZYc2VTaLCSNczaVtKQw6Q/U/FPYFJHYcnQeXQqfDlHp7BQ2RSS+lDULlsY1Gz9OVQqRfjb5pLApIrGlOZsiIoVPYVNEYsvdNWezwJglR4w0ZbOR/iAqLPpx5J/CpojElrveyKVwKXCLJClsikhsORqlKFSaryiFTH8I5JfCpojEVnJkU3FTCpuOUOnsFDZFJLYc1xt5gdFnozfSLihM9fOKJX8UNkUktlzn0UVECp7CpojEmrJmgdEPZBea6lF4NPKeXwqbIhJb7q43cilY+shOkSSFzSbMbLCZLQ6h3YSZlee6XZHOTDd1LzyasymFTr8y8k9hsx3MrCjqGkQkuBo96iJEWqE/iKSz6xp1AQWqq5k9CBwJvA9cDCwBHgFOBG4ysw3A9UAPoBL4obtXmdn/BZwG9ALeAP7dU86lmFkX4F7gM3e/No+vSTqIj9dvpmpbTdRl5M3yr2pZ9NlXzT72xebtOo1eoBav+oqeXTvH3+XpjtGvtlZHUI1k4ovNO9L+XumIhu7VJ9L+FTabNwy4zN1fN7N7gSuD9V+4+5FmtgfwN2Ccu282s2uAXwC/Af7g7r8BMLO/AKcCTwbP7wo8CCx296l5fD3SQSxbs4mT//uVqMvIv7mvpX1o3/698liItKZb1+Tf1ufcNTfiSvKshWO0Z7fOEbrjoldX+McHa/nHB2ujLiVvZv94TKT9myYw78zMBgOvuPt+wfcnAD8DyoD/5e6fmNmpwAzgs+Bp3YG57n6ZmZ0FXA30BkqA37v7/zGzBNAfmJkuaJrZRGAiQGlp6aiHH344lNdYr6qqiuLi4lD76GzC3qfLNtRy4z+3ceY3urHfbp1jFsy2rdvo2atn2sf37t2FgcXR7ouauho2btsYaQ2ZKqoporZrbWjtV9fC2q274Z1ollZLx2iRwfABRXTrEvEI/I6NULs12hraoKq6B8XdtofS9prtPVi9Y7dQ2i5UB/UvwrdvDvX96fjjj5/v7s1em6KRzeY1TeD1328OvhrwvLt/P3UjM+sJ3AGUu/sKM5sCpP4GegM43sxucfdtu3TqPh2YDlBeXu4VFRXZvo4WJRIJwu6jswl7n/b66Av455ucXXEkY76xR2j9FJI4HKfrNq9j9tLZUZeRkZK1JWwo3RBqHxNGjKNfz36h9lFI4nCM8uks2Lgs6ioyllg5jIpBIdVbPASG/Gs4bRewKI/TzvOnZ9vsZ2ajg+UfAE3Pj7wJjDGzbwCYWR8zO4jGYLnezIqBs5s870/A/wvMNDMFfWmz+r96NEtRRETiQmGzecuAH5vZByRPfd+Z+qC7fw5MAB4ys/eAucDB7r4RuBtYDDwLvNW0YXe/FXgX+EtwsZBIxlxpU0REYkaja024+3Lg4GYeGtxkuxeBo5p5/rXALleZu3tFyvJ1WZYpnZQHY5umtCkiIjGhkTWROAlGNnW3HxERiQuFTZEY0Vl0ERGJG4VNkRjxhpFNxU0REYkHhU2RGGmYs6msKSIiMaGwKRIjDSOb0ZYhIiKSMYVNkRhpmLOptCkiIjGhsCkSI64bbYqISMwobIrEiEY2RUQkbhQ2ReJEczZFRCRmFDZFYqTxanTFTRERiQeFTZEY0dXoIiISNwqbIjHi+rhKERGJGYVNkRhpvBZdaVNEROJBYVMkRupvfaSRTRERiQuFTZEY8dY3ERERKSgKmyIxojmbIiISNwqbIrESnEbXnE0REYkJhU2RGNHIpoiIxI3CpkiM6OMqRUQkbhQ2RWKk8abuSpsiIhIPCpsiMdL4cZURFyIiIpIhhU2RGNHHVYqISNwobIrEkEY2RUQkLhQ2RWKk8abuSpsiIhIPCpsiMaKPqxQRkbhR2BSJIWVNERGJC4VNkRhpvKm74qaIiMRDq2HTzN7IRyGFyMxmmNnZzayvMLOnguXTzezXrbTTsL1INhpufRRxHSIiIpnq2toG7n5sPgppjZkVuXtt1HU05e5PAE9EXYd0Dvq4ShERiZtWw6aZVbl7sZlVANcDG4FDgZnAImAS0As4w90rzew04FqgO/AFcIG7rzWzPYG/AvsAc4ETgVHuvt7MLgR+FjxnHnClu9eaWRXwR2AcMMvMjnT3M4K6Tgy2OzNd3cBtwKnAVmB8UMdg4F5gD+Bz4Ifu/mkLu2BcMHK5G/ALd99phNLMJgDl7v4TM5sBfA2UA3sDV7v7Y022PwqYDpzt7pUt9CuBW59bxkNvrYi6jIzs2LGD7q//I7T2t+1I/r3VRWlTRERiotWw2cThwHBgA/ARcI+7H21mk4CfAj8HXgOOcXc3s38DrgZ+CVwHvOjuN5rZKcBlAGY2HDgPGOPu1WZ2B3AB8GegDzDP3X9pyUlqH5jZnu7+OfBDkqExnT7Am+4+2cxuAi4HbgB+D9zv7veb2aXA7cAZLbQzGDgaGAq8ZGbfaGUfDQTGAgeTHPFsCJtmdmzQ//jmAq6ZTQQmApSWlpJIJFrpKjtVVVWh95ELz767lR07nCP2LIq6lFZV19TRrWu4A/B9e3Tjw4XzqOwkgTMOx2lNXQ0l20qiLiMjRTVFlKwNt9b5X8+nyAr//2uuxOEYZUcR1A6LuoqMVVX3ILEypHqLusOniXDaLmBRHqdtDZtvuftqADOrBJ4L1i8Cjg+W9wUeMbOBJEcqPw7WjwXOBHD3Z8zsy2D9t4FRwFvBRQ+9gHXBY7XArOA5bmZ/AS40s/uA0cDFLdS6A6gfhZxPciSV4Hn/Giz/Bbipldc8093rgA/N7COSIbIls4Ptl5hZacr64SRHNE9y91XNPdHdpwfbUF5e7hUVFa10lZ1EIkHYfeTCHcvm0q8fzPj30VGX0qq47NM4icM+Xbd5HbOXzo66jIyUrC1hQ+mGUPsYN2Ic/Xr2C7WPQhKHY5RPZ8HGZVFXkbHEymFUDAqp3uIhMOSscNouYFEep20Nm9tTlutSvq9Laev3wK3u/kRw6n1KK20ayZHG/2zmsW1N5mneBzwJbAMedfeaFtqt9vqbEiZDa1tfaz1v5fumUvdR6tDTaqAncATQbNiUNBxM900QERGJpTDewncHVgbLl6Ssfx04F8DMTgL6B+tfAM42s72Cx0rMbP/mGg5GBFeRnBN6XzvrewM4P1i+AHi1le3PMbMuZjYUGAK090+tjcD3gBuDEC4ZchzT9dciIiKxFEbYnAI8ambzgfUp668HTjKzxcA5wBpgk7svIRkenzOz94DnSc57TOdBYIW7f9DO+n4K/DDo6yKSFzi15FPgn8DTwI/cfVs7+8Xd15K8YGmamX2zve10Nu66+lpERCSuMrn1UXHwNQEkUtZXpCw3PObuc4A5zTT1FXCyu9eY2WjgKHffHjznEeCRdH03MRa4O9O6g+XHCC7UcfdPgBNae36w7YQ06xM0vt4ZwIzmtm9u3wUXBo3IpH9JchQ2RURE4qq98xjbYz9gppl1IXnxzuVtbSAYLd1M8up26STcdRpdREQkrvIWNt39Q5IXx2TTxqim68xsHtCjyeqL3H1Rpu2a2WSSp/ZTPeruU9tepeSaRjZFRETiK58jm6Fw96znPgahUsGyQHlr1/+LiIhIwdINZSQWTEObIiIisaSwKQXPQTM2RUREYkphUwqfu+ZsioiIxJTCphQ8jWyKiIjEl8KmFLzkTd0VN0VEROJIYVMKXvLjKkVERCSOFDal4OnjKkVEROJLYVMKXvI+m0qbIiIicaSwKQVPnyAkIiISXwqbUvCSn40uIiIicaSwKbGgkU0REZF4UtiUgucOprFNERGRWFLYlILn6BOERERE4kphUwqebn0kIiISXwqbUvCSH1eptCkiIhJHCptS8Dw5aVNERERiSGFTCl5yZFNERETiSGFTCp9HXYCIiIi0l8KmFLzkJwhpbFNERCSOFDal4OkThEREROJLYVMKnj4bXUREJL4UNqXg6WJ0ERGR+FLYlIKX/AQhxU0REZE4UthsgZklzKy8mfUTzOwPwfKPzOziVtpp2F7aTiObIiIi8dU16gLizt3virqGjs51o00REZHYijxsBqOCvyJ5Hch7QC3wlLs/Fjxe5e7FwfJVwLlAD+Bxd78uTZuDgaeB14BjgZXAeHffamZlwF1Ab6ASuNTdv2yhxIvM7B6S++pSd/9nk76mAFXufrOZJYB5wPFAP+Ayd3+1yfbfA64FTnP39a3tnyh9vmk7f3jxQ3bU1kVax5dbdujjKkVERGIq0rBpZiNIBq9j3X29mZUAt6bZ9iTgQOBokuNcT5jZce7+SprmDwS+7+6Xm9lM4CzgAeDPwE/d/WUz+w1wHfDzFsrs7e5lZnYccC8wspWX1dXdjzaz7wZtj0t5DWcCvwC+21zANbOJwESA0tJSEolEK11lp6qqqsU+Xl9Zzf2LdtC3OxRFOGeyu0Hf7etC3x+50No+lbaLwz6tqauhZFtJ1GVkpKimiJK14dY6/+v5FFlRqH0Ukjgco+wogtphUVeRsarqHiRWhlRvUXf4NBFO2wUsyuM06pHNE4BH60f43H1DCxeCnBT8ezf4vphkoEwXNj929wXB8nxgsJntDvRz95eD9fcDj7ZS40NBba+Y2W5m1q+V7f+W2mfK+hOAcuAkd/+6uSe6+3RgOkB5eblXVFS00lV2EokELfWxfv5nsGghf590PPsN6B1qLR1Fa/tU2i4O+3Td5nXMXjo76jIyUrK2hA2lG0LtY9yIcfTr2S/UPgpJHI5RPp0FG5dFXUXGEiuHUTEopHqLh8CQs8Jpu4BFeZxGHTabU0Nw4ZKZdQG6B+sNuNHd/5hhO9tTlmuBXu2sp+mHJbb24Yn1/day8/6tBIYABwFvt7OWvHJPvlRdCC4iIiLtFfXV6C8C55jZAIDgNPpyYFTw+OlAt2D5WeBSM6ufvznIzPZqS2fu/hXwpZl9K1h1EfByC08BOC/obyzwVdBGe3xC8lT+n4PpAwVPH0kuIiIi2Yp0ZNPd3zezqcDLZlZL8hT5NcAcM1sIPANsDrZ9zsyGA3ODU+1VwIXAujZ2ewlwl5n1Bj4CftjK9tvM7F2SoffSNva1E3dfamYXAI+a2WnuXplNe6EL0qZGNkVERKS9Ij+N7u73k5w7meqYlOVrUra9DbgtgzaXk3Ihj7vfnLK8oEn7LbVTkWb9DGBGsDylue2DeaiDm9n+XeCQTPqPmlN/Gl1pU0RERNon6tPoUsC8fmQz2jJEREQkxiIf2cxGMNfzhWYe+ra7f9GGdqYBY5qsvs3d78umvrirn7OpgU0RERFpr1iHzSBQluWgnR9nX03H47pCSERERLKk0+iSVsOcTZ1IFxERkXZS2JS0XFeji4iISJYUNiWthjmbkVYhIiIicaawKenpcnQRERHJksKmpNU4sqm0KSIiIu2jsClpac6miIiIZEthU9Jyr78aXURERKR9FDYlrcabuituioiISPsobEpauj5IREREsqWwKWnp4ypFREQkWwqb0ipdjS4iIiLtpbApabnOo4uIiEiWFDalVTqNLiIiIu2lsClpaWBTREREsqWwKWl5cImQbn0kIiIi7aWwKWlpZFNERESypbApaenWRyIiIpIthU1Jq3FkU2lTRERE2kdhU9JqnLMZcSEiIiISWwqbklb9yKaIiIhIeylsSqs0sikiIiLtpbApadV/gpDmbIqIiEh7KWxKWg0XCClrioiISDspbLbAzAab2eKo64hKw62PIq1CRERE4kxhU9JqHNlU3BQREZH26Rp1ATHQ1cweBI4E3gcuBoYDtwLFwHpggruvNrOhwDRgT2ALcLm7LzWzGcA2oBzYDfiFuz+V91eSYtmaTby7robqJWvTblP5eRWgkU0RERFpP3Pd3yYtMxsMfAyMdffXzexe4APgTGC8u39uZucBJ7v7pWb2AvAjd//QzL4J3OjuJwRhc2/gu8BQ4CXgG+6+rUl/E4GJAKWlpaMefvjh0F7bgx9s5/lPalrdrldXuOPbvTW6maGqqiqKi4ujLqNDicM+ramrYeO2jVGXkZGimiJqu9aG2kf/Xv0psqJQ+ygkcThG2bERardGXUXGqqp7UNxteziNF3WH7gPCabuAhX2cHn/88fPdvby5xzSy2boV7v56sPwA8L+BkcDzQQArAlabWTFwLPBoSjDrkdLOTHevAz40s4+Ag4EFqR25+3RgOkB5eblXVFSE8XoAOKhsK2NefoPy8maPiwZ79e3BXrv1DK2OjiaRSBDmz60zisM+Xbd5HbOXzo66jIyUrC1hQ+mGUPsYN2Ic/Xr2C7WPQhKHY5RPZ8HGZVFXkbHEymFUDAqp3uIhMOSscNouYFEepwqbrWs69LsJeN/dR6euNLPdgI3uXpZhO5EOKe/TrxeDdy9i5KDdoyxDREREOjhdINS6/cysPlj+AHgT2LN+nZl1M7MR7v418LGZnROsNzM7PKWdc8ysSzCvcwgQnz8xRURERNpJYbN1y4Afm9kHQH/g98DZwP9jZgtJngo/Ntj2AuCyYP37wPiUdj4F/gk8TXJe507zNUVEREQ6Ip1Gb4G7Lyc5t7KpBcBxzWz/MXBKmub+4e4/yllxIiIiIjGgkU0RERERCY1GNvPA3SdEXYOIiIhIFDSyKSIiIiKhUdgUERERkdAobIqIiIhIaBQ2RURERCQ0ukBIRCRLXbt0ZY/ee0RdRka6dOkSeq2d6XPRY6N7f+g1MOoqMtelW3j19uh8n4seNXOP9FMTJQ0z+xz4JORu9gDWh9xHZ6N9mnvap7ml/Zl72qe5p32ae2Hv0/3dfc/mHlDY7MTM7G13L4+6jo5E+zT3tE9zS/sz97RPc0/7NPei3KeasykiIiIioVHYFBEREZHQKGx2btOjLqAD0j7NPe3T3NL+zD3t09zTPs29yPap5myKiIiISGg0sikiIiIioVHY7KTMbLmZLTKzBWb2dtT1xJGZ3Wtm68xsccq6EjN73sw+DL72j7LGOEmzP6eY2crgOF1gZt+Nssa4MbN/MbOXzGyJmb1vZpOC9TpO26mFfapjtR3MrKeZ/dPMFgb78/pg/QFmNs/M/sfMHjGz7lHXGhct7NMZZvZxyjFalreadBq9czKz5UC5u+s+Zu1kZscBVcCf3X1ksO4mYIO7/x8z+zXQ392vibLOuEizP6cAVe5+c5S1xZWZDQQGuvs7ZtYXmA+cAUxAx2m7tLBPz0XHapuZmQF93L3KzLoBrwGTgF8Af3P3h83sLmChu98ZZa1x0cI+/RHwlLs/lu+aNLIp0k7u/gqwocnq8cD9wfL9JN+EJANp9qdkwd1Xu/s7wfIm4ANgEDpO262FfSrt4ElVwbfdgn8OnADUhyIdo23Qwj6NjMJm5+XAc2Y238wmRl1MB1Lq7quD5TVAaZTFdBA/MbP3gtPsOt3bTmY2GDgCmIeO05xosk9Bx2q7mFmRmS0A1gHPA5XARnevCTb5DAX6Nmm6T929/hidGhyjvzOzHvmqR2Gz8xrr7kcC3wF+HJzClBzy5BwVzVPJzp3AUKAMWA3cEmk1MWVmxcAs4Ofu/nXqYzpO26eZfapjtZ3cvdbdy4B9gaOBg6OtKP6a7lMzGwn8J8l9exRQAuRt6ozCZifl7iuDr+uAx0n+B5fsrQ3mdNXP7VoXcT2x5u5rg1+adcDd6Dhts2DO1izgQXf/W7Bax2kWmtunOlaz5+4bgZeA0UA/M+saPLQvsDKquuIsZZ+eEkwBcXffDtxHHo9Rhc1OyMz6BBPbMbM+wEnA4pafJRl6ArgkWL4EmBNhLbFXH4gCZ6LjtE2CCwX+BHzg7remPKTjtJ3S7VMdq+1jZnuaWb9guRdwIsl5sC8BZweb6RhtgzT7dGnKH5hGcg5s3o5RXY3eCZnZEJKjmQBdgb+6+9QIS4olM3sIqAD2ANYC1wGzgZnAfsAnwLnuroteMpBmf1aQPC3pwHLg31PmGkorzGws8CqwCKgLVv9vknMMdZy2Qwv79PvoWG0zMzuM5AVARSQHwGa6+2+C96mHSZ7ufRe4MBiRk1a0sE9fBPYEDFgA/CjlQqJwa1LYFBEREZGw6DS6iIiIiIRGYVNEREREQqOwKSIiIiKhUdgUERERkdAobIqIiIhIaBQ2RURERCQ0CpsiIiIiEhqFTREREREJzf8PthcLryT/FoUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "window = 30\n",
    "start_time = 5\n",
    "t = np.arange(0, window*fs, 1/fs)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "x = relative_eeg_timestamps[start_time*fs:(start_time + window)*fs]\n",
    "y = event_time_series[start_time*fs:(start_time + window)*fs]\n",
    "ax.plot(x, y, '-')\n",
    "\n",
