Fix ruff config

Signed-off-by: zethson <[email protected]>

pertpy/metadata/_cell_line.py

@@ -218,7 +218,7 @@ def annotate(
         Examples:
             >>> import pertpy as pt
             >>> adata = pt.dt.dialogue_example()
-            >>> adata.obs['cell_line_name'] = 'MCF7'
+            >>> adata.obs["cell_line_name"] = "MCF7"
             >>> pt_metadata = pt.md.CellLine()
             >>> adata_annotated = pt_metadata.annotate(adata=adata,
             >>>                                        reference_id='cell_line_name',
@@ -332,9 +332,11 @@ def annotate_bulk_rna(
         Examples:
             >>> import pertpy as pt
             >>> adata = pt.dt.dialogue_example()
-            >>> adata.obs['cell_line_name'] = 'MCF7'
+            >>> adata.obs["cell_line_name"] = "MCF7"
             >>> pt_metadata = pt.md.CellLine()
-            >>> adata_annotated = pt_metadata.annotate(adata=adata, reference_id='cell_line_name', query_id='cell_line_name', copy=True)
+            >>> adata_annotated = pt_metadata.annotate(
+            ...     adata=adata, reference_id="cell_line_name", query_id="cell_line_name", copy=True
+            ... )
             >>> pt_metadata.annotate_bulk_rna(adata_annotated)
         """
         if copy:
@@ -433,9 +435,11 @@ def annotate_protein_expression(
         Examples:
             >>> import pertpy as pt
             >>> adata = pt.dt.dialogue_example()
-            >>> adata.obs['cell_line_name'] = 'MCF7'
+            >>> adata.obs["cell_line_name"] = "MCF7"
             >>> pt_metadata = pt.md.CellLine()
-            >>> adata_annotated = pt_metadata.annotate(adata=adata, reference_id='cell_line_name', query_id='cell_line_name', copy=True)
+            >>> adata_annotated = pt_metadata.annotate(
+            ...     adata=adata, reference_id="cell_line_name", query_id="cell_line_name", copy=True
+            ... )
             >>> pt_metadata.annotate_protein_expression(adata_annotated)
         """
         if copy:
@@ -520,7 +524,7 @@ def annotate_from_gdsc(
             >>> import pertpy as pt
             >>> adata = pt.dt.mcfarland_2020()
             >>> pt_metadata = pt.md.CellLine()
-            >>> pt_metadata.annotate_from_gdsc(adata, query_id='cell_line')
+            >>> pt_metadata.annotate_from_gdsc(adata, query_id="cell_line")
         """
         if copy:
             adata = adata.copy()

pertpy/plot/_augur.py

@@ -79,7 +79,9 @@ def important_features(
             >>> adata = pt.dt.sc_sim_augur()
             >>> ag_rfc = pt.tl.Augur("random_forest_classifier")
             >>> loaded_data = ag_rfc.load(adata)
-            >>> v_adata, v_results = ag_rfc.predict(loaded_data, subsample_size=20, select_variance_features=True, n_threads=4)
+            >>> v_adata, v_results = ag_rfc.predict(
+            ...     loaded_data, subsample_size=20, select_variance_features=True, n_threads=4
+            ... )
             >>> ag_rfc.plot_important_features(v_results)
         """
         warnings.warn(
@@ -115,7 +117,9 @@ def lollipop(
             >>> adata = pt.dt.sc_sim_augur()
             >>> ag_rfc = pt.tl.Augur("random_forest_classifier")
             >>> loaded_data = ag_rfc.load(adata)
-            >>> v_adata, v_results = ag_rfc.predict(loaded_data, subsample_size=20, select_variance_features=True, n_threads=4)
+            >>> v_adata, v_results = ag_rfc.predict(
+            ...     loaded_data, subsample_size=20, select_variance_features=True, n_threads=4
+            ... )
             >>> ag_rfc.plot_lollipop(v_results)
         """
         warnings.warn(
@@ -152,7 +156,9 @@ def scatterplot(
             >>> ag_rfc = pt.tl.Augur("random_forest_classifier")
             >>> loaded_data = ag_rfc.load(adata)
             >>> h_adata, h_results = ag_rfc.predict(loaded_data, subsample_size=20, n_threads=4)
-            >>> v_adata, v_results = ag_rfc.predict(loaded_data, subsample_size=20, select_variance_features=True, n_threads=4)
+            >>> v_adata, v_results = ag_rfc.predict(
+            ...     loaded_data, subsample_size=20, select_variance_features=True, n_threads=4
+            ... )
             >>> ag_rfc.plot_scatterplot(v_results, h_results)
         """
         warnings.warn(

