Stack vertices in plot_volume_source_estimates

doc/changes/devel.rst

@@ -59,6 +59,7 @@ Bugs
 - Fix handling of channel information in annotations when loading data from and exporting to EDF file (:gh:`11960` :gh:`12017` by `Paul Roujansky`_)
 - Add missing ``overwrite`` and ``verbose`` parameters to :meth:`Transform.save() <mne.transforms.Transform.save>` (:gh:`12004` by `Marijn van Vliet`_)
 - Correctly prune channel-specific :class:`~mne.Annotations` when creating :class:`~mne.Epochs` without the channel(s) included in the channel specific annotations (:gh:`12010` by `Mathieu Scheltienne`_)
+- Fix :func:`~mne.viz.plot_volume_source_estimates` with :class:`~mne.VolSourceEstimate` which include a list of vertices (:gh:`12025` by `Mathieu Scheltienne`_)
 - Correctly handle passing ``"eyegaze"`` or ``"pupil"`` to :meth:`mne.io.Raw.pick` (:gh:`12019` by `Scott Huberty`_)
 
 API changes

mne/minimum_norm/inverse.py

@@ -145,6 +145,16 @@ def _repr_html_(self):
         )
         return html
 
+    @property
+    def ch_names(self):
+        """Name of channels attached to the inverse operator."""
+        return self["info"].ch_names
+
+    @property
+    def info(self):
+        """:class:`~mne.Info` attached to the inverse operator."""
+        return self["info"]
+
 
 def _pick_channels_inverse_operator(ch_names, inv):
     """Return data channel indices to be used knowing an inverse operator.

mne/viz/_3d.py

@@ -2650,10 +2650,9 @@ def plot_volume_source_estimates(
     %(subject_none)s
         If ``None``, ``stc.subject`` will be used.
     %(subjects_dir)s
-    mode : str
-        The plotting mode to use. Either 'stat_map' (default) or 'glass_brain'.
-        For "glass_brain", activation absolute values are displayed
-        after being transformed to a standard MNI brain.
+    mode : ``'stat_map'`` | ``'glass_brain'``
+        The plotting mode to use. For ``'glass_brain'``, activation absolute values are
+        displayed after being transformed to a standard MNI brain.
     bg_img : instance of SpatialImage | str
         The background image used in the nilearn plotting function.
         Can also be a string to use the ``bg_img`` file in the subject's
@@ -2714,10 +2713,11 @@ def plot_volume_source_estimates(
     >>> morph = mne.compute_source_morph(src_sample, subject_to='fsaverage')  # doctest: +SKIP
     >>> fig = stc_vol_sample.plot(morph)  # doctest: +SKIP
     """  # noqa: E501
-    from matplotlib import pyplot as plt, colors
     import nibabel as nib
-    from ..source_estimate import VolSourceEstimate
+    from matplotlib import pyplot as plt, colors
+
     from ..morph import SourceMorph
+    from ..source_estimate import VolSourceEstimate
     from ..source_space._source_space import _ensure_src
 
     if not check_version("nilearn", "0.4"):
@@ -2745,8 +2745,9 @@ def plot_volume_source_estimates(
         level="debug",
     )
     subject = _check_subject(src_subject, subject, first_kind=kind)
-    stc_ijk = np.array(np.unravel_index(stc.vertices[0], img.shape[:3], order="F")).T
-    assert stc_ijk.shape == (len(stc.vertices[0]), 3)
+    vertices = np.hstack(stc.vertices)
+    stc_ijk = np.array(np.unravel_index(vertices, img.shape[:3], order="F")).T
+    assert stc_ijk.shape == (vertices.size, 3)
     del kind
 
     # XXX this assumes zooms are uniform, should probably mult by zooms...
@@ -2756,12 +2757,11 @@ def _cut_coords_to_idx(cut_coords, img):
         """Convert voxel coordinates to index in stc.data."""
         ijk = _cut_coords_to_ijk(cut_coords, img)
         del cut_coords
-        logger.debug("    Affine remapped cut coords to [%d, %d, %d] idx" % tuple(ijk))
+        logger.debug("    Affine remapped cut coords to [%d, %d, %d] idx", tuple(ijk))
         dist, loc_idx = dist_to_verts.query(ijk[np.newaxis])
         dist, loc_idx = dist[0], loc_idx[0]
         logger.debug(
-            "    Using vertex %d at a distance of %d voxels"
-            % (stc.vertices[0][loc_idx], dist)
+            "    Using vertex %d at a distance of %d voxels", (vertices[loc_idx], dist)
         )
         return loc_idx
 
@@ -2848,7 +2848,7 @@ def _update_timeslice(idx, params):
         plot_map_callback(params["img_idx"], title="", cut_coords=cut_coords)
 
     def _update_vertlabel(loc_idx):
-        vert_legend.get_texts()[0].set_text(f"{stc.vertices[0][loc_idx]}")
+        vert_legend.get_texts()[0].set_text(f"{vertices[loc_idx]}")
 
     @verbose_dec
     def _onclick(event, params, verbose=None):
@@ -2932,7 +2932,7 @@ def _onclick(event, params, verbose=None):
             (stc.times[time_idx],)
             + tuple(cut_coords)
             + tuple(ijk)
-            + (stc.vertices[0][loc_idx],)
+            + (vertices[loc_idx],)
         )
     )
     del ijk
@@ -3046,8 +3046,7 @@ def plot_and_correct(*args, **kwargs):
 
     plot_and_correct(stat_map_img=params["img_idx"], title="", cut_coords=cut_coords)
 
-    if show:
-        plt.show()
+    plt_show(show)
     fig.canvas.mpl_connect(
         "button_press_event", partial(_onclick, params=params, verbose=verbose)
     )

mne/viz/tests/test_3d_mpl.py

@@ -13,13 +13,21 @@
 import pytest
 
 from mne import (
+    compute_covariance,
+    compute_source_morph,
+    make_fixed_length_epochs,
+    make_forward_solution,
+    read_bem_solution,
     read_forward_solution,
-    VolSourceEstimate,
+    read_trans,
+    setup_volume_source_space,
     SourceEstimate,
+    VolSourceEstimate,
     VolVectorSourceEstimate,
-    compute_source_morph,
 )
 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 catch_logging, _record_warnings
 from mne.viz import plot_volume_source_estimates
 from mne.viz.utils import _fake_click, _fake_keypress
@@ -148,3 +156,58 @@ def test_plot_volume_source_estimates_morph():
         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)