def preprocess_line(L):
for n in L:
loc = []
location = []
_data = data_io.data_input(_dir, n).values.tolist()
fixed_data = list(reversed(data_process.check(_data)))
for m in range(len(fixed_data) - 1):
location.append([fixed_data[m][4], fixed_data[m][3], fixed_data[m][11], fixed_data[m][2]])
dis = data_analysis.distance([fixed_data[m][4], fixed_data[m][3]], [fixed_data[m + 1][4], fixed_data[m + 1][3]])
if dis >= 0.3: # 两点间距离大于定为差异大点，即错误点的开始或结束点
loc.append(m)
block = data_process.block_g(loc)
print(block)
for i in block:
data_process.block_fix(location, i[0], i[1])
data_process.block_fix(location, 0, len(fixed_data))
distance = []
for k in location:
distance.append([k[0], k[1]])
data_io.map_g(_dir, distance, n, 'test', 'black')
def analysis_line(L):
choice = input('是否需要将数据处理结果输出至控制台？[Y/N]')
if choice == 'Y' or choice == 'y':
index = 1
elif choice == 'N' or choice == 'n':
index = 0
try:
with open(_dir + '\\' + 'dataset.csv', 'w', encoding='utf-8-sig') as dict_file:
# csv 头名
dict_file.write(u'文件名,车牌号,设备号,疲劳驾驶时间,疲劳驾驶次数,急加速累计时长,急加速累计次数,急减速累计时长,急减速累计次数,怠速预热累计时长,怠速预热累计次数,超长怠速累计时长,超长怠速累计次数,熄火滑行累计时长,熄火滑行累计次数,超速累计时长,超速累计次数,标准差,急变道次数\n')
num = len(L)
cur_num = 0
for n in L:
cur_num += 1
_data = data_io.data_input(_dir, n).values.tolist()
if index == 1:
print('\n' + n + ':')
print('\t车牌号：%s\n\t设备号：%s\n' % (_data[0][0], _data[0][1]))
fixed_data = list(reversed(data_process.check(_data)))
# 分模块来进行处理，这可以使各个部分的数据处理独立，后期便于调整和方法复用
# 这一块因为时间紧，所以没有做多进程
swift_times = data_analysis.road_swift(fixed_data)
fatigue_driving, fatigue_times = data_analysis.fatigue_driving(fixed_data)
time_last_acc, time_last_dec, times_acc, times_dec = data_analysis.speed_change_analysis(fixed_data)
idle_heating, idle_count, long_tickover, long_count, flameout, flameout_count, speed, speed_count = data_analysis.static_analysis(fixed_data)
variance = data_analysis.stable_analysis(fixed_data)
if index == 1:
print('\t疲劳驾驶时间：%d秒\n\t疲劳驾驶次数：%d次' % (fatigue_driving, fatigue_times))
print('\n\t急加速累计时长：%d秒\n\t急加速累计次数：%d次\n\t急减速累计时长：%d秒\n\t急减速累计次数：%d次' % (time_last_acc, times_acc, time_last_dec, times_dec))
print('\n\t怠速预热累计时长：%d秒\n\t怠速预热累计次数：%d次\n\t超长怠速累计时长：%d秒\n\t超长怠速累计次数：%d次'% (idle_heating, idle_count, long_tickover, long_count))
print('\n\t熄火滑行累计时长：%d秒\n\t熄火滑行累计次数：%d次\n\t超速累计时长：%d秒\n\t超速累计次数：%d次' % (flameout, flameout_count, speed, speed_count))
print('\n\t标准差：%d\n\t急变道次数：%d' % (variance, swift_times))
# 数据写入 csv
dict_file.write('%s,%s,%s,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d,%d\n' % (n.split('.')[0], fixed_data[0][0], fixed_data[0][1], fatigue_driving, fatigue_times, time_last_acc, times_acc, time_last_dec, times_dec, idle_heating, idle_count, long_tickover, long_count, flameout, flameout_count, speed, speed_count, variance, swift_times))
print('The data of %s has been written.(%d/%d)' % (n, cur_num, num))
except IOError as ioerr:
print("文件 %s 无法创建，请检查数据文件位置。" % (_dir))
print("\nDone.")
