CI update

.github/workflows/main.yml

@@ -0,0 +1,85 @@
+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
+
+name: CI
+
+on:
+  push:
+    branches: [ "master" ]
+  pull_request:
+    branches: [ "master" ]
+
+
+jobs:
+  linting:
+    name: Linting
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+      - name: Linting
+        run: |
+          pip install pre-commit
+          pre-commit run --all-files
+
+  build:
+    name: build
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v5
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: build
+      run: pip install .
+    - name: test import
+      run: python -c "import pysensors"
+
+  tests:
+      name: Tests
+      needs: linting
+      runs-on: ubuntu-latest
+      strategy:
+          fail-fast: false
+          max-parallel: 4
+          matrix:
+            python-version: ["3.9", "3.10", "3.11", "3.12"]
+
+      steps:
+        - uses: actions/checkout@v4
+        - name: Set up Python ${{ matrix.python-version }}
+          uses: actions/setup-python@v5
+          with:
+            python-version: ${{ matrix.python-version }}
+        - name: Install dependencies
+          run: |
+            pip install -r requirements-dev.txt
+        - name: Test with pytest
+          run: |
+            pytest tests --cov=pysensors --cov-report=xml
+        - name: Upload coverage to Codecov
+          uses: codecov/codecov-action@v5
+          with:
+            token: ${{ secrets.CODECOV_TOKEN }}
+            file: ./coverage.xml
+        - name: Execute feature notebook with papermill
+          run: |
+            pip install papermill
+            cd examples
+            papermill --report-mode pysensors_overview.ipynb out.json
+        - uses: actions/cache@v4
+          with:
+            path: ~/.cache/pip
+            key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements-dev.txt') }}
+            restore-keys: |
+              ${{ runner.os }}-pip-

.gitignore

@@ -17,6 +17,7 @@ coverage.xml
 
 # virtual environment
 venv/*
+env/*
 
 .ipynb_checkpoints
 */.ipynb_checkpoints/*

.pre-commit-config.yaml

@@ -1,22 +1,26 @@
-
 fail_fast: false
 repos:
 - repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v4.3.0
+  rev: v5.0.0
   hooks:
     - id: check-added-large-files
       args: ["--maxkb=775"]
     - id: check-merge-conflict
-- repo: https://github.com/asottile/reorder_python_imports
-  rev: v3.8.2
+- repo: https://github.com/PyCQA/isort
+  rev: 5.12.0
   hooks:
-    - id: reorder-python-imports
-- repo: https://github.com/ambv/black
-  rev: 22.8.0
+    - id: isort
+      args: [--profile=black]
+- repo: https://github.com/psf/black
+  rev: 25.1.0
   hooks:
     - id: black
-- repo: https://gitlab.com/pycqa/flake8
-  rev: 3.9.2
+-   repo: https://github.com/sphinx-contrib/sphinx-lint
+    rev: v0.6.1
+    hooks:
+    - id: sphinx-lint
+- repo: https://github.com/pycqa/flake8
+  rev: 7.1.1
   hooks:
     - id: flake8
       args: ["--config=setup.cfg"]

README.rst

@@ -32,11 +32,11 @@ PySensors provides the ``SSPOR`` (Sparse Sensor Placement Optimization for Recon
 Take representative examples of the types of data to be reconstructed (in this case polynomials)
 
 .. code-block:: python
-  
+
   x = numpy.linspace(0, 1, 1001)
   data = numpy.vander(x, 11).T  # Create an array whose rows are powers of x
 
-feed them to a ``SSPOR`` instance with 10 sensors, and 
+feed them to a ``SSPOR`` instance with 10 sensors, and
 
 .. code-block:: python
 
@@ -265,7 +265,7 @@ References
 
 .. |JOSS| image:: https://joss.theoj.org/papers/10.21105/joss.02828/status.svg
     :target: https://doi.org/10.21105/joss.02828
-    
+
 .. |Zenodo| image:: https://zenodo.org/badge/260577702.svg
     :target: https://zenodo.org/badge/latestdoi/260577702
 

docs/index.rst

@@ -14,4 +14,4 @@
 
 
    PySensors @ PyPI <https://pypi.org/project/python-sensors/>
-   Issue Tracker <https://github.com/dynamicslab/pysensors/issues>
+   Issue Tracker <https://github.com/dynamicslab/pysensors/issues>

pysensors/__init__.py

@@ -1,15 +1,13 @@
-from pkg_resources import DistributionNotFound
-from pkg_resources import get_distribution
+from importlib.metadata import PackageNotFoundError, distribution
 
 try:
-    __version__ = get_distribution("python-sensors").version
-except DistributionNotFound:
+    __version__ = distribution("python-sensors").version
+except PackageNotFoundError:
     pass
 
 from .classification import SSPOC
 from .reconstruction import SSPOR
 
-
 __all__ = [
     # Modules:
     "basis",

pysensors/basis/__init__.py

@@ -1,6 +1,6 @@
+from ._custom import Custom
 from ._identity import Identity
 from ._random_projection import RandomProjection
 from ._svd import SVD
-from ._custom import Custom
 
