CODEWITHSUNDEEP
pythonmultidimensional-arraynumpyshapesaverage
AJP
AJP

Reputation: 28543

average numpy array but retain shape

I have a Numpy 3 axis array whose elements are 3 dimensional. I'd like to average them and return the same shape of the array. The normal average function removes the 3 dimensions and replace it with the average (as expected):

a = np.array([[[0.1, 0.2, 0.3], [0.2, 0.3, 0.4]],
              [[0.4, 0.4, 0.4], [0.7, 0.6, 0.8]]], np.float32)

b = np.average(a, axis=2)
# b = [[0.2, 0.3],
#      [0.4, 0.7]]

Result required:

# b = [[[0.2, 0.2, 0.2], [0.3, 0.3, 0.3]],
#      [[0.4, 0.4, 0.4], [0.7, 0.7, 0.7]]]

Can you do this elegantly or do I just have to iterate over the array in Python (which will be a lot slower compared to a powerful Numpy function).

Can you set the Dtype argument, for the np.mean function, to a 1D array perhaps?

Thanks.

Upvotes: 10

Views: 6377

Answers (6)

Puco4
Puco4

Reputation: 643

You can use keepdims=True in numpy.mean to retain the original dimensions:

keepdims : bool, optional

If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.

If the default value is passed, then keepdims will not be passed through to the mean method of sub-classes of ndarray, however any non-default value will be. If the sub-class’ method does not implement keepdims any exceptions will be raised.

This already solves many broadcasting situations. However, if you need the same shape you can then use np.broadcast_to:

import numpy as np
a = np.array([[[0.1, 0.2, 0.3], [0.2, 0.3, 0.4]],
            [[0.4, 0.4, 0.4], [0.7, 0.6, 0.8]]], np.float32)
m1 = np.mean(a, axis=2, keepdims=True)
m2 = np.broadcast_to(m1, a.shape)
print('a.shape: %s\nm1.shape: %s\nm2.shape: %s' %(a.shape, m1.shape, m2.shape))

Output:

a.shape: (2, 2, 3)
m1.shape: (2, 2, 1)
m2.shape: (2, 2, 3)

Upvotes: 1

Bi Rico
Bi Rico

Reputation: 25833

Have you considered using broadcasting? Here is more info about broadcasting if you're new to the concept.

Here is an example using broadcast_arrays, keep in mind that the b produced here by broadcast_arrays should be treated as read only, you should make a copy if you want to write to it:

>>> b = np.average(a, axis=2)[:, :, np.newaxis]
>>> b, _ = np.broadcast_arrays(b, a)
>>> b
array([[[ 0.2       ,  0.2       ,  0.2       ],
        [ 0.29999998,  0.29999998,  0.29999998]],

       [[ 0.40000001,  0.40000001,  0.40000001],
        [ 0.69999999,  0.69999999,  0.69999999]]], dtype=float32)

Upvotes: 5

FraDega
FraDega

Reputation: 48

This is for an arbitrary axis:

array is the ndimentional array and axis is the axis to average

np.repeat( np.expand_dims( np.mean( array, axis ), axis ), array.shape[axis], axis )

Upvotes: 1

user545424
user545424

Reputation: 16189

Ok, CAUTION I don't have my masters in numpyology yet, but just playing around, I came up with:

>>> np.average(a,axis=-1).repeat(a.shape[-1]).reshape(a.shape)
array([[[ 0.2       ,  0.2       ,  0.2       ],
        [ 0.29999998,  0.29999998,  0.29999998]],

       [[ 0.40000001,  0.40000001,  0.40000001],
        [ 0.69999999,  0.69999999,  0.69999999]]], dtype=float32)

Upvotes: 7

kwgoodman
kwgoodman

Reputation: 2108

Here is a method that avoids making copies:

a = a.T
a[:] = a.mean(axis=0)
a = a.T

Or if you don't want to overwrite a:

b = np.empty_like(a)
b = b.T
b[:] = a.mean(axis=-1).T
b = b.T

Upvotes: 1

bossylobster
bossylobster

Reputation: 10163

>>> import numpy as np
>>> a = np.array([[[0.1, 0.2, 0.3], [0.2, 0.3, 0.4]],
...               [[0.4, 0.4, 0.4], [0.7, 0.6, 0.8]]], np.float32)
>>> b = np.average(a, axis=2)
>>> b
array([[ 0.2       ,  0.29999998],
       [ 0.40000001,  0.69999999]], dtype=float32)
>>> c = np.dstack((b, b, b))
>>> c
array([[[ 0.2       ,  0.2       ,  0.2       ],
        [ 0.29999998,  0.29999998,  0.29999998]],

       [[ 0.40000001,  0.40000001,  0.40000001],
        [ 0.69999999,  0.69999999,  0.69999999]]], dtype=float32)

Upvotes: 3

Related Questions