Correct codacy warnings

pina/collector.py

@@ -48,7 +48,8 @@ class Collector:
         for condition_name, condition in self.problem.conditions.items():
             # if the condition is not ready and domain is not attribute
             # of condition, we get and store the data
-            if (not self._is_conditions_ready[condition_name]) and (not hasattr(condition, "domain")):
+            if (not self._is_conditions_ready[condition_name]) and (
+            not hasattr(condition, "domain")):
                 # get data
                 keys = condition.__slots__
                 values = [getattr(condition, name) for name in keys]
@@ -69,7 +70,8 @@ class Collector:
                     already_sampled = []
                 # if we have sampled the condition but not all variables
                 else:
-                    already_sampled = [self.data_collections[loc]['input_points']]
+                    already_sampled = [
+                        self.data_collections[loc]['input_points']]
             # if the condition is ready but we want to sample again
             else:
                 self._is_conditions_ready[loc] = False
@@ -77,11 +79,13 @@ class Collector:
 
             # get the samples
             samples = [
-                          condition.domain.sample(n=n, mode=mode, variables=variables)
+                          condition.domain.sample(n=n, mode=mode,
+                                                  variables=variables)
                       ] + already_sampled
             pts = merge_tensors(samples)
             if (
-                    set(pts.labels).issubset(sorted(self.problem.input_variables))
+                    set(pts.labels).issubset(
+                        sorted(self.problem.input_variables))
             ):
                 pts = pts.sort_labels()
                 if sorted(pts.labels) == sorted(self.problem.input_variables):
@@ -89,7 +93,8 @@ class Collector:
                 values = [pts, condition.equation]
                 self.data_collections[loc] = dict(zip(keys, values))
             else:
-                raise RuntimeError('Try to sample variables which are not in problem defined in the problem')
+                raise RuntimeError(
+                    'Try to sample variables which are not in problem defined in the problem')
 
     def add_points(self, new_points_dict):
         """
@@ -100,5 +105,7 @@ class Collector:
         """
         for k, v in new_points_dict.items():
             if not self._is_conditions_ready[k]:
-                raise RuntimeError('Cannot add points on a non sampled condition')
+                raise RuntimeError(
-            self.data_collections[k]['input_points'] = self.data_collections[k]['input_points'].vstack(v)
+                    'Cannot add points on a non sampled condition')
+            self.data_collections[k]['input_points'] = self.data_collections[k][
+                'input_points'].vstack(v)

pina/condition/condition.py

@@ -5,6 +5,7 @@ from .input_equation_condition import InputPointsEquationCondition
 from .input_output_condition import InputOutputPointsCondition
 from .data_condition import DataConditionInterface
 
+
 class Condition:
     """
     The class ``Condition`` is used to represent the constraints (physical

pina/data/base_dataset.py

@@ -27,7 +27,7 @@ class BaseDataset(Dataset):
         if not hasattr(cls, '__slots__'):
             raise TypeError(
                 'Something is wrong, __slots__ must be defined in subclasses.')
-        return super(BaseDataset, cls).__new__(cls)
+        return object.__new__(cls)
 
     def __init__(self, problem, device):
         """"

pina/data/data_module.py

@@ -38,9 +38,11 @@ class PinaDataModule(LightningDataModule):
         :param datasets: list of datasets objects
         """
         super().__init__()
-        dataset_classes = [SupervisedDataset, UnsupervisedDataset, SamplePointDataset]
+        dataset_classes = [SupervisedDataset, UnsupervisedDataset,
+                           SamplePointDataset]
         if datasets is None:
-            self.datasets = [DatasetClass(problem, device) for DatasetClass in dataset_classes]
+            self.datasets = [DatasetClass(problem, device) for DatasetClass in
+                             dataset_classes]
         else:
             self.datasets = datasets
 
@@ -100,8 +102,6 @@ class PinaDataModule(LightningDataModule):
             for key, value in dataset.condition_names.items()
         }
 
