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annotate matchms_wrapper.py @ 2:a7c9fc186f8c draft

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"planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/matchms commit 557e6558b93e63fd0b70443164d2d624cc05c319"
author recetox
date Mon, 19 Apr 2021 08:31:42 +0000
parents 4aecfd6b319b
children 57959596262d
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1 import argparse
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2 import sys
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4 from matchms import calculate_scores
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5 from matchms.filtering import add_precursor_mz
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6 from matchms.importing import load_from_msp
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7 from matchms.similarity import (
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8 CosineGreedy,
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9 CosineHungarian,
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10 ModifiedCosine,
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11 )
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12 from pandas import DataFrame
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15 def main(argv):
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16 parser = argparse.ArgumentParser(description="Compute MSP similarity scores")
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17 parser.add_argument(
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18 "references_filename", type=str, help="Path to reference MSP library."
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19 )
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20 parser.add_argument("queries_filename", type=str, help="Path to query spectra.")
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21 parser.add_argument("similarity_metric", type=str, help='Metric to use for matching.')
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22 parser.add_argument("output_filename_scores", type=str, help="Path where to store the output .csv scores.")
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23 parser.add_argument("output_filename_matches", type=str, help="Path where to store the output .csv matches.")
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24 parser.add_argument("tolerance", type=float, help="Tolerance to use for peak matching.")
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25 parser.add_argument("mz_power", type=float, help="The power to raise mz to in the cosine function.")
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26 parser.add_argument("intensity_power", type=float, help="The power to raise intensity to in the cosine function.")
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28 args = parser.parse_args()
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30 reference_spectra = load_from_msp(args.references_filename)
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31 queries_spectra = load_from_msp(args.queries_filename)
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33 if args.similarity_metric == 'CosineGreedy':
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34 similarity_metric = CosineGreedy(args.tolerance, args.mz_power, args.intensity_power)
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35 elif args.similarity_metric == 'CosineHungarian':
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36 similarity_metric = CosineHungarian(args.tolerance, args.mz_power, args.intensity_power)
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37 elif args.similarity_metric == 'ModifiedCosine':
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38 similarity_metric = ModifiedCosine(args.tolerance, args.mz_power, args.intensity_power)
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39 reference_spectra = map(add_precursor_mz, reference_spectra)
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40 queries_spectra = map(add_precursor_mz, queries_spectra)
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41 else:
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42 return -1
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44 scores = calculate_scores(
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45 references=list(reference_spectra),
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46 queries=list(queries_spectra),
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47 similarity_function=similarity_metric,
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48 )
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50 query_names = [spectra.metadata['name'] for spectra in scores.queries]
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51 reference_names = [spectra.metadata['name'] for spectra in scores.references]
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53 # Write scores to dataframe
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54 dataframe_scores = DataFrame(data=[entry["score"] for entry in scores.scores], index=reference_names, columns=query_names)
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55 dataframe_scores.to_csv(args.output_filename_scores, sep=';')
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57 # Write number of matches to dataframe
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58 dataframe_matches = DataFrame(data=[entry["matches"] for entry in scores.scores], index=reference_names, columns=query_names)
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59 dataframe_matches.to_csv(args.output_filename_matches, sep=';')
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60 return 0
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63 if __name__ == "__main__":
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64 main(argv=sys.argv[1:])
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65 pass