def convert(self, fitted=True, deconvoluted=True):
precursor_information = self.precursor_information.convert(
) if self.precursor_information is not None else None
session = object_session(self)
conn = session.connection()
if fitted:
q = conn.execute(select([FittedPeak.__table__]).where(
FittedPeak.__table__.c.scan_id == self.id)).fetchall()
peak_set_items = list(
map(make_memory_fitted_peak, q))
peak_set = PeakSet(peak_set_items)
peak_set._index()
peak_index = PeakIndex(np.array([], dtype=np.float64), np.array(
[], dtype=np.float64), peak_set)
else:
peak_index = PeakIndex(np.array([], dtype=np.float64), np.array(
[], dtype=np.float64), PeakSet([]))
if deconvoluted:
q = conn.execute(select([DeconvolutedPeak.__table__]).where(
DeconvolutedPeak.__table__.c.scan_id == self.id)).fetchall()
deconvoluted_peak_set_items = list(
map(make_memory_deconvoluted_peak, q))
deconvoluted_peak_set = DeconvolutedPeakSet(
deconvoluted_peak_set_items)
deconvoluted_peak_set._reindex()
else:
deconvoluted_peak_set = DeconvolutedPeakSet([])
info = self.info or {}
scan = ProcessedScan(
self.scan_id, self.title, precursor_information, int(self.ms_level),
float(self.scan_time), self.index, peak_index, deconvoluted_peak_set,
activation=info.get('activation'))
return scan
def make_peak_index(fitted_peaks):
ps = PeakSet(fitted_peaks)
ps._index()
return PeakIndex(np.array([], dtype=float), np.array([], dtype=float), ps)
def make_peak_index(fitted_peaks):
ps = PeakSet(fitted_peaks)
ps._index()
return ps
def make_peak_index(fitted_peaks):
ps = PeakSet(fitted_peaks)
ps._index()
return PeakIndex(np.array([], dtype=float), np.array([], dtype=float), ps)
