clustering.py 文件源码

def minimalForCompleteness(self):
        print("Running estimator for minimal threshold for completeness")
        labels=self.createLabels()
        x = 0.00
        dx = 0.05
        countsList = {}
        x_list = []
        while x < 1:
            Arrays= {}
            FlatC = hierarchy.fcluster(self.Tree, x, criterion='distance')
            counter=collections.Counter(FlatC)
            Best = max(counter.iteritems(), key=operator.itemgetter(1))[0]
            toProcess=[Best]
            y=0
            for cluster, filename in zip(FlatC,labels):
                if cluster in toProcess:
                    hklFile = any_reflection_file(filename)
                    b= hklFile.as_miller_arrays()
                    for column in b:
                        if column.is_xray_intensity_array():
                            Arrays[y]=column
                            break
                    y+=1
            try:
                Arr = Arrays[0]
            except:
                countsList.append(0)
            for label in range(1, y):
                try:
                    Arr = Arr.concatenate(Arrays[label])
                except:
                    pass
            countsList[x]=(Arr.completeness())
            x+= dx
       # return minimal for max
        L = []
        for key in countsList:
            if countsList[key]>0.98:
                L.append(key)
        L.sort()
        return L[0]