implemented new filtering system based on a Filter Class. Previously, filtering was made expensive by extreme padding, now this has been fixed by choosing a more appropriate padding size. The gradient chain based on unitcells has been implemented but the results have yet to be tested using FDTD, which will be the next step. Added some utilities to visualize the effect of filter radius on given input masks and added all data concerning unit cell simulations. Still to be done is to fix materials in the MEEP simulations, so as to account for true material behavior instead of relying on constant refractive indices.

testing_filter_radius.py

@@ -0,0 +1,55 @@
+#use this file to import some test phase masks and observe 
+#the effect of changing the filtering radius
+import numpy as np
+import matplotlib.pyplot as plt
+from autograd import tensor_jacobian_product
+
+class TempGrid:
+    def __init__(self, Nx, Ny, res): self.Nx, self.Ny, self.res = Nx, Ny, res
+    def get_Nx(self): return self.Nx
+    def get_Ny(self): return self.Ny
+    def get_resolution(self): return self.res
+
+
+grid = TempGrid(Nx=1025, Ny=1025, res=0.7)
+radius_to_test = [2.0, 5.0, 11.0]
+
+input_mask = np.load('results/mask_2.npy')
+#make a plot of the input mask
+# fig, ax = plt.subplots()
+# ax.imshow(input_mask, cmap='inferno')
+# #zoom in the center region
+# center_x, center_y = grid.get_Nx() // 2, grid.get_Ny() // 2
+# zoom_size = 100  # adjust as needed
+# ax.set_xlim(center_x - zoom_size, center_x + zoom_size)
+# ax.set_ylim(center_y - zoom_size, center_y + zoom_size)
+# plt.savefig('input_mask.pdf')
+
+#apply the filter
+from filters import Filter
+
+center_x, center_y = grid.get_Nx() // 2, grid.get_Ny() // 2
+zoom_size = 80
+
+colormap = 'gray_r'
+fig, ax = plt.subplots(1,4, figsize=(12, 3))
+ax[0].imshow(input_mask, cmap=colormap) 
+ax[0].set_title('Input', fontsize=20)
+ax[0].set_xlim(center_x - zoom_size, center_x + zoom_size)
+ax[0].set_ylim(center_y - zoom_size, center_y + zoom_size)
+ax[0].set_xticks([])
+ax[0].set_yticks([])
+for i, radius in enumerate(radius_to_test, start=1):
+    filt = Filter(radius, grid)
+    output_mask = filt.apply_filter(input_mask)
+    ax[i].imshow(output_mask.reshape(grid.get_Nx(), grid.get_Ny()), cmap=colormap)    
+    ax[i].set_title(f'r={radius} \u03BCm', fontsize=20)
+    ax[i].set_xlim(center_x - zoom_size, center_x + zoom_size)
+    ax[i].set_ylim(center_y - zoom_size, center_y + zoom_size)
+    ax[i].set_xticks([])
+    ax[i].set_yticks([])
+plt.tight_layout()
+plt.savefig('filtered_masks.pdf')
+
+
+