def set_matching_peaks(library_spectrum, query_spectrum):
peak_matches = spectrum_match.get_best_match(query_spectrum,
[library_spectrum],
config.fragment_mz_tolerance,
config.allow_peak_shifts)[2]
query_spectrum.annotation = np.full_like(query_spectrum.mz, None, object)
for peak_match in peak_matches:
library_annotation = library_spectrum.annotation[peak_match[1]]
if library_annotation is not None and library_annotation.ion_type in 'by':
query_spectrum.annotation[peak_match[0]] = library_annotation
def get_matching_peaks(library_spectrum, query_spectrum):
_, score, peak_matches = spectrum_match.get_best_match(
query_spectrum, [library_spectrum],
allow_shift=config.allow_peak_shifts)
library_matches, query_matches = {}, {}
for peak_match in peak_matches:
query_matches[peak_match[0]] = library_matches[peak_match[1]] = (
'unknown' if library_spectrum.annotations[peak_match[1]] is None
else library_spectrum.annotations[peak_match[1]][0][0])
return library_matches, query_matches, score
def set_matching_peaks(library_spectrum, query_spectrum):
peak_matches = spectrum_match.get_best_match(
query_spectrum, [library_spectrum],
config.fragment_mz_tolerance, config.allow_peak_shifts)[2]
query_spectrum.annotation = np.full_like(query_spectrum.mz, None, object)
for peak_match in peak_matches:
library_annotation = library_spectrum.annotation[peak_match[1]]
if library_annotation is not None:
query_spectrum.annotation[peak_match[0]] = library_annotation
else:
fragment_annotation = PeptideFragmentAnnotation(1, 1, 'z', 0)
fragment_annotation.ion_type = 'unknown'
query_spectrum.annotation[peak_match[0]] =\
library_spectrum.annotation[peak_match[1]] =\
fragment_annotation
def _find_match(self, query, tol_mass, tol_mode):
"""
Identifies the given query Spectrum.
Args:
query: The query Spectrum to be identified.
Returns:
A SpectrumMatch identification. If the query couldn't be
identified SpectrumMatch.sequence will be None.
"""
# discard low-quality spectra
if not query.is_valid():
return spectrum.SpectrumMatch(query)
start_total = start_candidates = time.time()
# find all candidate library spectra
# for which a match has to be computed
candidates = self._filter_library_candidates(query, tol_mass, tol_mode)
stop_candidates = start_match = time.time()
if len(candidates) > 0:
# find the best matching candidate spectrum
match_candidate, match_score, _ = spectrum_match.get_best_match(
query, candidates)
identification = spectrum.SpectrumMatch(query, match_candidate,
match_score)
else:
identification = spectrum.SpectrumMatch(query)
stop_match = stop_total = time.time()
# store performance data
identification.num_candidates = len(candidates)
identification.time_candidates = stop_candidates - start_candidates
identification.time_match = stop_match - start_match
identification.time_total = stop_total - start_total
return identification
def _search_batch(self, query_spectra: List[MsmsSpectrum],
charge: int, mode: str)\
-> Iterator[SpectrumSpectrumMatch]:
"""
Generate spectrum-spectrum matches for a batch of query spectra with
the same precursor charge.
Parameters
----------
query_spectra : List[Spectrum]
The query spectra for which spectrum-spectrum matches are
generated.
charge : int
The precursor charge of the query spectra.
mode : {'std', 'open'}
The search mode. Either 'std' for a standard search with a small
precursor mass window, or 'open' for an open search with a wide
precursor mass window.
Returns
-------
Iterator[SpectrumSpectrumMatch]
An iterator of spectrum-spectrum matches for every query spectrum
that could be successfully matched to its most similar library
spectrum.
"""
# Find all library candidates for each query spectrum.
for query_spectrum, library_candidates in zip(
query_spectra,
self._get_library_candidates(query_spectra, charge, mode)):
# Find the best match candidate.
if library_candidates:
library_match, score, _ = spectrum_match.get_best_match(
query_spectrum, library_candidates,
config.fragment_mz_tolerance, config.allow_peak_shifts)
yield SpectrumSpectrumMatch(
query_spectrum,
library_match,
score,
num_candidates=len(library_candidates))