python处理csv数据动态显示曲线实例代码

本文研究的主要是python处理csv数据动态显示曲线，分享了实现代码，具体如下。

代码：

# -*- coding: utf-8 -*- 
""" 
Spyder Editor 
 
This temporary script file is located here: 
C:\Users\user\.spyder2\.temp.py 
""" 
""" 
Show how to modify the coordinate formatter to report the image "z" 
value of the nearest pixel given x and y 
""" 
# coding: utf-8 
 
import time 
import string 
import os  
import math  
import pylab 
 
import numpy as np 
from numpy import genfromtxt 
import matplotlib 
import matplotlib as mpl 
from matplotlib.colors import LogNorm 
from matplotlib.mlab import bivariate_normal 
 
import matplotlib.pyplot as plt 
import matplotlib.cm as cm 
 
 
import matplotlib.animation as animation 
 
    
  
metric = genfromtxt('D:\export.csv', delimiter=',') 
 
lines=len(metric)  
#print len(metric) 
#print len(metric[4]) 
#print metric[4]  
 
rowdatas=metric[:,0] 
for index in range(len(metric[4])-1): 
  a=metric[:,index+1] 
  rowdatas=np.row_stack((rowdatas,a)) 
   
#print len(rowdatas) 
#print len(rowdatas[4]) 
#print rowdatas[4]  
#   
 
#plt.figure(figsize=(38,38), dpi=80) 
#plt.plot(rowdatas[4] ) 
#plt.xlabel('time') 
#plt.ylabel('value') 
#plt.title("USBHID data analysis") 
#plt.show() 
 
linenum=1 
##如果是参数是list,则默认每次取list中的一个元素,即metric[0],metric[1],...  
listdata=rowdatas.tolist() 
print listdata[4] 
 
#fig = plt.figure()  
#window = fig.add_subplot(111)  
#line, = window.plot(listdata[4] )  
  
fig, ax = plt.subplots() 
line, = ax.plot(listdata[4],lw=2) 
ax.grid() 
 
time_template = 'Data ROW = %d' 
time_text = ax.text(0.05, 0.9, '', transform=ax.transAxes) 
  
#ax = plt.axes(xlim=(0, 700), ylim=(0, 255))  
#line, = ax.plot([], [], lw=2)  
  
def update(data):  
  global linenum 
  line.set_ydata(data) 
#  print 'this is line: %d'%linenum 
  time_text.set_text(time_template % (linenum)) 
  linenum=linenum+1 
#  nextitem = input(u'输入任意字符继续: ') 
  return line,  
 
def init(): 
#  ax.set_ylim(0, 1.1) 
#  ax.set_xlim(0, 10) 
#  line.set_data(xdata) 
  plt.xlabel('time') 
  plt.ylabel('Time') 
  plt.title('USBHID Data analysis') 
  return line, 
   
ani = animation.FuncAnimation(fig, update,listdata , interval=1*1000,init_func=init,repeat=False)  
plt.show()  

总结

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