[pull] main from mne-tools:main

.github/workflows/tests.yml

@@ -71,7 +71,7 @@ jobs:
             python: '3.13'
             kind: pip
           - os: ubuntu-latest
-            python: '3.13'
+            python: '3.14'
             kind: pip-pre
           - os: ubuntu-latest
             python: '3.13'

doc/changes/dev/13579.bugfix.rst

@@ -0,0 +1 @@
+Fix bug with montage test error message checking, by `Eric Larson`_.

doc/sphinxext/mne_doc_utils.py

@@ -80,6 +80,8 @@ def reset_warnings(gallery_conf, fname):
     for message in (
         # Matplotlib
         ".*is non-interactive, and thus cannot.*",
+        # nilearn
+        r"You are using the.*matplotlib backend that[.\n]*",
         # pybtex
         ".*pkg_resources is deprecated as an API.*",
     ):

examples/inverse/mixed_source_space_inverse.py

@@ -5,8 +5,7 @@
 Compute MNE inverse solution on evoked data with a mixed source space
 =====================================================================
 
-Create a mixed source space and compute an MNE inverse solution on an
-evoked dataset.
+Create a mixed source space and compute an MNE inverse solution on an evoked dataset.
 """
 # Author: Annalisa Pascarella <[email protected]>
 #

mne/channels/tests/test_standard_montage.py

@@ -292,7 +292,7 @@ def test_set_montage_artinis_basic():
     )
     new = RawArray(np.random.normal(size=(2, len(raw))), info_new)
     raw.add_channels([new], force_update_info=True)
-    with pytest.raises(ValueError, match="is not in list"):
+    with pytest.raises(ValueError, match="not in list"):
         raw.set_montage("artinis-brite23")
 
     # Detector not in montage: fail
@@ -302,5 +302,5 @@ def test_set_montage_artinis_basic():
     )
     new = RawArray(np.random.normal(size=(2, len(raw))), info_new)
     raw.add_channels([new], force_update_info=True)
-    with pytest.raises(ValueError, match="is not in list"):
+    with pytest.raises(ValueError, match="not in list"):
         raw.set_montage("artinis-brite23")

mne/report/report.py

@@ -3546,7 +3546,7 @@ def _add_projs(
         evoked = None
         if isinstance(info, Info):  # no-op
             pass
-        elif hasattr(info, "info"):  # try to get the file name
+        elif isinstance(getattr(info, "info", None), Info):  # try to get the file name
             if isinstance(info, Evoked):
                 evoked = info
             info = info.info

tools/install_pre_requirements.sh

@@ -28,12 +28,14 @@ python -m pip install $STD_ARGS --only-binary ":all:" --default-timeout=60 \
 	"scipy>=1.15.0.dev0" \
 	"scikit-learn>=1.6.dev0" \
 	"pandas>=3.0.0.dev0" \
-	"dipy>=1.10.0.dev0" \
-	"tables>=3.10.3.dev0" \
 	"statsmodels>=0.15.0.dev697" \
 	"pyarrow>=22.0.0.dev0" \
 	"matplotlib>=3.11.0.dev0" \
 	"h5py>=3.13.0"
+# Needs https://github.com/dipy/dipy/pull/3678
+# "dipy>=1.12.0.dev0"
+# Needs https://github.com/PyTables/PyTables/pull/1286
+# "tables>=3.10.3.dev0"
 echo "::endgroup::"
 # No Numba because it forces an old NumPy version
 

