snapshot...

modules/aeolus/aeolus_amv.py

@@ -1067,6 +1067,8 @@ def compare_amvs_bestfit_driver(all_list, bin_size=200):
 
     amvs_list = []
     bfs_list = []
+    rb_list = []
+    gfs_list = []
     prd_list = []
     for tup in all_list:
         ab_dct = tup[0]
@@ -1077,6 +1079,8 @@ def compare_amvs_bestfit_driver(all_list, bin_size=200):
             tup = ab_dct.get(key)
             amvs_list.append(tup[0])
             bfs_list.append(tup[1])
+            rb_list.append(tup[2])
+            gfs_list.append(tup[3])
 
         keys = list(pr_dct.keys())
         for key in keys:
@@ -1101,6 +1105,8 @@ def compare_amvs_bestfit_driver(all_list, bin_size=200):
 
     amvs = np.concatenate(amvs_list)
     bfs = np.concatenate(bfs_list)
+    rbm = np.concatenate(rb_list)
+    gfs_bfs = np.concatenate(gfs_list)
     prd = np.concatenate(prd_list)
 
     thin = np.logical_and(prd[:, 2] > 0, prd[:, 2] < 1)
@@ -1236,59 +1242,41 @@ def compare_amvs_bestfit(amvs, bfs, bin_size=200):
     return bin_ranges, bin_pres, bin_spd, bin_dir
 
 
-def make_plot():
-    # f = open('/Users/tomrink/amv_raob.pkl', 'rb')
-    f = open('/Users/tomrink/amv_bf_gfs_all_linear.pkl', 'rb')
-    tup_r = pickle.load(f)
-    f.close()
+def make_plot(bin_ranges, bin_values):
 
-    f = open('/Users/tomrink/amv_bf_gfs_all_nearest.pkl', 'rb')
-    tup_g = pickle.load(f)
-    f.close()
-
-    bin_ranges = tup_r[0]  # same for all
-    bin_pres_r = tup_r[1]
-    bin_pres_g = tup_g[1]
-    bin_spd_r = tup_r[2]
-    bin_spd_g = tup_g[2]
+    bin_vals_r = bin_values[0]
+    bin_vals_g = bin_values[1]
 
     x_values = []
-    num_pres_r = []
-    num_pres_g = []
-    num_spd = []
-    num_dir = []
-    pres_mad_r = []
-    pres_bias_r = []
-    pres_mad_g = []
-    pres_bias_g = []
-    spd_mad_r = []
-    spd_bias_r = []
-    spd_mad_g = []
-    spd_bias_g = []
+    num_vals_r = []
+    num_vals_g = []
+    mad_r = []
+    bias_r = []
+    mad_g = []
+    bias_g = []
+
 
     num_r = 0
     num_g = 0
     for i in range(len(bin_ranges)):
-        num_r += bin_pres_r[i].shape[0]
-        num_g += bin_pres_g[i].shape[0]
+        num_r += bin_vals_r[i].shape[0]
+        num_g += bin_vals_g[i].shape[0]
 
     for i in range(len(bin_ranges)):
         x_values.append(np.average(bin_ranges[i]))
-        num_pres_r.append((bin_pres_r[i].shape[0])/num_r)
-        num_pres_g.append((bin_pres_g[i].shape[0])/num_g)
 
-        pres_mad_r.append(np.average(np.abs(bin_pres_r[i])))
-        pres_bias_r.append(np.average(bin_pres_r[i]))
-        pres_mad_g.append(np.average(np.abs(bin_pres_g[i])))
-        pres_bias_g.append(np.average(bin_pres_g[i]))
+        num_vals_r.append((bin_vals_r[i].shape[0])/num_r)
+        num_vals_g.append((bin_vals_g[i].shape[0])/num_g)
+
+        mad_r.append(np.average(np.abs(bin_vals_r[i])))
+        bias_r.append(np.average(bin_vals_r[i]))
+
+        mad_g.append(np.average(np.abs(bin_vals_g[i])))
+        bias_g.append(np.average(bin_vals_g[i]))
 
-        spd_mad_r.append(np.average(np.abs(bin_spd_r[i])))
-        spd_bias_r.append(np.average(bin_spd_r[i]))
-        spd_mad_g.append(np.average(np.abs(bin_spd_g[i])))
-        spd_bias_g.append(np.average(bin_spd_g[i]))
 
 
-    do_plot(x_values, [pres_mad_r, pres_mad_g], ['GFS_linear', 'GFS_nearest'], ['blue', 'red'], title='ACHA - BestFit', x_axis_label='MAD', y_axis_label='hPa', invert=True, flip=True)
+    do_plot(x_values, [mad_r, mad_g], ['GFS_linear', 'GFS_nearest'], ['blue', 'red'], title='ACHA - BestFit', x_axis_label='MAD', y_axis_label='hPa', invert=True, flip=True)
     #do_plot(x_values, [pres_mad_r, pres_mad_g], ['RAOB', 'GFS'], ['blue', 'red'], title='ACHA - BestFit', x_axis_label='MAD', y_axis_label='hPa', invert=True, flip=True)
     #do_plot(x_values, [spd_mad_r, spd_mad_g], ['RAOB', 'GFS'], ['blue', 'red'], title='ACHA - BestFit', x_axis_label='MAE (m/s)', y_axis_label='hPa', invert=True, flip=True)
     #do_plot(x_values, [pres_bias_r, pres_bias_g], ['RAOB', 'GFS'], ['blue', 'red'], title='ACHA - BestFit', x_axis_label='BIAS', y_axis_label='hPa', invert=True, flip=True)