use meshgrid

modules/deeplearning/icing_cnn.py

@@ -1006,15 +1006,12 @@ def run_evaluate_static(h5f, ckpt_dir_s_path, prob_thresh=0.5, satellite='GOES16
     else:
         preds = np.argmax(probs, axis=1)
 
-    ll, cc = np.meshgrid(ll, cc, indexing='ij')
-    #cc = np.array(cc)
-    #ll = np.array(ll)
-    cc = cc.flatten()
-    ll = ll.flatten()
+    ll_grd, cc_grd = np.meshgrid(ll, cc, indexing='ij')
+    cc_grd = cc_grd.flatten()
+    ll_grd = ll_grd.flatten()
     ice_mask = preds == 1
-    print(cc.shape, ll.shape, ice_mask.shape)
-    ice_cc = cc[ice_mask]
-    ice_ll = ll[ice_mask]
+    ice_cc = cc_grd[ice_mask]
+    ice_ll = ll_grd[ice_mask]
 
     nav = get_navigation(satellite, domain)
     ice_lons = []