python處理csv數(shù)據(jù)動(dòng)態(tài)顯示曲線實(shí)例代碼

# -*- 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 
##如果是參數(shù)是list,則默認(rèn)每次取list中的一個(gè)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'輸入任意字符繼續(xù): ') 
  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()