{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CueingGroupAnalysis_Colab",
      "version": "0.3.2",
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kylemath/eeg-notebooks/blob/master/notebooks/CueingGroupAnalysis_Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "l_ZRAtVY8nJX",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# CueingGroupAnalysis_Colab\n",
        "\n",
        "This notebook is slightly different than the PC notebooks due to differences in the Mac operating system.\n",
        "\n",
        "First setup the computer by following the instructions in the mac_instructions_cueing.docx file, then return to this file.\n",
        "\n",
        "The cueing task can ellicit a number of reliable changes. A central cue indicates the location of an upcoming target onset. Here the task can be changed to be perfectly predictive, or have some level of cue validity. Task is to indicate the orientation of a spatial grating on the target, up for vertical, right for horizontal.\n",
        "\n",
        "ERP - Validly cued targets ellict larger ERP's than invalidly cued targets\n",
        "\n",
        "Response ERPs - Validly cued targets are more quickly identified and better identified\n",
        "\n",
        "Oscillations - Alpha power lateralizes after a spatial cue onset preceeding the upcoming onset of a target. Alpha power becomes smaller contraleral to the target side, and larger ipsilateral with the target."
      ]
    },
    {
      "metadata": {
        "id": "cRRt1FLy8xJs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "4577dce1-15c9-4865-a63f-290db307b36d"
      },
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/kylemath/eeg-notebooks --recurse-submodules\n",
        "%cd eeg-notebooks/notebooks\n",
        "\n",
        "\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fatal: destination path 'eeg-notebooks' already exists and is not an empty directory.\n",
            "/content/eeg-notebooks/notebooks\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "G_2iJn-A8hxR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5564c6c0-9a32-4993-caab-e5801c6c561a"
      },
      "cell_type": "code",
      "source": [
        "#from muselsl import stream, list_muses, view, record\n",
        "!pip install mne\n",
        "from multiprocessing import Process\n",
        "from mne import Epochs, find_events, concatenate_raws\n",
        "from mne.time_frequency import tfr_morlet\n",
        "import numpy as np\n",
        "from time import time, strftime, gmtime\n",
        "import os\n",
        "#from stimulus_presentation import cueing\n",
        "from utils import utils\n",
        "from collections import OrderedDict\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "from matplotlib import pyplot as plt\n",
        "import matplotlib.patches as patches"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: mne in /usr/local/lib/python3.6/dist-packages (0.17.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "BU6QcVVh8sXM",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Prepare the Data for Analysis\n",
        "Once a suitable data set has been collected, it is now time to analyze the data and see if we can identify the cueing effects\n",
        "\n",
        "# Load data into MNE objects\n",
        "MNE is a very powerful Python library for analyzing EEG data. It provides helpful functions for performing key tasks such as filtering EEG data, rejecting artifacts, and grouping EEG data into chunks (epochs).\n",
        "\n",
        "The first step after loading dependencies is use MNE to read the data we've collected into an MNE Raw object"
      ]
    },
    {
      "metadata": {
        "id": "ynN5eXFBAy-g",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 8939
        },
        "outputId": "ddca2e1c-04f2-4e5e-e384-e56bad55337e"
      },
      "cell_type": "code",
      "source": [
        "subs = [101, 102, 103, 104, 105, 106, 108, 109, 110, 111, 112,\n",
        "        202, 203, 204, 205, 207, 208, 209, 210, 211, \n",
        "        301, 302, 303, 304, 305, 306, 307, 308, 309]\n",
        "\n",
        "diff_out = []\n",
        "Ipsi_out = []\n",
        "Contra_out = []\n",
        "Ipsi_spectra_out = []\n",
        "Contra_spectra_out = []\n",
        "diff_spectra_out = []\n",
        "ERSP_diff_out = []\n",
        "ERSP_Ipsi_out = []\n",
        "ERSP_Contra_out = []\n",
        "\n",
        "frequencies =  np.linspace(6, 30, 100, endpoint=True)\n",
        "wave_cycles = 6\n",
        "\n",
        "# time frequency window for analysis\n",
        "f_low = 7 # Hz\n",
        "f_high = 10\n",
        "f_diff = f_high-f_low\n",
        " \n",
        "t_low = 0 # s\n",
        "t_high = 1\n",
        "t_diff = t_high-t_low\n",
        "\n",
        "bad_subs= [6, 7, 13, 26]\n",
        "really_bad_subs = [11, 12, 19]\n",
        "sub_count = 0    \n",
        "    \n",
        "    \n",
        "    \n",
        "for sub in subs:\n",
        "    print(sub)\n",
        "    \n",
        "    sub_count += 1\n",
        "\n",
        "    \n",
        "    if (sub_count in really_bad_subs):\n",
        "        rej_thresh_uV = 90\n",
        "    elif (sub_count in bad_subs):\n",
        "        rej_thresh_uV = 90\n",
        "    else:\n",
        "        rej_thresh_uV = 90\n",
        "\n",
        "    rej_thresh = rej_thresh_uV*1e-6\n",
        "    \n",
        "\n",
        "    \n",
        "    # Load both sessions\n",
        "    raw = utils.load_data('visual/cueing', sfreq=256., \n",
        "                          subject_nb=sub, session_nb=1)\n",
        "    raw.append(    utils.load_data('visual/cueing', sfreq=256., \n",
        "                          subject_nb=sub, session_nb=2)           )\n",
        "\n",
        "    # Filter Raw Data\n",
        "    raw.filter(1,30, method='iir')\n",
        "\n",
        "    #Select Events\n",
        "    events = find_events(raw)\n",
        "    event_id = {'LeftCue': 1, 'RightCue': 2}\n",
        "    epochs = Epochs(raw, events=events, event_id=event_id, \n",
        "                    tmin=-1, tmax=2, baseline=(-1, 0), \n",
        "                    reject={'eeg':rej_thresh}, preload=True,\n",
        "                    verbose=False, picks=[0, 3])\n",
        "    print('Trials Remaining: ' + str(len(epochs.events)) + '.')\n",
        "\n",
        "    # Compute morlet wavelet\n",
        "\n",
        "    # Left Cue\n",
        "    tfr, itc = tfr_morlet(epochs['LeftCue'], freqs=frequencies, \n",
        "                          n_cycles=wave_cycles, return_itc=True)\n",
        "    tfr = tfr.apply_baseline((-1,-.5),mode='mean')\n",
        "    #tfr.plot(picks=[0], mode='logratio', \n",
        "    #         title='TP9 - Ipsi');\n",
        "    #tfr.plot(picks=[3], mode='logratio', \n",
        "    #         title='TP10 - Contra');\n",
        "    power_Ipsi_TP9 = tfr.data[0,:,:]\n",
        "    power_Contra_TP10 = tfr.data[1,:,:]\n",
        "\n",
        "    # Right Cue\n",
        "    tfr, itc = tfr_morlet(epochs['RightCue'], freqs=frequencies, \n",
        "                          n_cycles=wave_cycles, return_itc=True)\n",
        "    tfr = tfr.apply_baseline((-1,-.5),mode='mean')\n",
        "    #tfr.plot(picks=[0], mode='logratio', \n",
        "    #         title='TP9 - Contra');\n",
        "    #tfr.plot(picks=[3], mode='logratio', \n",
        "    #         title='TP10 - Ipsi');\n",
        "    power_Contra_TP9 = tfr.data[0,:,:]\n",
        "    power_Ipsi_TP10 = tfr.data[1,:,:]\n",
        "\n",
        "    # Plot Differences\n",
        "    %matplotlib inline\n",
        "    times = epochs.times\n",
        "    power_Avg_Ipsi =   (power_Ipsi_TP9+power_Ipsi_TP10)/2;\n",
        "    power_Avg_Contra = (power_Contra_TP9+power_Contra_TP10)/2;\n",
        "    power_Avg_Diff = power_Avg_Ipsi-power_Avg_Contra;\n",
        "\n",
        "\n",
        "    #find max to make color range\n",
        "    plot_max = np.max([np.max(np.abs(power_Avg_Ipsi)), np.max(np.abs(power_Avg_Contra))])\n",
        "    plot_diff_max = np.max(np.abs(power_Avg_Diff))\n",
        "\n",
        "   \n",
        "    \n",
        "    #Ipsi\n",
        "    fig, ax = plt.subplots(1)\n",
        "    im = plt.imshow(power_Avg_Ipsi,\n",
        "               extent=[times[0], times[-1], frequencies[0], frequencies[-1]],\n",
        "               aspect='auto', origin='lower', cmap='coolwarm', vmin=-plot_max, vmax=plot_max)\n",
        "    plt.xlabel('Time (sec)')\n",
        "    plt.ylabel('Frequency (Hz)')\n",
        "    plt.title('Power Average Ipsilateral to Cue')\n",
        "    cb = fig.colorbar(im)\n",
        "    cb.set_label('Power')\n",
        "    # Create a Rectangle patch\n",
        "    rect = patches.Rectangle((t_low,f_low),t_diff,f_diff,linewidth=1,edgecolor='k',facecolor='none')\n",
        "    # Add the patch to the Axes\n",
        "    ax.add_patch(rect)\n",
        "\n",
        "    #TP10\n",
