classifier-multiclass-csp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pyxdf\n",
    "import scipy.io\n",
    "from scipy import signal\n",
    "\n",
    "from util import preprocess_util\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": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Read data\n",
    "streams, header = pyxdf.load_xdf(DATA_FILE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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",
    "\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": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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",
    "\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": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of events: 1213\n",
      "Event code for left MI: 7\n",
      "Event code for right 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 right 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": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def channel2name(channel_number):\n",
    "    mapping = {\n",
    "        0: 'Fz',\n",
    "        1: 'C3',\n",
    "        2: 'Cz',\n",
    "        3: 'C4',\n",
    "        4: 'Pz',\n",
    "        5: 'PO7',\n",
    "        6: 'Oz',\n",
    "        7: 'PO8',\n",
    "    }\n",
    "    return mapping[channel_number]\n",
    "\n",
    "\n",
    "def name2channel(name):\n",
    "    mapping = {\n",
    "        'Fz': 0,\n",
    "        'C3': 1,\n",
    "        'Cz': 2,\n",
    "        'C4': 3,\n",
    "        'Pz': 4,\n",
    "        'PO7': 5,\n",
    "        'Oz': 6,\n",
    "        'PO8': 7,\n",
    "    }\n",
    "    return mapping[name]\n",
    "\n",
    "\n",
    "def get_channel(channel_name: str, eeg_data):\n",
    "    '''\n",
    "    Params:\n",
    "        - channel_name: One of [Fz, C3, Cz, C4, Pz, P07, Oz, PO8]\n",
    "        - eeg_data: shape (num_channels, num_samples)\n",
    "    Returns:\n",
    "        - single channel\n",
    "    '''\n",
    "    return eeg_data[name2channel[channel_name], :]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8, 1023780)"
      ]
     },
     "execution_count": 7,
     "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": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "lf = 6\n",
    "hf = 35\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",
    "# Plot the notch filter TF\n",
    "# plt.figure()\n",
    "# plt.plot( freq, 20*np.log10(abs(h)))\n",
    "# plt.title('Notch Filter')\n",
    "# plt.xlabel('Frequency (Hz)')\n",
    "# plt.ylabel('|H(f)| dB')\n",
    "# plt.show()\n",
    "\n",
    "# Apply notch and bandpass filters\n",
    "eeg_notched = signal.filtfilt(b, a, eeg_data, axis=1)\n",
    "eeg_notched_bp = preprocess_util.bandpass(eeg_notched, lf=lf, hf=hf, fs=fs, ftype='butter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "channel_number = 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": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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'{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": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def find_closest_time(timestamp, eeg_timestamps):\n",
    "    return np.where(eeg_timestamps > timestamp)[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Get indices of events\n",
    "indices = [0]\n",
    "\n",
    "\n",
    "for timestamp in relative_event_time_stamps:\n",
    "    try:\n",
    "        indices.append(find_closest_time(timestamp, relative_eeg_timestamps))\n",
    "    except:\n",
    "        print(\"Index out of bound - continuing\")\n",
    "        continue\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": 15,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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 = 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",
    "\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": 16,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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",
    "\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": 17,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def normalise_eeg(eeg_data):\n",
    "    '''Normalise each channel to have std of 1'''\n",
    "    return eeg_notched_bp / eeg_notched_bp.std(axis=1)[:, None]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "eeg_notched_bp_norm = normalise_eeg(eeg_notched_bp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inspect EEG data with markers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": 20,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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",
    "    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(channel2name(i))\n",
    "        axs[i + 1].set_ylabel(r'$\\mu$ V')\n",
    "\n",
    "    plt.show()\n",
    "\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": 21,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "To MNE...\n",
      "Creating RawArray with float64 data, n_channels=8, n_times=1023780\n",
      "    Range : 0 ... 1023779 =      0.000 ...  4095.116 secs\n",
      "Ready.\n",
      "Filtering raw data in 1 contiguous segment\n",
      "Setting up band-pass filter from 7 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 7.00\n",
      "- Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 6.00 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 413 samples (1.652 sec)\n",
      "\n",
      "Setting up band-stop filter from 49 - 51 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandstop filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 49.38\n",
      "- Lower transition bandwidth: 0.50 Hz (-6 dB cutoff frequency: 49.12 Hz)\n",
      "- Upper passband edge: 50.62 Hz\n",
      "- Upper transition bandwidth: 0.50 Hz (-6 dB cutoff frequency: 50.88 Hz)\n",
      "- Filter length: 1651 samples (6.604 sec)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gQQC2bt1abn+3bt1Kenq6L93866+/TnFxcanXv8TChQt55plnSpWtXr2aI0eOMGfOnFLlPXv2JDs7m/nz5wPwzjvvnHJcVc+Y5r7EunXrmDp1Km+88UaptPfvvPMOR44cYenSpaSmpvLaa6/5ju3YsYMTJ06Qnp5OUVFRqfaWLFni216+fHmpY7feeitHjx4lJSWF9evXs3TpUjIyMkrVSU5O5osvvmDnzp2n9HXPnj1kZGSQnZ0NQHFxMYsXLy71nPfccw9JSUkUFxeTn58PQHp6OvPnzycpKYnc3FwyMjLIysoCvO/b1atX89FHH1U4R8XFxbz66qu+34Vly5b5fkf27NnDnj172LKl9G1wPB4PK1asoKioiJycHNLT00sdX7ly5SlzV95Tn6lCZdW7gGyMMT9RtcdLC8jGGOMSFpCNMcYlLCAbY4xLWEA2xhiXsIBsjDEuYQHZGGNcotIBWUROlsmrl+iUJ4vIBr/yJL9zbhGRNSKSLiKrReQtEWlaQfuPi8h6p40UEbmtsn02xhg3qoqs0wVOKqby3Kyqqf4FInIl8ChwlapmOTeqvx2IwXvDev+69wPDgQGqmufclH5UFfTZGGNcpyoC8rkaDzyuqlngy4s3vYK6TwEJTo69klx7M0QkHnjLqdMAuFBVpXq7bYwx1asqAnLZNE7P+2Wf9k/jtMDJPn0BsPJMjTqfhiNVdUvZY86n7r5OvcnA/ArauBe4F6BDhw5nNRhjjAmUGl+y8CciccC7QCTwlF8gPysiMgZvFuvLyzuuqtOAaQDx8fF6Lm0bY0xNC8RVFul4gyiqmuYE8y8ok33aWZ7IF5Eu5TUiIhcCHuBXzrKHMcbUaoEIyM8DU0SkvV9Z+GnqTnWWLxCRCBG5zbki4wPgNlXdV629NcaYGlIda8jzVTXR2fZfQ85R1WGqOk9EWgJfOFdYHALWAl+W0/ZrQASQIiJFQBHwv8B1QEfgTRHv3/JOs2xijDG1QqUDsqo2qKA84TTnzABmnEXbCrzoPMo64/nGGFOb2Df1jDHGJSwgG2OMS1hANsYYl7CAbIwxLmEB2RhjXKJeBWSPx0NUVBSvvvoqDRs2ZNiwYURERAAQGxtLVFQUjRo18mVJBhgzZgxNmzalXbt2jBs3jgsuuID9+/cTFxdHZGQk0dHRNGrUiOJib0LaO++8k9DQUHr27Enr1q2JiIggLi6OJk2aEBUVxaWXXkq3bt1o0aIFAJdf7v2SYU5ODmPGjGHcuHH06NGDpk2b0r9/fwDCwsJITEz09TMoKIgBAwYwfPhwX1+vuOIKDh48SNOmTQkPD6dt27a+zM8JCQnce++9LFy4kP79+9O3b1/f+J5++ulS8/P1119z7bXX0qxZM5555hkGDhwIwAMPPEBOTg5fffUVEydO9J3z8MMPM3z4cK677jreeOMNsrOz6dChAxdddBF33XUXCQkJvrodO3Zk7NixTJgwgePHj/syDQ8dOpTLL7+cLl283wFq2rQpY8eOBWD27Nl89dVX5Ofn06JFCyZPnkxsbCznn38+DzzwgK/tfv36AXD77bczdepUfvOb3zBixAj69euHx+MhODiY1atX+8YZHR1NcHAwHTp0KJUZ27+/d9xxB506dWLatGm0b9+eNm3asG/fPkaOHMk111zDkCFDAHjyySdp1qwZkZGRp7zfRIT169ejqvTq1YvzzjuPN998s9R7bOzYscTHx/PYY48B0LJlSwCCgoIYPXq0b+6GDBnCFVdcwYgRI+jcuTN9+vQBYNy4cSxatIiuXbsybNgwAIKDgxk/fjwFBQX069ePW2+9lWuuuYbly5czdOhQbr75ZoKCgujUqROtWrVi5MiRREVFERUVxZgxY3z9DwsLY/r06dx+++2+Ppf87Ny5M3l5eQwZMoTly5fTs2dP3/sawHuRFLRo0YLevXsTHh5OUpL3po9t27YlLi6Oq6++mpycHA4ePEhQUBDPPvss1157ra+NkJAQnnrqKW655RYiIyMZNWoU3377LTExMQwdOhTw/n5ERUUxaNAgLrnkEu6++27uv/9+OnXqRNu2bX39jYuLo2fPnoSFhZUaB8DPf/5zSi6hPQ3LOm2MMS5hWaeNMaa+sIBsjDEuYQHZGGNcwgKyMca4hAVkY4xxCQvIxhjjEhaQjTHGJSoVkEXkpIis8nskOuXJIrLBrzzJ75xbRGSNiKSLyGoRecu54Xx57T8uIuudNlJE5LYyx18WkfzKjMEYY9yisvdDPqd8eiJyJfAocJWqZjk3qL8diMF7o3r/uvcDw4EBqprnZA0Z5Xc8HmhWyf4bY4xrVEXGkHMxHnhcVbMAnFx40yuo+xSQ4OTWK8mxNwPACeSTgbH4BWljjKnNKruGHF5myWKM37GZfuWTnbILgJVnatT5NBypqlsqqPJrYK6q7jpDO/eKSKqIpO7bZ6n3jDHuVqNLFv5EJA54F4gEnlLVD8/mCUWkLTAaSDhTXVWdBkwDiI+P17Np3xhjAqWmr7JIBy4GUNU0J5h/QZms087yRL6IdCmnjYuArkCGiGwDGolIRnV22hhjakJNB+TngSki0t6vLPw0dac6yxeISISI3Kaqn6tqa1XtpKqdgKOq2rV6u22MMdWvsksW4SKyym9/vqomOtszRaTA2c5R1WGqOk9EWgJfOH+YOwSsBb4sp+3XgAggRUSKgCLgfyvZX2OMca1KBWRVbVBBecJpzpmBc7XEGdpW4EXncbp6EWdqyxhjagP7pp4xxriEBWRjjHEJC8jGGOMSFpCNMcYlLCAbY4xL1OuAnJOTUyp1+5EjR4iLi/PtFxUV8a9//Yt169b5UoY/8MADZGZmsnbtWt85R48epaCggHfffZdbbrmFPXv2ACAiREREMGLECFasWEFcXBwrV67ksssuY9euH7/1fdlll3H8+HE6dOgAeNOT9+7dm2nTptG6dWv++c9/smLFCkaNGuVL/V4iOTkZgN/97ndERkaybds2Dh06dMpYT548SYMGDRg8eDAhISF8+umnbN26lT/+8Y8kJSWRlpYGwOLFi9my5cdvrG/YsIGZM2fSp08fQkNDiY6O5ptvvgFg//79ZGZm8tBDD7Fy5Y/fiG/evDkRERF07tyZFStWANC6dWv27NnDxRdfzMsvv8zmzZt9Yz1x4gRz587lxIkTtGjRgiNHjvD222/ToEED2rRpw9dff01CQoLvNdu8eTPTpk1j0aJFRERE0K1bN3bv3g1AWloaGzduBODQoUO89NJLALRp04bGjRv7+piWlsbEiRN9+xs3buT999/nwQcfJDw8nAYNGvheQ3/Hjh2jU6dOfPLJJ4wcOdJXHhkZyfLlywGYOXPmKfOvqvTq1Yt///vfrFq1qtSx1NRUXn/9ddq3b8/UqVNLlY8cOZK+ffue0l5qamqpNPaNGjVCRDhw4ICv7KabbqJJkyZs374dj8fD7t27+fjjj5k1axbPP/88AKtWrSI+Pp6lS5f6zvN4PBQWFvKnP/3JV5aZmcngwYNJTU0lNjaWp59+2jfOkte4a9euxMTEADBp0iQSE71XwC5fvpyjR48SHR3Nc88953uvgfd1SU1NpWnTpoSFhXHy5Em+//571q1bR1KS7yaRLFu2jIEDB+LxeFixYgXbt2/3Hdu9ezfnn3++b3/mzJmsX7+e1atX88orr/Dmm28CsH79esLCwmjVqhXvvPNOqfnbvXs3LVq0OGWeyyg+U4XKqtcB2W3K/vIbY1yl2uOlBWRjjHEJC8jGGOMSFpCNMcYlLCAbY4xLWEA2xhiXsIBsjDEuUSU59UTkJJDmVzRLVSeJSDLQBii5DWeGqt7gnHML8CTQADgBpODNt3eoTNt/A4YAuXivA3xIVZdijDF1TFUlOa227NOOJ1Q1SUQuB94AeldRv40xxjVqOut0iXPJPu1vEdDVyas3z688DuiiqtvLP80YY9yvqgJy2cwhz/slLfXPHLJAVZ/gLLNPl2MEkKaq2UBfABF5CBhSXjAWkXuBewHf15KNMcatArJk4e8ss09PFpEJwD7gbr9zLwXGAYPLa9uyThtjapNAXWVxVtmn/Tyhqn1VdbiqrgUQkTbA28CNqppfA302xphqFaiAfC7Zp08hIsHAHOD3qrqxqjtnjDGBUF1ryFWZfbo8g4B4YKKIlNxD8WpnbdkYY2qlKgnI1Zx9+o5yyr4Fws6+h8YY4372TT1jjHEJC8jGGOMSFpCNMcYlLCAbY4xLWEA2xhiXsIBsjDEuUW8C8qFDh/jlL38JeNOcezweunTpwv79+0ulA1f1fsP6xRdfZMCAARw+fJiUlBQAXn75Zdq3b09ubi6vvfYaAwcOpGnTpgB07NiR1atXAxAREcGKFSs4fvw40dHR9O/fn4MHD9KtWzeysrLo0KEDx44dAyAlJYW2bdvSt29fDhw44HuuUaNGsXLlSuLi4pg/fz4AK1euZPz48Xz11Ve+cRQWFvL666+zadMm5syZA8DEiRPp2LEjf/3rX4mOjgZg6dKl5ObmMn36dPbu3UvDhg3Jz89n27ZtfPhhed9WhwcffJDNmzf79pcvX86+fft8Ke39j4E3/fqFF15I9+7d2bjx1O/rdOnShbi4OKKjo4mIiPD1DaBv376sX78ej8fD6tWrOXHiBPv37/cdT05OJj4+3pdm/sUXX2TevB/vL1Xyuv33f/83u3bt4u2332bWrFnk5OTg8XiIjo4mKiqKnj17cvToUUJCQrj11ltp06ZNqdd/8uTJXHPNNXg8Hl/6eIBjx44RHBzMgAEDmDt3rq98/fr1bNu2jWXLlpGZmUmfPn34xz/+gcfj4de//jWPPfYYf//73wkODiY+Pp7Vq1fz+uuvs3z5cubNm8ehQ4eYNGkSHo+HadOmsWzZMm688Ubuu+8+IiMjmThxIt988w3jxo3jww8/JC8vj127dtGiRQuOHTuGx+PhwIEDbN++nb179xIeHu5rLy0tjSFDhnDo0CEAHnjgAWbPnk1xcTFff/013333HUuWLOGFF17gwgsvpEePHvTo0YPvvvuOZ599loULFxISEkJ4eDjTpk2joKCA3bt3c+TIEd/4o6KiCAkJYd26dSxevJiEhATy8/PZvn2773UJDg6mSZMm7Nq1i8zMTKZOnUpCQgIAN998MwUFBRw8eBCAGTNmMHLkSIYMGcLs2bN9z7N48WK2bt3K9OnTWb9+PceOHSMnJ4e1a9f63sspKSlMmjSJEydOkJOTw7///W8A5s6di8fj4ZJLLkFV+eGHH9i0aRPvvfceWVlZABQUFBAcHHzKe7aM4jNVqKx6E5CNMaaSqj1eWkA2xhiXsIBsjDEuYQHZGGNcwgKyMca4hAVkY4xxCQvIxhjjEmcMyCJyUkRW+T0SnfJkEdngV57kd84tIrJGRNJFZLWIvCUiTctp+28istU5f6WI/MwpFxGZICKbRGSjiHwjIhc4xyLL9CdHRF6qqgkxxphAOZv7IZ9TvjwRuRJ4FLhKVbOcG9DfDsTgvRF9WU+oapKIXA68AfQGHsJ7E/o+qnrUOTZXRC5Q1cM4CU6d51sBfHQW4zDGGFerjiWL8cDjqpoFoKonVXW6qm44w3mLgK7O9u+BX6vqUaeNr4AlwM3+J4hId6AVsLgK+2+MMQFxNgE5vMwSwRi/YzP9yic7ZRcAK39CX0YAaSISBTRW1S1ljqc6bfv7FfChlnxvtgwRuVdEUkUkNTc39yd0yRhjak6VL1n4E5E44F0gEnhKVcu7acJkEZkA7APuPov++PsVcGtFB1V1GjANoGvXruUGbWOMcYvqWLJIBy4GUNU0J5h/QcVZpZ9Q1b6qOlxV16pqHnBERLqUqdfPaRsAEekDNFTVFVU+AmOMCYDqCMjPA1NEpL1fWUXBuCKTgZdFJBxARIYBg4H3/ercBHxQmY4aY4ybnM2SRbiIrPLbn6+qic72TBEpcLZzVHWYqs4TkZbAF84VFoeAtcCX59Cv/wOa4V1TPgnsBq5T1QK/OjcCV59Dm8YY42pnDMiq2qCC8oTTnDMDmHEWbd9RQbkCE51HReeWXdIwxphazb6pZ4wxLmEB2RhjXMICsjHGuIQFZGOMcQkLyMYY4xL1OiB36NCBo0eP+vaDg4PZu3evb3/w4MFkZGSUOqewsBAR8e137dqVn/3sZ779r776ijZt2gBQXFxMaGgoeXl5NGzYkIyMDKKjo1myZAlRUVGAN5txaGgoIsLKlStJSUlBVUlLSyMiIuKUPk+ePJlLL70UgMzMTCIjI33HvvvuO1q1agV4MzyXl/nZX6NGjUqNH2DZsmUsXLiQe+65h5tuusnXHkDjxo156KGH2LZtGydOnOA///kPAC1atPC1c9VVV9G+fXu2bdtWqt2kpCRfhl+Ayy67jJ49e/Lqq68C3ozV5513HgDNmjUjKSmp1PkJCQmlskP7e+WVV3yZlf/yl7+waNEi37ElS5awYMECBg8ezKhRowCYNWuWL7s3eLMSl2TsXrp0KZ9++inZ2dlMmTLFV6e4uBgRoUmTJhQWFpZ6/gULFgBQVFTE3Xf/+GXT7du3k5GRweLFi9m2bRs9evQgNjYWgDfffJOZM2dy6623cvLkyVLtLVu2jN/+9rfljrVESebx999/n82bN9OzZ086duxIx44dS9VLT0+nWbNmvv2MjAzefvtt3/6GDRt8GbtHjRrFhAkT2LJlC99++y15eXmsXLmS3/zmNwQFeUNFdnY2eXl5vszTcXFxHDhwwNdecnIyH3/8McePH+e7777zvWYHDhzwved37NjBzp07WbHC+52ukJAQOnfuzIkTJwA4fPgwM2bMOOV3r4Sq8vnnn/t+V7t27cq+ffvIzMw8pZ7H42Hr1q0ANG/enJMnT1JYWMj69etL9fssWdZpY4xxCcs6bYwx9YUFZGOMcQkLyMYY4xIWkI0xxiUsIBtjjEtYQDbGGJewgGyMMS5RqYAsIifL5NtLdMqTRWSDX3mS3zm3iMgaEUkXkdUi8paINC2nbRGRCSKySUQ2isg3IlI2p54xxtQZZ3OD+tM5p3x7InIl8ChwlapmOTewvx2IwXsje38PAYOAPqp6VEQuB+aKyAWqeqyS/TbGGNepbEA+V+OBx1U1C0BVTwLTK6j7e2CIqh516n4lIkuAm0VkH/CMUy8cCFHVztXbdWOMqV6VXUMOL7NkMcbv2Ey/8slO2QXAyjM1KiJRQGNV3VLmUCpwgarOdRKj9gVWA1PKtuG0c6+IpIpIam5u7jkPzhhjalKNLln4E5E44F0gEnhKVT881ycXkSedPkwt77iqTgOmAXTt2lXPtX1jjKlJNX2VRTpwMYCqpjnB/AvKZKVW1TzgiIiUzZvXz2mjJBP1aOD+au6zMcbUiJoOyM8DU0SkvV9ZeAV1JwMvi0g4+ALwYOB9EekITAVGl8lEbYwxtVZllyzCRWSV3/58VU10tmeKSEmwzFHVYao6T0RaAl84V1gcAtYCX5bT9v8BzYA0ETkJ7AauU9UCEbkDiAY+ce5NnK2qV1dyLMYYE1CVCsiq2qCC8oTTnDMDmHEWbSsw0XmUPVZuuTHG1Gb2TT1jjHEJC8jGGOMSFpCNMcYlLCAbY4xLWEA2xhiXqHcBuVOnTr7t9u3b065dO99+9+7dS9Vt0qQJV155Jddcc02pFPStWrWiVatWwI/p6aOiovjFL34BwPDhwwG48MILERGCg4Pp3r07MTEx9O7dG4CLLrrI194999xD586dOf/882nZsqXvuRISEmjVqhUXXHAB/fr1K9W3kjpXXHFFqfLBgwcDcN9995Wqe+WVVwLQoUMHLr/8cm677TY6d+7MkCFD6NWrF3Fxcb4+N2rUqNR8XXjhhb7++M9haGgo99xzDwBPPvlkqX707NmTdu3a+frdu3dv+vbt6+vPI4884ptLj8dDWFiYb27CwsLo1q2bbzzDhg0rNff9+/enUaNGDBgwAICOHTtyySWXABAbG0tMTAwAjRs3Jj4+HoCYmBhGjhzpayc6OrrUax8TE8PYsWMJCQkpNdfffvutb/vOO++kefPm9O7dm5EjR/LJJ5+UGvOgQYNo27atbxz+SuY0Pj6e1q1blzoWEhLi2/Z/7pL5LvlZ1h/+8IdSz924cWMmTJjgK7vhhhuIi4vzvV59+vShTZs2nHfeefzXf/0Xffr08c3Vb3/7W995Xbp0ISgoqNR7tHHjxsyePZsePXrQtm1bAGbPng38+Hvj388bb7zR97pde+21dO/enXbt2nHHHXcQHv7jVw/69OnDjTfeSMOGDZkwYQJBQUGMGzeOjh07lpqnX/ziF9xxxx3ExsbStWtXGjb88QKxkuctmfeRI0fSpUsXhg8fTkREBLGxsYD3fRcdHU14eDidO3emWbNmvjZK+ncGxWeqUFn1LiAbY8xPVO3x0gKyMca4hAVkY4xxCQvIxhjjEhaQjTHGJSwgG2OMS1hANsYYlzhjQK7mzNJ/E5GtzvkrReRnTvlpM06LyE0ikuY8x3wRaVEls2GMMQF0NrffrM7M0gBPqGqSk1X6DaA3p8k4DZwA/gr0UtUcEXkR+DXgOYuxGGOMa1XHksUpmaVVdbqqbjjDeYuArs7274Ff+2ecBpYANwPiPBqL9+70UUB21Q/DGGNq1tl8Qi6bFeR5v4Sk/llBFqjqE5xlZulyjMCbHeRMGaeLROQBIA04AmzC+4n6FCJyL3AvQIsWtqphjHG3s/mEXKCqff0e/tmhb/Yrf6LsiSIS56wPbxaRMRW0P9kJ+PcCd5+pMyISDDwAXAS0BdYAfyivrqpOU9V4VY1v0qTJmZo2xpiAqo4li7PKLO3nCSegD1fVtWeRcbqv0/ZmJ83TbLzrzcYYU6tVR0A+l8zSFakw4zSQBfRykqUCDAd+qFyXjTEm8H7KGnJVZpauSIUZp4ECEZkILBKRImA7cMc5tG2MMa50xoBczZml76igvMKM087x14HXz9S+McbUJvZNPWOMcQkLyMYY4xIWkI0xxiUsIBtjjEuczVUWxhhjHHv27Km2tu0TsjHGuIR4rzCr+0TkMHCmGxzVNi2AnEB3oorVxTFB3RxXXRxTR1Vt6V8gIiOq44lU9dOyZfVpyWKDqsYHuhNVSURSbUy1Q10cV10cU6DVp4BsjDHnrLxPstXF1pCNMcYl6lNAnhboDlQDG1PtURfHVRfHFFD15o96xhjjdvXpE7IxxriaBWRjjHGJOh+QReRKEdkgIhkiknjmM9xPRKaLyF4RWRvovlQVEYkVkW9EZJ2IpIvIbwPdp8oSkTARWS4iq50xlXs72dpIRBqIyPci8lmg+1KX1OmA7NwgfypwFdALuElEegW2V1Xib8CVge5EFTsB/E5VewEDgYfqwGt1HBiqqn3wph67UkQGBrZLVea3WKaeKlenAzIwAMhQ1S2qWgjMAq4LcJ8qTVUXAQcC3Y+qpKq7VHWls30Y7y97u8D2qnLUK9/ZDXYetf6v6E56tmuAtwLdl7qmrgfkdkCm3/5OavkveX0gIp3wZhX/T4C7UmnOf+1XAXuBBapa68cEvAQ8CRQHuB91Tl0PyKaWEZEI4B/AI04G8lpNVU86mdfbAwNE5MIAd6lSRORaYK+qrgh0X+qiuh6Qs4BYv/32TplxIREJxhuMZ6rqR4HuT1VS1UPAN9T+tf9LgV+KyDa8S4BDReS9wHap7qjrATkF6CYinUUkBPgVMDfAfTLlEBEB3gZ+UNU/B7o/VUFEWopIU2c7HBgOrA9opypJVf+gqu1VtRPe36d/qeotAe5WnVGnA7KqngB+DXyJ949Es1U1PbC9qjwR+QBYCvQQkZ0icneg+1QFLgVuxfuJa5XzuDrQnaqkNsA3IrIG74eDBapql4mZCtlXp40xxiXq9CdkY4ypTSp1P+RmzZppu3buuYosPT09p6K7/UdGRs6NiYmhSZMmgemcMcb1VqxYcUoMqUmVCsjt2rXjo4/c88fwHj16bK/oWExMDH/+858ZMaJasrEYY+oAEakwhtQEW7IwxhiXsIBsjDEuYQHZGGNcotoC8kUXXVRq/6OPPuKZZ5457TlnU8cYY+oq+4RsjDEuUamrLH6qAwcO8PTTT5OdnQ3AU089Rb9+/UrVSUxMJCQkhLVr13LkyBESExO57LLLAtFdY4ypEdUWkI8dO8Z11/146+Hc3FyGDh0KwHPPPcftt99OfHw82dnZ3H333XzxxRentJGVlUVSUhI7duzgtttuY9CgQYSGhlZXl40xJqCqLSCHhYXxz3/+07f/0UcfsXatN+PQkiVLyMjI8B3Lz8/nyJEjp7Rx1VVXERQURKdOnYiNjWXLli2cf/751dVlY4wJqIAsWRQXFzN79uwzftr13gCs4n1jjKlLAvJHvcGDB/Puu+/69n/4ofzUXPPnz6e4uJgdO3aQmZlJ586da6qLxhhT4wISkMePH8/atWsZMWIEV199NR988EG59dq0acMNN9zAuHHjmDhxoq0fG2PqtGpbsvj+++9L7V9//fVcf/31ADRv3pyXXnrplHP86wAMGjTIrks2xtQbdh2yMca4RED+qHc2Jk2aFOguGGNMjbJPyMYY4xIWkI0xxiUqlVNPRPYBAb2hcxkdK7rbv4gcBjbUcH/cpgWQE+hOBJjNgc0BVDwHFcaQmlBvkpyKSKqqxge6H4Fkc2BzADYH4N45sCULY4xxCQvIxhjjEvUpIE8LdAdcwObA5gBsDsClc1Bv1pCNMcbt6tMnZGOMcTULyMYY4xJ1PiCLyJUiskFEMkQkMdD9qUoiMl1E9orIWr+y5iKyQEQ2OT+bOeUiIi8787BGRC72O+d2p/4mEbk9EGP5qUQkVkS+EZF1IpIuIr91yuvNPIhImIgsF5HVzhxMdMo7i8h/nLF+KCIhTnmos5/hHO/k19YfnPINInJFgIb0k4lIAxH5XkQ+c/Zr1xyoap19AA2AzUAXIARYDfQKdL+qcHw/By4G1vqVvQgkOtuJwAvO9tXAF4AAA4H/OOXNgS3Oz2bOdrNAj+0c5qANcLGzHQlsBHrVp3lwxhLhbAcD/3HGNhv4lVP+OvCAs/0g8Lqz/SvgQ2e7l/M7Egp0dn53GgR6fOc4F48B7wOfOfu1ag7q+ifkAUCGqm5R1UJgFnDdGc6pNVR1EXCgTPF1wAxnewYw0q/87+q1DGgqIm2AK4AFqnpAVQ8CC4Arq73zVURVd6nqSmf7MPAD0I56NA/OWPKd3WDnocBQIMkpLzsHJXOTBPxCvOl4rgNmqepxVd0KZOD9HaoVRKQ9cA3wlrMv1LI5qOsBuR2Q6be/0ymry2JUdZezvRuIcbYrmos6M0fOfzsvwvsJsV7Ng/Nf9VXAXrz/mGwGDqnqCaeK/3h8Y3WO5wLR1PI5AF4CngSKnf1oatkc1PWAXK+p9/9g9eK6RhGJAP4BPKKqef7H6sM8qOpJVe0LtMf7ia5nYHtUs0TkWmCvqq4IdF8qo64H5Cwg1m+/vVNWl+1x/guO83OvU17RXNT6ORKRYLzBeKaqfuQU17t5AFDVQ8A3wM/wLseU3PPcfzy+sTrHmwD7qd1zcCnwSxHZhndpcijwV2rZHNT1gJwCdHP+0hqCd/F+boD7VN3mAiVXCNwO/NOv/DbnKoOBQK7zX/ovgctFpJlzJcLlTlmt4Kz7vQ38oKp/9jtUb+ZBRFqKSFNnOxwYjnct/RvgBqda2TkomZsbgH85/4uYC/zKuQKhM9ANWF4jg6gkVf2DqrZX1U54f8//pao3U9vmINB/Fa3uB96/qm/Eu6Y2PtD9qeKxfQDsAorwrnXdjXcdbCGwCfgaaO7UFWCqMw9pQLxfO3fh/eNFBnBnoMd1jnMwGO9yxBpglfO4uj7NA9Ab+N6Zg7XA/3PKu+ANJhnAHCDUKQ9z9jOc41382hrvzM0G4KpAj+0nzkcCP15lUavmwL46bYwxLlHXlyyMMabWsIBsjDEuYQHZGGNcwgKyMca4hAVkY4xxCQvIptYQkWgRWeU8dotIlrOdLyKvVtNzPiIit53m+LUi8kx1PLepf+yyN1MriYgHyFfVKdX4HA2BlXjvJneigjri1LlUVY9WV19M/WCfkE2tJyIJfve/9YjIDBFZLCLbReR6EXlRRNJEZL7zNWtEpJ+IfCsiK0Tky5KvWZcxFFhZEoxF5GHx3nd5jYjMAt99MpKBa2tksKZOs4Bs6qLz8AbTXwLvAd+oahxQAFzjBOX/A25Q1X7AdOC5ctq5FPC/WU0icJGq9gbu9ytPBf6rykdh6p2GZ65iTK3zhaoWiUga3iQF853yNKAT0AO4EFjgXXGgAd6voJfVBu89IUqsAWaKyCfAJ37le4G2Vdd9U19ZQDZ10XEAVS0WkSL98Q8lxXjf8wKkq+rPztBOAd57HpS4Bm+WlhHAeBGJc5Yzwpy6xlSKLVmY+mgD0FJEfgbe23eKyAXl1PsB6OrUCQJiVfUb4Pd4b9cY4dTrjvemPsZUigVkU++oN53XDcALIrIa7x3iBpVT9Qu8n4jBu6zxnrMM8j3wsnrvPQxwGfB5dfbZ1A922ZsxpyEiHwNPquqmCo7HAO+r6i9qtmemLrKAbMxpiEgPvPn5FlVwvD9QpKqrarRjpk6ygGyMMS5ha8jGGOMSFpCNMcYlLCAbY4xLWEA2xhiXsIBsjDEu8f8BDHBqLynx8PcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<MNEBrowseFigure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create MNE array from numpy data\n",
    "print('To MNE...')\n",
    "ch_names = ['EEG Fz', 'EEG C3', 'EEG Cz', 'EEG C4', 'EEG Pz', 'EEG PO7', 'EEG Oz', 'EEG PO8']\n",
    "ch_types = [\"eeg\", \"eeg\", \"eeg\", \"eeg\", \"eeg\", \"eeg\", \"eeg\", \"eeg\"]\n",
    "info = mne.create_info(\n",
    "    ch_names=ch_names,\n",
    "    sfreq=250, \n",
    "    ch_types=ch_types\n",
    ")\n",
    "raw = mne.io.RawArray(eeg_notched_bp, info)\n",
    "\n",
    "# Filter using MNE\n",
    "raw.filter(7., 30., fir_design='firwin')\n",
    "raw.notch_filter(50, filter_length='auto', fir_design='firwin')\n",
    "\n",
    "\n",
    "# Plot data\n",
    "fig, axs = plt.subplots(9)\n",
    "fig.suptitle('EEG Channels')\n",
    "for i in range(8):\n",
    "    axs[i].plot(raw._data[i])\n",
    "axs[8].plot(event_time_series)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "raw.plot(duration=5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ICA Experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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G7WloqnwAXxhjKoFKEckCUoDjgY+NMWWezz2j3vs8LCL/9Gx3kr+CGGOex6YEMW7cONNEmZVSSvlR7XbTLTaSM4Z1aeuiKKXUEa2ls/1U+jx2+/ztprYi8STwlDFmOHALELafbQrwqjFmlOffQGPMfZ7nKurl+b8MXAVcAbxvjHE1sd1qY4wTaNc0s3y+n8/3NU35rTFmAPB7mu6JUEop1UquGkNQgLR1MZRS6oh3KKb6jAF2ex5f67N8PnApgIicBsR5ls8BLhaRZM9z8SLS09+GjTGZQCY2Z//lg1y+xnwPXCAi4SISBZzbyHpPAQEicnory6WUUqoRNW5DUKDOTq2UUgfqUJxJ7wPeF5FlQI7P8r8Bp4nIWuASYC9QbIxZjw3mZ4vIauBroKl+3TeBdGPMhoNcPr+MMcuBd4FVwFfAkkbWM9gxBb9rZbmUUko1orrGTXCgtvwrpdSBktrMmEP8RiKhQI0xxiUik4BnjDGjWrGdp4AVxpgXD3YZD4dx48aZpUuXtnUxlFLqiDLm/q85e3gX7r9gWFsXRSml2j0RWWaMGefvuZYO+D0QPYD3RCQAOxj3ppZuwNNaX0rt7DxKKaU6gOoaN4Ga86+UUgfssAX/xpgtwOgD3MbY+stEZDFQ/+4JVxtj1hzIeymllGo/atxG036UUuogOJwt/4eEMWZCW5dBKaXUoeWq0QG/Sil1MOiZVCmlVLtX7XbrVJ9KKXUQaPCvlFKqXXO7DcZAUIBespRS6kDpmVQppVS7Vu12AxCkOf9KKXXANPhXSinVrrlq7JTUmvajlFIHToN/pZRS7ZrL7Qn+dcCvUkodMD2TKqWUatdcNTbtR6f6VEqpA6fBv1JKqXbN2/KvA36VUuqA6ZlUKaVUu1btafnXnH+llDpwGvwrpZRq12q8Of8a/Cul1IHS4F8ppVS7Vl2jA36VUupg0TOpUkqpds3l1rQfpZQ6WDT4V0op1a7pPP9KKXXwaPCvlFKqXXNm+wnWtB+llDpgeiZVSinVrjnz/OuAX6WUOnAa/CullGrXnAG/gZr2o5RSB+yoDP5FpJeIrD1E235FRC72PP5ORDaJyCoRWSIiow7FeyqlVEdWo2k/Sil10AS1dQGOAj83xiwVkeuBh4GpbV0g1dALP2zn241ZbV0MpVQr5JdVAzrgVymlDoY2D/5F5Brg/wADrAZqgM+NMR94ni8xxkR6Hv8WuBQIBT42xvy1iU0Hish04FhgN3C+MaZcRG4CbgZCgK3A1caYMhF5BSgCxgGdgd8ZYz4QEQGexAb16UBVI++3EPhtI5/xZs970qNHj/1/Keqge29pOtnFlfRLjmzroiilWigyNJBTBiXTV3+/Sil1wNo0+BeRocCfgGONMTkiEg/8t5F1TwP6A+MBAWaIyAnGmO8b2Xx/4ApjzE0i8h5wEfAG8JExZrpnm/8ApmGDe4AuwHHAIGAG8AFwITAQGAKkAOuBl/y83xnAJ/4KYox5HngeYNy4caaR8qpDyFVjmNwvkaeuHNPWRVFKKaWUajNt3fJ/CvC+MSYHwBiTZxva/TrN82+F5+9IbIDfWPC/wxiz0vN4GdDL83iYJ+iP9Wxjls9rPjHGuIH1IpLiWXYC8LYxpgbIFJFv673PmyIS4tnWqEY/qWpT1W63pgwopZRSqsNr6+DfHxeegcgiEoBNzwHb2v+gMea5Zm6n0udxDRDuefwKcIExZpWIXAec1Mhrmhsp/hxbuXgY24Pws2a+Th1GNTWGIB0sqJRSSqkOrq2joW+BS0QkAcCT9pMGjPU8fx4Q7Hk8C7hBRJz8/24iktyK94wC9ohIMDZw35/vgctEJFBEugAn11/BGGOAPwMTRWRQK8qkDrFqtyFY5whXSimlVAfXpi3/xph1IvIAME9EarApPb8HPhWRVcBMoNSz7mwRGQws9KQGlQBXAS2dwuXPwGIg2/N/1H7W/xibnrQe2IUd2Ovvs5SLyCPYQb/TWlgmdYi5atw6R7hSSimlOjyxjdbqcBk3bpxZunRpWxejwxl+3ywuGpPKfecNbeuiKKWUUkodUiKyzBgzzt9zbZ32o9Rh4arRtB+llFJKqfY44LfZPGMF5vh5aooxJvdwl0e1Xy63Wwf8KqWUUqrDO6KDf0+AP6qty6HaN2MM1TVGp/pUSimlVIenTaHqqOf2DGsJCtDDXSmllFIdm0ZD6qhXXeMGIEhz/pVSSinVwWnwr456Lk/Tv6b9KKWUUqqj0+BfHfVqajzBvw74VUoppVQHp9GQOupVu23aj071qZRSSqmOToN/ddRzOS3/OuBXKaWUUh2cRkPqqOcd8Ks5/0oppZTq4DT4V0e9GmfAr6b9KKWUUqqD0+BfHfVcbmeqTz3clVJKKdWxaTSkjnrVNTrVp1JKKaUUaPCvOoAanedfKaWUUgrQ4F91AM6A32BN+1FKKaVUB6fRkDrqOXf4DdSWf6WUUkp1cB0m+BeRXiKy9hBs9zsRGXewt6sOHu88/zrbj1JKKaU6uA4T/DeHiAS2dRnUwedya9qPUkoppRRAUFsX4DALEpE3gTHAOuAaYD3wLjAV+LeI5AF/A0KBbcD1xpgSEfkLcC4QDiwAbjHGGGfDIhIAvARkGGP+dBg/U7PM3ZSF2232v+JRaM3uQkAH/CqllFJKdbTgfyAwzRgzX0ReAn7pWZ5rjBkjIonAR8CpxphSEfk9cDfwd+ApY8zfAUTkdeAc4DPP64OAN4G1xpgH6r+piNwM3AzQo0ePQ/fpmvDLN5ZTXl3TJu/dXsRFhLR1EZRSSiml2lRHC/7TjTHzPY/fAO70PH7X8/9EYAgwX0QAQoCFnudOFpHfARFAPLbnwAn+nwPe8xf4AxhjngeeBxg3blybNL+/f+sk3KZjtvwDRIUF0yuxU1sXQymllFKqTXW04L9+9Ov8Xer5X4CvjTFX+K4kImHA08A4Y0y6iNwHhPmssgBbOXjEGFNx8It94IZ1i2nrIiillFJKqTbW0UZA9hCRSZ7HVwI/1nt+ETBZRPoBiEgnERlAbaCfIyKRwMX1Xvci8CXwnoh0tAqVUkoppZQ6QnS04H8TcJuIbADigGd8nzTGZAPXAW+LyGpsys8gY0wBMB1YC8wCltTfsDHmv8AK4HXP4F+llFJKKaXaFTEdOA+8LYwbN84sXbq0rYuhlFJKKaWOUiKyzBjj9z5UGvwfZiKSDexs63IAiUBOWxdCHXK6n49+uo87Bt3PHYPu56Pf4drHPY0xSf6e0OC/gxKRpY3VCNXRQ/fz0U/3cceg+7lj0P189GsP+1hz05VSSimllOogNPhXSimllFKqg9Dgv+N6vq0LoA4L3c9HP93HHYPu545B9/PRr833seb8K6WUUkop1UFoy79SSql2Q0TSRKRcREp8/nVt63IppdTRQu9Gq5RSqr051xjzTVsXQimljkba8q+UUqpdE5Gn6vUEuETkvrYul1JKHYk0+FdKKdWuGWNuN8ZEGmMigeOAfODTNi6WUkodkTT4V0op1d58IiIFnn+fOAtFJAn4BLjDGLOirQqnlFJHMs35V0op1d5cUD/nX0SCgQ+At4wx77RNsZRS6sinLf9KKaWOBE8CRcCf2rogSil1JNOWf6WUUu2aiNwCnAhMMMa427o8Sil1JNOWf6WUUu3dFUAfINNnxp8/tHWhlFLqSKR3+FVKKaWUUqqD0JZ/pZRSSimlOggN/pVSSimllOogNPgHROQMEdkkIltF5B4/z4eKyLue5xeLSC/P8qkiskxE1nj+P+WwF14ppZRSSqlm6vDBv4gEAv8DzgSGAFeIyJB6q00D8o0x/YBHgYc8y3OAc40xw4FrgdcPT6mVUkoppZRquQ4f/APjga3GmO3GmCrgHeD8euucD7zqefwBMEVExBizwhiT6Vm+DggXkdDDUmqllFJKKaVaSOf5h25Aus/fGcCExtYxxrhEpBBIwLb8Oy4ClhtjKuu/gYjcDNwM0KlTp7GDBg06eKVXSiml1MFjDFQUQnhsW5dEqVZbtmxZjjEmyd9zGvwfBCIyFJsKdJq/540xzwPPA4wbN84sXbr0MJZOKaWUUs325e/gp+fgqueg35S2Lo1SrSIiOxt7TtN+YDfQ3efvVM8yv+uISBAQA+R6/k4FPgauMcZsO+SlVUoppdShs+1b+/+uRW1bDqUOEQ3+YQnQX0R6i0gIcDkwo946M7ADegEuBr41xhgRiQW+AO4xxsw/XAVWSiml1CFSmm3/37Oqbcuh1CHS4YN/Y4wLuB2YBWwA3jPGrBORv4vIeZ7VXgQSRGQrcDfgTAd6O9AP+IuIrPT8Sz7MH0EppZRSB0NFEVQU2MfFmU2uqtSRSnP+AWPMl8CX9Zb9xedxBXCJn9f9A/jHIS+gUkoppQ69Qs/8H2GxULSnTYui1KHS4Vv+lVJKKaUAKMyw/3cfD2U54GowgZ9SRzwN/pVSSimlAEr22f+7jrH/F2vrvzr6aPCvlFJKKQVQkmX/Txli/y/NbbuyKHWIaPCvlFJKKQV2pp/QaIhOtX+X5TS9vlJHIA3+lVJKKaXAtvx3SoKIePt3mbb8q6OPBv9KKaWUUmBb/iOToVOi529t+VdHHw3+lVJKKaXADviNTIaQSAgMObrTfoyBOffDe9dCjautS6MOoyM2+BeR/4nI5LYuh1JKKaWOEiVZ0CkZRCAi8ehO+8ndCj/8B9Z/Ams/bOvSqMPoiA3+gc3Af0QkTUT+LSKj27pASimllDpCuars3X0jk+3fEQlH92w/W7+x/weGwIrX27Ys6rA6YoN/Y8zjxphJwIlALvCSiGwUkb+KyIA2Lp5SSimljiSl2fb/Tkme/xOO7pb/rA22d+O4uyHtRyjKbOsStW811ZA2H8ry2rokB+yIDf4dxpidxpiHjDGjgSuAC4ANbVsqpZRSSh1RSj1z/Pu2/B/NOf952yGhLwy/BDCa+tOUyhJ4+Ux45Sx45tja+0EcoY744F9EgkTkXBF5E/gK2AT8rI2LpZRSSqkjSfFe+39kZ/t/c3P+XVWwfgasfh+qSg9d+Q62vO0Q3wcS+0HX0bDybXC727pU7dPsP8HuZXDC72zgv+CJti7RATlig38RmSoiLwEZwE3AF0BfY8zlxphP27Z0SimllDpkSrJh1h9h5Vt21pqDoSDd/h/b3f4fkQAVhTbdozHFe+HFqfDe1fDRjfDciVCYcXDKcyhVl0PRbhv8A0y4FbLWwbyH2rZc7VF+mh0TccxNcMofYdDZtqJ0BM+QdMQG/8C9wAJgsDHmPGPMW8aYVlW5ReQMEdkkIltF5B4/z4eKyLue5xeLSC+f5+71LN8kIqe3+tMcaqvegfydbV0K1RGU5UFFUVuXQh1MhRkH3s298m149nhY8NTBC9bABmZFmQd3mx1V1kZIX3L4v8tdi+Hrv9j/m8MYeO8aWPgUfPIL+OSXUF1x4OUoTIfAUNviDzbnHxrP8a6ugDcvgZzNcPFL8PMP7FShL591ePLnjYFNX8Hy12ww72vfetuy35j8NPu/E/yPuAxGXgnz/gWLnm3e+6f9CPP+DdmbW1buVe/AnL/bitXhUlEEaz6w30tL/fgoSCAc92v794hLbTpY2vcHt4yH0REb/BtjTjHGvGCMyT+Q7YhIIPA/4ExgCHCFiAypt9o0IN8Y0w94FHjI89ohwOXAUOAM4GnP9tqXikL44jfw9CTbbQVQVQZf/hb+N9GeOA/kRibrPoaPboFtc/0/X17Q+MVkzyp49Vx47gT4afr+LzqZK1r34z3UijLh1fNg+pTml88YG1Rt+QbWf2q7jv3J3gSvnGNbuZpqadi7Ft66DD67q27wXV7Q9EXAl7sGts6x22qNzbPgkUHw3yGQ/lPLXlteANu/O3w31XG7oWCX/cwHQ3U5LH4eFj0DlcUHZ5stkTbfXtwORhDkqoQvfwePj7L78tGhdr/+NL112ytIh8/uhOyNMPuPNnd28+wDK2PWRpj5B/jvYPvvnZ83/htyVFfA9nlQ7rls5GyxwVNlScvf3/e9yvLsuXV/7384VVfA6vfg3avscdmUjGXw+oXw9AR48VT4cFrzKwB7VsNjI+yxsqOZwZAxtuW+xmXL+MpZMP9x+//Kt/b/+u1zYdcCOPsROPEeWPUWPHscrPvEPu922/NmS/dHYTrEpEKAJzRyKgGN5f3P+xfsXW0D/2EXQf+pcPUnNlXotQugaE/L3r8xu5fZlCJXZd3l3/0L3r4cZtxhf1NFe+xnn/0neGYSPDHGno/8ca4JTvAvAuc9CYPOgZm/hxVvNl4e5/4Ar5wNcx+AF6bUNi4WZsD/JsAzkyFvR8PXrn4PPr4FfngE3r++eceZMbaCM+uPtb0z9S16BmbcCcX7Gj6Xu82W58Np8Nzx9jrTXAXp9rsYcw1Ed7HL+k2FkChYc+SOkQhq6wK0A+OBrcaY7QAi8g5wPuAbwZ0P3Od5/AHwlIiIZ/k7xphKYIeIbPVsb+FhKnvzhMXALxbYIPuty+DK92DmPTY463uyDRj2rIab5kBQaMPXpy+BNe9DVQn0PgF6TLSB2r61NtjbMAMkwA4WunYG9DzWvq40Bz643l4QekyCC5+DuJ612y3cDa+db1taYrvDl/9n5x0+41/2RAT2RLZnJexaaHMq0xfZ5ac/CJN+aYOsz39tt3Xi7yCmO3z3oH39KX+y255xBxTshLMehj4n2ddXV8DKN2DnAhsoh0TYnMekwfZEu3c1pB4Dx94JQSH2NSVZdtt719gT5tjroecke0J+50objASFwJsXw41zak8U9e1bBz89bytNvi0fqcfA5W/VDjYDe4F88xJb/rQfbD7pOY/Wfj++23z1XHC77Do7F8CUv9iT3Mo3obrMDuo6/+naz+Nw19h9JWK/q80zAYEpf7azQGSth+I9nrzWT6GyCKb8FZIH2YC3eA/E9bYXvE9+aS+eNdX2xP6LHyEs1gZY1WUw5AIIDKr9bIufsRfp4r22IuiuhuAIOPVvtmt127e2a7rLKHvshUTULXtJFnz2K3vchEbbz1BZbN+z21jofbw98a9+F+J7wzmPQUS8/Y7euNgGEDHdYdLt0Os4SOgHwWG1LX2hUbD8Vfj+P7blJ2mArTB0HW33Q2iUXa8g3R4De1fbv398FE74LUR1sTOHdBtb+7md42/eQ7D0RTvNXp+TYeCZkDIUQjrZ98habyuSpdkw9jroN8W+ds8qG6z2mAgBnraGpS/Z3wFAbA845kYIjwfjtt9paa5t6ds2x773xFvtvg0O83+MzrzXlm3gWbY8XcfYY+nL/7Mtm4Eh0Hm4LbM/i561n2/A6Tag+P7fdvkdy2DLbPjxMXjrEps/e/IfGh7PxsDGL+DH/0JAMJz5EHQdZZ9zVcH3D9vgQQJg4BkQ29O2An/+azj/qYbbA3tcvHyW3Ufh8fY8tfELwEBcL7jkFbtfjbFd/DsX2s/YdbQ9hyYPttt118DHt8Ka9+zxEpEIGT/Z7zpxAJz3FPSYUPe9C3dD+mL7ewmPs8dgYIithKT9YGdd6T7B7ufwONi1yP4OXZWQOs5+zyGd7Lbyd9rjK2u93RfFe+157bynIDLJ/vbm/hOWvWy3HxgCGz6zzw29sOH38tN0u18jEuzvrqLQfu+9jodx19u/135ov4eu9WbULsmCt6+AmioIjbQB79hrYcj59vUBftrC3G746CZY+4H9TZkau+4Fz8Cnt9mW/OxNcNI9EBzu//j66QX7vY+6yh7D3cbAN3+D96+FdRfY3/y+NRDfF8593J6vI1Pq/gb9yd1qjwVHhNPy7yfvvyzPVqqGX1r3d5A6Fq58F9681AbFV75rjyNfxtjjMH+nrTA09jlrqu25Z95DgIHlx8NVH9rr9KaZtvIx8gr7/p/8EqafbD/zzh9h9NW2jDPvscf7yMvqbtsb/PeuXRYYZCsyb10GM263+3TI+Q3L9dPz9v4Ao6+GY++A6afA13+GS1+zDY05WyAw2KZCTfu69vMV74Ovfgep421laebvbWVv9M/9f37H0pfgi7vt42WvwLCf2d/8hFvsOXjnQvs5wf52fva8XR7Xy55LXzvfXn+ueNeW8+NfwC/m29/h/nz/sP3/uLtqlwWHweBz7O/qnP/6j5vaOTEdvKtURC4GzjDG3Oj5+2pggjHmdp911nrWyfD8vQ2YgK0QLDLGvOFZ/iLwlTHmg8beb9y4cWbp0qWH6uM0LWeLzU10LggXvWB/2Ju+sq0Hx/0aTr3PBsNLX7In/T0rbQAW3Mke4OX1uj9DIm2AfMyN8NJp9iR53Rf2QvvRzfbiNPa62rzMnsdCYn8Yd4M9SaQvhlt+sDMOzPojLPofnHSvza1b8RosexXyPa0HCf1g3DTb1bjpS1uZWPE67JxvT+zFewCxZcLYxwEB9gQaFms/92Wv20B37gOeVp4etmu3vKD2fSTAnjTyttuT9jmP2QrBtw/YE0iPibYCUFEAA86wF/0ts+GyN23l5qUz7IU6IsFeyE+7HzqPsIHGT9PtiTkoHIZeYC/sSYPtCerzX9v1ux9jg8n+U+12szba73TjZ/aif9o/7AnXVWlPwhs+h91L7Y1prv/Ctry8f71trQoMgWEX28+44Enod6rd75tm2kpb7jYbFNZU1n72KX+xn2/th7Y8TispQGiM/W6NgWOm2YpFaTZ0nwgBQZ79OQ9cFfDiadD3FPvdr3nPvn7wefbisnWOvbiU5dn9GtUZuoyEnpNhyfTa+ad9SaA9Tvqc7AkYA2xLYc5W6H+qDeiNsd99WS7sXl77uToPtwFFj4lw1cf2QrL8NTjh/2zlNH1x7ecPj6u92DvBSY9jIaabbbnulGRbjwecbvf57qU28HdV2YtOp0Tbq5a5vLbs4XEw4Ex77AcG2/fO2WyDscAQ2PJ1w9+W830HhdjveNwN9v8Nn9nnkofaY6u63KZA9D3Z/m7m/sPuv/oiU+znd9fAxs8hoT+c9W/ofZL9nRhjg9uVb8Mnt9rf9Wn3177eVWnvBLr5q9plZz4ME262j53Xb5oJb19mg5C8bTZg3LMKxt8CZ/7LrltTbY/3Fa/b72DEZbZs4XH2d/fF/9mKSkJ/ey6pKIQzHrQVyzl/t4HTyCvsb6GTp3V27oM2GBp2MZz+gD2myvLgm/ts2fO229/x1L/Dllm2YjXyctswMfMee1448192X2z8vOGx3/tEG9wsfMoGBKN+bs855QW2Yhbb01ZyCnfb33biAEDssbV9rj1P+BMQZM83uVvtebZTgj0fBATbY6W6zP6GJt1uK79z/2m31W2sPRYj4m3LZGQyXPwyfHu/fb8h59tjpudkG4SW5sIdS+35oXivXb7xc3vsDDgDLppuAyZj4LXzYPcKmDbbHgt7VtlyXvCsrUzOud8GQO4aWwGYNssG2F/dY88brnJbvotftufjr35rrz+TboPMlbbSP+4GW9HvlAgTf2mvL64q+PI39vcR3wcuf9s2MvjK2wFPjoHJd8Gpf61dXlMN3/7D9iDE9oAxV9vrR6GnpTihP1zzqf0d+1NdDg+m2u1O+bNdlrUBnp5oP8ewevOIzHvY/tZ+uchWDOvbs9oG0ZVFtkGrutz2UJTl2p53pzeh83Db6FNZAt/9EzKW2mMprqcNZAt22mO9yygbLA+90P6W3rrUHjfTZtvgeu9au69Ksu157Zgb7f557TzbY/6LBXUD/S9+Yxv+7vGTDlxVaitymSvghpn2OuUo2GVb9ntOhp+/b3/z3/7D/ibO+o+tSE75C6QMtxX8AWfaY9FVYdN9MlfArT/Y/fHK2Xacwdn/tXFGSZatBAu2AQug7xS7/Z7H2p6eWX+0v6myHOh5nN2nr55jj4vz/2d7ulyeFKiIBHtcBATa9bqMtO//wlR7vrzsTXt+N+7aynWdfbjKjuGY+At7/vG1dQ688TNbuRx7nb8jqs2JyDJjzDi/zx3pwb+IrAFW+/xbA1xrjHmgma8/5MG/iNwM3AzQo0ePsTt3tmHu/Z7VthV0+CW1LWlgW1xWvGkP5MXP2lYlCbQXl4m32qAiOAL2rLCBRVisPWnF9a7tIs3bYQPfEs+MCdGpcOmr9sSRt8O2mmett0FYjac79pzHbOsS2BahT2+zJ0hHz8kw+iob8Dkt6dXl9qSxexkgNuAadI4td1UJjL/Znmhm/8l2K0/9m235ePlMyN1it9FllA0C+pxY+14lWfYCnDwEwmNh45e2PE5Q1ucke3JL7G9P3oufsS2YrgrbYjbpl3a9nQtsq2RgiL3QlWbZlsOyXE+r7E32M9VvdchcCR/eaE+8Md0gY4ntubj4JdvK4HbbnpT1n9rX71pkP0/X0TYwGXdDbc9KZbE9cSUNrs1bXfaqbSXH85uP7Wn3YXxvWwlylduTaepYGwAsfs4GegPPst+XqbHvVZZrT7CZK6DbOBjk6bKvKIIz/10bDC54yqZ4ILZXJjjcBmIxPaBwl704XDS94YXTGPsZC9Nt5SG+j+352bnAXuC2zLaBbHisDaCveNcG//VVl9sLaWQyJA20x/env7SfZc9Ke5Gf+jf7ftmb7EUoe5NN4Ursb4Odkn02OBxwRt3W5MXP2RasPifZckV3sz1qSQNqj+Ws9bYnJj/NVlY3z6zt6YnrbS9kTmt+jcuWKXeb3Q9RXSFliN2uqwJm/9lWioIjbCU9toetwBbsqj2er/u8Nngrz7fHkYj9HOHxdXt8tn5jg++CXTbgDAyy+y8i3u7fXsfb9IX6LaVOqlpolG2h3TwLJv/Kbi9rg/097VoMCX3ghlm2lW7mvbZn78Zvbeuz77bmPWRbNt3V9jiJ723LFBRuB9Ydc5P9/X1wg20hBzsTy9mP2N9E/bLN+7dtkQwIhvE32oaNvO329+eqtK8beXnDY6Uk21bgMn6y573T7rcBaWG6DVqz1tuW5ZBOttI/+mrbo1G/h6Gi0DYSrPu4dtrIuN42cBxyvuc8kGc/U021/U10G2u/z71rbWW+NMdW/EdcZgPiXQttxX3zTLu93ifa9/btRc1YZoPBshxb/nMft8GvI22+rSjH9vCkTRh7bsjbbgOiaz6t26uWt8OmSVSX2uPn/KdtQL7zR/t8t7H2mCrLsb2AfU+ufW11Oaz9yFaojLGfsaLQBqo5m+w642+xvTn+emjA9jJ9dLPtpbrstdoeW/BUGt+AX63237taWWx/JwGB9pje+Ln9Pcx90J4HbphpK1X1bZsLr19gA0Ln2CrJgv/0t+f98TfVXf9/E21wef0X/j8D2DScd66sbQjoMtJ+74HBtqc3pBN8frdtpHBV2uNgwBn2uMvfac8nE35hGxpE7PXmG0+FJ76v7WmPSW38/cFWRp8caysNF/qkAL1xkT3Wbpnn/3Xl+fD0sbYSfdO3tfvqs1/ZBoI7ltUOjC4vsBWyslx7br5zhf1s8x+3vxvjSa0MCLY9ps6x6aTjuMptz210V9vAAvY6XFEERRk2lrhpji2LY9W78PHNtY0MZz9iKzz5O21Wg6vCHkeVxfY873udcXq7fCUNst/9+Jvsd1qaYxtLK4vh9qX2euPLGBvvZC63DQ6dh9n91+u42lSqNna0B//xwAjPv7HYVJx1xpjJzXz9JOA+Y8zpnr/vBTDGPOizzizPOgtFJAjYCyQB9/iu67teY+/Xpi3/TakstoPx8nfY1sZLX7Un3MZOzo0pzLCt/KHRNkANjWy4TtEem3IT37dha0qNy3ZXl2bbk5W/FhWwF9CVb0GXETYdpDkqiuxFOaqzzdlzKi1NKc2xLYExqfZHXf/7qK6wAZ6/zwn2BPrDI7albdhF0P80/13h/hRl2gqE07IJ9sL65f/ZFpSE/rbl01/g25jNs22L6sAzbRDR0v3rcLvtPopM9qTalNhjqP7FeN96G8Ak9LV/L3/dptH0Odm2TrWmu3T3cht4l2TB6f9sGAQ2ZdYfbctt7xPgqo/8BwHNYYzdD0tesC1TF72w/y5kY+wFqarMrtvS7740B4LCao+16grbo+KqtC2DjR2Djakqs4FR5grPMRxl92lEIhz/m/1vr7LYdqfvXmYvnD0m2daw6K72+3ACg9Icez6on27mqC6329i5wJYlaZC9AEd3rV3HXWMrGMZtK4RNHTe52+zg0Y2f2wD1stdtI4IxTf/mXVW2Fyi2R20lztf2eTDrD7aidfYjjadMOWpctryNfe6Wyt1mW5G7jPJ/7OSn2bzwflNsGkx9C5+2lbFBZ9lK5U/TbWXrgmf8H7sZS23P3tCf2fS56grbMxuRaM/t+zuPOT04NVW2oaXLSNsrEBAIQy7c//m3YJdNncneYPffmGtsit7rF8KoK2wFpyXWfWLTgsbfbNPNIuJrP8OGz2xvnbsG7lpdm6ZS44L7E2xv9Ek+c4HkbIGnxnkaO25p+n3dNTYYDY/1fz3L3WYrdxHxtnenqfOIk5JWlGkrxk7Dzv58frfdl7/ZVBvEPjnWphle+lrjr1v2ig32r/rIHlfF++Cx4TDqSjj3sbrr7l5uK4jjbrDXZUdFkT2vBIfbRsP6qZtZG+0+7n+arTA4vW3hcfY3uWuh3V54XMPyfXaXjRe6jIRp37Tst7b1G9tQ4fRg7Vpof+OBwbZXb8vXtgJ/zYyGaXyOkmzboLXxs9qGncBQ+Pl7dSusbeSoDv7rE5H+wJ+MMdc2c/0gYDMwBdgNLAGuNMas81nnNmC4MeZWEbkc+Jkx5lIRGQq8hc3z7wrMAfob41RzG2q3wT/YAHXTV54u7B5tXRrVFLe7eZUX1VBZnr2QtLbi46u6vPF83Y7A7bapL9FdD873eTDl7bAVZ2dchjryVBTZHq/lr9emZUamwC3f120Fbq4v/s9uD2wl5ox/AcYOQO08HM5+1PZ8+vpXT9tTfvZ/apf98IhNP/v1+sbTiNqT9J9sK7YzMNnthgdSbDrL1L83/jpXJTwx2vYS3/CVJzX3adsS7jTotCW326YAdRnhP22npQp22Z6KdR/ZdNQLnqmb8tQYp7e1NBvev85Wzm7+rm6aVRtoKvg/4gf8isgAY4x3niljzBYRGdHUa3wZY1wicjswCwgEXjLGrBORvwNLjTEzgBeB1z0DevOwM/zgWe897OBgF3BbU4F/uxfVuTYFR7VvGvi3XnMGeTVXRw78wR6H7TX4aeMLrzoIwqJtL9Rxd9ueoewNNsWzNYE/2Jb6AWfYNJE1H9h5+cGmuV35rv8AMjLFpv/52jbXpi2212O/vm5jbav71m9t8F+8x/bIxPZs+nVBoXbsz8zf21SuJS/ailB7CPzBnn96Tjp424vtARe/CBc+a9PdmtugIWKvKxHxcPmb8PzJtodq0Nm2l3nA6QevjAfJEd/yLyJzgb7YVvvVQBh2CtNbjDFlbVk2f9p1y79SSinVEdS47KQSVWW20auxluPXzrdpjTfNsX9Xl9vegPE32YHlR4r3rrWt5HdvsBWpV86qTedpSlUZPD7CtmoHhtqBw4n9Dk+Zj1Q7vrcpSUW7barn1Pth8p2HvRhHdcu/MeZkABHpAYwERnn+XykibmPMoCZerpRSSqmOJjDI5qfvT1RXyPEZFJv+kx2g29yxZu1Fv1Nh/Sd2WugCz6QjvtOaNiYkAi593c4AOOoKDfybo/cJcOfy2qlah17Q1iVq4IgP/h3GmF3ALuAzZ5mItHAEnFJKKaWUR3QXOx7OXWMHCO/43s6o1OMgppscDk4L/9ZvPNNAhzZ/bF/PSQc3vaajCAyGk+9t61L4ddQE//4YY0raugxKKaWUOkJFd7VTVZbss493fO/JoY9u65K1THRXSBlWew+VzsNaP9uZOuLpqEGllFJKKX+cOdtzt9nZh3Yvs1OfHon6nWrvl5H2g63AqA5Lg3+llFJKKX8SPDnuuVvt3eVNjb1PyZHI9wZ3wy9tu3KoNndUp/0opZRSSrVadKq9uZ5zp+fgCOg+vq1L1TrJg+GSV+3j7se0bVlUm9LgXymllFLKn4AAewfZjCX2Rk49J7fuzuTtRTuceUYdfpr2o5RSSinVmJ7HQsZPkLsFBp7R1qVR6oBp8K+UUkop1ZhRV0FAMITFwIjL2ro0Sh0wTftRSimllGpMYj/45UKISIDQqLYujVIHTIwxbV2GDkVEsoGdbV0OIBHIaetCqENO9/PRT/dxx6D7uWPQ/Xz0O1z7uKcxJsnfExr8d1AistQYM66ty6EOLd3PRz/dxx2D7ueOQffz0a897GPN+VdKKaWUUqqD0OBfKaWUUkqpDkKD/47r+bYugDosdD8f/XQfdwy6nzsG3c9Hvzbfx5rzr5RSSimlVAehLf9KKaWUUkp1EBr8K6WUajdE5EoRWSoiJSKyR0S+EpHjRORyEdkkIoUikiUir4pIdFuXVymljjQa/CullGoXRORu4DHgn0AK0AN4GjgfmA9MNsbEAH2wN6n8R9uUVCmljlya86+UUqrNiUgMsBu43hjz/n7WjcRWChKNMWcdjvIppdTRIqitC6CUUkoBk4Aw4OPGVhCR44AvgGigDLjw8BRNKaWOHhr8K6WUag8SgBxjjKuxFYwxPwIxItINuAlIO0xlU0qpo4bm/CullGoPcoFEEdlvo5QxZjcwE3jnkJdKKaWOMhr8K6WUag8WApXABc1cPwjoe8hKo5RSRylN+1FKKdXmjDGFIvIX4H8i4gJmA9XAqcDJwCrgB2PMLhHpCTwAzGmzAiul1BFKZ/tRSinVbojIz4FfA4OBYmAZNtA/G7gWiAPygS+Be40xuW1UVKWUOiJp8K+UUkoppVQHoTn/SimllFJKdRAa/CullFJKKdVBaPCvlFJKKaVUB6HBv1JKKaWUUh2ETvV5mCUmJppevXq1dTGUUkoppdRRatmyZTnGmCR/z2nwf5j16tWLpUuXtnUxOry9pXu5b8F9XDTgIqb2nNrWxVFKtZLL7eJP8//EBf0uYGKXiW1dHKWUahdEZGdjz2naj+qQ3t74NvMz5/PK2lfauihKqQPwzc5v+GL7Fzy4+MG2LopSSh0RNPhXHdLKrJUArMtdR7mrvG0Lo5RqtfW56wEorS5t45IopdSR4agL/kXkDBHZJCJbReQeP8+Hisi7nucXi0gvn+fu9SzfJCKne5Z1F5G5IrJeRNaJyK981r9PRHaLyErPv7MOy4dUB2xj3kZiQmOoMTVklmS2dXGUUq2UW2Fv8LuvbB8Vroo2Lo1SSrV/R1XwLyKBwP+AM4EhwBUiMqTeatOAfGNMP+BR4CHPa4cAlwNDgTOApz3bcwG/McYMASYCt9Xb5qPGmFGef18ewo+nDpLS6lLKXGWMSxkHQEZxRhuXSKmDr7iqmJKqkrYuxiGXX5HvfZxdnt2GJVGHktu4eWDRA95eW6VU6x1VwT8wHthqjNlujKkC3gHOr7fO+cCrnscfAFNERDzL3zHGVBpjdgBbgfHGmD3GmOUAxphiYAPQ7TB8FnWI5JTnADA6eTQAGSUa/B+oanc11e7qti6G8thbupdj3z6We3+8t62LcsjlVeQhCFD72+4IqmuquX3O7Tyx/Im2LsphkV6czjub3uHqr65u8Wtn7pjJqe+fSnpR+iEomTpS5VXk8enWT6lx17R1UQ67oy347wb4/rozaBioe9cxxriAQiChOa/1pAiNBhb7LL5dRFaLyEsiEuevUCJys4gsFZGl2dnaMtXWssvsPhgQN4DQwFD2lOxp1usySzI59+NzuW/BfYewdEeeXUW7GPP6GB5d9mhbF0V5/LT3JwC+S/8Ot3G3bWEOsbyKPAbGDwRqf9sdwQ+7f2Bexjymr5l+1O9jgE15m7yPCysLW/TaL3Z8wb6yfby96e2DXSx1BJs2axp/mv8nZqbNPCjbe2TpI9w+53aq3dW43C6mzZrGQz89dFC2fbAdbcH/ISMikcCHwF3GmCLP4meAvsAoYA/wiL/XGmOeN8aMM8aMS0ryO+WqOoyc1sGk8CQSwhK8OcP789m2z0grSuPDLR8elK7nsuoyluxdgjHmgLfVln7Y/QMAH2/5uI1LohzpxbXtGLuKdrVhSQ4tYwz5FfkMiBsAdKy0n+2F22sfF2xvYs2jw+b8zd7HLT2mnfU7wvfUFqrd1Vz5xZW8vfHIqVztLNrJ1oKtAMxKm3XA23MbN6+se4V5GfPYWbiT9OJ0ftr7E29seIOCioID3v7BdrQF/7uB7j5/p3qW+V1HRIKAGCC3qdeKSDA28H/TGPORs4IxZp8xpsYY4wamY9OOOrxyVzmvr3+93Q6+cwKExPBEEsITyC1vXvC/ZO8SekX3IjokmlfXvbr/F+zHr+b+ihtm3cB7m9474G21RI275qB2czpjJipqKqiu0dSf9sA3+E8rSmu7ghxi5a5yKmoq6BPThyAJ6lBpP2mFad7H2wq3tV1BDpOssizv413FzQ/+3cbNziI73fmOwh0HvVzKzri1JmcN/1z8T1xuV1sXp1lm7rCt/aOTR7Mlf8sBb8/33LO9cDvbCmp/k86MZO3J0Rb8LwH6i0hvEQnBDuCdUW+dGcC1nscXA98a2/Q6A7jcMxtQb6A/8JNnPMCLwAZjzH99NyQiXXz+vBBYe9A/0RHo5bUv8+8l/+a19a+1dVH8yi7PJjggmJjQmBa1/KcVpTEyaSSXDbyMObvmsK90X6vLUFBRwKI9iwB4bvVzVNVUtXpbLXXDrBu4buZ1B217u0ts/drldpFeojm17UFGcYa3Nfxobvl3fruJ4YnEh8fXCRCPdruKdzEsYRhw5AS12wq28V36d616bW5FLr2iewG0aIa2/Ip8akwN8WHxZJZmUllT2ar3V41bvm+59/Ge0ual0ba1mWkzGZM8huO7HU9GScYBTxXs+7nrB/9rc9tfaHhUBf+eHP7bgVnYgbnvGWPWicjfReQ8z2ovAgkishW4G7jH89p1wHvAemAmcJsxpgaYDFwNnOJnSs9/i8gaEVkNnAz8+vB80vZt/u75AMxLn9fGJfEvpyyHxPBERISE8ATyKvL2+xqX20V2eTYpnVI4s/eZGAzzM+e3ugzLspYBcOvIW8kuz+bLHQc2UVRZdVmzWvNLqkpYnrWcldkr2Vu694De05FZkklMaAwAu4vrd7Q131sb3uKJ5U90yMFXjalx17QqVSG3PJe+sX2JColqUSvp4VbuKj+gtDdnpp+4sDiSwpNa1fKfU57DT3t+anUZ2kpWWRY9Y3rStVPXdhX8N9byW1VTxQWfXsAd397RqoaT3PJcukV1IyokqkXpXc4xMTxxOABZpR2ngugwxhzSXjEnfQaOjMaGLflb2FqwlTN6n0HvmN7AgZfbN/hPL05nW8E2ukV244NzP+C6odcd0LYPhaMq+AcwxnxpjBlgjOlrjHnAs+wvxpgZnscVxphLjDH9jDHjjTHbfV77gOd1A40xX3mW/WiMEWPMiPpTehpjrjbGDPc8d54x5sio8h5CxhjvhWhLwZZ2ORAtpzyHpHA79iI+LJ78ivz9ljOnPAe3cdO5U2f6xfYjKTyJJXuXtLoMTqvA9UOvp1d0Lz7d+mmj6y7Zu4TvM75v9Pm3N77NxLcm8sgyv0NO6liTs8b7eGPexhaUuHE55TmMShoF1PYCtNS6nHU8+NODTF8znaX7lh6UcrXW8n3LufSzS5tVKWyMMYZV2asOuJXxXz/9i/M/PZ8NuRta9LrcilwSwhLoEdWj1VPZHuoW0mp3NWd8eAbXz7q+1dtw9lFCWAJJEUktzvk3xnDux+cybfY01uWua/br3Mbdpuc2Ywx5FXkkhiXSO6Z3uwn+F+xewJjXx/D0yqcbPOebWvHNrm9avO2c8hwSwhJIDk9u0cBuJ+gdlmh7SY6UlumD6Q8//oEp7085oGtWU3YV7fIG0U6K1cHmcruYv3v+QWm0+mrHVwRIAFN7TiU1KhU48Fn/csrscdYnpg+ZJZlsLdxK39i+DIwfSEhgyAGX+WA76oJ/1bbyKvIori5mQNwAyl3ldXKPHTXuGn7z3W+49etbWz0PuTGGx5Y9xhvr32jxa7PLs0kMTwQgITyBGlNDQWVBk69xTjgpESmICIPiB9UZgNZSO4t2khKRQkRwBGf2PpNl+5bVma/ckVOeww2zbuC2Obc1eK7GXcMnWz/hoZ8ewmB4b9N7lFWXNetzQMsC9fW561mwe0GD5W7jpqCygP5x/QkJCGl18D9jW212XlMVHX++2vEVl3x2CR9u/rBV713fn+f/mQ15G/hoy0f7X7kRT654kqu+vIrb59x+QC3bH2z5AIBPtzVeOayvrLqMclc5CeE2+G9Ny//La19m4psTWZi5EOCgDUovrCz07t9NeZvIq8hj2b5lrZ6lxwn+48Pibct/WctaNzOKMyiptuegpirg9d389c1c89U13grA/N3z+XH3jy167wNR5qrdx71jepNWlHbAlZH0onQumnER01dPb/U2vk3/FoPxm9rje75saQ60U9lJCLeVvKzy5rfeOxVCJ/jfW3Zwejzn7JrDrd/cWie9oz3KLc/l8+2f4zZuXlzz4iF5j13FuxidPLrZ426MMewr3dei88rDSx7m1m9u5eyPzuY/S/5zQI0T32V8x7iUcSSGJ9It0k7qeKD3+ymqsvPADIwfyK7iXaQVptE3pu8BbfNQOizBv4j8T0QmH473Um3LCTRO6XEK4D8XdX7mfGbvnM38zPk8u+rZVr3PvIx5vLj2RR5a8hBb87fu/wU+cspzSIqwLf8JYQkA+x30u6/MdlOnRKQA0D+uP9sLt7d6gGtaUZo3f3VClwkYDCuyVjRYb3Ne7QXT96SaU57DFV9cwZ/n/5nhicN54LgHqKyprNP96o8T/AcFBDX7ZFdYWchln1/GLd/c0mDWguKqYm8+bdfIrq0K/o0xzNk1h5O7n8zQhKH7/Qy+Vmat5J4f7mFj3kb+veTfFFcVt/j9fbncLu935G9/NEdJVQlvbLCV0kV7FrGloHWDyXLLc70pFC3ppfFtDU+NSiWzJLPF92B4bf1ruIyLJ1c8ySNLH2Hy25PrTLXYWld+cSW3zbmNtTlrWZW9yru8tQPinM/qpP3kV+a36De5KseWITY0ttkzeFXXVLN4z2JWZa9iQ94Gqt3V3PrNrfzim18c0DiglnDOBYnhifSK7kW5q7xV4x2q3dX84Yc/8MCiB3h61dNszt/M9DXTW33PDuc3s7Vga4P9sDl/M+FB4UzuNrlZ+9t3woiiqiKq3dUkhiWSHNHKln/P+Ijmthwv2rOo0WuLMYYnlj/B/N3zeWzZY80uiz8ZxRnMTpt9yNIdnbFl4zuPZ/HexQ0a3GrcNTyz6pk6s0e1RHFVMXkVefSM7kl8WHyzgv85u+Zw6gen8vDSh5v1HoWVhXyw+QOO7Xosp/U6jVfXv9rq772oqoit+Vs5pvMxAESFRBEbGntQgv/I4Ei6R3UnqyyLanc1fWM7ePAPbAb+IyJpIvJvERl9mN5XNdMb69/gwk8v9OYXF1QUMGPbDL8XUrdx87+V//OeVHw5QfL4znbiI39dgAszFxIeFM4Zvc7gvc37b632VVVTxcLMhfxn6X/oFNwJgFk7mz9NV3VNNQWVBSSE26Df+X9/g36di3rnTp0B6BvbF5fb1eyuQmMM729+35tbvKtoFz2iewC2RSooIIiV2SsbvM53+77B1z8W/YMdhTv41/H/4pUzXvHesGx/vRF7y/Z6A4bmBuq+QdHc9Ll1nvMNvrpFdWtV8L8udx37yvYxpccUekT38HvMVLureWfjOw16R15Y8wKxobE8dcpTlLnKWh2wO3YV76LKbQdft7b7em76XMpd5fz3JDs/QGtbhJ1KULfIbi3K+/cG/+EJ9IjuQY2padGFbW/pXnLKc+gV3Ys1OWt4Zd0rFFcX77fV8MfdPzY5c5XbuL2NAz/s/oFV2auICo4C/Fdu9pbu5dqvrm2yZTWvIo+IoAjCgsJIjLC9ec0dwA+wJnsN4UHhXNT/Ijbnb6bcVb7f1/jOrLOtYBs7C2uPE+f+Cs316rpXOeujs7h9zu2c9dFZze59cBorEsISvOkWrQnevt31LZ9t/4x3Nr3D59s/JyEsgXJXeavGmbjcLrYXbic5Iplqd3WDXt/N+ZvpH9ufYQnD2F64vcnz/v0L7+eEd0/w3oPF+3nDE2zwX57d7J6OnPIcIoMjiQ2LJS40rlnB/5rsNdw0+yZu/vpmv8+nF6ezvXA7nYI78cPuH1rd6OByu7hz7p38Zt5veHndy63axv5syN1ASEAIN4+42abO1Bur9snWT3h65dM8vuxxv693WrQb4+TK94zqaWfPa8bvb9k+O+atuWlIi/YsospdxS9G/oIHj3+QM3udyefbP2/VzEKrslZhMN5rJkBqZOoBp/0UVxUTHRJNv9h+3mW+j9ubwxL8G2MeN8ZMAk7ETqv5kohsFJG/isiAw1EG1ThjDM+ufpatBVt5d9O7ANz27W388cc/8o/F/2iw/qrsVTy76llumn1Tg643p9u9X2w/4kLj/E4zuCV/C/1i+/HzwT+n3FXO1zu/bnZZ7/j2Dm7++mZ2Fu3kgckPMCRhiPdEsrd07367EX3n+Ifmt/zvLdtLeFA40SHRgD1ZQG3qTGZJJt/sbDyPdUHmAv6+8O/cOfdOCioKKKgsoGd0TwBCA0PpFd3L7wXX94TkvNeekj18u+tbrhl6DWf3OZvAgEC6RXYjPCi8QQBQXFXMkyue9H7uvaV76RzRmW6RzQ/UnZkKAiSgQYudky4VFxpHt06tC/7n7JpDoARyUveT6BHVgz2lexpUOl9c8yIPLH6gTk9RhauCRXsWcVbvszim8zEESABrc5o3q0J+Rb7f4N7JSz6u23FkFGc0qxV5R+GOOvniCzIXEB8Wz5QeU+gd09t7fLaUE/yf1vM08ivz/aaF+eMcy/Fh8QyOHwzQonx2p5L554l/5pTup3BGrzM4tcepLNu3rNHfV055Dr/45hfcv+j+OlNQ1l/HsSprFauzVzOx60RSIlL87osX1rzA8qzlPLK08bEseRV5xIfFA7W/6Za0Cq/JWcPQhKEMTRxKjalpVgqHb2/m1oKtdSrcy7OW+3uJXyVVJTyx/An2lOxhRdYKiqqKuH/R/c1qOXXWcdJ+gEa/96Ys2rOIyOBIbhlxC2f0OoOHT7Qtsa2pSOwu2Y3L7eK4bscBkFlaOyOPMYZN+ZvoH9efIQlDcBt3ow0VLreL9za/R7mrnNk7ZwO1FbqE8ASSwpNwuV37TdV0ZJfVpnl27tS5WcG/85vNLs/2uz+c39Mdo++gxtTUaQircFXw8ZaPm3Ujsjc3vOk957yz8Z1Dcs+XDXkbGBA3gLEpY4kNjW2QkrV4r71nqb99viJrBZPfnsyTK56ss7y0upSf9vxEdU21t0LfPbo7CeEJzTp+N+TZMUxbC7bWmeluXe46v7/fFVkrCA8KZ2jiUMBmFhRUFjS7R/SRpY8wbdY0atw1rMhaQaAEegeAA3SL6nbgLf+VRUSHRnvTywDvb7M9Oqw5/8aYncaYh4wxo4ErgAuws/KoNrSlYIv3RPVt+rdsK9jG6uzVgG0VqH9S8J3FZ1XWqjrPZZVnERQQRGxoLD2je/oPsAps8D8yaSTJ4cnem0Ttz/bC7SzIXMBF/S/i/XPfZ0rPKYxNGcvq7NX8uPtHpn4wlf8s/U+T23DyP73Bf3gz035K93nz/YHaQUKeE8Zln1/Gr7/7daPBr3OyK60u9X5eJ+0HbE+Cv5NvZkkm3aO6EyRB3oFq8zLmYTCc1/c873oBEkByRHKDrv/Hlz/O86uf946N2Fu6l86dbPCfWZLZrIvNtoJt9IruxaikUd7P4ajf8l9YWbjfliKwQbZTcfxm5zeM6zyOmNAYUqNScRt3gwu0c3Fdsq+2pWhNzhoqayqZ2GUiEcER9I7u3eyBsf837/845+NzWJO9hn/99C8mvDmBDbkb2JK/hQAJ4OTuJ1NjauoMDtyUt4lbv7m1TmtVhauC8z45j8s/v5yc8hyMMSzas4gJnScQIAGMTBrJ6uzVrbqoby3YSkxojLd7uqmAzG3cfLr1U8pd5XXSfvrF9iM8KLzOVHz741zM+8f15/FTHufhEx/mmM7HkFWe1ehgSd/vZF6G/1m+nN9KckQy8zPns7tkNyOTRtrzRHHD84QTgDWVbpRXXhv8OwFecwf9VtVUsTFvI8OThjMwbmCj71VZU8n01dO9rdDObywlIoXtBdvZUrCFIAliTPKY/aazbC/c7k3vWJC5gCp3FS+e/iLzr5jP62e+TmVNZbNulucbDCeGJxIZHNmqgH1l1kpGJ4/m9tG38/CJDzMyaSQBEtCi1DuHUyma3NVm+PpOx5lVlkVhZSED4gYwJGEI0Hiql2/lykkNc87PTtoPNL+Sl1Oe4z02UjqlNCvn3zdVb11Ow4rz+tz1hASEcFH/i4gKjuKHjNpr2ENLHuIvC/7C777/XaPbf2HNC5z10Vn8Z+l/OCH1BO6bdB/7yvY1qHxmlmQ2q1L36rpXufPbOxv0hhhj2JC7gcEJgwkKCOKE1BP4PuP7OmldTuVjZ9HOBo0dTk/U2xve9p7Dqt3VXD/zeqbNnsZfFvyF7YXbEYTuUd3t1NnNuG/OjsIdhAeF43K7vDHCnF1zuPzzy7lwxoUN0kuX71vOiMQRBAcEAzA0wVYCmpOKWO2u5pV1r/DT3p/4Nv1bVmavZGD8QCKCI7zrpEba9MgDSb0qqioiOiSa1MhUju16LL8/5vd13qO9OazBv4gEici5IvIm8BWwCfjZIXifM0Rkk4hsFZF7/DwfKiLvep5fLCK9fJ6717N8k4icvr9teu4psNiz/F3P/QXaFWMMa7LX+B18C3gDpqsGX8Xe0r08vfJpAiSAj877iNDAUF5Y/UKd9bcXbiclIoVACWyQ+pNTZmfSERF7US+se1HPLc8lryKPfrH9EBEmdp3Ioj2LmvWjm502G0H4xchfMCh+EABjU8ZSWVPJnd/eCdiZb5rqDvbmynpSBKJDogkKCNpvV+Xesr2kdErx/p0YnkhoYCgZxRnkV+R7W6Hm7Jzj9/W+M118uMUOTHVa/sHOEJBRnNFgvv+8ijySwpNI6ZTiDbyW7VtGckQyPaJ61FnXXy6sc2JduGchxpg6wX9JdUmzAvX04nS6R3VnSMIQNudvrrOvnNbo+LB47wn5Dz/8wTvdqz8/7fmJn834GdNmTWN7wXbSitKY0mOK9zNAbfoY2JZAJ1DYUbjD+/7Odzo4wbZu94ntw44iGzhkl2Xz3qb3/B4LxVXF3vSMfy/5N29ueJMyVxlvbHiDLflb6BHVw7tvfMvx32X/Zf7u+XVaon17GmanzWZLwRZyynOY1HUSACOSRlBQWdBkq9KG3A1MmzWN/y79LwszF3pzcrcVbKNvTF9v3mhTrdJf7fiKP83/E9NXT/cey/Hh8QQGBDKlxxRmbJvR7GlU04vTbZpEaKx3mdNF3lhe/JK9S4gMjiQ5PLnRoM6pGJ/Z60zvspFJI+kR3aPBNHsVrgp2FO4gOCCYrPKsRo/T/Mr8BsF/c6c0dPL1RySOIDUqlYigCDblNwwmvt75NU+seMKbApJVlkVYYBijkkexrWAbm/M30zu2NyOTRrIlf0ujvUVL9y7l/E/O594f7wVsABkUEORtgewd05sJnSfw/ub397ufcspzCJAA4kLjEBEGxg9s8bgJl9tFWlEa/eP6e5eFBIaQGpnaql4Ep/IxvvP4Oo0VUJuOOCBuACkRKcSHxTdaXqeBwbc3tE7Lv2e8VlNjHH7a85M3MPQN/jtHdGZvSTOC//wtjEgaAeD3mNhRuIOeMT0JCwrj2G7HMi9jnk11Kkr3ThSwMHOh3zJmlWXx+PLHqXRVclrP0/jbsX/zni8W7qkdYL+3dC+XfX4Z5396fp0g123cPLPyGW+KY2FlIf9Z+h/mps9tkGKYUZJBcXWx9xw5tedUiqqKmLvLpm9W1VSRVphGYngiBlPnfAe1la/i6mLvteStDW+xIW8D3SK78fn2z/l4y8f0i7ONDInhieRW5DbZ2FFVU0VeRR6TukzyfpfGGJ5Z+Yz38zgTHYDtIduUv4nRKbVpOt2ibE+3s2/Si9J5ee3Lft/Xt0d97q65rMleUyflB2xjnsu4Gnz+nPIc/rHoH97fw4trXuTKL670ez53gn8R4bmpz3HVkKsa/Q7ag8M14HeqiLwEZAA3AV8AfY0xlxtjmj/FQvPeKxD4H3AmMAS4QkSG1FttGpBvjOkHPAo85HntEOyNwYYCZwBPi0jgfrb5EPCoZ1v5nm23KyLCDbNu4N2N7/p9fmPeRsICw7hy8JUAzN45m0ldJtE/rj/n9T2PWWmz6lx804rSGJY4jOGJwxsE/1nlWd6Tc6+YXmSVZ9XJ7XQCmH5xNhfu2K7HUlhZ2KBF2Z9ZabMYnTy6ThA+NnksYGv33SK7Ue2ubnK+3vppPyJCfFh8s1v+HQESQLfIbmSUZNQJ7Bu70+aWgi1M7jqZyOBIlu1bRkRQhDfnH6B7VHcMpsHNa/LK7QwXnTt19rY8rslZw6ikUd5eCEdSeFKDk5fTgr4lfwsFlQWUucps8B/lmeGgJINbv7mVcz8+17uP39n4DvctuA+3cWOMIaM4g9SoVAYnDKbcVV6nN8d3nvVRyaMICghiXsY8bv3m1ka71z/eals2V2Wv4vHlNs/0lO52gLjzHfu23m4v3E65q5wxyWO891twvtPokGjvvuwd09tbgbrnh3u4f9H9fmfscYKU5Ihk7ziLyd0mMy9jHmtz1zIwfqC3HM5nKKwsZGHmQoIkiHW567yf2/kuAiSABZkLvLPjTOwyEYARiTaAcAaW1uc2bv7w4x/4ae9PvLzuZW7++mau/upqqmqq2FqwlX6x/ejcqXOdlK7X1r3GNV9dUyc/3WltX5W9iryKPCKDIwkNDAXg7rF3M6nLJKavmc5XaV/5LYevXcW76B7Vvc7x1T+uP+FB4Y2OqViydwljUsYwNHFoo0FdRnEGgvDzwT/3LhuSMISeUT0pqCyokyaxtWArNaaGc/qcAzRe8ckrzyM+vDb4rx90NmVNtp32dnjicAIkgP5x/f22JDppEpklmbjcLrLKskiOSKZvbF92l+xmdfZqb4t2tbu60VZzJ71xdtpsXG4XG/M30jemL8GBwd51Lht0GXtK9+y3NzS3PJe40DgCAwIBW4nakLuhRXdV31W8C5fb1WBQYs/onvudIWph5kLuW3Cf95wENohLDE8kNiyWlE4pdc5lTpA2IH4AIsLghMGsz/N/nKzPXU94UDgndz+ZncU7cbld5JTnECRBxITGkBzesIHAV1l1GdNmT+Pizy6m2l1dZ3a3ntE9Ka4ubvJ8X+OuYXvhdkYljSI1MtXvMbGjcAe9o21Kx9m9zyavIo+ZO2byxIonCJIgnjv1OQyGxXsWN3itE6A/M/UZHjnpERLDE+ka2ZWe0T1ZkLmAubvmMuntSUz9YCoFlQW4jZvPtn3mff3MHTN5etXTXPPVNbjcrjrlqx/8Ow17Q+JtuHJ8t+NJjUzl9fWvez+Hy7g4MfVEoO40qNU11aQVpnFS6klAbU/r9DXTmdxtMm+f/TZg94Mz1XNCeAIut6vJRiVn/NyELhMAG0/sLNrJpvxN3DP+HgbHD67zOVZlr8Jt3IxJHuNdFiAB9Ivt5w3sL//icv677L9+jynnOOncqTOfbf+MipoKRiWPqrOO05Nfv+f+qRVP8e6md3ll3SvUuGt4bvVzrMlZw2fbP6M+J+3nSHG4Wv7vBRYAgz3z4b9ljDmw26k1bjyw1Riz3RhTBbwDnF9vnfOBVz2PPwCmeO7kez7wjjGm0hizA9jq2Z7fbXpec4pnG3i2ecEh+lwHJDYslvzK2pzhpXuXeqfc25y/mf5x/eke1d2br3n1kKsBOKfPOVS5q/gxw/4YXW4X6cXp9IzuyaSuk1iXu67ORdtp+Yfalm3fvH+nO7V/rG1tcoIkJ2hqbJaJbQXb2FqwldN7nV5neWxYLL8Z+xvGpozlX8f/C2jYnfzQTw9x19y7qKypJLs8G0G8rYXAfu/y6wSczmBfR2pUKhnFGd7P1D2qu98Ws2p3NTsKdzAgfoD3pDMgbgABUvvz8043Vm/QUV5FHnGhcXTp1IV9ZfsoqSphd8lub8+Hr5SIFLLLsr2tH8YY9pTuISEsgWp3tTe3M6VTivf9fsj4gfm755NWlMa3u74luyybBxY/wIdbPmTZvmUUVhZSUl1C96ju3vxx37sV5lfmExEUQWhgKKGBoUyfOp2bht9kt91IALN833Imd5tMaGAo36Z/y4ikEd4Knb9WPad13dn3TqvL9oLt9I3t6w1Se8f0psbUsLVgqzf3+ovtXwC2VcbpBXBuRPb3Y//OqKRR/HXSX7mg7wUUVhaSVZZVp4LpXDhWZdtBYjeNsJ/NuV/CzqKdBAcE87P+P+OnvT/xfcb39IruRZdIe/PvfrH9iAiKaLTFfF76PLYWbOWfx/2Tzy74jLvH3s3Wgq28seENiquK6RfXjwAJoE9MH7YXbKegooCHlz7MiqwVddJDnEBrU/4mcstzvelsznf6+CmP0z2qO1/t2H/wn16UXqdiCnZ2qJFJI/0G/9ll2aQVpXFMyjEMSRjCzqKdfu+WmVGSQUqnFLpEduHRkx7l4/M+JiQwxPtevpV2pzHA2edOr+XstNnc+8O93puDOb8Pp4wpnVKavNHcC2te4IU1L2CMYXXOapIjkr37emDcQDbnb27QW+ScT6rcVWwv3F4b/Mf0xWAoqCxgQNwAb+tqY5Uf55ipMTVszNvIxtyNDIwfWGedk7qfRHJ4snfsVWNyK+ru4/Gdx+MyrhYNOHYCJ3/B/86inX5bUavd1czcMZPb5tzGh1s+5A8//qF2e4XbvTnOXSO71gkkF2Uuom9MX++YqSHxQ9hesN1vZWVD7gYGxQ/yTqqwu2Q3ueW5xIfFEyABJEUkESRBjaZYOtcS53G5q7zORA1Ak2lNGSUZVNZU0i+2HwPjBzYYm1BVU0VGSQa9YnoBcHzq8QxNGMoffvwDM9NmMm34NCZ2nUhUSJTf8T6b8jbRKbiT9xroOCH1BH7c/SN3zr2TxPBELup/Ea+c8QrjO4+vc+8T38B4S/6WOr0q9d9vY95GgiTI29gWGBDIVUOuYmX2StZkr/G+9vjU44G6MyGlFaXhMi6m9ppKWGAYG/I28O2ubymsLOSaIdcQFxbHPePvoWunrlwz5Bqgeb1vTtpVn9g+pESkkFaY5r1WnJh6IhO7TmRV1irvsbFs3zICJZCRSSPrbMe5v0Vpdam3suFvmmjnM/2sf22SidPr4Ghsuk/n2jMvYx4b8jZ4G1x807wcTsv/keJwDfg9xRjzgjGmeSPWDkw3wDe/JcOzzO86nrsCFwIJTby2seUJQIFnG429FyJys4gsFZGl2dmtm8/6QMWFxnlTU7LLsrl+1vXcNuc2ssuy2ZhXexF64uQn+OLCL5jczeZtDk8cTnxYvPeE47R+9YruxeRuk3Ebd52TUVZ5VoPg37el2Mlj9p1nf0DcABbvWcx7m95j/JvjmZVWd/aemTtmcs8P9xAaGMppvU5r8NmuG3Ydr5zxCkMShhAgAXVyX11uF29seIM5u+aweM9issuyiQ+LJyggyLtOQnjTeYrODb58W/7B5gnuLtnN5vzNxIbGMi5lnN8BzjsLbetV/9j+XNT/IgAu7H9h3W3VG0MAtgWqoLKA+PB4G/yX7vOerH276h1JEUlUuau8J8L8ynwqayo5IfUEoLYFs1unbt6T3Tsb3wFsS8r3Gd/X2Zfz0ud5g67UyFT6xPShU3An73gQsC3/cWFx3r/HdR7HHaPvID4s3tuyWv+7zCzNZFKXSfxqzK8IDwrnjtF3eJ+PCo4iPCi8Tqve8n3LiQ6J9naNOwMJM4oz6B7V3bten5g+gA34XW4XvWN6szJrJe9sfIfJb0/mtXWvAbWtO6OTR/P6Wa9z8YCLObH7id7tTOo6ifCgcGJCY7wXjtXZqwmQAC4deClQm2u6s2gn3aO6c0K3Eyh3lfPT3p84ufvJ3m0FBgRyTOdjWJDZ8B4JpdWlPLz0YVIjUzmz95n0iunFtUOvpUunLjy67FGgdraIvrF92Va4rU6FyneqTOf7KqwsZEv+Fu9AdkeABDChywSW71veZEpJtbuazJLMBillYFPsNudvbpCP6wQm4zqPY0jCEAw2z7jcVc4La17w9oxlFGd4j7tTe57qDUi85wmfvP+NuRuJCo7imM7HIAgZxRm4jZvfzPsNn2//nCV7l1BUVYTLuOpU5FOjUtldWjcodLld5Ffk8/GWj3l8+eM8vvxxZqXNYmXWSm/PDMDwpOGUVJfUSRMoqSohvTidM3qdAcDW/K3e4N93Jo8BcQPoHtWd+LB4b+qGL2MMaYVpnNrjVMCmaeVW5Hor1I7ggGAuGnAR83fPbzRNEzw9gmF1g//I4EjvpAPV7mru+PYOLv/8cu9MNNsLtnPr17d6U/KcANhpwXb0iO5Buau8wdiJ6hqb6/3b739Ln5g+3DLiFpbuW8r63PXUuGvYkr+FAXF2/o4unbp4K6RFVUUs27eMk3vU/i6GJtgB1vUD6xp3DRvyNjA4fnDtLEYF2+tUdoICgugS2aXR72dzQe02ndnJnAqec95saqCoM73ngLgBDIwbyM6inXV6r9OL03Ebt7d8QQFBPHvqs/xqzK/453H/5NaRtxIgAYxOHu13APjO4p30iOrRoOf24v4XA3YCiMdOeoz7jr2PsSljGZowlM35m73pZMuzlnvHTazMXsnm/M3Eh8VzWs/T2JK/pU5j3LrcdfSJ7ePtBQS4oN8FRAZH8vqG19mQt4HQwFAmdLat8L7nXef4GBg3kIHxA1mTvYYPt3xI105dvY12Px/8c2ZeNNNbEao/gYbToOLLOad2juhMr5hepBWlsSBzAb2ie5EalcrIpJG4jMu7j5btW8ag+EEN8ud7x/Qmqzyrzngmf5NmZJVlESABXDHwCo7vdjzThk3z3pHe0blTZwIlsM4x5UydHRMaQ055DjN3zATgvL7nsT53fZ1joqqmioqaCqJCohq8f3ulN/k6DIwxzxtjxhljxiUlJbVJGeLC4rwXbd8pJV9Y8wJFVUXeAW/BgcF1Wv0CAwIZlTTKG2g4wW3vmN4MSxhmu9I8XZIVrgqKq4q9rbc9onogSJ2AeGv+Vm++v2Nyt8ks3ruY+xfdj8vtqjOjy5rsNfzu+9+RV57HXyf91Vtp8CckMIRukd3q9jT4pOSszVlbJ//Tsb+Wf99uQ1/do7pTUl3CosxFDIgbQGpUKnkVeQ2mC3ROov3j+nNqz1OZd9m8Oq0QUDuGwLc1q6CyAIMhPiyezp064zIubxDpXGR91c+Xd7rkj+16LAESwLe7vgXsxT0qJIrokGhyK3LpGd2Tc/qcw7J9y/hh9w8kRyQzNGEoG/M2ensiUqNSCQwIZFjisIbBf2icbzEQERus+knVcAbPDUscxtVDrmbhFQu9FxLntb5jF4wxLMy063SL7IYg7C7eTYWrgqzyrDrBvzOA+pOtnwDwy1G/xGVcPLD4AQBmbLc3Ettdspv4sPg6F5PwoHBeOeMVXjnjFW8lIiUixdtFvSV/Cz2je5IYbgccOsfYzqKd9IzuyXGpx9E3pq8dBDjgojqf+YTUE0gvTm8w484/F/+T3SW7+cdx//BWRgMkwJvqArUD2/rE9CGrLIsZ22aQEJbASakneVvHa9w1ZJdle9fdVrjNW5n0NTp5NCXVJQ0G4WeXZfPfpf9lXc469pbsxWVcdb5Xx0ndT8Jg+Hz753WWL8hcQGRwJIPiB3mDkg15G3hrw1s8vvxxbv76ZmrcdrpRZ5YsX6lRqQhSp+V/Tc4ahiQMISQwhM6dOpNRnFFnIOjSvUtrx5uE+wT/kakNWu9+PffXnPDuCfxlwV+Y0HkCvaJ78dvvf8ue0j3eRg6oTSH0HVTuBKdTe071nsuc4L97dO13NCh+kPeOoXN2zuGX3/ySG2bd4H19bkUuxdXFjOs8jt4xvXltva2I1m/5B7io/0UESAAfbP6gwXOO+i3/wYHBnNj9ROamz6XaXc0nWz/hu/TvWJe7jpfWvgTA3xb+jfmZ8/nd97+j3FXOtoJtdIvs1iCocipjm/I2ce7H53L7nNtxGzcLMhewKnsVvxrzK94++22uGXoN4UHhvLXhLXYU7qDcVe49BrtGdrVznddUM3/3/DqpJVB7w636Uz3uLN5JuaucwQmDvQHljqId9u6+Pp+3e1T3RsfR7CjYQbfIbsSHxXvPeZ0j7Lk7MTyRfrH9+GTrJ94g2RjDQz89xC1f30JOeQ5bCrYgCL1jejM6ZTQGw5T3p/D3hX+nxl3jPQ59Z3KJDYvlxuE3cm7fc709uqOTR7OjcEeDO4X761kD2xL+6fmf8tbZb3krxkCddLJ9pfvYXbKbs3ufTXJ4MiuzVnp77semjMVgvJXx6ppqVmStaJDf3im4Ez/r/zO+TvuaWWmzGJowlMiQSCKDI+uMGduSbwey94npwzGdj2Fl9koW71nMhf0vrNNr7Xst9235dxs3t3x9C1Pen1Jn0LT3hpmdUugV3YvN+ZtZtm9Z7TgpJ1UyexXlrnJW56z2Th1e5/vynKdnptmgvG9MX7/pavvK9pEYZtPRnj71ae4ae1eDdYIDgukZ3bPO+I7dJbupMTWc39cmjry6/lUSwxM5u/fZ1JiaOr0xToObtvy3rd2A75Ur1bPM7zoiEgTEYKcgbey1jS3PBWI922jsvdqF2NBY70loTc4aggOC6Rfbj7c2vgXAmJQxjb52ZPJIdhXvIq8iz5vW0jO6J4EBgZzf93wWZC5gT8meBjPphAWF0aVTF3YU2JOl27jZUrClQXfnhf1sK3iQBHFhvwvZWrDV23Lw6bZPCQ0MZcaFMzi377n7/Zz1b3Xv5OALwobcDTb/M6Ju8J8UkUReeV6jcwY7aQRdOnWps9wJwDNLM+kf19/7fP2c4835mwmUQO/Fwrel0hEgAXSN7FrnguabT+9se17GPCKDIxuUBWgwC4ZTjp7RPekRZVvzYkJjvK0ezn4a33k84zuPJ68ij693fs3x3Y5nSMIQ1uetr2359wSTIxJHsDl/s7fVI68ir07Lv6NvjG2pNsZQWl3K1V9ezT8W/YM1OWsIkABvi6eTs+wrKTzJ21q0rWAbWeVZHNv1WEICQ0iKSGJ3yW5vJck3yI0IjqBLpy4UVRWRGpnqHUQMNojekr+F0upSdpfs9rZA+xqbMpaxKWO9f3fu1NnbRe3k34OtZOws2kmNu8abAhccEMyrZ77KnEvm1BnIDXBm7zMJDwrn/U3ve5dtyN3AjG0zmDZsWp33BLhs4GUMiBvAVYOv8gZmzjqL9izihNQTGBg/0Ds7R25FLjWmxtt1D/gN3r0z2vhc4L7c/iWXf3G5d7yB0zDgb4q6QfGDGJcyjidXPOltYauuqWZu+lxO7H4iQQFBtnIUnsyKrBXevOKc8hzmZ84nqzzLb6UkNDCUrpFdvZWSsuoyNudv9g64TI2yc3A7lafwoHA252+uvbtvaO3vqV9sP/Iq8rxpBzuLdvJdxnf0iOrBxQMu5uETH+auMXd51/ftpUmNSqV3TG++3P6ld5nzXY1IGkGXTl1Ynb2aKncVyRHJ3nSvu8fe7Q16bhlxC/3i+rE5fzNrc9Zy7w/3YoypEzAek2Jnb6o/3aAjpVMKJ3U/iY+3fExVTRWl1aX88cc/8syqZzDGYIzx24hxZq8zKagsYOaOmTy76llGJY3ihNQT+HTrp2zK28TyrOWckHoCRVVFzNwxk415G/02IjjH7zOrniGtKI15GfNYtm8Zs9JmER0SzbVDriU4MJjokGjO63seX+740huAOZ+na6euGAx7y/YyN30u8WHxdT5rSqcUBscPbjAzlDN73NCEoUSHRJMYnmhb/stz6/R09IzuWWfmJF/bCrfRN7YvQxKGeI8R317bG4bdwLaCbVz46YVklWUxP3M+b2x4gwWZC3h02aNsLdhqB4AHRzC+83h6RfeipLqE9ze/zw+7f/DuS9/Z2vxxGjV8U0Sq3dXsLtntt2cNbAWg/j7xnR1pRbZNuxuTMoaRySNZnrWcrQVbGRA3gJFJI4kLjeP9ze/jcrtYsm8J5a7yOo0rjisHX4nL2PErzrklMTyxTm/PlgLb4BEcGFyn4nZBvwsa/cy+981Zl7PO+5t9dPmj3nX2lO4hNjSW8KBw+sX2o7KmknJXuTcVJykiia6durImZw0rs1bicru8M575cs5RM3fMJCY0hnGdx5FelN4gXc2prO/P8MThrMle432903N1cveTvUH9hC4TGNd5HNEh0dw25zZunHUjZdVlFFVq8N8kEVkjIm+KyO9F5EwRSRWRPx7kt1kC9PfMwhOCHcA7o946M4BrPY8vBr41do/PAC73zAbUG+gP/NTYNj2vmevZBp5tHtQBzAdLXFht2o/T/e6koEQGRzYIyH05NfHV2avZWbSTmNAYb8B3Qb8LMBhm75ztDdh8f2jDk4azLGuZ9wJYWl1aZx5csD/iD879gDfOesPbIu7kFv+4+0cmdZ3kvaHX/jiBmZO36wTTJ6aeyLbCbWSVZTVI3+kR1QOXcdUZvOarfgDs8G21G5cyzhtQ1t/OpvxN9IjuUafr1Z/6NxrxnbLRaQVzLtj1u4yhNph39oMT/Hfp1MUbbPtedO4ccydBAUH8fPDPOan7Sd7l5/Y9l8EJgymuKmb+7vkkhScRHhQOwKjkUdSYGm/+cmMn1n6x/SitLmVf2T6bYpG9knc3vctzq59jYNzAJqdAS45I9vZeODekObbrsQDe+xM4+6R+kOtcyEYmjyQ4IJiPzvuIv076q3ccwo7CHWSWZPoN/utzWv7LXeWkF6fXCf7TitLYW7aXKneVN1iKCY0hNiy2wXaiQqI4s/eZfLnjS28KxoxtMwgJCOHaodc2WD+lUwofnvchvx//e++yYYnDEOw+v2jARXSP6o7buMkszfS2pDl3MAX8Bhd9YvoQFBDk7U7/bNtn/P6H3xMbGstfJ/2Voqoi7ltwH4L4DQoBHjz+QcKCwrjru7uoqqliZtpMCisL6/RWDEkcwtc7vya3IpfHT36cQAn0jk/wF/w75XVa/n/c/SM1psZ7we8Waefg3lqwleCAYI7vdjxpRWm1wb9Py7/zm3Q+o3MeeeKUJ/jrpL8SFxbHlJ5TePKUJ3nvnPfqtCaLCJcOuJSV2St5f/P73u3EhMaQEpFCr5he3nEzzjH/t2P/xvXDrvduIykiiffPfZ9vLvmG3x/ze28lwBv8R/fmmC72cw1PHE5YUJjf7+PiAReTX5nP3PS5PLH8CWZsm8HTK59mXsY8SqtLqaypbJDadVy34+gZ3ZM//PgHssqyuHPMnfys38/ILs/mlq9vITggmPsn30+v6F68tv410orSvBUsX50jOhMSEMKanDWkRKQQERTBB5s/YG76XKb0mFJngPKVg66k2l3Nc6ufo29MX+95yhnzkl6Uzo8ZP3Ji6okNKvondz+ZlVkr66Rcfpv+LSkRKd7fWu+Y3mwr2EZuRW6dys7wxOG296JwG9Xuah5d9igvr32ZGncNaYVp9Inp4w2aQwJC6vTantv3XF478zWKqop4fPnjvL3xbRLCErhs4GV8vv1zZqXN8p4vAySA9899nx8v/5GEsAQ+3/45m/M30y2y236vSUMThtK1U1fvBAdgrw01psZvy39jukd1Jyo4ygb/++x89wPjBzIqaRR7S/dSWVPJkIQhBAcGc/WQq5m/ez6T357Mr779FfFh8XUaBRzdIrvx23G/ZXD8YG++flJEUp1cfd904FHJo/jPif9h+mnTG/SA+/LOnlee6+2ZvHrI1Szes9hbCdpbutfbeHVKj1O8r/UN8EckjWBF1gqW7F1CoAT6bZxMjUolKCCIKncVg+IG0SOqB8XVxQ3ur7CvdF+dSUIaMyJpBPmV+d5rsBP8p0aletONT+t5GiGBIdwz/h7iw+JZvHcx3+/+vrblXwf8NupEYDpQjg2g1wJnHcw38OTf3w7Mwt5D4D1jzDoR+buIOBOjvwgkiMhW4G7gHs9r1wHvAeuBmcBtxpiaxrbp2dbvgbs920rwbLvdiQ2NpaS6hOqaavaU7KFLpy5cOvBSbhlxCy+d/pLfYNIxNHEoQRLEquxVpBWl1WnZdFrLFu1Z5P2xdI3s6n1+YpeJZJVlsSZnjXcwkr8LzsD4gQxNHOqdj3htzlqyy7LZXbK7QctoU3rH9KayptIb+GYUZ5AckcyghEGkF6eTU57TIGD07V72J704vU4A7IgJjWFyNzuDz4QuE7yf28lJ31Oyh2dWPsP3Gd/77baszwlynJaHvErPHPqhcaRGpnpTQxrrpWmQ9lO6x5u7fmZvO73iqT1P9a5/So9T+OnnP9E3ti8xoTFMP20694y/hzHJY7yzQyzPWt6gkhMWGMbXO7+mqqaK3IpcvyfWPrG2S3ZrwVa+z/ie5PBk7ywdvmXwx3fg8sLMhfSO6e0NJpz7EziVuvppJNcNvY7juh3Hr0b/CrCpVhcPuNg70G9L/hYyS5sX/Hfu1Jn8ynw25G7AYLwBSc/onhRWFnrTn+q39Ptz6YBLKXeVe1NmFu1Z5L23QXMEBQTxxYVf8MTJTzAyaWSdMSLO/k7plMI1Q65hUPwgb2XJV3BgMH1j+rIpfxPGGF5Z9woD4wby7jnvcvGAizmu23FUuavo3Klzo5Wzzp0684/J9u7Sr69/nadXPk3/uP513u+SAZcA9nd+cveTGZY4jG922Vx0f2k/YL/DHUU7yCrL4umVT9O5U2fvbyY1MpXs8mzW56ynV0wv+sb2JbMk0/u5fdPOBsUPQhDvvnFmjqnfSntS95O8A3R9XT7ociZ3ncz9C+9nzs45LNm7hNFJoxERekX38vYO1m9A8Gdqr6kESiBz0+d65zVP6ZTC1B5T+c+J/+HJU55s9LWTukyiS6cu/Hbeb3lr41tcNvAyksKT+HDLh3WmvfQVGBDIA8c9QL/Yftw4/EaO6XwMJ6SeQPeo7uRW5HJBvwuID4vn/H7ne1MRfWdQ8d2OkypyYf8LOa3XaXy540tKqksaTLjQJ7YPZ/Y+E0G4bfRt3uVdO9lz4Rc7vqC4urhO44LDSSNzBmku2rOI79K/47y+53mvR31i+rA2dy0ut6vO78yZOGFh5kLe3vA2L619if8u+y9f7/qaKncVfWL6eGepGRg/sMH1bVTyKC4beBkzts3g+4zvuWTgJdww7AZvo5FvUBoWFEZMaAwndj+R+bvnszZnrbcXrSkiwjVDr2HZvmXeWX+c3q3GWv4b287ghME2+M9a4Z3v3nfGGmd/3Tj8Rh47+THO7XsuI5JG8ODxDzba6HTN0Gt479z3vA0WieGJ3l7jgooC9pburTOxxOm9Tvfbi1C/rAlh9kZf63PXEx0SzV1j7qJvTF/+vujvlFaXsqd0j/eakRyRzGMnP8YH535AZEikdztO3DB9zXRGJ4/2W9EKDgj2Tkc8KnlU7cQB9VJ/mtvy78Qlzli1zJJMggKCSApP4s8T/8x3l37nPS7O7Xsucy6ZQ6fgTizZs+SITPsJ2v8qB48xJg/4zvMPEekP/OkQvM+XwJf1lv3F53EFcEkjr30AeKA52/Qs346dDahdc1JNCioL2FO6h4HxAwkJDOH20bfv97XhQeEMiB/AquxV7CzcycSudU8AE7tM5JOtn3hbWnxTUs7odQZPLH+Cu+beRbmr3LYONdFdGhoYyoC4AazNWesdZ1B/Wq6mONtOK0zzTsWZGplaZ0aL+sGaM+Dttjm30TemL89Nfa5OQJtWlOY3jQLgmSnPUO4qJyI4goigCDvVoKfl//c//J4VWSsQhLP7nL3fsqdGpXrn3o8JjSGvvLZlU0SY0GUC83fP9w7grS8kMITY0FjvCdyZ019EOKn7Sbx6xqsNvkvnpilg96NzcvcdUOx7AYgIjuDk7iczK20WVw228xg7+bS+nEB5W8E2VmWvYmKXifys/89YtGeR93WNSY5Ipspdxb6yfSzdt5SLB1zsfa5rZFe+3PElaUVpRARFNEihGhg/kGdOfabBNlOjUgkOCGbhnoW43K46FdTGOBWE7zK+s5/Jk4frVBadlIXmBP9DE4cyJGEI7216j6k9p7K1YGud1vLm6B7d3Ztn7hyP6cXpdQLS3x7z2ya3MTB+IAszF5JRnMHm/M3cM/4eb6XyFyN/wY+7f2TasKZnKz4+9XgmdpnIY8sfA+C5qc/VyQE+IfUE3j/3ffrE9PEet85vubEehUldJ/HOpneY8v4UQgND+d+U/3lbip2KzuK9izmr91n0jO6JwXhbBX2D4KiQKAYnDOanvT/xS37pnTnGX3qZP0EBQTx28mPcMOsG7vruLgDv1KTOfgf/A+7riw6JZmTSSOZnzic6JJpe0b3s9yQ0CKLrCwwI5KrBV/Hw0ofp3Kkzd425i6CAID7Y/IG316t+8A92ys+Pz69taQ4ODOaxkx9jYeZCb6Xswn4X8snWT0iOSG6QD+64d8K9fLr1U24cfiOrs1d7x9GM79LwUvfQ8Q/xp4l/qhP4OK3DM7bNIDQw1G/QOCh+EJ07dWZu+lzO6XsOf53/V/rE9OHG4Td61/EdVO37nXeP6s6IxBFMXzOdqpoqRiSOYFP+Jh5eYu9Q3De2L8OThvP81OcbrXBOGz6NObvsfVmuGnwVMaExPHrSo+ws2ukd4O3rxNQT+WjLR5RUlzSZ+uLr4gEX8/Lal3lyxZOM7zzeG5i2pOUfbOrPK+teAeD2UfaaPTh+MN0iu9Enpo+3siUiTOkxpU7KY3MlhSd5b1S4Md/2nPmbVW5/nLn+8yvyGRw/mJDAEO479j6u/upq3t74NjuLdtbpjfBX1hO7nwiecfNNfdd/nPBH7l90P5cMuISSant/lF3Fu7yBfFl1GcXVxc2qrDs3RFyVvYqz+pxFZkkmXTp18Z476v/eggKCGJowlE35m7zX1SMp+D/caT91zvzGmC1Aw2ZgddA5NeS9pXvJrchtsuvOn5FJI1mydwlZ5VkNLuATu0yk3FXOzB0zSQxPrNOVHRkSydOnPk2f2D7EhcXx+/G/b7KXAWyX7rrcdazIWkFIQEiDGTGa4uQBOt3s6cXppEalegfzQcNgLTYs1tsCtq1wW51p9ipcFazLXee3twLsydZpJQ0MCLTzW5dmkl6czoqsFVwz5Bo+v/DzRi+yvurP+JNXkYcgxITY1uEnTn6CL3/2ZZPb8r3Lr9PD45RzTMqYOkFaU0ICQ7wn5ak9p9Z57qw+Z1FQWeDtzvbX8h8XFkd8WDw/7P6BnPIcRiaN5JjOx3DH6Dv2e9dD58L48daPqayprNOqnBpp7wC8eM9iekQ3nDGjMUEBQfSM7um9uc3+8nWhNtibuWMmoYGh3tY657XfpX9HeFC4N91qf5zxLO9teg+onee6NZLCk7w3mdtXto+QgJA6N+VqzIC4AWSXZ/PlDtuO4fvdjkgawdxL53LZoMv2u52/TLSDZ28bdZvfXoZB8YMICbT3O3SOn5FJIxvd95O7TWZw/GDiQuN48fQX63w3vgHg8MTh3u9/YeZCkiOS68zcBXgrG6XVpWzK3+RtlGiusKAw/n3Cv0kKt7nHzp20fcvR3DTEY7sey/rc9Szas4ihiUNbVI6rh1zNs6c+y3vnvEdkSCQndDuByppK7/mp/iw9jRkQN4Brh17r/e4TwhP47ILPmD51eqO/n76xfbl73N2EBoYyNmUsvxrzK9495906jQUOEWkQ9IQEhngD0gldJvjd7yLCiaknsmjPIj7b9hmZpZn8euyv66zrOyDbGeDpuHvc3VS4KggKCOK+Y+/jxNQTySrLIjgg2HvNmNR1Up2B2b7iw+L5/MLP+fSCT709cKf2PJVpw6f5rSz6VmD8zTrnT2hgKNOGT2NV9io25G1gV9EuIoIiGqRs7Y/vOd+ZNSk4MJiZF83k6VOfbvZ5sClJ4UlU1FRQUl3Cxlwb/Lfk2utICE9gX9k+tuRv8VYeRiWPYkKXCTy+/HGq3dV1fkv+JIYn8r8p/+OuMXc1OdbPmUAjpVOKd+KAnUU72VawjcqaSm/vYHNa/oMCghiWOMzbULG7dPd+G4l6RPdgZ9HOOuPzjhSHteUfeE5E+mIHxa4GwoC1IhJhjClr+qXqQDgHpZOH15yWT1/Hdj2Wtzfam3rUD1qO6XwMgRLIruJdflN0hiUO44XTXmiwvDFDE4by7qZ3+WDzBwxNHOoNIpojPiyeqJAo0orSqKypJKvMzgiTFJHEiMQRBAUE+R3f8N+T/suOwh08ueJJftj9A3eOsXcMnps+F5fbxbiUcc16/y6durCnZI93+rEL+13Y7FYep4UqoySDoYlDvdNoOheikMCQRnsgHL758k4PT2v987h/klWWVafFE2By18nEhMZ4ZxGpf1F29I3t6+3urj9Hc1OcytnbG94mOCC4znfvHLdpRWmc22f/A8Drl8dJd6j/mfxxgqs9pXsYnTzaG2SmRqUSGRxJSXUJIxJHNPvCe1L3k3hg8QM8s+oZokKiWtWq5hARUiNTSS9OJzQwlJROKc0qh3M8vLr+VZvHXq8S1NRsWr66R3fnhdOb95seFD+IGRfMaDJgDg0M5Z1z3qHaXd0gTcG3xXd0ymhvJazMVeY3dWdC5wm8vPZlPtj8AeWu8hYH/2D38ayLZuEyLm+637iUcdw0/CbvbGbNcVy343hq5VNA7cxNzSUidWcj6jyW8KBwvkv/joigiBY34NTfdqA0rzckQALqtMY31+WD7I2XfG/qVt/J3U/m3U3v8tcFf6VvTN8GvZrdo7pzx+g7/I4TGpsylq8v/prggGAiQyI5v9/5zN45m/Gdx9cZl9CUoIAggpoZBkUER/DheR+SXpzud0B8Y6b2nMo/F/+TBZkL7DSfLWi0cJzU/STO7nM2PaN7Ntp7dqCciTCyy7NZn7eelIgUv2OY9icpPMmbyuX7+7x+6PXe60FzPsMJqSc02svtT2hgKL1jevPR5o94dtWzjEkewy9G/QJoOFNfY0YljeLltS9T7ionsyRzv+/v3KAwrSiNoICgI6rl/3Cn/ZwMICI9gJHAKM//K0XEbYxp/dVQNclpFXRuQONvtpimHNftOEYkjqBzp84NfrhRIVH0i+3HpvxNfkflt5SzjTJXWZ1ZBppDROgdbWf8qT8jzKtnvoogfk+8CeEJJIQncGzXY3lq5VMUVBSws3gnf57/ZwbFD6pzEW5K18iuLN6zmBVZK4gKifLmvjdH/RuNZJdn+50ZqCkpESlsyN1AZU1lq3p4fEUER/gNkoMDgzm3z7m8seENUiJSGn2PfrH9vFP5NSdNwtE1sitBEkR+ZT4TOtdtNfQNVltasRmeONx7D4nmtNZHBEcQFRxFcXVxncpLgAQwKH4QS/ctbXKWrPo6d+rM5K6TmZ85n7N6n9Wgxbqlukd1J6Mkg8jgyGZ1a0PtjD/FVcWc0v2Ug9Ji2BzNCZYCJMBvfnKABPCfE/9DhavCG0AnhieSU57jbV32NTp5NKGBoTy5wubUO3cfbangwGCCqQ0iRcTbKNBcQxKGMDZlLFsLtja7tbgxznzs32V8R8/onodt37XWdUOv47y+5/lNT3JM7DLRO0PbbaNv89szefOImxt9vW9L6/HdjufD8z7cbwPJgRgQN6DFwXdieCID4wayIHMB+0r3tarSHyAB3ptYHirOOTGnLIfV2asb7e3eH99zve99NI7teiyD4wfbAcvNGDPRGsMTh/PpNjvnyvKs5WzOs1PtNqflH2wPhcu4WL5vOTnlOfuNk5yGvRVZK4gPi2/3v0lfbTLVpzFmlzHmM2PM/caYi40xA4DmNa2qVnFOkq0N/oMCgnjz7Dd55KRH/J6g7xl/D+M7j+fs3vvPbd+f1KhUzu5zNn1i+nB+v/o3Z96//nH92ZC3wTsdodNSGBQQtN/c33Gd7WG4PGs5Dy5+kISwBJ499dlmB2q9Y3qzr2wfP2T8wMikkc1OswGbIhUXGuedbcDfzET7kxyRTF5FXqPTkx4s1w29juO7Hc8tI29pdB1nfuSLB1zcokA3OCDYG9hP6Vk3H9Q3xailaTPOoMMTUk9o9kn67nF3ExUc1aD18taRtzIsYRhn9G6YG9yUP074I/837v/49dhft+h1/qRG2Zb/vaV7mzWbBdjzgFOBas1vq62c3uv0OuV1Ksr+Kl8RwRFM7DKRyppKIoMjD2kwuD8iwvTTpjPzZzMPSqvgL0f9km6R3Q7K8XOoiUiTgT/YVMkXT3uR9899v0F6YWveb0DcgAYTM7QHx3Y7liV7l7CreFeLGoQOJyf435C3gd0lu1vUW+vLt4fLd3YvEeGNs97ghdNeOGRB8iUDLyElIsU7wcXXO78Gmh/8O5UVJ6V1f40WTi/11oKtLU7lamuHO+2nUcaYkrYuw9HMyWnckLeBAAlodrDQXOM6j+PFzgdvoqMDaeUYkzKGD7d8yGfbPiNAAlrU6jw8cTihgaG8uu5V1uWu497x9+73AubLmSUnqzyLS5MubXHZu0V28wbuWWUNx1fsT3JEMgbD6hw728mhCv5TOqXw9KlPN7nO0MShfPWzr1pVhgePf5CvdnzlHaToa/pp08ksyWxxC1rP6J68duZrLcpjvXjAxVzY78IGlcYJXSbw9jlvt+j9wabL+JveszVSo1Ipd5VT7irnjIjmV0JeO/M1NuRuOCi9dG3l3gn38vyq5xsdSH/ryFtZtm8Zvx776zZvjQsOCCY4pHlpKPszOGEwX/3sqzb/TAdTUkRSi1KpjkSTu07m5bUvA3Vbw9sTpwfXmZGsJRNt+BqWOIxpw6YxudvkBsdpS1J4W2Nk0ki+ueQbyl3lzE6bzcrslUSHRDe7QhgbFkvvmN7eHuK+MX2bXN8ZZ2AwdaYcPhIcNTf5EpF4EflaRLZ4/vc78kJErvWss0VErvVZPtZzH4KtIvKEeI5aEXlYRDaKyGoR+VhEYj3Le4lIuYis9Px71t/7tRfBAcHeH3dqZKrfgVtHC2eKwG/Tv6VndM8WtQSFBIYwImkEy7OWExwQ3Kwbi/nyHdTnO11cczk3NHK5XeRW5Da7xcLhrO8MWjpUwX9zOXcGbqneMb355ahf+u0xcGYOao3RyaMbnVu9Ma0p/+Hg26LdkuMkLiyOY7s1HKR7JBmaMJTHT3m80d/2sMRhfH/591w6sOUV8PbuaAr8OwrfAbsjk1vXon6oRQRH0D2qOxvzNtYZNN1SQQFB3DX2rjZtXAgPCvc2Du1vcHF9vmmC+5vJLTQw1BtX+Zv1rj07aoJ/7Fz9c4wx/YE5nr/rEJF44K/ABOz0nH/1qSQ8A9yEvbFXf8BpSvsaGGaMGQFsBu712eQ2Y8woz79bD8FnOqicgZm+014ejTp36syEzjYl5LSeLc+zvXrw1QRIAL8c9UuiQqJa9NqY0BgeO+kx7hx9Z4t6HBw9onuQWZLJrqJduI27xcG/kybkBP8Hu4dHtR++XeqtvVAfzY7mBg51ZAkJDOGdc97hiwu/aNeDQp1AeUzKmEPeSn+oOZWPlvYQn9XH3nrq2K7HNmvguJNV0doxEm2l3aT9HATnAyd5Hr+KvZfA7+utczrwted+A4jI18AZIvIdEG2MWeRZ/hpwAfCVMWa2z+sXUXs33yNOn5g+LMhccMhmC2hPHj7xYb7Y/kWrWohP7nEyP1z+Q6tP0lN6TmEKLZ9nGWy3ZY2p8eYctrSi5lQWtuRvISUi5Yg/gavG+d4oqP5ds5VS7UtLZ3tqC9cMuYZ1Oeu4buh1bV2UA/aLkb+g3FXO5YMub9HrJnaZyIunvcighOZVGn53zO+45/t7WjQzUXtwNAX/KcaYPZ7HewF/TZ7dgHSfvzM8y7p5HtdfXt8NwLs+f/cWkRVAEfAnY8wPrSz7YXHloCtJCE/gikFXtHVRDrm4sDiuGtL0zaSa0latM6OSRxEogd4burS0ohYbGktcaBz5lfkHNJWkav+CAoL44NwPyCjO0EqeUuqAjes8jjmXzmnrYhwUEcER/Gli6+4h6+9mdo05pvMxR+R3dkSl/YjINyKy1s+/OtNWGGMMYA7ye/8RcAFvehbtAXoYY0YDdwNviYjfiFFEbhaRpSKyNDs7+2AWq0W6R3fnxuE3NvsGNerwiw6J5rhuxwF2WsuWph2JiHfcwYHM8a+ODAPjBzaYEUkppZRqyhHV8m+MObWx50Rkn4h0McbsEZEuQJaf1XZTmxoEkIpND9rteey7fLfPtq8DzgGmeCoWGGMqgUrP42Uisg0YACz1U+7ngecBxo0bd1ArJeroc/e4uymqKuLWka0bRnLFoCsIDQzlsoH7v1OrUkoppToW8cSyRzwReRjINcb8S0TuAeKNMb+rt048sAxwJodeDow1xuSJyE/AncBi4EvgSWPMl//P3n3HR1HmDxz/PFuzm03vCYHQu7SAYAEbAnZP7L1379SznWc5e/t51rN3VOy9K4KASu+hBUjvfTfbd5/fH7NZElIICCSQ5/0ysjv7zMwzO7Mz36fMM0KI6cCTwBQpZWWzZSUBNVLKgBCiHzAfGNl0P0F7srOz5dKlrcoHiqIoiqIoirJHCCGWSSnbfIbWgRT8JwAfAr2BfOCMUFCfDVwlpbwslO4S4F+h2R6UUr4Rmp4NvAlYgO+A66WUUgiRC5iB6tA8f0oprxJCnAbcB/iAIHCPlPKrTuSzMpS/rpYIVHV1JpS9Tu3nA5/axz2D2s89g9rPB759tY/7SCnbfIjGARP8K7tGCLG0vRKhcuBQ+/nAp/Zxz6D2c8+g9vOBrzvs4/3qhl9FURRFURRFUXafCv4VRVEURVEUpYdQwX/P9XJXZ0DZJ9R+PvCpfdwzqP3cM6j9fODr8n2s+vwriqIoiqIoSg+hav4VRVGUbkUIcU7owYgOIUSpEOI7IcRhXZ0vRVGUA4EK/hVFUZRuQwhxE/AU8BCQgjZ88/+AkzuYTVEURekk1e1HURRF6RaEEDFoT1e/WEr5URuf17H9yfQCsAJ9pZR5+yqPiqIo+zvDzpMoiqIoyj4xCYgAPmvrQyllbNNrIcRDwGFohQVFURSlk1TwryiKonQXCUCVlNLfUSIhxJnAOcB4KaVvn+RMURTlAKGCf0VRFKW7qAYShRCG9goAQogxwHPAsVLKyn2aO0VRlAOAuuFXURRF6S7+ADzAKW19KIRIBj4HrpVSrth32VIURTlwqJp/RVEUpVuQUtYLIe4GnhdC+IEfAR9wDDAVyAZmSSk/7MJsKoqi7NfUaD+KoihKtyKEOBe4ERgK2IFlwIPAQsAJNL9wDZNSFuzzTCqKouynVPCvKIqiKIqiKD2E6vOvKIqiKIqiKD2ECv4VRVEURVEUpYdQwb+iKIqiKIqi9BAq+FcURVEURVGUHkIF/4qiKIqiKIrSQ6hx/vexxMREmZWV1dXZUBRFURRlZ6REejyIiIiuzomi7JJly5ZVSSmT2vpMBf/7WFZWFkuXLu3qbCiKoiiKshM1b71F+cOPkPHAA0RPn97V2VGUThNC5Lf3mer2oyiKoiiK0gbHb/MBqP/q6y7OiaLsOT0q+BdCTBdCbBRC5Aohbm/jc7MQ4oPQ54uEEFnNPrsjNH2jEGJas+l5Qog1QoiVQghVpa8oiqIoBwj3hg3av2vXdnFOFGXP6THBvxBCDzwPzACGAWcLIYbtkOxSoFZKOQD4L/BoaN5hwFnAcGA68L/Q8pocKaUcLaXM3suboSiKoijKPiC9XgLV1ehsNvzl5fhra7s6S4qyR/SY4B+YAORKKbdKKb3AbODkHdKcDLwVev0xcLQQQoSmz5ZSeqSU24Dc0PIURVEURTkA+auqALCOGweAr6i4K7OjKHtMTwr+M4DCZu+LQtPaTCOl9AP1QMJO5pXAj0KIZUKIK9pasRDiCiHEUiHE0srKyr+8IYqiKIqi7F3+igoALGNGA+ArLenC3CjKntOTgv+95TAp5Vi07kTXCiEm75hASvmylDJbSpmdlNTmqEuKoiiKonQjvqbgf/Ro7X3JgR38Bz0eKp99Dte6dV2dFWUv60nBfzGQ2ex9r9C0NtMIIQxADFDd0bxSyqZ/K4DPUN2BFEVRFGW/F6jR+vib+vZFWK0HfPBf/dJLVD3/PAUXXYz0+bo6O8pe1JOC/yXAQCFEXyGECe0G3i93SPMlcGHo9UxgjpRShqafFRoNqC8wEFgshIgUQkQBCCEigWMBNSSAoiiKouznAvYGAPRRURjT0w744L/hm28BCNrtNC5a3MW5UfamHhP8h/rwXwf8AKwHPpRSrhNC3CeEOCmU7DUgQQiRC9wE3B6adx3wIZADfA9cK6UMACnAAiHEKmAx8I2U8vt9uV2KoiiKoux5QbsDDAaExYIxPf2ACf4dv/2Gt6ioxTRfaSne/HySbrwRhMC1cmXXZE7ZJ3rUE36llN8C3+4w7e5mr93A6e3M+yDw4A7TtgKj9nxOFUVRFEXpSgF7A3qbDSEExrR03KvXdHWW/jLn0qUUXnElluxxZM2aFZ7u2bQJAOu4sZiysnCvX99VWVT2gR5T868oyu6TUuJauRLp93d1VpRuwLV2HZXPPIsMBrs6K4qClJLGP/7Atfav3ajq3rQJX1lZ+H3Q7kAXHQ2AMT2dQF0dQafzL62jq9l/mQOAZ/0GtF7NGk9uLgDmAQMwDxkcLgwoB6YuD/6FEM8LIQ7t6nwoe0/NO7Mo+seNBByNXZ0VpQ0BhwNvfn6Haeo++JC8s86m8qmn9k2mdlPAbu/qLBzwpJTkzZxJ1f/+h/37nfdyDDgcNHz7rSo4HmCk30+grq6rswFA3UcfUXDxJeTNnEnNO7N2PkMb3Dk5bDvpZIr/cWN4WlPNP4AxLRWgReGgI9LrperlV6j/csdbC7uWa81qAIKNjfibDT3uzctHHx+PPjYWU+8++EpLO/Wb9VdXE3S59lp+d0egoYHa999XrRcd6PLgH9gEPCGEyBNCPCaEGNPVGTqQBd3uFj/4vc2bl0f5gw9i//57at56c5+ttzOCXi91n3yCdy8/uMW1Zi3OpUv36jp21PDdd5TedVd4qLqObD3xJLZMm96qD2hztbNnA1D32ectaou6k4r/+z82HTzxL9f+7WneoqIDquDrzcsLv3bMm7fT9MU33EDxTTdT+8EHezFXyr4UqKtj64knsenwyTiXLOnq7FDz1tuYBw7ENmUK5Y89hregYJeX0RQoNu/r3rzm35AaCv5LSzu1vIr/e5LKJ5+k5NbbaPz9913Oz94g/X7c63IwpqcD4C/ffn3wV1RgSEkBwJTZC/x+fGXlHS7PvX49ucdMJfeII3EsWNhuOsf8Bdh//nmfXDuCHg/5F1xI2X/uI+/c8/CV7/wauKtkMIh9zpxOHwvdUZcH/1LKp6WUk4ApaMNqvi6E2CCEuEcIMaiLs3dACbrdbD3xJHKnTcdbWLjzGXZC+v3Uzv6gwwDT/vPPgDZU2s5qCaWU1Lz9NrUffbRb+fHX1lL0jxtxrencgEuVT/6X0jv/TdF11+21k1LA4SDv9NPJP+/8TtcYNeevrcWdk7NL83jz8ym+6WbqPvqY0ttv73Db/JWV+EMnsMYFC9pM4ystxbNhA8bMTALV1fiKO1dYsv/8M7WzP9gnNb4yEKD61dcgGKR21u7V/DUXsNuxz/n1L+fdtXo1W2ccR97MmQTdbgD8NTVsO+NMSv5151/qNiODQYKN7RcqZDCIe8MGZCCw2+toi2v5CgCMfXrv9LcWaGgIjxrS3WpA96bGP//cacDn2bqVon/cuFuVD/ZffqHgyitp+OHHXZov4HDskSEcy594Au+2beDzUfXiS395eX+Ft6gY75YtxM48jdT77gMpqZ296wVNz9at4ddNLYhBux19VFPNfxoA/k6cxxt//52at94i9vSZGFJSqH7zzV3Oz64Kulw7vY55tmxFulzYjjkaAH9ls+C/shJDUiIAxl7ayOa+oo7jhOrX30C63eiioym84goa2rjGN/65iMLLL6fouuspf/ChvV4AqHnjTTwbNpB8yz+RTif1n326S/MH7HY8W7d2mM/ad96h6JpryTvzrG7X6tFZXR78N5FS5kspH5VSjgHOBk5BG5VH2UPsv/yCr7AQ6XRS8+Zbf3l5lc88S9m991J8403tpnEuW44pK4vYM87Aszm3w5Jy48LfKX/oYcruurvFibgt3vx83Bs3tphW/fIr2L//nuJ/3rzTvAfdbuo+/BAAz4YNuDuoLQ66XOSfdz7bTptJoL5+p8turnHh9gDAMe+3XZpXBoMUXnkV2/52GnWff97p+eo++RT0ehIuv4zG3//A2cGQba7Vq8OvnUuXtZnGMXcuAEn/+Ls2z4qVO82DY948iq67nrJ776X80cc6nfcdSSnxlZXtNID1bNoEoZO1a8WK3V4faAWJgosupuiaayi77/6/tKzaDz9E+nx48/LCw+hVPvss7tWrqf/0U+w//tRxXqSk6uVXqH7t9RYFBSklhVdfzeYjjmy3cFh273/YdsqpFF173W4XMqSUuNauI+j1hqe5N2xAWK3EHH8C3m3bOrz4NS5YAIEAluxxuHPWE/R4wsut/fBD3Bvb7lcccDjw19bucn7rPv+cDWPHUffpZ7s8r6+khPKHH8azZUubn7tWraLmrbd22ufbuXwFBRddTMEll+LZtq3ddBWPPY79+++pePSR8DR/dTUN33/fYeDhzsmh6Ia/0zjvN0puvbXNc2rQ7Sb/vPMpf/jh7fNt3Eju5ClsPfkUAg5Hh9vQEeeyZdR//Anxl15C/MUX41y8uFMBUP1XX1Fy2+34q6t3e91tca/VbsK1jMvGmJJM1FFHUf/ppy2O2c7wbt2+r7z5WstBwG5HZ4sC0GrFhcBX2nHwH2hooOT2OzD170/Kv/5FzIkn0Pj7H3u1i1TdJ5+wMXs8eWee1WErY9N3FXVUKPgv316z76+sxJCcDICxVy+ADisJpc+H45dfiJ15Gv2++JyI4cO14H6Hc3Xdhx+ij4kh9qwzqZ01i5q32o493Bs2YJ8zZ5fOVYGGBgouvYwt02fQ+Pvv+MrLqXr5ZaKmHkPCpZcSMWwYjnYqtdriWrWK3GOmsvW44yl/4MF209V+qFVQ+isqaPjhh04vvzvpNsG/EMIghDhRCPEu8B2wEfjbHl7HdCHERiFErhDi9jY+NwshPgh9vkgIkdXssztC0zcKIaZ1dpndiWPePPQJCURNnapdYHahVtO9YQP5F15ExdNPI6Uk6HKFu4K4li1r80Itg0Gcy5djyR5H5CGTAGhctKjddTR8u30gpo6CorrPP2fLccez7eRTqP/qq/D0pqDPl1+AZ2vLi+6OJ6TGP/8k6HSS/vhjoNdj/+XnlumlpPq11yi9+x4Kr7wK59KluNeto/6bb1qkq3n7Hbadfka46du5fDml994bLpi4li9DmM3ooqI6HDotYLfj+O036r/6OhwguVaswB0KzqtfebXTNSaNf/6JZdQoEq+7DkNyMlXPPdfuvE2FLGt2dqsbvAJ1ddTMepfqV17F1Lcv0dOmIazWnQ4BJ6Wk8rnnMfbprZ3w33kH+5xf203v2baNqpdfCdeMN19Oya23kXvEkRRcfEmHNZZNBZfYM8/Em5/fqpDW8MOP5J11NjXvvddh3gEavvkG97p16CIjqfv443DNrPT7qXn7bUrvvmenhVPQjjnHr3OJPu44TFlZ1H/xBUGPh4ZvviX6+OMxpKZS/8UXHS7D/t13VD75JBWPP05Ds2PPtWwZjfN+I2i3U/3Gm63m89fWUvfppwiLBcfcudR/2rr2S/r9lD3wIKX33tvqu29S+/bb5M2cSck/bwlP8+RuxjxgAKb+/UDKDrtY2OfORR8XR/x554PPh2fDBm36Tz9Rdvc9bJs5E7lDkOavqWHL9BlsnXFciy5GAO6Nmyi99942AykZDFL13PNIp5OKxx9vd5tAO7Z3DDTKH36YmrfebvO+Fk9uLvkXXUz5w49Qcvsd7S5XSknlk0+G39d98GGb6YIeT7hloHHR4vD5qfjmf1L8jxupaWOfNql64UV0NhtZn3yM9Hio//rrVmnqP/8c59Kl1Lz1dvhcVP3SSwSdTrxbt1L7/vvtLr8jQaeT0n/diTE9naRrryVy0kSkz4dr1aq203u9WmHhyy8pueVW6r/4gqr/vdDusiufeZbcY6ayYew48s48i/zzL6Ds/gc6bOFyb9wIOh3mgQMAiD3zDAJ1dTh++WWXts27dSuG9FDtfpXWNTZot6OP1oJ/ncmEPjEBX1nHXT1qP/gAf0UF6Y88jM5iIWr6DPD7sbeTn7rPPqfkzjt3qQWo4aefqHnvPaSUBByNlD/yKIbERNxr1lD9YtvfL4Br9Rp0UVFYx40FnQ5fKPiXgQD+6moMSUkAGFNTwGDA10Ge3OvXE3Q6iTz0UHRWKwmXXIy/srJV5YtzxQoiDz+c1HvuIXLy4VS98GKr/emYP59tfzuNomuuperFFzv9PVS9+BKNv/9O0Omk8MqrKLjgQvD7Sb71VgCs47Nxr13XZoEi6HJRctvt2sAFUmo9B/7+D/RRUUSfdCK1777b5vnZX12Nd8sWkm68EWNmJg37aYtmlwf/QoipQojXgSLgcuAboL+U8iwpZcdXxl1bjx54HpgBDAPOFkIM2yHZpUCtlHIA8F/g0dC8w9AeCjYcmA78Twih7+QyuwUpJc7FS7BOGE/0SScSqK6m8c/2A/Hmgm43xTfehHPRIqpfeJGaN9+i/uuvCTY0kHSzVuvfVr9PT24uwfp6rOOyMQ8ciD42tlUttAwECDQ0IL1e7D//TMzJJ2EeNpTGhdv7D/rKyym7/wFcq1bhLSyk7O57sI4Zgykri6oXXkRKqXVx2LSJqKlTAbD/uL05vGbWu2wcPyHcBQnA8etcdFYrUdOmYc3ObvGZNv9PVDz+BHUffohz8WLiL7oIU9++OJoFsb7iYsoffhj3mjUUXn0NVa+8QsEll1I3+wMKr7gydOFbjuWgg7COGxe+0WrH/VL2wINsOngihVdcScktt1Byi3bisv/0M8JoJOnmm/Bu2YJn8+ad7quAw4F73TqsE8ajM5tJuOIKnEuXUvHEE1S98EKrQM1XUIg+Ph7LmDF4tm1rEWAX33or5Q88gK+0lKS/34AwGLCMHLnT4N+1bBnuNWtIuPhiUv71L8xDh1Jyyy3YQy0ILbbf56PgooupfPJJKv/7VIvPat97j4avvsI6YQLOxYupefvtdtfpXLYMQ3oa0dOO1fKwdnt3FMe8eRTfdBOutWspv+/+VgW4Fvnxeql89jnMw4bS7yvtpF73wQcEXS4Kr7iC8ocepu6TT8g//4Kd1mC6Vq0iUF2N7eijiD7+eJxLlmjLamgg5m+nEnXsVBoXLuyw1rT6zbcw9emDeeBAql99LVyIq//yK4TFQvRJJ2L/6adWNbn2H34Ev5+sd2dhyR5HxeNPtCqg1332GbWzZlE3+wOqXth+wfUWFuIrKSHo8YS7dNh/+ik8xrlncy7mgQMw9+2rpW+ndlsGAjTO+w3b5MOJGDYU0EZTAe33B4DPR+Off7aYzzFnDoGqKgJ1dZTc8a9wJYX0+ym86irqZn9AxZP/1aZJSdWLL9L4++94Nm7EV1REzCmnEKitpe6jj1ss11tURP2XXxJoaCDvzLMouuba8DHnr6nBHsqT/de5LYKTgMNB0T/+gc5qJe6cc7D/+COO39puxWucPx/n0qWk3H0XtilTaPjxBy1Aa2jAuWRJeP+5Vq1Cer1EH3ccwYYGPJs24a+uxhn6Luo+/WR7vgsKcC5fruWlrg773LnEnnoqluHDiRg5Ekdo9JYW3+Gvc9HFxIBeT8O33xH0enHMnUfs6adjzc6m7sOPWgVEMhDAuXx5q+6JMhCg5q23qHj6aYpvuhlvQQFpDz+MzmrFctBB2va0MQRmwNFI3llnkX/ueZTcepvWJ//II2n4+us2W/KKb7qZqv/9D1PfvsT+7W9gNBD0eqh9/32Kb7q53Rphz8ZNmPr2RWc2AxA5cSKGtDTqPuu49SfoduOvqtK20evFW1SEdcxYLe+1dchAgGBjY7jmH8CYmoa/g5p/GQhQ9/5srBMmYBk5EoCI4cMw9upFw4+tu2i5Vq+m9I47qP/kUwouvhjHgoU7HbTAvWkTxdffQPl999O4YCH2H38kaLeT8dR/iT7hBGpmvYu/shLn0qWtrsuuNauJGDEcYTSij40NP8HYX10NwWA4+BcGg/Zcgw5q/p1LtPvYrOPGARB5+OEgRIu4wl9Vhb+0VFunECRcehnB+nocza7v9d98Q+HV12AeNAhrdjbVr7yKJzeX8scfb1G5t6NAfT11s2cTffzx9PvyCyxjx+KvribtwQcwZWrdlkwDBiDd7jafz1D34Yehwuj/qP/iC0pvv4NAdTUZTz9F+sMPY83Opuw/97WqSGyqaLJOGE/MSSfR+MefeIuKaPjxRyqeeKLV+ay76vLgH7gD+B0YKqU8SUr5npRyb9wdNwHIlVJulVJ6gdnAyTukORloapP6GDhaCCFC02dLKT1Sym1Abmh5nVlmt+ArLMRfVkbkhAnYJk9GFxVFw9df4ysupvK553GtWYuUEm9RUYtaYm9RESW33IJ32zYyX32VqKlTqXj0Ucr+cx8Rw4cTf8EFoNfjXte664FrWehHkj0OodNhHT8e5+Ltwb8MBim8/Ao2T55C+RNPEGxoIGr6dGyHHIJz5crwBbj8kUeoffddCi67nMIrrwKDgfQnHifuvPPwbt2Kr7gYX1ER0unENmUyllGjaPhRa4rz19ZS8fjjWm3gk//VCgpS4pg7V6uxMJmIOnYq3twt4cAEoHb2+xgzMxm8YjlZH31E8q23YBk3FveaNeHvp+H7H0BK+rw7C310NJX/9ySmrCzSH38Mf3k5dbNn416/Hkv2OCKGDcW7tXUXibrZs6mdNYvY0/5G7zffIOHyy7H/+CPOFSuw//IL1kkTiT3lFBBCC+jQ+rgWXHklRddfj7+mpsXynEuXQiBA5MEHAxB7+kys48dT89rrVD79DHlnnd3i4u4tKMCUmYl50CCtZjYUyHnz8mj8bT4Jl1/GwPm/ET19OgCWMaNxb9zYccD6+hvoY2OJOflkdCYTmS/8D1OfPhRdf0OrPuIN33+Pv7wcnc1G3aefhmtr3Zs2UfH4E0Qefji933oT29FHU/nMs61OxBAq2C5dinVcNhEjRmjzr1kT+q6KKL7lVsyDBjFowXwsY8dS+u+7aPjhRyqff75VjWXdp5/iKywk+R//wJieTtTRR1H7/vsUXHwJjX8uIu3BB+j72acE7XZK77q7w9YY+y+/gNGIbfJkoo8/HqSk/KGHMaanEzlxIrbDJyO93nZvBnetXIl79Wrizj+fuAvOx7NxI65ly7TWg++/J2rqMcSdfTbS7W51P03DN99g6tcP89ChpN59NwGHg4pHHmmZ5osvMQ8cQPRxM6h55x38tbU0LlrMlukzyJ02nYKLLiZQW0vKXf8GKXEsXIi/poZAVRXmgQMxZWVp3/EOtfPh/C9fTqC+HtuUKRh79UJYLOECrGv5ciInH66dh75rmXfHvHkYUlNJf/wxXCtWsO20mTR89x0NP/yg3Z9iNOKYO1cb3nH+fCqfepqCSy6l/kstUEi66UYiD5lE+SOPUPXCC+FjpPCKKym59TY2TTgYb34++thYat5+G39lJQ1ffbW9xtDvDx8XAUcjhVdciTcvn4wnHif59tswZWVRcutt1Mx6t0UQG3S7KXvwQUx9+hA3cyZR06fjLynFtWxZuBa75rXXgFBliRDEX3yR9n2sXYszdL6MPu44vLlb8BYUaK0gxx1P/jnn0rhosRZA+nxEn3gCoAW6rrVrWxRWpM+Hc8kSomdMxzpmDI75v+FctJig04nt6KOIPessfIWFNP7+R3ge55IlbDv9dPLPOZctM45r0aWy6n8vUP7wI1S/8CKO334j+Z//JPLgCQDoY2Mx9umNu42KjeoXX8CzfgOp995DxlNP0ef994g+4XgC9fXh32d4/StW4Jg7l6Sbb6L3Ky+T+u87yZo1i74ffEDKv/6FY9487Z6eNng2biRi8PbbA4VeT8zJJ9G4YGGL2nTnkiXhoDPo8ZB31tnkHjMV5/LlWqVIIIBlrDbeSKCujmCoQN1U8w9gTE3t8N4tx7zf8JWUEHfOOdvzIwS2o47E+eeiVufNyqefQR8XR+83XidQV0fhZZex5bjj2gxWmwo/dR99DDodIiKCmlnvUP/FFxj79MYyejRJ116D9PnYNlO7zyz//AvC3dgCDgeeDRuxjtG2UR8bG25B81doLR3GULcfAFOvXh0OAtG4eBGmfv3CBQa9zYapTx88GzeE07jXad1pLcOHA2AdOwZdZGS4O6xnyxZKbrsd6+jR9HnnbdIeehD8fraecCI1r71OyS234lzedjfO2vfeI+h0knD55ehjY+nz1psMXrqEmBNPDKcx99dag9qqOKv/5lvMw4ZiGT2a0tvvwDFvHsm33opl+HCEXk/6448hzGYKL7uMQENDeD7nkiWIiAgsw4cTe/pMhNHIlhnHUXzD36l+7XUKLrqY6tdeb/d76y66PPiXUh4lpXxVSrnrHTx3TQbQvBhbFJrWZprQE4HrgYQO5u3MMhFCXCGEWCqEWFq5D0faaa6pNGqdMAGd2UzUtGOp//xztp58ClXPPUf+uedScPElbDlmKqX/uhPQfpjbTjkV+69zSbrxRmyHHUraww9hzc7GlJFB2v33oTObMffrF27ODzgcOOYv0GqRli7DkJwc7j9onTABX3Fx+ITsXLqUxt9/R7rd1L79DrqoKGyHHkrkoYdqNYKLFxNsbMTx8y/YjjgCGQjg3bqV5H/8HWNqqtZ0iRZIuEPrNw8eTNS0aXhy1uPNz6d21rtIj4e488/Hu3Urno0bca9dh7+8HNtRRwFoga1eT8NXWvN5oK4O5+IlRM+Ygc5iwTJyBEKnwzJiJIH6+nBtiGvlSoy9e2MdN46+X35B77feIuuD2USfcALmYUMpf+jhcCBuHjoUgsEWXWuCLheV//sf1uxsUu+7j8iJE0m8+ir0sbEUXnY5vsJCoo45BkNSEtZx47D/+ANBr5eiq6/CuWgx9l/nUnj5FS0u/M7FSxBGI5bRowHQmc30fvstBi5cQL+vviTodFLx2OPh9N7CAoy9e2vBP1otGmy/UTvurLMwJCaG01tGjwa/v93aDc+2bTh+/ZW4c85GZ7EA2gWz9+uvYYiLo/Suuwg0NFDzzixq33+fymefw9S/P72efYag3Y79518INjZS/I8b0dlspD/0IEIIUu+5G11EBCX//Gd4e/21tTjmL8C7ZQuBqiqs2dnoo6Mx9e2La/UaZDCo3Y8iJb2eeRp9bCy9nn4Kvc1G8d//TtWzz1FwyaXh0a+k30/Vyy9jGTtWq8UCEq++Gun14lq1itR77yH2tNOIGDSIpBtvxDFnTpvdaZo4fplD5Pjx6KOiMPfrS9SM6SAEiTdcj9DrsY7PRpjNOObPD88j/X7qPv+cyuefp+TOf6OPiyPmlFOIOeEEdNHR1Lz7Lo6587TWgxNPwjJ6NKb+/al9f3Y4OPCVluJcupToE45HCEHEoEEkXnkl9V98ScN334W/O+fy5diOOYbE665Dut1UPvMMpXfdhT4+DnPfvrhWrMA6aSJx55yDITkZ5x9/4NncNBb4QHRWK4bU1BY1/57Nm6n/+huCbjf1X36JsFiwTZmC0OkwDxiAZ9Nm/FVVePPziTx4otY/+7PPyD12GqV33aXVUC/8HdsRU4g58UStW56UFN94EyU3/xNT376k3nkn/ooKvNvysP+8vStFzRtvEDF8OMbkZDKeeoroadOofPoZnEuW4Nm4Ee/WrRgzM0EI4s4/n6wPZiN9Pqpff4O6Tz4lYsQIYs84A3Q6GkM1pqV3/RvXqlVkPPEEkZMmoTOZ6PW//2Hs05vyBx6g/MGHtq//zbfw5ReQ+p97ESYTUUcdCUYjBZddHv6Oql97PVzgMw8eTMSIEeiionCvXYdzyVJERASJV1+lHT8LFtDw9dcQavmoevEFGr75FlNWFhHDtAZm68EHg9/fIkByrVmrdceYOInIyZPx5Kyn9v33ERYLkZMmEXXsVPRxcdR9MDucp/zzLyBQW0fKnXeii4gIt4gEXS5q3n6bqKlTGbxsKQMXLiDh0ktaHOeWESNbFeoD9fXUvvc+0TNmEHfWWURPn4beZiPykENACBwLFmL/9Veq33gT6fdT/8UXiIgI4psFzU3izj2HqBnTqXzqqVYF5YDdjq+4GPOgwS3nOfNMhMFA1XPPhb6TNeSffwGFl16GY8FCGr7+Bs+GDUi3m6rnng8Hh5bRo8FgIFBbG66Bb17zb0hLxV9a2m6hv/a99zCkpBB19FEtpkcdcQTS4wmfN2UwSPWbb9K4cCEJV1xB5KRJDJjzCxlPP00w1I0HtBaJxj/+YOtJJ7Np/AQafvoJ+/ffYzvqSOIvvojGeb/hXLSImJNOQgiBKSuLuDPPxF9ejj4uDtjeCu5avhyCQazjxwM7BP+hG3+bAnkAY2ZmuzX/0u/HtXQZ1gnjW0w3Dx2Ce8P2gqNr7VoQAvNQ7XgVRiPWiRNpXLBAu+/n3XcRBgMZzzyNPioKU+/eZDz7DNHHH0/vN99AFxVFXRujhPnKy6l+8y1sU6a0KPjtyDygPwDeHe7jCTQ04F67lqgjjyL9sUexHX00SX+/gbjzzt2+/WlpZL7wP3ylpS0Kns6lS7GMGY0wmTCmppL28ENYRh1Eyp13Mnj5MqKmT6fiiSc6PfBIV+lRT/jtKlLKl4GXAbKzs/f5OIlSSuo++hhTVhamfv0ASLr2WhoXLIRAgF7PPE35o4/h/PNPhMlE/WefkXDF5dqFKhCg/7ffYOrdG9BK931mvdNi+eYhQ8LNi0XXXKt1k7n0Ehr/+IPIQw5BazwBa6i2yLl4MaZep9Lw1dcIq5WsWe9Q8cT/EXfeeQiTCcu4cQiLhcb58yEQQPp8xF90ISn/vhN/WRmWUDOjedAgdJGROFeswBCfoJ1kBgzAkJhI5TPPUHjFlfhKS4maOpXEK6+gdtYsrTY2EACdDtsRUwAwJCQQecgh1H/1FYnXX6d1TwkEiJp6TIvtjBip1Sq71qzB1Ls3rlWrsE6YEP5emmrDABIvu4zim25GWK1Yx48P3yTmXr8eyyjtodA178wiUFlF0n//G/6OdFYrCZdfTsXjj4duqjwegKjp0yl/4AEKL70Mz+ZcMl/SujsVXXsdxTf/k17PP4fQ63EuWoRl1Ch0ERHhvAghMCQkYEhIIP7CC6l++WWS/n4DhjStCduUmYm5bxYYjXg2bQROwP7LHMzDhmLMaFmWjZw4EUNqKkVXX4NlzBgyX3wBfUxM+POqF15AmEwtar5Au9Ck3PVviq+/gU0TDm7xWa8XX8B68MEY09Op/+wzGufPx7ttm1ZgaOqDmpxM2iMPU3Td9RReex3pjz5K/nnnbb84CRG+r8Ry0EgcC3/X+u6vWUP644+Fm4ENSUn0eXcWjvnzMffvT8FFF1P32eckXnE59l9/xV9SSuq//hXeHxHDhtH/++8Iuj2Y+/UN5zn+wgtw/Por5Q8+hGXMWKTHTcm/7sSYlkb6Y4/hryjHm5dH3AXnh+fJePxx/LfeGh41RBcRgXX8eBrnb78hrfSee6j/RCtQiIgI0h9/DL0tEoDY006j5s03aVywUGs9mDQx1JR+KaX/+hcVjz5K8m230fDtdyBl+NgBSLzmauxzf6Xiv08RNXWqNkRnMEjUUUdh7tePmJNOou59LRjs/fZbWEaNonHhwvDvN3LSJBy//RYuVJoHDgTAlJWFJ1Tz7964kbyZpyN9Pu3YKisj9owz0EVq+TcPGohj7rxwFxbL2DHYJh+udSkqKKCuoAB9XHyoBU/7bcaceCLR06dT+fTTOJcuI+WO29GFxl13rViBc/kyrQUhwoL9xx+14B3QR0eT9vBDOBYsoO7jjzGkpoFeT9YHs9FZreHfR/QJx1PzxhsApD38MHpbJBHDhuFashRPbi72774n8ZpriJ4evs0Lc7++9P3gA8ruf4Da994j7rzzMKalUvOWFoxETpyo5SEmhshDJtE47zesEyYQf/FFFF19DfY5c3CtWEns6adrhbMRw3GvXYsMBrGMGY1pwACMvXrROH8BvrIyIkaMIHr6NCqe+D9tX1577fZz6tgxYDTiXLwI2+GHAaGbrIXAevAETH2zqHzySRxz5hB9wgnhrjGxp/2N6jfepPSee6n74AOiZkwn/aGH0Fks+CsrqX71VXzl5VpLk91O3LnnoouMDO/L5iJGjqDhm2/wVVSEa45rZs3SamWvvKJFWkNcHBEjRmj9ukOFmqCzEft33xN11FFtLl8IQdr9D+BetZryhx8h6+OPwtvfFLSbdwgAjWlpxJ17LjVvvknsmWdQ/eJL6KxWEIKq554j6PFgHjiQqOnTqHr2OfSJCaDXYx4wQAuKa2sJNgX/zWr+Tb16EXQ6CdTUYEhIaLFO17p1NC5YoBXujcYWn1mzs9FZrTh+nYvtiCMouf12Gr78isjJhxN//nna8RIVRfS0Y/Fu20rlU9o12f7jj/iKizEkJ6NPSKD4+htC++80LKNHU//Z59r18bzzwutKufNf2I48EuvYMeRfeBGOhQtJvPpq7RptMISvP/rY2PDIbU0VIC2C/14ZWiHI0Rg+BzVx5+QQbGwMtzA3iRg8BPt33xNwONDbbLjX5WDq27fF/JGHHoLjl1/wbttGww8/YpsyBUN8fPjzqCOOIOqII7TX047F/u13BO+9J1yZ5Fi4kJJbbkV6vSTf8k86oo+OxpCcjCe3ZfDvXLpMKwhNmICpd28yn3+uzfkto0djO/oo6j76iKQbrifY2Ihn40YSr78unCbm+ONbnGvT7r8P5+LFVD3/PJkd3H/R1bq85n8fKgYym73vFZrWZhohhAGIQRt+tL15O7PMLqf1W4wk/sILwidNY1oaA375mQHz5hJ5yCH0/eRjBs7/jQG//Aw6HbXvzKLhm2+IOemkcODfnoghg/GXleFcsiTcrafmtdcJ1NS0qAExDxyIPikR+88/E3S7afjhB6KOOZqIYcPo/fprWk0Z2o1VkZMm4Zj3G45589BFRmIdOxZTr15Ys7PD2yD0eiyjRuFavgLPpo2Y+vRBZ7FgTEsj479PIkxGrNnjSL3nbgyJiVhGjcLxyxzsP/2MdexYDKGaEYD4iy7EX1ZGzRtv0vDNtxhSU8NdSMLbOWgQwmTCvWYtvrIy/BUV4T6vO4qaNo3U+/5D348/Quj1GDPS0UVH487RBrDy19RQ/fLL2I44Amt2dot54y++iLQH7qfvhx+EL4bRxx+HPj4e55IlxJ5+OrYpU4g64ghS/30njrlzKX/0UfzV1bjXr9dqA9vRFJTXf/21djOXlBh7ZyJMJiKGDMG5YgX+6mpcK1eGR4RoThcRQe/XXiX+4otxrVpFxf9tv7nRuWQJDV99TfwFF7RoLWgSPXUqqffcTdTUY+jzzttkffQhfT/7lKgjjkDodMSccgqNCxdS/8UXJF5zDZGTJrX8To88krQHH8D555/kTpmCv6yMmJmnoYuMJO6cc8IBfsSoUQSqqii55VbMQ4ZoXW6aMfXuTfy55xI5cSKWMWO02lVCtXbpadhCF54mxvT0FoE/gNDpSH/4IUREhNav+YIL8W7bhmPOHCr/+19tWEudjqijt3+HwmAIB/5NbIcfhnfbNrxFxTiXL6f+k09JuOxShqxdw+Ali4kO3cMCWgBvGTcWYTaT9tCDCINWdxNz6inEnX8+NW+9TeFVV1Hz5ptEHHQQpj59Wqw78eqr8RUU0PD9Dzjm/IohOZmIUHN86j13k3TjjWS+9CKRTa2DRx0VDpIjD5lEoLaW2tkfYEhNxZiiBXmmvll4t27TbnR99ll0VitpDz2ELlIr9Cb9/YZwHswDBxKortbuZTGbiRg+HPPAgQyYN5fBy5ehT0ig+uWXwWgMB9Cg1RYm//OfZM1+H8uoUVr/7pgYHHPn4s3dgnXsONIeeoisDz8g9ozTw/PpzGaip02j4aefqf/0UyIPPhhDfHyLgnHyzf/Uuk+dfz4xJ58EgHX8eFyrV1P73vtgMLSoDWwu8eqrEAYDte++S93HnxCorW0V7Kb95z8kXnM16Y8/hu3ww9EnJmr3Mbjd4b7SlhEjca9bh2f9eiInTNAKW4cfhuPXX/GsX0/M304l9swztS4WycnhAg5olQXWMWOw/zJH69Lo99PwzTdYs7MxxMVhHjSIyMmHIywW4i+8MDxf3Nlnaze0f/ABUdOnk/HYY+HgKnbmaRAMUv/pp9R//Q2G5GSs41ueo5pr6tvuDt1nE2xspPbtd7AdeSQRgwe3Sh9z0kng9yMsFizZ46h69jkC9fXhrkxt0dsiSbjqStzr1mk12CGeUPekttaTeO01GNPTyT/nXBzz5pFw+WUk3XwTrpUr8axfT/zFF2tdKoGGL7/ClJWFzmzGEKfViAcatOBfH9Us+O+rVZ7tOCJU/VdfkX/e+RiSkog7++xWeREmE5GHHYZj7lzsP/9Mw5dfkXD1VWS+9FL4d9wk/uKLMWZmUvPGGwijkYyn/ku/b7+hz9tvEXHQQcSceiq2yZMxxMXR/8cfGPDLzy0qYIRej+3ww7Tr5rhxuNes1VqbFi/BMmKEVgii7W4/zc/bTedTX3ERNe/MYtPESdS8+y6g3Y+GTheu/GoSMXRIi/3i3rCeiKFDW6SxHao907Xyv08RqK4mesaMVt9Xk5gTTiTodGKfo93X4i0qpuj6GzAkxNP3ow8xDxjQ7rxNzAP6h59e3MS5eLFW0Th61E7njznpJAK1tTgXL9a65knZ6prdnD4qirizz9bOT+10iewOuk3NvxBiDbC62d8a4EIpZfvjLe2aJcBAIURftAD9LGDHNsYvgQuBP4CZwBwppRRCfAm8J4R4EkgHBgKLAdGJZXY5vc1GnzfeaNVUKfT67a8NhnCpP/KQQ6gNjYqyYw1uW8yDtR98U//a/j//RPE/btROQs2DHyGIPeVUql97jbL77yfY0KDd2NUG2+TJOObMoe6jj4k+bgbCZGoznWXsWKqefx5/RUWLoDfqyCOJOvLIlss8+igqQ8Fq6n/+0/KzQw8laupUKv+r3UiYeP114UJGOP9GIxFDh+JauwbXKq2Pq2VU28G/0OuJa3aRFkJgGTkS56JF+CoqKL7h7wQ9HpLbGJZU6HTEzpzZYpohLo6sDz/As3FjuFYUtIu4Ny+PmrfexvHzL1pt7rFTd1xkmDElGWt2Ng3ffYclVLhpKtxZx4+n9p13tJuspAwXxnZk7t+flNtuBSmpefNNZMCPc9FifEVFGNLSSLj8snbXH3f22W1eHEErgHnztmFITSPxmqvbTNP8Yh13wflac/r997fYV9EzZlDxyKNIr5eU225F6Nqv44g+4XjK738A+y+/4Pzjz/CNzZ1hzMigz6x3KLnjDnRGE2kPPUjN2++EnzNgO/JIjKGH5rQn8vDD4eFHaFywAEdoZJzEa69tMw/6qCiy2niGgRCC1Dv/hSmzF+UPPwJSkvbgA63SRR19NKb+/al44gkCtbXEnHpK+LvRWSwk7hC4NmedqBXEvFu3at2XQiIGD6Hu/dm4V6/GMXce8RdcQOzfTiX2b6e2WkZTjWPDV19hyR6HLvSbbgquEq+9hvL77ifuzDPDgWhbhE6HZfQo7D9pI4JZxo5Bb4tssyAec8rJ1H30EX6ns0VtXRNjSjK9nn225baOz6bmjTeofe89oqZObVEr2ZwhMZHo446j9t13tRrVceOwjh3bcvmpqSTdsL0AFH/euVQ+9TS66GgiD9Nq6q0TJlD9yisARB4+OZTuPOo++hhDfDyxp56KzmKh3+efIX2+VrXj0SccT9nd95B75FHhMeiTbtSeUiuEIPP55wk4HC0qO4wZGfT76ku8efla97NmvxFT795EHjKJyqef0fJy0UUtrhU7ihg6FPR6XGvWEHXUUdR++BGB+vp2j6e4s8/a3lInBAUXaoMpND+vtSXmhBOoePwJat+fHS44uTduRBcVhWGHQjVo172M/3uCon/ciLFXhrYdRiPe3C1Iv5+YU07W7kULDSjQNFiAPjZOq/l3tO72Y+6vBf/erVuJDAW+gYYG7R64wYNJf+LxFt9zc1HHHov9xx8pvv4GTAP6k9SsBac5ndlMn1mzcK1Yju2II8KFVb3NRt8PW3aB0bVzXWxiGTOamrfewrliJa5160i4+OLt30/z4L+qEn1sbIvrbNN1wbVyFRVPPol0uSi//wE8mzfT8NXXRB19VKvWD/MQLRZwr9+AecAA/CWlRJwzpEUaY+/eGPv0xv7TT+iio7FNmdxu/q3jszEkJdHw3XfEHH881a++Aj4fmS+/3KoipT2mfv2p//RTpJTh77tx4UIso0eHW8I6Yps8GWG10vD9D+giI7VCw6iOCw1xZ59F9csvU/P226TefXen8rnPNd0A2dV/QDxwBHAD2k23dcDCPbyO49CeKLwFuDM07T7gpNDrCOAjtBt6FwP9ms17Z2i+jcCMjpbZ0d+4ceNkd+dcsUJuGD9BFt9+R6fS+2trZc6w4TJn8BC5debpUkopgz6fDAYCrdL6amrkxsMOkzmDh8htZ58jg8Fgm8v01dTIDWPHyZzBQ6Rj0aJ21+34/XeZM3iIzBk8RFY8/3yH+fTV1MjcadPltrPPkQGXq/V21NXJwutvkEX/vEUGGhvbXEbp/Q/I9WPGyrKHH5HrR4yUAY+nw3U2VzP7g3Be148eI+u//6HT83Yk6PfLgquvkTmDh8j8iy9p9zttUv3uuzJn8BBZ9M9bZM7gIdJfVyellNK5alU4f1tOOGGnywk4HHLzkUdp+/Lcc2XVG29Ib3n5Htmmv8q5dq10rlq103S+qqrwsZszdJj0lpX9pfUGGhtlwdXXyC0nnyI9BQU7TR8MBuWmI4+Umw47XDuGn3vuL63fvWWLdG3Y0O7ndV9/Hd7HHaVry5YTT5I5g4dI+2/zw9Oca9Zqv/szzpA5g4dI9+bN7c4f9PnC66588aU207jWr5dBn2+neal84YXwstr6LYfXGQzKkn/fJfOvuEIGnM6dLldK7TzQtOyOzj1Sat/3+lGjZc6QobJx2fKdLjvo8cjqd9+VjYsXb58WCMj8Sy6VJffc0+I35ykokH67fefL9Hpl0c3/lFv/dpose/QxWff11zudZ2ccf/wR/g68xcU7Tb/l5FNk3nnny4DHIzcddrjMu+DCTq8rGAzu9FzTpPT+B+T6ESOlr7paW+8JJ8j8iy/p9Lra4i0rl5UvvBg+DxZed73MPf54WfvZZzJn8BDpyc9vkdcN4yfIkn//Ozyt/ocfZM7gIS32aVuCPp/cetpMuX7kQbJx2bK/lOfO8paWypzBQ2TBlVeFfru/hT+rfOll7ffjdIa3uUV+PR65/qBRMmfoMJkzeIh0rloli266WeYMHiI3H3tsm8dFMBiUGw+eKEv+fZd0/Lmo1fmiSe1HH3V4Hmiu9MEH5foRI6UnP1+uP2iULL7zzl36Dmref187jktKpJRSutatkzmDh8jqt97u9DKKbrxRbpw4SeZOn9HpY7v49ju06/w338iA271Led5TgKWyvXi4vQ+6+g+tdv2trs7Hnv7bH4J/KWWnT8ZN8i64UOYMHiJr3ntvp2m9JSWy9uNPpL++vsN0rpycnZ4kAw7H9mAmJ2en6w76/bu8bc3Vff65zBk8RG6ccLDcesYZuzRvwO2WRf+8ReZddFGHQdLuCHq90vHnok6dZHwVFTJnyNDwSby5socfkZuPPVY2Lt95ICOlVvBzrl79l77TrlZ43fUyZ/AQWfj3f3TJ+suf+D/tmDp44k5/E3tC7aefSfv8Bbs8n7+hQTr+bBkMBz0euWH8hHBhfmfqvv5a5l98STjQ2l3e8nKZO+O4nRb4d5dr3TpZ/+OPnctLSYn0bNu2V/LRlRyLFkn3li2dSlvx9NMyZ+gwWXr/A1qhaeHCvZIn9+bNMmfwEFny77uka8MGmTN4iKx69dU9uo6Su+6WGw85VFa/9bbMGTxE+mpqWnxecMWVMnf6jPD70vvul+tHj5HBTlQEBf3+v3zs76pNU47QKpxGjW5RqdVUGeUtKZHbzjlX5p1/Qat5t515lvbbPuvs8DR/XZ0M+v3tri/vggvl1tPPkNVvvaV9f5WVbabr7PfgXLlSu8aHrlm7eu10LNpeCAk0NsotJ5wgN046JFyA7IymAl7O4CGy+p1ZnZqn6fjMGTxEVjz9zC7leU/pKPjvTt1+Bkkpw0OhSCk3CyHa7lOh7HVtNUd2JP3RR3CvXbvTplvQ7jeIPW3nz2/bsa9gW3SRkWS++irutWs6lb6j5uvOiAj1bw3U1xMzdtwuzaszm8l4fPefdtsRYTS2uOG4I4akJKzZ2TiXLGnVdzHl9ttIuf22Tq9XHxuLJTZ2V7La7aQ/8jANR0wJD2e6ryVdfx3G9DSsEyeij47e6+uLPfWU3ZpPHxXV6hgTJhMJl15Kzdtvk3zTjTtdxo43x+0uY3Iy/b75epfPU50VMWxYeESdnealk90P9jeREzp3PgGI+dtpVL/+BrWzZmGbMgXrDvfr7CnmAQOIv+giat58k7qPPkJYrUSfcOLOZ9wF+rg4rc+/XRveUR+6wbyJddJEHPPm4dm6FXO/fjQu+hPr2LHtdk1tTuj1Lfrn7wvRM2ZQ88YbRB11ZLi/P2jnbtBGtwtUVWEe1vr6mXzLPym7/wFS/vWv7fPtJP8Rw4ZR++67ODPS0Scmtnn/V2eW08QyahSRUybTOO83oqZP71Q//xb5Cd0P4l63FvvPP+PJ3ULmq6+0252vLbYjjsA6caJ23Q91Pe3MelPvvRfX2jUddoXtKt0m+AdeEkL0R+s7vxqtC85aIYRVStnx89SVLmdMTcWYmtol67Yddii2ww7dJ+tqGtscaDUa0P4k7YH7qXrpZZJv3HnAdqDTRUYSe9ppXbZ+YTK1ex/E/iDxyis6vF9gb9lbgb+y60y9Mujz5hu4Vq8m9rTT9uq+Sb7tVmyTD8e5bDm2yYeHbz7fU/SxsRAI4CspQVgsrUbuiTn+eCoef4L6zz4n/sIL8OZuIebkbvl4H0AbHcqYkdEqaA0H//X1+GtqiExoHaRbx42j3+cdPyyt1Tzjx1PzxhvYv/ue6OPav5l3V/R65hmcf/65W4VKfUwMpgH9qZn1LoHqauIvuCB803Fn6Uwm+rz5BjIY7PAesh3FnXUmcZy5q1neJ7pN8C+lPBJACNEbGAWMDv27UggRlFIO6WB2RdknhE5HxlNPEWxsxBJ6WMr+yNSnD+kP7al76RVF6ekso0eHh4Ldm4QQRB5yiPbMgL1AHxcLhJ5+3myknyaGpCRshx9O/eefYx6o1ULvOORld6K3RRLfxohVTcG/v6KCoN2OIaHzNeEdaT4CUNSx0zpI2Xk6s7lTvQraE3XkkVS/8irCaiXx2mt2ezm7Evh3d90m+G8ipSwACoDwc52FELb251CUfav5mN+KoijKgaNptB5vYSG6NoJ/gNgzz6Do6msoufU2dDZbp7uIdSdNwb9ny1bt/Q4j9+z2cm2R9HnnbTy5uUSFRlDqaglXXknQ7SHmxBP2SdfK/UG3C/7bIqV0dHUeFEVRFEU5sIVrxMvK2m3JsE2ZQsSwYbhzcog55ZRODw/cnehjtT733q3aMwt2HLbzr7COHx9+knB3oLfZSL3zXztP2IMcOG0YHRBCxAshfhJCbA792+ZAvEKIC0NpNgshLmw2fZwQYo0QIlcI8YwIdWgUQtwrhCgWQqwM/R23r7ZJURRFUZQ9S99snP72av6FTkfmSy+Seu+9JLXx/Ij9gc5sRhcZiXujNs7Kngz+le6vRwT/wO3AL1LKgcAvofctCCHigXuAg4EJwD3NCgkvAJejDT86EGg+LMh/pZSjQ3/f7sVtUBRFURRlL2oe/Hc0Io0hKYm4s87c56P37EmG1FR8BQXAnuv2o+wfekrwfzLag8MI/XtKG2mmAT9JKWuklLXAT8B0IUQaEC2l/DM0burb7cyvKIqiKMp+TGezQejGTmNa14xgt680HylJ1fz3LD0l+E+RUpaGXpcBKW2kyQAKm70vCk3LCL3ecXqT64QQq4UQr3fQnegKIcRSIcTSysrK3d4IRVEURVH2HiEEBIOAVjN+IDOkaNuni4lp8QwA5cB3wAT/QoifhRBr2/hrMQBvqPZe7qHVvgD0RxuWtBT4v7YSSSlfllJmSymzk5KS9tCqFUVRFEXZW4xp6V2dhb2qqWXDmH5gb6fS2gET/Espj5FSjmjj7wugPNR9h9C/FW0sohjIbPa+V2hacej1jtORUpZLKQNSyiDwCtq9AoqiKIqi7Kfizj8fAFNWny7Oyd4Vdaw2FOeB+pRqpX0HTPC/E18CTaP3XAh80UaaH4BjhRBxoe47xwI/hLoLNQghJoZG+bmgaf6mAkXIqcDavbUBiqIoiqLsfSn/uoP+33+HuV+/rs7KXhUxZAi9/vc/0v5zb1dnRdnHhNYL5sAmhEgAPgR6A/nAGVLKGiFENnCVlPKyULpLgKbBYB+UUr4Rmp4NvAlYgO+A66WUUgjxDlqXHwnkAVc2u7egvbxUhvLQ1RKBqq7OhLLXqf184FP7uGdQ+7lnUPv5wLev9nEfKWWbfc17RPCvtCaEWCqlzO7qfCh7l9rPBz61j3sGtZ97BrWfD3zdYR/3lG4/iqIoiqIoitLjqeBfURRFURRFUXoIFfz3XC93dQaUfULt5wOf2sc9g9rPPYPazwe+Lt/Hqs+/oiiKoiiKovQQquZfURRFURRFUXoIFfwriqIo3YYQ4hwhxFIhhEMIUSqE+E4IcdgOaX4RQkghhKGr8qkoirK/UsG/oiiK0i0IIW4CngIeAlLQns3yP+DkZmnOBYxdkT9FUZQDgerzryiKonQ5IUQMUAxcLKX8qIM0S9CetP4HYJRS+vddLhVFUfZ/quZfURRF6Q4mARHAZx2keQh4ASjbJzlSFEU5AKngX1EURekOEoCq9mryhRDZwKHAs/s0V4qiKAcYFfwriqIo3UE1kNjWTbxCCB1a3/+/q24+iqIof40K/hVFUZTu4A/AA5zSxmfRQDbwgRCiDK3fP0CREOLwfZM9RVGUA4MaJk1RFEXpclLKeiHE3cDzQgg/8CPgA44BjgLSmyXPBBYD44DKfZ1XRVGU/Zka7UdRFEXpNkJDed4IDAXswDLgQSnl783SZAHbUKP9KIqi7DIV/CuKoiiKoihKD6H6/CuKoiiKoihKD6GCf0VRFEVRFEXpIVTwryiKoiiKoig9RI8K/oUQ04UQG4UQuUKI29v43CyE+CD0+aLQTWUIIaYKIZYJIdaE/j2q2TxzQ8tcGfpL3oebpCiKoiiKoiid1mOG+hRC6IHngalAEbBECPGllDKnWbJLgVop5QAhxFnAo8CZQBVwopSyRAgxAvgByGg237lSyqWdyUdiYqLMysr66xukKIqiKIqiKG1YtmxZlZQyqa3PekzwD0wAcqWUWwGEELOBk4Hmwf/JwL2h1x8DzwkhhJRyRbM06wCLEMIspfTsaiaysrJYurRT5QRFURRFUZQ9qqrIzrr5JRw2cyB6Y4/qANKjCCHy2/usJ+31DKCw2fsiWtbet0gTGju6HkjYIc1pwPIdAv83Ql1+7hJCiB1XLIS4QgixVAixtLJSPY9GURRFUZSu8dv7m1g7r5jVvxZ1dVaULtKTgv+/TAgxHK0r0JXNJp8rpRwJHB76O3/H+aSUL0sps6WU2UlJbbbAKIqiKIqi7FUyKKkosANQsrm2i3OjdJWeFPwXoz0Svkmv0LQ20wghDEAMUB163wv4DLhASrmlaQYpZXHoXzvwHlr3IkVRFEVRlG7FXusm4AsCUFXk6OLcKF2lJwX/S4CBQoi+QggTcBbw5Q5pvgQuDL2eCcyRUkohRCzwDXC7lHJhU2IhhEEIkRh6bQROANbu3c1QFEVRFGVP87j8XZ2Fva6+3AVAnxEJOGo9eJy+Ls6R0hV6TPAf6sN/HdpIPeuBD6WU64QQ9wkhTgolew1IEELkAjcBTcOBXgcMAO7eYUhPM/CDEGI1sBKt5eCVfbZRiqIoiqL8JcGg5KfX1/Hqjb+xdeXO78tzN/r224JCbbkTgN7D4wFoqHJ3ZXaULtKTRvtBSvkt8O0O0+5u9toNnN7GfA8AD7Sz2HF7Mo+KoiiKouw7v3+ay6bF5QAs/yGffqPbvzdv/e8lzH13Izq9YNplI8g6KHFfZXOPqKtwYjTrSe0XA0BDtYuk3lFdnCtlX+sxNf+KoiiKoijNbVtdxaqfCxl5ZC9GT+1NVaGDgD/YZtraskbmztpIar8YYlOs/PRGDi67dx/n+K+pL3cSm2IlOtECgL1a1fz3RCr4VxRFURSl26srd/LFUytY+XPBHlme3xtg/gebiEuL5NCZA0juHUXAH6SmtLHN9Mt/LEBn1DHt8hFMvWQ4Pk+AxV9t2yN52VfqKrTg32w1YIrQq24/e5EMSn56Yx1FG7vfqEoq+FcURVEUpdtb8PFmijbUsvDjXIo3/fWAatkP+dir3Uw5axB6vY7YFCsADZWuVmkDviC5yyoYmJ2MNdpEfFokww9PJ2dBCY7a/SOADviCNFS7iU22IIQgKtFCQ3XrbVU0XpefivwGZFDu1vz566rZtKicxrpdfh7sXqeCf0VRFEVRujVHrZuCtdWMOiYTS5SR1XP+2gOq6iqcrPihgIHjU8gYHAeALd4cWlfrYK10az1+T4C+o7bfDzBmam8k/OW87Cv1lS6QhAs50QkRe6zm3+8L4N1Pb4Jui6PWw3v/WcRHDy9l7rsbdmsZK38uJDLWzIDs5D2cu79OBf+KoiiKonRr638vRUoYOaUXgyemkbe6isb63atRlVIy/4PN6AyCQ2cOCE+PiDSiN+qwt1GTX5pbBwIyBsaGp0UnWhgwNol184v3i8C3rkIb6Wd78G/BXu1Cyt2r2W7SUOVi1r//4PVbFlCYU/OX89nVgkHJz2+sw+P0kXVQIjkLS6nIb9ilZVQW2ineWMtBR/ZCr+9+oXb3y9FeJISYLoTYKITIFULc3sbnZiHEB6HPFwkhspp9dkdo+kYhxLTOLlNRlO4tEAjudrOu0prPE6C+0qW+U2WPCQYlOQtL6DUkjpgkC8MOTSMYlGxaVL5by9u4qIyCddUcfGI/ImPM4elCCGxxZhw1rQsVNSWNRCdEYLK0HCRx9NTeeN0B1i0o2a287KpAIMiKnwr447MtuBt3bYz+utAwnzHJWvAflRiB3xvEZd/9sf593gDfvrgGjzuAwaRj/keb/3Jhoqst+y6P4k11TDl7MFMvHobBpNul/Sul5I/PtmCM0DP88PS9mNPd1+VDfQohngfea/7wrL20Hj3wPDAVKAKWCCG+lFLmNEt2KVArpRwghDgLeBQ4UwgxDO2hYMOBdOBnIcSg0Dw7W+Z+we/VLtjRSRaMJj1A+AcshOjKrLUpGAhSV+HCaNZjizPvVh49Th9VhQ4SMmxE2Ix7IZc7J6WkeGMtW1dWkZhpY8ikNHS6zm+L3xvA5fDhdfnR6QWxKdYu319SSoJBuVu1HfvymHM7fCz4eDObF5djjNAz5tjejD6mN3rD/l8nIqXE2eDVajJ3Y3sCviDVJQ5scRFYo02dW2dQsuKnApZ8sw2/N0hCho2plwwjwmbk909ysde4mXhyP9IHxrWat3xbA1tXVlJf4cQYoafPiET6jUlq97cgg5KSzXX4fUHi0yN3+xzQXfh9AfR6HWIXfvs1pY0U5tRgiTYSm2wlNsWKKWL3LukNVS4WfbUVe7WblKxohh6STnx6ZIfzeJw+CtfXUllgx2DSkTksnpSs6Db3g9ftZ/3CUgrWVVNb7kRv0BERaUCn1xGTbGHCCX2xxUW0u67C9TU4ajwc8jetlj4uNZLkrGg2LSljzLG9d3lbf5u9ifSBsYw8slerz22xZpwNbQT/pY3Ep9taTU/uE036wFhW/1rIqKN6odvJeS8QCJIzv4TNS8tpqHLjafSRmBnFoacPILVvTIfzyqDk5zdyyF1aAUBFfgMn3TA6fNz4vAHqyp0kZtjaPJbqyp1Yok2YQwWYphF/GqpdO/2d15U7KVxfg88bwNngpWh9LV63H587gNvp44TrRuFq8PLLW+spza1r83e+Mz5PACklRrO+3d9zwBekttxJTakDk9lA+qDY3T7u21KYU8OSr7cx6OAUhkxKA6DvqCS2LKtg8pmDOnU+XfVLIYU5NUw+axBma9fEFjvT5cE/sAl4QgiRBnwIvC+lXLEX1jMByJVSbgUQQswGTgaaB+onA/eGXn8MPCe0I/BkYLaU0gNsCz0EbEIo3c6W2S34fQEMRi2oL9tWT9H6WmzxZmKTrRSsq2bN3GLcjT6MZj0DxiVjjjSyaXEZfm+QEZMzGH54Orb4CHQ6QSAQpDS3nqpCO35vkGBQgpTojToiIo2YLAYa6zyU5zXgdfkxW41Yo01Yo00k9LIRFR9BQ5WLygI7NaWN2KvdNNZ7sMVGkJRpI7F3FBmDtBoeQBt9oaSRygK79ldop6rIEX5EeWSMifRBccSlWknIsNFrcFy4dsbl8LJtZRU+T4DETBuRMWaqSxzkrakmd0k5fl8QISBtQCyp/WMwWw3Ep0USlxqJ1+2nsdaD3qgjJslCeV4D5VsbMEcasEab8HuDeFx+gv4gdeVOqoocxKdHkjUyESklXneAtP4xpPTVLohuh4/CDTWUba3H6/SD0B6vXlXoQGcQBP2SnAUlTDl7MImZNoQQ2snVEwjXTvl9AfLXVJO3uorC9TU01rccZi46ycLoozPpPTwBg1FHMCgJBiRCQDAgqSpyULalnuoSBxE2I5lD48kamYjb4WP9H6UU5mgn98gYE7a4CGKSLMQkWYiKj8Dj8uOo9eCs92CyGOg9PB5LlIktyysp21KHJdqEQLBtTRWOajcDx6cwbkYfAn5J+bZ6An5Jcp8okvtE4fMEKFxfS/7aKurKnQT8koA/SH2lC4vNyNBD0xl8cCo6vfa9uRxeAn5JhNWAx+WnutiB2WrEYjNSkW+ndEsdHqefgD+I3xMAIcg6KJGDjuxFfFokgUCwRWEkb3UVc2ZtwOPwMfzwdOy1Hv78fCubFpdz1AVDScmKbvUbaipwVuQ1sGV5BdUljQR8QSSg0wkSMiJJ7RdDMCDZsqISR42bXkPiGHlEL1L6RqM36NDpBT5PgKpCB431HoQQ1JU3UlfuIjHTRkKGjS0rKinMqSYi0khUgoVgIEjAHyQmyUpa/xj0Rh1CgNlqJKl3FEazHketm+KNtTjqPDjrvRRtrKWmpJGISCMjpmQweGIqznov9moXkXHa77hsaz11FU5kQCtwxWfYGJidTH2lizlvrw/3BY5NsZIxKJbEXjb0Rh0Go55eQ+Ow2LYHC1JK5r2/kXXzS+g3OomMwbEs/TaPDx9egk6vQwYk5kgDXz6zipNuGBUODIKhYGbzknJ0OkFMsgWXw8eGP8qITbEydlofBk9MDRcC3I0+NvxRytp5xVr/5ZDIGBMZQ+IYe2wf4tMjcTZ4cdR6EAJ0eh1+XwCfO4DH6aehykV9lQu/J4DQCSKsRvQmHe5GH26HD4NJx7BD0kkfGEtloZ1tq6uoLnIghCAuzUpEpJHKQjtBv6T38HiyDkrEbDFQnmencH0NxRtraazzIHSC5Kwoeg2OC/2WA8SlWEnuG40pQk9VoYOchSVsWVGJq8GLwaijz8gERh+jBbN5a6rQ6QRxaZFahYyAtP4xmK1GchaW8Nv7m1oMSak36BgzrTfZx2WFj/WAT6slLs9rwBShR0ptuEqdXkffgxIYMC6FygI7897fSDAgSciIZPXcIlb9UsjYaX2YcFK/FgWwQCDIht9L2bS4nNIt9cigRKcXBIOSxV9tI21ADIec1jKIrS1r5Iv/rqCx3ktcWiRp/WMI+CUep4+AP8jmJeUU5tTwt1vGERXfdgFg1c8FWKKM9GvW337wwSnM/2Az1cVaBU5nNB1vAjj6oqFtFi4jbCaqix0tpgUC2jk+a2TbY/qPPiaTb19Yw5YVlQzMTml3/T5vgO9fWkPBuhoSM21kDonDbDWSu7yCTx9fzsEn9aXvqCQCviBuhw+fJ0BUQgSxqVZkQDL3vY3kLq1g0t/6Y7YYmPvuRlbNKWT0Mb1pqHLx5TMrqa9w0X9sEtMuH9EqgK6rcBKbbAm/j07Qvm97lbvDgse2VZV8//JagqFzhU4vSBsQS2KmjaA/yIDsFPoMT8AXGj0pZ0Fpp4N/j8vPpkVlrP+9lMpCO0hAgClCG43IaNbj9wXxeQJaeqe/RauiyWJg1FG9GHNsH4xmfbvraahykbOwhMY6T/jZDEu+3kZjnZcRUzIYdXQmlQV2vn9lLfHpkRxxzpDwvIMmpLB5STn5a6s7fAZEMBDkzy+2suLHAvqOSmTE5IxOfQddQXSX5hkhRB+02vWzAAvwPlpBYNMeWv5MYLqU8rLQ+/OBg6WU1zVLszaUpij0fgtwMFqB4E8p5azQ9NeA70KzdbjM0PQrgCsAevfuPS4/P39PbFKnBYOSd+/+g9gUKz5PgNLc+lZpsg5KZMDYJIo21pK7rIKAX9JneDx6g44tKyvDP0izxYDPGyDo3/lxY4s3Y40y4W704bT7tKBsB9GJEUQnWrDGmLBXu6kqcuBza+kyBsUSGWsmf201HqfWn9IUoScxM4qk3lEkZtrwuQOUbK6jbFu9dpOWRAuMIo0YjDocddq0HRnMegZNSCFrZCIVeQ3aBb7Y0Wba5vRGnXbBbZ5OaCfRhAwbFXkNrQJyW5wZY4SB2rJGkNq6I6wGpNS2f2B2CkMPTWPrikrmf7gZt8OHJcqIMcKAvcqFlJCQYSMuzUphTg0epx9zpIHMIfEkZtqw2EyYLAY8Th/rfy+lfFvHfRP1Rh0JzQKkJjqdIGNIHBFWA431Xuw1bhw1bnY8RQidaNWlwxZnxu3UCkK9hsRhi4tg46KycAGtxXdv1OEPTTdbDSRmRmEw6dDpBNEJFmrLnRSsq+54R+yQn5SsaCJjTOj0AoNZj9cVIH9NFX5fMFywSuodxZBJadSWNbJ2XjEJvWwcc9FQEntpD7jJW1PF3Hc30ljnITbFit6oAymRUqtxa6hyh4OtqPgIUvvHaBcboQVZTYVZAaT2jyE+3caW5RW4HTtvUrdEm3A1eMP7p/ewePzeAPYaDzq9QG/QUVvWiN/b8vsUOkFEpKFFs70xQk9iLxtZIxMp21rPtlVV7a7XGm1CZxDIgGxx3EYnWZhwfBZOu4+STbUUb64L/y4BdAbBsEPSGTu9D5GxZua9v5Gc+SWMndaHiaf0QwiBs8HLkq+34fMEGDejDxGRRj59Yjl+b4Cz7zkYU4SBZd/n8efnW8k+LosxU3tjshiQQcnWlZUs/S6PqkIHyVnRjD22NwXrqtm0WCuwp/WPYcSUDKLiI6gsdFC2tZ68NVX43AH0Bl2747Q3iYg0YrIaCAaCeBr9+H1BIiINRNhMOOs9eJx+7bceqhyIS4tEBiV1FVp3Jmu0dqw1FTCETmiBkYDk3lHEJFnw+4KUba1vs0uFwaTD7w2iN+joOzqRhHQbjfUeNi8pD5/rhE5oLWHNfmo6gyAh3UZlgZ3MoXEcce4QfJ4AdRVOtiyvZPOScpL7RDF2Wh90Bh2LvthKdbGDuLRI/F5t/8WlWvE4/S3OE8l9ojj2suHEJFlx2b388dkW1v9eSr8xSUy9eBh6oy40ys5mqosbiUuLpN/oRPoMTyC5bzR+T4CNi8pZ/n0eTruP7OOyyJ7Rh4YqN589uRwpYcaVI0nr3zrArCy08/n/LccWH8HM27JbBXCbl5Tz42vrOOS0AYyZur2W39ng5c3bFzJmam8mndof0IL0ovW1xCRZwv3am/vjsy0s/yGfYy4exuCDU9s8Nua9v5HNS8u57P8mh6fVlDTy/n2LOOaioQyemNZqHhmUvHvPnxgj9Jx+x/g2CxX2GjffvbiGykI7U84ezPDD08PBudfl55e317N1RTtPFxaEj4OJp/Rj7LQ+AHz34hry11VzyN8GsOz7fIL+IH1GJLBpcTlTLxnGoAnbt1FKyWv/nE//0Ukcef5QQKtpf/nv8zj4pH5kH5fV5qorC+x8+sQy4tMimXb5CCJs2rW1vRaOee9tZP0fpVz86KGYrUbKtzWw8ucCGus8eN0BGus9RMaY6T0sHpfDS+7SCvy+IImZNvqOSsJg0uFzB/C6/XhdWuWXwagPHxcRNiPx6ZHEp0XicvhYM7eIrSsqscaYmHhyP9IGxFJZYKdkUx3VJQ48Tm0Z9ho3AkLXSu03FpOsHSf5a6rD33FcWiQnXj+qRUE0EAjyxq0L6DM8gamXDG9zu50NXn58bS3FG+sYPjmDyWcO3Gkr0N4mhFgmpcxu87PuEvw3J4QYA7wOHCSlbL8ot2vL7LLgv7ns7Gy5dOnSPbFJnebzBlj2XR6bFpej0wtGTM5g6CFpNNZ5qS1rJKlPFNEJlhbpZVCGm9LqK13kr63G5fDicfgwmLSnA6YPjMUYoddOdKEAqKkLiiXK1KoZ0ePyU1Vgx1HnISohgsQMW6v+k9oF1smWFZVs+L0UnydA5tB4+oxMIKl3FDGJlnabxv3eAOV5DRRtrMVl9+Hz+IlNtpJ1UCKRMeZwS0RMkoWkzCgtuNuBu9FHTUkjdeVOzFYDkXFm/KE+zPHpNlL6RhMMBHE2eLWaCYuhxYleSkl9hQuDSYfeoCN/XTV5q6sJ+IMk94kic2g8yX2i2j0puBxetq6opDyvAZ9Hqyk0mg1sXVmJvdpFr6HxDJqQQq8h8W1eYKSUVOTbqS52IIMSoRPb0wmIT4skIcOG3qBDSkl1sYP8tdWYIgz0G5PUov8raK0u9mo39hq39n3EmrFEmXDZveSvqcbt9NFnRAIJ6bZW3X0ctW62rarCHGkgJSsag0mvFdS21mOxGckYFEdK3+g2v4vaskaKN9UhBFhsJiKitO4rHqcPvUFHUmYUXrcfZ4O33e4OLoeX9QtL8Th96PS6cA0uAkYdmcmkU/u3OgY8Lj/r5hdTtqUeGSpICp1ACC3gT+il1c6316zudfnDtVagtdRsW1WFo8ZDwB/UWiAMOhJ72YhOsBAMBolOsGCyGLDXuKkqtJM2IJaIyNZNxQFfkLoKZ6ilDRrrPeHgMjbFSu9h8cQkWTCYWp4yq4u14Dgy1kxMkgVHnYeAN0hK32gsUdt/o/WVLjYvKcdo1jP00LQW32kwKHE1eAkEtP7BOQtL2PB7KQCRMWbsNW7GTe/DwSf367D7TWluHZ8+sZwxU3szempvZt31B72GxHHc1Qe1SiulZPOS8nCB2GDUMWhCCiOO6EVSZusnkrodPtYtKMbd6Cc6IQJbfAQC7Rg2mLTgwWTRE5VgCXd7aL6upnz7vQE2Ly2nIt9OSt9o+oxICLdy+DwBAv4gZqs2f1Whg22rKgmECpe9hsS12HcyKEPdXAQGk56a4kbK8+pxN/qJT4uk3+ikFl0OPS4/easq0Rl0WuudQUdtuZNgIIjPHWDrykqKNtaSdVAiB5/Yt9VvJ3dZBQs+2hweWtAWZ2bK2YPbfAptQ7WLgrXVmCON9B+T1GpZq34pZMHHmzGZ9eiNOlx2H7Y4M4efOYi+oxLb7t7j8jNv9kY2LSonMdMWzsfJN44hoY0uM00K1lXz1XOrGJidwtRLhoWXvW5+Mb+9v4nkrGhOuXlMq66EXz+/iop8O+c/MAm3w8ePr66jbGs9QsC4GVkMmpDCtlVVVBXaCQYlW5ZXMuywdI44d3C7x+nir7ay5Js8rn7+iPB3krusgh9eWcsZ/xrf7tNwNy8t58dX1zH00DQOOXVAi/1akd/AN8+vxucNMPWS4fRtY39IKakssFNbpj2BNyLSiMGko6HKrVUqCOg9LIGUvttbJV0OLx8/spSGKjdR8RGccP0o4lKsfPjwEjxOP+feOzF8jmuodvHOnX8w5exBjJiyvbvTO3f9QVIvG9OvHNkqT/YaN588tgwhYObt2a2uD22pLLDz4UNLmHL2IOLSIvn62VUYI/TEp0diNBuwxpioK3NStrUevUHHwAkpDD8sneQ+rVtbO6t0Sz0LPtpMRd72Aq0xQk9SZhQRNiNGk56YZAtDD0nDGm2icEMtfm+ArIMS0et1VBU5yF1aTmSsmcETU9u8nsx5ez25yyu45PHDwr0oQPuNb1lRyYKPNuNu9HHEOYPD3YW62n4R/AshDMAMtJr/o4G5aDX/X+yh5U8C7pVSTgu9vwNASvlwszQ/hNL8EcpPGZAE3N48bVO60GwdLnNHXRH8K4qiXVxrS7U+5e11L1A6z17jZtWcQhoqXfQfm9xuTeqOfnl7PZv+LCM5K4ryPDvn3HNwm7W0TZq6eSWkR3bb/rPdScAfpCJUeZA+MLZVYXBXFG+sZfOyCgI+bVkDx6e0CHzas2lxGUu/zSPCZmTKOYM7DPybLP02j0VfbmXIIWkMnpDCugUl5C6toPfweI69dHib+76pMJnSN1orGPslh5w2gLIt9WxcVBZOZ4s3E/AFGZidwqEzB3RYI7tmbhG/zd7ExY8dFq7AWvpdHou+2MoVT09pt2uJlJI/v9jK8u/ztaA2O5ns47OoyLcz5+31WGwmjr/uoE59F7vC5wlQWWjXugCG9nVBTjVfPbOKw84YyKijMgHYsryC719ey2m3jWvRxefH19ZRsrmOCx8+pEWByF7j5vMnl+Nu9HPqzWNJ7NX5fH/w4GIaKl0Eg5KoBAun3DimVWWg3xdApxN7rHZcBiX566px2X3Ep0eSlGnbozXv+euq+frZVRx39cjwcK9l2+qZ89Z6asucxKVamXrp8DYrJrpKR8F/l/f5F0JMBc4GjgMWA7OBK6SUbT9ib/ctAQYKIfoCxWiFjHN2SPMlcCHwBzATmCOllEKIL4H3hBBPot3wOzCUV9GJZSqK0g0IIXZ6E6PSeVHxERw2c+AuzzfplP5sW1lJ2dYGRh7Rq8PAH7SuhukDYnczlz2P3qAjbQ99XxmD48Jj4O+KQRNSW3Q56Qzt/qAgS7/NY8PvpegNOiac2JdxM7LavfE7bUAsk88axLLv8kjKjGLK2YOJTbEyYnIGA7KTaahy02dEQvj+sc5oqrF32b3hgNVe49ZqkDvoUy6EYNIp/Rk0PoW1vxWz4Y9SNvypFUBS+kYz46qRnao531VGs77V7yNzaDy9hsSx9Ns8Bh+cSkSkkcL1NRjN+lbBaebQeDYvKeen19ZhshgYmJ1CdJJFC/wdPk76x5hdCvwBDps5kJ/eyCE2xcKxl45o82bizhQid4XQiXbvydgTeg2Jw2w1kLu8gr6jkmiocvHVM6swWw0ce9lw+o9N3qXBOrpalwf/wB3Ae8DNUsq99gxkKaVfCHEd8AOgB16XUq4TQtwHLJVSfgm8BrwTuqG3Bi2YJ5TuQ7Qbef3AtVLKAEBby9xb26AoirK/s0abmHl7NuVb6xk4vv2bI5WeRQjBwSf1Y2B2Cg3VrvDNzTsz8ohejDyi9ag9uxsINnWFczW7V8dR4+50a2FCho0pZw8me0YW638vwWw1Muyw9H06ipgQgkP+NoCPH13KV8+s5Ihzh7BlRSWZw+Jb5WPQ+BTWzisid1kFBpOedfO1IS1NEXpO+vuYNgc/2JmMwXFc9Mihe2Rbugu9Xkf/sclsWlyG+wwfCz7aTDAoOeXGMeFRk/Yn3abbT0+huv0oiqIoSvdUXexg9v2LOfay4eGRe977zyLiUqzMuKp1v/jubNvqKn58da02UICA024ZR2q/1jddSynDA1ls+LOM2rJGRkzOIC5VtZQ2V1lo58MHlxCXaqW2zMmkU/uHb77ujrp1tx9FURRFUZTuoKnmv2mULikl9ho3mUN3vftTV+t7UCLn/mcSmxaXkdI3us3AH7SWgqZuON15eMqulpQZxUFH9mL1r0WkD4xl1NGZXZ2l3aaCf0VRFEVRFCAiMvSMGLs29K3H6cfvCey3gwTY4szdunZ6f3PYGQMZdUwmttDzUvZX3Sb4F0KsAVY3+1sDXCilfLBLM6YoiqIoSo+g0+swN3t2hr1ae9jd/hr8K3uWEKLF0Oj7q+70HPspwCuAC+1G27VoIwApiqIoiqLsExabCZdDq/m314SC/wQV/CsHjm5T8y+lrEEb238ugBBiIPDvLsySoiiKoig9jCXKuL3mPxT82+JU8K8cOLpNzb8QYlDz91LKzUDrxz4qiqIoiqLsJZYoU3ioT3uNG4NRhyVKPWBOOXB0m+AfeEkIUSCE+EMI8ZIQ4i1grRCi4yfAdIIQIl4I8ZMQYnPo3zZv2xdCXBhKs1kIcWFomlUI8Y0QYoMQYp0Q4pFm6S8SQlQKIVaG/i77q3lVFEVRFKXrWGxG3E3dfqrdRCVEtHj6raLs77pN8C+lPFJK2Rs4E/gayAUswEohxIa/uPjbgV+klAOBX0LvWxBCxAP3AAcDE4B7mhUSnpBSDgHGAIcKIWY0m/UDKeXo0N+rfzGfiqIoiqJ0IUuUCbfDhwzKUPC//9/gqSjNdZs+/02klAVAAfBV0zQhxK49W7q1k4EjQq/fQruv4LYd0kwDfgrde4AQ4idgupTyfeDXUN68QojlQOvHCSqKoiiKst+LsBmREtxOHw3VLpJ34ym3itKddZua/45IKR1/cREpUsrS0OsyoK1nymcAhc3eF4WmhQkhYoET0VoPmpwmhFgthPhYCNHmEx+EEFcIIZYKIZZWVlbu7jYoiqIoirKXNfXvb6h042n0E61G+lEOMPtF8N8ZQoifhRBr2/g7uXk6KaUE5G4s3wC8DzwjpdwamvwVkCWlPAj4Ca1VoRUp5ctSymwpZXZSUtKurlpRFEVRlH3EFqsF+6Vb6gA1xr9y4Ol23X52l5TymPY+E0KUCyHSpJSlQog0oKKNZMVs7xoEWteeuc3evwxsllI+1Wyd1c0+fxV4bNdzriiKoihKd9E0pn/xproW7xXlQHHA1PzvxJfAhaHXFwJftJHmB+BYIURc6EbfY0PTEEI8AMQA/2g+Q6gg0eQkYP2ezbaiKIqiKPtSZKwZnU5QsE6r34tJVjf8KgeWnhL8PwJMFUJsBo4JvUcIkS2EeBXCDxm7H1gS+rtPSlkjhOgF3AkMA5bvMKTnDaHhP1cBNwAX7cuNUhRFURRlz9LpBLZ4M8GAxBZnxmIzdXWWFGWPOmC6/XQk1D3n6DamLwUua/b+deD1HdIUAW0O8CulvAO4Y49mVlEURVGULpWcFU1DlZvEzKiuzoqi7HE9peZfURRFURSlU4Ydmo7RrGfc9D5dnRVF2eN6RM2/oiiKoihKZ2UOjefypyarJ/sqByShjXyp7CtCiEogv6vzASQCVV2dCWWvU/v5wKf2cc+g9nPPoPbzgW9f7eM+Uso2x5dXwX8PJYRYKqXM7up8KHuX2s8HPrWPewa1n3sGtZ8PfN1hH6s+/4qiKIqiKIrSQ6jgX1EURVEURVF6CBX891wvd3UGlH1C7ecDn9rHPYPazz2D2s8Hvi7fx6rPv6IoiqIoiqL0EKrmX1EURVEURVF6CBX8K4qiKN2GECJPCOESQjiEEOVCiDeFELauzpeiKMqBQgX/iqIoSndzopTSBowFsoF/d3F+FEVRDhjqCb+KoihKtySlLBZCfAeMF0I4mn1kAMqklFldkzNFUZT9l6r5VxRFUbolIUQmcBzwu5TSFmoNiAMWAe93aeYURVH2U2q0H0VRFKXbEELkAYmAH6gHvgFullK6Qp+/AGQCJ0kpg12VT0VRlP2V6vajKIqidDenSCl/3nGiEOJK4AjgYBX4K4qi7B4V/CuKoijdnhDicOB+4DApZUNX50dRFGV/pfr8K4qiKN1aqO//h8AFUspNXZ0fRVGU/ZkK/hVFUZTu7mggBfg4NP6/QwixrqszpSiKsj9SN/wqiqIoiqIoSg+hav4VRVEURVEUpYdQwb+iKIqiKIqi9BAq+FcURVEURVGUHkIF/4qiKIqiKIrSQ6hx/vexxMREmZWV1dXZUBRFURRlB1JKhBBdnQ1F+cuWLVtWJaVMauszFfzvY1lZWSxdurSrs6EoiqIoSjNF69fy0f13MuO6mxlyyOSuzo6i/CVCiPz2PlPdfhRFURRF6fFW/PANwUCApV992tVZUZS9SgX/iqIoiqL0eGW5GwGoLS1BPQNJOZCp4F9RFEVRlB7N3eigobKCqIQkvC4njprqrs6Souw1KvhXFEVRFKVHqyrIA2DQxEMBqCsr6cLcKMrepYJ/RVEURVF6tMpQ8N93dDYAjrraLsyNouxdPSr4F0JMF0JsFELkCiFub+NzsxDig9Dni4QQWaHpU4UQy4QQa0L/HtVsnrmhZa4M/SXvw01SFEVRFOUvqszfRoQtipT+AwBoVN1+lANYjxnqUwihB54HpgJFwBIhxJdSypxmyS4FaqWUA4QQZwGPAmcCVcCJUsoSIcQI4Acgo9l850op1fidiqIoirIfqsrPI6lPX8zWSAwms6r5Vw5oPanmfwKQK6XcKqX0ArOBk3dIczLwVuj1x8DRQgghpVwhpWzqALgOsAghzPsk14qiKIqi7DXBYIDKAi34F0IQGRdHY21NV2drr1o37xdevPJ88lYt7+qsKF2gJwX/GUBhs/dFtKy9b5FGSukH6oGEHdKcBiyXUnqaTXsj1OXnLtHGowGFEFcIIZYKIZZWVlb+1e1QFEVRFGUPqSsrxe/1kNSnLwCRMXE46+u6NlN7kZSSPz+ZTWNdLX98Mrurs6N0gZ4U/P9lQojhaF2Brmw2+Vwp5Ujg8NDf+TvOJ6V8WUqZLaXMTkpq80nLiqIoiqJ0gcr8bQDh4N8SHY2rob4rs7RX2asrqSsvJTIunpKNOTgO8FYOpbWeFPwXA5nN3vcKTWszjRDCAMQA1aH3vYDPgAuklFuaZpBSFof+tQPvoXUvUhRFURRlP1CZvw2dXk9Cr94AWKJicNkbujhXe0/p5k0AHDLzHAAK167qyuwoXaDLg38hxPNCiEP3waqWAAOFEH2FECbgLODLHdJ8CVwYej0TmCOllEKIWOAb4HYp5cKmxEIIgxAiMfTaCJwArN27m6EoiqIoyp5Svm0L8em9MBiNgFbz72xoOGCf8ltVsA2h0zF08pFEREWTv2ZlV2epW8tfs5KNf8wnGAh0dVb2mC4P/oFNwBNCiDwhxGNCiDF7YyWhPvzXoY3Usx74UEq5TghxnxDipFCy14AEIUQucBPQNBzodcAA4O4dhvQ0Az8IIVYDK9FaDl7ZG/lXFEVRFAW8LidFOWvx+3x/eVnBQICSjTlkDBkWnmaNiiYY8ON1uXY6v9vhoCx3E173ztN2FzXFRcQkp2A0mek9YhT5a1YesAWdv2rFD1/z8QP/5uunHuWnV57v6uzsMV0+1KeU8mngaSFEH7Ta+NeFEBbgfeB9KeWmPbiub4Fvd5h2d7PXbuD0NuZ7AHigncWO21P5UxRFURSlfX6vl3fvvJma4kLSBgzm9Hsewmja/cH3KvK24nW5yBg6IjzNEh0DgKuhHrPV2u68q3/5gV/feAm/z0tEVDRHXXg5Qw8/crfzsq/UlBYTn94LgD4jR7Hpj/nUlBSRkJG5kzl7FkdtDfPfe4usUWOJTU1n5Q9fc9Ax00gbMLirs/aXdYeafwCklPlSykellGOAs4FT0GroFUVRFEU5wHhdTpZ/+wWrfvoWn9ez8xmALcsWUVNcyPApR1Oau5E5r7/0l/JQtF7rqZvZIviPBuiw33/B2lX89PKzZAwdzok33k58ei++fe7/WP7tF38pP3ubDAapKy0hLk0b7LDPyNEAbFuxZx5VtCdaY7qLPz56j4DPx1GXXMXh51yIMcLCqh+/6+ps7RHdJvgP9Z8/UQjxLvAdsBH42x5ex2494Tf02R2h6RuFENM6u0xFUZSerjJ/G989/yS/vvWKGllEASB/9UreuuU6fn3rFX5+9X98+J878HncO50vZ/6v2OITOPaqGzj41DNZ++uP5Mz/dbfzUbhuNbGpadjit4/qbYnSgn9nOyP+SCn57d03iElJ5eR/3smgiYdxxt0PMnDCIfz61ius+unbNufbU6SUrPj+Kz579D+snfvzLnXZaaiqxO/zEp+h1fzHJKeSOmAQq3/5gYDfv9t5Cvh9fPbYfTx9/t+Y+/Yr+303oobKCtbO/YmDjplGXGo6pggLQw45nI1/zsfjdHZ19v6yLg/+hRBThRCvo427fznajbX9pZRnSSn3WBG62RN+ZwDDgLOFEMN2SBZ+wi/wX7RhPQmlOwsYDkwH/ieE0HdymYqiKPu1gN+/2ze7lWzawOx7biV3yZ+s+vEb3rzpako3b9zDOVS6SsDvw15T1emae4/TyY8vP8vHD/4bvdHEWf95jBNvvJ2y3E0s/GBWh/M6G+rJW7mMIYdOQafTc8jp55AxZDg/vPAUK77/ipJNGyjMWdNu0L4jn9dDwdrVZI1q2XvX2tTtp52a/4I1qyjfmsuEk0/HaI4AQG8wcvzfb6Hf2PH8/Or/WDfvl07lYXcs//ZL5rzxEqW5m/jhhaf44cWnO13jXltSBEB8Wq/wtINPOYPakiKWfv3ZTuf3eT1sW7mMTYsW4nVtD4L//PRDti5bTPrAISz75gtWfLfjeCq7RkqJs6Eet8Ox28sI+H3UV5Tv1v0Yiz7/ECEEE07e3hN8xJHH4vd42PTngt3OU3fR5X3+gTvQhsi8WUq5N5+nHX7CL4AQoukJvznN0pwM3Bt6/THwXOihXScDs0MP9toWuiG4aUjPnS2zW6gqyEMCQgiE0IEAIXSI0L8IQdPzyfxeD163C7/Xi9Dp0BsMmCKsmK1WpJT4PB4Cfl94WUIXmlcIgoEAQb+fgM+nzWs0YjCa0JuM6PWGcG2ADAYJBoMgJVIGkVKrbTFZLAR8PhqqKqgpKcbncWO2WImw2TBbbegNBnQGPXqDEZ1ej05vQG8wIJH4XC68Lhdetwuvy4nX7SIYCKI3aGl0+tB8zd4D22soZPh/zabJ7R81f9+sVkP7Ttx4XU5kUGKLi8cWn0CEzYbeYGx3nwT8PjxOJx5nI411tVQX5lNVmE9taQl15aV4HA6ik1IYcuhkDjpmOmZrZJvLcdRUs2XZYioL8qgrK8FZX4fBaCLCZiM2LZ24tAyS+vQluU9fTJbW/VcDfj/1FWW4HXYCfj8y2HaNjU6nC3/3BrMZo9mMKcKKyWJBp9dr34PbhdvhwOWwIwMBCB1TTceH0RyB2WrFZLWi0+nwuT34vG78Hg8+jwe/1xOa5kHKILZY7buMjI0DwOdx43O7t3//AgSCYDBIMBAg4PPh93kJ+Lz4vT7tX58Xa0wccWnpmK2R6PT68LHexOdx01BZQU1pMXWlJThqazCazZgjbUQnJhGdlExMUgqW6JhW80opcdbX4aitwVlXi8u+fZQQg8msba/FitkaiTHCDDT9XrS8N//thb8r7UWLacFAALfDgdthx+dxa8e10Rg+poN+Pz6vh4DXRzDgxxxpwxIdTWRMLAaTWfudBSV6o7HVNjRtR2NtDZX526jI20pF/jbqy0uxV1fhrK9DCB22hATSBw5h8KGTGTDuYISudf1RwO9n/fxfKd6Yg8/tZuvyJUTGxXHGPQ/j93j45KG7+frpx7jwiecwRVhaze9sqKdgzUoaqiqRUhIRGYklOgaLLYpgMIiroR5nQwM+t4sIm43opBQSMjKJSkhskZ+A36edD1xOvG43AhB6vXbe0Om0/aATgMBg1I5pg8mEDAbxe73b/3zavwHf9vfBQBBLVDTWmBgiY2IxRljC36mjtoac3+bgqKkmKjGJpMw+RCenYjBq+8lgNIV/M6CdCwOhYzfg1/6Cfj96owmdXh/eBp/HQ9DvI9B0jg34tX/9fnxuN9boGBJ79yEmOTW87B011tVSX1FGQ2UFDVWVNNbWYLJGatsSHY0lKoYImw2DyYzBZERvNGGJikZv2B4qSCmpKSkif/UK8levoHDdGnweNwajiYEHH8Kk088hLjW9zfXXlBTx+WP3U1dWyviTTmPS6eeE++uPmjqD5d9+yZBDp5Daf2Cb82/6YwHBQIBhoX71Or2eU2+7my+eeJA5b2zv/mOyWJl55/2kDey4b3ZRzlr8Xg/9xo5vMb2p5r+9sf5X//wdlugYhk0+qsV0vcHIiTfewWeP/ocfXnganU7X6XsAAn4/NcWFuBsd1JeXUbY1l7qyElL6DWDY5KPC/fG3LFvM3HdeZcD4SZx44+388cls/vzkfWqKCxlxxFQctdXodHpGHDm1RWtGk5pQ8B+Xvv0ZpwPGT2TgwYfwx8fvMWjioe3uv9yli/jp5WfDD0CzJSQy7aq/E2GNZNFnHzD08COZcc2NfPnkQ8x95zV8Hg9x6RmYLZHEJKcQGRuHwWxu8/zTpL6inJzf5pDz2xzqyktDee3F0EOnMHzK0UQnJSOlpK6shLxVyynN3UQwECCxV28SevVGZzBgr66iUeaLrwAAXvtJREFUaP1a8lYuw+NsxGiOYPT0E5g08+xO3R9Smb+Ntb/+zMijjiUqITE8PW3gYOLSMsiZP4eRRx270+V0Z2J/b5rpLCHETGC6lPKy0PvzgYOllNc1S7M2lKYo9H4LcDBageBPKeWs0PTX0LomsbNl7ig7O1suXbpn+tbtiqcvOA2/p3M1M11JbzQSOID6DDZdNIOBIFIGMUZYMJhM+Fwu/D5vq/Qmi5X49AxiU9OJsNmo2LaVkk3rMVsjGXXscQw7/Eh0BgONdbUUb8hhy5I/Kc3ValHN1khiU9OJjIsj4PPhamigtrR4e1O6EMSlZZDStz+RsXHYa6qpDhU2goHdb+4FMJjNBP9C7fA+JQQGg1ELnI1GrbC7w6geRnMEfp8XGQy2mG4wmrDFJ2CLT0BvNOJ2OKgtLerUqCDdhdDpsERFa4XtCItWcPL7qK8sb7Ed0UkpxKdnEJWQiC0+ESmD1JYUU7R+LY11taT0G8jRl17V4ua32tJivn32Ccq2bMYSHYPZYiUhszfHXHpNOBAp3pDD7Htv46CjpzH18u2nyuqiQpZ98xnr589t87exMwaTGZPFohVC2/l97S0Rtiji0jMwmkwUrc8hGPBjslhb1Iy2ld9gIPCXf3s70un1RCUkEp2UQnRSMhGRkdSVl1G+ZXOrLlfGCIt2fugoDhACW2wctoREdDo9DZXl4eXEpqaRNWosiZlZVBXmsXbuzwT9fkYdexyTTjs7HESD1kf+y/97CJ3BwEk33kGvYSNarMbjbOSNG68iKiGRsx94Ap2udQHmvbv+ic/t5sLHn2sxXUpJWe4m3A47wWCQX998Ca/LxTkPPklsSmq7m/bL6y+w9tefuea191oEhVJKnj7/b4ydcRKTz724xTxel5MXLj+PEUcdy9GXXNXmcn1uN58+ci9F69cy7oRTOfTM81oFnT6Pm+KN6ylct5qSTespy92Mv1nricliJSY5heqiAoKBAL2GjiClX39W/fQ98Rm9OOveRzFGaK0OG/9YwA8vPKXtSyFASixR0Zz5n0db3cT7w4vPsGXZIq555d0W0x011bxx09WkDhjEzDvvbxWgF6xdzScP3UViZhaHnXU+OoOBOa+/SE1JETq9HmtsHBc89iwWWxQep5Mv/+8BCtaubvXd2BISGXXMDLJP/Ft4aFUZDLJ1xVJWfP8V+atXgBD0Hj6SfmMnEPD7yVu5jMKcNSAEiZl9cDc6cFRXhZen1+upryhvsZ7I2Dj6jskmbeAQinLWsH7BXOLSMph+zT9IHzS0zf0G2uhNH/zndpz1dVz4xPPhVqAmf34ym4UfzuKyZ18jJjml3eV0B0KIZVLK7DY/U8H/3g/+hRBXAFcA9O7de1x+fv5e2872bF7yBzIQQEpCNe0yVOu+/d+m18aICIzmiFAtmCQQ0Gqovc5GhE6H0WRGZzACEhkMtpi3ee06yBa1rwG/P1zTqdPpwv8KnQ4pJS57A876OszWSKISEolP7xW+gLobHXgaHeHuB8GAVuOl1YBpAafJYsEUYQn9u71GOpzO7w/P1/Q6XDOt7aim/RX61preN71tSiu2fxyaZjRHYA7Vqjtqq3HU1uBpbMTrciKECNfE+Txu/F4vxggLZmskZqtWI2yJiiYhszdRCUmtTrrlW3NZ/PlHbFr8e6uLdEq/gQycMIkB4ycSn5HZZq20o6Zaq8ndtoXybVuoyNuC2+HAGhNDQqi2JCEjE2tMLHqDoc3aXKRsWbvu1WrnfS6X1nrhcqI3GIiwRRFhsxERaUOnb6otlOHjzu9243G58DgbkcEgRnMExlArgsFsxmgK/RtqSnfU1uCoqaaxrkY79szasbk9jxIZlKFWIP32liajEYPJhCFUe9pYV0tdWQlelytUu+oP17TqDAYiY+OJTkwiLjWd2LR0IiJtSCnxupyhWtIK6iu0fx011TTW1hDw+zBGWIhP70VcWgZRCVoLhSUqGqHTg5T4vR7tt+PSWnh8Hk+oVUBq/4Veh3druKVJNmtl0j7S6XSYbTYstiiM5ojQcewLHd8+dAajVoNt0rbZ3ejA1dBAY10tQb9f+86EwOd24WpowGVvwOt2hVp0DEQlJBGfnkFS774k9skiItLW+jhAGxpx/YK5zH/vTRrr6xh55FRGHjWN4g3r+P2j99AbDBxz+XUMmnhouzV882a9ztKvPmXSzHPwedwU5ayhbMtmDEYTw6YcxcgjjyW+VyZCp8PT2Biq7a9Hp9NhiY7BGh2DMSJCqyUtK6OmpIiakiJ8bjcIrSBssjS1uFgxms3hvDcdx0iJDB0/Ab9Pq9X3eLYfRyYzBqMRvckUPpYMoelCCFwOO876unBtek1JEV6ni94jRzHqmBnEpqbhsjdQVZCHo6Y6vL/8Xq92TLhd6EPr0huMrVpyAj4vAX8g1HJkCZ93tdY3Q7jVU28wYDCZtVabwjzqy8uoryinvrKchsoKPM5GohKSSO03gJR+A4lLTyc6MZnopORQ4U9rUXI11GvHRaOdgFc7X/s8Hpz1tTRUVeKoqUYGg1hjYuk1dAR9DhrTKrBurKvl9w/fZc2cHzFbrYw/eSaZw0aSv2Ylf3z8HnFpGZx62z3tBk3rF8zl22ef4OhLrmb0tONbfFZXVsprf7+cw8+5iAknz2xz/iY1JcW89++biEpI4oy7H2pRCGkS8Pt56eoLyRw6ghNvuqPV5y9dfSFZo8Yy7aq/t8zj/F/59rn/46z/PNZieNAd+X0+5ob6/0fGxtHnoDFERNrwedzUlpZQsmkDwYAfnV5PclY/0gcNJXXgYCJj4rDFxxOXmo7Q6XDW17F27s+s/fVH6srL6Dt6HNOu+jvWmNgW6/O6XbjtdiLj4qivKGf23bcSlZDEOQ8+0aIF+t07b8JojuCMux9qlefl333Fr2++xCm33kX/cQeHp/s8bl7/x5WYIiyc8+D/hVuhfV4PS7/6lMbaGrJPPK3F8dB07XE77OHWDGdDPYU5a8hbuYy4tAwOPfM8GiorWP3z99SVl2KLi+egqTO0Gv7E5BZ50/rg/0z5tlxMERbSBw+l76hxxKamadvvclJbVqodo9ExRCW2vJbmr1nJDy8+jaO6mrHHnUjGkOEUb1xPUc5aAn4fcWnpxKWms2XZYurKSjjl1rvJGjW21XfUUFXJazdcxsijjuWYy65td/+DVqj58smHGXn0sfQbM77DtHuDCv4BIcQk4F4p5bTQ+zsA/r+9+46P66oT/v8502ekUZesaksuci9xSXea0wuEkJACKZAQskuA3WV5Fnj2AZaFpfx4YHmABUIS0ghJCCkOaTi9OY5r3OUqyeq9TC/3/P64o4lkSbYcyxpZ+r5fL700c+feO9+ZM3Pne8499xyt9Y/6rfNyYp21iRl+m4B8EuP9963bt15isyPu83CpavkXJ7/Oxnqa9u3BMAzcGRkUlM8gPTsn1WGJSSocCLD2r39m84urk2d8ypcs4+I7vzLgVPlQYtEoT/3Xdzi0cxsWq42iWbMpX7yURRdeOqilTZx82mqreePh+8xW3ISZK87g0n/8p2G7L4KZMP71v75D/a4dXP8fPxnQ/efdJx7h/ace54u/vp+MvPyjxlC9dTPP/PT7ZORP4Zp/+24ySeyzf+MHPPPT7w9KdPs89G9fxZubx6f+13cGLH/uv39C/a7tfOm3Dw7dUHKYQzu3semF1TTuqyIaCmF3OvHm5lE6byFTFyymZM68Ibu/DUVrfcQuM/3tXb+W1T/7IRd84S5OueRKc3vD4Fe3fYYFF1zEBbd9adA28ViMh75xN1prbv3Zr5OVhnXP/IV3/vwg13/3x4PO2Hwc1Vs28sr9v6W7uQmA4sq5LL38E8xcccaALmajLRwI8NYj97P11ZcAsFhtlMyZh93pNLvbNjWSkZ/PRV/8CtMWLRl2P6898Hs2v/gc05euoKBiJrNPP4u8qeWD1quv2sVj3/kGl3/lX5l79nkn5kUdwUmR/CultgFb+/1tA27VWv9wlPZvw5xQbBXmZFzrgZu01jv6rfNlYKHW+i6l1A3ANVrrzyil5mNel3AqUAy8CszCbPs94j4PJ8m/EGIi8XW007i3Cm9e/rB9tYeiDYOulibSs3OSZ3rExNLRUE9H/SGyi4rJLZ06om0CPd088s1/IhIMcNnd/8KMZacRDvi572t3UjSzkk/923dH/Px1u7bz7M9+iFKK67/34wExPPWj79J0YB9f+u2DQyacf/nBvxMNh7jpP3+WXGYYcX57x2eZsfx0Lv3HfxpxHKmgtebx732T7uZGbv9/92JzOOhqbuK+r97BRXfezaJVlw653YFN63n6J//BaZ+6nrNvuJlAdxf3fe1OSufOP6b3/miikTD1u3aQVVh8xK5ZJ0JvRxu+jnbySqclu06BWfkZ6pqww8UiEd5+9AEOfriJrsYGUHDOTbex/KqBA1S+ev/v2PbqS/zDHx494nwRJ8qRkv/xcMFvn3OBRYm/GzAn+doBjEryr7WOKaX6Zvi1Avf3zfALbNBar8ac4ffhxAW9HYk4SKz3BOaFvDHgy1rrOMBQ+xyNeIUQ4mSQnpPLrNPOPObtlMUy7IWFYmLIKS4hp9+FpSPhycjk+u/9mGd++n2e+el/kj+twrzYvbeXMz594zHtq3TuAm78z5/x+Pf+jSf/6zvc+P2fkpFXwIFN6zm4ZSNn33jrsC3Nbm8GPa0D+5E3799HyO+jfPEpxxRHKiilOPO6m/jLf/5vtr++hiWXXEFbbTUAeWXlw243fekKFpx/Meuefhy7y0Vr9QFikTDnfO4Loxqf3eEcslvNWPDm5OHNGXx2cqRnHWwOB+ffdifnY1ZWX7n3N7z5yP0oi4VlV1wNieU733qNytPPTknifzTjJvnXWncAbyT+UErNAv59lJ/jY83wm3jshwxRERlqn0IIIYT4eDILpvC5H/83215bQ9Xat4iFw1z1L9+kcGblMe8rp7iET3/7+zzxH9/iie9/m5nLT+fDV14kf1pFMlEbiicjc9BQn30Xo05bNP6Tf4Cy+YsorpzLB6ufZOGqi6mv2onVZiN/iC4q/a36wl2E/T7e+fODAJxx7Y0y++8wPBmZXPm1f+P5X/6UNx66F6cnjdlnruTl3/2SaDjEqVcPmVKm3LhJ/pVSlVrrPX33tdZ7lVKLUhmTEEIIIcae1WZnycWXs+Tiy497XwXl07nmW9/jpf/5BRtfeJbppyzn4i99NTnazFDc3gzCfj/xWCzZIly/Zxd5pVOHvIB4PFJKcfo11/PUj7/Hjjdf48Cm9RTPnjegq8tQbA4HV/3Lt6j5cBMWm42y+ZKKHYnFauWyu79OyO/j5d/9kjV/+DVGPM4Fn/8SeWXTUh3ekMZN8g/8Xik1A7Pv/FbABWxXSnm01if/dGpCCCGESIniyrl8/he/x4jHR9S9w5248Dzk6yUtKxttGDTu3c3s01ee6FBHVfmSZRTNnM2ae34FwIrD+qUPRylF+ZJlR19RAGaF6ZpvfY8db75KZ2MDM5effsTRoFIt5TP89tFan6+1ngpcD/wN2Ae4gS1Kqd3Hs2+lVI5Sao1Sam/if/Yw692aWGevUurWxDKPUup5pdRupdQOpdSP+61/m1KqVSm1JfF3x/HEKYQQQogTQyk14n7dnkwz+fd3mXOPdjTUEfb7Kaqcc8LiOxGUUlz+lX+ldN4C5p974YgnHRPHzmqzs2jVpZz7uS+M68QfxlfLPwBa61qgFniub5lSaugBp0fum8CrWusfK6W+mbj/b/1XUErlAN8FlmNO6LpRKbUaCAM/01q/rpRyAK8qpS7TWveN8//4kSb1EkIIIcTJpe+CUF9HOwXl06mv2gVwxAmixquswiKu/+6Pj76imDTGTcv/kWitfce5i08CDyZuPwhcPcQ6lwBrtNYdWutOYA3mBF4BrfXriTgiwCag9DjjEUIIIcQ4lZ5rzkjd294KQMOeXbi8GWQXyQhV4uR3UiT/o2CK1roxcbsJGGp6wRLgUL/7dYllSUqpLOAqzHH++3xaKbVVKfWkUmrIy+GVUncqpTYopTa0trZ+3NcghBBCiDGQlpWNsljobW8HoGHPbopnzR7xJFtCjGcTJvlXSr2ilNo+xN8n+6+nzVnNjnlms8QkYX8G/p/W+kBi8XNAudZ6EeaZggeH2lZrfY/WernWenl+/tFnJhRCCCFE6lgsVtKyc+hta8Hf1UlnQx0lc+anOiwhRsW46/P/cWmtLxzuMaVUs1KqSGvdqJQqAlqGWK0eOK/f/VIScw4k3APs1Vr/d7/nbO/3+L3AT489ciGEEEKMNznFpbTX13Fo5zYAyuYvTHFEQoyOCdPyfxSrgVsTt28Fnh1inZeBi5VS2YnRgC5OLEMp9QMgE/in/hskKhJ9PgHsGt2whRBCCJEKeWXTaK+vpXb7hzjcbqZUzEx1SEKMismS/P8YuEgptRe4MHEfpdRypdS9kJxh+D+B9Ym/72utO5RSpcD/BuYBmw4b0vOrieE/PwS+Ctw2li9KCCGEECdG/tRyYuEw2159mZI587FYrakOSYhRMWG6/RxJonvOqiGWbwDu6Hf/fuD+w9apA4a8wkdr/S3gW6MarBBCCCFSbvrSFcnbs884uSb3EuJIJkXyL4QQQghxLDyZWVx4x5fpbmli7tnnpTocIUaNJP9CCCGEEENYfNFlqQ5BiFGnzJEvxVhRSrUCNamOA8gD2lIdhDjhpJwnPinjyUHKeXKQcp74xqqMp2mthxxfXpL/SUoptUFrvTzVcYgTS8p54pMynhyknCcHKeeJbzyU8WQZ7UcIIYQQQohJT5J/IYQQQgghJglJ/ieve1IdgBgTUs4Tn5Tx5CDlPDlIOU98KS9j6fMvhBBCCCHEJCEt/0IIIcacUqpaKRVUSvmUUs1KqQeUUumJx65USn2glPIrpdqVUn9KzLbet+23E9v1/QWVUoZSKi91r0gIIU4OkvwLIYRIlau01unAUmA58O9KqWuBR4H/xhwSbz4QBt5RSmUDaK3/S2ud3vcH/AR4Q2stQyQKIcRRSPIvhBAipbTW9cCLwELg/wI/0Fo/qrUOaq2bgDsAH/DPh2+rlFLALcCDYxiyEEKctCT5F0IIkVJKqTLgciAATAX+0v9xrbUB/BW4aIjNVwIFiceFEEIchST/QgghUuUZpVQX8A7wJvD/Essbh1i3EbMb0OFuBZ7UWvtOSIRCCDHB2FIdgBBCiEnraq31K313lFJzEjeLgIOHrVsEDOjTr5TyANcBnzyRQQohxEQiLf9CCCHGiyqgDjOhT1JKWYBPA68etv6ngA7gjbEITgghJgJJ/oUQQowL2px45l8xR/25SSnlUkoVAvcCGcAvDtvkVuAhLRPWCCHEiEnyL4QQYtzQWj8O3Iw5sk87sBNwA2dprdv71lNKlQAXAA+lIk4hhDhZyQy/QgghhBBCTBLS8i+EEEIIIcQkIcm/EEIIIYQQk4Qk/0IIIYQQQkwSkvwLIYQQQggxScgkX2MsLy9Pl5eXpzoMIYQQQggxQW3cuLFNa50/1GOS/I+x8vJyNmzYkOowhBBCCDGM4M52LC4rzulZqQ5FiI9FKVUz3GPS7UcIIYQQop/2h3bSes824j2RVIcixKiT5F8IIYQQIiHu+yjhjzT4UhiJECeGJP9CCCGEEAnRRn/ydqwlkMJIhDgxJPkXQgghhEiId4eTt2OtwRRGIsSJIcm/EEIIIURC3BcFwFbgJtavIiDERCHJvxBCCCFEguGLohwWbDlujF654FdMPJL8CyGEEEIkGL4IlnQH1gwHcUn+xQQ0qZJ/pdSlSqkqpdQ+pdQ3h3jcqZR6PPH4OqVUeWL5RUqpjUqpbYn/F/Tb5o3EPrck/grG8CUJIYQQYhTF/VGsaXYsXgeGP4qO61SHJMSomjSTfCmlrMBvgIuAOmC9Umq11npnv9VuBzq11jOVUjcAPwGuB9qAq7TWDUqpBcDLQEm/7T6rtZaZu4QQQoiTnOGLYs10YvU6QIPhj2DNcKY6LCFGzWRq+T8V2Ke1PqC1jgCPAZ88bJ1PAg8mbj8JrFJKKa31Zq11Q2L5DsCtlJIjgRBCCDHBGMEYFo8Nq9cOIBN9iQlnMiX/JcChfvfrGNh6P2AdrXUM6AZyD1vn08AmrXX/IQD+mOjy83+UUmp0wxZCCCHEWDGCMSxuGxavA/ho9B8hJorJlPwfN6XUfMyuQF/qt/izWuuFwMrE381DbHenUmqDUmpDa2vr2AQrhBBCiGOiDY0Ox1EuG9YMM/k3pOVfTDDjPvlXSv1GKXXWKOyqHijrd780sWzIdZRSNiATaE/cLwWeBm7RWu/v20BrXZ/43ws8itm9aACt9T1a6+Va6+X5+fmj8FKEEEIIMdp0KAaAxW3Dmp5o+ZcRf8QEM+6Tf2AP8DOlVLVS6qdKqVM+5n7WA7OUUhVKKQdwA7D6sHVWA7cmbl8LvKa11kqpLOB54Jta63f7VlZK2ZRSeYnbduBKYPvHjE8IIYQQKWQEP0r+lc2CxWOT5F9MOOM++dda/1JrfQZwLmYr/P1Kqd1Kqe8qpSqPYT8x4G7MkXp2AU9orXcopb6vlPpEYrX7gFyl1D7gX4C+4UDvBmYC3zlsSE8n8LJSaiuwBfPMwR+O9zULIYQQYuz1T/4Bc7hPSf7FBHPSDPWpta7B7G//k0Tr//3AdwDrMezjBeCFw5Z9p9/tEHDdENv9APjBMLtdNtLnF0IIIcT4lUz+XWZ6ZE23ywW/YsIZ9y3/fRJdbK5SSv0JeBGoAq5JcVhCCCGEmCCSyb/no5Z/6fYjJppx3/KvlLoIuBG4HPgAc3z+O7XW/pQGJoQQQogJ5fDk35ruwPBJ8i8mlnGf/APfwhxF5+ta685UByOEEEKIickIDOzzb/U60BEDIxzH4hxxL+OTRrTJj++9BtJOLcRR6k11OGKMjPvkX2t9QapjEEIIMXqMUCzZp1oIMMfX93/QSLTeT9ppqUtEjWAMbBaU3Uz0LenmLL9GbwSL052SmE4UrTUdj1URbfIT+LCV/C8ulArAJHHS9PkXQghxctNa0/HkHhq+t5b2R3ehDZ3qkMQ40fNKDV3P7Me/qZmW320lXN191G201oSqOog2jV4vYCMQTXb5AbPlHyA+wq4/sbYgXc/tp/ulgyPeJlVirUGiTX6855VhSbPT8rsP6Vq9P9n1SQwWOdQ7os/meDepkn+l1KVKqSql1D6l1DeHeNyplHo88fg6pVR5v8e+lVhepZS6ZKT7FEIIYQrv6SSwoRl7URrBrW34NzSlOiQxDoT2ddH72iE8y6ZQ9K1TsWU5aX9oJ9HWwBG363mllrY/7qD5l5sI7mpPLg/X9tDww3U0/d8NROp6ibYG8L3fSKw9eNRYjGAs2eUHzAt+YWQTfYX2dNL8q8341jXS+1YdzT/fiH9zC1qf2EqujsaJ+wePSKRjBj2v1NDxeNWQrz18wExiPcunUHDXIjxLCvC930DbH7ej48bHjie4vY2eV2rGfeXnWAV3ttPymy20/m4rvrUNgx43QjF09OO/b2PppDnvqpTaBmzt97cNuFVr/cMRbm8FfgNcBNQB65VSq7XWO/utdjvQqbWeqZS6AXNo0euVUvMwJwWbDxQDr/SbY+Bo+xQnQLi6m0hND67ZOdgL01IdzgkTPtBF5zP7sLjteM8txTU3B6XUiLbVMYNYRwiL56OZKsXkYYRjKKsFZRu9Nh4d1/jWNhA51IvFY8NR6sW9IG/EfaH9G5uxpNko+PISWu/bTveL1bjn5crncwLQcYN4Z5hYZwgwW8xtee6jfv503KBr9T6sOS6yr56BslvJ+/x8Wv7nQ9r+uIOCf1icbH3vzwhE8b1Vh3NWFkYgRsfjVUz5yilY0u10PFYFWqMjBi2/3pLcRjms5N+1CEdx+rDxGIHYwJb/vm4/RxnuM1zdTdtDO7Hnucm9bT46HKPzr3vpfLyK4JYWsq+ZhTXTecR9DHhftCa8rwvDF8U1LweLc+h0LdYVouXXWzD8UTIvq8B7Tqm5vaHpeLyK4LY2UBCp72XK15airB+VR6SuF0uaDVuuC6UUOddW4qrMpuPR3fS8UkvmJeVHjC+0s53grg6cM7LwLMlHKUVgcwsdj1cB4N/QTN5t84/rN9o8u9NJYGMzymbBc0oBzplZKMvIfgeT+zE0sURl0pbvOebtjWCMzmf2Yct3Y/U66HruAPbCNJwVmWit6X2zjp6/V6McNnJvnI1rds4x7X+snTTJP+YkX4sSfzcAfwZ2ACNK/oFTgX1a6wMASqnHgE8C/RP1TwLfS9x+Evi1MjOtTwKPaa3DwMHEJGCnJtY72j7HBa01Sim0oc3py60WlN2CsijzILO/m+CHrWitsRd4sBelYXHZUA4Lthw3ym5J7idS20uoqoNYaxDDH0W5bHgW5+NemGfuLxon1h1BAcppNf/slgFJqxGIEq7uIXygm/C+TrBacM3JwTUry5xWPcs1ZEJhBKJ0vXCQwIZmALpfriHrEzNIP71o0Lpxf5TApmZibUGsGU7cC/OwF3jM12FogtvaCO3uAK2xZruwF3qwF6djy3UPOjAYwRjBHW3Eu8JYMhw4SrzYp3jAqjB6o0Sb/cRag9hL0nFM9SZfq44ZxDpDhA90E633oVxW7AVp2IvSsOW7+woH5bAOSup97zfQtfoA1gwH8ViE9od24piWQfpZxbjn5w44iB9e1v4Pmuj5ew2GPwoKnBWZuGZnYwRiGKEYaSsG96nVUYNYR5C4L4rFY8eW6UBr0OE4yqbMyW56IoSre7B4bDjLM0GZrV3hg90oq8JR6sVRnjEgmTOCMcLV3ehIHOfMbKxpdnTcIFzdQ7w9hI4bKKsFx1Qvtike83OqNfGeCLHWILG2AIbfbI2z5rpwzcgalFAYoRihXR3EeyNYvA6sXjtGIIYOxcGqcJZnYM1xoYMxIo1+lFVhn5I2oIVPxw0MfxStQVmVuX04jnJYiLUFidT5sOW6cM3JwZJmJ94TIdrkNz9fXgeuyuxkP3ZtaCK1PWYikWZHxwwMXxQdNbBmOFBOq/kdc1iTn9VYa4B4b4RInQ80OKd6cU7PRLltA7874TiRQ+a+nRWZAxIjIxInsLkF33sNxJoDKIeVtBVTSDu9yNyHAmuO+UNvBKLEusJYM51YPLajVipjnSE6HqsiUtODNcuJEYzhX9tI1+r9pJ9ZjPe80mETlL7YQrs68CwtQNksZH9qJs2/3ETXs/vJuWH2sJ/n/uI9EaKNPoxwnGijn9C+LuLdYazpdtzzckk7vQhruoO4P4rhi5ivbYhrC7RhJlaBLS0YgRjKbkFH4mC1kHFB2YDvho7GCVV1mt8LlxWUInywm2ijH8e0DNJWTMGa4TTjyHImyzTWHqTjiT3EWgO4KrPxLJ2CNdt83OJ1mMeG1iBGKIYtx4Ut25Us33B1NxaXDVuem2iTn+D2NohrPEsLzO9d4v00fFHzu7S/m+DWVrAovCtLcFZmm+9XewhtaPP4a7egHNZkfANe394uQrs7iNT7sLisOGdl456bgy3Pg7IO/7nQWhPa3YH//UbCB7vRkcNaPa0K99wcMi6twJ43dH9537sNxFqC5N4yL9nP3pbrJu+2+bTes5W2B3eQf+eiQXEHd3egowaZF5ebXVZ+vZm2h3Ziy3ER7wyR/6VF2PI9+Nc2gM2Cc3om7Q/vovPJPRTcfcqwyZ8OxrDmuJL3LWl2UEdu+TcCUdof3Y0t00HeHQsSxz8n+XctxvdeAz0vV9P0i41kXl5B2vLCoyae0bYgXc/sI7yvCwDltpFxwVTSTi0c8LuoDU3nE3vMY2tFJt0vHAQg/YxiOv66h+C2NjKvqMCW7aL9kV0Ed7bjWZj/0fM0+bEXpQ/47nsW5ROq6qT3jUO45uTgnJYx5Ovt+MseQrs6wGYhsKGZUFUH3rNL6Hx6L47yDDIvq6D9kV20/M+HpJ1aiGdxPvZSs9Klw3EzLzjCMUfHNcFtrfS8fohYcwCL1w6GJrC5BVueG9ecHKxeB8plxZrhwDU7Z8j3VWtNaFcH3S8cJNZmnv2wpNnM9Z1WjEAMa6a5vbMic9iy6X7xIEZvhLxblmDLddPymy20P7yTrE/MILy/G//6Jlzzcol3hmh/ZBd5X1yIc+rg9268UCf6dNSJopSaBfy71vrWEa5/LXCp1vqOxP2bgdO01nf3W2d7Yp26xP39wGmYFYL3tdaPJJbfhznXAEfb5+GWL1+uN2zYcEyvdTQ0/WIjyqKItYfMHzkAq8KW7UIbmnhHCOWyoexmMtufctlIW1aAvTgd3/uNRA/1gjIP0JY0O/GuMPHuMPbCNCxeO+GDPRA77EfAYra6WJxWtAajJ3EgtSmz5hw1iNT0QN/H0aZwzcrGOT0Li8uKEYgSqfcR2t2Bjhl4V5aSdkYRXU/vI1TVib3Qg5H44XFM9WJx2QhsbkGH41g8NrMPo8Y8mFVk4l/fRKwtiCXdjrJZiPeEIRGyslvMqd0TP5gA0dYgxA/7rlgVyqoG/eDZ8txYMxzE2kPmfhObWTw2jEgcYoO/c7Y8N55lU/AszEPHDXpeP0RwSyuu2dnk3DgHZbfg/6CJ3rfriXeEsGQ4yLigDPfcXHRcE+8Om4m+RRHY2ExwRzvO6Zl4lk8h1h4isKWFeHvIjNmi0FEDzykFWDMdRFuCxJr9xDpCH73/Q1AO60efncTrB8z3xWYBrZPvkS3PjSXdjhGMEWsJfLRfq8I5LYNosx/DP7hfqTXLicVlNT+nw5w+tXhsuOblooMxYj0R0JpoU2DwZ25Q/JZBZWUmwmYFpW+Uj+F3wIDXMejzYLPgXmC2Yge3mxXFI7JZcM3MMj/bh3oH7hs+2r8imbihlDnsYN+6FnDNysZe6iXeHSa4vR0dimEvSce9IJdYS5DAhy3JzzaALd+NcliJNviS+1EOi1kBnuJB2SxEDvUS746gHOZ3QUfixHujKLuZtHuWFCQbAnzv1hPc2oYl3U7WldNxLzZbAEP7u+h6Zh+u2TlkXlFBcGsbHX/eTf6dC3FOzwLMft49r9Ri8dhwL8zDs3QKOmYQ2mX241Y2C/Yis7Ic2t1BYEsr9F0nYAFHWQa2fDex9hCRg93mdzfDQbzfZ9ma6TS/xwpQCmVTxLvCZtLvMls9ddRAWRXx3ghGMEbW5RU4K7MJbG7Bv65x0GdVOazYp3iI1Ps+igczSfOeU4q9OI3Ov+xBxzXuOTkEd3WYjS59rMrcrt9HyJrjwp7vJlzdgw73+55hlj8WhQ7HcVRkYs1wENrZPuA7YitwoyMG8a4wtikeDF9kyO+YxevAmuEwW8ZjmlhHEGIa5bTimOrF8EWJNn7Uh145LFhcNrOSbrNgLzTLA6uF4PY2Ys0BrFlOXHNzcJR4seU4QSni3WEih3rxb2wGpcj7/PxByVC8J0zTzzbinJ5J3m3zB8Ua3NlO+8M7cc3JIffmeQMSs/ZHdxE+0E3Rt09DWRShPZ20PbgD4pqMi6aRsWrqoP0FtrbS8ehusj45g/Qzigc9DtD4o3U4Z2aTc11lclnDD9eZx+JrK4fcpuOJKgJbWij4xyVDXjAbawvS+dRewge6cUzLIOsTM3CUmImwEYoR2NJCaI9ZkdXhOLH2IMphJfPScuxFafS8Wkt4bxfKYcV7bine88tQFkXv2/V0P3+A7E/PwrO0gI7HzJZ+5bSiw3EyLi0n47wytKFp+skH2IvSk++zNjQN332PtNOKyLpy+oB4jXCM5l9sQjksTPnq0gGNLXFfhNbfbSXWGSLzsgrSzyii9616ev5eDdq8QHrKV07BmmlWiLue209wVwfEdbILldEbwVbgIeczlQPer7gvQmh3B6FdHYT2d6FDcWwFHrznleJZnA8agtvazDykwTfg828v9JB1zSwcZV6zQa7FT7Q5QHBrG5GaHmz5brznloIyPyvh/V1gaJTLRrw7bMaX4cC7spT0s4oHfNb6zmakn1NC1uXmexVtDdD2wA7zd1WB95xSMi4pxwhEafmfDyGumfK1U7B47EN+ZsaCUmqj1nr5kI+dLMm/UqpSa73nsGWbtdanjHD7lCX/Sqk7gTsBpk6duqympuaYXvvx0jGDruf2E+sMY8t1Yct1Q1ybLX8dIfMHam4OniUFKLuFuC9CrCWAETHQoRjBXR3mqUNDY8124j23FM8pBclWPm1ogltb6X2zzmyxnJGJvSQdNOhIHCMcRyf+jMSPmi3fjXOqF0dZRjLBjveEiTT60aEYkdpegjvaByRQFq8d99xc0s4oxlGUlnxu33sNhPZ0mq24hiZc3Y3hj+FemEfGBWXYp6QR742YyfM79ehgDHtRGt4LynDPT5ytiBlEWwJEG3xEG/1mzFHDPLgYGnuhB/eCPOzF6cQ7QkTqfebBJ66xZTuxFaZhy3UR3ttFYGsrOmJgy3ZizXVjy3LimGYmKWjzhyDa6Dd/dBMtH6GqDiIHez4qNKsi4/wyvBdMHXAQ0oYmtKeT3tcPmZWlodgsZFw4Fe85pclttdbokNnaoiNxel6rxfdeAxhgy3Nhn5KGrcCDPd9ttvD7o2byZwHltKHDMaJtQWw5LpwVmcR90WR/UeeMTFwzs0BDpN6XOMvRS9wfw+Ky4ihJx1GRicVhJbC5hXBtD/Z8D+4FudhL0lE2Czpstj6GD3Sho4bZEprvxpbnMU+zptkxQjEidT7865uIHOzGkmbHmuVEWRTWHJfZslSYRrwnQrw3gjXNjnKZsYcPdCcqew4cJeloQ5tl3eQHpcyzCul2LOkO80qoqIElzZ78EbVmu3AUpxNtDRCq6sAIxbFlO7EXmGdwYp0hAptaCGxpRcfiOKdnkXZKAbZ8t1kps1qwehMVzd4IRiBGeF9X8nPrmp2No8yLJd1hnp1S5oVlkdpejHAMHTHQkTja0FgznTinerF47AR3tBPY2pqsvLvnZJN2ehGOaRnJVrVYR4jQng6U3XwtwV3tYGicFZnYCjzEuyPEu0LEOkJEmwPoqIGjOA1bnhsdNTBCMZTditXrIO3UQmz9WkX7RA710rV6P5FDvbjm5+JZmEfnU3uTla3cz83Fv7mFSG0vRd86dcDnMrSnk8DmFoLb2z+qwFkVjuJ0dDROtCUAhpkAp60oxL0oD4vLZrai9zvTEG0J4FvbgOGLmmfWclzEOsPEWgJm/2VNMuG1eh04Z2XhnpubPP6AeQam84kqQlWJUaWV2WCQfmYx9ike87gQMZKVpHhvhMCWFnOfGWalL7Srw3wJOS7yPj8fe74HHTUIH+gyK5mhOPHOECQSaYvbRqwlQGhfF7H2II4yL57F+eaZuL7vXKIl3/9BE773GtCROO55uTimesGiPkrI4xr/+iaC29qwZjlxlmeinGalt68sY23BxBlBZTYA5bhwVWabrZ6JJC/eHSa0p5N4TwQjEMUIxc2zI4kzLtHWgHlcLPWSfnqR2eVjmDM3sfYgrfdvx/DHKLhr0YAuIO0P7yRY1UnhPy3FNuyZgXq6njtA1qdmkn6aeYZXxzUN//k+7vm5A5L0WEcIIxAddsQarTWt92wj1hKg8BvLhzwrVP9/3iXt9CKyrvgoIW7+zRYsLiv5ty8ctH64upvW323Fe0EZmReXD/m8fc8d2NhitiAHorgX5oFFEdphVuKsuS7seW7zrGCBh7RTi8xKGh+dcfe9XUdwe6JhZ0kBnc/uw1WZbZ41SZzZ973XQLTJj2dxPq5Z2cnn71q9H98HTRR/53QsDivRlgDNP99I9nWVpC2bMijeYFUH7X/cgXfVVDIvmmbGYWhaf/ch0UY/eV9YgLMiM7l+aF8XoaoO0k4rGnSWxwhECe7sMM/yK4Utz43/gybivggZ55eZ70NVJ5FaswGwryXeNS8XV2X2sC36OhxHR+KEq3vofv4A8e7IoIYZa44L78oS0k4tHPYz2ndW0r++ifC+LuxlXrI/NRN7vofed82KjbM8k7zPLxhwvNBxg2iDH4vXji3ro+Ni5FAvLb/9EM/SgmErjGNhoiT/rwMzgHrMPv8uzJ/pL2mtj3xVkLn9GcD3tNaXJO5/C0Br/aN+67ycWGetUsoGNAH5wDf7r9u3XmKzI+7zcKlq+T9ecV+EeHcEe6FnRKfnR4PW2uyGETWwuG0jHhpQa7NFbcgDRswwu2J47SPuOz9Wom1Bwge6UBaFc1Y2tiP0D9VaEznUa3YlslnM7g2J7jS2XDNZPhrzgi51xNP64tj0XSQ3Vt+R/s871s85KAZD43u7nu411RDT2PLc5H9xIa33biPeE0GH46SfXTKolbFP3B8lUt1jnh2qyEgm9kYkTqw5gC3XNSataH1dAo1gDFdl9pCVnSMJ1/YQ7wrjmp0zIceFB/M4itbJbjpHE+sI0fLbD0FrvOeWYs1yEanpwfdOfbJ1etjn0prW320l3hWi8H+tQFkthA920/r7reR8ds6AbiwjETnUS8tvtgyZrOuYQf2/v0vGJdPIOP+jMwftj+wk2hyg8OuD86jWe7cRbfJT+L9WDOqaNBQjEKXntUMENjWb3V1nZ5N+epHZEDKC3yT/hma6nt2HjhrYC9PI++LCER3vQ/u6aLt3G7mfm4t7QR6BD1vp+PNuCr56yrDXQLT/eTfBHe0U/utybFlOfGsb6Hp2P9nXzybtlIKjPueRxP1ROp/ck6ws20vScc/NwTU3F3tx2jH/PhuhGP6NzcR7IlgzHNineMyunekj/63X2mzI7Fq93zxrlqhIuBfkkn1d5RG7NR6u628H8L1bT8FXlyYbK8fakZL/k6bPv9b6fACl1FRgMbAk8X+LUsrQWs85yi7WA7OUUhWYFYgbgJsOW2c1cCuwFrgWeE1rrZVSq4FHlVI/x7zgdxbmbMNqBPucEKzpjjG/KE8p9bGes69v85CP2SzJ1pTxxp7nHrZf7OGUUjinZhxXn8JUJ4sTUare0/FQlsqi8J5bintBLpE6H6452VicNjIvKaf9kV0AQ16b08eaZsc9P3fQcovDiqNs7MYeVxZldjH4mJxTM2Bwj5MJ5VgvIrfluMj/4kI6nqii+/mDyeVpKwqTF6gO+1xK4T2nhPaHdxGq6sQ9L9e8VsuiBrRsj5SjzIt7cT6+t+tJP61owEW4ydl93QNTI2uWi1BVZ/LauT7h6m7C+7rIvLxiRIk/gMVjdo8brhJ8NGnLp+CanU2sNYBjasaIy8JZkYFy2QjubMe9IM/s2mVRyevghpJ5aTnBHW30/L2azEvK6X6pGudM8+Le42VNs5N7yzzzmqk0+3FX7C0uG96zSo5rH0opPIsLcM7MJrilhVhHCNe8XPPaq2OsjGRcUIZ/QzM9L1cP2aUt1U6a5L+P1roWqAWe61umlBr+0v2Ptosppe4GXgaswP1a6x1Kqe8DG7TWq4H7gIcTF/R2YCbzJNZ7AvNC3hjwZa11PPHcg/Y5eq9WCCFOLrZct9m1MMG9II+8z883LyIfYeVWTDz2Ag9T7j6FWFcII2hei3Wks5v9uebkYEm349/YjHteLsHdHTjLMz72RHGZl5QT3N5G95qaAd0yjIB5vZvFPTARtWY5zW5T/uiABqmeV2uxpNlJO0Kl9kSweh1DjoB0JMpqwT0n27xuLq6JNvqwFxx5NCZbtov0M0vwvV1HuKYHDE32p2aO2llzpRT2/OErH6liTbOTfpwVCYvHjvfcEnperiFc2zPuLv496ZL/oWitfSNc7wXghcOWfaff7RBw3TDb/pAhRhYaap9CCCE+Mt6HvRNjx5blgqxj20ZZLXiWFuB7p4FwTQ+x5gBpl1d8/BhyXKSfUYzv3Xq8Z5ckr0MYruW/r9Iaawsmk/9wTQ/hvV1kXjbyVv9Uc83LJbCllUhND5FGP67pmUfdJuOCMnP9eh/Zn5o5oGIvjiz9zBL865qINQfGXfKf+nPFQgghhBBHkLZsChia1t9+CIrj6poFZlKrnDa6/nYgOdN0vNsche7wrqH2xLDMsdaPJsoyW/1tpJ0xtq3+x8M1Oxusip5XazB6IjjKj578W1w28u9aRMn3zxzywmAxPIvTSuG/LidtRWGqQxlEkn8hhBBCjGv2KWl4lpoXmaadXnRME2YNxeKxk3lpOeF9XfS8WgtAvCsxQVn2wH1bs11gU0SbzbFFQns6Ce/pxHtO6UnT6g9gcdrwLMonvN8cqc09d2Rn5JRSxzwpljCN5iSLo2lCdPsRQgghxMSWfW0l3gumYss9thGYhpN2WiGRQ730vlqLoyiNWGcY5bYNGtVFWcwJDMPV3R/N9JrnPu5+4amQeVm5OZLVnOzjrkCJk5ck/0IIIYQY95RFjXhEtBHtTymyr55JtNlP59N7seV7sGUNnRC7ZmbR80otzb/aTLw7TP4XF47bVt0jsWY4x+XoM2JsnXyfXCGEEEKIUaDsFrKumoHhjxGp7hl2WNm004qSM9rnXFuJcwT95YUYr6TlXwghhBCTlnNaBq7Z2eY8AgvzhlzH6nVQ+I3l5my8xzjMphDjjST/QgghhJjUcm+ZT7wrdMShLC0uG4zO5QZCpJTSWqc6hklFKdUK1KQ6DiAPaEt1EOKEk3Ke+KSMJwcp58lBynniG6synqa1HnJMXEn+Jyml1Aat9fJUxyFOLCnniU/KeHKQcp4cpJwnvvFQxnLBrxBCCCGEEJOEJP9CCCGEEEJMEpL8T173pDoAMSaknCc+KePJQcp5cpBynvhSXsbS518IIYQQQohJQlr+hRBCCCGEmCQk+RdCCDFuKKVuU0ptU0oFlFJNSqnfKqWyUh2XEEJMFJL8CyGEGBeUUl8HfgJ8A8gETgemAWuUUjKtqhBCjALp8y+EECLllFIZQAPwBa31E/2WpwMHgX8Dfs5HM9MrwANUaK2rxzZaIYQ4edmOvooQQghxwp0JuICn+i/UWvuUUi8AF2mts/qWK6X+CzgbqB/LIIUQ4mQn3X6EEEKMB3lAm9Y6NsRjjYnHAVBKXQ/cBHxaax0do/iEEGJCkJZ/IYQQ40EbkKeUsg1RAShKPI5S6hTg18DFWuvWMY5RCCFOetLyL4QQYjxYC4SBa/ovTPT5vwx4VSlVADwDfFlrvXnMIxRCiAlAkn8hhBApp7XuBv4D+JVS6lKllF0pVQ48AdQBDwNPAo/0vyBYCCHEsZHRfoQQQowbSqnbgX8GZgA9mC3938Qc+vMgEAD6/3DN01rXjnGYQghx0pLkXwghhBBCiElCuv0IIYQQQggxSUjyL4QQQgghxCQhyb8QQgghhBCThCT/QgghhBBCTBIyydcYy8vL0+Xl5akOQwghhBBCTFAbN25s01rnD/WYJP9jrLy8nA0bNqQ6DCGEEEIcxdtvv83WrVu59dZbSU9PT3U4QoyYUqpmuMek2w+QmFCmSim1Tyn1zSEedyqlHk88vi4x8QxKqVyl1OtKKZ9S6tdjHrgQQgghTohoNMqrr75Ka2srH3zwQarDEWLUTPrkXyllBX6DOX38POBGpdS8w1a7HejUWs8EfgH8JLE8BPwf4F/HKFwhhBBCjIFDhw4lb+/fvz+FkQgxuiZ98g+cCuzTWh/QWkeAx4BPHrbOJ4EHE7efBFYppZTW2q+1fgezEiCEEEKICaK5uRmApUuX0tjYSCQSSXFEQowOSf6hBDjU735dYtmQ62itY0A3kDvSJ1BK3amU2qCU2tDa2nqc4QohhBDiROvo6MDpdDJz5kwMw0B+v8VEIcn/GNBa36O1Xq61Xp6fP+SF10IIIYQYRzo7O8nJyaGwsBCApqamFEckxOiQ5B/qgbJ+90sTy4ZcRyllAzKB9jGJTgghhBBjrrOzk+zsbLKysnA4HMluQEKc7CT5h/XALKVUhVLKAdwArD5sndXArYnb1wKvaa31GMYohBBCiDHk9/tJT0/HYrFQUFAgyb+YMCb9OP9a65hS6m7gZcAK3K+13qGU+j6wQWu9GrgPeFgptQ/owKwgAKCUqgYyAIdS6mrgYq31zjF+GUIIIYQYJbFYjFAoRFpaGgBTpkxh+/btaK1RSqU4OiGOz6RP/gG01i8ALxy27Dv9boeA64bZtvyEBieEEEKIMRUIBACSyX9hYSEbN26ku7ubrKysFEYmxPGTbj9CCCGEEP34/X4APB4PYLb8A9L1R0wIkvwLIYQQQvRzeMt/X/IvI/6IiWDcJv9Kqd8opc5KdRxCCCGEmFz6Wv77kn+n00l2dra0/IsJYdwm/8Ae4GdKqWql1E+VUqekOiAhhBBCnHwMw6CmpmbEyfvhyT+Yrf+TIflvaWnh73//O++88w6hUCjV4Zw0tNYnzSzQ4/aCX631L4FfKqWmYY6uc79Syg38Gfiz1npPSgMUQgghxLjX29vLk08+SU1NDQBnn302q1atOuKoPYFAAIvFgsvlSi6bMmUKu3fvJhKJ4HA4Tnjco6W3t5eXXnoJrTVXXnll8jqGodTV1fHAAw9gGAaGYbBp0yZuv/32AZUgAW+//TZvvvkmubm5LFmyBIvFwqZNm2hubmbGjBl8+tOfPuL7nGrjNvnvo7WuAX4C/CTR+n8/8B3MYTmFEEKchKLRKAcOHKCwsJDMzMxUhyPGkebmZp577jkCgQDFxcVorbHZbFx00UWkp6cf074OHjzIk08+SSQS4YorrqChoYF33nmH9PR0Tj/99GG38/v9eDyeARWEvn7/LS0tlJaWfrwXN8aCwSAPP/wwbW1tGIaBzWbjmmuuGXLdWCzGM888Q1paGnfccQft7e08/PDDvPjii1x77bWjGpfWmq6uLtLS0k6qihRAdXU1r776KtOnTycYDPLyyy8D5ohQp59+OuvXr+ePf/wjd9xxB06nM8XRDm3cJ/+JGXUvw2z9XwW8AXxvlJ/jUuCXmBWKe7XWPz7scSfwELAMc2bf67XW1YnHvgXcDsSBr2qtXx7N2IQQYryKx+O89957VFdXs3jxYhYtWjTi7R588EHq6uqwWq2ceeaZnHvuudhs4/4nSZxg3d3dPPLIIxiGwdSpU6mtrcVisdDd3U0gEOCzn/3siPe1efNmVq9eTU5ODrfeeisFBQUYhoHf72fNmjXMnDmTvLy8Ibf1+/2DWrsLCwsBs3Iy0uS/o6OD+vp6Ojo66Onpwefz4fP5yM/P54ILLiAjI2PEr+dI+lrq+3+HwuEwjz76KO3t7Xzuc5+jqqqK9evXc8kllwzZkr9u3Tra2tq46aab8Hq9eL1ezjrrLN566y1WrlyZrPwczu/343a7sVhG1pO8vb2dp59+mrq6OhwOB6tWrWLFihUj3j7V3n77bdLT07nxxhux2+20tLQAUFBQAMDMmTN55JFHePHFF7n66qtTGOnwxu2RVil1EXAjcDnwAfAYcKfW2j/Kz2MFfgNcBNQB65VSqw+bqOt2oFNrPVMpdQPmmYjrlVLzMCsl84Fi4BWlVKXWOj6aMaaC1pqqqipqa2tJT09n4cKFeL3eI27j8/loamrC6XRSVFQkP+SjSGtNe3s7vb29pKWlkZeXd8wHyng8jtUqJ8yi0ShVVVUA5OXlkZ+fP+L3pb29HZ/PR2lp6bh7Lw3D+Fg/nuFwmN7eXrKyskb8ndVa09rayvPPP09NTQ3p6ek89dRT2Gw25s2bd9TtN2zYQF1dHRdeeCEtLS28/fbb7NmzhxtvvDElY6gbhkEoFMLn8xEOhyfE8SsajVJXV0dOTs6Yn1nRWlNTU8MHH3xAe3s7mZmZTJ8+nUWLFh2xK0QoFOJPf/oTkUiEz3/+88lkG+Ctt97itddeo729ndzc3KPGsH37dp599llmzJjBZz7zmWQLrMVi4aqrruJXv/oVb7zxxrAt2oFAYFCsWVlZuN1uamtrWbZs2RGfPx6P87e//Y3Nmzcnl3k8HrxeLx6Phx07dnDw4EFuueWWEb2eI2lpaeHPf/4zXV1dzJ8/n/PPPx/DMHj66adpbGzkuuuuY/r06TgcDtatW8eBAwdYuHDhgH309PTw5ptvUllZSWVlZXL56aefzvvvv89bb73FddcNnO5Ia83zzz/Phg0bmDp1Krfccsuw35u+z8SOHTvYtGkTdrudiy66iP379/Piiy+ye/duPv3pTx/1zE4sFqOpqQmPx0NOTs6I36NDhw6xdu1ampubCQaDxONxysrKWLRoEXl5ebjdbrKyso46gVtPTw/79+/nnHPOwW63Ax8l/X1mzpzJWWedxbvvvsuKFSsoKSkZcZxjZTwf3b4FPAp8XWvdeQKf51Rgn9b6AIBS6jHgk0D/5P+TfHS24Ung18r8hHwSeExrHQYOJmYAPhVYewLj/VjuvfdeLBYLpaWlzJ8/n+LiYmKxGPv27aOnp4f8/HymTp2K1Wqlvr6eV155herqaqxWK/F4nLfeeotPfOITzJs3D8MwiEQiA/pCbty4kRdeeIF43Kz32Gw2KisrWbhwISUlJUQiEbq7u+no6MDn82G329Fa09PTQ2ZmJrNnzyY/P5+Ojg5qamrw+XzEYjHsdjv5+fkUFhYSDAbZsmULVVVVhEIh8vLymDp1KhkZGdjtdjIzMykqKkoesA3DoLGxkT179lBbW0ssFkNrjcViwWKxkJ+fT0VFBTk5OfT29hKNRikrK8Pr9RKNRtm5cyetra0opbBYLCilkrftdjtWq5VgMEgsFsPr9ZKRkYHX68XhcKC1pqmpiebmZvLy8igrK+PgwYOsX78ev99Pbm4uxcXFlJSUkJeXRyQSoaenh97eXqZMmUJFRQVKKUKhEO+++y5btmyht7c3+X7b7XaKioqYNWsWs2bNIicnZ9CpU8MwOHjwIDt27KCqqip5GjsnJ4ecnBxyc3MpKioiEong9/vJz8+nrKwMrTW7d++moaEBt9tNcXExoVCIjo4O7HY7brcbv9+P3+/HarXicDiSfxaLhWg0SmZmJh6Ph66uLnw+H1prtNYYhkFXVxcAxcXFlJWVEQwGqa+vJxAIkJaWRk5ODtnZ2cnKpFIKp9NJV1cX7e3t2Gw2PB4P4XCYxsZGtNYsWLCARYsWJcup7zPQVw51dXVYLBZCoRDvv//+gPfSarVSUVHBueeeS2lpKYFAAKfTOagF7bXXXmPdunUApKens3TpUmw2G01NTfh8PpxOZ/K97XsN4XCY1tZWAoEApaWllJSUYLVa0Vqza9cuduzYQW9vLzabDZvNRnp6Ojk5OYRCIUKhEPF4HJfLlXxfcnNzyc7Oxm63o5QiHo+zc+dO3nvvPRobG8nLy6OkpISioiLS09Nxu904HA5CoRDBYJD29nYcDgdTp05N9ufdtm0bhmFgt9uZMWMGU6ZMoaysjBkzZgz6IfT7/bz22mvs2bOH3t5enE4nn/rUp5g3bx4PPvggTz/9NFlZWRQXFw97LIrFYrzzzjtMmzaNs88+G4D58+fz1FNP8Yc//IEbbriBsrKyAduEQiGampro7OyksbGR9vb25PctEokQCoUwDIOMjIzkD/j+/ftpaGigrKyMVatWkZWVlTzmAHi9Xvbv38+7775LXV0dsVgs+XxpaWlcfPHFLF68OPk52rVrF5s2baK3t5cZM2awZMkSamtree+99wBYsGABs2bNSu4nKyuL1tZWDh06lHw+q9VKT08Pfr8fi8VCeno6WVlZyaSyq6uLjo4OOjs7sVqtZGdnk5eXlyzvzMxMDMNg9+7d7Nmzh2AwSE5ODuXl5UybNi3ZhWLv3r289NJLdHZ2opRi0aJFzJ8/n56eHqxWK5WVlaSlpdHb20tbWxttbW3s3r2bpqamZOJisVjYvHkzsViMefPmUVpaSnt7O11dXUSjUerr62lpaWHWrFksW7YMwzCoqqpi7969HDhwAJ/Ph9vtpqysjPb2dvbs2cMbb7zBLbfcMuTnIxaL8fjjj9PW1sbnPve5AYk/wOLFi3nttdfYsWMH55xzzqDta2treeGFFzAMg7y8PHbt2sXUqVO5/vrrBx0bvV4vK1as4L333uP8888fMvn2+/2D4rRYLMyYMYN9+/YdsbKttebZZ59l69atnHHGGSxZsoTs7OwBcTQ0NPDII4/wxz/+kdtuu23YMxCxWIwdO3YkjxUzZ84ckHhGo1Eef/xxotEoy5Yt48MPP2T79u2AOULRDTfcwOzZswHzmOtyuYZM/t944w3i8TiXXHLJgOUejyf5Xh1e8aqqqkom/rW1taxdu5aVK1cO2N7n87FlyxY2bdpER0cHNpuNBQsWcMEFF5CZmcmZZ57J5s2beeGFF7jnnnu4+eabyc/PH/K92L59O88//zzBYBCAsrIyVq5cycyZM+nq6koOw1pYWEh2dnby+LVp0yb+9re/4Xa7mTZtWvL3Ye/evTz11FPJ/efl5bFy5UoWLlw4bNlu27YNIHlsGM7KlStZv34969evH5fJv9JapzqGlFJKXQtcqrW+I3H/ZuA0rfXd/dbZnlinLnF/P3AaZoXgfa31I4nl9wEvaq2fPOw57gTuBJg6deqyvouOxorWmieffJKenh7q6+sxDAOv10soFCIajSbX60tAQqEQbrebVatWsXTpUtrb23nmmWeor6+nuLiY9vZ2wuEwc+bM4dRTT2XLli1s3bqV6dOnc8455xAIBDhw4AA7d+5MjpU8HKfTSTgcHnR7OBaLhZkzZ+L1emlubqahoQHDMAask5WVhdVqpbe3l0gkglKKwsJCXC4XSikMwyAWi9Hc3Dzg9QMopSgrK6OlpYVQKJQ8eIzW96SwsJCioiJaWlpoampKVpYOV1FRQWVlJWvXrqWnp4fZs2dTWVlJTk4O3d3dNDY2UltbS2NjY3Ibj8fDlClTSEtLIx6Pc+jQIXw+Hw6Hg8rKSnJzc+nt7aWjoyN5CvpwNpttwOnj/gnR4ex2O/F4fND7fzQulwut9aCyVkod9X3OyspKnra32WwUFRURi8Woq6sbsF56ejqFhYV0dnbS3t4+4LGpU6dy7rnnkp6eTktLCw0NDXz44YcEAoHka7bb7cyePZvp06fT1dXF5s2b6e3tZcWKFZSXl7N582b27duXjCkrK4tgMEhHR8egz1R/DoeD8vJyuru7aW5uxuv1kpOTQzweJxqN0tPTQzAYTF5oaLFYCIfDg/ZptVpxuVxEIhGi0Si5ubnMnj2btrY2Ghoa8Pl8Ry0HMMtwyZIlFBcXU19fz969e+nu7gbMZHbVqlXs3buXlpYWlFLs2LGDUCjEvHnzKCsrY968eckzgr29vdx7772Ew2GuuOIKFixYQFVVFc899xzZ2dnccMMNpKen8/777/PSSy9xyy23MH369GQsra2tPProo/T09HD22Wdz+umn43A42LBhA6+//npy1BG73Z78LPv9fhwOR/K73dvbm/w8ejwepk2bxt69ewGYO3cujY2NtLW1AeaxpK/CMG/ePDIzM0lPT8disfD+++9z6NAhFi5cyNy5c1m3bh01NTVkZWWRnZ1NTU1N8nnKyspwOBwcOHBgyM/vUMcQt9uN1nrYkVRsNhta62GPDwC5ublkZGTQ0tKSHJnm8MfPP/986uvrWb9+/YDvslKKtLS0AZ+T7OxsioqK2Lt3b/Lz1leZHioOq9VKRkYGnZ2deL1ewuEwkUgEj8fD9OnTmTlzJvPnz08mqY2NjTz22GNYLBbuuuuuAX2htdY8/fTTbN26lauvvpolS5YM+Zrvv/9+gsEgX/7ylwcs9/l8/OY3v8HhcJCXl0dLSwvTpk3jqquuGrbPdW9vLz//+c8566yzuPDCCwc9/qMf/YglS5Zw2WWXDVi+ZcsWnnnmGe68885hK7nr16/n+eef57zzzuO8884bch0wuw899NBDxGIxli5dSjQaxe/3U1FRwdy5c9m/fz9vvPEGXV1dZGZmkpOTw8GDBykuLuazn/0saWlpvPzyy6xdu5abb76ZGTNm0Nvby+bNm1FKsXTp0kHdex5++GH8fj933XXXgPfvF7/4BUuWLOGqq64a8r367//+b+bPn5+8XiASifA///M/2O127rrrLh577DHq6ur453/+ZxwOB4ZhsGbNGtatW5fswrV06VLmzZs3ZB//xsZGHnnkEbTWXHPNNcycOTP5WDweZ82aNbz//vuUlpZyxhln0N3dzbp165LHq8NlZGQwa9YsotEoW7duZcaMGVx33XUDGi37Ggj7Gt42bdpEU1MT+fn5XHHFFZSXlw/a729/+1tsNhtf/OIXhy3XPs888ww7d+7kG9/4RvJ7MJaUUhu11suHfEyS/xOf/Pe3fPlyvWHDhhP1co4qGAyyY8cODh06hMPhYM6cORQUFNDQ0EB1dTWxWIyCggIWLlw44EvS11p34MAB8vPzcTqdbNy4kXA4jMViYeXKlZx77rkDasvxeJzq6mo6OjpwOBxkZGSQk5OD1+tN/hA5HA56e3vZvn07nZ2d5ObmUlFRQVZWFna7nUgkQnNzM42NjTidTmbOnDngtGAsFiMSiRAOh+no6KChoYHm5ma01ng8HkpLS5k5c+aQ/RtjsVjyi5+eno7VamX37t1UV1eTk5PDKaecwrRp07BYLMmW677W62g0SiwWw+12Y7Va8fl8yQNINBpFa01+fj4FBQW0trZSV1eXbFHtSwZisRgtLS10dnYm35/09HS2bdvGW2+9RSAQoLCwkCuuuGJQS2ifnp4eqqur6erqSrZ8hEKh5JmNBQsWUFlZOeSBJxQK0dLSgtPpxO1209TUxIEDB7DZbMyYMYNp06YRiURobGzE7XaTm5tLPB4nGAzicDhIS0tLJiiRSIRIJEI8Hsdut9PV1UUgECArK4uMjIwBZ036zoy0trZSX1+Px+OhuLiY9PR0AoEA7e3tdHZ24vF4KCoqSrbYp6enJ380+o5bfe9lS0sLe/fuxWq1YhgGTU1NNDU14fV6mTNnDrNmzUpuN9Sp3XA4zKZNm+ju7iYzM5P29na2bduWrKBUVFRw3nnnMW3atOQ2gUAAq9U6KInx+Xx0dnYmP/cFBQW4XC5qa2vZv38/Bw8exOVysWLFChYvXjzgO9M3VJzdbh+wPBwO097enmx57TszYLVamTFjBrNmzUqur7XG7/cTCAQIBoOEw2FcLlfytHYkEqG6uhqAGTNmDPieg9mSuHbtWl5//fXk++x2u4nH4xQXF3PZZZcN2/e3o6ODv/71r9TX15Obm0tHRwfZ2dn09PRQWFjItddey29/+1tKSkq4+eabB5VDIBDgueeeY9euXckKUCAQYPr06Zxxxhnk5OQkK/cwuKuTYRjJCkBmZiYWi4Wuri7WrFlDdXU1BQUFzJ49G6vVSmdnJ0VFRcydO3dQVwXDMHj77bd544030FqTlpbGeeedx7Jly7BYLPT09LB7925ycnKSZ0h6enpoaGjA6XQmz3Dl5OQku4j5fL5kw0tf/H1nRLu6uggGg2RlZZGTk5P8bvX09NDW1pasZPedNSsvL6ewsDBZYe77LvWVd3Z2NgsXLkw+j9/vp62tjczMTILBIDt37qS3tzd5VrXveZVSBAIBNm3ahMViYdGiRdjtdnbv3k1bWxu5ubnk5uZis9nIy8vDZrOxb98+PvjgA7xeLwsXLkweM4dSU1PDAw88wIIFC7jmmmuSFYsXXniBjRs3cv7553PuuecOuS2YfdJffPFF7r777mRLudaaJ554gj179nDXXXcN22o8lEceeYS2tja+9rWvDfgsxmIxfvCDHwwZj8/n42c/+9mwiX1LSwu///3vqaio4KabbjpqV7z29nZeeukl9u7di9vtTp7l7FNYWMiqVauYMWMGFouFXbt28de//pXs7GxWrFjBCy+8wIoVK7jiiitG9JpfeeUV3nvvPb797W8nP/dvvvkmr7/++oD3dajt3nnnHW644QbmzJnD3//+d9577z1uu+02ysvLqa2t5f777+eSSy7hjDPO4N1332XNmjUsWbKEs846a0Tl0tbWxp/+9Cc6OzvJycmhsrKSgoIC1q9fT2NjI6eddhoXX3xx8nMdj8fZsWMH7e3teL1eioqKUEpRV1fHgQMHkhXy5cuXc+GFFx61q6ZhGOzatYtXXnmFrq4urrrqKpYuXZp8vKmpid/97ndcdtllnHbaaUd9PQcOHOChhx7i2muvZcGCBUddf7RJ8n8ESqkzgO9prS9J3P8WgNb6R/3WeTmxztrEBchNQD7wzf7r9l9vuOdLdfI/mgKBADU1NRQUFBx3n0UxUCQSobOzk/z8/JPmIqiJJhaL0dvbi9vtHpQgTwaHDh2ipqaGGTNmUFRUNOLt+roTbd++ncLCQi644AL27t3LX/7yF8Bs1b7rrruGTTLA/JHdtm0bPp+PuXPnMnv27KP2xT0R+irVpaWlJ/01AONFX6I5ffp0YrEYra2tBIPBZAv8kcq5u7ubX/ziF6xatSrZvWT79u08+eSTXHjhhcluZCPV14p/++23D2hg6enp4ec//zlXXnkly5cPzp0eeOABenp6+MpXvjIgXq01f/zjH2ltbeXuu+8+puEx+1+T1dTUxL59+ygoKGDWrFmD3pPq6moeffRRIpEIU6ZM4fbbbx/xiDk7duzgL3/5y4AzF/feey9a6yO2ZsdiMe69915aWlooLi6mrq6OZcuWDThT8MADD9De3s7111/P/fffT2VlJddff/0xfXdjsRhbtmxh9+7dHDx4kHg8TkZGBpdeeumIrifqr6/B7lh/Q8PhME888QQHDhzglltuoaKiAoDnn3+eTZs28fWvf31Ew3gahsFjjz3GsmXLkl2vxtKRkv9xfzRTSm0Dtvb72wbcqrX+4Sg9xXpgllKqAqjHvID3psPWWQ3citmX/1rgNa21VkqtBh5VSv0c84LfWZgXJ08KHo+HuXPnpjqMCcnhcAzbuirGhs1mIzs7O9VhpExZWdmwZ5yOxGKxsHz58gFJ0/z584lGo+zdu5dTTz31iIk/mK2dh/f5ToW+Ll1i9KxcuZJoNMquXbtIS0tjzpw5zJs3L3l27kgyMzMpKSlh586drFy5kt7eXp5//nlKSko444wzjjmWOXPmJFvT+3/Wh5rgq7/Fixfz7LPPUldXN2C7LVu2UFtbyyc+8YljHhe/f6v00T7/5eXl3H333TQ3N1NeXn5MXUr6KvINDQ3J67nq6+uPWnGy2WzccsstvPHGGzQ0NHD22Wdz/vnnD1hn5cqVPPzww9x7772kp6dz1VVXHXOl3WazJY8ffdfCZWdnf6wBFvrOOB8rp9PJddddxx/+8Aeefvpp/uEf/gGr1crWrVuZN2/eiMfvt1gs3HTT4enk+DDuk3/gXGBR4u8GzEm+dgCjkvxrrWNKqbuBlzGH+rxfa71DKfV9YIPWejVwH/Bw4oLejkQcJNZ7AvPi4Bjw5Ykw0o8QQoy2JUuWDNuXW0weFouFCy+8cMh+9iOxcOFCXnrpJfbv38/bb79NNBrl6quv/ljJocvlory8nD179nDxxRcnl/cl/8MlefPmzePFF1/kvffe4/rrr09us2bNGkpLS8fkc56RkfGxhgnNzs7G5XIlrxerqalBaz3g+pvheDweLr/88mEfnz59OqtWraK2tpZVq1Yd98RgfddwpILL5eKaa67hvvvu47nnniM3N5dwOMyKFStSEs9oG/fJv9a6A3Ns/zcAlFKzgH8f5ed4AXjhsGXf6Xc7BFx3+HaJx37IKFVEhBBCCDG8U045hbVr1/Lwww8D8KlPfeqY+vkfrrKykpdeemnASDZ9A1UMl7w6nU7OPPNM3njjDWprayktLWX16tWEw2GuvPLKcd1VUylFcXExDQ0NgDkJms1m+1hn+Yba9+Gj/ZzMSkpKuOCCC3jllVcAs+I5derUFEc1OsZ98p8YN39P332t9V6l1MhmkhFCCCHEhOF0Orn11lvZvHkz06ZNGzAqzMcxe/ZsXnrpJfbs2ZPsOnS0bj9gjn+/efNmHnnkEbKzs2lubuaSSy4ZF93Vjqa4uJj33nuPaDRKTU2NXNNyBGeddRZ5eXn4/f6jDu95MjkZSvv3SqkZmP3xtwIuYLtSyqO1PvI4kkIIIYSYUHJycli1atWo7Cs7O5v8/HyqqqoGJP99o00Nx+Vycdttt/HKK6/Q29vLlVdeedSJv8aL4uJiDMOgtraWpqamIedNECalFHPmzEl1GKNu3Cf/WuvzAZRSU4HFwJLE/y1KKUNrPfFKRQghhBBjYvbs2bz77rsEg0Hcbndydt+jXSyanZ09aNbbk0HfKD/r1q1Daz1gCGMxOYzfjmmH0VrXaq2f01r/p9b6Wq11JTDkEEZCCCGEECNRWVmJ1pr9+/cDZsv/8V6sOp5lZmaSkZHBnj17sFqtlJaWpjokMcZOmuR/KFrrkU1jKYQQQggxhJKSEhwOR3LyO7/fP+LhHE9GSqnkqDWLFy8e8RwBYuIY991+hBBCCCFOFKvVyrRp0zh48CBgTvLVN7HTRHX22WdTUVFBQUFBqkMRKXBSt/wfL6VUjlJqjVJqb+L/kDP6KKVuTayzVyl1a7/lP1RKHVJKyRkIIYQQ4iRVUVFBe3s7XV1d9Pb2kpmZmeqQTiilFKWlpdLqP0lN6uQf+CbwqtZ6FvBq4v4ASqkc4LvAacCpwHf7VRKeSywTQgghxEmqr6V/69ataK0nfPIvJrfJnvx/EngwcftB4Ooh1rkEWKO17tBadwJrgEsBtNbva60bxyJQIYQQQpwYU6ZMweVysXnzZgCysrJSG5AQJ9BkT/6n9Evem4ApQ6xTAhzqd78usUwIIYQQE4DFYqG8vJzOzk6A45o1WIjxbsJf8KuUegUYasq9/93/jtZaK6X0CYrhTuBOYMJMDS2EEEJMJBUVFezevRuXy0VGRkaqwxHihJnwyb/W+sLhHlNKNSulirTWjUqpIqBliNXqgfP63S8F3jjGGO4B7gFYvnz5CalgCCGEEOLjmzdvHnv27GHBggWpDkWIE2qyd/tZDfSN3nMr8OwQ67wMXKyUyk5c6HtxYpkQQgghJgiv18vNN9/MKaeckupQhDihlNaTtyFaKZULPAFMBWqAz2itO5RSy4G7tNZ3JNb7AvDtxGY/1Fr/MbH8p8BNQDHQANyrtf7eUZ6zNfFcqZYHtKU6CHHCSTlPfFLGk4OU8+Qg5TzxjVUZT9NaD3nxyqRO/iczpdQGrfXyVMchTiwp54lPynhykHKeHKScJ77xUMaTvduPEEIIIYQQk4Yk/0IIIYQQQkwSkvxPXvekOgAxJqScJz4p48lBynlykHKe+FJextLnXwghhBBCiElCWv6FEEIIIYSYJCT5F0IIIYQQYpKQ5H8SUErdr5RqUUpt77fse0qpeqXUlsTf5amMURyfoco4sfwrSqndSqkdiXkpxElsmO/y4/2+x9VKqS0pDFGMgmHKeYlS6v1EOW9QSp2ayhjF8RmmjBcrpdYqpbYppZ5TSmWkMkZx/JRSZUqp15VSOxO/w19LLM9RSq1RSu1N/M8ey7gk+Z8cHgAuHWL5L7TWSxJ/L4xxTGJ0PcBhZayUOh/4JLBYaz0f+FkK4hKj6wEOK2et9fV932Pgr8BTKYhLjK4HGHzM/inwH4ly/k7ivjh5PcDgMr4X+KbWeiHwNPCNsQ5KjLoY8HWt9TzgdODLSql5wDeBV7XWs4BXE/fHjCT/k4DW+i2gI9VxiBNnmDL+B+DHWutwYp2WMQ9MjKojfZeVUgr4DPDnMQ1KjLphylkDfS3BmZizyouT1DBlXAm8lbi9Bvj0mAYlRp3WulFrvSlxuxfYBZRgNsw9mFjtQeDqsYxLkv/J7W6l1NbE6ccxPeUkxkQlsFIptU4p9aZSakWqAxIn1EqgWWu9N9WBiBPin4D/Tyl1CPMs3rdSG444AXZgJoUA1wFlKYxFjDKlVDlwCrAOmKK1bkw81ARMGctYJPmfvH4LzACWAI3A/01pNOJEsAE5mKcavwE8kWgdFhPTjUir/0T2D8A/a63LgH8G7ktxPGL0fQH4R6XURsALRFIcjxglSql0zG6Z/6S17un/mDbH3B/Tcfcl+Z+ktNbNWuu41toA/gDIxWMTTx3wlDZ9ABhAXopjEieAUsoGXAM8nupYxAlzKx9dz/EX5Jg94Witd2utL9ZaL8OsyO9PdUzi+Cml7JiJ/5+01n3f4WalVFHi8SJgTLvlSvI/SfV96BI+BWwfbl1x0noGOB9AKVUJOIC2VAYkTpgLgd1a67pUByJOmAbg3MTtCwDp3jXBKKUKEv8twL8Dv0ttROJ4Jc623wfs0lr/vN9DqzEr9CT+PzumcckMvxOfUurPwHmYrb7NwHcT95dgnmqqBr7Ur/+ZOMkMU8YPA/djlnME+Fet9WspClGMgqHKWWt9n1LqAeB9rbUkCxPAMN/nKuCXmN35QsA/aq03pipGcXyGKeN04MuJVZ4CvqUlSTupKaXOBt4GtmGefQf4Nma//yeAqUAN8Bmt9ZgNzCLJvxBCCCGEEJOEdPsRQgghhBBikpDkXwghhBBCiElCkn8hhBBCCCEmCUn+hRBCCCGEmCQk+RdCCCGEEGKSkORfCCGEEEKISUKSfyGEEEIIISaJ/x+EfJP4/CWm5AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import FastICA\n",
    "\n",
    "transformer = FastICA(n_components=7, random_state=0, whiten=True)\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": 23,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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:', [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",
    "# print(event_codes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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 (trial dim: channels x samples x 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": 25,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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 trial')\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": 26,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": [
    "plt.figure()\n",
    "plt.title('Distribution of the std for all trial')\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": 27,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8, 500, 198)\n",
      "(8, 500, 196)\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)) < 1.3 and eeg_sampleL.max(axis=(0, 1)) < 4: #0.7\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)) < 1.3 and eeg_sampleR.max(axis=(0, 1)) < 4: #0.7\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": 28,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": 29,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.1837450832330974"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "good_trials['imagery_handR'].max(axis=(0, 1, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": 31,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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[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": 32,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hXiqE2C2E2CeE+ECT131CiJ9arz8ihFhpLX+jEGJL3U9FCLHJeu1ea5v2a13HY6xnEr759Dd5153vYnd896LuN1cyCXrcxAIeANKOWa0DB6cN/tD/B/7twX/j4SMPn+yhOHDgYBYcM0ETQriBLwMvA84F/lIIce6U1d4GJKSUa4HPAf8JIKX8oZRyk5RyE/Am4KCUckvd+95ovy6lHDvWsZ5puHX/rdw/dD8ff/jji7rffMkg6NOI+FUKY6ZoLOr+HThwcPTYl9gHwMPDDkFz4OBUxvFQ0C4F9kkpD0gpS8BPgFdPWefVwHetv28GXiiEEFPW+UvrvQ7miXJFKVcThYlF3W9ONwl53XUEzVHQHDg4XbA/tR+Ah4YfOskjceDAwWw4HgRtGXC47v9Ba1nTdaSUBpAC2qes8wbgx1OWfdsKb/6/JoQOACHE3wohHhdCPD4+Pn60n+G0RFJPApDWj2/Li7mQLxkEvRo+zY1XczkKmoMFo1KRSClP9jCeldif3I9AsCu+i3gxfrKH48CBgxlwShQJCCEuA/JSyu11i98opTwPuNL6eVOz90opvy6lvFhKeXFnZ+cijPbUgFExyJQyAGTKGYzK4pGknG4S8rkBiPo10g5Bc7BAvOHrD/H27z7ukLRFRq6cYzg3zCU9lwCKrDlw4GB+GBkZ4frrr2fNmjVs3ryZa6+9lj179gCQTqfp7e3l3e9+93Hb3/EgaEPA8rr/e61lTdcRQmhADJise/16pqhnUsoh63cG+BEqlOrAQrqkVLO+aF/D/4sBW0EDiPg9TojTwYJgmBUe609w164xfvTowMkezrMKB5IHALhi6RUAjOWd1F4HDuYDKSXXXXcdV199Nfv37+eJJ57gU5/6VLXv5v/7f/+Pq6666rju83gQtMeAdUKIVUIIL4ps3TplnVuBv7b+fh1wt7SmzkIIF/AX1OWfCSE0IUSH9bcHeAWwHQdVpPQUUCNo9v+LgVyppqBF/JoT4nSwIBxJ1tqD3bv72ZWWcLIxmB0EqCpoDkFz4GB+uOeee/B4PNxwww3VZRdccAFXXnklTzzxBKOjo7z4xS8+rvs85k4CUkpDCPFu4A+AG/iWlPIZIcTHgMellLcC3wS+L4TYB8RRJM7GVcBhKeWBumU+4A8WOXMDdwLfONaxnkmwCdmKyIqG/xcDeb1eQdMcBc3BgnAongPAp7kYSzu9XBcTdlrE0tBSglrQIWgOTk/8/gMw8vTx3WbPefCyTwNKLYsX4wQ9QQKa6pKzfft2Nm/ePO1tlUqF973vffzgBz/gzjvvPK5DOi6tnqSUvwN+N2XZR+r+LgKvn+G99wKXT1mWA6YfCQdV2AUCK6KKoC1miDNXUlWcABGfx+kk4GBBODSZB+DSVW3sHc2e5NE8u5Atq+Md8oToCnYxmh89ySNy4ODUQ0VWGMmN0B3qrhK0mfCVr3yFa6+9lt7e3uM+DqcX52kKm6D1RRY/xGn7oIET4nSwcAzE83g1FxuXxXhw/ySVisTlalqk7eA4I1vK4hIuAlqA7mC3o6A5OD1hKV0nChJVvCSo3Zc2bNjAzTffPG3dhx56iPvuu4+vfOUrZLNZSqUS4XCYT3/62Md4SlRxOlg4qjloMUXQbMJ2olEyKpRNWVPQnCIBBwvEwGSe5a0BlsT8mBXJZK50sof0rEGunCPkCSGEoCvY5RA0Bw6awG6D5hI1inTNNdeg6zpf//rXq8u2bdvGDTfcwMDAAP39/Xz2s5/lzW9+83EhZ+AQtNMWST2JJjR6gj0IxKIpaPmSUsvqc9ByJROz4tglOJgfDsXz9LWH6Ir4ABh18tAWDdlylrAnDEBXsIvxwrjTk9OBgyloRtCEENxyyy3ceeedrFmzhg0bNvDBD36Qnp6eEzYOJ8R5miKpJ4n6orhdbqK+6KIRtFzJBGio4gTIFg1iQc+ijMHB6Y3hVIHNfS10Rf0AjGecHMbFgq2ggSJoRsUgUUzQHpjqG+7AgcLO4TRru8J43M8ePadZiBNg6dKl3HTTTTO+7y1veQtvectbjts4nj1H/AxDSk/R4msBIOaNkSotkoKmNypoUb/TMN3B/GFWJKlCmbagl26LoDkK2uKhXkHrDnYDjtWGg5nx/Yf6edn/3MdX7nl2GRo3U9BOBhyCdpoipaeI+WIAxHyxRWv3NJOC5hQKOJgP0oUyUkJL0Etn2A5xOgraYiFXyhH2KoLWFmgDIFFMnMwhOThFMZYu8m+3PgPAwwcm51j7zILd4cQhaA6OCgWjQNATBFjUEKedgxbw1DoJgNMw3cH8kMirgoC2kBev5qIt5GUs4yhoi4V6Bc1W4BO6Q9AcTMd9eyeoSNjc18q2wSSG+ezJVbQVtBlagC8aHIK2QEwUJvjIAx8hWzq5/k1Fs4jfrUJEIS1Ezsgtyn51Q524Po86dRwFzcFCYBO0FitfsT3kZTLrVHEuFrLlbDUHrdXXCixeBbiD0wv375ugPeTlzVf0kSuZ7BhePK/Nkw07B811kimSQ9AWiHsO38Mt+27hroG7Tuo4ikYRv6YIWkALUDAKi7JfvWwRNG0KQdMdBc3B3Ejk1HnSGvQCEA14nHNnEZEr56oKWsQbwSVcTojTwTRIKblv7wTPWdvBZatUAckTh54954mjoJ2m2B3fDcD9Q/ef1HEUjZqCFtACFI3FCROVzKkEzQ5xOgqag7lhK2g2QXOMjhcPRsWgYBQIeZWC5na5iXljjoLmYBr2j2eZyOo8d0073VEfXs3FSOrZk4rgFAmcptiT2APAg0cexKicpAeLWW5U0DyLqaCpIgGfdgoXCZgGFJ49s73TCcm8UstaQorYR/we0gVHQVsM5MoqDcJW0ABa/C2OguZgGp48lATg4pVtCCHoCHmZeBalIsxkszEyMsL111/PmjVr2Lx5M9deey179uw5YeNwCNoCUJEV9iT20BnoJF1Kszexd/EHISV8/zqKpQx+6+sLaAF0U8esmCd897aC5rUUNL/HjdftOnVsNko5+OJF8KVLYBGOx8lAsphkNHd69lBM5EtoLkHEaRW26GhG0Fp9rY6Cdhyg79+PmVq8dnsnGk8cStAS9LC6Q6mt7WEfk7lnT7V1MwVNSsl1113H1Vdfzf79+3niiSf41Kc+xejoibsXOwRtARjKDpEr53jO0ucAEC/GF38Q++7C6L+PsgD/gT8BENRUNWfRPPES9NQcNDjFHrJ//AwkD0FuHAYfO9mjOSH4x3v/kXfe8c6TPYyjQiJfoiXoreZ2nFLnzhmO+kbpNlp8LU4V5zGiks9z4OWvYM8Vz8GIn4RnwgnAEwMJLlrRWu2R2x5+dhXzVGQFIURDDto999yDx+PhhhtuqC674IILuOOOO9i0aRObNm1i2bJlvPWtbz1u43A6CSwAe+JKyry452J+tf9XZEqZxR/EA59Hj/YCEEgcAqjmohWMQsPN90RgqoIGp9hDduRpaFsNyQHYcxusuPxkj+i4YufkTh4ffRxQFcUdgY6TPKKFIZEr01rXcSLq91AyKxTLJn6P+ySO7MyHXXlu+6ABtPpbeXri6ZM1pDMCuYceUn9UKkzceCM9H/rQyR3QMcCsmHz+iS+zP97OazZdWl3eEfaxZ+QkPO9mwH8++p/siu86rts8u+1s/uXSfwGUWja1gnP79u1s3rx52vs+9rGP8bGPfYxkMsmVV17Ju9/97uM2JkdBWwA2dW3is8//LBd1XQRAprzIJ2w+DoceoHDedQD48nHQswQ8AQBSxRNvtWEraF53PUGbo2H64cdU6HExkDwEPefBiitgzx8WZ5+LiJv23FTNi3hq7KmTPJqFI5EvVQsEAKKnYg7jGQpbQWvIQbMUNNuY08HCkb33XlzhMP7zzkPfsfNkD2dhSPSrnF0Lz0w+w3d2fANf121c1NdaXd4eVjloz5bzpEJlQRWcUkr+6q/+ive+971NSdzRwlHQFoD2QDsvWfkS8uU8wOIraPvvBllBX/k8OPIr/FLCxB6oqAfe+29+jFve0XdCh1AyTTSXQHPPU0F79Bvwu/fDNf8KV/3TCR0blQokD8NZ10LP+XD3x1WxQKB17veeJtib2MuFXReyY3IHT44+yYv6XnSyh7QgJPNl+tqD1f8jda3COq3m6Q5ODOwctHqVvdXfilExlP1GnbJ2JkJKyXhGr/aAPV7I/vFPhJ73PNzRKJk//AEp5Um3Z5gXRrbDjc8FXxRuuA9aV7JtfBsAWnQrHS1ZQCn0HSEfJbNCRjeq7f1OJmyl60RBSjmtgnPDhg3cfPPNTdf/6Ec/Sm9v73ENb4KjoB0VAloATWiLT9D23AbBDortawAUQRvfzS+eUL30th4ZO+GNp/VypSG8CYqgZZsRtHwcfm9dSBOLUFCRHQVTh5YV0HuxWjb05Inf7yJiJDdCb6SX8zvPPyMUtFOyCvgMhV3pHdAC1WXPpm4CDx+Ic/mn7mLf2PEzGTdTKYyxMQLnn49v3Tr1//j4cdv+CcWAFZrV07D9FwBsm9iGW4YQwF2Dv6+u2h5W1+xi5qHtmNzBTbtnbkx+ImHnoNXjmmuuQdd1vv71r1eXbdu2jY9//OPceeedfOELXzju43AI2lFACEHEG1lcgiYl7L8H1r6QYkVdJH5cML6LnUO6Na4S9+87sTeHkllpKBCAWUKcw1tBWpWUVr7cCUVyQP1uXQlLLwTEGUXQjIrBRGGC7mA3q2KrGMwOnuwhLRjJQrnaRQCUUS04rcIWA7ZXos9dUypb/UpdfjZYbewbz1KRsPVw8rhtszRwGADviuX41q1Vy/btO27bP6E48hSEOqFjPQw8DMC28W0YudW0aKt5cOhBQOUupuQ+EGUms4tTyWlUDN7wmzfw8Yc/Ttlc/HuDZHoOmhCCW265hTvvvJM1a9awYcMGPvjBD3L33XczNDTEpZdeyqZNm/jIRz5y3MbhhDiPEhFvhHRpEVtfjO+C/ASsvLJaremPLKU0soPxzDJCHRAJSu7bM8F1F/aesGHMpKA1VUBGrOTjc14Jhx46YWOqImmRwJY+8MfUjWfo8RO/30XCZGESU5r0hHoIaAFSeqrBD+9UR7FsUjIqVVIGjoK2mNBN9XCtP1+i3ihwEtI1TgLG0+q+uWf0+H3W8mE1KfQsX4HWoRz39b17CT3nOcdtHycMR55SE9nIEtjxSxL5SYayQ+i587lgdQv3jf+U/cn9vOW2t5DUk3har2UiuzhFV7898Nvq34ezh1kdW73gbRxLqLkiK01NapcuXcpNNy2eqndcFDQhxEuFELuFEPuEEB9o8rpPCPFT6/VHhBArreUrhRAFIcQW6+fGuvdsFkI8bb3nC+IUC+qHveHF7cfZb3UuWHVldSbsjyylNDmAtHLQ1nR5eHLgxM6EdcOsmtTaiPg9ZEsGlcqUBNLR7RBdBr2XKnJ5os1jbZWuZbn63XsxDD6u1MczACP5EQB6Qj10h7oBGMuPncwhLQi2IW2sgaA5CtpiwZ7Y1StozyaCNmalf+w+jgStqqAt70Vrb8fd0oJ+4OBx2/4JQymnJv1LL4S+50AxxeiQUtFkuZ2Xr3kBFVnh/7v7/6ueG27f6KJ5oT08/HD170OphUVfKhVJ/0SO7UNpSsbReWE2C3GeDBwzQRNCuIEvAy8DzgX+Ughx7pTV3gYkpJRrgc8B/1n32n4p5Sbr54a65V8F3gGss35eeqxjPZ5Y9BBn/30QWw4tfVWCFgi048qNV4sEYiHJcKp4QittSuZ0BS3q15ASsqUpKsjI06qisl1J/0weOGHjAiA1AKEusKpaWX6ZIoaTp0nIYQ7Y5rTdwW66g4qgjeZPH8PalEXQHAXt5EA3dDShoblqgZOINwKwuNGAk4RRW0E7jnYRpcMDuDs7cAVV4YvW3Y0xMXHctn/CMLYLZAWWXAC9lwCQHFY5rWFPjBetuYSrl1/NQGaA16x9DRd1bUZ4J4gvUg7aUHaIs9vOBuBQemEELV0sky6WkUgKluvAQtGsSOBk4HiM4FJgn5TygJSyBPwEePWUdV4NfNf6+2bghbMpYkKIJUBUSvmwVGzje8BrjsNYjxui3ujiErTDjyrrCCEomCrZ1xfqxF9OsCKmqq8CPhPdqFTb6ZwI6OVmOWhNHrLlIozvhu6NdQTtBBOlfFzlVNjos8IMhx48sftdJIzklILWHeymK9jVsOx0QKqJghb2agjBMbV7+tWWIf7+R2dOruGJgm7q+LTGSlmboD2bFLQjqWL1XKyHlJLfbDuCvgDVpTxwGO/yFdX/tfZ2jInToEgga903oktVSohLI5nsB+Dc7h40t8b/vOB/+PILv8w/XfJPrIz14fZOktUXZyI1mBnk7LazafO30Z/uX9B7i3WkrGQcHUGryMq0HLSTgeMxgmXA4br/B61lTdeRUhpACmi3XlslhHhKCPFHIcSVdevXZ0A32yYAQoi/FUI8LoR4fHwRq2cWVUHLjEJm2Ep8VzNhAH+oCxcVNraqkGPAq24sI+kT11GgmYLWNEyV6FcFAp1nQ6tl/ZEaOGHjAixLjZba/+1rFWE7QwjaaH4Uv9tPzBc7LRU0ux1YPUFzuQRhn0b6GBS0O3eO8dttwxyO5495jGcyimaxIbwJKtzpcXmeNQStw6pG7J+Y7sv45ECSd//oKT566zPz3mbp8GG8y5dX/9c6OzDHTwMFLWc9K0Od4NagpY/JtHrkXrhMPWpdwsVVvVcR8oRYHlmO0LIkiideaS0aRcYL4/SGe+mL9i1YQSsZ6hnldomjD3Eu0AftROFkU8RhYIWU8kLgvcCPhBDRhWxASvl1KeXFUsqLOzs7537DcULYE148o9rhLer30k1ALZckEFYP6XVBA01oeDzqITeSOnEEbd4KWsLKw2hbBZoPND+c6Iu7kGz0PBNCqY4DZw5B6w51I4Qg6AkS8UZOqxy0ZgoaqG4CxxLiHEwoYvbg/tPgwXgSoRt6g8UGnKSK9JMAsyKZzOqs71aKYTMFLZ5T4btfbTkyr21Kw8AYHcWzrKYduDs6MCYmTn1DV5ugBa1OJO1riGfVveTCZUunrd4XVZPsieL8js2x4EhW7WNZZBl90b4FK2i6YeJ1u/C6XZTMo/sezqQQ5xCwvO7/XmtZ03WEEBoQAyallLqUchJASvkEsB9Yb61fX4rYbJsnFRFvhIJRoFxZhOTmI1sAofK5qPkZEegBoM+bVd5smhrL8IkkaGYFb5MiAZiioMUtgta6Uv32RZXfzonEVAUNoO+5yn4jebjpW04nTBYmafe3V//vDnafVk3TU1bo3e4eYENVAR/9dTSYUNfDg/snj35wzwI0U9DgJKRrnARMZnUqEtZ0qnSQZhMCO0ctXzKZmIedhDEZBynRurqqy7SOTmSpRCVzih/P3IS6J3usit62NWTLKaTp4+ye6cbeKyIqjJsoNydoUkqOfPjDxL//g2Memm0f1BvupTvYzWRhstq8fC5IKSkZFXweN17NdVQhTimlKhLgzFDQHgPWCSFWCSG8wPXArVPWuRX4a+vv1wF3SymlEKLTKjJACLEaVQxwQEo5DKSFEJdbuWpvBn51HMZ63GDnbixKJefwFhWu86l92kUCaaEe1ks9GTUzFiVc4sSGOPWyOU1BsxWRhty3RD94IxC0CIU/euIVtGIS/C2Ny/quUL8HFsHm4wQjXUoT9dUE5u5g92kV4kwV1EMxOkVBC3rdFMpHF4ools2qOfPDBxyCNht0U29K0BbdMugkYDStzpE1naqLQrMJwVjdffORA3M3PbdzzbTOWj9craPDem1x1dzy2Bjx7/8Aac7zOsqNN+brtq8h7ZK4K36WxgLTVl8eURpMutz8flN4/HFSP/8Fo5/6FIWtWxc8/noMZiyCFukl6o0ikfOeQBgViSklPrdLETSzsmA1U6LWb6agud1uNm3axMaNG3n9619PPn9i0yqOmaBZOWXvBv4A7ARuklI+I4T4mBDiVdZq3wTahRD7UKFM24rjKmCbEGILqnjgBimlfWX8HfC/wD6UslazNT4FsKjl6aPbYcn51X91U4UqjpTVGLpcaQKeALpZpDPiYyRVOGFDaZaD1hZSeR12iABQIc7WlSrMCCdeQSsXoZyf3tape6Pa9xmQh5YupavnHUBnsJPx/GmQkGwhVSgT8rrxuBvPn5BPI3eUycdDSXWur+4IMZrWKZtHX7V1MgwxFxO6oTf1zHs2hDjHMop8remaTUGrqWbJwtzViqZFwmxSBjWyZixyHlr8u99l9BOfYOIrX53fG6YStLbVJF0uIsKLyzVdOQp6grikl2Kl+Xky+a1v425txd3WxsQ3vnE0H6GKoewQfrefdn97dUI63wmErZh5NUXQ1HW9sHuCTeia5aAFAgG2bNnC9u3b8Xq93HjjjdPWOZ44LkFWKeXvpJTrpZRrpJSfsJZ9REp5q/V3UUr5einlWinlpVLKA9byn0spN1gWGxdJKX9dt83HpZQbrW2+W55iQf1Fq34ydEgN1iohUSFOv9vP4YKXknTTKpMEtAAFo0BP1M9I+sR51TTLQYv6NTxuwUR9CXaiH9pW1v73R6GYOmHjophUv6eGOF1uZbdxBhC0lJ4i5otV/2/ztxHX46d+vouFdLE8Lf8MwO+R5EpHR47s8Ob5veq4JHILtwHYl9jHC256AZf+8FL2JhahJdlJQtEs4pPAr/9BTWgsPBsUNJuQLYkFVNVwEwVtNFNknUXg0oW5Jwx2S6cGglZV0E7cxOmZiWf43BOfawj7Ze++B1wuJr7yFcoj86jszk1AqDZu2laTcrloE+4Z36KJAHplumIkTZPcAw8QfeUrCF91FYXHHkdWjm6iBIqgLQsvQwhRnZDOm6CZdQTNmgiWF5iHVkFtY64qziuvvJJ9+/Zx4403smnTJjZt2sSqVat4wQtesKD9zQank8ACcdv2Ef71l0/zwgtVaNNuQHzCkDik/Graak7KRaOIT/MxnCoySYxOI0EgYBG0mJ+DTSqU5gsznSZz193EXn4twuud9nqzVk9CCNpCXuK2iWGlosa9/iW1lfwxSA8f9bjmRCGpfjdrjL78Uth3hzJn9Iamv34aoFwpUzAKDQpau78do2KQLqUbiNupilShPC28CbCTT1Pw9QBXL3ibdoHABctb+OWWI0xkSwtuhv3IyCNMFlV49LcHfst7Nr9nweM4HaCbOq35NOz8FZx/fTX8/2xQ0OwQesjnJuxr3vlkNK2zoi3Iocl8UwI3FXYY092EoJknMMT5w50/5NcHfs05befw0lUvRT9wkNLBg7S84Q0kf/pTcg89TMt1r5l9I7lxNXG1//V3k3S7WNJEPbPhdYXIy+nPlvLgILJUwn/W2eBykfrFL9D37cO/fv1Rfb7BzCDLIqrwokrQ9DQttFTXGfnkJ9F37pr23pJZwW1UGPFpVKTEXTIZ8bjRZvlcNnznnE3Phz40q4JmwzAMfv/73/PSl76UG264gRtuuIFyucw111zDe9/73oV83Flx8ssUTiNsPZzkPT99CrMi+dljapZiV1SeMMQtc9e2NdVFuqnjd/s5kiqSFC1ohYmqgtYW8hHPHX2oZvRTn2b4gx9k4O3vQJamqxEqB236LKs95Ks10s0Mq6bldoEAnPgQp92lYGoOGkDnWer3YjRsP0FIW8euQUELtAEQL86dL3MqoBlB25PYQ0rup6Qdnfv6YKKAxy04Z4m6kR+N0/mexB5afa1cseQK/tB/O9978OBR+yedyigaRXxZS9mx7ys8SwhaSRG0oEcj6vc0JWBj6SLdMT/RgDYvXz5jfAJXNIrLV8vrc8Vi4PGc0By0x0dV+7ovPvVFpJTkHlLRgfZ3vB13Wxv5h+fIt62YkJ9sCHEeShokXBo9zJzD5nOFMJiuoOlW71Hf2jUEL7kYgPxjjy3oM9mQUjKUHaI3rGoEFxriBKqp/fbvhcYXbILWLAetUCiwadMmLr74YlasWMHb3va26mv/8A//wDXXXMMrX/nKBe5xZjgK2gLgcbs4v7eF/3rd+VzzReXpZSfsnzDE96vfUxS0gBZgZKJAztMKuXEC2jLG8mO0BD2kCqWj6kNW3L2H1C9/ie/ss8k/+ij5xx+f1lOuWQ4aQHvYy6QdXkpblT7RukJcf2zeRQKpW29F6+4hdNml8x+8TdCaKWgd1kxuYm/VquR0Q6qkwsP1Clqbv0bQVsVWnZRxLQTpQpnlbcGGZbcdvA0AqY1hVkzcrplDLM0wmdVpC3npivis/xce4tyb2Mu61nW8eOWL+feH/p2P/uFuemIv58Ubeha8rVMZejmPz75O6gha1BulVCnNWERwJsBW0PxeV9PewSWjwmSuRHfEbxG4eYQ4JyYawpugVBetrU1VeJ4ADGWHGM4Ns7ZlLfuS+xjODeMeOIwIBPAsW0bwskvJPfzI7Pf/fFxFZeoI2pFUjqxb0GnMPMEJuENIMYlhVtDq8kj1feoZ5V2zBlcohNbTQ+GJJ+GNb5z7A+UmVOpLuxIgUnqKbDnLsvAUBW0KQev50Ieabm4wkadcMFi5NIphVtgxnKalJUBHeP7ntR3ibFbFaeegTcV3vvMdDh06xJe+9KV572c+cBS0BeDcpVF++reX09ceYlVbC1BrQHzCED+gyE2wrbqoYBbwa37GszqGR+V2+TU/uqnTEvBQNiX50sKr4pI334zweFj+lS+DppF76OGG16WU6Mb0ECdAe8hbUy8yNkGr89PxRaGcA3P2G195eJgjH/owR/75n6noCzi21Ry0JgStbTUIF0zsmf/2TjHYCtrUECecPgpaujA9B+3ewXsBgXAZDGYW7qST001CPo32kEXQFpiDVpEV9iX3sb51PZu7NwPg8g+y+zi2AzpVoJez+KUEl6dRQfMcfT6tmUrNu3Jw22CSQ5MnOCVkBhTLJi4BXrerqa3Lgfg47uB+uiIeIgFPzSdNSrj/85Cafm4a4+PTCBqAOxrFTJ+YfNvHR5R6dv1Z1wOwL7mP8vAwniVLEEIQuuxyjNFRSv39M2+kalJbG3t/Qil+7bO4EgS1MMJVIDfl2aLv34e2ZAnucBghBP5zz6W4e/f8PtDP3gJfuhj+9FlAEVCgaYhzPjBMieZWxMoudjCn9oieA/MJcdbjiSee4LOf/Sw/+MEPcLmOL6VyCNoCYX9p5y1Ts49C+QQraJP7VXiz7mQpGsrPKJ4tUfHFoJDE7/ZTNIq0BC3LiwW2zpGVCpk77iD0vOfhWbqUwHnnkXu4kaAZFYmUVJMv69EergtxVhW0uuYPfotYzHGhxb/7PTBNjNFRkj/5yfw/QFVBa5n+muaD1lUwMc+bxkKRHYPPbYT9d8+4SrpY5s3fepTbth9dayZ7Bjm1SACUP9rpgGQTgjaSG6HTuxKAnZP7F7zNrG4Q8WlEAxqaSzA5D/+qegxmBikYBda3rmdFZAVCenD7Rth1BhK0glnCJ6XyU5wS4oSF9+PU9+5l79UvIP7tb8+5rlmRvPXbj/Efv925sEEfJxRKJgGPWyWe+z2I/CTc8i4YeJiB9AD/5w/XEuz7Bv36fUT9dSHOyX1w57/Bt182bZvGxARaE3N0dyxGJXliCNq+5D58bh8vXfXS6v/lI0fwLFWT4dAVlwOQf+SRmTdSsCZ0wZqn4mBKEbTWQkKR0iYIeSLgLk5r91Tatx/fmloKju+s9ZQOHqRSnOPZOLFP9Zj2huG+/waz3OCBBih/T5c273PTrEjcFjFzCYFLiKMmaPM1qv3Sl75EPB7nBS94AZs2beLtb3/7gvY3GxyCdpS4qFeZEw6mTmBlIqhqyPpcLmo5aBO5EtIfg2KKgKYIWiygEvuT+YUpCcWnn8YYGSHy4hcBELriCorPPIOZrl0YupWX4/NMP23aQl7yJVPleqSPgNvXoPph+3fNUcmZ+u1viLz4xfg3bCB9xx3z/wCFBCBgpmT5jvUnLgftmVsgdRie/N6Mq/zjT7bwpz3j/OjRo2t3Zd+g6hW0Vr9SC08HBU03TPIlk9ZgjaCVzTKZUoaVIWUhsy9xYKa3z4icbhDyaQghVJh9gSHO/UlFCte0rMGsCEy9C5dvlF0jZ1ZVo5QSXZr4JaplXPxA9UFcJWgLyBGVlQpD//TPyEKB1K+m2l5Ox5bDSSZzJfaMnhziWyibBLwqfB7xa1yWuxu2/gi+9RKe2vdbypUSsuJhW/JeooG6HLWE1WYoeUiF4+rQLMQJKg+t/r55PBEvxmnzt1Xbve1LNBI0z4oVaEuWTIt+NEC3vgPLVxNgJJMEIFou1ia7UxD1RpSCVkfQpJSU+vvxrqqlWPjPOhsqlWroc0Y89X0QbnjBh5VF0vC2Bg80oFrJuRAftPqCALdLUFkgQZstxJnNTlcYv/3tb3PkyBG2bNnCli1b+N///d8F7W82OATtKHFOjyIfoyfSMVpKzPQw467GWVrRKOJx+SgZFdzBFpAmPlwUzZqCllpgw/T07beDphGxSoQDF25SF9meWljQTpxuViRg97ibzOmKoEWXNKh++C3iNMtDwJicxByfIHjRhQQuvJDijp3zN14sJNU+ZpKYO9aq2fAxlH/PiO0/V7/33A6l6Um0qUKZu3apNirZo3TMT+lWDlqdUa3m0mjxtZwWBM02MW4J1iqD7XEvCa2gYoQ4kFo4QctaBA2sQpUFFgkk9SQAnYFOdo9kMIvdeAOj9E/mKR6lee6pCKNiUEGqHLOOdeo6zCvlNexV1hL58vxNN0v9h9B37cJ37jnoe/dWE8Vnwj3W+T8Qt45rxYQf/yV8+bLa9XMCUSiZ+D02QfOwufykIgfA7iMPowkv5cQV7Eg+gc9bqNlsWA3EAdhZI6KVQgGZz+Nuq5uEWlAhzhNL0ADWtq6lf3wPZjyOZ+kSACvMeRn5Rx6Z2eqiStBq95KxnLq/hCpSTTabIOKLIFwmk7lamLqSzVLJ5/EsWVJd5jtL5fzqc4U5D/4J+p4DG16j/h94kMOZw7T6Wgl5atX2UW903gqaUamg1T0D3C6BuVCj2gUqaCcSJ38Epyl6W9TJnSieQCfhYhK3WeT2KddLwSggpSJinpC6WP1SWTFE/IoULSTEKaUkc/sdhC6/HHdMESl7RqQfrFXX6Vbj2WZFAm1WDlA8V7II2pTe9naIc5ZCgUNP/QmA29278W/cgMznKdXtf1YUk83DmzaivWCWZpwdHjVSQ3D4EVh9tcqxO3DPtFXsxsxdEd9RW6A0U9DA8kI7jQhaax1Bs60tOvwdyHIbI7mFh3/tECdMKVSZJ2ziG/PFGEoWMPUeKu40psiyb2wRuoQsEuxqc78WgBbVtsd+ENsPw2x5/p+3uHMHAF3veQ8Ambunn/f1+NPecdwugZSwfzwLD34Bdv9OWd/86v+CXV16glAoqxAnQIu3wma5A7n5reANsyd9iE7fSsqpTVSkSdq1tU5B668SOTI1F30zoe4j7rbpOa8nmqDZyvm6lnWkB5RKZStoAKHnPhczmZw5zFklaOHqosm8Gm9IVpTvZhO0WvfwiXwtCmKMqmPi6emuLvOuWIEIBCjunm6DUYVpwNgOWHIBRHpUCsqhh9gd38361kZ7jqg3Oi91V0qJWanloAG4jyLEOZuCtthwCNpRoiPsQ1Y8pE8gQdMTKmFyayrQYEZaNItIUxE0X0RdrAHr9YBX/U7OoqAZiQT6gTritWsX5cOHq+FNQCWder2UDvZXl9UUtOZVnGBV0WWONBYIQG22NsuF9svb/geAOz17CZxn9R19evuM6zdAzzRI9tMQtvrlZY9zayTbAPfK96vf8ekqkE3KXnhOF4l8+ajMVNN6mpAnhOZqLLxu87edFjloCSvkXh/itIllR7ANaQZJl5IL3m6uQUFbeIgzVUqhuTQCWoBkvkRFVw8at3fshPa0XWzYxUw+bwgsexZ7smITtIV4Ouo7d4LHQ+jyy9F6eijtnz2c1T+R4+K+VrTIVm7bdy889GVY92J40y1gFOD+zy38Qy0AhbJJ0ApxrintIih0SiuvRnauZ3c5QZvWR0XvocXXQqKyk5JRUUpf4pAqMvLFarlbqHsogNZEQXPFosh8vqlN0bEiUUxUFbTlkeXEEmof9QpW5MUvwt3ezuS3ZsgNbBLiTBbVslClUsshnoJWKwoStz0ngfKIup9q3TWCJtxufGvXou+dJaVkci8YReixOuQsv5TykafYk9jD2W1nN6wa8dWMlGcz5TYsIuaeEuI80UUCC93uQuAQtKOE5nYh8JDRT1xbpckRla90sBitOqaDatlSqagHXTCqEj39lpzt8yqVa6ZWJanf/pZ9z7+aA9dey5EPfZhKocDkN78FHg+RF76wup5wu/GuXEnpQI1w6HVtNKai0ypjHkkV1AUeWdK4wjwUtPDhSZIhyIc9eFeuxBUMUnz66RnXb4CebZDspyFiWSZkjy5Jf0YMPKR6jq64QlXH5ab7Hx2YyOEScNU6Fao+eBSVbFPbPNk4fRQ0dT7WhzhtYtkV7ECaQTLlhasODSHOsG/BRQIpPUXMG0MIQSJfRpZbABBaesF5nKcybDsgnydcq3S2CZqmCFremP9ks7hjJ751axFeL96+vlmrBgslk3TR4PLV7QR6f8x3Dv4rZm4cNr5WhVvPuhZ2nNhWy/UhzmVFFY5Nt1/AWPsaklQIu1bgEi4u7r6YEf0ZQCoVLXkIWvsg2KrsKSyYiSQA7tYmCpoVhTgRKlqimKDVp/bZGeikw9pFvYLm8vloe9ObyN13H+Nf+OL0UKeeUVXtHmV5Uyyb1e8+LF3Kx7IJ2gOW12Ch9rmMUXU/1bobLWl8q1dROjBL9GN4m/pttzBsW82B4gSlSolz2s9pWNUOcfr9fiYnJ2ckOjYR01yCiqyQLCZxuRZexWl3aDieIU4pJZOTk/j9CzPRdnzQjgFuvGSb5BzNBGkYjH7yU0jTpP1tf4N3xYpZ10+NDrAUGKGV7UMplrcFqcgKRbOIYaivLtyiklT9ln2FFCV8mqtpDlqlWGTs0/+Jb906QldczuQ3v0XuoYcwhofp+Pu/R2tvb1jfu2oV+q6aTD1bDtqSmB/NJZgYO6JCiVNDnHby/gwKWtEosmzU4FCnIF6Mq1nYOedQ3DPPyks9PZ0U1iNszfCyY/Pb3nwx8DAsvwTcmipbb0LQDk7k6G0Nsr5HzVgPjue4aEUTO5BZkNabdwtoD7QzOXw6KGh2Dtp0BW1ppANpBMmWpxeQGBMTlIdH8J99FsLTWAGqGyZlUxL2qfOxLeQlVzLRjeZmys1Q3z4rmS+hoR5CQsvMqkKfbrAVNL83Mp2gLVBBk1JS3LGD8J+pCZ23r4/M7bfPuL7dB7M9WgZLnHkg4OeqNdaEcNVVsOs3kByohV+PM4plk1arZ3Bn8QBxGSblaiEe7YQMhMsRwj6NS5dcyp0DdyI8CdIFg65EP/ReoshZoZ6gWSHOlmYhzhpBa1ZEAPCNbd/AkAbvuuBd8/4M+XKeolmsGlR3hbpotaLSU6tJ2976Fkr9/Ux85SvgctH57r+vvVjKqkmlpRCNpXWES50foVDXjB1fOoItAKTqJtl2WylPV+P+vavXkPrVrZjZHO5wk+4tI9tA80P7OvV/Sx87fer6nomg9fb2Mjg4yPh483C4XjYZz5aoxL1URIlEMYHPFUUvuZHJ6Q3gZ0K2nFUh1bHjS9L8fj+9vb1zr1gHh6AdAzwuHwV9/mGQ9O9+R+JHPwIhkLrO0k9/atb1i3GVCzAmW3l6KMXLzltSvdGWDPUAirWqG4DPVLN922qj2cMl+fOfY4yPs/S/P0vo0ksJXHwxiR//mPBVV9H+zr+dtr531Uoyd96JLJUQXu+sOWia28Wy1gDZcSthbmqIcw4FLVGIs2wS/niRp/rg9q1dS/q22+ZnulvKLn6Is5BUeRR2kmuwA/LNCFqWVR0hlrcGcbsE/UehoGXKGcKe8LTlbf42MqUMZbOMxz29jdKpglqIs7FIwOf20RaIIs0gpUqBklnC666tM/S+95N/5BFCz7mCFd/6VsM2c7o6H8OWgmZbeCTzZbqj8yRopRpBS+RLtPijlF0abk92Xg2zTxfYOWg+X7SWq2kRNI/bg8flmTdBMycmMJNJ/OtVhw5vXx9mMomZSlXVo3rYTchL7pqX2G86e7kqbD3UV6iWUxx66IQRtELZZKmloLVm97FTLsdTNIlbObytuQTRwCou6b4EAHdwP7mUZaLa0qdy0eomX1WC1toybV/umLrXmbNU+N+852aSepK/2fg38zYHtu+LtoLWHewmUpCYQd+0tnwun48ln/okCMHEl75E9CUvxrfOIkNT0kEmcjq4iriFB2+ka0YFrSusPmuqbpJtjIzi7uiYtn/vapXDXDp4oJqu0oCxHdB5tprYArT28bTPS8DlpS/S17CqXcWpaRqr6qpFp+K324b5+1uf5Lb3XMndIz/gqzu+yprA89n61MvY/4lrmzaBb4avbf0aX9rxJZ5805N4XCf3nuqEOI8BXpePwjxbPUnTZOIrX8V31lnErruOzB13UCnMHh6tpIZJyhDLu9rYM6qmSrrl9KyXVE85X1jdYAKGepgos1pv04dL9q678a1bS+hS5dAfufpqVnztayz594/iatJ307dqFZgmpcFBa99WiLOJDxrAirYgRtJKMJ1K0NweFQI0mn/m5OAB/GUwerspGAXy5Ty+deuopFLVpsSzQs80JL1OgzesJP3jqaANPgZIWKG8h5opaFJKDo7nWNURwqu5aAl4FpzIDmr2PBNBA/jvu55Y8DYXE8l8GZ/mqlodgApxtvvbCfs8uE31kLKrKgHMTIb8448jgkFyDz+CmUw2bDNrub3bIU6b/CUWEJpM62liXpuglWkL+mn3t+Pz5c4sBU1X9w+/v0X5AnpCtf61KBVtvgStNKBSL7yrVjb8Lh061HT90bS6R2Yr6t6wTi8z4KtTNLo3qPSEgQfn+WmaY+LGG9m9+WKO/Ou/Io1Gr6685YOGlIRTe9ld6SVTLJP0qHMmWsgS8XtY07KGiKcFLXgAPVFnuB1oa1TQkglwuXBHp6cd2MsqM4Q4U3qKI7kj5I08jwzP4lc2BYmiIoX2Nd/qayVaEOiR5gRPCEHne/4BgOx999de0NMNBC2VLyNcOkEtqFJBZiBodpFApq6YpDw6gqcu/8yGb7XqfFOfItOA+MGG7jj5cBe/D4W4Ktw3rZtI0KMiR3OZwtu9oNuCXvpT/QAM6o8jpUlminfbbNBNHbdwn3RyBg5BOyb4NR9GpTSvcvz8E09Q6u+n/e1vI/aqV1HJ5cjeM3vlkzs3QsLdTk/UX7UPsGfCxZKbtpC3mnflL6vXC0aBWBMFraLr5J94Ylrrptlg5zWUj6gLdjYfNIDlbUFczboIVDcYgHJzgpbdr+w8fCtXApa6snat2u9syaY25ioSEEKFOTNz56Cl5lsBO/AQuDRYphzoCU1X0JL5MrmSWW1xFAt45tXnbyqy5SxBT3DacrubwNcf2HbSPKbmg0Su1KCeQc0yIOB1826hQmTJ4a3V13MPPQSmSeffvQsqFfV/HWzDzHCVoHmsfc3/+Kb0VNW6JJkv0RL00BHowO3NLrgS+v0/28qff+UBHth34vowHi2KlqWG3+5VG2htqGgOeULzttko9Ssi5u3ra/g9F0Eb0/vxEmRjSWeUugemy62uoSNPzfvzTEXqV79i/PP/g9bTQ+rmn5N//PGG14tlE7/XDalB3OUse2UvmaJBwqrYaymkifiVn9557RfhDh2gmLImc8F29ZOvHS8jkcAdiyHc05Valx3inEFB25OoWRfdPTCzufVUJHS1/7Gkxn/etguXcNGue8iFZlaLPd3deFevJlffn1PPNkxmE/kSwq0T1ELqvj1DiNP2y8sbtfuMMTKK1jO9JZp3xQrQtIZitCpMQ1UQ1/l73jL6MBm3izd5pz83glrQ2u/s56c98W0NeelP96O5NPRKDpf/yILuuVNV/JMJh6AdA4KeALjKjKXnTkzO3HYbwu8ncs01BC+9BFc0OruZIODXx8l4OxsMOAuWApUvulTlpFsDbwS/1dGgaBZpqW9VYqHw1FNIXSd4+eXz/nyaVRlkjKgLtjwPBS1SHkcKdy3nqx6egDIkbAL9oJppta7fCFgEbb2S5EtzeCxhllVF0GxFAqDGNEeI8+5do1z8H3fMryXNwMOqTNxr5VgEpytoI9bDqSeqkkMjgfn1+ZuKXDnX4A1kQ5rqRiu0HL+452HlLXQKIpEvN+SfgUXQAm14jQwXSxUaTz34+errufvuxxUK0fqmN+GKxRpVACBXsgiaXxE0uwBhIcn9ST1Zl4NWpjXopSPQgcudXZCX4P7xLDc/MciTA0luery5j9TJhG4VZPjsCs4pBC3oCS5MQdO0mjnq8uUgRJW4TcVouojf4+JQej9LaKHHMJk08pTNuuPbvkaFEY8CRjzO6Cc/ReCii1j5058gAoFpOXF2JwHGVU7tnopF0IwCkUqFaClO1K/Oz4u6LsblSTGZtdSfYLsy3S5lwIpUmPFEUw80AHeLTdCaK2i74yqvdmP7RraMbZn357SLav75pgN89d79TGRLtBRdZAKzh+5CV1xB/rHHa1WlUyaziXwZXEUi3rDK49VTyv5kCgJaAKSgUEeUjNFRPN1d09YVHg/e5cspHWhS3ZsegooBbSpcKaXk5r0/Z6MhuCA3fZJpT0znmkAkciWiftVR5FD6EGe3qmpQ4c7XbFPmgaJZPGV60joE7RgQ8gQQosxoZvYwpzQM0n+4nfDVV+MKhRAuF4GNGyg8M7uFRMyYpOTroCPsY8KqTrOrsXK6qPYfJNCC3ypWmCkHLffII+B2E7zkknl/Pk9XFwhRLaUumTNXcQL0tQVZQhwj2KlmxdM2OLOCVjl0mKIHlq9SVT2ThUm0tjbcbW0U51LQ7LJx7ywhTlB5aHOEOO/ZNU7ZlNw/lwpi6DD0RC1/BiDUrnLh6tp/VQlaTH1XsSbkeT7IlXNNQ5w7Div1dnW35JydX0B+/88hH2c4VeA9P3mKv/vhE0dV3n28kcw3V9Bafa1w+DFarEqzRKbmwVR4ZjuBCy/E5fMRuvSSaarItBBnyFLQ5kmsymaZvJFvCHG2WASt4kovKFT6WL8iO+csifJ4/3H22jsO0K3wnN9u7xNoaVTQtIWEOA/hWbYUoanj7vJ6cXe0Y4w1n/yMpnW6o35GciMsqwi6DBOJZLxQl7rQ0qfyvY7Cp3DiK1/FzOVY8u8fxR0OE77yStJ33FGtXpRS1nzQJtVkb79cqkKcepI26SJSniRqEf0rllwGwJacVWkYbJ9WWGEmEk3zzwDcEUV+ZurHuSu+i3Z/O5cuuZRDmUMYlflN2GwFTRpqorZ3NEM4L0n4Zn9/8LJLkYUCxZ1Wmy0903CvTOZLuFw6UV+4VmjVJNIghEBIH0VT3cNlqaTyDqcUl9nwrlndXEGzibiloO2I72Bfch/XeTpU1ezU8VsK2lzn52SuRHvYx3hhnLyR57xOlfsm3AUyC5gUl8ySQ9DOBIQsBW0uX6v8449jTk4SfelLq8v8G89D37N31obgMTKUfG20h1UbpXzJqMbhM3lBu1WVhD+G35rxFIwCUb9n2oxB37ET3+rVuMNzkJg6COvGW7YUtFK9gtakefDytiDdIkHO10Q9A5UDNsMsyDU4ypE2WN2ierpVCwXOWo++a45KTrvB72whTpiXgvboQbXfxw7OYV0xvFWpdlb+WblS5j+SW/leNEI5WwsRjFpeWt2Wghb1a2QWSNDKlTK6qTcNce60voYLVrrZKPciKmV45hd89g97+OWWI/zu6RHGMguznjgRSBbKVQJlI1PKqPDiwENETMu/rzAJZtlqIXOommzsW38W5cOHG/r7TQ9xLiwHLVWqmdRKKashzvZAO2WRJpGf/3F77GCcjrCX123uZShZYOQU81ArWvlL3oBVVdgkxDlfglY+NIB3RWMit6ezi/LoTAStSFfER1yPs9TIQVkp3Q3GxHa4K9FchZsJxvg4yZ/9jNirX1VNgg8//yrM8QnKVq5cyaxQkaj8x8n9SF+UhIiSKRrE9TgtLg8tlUmiVpHJ2R1rMXKrudvYTglqChpU89DMRAKticUGKPXIFQzOmIPWn+5nTcsaVsVWYVSMaoPwuZDUkwg0on5F0PaMZghky0x4Zz9Pq/lg1vFQBVW1aEMiX8KtlZRCH7UJWvMwp8BHqaIImmFZjUyt/q/ud9VqSgMDyPKU+90Ugvab/b/B6/Ly0tjZkJyuPtuRg8IM+cs24rkSbaFa/tl5HTWCVijNnYZkQzd1h6CdCQh5/QhRnlMRSf/+NkQwSPj5V1WX+TduAMNQho9NIEs5guiU/W10WB5jk9lS9STNFETVHBZ/DL+VBFw0i0QDHvIlk7JZ878p7t2D76yzFvwZPT1LMIbVjdTeXmjsSfjcuaq1UR1WtAdZIuLE3c1Ly2dT0PxDk4x3eOgMqsoum6D5zz0Xfffu6Rd5PZo4Y9swK2ZNGg93qY4DRvMHeCJXYvdoBiHgkYPx2ZWnASunY7mabe+J7+Gnia38V3srNz79zepqtoLWFfEzWZhkt/wScfHYzNttAnv8zRS00ZRESI2IL80al7qpyq0/5Y97xqvnzV6rwMQYHyf+/R9QHm68+RqJBNkHHjihSpsiPzUFrVxR6lXUG4WBh5mQK9V6LgHJAYyxMWQ+j9fKSfStWwtSNiQd56YQNL/Hjd/jmj3EWUxDvwqV2u7kMV+MrG5gVCStVg4ayAUZ5z7aH+fivjYu7lMP7ccPnVredCXrs/pDzQla0BMkZ8xN0KSUlAYGplkEad3dGKPN1emxjE57xMSoGCzVE6QMlbM0mq8jdK0W4WuioMyGyW99G1ku0/G3tSp07yqLkFg5ccWSum/ZCppoW03E71UKWjFJqztAWyVBxFLQvJqLSuIFJIXOh7u7yVOpmftaXmhGMtHUYsOGKxZrHuL80RtIT+6hxdfC6pga58HU/Lql5Mt5qPi5cm0nsYCHfYcn0UomE/7yrOSlGoK2CdqUEGcyX8bl1tX9xVbQZshDc+OjVFH3NDOuQq4zhXq9q1dDuUzp8JTOBImDKnfXsmLaMbmDjR0bicZWqBxes/FeP98QZ9zKc7XPK9vwVrgK1XSI+UA39TMrB00I8VIhxG4hxD4hxAeavO4TQvzUev0RIcRKa/mLhBBPCCGetn5fU/eee61tbrF+pge6TzIiviC4Zido0jDI3H47kauvxhWoVS7N5ZSvp1WIrRJoq/a5nMjq1RCnYWq0Ww9g/C34LSdo3dCrUr0t65qZDMaRYXzrG1tozAeenp6q142toAUP3aVefPTrDetG/R6WuOIMy+YXrFLQpt9IzHSa0HiW8WUh/JqfkCfUQNBkuTx7rz99ZgXt2898m8t+dBn3Dd5XC1MUk00388Qh9cB6+XlLGE4VOTKbCjLwCLStqdp32Im/a0slfnr4DgpGAd3UuXf8O0RXfodPPfZxrv/t9YwYj2G03kxyhjE0g61sNMtBG04W8blilPL9ABz2rYWhJ5nMFnjHlUp92jumzo2Jr3+D0U98gv3Xvrzawiv30EPse+Gfcfhtbyf7xz/Oe0wLgVmRxHOlmuIL1ebHEW8Exncx4lmDJj0k3S6IH6x2sLCLRqoFI3Xnga2g2SFOUCrajCFOPQNf2ATfeTmM7aopaN5YQ6/QDktlypuJhknOTEjkSgwmClzU18K5S6N4NRdPD85ssTAf3Lt7jN9ua/6QPBro1nXnDdghTougWaR8vgqamUhQyWbx9k0laF0YY9MJmpSS0XSRaFipPEsKk4yWFBkbzdUTtJXqd6KfeK7EULLxPjG1+wlAbnyY+I9/RPTlL68WKkBdVallnluwirgCXjfE90P7WiJ+jXTRIFFMENMidIlkNQcNwGecw+sLndwe8PG+P74Pw+4lXFATNzORbGpSa8MdDmNmp+RTHdkCe24jXc4ScXlYGVPjnG8P2pSexTS8nNUT4azuCEcG1H05E6i1LGsGl8+H1t2tFEUpp1W8J60qzpA3VBfibN5NQBN+yhZBM+LqHj2jgrbGIsoHp3y+RL+yU7HSYA6lD9EX7aszE29UYudbJGDfY+xq165gFz63D+EuktcXpqD53QszlD1ROGaCJoRwA18GXgacC/ylEOLcKau9DUhIKdcCnwP+01o+AbxSSnke8NfA96e8741Syk3Wz3F2GD12RLwqB222CpHcI49gJhJEXvbShuVaTw/ulhb0GYxY7QoiGWivKiET2VK1ipOKtyHEGbD8xWwFDaiOy254bifdLwTakh6M4WGklLUqzgErEX3fncpcsjroNGEKDJSneyEBMxYJFHeovn7Jvlr5uN2nMbBhQ8M6TVHNQZtO0Owqqff/8f2U7LyLYvOb2cGJHOsTA/zNzZ9hZWqY4eQMs1IplYJWl3+2N7kXv8vLhycTpIw8n3nsM7zzjneyv/RrNG+W3+z/DV3BLq5u+3twFfjejh/O/HmmwH5wTg1xGmaF0XSRkNZCPKtuqL8rb0ZIgzYy/PlFvbQEPewZzSKlJHvXXXhXrUJoGiMf+xjG5CRHPvBBPD09aEuWMPmN/1Ubjh+Eb7zwqJO2p2Iyp1ORqhepDZugRV1eyE8w4V2KS4ZIuVwQP0CpXz2M7Z6w3r4+VRW2twlBq7PuaAl6Z1bQtv6k2iCcgQcb+nDW+7TZlbFCy80rX/CA1cprdUcYj9vF8tYAA/FjawH38d/s4JO/a66uHw106+Hmq6/iNEvVZPD5VnHaqpRnqoLW1aXIm52ILiV8/8/R7/si+ZJJMKCupTazwn5zNX53sFFB88fA3wKJQ7zrB0/w3E/fXTW4zT/1FAeufTkHr7uO4p5aBeS9n30flaLObVc1TlzcLS24otHqWPOWehJymSqE1r6GiN9DulgioSeIeFqIijwtnprKEvZq/EVW41+NMPcP3c8vxy0bm3ycSjYLhjErQXOFw1SyUwjvI18Dt4+McBFNHSHqjdIR6Ji3gjaeTSErXtZ3R1jXHWZiSB2/THB2ggaohP2Bw9b3LacUCZSQoqg6SvijKj9thmp3TfgwpK2gKYI2o4Jm93PeP4WgJQ9X/e6ypSyTxUmLoFkVnFP2PR+CJqUkkS/RFvaS1JO4hZuoN0rYEwF3fkEK2plWxXkpsE9KeUBKWQJ+Arx6yjqvBr5r/X0z8EIhhJBSPiWltKn6M0BACHFqBH/nAb/mR7iMxqq88T3w0Ziq8ENVb7qCQcJXXtnwXiGE1a+suTJUSqsEWhFqryplk3UKmqx4GkKcnmIKl3BVc9CAah6aTdD8RxPi7O6hks9TyWQom5IIedwjT8HZrwBkrRclVHu47S3OUE05Q4iz+MwzAOTWqBlUxBupkhLPihW4QiGKz8xC0ErTe8uBumgHMgO0+FrIG3l2mNZ6df5P9RicyPDeLT8jsGMrn37gRpJDM1hyTO5XuSgrLqsu2pvYy+rYSjYXdf5Py3ncvOdmdkzuoC33Vi50fYxH3/goP7z2h1zRdS2y3Ma+xDybwFMjaFNDnKMZRXxavK3EiwmKgR6eKqoZ8MtXQWfEx/quCPvGMqrf6pEjtL/9bXS9733kH3qYfS+4BiMeZ+lnPkP7W99C4YknlEK193YYehwe+J95j3E22FXOnZHarNQOL0atVmkp/zKQERKaVxG0g/0Iv7/a4094PHhX9qHX9XzM6QZ+jwutrqq4JdDcpFkNZKfqaBHqhIFHqg+1qC9ap6B5CFtEXriK86oI7bcI2qpORRRWtAWPiaANJQvsH88xlCwsqJJ0NpSsEJjXnqRMSXoPakHyRn7OMLed1/U3z3yY2/pvqy63vbCMMSvxf2Qb7L8L96NfRVDB61XHo71isquygqinozEHDZSKljzEM0fUufGxX6trfuLGGxGahisc5sj73k/FSk5fettWHjlb8Pn4zYznawUHQgjVpm6KgtZaGgJkVUFLFnOUK2UCHnUsOkhWtxHwugmUE7zOt5Rl4WXcP2nZvxRTs5rU2nBFwlQyUxS0gQfRz3opJZcgOqFIy+rYavYnZ+9jaiNeyELFx1k9Efrag7gz6jilA6Laq3ImeFYsp3T4cNM+nIl8kYooKQUNlJI1Qz9Ojwhgoq5nY1JNdmZS0NyRCFpn5/Q+rekhiCpH/UMZRaIbFLQp+7YnprMpvOmiQdmUSkHTE7T4WhBCEPNFEa4C+QXkoJ1pVZzLgPrMvkFrWdN1pJQGkAKmfquvBZ6UUtZnPH7bCm/+PzGDlbwQ4m+FEI8LIR6fqQXEiYJP84EwSeTrQmEPfUn93vErZLlM5vY7CL/whbia9ODyrV+Hvm9f05tiOaM+izvUXlXKJrJ6TUGTnoYqTqGn8bv9FI0isaCtoCniqO/dhyscbupXMxc8S9R7yiMjlIwKG10HEbICm9+ijGfHa62gSKtcg+3ZaPP+ZzMUCRS2byfe4ibQrsKFEW+ErJX4L1wu/Bs3kn/yyZkHOUMO2khuhJSe4vqzrwdga9Gasc8QXgw+ch99qWE8f/O3xEo5XHf+ofn+7M/cvbG6aG9iL+taz0IAH2jZxGeu+gy3vPoWMpPn0R3zVzshRAMalXJ0+sNpFswU4jxiKXwdwXYmKkV8bb2EO9Sl99bz1U1tbXeYPaNZsg88AED46qtpvf4NLPnEf+A/7zxW/uD7BDZuqPrjFZ95BoasY/3UDyF77NfUuFWk0BVtoqAV7YdML9L0k/UGIHGQUn8/3r4+hKt2i/KtWdtws8/qZjX/zEZryDNzkcDkXuhcr/IGDz/SoKDZalzEr9WIsEufl1lt/6Tqtbq8VR3zFW1BBibnJjsz4U97asd818jx6eeoG0U8UuKyq6unELSwN0xFVuZMxC4dOoR0CfYGUuyYrE2atC6boFnX2PZfAODJHuFisQeXR53DLa4AQ3QQcLUyUZhSKR3rhdRQNRfs8f4E0jQpPPEk4Re8gCWf+A/0vXuZ+MpXGPrsZ/DrFQ685iIAnhpr9FDz9vWhWwTN9qlsLVpqf9saon6tmmPo8ai817ZKLW8w5NMIGSlEqIPLl1zOo6NPKOe2cr5K0GYqEgBwhyONIU7TgORh0i3q+oykR0BKNrRvYFd8FyVz7olASs8ipI8VbUFag16ilvo5LwVtRR/mxASVpHVu1UUb7Ebp1fM+smRGBc3r8lOxCJo5GQePB1dk5uIs7+rV6P11k1GzrLYdU8dhIK2+E0XQmleQzicHLZ6rKeDJYpJWv/puYr4YLq1YzVedD0pmST3bTwGcEkUCQogNqLDnO+sWv9EKfV5p/byp2XullF+XUl4spby4c0o/shONgFvllCWLFrMvF2Hnr9Xfeob8U09hplJEXvRnTd/vXbuWSjaL0aT6ybT8tNyRTvweNxGfpkKcMyhoAH63l6JRnKaglQYG1MNurnZJTeC2esmZExOUTJOlLiuxuHUVtK+FsTqCllIE7bDZznCqyY1e8zdYUIDV1+/p7RxY4qoaIYY8IbJ1btWh5zwHfdcujIkZrC9myEHbGVchouctex694V62WLO1mRS06P4dlD1euv/+XexqXUHkvhn6C1ql+rSrvKhEMcFkcZJ1bWeBy4Mo5XjZqpfR4e8hnitVPdBA2WxII8Z4Yf4Re/tYTA1x2gRteaSHSWlSCXfzzlc8D4BVPvVg72sLkiqUKfQP4G5trc52W177Wlb+8AcELrgAAO/KlQifj+LOXXDkSRVuMPVaMcQxwA5V1Yc47Rl/JKceitlALxUzQMbthtw4pcFBvCuWN2zHu7yX8pEjVfuEYtls6EwAdohzBlI1sRc6LIKWOEgqO4xLuAh7wnXhUq1KhJWCNjdBs3ut2vYzy9uCZHRj1vDoMxPPzKic3L9vokpSdg4fO0G7bfswu0cT+Or5op1TZYX759swvXRogHJnC6ZbNKhWWpeaXFXvZTt/DSuuwHAHeKX7IaRLncOxjnMAgZdoNc+0ilAHMj9RJfTxfIni7t1UslmCl1xM5Oqrib785Uze+DVyP/sFt14meMmfvROf2zedoK3swxgeoVIsUrCKBKI5i6C1rybi95ApJwFwaWrsrbKuaMLrJlxJgUXQMuUsO0JRKGUxqgraAkKc6UGQJhkrZzVaykFqkPM7z6dcKbMrvmuGLdWQL+cJe0O4XYLWoJeYbhG0AHMqaPa1VOq37l3WvVI3zCoprxK06NIZc9B8bj/SVtDik2itrbM+V7yrVlI62I+Uks/fuYe3f/nXgKwWCPSn+wFYHlmuqmVd2rQKUr/bj0DMem7aBK0tXFPQQLWJcruLC1LQzrQqziGg/k7aay1ruo4QQgNiwKT1fy9wC/BmKWX1jiWlHLJ+Z4AfoUKppxRslp0sWCfO6DO1diCT+8nd/wBo2ozu/bM55ctcHFMK/GFLfo/4piloVV8pm6C5vFYOmrq52w+I0uEBVclzFLCb/RqTccqmpNtlzdQi3dB5VqOClhpECjdjtDQP8TQpEihu3Up5cJAnV1aqN4h6BQ0g9NznApCzVKBpmCEHbXd8Ny7hYn3rejZ1bWJLai8SmipoUkp6hg+S7F1DOOjjj30XEx7sb97FYHKfsuywWp/Yfk5LQkuUimfNbO3QXj1Bi/o9VMoxEvrEvBWWVFEdi6khziNJdS6saluGKWAi2MK61cqmxJ6Ftlnqa+HwIJ5lU4XtGoSm4Vu/nuLOZ5hI7OejK9aSEwLG59msfhbYD1w7lxJqD5RoZgx8MUSgBdPwkREgc5OUjxypGqHa8PT2IsvlauuvqvloHdqCXhL50nQFt5hWN/6OdaoHIJDKDBH1RnEJV0NFaJWgufUqcZsN/ZM5VnbU1M0VVteIQ5PNHyhls8w773wn1//meh4enm5WPTCZZ3NfK20hLzuHj607RLFs8vHf7KQsS3hk3YN0CkGbTxgJ1GQv2anO57F8bZJhm5UmDx+BSkXlL/Y9h8Mtl3KN+ylKxiRRs4Jv+SV43S5cMlLNM60i1An5ScyKybKWACWjQvJhVfEc3Ky6dSz51CdZ8qlPseevnsOPr/GwuXszGzumG756e3tBSsrDw9UQZzjXX/U0i/o18tbERxfqHhcxk9X3xzQTvyxCsI1LepR35FOBEJRymJa9xKwEbWqI07IPSYfUeyKVCoxu5/xO5fu4bXzbjNuyoZt5WvzqHtAaUgqaFIKsf24FzY6eGHYFtxXOTOXLCLe6j1QngJEedf9ocn/yuwNIV0kVSsQTM3qg2fCtWkUlncaMx7npscMkh5WaJi2Cdjh9mJ5QD37NDy4XhHumKWhCCIKe4LwUtPZQo4IW8UYQ7kI1D3E+0I0zi6A9BqwTQqwSQniB64Fbp6xzK6oIAOB1wN1SSimEaAF+C3xASll9+gohNCHUVSOE8ACvAGZ3dT0JsCs90rp14tjMv+c8mNxH7v77CWy6YEbvMdu3p1kemshNkCRM0K9OlM6Ij7GMykFz4SHq99YMY63EX79La8xBK5SRhkF56AjeoyVo1gVoTE5QMir0uJKKaHnD6kGX6K+RrtQgZrgHEzeD8SYKml0kUHfhJ372M0QgwP3n1EJ4UxU0/7nn4G5rI/un+5oPspQBLVBrvGvhSO4InYFOAlqATZ2bmCjGGdLcTRW0iVSOVYlBjHVnI4Rg1+pNANXQYAMm91fVM6jdHFt8Leq4WOTSttjojk1V0KKYslw1npwL33tEkaSQaPx8w6kCUb9GX0gpx6OBkOqzGGyvnos2KSoPDc1K0AD8Z5+FvnMnH+po4+f5fu7vWA7jx56oPpbRiQU8+OvIVJWgpY5A20oCPg3D8JHBxEwkkIXCdIJmjb9s9Yatmo/WoTPioyKbeKFNWkS7Y301vJLOj1e7COTqKkI1l6Zm7S59zh5+Ukr6J/Ksaq+pmyusv2fKQ3to+CFSegpTmnyzzpLFxlhG+Yad3RNh1zG277rlqSGGkgW8ngruCjVle6qCZl179dddM5QGBjgSU4pUPUEzwxF0t4fv3voYtz2xC6QJwQ62By+jV0yQHnuKNtNErHsR0YAHYao8UzsiAECwAyErtJDl7B412cps2YrW01M9F1xeLy3XvYb7rmxlWaSXoCfIhV0XsjO+syE8q1nRFGN8vErQ/Ol+VXkNRPyeqiN+ttJKRQpC5Zqi1+m2jkOwnfZAOzFfjAGvxyJocyto7kgEWSrViiasgpu0lYZhE7SuYBc9oR62jm+dYUsKqXyZiijSEVSTwtagh2gphxmK4HZ75lTQqsfDjkJ41TmqugioCVQtxLlUFZDkp1vF+LUAiBJlUyoFbYYCARt2oUBqzz6OpIpsDKvjOmCoYzdZnKQrUGfQMEMvUDtHciZU+3CGpitouAqEsv3wxYth203V9xgTE+Qfe6zBWxHOMAXNyil7N/AHYCdwk5TyGSHEx4QQr7JW+ybQLoTYB7wXsK043g2sBT4yxU7DB/xBCLEN2IJS4L5xrGM93vBr6sGbsRKdyVrMv+95GJMTFHfsIPy85834fq21FXd7e1MLCVGMk5CRao7Nkpif4VShStDq1YiqgibcytDU68btEqSLZWWRYRh4VhwdQXNFo+DxYE5OohsVukVSqUdCKAUNqUJHAKlBXC1qPwcTR3jtra/lf5/+35pS5AmoG7flc2OMj5P+7e/wvvSFFH2ieoMIe8Lkyrnq+4TLRfiaF5C55x4quSYz/BkapU8UJqqWCZu6NgGwJRhtqqAdeeoZfBUD30Zlf0JXN/G2JeQebNLAeXKvak1jwW7wHfPFFEGzFD3brLRBQbNCnDDFZmAGmBXJQFwpRr6fva3hteFUkSWxAN0WcRvVLEU13AMZte22kBekRIyNzEnQfGefjZnOsttQ4z0S7TwuCtpYWm8Ib4IiaB6XB1/yELSuJOjRMA0feWlSTKgbsTaNoKnE4vKQEugLZbOB9IEiaPY+G2Cfo+3rVK4TkComql0EsrqJ1+2qTnpCnjC4imTmaBEzkS2R1Y2mCtpMBO33B39P1BvlhSteOK2Cz6xIJrIluiJ+emJ+Jo7RZPi27SOsbA/i84ImBbtGLMI3A0GbTaUwEgkqqRR7Q+ohW0/QDk3mSfjCtBYz3PuklZsW6uRh94UAxMd30laRsOIKogEN03LDbwhzWh5t7SLN2UsUQSsfHqjaZtRjMDNIb0R9jxvbN2JKk72JmtpdT9B0i6B5Uwer123EryEtYpIr+0kSxlOn6HW6rOMUVGNaEVnBgOZWBC2ZVLlXoem2NzZcIXU/qmQtopfoB5dGxoq6REM9KuICXNpzKfcN3ddcBTuyBX7yRoq//AdwleiOKILWZilo5XCUqC86t4JmR0ImrM9oqWWZorLYgLoc16pZ7fQwZ1ALIlwGmWIRczKOu31+BG1om4q0XNunvov7xtRxqO+FW913E4I2V5Wx3YezJaiR0lM1guaLIkWRi+K/VfftX7yD3O9+yP6XvJS9z7uSQ296M/te9CKKu2vVwWdcJwEp5e+klOullGuklJ+wln1ESnmr9XdRSvl6KeVaKeWlUsoD1vL/kFKG6qw0Nkkpx6SUOSnlZinl+VLKDVLKf5BSzj+IvEiwv8RsyVbQRgEBKy4nN6JeCz13ZoIGSkVrRtC0YpxJogStHJuemJ/RlK5midJbDV0Bqm0L4MdF0SgihFBJsAWjWnXlXb5i6i7mBSEEWlsbxsQkZbNCB6latY0VKqo+xFOHcbUspyXo4e6Jb7AnsYf/efJ/+PKWL6vXbQndutAmvnoj0jDgjdcBVKuIwt4wpjQbZsQt112HzOdJ33HH9EHq2aYeaPUEbW3LWoJakC3BUFOCltiqBNq2i1TIoSXoYc/ycxp72IFS33LjTRW0mC9mhTjVTXk0PZ2gRfwaFcuGpP4BNxP2j2c5h2fwVSr4Dt3fMKsdy+h0RX10Wy78o24rhFU3C20Pe2nVM4hSCc+yJg3s6+Bboz7TkoTaXr8vqIiNufDeofUYyxQbCgTA6iLgjSKSA9C6koDXhTRVTme2oAjndAVN/V+yFLRmOWg2ERzPTiE2Vn4kLSsUOfFGSJWz1YdDTjcI+WrbCntDuNx6tZ3UTOi3erbWE7SgV6Mt5K3mCNZDSskDg/dzde/zWd+6ntH8aMODJ55T4dmuqM/ydKs79wYehq0/nXU89cgUyzy4f4IXnduNiYFLutg/ZudrWg9FO8RpWRnMViRQ3K6ukR0dBToDneSNfDUkumskQ8IXpU8UGD5iHetQO7sKLWzzXUTc7aYt0AGaj6jfg1GehaCR4ZwlanzyyBG8vdMnl4czh1XeEnB2u7oP1edxNRA0o0KAIlpupI6geRAudWzzBRdJEUPkatdjm7AJmoogrIiuYMAlqwqa1tIya+6VK2IRNDvMmTwEseVkLDPgSOc5VYL25nPfTK6c48e7flx9/61bj/DKL9zH4PfeAbt+Q8veHyJEhd6YUp6ifg+xUo5CMELUOzdBc/n9uCIRjHhSLfCoay1TNBAudZ+qErRZ2j3ZYdBEIYcRj6O1zk7QPEuXIrxekrvVM+7sYJoMQf54SO2zvhdudd9NCFpAC8yuoGVL+D0uTAqY0qyGOKPeKAjJOfk/QvdGikmNgX/6JLhcdP3zP7Ps859DFnUmv3ZjdVtnWhXnsxZ2iLNcKalKoeyIyqPoWE9uxIc7EsS/YaolXCN8a9dSalLJ6S0lSMhI1YRzSdRPyayQ1gtUTI2lLTXT26qChqiGDKIB1e6pNKAKbKcmXC8EWns7RnySklGhk0TVnJX2NSTcHuTYTpV3khqCWC/tsTRHyo/wrgvexWvWvoavbfsa/3jPP3JTZg9lgHKBSqlE8uc/J/bqV1HoUeOvV9CgMdwSuOgiPCtWkPrVr6YPcIozto16guZ2uTm/83y2ejUoJEnpKT7/xOf5pz/+E0WjSLH/EIZw0XuOuoG3Br1s616PLBTIb9lS26htj9G2urrIVtBUiDNUzUEbSakm0XZOIIDH7cIv1M2jwQdqBmwZSNLrHsIvUdWz+++uvjaeLtIZ8dFSzOKtSEalpfbUVWG1h3x051VIZi4FzdOrXu9KwspoH/3CVIUCx+iHNpbR6aqz2ABlsxHRAiqU0rqSgFdDVtQ6+Xxzguby+dA6O2sKWpMctJqCNsVkODuqrhOPNY5YLymj0BDirDe8DXlCeLTSnD38bt33G0JrP8mR0iMNy3ui/qbtno7s3cKHvjLBtT8+yMqoMle1E6WhsaCiNag6guiGCQf+CN99Fdzyt/DY/846Jhv37lZ9ZV90bg9lTDTc1a4SuDWVszklB202laKwdRsIwf4ewbpWlZ5hn8N7RjMk/RF6ZQF/2QrdBzsYSRX5zqrPkgy10bpSdVIJ+zSMktqf3QAcUPdOoF2kOLsngt/QcacS0/JnU3qKdCldJWhLQ0uJeqMNVaWuWAzh9VYJ2kphXWttNQXNVo6yBY20uxVytSKkVhSxklaLpxWRFQyLCqVSFjOZwN3SMuNxgrp+nPUKWmtfLbTfrdJgKBc4q+0snrfsefxs98+qz4HvPHAQz+hT9Bb3MHzWm8gJ9ajuiajz1eUStJbz5PwhYr7YnCFOUCqakbCInK2g6Ub1ODRUcYKyw5iCkE3QkuPIfH7OHDThduPt66Pc34/f4yKij5L1dfN4f5xKRZLW01W1S+27R52TpcbzcM4ctHyJ9pCv8V4M1cIzP+Nw2TtJ7G9FuKDvRz+k/W/eSvSlL6Xlta8lffsdlEdHqcgK5UrZIWhnAqqluC7LrDYzCpFuZLCD7IiP0IblDTYBoMqKy3WtLHxr11LJ5zGONMrJvlKKFGF8VsilJ6YIWaKQwzA0+upyXqoETcrqLCPq95AulCkdHkB4PFU/qaOBu6Mdc0IRtHaZUCE0YE/mEFetWML7hm/HyAxDpQyxXrwR5fFz7apr+dfL/5XXrnstO+M7+fjwXXyjJQblPMVnnkHqOuGrr64SMXsGZ19U9YUCQgii176M/KOPVR2sqyhlpxUImBWTeDFOe6B2A7mo+yL2uCr0Fyf4l/v+hW8/821u67+NH+78IXJokIlQG5GQeoC3hrw83rIK3G5yD9VVMlqhw6qpIops+Nw+FfL2hqtVpSPpIj1R/7SZdsDdisA1L4K2fWAUt7uIWwZIiSjsUdYfUkrGs4r4iNwo3abBqFnX0io3BlIS8LpZbtkJeOciaN3dSAFdScmFXRfRX7Zu5vWFIAuElJKxjF4lTjYypQxRYTm3t64k6HGDRdD0vBvh9zV9CHqWLaM8WAtxzkTQpilo2VEVmrcR6yUly3UhToOQt0bQwp4wLm2GIoHUINz8NyT338mvhj6Ly5PmzqFfNKyiUhKmVyxPvvu9LJuEtru30Hefuk7qw5x239TOiL/aGiuZL8Of/ksV5qz9M/j9B5Tf4hz42RODLIn52dzXii5NPEJj33hdjpk/BpYf3XzMQAtbt8Kq5RR9otqmyK7k3DWSwWhtw59J0CHUNgveFo6kCvR1REgZeVrC6sEf9mkU9SYKmhVO7PXl6YkF6LEqfL3LexvGMZhVCl1vWC0XQnBO+zkNCpoQQhGS8XFKRoWVwlKDLAUtGvCAS0fgIlWQ5DxtkK0paDGpzn3daxG06AokMGioKs7Z8s+gLsSZsY53dgwiS8mUMvjdfrw954OsKG8+4CUrX8JYYYxd8V3kdINtgyn+rechcvh578SrOSDUfay+kjtWypH2hon5YnMqaGARtKSl6FUVtFoOWs0HbQkId011rkPYWic9qo6nNkeIE1SY0zc8yLquCMLyQEvky+wdS5IpZ4h5Y6SLZR7aP0ncZd2vs43q3dw5aKoPp91FoEFBA9IuF2bXpaQOeomdG2qwSGm9/g1gGGTuuLNqd3ImGdU+a1Fl2cIqqc+OQrgH/dAYZtFNaH3j7OJw5jCv/uWr+eHOmou8b930FjYAfiNN3h2pPtyXWInmE7kMUnqqeS6AIgXCjb8iqwpaLOAhXTQwhofRli5BuBsfZAuB1t6BMTmJKOcJkVcPCuDR4UcBuKOS4g7bGb99HYZ3H8KM0hftw+f28dHnfJTf//nvuSjcx71BZVZbeGoLAMFNm6oEzZ7BzZSwHH3JS8A0ydx1V+MA9fQ0BS2hJ6jISlVBA3j9+tfjE4K3yWEeGHqAD176QZ7f+3y++fQ38Y4NkWqrPcBbgx6OGBqB885rzEOzwyDhmqVLg0zvizSEOLujjcoRQMjrQSM83WagCfJDO8i7BJo7zOPyHGWBgUruLZtShfQyo3SbFUat1kUEWqFiVJW8vrJ6YE5VpKZCeDzkIy56U7CmZQ3xckY5+x9DocB4RqdkVFjeGmhYni6liWAR19aVBL1upKmOlZHT8HTGmoaQPL29VQWtWDbxTwlxBr0aYZ82PQctO9ZA0MzoUjJC1hS0UmOIM+RRIc5pOWh6Br75Etj+c4ZveQcSidtYwpNjTzZ8nz0xf7VIxEZxxw48AyN8+yVuvBvOxf3z23AJV6OClq5X0CyClk7B4UfgnFeRuvYzHA6E4Tf/CJWZsz4GE3nu2zvO6y9ejtslKMkKPpeHfWPZmlrvj1UVtICmvp+ZQpxSSorbtlE+W+UUrYqp33aYfs9oBm9nJ6RS9HnU+b8v60dKWNHhwpRm9ViHfBrFgtpfQyWnFU5c7s0S8rrptYpoPFNCnIczKipg56ABnNN2DnsSeyhXat+X1tlpKWgmS4WljlkO9h1hL8JVwucOkMobFL1tDQpapJLGlIKCS92TVkTU+w6Z+TnbPEFdiDOXBSmRuXEqwXYypYyagNoeilaY88plVyIQ3Dt4L4/1x4lUUpyXuJPhvlfz0FCJ+6QyGQ9ZRFpKSVjPkfQEiXqj81PQOjswk1YOr/V9Z4tGrYrT2jZuTVltNGlcHraKCwrjikDN1EWgHt5Vq2hJjrE84oHUEMFOdSwfOKAIYNQX5d0/eoq//MbDfORu63yYEl6dKwctnivRWk/QfOr7sZ8pI64IuWcGkIYktryxOMvT14e7rY3ijh3oprpvOAraGQD7SxT1BC3STdZSXEIrGx9KP9n1Ewxp8OjIo7VtNOkxSLmIJksU3TXSYRO0RCGHrHjoa69LUBUC/DGCdUaT0YBGulCmPDKKp1spXlvGtvCRBz7Cg0eaJL7PAq29DXNykkDJuoFZD7qt41vpcgeImBUeHrwPhAu59EKScifl3OqGKm0hBM9tO5edPi+TuVEKTz2FZ/lytM7OaU75zRQ0UEnsnhUryEzNQ9Oz04oE7NBJPUHrCHTwVl8vcVHh7ee9nTec9QbecNYb1CwuOYreWTPybQ16MSsS9yWXUXx6O2baugHas+xQrfKogaBNqeLsiU0naEGvGxf+ebXW6crvIScEXk+MvUYXMjkAFbMWCov6IDtCNxojdtHBFBPSzkqBksc7a1KzjURM0J0SrIyuBKC/ZdkxFQocthL+e+snFFgKmmmAcEFsOQGvG1lR10sl58LT1rzy2bNsGeXhYaRhNA1xgiI3zRW02neWiSiCHbMeSlndbAhxhj1hEMXGLiEA++9RnlYv/29GrbvncveLqcgKdw3UJg5LYn7iuVLVJBUg84fbqbgEo5esouVlL6O0azfnmN2NClq164IKcQKY/Q+rUPCq5/OBJz/LG3o6SB1+AO79dNNjBPD/u30PAnj9ZmU3oVPB7/aSKpSZyFp5bXUEba4QZ/6RRzFTKfLnqIerTdAmChMYZoWBeJ5Qjzq+Z2tZMgTZNa4+S3erOgbVsJNfI1sUhDyhxhCnWyMjInS5swghWG0rv1MVtIx6sNshTlAErVwpcyBZayukdXVWQ5zLXZPq2rQq3nuifnDpaPhJFkqU/R2gp8Cwwn1miiRhcoa6idkEbUCWMBOJWbsIANXKfTOTIZlMIIwit+4rky6llarTulKFGS2C1h5o57zO87h/6H4ePhDnr7R7cFVKrL32PXz29RcQXK6eE6GyGl8ll0czDSbcARXi1OdD0DoxUnlwe6sV75migcul49f8aK66KvFYL6SmE7SYT91DShPqPjhTF4F6eFetxC0rrCqOQX6CaPdK2kJeHjusJlrxtMaf9oxzcV8ru/LWdd+km8CsRQJZ1YezGkK2ciztQr5dYimFbVsRmgt/YKwhl1cIgf/ccynu3FkVOAJagD88M8Jg4thath0rHIJ2DKhX0DJ53Zql95C7/wF8bQKPt/bllswSt+y9BYAt41uoSFWq7o7F0Do70ffU+W1ZSey6p1bd0h72obmEqhiteBtDnKD6cZpGlaDFAh6ShTLGyAhajyJUv9r/K27Zdwt/f9ffY1Tmn/jtbu9Alsu0ZKycBCvEuXV8KxdGVrO5WOTRzEHoOpd+fZJiJYmRWz3N6uC5HcoU9cHxJ8lveYrAhZuA6b0mZ1LQhBCEnnMFhSefQpp16kGTHDTbpbyeoAHcED2PP42k+IeL/gEhBBd0XUCoIAnpOmJZ7UFgqxflCy6CSoX841Yvvty4SrD21IhXfdUQ3hDoWatJtN5QIGAj6HUjpG9eBK1XP0De5SYQaOWQ7EKYJUgfqT7IuyJ+yIyywh1kODesvn+raMQmaG3lPLkmVa7NMB6TtKclXUH1sJ1oXXZMIU67ktF22beR1JPEyrp6ELg9BDw1Bc2ddeFtbT6D9fQuA9OkPDLSNMQJyjNwvKmCViPgKSu3KGqo6yCvGw1dCUKeENLVpEhg/10qnH7RXzOy7oUAvMgbZUVkBXf01yYOdkqC/T1JKUn/4Tb2rPKwYvlGwldfDcDlB7UGw9d6SxI7xOk7fB+4NLaFY9w/dD+Zis4P1z0H7vts01Dnjx4Z4BdPDfF/r1nH8rYgGEV0BEHrnLVJsyJoSXVcXR40oTWEkaSUZB94gPRttzH2mc+g9fQw8VyVkN8Z6EQgyJVzJPJlpASvRdCWGVkmKhHu3T2OT3MRDKhjYF8jYZ9GrmTS5m+b5oWWFDHaUQ/Z5cU4RV8QV6yxt+9gZpA2f1tDuM8uFLDNqcFW0JQ90DJXXJ1rlirbFvLidpdw4SORL1MJWUQjp76LkJEkLqNVg9OYL0ZQuBmRBmZyPgqauh9VMln++5f3A3DfERjLJtQE1OWGrnNheEvtM7SezUB6gNKhx3i3dgusfyl0n8vrNvey8bz1AAQtYmEm1bU97goQ88bIlDOYsyiqoEzHK7pBRdaEg6xu4PGUp3ksElvelKBFLYJmxNX35m6bm6B5+lYCsCauRAgR6+WyVW08ckgR7Tu2Z+gIe/nKX11Ewg5xNunHOVuIM5FXIU67Q0nEY+WeWabWu2Unha3b8K1ernq0T5l0+s85B33vXvJ5KwohPbzz+0/wm23TCxYWEw5BOwbYcWrhKlPKjIM0qXjayD/5JKHVwQaWfiR7hEw5w2VLLiNTyjS4iE+r5LR8ugxPjXS4XYLuqB+TEi7hmWZbQKCFgFGmYBSQUqqWF9ki5bGxqoI2nFUnm1ExprdZmQVah7po2uyy60g3Y/kxhnPDXLD0Ui4r6gy6BcNLNvDYiDKWNHKrq/k0Ns5pXY+/UmHv2D7M8Ql8a1WicVVBs/oE2hdXM0+mwKZNVLJZ9H11LuylrJod16FK0PyNBE0EW4kU09XKxKg3yoUllZsV6KtVurZY6kWmbz1oGoWnLKfy7Fg1mdlGA0HzhaFSJpHOUjIqTUOcQa8GFR85Y3ZTUCklK81+sm4fMX+YAWkpQImD1WPbFVEK2lpfOxLJgdSBaQpatJQj5Z1bPQM4EpWE05I2TU0OJsMdqpJzjpv/TDhs+eH11oU4zYpJppShpZhVSgLqmMiKn0BR4tZdeKLNQ/J2Ht0jT/4WV3j7tCpOaKKg6Vl1jtQpaCmferjHrPNgapFA2BvGpEBar5tkSAn77obVzwe3h4Ot63BJyZsGv8iLe6/m0ZFHqyEWW/G2fcf0PXsoHxrgj+sMVsdW412zBs+yZZy1O9/gh2d7oIFqWwUQGt8KPefz0wO3EvVGuXzJ5dwksuAJwV3/3vDZb9s+zId/+TTPX9/J/73GqjQu5SgJQcAiaNXq0joFTQhBQAs0hDgnvvhFDr/t7Qy95x8p7txJ1/veR8bKV4p4I9X17YlYwOomEC2kmSTGb58eZl13uNpSqT7EaVYkrb62RgUNiBOlxcr/6iokSYTbpoW66ys4bfRF+ghoAXZONhK0SipFuVBgqZisutfbn9fnMTBNLyWjgrAVcYug+ctJ4kSqBE0IwRItzGRZQqUya5snUJ0EAMxshv0H+wFIiBgDyXgt12v18+Hwo9XnRI9pktST/NPo/6XgjsCrvljdXs6adAWt1mu2F9swvmrEwSYnM0HrsCpbjdq1mCkaaJ7StDZytCxXKtaU6z7mt9az2121zX4cAIpL1MS3e8JSiqPL+ItLlpOyUjK2Hza44flr6Ir4OW/Ncop4p1Vy2lWczcy9i2WTfMlUBK1sta2yngeBeD8AhyrtFLdvJ3CBZaM0pQDCv+FcMAyKljF5Oq/OuZVThZBFhkPQjgH1ClrFSh7PPDMO5TKRc7tqXQWAkbyaEVy76lqgsXecb91a9AMHqi1s7Fmt4W2cOW7ua0WIEqbhmZ6f448RMHQkkqJZpC3kJVzIgGFUFbTh3HC18nQhvSDdVil1W9YKoYW7qwTz7OVXctl6ZZNxXyjMYyOP0ertRJbbqzYTNlzeMD2GSc5qB+NZopKGs6UsmkvD61KE176BTQ1xAgQvVL5KBauycufgJBjFmm2ABZug1RcJALX16m5mZ+WtB8ualdVltpqSFRr+c88l/5TVnzI33vCgB0iVUtVkVLtYYXzSuuk2CXGqcJ53Ttf2fMlklRim4HLTFghzSFo5VIn+xv6WmVHWhdTDZ19i3/Q+i3qWhBacs3OBaRoMtIAAIgm1/Ul/RB3fo6zkPBzP0x31TTOplUha8okqQQt43WD66bQiNZ5wpen2PL3qZv/TP36BQO8P0NzTleCuiL+xirPuvLWRsjzjYmUdWS5z3t5HOX/LPVT0mieUxCRbb2KZ6IfUAKx5AQAHM+O4jDAt+cO8aNuvMaVZDXPaxNzOQ0vfdhu4XDy2XtAXVW3XgpdeSs+BJIm6+8REtlT1OFQqriSW3g09G9k6vpVLei7hOUufw6SeIHXRG2H376pFKZNZnQ/dsp3zl8X42ps215rIl7IUXYKwRUqHrQ4U9QQNIOAJVFXdwvZnmPjKV4lddx2rfvUr1t1/H7FXvqJhMhX0KFXDdnGPLFXHV8tkSLvUvevsnpoFhJ24HbbaWLX6OqdZzUxUwkQtgtaaSzAebGEqmhE0t8vN2W1nT1PQANyJOEuYqPrf2fB4yuglNRYtYl3TVgqDr5wkLiPk64pElniiZMpq/TkVNK8X4fWSmkgSMtR1uHLFStJ6vpbrddbLlS/k3tth8HG6H/sOAP8pXsF3z/9ew30m71WkKphWxMUmaHEtiMelXptrwlf1QivV9cQtlnFr+nSCFluu8linECW7k4ErlUIEAriCcxOYhMtPwhcmOm6Rolgvz1/XSUdUHds2f4w3XqYqmjetaGVEtmKmG/cb8oSoyEo1R6wek3VdBLKlLAEtUA3X+m2lLOFGFosELrxY/Z9tPO/855wDQHmXUqTtVL2GVKKTAIegHQNqBK2MtGZeyT9uxbN8OYGzlzcoaLZ6dUn3Jfjd/obEYO/atchCoZr8bCtoZn35MfDJPz8PTTNZ39UkMdPfQsDqc1kwCrSFvHRa2/H09CClZDg3XDVstQnjfGBX08XycUxcqnzeIng9oR7WvuzzrPR3cpsZ57GRx9jUuRkQ0xQ0PAF6TIPSuDoudiP2bDlL2BOukk67L2AzBc2zYgXutjYKTz2FlJIP/1Tl+1WmKEQThQmCWnBa/0q7PRPFWs5GV0Ld4Fy99X5lloJWNAheeCHFp7crP7QpCpqUkqSebAxxAvGEUgaaK2huKubcIc5UOskSEafoErT4QyS1LkzhhvhBxjJFwj6NoCYgN8by2Eq8Li/7ktMJWiCfJekNztm2KJ7qZ7RFfQdyeJQWXwuTHqua6Sjz0Abi+WnhTVsxaimk6xQ0N+BmaUodd0+guUGrp6enWmkKMGFMH9fSFj+5ksmEraLZN+N6Bc0qmomV8ox8/OO858HvccnPv8bgu/6Oiq7XWb3UDJMZs2wclqhJwnB2BL3czuS13+Cc0b2srri4ebdyKu+pKmhFpJRkbvsDhfNWkw4JVsbUZw5cuAl/RicwkqqGpxK5Em1Wj12/x81yT5qAkSLVsZZD6UNs7NjIiqhSeg93rVWVgFaY7H/u2ku6UOYzr7ug0cDXUtCC3iBhn8ZQg4KWVhY5NIaR0r/+NcLjofsD/4L/rPXVXKNMKaMMht0+tX45T8I2CV1mtRNK5th8zlr++/UX8L4Xr59mfRC2ijFi3g5G86PV4yulJG4GCZiWV1gmzrivcZJaNsuM5EYaCgRsnN12Nrviu6rH0g6/+VLjtJFSpKMObneJom4RtFZre5ay4rGMwut7OC7xxciX1CPT3TK3cuSKREhOJGi3qlpX9a2kLIu4se4JSy9UFZMPfQl++Dp6rEne97mE9u7GseYsZTOUVGFBm6ClvCFcUm1vrgmf1mkRNL1WoZjVVQ7atBCnZTg+tVCgNaAmoFo6PWcXARvxXInBcCfeMes6jC7F5RK8+iJ1DL/3lqurSvjqzjCjspVivFHhqm9Fpu/dy9h//3fVnzJu5VS2WiHOSF3kyW/l+HWk1bHxn3cRuDy1Yi8LnmXLwO3GHFJRosmMOienpRItMhyCdgyohTgNyMcpJjXyW3YRu+41iGB7A0GzCVF3qJul4aVVwgb1PTmtMKeloEl/480p7NPwe02et3bJ9MH4YwStlksFo0BryEtHQc1Ete4eknqSglHgwi71cJmPi70Nm6BFiikyWhu4XLXPE+xGuN1ce9breWz0CSaLk1zZewWgEjcb4AmyxDCRcUW8tB71OfLlfMMMzu1yE9SCTSV7IQSB88+n+Mx2Htw/yZjVumSk6GlYL16M0+ZvcgOxc9X02ra9oyWyfjCjtRuc3aw6qxsELroIqesUd+5UF3b9zNbIY1SMuipOdaNLJmZW0IJeDdPwzknQ8iNKbtepEPQEWdIaZsLdA4mDjKYt89fcOMgKWmQJq1tWsze5dxpB8+bSpLyh6d/HFIwnDzAeUwStPDREu7+dSWEpWUdZyTmYKKg8qDpUFZVKpaagWYSiJ215oPmbVxMKr5dE1M2GUg9Sujlc3DJtnbOsNkF77DZJzRQ0qciq78GdJG/6GT9bezW7/urd5B58kMmvf6N6PpoU0A3rGNgErVNV1MX1cTBjtF78OsTrv8Mb4pM8E9/J0+NPE/ZpRPwaI6kipX37KB08yOHNvQhENeHcVoPXD1aqJCaeL9EWrD1AL/arB8Z2v5pEnNdxHn0RpTYcsh6WDD7OWLrITx47zOs291Y/fxWlHLoQ+DyBakcSwLLnkVU12Q5ZStMk/bvfEbrqKtxT8r9y5Vz1YW6HnWwFo60lhLulBSNTItbWxWs397IkFiCpJ3EJVzUUF/apazWitVMwCtWJmG5USMgQfjNDRdfx5zIc8UUblN+h7BASOU1BA1jfup6CUajem2wLiJaERTKmKGi4SsiKmmR3L12pGnUnD6sQpp4gToRcXQ/Hpf4OhG4RtDkUNFCFAtnJVDWn7ty1qxCiRMYKn+FywTX/CpMHwO2j51VfBUB4UtOIgR16DlkhO7the9obQlbU+TLX/aRq3qvXQvmZogEuffpENmale0zJQ2sLqO/em8nO6YFmQxG0LuR4SlXqWhYf0VAJgeCsrtr9dE1niDHZAlMUtHobmNFPfZrJb/wvY5//H7X9fJ2CVs5WzzMA/xHVQqs3lQaXG09fn5pgZ8cbti80TU3+htW9YjxdoSviU+koJxEOQVsIRnfAbR+qKlyaS8Mt3Hg0E1dqlCMPteJub6P1+ush2ArlXLUqaCQ3QkegA6/by9LwUoaytRnCtEpOa/tTCZqUKnxphykb4I8RsHqCFsoF2usImmdJD0dy6ka/vnU9AS2wsBCnRdBCxSxZTd30RnOjtPvbqyT1FatfQVALcv1Z13Pd+lcR8LiZnFpJ5wnQY5j4EkUQotpg2VbQ6hH2hmecEfrWrUPvP8TPHzlIp1eV1e+YbAyJJYqJGQiapaDVETRtNM14DIbzNd8fO8SZKZYJbNwAQPGZ7Yr01FVwNnQRgGqIM522KijrW3JZCHrdmIa3GpIwJiYY/c/PkPzlLxvWM8f3UgFKGAS0ACvaggzSBYl++ify9LUFa8m0kR7Wtqxl1+QuTLeXu8NRPjJyN6ViHnc+R9obqj5IZ8JE6jCTUZAuQWloiPZAO5OlNER756+gmeVqr1XdMBlOTSdoSWsCEjPNxhAn0JVyUfaAm+ZVaUWjyEjUpDMpMfN9HMg+OW2d9d3qO6iaslYVtBpBS5cy+EqS7C3b0M46m+9suJbMC19O9OUvZ/LrXyc2odRo4dZJ21YbY7uUTYMvrBLozUkiWgdul4D1L+FVHRcSkKoYB6xcuIxO+g+3gxA8dY6XJaEl1coy75o1mOEAZw1JEsUEhlkhVSg3dAnZqKlz8ulKDoFgQ/sGlkeXIxAM6FaIeOgJvv6nA5gVyd9dXetwYaOipykLgU8LsrQlwJH6ECc0VHLmy3mK27djjI8TfdnLpm0rU8pU83uCniCFcqGqoLUGvWgd7ZgF2eBLmNJT1ab0QNXOJOCq3UsA0sUyaRnEUyliDKnPPeaL1ggyNQ+0ZgStzzL+PZRSjcltAhFLW9f1FIImRRFZ8RHxaZyzrEVZS6QOg55CSLOhSACgJ9BF1OJAcxnVgspD01NpVvhz4IuxYUUXuErE6wMDF/4VvG8n/P3DdC9V4TeXlmy0UUIRYw2BJ9EPlQpmIol0ucl5/FRMdY+Zi6C5W1pAgFGspcdkdQMpik0UtBWqwjp+oGFx2OtHSoE/k1+QgjYU7kTmy5jemrhgt3lyu2pq76qOEEOyA3++Mf+tqqDteIbcgw+i9fQQ//a3KY+ONvThTJfS1fOTfBxPoh83gt5UmsqSpbi8XmWRNEVBA6WiiRFrwp+SJ109A4egLQyZI/Dwl2FkW3WRz+0jSJmem+5CT2ss/dQn1YkbsE5eS0UbyY2wJKROzmXhZVXCBMp1WuvqonTQSqK0WxFZJeE2ypUyFVmpehY1INBC0CKDBaNAa9BLRzFJRfPgbm1lxDL+WxJeQk+oZ14mqTZcoSBoGkE9T9ajZPKR/Ag9oVpV3PLocu7/y/v58OUfRnOpVjfxqYRAC7DEMGjPgOhoQ3jUTDpXzk3LgYh4IjM2bvatWwvlMpn9B7mwW13cW8Yak1kTeqKa89L4ZpugKQJgViT+eIKJqJuB9EB1NTthPFs00JYuxRWNUtxuNTSu80CrVg3ZDyRLQctlUnSEvWhlnZFPfrLBSy3odVM2POTLecx8nv6/eAPxb3+b4Q98kMRPa818mdxP0Qr7+jU/a///7P13uCRneeaPfyp3jifnyRqNRlkCBAKEwIiMARPMrrF3bdYRe3exl/Xaa+86rL0OeG1s7y4OYGxsME7YgMhIIktCWSONJp0wJ5/OqfL3j7equvt0nzMjbIP399NzXVyM+nRXV1W/9b73ez/3cz9jKZ4wR/BL5zm/3eTASKqHHZrg9rnb2ens8AeP/AHvGsnyN61F/vc9vwZATU8MAuZdUW6u4ckS8kge++JFUWXX3hGM0eZwBq1hNXjFX7+Ct378rZxZfwB+/Qi8+wQsf53z2008Hw6P9U/+UcrL8yAv7BoMVUaWYKTiU80pSHvYBqzUV9jKSqR3WritBdbbF/obbiOAUTau8WTEoK0L481Ed8dftar8m8/5OFUT5SfeiSfJJHWVsZ/6SXwg+1FReSfJnW4l5+YpUXmH2FR4mIzEu2MhdfK7uKXV4vMXPoXv+4ymDTZrbeqfvJPEDTdwSlqPQIQ4tox34ghHV3zKZplKW1RD9gK0Y9ISO3KRJ+qLzGfmSekpDMVgIjnBYn0Rpm/EXbmPP/vaEq+5Zipq1N4bVnAvdT0ZALRdDdODDWGY4mw/JMZ54qabBo7Vy6CF7y+1LNKGiq7KqMUCTluJUv1AvwQASAcMWkwSc2SoQ6t3HKqIz9nLAhhsx3N93RwiD7TUYIozsoYJ5CMhgEg3goKoTL8PoO13wNO5ejbw3MvOCXPWANDv7EpxTiUnSAe3Tg1sNsqdMheqF6Kq/N6Q02ncep05ownJETRFZFu2aru0oLEsxPMYioEhZZD1an+nGMR9T8g6ktOB5hZuuYyfzYIk4btivFxKgyYpCmpCxml1AVq9Y+NK7UENmhYT4H/Xcy/LMpKvk2h0LssDDUQj85VgzjStbtFW1apGRtFhJHSVUmwOxbeh0p2PQwbN/sxdoChM/9r/BN+n+cUvRpmBYtKgYfUwaKtC563LOjOVJs50wAomxwY0aCAAmrIhpCmrZefbrj+DZwDa04sJYRPBWj9Au+30RYzVMlPPM0k9/wXiD+FiEAiA15prEaCZTE5SNat9Inh9fh5rcTH4TIWGHydm9LsZd1yxEIU78L6IZYkHzEXLaVFMCQbNzBWQZDkChFPJKcYT408rxSlJEkouh2GatHRxXRvNDcYT/d0JNLmbZhxJ6WzvBmiKyoQHxRo4I7no5YbdGJggknpyaJEACOYBQF9ZZCouJu9HtvsnvVKntAdA609xPrVRZ7RZplVIR5M/gK7KGKpMw3SET86xY5hPBHYTPQxayPJF5x8sTK16lfG0wcpP/ATlP/kAyz/4QzS/Lvzv4rqC7xr4+Gy+74+wV1eZfe97STzrWWy9+914LbET1irnWQzaQsXVOEfG05z3xpA6FTS7yoHRZA+DNs5ts7cxm57lPQ++h7gv8SI5w6ce+isAakZyEDDvimpgtKpNTWCvBAxaZ0f0XN0+PbSS86Gth1iqL/Hw1sN85oH/IxjG1g585XcjBuvw6HCAlleTUTpWkiQSuspI1WEnJ/eJ13tjqb7EZhaMch25NYaP11cRHR7r6HiK0+s9Kc7UmEgpBVG4+1Fue8CjcEOM9jFhGpo0VLTxcTJ33IH2iXuImz7IpgAIri3uQdB/thKc30SqZ5E6/ipe1OqwaZZ5fOdxxtIxjAtnMJ86Q/rlL2exthjpz8LQrz3J3DaUt1a6TFQPQFtwznNGWmCxthh5j0HQvLu2BGNXoNRX8Z02P3zbIHsGYAbnamhJprIxdkJ/tjAVHmwIwxRn+6GHUScmIoa7N3YzaKEGLTxnJZ/B6ch9voS7+y2GDJpOf8uzeseh5ouF2Lko5sKtWLbPLHixtkhcjXPnQ01e9BtfwHa7wGgkPkJCTbBYE5+VEwmkeJxkQ1xfrzRBtPQRDNoP3Bq0bcvNihTntpAWXPAn+osEUtOkWz6eKiMlEtiuzav+9lW86m9fxXsffu/AvVLSKdR2izG5DsnRKE25WfXxvOEFOxp54rEGmtK/NLfsVmRSS2URt1xGzubEvXICgHYJDRqAkgC31b1n9Y6D6w9h0ECM9WHMuaeTbJmX1UUAhIi/VBRrn9XuzvM1s9bfhzMIO2gjFv4O0J1fvbMX0Ofnid94I+rYGI17vkipaaHIkvDXsxtdDdrSV0BSMKQ4U9VWVE0qOq30pzhBADS1VEN1fLZq/re9ghOeAWhPL1KjosVPD4OmyxovfXAFp2iQOdFTSZgIGbQdfN9nvdllnKZTouKul0XT5uewgsbmXrtChSQxtd9CIGQKhrocx3LEg4e+7bSJawqjnSrNjABU6811YkqMnJFjIjnxtIoEQPi1aZZNyxiJjtfLoO0OwaANMjYT6IzUfFo9u5PeXXkY+zJoBw+CJJHfXGYiJibv1bYqzIIRqeByZw8GLSoSEIvWg6eWBPM4McpSfanvremYRj2YoI3jV9A5u4jv0cfEDAI0cR1mq8YJu0Tzrrsp/sAPoI6Ps/FLv4zveSQ0Bd8ziJk+1T96P6kXvYjUrc9j9MffgVupUPmIAFWJ+nnOIHaecTXOkbGu1cactMnBkWSfvkqRFd5187t47eHX8iF/ghe7Gom2mIwvJ8VZMSvIvk9sZjbSoDXtJp2RQ6KSs7I08JmHtx5GQiKlpVjeeFD0Orz6jXD2c5xbryBJcHA0OfA9GhLx3FzkSwVCFF8sW2zlZHDakTygN5ZqS2zmJCTfJ18Wxz1dHvQCOzqe5vRGXeiXGv26wc7jj3PL+77B4qzK2Mk6TVMAzxA45N/yFqRWm+vP+EiSI4orSudFK7MxUe21GPhQTWVy3S+N53l+Yg4ZuGvlLsbSBtc+9iUkXcd+0bNo2s0+Bg0gc8PNANgPPxoB6EiD5tpM2Es87s+wVFuKGCIQrYeW68u0DLFJetMxdYCpDCNk0Aw9HTEzq5V23xwFXcDVfvhh4ldfPfRYvWx3COh2egCamkvhdBT8ng1XpdPPoIVVnJIrFucug2Z3GbSg4fpOPNvHoD2y/QjHC8e556ltzm01+dq5fsPR+cx8BNAA1HyeWKuBJel9Vjwdp4OPz0++5CQvPBaMjeyMyJIErNGSPEOzh0EbS8+Qa4GZVJEkicd2HoskDk+WhwCZRBLDapP1KpAciQCaZasslvZIR9o5FH1wc9JyWiRCVrKyFJjlivnNtruZiEuFGvdxGuKaLMfDdC08nEEGDQRzvnNGbE56ImnqqK5/WR5oIFKcidEUSD5WrQs5whTn7tDHhecbO12AFmaNpAsXMY4cEZ6Ytz6P5pe/TLkmMkayLPVtILjwJZi8humGhub5NMcDgJYcDbS7/SBZm5lG8mGkRuA1+gyD9v9eTF4Naw9F/3lkDaZKLfxjMaRkz4DtSXHWrBptpx0xTlMpQbX3Fgro8/O429u4jQZuq0zNTw54PPW6HA9ELEc8oNnbThtJkhjr1Kgkc4BYFPOxvHg9McZ2e/uSxoa9oWSSeKZMxxilYTVo2I19AVoxZQwVpY9LBoU6VHNd8WXTbg6IVJNack+AJsfjSNPTzNY2GAs0aA0/zoXtZnQ827MpGJcuErj7nkcByMzPsVJf6bsnwvFcLA6xK47jmxZWQ+mmhmCgC0J4fKdd46p1MWnn3/wmRt/xDswnn6R+550kDBXfM3jBIz5+o8HIv3s7AInrryd+7bWU/+Iv8F2HQuMpTkviHsfVOIfHUiwFVhvz0iYLIwGDFs9D0Bf2+TPP5xee+wtMxEeZM9tkWmISslPZblXjHlGz6mQ8D312AWdzkxFVXOdONvCPunj/wGce2n6Iw/nDHMseYsUswck3wJGXglnDX/4Ks/lEf0UhQcrL85FyC32vF32TuOmylg0mzp5K2zCW6ku0R8S9Hq95xJT40MXxmpkctY4j0py7+nBu/ua76RgKH//uUaTONq22eK7CtHb8mqshneLkBb/bZ3cn0IcWxe7+7JYANbO7xOK5yWs45Hg8sv0I43GZWxfvJ3bbi1hCvP9A5kDf+4vXPwtXAuXRpyI/sdD/jO2nUH2He/0ilmf1gbvRxCgVs8KdK+Jefd/JvVvTmEEaXtfTUQp0qdTqbjQCGUZCTSBXG9jLy+IeDIlevWiY4iy3hIs7gJpN4LsSntN9vnenOEN9Z8eWyBv5XQxaYIS6voafStFRjaj62HItTu2c4prRa3giYEfvfKxfTL6QWeirkFeKRYxOm7qS69sMRD2Lew2cs7OiKvb8XZCexNPTtHuKBBQjzVjTo5EQx/nGptA/nhw52QcKw7CMBAm7Q9KtQqIQfafvGTx6cThDbJpxfHkQaDXtJsmQbaos4lTK6EUx9kz78ooEAFTDwWkEc6bpIMnB2B8K0K4Qm5LS+b6X862g8vUyGbRS0+JwvIqecrB2unPQ7nERRm5kkpKfwt7sbrwSWgLN9lHXtiPNduq5z8Wr1VDPPRWNv6iVlt2Bi/fB/C3MlsTvVRsL5rHUmOjMEUqJggg9FkerPnNss7CHWfa3Mp4BaE83Jq8RqQ5LPAwLq2JBj42bXVAGfZV0oVv2aKBXCQFab6GAPicmX3tpCb9doeonBxa2cAc2NMUZz5HoYdB836fQqlAKfITKnTI5I4fr+RRiBTzfu6z+bWHICR3XkukYI9GEujvF2RvFpM5O0+qrwPJ9n1ZbJmbDk0G61vd9amZtYCeV1tN7pjgB7JkF5usbFFTxwDeJcT4AaKFZaCE+ZALREkKPZNY5vVFn55xghTLzh7E9u8+XKWWoUXrFOCp2dWZV6zr1M4RBC4Gm1WLhwqPo8/No09NkXvFyjCNH2Prt3yGhgGorvOJeD//KI8SvuSY6Xu4Nr8c6d47OPZ9A99osaoGJrhonaag4gcXCQWWTyUxMsFq7q9MA4nlmW/VIM6Pkc5dMcVbsJjnPQ5ubB99ntCYmtp3MhEjrPv63fe/3fI9Hth7h6pGrmZV0llUFDjwfDr4QZJXxra9wZAirU2mXyDk2jBzte306SH2uZDx8GJrm3GptIU2KcTferLCQOcSTpUGA9vyj4ln7wpNbosF9wKC1H3qI5he/yBduzWLk8oCPVRe/eQgcJEVBv+l6rlr0kbAEMxsBNJEOW6wIUHNwtw5n8hpOtFs8vv0oB556gIzdwn7JyyLQsDvFaaSyLE0qpB4+T6kpxloxGSwMgUXAY0p84LPFmABXn1gVz9GCtnezbCsAaIaeioTPizutAZ1sXI0zflHMa7ETVw0wDNAP0ELftHLTjjpvqBlx7k5DABvf90VFdc+zGNcUZEnoO8cSYz0ArYdB29iinU+SOPBb/NrDP0HLbnGqdArbszmSu5KlUgtJgk89ttE3x8xn51ltrEZNr9VCAb1t0lD7gXQIZvo2hqG1xIV7YOQoSV3pY9DQkxTaPuWYeO3+jfs5kD3ANaPXsFxfHvAZbGkGCcdEt6oQL0TztyLpPLY6OPc6rkezo+MyCLRadou4nhIN5StLuOUKeqGAJEGr46NK6uUxaIaN07DwPa+vUXpKH5biFNXKuzuJ5FtiXbpcBq3Ssjmo7mBkHcyLXWPiYRo0ECbP5/wpnI3uc51QE8zsgOT7Uf/qUCNZOPMohaSO6ZrYni0A2srXBQhbeB7TJfG7lAtBgUJkStxv1q4FAG2yqvJZ/ac4+uhvXNb1/XPGMwDt6cbESbHLCmwHZlctakmVlNHoS3316jtKgQ4tnKSKsSKGYrDa6KY49QUB0KylJehUqJEYaGOzbyPXHg1a22njlsuonsN6AHzO7mzyyJLDi3/zruihCLVAlxNyTME1Zaz4aJ8H2l5RSAqH7l7vrZ2mxVZFDLmVYOfWcTtYntX3oPq+vy+DBlCbmGW6sUXWF5NZU4pHAC0ExGHD3L6QJMFymTU+8JVFpgMwl5sTzEhv8UTKUKPz1xcWALDqKsSybNQ63PxLn+FD9wsavgvQ4vhIJPwWxaceJXGLsByRZJmRd/wY1oULjHzmo7ziiaeYqEDne17dd3rpO+5AisWofEQUC2zExKQSsqaHZsbZ8rOcTJaRZQlKZ0VacXfEc+RbZUYCzyOjkL+kzUbVaZH1JfQ5AQLzQSXjtlmCE98Jpz/Vx2ot1ZaoWTWuHr2aWavDlqrSHjsuqhxHjjHVfmpo2q3SXCfnupHgPoyJwBZkPeOL4oghAK1slpHGR/FlmfFWiSO5o5wunx5YHCeyMa6YSPOFU2uBubAYq7U7P4mkaXzyOsgGKXC/JsZzbyeBxLOfzVgVJppVAdBKZ8XzHTzXK5UKADPZXWNs4mpOmBYls0Luno+xHcuwffRaFquLkbh/dzxxVZbi2W2ayyKtl42reJaFv/4IrqSyrglmvJdBCw2Yv7oTFEjUV9krTEs8F4YWZzRlkNAVAdBUHfQ0TnObB5bKxNU4k1sCfBh5CX51Hv7mhyIjXN/3aViNrgZNTWB5FjvNFoWA9VMDDzcnGGt1u47t2RGghKBlW/BszWXmol6kvRq09tY2Z/RtJMnhXP0RfvqLP80DG0L0HfcESL5pvsBm3ez3KktO4uNHRtVKsYjScWjtBmjOEIA2elxYbQCMHiNhqLR6GDT0JOkWbMdcmnaTBzYf4Pqx65nLzNF22my1+3VNDTWGjA+WA4kuQJvJZnlsdXBsr9c6+G4cF2vAkLXpNIU/ZH4ev3QBt1JBLeRJ6Sp10yWhJS4N0HwfVTfB83ErFeqdSzBoI8fE/Vjtr5TOt8UcfjldBEAwddPSFkbWwbq4gdduR91EhmnQJrMxznpTqDtPRh59SS3JzJZ4xo0jYq5WR0fRDxxgevFUX5unlJaCx/5WNIRfeB4T2y7VuEI1aPQeFXntKhRQR0fxJYnRqosmuRj3v3eorONbGc8AtKcbhUBQGjirT62aLI/HyFLrajpAeL0oOrQrlALxdWj7IEkSk8nJPg2aPit2b9aFRaSOYNC+2RRny27hrItF56IqUm7b7RK+m+D8dhNdFq89HYAm6T6uJWNfJkArBvYSvazNmc0Gpikm8g1NTEC7bSq+639/mZ/76GOk9BRtp71nz9Ct4jSq7xErbQt/tVxykEEbZrMBYGSwmlU+cv8Kt8Q7yIkEI1MC5PQxaDE10r8oqSRqJobV0EFLcN+FMpt1kwcvinsRuYNLEq6aoNCoI5udPnYs/eIXk7z1VrJ/+B6+/4Gv8I2DEo0b+1kkJZUi8/KXU737ATqmTi1eZHLHJ/m+j1K785P86uuvJj52iBeNNYU2pLwIxWEALY9kt5h0kpgxhVz20hq0qtshi4J+UIzxdLDL32xtwsnvAteEB/8sen/o2n5l8UpmqwLYrphirDcKJ7hSOs+hoQzatqjgHO8HaOMBk7OZg4YsDaQgQIyXbCKPmR9lvFXmcP4QNas20NMR4IXHxji3tCzc2oMUZ/NLXyJ+w/VsSnUyAaPtBzq+VI/nUfpmoQ27YnsrYNDOQrErwl+vV4LP7FrYJk5ylWkR7/jEH3yAz89cz2bL5kLtAnOZuchqojfO3ygY9fRX7mJEstn8N9/Hk1dfw8r/+hi19CHQSyTUZB/ICf/dUhxcLTXgG9UbZsBEG4qBJEnMFRIs7gQWL7EcH/vqo3zn732ZrRrM7PhI2QzKqfeB3YaH/hy++G5AbKZc3+1LcYavFwLWT02JOcsJvNbCVk67n8V0ANCuKFzBcn2ZhtWg1nGoSeJ+1rdL7KQlWsvfx/OL38dnlz7Lu7/xbq4oXMFaSXzX844IPWxv6j5MmYVzgFosgOnT1vrBRNT/V+0BaJlJuOYtwb+nSOhKH/hDjWN0oBaHvzvzd9StOs+efHbkS9dbBQ5Qk8V5erYkGLTAp/JgscBjq7WBTcVqRQA0YKD5eeQVmZvD21gC10XN50kHc1RSS+7bqxIAx0SNi+txtrYFQFPFfRi6mdUTMHMTnLur7+VcyMpfpg9aveMw4W1gjOrgeZhnzkZgajhAi/Ml7yo0sySE/oixO10CT+5uIEGwaIfWz1CMKd2KejUBj/0NHHsZGGlGtkwuFlTa4W8ZGo3vKhSQNI12xmCs7vKIdrWwGfnGBy7rGv+54hmA9nQjF+xiy4t4lsXIWpuVSYMknf4UpyQJm4x2eQCgQWC10cOgyYkE6ugo1tISslmlSpKY1v/zRFWce/ig6T7ISLSdNva6WHROu3EapoPpNYgpApjZlpgEKkMWwL1C1hx8T8JTMqy31pGQGE2M7vn+UBOw3cPaPLXZwApMEstx4Z/UC9BWyi3uvVDmA19dpNUW79trV7iYFuDQu7gNRpoDI0nObYuFKHSqH1okABDLsLa5Qdt2uZI62twc40mxgPcCtHRM7WMA9dGEAGiSxKm1GpIEimqhYPR5+dhyjFw9SCsF/UZBAPOZ9/wO7qtfz5+eeC6//Rq5//rsDrgOhe/51/iWy+L5SUbbVX7hAy7S+z7C2s/8DGnPJjV9Jer2E2J357t7MGji2kc6Ko24RDFpXNJmo+JZ5CQNNZ9HyedRl9eRJVnck5kbRfry7l+LWLRTpVNossah9AFmtkQlZVgJezF2hFGpyhWp/kXD9322zSoFz4/0XGGM1HdoaxrNGNTl4ZWcZVOk6luFMcZbJY7mBWgKWZjeuH4uR8GviP9IjWFvbmKePo367BvxfI9sANrUoFl5WCQAkDh4BFeChXqjC9B67vNmU9yDAeYhluFobIxrL4Dsenxt4kq26iYXahf6RP5992R6nOW5OMc+91f8yud/m9aDD5J5+R00zrYorc0h6yVGY1NIkkT9s5/l7B0vI/4jv4Dk+yRiLeTs1L4MmhWMsdCzcKGY5EIA0Epeiowvxupm1Wd620eem0J6+MNw/ffA8VfC198LndpAOj9knyTZ6jJogT4rBGjh3Le75VrSUGkGAA1EoUe9Y6PrCVaUOLGGy+SBq/DtIoeNl/M9V34PVxSu4D0veg/nthokdIWT02Jx7wVo4RwbzgFKPg+eRMfvl1Dsudm943/As34Qrn6zAGhmF6D5nodiStTj8L7H3ociKTxn6jnMZsTmeneRUVkS99uz5T4G7dhYkVLTYq3abw+zWmnjewFAswYBWkJLQG4Od2s1urZ0TKPesQVAu5QGzW6hxsQm3tnaEho0JQBoe82VB18o7CoCdhu6AO1S/UjDqHdsRpwNjHkxZ5unT0fkwDCANp41+LR3PbYcg0f+EghIjbpCu5CM7JkAYjffTMLucGD7QiSJSW89KdwTTn4XAIWNFqsFtWs6HK7TPS3WwmhmVAp1nwdHXw3f/1l44X++rGv854pnANrTDSPQAZQvYJ05g+L5rEyIid2N7xqw8ZxIcXZKSEh9gsip1FQfQIPAauPCeRSnPVSDFk4qQzVoqo6kJYhLimCeNgSzsxnL8tffWEJS2hwbEQ9IqyN2dk+LQQu0XoZls9HcYCQ+0mersTuKQaqjj0HbqONb4ppq6TaPr9WiiSirZ7nnKZGW0GSZ+8+LWWCvNOeZWAFPkrBWS6CnODSa4txWE8/zo0Vhz0nHSNOolTkylsLYWEWfmyNn5NBlnc12D0Az1L4KMj2vRlVIp9ZqHB5NMZoBvP7foy3FyNbEZGkc7BeFy4ZB7J3/iQ8ev41WTOoCtE4Vfv858J4biO18itRUB+chhx/5079C9SDx8+/CazSo/f1HYfp6aG3D2c8FN3t4ihPEZFqJueSTCqVdmsDdUcUhE/RD1Q8dxDp3npHYiEjdSBK8+L+Jir+v/x9xD3ZOcTh3GK18npmWAFOhrvIJFgA47PZbYJQ6JWq+zYKeEym2nsjXd9hJZ0CShgI013OpmTVyRo56fpTJ1g6H84Lt2221AXByJsuoVBH/kRqn9dWvAuDcIIyHs2mhOdHamxiq3O1dieiluFWQmanW6bTqAgAF99lxPSodMS6HpYb0kcO84IxPJ65wbuwAq7Uay/VlDmYPDrvtZPQMf/xdedpGkrTVYvb3f4+pH3o58RET6+4lVKlMVh/D63RY+/mfxymX8R55nKvP+xSyFlJ6UjS23iPMXdXf8yMJlkttXM/nopVgWm8xk4+zXvGY2QZ/RBPi8GveAs/992BW4aE/jwBABNBC9km2Ig2arFhIso9bEaAvZNB62T8Q7HTDdDiWFzqnJ0pPUO84pGMqH9bzyMD1V72YuKbQMB1+8qaf5EOv/BDjyXG2GxZjaYPRoKl87yYwfOYjBi0T+Gc5/UVIe1oWGWl42a9CZpKkrtKyu8+/UyoBEl5SZa25xqHcIbJGlsnkJKqsDjBoO76YH92AQQsZrisnxMZ2tw7tYqXdZdB2AbSokGrkKE47aGUVMGgN07m8FKfdRg30c872FvWOHQG0PbMNB18I+HD+7uilbMujYYiuHpcKy/EwHY+8vY4+v4AUi2GePh01Sh+mQTNUhWQqw2OZ58Fjfx359I1VJRoj/YDauv5mXElm7vQD3RTnw38l/BWPvASnXCbesFktSl2wHVUvDwK0WloiVwdt9BBMXNVnzfPtiH+Sb5ck6Q5Jkp6UJOmMJEnvGvJ3Q5KkDwV//5okSQs9f/vPwetPSpL00ss95rc18vNQWYx8y9aDuaej7hpssVyU4swZuaiBKwiAVjbLfbsebX4uOmaV5IAGbV8GDYQODbnLoCkKlVia931VtKi5cU6IyWvNpw/QFCkQD7cbl7TYgK7ZZi9r89RmA9UNHPpTFg+tbPYxaPc8tcVEJsb18zm262Jo7lUosGFCOTuKuVYTrMW4MJW8WGlT7gg9zdBUMOBoKbx2jRcdK2KvrKDPzSJJghHcneJsmE4EavQcuB3RZuWJ9TrHJzNkEi62owlfqSCavkG6YaFNTw9tJhzX1ajFTDSpfvwnRbpSUuDTP8vUi+Cp8UNUs0l+8U0Kmde8GuPK46LbwEzQ8PfhwNR2HwYt0bCpxSGdcHA8f89CAdu1aeGTCxYs4+AhrLNnGU2MshUwTExfD0e+A776+/hmk1OlU1xZvBIu3kfO81AlJQLHX2sL8JPYEUL3cqeM7dkR03VwCJuUrWyxmRLP0DCAFjZZz8fyVIqTFDp1RtwESS3Jueq5geNNZGIcjAXjJzVG+8GHkBIJGvOBw3x8RDDP7e2oQKA31kc1ZkoN4vUL4oUAoK1WOnh0kFEjVqo3/MJhjp9zeeSATD4TZ7F+Bs/3OF44PvTeZ40sF5Itfu1NP8f//aFfJ3XrrUhnP0/xhAnVOs9aLJFSRqh85K9wt7aZ/o3fQCkWeen9EqlEGzLT+6Y4rQAYhABtoZjEcj1OrdVYahuMqU2umspSvdgk0wYnYwnt0fhVMHMDTFwND/5ZxACFz1WXQTOj512ym6hxD2cnaOa9B4MW6jvHEmPkjFzEoMUTVb5mi3mvOHdsgMUG2K6bFFNG1FR+WIoz/F45JuYRz+mfM83AwmXY7xdGfBeD5myK5+BNI3MktSRvuUKkQ1VZpWAUou8MY9MTAG03g3bV1AiSxEAl52qlHXl49aY4bc/G8iwBiEeP44btpnI9KU41eUmjWgHQBIPmbm8LBk1tIktKX3ukvpi+QaQEH/zz6KVsy6WWlIa/f1c0TQfwyXRWkQoLGIcP0zn95GAHll0xmY3z1/HXC7b+nl8HoFhxqe2qrKzIMR4tHqD4yNep20GKc/sMvPBdoGhY58S8sDZCl0FTDdCSQwFaJemTaUB8bMic+m2IfzRAkyRJAX4XeBlwJfAWSZKu3PW2fwuUfd8/DLwb+NXgs1cCbwZOAHcAvydJknKZx/z2RX4ByovYq2LXup0RP3xL282g5SMGbfcOZSopdCd9hQLzC7g7JTxbouYn9tSgDWXQAGI5EgT9IdfXUcdGSScMLpTFxHLl2ASaIrFV9dFk7WkBNDVo+Gt0mgNdBIZFOHlu1XcBNEfGU6Cjw6nNtb4H9evnS9xyuMhsPsFOUEG4F4NWaVlUxmYxN1sQy3JsQuhinlyvs9XaYiQ+Qsd2u42he2LT0knR4sUjMr5towWahrHEWBeMIHoGup5PxxaTmp4SVXblJ85wsdLm+GSGmGHjuzFOrXUn1Lqro9WsqBx8dyQ0BcL+eU5LWH48+ldw8w/AD38VXv7rKG/+A37uth/jj97+Us5MS8S1OMmbbsZ84kn84jFQY6JSychAcmTwSwKApjdM6nHIJMW5706rhBHuaHMBK6IfPIBbqTDn5vpYRZ73H6C1w8r9/4eqWRWgY+VepFiWfKy7SD2+47OuTsHaw3x26bO8+C9fzG/e95uc2xQWNQcmbuz7ft/3yZQ2WA2sahqKNqBBi5qsGzm2g4os+8IFDmYPDgVokiRxVdibJz1B+9FHiZ84QdURYyprZCE1Ttza6isQCGNr3GC80qZQDyo4A5Pa8ztNJNkkrg6CbwCbceJNiQdnXXLpKmttcW5XFK8Y+v6MkaFhN9gwXbKZFDR34ME/JfX8W2Figlfc3yHp5th573uJ33ADyefeQuKVr+Lacy5JqQ7pCdEtYQ92NGTQQjBy/ZwYG//5rx+h5KdJe3Wums5gLAu2y4yVRAGHFswz174V1h6itSnAdsicRd5UstXtfmA1UeKifRmIgp3d2QMIAFpHmEBfNXIV96zcQ7XTpJX6a0aCRtXaxHifDjSM7YbJSEqPvnO73t10ZPQMqqRGc5tvCIDl2/2/byjC33OzCyR1ta8Xp7MpZCMLKZ0vvflLvOHoG6K/5WK5gfl0wxXzd6RBCwBaIZ7m0GhqgEFbKrWYSIt1InwegX7mcvRYF6B9EylOWfORYnpXg6Y0yRv5odpI8SUa3PB9cPrOqO1TpuVQTfiXZdPUMB0K1NG8DuTmMI4exTz91GUAtBhfa83A1W+Cr78Xv90kW3cp5/t/x52mydcmrsRYOo8ZdOJJJ8fhKvHbdE6JCtTFMa9fT5goDE1xlpIucVOikLm89O0/d/xTMGg3A2d83z/n+74F/AXwml3veQ3w/uDfHwFulyRJCl7/C9/3Td/3zwNnguNdzjG/fZGbh+oy9soKZlKnaYiHuK7tEk3Gc8Jmo70zYPkQWm30FQoEQMFqKEMZtH1tNiCq5GzbbeyNDbTxCWo9NHY+nmciG2O92iFv5J8eQPPE4qi1BIO2n8UGCNPRfEJjoy4Wh3rHZqtuojkSfswHSWKxuh5NRJ4TZ7thcXwiw2whQbkhrn0vBq3csmlNzGBVHHwtzZGg/+LpzTrrrXXGExO8/QP384L/+Xn+8r7+hr+LDYWM3OGYI65f7wFouxk0gLopwI2eFPd/9ZSo3DwylkJVLXxP5+EVcR2+71OxVKSag354+C5MAG8FVQoapp+/BzwHjr1cpP1u/gE4fDsty0FSRENhQzEwrrgC3zSxVla7xqtXva7P36n7JWKCURom9QQk42JBWikPb0Ie9ccMBOChdu7wltIHWpl7NoxdyedOCW3ILdO3wMp9MH0j+VieUqeE7/uc3WqynboCd/0hfvqen8byLP7u7N/x5MWvEPc8xg/c1vf9ztYWqm2yEhdgs2YkBxi0cFLPGTk2c4Ge5dw5DmQPcL4yqEEDOGqU2fHTtB0F89QpYlefjI6T0TOQGidplYYCtPJEEsXzmdl6UjCbAVN5YVsAtIECgSDaGwLQn56W0JIXKTsXSGvpaFO2OzK60EdVOlXyCQ0+9TNgNpBe8vM4b30tV6zAy9/7WZyNDUZ++IeQJInKsatRPZhZXRfVpZ4TtS/bHbsB2rGJNNfM5njkYhUjM4Ji17lqPM5MkJZsqhdh6rruAU6+AZBonxdi8XjQ7Lo3xRkBNLOBmlQigFZqD2YPoKtBA/i3V/1bNtubnFZ+mYb6EK+ui/uqTkyQjmndXqhB7DQtRlIGuiqTjWvs9BhiS5JELpaLUpy+IsCbZPU/I/tWxAeRMPqLBJxNMTfUvBYv/PW7+PgjXdYyZwwCtIsBE+jaMsTzUYozrsY5MZUZqOQ8v93kQD4Y/z2/ZdQoXUuCkcKVxDqj5nMRg3a5KU7xuQzOtgBoitaksFuasztu/DcgK/D1PwAg1bKoJSRq5qVtPeodhxkpmD9yc8SOHcXd2aG1Kda9YT5oIADaarUNR18KTgf70XuQfNjK9kOWctPmCzPXgaKQ+rvPivO77l+BEnjtPfooZjbOdtqmYfaMo3h+KIO2kxRjctyqD/zt2xH/FABtGuhdAVeC14a+x/d9B6gCxX0+eznHBECSpLdLknSfJEn3bW0Ntm/4Z4n8PHgO9tI52sUkji9+1KqyK48fy0G7KtoO7aqSiboJ9DFoIUBTqQ3RoF1y1xfPEXc9oUFbX0edmOAXX3sVx2fEcXJGjqmsaJacjWUvv0jA99G9YDA3S7Sd9iUZNIDxTIz1qjjn5VLgAWT6yAHNvl7fpmpW0WSNlZK4h4fHUszk4/iBrmsYg+b7PtWWjT0zDx5YTZ1MTGMqG+P0ep215hrtdpq7T28xnY/zrr9+hPWAOfI8nycrEhmphbsibA3CCtrR+Cgbra6vUibW7ccJoKs1kKB9fjG6Pttvo0sJHloR93K12qHVUMEF40C//iyMsO+kKsXEpHrmM4Jyn3t29B7L8bBdH0myiavxoN1U4MX25JPwyt+Cl/wCvOI3h9/8eB7PBbnjUItL6HoALocwitBNd4eaEPfESaRYjIOP7FAxK5GvFJIE1/0rPuPscEV6gVktA5uPw8xNFAIG7cJOi4bp4I6dZL2+QstpccfCHdStOh/e/BoLjovcCwAAe1k87isxAfwbWmwAoIWLbi6WYyNVxJEVrHPnWcgssNneHMogjHsbrPijrH3jEXzbJn7yZGTBMBIfgdQ4aWeH5C62GqASsLLTpUVRvR1o5s5vN1FVi/ReAO1CGUn12BpTsbSnaLHEscIx0e9xSIQsQttrcHvpz+GhD8Kt/xHGrqD8HddwegqmT18g/ZIXk7zlFnEO4wfxgJkL232ei8OiE/x2vfPGW24SY/7aYwJ0zidN5qpV2jo0jVY/QEuOwNR1tNaEzUUIzMIUp6JYZGKBHtWqo6ZVnGAu3unsDKQ3IfAYDADajRM38rIDL8ORqszKL+eadhJZE83GM7sYNMf1KLesiKEvpvQBA+ZwowCAJECE3OnvlRnNpXttdiGq4gznA3tjAyThgbdcavOFJ7ubuayRHQBo607AoHlxUFTaThtd1lFkhaumsqxVO5EEJGT7j4wKfVqvBm13xakjFZBUkBIJ0oaK22mQUC8DoAVATy3kcLaEBk1VW8MNvXsjMwlXvhYe+ACYDRLNDrUklDp72yCF0TAdZnsAWugn6Z45H3UgGRaTuTj1jkNz9FoA7Me+BMBmtp8l3mmalGMZ9NteRPHuU8Qsj8R1b4v+3nnsUZqHJkD2aVk98o49GLSdpADk+Vblktf2rYj/54sEfN//v77v3+j7/o2jo3tXFf6TRn4BAHtlhc5YBtt3aPs6NW+X5imeA7M6NMVZjBeJq/G+6rOIQaurQRXnYIpTkZSB3WgUsRxxz6VttwSDNjHBW581z/fdKtiWvJFnOhfnYqX99Bg0s4amiIfbrQUmtcn9GTSAsUyMzYBBWymLxVMyXTRNPARla4dyR/TpO7slJpfDYylmCwlwA4A2hEFrWS6W68H8AgBWWTy0R8bTnFqvstXa4syqyo3zed7/fTfjej5/ca8Q8D60UmHLMtB8m86jjyCn06iTIl02nhin7bS73QECVqXeccD3kewqWiGJuyLAxGjaoGk3ycfTEYP2yEoFtyEWYn2+v61PGJIkkdRVZIS5Imc/BwdujboBAFFJuC9bUSpJP3QIFIXOE0/C4dvhue8QO9thYWRwLbFo1hPgSA3imrInQKuGDJqR5d4LJa7/tS/iXX8zY/dfAN/v83haOvg8HowZ3G4jUrO+B0e+QwC0domHlsWx8oduYDGouHrjsTdyKCvAwJya7qbPgrAWxe+zmhhHkRTqmjEA0KIm60aOlidRyo5hnT/HbFqAjZXGysB1ZTprrPgjVL7xIADxkyfZaG5gKEaU4sy5paEArT0mWNmRxg5+aNoJXNhpEjP2aI8DtJ84R7zocnt8knX3y7jaMteMXjP0vdAFxTl5i5sX3wtXvBJu+2kA1jub/NKbFD7yzl9k5nd+JwJ5Z1oSi8UEc+frA6Lnxl13cfE/vhO3Jhb5YWDkjTfO8lc/9ByOH1wAYFRpMlctcbEILVkSfo+9cehFtEuiECPSoAWAIRlzhCcfCAYtHRPNvG176NwH4tlq9ug7/+fz/yfayi9wbepf4TR81ISHJEl9XoRAUOgiev2CkFJs7/L365vbOiVkzUNp97+ns4tVHBYJXcX1fEwnqHzc2EROqMRkcaxeNjpn5CJmFsD1fLacQP+GuF8tuxWxjyemBGsapjkXd1r4PhwazZDSUsMBWgCIbTOOFneQ2mVeu/gL3K3+EIbr07Jb+xYBRQxasYATadAaexdT9cazflB0B3nwz4k1O1QTUGldmmWqd+weBm0W45h4jtRzK6T1dF/1e29MZsVYXWMEEkXsp0THl/V0P9DerptIEoy97XvQWy6vvg/kjGCqvWYT8+w5OocFGdK0e+a+eGGQQfM8toLja6V+E9tvV/xTALSLwGzPf88Erw19jyRJKpAFdvb57OUc89sXuXl8H6z1LezRHBYeG36OhrkrJx/LYSN2Q7tTnLIkc0XhCh7febz7WjKJkk1i1RXR6mlIijOmxvbciZMoEHcs/HoDv91GnRAgqrekeSIbY6PWGbrj2zPqG0iqjytJ2DWxa5xMTl7yYxMZg42amAiXg8lMatnEgpSwpNZZr5fJ6lnObDaIaTLTuTiz+UQkoh/GoIUtcWLzoujB3BHU9XMOFTm9vYrru1TrSd52ywILI0lecHSUD35tCdNx+djDa7SkwL/pkYeJXXUCKajUCW1DQs1VCNAapgN2CzwbbSyHvCaGYjGl07SbjKWynN1q0DAdHlqpIgenrO0B0ABiuoKERqddhvJ5YWHRE93qMTNaEGXDwDh4QDBolwpJwpUCwX1cpAencrGhmjyASmDQm48V+cypDVzP54H5a9C2q/zY33tUvut7OfvKV1L77Gf5lUf+DwkUvvPM1+Arv0ujc5T1P/4419y7Q61V4sHlCnFNYeqKZ3FBFffwQPYAH7z99/nZnSo/NP68ge+3lpfwZZnNRIGklqKu6nsCtLyRp225lEYmMc92AVpvs3sAPI9Ya5VlfwzrscdQCgXUqalIQylJEqTHiWFS1PvTaACkU5iaRKbVxC50LUHObzfRdXsoQPM6HTqnniA+k+aNtorjm+DpfOeh7x5634Goi8ZL9btRPEtUywbP+HpznbahUEr1s7HL5RZnpkY5uGLjhCxEu0T7oYdY+bF3UPvYx1j5kR/F9zw6XtANoyedJ8sSN8wXkIJUedLaYa6+w8WiREuWI71dFIdeRCuYdnYXCcSNnnnPaqBmxd+dUomd9s5ABScI+YDnQ7unuCas4rTKJlrSBt8nE9NEq60gQjAWMmijKWMogxalOJtbSIaP1uyfR0zXRJGUfSvRQ9AebpaczU2klEYCk6PjKWH2G0QI0LzAi7LWtvElGXQJN8gGtJ12BGpPTIlnMwRo5wOLoIMjKTJ6pi/FGaZGw/Hm2AnhZ/YHt3PFxsfISG1im0/i+A6Wt4/XYcAwqyMjUYoT5TIB2syNMHoF7n1/hez7VBMSlc6lU5wNU6Q4XSMHsSxqoYAyMkJscXNP/RmIIgGAtZoJ0zdgL5/HkyTWEv3Xt9WwKCR00jfdyMoBeMVXvaDaFtqPPgaeh31UEB/tXoA2jEFrbrKRCWxiNvtNbL9d8U8B0O4FjkiSdECSJB0h+v/orvd8FAh5xzcAn/MF1P8o8OagyvMAcAT4+mUe89sX2RlcW8E3bZyxPD6wRjbSVEQRz1NWgoqkIZPUlcUrebL8ZJ/YUi8msZsqDTmFpvQDsY7b2VfUSrxA0rHRt8XCpk2INGTFrGAoBnE1Tjau4Xg+Ke1pALTGBpIEHd3ArYudRWjOuF+MZ2Js1U1cz2e51CKpK3hNE83wyGo5JK3CdrtM1hAA7eBIClmWGEsb6LKBhDIUoFVaYsLOZxTUuIu1KSae77phBt0IxKfaKC89Ia7/+289wGbd5E+/usSH71tmYWYCz4XOmXPEr+oyBWEqJrQGiDRoHScq9dYnR4ltrlFI6miKTMNuMJ3J4fvwyEqVh1cqJEyQFB91H0Y3rilIvo7VCJrWzz+37+9hA28XK9p1A+iHD2OeGxTEDwvXEwt3M6EEAC2+J4NWbgYALTHCl8+I6/+T2BHUO17ErY/5tBIKkiSz8qM/hvepu/iRE9/LmJxg/VNbLP9tg/KHPsx17/0SP/ShOg8trnFyOouanWAxXSSJTDFWJHH+Ht5Yq3Lo5FsGvt9eXMIeGceVFRJqirqiDAK0TgVN1kSTbttle/IA1vnzTHmC6VpZfwC++Fvd3oHNTWTXZMUfRX3qFPGTJ5EkiY3mRldDGXihTciVgXOKaXFKOQW3JdPMiIIP2/VYKbeRZWsoQOs8/jg4DvFjM1xTWuG6/EvorL8Gz9m78XLIoF2pPUF59sUw0i0uWWuuofg56rs2f+vVDsuzM8Qt2FkMxlC7wvb/fS9yOs3oT/wErXvvpfX1r9HBRUUezrznxXPsXXySYrvGyohEO5EXdkK9MX0D7WAjEwGzAKjF9J55z2qi5oJ+tJtbA22ewujb/CBSfJbrkTFU7O0metIGq0E2oQkfuiBCMDaS7klx1ncBNKOb4pRaW3gxGb3eP5ZM19xXfwaCQYNu9Z+zsYGb1IlLJs8/MspqtY3piN8lZ+RwfTeyeqgE5yzr4LkCBLaddnTPsgmNmXycRwMd2rnAZHthJEHGyOyf4izVUccnoHSOpw59L192ryS7+kDfe4dGyKCNjuLVarQbdTypfXkATZLgytfinrkPgFqSyGpmvwg1aF62ay4bO3qU7FJ5qMVGGBGDVunAxNXYmxU6hQQNv3/+2m6Ywm6lvs49t1hoNmz8yq8AUP27v0NKJHCuEZuNltNTIBUviDm9t9ChvkbVkOkYCs76Bv8S4h8N0AJN2Y8CnwROAR/2ff8xSZL+uyRJYR+bPwSKkiSdAf4D8K7gs48BHwYeB+4EfsT3fXevY/5jz/WfLBQNWxI0qjMuJp9VKTtQDk48RynwVhpG858onqDttPvSnFrewGqqKNogU2Y65r6aCRJ5Mp6HURIPjjreZdCyRhZJkkgHWpG4nOnb8e0bdSGGbWhx3HqFtJ7ed/cTxlgmhucLq42VcouFrIbXsVENj/nkNLK+TaVTJWNkOLPZiNoCybLEVC6OQiya8HojBGhFpY2RcTDXxSRXTBk864i437/wyuehq+Lfzzs8wjWzOX7546eodRyec8UCZkUDxyF21VXRcUMQHbrSh7qahulEYEGfniDWqjOnuViuheM5HCgW0RSJTz62zsMrVRItFz3tsF8hugBoGp3WNhjZgZRSuGt3exg0AH1mBnttDd+79O/megEoyKapmBVm8vE9GbRSa4u45+HJGR5drTKZjfFk2UL9L7/KD//XUf7239/A+Affx5l5jR/5mMdLvyyz9PitlJ9K8pHDz+eH3/qbnP++O7jhjM9zP/V+rpkV42MxPcKcZSLZLTj196Ll0vSNA99vLS/jTgR9R5Vk0ElgkEHLG3kkSaJtu2wePCHa1zx8moyeYfmpT8Bnfg7+7wtEZWzQpqXqFUisLRM7Ke7xRmujq6EM+phOMqhfNRSDUgbspsJOTnx2udTC9Xx8qdPvQh9E+4EHxTVcfRKpvMgPHPmPOLXr9jUJDp8lRWnhHHhh3982W5vo5Km1++eW9WqH0iHB6lUfFU2lndVlGl/4ArnvfC2F7/nXSPE4tY/9A6YkEdtLFpGZBlnDeuIRANaK0EoOcYjXYrQSYiEPN4marIEvY2g9i5xZjwBae3ONht0YzqCFAC3Ql4U6s7zdxOvY6CkX2hWycQ3T8SIbmwighRq0pEGt42C73echH8tTs2o4noPc2sYyNJRape/7TfcScymiSACICgWczU3MRIykZHLVdBbf72prc7Ec0C1kqQQsv6L5uEGxQC9AA7h2NsfXz5fwPJ9zW01G0wbpmCYYtB6A1lvF6Xse9uYm2vV3wGv/N4vX/2c+5j2bVMAGhQUFQyMCaGJdcCtCEnBJDVoYJ16L0xGzWi0BNfPSzdkFg7aNnO8BaFcep7jWJK/uYe2B2NwDolAgM4ndlDGLoltCbxpXVPQacPF+lkZlvnbbCLWP/j1bv/u71D7+cbKvfCWxoCLTdDu4Qb9qIQvw++YYu7qKI0nU07GoYvfbHf8kGjTf9z/u+/5R3/cP+b7/S8Fr/9X3/Y8G/+74vv9dvu8f9n3/Zt/3z/V89peCzx3zff8T+x3zX1I4fsCOjImqm3UygwAtlqMU5NiHAbQri8I55PFSN82pZRSclkxCGdQSXJpBy5P2PBJlMYlFDFqnEhUppANWyJBTfTu+faMqHuSqloR6jfn0/N5p1p4YD3a567UOy6U2R4KdtmJ4HEpPouo7NOwacTnNxUqbK6e6bt+jaQPJjw/dEYYpzrzcRs/YWKs70UP7/CsFqLr1QL+D/39/9QledfUkP3LbIY7MTdHeFtqT+MkegLabQYsWETuyfAgtOQ45lYjdG0lkeOmJCT7w1UXqHQejYaKnnGhCHBYxTQZfxeyUYf45A1qycNfu+p2+SV2bngbbjkTY+4UTeD9JecGWTmVFpewA0wuUOzsUXI8zVeHW8L23LABwdqvJifGreWT7Ef5u5U5+6TtduPlaSr/3+7RPPcHv3/xmvvod/4q1lsP7p67k72+WeMVT9/GyHdEG6oLssWBZcO8fwulPCmf6IeaP9tISTAuwZChJ6pI/FKCF6cCO5VKbP4IUi9H8+teZTc+y3FwVgKNThSc+LnzlgFxHRvJ94lefxPVctlpbEYPm50XqcNIdNHqNq3F2Mj5mU2M7aFofOvDbfmcog9Z+6CG02VnUhZPgu0z4gt3arZPqjdCDqibL6Ef6q1u32lvE5XxfJaPv+6xW28Qn59nMgvmIsBKo3nU/uC7Z170OOZEg9cIXUP/0Z+n4MrG9UnmyAvkFOk8JfdlW0acVmBwPXFuiQMwn0g1JkgS+hqbvSnEWxedra+L+76VBgy5TXA+uL18VqSUtJTZF2bg475BFCwFaaISdifcDPeia1VbNKkp7h7ZhoFT7Cyg6Tmdf/RkImw0QAM3rdHCrVeqGQQKTuajpvBgPYTVimJUIGTRVc/CCCtLdAO22Y2Ns1U0eXa3ylbM7XB10RhhIcfY0dndLJXAc1NlDcO1bSMc1lvyxbh/mfeacMMWpjAt5ihaYG18WgwYixamIlHg1IVG/jCrORlto0OR8N+NiHD+O4vrM7+wNP3RVZiRliOKu1DhWU8EuJvF8L/IDha7lCjtnacgyj7zqOKkXvYjt33kPeB75t353F4hLdjelHrK6rW6LuFZNrHOtbAp74/93Upz/fxm2G4CJEfFQleTMkBTn/gzaQmaBvJHnw09+mDvP38lP3/PTLCZb4EtM2IPAKdSg7RnxAmnPo1D3QJZRRwR4rJiVaAIJAZqKWBSqK/fC3/wgPPKRvY9bu4ipZahqSZRmm7nM3N7v7YmJgKZer3YEg6YGu8qYx0J8FNQ6ba+M54h7GU5QIIxufTc24KgN3d1pWmpiZBy8jomzIXY8G601UloqaugcxjWzOX7rzdfxky+9AimWoboYxzgwhTbVtT7IGTkUSYkAWrK3SCBk0BbEgr7QKdG0uq1v3vqseVzP5wWHi0jlJnraiSbEYRHTFJJOW1TYHX/VwN9DBs32zUGABtgXLy3JdG2xuOn5AlWzyg3zYiIOOzb0RtmskPNcLgYttp57ONh41DpcPXo156vn+d0Hf5cjs9dy8o//nMOf/QzVP/t7Pjp1I+/8jmP8yAsPc+85mw++UOb8+DjJ3/4V2ovnWTPLzEox+PTPit60t/7HwfOsVnGrVeSZAKDJSeq+B05HtL8KombVuhWPtouRiBG/7lpaX/kKs0qSZdmH238OsrOiRczGIyApzFbEbxc7eZKdzg6O70QArWWM0vE1Ru1BgGYoBpsZH9+SqJZCrVAL8DHd1gBA832f9oMPiv6rI2KDMNoRLF6vFcTuUGWVuC+zKidIT/Vrv7ZaW6TUQl+ar9q26dge05kRnpiRkB5+El/LUvnSaeLXXosR9FLNfMd34FaqpLZljH20VhQOYC6t4xgG7YxPe3d6M4hWLEPcc8EV85zn+fiehqb0pziVoIl2c10seMOqOKNnK7A+CBm0zI5YGPWUC50KuUQ/QNtpWOiqTDr4fJgR6K30DCsDW3YLrbNDQ48j1ar4bhdImq65/2YXIh/KlulgXbggvj+eQMdmIS8+G/b/3Q3Qqi0bDQdFs3E73R7Jvc/yC4+NIknwns8JX8VQkjGQ4nS6Kc6whZ8W6IvTMY2L/giJgFG/LAZtMmCqm+KaDmSHV5sPhCThJEShTzUJ9UtVjSI0gHHJQuoBaLErBTExs7p/b+CpXIzVagc/NoLTVvAK3WKLMLbrQUVvdYW6opJMjjD9W+9m7o//iEOf/jSxY8eiVLYkW7TCNToRjMmeQoFSWWhYO4VC1Mv62x3PALRvMhwzBrKPaogHo6qmh6Q485QCDdowHYYiK/zUzT/FQ1sP8ZN3/yT/cO4f+EBa7PSmh5TMX5KWTwiAVqyDVMxHPcsqZiWi4MMJTfHF4lJ+7K9EQ+S/+rcRUzYQ1Ys0jQkaegyjZV02QAtp6nsvlGhaLgcCgKYaHvMhrS55SG2xmJ3oAWjFlIFrJ4ZagZSDFGfKb6EHJsHm2bP4vs9X177KyZGTA5/pjc7FEp2STu75V/e9Lksy+Vg+SnHqqoyhyuJ3DTRo2gFRJj7Z3IkYtJSW4tkHC/zx997Eu18wBq4nFpg9PNxYvpeb3G8wY69hygqceN3AW0IGzfY6xJUhAG1lj9+qJ1xTQdZ80glR1XbTgQLZuManHh+cfMpmjbzrsdJSiWkyV0ykkSXYqHW4Nih1r1k13n7126PzeHxbTPgnpjL8wPMP8vyDC7iKxKM/+p0gyyz/+3+PbLuMP/vHRGXiq34LMoNeYNaSmBhDdlKTEtQD65peb6+aVYvYprbtEtcUMi9/OeZTZ7jh66usqiruoReKHnxnPwcP/Ckcvp0TZx/k9Ngh1HyejUBrF6Y4m7bHoj9OwRwEvDHXYSMjmInyeQG0Lmw3Scd9PLxIixWGfXEVZ3OT+LXXRl0HUo0LgAAWe4bvk3MdFuU8Sk+7qZbdomE3yBsjfW26QrPh+dwYXzkuoVTqbDyQxtqok319dywlnvUsAEZXZOL76a3yB+isNfHHssR8n5o2mLoFaBtJEp4viloQoMj3dGQlAI+eC04HOZlFyWYxg3Zzwzan6djwFGdiR3xGS/YzaKGsYathMpoyIgY/PE4vwximnptmFc0sUzFSSL6PW6lE77kcDVovg2aeFmnkpbjYTOY1C0OV2Qz0bwMMWssiR0OkOINr282gFVMG183m+NTjYkzeflywU8NSnBIScTUepd7U8Yno+i/6I10GbV+A1gLFQJsQDFqmcxFVMi4foAGuOgGSTyMGDevSAE0LC3dy3TVDmZ2hrcPoyv4atolMjPVqG7utgC8h5cRYCIsmmqZD23aFHrG6TF2RSekpZF0n+ZznoI2L+xkBcdmhGfraJUJrmi5A2w5SvoyN4mxv4zuDmYZvdTwD0L7JsJsSWszFCErPG/qQFGeiSEmRUZGjFh674xUHXsE7b3wnv/6CX+c3XvgbPJ4VgG+yNQjQOs6liwQynkehBv5od1LsZdBCby8p0CdVSz0VgWsPDT9ubYWGMU7TkEl2YC59eQBtLG0wmY3xp18Vi9uxuLg2xXBZ0AQY812dux5JslBMRJMxiGbrthWP3ON7o9yySBkqmlXDCACadfYc56rnuFC7wO1zt+97XuW//iSS7JO5aWHgbyPxkYhBAzEB1ns0aK1kkZ1YhmJ1swvQ9BSSJHHbFWNoKyKto2dtsIYwaPUNeN/LeefmTzPhlTHjWdAHF8RQ92J5Zl+RQMj4XRaDZop7ndMzVMwKmiJz+/ExPvP4Bo/vcjEv23UKrsuFhsxMPoGqiBTDRq3DTRM38Vu3/RYffe1Hef5Mt9r01FqNYlJnNG0Q0xTe8ybxt9HjGaZ++ZdwH3+S137FZ3z0SnjznwWGp4NhLYl7ZsyJakxNStIIq9F60px1q05Gz+AF3R1imkLuda8jduWVnPzQBX73PS6nb34RZ/7HXZTO5vAbO5iF2yhsrvDZyavxPJ+NVr9NTNN0WfTHybZ3VYACsa3TrOaClmMBiLyw02RuRGy6djNorfvuBSBx003Cmyw1gbp9Spip7teofusJcq7D5i5PqF6/NtPxovRM6Ol3sDjGNw5LtKYLlB/1kGMymZe9LPq8WihgHJxhYlneF4z4+QXMskw620TzZSp7yBtbqkHc92BLzBk7TRPf05AC2wnCBVtLoI6NRWn4/Ri0cCMSmogam2uoo0VkFWhXyMVFGrKb4rQiiw3YA6AFALMVFOCUgrS4W+ouxqZrYqj7A7RQg9a0HMynngJVZTOVA0CyO+QTOuWgdVrI7IYbykrbJic1UHQPL2D82057ANT/2nddwy2HirzuummKga4uo2cwXTOyRwn7cEqShB0wO2oAPjIxDRMdWU5F37Fn2G3Q4iiFAqgqI51tRvWDe9s2DQnHzyDrHr4s0Rw2v+2KRCuYp3oAWtNtcWEM8ovDffvCmMrFWat0sCtB5iUtnsWQQQu71IymDLzqEk2JoS2rwlS2JNm0ws4QiaD7SrObTagGJuX6+CR4Hs7ODt/ueAagfZPhNFzUhIfx5McBuGjMDqY4FY2SHqcga3tqtiRJ4m0n3sZLF17K7XO3I6XAk2C0OTg4dlPkAxExaD5OkHp1PZeaVetJcQoQ5LsBg1ZdhKvfDJK8N0CrXqRqjNOKuyQ7sHAZFZzhtd12xZjY5aR0RoOdj2J4zKopZGSc5hE2ay4nZ3J9ny0mdTw3SWmImWC1ZYvUR6eKEvOQ02kevu/jvP3Tgt25be62gc+EYa1cpPL3nyB3sIVqDO6QirHiLoCmiV1+MPFuWQZrySKZ0ka3OW9POjXs/WbsleK87w/BtXn/9M/xJfkWTG044A6peNPt33XLsRjK6AjW5QC0jodieOQknUqngu/7vO05C8iyxGt+94s8tdFNo1ecFnnP41xNZiYvvm8iG2O9ZiJJErfP3T6w0358rcaVU5lobCe1JDElxlZ7i/SLX0zrudfwins9xrzhKbMwQpPa5IIYV4ofp+lZuBAxlyDc1TN6JvKliusKkqIw857fwbxW4ZEFCeetr0KbnGbj6waLd8+w+gefxVNU7pk8Sallcf/G/eiyHllzNE2HC/4EyeYy9BZeuDaxi/ezHZC69kVRKHN+u8lUXizcu4sEWvfei5zNYhwJqjCnroXVBykkdUqtITYeYZy/m4znUdP6NVGh99x4QizGYR/V1apYhOcLWeJakoe++wYyV2ZYeEsRJdV/r5MnDzO5JpHy9wYjjpvHs2VysYtYXmJP8XdblgWDFrT8Kbcs8HWQgmsLx7suABpb4tndT4MWMmi14P+V5UX0YBz0M2ji2ncaZgRkoFvI05viDEFQsyF+s42ArXd2unNJx+lckkHrrTQ1Tz+FNztPSwqeV7tJPqlHbH5aTyNLcg+DZjNttJB1D69t4jvOAIMGcGg0xQd/4Nn85puu7V5T0Fki1KE17EYEPJzVVdA01KIAvckARDqaABwhuzQ07BZoCSRZRhoZpdiuM504uu892B1uw0RNKEj+Jb4riHQnkA5ku65ZFbPChXGJxIXNvrTz7pjMxqibDvXlgFVNifeG39tb0duqruDBUCKkV4MWdYZIBhryZldrVguLw2bEuf5LSHM+A9C+yXBKddSMgRE0KfZi8UEGDShpBkX/8m6zLMkccx0qKYliYxCgNZ3mwA6sL7QEaVQKdegUxERdt+p4vjegQXMdMVFUXBPmngUjR4cDNKsF7RIVbYxmso3iwyF9eMuaYXH7FWJxefbBIm65DIqMovvorsXPPudnOR5/Pc86UODHbz/S97lCysB3RQNg2+1f3MotSwA0s4Yky3jzU2w/8RAJNcFbj7+VsWBBGxblD34QJIniSVM04t0VxXgxSnFC4HjesQVQ0NNstzxWk0WMzbUIoPVOCua5cyi5NIrhD6Y4fR/ufx8cfSlPFF9Czc1ETZt3R0jFt53OwKSuT01fHoPWdFANjywylmfRcTtcM5vj0//+BcQ0hf/xiSeC72jT9h1yrsfpCszmxRgbz8TY2KN3p+16nN5ocHyyW9gRNpzfbold6eLrbyZpQurjX9r3PK3FJdSxMRKZoIo3MPbsreS0PZuW0yJjZCImKfQJ1EbzZK/a4HdfpbD0lluZ+8CfMPlLv4TNGOZTZ1j7iZ+lHMtQanW488Kd3Dpza8R+NUyHJX8MxTNFP8sw7vqfxNpVKilwFBU21jAdl9VKm4mc+N7dcoPWffeRuOGGyFePqetg+zSTMScCGEPj/N0Ynoaj9M8fYYutqbQYz2Gab73aQZYEc5CP5XnqaJLp774KIzY4npNXzqK6EgeW914I21Vx32N5m6abobmHdrLl2cSRoCFYyFLTxvc0fGkXg6anUMfHUUs1Empi6KayC37CIgEHyffwz5/FuCJou9ypkN2lQYtE4UGke61wwmtWxW/bChiRVV2AGbfUfa4vleLsnDqF9Hd/RcpqUWnZdJ46TWtqnhbBb261yCe0qGBJlmSyejaq4qy2bab0NoouUo9uvT4UoA2LsBAmTHPWrXoE0DpPPIlx+DBSIJ1RFZmErmAqAnBcDoMGUM1oFBoeB9LD+8PuFU6pjJJNEff8S3cuAHLWOg05DbHuPFE1q5ybkJA7VmRQPSxCDXPlnGDYYzExV4YMWgjQxnSLxpDNchhdDZrdJVH0BOgpaHSLrVq2uN+jB4Q8wd749ldyPgPQvonwfV+UOs8dwAhy/4buDgdoqkohLO29VHgeRzpt1nOQrw5W6TXt5p7u5QBIEmkpQ8KCViBijdzXAw1aQldQZAnT1FElmYoiw8Q1MHnNcIAWVPqU1VHaKQE4tH0Ez7vjlkMjXDGR5jXXTuOWSiiZjPDgtNu84egb+Jvv/y4+9O+eE1lshDGS1PED76jdac5yyyafCIxMjQxPZVpMl+BPX/6nvOvmd+15Lr7vU7/zTpK3PActnxRWDLsiZNBCvU/kZN6piqKPpslacgSltE2zIc6rl1a3zp7DCAx0B1KczW2xuB28jbim4LpqX0VSb7QtF0V2cX1nYFLXpqexLw6K2neH0+ig6B6ZgBkKuzKMpg1+8AWH+NwTmyzuNCNTz7wPOx0pYtDGM0bUS3V3LJVaWI7HsfH+HetofDRifi6Mw7kJCfeLX9/3PK3lJbS5WRKBKFsKjD3rshwxlyEYzuiDAI3t00zaQTP45prox/j613H4s5/hyD13oz7/hcj6Jr/49Xex3d7mjgN3RN/dNB2e8ILd/XJwnmc+A3f/GrHZZ+NLEs1sjtjOJue2mng+TARpz165gbVyEXtxicTNN3UvbOo6wOekuhSxXwPhuXDhi7S8PK7Uv+CF93E+K/RG4THWqh3G0jFURSZvBKasicLQVk/xQyO4ss+Bc3sv3K37H0CKx/Hf+ItseOO092BG2k6buKJDPUgdNoUJr+MHYyQEdloCdWwUo9qmqA+vEIxpMoosRanNesdmolnC73SIHTsmrGc6VdKGiiQJwON5PjuNQBTuOtDc6WHQhqQ4gwq9FUOAl14Gbb8iAfPMGc6/4bvY+aVf5J0P/SWt1XWc1TVK47O0MaJrzSf0CKCBAFaRD1rLYlJro2ji2TMrJWzPvjyApg8BaFoI0J4gdkU/qErHVBpyAND2a5hud0BL8NDWQzyqrFCoqdw0NmgavV+4OzuoIyPkPBfTrlzy/QV7nZLWb2peNatcGBese+fxx4d9DBApToD2ufNoOZVEMBeEDNpWoOsc87bEXMHwFGf0O0tOf8P05Cg0g3XW92n64rhTgc74GQbt/9HwajXh1H/0ZoK2kuiaG5WM90ZJgsLlig3NKocti80cZKv96N33RSuPfQEaELfEw13Pil1mb3scoK99SlbSqcgyjF8JE1cLv7OenDwAQenxjjxKKy2OFbaQuZyI6wp3/sTzecmV4zjlEmo+mLD3KwcHCim9m4bt9C88lZZFLgRosSxLaYtcwye9TxoHoPPII9irq2ReegcYmeEALV7E8qyuvizsBdipQCzHTtNiLfCJcpbFvQl3bb7vY547103R7J4sd84EX3KYuC5jOwq2Z/cZFYfRtBwSgUP7UIC2trZvegDArTVQDI+UIyayXkuVFwXM5gNLlQgAZxFjZiZg0CYyMcEe2IPfExreTuf7z20kPhJppzZbm5w/mqb90EO4jb132/biEvrcPJoio8oSeCGDJkcMWh9ACybZWNieafMUCd8np6VZa3QbWEuKgpLNEjc8YjMf4FTlGzx36rm8YOYF0XsapsMD/hHcWAGe+AeoXoS/fjuMXUnsun8NQKdYJFvb4evnxQI/VxSgoJdBa3z+8wCkX/jC7oVNXgvAlZyNtEoDsf4IdCps2aPYfrPP42mrvYUma8xkRfoqBAPr1Q6TOfHduVhO/H7xvBiju8aSotgsTcDMU3s/s82vfo3ETTcSf96/Az+OucemoeW0RMFKBNBsfF/D8XczaCLFKXs+c+5wv0TR7kzpsdlwuCLQBxpHj0IsC+0KsiyRjQuz2mrbxvF8AdA+/0vwm1eQXvp09PkwwjmyFcgjFrUxUYG4m0HbQ4NW/8xnwHUpfO/38qyLj/C83/85UFWeOP4sbDlk0Jrkk1rEaoJg0msBC1Np24yqTWQ9qK4sCyDwtACa2c+gOVtbuNvbxI7vBmgaVYSmsj1EEhKF3QItzu89+HvUMzqFOowk9vYiGxZOqYQ6MUfa8/CsSzNMo+4mNaO/b3PVqrIyAmganVN7A7SJoMjMXzyPPpYmEQDukEFbq7TRFIm8vdkFaENSnOHv3MegQQDQghRnu0xdBsWTSY1PIicSWMuXLsT6545nANo3ESH1qR2/mdj3f0b8W3WGM2i4FOzhE95AtCscsWw2sxKpeg2vp7mr6Zq4vntJgKYHrFMlK6j/3vY4YaQD0JGTFCp6XNDeI4EWYeds/wHLFwC4oOg0E4I5c6uXD9B6wy2VhUAVLgnQikkD3030XUMYlbZNPqFBu4wfy3A2IcDUpdJ+jS98ARSF9O0vAiPdVyEYRqiXCUFG2lCp2du8zTzNL8Zd1mpVLqbEblVeXiOmxKJ2Mc7mJl6thnFIUOTsrnKKANoh4pqC54nfKBQD90bbcontA9Cw7X3bkXjtNn7HRDE80pYYf72VYUfGUsQ1hQeXKxEATgX6mtmgnH0smCDDdl29sVYRr03n+s9tLDEWMT+brU02T0yC49C6dziL5rVaOFtb6EGBQFxX8NxeBk0AtHCxyuiZCDBGDNrm46DoTKamWW0OMotf3/oEirHFG+d+hv/9kv/ddz+bpouLgnn4DuHT9pdvA8eEN74fIxSXjxUYa5f5+CNr5BMa2aQAUf0A7XPoBw+iLyx0vzg9Drl5TrTvj7RKA3H+bgAu2rP4uH0pqq3WFqPxUQpJscD0atBCp/VCrCB+v4AhHxjTZp0n5iWKi9WhGyt7cxPr3DmSz3oWkiQqBS1v+LMpRO7JKBVcblko6F1AFxUJJNECk+ypzt6AJB3TImBV79gcaW6CJGEcPgzxbPTbhwAttCopJjV4+MPgWqh//f1kNbefQYuqOCu4kkJVSqPk87il7kbPdPZOcTY+/wViV13F2E/9JPcefy657VXyb3wjF/Q8sWQAAAIGrdKy8IIMSVpPRxuJasumKDdQghRsJ+jtuK9EJYjdKc6werlzSngLxo4f33UfVUpeFtn3aXUGWdQo7DanNYUvr36Z3NR1xFybtHOZaxPgWxZerYY6c4SM5+G6+4DBIEb9Hdrx/r7NVbOKq0hoRw7ReWxvgDaeiSHjoa+tYEyPkKiL+S5Mra5W2kxkY8iNVSGHYP8UZ58GDSA11k1x1i5SlWV0XxjEawvzWIsXLnl9/9zxDED7JiL03FLHx4mlBX2rqk6fWSIIpN/Go9Bp9guQ94pOlQO2zVZWQiIQhAaxu93HXuF2xMRdClL+vX04w0jHNGodm5znU9GCwVsQ3kmhADiKnbOgGJzzqzRi4iFwa/0GopcbbrmMUgyqZy4B0PIJDd8RD1svg+Z6PtW2TS6uQWOTanKUxbSYZKzlwUq83mg/8ijG4cMo2eyeDFqoX4sAWkylIT/ONySLD0kNnqx+g1JxClSV+Nm1vgkhcpG/9lrxwjCAJmuQnSWmKfieAHaWO8iuNPcDaIFf2H6ANLQUUOIK6SDV2sugqYrMyZksDy5XoqKIuC/GTi+DBrBRGwSQq9U2ktS1UgljJD5C027SsltsNDewThxE0nVaX7936HmGu1Q9sNhI6ApeYLBbV7sN08PFKmMMA2inYOQok6mpPgYtjLP1x/DsHEX5qoG/hTtq/6rXC3Bz8X549W/DyJEIgPmTRYqdGt84s8m1s7no9wpTJ261SvPe+0jd9sLBCzzxWg7Uvo5hVyLmry/O34WZO0zDExuD3obbW+0tRhOjZOMakgTlwGpjvdphIiPGRM7IiecjNJftKaoAwKzz8IKE7Isiht1R/7RgoJK33AJAQo2L/qFDomW3iOvpHgbNwlDiUePxgSIBYKK9txls0lCi+1/vOByoraLNzSInEgJwBimtXFwwVVt1cd8PmE8KZv/4q8BucZO+1MegaYqGJmu0zBottYAiy6jFQp8GreMOLxJwSiXaDz9M6rYXIskyn3vVD/B/X/sfGPtPP8VGrUMyFcyjVotcQsfzuxWkGaNrMFtp2xSkBkomMFauCjDzTac49TTth0W3B2Mgxamx5aaI+z7tIbZEUdgtTgUG6MnRa8T/1y4NssIIqxqVqTkMT8H29teg2a0qaamNNQSgASROXk3n0Uf37IqiqzLHaKLYFvrsJIlArxumOFcrHaaycWhu75vilCUZXdZBcqKqYaA/xVldoSLL6AiCQ5+bx95HH/etimcA2jcRoQeVNj0dTeKKIjxZ3B69WSg2L7jdKsB9o1NBA0opsUBaK90FeHfD3D3PLTAa3UqIxSAs+w5TnCCsNmodh5xjUwnEpuTmRCXnMIBWOEDJuUDTEMPFexopzt5wSyXUYgEUY2+PsCBURSYT9Grr1aDV2ja+j0hxNjZZjqfYCC7NXtoboPm+T+exx4hddUK8EMsMLRIIG6aHdgypmIold4HyZmeFTC6FceQImfNbkSkmQPuBB5AMg9jJG8QLu69x5wwUDoCiEtMU8AVAG6ZDa1sOMW0vBu3SVhtuWdwzJZcm0xHAbHfXiGtnczy+WmMtEH3H3QRJXRHsJF239mEWEWuVDiMpI2qnFUbUcL61yWZrk2J2EuPw4T0bvNvLYhLUZkOApuKEDJqRGARovRo0PfjuzVMwdpyp1BSrzdW+NCHAE+VH8dqzkbt7b4Ssd+zo7fATj8B/fBKuer14LQBgsTmxqRhpV7hmNhexXOGzX//0p8G2ydzxst2Hh6vegOK7vFL5ap9eSXxwA859gc3J2yK2uGr1ALSAQVNkiVxco9yyqXWEjmYqSHEW40U6bodWWA28e54x65yalnB1heZXvtr3J9/3qfzFXxA7cSJiZVJGEiR3oDDH933BoMWyYlybdUpNi5ga67J+oeZSTyGNCBnASH3vjiPhRhGExmy6vErsaMDkx7oMWiZg0EJR+PTWXSCrcPvPA3CTerrPZgMEU9W0GjS1HLoioxSKfRo0y7WGatDMJ54A3ydxg3iGC0mDe/OHkA2D9VqHTDpk0JrRc9JbyVm36nieT6VlkfHryNmceHvl8gFa1FnCrOF6LmarTs6LUf7gB0k8+9ko6X4Qko6pbNhx4p5Pe7/OMHabC5KHKqlYWSHDiFUGTav3CmdbvFcdGUX2k3SkPdL2QbR3xDppJwc1aGk9TeK66/AaDcwzZ/Y8xlWumMeMhVkM30eR5CjFebHSFgx+a4e6Lu7rMIAGgkWTZbt/k5QcFZ0EXAeqK9QUGV3JAaDPz2NdvPht90J7BqB9E2GtrCDpOuroaGQiKsviIe1F6JtBFdGY6/a1lNgzgt3vVkLsoHrNSHv7se0XTkuinvCpeAIcVMwKqqz2fS5MLeSsNpVw/lR1AdJKu1KcpbNQOETNO4cZiD2/mRSn7zi41SpKviAaMV8CoAEUAlagl0GL2jwlVGhusaLr1BJAIo61sjdAc9bWcEslYicCgLZHijN0mA8r6FKGhqRvcIXlkJd0KvYqhaRO/KoTFBcrpHsAWuuBB4idvAopHgetCy6iKJ2DorBgiPcwaMNSnE3TxQhaY+2uFgy90Paz2nACgKbm8qQDT73djeevnsliuR5P7iyT8yU6XpyZfCKyzSgGqbWdIfqp1WqbqezgAjcaFwDt0Z1H6bgd5tJzGEeOCB+pIRFWcYUpzpimYNviext6fECDltbTXQ2apgiQXV2GseNMJidpO+2+VG6pU+Ji4yKaM091SCVl03SIa6JwhtycSHsEEd33SbFReNmoz2uunY4AdbjAVz/2MbT5uS74742Jk1TyJ3mn+mGaa7tA6kMfBM/h8YnX4LtBdd0QBg0gn9QptSzWAouNsMIt7HO5HaR4do8516zT0mSqV0zT/OpX+q/9S1/GfOoM+e/uNq/P6IF+a1ehQMft4OMTD9sC1TcoNS0SWpyO2xE9fcOqPi1BI6XgSZCr710gNZLSI9BVrdQpVjcxjh4Tf4zlovkwl9BFijN4b6pxHvILoql84SDX+Kf7GDQQlZwtp0VdKWBoAYMWMEC+79Nxh7d6Ms8LE179gLCUySe7XmebNZNMALiw2+ST4vPhnBQCtIbl4PmQ9upivgOsoNXU5QC0cL72zy1y/vVv4AO/ZvP8H34/bqnE2E/8+MD7MzGVNStO3PdoXQKgLWIzk56hkhKbDmXr8isVuwCtiEeatuSJyvQ9olMS65ef3gXQrCo5I0fiuuuAbuZhWBwKMhn6wUNIQEIRGwLX81mvdUQhQWuHhiHGbUobTHGC0KFp6i6deGoM8MXaXF2hLCsYmhjf+twcOA726qBk4lsZzwC0byLsi6toU1NIsowqqyiSEgG03jRnBNAcd1B8PyyCyXXHKODIYF/sArQoxXkJDYNdtWmmoB6Amp3ODgWj0OfDlomptNstcmaTim93GYfCwX4GzfOgdB6/cIC6fwFfnQdFeVpFAmFED/foqChvNi8N0EZSCWQ/0QfQQhZkRG2DZ7MsA5KENjODvY+os/3oowDEw+boe6Q4k1qShJqIfrtUTEUx1jlimszpGRruBoWkQezECeJNh6m6AFleu03n8cdJXHf98OP7gX9UkEqO6wr4gT5liA6kZbtoauCuviutLRsG6ujoJRi0CgBKcZR0U+zed7fNumJCbASWqmuMez4lR48qOAHySXFtwyoQVyvtqMqqN0KA9uWLXwbgaOEoxtGjOFtbEWjsDWt5CSUrnOdBpDjtoEVVTY3tz6BpCmwJqxDGrmQyJRaC1UZ3Un10W/zuKenAUAataTmRaeruCAFae0RM+u84keLASDL6vWJqDKdUovW1r5N5+cuHex1KEosv/B0kfA795UtEW7WnPgMr98GXfwfmn8eiNBUBtPA6O06HulWP7mchMEUNuwiEGrSRuFhod6TgGd6V4jSDMdg4eQDrzNlozPiWxcYv/zLa7CyZV74yen8uJq51t0t8yJIlwhY59TVhGB3MR6Zr9hUJlJ0alSSkanuzLGPpWGQ2alxcQvZ9USAAfQzaWNpgrdpms26iyBJ6bUkANIDZZ3HcOUWtPcigtVyTupLDUGWUfAEnMKq1AhPkYV1ZrAuLyIluiraQ1GlaLjsNk4bpkM/lgjc2RSU5RAAuo2ewPIvNurjnCbeKlC4gaVo0Z15KohJGkRQ3/NZncDY2+JvnSFRfdB2Tv/I/uvKJnkgZKhfNmGDQ9q3ibHHBN5nPzLOlp3EkGecyqsHD6AK0EVy5SEuWsGv7bRLFfKxk+22ZKmaFrJ5Fm5tDyedpP/jgnseY31mmHEujjAftqWSdltNisy4an0/lRIqzpsXRZG1PXaGhGCiq0zWqhR4vtC2cygplWSUTZJrCQi9rcXHPc/tWxDMA7ZsIe2UlarkjSZJ40EMGzRzGoDnQuhyAVgHAdItsZaW+FGcI0C6Z4iy3aKV96sGx1hpr0cIVRjqmEutsU3A9HPzurr1wEHbOdXdFtRVwTVbTo7hSk6R0ACWd/qY0aJFub2xMsFeX0SakmNSRvGRfijP0kxrxxfUtYzEaH8WYm8Na3lsz0Dl1ChQF41iwQw8ZtCE7wLHEWPTbqUobSatx2LaYN4rY0ibFpE7sKtFO6tCFQID/yU+C45B83vN6jt8D0Npl0VsyK/RjMU3G9/dm0NqWg6qJMTVs161NT2Ov7APQQg3ayDhGU1QD7k5xLhQT6KrMVmuDccdly9KZLXQXEENVSBvqAEDzfZ+1aofJ7BCAFjA+X14VAO1w7nC06JqnB1k0e2kJbb5rfJzQFdqWuOaGpkfMc82socs6Mc9DKz+Fji0YtM1AZDx2nKmkWAh6CwWeKAkAV1QP9lXchdEwXVKGMvA6dBmyRi4GshztpkOAFlfjNO6+GzyP9O0vHnoMgMTEYV5h/TJLc6+FJz4Gf/Z6+IPbQVLgVf+L7YaFJglgFD6LYaFFeD9zCZ1S04q6CIT3PnTp3446L1T6vrsTCs1fcA0oCqUP/CkAm//rf2GdO8fEz/wXZKO7qBWCqr6Nej+YDxn8eOgx2Nig1LBIBamljtPppji1JDudHcopiJf31pqOpg3KLeHuXtwQz65xNPBDjOcEI+faHBtP07E97n5qi4lMDKl8oQvQJq8l61VQ2/22RAk1QdOzqMo5dFVGKRbw6nV8y4p+v2GLuXX+PPrCQgS2QxD2jSVxX2eLWfG72a2BFGeoHVutifkqZpeRUqPI2WwE0C6HQQN4zd0dMptNpP/+H/jzFyq03vFWcq997dD3pmMa27YhNGj7mMd6dpslt8V8Zp6q5VNO5p8WQxQykEqxiKuKTENjfQ9zc8ALLJrU3Ezf61utLUYSI6Io5brraN1339DP+77P1IVTPFw8yE5Q0Z+QVZp2M6oin8rFBIOmGaT19J6G8DElhqK43VZP0GXLm5s45UXqikQuJjaKoSZ2P5+2b0U8A9C+ibBXViKhNgQTeZCPb+wCaHHFIO350Ni74i6KdgVfUrCcAptZaC9fiP7U2zB3r/B9H3u7ip3y2AlAzWpzNVq4wkjHNFLWJtNBfj1a0AoHwax207FBRecTgRg7Jc0jZzN430SK0w4qDtXxMcGg7UfFB1FM6bh2Oko3ApSbYjLMBwBtyzMZS4yhT89gr64N6I/CsC5cQJuZ7i5GRho8Z2ixQi9Aq/tiF3jYsplLjOOrFTJJn9iVx9koKpz4shClV/78L9APHOj6YO1OodYD8XpalJzHNAX2q+K0XRRlH4A2M3N5GrTRaaTWTl+FWRiqInN0PEXd2WHctqi4Rh+DBsLuZHeKs9bu10H1RkbPsJBZYKezw1RyirSe7gFopwfeby0to892XcbjmkLbcsX5anpkilqzaqK67aM/xsvveg0f1X+GuOzC6gOgJSE7F21E1ptd/6Lz1fNMJCfIJzLDGTTz0gxaR7JRx8Yi77mO20GWZDRZo3HXXSijI8SuPD70GCDSZCv+GHcf+y/wk2fgLR+C1/wuvP3zMHKY7bpJMUjnRwAtGPMhgzaVi7FSbrNaaQuT2rQYxxGD5gVjaFeK0wzS2srkOJmXvYzKhz/Mxq/8KqU//CNyb3kzqRe8oO/9oe3Car3/OOEGMRWMX6e6Rt10yMbEwtl22gJQKTooKqV2iVJaQivt/ZyH13Bqrc5CbR1PN6KFkWChpFPl2IQ4p0cv1njOlCJAaAjQRoRkYKTTv5AmlRgtfCpSFkNVUAuBNU65HBV57AnQDnQ7ZhQCFvm+RcG+HRpPg54URrVBirPSk+IEWG+UUXDRzAokR1EyGfxqwKpdBoNmX7zIc+4p8chNIzROHug79rDIxFR8ZOKSSnvIXAKA57HhW5h4zGfmqXdsKpmRpwXQnK1t5EwG2TCwdLH+1bb2rsKktkbFT5JI9Z/7RmsjkpIkb7kFe3l5KFNlX1xFL2/zaPEg55rit0qi0LJbXOytIm/t0FDUPdObIFKcimx3m6VDNBdTWaZZX8WVYDQhUtLKyAj5t75VVBR/G+MZgPY0w200cSsVtJnp6LWYGsPbA6CNxMdwJI2//vxXeXilsv/BOxUcPYtvZ9nM0ceQXI4GzatW8TsmWsJlx67TslusN9eZSPX70CQNlTFKTIUALUwJjQbVQZuinJuNxwBYUYLGxMokSib7zaU4NwTg0cbHhQbtMlKchaSBbWZZbXYr80K9R8YRE2bZNcnH8mjTU/itVl9D5N6wFhfRe5iaqOptSPHGaKJrtlp3xb05ZNuMxwTQVfSSqPC6VmH0qW3W/uvP0X7oIfJveXN3B7ebQYsAmgARcU25BIPmRU2o92TQ1tf3FLE629so+TzS2BHwPdKyMQDQAA6PxXCkOuO2ScOPDwK0pC4MSXtirdavg+oNSZJ49aFXA3AkL9gQdWxUNM/eJQb2LQt7dRV9vtunL6ErtG2XtJamoYhKXYJ2ZWktBU9+gp3kYa6Ql0k9+F547G/g2MtAFqatMSXWl+I8Xz3PgcwBcnFtTw3angAtYNA6TgdtaqqPQTMUAxyH5he/ROr5z+92DxgSuXhPqlg14NgdcN2/itjUrYbJSCqNJmtRinM3g3bVdJaG6fC5JzaZyMTQgqbqOSOHhMS2XRfC+V0pzk6g9YwpMUZ/9EdQRkYove99pO+4g/F3DZo6jyTFIhem6aL7FDL48VGQZFoV8TyPJFLRPcFqCfACEYMm7ext+zAatGx6fK3GQm0dZ3Y+csiPbEM6VY6Opwkfq+cWg3kjAmgC/I/b/QAtIck0ZYmylMVQZdSgaMHZ2o40hLsBmtfpiPHYY5USMmj3XSgjSzBfTAh9qd0kbaiostSnQQPYalbI00DCFwCtkEeqBAyadmkGbfM3fhMkiY+/ONunvdwrQqAYk1TaQyrCxYV3WNLEOJ/PzFNrOzTyI5fVkSQ6xM5O1GJK1sU8Vt8Zri0FUBqrrPuFqGsEiHFSNatRtXzq+bcC0Lj7noHPh71tHy0e5Ilq0F4NiZbTYmlHjMdQg1aT5X3vUUyJIcm7qjhzC6CnYe1BVi1BSswGoE2SJCZ+9mdIPvtZe9+Qb0E8A9CeZoS6ML2HQYurcbzArHF3ijOtjbDkjqDXF/nEo5dwJu5UsfUMnpMVVhuVGl5TDMTLSXHaawIEpOLiHB7cehDbswcYtJShMCGVI4B2sRE8pOOBPisAZqw9CJlplq0KkpcgoaZRMplvDqBtboKqCh80/fKKBEZSOp6dY7O1ieOJc622bWQJYsEDVXFa5I18TxPxwR2h7/tYFxbR5xe6L8YDP7bWYJl5yKD5vk/NuYjkKUw6LllD/Oaeso3t2XzqpEtzpkjlwx8m/ZIXk39LV2xNbJcGrR6IcVNi5xjXFQiLBIa0e+rYLnLQhHo4QJsCx9nTC83Z3BTp5BEBktKSQt0eBGizowIEjjsuDeJ9KU4QaeadRv+kH9puTGQGARrAKw++ElmSuaIgAL8kSegHDmAFAuww7NVV8LyoghPEfWkFDFpNAnyh36yYFfK+BHaTL8y/gy+5J9A+9/OCMbrhbdH3TCQnWAsAve/7XKhdYCG7QC6hRe2CeqNpOST14SnOcAHvuB0BiHsAWlyN0/rGA3j1Oqlec9ohoSoymZg6NMUKounzWMogo2ciBi20eQkZtKtnBKP02GqNZx3sNh9XZZV8LC8qxmPZwRRnsLEzVAN9YYFD//D3LHzkI0y/+zf7UpthzGSFSHq13n+cSAOrpyCep10T5zcWVBS23XZghCrmp1KnRDWt4JUrfX6OvREyaI+v1phubKH0Pp8hg9auENcV5oNxeXUyAHwhQMvMYMsGc95FLKdr15BEpi3JlMiiq3KkKXM2N6PnbbdRrb28DL7fB9AKAfi5f7HMbCGBoSqiTZDVEh0rEhqlZn+Kc6tVoSgFDGRyBLVQRKmIZ+9SKc7anZ+k9vGP88QrjnMh1ugaNGuZPT8TgkhD0mn7e/jtOR22A/A7Gh+l1rFpF0RD+71+n4FDbG+hjgjGNhVWmg6xtQlDb22w7heidlzQZYZDgKbPzaHPzwupwK5ofPZzKPk8lfEZntxxQI2RCAzbn1ivM1uIk5QssFs0JH+oB1oYhmIg7a7ilGWYvBqevJMvxXXw4Xmzt1zWvfhWxTMA7WmGdf4CANpMNy0TU2K4gXdQo6dKZLO1iSHlWfbHmJM2eXL9Emm9dgVLTUcMGhAtCpdTJBACtGJgpHnvutiBTKX6AVpCVxmXSqQlnZSW6jIOqTFIjMCGEFaz+gBMXstyfRnZHcFQFZRsBq/6zWnQ1NFRwTRcZpFAIanj2zk8340WrHLLIhvXkJubIGuUrSq5WC7SBNqrgztCZ2sLv9XqOvyDaI0DQ9vjjMXHsD2billhs7NCwk4gA5oqAJojlWlYDZpxiSf/1w8GC967kTStexBjl43H7hSn2mXQdtts+L5Py3KQ5P0ZNNjbasPZ2hIFGQHDkHbdoQza/HgI0BykWDoqHAhDMGj9E/hmYFw7lh4O0CZTk/zpy/6Ut514W/TaMIBmLQUVnD0MWlxTaVuucMj3g81OfY1Su0Sh0xCtvRLX8Z94Bxx7OczdAgu3Rp+fSk1F43mrvUXTbrKQWRAMWtAuqDeaprsng6bICpqsdRm0gLHsuB1iSozGXXeBppF8zqUn9WH3MYytuslYJkbWyEYM2mZrE1VWI3ucw6MpYpqYrm89MtL3+WJctCcT3mG7U5yC7QyrzSVdVCDvpdWZDTp9bOwGaE7PBjFewA56BU+GPl9OR2y6dDE/7bR3MIN+wM7mYNs66AK008s7jLYrJA4udP+4i+E+Op5GlSXm5OBYueBZlmVqiXkOSmt9m+O459GUJbb9jGDQegFawFjvttmwg9Y+oY0NCJZYD9jKgyPB5lhLRJ5voVktdFmucrtKUQqe/eQo6kgRtdoa+p3Rd1+8yOZv/AYX3/lOYidOUH7ji9jp7ERM6r4MWgDQNEmn7e/RXcRuUQ4AWiFWoN5xsEfGwfdx1vYGWb3hbu+gBExkBNCae1eBxjqbrPv5PgYttC/q7ZecfsmLaX75y9H9B2EGX//c58h+53dyeDzDU5sNiOdJeB5tp80T63WOjWciOU4Db2gXgTAM1Qh80Hbdn8lrob7KPfE4SmeCQ4XxoZ//dsUzAO1pRvuBB5B0HePY0ei1mBqL2p00Aj8e3/fZbG0iuRmW/DEOqtuXBmidCqaaxneTbAcNma3AaqNlt9BlPXKtHxb2qnjQpvJi0vz6mnBvn0gOpjgnpDJOYrxvQUOSYOIqAdA6NeHbNXUdy/VlJKeArsrI3yyDtrWJFkySl22zkRQMGhCxIlEfzuYWndQYbad9SQbNDvQNQxm0Ia1Rwvu12lhlubFIMTBOrTjj+J6KLZWivpYpIy0WPHXXIj+Q4lwXC2iQ4hAM2nANmuV6eD4gW+iyjiIPMjx6ANCsPQoFIgbNSENmmrRjDQVosbh4bdx1OTY3JewmeqKQNCi3rD5t32ZQeTeW2bu11snRk32Lin7ggGhV0+j+7lbgW9erQUvoCi3LoWAUKLmBPrCxQdksk29VYO7ZNB2Zll6Et/w5/JtPQA/YmExORmPlQvUCAAeyB8gGpqL1Xd0+GqbTt4AM3B81FgE0XBdnc5O20yamCoCWvOkmlNT+hTsgRP4DPmiA5XjsNC3G0wKghcbS2+1tRuOjEZBSFZkTU4JVet5ugBb0jyWe609xOlbEqOzV1mh3FOJi7ths9gO9PolFohAxzzOB7UTbaYsUZ7CBLHVK+EXxt71Y3tBnr3zmHDI+2cOHun+MNGjier7/1oP8zCuOo1UXIVHsa77dSh/goLTWJy9Jei4tSWbLzwgNWrEo2j31ALTdKc4IoI13F+l0TONNN4nxaajBc6gloiKn3n6c4XivmDWm1GCcJ0dRikW0RocEw5/l2p13cvZVr2bnD/+I9EtezNz7/pjJnPjOqmNv9QAAYVRJREFU02Wh29yPHQqrrRVJ6O6Ght2mpMgoSMJQt2Pjjol57nJ1aM72NuqIYHSzgUdlvb0z3GrDtUlYO2xQiHrsQrdwLtSgAeTe/GbwPMof+lD0Wun9fwKuS/7Nb+LwWIozmw38eJ6E69Kwm5zfbnJ8Mh0V39U9e18QaygGvmT1a9AAb/Ia3p3P8qhh4LRPCm3wv6D4RwE0SZIKkiR9WpKkp4L/H9oZV5KktwXveUqSpLcFryUkSfqYJElPSJL0mCRJv9Lz/u+VJGlLkqQHg/99/z/mPP8po/XAA8SuPomsdz10YmoMK2gYHCL0qlnF8izMTpotdZKUV6de2RowVOyLTpW2kgFknHExCYc6tJZz6T6c9toqkq6TK46QRubh7YcBBlKcSUNhQiphBgDtYrNnkR+/SmjQLt4PgDN5NWuNNTyrKMrVAw3aXmL8Pc9tYxM1nPjCFOcljjGSMvBDgBZQ6aIPpyacn9NBhVssJ8SrqdTQyca8cEF87TAGbUiK81heVHo+vP0wK/UVpj0NF5kLTQ3fztF0tymZ4nO9BsB9YWT6q0Tra5H+DOgzqt0N0DpWmKqx9tSsqFNTIElDGTTfdYVeZDQoIx85QrrTGArQluvLSEhMOw7XHp4d+HsxqWO7PrUe+5iNWodMTH1ak5lxUIide1k0a2kRKZFAGekCjriu4PmQMwqU7ToeohqsYlbIN3dg8hrattvtIrArZtOzIr1mVjlfFd91IHsg0oFVd6UZ99OggWCeTNfsbgBWVzFdk4mKhHX2LKkXvmDPz/ZGITkcoG0F3l7jGYOJ5ES0WVprrvUtYgCvvW6a1103PcBcjsRHhqc4rQamFDR2H2IpMSxCoXWp3b8J65NYxAsonbJoexUTgKzjdASrFGrQ2jvIo4Hua3M4y2KoCrmExlhNsER9DFqPBg3g5gMFvve5B0TrufxC72Ews4eYlTZptrqV4QmrgyVLrLkpdFVG0jSUkSLO1uaeGjRnfQMkqfvcBPHDtx1CV2Ref0Mga9ETUXFRLqFFhUvdDgB1ZrQuQAt1W+Pm4G/g1mqs/dzPYxw+zOFPf4qZd78bJZ1mOiU2YE+WniSuxlHlvcdoyKBJxOjgD5+b7RYlWSGnJcGXaJgO8sSlDa/D8DodvEYjupasETBonjnchL2+joRPRR3pY2uHMWj6zAyp226j/CcfwDxzhsZdd1H64z8m+7rXoc/NcXImS6lp0VEzJFybptXE9XzB9gcMWt0zL5ni9LEHGLRHUhn+KJflgDdJzrntkvfhWx3/WAbtXcBnfd8/Anw2+O++kCSpAPwc8CzgZuDneoDcr/u+fwVwHfBcSZJ6rbg/5Pv+tcH//uAfeZ7/JDHgdRVEXI1juSaaIkW7uHAgNltJzLRY+GalLZ7a6C6Sn3xsnX94eLWbF29XaMmB4d7YJLYuR2a1Tbt5SQ80Z20NdXICOT3BVLDGZ/TMwMBN6CrjlGkZY0ynpllt9LivT18v7CA+8VOgJVjPz+L4Dp4lGDQlmwHXxWvu47cz7Nw2NqI0A0YKfO/SDdOTOp6TA7oM2lbdZCSpw/rDVIrCUyz0edOmpoZONvbiIpKmoU322I3EgyE4hEGbSc+QNbLcef5OXN/lgCdRlzJcrJoofp7N9noEGHdbmERhpAG/yxTW17tVQ+wqEtilQWvZYgx5krWnZkXW9aCycPB63VIJXLcHoB0j064MB2i1Zcb1LIYPR2YHryXU4PSm5zZr5kCLp0tFWBnXC9DsoIKzdwIPgVday+P6HhVZplpdwvM90ZFj8hpaltO3K++NUPf2ZOlJTpVOkdEzjCfGBagHKu3udXieT8vaO8UJgnlqO230WbE4Wxcu0HE6nHhSjN3dVZB7Re9C3hthn9PxTIyZ1AzrzXUcz2Gxush8Zr7vvf/62fP85puuHThG2KDeN7L9KU6zTidgRPdKre0OQzGQUKju8gjsk1gkChh2lalcPBqfgkFrRgCt1CmhTQQVn/v0jB1JGUw3AjPS3iKeHg1aXwwBaE7hMIrkY291TbaTAcPV8myMoNuFNjqGvbGxtwZtYx1lpIjUs/kGYWny5C/ewUuuDACzluxLcYbAW1d0YkqMpl1nQmuIoo1YDiUANSOdQWPcnT/+Y7xqlcn//t8i2QJ0ZSlnKmcGNti7I6Er6KqMTxxfkugMMeAOGbS8lqZhOfg+6BPjffYx+4WzLYCQOio2U2kjieRL1GQZKkOsKAJJR10b63t5s7VJXI0PVFxO/OzPIMXjnPvO17H8734Q/dBBJv7LTwPwvMPiO7ecOAnbFHpHfFHd29zBBtquuW+KM6bE8LD7fdCAx+0KAIb344wmC5e8D9/q+McCtNcA7w/+/X7gtUPe81Lg077vl3zfLwOfBu7wfb/l+/7nAXzft4BvADNDPv8vJtqPPAKOQ/z66/pejwXuxilDjYxqQ+3ATjWGXBCL05y0yRNBmrPctPh3H7ifH/3gA/z+F86A50K7RE3OATCRmmA7r0b9JZt289IM2uoa2uQUpMZJOGIxePvVbx94X0oXDFrTGGMuPUfTbnYLBY6/Wrjdb5+GG76PZVtM+FangKEqyIHmxHsaXmhuvY7XaKBN9DBocMk0Zz6hI/kGupTqA2hH4xVolynnhXYpF+y2e4XcvWEtLqLNzXUrxECkGtX4UAZNkiROFE/wjc1vAHDc8yiTZqXcJqmMstZci6xJ9pw8gx1mlOasr/cxaJoiIYdGtbs0aCFg9zD3XVi16emhAM3ZEmNPHQsA2tyzSdsmpmsO9P1cri8zG7KAQ3aghVQI0LogcqPeefoAbXYWFAXzXNcI2VpaijoIhBECr4QqzqmULFCqi2cg73oweQ1N0yWxB6gKAdqp0ike2nqIk6MnkSSJbMCg9Qr1w4quvXzQQDBPpmsKU81cjtb936DjdDjyRB39wIF+ULFPFBLDNWiRni9jMJOewfVdzlXPsdneZCG7cFnHnkhOYLomO0a8H9CYdToB+L1cBk2SJDQpQcNq9DExLbuFoRhCYhHPk3CrTGbjXTPfEKBpCXzfZ6ezQ6o4gaRp+wK0mxYKTDW3MRPpyKwYEM+novcDTtcRXSN2AbSwEIaeisJE8DnTbUWpSXVsDGdza08NmrOxiTbeLwfpvS9R6D0pzqROpdU1+07raVpOgzG5JvS8shyxTiPt/jHr+z61j/49yRc8n9iu/pqj8dGINbt27Nqh59R7bvmEhuMFYLkxpBgt0KAV9Uxk6ptOxVHHxy+LQXO3xZwSgs2kriI7STZVdThACwxsG0Y/QAstNnZrILXJSeb/5P0U3/Y9jLzjxzjw4Q8jJ8V6N19MMluIs9Q2SNoCnOVTcGAkCa0dmvv04QzDUA08LGzX7ysmObVziryRp9pMR51T/iXFPxagjfu+HyoM14FhCrtpoLcHz0rwWhSSJOWAVyFYuDBeL0nSw5IkfUSSpMHcy7ch5Hic9B13RC0qwgh1KklDjYSqYa59sxwjPn4EX1a5QVuMdGj3L3bF6U9u1AVV63uU5RwJXWE8Oc5K3sNavACICfLSKc41wRJlZ/jPW5v88nP+W59QO4yUXycm2dS0UW6dFgLru1buEn9UNLjjV4Un2i0/ynKwOFqdvGDQMmISfTo6tNBeQT8UaEx2g5c9QpEl8gmdmFRkvbmO5XiUWzZXeIKFqaTFw583BBu2F4MmKjiHLKSJwtAiAYAjOTHpHy8c5zrXZMtLsVJuU9DH2GptsVRbGspORtF7jZ4HjXVIdx8PSZKIazoSykCKM3TKdzH3rfrSZqaHNoiPPOdCBu3Yy8kG6ZzeVkIAS/Ul5tTgXENhdk+EVghho2oQDNpY+ulNZpKuoy8sRGa1XqeDdeECxpEjfe+LhwAt2KjspEYoNcX1FNQkZGdp7VN5WYwXGYuPcf/G/ZytnOWaEdEUusug9QC0oKBnPwYtpohnW5JlEjfdSOvee/GaDWaeql42ewZiIW/bbtToPYywIjZk0KDbhWEuPcflRPi5i5oq0k0hsLIatKWnx6ABxJUkntTp87/r2yDG88SwmM9I0fjsTXG2nBama1KMjwiWd2NvgPY/XneSN4z75I4c7P+DJA2mbGsXhXfhLoCmjgk9sFrq2rjEWuJzpmdF/WIFQNtbg+asr3dlGPtFX5GAhuV6tIJNVVpP03EbokggcKoPAVq+3b/cWucvYF+8SHpIFbAiK0wGPSyvCRqb7xf5hI7piQxLe5gputWirMjkjVzUFisdU4M58zIYtMCkNtSgxXUF1yqwqip7ADQBC9q7GqWfqZxhNj18OTcOHmTsne9k9Id/GDnRny163uERnqpppEwBjF98Ii30sq1t6op4fvdLccaUrk68l0U7VTrF8eJxSg2LkdQgw/ntjksCNEmSPiNJ0qND/vea3vf5Ygvx9IRJ4vgq8OfAb/u+H26v/x5Y8H3/agTj9v59Pv92SZLukyTpvq2t4dVC/1QRP3mSmd96N0rY7iOIcJedMtSBFKdjpZgYLSDNPovbtEcjBu2+xTKaInHrkRHObTUjQ86SlBcpyMQ4K3kXa3EJ33UvmeL0bRtnc1MAtMw0xy2bVxWHP9gpS9ynilJkNjPLoewhPr/8+e4bjrwY3vEAZKZYaaygyRq+I6qhwl2uW7l8Bi3sw2gcCQorLpNBA6GBUrw8q81VSuuLjFJmwXkKJIVy0JYmYtCmpvDq9T7w6HueYGqGAbR4YSiDBvCcqecA8OPX/zgZr8a2l+b8dpPJ5AQ+Pg9uPjhQHdsXkci5JtKontPHoIGY5BT0gVZP4SLu+PsDNOPgQZy1tciKJYyQQYuKMrQY2VlxPeWeRaxpNyl1SsxIwUIV6n56IgRiW3Vxjr7vR1WHTzdix4+Ljg4EprWeh3G83+A1oYvJNq6I+7eTzFOuiiKP/NhJkCTBoOl7g6orilfw+eXP4+NzclR0fMjGxeTb64UWPquXKhKI2hzdeCP2ygov+OQGquORecXLL/vao7ZAu3RoG7UOqixRSOhMp8W+9UurXwIYSHHuFTNpAdBWZMQ4C58rs07jMtiF3ZHSk0hyJ3JrB9HHNTRZbQa/zULSGsqg7fx/7b15lCRXfef7uZERue+1d/W+q6XW2gghgdCGJGQQArPIBo+wzeYxGB/Gb8D2vPEY5s3gNz5jZt7Y2AzWAF4AG+NBjBdZIDBYoKW17+p9qe5as5bMyj3zvj9uRGRmVWZV1tZV1XU/59SpzMiIzIiMvBG/+/1tOXUzTwaSrlE0F8VTpxrjQx1mZqWOn1T/ZxhooUiM8zKJf9K+fUiJf1rtQ6mad12cZk83lVSKYl6Nl1lJAkNDDQkCLbGCbtcE57ym6ts9yWni1UkI2f0ubQMtPiMqZPrHqrRE6E03Nv0Y5/rSroGWLdnnopmBVsqRMjwkAx1uuZlowMLq30SxSeb7TMojtT6coK5dpVInZ01vSwWtiAVO71ZgaHqIE5MnuLb32nk/byYfuG4bo5UgsZIyrm/Ya08WsmOkg3FgHgXN40NSBSquMV2sFDk6fpRLkpeQmi66SStriXkNNCnlbVLKy5r8fQcYEkL0Adj/m43EAaDeZN5sL3P4EnBESvmFus8ck1I6ssKXgWvm2L8vSSkPSSkPdc0I7rxQOLPsegNN1UCLA6YK6t11C7vKRxk5fxYpJU+eSnHpphgH+qKcGstSmVIG2qiMEvJ56An2cD4p3Iatk8XJOWvhlIaGQUqsTX1uAUxa9EkLFNRnjXnUBeSmLTfx5OCTnE3P7mV5Nn2WvlA/YNg97dSAa1UQthmFI0cxgkG1b6Bi0KCx1EZ6qNYqpo7emJ9KMcbg1Gm6vvIGHvL9W3af+y50H2CiPI1AuMG5tVIbtRlheXAQWSg01DZyCSaaxqAB3NB/A/9y779wQ/8NBMqTjEs1+LfG1Gccmzw2d2yIq6BNzSqx4eC3PAjMWQqacwEpy8KchS29O5XqUDjeWL7CuSF66sZD/MC7AJh88k/dZY46ulV6wLDc+KF6OsI+DFFTeiayJYqVKj1zZHC2wn/JJZTPn6c8Pk7+5VfcZfU4MWgW6pymuvcxbpdYSe65E1Az4NAcbsnLOy93Hx/sdAy0Ji5Oe6yG5jD2fB6fe36C16oby5sfmWR4bxeBgwfnPN56ki36mg7ZaqRhCHqCPZjC5NHzjwKwNdqegubcyAewXTdOJ5BCmrRh4DWspo3BWxH1RcDIq4mjTb2CfySjjuXKjiqWYeG1+yMqBS1IKq/GVIe/A7OnZ04DrZrLUR4cbGj35eKPNbpsWxloPpNj1T5CaXscZFP4y+p7LlYL+KyaggZQGVHfT30MWnV6murUFGZvcxdnA15VqBYp67oJ2G5Db4QyWWLVcddAM0IhSqYgNt2oX2QeeQTvzp14N/fTjB3RHST9ybZc3cmQl3TJdnHaBnI9pcIUaY9Bwt/h/gY7wl6sTZsoDw23LHjtUB6zDbSkitMKWB6qpQQjHoOic17qSZ9nRHQQCdSqDjw+qKoKvL5v4cVfL90U4+Ce7USr6jfe32F/l9OjZGzlf84YNMfFb9Ti0E5PnaYsy2wO7aRclSQvQhfnA4DjQ7sP+E6TdR4EbhdCJOzkgNvtZQgh/iMQA369fgPH6LO5G3h5ifu5ogTMAGVZJuirXfRHsiNETDXb6Ah7YdctAFxdPMzpVJZnz05yaFuCnV0hipUqE8PKOBqScaWghWwDDSgcP85obpTOYGeTT1eUz9t9z/rqDLTJ5gaa165dM4IabPfuvxef6eN3f/q7szKAzqTP2AYaysWZiANQmWhdIXwmhSNH8O7ZXau27sx0nJl+tQpfejN899dmbbspFmB6OkK6UmAosoNRGcMwDHjbHzCeHyfqi7qxGk79onoDreiW2FiYggYQ88WUylOcIIXa56t7LnVLncypoNW7ONN2TMhMBc3yYOCd7eK0DbRSNT9naxif7TIuHj/WsLx07hyejo6GTON41wEAJo78o3vTOz2lZr5bK1Xl3mxSG8tjCLoiPoZtBW2wLqh9oTjtkPIvvUT+5ZcwIpGGwGiouTipBjCFyVi8n5SlLpzxS+4BVKb0XAraL132S/zJbX/C/Xfcr84h6rcb8npmuDhtA22uLE4zUGuOfskl9P7u73Km28OrP3tVy22aEQ823sgdhtN5V400DdPNbusN9bbdtzFgBujwdzBQtZXY6XoDTagODAugOxTDYxZ44mRtbEyXay7OZ0bVON4fsxVIb5jpwhRUiuCL1BQ0fxKzu2tOA80ttdJ0fMZnK2iGCdHG30zIa3JcbiI6fUK5dyfP4LevY+VqAZ9dx8xVlMfUcdW7fR03rBsnOxdWUCU5lQt1/TiL9nuGEEaOcGnUnZAJIUiHPUTqDDRZrZJ7+hmChw61/JhPXP0J/vyuP8cQ89+m40GLiYLt4szPvjY7RnMy2M2YnTmcDHnV+KtUKA22rmcGdmeSWMxNoAh6lYEmBQxONXdxDlErUpstZfm7E39H3BdnX3LfvMfTjDsOHSBiG2hOmSOyKdL2hH8uF6czQRGi5IY2ON+JR6prxLp0cc7D54G3CCGOALfZzxFCHBJCfBlASpkCPgc8Yf99VkqZEkJsBn4bOAA8NaOcxq/ZpTeeBX4N+OAS93NFcazzgLfaoKAFPcoA6gz7oO9KsrHdfMz8Ll995CjFcpU37ulkZ5f6UU2MKANtsBIl5FUK2jk7qSRz7DVy5ZxbVbwZTpFaq28TRG3DYXK2Iga48QHDMg6om8HHr/w4j55/1C3NAcqddSZ9hp6AuiD6TAPTdu+WU60Nm5kUXnutMdbIN8PFOfS8Uple+Pasfe5PBAhk1YB6tPcW7ix+nrFfehS2vE6VXvDVJHRXQaurDVZsVmLDIZhsqaC55CcQssK9b76S//yug9x5YBeHetRF1WlU3RSfrXbOp6BJq2UMWqk6d5KAd+tWME0Kx443LC+eOIl3x/aGZU45kHEh4bRSaBwFbUux2NS96dAd8bsK2lm7+XV/vD3joR5HLSu8/DL5l1/Gv3//rGBhJ0mgUJIk/UnGShlSfQeJ4MGyY/iyhdYxaACWx+L6/ut5Xe/rGpbHg94GAynjGmit38vn8TUkcSTe914+82Ev05dtb+OIazTLhoXZ8XyXdapuHm/Z9pYFvf/myGYGynZMp6Og5SfIzNMCpxlhbxift8TjJ9TYeHUwzfPnhjk5UuHocIZHzytDw7L7/QbNIBm7fhu+qCr5gRofVnc31elpKpnpWZ8DuDG2DTUKHfzxxvE5fhLiW2FGLTGPIThpbMFfyagkgqkB/NJWE40SPluVdeLLjNFxBKKhpmR5aNBepx0FzVaaS1nX8HYMNJMQhpHDrBYhUpvAjUcE0fHauS8eP041nSZw5ZUtPybqjbaM15pJMuRlLK/GZLZJ2Ytx22hLhnrc2MJk0FtXPmZuN6cqUlsTCIJeD7KkblAD04OzSyZNDXCuqorUvpJ6hXc98C4eGXiE9+17X1sGZzNEIOEqaE5BZ7KjpH3KMJ0vixMAo+ROzMbt36+sqO0vuiQB2xV5q5Ryj+0KTdnLD0spP1S33v1Syt323/+yl52VUgop5SUzy2lIKX9TSnmplPIKKeXNUspXlrKfK40Ty+D3Vhpi0EzbAOoIe8EwkDf/NnuMAfKH/5KA5eG6nR0qEwXIjg+CFSRV8hL0mXQFukgHoBTykT72KlBrjNwMp0it1derMqCCnTDVwkCbPM0oCabKtQvdPbvvwefx8d1j33WXTRQmmC5N0xVQg9hrGgivFyMSoTI+0dZ3UzxzhkoqhX9fXZaSd4aL85gT/ybhya80bL8pHuDKioqpesbsoYxJZ0xtP14Yb6hD5kkkEH5/o4J28iQiEKiV+KgnYCcJVKuzX3OwFbaunn5+7tqtmB6Dn9uvWjrNGcTtKGj5qZqCFm6cnQfsWmgzy2w4MWjFan7O7DthWXi3baMwQ0ErnjiBr67hM9Ti9CZNC079C6AMtKQ/SSg/VSs70oSeqM8tB3F2XLmhZ/bsbAdPPI539y7Gv/4N8s8977oM63EMtFypQjKQJJVPMd69h46oulFVq5JsqXUW51zEAhaTdWU2nLEa8bcu/uwkADlUqhVK1VLbWZEOCVdBm+HinJER+/kbP89jP/8Y//Z1/3ZB798f7uesbRi5Btq0CqAOe1uHRjQjbIUxPAWODGd4/ESK9/7JT8lXsqQygrv++4952lbQHOPJVdAAvGHXQEv4E65R1EpFcxXuZhOoSK8aO87Nv0mJDYejXvv6cvYwjB1zFTRh1MpsONcAY2xSlROpmxyUhpSCZPU0uU7MxIkFLmVrCppt9BgyCJ68CsaO1hTzgUSV6FAtpCP3zDMAcxpoCyER9JKp2G23CrPjg1PFSXu9LsYyqpak6THcgtfzldpQRWpr9x+/5aFaVNeMAWbUQpMSmR7kbCVB2Gfx6R99mlK1xFfv/Cofv+rjiz/IQIKo3QnELRmUHWPSVthjTtxvE5x7tBBlt1j1hL3P5aJtoF2ECpqGWisen7fCdKFCqVIilU8hKlEiftNN8w5d8U7OBC7hX4tv8eadEfyWh46Ql4jPRKYHIdztZqhZHouOQCcTfWGKduab0zi5GaXz5/EkEhgB+8YZ62+toE2cYcTT1ZDNEvaGuWXrLfzDiX+gVFEqg6OwdPrUhcY5Dk8iQWW8PRdn+nsqMbehoKczA3UUtOM/hK5LoPcgDDzVsP2muJ83VpSEfqxUVrK87bKYyE+4hgfYJQJmlJ4oHDmKb+fO5s2sg0nlqqi/uBz9HvzDZ2o12pybXbCmlt289Wa+e89351Y5/DFVxmPqnFLQAknVKLt+Fa+qhTazzIYTg5av5Od1c/l27nSTMEDFBlbGx2fF3Pk8PgJmgInYJjipDLTT6dNqhp6faJrB6dAd9TNidw8YGM8RsDyuIrRQOj/6MUoDA4hAgMQH3j/rdcfFmS1W6A32cmrqFMcmjrkB9PlyBSmZU0FrRTxoNSho4/Zj5ybbDL/H36BwtirR0M5nA27fRlCG+ES21BDPZxnWvPUOm7ElsoXB/BgFgVtdnewYGdM7p+unGWErTElmAckHvvwYlkeQDEvuPLCNW/d384tvsetA2pOXkBVi2hnLvgipXIqYL4ZlWJhdtRZLzSiePImnsxNPuMk+xjarmoxOGMIcBtp5/y6KwqsMtNM/JWAb9Iiia6B54nGwLMyx9KwaaGXbxddWFqd7/coSC1gIUfstUfWDkGSFcF2xxUqRswmJfyLrdtLIPvMMnlhsltK9WDbF/WSrtoHWpN7hpN2HN+5PqIB4e/yadm3I+Upt1DdKB5XMI8tRDARnZ5bayKYQlQJDMkHJGOb45HF+6bJf4uqeq5u88wIIJAjXK2jVCmRTTHhMPMIzf6snAFFyi1U7xcazdgFhbaBdpDizaa9VJlMoM2SXBSiXom6ZAgCEIHH3/8NmMconk4/aiwSb4gGs3CiEexoy1LZEtnC6z0IcOYmoyrldnAMDjYVYY1taG2iTZxgzexr6hgLcse0OpopTPD/6PACnptTstsNnK2i2YeRJxBdgoH0P3759De18GhQ0KeHsE7D9jdCxR7WXqqM/HuD66ik8EkYLww3uoPHCeIOLE+xSG3WzwcLRo/h2726+czF7n5yLy6v/CH/+s/DYF+GlB9SySbuMxYz4se2x7S37GQIqniuxDSZOzaqB5hCwDKjOThJQLk5JoTK3ggbgv+wySqdOU7bPh+vSnaGggXJzToQ74PyzUEhzJn1GqYC5ibkVtIifsekixXKVs+M5NicCcx/7HETveiuh66+n82Mfw0zM/kwnSSBXrHBF9xWcnDqpymXYmWxO/MhiFLR40GqIQZvMFhFiYQqak9G5UAXN8hhE/GZDFueI2zJr4fF8M9mX3EdFVjjiC85Q0DyLcnFWZJn/9K5LOLQ9wZ/8wiHylSxdoShf/MA1fOzWS5WKZJeoCVthMqWagTaWHyPpV+6vWg/M5jFOxVMtSuBALdZs6qyKRculaj04ZxDw+znh3QtnHoVTP8G/WQWiC6PkltkQhoHZ1Yl3PNMkg3MQTyxWm+DOhaugTWN6DBJBL6N2XFelrLafMgx3zGdKGc7Ztk3xpLqm5p58isCVVy56HM1kSzKIlMrAyDXJjp+yCw3HfDFGMwXXnWf4fJhdXe0paF11HT8sD+Chz7OZRwP+RgPNTk47Lzs4X1QT7hs3N89UXRCBBB4gYniVgpabACTjhiDui8/5XdZcnGW3WPVEfoKIN8LEtLqmJIPaQLsocU6+11In+ozt0srnwir+rI7wJbdCz0EuGf57d1lf3E+wNAahLqbyJbdw5o7YDp7vzGLki2xKze3iLJ4+3egmiG+D8VOz3XfVKkwOMGH1zOpLdk2PSpZ9cki1eToycQTLsIhbykBzs6HiCcrj88eglYaGyD31FJHbbmt8wWOqGK1cSilMxQx071cFcidOQ53LrzdYZZsYJU6AVGGYTXbsk5RyloIG4N2ymeKpU0gpqUxOUh4exrenhYGWtGsvpewYrhf/Vildsa3wnN0XbuRVEAZ07Gr+HnPhnIMZXQQc/JaHahMDLV+sIIwKVVmdV0EL2kWTc08/A9S3tWphoFl+kFXygy8wOD3IlugWdaGbKwbNVnhGMgXOTmQX5d50EB4PW+//Uzo/8uHmx2NPTrLFCld3qxm3RLr1zBzVdzEKWizQGIM2kSsRC1iz+o/Wo+onlSlV1XaO2rlQBQ1UyRjnRg6NXQSWyoEOlQTyYjgO046CNkrGEAs30OykgtsPxvjLD1/HVVtjs1vNOeEB2Apa2c7A9kVJ5VN0+JVFUt+kvBltGWiTA2ocQUsFLewzedWzT7Woy0/g33YDoILC3R6agNXdg298ummbp7YyOMHtp+tknXdHfG6MZqmk3nfK8Lhjfro4zTk74at44gSl4WGKJ040dfEvli3JIFRtA600Oxt+0p5YRL3RWSUl5quFVp2eRmazeDrqXZwGQsAW/3W86PNxbuSl2gZ2zO2QTHAie5hdsV1tx9LNiTcEhkVEmEpBs5XiSSFbt9yzcc636Sm5ZUbG82qCX+/yXWusvT1ahzizadNUN4+zU8pAy0wHm8umB98NA4chpdLC+2IBYpUUpUAn6XzZnVFvj23nuQ4189k75HHLScxElkpKQdtaFxPVtRfKuZoC5DA9ApUCU76+hubCoOKUdsd3c3joMKAa9e6K76JaVT8TV0FLJtuKQZv4m78BKYm94+7ZL8a2wMSZWvXvjj3KQEPWDCbAN3EcQ0h81SjTlRGu2BwHlJJRrBZnKWi+/fupZjKUzp6tFchtpaA5F/vUcWW4Hn0Idt8Gl78Xjv8AMsMw+iokdsxyT7aFq6Cdb6GgeahUzFkxaLlShYBXGfvzGWj+gwfBssg9rWaqxWPHweNpmrof98WZsEsxDJx/AoAtoX4oTM7p4nRccENTec6O5+hfgoE2Hx5D4DUNcqUKl3aqjFmBcOuZuQraHFmcrYgHVQyak6k8ni25PTpb4Yxt5xzlbNf3XOVPWtEd8buN5qG+SO3Sg5M3hTYR98V5yV/XHWN6hDRyVlud+XAMMae9k6Mahsw6Ay2YaHBxZux18IUZy9UUNE84pMpMNDHQKplpKiOjrQ20mKOgDbQsseHus8/kbz23u899O2zFpi4GDZTBGJjINVXQzHYyOKEhSQCUge1kOecL6nqfDiVU0W+UgjaUACkExRMnyD6uxt5yGmhRv0U8EMCQqm/zTKYqOfxSZTOOTRcbQhRadWBxcDNc6+LzVKFtD0lTGcLfG3my7sPsfrIywdnsa0t3bdY+VMWhYTBVmHKV4vFqcdZEfSZO2EDIX3UnaeOFcRL+BGPTBdflu9bQBtoy4Jx8w2NnutkK2mQmOEtBA+Cyn1X/X/w2ADuDeeJkSPlUeYy+mLop7IjuYKADipbgwIivpYRbGhiASgXv1roLXaddFHb0tcaVbYMtG+hzY53quabnGp4efppytcyR1BH2JvZSKKv1HFdBOzFoslpl8lt/Q/AN16lsw5nEtyi1bNQ20Dr31FSqejfniNr/yek4wprgmm3KIHMycGbOnPwHLgUg/+JLFI6o9/HtbqxW7+ILq8D98RNw7mk14PfcDpfeo2LTjjykPt/5LhdKfFsti3MOBa1ZDJrfNtDmU2oMvx//gUvIPvU0ANOPPELg4MFZ/QTBNtAqOfB4OTPyAgBbfXaq8Bwuzi0J9ft+7swEE9kSmxMLj5FaCEGvh1yxjM/j44quK9iT2OOqQFm3PdMiDLSARaki3d/9RLZIbB63hvP9O+fILVo7R/mTVnRFfa5bE+oUtMjSFTQhBAc6DvCSJdwbV2l6jBzVBcegOd+1U8rA+d8QGxeo1RAMW2Gmnd+wL6IUtLoMZ7O7m3KTbgKl03OUwAEIdav6fFMDaqIDLQ20iM/kaKUbPn0KPvQwRmI7luEFUXNxOvsSnMg3b/PU3aaB5nwPdrun3qjfPZfZvDLKpur6OmZKGUqmQPZ2Ujh6lOzjj2OEw27ZmeViWzKET0JuRuFrgKlKgSgGlapkPFuko+6+ZPVvonT+PLJFspTjnp4Znxf2mRjVLi6pGPzv6RO1Ek1T55DCYMyEXCXDvsTiymo0JZAgIu0YNFspnqjkZ03UZ+KM14C/4oY5OAraaKbx+1hLaANtGXCMhKpQA3Zoegiv4WUy421uoMW3wKarVMwTsFco//1LFSUD99oK2o7YDqQheKUfDhyf3WjZoXhabd/g4uy0B8VMA82OFcgGN7npxvW8vu/15Mo5Hj79MMO5YfbE95AvqYHrBHB7EnFkPk8127pheu7ZZymdO0f8Xe9qvkJ8q9qXsaMqJi3SZytoNBpoo69SxWCy2Iswp7i0X81enQychH+GgrZ3D5imqrX14gsYoVCtQG4zkjuVknnkn5Qrc/et0HOZ2p9X/17tS9ciDbRE3fnYfsOslwNeD5Vy8xg0x13eTqxT6PrryT39NNknniD/0kuEb72l6XoxX4yJwiQkd3FqUqm3Wx1VZI4Z6K6uMPGgxTeesMtyrLCBFrA8rhH1n974n/jCTV9wX3NU3+AcpTFaMbPq+2SuNGeCANSCi504NMdAa7dGWT3dEZ/bexNUBqfXY7gJBEvl0o5LOUqRQnYESjmmKzW31kJwFDQnrswpaRD11b1PXQ3BkBWiICuUgJLpZ6o45SpoQMtitXNmcAIYhsqEnBxQEzl/vKXSG3L6IAfisFmFangNP8IoNrg4zZ5ufPkK4UrNwK8Wi1TGxhagoNWyOEEpoCPpAuVKlUxWnct03X46Bq54w9Wkf/ADpv7+7wndcENjb+BlYEsyiLdqkKsWZr02WS0RFR7Gs0WkpEExsvr7oVRyO5DMpGxnuJozDNiw32QqX+Y93h6OyDzPjjyrXkifo+DroOpX77c3ucjrZzPsUhv1Ls7x8vS8CpozXgPespsk4Choqem12eYJtIG2LDgGWgk1EEdyIyT9nYBonRmy960qOH56lC0ldbP88ZSSkHtsBc3JXHt8r6BrKO+67GZSPGUbaPVKVahDXURHXm1c2VbQyuHNbrPoem7YdAMBM8AfPfNHajcTe92bpRPA7VSTnktFyzz8MJhm636F8a3KtXb2sFLOhAB/VClaDQraK5DciUkXQlQpSJUu3kpBM7xefLt3k3/hBdLff5jwm29snsHpkNypXJxHHoTNr1OZnUIoV+cr/weqpcUraPWz/e2zg2T9podK1STfpMyG31u215nfQEvcey8YBgP/5jcAiNx6a/P1/AnSxTTlzj0cyw2R9CeJO7PmORQ0wxC8bnuSVwbTeD0Gb9zdOhZyOQh4PWTtUiN94T4VJ2fj/Bbnqv7fii47wcSJAxvPFtt2cc400BYTg9Yd8TNdrNSKWU8V6I62VsYXyoGOA5SB10oTKkHAjq1bqIvTMegcw8ytoeWrGV31NQQdhS5rGKRs46BRQWterNY10Jop7O7O9CsF7fSjany2IOw3Z4VseA2fXQetNv6dYrXJujh6Z9+stmPQGl2c3VE/VQlj00Ump9Xv0imeCjVDN3Dfz4OUVAsFuj75yfY+awFsTQbxVA2yleKs16ZkiaiwGroIuIfj1EJrkcnZzMUJKrkmky/zM4nLCFYl3zlm16mfOs+0rxuPT3mSnL7Gy0IgQbRcUkkC6SEkMFlMz6+g2aqnz6uSBKSUjOfHifvjjNUlTaw1tIG2DATNIKZhUpRqII7lRoha6gLV2yoAeO8dgITXHqQre4wxGeHH54yGbSzD4lDPIU5d1QtCMPWPDzZ9q+Lp0xjBoNvzzaVrX82F6DB6BIIdeIJx8qUq5UqjrB20grx585s5NnmMmC/GZV2XuYVTawqaGgzlOeLQ0g//gODrDuGJtpi9OxmUA4dV/JlDx24Yq6vrNfIaRtc+PnaDimM4P60CUJ2bxkwFDSBw8DKmf/pTKqkUkdvvaLmP6g12KBfkuadhT13ZjP1vqz3uvXz2du3gZJx1H1CJETP302sgqxaFJi5Ob5sxaABWTw+xu++mPDxM+NZbm2ZwgkoykUhGEls4Vs2zK7qj1ix+jhg0gNfvUDfnm/d3EVsmxacVQa+HfBP3O9Sq/wcXkSTQ6TZ+r7Wtirfp4nRUzqUqaIAbhzaUzi+46fxcOIkCL8kCpAdJ2xOThbo4nTHljDFnMtQw1upqCLqKmy/CmL1uvYJm2f04Z3YpKZ48hdndPasxdgPxrTD4gooF3f7GlquFfSalinTDMQBMw4cQJTd2FmqFaJOTtete2S7y3VaRWqgpaMVaDBrA4GSe3KT6faT9tXg9x0CLbN1J77/7bfo++1l8O5uP0aWwsyuMR3rIVctMZIv8/oOv8pvffo50vsQUVWKG13XFdja4OO1aaC0SBcpDQxjhMEaosRVcxGeSzpcIJnZybS7H4+dUZQKmzpG2ujD859kU6l/w729OgkkipaKaPKTPkQ53UZGVeZMEnPFqWSpJIFvOUqqWiHsTjGdLa7LEBmgDbVkQQqX5lqSqNZMqjBAw1AWqN9bCQOu7Qt3An/5zQhOv8mp1C0dGpon4zIbWM1++/ct86xe/T+gN1zH+zW80dSsWjx/H2r5t9ky8az8Mv9iYyTn4PPQeJGy34JguzL4R3rv/XnbEdvDHt/0xUW+UXNG5KaptXAVtrElTXqB4doDisWNEbr65+bGDuvA6bL2u9rhjV82orJQgdQy69nLnfnXzOZ9RF9MJu3J5s4HZ+au/irVpEyIYJHzjm1rvA8Alb6893lMLMmbvHfCrT8Cv/BT6Fmmg+aPwgW/DB/+u6cuqUK1JqVqiKmvnKFesuAkn7RoCvf/hd9jzLz9myx/+j5aKzK64ivE7EoxwwrLY6UvWeh3O4yK4cW8XhoD3vW4ZsrHmIWiZTdVdqFPQFhGDVlPQipQrVdL58rzuxYZm4HX/F5Mk4NzIHTfn+Yl86+vDIugL9ZHwBHjR54Wzj9capc9RH6oZjiHmtGxqOhlyaggWJusMtFBDH04Hs7sHWSrN6t87Zwanw8H3KqUd5jXQAOXmtLGED4xio4K2WcX5dqRq6xVt5ahVT8xZuDFoyvBykjxeODdJvDBIuFplqi6pyEm2iHgjJO69l/g772nvcxbIWy7pwaha5Kjw0a89wRf/+RjfeOIMv//gq0xSJerxcWxY7fPOrpqxZc1TC608PNS0PlzEUS3jWzmUL3A6c5bB9DkYP8Gotx+P7zz7Esvo3gSloJVy5Mo5SpPnmIiq/ZrPxWkaJj6PD9NTYiJbcn+nplDGo45Bu8iJ++IUqhk8BqTLY3iqcaAW8D8LIeDaj8Dpn2Ccf4azfqUi9cxY32O3Nen8+CeojIyS+tqfNbwupbTb5jQJON3yelU/aMRuZVopwfDL0HvQ7ZGWLsyObbum5xoeuOcBt+3MLBen7QoonR9semjZx1VT3OB11zV9HWisZ3T5+2qPO3ar2ILcuJ1dWYau/fSG1GfWK2ge0bzGk9Xby/Zv/TU7vvmNuWfnoMp7/MZReP/fKKPZQQgVe9ZzYO7t52P3repm1gS/3UkAaIhDy5cqWB51Xtp1pRleb0Ol76a7Elcxfj8tj5P2GOzEqgVfO/1bW7C3J8KT/+4t3LK/zTidJRDyeZpOHADXcFuMgubMkkfSBTfVfl4X54wkASdDblEKmpMNa8crnU5l2d4xu0H9YhFCcCC+m5e8XjjxI1dBW2iZDcuwiPvibkcAR0Fz+poCSkEDyI3Xsj59QdeoazTQmpfaKJ461Tr+zGH3rSpe1xtuHJ8zcAy0+t+NKXyzy2z09lAxIJ6qjTenNZy5aY7euvUYHlUmyO4T6hjeP3pthC1imFilykSdapcpZjANE6+xsipNLGgR9vrJCcFLJ8/xX997Bf/qum187dFTTAmIevwcGc4QC1gN9TmNYBBPMtkyk7M0NNy0w0LYZ5LOKwPtdXk1Pg6feBDKeU5ZfRjeUfZ1LGOCAEAgTrKoJklj0+cYtwuIz6eggfJ0ecwS6XyZVNZu81RW94cubaBd3MR9cSaLE3RHJWVZoFqO4jWNuSuuX/UBpVz0X03qio8B0KokU/DqqwjfcgtjX/6yW5QUoDw4qFopXdrEkNh2vfp/6ifq/+gRqBSg5yARZ8bZJFFgJrlSRTVKt3fO7OoCj4fS4Pmm62cPH8YTj7cuEAs1o2X7m5TS5OC4O8eO1+LnOvcSskJEvVHXQJsoTBDzxVr2dTMTicb+n3MR7oI9t82/3jLjtzzIqm2g1cWhZRehoLVDzBejK9DFPw2rlPhdhYJyJ0f6av1R5yBxgVLRQz6zaQILQLZQwWOIhtIJ7WJ5DBJBi9FMwc3kmu+YZsagOf+X5OK0y5WUq5LtnctnoAEc6L2Go16L/IkfL9rFCcpFWa+gRbyRht6V7vjNjrsxbhnLX1PQAk0MtKFasdpKOk0llZpfQRMC3n0//PxfuWUrmuEoqvUTTgNrVpkNYZqkYgbRsVpYQWlgQLlam2Q+t8Qfdw20jpAXQ8APXh1hixghWakwVheonyllCFvhZYs1nIvecJScIfjT9+3hHVf28/Fb9mCICjlDEDNDHBnOsKd79r6oWmgtFLShoVkJAlCLQSO+lX3FEhHDy+GBRwB43giAkOxdbgUttpVe2409mB1mIqjuHfPFoIEas8JQ52Uwo+6h2bwak0up7biSaANtmYj74kwWJknGlHVfyEfoi/nnHpSBOHzyWfilB3nL65UbzamN1IyuX/8k1elpxr78ZXdZ/sUXAfAfaGKgxbdCdDOcUoOGIVVaod7Fmc63YaAVKw2KhfB4MHu63diNmWSfeILAoWvmDs4XQilXH/h24/L6TM7RmoEGyoVTb6C1MyjXMoE6Ba2+1Ea2WMFjtp8ksBB2xXcxnBtGSNidHlHfc8cchvQqEPbNDvh2mC6WCXo9i77ZdYZVxp1TCyk2j4LmpOc7ylmunEMgFpUkEAtYeE2DkXSBE2PK7bVjmQ20g91XUxGCl40SqQ4V59SOujCTjkCHa2yN58cbYsqAOgUt5RpoWcvPWG4Mv8ffYMA66ku9guZU1LfmM9BAJfI0yYKuJ9IkZMPAhzBKs9TW4bggPFJr3l4aGHBdn23jj7nhAabH4MotcYrlKnu9Y3RgkKprt+QYaBeCRDBCTgiu7VPfR1fExxt2KyMkYgY5OpxhT8/sfWlVC01WKqqLQBMXZ9hvkimWqfrieLwRrjFjPDGuvDWvoJIRlt1AS2x3DbTzlSzjdk26+VycYCcKCLVfjoHmZN06BdDXGtpAWyZivhjj+XHCYTWryk7HWicI1BOIg+FhV1eYX7tlN198f+uifv69e4ndfTfjf/4XlAbtbgUvvQSGgX///tkbCKFUtOP/DKW86sFoBqBzj9veJtOGgZYtVlz3poPV29fUxVkaHKR05gyh17XOuHIJd4E5Y9aa2K7KXYwdUQpabIur7myObHb7gw5OD87Zm3Q9EPDWKWiVegWtjGm7OJdTQYOam+oeI0bH6AnbQFtEl4QVJDyPgraYDE6HrohPKWh2y6XEPEkCTvZXtlQz0PzmPBOvFggh6In6ODuR4+ToChlodkHf53w+jsZ66A50L9jFCcpFWe/inGXkuQpayv2OMqbXrYFW//14utQ4rS9W62ZwtmOgtUHI9QjUFDSBqoMWqDPQpJQMxiSh0VoaZ2lgwA2Ub5tA3FXQAO67fjsAneVBOsyg+92B6iSwrIHyc+2WFSZrGA37dts+dY2ZzJikpovs7m4SFmK3yJuZyFEeHYNKBbOJizPqN5ESpksV2HQlh6YznC5NMRjuZKgygpBeNocXaPjOR2I7fWV1bRg0PZw3BQIxZxtEh6AZpCrUdXbEdnFOZEz8ljFvuZ3VQhtoy4SjoJl+NTBTE9EFW+Wfun0f189TwqDzE59AVquM/uEfApB98il8u3a17iF31ftVOvzjfwLP/zVc9i7wWG7MxlS+dX01B1XZfqaB1usaifVkn1BdCAKHDs37vk0xvZDcpZqmn3+uocTFtug2zqTPUKlWOJM+szztQ1YRv50kAI0G2nSxgrFCBto7d7+T3fHdfLLrepVAkh1rzKJdA4R8JtPFCtWqnPVaplheVA00h86wj5FMwVWq58veaqagLeWcXNoX4/mzk5wYVQlBy13BvDPQSb8Z5jmflyOizJ7E4s7tTBfnrGxppyxLnYI27TEZy481xJ+Bio/0JBINxWqLp04C85TYWADO9azBIyAthFFsyOIsV8sMxcA7lVctjMplSoODWO0mCDj4Y2DXYgR462V9mIbgivAkSV+C8fy4m/iTLqUvmIIW8CoFjcKUu+zSPrUfL5yxVa1mCtqmTchCgcrYWMNyp0it1UxBq//Od9zI64ZVuajDHf1k5GkCbHZjqJeNcDdhM0BYCgY9JqeqefpCfW15GgJWgIpUnorR7IT6P2nQH198b+GVRhtoy0TCn6Asy+TFaarlIOfGxbJmaDl4N/eTuPdeJv7m26QffpjsY48ReeudrTfY8WbouxIe+h1Vt+fajwA1l0BbMWgzXJwA1qY+yoODs6pPZ594QlXJbqbotcslb4dj31cuzv13uYu3R7dTrpZ5bfw1JgoTbI4s8+zsAuO3DKRsjEErV6oUy1WEUcIQRmPczzJwQ/8N/O07/paOrW+oLVyDLk6gaSbnlN0/c7F0RZSL8+hwhoDlYVNsbmMrYAYQiAYFbSkG2pVb45xOZXny1DjbO0MrcmM42HctTyf7OJ4ZWLSB1hHoIFPKUKgU3IrrDfjjqsp/ZpigFcSQkkmPh1Q+Ndsdiu1CO1NrO1c8dQqzrw/DvzzXyGZJAlQtDKPU8B0XKgWG4/Y+nB2gNDikurAsVEGri0ED1WXltc/dQUfxHB3Bbiqy4maaT5emL5yB5ouSFwKZq+1brqBc1fmCn9sP9PCGnR2ztnNLbcxwc7YqUgvUvDAFZaDtKxaJVKr8b7+HvOcoSWOZEwRAeYUS2+ktFRg0PZwqTrAt2p4KW6+gDU+P4/f4GZysrFn3JmgDbdlwXEejpSPIolLBWmZwLpHOX/kYRiDAwCd/HSyLxHve03plIeBdX4KrfwHe/GnYdCVQZ6C15eIsz3Jxmr19yGKRSqqxaXr28GEC11y9tCrZV9yr/ltBOFg7tq1RNdt+5JyKqVvvClrA8kBVnQc3S9CuOYco4vcszpXWFpe8o6acLbZTwgoRanaztVmqgdYZ9pEtVnj27AS7u8MYczRKB+WWDFrBhr6USzHQrtoSB+DFc1Ps712467EdXtd/AyPFKYrV4uINNFsFS+VSbsX1BgxDtS9Ln8eQ0FeucFYWGcuNNSQIOHh37qBw4oT7vHji5LK5NwE3prbexSmrFohGD0G+kudMpzrnhVdfoXhC9f21tixQyauLQXMw0gNQKdARU8flKJCpfKoxA3YFCfjiSCHI52pK2KQdS/jz1x7gj95/ddOm4FZ/82K1JcdAa5bF6cYxl6D/GjzAe9NpHiuOApJ9odtnbbMsJHbQU64waJqcyg61b6BZQYrVPH7LYDQ7QdQbZWAiT7820C5+nBiNVPE81ZK6QF2/a2UqrpvJJP1/8F8JXv8GOj/2UZVVORdd++Du/w9u/i13UcDyYIg2kwRKVQIz4n6svtmlNsqjoxSPH28v/my+/d3/NqX2+WsXNmcgPmJnCi17fMMFJuD11BQ0pxCqU6DVKC67e7MBw4CP/rNK0kjuXLnPWQQh24XZTN2dzJWI+hdvoG1Jqu/0mTMTTYOlmxE0gw110BbTh9Ph4Oba7/mjb16Z2L+7d93tPl5sFXfHyDqVPkW5Wm6qijkGGtlRtpdKvFROM5Yfoyc4W23x7dxJ+fx5161YeO01/PuWT2EJWh6EgEydUV+tqCzO+riqQqXA2U6oBLzknnmW3NPPqBjeSy9d2AcG4lCaVqWLHOwC2x1JpUin8ikq1QpjuTG6g7MNnJUgYAfL5/K1ifOU3fHh6q1bmxpnUNdNYJaCNgweD+bMIujMcHF6LPhX3+HD7/0u3cFuKpkD9K/U9dnw0Fuu8JLPS7qUYXtse1ubBcwA2VKWrckgE4VJwt4Io5nCmjbQFh9tq2mgPoh2W3Qbv/u269jdvXKydvjGGwnfOLt9ULsIIebMlqsnVyzTNyPhwdqi1KviieMEDqp6aZkf/RiA4BvewJK59y9mLerwdxC2whweUnFu693FqRS0RhenExwvKS17BucsvCFVZ2qNUXNXNTfQlqKgvWlPF5ZHUKpI9jQJlm5G0ArOShJYLEGvyV0He9mcCK7Y9cFv+vnCzV/g/hfud4sTLxTHIHt5TGXlNevYQaRXle6ZOseOUolHShNAraNBPd6dahJQOHES4bWQhQL+yxZoFM2BYQhCXrPBI1Cp1uI7nXNWKBeQhqC4Zyu5557DE43g27cPT3iByRpO1mB+EkL2RDxlG2hd6no4lhtTRpqsNDVaV4KgT/2ms7kUjkk9ZRf6jTRRNh08kQhGNDpLQSsPDWF2dTX1iERnVgLYeRMh4Gt3fJMb/vOPiV++QmV5rv5X9P7zK4DyOizExZktZ9mdDPJ0KU2focafdnFuAC7tvJT9SRV39amb3sh1Tfz8a42I32orSSBbnJ0k4Nu1CxEMknv2OXdZ5oc/xOzubl7yYxkQQjQMxsVkp60l/FZNQXNdnLaCVqWwsgraGibUwkCTUjKVLxMNLH5eGQtY3GAn4uxp00AKmkGmy8vj4gT4o/dfw2/d1aSw9DJy69Zb+Yu7/gKvZ3E3ye2x7RjC4JuvfhNQjdhnEelTClr6PDtKtXPVzEDz2QZa8cRx8i++BLQoDbQEZmb/VuyG6E7tOqgp1eVLdpB/8UWmH3+C4FVXLvzDHGW/3s05dgysIB2d6j4wlh9jOKcSIy5UxrnTNSJTl8AwVZwiXK1i+ua+Xlr9/bPaPZWGh5q6N6HerTxjnFaCIL0r1xJuz1u48z1/7T7dFmnfxZkr59iSCFCoZiiX1Ti+rP/CuJ8Xw5IMNCFEUgjxkBDiiP2/aWEqIcR99jpHhBD31S3/oRDiVSHEM/Zft73cJ4T4phDiqBDiMSHE9qXs54XAMiy+/jNf54u3fZHbtl74oqeLIeI324pByzfJ4hQeD4HLLiP3nDLQZLHI9COPEL7pphXNiPno5R8F4Oru1uVI1gs+00DYWZxFu8GxY6BVWGEX5xom3KKI8nSxQqUql6SgAbzzqn4sj+DS/hZ9YmdQr6Dly/kNcV6i3igHkgcYyAyQ8CXYGWviBo/0KgVp7BjbS2qi1x3obmqMWFu3gsdD4fhx8i+9hAgE8G7fvqz7HPJ5Gn4z5fLsGoNutvRl+0BKKJcJXLWIa4nTu7YuUYCxY5DcRcQXxRQmY7kxRrIjABdMQXMmrelCbb8mC5NEK1XV/WAOVKmNmQraMFaTBAGoJQmkZ0zynRqD83XpWArbY9v5zj3f4VPXfKptT4oTmrApaSKNLKm0h4jfbHuithos1cX5GeD7UsrPCyE+Yz//dP0KQogk8DvAIUACTwohHpBSOuXw3y+lPDzjfX8ZGJdS7hZC3Av8HvA+1jimYfLG/tb94tYabi+1eWhWBw0gcMXljH3lq1QLBdLf+x7V6Wkib1lZ4/TmrTfzo/f9aEU/40IhhMA3s5WQnblYkQVCK+3iXKO0MtCmcu0Vl52Pu6/YxPW7Ot3enPMRrKtrlS1nN4SBBvD6vtfzwtgLXNV9VfNJV0T1cOTcU2wvq2zuA53NVTHD68W7fTu5w09SzeXw79u3tESiJoT9Fum630ypZIKv1j8VauPMeMMhej/3WUqnTxO5ZY6ewa1wFLR8rasLqWPQcymGMEgGkozkRhjOXlgFzam3li7VCuVOldJEq9WGeN5mWP2byP70p0gp3fNdHhoidP31Tdd34/5mTPLdNmrz1BhcKjtjO5tPHFrgGGjdMRBGjnNjcN3WxLyJQqvJUl2c7wC+aj/+KnBPk3XuAB6SUqZso+whYI66ELPe91vArWKtFipZx7i91OZASkmuNLvMBoD/8suhVCL31FOk/tdX8O7YQeiGuSt+LwcJf6J5TMw6xG83VXZi0BwFrSTzi6pWfzHQysXpXPiXkiQAyjBu1zgDCFmhZSuzsZ64tu9aAK7uaaEwRVSiEANP0hXo4rKOy7hlyy0t3y92zzvIPvEE+RdeIDpXaaBFEvZ5Gn4zpbK6ZjW4OO1x5vMFSbznPXT/m3+DEVpEseD6GDSAchFSJ9ySNTtiOzg+cZzh7LAy2JolWawArouzVOuUMFWaJlZtT0GrZrNuU/tKJkN1erqli9MwVBzz1Ix7yLhdBDq+xoq/Biw1bjd3CISniKwGuXrr2r6PLNVA65FSOv1+BoFmWmg/cKbu+Vl7mcP/st2b/3edEeZuI6UsA5NA06AuIcRHhBCHhRCHR0ZGlnAoG4+w35pXQSuUq0jJLBcnQOi66zC7uzn78U+Qf+EFkh/84NztnTSzCJgzFTTbQKtu3Bi0moLWWGZjcpkUtIUyM0lgo5yX1/e+nt849Bu8c887m68QsZuLj59ERPv4+tu+3npdIPHe9yICAczeXuL33rvs+xv2NYZsFEq2gVbn4nQeLzkBx3FxOjFoqWMgK9ClYgv3xPdwdOIog9ODdPo7MY0Lk4/nKmh2YWWAyUqeqGR215YZzKyFVjp9GgDvltbljDpCXsamiw3LLoSLczE4CloRldW6I9nJ26/oW81dmpd576ZCiO8JIV5o8veO+vWkymWeXfp7bt4vpTwIvMn++4UFbo+U8ktSykNSykNd85Wb0DQQ8Zuz4gdm4hgMzVycnkiETb//XwDo+PCHib/7Z5d/Jy9yIj4fAo8bG+O4OIvVwspnca5R/JaBIWYraI6LM3qhDTQ7+6tULVGuljeMgeYxPNx36X1EvS2UF0dBA4humv/9YjE2/7cvsPm/fQHD176C2S5hX+OEs1hURlGDi7O8TAaa6+KcUP9H7L7BXap0yN7EXvKVPE8NP3VBW9LVDLSaUTpVKRAV8xuIbqkNO5PTbcc1R6xgMuQlNd3YP3pylcbpfDgG2uC0Kg31iZsuZ2fX2o0/gzZi0KSULYOKhBBDQog+KeV5IUQfMNxktQHgprrnm4Ef2u89YP9PCyH+ErgW+Jq9zRbgrBDCBGJAYw8KzZKJtOHidAyGZi5OgNC117L38ceWPZ5koxDyeRDSqjPQlEFcqGwcpWYmQghCTUrArKqCVs66KtpGPS+zCMRhzx1w5EEQ7Y3/pZQGmve965IEpJTkiwYBmmdxLjl8wAool2HargM58iogoFPVndubVMWfz6TPcNOWm5b2WQvZLcMiIDxkquq3KqVkUpaIGvPHgzmFg4snTqr/J9X/udpxdYR9nEllG5ZNZIsELI9qZbeGcHrGnphSBZNntiRbiyzVH/UA4GRl3gd8p8k6DwK3CyESdpbn7cCDQghTCNEJIISwgLcBLzR533cDD8uZXVw1SybsMymUVWuhVuTtyvYzC9XWo42zxRP2WyCtWgyafYPJlzduDBo0b5juxLqshoJWlVW3dY8Ty6IB7vkidB9QPX5XmbBf/WaklBTKVapOjcG6PreOmrYs6nRyB6RUJwJGXoHENmW4Abtitfpzb93+1qV/1gIIG17SogrlAvlKnhKSqGf+36wnHMbc1EfhyBEAiidPYfb0YARbF2Zu5eJca/FnAH0h5c587PxjwPqoo7lUx/jngb8SQvwycAp4L4AQ4hDwMSnlh6SUKSHE54An7G0+ay8LoQw1C/AA3wP+p73OnwJ/JoQ4CqSA5Q9Y0NARVm6GsekCfS16Es7l4tQsnbDPAwWzIQYt6DXIV/Ib2hBQDdNnK2hCKOX3QuLMvJ2MvJC5iKDyi5VQB/zrn672XgDqN1OuSvKlKvlSBVlVqlG9grZsLk6AxA4YfF49HnkVumr9h/2mn6u6ryJshXnrjgtsoHn8pA0D8lNMCTX5jrXZ/cK3ezeFo0cBpaDNVwqlI+wlNV2kWpVuNuTEEotJrxTdwW78Hj8vj72MR3joDfXOv9Eqs6QrnZRyDJhVitwum/Ghuuf3A/fPWGcauKbF++aBORpMapYDJ5NtJD2/gdbKxalZGiGviczVuThLFQK+KkWWwQ2zjmmWYTyVKxHxmRc8LT5kKYPsbPosAMnAhcnI0ywMxyiYypcoVyXYRaDrY9AKlQIe4cEylsGASO6AV/4OChkYfQ32Nvae/MqdX0EgVrQuZDMiZpCMISA3zqR9h49625tU+HbvIfvoY8hymeKpU0Run7ufZjLko1KVTOVLblmNyTWqoAkh2BLdwpHxI/SGepfnN7DC6JS7DUy9gdaKnOvi1AbaShDymciq2eDiDHjVrHejJgmASmCZmb4/lSutSuCxE1x8Jq2S0ddD7MpGJGEbCKnpIrliWTVLpzGLc6mtuhpI7oRqCV5+QP3fcl3Dy4YwLrhxBqpYbdowIDvKVGEKgKjVXlFm3549yGKR7FNPUZmYmFdB6wyr73w0U3NzTuSKa1JBA9gaUfF066WPszbQNjDtGGhOHFBojhg0zeKJ+E0qFbMhScDvtVXLJTTlXu8kQ14mso2xLals0b0JX0hmGWhz9DTUrB7Ob2M8W1TKv2ze6mnZlOnEDvX/abtv8NbXL8/7LpGwP05GGDA9wmRR1WmLOnXb5sG3W9VxG/3DPwIgdEPzIrUOyVDNKHYYThfojqzNyaVroK2D+DPQzdI3NM7sZy4DzRl4zkDULC8hn4mUJtlSLQbN71VG8UZW0BJBb8NFH2B4qkBf7MJ/J04M2pn0GQxhEPOu3d59G5lESKk2E9kShhCAgWV4Z8WgLZ+CZhtop/5FJUoE1kbR07A/qRS06RHGhMqw7Aq0V+rDv28v5qY+so89hnf7dnx79865fkfIjmPOqHtIrlhhIluidxXGaTtsiaqabuvFQNMK2gbGZ3qIBSxGMq0NNEe6TqzBmIKLgZDPBGmRK9daPXktpaBt5Bi0RNBLOl+mVKllGI9kCnRHl79+1nw4MWinp06T8CXwGNrdvxZpdHGqMeQ1/LNaPS3buIpsgmCnerx97bT4iwa7VAxaZoTh9FmElCSDzbsBzER4vfT8xm8AELnzjnldtB32JN/J5BycUtex1ZhItYPTWH29uDi1grbB6Yr45lHQCsSDFqZH2/IrQcRnIqsWubJqzZItVoiFnMzZjZvFWa+GdEVUIPJYpkBX+MIbaP1hVWE9XUqzNzy3oqBZPZzA9Ik6V7jf9DeU2VhWBc0w4ONPwMBT0L+IhusrRNgXpWAYFDNDjOKho1LFdDoftEHkrW9lU1USvvFN867rfM9j9kT+/IQyhteqgnZNzzV85trPXNDadEtB33U3OF3h+Qy0Ih3avbliOAqa44aZzJXw+2wX5wZX0AA3Dm1sukBVsqAemstF0Aq6RtqF6qmoWTg+00PI62E8W3KTm/wef6OLs5LH51nG31AwCXtuU//XCGHL7iYwPchwdoiuSmXePpz1CCGIve1n8ETn38ZrGrYXRn3H5yfV/00tqgKsNh7Dw/svef+6CR/RBtoGpyvim9fF6cQZaJafkM+DrNbKbEzmSgRsF+dGrlhf766CWpxk1yoFH++M7QR0gsBaJx70Mj5ddA37gOknV2ls9XSxj6u4Lw7AeHaUkdyYMtD8Kxc3ua0jyKkxFevmuDjXqoK23tAG2gan3sWZKZR58MVBqtVa04bUdFEnCKwgEZ8F0qRULVIoV8gWK1iWUtAu9hvJXDguznG78XLNQFudycLuuMpu0yU21jbJkJfxbJGhqTx+yyDkDcxS0NaLerJYNoVVT81zhTFGChN0lSvgb19BWyg7O0McH1EhGucncySC1ppr87Re0QbaBqc36idbrDCSLvDZ777IR//sSX71L59yjbTUdNENBNUsP46CVqoW3F6TXktncdaXTACVug/QvUoG2q64at2jFbS1TTxokcqWGJoq0BP14zf9s7I4l9XFuQZxMhRPltOkyhmloMVb99NcKju7wgxM5MgVKwxO5uldo+7N9Yg20DY4N+xWWUj//ftH+Osnz7KrK8Q/vDDIk6fHqVQl41kdg7aShH0mSJOyLDGRVUaIadrxM9pAcw201VbQ9iRUE+x2yxVoVgenft7QVJ6eiJ+AJ9BQqLZQLlz0ynSHv4OAMHnWqCKBLk9gRUuA7OxSWc7HRzOcm8iv2QzO9Yg20DY4l/RF2N4R5M8ePUUy6OUrv3gtHkPwg1eGGc8WkVLXQFtJwn7TbUkzklFuAsPjxM9c3DeSuQh4PQQsD+N1MWgRv7lqrpNLkpfwe2/6PW7bdtuqfL6mPRJ2DNrQVJ6e2GwFLVdZxk4CaxQhBP3eGE/71WSma4V7Tu7sVEkJL56b4uhwhl1dulftcqENtA2OEIK3HuwD4HP3XMaWZJBD2xL88NURN0C7YxVKG2wUApbHNdBGpzMAGEYJU5jrolfcSpIIWm4M2nA6v2rqGahxctfOuza00bweiActpvJlzk3k6Yn4lIFW2VguToDN4X5GTFVFqyu6bUU/a0dnCCHg64+fplip8qY9WmVeLrSBpuFjb97F/R88xF22oXbz/m5eOj/Fi+dUmxDt4lw5hBD4DHXDSGVVJhRG6aKf5bdDIuR1FbQTo1m2JTdu6ytNe/RE1bgpVqoqBq2uzEalWqFU3Rhja3PXZQB0lCvs7b5sRT8r4PWwryfC06cn8JoG1+5YOyVH1jvaQNMQC1jcsr/Hff6WA+rx//zRCWD14n42Cj77hpHKKRenpKiVGpRyO5wuUKlKjo9k2N0dXu1d0qxxrt9VS+LojvoaXJxOKZuA5+IfW367Pdnbpqfxdu5b8c/73bsvBeD1O5I6g3MZ0QaaZha7usLs64nw0vkp9vdG9I1xhYn51Q1jPDuNEFCRhQ0xy5+PXV0hjg5nOJPKUihX2dWlf4eaudnWUYt/crM4K3mqsuq2fPKZF/+E852738kdiUv56PgU9Kysggbw+p0d/I+fv4pP37l/xT9rI6FbPWma8taDvbw6lOYjN+6ctx+bZmkkgyGGJUzms8QCwQ1Rq6kdLumNkitVePiVYQA9UdC0RXdEKa/dER/+aTWOCpWCq6BthA4dW6Nb+f23fx1uOA4duy7IZ77t8k0X5HM2EtpA0zTlvjdsJ+Q1efsVetCtNF2hMK9kYLKQIxaIkSvntIsT2NcbAeDvnj8PaANN0x5/+eHr+MpPTrCtI4R/VBlj+XLedXVumLElxAUzzjQrg3ZxapqSCHn58I07sXST9BWnJ6IMj6F0mnjAIl/Jb4g4mfnY2xNBCHjy1DidYS/xoE5W0czP7u4w//Geg3gM4Rpj+XLebfm0EbI4NRcH+u6r0awyvRHVhuXM+BSxoJdc+eKv1dQOAa8HaXcdu2lf9+rujGZd4hho06VpCmXbxanHlmadsCQDTQiRFEI8JIQ4Yv9vWq5YCHGfvc4RIcR99rKIEOKZur9RIcQX7Nc+KIQYqXvtQ0vZT41mLbMpphS0YrVAV9hHppghZOlijwAfuXEnb9rTyX+8Z+UDnTUXHxGvcpNnSpmN5+LUrHuWGoP2GeD7UsrPCyE+Yz//dP0KQogk8DvAIUACTwohHpBSjgNX1q33JPDtuk2/KaX8+BL3T6NZ82yOx9QDUeLGvZ08/krGvbFsdH7rrktWexc06xhnHKWLaYpVVVNPuzg164WlujjfAXzVfvxV4J4m69wBPCSlTNlG2UPAnfUrCCH2At3Aj5e4PxrNuqM/pm4iwijz5j1dpItpbaBpNMtAxKoZaFOFKbVMjy3NOmGpBlqPlPK8/XgQ6GmyTj9wpu75WXtZPfeiFDNZt+xnhRDPCSG+JYTY0moHhBAfEUIcFkIcHhkZWcQhaDSrS8hrV8gXJbzeMhVZIWzpjEWNZqnUK2hTRWWgRX3R1dwljaZt5jXQhBDfE0K80OTvHfXr2caVbPE283Ev8PW6598FtkspL0cpbl9tupX63C9JKQ9JKQ91dekeYJr1h+Ny+dCbNpMpqX6cepav0Swd10ArKQNNIPTkR7NumDcGTUp5W6vXhBBDQog+KeV5IUQfMNxktQHgprrnm4Ef1r3HFYAppXyy7jPH6tb/MvD/zrefGs16xTRMTGES8EkyRWWg6ZuIRrN0fB4flmGRLqbJlrJEvBEMoYsXaNYHS/2lPgDcZz++D/hOk3UeBG4XQiTsLM/b7WUOP0ejeoZt7DncDby8xP3UaNY0Xo+XfCXvumG0gqbRLB0hBBFvxHVxRr3avalZPyw1i/PzwF8JIX4ZOAW8F0AIcQj4mJTyQ1LKlBDic8AT9jaflVKm6t7jvcBdM97314QQdwNlIAV8cIn7qdGsafymn0K5oF2cGs0yE/FGyBQzpEtpHX+mWVcsyUCzXZG3Nll+GPhQ3fP7gftbvMfOJst+E/jNpeybRrOe8Hl85Ct57eLUaJaZiBVhqjRFupjWCppmXaGd8RrNGiDsDbuzfOe5RqNZOmFv2C2zoQ00zXpCG2gazRog6U+SyqdcBU27ODWa5cFxcU4Vp7SLU7Ou0AaaRrMGcAy0dDGNIQyCZnC1d0mjuSiIeqNMFad0koBm3bHUJAGNRrMM1BtoYSuMEGK1d0mjuSgIW2FGc6MA2kDTrCu0gqbRrAGS/iSZUoZUPqXdmxrNMlI/nrSLU7Oe0AaaRrMGSPqTAJxJn9EZnBrNMtJgoGkFTbOO0AaaRrMGcAy0k1MntYKm0Swj9aqZNtA06wltoGk0awDHQMuVc7rEhkazjLyp/03u45gvtop7otEsDJ0koNGsARwDDWBfYt8q7olGc3ER88X43ru/xwPHHmBvYu9q745G0zbaQNNo1gD1Bto1Pdes4p5oNBcfPaEePnz5h1d7NzSaBaFdnBrNGiBkhdzHV3RdsYp7otFoNJq1gDbQNJo1QH3ds6Cli9RqNBrNRke7ODWaNcLnbvgcXYGu1d4NjUaj0awBtIGm0awR7tl9z2rvgkaj0WjWCNrFqdFoNBqNRrPG0AaaRqPRaDQazRpDG2gajUaj0Wg0awxtoGk0Go1Go9GsMZZkoAkhkkKIh4QQR+z/iRbr/aMQYkII8X9mLN8hhHhMCHFUCPFNIYTXXu6znx+1X9++lP3UaDQajUajWU8sVUH7DPB9KeUe4Pv282b8F+AXmiz/PeAPpJS7gXHgl+3lvwyM28v/wF5Po9FoNBqNZkOwVAPtHcBX7cdfBe5ptpKU8vtAun6ZUJU5bwG+1WT7+vf9FnCrqK/kqdFoNBqNRnMRs1QDrUdKed5+PAj0LGDbDmBCSlm2n58F+u3H/cAZAPv1SXt9jUaj0Wg0moueeQvVCiG+B/Q2eem3659IKaUQQi7XjrWLEOIjwEfspxkhxKsr/JGdwOgKf8ZaRh//xj3+jXzssLGPfyMfO2zs49/Ixw4rf/zbWr0wr4Empbyt1WtCiCEhRJ+U8rwQog8YXsBOjQFxIYRpq2SbgQH7tQFgC3BWCGECMXv9Zvv3JeBLC/jcJSGEOCylPHShPm+toY9/4x7/Rj522NjHv5GPHTb28W/kY4fVPf6lujgfAO6zH98HfKfdDaWUEvgB8O4m29e/77uBh+31NRqNRqPRaC56lmqgfR54ixDiCHCb/RwhxCEhxJedlYQQPwb+GhXsf1YIcYf90qeBTwkhjqJizP7UXv6nQIe9/FO0zg7VaDQajUajuehYUrN0KeUYcGuT5YeBD9U9f1OL7Y8D1zZZngfes5R9W0EumDt1jaKPf+OykY8dNvbxb+Rjh419/Bv52GEVj19oz6FGo9FoNBrN2kK3etJoNBqNRqNZY2gDTaPRaDQajWaNoQ20OoQQdwohXrV7gM5KTJirR6gQ4jft5a/WJUGsG9o49k8JIV4SQjwnhPi+EGJb3WsVIcQz9t8DF3bPl4c2jv+DQoiRuuP8UN1r99n9aI8IIe6bue16oI3j/4O6Y39NCDFR99q6Pv9CiPuFEMNCiBdavC6EEP/d/m6eE0JcXffauj73bRz7++1jfl4I8RMhxBV1r520lz8jhDh84fZ6+Wjj+G8SQkzW/b7/fd1rc46ZtU4bx/5/1R33C/Y4T9qvretzL4TYIoT4gX1Pe1EI8ckm66z+uJdS6j8Vh+cBjgE7AS/wLHBgxjr/Gvhj+/G9wDftxwfs9X3ADvt9PKt9TMt87DcDQfvxrzjHbj/PrPYxXIDj/yDwP5psmwSO2/8T9uPEah/Tch//jPU/Adx/EZ3/G4GrgRdavH4X8A+AAK4DHruIzv18x369c0zAW51jt5+fBDpX+xhW+PhvAv5Pk+ULGjNr8W++Y5+x7ttR5a4uinMP9AFX248jwGtNrvmrPu61glbjWuColPK4lLIIfAPVE7SeVj1C3wF8Q0pZkFKeAI7SJDt1DTPvsUspfyClzNpPH0UVFr5YaOfct+IO4CEpZUpKOQ48BNy5Qvu5Uiz0+H8O+PoF2bMLgJTyR0BqjlXeAXxNKh5FFdju4yI49/Mdu5TyJ/axwcU37ts5961YyjVjTbDAY7/Yxvx5KeVT9uM08DK1VpMOqz7utYFWw+3/aVPfG3TWOrKxR2g7265lFrr/v4yaWTj4hRCHhRCPCiHuWYH9W2naPf6ftaXubwkhtixw27VM28dgu7Z3AA/XLV7v538+Wn0/F8O5Xwgzx70E/kkI8aRQLfcuVt4ghHhWCPEPQohL7WUb5twLIYIoA+Rv6hZfNOdeqFClq4DHZry06uN+SXXQNBsPIcQHgEPAm+sWb5NSDgghdgIPCyGel1IeW509XDG+C3xdSlkQQnwUpaTessr7tBrcC3xLSlmpW7YRzv+GRghxM8pAe2Pd4jfa570beEgI8YqtylxMPIX6fWeEEHcB/xvYs7q7dMF5O/CIlLJebbsozr0QIowyPH9dSjm12vszE62g1XD6fzrU9wadtY5o7BHazrZrmbb2XwhxG/DbwN1SyoKzXEo5YP8/DvwQNRtZT8x7/FLKsbpj/jJwTbvbrgMWcgz3MsPVcRGc//lo9f1cDOd+XoQQl6N+8++Qqjg50HDeh4G/ZX2FdbSFlHJKSpmxH/89YAkhOtkg595mrjG/bs+9EMJCGWd/IaX8dpNVVn/cX+jgvLX6h1ITj6PcN07Q56Uz1vlVGpME/sp+fCmNSQLHWV9JAu0c+1WooNg9M5YnAJ/9uBM4wvoLlm3n+PvqHr8TeNR+nARO2N9Dwn6cXO1jWu7jt9fbjwoOFhfT+bf3fTutA8V/hsZg4ccvlnPfxrFvRcXUXj9jeQiI1D3+CXDnah/LChx/r/N7Rxkhp+3fQVtjZq3/zXXs9usxVJxa6GI69/Y5/BrwhTnWWfVxr12cNlLKshDi48CDqAyd+6WULwohPgscllI+gOoR+mdC9QhNoYw07PX+CngJKAO/KhtdQGuaNo/9vwBh4K9VXgSnpZR3A5cAfyKEqKIU2c9LKV9alQNZJG0e/68JIe5Gnd8UKqsTKWVKCPE54An77T4rG10Ba542jx/U7/0b0r5K2az78y+E+DoqW69TCHEW+B3AApBS/jHw96iMrqNAFvhF+7V1f+7bOPZ/j4qz/SN73JellIeAHuBv7WUm8JdSyn+84AewRNo4/ncDvyKEKAM54F779990zKzCISyaNo4d1GT0n6SU03WbXgzn/gbgF4DnhRDP2Mt+CzUhWTPjXrd60mg0Go1Go1lj6Bg0jUaj0Wg0mjWGNtA0Go1Go9Fo1hjaQNNoNBqNRqNZY2gDTaPRaDQajWaNoQ00jUaj0Wg0mjWGNtA0Go1Go9Fo1hjaQNNoNBqNRqNZY/z/wH+QvrrWwpEAAAAASUVORK5CYII=\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[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": [
    "# Continue with good trials"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example Solution of Adapting the CSP Tutorial for XDF Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# % pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the data\n",
    "\n",
    "The code below cuts trials for the two classes and should look familiar if you've completed the previous tutorials. Trials are cut in the interval [0.5–2.5 s] after the onset of the cue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample rate: 250\n",
      "Sample rate: 500\n",
      "Shape of trials[left]: (8, 500, 198)\n",
      "Shape of trials[right]: (8, 500, 196)\n"
     ]
    }
   ],
   "source": [
    "trials = good_trials.copy()\n",
    "sample_rate = fs\n",
    "print(\"Sample rate: \" + str(fs))\n",
    "\n",
    "nsamples_per_trial = trials['imagery_handL'].shape[1]\n",
    "print(\"Sample rate: \" + str(nsamples_per_trial))\n",
    "\n",
    "channel_names = ['Fz', 'C3', 'Cz', 'C4', 'Pz', 'PO7', 'Oz', 'PO8']\n",
    "\n",
    "# Some information about the dimensionality of the data (channels x time x trials)\n",
    "print('Shape of trials[left]:', trials['imagery_handL'].shape)\n",
    "print('Shape of trials[right]:', trials['imagery_handR'].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "Since the feature we're looking for (a decrease in $\\mu$-activity) is a frequency feature, lets plot the PSD of the trials in a similar manner as with the SSVEP data. The code below defines a function that computes the PSD for each trial (we're going to need it again later on):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from matplotlib import mlab\n",
    "\n",
    "\n",
    "def psd(trials):\n",
    "    '''\n",
    "    Calculates for each trial the Power Spectral Density (PSD).\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    trials : 3d-array (channels x samples x trials)\n",
    "        The EEG signal\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    trial_PSD : 3d-array (channels x PSD x trials)\n",
    "        the PSD for each trial.\n",
    "    freqs : list of floats\n",
    "        Yhe frequencies for which the PSD was computed (useful for plotting later)\n",
    "    '''\n",
    "\n",
    "    ntrials = trials.shape[2]\n",
    "    trials_PSD = np.zeros((nchannels, sample_rate + 1, ntrials))\n",
    "\n",
    "    # Iterate over trials and channels\n",
    "    for trial in range(ntrials):\n",
    "        for ch in range(nchannels):\n",
    "            # Calculate the PSD\n",
    "            (PSD, freqs) = mlab.psd(trials[ch, :, trial], NFFT=int(nsamples_per_trial), Fs=sample_rate)\n",
    "            trials_PSD[ch, :, trial] = PSD.ravel()\n",
    "\n",
    "    return trials_PSD, freqs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Apply the function\n",
    "psd_r, freqs = psd(trials['imagery_handR'])\n",
    "psd_f, freqs = psd(trials['imagery_handL'])\n",
    "trials_PSD = {'imagery_handR': psd_r, 'imagery_handL': psd_f}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function below plots the PSDs that are calculated with the above function. Since plotting it for 118 channels will clutter the display, it takes the indices of the desired channels as input, as well as some metadata to decorate the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def plot_psd(trials_PSD, freqs, chan_ind, chan_lab=None, maxy=None):\n",
    "    '''\n",
    "    Plots PSD data calculated with psd().\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    trials : 3d-array\n",
    "        The PSD data, as returned by psd()\n",
    "    freqs : list of floats\n",
    "        The frequencies for which the PSD is defined, as returned by psd()\n",
    "    chan_ind : list of integers\n",
    "        The indices of the channels to plot\n",
    "    chan_lab : list of strings\n",
    "        (optional) List of names for each channel\n",
    "    maxy : float\n",
    "        (optional) Limit the y-axis to this value\n",
    "    '''\n",
    "    plt.figure(figsize=(12, 5))\n",
    "\n",
    "    nchans = len(chan_ind)\n",
    "\n",
    "    # Maximum of 3 plots per row\n",
    "    nrows = np.ceil(nchans / 3)\n",
    "    ncols = min(3, nchans)\n",
    "\n",
    "    # Enumerate over the channels\n",
    "    for i, ch in enumerate(chan_ind):\n",
    "        # Figure out which subplot to draw to\n",
    "        plt.subplot(nrows, ncols, i + 1)\n",
    "\n",
    "        # Plot the PSD for each class\n",
    "        for cl in trials.keys():\n",
    "            plt.plot(freqs, np.mean(trials_PSD[cl][ch, :, :], axis=1), label=cl)\n",
    "\n",
    "        # All plot decoration below...\n",
    "\n",
    "        plt.xlim(1, 30)\n",
    "\n",
    "        if maxy != None:\n",
    "            plt.ylim(0, maxy)\n",
    "\n",
    "        plt.grid()\n",
    "\n",
    "        plt.xlabel('Frequency (Hz)')\n",
    "\n",
    "        if chan_lab == None:\n",
    "            plt.title('Channel %d' % (ch + 1))\n",
    "        else:\n",
    "            plt.title(chan_lab[i])\n",
    "\n",
    "        plt.legend()\n",
    "\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets put the `plot_psd()` function to use and plot three channels:\n",
    "\n",
    " 1. C3: Central, left\n",
    " 2. Cz: Central, central\n",
    " 3. C4: Central, right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-37-ae85fad4cb18>:32: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.subplot(nrows, ncols, i + 1)\n",
      "<ipython-input-37-ae85fad4cb18>:32: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.subplot(nrows, ncols, i + 1)\n",
      "<ipython-input-37-ae85fad4cb18>:32: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.subplot(nrows, ncols, i + 1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_psd(\n",
    "    trials_PSD,\n",
    "    freqs,\n",
    "    [channel_names.index(ch) for ch in ['C3', 'Cz', 'C4']],\n",
    "    chan_lab=['left', 'center', 'right'],\n",
    "    maxy=200  # problem: orders of magnitude\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A spike of mu activity can be seen on each channel for both classes. At the right hemisphere, the mu for the left hand movement is lower than for the right hand movement due to the ERD. At the left electrode, the mu for the right hand movement is reduced and at the central electrode the mu activity is about equal for both classes. This is in line with the theory that the left hand is controlled by the left hemiphere and the feet are controlled centrally."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classifying the data\n",
    "\n",
    "We will use a machine learning algorithm to construct a model that can distinguish between the right hand and foot movement of this subject. In order to do this we need to:\n",
    "\n",
    " 1. find a way to quantify the amount of mu activity present in a trial\n",
    " 2. make a model that describes expected values of mu activity for each class\n",
    " 3. finally test this model on some unseen data to see if it can predict the correct class label\n",
    "\n",
    "We will follow a classic BCI design by Blankertz et al. [1] where they use the logarithm of the variance of the signal in a certain frequency band as a feature for the classifier.\n",
    "\n",
    "[1] Blankertz, B., Dornhege, G., Krauledat, M., Müller, K.-R., & Curio, G. (2007). The non-invasive Berlin Brain-Computer Interface: fast acquisition of effective performance in untrained subjects. *NeuroImage*, 37(2), 539–550. doi:10.1016/j.neuroimage.2007.01.051\n",
    "\n",
    "The script below designs a band pass filter using [`scipy.signal.irrfilter`](http://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.iirfilter.html) that will strip away frequencies outside the 8--15Hz window. The filter is applied to all trials:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import scipy.signal\n",
    "\n",
    "\n",
    "def bandpass(trials, lo, hi, sample_rate):\n",
    "    '''\n",
    "    Designs and applies a bandpass filter to the signal.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    trials : 3d-array (channels x samples x trials)\n",
    "        The EEGsignal\n",
    "    lo : float\n",
    "        Lower frequency bound (in Hz)\n",
    "    hi : float\n",
    "        Upper frequency bound (in Hz)\n",
    "    sample_rate : float\n",
    "        Sample rate of the signal (in Hz)\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    trials_filt : 3d-array (channels x samples x trials)\n",
    "        The bandpassed signal\n",
    "    '''\n",
    "\n",
    "    # The iirfilter() function takes the filter order: higher numbers mean a sharper frequency cutoff,\n",
    "    # but the resulting signal might be shifted in time, lower numbers mean a soft frequency cutoff,\n",
    "    # but the resulting signal less distorted in time. It also takes the lower and upper frequency bounds\n",
    "    # to pass, divided by the niquist frequency, which is the sample rate divided by 2:\n",
    "    a, b = scipy.signal.iirfilter(6, [lo / (sample_rate / 2.0), hi / (sample_rate / 2.0)])\n",
    "\n",
    "    # Applying the filter to each trial\n",
    "    ntrials = trials.shape[2]\n",
    "    trials_filt = np.zeros((nchannels, nsamples_per_trial, ntrials))\n",
    "    for i in range(ntrials):\n",
    "        trials_filt[:, :, i] = scipy.signal.filtfilt(a, b, trials[:, :, i], axis=1)\n",
    "\n",
    "    return trials_filt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Apply the function\n",
    "trials_filt = {'imagery_handR': bandpass(trials['imagery_handR'], 8, 15, sample_rate),\n",
    "               'imagery_handL': bandpass(trials['imagery_handL'], 8, 15, sample_rate)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the PSD of the resulting `trials_filt` shows the suppression of frequencies outside the passband of the filter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-37-ae85fad4cb18>:32: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.subplot(nrows, ncols, i + 1)\n",
      "<ipython-input-37-ae85fad4cb18>:32: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.subplot(nrows, ncols, i + 1)\n",
      "<ipython-input-37-ae85fad4cb18>:32: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.subplot(nrows, ncols, i + 1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "psd_r, freqs = psd(trials_filt['imagery_handR'])\n",
    "psd_f, freqs = psd(trials_filt['imagery_handL'])\n",
    "trials_PSD2 = {'imagery_handR': psd_r, 'imagery_handL': psd_f}\n",
    "\n",
    "plot_psd(\n",
    "    trials_PSD2,\n",
    "    freqs,\n",
    "    [channel_names.index(ch) for ch in ['C3', 'Cz', 'C4']],\n",
    "    chan_lab=['left', 'center', 'right'],\n",
    "    maxy=200  # problem: orders of magnitude\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a feature for the classifier, we will use the logarithm of the variance of each channel. The function below calculates this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Calculate the log(var) of the trials\n",
    "def logvar(trials):\n",
    "    '''\n",
    "    Calculate the log-var of each channel.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    trials : 3d-array (channels x samples x trials)\n",
    "        The EEG signal.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    logvar - 2d-array (channels x trials)\n",
    "        For each channel the logvar of the signal\n",
    "    '''\n",
    "    return np.log(np.var(trials, axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Apply the function\n",
    "trials_logvar = {'imagery_handR': logvar(trials_filt['imagery_handR']),\n",
    "                 'imagery_handL': logvar(trials_filt['imagery_handL'])}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below is a function to visualize the logvar of each channel as a bar chart:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def plot_logvar(trials):\n",
    "    '''\n",
    "    Plots the log-var of each channel/component.\n",
    "    arguments:\n",
    "        trials - Dictionary containing the trials (log-vars x trials) for 2 classes.\n",
    "    '''\n",
    "    plt.figure(figsize=(12, 5))\n",
    "\n",
    "    x0 = np.arange(nchannels)\n",
    "    x1 = np.arange(nchannels) + 0.4\n",
    "\n",
    "    y0 = np.mean(trials['imagery_handR'], axis=1)\n",
    "    y1 = np.mean(trials['imagery_handL'], axis=1)\n",
    "\n",
    "    plt.bar(x0, y0, width=0.5, color='b', label='imagery_handR')\n",
    "    plt.bar(x1, y1, width=0.4, color='r', label='imagery_handL')\n",
    "\n",
    "    plt.xlim(-0.5, nchannels + 0.5)\n",
    "\n",
    "    plt.gca().yaxis.grid(True)\n",
    "    plt.title('log-var of each channel/component')\n",
    "    plt.xlabel('channels/components')\n",
    "    plt.ylabel('log-var')\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the log-vars\n",
    "plot_logvar(trials_logvar)  ## but why is it negative? Order of magnitude problem again?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that most channels show a small difference in the log-var of the signal between the two classes. The next step is to go from 118 channels to only a few channel mixtures. The CSP algorithm calculates mixtures of channels that are designed to maximize the difference in variation between two classes. These mixures are called spatial filters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from numpy import linalg\n",
    "\n",
    "\n",
    "def cov(trials):\n",
    "    ''' Calculate the covariance for each trial and return their average '''\n",
    "    ntrials = trials.shape[2]\n",
    "    covs = [trials[:, :, i].dot(trials[:, :, i].T) / nsamples for i in range(ntrials)]\n",
    "    return np.mean(covs, axis=0)\n",
    "\n",
    "\n",
    "def whitening(sigma):\n",
    "    ''' Calculate a whitening matrix for covariance matrix sigma. '''\n",
    "    U, l, _ = linalg.svd(sigma)\n",
    "    return U.dot(np.diag(l ** -0.5))\n",
    "\n",
    "\n",
    "def csp(trials_r, trials_f):\n",
    "    '''\n",
    "    Calculate the CSP transformation matrix W.\n",
    "    arguments:\n",
    "        trials_r - Array (channels x samples x trials) containing right hand movement trials\n",
    "        trials_f - Array (channels x samples x trials) containing foot movement trials\n",
    "    returns:\n",
    "        Mixing matrix W\n",
    "    '''\n",
    "    cov_r = cov(trials_r)\n",
    "    cov_f = cov(trials_f)\n",
    "    P = whitening(cov_r + cov_f)\n",
    "    B, _, _ = linalg.svd(P.T.dot(cov_f).dot(P))\n",
    "    W = P.dot(B)\n",
    "    return W\n",
    "\n",
    "\n",
    "def apply_mix(W, trials):\n",
    "    ''' Apply a mixing matrix to each trial (basically multiply W with the EEG signal matrix)'''\n",
    "    ntrials = trials.shape[2]\n",
    "    trials_csp = np.zeros((nchannels, nsamples_per_trial, ntrials))\n",
    "    for i in range(ntrials):\n",
    "        trials_csp[:, :, i] = W.T.dot(trials[:, :, i])\n",
    "    return trials_csp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-2.78625722e+00 -9.00610862e+01 -3.63469882e+01 -2.65692437e+02\n",
      "  -1.58050513e+02  6.12980833e+02  2.91366271e+02 -7.26223879e+00]\n",
      " [-2.32488998e+00 -6.08976523e+01 -2.27370967e+01 -1.69476090e+02\n",
      "  -1.03673854e+02  3.90893243e+02  1.88586679e+02 -2.92167881e+00]\n",
      " [-1.39418284e+00 -4.05996612e+01 -1.18200469e+01 -1.14268308e+02\n",
      "  -6.84609587e+01  2.64079605e+02  1.26216017e+02 -2.62907107e+00]\n",
      " [-2.38075863e+00 -6.86475890e+01 -2.55587667e+01 -1.97678270e+02\n",
      "  -1.18583628e+02  4.56168672e+02  2.19598322e+02  6.31335933e-01]\n",
      " [-1.38737274e+01 -2.58035670e+02 -9.61627067e+01 -7.43695195e+02\n",
      "  -4.48703493e+02  1.72094314e+03  8.23207728e+02 -1.19977708e+01]\n",
      " [-2.42761361e+00 -6.26572082e+01 -2.27030788e+01 -1.76232734e+02\n",
      "  -1.05842543e+02  4.11908298e+02  1.95984794e+02 -2.64617152e+00]\n",
      " [-2.30382368e+00 -9.16601160e+01 -3.39419376e+01 -2.64592652e+02\n",
      "  -1.56604108e+02  6.09891482e+02  2.97209964e+02 -5.96639217e+00]\n",
      " [-2.12209395e+00 -5.94800641e+01 -2.15835719e+01 -1.71280392e+02\n",
      "  -1.05882696e+02  3.99706076e+02  1.90517768e+02 -3.91767230e+00]]\n"
     ]
    }
   ],
   "source": [
    "# Apply the functions\n",
    "W = csp(trials_filt['imagery_handR'], trials_filt['imagery_handL'])\n",
    "trials_csp = {'imagery_handR': apply_mix(W, trials_filt['imagery_handR']),\n",
    "              'imagery_handL': apply_mix(W, trials_filt['imagery_handL'])}\n",
    "print(W)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Multiclass CSP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://github.com/spolsley/common-spatial-patterns\n",
    "\n",
    "# Common Spatial Pattern implementation in Python, used to build spatial filters for identifying task-related activity.\n",
    "import numpy as np\n",
    "import scipy.linalg as la\n",
    "\n",
    "# CSP takes any number of arguments, but each argument must be a collection of trials associated with a task\n",
    "# That is, for N tasks, N arrays are passed to CSP each with dimensionality (# of trials of task N) x (feature vector)\n",
    "# Trials may be of any dimension, provided that each trial for each task has the same dimensionality,\n",
    "# otherwise there can be no spatial filtering since the trials cannot be compared\n",
    "def CSP(*tasks):\n",
    "\tif len(tasks) < 2:\n",
    "\t\tprint (\"Must have at least 2 tasks for filtering.\")\n",
    "\t\treturn (None,) * len(tasks)\n",
    "\telse:\n",
    "\t\tfilters = ()\n",
    "\t\t# CSP algorithm\n",
    "\t\t# For each task x, find the mean variances Rx and not_Rx, which will be used to compute spatial filter SFx\n",
    "\t\titerator = range(0,len(tasks))\n",
    "\t\tfor x in iterator:\n",
    "\t\t\t# Find Rx\n",
    "\t\t\tRx = covarianceMatrix(tasks[x][0])\n",
    "\t\t\tfor t in range(1,len(tasks[x])):\n",
    "\t\t\t\tRx += covarianceMatrix(tasks[x][t])\n",
    "\t\t\tRx = Rx / len(tasks[x])\n",
    "\n",
    "\t\t\t# Find not_Rx\n",
    "\t\t\tcount = 0\n",
    "\t\t\tnot_Rx = Rx * 0\n",
    "\t\t\tfor not_x in [element for element in iterator if element != x]:\n",
    "\t\t\t\tfor t in range(0,len(tasks[not_x])):\n",
    "\t\t\t\t\tnot_Rx += covarianceMatrix(tasks[not_x][t])\n",
    "\t\t\t\t\tcount += 1\n",
    "\t\t\tnot_Rx = not_Rx / count\n",
    "\n",
    "\t\t\t# Find the spatial filter SFx\n",
    "\t\t\tSFx = spatialFilter(Rx,not_Rx)\n",
    "\t\t\tfilters += (SFx,)\n",
    "\n",
    "\t\t\t# Special case: only two tasks, no need to compute any more mean variances\n",
    "\t\t\tif len(tasks) == 2:\n",
    "\t\t\t\tfilters += (spatialFilter(not_Rx,Rx),)\n",
    "\t\t\t\tprint(\"We have 2 tasks\")\n",
    "\t\t\t\tbreak\n",
    "\t\treturn filters\n",
    "\n",
    "# covarianceMatrix takes a matrix A and returns the covariance matrix, scaled by the variance\n",
    "def covarianceMatrix(A):\n",
    "\tCa = np.dot(A,np.transpose(A))/np.trace(np.dot(A,np.transpose(A)))\n",
    "\treturn Ca\n",
    "\n",
    "# spatialFilter returns the spatial filter SFa for mean covariance matrices Ra and Rb\n",
    "def spatialFilter(Ra,Rb):\n",
    "\tR = Ra + Rb\n",
    "\tE,U = la.eig(R)\n",
    "\n",
    "\t# CSP requires the eigenvalues E and eigenvector U be sorted in descending order\n",
    "\tord = np.argsort(E)\n",
    "\tord = ord[::-1] # argsort gives ascending order, flip to get descending\n",
    "\tE = E[ord]\n",
