Bump pre commit tool versions

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.4.0
+    rev: v6.0.0
     hooks:
       - id: end-of-file-fixer
       - id: trailing-whitespace
@@ -9,18 +9,18 @@ repos:
       - id: check-executables-have-shebangs
       - id: check-toml
   - repo: https://github.com/google/yapf
-    rev: v0.33.0
+    rev: v0.43.0
     hooks:
       - id: yapf
         additional_dependencies:
           - pyproject.toml
   - repo: https://github.com/PyCQA/isort
-    rev: 5.12.0
+    rev: 8.0.1
     hooks:
       - id: isort
         additional_dependencies:
           - pyproject.toml
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.0.280
+    rev: v0.15.18
     hooks:
       - id: ruff

README.md

@@ -50,9 +50,9 @@ Steffen Schneider, Rodrigo González Laiz, Anastasiia Filipova, Markus Frey, Mac
   [Learnable latent embeddings for joint behavioral and neural analysis.](https://arxiv.org/abs/2204.00673)
   Steffen Schneider*, Jin Hwa Lee* and Mackenzie Weygandt Mathis
 
-# Patent Information 
+# Patent Information
 
-- [Dimensionality reduction of time-series data, and systems and devices that use the resultant embeddings](https://patents.google.com/patent/US12499131B2/en).  Steffen Schneider* & Mackenzie Weygandt Mathis*. Awarded Dec 2025. Please contact the [TTO office](adam.swetloff@epfl.ch) at EPFL for licensing.  
+- [Dimensionality reduction of time-series data, and systems and devices that use the resultant embeddings](https://patents.google.com/patent/US12499131B2/en).  Steffen Schneider* & Mackenzie Weygandt Mathis*. Awarded Dec 2025. Please contact the [TTO office](adam.swetloff@epfl.ch) at EPFL for licensing.
 
 # License
 

cebra/helper.py

@@ -169,8 +169,7 @@ def wrapper(*args, patched_version=None, **kwargs):
                 installed_version = packaging.version.parse(
                     patched_version)  # Use the patched version if provided
             else:
-                installed_version = packaging.version.parse(
-                    module.__version__)
+                installed_version = packaging.version.parse(module.__version__)
 
             if installed_version < required_version:
                 raise ImportError(

cebra/integrations/plotly.py

@@ -87,7 +87,8 @@ def _define_colorscale(self, cmap: str):
         Returns:
             colorscale: List of scaled colors to plot the embeddings
         """
-        colorscale = _convert_cmap2colorscale(matplotlib.colormaps.get_cmap(cmap))
+        colorscale = _convert_cmap2colorscale(
+            matplotlib.colormaps.get_cmap(cmap))
 
         return colorscale
 

docs/source/api/integrations/data.rst

@@ -3,4 +3,4 @@ Data Loading
 
 .. automodule:: cebra.data.load
    :show-inheritance:
-   :members:
+   :members:

docs/source/api/integrations/deeplabcut.rst

@@ -4,5 +4,3 @@ DeepLabCut
 .. automodule:: cebra.integrations.deeplabcut
    :show-inheritance:
    :members:
-
-

docs/source/api/integrations/matplotlib.rst

@@ -4,4 +4,3 @@ Plotting with ``matplotlib``
 .. automodule:: cebra.integrations.matplotlib
    :show-inheritance:
    :members:
-

docs/source/api/integrations/plotly.rst

@@ -4,4 +4,3 @@ Plotting with ``plotly``
 .. automodule:: cebra.integrations.plotly
    :show-inheritance:
    :members:
-

tests/test_datasets.py

@@ -148,9 +148,8 @@ def test_allen():
 
 
 @pytest.mark.requires_dataset
-@pytest.mark.parametrize("options",
-                         cebra.datasets.get_options("*",
-                                                    expand_parametrized=False))
+@pytest.mark.parametrize(
+    "options", cebra.datasets.get_options("*", expand_parametrized=False))
 def test_options(options):
     assert len(options) > 0
     assert len(multisubject_options) > 0

tests/test_solver.py

@@ -41,8 +41,10 @@ def _get_loader(data_name, loader_initfunc):
     loader = loader_initfunc(data, **kwargs)
     return loader, data
 
+
 OUTPUT_DIMENSION = 3
 
+
 def _make_model(dataset, model_architecture="offset10-model"):
     # TODO flexible input dimension
     # return nn.Sequential(
@@ -398,4 +400,4 @@ def test_unified_session(data_name, model_architecture, loader_initfunc,
         assert emb.shape == (loader.dataset.num_timepoints, 3)
 
         emb = solver.transform(data, labels, session_id=i, batch_size=300)
-        assert emb.shape == (loader.dataset.num_timepoints, 3)
+        assert emb.shape == (loader.dataset.num_timepoints, 3)