How to down sample an array in python without a for loop

Question

Is there a 'pythonic' way to cleanly down-sample without multiple for loops?

This example below is the type of for loop I wish to get rid of.

Minimum working example:

import numpy as np
unsampled_array = [1,3,5,7,9,11,13,15,17,19]
number_of_samples = 7
downsampled_array = []
downsampling_indices = np.linspace(0, len(unsampled_array)-1, number_of_samples).round()
for index in downsampling_indices:
    downsampled_array.append(unsampled_array[int(index)])
print(downsampled_array)

Result:

>>> [ 1  5  7  9 13 17 19]

Noam Peled · Accepted Answer · 2019-02-25 14:48:23Z

3

If you want "real" downsampling, where each value is the mean of k values, you can use

unsampled_array.reshape(-1, k).mean(1)

Make sure unsampled_array is a np.array. In your case, k=2. That will give you:

[ 2. 6. 10. 14. 18.]

* Update: If you just want to take the first item for each k items, you can use this code:

unsampled_array.reshape(-1, 2)[:, 0]

Take a look at this plot:

edited Feb 25, 2019 at 14:48

answered Feb 25, 2019 at 2:33

Noam Peled

4,6525 gold badges46 silver badges48 bronze badges

Sign up to request clarification or add additional context in comments.

1 Comment

Jesse Reza Khorasanee Over a year ago

You are correct. I didn't mention it in the question but I was initially interested in downsampling objects and downsampling between and to sample sizes. I guess I over simplified the example.

Happy Boy · Accepted Answer · 2019-02-25 02:31:48Z

2

You need function np.ix_, as follows:

import numpy as np


unsampled_array = np.array([1,3,5,7,9,11,13,15,17,19])
number_of_samples = 5
downsampling_indices = np.linspace(0, len(unsampled_array)-1, number_of_samples).round()
downsampling_indices = np.array(downsampling_indices, dtype=np.int64)

indices = np.ix_(downsampling_indices)
downsampled_array = unsampled_array[indices]

print(downsampled_array)

answered Feb 25, 2019 at 2:31

Happy Boy

5262 silver badges9 bronze badges

8 Comments

nico Over a year ago

I don't think the np.ix_ is necessary at all. You can just go unsampled_array[ downsampling_indices].

Jesse Reza Khorasanee Over a year ago

This is great as it also works with non integer down sampling ( ie 10->7 )

Happy Boy Over a year ago

np.ix_ is very quick. You can compare both approaches.

Noam Peled Over a year ago

Instead of calling np.array with dtype=np.int64, add to the end of the np.linspace command astype(int). Also, instead of using round, I would use np.rint.

nico Over a year ago

@NoamPeled You are right about astype(int) but np.rint returns a float for some reason so there is no benefit of np.rint(x).astype(int) over x.round().astype(int)

|

Collectives™ on Stack Overflow

How to down sample an array in python without a for loop

Minimum working example:

Result:

2 Answers 2

1 Comment

8 Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

Minimum working example:

Result:

2 Answers 2

1 Comment

8 Comments

Your Answer

Sign up or log in

Post as a guest

Related