def envmono(isotopes, charge, intensity='maximum'):
"""Calculate envelope centroid for given isotopes.
isotopes (mspy.peaklist or list of mspy.peak) - envelope isotopes
charge (int) - peak charge
intensity (maximum | sum | average) - envelope intensity type
"""
# check isotopes
if len(isotopes) == 0:
return None
# check peaklist object
if not isinstance(isotopes, obj_peaklist.peaklist):
isotopes = obj_peaklist.peaklist(isotopes)
# calc averagine
avFormula = averagine(isotopes.basepeak.mz, charge=charge, composition=AVERAGE_AMINO)
avPattern = avFormula.pattern(fwhm=0.1, threshold=0.001, charge=charge)
avPattern = obj_peaklist.peaklist(avPattern)
# get envelope centroid
points = numpy.array([(p.mz, p.intensity) for p in isotopes])
centroid = labelpeak(points, mz=isotopes.basepeak.mz, pickingHeight=0.8)
if not centroid:
centroid = isotopes.basepeak
# get averagine centroid
points = numpy.array([(p.mz, p.intensity) for p in avPattern])
avCentroid = labelpeak(points, mz=avPattern.basepeak.mz, pickingHeight=0.8)
if not avCentroid:
avCentroid = avPattern.basepeak
# align profiles and get monoisotopic mass
shift = centroid.mz - avCentroid.mz
errors = [(abs(p.mz - avPattern.basepeak.mz - shift), p.mz) for p in isotopes]
mz = min(errors)[1] - (avPattern.basepeak.mz - avFormula.mz(charge)[0])
# sum intensities
sumIntensity = 0
for isotope in isotopes:
sumIntensity += isotope.intensity
# get ai, base and sn
base = isotopes.basepeak.base
sn = isotopes.basepeak.sn
fwhm = isotopes.basepeak.fwhm
if intensity == 'sum':
ai = base + sumIntensity
elif intensity == 'average':
ai = base + sumIntensity / len(isotopes)
else:
ai = isotopes.basepeak.ai
if isotopes.basepeak.sn:
sn = (ai - base) * isotopes.basepeak.sn / (isotopes.basepeak.ai - base)
# make peak
peak = obj_peak.peak(mz=mz, ai=ai, base=base, sn=sn, fwhm=fwhm, isotope=0)
return peak
def envmono(isotopes, charge, intensity='maximum'):
"""Calculate envelope centroid for given isotopes.
isotopes (mspy.peaklist or list of mspy.peak) - envelope isotopes
charge (int) - peak charge
intensity (maximum | sum | average) - envelope intensity type
"""
# check isotopes
if len(isotopes) == 0:
return None
# check peaklist object
if not isinstance(isotopes, obj_peaklist.peaklist):
isotopes = obj_peaklist.peaklist(isotopes)
# calc averagine
avFormula = averagine(isotopes.basepeak.mz, charge=charge, composition=AVERAGE_AMINO)
avPattern = avFormula.pattern(fwhm=0.1, threshold=0.001, charge=charge)
avPattern = obj_peaklist.peaklist(avPattern)
# get envelope centroid
points = numpy.array([(p.mz, p.intensity) for p in isotopes])
centroid = labelpeak(points, mz=isotopes.basepeak.mz, pickingHeight=0.8)
if not centroid:
centroid = isotopes.basepeak
# get averagine centroid
points = numpy.array([(p.mz, p.intensity) for p in avPattern])
avCentroid = labelpeak(points, mz=avPattern.basepeak.mz, pickingHeight=0.8)
if not avCentroid:
avCentroid = avPattern.basepeak
# align profiles and get monoisotopic mass
shift = centroid.mz - avCentroid.mz
errors = [(abs(p.mz - avPattern.basepeak.mz - shift), p.mz) for p in isotopes]
mz = min(errors)[1] - (avPattern.basepeak.mz - avFormula.mz(charge)[0])
# sum intensities
sumIntensity = 0
for isotope in isotopes:
sumIntensity += isotope.intensity
# get ai, base and sn
base = isotopes.basepeak.base
sn = isotopes.basepeak.sn
fwhm = isotopes.basepeak.fwhm
if intensity == 'sum':
ai = base + sumIntensity
elif intensity == 'average':
ai = base + sumIntensity / len(isotopes)
else:
ai = isotopes.basepeak.ai
if isotopes.basepeak.sn:
sn = (ai - base) * isotopes.basepeak.sn / (isotopes.basepeak.ai - base)
# make peak
peak = obj_peak.peak(mz=mz, ai=ai, base=base, sn=sn, fwhm=fwhm, isotope=0)
return peak
def _makeScan(self, scanData):
"""Make scan object from raw data."""
