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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import re
import numpy as np
import pytest
from mne import (
SourceEstimate,
VolSourceEstimate,
VolVectorSourceEstimate,
compute_covariance,
compute_source_morph,
make_fixed_length_epochs,
make_forward_solution,
read_bem_solution,
read_forward_solution,
read_trans,
setup_volume_source_space,
)
from mne.datasets import testing
from mne.io import read_raw_fif
from mne.minimum_norm import apply_inverse, make_inverse_operator
from mne.utils import _record_warnings, catch_logging
from mne.viz import plot_volume_source_estimates
from mne.viz.utils import _fake_click, _fake_keypress
data_dir = testing.data_path(download=False)
subjects_dir = data_dir / "subjects"
fwd_fname = data_dir / "MEG" / "sample" / "sample_audvis_trunc-meg-vol-7-fwd.fif"
@pytest.mark.slowtest # can be slow on OSX
@testing.requires_testing_data
@pytest.mark.parametrize(
"mode, stype, init_t, want_t, init_p, want_p, bg_img",
[
("glass_brain", "s", None, 2, None, (-30.9, 18.4, 56.7), None),
("stat_map", "vec", 1, 1, None, (15.7, 16.0, -6.3), None),
("glass_brain", "vec", None, 1, (10, -10, 20), (6.6, -9.0, 19.9), None),
("stat_map", "s", 1, 1, (-10, 5, 10), (-12.3, 2.0, 7.7), "brain.mgz"),
],
)
def test_plot_volume_source_estimates_basic(
mode, stype, init_t, want_t, init_p, want_p, bg_img
):
"""Test interactive plotting of volume source estimates."""
pytest.importorskip("nibabel")
pytest.importorskip("dipy")
pytest.importorskip("nilearn")
forward = read_forward_solution(fwd_fname)
sample_src = forward["src"]
if init_p is not None:
init_p = np.array(init_p) / 1000.0
vertices = [s["vertno"] for s in sample_src]
n_verts = sum(len(v) for v in vertices)
n_time = 2
data = np.random.RandomState(0).rand(n_verts, n_time)
if stype == "vec":
stc = VolVectorSourceEstimate(
np.tile(data[:, np.newaxis], (1, 3, 1)), vertices, 1, 1
)
else:
assert stype == "s"
stc = VolSourceEstimate(data, vertices, 1, 1)
# sometimes get scalars/index warning
with _record_warnings():
with catch_logging(verbose="debug") as log:
fig = stc.plot(
sample_src,
subject="sample",
subjects_dir=subjects_dir,
mode=mode,
initial_time=init_t,
initial_pos=init_p,
bg_img=bg_img,
verbose=True,
)
log = log.getvalue()
want_str = f"t = {want_t:0.3f} s"
assert want_str in log, (want_str, init_t)
want_str = f"({want_p[0]:0.1f}, {want_p[1]:0.1f}, {want_p[2]:0.1f}) mm"
assert want_str in log, (want_str, init_p)
for ax_idx in [0, 2, 3, 4]:
_fake_click(fig, fig.axes[ax_idx], (0.3, 0.5))
_fake_keypress(fig, "left")
_fake_keypress(fig, "shift+right")
if bg_img is not None:
with pytest.raises(FileNotFoundError, match="MRI file .* not found"):
stc.plot(
sample_src,
subject="sample",
subjects_dir=subjects_dir,
mode="stat_map",
bg_img="junk.mgz",
)
use_ax = None
for ax in fig.axes:
if ax.get_xlabel().startswith("Time"):
use_ax = ax
break
assert use_ax is not None
label = use_ax.get_legend().get_texts()[0].get_text()
assert re.match("[0-9]*", label) is not None, label
@pytest.mark.slowtest # can be slow on OSX
@testing.requires_testing_data
def test_plot_volume_source_estimates_morph():
"""Test interactive plotting of volume source estimates with morph."""
pytest.importorskip("nibabel")
pytest.importorskip("dipy")
pytest.importorskip("nilearn")
forward = read_forward_solution(fwd_fname)
sample_src = forward["src"]
vertices = [s["vertno"] for s in sample_src]
n_verts = sum(len(v) for v in vertices)
n_time = 2
data = np.random.RandomState(0).rand(n_verts, n_time)
stc = VolSourceEstimate(data, vertices, 1, 1)
sample_src[0]["subject_his_id"] = "sample" # old src
morph = compute_source_morph(
sample_src, "sample", "fsaverage", zooms=5, subjects_dir=subjects_dir
)
initial_pos = (-0.05, -0.01, -0.006)
# sometimes get scalars/index warning
with _record_warnings():
with catch_logging() as log:
stc.plot(
morph,
subjects_dir=subjects_dir,
mode="glass_brain",
initial_pos=initial_pos,
verbose=True,
)
log = log.getvalue()
assert "t = 1.000 s" in log
assert "(-52.0, -8.0, -7.0) mm" in log
with pytest.raises(ValueError, match="Allowed values are"):
stc.plot(sample_src, "sample", subjects_dir, mode="abcd")
vertices.append([])
surface_stc = SourceEstimate(data, vertices, 1, 1)
with pytest.raises(TypeError, match="an instance of VolSourceEstimate"):
plot_volume_source_estimates(surface_stc, sample_src, "sample", subjects_dir)
with pytest.raises(ValueError, match="Negative colormap limits"):
stc.plot(
sample_src, "sample", subjects_dir, clim=dict(lims=[-1, 2, 3], kind="value")
)
@testing.requires_testing_data
def test_plot_volume_source_estimates_on_vol_labels():
"""Test plot of source estimate on srcs setup on 2 labels."""
pytest.importorskip("nibabel")
pytest.importorskip("dipy")
pytest.importorskip("nilearn")
raw = read_raw_fif(
data_dir / "MEG" / "sample" / "sample_audvis_trunc_raw.fif", preload=False
)
raw.pick("meg").crop(0, 10)
raw.pick(raw.ch_names[::2]).del_proj().load_data()
epochs = make_fixed_length_epochs(raw, preload=True).apply_baseline((None, None))
evoked = epochs.average()
subject = "sample"
bem = read_bem_solution(
subjects_dir / f"{subject}" / "bem" / "sample-320-bem-sol.fif"
)
pos = 25.0 # 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=False,
)
trans = read_trans(data_dir / "MEG" / "sample" / "sample_audvis_trunc-trans.fif")
fwd = make_forward_solution(
evoked.info,
trans,
src,
bem,
meg=True,
eeg=False,
mindist=0,
n_jobs=1,
)
cov = compute_covariance(
epochs,
tmin=None,
tmax=None,
method="empirical",
)
inverse_operator = make_inverse_operator(evoked.info, fwd, cov, loose=1, depth=0.8)
stc = apply_inverse(
evoked, inverse_operator, 1.0 / 3**2, method="sLORETA", pick_ori=None
)
stc.plot(src, subject, subjects_dir, initial_time=0.03)
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