{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CueingGroupAnalysis_Colab_Winter2019",
      "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_Winter2019.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_Winter2019\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",
        "outputId": "1f9960f8-c74a-4550-ea62-784c66857974",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/kylemath/eeg-notebooks --recurse-submodules\n",
        "%cd eeg-notebooks/notebooks"
      ],
      "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",
        "outputId": "88581a9e-cdf5-4380-f325-6ddccf434fe7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "!pip install mne\n",
        "from mne import Epochs, find_events, concatenate_raws\n",
        "from mne.time_frequency import tfr_morlet\n",
        "import numpy as np\n",
        "import os\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 (0.17.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "BU6QcVVh8sXM",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# 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": {}
      },
      "cell_type": "code",
      "source": [
        "# Fall 2018\n",
        "# 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",
        "# Winter 2019\n",
        "subs = [1101, 1102, 1103, 1104, 1105, 1106, 1108, 1109, 1110,\n",
        "        1202, 1203, 1205, 1206, 1209, 1210, 1211, 1215,\n",
        "        1301, 1302, 1313, \n",
        "        1401, 1402, 1403, 1404, 1405,  1408, 1410, 1411, 1412, 1413, 1413, 1414, 1415, 1416]\n",
        "\n",
        "# Both\n",
        "# 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",
        "#         1101, 1102, 1103, 1104, 1105, 1106, 1108, 1109, 1110,\n",
        "#         1202, 1203, 1205, 1206, 1209, 1210, 1211, 1215,\n",
        "#         1301, 1302, 1313, \n",
        "#         1401, 1402, 1403, 1404, 1405,  1408, 1410, 1411, 1412, 1413, 1413, 1414, 1415, 1416]\n",
        "\n",
        "\n",
        "# placeholders to add to for each subject\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",
        "    # 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",
        "    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",
        "    power_Contra_TP9 = tfr.data[0,:,:]\n",
        "    power_Ipsi_TP10 = tfr.data[1,:,:]\n",
        "\n",
        "    # Compute averages Differences\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",
        "    \n",
        "    #output data into array\n",
        "    times = epochs.times\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",
        "    #save the spectrograms to average over after\n",
        "    ERSP_diff_out.append(power_Avg_Diff)\n",
        "    ERSP_Ipsi_out.append(power_Avg_Ipsi)\n",
        "    ERSP_Contra_out.append(power_Avg_Contra)\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "z1bT1OkuBAkf",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Combine Subjects"
      ]
    },
    {
      "metadata": {
        "id": "IZRv31hVA9Xk",
        "colab_type": "code",
        "outputId": "13827c58-876f-494b-d17e-abdf416e4a47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1462
        }
      },
      "cell_type": "code",
      "source": [
        "#average spectrograms\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",
        "#average spectra\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",
        "#error bars for spectra (standard error)\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",
        "\n",
        "##\n",
        "\n",
        "#Plot 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",
        "plt.legend(('Ipsi','Contra'))\n",
        "plt.xlabel('Frequency (Hz)')\n",
        "plt.ylabel('Power (uV^2)')   \n",
        "plt.hlines(0,3,33)\n",
        "\n",
        "#Plot Spectra Diff error bars\n",
        "fig, ax = plt.subplots(1)\n",
