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

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>>> 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