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])