# parse peaks
points = self._parsePoints(scanData)
if scanData['spectrumType'] == 'discrete':
for x, p in enumerate(points):
points[x] = obj_peak.peak(p[0], p[1])
scan = obj_scan.scan(peaklist=obj_peaklist.peaklist(points))
else:
scan = obj_scan.scan(profile=points)
# set metadata
scan.title = scanData['title']
scan.scanNumber = scanData['scanNumber']
scan.parentScanNumber = scanData['parentScanNumber']
scan.msLevel = scanData['msLevel']
scan.polarity = scanData['polarity']
scan.retentionTime = scanData['retentionTime']
scan.totIonCurrent = scanData['totIonCurrent']
scan.basePeakMZ = scanData['basePeakMZ']
scan.basePeakIntensity = scanData['basePeakIntensity']
scan.precursorMZ = scanData['precursorMZ']
scan.precursorIntensity = scanData['precursorIntensity']
scan.precursorCharge = scanData['precursorCharge']
return scan
def _makeScan(self, scanData):
"""Make scan object from raw data."""
# parse peaks
points = self._parsePoints(scanData)
if scanData['spectrumType'] == 'discrete':
for x, p in enumerate(points):
points[x] = obj_peak.peak(p[0], p[1])
scan = obj_scan.scan(peaklist=obj_peaklist.peaklist(points))
else:
scan = obj_scan.scan(profile=points)
# set metadata
scan.title = scanData['title']
scan.scanNumber = scanData['scanNumber']
scan.parentScanNumber = scanData['parentScanNumber']
scan.msLevel = scanData['msLevel']
scan.polarity = scanData['polarity']
scan.retentionTime = scanData['retentionTime']
scan.totIonCurrent = scanData['totIonCurrent']
scan.basePeakMZ = scanData['basePeakMZ']
scan.basePeakIntensity = scanData['basePeakIntensity']
scan.precursorMZ = scanData['precursorMZ']
scan.precursorIntensity = scanData['precursorIntensity']
scan.precursorCharge = scanData['precursorCharge']
return scan
def __init__(self, profile=[], peaklist=[], **attr):
self.title = ''
self.scanNumber = None
self.parentScanNumber = None
self.polarity = None
self.msLevel = None
self.retentionTime = None
self.totIonCurrent = None
self.basePeakMZ = None
self.basePeakIntensity = None
self.precursorMZ = None
self.precursorIntensity = None
self.precursorCharge = None
# buffers
self._baseline = None
self._baselineParams = {'window': None, 'offset': None}
# convert profile to numPy array
if not isinstance(profile, numpy.ndarray):
profile = numpy.array(profile)
self.profile = profile
# convert peaks to peaklist
if not isinstance(peaklist, obj_peaklist.peaklist):
peaklist = obj_peaklist.peaklist(peaklist)
self.peaklist = peaklist
# get additional attributes
self.attributes = {}
for name, value in attr.items():
self.attributes[name] = value
def topeaklist(self, peaklist, fwhm=0.1, forceFwhm=True, autoAlign=True, iterLimit=None, relThreshold=0.):
"""Fit modeled profiles to peaklist.
