Python實時繪圖內存泄漏

Question

我有一些簡單的python代碼，可以爲每秒查看數據而生成實時監視器。它使用matplotlib，工作得很好，除了有內存泄漏。腳本的內存使用情況在一天內緩慢上升，似乎沒有限制。我承認編程python是新手，所以想知道是否有人能看到我正在做的事情，顯然是很糟糕的。預先感謝您的幫助。

import time 
import numpy as np 
import matplotlib 
from matplotlib import figure 
import matplotlib.pyplot as plt 
import pylab as p 
import os 
import subprocess as sp 
from subprocess import Popen, PIPE 

def main(): 
    #####Initialize the plot##### 
    fig = plt.figure() 
    ax1 = fig.add_subplot(1,1,1,axisbg='black') #Set up basic plot attributes 
    ax1.set_title('Blip Current vs. Time',color='blue')  
    ax1.set_xlabel('Time (hrs)',color='blue') 
    ax1.set_ylabel('Blip Current',color='blue') 
    for t in ax1.xaxis.get_ticklines(): t.set_color('yellow') 
    for t in ax1.xaxis.get_ticklabels(): t.set_color('yellow') 
    for t in ax1.yaxis.get_ticklines(): t.set_color('white') 
    for t in ax1.yaxis.get_ticklabels(): t.set_color('purple') 
    plt.ion() #Set interactive mode 
    plt.show(False) #Set to false so that the code doesn't stop here 
    i=0 #initialize counter variable (this will help me to limit the number of points displayed on graph 

    ###Update the plot continuously### 
    while True: #This is a cheap trick to keep updating the plot, i.e. create a real time data monitor 
     blip=Popen('adoIf -vo -6 lxf.blip_b3 dataBarM', shell=True, stdout=PIPE).communicate()[0] #Get data to plot 
     hr=float(time.strftime('%H')) 
     mins=time.strftime('%M') 
     secs=time.strftime('%S') 
     secadj=float(secs)/3600 
     minadj=float(mins)/60 
     currenttime=float(hr+minadj+secadj) #Put time into format for easier plotting, i.e. 21.50 for 9:30 pm 
     if currenttime >= 0 and currenttime < 0.22: #Set x range properly when rolling over to midnight 
      xmin=0 
      xmax=currenttime+.01 
     else: 
      xmin=currenttime-.22 #Limit data to be displayed, only care about recent past 
      xmax=currenttime+.01 
     try: 
      blip =float(blip) #This throws an error if for some reason the data wasn't received at the top of the while statement 
     except ValueError: 
      blip=0.0 
     if i>300: #Limit displayed points to save memory (hopefully...) 
      del ax1.lines[0] #After 300 points, start deleting the first point each time 
     else: 
      i +=1 
     if blip > 6: #Plot green points if current is above threshold 
      ax1.plot(currenttime,blip,marker='o', linestyle='--',c='g') 
     else: #Plot red points if current has fallen off 
      ax1.plot(currenttime,blip,marker='o', linestyle='--',c='r') 
     plt.axis([xmin,xmax,None,None]) #Set xmin/xmax to limit displayed data to a reasonable window 
     plt.draw() 
     time.sleep(2) #Update every 2 seconds 

if __name__=='__main__': 
    print 'Starting Monitor' 
    main() 

Answer 1

我敢肯定，你需要每次都清除圖形，否則matplotlib將繼續創建一大堆新對象，並且事物不會被垃圾收集。嘗試是這樣的：

fig.clf() 

爲while循環中的第一件事情。

Answer 2

尤里卡！我想通了（至少，一個解決方法）。我從while循環中取出了ax1.plot命令，並使用'set_xdata'和'set_ydata'命令以及fig.canvas.draw（）命令。感謝大家的幫助，尤其是reptilicus，指出ax.plot命令每次調用它時都會創建一個新對象。

