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cdpop_from_R_function2.R
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#' @author Bill Peterman
#' @title Internal CDPOP function
#' @description Function to run CDPOP from R
#'
#' @param CDPOP.py Full path to the CDPOP.py file
#' @param sim_name Name for simulation results. Defaults to 'output'
#' @param pts Spatial points object
#' @param sim_dir Directory where simulation results will be written
#' @param resist_rast Resistance surface
#' @param agefilename Path to age file. Default will create and use a non-overlapping generations file.
#' @param mcruns Default = 1
#' @param looptime Default = 401; Number generations to conduct simulation
#' @param output_years Default = 50; Interval to write out simulation results
#' @param gridformat Default = 'genepop'; c('genepop', 'genalex', 'structure', 'cdpop')
#' @param cdclimgentime Default = 0. To initiate the CDClimate module, this is the generation/year that the next effective distance matrix will be read in at. You can specify multiple generations by separating each generation to read in the next cost distance matrix by ‘|’. Then in the following surface columns, a separate file can be given for each generation.
#' @param matemoveno Default = 2; Uses Inverse Square (1 / (Cost Distance^2)). This function gets rescaled to min and threshold of the inverse square cost distance.
#' @param matemoveparA Not used with inverse square movement
#' @param matemoveparB Not used with inverse square movement
#' @param matemoveparC Not used with inverse square movement
#' @param matemovethresh Default = 'max'; The maximum movement is the maximum resistance distance
#' @param output_matedistance Does mate movement distance differ (Default = 'N')
#' @param sexans Default = 'N'; No selfing
#' @param Freplace Default = 'N'; Females mate without replacement
#' @param Mreplace Default = 'N'; Males mate without replacement
#' @param philopatry Default = 'N';
#' @param multiple_paternity Default = 'N'; No philopatry of Males or Females
#' @param selfans Default = 'N'; No selfing
#' @param Fdispmoveno Default = NULL. Will be set equal to matemoveno
#' @param FdispmoveparA Not used with inverse square movement
#' @param FdispmoveparB Not used with inverse square movement
#' @param FdispmoveparC Not used with inverse square movement
#' @param Fdispmovethresh Default = NULL. Will be set to matemovethresh
#' @param Mdispmoveno Default = NULL. Will be set equal to matemoveno
#' @param MdispmoveparA Not used with inverse square movement
#' @param MdispmoveparB Not used with inverse square movement
#' @param MdispmoveparC Not used with inverse square movement
#' @param Mdispmovethresh Default = NULL. Will be set to matemovethresh
#' @param offno Default = 2; Poisson draw around ‘mean fecundity’
#' @param MeanFecundity Default = 5; Specifies mean fecundity in age variable file.
#' @param Femalepercent Default = 50
#' @param EqualsexratioBirth Default = 'N'
#' @param TwinningPercent Default = 0
#' @param popModel Default = 'exp'
#' @param r Population growth rate. No applicable when using exponential growth rate
#' @param K_env Equal to the number of individuals simulated
#' @param subpopmortperc Default = 0|0|0|0; Not using subpopulation features
#' @param muterate Default = 0.0005
#' @param mutationtype Default = 'forward'
#' @param loci Default = 1000; For simulating SNP-like markers
#' @param intgenesans Default = 'random'; Random initiation of alleles
#' @param allefreqfilename Default = 'N'
#' @param alleles Default = 2; For simulating SNP-like markers
#' @param mtdna Default = 'N'
#' @param startGenes Default = 0;
#' @param cdevolveans Default = 'N'; No loci are under selection
#' @param startSelection Default = 0; No selection
#' @param betaFile_selection Default = 'N'; No selection
#' @param epistasis Default = 'N'; No epigenetics
#' @param epigeneans Default = 'N'; No epigenetics
#' @param startEpigene Default = 0; No epigenetics
#' @param betaFile_epigene Default = 'N'; No epigenetics
#' @param cdinfect Default = 'N'; No epigenetics
#' @param transmissionprob Default = 0; No epigenetics
#' @param deme_size Number of individuals within each deme
#' @param n_demes Number of demes on the landscape
#' @param python Optional. Only needed if Python 2.7 is not in your system path. If needed, specify the full path to the Python 2.7x program file.
