Note

This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the user guide for the big picture.

nilearn.image.iter_img¶

nilearn.image.iter_img(imgs)[source]¶

Iterate over images.

Could be along the 4th dimension for 4D Niimg-like object or the 2nd dimension for 2D Surface images..

Parameters:

imgs4D Niimg-like object or SurfaceImage: See Input and output: neuroimaging data representation.

Returns:

Iterator of Nifti1Image or SurfaceImage

See also

nilearn.image.index_img

Examples

>>> import numpy as np
>>> from nibabel import Nifti1Image
>>> from nilearn.image import iter_img
>>>
>>> # Create dummy 4D image.
>>> affine = np.eye(4)
>>> data = np.ones((3, 3, 3, 2))
>>>
>>> # Set different values for each 3D image in 4D series.
>>> for i in range(data.shape[-1]):
...     data[:, :, :, i] = data[:, :, :, i] * i
>>>
>>> # Create 4D Nifti Image and print the shape of the data.
>>> img_4d = Nifti1Image(data, affine)
>>> f"{img_4d.__class__.__name__} with shape= {img_4d.shape}"
'Nifti1Image with shape= (3, 3, 3, 2)'
>>>
>>> # iter_img creates a generator object.
>>> all_images = iter_img(img_4d)
>>> type(all_images)
<class 'generator'>
>>>
>>> # Let's check its content.
>>> [f"{x.__class__.__name__} with shape= {x.shape}" for x in all_images]
['Nifti1Image with shape= (3, 3, 3)', 'Nifti1Image with shape= (3, 3, 3)']

Examples using `nilearn.image.iter_img`¶

3D and 4D niimgs: handling and visualizing

Deriving spatial maps from group fMRI data using ICA and Dictionary Learning

nilearn.image.iter_img¶

Examples using nilearn.image.iter_img¶

Examples using `nilearn.image.iter_img`¶