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

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

src/scvi/model/base/_de_core.py

@@ -61,6 +61,7 @@ def ravel_idx(my_idx, obs_df):
         raise ValueError("One of idx1 or idx2 has size zero.")
     return obs_col, group1, group2
 
+
 def _subset_group(
     subset_idx: list[bool] | np.ndarray | str,
     groupby: list[bool] | np.ndarray | str,
@@ -98,7 +99,7 @@ def ravel_idx(my_idx, obs_df):
     obs_col = obs_df[groupby].astype(str)
     mask = np.ones_like(obs_col, dtype=bool)
     mask[subset_idx] = False
-    obs_col[mask] = 'other'
+    obs_col[mask] = "other"
     return obs_col
 
 

src/scvi/model/base/_differential.py

@@ -364,14 +364,14 @@ def m1_domain_fn(samples):
                 proba_m2 = np.mean(is_de_minus, 0)
                 if test_mode == "two":
                     proba_de = proba_m1 + proba_m2
-                    sign = 1.
+                    sign = 1.0
                 else:
                     proba_de = np.maximum(proba_m1, proba_m2)
                     sign = np.sign(proba_m1 - proba_m2)
                 change_distribution_props = describe_continuous_distrib(
-                    samples=change_fn(scales_1, scales_2, 1e-3*pseudocounts),
+                    samples=change_fn(scales_1, scales_2, 1e-3 * pseudocounts),
                     credible_intervals_levels=cred_interval_lvls,
-                ) # reduced pseudocounts to correctly estimate lfc
+                )  # reduced pseudocounts to correctly estimate lfc
                 change_distribution_props = {
                     "lfc_" + key: val for (key, val) in change_distribution_props.items()
                 }

src/scvi/model/base/_rnamixin.py

@@ -112,7 +112,7 @@ def _get_importance_weights(
             return_mean=False,
             n_mc_samples=n_mc_samples,
             n_mc_samples_per_pass=n_mc_samples_per_pass,
-        ) # n_anchors
+        )  # n_anchors
         mask = torch.tensor(anchor_cells)
         qz_anchor = subset_distribution(qz, mask, 0)  # n_anchors, n_latent
         log_qz = qz_anchor.log_prob(zs.unsqueeze(-2)).sum(dim=-1)  # n_samples, n_cells, n_anchors
@@ -129,7 +129,7 @@ def _get_importance_weights(
             log_px_z.append(
                 distributions_px.log_prob(x_anchor).sum(dim=-1)[..., None].cpu()
             )  # n_samples, n_cells, 1
-        log_px_z = torch.cat(log_px_z, dim=-1) # n_samples, n_cells, n_anchors
+        log_px_z = torch.cat(log_px_z, dim=-1)  # n_samples, n_cells, n_anchors
 
         log_pz = log_pz.reshape(-1, 1)
         log_px_z = log_px_z.reshape(-1, len(anchor_cells))