Merge branch 'main' into dev-headsurface

.github/workflows/autofix.yml

@@ -21,4 +21,4 @@ jobs:
       - run: pip install --upgrade towncrier pygithub gitpython numpy
       - run: python ./.github/actions/rename_towncrier/rename_towncrier.py
       - run: python ./tools/dev/ensure_headers.py
-      - uses: autofix-ci/action@ff86a557419858bb967097bfc916833f5647fa8c
+      - uses: autofix-ci/action@551dded8c6cc8a1054039c8bc0b8b48c51dfc6ef

.github/workflows/check_changelog.yml

@@ -5,6 +5,9 @@ on:  # yamllint disable-line rule:truthy
     types: [opened, synchronize, labeled, unlabeled]
     branches: ["main"]
 
+permissions:
+  contents: read
+
 jobs:
   changelog_checker:
     name: Check towncrier entry in doc/changes/devel/

.github/workflows/circle_artifacts.yml

@@ -1,4 +1,7 @@
 on: [status]  # yamllint disable-line rule:truthy
+permissions:
+  contents: read
+  statuses: write
 jobs:
   circleci_artifacts_redirector_job:
     if: "${{ startsWith(github.event.context, 'ci/circleci: build_docs') }}"

.pre-commit-config.yaml

@@ -1,7 +1,7 @@
 repos:
   # Ruff mne
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.9.1
+    rev: v0.9.3
     hooks:
       - id: ruff
         name: ruff lint mne
@@ -23,7 +23,7 @@ repos:
 
   # Codespell
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.3.0
+    rev: v2.4.0
     hooks:
       - id: codespell
         additional_dependencies:
@@ -82,7 +82,7 @@ repos:
 
   # zizmor
   - repo: https://github.com/woodruffw/zizmor-pre-commit
-    rev: v1.1.1
+    rev: v1.2.2
     hooks:
       - id: zizmor
 

doc/changes/devel/12071.newfeature.rst

@@ -0,0 +1 @@
+Add new ``select`` parameter to :func:`mne.viz.plot_evoked_topo` and :meth:`mne.Evoked.plot_topo` to toggle lasso selection of sensors, by `Marijn van Vliet`_.

doc/changes/devel/12656.bugfix.rst

@@ -0,0 +1 @@
+Fix bug where :func:`mne.export.export_raw` does not correct for recording start time (:attr:`raw.first_time <mne.io.Raw.first_time>`) when exporting Raw instances to EDF or EEGLAB formats, by `Qian Chu`_.

doc/changes/devel/13065.bugfix.rst

@@ -0,0 +1,7 @@
+Improved sklearn class compatibility and compliance, which resulted in some parameters of classes having an underscore appended to their name during ``fit``, such as:
+
+- :class:`mne.decoding.FilterEstimator` parameter ``picks`` passed to the initializer is set as ``est.picks_``
+- :class:`mne.decoding.UnsupervisedSpatialFilter` parameter ``estimator`` passed to the initializer is set as ``est.estimator_``
+
+Unused ``verbose`` class parameters (that had no effect) were removed from :class:`~mne.decoding.PSDEstimator`, :class:`~mne.decoding.TemporalFilter`, and :class:`~mne.decoding.FilterEstimator` as well.
+Changes by `Eric Larson`_.

doc/changes/devel/13070.bugfix.rst

@@ -0,0 +1 @@
+Return events when requested even when current matches the desired sfreq in :meth:`mne.io.Raw.resample` by :newcontrib:`Roy Eric Wieske`.

doc/changes/devel/13082.bugfix.rst

@@ -0,0 +1 @@
+Fix bug with automated Mesa 3D detection for proper 3D option setting on systems with software rendering, by `Eric Larson`_.

