Improving the pre-commit and making sure that everybody can install eegdash

.github/workflows/pre-commit.yaml

@@ -3,7 +3,7 @@ concurrency:
   group: ${{ github.workflow }}-${{ github.event.number }}-${{ github.event.ref }}
   cancel-in-progress: true
 
-on: [pull_request]
+on: [push, pull_request]
 
 jobs:
   pre-commit:

.github/workflows/tests.yml

@@ -5,7 +5,7 @@ concurrency:
 on:
   push:
     branches:
-      - "master"
+      - "*"
   pull_request:
     branches:
       - '*' # all branches, including forks
@@ -17,7 +17,7 @@ jobs:
       fail-fast: false
       matrix:
         os: [ "ubuntu-latest", "macos-latest", "windows-latest" ]
-        python-version: ["3.10", "3.11"]
+        python-version: ["3.10", "3.12"] # first and last supported Python version
     steps:
     ## Install Braindecode
     - name: Checking Out Repository
@@ -31,15 +31,18 @@ jobs:
       with:
         path: ~/mne_data
         key: ${{ runner.os }}-v3
-    - uses: actions/setup-python@v4
+
+    - name: Install uv and set the python version
+      uses: astral-sh/setup-uv@v5
       with:
+        enable-cache: true
         python-version: ${{ matrix.python-version }}
     - name: Show Python Version
       run: python --version
-    # Update pip
-    - name: Update pip
-      run: python -m pip install --upgrade pip
     - name: Install EEGDash from Current Checkout
-      run: pip install -e .
-    # Show Braindecode Version
-    - run: python -c "import eegdash; print(eegdash.__version__)"
+      run: uv pip install -e .[tests]
+    # Show EEGDash Version
+    - name: Show EEGDash Version
+      run: python -c "import eegdash; print(eegdash.__version__)"
+    - name: Run Tests
+      run: pytest -vvv tests/

.gitignore

@@ -1,4 +1,3 @@
-old
 *.egg-info
 *.npy
 */*.npy
@@ -14,3 +13,4 @@ dist/
 **/*/eegdash.egg-info/
 .env
 tests/data/
+.ruff_cache/

.pre-commit-config.yaml

@@ -1,44 +1,30 @@
 default_language_version:
     python: python3
 ci:
-    autofix_commit_msg: '[pre-commit.ci] auto fixes from pre-commit.com hooks
-
-        '
+    autofix_commit_msg: '[pre-commit.ci] auto fixes from pre-commit.com hooks'
     autofix_prs: true
     autoupdate_branch: master
     autoupdate_commit_msg: '[pre-commit.ci] pre-commit autoupdate'
-    autoupdate_schedule: quarterly
+    autoupdate_schedule: weekly
     skip: []
     submodules: false
 repos:
--   repo: https://github.com/charliermarsh/ruff-pre-commit
-    rev: v0.11.7
+-   repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.11.13
     hooks:
     -   id: ruff
-        name: ruff lint docs & examples
+        name: ruff lint & format
         args:
         - --fix
-        - --select=E,W,F,I,D
-        - --ignore=E402,E501,F401,D103,D400,D100,D101,D102,D105,D107,D415,D417,D205
-        files: ^(docs|examples)/
-    -   id: ruff
-        name: ruff lint eegdash preview
-        args:
-        - --fix
-        - --preview
-        - --select=NPY201
-        - --ignore=D100,D101,D102,D105,D107,D415,D417,D205
-        files: ^eegdash/
-    -   id: ruff
-        name: ruff lint docs & examples
-        args:
-        - --fix
-        - --select=D
-        - --ignore=D103,D400,E402,D100,D101,D102,D105,D107,D415,D417,D205
-        files: ^(docs|examples)/
+        - --show-fixes
+        # Selects all rules from your original config (E,W,F,I,D) and enables import sorting (I)
+        # and unused import removal (F401 is part of 'F' and is no longer ignored).
+        - --select=E,W,F,D,NPY201
+        - --ignore=E402,E501,D100,D101,D102,D103,D104,D105,D107,D205,D400,D415,D417,F403,D401,E741,E722,D419
+        files: ^(eegdash|docs|examples|notebooks|scripts|tests)/
+        exclude: \.ipynb$
     -   id: ruff-format
         name: ruff format code
-        files: ^(eegdash|docs|examples)/
 -   repo: https://github.com/codespell-project/codespell
     rev: v2.4.1
     hooks:
@@ -50,7 +36,6 @@ repos:
         exclude_types:
         - csv
         - json
-        exclude: ^tests/|generated/^.github
 -   repo: https://github.com/asottile/blacken-docs
     rev: 1.19.1
     hooks:

