def centroid(signal, x, height):
"""Find peak centroid for searched x-value measured at y-value.
signal (numpy array) - signal data points
x (float) - x-value
height (float) - y-value for width determination
"""
# check signal type
if not isinstance(signal, numpy.ndarray):
raise TypeError("Signal must be NumPy array!")
if signal.dtype.name != 'float64':
raise TypeError("Signal data must be float64!")
# check signal data
if len(signal) == 0:
raise ValueError("Signal contains no data!")
# determine centroid
return calculations.signal_centroid(signal, float(x), float(height))
def centroid(signal, x, height):
"""Find peak centroid for searched x-value measured at y-value.
signal (numpy array) - signal data points
x (float) - x-value
height (float) - y-value for width determination
"""
# check signal type
if not isinstance(signal, numpy.ndarray):
raise TypeError, "Signal must be NumPy array!"
if signal.dtype.name != 'float64':
raise TypeError, "Signal data must be float64!"
# check signal data
if len(signal) == 0:
raise ValueError, "Signal contains no data!"
# determine centroid
return calculations.signal_centroid(signal, float(x), float(height))
def checkpattern_fast(signal, pattern, pickingHeight=0.75):
"""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
"""
peaklist = []
num_theo = 0
num_found = 0
for isotope in pattern:
num_theo += 1
signal_intensity = calculations.signal_intensity(signal, isotope[0])
if signal_intensity == 0:
peaklist.append(0.0)
continue
centroid = calculations.signal_centroid(signal,
isotope[0],
signal_intensity * pickingHeight)
peaklist.append(calculations.signal_intensity(signal, centroid))
num_found += 1
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)))