def lookup_standards(self):
mztheo = []
dfo = self.m.preproc.extractor.dataframe
dfo.sort_values(by='mz', inplace=True)
dfo.reset_index(drop=True, inplace=True)
mzo = np.array(dfo['mz'])
for i in self.standards:
a = lookup.find(mzo, i, t=50)
mztheo.append(a)
def collect_convex_hulls(self):
self.convex_hulls = []
# opening featureXML
xml_file = oms.FeatureXMLFile()
self.fmap = oms.FeatureMap()
xml_file.load(self.feature_xml_fname, self.fmap)
feature_mzs = []
for i, fe in enumerate(self.fmap):
feature_mzs.append([i, fe.getMZ()])
feature_mzs = np.array(feature_mzs)
feature_mzs = feature_mzs[feature_mzs[:, 1].argsort(), :]
# looking up the example features in the featureXML
self.examples_oms_features = {}
for ex, (mz_t, mz_m) in self.peaks_peaks.items():
i_mz_fe = lookup.find(feature_mzs[:, 1], mz_m, t=10)
if i_mz_fe:
self.examples_oms_features[feature_mzs[i_mz_fe, 0]] = ex
# collecting convex hulls
for ife, fe in enumerate(self.fmap):
if ife in self.examples_oms_features:
hull_list = fe.getConvexHulls()
self.extend_hulls(hull_list, ife, 0)
subord_feature = fe.getSubordinates()
if subord_feature:
for subfe in subord_feature:
hull_list = subfe.getConvexHulls()
self.extend_hulls(hull_list, ife, 1)
# columns: rt, mz, feature index, hull index, is sub-feature
self.convex_hulls = np.vstack(self.convex_hulls)
self.oms_feature_mzs = feature_mzs[feature_mzs[:, 0].argsort(), :]
def background_correction_by_other_chromatograms(
self,
others,
start = None,
end = None,
):
"""
Subtracts corresponding values of other chromatograms.
Parameters
----------
other : list,SECReader
One or more instances of ``SECReader``.
start : float
Start at this volume. By default the beginning of the
chromatogram.
end : float
Do until this volume. By default the end of the chromatogram.
"""
def get_closest(vol):
return lookup.find(self.volume, vol, np.inf)
istart = 0 if start is None else get_closest(start)
iend = 0 if end is None else get_closest(end)
others = (
(others,)
if not isinstance(others, (tuple, list, np.ndarray)) else
others
)
for i in xrange(istart, iend + 1):
vol = self.volume[i]
i_other = (
lookup.find(other.volume, vol, np.inf)
for other in others
)
background = np.median([
others[j].absorbance[io]
for j, io in enumerate(i_other)
])
self.absorbance[i] = self.absorbance[i] - background
def lookup_progenesis(self):
self.progenesis_peaks = {}
if not hasattr(self, 'progenesis_mzs'):
return
for ex in self.examples:
mz_theoretical = self.pc_adduct_masses[(ex, 0)]
i = lookup.find(
self.progenesis_mzs, # all masses in the sample
mz_theoretical, # mass to search for
t=10, # tolerance in ppm
)
if i:
self.progenesis_peaks[ex] = self.progenesis_mzs[i]
def lookup_peaks(self):
self.peaks_peaks = {}
for ex in self.examples:
mz_theoretical = self.pc_adduct_masses[(ex, 0)]
i = lookup.find(
self.sample_selected, # all masses in the sample
mz_theoretical, # mass to search for
t=10, # tolerance in ppm
)
if i is not None:
self.peaks_peaks[ex] = (
mz_theoretical,
self.sample_selected[i],
)
else:
print('Example not found: %s %.04f' % (ex, mz_theoretical))
def get_closest(vol):
return lookup.find(self.volume, vol, np.inf)