Remove self references to general data. Maybe faster?

app.py

@@ -1101,15 +1101,15 @@ class Network(object):
 
   def __init__(self, structure, weights):
       # Instantiate variables
-      self.curves = curves
-      self.new_curves = new_curves
-      self.geometry = geometry
-      self.new_geometry = new_geometry
-      self.S = S
-      self.N = N
-      self.D = D
-      self.F = F
-      self.G = G
+      # self.curves = curves
+      # self.new_curves = new_curves
+      # self.geometry = geometry
+      # self.new_geometry = new_geometry
+      # self.S = S
+      # self.N = N
+      # self.D = D
+      # self.F = F
+      # self.G = G
 
       # Load network
       with open(structure, 'r') as file:
@@ -1120,19 +1120,19 @@ class Network(object):
       print(idx)
 
       if idx is None:
-          idx = numpy.random.randint(1, self.S * self.N)
+          idx = numpy.random.randint(1, S * N)
       else:
         idx = int(idx)
 
       # Get the input
-      data_input = self.new_geometry[idx:(idx+1), :]
-      other_data_input = data_input.reshape((self.G, self.G, self.G), order='F')
+      data_input = new_geometry[idx:(idx+1), :]
+      other_data_input = data_input.reshape((G, G, G), order='F')
 
       # Get the outputs
       print(data_input.shape)
       predicted_output = self.network.predict(data_input)
-      true_output = self.new_curves[idx].reshape((3, self.F))
-      predicted_output = predicted_output.reshape((3, self.F))
+      true_output = new_curves[idx].reshape((3, F))
+      predicted_output = predicted_output.reshape((3, F))
 
       f = numpy.linspace(0.05, 2.0, 64)
       fd = pandas.DataFrame(f).rename(columns={0: "Frequency"})
@@ -1145,9 +1145,8 @@ class Network(object):
 
   def analysis_from_geometry(self, geometry):
       # Get the outputs
-      print(numpy.array([geometry.flatten().tolist()]).shape)
       predicted_output = self.network.predict(numpy.array([geometry.flatten().tolist()]))
-      predicted_output = predicted_output.reshape((3, self.F))
+      predicted_output = predicted_output.reshape((3, F))
 
       f = numpy.linspace(0.05, 2.0, 64)
       fd = pandas.DataFrame(f).rename(columns={0: "Frequency"})
@@ -1160,18 +1159,18 @@ class Network(object):
       print(idx)
 
       if idx is None:
-          idx = numpy.random.randint(1, self.S * self.N)
+          idx = numpy.random.randint(1, S * N)
       else:
         idx = int(idx)
 
       # Get the input
-      data_input = self.new_curves[idx:(idx+1), :]
-      other_data_input = data_input.reshape((3, self.F))
+      data_input = new_curves[idx:(idx+1), :]
+      other_data_input = data_input.reshape((3, F))
 
       # Get the outputs
       predicted_output = self.network.predict(data_input)
-      true_output = self.new_geometry[idx].reshape((self.G, self.G, self.G), order='F')
-      predicted_output = predicted_output.reshape((self.G, self.G, self.G), order='F')
+      true_output = new_geometry[idx].reshape((G, G, G), order='F')
+      predicted_output = predicted_output.reshape((G, G, G), order='F')
 
       # return idx, other_data_input, true_output, predicted_output
       return predicted_output, true_output
@@ -1179,11 +1178,11 @@ class Network(object):
   
   def synthesis_from_spectrum(self, other_data_input):
       # Get the input
-      data_input = other_data_input.reshape((1, 3*self.F))
+      data_input = other_data_input.reshape((1, 3*F))
  
       # Get the outputs
       predicted_output = self.network.predict(data_input)
-      predicted_output = predicted_output.reshape((self.G, self.G, self.G), order='F')
+      predicted_output = predicted_output.reshape((G, G, G), order='F')
 
       # return idx, other_data_input, true_output, predicted_output
       return predicted_output
@@ -1191,15 +1190,15 @@ class Network(object):
   def get_geometry(self, idx=None):
 
       if idx is None:
-          idx = numpy.random.randint(1, self.S * self.N)
+          idx = numpy.random.randint(1, S * N)
       else:
         idx = int(idx)
 
       idx = int(idx)
 
       # Get the input
-      data_input = self.new_geometry[idx:(idx+1), :]
-      other_data_input = data_input.reshape((self.G, self.G, self.G), order='F')
+      data_input = new_geometry[idx:(idx+1), :]
+      other_data_input = data_input.reshape((G, G, G), order='F')
 
       # return idx, other_data_input, true_output, predicted_output
       return other_data_input
@@ -1208,15 +1207,15 @@ class Network(object):
   def get_performance(self, idx=None):
 
       if idx is None:
-          idx = numpy.random.randint(1, self.S * self.N)
+          idx = numpy.random.randint(1, S *N)
       else:
         idx = int(idx)
 
       idx = int(idx)
 
       # Get the input
-      data_input = self.new_curves[idx:(idx+1), :]
-      other_data_input = data_input.reshape((3, self.F))
+      data_input = new_curves[idx:(idx+1), :]
+      other_data_input = data_input.reshape((3,  F))
 
       f = numpy.linspace(0.05, 2.0, 64)
       fd = pandas.DataFrame(f).rename(columns={0: "Frequency"})