From daadf4161342b25aad77a683be1ecde57d0c2664 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Tue, 3 Jan 2023 11:32:38 -0600
Subject: [PATCH] snapshot...
---
modules/icing/pirep_goes.py | 121 +++++++++++++++++++++++++-----------
1 file changed, 85 insertions(+), 36 deletions(-)
diff --git a/modules/icing/pirep_goes.py b/modules/icing/pirep_goes.py
index 08e84c2b..e1564816 100644
--- a/modules/icing/pirep_goes.py
+++ b/modules/icing/pirep_goes.py
@@ -127,7 +127,10 @@ def get_goes_datasource(timestamp):
def get_grid_values(h5f, grid_name, j_c, i_c, half_width, num_j=None, num_i=None, scale_factor_name='scale_factor', add_offset_name='add_offset',
fill_value_name='_FillValue', range_name='actual_range', fill_value=None):
- hfds = h5f[grid_name]
+ try:
+ hfds = h5f[grid_name]
+ except Exception:
+ return None
attrs = hfds.attrs
if attrs is None:
raise GenericException('No attributes object for: '+grid_name)
@@ -337,9 +340,11 @@ def run(pirep_dct, platform, outfile=None, outfile_l1b=None, dt_str_start=None,
cnt_b += 1
if cnt_a > 0 and cnt_a != len(ds_list):
- raise GenericException('weirdness')
+ continue
+ #raise GenericException('weirdness')
if cnt_b > 0 and cnt_b != len(l1b_ds_list):
- raise GenericException('weirdness')
+ continue
+ #raise GenericException('weirdness')
if cnt_a == len(ds_list) and cnt_b == len(l1b_ds_list):
lon_c.append(lon_s[0])
@@ -2557,9 +2562,15 @@ 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 < 15000)] = 2
+ # 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 < 5000)] = 0
+ nda[np.logical_and(nda >= 5000, nda < 8000)] = 1
+ # nda[np.logical_and(nda >= 5200, 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))
print('No icing: ', np.sum((iint == 0) & (nda == 0)), np.sum((iint == 0) & (nda == 1)))
@@ -2599,27 +2610,63 @@ 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))
+
+ tn_0 = np.sum(true_no_ice & (nda == 0))
+ tn_1 = np.sum(true_no_ice & (nda == 1))
+
+ fp_0 = np.sum(false_ice & (nda == 0))
+ fp_1 = np.sum(false_ice & (nda == 1))
+
+ fn_0 = np.sum(false_no_ice & (nda == 0))
+ fn_1 = np.sum(false_no_ice & (nda == 1))
+
+ recall_0 = tp_0 / (tp_0 + fn_0)
+ recall_1 = tp_1 / (tp_1 + fn_1)
+
+ precision_0 = tp_0 / (tp_0 + fp_0)
+ precision_1 = tp_1 / (tp_1 + fp_1)
+
+ 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))
+
print('Total (Positive/Icing Prediction: ')
print('True icing: ', np.sum(true_ice & (nda == 0)), np.sum(true_ice & (nda == 1)))
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('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('---------------------------------------------------')
print('---------------------------------------------------')
@@ -2630,22 +2677,24 @@ def analyze(preds_file, labels, prob_avg, test_file):
print('* False icing (False Positive/False Alarm) *: ', np.sum(false_ice & (nda == 0)), np.sum(false_ice & (nda == 1)))
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('Recall: ', recall_0, recall_1)
+ print('Precision: ', precision_0, precision_1)
+ print('MCC: ', mcc_0, mcc_1)
- # 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('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('------------')
-
- return flt_alt[iint == 0], flt_alt[iint == 1]
+ #
+ # 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