peaklist (mspy.peaklist) - peak list
fwhm (float) - defaut fwhm
forceFwhm (bool) - use default fwhm
autoAlign (bool) - automatic m/z shift
iterLimit (int) - maximum number of iterations
"""
# check peaklist object
if not isinstance(peaklist, obj_peaklist.peaklist):
peaklist = obj_peaklist.peaklist(peaklist)
# crop peaklist to relevant m/z range
peaklist = peaklist.duplicate()
peaklist.crop(self.mzrange[0], self.mzrange[1])
# remove peaks below threshold
peaklist.remthreshold(relThreshold=relThreshold)
# get fwhm from basepeak
if not forceFwhm and peaklist.basepeak and peaklist.basepeak.fwhm:
fwhm = peaklist.basepeak.fwhm
# make data to fit
points = numpy.array([(p.mz, p.intensity) for p in peaklist])
# fit
return self.topoints(
points = points,
fwhm = fwhm,
autoAlign = autoAlign,
iterLimit = iterLimit,
)
def __init__(self, profile=[], peaklist=[], **attr):
self.title = ""
self.scanNumber = None
self.parentScanNumber = None
self.polarity = None
self.msLevel = None
self.retentionTime = None
self.totIonCurrent = None
self.basePeakMZ = None
self.basePeakIntensity = None
self.precursorMZ = None
self.precursorIntensity = None
self.precursorCharge = None
# buffers
self._baseline = None
self._baselineParams = {"window": None, "offset": None}
# convert profile to numPy array
if not isinstance(profile, numpy.ndarray):
profile = numpy.array(profile)
self.profile = profile
# convert peaks to peaklist
if not isinstance(peaklist, obj_peaklist.peaklist):
peaklist = obj_peaklist.peaklist(peaklist)
self.peaklist = peaklist
# get additional attributes
self.attributes = {}
for name, value in attr.items():
self.attributes[name] = value
def _makeScan(self, scanData, dataType):
"""Make scan object from raw data."""
# parse data as peaklist (discrete points)
if dataType == 'peaklist' or (dataType==None and len(scanData['data'])<3000):
buff = []
for point in scanData['data']:
buff.append(obj_peak.peak(point[0], point[1]))
scan = obj_scan.scan(peaklist=obj_peaklist.peaklist(buff))
# parse data as spectrum (continuous line)
else:
scan = obj_scan.scan(profile=scanData['data'])
# set metadata
scan.title = scanData['title']
scan.scanNumber = scanData['scanNumber']
scan.parentScanNumber = scanData['parentScanNumber']
scan.msLevel = scanData['msLevel']
scan.polarity = scanData['polarity']
scan.retentionTime = scanData['retentionTime']
scan.totIonCurrent = scanData['totIonCurrent']
scan.basePeakMZ = scanData['basePeakMZ']
scan.basePeakIntensity = scanData['basePeakIntensity']
scan.precursorMZ = scanData['precursorMZ']
scan.precursorIntensity = scanData['precursorIntensity']
scan.precursorCharge = scanData['precursorCharge']
return scan
def _makeScan(self, scanData, dataType):
"""Make scan object from raw data."""
# parse data as peaklist (discrete points)
if dataType == 'peaklist' or (dataType == None
and len(scanData['data']) < 3000):
buff = []
for point in scanData['data']:
buff.append(obj_peak.peak(point[0], point[1]))
scan = obj_scan.scan(peaklist=obj_peaklist.peaklist(buff))
# parse data as spectrum (continuous line)
else:
scan = obj_scan.scan(profile=scanData['data'])
# set metadata
scan.title = scanData['title']
scan.scanNumber = scanData['scanNumber']
scan.parentScanNumber = scanData['parentScanNumber']
scan.msLevel = scanData['msLevel']
scan.polarity = scanData['polarity']
scan.retentionTime = scanData['retentionTime']
scan.totIonCurrent = scanData['totIonCurrent']
scan.basePeakMZ = scanData['basePeakMZ']
scan.basePeakIntensity = scanData['basePeakIntensity']
scan.precursorMZ = scanData['precursorMZ']
scan.precursorIntensity = scanData['precursorIntensity']
scan.precursorCharge = scanData['precursorCharge']
return scan
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 setpeaklist(self, peaks):
"""Set new peaklist."""