        "    fig, ax = plt.subplots(1)\n",
        "    im = plt.imshow(power_Avg_Contra,\n",
        "               extent=[times[0], times[-1], frequencies[0], frequencies[-1]],\n",
        "               aspect='auto', origin='lower', cmap='coolwarm', vmin=-plot_max, vmax=plot_max)\n",
        "    plt.xlabel('Time (sec)')\n",
        "    plt.ylabel('Frequency (Hz)')\n",
        "    plt.title(str(sub) + ' - Power Average Contra to Cue')\n",
        "    cb = fig.colorbar(im)\n",
        "    cb.set_label('Power')\n",
        "    # Create a Rectangle patch\n",
        "    rect = patches.Rectangle((t_low,f_low),t_diff,f_diff,linewidth=1,edgecolor='k',facecolor='none')\n",
        "    # Add the patch to the Axes\n",
        "    ax.add_patch(rect)\n",
        "\n",
        "    #difference between conditions\n",
        "    fig, ax = plt.subplots(1)\n",
        "    im = plt.imshow(power_Avg_Diff,\n",
        "               extent=[times[0], times[-1], frequencies[0], frequencies[-1]],\n",
        "               aspect='auto', origin='lower', cmap='coolwarm', vmin=-plot_diff_max, vmax=plot_diff_max)\n",
        "    plt.xlabel('Time (sec)')\n",
        "    plt.ylabel('Frequency (Hz)')\n",
        "    plt.title('Power Difference Ipsi-Contra')\n",
        "    cb = fig.colorbar(im)\n",
        "    cb.set_label('Ipsi-Contra Power')\n",
        "    # Create a Rectangle patch\n",
        "    rect = patches.Rectangle((t_low,f_low),t_diff,f_diff,linewidth=1,edgecolor='k',facecolor='none')\n",
        "    # Add the patch to the Axes\n",
        "    ax.add_patch(rect)\n",
        "     \n",
        "        \n",
        "   \n",
        "        \n",
        "    #output data into array\n",
        "    Ipsi_out.append(np.mean(power_Avg_Ipsi[np.argmax(frequencies>f_low):\n",
        "                                           np.argmax(frequencies>f_high)-1,\n",
        "                            np.argmax(times>t_low):np.argmax(times>t_high)-1 ]\n",
        "                           )\n",
        "                   )            \n",
        "    Ipsi_spectra_out.append(np.mean(power_Avg_Ipsi[:,np.argmax(times>t_low):\n",
        "                                                   np.argmax(times>t_high)-1 ],1\n",
        "                                   )\n",
        "                           )\n",
        "        \n",
        "    Contra_out.append(np.mean(power_Avg_Contra[np.argmax(frequencies>f_low):\n",
        "                                               np.argmax(frequencies>f_high)-1,\n",
        "                            np.argmax(times>t_low):np.argmax(times>t_high)-1 ]\n",
        "                             )\n",
        "                     )\n",
        "    \n",
        "    Contra_spectra_out.append(np.mean(power_Avg_Contra[:,np.argmax(times>t_low):\n",
        "                                                       np.argmax(times>t_high)-1 ],1))\n",
        "    \n",
        "    \n",
        "    diff_out.append(np.mean(power_Avg_Diff[np.argmax(frequencies>f_low):\n",
        "                                           np.argmax(frequencies>f_high)-1,\n",
        "                            np.argmax(times>t_low):np.argmax(times>t_high)-1 ]\n",
        "                           )\n",
        "                   )\n",
        "    diff_spectra_out.append(np.mean(power_Avg_Diff[:,np.argmax(times>t_low):\n",
        "                                                   np.argmax(times>t_high)-1 ],1\n",
        "                                   )\n",
        "                           )\n",
        "    \n",
        "    \n",
        "    ERSP_diff_out.append(power_Avg_Diff)\n",
        "    ERSP_Ipsi_out.append(power_Avg_Ipsi)\n",
        "    ERSP_Contra_out.append(power_Avg_Contra)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "101\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61284\n",
            "    Range : 0 ... 61283 =      0.000 ...   239.387 secs\n",
            "Ready.\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61284\n",
            "    Range : 0 ... 61283 =      0.000 ...   239.387 secs\n",
            "Ready.\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61284\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61284\n",
            "122 events found\n",
            "Event IDs: [ 1  2 11 12 21 22]\n",
            "Trials Remaining: 10.\n",
            "Applying baseline correction (mode: mean)\n",
            "Applying baseline correction (mode: mean)\n",
            "102\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "92 events found\n",
            "Event IDs: [ 1  2 11 12 21 22]\n",
            "Trials Remaining: 40.\n",
            "Applying baseline correction (mode: mean)\n",
            "Applying baseline correction (mode: mean)\n",
            "103\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "160 events found\n",
            "Event IDs: [ 1  2 11 12 21 22]\n",
            "Trials Remaining: 58.\n",
            "Applying baseline correction (mode: mean)\n",
            "Applying baseline correction (mode: mean)\n",
            "104\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61284\n",
            "    Range : 0 ... 61283 =      0.000 ...   239.387 secs\n",
            "Ready.\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61284\n",
            "    Range : 0 ... 61283 =      0.000 ...   239.387 secs\n",
            "Ready.\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61284\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61284\n",
            "272 events found\n",
            "Event IDs: [ 1  2 11 12 21 22]\n",
            "Trials Remaining: 39.\n",
            "Applying baseline correction (mode: mean)\n",
            "Applying baseline correction (mode: mean)\n",
            "105\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "111 events found\n",
            "Event IDs: [ 1  2 11 12 21 22]\n",
            "Trials Remaining: 21.\n",
            "Applying baseline correction (mode: mean)\n",
            "Applying baseline correction (mode: mean)\n",
            "106\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61284\n",
            "    Range : 0 ... 61283 =      0.000 ...   239.387 secs\n",
            "Ready.\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61284\n",
            "    Range : 0 ... 61283 =      0.000 ...   239.387 secs\n",
            "Ready.\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61284\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61284\n",
            "201 events found\n",
            "Event IDs: [ 1  2 11 12 21 22]\n",
            "Trials Remaining: 2.\n",
            "Applying baseline correction (mode: mean)\n",
            "Applying baseline correction (mode: mean)\n",
            "108\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61284\n",
            "    Range : 0 ... 61283 =      0.000 ...   239.387 secs\n",
            "Ready.\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61284\n",
            "130 events found\n",
            "Event IDs: [ 1  2 11 12 21 22]\n",
            "Trials Remaining: 0.\n",
            "Applying baseline correction (mode: mean)\n",
            "Applying baseline correction (mode: mean)\n",
            "109\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61284\n",
            "    Range : 0 ... 61283 =      0.000 ...   239.387 secs\n",
            "Ready.\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61284\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "160 events found\n",
            "Event IDs: [ 1  2 11 12 21 22]\n",
            "Trials Remaining: 5.\n",
            "Applying baseline correction (mode: mean)\n",
            "Applying baseline correction (mode: mean)\n",
            "110\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61284\n",
            "    Range : 0 ... 61283 =      0.000 ...   239.387 secs\n",
            "Ready.\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61284\n",
            "107 events found\n",
            "Event IDs: [ 1  2 11 12 21 22]\n",
            "Trials Remaining: 28.\n",
            "Applying baseline correction (mode: mean)\n",
            "Applying baseline correction (mode: mean)\n",
            "111\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "223 events found\n",
            "Event IDs: [ 1  2 11 12 21 22]\n",
            "Trials Remaining: 32.\n",
            "Applying baseline correction (mode: mean)\n",
            "Applying baseline correction (mode: mean)\n",
            "112\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61284\n",
            "    Range : 0 ... 61283 =      0.000 ...   239.387 secs\n",
            "Ready.\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61284\n",
            "86 events found\n",
            "Event IDs: [ 1  2 11 12 21 22]\n",
            "Trials Remaining: 0.\n",
            "Applying baseline correction (mode: mean)\n",
            "Applying baseline correction (mode: mean)\n",