要繪製的x和y值現在存儲在數組中，每個數組中的第一個元素在while循環的每次迭代中都被刪除（在繪製了特定數量的點之後，其數目在該代碼使用簡單的索引號i）。內存使用情況持平，CPU使用率較低。代碼如下：

def main(): 
    #####Initialize the plot attributes##### 
    fig = plt.figure() 
    ax1 = fig.add_subplot(1,1,1, axisbg='black')#Set up basic plot attributes 
    ax1.set_title('Blip Current vs. Time',color='blue')  
    ax1.set_xlabel('Time (hrs)',color='blue') 
    ax1.set_ylabel('Blip Current',color='blue') 
    for t in ax1.xaxis.get_ticklines(): t.set_color('yellow') 
    for t in ax1.xaxis.get_ticklabels(): t.set_color('yellow') 
    for t in ax1.yaxis.get_ticklines(): t.set_color('white') 
    for t in ax1.yaxis.get_ticklabels(): t.set_color('purple') 
    plt.ion() #Set interactive mode 
    plt.show(False) #Set to false so that the code doesn't stop here 
    i=0 #initialize counter variable (this will help me to limit the number of points displayed on graph 

    ###Initialize x values#### 
    times=[] #Create blank array to hold x values 
    hr=float(time.strftime('%H')) #Hours 
    mins=time.strftime('%M') #Minutes 
    secs=time.strftime('%S') #Seconds 
    secadj=float(secs)/3600 
    minadj=float(mins)/60 
    currenttime=float(hr+minadj+secadj) #Put time into format for easier plotting, i.e. 21.50 for 9:30 pm 
    times.append(currenttime) #Add first x value to x value array 
    if currenttime >= 0 and currenttime < 0.22: #Set x range properly when rolling over to midnight 
     xmin=0 
     xmax=currenttime+.01 
    else: 
     xmin=currenttime-.22 #Limit data to be displayed, only care about recent past 
     xmax=currenttime+.01 

    ###Initialize y values### 
    blipcur=[] #Create blank array to hold y values 
    blip=Popen('adoIf -vo -6 lxf.blip_b3 dataBarM', shell=True, stdout=PIPE).communicate()[0] #Get first datapoint for plot 
    try: 
     blip =float(blip) #This throws an error if for some reason the data wasn't received at the top of the while statement 
    except ValueError: 
     blip=0.0 
    blipcur.append(blip) #Add first y value to y value array 

    ###Initialize plot### 
    line1, = ax1.plot(times, blipcur, 'g-', marker='o') 

    ###Update the plot continuously### 
    while True: #This is a cheap trick to keep updating the plot, i.e. create a real time data monitor 
     hr=float(time.strftime('%H')) #Get new x data for plotting (get current time) 
     mins=time.strftime('%M') 
     secs=time.strftime('%S') 
     secadj=float(secs)/3600 
     minadj=float(mins)/60 
     currenttime=float(hr+minadj+secadj) #Put time into format for easier plotting, i.e. 21.50 for 9:30 pm 
     times.append(currenttime) #Add latest point to x value array 
     if currenttime >= 0 and currenttime < 0.22: #Set x range properly when rolling over to midnight 
      xmin=0 
      xmax=currenttime+.01 
     else: 
      xmin=currenttime-.22 #Limit data to be displayed, only care about recent past 
      xmax=currenttime+.01 

     blip=Popen('adoIf -vo -6 lxf.blip_b3 dataBarM', shell=True, stdout=PIPE).communicate()[0] #Get new y data for plotting 
     try: 
      blip =float(blip) #This throws an error if for some reason the data wasn't received from previous line of code 
     except ValueError: #Just set to zero if there was some temporary error 
      blip=0.0 
     blipcur.append(blip) #Add latest point to y value array 

     if i>285: #Limit data points shown on graph. Saves memory. 
      del blipcur[0] #Delete first element in y value array (oldest/first plotted point) 
      del times[0] #Delete first element in x value array 
     else: 
      i +=1 #Only want to keep making number bigger until I get over the threshold for # points I want, then why bother 

     line1.set_xdata(times) #Set x data for plot from x value array 
     plt.axis([xmin,xmax,-2,50]) #Set xmin/xmax to limit displayed data to a reasonable window 
     line1.set_ydata(blipcur) #Set y data for plot from y value array 
     fig.canvas.draw() #Update plot 
     time.sleep(2.6) #Update every 2.6 seconds 

if __name__=='__main__': 
    print 'Starting Monitor' 
    main() 

