From e2d7db05c2c6ba7b232458cd288ce00710f6d8af Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Fri, 19 May 2023 09:42:54 -0500
Subject: [PATCH] snapshot...
---
.../deeplearning/cloud_fraction_fcn_abi.py | 33 +++++++++----------
1 file changed, 16 insertions(+), 17 deletions(-)
diff --git a/modules/deeplearning/cloud_fraction_fcn_abi.py b/modules/deeplearning/cloud_fraction_fcn_abi.py
index f3a0f7ff..dabad740 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)
--
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