IntervalProd.contains_all¶

IntervalProd.contains_all(other, atol=0.0)[source]¶

Return True if all points defined by other are contained.

Parameters:

other: Collection of points to be tested. Can be given as a single point, a (d, N) array-like where d is the number of dimensions, or a length-d meshgrid tuple.
atolfloat, optional: The maximum allowed distance in 'inf'-norm between the other set and this interval product.

Returns:

containsbool: True if all points are contained, False otherwise.

Examples

>>> min_pt, max_pt = [-1, 0, 2], [-0.5, 0, 3]
>>> rbox = IntervalProd(min_pt, max_pt)

Arrays are expected in (ndim, npoints) shape:

>>> arr = np.array([[-1, 0, 2],   # defining one point at a time
...                 [-0.5, 0, 2]])
>>> rbox.contains_all(arr.T)
True

Implicit meshgrids defined by coordinate vectors:

>>> from odl.discr.grid import sparse_meshgrid
>>> vec1 = (-1, -0.9, -0.7)
>>> vec2 = (0, 0, 0)
>>> vec3 = (2.5, 2.75, 3)
>>> mg = sparse_meshgrid(vec1, vec2, vec3)
>>> rbox.contains_all(mg)
True

Works also with an arbitrary iterable:

>>> rbox.contains_all([[-1, -0.5], # define points by axis
...                    [0, 0],
...                    [2, 2]])
True

Grids are also accepted as input:

>>> agrid = odl.uniform_grid(rbox.min_pt, rbox.max_pt, [3, 1, 3])
>>> rbox.contains_all(agrid)
True