fixup! Format Python code with psf/black pull_request

deeprvat/annotations/annotations.py

@@ -2436,4 +2436,4 @@ def select_rename_fill_annotations(
 
 
 if __name__ == "__main__":
-    cli()
+    cli()

pipelines/resources/absplice_dna.py

@@ -1,8 +1,8 @@
 from absplice import SplicingOutlierResult
 
 splicing_result = SplicingOutlierResult(
-        df_mmsplice=snakemake.input['mmsplice_splicemap'], 
-        df_spliceai=snakemake.input['spliceai'],
-    )
-splicing_result.predict_absplice_dna(extra_info=snakemake.params['extra_info'])
-splicing_result._absplice_dna.to_csv(snakemake.output['absplice_dna'])
+    df_mmsplice=snakemake.input["mmsplice_splicemap"],
+    df_spliceai=snakemake.input["spliceai"],
+)
+splicing_result.predict_absplice_dna(extra_info=snakemake.params["extra_info"])
+splicing_result._absplice_dna.to_csv(snakemake.output["absplice_dna"])

pipelines/resources/coding_genes.py

@@ -1,17 +1,23 @@
 import pandas as pd
 import pyranges as pr
 
-gr = pr.read_gtf(snakemake.input['gtf_file'])
-gr = gr[(gr.Feature == 'gene') & (gr.gene_type == 'protein_coding')]
+gr = pr.read_gtf(snakemake.input["gtf_file"])
+gr = gr[(gr.Feature == "gene") & (gr.gene_type == "protein_coding")]
 df_genes = gr.df
 
-df_genes['gene_id_orig'] = df_genes['gene_id']
-df_genes['PAR_Y'] = df_genes['gene_id'].apply(lambda x: 'PAR_Y' in x)
-df_genes = df_genes[df_genes['PAR_Y'] == False]
-df_genes['gene_id'] = df_genes['gene_id'].apply(lambda x: x.split('.')[0])
+df_genes["gene_id_orig"] = df_genes["gene_id"]
+df_genes["PAR_Y"] = df_genes["gene_id"].apply(lambda x: "PAR_Y" in x)
+df_genes = df_genes[df_genes["PAR_Y"] == False]
+df_genes["gene_id"] = df_genes["gene_id"].apply(lambda x: x.split(".")[0])
 
 columns = [
-    'Chromosome', 'Start', 'End', 'Strand',
-    'gene_id', 'gene_id_orig', 'gene_name', 'gene_type'
+    "Chromosome",
+    "Start",
+    "End",
+    "Strand",
+    "gene_id",
+    "gene_id_orig",
+    "gene_name",
+    "gene_type",
 ]
-df_genes[columns].to_csv(snakemake.output['coding_genes'], index=False)
+df_genes[columns].to_csv(snakemake.output["coding_genes"], index=False)

pipelines/resources/mmsplice_splicemap.py

@@ -1,10 +1,11 @@
 from absplice import SpliceOutlier, SpliceOutlierDataloader
 
 dl = SpliceOutlierDataloader(
-    snakemake.input['fasta'], snakemake.input['vcf'],
-    splicemap5=list(snakemake.input['splicemap_5']),
-    splicemap3=list(snakemake.input['splicemap_3'])
+    snakemake.input["fasta"],
+    snakemake.input["vcf"],
+    splicemap5=list(snakemake.input["splicemap_5"]),
+    splicemap3=list(snakemake.input["splicemap_3"]),
 )
 
 model = SpliceOutlier()
-model.predict_save(dl, snakemake.output['result'])
+model.predict_save(dl, snakemake.output["result"])

pipelines/resources/spliceai.py

@@ -1,13 +1,14 @@
 from spliceai_rocksdb.spliceAI import SpliceAI
 
 
-if snakemake.params['lookup_only']:
-    model = SpliceAI(db_path=snakemake.params['db_path'])
+if snakemake.params["lookup_only"]:
+    model = SpliceAI(db_path=snakemake.params["db_path"])
 else:
-    model = SpliceAI(snakemake.input['fasta'],
-                     annotation=snakemake.params['genome'],
-                     db_path=snakemake.params['db_path'])
+    model = SpliceAI(
+        snakemake.input["fasta"],
+        annotation=snakemake.params["genome"],
+        db_path=snakemake.params["db_path"],
+    )
 
 
-model.predict_save(snakemake.input['vcf'],
-                   snakemake.output['result'])
+model.predict_save(snakemake.input["vcf"], snakemake.output["result"])