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modified_consumptionana.py
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modified_consumptionana.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Mar 2 18:20:09 2019
改进版consumptionana
只选取fuel_consumption从0开始的段
@author: yulu
"""
import numpy as np
import modified_trip
import getTimeDiff
def modified_ct(ddata,datareinx):
#先处理index和“index”不符合的情况
# datareinx=datareinx[datareinx['index']<datareinx.shape[0]]
datareinx['index']=datareinx.index
data=np.zeros((1,16))
##
data_divert_T=np.zeros((1,4))
T=5 #时间间隔
datereinx_temp=datareinx[datareinx['fuel_consumption']==0]
# datereinx_temp=datereinx_temp[datereinx_temp['status_basic']==1]
if datereinx_temp.shape[0]!=0:
index=np.array(datereinx_temp['index'])
index_2=[] #放入燃油从0到非0 的全部索引
for i in range(1,len(index)-1):
# if index[i] in datereinx_temp['index'] and index[i+1] in datereinx_temp['index']:
if datereinx_temp['index'].loc[index[i]]+1<datereinx_temp['index'].loc[index[i+1]]:
index_2.append((datereinx_temp['index'].loc[index[i]],datereinx_temp['index'].loc[index[i+1]]))
# else:
# continue
if (datareinx.shape[0]-1) not in index_2:
index_2.append((index[-1],datareinx.shape[0]-1))
# =============================================================================
########提取纯用电的
# # ddata_0_temp=np.zeros((1,15))
# # for i in range(len(ddata)):
# # if ddata[i,2] in index:
# # ddata_0_temp=np.vstack((ddata_0_temp,ddata[i,:]))
# #
# # ddata_0_temp=np.delete(ddata_0_temp,[0],axis=0)
# # cg=consumptionana.ct(ddata_0_temp,datareinx)
# # ce=np.array(cg[1])/1000 ##使用的燃油量‘
# #
# ##矩阵中添加行：numpy.row_stack(mat, a)
# ##矩阵中添加列：numpy.column_stack(mat,a)
# #
# # ddata_0_temp=np.column_stack((ddata_0_temp,ce))
# # ddata_pure_electricity=ddata_0_temp[ddata_0_temp[:,15]==0,:]
# # temp1=np.array(datareinx['quqantity_electricity'].loc[ddata_pure_electricity[:,2]])
# # temp2=np.array(datareinx['quqantity_electricity'].loc[ddata_pure_electricity[:,3]])
# # ddata_pure_electricity[:,15]=temp1-temp2 #消耗的quqantity_electricity
# =============================================================================
#######用油的
#从燃油消耗量为0的地方开始计算，下一个燃油量减上一个
#当100km以上时，
#如果下一个行程和上一个行程之间的distance_accumulate大于30？，停止，从下一个为0 的开始
#或者一直进行到fuel为0，该段行程舍去
da=np.array(datareinx['distance_accumulative'])
fc=np.array(datareinx['fuel_consumption'])
for i in range(len(index_2)):
index_start=index_2[i][0]
index_end=index_2[i][1]
#前100公里
datareinx_temp=datareinx.iloc[index_start:index_end] #从index_start到index_end；前闭后闭
#判断是否存在fuel_consumption跳跃的点
fc=datareinx_temp['fuel_consumption']
fca=fc.loc[index_start+1:index_end].reset_index(drop=True)
fcu=fc.loc[index_start:index_end-1].reset_index(drop=True)
fcminus=fca-fcu
##找到第一个跳跃的点，往后全部舍弃
fcminus=fcminus[fcminus>20]
if len(fcminus)>0: #如果存在，舍弃后面的，重新赋值
index_end=fcminus[fcminus == fcminus.iloc[0]].index.tolist()
index_end=index_end[0]+index_start
datareinx_temp=datareinx.iloc[index_start:index_end] #从index_start到index_end；前闭后闭
##增加一列累计燃油
datareinx_temp['fuel_accumulate']=0
distance_accumulate_start=np.array(datareinx['distance_accumulative'].loc[index_start])
distance_accumulate_100=distance_accumulate_start+100 #找100km开外的公里数
distance_accumulate_start=distance_accumulate_100-100 #找开始的公里数
