snapshot...

modules/aeolus/aeolus_amv.py

@@ -947,22 +947,59 @@ def compare_amvs_bestfit(amvs_list, bfs_list, bin_size=200):
     amvs = np.transpose(amvs, axes=[1, 0])
     bfs = np.stack(bfs_list, axis=0)
 
-    didx = 4
-    sidx = 3
-    pidx = 2
+    good_amvs = amvs
+    num_good = good_amvs.shape[0]
+    didx = 6
+    sidx = 5
+    pidx = 4
 
-    vld_bf = bfs[:, 3] == 0
-    keep_idxs = vld_bf
+    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: {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: {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)
+    hgh = good_amvs[:, pidx] <= 400
+
+    n_low = np.sum(low)
+    n_mid = np.sum(mid)
+    n_hgh = np.sum(hgh)
+
+    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: {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: {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: {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()))
+
+    # Comparison to Level of Best Fit (LBF) ------------------------------------------------------------------
+    # --------------------------------------------------------------------------------------------------------
     bin_ranges = get_press_bin_ranges(50, 1050, bin_size=bin_size)
 
-    amv_p = amvs[keep_idxs, pidx]
+    vld_bf = bfs[:, 3] == 0
+    keep_idxs = vld_bf
+
+    amv_p = good_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('Number of good best fits to RAOB: ', bf_p.shape[0])
     print('press, MAD: {0:.2f}'.format(mad))
     print('press, bias: {0:.2f}'.format(bias))
     pd_std = np.std(diff)
@@ -972,8 +1009,8 @@ def compare_amvs_bestfit(amvs_list, bfs_list, bin_size=200):
 
     bin_pres = bin_data_by(diff, amv_p, bin_ranges)
 
-    amv_spd = amvs[keep_idxs, sidx]
-    amv_dir = amvs[keep_idxs, didx]
+    amv_spd = good_amvs[keep_idxs, sidx]
+    amv_dir = good_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
@@ -994,7 +1031,7 @@ def compare_amvs_bestfit(amvs_list, bfs_list, bin_size=200):
     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])
+    amv_u, amv_v = uv_from_spd_dir(good_amvs[keep_idxs, sidx], good_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'))