tutorials/preprocessing/14_quality_control_report.py

@@ -0,0 +1,267 @@
+"""
+.. _tut-qc-report:
+
+============================================
+Quality control (QC) reports with mne.Report
+============================================
+
+Quality control (QC) is the process of systematically inspecting M/EEG data
+**throughout all stages of an analysis pipeline**, including raw data,
+intermediate preprocessing steps, and derived results.
+
+While QC often begins with an initial inspection of the raw recording,
+it is equally important to verify that signals continue to "look reasonable"
+after operations such as filtering, artifact correction, epoching, and
+averaging. Issues introduced or missed at any stage can propagate downstream
+and invalidate later analyses.
+
+This tutorial demonstrates how to create a **single, narrative QC report**
+using :class:`mne.Report`, focusing on **what should be inspected and how the
+results should be interpreted**, rather than exhaustively covering the API.
+
+For clarity and reproducibility, the examples below focus on common QC checks
+applied at representative stages of an analysis pipeline. The same reporting
+approach can—and should—be reused whenever new processing steps are applied.
+
+We use the MNE sample dataset for demonstration. Not all QC sections are
+applicable to every dataset (e.g., continuous head-position tracking), and
+this tutorial explicitly handles such cases.
+
+.. note:: For several additional examples of complete reports, see the
+   `MNE-BIDS-Pipeline QC reports <https://mne.tools/mne-bids-pipeline/stable/examples/examples.html>`_.
+"""  # noqa: E501
+
+# Authors: The MNE-Python contributors
+# License: BSD-3-Clause
+# Copyright the MNE-Python contributors.
+
+# %%
+
+from pathlib import Path
+
+import mne
+from mne.preprocessing import ICA, create_eog_epochs
+
+# %%
+# Load the sample dataset
+# -----------------------
+# We load a pre-filtered MEG/EEG recording from the MNE sample dataset.
+# Only channels relevant for QC (MEG, EEG, EOG, stimulus) are retained.
+
+data_path = Path(mne.datasets.sample.data_path(verbose=False))
+subject = "sample"
+sample_dir = data_path / "MEG" / subject
+subjects_dir = data_path / "subjects"
+
+raw_path = sample_dir / "sample_audvis_filt-0-40_raw.fif"
+
+
+raw = mne.io.read_raw(raw_path)
+
+# We will also crop the dataset for speed
+raw.crop(0, 60).load_data()
+
+# Retain only channels relevant for QC to simplify visualization and
+# focus inspection on signals typically reviewed during data quality checks.
+raw.pick(["meg", "eeg", "eog", "stim"])
+
+sfreq = raw.info["sfreq"]  # Sampling Frequency (Hz)
+
+# %%
+# Create the QC report
+# --------------------
+# The report acts as a container that collects figures, tables, and text
+# into a single HTML document.
+
+report = mne.Report(
+    title="Sample dataset - Quality Control report",
+    subject=subject,
+    subjects_dir=subjects_dir,
+)
+
+# %%
+# Dataset overview
+# ----------------
+# A brief overview helps the reviewer immediately understand the scale and
+# basic properties of the dataset.
+
+html_overview = """
+This report presents a quality control (QC) overview of the MNE sample dataset.<br><br>
+For information about the paradigm, see
+<a href="https://mne.tools/stable/documentation/datasets.html#sample">the MNE docs</a>.
+"""
+
+report.add_html(
+    title="Overview",
+    html=html_overview,
+    tags=("overview"),
+)
+
+# %%
+# Raw data inspection
+# -------------------
+# Visual inspection of raw data is the single most important QC step.
+# Here we inspect both the time series and the power spectral density (PSD).
+#
+# - Look for channels with unusually large amplitudes or flat signals.
+# - In the PSD, check for excessive low-frequency drift, strong line noise,
+#   or abnormal spectral shapes compared to neighboring channels.
+
+report.add_raw(
+    raw,
+    title="Raw data overview",
+    psd=False,  # omit just for speed here
+)
+
+# %%
+# Events and stimulus timing
+# --------------------------
+# Correct event detection is crucial for all subsequent epoch-based analyses.
+#