    "import matplotlib.transforms as mtransforms\n",
    "trans = mtransforms.blended_transform_factory(ax.transData, ax.transAxes)\n",
    "\n",
    "ax.fill_between(x, 0, 1, where= (y == 5),\n",
    "                facecolor='green', alpha=0.4, transform=trans, label='Right Hand')\n",
    "ax.fill_between(x, 0, 1, where= (y == 7),\n",
    "                facecolor='orange', alpha=0.5, transform=trans, label='Left Hand')\n",
    "\n",
    "event_labels = list(label2code.keys())\n",
    "plt.yticks(range(len(event_labels)), event_labels)\n",
    "plt.grid()\n",
    "plt.legend(shadow=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "window = 100\n",
    "start_time = 15\n",
    "x = relative_eeg_timestamps[start_time*fs:(start_time + window)*fs]\n",
    "y = event_time_series[start_time*fs:(start_time + window)*fs]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "ax.plot(x, y)\n",
    "\n",
    "import matplotlib.transforms as mtransforms\n",
    "trans = mtransforms.blended_transform_factory(ax.transData, ax.transAxes)\n",
    "\n",
    "ax.fill_between(x, 0, 1, where= (y == 5),\n",
    "                facecolor='green', alpha=0.4, transform=trans, label='Right Hand')\n",
    "ax.fill_between(x, 0, 1, where= (y == 7),\n",
    "                facecolor='orange', alpha=0.5, transform=trans, label='Left Hand')\n",
    "\n",
    "event_labels = list(label2code.keys())\n",
    "plt.yticks(range(len(event_labels)), event_labels)\n",
    "plt.ylim([2.8,7.2])\n",
    "plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.05), shadow=True, fancybox=True)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Normalise EEG data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "eeg_notched_bp_norm = preprocess.normalise_eeg(eeg_notched_bp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inspect EEG data with markers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "window = 26\n",
    "start_time = 0\n",
    "number_of_channels = 8\n",
    "\n",
    "plt.subplots(figsize=(10,20), sharex=True)\n",
    "plt.tight_layout()\n",
    "\n",
    "x = relative_eeg_timestamps[start_time*fs:(start_time + window)*fs]\n",
    "y1 = event_time_series[start_time*fs:(start_time + window)*fs]\n",
    "plt.subplot((number_of_channels + 1),1,1)\n",
    "plt.plot(x, y1)\n",
    "\n",
    "for i in range(number_of_channels):\n",
    "    y2 = eeg_notched_bp_norm[i ,start_time*fs:(start_time + window)*fs]\n",
    "    plt.subplot((number_of_channels + 1), 1, 2)\n",
    "    plt.plot(x, y2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_all(eeg_timestamps, event_time_series, eeg_data, number_of_channels=None, window=30, start_time = 15):\n",
    "\n",
    "    if number_of_channels == None:\n",
    "        number_of_channels = eeg_data.shape[0]\n",
    "\n",
    "    colors = plt.rcParams[\"axes.prop_cycle\"]()\n",
    "\n",
    "    height_ratios = [1 if i>0 else 3 for i in range(number_of_channels+1)]\n",
    "\n",
    "    fig, axs = plt.subplots(\n",
    "        number_of_channels+1, 1,\n",
    "        figsize=(10,8), \n",
    "        sharex=True, \n",
    "        gridspec_kw={'height_ratios': height_ratios}\n",
    "    )\n",
    "    plt.tight_layout()\n",
    "\n",
    "    x = eeg_timestamps[start_time*fs:(start_time + window)*fs]\n",
    "    y = event_time_series[start_time*fs:(start_time + window)*fs]\n",
    "\n",
    "    c = next(colors)[\"color\"]\n",
    "    axs[0].plot(x, y, color=c)\n",
    "    axs[0].set_title('Marker Stream')\n",
    "    plt.sca(axs[0])\n",
    "    plt.yticks(range(len(event_labels)), event_labels)\n",
    "    axs[0].set_ylim([2.8,7.2])\n",
    "\n",
    "    for i in range(number_of_channels):\n",
    "        axs[i+1].plot(\n",
    "            x, \n",
    "            eeg_data[i ,start_time*fs:(start_time + window)*fs], \n",
    "            color=next(colors)[\"color\"]\n",
    "        )\n",
    "        axs[i+1].set_title(preprocess.channel2name(i))\n",
    "        axs[i+1].set_ylabel(r'$\\mu$ V')\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "plot_all(relative_eeg_timestamps, event_time_series, eeg_notched_bp_norm, start_time=40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_file_name = DATA_FILE_NAME.split('.')[0]\n",
    "np.save(DATA_DIR / f'{data_file_name}_eeg_data', eeg_notched_bp, allow_pickle=True)\n",
    "np.save(DATA_DIR / f'{data_file_name}_marker_stream', eeg_notched_bp, allow_pickle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ICA Experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.decomposition import FastICA\n",
    "\n",
    "# transformer = FastICA(n_components=7, random_state=0, whiten=True)\n",
    "\n",
    "\n",
    "# _transformed = transformer.fit_transform(eeg_notched_bp.T)\n",
    "# _transformed.shape\n",
    "\n",
    "# plot_all(relative_eeg_timestamps, event_time_series, _transformed.T, window=5, number_of_channels=7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split into trials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of EEG: (8, 1023780)\n",
      "Sample rate: 250\n",
      "Number of channels: 8\n",
      "Channel names: ['Fz', 'C3', 'Cz', 'C4', 'Pz', 'PO7', 'Oz', 'PO8']\n",
      "Number of events: 1213\n",
      "Event codes: [0 1 2 3 4 5 6 7]\n",
      "Class labels: ['imagery_handL', 'imagery_handR']\n",
      "Class codes: [7, 5]\n",
      "Number of classes: 2\n"
     ]
    }
   ],
   "source": [
    "EEG = eeg_notched_bp_norm.copy()\n",
    "event_onsets = event_time_stamps.copy()\n",
    "cl_labs = ['imagery_handL', 'imagery_handR']\n",
    "cl_codes = [label2code[code] for code in cl_labs]\n",
    "nclasses = len(cl_labs)\n",
    "nchannels = EEG.shape[0]\n",
    "\n",
    "# Print some information\n",
    "print('Shape of EEG:', EEG.shape)\n",
    "print('Sample rate:', fs)\n",
    "print('Number of channels:', EEG.shape[0])\n",
    "print('Channel names:', [preprocess.channel2name(channel) for channel in range(EEG.shape[0])])\n",
    "print('Number of events:', len(event_onsets))\n",
    "print('Event codes:', np.unique(event_codes))\n",
    "print('Class labels:', cl_labs)\n",
    "print('Class codes:', cl_codes)\n",
    "print('Number of classes:', nclasses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8, 500, 200) (8, 500, 200)\n"
     ]
    }
   ],
   "source": [
    "# Dictionary to store the trials in, each class gets an entry\n",
    "trials = {}\n",
    "markers = {} \n",
    "\n",
    "# The time window (in samples) to extract for each trial, here 0.5 -- 2.5 seconds\n",
    "win = np.arange(int(0.5*fs), int(2.5*fs))\n",
    "\n",
    "# Length of the time window\n",
    "nsamples = len(win)\n",
    "\n",
    "for cl_lab in cl_labs:\n",
    "    event_onset_mask =  np.roll(event_time_series,1)!=event_time_series\n",
    "    class_onset_mask = event_time_series == label2code[cl_lab]\n",
    "    class_onset_indices = np.where(np.logical_and(\n",
    "                                event_onset_mask, class_onset_mask))[0]\n",
    "    \n",
    "    # Allocate memory for the trials\n",
    "    trials[cl_lab] = np.zeros((nchannels, nsamples, len(class_onset_indices)))\n",
    "    markers[cl_lab] = np.zeros((nsamples, len(class_onset_indices)))\n",
    "\n",
    "    # Extract each trial\n",
    "    for i, onset in enumerate(class_onset_indices):\n",
    "        trials[cl_lab][:,:,i] = EEG[:, win+onset]\n",
    "        markers[cl_lab][:, i] = event_time_series[win+onset]\n",
    "\n",
    "print(trials['imagery_handL'].shape, trials['imagery_handR'].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Some statistical Analysis to remove noisy trials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check the distribution of the max_val\n",
    "plt.figure()\n",
    "plt.title('Distribution of the max for all trials')\n",
    "plt.hist(trials['imagery_handL'].max(axis = (0,1)), bins='auto', label='Left MI Trials')\n",
