Make a grayplot from a 3D+time dataset, sort of like Jonathan Power:
Result is saved to a PNG image for your viewing delight.
* This style of plot is also called a carpet plot,
but REAL carpets are much more attractive, IMHO.
* The horizontal axis of the grayplot is time, and the
vertical axis is all 3 spatial dimensions collapsed into 1.
* Also see AFNI script @grayplot, as well as the QC output
generated by afni_proc.py.
3dGrayplot [options] inputdataset
OPTIONS: [lots of them]
-mask mset = Name of mask dataset
* Voxels that are 0 in mset will not be processed.
* Dataset must be byte-valued (8 bits: 0..255);
shorts (16 bits) are also acceptable, but only
values from 1.255 will be processed.
* Each distinct value from 1..255 will be processed
separately, and the output image will be ordered
with the mask=1 voxels on top, mask=2 voxels next,
and so on down the image.
* A partition (e.g., mask=3) with fewer than 9 voxels
will not be processed at all.
* If there is more than one partition, horizontal dashed
lines will drawn between them.
* If '-mask' is not given, then all voxels will be used,
except those at the very edge of a volume. Doing this is
usually not a good idea, as the non-brain tissue will
take up a lot of useless space in the output grayplot.
-input dataset = Alternative way to input the dataset to process.
-prefix ppp.png = Name for output file.
* Default is Grayplot.png (if you don't use this option)
* If the filename ends in '.jpg', a JPEG file is output.
* If the filename ends in '.pgm', a PGM file is output.
[PGM files can be manipulated with the NETPBM package.]
* If the filename does not end in '.jpg' OR in '.png'
OR in '.pgm', then '.png' will be added at the end.
-dimen X Y = Output size of image in pixels.
* X = width = time axis direction
* Y = height = voxel/space dimensions
* Defaults are X=1024 Y=512 -- suitable for screen display.
* For publication, you might want more pixels, as in
-dimen 1800 1200
which would be 6 inches wide by 4 inches high, at the usual
300 dots-per-inch (dpi) of high resolution image printing.
** Note that there are usually many more voxels in the Y direction
than there are pixels in the output image. This fact requires
coarsening the Y output grid and resampling the data to match.
See the next option for a little more information about
how this resampling is implemented.
-oldresam = The method for resampling the processed dataset to the final
grayscale image size was changed/improved in a major way.
If you want to use the original method, then give this option.
* The only reason for using this option is for
comparison with the new method.
* The new resampling method uses minimum-sidelobe local averaging
when coarsening the grid (vertical direction Y = voxels/space)
-- whose purpose is to reduce aliasing artifacts
* And uses cubic interpolation when refining the grid
(usually horizontal direction = time) -- whose purpose
is purely beauty -- compared to the older linear interpolation.
* Note that the collapsing of multiple voxels into one pixel in
the Y direction will tend to cancel out signals that change sign
within neighbors in the voxel ordering method you choose.
(See the 'order' options below.)
-polort p = Order of polynomials for detrending.
* Default value is 2 (mean, slope, quadratic curve).
* Use '-1' if data is already detrended and de-meaned.
(e.g., is an AFNI errts.* file or other residual dataset)
-fwhm f = FWHM of blurring radius to use in the dataset before
making the image.
* Each partition (i.e., mask=1, mask=2, ...) is blurred
independently, as in program 3dBlurInMask.
* Default value is 0 mm = no blurring.
[In the past, the default value was 6.]
* If the dataset was NOT previously blurred, a little
spatial blurring here will help bring out larger scale
features in the times series, which might otherwise
look very noisy.
** The following four options control the ordering of **
** voxels in the grayplot, in the vertical direction. **
-pvorder = Within each mask partition, order the voxels (top to
bottom) by how well they match the two leading principal
components of that partition. The result is to make the
top part of each partition be made up of voxels with
similar time series, and the bottom part will be more
++ The presence of a lot of temporal structure in a
grayplot of a 'errts' residual dataset indicates
that the 'removal' of unwanted time series components
did not work well.
