Release aftermath

.gitignore

@@ -6,6 +6,7 @@ __pycache__
 *.swp
 *.swo
 *DS_Store
+_version.py
 
 .tox/
 build/

CHANGELOG

@@ -11,6 +11,9 @@ The rules for CHANGELOG file:
 
 .. inclusion-marker-changelog-start
 
+Unreleased
+----------
+
 0.3.0 (2025/06/12)
 ------------------
 - Add ``_BasePCov`` class (#248)

CITATION.cff

@@ -0,0 +1,28 @@
+cff-version: 1.2.0
+message: "If you use scikit-matter for your work, please read and cite it as below."
+title: >-
+  scikit-matter : a suite of generalisable machine learning methods born out of chemistry and materials science [version 2; peer review: 3 approved, 1 approved with reservations]
+type: journalArticle
+issue: 81
+volume: 3
+authors: 
+  - family-names: Goscinski
+    given-names: Alexander
+  - family-names: Principe
+    given-names: Victor P.
+  - family-names: Fraux
+    given-names: Guillaume
+  - family-names: Kliavinek
+    given-names: Sergei
+  - family-names: Helfrecht
+    given-names: Benjamin A.
+  - family-names: Loche
+    given-names: Philip
+  - family-names: Ceriotti
+    given-names: Michele
+  - family-names: Cersonsky
+    given-names: Rose K.
+date-published: 2023
+identifiers: 
+  - type: doi
+    value: 10.12688/openreseurope.15789.2

MANIFEST.in

@@ -1,6 +1,7 @@
 graft src
 
 include LICENSE
+include CITATION.cff
 include README.rst
 
 prune docs

README.rst

@@ -1,13 +1,16 @@
 scikit-matter
 =============
-|tests| |codecov| |pypi| |conda| |docs| |doi|
+
+|tests| |codecov| |pypi| |conda| |docs-stable| |docs-latest| |doi|
 
 A collection of ``scikit-learn`` compatible utilities that implement methods born out of
 the materials science and chemistry communities.
 
-For details, tutorials, and examples, please have a look at our `documentation`_.
+For details, tutorials, and examples, please have a look at our documentation_. We also
+provide a `latest documentation`_ from the current unreleased development version.
 
-.. _`documentation`: https://scikit-matter.readthedocs.io
+.. _`documentation`: https://scikit-matter.readthedocs.io/en/v0.3/
+.. _`latest documentation`: https://scikit-matter.readthedocs.io/en/latest
 
 .. marker-installation
 
@@ -98,9 +101,13 @@ Thanks goes to all people that make scikit-matter possible:
    :alt: Code coverage
    :target: https://codecov.io/gh/scikit-learn-contrib/scikit-matter/
 
-.. |docs| image:: https://img.shields.io/badge/📚_documentation-latest-sucess
-   :alt: Python
-   :target: documentation_
+.. |docs-stable| image:: https://img.shields.io/badge/📚_Documentation-stable-sucess
+   :alt: Documentation of stable released version
+   :target: `documentation`_
+
+.. |docs-latest| image:: https://img.shields.io/badge/📒_Documentation-latest-yellow.svg
+   :alt: Documentation of latest unreleased version
+   :target: `latest documentation`_
 
 .. |pypi| image:: https://img.shields.io/pypi/v/skmatter.svg
    :alt: Latest PYPI version

docs/src/contributing.rst

@@ -212,3 +212,49 @@ properly. It should look something like this:
 
 You're good to go! Time to submit a `pull request.
 <https://github.com/lab-cosmo/scikit-matter/pulls>`_
+
+How to Perform a Release
+-------------------------
+
+1. **Prepare a Release Pull Request**
+
+   - Based on the main branch create branch ``release-x.y.z`` and a PR.
+   - Ensure that all `CI tests
+     <https://github.com/scikit-learn-contrib/scikit-matter/actions>`_ pass.
+   - Optionally, run the tests locally to double-check.
+
+2. **Update the Changelog**
+
+   - Edit the changelog located in ``CHANGELOG``:
+      - Add a new section for the new version, summarizing the changes based on the
+        PRs merged since the last release.
+      - Leave a placeholder section titled *Unreleased* for future updates.
+
+3. **Merge the PR and Create a Tag**
+
+   - Merge the release PR.
+   - Update the ``main`` branch and check that the latest commit is the release PR with
+     ``git log``
+   - Create a tag on directly the ``main`` branch.
+   - Push the tag to GitHub. For example for a release of version ``x.y.z``:
+
+     .. code-block:: bash
+
+        git checkout main
+        git pull
+        git tag -a vx.y.z -m "Release vx.y.z"
+        git push --tags
+
+4. **Finalize the GitHub Release**
+
+   - Once the PR is merged, the CI will automatically:
+      - Publish the package to PyPI.
+      - Create a draft release on GitHub.
+   - Update the GitHub release notes by pasting the changelog for the version.
+
+5. **Merge Conda Recipe Changes**
+
+   - May resolve and then merge an automatically created PR on the `conda recipe
+     <https://github.com/conda-forge/skmatter-feedstock>`_.
+   - Once thus PR is merged and the new version will be published automatically on the
+     `conda-forge <https://anaconda.org/conda-forge/skmatter>`_ channel.

