Formatting

* Adding black as dev dependency
* Formatting pina code
* Formatting tests

pina/solver/physic_informed_solver/rba_pinn.py

@@ -1,4 +1,4 @@
-""" Module for Residual-Based Attention PINN. """
+"""Module for Residual-Based Attention PINN."""
 
 from copy import deepcopy
 import torch
@@ -66,15 +66,17 @@ class RBAPINN(PINN):
         j.cma.2024.116805 <https://doi.org/10.1016/j.cma.2024.116805>`_.
     """
 
-    def __init__(self,
-                 problem,
-                 model,
-                 optimizer=None,
-                 scheduler=None,
-                 weighting=None,
-                 loss=None,
-                 eta=0.001,
-                 gamma=0.999):
+    def __init__(
+        self,
+        problem,
+        model,
+        optimizer=None,
+        scheduler=None,
+        weighting=None,
+        loss=None,
+        eta=0.001,
+        gamma=0.999,
+    ):
         """
         :param torch.nn.Module model: The neural network model to use.
         :param AbstractProblem problem: The formulation of the problem.
@@ -86,17 +88,19 @@ class RBAPINN(PINN):
             default `None`.
         :param torch.nn.Module loss: The loss function to be minimized;
             default `None`.
-        :param float | int eta: The learning rate for the weights of the 
+        :param float | int eta: The learning rate for the weights of the
             residual; default 0.001.
         :param float gamma: The decay parameter in the update of the weights
             of the residual. Must be between 0 and 1; default 0.999.
         """
-        super().__init__(model=model,
-                         problem=problem,
-                         optimizer=optimizer,
-                         scheduler=scheduler,
-                         weighting=weighting,
-                         loss=loss)
+        super().__init__(
+            model=model,
+            problem=problem,
+            optimizer=optimizer,
+            scheduler=scheduler,
+            weighting=weighting,
+            loss=loss,
+        )
 
         # check consistency
         check_consistency(eta, (float, int))
@@ -119,9 +123,11 @@ class RBAPINN(PINN):
     # for now RBAPINN is implemented only for batch_size = None
     def on_train_start(self):
         if self.trainer.batch_size is not None:
-            raise NotImplementedError("RBAPINN only works with full batch "
-                                      "size, set batch_size=None inside the "
-                                      "Trainer to use the solver.")
+            raise NotImplementedError(
+                "RBAPINN only works with full batch "
+                "size, set batch_size=None inside the "
+                "Trainer to use the solver."
+            )
         return super().on_train_start()
 
     def _vect_to_scalar(self, loss_value):
@@ -160,10 +166,11 @@ class RBAPINN(PINN):
         cond = self.current_condition_name
 
         r_norm = (
-            self.eta * torch.abs(residual)
+            self.eta
+            * torch.abs(residual)
             / (torch.max(torch.abs(residual)) + 1e-12)
         )
-        self.weights[cond] = (self.gamma*self.weights[cond] + r_norm).detach()
+        self.weights[cond] = (self.gamma * self.weights[cond] + r_norm).detach()
 
         loss_value = self._vectorial_loss(
             torch.zeros_like(residual, requires_grad=True), residual