# -*- coding: utf-8 -*-
"""
.. _ex-iterative-reweighted-tf-mxne:

==============================================================================
Compute iterative reweighted TF-MxNE with multiscale time-frequency dictionary
==============================================================================

The iterative reweighted TF-MxNE solver is a distributed inverse method
based on the TF-MxNE solver, which promotes focal (sparse) sources
:footcite:`StrohmeierEtAl2015`. The benefits of this approach are that:

- it is spatio-temporal without assuming stationarity (source properties
  can vary over time),
- activations are localized in space, time, and frequency in one step,
- the solver uses non-convex penalties in the TF domain, which results in a
  solution less biased towards zero than when simple TF-MxNE is used,
- using a multiscale dictionary allows to capture short transient
  activations along with slower brain waves :footcite:`BekhtiEtAl2016`.
"""
# Author: Mathurin Massias <mathurin.massias@gmail.com>
#         Yousra Bekhti <yousra.bekhti@gmail.com>
#         Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>
#         Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause

# %%

import mne
from mne.datasets import somato
from mne.inverse_sparse import tf_mixed_norm, make_stc_from_dipoles
from mne.viz import plot_sparse_source_estimates

print(__doc__)


# %%
# Load somatosensory MEG data

data_path = somato.data_path()
subject = '01'
task = 'somato'
raw_fname = (data_path / f'sub-{subject}' / 'meg' /
             f'sub-{subject}_task-{task}_meg.fif')
fwd_fname = (data_path / 'derivatives' / f'sub-{subject}' /
             f'sub-{subject}_task-{task}-fwd.fif')

# Read evoked
raw = mne.io.read_raw_fif(raw_fname)
raw.pick_types(meg=True, eog=True, stim=True)
events = mne.find_events(raw, stim_channel='STI 014')

reject = dict(grad=4000e-13, eog=350e-6)
event_id, tmin, tmax = dict(unknown=1), -0.5, 0.5
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, reject=reject,
                    baseline=(None, 0))
evoked = epochs.average()

evoked.crop(tmin=0.0, tmax=0.2)

# Compute noise covariance matrix
cov = mne.compute_covariance(epochs, rank='info', tmax=0.)
del epochs, raw

# Handling forward solution
forward = mne.read_forward_solution(fwd_fname)

# %%
# Run iterative reweighted multidict TF-MxNE solver

alpha, l1_ratio = 20, 0.05
loose, depth = 0.9, 1.
# Use a multiscale time-frequency dictionary
wsize, tstep = [4, 16], [2, 4]


n_tfmxne_iter = 10
# Compute TF-MxNE inverse solution with dipole output
dipoles, residual = tf_mixed_norm(
    evoked, forward, cov, alpha=alpha, l1_ratio=l1_ratio,
    n_tfmxne_iter=n_tfmxne_iter, loose=loose,
    depth=depth, tol=1e-3,
    wsize=wsize, tstep=tstep, return_as_dipoles=True,
    return_residual=True)

# %%
# Generate stc from dipoles

stc = make_stc_from_dipoles(dipoles, forward['src'])
plot_sparse_source_estimates(
    forward['src'], stc, bgcolor=(1, 1, 1), opacity=0.1,
    fig_name=f"irTF-MxNE (cond {evoked.comment})")

# %%
# Show the evoked response and the residual for gradiometers
ylim = dict(grad=[-300, 300])
evoked.copy().pick_types(meg='grad').plot(
    titles=dict(grad='Evoked Response: Gradiometers'), ylim=ylim)
residual.copy().pick_types(meg='grad').plot(
    titles=dict(grad='Residuals: Gradiometers'), ylim=ylim)

# %%
# References
# ----------
# .. footbibliography::