From 8a24d891cc4cd063c2db7b841cbe5ed36dc63d78 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Mon, 28 Nov 2022 15:25:32 -0600
Subject: [PATCH] snapshot...
---
modules/deeplearning/srcnn_l1b_l2.py | 52 +++++++++++++++++++++++++---
1 file changed, 48 insertions(+), 4 deletions(-)
diff --git a/modules/deeplearning/srcnn_l1b_l2.py b/modules/deeplearning/srcnn_l1b_l2.py
index 2973c69c..4c730626 100644
--- a/modules/deeplearning/srcnn_l1b_l2.py
+++ b/modules/deeplearning/srcnn_l1b_l2.py
@@ -713,6 +713,50 @@ def run_restore_static(directory, ckpt_dir):
nn.run_restore(directory, ckpt_dir)
+# def run_evaluate_static(in_file, out_file, ckpt_dir):
+# N = 8
+# sub_y, sub_x = (N+1) * 128, (N+1) * 128
+# y_0, x_0, = 2500 - int(sub_y/2), 2500 - int(sub_x/2)
+#
+# slc_y_2, slc_x_2 = slice(1, 128*N + 6, 2), slice(1, 128*N + 6, 2)
+# y_2, x_2 = np.arange((128*N)/2 + 3), np.arange((128*N)/2 + 3)
+# t, s = np.arange(1, (128*N)/2 + 2, 0.5), np.arange(1, (128*N)/2 + 2, 0.5)
+#
+# h5f = h5py.File(in_file, 'r')
+# grd_a = get_grid_values_all(h5f, 'temp_11_0um_nom')
+# grd_a = grd_a[y_0:y_0+sub_y, x_0:x_0+sub_x]
+# grd_a = grd_a[slc_y_2, slc_x_2]
+# bt = grd_a
+# grd_a = normalize(grd_a, 'temp_11_0um_nom', mean_std_dct)
+# grd_a = resample_2d_linear_one(x_2, y_2, grd_a, t, s)
+#
+# grd_b = get_grid_values_all(h5f, 'refl_0_65um_nom')
+# grd_b = grd_b[y_0:y_0+sub_y, x_0:x_0+sub_x]
+# grd_b = grd_b[slc_y_2, slc_x_2]
+# refl = grd_b
+# grd_b = normalize(grd_b, 'refl_0_65um_nom', mean_std_dct)
+# grd_b = resample_2d_linear_one(x_2, y_2, grd_b, t, s)
+#
+# grd_c = get_grid_values_all(h5f, label_param)
+# grd_c = grd_c[y_0:y_0+sub_y, x_0:x_0+sub_x]
+# grd_c = grd_c[slc_y_2, slc_x_2]
+# if label_param != 'cloud_probability':
+# grd_c = normalize(grd_c, label_param, mean_std_dct)
+# grd_c = resample_2d_linear_one(x_2, y_2, grd_c, t, s)
+#
+# data = np.stack([grd_a, grd_b, grd_c], axis=2)
+# data = np.expand_dims(data, axis=0)
+#
+# nn = SRCNN()
+# out_sr = nn.run_evaluate(data, ckpt_dir)
+# if label_param != 'cloud_probability':
+# out_sr = denormalize(out_sr, label_param, mean_std_dct)
+# if out_file is not None:
+# np.save(out_file, out_sr)
+# else:
+# return out_sr, bt, refl
+
+
def run_evaluate_static(in_file, out_file, ckpt_dir):
N = 8
sub_y, sub_x = (N+1) * 128, (N+1) * 128
@@ -725,17 +769,17 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
h5f = h5py.File(in_file, 'r')
grd_a = get_grid_values_all(h5f, 'temp_11_0um_nom')
grd_a = grd_a[y_0:y_0+sub_y, x_0:x_0+sub_x]
- grd_a = grd_a[slc_y_2, slc_x_2]
+ #grd_a = grd_a[slc_y_2, slc_x_2]
bt = grd_a
grd_a = normalize(grd_a, 'temp_11_0um_nom', mean_std_dct)
- grd_a = resample_2d_linear_one(x_2, y_2, grd_a, t, s)
+ #grd_a = resample_2d_linear_one(x_2, y_2, grd_a, t, s)
grd_b = get_grid_values_all(h5f, 'refl_0_65um_nom')
grd_b = grd_b[y_0:y_0+sub_y, x_0:x_0+sub_x]
- grd_b = grd_b[slc_y_2, slc_x_2]
+ #grd_b = grd_b[slc_y_2, slc_x_2]
refl = grd_b
grd_b = normalize(grd_b, 'refl_0_65um_nom', mean_std_dct)
- grd_b = resample_2d_linear_one(x_2, y_2, grd_b, t, s)
+ #grd_b = resample_2d_linear_one(x_2, y_2, grd_b, t, s)
grd_c = get_grid_values_all(h5f, label_param)
grd_c = grd_c[y_0:y_0+sub_y, x_0:x_0+sub_x]
--
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