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CHANGELOG.md

@@ -12,4 +12,4 @@ and this project adheres to [Semantic Versioning][].
 
 ### Added
 
--   Basic tool, preprocessing and plotting functions
+- Basic tool, preprocessing and plotting functions

README.md

@@ -22,15 +22,15 @@ TorchGMM models against scikit-learn models is available on the
 
 ## Features
 
--   Support for GPU and multi-node training by implementing models in PyTorch and relying on
-    [PyTorch Lightning](https://lightning.ai/docs/pytorch/stable/)
--   Mini-batch training for all models such that they can be used on huge datasets
--   Well-structured implementation of models
+- Support for GPU and multi-node training by implementing models in PyTorch and relying on
+  [PyTorch Lightning](https://lightning.ai/docs/pytorch/stable/)
+- Mini-batch training for all models such that they can be used on huge datasets
+- Well-structured implementation of models
 
-    -   High-level `Estimator` API allows for easy usage such that models feel and behave like in
-        scikit-learn
-    -   Medium-level `LightingModule` implements the training algorithm
-    -   Low-level PyTorch `Module` manages the model parameters
+    - High-level `Estimator` API allows for easy usage such that models feel and behave like in
+      scikit-learn
+    - Medium-level `LightingModule` implements the training algorithm
+    - Low-level PyTorch `Module` manages the model parameters
 
 ## Getting started
 
@@ -110,8 +110,8 @@ In fact, **you do not need to change anything else in your code**.
 
 Currently, TorchGMM implements two different models:
 
--   [GaussianMixture](https://pycave.borchero.com/sites/generated/bayes/gmm/pycave.bayes.GaussianMixture.html)
--   [K-Means](https://pycave.borchero.com/sites/generated/clustering/kmeans/pycave.clustering.KMeans.html)
+- [GaussianMixture](https://pycave.borchero.com/sites/generated/bayes/gmm/pycave.bayes.GaussianMixture.html)
+- [K-Means](https://pycave.borchero.com/sites/generated/clustering/kmeans/pycave.clustering.KMeans.html)
 
 ## Contribution
 

docs/contributing.md

@@ -99,10 +99,10 @@ Specify `vX.X.X` as a tag name and create a release. For more information, see [
 
 Please write documentation for new or changed features and use-cases. This project uses [sphinx][] with the following features:
 
--   the [myst][] extension allows to write documentation in markdown/Markedly Structured Text
--   [Numpy-style docstrings][numpydoc] (through the [napoloen][numpydoc-napoleon] extension).
--   Jupyter notebooks as tutorials through [myst-nb][] (See [Tutorials with myst-nb](#tutorials-with-myst-nb-and-jupyter-notebooks))
--   [Sphinx autodoc typehints][], to automatically reference annotated input and output types
+- the [myst][] extension allows to write documentation in markdown/Markedly Structured Text
+- [Numpy-style docstrings][numpydoc] (through the [napoloen][numpydoc-napoleon] extension).
+- Jupyter notebooks as tutorials through [myst-nb][] (See [Tutorials with myst-nb](#tutorials-with-myst-nb-and-jupyter-notebooks))
+- [Sphinx autodoc typehints][], to automatically reference annotated input and output types
 
 See the [scanpy developer docs](https://scanpy.readthedocs.io/en/latest/dev/documentation.html) for more information
 on how to write documentation.
@@ -119,10 +119,10 @@ repository.
 
 #### Hints
 
--   If you refer to objects from other packages, please add an entry to `intersphinx_mapping` in `docs/conf.py`. Only
-    if you do so can sphinx automatically create a link to the external documentation.
--   If building the documentation fails because of a missing link that is outside your control, you can add an entry to
-    the `nitpick_ignore` list in `docs/conf.py`
+- If you refer to objects from other packages, please add an entry to `intersphinx_mapping` in `docs/conf.py`. Only
+  if you do so can sphinx automatically create a link to the external documentation.
+- If building the documentation fails because of a missing link that is outside your control, you can add an entry to
+  the `nitpick_ignore` list in `docs/conf.py`
 
 #### Building the docs locally
 

docs/extensions/typed_returns.py

@@ -12,7 +12,7 @@
 def _process_return(lines: Iterable[str]) -> Generator[str, None, None]:
     for line in lines:
         if m := re.fullmatch(r"(?P<param>\w+)\s+:\s+(?P<type>[\w.]+)", line):
-            yield f'-{m["param"]} (:class:`~{m["type"]}`)'
+            yield f"-{m['param']} (:class:`~{m['type']}`)"
         else:
             yield line
 

src/torchgmm/base/estimator/base.py

@@ -173,7 +173,7 @@ def save_parameters(self, path: Path) -> None:
                 f.write(data)
         except TypeError:
             warnings.warn(
-                f"Failed to serialize parameters of `{self.__class__.__name__}` to JSON. " "Falling back to `pickle`.",
+                f"Failed to serialize parameters of `{self.__class__.__name__}` to JSON. Falling back to `pickle`.",
                 stacklevel=2,
             )
             with (path / "params.pickle").open("wb+") as f:

src/torchgmm/base/utils/generics.py

@@ -18,6 +18,6 @@ def get_generic_type(cls: Type[Any], origin: Type[Any], index: int = 0) -> Type[
         if get_origin(base) == origin:
             args = get_args(base)
             if not args:
-                raise ValueError(f"`{cls.__name__}` does not provide a generic parameter " f"for `{origin.__name__}`")
+                raise ValueError(f"`{cls.__name__}` does not provide a generic parameter for `{origin.__name__}`")
             return get_args(base)[index]
     raise ValueError(f"`{cls.__name__}` does not inherit from `{origin.__name__}`")