    "plt.hist(trials['imagery_handR'].max(axis = (0,1)), bins='auto', label='Right MI Trials')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAEICAYAAABGaK+TAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAAAUXUlEQVR4nO3de7SldX3f8ffHGchwk0vmlMIMcjBYU2oSdY1ES5O6uDQoVFkrLgMonaZYapMYjaY6GJMMViuuJsR0pWkWBcsIChJQIWJaiULVRtHhFm5GcBhghssMIpcxXrh8+8d+Bg5nzmWfmXPOc34z79dae83ez/Ps3+979rP35/z273meM6kqJEnteUHfBUiSto8BLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUKAN8gUryF0l+f5baelGSLUkWdY+vTfK22Wi7a++vk6ycrfZm0O+Hkjyc5MEht1+d5KK5rmuIOl6bZMMU649Kcme3z06a41pGk1SSxd3jHX5vzOT9kGR9kmN3pL9dmQHeg+5N+8MkTyR5NMnfJnl7kmf3R1W9var+85BtTfkBqKp7q2rvqnp6FmrfJgSr6nVVtWZH255hHS8C3gMcUVX/eIL1U4bkHNY1G/1+EPizbp99bhbKmhXD/gLs4/2wqzLA+/Ovq2of4FDgbOB9wPmz3cnWkdVO6EXA96pqU9+FzIFDgdu254l97u8MmCnzyBe7Z1X1WFVdCfwasDLJywCSXJDkQ939pUk+343WH0ny1SQvSHIhgyD7q+7r9nvHfCU+Pcm9wJfHf03u/EySbyZ5PMkVSQ7o+tpmBLl1lJ/keOD9wK91/d3crX/2a3dX1weS3JNkU5JPJNm3W7e1jpVJ7u2mP35vstcmyb7d8zd37X2ga/9Y4Grg4K6OC8Y9by/gr8es35Lk4G717l2bTyS5LcmKMc87OMnlXX93J/ntKWp7fZLbu3Y2JvndyfpNske3P7+f5HbgVVO0+13gxWP26U91bVzZ7fu7kvz7MduvTnJZkouSPA782wnaPCHJjd2+vi/J6sn6n6Kuqfb9h5P8P+AfgBePez/8TJIvJ/let78/mWS/Sfo4Msnars6Hkpwz0zp3OVXlbZ5vwHrg2AmW3wv8x+7+BcCHuvsfAf4C2K27/RKQidoCRoECPgHsBewxZtnibptrgY3Ay7ptLgcu6ta9FtgwWb3A6q3bjll/LfC27v6/A+5iEEJ7A58BLhxX2//s6voF4MfAP53kdfoEcAWwT/fc7wCnT1bnuOdO9HOsBn4EvB5Y1L2u3+jWvQC4HvgDYPeu/nXAr0zS/gPAL3X39wdeOUW/ZwNfBQ4ADgFunab28fv0K8CfA0uAlwObgaPH/ExPAid1P8Mek7wWP9et/3ngIeCkcftk7HvjbZPUNdm+vxf4Z8BiBu/Pse+Hw4HjgJ8CRrqf5WOTvLe+DpzW3d8beHXfn9WFfnMEvrDcz+BDPt6TwEHAoVX1ZFV9tbp3+RRWV9UPquqHk6y/sKpuraofAL8PvDndQc4d9BbgnKpaV1VbgDOBk8eN/s+qqh9W1c3AzQyC/Hm6Wk4GzqyqJ6pqPfDHwGk7WN/XquoLNTgecOGYvl8FjFTVB6vqJ1W1jsEvmpMnaedJ4IgkL6yq71fVDVP0+Wbgw1X1SFXdB/y3YYtNcghwFPC+qvpRVd0EnAf8mzGbfb2qPldVz0y0v6vq2qq6pVv/d8DFwL8ctoYhXFBVt1XVU1X15Li+76qqq6vqx1W1GThnir6fBA5PsrSqtlTVN2axxp2SAb6wLAMemWD5f2Uwqv1iknVJVg3R1n0zWH8Pg5HT0qGqnNrBXXtj214MHDhm2dizRv6BwWhrvKVdTePbWraD9Y3ve0n3y+VQBlMfj269MZgyOHCCNgB+lcFI/p4k/zfJa6bo82C2fb2HdTDwSFU9Me75Y1+HKfd1kl9Mck03NfQY8HZmZ19P23+SA5Nc0k0zPQ5cNEXfpwP/BPh2km8lOXEWa9wpGeALRJJXMfhQfm38um4E+p6qejHwBuDdSY7ZunqSJqcboR8y5v6LGIx+HgZ+AOw5pq5FDL76Dtvu/QzCcGzbTzH42j4TD3c1jW9r45DPn+mf2bwPuLuq9htz26eqXj9h41Xfqqo3Av8I+Bxw6RT9PsC2r/ew7gcOSLLPuOePfR2m+1k/BVwJHFJV+zKYjssMapiun6n6/y/d+p+rqhcCb52s76q6s6pOYfCafhS4rDuuoEkY4D1L8sJupHEJg/nFWybY5sQkhycJ8BjwNPBMt/ohBvO1M/XWJEck2ZPBaWuXddMK32EwKj0hyW7ABxjMX271EDCayc82uBj4nSSHJdmbwQf401X11EyK62q5FPhwkn2SHAq8m8EIbhgPAT+99QDqEL4JPJHkfd1Bx0VJXtb9Yn2eJLsneUuSfbspg8d5/v4Y3++lwJlJ9k+yHHjHkDXRTbn8LfCRJEuS/DyDkepMzmffh8Eo/kdJjgROncFzx5pu30/W9xbgsSTLgP802YZJ3ppkpKqeAR7tFj8z2fYywPv0V0meYDDy+z0Gc4O/Psm2LwH+hsEH4evAn1fVNd26jwAf6L72/+4M+r+QwYHSBxkcHPttGJwVA/wGg3nWjQxG5GPPSvnL7t/vJZlo3vfjXdtfAe5mcNBw6MAa5x1d/+sYfDP5VNf+tKrq2wx+mazrXpuDp9n+aeBEBgcJ72bwDeA8YLJfAKcB67tpgbczmPufrN+zGEx73A18kcHrMxOnMDjYeD/wWeAPq+pvZvD83wA+2L3f/oDnvi3M1HT7fiJnAa9kMPC4isFB7ckcD9yWZAvwp8DJUxzDEc+dySBJaowjcElqlAEuSY0ywCWpUQa4JDVqXv/wzdKlS2t0dHQ+u5Sk5l1//fUPV9XI+OXzGuCjo6OsXbt2PruUpOYlmfDqXadQJKlRBrgkNcoAl6RGGeCS1CgDXJIaZYBLUqMMcElqlAEuSY0ywCWpUc0G+OiqqxhddVXfZUhSb5oNcEna1RngktQoA1ySGmWAS1KjDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUKANckhplgEtSowxwSWqUAS5JjTLAJalRBrgkNaq9AF+9b98VSNKC0F6AS5IAA1ySmmWAS1KjDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUqKEDPMmiJDcm+Xz3+LAk1yW5K8mnk+w+d2VKksabyQj8ncAdYx5/FPiTqjoc+D5w+mwWJkma2lABnmQ5cAJwXvc4wNHAZd0ma4CT5qA+SdIkhh2Bfwx4L/BM9/ingUer6qnu8QZg2eyWJkmayrQBnuREYFNVXb89HSQ5I8naJGs3b968PU1sY3TVVbPSjiS1bJgR+FHAG5KsBy5hMHXyp8B+SRZ32ywHNk705Ko6t6pWVNWKkZGRWShZkgRDBHhVnVlVy6tqFDgZ+HJVvQW4BnhTt9lK4Io5q1KStI0dOQ/8fcC7k9zFYE78/NkpSZI0jMXTb/KcqroWuLa7vw44cvZLkiQNwysxJalRBrgkNcoAl6RGGeCS1CgDXJIaZYBLUqMMcElqlAEuSY1qMsDXLzm17xIkqXdNBrgkyQCXpGYZ4JLUKANckhplgEtSowxwSWqUAS5JjTLAJalRTQW4/xu9JD2nqQCXJD3HAJekRhngktQoA1ySGmWAS1KjDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUKANckhplgEtSowxwSWqUAS5JjTLAJalRBrgkNWraAE+yJMk3k9yc5LYkZ3XLD0tyXZK7knw6ye5zX64kaathRuA/Bo6uql8AXg4cn+TVwEeBP6mqw4HvA6fPWZWSpG1MG+A1sKV7uFt3K+Bo4LJu+RrgpLkoUJI0saHmwJMsSnITsAm4Gvgu8GhVPdVtsgFYNicVSpImNFSAV9XTVfVyYDlwJPCzw3aQ5Iwka5Os3bx58/ZVKUnaxozOQqmqR4FrgNcA+yVZ3K1aDmyc5DnnVtWKqloxMjKyI7VKksYY5iyUkST7dff3AI4D7mAQ5G/qNlsJXDFHNUqSJrB4+k04CFiTZBGDwL+0qj6f5HbgkiQfAm4Ezp/DOiVJ40wb4FX1d8ArJli+jsF8uCSpB16JKUmNMsAlqVEGuCQ1ygCXpEYZ4JLUKANckhplgEtSowxwSWqUAS5JjTLAJalRBrgkNcoAl6RGGeCS1CgDXJIaZYBLUqMMcElqlAEuSY1qNsDXLzm17xIkqVfNBrgk7eoMcElqlAEuSY0ywCWpUQa4JDXKAJekRhngktQoA1ySGmWAS1KjDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUKANckhplgEtSowxwSWrUtAGe5JAk1yS5PcltSd7ZLT8gydVJ7uz+3X/uy5UkbTXMCPwp4D1VdQTwauA3kxwBrAK+VFUvAb7UPZYkzZNpA7yqHqiqG7r7TwB3AMuANwJrus3WACfNUY2SpAnMaA48ySjwCuA64MCqeqBb9SBw4CTPOSPJ2iRrN2/evCO1Tmh01VWMrrpq1tuVpIVu6ABPsjdwOfCuqnp87LqqKqAmel5VnVtVK6pqxcjIyA4VK0l6zlABnmQ3BuH9yar6TLf4oSQHdesPAjbNTYmSpIkMcxZKgPOBO6rqnDGrrgRWdvdXAlfMfnmSpMksHmKbo4DTgFuS3NQtez9wNnBpktOBe4A3z0mFkqQJTRvgVfU1IJOsPmZ2y5EkDcsrMSWpUQa4JDWqqQBfv+TUvkuQpAWjqQCXJD3HAJekRhngktQoA1ySGmWAS1KjDHBJapQBLkmNMsAlqVFNB7gX9kjalTUd4JK0KzPAJalRBrgkNcoAl6RGGeCS1CgDXJIaZYBLUqMMcElqlAEuSY0ywCWpUQa4JDXKAJekRhngktQoA1ySGmWAS1KjDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUqOYD3P+ZXtKuqvkAl6Rd1bQBnuTjSTYluXXMsgOSXJ3kzu7f/ee2TEnSeMOMwC8Ajh+3bBXwpap6CfCl7rEkaR5NG+BV9RXgkXGL3wis6e6vAU6a3bIkSdPZ3jnwA6vqge7+g8CBk22Y5Iwka5Os3bx583Z2J0kab4cPYlZVATXF+nOrakVVrRgZGdnR7iRJne0N8IeSHATQ/btp9kqSJA1jewP8SmBld38lcMXslCNJGtYwpxFeDHwdeGmSDUlOB84GjktyJ3Bs97g365ecyuiqqxhddVWfZUjSvFo83QZVdcokq46Z5VokSTPglZiS1CgDXJIaZYBLUqMMcElqlAEuSY0ywCWpUQa4JDXKAJekRhngktQoA1ySGmWAS1KjmgnwYf9QlX/QStKuopkAlyQ9nwEuSY0ywCWpUQa4JDVqpwnw9UtO7bsESZpXO02AS9KuxgCXpEYZ4JLUqJ06wL2oR9LObKcOcEnamRngktQoA1ySGmWAS1KjdvoA90CmpJ3VTh/gkrSzMsAlqVEGuCQ1anHfBcyJ1fsy+qNPPftw6zz4+rNPeN6c+PqzT5j30iRptjgCl6RGGeCS1CgDXJIatVMF+Pj/1GGy/+Rh/ZJTt1k3Z+eLr953YbQxX1qqVZorYz4Hc3ktyk4V4JK0K9mhAE9yfJK/T3JXklWzVZQkaXrbHeBJFgH/HXgdcARwSpIjZqswSdLUdmQEfiRwV1Wtq6qfAJcAb5ydsiRJ00lVbd8TkzcBx1fV27rHpwG/WFW/NW67M4AzuocvBf5+guaWAg9vVyFzy7pmxrpmxrpmZleu69CqGhm/cM6vxKyqc4Fzp9omydqqWjHXtcyUdc2Mdc2Mdc2MdW1rR6ZQNgKHjHm8vFsmSZoHOxLg3wJekuSwJLsDJwNXzk5ZkqTpbPcUSlU9leS3gP8DLAI+XlW3bWdzU06x9Mi6Zsa6Zsa6Zsa6xtnug5iSpH55JaYkNcoAl6RG9R7gC/Fy/CQfT7Ipya191zJWkkOSXJPk9iS3JXln3zUBJFmS5JtJbu7qOqvvmrZKsijJjUk+33ctYyVZn+SWJDclWdt3PVsl2S/JZUm+neSOJK9ZADW9tHudtt4eT/KuvusCSPI73Xv+1iQXJ1kyr/33OQfeXY7/HeA4YAODM1tOqarbeytqUNcvA1uAT1TVy/qsZawkBwEHVdUNSfYBrgdOWgCvV4C9qmpLkt2ArwHvrKpv9FkXQJJ3AyuAF1bViX3Xs1WS9cCKqlpQF6YkWQN8tarO684u27OqHu25rGd1mbGRwUWD9/RcyzIG7/UjquqHSS4FvlBVF8xXDX2PwBfk5fhV9RXgkb7rGK+qHqiqG7r7TwB3AMv6rQpqYEv3cLfu1vvR8STLgROA8/qupQVJ9gV+GTgfoKp+spDCu3MM8N2+w3uMxcAeSRYDewL3z2fnfQf4MuC+MY83sAACqQVJRoFXANf1XArw7FTFTcAm4OqqWgh1fQx4L/BMz3VMpIAvJrm++3MTC8FhwGbgf3XTTucl2avvosY5Gbi47yIAqmoj8EfAvcADwGNV9cX5rKHvANd2SLI3cDnwrqp6vO96AKrq6ap6OYMrco9M0uvUU5ITgU1VdX2fdUzhX1TVKxn8Nc/f7Kbt+rYYeCXwP6rqFcAPgAVxXAqgm9J5A/CXfdcCkGR/BjMGhwEHA3sleet81tB3gHs5/gx1c8yXA5+sqs/0Xc943Vfua4Djey7lKOAN3VzzJcDRSS7qt6TndKM3qmoT8FkG04l92wBsGPPt6TIGgb5QvA64oaoe6ruQzrHA3VW1uaqeBD4D/PP5LKDvAPdy/BnoDhaeD9xRVef0Xc9WSUaS7Nfd34PBQelv91lTVZ1ZVcurapTB++rLVTWvo6PJJNmrOwhNN0Xxr4Dez3iqqgeB+5K8tFt0DNDrAfJxTmGBTJ907gVenWTP7rN5DIPjUvNmzv8a4VRm+XL8WZPkYuC1wNIkG4A/rKrz+60KGIwqTwNu6eabAd5fVV/oryQADgLWdGcIvAC4tKoW1Gl7C8yBwGcHn3kWA5+qqv/db0nPegfwyW5AtQ749Z7rAZ79RXcc8B/6rmWrqrouyWXADcBTwI3M82X1XkovSY3qewpFkrSdDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUqP8P9OZ8O32BxTgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.title('Distribution of the std for all trials')\n",
    "plt.hist(trials['imagery_handL'].std(axis = (0,1)), bins='auto', label='Left MI Trials')\n",
    "plt.hist(trials['imagery_handR'].std(axis = (0,1)), bins='auto', label='Right MI Trials')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8, 500, 194)\n",
      "(8, 500, 194)\n"
     ]
    }
   ],
   "source": [
    "# remove bad trials\n",
    "\n",
    "good_trials = {\n",
    "    'imagery_handL': np.zeros((nchannels, nsamples,1)),\n",
    "    'imagery_handR': np.zeros((nchannels, nsamples,1)),\n",
    "} \n",
    "\n",
    "for trial in range(trials['imagery_handL'].shape[2]):\n",
    "    eeg_sampleL = trials['imagery_handL'][:,:,trial]\n",
    "    if eeg_sampleL.std(axis = (0,1)) < 0.7 and eeg_sampleL.max(axis = (0,1)) < 4:\n",
    "        good_trials['imagery_handL'] = np.concatenate(\n",
    "            [good_trials['imagery_handL'], np.expand_dims(eeg_sampleL, -1)], \n",
    "            axis=2\n",
    "        )\n",
    "\n",
    "    eeg_sampleR = trials['imagery_handR'][:,:,trial]\n",
    "    if eeg_sampleR.std(axis = (0,1)) < 0.7 and eeg_sampleR.max(axis = (0,1)) < 4:\n",
    "        good_trials['imagery_handR'] = np.concatenate(\n",
    "            [good_trials['imagery_handR'], np.expand_dims(eeg_sampleR, -1)],\n",
    "            axis=2\n",
    "        )\n",
    "\n",
    "good_trials['imagery_handR'] = good_trials['imagery_handR'][:,:,1:]\n",
    "good_trials['imagery_handL'] = good_trials['imagery_handL'][:,:,1:]\n",
    "\n",
    "print(good_trials['imagery_handL'].shape)\n",
    "print(good_trials['imagery_handR'].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check the distribution of the max_val\n",