examples/neighbors/pamm.py

@@ -17,7 +17,6 @@
 of the H-bond motif.
 """
 
-
 # %%
 from typing import Callable, Union
 
@@ -150,7 +149,6 @@ def _update_cluster_cov(
         idxroot: np.ndarray,
         center_idx: np.ndarray,
     ):
-
         if cell is not None:
             cov = _get_lcov_clusterp(
                 len(X), nsamples, X, idxroot, center_idx[k], probs, cell
@@ -194,7 +192,6 @@ def _get_lcov_cluster(
         probs: np.ndarray,
         cell: np.ndarray,
     ):
-
         ww = np.zeros(N)
         normww = logsumexp(probs[clroots == idcl])
         ww[clroots == idcl] = np.exp(probs[clroots == idcl] - normww)
@@ -211,7 +208,6 @@ def _get_lcov_clusterp(
         probs: np.ndarray,
         cell: np.ndarray,
     ):
-
         ww = np.zeros(N)
         totnormp = logsumexp(probs)
         cov = np.zeros((x.shape[1], x.shape[1]), dtype=float)

examples/neighbors/sparse-kde.py

@@ -20,7 +20,6 @@
 Here we first sample from these three Gaussians.
 """
 
-
 # %%
 import time
 
@@ -98,7 +97,6 @@
 
 # %%
 class GaussianMixtureModel:
-
     def __init__(
         self,
         weights: np.ndarray,
@@ -116,7 +114,6 @@ def __init__(
         self.norm = 1 / np.sqrt((2 * np.pi) ** self.dimension * self.cov_det)
 
     def __call__(self, x: np.ndarray, i: int = None):
-
         if len(x.shape) == 1:
             x = x[np.newaxis, :]
         if self.period is not None:

examples/pcovr/PCovR.py

@@ -8,7 +8,6 @@
 # %%
 #
 
-
 import numpy as np
 from matplotlib import cm
 from matplotlib import pyplot as plt

examples/pcovr/PCovR_Regressors.py

@@ -4,6 +4,7 @@
 Choosing Different Regressors for PCovR
 =======================================
 """
+
 # %%
 #
 import time

examples/reconstruction/PlotLFRE.py

@@ -20,7 +20,6 @@
 # %%
 #
 
-
 import matplotlib as mpl
 import matplotlib.pyplot as plt
 import numpy as np

examples/reconstruction/PlotPointwiseGFRE.py

@@ -16,7 +16,6 @@
 # %%
 #
 
-
 import matplotlib as mpl
 import matplotlib.pyplot as plt
 import numpy as np

examples/regression/Ridge2FoldCVRegularization.py

@@ -114,11 +114,15 @@
 )
 
 sklearn_ridge_2foldcv_tikhonov = RidgeCV(
-    alphas=alphas, cv=cv, fit_intercept=False  # remove the incluence of learning bias
+    alphas=alphas,
+    cv=cv,
+    fit_intercept=False,  # remove the incluence of learning bias
 )
 
 sklearn_ridge_loocv_tikhonov = RidgeCV(
-    alphas=alphas, cv=None, fit_intercept=False  # remove the incluence of learning bias
+    alphas=alphas,
+    cv=None,
+    fit_intercept=False,  # remove the incluence of learning bias
 )
 