doc/changes/names.inc

@@ -258,6 +258,7 @@
 .. _Roman Goj: https://romanmne.blogspot.co.uk
 .. _Ross Maddox: https://www.urmc.rochester.edu/labs/maddox-lab.aspx
 .. _Rotem Falach: https://github.com/Falach
+.. _Roy Eric Wieske: https://github.com/Randomidous
 .. _Sammi Chekroud: https://github.com/schekroud
 .. _Samu Taulu: https://phys.washington.edu/people/samu-taulu
 .. _Samuel Deslauriers-Gauthier: https://github.com/sdeslauriers

doc/sphinxext/mne_doc_utils.py

@@ -97,6 +97,8 @@ def reset_warnings(gallery_conf, fname):
         r"numpy\.core is deprecated and has been renamed to numpy\._core",
         # matplotlib
         "__array_wrap__ must accept context and return_scalar.*",
+        # nibabel
+        "__array__ implementation doesn't accept.*",
     ):
         warnings.filterwarnings(  # deal with other modules having bad imports
             "ignore", message=f".*{key}.*", category=DeprecationWarning

doc/sphinxext/related_software.py

@@ -173,6 +173,7 @@ def _get_packages() -> dict[str, str]:
     packages = sorted(packages, key=lambda x: x.lower())
     packages = [RENAMES.get(package, package) for package in packages]
     out = dict()
+    reasons = []
     for package in status_iterator(
         packages, f"Adding {len(packages)} related software packages: "
     ):
@@ -183,12 +184,17 @@ def _get_packages() -> dict[str, str]:
             else:
                 md = importlib.metadata.metadata(package)
         except importlib.metadata.PackageNotFoundError:
-            pass  # raise a complete error later
+            reasons.append(f"{package}: not found, needs to be installed")
+            continue  # raise a complete error later
         else:
             # Every project should really have this
+            do_continue = False
             for key in ("Summary",):
                 if key not in md:
-                    raise ExtensionError(f"Missing {repr(key)} for {package}")
+                    reasons.extend(f"{package}: missing {repr(key)}")
+                    do_continue = True
+            if do_continue:
+                continue
             # It is annoying to find the home page
             url = None
             if "Home-page" in md:
@@ -204,15 +210,17 @@ def _get_packages() -> dict[str, str]:
                     if url is not None:
                         break
                 else:
-                    raise RuntimeError(
-                        f"Could not find Home-page for {package} in:\n"
-                        f"{sorted(set(md))}\nwith Summary:\n{md['Summary']}"
+                    reasons.append(
+                        f"{package}: could not find Home-page in {sorted(md)}"
                     )
+                    continue
             out[package]["url"] = url
             out[package]["description"] = md["Summary"].replace("\n", "")
-    bad = [package for package in packages if not out[package]]
-    if bad and REQUIRE_METADATA:
-        raise ExtensionError(f"Could not find metadata for:\n{' '.join(bad)}")
+    reason_str = "\n".join(reasons)
+    if reason_str and REQUIRE_METADATA:
+        raise ExtensionError(
+            f"Could not find suitable metadata for related software:\n{reason_str}"
+        )
 
     return out
 

examples/decoding/linear_model_patterns.py

@@ -79,7 +79,7 @@
 
 # Extract and plot spatial filters and spatial patterns
 for name, coef in (("patterns", model.patterns_), ("filters", model.filters_)):
-    # We fitted the linear model onto Z-scored data. To make the filters
+    # We fit the linear model on Z-scored data. To make the filters
     # interpretable, we must reverse this normalization step
     coef = scaler.inverse_transform([coef])[0]
 

examples/preprocessing/movement_detection.py

@@ -81,6 +81,7 @@
 ##############################################################################
 # After checking the annotated movement artifacts, calculate the new transform
 # and plot it:
+
 new_dev_head_t = compute_average_dev_head_t(raw, head_pos)
 raw.info["dev_head_t"] = new_dev_head_t
 fig = mne.viz.plot_alignment(

mne/_fiff/proj.py

@@ -1100,7 +1100,7 @@ def _has_eeg_average_ref_proj(
 
 
 def _needs_eeg_average_ref_proj(info):
-    """Determine if the EEG needs an averge EEG reference.
+    """Determine if the EEG needs an average EEG reference.
 
