Modern packaging + ruff + numpy 2 compat

.github/workflows/python_tests.yml

@@ -2,45 +2,47 @@ name: Python tests
 
 on:
   push:
-    branches: [master, develop]
+    branches: [master]
   pull_request:
-    branches: [master, develop]
+    branches: [master]
 
 jobs:
   build:
     strategy:
       fail-fast: false
       matrix:
-        platform: [ubuntu-latest, windows-latest]  # macos-latest
-        python-version: ["3.9", "3.10", "3.11"]
+        platform: [ubuntu-latest, macos-latest, windows-latest]
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
 
     runs-on: ${{ matrix.platform }}
 
     env:
       FORCE_COLOR: true
 
     steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@v4
 
     - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v1
+      uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
 
+    - name: Install libomp (macOS)
+      if: ${{ matrix.platform == 'macos-latest' }}
+      run: brew install libomp
+
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-        pip install -r requirements.txt
-        pip install -r requirements-test.txt
-        pip install .
+        pip install ".[test]"
 
     - name: Test with pytest
       run: |
-        pytest --cov --cov-report=xml --cov-config=setup.cfg --verbose
+        pytest --cov --cov-report=xml --verbose
 
     - name: Upload coverage report
       if: ${{ matrix.platform == 'ubuntu-latest' && matrix.python-version == 3.9 }}
-      uses: codecov/codecov-action@v1
+      uses: codecov/codecov-action@v4
       with:
         token: a58a0c62-fb11-4429-977b-65bec01ecb44
         file: ./coverage.xml

.github/workflows/ruff.yml

@@ -0,0 +1,13 @@
+name: Ruff
+on: [push, pull_request]
+jobs:
+  ruff:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: "Linting"
+        uses: astral-sh/ruff-action@v1
+      - name: "Formatting"
+        uses: astral-sh/ruff-action@v1
+        with:
+          args: "format --check"

.gitignore

@@ -142,4 +142,6 @@ notebooks/20_catch_errors.ipynb
 *.pptx
 
 # Custom
+*/.virtual_documents/
 notebooks/debug*
+notebooks/my_hypno.csv

MANIFEST.in

@@ -1,21 +1,6 @@
-# Add README, LICENSE and requirements :
+# Add README and LICENSE :
 include README.rst
 include LICENSE
-include requirements.txt
 
 # Add trained classifiers
-# v0.4.0
-include yasa/classifiers/clf_eeg_lgb_0.4.0.joblib
-include yasa/classifiers/clf_eeg+eog_lgb_0.4.0.joblib
-include yasa/classifiers/clf_eeg+eog+emg_lgb_0.4.0.joblib
-include yasa/classifiers/clf_eeg+eog+emg+demo_lgb_0.4.0.joblib
-
-# v0.5.0
-include yasa/classifiers/clf_eeg_lgb_0.5.0.joblib
-include yasa/classifiers/clf_eeg+demo_lgb_0.5.0.joblib
-include yasa/classifiers/clf_eeg+eog_lgb_0.5.0.joblib
-include yasa/classifiers/clf_eeg+eog+demo_lgb_0.5.0.joblib
-include yasa/classifiers/clf_eeg+emg_lgb_0.5.0.joblib
-include yasa/classifiers/clf_eeg+emg+demo_lgb_0.5.0.joblib
-include yasa/classifiers/clf_eeg+eog+emg_lgb_0.5.0.joblib
-include yasa/classifiers/clf_eeg+eog+emg+demo_lgb_0.5.0.joblib
+recursive-include yasa/classifiers/ *.joblib

README.rst

@@ -14,6 +14,10 @@
 .. image:: https://pepy.tech/badge/yasa
     :target: https://pepy.tech/badge/yasa
 
+.. image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json
+  :target: https://github.com/astral-sh/ruff
+  :alt: Ruff
+
 ----------------
 
 .. figure::  /docs/pictures/yasa_logo.png
@@ -48,6 +52,15 @@ Alternatively, YASA can be installed with conda:
   conda config --set channel_priority strict
   conda install yasa
 
+To build and install from source, clone this repository or download the source archive and decompress the files
+
+.. code-block:: shell
+
+  cd yasa
+  pip install ".[test]"              # install the package
+  pip install --editable ".[test]"   # or editable install
+  pytest                             # test the package
+
 **What are the prerequisites for using YASA?**
 
