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docs/tutorial.ipynb

@@ -507,7 +507,7 @@
    ],
    "source": [
     "sc.pl.umap(\n",
-    "    adata, \n",
+    "    adata,\n",
     "    color=\"directional_cosine_sim_variance\",\n",
     "    cmap=\"Greys\",\n",
     "    vmin=\"p1\",\n",
@@ -537,7 +537,7 @@
     "    extrapolated_cells_list = []\n",
     "    for i in track(range(n_samples)):\n",
     "        with io.StringIO() as buf, redirect_stdout(buf):\n",
-    "            vkey = \"velocities_velovi_{i}\".format(i=i)\n",
+    "            vkey = f\"velocities_velovi_{i}\"\n",
     "            v = vae.get_velocity(n_samples=1, velo_statistic=\"mean\")\n",
     "            adata.layers[vkey] = v\n",
     "            scv.tl.velocity_graph(adata, vkey=vkey, sqrt_transform=False, approx=True)\n",
@@ -1134,10 +1134,10 @@
    ],
    "source": [
     "sc.pl.umap(\n",
-    "    adata, \n",
+    "    adata,\n",
     "    color=\"directional_cosine_sim_variance_extrinisic\",\n",
-    "    vmin=\"p1\", \n",
-    "    vmax=\"p99\", \n",
+    "    vmin=\"p1\",\n",
+    "    vmax=\"p99\",\n",
     ")"
    ]
   },

velovi/_model.py

@@ -49,6 +49,7 @@ class VELOVI(VAEMixin, UnsupervisedTrainingMixin, BaseModelClass):
         Use a linear decoder from latent space to time.
     **model_kwargs
         Keyword args for :class:`~velovi.VELOVAE`
+
     """
 
     def __init__(
@@ -108,13 +109,8 @@ def __init__(
             **model_kwargs,
         )
         self._model_summary_string = (
-            "VELOVI Model with the following params: \nn_hidden: {}, n_latent: {}, n_layers: {}, dropout_rate: "
-            "{}"
-        ).format(
-            n_hidden,
-            n_latent,
-            n_layers,
-            dropout_rate,
+            f"VELOVI Model with the following params: \nn_hidden: {n_hidden}, n_latent: {n_latent}, n_layers: {n_layers}, dropout_rate: "
+            f"{dropout_rate}"
         )
         self.init_params_ = self._get_init_params(locals())
 
@@ -164,6 +160,7 @@ def train(
             `train()` will overwrite values present in `plan_kwargs`, when appropriate.
         **trainer_kwargs
             Other keyword args for :class:`~scvi.train.Trainer`.
+
         """
         user_plan_kwargs = plan_kwargs.copy() if isinstance(plan_kwargs, dict) else {}
         plan_kwargs = {"lr": lr, "weight_decay": weight_decay, "optimizer": "AdamW"}
@@ -238,6 +235,7 @@ def get_state_assignment(
         -------
         If `n_samples` > 1 and `return_mean` is False, then the shape is `(samples, cells, genes)`.
         Otherwise, shape is `(cells, genes)`. In this case, return type is :class:`~pandas.DataFrame` unless `return_numpy` is True.
+
         """
         adata = self._validate_anndata(adata)
         scdl = self._make_data_loader(
@@ -342,6 +340,7 @@ def get_latent_time(
         -------
         If `n_samples` > 1 and `return_mean` is False, then the shape is `(samples, cells, genes)`.
         Otherwise, shape is `(cells, genes)`. In this case, return type is :class:`~pandas.DataFrame` unless `return_numpy` is True.
+
         """
         adata = self._validate_anndata(adata)
         if indices is None:
@@ -484,6 +483,7 @@ def get_velocity(
         -------
         If `n_samples` > 1 and `return_mean` is False, then the shape is `(samples, cells, genes)`.
         Otherwise, shape is `(cells, genes)`. In this case, return type is :class:`~pandas.DataFrame` unless `return_numpy` is True.
+
         """
         adata = self._validate_anndata(adata)
         if indices is None:
@@ -658,6 +658,7 @@ def get_expression_fit(
         -------
         If `n_samples` > 1 and `return_mean` is False, then the shape is `(samples, cells, genes)`.
         Otherwise, shape is `(cells, genes)`. In this case, return type is :class:`~pandas.DataFrame` unless `return_numpy` is True.
+
         """
         adata = self._validate_anndata(adata)
 
@@ -813,6 +814,7 @@ def get_gene_likelihood(
         -------
         If `n_samples` > 1 and `return_mean` is False, then the shape is `(samples, cells, genes)`.
         Otherwise, shape is `(cells, genes)`. In this case, return type is :class:`~pandas.DataFrame` unless `return_numpy` is True.
+
         """
         adata = self._validate_anndata(adata)
         scdl = self._make_data_loader(
@@ -919,6 +921,7 @@ def setup_anndata(
         Returns
         -------
         %(returns)s
+
         """
         setup_method_args = cls._get_setup_method_args(**locals())
         anndata_fields = [
@@ -969,6 +972,7 @@ def get_permutation_scores(
         -------
         Tuple of DataFrame and AnnData. DataFrame is genes by cell types with score per cell type.
         AnnData is the permutated version of the original AnnData.
+
         """
         adata = self._validate_anndata(adata)
         adata_manager = self.get_anndata_manager(adata)
@@ -1092,6 +1096,7 @@ def _directional_statistics_per_cell(
     ----------
     tensor
         Shape of samples by genes for a given cell.
+
     """
     n_samples = tensor.shape[0]
     # over samples axis

velovi/_module.py

@@ -1,4 +1,5 @@
 """Main module."""
+
 from typing import Callable, Iterable, Literal, Optional
 
 import numpy as np
@@ -44,6 +45,7 @@ class DecoderVELOVI(nn.Module):
         Whether to use layer norm in layers
     linear_decoder
         Whether to use linear decoder for time
+
     """
 
     def __init__(
@@ -183,6 +185,7 @@ class VELOVAE(BaseModuleClass):
     var_activation
         Callable used to ensure positivity of the variational distributions' variance.
         When `None`, defaults to `torch.exp`.
+
     """
 
     def __init__(

velovi/_utils.py

@@ -56,6 +56,7 @@ def preprocess_data(
     Returns
     -------
     Preprocessed adata.
+
     """
     if min_max_scale:
         scaler = MinMaxScaler()