--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/matchms_similarity_wrapper.py	Tue Jun 27 14:25:59 2023 +0000
@@ -0,0 +1,136 @@
+import argparse
+import json
+import sys
+
+from matchms import calculate_scores
+from matchms.importing import load_from_mgf, load_from_msp
+from matchms.similarity import (CosineGreedy, CosineHungarian, MetadataMatch,
+                                ModifiedCosine, NeutralLossesCosine)
+from spec2vec import Spec2Vec
+from spec2vec.serialization.model_importing import load_weights, Word2VecLight
+
+
+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 load_model(model_file, weights_file) -> Word2VecLight:
+    """
+    Read a lightweight version of a :class:`~gensim.models.Word2Vec` model from disk.
+
+    Parameters
+    ----------
+    model_file:
+        A path of json file to load the model.
+    weights_file:
+        A path of `.npy` file to load the model's weights.
+
+    Returns
+    -------
+    :class:`~spec2vec.serialization.model_importing.Word2VecLight` – a lightweight version of a
+    :class:`~gensim.models.Word2Vec`
+    """
+    with open(model_file, "r", encoding="utf-8") as f:
+        model: dict = json.load(f)
+        del (model["mapfile_path"])
+
+    weights = load_weights(weights_file, model["__weights_format"])
+    return Word2VecLight(model, weights)
+
+
+def main(argv):
+    parser = argparse.ArgumentParser(description="Compute MSP similarity scores")
+    parser.add_argument("-r", dest="ri_tolerance", type=float, help="Use RI filtering with given tolerance.")
+    parser.add_argument("-s", dest="symmetric", action='store_true', help="Computation is symmetric.")
+    parser.add_argument("--array_type", type=str, help="Type of array to use for storing scores (numpy or sparse).")
+    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("--spec2vec_model", dest="spec2vec_model", type=str, help="Path to spec2vec model.")
+    parser.add_argument("--spec2vec_weights", dest="spec2vec_weights", type=str, help="Path to spec2vec weights.")
+    parser.add_argument("--allow_missing_percentage", dest="allowed_missing_percentage", type=lambda x: float(x) * 100.0, help="Maximum percentage of missing peaks in model corpus.")
+    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 .json scores.")
+    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 = queries_spectra.copy()
+    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))
+    elif args.similarity_metric == 'NeutralLossesCosine':
+        similarity_metric = NeutralLossesCosine(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))
+    elif args.similarity_metric == 'Spec2Vec':
+        model = load_model(args.spec2vec_model, args.spec2vec_weights)
+        similarity_metric = Spec2Vec(model, intensity_weighting_power=args.intensity_power, allowed_missing_percentage=args.allowed_missing_percentage)
+    else:
+        return -1
+
+    print("Calculating scores...")
+    scores = calculate_scores(
+        references=reference_spectra,
+        queries=queries_spectra,
+        array_type=args.array_type,
+        similarity_function=similarity_metric,
+        is_symmetric=args.symmetric
+    )
+
+    if args.ri_tolerance is not None:
+        print("RI filtering with tolerance ", args.ri_tolerance)
+        ri_matches = calculate_scores(references=reference_spectra,
+                                      queries=queries_spectra,
+                                      similarity_function=MetadataMatch("retention_index", "difference", args.ri_tolerance),
+                                      array_type="numpy",
+                                      is_symmetric=args.symmetric).scores
+        scores.scores.add_coo_matrix(ri_matches, "MetadataMatch", join_type="inner")
+
+    write_outputs(args, scores)
+    return 0
+
+
+def write_outputs(args, scores):
+    """Write Scores to json file."""
+    print("Storing outputs...")
+    scores.to_json(args.output_filename_scores)
+
+
+if __name__ == "__main__":
+    main(argv=sys.argv[1:])
+    pass