spatial.py 文件源码

def threshold_components_parallel(pars):


        A_i, i , dims, medw, d, thr_method, se, ss, maxthr, nrgthr, extract_cc  = pars
        A_temp = np.reshape(A_i, dims[::-1])
        A_temp = median_filter(A_temp, medw)
        if thr_method == 'max':
            BW = (A_temp>maxthr*np.max(A_temp))                        
        elif thr_method == 'nrg':        
            Asor = np.sort(np.squeeze(np.reshape(A_temp, (d, 1))))[::-1]
            temp = np.cumsum(Asor**2)
            ff = np.squeeze(np.where(temp < (1 - nrgthr) * temp[-1]))    
            if ff.size > 0:
                if ff.ndim == 0:
                    ind = ff
                else:
                    ind = ff[-1]
                A_temp[A_temp < Asor[ind]] = 0
                BW = (A_temp >= Asor[ind])
            else:
                BW = (A_temp >= 0)

        Ath = np.squeeze(np.reshape(A_temp, (d, 1)))
        Ath2 = np.zeros((d))
        BW = binary_closing(BW.astype(np.int), structure=se)
        if extract_cc:        
            labeled_array, num_features = label(BW, structure=ss)
            BW = np.reshape(BW, (d, 1))
            labeled_array = np.squeeze(np.reshape(labeled_array, (d, 1)))
            nrg = np.zeros((num_features, 1))
            for j in range(num_features):
                nrg[j] = np.sum(Ath[labeled_array == j + 1]**2)

            indm = np.argmax(nrg)         
            #Ath2[labeled_array == indm + 1] = A_i[labeled_array == indm + 1]
            Ath2[labeled_array == indm + 1] = Ath[labeled_array == indm + 1]
        else:            
            BW = BW.flatten()
            Ath2[BW] = Ath[BW]

        return Ath2, i

#%% lars_regression_noise