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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!!