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ResistanceGA_CDPOP_simFunction_JW.R
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################################################################################
########## Julian Wittische - November 2021 - Simulating connectivity ##########
################################################################################
# This is largely based on work by William Peterman and Kristopher Winiarski
ResistanceGAMS.sim <- function(r.dim = 100,
RMexpvar_1 = 1,
RMexpscale_1 = 1,
RMexpvar_2 = 1,
RMexpscale_2 = 2,
transformation_1 = 3,
r.tran_shape_1 = 3,
r.tran_max_1 = 100,
transformation_2 = 8,
r.tran_shape_2 = 3,
r.tran_max_2 = 100,
RMexpvar_r = 1,
RMexpscale_r = 15,
RMexpvar_r2 = 1,
RMexpscale_r2 = 10,
habitat = 0.5,
n_ind = 500,
n_samplepoints = 100,
start = 1,
seed = 1,
iters = 25,
parallel = 24,
method = 'standard',
maxiter = 100,
JULIA_HOME = "C:/Users/jwittische/AppData/Local/Programs/Julia-1.6.3/bin/",
CDPOP.py = 'C:/Users/jwittische/Desktop/Projects/BestDistance/CDPOP-master/src/CDPOP.py',
sim_name = 'output_',
sim_dir = "C:/Users/jwittische/Desktop/Projects/BestDistance/cdpop_sim_TEST/",
looptime = 101,
output_years = 100,
gridformat = 'cdpop',
loci = 30,
alleles = 30,
matemoveno = 2, ## 1 = Linear, 2 = Inv sq; 9 = custom prob matrix
matemovethresh = 0.025,
MeanFecundity = 4,
n_axes = 64
){
library(ResistanceGA)
library(RandomFields)
# source("U:/CDPOP/cdpop_function.R")
# library(PopGenReport)
if(is.null(start)) {
stop("Specify integer `start` value!!!")
}
# Helper Function ---------------------------------------------------------
surface_corrs <- function(A, orig, rep) {
rep <- rep * A
rep <- orig + rep
}
# Main Function -----------------------------------------------------------
# for(i in sd) {
for(z in start:iters){
# seed <- seed + 1
# >> Make Random Surfaces -------------------------------------------------
# Specify random model
# * Surface 1 ------------------------------------------------------------
# Create original and correlated surfaces for surface 1.
set.seed(z)
corr = c(0.9, 0.5, 0.1)
A <- sqrt((1/(corr^2)) - 1) # calculate A
coo <- 1:r.dim
model <- RMexp(var=RMexpvar_1, scale=RMexpscale_1)
rf.sim <- RFsimulate(model = model, x=coo, y=coo, grid=TRUE)
surface_orig <- as.matrix(rf.sim)
rf.sim <- RFsimulate(model = model, x=coo, y=coo, grid=TRUE)
surface_rep <- as.matrix(rf.sim)
corr_surfaces <- lapply(A, surface_corrs, orig = surface_orig, rep = surface_rep)
out_rasts <- lapply(c(list(surface_orig), corr_surfaces), function(r) raster(scale(r)))
names(out_rasts) <- c("orig", paste0("cor_", corr))
bb <- extent(0.5, r.dim + 0.5, 0.5, r.dim + 0.5)
extent(out_rasts$orig) <- bb
out_rasts$orig <- setExtent(out_rasts$orig, bb, keepres=TRUE)
orig <- out_rasts$orig
extent(out_rasts$cor_0.9) <- bb
out_rasts$cor_0.9 <- setExtent(out_rasts$cor_0.9, bb, keepres=TRUE)
cor_0.9 <- out_rasts$cor_0.9
extent(out_rasts$cor_0.5) <- bb
out_rasts$cor_0.5 <- setExtent(out_rasts$cor_0.5, bb, keepres=TRUE)
cor_0.5 <- out_rasts$cor_0.5
extent(out_rasts$cor_0.1) <- bb
out_rasts$cor_0.1 <- setExtent(out_rasts$cor_0.1, bb, keepres=TRUE)
