def main(options):
# load TraML file
targeted = pyopenms.TargetedExperiment()
pyopenms.TraMLFile().load(options.traml_in, targeted)
# Create empty files as input and finally as output
empty_swath = pyopenms.MSExperiment()
trafo = pyopenms.TransformationDescription()
output = pyopenms.MSExperiment()
# load input
for infile in options.infiles:
exp = pyopenms.MSExperiment()
pyopenms.FileHandler().loadExperiment(infile, exp)
transition_exp_used = pyopenms.TargetedExperiment()
do_continue = True
if options.is_swath:
do_continue = pyopenms.OpenSwathHelper(
).checkSwathMapAndSelectTransitions(exp, targeted,
transition_exp_used,
options.min_upper_edge_dist)
else:
transition_exp_used = targeted
if do_continue:
# set up extractor and run
tmp_out = pyopenms.MSExperiment()
extractor = pyopenms.ChromatogramExtractor()
extractor.extractChromatograms(exp, tmp_out, targeted,
options.extraction_window,
options.ppm, trafo,
options.rt_extraction_window,
options.extraction_function)
# add all chromatograms to the output
for chrom in tmp_out.getChromatograms():
output.addChromatogram(chrom)
dp = pyopenms.DataProcessing()
pa = pyopenms.DataProcessing().ProcessingAction().SMOOTHING
dp.setProcessingActions(set([pa]))
chromatograms = output.getChromatograms()
for chrom in chromatograms:
this_dp = chrom.getDataProcessing()
this_dp.append(dp)
chrom.setDataProcessing(this_dp)
output.setChromatograms(chromatograms)
pyopenms.MzMLFile().store(options.outfile, output)
def algorithm(chromatogram_map, targeted, precursor_tolerance, product_tolerance, allow_unmapped=True, allow_double_mappings=False):
output = copy.copy(chromatogram_map)
output.clear(False);
empty_chromats = []
output.setChromatograms(empty_chromats);
notmapped = 0
for chrom in chromatogram_map.getChromatograms():
mapped_already = False
for transition in targeted.getTransitions():
if (abs(chrom.getPrecursor().getMZ() - transition.getPrecursorMZ()) < precursor_tolerance and
abs(chrom.getProduct().getMZ() - transition.getProductMZ()) < product_tolerance):
if mapped_already:
this_peptide = targeted.getPeptideByRef(transition.getPeptideRef() ).sequence
other_peptide = chrom.getPrecursor().getMetaValue("peptide_sequence")
print "Found mapping of", chrom.getPrecursor().getMZ(), "/", chrom.getProduct().getMZ(), "to", transition.getPrecursorMZ(), "/",transition.getProductMZ()
print "Of peptide", this_peptide
print "But the chromatogram is already mapped to", other_peptide
if not allow_double_mappings: raise Exception("Cannot map twice")
mapped_already = True
precursor = chrom.getPrecursor();
peptide = targeted.getPeptideByRef(transition.getPeptideRef() )
precursor.setMetaValue("peptide_sequence", peptide.sequence)
chrom.setPrecursor(precursor)
chrom.setNativeID(transition.getNativeID())
if not mapped_already:
notmapped += 1
print "Did not find a mapping for chromatogram", chrom.getNativeID()
if not allow_unmapped: raise Exception("No mapping")
else:
output.addChromatogram(chrom)
if notmapped > 0:
print "Could not find mapping for", notmapped, "chromatogram(s)"
dp = pyopenms.DataProcessing()
# dp.setProcessingActions(ProcessingAction:::FORMAT_CONVERSION)
pa = pyopenms.DataProcessing().ProcessingAction().FORMAT_CONVERSION
dp.setProcessingActions(set([pa]))
chromatograms = output.getChromatograms();
for chrom in chromatograms:
this_dp = chrom.getDataProcessing()
this_dp.append(dp)
chrom.setDataProcessing(this_dp)
output.setChromatograms(chromatograms);
return output
def testDataProcessing(dp=pyopenms.DataProcessing()):
"""
@tests:
DataProcessing.__init__
DataProcessing.getKeys
DataProcessing.getMetaValue
DataProcessing.getProcessingActions
DataProcessing.getSoftware
DataProcessing.isMetaEmpty
DataProcessing.metaValueExists
DataProcessing.removeMetaValue
DataProcessing.setCompletionTime
DataProcessing.setMetaValue
DataProcessing.setProcessingActions
DataProcessing.setSoftware
DataProcessing.__eq__
DataProcessing.__ge__
DataProcessing.__gt__
DataProcessing.__le__
DataProcessing.__lt__
DataProcessing.__ne__
DataProcessing.clearMetaInfo
DataProcessing.getCompletionTime
"""
_testMetaInfoInterface(dp)
assert dp == dp
assert not dp != dp
assert isinstance(dp.getCompletionTime().getDate(), str)
assert isinstance(dp.getCompletionTime().getTime(), str)
dp.clearMetaInfo()
k = []
dp.getKeys(k)
assert k == []
dp.getMetaValue
ac = dp.getProcessingActions()
assert ac == set(())
dp.setProcessingActions(ac)
assert isinstance(dp.getSoftware().getName(), str)
assert isinstance(dp.getSoftware().getVersion(), str)
dp.isMetaEmpty()
dp.metaValueExists
dp.removeMetaValue
dp.setCompletionTime(pyopenms.DateTime.now())
s = dp.getSoftware()
