From 8d5ee76e76ee1fd5492ef25ce485b1832783388d Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Mon, 15 May 2023 11:18:43 -0500
Subject: [PATCH] snapshot...
---
.../deeplearning/cloud_fraction_fcn_abi.py | 26 +++++++++----------
1 file changed, 13 insertions(+), 13 deletions(-)
diff --git a/modules/deeplearning/cloud_fraction_fcn_abi.py b/modules/deeplearning/cloud_fraction_fcn_abi.py
index b25cab83..afdf02cd 100644
--- a/modules/deeplearning/cloud_fraction_fcn_abi.py
+++ b/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):
--
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