From d2ef927c02daab917953aa558e267722ec921b83 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Thu, 3 Nov 2022 16:41:08 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/srcnn_l1b_l2.py | 30 ++++++++++------------------
1 file changed, 11 insertions(+), 19 deletions(-)
diff --git a/modules/deeplearning/srcnn_l1b_l2.py b/modules/deeplearning/srcnn_l1b_l2.py
index 1c778e95..a8dcf555 100644
--- a/modules/deeplearning/srcnn_l1b_l2.py
+++ b/modules/deeplearning/srcnn_l1b_l2.py
@@ -752,30 +752,22 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
def run_evaluate_static_2(in_file, out_file, ckpt_dir):
nda = np.load(in_file)
- grd_a = nda[:, 0, :, :]
- grd_a = grd_a[:, 3:131:2, 3:131:2]
-
- grd_b = nda[:, 2, 3:131, 3:131]
-
- grd_c = nda[:, 3, :, :]
- grd_c = grd_c[:, 3:131:2, 3:131:2]
-
- num, leny, lenx = grd_a.shape
- x = np.arange(lenx)
- y = np.arange(leny)
- x_up = np.arange(0, lenx, 0.5)
- y_up = np.arange(0, leny, 0.5)
+ grd_a = nda[:, 0, :, :]
+ grd_a = grd_a[:, slc_y_2, slc_x_2]
grd_a = normalize(grd_a, 'temp_11_0um_nom', mean_std_dct)
- grd_a = resample_2d_linear(x, y, grd_a, x_up, y_up)
+ grd_a = resample_2d_linear(x_2, y_2, grd_a, t, s)
+ grd_b = nda[:, 2, :, :]
+ grd_b = grd_b[:, slc_y_2, slc_x_2]
grd_b = normalize(grd_b, 'refl_0_65um_nom', mean_std_dct)
+ grd_b = resample_2d_linear(x_2, y_2, grd_b, t, s)
- if label_param == 'cloud_fraction':
- grd_c = np.where(np.isnan(grd_c), 0, grd_c)
- else:
+ grd_c = nda[:, 3, :, :]
+ grd_c = grd_c[:, slc_y_2, slc_x_2]
+ if label_param != 'cloud_fraction':
grd_c = normalize(grd_c, label_param, mean_std_dct)
- grd_c = resample_2d_linear(x, y, grd_c, x_up, y_up)
+ grd_c = resample_2d_linear(x_2, y_2, grd_c, t, s)
data = np.stack([grd_a, grd_b, grd_c], axis=3)
print(data.shape)
@@ -783,7 +775,7 @@ def run_evaluate_static_2(in_file, out_file, ckpt_dir):
nn = SRCNN()
out_sr = nn.run_evaluate(data, ckpt_dir)
if label_param != 'cloud_fraction':
- # out_sr = denormalize(out_sr, label_param, mean_std_dct)
+ out_sr = denormalize(out_sr, label_param, mean_std_dct)
pass
if out_file is not None:
np.save(out_file, out_sr)
--
GitLab