{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n\n# Plot point-spread functions (PSFs) for a volume\n\nVisualise PSF at one volume vertex for sLORETA.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# Authors: Olaf Hauk <olaf.hauk@mrc-cbu.cam.ac.uk>\n#          Alexandre Gramfort <alexandre.gramfort@inria.fr>\n#          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": [
        "import numpy as np\n\nimport mne\nfrom mne.datasets import sample\nfrom mne.minimum_norm import get_point_spread, make_inverse_resolution_matrix\n\nprint(__doc__)\n\ndata_path = sample.data_path()\nsubjects_dir = data_path / \"subjects\"\nmeg_path = data_path / \"MEG\" / \"sample\"\nfname_cov = meg_path / \"sample_audvis-cov.fif\"\nfname_evo = meg_path / \"sample_audvis-ave.fif\"\nfname_trans = meg_path / \"sample_audvis_raw-trans.fif\"\nfname_bem = subjects_dir / \"sample\" / \"bem\" / \"sample-5120-bem-sol.fif\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "For the volume, create a coarse source space for speed (don't do this in\nreal code!), then compute the forward using this source space.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# read noise cov and and evoked\nnoise_cov = mne.read_cov(fname_cov)\nevoked = mne.read_evokeds(fname_evo, 0)\n\n# create a coarse source space\nsrc_vol = mne.setup_volume_source_space(  # this is a very course resolution!\n    \"sample\", pos=15.0, subjects_dir=subjects_dir, add_interpolator=False\n)  # usually you want True, this is just for speed\n\n# compute the forward\nforward_vol = mne.make_forward_solution(  # MEG-only for speed\n    evoked.info, fname_trans, src_vol, fname_bem, eeg=False\n)\ndel src_vol"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now make an inverse operator and compute the PSF at a source.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "inverse_operator_vol = mne.minimum_norm.make_inverse_operator(\n    info=evoked.info, forward=forward_vol, noise_cov=noise_cov\n)\n\n# compute resolution matrix for sLORETA\nrm_lor_vol = make_inverse_resolution_matrix(\n    forward_vol, inverse_operator_vol, method=\"sLORETA\", lambda2=1.0 / 9.0\n)\n\n# get PSF and CTF for sLORETA at one vertex\nsources_vol = [100]\nstc_psf_vol = get_point_spread(rm_lor_vol, forward_vol[\"src\"], sources_vol, norm=True)\ndel rm_lor_vol"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Visualize\nPSF:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# Which vertex corresponds to selected source\nsrc_vol = forward_vol[\"src\"]\nverttrue_vol = src_vol[0][\"vertno\"][sources_vol]\n\n# find vertex with maximum in PSF\nmax_vert_idx, _ = np.unravel_index(stc_psf_vol.data.argmax(), stc_psf_vol.data.shape)\nvert_max_ctf_vol = src_vol[0][\"vertno\"][[max_vert_idx]]\n\n# plot them\nbrain_psf_vol = stc_psf_vol.plot_3d(\n    \"sample\",\n    src=forward_vol[\"src\"],\n    views=\"ven\",\n    subjects_dir=subjects_dir,\n    volume_options=dict(alpha=0.5),\n)\nbrain_psf_vol.add_text(0.1, 0.9, \"Volumetric sLORETA PSF\", \"title\", font_size=16)\nbrain_psf_vol.add_foci(\n    verttrue_vol, coords_as_verts=True, scale_factor=1, hemi=\"vol\", color=\"green\"\n)\nbrain_psf_vol.add_foci(\n    vert_max_ctf_vol,\n    coords_as_verts=True,\n    scale_factor=1.25,\n    hemi=\"vol\",\n    color=\"black\",\n    alpha=0.3,\n)"
      ]
    }
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