s.setName("pyopenms")
dp.setSoftware(s)
assert dp.getSoftware().getName() == "pyopenms"
def _addDataProcessing(item, params):
dp = item.getDataProcessing()
p = pms.DataProcessing()
p.setProcessingActions(set([pms.ProcessingAction.ALIGNMENT]))
sw = p.getSoftware()
sw.setName(os.path.basename(sys.argv[0]))
sw.setVersion(pms.VersionInfo.getVersion())
p.setSoftware(sw)
p.setCompletionTime(pms.DateTime.now())
for k, v in params.asDict().items():
p.setMetaValue("parameter: "+k, pms.DataValue(v))
dp.append(p)
item.setDataProcessing(dp)
return item
def _addDataProcessing(item, params, action):
dp = item.getDataProcessing()
p = pms.DataProcessing()
p.setProcessingActions(set([action]))
sw = p.getSoftware()
sw.setName(os.path.basename(sys.argv[0]))
sw.setVersion(pms.VersionInfo.getVersion())
p.setSoftware(sw)
p.setCompletionTime(
pms.DateTime.now()
) # TODO: check if this is the reason for the many data processing entries
for k, v in params.asDict().items():
p.setMetaValue(b"parameter: " + k, v)
dp.append(p)
item.setDataProcessing(dp)
return item
def testFeatureMap():
"""
@tests:
FeatureMap.__init__
FeatureMap.__add__
FeatureMap.__iadd__
FeatureMap.__radd__
FeatureMap.__getitem__
FeatureMap.clear
FeatureMap.clearUniqueId
FeatureMap.ensureUniqueId
FeatureMap.getDataProcessing
FeatureMap.getProteinIdentifications
FeatureMap.getUnassignedPeptideIdentifications
FeatureMap.getUniqueId
FeatureMap.hasInvalidUniqueId
FeatureMap.hasValidUniqueId
FeatureMap.push_back
FeatureMap.setDataProcessing
FeatureMap.setProteinIdentifications
FeatureMap.setUnassignedPeptideIdentifications
FeatureMap.setUniqueIds
FeatureMap.size
FeatureMap.sortByIntensity
FeatureMap.sortByMZ
FeatureMap.sortByOverallQuality
FeatureMap.sortByPosition
FeatureMap.sortByRT
FeatureMap.swap
FeatureMap.updateRanges
"""
fm = pyopenms.FeatureMap()
_testUniqueIdInterface(fm)
fm.clear()
fm.clearUniqueId()
f = pyopenms.Feature()
fm.push_back(f)
assert fm.size() == 1
assert fm[0] == f
fm.sortByIntensity()
assert fm.size() == 1
assert fm[0] == f
fm.sortByIntensity(False)
assert fm.size() == 1
assert fm[0] == f
fm.sortByPosition()
assert fm.size() == 1
assert fm[0] == f
fm.sortByRT()
assert fm.size() == 1
assert fm[0] == f
fm.sortByMZ()
assert fm.size() == 1
assert fm[0] == f
fm.sortByOverallQuality()
assert fm.size() == 1
assert fm[0] == f
fm2 = pyopenms.FeatureMap()
fm.swap(fm2)
assert fm2.size() == 1
assert fm2[0] == f
assert fm.size() == 0
fm2.updateRanges()
assert fm2.getProteinIdentifications() == []
fm2.setProteinIdentifications([])
assert fm2.getUnassignedPeptideIdentifications() == []
fm2.setUnassignedPeptideIdentifications([])
fm2.clear()
assert fm2.size() == 0
dp = pyopenms.DataProcessing()
fm2.setDataProcessing([dp])
assert fm2.getDataProcessing() == [dp]
testDataProcessing(dp)
fm2.setUniqueIds()
fm += fm
assert fm + fm != fm
def main(options):
# make sure that the ids are "correct" for the testcase
date_time = pyopenms.DateTime()
if options.test:
date_time.set("1999-12-31 23:59:59")
pyopenms.UniqueIdGenerator().setSeed(date_time)
else:
date_time = pyopenms.DateTime.now()
exp = pyopenms.MSExperiment()
out_map = pyopenms.ConsensusMap()
pyopenms.FileHandler().loadExperiment(options.infile, exp)
exp.updateRanges()
#
# 1. filter MS1 level (only keep MS1)
#
tmp = copy.copy(exp)
tmp.clear(False)
for spectrum in exp:
if spectrum.getMSLevel() == 1:
tmp.push_back(spectrum)
exp = tmp
exp.sortSpectra(True)
#
# 2. set parameters
#
analyzer = pyopenms.SILACAnalyzer()
analyzer.initialize(
# section sample
options.selected_labels,
options.charge_min,
options.charge_max,
options.missed_cleavages,
options.isotopes_per_peptide_min,
options.isotopes_per_peptide_max,
# section "algorithm"
options.rt_threshold,
options.rt_min,
options.intensity_cutoff,
options.intensity_correlation,
options.model_deviation,
options.allow_missing_peaks,
# labels
options.label_identifiers)
#
# 3. run
#
analyzer.run_all(exp, out_map)
#
# 4. set dataprocessing and output meta information
#
out_map.sortByPosition()
dp = out_map.getDataProcessing()
p = pyopenms.DataProcessing()
p.setProcessingActions(
set([
pyopenms.ProcessingAction().DATA_PROCESSING,
pyopenms.ProcessingAction().PEAK_PICKING,
pyopenms.ProcessingAction().FILTERING,
pyopenms.ProcessingAction().QUANTITATION
]))
p.setCompletionTime(date_time)
sw = p.getSoftware()
sw.setName("SILACAnalyzer")
if options.test:
sw.setVersion("version_string")
p.setSoftware(sw)
p.setMetaValue("parameter: mode", "test_mode")
else:
sw.setVersion("pyTOPP v1.10")
p.setSoftware(sw)
dp.append(p)
out_map.setDataProcessing(dp)
#
# 5. write output
#
analyzer.writeConsensus(pyopenms.String(options.outfile), out_map)
