From ae0f9f719808156426300e2856044149237ddc7e Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Wed, 14 Jun 2023 12:33:11 -0500
Subject: [PATCH] snapshot...
---
.../deeplearning/cloud_fraction_fcn_abi.py | 64 +++++++++++++++++++
1 file changed, 64 insertions(+)
diff --git a/modules/deeplearning/cloud_fraction_fcn_abi.py b/modules/deeplearning/cloud_fraction_fcn_abi.py
index c140172a..4614aaf6 100644
--- a/modules/deeplearning/cloud_fraction_fcn_abi.py
+++ b/modules/deeplearning/cloud_fraction_fcn_abi.py
@@ -822,6 +822,70 @@ class SRCNN:
# return [cld_frac_out, bt, refl, cp, lons, lats]
return cld_frac_out
+ def run_inference_full_disk(self, in_file, out_file):
+ gc.collect()
+
+ h5f = h5py.File(in_file, 'r')
+
+ bt = get_grid_values_all(h5f, 'temp_11_0um_nom')
+ y_len, x_len = bt.shape
+ h_y_len = int(y_len / 2)
+ refl = get_grid_values_all(h5f, 'refl_0_65um_nom')
+ 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')
+
+ bt_nh = bt[0:h_y_len + 1, :]
+ refl_nh = refl[0:h_y_len + 1, :]
+ refl_lo_nh = refl_lo[0:h_y_len + 1, :]
+ refl_hi_nh = refl_hi[0:h_y_len + 1, :]
+ refl_std_nh = refl_std[0:h_y_len + 1, :]
+ cp_nh = cp[0:h_y_len + 1, :]
+
+ bt_sh = bt[h_y_len - 1:y_len, :]
+ refl_sh = refl[h_y_len - 1:y_len, :]
+ refl_lo_sh = refl_lo[h_y_len - 1:y_len, :]
+ refl_hi_sh = refl_hi[h_y_len - 1:y_len, :]
+ refl_std_sh = refl_std[h_y_len - 1:y_len, :]
+ cp_sh = cp[h_y_len - 1:y_len, :]
+
+ cld_frac_nh = self.run_inference_(bt_nh, refl_nh, refl_lo_nh, refl_hi_nh, refl_std_nh, cp_nh)
+
+ cld_frac_sh = self.run_inference_(bt_sh, refl_sh, refl_lo_sh, refl_hi_sh, refl_std_sh, cp_sh)
+
+ cld_frac_out = np.zeros((y_len, x_len), dtype=np.int8)
+ border = int((KERNEL_SIZE - 1) / 2)
+ cld_frac_out[border:h_y_len, border:x_len - border] = cld_frac_nh[0, :, :]
+ cld_frac_out[h_y_len:y_len - border, border:x_len - border] = cld_frac_sh[0, :, :]
+
+ # Use this hack for now.
+ off_earth = (bt <= 161.0)
+ night = np.isnan(refl)
+ cld_frac_out[off_earth] = -1
+ cld_frac_out[np.invert(off_earth) & night] = -1
+
+ # --- Make DataArray -------------------------------------------------
+ # 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)
+ # })
+ # ------------------------------------------------------------------------
+
+ h5f.close()
+
+ if out_file is not None:
+ np.save(out_file, (cld_frac_out, bt, refl, cp))
+ else:
+ # return [cld_frac_out, bt, refl, cp, lons, lats]
+ return cld_frac_out
+
def run_inference_(self, bt, refl, refl_lo, refl_hi, refl_std, cp):
bt = normalize(bt, 'temp_11_0um_nom', mean_std_dct)
refl = normalize(refl, 'refl_0_65um_nom', mean_std_dct)
--
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