--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/matchms_wrapper.py	Mon Dec 07 20:12:13 2020 +0000
@@ -0,0 +1,61 @@
+import argparse
+import sys
+
+import pandas
+from matchms import calculate_scores
+from matchms.importing import load_from_msp
+from matchms.similarity import (
+    CosineGreedy,
+    CosineHungarian,
+    FingerprintSimilarity,
+    IntersectMz,
+    ModifiedCosine,
+    ParentMassMatch
+)
+
+
+def main(argv):
+    parser = argparse.ArgumentParser(description="Compute MSP similarity scores")
+    parser.add_argument(
+        "references_filename", type=str, help="Path to reference MSP library."
+    )
+    parser.add_argument("queries_filename", type=str, help="Path to query spectra.")
+    parser.add_argument("output_filename", type=str, help="Path where to store the output .csv.")
+    parser.add_argument("similarity_metric", type=str, help='Metric to use for matching.')
+
+    args = parser.parse_args()
+
+    if args.similarity_metric == 'CosineGreedy':
+        similarity_metric = CosineGreedy()
+    elif args.similarity_metric == 'CosineHungarian':
+        similarity_metric = CosineHungarian()
+    elif args.similarity_metric == 'FingerprintSimilarity':
+        similarity_metric = FingerprintSimilarity()
+    elif args.similarity_metric == 'IntersectMz':
+        similarity_metric = IntersectMz()
+    elif args.similarity_metric == 'ModifiedCosine':
+        similarity_metric = ModifiedCosine()
+    else:
+        similarity_metric = ParentMassMatch()
+
+    reference_spectra = [
+        spectrum for spectrum in load_from_msp(args.references_filename)
+    ]
+    queries_spectra = [spectrum for spectrum in load_from_msp(args.queries_filename)]
+
+    scores = calculate_scores(
+        references=reference_spectra,
+        queries=queries_spectra,
+        similarity_function=similarity_metric,
+    )
+
+    query_names = [spectra.metadata['name'] for spectra in scores.queries]
+    reference_names = [spectra.metadata['name'] for spectra in scores.references]
+    dataframe = pandas.DataFrame(data=scores.scores, index=reference_names, columns=query_names)
+    dataframe.to_csv(args.output_filename, sep=';')
+    return 0
+
+
+if __name__ == "__main__":
+    main(argv=sys.argv[1:])
+    pass