pertpy/plot/_coda.py

@@ -594,7 +594,9 @@ def effects_umap(  # pragma: no cover
             >>>     pen_args={"phi": 0, "lambda_1": 3.5},
             >>>     tree_key="tree"
             >>> )
-            >>> tasccoda_model.run_nuts(tasccoda_data, modality_key="coda", rng_key=1234, num_samples=10000, num_warmup=1000)
+            >>> tasccoda_model.run_nuts(
+            ...     tasccoda_data, modality_key="coda", rng_key=1234, num_samples=10000, num_warmup=1000
+            ... )
             >>> tasccoda_model.plot_effects_umap(tasccoda_data,
             >>>                         effect_name=["effect_df_condition[T.Salmonella]",
             >>>                                      "effect_df_condition[T.Hpoly.Day3]",

pertpy/plot/_guide_rna.py

@@ -46,7 +46,7 @@ def heatmap(
 
             >>> import pertpy as pt
             >>> mdata = pt.dt.papalexi_2021()
-            >>> gdo = mdata.mod['gdo']
+            >>> gdo = mdata.mod["gdo"]
             >>> ga = pt.pp.GuideAssignment()
             >>> ga.assign_by_threshold(gdo, assignment_threshold=5)
             >>> ga.plot_heatmap(gdo)

pertpy/plot/_milopy.py

@@ -155,7 +155,7 @@ def da_beeswarm(
             >>> milo.make_nhoods(mdata["rna"])
             >>> mdata = milo.count_nhoods(mdata, sample_col="orig.ident")
             >>> milo.da_nhoods(mdata, design="~label")
-            >>> milo.annotate_nhoods(mdata, anno_col='cell_type')
+            >>> milo.annotate_nhoods(mdata, anno_col="cell_type")
             >>> milo.plot_da_beeswarm(mdata)
         """
         warnings.warn(

