general testing of parameters. Regarding metaprop_fibermode_doublet, the spatial sampling has been fixed and the grid size is controlled by the number of pixels. I have seen that the mask-mask and mask-output plane distance is crucial and should not be chosen randomly, at the moment i have tested empirically some dimensions. The parameters in this commit seem to work ok, but we need to understand. Then, intensity conversation is still an issue, since the pattern at the output has 95/100, meaning 5/100 is lost somewhere. We really need to understand if this is an issue or not. With these settings, we might want to increase the window size, but then see the next point. I have introduced boundary boxes on phase masks, this is to avoid the algorithm using pixels that are clearly outside and only see noise. If we extend the simulation domain, we ought to extend these boundaries, otherwise the issue will present itself again. For what pixelation is concerned, honestly i expected worse, though there is still the need to apply some kind of filter to eradicate spurious single pixels. That should be doable using autograd, since the heavylifting is done by the adjoint, using autograd just for the filter part should not require too much computing effort. This needs to be tested.

metaprop_asm.py

@@ -6,14 +6,12 @@ k0 = 2 * np.pi / wavelength
 
 unit_cell_pitch = 400e-9
 
-Nx = 1024*10
+Nx = 1024*2
 Ny = Nx
 S = Nx * Ny
 
-#size_x = unit_cell_pitch * Nx
-#size_y = unit_cell_pitch * Ny
-size_x = 7968e-6
-size_y = size_x
+size_x = unit_cell_pitch * Nx
+size_y = unit_cell_pitch * Ny
 
 norm_phase_min = 0
 norm_phase_max = 1
@@ -63,8 +61,8 @@ def forward_propagate(phase_mask):
     KX, KY = np.meshgrid(kappas, kappas)
     K_parallel = np.sqrt(KX**2 + KY**2)
     nu_parallel = K_parallel / (2 * np.pi)
-    #phase_factor = k0 * z_prop * np.sqrt(1 - (wavelength * nu_parallel) ** 2 + 0*1j)
-    phase_factor = k0 * z_prop * (1 - 0.5*((wavelength * nu_parallel) ** 2 + 0*1j))
+    phase_factor = k0 * z_prop * np.sqrt(1 - (wavelength * nu_parallel) ** 2 + 0*1j)
+    #phase_factor = k0 * z_prop * (1 - 0.5*((wavelength * nu_parallel) ** 2 + 0*1j))
     P = np.exp(1j * phase_factor)
     fft_field_propagated = np.multiply(field_fft,P) #element wise product
 
@@ -79,7 +77,7 @@ if __name__ == "__main__":
 
     output_field, P = forward_propagate(phase_mask_test)
 
-    plt.figure(figsize=(10, 12))
+    plt.figure(figsize=(10, 10))
     plt.subplot(4, 2, 1)
     #plt.imshow(np.abs(field_fft), extent=(-ks/2, ks/2, -ks/2, ks/2))
     plt.imshow(np.abs(output_field.reshape((Nx,Ny))), extent=(-size_x/2*1e6, size_x/2*1e6, -size_y/2*1e6, size_y/2*1e6))
@@ -103,16 +101,16 @@ if __name__ == "__main__":
     #make this subplot take two places in the figure
     
     plt.subplot(4, 2, 3)
-    plt.plot(xs*1e6, np.abs(output_field.reshape((Nx,Ny))[Nx//2, :])**2, 'b-', label='output')
-    plt.plot(xs*1e6, gaussian_profile**2, 'r--', label='theory')
+    plt.plot(xs*1e6, np.abs(output_field.reshape((Nx,Ny))[Nx//2, :])**2 / np.max(np.abs(output_field.reshape((Nx,Ny))[Nx//2, :])**2), 'b-', label='output')
+    plt.plot(xs*1e6, gaussian_profile**2 / np.max(gaussian_profile**2), 'r--', label='theory')
     plt.xlabel('x (um)')
     plt.ylabel('Intensity (a.u.)')
-    plt.legend()
+    plt.legend(loc='center left')
     plt.subplot(4, 2, 4)
     plt.plot(xs*1e6, np.abs(input_field.reshape((Nx,Ny))[Nx//2, :])**2, 'g--', label='input')
     plt.xlabel('x (um)')
     plt.ylabel('Intensity (a.u.)')
-    plt.legend()
+    plt.legend(loc='center left')
 
     plt.subplot(4,1,3)
     plt.plot(xs*1e6, np.angle(output_field.reshape((Nx,Ny))[Nx//2, :])%(2*np.pi), 'b-', label='output phase')
@@ -120,7 +118,8 @@ if __name__ == "__main__":
     plt.plot(xs*1e6, np.angle(input_field.reshape((Nx,Ny))[Nx//2, :]), 'g--', label='input phase')
     plt.xlabel('x (um)')
     plt.ylabel('Phase (rad)')
-    plt.legend()
+    plt.legend(loc='center left')
+
 
     plt.subplot(4,1,4)
     plt.plot(xs*1e6, np.angle(output_field.reshape((Nx,Ny))[Nx//2, :])%(2*np.pi), 'b-', label='output phase')
@@ -129,5 +128,8 @@ if __name__ == "__main__":
     plt.xlabel('x (um)')
     plt.xlim(np.max(size_x/4*1e6), np.max(size_x/2*1e6))
     plt.ylabel('Phase (rad)')
+    plt.legend(loc='center left')
 
+
+    plt.tight_layout()
     plt.savefig("propagation_verification.pdf")