    "plt.figure()\n",
    "plt.title('Distribution of the max for good trials')\n",
    "plt.hist(good_trials['imagery_handL'].max(axis = (0,1)), bins='auto', label='Left MI Trials')\n",
    "plt.hist(good_trials['imagery_handR'].max(axis = (0,1)), bins='auto', label='Right MI Trials')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check the distribution of the max_val\n",
    "plt.figure()\n",
    "plt.title('Distribution of the std for good trials')\n",
    "plt.hist(good_trials['imagery_handL'].std(axis = (0,1)), bins='auto', label='Left MI Trials')\n",
    "plt.hist(good_trials['imagery_handR'].std(axis = (0,1)), bins='auto', label='Right MI Trials')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "av_left_trials = trials['imagery_handL'].mean(axis = 2)\n",
    "av_left_trials.shape\n",
    "plt.figure(figsize=(10,4))\n",
    "plt.tight_layout()\n",
    "\n",
    "t = np.arange(0, int(av_left_trials.shape[1]/250), 1/fs)\n",
    "for name in ['C3', 'C4', 'Cz', 'Pz']:\n",
    "    plt.plot(t, av_left_trials[preprocess.name2channel(name)], label=name)\n",
    "    \n",
    "plt.title('Average time series of all left trials')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "av_left_trials = good_trials['imagery_handL'].mean(axis = 2)\n",
    "plt.figure(figsize=(10,4))\n",
    "plt.tight_layout()\n",
    "\n",
    "t = np.arange(0, int(av_left_trials.shape[1]/250), 1/fs)\n",
    "for name in ['C3', 'C4', 'Cz', 'Pz']:\n",
    "    plt.plot(t, av_left_trials[preprocess.name2channel(name)], label=name)\n",
    "    \n",
    "plt.title('Average time series of \"good\" left trials')    \n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time series classification using RandomForrest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "import sktime\n",
    "from sktime.classification.compose import ColumnEnsembleClassifier\n",
    "from sktime.classification.dictionary_based import BOSSEnsemble\n",
    "from sktime.classification.interval_based import TimeSeriesForestClassifier\n",
    "from sktime.classification.shapelet_based import MrSEQLClassifier\n",
    "from sktime.datasets import load_basic_motions\n",
    "from sktime.transformations.panel.compose import ColumnConcatenator\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60, 6) (60,) (20, 6) (20,)\n"
     ]
    }
   ],
   "source": [
    "X, y = load_basic_motions(return_X_y=True)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n",
    "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>dim_0</th>\n",
       "      <th>dim_1</th>\n",
       "      <th>dim_2</th>\n",
       "      <th>dim_3</th>\n",
       "      <th>dim_4</th>\n",
       "      <th>dim_5</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0    -0.407421\n",
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       "2     2.355158\n",
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       "      <td>0     1.413374\n",
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       "      <td>0    -0.066584\n",
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       "      <td>0     0.302612\n",
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       "      <td>0    -0.087891\n",
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       "2    -0.503377\n",
       "3...</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0    -0.357300\n",
       "1    -0.357300\n",
       "2    -0.005055\n",
       "3...</td>\n",
       "      <td>0    -0.584885\n",
       "1    -0.584885\n",
       "2     0.295037\n",
       "3...</td>\n",
       "      <td>0    -0.792751\n",
       "1    -0.792751\n",
       "2     0.213664\n",
       "3...</td>\n",
       "      <td>0     0.074574\n",
       "1     0.074574\n",
       "2    -0.157139\n",
       "3...</td>\n",
       "      <td>0     0.159802\n",
       "1     0.159802\n",
       "2    -0.306288\n",
       "3...</td>\n",
       "      <td>0     0.023970\n",
       "1     0.023970\n",
       "2     1.230478\n",
       "3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0    -0.352746\n",
       "1    -0.352746\n",
       "2    -1.354561\n",
       "3...</td>\n",
       "      <td>0     0.316845\n",
       "1     0.316845\n",
       "2     0.490525\n",
       "3...</td>\n",
       "      <td>0    -0.473779\n",
       "1    -0.473779\n",
       "2     1.454261\n",
       "3...</td>\n",
       "      <td>0    -0.327595\n",
       "1    -0.327595\n",
       "2    -0.269001\n",
       "3...</td>\n",
       "      <td>0     0.106535\n",
       "1     0.106535\n",
       "2     0.021307\n",
       "3...</td>\n",
       "      <td>0     0.197090\n",
       "1     0.197090\n",
       "2     0.460763\n",
       "3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0      0.052231\n",
       "1      0.052231\n",
       "2     -0.54804...</td>\n",
       "      <td>0     -0.730486\n",
       "1     -0.730486\n",
       "2      0.70700...</td>\n",
       "      <td>0    -0.518104\n",
       "1    -0.518104\n",
       "2    -1.179430\n",
       "3...</td>\n",
       "      <td>0    -0.159802\n",
       "1    -0.159802\n",
       "2    -0.239704\n",
       "3...</td>\n",
       "      <td>0    -0.045277\n",
       "1    -0.045277\n",
       "2     0.023970\n",
       "3...</td>\n",
       "      <td>0     -0.029297\n",
       "1     -0.029297\n",
       "2      0.29829...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                dim_0  \\\n",
       "9   0    -0.407421\n",
       "1    -0.407421\n",
       "2     2.355158\n",
       "3...   \n",
       "24  0     0.383922\n",
       "1     0.383922\n",
       "2    -0.272575\n",
       "3...   \n",
       "5   0    -0.357300\n",
       "1    -0.357300\n",
       "2    -0.005055\n",
       "3...   \n",
       "7   0    -0.352746\n",
       "1    -0.352746\n",
       "2    -1.354561\n",
       "3...   \n",
       "34  0      0.052231\n",
       "1      0.052231\n",
       "2     -0.54804...   \n",
       "\n",
       "                                                dim_1  \\\n",
       "9   0     1.413374\n",
       "1     1.413374\n",
       "2    -3.928032\n",
       "3...   \n",
       "24  0     0.302612\n",
       "1     0.302612\n",
       "2    -1.381236\n",
       "3...   \n",
       "5   0    -0.584885\n",
       "1    -0.584885\n",
       "2     0.295037\n",
       "3...   \n",
       "7   0     0.316845\n",
       "1     0.316845\n",
       "2     0.490525\n",
       "3...   \n",
       "34  0     -0.730486\n",
       "1     -0.730486\n",
       "2      0.70700...   \n",
       "\n",
       "                                                dim_2  \\\n",
       "9   0     0.092782\n",
       "1     0.092782\n",
       "2    -0.211622\n",
       "3...   \n",
       "24  0    -0.398075\n",
       "1    -0.398075\n",
       "2    -0.681258\n",
       "3...   \n",
       "5   0    -0.792751\n",
       "1    -0.792751\n",
       "2     0.213664\n",
       "3...   \n",
       "7   0    -0.473779\n",
       "1    -0.473779\n",
       "2     1.454261\n",
       "3...   \n",
       "34  0    -0.518104\n",
       "1    -0.518104\n",
       "2    -1.179430\n",
       "3...   \n",
       "\n",
       "                                                dim_3  \\\n",
       "9   0    -0.066584\n",
       "1    -0.066584\n",
       "2    -3.630177\n",
       "3...   \n",
       "24  0     0.071911\n",
       "1     0.071911\n",
       "2    -0.761725\n",
       "3...   \n",
       "5   0     0.074574\n",
       "1     0.074574\n",
       "2    -0.157139\n",
       "3...   \n",
       "7   0    -0.327595\n",
       "1    -0.327595\n",
       "2    -0.269001\n",
       "3...   \n",
       "34  0    -0.159802\n",
       "1    -0.159802\n",
       "2    -0.239704\n",
       "3...   \n",
       "\n",
       "                                                dim_4  \\\n",
       "9   0     0.223723\n",
       "1     0.223723\n",
       "2    -0.026634\n",
       "3...   \n",
       "24  0     0.175783\n",
       "1     0.175783\n",
       "2    -0.114525\n",
       "3...   \n",
       "5   0     0.159802\n",
       "1     0.159802\n",
       "2    -0.306288\n",
       "3...   \n",
       "7   0     0.106535\n",
       "1     0.106535\n",
       "2     0.021307\n",
       "3...   \n",
       "34  0    -0.045277\n",
       "1    -0.045277\n",
       "2     0.023970\n",
       "3...   \n",
       "\n",
       "                                                dim_5  \n",
       "9   0     0.135832\n",
