我必须遍历两个1000×1000大的数组.我已经将分辨率降低到100×100,以加快迭代速度,但是一个阵列仍然需要15分钟左右！
因此,我尝试同时遍历两个对象,为此我发现了这一点：

for index, (x,y) in ndenumerate(izip(x_array,y_array)):

但是我得到了错误：

ValueError: too many values to unpack

这是我完整的python代码：我希望您能帮助我更快地完成它,因为这是为了我的硕士论文,最后我必须运行它大约100次…

area_length=11
d_circle=(area_length-1)/2

xdis_new=xdis.copy()
ydis_new=ydis.copy()
ie,je=xdis_new.shape


while (np.isnan(np.sum(xdis_new))) and (np.isnan(np.sum(ydis_new))):
xdis_interpolated=xdis_new.copy()
ydis_interpolated=ydis_new.copy()
# itx=np.nditer(xdis_new,flags=['multi_index'])
# for x in itx:
    # print 'next x and y'
for index, (x,y) in ndenumerate(izip(xdis_new,ydis_new)):
    if np.isnan(x):
        print 'index',index[0],index[1]
        print 'interpolate'
        # define indizes of interpolation area
        i1=index[0]-(area_length-1)/2
        if i1<0:
            i1=0
        i2=index[0]+((area_length+1)/2)
        if i2>ie:
            i2=ie
        j1=index[1]-(area_length-1)/2
        if j1<0:
            j1=0
        j2=index[1]+((area_length+1)/2)
        if j2>je:
            j2=je
        # -->
        print 'i1',i1,'','i2',i2
        print 'j1',j1,'','j2',j2

        area_values=xdis_new[i1:i2,j1:j2]
        print area_values

        b=area_values[~np.isnan(area_values)]

        if len(b)>=((area_length-1)/2)*4:

            xi,yi=meshgrid(arange(len(area_values[0,:])),arange(len(area_values[:,0])))

            weight=zeros((len(area_values[0,:]),len(area_values[:,0])))
            d=zeros((len(area_values[0,:]),len(area_values[:,0])))
            weight_fac=zeros((len(area_values[0,:]),len(area_values[:,0])))
            weighted_area=zeros((len(area_values[0,:]),len(area_values[:,0])))

            d=sqrt((xi-xi[(area_length-1)/2,(area_length-1)/2])*(xi-xi[(area_length-1)/2,(area_length-1)/2])+(yi-yi[(area_length-1)/2,(area_length-1)/2])*(yi-yi[(area_length-1)/2,(area_length-1)/2]))
            weight=1/d
            weight[where(d==0)]=0
            weight[where(d>d_circle)]=0
            weight[where(np.isnan(area_values))]=0

            weight_sum=np.sum(weight.flatten())
            weight_fac=weight/weight_sum
            weighted_area=area_values*weight_fac

            print 'weight'
            print weight_fac
            print 'values'
            print area_values
            print 'weighted'
            print weighted_area

            m=nansum(weighted_area)
            xdis_interpolated[index]=m
            print 'm',m

        else:
            print 'insufficient elements'

    if np.isnan(y):
        print 'index',index[0],index[1]
        print 'interpolate'
        # define indizes of interpolation area
        i1=index[0]-(area_length-1)/2
        if i1<0:
            i1=0
        i2=index[0]+((area_length+1)/2)
        if i2>ie:
            i2=ie
        j1=index[1]-(area_length-1)/2
        if j1<0:
            j1=0
        j2=index[1]+((area_length+1)/2)
        if j2>je:
            j2=je
        # -->
        print 'i1',i1,'','i2',i2
        print 'j1',j1,'','j2',j2

        area_values=ydis_new[i1:i2,j1:j2]
        print area_values

        b=area_values[~np.isnan(area_values)]

        if len(b)>=((area_length-1)/2)*4:

            xi,yi=meshgrid(arange(len(area_values[0,:])),arange(len(area_values[:,0])))

            weight=zeros((len(area_values[0,:]),len(area_values[:,0])))
            d=zeros((len(area_values[0,:]),len(area_values[:,0])))
            weight_fac=zeros((len(area_values[0,:]),len(area_values[:,0])))
            weighted_area=zeros((len(area_values[0,:]),len(area_values[:,0])))

            d=sqrt((xi-xi[(area_length-1)/2,(area_length-1)/2])*(xi-xi[(area_length-1)/2,(area_length-1)/2])+(yi-yi[(area_length-1)/2,(area_length-1)/2])*(yi-yi[(area_length-1)/2,(area_length-1)/2]))
            weight=1/d
            weight[where(d==0)]=0
            weight[where(d>d_circle)]=0
            weight[where(np.isnan(area_values))]=0

            weight_sum=np.sum(weight.flatten())
            weight_fac=weight/weight_sum
            weighted_area=area_values*weight_fac

            print 'weight'
            print weight_fac
            print 'values'
            print area_values
            print 'weighted'
            print weighted_area

            m=nansum(weighted_area)
            ydis_interpolated[index]=m
            print 'm',m

        else:
            print 'insufficient elements'

    else:
        print 'no need to interpolate'

xdis_new=xdis_interpolated
ydis_new=ydis_interpolated

解决方法:

一些忠告：

>分析您的代码以查看最慢的部分.可能不是迭代,而是每次都需要完成的计算.
>尽可能减少函数调用.在Python中,函数调用不是免费的.
>将最慢的部分重写为C扩展,然后在Python代码中调用该C函数(请参见Extending and Embedding the Python interpreter).
> This page也有一些好的建议.