            "202\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "156 events found\n",
            "Event IDs: [ 1  2 11 12 21 22]\n",
            "Trials Remaining: 61.\n",
            "Applying baseline correction (mode: mean)\n",
            "Applying baseline correction (mode: mean)\n",
            "203\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "120 events found\n",
            "Event IDs: [ 1  2 11 12 21 22]\n",
            "Trials Remaining: 4.\n",
            "Applying baseline correction (mode: mean)\n",
            "Applying baseline correction (mode: mean)\n",
            "204\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61308\n",
            "    Range : 0 ... 61307 =      0.000 ...   239.480 secs\n",
            "Ready.\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61308\n",
            "    Range : 0 ... 61307 =      0.000 ...   239.480 secs\n",
            "Ready.\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61308\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61308\n",
            "183 events found\n",
            "Event IDs: [ 1  2 11 12 21 22]\n",
            "Trials Remaining: 49.\n",
            "Applying baseline correction (mode: mean)\n",
            "Applying baseline correction (mode: mean)\n",
            "205\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "194 events found\n",
            "Event IDs: [ 1  2 11 12 21 22]\n",
            "Trials Remaining: 87.\n",
            "Applying baseline correction (mode: mean)\n",
            "Applying baseline correction (mode: mean)\n",
            "207\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61284\n",
            "    Range : 0 ... 61283 =      0.000 ...   239.387 secs\n",
            "Ready.\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61284\n",
            "    Range : 0 ... 61283 =      0.000 ...   239.387 secs\n",
            "Ready.\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61284\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61284\n",
            "181 events found\n",
            "Event IDs: [ 1  2 11 12 21 22]\n",
            "Trials Remaining: 70.\n",
            "Applying baseline correction (mode: mean)\n",
            "Applying baseline correction (mode: mean)\n",
            "208\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "181 events found\n",
            "Event IDs: [ 1  2 11 12 21 22]\n",
            "Trials Remaining: 70.\n",
            "Applying baseline correction (mode: mean)\n",
            "Applying baseline correction (mode: mean)\n",
            "209\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "118 events found\n",
            "Event IDs: [ 1  2 11 12 21 22]\n",
            "Trials Remaining: 5.\n",
            "Applying baseline correction (mode: mean)\n",
            "Applying baseline correction (mode: mean)\n",
            "210\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61284\n",
            "    Range : 0 ... 61283 =      0.000 ...   239.387 secs\n",
            "Ready.\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61284\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "170 events found\n",
            "Event IDs: [ 1  2 11 12 21 22]\n",
            "Trials Remaining: 67.\n",
            "Applying baseline correction (mode: mean)\n",
            "Applying baseline correction (mode: mean)\n",
            "211\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "96 events found\n",
            "Event IDs: [ 1  2 11 12 21 22]\n",
            "Trials Remaining: 9.\n",
            "Applying baseline correction (mode: mean)\n",
            "Applying baseline correction (mode: mean)\n",
            "301\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=30564\n",
            "    Range : 0 ... 30563 =      0.000 ...   119.387 secs\n",
            "Ready.\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=30564\n",
            "    Range : 0 ... 30563 =      0.000 ...   119.387 secs\n",
            "Ready.\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 30564\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 30564\n",
            "54 events found\n",
            "Event IDs: [ 1  2 11 12 21 22]\n",
            "Trials Remaining: 26.\n",
            "Applying baseline correction (mode: mean)\n",
            "Applying baseline correction (mode: mean)\n",
            "302\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "213 events found\n",
            "Event IDs: [ 1  2 11 12 21 22]\n",
            "Trials Remaining: 11.\n",
            "Applying baseline correction (mode: mean)\n",
            "Applying baseline correction (mode: mean)\n",
            "303\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "198 events found\n",
            "Event IDs: [ 1  2 11 12 21 22]\n",
            "Trials Remaining: 8.\n",
            "Applying baseline correction (mode: mean)\n",
            "Applying baseline correction (mode: mean)\n",
            "304\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61284\n",
            "    Range : 0 ... 61283 =      0.000 ...   239.387 secs\n",
            "Ready.\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61284\n",
            "    Range : 0 ... 61283 =      0.000 ...   239.387 secs\n",
            "Ready.\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61284\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61284\n",
            "195 events found\n",
            "Event IDs: [ 1  2 11 12 21 22]\n",
            "Trials Remaining: 24.\n",
            "Applying baseline correction (mode: mean)\n",
            "Applying baseline correction (mode: mean)\n",
            "305\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "166 events found\n",
            "Event IDs: [ 1  2 11 12 21 22]\n",
            "Trials Remaining: 9.\n",
            "Applying baseline correction (mode: mean)\n",
            "Applying baseline correction (mode: mean)\n",
            "306\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61308\n",
            "    Range : 0 ... 61307 =      0.000 ...   239.480 secs\n",
            "Ready.\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61308\n",
            "    Range : 0 ... 61307 =      0.000 ...   239.480 secs\n",
            "Ready.\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61308\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61308\n",
            "147 events found\n",
            "Event IDs: [ 1  2 11 12 21 22]\n",
            "Trials Remaining: 1.\n",
            "Applying baseline correction (mode: mean)\n",
            "Applying baseline correction (mode: mean)\n",
            "307\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61308\n",
            "    Range : 0 ... 61307 =      0.000 ...   239.480 secs\n",
            "Ready.\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61308\n",
            "    Range : 0 ... 61307 =      0.000 ...   239.480 secs\n",
            "Ready.\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61308\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61308\n",
            "171 events found\n",
            "Event IDs: [ 1  2 11 12 21 22]\n",
            "Trials Remaining: 49.\n",
            "Applying baseline correction (mode: mean)\n",
            "Applying baseline correction (mode: mean)\n",
            "308\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61296\n",
            "    Range : 0 ... 61295 =      0.000 ...   239.434 secs\n",
            "Ready.\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61308\n",
            "    Range : 0 ... 61307 =      0.000 ...   239.480 secs\n",
            "Ready.\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61296\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61308\n",
            "180 events found\n",
            "Event IDs: [ 1  2 11 12 21 22]\n",
            "Trials Remaining: 71.\n",
            "Applying baseline correction (mode: mean)\n",
            "Applying baseline correction (mode: mean)\n",
            "309\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61308\n",
            "    Range : 0 ... 61307 =      0.000 ...   239.480 secs\n",
            "Ready.\n",
            "Creating RawArray with float64 data, n_channels=5, n_times=61308\n",
            "    Range : 0 ... 61307 =      0.000 ...   239.480 secs\n",
            "Ready.\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61308\n",
            "Setting up band-pass filter from 1 - 30 Hz\n",
            "Using filter length: 61308\n",
            "196 events found\n",
            "Event IDs: [ 1  2 11 12 21 22]\n",
            "Trials Remaining: 74.\n",
            "Applying baseline correction (mode: mean)\n",
            "Applying baseline correction (mode: mean)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zMWCXJ/E1wGdaa9+8q4H48HiAMeanCUuQXpCHxKzVEuOdoKsicVL9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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+m4SZp0LWJqf0723qa63eD3QEM6r9W4czQ/cDZaowW0xOM/0smYW0gzXRMGsH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mfJYx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\nQ0NDQ8OFaItEQ0NDQ8OF+P8BNQNrES1iVQ0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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y42Jed24z2vdLHyUVeebFS8lWSKtVO7m0qoaYfkAOI0hnG2zNIj3HhEseS6Im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\nkAV2uSZhjLku4Uf9ztbaC40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5C/Br1lpvrf0M8BQO4N+YtZHKpH9QpB2knThJPA3DSTBg\nctlIVUstrcRSxR94IYlnCbKkCp+MP5XUnJHUUvGhxK8vRX9qVpUT8RqT+xCslE3rd1YCI2kw0Y7u\nVJZ6XXrvVeD6p/uM4zwwvPQ91DEQkvs/d11JnV3SSstzpS2/zEftc6p8UcIfsWLNSq1ZcZqVP83K\nnWblLg8+iVx4R7B4cmEqUWjK1y85j2m9JOgUH5F8EpEXJX1wiSXkozYxSSyXiw/NrCvKWpHayZy2\nOVdUqfIjpPZmfDFODUKzGPBavlYQU7q3KTnyKAVjUcZB7Uraecb8Ekdcme4oR2atzQ8pCviHGsix\nRVxbay8GnpfeR03i5sBb4qF3i9PfQTBtdXR0dJx1aCnzm845H3AiaTmMMdcHXkiIBvTA5Y3/4VLg\nyicxto6Ojo5dSJaIXeecAC4wxvzxrmPW2v+ytMFFd2GMuX90Lh8axphbAW8AnhGD9T5NSPchx3Ll\neHwxshoc1dJN6nW5oDjINlJgM/Ww0A6r7JuiXxkANXVeNw7t6OhOn6sZM4dEVatCUGBdvL6Yoabn\nhjG6QmZtzGWyDyf+rvoVprPRw+gIlM1odlo7zeiGqsKaMGwBop602uOr05qaxhRct6yqWW5j5nhb\n23rOvKKpndarRH/1awa3ZuUuD6Ymt0bHV73mhOmlmY+0LpKpaXRh/kOwomghB/KVgL52vMHMtIIh\nmm42mTq20FUPguKQVsWkNJeGRJjcvNKBMq0GnFrhh1ON6WnIhlFQVZBsRTxoTU8zkN+xo5bqvdbT\ncU9eJ7JJ/B6BcSpfc8cWY6kmcR/gV4wxLwWeAbwg1rjeC3GDeDHwI9ba58bD7yD4K24a/wa4GfC2\nfdvv6OjoOBYsoMCehE/CWnvPo25z0V1Yaw3BwfxW4BeAfzLG/KYx5vZLO4qFuJ9NvUEQHdXPAX7K\nGKOMMZ9P2JR+Z/ltBGzTHGSAzmxiv5QqILUlHL1VUjfmHaCT9iopvNSNqLQIcXkiDea+hbSfNAep\nRXhPdhjnz3wh6E7mRrjNl6DWesJYx+SwdorRBY1inZLSSQesnzrP5Tjk/+H+miRw1UCcSFYXqK9V\nYj8IztpEAZV9bUg6V8biJulX6owtxck/qBHNyEDRIFbj5Qz5FSiwud6BmMc8l6qWbFPQZXJaF8d1\nZISqErM16KRF1HOXg0K1CpTMlHhOD+FYPnFGe5yR+ttnU50v1zRCi5BOahUc01KrcPF40SAGnF7h\nh1WoQT6sAg1WDyLQTjyniojit3/vkvZ2GNLEEsisi9te5wEW+ySstX8F/BXws8aYmwPfDbzQGPMv\nhHQcT7HWfmJLE99BoGI9whjzCHH8WcCPAL8FvIugVTwLePry2+jo6Og4PpzFwXRHjr0d18aYLyVE\n9H0PQQB+BXA74AHGmP9irX393HXW2mcCz9zS9F33HYvEUltrXSEsST5Ckm1TWCuKH6JKrLZdmklt\n5VQZ3uOqfuav1YBX8ydILcILbSQF2ykhnXrUJG5IWIk3jr3ur0iOY9IOPDhX6nAPGtZoBu0Z1BA0\nIu1RKlTLk2PIWoSq/89zsulL5V0IEptJ5IaKutEM7XDbl7Sle1aU4RhA5knJ/Uog3eDWke4aKK9a\nBHfFi0mV2WoJuwTSZSk8+ZhykGTVDBD9IrqsvyFrtCV5pdcDfjhVa1gzfgmV0qIjJO4NlOe5Y5UG\nE30QToUEeymGL2gRWnxe7jtONkoNOD0w6lMovY4pNuKc6CF/LyfPS34nvAMxz7nxNCfCDyi/h0fl\nkfHsZi/tn/jk7MSiTSIW0r4b8P3ALYBXEiobPddae2k8578DT6VTVzs6Os5znMuahDHmltbaty49\nf6km8SHgAwSn9Xc0JfEAsNY+wxjz5KUdHyeknTOkKJ6XlFrJIBQcoqRkwGXfRIuNaTWEBLadbJX4\nHOH/VqORSd+SHVsrXwXraVzUKogJOrbbmGXbunofCyZJtpQns29KURzFOgrRK6dYK4VWCu01vqkz\nLO8zSXbtPKX//Vz9YO9Iydyqz7SClJw82uarL7BIGDf3pQ3pOEQ3Ipgt+6MIz36gaBHDeDqwmdw4\nkerb+55bW7WPSb5KSpD8wqN1rK1NYacVf0S0569WZZHl4LoSWFdpzFLjkpL+DFpGlmJA47KPQSmH\nEv4Wp4KW4FSYuZbVhSb4b/SIW10QnoNfAwTG05Z0F1UAobgPyZ5K2ppk+M2tucPAq4G2lvfcOSeN\nmIT188Sh6wMvAa66tI2lm8SdrLWvMsYM1toxdv551trPypOstVdc2nFHR0fHuYoszOw456RgjPkq\nAiHo3818fOE+bS3Vhz5gjPkLgvM54UeMMW82xtx4nw6PE0kiLdLGcvVPCamt2PJd87+vGDIbxyFi\nFyb9CMkwF5ZhnhyRpCIn/k921pb5VF23TUr0UwlXIrdJYuBQmE1jeIW/NWtXEtZJf0aeT8Ekmowj\nnV+li57j+ke7e2Kr6SH8rYcQK5CkasGWKYFPKn+5l3yB6/iYlMivaBHDeBnanUaP8eXXVQlNmdgv\n3+OG+S4MNdl/YTYNYv8HTrAAACAASURBVB1q2X68Vzecwq2ugF9dENhCwymR6qIspqJVibgGqcU1\nf1dxN6gcAxOe8cCoVoz6VPg//S20CJnEMCczZMVaX8B6uIAxs5xO4XSMmRDrYPL4G59TsgxMS8Ru\nuKcjYj1NWV1zrxNlNz2GwCa9LSEL7G2AHyVoEd+1T0NLfzWfSKiZ+ipx7OmEoLgn7NNhR0dHx7kO\nuRlte50gbg48xFr7F4C31r7ZWvskQhGip+7T0FJz0+2Aa8sAOmvtx4wxDwQ+svmyE4SIBq3s0env\nhbt84c+LRx+jPSfpxUVBnyTh61jIB09hOjGVpmcZNjOQrKZ4YZCOoq2/laYcqpIElizeVHq1MJui\nT8KnJH9Be1AK1qOK0i6svGLtFIMurKv2PlOq8GpMcY6y9JWemVYoNcNqEv6G0KjOcQJeahHiJXn7\nm+97RtOTRYfG0yg/RmbTafS4JkX2Z6lR1V8pGZUsn4H8322Qbitmk1iHsh2nghahvIfxdPAFTW9C\nsL9q231iIsk1kTXWRhsMbcHodbhPT3xuKrP9kuaWNAjpHyjtjzFx4ogeok9CBaaYVxqnB2YdWnlO\n01yHdeIiWyrNR1r3KaX+Ut/cXjhLg+kE/hW4IiFzxWeMMde21v4zgY367H0aWnoXnwZuOHP8i4HP\nzhzv6OjoOG+RqPO7XieIVwAvMcZcCfhz4Ikx+PmBnKG0HE+LHT4VeC9hczHAvYHH7dNhR0dHx7mO\nFCOy65wTxH0JCVQvBx5ASIf0auBTwA/s09DSTeKhhEp09yTUsnaE1N6PiHauswfZvFQcda2Da3FT\nFEfrgKtMTkAOAkoK9Qj5f5iqaZtoeMnUJGmwqQrdLqR6xU6BQpVKdSkthkoO+9BaSQ/CpP1krgp/\nN/TXXBujOK61glGBGoOJaa1DUF0yM8zdZzDJNc5bacdVOtAghXkkXFzMhShdp5wQVFA/nMpJ45we\nMqUy/F0Hs6X5SGOQ/1fjVqWGg441LfR4OgTR5VraKq6FOiVHub/G5NL043wdTJduL8xZMXuV63U2\nzTh9Clag9NDUdqcKrnMpUE0PkcIpAvx8PTYv6pJIc5SKddvz2vIKrXSh5VbObs04U99EKxVMVbp8\nx7xa43wbhKgAjfJjehDhOeeUILKWRXLE12lh8r0pJuM4DM5EMJ0x5jaEsqPXIjib/7e19ncPMr5Y\nVTRtBu8wxvx74NrARxNDdSmWVqbzBIfHY/dpvKOjo+N8xFEH0xljrkCot/Mga+2zjDE3BS4yxrzF\nWvvX+47PGPNP1trrpvfxN/xA/uPFaTmiPetmBGdIBWvtiZucSoqNRJ8UQUVbtAgVE4ZtcmoV7aFO\nh5ClfxE0ljWMDYF18+0H6Txdo2PLKBZrE5CkpMLfDg5rJbSDJlVB/DvTZ4UzuThUi0TmmsR+ztWa\nyKBhPShONVXXkvM6ScThHufH71KAkipOaKVXMEjaa9QiZICcUnhdksS5mDAupKJe5XaTiaDMQ+3Y\nbFO6F3k4PPeUwC9QXX2plAexTvM0bXUrbaaVVO5bkBAapISDKb26NHFnp7U+xTh4UAqlxtxoXqcp\n3XqcK6dPFYlbDbmSoaM2jYyiwmCiNCdoijah0UHLpmgSaR7Te9/Mq/OFHqr0FcL6QaHj/M3RR7NW\nFrW1cM5QtMRITkhhjymp/xid19oXSvJRYBEBZL++7ghgrX1W/P9dxpgXAd9LYJbui4uMMd9prX3O\nAa6tsDQtxxMImVkvJnjNJTzdL9HR0fE5hDOQluNLgHc2x94B3Gq/kWVcTHBWP5zgR65KO+xTdGip\nJnE34JustS9bPMRjRrJn5wRrKeFYTpUgxTBfpC3hXJLahIrSfLaji89babGy3CYabKSkTsYp/BKS\nyYcX/VFoiMWbsP3eQwJBsj/C+aRR6Go8Mrhp1hYtNA0HJf13mbbgl2ismoNWjM7n2tctnVemukj+\nF0DoZ0m61TWNdRhQbh2eU/JTpCAxESwmg8p8stMrFQK79CqnkGjTYlcSry9zIH1HCl80TlEIqSp4\n42tdtKr7XNGSa+1lG2SSv7k0JiH9RfgKu8YfoXzQfBD13ZM/wukQBJcT8cXUGQkur6EY/CYS5UGg\nMCuvGKIvYvR1QKmc0zK3ScMI9+WEj0bpkJ5yiMGI1Rzg41yG//G+euatluii9ygVwEpamlPFV3cU\nSCnQd52zB65MqMgpcdgKnS8+xLUZSzeJ04Skfh0dHR2f85B5tradswc+zdSUv3eFToHHziXxi76P\nr9unoaWbxG8S6K5nF5NJINgwg3QlC/a0yd2mBUxqiTAk9auTvlXFJ30jYSbtAkewrAoGVGND3uT3\naKEh+yUmqcvxaEWWymT7PrpCXPRnjF4XjSiyUkY/5AJBdQoPla8l2nFTSdQ5ZtMYBerEyNEaTo2y\ncM7MuPFBCm3mwWetZ4gSWpB2ddQmWIU02HXA3KrSILImEVM75ACrKG2P0RZfEs6JdBRpLoSfJgdB\nbsCmVPGt9DjHIGr/T9AqzKcMmtMz5WazpKxWBEVRoVhVa1vndT1WwZ9eqahZRdt9SrMx40dw6Fwa\nN/gl0k2FNTqKQEOolfVqDoSGFrTeINkndpPCh2flNYM7HdrKPg4fYilTIsP8AIqWOOpV1IyGRksW\nGptkZR0BYsKWnefsgb8FHtQcO0yFztcDV5o5fhXgj+P/i7B0k/gC4AeNMT9CKQyUsY99q6Ojo+Nc\nxxlwXL8SWBtj7mmt/R1jzC2AbwR+Zp9GjDE/CPwQcAVjzJtnTrkO8NF92ly6SVwBeNE+DR83PCra\nZYM0n7j4MoEZELjsscSkjzEOUnZKSSyKhiAYSxsg/RHKR0YK8z4JWRITyMnaXKQ5JZv9EqQ2ZNpw\np0BFqU95n22/qd3Ra0Y/VLbmMFE+X4tyOR3H6HVVtjTl1xudjxR8xek1rIaYRtyHtA1LWFlBsvSZ\njVUYOyK9xnAqPitfx0JM0m6keIhV1iAy46VhNlXSs3jySYsAsjax6VkE1hbC7xCZV1lDqTxV05dY\nG0pFLYJJBdYNcxb9EWnuhqQl11puYWONVdqMkIhvFe33Q7DdT/wHIWFj0jwT063WsOV69vkY4rhs\nU643FVlVDMVnpVRQUVKSROWDryKl3HZqQMcO0joZk1akhlRcNvsj0nNO2nTiJR4FjnqTsNaeNsbc\nGXiSMeanCJks/qe19h17Du1ZhE3gWcDzZz6/lEC1XYylcRL33KfRjo6OjvMZZ0CTIPoQbneIYWGt\nvQR4btRIfv8wbSXsEydxU+DuwA2stfc0xijg66y1f3YUA+no6Og4V+CjprLrnJOCtfb3jyq2bWmc\nxF2BPyCkCv86QnqO6wPPM8bc11r7B0s7PFNIKqdWA6MGnYKcRGCOdjVvMwVCaTWitMuBcdLU1KZD\nyNfOZIEdcIzR8e0pdM9N0DPt5vY3UGjztcpX2UODySkEuQWHoEJ5zZpihkimlmBGCNTW0YU2cuZW\nKAFWTlVO6+SoHl0yOYHznlMrJWpLFGd4m4E2meMksrnHx8yhMUDM6ZBeA8A3FM5JwFw0OY16VZmZ\nsvlK0iN9MAnJwLmW/prNc6LaWknaluozhLHk+tYpFYxSJf2FrHEQg/fcAglUPuMW2aGMB7/C4VAx\nEK6iHePROlTT04kOGzGqFSMrRr+KpkE9cVonc83oFaMbWLv0nMrzDWYyQd6oAv/q701yfLu43kJm\nDkGLVsUsFGp3jNF856PzOs5rNiOXOtmplkVxsOtcayU9T43D4xmPigLLbk1h37QcR4mjjG1bqkk8\nHPhea+3zjDGXAlhrP2iMuQuB8XTim0RHR0fHceFMmJuOGEcW27Z0k7gxxQkiN8jXADc67CCOAjmg\nRg9oB+MwV9XKl2RhKZjOq+Iok25MpbdK8gnKO1Ssge1jsj2tHI7kQN9Of80O8RRstiClR6uhVJJ7\n1Cac1qwdDKoEhnmvWGfpMDikUzujKHSdgvmy0zpqHE4G0zkYR4/WRMe1iuk6po7ZTePPErugwLoY\npOSjtpCDCVNqieFUTmona0W45LRundS5vkBJP9E6aZO0WQWNZX9+cnBr0fYKtEcLymnlSI/3UFNu\nVdWfY5pypThw5+mkqUaH94ox30d06lKv10DldqzUyKDWVW32QG0dGEXdBzkfjrA+1l5z2g15rThf\naxKlr0TVDc821cDQWmgScV3INefEmg+1Q3zWJiDUwc7fSD3Euh3Jia3rini+OK1ThcRE8w7fKZ3n\n8ChwDmwSRxbbttRo9iFCavAWX0vIDtvR0dHxOYMqP9mW1wkixbYdGks1iWcALzbGPBbQxpi7AV8B\n3IuQs/zE4YT92WtVSXmQfAghkCdoDjGZWEypp92I1mNIe8wQA79SIN08oTO0OUTaawkscl6H63f4\nIyStNvRXpLRSHax+3yKfn+zqnpC22jm01jk4C2KKDZfszcUfAcEnkQLjlE/+i1TXuvVLeLz3Ij1H\nSMfhHEXbSONR278sIbGfarSJ4F/Q+lScm3iuUqGW84aAuW0aRHw6SU+sbPAlfXqhwWoh8Ycx6izR\n+mGVn4uOfqmk3YQxpbE09aCT/Z85aTx4seao1jJZYhpLojcDWWqWKdrTWjylRwa1YqVGdAxvSim8\nExVaahDh+UZJPGoRa6dZj4nGGp+ZGKZWKQAQBuVx3qOVZxAqcdIgUjsAfkjjHOJzXgXtIzCI6/kR\n38MUcCm1iOBjGSp6d9aQvALlSKkmjwLxF2TnOSeII4ttW0qBfaQx5pOE/OSeUNf6ncD9D5rvvKOj\no+NcxdnObuIIY9sWU2CttU8gbA5nJZwPduBRrQL3o0nDoaH4I9Jx79Gs8W6AwecEaYmFUzFzNvkU\npMU5MptygJEvvoNtfgmJKuGeSN+wzZYqJdPQT1igA6EAUvIBJJvy2KTOUCpoAuiiiaTzHEU7SIVx\nfEwVnuophzTiPmoeIJP8Sft+e5+p5nfSOEZfbPk+1m4GGbCWWEynctBcqz3k9NeC2SJTcLSspjAW\nGU6ZtBtfkhqmz0QyOT0ELS+likjjHYdTURM6FbWHoQpYmxTDIdnzVQmw3CKAOhR4lTVV5wtjbZ1S\nuwf3DSvtWDvHSq85pcfKL5E1HK+zBpE1Ch/aWjuVtYh1DKZ0wjeVEHwS4Xk6rRi9Z6UDKykhXTt6\nsgabNKF03yvlWPvA3QqaiatSmOf1knw+cX5HVqwbDUIG/+l4vyPs/GFfirPdJ3GUsW1LKbD32/b5\n2VBPoqOjo+O4cAYS/B05YpzE9xCqiXqC9efp1tq37NPOUk3iAc37AbguoV7qOzkL6kk4gkQRpGEt\nGEspJYV4YilVePJJuDGUpRxqhpNvCrHMIfSjUCqX+VmkMeTrBVOpsJSopFqZqGyubZmED5JkpnAx\nRUhuN/kYKIs8aD0qaB0usKPw4twsPdbajPcxJiP2l7QLJ237XsdiRkIjqiR4oU0I6Tr5JFRktySO\nv1NDkNT3ZDHVRX7qv+VYNn2ebfQ6xBao4QK8Wwf/VdRU09gS48YpXWkRJUZDxRTW0x8YqUG08m7y\nk7i4BtbxPiUD6fSo83NTyrMaPCvtuGDQjH5kUCOD8HnIJJFSi0gJIEdftIj1qLLfScbCpOefUov4\nIRT4SWskrb8xrp/UTvocQslbpQZWkSW4Vquo0Q9oNVbahI9FxBJDax19EUH7GSYJLMOzCeymmKxm\nMu8HwdmuSRhj/gfwFALDKdWpuCXw58aYb7XWvnxpW0t9EjeeGcSVgEcAe+1KHR0dHec6lrCXTpjd\n9BDgu6y1VZ6mSDp6BHC0m8QcrLX/aoz5aeDtwIk7r1P6ZKWGGDWduAyqijYFMrNJuTFytIdcpEX7\nUZQsLeLSNqkg+R+CzVfEXESpfOn4ZT9FGldVZHVrr06+Bqn+jj4VK2oZUnNSYNQeNAyCkeU92R+R\n3lf3rMpx76M/ImoXoy8aRShYFIrTbGN7JN9FsDmnRH+ngv3ZpwjmoUoLndOLCy0kaBODuI8NrDCp\nXUy0CYDCJiptBkaN0i6OaxQFfUpK8rW+gJHaVl40nFozTHM5KM+u3xQfJijf7+gVp93A5euB06Pm\nsrXOPgOA1eCDFrFSjCvFSmkGFdfpTJGgHBXtdY6NGH3QImpNovip0joYdPBLgGeFCinnddAqUqT1\n6KaahFJwWmu08qy15lTyk6ghf/9ynI/QOEe/ygyt8IpjTiwvofl6r0CH34Kl5YB3IWkru845QdyQ\n+QR/f8ieJR8O68W5NnCNQ7bR0dHRcY6hoqzMvo7KtHVAfIAQptDi5oRUHYux1HH9xzOHrwTcBnjF\nPh12dHR0nOuQ7LRt55wgfht4kTHmScDfxWNfRgiw+819Glpqbrpk5thHgJcCT92nwzOFFLCUHMHJ\n9JOqvE0qpflEUXXZ1JQqeC11PBc6YQn2SVTYuSbmaLCbzB5B7Z86jGsnLJVKLR3SUq0u6TRq+qJS\nMGgfa0/UVFpZ09qJ8wFkXsPK7JTNEJFC60OSweA4rE1i5R4KRbbQhXWmOSqlQuBYCqBKjuEZmmtq\now2Wy2MVcy8JA9tQzBuRYh1rSgfTpM7t+Ei/HvUqmJr8KjtS52imOZGgSokkFYMiUzZTcNosUYGU\nKmXg9BhMTZ89rbnstM7mIAipUtYrn81/FwwjWntWSjE0Fe/K+ilrL9NWXW1qmlQlTM9PEam8PpMn\nfEw1MiZqdGwn9akUrAfF4ELw3lqPDF4zKh1rm8jv2TQwcfRDTsOxTgGAwkybkiSm5JVH5Sc42x3X\nwKMJxKJ7Aw8kxE28G3gU8Jh9Gur1JDo6Ojr2hBOb5LZzTgrWWg88Ob4OhaXmpl9b2qC19oEHH87B\nEYJlgvSh8DHlcKrUVuTqXM86vlSKIBNaREjwVyiyVT8xtYdEosGidNYiKqe3CFrLCcdkm0LKztTR\nmYRqiU4oJatwPbUWMaM5zNFkdUwFMWifA8jaceV7jBTHQcOgFcNQnNYyECwl/0tBXqP3aK8qyb1N\nRJf/xpNTZ6gh0orDs2zTMCSH9ZwzfE6LmMPcs5pDSokx+lWunub8UBEiqupo/lTWIoLjujZNSDqz\nUiEhXqJop+lISfLSOFvnoU80VaeC0/q05rLT5JTtEKoFXjAqZOTv4EMyPa9dJJI295k1nXr9tFrE\nKFRVl/7R4XhZD2FtJMKEa65N6emztpLWPsV5nZ6PDIrM1RVF9TxJ35Xfm5D0slRtPCrp/mzWJIwx\ndwCuba19bnP8kcAfxeJGi7HU3PQlwO0JecnfRYiTMITfYUmBPcG9s6Ojo+N44FypjbHtnOOGMeYr\nCW6ARwLPbT6+MvAKY8xXWWvftbTNpZvEG4A3WGt/QQzmAuDngU9ba39xaYfGmHsTbGIPtdY+Oh57\nH2HDkcUxHmitffHSdp2gzoWSLMEjMSC1iMLnVNmg77J2kWpfp9QcwIQ0V9URThpKTgHiK0JDS3WU\nWoRD5aAjmah8TLZh6hQaIKR/JTQIIaUmyU+mTpC01zYpGzrY+8N1HtF07C/aynWUxLSKmkSUAof5\nL4r3RTJUXjM60CpSRWekq+zHaeY71CcnaxZrdapKCd22sQktlTIORPSd7rXI+PLcqha216CCRuHU\nIM4J6+80p7If4rQb8rpskRMEAo6QEC8ptWGsQZvQUqNo7jGtj/UY6oxffjqkbV+P4by1Tmsn8lGB\nU0NpY6WDv671Tch1K/1epd/6vQ8KaXVe8T/53KaLvgmZSDKkIPdlrcYgvkElLbH4GRPN3flSe1vW\n4A6+iTodOT5Qrwe1eX0cBJu02PacE8BPAk+Qv9UJ1tr7G2Mc8DPAPZY2uHSTuA9wo6bDy40xP0dI\nI75okzDGPJGQnfDvZz6+ey+F2tHRcS7gLA6muy3wo1s+fzRB6F+MpXESVyaYnFrMHduGZ1prv4vg\ndT9iSH7RNO1BlsJmEqokrUL5Kdu5unYPSCnCIQLaZmyZ0h/haRklhQ0jj0sfQ/JHSOaS1CJGYU8O\nJUaLbXkOmsSwCdJs0hxWg2c1wGoFp04pVoNiGBSrVfBRJNZT0WZUDjqSSdfyvOfALoJ9XLlq3rP0\nnvwRIg2DTAu9yT5cghp9tOt7cvo/kdo9tRD+DoFtmqlfaczMmpRYbmDtV/F1KmsRa7cKqTL8kIs8\npf/lWkjjWmnPoBwr7eJ8h3QaWpQCTQFwMrAMiMFu4blefhpOn/Zcdll8Xe657HK4fA2n14rTa83p\nFBSXUlcskHYzq00V35RqXvK8kgZm6hfbhDpFivDPRV/jJi1inVhRjRaxqb8jYzf5Za8TwNWstR/e\n9KG19h+Aa+7T4FJN4pnAy40xzwbeF499IfCdwB8t7cxa+9otHz/AGPNowob0POBh1trLl7bd0dHR\ncVw4ix3XHzHG3MRa+565D40xX86ZCKYjmJv+HLgrwYGtgA8TfBJ7BWZswHOANxIcLdcHXgJ8Nra/\nF3JxljnJ0rua3RQuiCwnF8+JLKcktQnbdW5HSLtV39lXIHQRoZY6VVIGBrZFYeH4OIIk3bVaAkQp\nLtp5U8j/nGTksiRXJKqUMkPcRCXpJOkw2W6VikybxKiK/glX9acYRJI/LSRJR9BiFAqnY5IQIRkr\nIc0PMbFbkfBLcZk0r4GLU6R4yRjT3mU+fVvIqX5mLt06Cp3Tbmjl0EoF67dypHiWXE5TPKMRjY6f\n44ttWkq56/h34u8X/4MKMrFkK8X+VloH34SY30F5Bj2KVBqF3abwZY1EW/967VmPnvXa5+cQtEEV\nNMAxMI9WWqFVeKGmMUR5vpRn0KFtnerIphK3QmOdK7eajpc08/M/mLrxFUhmmqcwCUuMhJ74IhKj\nSZZYnWsbGr/UIXAWU2BfAPwS8F3tB8aYFfAbwAv3aXBpnMQIPC2+jhzW2geJtx80xjyeUPVu702i\no6Oj40wjmX93nXMCeARwkfl/7b17lGzLXd/3qaq9e+acqwcSCKQAMljgEkZgbMUYYuzF24EYsHlI\nwgEiZWGMESjYgAAtEgPCvCwbWYDih4h5cwkRAhuECRCwjWzHMqxAkFFJCAchARborXvPTPeuqvzx\nq9fe3TPTc++cOTOj+p7Vq/t07967du09Xb/H9/f9WftrwHcCr0HYqB8K/E/p9TecZ4d7azdZaz/O\nWvv91tpfTP8frLXPPM/BTtjvYXKBluPaPNx9d3R0dNwNZCnPsx6XDefcHwJ/DilNeAHwS8AvIAvD\n/wX8OefcH51nn/sW0/1NxIW5H/io9PZ7A99grX2sc27vYrsdeATw76y1n+2c+xlr7WOQNqk/fJ6d\nlLDOjuuiiDPq6kko4agmYR0bOmROi28dG1VDI3EeZsp0VpWpgOn/KEq8pySt06NNLGY6q06hhpj6\nP5y2umslSqzQdJNr3GOd3m+7ZSgVU3hjWw4iotAhF0iFpPaZJBUagV2t58nLkJK9Er6bS5JIMlZC\nTUZJ1zTDJOGYFPKDTIPVNXEc82s5kEHCUXnfurl2+Zrl/8+pnnJthzxerUo4pQ1lzb6TrqtP90Qb\nVszhpqmhvvqltZmUSFtK7kBMYS8JIeaQZQn3qFj6QOTeGxK603PJiXR9vQcfIqnxIsaQaLLyrIOE\nNIeS3J3TfXNYsSalhbgAUoyWe44su9PJd2vYMo+rfS7TkD43uv3OYp4XMf9KEddlbrMExxR0ud9m\nMjYzOvfDVzNtsU9i+h4lrnHO/QHwTGutQtikOOfe9FD3t++8fQXw3znn/kYzkN8DPh3JV5wJa62x\n1r7aWvtq4COB56bXXwF8BvB8a60D/i3wU5xTX6Sjo6PjsrA07E563Es456Jz7k3OuTdZa5/3UPez\nb+L68cAr0ut2fXwV8F/ts4OU1ziNMvtf7zmWc6EkovcQ7it2fhL8i0qVIrCTvBHpc5CkJJjLRWTL\nE6o3Ib2xFjIVVJpoFYGb01p9zNaQJK53hUOzMZyThvk9v72pnFNjLZbireRNbCUUNUxBY5RGKZ2+\np4q0AognkS3JLPKnS6GUeE+Zzpm9iCTCwMCUktepp8diftpHS93M8zmbh3TG8yK9Oa25kBsiKWnt\nc2VbEasoNNmFVwULjxFV5Dd8MPhgUuJez5KoUnRWvYhZz2ml0AtKaqblDklCI28HulB0lwnjFiHG\n0n88i+wNLUUTRU7T7/TyUhFlvleiBh1az0Wd21ouDlsmSmjQut5zM7JAumfaDoFyXM0uCQ4f6t/b\njMDQzPlDobPvwhVOXJ+Er0OqsM+NfT2J30biXEt8BlJM19HR0fFug7Yh2EmPe+1JXBT29SReBLzc\nWvv9gEkd6T4CCTf9zbs1uPOgyCrssBx3Wg9KQ/TF5M4x8CryN/+exidvIizi5bXgK9nBydJpBcvS\natzE+meCb9lSgtT/OPcErnHjtGXal3gTcwkN+VyjUjuwKtzXWm9Leeci2pesx9GEQrmc90OGIZhU\n8GU41uJRTF6l3tayL92YHUtZkva6lFwEgVFtJO7OhAlTFc5T1YbP4m6tBIkq1nlzD+Qr0liNTeZg\n5g3GLGvdyqnoms+R/25TYdvrJpRMXbyIaUHJXHoGZb+NNzXfX91ek72uek9mL6cWIe6moLbXYPmc\nCzKNzt7LwvJGcl+DTnebosiYD0XGhXLdTy5ck+elx5FzEVKcKcWE2YvYJWTYXL3y9xSbOc7Fpa1E\nfs63VSn2el9cBK6hJ/GQV6y9PAnn3EuALwA+EPEqPgu5Zz7JOXdXaLEdHR0dVxVXuOJ6J5xztx7q\nd/dlN/0p59xPIQnlK4qF5bgVf2wtyFwRtLByk7hf7nUtrCVV5ToaL6PdV/YiKsOlCrsVq0fFEnOO\nMsgSd8/btEJosEtITTyIpU1QC7OkYK8aS8KaiVHMwJgswnzqwmQSEbnBiBcxai/eQsOoyceetMcH\ng9YRrQxGwdqr0r94ud/2yogvkBg9Zf9esjgLL6Ja7bvR5gUyy2g5J3NPMqB35KUKsydNa/FIGq/h\npBj2Ula7MG52ILNd1wAAIABJREFUFHZpFcs1z9er9aaaQc88zKV0SJkURfH0cjxfKZFGCQGMV3gi\nupFwlzFnqz4WS1yKOmueTKX7K1viIg+iiLrmykoOoGHfLUUlM5ZFdNnryfIjefxGx3K/FXZUm5Mq\nYZwT8hGNt7LLqzLFU7mYX+6ryG6y1n6Jc+7F6fVzTtvWOfeiffe7b7jp3ySq67Tvjjs6OjpuKnL3\nvrO2uQhYazVS5/DZyPr9h8BznHO/stj0OcCL0+u/dcouI5JC2Av7LhIvAL7ZWvvt5y3EuCxkKyjz\njGoMuuYQWgi3fhlAT8ym4DFs5pz51pOIoTKacrOZRnwuJO72nCcv3oRKjBLdMjdyPcWC2STD2r4R\nQ6weQbbOoXLBiySCkuNK/qJ6J1rN2SUmWfej9qzMxKh9sfSz5RVQjFEz6QHth2SZgdaaKcy9iRxz\nVo1lXkT2VNxiNLVeRMtsKv9UvaZb1zGdXxGIa/M0Cy8i7ztfv/a+yd6EVjEJytW6l51NohZx8pJ/\nCnMvQiRKahvNPBe5/kETZnmJsDCDpUqkYXule6bkjRoBRqMVXkttBCS2mSbVtbS3udyDYoHr1FyL\nRV2BSMgYFYunW75PPT8flFx/lYUj63a72E/Zi2jzEZLnqrmX+bWtebqlt1ZyEovc3ezvQVW59aVo\n48PBJXsSz0ZIQh/tnHubtfa5SB2ZbTdyzj25ef2BF3XwfReJzwfeC/gKa+0dYCa855x77EUNqKOj\no+Oqo+0lfto2F4R/D/yyc+5t6f//Avg2a+2Bc+74pC9Zaz819+RJzYg+H3gt8GLn3N6j23eR2Lup\n0L1CbRrTPOI2sykqMXOjNhB8YrYkqzJ4ib8Giaq1XP38/7IfKrtJIuriRWyCPKZsVaaGMyYx+iPJ\nEks5ilyRXWPc23HpeQVww0wiV8TGWZV0RGFixCdOPWRruFp2Ost/J6vOZE9CT4x6w4gwjrK9HNF4\nJa05W1lvo0SmeeM1OjW5yUKBujBK5qwmrQJD8VTE0m+9iMw2OsvqyzkDaUJbr/zsvsje4XL/mc2m\nAkoF0MkCjaCUwWfvpLXCF/tuG/Rkb9An6fKTrOiWZaORedD4cp1Nc6ySY0vfidS6jvZ+l5ySYhjq\nmEqdjK55ibbyPgSFTzmLHDYxNFZ44/1VUcqaI8heskrC8uUvJgo7rBWXrOdOqr9o7j2dWV5x5hUX\nUcwyl2rLW8t1EcU7ivVvo8x5kwvLTLGLwGX2k3DOvXLx1mcCrzxjgfhG4K8hrNT3A34ReCXwKcD7\nA1+97/FPXSSstY9xzr3VOfd9++6wo6Oj46bjosNN1tpnAN+146O3O+ee1Gz3dCTf8PFn7PJZwMem\n188EfsM59/HW2icC/4aLWiSANwK32zestb/tnPvj+x6go6Oj46bhousknHP3I9p4J8Ja+7WIDNIn\nOud+/YxdPsY597r0+pOBH0vHeb219r32H9nZi8Quf+kJ5znAZWGWtG7DTg3tMaJQSqWQU0yCaW0R\nXYDg0UonakIse285kvL9HDbKYm7SkcxHk6QZdCmmUkgy1BBmdMOTqK9b57ZTF7+GmnSW1Wh6DkRU\noUAqVQX6cpxUl05zoSSt5bHhQB0zxA2DX5fCtkz13egDtPYodVDGolO4K4cA8vh0CYXF4u5XSqeE\nfmZhoBwKOu06p5BQS4OVbm86zXNEqdoXOSpVLqMiooOXO6GEmzRKx9orQSH7iJnaXEOBZZ9N0roN\nFYZQQyy7E6kS+tBJjkQosEnUMCfP2Q5r5fs3n2Mu4cyCjDl5PRiJooVIISq04ZcaapJ4uiqhNJ0K\n52QIuYjSNCEtAKUrQWCIiinJs8i+tYTsQt7Lbks637dL6mtbNCjnWkN5raDfFFQpXpRwbk2Wbx0n\nhzrVNr324eKyKbDW2ucDfxlRcT2x81yDN1hrPw54AGlp+sy0n6cAbznPsc9aJHad5iWzfzs6Ojqu\nFkLcI3F9Qb+U1tpPRpLOT3XOvXnPr30L8HPIiv0S59xvJ4XtlwPfc57j75u4vvKYF09V+utsC6WJ\nRKIyKBW2aLAqiMib9qCUxwRVJcDz99tEphKqZE5aZ5noKSQKbKjWnE7eRKYbtoVCNXm9u/hoKdin\ntVik2YsYdGiSxaHs12uFCRGtNJPXIvbX0FRHIxTKsXgSG1ZqzRjXrPwdjF8nyzudqx7QxqPMobyn\nk4UdcmrTbEmFFIu3JN1rB7rsTSyRxfeWFvXWdsmq1YQk3914E1HXgkWlUXFKXop4E/XekNdK54y+\nHL84jxihEse5hd8W0YVmLDC/hpn2WyU02nlIFNg4Fa+3vd/a+QDxQMt+s3VPxKjsFbb3kHzeFtTF\nWGmqakv1UVeZEA1Dk8CuEiDi7eRkcva+hI4aiX5OtW1/JFv5kDwXOlOiVWj+Clsq+JxenP+uQlRM\nXhcvYmaxL+4/00QVcoL8InDJnsRXAI8CXmHtjPX6dOfcr+36gnPu+6y1Pw88yjn3m+nttwHPTaGt\nvXFjFomOjo6Oy0IIZ1NcL4oC65z7Sw/xq78PPNFa+1mI1fCG8y4QcPYiMVhrv4x5bsIs3ztPiffd\nQim4ypbDTlnvFD9WmqCG1LfXJ5pkBALKB6IS4b9q1VXabFSayWSPQDX5CJ1iprp4EbmQLiYmqiYW\nGQSiLrTHLDmwRBbka2mvJa7deBEikFaF+dqGOBtlSm5gCGKB5f2MJjAaX6ivrRcxbsSTUDkngSaa\nAXJc3miCNrOis6jnPYaNCqWQqY2l7yqYyq9Ln/AswV5EFOcyY62YW4ha+lOH9P2UWzAx95OuVn4p\nqstdeZRKkiUaExUx7UspM/MmfJMDab3A5VhmlGkVq+Xc5GVKT28lXoSJU82dZSu69WDRBLU4f3If\n7pybEu8wmuwtzi3skmgNuVFUpY5GE4lR8lNRaQYCQWuU8sUDME2+QPxvmecQQDe5iWXMv/WCM7I3\nXGXqY6W+Fm+s9SB0LVDNxXszGZB57id72Sb9LcwlOa6lJ3FuWGs/EvhJ4H2QujYFjNbaNwKf4Zz7\n1X33ddYi8XvA3z7jvcg5Srw7Ojo6rjuu+iIBfB/wUuDvOud+H8Ba+77A84AfAj5k3x2dukg45z7g\noY/x3mBpqUJrmUWiilJIB8QQEDW81DIz5GY1VaoakgeiNDEagh7RyuP1SFSKEBt2U7J65FEtOYVC\nJYsqyyDMiuliHecSrQdRYrkNM0Re+ySt4WcWX0RhlGdSA14rNsEwmlBE54wOHGjPaCYGJY/RHzNM\nx5jpGOOPUcnTitoQ4pDmw+D1KHIaaiAqhVeR0MSxocmdLKzFel2EDhNSuVYbcy/CicmTKO/H+etq\nzUdMnqAwSG4BhcYTlNnKJ2UJFmJtSgOIR6KzN6Maiz3Ojl1YT43QX71msUh+17h7bbakS3sqYXaZ\nsJl5v3OPVyhLShIshVWVUe4LHTGpmHFIKSLf5B1igInqnYYoMh45AeuNsKdyzF6pgIpa/l4qsa/8\nfQWlRQyTOXurZTa1P5JFIkNVL9gschHtNW3ZTLvyENmTqHPezkcuWKxFgcuGRg8Xy5zLSdvcQ/wx\n4Cudc0f5DefcG621XwWcS1rpItu+dnR0dLxbQLr+nfG4t6vEfwSetOP9JwJLYcBTcWMS16WdY8P6\n2LYaFEEblA+pXkJYToVHX0z6MDMDFIA2BCPTFfQws9Zz/LTGTueWjtGx5CdUaQkp0hwnle7nJkLt\n/6u8QCyewKCEmaRVZFRTEuaTCHlAMSiN154