       "1     0.135832\n",
       "2    -1.946925\n",
       "3...  \n",
       "24  0    -0.087891\n",
       "1    -0.087891\n",
       "2    -0.503377\n",
       "3...  \n",
       "5   0     0.023970\n",
       "1     0.023970\n",
       "2     1.230478\n",
       "3...  \n",
       "7   0     0.197090\n",
       "1     0.197090\n",
       "2     0.460763\n",
       "3...  \n",
       "34  0     -0.029297\n",
       "1     -0.029297\n",
       "2      0.29829...  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9      standing\n",
       "24      walking\n",
       "5      standing\n",
       "7      standing\n",
       "34    badminton\n",
       "dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy array before swapping into correct shape:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 500, 194)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "good_trials['imagery_handL'].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Swapping into correct shape:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(194, 8, 500)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "good_trials_np_nested = {}\n",
    "good_trials_np_nested['imagery_handL'] = np.swapaxes(good_trials['imagery_handL'],0,2)\n",
    "good_trials_np_nested['imagery_handL'] = np.swapaxes(good_trials_np_nested['imagery_handL'],1,2)\n",
    "\n",
    "good_trials_np_nested['imagery_handR'] = np.swapaxes(good_trials['imagery_handR'],0,2)\n",
    "good_trials_np_nested['imagery_handR'] = np.swapaxes(good_trials_np_nested['imagery_handR'],1,2)\n",
    "\n",
    "# Drop some trials for test purposes:\n",
    "# good_trials_np_nested['imagery_handL'] = good_trials_np_nested['imagery_handL'][0:100,:,:]\n",
    "# good_trials_np_nested['imagery_handR'] = good_trials_np_nested['imagery_handR'][0:100,:,:]\n",
    "\n",
    "good_trials_np_nested['imagery_handL'].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting 3d numpy array into nested dataset for sktime:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "good_trials_nested = {}\n",
    "good_trials_nested['imagery_handL'] = sktime.utils.data_processing.from_3d_numpy_to_nested(good_trials_np_nested['imagery_handL'], column_names=None, cells_as_numpy=False)\n",
    "good_trials_nested['imagery_handR'] = sktime.utils.data_processing.from_3d_numpy_to_nested(good_trials_np_nested['imagery_handR'], column_names=None, cells_as_numpy=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>var_0</th>\n",
       "      <th>var_1</th>\n",
       "      <th>var_2</th>\n",
       "      <th>var_3</th>\n",
       "      <th>var_4</th>\n",
       "      <th>var_5</th>\n",
       "      <th>var_6</th>\n",
       "      <th>var_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0     -0.002351\n",
       "1      0.162828\n",
       "2      0.32019...</td>\n",
       "      <td>0     -0.160221\n",
       "1     -0.187490\n",
       "2     -0.18103...</td>\n",
       "      <td>0     -0.066853\n",
       "1     -0.136254\n",
       "2     -0.19861...</td>\n",
       "      <td>0      0.124155\n",
       "1      0.333704\n",
       "2      0.52936...</td>\n",
       "      <td>0      0.082866\n",
       "1     -0.005444\n",
       "2     -0.10014...</td>\n",
       "      <td>0      0.164893\n",
       "1      0.169971\n",
       "2      0.15965...</td>\n",
       "      <td>0     -0.106920\n",
       "1     -0.155770\n",
       "2     -0.20933...</td>\n",
       "      <td>0     -0.300756\n",
       "1     -0.283362\n",
       "2     -0.22602...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0      0.311350\n",
       "1      0.296585\n",
       "2      0.26701...</td>\n",
       "      <td>0      0.304720\n",
       "1      0.125742\n",
       "2     -0.05300...</td>\n",
       "      <td>0     -0.309294\n",
       "1     -0.366519\n",
       "2     -0.40074...</td>\n",
       "      <td>0     -0.068097\n",
       "1     -0.047299\n",
       "2     -0.02906...</td>\n",
       "      <td>0     -0.075980\n",
       "1     -0.082200\n",
       "2     -0.08649...</td>\n",
       "      <td>0     -0.335307\n",
       "1     -0.321827\n",
       "2     -0.26733...</td>\n",
       "      <td>0      0.084043\n",
       "1      0.204241\n",
       "2      0.30477...</td>\n",
       "      <td>0      0.038526\n",
       "1      0.084348\n",
       "2      0.11990...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0      0.088491\n",
       "1      0.115101\n",
       "2      0.14124...</td>\n",
       "      <td>0      0.242328\n",
       "1      0.300805\n",
       "2      0.33722...</td>\n",
       "      <td>0     -0.412223\n",
       "1     -0.379501\n",
       "2     -0.29994...</td>\n",
       "      <td>0      0.023906\n",
       "1      0.041966\n",
       "2      0.06418...</td>\n",
       "      <td>0     -0.051356\n",
       "1     -0.060743\n",
       "2     -0.07129...</td>\n",
       "      <td>0     -0.163073\n",
       "1     -0.310198\n",
       "2     -0.44598...</td>\n",
       "      <td>0     -0.222886\n",
       "1     -0.182086\n",
       "2     -0.12459...</td>\n",
       "      <td>0      0.574146\n",
       "1      0.564259\n",
       "2      0.51079...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0     -0.229008\n",
       "1     -0.226172\n",
       "2     -0.20242...</td>\n",
       "      <td>0      0.786457\n",
       "1      0.785095\n",
       "2      0.72480...</td>\n",
       "      <td>0      0.253241\n",
       "1     -0.011489\n",
       "2     -0.24152...</td>\n",
       "      <td>0     -0.259709\n",
       "1     -0.409964\n",
       "2     -0.52352...</td>\n",
       "      <td>0      0.046520\n",
       "1      0.045167\n",
       "2      0.04229...</td>\n",
       "      <td>0      0.044789\n",
       "1      0.485555\n",
       "2      0.85506...</td>\n",
       "      <td>0      0.151397\n",
       "1      0.109058\n",
       "2      0.04455...</td>\n",
       "      <td>0     -0.742927\n",
       "1     -0.787615\n",
       "2     -0.75938...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0     -0.071439\n",
       "1     -0.089475\n",
       "2     -0.10628...</td>\n",
       "      <td>0     -0.400932\n",
       "1     -0.278522\n",
       "2     -0.11505...</td>\n",
       "      <td>0      0.379686\n",
       "1      0.180676\n",
       "2     -0.03060...</td>\n",
       "      <td>0      0.140490\n",
       "1      0.006344\n",
       "2     -0.14456...</td>\n",
       "      <td>0      0.175059\n",
       "1      0.215795\n",
       "2      0.24061...</td>\n",
       "      <td>0     -0.462487\n",
       "1     -0.313289\n",
       "2     -0.14452...</td>\n",
       "      <td>0      0.037046\n",
       "1      0.102471\n",
       "2      0.16272...</td>\n",
       "      <td>0     -0.225277\n",
       "1     -0.458798\n",
       "2     -0.65217...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               var_0  \\\n",
       "0  0     -0.002351\n",
       "1      0.162828\n",
       "2      0.32019...   \n",
       "1  0      0.311350\n",
       "1      0.296585\n",
       "2      0.26701...   \n",
       "2  0      0.088491\n",
       "1      0.115101\n",
       "2      0.14124...   \n",
       "3  0     -0.229008\n",
       "1     -0.226172\n",
       "2     -0.20242...   \n",
       "4  0     -0.071439\n",
       "1     -0.089475\n",
       "2     -0.10628...   \n",
       "\n",
       "                                               var_1  \\\n",
       "0  0     -0.160221\n",
       "1     -0.187490\n",
       "2     -0.18103...   \n",
       "1  0      0.304720\n",
       "1      0.125742\n",
       "2     -0.05300...   \n",
       "2  0      0.242328\n",
       "1      0.300805\n",
       "2      0.33722...   \n",
       "3  0      0.786457\n",
       "1      0.785095\n",
       "2      0.72480...   \n",
       "4  0     -0.400932\n",
       "1     -0.278522\n",
       "2     -0.11505...   \n",
       "\n",
       "                                               var_2  \\\n",
       "0  0     -0.066853\n",
       "1     -0.136254\n",
       "2     -0.19861...   \n",
       "1  0     -0.309294\n",
       "1     -0.366519\n",
       "2     -0.40074...   \n",
       "2  0     -0.412223\n",
       "1     -0.379501\n",
       "2     -0.29994...   \n",
       "3  0      0.253241\n",
       "1     -0.011489\n",
       "2     -0.24152...   \n",
       "4  0      0.379686\n",
       "1      0.180676\n",
       "2     -0.03060...   \n",
       "\n",
