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>
98 lines
3.6 KiB
Python
98 lines
3.6 KiB
Python
"""Module for Location class."""
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import torch
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from .exclusion_domain import Exclusion
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from .operation_interface import OperationInterface
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from ..label_tensor import LabelTensor
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class Difference(OperationInterface):
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""" PINA implementation of Difference of Domains."""
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def __init__(self, geometries):
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"""
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PINA implementation of Difference 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 \setminus B = \{x \mid x \in A \text{ and } x \not\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'. The first
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geometry in the list is the geometry from which points are
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sampled. The rest of the geometries are the geometries that
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are excluded from the first geometry to find the difference.
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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 Difference of the ellipsoid domains
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>>> difference = Difference([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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for geometry in self.geometries[1:]:
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if geometry.is_inside(point):
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return False
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return self.geometries[0].is_inside(point, check_border)
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def sample(self, n, mode='random', variables='all'):
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"""Sample routine for difference 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 Cartesian 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 Difference of the ellipsoid domains
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>>> difference = Difference([cartesian1, cartesian2])
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>>> difference.sample(n=5)
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LabelTensor([[0.8400, 0.9179],
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[0.9154, 0.5769],
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[1.7403, 0.4835],
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[0.9545, 1.2851],
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[1.3726, 0.9831]])
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>>> len(difference.sample(n=5)
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5
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"""
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if mode != 'random':
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raise NotImplementedError(
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f'{mode} is not a valid mode for sampling.')
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sampled = []
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# sample the points
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while len(sampled) < n:
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# get sample point from first geometry
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point = self.geometries[0].sample(1, mode, variables)
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is_inside = False
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# check if point is inside any other geometry
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for geometry in self.geometries[1:]:
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# if point is inside any other geometry, break
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if geometry.is_inside(point):
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is_inside = True
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break
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# if point is not inside any other geometry, add to sampled
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if not is_inside:
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sampled.append(point)
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return LabelTensor(torch.cat(sampled), labels=self.variables)
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