calculate mean of multidimensional array using indices and masking array

Question

m =  np.array([[[0.4,0.5],[0.2,0.3]], [[0.8,0.1],[0.7,0.9]]])
id = np.array([[[1,2],[2,3]], [[3,1],[3,2]]])
mask = np.array([[[0,1],[1,0]], [[1,1],[1,1]]])

I'd like to calculate the mean of m for each index in id, and only for nonzero elements in mask.

For example for id==3, that would be the mean of [0.8,0.7].

I thought np.ma.masked_array.mean would do the trick but this does not give me the expected output

 >> np.ma.masked_array(m[id==3],mask=mask[id==3]).mean()
    0.29999999

iz_ · Accepted Answer · 2019-03-28 00:26:46Z

3

Simple array indexing should work:

import numpy as np

m =  np.array([[[0.4,0.5],[0.2,0.3]], [[0.8,0.1],[0.7,0.9]]])
my_id = np.array([[[1,2],[2,3]], [[3,1],[3,2]]])
mask = np.array([[[0,1],[1,0]], [[1,1],[1,1]]])

print(m[(mask != 0) & (my_id == 3)])          # [0.8 0.7]
print(m[(mask != 0) & (my_id == 3)].mean())   # 0.75

Also, id is a built-in function, so don't name a variable that. It will shadow the built-in.

answered Mar 28, 2019 at 0:26

iz_

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Paul Panzer · Accepted Answer · 2019-03-28 01:05:34Z

2

Apply the mask to both m and id_ and then use np.bincount

m =  np.array([[[0.4,0.5],[0.2,0.3]], [[0.8,0.1],[0.7,0.9]]])
id_ = np.array([[[1,2],[2,3]], [[3,1],[3,2]]])
mask = np.array([[[0,1],[1,0]], [[1,1],[1,1]]])

mask = mask.astype(bool)
mm, idm = m[mask], id_[mask]
result = np.bincount(idm, mm)/np.bincount(idm)

result
# array([       nan, 0.1       , 0.53333333, 0.75      ])

result contains all the means, for example result[3] is the mean for id_ 3.

Note: your approach does not work because by convention in a masked array the mask specifies the missing not the valid data, in other words you'd have to invert the mask.

edited Mar 28, 2019 at 1:05

answered Mar 28, 2019 at 0:57

Paul Panzer

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