       "                                               var_3  \\\n",
       "0  0      0.124155\n",
       "1      0.333704\n",
       "2      0.52936...   \n",
       "1  0     -0.068097\n",
       "1     -0.047299\n",
       "2     -0.02906...   \n",
       "2  0      0.023906\n",
       "1      0.041966\n",
       "2      0.06418...   \n",
       "3  0     -0.259709\n",
       "1     -0.409964\n",
       "2     -0.52352...   \n",
       "4  0      0.140490\n",
       "1      0.006344\n",
       "2     -0.14456...   \n",
       "\n",
       "                                               var_4  \\\n",
       "0  0      0.082866\n",
       "1     -0.005444\n",
       "2     -0.10014...   \n",
       "1  0     -0.075980\n",
       "1     -0.082200\n",
       "2     -0.08649...   \n",
       "2  0     -0.051356\n",
       "1     -0.060743\n",
       "2     -0.07129...   \n",
       "3  0      0.046520\n",
       "1      0.045167\n",
       "2      0.04229...   \n",
       "4  0      0.175059\n",
       "1      0.215795\n",
       "2      0.24061...   \n",
       "\n",
       "                                               var_5  \\\n",
       "0  0      0.164893\n",
       "1      0.169971\n",
       "2      0.15965...   \n",
       "1  0     -0.335307\n",
       "1     -0.321827\n",
       "2     -0.26733...   \n",
       "2  0     -0.163073\n",
       "1     -0.310198\n",
       "2     -0.44598...   \n",
       "3  0      0.044789\n",
       "1      0.485555\n",
       "2      0.85506...   \n",
       "4  0     -0.462487\n",
       "1     -0.313289\n",
       "2     -0.14452...   \n",
       "\n",
       "                                               var_6  \\\n",
       "0  0     -0.106920\n",
       "1     -0.155770\n",
       "2     -0.20933...   \n",
       "1  0      0.084043\n",
       "1      0.204241\n",
       "2      0.30477...   \n",
       "2  0     -0.222886\n",
       "1     -0.182086\n",
       "2     -0.12459...   \n",
       "3  0      0.151397\n",
       "1      0.109058\n",
       "2      0.04455...   \n",
       "4  0      0.037046\n",
       "1      0.102471\n",
       "2      0.16272...   \n",
       "\n",
       "                                               var_7  \n",
       "0  0     -0.300756\n",
       "1     -0.283362\n",
       "2     -0.22602...  \n",
       "1  0      0.038526\n",
       "1      0.084348\n",
       "2      0.11990...  \n",
       "2  0      0.574146\n",
       "1      0.564259\n",
       "2      0.51079...  \n",
       "3  0     -0.742927\n",
       "1     -0.787615\n",
       "2     -0.75938...  \n",
       "4  0     -0.225277\n",
       "1     -0.458798\n",
       "2     -0.65217...  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "good_trials_nested['imagery_handL'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combine both 'imagery_handL' and 'imagery_handR' into one 3d array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(388, 8, 500)\n"
     ]
    }
   ],
   "source": [
    "good_trials_np_MI = np.concatenate((good_trials_np_nested['imagery_handL'], good_trials_np_nested['imagery_handR']), axis = 0)\n",
    "print(good_trials_np_MI.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert into nested dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0     -0.075980\n",
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       "      <td>0     -0.335307\n",
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       "      <td>0     -0.051356\n",
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       "      <td>0     -0.163073\n",
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       "      <td>0     -0.222886\n",
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       "2     -0.12459...</td>\n",
       "      <td>0      0.574146\n",
       "1      0.564259\n",
       "2      0.51079...</td>\n",
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       "      <th>3</th>\n",
       "      <td>0     -0.229008\n",
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       "      <td>0      0.786457\n",
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       "      <td>0      0.253241\n",
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       "      <td>0     -0.259709\n",
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       "2     -0.52352...</td>\n",
       "      <td>0      0.046520\n",
       "1      0.045167\n",
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       "      <td>0      0.044789\n",
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       "2      0.85506...</td>\n",
       "      <td>0      0.151397\n",
       "1      0.109058\n",
       "2      0.04455...</td>\n",
       "      <td>0     -0.742927\n",
       "1     -0.787615\n",
       "2     -0.75938...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0     -0.071439\n",
       "1     -0.089475\n",
       "2     -0.10628...</td>\n",
       "      <td>0     -0.400932\n",
       "1     -0.278522\n",
       "2     -0.11505...</td>\n",
       "      <td>0      0.379686\n",
       "1      0.180676\n",
       "2     -0.03060...</td>\n",
       "      <td>0      0.140490\n",
       "1      0.006344\n",
       "2     -0.14456...</td>\n",
       "      <td>0      0.175059\n",
       "1      0.215795\n",
       "2      0.24061...</td>\n",
       "      <td>0     -0.462487\n",
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       "2     -0.14452...</td>\n",
       "      <td>0      0.037046\n",
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       "                                               var_0  \\\n",
       "0  0     -0.002351\n",
       "1      0.162828\n",
       "2      0.32019...   \n",
       "1  0      0.311350\n",
       "1      0.296585\n",
       "2      0.26701...   \n",
       "2  0      0.088491\n",
       "1      0.115101\n",
       "2      0.14124...   \n",
       "3  0     -0.229008\n",
       "1     -0.226172\n",
       "2     -0.20242...   \n",
       "4  0     -0.071439\n",
       "1     -0.089475\n",
       "2     -0.10628...   \n",
       "\n",
       "                                               var_1  \\\n",
       "0  0     -0.160221\n",
       "1     -0.187490\n",
       "2     -0.18103...   \n",
       "1  0      0.304720\n",
       "1      0.125742\n",
       "2     -0.05300...   \n",
       "2  0      0.242328\n",
       "1      0.300805\n",
       "2      0.33722...   \n",
       "3  0      0.786457\n",
       "1      0.785095\n",
       "2      0.72480...   \n",
       "4  0     -0.400932\n",
       "1     -0.278522\n",
       "2     -0.11505...   \n",
       "\n",
       "                                               var_2  \\\n",
       "0  0     -0.066853\n",
       "1     -0.136254\n",
       "2     -0.19861...   \n",
       "1  0     -0.309294\n",
       "1     -0.366519\n",
       "2     -0.40074...   \n",
       "2  0     -0.412223\n",
       "1     -0.379501\n",
       "2     -0.29994...   \n",
       "3  0      0.253241\n",
       "1     -0.011489\n",
       "2     -0.24152...   \n",
       "4  0      0.379686\n",
       "1      0.180676\n",
       "2     -0.03060...   \n",
       "\n",
       "                                               var_3  \\\n",
       "0  0      0.124155\n",
       "1      0.333704\n",
       "2      0.52936...   \n",
       "1  0     -0.068097\n",
       "1     -0.047299\n",
       "2     -0.02906...   \n",
       "2  0      0.023906\n",
       "1      0.041966\n",
       "2      0.06418...   \n",
       "3  0     -0.259709\n",
       "1     -0.409964\n",
       "2     -0.52352...   \n",
       "4  0      0.140490\n",
       "1      0.006344\n",
       "2     -0.14456...   \n",
       "\n",
       "                                               var_4  \\\n",
       "0  0      0.082866\n",
       "1     -0.005444\n",
       "2     -0.10014...   \n",
       "1  0     -0.075980\n",
       "1     -0.082200\n",
       "2     -0.08649...   \n",
       "2  0     -0.051356\n",
       "1     -0.060743\n",
       "2     -0.07129...   \n",
       "3  0      0.046520\n",
       "1      0.045167\n",
       "2      0.04229...   \n",
       "4  0      0.175059\n",
       "1      0.215795\n",
       "2      0.24061...   \n",
       "\n",
       "                                               var_5  \\\n",
       "0  0      0.164893\n",
       "1      0.169971\n",
       "2      0.15965...   \n",
       "1  0     -0.335307\n",
       "1     -0.321827\n",
       "2     -0.26733...   \n",
