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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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23
pina/operator.py
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@@ -1,9 +1,11 @@
"""
Module for operator vectorize implementation. Differential operator are used to write any differential problem.
These operator are implemented to work on different accellerators: CPU, GPU, TPU or MPS.
All operator take as input a tensor onto which computing the operator, a tensor with respect
to which computing the operator, the name of the output variables to calculate the operator
for (in case of multidimensional functions), and the variables name on which the operator is calculated.
Module for operator vectorize implementation. Differential operator are used to
write any differential problem. These operator are implemented to work on
different accellerators: CPU, GPU, TPU or MPS. All operator take as input a
tensor onto which computing the operator, a tensor with respect to which
computing the operator, the name of the output variables to calculate the
operator for (in case of multidimensional functions), and the variables name
on which the operator is calculated.
"""
import torch
@@ -50,7 +52,7 @@ def grad(output_, input_, components=None, d=None):
if len(output_.labels) != 1:
raise RuntimeError("only scalar function can be differentiated")
if not all([di in input_.labels for di in d]):
if not all(di in input_.labels for di in d):
raise RuntimeError("derivative labels missing from input tensor")
output_fieldname = output_.labels[0]
@@ -139,8 +141,8 @@ def div(output_, input_, components=None, d=None):
grad_output = grad(output_, input_, components, d)
labels = [None] * len(components)
tensors_to_sum = []
for i, (c, d) in enumerate(zip(components, d)):
c_fields = f"d{c}d{d}"
for i, (c, d_) in enumerate(zip(components, d)):
c_fields = f"d{c}d{d_}"
tensors_to_sum.append(grad_output.extract(c_fields))
labels[i] = c_fields
div_result = LabelTensor.summation(tensors_to_sum)
@@ -205,11 +207,8 @@ def laplacian(output_, input_, components=None, d=None, method="std"):
if method == "divgrad":
raise NotImplementedError("divgrad not implemented as method")
# TODO fix
# grad_output = grad(output_, input_, components, d)
# result = div(grad_output, input_, d=d)
elif method == "std":
if method == "std":
if len(components) == 1:
result = scalar_laplace(output_, input_, components, d)
labels = [f"dd{components[0]}"]