Run black formatting on hpc-prune.py.

Run black formatting on hpc-prune.py.

scripts/hpc-prune.py

@@ -36,7 +36,9 @@ def create_logging_handler(debug: bool) -> logging.Handler:
     # to DEBUG, information will still be outputted. In addition, errors and warnings are more
     # severe than info and therefore will always be outputted to the log.
     ch.setLevel(logging.DEBUG if debug else logging.INFO)
-    ch.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
+    ch.setFormatter(
+        logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
+    )
     return ch
 
 
@@ -49,19 +51,23 @@ def __exit__(*x):
 
 
 def create_argument_parser():
-    parser = argparse.ArgumentParser(prog='hpc-prune')
-    parser.add_argument('config_filename',
-                        type=str, default=CONFIG_FILENAME,
-                        help='Name of configuration file to use.')
-    parser.add_argument('-i', '--input',
-                        type=str,
-                        help='Intput file/stream.')
-    parser.add_argument('-o', '--output',
-                        type=str,
-                        help='Output file/stream.')
-    parser.add_argument('--num_workers',
-                       type=int, default=cpu_count(),
-                       help='The number of workers to use. Only valid of using dask_multiprocessing, custom_multiprocessing or local as mode. (default: {}).'.format(cpu_count()))
+    parser = argparse.ArgumentParser(prog="hpc-prune")
+    parser.add_argument(
+        "config_filename",
+        type=str,
+        default=CONFIG_FILENAME,
+        help="Name of configuration file to use.",
+    )
+    parser.add_argument("-i", "--input", type=str, help="Intput file/stream.")
+    parser.add_argument("-o", "--output", type=str, help="Output file/stream.")
+    parser.add_argument(
+        "--num_workers",
+        type=int,
+        default=cpu_count(),
+        help="The number of workers to use. Only valid of using dask_multiprocessing, custom_multiprocessing or local as mode. (default: {}).".format(
+            cpu_count()
+        ),
+    )
 
     return parser
 
@@ -76,40 +82,46 @@ def run(args):
     cfg = ConfigParser()
     cfg.read(args.config_filename)
 
-    in_fname = cfg['data']['modules'] if not args.input else args.input
+    in_fname = cfg["data"]["modules"] if not args.input else args.input
     LOGGER.info("Loading modules from {}.".format(in_fname))
     # Loading from YAML is extremely slow. Therefore this is a potential performance improvement.
     # Potential improvements are switching to JSON or to use a CLoader:
     # https://stackoverflow.com/questions/27743711/can-i-speedup-yaml
-    if in_fname.endswith('.yaml'):
+    if in_fname.endswith(".yaml"):
         modules = load_from_yaml(in_fname)
     else:
-        with open(in_fname, 'rb') as f:
+        with open(in_fname, "rb") as f:
             modules = pickle.load(f)
     # Filter out modules with to few genes.
-    min_genes = int(cfg['parameters']['min_genes'])
+    min_genes = int(cfg["parameters"]["min_genes"])
     modules = list(filter(lambda m: len(m) >= min_genes, modules))
 
     LOGGER.info("Loading databases.")
+
     def name(fname):
         return os.path.splitext(os.path.basename(fname))[0]
-    db_fnames = list(mapcat(glob.glob, cfg['data']['databases'].split(";")))
+
+    db_fnames = list(mapcat(glob.glob, cfg["data"]["databases"].split(";")))
     dbs = [RankingDatabase(fname=fname, name=name(fname)) for fname in db_fnames]
 
     LOGGER.info("Calculating regulons.")
-    motif_annotations_fname = cfg['data']['motif_annotations']
-    mode= cfg['parameters']['mode']
+    motif_annotations_fname = cfg["data"]["motif_annotations"]
+    mode = cfg["parameters"]["mode"]
     with ProgressBar() if mode == "dask_multiprocessing" else NoProgressBar():
-        df = prune2df(dbs, modules, motif_annotations_fname,
-                                  rank_threshold=int(cfg['parameters']['rank_threshold']),
-                                  auc_threshold=float(cfg['parameters']['auc_threshold']),
-                                  nes_threshold=float(cfg['parameters']['nes_threshold']),
-                                  client_or_address=mode,
-                                  module_chunksize=cfg['parameters']['chunk_size'],
-                                  num_workers=args.num_workers)
+        df = prune2df(
+            dbs,
+            modules,
+            motif_annotations_fname,
+            rank_threshold=int(cfg["parameters"]["rank_threshold"]),
+            auc_threshold=float(cfg["parameters"]["auc_threshold"]),
+            nes_threshold=float(cfg["parameters"]["nes_threshold"]),
+            client_or_address=mode,
+            module_chunksize=cfg["parameters"]["chunk_size"],
+            num_workers=args.num_workers,
+        )
 
     LOGGER.info("Writing results to file.")
-    df.to_csv(cfg['parameters']['output'] if not args.output else args.output)
+    df.to_csv(cfg["parameters"]["output"] if not args.output else args.output)
 
 
 if __name__ == "__main__":