add tests on solutions and renamed by software standards

Author: GiovanniCanali

…s/test_problem_zoo/test_acoustic_wave.py …roblem_zoo/test_acoustic_wave_problem.pytests/test_problem_zoo/test_acoustic_wave.py renamed to tests/test_problem_zoo/test_acoustic_wave_problem.py tests/test_problem_zoo/test_acoustic_wave.py renamed to tests/test_problem_zoo/test_acoustic_wave_problem.py

@@ -1,4 +1,5 @@
 import pytest
+import torch
 from pina.problem.zoo import AcousticWaveProblem
 from pina.problem import SpatialProblem, TimeDependentProblem
 
@@ -17,3 +18,19 @@ def test_constructor(c):
     # Should fail if c is not a float or int
     with pytest.raises(ValueError):
         AcousticWaveProblem(c="invalid")
+
+
+@pytest.mark.parametrize("c", [0.1, 1])
+def test_solution(c):
+
+    # Find the solution to the problem
+    problem = AcousticWaveProblem(c=c)
+    problem.discretise_domain(n=10, mode="grid", domains=None)
+    pts = problem.discretised_domains["D"]
+    solution = problem.solution(pts.requires_grad_())
+
+    # Compute the residual
+    residual = problem.conditions["D"].equation.residual(pts, solution).tensor
+
+    # Assert the residual of the PDE is close to zero
+    assert torch.allclose(residual, torch.zeros_like(residual), atol=5e-5)

tests/test_problem_zoo/test_advection.py …st_problem_zoo/test_advection_problem.pytests/test_problem_zoo/test_advection.py renamed to tests/test_problem_zoo/test_advection_problem.py tests/test_problem_zoo/test_advection.py renamed to tests/test_problem_zoo/test_advection_problem.py

@@ -1,4 +1,5 @@
 import pytest
+import torch
 from pina.problem.zoo import AdvectionProblem
 from pina.problem import SpatialProblem, TimeDependentProblem
 
@@ -17,3 +18,19 @@ def test_constructor(c):
     # Should fail if c is not a float or int
     with pytest.raises(ValueError):
         AdvectionProblem(c="invalid")
+
+
+@pytest.mark.parametrize("c", [1.5, 3])
+def test_solution(c):
+
+    # Find the solution to the problem
+    problem = AdvectionProblem(c=c)
+    problem.discretise_domain(n=10, mode="grid", domains=None)
+    pts = problem.discretised_domains["D"]
+    solution = problem.solution(pts.requires_grad_())
+
+    # Compute the residual
+    residual = problem.conditions["D"].equation.residual(pts, solution).tensor
+
+    # Assert the residual of the PDE is close to zero
+    assert torch.allclose(residual, torch.zeros_like(residual), atol=5e-5)

tests/test_problem_zoo/test_allen_cahn.py …t_problem_zoo/test_allen_cahn_problem.pytests/test_problem_zoo/test_allen_cahn.py renamed to tests/test_problem_zoo/test_allen_cahn_problem.py tests/test_problem_zoo/test_allen_cahn.py renamed to tests/test_problem_zoo/test_allen_cahn_problem.py

@@ -1,4 +1,5 @@
 import pytest
+import torch
 from pina.problem.zoo import DiffusionReactionProblem
 from pina.problem import TimeDependentProblem, SpatialProblem
 
@@ -17,3 +18,19 @@ def test_constructor(alpha):
     # Should fail if alpha is not a float or int
     with pytest.raises(ValueError):
         problem = DiffusionReactionProblem(alpha="invalid")
+
+
+@pytest.mark.parametrize("alpha", [0.1, 1])
+def test_solution(alpha):
+
+    # Find the solution to the problem
+    problem = DiffusionReactionProblem(alpha=alpha)
+    problem.discretise_domain(n=10, mode="grid", domains=None)
+    pts = problem.discretised_domains["D"]
+    solution = problem.solution(pts.requires_grad_())
+
+    # Compute the residual
+    residual = problem.conditions["D"].equation.residual(pts, solution).tensor
+
+    # Assert the residual of the PDE is close to zero
+    assert torch.allclose(residual, torch.zeros_like(residual), atol=5e-5)

…t_problem_zoo/test_diffusion_reaction.py …m_zoo/test_diffusion_reaction_problem.pytests/test_problem_zoo/test_diffusion_reaction.py renamed to tests/test_problem_zoo/test_diffusion_reaction_problem.py tests/test_problem_zoo/test_diffusion_reaction.py renamed to tests/test_problem_zoo/test_diffusion_reaction_problem.py

@@ -0,0 +1,38 @@
+import pytest
+import torch
+from pina.problem.zoo import HelmholtzProblem
+from pina.problem import SpatialProblem
+
+
+@pytest.mark.parametrize("k", [1.5, 3])
+@pytest.mark.parametrize("alpha_x", [1, 3])
+@pytest.mark.parametrize("alpha_y", [1, 3])
+def test_constructor(k, alpha_x, alpha_y):
+
+    problem = HelmholtzProblem(k=k, alpha_x=alpha_x, alpha_y=alpha_y)
+    problem.discretise_domain(n=10, mode="random", domains=None)
+    assert problem.are_all_domains_discretised
+    assert isinstance(problem, SpatialProblem)
+    assert hasattr(problem, "conditions")
+    assert isinstance(problem.conditions, dict)
+
+    with pytest.raises(ValueError):
+        HelmholtzProblem(k=1, alpha_x=1.5, alpha_y=1)
+
+
+@pytest.mark.parametrize("k", [1.5, 3])
+@pytest.mark.parametrize("alpha_x", [1, 3])
+@pytest.mark.parametrize("alpha_y", [1, 3])
+def test_solution(k, alpha_x, alpha_y):
+
+    # Find the solution to the problem
+    problem = HelmholtzProblem(k=k, alpha_x=alpha_x, alpha_y=alpha_y)
+    problem.discretise_domain(n=10, mode="grid", domains=None)
+    pts = problem.discretised_domains["D"]
+    solution = problem.solution(pts.requires_grad_())
+
+    # Compute the residual
+    residual = problem.conditions["D"].equation.residual(pts, solution).tensor
+
+    # Assert the residual of the PDE is close to zero
+    assert torch.allclose(residual, torch.zeros_like(residual), atol=5e-5)

tests/test_problem_zoo/test_helmholtz.py

@@ -0,0 +1,28 @@
+import torch
+from pina.problem.zoo import Poisson2DSquareProblem
+from pina.problem import SpatialProblem
+
+
+def test_constructor():
+
+    problem = Poisson2DSquareProblem()
+    problem.discretise_domain(n=10, mode="random", domains=None)
+    assert problem.are_all_domains_discretised
+    assert isinstance(problem, SpatialProblem)
+    assert hasattr(problem, "conditions")
+    assert isinstance(problem.conditions, dict)
+
+
+def test_solution():
+
+    # Find the solution to the problem
+    problem = Poisson2DSquareProblem()
+    problem.discretise_domain(n=10, mode="grid", domains=None)
+    pts = problem.discretised_domains["D"]
+    solution = problem.solution(pts.requires_grad_())
+
+    # Compute the residual
+    residual = problem.conditions["D"].equation.residual(pts, solution).tensor
+
+    # Assert the residual of the PDE is close to zero
+    assert torch.allclose(residual, torch.zeros_like(residual), atol=5e-5)