began argument parsing for new training pipeline

src/depht_utils/pipelines/train_model.py

@@ -7,13 +7,26 @@
 import pathlib
 
 
-def parse_args():
+def parse_args(unparsed_args=None):
     p = argparse.ArgumentParser(description=__doc__)
-    return p.parse_args()
+    p.add_argument("-b", "--bact-dir", type=pathlib.Path,
+                   help="path to directory containing bacterial file(s) for "
+                        "training")
+    p.add_argument("-p", "--phage-dir", type=pathlib.Path,
+                   help="path to directory containing phage file(s) for "
+                        "training")
+    p.add_argument("-n", "--model-name", type=str,
+                   help="name for the new model (e.g. 'Streptomyces')")
 
+    if unparsed_args:
+        return p.parse_args(unparsed_args)
+    else:
+        return p.parse_args()
 
-def main():
-    pass
+
+def main(unparsed_args=None):
+    args = parse_args(unparsed_args)
+    print(args)
 
 
 if __name__ == "__main__":

src/depht_utils/run.py

@@ -4,15 +4,15 @@
 from depht_utils.pipelines import (
     annotate_gene_clusters, benchmark_output, build_reference_db,
     build_functions_db, curate_functions, index_functions, phamerate,
-    pull_sequences, screen_conserved_phams, train_prophage_model)
+    pull_sequences, screen_conserved_phams, train_prophage_model, train_model)
 
 # GLOBAL VARIABLES
 # -----------------------------------------------------------------------------
 PIPELINES = [
         "annotate_gene_clusters", "benchmark_output", "build_reference_db",
         "build_functions_db", "curate_functions", "index_functions",
         "phamerate", "pull_sequences", "screen_conserved_phams",
-        "train_prophage_model"]
+        "train_prophage_model", "train_model"]
 
 
 def main(unparsed_args):
@@ -40,6 +40,8 @@ def main(unparsed_args):
         screen_conserved_phams.main(unparsed_args[1:])
     elif args.pipeline == "train_prophage_model":
         train_prophage_model.main(unparsed_args[1:])
+    elif args.pipeline == "train_model":
+        train_model.main(unparsed_args[1:])
     else:
         raise NotImplementedError(
                    f"Prophicient Utility pipeline '{args.build_reference_db}' "