snapshot...

modules/deeplearning/cloud_opd_srcnn_abi.py

@@ -685,25 +685,25 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
 
     refl = get_grid_values_all(h5f, 'refl_0_65um_nom')
     ylen, xlen = refl.shape
-    refl = refl[int(ylen/2):ylen, :]
+    # refl = refl[int(ylen/2):ylen, :]
     LEN_Y, LEN_X = refl.shape
 
     bt = get_grid_values_all(h5f, 'temp_11_0um_nom')
     ylen, xlen = bt.shape
-    bt = bt[int(ylen/2):ylen, :]
+    # bt = bt[int(ylen/2):ylen, :]
 
     cld_opd = get_grid_values_all(h5f, label_param)
     ylen, xlen = cld_opd.shape
-    cld_opd = cld_opd[int(ylen/2):ylen, :]
+    # cld_opd = cld_opd[int(ylen/2):ylen, :]
     cld_opd_hres = cld_opd.copy()
 
-    refl = np.where(np.isnan(refl), 0, refl)
-    refl = normalize(refl, 'refl_0_65um_nom', mean_std_dct)
-
     nn = SRCNN(LEN_Y=LEN_Y-16, LEN_X=LEN_X-16)
 
-    refl = refl[nn.slc_y, nn.slc_x]
+    refl = np.where(np.isnan(refl), 0, bt)
+    refl = refl[nn.slc_y_m, nn.slc_x_m]
     refl = np.expand_dims(refl, axis=0)
+    refl = nn.upsample(refl)
+    refl = normalize(refl, 'refl_0_65um_nom', mean_std_dct)
 
     bt = np.where(np.isnan(bt), 0, bt)
     bt = bt[nn.slc_y_m, nn.slc_x_m]
@@ -723,7 +723,7 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
     # refl_avg = np.squeeze(refl_avg)
 
     cld_opd = np.where(np.isnan(cld_opd), 0, cld_opd)
-    cld_opd = cld_opd[nn.slc_y_2, nn.slc_x_2]
+    cld_opd = cld_opd[nn.slc_y_m, nn.slc_x_m]
     cld_opd = np.expand_dims(cld_opd, axis=0)
     cld_opd = nn.upsample(cld_opd)
     cld_opd = normalize(cld_opd, label_param, mean_std_dct)