apply ruff auto-fixes

Author: stes

cebra/__init__.py

@@ -33,7 +33,7 @@
     from cebra.integrations.sklearn.decoder import L1LinearRegressor
 
     is_sklearn_available = True
-except ImportError as e:
+except ImportError:
     # silently fail for now
     pass
 
@@ -42,7 +42,7 @@
     from cebra.integrations.matplotlib import *
 
     is_matplotlib_available = True
-except ImportError as e:
+except ImportError:
     # silently fail for now
     pass
 
@@ -51,7 +51,7 @@
     from cebra.integrations.plotly import *
 
     is_plotly_available = True
-except ImportError as e:
+except ImportError:
     # silently fail for now
     pass
 

cebra/__main__.py

@@ -27,11 +27,7 @@
 import argparse
 import sys
 
-import numpy as np
-import torch
-
 import cebra
-import cebra.distributions as cebra_distr
 
 
 def train(parser, kwargs):

cebra/config.py

@@ -21,7 +21,6 @@
 #
 import argparse
 import json
-from dataclasses import MISSING
 from typing import Literal, Optional
 
 import literate_dataclasses as dataclasses

cebra/data/base.py

@@ -22,11 +22,8 @@
 """Base classes for datasets and loaders."""
 
 import abc
-import collections
-from typing import List
 
 import literate_dataclasses as dataclasses
-import numpy as np
 import torch
 
 import cebra.data.assets as cebra_data_assets

cebra/data/datasets.py

@@ -21,21 +21,14 @@
 #
 """Pre-defined datasets."""
 
-import abc
-import collections
 import types
 from typing import List, Tuple, Union
 
-import literate_dataclasses as dataclasses
 import numpy as np
 import numpy.typing as npt
 import torch
-from numpy.typing import NDArray
 
 import cebra.data as cebra_data
-import cebra.distributions
-from cebra.data.datatypes import Batch
-from cebra.data.datatypes import BatchIndex
 
 
 class TensorDataset(cebra_data.SingleSessionDataset):

cebra/data/datatypes.py

@@ -20,9 +20,6 @@
 # limitations under the License.
 #
 import collections
-from typing import Tuple
-
-import torch
 
 __all__ = ["Batch", "BatchIndex", "Offset"]
 

cebra/data/helper.py

@@ -181,14 +181,14 @@ def fit(
         elif ref_data.shape[0] == data.shape[0] and (ref_label is None or
                                                      label is None):
             raise ValueError(
-                f"Missing labels: the data to align are the same shape but you provided only "
-                f"one of the sets of labels. Either provide both the reference and alignment "
-                f"labels or none.")
+                "Missing labels: the data to align are the same shape but you provided only "
+                "one of the sets of labels. Either provide both the reference and alignment "
+                "labels or none.")
         else:
             if ref_label is None or label is None:
                 raise ValueError(
-                    f"Missing labels: the data to align are not the same shape, "
-                    f"provide labels to align the data and reference data.")
+                    "Missing labels: the data to align are not the same shape, "
+                    "provide labels to align the data and reference data.")
 
             if len(ref_label.shape) == 1:
                 ref_label = np.expand_dims(ref_label, axis=1)

cebra/data/multi_session.py

@@ -22,11 +22,9 @@
 """Datasets and loaders for multi-session training."""
 
 import abc
-import collections
 from typing import List
 
 import literate_dataclasses as dataclasses
-import numpy as np
 import torch
 
 import cebra.data as cebra_data

cebra/data/single_session.py

@@ -26,12 +26,9 @@
 """
 
 import abc
-import collections
 import warnings
-from typing import List
 
 import literate_dataclasses as dataclasses
-import numpy as np
 import torch
 
 import cebra.data as cebra_data
@@ -365,8 +362,8 @@ def __post_init__(self):
 
         if self.conditional != "time_delta":
             raise NotImplementedError(
-                f"Hybrid training is currently only implemented using the ``time_delta`` "
-                f"continual distribution.")
+                "Hybrid training is currently only implemented using the ``time_delta`` "
+                "continual distribution.")
 
         self.time_distribution = cebra.distributions.TimeContrastive(
             time_offset=self.time_offset,

cebra/datasets/allen/ca_movie.py

@@ -29,11 +29,8 @@
 
 """
 
-import glob
-import hashlib
 import pathlib
 
-import h5py
 import joblib
 import numpy as np
 import pandas as pd
@@ -46,7 +43,6 @@
 import cebra.data
 from cebra.datasets import get_datapath
 from cebra.datasets import parametrize
-from cebra.datasets import register
 from cebra.datasets.allen import NUM_NEURONS
 from cebra.datasets.allen import SEEDS