Merge pull request #19 from su2code/chore_whitespaces

remove trailing whitespace

Author: bigfooted

Common/CommonMethods.py

@@ -24,7 +24,7 @@
 #=============================================================================================#
 
 import numpy as np
-import cantera as ct 
+import cantera as ct
 
 
 def ComputeLewisNumber(flame:ct.Solution):
@@ -33,11 +33,11 @@ def ComputeLewisNumber(flame:ct.Solution):
 
 def avg_Le_start_end(Le_sp:np.ndarray):
     Le_av = 0.5*(Le_sp[0] + Le_sp[-1])
-    return Le_av 
+    return Le_av
 
 def avg_Le_arythmic(Le_sp:np.ndarray):
     Le_av = np.average(Le_sp)
-    return Le_av 
+    return Le_av
 
 def avg_Le_min_max(Le_sp:np.ndarray):
     Le_av = 0.5*(np.min(Le_sp)+np.max(Le_sp))
@@ -49,7 +49,7 @@ def avg_Le_unity(Le_sp:np.ndarray):
 
 def avg_Le_const(Le_sp:np.ndarray, Le_const:float):
     Le_av = Le_const * np.ones(np.shape(Le_sp))
-    return Le_av 
+    return Le_av
 
 def avg_Le_local(Le_sp:np.ndarray):
     return Le_sp
@@ -188,4 +188,4 @@ def write_SU2_MLP(file_out:str, weights:list[np.ndarray], biases:list[np.ndarray
         fid.write("\t".join("%+.16e" % float(b) for b in B) + "\n")
 
     fid.close()
-    return 
+    return

Common/Config_base.py

@@ -23,10 +23,10 @@
 #                                                                                             |
 #=============================================================================================#
 
-import os 
+import os
 import pyfiglet
 import pickle
-import numpy as np 
+import numpy as np
 
 from Common.Properties import DefaultProperties, ActivationFunctionOptions
 from Common.CommonMethods import write_SU2_MLP
@@ -65,7 +65,7 @@ class Config:
 
     def __init__(self):
         self._output_dir = os.getcwd()
-        return 
+        return
 
     def PrintBanner(self):
         """Print the main banner for the SU2 DataMiner configuration in the terminal.
@@ -74,7 +74,7 @@ def PrintBanner(self):
         customfig = pyfiglet.Figlet(font="slant")
         print(customfig.renderText(self.__banner_header))
 
-        return 
+        return
 
     def SetOutputDir(self, output_dir:str):
         """Define the output directory where all raw and processed fluid data and manifold data are saved.
@@ -88,7 +88,7 @@ def SetOutputDir(self, output_dir:str):
 
         self._output_dir = output_dir
 
-        return 
+        return
 
     def GetOutputDir(self):
         """Get the output directory where raw and processed fluid data and manifold data are stored.
@@ -110,9 +110,9 @@ def SetConcatenationFileHeader(self, header:str=DefaultProperties.output_file_he
         :type header: str, optional
         """
 
-        self.__concatenated_file_header = header 
+        self.__concatenated_file_header = header
 
-        return 
+        return
 
     def GetConcatenationFileHeader(self):
         """Get the file name header of the processed fluid manifold data.
@@ -130,17 +130,17 @@ def SetConfigName(self, config_name:str):
         :type config_name: str
         """
 
-        self._config_name = config_name 
+        self._config_name = config_name
 
-        return 
+        return
 
     def GetConfigName(self):
         """Get the name of the current SU2 DataMiner configuration.
 
         :return: SU2 DataMiner configuration name.
         :rtype: str
         """
-        return self._config_name 
+        return self._config_name
 
     def SetControllingVariables(self, names_cv:list[str]):
         """Define the set of controlling variable names used as inputs for the networks of the data-driven fluid model.
@@ -153,7 +153,7 @@ def SetControllingVariables(self, names_cv:list[str]):
         for c in names_cv:
             self._controlling_variables.append(c)
 
-        return 
+        return
 
     def GetControllingVariables(self):
         """Retrieve the set of controlling variable names used as inputs for the networks of the data-driven fluid model.
@@ -174,9 +174,9 @@ def SetTrainFraction(self, input:float=DefaultProperties.train_fraction):
 
         if input >= 1 or input <=0:
             raise Exception("Training data fraction should be between zero and one.")
-        self.__train_fraction = input 
+        self.__train_fraction = input
 
-        return 
+        return
 
     def SetTestFraction(self, input:float=DefaultProperties.test_fraction):
         """Define the fraction of fluid data used for MLP prediction accuracy evaluation.
@@ -188,9 +188,9 @@ def SetTestFraction(self, input:float=DefaultProperties.test_fraction):
 
         if input >= 1 or input <=0:
             raise Exception("Test data fraction should be between zero and one.")
-        self.__test_fraction = input 
+        self.__test_fraction = input
 
