def test_run(self):
thisfilter = pyopenms.GaussFilter()
old_firstspec = self.exp[0]
thisfilter.filterExperiment(self.exp)
self.assertNotEqual(self.exp.size(), 0)
self.assertNotEqual(old_firstspec, self.exp[0])
# MZ should not change, Intensity should
self.assertEqual(old_firstspec[10].getMZ(), self.exp[0][10].getMZ())
self.assertNotEqual(old_firstspec[10].getIntensity(),
self.exp[0][10].getIntensity())
def smoothing(self):
if not self.smooth_profile_data:
return
self._log('Smoothing profile data by Gaussian filter.')
gs = oms.GaussFilter()
param = gs.getDefaults()
self._oms_set_param(self.gs_param, param)
gs.setParameters(param)
gs.filterExperiment(self.profile_map)
if self.export_smoothed_profile:
self.smoothed_profile_mzml = os.path.join(
self.wd,
'%s__smoothed.mzML' % self.name,
)
self._log('Exporting smoothed profile '
'data into `%s`.' % self.smoothed_profile_mzml)
oms.MzMLFile().store(self.smoothed_profile_mzml, self.profile_map)
def find_features(self, pp_type: str, peak_radius: int,
window_radius: float, pp_mode: str, ff_type: str,
dir: str, filter: str,
debug: bool) -> List[List[ms.FeatureMap]]:
"""Runs optional peak picking and then an existing feature finder on each IM bin.
Keyword arguments:
pp_type: the peak picker to use ('none', 'pphr', or 'custom')
peak_radius: for the custom peak picker, the minimum peak radius of a peak set
window_radius: for the custom peak picker, the maximum m/z window radius to consider
pp_mode: for the custom peak picker, the mode to use ('ltr' or 'int')
ff_type: the existing feature finder to use ('centroided' or 'multiplex')
dir: the directory to write the intermediate output files to
filter: the noise filter to use ('none', 'gauss', or 'sgolay')
debug: determines if intermediate output files should be written
Returns: a list of two lists (for the passes), each containing the features for all of
their bins.
"""
features = [[], []]
total_features = [ms.FeatureMap(),
ms.FeatureMap()] # Only used for debug output
if filter == 'gauss':
filter_g = ms.GaussFilter()
params_g = filter_g.getDefaults()
params_g.setValue(b'ppm_tolerance', 20.0)
params_g.setValue(b'use_ppm_tolerance', b'true')
filter_g.setParameters(params_g)
if filter == 'sgolay':
filter_s = ms.SavitzkyGolayFilter()
params_s = filter_s.getDefaults()
params_s.setValue(b'frame_length', 7)
params_s.setValue(b'polynomial_order', 3)
filter_s.setParameters(params_s)
pick_hr = ms.PeakPickerHiRes()
pick_im = ppim.PeakPickerIonMobility()
nb = [self.num_bins, 0 if self.num_bins == 1 else self.num_bins + 1
] # Size of each pass
for j in range(2): # Pass index
for i in range(nb[j]): # Bin index
exp, new_exp = ms.MSExperiment(), ms.MSExperiment()
ms.MzMLFile().load(
dir + '/b-' + str(j) + '-' + str(i) + '.mzML', exp)
# Optional noise filtering
if filter == 'gauss':
filter_g.filterExperiment(exp)
elif filter == 'sgolay':
filter_s.filterExperiment(exp)
if filter != 'none' and debug:
ms.MzMLFile().store(
dir + '/pass' + str(j) + '-bin' + str(i) +
'-filtered.mzML', exp)
# Optional peak picking
if pp_type == 'pphr':
pick_hr.pickExperiment(exp, new_exp)
elif pp_type == 'custom':
new_exp = pick_im.pick_experiment(exp,
peak_radius,
window_radius,
pp_mode,
self.MIN_INTENSITY,
strict=True)
else:
new_exp = exp
if pp_type != 'none' and debug:
ms.MzMLFile().store(
dir + '/pass' + str(j) + '-bin' + str(i) +
'-picked.mzML', new_exp)
# Feature finding
temp_features = ms.FeatureMap()
if util.has_peaks(new_exp):
temp_features = self.run_ff(new_exp, ff_type)
temp_features = self.match_features_internal(temp_features)
temp_features.setUniqueIds()
if debug:
ms.FeatureXMLFile().store(
dir + '/pass' + str(j) + '-bin' + str(i) +
'.featureXML', temp_features)
features[j].append(temp_features)
total_features[j] += temp_features
if debug:
for j in range(2):
total_features[j].setUniqueIds()
ms.FeatureXMLFile().store(
dir + '/pass' + str(j) + '.featureXML', total_features[j])
return features[0], features[1]
def test_init(self):
thisfilter = pyopenms.GaussFilter()