Merge branch 'main' into add_tfr_weights

.pre-commit-config.yaml

@@ -1,7 +1,7 @@
 repos:
   # Ruff mne
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.7.0
+    rev: v0.7.1
     hooks:
       - id: ruff
         name: ruff lint mne

doc/changes/devel/12911.bugfix.rst

@@ -0,0 +1 @@
+Allow exporting edf where a channel contains only constant values, by `Florian Hofer`_.

doc/changes/devel/12918.apichange.rst

@@ -0,0 +1 @@
+Deprecate ``subject`` parameter in favor of ``subjects`` in :func:`mne.datasets.eegbci.load_data`, by `Stefan Appelhoff`_.

doc/documentation/datasets.rst

@@ -161,17 +161,23 @@ EEGBCI motor imagery
 ====================
 :func:`mne.datasets.eegbci.load_data`
 
-The EEGBCI dataset is documented in :footcite:`SchalkEtAl2004`. The data set is
-available at PhysioNet :footcite:`GoldbergerEtAl2000`. The dataset contains
-64-channel EEG recordings from 109 subjects and 14 runs on each subject in EDF+
-format. The recordings were made using the BCI2000 system. To load a subject,
-do::
+The EEGBCI dataset is documented in :footcite:`SchalkEtAl2004` and on the
+`PhysioNet documentation page <https://physionet.org/content/eegmmidb/1.0.0/>`_.
+The data set is available at PhysioNet :footcite:`GoldbergerEtAl2000`.
+It contains 64-channel EEG recordings from 109 subjects and 14 runs on each
+subject in EDF+ format. The recordings were made using the BCI2000 system.
+To load a subject, do::
 
     from mne.io import concatenate_raws, read_raw_edf
     from mne.datasets import eegbci
-    raw_fnames = eegbci.load_data(subject, runs)
+    subjects = [1]  # may vary
+    runs = [4, 8, 12]  # may vary
+    raw_fnames = eegbci.load_data(subjects, runs)
     raws = [read_raw_edf(f, preload=True) for f in raw_fnames]
+    # concatenate runs from subject
     raw = concatenate_raws(raws)
+    # make channel names follow standard conventions
+    eegbci.standardize(raw)
 
 .. topic:: Examples
 

doc/install/advanced.rst

@@ -116,6 +116,37 @@ use a different binding, you can instead install MNE-Python via ``pip``:
 
 .. _CUDA:
 
+Fixing dock icons on Linux
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+On newer versions of Ubuntu (e.g., 24.04), applications must supply a ``.desktop``
+file associated with them, otherwise a generic icon will be used like:
+
+.. image:: ../_static/default_linux_dock_icon.png
+   :alt: Default Linux dock icon
+
+To fix this, you can create a ``.desktop`` file for MNE-Python. Here is an example
+file that you can save as ``~/.local/share/applications/mne-python.desktop`` after
+fixing the path to the MNE-Python icon, which you can download
+`here <https://github.com/mne-tools/mne-python/blob/922a7801a0ca6af225c7b861fe6bd97b1518af3a/mne/icons/mne_default_icon.png>`__
+if needed:
+
+.. code-block:: ini
+
+    [Desktop Entry]
+    Type=Application
+    Version=1.5
+    Name=MNE-Python
+    StartupWMClass=MNE-Python
+    Icon=/path/to/mne-python/mne/icons/mne_default_icon.png
+    SingleMainWindow=true
+    NoDisplay=true
+
+It should make the icon appear correctly in the dock:
+
+.. image:: ../_static/mne_python_dock_icon.png
+   :alt: MNE-Python dock icon
+
 GPU acceleration with CUDA
 ^^^^^^^^^^^^^^^^^^^^^^^^^^
 

examples/decoding/decoding_csp_eeg.py

@@ -10,8 +10,9 @@
 
 See https://en.wikipedia.org/wiki/Common_spatial_pattern and
 :footcite:`Koles1991`. The EEGBCI dataset is documented in
-:footcite:`SchalkEtAl2004` and is available at PhysioNet
-:footcite:`GoldbergerEtAl2000`.
+:footcite:`SchalkEtAl2004` and on the
+`PhysioNet documentation page <https://physionet.org/content/eegmmidb/1.0.0/>`_.
+The dataset is available at PhysioNet :footcite:`GoldbergerEtAl2000`.
 """
 # Authors: Martin Billinger <martin.billinger@tugraz.at>
 #
@@ -40,15 +41,15 @@
 # avoid classification of evoked responses by using epochs that start 1s after
 # cue onset.
 tmin, tmax = -1.0, 4.0
-subject = 1
+subjects = 1
 runs = [6, 10, 14]  # motor imagery: hands vs feet
 
