black cleanup

src/baskerville/scripts/hound_ism_snp.py

@@ -36,6 +36,7 @@
 given in a VCF file.
 """
 
+
 ################################################################################
 # main
 ################################################################################
@@ -211,7 +212,7 @@ def main():
         ref_1hot = np.expand_dims(seqs_1hot[si], axis=0)
 
         # save sequence
-        scores_h5['seqs'][si] = ref_1hot[0, mut_start:mut_end].astype('bool')
+        scores_h5["seqs"][si] = ref_1hot[0, mut_start:mut_end].astype("bool")
 
         # predict reference
         ref_preds = []
@@ -250,11 +251,15 @@ def main():
                             options.untransform_old,
                         )
                         alt_preds.append(alt_preds_shift)
-                    alt_preds = np.array(alt_preds)                    
+                    alt_preds = np.array(alt_preds)
 
-                    ism_scores = snps.compute_scores(ref_preds, alt_preds, options.snp_stats)
+                    ism_scores = snps.compute_scores(
+                        ref_preds, alt_preds, options.snp_stats
+                    )
                     for snp_stat in options.snp_stats:
-                        scores_h5[snp_stat][si, mi-mut_start, ni] = ism_scores[snp_stat]
+                        scores_h5[snp_stat][si, mi - mut_start, ni] = ism_scores[
+                            snp_stat
+                        ]
 
     # close output HDF5
     scores_h5.close()

src/baskerville/scripts/hound_snp.py

@@ -185,7 +185,7 @@ def main():
         print("Running on CPU")
         if options.require_gpu:
             raise SystemExit("Job terminated because it's running on CPU")
-    
+
     #################################################################
     # download input files from gcs to a local file
     if options.gcs:
@@ -200,7 +200,7 @@ def main():
             options.targets_file = download_rename_inputs(
                 options.targets_file, temp_dir
             )
-    
+
     #################################################################
     # calculate SAD scores:
     if options.processes is not None:

src/baskerville/seqnn.py

@@ -970,14 +970,14 @@ def predict(
         return preds
 
     def predict_transform(
-        self, 
-        seq_1hot: np.array, 
+        self,
+        seq_1hot: np.array,
         targets_df,
         strand_transform: np.array = None,
         untransform_old: bool = False,
     ):
         """Predict a single sequence and transform.
-        
+
         Args:
             seq_1hot (np.array): 1-hot encoded sequence.
             targets_df (pd.DataFrame): Targets dataframe.
@@ -986,19 +986,19 @@ def predict_transform(
         """
         # predict
         preds = self(seq_1hot)[0]
-        
+
         # untransform predictions
         if untransform_old:
             preds = dataset.untransform_preds1(preds, targets_df)
         else:
             preds = dataset.untransform_preds(preds, targets_df)
-        
+
         # sum strand pairs
         if strand_transform is not None:
             preds = preds * strand_transform
 
         return preds
-    
+
     def restore(self, model_file, head_i=0, trunk=False):
         """Restore weights from saved model."""
         if trunk:

src/baskerville/snps.py

@@ -37,7 +37,7 @@ def score_snps(params_file, model_file, vcf_file, worker_index, options):
 
     # read targets
     if options.targets_file is None:
-        print('Must provide targets file to clarify stranded datasets', file=sys.stderr)
+        print("Must provide targets file to clarify stranded datasets", file=sys.stderr)
         exit(1)
     targets_df = pd.read_csv(options.targets_file, sep="\t", index_col=0)
 

tests/test_ism.py

@@ -12,6 +12,7 @@
 snp_out_dir = "tests/data/ism/snp_out"
 bed_out_dir = "tests/data/ism/bed_out"
 
+
 def test_snp():
     cmd = [
         "src/baskerville/scripts/hound_ism_snp.py",
@@ -46,6 +47,7 @@ def test_snp():
             # verify variance
             assert (score_var > 0).all()
 
+
 def test_bed():
     cmd = [
         "src/baskerville/scripts/hound_ism_bed.py",
@@ -78,4 +80,4 @@ def test_bed():
             assert not np.isnan(score).any()
 
             # verify variance
-            assert (score_var > 0).all()
+            assert (score_var > 0).all()