Merge branch 'main' into cola5

.pre-commit-config.yaml

@@ -1,7 +1,7 @@
 repos:
   # Ruff mne
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.9.2
+    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:

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

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/io/base.py

@@ -1386,7 +1386,10 @@ def resample(
         sfreq = float(sfreq)
         o_sfreq = float(self.info["sfreq"])
         if _check_resamp_noop(sfreq, o_sfreq):
-            return self
+            if events is not None:
+                return self, events.copy()
+            else:
+                return self
 
         # When no event object is supplied, some basic detection of dropped
         # events is performed to generate a warning. Finding events can fail

mne/io/fiff/tests/test_raw_fiff.py

@@ -23,6 +23,7 @@
     concatenate_events,
     create_info,
     equalize_channels,
+    events_from_annotations,
     find_events,
     make_fixed_length_epochs,
     pick_channels,
@@ -1318,6 +1319,15 @@ def test_crop():
     assert raw.n_times - 1 == raw3.n_times
 
 
+@testing.requires_testing_data
+def test_resample_with_events():
+    """Test resampling raws with events."""
+    raw = read_raw_fif(fif_fname)
+    raw.resample(250)  # pretend raw is recorded at 250 Hz
+    events, _ = events_from_annotations(raw)
+    raw, events = raw.resample(250, events=events)
+
+
 @testing.requires_testing_data
 def test_resample_equiv():
     """Test resample (with I/O and multiple files)."""

mne/viz/_figure.py

@@ -424,7 +424,7 @@ def _redraw(self, update_data=True, annotations=False):
         if annotations and not self.mne.is_epochs:
             self._draw_annotations()
 
-    def _close(self, event):
+    def _close(self, event=None):
         """Handle close events (via keypress or window [x])."""
         from matplotlib.pyplot import close
 

mne/viz/_mpl_figure.py

@@ -186,7 +186,7 @@ def _inch_to_rel(self, dim_inches, horiz=True):
 class MNEAnnotationFigure(MNEFigure):
     """Interactive dialog figure for annotations."""
 
-    def _close(self, event):
+    def _close(self, event=None):
         """Handle close events (via keypress or window [x])."""
         parent = self.mne.parent_fig
         # disable span selector
@@ -275,7 +275,7 @@ def _set_active_button(self, idx, *, draw=True):
 class MNESelectionFigure(MNEFigure):
     """Interactive dialog figure for channel selections."""
 
-    def _close(self, event):
+    def _close(self, event=None):
         """Handle close events."""
         self.mne.parent_fig.mne.child_figs.remove(self)
         self.mne.fig_selection = None

mne/viz/backends/_pyvista.py

@@ -1344,7 +1344,7 @@ def _is_osmesa(plotter):
                     "surface rendering, consider upgrading to 18.3.6 or "
                     "later."
                 )
-        is_osmesa = "via llvmpipe" in gpu_info
+        is_osmesa = "llvmpipe" in gpu_info
     return is_osmesa
 
 

mne/viz/utils.py

@@ -1185,7 +1185,7 @@ def _onpick_sensor(event, fig, ax, pos, ch_names, show_names):
     fig.canvas.draw()
 
 
-def _close_event(event, fig):
+def _close_event(event=None, fig=None):
     """Listen for sensor plotter close event."""
     if getattr(fig, "lasso", None) is not None:
         fig.lasso.disconnect()

tutorials/epochs/60_make_fixed_length_epochs.py

@@ -5,13 +5,12 @@
 =================================================
 
 This tutorial shows how to segment continuous data into a set of epochs spaced
-equidistantly in time. The epochs will not be created based on experimental
-events; instead, the continuous data will be "chunked" into consecutive epochs
-(which may be temporally overlapping, adjacent, or separated).
-We will also briefly demonstrate how to use these epochs in connectivity
-analysis.
+equidistantly in time. The epochs will not be created based on experimental events;
+instead, the continuous data will be "chunked" into consecutive epochs (which may be
+temporally overlapping, adjacent, or separated). We will also briefly demonstrate how
+to use these epochs in connectivity analysis.
 
-First, we import necessary modules and read in a sample raw data set.
+First, we import the necessary modules and read in a sample raw data set.
 This data set contains brain activity that is event-related, i.e.,
 synchronized to the onset of auditory stimuli. However, rather than creating
 epochs by segmenting the data around the onset of each stimulus, we will

tutorials/evoked/10_evoked_overview.py

@@ -5,12 +5,11 @@
 The Evoked data structure: evoked/averaged data
 ===============================================
 
-This tutorial covers the basics of creating and working with :term:`evoked`
-data. It introduces the :class:`~mne.Evoked` data structure in detail,
-including how to load, query, subset, export, and plot data from an
-:class:`~mne.Evoked` object. For details on creating an :class:`~mne.Evoked`
-object from (possibly simulated) data in a :class:`NumPy array
-<numpy.ndarray>`, see :ref:`tut-creating-data-structures`.
+This tutorial covers the basics of creating and working with :term:`evoked` data. It
+introduces the :class:`~mne.Evoked` data structure in detail, including how to load,
+query, subset, export, and plot data from an :class:`~mne.Evoked` object. For details
+on creating an :class:`~mne.Evoked` object from (possibly simulated) data in a
+:class:`NumPy array <numpy.ndarray>`, see :ref:`tut-creating-data-structures`.
 
 As usual, we start by importing the modules we need:
 """