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https://github.com/edoverg/modeconverter.git
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after the first tests, I increase the number of iterations per epoch
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+19
-16
@@ -395,11 +395,11 @@ def normalization_sim() -> np.ndarray:
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k_point=mp.Vector3(),
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k_point=mp.Vector3(),
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)
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)
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plt.figure()
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#plt.figure()
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ax = plt.gca()
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#ax = plt.gca()
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norm_sim.plot2D(output_plane=view_2D_plane, ax=ax)
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#norm_sim.plot2D(output_plane=view_2D_plane, ax=ax)
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plt.savefig("results/normalization_sim_setup.pdf")
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#plt.savefig("results/normalization_sim_setup.pdf")
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#plt.close()
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NearRegions = [mp.Near2FarRegion(
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NearRegions = [mp.Near2FarRegion(
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center=NEAR_REGION_MONITOR_CENTER,
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center=NEAR_REGION_MONITOR_CENTER,
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@@ -418,11 +418,12 @@ def normalization_sim() -> np.ndarray:
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until_after_sources=stop_cond
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until_after_sources=stop_cond
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)
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)
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#make a plot of the fields when simulation finished
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#make a plot of the fields when simulation finished
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plt.figure()
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#plt.figure()
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ax = plt.gca()
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#ax = plt.gca()
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norm_sim.plot2D(output_plane=view_2D_plane, fields=mp.Ex, ax=ax)
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#norm_sim.plot2D(output_plane=view_2D_plane, fields=mp.Ex, ax=ax)
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plt.savefig("results/fields_after_norm_sim.pdf")
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#plt.savefig("results/fields_after_norm_sim.pdf")
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#plt.close()
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ref_fields = np.array(
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ref_fields = np.array(
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[norm_sim.get_farfield(norm_near2far, point) for point in ff_points]
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[norm_sim.get_farfield(norm_near2far, point) for point in ff_points]
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)
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)
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@@ -615,9 +616,9 @@ if __name__ == "__main__":
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#sigmoid_biases = [8, 16, 32, 64, 128, 256]
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#sigmoid_biases = [8, 16, 32, 64, 128, 256]
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sigmoid_biases = [8, 16, 32]
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sigmoid_biases = [8, 16, 32]
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#max_evals = [80, 80, 100, 120, 120, 100]
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#max_evals = [48, 48, 60, 60, 60, 60]
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max_evals = [10, 7, 7]
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max_evals = [48, 48, 60]
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#max_evals = [1, 1]
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epigraph_tolerance = np.array([1e-4]*num_wavelengths)
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epigraph_tolerance = np.array([1e-4]*num_wavelengths)
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tolerance_width_and_spacing = np.array([1e-8]*2)
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tolerance_width_and_spacing = np.array([1e-8]*2)
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@@ -743,9 +744,11 @@ if __name__ == "__main__":
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plt.figure()
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plt.figure()
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plt.subplot(1,2,1)
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plt.subplot(1,2,1)
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plt.plot(objfunc_history, label="Objective function value")
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plt.plot(objfunc_history[:,0], label="f1 - 1.50 um")
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plt.plot(objfunc_history[:,1], label="f2 - 1.55 um")
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plt.plot(objfunc_history[:,2], label="f3 - 1.60 um")
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plt.subplot(1,2,2)
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plt.subplot(1,2,2)
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plt.plot(epivar_history, label="Epigraph variable value")
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plt.plot(epivar_history, label="Epigraph")
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plt.xlabel("Iteration")
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plt.xlabel("Iteration")
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plt.ylabel("Value")
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plt.ylabel("Value")
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plt.legend()
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plt.legend()
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@@ -814,4 +817,4 @@ if __name__ == "__main__":
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'optimal_design_weights': optimal_design_weights,
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'optimal_design_weights': optimal_design_weights,
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}, f)
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}, f)
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print("Normalization simulation completed")
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print("Simulation completed")
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