formatting

pina/data/data_module.py

@@ -20,18 +20,18 @@ class DummyDataloader:
     def __init__(self, dataset):
         """
         Prepare a dataloader object that returns the entire dataset in a single
-        batch. Depending on the number of GPUs, the dataset is managed 
+        batch. Depending on the number of GPUs, the dataset is managed
         as follows:
 
-        - **Distributed Environment** (multiple GPUs): Divides dataset across 
-            processes using the rank and world size. Fetches only portion of 
+        - **Distributed Environment** (multiple GPUs): Divides dataset across
+            processes using the rank and world size. Fetches only portion of
             data corresponding to the current process.
-        - **Non-Distributed Environment** (single GPU): Fetches the entire 
+        - **Non-Distributed Environment** (single GPU): Fetches the entire
             dataset.
 
         :param PinaDataset dataset: The dataset object to be processed.
 
-        .. note:: 
+        .. note::
            This dataloader is used when the batch size is ``None``.
         """
 
@@ -78,8 +78,8 @@ class Collator:
         Initialize the object, setting the collate function based on whether
         automatic batching is enabled or not.
 
-        :param dict max_conditions_lengths: ``dict`` containing the maximum 
-            number  of data points to consider in a single batch for 
+        :param dict max_conditions_lengths: ``dict`` containing the maximum
+            number  of data points to consider in a single batch for
             each condition.
         :param bool automatic_batching: Whether to enable automatic batching.
         :param PinaDataset dataset: The dataset where the data is stored.

pina/data/dataset.py

@@ -276,7 +276,7 @@ class PinaGraphDataset(PinaDataset):
         :param data: List of items to collate in a single batch.
         :type data: list[Data] | list[Graph]
         :return: Batch object.
-        :rtype: :class:`~torch_geometric.data.Batch` 
+        :rtype: :class:`~torch_geometric.data.Batch`
             | :class:`~pina.graph.LabelBatch`
         """
 

pina/label_tensor.py

@@ -389,7 +389,7 @@ class LabelTensor(torch.Tensor):
 
     def requires_grad_(self, mode=True):
         """
-        Override the :meth:`~torch.Tensor.requires_grad_` method to handle 
+        Override the :meth:`~torch.Tensor.requires_grad_` method to handle
         the labels in the new tensor.
         For more details, see :meth:`~torch.Tensor.requires_grad_`.
 

pina/model/fourier_neural_operator.py

@@ -325,14 +325,14 @@ class FNO(KernelNeuralOperator):
         ``projection_net`` maps the hidden representation to the output
         function.
 
-        :param x: The input tensor for performing the computation. Depending 
+        :param x: The input tensor for performing the computation. Depending
             on the ``dimensions`` in the initialization, it expects a tensor
             with the following shapes:
-            
+
             * 1D tensors: ``[batch, X, channels]``
             * 2D tensors: ``[batch, X, Y, channels]``
             * 3D tensors: ``[batch, X, Y, Z, channels]``
-    
+
         :type x: torch.Tensor | LabelTensor
         :return: The output tensor.
         :rtype: torch.Tensor

pina/optim/torch_optimizer.py

@@ -18,7 +18,7 @@ class TorchOptimizer(Optimizer):
         :param torch.optim.Optimizer optimizer_class: A
             :class:`torch.optim.Optimizer` class.
         :param dict kwargs: Additional parameters passed to ``optimizer_class``,
-            see more 
+            see more
             `here <https://pytorch.org/docs/stable/optim.html#algorithms>`_.
         """
         check_consistency(optimizer_class, torch.optim.Optimizer, subclass=True)

pina/optim/torch_scheduler.py

@@ -24,7 +24,7 @@ class TorchScheduler(Scheduler):
         :param torch.optim.LRScheduler scheduler_class: A
             :class:`torch.optim.LRScheduler` class.
         :param dict kwargs: Additional parameters passed to ``scheduler_class``,
-            see more 
+            see more
             `here <https://pytorch.org/docs/stable/optim.html#algorithms>_`.
         """
         check_consistency(scheduler_class, LRScheduler, subclass=True)

pina/problem/abstract_problem.py

@@ -178,14 +178,14 @@ class AbstractProblem(metaclass=ABCMeta):
             chebyshev sampling, ``chebyshev``; grid sampling ``grid``.
         :param domains: The domains from which to sample. Default is ``all``.
         :type domains: str | list[str]
-        :param dict sample_rules: A dictionary defining custom sampling rules 
-            for input variables. If provided, it must contain a dictionary 
-            specifying the sampling rule for each variable, overriding the 
-            ``n`` and ``mode`` arguments. Each key must correspond to the 
-            input variables from 
-            :meth:~pina.problem.AbstractProblem.input_variables, and its value 
-            should be another dictionary with 
-            two keys: ``n`` (number of points to sample) and ``mode`` 
+        :param dict sample_rules: A dictionary defining custom sampling rules
+            for input variables. If provided, it must contain a dictionary
+            specifying the sampling rule for each variable, overriding the
+            ``n`` and ``mode`` arguments. Each key must correspond to the
+            input variables from
+            :meth:~pina.problem.AbstractProblem.input_variables, and its value
+            should be another dictionary with
+            two keys: ``n`` (number of points to sample) and ``mode``
             (sampling method). Defaults to None.
         :raises RuntimeError: If both ``n`` and ``sample_rules`` are specified.
         :raises RuntimeError: If neither ``n`` nor ``sample_rules`` are set.
@@ -214,8 +214,8 @@ class AbstractProblem(metaclass=ABCMeta):
             implemented for :class:`~pina.domain.cartesian.CartesianDomain`.
 
         .. warning::
-            If custom discretisation is applied by setting ``sample_rules`` not 
-            to ``None``, then the discretised domain must be of class 
+            If custom discretisation is applied by setting ``sample_rules`` not
+            to ``None``, then the discretised domain must be of class
             :class:`~pina.domain.cartesian.CartesianDomain`
         """
 

pina/trainer.py

@@ -13,8 +13,8 @@ class Trainer(lightning.pytorch.Trainer):
     PINA custom Trainer class to extend the standard Lightning functionality.
 
     This class enables specific features or behaviors required by the PINA
-    framework. It modifies the standard 
-    :class:`lightning.pytorch.Trainer <lightning.pytorch.trainer.trainer.Trainer>` 
+    framework. It modifies the standard
+    :class:`lightning.pytorch.Trainer <lightning.pytorch.trainer.trainer.Trainer>`
     class to better support the training process in PINA.
     """
 
@@ -209,7 +209,7 @@ class Trainer(lightning.pytorch.Trainer):
         Manage the training process of the solver.
 
         :param dict kwargs: Additional keyword arguments. See `pytorch-lightning
-            Trainer API <https://lightning.ai/docs/pytorch/stable/common/trainer.html#trainer-class-api>`_ 
+            Trainer API <https://lightning.ai/docs/pytorch/stable/common/trainer.html#trainer-class-api>`_
             for details.
         """
         return super().fit(self.solver, datamodule=self.data_module, **kwargs)
@@ -219,7 +219,7 @@ class Trainer(lightning.pytorch.Trainer):
         Manage the test process of the solver.
 
         :param dict kwargs: Additional keyword arguments. See `pytorch-lightning
-            Trainer API <https://lightning.ai/docs/pytorch/stable/common/trainer.html#trainer-class-api>`_ 
+            Trainer API <https://lightning.ai/docs/pytorch/stable/common/trainer.html#trainer-class-api>`_
             for details.
         """
         return super().test(self.solver, datamodule=self.data_module, **kwargs)