🎨 Format Python code with psf/black

pina/adaptive_functions/__init__.py

@@ -1,21 +1,31 @@
 __all__ = [
-    'AdaptiveActivationFunctionInterface',
-    'AdaptiveReLU',
-    'AdaptiveSigmoid',
-    'AdaptiveTanh',
-    'AdaptiveSiLU',
-    'AdaptiveMish',
-    'AdaptiveELU',
-    'AdaptiveCELU',
-    'AdaptiveGELU',
-    'AdaptiveSoftmin',
-    'AdaptiveSoftmax',
-    'AdaptiveSIREN',
-    'AdaptiveExp']
+    "AdaptiveActivationFunctionInterface",
+    "AdaptiveReLU",
+    "AdaptiveSigmoid",
+    "AdaptiveTanh",
+    "AdaptiveSiLU",
+    "AdaptiveMish",
+    "AdaptiveELU",
+    "AdaptiveCELU",
+    "AdaptiveGELU",
+    "AdaptiveSoftmin",
+    "AdaptiveSoftmax",
+    "AdaptiveSIREN",
+    "AdaptiveExp",
+]
 
-from .adaptive_func import (AdaptiveReLU, AdaptiveSigmoid, AdaptiveTanh,
-                            AdaptiveSiLU, AdaptiveMish, AdaptiveELU,
-                            AdaptiveCELU, AdaptiveGELU, AdaptiveSoftmin,
-                            AdaptiveSoftmax, AdaptiveSIREN, AdaptiveExp)
+from .adaptive_func import (
+    AdaptiveReLU,
+    AdaptiveSigmoid,
+    AdaptiveTanh,
+    AdaptiveSiLU,
+    AdaptiveMish,
+    AdaptiveELU,
+    AdaptiveCELU,
+    AdaptiveGELU,
+    AdaptiveSoftmin,
+    AdaptiveSoftmax,
+    AdaptiveSIREN,
+    AdaptiveExp,
+)
 from .adaptive_func_interface import AdaptiveActivationFunctionInterface
-

pina/adaptive_functions/adaptive_func.py

@@ -40,6 +40,7 @@ class AdaptiveReLU(AdaptiveActivationFunctionInterface):
         DOI: `JCP 10.1016
         <https://doi.org/10.1016/j.jcp.2019.109136>`_.
     """
+
     def __init__(self, alpha=None, beta=None, gamma=None, fixed=None):
         super().__init__(alpha, beta, gamma, fixed)
         self._func = torch.nn.ReLU()
@@ -80,6 +81,7 @@ class AdaptiveSigmoid(AdaptiveActivationFunctionInterface):
         DOI: `JCP 10.1016
         <https://doi.org/10.1016/j.jcp.2019.109136>`_.
     """
+
     def __init__(self, alpha=None, beta=None, gamma=None, fixed=None):
         super().__init__(alpha, beta, gamma, fixed)
         self._func = torch.nn.Sigmoid()
@@ -120,6 +122,7 @@ class AdaptiveTanh(AdaptiveActivationFunctionInterface):
         DOI: `JCP 10.1016
         <https://doi.org/10.1016/j.jcp.2019.109136>`_.
     """
+
     def __init__(self, alpha=None, beta=None, gamma=None, fixed=None):
         super().__init__(alpha, beta, gamma, fixed)
         self._func = torch.nn.Tanh()
@@ -161,6 +164,7 @@ class AdaptiveSiLU(AdaptiveActivationFunctionInterface):
         DOI: `JCP 10.1016
         <https://doi.org/10.1016/j.jcp.2019.109136>`_.
     """
+
     def __init__(self, alpha=None, beta=None, gamma=None, fixed=None):
         super().__init__(alpha, beta, gamma, fixed)
         self._func = torch.nn.SiLU()
@@ -201,6 +205,7 @@ class AdaptiveMish(AdaptiveActivationFunctionInterface):
         DOI: `JCP 10.1016
         <https://doi.org/10.1016/j.jcp.2019.109136>`_.
     """
+
     def __init__(self, alpha=None, beta=None, gamma=None, fixed=None):
         super().__init__(alpha, beta, gamma, fixed)
         self._func = torch.nn.Mish()
@@ -244,6 +249,7 @@ class AdaptiveELU(AdaptiveActivationFunctionInterface):
         DOI: `JCP 10.1016
         <https://doi.org/10.1016/j.jcp.2019.109136>`_.
     """
+
     def __init__(self, alpha=None, beta=None, gamma=None, fixed=None):
         super().__init__(alpha, beta, gamma, fixed)
         self._func = torch.nn.ELU()
@@ -284,10 +290,12 @@ class AdaptiveCELU(AdaptiveActivationFunctionInterface):
         DOI: `JCP 10.1016
         <https://doi.org/10.1016/j.jcp.2019.109136>`_.
     """
+
     def __init__(self, alpha=None, beta=None, gamma=None, fixed=None):
         super().__init__(alpha, beta, gamma, fixed)
         self._func = torch.nn.CELU()
 
