Backport PR #2661: [pre-commit.ci] pre-commit autoupdate (#2677)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
scverse · Oct 9, 2023 · fc498c3 · fc498c3
1 parent 46969b4
commit fc498c3
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Showing 17 changed files with 6 additions and 25 deletions.
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -1,10 +1,10 @@
 repos:
 -   repo: https://github.com/psf/black
-    rev: 22.3.0
+    rev: 23.9.1
     hooks:
     -   id: black
 -   repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.0.287
+    rev: v0.0.292
     hooks:
     -   id: ruff
         args: ["--fix"]

diff --git a/pyproject.toml b/pyproject.toml
@@ -57,7 +57,7 @@ dependencies = [
     "matplotlib>=3.4",
     "pandas>=1.0",
     "scipy>=1.4",
-    "seaborn",
+    "seaborn!=0.13.0",
     "h5py>=3",
     "tqdm",
     "scikit-learn>=0.24",

diff --git a/scanpy/experimental/pp/_highly_variable_genes.py b/scanpy/experimental/pp/_highly_variable_genes.py
@@ -89,7 +89,6 @@ def _highly_variable_pearson_residuals(
     # Get pearson residuals for each batch separately
     residual_gene_vars = []
     for batch in np.unique(batch_info):
-
         adata_subset_prefilter = adata[batch_info == batch]
         X_batch_prefilter = _get_obs_rep(adata_subset_prefilter, layer=layer)
 

diff --git a/scanpy/experimental/pp/_normalization.py b/scanpy/experimental/pp/_normalization.py
@@ -24,7 +24,6 @@
 
 
 def _pearson_residuals(X, theta, clip, check_values, copy=False):
-
     X = X.copy() if copy else X
 
     # check theta

diff --git a/scanpy/external/pl.py b/scanpy/external/pl.py
@@ -417,10 +417,8 @@ def _plot_scores(
     fig, axs = plt.subplots(n_batches, 2, figsize=figsize)
 
     for idx, (batch_key, sub_obs) in enumerate(adata.obs.groupby(batches)):
-
         # We'll need multiple rows if Scrublet was run in multiple batches
         if 'batched_by' in adata.uns['scrublet']:
-
             threshold = adata.uns["scrublet"]['batches'][batch_key].get(
                 "threshold", None
             )

diff --git a/scanpy/external/pp/_scrublet.py b/scanpy/external/pp/_scrublet.py
@@ -166,12 +166,10 @@ def scrublet(
     adata_obs = adata.copy()
 
     def _run_scrublet(ad_obs, ad_sim=None):
-
         # With no adata_sim we assume the regular use case, starting with raw
         # counts and simulating doublets
 
         if ad_sim is None:
-
             pp.filter_genes(ad_obs, min_cells=3)
             pp.filter_cells(ad_obs, min_genes=3)
 
@@ -238,9 +236,7 @@ def _run_scrublet(ad_obs, ad_sim=None):
         batches = np.unique(adata.obs[batch_key])
         scrubbed = [
             _run_scrublet(
-                adata_obs[
-                    adata_obs.obs[batch_key] == batch,
-                ],
+                adata_obs[adata_obs.obs[batch_key] == batch,],
                 adata_sim,
             )
             for batch in batches

diff --git a/scanpy/plotting/_anndata.py b/scanpy/plotting/_anndata.py
@@ -796,7 +796,7 @@ def violin(
             y=y,
             data=obs_tidy,
             kind="violin",
-            scale=scale,
+            density_norm=scale,
             col=x,
             col_order=keys,
             sharey=False,

diff --git a/scanpy/plotting/_baseplot_class.py b/scanpy/plotting/_baseplot_class.py
@@ -519,7 +519,6 @@ def _plot_colorbar(self, color_legend_ax: Axes, normalize):
         color_legend_ax.xaxis.set_tick_params(labelsize='small')
 
     def _plot_legend(self, legend_ax, return_ax_dict, normalize):
-
         # to maintain the fixed height size of the legends, a
         # spacer of variable height is added at top and bottom.
         # The structure for the legends is:

diff --git a/scanpy/plotting/_dotplot.py b/scanpy/plotting/_dotplot.py
@@ -471,7 +471,6 @@ def _plot_size_legend(self, size_legend_ax: Axes):
         size_legend_ax.set_xlim(xmin - 0.15, xmax + 0.5)
 
