Switch linting to ruff

.pre-commit-config.yaml

@@ -5,7 +5,7 @@ repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v4.0.1
     hooks:
-      - id: check-byte-order-marker
+      - id: fix-byte-order-marker
       - id: check-case-conflict
       - id: check-merge-conflict
       - id: check-yaml
@@ -18,24 +18,16 @@ repos:
       - id: trailing-whitespace
       - id: requirements-txt-fixer
 
-  - repo: https://github.com/psf/black
-    rev: 23.10.1
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.1.14
     hooks:
-      - id: black
+      - id: ruff
+        types_or: [ python, pyi, jupyter ]
+        args: [ --fix ]
+      - id: ruff-format
+        types_or: [ python, pyi, jupyter ]
 
-  - repo: https://github.com/pycqa/isort
-    rev: 5.12.0
+  - repo: https://github.com/numpy/numpydoc
+    rev: v1.6.0
     hooks:
-      - id : isort
-        args : ["--profile=black", "--filter-files"]
-
-  - repo: https://github.com/asottile/blacken-docs
-    rev: v1.12.0
-    hooks:
-    -   id: blacken-docs
-        additional_dependencies: [black==20.8b0]
-  - repo: https://github.com/pycqa/flake8
-    rev: 6.0.0
-    hooks:
-    -   id: flake8
-        additional_dependencies: [flake8-docstrings, Flake8-pyproject]
+      - id: numpydoc-validation

pyproject.toml

@@ -44,14 +44,8 @@ doc = [
     "pydata-sphinx-theme"
 ]
 lint = [
-    "black < 24.0",
-    "black[jupyter]",
-    "flake8",
-    "flake8-docstrings",
-    "Flake8-pyproject",
-    "isort",
     "pre-commit",
-    "tomli"
+    "ruff"
 ]
 test = [
     "pytest",
@@ -68,6 +62,30 @@ all = ["TopoEmbedX[dev, doc]"]
 homepage="https://github.com/pyt-team/TopoEmbedX"
 repository="https://github.com/pyt-team/TopoEmbedX"
 
+[tool.ruff]
+target-version = "py310"
+extend-include = ["*.ipynb"]
+
+[tool.ruff.format]
+docstring-code-format = true
+
+[tool.ruff.lint]
+select = [
+    "F", # pyflakes errors
+    "E", # code style
+    "W", # warnings
+    "I", # import order
+    "UP", # pyupgrade rules
+    "SIM", # code simplifications
+]
+ignore = ["E501"] # line too long
+
+[tool.ruff.lint.pydocstyle]
+convention = "numpy"
+
+[tool.ruff.per-file-ignores]
+"__init__.py" = ["F403"]
+
 [tool.setuptools.dynamic]
 version = {attr = "topoembedx.__version__"}
 
@@ -96,19 +114,3 @@ ignore_missing_imports = true
 
 [tool.pytest.ini_options]
 addopts = "--capture=no"
-
-[tool.isort]
-multi_line_output = 3
-include_trailing_comma = true
-skip = [".gitignore", "__init__.py"]
-
-[tool.flake8]
-application_import_names = "topoembedx"
-docstring-convention = "numpy"
-exclude = ["examples/*.ipynb"]
-import_order_style = "smarkets"
-max-line-length = 88
-extend-ignore = ["E501", "F401", "E203"]
-per-file-ignores = [
-    "*/__init__.py: D104,F401,F403"
-]

test/classes/test_cell2vec.py

@@ -1,7 +1,6 @@
 """Test Cell2Vec class."""
 
 import numpy as np
-import pytest
 import toponetx as tnx
 
 from topoembedx.classes.cell2vec import Cell2Vec

test/classes/test_cell_diff2vec.py

@@ -1,6 +1,5 @@
 """Test CellDiff2Vec class."""
 
-import pytest
 import toponetx as tnx
 
 from topoembedx.classes.cell_diff2vec import CellDiff2Vec

test/classes/test_deepcell.py

@@ -1,7 +1,6 @@
 """Test the DeepCell class."""
 
 import numpy as np
-import pytest
 import toponetx as tnx
 
 from topoembedx.classes.deepcell import DeepCell

test/classes/test_higher_order_laplacian_eigenmaps.py

@@ -1,7 +1,6 @@
 """Test HigherOrderLaplacianEigenmaps class."""
 
 import numpy as np
-import pytest
 import toponetx as tnx
 
 from topoembedx.classes.higher_order_laplacian_eigenmaps import (

test/classes/test_hoglee.py

@@ -1,7 +1,6 @@
 """Test the HOGLEE class."""
 
 import numpy as np
-import pytest
 import toponetx as tnx
 
 from topoembedx.classes.hoglee import HOGLEE

test/classes/test_hope.py

@@ -1,7 +1,5 @@
 """Test HOPE class."""
 
