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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
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