Python 2.7: looping over 1D fibers in a multidimensional Numpy array

I think you might be looking for numpy.apply_along_axis

In [10]: def my_func(x):   ...:     return x**2 + xIn [11]: np.apply_along_axis(my_func, 2, A)Out[11]: array([[[  0,   2,   6],        [ 12,  20,  30],        [ 42,  56,  72]],       [[ 90, 110, 132],        [156, 182, 210],        [240, 272, 306]],       [[342, 380, 420],        [462, 506, 552],        [600, 650, 702]]])

Although many NumPy functions (including sum) have their own axis argument to specify which axis to use:

In [12]: np.sum(A, axis=2)Out[12]: array([[ 3, 12, 21],       [30, 39, 48],       [57, 66, 75]])

numpy provides a number of different ways of looping over 1 or more dimensions.

Your example:

func = np.sumfor fiber_index in np.ndindex(A.shape[:-1]):    print func(fiber_index)    print A[fiber_index]

produces something like:

(0, 0)[0 1 2](0, 1)[3 4 5](0, 2)[6 7 8]...

generates all index combinations over the 1st 2 dim, giving your function the 1D fiber on the last.

Look at the code for ndindex. It's instructive. I tried to extract it's essence in https://stackoverflow.com/a/25097271/901925.

It uses as_strided to generate a dummy matrix over which an nditer iterate. It uses the 'multi_index' mode to generate an index set, rather than elements of that dummy. The iteration itself is done with a __next__ method. This is the same style of indexing that is currently used in numpy compiled code.

http://docs.scipy.org/doc/numpy-dev/reference/arrays.nditer.htmlIterating Over Arrays has good explanation, including an example of doing so in cython.

Many functions, among them sum, max, product, let you specify which axis (axes) you want to iterate over. Your example, with sum, can be written as:

np.sum(A, axis=-1)np.sum(A, axis=(1,2))   # sum over 2 axes

An equivalent is

np.add.reduce(A, axis=-1)

np.add is a ufunc, and reduce specifies an iteration mode. There are many other ufunc, and other iteration modes - accumulate, reduceat. You can also define your own ufunc.

xnx suggests

np.apply_along_axis(np.sum, 2, A)

It's worth digging through apply_along_axis to see how it steps through the dimensions of A. In your example, it steps over all possible i,j in a while loop, calculating:

outarr[(i,j)] = np.sum(A[(i, j, slice(None))])

Including slice objects in the indexing tuple is a nice trick. Note that it edits a list, and then converts it to a tuple for indexing. That's because tuples are immutable.

Your iteration can applied along any axis by rolling that axis to the end. This is a 'cheap' operation since it just changes the strides.

def with_ndindex(A, func, ax=-1):    # apply func along axis ax    A = np.rollaxis(A, ax, A.ndim) # roll ax to end (changes strides)    shape = A.shape[:-1]    B = np.empty(shape,dtype=A.dtype)    for ii in np.ndindex(shape):        B[ii] = func(A[ii])    return B

I did some timings on 3x3x3, 10x10x10 and 100x100x100 A arrays. This np.ndindex approach is consistently a third faster than the apply_along_axis approach. Direct use of np.sum(A, -1) is much faster.

So if func is limited to operating on a 1D fiber (unlike sum), then the ndindex approach is a good choice.