{
  "cells": [
    {
      "cell_type": "markdown",
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      "source": [
        "\n\n# Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method\n\nSee :footcite:`WipfNagarajan2009` for details.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# Author: Martin Luessi <mluessi@nmr.mgh.harvard.edu>\n#         Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>\n#\n# License: BSD-3-Clause\n# Copyright the MNE-Python contributors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import numpy as np\n\nimport mne\nfrom mne.datasets import sample\nfrom mne.inverse_sparse import gamma_map, make_stc_from_dipoles\nfrom mne.viz import (\n    plot_dipole_amplitudes,\n    plot_dipole_locations,\n    plot_sparse_source_estimates,\n)\n\ndata_path = sample.data_path()\nsubjects_dir = data_path / \"subjects\"\nmeg_path = data_path / \"MEG\" / \"sample\"\nfwd_fname = meg_path / \"sample_audvis-meg-eeg-oct-6-fwd.fif\"\nevoked_fname = meg_path / \"sample_audvis-ave.fif\"\ncov_fname = meg_path / \"sample_audvis-cov.fif\"\n\n# Read the evoked response and crop it\ncondition = \"Left visual\"\nevoked = mne.read_evokeds(evoked_fname, condition=condition, baseline=(None, 0))\nevoked.crop(tmin=-50e-3, tmax=300e-3)\n\n# Read the forward solution\nforward = mne.read_forward_solution(fwd_fname)\n\n# Read noise noise covariance matrix and regularize it\ncov = mne.read_cov(cov_fname)\ncov = mne.cov.regularize(cov, evoked.info, rank=None)\n\n# Run the Gamma-MAP method with dipole output\nalpha = 0.5\ndipoles, residual = gamma_map(\n    evoked,\n    forward,\n    cov,\n    alpha,\n    xyz_same_gamma=True,\n    return_residual=True,\n    return_as_dipoles=True,\n)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Plot dipole activations\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "plot_dipole_amplitudes(dipoles)\n\n# Plot dipole location of the strongest dipole with MRI slices\nidx = np.argmax([np.max(np.abs(dip.amplitude)) for dip in dipoles])\nplot_dipole_locations(\n    dipoles[idx],\n    forward[\"mri_head_t\"],\n    \"sample\",\n    subjects_dir=subjects_dir,\n    mode=\"orthoview\",\n    idx=\"amplitude\",\n)\n\n# # Plot dipole locations of all dipoles with MRI slices\n# for dip in dipoles:\n#     plot_dipole_locations(dip, forward['mri_head_t'], 'sample',\n#                           subjects_dir=subjects_dir, mode='orthoview',\n#                           idx='amplitude')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Show the evoked response and the residual for gradiometers\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "ylim = dict(grad=[-120, 120])\nevoked.pick(picks=\"grad\", exclude=\"bads\")\nevoked.plot(\n    titles=dict(grad=\"Evoked Response Gradiometers\"),\n    ylim=ylim,\n    proj=True,\n    time_unit=\"s\",\n)\n\nresidual.pick(picks=\"grad\", exclude=\"bads\")\nresidual.plot(\n    titles=dict(grad=\"Residuals Gradiometers\"), ylim=ylim, proj=True, time_unit=\"s\"\n)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Generate stc from dipoles\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "stc = make_stc_from_dipoles(dipoles, forward[\"src\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "View in 2D and 3D (\"glass\" brain like 3D plot)\nShow the sources as spheres scaled by their strength\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "scale_factors = np.max(np.abs(stc.data), axis=1)\nscale_factors = 0.5 * (1 + scale_factors / np.max(scale_factors))\n\nplot_sparse_source_estimates(\n    forward[\"src\"],\n    stc,\n    bgcolor=(1, 1, 1),\n    modes=[\"sphere\"],\n    opacity=0.1,\n    scale_factors=(scale_factors, None),\n    fig_name=\"Gamma-MAP\",\n)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## References\n.. footbibliography::\n\n"
      ]
    }
  ],
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