#'
#' @keywords internal
#'
cdpop <- function(CDPOP.py,
sim_name = 'output_',
pts,
sim_dir,
resist_rast,
resist_mat = NULL,
agefilename = NULL,
mcruns = 1,
looptime = 400,
output_years = 50,
gridformat = 'genepop',
cdclimgentime = 0,
matemoveno = 2,
matemoveparA = 0,
matemoveparB = 0,
matemoveparC = 0,
matemovethresh = 'max',
output_matedistance = 'N',
sexans = 'Y',
Freplace = 'N',
Mreplace = 'N',
philopatry = 'N',
multiple_paternity = 'N',
selfans = 'N',
Fdispmoveno = NULL,
FdispmoveparA = 0,
FdispmoveparB = 0,
FdispmoveparC = 0,
Fdispmovethresh = NULL,
Mdispmoveno = NULL,
MdispmoveparA = 0,
MdispmoveparB = 0,
MdispmoveparC = 0,
Mdispmovethresh = NULL,
offno = 2,
MeanFecundity = 5,
Femalepercent = 50,
EqualsexratioBirth = 'N',
TwinningPercent = 0,
popModel = 'exp',
r = 1,
K_env = length(pts),
subpopmortperc = 0,
muterate = 0.0005,
mutationtype = 'random',
loci = 1000,
intgenesans = 'random',
allefreqfilename = 'N',
alleles = 2,
mtdna = 'N',
startGenes = 0,
cdevolveans = 'N',
startSelection = 0,
betaFile_selection = 'N',
epistasis = 'N',
epigeneans = 'N',
startEpigene = 0,
betaFile_epigene = 'N',
cdinfect = 'N',
transmissionprob = 0,
deme_size = 1,
n_demes = NULL,
python = NULL){
# Install / Load Libraries ------------------------------------------------
# list.of.packages <- c("PopGenReport",
# "adegenet",
# "readr",
# "raster")
#
# new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
# if(length(new.packages)) install.packages(new.packages)
# Create directories ------------------------------------------------------
if(!dir.exists(sim_dir)) dir.create(sim_dir, recursive = TRUE)
suppressWarnings(
dir.create(paste0(sim_dir,"/data/"), recursive = TRUE)
)
data_dir <- paste0(sim_dir,"/data/")
# Fill NULL ---------------------------------------------------------------
if(is.null(n_demes)){
n_demes <- length(pts)
}
if(matemoveno == 9){
if(class(resist_rast) == "RasterLayer"){
stop('Specify a probability matrix instead of a raster layer!')
}
write.table(resist_rast,
paste0(data_dir, "move_prob.csv"),
sep = ",",
row.names = FALSE,
col.names = FALSE)
cdmat <- 'move_prob'
# write.table(matemoveno, paste0(data_dir, 'DispProb.csv'),
# sep = ",",
# row.names = F)
# matemoveno <- 'DispProb'
}
if(is.null(Fdispmoveno)){
Fdispmoveno <- matemoveno
}
if(is.null(Mdispmoveno)){
Mdispmoveno <- matemoveno
}
if(is.null(Fdispmovethresh)){
Fdispmovethresh <- matemovethresh
}
if(is.null(Mdispmovethresh)){
Mdispmovethresh <- matemovethresh
}
# Age file ----------------------------------------------------------------
if(is.null(agefilename)){
age_df <- data.frame(`Age class` = c(0,1),
Distribution = c(0,1),
`Male Mortality` = c(0,100),
`Female Mortality` = c(0,100),
`Mean Fecundity` = c(0,MeanFecundity),
`Std Fecundity` = c(0,0),
`Male Maturation` = c(0,1),
`Female Maturation` = c(0,1),
check.names = F)
write.table(age_df, paste0(data_dir, 'AgeVars.csv'),
sep = ",",
row.names = F)
# age_file <- paste0(data_dir, 'AgeVars.csv')
age_file <- 'AgeVars.csv'
}
# XY File -----------------------------------------------------------------
if(deme_size != 1){
sub_pop <- rep(1:n_demes, each = deme_size)
subpopmortperc <- rep(0, n_demes)
subpopmortperc <- paste(subpopmortperc, collapse = " | ")
} else {
sub_pop <- rep(1, length(pts))
}
xyFile_df <- data.frame(Subpopulation = sub_pop,
XCOORD = pts@coords[,1],
YCOORD = pts@coords[,2],
ID = paste0('initial',1:length(pts) - 1),
sex = sample(c(0,1),
replace = T,
size = length(pts)),
Fitness_AA = rep(0, length(pts)),
Fitness_Aa = rep(0, length(pts)),
Fitness_aa = rep(0, length(pts)),
Fitness_AABB = rep(0, length(pts)),
Fitness_AaBB = rep(0, length(pts)),
Fitness_aaBB = rep(0, length(pts)),
Fitness_AABb = rep(0, length(pts)),
Fitness_AaBb = rep(0, length(pts)),
Fitness_aaBb = rep(0, length(pts)),
Fitness_AAbb = rep(0, length(pts)),
Fitness_Aabb = rep(0, length(pts)),
Fitness_aabb = rep(0, length(pts))
)
write.table(xyFile_df, paste0(data_dir, 'xyFile.csv'),
sep = ',',
row.names = F)
# xyFile <- paste0(data_dir, 'xyFile')
xyFile <- 'xyFile'
# Resistance Distance -----------------------------------------------------
if(matemoveno == 9){
write.table(resist_mat,
paste0(data_dir, "resist_mat.csv"),
sep = ",",
row.names = FALSE,
col.names = FALSE)
cdmat <- 'resist_mat'
}
if(matemoveno != 9){
if(!is.null(resist_mat)){
write.table(resist_mat,
paste0(data_dir, "resist_mat.csv"),
sep = ",",
row.names = FALSE,
col.names = FALSE)
cdmat <- 'resist_mat'
} else {
print("Calculating resistance distance with `gdistance`...")
