edoardo/modeconverter
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code alignment between the two versions. There are now two possibilities: 1)optimize the average objective function, this generally leads to different performance of the converted modes; 2) optimize using an epigraph formulation. This assures that whatever the final epigraph value, the performance of the converted modes will be greater (in terms of mode overlap) than the epigraph itself. The epigraph is implemented as a constrained optimization, which is stricter than optimizing the average

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edoardo
2026-07-28 22:41:42 +02:00
parent ebcaeb5643
commit 36c1db0df2
2 changed files with 109 additions and 61 deletions
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metaprop_fibermode_doublet_multi.py
+8 -3
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@@ -52,7 +52,7 @@ rho = np.sqrt(X**2 + Y**2)
sampling_period = xs[1] - xs[0]
d0 = 345e-6 #propagation distance: source - MS1
d1 = 500e-6 #propagation distance: MS1 - MS2
d1 = 100e-6 #propagation distance: MS1 - MS2
d2 = 300e-6 #propagation distance: MS2 - target
d = [d1, d2] #d = [d1,d2] d1:distance MS1-MS2, d2: distance MS2-target
@@ -510,6 +510,8 @@ def value_and_grad(weights):
return np.array(values), np.array(gradients)
opt_history = []
opt_history1 = []
opt_history2 = []
def obj_fun(weights, grad):
'''
Objective function for the optimization. Computes the overall cost function by taking the mean value
@@ -525,6 +527,7 @@ def obj_fun(weights, grad):
obj_val, grads = value_and_grad(weights)
average_obj_val = np.mean(obj_val)
if grads.size > 0:
@@ -540,7 +543,9 @@ def obj_fun(weights, grad):
make_2Dplot_of(np.abs(grad[S:].reshape((Nx, Ny))), choose_quantity="amplitude", save_name="gradient_magnitude", plot_zoom_x=full_view_x, plot_zoom_y=full_view_y)
opt_history.append(average_obj_val)
make_1Dplot_of([np.arange(len(opt_history))], [opt_history], plot_zoom_x=len(opt_history), save_name="optimization_history")
opt_history1.append(obj_val[0])
opt_history2.append(obj_val[1])
make_1Dplot_of([np.arange(len(opt_history))]*3, [opt_history,opt_history1,opt_history2], plot_zoom_x=len(opt_history), save_name="optimization_history")
return average_obj_val
@@ -619,7 +624,7 @@ if __name__ == "__main__":
make_polarPlot_of(verify_out_field_all, choose_quantity="amplitude", save_name="modeconv1_optimized_output_field_allSource_amplitude", plot_zoom_r=zoom_core_diameter)
make_polarPlot_of(verify_out_field_all, choose_quantity="phase", save_name="modeconv1_optimized_output_field_allSource_phase", plot_zoom_r=zoom_core_diameter)
#make 1D slices of target/outpu fields to compare
#make 1D slices of target/output fields to compare
slice_y_index = Ny//2
slice_x = xs * 1e6
slice_target_1 = target_Efield_1.reshape((Nx, Ny))[slice_y_index,:]