FastNoiseDemo([numRects=1][, rectSize=128][, scale=1][, syncToVBL=1][, dontclear=0])
Demonstrates how to generate and draw noise patches on-the-fly in a fast way. Can be
used to benchmark your system by varying the load. If you like this demo
then also have a look at FastMaskedNoiseDemo that shows how to
efficiently draw a masked stimulus by use of alpha-blending.
numRects = Number of random patches to generate and draw per frame.
rectSize = Size of the generated random noise image: rectSize by rectSize
pixels. This is also the size of the Psychtoolbox noise
scale = Scalefactor to apply to texture during drawing: E.g. if you’d set
scale = 2, then each noise pixel would be replicated to draw an image
that is twice the width and height of the input noise image. In this
demo, a nearest neighbour filter is applied, i.e., pixels are just
replicated, not bilinearly filtered – Important to preserve statistical
independence of the random pixel values!
syncToVBL = 1=Synchronize bufferswaps to retrace. 0=Swap immediately when
drawing is finished. Value zero is useful for benchmarking the whole
system, because your measured framerate will not be limited by the
monitor refresh rate – Gives you a feeling of how much headroom is left
in your loop.
dontclear = If set to 1 then the backbuffer is not automatically cleared
to background color after a flip. Can save up to 1 millisecond on old
Two patches, 256 by 256 noise pixels each, scaled by any factor between 1
and 5 yields a redraw rate of 100 Hz.
One patch, 256 by 256 noise pixels, scaled by any factor between 1
and 5 yields a redraw rate of 196 Hz.
Two patches, 128 by 128 noise pixels each, scaled by any factor between 1
and 5 yields a redraw rate of 360 - 380 Hz.
One patch, 128 by 128 noise pixels, scaled by any factor between 1
and 5 yields a redraw rate of 670 Hz.