`skimage.util._regular_grid:regular_grid`

source: /dev/scikit-image/src/skimage/util/_regular_grid.py :4

Signature

def regular_grid ( ar_shape , n_points )

Summary

Find n_points regularly spaced along ar_shape.

Extended Summary

The returned points (as slices) should be as close to cubically-spaced as possible. Essentially, the points are spaced by the Nth root of the input array size, where N is the number of dimensions. However, if an array dimension cannot fit a full step size, it is "discarded", and the computation is done for only the remaining dimensions.

Parameters

ar_shape : array_like of dtype int: The shape of the space embedding the grid. len(ar_shape) is the number of dimensions.
n_points : int: The (approximate) number of points to embed in the space.

Returns

slices : tuple of (slice, ...)

A slice along each dimension of ar_shape, such that the intersection of all the slices give the coordinates of regularly spaced points.

versionchanged 0.14.1

In scikit-image 0.14.1 and 0.15, the return type was changed from a list to a tuple to ensure compatibility with Numpy 1.15 and higher. If your code requires the returned result to be a list, you may convert the output of this function to a list with:

>>> result = list(regular_grid(ar_shape=(3, 20, 40), n_points=8))

Examples

ar = np.zeros((20, 40))
g = regular_grid(ar.shape, 8)

✓

✗

ar[g] = 1

✓

ar.sum()

✗

ar = np.zeros((20, 40))
g = regular_grid(ar.shape, 32)

✓

✗

ar[g] = 1

✓

ar.sum()

✗

ar = np.zeros((3, 20, 40))
g = regular_grid(ar.shape, 8)

✓

✗

ar[g] = 1

✓

ar.sum()

✗

Aliases

skimage.util.regular_grid