Iterate over two big arrays at once

Question

I have to iterate over two arrays which are 1000x1000 big. I already reduced the resolution to 100x100 to make the iteration faster, but it still takes about 15 minutes for ONE array! So I tried to iterate over both at the same time, for which I found this:

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

but then I get the error:

ValueError: too many values to unpack

Here is my full python code: I hope you can help me make this a lot faster, because this is for my master thesis and in the end I have to run it about a 100 times...

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

Answer 1

Some advice:

• Profile your code to see what is the slowest part. It may not be the iteration but the computations that need to be done each time.
• Reduce function calls as much as possible. Function calls are not for free in Python.
• Rewrite the slowest part as a C extension and then call that C function in your Python code (see Extending and Embedding the Python interpreter).
• This page has some good advice as well.

Answer 2

You specifically asked for iterating two arrays in a single loop. Here is a way to do that

l1 = ["abc", "def", "hi"]
l2 = ["ghi", "jkl", "lst"]
for f,s in zip(l1,l2):
    print "%s : %s" %(f,s)

The above is for python 3, you can use izip for python 2