driving_simulation/analysis.py at master · JunhaoWang/driving_simulation · GitHub

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"""
The MIT License (MIT)
Copyright (c) <2016> <Junhao Wang>

Permission is hereby granted, free of charge, to any person obtaining
a copy of this software and associated documentation files (the "Software"),
to deal in the Software without restriction, including without limitation the
rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
sell copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included
in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
IN THE SOFTWARE.
"""
#############################################################################################################
# PURPOSE: Calculate stats and steering_entropy for OpenDS data file
# USAGE: python analysis.py
# NOTE: !!! change path to your absolute carData path such as below !!!
#############################################################################################################

import pandas
import numpy as np
import math
import scipy as sc
criteria = []

########################### !!! change path to your absolute carData path such as below !!! #################
path = '/Users/juwang/Desktop/driving_simulation/opends_2.0_bin/analyzerData/2016_11_20-16_14_15/carData.txt'
#############################################################################################################

def convfloat(input):
	try:
		return float(input)
	except:
		return input

def readlog(file = path):
	lines = [line.rstrip('\n').rstrip('\r') for line in open(file)][4:]
	data = [i.split(':') for i in lines]
	frame = pandas.DataFrame(data)
	frame.rename(inplace=True,columns={0:'time(ms)',1:'pos(x)',2:'pos(y)',3:'pos(z)',4:'rot(x)',5:'rot(y)',6:'rot(z)',7:'rot(w)',8:'speed(km/h)',9:'wheel',10:'gas',11:'brake',12:'engine'})
	frame = frame.convert_objects(convert_numeric=True)
	mintime = list(frame['time(ms)'])[0]
	frame['time(ms)'] = frame['time(ms)'].apply(lambda x: int(x) - int(mintime))
	frame.to_pickle(file.split('/')[-2]+'_result')
	return frame


def convmaxbin(input):
	for i in criteria:
		if input <= i:
			return i

def steering_entropy(data = readlog()):
	# Bin the data frame by "a" with 10 bins...
	bins = np.linspace(data.wheel.min(), data.wheel.max(), 40)
	groups = data.groupby(pandas.cut(data.wheel, bins))
	global criteria
	criteria = [-99999999 if math.isnan(x) else x for x in groups.max().wheel]
	data['entropy_wheel'] = data['wheel'].apply(lambda x: convmaxbin(x))
	p_wheel= data['entropy_wheel'].value_counts()/len(data['entropy_wheel'])
	entropy=sc.stats.entropy(p_wheel)
	return entropy

def rollmean(bin=10, data = readlog()):
	return pandas.rolling_mean(data, bin)

def summary(data = readlog()):
	return data.describe()


# summary of stats
print '======================= SUMMARY =======================\n'
print summary()
print '\n'
# steering_entropy
print '======================= STEERING ENTROPY =======================\n'
print steering_entropy()
print '\n'