def main():
# register command line arguments
model = CTDModel(
name='NameOfThePyTOPPTool', # required
version='1.0', # required
description=
'This is an example tool how to write pyTOPP tools compatible with the OpenMS workflow ecosystem.',
manual='RTF',
docurl='http://dummy.url/docurl.html',
category='Example',
executableName='exampletool',
executablePath='/path/to/exec/exampletool-1.0/exampletool')
# Register in / out etc. with CTDModel
model.add(
'input',
required=True,
type='input-file',
is_list=False,
file_formats=['mzML'], # filename restrictions
description='Input file')
model.add(
'output',
required=True,
type='output-file',
is_list=False,
file_formats=['mzML'], # filename restrictions
description='Output file')
defaults = pms.PeakPickerHiRes().getDefaults()
# expose algorithm parameters in command line options
addParamToCTDopts(defaults, model)
# parse command line
# if -write_ini is provided, store model in CTD file, exit with error code 0
# if -ini is provided, load CTD file into defaults Param object and return new model with paraneters set as defaults
arg_dict, openms_params = parseCTDCommandLine(sys.argv, model, defaults)
# data processing
fh = pms.MzMLFile()
fh.setLogType(pms.LogType.CMD)
input_map = pms.MSExperiment()
fh.load(arg_dict["input"], input_map)
pp = pms.PeakPickerHiRes()
pp.setParameters(openms_params)
out_map = pms.MSExperiment()
pp.pickExperiment(input_map, out_map)
out_map = addDataProcessing(
out_map, openms_params,
pms.DataProcessing.ProcessingAction.PEAK_PICKING)
fh = pms.FileHandler()
fh.storeExperiment(arg_dict["output"], out_map)
def run_featurefinder_centroided(input_path, params, seeds, out_path):
fh = pms.MzMLFile()
options = pms.PeakFileOptions()
options.setMSLevels([1,1])
fh.setOptions(options)
input_map = pms.MSExperiment()
fh.load(input_path, input_map)
input_map.updateRanges()
ff = pms.FeatureFinder()
ff.setLogType(pms.LogType.CMD)
features = pms.FeatureMap()
name = pms.FeatureFinderAlgorithmPicked.getProductName()
ff.run(name, input_map, features, params, seeds)
features.setUniqueIds()
addDataProcessing(features, params, pms.ProcessingAction.QUANTITATION)
fh = pms.FeatureXMLFile()
fh.store(out_path, features)
def run_peak_picker(input_map, params, out_path):
spec0 = input_map[0]
if pms.PeakTypeEstimator().estimateType(spec0) == \
pms. SpectrumSettings.SpectrumType.PEAKS:
logging.warn("input peak map does not look like profile data")
if any(not s.isSorted() for s in input_map):
raise Exception("Not all spectra are sorted according to m/z")
pp = pms.PeakPickerHiRes()
pp.setParameters(params)
out_map = pms.MSExperiment()
pp.pickExperiment(input_map, out_map)
out_map = addDataProcessing(out_map, params, pms.ProcessingAction.PEAK_PICKING)
fh = pms.FileHandler()
fh.storeExperiment(out_path, out_map)
def run_peak_picker(input_map, params, out_path):
spec0 = input_map[0]
if pms.PeakTypeEstimator().estimateType(spec0) == \
pms. SpectrumSettings.SpectrumType.PEAKS:
logging.warn("input peak map does not look like profile data")
if any(not s.isSorted() for s in input_map):
raise Exception("Not all spectra are sorted according to m/z")
pp = pms.PeakPickerHiRes()
pp.setParameters(params)
out_map = pms.MSExperiment()
pp.pickExperiment(input_map, out_map)
out_map = addDataProcessing(
out_map, params, pms.DataProcessing.ProcessingAction.PEAK_PICKING)
fh = pms.FileHandler()
fh.storeExperiment(out_path, out_map)
def id_mapper(in_file, id_file, out_file, params, use_centroid_rt,
use_centroid_mz, use_subelements):
in_type = pms.FileHandler.getType(in_file)
protein_ids = []
peptide_ids = []
pms.IdXMLFile().load(id_file, protein_ids, peptide_ids)
mapper = pms.IDMapper()
mapper.setParameters(params)
if in_type == pms.Type.CONSENSUSXML:
file_ = pms.ConsensusXMLFile()
map_ = pms.ConsensusMap()
