diff --git a/modules/deeplearning/cloud_fraction_fcn_abi.py b/modules/deeplearning/cloud_fraction_fcn_abi.py
index f3a0f7ff9719f1879f4ea3715d18fef299259065..dabad740509730210402ee04cf4b099021cc57b8 100644
--- a/modules/deeplearning/cloud_fraction_fcn_abi.py
+++ b/modules/deeplearning/cloud_fraction_fcn_abi.py
@@ -778,30 +778,29 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
 
     bt = get_grid_values_all(h5f, 'temp_11_0um_nom')
     refl = get_grid_values_all(h5f, 'refl_0_65um_nom')
-    bt = bt[0:2500, :]
-    refl = refl[0:2500, :]
-    y_len, x_len = bt.shape[0], bt.shape[1]
+    refl_lo = get_grid_values_all(h5f, 'refl_0_65um_nom_min_sub')
+    refl_hi = get_grid_values_all(h5f, 'refl_0_65um_nom_max_sub')
+    refl_std = get_grid_values_all(h5f, 'refl_0_65um_nom_stddev_sub')
+    cp = get_grid_values_all(h5f, label_param)
     lons = get_grid_values_all(h5f, 'longitude')
     lats = get_grid_values_all(h5f, 'latitude')
-    lons = lons[0:2500, :]
-    lats = lats[0:2500, :]
-    bt = np.where(np.isnan(bt), 0, bt)
+
+    # bt = bt[0:2500, :]
+    # refl = refl[0:2500, :]
+    # lons = lons[0:2500, :]
+    # lats = lats[0:2500, :]
+    # refl_lo = refl_lo[0:2500, :]
+    # refl_hi = refl_hi[0:2500, :]
+    # refl_std = refl_std[0:2500, :]
+    # cp = cp[0:2500, :]
+
+    y_len, x_len = bt.shape[0], bt.shape[1]
+
     bt = normalize(bt, 'temp_11_0um_nom', mean_std_dct)
-    refl = np.where(np.isnan(refl), 0, refl)
     refl = normalize(refl, 'refl_0_65um_nom', mean_std_dct)
-
-    refl_lo = get_grid_values_all(h5f, 'refl_0_65um_nom_min_sub')
-    refl_lo = refl_lo[0:2500, :]
     refl_lo = normalize(refl_lo, 'refl_0_65um_nom', mean_std_dct)
-    refl_hi = get_grid_values_all(h5f, 'refl_0_65um_nom_max_sub')
-    refl_hi = refl_hi[0:2500, :]
     refl_hi = normalize(refl_hi, 'refl_0_65um_nom', mean_std_dct)
-    refl_std = get_grid_values_all(h5f, 'refl_0_65um_nom_stddev_sub')
-    refl_std = refl_std[0:2500, :]
     refl_std = np.where(np.isnan(refl_std), 0, refl_std)
-
-    cp = get_grid_values_all(h5f, label_param)
-    cp = cp[0:2500, :]
     cp = np.where(np.isnan(cp), 0, cp)
 
     data = np.stack([bt, refl, refl_lo, refl_hi, refl_std, cp], axis=2)