Obtains the CPU usage trend chart of a single program.

This article positioning: CPU history data has been stored in the disk, waiting for visual analysis, there is no idea for now.
Previous ArticleArticle(Http://www.cnblogs.com/MikeZhang/archive/2012/02/01/cpuRatePythonTop.html) mentioned how to use
Python stores the result of the top command. This article describes how to save the result.

In python, we can use matplotlib to conveniently visualize data, for exampleCode:

 
1 ImportMatplotlib. pyplot as PLT
2 
3List1 = [1, 2, 3]
4List2 = [4, 5, 9]
5PLT. Plot (list1, list2)
6PLT. Show ()
 

The execution result is as follows:

The above is just to pass two list data structures to the plot function, show will come out ...... Haha, it's very convenient!
This is used to obtain the CPU trend chart!
But the data we get is not that friendly now. For example, if I have a file (file.txt), the content is as follows:

1328503984 CPU (s): 0.0% us, 0.0% Sy, 0.0% Ni, 100.0% ID, 0.0% wa, 0.0% hi, 0.0% Si, 0.0% St
1328503997 CPU (s): 7.7% us, 7.7% Sy, 0.0% Ni, 76.9% ID, 0.0% wa, 0.0% hi, 7.7% Si, 0.0% St
1328504000 CPU (s): 0.0% us, 9.1% Sy, 0.0% Ni, 90.9% ID, 0.0% wa, 0.0% hi, 0.0% Si, 0.0% St
1328504012 CPU (s): 9.1% us, 0.0% Sy, 0.0% Ni, 90.9% ID, 0.0% wa, 0.0% hi, 0.0% Si, 0.0% St
1328504016 CPU (s): 8.3% us, 8.3% Sy, 0.0% Ni, 83.3% ID, 0.0% wa, 0.0% hi, 0.0% Si, 0.0% St
1328504019 CPU (s): 0.0% us, 0.0% Sy, 0.0% Ni, 100.0% ID, 0.0% wa, 0.0% hi, 0.0% Si, 0.0% St
1328504022 CPU (s): 0.0% us, 9.1% Sy, 0.0% Ni, 90.9% ID, 0.0% wa, 0.0% hi, 0.0% Si, 0.0% St
1328504228 CPU (s): 0.0% us, 0.0% Sy, 0.0% Ni, 100.0% ID, 0.0% wa, 0.0% hi, 0.0% Si, 0.0% St
 

The first column is the time column, and the sixth column is the idle value of the CPU.

We have to do some work to figure out the CPU usage trend from this set of data.

The following is the code. Here is an idea. You need to copy it back and modify it:

 1   # Coding: UTF-8  
  2   '''  
  3   File: cpuusage. py
  4   Author: Mike
  5   E-mail: Mike_Zhang@live.com
  6   ''' 
 7   Import Matplotlib. pyplot as PLT
8   Import String
 9  
 10   Def Getcpuinfdata (filename ):
 11 Ret = {}
 12 F = open (filename, "  R  " )
 13 Linelist = f. readlines ()
 14       For Line In Linelist:
 15 TMP = line. Split ()
 16 SZ = Len (TMP)
 17 T_key = string. atoi (TMP [0]) #  Get key  
  18 T_value = 100.001-string. atof (line. Split ( '  :  ' ) [1]. Split ( ' ,  ' ) [3]. Split ( '  %  ' ) [0]) #  Get value  
  19           Print T_key, t_value
 20           If   Not Ret. has_key (t_key ):
 21 RET [t_key] = []
22 RET [t_key]. append (t_value)
 23 F. Close ()
 24       Return RET
 25      
 26 Retmap1 = getcpuinfdata ( "  File.txt  " )
 27   #  Generate CPU usage trend chart  
 28 List1 = retmap1.keys ()
 29 List1.sort ()
 30 List2 = []
 31   For I In List1: list2.append (retmap1 [I])
 32 PLT. Plot (list1, list2)
 33 PLT. Show ()

Okay, that's all. I hope it will help you.