+# - Verify that the number of events matches expectations.
+# - Check that event timing is plausible and evenly distributed.
+# - Missing or duplicated events often indicate trigger channel issues.
+
+events = mne.find_events(raw)
+
+report.add_events(
+    events,
+    sfreq=sfreq,
+    title="Detected events",
+)
+
+# %%
+# Epoching and rejection statistics
+# ---------------------------------
+# Epoching allows inspection of data segments time-locked to events, along
+# with automated rejection based on amplitude thresholds.
+
+event_id = {
+    "auditory/left": 1,
+    "auditory/right": 2,
+    "visual/left": 3,
+    "visual/right": 4,
+}
+
+epochs = mne.Epochs(
+    raw,
+    events,
+    event_id=event_id,
+    tmin=-0.2,
+    tmax=0.5,
+    baseline=(None, 0),
+    reject=dict(eeg=150e-6),
+    preload=True,
+)
+
+report.add_epochs(
+    epochs,
+    title="Epochs and rejection statistics",
+)
+
+# %%
+# Evoked responses
+# ----------------
+# Averaged responses should show physiologically plausible waveforms and
+# reasonable signal-to-noise ratios.
+#
+# - Check that evoked responses have the expected polarity and timing.
+# - Absence of clear evoked structure may indicate poor data quality or
+#   incorrect event definitions.
+
+cov_path = sample_dir / "sample_audvis-cov.fif"
+evoked = mne.read_evokeds(
+    sample_dir / "sample_audvis-ave.fif",
+    baseline=(None, 0),
+)[0]  # just one for speed
+evoked.decimate(4)  # also for speed
+
+report.add_evokeds(
+    evokeds=evoked,
+    noise_cov=cov_path,
+    n_time_points=5,
+)
+
+
+# %%
+# ICA for artifact inspection
+# ---------------------------
+# Independent Component Analysis (ICA) can be used during QC to identify
+# stereotypical artifacts such as eye blinks and eye movements.
+#
+# For QC purposes, ICA is typically run with a lightweight configuration
+# (e.g., fewer components or temporal decimation) to provide rapid feedback
+# on data quality, rather than an optimized decomposition for final analysis.
+#
+# - Use the topographic maps to identify spatial patterns characteristic
+#   of artifacts (e.g., frontal patterns for eye blinks).
+# - The component property viewer is intended for detailed inspection of
+#   individual components and is most informative when combined with
+#   epoched data or explicit artifact scoring.
+# - Components correlated with EOG should show frontal topographies and
+#   stereotyped time courses.
+# - Only components clearly associated with artifacts should be excluded.
+
+ica = ICA(
+    n_components=15,
+    random_state=97,
+    max_iter=50,  # just for speed!
+)
+
+# Fit ICA using a decimated signal for speed
+ica.fit(raw, picks=("meg", "eeg"), decim=10, verbose="error")
+
+
+# Identify EOG-related components
+eog_epochs = create_eog_epochs(raw)
+eog_inds, eog_scores = ica.find_bads_eog(eog_epochs)
+ica.exclude = eog_inds
+
+report.add_ica(
+    ica=ica,
+    inst=epochs,
+    eog_evoked=eog_epochs.average(),
+    eog_scores=eog_scores,
+    title="ICA components (artifact inspection)",
+)
+
+# %%
+# MEG–MRI coregistration
+# ----------------------
+# Accurate coregistration is critical for source localization.
+#
+# - Head shape points should align well with the MRI scalp surface.
+# - Systematic misalignment indicates digitization or transformation errors.
+
+
+trans = sample_dir / "sample_audvis_raw-trans.fif"
+report.add_trans(
+    trans,
+    info=raw.info,
+    title="MEG–MRI-head coregistration",
+    subject=subject,
+    subjects_dir=subjects_dir,
+)
+
+# %%
+# MRI and BEM surfaces
+# --------------------
+# Boundary Element Method (BEM) surfaces define the head model used for
+# forward and inverse solutions.
+#
+# - Surfaces should be smooth, closed, and non-intersecting.
+# - Poorly formed surfaces can severely degrade source estimates.
+
+report.add_bem(
+    subject,
+    subjects_dir=subjects_dir,
+    title="BEM surfaces",
+    decim=20,  # for speed
+)
+
+# %%
+# View the final report
+# ---------------------
+# You can set ``open_browser=True`` to have it pop open a browser tab if you want:
+
+report.save("qc_report.html", overwrite=True, open_browser=False)