From d5ff6d1caa53982a719bc7e88b9097d2fd6dd9db Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Mon, 9 May 2022 10:05:57 -0500
Subject: [PATCH] snapshot...
---
modules/icing/pirep_goes.py | 306 ++++++++++++++++++++++++++++++------
1 file changed, 262 insertions(+), 44 deletions(-)
diff --git a/modules/icing/pirep_goes.py b/modules/icing/pirep_goes.py
index 38839a46..9bcab5bd 100644
--- a/modules/icing/pirep_goes.py
+++ b/modules/icing/pirep_goes.py
@@ -381,6 +381,149 @@ def run(pirep_dct, platform, outfile=None, outfile_l1b=None, dt_str_start=None,
create_file(outfile_l1b, data_dct, l1b_ds_list, l1b_ds_types, lon_c, lat_c, time_s, fl_alt_s, ice_int_s, unq_ids)
+def run_viirs(pirep_dct, platform='VIIRS', outfile=None, outfile_l1b=None, dt_str_start=None, dt_str_end=None):
+ time_keys = list(pirep_dct.keys())
+ l1b_grd_dct = {name: [] for name in l1b_ds_list}
+ ds_grd_dct = {name: [] for name in ds_list}
+
+ t_start = None
+ t_end = None
+ if (dt_str_start is not None) and (dt_str_end is not None):
+ dto = datetime.datetime.strptime(dt_str_start, '%Y-%m-%d_%H:%M').replace(tzinfo=timezone.utc)
+ dto.replace(tzinfo=timezone.utc)
+ t_start = dto.timestamp()
+
+ dto = datetime.datetime.strptime(dt_str_end, '%Y-%m-%d_%H:%M').replace(tzinfo=timezone.utc)
+ dto.replace(tzinfo=timezone.utc)
+ t_end = dto.timestamp()
+
+ lon_s = np.zeros(1)
+ lat_s = np.zeros(1)
+ last_clvr_file = None
+ last_h5f = None
+ nav = None
+
+ lon_c = []
+ lat_c = []
+ time_s = []
+ fl_alt_s = []
+ ice_int_s = []
+ unq_ids = []
+ num_rpts = 0
+ num_rpts_match = 0
+
+ for idx, time in enumerate(time_keys):
+ if t_start is not None:
+ if time < t_start:
+ continue
+ if time > t_end:
+ continue
+ num_rpts += 1
+
+ try:
+ clvr_ds = get_clavrx_datasource(time, platform)
+ except Exception:
+ print('run: Problem retrieving Datasource')
+ continue
+
+ clvr_file = clvr_ds.get_file_containing_time(time)[0]
+ if clvr_file is None:
+ continue
+
+ if clvr_file != last_clvr_file:
+ try:
+ h5f = h5py.File(clvr_file, 'r')
+ nav = clvr_ds.get_navigation(h5f)
+ except Exception:
+ if h5f is not None:
+ h5f.close()
+ print('Problem with file: ', clvr_file)
+ continue
+ if last_h5f is not None:
+ last_h5f.close()
+ last_h5f = h5f
+ last_clvr_file = clvr_file
+ else:
+ h5f = last_h5f
+
+ cc = ll = -1
+ reports = pirep_dct[time]
+ for tup in reports:
+ lat, lon, fl, I, uid, rpt_str = tup
+ lat_s[0] = lat
+ lon_s[0] = lon
+ print(' ',lon, lat)
+ if not nav.check_inside(lon, lat):
+ print('missed range check')
+ continue
+
+ cc_a, ll_a = nav.earth_to_lc_s(lon_s, lat_s) # non-navigable, skip
+ if cc_a[0] < 0:
+ print('cant navigate')
+ continue
+
+ if cc_a[0] == cc and ll_a[0] == ll: # time adjacent duplicate, skip
+ continue
+ else:
+ cc = cc_a[0]
+ ll = ll_a[0]
+
+ cnt_a = 0
+ for didx, ds_name in enumerate(ds_list):
+ gvals = get_grid_values(h5f, ds_name, ll_a[0], cc_a[0], 20, fill_value_name=None, range_name=ds_range[didx], fill_value=ds_fill[didx])
+ if gvals is not None:
+ ds_grd_dct[ds_name].append(gvals)
+ cnt_a += 1
+
+ cnt_b = 0
