def centroid(scan, threshold = None):
"""
Centroids profile-mode data given in [(M/Z, intensity)] format.
"""
from internalAlgorithms import average
if not scan:
return scan
if not threshold:
assert not isinstance(scan, Iterator), ("centroid() requires explicit "
"threshold value if given an "
"Iterator argument.")
threshold = average(list(zip(*scan))[1])
peaks = []
peak = []
for pt in scan:
if pt[1] > threshold:
peak.append(pt)
elif peak:
centMZ = average(list(zip(*peak))[0], weights = list(zip(*peak))[1])
centInt = max(list(zip(*peak))[1])
if len(pt) == 4:
centNoise = average(list(zip(*peak))[2], weights = list(zip(*peak))[1])
centCharge = max(list(zip(*peak))[3])
peaks.append((centMZ, centInt, centNoise, centCharge))
else:
peaks.append((centMZ, centInt))
peak = []
return peaks
def calculate_bounds(self, absolute_scan_lookup):
regionIndices = zip(*self.regions)[0]
self.scans = [absolute_scan_lookup[x] for x in regionIndices]
self.scanrange = min(self.scans), max(self.scans)
self.regions = [(absolute_scan_lookup[x], y) for x, y in self.regions]
minMZs = [min(zip(*peaks)[0]) for y, peaks in self.regions]
avgC12 = average(minMZs)
while not all([abs(x - avgC12) < 0.05 for x in minMZs]):
oddOneOut = max(minMZs, key=lambda x: abs(x - avgC12))
minMZs.remove(oddOneOut)
avgC12 = average(minMZs)
self.mz = avgC12
xic = pts_to_bins([(x[0], min(x[1], key=lambda pt:
(pt[0] - avgC12))[1])
for x in self.regions], 100)
ints = [0.0] + list(zip(*xic)[1]) + [0.0]
self.skewness = skew(ints)
self.kurtosis = kurtosis(ints)
def signal_noise(spectrum, minSN):
"""
Filters the spectrum by a signal-to-noise threshold, by finding the subset
of lowest-intensity peaks that match a specified minimum signal-to-noise
ratio and discarding those.
"""
# Ought to do a binary search!
if not minSN or not float(minSN):
return spectrum
minSN = float(minSN)
spectrum.sort(key = lambda x: x[1])
for i in range(0, len(spectrum)):
ints = [x[1] for x in spectrum[i:]]
SN = average(ints) / std(ints)
if SN > minSN:
return spectrum[i:]
return spectrum