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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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11
tests/test_callback/test_adaptive_refinement_callback.py
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@@ -7,12 +7,13 @@ from pina.callback import R3Refinement
# make the problem
poisson_problem = Poisson()
boundaries = ['g1', 'g2', 'g3', 'g4']
boundaries = ["g1", "g2", "g3", "g4"]
n = 10
poisson_problem.discretise_domain(n, 'grid', domains=boundaries)
poisson_problem.discretise_domain(n, 'grid', domains='D')
model = FeedForward(len(poisson_problem.input_variables),
len(poisson_problem.output_variables))
poisson_problem.discretise_domain(n, "grid", domains=boundaries)
poisson_problem.discretise_domain(n, "grid", domains="D")
model = FeedForward(
len(poisson_problem.input_variables), len(poisson_problem.output_variables)
)
# make the solver
solver = PINN(problem=poisson_problem, model=model)

14
tests/test_callback/test_metric_tracker.py
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@@ -7,12 +7,13 @@ from pina.problem.zoo import Poisson2DSquareProblem as Poisson
# make the problem
poisson_problem = Poisson()
boundaries = ['g1', 'g2', 'g3', 'g4']
boundaries = ["g1", "g2", "g3", "g4"]
n = 10
poisson_problem.discretise_domain(n, 'grid', domains=boundaries)
poisson_problem.discretise_domain(n, 'grid', domains='D')
model = FeedForward(len(poisson_problem.input_variables),
len(poisson_problem.output_variables))
poisson_problem.discretise_domain(n, "grid", domains=boundaries)
poisson_problem.discretise_domain(n, "grid", domains="D")
model = FeedForward(
len(poisson_problem.input_variables), len(poisson_problem.output_variables)
)
# make the solver
solver = PINN(problem=poisson_problem, model=model)
@@ -21,6 +22,7 @@ solver = PINN(problem=poisson_problem, model=model)
def test_metric_tracker_constructor():
MetricTracker()
# def test_metric_tracker_routine(): #TODO revert
# # make the trainer
# trainer = Trainer(solver=solver,
@@ -35,5 +37,3 @@ def test_metric_tracker_constructor():
# # assert the logged metrics are correct
# logged_metrics = sorted(list(metrics.keys()))
# assert logged_metrics == ['train_loss_epoch', 'train_loss_step', 'val_loss']

14
tests/test_callback/test_optimizer_callback.py
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@@ -10,18 +10,20 @@ from pina.optim import TorchOptimizer
# make the problem
poisson_problem = Poisson()
boundaries = ['g1', 'g2', 'g3', 'g4']
boundaries = ["g1", "g2", "g3", "g4"]
n = 10
poisson_problem.discretise_domain(n, 'grid', domains=boundaries)
poisson_problem.discretise_domain(n, 'grid', domains='D')
model = FeedForward(len(poisson_problem.input_variables),
len(poisson_problem.output_variables))
poisson_problem.discretise_domain(n, "grid", domains=boundaries)
poisson_problem.discretise_domain(n, "grid", domains="D")
model = FeedForward(
len(poisson_problem.input_variables), len(poisson_problem.output_variables)
)
# make the solver
solver = PINN(problem=poisson_problem, model=model)
adam_optimizer = TorchOptimizer(torch.optim.Adam, lr=0.01)
lbfgs_optimizer = TorchOptimizer(torch.optim.LBFGS, lr= 0.001)
lbfgs_optimizer = TorchOptimizer(torch.optim.LBFGS, lr=0.001)
def test_switch_optimizer_constructor():
SwitchOptimizer(adam_optimizer, epoch_switch=10)

2
tests/test_callback/test_progress_bar.py
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@@ -30,4 +30,4 @@ from pina.problem.zoo import Poisson2DSquareProblem as Poisson
# accelerator='cpu',
# max_epochs=5)
# trainer.train()
# # TODO there should be a check that the correct metrics are displayed
# # TODO there should be a check that the correct metrics are displayed