-raw_fnames = eegbci.load_data(subject, runs)
+raw_fnames = eegbci.load_data(subjects, runs)
 raw = concatenate_raws([read_raw_edf(f, preload=True) for f in raw_fnames])
 eegbci.standardize(raw)  # set channel names
 montage = make_standard_montage("standard_1005")
 raw.set_montage(montage)
-raw.annotations.rename(dict(T1="hands", T2="feet"))
+raw.annotations.rename(dict(T1="hands", T2="feet"))  # as documented on PhysioNet
 raw.set_eeg_reference(projection=True)
 
 # Apply band-pass filter

examples/preprocessing/eeg_bridging.py

@@ -65,7 +65,7 @@
 raw_data = dict()  # store infos for electrode positions
 for sub in range(1, 11):
     print(f"Computing electrode bridges for subject {sub}")
-    raw_fname = mne.datasets.eegbci.load_data(subject=sub, runs=(1,))[0]
+    raw_fname = mne.datasets.eegbci.load_data(subjects=sub, runs=(1,))[0]
     raw = mne.io.read_raw(raw_fname, preload=True, verbose=False)
     mne.datasets.eegbci.standardize(raw)  # set channel names
     raw.set_montage(montage, verbose=False)

examples/preprocessing/muscle_ica.py

@@ -97,7 +97,7 @@
 
 for sub in (1, 2):
     raw = mne.io.read_raw_edf(
-        mne.datasets.eegbci.load_data(subject=sub, runs=(1,))[0], preload=True
+        mne.datasets.eegbci.load_data(subjects=sub, runs=(1,))[0], preload=True
     )
     mne.datasets.eegbci.standardize(raw)  # set channel names
     montage = mne.channels.make_standard_montage("standard_1005")

examples/time_frequency/time_frequency_erds.py

@@ -50,7 +50,7 @@
 # First, we load and preprocess the data. We use runs 6, 10, and 14 from
 # subject 1 (these runs contains hand and feet motor imagery).
 
-fnames = eegbci.load_data(subject=1, runs=(6, 10, 14))
+fnames = eegbci.load_data(subjects=1, runs=(6, 10, 14))
 raw = concatenate_raws([read_raw_edf(f, preload=True) for f in fnames])
 
 raw.rename_channels(lambda x: x.strip("."))  # remove dots from channel names

mne/datasets/eegbci/eegbci.py

@@ -9,7 +9,7 @@
 from os import path as op
 from pathlib import Path
 
-from ...utils import _url_to_local_path, logger, verbose
+from ...utils import _url_to_local_path, logger, verbose, warn
 from ..utils import _do_path_update, _downloader_params, _get_path, _log_time_size
 
 EEGMI_URL = "https://physionet.org/files/eegmmidb/1.0.0/"
@@ -21,7 +21,9 @@ def data_path(url, path=None, force_update=False, update_path=None, *, verbose=N
 
     This is a low-level function useful for getting a local copy of a remote EEGBCI
     dataset :footcite:`SchalkEtAl2004`, which is also available at PhysioNet
-    :footcite:`GoldbergerEtAl2000`.
+    :footcite:`GoldbergerEtAl2000`. Metadata, such as the meaning of event markers
+    may be obtained from the
+    `PhysioNet documentation page <https://physionet.org/content/eegmmidb/1.0.0/>`_.
 
     Parameters
     ----------
@@ -92,8 +94,10 @@ def data_path(url, path=None, force_update=False, update_path=None, *, verbose=N
 
 @verbose
 def load_data(
-    subject,
-    runs,
+    subjects=None,
+    runs=None,
+    *,
+    subject=None,
     path=None,
     force_update=False,
     update_path=None,
@@ -103,14 +107,19 @@ def load_data(
     """Get paths to local copies of EEGBCI dataset files.
 