# convert peaks to peaklist
if isinstance(peaks, obj_peaklist.peaklist):
self.peaklist = peaks
else:
self.peaklist = obj_peaklist.peaklist(peaks)
def setpeaklist(self, peaks):
"""Set new peaklist."""
# convert peaks to peaklist
if isinstance(peaks, obj_peaklist.peaklist):
self.peaklist = peaks
else:
self.peaklist = obj_peaklist.peaklist(peaks)
def deconvolute(peaklist, massType=0):
"""Recalculate peaklist to singly charged.
peaklist (mspy.peaklist) - peak list to deconvolute
massType (0 or 1) - mass type used for m/z re-calculation, 0 = monoisotopic, 1 = average
"""
# recalculate peaks
buff = []
for peak in copy.deepcopy(peaklist):
CHECK_FORCE_QUIT()
# uncharged peak
if not peak.charge:
continue
# charge is correct
elif abs(peak.charge) == 1:
buff.append(peak)
# recalculate peak
else:
# set fwhm
if peak.fwhm:
newFwhm = abs(peak.fwhm*peak.charge)
peak.setfwhm(newFwhm)
# set m/z and charge
if peak.charge < 0:
newMz = mod_basics.mz(mass=peak.mz, charge=-1, currentCharge=peak.charge, massType=massType)
peak.setmz(newMz)
peak.setcharge(-1)
else:
newMz = mod_basics.mz(mass=peak.mz, charge=1, currentCharge=peak.charge, massType=massType)
peak.setmz(newMz)
peak.setcharge(1)
# store peak
buff.append(peak)
# remove baseline
if buff:
for peak in buff:
peak.setsn(None)
peak.setai(peak.intensity)
peak.setbase(0.)
# update peaklist
peaklist = obj_peaklist.peaklist(buff)
return peaklist
def deconvolute(peaklist, massType=0):
"""Recalculate peaklist to singly charged.
peaklist (mspy.peaklist) - peak list to deconvolute
massType (0 or 1) - mass type used for m/z re-calculation, 0 = monoisotopic, 1 = average
"""
# recalculate peaks
buff = []
for peak in copy.deepcopy(peaklist):
CHECK_FORCE_QUIT()
# uncharged peak
if not peak.charge:
continue
# charge is correct
elif abs(peak.charge) == 1:
buff.append(peak)
# recalculate peak
else:
# set fwhm
if peak.fwhm:
newFwhm = abs(peak.fwhm*peak.charge)
peak.setfwhm(newFwhm)
# set m/z and charge
if peak.charge < 0:
newMz = mod_basics.mz(mass=peak.mz, charge=-1, currentCharge=peak.charge, massType=massType)
peak.setmz(newMz)
peak.setcharge(-1)
else:
newMz = mod_basics.mz(mass=peak.mz, charge=1, currentCharge=peak.charge, massType=massType)
peak.setmz(newMz)
peak.setcharge(1)
# store peak
buff.append(peak)
# remove baseline
if buff:
for peak in buff:
peak.setsn(None)
peak.setai(peak.intensity)
peak.setbase(0.)
# update peaklist
peaklist = obj_peaklist.peaklist(buff)
return peaklist
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 _makeScan(self, scanData, dataType):
"""Make scan object from raw data."""
# parse data as peaklist (discrete points)
if dataType == 'discrete':
buff = []
for point in scanData:
buff.append(obj_peak.peak(point[0], point[1]))
scan = obj_scan.scan(peaklist=obj_peaklist.peaklist(buff))
# parse data as spectrum (continuous line)
else:
scan = obj_scan.scan(profile=scanData)
return scan
def swap(self):
"""Swap data between profile and peaklist."""
# make new profile
profile = [[i.mz, i.ai] for i in self.peaklist]
profile = numpy.array(profile)
# make new peaklist
peaks = [obj_peak.peak(i[0], i[1]) for i in self.profile]
peaks = obj_peaklist.peaklist(peaks)
# update scan
self.profile = profile
self.peaklist = peaks
# clear buffers
self.reset()
def swap(self):
"""Swap data between profile and peaklist."""
