:orphan:
.. _ahelp_3dNwarpFuncs:
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3dNwarpFuncs
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.. contents:: :local:

.. codeblock:: none
Usage: 3dNwarpFuncs [options]
This program reads in a nonlinear 3D warp (from 3dQwarp, etc.) and
computes some functions of the displacements. See the OPTIONS below
for information on what can be computed. The NOTES sections describes
the formulae of the functions that are available.

OPTIONS:

nwarp www = 'www' is the name of the 3D warp dataset
(this is a mandatory option!)
++ This can be computed on the fly, as in 3dNwarpApply.
prefix ppp = 'ppp' is the name of the new output dataset
bulk = Compute the (fractional) bulk volume change.
++ e.g., Jacobian determinant minus 1.
++ see 'MORE...' (below) for interpreting the sign of 'bulk'.
shear = Compute the shear energy.
vorticity = Compute the vorticity enerty.
all = Compute all 3 of these fun fun functions.
If none of 'bulk', 'shear', or 'vorticity' are given, then 'bulk'
will be assumed.

NOTES:

Denote the displacement vector field (warp) by
[ p(x,y,z) , q(x,y,z) , r(x,y,z) ]
Define the Jacobian matrix by
[ 1+dp/dx dp/dy dp/dz ] [ Jxx Jxy Jxz ]
J = [ dq/dx 1+dq/dy dq/dz ] = [ Jyx Jyy Jyz ]
[ dr/dx dr/dy 1+dr/dz ] [ Jzx Jzy Jzz ]
* The 'bulk' output is the determinant of this matrix (det[J]), minus 1.
* It measures the fractional amount of volume distortion.
* Negative means the warped coordinates are shrunken (closer together)
than the input coordinates. Also see the 'MORE...' section below.
* The 'shear' output is the sum of squares of the J matrix elements 
which equals the sum of squares of its eigenvalues  divided by
det[J]^(2/3), then minus 3.
* It measures the amount of shearing distortion (normalized by the amount
of volume distortion).
* The 'vorticity' output is the sum of squares of the skew part of
the J matrix = [ JxyJyx , JxzJzx , JyzJzy ], divided by det[J]^(2/3).
* It measures the amount of twisting distortion (also normalized).
* All 3 of these functions are dimensionless.
* The penalty used in 3dQwarp is a combination of the bulk, shear,
and vorticity functions.

MORE about interpreting bulk:

If the warp N(x,y,z) is the '_WARP' output from 3dQwarp, then N(x,y,z)
maps the base dataset (x,y,z) coordinates to the source dataset (x,y,z)
coordinates. If the source dataset has to expand in size to match
the base dataset, then going from base coordinates to source must
be a shrinkage. Thus, negative 'bulk' in this '_WARP' dataset
corresponds to expansion going from source to base. Conversely,
in this situation, positive 'bulk' will show up in the '_WARPINV'
dataset from 3dQwarp as that is the map from source (x,y,z) to
base (x,y,z).
The situation above happens a lot when using one of the MNI152 human
brain templates as the base dataset. This family of datasets is larger
than the average human brain, due to the averaging process used to
define the first MNI152 template back in the 1990s.
I have no easy interpretation handy for the 'shear' and 'vorticity'
outputs, alas. They are computed as part of the penalty function used
to control weirdness in the 3dQwarp optimization process.

AUTHOR  RWCox == @AFNIman

++ Compile date = Sep 18 2022 {AFNI_22.2.12:linux_ubuntu_16_64}