def test_run_mrmfeaturefinder(self):
# load chromatograms
chromatograms = pyopenms.MSExperiment()
fh = pyopenms.FileHandler()
fh.loadExperiment(self.chromatograms, chromatograms)
# load TraML file
targeted = pyopenms.TargetedExperiment()
tramlfile = pyopenms.TraMLFile()
tramlfile.load(self.tramlfile, 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()
featurefinder.pickExperiment(chromatograms, output, targeted, trafo,
empty_swath)
self.assertAlmostEqual(output.size(), 3)
self.assertAlmostEqual(output[0].getRT(), 3119.092041015, eps)
self.assertAlmostEqual(output[0].getIntensity(), 3614.99755859375, eps)
self.assertAlmostEqual(
output[0].getMetaValue(b"var_xcorr_shape_weighted"),
0.997577965259552, eps)
self.assertAlmostEqual(output[0].getMetaValue(b"sn_ratio"),
86.00413513183594, eps)
def reader(path: str, on_disc: bool = True):
"""
Load `path` file into an OnDiskExperiment. If the file is not indexed, load
the file.
Parameters
----------
path : str
path to read mzML file from.
on_disc : bool
if True doesn't load the whole file on memory.
Returns
-------
pyopenms.OnDiskMSExperiment or pyopenms.MSExperiment
"""
if on_disc:
try:
exp_reader = pyopenms.OnDiscMSExperiment()
exp_reader.openFile(path)
except RuntimeError:
msg = "{} is not an indexed mzML file, switching to MSExperiment"
print(msg.format(path))
exp_reader = pyopenms.MSExperiment()
pyopenms.MzMLFile().load(path, exp_reader)
else:
exp_reader = pyopenms.MSExperiment()
pyopenms.MzMLFile().load(path, exp_reader)
return exp_reader
def test_extractor(self):
targeted = pyopenms.TargetedExperiment()
tramlfile = pyopenms.TraMLFile()
tramlfile.load(self.filename, targeted)
exp = pyopenms.MSExperiment()
pyopenms.MzMLFile().load(self.filename_mzml, exp)
trafo = pyopenms.TransformationDescription()
tmp_out = pyopenms.MSExperiment()
extractor = pyopenms.ChromatogramExtractor()
extractor.extractChromatograms(exp, tmp_out, targeted, 10, False,
trafo, -1, "tophat")
# Basically test that the output is non-zero (e.g. the data is
# correctly relayed to python)
# The functionality is not tested here!
self.assertEqual(len(tmp_out.getChromatograms()),
len(targeted.getTransitions()))
self.assertNotEqual(len(tmp_out.getChromatograms()), 0)
self.assertEqual(tmp_out.getChromatograms()[0].size(), exp.size())
self.assertNotEqual(tmp_out.getChromatograms()[0].size(), 0)
self.assertNotEqual(tmp_out.getChromatograms()[0][0].getRT(), 0)
self.assertNotEqual(tmp_out.getChromatograms()[0][0].getIntensity(), 0)
def main(self):
#after path_parsing method we have self.src_full_name_list
print("Peak Picking implementation")
for f in get_list_full_names(self.src):
# to prepare(init) empty list and entity;
self.init_entity(**self.kw)
print("source file:", f)
input_map = oms.MSExperiment() # the 1st step: load map;
oms.MzMLFile().load(f, input_map)
centroid_out_map = oms.MSExperiment()
# the 2nd step: apply_ffm;
self.pp.entity.pickExperiment(input_map, centroid_out_map)
centroid_out_map.updateRanges()
# the 3d step: is store result into file:
#convert_src_to_dst_file_name(src, dst, suffix_dst_files, ext_dst_files)
dst_full_file_name = os.path.join(self.dst,\
convert_src_to_dst_file_name(f,
self.dst,
self.suffix_dst_files,
self.ext_dst_files
) ) #call 'global' function;
#print("dst=",dst_full_file_name)
oms.MzMLFile().store(dst_full_file_name, centroid_out_map)