+ for didx, ds_name in enumerate(l1b_ds_list):
+ gvals = get_grid_values(h5f, ds_name, ll_a[0], cc_a[0], 20, fill_value_name=None, range_name=l1b_ds_range[didx], fill_value=l1b_ds_fill[didx])
+ if gvals is not None:
+ l1b_grd_dct[ds_name].append(gvals)
+ cnt_b += 1
+
+ if cnt_a > 0 and cnt_a != len(ds_list):
+ raise GenericException('weirdness')
+ if cnt_b > 0 and cnt_b != len(l1b_ds_list):
+ raise GenericException('weirdness')
+
+ if cnt_a == len(ds_list) and cnt_b == len(l1b_ds_list):
+ lon_c.append(lon_s[0])
+ lat_c.append(lat_s[0])
+ time_s.append(time)
+ fl_alt_s.append(fl)
+ ice_int_s.append(I)
+ unq_ids.append(uid)
+ print('num reports: ', num_rpts)
+ if len(time_s) == 0:
+ return
+
+ t_start = time_s[0]
+ t_end = time_s[len(time_s)-1]
+
+ data_dct = {}
+ for ds_name in ds_list:
+ data_dct[ds_name] = np.array(ds_grd_dct[ds_name])
+ lon_c = np.array(lon_c)
+ lat_c = np.array(lat_c)
+ time_s = np.array(time_s)
+ fl_alt_s = np.array(fl_alt_s)
+ ice_int_s = np.array(ice_int_s)
+ unq_ids = np.array(unq_ids)
+
+ if outfile is not None:
+ outfile = add_time_range_to_filename(outfile, t_start, t_end)
+ create_file(outfile, data_dct, ds_list, ds_types, lon_c, lat_c, time_s, fl_alt_s, ice_int_s, unq_ids)
+
+ data_dct = {}
+ for ds_name in l1b_ds_list:
+ data_dct[ds_name] = np.array(l1b_grd_dct[ds_name])
+
+ if outfile_l1b is not None:
+ outfile_l1b = add_time_range_to_filename(outfile_l1b, t_start, t_end)
+ create_file(outfile_l1b, data_dct, l1b_ds_list, l1b_ds_types, lon_c, lat_c, time_s, fl_alt_s, ice_int_s, unq_ids)
+
+
def pirep_info(pirep_dct):
time_keys = list(pirep_dct.keys())
@@ -1710,6 +1853,26 @@ def run_mean_std_3(train_file_path, check_cloudy=False, params=train_params_day)
pickle.dump(mean_std_lo_hi_dct, f)
f.close()
+# Special VIIRS date/times -----------------------------------------------------------
+# time_ranges = [[get_timestamp('2019-03-12_00:00'), get_timestamp('2019-03-15_23:59')],
+# [get_timestamp('2019-03-20_00:00'), get_timestamp('2019-03-26_23:59')],
+# [get_timestamp('2019-03-29_00:00'), get_timestamp('2019-03-29_23:59')],
+# [get_timestamp('2019-04-17_00:00'), get_timestamp('2019-04-17_23:59')],
+# [get_timestamp('2019-04-20_00:00'), get_timestamp('2019-04-20_23:59')],
+# [get_timestamp('2019-04-23_00:00'), get_timestamp('2019-04-23_23:59')],
+# [get_timestamp('2019-04-27_00:00'), get_timestamp('2019-04-27_23:59')],
+# [get_timestamp('2019-04-29_00:00'), get_timestamp('2019-04-29_23:59')],
+# [get_timestamp('2019-05-09_00:00'), get_timestamp('2019-05-09_23:59')],
+# [get_timestamp('2019-05-12_00:00'), get_timestamp('2019-05-12_23:59')],
+# [get_timestamp('2019-05-14_00:00'), get_timestamp('2019-05-14_23:59')],
+# [get_timestamp('2019-05-19_00:00'), get_timestamp('2019-05-19_23:59')],
+# [get_timestamp('2019-06-13_00:00'), get_timestamp('2019-06-13_23:59')],
+# [get_timestamp('2019-07-22_00:00'), get_timestamp('2019-07-22_23:59')],
+# [get_timestamp('2019-07-24_00:00'), get_timestamp('2019-07-24_29:59')],
+# [get_timestamp('2019-08-20_00:00'), get_timestamp('2019-08-20_23:59')],