     This returns True if no custom reference has been applied and no average
     reference projection is present in the list of projections.

mne/conftest.py

@@ -174,6 +174,7 @@ def pytest_configure(config: pytest.Config):
     # pandas
     ignore:\n*Pyarrow will become a required dependency of pandas.*:DeprecationWarning
     ignore:np\.find_common_type is deprecated.*:DeprecationWarning
+    ignore:Python binding for RankQuantileOptions.*:
     # pyvista <-> NumPy 2.0
     ignore:__array_wrap__ must accept context and return_scalar arguments.*:DeprecationWarning
     # nibabel <-> NumPy 2.0

mne/cov.py

@@ -1226,7 +1226,7 @@ def _compute_rank_raw_array(
     from .io import RawArray
 
     return _compute_rank(
-        RawArray(data, info, copy=None, verbose=_verbose_safe_false()),
+        RawArray(data, info, copy="auto", verbose=_verbose_safe_false()),
         rank,
         scalings,
         info,
@@ -1405,7 +1405,7 @@ def _compute_covariance_auto(
             # project back
             cov = np.dot(eigvec.T, np.dot(cov, eigvec))
             # undo bias
-            cov *= data.shape[0] / (data.shape[0] - 1)
+            cov *= data.shape[0] / max(data.shape[0] - 1, 1)
             # undo scaling
             _undo_scaling_cov(cov, picks_list, scalings)
             method_ = method[ei]

mne/decoding/base.py

@@ -19,7 +19,7 @@
 from sklearn.linear_model import LogisticRegression
 from sklearn.metrics import check_scoring
 from sklearn.model_selection import KFold, StratifiedKFold, check_cv
-from sklearn.utils import check_array, indexable
+from sklearn.utils import check_array, check_X_y, indexable
 
 from ..parallel import parallel_func
 from ..utils import _pl, logger, verbose, warn
@@ -76,9 +76,9 @@ class LinearModel(MetaEstimatorMixin, BaseEstimator):
     )
 
     def __init__(self, model=None):
+        # TODO: We need to set this to get our tag checking to work properly
         if model is None:
             model = LogisticRegression(solver="liblinear")
-
         self.model = model
 
     def __sklearn_tags__(self):
@@ -122,7 +122,11 @@ def fit(self, X, y, **fit_params):
         self : instance of LinearModel
             Returns the modified instance.
         """
-        X = check_array(X, input_name="X")
+        if y is not None:
+            X = check_array(X)
+        else:
+            X, y = check_X_y(X, y)
+        self.n_features_in_ = X.shape[1]
         if y is not None:
             y = check_array(y, dtype=None, ensure_2d=False, input_name="y")
             if y.ndim > 2:
@@ -133,6 +137,7 @@ def fit(self, X, y, **fit_params):
 
         # fit the Model
         self.model.fit(X, y, **fit_params)
+        self.model_ = self.model  # for better sklearn compat
 
         # Computes patterns using Haufe's trick: A = Cov_X . W . Precision_Y
 

mne/decoding/csp.py

@@ -6,7 +6,8 @@
 
 import numpy as np
 from scipy.linalg import eigh
-from sklearn.base import BaseEstimator, TransformerMixin
+from sklearn.base import BaseEstimator
+from sklearn.utils.validation import check_is_fitted
 
 from .._fiff.meas_info import create_info
 from ..cov import _compute_rank_raw_array, _regularized_covariance, _smart_eigh
@@ -19,10 +20,11 @@
     fill_doc,
     pinv,
 )
+from .transformer import MNETransformerMixin
 
 
 @fill_doc
-class CSP(TransformerMixin, BaseEstimator):
+class CSP(MNETransformerMixin, BaseEstimator):
     """M/EEG signal decomposition using the Common Spatial Patterns (CSP).
 
