Fix Codacy Warnings (#477)

---------

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

pina/equation/__init__.py

@@ -1,3 +1,7 @@
+"""
+Module for defining equations and system of equations.
+"""
+
 __all__ = [
     "SystemEquation",
     "Equation",

pina/equation/equation.py

@@ -4,10 +4,12 @@ from .equation_interface import EquationInterface
 
 
 class Equation(EquationInterface):
+    """
+    Equation class for specifing any equation in PINA.
+    """
 
     def __init__(self, equation):
         """
-        Equation class for specifing any equation in PINA.
         Each ``equation`` passed to a ``Condition`` object
         must be an ``Equation`` or ``SystemEquation``.
 

pina/equation/equation_factory.py

@@ -1,14 +1,17 @@
-"""Module"""
+"""Module for defining different equations."""
 
 from .equation import Equation
 from ..operator import grad, div, laplacian
 
 
 class FixedValue(Equation):
+    """
+    Fixed Value Equation class.
+    """
 
     def __init__(self, value, components=None):
         """
-        Fixed Value Equation class. This class can be
+        This class can be
         used to enforced a fixed value for a specific
         condition, e.g. Dirichlet Boundary conditions.
 
@@ -29,11 +32,13 @@ class FixedValue(Equation):
 
 
 class FixedGradient(Equation):
+    """
+    Fixed Gradient Equation class.
+    """
 
     def __init__(self, value, components=None, d=None):
         """
-        Fixed Gradient Equation class. This class can be
-        used to enforced a fixed gradient for a specific
+        This class can beused to enforced a fixed gradient for a specific
         condition.
 
         :param float value: Value to be mantained fixed.
@@ -55,11 +60,13 @@ class FixedGradient(Equation):
 
 
 class FixedFlux(Equation):
+    """
+    Fixed Flux Equation class.
+    """
 
     def __init__(self, value, components=None, d=None):
         """
-        Fixed Flux Equation class. This class can be
-        used to enforced a fixed flux for a specific
+        This class can be used to enforced a fixed flux for a specific
         condition.
 
         :param float value: Value to be mantained fixed.
@@ -81,10 +88,13 @@ class FixedFlux(Equation):
 
 
 class Laplace(Equation):
+    """
+    Laplace Equation class.
+    """
 
     def __init__(self, components=None, d=None):
         """
-        Laplace Equation class. This class can be
+        This class can be
         used to enforced a Laplace equation for a specific
         condition (force term set to zero).
 

pina/equation/equation_interface.py

@@ -19,10 +19,10 @@ class EquationInterface(metaclass=ABCMeta):
         Residual computation of the equation.
 
         :param LabelTensor input_: Input points to evaluate the equation.
-        :param LabelTensor output_: Output vectors given by my model (e.g., a ``FeedForward`` model).
+        :param LabelTensor output_: Output vectors given by my model (e.g.,
+        a ``FeedForward`` model).
         :param dict params_: Dictionary of unknown parameters, eventually
             related to an ``InverseProblem``.
         :return: The residual evaluation of the specified equation.
         :rtype: LabelTensor
         """
-        pass

pina/equation/system_equation.py

@@ -1,16 +1,19 @@
 """Module for SystemEquation."""
 
 import torch
+from .equation_interface import EquationInterface
 from .equation import Equation
 from ..utils import check_consistency
 
 
-class SystemEquation(Equation):
+class SystemEquation(EquationInterface):
+    """
+    System of Equation class for specifing any system
+    of equations in PINA.
+    """
 
     def __init__(self, list_equation, reduction=None):
         """
-        System of Equation class for specifing any system
-        of equations in PINA.
         Each ``equation`` passed to a ``Condition`` object
         must be an ``Equation`` or ``SystemEquation``.
         A ``SystemEquation`` is specified by a list of
@@ -37,7 +40,7 @@ class SystemEquation(Equation):
             self.reduction = torch.mean
         elif reduction == "sum":
             self.reduction = torch.sum
-        elif (reduction == None) or callable(reduction):
+        elif (reduction is None) or callable(reduction):
             self.reduction = reduction
         else:
             raise NotImplementedError(