+# [get_timestamp('2019-09-09_00:00'), get_timestamp('2019-09-09_23:59')],
+# [get_timestamp('2019-09-09_00:00'), get_timestamp('2019-09-11_23:59')]]
+
def split_data(times):
time_idxs = np.arange(times.shape[0])
@@ -1732,6 +1895,35 @@ def split_data(times):
[get_timestamp('2022-03-01_00:00'), get_timestamp('2022-03-07_23:59')],
[get_timestamp('2022-04-01_00:00'), get_timestamp('2022-04-04_23:59')]]
+ # time_ranges_lunar = [[get_timestamp('2018-01-01_00:00'), get_timestamp('2018-01-06_23:59')],
+ # [get_timestamp('2018-03-01_00:00'), get_timestamp('2018-03-06_23:59')],
+ # [get_timestamp('2018-05-01_00:00'), get_timestamp('2018-05-06_23:59')],
+ # [get_timestamp('2018-07-01_00:00'), get_timestamp('2018-07-06_23:59')],
+ # [get_timestamp('2018-09-01_00:00'), get_timestamp('2018-09-06_23:59')],
+ # [get_timestamp('2018-11-01_00:00'), get_timestamp('2018-11-06_23:59')],
+ # [get_timestamp('2019-01-01_00:00'), get_timestamp('2019-01-06_23:59')],
+ # #[get_timestamp('2019-03-01_00:00'), get_timestamp('2019-03-07_23:59')],
+ # [get_timestamp('2019-03-12_00:00'), get_timestamp('2019-03-15_23:59')],
+ # [get_timestamp('2019-03-20_00:00'), get_timestamp('2019-03-26_23:59')],
+ # [get_timestamp('2019-03-29_00:00'), get_timestamp('2019-03-29_23:59')],
+ # [get_timestamp('2019-05-01_00:00'), get_timestamp('2019-05-03_23:59')],
+ # [get_timestamp('2019-05-09_00:00'), get_timestamp('2019-05-09_23:59')],
+ # [get_timestamp('2019-05-12_00:00'), get_timestamp('2019-05-12_23:59')],
+ # [get_timestamp('2019-05-14_00:00'), get_timestamp('2019-05-14_23:59')],
+ # [get_timestamp('2019-05-19_00:00'), get_timestamp('2019-05-19_23:59')],
+ # [get_timestamp('2019-07-01_00:00'), get_timestamp('2019-07-05_23:59')],
+ # [get_timestamp('2019-07-22_00:00'), get_timestamp('2019-07-22_23:59')],
+ # [get_timestamp('2019-07-24_00:00'), get_timestamp('2019-07-24_23:59')],
+ # [get_timestamp('2019-09-01_00:00'), get_timestamp('2019-09-05_23:59')],
+ # [get_timestamp('2019-09-09_00:00'), get_timestamp('2019-09-09_23:59')],
+ # [get_timestamp('2019-09-09_00:00'), get_timestamp('2019-09-11_23:59')],
+ # [get_timestamp('2019-11-01_00:00'), get_timestamp('2019-11-07_23:59')],
+ # [get_timestamp('2021-09-24_00:00'), get_timestamp('2021-10-01_23:59')],
+ # [get_timestamp('2021-11-01_00:00'), get_timestamp('2021-11-07_23:59')],
+ # [get_timestamp('2022-01-01_00:00'), get_timestamp('2022-01-07_23:59')],
+ # [get_timestamp('2022-03-01_00:00'), get_timestamp('2022-03-07_23:59')],
+ # [get_timestamp('2022-04-01_00:00'), get_timestamp('2022-04-04_23:59')]]
+
keep_out = 10800 # 3 hrs
vld_time_idxs = []
@@ -1762,6 +1954,32 @@ def split_data(times):
[get_timestamp('2022-03-26_00:00'), get_timestamp('2022-03-30_23:59')],
[get_timestamp('2022-04-07_00:00'), get_timestamp('2022-04-10_23:59')]]
+ # time_ranges_lunar = [[get_timestamp('2018-02-01_00:00'), get_timestamp('2018-02-04_23:59')],
+ # [get_timestamp('2018-04-01_00:00'), get_timestamp('2018-04-04_23:59')],
+ # [get_timestamp('2018-06-01_00:00'), get_timestamp('2018-06-04_23:59')],
