feat: add libraries input to convert

AMDirT/cli.py

@@ -10,6 +10,31 @@
 from json import load
 
 
+class MutuallyExclusiveOption(click.Option):
+    # Credits goes to Stan Chang for this code snippet
+    # https://gist.github.com/stanchan/bce1c2d030c76fe9223b5ff6ad0f03db
+
+    def __init__(self, *args, **kwargs):
+        self.mutually_exclusive = set(kwargs.pop("mutually_exclusive", []))
+        help = kwargs.get("help", "")
+        if self.mutually_exclusive:
+            ex_str = ", ".join(self.mutually_exclusive)
+            kwargs["help"] = help + (
+                " NOTE: This argument is mutually exclusive with "
+                " arguments: [" + ex_str + "]."
+            )
+        super(MutuallyExclusiveOption, self).__init__(*args, **kwargs)
+
+    def handle_parse_result(self, ctx, opts, args):
+        if self.mutually_exclusive.intersection(opts) and self.name in opts:
+            raise click.UsageError(
+                "Illegal usage: `{}` is mutually exclusive with "
+                "arguments `{}`.".format(self.name, ", ".join(self.mutually_exclusive))
+            )
+
+        return super(MutuallyExclusiveOption, self).handle_parse_result(ctx, opts, args)
+
+
 def get_table_list():
     json_path = get_json_path()
     with open(json_path, "r") as f:
@@ -110,6 +135,20 @@ def viewer(ctx, no_args_is_help=True, **kwargs):
     type=click.Path(exists=True),
     help="(Optional) JSON file listing AncientMetagenomeDir tables",
 )
+@click.option(
+    "--libraries",
+    type=click.Path(readable=True, file_okay=True, dir_okay=False, exists=True),
+    help=("(Optional) Path to libraries table"),
+    cls=MutuallyExclusiveOption,
+    mutually_exclusive=["librarymetadata"],
+)
+@click.option(
+    "--librarymetadata",
+    is_flag=True,
+    help="Generate AncientMetagenomeDir libraries table of all samples in input table",
+    cls=MutuallyExclusiveOption,
+    mutually_exclusive=["libraries"],
+)
 @click.option(
     "-o",
     "--output",
@@ -123,11 +162,6 @@ def viewer(ctx, no_args_is_help=True, **kwargs):
     is_flag=True,
     help="Generate BibTeX file of all publications in input table",
 )
-@click.option(
-    "--librarymetadata",
-    is_flag=True,
-    help="Generate AncientMetagenomeDir libraries table of all samples in input table",
-)
 @click.option(
     "--curl",
     is_flag=True,

AMDirT/convert/__init__.py

@@ -22,6 +22,7 @@
 
 def run_convert(
     samples,
+    libraries,
     table_name,
     tables=None,
     output=".",
@@ -40,9 +41,10 @@ def run_convert(
     """Run the AMDirT conversion application to input samplesheet tables for different pipelines
 
     Args:
-        tables (str): Path to JSON file listing tables
         samples (str): Path to AncientMetagenomeDir filtered samples tsv file
+        libraries(str): Optional path to AncientMetagenomeDir filtered libraries tsv file
         table_name (str): Name of the table of the table to convert
+        tables (str): Path to JSON file listing tables
         output (str): Path to output table. Defaults to "."
     """
     os.makedirs(output, exist_ok=True)
@@ -79,14 +81,33 @@ def run_convert(
     else:
         logger.info("Input sample dataset is valid")
         samples = pd.read_csv(samples, sep="\t")
-        libraries = pd.read_csv(remote_resources["libraries"][table_name], sep="\t")
+        remote_libraries = pd.read_csv(
+            remote_resources["libraries"][table_name], sep="\t"
+        )
 
-    selected_libraries = get_libraries(
-        samples=samples,
-        libraries=libraries,
-        table_name=table_name,
-        supported_archives=supported_archives,
-    )
+    if not libraries:
+        selected_libraries = get_libraries(
+            samples=samples,
+            libraries=remote_libraries,
+            table_name=table_name,
+            supported_archives=supported_archives,
+        )
+    else:
+        dataset_valid = list()
+        v = AMDirValidator(schema, libraries)
+        dataset_valid.append(v.parsing_ok)
+        if v.parsing_ok:
+            dataset_valid.append(v.validate_schema())
+            dataset_valid.append(v.check_duplicate_rows())
+            dataset_valid.append(v.check_columns())
+
+        dataset_valid = all(dataset_valid)
+        if dataset_valid is False:
+            v.to_rich()
+            raise DatasetValidationError("Input libraries dataset is not valid")
+        else:
+            logger.info("Input libraries dataset is valid")
+            selected_libraries = pd.read_csv(libraries, sep="\t")
 
     accession_table = prepare_accession_table(
         samples=samples,