        "plt.errorbar(frequencies,GrandAvg_spec_diff,yerr=GrandAvg_spec_diff_ste)\n",
        "plt.legend('Ipsi-Contra')\n",
        "plt.xlabel('Frequency (Hz)')\n",
        "plt.ylabel('Power (uV^2)')   \n",
        "plt.hlines(0,3,33)\n",
        "\n",
        "##\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(\n",
        "    ,\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": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.patches.Rectangle at 0x7f5948b01d68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0rW5f2wDA28TEUYPZzIe4Btbizt8D3mbOmXohD615vt2pX9vnQh5cfR/BxoFO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\nMXGzJGKMMSZu/x+viUkDIwDizQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DlwA9gWZgkrv/thzvS3uUHArM3bePOoaOMLPrgQHA221emgQsBD4P9ACeBs4E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\nICIiIUoOIiIS8v8BYhCeQc8LHNMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TSqmXY/ajvgNw4Rp1VFRUVOwGqrlpgL/xPyilPozxUbwfODl3p+bAILhuxBex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\n0AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9jhhW6n5q3b8WQc2TACnlo4CnYory3TE3AUgpvwh4GPAXUsr/rpT6jSMZaUVF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\nikQIIYRSUSRCCCGU+j+NyJp/EHJmUwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zFCUdLcC070MXqeRuWBBoqOHcZSPJAw7Tax/ycQy20bHVtwn/Uu1k6PghGnCw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\n+P+66v0H3qvRSgAAAABJRU5ErkJggg==\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": {}
      },
      "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": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "lUL3-i3iBbzG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Save average powers ipsi and contra\n"
      ]
    },
    {
      "metadata": {
        "id": "OjARyZcRBMml",
        "colab_type": "code",
        "outputId": "da1b9126-648f-4bba-aad8-c150344c07b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 666
        }
      },
      "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",
        "print(df)\n",
        "df.to_csv('375CueingEEG.csv')\n",
        "print('Saved subject averages for each condition to 375CueingEEG.csv file in present directory')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nan, 1.7890047693694025e-11, 6.40257192463808e-11, 1.2016988558064774e-10, 1.643681129897095e-10, 1.1465401250727357e-10, 8.107123680349033e-11, -3.1169419915325006e-11, 6.493727108357483e-11, 4.3529314385415755e-11, 2.8760356184521284e-11, 3.773350340044309e-11, 2.1131349959479695e-11, 1.5571207459949601e-10, -4.629115067550113e-10, -8.0522323353216e-11, -4.6644221372820765e-11, nan, nan, -3.4389510978749296e-11, 7.091042185483415e-11, 1.2983383558014348e-11, 1.3604198590381146e-10, -5.861633427481697e-11, -6.204912953285913e-11, 1.8344086913139663e-10, 2.380242500968211e-11, -8.8050332977192e-11, -7.508128947253718e-11, 2.9079648598434818e-11, 2.9079648598434818e-11, -4.146716305783257e-11, -1.5307351374033267e-10, 2.3393198398388223e-11]\n",
            "      Ipsi Power  Contra Power\n",
            "0            NaN           NaN\n",
            "1  -3.901325e-11 -5.690330e-11\n",
            "2   4.568905e-11 -1.833667e-11\n",
            "3   2.037251e-11 -9.979737e-11\n",
            "4   2.567893e-10  9.242117e-11\n",
            "5   1.128359e-10 -1.818109e-12\n",
            "6   3.090019e-10  2.279306e-10\n",
            "7  -1.325457e-10 -1.013762e-10\n",
            "8   4.086011e-11 -2.407716e-11\n",
            "9   8.168927e-11  3.815995e-11\n",
            "10  1.073335e-10  7.857313e-11\n",
            "11  5.134699e-10  4.757364e-10\n",
            "12 -1.088382e-11 -3.201517e-11\n",
            "13  1.006811e-10 -5.503096e-11\n",
            "14 -2.543183e-10  2.085932e-10\n",
            "15 -3.615777e-11  4.436455e-11\n",
            "16 -5.354581e-11 -6.901593e-12\n",
            "17           NaN           NaN\n",
            "18           NaN           NaN\n",
