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docs/notebooks/Tutorial.ipynb

@@ -73,7 +73,7 @@
    "source": [
     "# In this case we define the two datasets based on the subgroup category\n",
     "simulated_data.obs['Dataset'] = simulated_data.obs.Subgroup.replace(\n",
-    "    {'Group1': 'Dataset 1', 'Group2': 'Dataset 1', \n",
+    "    {'Group1': 'Dataset 1', 'Group2': 'Dataset 1',\n",
     "     'Group3': 'Dataset 2', 'Group4': 'Dataset 2'})"
    ]
   },
@@ -86,8 +86,8 @@
    "outputs": [],
    "source": [
     "simulated_data.obs['Celltypes'] = simulated_data.obs.Group.replace(\n",
-    "    {'Group1': 'Cell type 1', 'Group2': 'Cell type 2', \n",
-    "     'Group3': 'Cell type 3', 'Group4': 'Cell type 4', \n",
+    "    {'Group1': 'Cell type 1', 'Group2': 'Cell type 2',\n",
+    "     'Group3': 'Cell type 3', 'Group4': 'Cell type 4',\n",
     "     'Group5': 'Cell type 5'})"
    ]
   },
@@ -107,7 +107,7 @@
    "outputs": [],
    "source": [
     "simulated_data.obs['Gene_programs'] = simulated_data.obs.Subgroup.replace(\n",
-    "    {'Group1': 'Gene program 1a', 'Group2': 'Gene program 2a', \n",
+    "    {'Group1': 'Gene program 1a', 'Group2': 'Gene program 2a',\n",
     "     'Group3': 'Gene program 1b', 'Group4': 'Gene program 2b'})"
    ]
   },
@@ -246,7 +246,7 @@
    },
    "outputs": [],
    "source": [
-    "plt.plot(spvipes.history['elbo_train']['elbo_train'], label='elbo_train');\n",
+    "plt.plot(spvipes.history['elbo_train']['elbo_train'], label='elbo_train')\n",
     "plt.legend()"
    ]
   },

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