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      "cell_type": "markdown",
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      "source": [
        "\n\n# Compare evoked responses for different conditions\n\nIn this example, an Epochs object for visual and auditory responses is created.\nBoth conditions are then accessed by their respective names to create a sensor\nlayout plot of the related evoked responses.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "source": [
        "# Authors: Denis Engemann <denis.engemann@gmail.com>\n#          Alexandre Gramfort <alexandre.gramfort@inria.fr>\n\n# License: BSD-3-Clause\n# Copyright the MNE-Python contributors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n\nimport mne\nfrom mne.datasets import sample\nfrom mne.viz import plot_evoked_topo\n\nprint(__doc__)\n\ndata_path = sample.data_path()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Set parameters.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "meg_path = data_path / \"MEG\" / \"sample\"\nraw_fname = meg_path / \"sample_audvis_filt-0-40_raw.fif\"\nevent_fname = meg_path / \"sample_audvis_filt-0-40_raw-eve.fif\"\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 amplitude-peak rejection values for MEG channels.\nreject = dict(grad=4000e-13, mag=4e-12)\n\n# Create epochs including different events.\nevent_id = {\"audio/left\": 1, \"audio/right\": 2, \"visual/left\": 3, \"visual/right\": 4}\nepochs = mne.Epochs(\n    raw, events, event_id, tmin, tmax, picks=\"meg\", baseline=(None, 0), reject=reject\n)\n\n# Generate list of evoked objects from conditions names\nevokeds = [epochs[name].average() for name in (\"left\", \"right\")]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Show topography for two different conditions.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "colors = \"blue\", \"red\"\ntitle = \"MNE sample data\\nleft vs right (A/V combined)\"\n\nplot_evoked_topo(evokeds, color=colors, title=title, background_color=\"w\")\n\nplt.show()"
      ]
    }
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