     This class can be used as a supervised decomposition to estimate spatial
@@ -112,49 +114,44 @@ def __init__(
         component_order="mutual_info",
     ):
         # Init default CSP
-        if not isinstance(n_components, int):
-            raise ValueError("n_components must be an integer.")
         self.n_components = n_components
         self.rank = rank
         self.reg = reg
-
-        # Init default cov_est
-        if not (cov_est == "concat" or cov_est == "epoch"):
-            raise ValueError("unknown covariance estimation method")
         self.cov_est = cov_est
-
-        # Init default transform_into
-        self.transform_into = _check_option(
-            "transform_into", transform_into, ["average_power", "csp_space"]
-        )
-
-        # Init default log
-        if transform_into == "average_power":
-            if log is not None and not isinstance(log, bool):
-                raise ValueError(
-                    'log must be a boolean if transform_into == "average_power".'
-                )
-        else:
-            if log is not None:
-                raise ValueError('log must be a None if transform_into == "csp_space".')
+        self.transform_into = transform_into
         self.log = log
-
-        _validate_type(norm_trace, bool, "norm_trace")
         self.norm_trace = norm_trace
         self.cov_method_params = cov_method_params
-        self.component_order = _check_option(
-            "component_order", component_order, ("mutual_info", "alternate")
+        self.component_order = component_order
+
+    def _validate_params(self, *, y):
+        _validate_type(self.n_components, int, "n_components")
+        if hasattr(self, "cov_est"):
+            _validate_type(self.cov_est, str, "cov_est")
+            _check_option("cov_est", self.cov_est, ("concat", "epoch"))
+        if hasattr(self, "norm_trace"):
+            _validate_type(self.norm_trace, bool, "norm_trace")
+        _check_option(
+            "transform_into", self.transform_into, ["average_power", "csp_space"]
         )
-
-    def _check_Xy(self, X, y=None):
-        """Check input data."""
-        if not isinstance(X, np.ndarray):
-            raise ValueError(f"X should be of type ndarray (got {type(X)}).")
-        if y is not None:
-            if len(X) != len(y) or len(y) < 1:
-                raise ValueError("X and y must have the same length.")
-        if X.ndim < 3:
-            raise ValueError("X must have at least 3 dimensions.")
+        if self.transform_into == "average_power":
+            _validate_type(
+                self.log,
+                (bool, None),
+                "log",
+                extra="when transform_into is 'average_power'",
+            )
+        else:
+            _validate_type(
+                self.log, None, "log", extra="when transform_into is 'csp_space'"
+            )
+        _check_option(
+            "component_order", self.component_order, ("mutual_info", "alternate")
+        )
+        self.classes_ = np.unique(y)
+        n_classes = len(self.classes_)
+        if n_classes < 2:
+            raise ValueError(f"n_classes must be >= 2, but got {n_classes} class")
 
     def fit(self, X, y):
         """Estimate the CSP decomposition on epochs.
@@ -171,12 +168,9 @@ def fit(self, X, y):
         self : instance of CSP
             Returns the modified instance.
         """
-        self._check_Xy(X, y)
-
-        self._classes = np.unique(y)
-        n_classes = len(self._classes)
-        if n_classes < 2:
-            raise ValueError("n_classes must be >= 2.")
+        X, y = self._check_data(X, y=y, fit=True, return_y=True)
+        self._validate_params(y=y)
+        n_classes = len(self.classes_)
         if n_classes > 2 and self.component_order == "alternate":
             raise ValueError(
                 "component_order='alternate' requires two classes, but data contains "
@@ -225,13 +219,8 @@ def transform(self, X):
             If self.transform_into == 'csp_space' then returns the data in CSP
             space and shape is (n_epochs, n_components, n_times).
         """
-        if not isinstance(X, np.ndarray):
-            raise ValueError(f"X should be of type ndarray (got {type(X)}).")
-        if self.filters_ is None:
-            raise RuntimeError(
-                "No filters available. Please first fit CSP decomposition."
-            )
-
+        check_is_fitted(self, "filters_")
+        X = self._check_data(X)
         pick_filters = self.filters_[: self.n_components]
         X = np.asarray([np.dot(pick_filters, epoch) for epoch in X])
 
@@ -577,7 +566,7 @@ def _compute_covariance_matrices(self, X, y):
 
         covs = []
         sample_weights = []
-        for ci, this_class in enumerate(self._classes):
+        for ci, this_class in enumerate(self.classes_):
             cov, weight = cov_estimator(
                 X[y == this_class],
                 cov_kind=f"class={this_class}",
@@ -689,7 +678,7 @@ def _normalize_eigenvectors(self, eigen_vectors, covs, sample_weights):
     def _order_components(
         self, covs, sample_weights, eigen_vectors, eigen_values, component_order
     ):
-        n_classes = len(self._classes)
+        n_classes = len(self.classes_)
         if component_order == "mutual_info" and n_classes > 2:
             mutual_info = self._compute_mutual_info(covs, sample_weights, eigen_vectors)
             ix = np.argsort(mutual_info)[::-1]
@@ -889,10 +878,8 @@ def fit(self, X, y):
         self : instance of SPoC
             Returns the modified instance.
         """
-        self._check_Xy(X, y)
-
-        if len(np.unique(y)) < 2:
-            raise ValueError("y must have at least two distinct values.")
+        X, y = self._check_data(X, y=y, fit=True, return_y=True)
+        self._validate_params(y=y)
 
         # The following code is directly copied from pyRiemann