From aa22e3ecd08bce6f42e63e50c93591c0818e44e0 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Tue, 29 Nov 2022 10:55:55 -0600
Subject: [PATCH] snapshot...
---
modules/icing/pirep_goes.py | 204 +++++++++++++++++++++++++++---------
1 file changed, 155 insertions(+), 49 deletions(-)
diff --git a/modules/icing/pirep_goes.py b/modules/icing/pirep_goes.py
index 8d2f9684..6b3d13e9 100644
--- a/modules/icing/pirep_goes.py
+++ b/modules/icing/pirep_goes.py
@@ -2258,6 +2258,138 @@ def tiles_info(filename):
print('Icing 6: ', np.sum(iint == 6))
+# def analyze(preds_file, labels, prob_avg, test_file):
+#
+# if preds_file is not None:
+# labels, prob_avg, cm_avg = pickle.load(open(preds_file, 'rb'))
+#
+# h5f = h5py.File(test_file, 'r')
+# nda = h5f['flight_altitude'][:]
+# iint = h5f['icing_intensity'][:]
+# cld_hgt = h5f['cld_height_acha'][:]
+# cld_dz = h5f['cld_geo_thick'][:]
+# cld_tmp = h5f['cld_temp_acha'][:]
+#
+# print('report altitude (m): ', np.histogram(nda, bins=12))
+#
+# iint = np.where(iint == -1, 0, iint)
+# iint = np.where(iint != 0, 1, iint)
+#
+# nda[np.logical_and(nda >= 0, nda < 2000)] = 0
+# nda[np.logical_and(nda >= 2000, nda < 4000)] = 1
+# nda[np.logical_and(nda >= 4000, nda < 6000)] = 2
+# nda[np.logical_and(nda >= 6000, nda < 8000)] = 3
+# nda[np.logical_and(nda >= 8000, nda < 15000)] = 4
+#
+# print(np.sum(nda == 0), np.sum(nda == 1), np.sum(nda == 2), np.sum(nda == 3), np.sum(nda == 4))
+# print('No icing: ', np.histogram(nda[iint == 0], bins=5)[0])
+# print('---------------------------')
+# print('Icing: ', np.histogram(nda[iint == 1], bins=5)[0])
+# print('---------------------------')
+#
+# print('No Icing(Negative): mean cld_dz, cld_hgt')
+# print('Icing(Positive): ", "')
+# print('level 0: ')
+# print(np.nanmean(cld_dz[(nda == 0) & (iint == 0)]), np.nanmean(cld_hgt[(nda == 0) & (iint == 0)]), np.nanmean(cld_tmp[(nda == 0) & (iint == 0)]))
+# print(np.nanmean(cld_dz[(nda == 0) & (iint == 1)]), np.nanmean(cld_hgt[(nda == 0) & (iint == 1)]), np.nanmean(cld_tmp[(nda == 0) & (iint == 1)]))
+# print('------------')
+#
+# print('level 1: ')
+# print(np.nanmean(cld_dz[(nda == 1) & (iint == 0)]), np.nanmean(cld_hgt[(nda == 1) & (iint == 0)]), np.nanmean(cld_tmp[(nda == 1) & (iint == 0)]))
+# print(np.nanmean(cld_dz[(nda == 1) & (iint == 1)]), np.nanmean(cld_hgt[(nda == 1) & (iint == 1)]), np.nanmean(cld_tmp[(nda == 1) & (iint == 1)]))
+# print('------------')
+#
+# print('level 2: ')
+# print(np.nanmean(cld_dz[(nda == 2) & (iint == 0)]), np.nanmean(cld_hgt[(nda == 2) & (iint == 0)]), np.nanmean(cld_tmp[(nda == 2) & (iint == 0)]))
+# print(np.nanmean(cld_dz[(nda == 2) & (iint == 1)]), np.nanmean(cld_hgt[(nda == 2) & (iint == 1)]), np.nanmean(cld_tmp[(nda == 2) & (iint == 1)]))
+# print('------------')
+#
+# print('level 3: ')
+# print(np.nanmean(cld_dz[(nda == 3) & (iint == 0)]), np.nanmean(cld_hgt[(nda == 3) & (iint == 0)]), np.nanmean(cld_tmp[(nda == 3) & (iint == 0)]))
+# print(np.nanmean(cld_dz[(nda == 3) & (iint == 1)]), np.nanmean(cld_hgt[(nda == 3) & (iint == 1)]), np.nanmean(cld_tmp[(nda == 3) & (iint == 1)]))