Answer 3

你原來的代碼表明您想取決於blip值不同顏色的圓點。使用您的新解決方案，使用set_data，您需要爲每種顏色提供新的Line2D。另一種方法是使用散點圖而不是線圖。散點圖可以爲圖中的每個點分配不同的顏色。

---

如果你想有一個固定大小的名單，說285元，而不是 這樣做：

if i>285: #Limit data points shown on graph. Saves memory. 
     del blipcur[0] #Delete first element in y value array (oldest/first plotted point) 
     del times[0] #Delete first element in x value array 

你可以使用collection.deques與maxlen=285。 deques，你可以追加到你的心臟的喜悅和deque將放棄最舊的元素，當它太滿。這會讓你的代碼更簡單一些，因爲你不必自己管理它的大小。 deques也可以在彈出的第一個元素在關O（1）時間，而在lists O（n）的時間彈出的第一個元素了，所以有theoretical performance增益也在這裏，雖然只有約300個元素是不會做出有顯着差異。

---

如果你有Matplotlib 1.2版或更新版本，您可以使用它FuncAnimation類。這將爲您處理大部分樣板代碼。此外，它可以讓你避免調用plt.ion()。

---

import numpy as np 
import matplotlib.pyplot as plt 
import collections 
import datetime as DT 
import matplotlib.animation as animation 


def currenttime(): 
    now = DT.datetime.now() 
    hr = now.hour 
    mins = now.minute/60 
    secs = now.second/3600 
    return float(hr + mins + secs) 


def main(): 
    def animate(data, pathcol): 
     xvals, yvals, colors = data 
     assert len(xvals)<=300 
     if len(xvals) > 1: 
      ax.set_xlim(xvals.min(), xvals.max()) 
     pathcol.set_array(colors) 
     pathcol.set_offsets(np.column_stack([xvals, yvals])) 

    def step(): 
     xvals = collections.deque([], maxlen=N) 
     yvals = collections.deque([], maxlen=N) 
     colors = collections.deque([], maxlen=N) 
     fudge = 0 
     while True: 
      blip = np.random.random() * 10 
      xvals.append(currenttime() + fudge) 
      yvals.append(blip) 
      colors.append(1 if blip > 6 else 0) 
      yield np.asarray(xvals), np.asarray(yvals), np.asarray(colors) 
      # make time go faster 
      fudge += 0.0001 

    # Initialize the plot##### 
    N = 300 
    fig = plt.figure() 
    ax = fig.add_subplot(
     1, 1, 1, axisbg='black') # Set up basic plot attributes 
    ax.set_title('Blip Current vs. Time', color='blue') 
    ax.set_xlabel('Time (hrs)', color='blue') 
    ax.set_ylabel('Blip Current', color='blue') 

    for t in ax.xaxis.get_ticklines(): 
     t.set_color('yellow') 
    for t in ax.xaxis.get_ticklabels(): 
     t.set_color('yellow') 
    for t in ax.yaxis.get_ticklines(): 
     t.set_color('white') 
    for t in ax.yaxis.get_ticklabels(): 
     t.set_color('purple') 
    pathcol = ax.scatter([0,24], [0,10], 
          c=[0,1], s=100, 
          cmap=plt.get_cmap('RdYlGn'), vmin=0, vmax=1) 
    ani = animation.FuncAnimation(
     fig, animate, step, interval=20, fargs=(pathcol,)) 
    plt.show() 


if __name__ == '__main__': 
    print 'Starting Monitor' 
    main()