snapshot...

modules/deeplearning/cloud_fraction_fcn_abi.py

@@ -71,25 +71,13 @@ print('data_params_full: ', data_params_full)
 print('label_param: ', label_param)
 
 KERNEL_SIZE = 3
-N_X = N_Y = 1
 X_LEN = Y_LEN = 64
 
 if KERNEL_SIZE == 3:
-    slc_x = slice(0, int((N_X*X_LEN)/4) + 2)
-    slc_y = slice(0, int((N_Y*Y_LEN)/4) + 2)
-    x_64 = slice(4, N_X*X_LEN + 4)
-    y_64 = slice(4, N_Y*Y_LEN + 4)
-# elif KERNEL_SIZE == 5: These no longer apply here
-#     slc_x = slice(3, 135)
-#     slc_y = slice(3, 135)
-#     slc_x_2 = slice(2, 137, 2)
-#     slc_y_2 = slice(2, 137, 2)
-#     x_128 = slice(5, 133)
-#     y_128 = slice(5, 133)
-#     t = np.arange(1, 67, 0.5)
-#     s = np.arange(1, 67, 0.5)
-#     x_2 = np.arange(68)
-#     y_2 = np.arange(68)
+    slc_x = slice(0, int(X_LEN/4) + 2)
+    slc_y = slice(0, int(Y_LEN/4) + 2)
+    x_64 = slice(4, X_LEN + 4)
+    y_64 = slice(4, Y_LEN + 4)
 # ----------------------------------------
 
 
@@ -342,21 +330,6 @@ class SRCNN:
                 tmp = normalize(tmp, 'refl_0_65um_nom', mean_std_dct)
             data_norm.append(tmp)
 
-        # for param in data_params_full:
-        #     idx = params_i.index(param)
-        #     tmp = input_label[:, idx, :, :]
-        #
-        #     lo, hi, std, avg = get_min_max_std(tmp)
-        #     lo = normalize(lo, param, mean_std_dct)
-        #     hi = normalize(hi, param, mean_std_dct)
-        #     # avg = normalize(avg, param, mean_std_dct)
-        #
-        #     data_norm.append(lo[:, slc_y, slc_x])
-        #     data_norm.append(hi[:, slc_y, slc_x])
-        #     data_norm.append(std[:, slc_y, slc_x])
-        # ---------------------------------------------------
-        # If next uncommented, take out get_grid_cell_mean
-        # tmp = input_data[:, label_idx, :, :]
         tmp = input_label[:, label_idx_i, :, :]
         tmp = get_grid_cell_mean(tmp)
         tmp = tmp[:, slc_y, slc_x]