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data_generator.py
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import pandas as pd
import os
import random
from parameters import dataExecute_conf
def saveCsv(df, file_name):
'''
:param df: 存入df
:param file_name: 文件名
:return: 无
'''
df=df.reset_index(drop=True) #重置索引
if not os.path.exists('\\'):
os.makedirs('\\')
df.to_csv('data\\' + file_name + '.csv', mode='a', header=True)
def cutbyRoad(df=None, road=None):
'''
:param df: 打开后文件
:param road: 路段名称
:return: 切路df,按照全局时间排序
'''
road_df = df[df['Location'] == road]
return road_df.sort_values(by='Global_Time', ascending=True)
def cutbyPosition(road_df, start_y=0, start_time=0, area_length=50):
'''
给定起始时间，起始y，区间长度，输出区间内车辆list
:param road_df:限定路段后的df
:param start_y: 区域开始段，单位为ft
:param start_time: 起始时间，0.1s
:param area_length: 区域长度单位为m
:return: vehicle_list为起始框内部车辆编号
'''
area_df = road_df[road_df['Global_Time'] == start_time]
area_df = area_df[(area_df['Global_Y'] - start_y <= area_length) & (
area_df['Global_Y'] - start_y >= 0)]
vehicle_list = area_df['Vehicle_ID'].unique()
if len(list(vehicle_list)) <= 2:
return None
else:
return list(vehicle_list)
def cutbyTime(road_df, start_time=0, vehicle_list=None, time_length=10.0, stride=1):
'''
:param road_df:road_df
:param start_time: 开始实践 :param time_length: 采样时间长度,单位为s
:param stride: 采样步长
:return: 返回一组清洗完数据time
'''
temp_df = road_df[road_df['Vehicle_ID'].isin(vehicle_list)]
one_sequence = pd.DataFrame()
for vehicle in vehicle_list:
for time in range(int(time_length * 10 / stride)):
df = temp_df[
(temp_df['Vehicle_ID'] == vehicle) & (temp_df['Global_Time'] == (start_time + time * stride))]
if df.shape[0] == 1 and int(df['Lane_ID'])<=5:
one_sequence = pd.concat([one_sequence, df])
else:
return None
return one_sequence
def unitConversion(df):
'''
转换后长度单位为m，时间单位为0.1秒
:param df: 被转换df
:return: 转换后df
'''
ft_to_m = 0.3048
df['Global_Time'] = df['Global_Time'] / 100
for strs in ["Global_X", "Global_Y", "Local_X", "Local_Y", "v_length", "v_Width"]:
df[strs] = df[strs] * ft_to_m
df["v_Vel"] = df["v_Vel"] * ft_to_m*3.6
return df
def main():
conf = dataExecute_conf()
init_df = pd.read_csv(conf.data_source, usecols=conf.useCols)
road_df = cutbyRoad(init_df, road=conf.road)
road_df = unitConversion(road_df)
min_Global_Y, max_Global_Y = road_df['Global_Y'].min()+100, road_df['Global_Y'].max()
min_Global_Time, max_Global_Time = road_df['Global_Time'].min(), road_df['Global_Time'].max()
total_dist=int((max_Global_Y - min_Global_Y) / (conf.area_step))
total_time=int((max_Global_Time - min_Global_Time) / (conf.time_step * 10))
print("共计{}--{}组数据，时间步长为{}，距离步长为{}".format(total_dist,total_time,conf.time_step,conf.area_step))
total_data=0
for dist_index in range(conf.hist_dist,total_dist):
for time_index in range(conf.hist_time,total_time):
if conf.noise:
time_noise=random.randint(0,200)
dist_noise=random.randint(0,200)
else:
time_noise,dist_noise=0,0
start_time = min_Global_Time + time_index * conf.time_step * 10+time_noise*10
start_y = min_Global_Y + dist_index * conf.area_step+dist_noise
vehicle_list = cutbyPosition(road_df, start_y=start_y, start_time=start_time,
area_length=conf.area_length)
if vehicle_list is None:
# print('{}秒时刻，{}为起点区域内车辆过少，进入下个时段'.format(start_time * 0.1, start_y))
print('{}--{}内车辆过少，进入下个时段'.format(dist_index, time_index))
continue
one_sequence = cutbyTime(road_df, start_time=start_time, vehicle_list=vehicle_list,
time_length=conf.time_length,
stride=conf.stride)
if one_sequence is None:
# print('{}时刻，{}为起点区域内车辆存在消失，进入下个时段'.format(start_time * 0.1, start_y))
print('{}--{}内车辆存在消失或驶入辅助匝道，进入下个时段'.format(dist_index, time_index))
# TODO 过滤出有变道信息
# elif one_sequence.groupby("Lane_ID")
pass
else:
total_data+=1
saveCsv(one_sequence, file_name=conf.road)
print('{}/{} - {}/{} saved! Exist {} data! '.format(dist_index, total_dist,time_index, total_time,total_data))
if total_data == conf.need_num:
print("数据采集完成")
break
if total_data ==conf.need_num:
print("数据采集完成")
break
main()