-
-
     def train_dataloader(self):
         """
         Return the training dataloader for the dataset
@@ -158,11 +158,13 @@ class PinaDataModule(LightningDataModule):
             if seed is not None:
                 generator = torch.Generator()
                 generator.manual_seed(seed)
-                indices = torch.randperm(sum(lengths), generator=generator).tolist()
+                indices = torch.randperm(sum(lengths),
+                                         generator=generator).tolist()
             else:
                 indices = torch.arange(sum(lengths)).tolist()
         else:
-            indices = torch.arange(0, sum(lengths), 1, dtype=torch.uint8).tolist()
+            indices = torch.arange(0, sum(lengths), 1,
+                                   dtype=torch.uint8).tolist()
         offsets = [
             sum(lengths[:i]) if i > 0 else 0 for i in range(len(lengths))
         ]

pina/data/pina_batch.py

@@ -5,6 +5,9 @@ from .pina_subset import PinaSubset
 
 
 class Batch:
+    """
+    Implementation of the Batch class used during training to perform SGD optimization.
+    """
 
     def __init__(self, dataset_dict, idx_dict):
 

pina/data/pina_dataloader.py

@@ -33,7 +33,7 @@ class PinaDataLoader:
         Create batches according to the batch_size provided in input.
         """
         self.batches = []
-        n_elements = sum([len(v) for v in self.dataset_dict.values()])
+        n_elements = sum(len(v) for v in self.dataset_dict.values())
         if batch_size is None:
             batch_size = n_elements
         indexes_dict = {}

pina/data/pina_subset.py

@@ -1,3 +1,8 @@
+"""
+Module for PinaSubset class
+"""
+
+
 class PinaSubset:
     """
     TODO

pina/data/unsupervised_dataset.py

@@ -6,8 +6,9 @@ from .base_dataset import BaseDataset
 
 class UnsupervisedDataset(BaseDataset):
     """
-    This class extend BaseDataset class to handle unsupervised dataset,
+    This class extend BaseDataset class to handle
-    composed of input points and, optionally, conditional variables
+    unsupervised dataset,composed of input points
+    and, optionally, conditional variables
     """
     data_type = 'unsupervised'
     __slots__ = ['input_points', 'conditional_variables']

pina/optim/torch_optimizer.py

@@ -13,6 +13,7 @@ class TorchOptimizer(Optimizer):
 
         self.optimizer_class = optimizer_class
         self.kwargs = kwargs
+        self.optimizer_instance = None
 
     def hook(self, parameters):
         self.optimizer_instance = self.optimizer_class(parameters,

pina/trainer.py

@@ -9,7 +9,8 @@ from .solvers.solver import SolverInterface
 
 class Trainer(pytorch_lightning.Trainer):
 
-    def __init__(self, solver, batch_size=None, train_size=.7, test_size=.2, eval_size=.1, **kwargs):
+    def __init__(self, solver, batch_size=None, train_size=.7, test_size=.2,
+                 eval_size=.1, **kwargs):
         """
         PINA Trainer class for costumizing every aspect of training via flags.
 
@@ -39,7 +40,6 @@ class Trainer(pytorch_lightning.Trainer):
         self._create_loader()
         self._move_to_device()
 
-
     def _move_to_device(self):
         device = self._accelerator_connector._parallel_devices[0]
 
@@ -59,8 +59,10 @@ class Trainer(pytorch_lightning.Trainer):
         """
         if not self.solver.problem.collector.full:
             error_message = '\n'.join(
-                [f'{" " * 13} ---> Condition {key} {"sampled" if value else "not sampled"}' 
+                [
-                for key, value in self.solver.problem.collector._is_conditions_ready.items()])
+                    f'{" " * 13} ---> Condition {key} {"sampled" if value else "not sampled"}'
+                    for key, value in
+                    self.solver.problem.collector._is_conditions_ready.items()])
             raise RuntimeError('Cannot create Trainer if not all conditions '
                                'are sampled. The Trainer got the following:\n'
                                f'{error_message}')
@@ -72,7 +74,8 @@ class Trainer(pytorch_lightning.Trainer):
         device = devices[0]
 
         data_module = PinaDataModule(problem=self.solver.problem, device=device,
-                                     train_size=self.train_size, test_size=self.test_size,
+                                     train_size=self.train_size,
+                                     test_size=self.test_size,
                                      eval_size=self.eval_size)
         data_module.setup()
         self._loader = data_module.train_dataloader()