def match_decon2ls_isos(spectrum_db, hypothesis, adducts=None, ms1_match_tolerance=1e-5, output_path=None):
if adducts is None:
adducts = [NoShift]
results = RecordDatabase(output_path, record_type=hypothesis.record_type)
results.apply_schema()
for composition in hypothesis:
matches = []
scans_searched = set()
charge_states = set()
for adduct in adducts:
for spectrum in spectrum_db.ppm_match_tolerance_search(
composition.intact_mass + adduct.mass, ms1_match_tolerance):
match_ppm = ppm_error(composition.intact_mass + adduct.mass, spectrum.neutral_mass)
match = PrecursorMatch(
str(composition.id) + ":" + adduct.name, spectrum.neutral_mass, match_ppm,
spectrum.get("abundance"), spectrum.charge, spectrum.other_data,
spectrum.scan_ids[0])
matches.append(match)
scans_searched.update(spectrum.scan_ids)
charge_states.add(spectrum.charge)
groups = groupby(matches, match_key_getter)
composition.precursor_matches = {key: combine_results(group) for key, group in groups.items()}
composition.precursor_scans_searched = scans_searched
composition.precursor_scan_density = density(scans_searched)
composition.precursor_charge_states = charge_states
results.load_data([composition], set_id=False, cast=True, commit=False)
results.commit()
results.apply_indices()
return results
def distinct_fragments(self, other, fragmentation_parameters=None):
"""Compute the set of distinct masses observed between two structures
Parameters
----------
self: Glycan or list
other : Glycan or list
Glycan objects whose fragments will be compared or lists of Fragment
to compare.
fragmentation_parameters : dict, optional
If `self` and `other` are |Glycan| objects, these parameters will be used
to call :meth:`Glycan.fragments`.
Returns
-------
set: The distinct fragment masses of `self`
set: The distinct fragment masses of `other`
"""
self_fragments = []
other_fragments = []
if isinstance(self, (tuple, list)):
self_fragments = self
other_fragments = other
else:
if fragmentation_parameters is None:
fragmentation_parameters = dict(kind="BY", max_cleavages=1, average=False, charge=0)
self_fragments = list(self.fragments(**fragmentation_parameters))
other_fragments = list(other.fragments(**fragmentation_parameters))
def grouper(fragment):
return round(fragment.mass, 4)
self_masses = set(groupby(self_fragments, grouper))
other_masses = set(groupby(other_fragments, grouper))
self_unique = self_masses - other_masses
other_unique = other_masses - self_masses
return self_unique, other_unique
