import numpy as np
import matplotlib.pyplot as plt
import csv
import expert as expert
import pickle
type_of_filter = "nt2012q1_ukie_201206"
type_of_filter = "nt2012q1_ukie_20130618"
if type_of_filter == "nt2012q1_ukie_201206":
filename_advanced = "../../sav_files/nt2012q1_ukie_201206_rospa_2013-09-05__for_stats.sav"
filename_group = "../../sav_files/nt2012q1_ukie_201206_axaie_2013-12-14_for_stats.sav"
else:
filename_advanced = "../../sav_files/rospa-driver-reports-2014-01-07.sav"
filename_group = "../../sav_files/nt2012q1_ukie_20130618_ares_2014-01-10_for_stats.sav"
stats_rospa,mad,road_types = stats.get_stats_without_scores(filename_advanced)
stats_axaie,mad,road_types = stats.get_stats_without_scores(filename_group)
for metric in stats_rospa:
if metric!="distance":
plt.figure(metric)
for i,road_type in enumerate(stats_rospa[metric]):
plt.subplot(6,2,i+1)
if stats_axaie[metric][road_type]!=-100:
percentiles = stats_rospa[metric][road_type].keys()
percentiles.sort()
rospa_temp = []
axaie_temp = []
for percent in percentiles:
rospa_temp.append(stats_rospa[metric][road_type][percent])
