[pre-commit.ci] pre-commit autoupdate (#553)

* [pre-commit.ci] pre-commit autoupdate

updates:
- [github.com/psf/black: 23.12.1 → 24.3.0](psf/black@23.12.1...24.3.0)
- [github.com/PyCQA/flake8: 6.1.0 → 7.0.0](PyCQA/flake8@6.1.0...7.0.0)
- [github.com/astral-sh/ruff-pre-commit: v0.1.9 → v0.3.5](astral-sh/ruff-pre-commit@v0.1.9...v0.3.5)
- [github.com/adrienverge/yamllint.git: v1.33.0 → v1.35.1](https://github.com/adrienverge/yamllint.git/compare/v1.33.0...v1.35.1)

* [pre-commit.ci] auto fixes from pre-commit.com hooks

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

.pre-commit-config.yaml

@@ -35,7 +35,7 @@ repos:
 
 
   - repo: https://github.com/psf/black
-    rev: 23.12.1
+    rev: 24.3.0
     hooks:
       - id: black
         language_version: python3.8
@@ -54,7 +54,7 @@ repos:
       - id: isort
 
   - repo: https://github.com/PyCQA/flake8
-    rev: 6.1.0
+    rev: 7.0.0
     hooks:
       - id: flake8
         additional_dependencies: [
@@ -69,7 +69,7 @@ repos:
         exclude: ^docs/ | ^setup\.py$ |
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.1.9
+    rev: v0.3.5
     hooks:
       - id: ruff
         args: [ --fix, --exit-non-zero-on-fix, --ignore, E501 ]
@@ -86,7 +86,7 @@ repos:
         exclude: ^tests/|generated/
 
   - repo: https://github.com/adrienverge/yamllint.git
-    rev: v1.33.0
+    rev: v1.35.1
     hooks:
       - id: yamllint
         exclude: >

examples/advanced_examples/plot_filterbank_csp_vs_csp.py

@@ -6,6 +6,7 @@
 This example show a comparison of CSP versus FilterBank CSP on the
 very popular dataset 2a from the BCI competition IV.
 """
+
 # Authors: Alexandre Barachant <[email protected]>
 #
 # License: BSD (3-clause)

examples/advanced_examples/plot_grid_search_withinsession.py

@@ -7,6 +7,7 @@
 for finding the best model parameter, using grid search. Two models
 are compared, one "vanilla" model with model tuned via grid search.
 """
+
 import os
 from pickle import load
 

examples/advanced_examples/plot_mne_and_scikit_estimators.py

@@ -13,6 +13,7 @@
 - XDAWN extended covariance and LR on tangent space, based on Numpy
 
 """
+
 # Authors: Sylvain Chevallier
 #
 # License: BSD (3-clause)

examples/advanced_examples/plot_select_electrodes_resample.py

@@ -8,6 +8,7 @@
 utility function to select common electrodes shared between datasets.
 This tutorial demonstrates how to use this functionality.
 """
+
 # Authors: Sylvain Chevallier <[email protected]>
 #
 # License: BSD (3-clause)

examples/advanced_examples/plot_statistical_analysis.py

@@ -8,6 +8,7 @@
 they can be used.
 
 """
+
 # Authors: Vinay Jayaram <[email protected]>
 #
 # License: BSD (3-clause)

examples/changing_download_directory.py

@@ -5,6 +5,7 @@
 
 This is a minimal example to demonstrate how to change the default data
 download directory to a custom path/location. """
+
 # Authors: Divyesh Narayanan <[email protected]>
 #
 # License: BSD (3-clause)

examples/example_codecarbon.py

@@ -11,6 +11,7 @@
 generation, the results is computed on a local cluster but could be easily
 replicated on your infrastructure.
 """
+
 # Authors: Igor Carrara <[email protected]>
 #          Bruno Aristimunha <[email protected]>
 #

examples/external/plot_learning_curve_p300_external.py

@@ -16,6 +16,7 @@
 
 We will use the P300 paradigm, which uses the AUC as metric.
 """
+
 # Authors: Jan Sosulski
 #
 # License: BSD (3-clause)

examples/learning_curve/plot_learning_curve_motor_imagery.py

@@ -14,6 +14,7 @@
 We will use the LeftRightImagery paradigm. This will restrict the analysis
 to two classes (left- vs right-hand) and use AUC as metric.
 """
+
 # Original author: Alexandre Barachant <[email protected]>
 # Learning curve modification: Jan Sosulski
 #

examples/learning_curve/plot_learning_curve_p300.py

@@ -15,6 +15,7 @@
 
 We will use the P300 paradigm, which uses the AUC as metric.
 """
+
 # Authors: Jan Sosulski
 #
 # License: BSD (3-clause)

