Development Log

Scattered interpolation

SWMF outputs may be in generalized coordinates. For the purpose of plotting, we often need to first interpolate onto a uniform mesh. Even thought this seems to be a very basic job, I have not yet found a simple solution to do this in native Julia. My current workaround is to call Python:

using PyPlot
n = 100
X, Y, W = rand(n), rand(n), rand(n)
interval = 0.02
xi = range(minimum(X), stop=maximum(X), step=interval)
yi = range(minimum(Y), stop=maximum(Y), step=interval)
# Perform linear interpolation of the data (x,y) on grid(xi,yi)
triang = matplotlib.tri.Triangulation(X,Y)
interpolator = matplotlib.tri.LinearTriInterpolator(triang, W)
Xi = [y for _ in xi, y in yi]
Yi = [x for x in xi, _ in yi]
wi = interpolator(Xi, Yi)

There is a package in Julia called ScatteredInterpolation.jl, but unfortunately it does not have simple bilinear interpolation method, and the existing methods in the package costs too much memory. From the author of the package:

I would really like to have an implementation of a fast linear method corresponding to the ones in Python or MATLAB. However, these use a Delaunay triangulation of the sampling points, and as far as I know, the only Julia library providing such a triangulation works only in 2D. I started an implementation two years ago by wrapping the Qhull library that both Python and MATLAB use for the Delaunay triangulation, but that was a major pain and I gave up.

Actually there is a QHull wrapper in Julia now. But again this is a wrapper over a Python library. In this case I would say: do not reinvent the wheel for no good reasons.

Macros

Several places where macros can be used:

• Create a subarray with a name symbol
• Reduce wrapper code duplicates