From 57daf95ab5a085f66074cc3c623fb0809cdcf6fc Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Tue, 14 Feb 2023 10:56:13 -0600
Subject: [PATCH] snapshot...
---
modules/deeplearning/cnn_cld_frac.py | 59 ++++++++++++++++++++--------
1 file changed, 42 insertions(+), 17 deletions(-)
diff --git a/modules/deeplearning/cnn_cld_frac.py b/modules/deeplearning/cnn_cld_frac.py
index d51c45ae..12f7147e 100644
--- a/modules/deeplearning/cnn_cld_frac.py
+++ b/modules/deeplearning/cnn_cld_frac.py
@@ -201,29 +201,54 @@ def get_grid_cell_mean(grd_k):
return s
+# def get_label_data(grd_k):
+# grd_k = np.where(np.isnan(grd_k), 0, grd_k)
+#
+# a = grd_k[:, 0::2, 0::2]
+# b = grd_k[:, 1::2, 0::2]
+# c = grd_k[:, 0::2, 1::2]
+# d = grd_k[:, 1::2, 1::2]
+# s_t = a + b + c + d
+# s_t /= 4.0
+#
+# blen, ylen, xlen = s_t.shape
+# s_t = s_t.flatten()
+# cat_0 = np.logical_and(s_t >= 0.0, s_t < 0.2)
+# cat_1 = np.logical_and(s_t >= 0.2, s_t < 0.7)
+# cat_2 = np.logical_and(s_t >= 0.7, s_t <= 1.0)
+#
+# s_t[cat_0] = 0
+# s_t[cat_1] = 1
+# s_t[cat_2] = 2
+#
+# s_t = s_t.reshape((blen, ylen, xlen))
+#
+# return s_t
+
+
def get_label_data(grd_k):
grd_k = np.where(np.isnan(grd_k), 0, grd_k)
+ cat_0 = np.logical_and(grd_k >= 0.0, grd_k < 0.2)
+ cat_1 = np.logical_and(grd_k >= 0.2, grd_k < 0.7)
+ cat_2 = np.logical_and(grd_k >= 0.7, grd_k <= 1.0)
+ grd_k[cat_0] = -1
+ grd_k[cat_1] = 0
+ grd_k[cat_2] = 1
a = grd_k[:, 0::2, 0::2]
b = grd_k[:, 1::2, 0::2]
c = grd_k[:, 0::2, 1::2]
d = grd_k[:, 1::2, 1::2]
- s_t = a + b + c + d
- s_t /= 4.0
-
- blen, ylen, xlen = s_t.shape
- s_t = s_t.flatten()
- cat_0 = np.logical_and(s_t >= 0.0, s_t < 0.2)
- cat_1 = np.logical_and(s_t >= 0.2, s_t < 0.7)
- cat_2 = np.logical_and(s_t >= 0.7, s_t <= 1.0)
-
- s_t[cat_0] = 0
- s_t[cat_1] = 1
- s_t[cat_2] = 2
+ s = a + b + c + d
- s_t = s_t.reshape((blen, ylen, xlen))
+ cat_0 = s <= -3
+ cat_1 = np.logical_and(s > -3, s < 3)
+ cat_2 = s >= 3
+ s[cat_0] = 0
+ s[cat_1] = 1
+ s[cat_2] = 2
- return s_t
+ return s
class SRCNN:
@@ -384,9 +409,9 @@ class SRCNN:
if DO_ESPCN:
tmp = tmp[:, slc_y_2, slc_x_2]
else: # Half res upsampled to full res:
- tmp = upsample(tmp)
- # tmp = upsample_mean(tmp)
- # tmp = tmp[:, slc_y, slc_x]
+ # tmp = upsample(tmp)
+ tmp = upsample_mean(tmp)
+ tmp = tmp[:, slc_y, slc_x]
if label_param != 'cloud_probability':
tmp = normalize(tmp, label_param, mean_std_dct)
if DO_ADD_NOISE:
--
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