examples/load_model.py

@@ -5,6 +5,7 @@
 
 This example shows how to use load the pretrained pipeline in MOABB.
 """
+
 # Authors: Igor Carrara <[email protected]>
 #
 # License: BSD (3-clause)

examples/noplot_bids_conversion.py

@@ -21,6 +21,7 @@
 We will use the AlexMI dataset [3]_, one of the smallest in
 people and one that can be downloaded quickly.
 """
+
 # Authors: Pierre Guetschel <[email protected]>
 #
 # License: BSD (3-clause)

examples/noplot_vr_pc_p300_different_epoch_size.py

@@ -11,6 +11,7 @@
 This example demonstrates the use of `get_block_repetition`, which allows
 to specify the experimental blocks and repetitions for analysis.
 """
+
 # Authors: Pedro Rodrigues <[email protected]>
 # Modified by: Gregoire Cattan <[email protected]>
 # License: BSD (3-clause)

examples/plot_benchmark.py

@@ -8,6 +8,7 @@
 dataset to keep the computation time low, but this benchmark is designed
 to easily scale to many datasets.
 """
+
 # Authors: Sylvain Chevallier <[email protected]>
 #
 # License: BSD (3-clause)

examples/plot_benchmark_DL.py

@@ -7,6 +7,7 @@
 For this example, we will use only one dataset to keep the computation time low, but this benchmark is designed
 to easily scale to many datasets.
 """
+
 # Authors: Igor Carrara <[email protected]>
 #
 # License: BSD (3-clause)

examples/plot_benchmark_braindecode.py

@@ -7,6 +7,7 @@
 For this example, we will use only 2 datasets to keep the computation time low, but this benchmark is designed
 to easily scale to many datasets.
 """
+
 # Authors: Igor Carrara <[email protected]>
 #          Bruno Aristimunha <[email protected]>
 #          Sylvain Chevallier <[email protected]>

examples/plot_benchmark_grid_search.py

@@ -9,6 +9,7 @@
 For this example, we will use only one dataset to keep the computation time low, but this benchmark is designed
 to easily scale to many datasets.
 """
+
 # Authors: Igor Carrara <[email protected]>
 #
 # License: BSD (3-clause)

examples/plot_braindecode.py

@@ -5,6 +5,7 @@
 This example shows how to use BrainDecode in combination with MOABB evaluation.
 In this example, we use the architecture EEGNetv4.
 """
+
 # Authors: Igor Carrara <[email protected]>
 #          Bruno Aristimunha <[email protected]>
 #

examples/plot_cross_session_motor_imagery.py

@@ -19,6 +19,7 @@
 is trained on every other session and performance are evaluated on the current
 session.
 """
+
 # Authors: Alexandre Barachant <[email protected]>
 #          Sylvain Chevallier <[email protected]>
 #

examples/plot_cross_session_multiple_datasets.py

@@ -11,6 +11,7 @@
 is trained on every other session and performance are evaluated on the current
 session.
 """
+
 # Authors: Sylvain Chevallier <[email protected]>
 #
 # License: BSD (3-clause)

examples/plot_cross_subject_ssvep.py

@@ -12,6 +12,7 @@
 
 We will use the SSVEP paradigm, which uses the AUC as metric.
 """
+
 # Authors: Sylvain Chevallier <[email protected]>
 #
 # License: BSD (3-clause)

examples/plot_disk_cache.py

@@ -61,6 +61,7 @@
     method of the datasets. It works the same way as for a
     paradigm except that it will save un-processed raw recordings.
 """
+
 # Authors: Pierre Guetschel <[email protected]>
 #
 # License: BSD (3-clause)

examples/plot_explore_paradigm.py

@@ -19,6 +19,7 @@
      - LeftRightImagery
 
 """
+
 # Authors: Alexandre Barachant <[email protected]>
 #          Sylvain Chevallier <[email protected]>
 #

examples/plot_fixed_interval_windows.py

@@ -15,6 +15,7 @@
 In this example, we will use the Zhou2016 dataset because
 it is relatively small and can be downloaded quickly.
 """
+
 # Authors: Pierre Guetschel <[email protected]>
 #
 # License: BSD (3-clause)

examples/plot_within_session_p300.py

@@ -14,6 +14,7 @@
 We will use the P300 paradigm, which uses the AUC as metric.
 
