{
  "nbformat_minor": 0, 
  "nbformat": 4, 
  "cells": [
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "%matplotlib inline"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }, 
    {
      "source": [
        "\n\nThe :class:`Info <mne.Info>` data structure\n==============================================\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "from __future__ import print_function\n\nimport mne\nimport os.path as op"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }, 
    {
      "source": [
        "The :class:`Info <mne.Info>` data object is typically created\nwhen data is imported into MNE-Python and contains details such as:\n\n - date, subject information, and other recording details\n - the sampling rate\n - information about the data channels (name, type, position, etc.)\n - digitized points\n - sensor\u2013head coordinate transformation matrices\n\nand so forth. See the :class:`the API reference <mne.Info>`\nfor a complete list of all data fields. Once created, this object is passed\naround throughout the data analysis pipeline.\n\nIt behaves as a nested Python dictionary:\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "# Read the info object from an example recording\ninfo = mne.io.read_info(\n    op.join(mne.datasets.sample.data_path(), 'MEG', 'sample',\n            'sample_audvis_raw.fif'), verbose=False)"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }, 
    {
      "source": [
        "List all the fields in the info object\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "print('Keys in info dictionary:\\n', info.keys())"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }, 
    {
      "source": [
        "Obtain the sampling rate of the data\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "print(info['sfreq'], 'Hz')"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }, 
    {
      "source": [
        "List all information about the first data channel\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "print(info['chs'][0])"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }, 
    {
      "source": [
        "\nObtaining subsets of channels\n-----------------------------\n\nThere are a number of convenience functions to obtain channel indices, given\nan :class:`mne.Info` object.\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "source": [
        "Get channel indices by name\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "channel_indices = mne.pick_channels(info['ch_names'], ['MEG 0312', 'EEG 005'])"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }, 
    {
      "source": [
        "Get channel indices by regular expression\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "channel_indices = mne.pick_channels_regexp(info['ch_names'], 'MEG *')"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }, 
    {
      "source": [
        "Get channel indices by type\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "channel_indices = mne.pick_types(info, meg=True)  # MEG only\nchannel_indices = mne.pick_types(info, eeg=True)  # EEG only"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }, 
    {
      "source": [
        "MEG gradiometers and EEG channels\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "channel_indices = mne.pick_types(info, meg='grad', eeg=True)"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }, 
    {
      "source": [
        "Get a dictionary of channel indices, grouped by channel type\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "channel_indices_by_type = mne.io.pick.channel_indices_by_type(info)\nprint('The first three magnetometers:', channel_indices_by_type['mag'][:3])"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }, 
    {
      "source": [
        "Obtaining information about channels\n------------------------------------\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "# Channel type of a specific channel\nchannel_type = mne.io.pick.channel_type(info, 75)\nprint('Channel #75 is of type:', channel_type)"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }, 
    {
      "source": [
        "Channel types of a collection of channels\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "meg_channels = mne.pick_types(info, meg=True)[:10]\nchannel_types = [mne.io.pick.channel_type(info, ch) for ch in meg_channels]\nprint('First 10 MEG channels are of type:\\n', channel_types)"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }, 
    {
      "source": [
        "Dropping channels from an info structure\n----------------------------------------\n\nIt is possible to limit the info structure to only include a subset of\nchannels with the :func:`mne.pick_info` function:\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "# Only keep EEG channels\neeg_indices = mne.pick_types(info, meg=False, eeg=True)\nreduced_info = mne.pick_info(info, eeg_indices)\n\nprint(reduced_info)"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }
  ], 
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      "display_name": "Python 2", 
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      "mimetype": "text/x-python", 
      "nbconvert_exporter": "python", 
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      "file_extension": ".py", 
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      "codemirror_mode": {
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