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Fix Codacy Warnings (#477)

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Co-authored-by: Dario Coscia <dariocos99@gmail.com>
This commit is contained in:
Filippo Olivo
2025-03-10 15:38:45 +01:00
committed by Nicola Demo
parent e3790e049a
commit 4177bfbb50
157 changed files with 3473 additions and 3839 deletions
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94
tests/test_problem.py
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@@ -9,24 +9,24 @@ from pina.domain import CartesianDomain
def test_discretise_domain():
n = 10
poisson_problem = Poisson()
boundaries = ['g1', 'g2', 'g3', 'g4']
poisson_problem.discretise_domain(n, 'grid', domains=boundaries)
boundaries = ["g1", "g2", "g3", "g4"]
poisson_problem.discretise_domain(n, "grid", domains=boundaries)
for b in boundaries:
assert poisson_problem.discretised_domains[b].shape[0] == n
poisson_problem.discretise_domain(n, 'random', domains=boundaries)
poisson_problem.discretise_domain(n, "random", domains=boundaries)
for b in boundaries:
assert poisson_problem.discretised_domains[b].shape[0] == n
poisson_problem.discretise_domain(n, 'grid', domains=['D'])
assert poisson_problem.discretised_domains['D'].shape[0] == n ** 2
poisson_problem.discretise_domain(n, 'random', domains=['D'])
assert poisson_problem.discretised_domains['D'].shape[0] == n
poisson_problem.discretise_domain(n, "grid", domains=["D"])
assert poisson_problem.discretised_domains["D"].shape[0] == n**2
poisson_problem.discretise_domain(n, "random", domains=["D"])
assert poisson_problem.discretised_domains["D"].shape[0] == n
poisson_problem.discretise_domain(n, 'latin', domains=['D'])
assert poisson_problem.discretised_domains['D'].shape[0] == n
poisson_problem.discretise_domain(n, "latin", domains=["D"])
assert poisson_problem.discretised_domains["D"].shape[0] == n
poisson_problem.discretise_domain(n, 'lh', domains=['D'])
assert poisson_problem.discretised_domains['D'].shape[0] == n
poisson_problem.discretise_domain(n, "lh", domains=["D"])
assert poisson_problem.discretised_domains["D"].shape[0] == n
poisson_problem.discretise_domain(n)
@@ -34,61 +34,53 @@ def test_discretise_domain():
def test_variables_correct_order_sampling():
n = 10
poisson_problem = Poisson()
poisson_problem.discretise_domain(n,
'grid',
domains=['D'])
assert poisson_problem.discretised_domains['D'].labels == sorted(
poisson_problem.input_variables)
poisson_problem.discretise_domain(n, "grid", domains=["D"])
assert poisson_problem.discretised_domains["D"].labels == sorted(
poisson_problem.input_variables
)
poisson_problem.discretise_domain(n, 'grid', domains=['D'])
assert poisson_problem.discretised_domains['D'].labels == sorted(
poisson_problem.input_variables)
poisson_problem.discretise_domain(n, "grid", domains=["D"])
assert poisson_problem.discretised_domains["D"].labels == sorted(
poisson_problem.input_variables
)
def test_add_points():
poisson_problem = Poisson()
poisson_problem.discretise_domain(0,
'random',
domains=['D'])
new_pts = LabelTensor(torch.tensor([[0.5, -0.5]]), labels=['x', 'y'])
poisson_problem.add_points({'D': new_pts})
assert torch.isclose(poisson_problem.discretised_domains['D'].extract('x'),
new_pts.extract('x'))
assert torch.isclose(poisson_problem.discretised_domains['D'].extract('y'),
new_pts.extract('y'))
poisson_problem.discretise_domain(0, "random", domains=["D"])
new_pts = LabelTensor(torch.tensor([[0.5, -0.5]]), labels=["x", "y"])
poisson_problem.add_points({"D": new_pts})
assert torch.isclose(
poisson_problem.discretised_domains["D"].extract("x"),
new_pts.extract("x"),
)
assert torch.isclose(
poisson_problem.discretised_domains["D"].extract("y"),
new_pts.extract("y"),
)
@pytest.mark.parametrize(
"mode",
[
'random',
'grid'
]
)
@pytest.mark.parametrize("mode", ["random", "grid"])
def test_custom_sampling_logic(mode):
poisson_problem = Poisson()
sampling_rules = {
'x': {'n': 100, 'mode': mode},
'y': {'n': 50, 'mode': mode}
"x": {"n": 100, "mode": mode},
"y": {"n": 50, "mode": mode},
}
poisson_problem.discretise_domain(sample_rules=sampling_rules)
for domain in ['g1', 'g2', 'g3', 'g4']:
for domain in ["g1", "g2", "g3", "g4"]:
assert poisson_problem.discretised_domains[domain].shape[0] == 100 * 50
assert poisson_problem.discretised_domains[domain].labels == ['x', 'y']
assert poisson_problem.discretised_domains[domain].labels == ["x", "y"]
@pytest.mark.parametrize(
"mode",
[
'random',
'grid'
]
)
@pytest.mark.parametrize("mode", ["random", "grid"])
def test_wrong_custom_sampling_logic(mode):
d2 = CartesianDomain({'x': [1,2], 'y': [0,1] })
d2 = CartesianDomain({"x": [1, 2], "y": [0, 1]})
poisson_problem = Poisson()
poisson_problem.domains['D'] = Union([poisson_problem.domains['D'], d2])
poisson_problem.domains["D"] = Union([poisson_problem.domains["D"], d2])
sampling_rules = {
'x': {'n': 100, 'mode': mode},
'y': {'n': 50, 'mode': mode}
"x": {"n": 100, "mode": mode},
"y": {"n": 50, "mode": mode},
}
with pytest.raises(RuntimeError):
poisson_problem.discretise_domain(sample_rules=sampling_rules)
poisson_problem.discretise_domain(sample_rules=sampling_rules)