"""
.. _ex-psd-ctf:

==================================================================
Plot point-spread functions (PSFs) and cross-talk functions (CTFs)
==================================================================

Visualise PSF and CTF at one vertex for sLORETA.
"""
# Authors: Olaf Hauk <olaf.hauk@mrc-cbu.cam.ac.uk>
#          Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

# %%

import mne
from mne.datasets import sample
from mne.minimum_norm import (
    get_cross_talk,
    get_point_spread,
    make_inverse_resolution_matrix,
)

print(__doc__)

data_path = sample.data_path()
subjects_dir = data_path / "subjects"
meg_path = data_path / "MEG" / "sample"
fname_fwd = meg_path / "sample_audvis-meg-eeg-oct-6-fwd.fif"
fname_cov = meg_path / "sample_audvis-cov.fif"
fname_evo = meg_path / "sample_audvis-ave.fif"

# read forward solution
forward = mne.read_forward_solution(fname_fwd)
# forward operator with fixed source orientations
mne.convert_forward_solution(forward, surf_ori=True, force_fixed=True, copy=False)

# noise covariance matrix
noise_cov = mne.read_cov(fname_cov)

# evoked data for info
evoked = mne.read_evokeds(fname_evo, 0)

# make inverse operator from forward solution
# free source orientation
inverse_operator = mne.minimum_norm.make_inverse_operator(
    info=evoked.info, forward=forward, noise_cov=noise_cov, loose=0.0, depth=None
)

# regularisation parameter
snr = 3.0
lambda2 = 1.0 / snr**2

# compute resolution matrix for sLORETA
rm_lor = make_inverse_resolution_matrix(
    forward, inverse_operator, method="sLORETA", lambda2=lambda2
)

# get PSF and CTF for sLORETA at one vertex
sources = [1000]

stc_psf = get_point_spread(rm_lor, forward["src"], sources, norm=True)

stc_ctf = get_cross_talk(rm_lor, forward["src"], sources, norm=True)
del rm_lor

##############################################################################
# Visualize
# ---------
# PSF:

# Which vertex corresponds to selected source
vertno_lh = forward["src"][0]["vertno"]
verttrue = [vertno_lh[sources[0]]]  # just one vertex

# find vertices with maxima in PSF and CTF
vert_max_psf = vertno_lh[stc_psf.data.argmax()]
vert_max_ctf = vertno_lh[stc_ctf.data.argmax()]

brain_psf = stc_psf.plot("sample", "inflated", "lh", subjects_dir=subjects_dir)
brain_psf.show_view("ventral")
brain_psf.add_text(0.1, 0.9, "sLORETA PSF", "title", font_size=16)

# True source location for PSF
brain_psf.add_foci(
    verttrue, coords_as_verts=True, scale_factor=1.0, hemi="lh", color="green"
)

# Maximum of PSF
brain_psf.add_foci(
    vert_max_psf, coords_as_verts=True, scale_factor=1.0, hemi="lh", color="black"
)

# %%
# CTF:

brain_ctf = stc_ctf.plot("sample", "inflated", "lh", subjects_dir=subjects_dir)
brain_ctf.add_text(0.1, 0.9, "sLORETA CTF", "title", font_size=16)
brain_ctf.show_view("ventral")
brain_ctf.add_foci(
    verttrue, coords_as_verts=True, scale_factor=1.0, hemi="lh", color="green"
)

# Maximum of CTF
brain_ctf.add_foci(
    vert_max_ctf, coords_as_verts=True, scale_factor=1.0, hemi="lh", color="black"
)


# %%
# The green spheres indicate the true source location, and the black
# spheres the maximum of the distribution.