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Commit 654bb9bc authored by tomrink's avatar tomrink
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......@@ -944,6 +944,80 @@ def analyze2(amvs_list, bf_list, raob_match_list, bf_gfs_list, amv_prod_list):
np.average(np.abs(bin_spd[i])), np.average(bin_spd[i]), np.average(np.abs(bin_dir[i])), np.average(bin_dir[i])))
# Comparison to Level of Best Fits (LBF) GFS to RAOB ------------------------------------------------------------------
# ------------------------------------------------------------------------------------------------------------
vld_bf = bfs[:, 3] == 0
vld_bf_gfs = bfs_gfs[:, 3] == 0
keep_idxs = np.logical_and(vld_bf, vld_bf_gfs)
amv_p = good_amvs[keep_idxs, pidx]
bf_p = bfs[keep_idxs, 2]
bf_p_gfs = bfs_gfs[keep_idxs, 2]
diff = bf_p - bf_p_gfs
mad = np.average(np.abs(diff))
bias = np.average(diff)
print('********************************************************')
print('Number of good best fits to GFS: ', 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: {0:.2f} {1:.2f} '.format(pd_mean, np.sqrt(pd_mean**2 + pd_std**2)))
print('------------------------------------------')
bin_pres = bin_data_by(diff, amv_p, bin_ranges)
amv_spd = good_amvs[keep_idxs, sidx]
amv_dir = good_amvs[keep_idxs, didx]
bf_gfs_spd, bf_gfs_dir = spd_dir_from_uv(bfs_gfs[keep_idxs, 0], bfs_gfs[keep_idxs, 1])
bf_spd, bf_dir = spd_dir_from_uv(bfs[keep_idxs, 0], bfs[keep_idxs, 1])
diff = bf_spd - bf_gfs_spd
diff = diff.magnitude
spd_mad = np.average(np.abs(diff))
spd_bias = np.average(diff)
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: {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(bf_dir.magnitude, bf_gfs_dir.magnitude)
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)
u_diffs = bfs[keep_idxs, 0] - bfs_gfs[keep_idxs, 0]
v_diffs = bfs[keep_idxs, 1] - bfs_gfs[keep_idxs, 1]
vd = np.sqrt(u_diffs**2 + v_diffs**2)
vd_mean = np.mean(vd)
vd_std = np.std(vd)
print('VD bias/rms: {0:.2f} {1:.2f}'.format(vd_mean, np.sqrt(vd_mean**2 + vd_std**2)))
print('******************************************************')
x_values = []
num_pres = []
num_spd = []
num_dir = []
print('level num cases hgt MAD/bias spd MAD/bias dir MAD/bias')
print('-------------------------------------------------------------------')
for i in range(len(bin_ranges)):
x_values.append(np.average(bin_ranges[i]))
num_pres.append(bin_pres[i].shape[0])
num_spd.append(bin_spd[i].shape[0])
num_dir.append(bin_dir[i].shape[0])
print('{0:d} {1:d} {2:.2f}/{3:.2f} {4:.2f}/{5:.2f} {6:.2f}/{7:.2f}'
.format(int(x_values[i]), num_pres[i], np.average(np.abs(bin_pres[i])), np.average(bin_pres[i]),
np.average(np.abs(bin_spd[i])), np.average(bin_spd[i]), np.average(np.abs(bin_dir[i])), np.average(bin_dir[i])))
return bin_ranges, bin_pres, bin_spd, bin_dir
def compare_amvs_bestfit(amvs_list, bfs_list, bin_size=200):
amvs = np.concatenate(amvs_list, axis=1)
amvs = np.transpose(amvs, axes=[1, 0])
......
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