modified update function to include variables that were previously not included but that should also be updated at every distance change.

metaprop_fibermode_doublet_epigraph_ego.py

@@ -25,6 +25,7 @@ from smt.design_space import (
     DesignSpace,
 ) 
 
+
 ##############################
 #physics and simulation domain
 wavelength = 1.55e-6
@@ -80,6 +81,15 @@ fft_operator = pyfftw.FFTW(fft_input_array, fft_output_array, axes=(0,1), direct
 ifft_operator = pyfftw.FFTW(ifft_input_array, ifft_output_array, axes=(0,1), direction='FFTW_BACKWARD', threads=n_cpu, flags=('FFTW_MEASURE','FFTW_DESTROY_INPUT',))
 ##############################
 
+##############################
+#Plot zoom parameters [um]
+zoom_x = 60
+zoom_y = 60
+zoom_core_diameter = 2 * 6
+full_view_x = size_x * 1e6 / 2
+full_view_y = size_y * 1e6 / 2
+##############################
+
 ##############################
 #setting up spatial frequencies
 ks = 2 * np.pi / sampling_period
@@ -104,15 +114,6 @@ P_1_dagger_nat = np.fft.ifftshift(P_1).T.conj()
 P_2_dagger_nat = np.fft.ifftshift(P_2).T.conj()
 ##############################
 
-##############################
-#Plot zoom parameters [um]
-zoom_x = 60
-zoom_y = 60
-zoom_core_diameter = 2 * 6
-full_view_x = size_x * 1e6 / 2
-full_view_y = size_y * 1e6 / 2
-##############################
-
 def update_phase_factors():
     print("Updating phase factors using d=",d)
     phase_factor_0[:,:] = k0 * d[0] * np.sqrt(1 - (wavelength * nu_parallel) ** 2 + 0*1j)
@@ -125,6 +126,11 @@ def update_phase_factors():
     P_2[:,:] = np.exp(1j * phase_factor_2)
     P_2_nat[:,:] = np.fft.ifftshift(P_2)
 
+    P_1_dagger[:] = P_1.T.conj()
+    P_2_dagger[:] = P_2.T.conj()
+    P_1_dagger_nat[:] = np.fft.ifftshift(P_1).T.conj()
+    P_2_dagger_nat[:] = np.fft.ifftshift(P_2).T.conj()
+
 def update_input_fields():
     input_field_1[:] = propagate_source(source_field_1)
     input_field_1_2d[:] = input_field_1.reshape((Nx, Ny)) #reshape to 2D for plotting
@@ -588,7 +594,7 @@ def obj_fun(epigraph_and_weights, grad):
     
     opt_history.append(epigraph)
     #make a 1d plot of the optimization history together with the objective values
-    #make_1Dplot_of(x_axis=[np.arange(len(obj_values1)), np.arange(len(obj_values2)), np.arange(len(opt_history))], y_axis=[obj_values1, obj_values2, opt_history], legend_labels=["ob1","ob2","t"], save_name="optimization_history", plot_zoom_x=len(opt_history))
+    make_1Dplot_of(x_axis=[np.arange(len(obj_values1)), np.arange(len(obj_values2)), np.arange(len(opt_history))], y_axis=[obj_values1, obj_values2, opt_history], legend_labels=["ob1","ob2","t"], save_name="optimization_history", plot_zoom_x=len(opt_history))
 
     return epigraph
 
@@ -771,4 +777,7 @@ if __name__ == "__main__":
     #if the results folder does not exist, create it
     if not os.path.exists('results'):
         os.makedirs('results')
+
+    
+
     run_ego()