snapshot...

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 = []