 To use YASA, all you need is:

docs/changelog.rst

@@ -29,18 +29,23 @@ which comes with several pre-built functions (aka methods) and attributes. See f
     hyp.duration  # Total duration of the hypnogram, in minutes
     hyp.sampling_frequency  # Sampling frequency of the hypnogram
     hyp.mapping  # Mapping from strings to integers
+    hyp.proba  # Probability of each sleep stage, if specified
     # Below are some class methods
     hyp.sleep_statistics()  # Calculate the sleep statistics
     hyp.plot_hypnogram()  # Plot the hypnogram
     hyp.upsample_to_data()  # Upsample to data
 
-Please see the documentation of :py:class:`yasa.Hypnogram` for more details.
+This brings along critical changes to several YASA function, for example:
 
-.. important::
-  The adoption of object-oriented :py:class:`yasa.Hypnogram` usage brings along critical changes to several YASA function, for example:
+* :py:class:`yasa.SleepStaging` now returns a :py:class:`yasa.Hypnogram` instead of a :py:class:`numpy.ndarray`. The probability of each sleep stage for each epoch can now be accessed with :py:attr:`yasa.Hypnogram.proba`.
+* :py:func:`yasa.simulate_hypnogram` now returns a :py:class:`yasa.Hypnogram` instead of a :py:class:`numpy.ndarray`.
+* The suggested approach to plotting hypnograms is through the :py:meth:`yasa.Hypnogram.plot_hypnogram` method. The old function :py:func:`yasa.plot_hypnogram` still exists, but now *requires* a :py:class:`yasa.Hypnogram` instance as input.
+
+**Other improvements**
 
-  * :py:func:`yasa.simulate_hypnogram` now returns a :py:class:`yasa.Hypnogram` instead of a :py:class:`numpy.ndarray`.
-  * The suggested approach to plotting hypnograms is through the :py:meth:`yasa.Hypnogram.plot_hypnogram` method. The old function :py:func:`yasa.plot_hypnogram` still exists, but now *requires* a :py:class:`yasa.Hypnogram` instance as input.
+* Added helpful string representation (__repr__) to :py:class:`yasa.SleepStaging`.
+* :py:func:`yasa.simulate_hypnogram` now returns a :py:class:`yasa.Hypnogram` instead of a :py:class:`numpy.ndarray`.
+* The suggested approach to plotting hypnograms is through the :py:meth:`yasa.Hypnogram.plot_hypnogram` method. The old function :py:func:`yasa.plot_hypnogram` still exists, but now *requires* a :py:class:`yasa.Hypnogram` instance as input.
 
 ----------------------------------------------------------------------------------------
 

docs/conf.py

@@ -3,9 +3,11 @@
 import os
 import sys
 import time
-import yasa
+
 import sphinx_bootstrap_theme
 
+import yasa
+
 # Add any Sphinx extension module names here, as strings. They can be
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.

docs/contributing.rst

@@ -12,11 +12,11 @@ Code guidelines
 
 Before starting new code, we highly recommend opening an issue on `GitHub <https://github.com/raphaelvallat/yasa>`_ to discuss potential changes.
 
-* Please use standard `pep8 <https://pypi.python.org/pypi/pep8>`_ and `flake8 <http://flake8.pycqa.org/>`_ Python style guidelines. To test that your code complies with those, you can run:
+* Please use standard `pep8 <https://pypi.python.org/pypi/pep8>`_ and `flake8 <http://flake8.pycqa.org/>`_ Python style guidelines. YASA uses `ruff <https://github.com/astral-sh/ruff>`_ for code formatting. Before submitting a PR, please make sure to run the following command in the root folder of YASA:
 
   .. code-block:: bash
 
-     $ flake8
+     $ ruff format --line-length=100
 
 * Use `NumPy style <https://numpydoc.readthedocs.io/en/latest/format.html>`_ for docstrings. Follow existing examples for simplest guidance.
 

docs/index.rst

@@ -14,6 +14,10 @@
 .. image:: https://pepy.tech/badge/yasa
     :target: https://pepy.tech/badge/yasa
 
+.. image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json
+  :target: https://github.com/astral-sh/ruff
+  :alt: Ruff
+
 ----------------
 
 .. figure::  /pictures/yasa_logo.png
@@ -48,6 +52,15 @@ Alternatively, YASA can be installed with conda:
   conda config --set channel_priority strict
   conda install yasa
 
+To build and install from source, clone this repository or download the source archive and decompress the files
+
+.. code-block:: shell
+
+  cd yasa
+  pip install ".[test]"      # install the package
+  pip install -e ".[test]"   # or editable install
+  pytest                     # test the package
+
 **What are the prerequisites for using YASA?**
 
 To use YASA, all you need is: