changeset 5:c5ed6bfcd806 draft

planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/matchms commit a493624c962d820d962bd81c14aceede5d5be5b4

--- a/macros.xml	Thu Oct 12 13:30:20 2023 +0000
+++ b/macros.xml	Thu Oct 19 15:25:57 2023 +0000
@@ -1,5 +1,5 @@
 <macros>
-    <token name="@TOOL_VERSION@">0.22.0</token>
+    <token name="@TOOL_VERSION@">0.23.1</token>
 
     <xml name="creator">
         <creator>

--- a/matchms_convert.xml	Thu Oct 12 13:30:20 2023 +0000
+++ b/matchms_convert.xml	Thu Oct 19 15:25:57 2023 +0000
@@ -1,4 +1,4 @@
-<tool id="matchms_convert" name="matchms convert" version="@TOOL_VERSION@+galaxy2" profile="21.05">
+<tool id="matchms_convert" name="matchms convert" version="@TOOL_VERSION@+galaxy0" profile="21.05">
     <description>convert between mass spectral library formats (.mgf/.msp/.json) using matchms</description>
     
     <macros>

--- a/matchms_similarity_wrapper.py	Thu Oct 12 13:30:20 2023 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,136 +0,0 @@
-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