# make new profile
profile = [[i.mz, i.ai] for i in self.peaklist]
profile = numpy.array(profile)
# make new peaklist
peaks = [obj_peak.peak(i[0], i[1]) for i in self.profile]
peaks = obj_peaklist.peaklist(peaks)
# update scan
self.profile = profile
self.peaklist = peaks
# clear buffers
self.reset()
def topeaklist(self,
peaklist,
fwhm=0.1,
forceFwhm=True,
autoAlign=True,
iterLimit=None,
relThreshold=0.):
"""Fit modeled profiles to peaklist.
peaklist (mspy.peaklist) - peak list
fwhm (float) - defaut fwhm
forceFwhm (bool) - use default fwhm
autoAlign (bool) - automatic m/z shift
iterLimit (int) - maximum number of iterations
"""
# check peaklist object
if not isinstance(peaklist, obj_peaklist.peaklist):
peaklist = obj_peaklist.peaklist(peaklist)
# crop peaklist to relevant m/z range
peaklist = peaklist.duplicate()
peaklist.crop(self.mzrange[0], self.mzrange[1])
# remove peaks below threshold
peaklist.remthreshold(relThreshold=relThreshold)
# get fwhm from basepeak
if not forceFwhm and peaklist.basepeak and peaklist.basepeak.fwhm:
fwhm = peaklist.basepeak.fwhm
# make data to fit
points = numpy.array([(p.mz, p.intensity) for p in peaklist])
# fit
return self.topoints(
points=points,
fwhm=fwhm,
autoAlign=autoAlign,
iterLimit=iterLimit,
)
def labelscan(signal, minX=None, maxX=None, pickingHeight=0.75, absThreshold=0., relThreshold=0., snThreshold=0., baseline=None):
"""Return centroided peaklist for given data points.
signal (numpy array) - signal data points
minX (float) - x-range start
maxX (float) - x-range end
pickingHeight (float) - centroiding height
absThreshold (float) - absolute intensity threshold
relThreshold (float) - relative intensity threshold
snThreshold (float) - signal to noise threshold
baseline (numpy array) - signal baseline
"""
# check signal type
if not isinstance(signal, numpy.ndarray):
raise TypeError, "Signal must be NumPy array!"
# check baseline type
if baseline != None and not isinstance(baseline, numpy.ndarray):
raise TypeError, "Baseline must be NumPy array!"
# crop data
if minX != None and maxX != None:
i1 = mod_signal.locate(signal, minX)
i2 = mod_signal.locate(signal, maxX)
signal = signal[i1:i2]
# check data points
if len(signal) == 0:
return obj_peaklist.peaklist([])
# get local maxima
buff = []
basepeak = mod_signal.basepeak(signal)
threshold = max(signal[basepeak][1] * relThreshold, absThreshold)
for peak in mod_signal.maxima(signal):
if peak[1] >= threshold:
buff.append( [peak[0], peak[1], 0., None, None] ) # mz, ai, base, sn, fwhm
CHECK_FORCE_QUIT()
# get peaks baseline and s/n
basepeak = 0.0
if baseline != None:
for peak in buff:
idx = mod_signal.locate(baseline, peak[0])
if (idx > 0) and (idx < len(baseline)):
p1 = baseline[idx-1]
p2 = baseline[idx]
peak[2] = mod_signal.interpolate( (p1[0], p1[1]), (p2[0], p2[1]), x=peak[0])
noise = mod_signal.interpolate( (p1[0], p1[2]), (p2[0], p2[2]), x=peak[0])
intens = peak[1] - peak[2]
if noise:
peak[3] = intens / noise
if intens > basepeak:
basepeak = intens
CHECK_FORCE_QUIT()
# remove peaks bellow threshold
threshold = max(basepeak * relThreshold, absThreshold)
candidates = []
for peak in buff:
if peak[0] > 0 and (peak[1] - peak[2]) >= threshold and (not peak[3] or peak[3] >= snThreshold):
candidates.append(peak)
# make centroides
if pickingHeight < 1.:
buff = []
previous = None
for peak in candidates:
CHECK_FORCE_QUIT()
# calc peak height
h = ((peak[1]-peak[2]) * pickingHeight) + peak[2]
# get centroid indexes
idx = mod_signal.locate(signal, peak[0])
if (idx == 0) or (idx == len(signal)):
continue
ileft = idx-1
while (ileft > 0) and (signal[ileft][1] > h):
ileft -= 1
iright = idx
while (iright < len(signal)-1) and (signal[iright][1] > h):
iright += 1
# calculate peak mz
leftMZ = mod_signal.interpolate(signal[ileft], signal[ileft+1], y=h)
rightMZ = mod_signal.interpolate(signal[iright-1], signal[iright], y=h)
peak[0] = (leftMZ + rightMZ)/2.