print("Picked data stored into:", dst_full_file_name)
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 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.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 main(options):
# generate fragmentationtype lookup
lookup = {}
methods = pyopenms.ActivationMethod()
for attr in dir(methods):
value = getattr(methods,attr)
if isinstance(value,int):
lookup[value] = attr
print "loading MS Experiment "
exp = pyopenms.MSExperiment()
fh = pyopenms.FileHandler()
fh.loadExperiment(options.infile,exp)
print "checking spectra types:"
fragmentationTypes = {}
for s in exp:
typ = getSpectrumType(s,lookup)
cont = continousSpectrumCheck(s)
fragmentationTypes[typ] = fragmentationTypes.get(typ, [] ) + [cont]
isContinousSpectrum = {}
for typ in fragmentationTypes:
check = percentile75(fragmentationTypes[typ])
isContinousSpectrum[typ] = check
if check == True:
print "\t" + typ + " has continous spectra data"
else:
print "\t" + typ + " has centroided spectra data"
print "picking spectra"
expNew = pyopenms.MSExperiment()
picker = pyopenms.PeakPickerHiRes()
for s in exp:
typ = getSpectrumType(s,lookup)
if isContinousSpectrum[typ] == True:
newSpec = pyopenms.MSSpectrum()
picker.pick(s,newSpec)
expNew.addSpectrum(newSpec)
else:
expNew.addSpectrum(s)
print "saving file to ",options.outfile
mzFile = pyopenms.MzMLFile()
fileoptions = mzFile.getOptions()
fileoptions.setCompression(True)
mzFile.setOptions(fileoptions)
mzFile.store(options.outfile,expNew)
print "finished"
def compute_bin_im(self, run: int, bin: int, dir: str = '.') -> float:
"""Computes the intensity-weighted average IM value for a given bin.
Keyword arguments:
run: the pass that the bin is in (1 or 2)
bin: the bin to compute the average IM for
dir: the directory to write and read temporary files to
Returns: the intensity-weighted average IM value for a given bin.
"""
exp = ms.MSExperiment()
ms.MzMLFile().load(dir + '/b-' + str(run) + '-' + str(bin) + '.mzML',
exp)
total_intensity, average_im = 0, 0
all_points = []
for i in range(exp.getNrSpectra()):
spec = exp.getSpectrum(i)
all_points.extend(util.get_spectrum_points(spec))
for i in range(len(all_points)):
total_intensity += all_points[i][2]
if total_intensity != 0:
for i in range(len(all_points)):
average_im += all_points[i][3] * (all_points[i][2] /
total_intensity)
return average_im
def _read_openMS_file(self, file):
exp = pyopenms.MSExperiment()
file.load(self._file, exp)
file.store(self._file, exp)
def bin_highres(df_in):
df = df_in.groupby(df_in['mz'].round()).sum()
I = df['I'].loc[self.mz].as_matrix()
I[np.isnan(I)] = 0
return I
# TODO: change hardcoded min/max masses to be either taken from config
# or automatically derived from the data
self.mz = np.arange(70, 601, 1)
rt = []
intensities = []
spectra = exp.getSpectra()
print('Binning:')
for idx, spectrum in enumerate(spectra):
if ((idx + 1) % 100) == 0:
print('\t{:>4} / {}'.format(idx + 1, len(spectra)))
rt.append(spectrum.getRT())
spectrum_df = pd.DataFrame(list(spectrum.get_peaks()),
index=['mz', 'I']).T
intensities.append(bin_highres(spectrum_df))
self.data_raw = np.array(intensities).T
self.rt = np.array(rt)
self.scans_per_sec = pd.Series(self.rt.round()) \
.value_counts().value_counts().index[0]
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 __get_mzml(self, file):
b_content = file.bcore
