Time different kinds of slicing.

time_slice.py

import time

import numpy

def mask_op(array_a, array_b, nodata):
    result = numpy.full(array_a.shape, nodata, dtype=numpy.float)
    valid_mask = numpy.ones(array_a.shape, dtype=numpy.bool)
    valid_mask &= (array_a != nodata) & (array_b != nodata)
    result[valid_mask] = array_a[valid_mask]+array_b[valid_mask]
    return result

def mask_op_noneslice(array_a, array_b, nodata):
    result = numpy.empty(array_a.shape, dtype=numpy.float)
    result[slice(None)] = nodata
    valid_mask = numpy.empty(array_a.shape, dtype=numpy.bool)
    valid_mask[slice(None)] = True
    valid_mask &= (array_a != nodata) & (array_b != nodata)
    result[valid_mask] = array_a[valid_mask]+array_b[valid_mask]
    return result


op_time = 0
noneslice_op_time = 0
nodata = -1
N = 1000
D = 2**8
for _ in range(N):
    array_a = numpy.random.random((D, D))
    array_b = numpy.random.random((D, D))
    nodata_a_mask = numpy.random.random((D, D)) > 0.5
    nodata_b_mask = numpy.random.random((D, D)) > 0.5
    array_a[nodata_a_mask] = nodata
    array_b[nodata_b_mask] = nodata

    start_time = time.time()
    result_base = mask_op(array_a, array_b, nodata)
    op_time += time.time() - start_time
    start_time = time.time()
    result_noneslice = mask_op_noneslice(array_a, array_b, nodata)
    noneslice_op_time += time.time() - start_time

print(f'D: {D}')
print(f'op_time: {op_time}\nnoneslice_op_time: {noneslice_op_time}')
print(f'noneslice performance increase: {op_time/noneslice_op_time*100:.2f}')