# get peak intensity
intens = mod_signal.intensity(signal, peak[0])
if intens and intens <= peak[1]:
peak[1] = intens
else:
continue
# try to group with previous peak
if previous != None and leftMZ < previous:
if peak[1] > buff[-1][1]:
buff[-1] = peak
previous = rightMZ
else:
buff.append(peak)
previous = rightMZ
# store as candidates
candidates = buff
CHECK_FORCE_QUIT()
# get peaks baseline and s/n
basepeak = 0.0
if baseline != None:
for peak in candidates:
idx = mod_signal.locate(baseline, peak[0])
if (idx > 0) and (idx < len(baseline)):
p1 = baseline[idx-1]
p2 = baseline[idx]
peak[2] = mod_signal.interpolate( (p1[0], p1[1]), (p2[0], p2[1]), x=peak[0])
noise = mod_signal.interpolate( (p1[0], p1[2]), (p2[0], p2[2]), x=peak[0])
intens = peak[1] - peak[2]
if noise:
peak[3] = intens / noise
if intens > basepeak:
basepeak = intens
CHECK_FORCE_QUIT()
# remove peaks bellow threshold and calculate fwhm
threshold = max(basepeak * relThreshold, absThreshold)
centroides = []
for peak in candidates:
if peak[0] > 0 and (peak[1] - peak[2]) >= threshold and (not peak[3] or peak[3] >= snThreshold):
peak[4] = mod_signal.width(signal, peak[0], (peak[2] + ((peak[1] - peak[2]) * 0.5)))
centroides.append(obj_peak.peak(mz=peak[0], ai=peak[1], base=peak[2], sn=peak[3], fwhm=peak[4]))
# return peaklist object
return obj_peaklist.peaklist(centroides)
def labelscan(signal, minX=None, maxX=None, pickingHeight=0.75, absThreshold=0., relThreshold=0., snThreshold=0., baseline=None):
"""Return centroided peaklist for given data points.
signal (numpy array) - signal data points
minX (float) - x-range start
maxX (float) - x-range end
pickingHeight (float) - centroiding height
absThreshold (float) - absolute intensity threshold
relThreshold (float) - relative intensity threshold
snThreshold (float) - signal to noise threshold
baseline (numpy array) - signal baseline
"""
# check signal type
if not isinstance(signal, numpy.ndarray):
raise TypeError, "Signal must be NumPy array!"
# check baseline type
if baseline != None and not isinstance(baseline, numpy.ndarray):
raise TypeError, "Baseline must be NumPy array!"