+# print('------------')
+#
+# print('level 4: ')
+# print(np.nanmean(cld_dz[(nda == 4) & (iint == 0)]), np.nanmean(cld_hgt[(nda == 4) & (iint == 0)]), np.nanmean(cld_tmp[(nda == 4) & (iint == 0)]))
+# print(np.nanmean(cld_dz[(nda == 4) & (iint == 1)]), np.nanmean(cld_hgt[(nda == 4) & (iint == 1)]), np.nanmean(cld_tmp[(nda == 4) & (iint == 1)]))
+# print('----------------------------------------------------------')
+# print('----------------------------------------------------------')
+#
+# if prob_avg is None:
+# return
+#
+# preds = np.where(prob_avg > 0.5, 1, 0)
+#
+# true_ice = (labels == 1) & (preds == 1)
+# false_ice = (labels == 0) & (preds == 1)
+#
+# true_no_ice = (labels == 0) & (preds == 0)
+# false_no_ice = (labels == 1) & (preds == 0)
+#
+# print('Total (Positive/Icing Prediction: ')
+# print('True icing: ', np.histogram(nda[true_ice], bins=5)[0])
+# print('-------------------------')
+# print('False no icing (False Negative/Miss): ', np.histogram(nda[false_no_ice], bins=5)[0])
+# print('By flight level:')
+# print('No Icing(Negative): mean cld_dz, cld_hgt')
+# print('Icing(Positive): ", "')
+# print('level 0: ')
+# print(np.nanmean(cld_dz[(nda == 0) & false_no_ice]), np.nanmean(cld_hgt[(nda == 0) & false_no_ice]), np.nanmean(cld_tmp[(nda == 0) & false_no_ice]))
+# print(np.nanmean(cld_dz[(nda == 0) & true_ice]), np.nanmean(cld_hgt[(nda == 0) & true_ice]), np.nanmean(cld_tmp[(nda == 0) & true_ice]))
+# print('------------')
+#
+# print('level 1: ')
+# print(np.nanmean(cld_dz[(nda == 1) & false_no_ice]), np.nanmean(cld_hgt[(nda == 1) & false_no_ice]), np.nanmean(cld_tmp[(nda == 1) & false_no_ice]))
+# print(np.nanmean(cld_dz[(nda == 1) & true_ice]), np.nanmean(cld_hgt[(nda == 1) & true_ice]), np.nanmean(cld_tmp[(nda == 1) & true_ice]))
+# print('------------')
+#
+# print('level 2: ')
+# print(np.nanmean(cld_dz[(nda == 2) & false_no_ice]), np.nanmean(cld_hgt[(nda == 2) & false_no_ice]), np.nanmean(cld_tmp[(nda == 2) & false_no_ice]))
+# print(np.nanmean(cld_dz[(nda == 2) & true_ice]), np.nanmean(cld_hgt[(nda == 2) & true_ice]), np.nanmean(cld_tmp[(nda == 2) & true_ice]))
+# print('------------')
+#
+# print('level 3: ')
+# print(np.nanmean(cld_dz[(nda == 3) & false_no_ice]), np.nanmean(cld_hgt[(nda == 3) & false_no_ice]), np.nanmean(cld_tmp[(nda == 3) & false_no_ice]))
+# print(np.nanmean(cld_dz[(nda == 3) & true_ice]), np.nanmean(cld_hgt[(nda == 3) & true_ice]), np.nanmean(cld_tmp[(nda == 3) & true_ice]))
+# print('------------')
+#
+# print('level 4: ')
+# print(np.nanmean(cld_dz[(nda == 4) & false_no_ice]), np.nanmean(cld_hgt[(nda == 4) & false_no_ice]), np.nanmean(cld_tmp[(nda == 4) & false_no_ice]))
+# print(np.nanmean(cld_dz[(nda == 4) & true_ice]), np.nanmean(cld_hgt[(nda == 4) & true_ice]), np.nanmean(cld_tmp[(nda == 4) & true_ice]))
+# print('---------------------------------------------------')
+# print('---------------------------------------------------')
+#
+# print('Total (Negative/No Icing Prediction: ')
+# print('True no icing: ', np.histogram(nda[true_no_ice], bins=5)[0])
