Python之csv数据读取、创建和追加

import csv
import os
import numpy as np
import random
from scipy.optimize import leastsq

TotalVehicleFlow_str = []
AvgSharr_dstr = []

csv_open_file = r'C:\Users\psqk\Desktop\my_query.csv'

#load original data , format:string
with open(csv_open_file,'r') as inputfile:
    reader  = csv.DictReader(inputfile)
    TotalVehicleFlow_str = [row['TotalVehicleFlow'] for row in reader]
with open(csv_open_file,'r') as inputfile:
    reader  = csv.DictReader(inputfile)
    AvgSharr_str = [row['AvgSharr'] for row in reader]
with open(csv_open_file,'r') as inputfile:
    reader  = csv.DictReader(inputfile)
    RealTime_str = [row['RealTime'] for row in reader]
with open(csv_open_file,'r') as inputfile:
    reader  = csv.DictReader(inputfile)
    Speed_str = [row['AvgSpeed'] for row in reader]
with open(csv_open_file,'r') as inputfile:
    reader  = csv.DictReader(inputfile)
    EquID_str = [row['EquID'] for row in reader]
with open(csv_open_file,'r') as inputfile:
    reader  = csv.DictReader(inputfile)
    Lane_str = [row['Lane'] for row in reader]
#TotalVehicleFlow_tuple = tuple(TotalVehicleFlow_str)
#AvgSharr_tuple = tuple(AvgSharr_str)

#print (TotalVehicleFlow_str[-10:])
#print (AvgSharr_str[-10:])

#convert the format from string into integer & float
TotalVehicleFlow_num = []
for n in TotalVehicleFlow_str:
    TotalVehicleFlow_num.append(int(n))
AvgSharr_num = []
for n in AvgSharr_str:
    AvgSharr_num.append(float(n))
print(len(RealTime_str),len(AvgSharr_num))  
print('EquID: {}\n'.format(EquID_str[1]),'Lane: {}'.format(Lane_str[1]))
#print (TotalVehicleFlow_num[-10:])
#print (AvgSharr_num[-10:])

#plt.scatter(AvgSharr_num,TotalVehicleFlow_num,c=['r','g','b'],s=10,marker='*')
#plt.show()

#add to np.array
AvgSharr_np = np.array(AvgSharr_num)
TotalVehicleFlow_np = np.array(TotalVehicleFlow_num)


import numpy as np
import matplotlib.pyplot as plt

# y = 2 + 3x + 4x^2
X = AvgSharr_np
Y = TotalVehicleFlow_np

#拟合曲线的参数
parameter_k = 6
parameter_b = -16


_X = np.arange(0, 24, 0.1)
_Y = np.array([parameter_b + parameter_k*x for x in _X])


#设置画布大小
plt.figure(figsize=(12, 8))
axes = plt.axes()
axes.spines['top'].set_visible(False)
axes.spines['right'].set_visible(False)
#axes.set_xticks([0,5,10,15,20,25,30,35,40,45])
plt.xlim(0,80)
plt.ylim(0,120)
font = {'family' : 'Times New Roman',
'weight' : 'normal',
'size'   : 20,}

plot1=plt.plot(X, Y, 'r*',label='original values')
plot2=plt.plot(_X, _Y, 'b',linewidth = 2,label='curve_fit values')


plt.tick_params(labelsize=20)
plt.xlabel('OCCUPANCY, %',font)
plt.ylabel('FLOW RATE',font)

#plt.plot(X, Y, 'ro', _X, _Y, 'b', linewidth=5)
#plt.title("y = {} + {}x + {}$x^2$ ".format(a0, a1, a2))
#plt.title("y = {}$x^2$ + {}x + ({})".format(a2, a1, a0),fontsize=20,)
plt.title("MC Master",fontsize=20,)
plt.axvline(24,ls='--',linewidth = 1,color='purple')
savefig_name = "{}-lane{}.png".format(EquID_str[1][-4:],Lane_str[1])
plt.savefig(savefig_name) 
plt.show()


#create csv file 生成一个csv文件
def create_csv(path):
    with open(path, "w+", newline='') as file:
        csv_file = csv.writer(file)
        head = ["time","avgsharr","vehicleflow","speed","congestion_status"]
        csv_file.writerow(head)
#append csv file  追加行数据      
def append_csv(path,data):
    with open(path, "a+", newline='') as file: # 处理csv读写时不同换行符  linux:\n    windows:\r\n    mac:\r
        csv_file = csv.writer(file)
        datas = data
        csv_file.writerows(datas)
        
        
output_csv = 'csv_{}-lane{}_output.csv'.format(EquID_str[1][-4:],Lane_str[1])
#创建一个csv文件，会自动替换之前的csv文件
create_csv(output_csv) 
      
for n in range(0,len(TotalVehicleFlow_str)):
    data_time_csv = '{}'.format(RealTime_str[n])
    data_sharr_csv = '{}'.format(AvgSharr_num[n])
    data_vflow_csv = '{}'.format(TotalVehicleFlow_num[n])
    data_speed_csv = '{}'.format(Speed_str[n])
    if parameter_k*X[n] + parameter_b > Y[n] and X[n]<25:
        #print("\033[0;34mlight   |  {:19}, |  {:5.2f}, |{:5d}, |   {:8}  |".format(RealTime_str[n],X[n],Y[n],Speed_str[n]))
        data_congestion_csv = '{}'.format('light') 
    elif parameter_k*X[n] + parameter_b > Y[n] and X[n]>25:
        #print("\033[1;31mserious |  {:19}, |  {:6.2f}, |{:5d}, |   {:8}  |\033[0m".format(RealTime_str[n],X[n],Y[n],Speed_str[n]))
        data_congestion_csv = '{}'.format('serious')
    else:
        n=0
        data_congestion_csv = '{}'.format('normal')
    append_csv(output_csv,zip([data_time_csv],[data_sharr_csv],[data_vflow_csv],[data_speed_csv],[data_congestion_csv]))

      #  print("\033[1;30mnormal  |  {:19}, |  {:5.2f}, |{:5d}, |   {:8}  |\033[0m".format(RealTime_str[n],X[n],Y[n],Speed_str[n]))

print("end")

'''
create_csv('output_csv.csv')
for n in range(0,len(TotalVehicleFlow_str)):
    data_time_csv = '{}'.format(RealTime_str[n])
    data_sharr_csv = '{}'.format(AvgSharr_num[n])
    data_vflow_csv = '{}'.format(TotalVehicleFlow_num[n])
    data_speed_csv = '{}'.format(Speed_str[n])
    append_csv('output_csv.csv',zip([data_time_csv],[data_sharr_csv],[data_vflow_csv],[data_speed_csv]))
'''