{
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    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "%matplotlib inline"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }, 
    {
      "source": [
        "\n# Compare evoked responses for different conditions\n\n\nIn this example, an Epochs object for visual and\nauditory responses is created. Both conditions\nare then accessed by their respective names to\ncreate a sensor layout plot of the related\nevoked responses.\n\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "# Authors: Denis Engemann <denis.engemann@gmail.com>\n#          Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>\n\n# License: BSD (3-clause)\n\n\nimport matplotlib.pyplot as plt\nimport mne\n\nfrom mne.viz import plot_evoked_topo\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 = 1\ntmin = -0.2\ntmax = 0.5\n\n#   Setup for reading the raw data\nraw = mne.io.read_raw_fif(raw_fname)\nevents = mne.read_events(event_fname)\n\n#   Set up pick list: MEG + STI 014 - bad channels (modify to your needs)\ninclude = []  # or stim channels ['STI 014']\n# bad channels in raw.info['bads'] will be automatically excluded\n\n#   Set up amplitude-peak rejection values for MEG channels\nreject = dict(grad=4000e-13, mag=4e-12)\n\n# pick MEG channels\npicks = mne.pick_types(raw.info, meg=True, eeg=False, stim=False, eog=True,\n                       include=include, exclude='bads')\n\n# Create epochs including different events\nevent_id = {'audio/left': 1, 'audio/right': 2,\n            'visual/left': 3, 'visual/right': 4}\nepochs = mne.Epochs(raw, events, event_id, tmin, tmax,\n                    picks=picks, baseline=(None, 0), reject=reject)\n\n# Generate list of evoked objects from conditions names\nevokeds = [epochs[name].average() for name in ('left', 'right')]"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }, 
    {
      "source": [
        "Show topography for two different conditions\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "colors = 'yellow', 'green'\ntitle = 'MNE sample data - left vs right (A/V combined)'\n\nplot_evoked_topo(evokeds, color=colors, title=title)\n\nplt.show()"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }
  ], 
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