snapshot...

modules/deeplearning/cnn_cld_frac.py

@@ -1011,6 +1011,44 @@ def analyze(file='/Users/tomrink/cld_opd_out.npy'):
     return np.array(values), bin_edges
 
 
+def analyze2(nda_m, nda_i):
+    nda_m = np.where(nda_m < 0.5, 0, 1)
+    nda_i = np.where(nda_i < 0.5, 0, 1)
+
+    cf_m = np.zeros((64, 64))
+    cf_i = np.zeros((64, 64))
+
+    for j in range(1, 65):
+        for i in range(1, 65):
+            sum = nda_m[j-1:j+2, i-1:i+2]
+            cf_m[j-1, i-1] = sum/9
+
+            sum = nda_i[j*2-1:j*2+3, i*2-1:i*2+3]
+            cf_i[j-1, i-1] = sum/16
+
+    cat_0 = cf_m == 0
+    cat_1 = (cf_m >= 0.1) & (cf_m < 0.13)
+    cat_2 = (cf_m >= 0.2) & (cf_m < 0.23)
+    cat_3 = (cf_m >= 0.3) & (cf_m < 0.34)
+    cat_4 = (cf_m >= 0.4) & (cf_m < 0.45)
+    cat_5 = (cf_m >= 0.5) & (cf_m < 0.56)
+    cat_6 = (cf_m >= 0.6) & (cf_m < 0.67)
+    cat_7 = (cf_m >= 0.7) & (cf_m < 0.78)
+    cat_8 = (cf_m >= 0.8) & (cf_m < 0.89)
+    cat_9 = cf_m == 1.0
+
+    cf_m[cat_0] = 0
+    cf_m[cat_1] = 1
+    cf_m[cat_2] = 2
+    cf_m[cat_3] = 3
+    cf_m[cat_4] = 4
+    cf_m[cat_5] = 5
+    cf_m[cat_6] = 6
+    cf_m[cat_7] = 7
+    cf_m[cat_8] = 8
+    cf_m[cat_9] = 9
+
+
 if __name__ == "__main__":
     nn = SRCNN()
     nn.run('matchup_filename')