Usage: 3dLocalSVD [options] inputdataset
* You may want to use 3dDetrend before running this program,
   or at least use the '-polort' option.
* This program is highly experimental.  And slowish.
* Computes the SVD of the time series from a neighborhood of each
   voxel.  An inricate way of 'smoothing' 3D+time datasets,
   in some sense, maybe.
* For most purposes, program 3dLocalPV does the same thing, but faster.
   The only reason to use 3dLocalSVD is if you are using -vproj
   with the projection dimension ndim > 2.

 -mask mset           = restrict operations to this mask
 -automask            = create a mask from time series dataset
 -prefix ppp          = save SVD vector result into this new dataset
 -input inputdataset  = input time series dataset
 -nbhd nnn            = e.g., 'SPHERE(5)' 'TOHD(7)' etc.
 -polort p [+]        = detrending ['+' means to add trend back]
 -vnorm               = normalize data vectors
                         [strongly recommended]
 -vproj [ndim]        = project central data time series onto local SVD subspace
                         of dimension 'ndim'
                         [default: just output principal singular vector]
                         [for 'smoothing' purposes, '-vnorm -vproj 2' is a good idea]

++ Compile date = Oct 13 2022 {AFNI_22.3.03:linux_ubuntu_16_64}