cor_0.1 <- out_rasts$cor_0.1
orig_1 <- orig
cor_0.9_1 <- cor_0.9
cor_0.5_1 <- cor_0.5
cor_0.1_1 <- cor_0.1
# * Surface 2 ------------------------------------------------------------
# Create original and correlated surfaces for surface 2.
corr = c(0.9, 0.5, 0.1)
A <- sqrt((1/(corr^2)) - 1) # calcualate A
coo <- 1:r.dim
model <- RMexp(var=RMexpvar_2, scale=RMexpscale_2)
rf.sim <- RFsimulate(model = model, x=coo, y=coo, grid=TRUE)
surface_orig <- as.matrix(rf.sim)
rf.sim <- RFsimulate(model = model, x=coo, y=coo, grid=TRUE)
surface_rep <- as.matrix(rf.sim)
corr_surfaces <- lapply(A, surface_corrs, orig = surface_orig, rep = surface_rep)
out_rasts <- lapply(c(list(surface_orig), corr_surfaces), function(r) raster(scale(r)))
names(out_rasts) <- c("orig", paste0("cor_", corr))
extent(out_rasts$orig) <- bb
out_rasts$orig <- setExtent(out_rasts$orig, bb, keepres=TRUE)
orig <- out_rasts$orig
extent(out_rasts$cor_0.9) <- bb
out_rasts$cor_0.9 <- setExtent(out_rasts$cor_0.9, bb, keepres=TRUE)
cor_0.9 <- out_rasts$cor_0.9
extent(out_rasts$cor_0.5) <- bb
out_rasts$cor_0.5 <- setExtent(out_rasts$cor_0.5, bb, keepres=TRUE)
cor_0.5 <- out_rasts$cor_0.5
extent(out_rasts$cor_0.1) <- bb
out_rasts$cor_0.1 <- setExtent(out_rasts$cor_0.1, bb, keepres=TRUE)
cor_0.1 <- out_rasts$cor_0.1
#Create original and correlated surfaces for surface 2.
orig_2 <- orig
cor_0.9_2 <- cor_0.9
cor_0.5_2 <- cor_0.5
cor_0.1_2 <- cor_0.1
# * Random surface ------------------------------------------------------------
corr = c(0.9, 0.5, 0.1)
A <- sqrt((1/(corr^2)) - 1) # calcualate A
coo <- 1:r.dim
model <- RMexp(var=RMexpvar_r, scale=RMexpscale_r)
rf.sim <- RFsimulate(model = model, x=coo, y=coo, grid=TRUE)
surface_orig <- as.matrix(rf.sim)
rf.sim <- RFsimulate(model = model, x=coo, y=coo, grid=TRUE)
surface_rep <- as.matrix(rf.sim)
corr_surfaces <- lapply(A, surface_corrs, orig = surface_orig, rep = surface_rep)
out_rasts <- lapply(c(list(surface_orig), corr_surfaces), function(r) raster(scale(r)))
names(out_rasts) <- c("orig", paste0("cor_", corr))
extent(out_rasts$orig) <- bb
out_rasts$orig <- setExtent(out_rasts$orig, bb, keepres=TRUE)
orig <- out_rasts$orig
extent(out_rasts$cor_0.9) <- bb
out_rasts$cor_0.9 <- setExtent(out_rasts$cor_0.9, bb, keepres=TRUE)
cor_0.9 <- out_rasts$cor_0.9
extent(out_rasts$cor_0.5) <- bb
out_rasts$cor_0.5 <- setExtent(out_rasts$cor_0.5, bb, keepres=TRUE)
cor_0.5 <- out_rasts$cor_0.5
extent(out_rasts$cor_0.1) <- bb
out_rasts$cor_0.1 <- setExtent(out_rasts$cor_0.1, bb, keepres=TRUE)
cor_0.1 <- out_rasts$cor_0.1
#Create original and correlated surfaces for surface 2.
random_1 <- orig
cor_0.9_3 <- cor_0.9
cor_0.5_3 <- cor_0.5
cor_0.1_3 <- cor_0.1
# >> Create Directory -----------------------------------------------------
# SD <- ifelse(i == 0.5, '0_5', '1_25')
dir.create(paste0(sim_dir, "Results/",
'iter__', z),
recursive = TRUE)
out <- paste0(sim_dir, "Results/",
'iter__', z, "/")
tmp <- paste0(tempdir(), '\\')
# >> Create Truth ---------------------------------------------------------
r.stack <- stack(orig_1,
orig_2,
random_1)
names(r.stack) <- c('cont1', 'cont2', 'rand')
# #Run 'GA.prep'
GA.inputs <- GA.prep(ASCII.dir = r.stack[[-3]],
select.trans = list('A','A'),
max.cat = 500,
max.cont = 500,
Results.dir = tmp,
quiet = TRUE)
# * Julia Prep -----------------------------------------------------------
x.coord <- SpatialPoints(cbind(runif(n_samplepoints, 10, 40),
runif(n_samplepoints, 10, 40)))
jl.inputs <- jl.prep(n.Pops = n_samplepoints,
CS_Point.File = x.coord,
JULIA_HOME = JULIA_HOME)
PARM <- c(transformation_1, #Transformation for continuous surface 1
r.tran_shape_1, #shape parameter for continuous surface 1
r.tran_max_1, #max parameter for continuous surface 1
