{
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    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "%matplotlib inline"
      ], 
      "outputs": [], 
      "metadata": {
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      }
    }, 
    {
      "source": [
        "\n# Plot an estimate of data SNR\n\n\nThis estimates the SNR as a function of time for a set of data.\n\n"
      ], 
      "cell_type": "markdown", 
      "metadata": {}
    }, 
    {
      "execution_count": null, 
      "cell_type": "code", 
      "source": [
        "# Author: Eric Larson <larson.eric.d@gmail.com>\n#\n# License: BSD (3-clause)\n\nfrom os import path as op\n\nfrom mne.datasets.sample import data_path\nfrom mne.minimum_norm import read_inverse_operator\nfrom mne import read_evokeds\nfrom mne.viz import plot_snr_estimate\n\nprint(__doc__)\n\ndata_dir = op.join(data_path(), 'MEG', 'sample')\nfname_inv = op.join(data_dir, 'sample_audvis-meg-oct-6-meg-inv.fif')\nfname_evoked = op.join(data_dir, 'sample_audvis-ave.fif')\n\ninv = read_inverse_operator(fname_inv)\nevoked = read_evokeds(fname_evoked, baseline=(None, 0))[0]\n\nplot_snr_estimate(evoked, inv)"
      ], 
      "outputs": [], 
      "metadata": {
        "collapsed": false
      }
    }
  ], 
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      "name": "python2", 
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