+# print('-------------------------')
+# print('* False icing (False Positive/False Alarm) *: ', np.histogram(nda[false_ice], bins=5)[0])
+# print('-------------------------')
+#
+# print('level 0: ')
+# print(np.nanmean(cld_dz[(nda == 0) & false_ice]), np.nanmean(cld_hgt[(nda == 0) & false_ice]), np.nanmean(cld_tmp[(nda == 0) & false_ice]))
+# print(np.nanmean(cld_dz[(nda == 0) & true_no_ice]), np.nanmean(cld_hgt[(nda == 0) & true_no_ice]), np.nanmean(cld_tmp[(nda == 0) & true_no_ice]))
+# print('------------')
+#
+# print('level 1: ')
+# print(np.nanmean(cld_dz[(nda == 1) & false_ice]), np.nanmean(cld_hgt[(nda == 1) & false_ice]), np.nanmean(cld_tmp[(nda == 1) & false_ice]))
+# print(np.nanmean(cld_dz[(nda == 1) & true_no_ice]), np.nanmean(cld_hgt[(nda == 1) & true_no_ice]), np.nanmean(cld_tmp[(nda == 1) & true_no_ice]))
+# print('------------')
+#
+# print('level 2: ')
+# print(np.nanmean(cld_dz[(nda == 2) & false_ice]), np.nanmean(cld_hgt[(nda == 2) & false_ice]), np.nanmean(cld_tmp[(nda == 2) & false_ice]))
+# print(np.nanmean(cld_dz[(nda == 2) & true_no_ice]), np.nanmean(cld_hgt[(nda == 2) & true_no_ice]), np.nanmean(cld_tmp[(nda == 2) & true_no_ice]))
+# print('------------')
+#
+# print('level 3: ')
+# print(np.nanmean(cld_dz[(nda == 3) & false_ice]), np.nanmean(cld_hgt[(nda == 3) & false_ice]), np.nanmean(cld_tmp[(nda == 3) & false_ice]))
+# print(np.nanmean(cld_dz[(nda == 3) & true_no_ice]), np.nanmean(cld_hgt[(nda == 3) & true_no_ice]), np.nanmean(cld_tmp[(nda == 3) & true_no_ice]))
+# print('------------')
+#
+# print('level 4: ')
+# print(np.nanmean(cld_dz[(nda == 4) & false_ice]), np.nanmean(cld_hgt[(nda == 4) & false_ice]), np.nanmean(cld_tmp[(nda == 4) & false_ice]))
+# print(np.nanmean(cld_dz[(nda == 4) & true_no_ice]), np.nanmean(cld_hgt[(nda == 4) & true_no_ice]), np.nanmean(cld_tmp[(nda == 4) & true_no_ice]))
+#
+
def analyze(preds_file, labels, prob_avg, test_file):
if preds_file is not None:
@@ -2275,16 +2407,15 @@ def analyze(preds_file, labels, prob_avg, test_file):
iint = np.where(iint == -1, 0, iint)
iint = np.where(iint != 0, 1, iint)
- nda[np.logical_and(nda >= 0, nda < 2000)] = 0
- nda[np.logical_and(nda >= 2000, nda < 4000)] = 1
- nda[np.logical_and(nda >= 4000, nda < 6000)] = 2
- nda[np.logical_and(nda >= 6000, nda < 8000)] = 3
- nda[np.logical_and(nda >= 8000, nda < 15000)] = 4
+ nda[np.logical_and(nda >= 0, nda < 2500)] = 0
+ nda[np.logical_and(nda >= 2500, nda < 15000)] = 1
+ # nda[np.logical_and(nda >= 8000, nda < 15000)] = 2
- print(np.sum(nda == 0), np.sum(nda == 1), np.sum(nda == 2), np.sum(nda == 3), np.sum(nda == 4))
- print('No icing: ', np.histogram(nda[iint == 0], bins=5)[0])
+ #print(np.sum(nda == 0), np.sum(nda == 1), np.sum(nda == 2))
+ print(np.sum(nda == 0), np.sum(nda == 1))
+ print('No icing: ', np.histogram(nda[iint == 0], bins=2)[0])
print('---------------------------')
- print('Icing: ', np.histogram(nda[iint == 1], bins=5)[0])
+ print('Icing: ', np.histogram(nda[iint == 1], bins=2)[0])
print('---------------------------')
print('No Icing(Negative): mean cld_dz, cld_hgt')
