How can I loop over a multidimensional array and save the result in an array?

Question

I want to fill in an empty 4D array. I have created a pre-allocated array (data_4d_smoothed) with 80 x 80 x 44 x 50. I want to loop through all (50) volumes of the data (data_4d), smooth them separately and store the results in data_4d_smoothed. Basically:

data_4d_smoothed = np.zeros(data_4d.shape)

sigma = 0.7
for i in data_4d[:, :, :, i]:
    smoothed_vol = gaussian_filter(i, sigma=sigma)
    data_4d_smoothed.append(smoothed_vol)

The gaussian_filter should take every volume (the last dimension of the 4d array), do the operation, and save it into data_4d_smoothed. But obviously, this is not a 2D array and I think I need a nested loop to fill this empty list.

Daniel F · Accepted Answer · 2020-11-23 12:33:45Z

1

I think this should work without looping:

from scipy.ndimage import gaussian_filter
data_4d = np.random.rand(80,80,44,50)
data_4d_smoothed = gaussian_filter(data_4d, sigma = (sigma, sigma, sigma, 0))

Basically make the last dimension's sigma = 0, so that it doesn't do the convolution in that dimension.

Checking:

data_4d_0 = gaussian_filter(data_4d[..., 0], sigma = sigma)  #filter first image

np.allclose(data_4d_0, data_4d_smoothed[..., 0])             #first image from global filter

True

edited Nov 23, 2020 at 12:33

answered Nov 23, 2020 at 11:35

Daniel F

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4 Comments

e. erhan Over a year ago

This gives me False. Sigma is a property of the function - irrespective of the dimentions (gaussian_filter). So I don't think this fits :( the function should take 50 volumes one-by-one - the last dimension - apply the function property, and then save it in the output again as a 4D array.

Daniel F Over a year ago

You're not using scipy.ndimage.gaussian_filter? Then what are you using?

e. erhan Over a year ago

SORRY IT WORKED! can you explain what happens here?

Daniel F Over a year ago

A convolution with a sigma = 0 gaussian kernel just returns the original array, so by using 0 as the sigma in the 4th dimension, you only convolve over the first three dimensions.

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