snapshot...

modules/deeplearning/cnn_cld_frac.py

@@ -219,55 +219,6 @@ def get_min_max_std(grd_k):
 
     return lo, hi, std
 
-# 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 < 0.15)
-#     cat_1 = np.logical_and(grd_k >= 0.15, grd_k < 0.85)
-#     cat_2 = np.logical_and(grd_k >= 0.85, 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 = a + b + c + d
-#
-#     cat_0 = s <= -3
-#     cat_1 = np.logical_and(s > -3, s < 2)
-#     cat_2 = s >= 2
-#     s[cat_0] = 0
-#     s[cat_1] = 1
-#     s[cat_2] = 2
-#
-#     return s
-
 
 def get_label_data(grd_k):
     grd_k = np.where(np.isnan(grd_k), 0, grd_k)