Principal Component Analysis of 3D Datasets
Usage: 3dpc [options] dataset dataset ...

Each input dataset may have a sub-brick selector list.
Otherwise, all sub-bricks from a dataset will be used.

  -dmean        = remove the mean from each input brick (across space)
  -vmean        = remove the mean from each input voxel (across bricks)
                    [N.B.: -dmean and -vmean are mutually exclusive]
                    [default: don't remove either mean]
  -vnorm        = L2 normalize each input voxel time series
                    [occurs after the de-mean operations above,]
                    [and before the brick normalization below. ]
  -normalize    = L2 normalize each input brick (after mean subtraction)
                    [default: don't normalize]
  -nscale       = Scale the covariance matrix by the number of samples
                  This is not done by default for backward compatibility.
                  You probably want this option on.
  -pcsave sss   = 'sss' is the number of components to save in the output;
                    it can't be more than the number of input bricks
                    [default = none of them]
                  * To get all components, set 'sss' to a very large
                    number (more than the time series length), like 99999
                    You can also use the key word ALL, as in -pcsave ALL
                    to save all the components.
  -reduce r pp  = Compute a 'dimensionally reduced' dataset with the top
                    'r' eigenvalues and write to disk in dataset 'pp'
                    [default = don't compute this at all]
                  * If '-vmean' is given, then each voxel's mean will
                    be added back into the reduced time series.  If you
                    don't want this behaviour, you could remove the mean
                    with 3dDetrend before running 3dpc.
                  * On the other hand, the effects of '-vnorm' and '-dmean'
                    and '-normalize' are not reversed in this output
                    (at least at present -- send some cookies and we'll talk).
  -prefix pname = Name for output dataset (will be a bucket type);
                  * Also, the eigen-timeseries will be in 'pname'_vec.1D
                    (all of them) and in 'pnameNN.1D' for eigenvalue
                    #NN individually (NN=00 .. 'sss'-1, corresponding
                    to the brick index in the output dataset)
                  * The eigenvalues will be printed to file 'pname'_eig.1D
                    All eigenvalues are printed, regardless of '-pcsave'.
                    [default value of pname = 'pc']
  -1ddum ddd    = Add 'ddd' dummy lines to the top of each *.1D file.
                    These lines will have the value 999999, and can
                    be used to align the files appropriately.
                    [default value of ddd = 0]
  -verbose      = Print progress reports during the computations
  -quiet        = Don't print progress reports [the default]
  -eigonly      = Only compute eigenvalues, then
                    write them to 'pname'_eig.1D, and stop.
  -float        = Save eigen-bricks as floats
                    [default = shorts, scaled so that |max|=10000]
  -mask mset    = Use the 0 sub-brick of dataset 'mset' as a mask
                    to indicate which voxels to analyze (a sub-brick
                    selector is allowed) [default = use all voxels]

Example using 1D data a input, with each column being the equivalent
of a sub-brick:
 3dpc -prefix mmm -dmean -nscale -pcsave ALL datafile.1D

++ Compile date = Aug 10 2020 {AFNI_20.2.11:linux_ubuntu_16_64}