From 563b761d8d9ed87d93e51514ee57b7e22164c3b5 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Thu, 24 Nov 2022 09:29:49 -0600
Subject: [PATCH] snapshot...

---
 modules/deeplearning/icing_fcn.py | 14 +-------------
 1 file changed, 1 insertion(+), 13 deletions(-)

diff --git a/modules/deeplearning/icing_fcn.py b/modules/deeplearning/icing_fcn.py
index 07edb445..1f25f8f8 100644
--- a/modules/deeplearning/icing_fcn.py
+++ b/modules/deeplearning/icing_fcn.py
@@ -555,17 +555,6 @@ class IcingIntensityFCN:
         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)
@@ -1103,7 +1092,7 @@ class IcingIntensityFCN:
 
 
 def run_restore_static(filename_l1b, filename_l2, ckpt_dir_s_path, day_night='DAY', l1b_or_l2='both',
-                       satellite='GOES16', use_flight_altitude=False, flight_level=0):
+                       satellite='GOES16', use_flight_altitude=False):
     ckpt_dir_s = os.listdir(ckpt_dir_s_path)
     cm_s = []
     prob_s = []
@@ -1114,7 +1103,6 @@ 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, satellite=satellite, 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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