Hi all, I am very new to Julia, and I am trying it with a dive-in approach,
translating of a python script of mine. The results are already
encouraging, since I get almost the same performance as the highly
optimized numpy code with trivial Julia code (finally I can write for
cycles!), but I think (because I profiled) I would gain a lot from
optimizing the following linearly interpolating function:
function interp1d(x::Array{Float64,1},x0::Array{Float64,1},y0::Array{Float64
,1})
y = zeros(x)*NaN;
jj = 1
for ii in 1:length(y)
if x0[1]<x[ii]<x0[end]
jj = findnext(val->val>x[ii],x0,jj)-1;
y[ii] = y0[jj] + (y0[jj+1]-y0[jj])/(x0[jj+1]-x0[jj])*(x[ii]-x0[
jj]);
end
end
return y;
end
I guess that findnext may not be the best choice, and possibly
metaprogramming could help, but I am mostly new to it and I would
appreciate some sort of introduction. Could someone point me to resources
to make progress, or do you have any suggestion?
Thanks,
Andrea
