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Copy path论文实验.py
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论文实验.py
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# -*- coding: utf-8 -*-
import csv
import numpy as np
import pandas as pd
"""
变量说明：day： 当前日期
finallist ：所有两两关系九十天的相关度字典
List ：记录一天的路线（key）与编号（value）
Lines ：二维列表，记录一天中所有线路的刷卡序列
currday ： 统计天数
sheet ：{车牌号：[时间戳,线路,刷卡人数]}
"""
with open(r"E:\所有天数所有线路.csv", 'r') as f:
reader = csv.reader(f)
day = '20181001'
List = {}
Lines = []
finallist = {}
sheet = {}
currday = 0
for line in reader:
if line[0] == day:
if line[5] not in sheet: # 记录刷卡信息
# line[5]车牌号 , line[4]线路
sheet.setdefault(line[5], []).append(line[1])
sheet.setdefault(line[5], []).append(line[4])
sheet.setdefault(line[5], []).append(1)
# 该车第一次出现
sheet.setdefault(line[5], []).append(1)
# 该车最后一次出现的时间戳
sheet.setdefault(line[5], []).append(line[1])
# 特别地，第n趟首次刷卡。此时flag=0 && 与上一次车时间戳相隔超过1000
elif (eval(line[1]) - eval(sheet[line[5]][4])) > 1000:
sheet[line[5]][4] = line[1]
# times++;
sheet[line[5]][3] += 1
# 修改当前车辆车牌号 = 车牌号 + 出现次数
line[5] = line[5] + str(sheet[line[5]][3])
sheet.setdefault(line[5], []).append(line[1])
sheet.setdefault(line[5], []).append(line[4])
sheet.setdefault(line[5], []).append(1)
sheet.setdefault(line[5], []).append(1)
# 平凡的情况
elif (line[5] + str(sheet[line[5]][3])) in sheet:
line[5] = line[5] + str(sheet[line[5]][3])
sheet[line[5]][2] += 1
# 统计第一趟车的刷卡人数
else:
sheet[line[5]][2] += 1
else:
# 此时进入第二天
day = line[0]
# 统计同一线路刷卡数据，并汇总成二维列表
i = 0
for key in sheet.keys():
# 路线刷卡数据不存在，写入List
if sheet[key][1] not in List:
Lines.append([])
Lines[i].append(sheet[key][2])
List.setdefault(sheet[key][1], i)
i += 1
flag = sheet[key][1]
# 两个相邻的同路线刷卡数据合并
elif sheet[key][1] in List and flag == sheet[key][1]:
Lines[List.get(flag)][-1] = Lines[List.get(flag)][-1] + sheet[key][2]
elif sheet[key][1] in List and flag != sheet[key][1]:
Lines[List.get(sheet[key][1])].append(sheet[key][2])
flag = sheet[key][1]
# 将线路与相应刷卡数序列匹配成列表形式
a = list(zip(List.keys(), Lines))
for m in range(len(a) - 1):
for n in range(m + 1, len(a)):
# 删除元素使列表长度相等
if len(a[m][1]) > len(a[n][1]):
L1 = a[m][1][0:len(a[n][1])]
L2 = a[n][1]
else:
L2 = a[n][1][0:len(a[m][1])]
L1 = a[m][1]
# 计算标准差
A = np.array([L1, L2])
var = np.corrcoef(A)
s = a[m][0], a[n][0] # 103699
q = a[n][0], a[m][0] # 699103
if s in finallist:
finallist.setdefault(s, []).append(var[0][1])
elif q in finallist:
finallist.setdefault(s, []).append(var[0][1])
elif s and q not in finallist:
for i in range(currday):
finallist.setdefault(s, []).append(np.nan)
finallist.setdefault(s, []).append(var[0][1])
else:
finallist.setdefault(s, []).append(np.nan)
"""
print(sheet)
print(a)
"""
# 清空用于计算每天信息的列表字典，为第二天做准备
currday += 1
# 这一天没出现的线路置空
for k in finallist:
lens = len(finallist.get(k))
for i in range(currday - lens):
if lens < currday:
finallist.setdefault(k, []).append(np.nan)
else:
break
sheet.clear()
sheet.setdefault(line[5], []).append(line[1])
sheet.setdefault(line[5], []).append(line[4])
sheet.setdefault(line[5], []).append(1)
# 该车第一次出现
sheet.setdefault(line[5], []).append(1)
# 该车最后一次出现的时间戳
sheet.setdefault(line[5], []).append(line[1])
List.clear()
Lines.clear()
day = line[0]
i = 0
# 统计同一线路刷卡数据，并汇总成二维列表
for key in sheet.keys():
# 路线刷卡数据不存在，写入List
if sheet[key][1] not in List:
Lines.append([])
Lines[i].append(sheet[key][2])
List.setdefault(sheet[key][1], i)
i += 1
flag = sheet[key][1]
# 两个相邻的同路线刷卡数据合并
elif sheet[key][1] in List and flag == sheet[key][1]:
Lines[List.get(flag)][-1] = Lines[List.get(flag)][-1] + sheet[key][2]
elif sheet[key][1] in List and flag != sheet[key][1]:
Lines[List.get(sheet[key][1])].append(sheet[key][2])
flag = sheet[key][1]
# 将线路与相应刷卡数序列匹配成列表形式
a = list(zip(List.keys(), Lines))
for m in range(len(a) - 1):
for n in range(m + 1, len(a)):
# 删除元素使列表长度相等
if len(a[m][1]) > len(a[n][1]):
L1 = a[m][1][0:len(a[n][1])]
L2 = a[n][1]
else:
L2 = a[n][1][0:len(a[m][1])]
L1 = a[m][1]
# 计算标准差
A = np.array([L1, L2])
var = np.corrcoef(A)
s = a[m][0], a[n][0] # 103699
q = a[n][0], a[m][0] # 699103
if s in finallist:
finallist.setdefault(s, []).append(var[0][1])
elif q in finallist:
finallist.setdefault(s, []).append(var[0][1])
elif s and q not in finallist:
# 之前没来的 加入 nan表识
for i in range(currday):
finallist.setdefault(s, []).append(np.nan)
finallist.setdefault(s, []).append(var[0][1])
else:
finallist.setdefault(s, []).append(np.nan)
"""
print(sheet)
print(a)
"""
# 清空用于计算每天信息的列表字典，为第二天做准备
currday += 1
for k in finallist:
lens = len(finallist.get(k))
for i in range(currday - lens):
if lens < currday:
finallist.setdefault(k, []).append(np.nan)
else:
break
'''
for k in finallist:
print(finallist.get(k))
'''
pd.DataFrame(finallist).to_csv(r"C:\Users\lynn\Desktop\92days_correlation.csv")
s = 0
finallistz = {}
finallistf = {}
for k in finallist:
for i in range(len(finallist.get(k))):
if np.isnan(finallist.get(k)[i]):
pass
else:
s += float(finallist.get(k)[i])
print(s)
if (s >= 0):
for i in range(len(finallist.get(k))):
finallistz.setdefault(k,[]).append(finallist.get(k)[i])
elif(s < 0):
for i in range(len(finallist.get(k))):
finallistf.setdefault(k,[]).append(finallist.get(k)[i])
s = 0
pd.DataFrame(finallistz).to_csv(r"C:\Users\lynn\Desktop\92days_zcorrelation.csv")
pd.DataFrame(finallistf).to_csv(r"C:\Users\lynn\Desktop\92days_fcorrelation.csv")