From f60313d511c2e3419d104a319bff719120faf937 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Fri, 30 Sep 2022 15:09:44 -0500
Subject: [PATCH] snapshot...
---
modules/deeplearning/srcnn_l1b_l2.py | 17 ++++++++++++-----
1 file changed, 12 insertions(+), 5 deletions(-)
diff --git a/modules/deeplearning/srcnn_l1b_l2.py b/modules/deeplearning/srcnn_l1b_l2.py
index b7d51531..560e9f61 100644
--- a/modules/deeplearning/srcnn_l1b_l2.py
+++ b/modules/deeplearning/srcnn_l1b_l2.py
@@ -672,7 +672,7 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
nn = SRCNN()
out_sr = nn.run_evaluate(data, ckpt_dir)
- #out_sr = denormalize(out_sr, label_params[0], mean_std_dct)
+ # out_sr = denormalize(out_sr, label_params[0], mean_std_dct)
if out_file is not None:
np.save(out_file, out_sr)
else:
@@ -682,19 +682,26 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
def analyze():
h5f = h5py.File('/Users/tomrink/clavrx_snpp_viirs.A2019071.0000.001.2019071061610.uwssec_B00038187.level2.h5', 'r')
grd = get_grid_values_all(h5f, 'cloud_fraction')
- grd = grd[::2, ::2]
+ grd = np.where(np.isnan(grd), 0, grd)
+ grd = grd[1600:, 400:1200]
print(grd.shape)
- leny, lenx = grd.shape
+
+ grd_lr = grd[::2, ::2]
+ print(grd_lr.shape)
+ leny, lenx = grd_lr.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 = resample_2d_linear_one(x, y, grd, x_up, y_up)
- print(grd_a.shape)
+ grd_hr = resample_2d_linear_one(x, y, grd_lr, x_up, y_up)
+ print(grd_hr.shape)
h5f.close()
+ return grd, grd_hr
+
if __name__ == "__main__":
nn = SRCNN()
--
GitLab