#! /usr/bin/python2
import Gnuplot, Gnuplot.funcutils
def demo():
"""Demonstrate the Gnuplot package."""
from numpy import *
# 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.
print Gnuplot.Gnuplot.__init__.__doc__
g = Gnuplot.Gnuplot(debug=1)
g.title('A simple example') # (optional)
#g('set data style linespoints') # give gnuplot an arbitrary command
# Plot a list of (x, y) pairs (tuples or a Numeric array would
# also be OK):
g.splot([[0,1.1], [1,5.8], [2,3.3], [3,4.2]])
g.hardcopy('gp_test.ps', enhanced=1, color=1)
raw_input('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:
x = arange(10, typecode=Float)
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 3 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)
raw_input('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')
raw_input('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 data style 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))
raw_input('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))
raw_input('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()