pertpy/plot/_mixscape.py

@@ -47,9 +47,9 @@ def barplot(  # pragma: no cover
             >>> import pertpy as pt
             >>> mdata = pt.dt.papalexi_2021()
             >>> ms = pt.tl.Mixscape()
-            >>> ms.perturbation_signature(mdata['rna'], 'perturbation', 'NT', 'replicate')
-            >>> ms.mixscape(adata = mdata['rna'], control = 'NT', labels='gene_target', layer='X_pert')
-            >>> ms.plot_barplot(mdata['rna'], guide_rna_column='NT')
+            >>> ms.perturbation_signature(mdata["rna"], "perturbation", "NT", "replicate")
+            >>> ms.mixscape(adata=mdata["rna"], control="NT", labels="gene_target", layer="X_pert")
+            >>> ms.plot_barplot(mdata["rna"], guide_rna_column="NT")
         """
         warnings.warn(
             "This function is deprecated and will be removed in pertpy 0.8.0!"
@@ -109,9 +109,11 @@ def heatmap(  # pragma: no cover
             >>> import pertpy as pt
             >>> mdata = pt.dt.papalexi_2021()
             >>> ms = pt.tl.Mixscape()
-            >>> ms.perturbation_signature(mdata['rna'], 'perturbation', 'NT', 'replicate')
-            >>> ms.mixscape(adata = mdata['rna'], control = 'NT', labels='gene_target', layer='X_pert')
-            >>> ms.plot_heatmap(adata = mdata['rna'], labels='gene_target', target_gene='IFNGR2', layer='X_pert', control='NT')
+            >>> ms.perturbation_signature(mdata["rna"], "perturbation", "NT", "replicate")
+            >>> ms.mixscape(adata=mdata["rna"], control="NT", labels="gene_target", layer="X_pert")
+            >>> ms.plot_heatmap(
+            ...     adata=mdata["rna"], labels="gene_target", target_gene="IFNGR2", layer="X_pert", control="NT"
+            ... )
         """
         warnings.warn(
             "This function is deprecated and will be removed in pertpy 0.8.0!"
@@ -173,9 +175,11 @@ def perturbscore(  # pragma: no cover
             >>> import pertpy as pt
             >>> mdata = pt.dt.papalexi_2021()
             >>> mixscape_identifier = pt.tl.Mixscape()
-            >>> mixscape_identifier.perturbation_signature(mdata['rna'], 'perturbation', 'NT', 'replicate')
-            >>> mixscape_identifier.mixscape(adata = mdata['rna'], control = 'NT', labels='gene_target', layer='X_pert')
-            >>> mixscape_identifier.perturbscore(adata = mdata['rna'], labels='gene_target', target_gene='IFNGR2', color = 'orange')
+            >>> mixscape_identifier.perturbation_signature(mdata["rna"], "perturbation", "NT", "replicate")
+            >>> mixscape_identifier.mixscape(adata=mdata["rna"], control="NT", labels="gene_target", layer="X_pert")
+            >>> mixscape_identifier.perturbscore(
+            ...     adata=mdata["rna"], labels="gene_target", target_gene="IFNGR2", color="orange"
+            ... )
         """
         warnings.warn(
             "This function is deprecated and will be removed in pertpy 0.8.0!"
@@ -247,9 +251,11 @@ def violin(  # pragma: no cover
             >>> import pertpy as pt
             >>> mdata = pt.dt.papalexi_2021()
             >>> ms = pt.tl.Mixscape()
-            >>> ms.perturbation_signature(mdata['rna'], 'perturbation', 'NT', 'replicate')
-            >>> ms.mixscape(adata = mdata['rna'], control = 'NT', labels='gene_target', layer='X_pert')
-            >>> ms.plot_violin(adata = mdata['rna'], target_gene_idents=['NT', 'IFNGR2 NP', 'IFNGR2 KO'], groupby='mixscape_class')
+            >>> ms.perturbation_signature(mdata["rna"], "perturbation", "NT", "replicate")
+            >>> ms.mixscape(adata=mdata["rna"], control="NT", labels="gene_target", layer="X_pert")
+            >>> ms.plot_violin(
+            ...     adata=mdata["rna"], target_gene_idents=["NT", "IFNGR2 NP", "IFNGR2 KO"], groupby="mixscape_class"
+            ... )
         """
         warnings.warn(
             "This function is deprecated and will be removed in pertpy 0.8.0!"
@@ -319,10 +325,10 @@ def lda(  # pragma: no cover
             >>> import pertpy as pt
             >>> mdata = pt.dt.papalexi_2021()
             >>> ms = pt.tl.Mixscape()
-            >>> ms.perturbation_signature(mdata['rna'], 'perturbation', 'NT', 'replicate')
-            >>> ms.mixscape(adata = mdata['rna'], control = 'NT', labels='gene_target', layer='X_pert')
-            >>> ms.lda(adata=mdata['rna'], control='NT', labels='gene_target', layer='X_pert')
-            >>> ms.plot_lda(adata=mdata['rna'], control='NT')
+            >>> ms.perturbation_signature(mdata["rna"], "perturbation", "NT", "replicate")
+            >>> ms.mixscape(adata=mdata["rna"], control="NT", labels="gene_target", layer="X_pert")
+            >>> ms.lda(adata=mdata["rna"], control="NT", labels="gene_target", layer="X_pert")
+            >>> ms.plot_lda(adata=mdata["rna"], control="NT")
         """
         warnings.warn(
             "This function is deprecated and will be removed in pertpy 0.8.0!"

pertpy/preprocessing/_guide_rna.py

@@ -41,7 +41,7 @@ def assign_by_threshold(
 
             >>> import pertpy as pt
             >>> mdata = pt.data.papalexi_2021()
-            >>> gdo = mdata.mod['gdo']
+            >>> gdo = mdata.mod["gdo"]
             >>> ga = pt.pp.GuideAssignment()
             >>> ga.assign_by_threshold(gdo, assignment_threshold=5)
         """
@@ -86,7 +86,7 @@ def assign_to_max_guide(
 
             >>> import pertpy as pt
             >>> mdata = pt.dt.papalexi_2021()
-            >>> gdo = mdata.mod['gdo']
+            >>> gdo = mdata.mod["gdo"]
             >>> ga = pt.pp.GuideAssignment()
             >>> ga.assign_to_max_guide(gdo, assignment_threshold=5)
         """
@@ -143,7 +143,7 @@ def plot_heatmap(
 
             >>> import pertpy as pt
             >>> mdata = pt.dt.papalexi_2021()
-            >>> gdo = mdata.mod['gdo']
+            >>> gdo = mdata.mod["gdo"]
             >>> ga = pt.pp.GuideAssignment()
             >>> ga.assign_by_threshold(gdo, assignment_threshold=5)
             >>> ga.heatmap(gdo)