# crop data
if minX != None and maxX != None:
i1 = mod_signal.locate(signal, minX)
i2 = mod_signal.locate(signal, maxX)
signal = signal[i1:i2]
# check data points
if len(signal) == 0:
return obj_peaklist.peaklist([])
# get local maxima
buff = []
basepeak = mod_signal.basepeak(signal)
threshold = max(signal[basepeak][1] * relThreshold, absThreshold)
for peak in mod_signal.maxima(signal):
if peak[1] >= threshold:
buff.append( [peak[0], peak[1], 0., None, None] ) # mz, ai, base, sn, fwhm
CHECK_FORCE_QUIT()
# get peaks baseline and s/n
basepeak = 0.0
if baseline != None:
for peak in buff:
idx = mod_signal.locate(baseline, peak[0])
if (idx > 0) and (idx < len(baseline)):
p1 = baseline[idx-1]
p2 = baseline[idx]
peak[2] = mod_signal.interpolate( (p1[0], p1[1]), (p2[0], p2[1]), x=peak[0])
noise = mod_signal.interpolate( (p1[0], p1[2]), (p2[0], p2[2]), x=peak[0])
intens = peak[1] - peak[2]
if noise:
peak[3] = intens / noise
if intens > basepeak:
basepeak = intens
CHECK_FORCE_QUIT()
# remove peaks bellow threshold
threshold = max(basepeak * relThreshold, absThreshold)
candidates = []
for peak in buff:
if peak[0] > 0 and (peak[1] - peak[2]) >= threshold and (not peak[3] or peak[3] >= snThreshold):
candidates.append(peak)
# make centroides
if pickingHeight < 1.:
buff = []
previous = None
for peak in candidates:
CHECK_FORCE_QUIT()
# calc peak height
h = ((peak[1]-peak[2]) * pickingHeight) + peak[2]
# get centroid indexes
idx = mod_signal.locate(signal, peak[0])
if (idx == 0) or (idx == len(signal)):
continue
ileft = idx-1
while (ileft > 0) and (signal[ileft][1] > h):
ileft -= 1
iright = idx
while (iright < len(signal)-1) and (signal[iright][1] > h):
iright += 1
# calculate peak mz
leftMZ = mod_signal.interpolate(signal[ileft], signal[ileft+1], y=h)
rightMZ = mod_signal.interpolate(signal[iright-1], signal[iright], y=h)
peak[0] = (leftMZ + rightMZ)/2.
# get peak intensity
intens = mod_signal.intensity(signal, peak[0])
if intens and intens <= peak[1]:
peak[1] = intens
else:
continue
# try to group with previous peak
if previous != None and leftMZ < previous:
if peak[1] > buff[-1][1]:
buff[-1] = peak
previous = rightMZ
else:
buff.append(peak)
previous = rightMZ
# store as candidates
candidates = buff
CHECK_FORCE_QUIT()
# get peaks baseline and s/n
basepeak = 0.0
if baseline != None:
for peak in candidates:
idx = mod_signal.locate(baseline, peak[0])
if (idx > 0) and (idx < len(baseline)):
p1 = baseline[idx-1]
p2 = baseline[idx]
peak[2] = mod_signal.interpolate( (p1[0], p1[1]), (p2[0], p2[1]), x=peak[0])
noise = mod_signal.interpolate( (p1[0], p1[2]), (p2[0], p2[2]), x=peak[0])
intens = peak[1] - peak[2]
if noise:
peak[3] = intens / noise
if intens > basepeak:
basepeak = intens
CHECK_FORCE_QUIT()
# remove peaks bellow threshold and calculate fwhm
threshold = max(basepeak * relThreshold, absThreshold)
centroides = []
for peak in candidates:
if peak[0] > 0 and (peak[1] - peak[2]) >= threshold and (not peak[3] or peak[3] >= snThreshold):
peak[4] = mod_signal.width(signal, peak[0], (peak[2] + ((peak[1] - peak[2]) * 0.5)))
centroides.append(obj_peak.peak(mz=peak[0], ai=peak[1], base=peak[2], sn=peak[3], fwhm=peak[4]))
# return peaklist object
return obj_peaklist.peaklist(centroides)
def envcentroid(isotopes, pickingHeight=0.5, intensity='maximum'):
"""Calculate envelope centroid for given isotopes.
isotopes (mspy.peaklist or list of mspy.peak) envelope isotopes
pickingHeight (float) - centroiding height
intensity (maximum | sum | average) envelope intensity type
"""
# check isotopes
if len(isotopes) == 0:
return None
elif len(isotopes) == 1:
return isotopes[0]
# check peaklist object
if not isinstance(isotopes, obj_peaklist.peaklist):
isotopes = obj_peaklist.peaklist(isotopes)
# get sums
sumMZ = 0.
