Hi there, xdata (or any other grid) does not need to be on a grid, that's just the way the example is written. but no extrapolation, that's right.
On Thursday, 14 May 2015 01:05:55 UTC+1, Yakir Gagnon wrote: > > Thanks a ton people!!! > > ExtremelyRandomizedTrees.jl: Might be really good, but errored a lot on > version 4. > ApproXD.jl: Very cool, I'll come back to that later, but for now: "What > Lininterp cannot do: Multidimensional extrapolation" which I need (and > accept is prone to silliness). Plus, according to the examples, the known > xdata needs to be on a grid. My x,y,z are not equally spaced that way, they > are scattered. > compute the distances: I started with that, and in light of these results > I might fall back to doing just that. > Polyharmonic Splines: does exactly what I want, but not faster than just > distances (but probably a lot more accurate). > CHull.jl: might be awesome for this, but I didn't quickly see how I can > use it to interpolate and extrapolate. > > I feel like I owe you guys some background: I have some color-map > functions that take a value, let's call it X (for some 0<X<1 and for some > -pi/2<X<pi/2) and return an RGB. I need to use these to convert images that > have colors that are similar (but not always identical) to the RGB values > from these color-maps back to the value (X) that would have resulted in > said RGB (so it's like the inverse of those color-map functions). After > much trial and error, I think that since the images I want to convert this > way are all 8-bit, the fastest way would be to construct a lookup table for > all 2^(8*3) possible RGB combinations. This table (Dict) will take any RGB > value from those images I need to convert and return X. This makes sense > because I have only 4 such color-map functions, and potentially endless > amounts of images to convert (currently a couple of tera). > So I think I'll use simple distances, run that once, save those lookup > tables and use them again and again on new images. > > > > > On Thursday, May 14, 2015 at 4:19:18 AM UTC+10, Luke Stagner wrote: >> >> You can use Polyharmonic Splines >> <http://nbviewer.ipython.org/gist/lstagner/04a05b120e0be7de9915> >> >> >> On Wednesday, May 13, 2015 at 5:33:08 AM UTC-7, Yakir Gagnon wrote: >>> >>> I have a bunch (~1000) of x,y,z and a corresponding value, V. One unique >>> V for each x,y,z. I want to interpolate and extrapolate "wildly" (so I >>> really don't care about how accurate or correct it is). The x,y,z I have >>> are not regularly spaced or anything. They're scattered across some range >>> (they all share similar ranges), and I want to know what value (i.e. new V) >>> I should be getting at new and regularly spaced x,y,z. I think >>> Matlab's griddata would do what I want. >>> I tired following the "lower-level" functionality from Grid.jl, >>> Interpolations.jl, and Dierckx.jl, but couldn't figure out how to get this >>> to work. >>> I think I need to fit some curve to my scattered points and then use >>> that to find the values at the new xyz (at least that's how I think >>> griddata works)... Any good simple ideas out there? >>> >>>
