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import unittest
import os,sys
import pdb
import pyopenms
"""
python pyTOPP/OpenSwathRTNormalizer.py --in ../source/TEST/TOPP/OpenSwathRTNormalizer_1_input.mzML \
--tr ../source/TEST/TOPP/OpenSwathRTNormalizer_1_input.TraML --out OpenSwathRTNormalizer.tmp.out \
&& diff OpenSwathRTNormalizer.tmp.out ../source/TEST/TOPP/OpenSwathRTNormalizer_1_output.trafoXML
"""
def simple_find_best_feature(output, pairs, targeted):
f_map = {}
for f in output:
key = f.getMetaValue("PeptideRef")
if f_map.has_key(key):
f_map[key].append(f)
else:
f_map[key] = [f]
for v in f_map.values():
bestscore = -10000
for feature in v:
score = feature.getMetaValue("main_var_xx_lda_prelim_score")
if score > bestscore:
best = feature
bestscore = score
pep = targeted.getPeptideByRef( feature.getMetaValue("PeptideRef") )
pairs.append( [best.getRT(), pep.getRetentionTime() ] )
def algorithm(chromatograms, targeted):
# Create empty files as input and finally as output
empty_swath = pyopenms.MSExperiment()
trafo = pyopenms.TransformationDescription()
output = pyopenms.FeatureMap();
# set up featurefinder and run
featurefinder = pyopenms.MRMFeatureFinderScoring()
# set the correct rt use values
scoring_params = pyopenms.MRMFeatureFinderScoring().getDefaults();
scoring_params.setValue("Scores:use_rt_score",'false', '')
featurefinder.setParameters(scoring_params);
featurefinder.pickExperiment(chromatograms, output, targeted, trafo, empty_swath)
# get the pairs
pairs=[]
simple_find_best_feature(output, pairs, targeted)
pairs_corrected = pyopenms.MRMRTNormalizer().rm_outliers( pairs, 0.95, 0.6)
pairs_corrected = [ list(p) for p in pairs_corrected]
# // store transformation, using a linear model as default
trafo_out = pyopenms.TransformationDescription()
trafo_out.setDataPoints(pairs_corrected);
model_params = pyopenms.Param()
model_params.setValue("symmetric_regression", 'false', '');
model_type = "linear";
trafo_out.fitModel(model_type, model_params);
return trafo_out
def main(options):
# load chromatograms
chromatograms = pyopenms.MSExperiment()
fh = pyopenms.FileHandler()
fh.loadExperiment(options.infile, chromatograms)
# load TraML file
targeted = pyopenms.TargetedExperiment();
tramlfile = pyopenms.TraMLFile();
tramlfile.load(options.traml_in, targeted);
trafo_out = algorithm(chromatograms, targeted)
pyopenms.TransformationXMLFile().store(options.outfile, trafo_out);
def handle_args():
import argparse
usage = ""
usage += "\nMRMMapper maps measured chromatograms (mzML) and the transitions used (TraML)"
parser = argparse.ArgumentParser(description = usage )
parser.add_argument('--in', dest="infile", help = 'An input file containing chromatograms')
parser.add_argument("--tr", dest="traml_in", help="TraML input file containt the transitions")
parser.add_argument("--out", dest="outfile", help="Output trafoXML file with annotated chromatograms")
args = parser.parse_args(sys.argv[1:])
return args
if __name__ == '__main__':
options = handle_args()
main(options)
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