MAINT: Remove tomli, tomlsort, and ruff from test group (#13809)

Author: larsoner

examples/inverse/multi_dipole_model.py

@@ -45,6 +45,7 @@
 cov_fname = meg_path / "sample_audvis-shrunk-cov.fif"
 bem_dir = data_path / "subjects" / "sample" / "bem"
 bem_fname = bem_dir / "sample-5120-5120-5120-bem-sol.fif"
+trans_fname = meg_path / "sample_audvis_raw-trans.fif"
 
 ###############################################################################
 # Read the MEG data from the audvis experiment. Make epochs and evokeds for the
@@ -102,8 +103,9 @@
 cov_fit_right["projs"] = evoked_fit_right.info["projs"]
 
 # Fit the dipoles with the subset of sensors.
-dip_left, _ = mne.fit_dipole(evoked_fit_left, cov_fit_left, bem)
-dip_right, _ = mne.fit_dipole(evoked_fit_right, cov_fit_right, bem)
+kwargs = dict(bem=bem, trans=trans_fname)
+dip_left, _ = mne.fit_dipole(evoked_fit_left, cov_fit_left, **kwargs)
+dip_right, _ = mne.fit_dipole(evoked_fit_right, cov_fit_right, **kwargs)
 
 ###############################################################################
 # Now that we have the location and orientations of the dipoles, compute the

examples/stats/r_interop.py

@@ -1,9 +1,9 @@
 """
 .. _r-interop:
 
-============================
-Integrating with R via rpy2
-============================
+===============================
+Integrating with R via ``rpy2``
+===============================
 
 This example shows how to run a mass-univariate 2-sample t-test on
 :class:`~mne.Epochs` data in Python using :func:`scipy.stats.ttest_ind`,

pyproject.toml

@@ -24,7 +24,6 @@ doc = [
   "pyvistaqt >= 0.11",  # released 2023-06-30, no newer version available
   "pyxdf",
   "pyzmq != 24.0.0",
-  "rpy2",
   "scikit-learn >= 1.4",  # released 2024-01-18, will become 1.5 on 2026-05-21
   "seaborn >= 0.5, != 0.11.2",
   "selenium >= 4.27.1",
@@ -36,6 +35,7 @@ doc = [
   "sphinxcontrib-towncrier >= 0.5.0a0",
   "sphinxcontrib-youtube",
 ]
+doc-full = ["rpy2", {include-group = "doc"}]
 # Dependencies in a separate group for uv lockfile generation, currently only used for
 # the 'old' environment CI test run
 lockfile_extras = [
@@ -55,8 +55,6 @@ test = [
   "pytest-rerunfailures",
   "pytest-timeout >= 2.2",
   "ruff >= 0.1",
-  "toml-sort",
-  "tomli; python_version < '3.11'",
   "twine",
   "vulture",
   "wheel >= 0.21",

tools/circleci_dependencies.sh

@@ -4,10 +4,12 @@ ONLY_BINARY="--only-binary \"numpy,dipy,scipy,matplotlib,pandas,statsmodels,netC
 
 set -x
 python -m pip install --upgrade "pip>=25.1" build
+# rpy2 3.6.7 (or its deps) cause problems with our installed R version, so pin them
 python -m pip install --upgrade --progress-bar off $ONLY_BINARY \
     -ve .[full] \
     --group=test \
-    --group=doc \
+    --group=doc-full \
+    "rpy2==3.6.6" "rpy2-rinterface==3.6.5" "rpy2-robjects==3.6.4" \
     -r doc/sphinxext/related_software.txt \
     "git+https://github.com/mne-tools/mne-bids.git" \
     "git+https://github.com/mne-tools/mne-qt-browser.git" \

tutorials/preprocessing/90_eyetracking_data.py

@@ -8,7 +8,6 @@
 In this tutorial we will explore simultaneously recorded eye-tracking and EEG data from
 a pupillary light reflex task. We will combine the eye-tracking and EEG data, and plot
 the ERP and pupil response to the light flashes (i.e. the pupillary light reflex).
-
 """
 
 # Authors: Scott Huberty <seh33@uw.edu>
@@ -230,7 +229,13 @@
 
 # Skip baseline correction for now. We will apply baseline correction later.
 epochs = mne.Epochs(
-    raw_et, events=et_events, event_id=event_dict, tmin=-0.3, tmax=3, baseline=None
+    raw_et,
+    events=et_events,
+    event_id=event_dict,
+    tmin=-0.3,
+    tmax=3,
+    baseline=None,
+    picks="all",  # include eyetracking channels in the epochs
 )
 del raw_et  # free up some memory
 epochs[:8].plot(
@@ -251,4 +256,4 @@
 # Finally, let's plot the evoked responses to the light flashes to get a sense of the
 # average pupillary light response, and the associated ERP in the EEG data.
 
-epochs.apply_baseline().average().plot(picks=occipital + pupil)
+epochs.apply_baseline().average(picks="all").plot(picks=occipital + pupil)