Difference#

Module for Difference class.

class Difference(geometries)[source]#

Bases: OperationInterface

PINA implementation of Difference of Domains. Given two sets \(A\) and \(B\) then the domain difference is defined as:

\[A - B = \{x \mid x \in A \land x \not\in B\},\]

with \(x\) a point in \(\mathbb{R}^N\) and \(N\) the dimension of the geometry space.

Parameters:

geometries (list) – A list of geometries from pina.geometry such as EllipsoidDomain or CartesianDomain. The first geometry in the list is the geometry from which points are sampled. The rest of the geometries are the geometries that are excluded from the first geometry to find the difference.

Example:
>>> # Create two ellipsoid domains
>>> ellipsoid1 = EllipsoidDomain({'x': [-1, 1], 'y': [-1, 1]})
>>> ellipsoid2 = EllipsoidDomain({'x': [0, 2], 'y': [0, 2]})
>>> # Create a Difference of the ellipsoid domains
>>> difference = Difference([ellipsoid1, ellipsoid2])
is_inside(point, check_border=False)[source]#

Check if a point is inside the Difference domain.

Parameters:

  • point (torch.Tensor) – Point to be checked.

  • check_border (bool) – If True, the border is considered inside.

Returns:

True if the point is inside the Exclusion domain, False otherwise.

Return type:

bool

sample(n, mode='random', variables='all')[source]#

Sample routine for Difference domain.

Parameters:

  • n (int) – Number of points to sample in the shape.

  • mode (str) – Mode for sampling, defaults to random. Available modes include: random.

  • variables (str | list[str]) – Variables to be sampled, defaults to all.

Returns:

Returns LabelTensor of n sampled points.

Return type:

LabelTensor

Example:
>>> # Create two Cartesian domains
>>> cartesian1 = CartesianDomain({'x': [0, 2], 'y': [0, 2]})
>>> cartesian2 = CartesianDomain({'x': [1, 3], 'y': [1, 3]})
>>> # Create a Difference of the ellipsoid domains
>>> difference = Difference([cartesian1, cartesian2])
>>> # Sampling
>>> difference.sample(n=5)
    LabelTensor([[0.8400, 0.9179],
                [0.9154, 0.5769],
                [1.7403, 0.4835],
                [0.9545, 1.2851],
                [1.3726, 0.9831]])
>>> len(difference.sample(n=5)
    5