From 73224230eb2c2ad75932b2fe58d08b8ce127d8f7 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Sat, 25 Feb 2023 12:48:37 -0600
Subject: [PATCH] snapshot...
---
modules/deeplearning/cnn_cld_frac.py | 22 ++++++++++++++++++++--
1 file changed, 20 insertions(+), 2 deletions(-)
diff --git a/modules/deeplearning/cnn_cld_frac.py b/modules/deeplearning/cnn_cld_frac.py
index 2e78ba0e..9f922b61 100644
--- a/modules/deeplearning/cnn_cld_frac.py
+++ b/modules/deeplearning/cnn_cld_frac.py
@@ -86,8 +86,10 @@ if KERNEL_SIZE == 3:
s = np.arange(0, int((N*128)/2) + 3, 0.5)
x_k = slice(1, N*128 + 3)
y_k = slice(1, N*128 + 3)
- x_128 = slice(3, N*128 + 3)
- y_128 = slice(3, N*128 + 3)
+ #x_128 = slice(3, N*128 + 3)
+ #y_128 = slice(3, N*128 + 3)
+ x_128 = slice(2, N*128 + 2)
+ y_128 = slice(2, N*128 + 2)
elif KERNEL_SIZE == 5:
slc_x = slice(3, 135)
slc_y = slice(3, 135)
@@ -201,6 +203,22 @@ def get_grid_cell_mean(grd_k):
return s
+def get_min_max_std(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]
+
+ lo = np.nanmin([a[:, ], b[:, ], c[:, ], d[:, ]])
+ hi = np.nanmax([a[:, ], b[:, ], c[:, ], d[:, ]])
+ std = np.nanstd([a[:, ], b[:, ], c[:, ], d[:, ]])
+
+ lo = np.where(np.isnan(lo), lo)
+ hi = np.where(np.isnan(hi), hi)
+ std = np.where(np.isnan(std), std)
+
+ return lo, hi, std
+
# def get_label_data(grd_k):
# grd_k = np.where(np.isnan(grd_k), 0, grd_k)
#
--
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