Mercurial > repos > recetox > matchms_similarity
diff matchms_networking_wrapper.py @ 0:e5010b19d64d draft
planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/matchms commit f79a5b51599254817727bc9028b9797ea994cb4e
author | recetox |
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date | Tue, 27 Jun 2023 14:26:29 +0000 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/matchms_networking_wrapper.py Tue Jun 27 14:26:29 2023 +0000 @@ -0,0 +1,39 @@ +import argparse +import sys + +from matchms.importing import scores_from_json +from matchms.networking import SimilarityNetwork + + +def main(argv): + parser = argparse.ArgumentParser(description="Create network-graph from similarity scores.") + parser.add_argument("--graph_format", type=str, help="Format of the output similarity network.") + parser.add_argument("--score_name", type=str, help="Name of the score layer to use for creating the network graph.") + parser.add_argument("--identifier", type=str, help="Unique metadata identifier of each spectrum from which scores are computed.") + parser.add_argument("--top_n", type=int, help="Number of highest-score edges to keep.") + parser.add_argument("--max_links", type=int, help="Maximum number of links to add per node.") + parser.add_argument("--score_cutoff", type=float, help="Minimum similarity score value to link two spectra.") + parser.add_argument("--link_method", type=str, help="Method for selecting top N edges for each node.") + parser.add_argument("--keep_unconnected_nodes", help="Keep unconnected nodes in the network.", action="store_true") + parser.add_argument("scores", type=str, help="Path to matchms similarity-scores .json file.") + parser.add_argument("output_filename", type=str, help="Path where to store the output similarity network.") + args = parser.parse_args() + + scores = scores_from_json(args.scores) + + network = SimilarityNetwork(identifier_key=args.identifier, + top_n=args.top_n, + max_links=args.max_links, + score_cutoff=args.score_cutoff, + link_method=args.link_method, + keep_unconnected_nodes=args.keep_unconnected_nodes) + + network.create_network(scores, args.score_name) + network.export_to_file(filename=args.output_filename, graph_format=args.graph_format) + + return 0 + + +if __name__ == "__main__": + main(argv=sys.argv[1:]) + pass