From 2ff47ddebdaa2b303600308ed23c4892a6b2153f Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Mon, 30 Nov 2020 22:36:19 -0600
Subject: [PATCH] snapshot...
---
modules/aeolus/aeolus_amv.py | 43 ++++++++++++++++++------------------
1 file changed, 22 insertions(+), 21 deletions(-)
diff --git a/modules/aeolus/aeolus_amv.py b/modules/aeolus/aeolus_amv.py
index 76f75cdc..84569669 100644
--- a/modules/aeolus/aeolus_amv.py
+++ b/modules/aeolus/aeolus_amv.py
@@ -371,13 +371,14 @@ def analyze2(raob_to_amv_dct, raob_dct):
sidx = 5
pidx = 4
+ print('Number of AMVs: {0:d}'.format(num_good))
spd_min = good_amvs[:, sidx].min()
spd_max = good_amvs[:, sidx].max()
- print('spd min/max/mean: ', spd_min, spd_max, np.average(good_amvs[:, sidx]))
+ print('spd min/max/mean: {0:.2f} {1:.2f} {2:.2f}'.format(spd_min, spd_max, np.average(good_amvs[:, sidx])))
p_min = good_amvs[:, pidx].min()
p_max = good_amvs[:, pidx].max()
- print('pres min/max/mean: ', p_min, p_max, np.average(good_amvs[:, pidx]))
+ print('pres min/max/mean: {0:.2f} {1:.2f} {2:.2f}'.format(p_min, p_max, np.average(good_amvs[:, pidx])))
low = good_amvs[:, pidx] >= 700
mid = np.logical_and(good_amvs[:, pidx] < 700, good_amvs[:, pidx] > 400)
@@ -387,21 +388,21 @@ def analyze2(raob_to_amv_dct, raob_dct):
n_mid = np.sum(mid)
n_hgh = np.sum(hgh)
- print('% low: ', 100.0*(n_low/num_good))
- print('% mid: ', 100.0*(n_mid/num_good))
- print('% hgh: ', 100.0*(n_hgh/num_good))
+ print('% low: {0:.2f}'.format(100.0*(n_low/num_good)))
+ print('% mid: {0:.2f}'.format(100.0*(n_mid/num_good)))
+ print('% hgh: {0:.2f}'.format(100.0*(n_hgh/num_good)))
print('---------------------------')
- print('Low Spd min/max/mean: ', good_amvs[low, sidx].min(), good_amvs[low, sidx].max(), good_amvs[low,sidx].mean())
- print('Low Press min/max/mean: ', good_amvs[low, pidx].min(), good_amvs[low, pidx].max(), good_amvs[low, pidx].mean())
+ print('Low Spd min/max/mean: {0:.2f} {1:.2f} {2:.2f}'.format(good_amvs[low, sidx].min(), good_amvs[low, sidx].max(), good_amvs[low,sidx].mean()))
+ print('Low Press min/max/mean: {0:.2f} {1:.2f} {2:.2f}'.format(good_amvs[low, pidx].min(), good_amvs[low, pidx].max(), good_amvs[low, pidx].mean()))
print('---------------------------')
- print('Mid Spd min/max/mean: ', good_amvs[mid, sidx].min(), good_amvs[mid, sidx].max(), good_amvs[mid, sidx].mean())
- print('Mid Press min/max/mean: ', good_amvs[mid, pidx].min(), good_amvs[mid, pidx].max(), good_amvs[mid, pidx].mean())
+ print('Mid Spd min/max/mean: {0:.2f} {1:.2f} {2:.2f}'.format(good_amvs[mid, sidx].min(), good_amvs[mid, sidx].max(), good_amvs[mid, sidx].mean()))
+ print('Mid Press min/max/mean: {0:.2f} {1:.2f} {2:.2f}'.format(good_amvs[mid, pidx].min(), good_amvs[mid, pidx].max(), good_amvs[mid, pidx].mean()))