 # %%
@@ -331,7 +335,9 @@ def get_train_test_error(estimator):
 )
 
 sklearn_ridge_loocv_tikhonov = RidgeCV(
-    alphas=alphas, cv=None, fit_intercept=False  # remove the incluence of learning bias
+    alphas=alphas,
+    cv=None,
+    fit_intercept=False,  # remove the incluence of learning bias
 )
 
 print("skmatter 2-fold CV cutoff")

examples/selection/FeatureSelection.py

@@ -4,6 +4,7 @@
 PCovR-Inspired Feature Selection
 ================================
 """
+
 # %%
 #
 import numpy as np

examples/selection/Selectors-Pipelines.py

@@ -9,7 +9,6 @@
 # %%
 #
 
-
 import numpy as np
 from matplotlib import pyplot as plt
 from sklearn.datasets import load_diabetes

pyproject.toml

@@ -1,6 +1,7 @@
 [build-system]
 requires = [
-    "setuptools",
+    "setuptools >= 77",
+    "setuptools_scm >= 8",
     "wheel",
 ]
 build-backend = "setuptools.build_meta"
@@ -57,12 +58,18 @@ repository = "https://github.com/scikit-learn-contrib/scikit-matter"
 issues = "https://github.com/scikit-learn-contrib/scikit-matterissues"
 changelog = "http://scikit-matter.readthedocs.io/en/latest/changelog.html"
 
+[tool.check-manifest]
+ignore = ["src/skmatter/_version.py"]
+
 [tool.setuptools.packages.find]
 where = ["src"]
 
 [tool.setuptools.dynamic]
 version = {attr = "skmatter.__version__"}
 
+[tool.setuptools_scm]
+version_file = "src/skmatter/_version.py"
+
 [tool.coverage.run]
 branch = true
 data_file = 'tests/.coverage'
@@ -75,15 +82,6 @@ include = [
 [tool.coverage.xml]
 output = 'tests/coverage.xml'
 
-[tool.isort]
-skip = "__init__.py"
-profile = "black"
-line_length = 88
-indent = 4
-include_trailing_comma = true
-lines_after_imports = 2
-known_first_party = "skmatter"
-
 [tool.pytest.ini_options]
 testpaths = ["tests"]
 addopts = [
@@ -94,7 +92,7 @@ addopts = [
 ]
 
 [tool.ruff]
-exclude = ["docs/src/examples/"]
+exclude = ["docs/src/examples/", "src/torchpme/_version.py"]
 lint.ignore = [
     "F401",
     "E203",

src/skmatter/__init__.py

@@ -7,4 +7,4 @@
 coding guidelines to promote usability and interoperability with existing workflows.
 """
 
-__version__ = "0.3.0"
+from ._version import __version__  # noqa: F401

src/skmatter/_selection.py

@@ -1059,7 +1059,6 @@ def __init__(
         full=False,
         random_state=0,
     ):
-
         self.mixing = mixing
         self.initialize = initialize
 

src/skmatter/decomposition/_kernel_pcovr.py

@@ -238,7 +238,7 @@ def _fit(self, K, Yhat, W):
             _, S, Vt = self._decompose_truncated(K_tilde)
         else:
             raise ValueError(
-                "Unrecognized svd_solver='{0}'" "".format(self._fit_svd_solver)
+                "Unrecognized svd_solver='{0}'".format(self._fit_svd_solver)
             )
 
         U = Vt.T

src/skmatter/decomposition/_pcov.py

@@ -136,7 +136,7 @@ def _fit_sample_space(self, X, Y, Yhat, W, compute_pty_=True):
             U, S, Vt = self._decompose_truncated(Kt)
         else:
             raise ValueError(
-                "Unrecognized svd_solver='{0}'" "".format(self.fit_svd_solver_)
+                "Unrecognized svd_solver='{0}'".format(self.fit_svd_solver_)
             )
 
         self.singular_values_ = np.sqrt(S.copy())
@@ -231,7 +231,7 @@ def _decompose_full(self, mat):
         if self.n_components_ == "mle":
             if self.n_samples_in_ < self.n_features_in_:
                 raise ValueError(
-                    "n_components='mle' is only supported " "if n_samples >= n_features"
+                    "n_components='mle' is only supported if n_samples >= n_features"
                 )
         elif (
             not 0 <= self.n_components_ <= min(self.n_samples_in_, self.n_features_in_)

src/skmatter/metrics/_pairwise.py

@@ -150,7 +150,6 @@ def pairwise_mahalanobis_distances(
     def _mahalanobis(
         cell: np.ndarray, X: np.ndarray, Y: np.ndarray, cov_inv: np.ndarray
     ):
-
         XY = np.concatenate([x - Y for x in X])
         if cell is not None:
             XY -= np.round(XY / cell) * cell