pGsbgmaIuonVGRVZ6YtATB/qYMRwz+GOG6YhhOkJN\n6+JdoY0Y6WpCmw0mbNBmJZIaJcYuyZdMW881AUsLrjBXlJ5ZyJmNVOZVmTK/LdtplgMor6WKIYTK\nBjLK49WAJpR9yXUOJSeRv62jJwZNVKbUhgRlZOyxtI0qx8xY5ial3kFVixyxmId0rbIciVEiaGji\nlAQIK+NKvF65V/M4yryhq/cy81YoTaZMyhVEtWh/G6WVbZYU11q8iNHU/MRg2ntOvF2dPJvCFtvh\n8cp1O7sIIefUKtOpZpvyfZFzEZvslXt5b2pk6ZdeSgAG3e6XwqrLjDqdcpYXgctMXD9EfC/wv1tr\nfwh4DaK68iTgvwf+ibX20/OGzrl/ftqObswi0dHR0XFZuAY5iZek5126e9/RvI7U9iE70ReJjo6O\njnPiqucknHMXlkq4QYtEGx7a3T+CVMwWtIGQEoxqkKRW9CXMpHKxVS64UpoYJUSllK49mEmFRTkk\n0iTVxCVuEowmohK90UddVGF3QaQn5l3FWsXXnPTLfSCG1OFs0NNMvVVCNhofBzwar6XvRR5X+x2h\nvh4xTkdov0ZNa7TfQE7mx0BQCm0GdJCEa5aW8DFTO6VYUcccBtkdapI0rCmXLCi9OySVw1JUeYz5\nFU+SHIVqrJrkNQStUnLVy9iah4oTs4LLROUM0aOTcq+OviSvNXF2V20rvMr1NbrtIy5nMego1yh1\n5BuUxyChJhMmdNhIgV8batMGHaRgETU39JaJ8ip1oVChCbloiFP+QasDDkmiJYelQiqwq1XEuuxX\n5//pWkwn5396aKntJ5J7l2Tl4V2J5Kr6yizUtJn0rPvhsnguh5UyWvprDYNWari+oFZA18CTuDDc\noEWio6Oj43IgavOnrwL3YpGw1v74Pts55z5z333eqEXiJIpbVFq8AslA01JhVQyzap+S1Ay+Jm2z\nLEIS//Ol+C7tPyX9agctir697LOVGogzB2JmFSEJSB/VFu2sLczK1Fetml7RyotHoNaly1s+90wl\n9UrkQ3KqUCxszxA3jF6S1dqvMcmLUH5TqqFiVMV8Ws5xHVuc9YKoScq5sFqIVSBR5icneXd4gLkE\n7wTLtYjrRVWKFcFUb0JpJjUwMKUEcFP1Va6P9MvIXQljstx19E3y+nT6pFjbcvzShS4lTgcVxGNT\nU0pcT8mL2KDDBhOm6r2ma6aIeA2KfI/m+didNM4Fl+JpqsV8R7xv7zXZ3hgwQeE9+EHu2zG3PEQX\n2mr5lgrExkPIkiRlHKp6DdlTye8ve1prNafPQvUifOo4l70ISVgjz40F3x6jdlvMRaY1aV28igtM\nXPs9mg6d9fldwtsveoc3apHo6OjouAxkmutZ21w2nHPPuuh93phFou2BnLuRLZFplmAImiTBoUuh\nVTG+YhAvIgZUiCkurFHBV+/jBJTYamPxFDE4FcXijTFJWDex1FSApdimv0Ij7ZCi9Znel62lQXkp\niAtCTzVhavYhMe7cjzs0lrIOHhMmhulICuimtXgQwWefmn3LafK4Usfkubhaes5USqG1Lv+IzE5L\nry2ak7mIO9UVfBApePEGxcqdosFET8gelTYzIbo69ioTH6JHKVVkzIWqG1BKIwxXKbEjpplRIRXd\nKWi82dqHOjCqiZVOnh4TQ1iX66SndcmPiNebihFRROUJ5mTyiW6exWNTMxmXGKWoTn7U5lZ4CJSe\n2FlKJdOzi8R8IwditBQbZv9hl4eXJULamL3kImLpp148iVRM1xYqZomaEEUGfGo8ic3MG6peRJmL\nIj2eJE+0l853VGl2fYo3eC7skZO4qEPda9yYRaKjo6PjstAT19cQtbxot5UvjJZQi7ZiKqwjEmMW\n9dNAZS6pEJM3oUEHYpOLOM2bONe4syEWJR8RE0NmKRteGgs1/ayzjHORnY5V5sH4DTr16s4HyvmJ\nIj0dpemPCl5i49MavTlC+Y3EyL0vEhVlsFnGeyE4pxFZDpXYTW0v55yvkF1UBs8uuY22GLLFzJNI\nHkuMsbCKmO0rWaQ5jbIoyBPmkK7FdOm5zUuAIhiTvKBQhAyzF5PPVyf/r5xVc96VheYZ1cSYBBRN\nkIZOg1+jp3VhNwFFAiYAShvZZ9M4axdqY6e5F9HOh7CY4qzAS2t5yNzFVDTHzIsoTCkiAUNUQRhk\nC+R7MwSFkMNiKdpUSqTMjRJRSRGlDPOmRgrIDaRi28O6NkkKgaZAdX7sVj4/987OrL+2l7hmSyP9\nISHEsyuq73HF9YXhxiwSHR0dHZeFECLBX72cxN3AjVkkiqUaaxx8ae0X67c0d4nVKlZJZlrpPcQF\nKMeSY8v/M/cblAipLcY2+y5zC5tsjyaefWz2m6Fn1nkVzMtNfLJnoINHJ/79kjUzGzAUAT/lNyg/\nzbyI+YC11IvM5Lur9d8ygGq70tg0GJrXTLQSG7v+n/ezNW8pH6Bi8iZyYiCEuYAfNZfRiuVlsb/q\nFekih67I3qLIZMxyEiqio+RbsuWbWlhV7zSzhpo2rUZ5BpKAYmKRmTAx+OPqRfhNZcspsb5RihgM\nWnmRgYmLtryqepEKVdhDuQZil2R3m5NApizlJsSLyGycSWemFGit0GjUEDHE5HnMWUIi4ii5q8FI\nPZHW4p2A5CEG00rbJytf13oekje4Lfh3ej/p7DlpBYNJLWG1n7H+DNImVjzQC/Ikrr4sx4XhxiwS\nHR0dHZeGKOHOs7a5Cbgxi8RM3O8kZlPaLsd8NRC0WH0hDCidZbGHwuxRQUubztbbaJDZEq08scl8\n+XzMzN+m5hZypWixyFSN4erC8Jifgy6ewxy7qpFVYmjp5BkUaWxqfkb+E2d1Icr7WmkOgCn5jJhE\nEbPw3Nb8q2xRsuVBLDnqhbGktr2J1rosHmK23lPNQMxeWK4QrqSgLU8r77vmUvL1jOJFLOtkiKUR\nlXgVcW7FUz04EhMtf575+CblicSSFdbZENYYvy6MJuOP0X5Kja/SnGsj41IKpTxKh+ohppi6UQof\nA4rk4ehYKq2lmc/u3ES5X2JMYoykH7paf9A+i3oAeB1Lu9Is4theTxmDzJ1WIgg4NNeyqgSIrP2Q\n2Ufp+9K+N2IQ0cu2NmMX2loMnc5/KE24cl1K9eK0CqltVLiwiuvTvJt2m5uAG7NIdHR0dFwWYox7\nVFzfjFWiLxIdHR0d50SnwF5DZPrrTlmOlh6ZXycxuqgMESmSi9HI6p8L5mIgqsIJTCEKPaN/5n4B\nWgW0VjMaYEi0VpFnmHfkavtR570FBTqq0pnMR7V1o82bq7WJWVUKxjKNEigd92pxoM9fTs+hUH2X\nfTTkYKa8zj2+oSbB2xDMoHwqKquhlzI/+LJ9DvvkKxYa6Y1lMjy/Ls8p5DRo6fJHChHq1IOg/a45\nIXSRk9hGJdrrIgShUie4Enpq6NUqSWYQRcCwPV4OM5kS4hABPxMnobuGSfqGBz8PNYW2hVykdEok\nh748iqGGvYqgopyjhxLOCUrNpDnah06CfpnCqlNiWuiuNQm8g+Fauhmm6JR8XyYLoc8292MT58ph\nuLaw0DQSGTGqdAVEQlGljuRyDlUUsF6bdOySWK8yNYNOSWsdUqdFXyiwuY/4jBb+MOB9wJ+hu3HW\n59cFl75IWGu/CNEz/zvOuRek994L+B7gKUiI8p8DX+Wcuxmz3NHRcbPQiESfts1NwKUuEtba7wYe\nB7x68dE/An4P+CvAbeBfAV8MvHjffSsqJXUXlsVfxFDE23JRXQgD6Ig2Q0lsizCglr7YOiVtk6Xe\nei9ixaXkmZFv5320Et9CAax9qbM1CmKztz1+s3XcdmErxWjJe5iCRqdEsngzAW0SDVabmdyICl4s\nV19l0YlNhVJ71xtDlqiOC++J1tqnFleVgrNM/yQV+alqkQNz2zwmamqyJvPny6K65XnnpLYcIaLI\n/ZhrIV8r7jY7dk7aKo1KNNjzIHs07Ri1CgxMJVGtY5CiuUbET/tNoSW3XsSSUBAbafD8WfZqNIGI\nRyUJE12SwEnGPiWujc4FbnIpC7V4URtpjGw7DIphSNtrGE1bpDcnXFQvrSavTxIOab2IpXcJEJTc\nAyr5ESYlonXIYoAQSsEfpdjPaCnQG0xTPJeT1lmOXU2za6GImHhRxXRnF8vdlMT1Zfe4/hHn3NOA\nd+Y3rLWPRBaHf+Cci865B4B/DHzeJY+to6OjYy9EhAJ76mNH6Ps64lI9CefcL+94+4PT8+ua914D\nfOjDPd7Se2hzEyT5cCmUMiIpkTwISPTYVoQv9bhu4/JlV5n6qBSDFv9BqVC/uvQkUsFPzk+0eRTx\nIjQmJinmGCU3sci2ZG9DodnEgYjC5IIrHVBDLSgsHlbwKJIXETz4ZMVuc22JQdcAMPCUv/qFvP4P\n3nS+C9BxLjzx8Y/jN172PSnvJX7DLP/VSIZvFy8mOig1v2A0hCQHziAeRivwp5MnMRjxNgYjMf7R\niJU+DtVSz8J5J92/7d3Z5pKy19B63dnTaz1JT/IKU/Mqk6S+g1b4JPMBqvEkkmBgHptuJGoy/Rhf\nZPN1lMI9fVGexBVVgb0buAqJ6/uA9SL/cCe933FF8Po/eBNv+48/yzTeZjPeYmMO2ZgDJsaki6TL\nIpxDTDqH4lLSsP0hyT0uYtbojFq0gdJPScYu1c5cF1J+ZNCi1cTiR5BYe22oDaOSWoUxHGP8ulSm\nqyAc+ojEaIIZ5YfLrJj0yKRXTGrER8PEUBLkUJPWOdQ0xA0myg9TSVYHqWYvHehy2K+tjwAe8TGf\nddGXreMuobObLhfvAg6stbpZKO5L7++NJUOljWsXyYfWA0i5BhFSq0V4qbOpyCMkyQr5rvx4xCLt\nUOPyuUXioAJRe4mVB1XGoJsGKLmNZWZeVGZMLD9+Q4rT+mikaCq1HPXZNosq/ShqJpAkmgLFCFrO\np3oRIYkHikS5WKOeImRYmE3Nr2vQoAInRZpVDElIcCJqjVZhlo/IC4NupC10Hgs5x6FkHlF4PQoz\nK2UxdhXXtddV532g8FQBPt3kG1qG1S5vTcbQMNdo8i9pP5klVnIYzTkWFpeKMurMZooiA66DTwtR\nlUfRLcvsJKkUbcRbzbkvybbtyAvVecl5rywLk2P5g6mL5mZS26J4ipKHGJInYXRMr1Os3wSMjozG\nzxooFQnuYrk3DLDGWxB/qP4dZWYYUNh4PkZQAwGP0RodA1ppBhPxMaZxV5kPyMys6uVkdlcuotMq\n7Lz/1AVpZYQ92E3hhrCbLjsnsQuvQX6xPqh570OAX783w+no6Og4HbmY7tTHDXEl7rkn4Zx7wFr7\nfwDPs9Y+C3g08CXA33+o+5zJglOttWVOIpK7XKZ2l4yEaDBKE4MRWYTSYEUXCy838Imowo8flBdp\nCER8LjR1BFmOQLyN1DI0Sxc3Md1iwUaFVwNTDOhoUFF46ipKc3gyJ73xJgyKjVDsa/f6QUZOzK1a\nRRa9SnFE0CHRR8KJ/nFl2MRiEWst3HPxYmqoKcd9s6eRuenZ+8jXIyKS3VFpTJxmDZFyGGp2vZrr\nJ95ULPOfPYpsu+ZxAHV+VVx4m8290cp3pDG1Ioa7xlKt+JjkMoJYwK3AYpJhX3oRxJiO2+wwMdRy\n3ivoUcJei/utPcet66RqjH4wVWoj10gs8xG5BiF7DjnO33oQrSCfXnjBQ2ZyldBilRDJkiY5F5Dv\nH5nz5HMog9djcRI8utQQ5RqXnJeQWowqVZPPM+diZgyqHMSMvs5Y9CnkeTE5ibwQnLXNTcClLRLW\nWgO8Kv33icCftNZ+IfAy4NnAS4DfQryK+4HvvayxdXR0dJwHXbvpLsA554Enn7LJhWTtcqy4jd/O\nKq5z7LfERU3ZJmqFTjLiOourLZKtWeRuZjmTefKI1lxUYrFDqYzNsdsxcbeN8hK3D352jGxhBTUx\nKMMUR3SUZOkUpZ5jSsna8p0o9pFKNRObmOLA+gBtxIIavMKPUgGUv6mAOAEqoKKqd3Vq17o9uQEd\nJ2IwUjkcA1r7EuMvXP5dlnSctqq5gxqqd6YMQQ9zq7nkB6onWFk/2UYMwrNPHkVYWNm5VmNZd5Hv\nhRhVqbCfezhmZx6gDj9WL0KJV1XakTbnrlPSflaDku/BnO9o8h7eHCRPYsCbUZ7VUL2sRBLITZQy\nsnhkUAqjIJrsSVGb9zSXt20p2uYf2loe3QjllYrphQeRq8pb9pAOPlnusRFMbPJRaY6DHuVvVYvQ\n44DHRzOr/TBa4aN4DD5Up09aoTJvhdowm4oPWMYTimjjRaB7Eh0dHR0dJyKEeHbi+i4sEtbapwE/\nCnycc+6XLvwAO9AXiY6Ojo5zIhfMnbXNRcJa+97ANwNvudAdn4EbtUhEJUnjXfTX/PnW6yhhJJXC\nGiEGdNSpG1h1k+U7lbrZ0mCzxn8JgCzE77SqPXYH1Qi+peQuyR1vex34FGYw2mOQ1zoOeCX9mYUe\nOw83QK0hmNSAJjDpFdo0on5jCkPkZLTSELyExzIdVumdSm8ldKQ2aR+e6Gs4qCQqU7ghd1xTflOT\n5SmTGpXGZNHAQggYm9BTG/JRJeyUQ1El9Jd6KoRy3Zc00aajW2xDTakzoarn3Ia2yvfbAjahOMz/\nX2Q4UsFWfk5zQBPikNBSFkzM/TkSISK974eDcu5eDyW569VA6rGGbwincg1jocAOOsAAyqvSIU5+\n0NptU6hJcWKIKdO1a+hHjppDpToVqhWqc5DQaRtilET2tmhkUAPaDEJ9pYZ8PSaFswy+kecYkmgm\net43WzfU19JLvaXilkLS2PQGuRha6j0KN70YeAHwNRe949NwFSiwHR0dHdcKZ9Jf91hEzgNr7dOB\n90Akiy4VN8qTWCalTvQi2qRvkTlIVke2RhYJt0pR1dWSbYqrpKAq4omYLIudkput9ZU7lIls9KbQ\nQ1t+YlS6JC4HvWbSK7weGNTIFEcmZaT6Nxqp/CyFXam/ckPpDErj1YDSkaA9So+oISfV16l4Lon+\n6YWVpYdZAjsL06kYUdqXscrcp6K90uFuUz2IXR3vlE4JS000A1GnJLZpk9lDKSprvYuc3M4PlaVV\n0IUaWw7TEEdPwxbFNRdfKSkMRAkJYZ4sTkWFJWm9weQe4Tsqqst5LzyI7D0BTMNh8VbbhLXHlGR1\n24VQKYp0uMinpwJOpRliTlgnj1fF8qwUKSldxSaXCeqlSGM+Xx38zIMwflMT9aE5/ywg2c6zNijt\nCZlarhQmjJAvAAAcIklEQVRGSWdBo4damKg9JmoGXedcNVXOuqHnliLVLObYSpbk65hJFRfkSYQY\n9xD423+RsNY+A/iuHR+9Hfho4FuQPES01p5jpA8fN2qR6Ojo6LgMXHRnOufc/Qj1fwvW2pcC3+qc\n+53zjPGicHMWibhtLbZ5CRVjiWHn99pn0bNovlfim7WQrBwq0zCbmLCOvhR3yT5Ctb6SB5H1fExp\nQCONZ0qRVd5/tqTNSNAj3qwIemAyK9ER0qIj5BnwscaniyRGSwNsZUW0STLo4jUFJCehYiAavzUO\nSl/v5C3k5jjBzwQ7shehsg7SNEGYYJpQYQLv2ZIl1yJtnfSqicMokhT52Yw1X1GKykaiSXH6JJGS\nczcSk86FeLs8iYVnuLAoy70TAzokCzeQckwGw2araLCVJykaTcmazl5Vmc80h6F4RtVj8nooOYlJ\njyX30kqVFOrr4tyMCnLrJst2iIqgmTWkKtvn3ExTfJZF+nTKrxSZkR3eQ6aUSpGkrx5E0qVSMcx6\ndtcGVynnozQqeKIZ09+elsLVlHsSUb4JowxamSRzowCPVpoQJb+Scw+l0E/XvEmarXmOKOVKsld7\nEQh+D3aTf/jhJmvto4CPB55qrX1eevv9gPuttX/POfeQi473xc1ZJDo6OjouCZeVuHbOvQN4TPue\ntfb/A57ZKbDnhI474r9QmgPN8hNtpLrIcG/n8IsXkayv/Hp7u9bzWBTypJj14NeorAzq15hpjZrW\naL/ZbmGZY9ZGLOdoRoIZ8OZg5lW0MeulJEV7/OJRJQsVPcKAtLjUpsSQY2EgnSDREQPKV+lxlSTH\nVYjVc5g24DfEzQamiRiDvPayf7LAmpaGP2iNMgaGEYxBDyMMg3gUjXcRh5UILA4r/LBCh0kYaGZE\n6VBYQGoh6ZG9iCyZUfMTNV5drMwdonuhYVjNrnkrO5GLxxoLejs/Fpr8ylAK5rKHlBlxXo+1oJLt\nHIQmpja3oRQQaoSB1d4D7XfkVOby3W3zH9UUBebXKnlCbZGcvLep7/sJHafqDZdczDRns6W5FNac\nKc2etJ6IcYByr1bve1CeWC6DRsfEcMrzkEQzR+0ZtC9SISaxB3Ohasu4Em/2gmQ57gEF9l7hxiwS\nHR0dHZeFGAPxDEXZeEFJ8iWccx9wV3Z8Am7MIpEtxYxiDZb6A4qFGVGFKSIxX7Ulc5DRsmNOk0Nu\nY99F6K3EqzfiSfiUh5jW6ORJqGlTGgBtSUgbA2YkGonLD40lHfRIGFYlnt3KWSzHn63AvN+oDSHN\nh9bTLH6eLa6SZ2hu9PJZZir5TfIcvHgN04a4XhPXx8RpIhyvid7LY0rPzR+W0pKT0INBjSN6HFDj\niBoG1OoAViuUMajVIXG1Qg8HhNUBajggjAcEM8q5mVAkHlrPajkH5bGD09/KhixrY7buiUbwcNec\nzTdOkiXazH3QIkNuimS23KNZemSeSxBnVe5vLW5re5F33rez7zfvzWsJWhbQnNWnSPdFei6eU/Ig\n2jas2m/mc+Gbv0Wt0qiD1DnEUEcX67F19KUNbJSGIGmezSzXIqxBMNoX6XKjPCu1ZkDYg0UepYhM\nbrbu54eD3nSoo6Ojo+Nk7BFuuildh/oi0dHR0XFOhCkSpjPYTVNfJK4UtgqmmjATQFUNzS0vRd5A\n+kgnomrKlC1UBIrKa074lSKj6EvIqU3wmbCRkNMyzOQnSVZPx6jNGjbrkuglhWJipoomeqgygyR2\ntSGuVmBGzLiSZPawkkfumJfabhal2q1QSVI4jQqNJK1jHCBGQpZRaJOvOZndJNWV95KkzmPfrCXM\ndHREXK8JR8eE9Rp/5wh/vCZOHr+ZCOuJMPkZ40NphdISbtLjiFkN6IMV5mCFPlihDw8kDHVwCAcH\nqINDtL+NWqVQx7DCDxLe8EZCCUsV2RxeqN3J/Kz4q8iHBH9yqK08L8JRTbGYas4rFwmSZEfQAxGk\nx7TK/c/z/ufFfgoJLWnl52SKRgpExtCEjmJVKz759XYoLY12foe0vUPa0FOam2Wieka8iGG7aBJQ\nURMNtAIPEqqbh7lKiDaRD+QeiYQUpmrpvyZTXpWftY4dwprRH+/sCkgMMt4LQCAQzghd5YLB644b\ns0h0dHR0XBa6VPg1RLZCSsJP6eRNZHqonnkRUxRpi4B0e6sexZw+uFV4lHwQyR1mimwoMgWnJaoJ\nHr1Zw3SMWq+LFc5UPQmahB9aqJfKDJLEHlJid1wRxwPUuEIPq0SVrTTZll7ZigaWBL6qgnIqJftz\nsVRJ5OqpWtcL+ifBo6YN8fgIjo+J62PC0TH+zh2mB+7g7xwx3Tlm8+Axfj0xHW3w6wm/8TMXPXsS\nZjSY1cBwODIcrhhvHzDcOsDcOsAcrDC3b6Fv3UrFeZLk1zEQw1Qt+RjwZgWMQg1VqhjJVdytdo3L\nyWrtJ5EQyVIihcqZi/98LQJsBeJO+gHQSuRMdOpToQfiIAVkuWlgYb0oVVqN5+TyENaVxtpKnrCU\ni6nn1FriM09oIbC3RfFNY2iJHS3a77feZbkvigRL8i53yHCw7MC3A+XaJLp4ng9NliTR23+TqeAv\nexA6+CJ3M3j5e8t03bYjYJtQfzjoi0RHR0dHx4nodRLXEU2MtrXEirx3zj8kL2KKg3gTyYuYosan\nvtHLnET2JAYdRBBQUwqYFFEkk1NXMuPXmOl4m+q6ORbLa72WYrP1GqZNLTSbJvEkFtaHGgzRTCil\nUONInCaUT9ab96hVJIwS39XpnANIkVySIgm6xuihSpG0MhVaJxE0LdZ2jINQHdWm2JdSmEjq3Zho\nr60X8c4H2DxwxPqdD7J+1xGbB4+ZjifW71ozHU9MRx6/DoREgzWjQRuFHjWr2yPj7ZHx1sjqEYeM\ntw9YPfI25nDNMHmGEEUKJMt5GPGEdKL0thZx0FV2nXzGMcxor8XK9BtU603kHFGIVVIkeAiR6Kf6\nh7/LYgah7ObxpUJAFQYY0rwGkRspFrke0WGScwDG6U6Za9jOEbTegirn0lj2OX+QxQVPtO7TfOXO\neLrJYS09x9Y7CU3hXM4/pPnZeRzROEGFnJdoPs6ikNGnXukTA6BUQCuPaenMjWTOjGYeqkyIFKpu\n5Nq2BX6NN6TDxEUg+ICfTvdKwhmyHdcFN2eR6Ojo6LgkxBjOLJa7W8V0l42bs0ioKthXBdIakbSo\niyBe9iKmYJiCeBEhKBHLS9LbqTeOFGilXsBSeJTkqFOTIU2Vih78GjMdYTZH4kVsjiqLKXkSWx5E\neuRcxJL9E4M0j4laQ1h2cGZmvUlTG2HURFTJS3g1zPpFbwngFRnllNdRnhgmDElOoo2h52OGWD0g\n7wnrDf54zXRHPIjNg8ccveOYzZ0Nmwc2rN+5YfNOj78TiJvk9Y0KPSiGRxmmR3lWxxN+U89HD5oY\nA9oY4sFKvKjNBGPyorQnDvNithyvlxB25MSAuFLpGi7mMp2bSjIjco2mWvCYLOZSFNjITiitaxFk\nzkmMI0wjDBNq2MwEDLVeFyHDkDyJ1fpd20Nd5gVinElgzEQVY4RpM7fyYd7gGkAb8SCMkfEmyYxW\nwr1e7+o9FQmW7KH4Rg6n9YJ15lUt3Ie8v1JANxGCKjLmKooXIX/DOYdWvaqaj/HbBZGtd7jIRRRc\nkMBfz0l0dHR0dJyIvkhcQ+QmLW1ToCyS5qNhYhA2U9RsQmrak7wIH4ThlBu0tJpkRouBNJPGVk29\nRJgwDaPCbI7Qm2P0+kFU9iA2a/EgvBcvovEelnkIla04rSS+nSw9ZQYYB9R4AONKaiaSTEUYDohm\nwI+HBLNiMiLbkWW0c+1EYFuuQuRFxKorAnEaIMufi3X8xCe8N4/4C59zsRftKD2/9WJ3e13xxMe/\nN8PxA9sfLHICs0ZOflM9B7/tnUafrP5GWDF7PcWDGIbKoFNy30m+QlfPIyM0XsVJTC+t6vfTI+5o\nh5tlPHIuTXIGNV+yvFeBrZqNWYOj8jrUXESaP/myujhZjrhHnUQPN3W8O+E3fuKfoTdH6OM7cPwg\n6ugO8cEHCA8+QLhzxPTAg2ze+QDHb3+A9buOOHr7HY7fecTmwQ3rBybW79gwvUPCTSGFm/SoUKNi\nfKRh9R4j430Dh48+ZHXfisNH3+LgUbcYbh0yPvI24yNuYx75CPR996Fu30e8dZ8skge38KtbomM1\nHOL12PTHbn5kmmSvCVPqvy3FYLnIMXfTUxtZ0Iuu1rQRqnK7sKcf3pbBklVtZz/CxqCSwi1mlHBT\nk9SWYjtTNLU6rgn2aDrUZTmuGDZ6xcYc1HqIBZspRPEofFRbXoQP4j34UGWWW1E0acrCrLXjwIRh\nEjGxnIuY1uJFpFyEOj7aroXwvlp1yWorbspSPjtJaOcfmTiu5DGMxPGwVFx7I55E9iAmPcqPZdO0\nJkuiZ8wFChVa+Vnx7ZbFleP1LZJ1qAbxeNRgGA5X+PXEeGskhoDSGmU02ig2tyb8nTbnkBaJw4Hx\nPmE2HT7qQJhNjzhkvO8W5nDFcN8t9O1b6MND1MEhcZUew4owHs48KN9IqMO8JmBW1Wum2YKhx40w\n0fyEHo7FWp0mqWmZBtQ0phxME+/P17K11OUGknPP/4WSTxIr1xD1kNhkA9GYlHcyhSW1ZDcV8bwz\npNnzGIsnkYQVSWNAVxbWzFsdx7KooZPn2uYm8jwuPYv0XmVG6fn30vkVBlpqU1uvSWLKKfFoTxNU\n3MrLbIkrhpnXNd+JutCFOEyBYM5gN50h23FdcGMWiY6Ojo7LQmc3dXR0dHSciBD3kArv4aarhQ0r\n1rGGm3wS7ctyGznMFKMqhXMhKElU5+Ky5EkbHcRTTv1/RxMYtWdVdOs3jGrD6I8Z/DFDCjVV8b5j\nCS+FnDhsktM5nAQ1UdgmEbP733Roi8OBuOxZ0G8RYvJmVRL3Xg9bfZG3k9VV5kG6JqcEfJYW8UmP\nP0sbtJTCpM8vO9AlVKEPVgyN5IEeDMPhWGQ5pkRvDU0BktIaM2r0oBlvrRgOR8bbB4wp1DTcviWC\nf/fdnuUi4sFt/MF9xGHFNBwyjbfwRsKNXqVkfQqxhZZyoEQ4TyQfkqxDOu+lnEo+b51zFbn3Rw7x\n5ORoDiHGsDOBW+aoTeI2CeGYnxdhpoxWpLC+2RTJZWruNFVSxGZD3EzynBPZS3JECm0qIzRdNU2o\nYZD3h5FopprQ1onM0I6xTWq3IamcpM7Fect8i065olTkOjvfRG2FeRFhpssuw0nLLoqzRLWaz2sN\ndV3MT14MezQdOuPz64Ibs0h0dHR0XBZkfTqLAntJg7nLuDGLxFE45E64VazmXBiXO875mLrPRbGj\nQ1j0hE6eA4DRktAddMRoz5gfasOgPCt1zMrfYfRHpWd126+6tXKUluI7kZEwMyt8Zmm2jJdhBDMS\nkiR4HFZEbaQjXUNx3ZWgbr2HnDrVjQx17mWsk9T5UpiwJHKTxIHyiQmUWT8pOVr+AowROuJqJccw\nIvs93DrAr6XQzq83kuhrpMKzuJ/SWiTCVwNDkggfbh1gbt9CHR7Kfg9viRdxcJtwcIuwus20ukXQ\nI5vxFpM5YNIrJjWmQsmheI3Vo6hzkT2J/Gx0wOgqNy1S7yL5bvwmyU43ie0sj+1TEntHZ8GM2HoO\nWTY8W9ra1ERuY123UDESc4I2yaJEmMm3C9sqVC9ivSZsapFjTqTP517uO2F9RXQc6vVUcm0IPiW6\nTblfox7KvVqK8Zqke/EaZu9VCZCTxATl/mw6M6Lr39BpXsRirtOOmvmsku1RG/zqcNfPx7kRvSec\nIRYYz/j8uuAmLBIG4M1/9Kb0o58XCTVbJEqL0rRIxEBShVVbRIi8SBidWyR6Rh0Y1AajAiu1TgvE\nMaM/Rk9HmPWRqItujiGFntjkkFOj7rpzkUjVrtoQzQDDUOiS0QxEsyJqjTcj0ayYzEhQAz7VQohe\nk54tEpSgUqvi3y4S4trLIiGaNjqHXfyEymEmn1RTEx+fjYRemNbEdeon4Sfi8Yaw2RDWG8J6LRXY\nGwl1hM0ki4RfLBIp1GYmj5o8wxTQG4+ZAmYKqOMJNa7gYEIdTcTVmrg6IoxHTOMBUY9shkO8WcmC\nqdICgSmLRKRe8zwXwupqFgkV0aknQQk9pTaneaFQYRJjIM2HLBKpEjt48OkHbdFDINKEZrQWhdgU\ncsw/XFG1i8T8XlSRwuJRMch1CAGmdQl9xVTVz2YjFfCbNWHjicET1rU6up37bJyoQVqr6jFTdbWE\nmIYh0XcHuTdT2CmqQYqHMhNL6x0LQl4k9HyRSK1Zl3dlRSh9L6BlMjFfJFLV9bLPx3zi2kU3LxKa\n//LWd5Q/8x0D2Bt+8+YzE9NhuhkFQDdhkXgCwNd/zbPv9Tg6OjquD54AvO4hfO8dwFvf9rt/7zF7\nbv/W9J1ri5uwSLwS+AvA71PU+Ts6Ojp2wiALxCsfypedc2+x1n4Q8Kg9v/IO59xbHsqxrgrUTdE8\n7+jo6Oi4eOwKDHZ0dHR0dAB9kejo6OjoOAV9kejo6OjoOBF9kejo6OjoOBF9kejo6OjoOBF9kejo\n6OjoOBF9kejo6OjoOBHXspjOWvsY4B8DnwM8zjn3Rydsdwv4R8DHIJI3rwC+2Dl357LGmsbxXOAL\nkUX59cBfd85tVXtaa38JsMDbm7f/vnPun17SOP8s8J3AewEb4Fucc9+/Y7svAL4WGIE3A1/qnHtI\nxUkPB/uM11r7TODFyLxn/LZz7lMva5xLWGu/CPgO4O84515wwjZXYo6b8Zw65qs0z9baTwC+GXg0\nUjz3Yufcd+zY7krN8VXFtfMk0gLx74Hf3GPz5wOPBZ6cHo8BvuHujW4b1tq/DHwp8DHOuQ8Cfhb4\nkVO+8rXOuSc3j8taIA6AlwEvTOP8NOBF1toPW2z34cCLgE9P2/0D4MettavLGOd5x5vwHxZzei8X\niO8GPhF49SnbXIk5bsZz5pgT7vk8W2sfD/wk8Dzn3JOB/xb4RmvtRy+2u1JzfJVx7RaJhM8E/tke\n230B8CLn3MY5NyFW5+fd1ZHtHsMPOOfelP7/ncCfttb+iUsex1n4BADn3P3p+beAnwY+d7Hd5wE/\n7Zx7bdruRxFVxY+9tJEK9h3vVcOPOOeeBrzzlG2uyhxn7DPmqwIPfL5z7hcAksf+n4APX2x31eb4\nyuLaLRLOubc651511nbW2scCjwNe07z9GuAJyRu5LDy5HYNz7kHgDcCHnrD951pr/29r7WustS+2\n1u6rEfNw8WTgtYv3XsP2OGfnk/DaHdvdbew7XoD3t9b+jLXWWWt/3lr7kXd/eLvhnPvlPTa7KnMM\n7D1muALz7Jz7Q+fcy/L/rbVPAp6ChJpbXKk5vsq4kjkJa+0zgO/a8dHbnXNP2nM396XnNv9wp/ns\nwnR8TxvvjjHk/9/HNv4l8Bbgf0MExH4ceCHwP17MSE/Ffew3zn23u9vYdxy/hYQfvg34Q+A5wMut\ntR/snLuqWs5XZY7Pgys3z9ba9wP+BfDtzrnfWHx8Hef4nuBKLhIphHD/w9zNu9Lzrea9+xafXQhO\nG6+19tcWY8jj2BqDc+5bm/++xVr7rcAPXtQ4z8C72G+c+253t7HXOJIV3FrCL7TWfg3w54Gfuqsj\nfOi4KnO8N67aPFtr/wyyaH2Xc+7bdmxy7eb4XuHahZv2RbJefh9hC2V8CPC7zrm3XeJQXtWOwVr7\nSOB9gf+33chaO1hrP8Ja214TjbB2LgOvApZ5kg8Bfn3Hdu35KMR1X253t7HXeK21T0zJzBaKy5vX\nh4KrMsd74yrNc1ogXg58+QkLBFzDOb5XuLGLRML3Al9lrV0lNsxXsl/C+6LH8D8k1xfga4BX7KLA\nIjf2F0Gh7z4HCTldBn4RmKy1z0rH/1PAJ7Ptyfwg8KkNi+gLEevrX1/SODP2He+XAT9srb2dtnsW\nEIB/d4ljPS+uyhyfB1dinq21h8CPAc92zr30lE2v4xzfE1y7fhLW2s8GvgnhNv9xJBbqgS9wzv0H\na+2rgc9yzr0qLQzfjTAWIvBziHWxvuQx/y3gi5FF+bXAFznn3pA+a8f7Z4B/CLwP8gf2C8BXO+cu\nxQW21n4EwnV/HHAEfL1z7qXW2m8BHnDOfVPa7nOBrwNWiLf2JTtivldivOlH4x8ibKgJ+APgbzvn\nfvUejNcgFizAE5EfpbcgVF64mnO815ivyjyneftBtkkN9wMHXME5vuq4dotER0dHR8fl4aaHmzo6\nOjo6Hgb6ItHR0dHRcSL6ItHR0dHRcSL6ItHR0dHRcSL6ItHR0dHRcSL6ItHR0dHRcSL6ItFxT2Ct\n/YvW2iNr7aMv8Zi3rbWvSvz4u3WMb7fWvuzsLTs6rgd6nUTHhcNa+0+Bz0//1Ujh43GzyV93zv3A\nPRrXo5xzT7+Lx1gBv4o0unnx3TpOR8dloS8SHXcV1tqPRSQ0HnPJmlnLcTwZ0cv6MOfcWc1zHu6x\nnoGo937gZXdB7Oi4aFxJFdiOm4/l4mGtjUgjmC8FPgL4FeAZSBvKv4JIQfwN59zPpu+/L9JZ7GOA\n28AvIXo9r2c3ng3867xAJCHFbwf+GtLm8vWIrMePps//JNKt7M8iLTB/CviyLHudtKK+E3gqIo39\nQufcC9Oxfix99jnAVvvXjo7rhJ6T6LhK+FLg6cAHI4quvwz8MNLD+hVA21v5J4B3pG3fD+nd8UOn\n7PuTgJ9v/v8MZIH4KOARwJcDL7HWvmcSV/xZZKF6X0Qt9PHIokQSsXs5ogX2nkinxOdbaz8NwDnn\nEaG4T3oIc9DRcaXQF4mOq4T7nXOvT+KHvwK8zjn3L5Mg408jC0KWgn4q8JXOuXc4594OPBf4GGvt\nBy13aq0dkR/6Vgb6PRARxQedczF5KI92zr0Z+BTgkcD/7Jw7cs79F+B/AZ6eFpC/hDSF+rb0+a8C\nfxX4z83+fx3Y1W+7o+NaoYebOq4Sfrd5/SDiKbT/P0iv/wTSq+CN1rbtQpiAD0CUgVs8Nj2/pXnv\nfqT/+O9Ya38e+BngB4AH0v4fBTy42L9GPIsnAW9s1YSdc62XAvBHiDptR8e1Rl8kOq4Swhn/z7iD\nLAi3nHPnYV6UbZ1zbwE+ylr73wCfBnw18JXW2qem/f/nk1rlWmsDZ3vhEVnIOjquNXq4qeM64rWI\ngfOU/Ia11lhr3/+E7bMH8Z7N9gfW2kc65/6tc+5r074eD3xi2v/7W2vb7e+z1mbP4HXp89vN559u\nrf3E5piPQxLaHR3XGn2R6Lh2cM79J4TN9EJr7eNTnuDvAv8qNclZbr8BHPMcwYuAl1pr3yf9/08j\n4azXAf8n8Dtp/4+x1r4H8L8CudPZzwBvBr4hFeh9ONKB8BHN/j+M3gqz4wagLxId1xWfh+QsXgv8\nHkKb/ZTELNqFn0O8hIyvRvIGv2mtfQD4J0jHwP/HOTcBnwE8AXhDOsYh8DSAlIv4BODPI4vFTwLP\nd879BBR67V9Mx+zouNboxXQd7xZoiume4pxzd/lYT0M8lV5M13Ht0T2JjncLpCK67wW+/m4eJ9Ft\nvw74xr5AdNwE9EWi490JXw58eJLNuFv4JqS+o+s2ddwI9HBTR0dHR8eJ6J5ER0dHR8eJ6ItER0dH\nR8eJ6ItER0dHR8eJ6ItER0dHR8eJ6ItER0dHR8eJ+P8Bowp1/e5gd5gAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "z1bT1OkuBAkf",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Combine Subjects"
      ]
    },
    {
      "metadata": {
        "id": "IZRv31hVA9Xk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1533
        },
        "outputId": "d5a8d37a-081e-47a8-aa48-f8718521f2e5"
      },
      "cell_type": "code",
      "source": [
        "print(np.shape(ERSP_diff_out))\n",
        "print(np.shape(Contra_spectra_out))\n",
        "\n",
        "print(diff_out)\n",
        "\n",
        "\n",
        "GrandAvg_diff = np.nanmean(ERSP_diff_out,0)\n",
        "GrandAvg_Ipsi = np.nanmean(ERSP_Ipsi_out,0)\n",
        "GrandAvg_Contra = np.nanmean(ERSP_Contra_out,0)\n",
        "\n",
        "GrandAvg_spec_Ipsi = np.nanmean(Ipsi_spectra_out,0)\n",
        "GrandAvg_spec_Contra = np.nanmean(Contra_spectra_out,0)\n",
