Fix Codacy Warnings (#477)

---------

Co-authored-by: Dario Coscia <dariocos99@gmail.com>

pina/domain/simplex.py

@@ -1,6 +1,10 @@
+"""
+Module for Simplex Domain.
+"""
+
 import torch
 from .domain_interface import DomainInterface
-from pina.domain import CartesianDomain
+from .cartesian import CartesianDomain
 from ..label_tensor import LabelTensor
 from ..utils import check_consistency
 
@@ -51,23 +55,20 @@ class SimplexDomain(DomainInterface):
         # check consistency of labels
         matrix_labels = simplex_matrix[0].labels
         if not all(vertex.labels == matrix_labels for vertex in simplex_matrix):
-            raise ValueError(f"Labels don't match.")
+            raise ValueError("Labels don't match.")
 
         # check consistency dimensions
         dim_simplex = len(matrix_labels)
         if len(simplex_matrix) != dim_simplex + 1:
             raise ValueError(
-                "An n-dimensional simplex is composed by n + 1 tensors of dimension n."
+                "An n-dimensional simplex is composed by n + 1 tensors of "
+                "dimension n."
             )
 
         # creating vertices matrix
         self._vertices_matrix = LabelTensor.vstack(simplex_matrix)
 
         # creating basis vectors for simplex
-        # self._vectors_shifted = (
-        #     (self._vertices_matrix.T)[:, :-1] - (self._vertices_matrix.T)[:, None, -1]
-        # )  ### TODO: Remove after checking
-
         vert = self._vertices_matrix
         self._vectors_shifted = (vert[:-1] - vert[-1]).T
 
@@ -92,7 +93,7 @@ class SimplexDomain(DomainInterface):
         """
 
         span_dict = {}
-        for i, coord in enumerate(self.variables):
+        for coord in self.variables:
             sorted_vertices = torch.sort(vertices[coord].tensor.squeeze())
             # respective coord bounded by the lowest and highest values
             span_dict[coord] = [
@@ -133,6 +134,7 @@ class SimplexDomain(DomainInterface):
         point_shift = point_shift.tensor.reshape(-1, 1)
 
         # compute barycentric coordinates
+
         lambda_ = torch.linalg.solve(
             self._vectors_shifted * 1.0, point_shift * 1.0
         )
@@ -222,7 +224,8 @@ class SimplexDomain(DomainInterface):
         Sample n points from Simplex domain.
 
         :param int n: Number of points to sample in the shape.
-        :param str mode: Mode for sampling, defaults to ``random``. Available modes include: ``random``.
+        :param str mode: Mode for sampling, defaults to ``random``. Available
+            modes include: ``random``.
         :param variables: Variables to be sampled, defaults to ``all``.
         :type variables: str | list[str]
         :return: Returns ``LabelTensor`` of n sampled points.