Source code for PyFoam.ThirdParty.Gnuplot.demo

#! /usr/bin/env python
# $Id: demo.py 299 2007-03-30 12:52:17Z mhagger $

# Copyright (C) 1999-2003 Michael Haggerty <mhagger@alum.mit.edu>
#
# This file is licensed under the GNU Lesser General Public License
# (LGPL).  See LICENSE.txt for details.

"""demo.py -- Demonstrate the Gnuplot python module.

Run this demo by typing 'python demo.py'.  For a more complete test of
the Gnuplot package, see test.py.

"""

from numpy import *

# If the package has been installed correctly, this should work:
from PyFoam.ThirdParty import Gnuplot
from PyFoam.ThirdParty.Gnuplot import funcutils

from PyFoam.ThirdParty.six.moves import input as rinput

[docs]def demo(): """Demonstrate the Gnuplot package.""" # A straightforward use of gnuplot. The `debug=1' switch is used # in these examples so that the commands that are sent to gnuplot # are also output on stderr. g = Gnuplot.Gnuplot(debug=1) g.title('A simple example') # (optional) g('set style data linespoints') # give gnuplot an arbitrary command # Plot a list of (x, y) pairs (tuples or a numpy array would # also be OK): g.plot([[0,1.1], [1,5.8], [2,3.3], [3,4.2]]) rinput('Please press return to continue...\n') g.reset() # Plot one dataset from an array and one via a gnuplot function; # also demonstrate the use of item-specific options: try: x = arange(10, dtype='float_') except TypeError: x = arange(10, typecode='d') y1 = x**2 # Notice how this plotitem is created here but used later? This # is convenient if the same dataset has to be plotted multiple # times. It is also more efficient because the data need only be # written to a temporary file once. d = Gnuplot.Data(x, y1, title='calculated by python', with_='points pt 3 ps 3') g.title('Data can be computed by python or gnuplot') g.xlabel('x') g.ylabel('x squared') # Plot a function alongside the Data PlotItem defined above: g.plot(Gnuplot.Func('x**2', title='calculated by gnuplot'), d) rinput('Please press return to continue...\n') # Save what we just plotted as a color postscript file. # With the enhanced postscript option, it is possible to show `x # squared' with a superscript (plus much, much more; see `help set # term postscript' in the gnuplot docs). If your gnuplot doesn't # support enhanced mode, set `enhanced=0' below. g.ylabel('x^2') # take advantage of enhanced postscript mode g.hardcopy('gp_test.ps', enhanced=1, color=1) print ('\n******** Saved plot to postscript file "gp_test.ps" ********\n') rinput('Please press return to continue...\n') g.reset() # Demonstrate a 3-d plot: # set up x and y values at which the function will be tabulated: x = arange(35)/2.0 y = arange(30)/10.0 - 1.5 # Make a 2-d array containing a function of x and y. First create # xm and ym which contain the x and y values in a matrix form that # can be `broadcast' into a matrix of the appropriate shape: xm = x[:,newaxis] ym = y[newaxis,:] m = (sin(xm) + 0.1*xm) - ym**2 g('set parametric') g('set style data lines') g('set hidden') g('set contour base') g.title('An example of a surface plot') g.xlabel('x') g.ylabel('y') # The `binary=1' option would cause communication with gnuplot to # be in binary format, which is considerably faster and uses less # disk space. (This only works with the splot command due to # limitations of gnuplot.) `binary=1' is the default, but here we # disable binary because older versions of gnuplot don't allow # binary data. Change this to `binary=1' (or omit the binary # option) to get the advantage of binary format. g.splot(Gnuplot.GridData(m,x,y, binary=0)) rinput('Please press return to continue...\n') # plot another function, but letting GridFunc tabulate its values # automatically. f could also be a lambda or a global function: def f(x,y): return 1.0 / (1 + 0.01 * x**2 + 0.5 * y**2) g.splot(Gnuplot.funcutils.compute_GridData(x,y, f, binary=0)) rinput('Please press return to continue...\n')
# Explicit delete shouldn't be necessary, but if you are having # trouble with temporary files being left behind, try uncommenting # the following: #del g, d # when executed, just run demo(): if __name__ == '__main__': demo() # Should work with Python3 and Python2