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Correct codacy warnings

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FilippoOlivo
2024-10-22 14:26:39 +02:00
committed by Nicola Demo
parent c9304fb9bb
commit 1bc1b3a580
15 changed files with 252 additions and 210 deletions
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69
tests/test_problem.py
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@@ -27,42 +27,42 @@ 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
@@ -79,7 +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
@@ -91,6 +91,7 @@ def test_discretise_domain():
poisson_problem.discretise_domain(n)
def test_sampling_few_variables():
n = 10
poisson_problem = Poisson()
@@ -115,9 +116,8 @@ 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'])
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,
@@ -131,6 +131,7 @@ def test_variables_correct_order_sampling():
assert poisson_problem.input_pts['D'].labels == sorted(
poisson_problem.input_variables)
def test_add_points():
poisson_problem = Poisson()
poisson_problem.discretise_domain(0,
@@ -139,8 +140,10 @@ def test_add_points():
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'))
def test_collector():