add option to, or not, copy from /arcdata to local

modules/deeplearning/amv_raob.py

@@ -740,6 +740,8 @@ def get_images(lons, lats, timestamp, channel_list, half_width, step, do_norm=Fa
             return None, None, None, None
 
         # copy from archive to local drive
+        t0 = datetime.datetime.now().timestamp()
+        local_path = fC
         if COPY_GOES:
             local_path = goes_cache_dir + '/' + os.path.split(fC)[1]
             if not os.path.exists(local_path):
@@ -752,6 +754,8 @@ def get_images(lons, lats, timestamp, channel_list, half_width, step, do_norm=Fa
         except Exception as exc:
             print(exc)
             return None, None, None, None
+        t1 = datetime.datetime.now().timestamp()
+        print((t1-t0)*1000)
 
         bt_or_refl = None
         if pug_l1b_c.bt_or_refl == 'bt':
@@ -761,6 +765,7 @@ def get_images(lons, lats, timestamp, channel_list, half_width, step, do_norm=Fa
 
         if TRIPLET or CONV3D:
             # copy from archive to local drive
+            local_path_l = fL
             if COPY_GOES:
                 local_path_l = goes_cache_dir + '/' + os.path.split(fL)[1]
                 if not os.path.exists(local_path_l):
@@ -780,6 +785,7 @@ def get_images(lons, lats, timestamp, channel_list, half_width, step, do_norm=Fa
                 bt_or_refl_l = pug_l1b_l.refl
 
             # copy from archive to local drive
+            local_path_r = fR
             if COPY_GOES:
                 local_path_r = goes_cache_dir + '/' + os.path.split(fR)[1]
                 if not os.path.exists(local_path_r):
@@ -798,6 +804,7 @@ def get_images(lons, lats, timestamp, channel_list, half_width, step, do_norm=Fa
             elif pug_l1b_r.bt_or_refl == 'refl':
                 bt_or_refl_r = pug_l1b_r.refl
 
+        t0 = datetime.datetime.now().timestamp()
         if daynight != 'ANY':
             if step[ch_idx] == 1:
                 if geoloc_2km is None:
@@ -881,6 +888,8 @@ def get_images(lons, lats, timestamp, channel_list, half_width, step, do_norm=Fa
             if TRIPLET or CONV3D:
                 subprocess.call(['rm', local_path_l])
                 subprocess.call(['rm', local_path_r])
+        t1 = datetime.datetime.now().timestamp()
+        print((t1-t0)*1000)
 
     return np.array(data), np.array(data_l), np.array(data_r), np.array(idxs)