if self.ext == 'raw':
self.tf = store_byte_in_tmp(
b_content,
prefix=self.fname,
suffix='.RAW',
directory=self.target_dir.absolute().as_posix()
)
self.cmd_msconvert = self.__build_cmd_msconvert()
self.__run_cmd()
elif self.ext == 'mzml':
self.tf = store_byte_in_tmp(
b_content,
prefix=self.fname,
suffix='.mzML',
directory=self.target_dir.absolute().as_posix()
)
elif self.ext == 'mzxml':
self.tf = store_byte_in_tmp(
b_content,
prefix=self.fname,
suffix='.mzXML',
directory=self.target_dir.absolute().as_posix()
)
exp = pyopenms.MSExperiment()
pyopenms.MzXMLFile().load(self.tf.name, exp)
target_path = self.__get_mzml_path().absolute().as_posix()
pyopenms.MzMLFile().store(target_path, exp)
def get_spectrum(self, filepath, retention_time):
# See: https://pypi.org/project/pyopenms/
peak_list = []
mz_list = []
rt_list = []
intensity_list = []
path = str.encode(filepath)
try:
exp = pyopenms.MSExperiment()
pyopenms.FileHandler().loadExperiment(path, exp)
except Exception as error:
print(str(error))
for spectrum in exp:
rt = str(spectrum.getRT())
# So either no rt param passed, or the decimal input exists in the start of the mzml rt float (no rounding)
if not retention_time or retention_time in rt:
if rt not in rt_list:
rt_list.append(rt)
for peak in spectrum:
mz = peak.getMZ()
intensity = peak.getIntensity()
peak_list.append({"intensity": intensity, "mz": mz})
mz_list.append(mz)
intensity_list.append(intensity)
return peak_list, mz_list, rt_list, intensity_list
def run_MSExperiment_copy(self):
p = pyopenms.FileHandler()
e1 = pyopenms.MSExperiment()
p.loadExperiment(self.testfile, e1)
show_mem("data loaded")
specs = list(e1)
for s in specs:
for _ in range(10):
e1.addSpectrum(s)
li = []
print("please be patient :")
N = 5
for k in range(N):
sys.stdout.flush()
for __ in range(400):
e2 = copy.copy(e1)
li.append(e2)
e1 = copy.copy(e2)
li.append(e1)
print(int(100.0 * (k + 1) / N), "%")
print("\n")
show_mem("experiment list generated")
del li
del e1
del e2
del p
show_mem("experiment list deleted")
def testFunction(self):
for method in [self.eightplex, self.fourplex, self.tmt]:
inst = pyopenms.IsobaricChannelExtractor(method)
map1 = pyopenms.ConsensusMap()
exp = pyopenms.MSExperiment()
assert inst.extractChannels is not None
def maxq(ctx, filename, zipurl, rawname):
"""Calculate all possible metrics for these files. These data sources will be included in set metrics."""
exp = oms.MSExperiment()
oms.MzMLFile().load(click.format_filename(filename), exp)
rq = basicqc.getBasicQuality(exp)
ms2num = 0
for x in rq.qualityMetrics:
if x.name == "Number of MS2 spectra":
ms2num = x.value
if ms2num < 1:
logging.warn(
"We seem to have found no MS2 spectra which is unlikely to be true since you have also given some identifications. \
We continue with symbolic value of 1 for the number of MS2 spectra, \
however this means some metrics will invariably be incorrect!\
Please make sure, we have the right inputs.")
ms2num = 1
try:
mq, params = idqcmq.loadMQZippedResults(zipurl)
if not rawname:
logging.warning("Infering rawname from mzML")
rawname = basename(
exp.getExperimentalSettings().getSourceFiles()
[0].getNameOfFile().decode()) # TODO split extensions
rq.qualityMetrics.extend(
idqcmq.getMQMetrics(rawname, params, mq, ms2num))
rqs.append(rq)
except:
logging.warn("Retrieving any results from the URL failed.")
