[SciPy-User] 2D integration of irregularly gridded data

Andrew York Andrew.G.York+scipy@gmail....
Wed Aug 29 08:37:30 CDT 2012


I have 2D data sampled at irregular points. I want to estimate the integral
of this data over a finite x-y region. Is there a standard way to do this
in python/scipy, or should I roll my own integration?

The algorithm I imagine is to use Delaunay triangulation to construct a
surface f(x, y), and then sum the volume under this approximate surface.

The form my data currently takes:

x = [1.1, 1.3, 1.31, 1.33, 2, 2.05] #x-coordinates at which data is known
y = [0.15, 0.7, 0.01, 0.01, 0.9, 0] #y-coordinates at which data is known
f = [1.1, 1.3, 1.15, 1.2, 1.18, 1.3] # Data at these x-y coordinates
(This isn't real data, just meant to concretely illustrate the type of data
I have)

I have to process roughly a million of these datasets to produce a single
image, so a vectorized solution would be nice.

A version of my question is described here:
http://math.stackexchange.com/questions/187730/efficiently-estimate-a-2d-integral-from-irregularly-sampled-limited-data
-------------- next part --------------
An HTML attachment was scrubbed...
URL: http://mail.scipy.org/pipermail/scipy-user/attachments/20120829/3aad9b95/attachment.html 


More information about the SciPy-User mailing list