        "GrandAvg_spec_diff = np.nanmean(diff_spectra_out,0)\n",
        "\n",
        "num_good = len(diff_out) - sum(np.isnan(diff_out))   \n",
        "GrandAvg_spec_Ipsi_ste = np.nanstd(Ipsi_spectra_out,0)/np.sqrt(num_good)\n",
        "GrandAvg_spec_Contra_ste = np.nanstd(Contra_spectra_out,0)/np.sqrt(num_good)\n",
        "GrandAvg_spec_diff_ste = np.nanstd(diff_spectra_out,0)/np.sqrt(num_good)\n",
        "\n",
        "#Spectra error bars\n",
        "fig, ax = plt.subplots(1)\n",
        "plt.errorbar(frequencies,GrandAvg_spec_Ipsi,yerr=GrandAvg_spec_Ipsi_ste)\n",
        "plt.errorbar(frequencies,GrandAvg_spec_Contra,yerr=GrandAvg_spec_Contra_ste)\n",
        "\n",
        "plt.legend(('Ipsi','Contra'))\n",
        "plt.xlabel('Frequency (Hz)')\n",
        "plt.ylabel('Power (uV^2)')   \n",
        "plt.hlines(0,3,33)\n",
        "\n",
        "#Spectra Diff error bars\n",
        "fig, ax = plt.subplots(1)\n",
        "plt.errorbar(frequencies,GrandAvg_spec_diff,yerr=GrandAvg_spec_diff_ste)\n",
        "\n",
        "plt.legend('Ipsi-Contra')\n",
        "plt.xlabel('Frequency (Hz)')\n",
        "plt.ylabel('Power (uV^2)')   \n",
        "plt.hlines(0,3,33)\n",
        "\n",
        "#Grand Average Ipsi\n",
        "plot_max = np.max([np.max(np.abs(GrandAvg_Ipsi)), np.max(np.abs(GrandAvg_Contra))])  \n",
        "fig, ax = plt.subplots(1)\n",
        "im = plt.imshow(GrandAvg_Ipsi,\n",
        "           extent=[times[0], times[-1], frequencies[0], frequencies[-1]],\n",
        "           aspect='auto', origin='lower', cmap='coolwarm', vmin=-plot_max, vmax=plot_max)\n",
        "plt.xlabel('Time (sec)')\n",
        "plt.ylabel('Frequency (Hz)')\n",
        "plt.title('Power Ipsi')\n",
        "cb = fig.colorbar(im)\n",
        "cb.set_label('Power')\n",
        "# Create a Rectangle patch\n",
        "rect = patches.Rectangle((t_low,f_low),t_diff,f_diff,linewidth=1,edgecolor='k',facecolor='none')\n",
        "# Add the patch to the Axes\n",
        "ax.add_patch(rect)\n",
        "\n",
        "#Grand Average Contra\n",
        "fig, ax = plt.subplots(1)\n",
        "im = plt.imshow(GrandAvg_Contra,\n",
        "           extent=[times[0], times[-1], frequencies[0], frequencies[-1]],\n",
        "           aspect='auto', origin='lower', cmap='coolwarm', vmin=-plot_max, vmax=plot_max)\n",
        "plt.xlabel('Time (sec)')\n",
        "plt.ylabel('Frequency (Hz)')\n",
        "plt.title('Power Contra')\n",
        "cb = fig.colorbar(im)\n",
        "cb.set_label('Power')\n",
        "# Create a Rectangle patch\n",
        "rect = patches.Rectangle((t_low,f_low),t_diff,f_diff,linewidth=1,edgecolor='k',facecolor='none')\n",
        "# Add the patch to the Axes\n",
        "ax.add_patch(rect)\n",
        "\n",
        "#Grand Average Ipsi-Contra Difference\n",
        "plot_max_diff = np.max(np.abs(GrandAvg_diff))\n",
        "fig, ax = plt.subplots(1)\n",
        "im = plt.imshow(GrandAvg_diff,\n",
        "           extent=[times[0], times[-1], frequencies[0], frequencies[-1]],\n",
        "           aspect='auto', origin='lower', cmap='coolwarm', vmin=-plot_max_diff, vmax=plot_max_diff)\n",
        "plt.xlabel('Time (sec)')\n",
        "plt.ylabel('Frequency (Hz)')\n",
        "plt.title('Power Difference Ipsi-Contra')\n",
        "cb = fig.colorbar(im)\n",
        "cb.set_label('Ipsi-Contra Power')\n",
        "# Create a Rectangle patch\n",
        "rect = patches.Rectangle((t_low,f_low),t_diff,f_diff,linewidth=1,edgecolor='k',facecolor='none')\n",
        "# Add the patch to the Axes\n",
        "ax.add_patch(rect)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(29, 100, 769)\n",
            "(29, 100)\n",
            "[-1.8633016529355503e-10, -5.19449269804164e-11, 3.965717366845645e-11, -9.439937924298804e-11, 1.5395426956926097e-10, nan, nan, 1.437520530470469e-10, 6.156413801292706e-12, -3.454228962737695e-11, nan, 1.4040022690241467e-10, 4.5250575579733986e-11, 8.133414421717266e-11, 1.7562617842729205e-12, 3.4989854712143306e-11, -7.35844888841272e-11, -2.0667398674718962e-11, 5.83383223147634e-11, -1.1353507280272532e-10, 2.057854566935435e-10, 8.136129078256774e-11, 2.2311245651295555e-09, 4.417204016506356e-13, 1.5824220701839011e-10, nan, 2.972694444208326e-11, -2.705345319003894e-11, 1.0032262566341921e-10]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.patches.Rectangle at 0x7fd9b86f23c8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Bj767klfUK+yptdXb2bZhJds2rmTbxm/Yvnk1O7asaVNydHncFO3cja79e1BU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\njVtjuAhyAAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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poI+Z3Q+8DkwATuzE/Sa7+5cILZFSjgDucvdZAO5+I2G9xj6duE9DGLaZfqsXkepLlCjc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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AUuoeAP7cj9vXwBoaGhouLEJ009LrEmBSk9Bax01XKdUDTwQeqrUuy4r+OnDb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\nUUq9LfDDwIO01m9Z087are6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zek+fIJHCuBBVhrrIQDVsN3NoJrUzRsEAldrOISTWQWbn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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kllBB8xurl0jGN8OYRCpvYVzvZ2babl5tfZA4y1w9R4xHbE/SWMoiGjwfI9et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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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K84vvTGfabNx6rsfmoB9jmz1zXTi/8/l3UJ8f7+nnPvsZHvHwh0P43dgFrnJJCw/4J/ia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\nLNoi0dDQ0NBwLNoi0dDQ0NBwLP4/4IsiFUZ42z8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Tl0BbedYBhmK",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Compute T-test "
      ]
    },
    {
      "metadata": {
        "id": "4K8t7xIXBMQy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "bda23af3-4d6e-4cb7-cab3-975d01a25867"
      },
      "cell_type": "code",
      "source": [
        "import scipy\n",
        "num_good = len(diff_out) - sum(np.isnan(diff_out))\n",
        "\n",
        "[tstat, pval] = scipy.stats.ttest_ind(diff_out,np.zeros(len(diff_out)),nan_policy='omit')\n",
        "print('Ipsi Mean: '+  str(np.nanmean(Ipsi_out))) \n",
        "print('Contra Mean: '+  str(np.nanmean(Contra_out))) \n",
        "print('Mean Diff: '+  str(np.nanmean(diff_out))) \n",
        "print('t(' + str(num_good-1) + ') = ' + str(round(tstat,3)))\n",
        "print('p = ' + str(round(pval,3)))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Ipsi Mean: 9.024561362956465e-11\n",
            "Contra Mean: -2.6175863611708235e-11\n",
            "Mean Diff: 1.164214772412728e-10\n",
            "t(24) = 1.394\n",
            "p = 0.169\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "lUL3-i3iBbzG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Save average powers ipsi and contra\n"
      ]
    },
    {
      "metadata": {
        "id": "OjARyZcRBMml",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "5963100f-ce1d-4a44-848a-6bef17475a01"
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "print(diff_out)\n",
        "raw_data = {'Ipsi Power': Ipsi_out, \n",
        "        'Contra Power': Contra_out}\n",
        "df = pd.DataFrame(raw_data, columns = ['Ipsi Power', 'Contra Power'])\n",
        "df.to_csv('375CueingEEG.csv')\n",
        "print('Saved subject averages for each condition to 375CueingEEG.csv file in present directory')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-1.8633016529355503e-10, -5.19449269804164e-11, 3.965717366845645e-11, -9.439937924298804e-11, 1.5395426956926097e-10, nan, nan, 1.437520530470469e-10, 6.156413801292706e-12, -3.454228962737695e-11, nan, 1.4040022690241467e-10, 4.5250575579733986e-11, 8.133414421717266e-11, 1.7562617842729205e-12, 3.4989854712143306e-11, -7.35844888841272e-11, -2.0667398674718962e-11, 5.83383223147634e-11, -1.1353507280272532e-10, 2.057854566935435e-10, 8.136129078256774e-11, 2.2311245651295555e-09, 4.417204016506356e-13, 1.5824220701839011e-10, nan, 2.972694444208326e-11, -2.705345319003894e-11, 1.0032262566341921e-10]\n",
            "Saved subject averages for each condition to 375CueingEEG.csv file in present directory\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "uuOjOxR_BXJk",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Save Spectra"
      ]
    },
    {
      "metadata": {
        "id": "5wDJboCiBMsn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c514d5e0-9894-412b-959a-e3932cd3ccbf"
      },
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(Ipsi_spectra_out,columns=frequencies)\n",
        "df.to_csv('375CueingIpsiSpec.csv')\n",
        "\n",
        "df = pd.DataFrame(Contra_spectra_out,columns=frequencies)\n",
        "df.to_csv('375CueingContraSpec.csv')\n",
        "print('Saved Spectra to 375Cueing*Spec.csv file in present directory')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saved Spectra to 375Cueing*Spec.csv file in present directory\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}