print('---------------------------')
- print('Hgh Spd min/max/mean: ', good_amvs[hgh, sidx].min(), good_amvs[hgh, sidx].max(), good_amvs[hgh, sidx].mean())
- print('Hgh Press min/max/mean: ', good_amvs[hgh, pidx].min(), good_amvs[hgh, pidx].max(), good_amvs[hgh, pidx].mean())
+ print('Hgh Spd min/max/mean: {0:.2f} {1:.2f} {2:.2f}'.format(good_amvs[hgh, sidx].min(), good_amvs[hgh, sidx].max(), good_amvs[hgh, sidx].mean()))
+ print('Hgh Press min/max/mean: {0:.2f} {1:.2f} {2:.2f}'.format(good_amvs[hgh, pidx].min(), good_amvs[hgh, pidx].max(), good_amvs[hgh, pidx].mean()))
bin_size = 200.0
vld_bf = bfs[:, 3] == 0
@@ -413,12 +414,12 @@ def analyze2(raob_to_amv_dct, raob_dct):
mad = np.average(np.abs(diff))
bias = np.average(diff)
print('********************************************************')
- print('num of best fits: ', bf_p.shape[0])
- print('press, MAD: ', mad)
- print('press, bias: ', bias)
+ print('Number of good best fits: ', bf_p.shape[0])
+ print('press, MAD: {0:.2f}'.format(mad))
+ print('press, bias: {0:.2f}'.format(bias))
pd_std = np.std(diff)
pd_mean = np.mean(diff)
- print('press bias/rms ', pd_mean, np.sqrt(pd_mean**2 + pd_std**2))
+ print('press bias/rms: {0:.2f} {1:.2f} '.format(pd_mean, np.sqrt(pd_mean**2 + pd_std**2)))
print('------------------------------------------')
bin_ranges = get_press_bin_ranges(50, 1050, bin_size=bin_size)
@@ -431,17 +432,17 @@ def analyze2(raob_to_amv_dct, raob_dct):
diff = amv_spd * units('m/s') - bf_spd
spd_mad = np.average(np.abs(diff))
spd_bias = np.average(diff)
- print('spd, MAD: ', spd_mad)
- print('spd, bias: ', spd_bias)
+ print('spd, MAD: {0:.2f}'.format(spd_mad))
+ print('spd, bias: {0:.2f}'.format(spd_bias))
spd_mean = np.mean(diff)
spd_std = np.std(diff)
- print('spd MAD/bias/rms: ', np.average(np.abs(diff)), spd_mean, np.sqrt(spd_mean**2 + spd_std**2))
+ print('spd MAD/bias/rms: {0:.2f} {1:.2f} {2:.2f}'.format(np.average(np.abs(diff)), spd_mean, np.sqrt(spd_mean**2 + spd_std**2)))
print('-----------------')
bin_spd = bin_data_by(diff, amv_p, bin_ranges)
dir_diff = direction_difference(amv_dir, bf_dir.magnitude)
- print('dir, MAD: ', np.average(np.abs(dir_diff)))
- print('dir bias: ', np.average(dir_diff))
+ print('dir, MAD: {0:.2f}'.format(np.average(np.abs(dir_diff))))
+ print('dir bias: {0:.2f}'.format(np.average(dir_diff)))
print('-------------------------------------')
bin_dir = bin_data_by(dir_diff, amv_p, bin_ranges)
@@ -452,7 +453,7 @@ def analyze2(raob_to_amv_dct, raob_dct):
vd = np.sqrt(u_diffs**2 + v_diffs**2)
vd_mean = np.mean(vd)
vd_std = np.std(vd)
- print('VD bias/rms: ', vd_mean, np.sqrt(vd_mean**2 + vd_std**2))
+ print('VD bias/rms: {0:.2f} {1:.2f}'.format(vd_mean, np.sqrt(vd_mean**2 + vd_std**2)))
print('------------------------------------------')
x_values = []
--
GitLab