simLife (version 0.5.2)

simCluster: Generate clustered regions

Description

Construct regions of clustered objects

Usage

simCluster(S, CL, cond = list(eps = 1e-06, minSize = 1L),
  do.rsa = FALSE, verbose = FALSE, pl = 0L)

Arguments

S

(non-overlapping) geometry objects system

CL

cluster regions, i.e. objects of class 'spheres', possibly overlapping

cond

conditioning object for cluster algorithm, see details

do.rsa

optional, whether to apply rsa algorithm to cluster spheres

verbose

optional, verbose output, verbose=FALSE (default)

pl

integer, pl>0 for printing information

Value

a list of the following element:

• id numeric vector of ids of including spheroids

• center the center of enclosing ball

• r the radius

• interior equals to 0 if any of the enclosed ball objects hit the boundaries of the simulation box and equals 1 otherwise

Details

The function takes a non-overlapping system of spheres, cylinders, spheroids of type "prolate" or "oblate and a random ball configuration CL of class "spheres" which defines clustered regions of the objects given in S according to the minimum required distance eps between these objects and a number of objects required in that region by minSize in the list cond. The function rsa is internally called if do.rsa=TRUE in order to make these cluster regions non-overlapping.

Examples

# NOT RUN {
## Simulate and densify particle system
## densify and include a secondary phase

library(unfoldr)

## Unless MS-Windows platform	
# library(parallel)
# options(simLife.mc=2L)
 
# simulation box either this kind of list
# or use object spatstat::box3 
box <- list("xrange"=c(0,3),"yrange"=c(0,3),"zrange"=c(0,9))

# parameter for spheroids simulation 
theta <- list("size"=list(0.25),"shape"=list("s"=0.25), "orientation"=list("kappa"=1))

# for ease of use: constant size particles
S <- simPoissonSystem(theta,lam=15,size="const",type="prolate",
			orientation="rbetaiso",box=box,mu=c(0,1,0),pl=1,label="P")
	
## 2nd. phase (ferrit)	
param <- list("size"=list(0.075), "shape"=list("s"=0.75))
F <- simPoissonSystem(param,lam=2,size="const",type="prolate",
		box=box,mu=c(0,1,0),pl=1,label="P")

# apply RSA, this may take some 
RSA <- rsa(S,F,verbose=TRUE)

## show 3D spheroids (non-overlapping)
# library(rgl)
# cols <- c("#0000FF","#00FF00","#FF0000","#FF00FF","#FFFF00","#00FFFF")
# spheroids3d(RSA[1:length(S)], box, col=cols)	
# spheroids3d(RSA[length(S):(length(S)+length(F))], box, col="gray")

## construct clusters
CL <- simPoissonSystem(list("size"=list(0.35)), lam=0.1, size="const",
			type="spheres", box=box, pl=1, label="F")

CLUST <- simCluster(RSA, CL, verbose=TRUE)
cat("cluster length: ",length(CLUST),"\n")

## show cluster regions
# library(rgl)
# open3d()
# X <- do.call(rbind,lapply(CLUST, function(x) c(x$center,x$r)))
# invisible(lapply(CLUST, function(x) rgl::spheres3d(X[,1:3],radius=X[,4],col="gray",alpha=0.2)))
# cols <- c("#0000FF","#00FF00","#FF0000","#FF00FF","#FFFF00","#00FFFF")
# invisible(lapply(CLUST, function(x) spheroids3d(RSA[x$id],box,col=cols)))

# some controls for 'GenSA'
ctrl <- list(threshold.stop=0.01,max.call=10000)
# densify region to clustered particles
RET <- densifyCluster(RSA, CLUST, ctrl, weight=100, cores = 1L, cl = NULL)	

S <- RET$S
CL <- RET$cluster

## show densified clusters 
# library(rgl)
# open3d()
# X <- do.call(rbind,lapply(CLUST, function(x) c(x$center,x$r)))
# invisible(lapply(CLUST, function(x)
# rgl::spheres3d(x=X[,1:3],radius=X[,4],col="gray",alpha=0.2)))
# invisible(lapply(CL, function(x) { spheroids3d(x, box, col=cols) }))
	
# }