--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/matchms_similarity_wrapper.py	Fri Jan 28 16:22:06 2022 +0000
@@ -0,0 +1,100 @@
+import argparse
+import sys
+
+from matchms import calculate_scores
+from matchms.importing import load_from_mgf, load_from_msp
+from matchms.similarity import (
+    CosineGreedy,
+    CosineHungarian,
+    ModifiedCosine,
+)
+from pandas import DataFrame
+
+
+def convert_precursor_mz(spectrum):
+    """
+    Check the presence of precursor m/z since it is needed for ModifiedCosine similarity metric. Convert to float if
+    needed, raise error if missing.
+    """
+
+    if "precursor_mz" in spectrum.metadata:
+        metadata = spectrum.metadata
+        metadata["precursor_mz"] = float(metadata["precursor_mz"])
+        spectrum.metadata = metadata
+        return spectrum
+    else:
+        raise ValueError("Precursor_mz missing. Apply 'add_precursor_mz' filter first.")
+
+
+def main(argv):
+    parser = argparse.ArgumentParser(description="Compute MSP similarity scores")
+    parser.add_argument("-s", dest="symmetric", action='store_true', help="Computation is symmetric.")
+    parser.add_argument("--ref", dest="references_filename", type=str, help="Path to reference spectra library.")
+    parser.add_argument("--ref_format", dest="references_format", type=str, help="Reference spectra library file format.")
+    parser.add_argument("queries_filename", type=str, help="Path to query spectra.")
+    parser.add_argument("queries_format", type=str, help="Query spectra file format.")
+    parser.add_argument("similarity_metric", type=str, help='Metric to use for matching.')
+    parser.add_argument("tolerance", type=float, help="Tolerance to use for peak matching.")
+    parser.add_argument("mz_power", type=float, help="The power to raise mz to in the cosine function.")
+    parser.add_argument("intensity_power", type=float, help="The power to raise intensity to in the cosine function.")
+    parser.add_argument("output_filename_scores", type=str, help="Path where to store the output .tsv scores.")
+    parser.add_argument("output_filename_matches", type=str, help="Path where to store the output .tsv matches.")
+    args = parser.parse_args()
+
+    if args.queries_format == 'msp':
+        queries_spectra = list(load_from_msp(args.queries_filename))
+    elif args.queries_format == 'mgf':
+        queries_spectra = list(load_from_mgf(args.queries_filename))
+    else:
+        raise ValueError(f'File format {args.queries_format} not supported for query spectra.')
+
+    if args.symmetric:
+        reference_spectra = []
+    else:
+        if args.references_format == 'msp':
+            reference_spectra = list(load_from_msp(args.references_filename))
+        elif args.references_format == 'mgf':
+            reference_spectra = list(load_from_mgf(args.references_filename))
+        else:
+            raise ValueError(f'File format {args.references_format} not supported for reference spectra library.')
+
+    if args.similarity_metric == 'CosineGreedy':
+        similarity_metric = CosineGreedy(args.tolerance, args.mz_power, args.intensity_power)
+    elif args.similarity_metric == 'CosineHungarian':
+        similarity_metric = CosineHungarian(args.tolerance, args.mz_power, args.intensity_power)
+    elif args.similarity_metric == 'ModifiedCosine':
+        similarity_metric = ModifiedCosine(args.tolerance, args.mz_power, args.intensity_power)
+        reference_spectra = list(map(convert_precursor_mz, reference_spectra))
+        queries_spectra = list(map(convert_precursor_mz, queries_spectra))
+    else:
+        return -1
+
+    print("Calculating scores...")
+    scores = calculate_scores(
+        references=queries_spectra if args.symmetric else reference_spectra,
+        queries=queries_spectra,
+        similarity_function=similarity_metric,
+        is_symmetric=args.symmetric
+    )
+
+    write_outputs(args, scores)
+    return 0
+
+
+def write_outputs(args, scores):
+    print("Storing outputs...")
+    query_names = [spectra.metadata['name'] for spectra in scores.queries]
+    reference_names = [spectra.metadata['name'] for spectra in scores.references]
+
+    # Write scores to dataframe
+    dataframe_scores = DataFrame(data=[entry["score"] for entry in scores.scores], index=reference_names, columns=query_names)
+    dataframe_scores.to_csv(args.output_filename_scores, sep='\t')
+
+    # Write number of matches to dataframe
+    dataframe_matches = DataFrame(data=[entry["matches"] for entry in scores.scores], index=reference_names, columns=query_names)
+    dataframe_matches.to_csv(args.output_filename_matches, sep='\t')
+
+
+if __name__ == "__main__":
+    main(argv=sys.argv[1:])
+    pass