file_.load(in_file, map_)
mapper.annotate(map_, peptide_ids, protein_ids, use_subelements)
addDataProcessing(
map_, params,
pms.DataProcessing.ProcessingAction.IDENTIFICATION_MAPPING)
file_.store(out_file, map_)
elif in_type == pms.Type.FEATUREXML:
file_ = pms.FeatureXMLFile()
map_ = pms.FeatureMap()
file_.load(in_file, map_)
mapper.annotate(map_, peptide_ids, protein_ids, use_centroid_rt,
use_centroid_mz)
addDataProcessing(
map_, params,
pms.DataProcessing.ProcessingAction.IDENTIFICATION_MAPPING)
file_.store(out_file, map_)
elif in_type == pms.Type.MZQ:
file_ = pms.MzQuantMLFile()
msq = pms.MSQuantifications()
file_.load(in_file, msq)
maps = msq.getConsensusMaps()
for map_ in maps:
mapper.annotate(map_, peptide_ids, protein_ids, use_subelements)
addDataProcessing(
map_, params,
pms.DataProcessing.ProcessingAction.IDENTIFICATION_MAPPING)
msq.setConsensusMaps(maps)
file_.store(out_file, msq)
else:
raise Exception("invalid input file format")
def id_mapper(in_file, id_file, out_file, params, use_centroid_rt,
use_centroid_mz, use_subelements ):
in_type = pms.FileHandler.getType(in_file)
protein_ids = []
peptide_ids = []
pms.IdXMLFile().load(id_file, protein_ids, peptide_ids)
mapper = pms.IDMapper()
mapper.setParameters(params)
if in_type == pms.Type.CONSENSUSXML:
file_ = pms.ConsensusXMLFile()
map_ = pms.ConsensusMap()
file_.load(in_file, map_)
mapper.annotate(map_, peptide_ids, protein_ids, use_subelements)
addDataProcessing(map_, params, pms.ProcessingAction.IDENTIFICATION_MAPPING)
file_.store(out_file, map_)
elif in_type == pms.Type.FEATUREXML:
file_ = pms.FeatureXMLFile()
map_ = pms.FeatureMap()
file_.load(in_file, map_)
mapper.annotate(map_, peptide_ids, protein_ids, use_centroid_rt,
use_centroid_mz)
addDataProcessing(map_, params, pms.ProcessingAction.IDENTIFICATION_MAPPING)
file_.store(out_file, map_)
elif in_type == pms.Type.MZQ:
file_ = pms.MzQuantMLFile()
msq = pms.MSQuantifications()
file_.load(in_file, msq)
maps = msq.getConsensusMaps()
for map_ in maps:
mapper.annotate(map_, peptide_ids, protein_ids, use_subelements)
addDataProcessing(map_, params, pms.ProcessingAction.IDENTIFICATION_MAPPING)
msq.setConsensusMaps(maps)
file_.store(out_file, msq)
else:
raise Exception("invalid input file format")
def align(in_files, out_files, out_trafos, reference_index,
reference_file, params):
in_types = set(pms.FileHandler.getType(in_) for in_ in in_files)
if in_types <= set((pms.Type.MZML, pms.Type.MZXML, pms.Type.MZDATA)):
align_features = False
elif in_types == set((pms.Type.FEATUREXML,)):
align_features = True
else:
raise Exception("different kinds of input files")
algorithm = pms.MapAlignmentAlgorithmPoseClustering()
alignment_params = params.copy("algorithm:", True)
algorithm.setParameters(alignment_params)
algorithm.setLogType(pms.LogType.CMD)
plog = pms.ProgressLogger()
plog.setLogType(pms.LogType.CMD)
if reference_file:
file_ = reference_file
elif reference_index > 0:
file_ = in_files[reference_index-1]
else:
sizes = []
if align_features:
fh = pms.FeatureXMLFile()
plog.startProgress(0, len(in_files), "Determine Reference map")
for i, in_f in enumerate(in_files):
sizes.append((fh.loadSize(in_f), in_f))
plog.setProgress(i)
else:
fh = pms.MzMLFile()
mse = pms.MSExperiment()
plog.startProgress(0, len(in_files), "Determine Reference map")
for i, in_f in enumerate(in_files):
fh.load(in_f, mse)
mse.updateRanges()
sizes.append((mse.getSize(), in_f))
plog.setProgress(i)
plog.endProgress()
__, file_ = max(sizes)
f_fmxl = pms.FeatureXMLFile()
if not out_files:
options = f_fmxl.getOptions()
options.setLoadConvexHull(False)
options.setLoadSubordinates(False)
f_fmxl.setOptions(options)
if align_features:
map_ref = pms.FeatureMap()
f_fxml_tmp = pms.FeatureXMLFile()
options = f_fmxl.getOptions()
