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]]