snapshot...

modules/deeplearning/cloud_fraction_fcn_abi.py

@@ -865,6 +865,44 @@ def run_evaluate_static_full_disk(in_file, out_file, ckpt_dir):
         return [cld_frac_out, bt, refl, cp, lons, lats]
 
 
+def run_evaluate_static_valid(in_file, out_file, ckpt_dir):
+    gc.collect()
+
+    h5f = h5py.File(in_file, 'r')
+
+    bt = get_grid_values_all(h5f, 'orig/temp_ch38')
+    y_len, x_len = bt.shape
+    refl = get_grid_values_all(h5f, 'orig/refl_ch01')
+    refl_lo = get_grid_values_all(h5f, 'orig/refl_submin_ch01')
+    refl_hi = get_grid_values_all(h5f, 'orig/refl_submax_ch01')
+    refl_std = get_grid_values_all(h5f, 'orig/refl_substddev_ch01')
+    cp = get_grid_values_all(h5f, 'orig/'+label_param)
+    lons = get_grid_values_all(h5f, 'orig/longitude')
+    lats = get_grid_values_all(h5f, 'orig/latitude')
+
+    h5f.close()
+
+    cld_frac = run_evaluate_static_(bt, refl, refl_lo, refl_hi, refl_std, cp, ckpt_dir)
+
+    cld_frac_out = np.zeros((y_len, x_len), dtype=np.int8)
+    border = int((KERNEL_SIZE - 1)/2)
+    cld_frac_out[border:y_len-border, border:x_len - border] = cld_frac[0, :, :]
+
+    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], axis=0), dims=dims)
+    da.assign_coords({
+        'num_params': var_names,
+        'lat': (['y', 'x'], lats),
+        'lon': (['y', 'x'], lons)
+    })
+
+    if out_file is not None:
+        np.save(out_file, (cld_frac_out, bt, refl, cp, lons, lats))
+    else:
+        return [cld_frac_out, bt, refl, cp, lons, lats]
+
+
 def run_evaluate_static_(bt, refl, refl_lo, refl_hi, refl_std, cp, ckpt_dir):
     bt = normalize(bt, 'temp_11_0um_nom', mean_std_dct)
     refl = normalize(refl, 'refl_0_65um_nom', mean_std_dct)