From be2fe53aaeb93adfda6ce5ba25133c8e7b70be48 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Mon, 21 Nov 2022 14:34:40 -0600
Subject: [PATCH] minor...
---
modules/deeplearning/icing_fcn.py | 18 +++++++++++++++++-
1 file changed, 17 insertions(+), 1 deletion(-)
diff --git a/modules/deeplearning/icing_fcn.py b/modules/deeplearning/icing_fcn.py
index ccceb7de..0da9aa42 100644
--- a/modules/deeplearning/icing_fcn.py
+++ b/modules/deeplearning/icing_fcn.py
@@ -543,8 +543,22 @@ class IcingIntensityFCN:
h5f = self.h5f_l1b_tst
else:
h5f = self.h5f_l2_tst
+
time = h5f['time']
+ flt_alt = h5f['flight_altitude'][:]
tst_idxs = np.arange(time.shape[0])
+
+ if self.flight_level == 0:
+ tst_idxs = tst_idxs[np.logical_and(flt_alt >= 0, flt_alt < 2000)]
+ elif self.flight_level == 1:
+ tst_idxs = tst_idxs[np.logical_and(flt_alt >= 2000, flt_alt < 4000)]
+ elif self.flight_level == 2:
+ tst_idxs = tst_idxs[np.logical_and(flt_alt >= 4000, flt_alt < 6000)]
+ elif self.flight_level == 3:
+ tst_idxs = tst_idxs[np.logical_and(flt_alt >= 6000, flt_alt < 8000)]
+ elif self.flight_level == 4:
+ tst_idxs = tst_idxs[np.logical_and(flt_alt >= 8000, flt_alt < 15000)]
+
self.num_data_samples = len(tst_idxs)
self.get_test_dataset(tst_idxs)
@@ -1081,7 +1095,8 @@ class IcingIntensityFCN:
self.do_evaluate(ckpt_dir)
-def run_restore_static(filename_l1b, filename_l2, ckpt_dir_s_path, day_night='DAY', l1b_or_l2='both', use_flight_altitude=False):
+def run_restore_static(filename_l1b, filename_l2, ckpt_dir_s_path, day_night='DAY', l1b_or_l2='both',
+ use_flight_altitude=False, flight_level=0):
ckpt_dir_s = os.listdir(ckpt_dir_s_path)
cm_s = []
prob_s = []
@@ -1092,6 +1107,7 @@ def run_restore_static(filename_l1b, filename_l2, ckpt_dir_s_path, day_night='DA
if not os.path.isdir(ckpt_dir):
continue
nn = IcingIntensityFCN(day_night=day_night, l1b_or_l2=l1b_or_l2, use_flight_altitude=use_flight_altitude)
+ nn.flight_level = flight_level
nn.run_restore(filename_l1b, filename_l2, ckpt_dir)
cm_s.append(tf.math.confusion_matrix(nn.test_labels.flatten(), nn.test_preds.flatten()))
prob_s.append(nn.test_probs.flatten())
--
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