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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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15
tests/test_equations/test_equation.py
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@@ -7,15 +7,16 @@ import pytest
def eq1(input_, output_):
u_grad = grad(output_, input_)
u1_xx = grad(u_grad, input_, components=['du1dx'], d=['x'])
u2_xy = grad(u_grad, input_, components=['du2dx'], d=['y'])
u1_xx = grad(u_grad, input_, components=["du1dx"], d=["x"])
u2_xy = grad(u_grad, input_, components=["du2dx"], d=["y"])
return torch.hstack([u1_xx, u2_xy])
def eq2(input_, output_):
force_term = (torch.sin(input_.extract(['x']) * torch.pi) *
torch.sin(input_.extract(['y']) * torch.pi))
delta_u = laplacian(output_.extract(['u1']), input_)
force_term = torch.sin(input_.extract(["x"]) * torch.pi) * torch.sin(
input_.extract(["y"]) * torch.pi
)
delta_u = laplacian(output_.extract(["u1"]), input_)
return delta_u - force_term
@@ -36,10 +37,10 @@ def test_residual():
eq_1 = Equation(eq1)
eq_2 = Equation(eq2)
pts = LabelTensor(torch.rand(10, 2), labels=['x', 'y'])
pts = LabelTensor(torch.rand(10, 2), labels=["x", "y"])
pts.requires_grad = True
u = torch.pow(pts, 2)
u.labels = ['u1', 'u2']
u.labels = ["u1", "u2"]
eq_1_res = eq_1.residual(pts, u)
eq_2_res = eq_2.residual(pts, u)

23
tests/test_equations/test_system_equation.py
View File

@@ -7,15 +7,16 @@ import pytest
def eq1(input_, output_):
u_grad = grad(output_, input_)
u1_xx = grad(u_grad, input_, components=['du1dx'], d=['x'])
u2_xy = grad(u_grad, input_, components=['du2dx'], d=['y'])
u1_xx = grad(u_grad, input_, components=["du1dx"], d=["x"])
u2_xy = grad(u_grad, input_, components=["du2dx"], d=["y"])
return torch.hstack([u1_xx, u2_xy])
def eq2(input_, output_):
force_term = (torch.sin(input_.extract(['x']) * torch.pi) *
torch.sin(input_.extract(['y']) * torch.pi))
delta_u = laplacian(output_.extract(['u1']), input_)
force_term = torch.sin(input_.extract(["x"]) * torch.pi) * torch.sin(
input_.extract(["y"]) * torch.pi
)
delta_u = laplacian(output_.extract(["u1"]), input_)
return delta_u - force_term
@@ -25,25 +26,25 @@ def foo():
def test_constructor():
SystemEquation([eq1, eq2])
SystemEquation([eq1, eq2], reduction='sum')
SystemEquation([eq1, eq2], reduction="sum")
with pytest.raises(NotImplementedError):
SystemEquation([eq1, eq2], reduction='foo')
SystemEquation([eq1, eq2], reduction="foo")
with pytest.raises(ValueError):
SystemEquation(foo)
def test_residual():
pts = LabelTensor(torch.rand(10, 2), labels=['x', 'y'])
pts = LabelTensor(torch.rand(10, 2), labels=["x", "y"])
pts.requires_grad = True
u = torch.pow(pts, 2)
u.labels = ['u1', 'u2']
u.labels = ["u1", "u2"]
eq_1 = SystemEquation([eq1, eq2], reduction='mean')
eq_1 = SystemEquation([eq1, eq2], reduction="mean")
res = eq_1.residual(pts, u)
assert res.shape == torch.Size([10])
eq_1 = SystemEquation([eq1, eq2], reduction='sum')
eq_1 = SystemEquation([eq1, eq2], reduction="sum")
res = eq_1.residual(pts, u)
assert res.shape == torch.Size([10])