Merge branch 'main' into doc-ref-channel-clarification

Author: tsbinns

.github/workflows/codeql-analysis.yml

@@ -44,7 +44,7 @@ jobs:
 
     # Initializes the CodeQL tools for scanning.
     - name: Initialize CodeQL
-      uses: github/codeql-action/init@v4.35.2
+      uses: github/codeql-action/init@v4.35.3
       with:
         languages: ${{ matrix.language }}
         # If you wish to specify custom queries, you can do so here or in a config file.
@@ -58,7 +58,7 @@ jobs:
     # Autobuild attempts to build any compiled languages  (C/C++, C#, or Java).
     # If this step fails, then you should remove it and run the build manually (see below)
     - name: Autobuild
-      uses: github/codeql-action/autobuild@v4.35.2
+      uses: github/codeql-action/autobuild@v4.35.3
 
     # ℹ️ Command-line programs to run using the OS shell.
     # 📚 See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsrun
@@ -71,4 +71,4 @@ jobs:
     #   ./location_of_script_within_repo/buildscript.sh
 
     - name: Perform CodeQL Analysis
-      uses: github/codeql-action/analyze@v4.35.2
+      uses: github/codeql-action/analyze@v4.35.3

doc/changes/dev/13894.bugfix.rst

@@ -0,0 +1 @@
+Fix bug where ``picks`` was ignored in :meth:`mne.Epochs.apply_function` when ``channel_wise=False``, by `Thomas Binns`_.

doc/development/contributing.rst

@@ -587,12 +587,14 @@ deprecation period when the stated release is being prepared:
   parameter, and (if found) update them to use the new function / parameter.
 
 
+.. _`changelog-guide`:
+
 Describe your changes in the changelog
 --------------------------------------
 
-Include in your changeset a brief description of the change in the
-:ref:`changelog <whats_new>` using towncrier_ format, which aggregates small,
-properly-named ``.rst`` files to create a changelog. This can be
+Include in your changeset a brief description of the changes, which will appear in the
+:ref:`changelog <whats_new>`, using towncrier_ format. This aggregates small,
+properly named ``.rst`` files to create a changelog. This can be
 skipped for very minor changes like correcting typos in the documentation.
 
 There are six separate sections for changes, based on change type.
@@ -1024,8 +1026,9 @@ down the road. Here are the guidelines:
   you are specifically asked by a maintainer to PR into another branch (e.g.,
   for backports or maintenance bugfixes to the current stable version).
 
-- Don't forget to include in your PR a brief description of the change in the
-  :ref:`changelog <whats_new>` (:file:`doc/whats_new.rst`).
+- Don't forget to include in your PR a brief description of the change in a
+  changelog entry (see :ref:`the changelog section <changelog-guide>` above
+  for instructions).
 
 - Our community uses the following commit tags and conventions:
 

doc/sphinxext/related_software.py

@@ -5,6 +5,10 @@
 1. Add it to the MNE-installers if possible, and it will automatically appear.
 2. If it's on PyPI and not in the MNE-installers, add it to the PYPI_PACKAGES set.
 3. If it's not on PyPI, add it to the MANUAL_PACKAGES dictionary.
+
+If PyPI or manual, also add package name to `related_software.txt` or
+`related_software_nodeps.txt` so that it's installed at doc-build time (for package
+metadata querying).
 """
 
 # Authors: The MNE-Python contributors.
@@ -35,6 +39,7 @@
     "niseq",
     "sesameeg",
     "mne-kit-gui",  # moved to its own env in the installers
+    "zuna",
 }
 
 # If it's not available on PyPI, add it to this dict:
@@ -221,7 +226,7 @@ def run(self):
         return [my_list]
 
 
-def setup(app):
+def setup(app):  # noqa: D103
     app.add_directive("related-software", RelatedSoftwareDirective)
     # Run it as soon as this is added as a Sphinx extension so that any errors
     # / new packages are reported early. The next call in run() will be cached.

doc/sphinxext/related_software_nodeps.txt

@@ -4,3 +4,4 @@ fsleyes
 mne-kit-gui
 mne-videobrowser
 hedtools  # because it limits our Pandas version currently
+zuna  # requires pytorch

environment.yml

@@ -62,6 +62,7 @@ dependencies:
   - tqdm >=4.66
   - traitlets
   - trame
+  - trame-pyvista
   - trame-vtk
   - trame-vuetify
   - vtk ==9.6.0

mne/beamformer/tests/test_lcmv.py

@@ -564,6 +564,20 @@ def test_make_lcmv_bem(tmp_path, reg, proj, kind):
     make_lcmv(epochs.info, forward_fixed, data_cov_grad, reg=0.01, noise_cov=noise_cov)
 
