Merge branch 'main' into custom-annotation-colors

Author: cbrnr

.pre-commit-config.yaml

@@ -1,7 +1,7 @@
 repos:
   # Ruff mne
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.15.9
+    rev: v0.15.10
     hooks:
       - id: ruff-check
         name: ruff lint mne
@@ -74,7 +74,7 @@ repos:
 
   # zizmor
   - repo: https://github.com/woodruffw/zizmor-pre-commit
-    rev: v1.23.1
+    rev: v1.24.1
     hooks:
       - id: zizmor
         args: [--fix]

doc/changes/dev/13825.bugfix.rst

@@ -0,0 +1 @@
+Improved colocated OPM topomap handling by recognizing additional OPM coil types (FieldLine and Kernel), by `Pragnya Khandelwal`_.

doc/changes/dev/13842.other.rst

@@ -0,0 +1 @@
+Added regression coverage for colocated triaxial OPM topomap handling in ``plot_joint()`` and ``ICA.plot_components()``, by `Pragnya Khandelwal`_.

doc/changes/dev/13843.bugfix.rst

@@ -0,0 +1 @@
+Fix bug where logging messages were sometimes errantly printed, by `Eric Larson`_.

doc/conf.py

@@ -180,15 +180,13 @@
     "picard": ("https://mind-inria.github.io/picard/", None),
     "eeglabio": ("https://eeglabio.readthedocs.io/en/latest", None),
     "pybv": ("https://pybv.readthedocs.io/en/latest", None),
-    # should go back below after https://github.com/dipy/dipy/issues/3870 is fixed
-    "dipy": ("https://docs.dipy.org/1.12.0", None),
 }
 intersphinx_mapping.update(
     get_intersphinx_mapping(
         packages=set(
             """
 imageio matplotlib numpy pandas python scipy statsmodels sklearn numba joblib nibabel
-seaborn patsy pyvista nilearn pyqtgraph
+seaborn patsy pyvista dipy nilearn pyqtgraph
 """.strip().split()
         ),
     )

doc/sphinxext/credit_tools.py

@@ -315,6 +315,9 @@ def generate_credit_rst(app=None, *, verbose=False):
         logo/LICENSE doc/credit.rst
     """.strip().split():
         globs[key] = "null"
+    # A few remaps
+    globs["mne/io/edf/_open.py"] = "mne.io"
+    globs["mne/_edf/open.py"] = "mne.io"
     # Now onto the actual module organization
     root_path = pathlib.Path(mne.__file__).parent
     mod_file_map = dict()
@@ -345,8 +348,6 @@ def generate_credit_rst(app=None, *, verbose=False):
     globs["bin/*"] = "mne.commands"
     globs["mne/morph_map.py"] = "mne.surface"
     globs["mne/baseline.py"] = "mne.epochs"
-    globs["mne/io/edf/_open.py"] = "mne.io"
-    globs["mne/_edf/open.py"] = "mne.io"
     for key in """
         parallel.py rank.py misc.py data/*.* defaults.py fixes.py icons/*.* icons.*
     """.strip().split():

examples/preprocessing/plot_epoch_quality.py

@@ -111,6 +111,14 @@
 # :meth:`mne.Epochs.drop` method. Here, we remove the worst scoring epochs (≥ 0.6)
 # which contained strong artifact activity. The remaining good epochs can then be used
 # for further analysis.
+#
+# .. admonition:: Repairing and retaining epochs with artifacts
+#
+#     In this case, the large frontal deflections are likely blink artifacts, so
+#     dropping is not the only option. For artifacts like these, it can be
+#     preferable to repair the data and keep more epochs, for example with
+#     :ref:`ICA <tut-artifact-ica>` or
+#     :ref:`regression-based artifact correction <tut-artifact-regression>`.
 epochs.drop(np.where(scores >= 0.6)[0])
 print(f"Epochs remaining after dropping scores ≥ 0.6: {len(epochs)}")
 