       "2  0     -0.163073\n",
       "1     -0.310198\n",
       "2     -0.44598...   \n",
       "3  0      0.044789\n",
       "1      0.485555\n",
       "2      0.85506...   \n",
       "4  0     -0.462487\n",
       "1     -0.313289\n",
       "2     -0.14452...   \n",
       "\n",
       "                                               var_6  \\\n",
       "0  0     -0.106920\n",
       "1     -0.155770\n",
       "2     -0.20933...   \n",
       "1  0      0.084043\n",
       "1      0.204241\n",
       "2      0.30477...   \n",
       "2  0     -0.222886\n",
       "1     -0.182086\n",
       "2     -0.12459...   \n",
       "3  0      0.151397\n",
       "1      0.109058\n",
       "2      0.04455...   \n",
       "4  0      0.037046\n",
       "1      0.102471\n",
       "2      0.16272...   \n",
       "\n",
       "                                               var_7  \n",
       "0  0     -0.300756\n",
       "1     -0.283362\n",
       "2     -0.22602...  \n",
       "1  0      0.038526\n",
       "1      0.084348\n",
       "2      0.11990...  \n",
       "2  0      0.574146\n",
       "1      0.564259\n",
       "2      0.51079...  \n",
       "3  0     -0.742927\n",
       "1     -0.787615\n",
       "2     -0.75938...  \n",
       "4  0     -0.225277\n",
       "1     -0.458798\n",
       "2     -0.65217...  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "good_trials_nested_complete = sktime.utils.data_processing.from_3d_numpy_to_nested(good_trials_np_MI, column_names=None, cells_as_numpy=False)\n",
    "good_trials_nested_complete.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate the y-vector containing the labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7\n",
      " 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7\n",
      " 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7\n",
      " 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7\n",
      " 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7\n",
      " 7 7 7 7 7 7 7 7 7 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5\n",
      " 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5\n",
      " 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5\n",
      " 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5\n",
      " 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5\n",
      " 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5]\n",
      "[5 7] [194 194]\n"
     ]
    }
   ],
   "source": [
    "# Test purposes, change back!\n",
    "\n",
    "# y_labels_np_imagery_handL = np.full(20, 7) #(good_trials['imagery_handL'].shape[2])\n",
    "# y_labels_np_imagery_handR = np.full(20, 5) # (good_trials['imagery_handR'].shape[2])\n",
    "y_labels_np_imagery_handL = np.full(good_trials_np_nested['imagery_handR'].shape[0], 7) #\n",
    "y_labels_np_imagery_handR = np.full(good_trials_np_nested['imagery_handR'].shape[0], 5)\n",
    "y_labels_np = np.concatenate((y_labels_np_imagery_handL, y_labels_np_imagery_handR))\n",
    "y_labels_np_imagery_handL.shape\n",
    "print(y_labels_np)\n",
    "\n",
    "labels, counts = np.unique(y_labels_np, return_counts=True)\n",
    "print(labels, counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define train and testset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(291, 8) (291,) (97, 8) (97,)\n",
      "[5 7] [52 45]\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(good_trials_nested_complete,y_labels_np, test_size=0.25, random_state=10000, shuffle=True)\n",
    "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)\n",
    "# TODO: Shuffle / random_state: Check whether these are applied correctly.\n",
    "\n",
    "test_labels, test_counts = np.unique(y_test, return_counts=True)\n",
    "print(test_labels, test_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6185567010309279"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "steps = [\n",
    "    (\"concatenate\", ColumnConcatenator()),\n",
    "    (\"classify\", TimeSeriesForestClassifier(n_estimators=250)),\n",
    "]\n",
    "clf = Pipeline(steps)\n",
    "clf.fit(X_train, y_train)\n",
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = ColumnEnsembleClassifier(\n",
    "    estimators=[\n",
    "        (\"TSF0\", TimeSeriesForestClassifier(n_estimators=100), [0]),\n",
    "        (\"BOSSEnsemble3\", BOSSEnsemble(max_ensemble_size=5), [4]), # What is 2 for?\n",
    "    ]\n",
    ")\n",
    "clf.fit(X_train, y_train)\n",
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Print some information\n",
    "print('Shape of EEG:', EEG.shape)\n",
    "print('Sample rate:', fs)\n",
    "print('Number of channels:', EEG.shape[0])\n",
    "print('Channel names:', [preprocess.channel2name(channel) for channel in range(EEG.shape[0])])\n",
    "print('Number of events:', len(event_onsets))\n",
    "print('Event codes:', np.unique(event_codes))\n",
    "print('Class labels:', cl_labs)\n",
    "print('Class codes:', cl_codes)\n",
    "print('Number of classes:', nclasses)\n",
    "\n",
    "cl0 = cl_labs[0]\n",
    "cl1 = cl_labs[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "psd0, freqs, = analysis.psd(good_trials[cl0], nchannels, nsamples, fs)\n",
    "psd1, freqs, = analysis.psd(good_trials[cl1], nchannels, nsamples, fs)\n",
    "trials_PSD = {\n",
    "    cl0: psd0, \n",
    "    cl1: psd1\n",
    "}\n",
    "\n",
    "analysis.plot_psd(\n",
    "    trials_PSD,\n",
    "    freqs,\n",
    "    [preprocess.name2channel(name) for name in ['C3', 'Cz', 'C4']],\n",
    "    chan_lab=['left (C3)', 'center', 'right'],\n",
    "    maxy=0.05\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "trials_logvar = {\n",
    "    cl0: analysis.logvar(good_trials[cl0]),\n",
    "    cl1: analysis.logvar(good_trials[cl1])\n",
    "}\n",
    "\n",
    "analysis.plot_logvar(trials_logvar, nchannels, cl_labs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CSP\n",
    "Using CSP generates components which are easier to classify than the channels. \n",
    "\n",
    "Instead of 8 channels, we now have 8 mixtures of channels, called components. They are the result of 8 spatial filters applied to the data.\n",
    "\n",
    "The first filters maximize the variation of the first class, while minimizing the variation of the second. The last filters maximize the variation of the second class, while minimizing the variation of the first.\n",
    "\n",
    "This is also visible in a PSD plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "csp = preprocess.CSP(good_trials, cl_labs, nsamples, nchannels)\n",
    "trials_csp = csp.get_csp_trials()\n",
    "\n",
    "trials_csp_logvar = {\n",
    "    cl0: analysis.logvar(trials_csp[cl0]),\n",
    "    cl1: analysis.logvar(trials_csp[cl1])\n",
    "}\n",
    "analysis.plot_logvar(trials_csp_logvar, nchannels, cl_labs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "psd0, freqs, = analysis.psd(trials_csp[cl0], nchannels, nsamples, fs)\n",
    "psd1, freqs, = analysis.psd(trials_csp[cl1], nchannels, nsamples, fs)\n",
    "trials_csp_PSD = {\n",
    "    cl0: psd0, \n",
    "    cl1: psd1\n",
    "}\n",
    "\n",
    "analysis.plot_psd(\n",
    "    trials_csp_PSD,\n",
    "    freqs,\n",
    "    [0,3,-1],\n",
    "    chan_lab=['first component', 'middle component', 'last component'],\n",
    "    maxy=0.22\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "analysis.plot_scatter(trials_csp_logvar[cl0], trials_csp_logvar[cl1], cl_labs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_percentage = 0.8\n",
    "\n",
    "\n",
    "# Calculate the number of trials for each class the above percentage boils down to\n",
    "ntrain_cl0 = int(good_trials[cl0].shape[2] * train_percentage)\n",
    "ntrain_cl1 = int(good_trials[cl1].shape[2] * train_percentage)\n",
    "ntest_cl0 = good_trials[cl0].shape[2] - ntrain_cl0\n",
    "ntest_cl1 = good_trials[cl1].shape[2] - ntrain_cl1\n",
    "\n",
    "print(f'Training datapoints: ({cl0}: {ntrain_cl0}; {cl1}: {ntrain_cl1})')\n",
    "print(f'Testing datapoints: ({cl0}: {ntest_cl0}; {cl1}: {ntest_cl1})')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the filtered and cleaned signal into a train and test set\n",
    "train = {\n",
    "    cl0: good_trials[cl0][:,:,:ntrain_cl0],\n",
    "    cl1: good_trials[cl1][:,:,:ntrain_cl1]\n",
    "}\n",
    "\n",
    "test = {\n",
    "    cl0: good_trials[cl0][:,:,ntrain_cl0:],\n",
    "    cl1: good_trials[cl1][:,:,ntrain_cl1:]\n",
    "}\n",
    "\n",