def means(L):
speed = []
means = []
all_num = len(L)
cur = 0
for n in L:
cur += 1
_data = data_io.data_input(_dir, n).values.tolist()
fixed_data = list(reversed(data_process.check(_data)))
for m in fixed_data:
if m[5] == 1: # or n[11] != 0:
speed.append(m[11])
means.append([n.split('.')[0], sum(speed) / len(speed)])
speed = []
print('%s is OK. (%d/%d)' % (n, cur, all_num))
for n in means:
print(n)
print("\nDone.")
def info_extract_line(self):
climate = []
wind_direction = []
level = []
_data = data_io.data_input(_dir, 'climate.csv').values.tolist()
for n in _data:
if n[7] not in climate:
climate.append(n[7])
if n[3] not in wind_direction:
wind_direction.append(n[3])
if n[4] not in level:
level.append(n[4])
for m in climate:
data_io.save_csv(_data, m, _dir + '\\weather', m)
for i in wind_direction:
data_io.save_csv(_data, i, _dir + '\\wind_direction', i)
for j in level:
k = j.replace('<', '小于').replace('～', '到')
print(k)
data_io.save_csv(_data, j, _dir, k)
def analysis_with_weather_line(L):
all_num = len(L)
cur_num = 0
weather_data = []
didian = []
weather = []
spd_lim = {'大雨': 90, '暴雨': 70, '浮尘': 0, '扬沙': 0, '大暴雨': 40, '雾': 100}
spd_set = []
default_spd = 100
final = []
for n in L:
cur_num += 1
print('%s started.' % n)
_data = data_io.data_input(_dir, n).values.tolist()
_data = list(reversed(data_process.check(_data))) # 修正漂移经纬度
for m in range(len(_data) - 1):
_data[m + 1][4], _data[m + 1][3] = data_process.location_fix([_data[m][4], _data[m][3], _data[m][11], _data[m][2]])
temp_data = data_io.data_input(_dir, 'climate.csv').values.tolist()
for m in temp_data: # 预处理天气数据
weather_data.append([m[1], m[2], m[7], m[10]])
# 以10分钟为间隔获取地点
for m in range(len(_data)//600):
loc = data_convert.wgs84_to_gcj02(_data[m * 600][3], _data[m * 600][4])
city, district = data_analysis.get_location(loc)
if [city, district] not in didian:
didian.append([str(city).replace('市', ''), str(district).replace('区', '').replace('县', '')])
# 找出符合地理条件的天气和时间
for m in weather_data: # ['绍兴', '诸暨', '多云', '24/9/2018']
for i in didian:
if i[0] == m[0] and i[1] == m[1]:
if [m[2], m[3], m[0], m[1]] not in weather:
weather.append([m[2], m[3], m[0], m[1]]) # ['晴', '30/7/2018', '抚州', '崇仁']
# 检查找出每个日期对应的最小的速度值
for m in range(len(weather) - 1):
for i in spd_lim:
if i in weather[m][0]:
default_spd = min(default_spd, spd_lim.get(i))
if weather[m][1] == weather[m + 1][1] and weather[m][2] == weather[m + 1][2] and weather[m][3] == weather[m + 1][3]:
continue
else:
spd_set.append([weather[m][1], default_spd, weather[m][2], weather[m][3]]) # ['30/7/2018', 100, '南昌', '新建']
export = [n.split('.')[0], _data[0][0], _data[0][1], data_analysis.speed_analysis(_data, spd_set, weather_data)]
final.append(export)
print(export)
print('The data of %s was generated. (%d/%d)' % (n, cur_num, all_num))
try:
with open(_dir + '\\weather_dataset.csv', 'w') as dict_file:
dict_file.write('文件名,车牌号,设备号,晴,小雨,中雨,大雨,暴雨,大暴雨,雾,超速次数')
for m in final:
dict_file.write('%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s\n' % (
m[0], m[1], m[2], m[1][0], m[1][1], m[1][2], m[1][3], m[1][4], m[1][5], m[1][6], m[2],
m[11], m[12]))
except IOError as ioerr:
print("文件 %s 无法创建，请检查数据文件位置。" % _dir)