regular-grid

regular_grid

skimage.util.regular_grid(ar_shape, n_points) [source]

Find n_points regularly spaced along ar_shape.

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

Examples

>>> ar = np.zeros((20, 40))
>>> g = regular_grid(ar.shape, 8)
>>> g
[slice(5, None, 10), slice(5, None, 10)]
>>> ar[g] = 1
>>> ar.sum()
8.0
>>> ar = np.zeros((20, 40))
>>> g = regular_grid(ar.shape, 32)
>>> g
[slice(2, None, 5), slice(2, None, 5)]
>>> ar[g] = 1
>>> ar.sum()
32.0
>>> ar = np.zeros((3, 20, 40))
>>> g = regular_grid(ar.shape, 8)
>>> g
[slice(1, None, 3), slice(5, None, 10), slice(5, None, 10)]
>>> ar[g] = 1
>>> ar.sum()
8.0