{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n\n# Estimate data SNR using an inverse\n\nThis estimates the SNR as a function of time for a set of data\nusing a minimum-norm inverse operator.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# Author: Eric Larson <larson.eric.d@gmail.com>\n#\n# License: BSD-3-Clause\n# Copyright the MNE-Python contributors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "from mne import read_evokeds\nfrom mne.datasets.sample import data_path\nfrom mne.minimum_norm import read_inverse_operator\nfrom mne.viz import plot_snr_estimate\n\nprint(__doc__)\n\ndata_dir = data_path() / \"MEG\" / \"sample\"\nfname_inv = data_dir / \"sample_audvis-meg-oct-6-meg-inv.fif\"\nfname_evoked = 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)"
      ]
    }
  ],
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      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
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      },
      "file_extension": ".py",
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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