| Previous changeset 3:7504a8f93699 (2023-10-12) Next changeset 5:811104b25490 (2023-11-19) |
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Commit message:
planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/matchms commit a493624c962d820d962bd81c14aceede5d5be5b4 |
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modified:
macros.xml matchms_metadata_export.xml |
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removed:
matchms_similarity_wrapper.py |
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| diff -r 7504a8f93699 -r 73c902331bbc macros.xml --- a/macros.xml Thu Oct 12 13:27:11 2023 +0000 +++ b/macros.xml Thu Oct 19 15:24:22 2023 +0000 |
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| @@ -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> |
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| diff -r 7504a8f93699 -r 73c902331bbc matchms_metadata_export.xml --- a/matchms_metadata_export.xml Thu Oct 12 13:27:11 2023 +0000 +++ b/matchms_metadata_export.xml Thu Oct 19 15:24:22 2023 +0000 |
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| @@ -1,4 +1,4 @@ -<tool id="matchms_metadata_export" name="matchms metadata export" version="@TOOL_VERSION@+galaxy2" profile="21.09"> +<tool id="matchms_metadata_export" name="matchms metadata export" version="@TOOL_VERSION@+galaxy0" profile="21.09"> <description>extract all metadata from mass spectra file to tabular format</description> <macros> <import>macros.xml</import> |
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| diff -r 7504a8f93699 -r 73c902331bbc matchms_similarity_wrapper.py --- a/matchms_similarity_wrapper.py Thu Oct 12 13:27:11 2023 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 |
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| @@ -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 |