+ # [get_timestamp('2018-08-01_00:00'), get_timestamp('2018-08-04_23:59')],
+ # [get_timestamp('2018-10-01_00:00'), get_timestamp('2018-10-04_23:59')],
+ # [get_timestamp('2018-12-01_00:00'), get_timestamp('2018-12-04_23:59')],
+ # [get_timestamp('2019-02-01_00:00'), get_timestamp('2019-02-04_23:59')],
+ # #[get_timestamp('2019-04-01_00:00'), get_timestamp('2019-04-04_23:59')],
+ # [get_timestamp('2019-04-17_00:00'), get_timestamp('2019-04-17_23:59')],
+ # [get_timestamp('2019-04-20_00:00'), get_timestamp('2019-04-20_23:59')],
+ # [get_timestamp('2019-04-23_00:00'), get_timestamp('2019-04-23_23:59')],
+ # [get_timestamp('2019-04-27_00:00'), get_timestamp('2019-04-27_23:59')],
+ # [get_timestamp('2019-04-29_00:00'), get_timestamp('2019-04-29_23:59')],
+ # [get_timestamp('2019-06-01_00:00'), get_timestamp('2019-06-03_23:59')],
+ # [get_timestamp('2019-06-13_00:00'), get_timestamp('2019-06-13_23:59')],
+ # [get_timestamp('2019-08-01_00:00'), get_timestamp('2019-08-03_23:59')],
+ # [get_timestamp('2019-08-20_00:00'), get_timestamp('2019-08-20_23:59')],
+ # [get_timestamp('2019-10-01_00:00'), get_timestamp('2019-10-04_23:59')],
+ # [get_timestamp('2019-12-01_00:00'), get_timestamp('2019-12-04_23:59')],
+ #
+ # [get_timestamp('2021-10-05_00:00'), get_timestamp('2021-10-10_23:59')],
+ # [get_timestamp('2021-12-01_00:00'), get_timestamp('2021-12-04_23:59')],
+ # [get_timestamp('2022-02-01_00:00'), get_timestamp('2022-02-04_23:59')],
+ # [get_timestamp('2022-03-26_00:00'), get_timestamp('2022-03-30_23:59')],
+ # [get_timestamp('2022-04-07_00:00'), get_timestamp('2022-04-10_23:59')]]
+
tst_time_idxs = []
for t_rng in time_ranges:
t_rng[0] -= keep_out
@@ -2002,7 +2220,7 @@ def tiles_info(filename):
print('Icing 6: ', np.sum(iint == 6))
-def analyze(preds_file, test_file='/Users/tomrink/data/icing_ml/tiles_202109240000_202111212359_l2_test_v3_DAY.h5'):
+def analyze(preds_file, labels, prob_avg, test_file):
h5f = h5py.File(test_file, 'r')
nda = h5f['flight_altitude'][:]
iint = h5f['icing_intensity'][:]
@@ -2010,6 +2228,8 @@ def analyze(preds_file, test_file='/Users/tomrink/data/icing_ml/tiles_2021092400
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)
@@ -2028,106 +2248,104 @@ def analyze(preds_file, test_file='/Users/tomrink/data/icing_ml/tiles_2021092400
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)]))
- print(np.nanmean(cld_dz[(nda == 0) & (iint == 1)]), np.nanmean(cld_hgt[(nda == 0) & (iint == 1)]))
+ 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)]))
- print(np.nanmean(cld_dz[(nda == 1) & (iint == 1)]), np.nanmean(cld_hgt[(nda == 1) & (iint == 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)]))
- print(np.nanmean(cld_dz[(nda == 2) & (iint == 1)]), np.nanmean(cld_hgt[(nda == 2) & (iint == 1)]))
+ 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)]))
- print(np.nanmean(cld_dz[(nda == 3) & (iint == 1)]), np.nanmean(cld_hgt[(nda == 3) & (iint == 1)]))
+ 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)]))
- print(np.nanmean(cld_dz[(nda == 4) & (iint == 1)]), np.nanmean(cld_hgt[(nda == 4) & (iint == 1)]))
