From 5708ab26b9b877ef0065300e7dc5cb4a68beb363 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Wed, 24 May 2023 14:28:11 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/cloud_opd_srcnn_abi.py | 25 +++++++++++++--------
1 file changed, 16 insertions(+), 9 deletions(-)
diff --git a/modules/deeplearning/cloud_opd_srcnn_abi.py b/modules/deeplearning/cloud_opd_srcnn_abi.py
index 56b816f2..5d888712 100644
--- a/modules/deeplearning/cloud_opd_srcnn_abi.py
+++ b/modules/deeplearning/cloud_opd_srcnn_abi.py
@@ -141,10 +141,10 @@ def get_min_max_std(grd_k):
return lo, hi, std, avg
-# def upsample_static(grd, x_2, y_2, t, s, y_k, x_k):
-# grd = resample_2d_linear(x_2, y_2, grd, t, s, y_k, x_k)
-# grd = grd[:, y_k, x_k]
-# return grd
+def upsample_static(grd, x_2, y_2, t, s, y_k, x_k):
+ grd = resample_2d_linear(x_2, y_2, grd, t, s)
+ # grd = grd[:, y_k, x_k]
+ return grd
class SRCNN:
@@ -705,12 +705,19 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
cld_opd = cld_opd[int(ylen/2):ylen, :]
cld_opd_hres = cld_opd.copy()
- nn = SRCNN(LEN_Y=2*LEN_Y, LEN_X=2*LEN_X)
+ nn = SRCNN()
+
+ slc_x = slice(0, (LEN_X - 16) + 4)
+ slc_y = slice(0, (LEN_Y - 16) + 4)
+ x_2 = np.arange((LEN_X - 16) + 4)
+ y_2 = np.arange((LEN_Y - 16) + 4)
+ t = np.arange(0, (LEN_X - 16) + 4, 0.5)
+ s = np.arange(0, (LEN_Y - 16) + 4, 0.5)
refl = np.where(np.isnan(refl), 0, bt)
- refl = refl[nn.slc_y, nn.slc_x]
+ refl = refl[slc_y, slc_x]
refl = np.expand_dims(refl, axis=0)
- refl = nn.upsample(refl)
+ refl = upsample_static(refl, x_2, y_2, t, s)
print(refl.shape)
refl = normalize(refl, 'refl_0_65um_nom', mean_std_dct)
print('REFL done')
@@ -718,7 +725,7 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
bt = np.where(np.isnan(bt), 0, bt)
bt = bt[nn.slc_y, nn.slc_x]
bt = np.expand_dims(bt, axis=0)
- bt = nn.upsample(bt)
+ bt = upsample_static(bt, x_2, y_2, t, s)
bt = normalize(bt, 'temp_11_0um_nom', mean_std_dct)
print('BT done')
@@ -736,7 +743,7 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
cld_opd = np.where(np.isnan(cld_opd), 0, cld_opd)
cld_opd = cld_opd[nn.slc_y, nn.slc_x]
cld_opd = np.expand_dims(cld_opd, axis=0)
- cld_opd = nn.upsample(cld_opd)
+ cld_opd = upsample_static(cld_opd, x_2, y_2, t, s)
cld_opd = normalize(cld_opd, label_param, mean_std_dct)
print('OPD done')
--
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