     def _plot_legend(self, legend_ax, return_ax_dict, normalize):
-
         # to maintain the fixed height size of the legends, a
         # spacer of variable height is added at the bottom.
         # The structure for the legends is:

diff --git a/scanpy/plotting/_stacked_violin.py b/scanpy/plotting/_stacked_violin.py
@@ -439,7 +439,6 @@ def _make_rows_of_violinplots(
         )
         axs_list = []
         for idx, row_label in enumerate(_color_df.index):
-
             row_ax = fig.add_subplot(gs[idx + 1, 1:-1])
             axs_list.append(row_ax)
 

diff --git a/scanpy/plotting/_tools/__init__.py b/scanpy/plotting/_tools/__init__.py
@@ -1617,7 +1617,6 @@ def _get_values_to_plot(
     if groups is None:
         groups = adata.uns[key]['names'].dtype.names
     if values_to_plot is not None:
-
         df_list = []
         for group in groups:
             df = rank_genes_groups_df(adata, group, key=key, gene_symbols=gene_symbols)

diff --git a/scanpy/plotting/_tools/scatterplots.py b/scanpy/plotting/_tools/scatterplots.py
@@ -871,7 +871,6 @@ def pca(
             adata, 'pca', show=show, return_fig=return_fig, save=save, **kwargs
         )
     else:
-
         if 'pca' not in adata.obsm.keys() and 'X_pca' not in adata.obsm.keys():
             raise KeyError(
                 f"Could not find entry in `obsm` for 'pca'.\n"

diff --git a/scanpy/plotting/_utils.py b/scanpy/plotting/_utils.py
@@ -472,7 +472,6 @@ def _set_default_colors_for_categorical_obs(adata, value_to_plot):
 def add_colors_for_categorical_sample_annotation(
     adata, key, palette=None, force_update_colors=False
 ):
-
     color_key = f"{key}_colors"
     colors_needed = len(adata.obs[key].cat.categories)
     if palette and force_update_colors:
@@ -1180,7 +1179,6 @@ def fix_kwds(kwds_dict, **kwargs):
 
 
 def _get_basis(adata: anndata.AnnData, basis: str):
-
     if basis in adata.obsm.keys():
         basis_key = basis
 

diff --git a/scanpy/preprocessing/_simple.py b/scanpy/preprocessing/_simple.py
@@ -683,7 +683,6 @@ def _regress_out_chunk(data):
     from statsmodels.tools.sm_exceptions import PerfectSeparationError
 
     for col_index in range(data_chunk.shape[1]):
-
         # if all values are identical, the statsmodel.api.GLM throws an error;
         # but then no regression is necessary anyways...
         if not (data_chunk[:, col_index] != data_chunk[0, col_index]).any():

diff --git a/scanpy/tests/external/test_scrublet.py b/scanpy/tests/external/test_scrublet.py
@@ -85,7 +85,6 @@ def test_scrublet_data():
     # Replicate the preprocessing steps used by the main function
 
     def preprocess_for_scrublet(adata):
-
         adata_pp = adata.copy()
         pp.filter_genes(adata_pp, min_cells=3)
         pp.filter_cells(adata_pp, min_genes=3)
@@ -100,7 +99,6 @@ def preprocess_for_scrublet(adata):
     # Simulate doublets using the same parents
 
     def create_sim_from_parents(adata, parents):
-
         # Now simulate doublets based on the randomly selected parents used
         # previously
 

diff --git a/scanpy/tests/test_utils.py b/scanpy/tests/test_utils.py
@@ -26,7 +26,6 @@ def test_descend_classes_and_funcs():
 
 
 def test_check_nonnegative_integers():
-
     X = np.random.poisson(size=(100, 100)).astype(np.float64)
     assert check_nonnegative_integers(X) is True
     assert check_nonnegative_integers(-X) is False

diff --git a/scanpy/tools/_paga.py b/scanpy/tools/_paga.py
@@ -502,7 +502,7 @@ def paga_compare_paths(
     n_paths = 0
     n_agreeing_paths = 0
     # loop over all pairs of leaf nodes in the reference adata1
-    for (r, s) in itertools.combinations(leaf_nodes1, r=2):
+    for r, s in itertools.combinations(leaf_nodes1, r=2):
         r2, s2 = asso_groups1[r][0], asso_groups1[s][0]
         on1_g1, on2_g1 = [orig_names1[int(i)] for i in [r, s]]
         on1_g2, on2_g2 = [orig_names2[int(i)] for i in [r2, s2]]