style: formatting

workflow/rules/clustering.smk

@@ -1,9 +1,10 @@
 if config["clustering_preset"] == "mycobacterium_tuberculosis":
+
     rule copy_or_touch_list_excluded_samples:
         output:
             temp(OUT + "/previous_list_excluded_samples.tsv"),
         params:
-            previous_list = PREVIOUS_CLUSTERING + "/list_excluded_samples.tsv"
+            previous_list=PREVIOUS_CLUSTERING + "/list_excluded_samples.tsv",
         shell:
             """
 if [ -f {params.previous_list} ]
@@ -13,11 +14,13 @@ else
     touch {output}
 fi
             """
-    
+
     rule list_excluded_samples:
         input:
-            seq_exp_json = expand(INPUT + "/mtb_typing/seq_exp_json/{sample}.json", sample=SAMPLES),
-            exclude_list = OUT + "/previous_list_excluded_samples.tsv",
+            seq_exp_json=expand(
+                INPUT + "/mtb_typing/seq_exp_json/{sample}.json", sample=SAMPLES
+            ),
+            exclude_list=OUT + "/previous_list_excluded_samples.tsv",
         output:
             OUT + "/list_excluded_samples.tsv",
         log:
@@ -45,7 +48,9 @@ python workflow/scripts/list_excluded_samples.py \
 --coverage-threshold {params.coverage_threshold} \
 2>&1> {log}
             """
+
 else:
+
     rule touch_list_excluded_samples:
         output:
             temp(OUT + "/list_excluded_samples.tsv"),

workflow/scripts/cluster.py

@@ -66,6 +66,7 @@ def read_data(distances, previous_clustering):
         )
     return df_distances, df_previous_clustering
 
+
 @timing
 def clean_sample_columns(df, cols, fixed_string):
     """
@@ -119,6 +120,7 @@ def exclude_samples(df_distances, exclude_list):
         ]
     return df_distances
 
+
 @timing
 def emit_and_save_critical_warning(message, output_path):
     """
@@ -140,6 +142,7 @@ def emit_and_save_critical_warning(message, output_path):
     with open(output_path, "a") as f:
         f.write(message + "\n")
 
+
 @timing
 def get_df_nodes(df_distances, df_previous_clustering):
     """
@@ -369,7 +372,10 @@ def infer_clusters(graph, merged_cluster_separator, warnings_path):
         set_curated_clusters = enlist_clusters(subgraph, "curated_cluster")
         set_final_clusters = enlist_clusters(subgraph, "final_cluster")
         if len(set_curated_clusters) > 1:
-            emit_and_save_critical_warning(f"WARNING: Curated clusters {set_curated_clusters} have merged!", warnings_path)
+            emit_and_save_critical_warning(
+                f"WARNING: Curated clusters {set_curated_clusters} have merged!",
+                warnings_path,
+            )
             inferred_cluster = construct_merged_cluster_name(
                 set_curated_clusters, merged_cluster_separator
             )
@@ -379,7 +385,10 @@ def infer_clusters(graph, merged_cluster_separator, warnings_path):
                 f"Cluster {inferred_cluster} is curated and not merged with others"
             )
         elif len(set_final_clusters) > 1:
-            emit_and_save_critical_warning(f"WARNING: Final clusters {set_final_clusters} have merged!", warnings_path)
+            emit_and_save_critical_warning(
+                f"WARNING: Final clusters {set_final_clusters} have merged!",
+                warnings_path,
+            )
             inferred_cluster = construct_merged_cluster_name(
                 set_final_clusters, merged_cluster_separator
             )
@@ -452,7 +461,9 @@ def main(args):
         args.distances, args.previous_clustering
     )
 
-    df_distances = clean_sample_columns(df_distances, ["sample1", "sample2"], "_contig1")
+    df_distances = clean_sample_columns(
+        df_distances, ["sample1", "sample2"], "_contig1"
+    )
 
     if args.exclude_list:
         df_distances = exclude_samples(df_distances, args.exclude_list)
@@ -463,7 +474,9 @@ def main(args):
 
     G = create_graph(df_distances_filtered, df_nodes)
 
