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modules/aeolus/aeolus_amv.py

@@ -686,6 +686,82 @@ def analyze2(raob_to_amv_dct, raob_dct, gfs_filename=None):
                       np.average(np.abs(bin_spd[i])), np.average(bin_spd[i]), np.average(np.abs(bin_dir[i])), np.average(bin_dir[i])))
 
 
+def compare_amvs_bestfit(amvs, bfs):
+    didx = 4
+    sidx = 3
+    pidx = 2
+
+    vld_bf = bfs[:, 3] == 0
+    keep_idxs = vld_bf
+
+    bin_size = 200.0
+    bin_ranges = get_press_bin_ranges(50, 1050, bin_size=bin_size)
+
+    amv_p = amvs[keep_idxs, pidx]
+    bf_p = bfs[keep_idxs, 2]
+    diff = amv_p - bf_p
+    mad = np.average(np.abs(diff))
+    bias = np.average(diff)
+    print('********************************************************')
+    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: {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 = amvs[keep_idxs, sidx]
+    amv_dir = amvs[keep_idxs, didx]
+    bf_spd, bf_dir = spd_dir_from_uv(bfs[keep_idxs, 0], bfs[keep_idxs, 1])
+
+    diff = amv_spd * units('m/s') - bf_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(amv_dir, bf_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)
+
+    amv_u, amv_v = uv_from_spd_dir(amvs[keep_idxs, sidx], amvs[keep_idxs, didx])
+    u_diffs = amv_u - (bfs[keep_idxs, 0] * units('m/s'))
+    v_diffs = amv_v - (bfs[keep_idxs, 1] * units('m/s'))
+
+    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])))
+
+
 # imports the S4 NOAA output
 # filename: full path as a string, '/home/user/filename'
 # returns a dict: time -> list of profiles (a profile is a list of levels)