mne/_fiff/constants.py

@@ -1183,6 +1183,7 @@
         FIFF.FIFFV_COIL_QUSPIN_ZFOPM_MAG2,
         FIFF.FIFFV_COIL_FIELDLINE_OPM_MAG_GEN1,
         FIFF.FIFFV_COIL_KERNEL_OPM_MAG_GEN1,
+        # If more OPM coil types are added here, update mne/viz/topomap.py too.
         FIFF.FIFFV_COIL_KRISS_GRAD,
         FIFF.FIFFV_COIL_COMPUMEDICS_ADULT_GRAD,
         FIFF.FIFFV_COIL_COMPUMEDICS_PEDIATRIC_GRAD,

mne/conftest.py

@@ -27,12 +27,13 @@
 
 # ruff: noqa: E402
 import mne
-from mne import Epochs, pick_types, read_events
+from mne import Epochs, create_info, make_fixed_length_epochs, pick_types, read_events
+from mne._fiff.constants import FIFF
 from mne.channels import read_layout
 from mne.coreg import create_default_subject
 from mne.datasets import testing
 from mne.fixes import _compare_version, has_numba
-from mne.io import read_raw_ctf, read_raw_fif, read_raw_nirx, read_raw_snirf
+from mne.io import RawArray, read_raw_ctf, read_raw_fif, read_raw_nirx, read_raw_snirf
 from mne.stats import cluster_level
 from mne.utils import (
     Bunch,
@@ -546,6 +547,47 @@ def _bias_params(evoked, noise_cov, fwd):
     return evoked, fwd, noise_cov, data_cov, want
 
 
+@pytest.fixture
+def triaxial_raw():
+    """Create a small triaxial OPM raw for regression tests."""
+    ch_names = ["OPM001", "OPM002", "OPM003", "OPM004", "OPM005", "OPM006"]
+    info = create_info(ch_names, 1000.0, ch_types="mag")
+    positions = np.array(
+        [
+            [0.03, 0.00, 0.05],
+            [0.03, 0.00, 0.05],
+            [0.03, 0.00, 0.05],
+            [-0.03, 0.00, 0.05],
+            [-0.03, 0.00, 0.05],
+            [-0.03, 0.00, 0.05],
+        ]
+    )
+    orientations = np.array(
+        [
+            [0.5145, 0.0000, 0.8575],
+            [0.0000, 1.0000, 0.0000],
+            [0.0000, 0.0000, 1.0000],
+            [-0.5145, 0.0000, 0.8575],
+            [0.0000, 1.0000, 0.0000],
+            [0.0000, 0.0000, 1.0000],
+        ]
+    )
+    with info._unlock():
+        for idx, ch in enumerate(info["chs"]):
+            ch["coil_type"] = FIFF.FIFFV_COIL_FIELDLINE_OPM_MAG_GEN1
+            ch["loc"][:3] = positions[idx]
+            ch["loc"][9:12] = orientations[idx]
+    rng = np.random.default_rng(0)
+    data = rng.standard_normal((len(ch_names), 2000))
+    return RawArray(data, info, verbose="error")
+
+
+@pytest.fixture
+def triaxial_evoked(triaxial_raw):
+    """Create a small triaxial OPM evoked for regression tests."""
+    return make_fixed_length_epochs(triaxial_raw).average()
+
+
 @pytest.fixture
 def garbage_collect():
     """Garbage collect on exit."""

mne/utils/_logging.py

@@ -166,8 +166,9 @@ def __init__(self, verbose=None, *, add_frames=None):
         self._old_frames = _filter.add_frames
 
     def __enter__(self):  # noqa: D105
+        level = logger.level if self._level is None else self._level
         self._old_level = set_log_level(
-            self._level, return_old_level=True, add_frames=self._add_frames
+            level, return_old_level=True, add_frames=self._add_frames
         )
 
     def __exit__(self, *args):  # noqa: D105
@@ -319,10 +320,7 @@ def __init__(self, verbose=None):
         self.verbose = verbose
 
     def __enter__(self):  # noqa: D105
-        if self.verbose is not None:
-            self._ctx = use_log_level(self.verbose)
-        else:
-            self._ctx = contextlib.nullcontext()
+        self._ctx = use_log_level(self.verbose)
         self._data = ClosingStringIO()
         self._lh = logging.StreamHandler(self._data)
         self._lh.setFormatter(logging.Formatter("%(message)s"))