def getMax(self):
localXmin =[]
localXmax = []
localYmax = []
for el in self.data:
if el is None:
continue
localXmin.append(min_f(el.x_data))
localXmax.append(max_f(el.x_data))
localYmax.append(max_l(el.y_data))
return min_f(np.array(localXmin)), max_f(np.array(localXmax)), max_l(np.array(localYmax))
def getMax(self):
localXmin = []
localXmax = []
localYmax = []
for el in self.data:
if el is None:
continue
localXmin.append(min_f(el.x_data))
localXmax.append(max_f(el.x_data))
localYmax.append(max_l(el.y_data))
return min_f(np.array(localXmin)), max_f(np.array(localXmax)), max_l(
np.array(localYmax))
def clusterComparison(list_):#receive a list of peak with clusters identified
"""
return the best peak
WARNING: p_ydata and p_.y_data are None
TODO:
"""
sortedList = sorted(list_, key=lambda x: len(x.fragCluster))
longest=len(sortedList[-1].fragCluster)
sameSizePeaks=MSPeakList()
for p in sortedList:
if len(p.fragCluster) == longest:
sameSizePeaks.append(p)
if len(sameSizePeaks) == 1:
return sameSizePeaks[0]
corr=np.array([0.] * len(sameSizePeaks))
#for i, p in enumerate(sameSizePeaks):
# for p_ in p.fragCluster:
# corr[i] += r_coef(p_.y_data, p.y_data)
m=max_f(corr)
return sameSizePeaks[np.where(corr == m)[0][0]]
def clusterComparison(
list_): #receive a list of peak with clusters identified
"""
return the best peak
WARNING: p_ydata and p_.y_data are None
TODO:
"""
sortedList = sorted(list_, key=lambda x: len(x.fragCluster))
longest = len(sortedList[-1].fragCluster)
sameSizePeaks = MSPeakList()
for p in sortedList:
if len(p.fragCluster) == longest:
sameSizePeaks.append(p)
if len(sameSizePeaks) == 1:
return sameSizePeaks[0]
corr = np.array([0.] * len(sameSizePeaks))
#for i, p in enumerate(sameSizePeaks):
# for p_ in p.fragCluster:
# corr[i] += r_coef(p_.y_data, p.y_data)
m = max_f(corr)
return sameSizePeaks[np.where(corr == m)[0][0]]