            "19  5.637907e-13  3.495330e-11\n",
            "20 -9.265134e-12 -8.017556e-11\n",
            "21 -2.448927e-10 -2.578761e-10\n",
            "22  2.682534e-10  1.322114e-10\n",
            "23  4.775386e-11  1.063702e-10\n",
            "24 -5.500639e-11  7.042736e-12\n",
            "25  1.907671e-11 -1.643642e-10\n",
            "26  2.746872e-11  3.666293e-12\n",
            "27 -9.699470e-11 -8.944371e-12\n",
            "28  1.345766e-10  2.096579e-10\n",
            "29  3.654300e-11  7.463347e-12\n",
            "30  3.654300e-11  7.463347e-12\n",
            "31 -3.302737e-11  8.439791e-12\n",
            "32 -4.426000e-11  1.088135e-10\n",
            "33 -4.177686e-11 -6.517005e-11\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",
        "outputId": "9ab7e879-295b-4db1-8ca2-4187bbf37a01",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3111
        }
      },
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(Ipsi_spectra_out,columns=frequencies)\n",
        "print(df)\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 375CueingContraSpec.csv file in present directory')"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "       6.000000      6.242424      6.484848      6.727273      6.969697   \\\n",
            "0            NaN           NaN           NaN           NaN           NaN   \n",
            "1   7.991890e-11  8.036284e-11  7.548816e-11  6.696746e-11  5.489871e-11   \n",
            "2   8.482874e-11  8.635330e-11  9.420775e-11  1.024297e-10  1.064200e-10   \n",
            "3   1.394826e-10  1.324403e-10  1.241198e-10  1.121430e-10  9.387917e-11   \n",
            "4   1.355015e-10  1.519385e-10  1.523882e-10  1.378647e-10  1.137803e-10   \n",
            "5   4.146935e-11  4.747567e-11  5.477697e-11  6.351733e-11  7.302017e-11   \n",
            "6   1.591767e-10  1.802644e-10  1.960184e-10  2.090976e-10  2.218464e-10   \n",
            "7   2.051924e-10  1.359738e-10  7.068325e-11  1.696284e-11 -2.275222e-11   \n",
            "8  -2.289620e-11 -1.966269e-11 -1.143603e-11 -6.869489e-13  1.008869e-11   \n",
            "9   2.639212e-10  2.390503e-10  2.144125e-10  1.905306e-10  1.680688e-10   \n",
            "10  1.734091e-10  1.751927e-10  1.753140e-10  1.718332e-10  1.640584e-10   \n",
            "11 -4.155194e-11  5.554974e-11  1.555793e-10  2.462566e-10  3.218309e-10   \n",
            "12  1.132429e-10  9.957683e-11  9.229583e-11  8.893916e-11  8.553388e-11   \n",
            "13  1.521996e-10  2.196098e-10  2.624162e-10  2.803374e-10  2.764403e-10   \n",
            "14 -1.992109e-12  8.659712e-12  1.729436e-11  2.559822e-11  2.899818e-11   \n",
            "15 -1.632265e-10 -1.465498e-10 -1.333522e-10 -1.212212e-10 -1.078400e-10   \n",
            "16  1.051757e-10  9.010762e-11  7.191888e-11  5.248310e-11  3.280272e-11   \n",
            "17           NaN           NaN           NaN           NaN           NaN   \n",
            "18           NaN           NaN           NaN           NaN           NaN   \n",
            "19 -3.037587e-11 -3.195487e-11 -3.150411e-11 -2.845690e-11 -2.399326e-11   \n",
            "20 -4.576869e-11 -7.575594e-11 -1.010965e-10 -1.151713e-10 -1.153312e-10   \n",
            "21 -3.017372e-10 -2.806035e-10 -2.604053e-10 -2.436529e-10 -2.315371e-10   \n",
            "22  1.892637e-10  1.928711e-10  1.834239e-10  1.687836e-10  1.565444e-10   \n",
            "23 -1.863042e-10 -1.441354e-10 -1.077887e-10 -7.797507e-11 -5.404857e-11   \n",
            "24 -1.099290e-10 -1.131996e-10 -1.189890e-10 -1.243301e-10 -1.263736e-10   \n",
            "25 -2.486010e-10 -1.910190e-10 -1.426405e-10 -9.719711e-11 -5.511673e-11   \n",
            "26 -2.444701e-10 -2.199233e-10 -1.877827e-10 -1.515040e-10 -1.140170e-10   \n",
            "27 -1.015551e-10 -9.978091e-11 -9.461620e-11 -8.792444e-11 -8.189433e-11   \n",
            "28 -1.236814e-10 -1.335374e-10 -1.266249e-10 -1.085890e-10 -8.551483e-11   \n",
