minor fixes in mask

src/ich/algo.py

@@ -268,12 +268,12 @@ def process_block(i, j, cohArray, lidarArray, initial_ws, htl, htg, parm_):
             # if np.all(np.array(parm) == 0):
             #     parm = parm_.copy()
             #     del parm_
-            if lidarBlock.shape[0]*lidarBlock.shape[1]>initial_ws*initial_ws:
-                # mask[np.shape(mask)[0]//2,np.shape(mask)[1]//2]=1
-                print('blank_blocks',lidarBlock.shape,parm)
-                np.fill_diagonal(mask, 1)
-                mask = np.flipud(mask)
-                np.fill_diagonal(mask, 1)
+            # if lidarBlock.shape[0]*lidarBlock.shape[1]>initial_ws*initial_ws:
+            #     mask[np.shape(mask)[0]//2,np.shape(mask)[1]//2]=1
+            #     # print('blank_blocks',lidarBlock.shape,parm)
+            #     # np.fill_diagonal(mask, 1)
+            #     # mask = np.flipud(mask)
+            #     # np.fill_diagonal(mask, 1)
 
             s_parm = np.full(lidarBlock.shape, parm[1]) 
             c_parm = np.full(lidarBlock.shape, parm[2]) 

src/ich/args_in.py

@@ -150,18 +150,30 @@ def rvog_inverse(args):
 
     temp_lidar = blockshaped(lidar_ht_cal, args.window_size, args.window_size)
     temp_mask = np.zeros(temp_lidar.shape)
+
+    """ uncomment below to get cal coefficents uniformly across all windows """    
+    # for win in tqdm(range(np.shape(temp_lidar)[0])):
+    #     mask = np.zeros(temp_mask[win,:,:].shape)
+    #     np.fill_diagonal(mask, 1)
+    #     mask = np.flipud(mask)
+    #     np.fill_diagonal(mask, 1)
+    #     temp_mask[win,:,:] = mask
+
     
     for win in tqdm(range(np.shape(temp_lidar)[0])):
         mask = temp_lidar[win,:,:].copy()
         mask[~np.isnan(mask)] = 1 
         temp_mask[win,:,:] = mask
+
         if np.all(temp_lidar[win,:,:]==0) or np.all(np.isnan(temp_lidar[win,:,:])):
             mask = np.zeros(temp_mask[win,:,:].shape)
+            # mask[np.shape(mask)[0]//2,np.shape(mask)[1]//2]=1
             np.fill_diagonal(mask, 1)
             mask = np.flipud(mask)
             np.fill_diagonal(mask, 1)
             temp_mask[win,:,:] = mask
 
+
     temp_mask = unblockshaped(temp_mask, rows,cols)
 
     s = s*temp_mask