Source code for pina.geometry.union_domain

"""Module for Union class. """

import torch
from .operation_interface import OperationInterface
from ..label_tensor import LabelTensor
import random


[docs] class Union(OperationInterface): def __init__(self, geometries): r""" PINA implementation of Unions of Domains. Given two sets :math:`A` and :math:`B` then the domain difference is defined as: .. math:: A \cup B = \{x \mid x \in A \lor x \in B\}, with :math:`x` a point in :math:`\mathbb{R}^N` and :math:`N` the dimension of the geometry space. :param list geometries: A list of geometries from ``pina.geometry`` such as ``EllipsoidDomain`` or ``CartesianDomain``. :Example: >>> # Create two ellipsoid domains >>> ellipsoid1 = EllipsoidDomain({'x': [-1, 1], 'y': [-1, 1]}) >>> ellipsoid2 = EllipsoidDomain({'x': [0, 2], 'y': [0, 2]}) >>> # Create a union of the ellipsoid domains >>> union = GeometryUnion([ellipsoid1, ellipsoid2]) """ super().__init__(geometries)
[docs] def is_inside(self, point, check_border=False): """ Check if a point is inside the ``Union`` domain. :param point: Point to be checked. :type point: LabelTensor :param check_border: Check if the point is also on the frontier of the ellipsoid, default ``False``. :type check_border: bool :return: Returning ``True`` if the point is inside, ``False`` otherwise. :rtype: bool """ for geometry in self.geometries: if geometry.is_inside(point, check_border): return True return False
[docs] def sample(self, n, mode="random", variables="all"): """ Sample routine for ``Union`` domain. :param int n: Number of points to sample in the shape. :param str mode: Mode for sampling, defaults to ``random``. Available modes include: ``random``. :param variables: Variables to be sampled, defaults to ``all``. :type variables: str | list[str] :return: Returns ``LabelTensor`` of n sampled points. :rtype: LabelTensor :Example: >>> # Create two ellipsoid domains >>> cartesian1 = CartesianDomain({'x': [0, 2], 'y': [0, 2]}) >>> cartesian2 = CartesianDomain({'x': [1, 3], 'y': [1, 3]}) >>> # Create a union of the ellipsoid domains >>> union = Union([cartesian1, cartesian2]) >>> # Sample >>> union.sample(n=5) LabelTensor([[1.2128, 2.1991], [1.3530, 2.4317], [2.2562, 1.6605], [0.8451, 1.9878], [1.8623, 0.7102]]) >>> len(union.sample(n=5) 5 """ sampled_points = [] # calculate the number of points to sample for each geometry and the remainder remainder = n % len(self.geometries) num_points = n // len(self.geometries) # sample the points # NB. geometries as shuffled since if we sample # multiple times just one point, we would end # up sampling only from the first geometry. iter_ = random.sample(self.geometries, len(self.geometries)) for i, geometry in enumerate(iter_): # int(i < remainder) is one only if we have a remainder # different than zero. Notice that len(geometries) is # always smaller than remaider. sampled_points.append( geometry.sample( num_points + int(i < remainder), mode, variables ) ) # in case number of sampled points is smaller than the number of geometries if len(sampled_points) >= n: break return LabelTensor(torch.cat(sampled_points), labels=self.variables)