python - Efficient way to iterate through numpy arrays in parallel and create a new resultant array -


i have 3 numpy arrays dm_w, dm_s , dm_p. in need of iterating through these arrays in parallel, computation based on check condition shown in code below.

my code works smaller arrays, takes long larger arrays. need efficient , faster method achieve this. need expert opinion.

my code: 

prox_mat = [] w_dist, s_dist, pi in zip(np.nditer(dm_w), np.nditer(dm_s), np.nditer(dm_p)):     if pi == 0.0:          proximity_score = ((w_dist + len(np.unique(dm_s) * s_dist)) /                             (dm_w.shape[0] * len(np.unique(dm_s))))          prox_mat.append(proximity_score)     else:          proximity_score = ((w_dist + len(np.unique(dm_s) * s_dist)) /                             (dm_w.shape[0] * len(np.unique(dm_s)))) * log10(10 * pi)          prox_mat.append(proximity_score)  ps = np.array(prox_mat) ps = np.reshape(ps, dm_w.shape)

several things. one, computation of np.unique(dm_s) should pulled outside of loop. further, looks like:

len(np.unique(dm_s) * s_dist) == len(np.unique(dm_s))

which should either pulled out of loop or mistake. in case..

we should vectorize forloop/append construct:

dm_s_uniques = len(np.unique(dm_s)) logs = np.log10(10 * dm_p) logs[logs == -np.inf] = 1 prox_mat = ((dm_w +  dm_s_uniques) / (dm_w.shape[0] * dm_s_uniques)) * logs  ps = np.reshape(ps, dm_w.shape)

it looks map


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