+
 class AdaptiveGELU(AdaptiveActivationFunctionInterface):
     r"""
     Adaptive trainable :class:`~torch.nn.GELU` activation function.
@@ -324,6 +332,7 @@ class AdaptiveGELU(AdaptiveActivationFunctionInterface):
         DOI: `JCP 10.1016
         <https://doi.org/10.1016/j.jcp.2019.109136>`_.
     """
+
     def __init__(self, alpha=None, beta=None, gamma=None, fixed=None):
         super().__init__(alpha, beta, gamma, fixed)
         self._func = torch.nn.GELU()
@@ -364,6 +373,7 @@ class AdaptiveSoftmin(AdaptiveActivationFunctionInterface):
         DOI: `JCP 10.1016
         <https://doi.org/10.1016/j.jcp.2019.109136>`_.
     """
+
     def __init__(self, alpha=None, beta=None, gamma=None, fixed=None):
         super().__init__(alpha, beta, gamma, fixed)
         self._func = torch.nn.Softmin()
@@ -404,10 +414,12 @@ class AdaptiveSoftmax(AdaptiveActivationFunctionInterface):
         DOI: `JCP 10.1016
         <https://doi.org/10.1016/j.jcp.2019.109136>`_.
     """
+
     def __init__(self, alpha=None, beta=None, gamma=None, fixed=None):
         super().__init__(alpha, beta, gamma, fixed)
         self._func = torch.nn.Softmax()
 
+
 class AdaptiveSIREN(AdaptiveActivationFunctionInterface):
     r"""
     Adaptive trainable :obj:`~torch.sin` function.
@@ -439,10 +451,12 @@ class AdaptiveSIREN(AdaptiveActivationFunctionInterface):
         DOI: `JCP 10.1016
         <https://doi.org/10.1016/j.jcp.2019.109136>`_.
     """
+
     def __init__(self, alpha=None, beta=None, gamma=None, fixed=None):
         super().__init__(alpha, beta, gamma, fixed)
         self._func = torch.sin
 
+
 class AdaptiveExp(AdaptiveActivationFunctionInterface):
     r"""
     Adaptive trainable :obj:`~torch.exp` function.
@@ -474,15 +488,16 @@ class AdaptiveExp(AdaptiveActivationFunctionInterface):
         DOI: `JCP 10.1016
         <https://doi.org/10.1016/j.jcp.2019.109136>`_.
     """
+
     def __init__(self, alpha=None, beta=None, fixed=None):
 
         # only alpha, and beta parameters (gamma=0 fixed)
         if fixed is None:
-            fixed = ['gamma']
+            fixed = ["gamma"]
         else:
             check_consistency(fixed, str)
-            fixed = list(fixed) + ['gamma']
+            fixed = list(fixed) + ["gamma"]
 
         # calling super
-        super().__init__(alpha, beta, 0., fixed)
+        super().__init__(alpha, beta, 0.0, fixed)
         self._func = torch.exp