.readthedocs.yaml

@@ -1,3 +1,4 @@
+
 # Read the Docs configuration file
 # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
 
@@ -20,4 +21,4 @@ sphinx:
 # python:
 #    install:
 #    - requirements: docs/requirements.txt
-        
+

LICENSE

@@ -4,6 +4,7 @@ Copyright (C) 2024-2025
 
 Young Truong, UCSD, [email protected]
 Arnaud Delorme, UCSD, [email protected]
+Bruno Aristimunha, [email protected]
 
 This program is free software; you can redistribute it and/or modify
 it under the terms of the GNU General Public License as published by

README.md

@@ -1,7 +1,9 @@
 # EEG-Dash
+
 To leverage recent and ongoing advancements in large-scale computational methods and to ensure the preservation of scientific data generated from publicly funded research, the EEG-DaSh data archive will create a data-sharing resource for MEEG (EEG, MEG) data contributed by collaborators for machine learning (ML) and deep learning (DL) applications. 
 
 ## Data source
+
 The data in EEG-DaSh originates from a collaboration involving 25 laboratories, encompassing 27,053 participants. This extensive collection includes MEEG data, which is a combination of EEG and MEG signals. The data is sourced from various studies conducted by these labs, involving both healthy subjects and clinical populations with conditions such as ADHD, depression, schizophrenia, dementia, autism, and psychosis. Additionally, data spans different mental states like sleep, meditation, and cognitive tasks. In addition, EEG-DaSh will incorporate a subset of the data converted from NEMAR, which includes 330 MEEG BIDS-formatted datasets, further expanding the archive with well-curated, standardized neuroelectromagnetic data.
 
 ## Featured data
@@ -21,9 +23,11 @@ The following HBN datasets are currently featured on EEGDash. Documentation abou
 A total of [246 other datasets](datasets.md) are also available through EEGDash. 
 
 ## Data format
+
 EEGDash queries return a **Pytorch Dataset** formatted to facilitate machine learning (ML) and deep learning (DL) applications. PyTorch Datasets are the best format for EEGDash queries because they provide an efficient, scalable, and flexible structure for machine learning (ML) and deep learning (DL) applications. They allow seamless integration with PyTorch’s DataLoader, enabling efficient batching, shuffling, and parallel data loading, which is essential for training deep learning models on large EEG datasets.
 
 ## Data preprocessing
+
 EEGDash datasets are processed using the popular [BrainDecode](https://braindecode.org/stable/index.html) library. In fact, EEGDash datasets are BrainDecode datasets, which are themselves PyTorch datasets. This means that any preprocessing possible on BrainDecode datasets is also possible on EEGDash datasets. Refer to [BrainDecode](https://braindecode.org/stable/index.html) tutorials for guidance on preprocessing EEG data.
 
 ## EEG-Dash usage

eegdash/__init__.py

@@ -1,4 +1,4 @@
-from .main import EEGDash, EEGDashDataset
+from .api import EEGDash, EEGDashDataset
 
 __all__ = ["EEGDash", "EEGDashDataset"]
 __version__ = "0.1.0"