## This file is part of mvSLOUCH

## This software comes AS IS in the hope that it will be useful WITHOUT ANY WARRANTY, 
## NOT even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. 
## Please understand that there may still be bugs and errors. Use it at your own risk. 
## We take no responsibility for any errors or omissions in this package or for any misfortune 
## that may befall you or others as a result of its use. Please send comments and report 
## bugs to Krzysztof Bartoszek at krzbar@protonmail.ch .

library(testthat)
context("mvSLOUCH: simulOUCHProcPhylTree")

library(mvSLOUCH)
library(PCMBase)

RNGversion(min(as.character(getRversion()),"3.6.1"))
set.seed(12345, kind = "Mersenne-Twister", normal.kind = "Inversion")
### We will first simulate a small phylogenetic tree using functions from ape.
### For simulating the tree one could also use alternative functions, e.g. sim.bd.taxa 
### from the TreeSim package
phyltree<-ape::rtree(5)

## The line below is not necessary but advisable for speed
phyltree<-phyltree_paths(phyltree)

### Define a vector of regimes.
regimes<-c("small","small","large","small","small","large","large","large")

### Define SDE parameters to be able to simulate data under the OUOU model.
OUOUparameters<-list(vY0=matrix(c(1,-1,0.5),nrow=3,ncol=1),
A=rbind(c(9,0,0),c(0,5,0),c(0,0,1)),mPsi=cbind("small"=c(1,-1,0.5),
"large"=c(-1,1,0.5)),Syy=rbind(c(1,0.25,0.3),c(0,1,0.2),c(0,0,1)))

### Now simulate the data.
jumpobj<-list(jumptype="RandomLineage",jumpprob=0.5,jumpdistrib="Normal",vMean=rep(0,3),mCov=diag(1,3,3))
OUOUdata<-simulOUCHProcPhylTree(phyltree,OUOUparameters,regimes,NULL,jumpsetup=jumpobj)


testthat::expect_identical(length(OUOUdata),phyltree$Ntips*length(OUOUparameters$vY0))
testthat::expect_equivalent(row.names(OUOUdata),phyltree$tip.label)
testthat::expect_equivalent(OUOUdata[1,1], 0.6345661643506110838331)