From 6f6ac7e642317adba35ccb6a335d0afd59a8a658 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Tue, 10 Jan 2023 14:48:49 -0600
Subject: [PATCH] snapshot...
---
modules/icing/pirep_goes.py | 234 ++++--------------------------------
1 file changed, 21 insertions(+), 213 deletions(-)
diff --git a/modules/icing/pirep_goes.py b/modules/icing/pirep_goes.py
index 31610465..51b99f7c 100644
--- a/modules/icing/pirep_goes.py
+++ b/modules/icing/pirep_goes.py
@@ -2422,138 +2422,6 @@ 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:
@@ -2573,42 +2441,20 @@ 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 < 15000)] = 2
-
- nda[np.logical_and(nda >= 0, nda < 3000)] = 0
- nda[np.logical_and(nda >= 3000, nda < 6000)] = 1
- # nda[np.logical_and(nda >= 5200, nda < 6000)] = 2
+ nda[np.logical_and(nda >= 300, nda < 4000)] = 0
+ nda[np.logical_and(nda >= 4000, nda < 7000)] = 1
+ nda[np.logical_and(nda >= 7000, nda < 15000)] = 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))
- print('No icing: ', np.sum((iint == 0) & (nda == 0)), np.sum((iint == 0) & (nda == 1)))
+ print('No icing: ', np.sum((iint == 0) & (nda == 0)), np.sum((iint == 0) & (nda == 1)), np.sum((iint == 0) & (nda == 2)))
print('---------------------------')
- print('Icing: ', np.sum((iint == 1) & (nda == 0)), np.sum((iint == 1) & (nda == 1)))
+ print('Icing: ', np.sum((iint == 1) & (nda == 0)), np.sum((iint == 1) & (nda == 1)), np.sum((iint == 1) & (nda == 2)))
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('----------------------------------------------------------')
print('----------------------------------------------------------')
- # return flt_alt[iint == 0], flt_alt[iint == 1]
if prob_avg is None:
return
@@ -2621,91 +2467,53 @@ def analyze(preds_file, labels, prob_avg, test_file):
true_no_ice = (labels == 0) & (preds == 0)
false_no_ice = (labels == 1) & (preds == 0)
- # tp = true_ice
- # tn = true_no_ice
- # fp = false_ice
- # fn = false_no_ice
-
tp_0 = np.sum(true_ice & (nda == 0))
tp_1 = np.sum(true_ice & (nda == 1))
+ tp_2 = np.sum(true_ice & (nda == 2))
tn_0 = np.sum(true_no_ice & (nda == 0))
tn_1 = np.sum(true_no_ice & (nda == 1))
+ tn_2 = np.sum(true_no_ice & (nda == 2))
fp_0 = np.sum(false_ice & (nda == 0))
fp_1 = np.sum(false_ice & (nda == 1))
+ fp_2 = np.sum(false_ice & (nda == 2))
fn_0 = np.sum(false_no_ice & (nda == 0))
fn_1 = np.sum(false_no_ice & (nda == 1))
+ fn_2 = np.sum(false_no_ice & (nda == 2))
recall_0 = tp_0 / (tp_0 + fn_0)
recall_1 = tp_1 / (tp_1 + fn_1)
+ recall_2 = tp_2 / (tp_2 + fn_2)
precision_0 = tp_0 / (tp_0 + fp_0)
precision_1 = tp_1 / (tp_1 + fp_1)
+ precision_2 = tp_2 / (tp_2 + fp_2)
mcc_0 = ((tp_0 * tn_0) - (fp_0 * fn_0)) / np.sqrt((tp_0 + fp_0) * (tp_0 + fn_0) * (tn_0 + fp_0) * (tn_0 + fn_0))
mcc_1 = ((tp_1 * tn_1) - (fp_1 * fn_1)) / np.sqrt((tp_1 + fp_1) * (tp_1 + fn_1) * (tn_1 + fp_1) * (tn_1 + fn_1))
-
-
- # precision = true_ice / (true_ice + false_ice)
- # recall = true_ice / (true_ice + false_no_ice)
- # f1 = 2 * (precision * recall) / (precision + recall)
- # tp = true_ice
- # tn = true_no_ice
- # fp = false_ice
- # fn = false_no_ice
- # mcc = ((tp * tn) - (fp * fn)) / np.sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))
+ mcc_2 = ((tp_2 * tn_2) - (fp_2 * fn_2)) / np.sqrt((tp_2 + fp_2) * (tp_2 + fn_2) * (tn_2 + fp_2) * (tn_2 + fn_2))
print('Total (Positive/Icing Prediction: ')
- print('True icing: ', np.sum(true_ice & (nda == 0)), np.sum(true_ice & (nda == 1)))
+ print('True icing: ', np.sum(true_ice & (nda == 0)), np.sum(true_ice & (nda == 1)), np.sum(true_ice & (nda == 2)))
print('-------------------------')
- print('False no icing (False Negative/Miss): ', np.sum(false_no_ice & (nda == 0)), np.sum(false_no_ice & (nda == 1)))
- # 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('False no icing (False Negative/Miss): ', np.sum(false_no_ice & (nda == 0)), np.sum(false_no_ice & (nda == 1)), np.sum(false_no_ice & (nda == 2)))
print('---------------------------------------------------')
print('---------------------------------------------------')
print('Total (Negative/No Icing Prediction: ')
- print('True no icing: ', np.sum(true_no_ice & (nda == 0)), np.sum(true_no_ice & (nda == 1)))
+ print('True no icing: ', np.sum(true_no_ice & (nda == 0)), np.sum(true_no_ice & (nda == 1)), np.sum(true_no_ice & (nda == 2)))
print('-------------------------')
- print('* False icing (False Positive/False Alarm) *: ', np.sum(false_ice & (nda == 0)), np.sum(false_ice & (nda == 1)))
+ print('* False icing (False Positive/False Alarm) *: ', np.sum(false_ice & (nda == 0)), np.sum(false_ice & (nda == 1)), np.sum(false_ice & (nda == 2)))
print('-------------------------')
- print('Recall: ', recall_0, recall_1)
- print('Precision: ', precision_0, precision_1)
- print('MCC: ', mcc_0, mcc_1)
+ print('Recall: ', recall_0, recall_1, recall_2)
+ print('Precision: ', precision_0, precision_1, precision_2)
+ print('MCC: ', mcc_0, mcc_1, mcc_2)
+
+ return labels[nda == 0], prob_avg[nda == 0], labels[nda == 1], prob_avg[nda == 1], labels[nda == 2], prob_avg[nda == 2]
- # 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('------------')
def get_training_parameters(day_night='DAY', l1b_andor_l2='both'):
--
GitLab