fixed conflict on spatial dimensions and filtering

filters.py

@@ -2,7 +2,6 @@ import autograd.numpy as npa
 from numpy.typing import ArrayLike as ArrayLikeType
 import matplotlib.pyplot as plt
 
-filter_radius_um = 1
 def _centered(arr: npa.ndarray, newshape: ArrayLikeType) -> npa.ndarray:
     """Formats the output of an FFT to center the zero-frequency component.
 

mpid.py

@@ -137,13 +137,13 @@ class SpaceGrid:
         '''
         Returns the resolution of the spatial grid in micrometers [um].
         '''
-        return self.unit_cell_pitch * 1e6 # convert to [um]
+        return self.unit_cell_pitch * 1e6 # returns [um]
 
     def get_unit_cell_pitch(self) -> float:
         '''
         Returns the unit cell pitch of the spatial grid in micrometers [um].
         '''
-        return self.unit_cell_pitch * 1e6 # convert back to [um]
+        return self.unit_cell_pitch * 1e6 # returns [um]
 
     def get_size_x(self):
         return self.size_x
@@ -396,7 +396,7 @@ class Simulation:
             self.filter_radius = None
 
         if conic_filter_radius is not None:
-            self.filter_radius = conic_filter_radius * 1e-6
+            self.filter_radius = conic_filter_radius 
         else:
             self.filter_radius = None
 

single_converter_symm.py

@@ -75,7 +75,7 @@ simulation.set_optimization(
     ms_max_avail_diameter=ms_max_available_diameter,
     phase_mask_init_size=output_fiber_core_diameter/3,
     opt_max_eval=opt_max_eval,
-    #conic_filter_radius=1.0,    
+    conic_filter_radius=1.0,    
 )
 
 simulation.run()

single_converter_symm_ego.py

@@ -88,7 +88,7 @@ def setup_optical_system()->pid.Simulation:
         ms_max_avail_diameter=ms_max_available_diameter,
         phase_mask_init_size=output_fiber_core_diameter/3,
         opt_max_eval=opt_max_eval,
-        conic_filter_radius=1.0,
+        conic_filter_radius=1.5,
         results_while_solving = False,
         save_results = False,
     )
@@ -299,7 +299,7 @@ def run_ego(initial_dataset, surrogate_model, n_ego_iter, xlimits, simulation):
         y_pred_grid = np.zeros_like(d0_grid)
         for i in range(d0_grid.shape[0]):
             for j in range(d0_grid.shape[1]):
-                y_pred_grid[i,j] = sm.predict_values(np.atleast_2d([d0_grid[i,j], d1_grid[i,j]]))
+                y_pred_grid[i,j] = sm.predict_values(np.atleast_2d([d0_grid[i,j], d1_grid[i,j]]))[0,0]
         contour = ax.contourf(d0_grid, d1_grid, y_pred_grid, levels=50, cmap='viridis')
         fig.colorbar(contour, ax=ax, label='predicted overlap')
         ax.scatter(xdoe_train_loaded[:,0], xdoe_train_loaded[:,1], color='blue', label='DOE Points')
@@ -328,21 +328,6 @@ def run_ego(initial_dataset, surrogate_model, n_ego_iter, xlimits, simulation):
     y_opt = y_data[ind_best]
     np.savetxt(f'{surrogate_folder_name}/ego_opt_results.txt', np.hstack((x_opt,y_opt)))
     
-    ims = []
-    fig = plt.figure(figsize=[10, 10])
-    ax = plt.gca()
-    ax.axes.get_xaxis().set_visible(False)
-    ax.axes.get_yaxis().set_visible(False)
-
-    for k in range(n_ego_iter):
-        image_pt = mpimg.imread(f"{surrogate_folder_name}/optimisation_{k}.pdf")
-        im = plt.imshow(image_pt)
-        ims.append([im])
-
-    ani = animation.ArtistAnimation(fig, ims, interval=500)
-    #save the animation to a gif file
-    ani.save(f'{surrogate_folder_name}/ego_optimization.gif', writer='pillow')
-
     #plot of the EI history
     fig, ax = plt.subplots(figsize=(8,6))
     ax.plot(range(1, n_ego_iter+1), EI_history, marker='o')
@@ -459,8 +444,8 @@ if __name__ == "__main__":
     n_ego_iter = 15 #number of EGO refinement iterations
     
     corr_function_name = 'matern32'
-    doe_folder_name = 'DOE'
+    doe_folder_name = 'DOE_1_5'
-    surrogate_folder_name = corr_function_name + '_surrogate_filtered'
+    surrogate_folder_name = corr_function_name + '_surrogate_filtered_1_5'
     if not os.path.exists(doe_folder_name):
         os.makedirs(doe_folder_name)
     if not os.path.exists(surrogate_folder_name):
@@ -486,4 +471,4 @@ if __name__ == "__main__":
     
     #EVALUATE THE MODEL PERFORMANCE BY RUNNING A LOO CROSS-VALIDATION
     refined_dataset = pickle.load(open(f'{surrogate_folder_name}/refined_dataset.pkl', 'rb'))
-    leave_one_out(refined_dataset, corr_function_name)
+    leave_one_out(refined_dataset, corr_function_name)