snapshot...

modules/deeplearning/cloud_opd_srcnn_abi.py

@@ -693,19 +693,21 @@ 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, :]
     LEN_Y, LEN_X = refl.shape
     print(LEN_Y, LEN_X)
 
     bt = get_grid_values_all(h5f, 'temp_11_0um_nom')
     ylen, xlen = bt.shape
-    bt = bt[int(ylen/2):ylen, :]
 
     cld_opd = get_grid_values_all(h5f, 'cld_opd_dcomp_1')
     ylen, xlen = cld_opd.shape
-    cld_opd = cld_opd[int(ylen/2):ylen, :]
+    cld_opd = cld_opd[::2, ::2]
     cld_opd_hres = cld_opd.copy()
 
+    refl_sub_lo = get_grid_values_all(h5f, 'refl_submin_ch01')
+    refl_sub_hi = get_grid_values_all(h5f, 'refl_submax_ch01')
+    refl_sub_std = get_grid_values_all(h5f, 'refl_substddev_ch01')
+
     nn = SRCNN()
 
     slc_x = slice(0, (LEN_X - 16) + 4)
@@ -730,16 +732,19 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
     bt_us = normalize(bt_us, 'temp_11_0um_nom', mean_std_dct)
     print('BT done')
 
-    # refl = get_grid_values_all(h5f, 'super/refl_0_65um')
-    # refl = np.where(np.isnan(refl), 0, refl)
-    # refl = np.expand_dims(refl, axis=0)
-    # refl_lo, refl_hi, refl_std, refl_avg = get_min_max_std(refl)
-    # refl_lo = normalize(refl_lo, 'refl_0_65um_nom', mean_std_dct)
-    # refl_hi = normalize(refl_hi, 'refl_0_65um_nom', mean_std_dct)
-    # refl_avg = normalize(refl_avg, 'refl_0_65um_nom', mean_std_dct)
-    # refl_lo = np.squeeze(refl_lo)
-    # refl_hi = np.squeeze(refl_hi)
-    # refl_avg = np.squeeze(refl_avg)
+    refl_sub_lo = np.expand_dims(refl_sub_lo, axis=0)
+    refl_sub_lo = upsample_nearest(refl_sub_lo)
+    refl_sub_lo = refl_sub_lo[:, slc_y, slc_x]
+    refl_sub_lo = normalize(refl_sub_lo, 'refl_0_65um_nom', mean_std_dct)
+
+    refl_sub_hi = np.expand_dims(refl_sub_hi, axis=0)
+    refl_sub_hi = upsample_nearest(refl_sub_hi)
+    refl_sub_hi = refl_sub_hi[:, slc_y, slc_x]
+    refl_sub_hi = normalize(refl_sub_hi, 'refl_0_65um_nom', mean_std_dct)
+
+    refl_sub_std = np.expand_dims(refl_sub_std, axis=0)
+    refl_sub_std = upsample_nearest(refl_sub_std)
+    refl_sub_std = refl_sub_std[:, slc_y, slc_x]
 
     cld_opd = np.where(np.isnan(cld_opd), 0, cld_opd)
     cld_opd = cld_opd[slc_y, slc_x]
@@ -748,7 +753,7 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
     cld_opd_us = normalize(cld_opd_us, label_param, mean_std_dct)
     print('OPD done')
 
-    data = np.stack([bt_us, refl_us, cld_opd_us], axis=3)
+    data = np.stack([bt_us, refl_us, refl_sub_lo, refl_sub_hi, refl_sub_std, cld_opd_us], axis=3)
 
     cld_opd_sres = nn.run_evaluate(data, ckpt_dir)
     cld_opd_sres = descale(cld_opd_sres, label_param, mean_std_dct)