options.setLoadConvexHull(False)
options.setLoadSubordinates(False)
f_fxml_tmp.setOptions(options)
f_fxml_tmp.load(file_, map_ref)
algorithm.setReference(map_ref)
else:
map_ref = pms.MSExperiment()
pms.MzMLFile().load(file_, map_ref)
algorithm.setReference(map_ref)
plog.startProgress(0, len(in_files), "Align input maps")
for i, in_file in enumerate(in_files):
trafo = pms.TransformationDescription()
if align_features:
map_ = pms.FeatureMap()
f_fxml_tmp = pms.FeatureXMLFile()
f_fxml_tmp.setOptions(f_fmxl.getOptions())
f_fxml_tmp.load(in_file, map_)
if in_file == file_:
trafo.fitModel("identity")
else:
algorithm.align(map_, trafo)
if out_files:
pms.MapAlignmentTransformer.transformSingleFeatureMap(map_, trafo)
addDataProcessing(map_, params, pms.ProcessingAction.ALIGNMENT)
f_fxml_tmp.store(out_files[i], map_)
else:
map_ = pms.MSExperiment()
pms.MzMLFile().load(in_file, map_)
if in_file == file_:
trafo.fitModel("identity")
else:
algorithm.align(map_, trafo)
if out_files:
pms.MapAlignmentTransformer.transformSinglePeakMap(map_, trafo)
addDataProcessing(map_, params, pms.ProcessingAction.ALIGNMENT)
pms.MzMLFile().store(out_files[i], map_)
if out_trafos:
pms.TransformationXMLFile().store(out_trafos[i], trafo)
plog.setProgress(i+1)
plog.endProgress()
def align(in_files, out_files, out_trafos, reference_index, reference_file,
params):
in_types = set(pms.FileHandler.getType(in_) for in_ in in_files)
if in_types <= set((pms.Type.MZML, pms.Type.MZXML, pms.Type.MZDATA)):
align_features = False
elif in_types == set((pms.Type.FEATUREXML, )):
align_features = True
else:
raise Exception("different kinds of input files")
algorithm = pms.MapAlignmentAlgorithmPoseClustering()
alignment_params = params.copy("algorithm:", True)
algorithm.setParameters(alignment_params)
algorithm.setLogType(pms.LogType.CMD)
plog = pms.ProgressLogger()
plog.setLogType(pms.LogType.CMD)
if reference_file:
file_ = reference_file
elif reference_index > 0:
file_ = in_files[reference_index - 1]
else:
sizes = []
if align_features:
fh = pms.FeatureXMLFile()
plog.startProgress(0, len(in_files), "Determine Reference map")
for i, in_f in enumerate(in_files):
sizes.append((fh.loadSize(in_f), in_f))
plog.setProgress(i)
else:
fh = pms.MzMLFile()
mse = pms.MSExperiment()
plog.startProgress(0, len(in_files), "Determine Reference map")
for i, in_f in enumerate(in_files):
fh.load(in_f, mse)
mse.updateRanges()
sizes.append((mse.getSize(), in_f))
plog.setProgress(i)
plog.endProgress()
__, file_ = max(sizes)
f_fmxl = pms.FeatureXMLFile()
if not out_files:
options = f_fmxl.getOptions()
options.setLoadConvexHull(False)
options.setLoadSubordinates(False)
f_fmxl.setOptions(options)
if align_features:
map_ref = pms.FeatureMap()
f_fxml_tmp = pms.FeatureXMLFile()
options = f_fmxl.getOptions()
options.setLoadConvexHull(False)
options.setLoadSubordinates(False)
f_fxml_tmp.setOptions(options)
f_fxml_tmp.load(file_, map_ref)
algorithm.setReference(map_ref)
else:
map_ref = pms.MSExperiment()
pms.MzMLFile().load(file_, map_ref)
algorithm.setReference(map_ref)
plog.startProgress(0, len(in_files), "Align input maps")
for i, in_file in enumerate(in_files):
trafo = pms.TransformationDescription()
if align_features:
map_ = pms.FeatureMap()
f_fxml_tmp = pms.FeatureXMLFile()
f_fxml_tmp.setOptions(f_fmxl.getOptions())
f_fxml_tmp.load(in_file, map_)
if in_file == file_:
trafo.fitModel("identity")
else:
algorithm.align(map_, trafo)
if out_files:
pms.MapAlignmentTransformer.transformSingleFeatureMap(
map_, trafo)
addDataProcessing(map_, params, pms.ProcessingAction.ALIGNMENT)
f_fxml_tmp.store(out_files[i], map_)
else:
map_ = pms.MSExperiment()
pms.MzMLFile().load(in_file, map_)
if in_file == file_:
trafo.fitModel("identity")
else:
algorithm.align(map_, trafo)
if out_files:
pms.MapAlignmentTransformer.transformSinglePeakMap(map_, trafo)
addDataProcessing(map_, params, pms.ProcessingAction.ALIGNMENT)
pms.MzMLFile().store(out_files[i], map_)
if out_trafos:
pms.TransformationXMLFile().store(out_trafos[i], trafo)
plog.setProgress(i + 1)
plog.endProgress()
def link(in_files, out_file, keep_subelements, params):
in_types = set(pms.FileHandler.getType(in_) for in_ in in_files)
if in_types == set((pms.Type.CONSENSUSXML, )):
link_features = False
elif in_types == set((pms.Type.FEATUREXML, )):
link_features = True
else:
raise Exception("different kinds of input files")
algorithm_parameters = params.copy("algorithm:", True)
algorithm = pms.FeatureGroupingAlgorithmQT()
algorithm.setParameters(algorithm_parameters)
out_map = pms.ConsensusMap()
fds = out_map.getColumnHeaders()
if link_features:
f = pms.FeatureXMLFile()
maps = []
for i, in_file in enumerate(in_files):
map_ = pms.FeatureMap()
f.load(in_file, map_)
# set filedescriptions
fd = fds.get(i, pms.ColumnHeader())
fd.filename = in_file
fd.size = map_.size()
fd.unique_id = map_.getUniqueId()
fds[i] = fd
maps.append(map_)
out_map.setColumnHeaders(fds)
algorithm.group(maps, out_map)
else:
f = pms.ConsensusXMLFile()
maps = []
for i, in_file in enumerate(in_files):
map_ = pms.ConsensusMap()
f.load(in_file, map_)
maps.append(map_)
algorithm.group(maps, out_map)
if not keep_subelements:
for i in range(len(in_files)):
# set filedescriptions
fd = fds.get(i, pms.ColumnHeader())
fd.filename = in_files[i]
fd.size = maps[i].size()
fd.unique_id = maps[i].getUniqueId()
fds[i] = fd
out_map.setColumnHeaders(fds)
else:
algorithm.transferSubelements(maps, out_map)
out_map.setUniqueIds()
addDataProcessing(out_map, params,
pms.DataProcessing.ProcessingAction.FEATURE_GROUPING)
pms.ConsensusXMLFile().store(out_file, out_map)
sizes = []
for feat in out_map:
sizes.append(feat.size())
c = Counter(sizes)
print "Number of consensus features:"
for size, count in c.most_common():
print " of size %2d : %6d" % (size, count)
print " total : %6d" % out_map.size()
def link(in_files, out_file, keep_subelements, params):
in_types = set(pms.FileHandler.getType(in_) for in_ in in_files)
if in_types == set((pms.Type.CONSENSUSXML,)):
link_features = False
elif in_types == set((pms.Type.FEATUREXML,)):
link_features = True
else:
raise Exception("different kinds of input files")
algorithm_parameters = params.copy("algorithm:", True)
algorithm = pms.FeatureGroupingAlgorithmQT()
algorithm.setParameters(algorithm_parameters)
out_map = pms.ConsensusMap()
fds = out_map.getFileDescriptions()
if link_features:
f = pms.FeatureXMLFile()
maps = []
for i, in_file in enumerate(in_files):
map_ = pms.FeatureMap()
f.load(in_file, map_)
# set filedescriptions
fd = fds.get(i, pms.FileDescription())
fd.filename = in_file
fd.size = map_.size()
fd.unique_id = map_.getUniqueId()
fds[i] = fd
maps.append(map_)
out_map.setFileDescriptions(fds)
algorithm.group(maps, out_map)
else:
f = pms.ConsensusXMLFile()
maps = []
for i, in_file in enumerate(in_files):
map_ = pms.ConsensusMap()
f.load(in_file, map_)
maps.append(map_)
algorithm.group(maps, out_map)
if not keep_subelements:
for i in range(len(in_files)):
# set filedescriptions
fd = fds.get(i, pms.FileDescription())
fd.filename = in_files[i]
fd.size = maps[i].size()
fd.unique_id = maps[i].getUniqueId()
fds[i] = fd
out_map.setFileDescriptions(fds)
else:
algorithm.transferSubelements(maps, out_map)
out_map.setUniqueIds()
addDataProcessing(out_map, params, pms.ProcessingAction.FEATURE_GROUPING)
pms.ConsensusXMLFile().store(out_file, out_map)
sizes = []
for feat in out_map:
sizes.append(feat.size())
c = Counter(sizes)
print "Number of consensus features:"
for size, count in c.most_common():
print " of size %2d : %6d" % (size, count)
print " total : %6d" % out_map.size()