finale()
def run_fileformats_io(self):
p = pyopenms.FileHandler()
e = pyopenms.MSExperiment()
p.loadExperiment(self.testfile, e)
show_mem("after load mzXML")
ct = pyopenms.ChromatogramTools()
ct.convertChromatogramsToSpectra(e)
p.storeExperiment(self.testfile, e)
show_mem("after store mzXML")
p.loadExperiment(self.testfile, e)
show_mem("after load mzXML")
p = pyopenms.FileHandler()
ct.convertSpectraToChromatograms(e, True)
p.storeExperiment("../test.mzML".encode(), e)
show_mem("after store mzML")
p.loadExperiment("../test.mzML".encode(), e)
show_mem("after load mzML")
p = pyopenms.FileHandler()
ct.convertChromatogramsToSpectra(e)
p.storeExperiment("../test.mzData".encode(), e)
show_mem("after store mzData")
p.loadExperiment("../test.mzData".encode(), e)
show_mem("after load mzData")
del e
del p
del ct
def pick_experiment(self, exp: ms.MSExperiment, peak_radius: int = 1, window_radius: float = 0.015,
pp_mode: str = 'int', min_int_mult: float = 0.10, strict: bool = True) -> ms.MSExperiment():
"""Peak picks an experiment.
Keyword arguments:
exp: the experiment to peak pick
peak_radius: the minimum peak radius of a peak set
window_radius: the maximum m/z window radius of a peak set
pp_mode: the mode to use ('ltr' or 'int')
min_int_mult: a multiplier to the maximum peak intensity in a set (for differentiating
between signal and noise)
strict: if False, allow a single increase in intensity in either direction
Returns: the peak picked experiment.
"""
exp.sortSpectra()
spectra = exp.getSpectra()
picked_exp = ms.MSExperiment()
for spec in spectra:
if spec.getMSLevel() != 1:
continue
picked_exp.addSpectrum(self.pick_spectra(spec, peak_radius, window_radius, pp_mode, min_int_mult, strict))
return picked_exp
def main(options):
precursor_tolerance = options.precursor_tolerance
product_tolerance = options.product_tolerance
out = options.outfile
chromat_in = options.infile
traml_in = options.traml_in
# precursor_tolerance = 0.05
# product_tolerance = 0.05
# out = "/tmp/out.mzML"
# chromat_in = "../source/TEST/TOPP/MRMMapping_input.chrom.mzML"
# traml_in = "../source/TEST/TOPP/MRMMapping_input.TraML"
ff = pyopenms.MRMFeatureFinderScoring()
chromatogram_map = pyopenms.MSExperiment()
fh = pyopenms.FileHandler()
fh.loadExperiment(chromat_in, chromatogram_map)
targeted = pyopenms.TargetedExperiment()
tramlfile = pyopenms.TraMLFile()
tramlfile.load(traml_in, targeted)
output = algorithm(chromatogram_map, targeted, precursor_tolerance,
product_tolerance)
pyopenms.MzMLFile().store(out, output)
def _write_spectra_mzml(filename: str, spectra: Iterable[sus.MsmsSpectrum]) \
-> None:
"""
Write the given spectra to an mzML file.
Parameters
----------
filename : str
The mzML file name where the spectra will be written.
spectra : Iterable[sus.MsmsSpectrum]
The spectra to be written to the mzML file.
