Merge remote-tracking branch 'upstream/main' into feature/TD-nirs_snirf

* upstream/main: eegbci api: allow downloading multiple subjects (mne-tools#12918) DOC: Document Linux desktop workaround (mne-tools#12900) Allow exporting edf where a channel contains only constant values (mne-tools#12911) Autogenerate environment.yml file from pyproject.toml (mne-tools#12914)
larsoner · Oct 28, 2024 · 9723621 · 9723621
2 parents b3b3f2e + e15292f
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diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
@@ -106,6 +106,7 @@ jobs:
           create-args: >-
             python=${{ env.PYTHON_VERSION }}
             mamba
+            nomkl
         if: ${{ !startswith(matrix.kind, 'pip') }}
       # Make sure we have the right Python
       - run: python -c "import platform; assert platform.machine() == 'arm64', platform.machine()"

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -64,10 +64,17 @@ repos:
         files: pyproject.toml
 
   # dependencies
+  - repo: local
+    hooks:
+      - id: update-env-file
+        name: Copy dependency changes from pyproject.toml to environment.yml
+        language: python
+        entry: ./tools/hooks/update_environment_file.py
+        files: pyproject.toml
   - repo: local
     hooks:
       - id: dependency-sync
-        name: Sync dependency list between pyproject.toml and README.rst
+        name: Copy core dependencies from pyproject.toml to README.rst
         language: python
         entry: ./tools/hooks/sync_dependencies.py
         files: pyproject.toml

diff --git a/doc/_static/default_linux_dock_icon.png b/doc/_static/default_linux_dock_icon.png
diff --git a/doc/_static/mne_python_dock_icon.png b/doc/_static/mne_python_dock_icon.png
diff --git a/doc/changes/devel/12911.bugfix.rst b/doc/changes/devel/12911.bugfix.rst
@@ -0,0 +1 @@
+Allow exporting edf where a channel contains only constant values, by `Florian Hofer`_.
diff --git a/doc/changes/devel/12918.apichange.rst b/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`_.
diff --git a/doc/documentation/datasets.rst b/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
 

diff --git a/doc/install/advanced.rst b/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
 ^^^^^^^^^^^^^^^^^^^^^^^^^^
 

diff --git a/environment.yml b/environment.yml
@@ -1,67 +1,61 @@
+# THIS FILE IS AUTO-GENERATED BY tools/hooks/update_environment_file.py AND WILL BE OVERWRITTEN
 name: mne
 channels:
   - conda-forge
 dependencies:
   - python >=3.10
-  - pip
-  - numpy
-  - scipy
-  - openblas
-  - matplotlib
-  - tqdm
-  - pooch>=1.5
+  - antio >=0.4.0
+  - darkdetect
   - decorator
-  - h5io
-  - packaging
-  - numba
-  - pandas
-  - pyarrow
-  - xlrd
-  - scikit-learn
+  - defusedxml
+  - dipy
+  - edfio >=0.2.1
+  - eeglabio
+  - h5io >=0.2.4
   - h5py
-  - jinja2
-  - pillow
-  - statsmodels
-  - jupyter
-  - ipython !=8.7.0
-  - joblib
-  - psutil
-  - numexpr
-  - imageio
-  - spyder-kernels >=1.10.0
   - imageio >=2.6.1
   - imageio-ffmpeg >=0.4.1
-  - vtk >=9.2
-  - traitlets
-  - pyvista >=0.32,!=0.35.2,!=0.38.0,!=0.38.1,!=0.38.2,!=0.38.3,!=0.38.4,!=0.38.5,!=0.38.6,!=0.42.0
-  - pyvistaqt >=0.4
-  - qdarkstyle !=3.2.2
-  - darkdetect
-  - dipy
+  - ipyevents
+  - ipympl
+  - ipython !=8.7.0
+  - ipywidgets
+  - jinja2
+  - joblib
+  - jupyter
+  - lazy_loader >=0.3
+  - matplotlib >=3.6
+  - mffpy >=0.5.7
+  - mne-qt-browser
   - nibabel
-  - openmeeg >=2.5.5
   - nilearn
+  - numba
+  - numpy >=1.23,<3
+  - openmeeg >=2.5.5
+  - packaging
+  - pandas
+  - pillow
+  - pip
+  - pooch >=1.5
+  - pyarrow
+  - pybv
+  - pymatreader
+  - PySide6 !=6.8.0,!=6.8.0.1
+  - python-neo
   - python-picard
+  - pyvista >=0.32,!=0.35.2,!=0.38.0,!=0.38.1,!=0.38.2,!=0.38.3,!=0.38.4,!=0.38.5,!=0.38.6,!=0.42.0
+  - pyvistaqt >=0.4
+  - qdarkstyle !=3.2.2
   - qtpy
-  - pyside6 !=6.8.0,!=6.8.0.1
-  - mne-base
-  - seaborn-base
-  - mffpy >=0.5.7
-  - ipyevents
-  - ipywidgets
-  - ipympl
+  - scikit-learn
+  - scipy >=1.9
+  - sip
+  - snirf
+  - statsmodels
+  - threadpoolctl
+  - tqdm
+  - traitlets
   - trame
   - trame-vtk
   - trame-vuetify
-  - jupyter_client
-  - nbformat
-  - nbclient
-  - mne-qt-browser
-  - pymatreader
-  - eeglabio
-  - edfio >=0.2.1
-  - pybv
-  - mamba
-  - lazy_loader
-  - defusedxml
-  - python-neo
+  - vtk >=9.2
+  - xlrd
diff --git a/examples/decoding/decoding_csp_eeg.py b/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

diff --git a/examples/preprocessing/eeg_bridging.py b/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)

diff --git a/examples/preprocessing/muscle_ica.py b/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")

diff --git a/examples/time_frequency/time_frequency_erds.py b/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