103 lines
3.5 KiB
Python
103 lines
3.5 KiB
Python
import torch
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import pytest
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from pina.problem import SpatialProblem
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from pina.operators import nabla
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from pina import LabelTensor, Condition
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from pina.geometry import CartesianDomain
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from pina.equation.equation import Equation
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from pina.equation.equation_factory import FixedValue
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def laplace_equation(input_, output_):
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force_term = (torch.sin(input_.extract(['x'])*torch.pi) *
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torch.sin(input_.extract(['y'])*torch.pi))
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nabla_u = nabla(output_.extract(['u']), input_)
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return nabla_u - force_term
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my_laplace = Equation(laplace_equation)
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in_ = LabelTensor(torch.tensor([[0., 1.]], requires_grad=True), ['x', 'y'])
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out_ = LabelTensor(torch.tensor([[0.]], requires_grad=True), ['u'])
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class Poisson(SpatialProblem):
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output_variables = ['u']
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spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]})
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conditions = {
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'gamma1': Condition(
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location=CartesianDomain({'x': [0, 1], 'y': 1}),
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equation=FixedValue(0.0)),
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'gamma2': Condition(
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location=CartesianDomain({'x': [0, 1], 'y': 0}),
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equation=FixedValue(0.0)),
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'gamma3': Condition(
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location=CartesianDomain({'x': 1, 'y': [0, 1]}),
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equation=FixedValue(0.0)),
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'gamma4': Condition(
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location=CartesianDomain({'x': 0, 'y': [0, 1]}),
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equation=FixedValue(0.0)),
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'D': Condition(
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location=CartesianDomain({'x': [0, 1], 'y': [0, 1]}),
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equation=my_laplace),
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'data': Condition(
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input_points=in_,
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output_points=out_)
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}
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def poisson_sol(self, pts):
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return -(
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torch.sin(pts.extract(['x'])*torch.pi) *
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torch.sin(pts.extract(['y'])*torch.pi)
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)/(2*torch.pi**2)
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truth_solution = poisson_sol
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# make the problem
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poisson_problem = Poisson()
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def test_discretise_domain():
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n = 10
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boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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poisson_problem.discretise_domain(n, 'grid', locations=boundaries)
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for b in boundaries:
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assert poisson_problem.input_pts[b].shape[0] == n
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poisson_problem.discretise_domain(n, 'random', locations=boundaries)
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for b in boundaries:
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assert poisson_problem.input_pts[b].shape[0] == n
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poisson_problem.discretise_domain(n, 'grid', locations=['D'])
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assert poisson_problem.input_pts['D'].shape[0] == n**2
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poisson_problem.discretise_domain(n, 'random', locations=['D'])
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assert poisson_problem.input_pts['D'].shape[0] == n
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poisson_problem.discretise_domain(n, 'latin', locations=['D'])
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assert poisson_problem.input_pts['D'].shape[0] == n
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poisson_problem.discretise_domain(n, 'lh', locations=['D'])
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assert poisson_problem.input_pts['D'].shape[0] == n
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def test_sampling_few_variables():
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n = 10
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poisson_problem.discretise_domain(n, 'grid', locations=['D'], variables=['x'])
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assert poisson_problem.input_pts['D'].shape[1] == 1
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assert poisson_problem._have_sampled_points['D'] is False
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# def test_sampling_all_args():
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# n = 10
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# poisson_problem.discretise_domain(n, 'grid', locations=['D'])
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# def test_sampling_all_kwargs():
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# n = 10
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# poisson_problem.discretise_domain(n=n, mode='latin', locations=['D'])
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# def test_sampling_dict():
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# n = 10
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# poisson_problem.discretise_domain(
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# {'variables': ['x', 'y'], 'mode': 'grid', 'n': n}, locations=['D'])
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# def test_sampling_mixed_args_kwargs():
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# n = 10
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# with pytest.raises(ValueError):
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# poisson_problem.discretise_domain(n, mode='latin', locations=['D']) |