def compute_alignment(self, other, mz_tolerance):
assert isinstance(other, Spectrum), "need spectrum as argument"
aligner = pyopenms.SpectrumAlignment()
alignment = []
conf = aligner.getDefaults()
conf_d = conf.asDict()
conf_d[
"is_relative_tolerance"] = "false" # "true" not implemented yet !
conf_d["tolerance"] = mz_tolerance
conf.update(conf_d)
aligner.setParameters(conf)
s0 = self.toMSSpectrum()
s1 = other.toMSSpectrum()
alignment = []
aligner.getSpectrumAlignment(alignment, s0, s1)
return alignment
def compute_alignments(spectra, mz_tolerance):
"""takes a list of spectra and groups peaks given mz_tolerance.
it returns a list of lists. every inner list specifies the alignment of one input
spectrum to its follower in the list.
One assignment is a list of tuples, where the first entry is a peak index from the first
list, the second entry is the index of a peak from the second spectrum.
For example:
if you run this method with a list or tuple of three spectra (s0, s1, s2) the return
values will be [align_0_to_1, align_1_to_2]
an alignment is a list [(i0, j0), (i1, j1), ...]
so that s0.peaks[i0, :] is assigned to s1.peaks[j0, :] and so on.
"""
aligner = pyopenms.SpectrumAlignment()
alignment = []
conf = aligner.getDefaults()
conf_d = conf.asDict()
conf_d[
"is_relative_tolerance"] = "false" # "true" not implemented yet !
conf_d["tolerance"] = mz_tolerance
conf.update(conf_d)
aligner.setParameters(conf)
openms_spectra = [s.toMSSpectrum() for s in spectra]
# create pairwise alignments
alignments = []
for s0, s1 in zip(openms_spectra, openms_spectra[1:]):
alignment = []
aligner.getSpectrumAlignment(alignment, s0, s1)
alignments.append(alignment)
return alignments
mz.append(float(i))
spectrums = []
based_intensity = np.transpose(source['mz_exp'])
for i in range(based_intensity.shape[0]):
peak_intensity = based_intensity[i]
spectrum = pyopenms.MSSpectrum()
spectrum.set_peaks([mz, peak_intensity])
spectrum.sortByPosition()
spectrums.append(spectrum)
super_spectrum_mz = np.array(super_spectrum_mz)
print(super_spectrum_mz.shape)
aligned_intensities = []
aligner = pyopenms.SpectrumAlignment()
target_spectrum = pyopenms.MSSpectrum()
target_spectrum.set_peaks(
[super_spectrum_mz, np.zeros_like(super_spectrum_mz)])
target_spectrum.sortByPosition()
for spectrum in spectrums:
alignment = []
aligner.getSpectrumAlignment(alignment, spectrum, target_spectrum)
_, to_align_intensity = spectrum.get_peaks()
aligned_intensity = np.zeros_like(super_spectrum_mz)
for t in alignment:
aligned_intensity[t[1]] = to_align_intensity[t[0]]
aligned_intensities.append(aligned_intensity)
aligned_intensities = np.array(aligned_intensities)