pertpy/tools/_augur.py

@@ -220,7 +220,9 @@ def sample(self, adata: AnnData, categorical: bool, subsample_size: int, random_
             >>> loaded_data = ag_rfc.load(adata)
             >>> ag_rfc.select_highly_variable(loaded_data)
             >>> features = loaded_data.var_names
-            >>> subsample = ag_rfc.sample(loaded_data, categorical=True, subsample_size=20, random_state=42, features=loaded_data.var_names)
+            >>> subsample = ag_rfc.sample(
+            ...     loaded_data, categorical=True, subsample_size=20, random_state=42, features=loaded_data.var_names
+            ... )
         """
         # export subsampling.
         random.seed(random_state)
@@ -1051,7 +1053,9 @@ def plot_important_features(
             >>> adata = pt.dt.sc_sim_augur()
             >>> ag_rfc = pt.tl.Augur("random_forest_classifier")
             >>> loaded_data = ag_rfc.load(adata)
-            >>> v_adata, v_results = ag_rfc.predict(loaded_data, subsample_size=20, select_variance_features=True, n_threads=4)
+            >>> v_adata, v_results = ag_rfc.predict(
+            ...     loaded_data, subsample_size=20, select_variance_features=True, n_threads=4
+            ... )
             >>> ag_rfc.plot_important_features(v_results)
         """
         if isinstance(data, AnnData):
@@ -1101,7 +1105,9 @@ def plot_lollipop(
             >>> adata = pt.dt.sc_sim_augur()
             >>> ag_rfc = pt.tl.Augur("random_forest_classifier")
             >>> loaded_data = ag_rfc.load(adata)
-            >>> v_adata, v_results = ag_rfc.predict(loaded_data, subsample_size=20, select_variance_features=True, n_threads=4)
+            >>> v_adata, v_results = ag_rfc.predict(
+            ...     loaded_data, subsample_size=20, select_variance_features=True, n_threads=4
+            ... )
             >>> ag_rfc.plot_lollipop(v_results)
         """
         if isinstance(data, AnnData):
@@ -1149,7 +1155,9 @@ def plot_scatterplot(
             >>> ag_rfc = pt.tl.Augur("random_forest_classifier")
             >>> loaded_data = ag_rfc.load(adata)
             >>> h_adata, h_results = ag_rfc.predict(loaded_data, subsample_size=20, n_threads=4)
-            >>> v_adata, v_results = ag_rfc.predict(loaded_data, subsample_size=20, select_variance_features=True, n_threads=4)
+            >>> v_adata, v_results = ag_rfc.predict(
+            ...     loaded_data, subsample_size=20, select_variance_features=True, n_threads=4
+            ... )
             >>> ag_rfc.plot_scatterplot(v_results, h_results)
         """
         cell_types = results1["summary_metrics"].columns

pertpy/tools/_coda/_base_coda.py

@@ -2141,7 +2141,9 @@ def plot_effects_umap(  # pragma: no cover
             >>>     pen_args={"phi": 0, "lambda_1": 3.5},
             >>>     tree_key="tree"
             >>> )
-            >>> tasccoda_model.run_nuts(tasccoda_data, modality_key="coda", rng_key=1234, num_samples=10000, num_warmup=1000)
+            >>> tasccoda_model.run_nuts(
+            ...     tasccoda_data, modality_key="coda", rng_key=1234, num_samples=10000, num_warmup=1000
+            ... )
             >>> tasccoda_model.plot_effects_umap(tasccoda_data,
             >>>                         effect_name=["effect_df_condition[T.Salmonella]",
             >>>                                      "effect_df_condition[T.Hpoly.Day3]",

pertpy/tools/_coda/_tasccoda.py

@@ -334,7 +334,7 @@ def set_init_mcmc_states(self, rng_key: None, ref_index: np.ndarray, sample_adat
             >>> mdata = tasccoda.prepare(
             >>>     mdata, formula="Health", reference_cell_type="automatic", tree_key="lineage", pen_args={"phi": 0}
             >>> )
-            >>> adata = tasccoda.set_init_mcmc_states(rng_key=42, ref_index=[0,1], sample_adata=mdata['coda'])
+            >>> adata = tasccoda.set_init_mcmc_states(rng_key=42, ref_index=[0, 1], sample_adata=mdata["coda"])
         """
         N, D = sample_adata.obsm["covariate_matrix"].shape
         P = sample_adata.X.shape[1]

pertpy/tools/_dialogue.py

@@ -623,7 +623,9 @@ def calculate_multifactor_PMD(
             >>> import scanpy as sc
             >>> adata = pt.dt.dialogue_example()
             >>> sc.pp.pca(adata)
-            >>> dl = pt.tl.Dialogue(sample_id = "clinical.status", celltype_key = "cell.subtypes", n_counts_key = "nCount_RNA", n_mpcs = 3)
+            >>> dl = pt.tl.Dialogue(
+            ...     sample_id="clinical.status", celltype_key="cell.subtypes", n_counts_key="nCount_RNA", n_mpcs=3
+            ... )
             >>> adata, mcps, ws, ct_subs = dl.calculate_multifactor_PMD(adata, normalize=True)
         """
         # IMPORTANT NOTE: the order in which matrices are passed to multicca matters.

