def profile(peaklist,
fwhm=0.1,
points=10,
noise=0,
raster=None,
forceFwhm=False,
model='gaussian'):
"""Make profile spectrum for given peaklist.
peaklist (mspy.peaklist) - peaklist
fwhm (float) - default peak fwhm
points (int) - default number of points per peak width (not used if raster is given)
noise (float) - random noise width
raster (1D numpy.array) - m/z raster
forceFwhm (bool) - use default fwhm for all peaks
model (gaussian, lorentzian, gausslorentzian) - peak shape function
"""
# check peaklist type
if not isinstance(peaklist, obj_peaklist.peaklist):
peaklist = obj_peaklist.peaklist(peaklist)
# check raster type
if raster != None and not isinstance(raster, numpy.ndarray):
raster = numpy.array(raster)
# get peaks
peaks = []
for peak in peaklist:
peaks.append([peak.mz, peak.intensity, peak.fwhm])
if forceFwhm or not peak.fwhm:
peaks[-1][2] = fwhm
# get model
shape = 0
if model == 'gaussian':
shape = 0
elif model == 'lorentzian':
shape = 1
elif model == 'gausslorentzian':
shape = 2
# make profile
if raster != None:
data = calculations.signal_profile_to_raster(numpy.array(peaks),
raster, float(noise),
shape)
else:
data = calculations.signal_profile(numpy.array(peaks), int(points),
float(noise), shape)
# make baseline
baseline = []
for peak in peaklist:
if not baseline or baseline[-1][0] != peak.mz:
baseline.append([peak.mz, -peak.base])
# apply baseline
data = mod_signal.subbase(data, numpy.array(baseline))
return data
def profile(peaklist, fwhm=0.1, points=10, noise=0, raster=None, forceFwhm=False, model='gaussian'):
"""Make profile spectrum for given peaklist.
peaklist (mspy.peaklist) - peaklist
fwhm (float) - default peak fwhm
points (int) - default number of points per peak width (not used if raster is given)
noise (float) - random noise width
raster (1D numpy.array) - m/z raster
forceFwhm (bool) - use default fwhm for all peaks
model (gaussian, lorentzian, gausslorentzian) - peak shape function
"""
# check peaklist type
if not isinstance(peaklist, obj_peaklist.peaklist):
peaklist = obj_peaklist.peaklist(peaklist)
# check raster type
if raster != None and not isinstance(raster, numpy.ndarray):
raster = numpy.array(raster)
# get peaks
peaks = []
for peak in peaklist:
peaks.append([peak.mz, peak.intensity, peak.fwhm])
if forceFwhm or not peak.fwhm:
peaks[-1][2] = fwhm
# get model
shape = 0
if model == 'gaussian':
shape = 0
elif model == 'lorentzian':
shape = 1
elif model == 'gausslorentzian':
shape = 2
# make profile
if raster != None:
data = calculations.signal_profile_to_raster(numpy.array(peaks), raster, float(noise), shape)
else:
data = calculations.signal_profile(numpy.array(peaks), int(points), float(noise), shape)
# make baseline
baseline = []
for peak in peaklist:
if not baseline or baseline[-1][0] != peak.mz:
baseline.append([peak.mz, -peak.base])
# apply baseline
data = mod_signal.subbase(data, numpy.array(baseline))
return data
def tospectrum(self,
signal,
fwhm=0.1,
forceFwhm=True,
autoAlign=True,
iterLimit=None,
relThreshold=0.,
pickingHeight=0.90,
baseline=None):
"""Fit modeled profiles to spectrum using tmp peaklist.
signal (numpy array) - m/z / intensity pairs
fwhm (float) - defaut fwhm
forceFwhm (bool) - use default fwhm
autoAlign (bool) - automatic m/z shift
iterLimit (int) - maximum number of iterations
pickingHeight (float) - peak picking height for centroiding
relThreshold (float) - relative intensity threshold
baseline (numpy array) - signal baseline
"""
# crop signal to relevant m/z range
i1 = mod_signal.locate(signal, self.mzrange[0])
i2 = mod_signal.locate(signal, self.mzrange[1])
signal = signal[i1:i2]
# get peaklist from signal
peaklist = mod_peakpicking.labelscan(signal=signal,
pickingHeight=pickingHeight,
relThreshold=relThreshold,
baseline=baseline)
# remove shoulder peaks
peaklist.remshoulders(fwhm=fwhm)
# correct signal baseline
if baseline != None:
self.spectrum = mod_signal.subbase(signal, baseline)
# fit to peaklist
return self.topeaklist(
peaklist=peaklist,
fwhm=fwhm,
forceFwhm=forceFwhm,
autoAlign=autoAlign,
iterLimit=iterLimit,
relThreshold=relThreshold,
)
def subbase(self, window=0.1, offset=0.0):
"""Subtract baseline from profile.
window (float or None) - noise calculation window (%/100)
offset (float) - baseline offset, relative to noise width (in %/100)
"""
# get baseline
baseline = self.baseline(window=window, offset=offset)
# subtract baseline
profile = mod_signal.subbase(signal=self.profile, baseline=baseline)
# store data
self.profile = profile
self.peaklist.empty()
# clear buffers
self.reset()
def subbase(self, window=0.1, offset=0.):
"""Subtract baseline from profile.
window (float or None) - noise calculation window (%/100)
offset (float) - baseline offset, relative to noise width (in %/100)
"""
# get baseline
baseline = self.baseline(window=window, offset=offset)
# subtract baseline
profile = mod_signal.subbase(signal=self.profile, baseline=baseline)
# store data
self.profile = profile
self.peaklist.empty()
# clear buffers
self.reset()
def tospectrum(self, signal, fwhm=0.1, forceFwhm=True, autoAlign=True, iterLimit=None, relThreshold=0., pickingHeight=0.90, baseline=None):
"""Fit modeled profiles to spectrum using tmp peaklist.
signal (numpy array) - m/z / intensity pairs
fwhm (float) - defaut fwhm
forceFwhm (bool) - use default fwhm
autoAlign (bool) - automatic m/z shift
iterLimit (int) - maximum number of iterations
pickingHeight (float) - peak picking height for centroiding
relThreshold (float) - relative intensity threshold
baseline (numpy array) - signal baseline
"""
# crop signal to relevant m/z range
i1 = mod_signal.locate(signal, self.mzrange[0])
i2 = mod_signal.locate(signal, self.mzrange[1])
signal = signal[i1:i2]
# get peaklist from signal
peaklist = mod_peakpicking.labelscan(
signal = signal,
pickingHeight = pickingHeight,
relThreshold = relThreshold,
baseline = baseline
)
# remove shoulder peaks
peaklist.remshoulders(fwhm=fwhm)
# correct signal baseline
if baseline != None:
self.spectrum = mod_signal.subbase(signal, baseline)
# fit to peaklist
return self.topeaklist(
peaklist = peaklist,
fwhm = fwhm,
forceFwhm = forceFwhm,
autoAlign = autoAlign,
iterLimit = iterLimit,
relThreshold = relThreshold,
)