            "29 -1.196307e-10 -1.035983e-10 -8.547074e-11 -6.942479e-11 -5.634324e-11   \n",
            "30 -1.196307e-10 -1.035983e-10 -8.547074e-11 -6.942479e-11 -5.634324e-11   \n",
            "31  4.570382e-11  2.986854e-11  1.272615e-11 -3.008220e-12 -1.543231e-11   \n",
            "32  6.194128e-10  6.055475e-10  5.266180e-10  4.074218e-10  2.767420e-10   \n",
            "33 -1.126135e-10 -1.308744e-10 -1.333577e-10 -1.263601e-10 -1.162239e-10   \n",
            "\n",
            "       7.212121      7.454545      7.696970      7.939394      8.181818   \\\n",
            "0            NaN           NaN           NaN           NaN           NaN   \n",
            "1   3.928643e-11  2.071860e-11  3.181606e-13 -2.052093e-11 -4.029148e-11   \n",
            "2   1.039261e-10  9.499544e-11  8.129098e-11  6.521155e-11  4.913061e-11   \n",
            "3   6.952496e-11  4.237734e-11  1.715320e-11 -1.951685e-12 -1.242241e-11   \n",
            "4   8.885054e-11  7.355947e-11  7.767381e-11  1.071070e-10  1.614869e-10   \n",
            "5   8.233485e-11  9.080593e-11  9.834153e-11  1.052699e-10  1.119386e-10   \n",
            "6   2.360274e-10  2.528666e-10  2.726663e-10  2.943405e-10  3.154951e-10   \n",
            "7  -5.068669e-11 -7.162195e-11 -9.029272e-11 -1.094741e-10 -1.293937e-10   \n",
            "8   1.927783e-11  2.647718e-11  3.208575e-11  3.667147e-11  4.058414e-11   \n",
            "9   1.476014e-10  1.295449e-10  1.140981e-10  1.011510e-10  9.021884e-11   \n",
            "10  1.528162e-10  1.399273e-10  1.273332e-10  1.163920e-10  1.076142e-10   \n",
            "11  3.825605e-10  4.319614e-10  4.736947e-10  5.095872e-10  5.392423e-10   \n",
            "12  7.853530e-11  6.599330e-11  4.780540e-11  2.535703e-11  9.142596e-13   \n",
            "13  2.553082e-10  2.221843e-10  1.825206e-10  1.414754e-10  1.033338e-10   \n",
            "14  2.030075e-11 -6.441448e-12 -5.346427e-11 -1.185210e-10 -1.955033e-10   \n",
            "15 -9.194498e-11 -7.375521e-11 -5.482577e-11 -3.740813e-11 -2.363154e-11   \n",
            "16  1.354064e-11 -4.648259e-12 -2.122263e-11 -3.599269e-11 -4.914679e-11   \n",
            "17           NaN           NaN           NaN           NaN           NaN   \n",
            "18           NaN           NaN           NaN           NaN           NaN   \n",
            "19 -1.954894e-11 -1.589403e-11 -1.302493e-11 -1.048426e-11 -7.694647e-12   \n",
            "20 -1.030080e-10 -8.228831e-11 -5.793932e-11 -3.384502e-11 -1.234650e-11   \n",
            "21 -2.245237e-10 -2.226341e-10 -2.254633e-10 -2.321171e-10 -2.412028e-10   \n",
            "22  1.526209e-10  1.605196e-10  1.810823e-10  2.124974e-10  2.506242e-10   \n",
            "23 -3.448216e-11 -1.750570e-11 -1.625271e-12  1.411057e-11  3.012498e-11   \n",
            "24 -1.233505e-10 -1.149077e-10 -1.019163e-10 -8.594513e-11 -6.867757e-11   \n",
            "25 -2.055605e-11  2.719007e-12  1.394190e-11  1.571367e-11  1.277618e-11   \n",
            "26 -7.777898e-11 -4.476871e-11 -1.633497e-11  7.000955e-12  2.558695e-11   \n",
            "27 -7.836279e-11 -7.829831e-11 -8.157123e-11 -8.713611e-11 -9.354055e-11   \n",
            "28 -6.064853e-11 -3.372653e-11 -2.503652e-12  3.501561e-11  7.902951e-11   \n",
            "29 -4.514284e-11 -3.423873e-11 -2.230347e-11 -8.399179e-12  8.085575e-12   \n",
            "30 -4.514284e-11 -3.423873e-11 -2.230347e-11 -8.399179e-12  8.085575e-12   \n",
            "31 -2.381502e-11 -2.852306e-11 -3.061445e-11 -3.133040e-11 -3.169920e-11   \n",
            "32  1.565924e-10  5.866081e-11 -1.389431e-11 -6.283206e-11 -9.186853e-11   \n",
            "33 -1.067093e-10 -9.839175e-11 -8.975175e-11 -7.880157e-11 -6.433280e-11   \n",
            "\n",
            "        ...          27.818182     28.060606     28.303030     28.545455  \\\n",
