inst/examples/testCrash.R

In stranda/quantsel: Spatially explicit population genetic simulations with polygenic traits and selection

library(kernelPop2)
library(ggplot2)
library(dplyr)
source("helpers.R")
source("analysis.R")
### this script maks a 250 population grid and 
###


gapprop = 0



rland <- NULL
rland <- landscape.new.empty()
rland <- landscape.new.intparam(rland, h=1024, s=2,np=0,totgen=20000,maxland=3e5)
rland <- landscape.new.switchparam(rland,mp=0)
rland <- landscape.new.floatparam(rland,s=0,seedscale=c(40,290),
                                  seedshape=c(1,300),seedmix=c(0.12),
                                  pollenscale=c(40,100),pollenshape=c(1,10),
                                  pollenmix=0.2 , asp=0.5)


S <- matrix(c(
    0, 0,
    0.8, 0.0
), byrow=T, nrow = 2)
  R <- matrix(c(
      0, 12,
      0,   0
  ), byrow=T, nrow = 2)
M <- matrix(c(
    0, 0,
                0, 1
), byrow=T, nrow = 2)

rland <- landscape.new.local.demo(rland,S,R,M)

S <- matrix(0,ncol = (rland$intparam$habitats*rland$intparam$stages),
            nrow = (rland$intparam$habitats*rland$intparam$stages))

R <- S
M <- S

rights <- floor(seq(00,30000,length=33))
tops <-   floor(seq(0,30000,length=33))
locs=NULL
for (i in 1:(length(tops)-1))
{
    locs <- rbind(locs,
                  data.frame(lft=c(rights[-1]-diff(rights[])+1),
                             bot=rep(tops[i+1]-(tops[2]-tops[1]),16),
                             rgt=rights[-1],
                             top=tops[i+1]))
}

lfts=which(locs$lft==1)
k=(0.10 * (sqrt((locs[,3]-locs[,1])*(locs[,4]-locs[,2]))))
e=rep(gapprop,rland$intparam$habitat)
#k[525:526] <- k[525:526]*2
rland <- landscape.new.epoch(rland,S=S,R=R,M=M,
                             carry=k,
                             extinct=e,
                             leftx=locs[,1],
                             rightx=locs[,3],
                             boty=locs[,2],
                             topy=locs[,4],
                             maxland=c(min(locs[1]),min(locs[2]),max(locs[3]),max(locs[4])))


for (i in 1:16)
    rland <- landscape.new.locus(rland,type=1,ploidy=2,mutationrate=0.00,transmission=0,numalleles=2)

expmat <- matrix(c(
    0.5,0,0.5,0,
    0.5,0,0.5,0,
    1,0,0,0,
    1,0,0,0,
    0,1,0,0,
    0,1,0,0,
    0,1,0,0,
    0,1,0,0,
    0.5,0,0.5,0,
    0.5,0,0.5,0,
    0,0,1,0,
    0,0,1,0,
    0,0,0,1,
    0,0,0,1,
    0,0,0,1,
    0,0,0,1
                   ),byrow=T,ncol=4)
hsq <- c(1,1,1,1)
rland <- landscape.new.expression(rland,expmat=expmat*0.125,hsq=hsq) #0.125 -> 4 diploid loci, up to 8 alelle additive doses
rland <- landscape.new.gpmap(rland,
                             ## 4 cols 5 rows.  Cols correspond to
                             ##phenotype (-1 is none), curvature, range of effect on vital rate
                             ##phenotypes are in C indexing so, add 1 to compare to pehnotypes above
                             matrix(c(-1,   0,    .1, 1,  #short scale #no selection
                                       -1, 0.01, .1, 1,#long scale  #no selection
                                      -1,   0,    .1, 1, #long shape
                                       1,   1,    .1, 1,   #mixture   #phenotype 2
                                      -1,   0,    .1, 1 ),
                                    ncol=4,byrow=T),
                             
                             matrix(c( 2,   0.01,  0.2, -1, #survival
                                      -1,     0,    1,  1, #unused at the moment
                                       0,   0.01,  0.2,  1), #reproduction #phenotype 1
                                    ncol=4,byrow=T))

pop1=460
pop2=468

inits <- matrix(0,ncol=rland$intparam$habitats,nrow=2)
inits[1:2,c(pop1,pop2)] <- initpopsize  
rland <- landscape.new.individuals(rland,c(inits))

if (FALSE)
    {
rland$individuals[landscape.populations(rland)==pop1,c(10,11,12,13)]=1L
rland$individuals[landscape.populations(rland)==pop2,c(10,11,12,13)]=2L

rland$individuals[landscape.populations(rland)==pop1,c(14,15,16,17)]=2L
rland$individuals[landscape.populations(rland)==pop2,c(14,15,16,17)]=1L
}
#rland$individuals[,5] <- 4500+floor(rland$individuals[,5]/10)
###############################

l=landscape.simulate(rland,1)

landscape.plot.phenotypes(l,1)

locs <- landscape.generate.locations(npop=1024,
                                     xrange=c(0,40000),yrange=c(0,40000),
                                     sizexkernel=c(400,65),sizeykernel=c(400,65)
                                     )

phens=c(1,2,3,4) #represented as 0 in c++
gen=100
sumlst=list()[1:gen]

#pdf(paste0("gaps_",gapprop,".pdf"), width=15,height=7.5)

for (i in 1:gen)
{
    print(dim(l$individuals))
 #   l=landscape.kill.locs(l,locs)
    print(dim(l$individuals))
    if (dim(l$individuals)[1]>0) l.old=l
    l=landscape.simulate(l,1)
    if ((i %% 1)==0)
    {
        par(mfrow=c(2,2))
        for (phen in phens)
            landscape.plot.phenotypes(l,phen)
        par(mfrow=c(1,1))
    }
    print(i)
    print(dim(l$individuals))
#    print(landscape.allelefreq(l) )
    print(colMeans(landscape.phenotypes.c(l)))
    sumlst[[i]] <- data.frame(landscape.phenosummary(l))
    sumlst[[i]]$gen=i
}
#dev.off()

sumdf <- do.call(rbind,sumlst)

save(file=paste0("gap_",gapprop,"_res.rda"),sumlst,sumdf)


p = sumdf %>%
    ggplot(aes(y=gen,x=pop,z=phen1_mean))+
    geom_tile(aes(fill = phen1_mean)) +
    geom_contour() +
    scale_fill_gradientn(colors = rev(cm.colors(100)))