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pina/utils.py

@@ -1,4 +1,5 @@
 """Utils module"""
+
 from torch.utils.data import Dataset, DataLoader
 from functools import reduce
 import types
@@ -12,13 +13,13 @@ import torch
 
 
 def check_consistency(object, object_instance, subclass=False):
-    """Helper function to check object inheritance consistency. 
+    """Helper function to check object inheritance consistency.
        Given a specific ``'object'`` we check if the object is
        instance of a specific ``'object_instance'``, or in case
        ``'subclass=True'`` we check if the object is subclass
        if the ``'object_instance'``.
 
-    :param (iterable or class object) object: The object to check the inheritance 
+    :param (iterable or class object) object: The object to check the inheritance
     :param Object object_instance: The parent class from where the object
         is expected to inherit
     :param str object_name: The name of the object
@@ -39,9 +40,9 @@ def check_consistency(object, object_instance, subclass=False):
             raise ValueError(f"{type(obj).__name__} must be {object_instance}.")
 
 
-def number_parameters(model,
-                      aggregate=True,
-                      only_trainable=True):  # TODO: check
+def number_parameters(
+    model, aggregate=True, only_trainable=True
+):  # TODO: check
     """
     Return the number of parameters of a given `model`.
 
@@ -79,8 +80,9 @@ def merge_two_tensors(tensor1, tensor2):
     n2 = tensor2.shape[0]
 
     tensor1 = LabelTensor(tensor1.repeat(n2, 1), labels=tensor1.labels)
-    tensor2 = LabelTensor(tensor2.repeat_interleave(n1, dim=0),
-                          labels=tensor2.labels)
+    tensor2 = LabelTensor(
+        tensor2.repeat_interleave(n1, dim=0), labels=tensor2.labels
+    )
     return tensor1.append(tensor2)
 
 
@@ -95,13 +97,13 @@ def torch_lhs(n, dim):
     """
 
     if not isinstance(n, int):
-        raise TypeError('number of point n must be int')
+        raise TypeError("number of point n must be int")
 
     if not isinstance(dim, int):
-        raise TypeError('dim must be int')
+        raise TypeError("dim must be int")
 
     if dim < 1:
-        raise ValueError('dim must be greater than one')
+        raise ValueError("dim must be greater than one")
 
     samples = torch.rand(size=(n, dim))