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#!/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")
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