fixed ruff B028

src/multimil/data/_preprocessing.py

@@ -1,14 +1,13 @@
 import warnings
-from typing import List, Optional, Union
 
 import anndata as ad
 import numpy as np
 import pandas as pd
 
 
 def organize_multiome_anndatas(
-    adatas: List[List[Union[ad.AnnData, None]]],
-    layers: Optional[List[List[Union[str, None]]]] = None,
+    adatas: list[list[ad.AnnData | None]],
+    layers: list[list[str | None]] | None = None,
 ):
     """Concatenate all the input anndata objects.
 
@@ -26,7 +25,6 @@ def organize_multiome_anndatas(
     # TODO: add checks for layers
     # TODO: add check that len of modalities is the same as len of losses, etc
 
-
     # needed for scArches operation setup
     datasets_lengths = {}
     datasets_obs_names = {}
@@ -45,8 +43,8 @@ def organize_multiome_anndatas(
                         stacklevel=2,
                     )
                 # check that all adatas in the same modality have the same number of features
-                if (mod_length := modality_lengths.get(f'{mod}', None)) is None:
-                    modality_lengths[f'{mod}'] = adata.shape[1]
+                if (mod_length := modality_lengths.get(f"{mod}", None)) is None:
+                    modality_lengths[f"{mod}"] = adata.shape[1]
                 else:
                     if adata.shape[1] != mod_length:
                         raise ValueError(
@@ -78,7 +76,7 @@ def organize_multiome_anndatas(
     for mod, modality_adatas in enumerate(adatas):
         for i, adata in enumerate(modality_adatas):
             if not isinstance(adata, ad.AnnData) and adata is None:
-                X_zeros = np.zeros((datasets_lengths[i], modality_lengths[f'{mod}']))
+                X_zeros = np.zeros((datasets_lengths[i], modality_lengths[f"{mod}"]))
                 adatas[mod][i] = ad.AnnData(X_zeros, dtype=X_zeros.dtype)
                 adatas[mod][i].obs_names = datasets_obs_names[i]
                 adatas[mod][i].var_names = modality_var_names[mod]

src/multimil/dataloaders/_ann_dataloader.py

@@ -1,6 +1,5 @@
 import copy
 import itertools
-from typing import Optional, Union
 
 import numpy as np
 import torch
@@ -34,7 +33,7 @@ def __init__(
         batch_size: int,
         min_size_per_class: int,
         shuffle: bool = True,
-        drop_last: Union[bool, int] = True,
+        drop_last: bool | int = True,
         shuffle_classes: bool = True,
     ):
         if drop_last > batch_size:
@@ -168,9 +167,9 @@ def __init__(
         indices=None,
         batch_size=128,
         min_size_per_class=None,
-        data_and_attributes: Optional[dict] = None,
-        drop_last: Union[bool, int] = True,
-        sampler: Optional[torch.utils.data.sampler.Sampler] = StratifiedSampler,
+        data_and_attributes: dict | None = None,
+        drop_last: bool | int = True,
+        sampler: torch.utils.data.sampler.Sampler | None = StratifiedSampler,
         **data_loader_kwargs,
     ):
         if adata_manager.adata is None:

src/multimil/dataloaders/_data_splitting.py

@@ -1,9 +1,7 @@
-from typing import Optional
-
 from scvi.data import AnnDataManager
 from scvi.dataloaders import DataSplitter
 
-from ..dataloaders._ann_dataloader import GroupAnnDataLoader
+from multimil.dataloaders._ann_dataloader import GroupAnnDataLoader
 
 
 # adjusted from scvi-tools
@@ -33,7 +31,7 @@ def __init__(
         adata_manager: AnnDataManager,
         group_column: str,
         train_size: float = 0.9,
-        validation_size: Optional[float] = None,
+        validation_size: float | None = None,
         use_gpu: bool = False,
         **kwargs,
     ):

src/multimil/distributions/_mmd.py

@@ -1,5 +1,3 @@
-from typing import List, Optional
-
 import torch
 
 
@@ -21,7 +19,7 @@ def gaussian_kernel(
         self,
         x: torch.Tensor,
         y: torch.Tensor,
-        gamma: Optional[List[float]] = None,
+        gamma: list[float] | None = None,
     ) -> torch.Tensor:
         """Apply Guassian kernel.
 

src/multimil/model/__init__.py

@@ -1,6 +1,5 @@
-from ._multivae import MultiVAE
 from ._mil import MILClassifier
+from ._multivae import MultiVAE
 from ._multivae_mil import MultiVAE_MIL
 
-
 __all__ = ["MultiVAE", "MILClassifier", "MultiVAE_MIL"]