-    inferred_cluster_dict = infer_clusters(G, args.merged_cluster_separator, args.warnings_path)
+    inferred_cluster_dict = infer_clusters(
+        G, args.merged_cluster_separator, args.warnings_path
+    )
 
     create_output(inferred_cluster_dict, df_previous_clustering, args.output)
 
@@ -493,14 +506,14 @@ def main(args):
         default="|",
     )
     parser.add_argument(
-        "--exclude-list", type=Path, help="Path to list of samples to exclude from clustering"
+        "--exclude-list",
+        type=Path,
+        help="Path to list of samples to exclude from clustering",
     )
     parser.add_argument(
         "--log", type=Path, help="Path to log file", default="cluster.log"
     )
-    parser.add_argument(
-        "--warnings-path", type=Path, help="Path to warnings file"
-    )
+    parser.add_argument("--warnings-path", type=Path, help="Path to warnings file")
     parser.add_argument(
         "-v", "--verbose", action="count", default=0, help="Verbosity level"
     )

workflow/scripts/list_excluded_samples.py

@@ -5,53 +5,64 @@
 import datetime
 import logging
 
+
 def read_input_data(input_files):
     data = {}
     for file in input_files:
         with open(file) as f:
             data[file.stem] = json.load(f)
-    df = pd.DataFrame.from_dict(data, orient='index').reset_index(names="sample")
-    df['mean_coverage'] = df['mean_coverage'].astype(float)
+    df = pd.DataFrame.from_dict(data, orient="index").reset_index(names="sample")
+    df["mean_coverage"] = df["mean_coverage"].astype(float)
     return df
 
+
 def exclude_on_coverage(df, threshold):
     df_copy = df.copy()
-    return df_copy[df_copy['mean_coverage'] < threshold]
+    return df_copy[df_copy["mean_coverage"] < threshold]
+
 
 def exclude_on_pattern(df, pattern):
     df_copy = df.copy()
-    return df_copy[~df_copy['sample'].str.contains(pattern)]
+    return df_copy[~df_copy["sample"].str.contains(pattern)]
+
 
 def read_previous_exclude_list(file):
     with open(file) as f:
         lines = f.readlines()
     if len(lines) == 0:
-        return pd.DataFrame(columns=['sample', 'reason', 'date'])
+        return pd.DataFrame(columns=["sample", "reason", "date"])
     else:
         df = pd.read_csv(file, sep="\t")
         return df
 
+
 def main(args):
     df = read_input_data(args.input)
     df_coverage_excluded = exclude_on_coverage(df, args.coverage_threshold)
     df_coverage_excluded["reason"] = "low_coverage"
     df_pattern_excluded = exclude_on_pattern(df, args.inclusion_pattern)
     df_pattern_excluded["reason"] = "not_NLA"
-    df_excluded = pd.concat([df_coverage_excluded[['sample', 'reason']], df_pattern_excluded[['sample', 'reason']]])
-    df_excluded['date'] = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    df_excluded = pd.concat(
+        [
+            df_coverage_excluded[["sample", "reason"]],
+            df_pattern_excluded[["sample", "reason"]],
+        ]
+    )
+    df_excluded["date"] = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
     df_previous_excluded = read_previous_exclude_list(args.previous_exclude_list)
     df_final = pd.concat([df_previous_excluded, df_excluded])
-    df_final.to_csv(args.output, sep="\t", index=False) 
+    df_final.to_csv(args.output, sep="\t", index=False)
+
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     parser = argparse.ArgumentParser()
 
-    parser.add_argument('--input', type=Path, required=True, nargs='+')
-    parser.add_argument('--previous-exclude-list', type=Path, required=True)
-    parser.add_argument('--output', type=Path, required=True)
-    parser.add_argument('--inclusion-pattern', type=str, required=True)
-    parser.add_argument('--coverage-threshold', type=float, required=True)
+    parser.add_argument("--input", type=Path, required=True, nargs="+")
+    parser.add_argument("--previous-exclude-list", type=Path, required=True)
+    parser.add_argument("--output", type=Path, required=True)
+    parser.add_argument("--inclusion-pattern", type=str, required=True)
+    parser.add_argument("--coverage-threshold", type=float, required=True)
 
     args = parser.parse_args()
 
-    main(args)
+    main(args)