[SciPy-user] How to Enable Delaunay Package

Lorenzo Isella lorenzo.isella@gmail....
Tue Jun 26 15:01:53 CDT 2007

Dear All,
I am posting this after a discussion originated on the matplotlib 
mailing list.
Fundamentally, I need to plot data on irregular (i.e. non equi-spaced 
rectangular) grids.
I finally was recommended to look at the Delaunay package (see approach2 
at the link:  


The problem is that the approach:

from scipy.sandbox.delaunay import *

does not work (the system does not find the requested module).

Now, I am running Debian testing on my box and I have Python2.3,2.4,2.5 
installed beside SciPy as taken from the standard repositories.
Under /usr/lib/python2.4/site-packages/scipy/sandbox I have the file 
setup.py which I copy and paste at the end of the email.
I try uncommenting the line dealing with Delaunay, but that did not help 
me out (probably it is useful only if I am rebuilding SciPy, which I 
would like to avoid).
Anyone has experienced the same problem or has any suggestions?
I am really in need to get this working in order to be able to perform 
some non-trivial data plotting with matplotlib.
Many thanks


import os

def configuration(parent_package='',top_path=None):
    from numpy.distutils.misc_util import Configuration
    config = Configuration('sandbox',parent_package,top_path)

    sandbox_packages = []
        sandbox_file = open(os.path.join(config.package_path,
                                         'enabled_packages.txt'), 'rU')
    except IOError:
        for line in sandbox_file:
            p = line.strip()
            if line.startswith('#'):

    for p in sandbox_packages:

    # All subpackages should be commented out in the version
    # committed to the repository. This prevents build problems
    # for people who are not actively working with these
    # potentially unstable packages.

    # You can put a list of modules you want to always enable in the
    # file 'enabled_packages.txt' in this directory (you'll have to 
create it).
    # Since this isn't under version control, it's less likely you'll
    # check it in and screw other people up :-)

    # An example package:

    # Monte Carlo package

    # PySparse fork with NumPy compatibility

    # Robert Kern's corner:


    # Delaunay triangulation and Natural Neighbor interpolation

    # Gist-based plotting library for X11

    # elementwise numerical expressions

    # Statistical models

    # Adaptation of Scientific.IO (2.4.9) to use NumPy

    # Finite Difference Formulae package

    # Package with useful constants and unit-conversions defined

    # Interpolating between sparse samples

    # Package for Support Vector Machine

    # Package for Gaussian Mixture Models

    # David Cournapeau's corner: autocorrelation, lpc, lpc residual

    # New spline package (based on scipy.interpolate)

    return config

if __name__ == '__main__':
    from numpy.distutils.core import setup

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