from mne import (
    compute_covariance,
    make_fixed_length_epochs,
    make_forward_solution,
    read_bem_solution,
    setup_volume_source_space,
)
from mne.datasets import sample
from mne.io import read_raw_fif
from mne.minimum_norm import apply_inverse, make_inverse_operator


directory = sample.data_path() / "MEG" / "sample"
raw = read_raw_fif(directory / "sample_audvis_raw.fif", preload=False)
raw.pick("meg").crop(0, 60).load_data()
epochs = make_fixed_length_epochs(raw, preload=True)
evoked = epochs.average()

subjects_dir = sample.data_path() / "subjects"
subject = "sample"

bem = read_bem_solution(subjects_dir / f"{subject}" / "bem" / "sample-5120-bem-sol.fif")

pos = 10.0  # spacing in mm
volume_label = [
    "Right-Cerebral-Cortex",
    "Left-Cerebral-Cortex",
]
src = setup_volume_source_space(
    subject,
    subjects_dir=subjects_dir,
    pos=pos,
    mri=subjects_dir / subject / "mri" / "aseg.mgz",
    bem=bem,
    volume_label=volume_label,
    add_interpolator=True,
)

trans = sample.data_path() / "MEG" / "sample" / "sample_audvis_raw-trans.fif"
fwd = make_forward_solution(
    evoked.info,
    trans,
    src,
    bem,
    meg=True,
    eeg=False,
    mindist=5.0,  # ignore sources <= 5mm from the inner skull
    n_jobs=6,
)

cov = compute_covariance(
    epochs,
    tmin=None,
    tmax=None,
    method="empirical",
)
inverse_operator = make_inverse_operator(evoked.info, fwd, cov, loose=1, depth=0.8)

lambda2 = 1.0 / 3**2
stc = apply_inverse(evoked, inverse_operator, lambda2, method="sLORETA", pick_ori=None)

stc.plot(src, subject, subjects_dir, initial_time=0.03)