.. _old_animation-movie_demo:

old_animation example code: movie_demo.py
=========================================

[`source code <movie_demo.py>`_]

::

    #!/usr/bin/python
    #
    # Josh Lifton 2004
    #
    # Permission is hereby granted to use and abuse this document
    # so long as proper attribution is given.
    #
    # This Python script demonstrates how to use the numarray package
    # to generate and handle large arrays of data and how to use the
    # matplotlib package to generate plots from the data and then save
    # those plots as images.  These images are then stitched together
    # by Mencoder to create a movie of the plotted data.  This script
    # is for demonstration purposes only and is not intended to be
    # for general use.  In particular, you will likely need to modify
    # the script to suit your own needs.
    #
    
    from __future__ import print_function
    
    import matplotlib
    matplotlib.use('Agg')
    import matplotlib.pyplot as plt   # For plotting graphs.
    import numpy as np
    import subprocess                 # For issuing commands to the OS.
    import os
    import sys                        # For determining the Python version.
    
    #
    # Print the version information for the machine, OS,
    # Python interpreter, and matplotlib.  The version of
    # Mencoder is printed when it is called.
    #
    print('Executing on', os.uname())
    print('Python version', sys.version)
    print('matplotlib version', matplotlib.__version__)
    
    not_found_msg = """
    The mencoder command was not found;
    mencoder is used by this script to make an avi file from a set of pngs.
    It is typically not installed by default on linux distros because of
    legal restrictions, but it is widely available.
    """
    
    try:
        subprocess.check_call(['mencoder'])
    except subprocess.CalledProcessError:
        print("mencoder command was found")
        pass # mencoder is found, but returns non-zero exit as expected
        # This is a quick and dirty check; it leaves some spurious output
        # for the user to puzzle over.
    except OSError:
        print(not_found_msg)
        sys.exit("quitting\n")
    
    
    #
    # First, let's create some data to work with.  In this example
    # we'll use a normalized Gaussian waveform whose mean and
    # standard deviation both increase linearly with time.  Such a
    # waveform can be thought of as a propagating system that loses
    # coherence over time, as might happen to the probability
    # distribution of a clock subjected to independent, identically
    # distributed Gaussian noise at each time step.
    #
    
    print('Initializing data set...')   # Let the user know what's happening.
    
    # Initialize variables needed to create and store the example data set.
    numberOfTimeSteps = 100   # Number of frames we want in the movie.
    x = np.arange(-10,10,0.01)   # Values to be plotted on the x-axis.
    mean = -6                 # Initial mean of the Gaussian.
    stddev = 0.2              # Initial standard deviation.
    meaninc = 0.1             # Mean increment.
    stddevinc = 0.1           # Standard deviation increment.
    
    # Create an array of zeros and fill it with the example data.
    y = np.zeros((numberOfTimeSteps,len(x)), float)
    for i in range(numberOfTimeSteps) :
        y[i] = (1/np.sqrt(2*np.pi*stddev))*np.exp(-((x-mean)**2)/(2*stddev))
        mean = mean + meaninc
        stddev = stddev + stddevinc
    
    print('Done.')                       # Let the user know what's happening.
    
    #
    # Now that we have an example data set (x,y) to work with, we can
    # start graphing it and saving the images.
    #
    
    for i in range(len(y)) :
        #
        # The next four lines are just like MATLAB.
        #
        plt.plot(x,y[i],'b.')
        plt.axis((x[0],x[-1],-0.25,1))
        plt.xlabel('time (ms)')
        plt.ylabel('probability density function')
    
        #
        # Notice the use of LaTeX-like markup.
        #
        plt.title(r'$\cal{N}(\mu, \sigma^2)$', fontsize=20)
    
        #
        # The file name indicates how the image will be saved and the
        # order it will appear in the movie.  If you actually wanted each
        # graph to be displayed on the screen, you would include commands
        # such as show() and draw() here.  See the matplotlib
        # documentation for details.  In this case, we are saving the
        # images directly to a file without displaying them.
        #
        filename = str('%03d' % i) + '.png'
        plt.savefig(filename, dpi=100)
    
        #
        # Let the user know what's happening.
        #
        print('Wrote file', filename)
    
        #
        # Clear the figure to make way for the next image.
        #
        plt.clf()
    
    #
    # Now that we have graphed images of the dataset, we will stitch them
    # together using Mencoder to create a movie.  Each image will become
    # a single frame in the movie.
    #
    # We want to use Python to make what would normally be a command line
    # call to Mencoder.  Specifically, the command line call we want to
    # emulate is (without the initial '#'):
    # mencoder mf://*.png -mf type=png:w=800:h=600:fps=25 -ovc lavc -lavcopts vcodec=mpeg4 -oac copy -o output.avi
    # See the MPlayer and Mencoder documentation for details.
    #
    
    command = ('mencoder',
               'mf://*.png',
               '-mf',
               'type=png:w=800:h=600:fps=25',
               '-ovc',
               'lavc',
               '-lavcopts',
               'vcodec=mpeg4',
               '-oac',
               'copy',
               '-o',
               'output.avi')
    
    #os.spawnvp(os.P_WAIT, 'mencoder', command)
    
    print("\n\nabout to execute:\n%s\n\n" % ' '.join(command))
    subprocess.check_call(command)
    
    print("\n\n The movie was written to 'output.avi'")
    
    print("\n\n You may want to delete *.png now.\n\n")
    
    

Keywords: python, matplotlib, pylab, example, codex (see :ref:`how-to-search-examples`)