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pythonnumpyiteration
Chris
Chris

Reputation: 1531

Numpy.select from 3D array

Suppose I have the following numpy arrays:

>>a
array([[0, 0, 2],
       [2, 0, 1],
       [2, 2, 1]])
>>b
array([[2, 2, 0],
       [2, 0, 2],
       [1, 1, 2]])

that I then vertically stack 

c=np.dstack((a,b))

resulting in:

>>c
array([[[0, 2],
        [0, 2],
        [2, 0]],

       [[2, 2],
        [0, 0],
        [1, 2]],

       [[2, 1],
        [2, 1],
        [1, 2]]])

From this I wish to, for each 3rd dimension of c, check which combination is present in this subarray, and then number it accordingingly with the index of the list-match. I've tried the following, but it is not working. The algorithm is simple enough with double for-loops, but because c is very large, it is prohibitively slow. 

classes=[(0,0),(2,1),(2,2)]
out=np.select( [h==c for h in classes], range(len(classes)), default=-1)

My desired output would be

out = [[-1,-1,-1],
       [3,  1,-1],
       [2,  2,-1]]

Upvotes: 0

Views: 866

Answers (3)

Bi Rico
Bi Rico

Reputation: 25813

Here is another way to get what you want, thought I would post it in case it's useful to anyone.

import numpy as np

a = np.array([[0, 0, 2],
              [2, 0, 1],
              [2, 2, 1]])
b = np.array([[2, 2, 0],
              [2, 0, 2],
              [1, 1, 2]])
classes=[(0,0),(2,1),(2,2)]

c = np.empty(a.shape, dtype=[('a', a.dtype), ('b', b.dtype)])
c['a'] = a
c['b'] = b
classes = np.array(classes, dtype=c.dtype)
classes.sort()
out = classes.searchsorted(c)
out = np.where(c == classes[out], out+1, -1)
print out
#array([[-1, -1, -1]
#       [ 3,  1, -1]
#       [ 2,  1, -1]])

Upvotes: 1

lxop
lxop

Reputation: 8595

You can test the a and b arrays separately like this:

clsa = (0,2,2)
clesb = (0,1,2)

np.select ( [(ca==a) & (cb==b) for ca,cb in zip (clsa, clsb)], range (3), default = -1)

which gets your desired result (except returns 0,1,2 instead of 1,2,3).

Upvotes: 1

sega_sai
sega_sai

Reputation: 8538

How about this:

(np.array([np.array(h)[...,:] == c for h in classes]).all(axis = -1) *
         (2 + np.arange(len(classes)))[:, None, None]).max(axis=0) - 1

It returns, what you actually need

array([[-1, -1, -1],
       [ 3,  1, -1],
       [ 2,  2, -1]])

Upvotes: 1

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