snapshot...

modules/deeplearning/cloud_fraction_fcn_abi.py

@@ -324,6 +324,8 @@ class SRCNN:
             tmp = tmp[:, slc_y, slc_x]
             if param != 'refl_substddev_ch01':
                 tmp = normalize(tmp, 'refl_0_65um_nom', mean_std_dct)
+            else:
+                tmp = np.where(np.isnan(tmp), 0, tmp)
             data_norm.append(tmp)
 
         tmp = input_label[:, label_idx_i, :, :]
@@ -774,27 +776,25 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
     h5f = h5py.File(in_file, 'r')
 
     bt = get_grid_values_all(h5f, 'orig/temp_11_0um')
+    refl = get_grid_values_all(h5f, 'orig/refl_0_65um')
     y_len, x_len = bt.shape[0], bt.shape[1]
     lons = get_grid_values_all(h5f, 'orig/longitude')
     lats = get_grid_values_all(h5f, 'orig/latitude')
     bt = np.where(np.isnan(bt), 0, bt)
     bt = normalize(bt, 'temp_11_0um_nom', mean_std_dct)
 
-    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 = get_grid_values_all(h5f, 'orig/refl_submin_ch01')
     refl_lo = normalize(refl_lo, 'refl_0_65um_nom', mean_std_dct)
+    refl_hi = get_grid_values_all(h5f, 'orig/refl_submax_ch01')
     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_std = get_grid_values_all(h5f, 'orig/refl_substdev_ch01')
+    refl_std = np.where(np.isnan(refl_std), 0, refl_std)
 
     cp = get_grid_values_all(h5f, 'orig/'+label_param)
     cp = np.where(np.isnan(cp), 0, cp)
 
-    data = np.stack([bt, refl_lo, refl_hi, refl_avg, cp], axis=2)
+    # data = np.stack([bt, refl, refl_lo, refl_hi, refl_std, cp], axis=2)
+    data = np.stack([bt, refl_lo, refl_hi, refl_std, cp], axis=2)
     data = np.expand_dims(data, axis=0)
 
     h5f.close()
@@ -808,12 +808,12 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
     cld_frac_out[border:y_len - border, border:x_len - border] = cld_frac[0, :, :]
 
     bt = denormalize(bt, 'temp_11_0um_nom', mean_std_dct)
-    refl_avg = denormalize(refl_avg, 'refl_0_65um_nom', mean_std_dct)
+    refl = denormalize(refl, 'refl_0_65um_nom', mean_std_dct)
 
     var_names = ['cloud_fraction', 'temp_11_0um', 'refl_0_65um']
     dims = ['num_params', 'y', 'x']
 
-    da = xr.DataArray(np.stack([cld_frac_out, bt, refl_avg], axis=0), dims=dims)
+    da = xr.DataArray(np.stack([cld_frac_out, bt, refl], axis=0), dims=dims)
     da.assign_coords({
         'num_params': var_names,
         'lat': (['y', 'x'], lats),
@@ -821,9 +821,9 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
     })
 
     if out_file is not None:
-        np.save(out_file, (cld_frac_out, bt, refl_avg, cp, lons, lats))
+        np.save(out_file, (cld_frac_out, bt, refl, cp, lons, lats))
     else:
-        return [cld_frac_out, bt, refl_avg, cp, lons, lats]
+        return [cld_frac_out, bt, refl, cp, lons, lats]
 
 
 def analyze_3cat(file):