snapshot...

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)
 #