    "print(f'Training dataset: ({cl0}: {train[cl0].shape}; {cl1}: {train[cl0].shape})')\n",
    "print(f'Testing dataset: ({cl0}: {test[cl0].shape}; {cl1}: {test[cl1].shape})')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_csp = preprocess.CSP(train, cl_labs, nsamples, nchannels)\n",
    "train_csp_components = train_csp.get_csp_trials()\n",
    "W = train_csp.get_W()\n",
    "print(W.shape)\n",
    "print(train_csp_components[cl0].shape)\n",
    "\n",
    "test_csp = preprocess.CSP(test, cl_labs, nsamples, nchannels, W=W)\n",
    "test_csp_components = test_csp.get_csp_trials()\n",
    "print(test_csp_components[cl0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_csp_logvar = {\n",
    "    cl0: analysis.logvar(train_csp_components[cl0]),\n",
    "    cl1: analysis.logvar(train_csp_components[cl1])\n",
    "}\n",
    "test_csp_logvar = {\n",
    "    cl0: analysis.logvar(test_csp_components[cl0]),\n",
    "    cl1: analysis.logvar(test_csp_components[cl1])\n",
    "}\n",
    "\n",
    "print(train_csp_logvar[cl0].shape)\n",
    "\n",
    "analysis.plot_logvar(train_csp_logvar, nchannels, cl_labs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select only the first and last components for classification\n",
    "comp = np.array([0,-1])\n",
    "\n",
    "train_csp_selection = {\n",
    "    cl0: train_csp_components[cl0][comp,:,:],\n",
    "    cl1: train_csp_components[cl1][comp,:,:]\n",
    "}\n",
    "print(train_csp_selection[cl0].shape)\n",
    "\n",
    "test_csp_selection = {\n",
    "    cl0: test_csp_components[cl0][comp,:,:],\n",
    "    cl1: test_csp_components[cl1][comp,:,:]\n",
    "}\n",
    "print(test_csp_selection[cl0].shape)\n",
    "\n",
    "# Recalculate the log-var\n",
    "train_csp_logvar = {\n",
    "    cl0: analysis.logvar(train_csp_selection[cl0]),\n",
    "    cl1: analysis.logvar(train_csp_selection[cl1])\n",
    "}\n",
    "print(train_csp_logvar[cl0].shape)\n",
    "\n",
    "test_csp_logvar = {\n",
    "    cl0: analysis.logvar(test_csp_selection[cl0]),\n",
    "    cl1: analysis.logvar(test_csp_selection[cl1])\n",
    "}\n",
    "\n",
    "\n",
    "analysis.plot_logvar(train_csp_logvar, nchannels, cl_labs)\n",
    "analysis.plot_logvar(test_csp_logvar, nchannels, cl_labs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_lda(class1, class2):\n",
    "    '''\n",
    "    Trains the LDA algorithm.\n",
    "    arguments:\n",
    "        class1 - An array (observations x features) for class 1\n",
    "        class2 - An array (observations x features) for class 2\n",
    "    returns:\n",
    "        The projection matrix W\n",
    "        The offset b\n",
    "    '''\n",
    "    nclasses = 2\n",
    "    \n",
    "    nclass1 = class1.shape[0]\n",
    "    nclass2 = class2.shape[0]\n",
    "    \n",
    "    # Class priors: in this case, we have an equal number of training\n",
    "    # examples for each class, so both priors are 0.5\n",
    "    prior1 = nclass1 / float(nclass1 + nclass2)\n",
    "    prior2 = nclass2 / float(nclass1 + nclass1)\n",
    "   \n",
    "    mean1 = np.mean(class1, axis=0)\n",
    "    mean2 = np.mean(class2, axis=0)\n",
    "    \n",
    "    class1_centered = class1 - mean1\n",
    "    class2_centered = class2 - mean2\n",
    "    \n",
    "    # Calculate the covariance between the features\n",
    "    cov1 = class1_centered.T.dot(class1_centered) / (nclass1 - nclasses)\n",
    "    cov2 = class2_centered.T.dot(class2_centered) / (nclass2 - nclasses)\n",
    "   \n",
    "    W = (mean2 - mean1).dot(np.linalg.pinv(prior1*cov1 + prior2*cov2))\n",
    "    b = (prior1*mean1 + prior2*mean2).dot(W)\n",
    "    \n",
    "    return (W,b)\n",
    "\n",
    "def apply_lda(test, W, b):\n",
    "    '''\n",
    "    Applies a previously trained LDA to new data.\n",
    "    arguments:\n",
    "        test - An array (features x trials) containing the data\n",
    "        W    - The project matrix W as calculated by train_lda()\n",
    "        b    - The offsets b as calculated by train_lda()\n",
    "    returns:\n",
    "        A list containing a classlabel for each trial\n",
    "    '''\n",
    "    ntrials = test.shape[1]\n",
    "    \n",
    "    prediction = []\n",
    "    for i in range(ntrials):\n",
    "        # The line below is a generalization for:\n",
    "        # result = W[0] * test[0,i] + W[1] * test[1,i] - b\n",
    "        result = W.dot(test[:,i]) - b\n",
    "        if result <= 0:\n",
    "            prediction.append(1)\n",
    "        else:\n",
    "            prediction.append(2)\n",
    "    \n",
    "    return np.array(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "W,b = train_lda(train_csp_logvar[cl0].T, train_csp_logvar[cl1].T)\n",
    "\n",
    "print('W:', W)\n",
    "print('b:', b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scatterplot like before\n",
    "analysis.plot_scatter(train_csp_logvar[cl0], train_csp_logvar[cl1], cl_labs)\n",
    "\n",
    "# Calculate decision boundary (x,y)\n",
    "x = np.arange(-5, 1, 0.1)\n",
    "y = (b - W[0]*x) / W[1]\n",
    "\n",
    "# Plot the decision boundary\n",
    "plt.plot(x,y, linestyle='--', linewidth=2, color='k')\n",
    "plt.xlim(-5, 1)\n",
    "plt.ylim(-2.2, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "analysis.plot_scatter(test_csp_logvar[cl0], test_csp_logvar[cl1], cl_labs)\n",
    "plt.plot(x,y, linestyle='--', linewidth=2, color='k')\n",
    "plt.xlim(-5, 1)\n",
    "plt.ylim(-2.2, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Print confusion matrix\n",
    "conf = np.array([\n",
    "    [(apply_lda(test_csp_logvar[cl0], W, b) == 1).sum(), \n",
    "    (apply_lda(test_csp_logvar[cl1], W, b) == 1).sum()],\n",
    "    [(apply_lda(test_csp_logvar[cl0], W, b) == 2).sum(), \n",
    "    (apply_lda(test_csp_logvar[cl1], W, b) == 2).sum()],\n",
    "])\n",
    "\n",
    "print('Confusion matrix:')\n",
    "print(conf)\n",
    "print()\n",
    "print('Accuracy: %.3f' % (np.sum(np.diag(conf)) / float(np.sum(conf))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Replicate results with sklearn "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as skLDA\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train\n",
    "X_cl0 = train_csp_logvar[cl0].T\n",
    "y_cl0 = np.zeros(X_cl0.shape[0])\n",
    "X_cl1 = train_csp_logvar[cl1].T\n",
    "y_cl1 = np.ones(X_cl0.shape[0])\n",
    "\n",
    "X_train = np.concatenate([X_cl0, X_cl1], axis=0)\n",
    "y_train = np.concatenate([y_cl0, y_cl1], axis=0)\n",
    "\n",
    "print(X_train.shape, y_train.shape)\n",
    "\n",
    "# Test\n",
    "X_cl0 = test_csp_logvar[cl0].T\n",
    "y_cl0 = np.zeros(X_cl0.shape[0])\n",
    "X_cl1 = test_csp_logvar[cl1].T\n",
    "y_cl1 = np.ones(X_cl0.shape[0])\n",
    "\n",
    "X_test = np.concatenate([X_cl0, X_cl1], axis=0)\n",
    "y_test = np.concatenate([y_cl0, y_cl1], axis=0)\n",
    "print(X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_lda = skLDA()\n",
    "clf_lda.fit(X_train, y_train)\n",
    "\n",
    "ypred = clf_lda.predict(X_test)\n",
    "\n",
    "print(f'LDA Accuracy: {np.sum(ypred == y_test) / ypred.size}')\n",
    "\n",
    "print(confusion_matrix(y_test, ypred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred = clf_lda.predict(X_train)\n",
    "print(f'LDA Accuracy: {np.sum(y_train_pred == y_train) / y_train_pred.size}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Try out an SVM\n",
    "\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_svm = make_pipeline(StandardScaler(), SVC(gamma='auto'))\n",
    "clf_svm.fit(X_train, y_train)\n",
    "y_pred = clf_svm.predict(X_test)\n",
    "\n",
    "print(f'SVM Accuracy: {np.sum(y_pred == y_test) / y_pred.size}')\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "X = np.concatenate([X_test, X_train], axis=0)\n",
    "y = np.concatenate([y_test, y_train], axis=0)\n",
    "print('All', X.shape,y.shape)\n",
    "\n",
    "cv = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SVM\n",
    "clf = SVC(kernel='linear', C=1, random_state=42)\n",
    "scores = cross_val_score(clf, X, y, cv=cv)\n",
    "\n",
    "print(f'SVM mean score for {cv} folds: {scores.mean()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LDA\n",
    "clf_lda = skLDA()\n",
    "scores = cross_val_score(clf_lda, X, y, cv=5)\n",
    "print(f'LDA mean score for {cv} folds: {scores.mean()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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