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)