snapshot...

modules/aeolus/aeolus_amv.py

@@ -13,6 +13,7 @@ from metpy.units import units
 from util.gfs_reader import get_vert_profile_s
 from amv.intercompare import get_raob_dict_cdf
 from util.line_plot import do_plot
+import pickle
 
 
 amv_file_duration = 60  # minutes
@@ -1061,23 +1062,49 @@ def compare_amvs_bestfit(amvs_list, bfs_list, bin_size=200):
     return bin_ranges, bin_pres, bin_spd, bin_dir
 
 
-def make_plot(bin_ranges, bin_pres, bin_spd, bin_dir):
+def make_plot():
+    f = open('/Users/tomrink/amv_raob.pkl', 'rb')
+    tup_r = pickle.load(f)
+    f.close()
+
+    f = open('/Users/tomrink/amv_gfs.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]
+
     x_values = []
-    num_pres = []
+    num_pres_r = []
+    num_pres_g = []
     num_spd = []
     num_dir = []
-    pres_mad = []
-    pres_bias = []
+    pres_mad_r = []
+    pres_bias_r = []
+    pres_mad_g = []
+    pres_bias_g = []
 
+    num_r = 0
+    num_g = 0
     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])
-        pres_mad.append(np.average(np.abs(bin_pres[i])))
-        pres_bias.append(np.average(bin_pres[i]))
+        num_r += bin_pres_r[i].shape[0]
+        num_g += bin_pres_g[i].shape[0]
 
-    do_plot(x_values, [pres_mad, pres_bias], ['mad', 'bias'], ['blue', 'red'], invert=True, flip=True)
+    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)
+        #num_spd.append(bin_spd[i].shape[0])
+        #num_dir.append(bin_dir[i].shape[0])
+        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]))
+
+    #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, [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)
+    #do_plot(x_values, [num_pres_r, num_pres_g], ['RAOB:'+str(num_r), 'GFS:'+str(num_g)], ['blue', 'red'], x_axis_label='Normalized Count', y_axis_label='hPa', invert=True, flip=True)
 
 
 # imports the S4 NOAA output