[Scilab-users] interp and memory

[Federico Miyara]

Jean-Ives,

It seems that the variables are not so huge, xp has 1000000 components 
as well as z, while x and y have 10000 components.

But I guess splin() gets the derivatives through solving a linear 
equation system of, in this case, 10000 x 10000, and even if the 
system's matrix is quasi diagonal (it has only the diagonal, 
sub-diagonal and supra-diagonal components different from 0) and this 
surely reduces the computational load, it seems quite a large system and 
probably it gets stuck here.

Probably a lighter (and better) way to do what you seem to be looking 
for is to try to resample your x-y data by a factor of 100 using 
intdec(). However, doing that 1000 times may take quite a long time (I 
haven't tested it). If you really need that, it would probably be better 
to do the oversampling algorithm from scratch in such a way to design 
only once the smoothing filter instead of letting intdec() design the 
same filter over and over again.

Regards,

Federico Miyara


On 08/03/2021 04:33, Jean-Yves Baudais wrote:
> Hello,
>
>   Is my code wrong or is there a real memory problem with the function 
> interp in the following code? (Scilab fulfills the memory and of 
> course stops.)
>
> for i=1:1000
>   mprintf("%d\n",i);
>   n=1e6;
>   xp=1:n;
>   x=1:100:n;
>   y=rand(x);
>   d=splin(x,y);
>   z=interp(xp,x,y,d);
> end
>
> Thanks,
>
> Jean-Yves
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