This may seem like a strange question, but how do you rewrite in pure python next line:
np.sum(three_dim_matrix, axis=1).cumsum(axis=1)
cumsum is supposed to be applied to a two-dimensional matrix, so the code for cumsum I could already find:
from itertools import accumulate
[list(accumulate(row)) for row in two_dim_matrix]
If you're really wondering why I don't use numpy, the problem is that optimizers for MINLP (such, GEKKO) don't support defining objective functions in numpy features
Example:
example = np.array([[[ 70, 110, 130],
[-50, -100, -200]],
[[300, 140, 120],
[300, 140, 120]],
[[ 400, 180, -240],
[1000, 320, 560]]])
first_step = np.sum(example, axis=1)
# [[ 20 10 -70]
# [ 600 280 240]
# [1400 500 320]]
second_step = np.cumsum(first_step, axis=1)
# [[ 20 30 -40]
# [ 600 880 1120]
# [1400 1900 2220]]
In the two steps your example shows, data being the input list:
first_step = [list(map(sum, zip(*rows))) for rows in data]
second_step = [list(accumulate(row)) for row in first_step]
Or both steps combined (should be faster, as it doesn't build intermediate lists):
both_steps = [list(accumulate(map(sum, zip(*rows)))) for rows in data]