| Re: FOR loops and efficiency [message #66533] |
Fri, 22 May 2009 05:35  |
Jeremy Bailin
Messages: 618
Registered: April 2008
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Senior Member |
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On May 21, 12:04 pm, Rachel <lotsofche...@gmail.com> wrote:
> It was impressed upon me sometime or another that the use of FOR loops
> in IDL applications tends to be an inefficient solution for doing many
> tasks, yet sometimes I have difficulty finding a reasonable
> alternative to the FOR loop. I was wondering if anyone could give me
> advice on the following example code.
>
> I am trying to make a function that takes arrays of parameters and
> then generates a mathematical model. In the following example I use
> gaussian curves, but generally I would want to expand an
> implementation to other mathematical functions (gaussians are just
> easy for this example).
> So basically I can accomplish what I want to do using something like
> the following:
>
> x = findgen(2000)*0.1 + 900.0
> y = fltarr(2000)+1.0
>
> lam0 = findgen(10)*50.0 + 900.0
> depth = findgen(10)/10.0
> width = findgen(10)
>
> for i = 0,n_elements(lam0)-1 do y = y *(1.0 - depth[i]*exp(-(x-width
> [i])^2/2.0/width[i]))
>
> I was thinking about how one might accomplish the same things without
> a for loop and I came up with the following... problem being that for
> large arrays of lam0 this is actually more inefficient (I'm assuming
> because of the use of extraordinarily large arrays)
>
> n = n_elements(x)
> nlines = n_elements(lam0)
> y = product(1.0 - rebin(transpose(depth),n,nlines)*exp(-(rebin
> (x,n,nlines)-rebin(transpose(lam0),n,nlines))^2/2.0/rebin(tr anspose
> (width),n,nlines)),2)
>
> any advise?
>
> Thanks!
> Josh
I don't know about the more complicated functions, but one thing here
is that you're probably spending a lot of effort calculating array
elements that are far enough away from the center of what is
effectively a 10D Gaussian that the their value is certainly 0 to
machine precision. If you restrict the part of it you compute to the
innermost N sigmas (where you can figure out an appropriate value of
N, and probably just use max(width) to be safe for sigma), it'll take
a lot longer to run into memory-related slowdowns.
That said, if your better model functions don't fall to zero as
quickly as a Gaussian, that may not be relevant... and as Craig says,
the for loop may not be the limiting factor in your case.
-Jeremy.
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