 
+@pytest.fixture(scope="module")
+def evoked_fwd_noise_data():
+    """Fixture to supply reusable data."""
+    _, _, evoked, data_cov, noise_cov, _, _, _, _, _ = _get_data(proj=True)
+    assert "eeg" not in evoked
+    assert "meg" in evoked
+    sphere = mne.make_sphere_model(r0=(0.0, 0.0, 0.0), head_radius=0.080)
+    src = mne.setup_volume_source_space(
+        pos=25.0, sphere=sphere, mindist=5.0, exclude=2.0
+    )
+    fwd_sphere = mne.make_forward_solution(evoked.info, None, src, sphere)
+    return evoked, fwd_sphere, noise_cov, data_cov
+
+
 @testing.requires_testing_data
 @pytest.mark.slowtest
 @pytest.mark.parametrize(
@@ -576,24 +590,16 @@ def test_make_lcmv_bem(tmp_path, reg, proj, kind):
         (None, "max-power"),
     ],
 )
-def test_make_lcmv_sphere(pick_ori, weight_norm):
+def test_make_lcmv_sphere(pick_ori, weight_norm, evoked_fwd_noise_data):
     """Test LCMV with sphere head model."""
     # unit-noise gain beamformer and orientation
     # selection and rank reduction of the leadfield
-    _, _, evoked, data_cov, noise_cov, _, _, _, _, _ = _get_data(proj=True)
-    assert "eeg" not in evoked
-    assert "meg" in evoked
-    sphere = mne.make_sphere_model(r0=(0.0, 0.0, 0.0), head_radius=0.080)
-    src = mne.setup_volume_source_space(
-        pos=25.0, sphere=sphere, mindist=5.0, exclude=2.0
-    )
-    fwd_sphere = mne.make_forward_solution(evoked.info, None, src, sphere)
-
+    evoked, fwd_sphere, noise_cov, data_cov = evoked_fwd_noise_data
     # Test that we get an error if not reducing rank
     with (
         pytest.raises(ValueError, match="Singular matrix detected"),
         _record_warnings(),
-        pytest.warns(RuntimeWarning, match="positive semidefinite"),
+        pytest.warns(RuntimeWarning, match="(positive semidefinite|largest eigenvalu)"),
     ):
         make_lcmv(
             evoked.info,

mne/epochs.py

@@ -2045,7 +2045,7 @@ def apply_function(
                 for run_idx, ch_idx in enumerate(picks):
                     self._data[:, ch_idx, :] = data_picks_new[run_idx]
         else:
-            self._data = _check_fun(fun, data_in, **kwargs)
+            self._data[:, picks, :] = _check_fun(fun, data_in[:, picks, :], **kwargs)
 
         return self
 

mne/source_estimate.py

@@ -3219,18 +3219,21 @@ def spatio_temporal_dist_adjacency(src, n_times, dist, verbose=None):
         vertices are time 1, the nodes from 2 to 2N are the vertices
         during time 2, etc.
     """
+    from scipy import sparse
+
     if src[0]["dist"] is None:
         raise RuntimeError(
             "src must have distances included, consider using "
             "setup_source_space with add_dist=True"
         )
     blocks = [s["dist"][s["vertno"], :][:, s["vertno"]] for s in src]
     # Ensure we keep explicit zeros; deal with changes in SciPy
-    for block in blocks:
+    for bi, block in enumerate(blocks):
         if isinstance(block, np.ndarray):
             block[block == 0] = -np.inf
         else:
             block.data[block.data == 0] == -1
+        blocks[bi] = sparse.csr_array(block)  # avoid SciPy dep warning about mat->arr
     edges = sparse.block_diag(blocks)
     edges.data[:] = np.less_equal(edges.data, dist)
     # clean it up and put it in coo format

mne/tests/test_epochs.py

@@ -4832,20 +4832,21 @@ def test_apply_function():
     info = mne.create_info(n_channels, 1000.0, "eeg")
     epochs = mne.EpochsArray(data, info, events)
     data_epochs = epochs.get_data()
+    picks = np.arange(3)
+    non_picks = np.arange(3, n_channels)
 
     # apply_function to all channels at once
     def fun(data):
         """Reverse channel order without changing values."""
         return np.eye(data.shape[1])[::-1] @ data
 
-    want = data_epochs[:, ::-1]
-    got = epochs.apply_function(fun, channel_wise=False).get_data()
+    want = np.concatenate(
+        [data_epochs[:, picks][:, ::-1], data_epochs[:, non_picks]], axis=1
+    )  # only reverse channel order of picks
+    got = epochs.apply_function(fun, picks=picks, channel_wise=False).get_data()
     assert_array_equal(want, got)
 
     # apply_function channel-wise (to first 3 channels) by replacing with mean
-    picks = np.arange(3)
-    non_picks = np.arange(3, n_channels)
-
     def fun(data):
         return np.full_like(data, data.mean())