Correct codacy warnings

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

@@ -26,7 +26,7 @@ class PinaDataModule(LightningDataModule):
                  eval_size=.1,
                  batch_size=None,
                  shuffle=True,
-                 datasets = None):
+                 datasets=None):
         """
         Initialize the object, creating dataset based on input problem
         :param AbstractProblem problem: PINA problem
@@ -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,
-    composed of input points and, optionally, conditional variables
+    This class extend BaseDataset class to handle
+    unsupervised dataset,composed of input points
+    and, optionally, conditional variables
     """
     data_type = 'unsupervised'
     __slots__ = ['input_points', 'conditional_variables']