- print('-----------------------------')
- print('----------------------------')
-
- if preds_file is None:
- return
+ 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('----------------------------------------------------------')
- labels, prob_avg, cm_avg = pickle.load(open(preds_file, 'rb'))
+ if preds_file is not None:
+ labels, prob_avg, cm_avg = pickle.load(open(preds_file, 'rb'))
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 icing (False Positive/Alarm): ', np.histogram(nda[false_ice], bins=5)[0])
+ 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_ice]), np.nanmean(cld_hgt[(nda == 0) & false_ice]))
- print(np.nanmean(cld_dz[(nda == 0) & true_ice]), np.nanmean(cld_hgt[(nda == 0) & true_ice]))
+ 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_ice]), np.nanmean(cld_hgt[(nda == 1) & false_ice]))
- print(np.nanmean(cld_dz[(nda == 1) & true_ice]), np.nanmean(cld_hgt[(nda == 1) & true_ice]))
+ 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_ice]), np.nanmean(cld_hgt[(nda == 2) & false_ice]))
- print(np.nanmean(cld_dz[(nda == 2) & true_ice]), np.nanmean(cld_hgt[(nda == 2) & true_ice]))
+ 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_ice]), np.nanmean(cld_hgt[(nda == 3) & false_ice]))
- print(np.nanmean(cld_dz[(nda == 3) & true_ice]), np.nanmean(cld_hgt[(nda == 3) & true_ice]))
+ 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_ice]), np.nanmean(cld_hgt[(nda == 4) & false_ice]))
- print(np.nanmean(cld_dz[(nda == 4) & true_ice]), np.nanmean(cld_hgt[(nda == 4) & true_ice]))
- print('-------------')
- print('-------------')
-
- true_no_ice = (labels == 0) & (preds == 0)
- false_no_ice = (labels == 1) & (preds == 0)
+ 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 no icing (False Negative/Miss) *: ', np.histogram(nda[false_no_ice], bins=5)[0])
+ 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_no_ice]), np.nanmean(cld_hgt[(nda == 0) & false_no_ice]))
- print(np.nanmean(cld_dz[(nda == 0) & true_no_ice]), np.nanmean(cld_hgt[(nda == 0) & true_no_ice]))
+ 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_no_ice]), np.nanmean(cld_hgt[(nda == 1) & false_no_ice]))
- print(np.nanmean(cld_dz[(nda == 1) & true_no_ice]), np.nanmean(cld_hgt[(nda == 1) & true_no_ice]))
+ 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_no_ice]), np.nanmean(cld_hgt[(nda == 2) & false_no_ice]))
- print(np.nanmean(cld_dz[(nda == 2) & true_no_ice]), np.nanmean(cld_hgt[(nda == 2) & true_no_ice]))
+ 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_no_ice]), np.nanmean(cld_hgt[(nda == 3) & false_no_ice]))
- print(np.nanmean(cld_dz[(nda == 3) & true_no_ice]), np.nanmean(cld_hgt[(nda == 3) & true_no_ice]))
+ 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_no_ice]), np.nanmean(cld_hgt[(nda == 4) & false_no_ice]))
- print(np.nanmean(cld_dz[(nda == 4) & true_no_ice]), np.nanmean(cld_hgt[(nda == 4) & true_no_ice]))
+ 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 get_training_parameters(day_night='DAY', l1b_andor_l2='both'):
--
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