On Sun, Oct 4, 2009 at 4:21 PM, Robert Kern <robert.k...@gmail.com> wrote: > On 2009-10-04 15:27 PM, Christopher Barker wrote: >> Václav Šmilauer wrote: >> >>> about a year ago I developed for my own purposes a routine for averaging >>> irregularly-sampled data using gaussian average. >> >> is this similar to Kernel Density estimation? >> >> http://www.scipy.org/doc/api_docs/SciPy.stats.kde.gaussian_kde.html > > No. It is probably closer to radial basis function interpolation (in fact, it > almost certainly is a form of RBFs): > > http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html#id1
Except in radial basis function interpolation, you solve for the weights that give the original values at the original data points. Here, it's just a inverse-distance weighted average, where the weights are chosen using an exp(-x^2/A) relation. There's a huge difference between the two when you're dealing with data with noise. Ryan -- Ryan May Graduate Research Assistant School of Meteorology University of Oklahoma ------------------------------------------------------------------------------ Come build with us! The BlackBerry® Developer Conference in SF, CA is the only developer event you need to attend this year. Jumpstart your developing skills, take BlackBerry mobile applications to market and stay ahead of the curve. Join us from November 9-12, 2009. Register now! http://p.sf.net/sfu/devconf _______________________________________________ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
