Generating Multi-Plot Real-Time Plots with Python

In my last post the real-time plotting capabilities were demonstrated, we're extending on this by showing how to generate multiple plots simultaneously.  A couple noteworthy observations, in the past post the X and Y scaling was automatically scaled after each element addition.  While you can still do this, typically for multiplots we would prefer maintaining a shared X range.  While somewhat unnecessary, I've elected to maintain a uniform Y range.

#!/usr/bin/python
from pylab import *;
import time;

def log(M):
  print "__(log) " + M;

def test02():
  plt.ion();
  fig=plt.figure(1);
  ax1=fig.add_subplot(311);
  ax2=fig.add_subplot(312);
  ax3=fig.add_subplot(313);
  l1,=ax1.plot(100,100,'r-');
  l2,=ax2.plot(100,100,'r-');
  l3,=ax3.plot(100,100,'r-');
  time.sleep(3);

  D=[];
  i=0.0;
  while (i < 50.0):
    D.append((i,sin(i),cos(i),cos(i*2)));
    T1=[x[0] for x in D];
    L1=[x[1] for x in D];
    L2=[x[2] for x in D];
    L3=[x[3] for x in D];

    l1.set_xdata(T1);
    l1.set_ydata(L1);

    l2.set_xdata(T1);
    l2.set_ydata(L2);

    l3.set_xdata(T1);
    l3.set_ydata(L3);

    ax1.set_xlim([0,50]);
    ax2.set_xlim([0,50]);
    ax3.set_xlim([0,50]);
    ax1.set_ylim([-1.5,1.5]);
    ax2.set_ylim([-1.5,1.5]);
    ax3.set_ylim([-1.5,1.5]);

    plt.draw();
    i+=0.10;
  show(block=True);

#---main---
log("main process initializing");
test02();
log("main process terminating");

Easy Peasy;

Generating Real-Time Plots with Python

In my previous post we described plotting data using MatplotLib utilities and Python.  While this may be valuable, it becomes notably more valuable when you can generate 'live' plots during run-time.  In a past employment I worked with a series of controls engineers that utilized real-time data plots to debug and develop a highly complex multi-axis weapons system and it was the first time I understood how a real-time plot of sequence of steps simplified the development effort.

Let's get started.
Unlike the previous post, let's create the data and plot it as it is generated.

#!/usr/bin/python
from pylab import *;
import time;

def log(M):
  print "__(log) " + M;

def test01():
  plt.ion();
  fig=plt.figure(1);
  ax1=fig.add_subplot(111);
  l1,=ax1.plot(100,100,'r-');

  D=[];
  i=0.0;
  while (i < 50.0):
    D.append((i,sin(i)));
    T=[x[0] for x in D];
    L=[x[1] for x in D];
    l1.set_xdata(T);
    l1.set_ydata(L);
    ax1.relim();
    ax1.autoscale_view();
    plt.draw();
    i+=0.10;
    time.sleep(1/10.0);
  show(block=True);

#---main---
log("main process initializing");
test01();
log("main process terminating");

The result is a dynamically generated plot that resembles the following;

Tie this plotting routine to a system providing run-time information via a socket, or perhaps monitoring network traffic via pcapture libraries and you've got yourself the foundation of a real-time data monitoring system.

Cheers.

Plotting with Python

Scripting languages are incredibly powerful, but more powerful when you can visualize the data you are processing.  In this post, we will demonstrate how to quickly plot data sets via Python.

Start with installing Python and a plotting utility known as MatplotLib;

$ sudo apt-get install python python-matplotlib 

Then, let's start with a classic plot, sin(x);

#!/usr/bin/python
from pylab import *;
import time;

def log(M):
  print "__(log) " + M;


def test00():
  D=[];
  i=0.0;
  while (i < 50.0):
    D.append((i,sin(i)));
    i+=0.10;
 
  plt.ion();
  xlabel('radians');
  ylabel('sin(x)');
  grid(True);
  plt.figure(1);
  show();
  T=[x[0] for x in D];
  L=[x[1] for x in D];
  plt.plot(T,L,'b-');
  show(block=True);

#---main---
log("main process initializing");
test00();
log("main process terminating");

The result is calculating a data set followed by plotting the data and allowing the user to manipulate the plots (e.g. zooming, panning, ...).

For more detailed features, refer to the MatlabLib site: http://matplotlib.org/contents.html

Cheers.