-        return 
+        return
 
     def GetTrainFraction(self):
         """Get the fraction of fluid data used for MLP training.
@@ -229,7 +229,7 @@ def SetAlphaExpo(self, alpha_expo_in:float=DefaultProperties.init_learning_rate_
         if alpha_expo_in >= 0:
             raise Exception("Initial learning rate exponent should be negative.")
         self._alpha_expo = alpha_expo_in
-        return 
+        return
 
     def GetLRDecay(self):
         """Get the exponential learning rate decay parameter for MLP training.
@@ -250,7 +250,7 @@ def SetLRDecay(self, lr_decay_in:float=DefaultProperties.learning_rate_decay):
         if lr_decay_in <= 0 or lr_decay_in > 1.0:
             raise Exception("Learning rate decay parameter should be between zero and one.")
         self._lr_decay = lr_decay_in
-        return 
+        return
 
     def SetNEpochs(self, n_epochs_in:int=DefaultProperties.N_epochs):
         """Specify the maximum number of epochs for training of the networks.
@@ -263,7 +263,7 @@ def SetNEpochs(self, n_epochs_in:int=DefaultProperties.N_epochs):
             raise Exception("Number of epochs should be higher than 1")
 
         self._n_epochs = int(n_epochs_in)
-        return 
+        return
 
     def GetNEpochs(self):
         """Retrieve the maximum number of epochs the networks are trained for.
@@ -284,15 +284,15 @@ def SetBatchExpo(self, batch_expo_in:int=DefaultProperties.batch_size_exponent):
         if batch_expo_in <= 0:
             raise Exception("Mini-batch size exponent should be positive.")
         self._batch_expo = int(batch_expo_in)
-        return 
+        return
 
     def GetBatchExpo(self):
         """Get the MLP training mini-batch size exponent.
 
         :return: mini-batch size exponent (base 2)
         :rtype: int
         """
-        return self._batch_expo 
+        return self._batch_expo
 
     def __HiddenLayerChecks(self,hidden_layer_architecture:list[int]):
         if not hidden_layer_architecture:
@@ -302,7 +302,7 @@ def __HiddenLayerChecks(self,hidden_layer_architecture:list[int]):
                 raise Exception("Number of nodes in the hidden layers should be positive.")
             if type(h) is not int:
                 raise Exception("Nodes in the hidden layers should be integers.")
-        return 
+        return
 
     def SetHiddenLayerArchitecture(self, hidden_layer_architecture:list[int]=DefaultProperties.hidden_layer_architecture):
         """
@@ -318,7 +318,7 @@ def SetHiddenLayerArchitecture(self, hidden_layer_architecture:list[int]=Default
         self._hidden_layer_architecture = []
         for n in hidden_layer_architecture:
             self._hidden_layer_architecture.append(n)
-        return 
+        return
 
     def GetHiddenLayerArchitecture(self):
         """Get the hidden layer architecture of the multi-layer perceptron used for the MLP-based manifold.
@@ -336,7 +336,7 @@ def __WeightsCheck(self, weights:list[np.ndarray[float]]):
             w_ip = weights[i+1]
             if np.shape(w_i)[1] != np.shape(w_ip)[0]:
                 raise Exception("Weight arrays are improperly formatted. Check rows and columns.")
-        return 
+        return
 
 
     def SetWeights(self, weights:list[np.ndarray[float]]):
@@ -351,15 +351,15 @@ def SetWeights(self, weights:list[np.ndarray[float]]):
         self._MLP_weights = []
         for w in weights:
             self._MLP_weights.append(w)
-        return 
+        return
 
     def __BiasesCheck(self, biases:list[np.ndarray[float]]):
         if not biases:
             raise Exception("Biases list should contain at least one entry.")
         for b in biases:
             if b.size == 0:
                 raise Exception("Biases for hidden layers should contain at least one value.")
-        return 
+        return
 
     def SetBiases(self, biases:list[np.ndarray[float]]):
         """Store the bias values of the neural network.
@@ -373,7 +373,7 @@ def SetBiases(self, biases:list[np.ndarray[float]]):
         self._MLP_biases = []
         for w in biases:
             self._MLP_biases.append(w)
-        return 
+        return
 
     def SetActivationFunction(self, activation_function_in:str=DefaultProperties.activation_function):
         """Define the hidden layer activation function for the MLP-based manifold. See Common.Properties.ActivationFunctionOptions for the supported options.
@@ -384,8 +384,8 @@ def SetActivationFunction(self, activation_function_in:str=DefaultProperties.act
         """
         if activation_function_in not in ActivationFunctionOptions.keys():
             raise Exception("Activation function " + activation_function_in + " not in available options.")
-        self._activation_function = activation_function_in 
-        return 
+        self._activation_function = activation_function_in
+        return
 
     def GetActivationFunction(self):
         """Get the hidden layer activation function name.
@@ -418,7 +418,7 @@ def UpdateMLPHyperParams(self, trainer):
         self._MLP_biases = trainer.GetBiases().copy()
         self._hidden_layer_architecture =[h for h in trainer.architecture]
 
-        return 
+        return
 
     def GetWeightsBiases(self):
         """Return values for weights and biases for the hidden layers in the MLP.
@@ -453,5 +453,5 @@ def SaveConfig(self):
         file = open(self._config_name+'.cfg','wb')
         pickle.dump(self, file)
         file.close()
-        return 
+        return