def _alignData(self):
"""Re-calibrate data using theoretical envelope."""
# split data to raster and intensities
xAxis, yAxis = numpy.hsplit(self.data, 2)
raster = xAxis.flatten()
intensities = yAxis.flatten()
# model profiles
models, exchanged = self._makeModels(raster)
# fit current data
fit = self._leastSquare(intensities, models, iterLimit=len(self.models))
# get all isotopes for all used models
isotopes = []
for i, abundance in enumerate(fit):
pattern = self.models[exchanged[i]][1]
isotopes += [(p[0], p[1]*abundance) for p in pattern]
# check isotopes
if not isotopes:
return
# make total envelope
profile = mod_pattern.profile(isotopes, fwhm=self.fwhm, points=10, model=self.peakShape)
# label peaks in profile
peaklist = mod_peakpicking.labelscan(profile, pickingHeight=0.95, relThreshold=0.01)
# find useful calibrants
calibrants = []
tolerance = self.fwhm/1.5
for peak in peaklist:
for point in self.data:
error = point[0] - peak.mz
if abs(error) <= tolerance:
if calibrants and calibrants[-1][0] == peak.mz and calibrants[-1][1] < point[1]:
calibrants[-1] = (point[0], peak.mz)
else:
calibrants.append((point[0], peak.mz))
elif error > tolerance:
break
# calc calibration
if len(calibrants) > 3:
model, params, chi = mod_calibration.calibration(calibrants, model='quadratic')
elif len(calibrants) > 1:
model, params, chi = mod_calibration.calibration(calibrants, model='linear')
else:
return
# apply calibration to data
for x in range(len(self.data)):
self.data[x][0] = model(params, self.data[x][0])
for x in range(len(self.spectrum)):
self.spectrum[x][0] = model(params, self.spectrum[x][0])
def labelscan(
self,
pickingHeight=0.75,
absThreshold=0.0,
relThreshold=0.0,
snThreshold=0.0,
baselineWindow=0.1,
baselineOffset=0.0,
smoothMethod=None,
smoothWindow=0.2,
smoothCycles=1,
):
"""Label centroides in current scan.
pickingHeight (float) - peak picking height for centroiding
absThreshold (float) - absolute intensity threshold
relThreshold (float) - relative intensity threshold
snThreshold (float) - signal to noise threshold
baselineWindow (float) - noise calculation window (in %/100)
baselineOffset (float) - baseline offset, relative to noise width (in %/100)
smoothMethod (None, MA, GA or SG) - smoothing method
smoothWindow (float) - m/z window size for smoothing
smoothCycles (int) - number of smoothing cycles
"""
# get baseline
baseline = self.baseline(window=baselineWindow, offset=baselineOffset)
# pre-smooth profile
profile = self.profile
if smoothMethod:
profile = mod_signal.smooth(signal=profile, method=smoothMethod, window=smoothWindow, cycles=smoothCycles)
# label peaks
peaklist = mod_peakpicking.labelscan(
signal=profile,
pickingHeight=pickingHeight,
absThreshold=absThreshold,
relThreshold=relThreshold,
snThreshold=snThreshold,
baseline=baseline,
)
# check peaklist
if peaklist == None:
return False
# update peaklist
self.peaklist = peaklist
return True
def labelscan(self,
pickingHeight=0.75,
absThreshold=0.,
relThreshold=0.,
snThreshold=0.,
baselineWindow=0.1,
baselineOffset=0.,
smoothMethod=None,
smoothWindow=0.2,
smoothCycles=1):
"""Label centroides in current scan.
pickingHeight (float) - peak picking height for centroiding
absThreshold (float) - absolute intensity threshold
relThreshold (float) - relative intensity threshold
snThreshold (float) - signal to noise threshold
baselineWindow (float) - noise calculation window (in %/100)
baselineOffset (float) - baseline offset, relative to noise width (in %/100)
smoothMethod (None, MA, GA or SG) - smoothing method
smoothWindow (float) - m/z window size for smoothing
smoothCycles (int) - number of smoothing cycles
"""
# get baseline
baseline = self.baseline(window=baselineWindow, offset=baselineOffset)
# pre-smooth profile
profile = self.profile
if smoothMethod:
profile = mod_signal.smooth(signal=profile,
method=smoothMethod,
window=smoothWindow,
cycles=smoothCycles)
# label peaks
peaklist = mod_peakpicking.labelscan(signal=profile,
pickingHeight=pickingHeight,
absThreshold=absThreshold,
relThreshold=relThreshold,
snThreshold=snThreshold,
baseline=baseline)
# check peaklist
if peaklist == None:
return False
# update peaklist
self.peaklist = peaklist
return True
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 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 _alignData(self):
"""Re-calibrate data using theoretical envelope."""
# split data to raster and intensities
xAxis, yAxis = numpy.hsplit(self.data, 2)
raster = xAxis.flatten()
intensities = yAxis.flatten()
# model profiles
models, exchanged = self._makeModels(raster)
# fit current data
fit = self._leastSquare(intensities,
models,
iterLimit=len(self.models))
# get all isotopes for all used models
isotopes = []
for i, abundance in enumerate(fit):
pattern = self.models[exchanged[i]][1]
isotopes += [(p[0], p[1] * abundance) for p in pattern]
# check isotopes
if not isotopes:
return
# make total envelope
profile = mod_pattern.profile(isotopes,
fwhm=self.fwhm,
points=10,
model=self.peakShape)
# label peaks in profile
peaklist = mod_peakpicking.labelscan(profile,
pickingHeight=0.95,
relThreshold=0.01)
# find useful calibrants
calibrants = []
tolerance = self.fwhm / 1.5
for peak in peaklist:
for point in self.data:
error = point[0] - peak.mz
if abs(error) <= tolerance:
if calibrants and calibrants[-1][
0] == peak.mz and calibrants[-1][1] < point[1]:
calibrants[-1] = (point[0], peak.mz)
else:
calibrants.append((point[0], peak.mz))
elif error > tolerance:
break
# calc calibration
if len(calibrants) > 3:
model, params, chi = mod_calibration.calibration(calibrants,
model='quadratic')
elif len(calibrants) > 1:
model, params, chi = mod_calibration.calibration(calibrants,
model='linear')
else:
return
# apply calibration to data
for x in range(len(self.data)):
self.data[x][0] = model(params, self.data[x][0])
for x in range(len(self.spectrum)):
self.spectrum[x][0] = model(params, self.spectrum[x][0])