@@ -2299,19 +2430,11 @@ def analyze(preds_file, labels, prob_avg, test_file):
print(np.nanmean(cld_dz[(nda == 1) & (iint == 1)]), np.nanmean(cld_hgt[(nda == 1) & (iint == 1)]), np.nanmean(cld_tmp[(nda == 1) & (iint == 1)]))
print('------------')
- print('level 2: ')
- print(np.nanmean(cld_dz[(nda == 2) & (iint == 0)]), np.nanmean(cld_hgt[(nda == 2) & (iint == 0)]), np.nanmean(cld_tmp[(nda == 2) & (iint == 0)]))
- print(np.nanmean(cld_dz[(nda == 2) & (iint == 1)]), np.nanmean(cld_hgt[(nda == 2) & (iint == 1)]), np.nanmean(cld_tmp[(nda == 2) & (iint == 1)]))
- print('------------')
+ # print('level 2: ')
+ # print(np.nanmean(cld_dz[(nda == 2) & (iint == 0)]), np.nanmean(cld_hgt[(nda == 2) & (iint == 0)]), np.nanmean(cld_tmp[(nda == 2) & (iint == 0)]))
+ # print(np.nanmean(cld_dz[(nda == 2) & (iint == 1)]), np.nanmean(cld_hgt[(nda == 2) & (iint == 1)]), np.nanmean(cld_tmp[(nda == 2) & (iint == 1)]))
+ # print('------------')
- print('level 3: ')
- print(np.nanmean(cld_dz[(nda == 3) & (iint == 0)]), np.nanmean(cld_hgt[(nda == 3) & (iint == 0)]), np.nanmean(cld_tmp[(nda == 3) & (iint == 0)]))
- print(np.nanmean(cld_dz[(nda == 3) & (iint == 1)]), np.nanmean(cld_hgt[(nda == 3) & (iint == 1)]), np.nanmean(cld_tmp[(nda == 3) & (iint == 1)]))
- print('------------')
-
- print('level 4: ')
- print(np.nanmean(cld_dz[(nda == 4) & (iint == 0)]), np.nanmean(cld_hgt[(nda == 4) & (iint == 0)]), np.nanmean(cld_tmp[(nda == 4) & (iint == 0)]))
- print(np.nanmean(cld_dz[(nda == 4) & (iint == 1)]), np.nanmean(cld_hgt[(nda == 4) & (iint == 1)]), np.nanmean(cld_tmp[(nda == 4) & (iint == 1)]))
print('----------------------------------------------------------')
print('----------------------------------------------------------')
@@ -2327,9 +2450,9 @@ def analyze(preds_file, labels, prob_avg, test_file):
false_no_ice = (labels == 1) & (preds == 0)
print('Total (Positive/Icing Prediction: ')
- print('True icing: ', np.histogram(nda[true_ice], bins=5)[0])
+ print('True icing: ', np.histogram(nda[true_ice], bins=2)[0])
print('-------------------------')
- print('False no icing (False Negative/Miss): ', np.histogram(nda[false_no_ice], bins=5)[0])
+ print('False no icing (False Negative/Miss): ', np.histogram(nda[false_no_ice], bins=2)[0])
print('By flight level:')
print('No Icing(Negative): mean cld_dz, cld_hgt')
print('Icing(Positive): ", "')
@@ -2343,26 +2466,18 @@ def analyze(preds_file, labels, prob_avg, test_file):
print(np.nanmean(cld_dz[(nda == 1) & true_ice]), np.nanmean(cld_hgt[(nda == 1) & true_ice]), np.nanmean(cld_tmp[(nda == 1) & true_ice]))
print('------------')
- print('level 2: ')
- print(np.nanmean(cld_dz[(nda == 2) & false_no_ice]), np.nanmean(cld_hgt[(nda == 2) & false_no_ice]), np.nanmean(cld_tmp[(nda == 2) & false_no_ice]))
- print(np.nanmean(cld_dz[(nda == 2) & true_ice]), np.nanmean(cld_hgt[(nda == 2) & true_ice]), np.nanmean(cld_tmp[(nda == 2) & true_ice]))
- print('------------')
-
- print('level 3: ')
- print(np.nanmean(cld_dz[(nda == 3) & false_no_ice]), np.nanmean(cld_hgt[(nda == 3) & false_no_ice]), np.nanmean(cld_tmp[(nda == 3) & false_no_ice]))