-import numpy as np
-import pytest
 import toponetx as tnx
 
 from topoembedx.classes.hope import HOPE

topoembedx/__init__.py

@@ -1,3 +1,4 @@
+"""Initialize the library with modules and other content."""
 __version__ = "0.0.1"
 
 from .classes.cell2vec import Cell2Vec

topoembedx/classes/__init__.py

@@ -0,0 +1 @@
+"""Initialize the classes module of TopoEmbedX."""

topoembedx/classes/cell2vec.py

@@ -1,5 +1,5 @@
 """Cell2Vec: a class that extends the Node2Vec class."""
-from typing import Literal, Union
+from typing import Literal
 
 import networkx as nx
 import numpy as np
@@ -94,9 +94,9 @@ def fit(
 
         g = nx.from_numpy_matrix(self.A)
 
-        super(Cell2Vec, self).fit(g)
+        super().fit(g)
 
-    def get_embedding(self, get_dict: bool = False) -> Union[dict, np.ndarray]:
+    def get_embedding(self, get_dict: bool = False) -> dict | np.ndarray:
         """Get embedding.
 
         Parameters
@@ -109,7 +109,7 @@ def get_embedding(self, get_dict: bool = False) -> Union[dict, np.ndarray]:
         dict or numpy.ndarray
             Embedding.
         """
-        emb = super(Cell2Vec, self).get_embedding()
+        emb = super().get_embedding()
         if get_dict:
             return dict(zip(self.ind, emb))
         return emb

topoembedx/classes/cell_diff2vec.py

@@ -1,5 +1,5 @@
 """Class CellDiff2Vec."""
-from typing import Literal, Union
+from typing import Literal
 
 import networkx as nx
 import numpy as np
@@ -82,9 +82,9 @@ def fit(
             raise ValueError(
                 "The diffusion_cover is too large for the size of the graph."
             )
-        super(CellDiff2Vec, self).fit(g)
+        super().fit(g)
 
-    def get_embedding(self, get_dict: bool = False) -> Union[dict, np.ndarray]:
+    def get_embedding(self, get_dict: bool = False) -> dict | np.ndarray:
         """Get embedding.
 
         Parameters
@@ -97,7 +97,7 @@ def get_embedding(self, get_dict: bool = False) -> Union[dict, np.ndarray]:
         dict or numpy.ndarray
             Embedding.
         """
-        emb = super(CellDiff2Vec, self).get_embedding()
+        emb = super().get_embedding()
         if get_dict:
             return dict(zip(self.ind, emb))
         return emb

topoembedx/classes/deepcell.py

@@ -1,5 +1,5 @@
 """DeepCell class for embedding complex networks using DeepWalk."""
-from typing import Literal, Union
+from typing import Literal
 
 import networkx as nx
 import numpy as np
@@ -79,9 +79,9 @@ def fit(
 
         g = nx.from_numpy_matrix(self.A)
 
-        super(DeepCell, self).fit(g)
+        super().fit(g)
 
-    def get_embedding(self, get_dict: bool = False) -> Union[dict, np.ndarray]:
+    def get_embedding(self, get_dict: bool = False) -> dict | np.ndarray:
         """Get embeddings.
 
         Parameters
@@ -94,7 +94,7 @@ def get_embedding(self, get_dict: bool = False) -> Union[dict, np.ndarray]:
         dict or np.ndarray
             The embedding of the complex.
         """
-        emb = super(DeepCell, self).get_embedding()
+        emb = super().get_embedding()
         if get_dict:
             return dict(zip(self.ind, emb))
         return emb

topoembedx/classes/higher_order_laplacian_eigenmaps.py

@@ -1,5 +1,5 @@
 """Higher Order Laplacian Eigenmaps."""
-from typing import Literal, Union
+from typing import Literal
 
 import networkx as nx
 import numpy as np
@@ -76,9 +76,9 @@ def fit(
 
         g = nx.from_numpy_matrix(self.A)
 
-        super(HigherOrderLaplacianEigenmaps, self).fit(g)
+        super().fit(g)
 
-    def get_embedding(self, get_dict: bool = False) -> Union[dict, np.ndarray]:
+    def get_embedding(self, get_dict: bool = False) -> dict | np.ndarray:
         """Get embeddings.
 