"""
experiment = pyopenms.MSExperiment()
for spectrum in tqdm.tqdm(spectra, desc='Spectra written', unit='spectra'):
mzml_spectrum = pyopenms.MSSpectrum()
mzml_spectrum.setMSLevel(2)
mzml_spectrum.setNativeID(spectrum.identifier)
precursor = pyopenms.Precursor()
precursor.setMZ(spectrum.precursor_mz)
precursor.setCharge(spectrum.precursor_charge)
mzml_spectrum.setPrecursors([precursor])
mzml_spectrum.set_peaks([spectrum.mz, spectrum.intensity])
if hasattr(spectrum, 'retention_time'):
mzml_spectrum.setRT(spectrum.retention_time)
if hasattr(spectrum, 'filename'):
mzml_spectrum.setMetaValue('filename',
str.encode(spectrum.filename))
if hasattr(spectrum, 'scan'):
mzml_spectrum.setMetaValue('scan', str.encode(str(spectrum.scan)))
if hasattr(spectrum, 'cluster'):
mzml_spectrum.setMetaValue('cluster',
str.encode(str(spectrum.cluster)))
experiment.addSpectrum(mzml_spectrum)
pyopenms.MzMLFile().store(filename, experiment)
def main(options):
# generate fragmentationtype lookup
lookup = {}
methods = pyopenms.ActivationMethod()
for attr in dir(methods):
value = getattr(methods, attr)
if isinstance(value, int):
lookup[value] = attr
print "loading MS Experiment "
exp = pyopenms.MSExperiment()
fh = pyopenms.FileHandler()
fh.loadExperiment(options.infile, exp)
print "getting fragment spectra types:"
fragmentationTypes = set()
for s in exp:
if s.getMSLevel() != 1:
typ = getSpectrumType(s, lookup)
fragmentationTypes.add(typ)
# writing new files
filepart, suffix = os.path.splitext(options.outfile)
filenames = []
for typ in fragmentationTypes:
expNew = pyopenms.MSExperiment()
for s in exp:
if s.getMSLevel() == 1 or getSpectrumType(s, lookup) == typ:
expNew.addSpectrum(s)
print "saving file"
mzFile = pyopenms.MzMLFile()
fileoptions = mzFile.getOptions()
fileoptions.setCompression(True)
mzFile.setOptions(fileoptions)
name = filepart + "_" + typ + ".mzML"
filenames.append(name)
mzFile.store(name, expNew)
del expNew
print "create zip file", options.outfile
zipFile = zipfile.ZipFile(options.outfile, "w", allowZip64=True)
for name in filenames:
zipFile.write(name, os.path.basename(name))
zipFile.close()
print "finished"
def read_mzml(infile):
# init variables
mzml_file = oms.MzMLFile()
exp = oms.MSExperiment()
# load spectra into exp
mzml_file.load(infile, exp)
return (exp)
def toMSExperiment(self):
"""converts peakmap to pyopenms.MSExperiment"""
exp = pyopenms.MSExperiment()
for spec in self.spectra:
exp.push_back(spec.toMSSpectrum())
exp.updateRanges()
exp.setLoadedFilePath(self.meta.get("source", ""))
return exp
def main(options):
# generate fragmentationtype lookup
lookup = {}
methods = pyopenms.ActivationMethod()
for attr in dir(methods):
value = getattr(methods, attr)
if isinstance(value, int):
lookup[value] = attr
print "loading MS Experiment "
exp = pyopenms.MSExperiment()
fh = pyopenms.FileHandler()
fh.loadExperiment(options.infile, exp)
print "getting fragment spectra types:"
fragmentationTypes = {}
for s in exp:
if s.getMSLevel() != 1:
typ = getSpectrumType(s, lookup)
fragmentationTypes[typ] = fragmentationTypes.get(typ, 0) + 1
print "found the following spectra types:"
for typ in fragmentationTypes:
print "typ '" + typ + "' with " + str(
fragmentationTypes[typ]) + " spectra"
print "extracting all " + str(
fragmentationTypes.get(
options.extractType,
0)) + " spectra with type " + options.extractType
expNew = pyopenms.MSExperiment()
for s in exp:
if s.getMSLevel() == 1 or getSpectrumType(
s, lookup) == options.extractType:
expNew.addSpectrum(s)
print "saving file"
mzFile = pyopenms.MzMLFile()
fileoptions = mzFile.getOptions()
fileoptions.setCompression(True)
mzFile.setOptions(fileoptions)
mzFile.store(options.outfile, expNew)
print "finished"
def basic(filename):
"""Calculate the basic metrics available from virtually every mzML file."""