 """
+
 # Authors: Pedro Rodrigues <[email protected]>
 #
 # License: BSD (3-clause)

examples/plot_within_session_ssvep.py

@@ -10,6 +10,7 @@
 pipeline using a 5-fold cross-validation. The reported metric (here, accuracy)
 is the average of all fold.
 """
+
 # Authors: Sylvain Chevallier <[email protected]>
 #
 # License: BSD (3-clause)

moabb/analysis/meta_analysis.py

@@ -1,4 +1,5 @@
 """Meta-analysis functions for MOABB."""
+
 import itertools
 import logging
 

moabb/datasets/Lee2019.py

@@ -1,4 +1,5 @@
 """BMI/OpenBMI dataset."""
+
 from functools import partialmethod
 
 import numpy as np

moabb/datasets/__init__.py

@@ -6,6 +6,7 @@
 See https://github.com/NeuroTechX/moabb/wiki/Datasets-Support for detail
 on datasets (electrodes, number of trials, sessions, etc.)
 """
+
 from . import compound_dataset
 
 # flake8: noqa

moabb/datasets/base.py

@@ -1,4 +1,5 @@
 """Base class for a dataset."""
+
 import abc
 import logging
 import re

moabb/evaluations/__init__.py

@@ -2,6 +2,7 @@
 generalization statistic (AUC score, f-score, accuracy, etc) -- it can be
 either within-recording-session accuracy, across-session within-subject
 accuracy, across-subject accuracy, or other transfer learning settings."""
+
 # flake8: noqa
 from .evaluations import (
     CrossSessionEvaluation,

moabb/paradigms/__init__.py

@@ -5,6 +5,7 @@
 have two-class, multi-class, or continuous paradigms; similarly,
 different preprocessing is necessary for ERP vs ERD paradigms.
 """
+
 # flake8: noqa
 from moabb.paradigms.cvep import *
 from moabb.paradigms.fixed_interval_windows import *

moabb/tests/download.py

@@ -1,4 +1,5 @@
 """Tests to ensure that datasets download correctly."""
+
 import unittest
 
 import mne

moabb/utils.py

@@ -1,4 +1,5 @@
 """Util functions for moabb."""
+
 import inspect
 import logging
 import os
@@ -50,9 +51,9 @@ def _set_tensorflow_seed(seed: int) -> None:
         import tensorflow as tf
 
         tf.random.set_seed(seed)  # tf cpu fix seed
-        os.environ[
-            "TF_DETERMINISTIC_OPS"
-        ] = "1"  # tf gpu fix seed, please `pip install tensorflow-determinism` first
+        os.environ["TF_DETERMINISTIC_OPS"] = (
+            "1"  # tf gpu fix seed, please `pip install tensorflow-determinism` first
+        )
         tf.keras.utils.set_random_seed(seed)
 
     except ImportError:

scripts/data_visualization_p300.py

@@ -11,6 +11,7 @@
 
 .. versionadded:: 0.4.5
 """
+
 import warnings
 
 # Authors: Jan Sosulski <[email protected]>

tutorials/4_adding_a_dataset.py

@@ -3,6 +3,7 @@
 Tutorial 4: Creating a dataset class
 ====================================
 """
+
 # Authors: Pedro L. C. Rodrigues, Sylvain Chevallier
 #
 # https://github.com/plcrodrigues/Workshop-MOABB-BCI-Graz-2019

tutorials/noplot_tutorial_5_build_a_custom_dataset.py

@@ -3,6 +3,7 @@
 Tutorial 5: Creating a dataset class
 ====================================
 """
+
 # Author: Gregoire Cattan
 #
 # https://github.com/plcrodrigues/Workshop-MOABB-BCI-Graz-2019

tutorials/plot_Getting_Started.py

@@ -9,6 +9,7 @@
 next tutorial.
 
 """
+
 # Authors: Vinay Jayaram <[email protected]>
 #
 # License: BSD (3-clause)

tutorials/tutorial_1_simple_example_motor_imagery.py

@@ -9,6 +9,7 @@
 use a CSP to enhance the signal-to-noise ratio of the EEG epochs and a LDA to
 classify these signals.
 """
+
 # Authors: Pedro L. C. Rodrigues, Sylvain Chevallier
 #
 # https://github.com/plcrodrigues/Workshop-MOABB-BCI-Graz-2019

tutorials/tutorial_2_using_mulitple_datasets.py

@@ -7,6 +7,7 @@
 a classifier with three different MI datasets instead of just one. As before,
 we begin by importing all relevant libraries.
 """
+
 # Authors: Pedro L. C. Rodrigues, Sylvain Chevallier
 #
 # https://github.com/plcrodrigues/Workshop-MOABB-BCI-Graz-2019

tutorials/tutorial_3_benchmarking_multiple_pipelines.py

@@ -6,6 +6,7 @@
 In this last part, we extend the previous example by assessing the
 classification score of not one but three classification pipelines.
 """
+
 # Authors: Pedro L. C. Rodrigues, Sylvain Chevallier
 #
 # https://github.com/plcrodrigues/Workshop-MOABB-BCI-Graz-2019