     This will fetch data for the EEGBCI dataset :footcite:`SchalkEtAl2004`, which is
-    also available at PhysioNet :footcite:`GoldbergerEtAl2000`.
+    also available at PhysioNet :footcite:`GoldbergerEtAl2000`. Metadata, such as the
+    meaning of event markers may be obtained from the
+    `PhysioNet documentation page <https://physionet.org/content/eegmmidb/1.0.0/>`_.
 
     Parameters
     ----------
-    subject : int
-        The subject to use. Can be in the range of 1-109 (inclusive).
+    subjects : int | list of int
+        The subjects to use. Can be in the range of 1-109 (inclusive).
     runs : int | list of int
         The runs to use (see Notes for details).
+    subject : int
+        This parameter is deprecated and will be removed in mne version 1.9.
+        Please use ``subjects`` instead.
     path : None | path-like
         Location of where to look for the EEGBCI data. If ``None``, the environment
         variable or config parameter ``MNE_DATASETS_EEGBCI_PATH`` is used. If neither
@@ -149,20 +158,39 @@ def load_data(
     For example, one could do::
 
         >>> from mne.datasets import eegbci
-        >>> eegbci.load_data(1, [6, 10, 14], "~/datasets") # doctest:+SKIP
+        >>> eegbci.load_data([1, 2], [6, 10, 14], "~/datasets") # doctest:+SKIP
 
-    This would download runs 6, 10, and 14 (hand/foot motor imagery) runs from subject 1
-    in the EEGBCI dataset to "~/datasets" and prompt the user to store this path in the
-    config (if it does not already exist).
+    This would download runs 6, 10, and 14 (hand/foot motor imagery) runs from subjects
+    1 and 2 in the EEGBCI dataset to "~/datasets" and prompt the user to store this path
+    in the config (if it does not already exist).
 
     References
     ----------
     .. footbibliography::
     """
     import pooch
 
+    # XXX: Remove this with mne 1.9 ↓↓↓
+    # Also remove the subject parameter at that point.
+    # Also remove the `None` default for subjects and runs params at that point.
+    if subject is not None:
+        subjects = subject
+        warn(
+            "The ``subject`` parameter is deprecated and will be removed in version "
+            "1.9. Use the ``subjects`` parameter (note the `s`) to suppress this "
+            "warning.",
+            FutureWarning,
+        )
+        del subject
+    if subjects is None or runs is None:
+        raise ValueError("You must pass the parameters ``subjects`` and ``runs``.")
+    # ↑↑↑
+
     t0 = time.time()
 
+    if not hasattr(subjects, "__iter__"):
+        subjects = [subjects]
+
     if not hasattr(runs, "__iter__"):
         runs = [runs]
 
@@ -198,20 +226,22 @@ def load_data(
     # fetch the file(s)
     data_paths = []
     sz = 0
-    for run in runs:
-        file_part = f"S{subject:03d}/S{subject:03d}R{run:02d}.edf"
-        destination = Path(base_path, file_part)
-        data_paths.append(destination)
-        if destination.exists():
-            if force_update:
-                destination.unlink()
-            else:
-                continue
-        if sz == 0:  # log once
-            logger.info("Downloading EEGBCI data")
-        fetcher.fetch(file_part)
-        # update path in config if desired
-        sz += destination.stat().st_size
+    for subject in subjects:
+        for run in runs:
+            file_part = f"S{subject:03d}/S{subject:03d}R{run:02d}.edf"
+            destination = Path(base_path, file_part)
+            data_paths.append(destination)
+            if destination.exists():
+                if force_update:
+                    destination.unlink()
+                else:
+                    continue
+            if sz == 0:  # log once
+                logger.info("Downloading EEGBCI data")
+            fetcher.fetch(file_part)
+            # update path in config if desired
+            sz += destination.stat().st_size
+
     _do_path_update(path, update_path, config_key, name)
     if sz > 0:
         _log_time_size(t0, sz)

mne/datasets/eegbci/tests/test_eegbci.py

@@ -2,12 +2,26 @@
 # License: BSD-3-Clause
 # Copyright the MNE-Python contributors.
 
