0ea17d8ff4
* Operation Interface Enhancement + Clarification - added set notation to all the geometry operations - added a warning to say sample_surface=True doesn't work * minor fix docs * fix operation_interface.py doc --------- Co-authored-by: Dario Coscia <dariocoscia@Dario-Coscia.local> Co-authored-by: Dario Coscia <93731561+dario-coscia@users.noreply.github.com>
105 lines
3.8 KiB
Python
105 lines
3.8 KiB
Python
import torch
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from .location import Location
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from .operation_interface import OperationInterface
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from ..utils import check_consistency
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from ..label_tensor import LabelTensor
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import random
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class Union(OperationInterface):
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""" PINA implementation of Unions of Domains."""
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def __init__(self, geometries):
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"""
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PINA implementation of Unions of Domains.
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Given two sets :math:`A` and :math:`B` then the
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domain difference is defined as:
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..:math:
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A \cup B = \{x \mid x \in A \text{ or } x \in B\},
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with :math:`x` a point in :math:`\mathbb{R}^N` and :math:`N`
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the dimension of the geometry space.
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:param list geometries: A list of geometries from 'pina.geometry'
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such as 'EllipsoidDomain' or 'CartesianDomain'.
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:Example:
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# Create two ellipsoid domains
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>>> ellipsoid1 = EllipsoidDomain({'x': [-1, 1], 'y': [-1, 1]})
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>>> ellipsoid2 = EllipsoidDomain({'x': [0, 2], 'y': [0, 2]})
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# Create a union of the ellipsoid domains
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>>> union = GeometryUnion([ellipsoid1, ellipsoid2])
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"""
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super().__init__(geometries)
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def is_inside(self, point, check_border=False):
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"""Check if a point is inside the union domain.
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:param point: Point to be checked.
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:type point: LabelTensor
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:param check_border: Check if the point is also on the frontier
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of the ellipsoid, default False.
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:type check_border: bool
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:return: Returning True if the point is inside, False otherwise.
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:rtype: bool
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"""
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for geometry in self.geometries:
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if geometry.is_inside(point, check_border):
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return True
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return False
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def sample(self, n, mode='random', variables='all'):
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"""Sample routine for union domain.
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:param n: Number of points to sample in the shape.
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:type n: int
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:param mode: Mode for sampling, defaults to 'random'.
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Available modes include: random sampling, 'random'.
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:type mode: str, optional
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:param variables: pinn variable to be sampled, defaults to 'all'.
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:type variables: str or list[str], optional
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:Example:
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# Create two ellipsoid domains
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>>> cartesian1 = CartesianDomain({'x': [0, 2], 'y': [0, 2]})
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>>> cartesian2 = CartesianDomain({'x': [1, 3], 'y': [1, 3]})
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# Create a union of the ellipsoid domains
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>>> union = Union([cartesian1, cartesian2])
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>>> union.sample(n=5)
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LabelTensor([[1.2128, 2.1991],
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[1.3530, 2.4317],
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[2.2562, 1.6605],
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[0.8451, 1.9878],
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[1.8623, 0.7102]])
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>>> len(union.sample(n=5)
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5
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"""
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sampled_points = []
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# calculate the number of points to sample for each geometry and the remainder
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remainder = n % len(self.geometries)
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num_points = n // len(self.geometries)
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# sample the points
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# NB. geometries as shuffled since if we sample
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# multiple times just one point, we would end
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# up sampling only from the first geometry.
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iter_ = random.sample(self.geometries, len(self.geometries))
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for i, geometry in enumerate(iter_):
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# int(i < remainder) is one only if we have a remainder
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# different than zero. Notice that len(geometries) is
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# always smaller than remaider.
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sampled_points.append(geometry.sample(
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num_points + int(i < remainder), mode, variables))
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# in case number of sampled points is smaller than the number of geometries
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if len(sampled_points) >= n:
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break
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return LabelTensor(torch.cat(sampled_points), labels=self.variables)
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