            "0       ...                NaN           NaN           NaN           NaN   \n",
            "1       ...      -2.110801e-11 -2.141138e-11 -2.161187e-11 -2.171384e-11   \n",
            "2       ...      -5.210840e-12 -5.030915e-12 -4.853308e-12 -4.678700e-12   \n",
            "3       ...       5.470784e-12  4.363229e-12  3.233237e-12  2.092435e-12   \n",
            "4       ...      -4.556771e-12 -4.779064e-12 -4.955921e-12 -5.090998e-12   \n",
            "5       ...      -1.087794e-11 -1.029440e-11 -9.726796e-12 -9.177509e-12   \n",
            "6       ...      -1.102624e-12 -1.486730e-12 -1.819941e-12 -2.107474e-12   \n",
            "7       ...       1.738099e-11  1.634645e-11  1.533262e-11  1.434358e-11   \n",
            "8       ...      -6.907351e-12 -6.883115e-12 -6.830596e-12 -6.750004e-12   \n",
            "9       ...       3.397888e-12  3.071010e-12  2.761509e-12  2.469625e-12   \n",
            "10      ...       1.795384e-12  1.715378e-12  1.633663e-12  1.550629e-12   \n",
            "11      ...       4.430557e-11  4.284110e-11  4.135584e-11  3.985125e-11   \n",
            "12      ...      -2.090928e-12 -1.916058e-12 -1.738928e-12 -1.563288e-12   \n",
            "13      ...      -2.142111e-11 -2.068772e-11 -1.989041e-11 -1.904290e-11   \n",
            "14      ...       8.122216e-12  7.302915e-12  6.478321e-12  5.658976e-12   \n",
            "15      ...      -6.737278e-12 -6.474572e-12 -6.214444e-12 -5.960195e-12   \n",
            "16      ...       8.821102e-12  8.426885e-12  8.029811e-12  7.631691e-12   \n",
            "17      ...                NaN           NaN           NaN           NaN   \n",
            "18      ...                NaN           NaN           NaN           NaN   \n",
            "19      ...       2.160869e-12  1.652602e-12  1.182025e-12  7.478319e-13   \n",
            "20      ...       9.866176e-12  9.106445e-12  8.401268e-12  7.747526e-12   \n",
            "21      ...       1.034560e-12  8.441881e-13  6.669552e-13  5.031984e-13   \n",
            "22      ...      -8.631284e-14 -4.163090e-13 -6.950198e-13 -9.272307e-13   \n",
            "23      ...       1.610265e-11  1.510977e-11  1.418317e-11  1.331900e-11   \n",
            "24      ...       1.027779e-11  9.697388e-12  9.139330e-12  8.602132e-12   \n",
            "25      ...       9.857953e-12  1.002092e-11  1.011396e-11  1.014096e-11   \n",
            "26      ...       7.691710e-12  9.181761e-12  1.048695e-11  1.161431e-11   \n",
            "27      ...      -1.753326e-11 -1.679225e-11 -1.603954e-11 -1.528187e-11   \n",
            "28      ...       5.794606e-12  5.116645e-12  4.528455e-12  4.018316e-12   \n",
            "29      ...       1.449307e-11  1.255506e-11  1.078479e-11  9.176391e-12   \n",
            "30      ...       1.449307e-11  1.255506e-11  1.078479e-11  9.176391e-12   \n",
            "31      ...       4.083996e-12  3.934985e-12  3.764528e-12  3.576713e-12   \n",
            "32      ...      -4.863543e-12 -4.295936e-12 -3.782809e-12 -3.319118e-12   \n",
            "33      ...      -3.605396e-11 -3.623576e-11 -3.626747e-11 -3.615323e-11   \n",
            "\n",
            "       28.787879     29.030303     29.272727     29.515152     29.757576  \\\n",
            "0            NaN           NaN           NaN           NaN           NaN   \n",
            "1  -2.172236e-11 -2.164310e-11 -2.148219e-11 -2.124607e-11 -2.094144e-11   \n",
            "2  -4.507663e-12 -4.340669e-12 -4.178092e-12 -4.020207e-12 -3.867214e-12   \n",
            "3   9.518999e-13 -1.779517e-13 -1.287490e-12 -2.367939e-12 -3.411441e-12   \n",
            "4  -5.188059e-12 -5.250875e-12 -5.283165e-12 -5.288527e-12 -5.270396e-12   \n",
            "5  -8.648437e-12 -8.141057e-12 -7.656433e-12 -7.195249e-12 -6.757861e-12   \n",
            "6  -2.354097e-12 -2.564129e-12 -2.741481e-12 -2.889698e-12 -3.011956e-12   \n",
            "7   1.338294e-11  1.245381e-11  1.155877e-11  1.069994e-11  9.878931e-12   \n",