         Parameters
@@ -91,7 +91,7 @@ def get_embedding(self, get_dict: bool = False) -> Union[dict, np.ndarray]:
         dict or np.ndarray
             The embedding of the complex.
         """
-        emb = super(HigherOrderLaplacianEigenmaps, self).get_embedding()
+        emb = super().get_embedding()
         if get_dict:
             return dict(zip(self.ind, emb))
         return emb

topoembedx/classes/hoglee.py

@@ -1,5 +1,5 @@
 """Higher Order Geometric Laplacian EigenMaps (HOGLEE) class."""
-from typing import Literal, Union
+from typing import Literal
 
 import networkx as nx
 import numpy as np
@@ -65,9 +65,9 @@ def fit(
 
         g = nx.from_numpy_matrix(self.A)
 
-        super(HOGLEE, self).fit(g)
+        super().fit(g)
 
-    def get_embedding(self, get_dict: bool = False) -> Union[dict, np.ndarray]:
+    def get_embedding(self, get_dict: bool = False) -> dict | np.ndarray:
         """Get embedding.
 
         Parameters
@@ -80,7 +80,7 @@ def get_embedding(self, get_dict: bool = False) -> Union[dict, np.ndarray]:
         dict or numpy.ndarray
             Embedding.
         """
-        emb = super(HOGLEE, self).get_embedding()
+        emb = super().get_embedding()
         if get_dict:
             return dict(zip(self.ind, emb))
         return emb

topoembedx/classes/hope.py

@@ -1,5 +1,5 @@
 """Higher Order Laplacian Positional Encoder (HOPE) class."""
-from typing import Literal, Union, overload
+from typing import Literal, overload
 
 import numpy as np
 from scipy import sparse
@@ -41,7 +41,7 @@ def _laplacian_pe(
     @staticmethod
     def _laplacian_pe(
         A: np.ndarray, n_eigvecs: int, return_eigenval: bool = False
-    ) -> Union[np.ndarray, tuple[np.ndarray, np.ndarray]]:
+    ) -> np.ndarray | tuple[np.ndarray, np.ndarray]:
         """Compute Laplacian Positional Encodings (PE) for a given adjacency matrix.
 
         Parameters
@@ -148,29 +148,28 @@ def fit(
         >>> import toponetx as tnx
         >>> from topoembedx import HOPE
         >>> ccc = tnx.classes.CombinatorialComplex()
-        >>> ccc.add_cell([2,5],rank=1)
-        >>> ccc.add_cell([2,4],rank=1)
-        >>> ccc.add_cell([7,8],rank=1)
-        >>> ccc.add_cell([6,8],rank=1)
-        >>> ccc.add_cell([2,4,5],rank=3)
-        >>> ccc.add_cell([6,7,8],rank=3)
+        >>> ccc.add_cell([2, 5], rank=1)
+        >>> ccc.add_cell([2, 4], rank=1)
+        >>> ccc.add_cell([7, 8], rank=1)
+        >>> ccc.add_cell([6, 8], rank=1)
+        >>> ccc.add_cell([2, 4, 5], rank=3)
+        >>> ccc.add_cell([6, 7, 8], rank=3)
 
         >>> model = HOPE()
-        >>> model.fit(ccc, neighborhood_type="adj", neighborhood_dim={"rank": 0, "via_rank" :3})
+        >>> model.fit(
+        ...     ccc,
+        ...     neighborhood_type="adj",
+        ...     neighborhood_dim={"rank": 0, "via_rank": 3},
+        ... )
         >>> em = model.get_embedding(get_dict=True)
-
-
-        Returns
-        -------
-        None
         """
         self.ind, self.A = neighborhood_from_complex(
             complex, neighborhood_type, neighborhood_dim
         )
 
         self._embedding = self._laplacian_pe(self.A, self.dimensions)
 
-    def get_embedding(self, get_dict: bool = False) -> Union[dict, np.ndarray]:
+    def get_embedding(self, get_dict: bool = False) -> dict | np.ndarray:
         """Get embedding.
 
         Parameters