- print(np.nanmean(cld_dz[(nda == 3) & true_ice]), np.nanmean(cld_hgt[(nda == 3) & true_ice]), np.nanmean(cld_tmp[(nda == 3) & true_ice]))
- print('------------')
+ # print('level 2: ')
+ # print(np.nanmean(cld_dz[(nda == 2) & false_no_ice]), np.nanmean(cld_hgt[(nda == 2) & false_no_ice]), np.nanmean(cld_tmp[(nda == 2) & false_no_ice]))
+ # print(np.nanmean(cld_dz[(nda == 2) & true_ice]), np.nanmean(cld_hgt[(nda == 2) & true_ice]), np.nanmean(cld_tmp[(nda == 2) & true_ice]))
+ # print('------------')
- print('level 4: ')
- print(np.nanmean(cld_dz[(nda == 4) & false_no_ice]), np.nanmean(cld_hgt[(nda == 4) & false_no_ice]), np.nanmean(cld_tmp[(nda == 4) & false_no_ice]))
- print(np.nanmean(cld_dz[(nda == 4) & true_ice]), np.nanmean(cld_hgt[(nda == 4) & true_ice]), np.nanmean(cld_tmp[(nda == 4) & true_ice]))
print('---------------------------------------------------')
print('---------------------------------------------------')
print('Total (Negative/No Icing Prediction: ')
- print('True no icing: ', np.histogram(nda[true_no_ice], bins=5)[0])
+ print('True no icing: ', np.histogram(nda[true_no_ice], bins=2)[0])
print('-------------------------')
- print('* False icing (False Positive/False Alarm) *: ', np.histogram(nda[false_ice], bins=5)[0])
+ print('* False icing (False Positive/False Alarm) *: ', np.histogram(nda[false_ice], bins=2)[0])
print('-------------------------')
print('level 0: ')
@@ -2375,19 +2490,10 @@ def analyze(preds_file, labels, prob_avg, test_file):
print(np.nanmean(cld_dz[(nda == 1) & true_no_ice]), np.nanmean(cld_hgt[(nda == 1) & true_no_ice]), np.nanmean(cld_tmp[(nda == 1) & true_no_ice]))
print('------------')
- print('level 2: ')
- print(np.nanmean(cld_dz[(nda == 2) & false_ice]), np.nanmean(cld_hgt[(nda == 2) & false_ice]), np.nanmean(cld_tmp[(nda == 2) & false_ice]))
- print(np.nanmean(cld_dz[(nda == 2) & true_no_ice]), np.nanmean(cld_hgt[(nda == 2) & true_no_ice]), np.nanmean(cld_tmp[(nda == 2) & true_no_ice]))
- print('------------')
-
- print('level 3: ')
- print(np.nanmean(cld_dz[(nda == 3) & false_ice]), np.nanmean(cld_hgt[(nda == 3) & false_ice]), np.nanmean(cld_tmp[(nda == 3) & false_ice]))
- print(np.nanmean(cld_dz[(nda == 3) & true_no_ice]), np.nanmean(cld_hgt[(nda == 3) & true_no_ice]), np.nanmean(cld_tmp[(nda == 3) & true_no_ice]))
- print('------------')
-
- print('level 4: ')
- print(np.nanmean(cld_dz[(nda == 4) & false_ice]), np.nanmean(cld_hgt[(nda == 4) & false_ice]), np.nanmean(cld_tmp[(nda == 4) & false_ice]))
- print(np.nanmean(cld_dz[(nda == 4) & true_no_ice]), np.nanmean(cld_hgt[(nda == 4) & true_no_ice]), np.nanmean(cld_tmp[(nda == 4) & true_no_ice]))
+ # print('level 2: ')
+ # print(np.nanmean(cld_dz[(nda == 2) & false_ice]), np.nanmean(cld_hgt[(nda == 2) & false_ice]), np.nanmean(cld_tmp[(nda == 2) & false_ice]))
+ # print(np.nanmean(cld_dz[(nda == 2) & true_no_ice]), np.nanmean(cld_hgt[(nda == 2) & true_no_ice]), np.nanmean(cld_tmp[(nda == 2) & true_no_ice]))
+ # print('------------')
def get_training_parameters(day_night='DAY', l1b_andor_l2='both'):
--
GitLab