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convones [2010/06/22 05:10] (current)
jochen created
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 +====== convones ======
 +===== Motivation =====
 +The main motivation to use convones is to have a faster routine for the special case where one of the arguments of a one-dimensional convolution is a series of all-1 values.
 +===== Reference =====
 +<​file> ​ convones ​ - convoles a function with a number of consecutive ones
 +  FORMAT: ​      cfunc = convones(f, cnum [, w])
 +  Input fields:
 +        f           ​function to convolve
 +        cnum        number of consecutive ones to use
 +        w           if 1x1 true, weight (divide by number of samples)
 +  Output fields:
 +        cfunc       ​ones-convolved function
 +  See also conv</​file>​
 +===== Used by =====
 +The main internal use for this function is the creation of design matrices. Opposed to SPM (which typically uses a 16 timebin-per-TR resolution),​ the [[prt.CreateSDM|design matrix creation]] in NeuroElf uses a millisecond resolution. While differences between convolved time courses are small (the HRF does not have frequency components for which the maximally occurring shift would matter much w.r.t. their phase), I like the idea of being precise wherever possible. And since onset times are commonly given in milliseconds (and people seem to make a whole lot of fuss about stimulus timing these days), I figured it would be nice to have this implemented...
 +===== Exemplary use =====
 +<code matlab conv_ones_rectwindow.m>​% create a large gaussian kernel
 +k = smoothkern(2000);​
 +% convolve with a all-ones vector of equal length
 +tic, ko = convones(k, numel(k)); toc
 +% reference: Elapsed time is 0.024565 seconds.
 +% and now the same with conv
 +tic, koc = conv(k, ones(numel(k),​ 1)); toc
 +% reference: Elapsed time is 0.240042 seconds.</​code>​
 +Note: the difference gets larger the longer the vectors become!!
convones.txt ยท Last modified: 2010/06/22 05:10 by jochen