Merge branch 'main' into xfit

Author: wmvanvliet

.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:

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/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/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:
 """