pervertexdata.smoothnn: Smooth per-vertex data based on mesh.

In dfsp-spirit/haze: Smoothing of per-Vertex Data on Triangular Meshes

Smooth per-vertex data based on mesh.

Description

Smooth per-vertex data based on mesh.

Usage

pervertexdata.smoothnn(surface, pvdata, num_iter, k = 1L, method = "C++")

Arguments

surface	a mesh, represented as an fs.surface instance from the freesurferformats package or a tmesh3d instance from rgl, or a character string representing the path of a mesh to load with freesurferformats::read.fs.surface.
pvdata	numerical vector of per-vertex-data for the mesh, one value per vertex. Data values of NA will be ignored, allowing you to mask parts of the data. If you pass an n x m matrix or data.frame, the n rows will be treated as (independent) overlays that should be smoothed in parallel. To set the number of cores to use for parallel processing, set the 'mc_cores' option like this: options("mc.cores"=22L); before calling this function. Data.frames and matrices with a single row will be converted to vectors, and the return value will be a vector in that case.
num_iter	positive integer, number of smoothing iterations.
k	scalar positive integer, the k value for the k-ring neighborhood. For k=1, this function computes the adjacency list representation of the graph (where the neighbors include the vertex itself).
method	character string, one of 'C++' or 'R'. The C++ version is much faster (about 30 times faster on our test machine), and there is little reason to ever use the R version in production code, so just ignore this.

Value

numerical vector, the smoothed data.

See Also

pervertexdata.smoothnn.adj if you already have pre-computed adjacency data for the mesh. Using that data can increase performance considerably, especially if you need to smooth many data sets.

Examples

## Not run: 
mesh = rgl::tetrahedron3d();
pvd = rnorm(nrow(mes2$vb), mean = 5.0, sd = 1.0);
pvd_smoothed = pervertexdata.smoothnn(mesh, pvd, num_iter = 30L);

## End(Not run)

dfsp-spirit/haze documentation built on Feb. 28, 2023, 11:45 p.m.