From cbf3d8ee53e56b33d3fc0f5975b54eef7852b2a4 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Mon, 29 Nov 2021 16:24:53 -0600
Subject: [PATCH] throw away unused code
---
modules/icing/pirep_goes.py | 82 +++++++++++++++++--------------------
1 file changed, 37 insertions(+), 45 deletions(-)
diff --git a/modules/icing/pirep_goes.py b/modules/icing/pirep_goes.py
index 377843f6..1d23dc59 100644
--- a/modules/icing/pirep_goes.py
+++ b/modules/icing/pirep_goes.py
@@ -1796,51 +1796,6 @@ def analyze_moon_phase(icing_dict):
print(len(icing_dict), cnt)
-def collect(icing_times, icing_intensity, jdays=['071', '072', '074', '079', '080', '081', '082', '084', '085', '088',
- '107', '110', '113', '117', '119', '129', '132', '134', '139', '150', '164', '203', '205', '232', '252', '254']):
-
- nbins = 6
-
- keep_idxs_yes = [[[] for j in range(nbins)] for i in range(len(jdays))]
- keep_idxs_no = [[[] for j in range(nbins)] for i in range(len(jdays))]
- ice_intsy = [[[] for j in range(7)] for i in range(len(jdays))]
-
- for jidx, jd in enumerate(jdays):
- _, ts_edges = make_times('2019-' + jd + '_00:00', None, num_steps=6, format_code='%Y-%j_%H:%M', hours=4)
-
- fltr_times, fltr_idxs = time_filter_2(icing_times, '2019-'+jd+'_00:00', '2019-'+jd+'_23:59', format_code='%Y-%j_%H:%M')
- bin_idxs = find_bin_index(np.array(ts_edges), np.array(fltr_times))
-
- for cnt, bi in enumerate(bin_idxs):
- #keep_idxs[jidx][bi].append(fltr_idxs[cnt])
- k = fltr_idxs[cnt]
- if icing_intensity[k] == -1:
- keep_idxs_no[jidx][bi].append(k)
- else:
- keep_idxs_yes[jidx][bi].append(k)
-
- ice_intsy[jidx][0] = np.sum(icing_intensity[fltr_idxs] == -1)
- ice_intsy[jidx][1] = np.sum(icing_intensity[fltr_idxs] == 1)
- ice_intsy[jidx][2] = np.sum(icing_intensity[fltr_idxs] == 2)
- ice_intsy[jidx][3] = np.sum(icing_intensity[fltr_idxs] == 3)
- ice_intsy[jidx][4] = np.sum(icing_intensity[fltr_idxs] == 4)
- ice_intsy[jidx][5] = np.sum(icing_intensity[fltr_idxs] == 5)
- ice_intsy[jidx][6] = np.sum(icing_intensity[fltr_idxs] == 6)
-
- print(jd, ' --------------------')
- for j in range(nbins):
- print(len(keep_idxs_no[jidx][j]), len(keep_idxs_yes[jidx][j]))
- print('no icing: ', ice_intsy[jidx][0])
- print('icing 1: ', ice_intsy[jidx][1])
- print('icing 2: ', ice_intsy[jidx][2])
- print('icing 3: ', ice_intsy[jidx][3])
- print('icing 4: ', ice_intsy[jidx][4])
- print('icing 5: ', ice_intsy[jidx][5])
- print('icing 6: ', ice_intsy[jidx][6])
-
- return keep_idxs_no, keep_idxs_yes, ice_intsy
-
-
def tiles_info(filename):
h5f = h5py.File(filename, 'r')
iint = h5f['icing_intensity'][:]
@@ -1854,6 +1809,43 @@ def tiles_info(filename):
print('Icing 6: ', np.sum(iint == 6))
+def analyze(preds_file):
+ h5f = h5py.File('/Users/tomrink/data/icing_ml/tiles_202109240000_202111152359_l1b_test_v3_DAY.h5', 'r')
+ nda = h5f['flight_altitude'][:]
+
+ 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
+
+ labels, prob_avg, cm_avg = pickle.load(open(preds_file, 'rb'))
+
+ print(np.sum(nda == 0), np.sum(nda == 1), np.sum(nda == 2), np.sum(nda == 3), np.sum(nda == 4))
+ print('---------------------------')
+ print(np.histogram(nda[labels == 0]))
+ print('---------------------------')
+ print(np.histogram(nda[labels == 1]))
+ print('-----------------------------')
+
+ preds = np.where(prob_avg > 0.5, 1, 0)
+
+ true_ice = (labels == 1) & (preds == 1)
+ false_ice = (labels == 0) & (preds == 1)
+
+ print(np.histogram(nda[true_ice]))
+ print('---------------------------')
+ print(np.histogram(nda[false_ice]))
+ print('---------------------------')
+
+ true_no_ice = (labels == 0) & (preds == 0)
+ false_no_ice = (labels == 1) & (preds == 0)
+
+ print(np.histogram(nda[true_no_ice]))
+ print('---------------------------')
+ print(np.histogram(nda[false_no_ice]))
+
+
def get_training_parameters(day_night='DAY', l1b_andor_l2='BOTH'):
if day_night == 'DAY':
train_params_l2 = ['cld_height_acha', 'cld_geo_thick', 'cld_temp_acha', 'cld_press_acha', 'supercooled_cloud_fraction',
--
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