sumIntensity = 0.
for isotope in isotopes:
sumMZ += isotope.mz * isotope.intensity
sumIntensity += isotope.intensity
# get average m/z
mz = sumMZ / sumIntensity
# get ai, base and sn
base = isotopes.basepeak.base
sn = isotopes.basepeak.sn
fwhm = isotopes.basepeak.fwhm
if intensity == 'sum':
ai = base + sumIntensity
elif intensity == 'average':
ai = base + sumIntensity / len(isotopes)
else:
ai = isotopes.basepeak.ai
if isotopes.basepeak.sn:
sn = (ai - base) * isotopes.basepeak.sn / (isotopes.basepeak.ai - base)
# get envelope width
minInt = isotopes.basepeak.intensity * pickingHeight
i1 = None
i2 = None
for x, isotope in enumerate(isotopes):
if isotope.intensity >= minInt:
i2 = x
if i1 == None:
i1 = x
mz1 = isotopes[i1].mz
mz2 = isotopes[i2].mz
if i1 != 0:
mz1 = mod_signal.interpolate((isotopes[i1-1].mz, isotopes[i1-1].ai), (isotopes[i1].mz, isotopes[i1].ai), y=minInt)
if i2 < len(isotopes)-1:
mz2 = mod_signal.interpolate((isotopes[i2].mz, isotopes[i2].ai), (isotopes[i2+1].mz, isotopes[i2+1].ai), y=minInt)
if mz1 != mz2:
fwhm = abs(mz2 - mz1)
# make peak
peak = obj_peak.peak(mz=mz, ai=ai, base=base, sn=sn, fwhm=fwhm)
return peak
def envcentroid(isotopes, pickingHeight=0.5, intensity='maximum'):
"""Calculate envelope centroid for given isotopes.
isotopes (mspy.peaklist or list of mspy.peak) envelope isotopes
pickingHeight (float) - centroiding height
intensity (maximum | sum | average) envelope intensity type
"""
# check isotopes
if len(isotopes) == 0:
return None
elif len(isotopes) == 1:
return isotopes[0]
# check peaklist object
if not isinstance(isotopes, obj_peaklist.peaklist):
isotopes = obj_peaklist.peaklist(isotopes)
# get sums
sumMZ = 0.
sumIntensity = 0.
for isotope in isotopes:
sumMZ += isotope.mz * isotope.intensity
sumIntensity += isotope.intensity
# get average m/z
mz = sumMZ / sumIntensity
# get ai, base and sn
base = isotopes.basepeak.base
sn = isotopes.basepeak.sn
fwhm = isotopes.basepeak.fwhm
if intensity == 'sum':
ai = base + sumIntensity
elif intensity == 'average':
ai = base + sumIntensity / len(isotopes)
else:
ai = isotopes.basepeak.ai
if isotopes.basepeak.sn:
sn = (ai - base) * isotopes.basepeak.sn / (isotopes.basepeak.ai - base)
# get envelope width
minInt = isotopes.basepeak.intensity * pickingHeight
i1 = None
i2 = None
for x, isotope in enumerate(isotopes):
if isotope.intensity >= minInt:
i2 = x
if i1 == None:
i1 = x
mz1 = isotopes[i1].mz
mz2 = isotopes[i2].mz
if i1 != 0:
mz1 = mod_signal.interpolate((isotopes[i1-1].mz, isotopes[i1-1].ai), (isotopes[i1].mz, isotopes[i1].ai), y=minInt)
if i2 < len(isotopes)-1:
mz2 = mod_signal.interpolate((isotopes[i2].mz, isotopes[i2].ai), (isotopes[i2+1].mz, isotopes[i2+1].ai), y=minInt)
if mz1 != mz2:
fwhm = abs(mz2 - mz1)
# make peak
peak = obj_peak.peak(mz=mz, ai=ai, base=base, sn=sn, fwhm=fwhm)
return peak
