From b5543be6a48593854fb4b32b79472bc9547a68b6 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Thu, 11 Feb 2021 12:22:47 -0600
Subject: [PATCH] add option to, or not, copy from /arcdata to local
---
modules/deeplearning/amv_raob.py | 9 +++++++++
1 file changed, 9 insertions(+)
diff --git a/modules/deeplearning/amv_raob.py b/modules/deeplearning/amv_raob.py
index 6317bdcb..52c16a22 100644
--- a/modules/deeplearning/amv_raob.py
+++ b/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)
--
GitLab