From 2c9bd34ee40c506bdc990887c2f74807844833c8 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Sat, 25 Sep 2021 08:27:03 -0500
Subject: [PATCH] snapshot...
---
modules/icing/pirep_goes.py | 44 +++++++++++++++++++++++++++++++++++++
1 file changed, 44 insertions(+)
diff --git a/modules/icing/pirep_goes.py b/modules/icing/pirep_goes.py
index b808b62d..92b3be03 100644
--- a/modules/icing/pirep_goes.py
+++ b/modules/icing/pirep_goes.py
@@ -1490,6 +1490,50 @@ def run_mean_std_2(check_cloudy=False, no_icing_to_icing_ratio=5, params=train_p
pickle.dump(mean_std_dct, f)
f.close()
+
+def run_mean_std_3(train_file_path, check_cloudy=False, params=train_params_day):
+
+ # params = ['cld_height_acha', 'cld_geo_thick', 'cld_press_acha', 'supercooled_cloud_fraction', 'cld_temp_acha', 'cld_opd_acha',
+ # 'cld_reff_acha', 'cld_reff_dcomp', 'cld_reff_dcomp_1', 'cld_reff_dcomp_2', 'cld_reff_dcomp_3',
+ # 'cld_opd_dcomp', 'cld_opd_dcomp_1', 'cld_opd_dcomp_2', 'cld_opd_dcomp_3', 'cld_cwp_dcomp', 'iwc_dcomp',
+ # 'lwc_dcomp', 'cld_emiss_acha', 'conv_cloud_fraction']
+ #check_cloudy = True
+
+ params = ['temp_10_4um_nom', 'temp_11_0um_nom', 'temp_12_0um_nom', 'temp_13_3um_nom', 'temp_3_75um_nom',
+ 'temp_6_2um_nom', 'temp_6_7um_nom', 'temp_7_3um_nom', 'temp_8_5um_nom', 'temp_9_7um_nom',
+ 'refl_0_47um_nom', 'refl_0_65um_nom', 'refl_0_86um_nom', 'refl_1_38um_nom', 'refl_1_60um_nom']
+ check_cloudy = False
+
+ mean_std_lo_hi_dct = {}
+
+ h5f = h5py.File(train_file_path, 'r')
+
+ if check_cloudy:
+ cld_msk = h5f['cloud_mask'][:].flatten()
+
+ for dname in params:
+ data = h5f[dname][:,].flatten()
+
+ if check_cloudy:
+ keep = np.logical_or(cld_msk == 2, cld_msk == 3)
+ data = data[keep]
+
+ lo = np.nanmin(data)
+ hi = np.nanmax(data)
+ mean = np.nanmean(data)
+ data -= mean
+ std = np.nanstd(data)
+
+ print(dname,': ', mean, std, lo, hi)
+
+ mean_std_lo_hi_dct[dname] = (mean, std, lo, hi)
+
+ h5f.close()
+
+ f = open('/Users/tomrink/data/icing/mean_std_lo_hi_test.pkl', 'wb')
+ pickle.dump(mean_std_lo_hi_dct, f)
+ f.close()
+
# def split_data(num_obs, perc=0.2, skip=1, shuffle=True, seed=None):
# idxs = np.arange(num_obs)
# idxs = list(idxs)
--
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