transformation_2, #Transformation for continuous surface 1
r.tran_shape_2, #shape parameter for continuous surface 1
r.tran_max_2) #max parameter for continuous surface 2
# Combine resistance surfaces
Resist <- Combine_Surfaces(PARM = PARM,
jl.inputs = jl.inputs,
GA.inputs = GA.inputs,
out = NULL,
rescale = TRUE)
print(Combine_Surfaces(PARM = PARM,
jl.inputs = jl.inputs,
GA.inputs = GA.inputs,
out = NULL,
rescale = TRUE,
p.contribution = TRUE))
plot(stack(r.stack,Resist))
# Generate Points ---------------------------------------------------------
pts <- unique(floor(cbind(runif(10000, 0.15 * r.dim, 0.85 * r.dim),
runif(10000, 0.15 * r.dim, 0.85 * r.dim))))
sample.thresh <- as.numeric(quantile(Resist, habitat))
sample.extract <- extract(Resist, pts)
sample.suit <- pts[sample.extract <= sample.thresh,]
pts <- SpatialPoints(sample.suit[sample(nrow(sample.suit), n_ind, replace = F),])
###
# >> Calculate cost distance -------------------------------------------------
jl.inputs <- jl.prep(n.Pops = length(pts),
CS_Point.File = pts,
JULIA_HOME = JULIA_HOME)
## Pairwise resistance distances
r.dist <- Run_CS.jl(jl.inputs = jl.inputs,
r = Resist,
full.mat = T
)
plot(Resist)
plot(pts, add = T, pch = 19)
m_thresh <- quantile(lower(r.dist), matemovethresh)
cdpop_sim <- cdpop(CDPOP.py = "C:/Users/jwittische/Desktop/Projects/BestDistance/CDPOP-master/src/CDPOP.py",
sim_name = sim_name,
pts = pts,
resist_rast = Resist,
resist_mat = r.dist,
sim_dir = out,
looptime = looptime,
output_years = output_years,
gridformat = gridformat,
loci = loci,
alleles = alleles,
matemoveno = matemoveno, ## 1 = Linear, 5 = Neg exp; 9 = custom prob matrix
matemovethresh = m_thresh,
MeanFecundity = MeanFecundity)
cdpop_grid <- cdpop_sim$grid_list[[length(cdpop_sim$pop_list)]]
pops <- cdpop_sim$pop_list[[length(cdpop_sim$pop_list)]]
# Subsample ---------------------------------------------------------------
ind_samp <- sort(sample(1:nInd(cdpop_grid), n_samplepoints, replace = F))
pops_ <- pops[ind_samp]
s_pts <- pts[pops_]
s_pops <- data.frame(pop = pops_,
x = pts@coords[pops_,1],
y = pts@coords[pops_,2])
write.table(s_pops,
paste0(out, "sampled_pops.csv"),
sep = ",",
row.names = FALSE,
col.names = TRUE)
# PCA dist ---------------------------------------------------------------
Dps <- 1-propShared(cdpop_grid[ind_samp])
pca <- pca_dist(cdpop_grid[ind_samp], n_axes = n_axes)
## Quick plot select
par(mfrow = c(2,2))
plot(lower(Dps) ~ lower(r.dist[pops_,pops_]), main = "Dps ~ Resist")
plot(lower(pca) ~ lower(r.dist[pops_,pops_]), main = "PCA ~ Resist")
plot(lower(Dps) ~ c(dist(pts@coords[pops_,])), main = "Dps ~ Euc dist")
plot(lower(pca) ~ c(dist(pts@coords[pops_,])), main = "PCA ~ Euc dist")
par(mfrow = c(1,1))
graphics.off()
# Optimize: All Comb ----------------------------------------------------------------
if(method == 'island') {
GA.inputs <- GA.prep(ASCII.dir = r.stack,
Results.dir = 'all_comb',
max.cont = 500,
select.trans = list('A', 'A', 4),
maxiter = 52,
run = 8,
island.pop = island.pop,
numIslands = 4,
migrationInterval = 4,
gaisl = T
)
} else {
GA.inputs <- GA.prep(ASCII.dir = r.stack,
Results.dir = 'all_comb',
max.cont = 2500,
select.trans = list('A', 'A', 'A'),
maxiter = maxiter, #65
run = 25,
# pop.size = 5,
parallel = parallel
)
}
jl.inputs <- jl.prep(n.Pops = n_samplepoints,
CS_Point.File = s_pts,
response = lower(pca),
JULIA_HOME = JULIA_HOME)
dir.create(paste0(out,'rga/'), recursive = T)
Rga_out <- all_comb(jl.inputs = jl.inputs,
GA.inputs = GA.inputs,
# iters = 10,
results.dir = paste0(out,'rga/')
)
write.csv(pca,
file = paste0(out, "pca_dist.csv"))
writeRaster(Resist,
paste0(out, "true_resist.asc"))
write.csv(r.dist[pops_,pops_],
file = paste0(out, "true_ResistDist.csv"))
} # end iteration loop (z)
} # end function