#找100km开外的公里数的index
index_100 = datareinx[datareinx.distance_accumulative == distance_accumulate_100].index.tolist() # index
if len(index_100)>0: #要是找到了
index_100=index_100[0]
else: #要是没找到，找最近的那个
x=distance_accumulate_100
a=abs(da-x)
b=a.min()
###还是要用插值法？？
if b>10: #(距离小于10) 要是找到最近的那个了，但是它大于10km远，我们就不找了，索引的end
index_100=index_end+1 #为了创造条件，使得下面一块运行if后面的语句
else:
c=abs(x-b) #最近里程的数字
index_100 = datareinx[datareinx.distance_accumulative == c].index.tolist() # index
if len(index_100)==0:
c=abs(x+b) #最近里程的数字
index_100 = datareinx[datareinx.distance_accumulative == c].index.tolist() # index
index_100=index_100[0] #第100公里所在的行号index
#如果第100公里所在的行号index超过了index_end
if index_100>index_end:
datareinx_temp['fuel_accumulate'].loc[index_start:index_end]=fc[index_start-index_start:index_end+1-index_start]
else: #如果没超过
datareinx_temp['fuel_accumulate'].loc[index_start:index_100]=fc[index_start-index_start:index_100+1-index_start]
#100公里外的
j=index_100
while j <index_end:
index_self = np.where(da==da[j]) #本身的最后一个值
index_self = index_self[0][-1]
index_temp = np.where(da==da[j]-100) # index #找100公里之前的那个数字
#if fc[j]!=0:
if len(index_temp[0])!=0:
#100km之前的那个索引
k=-1
index_temp_1 = index_temp[0][k]
while index_temp_1>index_end: #避免有一些里程不规律，出现递减
index_temp_1 = index_temp[0][k]
k=k-1
if index_temp_1>index_start and index_temp_1<=index_end:
datareinx_temp['fuel_accumulate'].loc[j:index_self]=fc[index_temp_1]+fc[j]
else: #如果找不到的话，找最近的里程的数
x=da[j]-100
a=abs(da-x)
b=a.min()
if b<=10:
c=abs(x-b) #最近里程的数字
index_temp_2 = np.where(da==c) # index
if index_temp_2[0].size==0:
c=abs(x+b) #最近里程的数字
index_temp_2 = np.where(da==c) # index
index_temp_2 = index_temp_2[0][-1] #100km之前的那个索引
if index_temp_2>index_start:
datareinx_temp['fuel_accumulate'].loc[j:index_self]=fc[index_temp_2]+fc[j]
else:
break
j=index_self+1
data=np.vstack((data,modified_trip.trip(datareinx_temp)))
# =============================================================================
# 不同速度区间下的电动车的能耗？速度跟油耗/电耗的关系？每五分钟，算平均速度（里程/5min），
## 耗电量/耗油量/状态为1
# datareinx_temp=datareinx_temp[datareinx_temp['statusn2']==1]
k=index_start
# for k in range((index_start,index_end)):
while k<index_end:
time_start=datareinx['time_collect'].loc[k]
da_start=datareinx['distance_accumulative'].loc[k]
qep_start=datareinx['quqantity_electricity_percent'].loc[k]
fa_start=datareinx_temp['fuel_accumulate'].loc[k]
while getTimeDiff.GetTimeDiff(time_start,datareinx['time_collect'].loc[k])<T*60 and k<index_end :#如果在5分钟内
k=k+1
#break出来的时候，就是找到了那个大于5分钟的值？
#持续时间、路程、soc变化、燃油变化
data_divert_T_temp=np.hstack((getTimeDiff.GetTimeDiff(time_start,datareinx['time_collect'].loc[k-1]), \
datareinx['distance_accumulative'].loc[k-1]-da_start, \
qep_start-datareinx['quqantity_electricity_percent'].loc[k-1], \
datareinx_temp['fuel_accumulate'].loc[k-1]-fa_start))
data_divert_T=np.vstack((data_divert_T,data_divert_T_temp))
# =============================================================================
data=data[data[:,15]>=0]
data=np.delete(data,0,axis = 0)
data_divert_T=data_divert_T[data_divert_T[:,0]<=(T+1)*60]#小于t+1分钟
data_divert_T[:,0]=data_divert_T[:,0]/60 #换算成分钟
data_divert_T=data_divert_T[data_divert_T[:,1]>0] #路程大于0
data_divert_T=data_divert_T[data_divert_T[:,3]>=0]
return data,data_divert_T