exp = oms.MSExperiment()
oms.MzMLFile().load(click.format_filename(filename), exp)
rq = basicqc.getBasicQuality(exp)
rqs.append(rq)
finale()
def open_profile_mzml(self):
self._log('Loading profile data from `%s`.' % self.profile_mzml)
# As I understand this belongs to the peak picking
# hence I moved here, we don't need these attributes in __init__
self.profile_map = oms.MSExperiment()
oms.MzMLFile().load(self.profile_mzml, self.profile_map)
def test_acquisitioninfomemberaccess(self):
exp = pyopenms.MSExperiment()
pyopenms.MzMLFile().load(self.filename_mzml, exp)
# Basically test that the output is non-zero (e.g. the data is
# correctly relayed to python)
# The functionality is not tested here!
# starting point
self.assertEqual(exp[0].getAcquisitionInfo().size(), 1)
self.assertNotEqual(exp[0].getAcquisitionInfo().size(), 0)
# metainfo
exp[0].getAcquisitionInfo().size() # is 1
self.assertEqual(exp[0].getAcquisitionInfo()[0].isMetaEmpty(),
True) # is True
spectra = exp.getSpectra()
aqis = spectra[0].getAcquisitionInfo()
aqi = aqis[0] # get a copy
aqi.setMetaValue('key', 420) # modify it
aqis[0] = aqi # and set entry
spectra[0].setAcquisitionInfo(aqis)
exp.setSpectra(spectra)
self.assertEqual(exp[0].getAcquisitionInfo()[0].getMetaValue('key'),
420) # should be 420
acin = pyopenms.Acquisition()
acin.setMetaValue('key', 42)
self.assertEqual(acin.getMetaValue('key'), 42) # is 42
self.assertEqual(acin.isMetaEmpty(), False) # is False
# list/vector assignment
magicnumber = 3
neac = pyopenms.AcquisitionInfo()
for i in range(0, magicnumber):
neac.push_back(acin)
self.assertEqual(neac.size(), magicnumber) # is magicnumber
# iteration
for i in neac:
self.assertEqual(i.isMetaEmpty(), False) # always is False
# accession already tested in 2nd section
tmp = exp.getSpectra()
tmp[0].setAcquisitionInfo(neac)
exp.setSpectra(tmp)
self.assertEqual(exp[0].getAcquisitionInfo().size(),
magicnumber) # should be magicnumber
for i in exp[0].getAcquisitionInfo():
self.assertEqual(i.isMetaEmpty(), False) # should always be False
# resize
neac.resize(0)
self.assertEqual(neac.size(), 0)
def addDataProcessing(what, params):
if isinstance(what, pms.MSExperiment):
result = pms.MSExperiment()
for spec in what:
spec = _addDataProcessing(spec, params)
result.push_back(spec)
else:
result = _addDataProcessing(what, params)
return result
def addDataProcessing(obj, params, action):
if isinstance(obj, pms.MSExperiment):
result = pms.MSExperiment()
for spec in obj:
spec = _addDataProcessing(spec, params, action)
result.addSpectrum(spec)
else:
result = _addDataProcessing(obj, params, action)
return result
def pickPeakMap(self, pm, showProgress=False):
assert isinstance(pm, PeakMap)
eout = pyopenms.MSExperiment()
if showProgress:
self.pp.setLogType(pyopenms.LogType.CMD)
else:
self.pp.setLogType(pyopenms.LogType.NONE)
self.pp.pickExperiment(pm.toMSExperiment(), eout)
return PeakMap.fromMSExperiment(eout)
def reset(self) -> None:
"""Resets the feature finder to its default state."""
self.num_bins, self.bin_size = 0, 0
self.im_start, self.im_end = 0, 0
self.im_delta, self.im_offset = 0, 0
self.im_scan_nums = [
[], []
] # Keep the intensity-weighted average IM value for each bin
self.exps = [[], [ms.MSExperiment()]] # To "cache" mzML writes
