frequency_vs_time_for_epoch.ipynb

{
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  "metadata": {
    "colab": {
      "name": "frequency vs time for epoch.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNO545EaJn1JuWPmkVB7rbg",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Zoro114/mne/blob/main/frequency_vs_time_for_epoch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s_3Oqk-FdvkC",
        "outputId": "ac6ff1a6-a955-40b1-9c3f-bdc0d367e05f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting mne\n",
            "  Downloading mne-1.0.3-py3-none-any.whl (7.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 7.5 MB 5.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from mne) (3.2.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from mne) (4.64.0)\n",
            "Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.7/dist-packages (from mne) (1.21.6)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from mne) (4.4.2)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from mne) (21.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from mne) (2.11.3)\n",
            "Requirement already satisfied: pooch>=1.5 in /usr/local/lib/python3.7/dist-packages (from mne) (1.6.0)\n",
            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from mne) (1.4.1)\n",
            "Requirement already satisfied: appdirs>=1.3.0 in /usr/local/lib/python3.7/dist-packages (from pooch>=1.5->mne) (1.4.4)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.7/dist-packages (from pooch>=1.5->mne) (2.23.0)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->mne) (3.0.9)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pooch>=1.5->mne) (2022.5.18.1)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pooch>=1.5->mne) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pooch>=1.5->mne) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.19.0->pooch>=1.5->mne) (3.0.4)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->mne) (2.0.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mne) (1.4.2)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mne) (2.8.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mne) (0.11.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->mne) (4.2.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->mne) (1.15.0)\n",
            "Installing collected packages: mne\n",
            "Successfully installed mne-1.0.3\n"
          ]
        }
      ],
      "source": [
        "!pip install mne"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import mne"
      ],
      "metadata": {
        "id": "BmKQqlUBdyRi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_path=mne.datasets.sample.data_path()\n",
        "meg_fname=data_path/'MEG'/'sample'\n",
        "raw_fname=meg_fname/'sample_audvis_filt-0-40_raw.fif'\n",
        "event_fname=meg_fname/'sample_audvis_filt-0-40_raw-eve.fif'\n",
        "\n",
        "raw=mne.io.read_raw_fif(raw_fname,preload=True)\n",
        "events=mne.read_events(event_fname)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Htyv2I45fHOv",
        "outputId": "b9e0c0cc-5904-4fe7-9999-6179dbeabf6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using default location ~/mne_data for sample...\n",
            "Creating ~/mne_data\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading file 'MNE-sample-data-processed.tar.gz' from 'https://osf.io/86qa2/download?version=6' to '/root/mne_data'.\n",
            "100%|██████████████████████████████████████| 1.65G/1.65G [00:00<00:00, 480GB/s]\n",
            "Untarring contents of '/root/mne_data/MNE-sample-data-processed.tar.gz' to '/root/mne_data'\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Attempting to create new mne-python configuration file:\n",
            "/root/.mne/mne-python.json\n",
            "Opening raw data file /root/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif...\n",
            "    Read a total of 4 projection items:\n",
            "        PCA-v1 (1 x 102)  idle\n",
            "        PCA-v2 (1 x 102)  idle\n",
            "        PCA-v3 (1 x 102)  idle\n",
            "        Average EEG reference (1 x 60)  idle\n",
            "    Range : 6450 ... 48149 =     42.956 ...   320.665 secs\n",
            "Ready.\n",
            "Reading 0 ... 41699  =      0.000 ...   277.709 secs...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "event_id = {'Auditory/Left': 1, 'Auditory/Right': 2,\n",
        "            'Visual/Left': 3, 'Visual/Right': 4}"
      ],
      "metadata": {
        "id": "CwdS8yTcfM4C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reject = dict(mag=3000e-15,grad=3000e-13,eeg=100e-6,eog=200e-6)"
      ],
      "metadata": {
        "id": "7BbKvduJYOkL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "raw.filter(10,12)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "id": "gM3Xt3LOg8bO",
        "outputId": "1c9365cf-39bf-48c9-8130-558c7e2b8a44"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Filtering raw data in 1 contiguous segment\n",
            "Setting up band-pass filter from 10 - 12 Hz\n",
            "\n",
            "FIR filter parameters\n",
            "---------------------\n",
            "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
            "- Windowed time-domain design (firwin) method\n",
            "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
            "- Lower passband edge: 10.00\n",
            "- Lower transition bandwidth: 2.50 Hz (-6 dB cutoff frequency: 8.75 Hz)\n",
            "- Upper passband edge: 12.00 Hz\n",
            "- Upper transition bandwidth: 3.00 Hz (-6 dB cutoff frequency: 13.50 Hz)\n",
            "- Filter length: 199 samples (1.325 sec)\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Raw | sample_audvis_filt-0-40_raw.fif, 376 x 41700 (277.7 s), ~122.9 MB, data loaded>"
            ],
            "text/html": [
              "<table class=\"table table-hover table-striped table-sm table-responsive small\">\n",
              "    <tr>\n",
              "        <th>Measurement date</th>\n",
              "        \n",
              "        <td>December 03, 2002  19:01:10 GMT</td>\n",
              "        \n",
              "    </tr>\n",
              "    <tr>\n",
              "        <th>Experimenter</th>\n",
              "        \n",
              "        <td>Unknown</td>\n",
              "        \n",
              "    </tr>\n",
              "        <th>Participant</th>\n",
              "        \n",
              "        <td>Unknown</td>\n",
              "        \n",
              "    </tr>\n",
              "    <tr>\n",
              "        <th>Digitized points</th>\n",
              "        \n",
              "        <td>0 points</td>\n",
              "        \n",
              "    </tr>\n",
              "    <tr>\n",
              "        <th>Good channels</th>\n",
              "        <td>204 Gradiometers, 102 Magnetometers, 9 Stimulus, 60 EEG, 1 EOG</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "        <th>Bad channels</th>\n",
              "        <td>MEG 2443, EEG 053</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "        <th>EOG channels</th>\n",
              "        <td>EOG 061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "        <th>ECG channels</th>\n",
              "        <td>Not available</td>\n",
              "    \n",
              "    <tr>\n",
              "        <th>Sampling frequency</th>\n",
              "        <td>150.15 Hz</td>\n",
              "    </tr>\n",
              "    \n",
              "    \n",
              "    <tr>\n",
              "        <th>Highpass</th>\n",
              "        <td>10.00 Hz</td>\n",
              "    </tr>\n",
              "    \n",
              "    \n",
              "    <tr>\n",
              "        <th>Lowpass</th>\n",
              "        <td>12.00 Hz</td>\n",
              "    </tr>\n",
              "    \n",
              "    \n",
              "    <tr>\n",
              "        <th>Projections</th>\n",
              "        <td>PCA-v1 : off<br/>PCA-v2 : off<br/>PCA-v3 : off<br/>Average EEG reference : off</td>\n",
              "    </tr>\n",
              "    \n",
              "    \n",
              "    <tr>\n",
              "        <th>Filenames</th>\n",
              "        <td>sample_audvis_filt-0-40_raw.fif</td>\n",
              "    </tr>\n",
              "    \n",
              "    <tr>\n",
              "        <th>Duration</th>\n",
              "        <td>00:04:37 (HH:MM:SS)</td>\n",
              "    </tr>\n",
              "</table>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "epochs=mne.Epochs(raw,events,event_id,tmin=-0.5,tmax=1.5,baseline=None,proj=True,reject=reject,preload=True,verbose='error')\n",
        "del raw"
      ],
      "metadata": {
        "id": "5fV_maDdhKc6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = epochs.to_data_frame(time_format=None,scalings=dict(eeg=1, mag=1, grad=1))\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "00sjtn2ijXIi",
        "outputId": "fe537ba9-de3e-4ba9-9a2e-ce054829cc23"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       time    condition  epoch      MEG 0113      MEG 0112      MEG 0111  \\\n",
              "0 -0.499488  Visual/Left      1  4.610698e-12  1.490138e-12  8.786036e-15   \n",
              "1 -0.492828  Visual/Left      1  3.167765e-12  1.469077e-12 -2.062531e-14   \n",
              "2 -0.486168  Visual/Left      1  9.218060e-13  1.137313e-12 -5.249979e-14   \n",
              "3 -0.479509  Visual/Left      1 -1.649358e-12  5.722488e-13 -8.227664e-14   \n",
              "4 -0.472849  Visual/Left      1 -3.969608e-12 -1.013922e-13 -1.037398e-13   \n",
              "\n",
              "       MEG 0122      MEG 0123      MEG 0121      MEG 0132  ...   EEG 052  \\\n",
              "0  4.872627e-13  3.811255e-14 -1.050202e-13  8.417231e-13  ... -0.000001   \n",
              "1 -6.489358e-13  1.097027e-12 -1.075587e-13 -5.621832e-13  ... -0.000003   \n",
              "2 -1.668619e-12  1.884995e-12 -9.028214e-14 -1.910436e-12  ... -0.000003   \n",
              "3 -2.356499e-12  2.254338e-12 -5.920207e-14 -2.906130e-12  ... -0.000004   \n",
              "4 -2.564293e-12  2.151141e-12 -2.202070e-14 -3.318857e-12  ... -0.000003   \n",
              "\n",
              "        EEG 053       EEG 054       EEG 055       EEG 056       EEG 057  \\\n",
              "0 -2.738145e-07  1.411756e-06  1.177853e-06  2.033776e-07  1.689465e-06   \n",
              "1 -2.280304e-07  2.616206e-07 -6.369013e-07 -1.592302e-06  4.585942e-07   \n",
              "2 -1.509298e-07 -9.470013e-07 -2.338243e-06 -3.063998e-06 -8.890423e-07   \n",
              "3 -5.465201e-08 -1.979034e-06 -3.611760e-06 -3.949454e-06 -2.101789e-06   \n",
              "4  4.704873e-08 -2.633380e-06 -4.225623e-06 -4.105385e-06 -2.947032e-06   \n",
              "\n",
              "        EEG 058       EEG 059       EEG 060    EOG 061  \n",
              "0  1.901676e-06  1.543722e-06  5.667438e-07 -71.982720  \n",
              "1  4.239294e-07 -2.210197e-07 -6.505732e-07 -63.912281  \n",
              "2 -1.155060e-06 -1.960340e-06 -1.758163e-06 -54.352427  \n",
              "3 -2.551734e-06 -3.354865e-06 -2.594119e-06 -46.923936  \n",
              "4 -3.511828e-06 -4.149343e-06 -3.037180e-06 -43.465338  \n",
              "\n",
              "[5 rows x 379 columns]"
            ],
            "text/html": [
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              "  <div id=\"df-b93295b0-e485-4863-83d9-081e478f9153\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>time</th>\n",
              "      <th>condition</th>\n",
              "      <th>epoch</th>\n",
              "      <th>MEG 0113</th>\n",
              "      <th>MEG 0112</th>\n",
              "      <th>MEG 0111</th>\n",
              "      <th>MEG 0122</th>\n",
              "      <th>MEG 0123</th>\n",
              "      <th>MEG 0121</th>\n",
              "      <th>MEG 0132</th>\n",
              "      <th>...</th>\n",
              "      <th>EEG 052</th>\n",
              "      <th>EEG 053</th>\n",
              "      <th>EEG 054</th>\n",
              "      <th>EEG 055</th>\n",
              "      <th>EEG 056</th>\n",
              "      <th>EEG 057</th>\n",
              "      <th>EEG 058</th>\n",
              "      <th>EEG 059</th>\n",
              "      <th>EEG 060</th>\n",
              "      <th>EOG 061</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.499488</td>\n",
              "      <td>Visual/Left</td>\n",
              "      <td>1</td>\n",
              "      <td>4.610698e-12</td>\n",
              "      <td>1.490138e-12</td>\n",
              "      <td>8.786036e-15</td>\n",
              "      <td>4.872627e-13</td>\n",
              "      <td>3.811255e-14</td>\n",
              "      <td>-1.050202e-13</td>\n",
              "      <td>8.417231e-13</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.000001</td>\n",
              "      <td>-2.738145e-07</td>\n",
              "      <td>1.411756e-06</td>\n",
              "      <td>1.177853e-06</td>\n",
              "      <td>2.033776e-07</td>\n",
              "      <td>1.689465e-06</td>\n",
              "      <td>1.901676e-06</td>\n",
              "      <td>1.543722e-06</td>\n",
              "      <td>5.667438e-07</td>\n",
              "      <td>-71.982720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.492828</td>\n",
              "      <td>Visual/Left</td>\n",
              "      <td>1</td>\n",
              "      <td>3.167765e-12</td>\n",
              "      <td>1.469077e-12</td>\n",
              "      <td>-2.062531e-14</td>\n",
              "      <td>-6.489358e-13</td>\n",
              "      <td>1.097027e-12</td>\n",
              "      <td>-1.075587e-13</td>\n",
              "      <td>-5.621832e-13</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.000003</td>\n",
              "      <td>-2.280304e-07</td>\n",
              "      <td>2.616206e-07</td>\n",
              "      <td>-6.369013e-07</td>\n",
              "      <td>-1.592302e-06</td>\n",
              "      <td>4.585942e-07</td>\n",
              "      <td>4.239294e-07</td>\n",
              "      <td>-2.210197e-07</td>\n",
              "      <td>-6.505732e-07</td>\n",
              "      <td>-63.912281</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.486168</td>\n",
              "      <td>Visual/Left</td>\n",
              "      <td>1</td>\n",
              "      <td>9.218060e-13</td>\n",
              "      <td>1.137313e-12</td>\n",
              "      <td>-5.249979e-14</td>\n",
              "      <td>-1.668619e-12</td>\n",
              "      <td>1.884995e-12</td>\n",
              "      <td>-9.028214e-14</td>\n",
              "      <td>-1.910436e-12</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.000003</td>\n",
              "      <td>-1.509298e-07</td>\n",
              "      <td>-9.470013e-07</td>\n",
              "      <td>-2.338243e-06</td>\n",
              "      <td>-3.063998e-06</td>\n",
              "      <td>-8.890423e-07</td>\n",
              "      <td>-1.155060e-06</td>\n",
              "      <td>-1.960340e-06</td>\n",
              "      <td>-1.758163e-06</td>\n",
              "      <td>-54.352427</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.479509</td>\n",
              "      <td>Visual/Left</td>\n",
              "      <td>1</td>\n",
              "      <td>-1.649358e-12</td>\n",
              "      <td>5.722488e-13</td>\n",
              "      <td>-8.227664e-14</td>\n",
              "      <td>-2.356499e-12</td>\n",
              "      <td>2.254338e-12</td>\n",
              "      <td>-5.920207e-14</td>\n",
              "      <td>-2.906130e-12</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.000004</td>\n",
              "      <td>-5.465201e-08</td>\n",
              "      <td>-1.979034e-06</td>\n",
              "      <td>-3.611760e-06</td>\n",
              "      <td>-3.949454e-06</td>\n",
              "      <td>-2.101789e-06</td>\n",
              "      <td>-2.551734e-06</td>\n",
              "      <td>-3.354865e-06</td>\n",
              "      <td>-2.594119e-06</td>\n",
              "      <td>-46.923936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.472849</td>\n",
              "      <td>Visual/Left</td>\n",
              "      <td>1</td>\n",
              "      <td>-3.969608e-12</td>\n",
              "      <td>-1.013922e-13</td>\n",
              "      <td>-1.037398e-13</td>\n",
              "      <td>-2.564293e-12</td>\n",
              "      <td>2.151141e-12</td>\n",
              "      <td>-2.202070e-14</td>\n",
              "      <td>-3.318857e-12</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.000003</td>\n",
              "      <td>4.704873e-08</td>\n",
              "      <td>-2.633380e-06</td>\n",
              "      <td>-4.225623e-06</td>\n",
              "      <td>-4.105385e-06</td>\n",
              "      <td>-2.947032e-06</td>\n",
              "      <td>-3.511828e-06</td>\n",
              "      <td>-4.149343e-06</td>\n",
              "      <td>-3.037180e-06</td>\n",
              "      <td>-43.465338</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 379 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b93295b0-e485-4863-83d9-081e478f9153')\"\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "long_df = epochs.to_data_frame(time_format=None, index='condition',\n",
        "                               long_format=True)\n",
        "long_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
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        "id": "ruVjDp3cj4sN",
        "outputId": "146e66c2-80cb-4ec3-c2c1-b09ecf3e60b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Converting \"epoch\" to \"category\"...\n",
            "Converting \"channel\" to \"category\"...\n",
            "Converting \"ch_type\" to \"category\"...\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            epoch      time   channel ch_type      value\n",
              "condition                                               \n",
              "Visual/Left     1 -0.499488  MEG 0113    grad  46.106980\n",
              "Visual/Left     1 -0.499488  MEG 0112    grad  14.901382\n",
              "Visual/Left     1 -0.499488  MEG 0111     mag   8.786036\n",
              "Visual/Left     1 -0.499488  MEG 0122    grad   4.872627\n",
              "Visual/Left     1 -0.499488  MEG 0123    grad   0.381126"
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              "      <td>MEG 0113</td>\n",
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              "      <td>-0.499488</td>\n",
              "      <td>MEG 0111</td>\n",
              "      <td>mag</td>\n",
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              "      <td>MEG 0122</td>\n",
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              "\n",
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              "          const element = document.querySelector('#df-ccfaf8f8-a1df-4269-a9f2-eed645abba06');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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          "metadata": {},
          "execution_count": 28
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure()\n",
        "channels = ['MEG 1332', 'MEG 1342']\n",
        "data = long_df.loc['Auditory/Left'].query('channel in @channels')\n",
        "data['channel'] = data['channel'].astype(str)\n",
        "data.reset_index(drop=True, inplace=True) \n",
        "sns.lineplot(x='time', y='value', hue='channel', data=data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "o-4M7VfOqepB",
        "outputId": "27cc8c03-c649-49e0-fe42-74c34e7c9a99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0fedb37210>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n"
      ],
      "metadata": {
        "id": "A2njxCJRiLfe"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}