def labelpeak(self, mz=None, minX=None, maxX=None, pickingHeight=0.75, baselineWindow=0.1, baselineOffset=0.0):
"""Return labeled peak in given m/z range.
mz (float) - m/z value to label
minX (float) - m/z range start
maxX (float) - m/z range end
pickingHeight (float) - centroiding height
baselineWindow (float) - noise calculation window (in %/100)
baselineOffset (float) - baseline offset, relative to noise width (in %/100)
"""
# get baseline
baseline = self.baseline(window=baselineWindow, offset=baselineOffset)
# label peak
peak = mod_peakpicking.labelpeak(
signal=self.profile, mz=mz, minX=minX, maxX=maxX, pickingHeight=pickingHeight, baseline=baseline
)
# check peak
if not peak:
return False
# append peak
self.peaklist.append(peak)
return True
def labelpeak(self,
mz=None,
minX=None,
maxX=None,
pickingHeight=0.75,
baselineWindow=0.1,
baselineOffset=0.):
"""Return labeled peak in given m/z range.
mz (float) - m/z value to label
minX (float) - m/z range start
maxX (float) - m/z range end
pickingHeight (float) - centroiding height
baselineWindow (float) - noise calculation window (in %/100)
baselineOffset (float) - baseline offset, relative to noise width (in %/100)
"""
# get baseline
baseline = self.baseline(window=baselineWindow, offset=baselineOffset)
# label peak
peak = mod_peakpicking.labelpeak(signal=self.profile,
mz=mz,
minX=minX,
maxX=maxX,
pickingHeight=pickingHeight,
baseline=baseline)
# check peak
if not peak:
return False
# append peak
self.peaklist.append(peak)
return True
def checkpattern(signal, pattern, pickingHeight=0.75, baseline=None):
"""Compare signal with given isotopic pattern. Return rms, basepeak
intensity
signal (numpy array) - signal data points
pattern (list of [mz,intens]) - theoretical pattern to compare
pickingHeight (float) - centroiding height
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 is not None and not isinstance(baseline, numpy.ndarray):
raise TypeError, "Baseline must be NumPy array!"
# check signal data
if len(signal) == 0:
return None
# get signal intensites for isotopes
peaklist = []
num_theo = 0
num_found = 0
for isotope in pattern:
peak = mod_peakpicking.labelpeak(
signal = signal,
mz = isotope[0],
pickingHeight = pickingHeight,
baseline = baseline
)
num_theo += 1
if peak:
peaklist.append(peak.intensity)
num_found += 1
else:
peaklist.append(0.0)
# normalize peaklist
basepeak = max(peaklist)
if basepeak:
peaklist = [p/basepeak for p in peaklist]
else:
return None
# get rms
rms = 0
for x, isotope in enumerate(pattern):
rms += (isotope[1] - peaklist[x])**2
if len(pattern) > 1:
rms = math.sqrt(rms / (len(pattern)-1))
return (CheckPatternResult(rms, basepeak, (num_found, num_theo)))
def matchpattern(signal, pattern, pickingHeight=0.75, baseline=None):
"""Compare signal with given isotopic pattern.
signal (numpy array) - signal data points
pattern (list of [mz,intens]) - theoretical pattern to compare
pickingHeight (float) - centroiding height
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!"
# check signal data
if len(signal) == 0:
return None
# get signal intensites for isotopes
peaklist = []
for isotope in pattern:
peak = mod_peakpicking.labelpeak(signal=signal,
mz=isotope[0],
pickingHeight=pickingHeight,
baseline=baseline)
if peak:
peaklist.append(peak.intensity)
else:
peaklist.append(0.0)
# normalize peaklist
basepeak = max(peaklist)
if basepeak:
peaklist = [p / basepeak for p in peaklist]
else:
return None
# get rms
rms = 0
for x, isotope in enumerate(pattern):
rms += (isotope[1] - peaklist[x])**2
if len(pattern) > 1:
rms = math.sqrt(rms / (len(pattern) - 1))
return rms
