By the way, I forgot to mention that I have read the performance tips, in
particular concerning allocation of output. I do have an interp1d! function
but I need both.
Il giorno martedì 26 maggio 2015 16:17:35 UTC+2, Andrea Cimatoribus ha
scritto:
>
> 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
>