+import pytest
+
 from mne.datasets import eegbci
 
 
 def test_eegbci_download(tmp_path, fake_retrieve):
     """Test Sleep Physionet URL handling."""
-    for subj in range(4):
-        fnames = eegbci.load_data(subj + 1, runs=[3], path=tmp_path, update_path=False)
+    subjects = range(1, 5)
+    for subj in subjects:
+        fnames = eegbci.load_data(subj, runs=[3], path=tmp_path, update_path=False)
         assert len(fnames) == 1, subj
     assert fake_retrieve.call_count == 4
+
+    # XXX: remove in version 1.9
+    with pytest.warns(FutureWarning, match="The ``subject``"):
+        fnames = eegbci.load_data(
+            subject=subjects, runs=[3], path=tmp_path, update_path=False
+        )
+        assert len(fnames) == 4
+
+    # XXX: remove in version 1.9
+    with pytest.raises(ValueError, match="You must pass the parameters"):
+        fnames = eegbci.load_data(path=tmp_path, update_path=False)

mne/datasets/utils.py

@@ -347,9 +347,8 @@ def _download_all_example_data(verbose=True):
         sleep_physionet,
     )
 
-    eegbci.load_data(1, [6, 10, 14], update_path=True)
-    for subj in range(4):
-        eegbci.load_data(subj + 1, runs=[3], update_path=True)
+    eegbci.load_data(subjects=1, runs=[6, 10, 14], update_path=True)
+    eegbci.load_data(subjects=range(1, 5), runs=[3], update_path=True)
     logger.info("[done eegbci]")
 
     sleep_physionet.age.fetch_data(subjects=[0, 1], recording=[1])

mne/export/_edf.py

@@ -138,6 +138,8 @@ def _export_raw(fname, raw, physical_range, add_ch_type):
         if physical_range == "auto":  # per channel type
             pmin = ch_types_phys_min[ch_type]
             pmax = ch_types_phys_max[ch_type]
+            if pmax == pmin:
+                pmax = pmin + 1
             prange = pmin, pmax
 
         signals.append(

mne/export/tests/test_export.py

@@ -379,6 +379,16 @@ def test_export_edf_signal_clipping(tmp_path, physical_range, exceeded_bound):
     assert raw_read.get_data().max() <= physical_range[1]
 
 
+@edfio_mark()
+def test_export_edf_with_constant_channel(tmp_path):
+    """Test if exporting to edf works if a channel contains only constant values."""
+    temp_fname = tmp_path / "test.edf"
+    raw = RawArray(np.zeros((1, 10)), info=create_info(1, 1))
+    raw.export(temp_fname)
+    raw_read = read_raw_edf(temp_fname, preload=True)
+    assert_array_equal(raw_read.get_data(), np.zeros((1, 10)))
+
+
 @edfio_mark()
 @pytest.mark.parametrize(
     ("input_path", "warning_msg"),

mne/viz/backends/_qt.py

@@ -965,7 +965,7 @@ def func(button):
                         callback(button_name)
                     finally:
                         widget.unsetCursor()
-                        break
+                    break
 
         widget.buttonClicked.connect(func)
         return _QtDialogWidget(widget, modal)

tutorials/forward/35_eeg_no_mri.py

@@ -48,7 +48,7 @@
 # .. note:: See :ref:`plot_montage` to view all the standard EEG montages
 #           available in MNE-Python.
 
-(raw_fname,) = eegbci.load_data(subject=1, runs=[6])
+(raw_fname,) = eegbci.load_data(subjects=1, runs=[6])
 raw = mne.io.read_raw_edf(raw_fname, preload=True)
 
 # Clean channel names to be able to use a standard 1005 montage

tutorials/preprocessing/40_artifact_correction_ica.py

@@ -532,9 +532,9 @@
 raws = list()
 icas = list()
 
-for subj in range(4):
+for subj in range(1, 5):
     # EEGBCI subjects are 1-indexed; run 3 is a left/right hand movement task
-    fname = mne.datasets.eegbci.load_data(subj + 1, runs=[3])[0]
+    fname = mne.datasets.eegbci.load_data(subj, runs=[3])[0]
     raw = mne.io.read_raw_edf(fname).load_data().resample(50)
     # remove trailing `.` from channel names so we can set montage
     mne.datasets.eegbci.standardize(raw)