pertpy/tools/_distances/_distance_tests.py

@@ -37,8 +37,8 @@ class DistanceTest:
     Examples:
         >>> import pertpy as pt
         >>> adata = pt.dt.distance_example_data()
-        >>> distance_test = pt.tl.DistanceTest('edistance', n_perms=1000)
-        >>> tab = distance_test(adata, groupby='perturbation', contrast='control')
+        >>> distance_test = pt.tl.DistanceTest("edistance", n_perms=1000)
+        >>> tab = distance_test(adata, groupby="perturbation", contrast="control")
     """
 
     def __init__(
@@ -100,8 +100,8 @@ def __call__(
         Examples:
             >>> import pertpy as pt
             >>> adata = pt.dt.distance_example_data()
-            >>> distance_test = pt.tl.DistanceTest('edistance', n_perms=1000)
-            >>> tab = distance_test(adata, groupby='perturbation', contrast='control')
+            >>> distance_test = pt.tl.DistanceTest("edistance", n_perms=1000)
+            >>> tab = distance_test(adata, groupby="perturbation", contrast="control")
         """
         if self.distance.metric_fct.accepts_precomputed:
             # Much faster if the metric can be called on the precomputed
@@ -134,8 +134,8 @@ def test_xy(self, adata: AnnData, groupby: str, contrast: str, show_progressbar:
         Examples:
             >>> import pertpy as pt
             >>> adata = pt.dt.distance_example_data()
-            >>> distance_test = pt.tl.DistanceTest('edistance', n_perms=1000)
-            >>> test_results = distance_test.test_xy(adata, groupby='perturbation', contrast='control')
+            >>> distance_test = pt.tl.DistanceTest("edistance", n_perms=1000)
+            >>> test_results = distance_test.test_xy(adata, groupby="perturbation", contrast="control")
         """
         groups = adata.obs[groupby].unique()
         if contrast not in groups:
@@ -226,8 +226,8 @@ def test_precomputed(self, adata: AnnData, groupby: str, contrast: str, verbose:
         Examples:
             >>> import pertpy as pt
             >>> adata = pt.dt.distance_example_data()
-            >>> distance_test = pt.tl.DistanceTest('edistance', n_perms=1000)
-            >>> test_results = distance_test.test_precomputed(adata, groupby='perturbation', contrast='control')
+            >>> distance_test = pt.tl.DistanceTest("edistance", n_perms=1000)
+            >>> test_results = distance_test.test_precomputed(adata, groupby="perturbation", contrast="control")
         """
         if not self.distance.metric_fct.accepts_precomputed:
             raise ValueError(f"Metric {self.metric} does not accept precomputed distances.")

pertpy/tools/_milo.py

@@ -429,7 +429,7 @@ def annotate_nhoods(
             >>> sc.pp.neighbors(mdata["rna"])
             >>> milo.make_nhoods(mdata["rna"])
             >>> mdata = milo.count_nhoods(mdata, sample_col="orig.ident")
-            >>> milo.annotate_nhoods(mdata, anno_col='cell_type')
+            >>> milo.annotate_nhoods(mdata, anno_col="cell_type")
         """
         try:
             sample_adata = mdata["milo"]
@@ -480,7 +480,7 @@ def annotate_nhoods_continuous(self, mdata: MuData, anno_col: str, feature_key:
             >>> sc.pp.neighbors(mdata["rna"])
             >>> milo.make_nhoods(mdata["rna"])
             >>> mdata = milo.count_nhoods(mdata, sample_col="orig.ident")
-            >>> milo.annotate_nhoods_continuous(mdata, anno_col='nUMI')
+            >>> milo.annotate_nhoods_continuous(mdata, anno_col="nUMI")
         """
         if "milo" not in mdata.mod:
             raise ValueError(
@@ -845,7 +845,7 @@ def plot_da_beeswarm(
             >>> milo.make_nhoods(mdata["rna"])
             >>> mdata = milo.count_nhoods(mdata, sample_col="orig.ident")
             >>> milo.da_nhoods(mdata, design="~label")
-            >>> milo.annotate_nhoods(mdata, anno_col='cell_type')
+            >>> milo.annotate_nhoods(mdata, anno_col="cell_type")
             >>> milo.plot_da_beeswarm(mdata)
         """
         try: