{
  "nbformat_minor": 0, 
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  "cells": [
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "%matplotlib inline"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }, 
    {
      "source": [
        "\n# Reading epochs from a raw FIF file\n\n\nThis script shows how to read the epochs from a raw file given\na list of events. For illustration, we compute the evoked responses\nfor both MEG and EEG data by averaging all the epochs.\n\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>\n#          Matti Hamalainen <msh@nmr.mgh.harvard.edu>\n#\n# License: BSD (3-clause)\n\nimport mne\nfrom mne import io\nfrom mne.datasets import sample\n\nprint(__doc__)\n\ndata_path = sample.data_path()"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }, 
    {
      "source": [
        "Set parameters\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'\nevent_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'\nevent_id, tmin, tmax = 1, -0.2, 0.5\n\n# Setup for reading the raw data\nraw = io.read_raw_fif(raw_fname)\nevents = mne.read_events(event_fname)\n\n# Set up pick list: EEG + MEG - bad channels (modify to your needs)\nraw.info['bads'] += ['MEG 2443', 'EEG 053']  # bads + 2 more\npicks = mne.pick_types(raw.info, meg=True, eeg=False, stim=True, eog=True,\n                       exclude='bads')\n\n# Read epochs\nepochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,\n                    picks=picks, baseline=(None, 0), preload=True,\n                    reject=dict(grad=4000e-13, mag=4e-12, eog=150e-6))\n\nevoked = epochs.average()  # average epochs to get the evoked response"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }, 
    {
      "source": [
        "Show result\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "evoked.plot()"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }
  ], 
  "metadata": {
    "kernelspec": {
      "display_name": "Python 2", 
      "name": "python2", 
      "language": "python"
    }, 
    "language_info": {
      "mimetype": "text/x-python", 
      "nbconvert_exporter": "python", 
      "name": "python", 
      "file_extension": ".py", 
      "version": "2.7.13", 
      "pygments_lexer": "ipython2", 
      "codemirror_mode": {
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