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Update of LabelTensor class and fix Simplex domain (#362)

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*Implement new methods in LabelTensor and fix operators
This commit is contained in:
Filippo Olivo
2024-10-10 18:26:52 +02:00
committed by Nicola Demo
parent fdb8f65143
commit 7528f6ef74
19 changed files with 551 additions and 217 deletions
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87
tests/test_problem.py
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@@ -27,50 +27,46 @@ class Poisson(SpatialProblem):
conditions = {
'gamma1':
Condition(domain=CartesianDomain({
'x': [0, 1],
'y': 1
}),
equation=FixedValue(0.0)),
Condition(domain=CartesianDomain({
'x': [0, 1],
'y': 1
}),
equation=FixedValue(0.0)),
'gamma2':
Condition(domain=CartesianDomain({
'x': [0, 1],
'y': 0
}),
equation=FixedValue(0.0)),
Condition(domain=CartesianDomain({
'x': [0, 1],
'y': 0
}),
equation=FixedValue(0.0)),
'gamma3':
Condition(domain=CartesianDomain({
'x': 1,
'y': [0, 1]
}),
equation=FixedValue(0.0)),
Condition(domain=CartesianDomain({
'x': 1,
'y': [0, 1]
}),
equation=FixedValue(0.0)),
'gamma4':
Condition(domain=CartesianDomain({
'x': 0,
'y': [0, 1]
}),
equation=FixedValue(0.0)),
Condition(domain=CartesianDomain({
'x': 0,
'y': [0, 1]
}),
equation=FixedValue(0.0)),
'D':
Condition(domain=CartesianDomain({
'x': [0, 1],
'y': [0, 1]
}),
equation=my_laplace),
Condition(domain=CartesianDomain({
'x': [0, 1],
'y': [0, 1]
}),
equation=my_laplace),
'data':
Condition(input_points=in_, output_points=out_)
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)
torch.sin(pts.extract(['y']) * torch.pi)) / (2 * torch.pi ** 2)
truth_solution = poisson_sol
# make the problem
poisson_problem = Poisson()
print(poisson_problem.input_pts)
def test_discretise_domain():
n = 10
poisson_problem = Poisson()
@@ -83,7 +79,7 @@ def test_discretise_domain():
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
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
@@ -94,14 +90,15 @@ def test_discretise_domain():
assert poisson_problem.input_pts['D'].shape[0] == n
# def test_sampling_few_variables():
# n = 10
# 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_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():
@@ -117,13 +114,11 @@ def test_variables_correct_order_sampling():
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'],
@@ -140,8 +135,8 @@ def test_add_points():
poisson_problem.discretise_domain(0,
'random',
locations=['D'],
variables=['x','y'])
new_pts = LabelTensor(torch.tensor([[0.5,-0.5]]),labels=['x','y'])
variables=['x', 'y'])
new_pts = LabelTensor(torch.tensor([[0.5, -0.5]]), labels=['x', 'y'])
poisson_problem.add_points({'D': new_pts})
assert torch.isclose(poisson_problem.input_pts['D'].extract('x'),new_pts.extract('x'))
assert torch.isclose(poisson_problem.input_pts['D'].extract('y'),new_pts.extract('y'))
assert torch.isclose(poisson_problem.input_pts['D'].extract('x'), new_pts.extract('x'))
assert torch.isclose(poisson_problem.input_pts['D'].extract('y'), new_pts.extract('y'))