            "8  -6.642258e-12 -6.508836e-12 -6.351703e-12 -6.173232e-12 -5.976043e-12   \n",
            "9   2.195466e-12  1.939016e-12  1.700131e-12  1.478547e-12  1.273891e-12   \n",
            "10  1.466625e-12  1.381976e-12  1.296983e-12  1.211934e-12  1.127110e-12   \n",
            "11  3.832983e-11  3.679501e-11  3.525095e-11  3.370241e-11  3.215457e-11   \n",
            "12 -1.392281e-12 -1.228462e-12 -1.073835e-12 -9.298909e-13 -7.976622e-13   \n",
            "13 -1.815816e-11 -1.724826e-11 -1.632433e-11 -1.539644e-11 -1.447362e-11   \n",
            "14  4.854071e-12  4.071474e-12  3.317770e-12  2.598338e-12  1.917412e-12   \n",
            "15 -5.714203e-12 -5.478063e-12 -5.252720e-12 -5.038602e-12 -4.835722e-12   \n",
            "16  7.234350e-12  6.839574e-12  6.449076e-12  6.064472e-12  5.687247e-12   \n",
            "17           NaN           NaN           NaN           NaN           NaN   \n",
            "18           NaN           NaN           NaN           NaN           NaN   \n",
            "19  3.486066e-13 -1.717036e-14 -3.510517e-13 -6.545685e-13 -9.292653e-13   \n",
            "20  7.142027e-12  6.581573e-12  6.063012e-12  5.583290e-12  5.139473e-12   \n",
            "21  3.530098e-13  2.162732e-13  9.269572e-14 -1.815771e-14 -1.168381e-13   \n",
            "22 -1.117640e-12 -1.270824e-12 -1.391155e-12 -1.482759e-12 -1.549501e-12   \n",
            "23  1.251317e-11  1.176147e-11  1.105966e-11  1.040361e-11  9.789338e-12   \n",
            "24  8.084637e-12  7.585968e-12  7.105497e-12  6.642819e-12  6.197682e-12   \n",
            "25  1.010643e-11  1.001537e-11  9.873057e-12  9.684958e-12  9.456576e-12   \n",
            "26  1.257263e-11  1.337196e-11  1.402313e-11  1.453720e-11  1.492537e-11   \n",
            "27 -1.452529e-11 -1.377513e-11 -1.303605e-11 -1.231204e-11 -1.160645e-11   \n",
            "28  3.575487e-12  3.190260e-12  2.853944e-12  2.558838e-12  2.298211e-12   \n",
            "29  7.722905e-12  6.416491e-12  5.248686e-12  4.210642e-12  3.293275e-12   \n",
            "30  7.722905e-12  6.416491e-12  5.248686e-12  4.210642e-12  3.293275e-12   \n",
            "31  3.375482e-12  3.164543e-12  2.947320e-12  2.726895e-12  2.506011e-12   \n",
            "32 -2.900319e-12 -2.522325e-12 -2.181518e-12 -1.874737e-12 -1.599190e-12   \n",
            "33 -3.589927e-11 -3.551351e-11 -3.500525e-11 -3.438482e-11 -3.366325e-11   \n",
            "\n",
            "       30.000000  \n",
            "0            NaN  \n",
            "1  -2.057510e-11  \n",
            "2  -3.719235e-12  \n",
            "3  -4.411093e-12  \n",
            "4  -5.232011e-12  \n",
            "5  -6.344323e-12  \n",
            "6  -3.111111e-12  \n",
            "7   9.096915e-12  \n",
            "8  -5.762940e-12  \n",
            "9   1.085691e-12  \n",
            "10  1.042786e-12  \n",
            "11  3.061286e-11  \n",
            "12 -6.777715e-13  \n",
            "13 -1.356378e-11  \n",
            "14  1.278172e-12  \n",
            "15 -4.643791e-12  \n",
            "16  5.318740e-12  \n",
            "17           NaN  \n",
            "18           NaN  \n",
            "19 -1.176655e-12  \n",
            "20  4.728775e-12  \n",
            "21 -2.039849e-13  \n",
            "22 -1.594952e-12  \n",
            "23  9.213076e-12  \n",
            "24  5.769963e-12  \n",
            "25  9.193358e-12  \n",
            "26  1.519870e-11  \n",
            "27 -1.092205e-11  \n",
            "28  2.066240e-12  \n",
            "29  2.487449e-12  \n",
            "30  2.487449e-12  \n",
            "31  2.287051e-12  \n",
            "32 -1.352438e-12  \n",
            "33 -3.285204e-11  \n",
            "\n",
            "[34 rows x 100 columns]\n",
            "Saved Spectra to 375CueingContraSpec.csv file in present directory\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}