++ Using '-pvorder' to put all the structured time series
close together will make such problems more visible.
++ IMHO, this is the most useful ordering.
-LJorder = Within each mask partition, order the voxels (top to
bottom) by their Ljung-Box statistics, which is a measure
of temporal correlation.
++ Experimental; probably not useful.
-peelorder = Within each mask partition, order the voxels by how
many 'peel' steps are needed to get from the partition
boundary to a given voxel.
++ This ordering puts voxels in 'similar' geometrical
positions sort-of close together in the image.
And is usually not very interesting, IMHO.
-ijkorder = Set the intra-partition ordering to the default, by
dataset 3D index ('ijk').
++ In AFNI's +tlrc ordering, this ordering primarily will
be from Inferior to Superior in the brain (from top to
bottom in the grayplot image).
++ This is the default ordering method, but not the best.
** These options control the scaling from voxel value to gray level **
-range X = Set the range of the data to be plotted to be 'X'.
Each time series is first normalized by its values to:
Z[i] = (t[i] - mean_t)/stdev_t.
When this option is used, then:
* a value of 0 will be plotted as middle-gray
* a value of +X (or above) will be plotted as white
* a value of -X (or below) will be plotted as black
Thus, this option should be used with data that is centered
around zero -- or will be so after '-polort' detrending.
* For example, if you are applying this option to an
afni_proc.py 'errts' (residuals) dataset, a good value
of X to use is 3 or 4, since those values are in percents.
* The @grayplot script uses '-range 3.89' since that is the
value at which a standard normal N(0,1) deviate has a 1e-4
two-sided tail probability. (If nothing else, this sounds cool.)
If you do NOT use '-range', then the data will be automatically
normalized so each voxel time series has RMS value 1, and then
the grayscale plot will be black-to-white being the min-to-max,
where the min and max computed over the entire detrended
and normalized dataset.
* This default automatic normalizing and scaling makes it
almost impossible to directly compare grayplots from
different datasets. This difficulty is why the '-range'
and '-percent' options were added.
-percent = Use this option on 'raw' time series datasets, to compute
the mean of each voxel timeseries and then use that value
to scale the values to percent differences from the mean.
* NOT suitable for use with a residual 'errts' dataset!
* Should be combined with '-range'.
* Detrending will be applied while calculating the mean.
By default, that will be quadratic detrending of each
voxel time series, but that can be changed with the
-raw_with_bounds A B
= Use this option on 'raw' time series datasets, map values
<= A to black, those >= B to white, and intermediate values
* Can be used with any kind of dataset, but probably makes
most sense to use with scaled ones (errts, fitts or
* Should NOT be combined with '-range' or '-percent'.
** Quick hack for Cesar Caballero-Gaudes, April 2018, by @AFNIman.
As such, this program may be modified in the future to be more useful,
or at least more beautifully gorgeous.
** Applied to 'raw' EPI data, the results may not be very informative.
It seems to be more useful to look at the grayplot calculated from
pre-processed data (e.g., time series registered, filtered, etc.).
** See also the script @grayplot, which can process the results from
afni_proc.py and produce an image with the grayplot combined with
a graph of the motion magnitude, and with the GM, WM, and CSF in
** afni_proc.py uses this program to create grayplots of the residuals
from regression analysis, as part of its Quality Control (QC) output.
The following commands first generate a time series dataset,
then create grayplots using each of the ordering methods
(so you can compare them). No mask is given.
3dcalc -a jRandomDataset:64:64:30:256 -datum float \
-prefix Qsc.nii -expr 'abs(.3+cos(0.1*i))*sin(0.1*t+0.1*i)+gran(0,3)'
3dGrayplot -pvorder -prefix QscPV.png -input Qsc.nii -fwhm 8
3dGrayplot -ijkorder -prefix QscIJK.png -input Qsc.nii -fwhm 8
3dGrayplot -peelorder -prefix QscPEEL.png -input Qsc.nii -fwhm 8