{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n\n# Plot point-spread functions (PSFs) and cross-talk functions (CTFs)\n\nVisualise PSF and CTF at one 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#\n# License: BSD-3-Clause\n# Copyright the MNE-Python contributors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import mne\nfrom mne.datasets import sample\nfrom mne.minimum_norm import (\n    get_cross_talk,\n    get_point_spread,\n    make_inverse_resolution_matrix,\n)\n\nprint(__doc__)\n\ndata_path = sample.data_path()\nsubjects_dir = data_path / \"subjects\"\nmeg_path = data_path / \"MEG\" / \"sample\"\nfname_fwd = meg_path / \"sample_audvis-meg-eeg-oct-6-fwd.fif\"\nfname_cov = meg_path / \"sample_audvis-cov.fif\"\nfname_evo = meg_path / \"sample_audvis-ave.fif\"\n\n# read forward solution\nforward = mne.read_forward_solution(fname_fwd)\n# forward operator with fixed source orientations\nmne.convert_forward_solution(forward, surf_ori=True, force_fixed=True, copy=False)\n\n# noise covariance matrix\nnoise_cov = mne.read_cov(fname_cov)\n\n# evoked data for info\nevoked = mne.read_evokeds(fname_evo, 0)\n\n# make inverse operator from forward solution\n# free source orientation\ninverse_operator = mne.minimum_norm.make_inverse_operator(\n    info=evoked.info, forward=forward, noise_cov=noise_cov, loose=0.0, depth=None\n)\n\n# regularisation parameter\nsnr = 3.0\nlambda2 = 1.0 / snr**2\n\n# compute resolution matrix for sLORETA\nrm_lor = make_inverse_resolution_matrix(\n    forward, inverse_operator, method=\"sLORETA\", lambda2=lambda2\n)\n\n# get PSF and CTF for sLORETA at one vertex\nsources = [1000]\n\nstc_psf = get_point_spread(rm_lor, forward[\"src\"], sources, norm=True)\n\nstc_ctf = get_cross_talk(rm_lor, forward[\"src\"], sources, norm=True)\ndel rm_lor"
      ]
    },
    {
      "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\nvertno_lh = forward[\"src\"][0][\"vertno\"]\nverttrue = [vertno_lh[sources[0]]]  # just one vertex\n\n# find vertices with maxima in PSF and CTF\nvert_max_psf = vertno_lh[stc_psf.data.argmax()]\nvert_max_ctf = vertno_lh[stc_ctf.data.argmax()]\n\nbrain_psf = stc_psf.plot(\"sample\", \"inflated\", \"lh\", subjects_dir=subjects_dir)\nbrain_psf.show_view(\"ventral\")\nbrain_psf.add_text(0.1, 0.9, \"sLORETA PSF\", \"title\", font_size=16)\n\n# True source location for PSF\nbrain_psf.add_foci(\n    verttrue, coords_as_verts=True, scale_factor=1.0, hemi=\"lh\", color=\"green\"\n)\n\n# Maximum of PSF\nbrain_psf.add_foci(\n    vert_max_psf, coords_as_verts=True, scale_factor=1.0, hemi=\"lh\", color=\"black\"\n)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "CTF:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "brain_ctf = stc_ctf.plot(\"sample\", \"inflated\", \"lh\", subjects_dir=subjects_dir)\nbrain_ctf.add_text(0.1, 0.9, \"sLORETA CTF\", \"title\", font_size=16)\nbrain_ctf.show_view(\"ventral\")\nbrain_ctf.add_foci(\n    verttrue, coords_as_verts=True, scale_factor=1.0, hemi=\"lh\", color=\"green\"\n)\n\n# Maximum of CTF\nbrain_ctf.add_foci(\n    vert_max_ctf, coords_as_verts=True, scale_factor=1.0, hemi=\"lh\", color=\"black\"\n)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The green spheres indicate the true source location, and the black\nspheres the maximum of the distribution.\n\n"
      ]
    }
  ],
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    "kernelspec": {
      "display_name": "Python 3",
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