    "\tU = U[:,ord]\n",
    "\n",
    "\t# Find the whitening transformation matrix\n",
    "\tP = np.dot(np.sqrt(la.inv(np.diag(E))),np.transpose(U))\n",
    "\n",
    "\t# The mean covariance matrices may now be transformed\n",
    "\tSa = np.dot(P,np.dot(Ra,np.transpose(P)))\n",
    "\tSb = np.dot(P,np.dot(Rb,np.transpose(P)))\n",
    "\n",
    "\t# Find and sort the generalized eigenvalues and eigenvector\n",
    "\tE1,U1 = la.eig(Sa,Sb)\n",
    "\tord1 = np.argsort(E1)\n",
    "\tord1 = ord1[::-1]\n",
    "\tE1 = E1[ord1]\n",
    "\tU1 = U1[:,ord1]\n",
    "\n",
    "\t# The projection matrix (the spatial filter) may now be obtained\n",
    "\tSFa = np.dot(np.transpose(U1),P)\n",
    "\treturn SFa.astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8, 500, 196)\n",
      "(500, 8, 196)\n",
      "(500, 8, 196)\n",
      "We have 2 tasks\n",
      "(8, 8)\n",
      "[[-2.78625722e+00 -9.00610862e+01 -3.63469882e+01 -2.65692437e+02\n",
      "  -1.58050513e+02  6.12980833e+02  2.91366271e+02 -7.26223879e+00]\n",
      " [-2.32488998e+00 -6.08976523e+01 -2.27370967e+01 -1.69476090e+02\n",
      "  -1.03673854e+02  3.90893243e+02  1.88586679e+02 -2.92167881e+00]\n",
      " [-1.39418284e+00 -4.05996612e+01 -1.18200469e+01 -1.14268308e+02\n",
      "  -6.84609587e+01  2.64079605e+02  1.26216017e+02 -2.62907107e+00]\n",
      " [-2.38075863e+00 -6.86475890e+01 -2.55587667e+01 -1.97678270e+02\n",
      "  -1.18583628e+02  4.56168672e+02  2.19598322e+02  6.31335933e-01]\n",
      " [-1.38737274e+01 -2.58035670e+02 -9.61627067e+01 -7.43695195e+02\n",
      "  -4.48703493e+02  1.72094314e+03  8.23207728e+02 -1.19977708e+01]\n",
      " [-2.42761361e+00 -6.26572082e+01 -2.27030788e+01 -1.76232734e+02\n",
      "  -1.05842543e+02  4.11908298e+02  1.95984794e+02 -2.64617152e+00]\n",
      " [-2.30382368e+00 -9.16601160e+01 -3.39419376e+01 -2.64592652e+02\n",
      "  -1.56604108e+02  6.09891482e+02  2.97209964e+02 -5.96639217e+00]\n",
      " [-2.12209395e+00 -5.94800641e+01 -2.15835719e+01 -1.71280392e+02\n",
      "  -1.05882696e+02  3.99706076e+02  1.90517768e+02 -3.91767230e+00]]\n",
      "(8, 8)\n",
      "[[  -3.174517    -2.415906    -1.5172136   -2.5735009  -13.089917\n",
      "    -2.532178    -2.843156    -2.2714632]\n",
      " [ -63.930504   -43.0012     -28.583847   -48.60422   -182.51605\n",
      "   -44.2707     -64.94388    -42.006794 ]\n",
      " [  31.340101    19.610462    10.902074    22.358997    84.03449\n",
      "    19.869255    29.695076    19.030592 ]\n",
      " [-211.31221   -134.7377     -90.897156  -157.30191   -591.8075\n",
      "  -140.4649    -210.57033   -136.45624  ]\n",
      " [-231.52939   -146.43047    -99.6318    -171.87619   -648.0648\n",
      "  -155.85262   -230.70123   -149.93124  ]\n",
      " [ 323.26947    207.62575    139.5132     241.06258    910.22424\n",
      "   217.14633    321.2106     212.30289  ]\n",
      " [ 186.12347    120.51836     80.656586   140.15443    525.91406\n",
      "   125.186516   189.93091    121.82538  ]\n",
      " [   3.8464067    3.645708     2.051641     6.9135423   16.53197\n",
      "     4.0961637    4.9199433    3.095636 ]]\n",
      "(8, 8)\n",
      "[[   3.8464067    3.645708     2.051641     6.9135423   16.53197\n",
      "     4.0961637    4.9199433    3.095636 ]\n",
      " [-186.12347   -120.51836    -80.656586  -140.15443   -525.91406\n",
      "  -125.186516  -189.93091   -121.82538  ]\n",
      " [ 323.26947    207.62575    139.5132     241.06258    910.22424\n",
      "   217.14633    321.2106     212.30289  ]\n",
      " [ 231.52939    146.43047     99.6318     171.87619    648.0648\n",
      "   155.85262    230.70123    149.93124  ]\n",
      " [-211.31221   -134.7377     -90.897156  -157.30191   -591.8075\n",
      "  -140.4649    -210.57033   -136.45624  ]\n",
      " [  31.340101    19.610462    10.902074    22.358997    84.03449\n",
      "    19.869255    29.695076    19.030592 ]\n",
      " [ -63.930504   -43.0012     -28.583847   -48.60422   -182.51605\n",
      "   -44.2707     -64.94388    -42.006794 ]\n",
      " [  -3.174517    -2.415906    -1.5172136   -2.5735009  -13.089917\n",
      "    -2.532178    -2.843156    -2.2714632]]\n",
      "<class 'tuple'>\n",
      "[[   3.8464067    3.645708     2.051641     6.9135423   16.53197\n",
      "     4.0961637    4.9199433    3.095636 ]\n",
      " [-186.12347   -120.51836    -80.656586  -140.15443   -525.91406\n",
      "  -125.186516  -189.93091   -121.82538  ]\n",
      " [ 323.26947    207.62575    139.5132     241.06258    910.22424\n",
      "   217.14633    321.2106     212.30289  ]\n",
      " [ 231.52939    146.43047     99.6318     171.87619    648.0648\n",
      "   155.85262    230.70123    149.93124  ]\n",
      " [-211.31221   -134.7377     -90.897156  -157.30191   -591.8075\n",
      "  -140.4649    -210.57033   -136.45624  ]\n",
      " [  31.340101    19.610462    10.902074    22.358997    84.03449\n",
      "    19.869255    29.695076    19.030592 ]\n",
      " [ -63.930504   -43.0012     -28.583847   -48.60422   -182.51605\n",
      "   -44.2707     -64.94388    -42.006794 ]\n",
      " [  -3.174517    -2.415906    -1.5172136   -2.5735009  -13.089917\n",
      "    -2.532178    -2.843156    -2.2714632]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-48-6e5007153ff6>:79: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  return SFa.astype(np.float32)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "class_0_input = trials_filt['imagery_handL'].copy()\n",
    "class_1_input = trials_filt['imagery_handR'].copy()\n",
    "\n",
    "# cut such that same number of trials:\n",
    "min_dim = min(class_0_input.shape[2], class_1_input.shape[2])\n",
    "class_0_input = class_0_input[:,:,0:min_dim]\n",
    "class_1_input = class_1_input[:,:,0:min_dim]\n",
    "\n",
    "print(class_0_input.shape)\n",
    "class_0_input = np.swapaxes(class_0_input,0,1) # dim: Nr. of trials of task N x deature vectors\n",
    "print(class_0_input.shape)\n",
    "class_1_input = np.swapaxes(class_1_input,0,1)\n",
    "print(class_1_input.shape)\n",
    "\n",
    "W_multiclass = CSP(class_0_input, class_1_input)\n",
    "\n",
    "\n",
    "print(W.shape)\n",
    "print(W)\n",
    "print(W_multiclass[0].shape)\n",
    "print(W_multiclass[0])\n",
    "print(W_multiclass[1].shape)\n",
    "print(W_multiclass[1])\n",
    "print(type(W_multiclass))\n",
    "\n",
    "W = W_multiclass[1]\n",
    "\n",
    "# Apply the functions\n",
    "# W = csp(trials_filt['imagery_handR'], trials_filt['imagery_handL'])\n",
    "trials_csp = {'imagery_handR': apply_mix(W, trials_filt['imagery_handR']),\n",
    "              'imagery_handL': apply_mix(W, trials_filt['imagery_handL'])}\n",
    "print(W)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import from numpy array into MNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Data must be a 2D array of shape (n_channels, n_samples), got shape (8, 500, 196)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-65-9f4aa8d473c5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     10\u001b[0m )\n\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m \u001b[0mraw\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmne\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mRawArray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrials_filt\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'imagery_handR'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<decorator-gen-177>\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, info, first_samp, copy, verbose)\u001b[0m\n",
      "\u001b[1;32mC:\\Anaconda\\envs\\default_env\\lib\\site-packages\\mne\\io\\array\\array.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, info, first_samp, copy, verbose)\u001b[0m\n\u001b[0;32m     60\u001b[0m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masanyarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morig_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     61\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 62\u001b[1;33m             raise ValueError('Data must be a 2D array of shape (n_channels, '\n\u001b[0m\u001b[0;32m     63\u001b[0m                              'n_samples), got shape %s' % (data.shape,))\n\u001b[0;32m     64\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'ch_names'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Data must be a 2D array of shape (n_channels, n_samples), got shape (8, 500, 196)"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see the result of the CSP algorithm, we plot the log-var like we did before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LL774YmbPns1ee+3FDTfcwLve9S6GDBnCHnvswaWXXsrkyZPZe++9OeKII1i+fHmH7Z9++umMGDGC3XffvVvr68i+++7LqaeeyujRoznqqKPWTiNZV0Twuc997k1zpDdUrOemfE1j3LhxuWDBgqrLkKQe1yznwyUVFNIHfh5JUDtJbkPDYbN59dVXaWlpoX///sybN4+PfexjtLW1dbqd888/nzFjxnD22Wd3f5E9qL3vMiJaM3Pcuut6IqAkSdIm6oknnuCUU07hjTfeYODAgWuvZtEZY8eOZfPNN+frX/96D1TYPAzNkiRJm6hdd92VhQsXdqmN1tbWtyybMGECr7766puWXXfddYwaNapLfVXJ0CxJkqRudd9991VdQrfzREBJkiSphKFZkiRJKmFoliRJkkoYmiVJkqQShmZJkqSuiOjeRwPe//739/CH6hlbbLFFj7Q7depULr/8cgDOPPNMZs2a1e19GJolSZL6mHvuuafqEgBYvXp11SX0GkOzJElSH7PmiO3cuXM5+OCDOe6449h55535zGc+w/Tp0xk/fjyjRo3i0UcfBeDmm29mwoQJjBkzhsMPP5ynnnoKgJUrV3LEEUew5557cs4557Djjjvy9NNPAzBt2jTGjx/PPvvsw3nnnbc2IG+xxRZceOGFjB49mssuu4zjjz9+bV1z5szhhBNOWG/tF110EaNHj2b//fdfW0dH9U2dOpWzzjqLSZMmsfPOO/ONb3xjbTuXXXYZu+22GwcddBBLly7thlFdP0OzJElSH7Zo0SKuvPJKFi9ezHXXXcfDDz/M/PnzOeecc7jiiisAOOigg7j33ntZuHAhp512Gl/96lcBuOSSSzj00EN56KGHOOmkk3jiiSeA2u2lf/jDH3L33XfT1tZGS0sL06dPB+Dll19mwoQJLFq0iM9//vMsWbKElStXAvC9732Ps846q8NaX375Zfbff38WLVrExIkT196BsKP6AJYsWcJtt93G/PnzueSSS1i1ahWtra3MnDmTtrY2br31Vu6///7uH9h1eHMTSZKkPmy//fZj++23B2CXXXZh8uTJAIwaNYo77rgDgGXLlnHqqaeyfPlyXnvtNXbaaScA7rrrLm666SYAjjzySLbZZhsAbr/9dlpbW9lvv/0AeOWVV9huu+0AaGlp4cQTTwQgIjjjjDOYNm0aU6ZMYd68eXz/+9/vsNaBAwdyzDHHALXbb8+ZM2e99QEcffTRDBo0iEGDBrHddtvx1FNPceedd3LCCSew2WabAXDsscd2dRhLeaRZkiSpDxs0aNDa5/369Vv7ul+/frz++usAXHDBBZx//vk8+OCDfOtb3+KPf/zjetvMTD7ykY/Q1tZGW1sbS5cuZerUqQAMHjyYlpaWtetOmTKFadOmMWPGDE4++WT69+/4mOyAAQOI4mTHlpaWhuqr/3z12/Q2Q7MkSdJG7oUXXmCHHXYA4Nprr127/MADD+T6668HYPbs2Tz33HMAHHbYYcyaNYsVK1YA8Oyzz/L444+32/awYcMYNmwYl156KVOmTOnW+joyceJEfvzjH/PKK6/w4osvcvPNN29Qv51haJYkSeqKzO599ICpU6dy8sknM3bsWLbddtu1yy+++GJmz57NXnvtxQ033MC73vUuhgwZwh577MGll17K5MmT2XvvvTniiCNYvnx5h+2ffvrpjBgxgt13371b6+vIvvvuy6mnnsro0aM56qij1k4jWeO8885j+PDhDB8+nAMOOGCDalpXZA99Od1p3LhxuWDBgqrLkKQe1+AlWntcUkEhfeDnkQS1k+Q2NBw2m1dffZWWlhb69+/PvHnz+NjHPkZbW1un2zn//PMZM2YMZ599dvcX2YPa+y4jojUzx627ricCSpIkbaKeeOIJTjnlFN544w0GDhy49moWnTF27Fg233xzvv71r/dAhc3D0CxJkrSJ2nXXXVm4cGGX2mhtbX3LsgkTJvDqq6++adl1113HqFGjutRXlQzNkiRJ6lb33Xdf1SV0O08ElCRJ6qS+cE6Y1q+z36GhWZIkqRMGDx7MM888Y3DuwzKTZ555hsGDBze8jdMzJEmSOmH48OEsW7Zs7a2j1TcNHjyY4cOHN7y+oVmSJKkTBgwY8KbbPGvT4PQMSZIkqYShWZIkSSphaJYkSZJKGJolSZKkEoZmSZIkqYShWZIkSSrRY6E5Ir4bESsi4pd1y94eEXMi4pHiv9v0VP+SJElSd+nJI83XAEeus+wzwO2ZuStwe/FakiRJamo9Fpoz87+AZ9dZfBxwbfH8WuD4nupfkiRJ6i69fUfAoZm5vHj+e2BoRytGxLnAuQBDhw5l7ty5PV+dJFXs8surrqBmLhUU4r/zkppYZGbPNR4xErglM/cqXj+fmVvXvf9cZpbOax43blwuWLCgx+qUpGYRUXUFNUkFhfTgzyNJalREtGbmuHWX9/bVM56KiO2LgrYHVvRy/5IkSVKn9XZo/gnwkeL5R4B/6+X+JUmSpE7ryUvOzQDmAe+NiGURcTbwZeCIiHgEOLx4LUmSJDW1HjsRMDM/1MFbh/VUn5IkSVJP8I6AkiRJUglDsyRJklSit6/TLKjumlJezkmSVAV/7mkj4JFmSZIkqYShWZIkSSphaJYkSZJKbHJzmpvhFrXOsJIk9RZ/7kndwyPNkiRJUglDsyRJklTC0CxJkiSVMDRLkiRJJQzNkiRJUglDsyRJklTC0CxJkiSV2OSu0yxpE1bVBWvTq9RKUl/nkWZJkiSphKFZkiRJKmFoliRJkkoYmiVJkqQSnggoqVdUdQ5ePU/HU7fz5FJpk2FoliRJahb+Ita0DM1qXv7DIUmSmoRzmiVJkqQShmZJkiSphNMzJEl9kieXSupNhmZJkrTJa4ZfwsBfxJqZoVntaoZ/PPyHQ5IkNQvnNEuSJEklDM2SJElSCUOzJEmSVMLQLEmSJJUwNEuSJEklDM2SJElSCUOzJEmSVMLQLEmSJJWoJDRHxCcj4qGI+GVEzIiIwVXUIUmSJDWi10NzROwA/C0wLjP3AlqA03q7DkmSJKlRVU3P6A+8LSL6A5sBT1ZUhyRJklQqMrP3O434OHAZ8AowOzNPb2edc4FzAYYOHTp25syZ3dJ3a2u3NNMlY6moiLFjG17VcepjqvrC3Kca7LhvjRNUNFad/H+vGcbKfaoxjlPj+sJYbewOOeSQ1swct+7yXg/NEbEN8CPgVOB54AZgVmZO62ibcePG5YIFC7qp/25ppkuSioroxHftOPUxVX1h7lMNdty3xgkqGqtO/r/XDGPlPtUYx6lxfWGsNnYR0W5ormJ6xuHAf2fmysxcBdwIvL+COiRJkqSGVBGanwD2j4jNIiKAw4DFFdQhSZIkNaTXQ3Nm3gfMAh4AHixq+HZv1yFJkiQ1qn8VnWbmxcDFVfQtdafmmQMnSZJ6kncElCRJkkoYmiVJkqQShmZJkiSphKFZkiRJKmFoliRJkkoYmiVJkqQShmZJkiSphKFZkiRJKmFoliRJkkoYmiVJkqQShmZJkiSphKFZkiRJKmFoliRJkkoYmiVJkqQShmZJkiSphKFZkiRJKmFoliRJkkoYmiVJkqQShmZJkiSpRGlojoiWiJjeG8VIkiRJzag0NGfmamDHiBjYC/VIkiRJTad/g+v9Brg7In4CvLxmYWb+Y49UJUmSJDWRRkPzo8WjHzCk58qRJEmSmk9DoTkzL+npQiRJkqRm1VBojoh3Ap8G9gQGr1memYf2UF2SJElS02j0knPTgSXATsAlwGPA/T1UkyRJktRUGg3N78jMq4FVmfnzzDwL8CizJEmSNgmNngi4qvjv8og4GngSeHvPlCRJkiQ1l0ZD86URsRVwIXAFsCXwyR6rSpIkSWoijYbm+zLzBeAF4JAerEeSJElqOo3Oab47ImZHxNkRsU2PViRJkiQ1mYZCc2buBnyO2iXnWiPiloj4cI9WJkmSJDWJRo80k5nzM/PvgPHAs8C1PVaVJEmS1EQaCs0RsWVEfCQi/gO4B1hOLTxLkiRJG71GTwRcBPwY+EJmzuu5ciRJkqTm02ho3jkzEyAijsnMW7rSaURsDVwF7AUkcJZhXJIkSc2q0RMBs+7lF7qh338GfpqZ7wNGA4u7oU1JkiSpRzR6pLledKXD4iYpE4EzATLzNeC1rrQpSZIk9aSGr55R57wu9rkTsBL4XkQsjIirImLzLrYpSZIk9Zh488yLDlaK+GA7i18AHszMFZ3qMGIccC9wYGbeFxH/DPwhMz+/znrnAucCDB06dOzMmTM7002HWlu7pZkuGUtFRYwd2/CqjlNjmmGcwLFqlOPUuErGqhPjBM0xVu5TjXGcGtcXxmpjd8ghh7Rm5rh1lzcamv8dOAC4o1g0CWildtT4C5l5XaOFRMS7gHszc2Tx+s+Az2Tm0R1tM27cuFywYEGjXZT03y3NdEl2bYZLFzou/67XcJwa0wzjBI5VoxynxlUyVp0YJ2iOsXKfaozj1Li+MFYbu4hoNzQ3Oj2jP7B7Zp6YmScCe1C76sUE4H91ppDM/D3w24h4b7HoMOBXnWlDkiRJ6k2Nngg4IjOfqnu9olj2bESs2oB+LwCmR8RA4DfAlA1oQ5IkSeoVjYbmuRFxC3BD8fqkYtnmwPOd7TQz24C3HPaWJEmSmlGjoflvgA8CBxWvrwV+VFy/+ZCeKEySJElqFg2F5szMiLiL2vWUE5ifjZxBKEmSJG0EGjoRMCJOAeZTm5ZxCnBfRJzUk4VJkiRJzaLR6RkXAfutuSZzRLwT+E9gVk8VJkmSJDWLRi8512+dm5g804ltJUmSpD6t0SPNP42I24AZxetTgVt7piRJkiSpuTR6IuCnIuJE4MBi0bcz86aeK0uSJElqHo0eaSYzfwT8qAdrkSRJkprSekNzRLxI7RJzb3mL2pXotuyRqiRJkqQmst7QnJlDeqsQSZIkqVl5BQxJkiSphKFZkiRJKmFoliRJkkoYmiVJkqQShmZJkiSphKFZkiRJKmFoliRJkkoYmiVJkqQShmZJkiSphKFZkiRJKmFoliRJkkoYmiVJkqQShmZJkiSphKFZkiRJKmFoliRJkkoYmiVJkqQShmZJkiSphKFZkiRJKmFoliRJkkoYmiVJkqQShmZJkiSphKFZkiRJKmFoliRJkkoYmiVJkqQShmZJkiSpRGWhOSJaImJhRNxSVQ2SJElSI6o80vxxYHGF/UuSJEkNqSQ0R8Rw4Gjgqir6lyRJkjqjqiPN/wR8Gnijov4lSZKkhkVm9m6HEccAH8jMv46IScDfZ+Yx7ax3LnAuwNChQ8fOnDmzW/pvbe2WZrpkLBUVMXZsw6s6To1phnECx6pRjlPjKhmrTowTNMdYuU81xnFqXF8Yq43dIYcc0pqZ49ZdXkVo/hJwBvA6MBjYErgxMz/c0Tbjxo3LBQsWdFP/3dJMlyQVFdGJ79pxakwzjBM4Vo1ynBpXyVh18udRM4yV+1RjHKfG9YWx2thFRLuhudenZ2TmP2Tm8MwcCZwG/Gx9gVmSJEmqmtdpliRJkkr0r7LzzJwLzK2yBkmSJKmMR5olSZKkEoZmSZIkqYShWZIkSSphaJYkSZJKGJolSZKkEoZmSZIkqYShWZIkSSphaJYkSZJKGJolSZKkEoZmSZIkqYShWZIkSSphaJYkSZJKGJolSZKkEoZmSZIkqYShWZIkSSphaJYkSZJKGJolSZKkEoZmSZIkqYShWZIkSSphaJYkSZJKGJolSZKkEoZmSZIkqYShWZIkSSphaJYkSZJKGJolSZKkEoZmSZIkqYShWZIkSSphaJYkSZJKGJolSZKkEoZmSZIkqYShWZIkSSphaJYkSZJKGJolSZKkEoZmSZIkqYShWZIkSSrR66E5IkZExB0R8auIeCgiPt7bNUiSJEmd0b+CPl8HLszMByJiCNAaEXMy81cV1CJJkiSV6vUjzZm5PDMfKJ6/CCwGdujtOiRJkqRGVTqnOSJGAmOA+6qsQ5IkSVqfyMxqOo7YAvg5cFlm3tjO++cC5wIMHTp07MyZM7ul39bWbmmmS8ZSURFjxza8quPUmGYYJ3CsGuU4Na6SserEOEFzjJX7VGMcp8b1hbHa2B1yyCGtmTlu3eWVhOaIGADcAtyWmf9Ytv64ceNywYIF3dR3tzTTJUlFRXTiu3acGtMM4wSOVaMcp8ZVMlad/HnUDGPlPtUYx6lxfWGsNnYR0W5oruLqGQFcDSxuJDBLkiRJVatiTvOBwBnAoRHRVjw+UEEdkiRJUkN6/ZJzmXkXVPW3B0mSJKnzvCOgJEmSVMLQLEmSJJUwNEuSJEklDM2SJElSCUOzJEmSVMLQLEmSJJUwNEuSJEklDM2SJElSCUOzJEmSVMLQLEmSJJUwNEuSJEklDM2SJElSCUOzJEmSVMLQLEmSJJUwNEuSJEklDM2SJElSCUOzJEmSVMLQLEmSJJUwNEuSJEklDM2SJElSCUOzJEmSVMLQLEmSJJUwNEuSJEklDM2SJElSCUOzJEmSVMLQLEmSJJUwNEuSJEklDM2SJElSCUOzJEmSVMLQLEmSJJUwNEuSJEklDM2SJElSCUOzJEmSVMLQLEmSJJUwNEuSJEklKgnNEXFkRCyNiF9HxGeqqEGSJElqVK+H5ohoAf4FOArYA/hQROzR23VIkiRJjariSPN44NeZ+ZvMfA2YCRxXQR2SJElSQ6oIzTsAv617vaxYJkmSJDWl/lUX0JGIOBc4t3j5UkQsrbKedmwLPL0hG0Y3F9J4x5X1vEFj5Tg1bhMbK8epcX1rrBynTnTct8bKcWrcJjZWGzxOPWzH9hZWEZp/B4yoez28WPYmmflt4Nu9VVRnRcSCzBxXdR19gWPVGMepMY5T4xyrxjhOjXOsGuM4NaavjVMV0zPuB3aNiJ0iYiBwGvCTCuqQJEmSGtLrR5oz8/WIOB+4DWgBvpuZD/V2HZIkSVKjKpnTnJm3ArdW0Xc3atqpI03IsWqM49QYx6lxjlVjHKfGOVaNcZwa06fGKTKz6hokSZKkpuZttCVJkqQShuYN4G3Ay0XEdyNiRUT8supaml1EjIiIOyLiVxHxUER8vOqamlFEDI6I+RGxqBinS6quqZlFREtELIyIW6qupZlFxGMR8WBEtEXEgqrraVYRsXVEzIqIJRGxOCIOqLqmZhQR7y32pTWPP0TEJ6quqxlFxCeLf8t/GREzImJw1TWVcXpGJxW3AX8YOILajVnuBz6Umb+qtLAmExETgZeA72fmXlXX08wiYntg+8x8ICKGAK3A8e5TbxYRAWyemS9FxADgLuDjmXlvxaU1pYj4O2AcsGVmHlN1Pc0qIh4DxmVmM14rtmlExLXAnZl5VXHlq80y8/mKy2pqRV74HTAhMx+vup5mEhE7UPs3fI/MfCUirgduzcxrqq1s/TzS3HneBrwBmflfwLNV19EXZObyzHygeP4isBjvkvkWWfNS8XJA8fC3/nZExHDgaOCqqmtR3xcRWwETgasBMvM1A3NDDgMeNTB3qD/wtojoD2wGPFlxPaUMzZ3nbcDVYyJiJDAGuK/iUppSMeWgDVgBzMlMx6l9/wR8Gnij4jr6ggRmR0RrcSdavdVOwErge8WUn6siYvOqi+oDTgNmVF1EM8rM3wGXA08Ay4EXMnN2tVWVMzRLTSIitgB+BHwiM/9QdT3NKDNXZ+Y+1O4kOj4inPqzjog4BliRma1V19JHHJSZ+wJHAX9TTC3Tm/UH9gX+NTPHAC8Dns+zHsUUlmOBG6qupRlFxDbU/kq/EzAM2DwiPlxtVeUMzZ3X0G3Apc4o5uj+CJiemTdWXU+zK/40fAdwZMWlNKMDgWOLubozgUMjYlq1JTWv4ogXmbkCuInaFDy92TJgWd1fdmZRC9Hq2FHAA5n5VNWFNKnDgf/OzJWZuQq4EXh/xTWVMjR3nrcBV7cqTnC7Glicmf9YdT3NKiLeGRFbF8/fRu1k3CWVFtWEMvMfMnN4Zo6k9u/TzzKz6Y/gVCEiNi9OvqWYbjAZ8Io/68jM3wO/jYj3FosOAzxRef0+hFMz1ucJYP+I2Kz4GXgYtfN5mloldwTsy7wNeGMiYgYwCdg2IpYBF2fm1dVW1bQOBM4AHizm6wJ8trhzpv5ke+Da4oz0fsD1menl1NQVQ4Gbaj+z6Q/8IDN/Wm1JTesCYHpxsOg3wJSK62laxS9gRwDnVV1Ls8rM+yJiFvAA8DqwkD5wd0AvOSdJkiSVcHqGJEmSVMLQLEmSJJUwNEuSJEklDM2SJElSCUOzJEmSVMLQLEnriIhrIuKkivoeGRGl1wqOiCsj4sDeqKk3RcSkiGj6mxxI2vQYmiWpb9ofuLfqInrAJPrAncEkbXoMzZI2eRHxlxHxi4hYFBHXFYsnRsQ9EfGbNUedI2KLiLg9Ih6IiAcj4rhi+ciIWBwR34mIhyJidnHXQiJibkR8JSLmR8TDEfFnxfKWiPhaRNxf9P2WGyFExJ7Fdm3FOrsWy3cHHs7M1RHxnoj4z6L2ByJil6j5WkT8sqjz1GK7SRHx84j4t+JzfTkiTi/6eDAidinWu6Y4kr2gqPmYYvngiPhese7CiDikWH5mRNwYET+NiEci4qt1n2FyRMwrarshIrYolj8WEZfUjeX7ImIk8FHgk8Vn/rOIOLn4HIsi4r+6/cuXpAZ5R0BJm7SI2BP4HPD+zHw6It4O/CO1OxAeBLwP+AkwC/gjcEJm/iEitgXujYifFE3tCnwoM/8qIq4HTgSmFe/1z8zxEfEB4GLgcOBs4IXM3C8iBgF3R8RsoP6OUx8F/jkz19yJraVYfhSw5s5104EvZ+ZNETGY2sGQDwL7AKOBbYH76wLnaGB34Flqd3a7qqjt49Tu+vaJYr2RwHhgF+COiHgP8DdAZuaoiHgfMDsidivW3wcYA7wKLI2IK4BXirE9PDNfjoj/Bfwd8IVim6czc9+I+Gvg7zPznIi4EngpMy8vvp8HgT/PzN9FcRt1SaqCoVnSpu5Q4IbMfBogM58tbqv848x8A/hVRAwt1g3gixExEXgD2IHarZgB/jsz24rnrdRC5xo3trN8MrB33dzpragF74frtpsHXBQRw4EbM/ORYvmfA1MiYgiwQ2beVNT+R4CIOAiYkZmrgaci4ufAfsAfgPszc3mx3qPA7KLNB4FD6vq+vvj8j0TEb6j98nAQcEXR15KIeBxYE5pvz8wXinZ/BewIbA3sQe0XAoCBxWdqb1w+SPvuBq4pfhG5sYN1JKnHGZolqX2v1j2P4r+nA+8Exmbmqoh4DBjczvqrgbe109Zq/vTvbgAXZOZt9Z0WUxQAyMwfRMR9wNHArcUUjnuBrTPzySI0d+VzvVH3+g3e/DOh/oh3e6/X1+6azxnAnMz8UMk29ePy5k4zPxoRE6iNQWtEjM3MZ0pqkaRu55xmSZu6nwEnR8Q7AIrpGR3ZClhRBOZDqB1N3VC3AR+LiAFFv7tFxOb1K0TEzsBvMvMbwL8Be1M7GnwHQGa+CCyLiOOL9QdFxGbAncCpxbzpdwITgfmdrO/kiOhXzHPeGVhatHv6mnqBdxfLO3IvcGAxtYOI2LxuOkdHXgTW/jIQEbtk5n2Z+b+BlcCITn4OSeoWHmmWtEnLzIci4jLg5xGxGli4ntWnAzcX82wXAEu60PVV1KZqPBC1uQsrgePXWecU4IyIWAX8HvgitfnAs+rWOQP4VkR8AVgFnAzcBBwALKJ2hPjTmfn7Yh5yo56gFrS3BD6amX+MiP8L/Gvx+V8HzszMV4upF2+RmSsj4kxgRjFvG2pznB9ud4Oam4FZUTvJ8gJqJwXuSu2o9e3FZ5KkXheZZX9xkyQ1i4h4AJiQmat6sI9rgFsyc1bZupK0qfBIsyT1IZm5b9U1SNKmyCPNkiRJUglPBJQkSZJKGJolSZKkEoZmSZIkqYShWZIkSSphaJYkSZJKGJolSZKkEv8PrdHJyC/2HFUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trials_logvar = {'imagery_handR': logvar(trials_csp['imagery_handR']),\n",
    "                 'imagery_handL': logvar(trials_csp['imagery_handL'])}\n",
    "plot_logvar(trials_logvar)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of 118 channels, we now have 118 mixtures of channels, called components. They are the result of 118 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. The code below plots the PSD for the first and last components as well as one in the middle:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-37-ae85fad4cb18>:32: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.subplot(nrows, ncols, i + 1)\n",
      "<ipython-input-37-ae85fad4cb18>:32: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.subplot(nrows, ncols, i + 1)\n",
      "<ipython-input-37-ae85fad4cb18>:32: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.subplot(nrows, ncols, i + 1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "psd_r, freqs = psd(trials_csp['imagery_handR'])\n",
    "psd_f, freqs = psd(trials_csp['imagery_handL'])\n",
    "trials_PSD2 = {'imagery_handR': psd_r, 'imagery_handL': psd_f}\n",
    "\n",
    "plot_psd(trials_PSD2, freqs, [0, 3, -1], chan_lab=['first component', 'middle component', 'last component'], maxy=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to see how well we can differentiate between the two classes, a scatter plot is a useful tool. Here both classes are plotted on a 2-dimensional plane: the x-axis is the first CSP component, the y-axis is the last."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def plot_scatter(left, right):\n",
    "    plt.figure()\n",
    "    plt.scatter(left[0, :], left[-1, :], color='b', label='imagery_handR')\n",
    "    plt.scatter(right[0, :], right[-1, :], color='r', label='imagery_handL')\n",
    "    plt.xlabel('Last component')\n",
    "    plt.ylabel('First component')\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kZh9OeulNQFmmAxOR/DN3Luzevf/5dYynii7mOSg5FKzuehBHA+cCBwMfSDreAXwy45GJSN7patrC1cxnOxqlVGy6TBDu/mt3nw6c6+7Tk44r3P2PWYxRRHIlCGDUqHDkkRmbbBQXEezXbBFT+SRNrC/VKKViEmcU07NmdjVQldze3WdkKigRyQOJgkPSPaWRHZu5jfC//iL2/eF/1+CpvP+2qcoJRSROkfrXwEHAYuC3SYeIFLMuCg5D2cV1JXP3OVdRAbfdpg5DsYnTgyh39//KeCQikl+6WSfpcF8Xa2dQKWxxehC/MbNzMh6JiOROqgX2ulsnSWsoDQhxEsQcwiSxw8xeMbNXzeyVTAcmIlnS1QJ755wDgwfv337IEI1OGiDSJgh3H+7uJe5e5u5vip6/KRvBiUhmBQGsr+9igb377gsLCxUVb5yvqIBbb1WxYYCIs9SGAVOBI9z9a2Z2OHCYu/85GwHGpaU2RGIKApg9G9+2be8pS9XODDo6shaW5EZfl9q4ETgNuDh6vg34QT/FJiLZFAQwbRps24bB3iMl1RkGvDijmE5x93eY2ZMA7v6SmQ3JcFwi0t+CAOrrob09fVvNghbi9SB2m1kp0e5vZjYaUL9TpJAkCtHdJAcHOrBwNrRmQQvxEsR3gbuBQ8xsPrAE+EZGoxKR/pEYvlpXt38hupN2Shle3sEjC9coOQgQ4xaTuwdmthSoIbxdeb67P5PxyESkbxK9hjSJAcLeQzBsJk03KzfIG+LuKPd34JVEezMb7+7dLAAvIjkRBOESGevWhZPe4tQbzLDZs6m/8cbMxycFJW2CMLNPE24X+gLQTtiLcODtmQ1NRHokCGDGDNi1K3yeLjlopzdJI04PYg5wtLtvTttSRHJnzpw3kkM6lZXhKCUlB+lGnCL1c8DW/vxQM5tjZivMbKWZfSbF61PMbKuZLYuOL/fn54sUiyWNAesHVdFhJXjnfUBTKS+H5mZYs0bJQdKK04P4J/Cwmf0W2Jk46e7/3ZsPNLPjCHekOxnYBfzezH7j7s92avoHdz+3N58hMhDcWhtwYctMhpG+CI1ZOPFNvQbpgTgJYl10DImOvjoWeNzdXwMws0eADwPf7IdrixS3IIA5c/DNm5lON7Ogk1VUwKZNGQ5MilGcYa5fATCzA6Pn27p/R1orgPlmVgG8DpwDpFpE6TQzewrYCHzB3Vd2bmBmM4GZAOO1LIAUsygxEN1GipUYIFyN9YYbMhaWFLe0NQgzOy5aZmMlsNLMlprZxN5+YDSH4jrgAeD3wDLC0VHJngAq3X0S8D3gV11cq8ndq929evTo0b0NSSRvBQEsPLCRjrq6vckhnT2UvrEvtLZ5kz6IU6RuAj7n7pXuXgl8HrilLx/q7j9y98nu/m7gJeBvnV5/JdFTcff7gMFmNqovnylSUIKAzcOruLjO+MT2BbH+owJsp5w/NSwMV2FVIVr6KM6/u2Hu/lDiibs/DAzry4ea2SHR1/GE9YefdHr9zdEy45jZyVGcGmYrA0MQ0F4/nYpta7tfbTWJA2uo5Gc1TZxxo5KC9I9Yo5jM7EvAHdHzOsKRTX3xi6gGsRv4lLu/bGazAdz9JuCjQIOZ7SGsU3zc021cIVIkdsyaQ1n77tjtHZhT0cwpN0xlhnKD9KM4GwaNAL4CnBGd+gMwz91fynBsPaINg6QYBAFcXGfxi9AADQ2gZTKkl7rbMCjOKKaXgCvM7CCgw91f7e8ARQa6IIDH5wR8bvPc+G+qqAhHKKnOIBkSZy2mk4BbgeHR863ADHdfmuHYRAaEJY0BH1gwi4vZ3m3PIdHXNyUGyZI4ReofAY3uXuXuVcCngNsyGpVIEUts0TDVAjaXjOL0BXW8KU1y2MkQZg1rxtzDSW9KDpIFcYrU7e7+h8QTd18SFY9FpIeCABZPD/jL7jmMYjOWZuiFA2up5Esl8zn7ZiUFya44CeIRM7sZWET47/VCwrWZ3gHg7k9kMD6R4hEEnH3JHC72zbGL0GuppLpije4oSU7ESRCToq/XdDp/ImHC+M9+jUikCCUW1quIs7BexDGqmuezSYlBciTOKKb/yEYgIsWqthZ+2DI33qqrEQesYba6DZJTcUYxHQx8AqhKbu/uV2QsKpEicGttwH+2zOUB1mH0YJ5nRQWme0qSB+LcYroP+BOwHOjIbDgiRSAI2DFtNtP3bItda3DAmpuVFCSvxEkQZe7+uYxHIlIEljQGnLxgBmXE3PqTMDmsrmngGCUHyTNx5kHcYWafNLPDzGxk4sh4ZCIFJghg/E1zGdJNcnDCbrhHxx5Kw+SwWEtlSP6JkyB2AdcDjwFLo0OLHokkNDbCoEFcXGcc7mu7bbqWSkpxzqxxzJ1BvkfJQfJWnFtMnwfe6u7as1AkEgSwZ1Yjl2xfsHdJ7nT1hg6Mq5mvtfWkYMRJEM9CD8bniRS5JY0BH11QzxDaYxehO4BbSmfz/oVTVYeWghEnQWwHlpnZQ8DOxEkNc5WB6K+1jZzesiBtYkge1LpzWAVlN9/ALGUGKTBxEsSv6GJPaJGBIghgW30jM9vTJweAdkp5a+Ue5s/XyFUpXHFmUi80syHA26JTq909/nZXIgVu4kSYtCqgmZti31Ia1DCTNaozSIGLM5N6CrAQWENYhzvczOrd/X8zGplILgUBO2bNYej2zayITsXe5a2mRlVoKQpxbjF9GzjL3VcDmNnbCFd2nZzJwERyprERX7CAsh68xQErLYWFC3VPSYpGnAQxOJEcANz9b2Y2OIMxieRGELB12hzetCf+ctwQJoeNE2oYu3JxpiITyYk4E+VazeyHZjYlOm6hjxPlzGyOma0ws5Vm9pkUr5uZfdfMnjWzpxN7T4hkypLGgB110zmoB8nBgVc4kNtqmpUcpCjFSRANwCrgiuhYFZ3rFTM7DvgkcDLhXhPnmtlbOzV7H3BUdMwEFvT280TSqa2FoxfMoYx4Yy8cWEMls4Y1c2/zq8xYrFtKUpzi3GIaBNzg7v8NYGalwNA+fOaxwOPu/lp0vUeADwPfTGpzHnC7uzvwJzM72MwOc/fn+/C5IvuKbik9uGdz7Ld0AP81ppnrN0ylKXORieSFOD2IFuCApOcHAH3pT68A3mVmFWZWDpwDHN6pzVjguaTn66Nz+zCzmWbWamatbW1tfQhJBpqHJzbidXV7bynFua3kwD1jGrh+g3oMMjDESRBl7r4t8SR6XN7bD3T3Z4DrgAeA3wPLgPZeXqvJ3avdvXr06NG9DUkGksZGOsx4z6p4E94SHHhkQgPnb9DwVRk44iSI7clFYjObDLzelw919x+5+2R3fzfwEvC3Tk02sG+vYlx0TqT3amvxBQsoIX6PoQN4oawSa25mykolBxlY4tQgPgPcaWYbCf9fvRm4sC8famaHuPuLZjaesP5waqcm9wCXm9lPgVOArao/SK8lTXqL22top5TS5oXY1KkcmtHgRPJXnKU2/mJmxwBHR6f6Y6mNX5hZBbAb+JS7v2xms6PPu4lwm9NzeGMl2el9/DwZqBob8QU3UdaDPaF3MZghzbdpwpsMeHF6EEQJYUXahjG5+7tSnLsp6bEDn+qvz5OBp7ERzl9Qy5m09Ghew85Bwyj78c1KDiLEq0GIFIy/1jbSYSX8YIHFTg4OvMZQHm1opmz3NiUHkUisHoRIIdgydiJHb1zV49FJGw6ewLiXVnJGpgITKVBpexBm1hLnnEgubRsxlhExk4NHR4eVYA0NjHtpZYajEylMXSYIMyszs5HAKDMbYWYjo6OKFJPWRHJhSWPA63YAw17eGDs53D6sgZ80OyUd7VqWW6Qb3d1imkU4xHUMsJQ3ho6/Anw/s2GJpNHYSMeCBZxO/H0aEquu1ms+g0gsXSYId78BuMHMPu3u38tiTCLdWj9iImNfXhV7hEV4S8n4W81sjlms5CASV5z/Y/82s+EAZvZFM/ullt+WXHnOxjL25fi1hg5gdU0DJd6h5CDSQ3ESxJfc/VUzOwOoBX6Elt+WLJs2JGCnDWIc8WsNzzGGRc2uxCDSS3ESRGIhvfcDTe7+W2BI5kISecPEiXC/1XLb7jqG0h47ObRYDX9o3qApDSJ9EGcexAYzuxk4E7jOzIaiCXaSYUEAv60L+AszOIBdsQvRuyjhzw23U3ujMoNIX8X5Qf8x4H7gve7+MjASuDKTQcnA1tgIZ9SNJaCO8pjJwYHXbBhDmm/nDCUHkX7RbQ8i2j3uCXc/JnEuWlVVK6tKvwsCeGRWwPe21zMk5u0kCJPDbTXN2vpTpJ9124Nw93ZgdbQst0jGLGkMeG/dKG7eHr/WAGGBzJqVHEQyIU4NYgSw0sz+DGxPnHT3D2YsKhkwEr2GG7dfwqCYS3InWm0bWsHwH92gxfVEMiROgvhSxqOQAWn1kIlcvHsVF9Pz2dBjVy5meAZjE5F4GwY9ko1AZOC4tTZgWksdb6NniWEPxuDmOxirHoNIVnS3WN+S6OurZvZK0vGqmb2SvRClaAThwnrTW+p6tC+0A08zgZ83d+h2kkgWdVekngrg7sPd/U1Jx3B3f1OW4pMisaQxoKPuEg5gR496DVNppgTn5oaVyg0iWdZdgrg78cDMfpGFWKRIrRpby+kL6ijpwb7QDjxADX+snEpzs1blFsmF7hJE8i96R/bnh5rZZ81spZmtMLNFZlbW6fVpZtZmZsui47L+/HzJjiWNAXvMOHZjz/aFThSi3+uLWbNGd5VEcqW7BOFdPO4TMxsLXAFUu/txQCnw8RRNf+buJ0THD/vr8yU7NkwMew2DiF9r6ACaSsPNfMauXJzZAEUkre5GMU2KitEGHJBUmDbA+1iHGBRdczdQDmzsw7UkjyxpDJi0YBZj2B47MUBYhL65YaVuJYnkkS57EO5emlSUHtRfRWp33wB8C1hHuGTHVnd/IEXTj5jZ02Z2l5kdnupaZjbTzFrNrLWtra23IUl/CAJeLRvF6QvqGN6D5LCcCfyk2ZnkSg4i+Sbrq7Ka2QjgPOAIwu1Mh5lZXadm9wJV7v524EFgYapruXuTu1e7e/Xo0aMzGbZ049bagO11Mxm+c3OPbic9MqGBt7tGJ4nkq1ws210L/Mvd29x9N/BL4J3JDdx9s7vvjJ7+EJic5RgljsZGOqyE6S11DOO1WG9xYBMHc3mDM0V7Q4vktThLbfS3dcCpZlYOvA7UAK3JDczssGjVWIAPAs9kN0TpThDAYfW1/Ed7S49+w3DgmZIJTGhfiVKDSP7Leg/C3R8H7gKeAJZHMTSZ2VfNLLEA4BXRMNinCEc8Tct2nJLarbUBH6g7kP9ojzd0NTFs9RWGcVtNMxPaV2Y4QhHpL+bebyNYc6q6utpbW1vTN5Reu3JswPyNn2AIHWnbhreSKpjDDTxQMZUbtOiqSF4ys6XuXp3qtVzcYpIC89faRo5quZlv0hF7wtsmKpgyYRMr1WEQKVhKENKtLWMncvTGVbETA8BuBnHLhBuUHEQKXC5GMUkBWNIYsMsGMaIHycGBXaVlDG7+MVev1P0kkUKnHoTs5+GJjbxn1YIe9RocaD24hpNe0hIZIsVCPQjZ6xsTA16xA3uUHMJJb8avxzQoOYgUGfUgBIBHy2v5P6/HX3UVwuTwyIQGpqy8kfMzFJeI5I56EMLDExt5Zw+SgwOvMoxHG5o1G1qkiClBDFBBAFVVcLEFsW8pOUBpKdbczHDfxhk3qhAtUsx0i2kA+sbEgE+umsPFbAbS79cQzoY2Shpma2s3kQFECWIACQJYPCOgaVc9g2mP9Z7kOoOIDCxKEAPEX0bUcvHLLVxMvB3eIEwOGw6eoOQgMkCpBjEArBpbS/XLYRG6JwvsbZxQw7iXNB1aZKBSgihSSxoDXrUDcTOO3Rh/hNJ2yrmtphlz7QstMtApQRShJY0Bpyyo37v1Z9xew5aSCp5saGLGYo1OEhEliOISjV09fUFd7CI0vFGIHtm+SUNXRWQvFamLRRDAtGmwZ0/8OQ3AOippqZmvXoOI7Ec9iCJwa21AR90lsGdP2raJAnSL1fCTZqfS1yg5iEhK6kEUuCvHBszbOJMS0u8M6MDqmgaOWXwjtZkPTUQKnBJEARs7Fh7dOJdhvNZlm0Ta6KCEv9fM4pjFmtMgIvHoFlMBCgL4xKCARzdWUcnabtt2YBxY7vy0uV3JQUR6JCcJwsw+a2YrzWyFmS0ys7JOrw81s5+Z2bNm9riZVeUiznzT2BgurndW3SgWttdRxdpuC9IONA+bTVMTTFWZQUR6KOsJwszGAlcA1e5+HFAKfLxTs0uBl9z9rcD/A67LbpT5p7ERXl4QcAszGc3mtImhgxKsoYH6bTcqOYhIr+SqBjEIOMDMdgPlwMZOr58HzIse3wV838zM3dNXYotMbS20tISP/0X6esNaKvnBmPlcv0FZQUT6Jus9CHffAHwLWAc8D2x19wc6NRsLPBe13wNsBSo6X8vMZppZq5m1trW1ZTbwbAsCtg4exYMtRgfGi4xifJp6w1oqOX3MGiUHEekXubjFNIKwh3AEMAYYZmZ1vbmWuze5e7W7V48ePbo/w8ydIIBRo6CujoP2bN67VMboaO+GrmynnJ9MmM+GDVmJUkQGgFwUqWuBf7l7m7vvBn4JvLNTmw3A4QBmNgg4CNL8hCwGQcCeGTNhc+o/agnhqKRkDmwdVMGw5iauXqmeg4j0n1wkiHXAqWZWbmYG1ADPdGpzD1AfPf4o8D9FX38IAjo+Uc+gXV3XGELOGirpwFhDJbfVNHPQ7k0apiQi/S7rRWp3f9zM7gKeAPYATwJNZvZVoNXd7wF+BNxhZs8CW9h/lFNxCQKYOZOSjvQL7K2jkstq1rB4MVQBMzIenIgMVDkZxeTu1wDXdDr95aTXdwAXZDWoHLi1NqCmZS7j08xnSNjBYH4yYT6LtU2DiGSBZlJnWbQiNxdbwIUtM6mMMdnNASoqKGu+TXUGEckarcWURUEAW+saeZYmSmlP22vYQymfHLyQ2tumqsQgIlmnHkSWBAG8XNdIAwsYFCM5bKecz1UoOYhI7qgHkQVBAL//RMDtLEibGBywykqGzZ/Pd5UZRCSH1IPIoES94bd1ATd1zIzVa7itphnWrNGwVRHJOfUgMiQaucprr8HDMdZQ0tafIpJvlCAyZO7cMDkAjGddl+0ceGRCA1NW3qg5DSKSV3SLKUPWJeWEdYxP2SY5OYiI5BsliL5KFBpKSsKvQQDA+KSccDXz2U75Pm/bTjmPNjQrOYhI3lKC6ItEoWHtWnAPv86cCUHA/PlQHuWERUzlkzTtXUNpfWklTzY0ccaNqjeISP6yYlkDr7q62ltbW7P7oVVVYVLorLIS1qwhCMJaxLp1YY9i/nwNThKR/GJmS929OuVrShB9UFIS9hw6M4OOjuzGIiLSC90lCN1i6ovxqYvPXZ4XESkgShAxdFGHZp9CQ0J5eXheRKTAaR5EGskT3uCNOjTA1ERBQYUGESlCqkGkkaYOLSJS0FSD6IN1XUyC7uq8iEixUIJIQ3VoERmolCAiXRWiVYcWkYEq6wnCzI42s2VJxytm9plObaaY2dakNl/u4nL9opsJ0UydCk1NYc3BLPza1KQ6tIgUv5wWqc2sFNgAnOLua5POTwG+4O7nxr1Wb4vUQQD19dDevv9rKkSLSLHL5yJ1DfCP5OSQTYmeQ6rkACpEi8jAlusE8XFgURevnWZmT5nZ78xsYqoGZjbTzFrNrLWtra3HH568Z0MqKkSLyECWswRhZkOADwJ3pnj5CaDS3ScB3wN+leoa7t7k7tXuXj169Ogex9BdD0GFaBEZ6HLZg3gf8IS7v9D5BXd/xd23RY/vAwab2aj+DqCrHkJpqQrRIiK5TBAX0cXtJTN7s5lZ9Phkwjg393cAXQ1hXbhQyUFEJCcJwsyGAWcCv0w6N9vMZkdPPwqsMLOngO8CH/cMDLfSEFYRka5pLSYRkQEsn4e5iohInlKCEBGRlJQgREQkJSUIERFJSQlCRERSKppRTGbWBmR7TadRwKYsf2ZfKN7MKaRYQfFmWiHFW+nuKZeiKJoEkQtm1trV8LB8pHgzp5BiBcWbaYUWb1d0i0lERFJSghARkZSUIPqmKdcB9JDizZxCihUUb6YVWrwpqQYhIiIpqQchIiIpKUGIiEhKShAxmNlnzWylma0ws0VmVtbp9Wlm1mZmy6LjshzGOieKc6WZfSbF62Zm3zWzZ83saTN7Rw7CTI4nXbxTzGxr0vf2y1mO71Yze9HMViSdG2lmD5rZ36OvI7p4b33U5u9mVl8A8bYnfZ/vyWG8F0T/HjrMrMuhomZ2tpmtjv4tX1UA8a4xs+XR97cwlp52dx3dHMBY4F/AAdHznwPTOrWZBnw/D2I9DlgBlAODgMXAWzu1OQf4HWDAqcDjeR7vFOA3OYzx3cA7gBVJ574JXBU9vgq4LsX7RgL/jL6OiB6PyNd4o9e25cn391jgaOBhoLqL95UC/wCOBIYATwET8jXeqN0aYFS2v8d9OdSDiGcQcICZDSL8YbYxx/F05VjCH/ivufse4BHgw53anAfc7qE/AQeb2WHZDjQSJ96ccvf/BbZ0On0esDB6vBA4P8Vb3ws86O5b3P0l4EHg7EzFmdCHeHMiVbzu/oy7r07z1pOBZ939n+6+C/gp4Z8zo/oQb0FSgkjD3TcA3wLWAc8DW939gRRNPxLdsrnLzA7PapBvWAG8y8wqzKycsLfQOZaxwHNJz9dH53IhTrwAp5nZU2b2OzObmN0QUzrU3Z+PHv8bODRFm3z6PseJF6DMzFrN7E9mdn52Quu1fPr+xuXAA2a21Mxm5jqYOAblOoB8F92vPQ84AngZuNPM6ty9OanZvcAid99pZrMIf0v7z2zH6u7PmNl1wAPAdmAZ0J7tOOKKGe8ThGvFbDOzc4BfAUdlM87uuLubWcGMFU8Tb6W7bzCzI4H/MbPl7v6PbMZX5M6Ivr+HAA+a2V+jHkneUg8ivVrgX+7e5u67CffRfmdyA3ff7O47o6c/BCZnOcbkWH7k7pPd/d3AS8DfOjXZwL6/pY+LzuVEunjd/RV33xY9vg8YbGajchBqshcSt+Wiry+maJNP3+c48SZ6y7j7Pwnvp5+YrQB7IZ++v7EkfX9fBO4mvE2W15Qg0lsHnGpm5WZmQA3wTHKDTvfwP9j59WyKfjvBzMYT3s//Sacm9wCfiEYznUp4y+x5ciRdvGb25uj7jpmdTPhvdnO24+zkHiAxKqke+HWKNvcDZ5nZiKgXelZ0LhfSxhvFOTR6PAo4HViVtQh77i/AUWZ2hJkNAT5O+OfMS2Y2zMyGJx4T/ntY0f278kCuq+SFcABfAf5K+Bd6BzAU+Crwwej1/wusJBxJ8RBwTA5j/QPhf+yngJro3GxgdvTYgB8QjgBZTjejLvIk3suTvrd/At6Z5fgWEdaedhPe574UqABagL8TjrwaGbWtBn6Y9N4ZwLPRMT2f4yXsFS+Pvs/LgUtzGO+Hosc7gReA+6O2Y4D7kt57DmGP8x/A3HyOl3C01VPRsTJb8fb10FIbIiKSkm4xiYhISkoQIiKSkhKEiIikpAQhIiIpKUGIiEhKShBSNMxsWx/fX2VmF/dXPPnOzK7OdQyS35QgRN5QBQyYBAEoQUi3lCCkqJnZB8zscTN70swWm9mh0fn3JO198GQ0y/VawsUDl5nZZ1Nc67+i9fyfMrNro3MnRIvbPW1mdyf2WjCzh83s/0WL3z1jZieZ2S+jPRm+HrWpMrO/mlkQtbkrWrQQM6uJ4loe7UGQmOW8xsy+YmZPRK8dE50fFrX7c/S+86Lz06LP/X302d+Mzl9LuELxMjMLMvzXIIUq1zP1dOjor4MU+xkQ7sWQmBB6GfDt6PG9wOnR4wMJF66cQhd7TwDvA/4IlEfPE7ORnwbeEz3+KvCd6PHDRPsuAHMIl4g/jHAW/nrC2c1VhCt8JuK4FfgCUEa4UunbovO3A5+JHq8BPh09buSNmdDfAOqixwcTzjAeRrhXyT+Bg6LrrgUO7+r7pUNH8qEehBS7ccD9ZrYcuBJILBf+KPDfZnYFcLCH+1F0pxa4zd1fA3D3LWZ2UPTeR6I2Cwk3lElIrA20HFjp7s97uKjjP3ljobnn3P3R6HEzcAbh5jP/cvfEwoWdr/vL6OtSwiQD4do+V5nZMsLkVAaMj15rcfet7r6DcFmTyjR/VhFAt5ik+H2PcLe/44FZhD84cfdrCXsUBwCPJm7V9LPECr8dSY8TzxNL7Xde6ybO2jeJa7UnXceAj7j7CdEx3t2f6dS+83tEuqUEIcXuIN5YBnrvvtBm9hZ3X+7u1xGuDHoM8CowvIvrPAhMT6oRjHT3rcBLZvauqM0lhLvi9cR4MzstenwxsARYDVSZ2Vt7cN37gU8nrXwbZ6nu3WY2uIfxygCiBCHFpNzM1icdnwPmEW7ytBTYlNT2M2a2wsyeJlyZ83eE9YT2qAi9T5Ha3X9PeMuoNbqN84XopXrg+ug6JxDWIXpiNfApM3uGsF6yILoVND2Kezlhj+OmNNf5GjAYeNrMVkbP02mK2qtILSlpNVeRHDGzKsKi+HG5jkUkFfUgREQkJfUgREQkJfUgREQkJSUIERFJSQlCRERSUoIQEZGUlCBERCSl/w+KSyGGv8FMtAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scatter(trials_logvar['imagery_handR'], trials_logvar['imagery_handL'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will apply a linear classifier to this data. A linear classifier can be thought of as drawing a line in the above plot to separate the two classes. To determine the class for a new trial, we just check on which side of the line the trial would be if plotted as above.\n",
    "\n",
    "The data is split into a train and a test set. The classifier will fit a model (in this case, a straight line) on the training set and use this model to make predictions about the test set (see on which side of the line each trial in the test set falls). Note that the CSP algorithm is part of the model, so for fairness sake it should be calculated using only the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Percentage of trials to use for training (50-50 split here)\n",
    "train_percentage = 0.75\n",
    "\n",
    "# Calculate the number of trials for each class the above percentage boils down to\n",
    "ntrain_r = int(trials_filt['imagery_handR'].shape[2] * train_percentage)\n",
    "ntrain_f = int(trials_filt['imagery_handL'].shape[2] * train_percentage)\n",
    "ntest_r = trials_filt['imagery_handR'].shape[2] - ntrain_r\n",
    "ntest_f = trials_filt['imagery_handL'].shape[2] - ntrain_f\n",
    "\n",
    "# Splitting the frequency filtered signal into a train and test set\n",
    "train = {'imagery_handR': trials_filt['imagery_handR'][:, :, :ntrain_r],\n",
    "         'imagery_handL': trials_filt['imagery_handL'][:, :, :ntrain_f]}\n",
    "\n",
    "test = {'imagery_handR': trials_filt['imagery_handR'][:, :, ntrain_r:],\n",
    "        'imagery_handL': trials_filt['imagery_handL'][:, :, ntrain_f:]}\n",
    "\n",
    "# Train the CSP on the training set only\n",
    "W = csp(train['imagery_handR'], train['imagery_handL'])\n",
    "\n",
    "# Apply the CSP on both the training and test set\n",
    "train['imagery_handR'] = apply_mix(W, train['imagery_handR'])\n",
    "train['imagery_handL'] = apply_mix(W, train['imagery_handL'])\n",
    "test['imagery_handR'] = apply_mix(W, test['imagery_handR'])\n",
    "test['imagery_handL'] = apply_mix(W, test['imagery_handL'])\n",
    "\n",
    "# Select only the first and last components for classification\n",
    "comp = np.array([0, -1])\n",
    "train['imagery_handR'] = train['imagery_handR'][comp, :, :]\n",
    "train['imagery_handL'] = train['imagery_handL'][comp, :, :]\n",
    "test['imagery_handR'] = test['imagery_handR'][comp, :, :]\n",
    "test['imagery_handL'] = test['imagery_handL'][comp, :, :]\n",
    "\n",
    "# Calculate the log-var\n",
    "train['imagery_handR'] = logvar(train['imagery_handR'])\n",
    "train['imagery_handL'] = logvar(train['imagery_handL'])\n",
    "test['imagery_handR'] = logvar(test['imagery_handR'])\n",
    "test['imagery_handL'] = logvar(test['imagery_handL'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a classifier the Linear Discriminant Analysis (LDA) algorithm will be used. It fits a gaussian distribution to each class, characterized by the mean and covariance, and determines an optimal separating plane to divide the two. This plane is defined as $r = W_0 \\cdot X_0 + W_1 \\cdot X_1 + \\ldots + W_n \\cdot X_n - b$, where $r$ is the classifier output, $W$ are called the feature weights, $X$ are the features of the trial, $n$ is the dimensionality of the data and $b$ is called the offset.\n",
    "\n",
    "In our case we have 2 dimensional data, so the separating plane will be a line: $r = W_0 \\cdot X_0 + W_1 \\cdot X_1 - b$. To determine a class label for an unseen trial, we can calculate whether the result is positive or negative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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",
    "\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": "markdown",
   "metadata": {},
   "source": [
    "Training the LDA using the training data gives us $W$ and $b$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W: [ 1.36009332 -1.99621462]\n",
      "b: 0.4255365330277503\n"
     ]
    }
   ],
   "source": [
    "W, b = train_lda(train['imagery_handR'].T, train['imagery_handL'].T)\n",
    "\n",
    "print('W:', W)\n",
    "print('b:', b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be informative to recreate the scatter plot and overlay the decision boundary as determined by the LDA classifier. The decision boundary is the line for which the classifier output is exactly 0. The scatterplot used $X_0$ as $x$-axis and $X_1$ as $y$-axis. To find the function $y = f(x)$ describing the decision boundary, we set $r$ to 0 and solve for $y$ in the equation of the separating plane:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width:600px\">\n",
    "$$\\begin{align}\n",
    "W_0 \\cdot X_0 + W_1 \\cdot X_1 - b &= r &&\\text{the original equation} \\\\\\\n",
    "W_0 \\cdot x + W_1 \\cdot y - b &= 0     &&\\text{filling in $X_0=x$, $X_1=y$ and $r=0$} \\\\\\\n",
    "W_0 \\cdot x + W_1 \\cdot y &= b         &&\\text{solving for $y$}\\\\\\\n",
    "W_1 \\cdot y &= b - W_0 \\cdot x \\\\\\\n",
    "\\\\\\\n",
    "y &= \\frac{b - W_0 \\cdot x}{W_1}\n",
    "\\end{align}$$\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first plot the decision boundary with the training data used to calculate it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2d5a7c5dd60>]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Scatterplot like before\n",
    "plot_scatter(train['imagery_handR'], train['imagery_handL'])\n",
    "plt.title('Training data')\n",
    "\n",
    "# Calculate decision boundary (x,y)\n",
    "x = np.arange(0, 6, 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": "markdown",
   "metadata": {},
   "source": [
    "The code below plots the boundary with the test data on which we will apply the classifier. You will see the classifier is going to make some mistakes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2d5a7cd49d0>]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plot_scatter(test['imagery_handR'], test['imagery_handL'])\n",
    "plt.title('Test data')\n",
    "plt.plot(x, y, linestyle='--', linewidth=2, color='k')\n",
    "#plt.xlim(-5, 1)\n",
    "#plt.ylim(-2.2, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the LDA is constructed and fitted to the training data. We can now apply it to the test data. The results are presented as a confusion matrix:\n",
    "\n",
    "<table>\n",
    "    <tr><td></td><td colspan='2' style=\"font-weight:bold\">True labels →</td></tr>\n",
    "    <tr><td style=\"font-weight:bold\">↓ Predicted labels</td><td>Right</td><td>Left</td></tr>\n",
    "    <tr><td>Right</td><td></td><td></td></tr>\n",
    "    <tr><td>Left</td><td></td><td></td></tr>\n",
    "</table>\n",
    "\n",
    "The number at the diagonal will be trials that were correctly classified, any trials incorrectly classified (either a false positive or false negative) will be in the corners."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      "[[41 17]\n",
      " [ 8 33]]\n",
      "\n",
      "Accuracy: 0.747\n"
     ]
    }
   ],
   "source": [
    "# Print confusion matrix\n",
    "conf = np.array([\n",
    "    [(apply_lda(test['imagery_handR'], W, b) == 1).sum(), (apply_lda(test['imagery_handL'], W, b) == 1).sum()],\n",
    "    [(apply_lda(test['imagery_handR'], W, b) == 2).sum(), (apply_lda(test['imagery_handL'], 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))))\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}