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# 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
# 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
# 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
# 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 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";
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";
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()
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
rt = float(rt_[2])*60
# Convert to mz/int pairs, then into separate arrays filtering out zero-intensity peaks
pairs = [it.split() for it in stack[3:] if len(it.strip()) > 0 and float(it.split()[1]) > 0.0]
try:
mz = [float(it[0]) for it in pairs]
intensity = [float(it[1]) for it in pairs]
except ValueError:
print("Could not convert", len(stack), "with pairs" , len(pairs))
return
assert len(mz) == len(intensity)
#
print("Handle scan at rt", rt)
peaks = np.ndarray(shape=(len(mz), 2), dtype=np.float32)
peaks[:,0] = mz
peaks[:,1] = intensity
s = pyopenms.MSSpectrum()
s.set_peaks(peaks)
s.setRT(rt)
# set MSLevel to 1 for all
s.setMSLevel(1)
outexp.addSpectrum(s)