[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

src/spVIPES/data/_utils.py

@@ -158,9 +158,7 @@ def _make_column_categorical(
     if np.min(counts) < 3:
         category = unique[np.argmin(counts)]
         warnings.warn(
-            "Category {} in adata.obs['{}'] has fewer than 3 cells. Models may not train properly.".format(
-                category, alternate_column_key
-            ),
+            f"Category {category} in adata.obs['{alternate_column_key}'] has fewer than 3 cells. Models may not train properly.",
             UserWarning,
             stacklevel=settings.warnings_stacklevel,
         )

src/spVIPES/dataloaders/.ipynb_checkpoints/_ann_dataloader-checkpoint.py

@@ -75,11 +75,11 @@ def __init__(
             sampler_cls = (
                 SequentialSampler
                 if not shuffle and not weighted
-                else WeightedRandomSampler
-                if shuffle and weighted
-                else RandomSampler
-                if shuffle and not weighted
-                else None
+                else (
+                    WeightedRandomSampler
+                    if shuffle and weighted
+                    else RandomSampler if shuffle and not weighted else None
+                )
             )
             if sampler_cls != WeightedRandomSampler:
                 sampler = BatchSampler(

src/spVIPES/dataloaders/_ann_dataloader.py

@@ -74,11 +74,11 @@ def __init__(
             sampler_cls = (
                 SequentialSampler
                 if not shuffle and not weighted
-                else WeightedRandomSampler
-                if shuffle and weighted
-                else RandomSampler
-                if shuffle and not weighted
-                else None
+                else (
+                    WeightedRandomSampler
+                    if shuffle and weighted
+                    else RandomSampler if shuffle and not weighted else None
+                )
             )
             if sampler_cls != WeightedRandomSampler:
                 sampler = BatchSampler(

src/spVIPES/model/base/.ipynb_checkpoints/training_mixin-checkpoint.py

@@ -4,7 +4,6 @@
 https://docs.scvi-tools.org/en/0.9.0/user_guide/notebooks/model_user_guide.html#Mixing-in-pre-coded-features
 """
 
-
 from typing import List, Optional, Union
 
 import numpy as np

src/spVIPES/model/base/training_mixin.py

@@ -4,7 +4,6 @@
 https://docs.scvi-tools.org/en/0.9.0/user_guide/notebooks/model_user_guide.html#Mixing-in-pre-coded-features
 """
 
-
 from typing import Optional, Union
 
 import numpy as np

src/spVIPES/model/spvipes.py

@@ -88,8 +88,8 @@ def __init__(
         )
 
         self._model_summary_string = (
-            "spVIPES Model with the following params: \nn_hidden: {}, n_dimensions_shared: {}, n_dimensions_private: {}, dropout_rate: {}"
-        ).format(n_hidden, n_dimensions_shared, n_dimensions_private, dropout_rate)
+            f"spVIPES Model with the following params: \nn_hidden: {n_hidden}, n_dimensions_shared: {n_dimensions_shared}, n_dimensions_private: {n_dimensions_private}, dropout_rate: {dropout_rate}"
+        )
         # necessary line to get params that will be used for saving/loading
         self.init_params_ = self._get_init_params(locals())
 

src/spVIPES/module/spVIPESmodule.py

@@ -1,4 +1,5 @@
 """Main module."""
+
 from typing import Literal
 
 import numpy as np