import torch import pytest from pina.problem import SpatialProblem from pina.operators import laplacian from pina import LabelTensor, Condition from pina.geometry import CartesianDomain from pina.equation.equation import Equation from pina.equation.equation_factory import FixedValue def laplace_equation(input_, output_): force_term = (torch.sin(input_.extract(['x']) * torch.pi) * torch.sin(input_.extract(['y']) * torch.pi)) delta_u = laplacian(output_.extract(['u']), input_) return delta_u - force_term my_laplace = Equation(laplace_equation) in_ = LabelTensor(torch.tensor([[0., 1.]], requires_grad=True), ['x', 'y']) out_ = LabelTensor(torch.tensor([[0.]], requires_grad=True), ['u']) class Poisson(SpatialProblem): output_variables = ['u'] spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]}) conditions = { 'gamma1': Condition(location=CartesianDomain({ 'x': [0, 1], 'y': 1 }), equation=FixedValue(0.0)), 'gamma2': Condition(location=CartesianDomain({ 'x': [0, 1], 'y': 0 }), equation=FixedValue(0.0)), 'gamma3': Condition(location=CartesianDomain({ 'x': 1, 'y': [0, 1] }), equation=FixedValue(0.0)), 'gamma4': Condition(location=CartesianDomain({ 'x': 0, 'y': [0, 1] }), equation=FixedValue(0.0)), 'D': Condition(location=CartesianDomain({ 'x': [0, 1], 'y': [0, 1] }), equation=my_laplace), 'data': Condition(input_points=in_, output_points=out_) } def poisson_sol(self, pts): return -(torch.sin(pts.extract(['x']) * torch.pi) * torch.sin(pts.extract(['y']) * torch.pi)) / (2 * torch.pi**2) truth_solution = poisson_sol def test_discretise_domain(): n = 10 poisson_problem = Poisson() boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] poisson_problem.discretise_domain(n, 'grid', locations=boundaries) for b in boundaries: assert poisson_problem.input_pts[b].shape[0] == n poisson_problem.discretise_domain(n, 'random', locations=boundaries) for b in boundaries: assert poisson_problem.input_pts[b].shape[0] == n poisson_problem.discretise_domain(n, 'grid', locations=['D']) assert poisson_problem.input_pts['D'].shape[0] == n**2 poisson_problem.discretise_domain(n, 'random', locations=['D']) assert poisson_problem.input_pts['D'].shape[0] == n poisson_problem.discretise_domain(n, 'latin', locations=['D']) assert poisson_problem.input_pts['D'].shape[0] == n poisson_problem.discretise_domain(n, 'lh', locations=['D']) assert poisson_problem.input_pts['D'].shape[0] == n def test_sampling_few_variables(): n = 10 poisson_problem = Poisson() poisson_problem.discretise_domain(n, 'grid', locations=['D'], variables=['x']) assert poisson_problem.input_pts['D'].shape[1] == 1 assert poisson_problem._have_sampled_points['D'] is False def test_variables_correct_order_sampling(): n = 10 poisson_problem = Poisson() poisson_problem.discretise_domain(n, 'grid', locations=['D'], variables=['x']) poisson_problem.discretise_domain(n, 'grid', locations=['D'], variables=['y']) assert poisson_problem.input_pts['D'].labels == sorted( poisson_problem.input_variables) poisson_problem.discretise_domain(n, 'grid', locations=['D']) assert poisson_problem.input_pts['D'].labels == sorted( poisson_problem.input_variables) poisson_problem.discretise_domain(n, 'grid', locations=['D'], variables=['y']) poisson_problem.discretise_domain(n, 'grid', locations=['D'], variables=['x']) assert poisson_problem.input_pts['D'].labels == sorted( poisson_problem.input_variables)