-__all__ = ["Identity", "SVD", "RandomProjection","Custom"]
+__all__ = ["Identity", "SVD", "RandomProjection", "Custom"]

pysensors/basis/_base.py

@@ -1,5 +1,4 @@
-from abc import ABC
-from abc import abstractmethod
+from abc import ABC, abstractmethod
 
 from sklearn.utils.validation import check_is_fitted
 

pysensors/basis/_custom.py

@@ -1,8 +1,9 @@
 """
 custom mode basis class.
 """
-from ._base import InvertibleBasis
-from ._base import MatrixMixin
+
+from ._base import InvertibleBasis, MatrixMixin
+
 
 class Custom(InvertibleBasis, MatrixMixin):
     """
@@ -27,11 +28,11 @@ class Custom(InvertibleBasis, MatrixMixin):
     """
 
     def __init__(self, U, n_basis_modes=10, **kwargs):
-        '''
-            kwargs : Not defined but added to remain consistent with prior basis functions. 
-        '''
+        """
+        kwargs : Not defined but added to remain consistent with prior basis functions.
+        """
         if isinstance(n_basis_modes, int) and n_basis_modes > 0:
-            super(Custom, self).__init__()#n_components=n_basis_modes, **kwargs
+            super(Custom, self).__init__()  # n_components=n_basis_modes, **kwargs
             self._n_basis_modes = n_basis_modes
             self.custom_basis_ = U
         else:
@@ -48,13 +49,22 @@ def fit(self, X):
         -------
         self : instance
         """
-        # self.basis_matrix_ = self.custom_basis_[:,: self.n_basis_modes] @ self.custom_basis_[:,: self.n_basis_modes].T @ X[: self.n_basis_modes, :].T.copy()
-        # self.basis_matrix_ = self.custom_basis_ @ self.custom_basis_.T @ X[: self.n_basis_modes, :].T.copy()
-        self.basis_matrix_ = self.custom_basis_[:,:self.n_basis_modes]
-        # self.basis_matrix_ = (X @ self.custom_basis_[:,:self.n_basis_modes] @ self.custom_basis_[:,:self.n_basis_modes].T)[:self.n_basis_modes,:].T
-
-        # self.basis_matrix_ = ((X @ self.custom_basis_).T)[:,:self.n_basis_modes]
-        # self.basis_matrix_ = ((X @ self.custom_basis_ @ self.custom_basis_.T).T)[:,:self.n_basis_modes]
+        # self.basis_matrix_ = self.custom_basis_[
+        #     :,: self.n_basis_modes] @ self.custom_basis_[
+        #         :,: self.n_basis_modes].T @ X[
+        #             : self.n_basis_modes, :].T.copy()
+        # self.basis_matrix_ = self.custom_basis_ @ self.custom_basis_.T @ X[
+        #     : self.n_basis_modes, :].T.copy()
+        self.basis_matrix_ = self.custom_basis_[:, : self.n_basis_modes]
+        # self.basis_matrix_ = (X @ self.custom_basis_[
+        #     :,:self.n_basis_modes] @ self.custom_basis_[
+        #         :,:self.n_basis_modes].T)[:self.n_basis_modes,:].T
+
+        # self.basis_matrix_ = ((X @ self.custom_basis_).T)[
+        #     :,:self.n_basis_modes]
+        # self.basis_matrix_ = ((
+        #     X @ self.custom_basis_ @ self.custom_basis_.T).T)[
+        #         :,:self.n_basis_modes]
         return self
 
     def matrix_inverse(self, n_basis_modes=None):

pysensors/basis/_identity.py

@@ -3,14 +3,14 @@
 
 This is essentially a dummy basis which just uses raw, unaltered features.
 """
+
 from warnings import warn
 
 from numpy import identity
 from sklearn.base import BaseEstimator
 from sklearn.utils import check_array
 
-from ._base import InvertibleBasis
-from ._base import MatrixMixin
+from ._base import InvertibleBasis, MatrixMixin
 
 
 class Identity(BaseEstimator, InvertibleBasis, MatrixMixin):

pysensors/basis/_random_projection.py

@@ -3,11 +3,11 @@
 
 Project data onto random features.
 """
+
 from numpy.linalg import pinv
 from sklearn.random_projection import GaussianRandomProjection
 
-from ._base import InvertibleBasis
-from ._base import MatrixMixin
+from ._base import InvertibleBasis, MatrixMixin
 
 
 class RandomProjection(GaussianRandomProjection, InvertibleBasis, MatrixMixin):

pysensors/basis/_svd.py

@@ -1,10 +1,10 @@
 """
 SVD mode basis class.
 """
+
 from sklearn.decomposition import TruncatedSVD
 
-from ._base import InvertibleBasis
-from ._base import MatrixMixin
+from ._base import InvertibleBasis, MatrixMixin
 
 
 class SVD(TruncatedSVD, InvertibleBasis, MatrixMixin):

pysensors/classification/_sspoc.py

@@ -14,6 +14,7 @@
     Proceedings of the National Academy of Sciences
     115.42 (2018): 10564-10569.
 """
+
 import warnings
 
 import numpy as np
@@ -24,10 +25,11 @@
 from sklearn.utils.validation import check_is_fitted
 
 from ..basis import Identity
-from ..utils import constrained_binary_solve
-from ..utils import constrained_multiclass_solve
-from ..utils import validate_input
-
+from ..utils import (
+    constrained_binary_solve,
+    constrained_multiclass_solve,
+    validate_input,
+)
 
 INT_DTYPES = (int, np.int64, np.int32, np.int16, np.int8)
 
@@ -259,7 +261,7 @@ def fit(
 
         if self.threshold is None:
             # Chosen as in Brunton et al. (2016)
-            threshold = np.sqrt(np.sum(s ** 2)) / (
+            threshold = np.sqrt(np.sum(s**2)) / (
                 2 * self.basis_matrix_inverse_.shape[0] * n_classes
             )
         else:

pysensors/optimizers/__init__.py

@@ -1,9 +1,5 @@
 from ._ccqr import CCQR
-from ._qr import QR
 from ._gqr import GQR
+from ._qr import QR
 
-__all__ = [
-    "CCQR",
-    "QR",
-    "GQR"
-]
+__all__ = ["CCQR", "QR", "GQR"]