snapshot...

modules/deeplearning/cloud_fraction_fcn_abi.py

@@ -1374,14 +1374,30 @@ def analyze_5cat(file):
 
 
 # Fig, ax = plt.subplots()
-# lbls = lbls.flatten()
-# pred = pred.flatten()
-# cld_prob = cld_prob.flatten()
+# import numpy as np
+# tup = np.load('/Users/tomrink/cld_frac_viirs.npy', allow_pickle=True)
+# lbls = tup[0].flatten()
+# pred = tup[1].flatten()
+# bt = tup[2].flatten()
+# refl = tup[3].flatten()
+# refl_rng = tup[4].flatten()
+# refl_std = tup[5].flatten()
+# cld_prob = tup[6].flatten()
 # cat_0 = lbls == 0
 # cat_1 = lbls == 1
 # cat_2 = lbls == 2
 # cat_3 = lbls == 3
 # cat_4 = lbls == 4
+# cat_0_hit = (lbls == 0) & (pred == 0)
+# cat_1_hit = (lbls == 1) & (pred == 1)
+# cat_2_hit = (lbls == 2) & (pred == 2)
+# cat_3_hit = (lbls == 3) & (pred == 3)
+# cat_4_hit = (lbls == 4) & (pred == 4)
+# cat_0_miss = (lbls == 0) & (pred != 0)
+# cat_1_miss = (lbls == 1) & (pred != 1)
+# cat_2_miss = (lbls == 2) & (pred != 2)
+# cat_3_miss = (lbls == 3) & (pred != 3)
+# cat_4_miss = (lbls == 4) & (pred != 4)
 # plt.hist(cld_prob[cat_0], log=True, histtype='step', linewidth=1.4, color='blue', label='CLR')
 # plt.hist(cld_prob[cat_1], log=True, histtype='step', linewidth=1.4, color='orange', label='1/4')
 # plt.hist(cld_prob[cat_2], log=True, histtype='step', linewidth=1.4, color='green', label='1/2')