trans <- transition(x = resist_rast,
transitionFunction = function(x) 1 / mean(x),
directions = 8)
trR <- geoCorrection(trans, "r", scl = T)
resist_mat <- as.matrix(commuteDistance(trR, pts) / 1000)
## Check file format, row/col names?
write.table(resist_mat,
paste0(data_dir, "resist_mat.csv"),
sep = ",",
row.names = FALSE,
col.names = FALSE)
cdmat <- 'resist_mat'
}
}
# CDPOP input ----------------------------------------------------------
cdpop_df <- data.frame(xyfilename = xyFile,
agefilename = age_file,
mcruns = mcruns,
looptime = looptime,
output_years = output_years,
gridformat = gridformat,
cdclimgentime = cdclimgentime,
matecdmat = cdmat,
dispcdmat = cdmat,
matemoveno = matemoveno,
matemoveparA = matemoveparA,
matemoveparB = matemoveparB,
matemoveparC = matemoveparC,
matemovethresh = matemovethresh,
output_matedistance = output_matedistance,
sexans = sexans,
Freplace = Freplace,
Mreplace = Mreplace,
philopatry = philopatry,
multiple_paternity = multiple_paternity,
selfans = selfans,
Fdispmoveno = Fdispmoveno,
FdispmoveparA = FdispmoveparA,
FdispmoveparB = FdispmoveparB,
FdispmoveparC = FdispmoveparC,
Fdispmovethresh = Fdispmovethresh,
Mdispmoveno = Mdispmoveno,
MdispmoveparA = MdispmoveparA,
MdispmoveparB = MdispmoveparB,
MdispmoveparC = MdispmoveparC,
Mdispmovethresh = Mdispmovethresh,
offno = offno,
Femalepercent = Femalepercent,
EqualsexratioBirth = EqualsexratioBirth,
TwinningPercent = TwinningPercent,
popModel = popModel,
r = r,
K_env = K_env,
subpopmortperc = subpopmortperc,
muterate = muterate,
mutationtype = mutationtype,
loci = loci,
intgenesans = intgenesans,
allefreqfilename = allefreqfilename,
alleles = alleles,
mtdna = mtdna,
startGenes = startGenes,
cdevolveans = cdevolveans,
startSelection = startSelection,
betaFile_selection = betaFile_selection,
epistasis = epistasis,
epigeneans = epigeneans,
startEpigene = startEpigene,
betaFile_epigene = betaFile_epigene,
cdinfect = cdinfect,
transmissionprob = transmissionprob,
check.names = F)
write.table(cdpop_df,
paste0(data_dir, "CDPOP_inputs.csv"),
sep = ",",
row.names = FALSE,
col.names = TRUE,
quote = F)
# Run CDPOP ---------------------------------------------------------------
print("Running CDPOP...")
if(!is.null(python)){
system(paste(python, CDPOP.py, data_dir, "CDPOP_inputs.csv", sim_name))
} else {
system(paste("python", CDPOP.py, data_dir, "CDPOP_inputs.csv", sim_name))
}
# Import Results ----------------------------------------------------------
fi <- file.info(list.files(path = sim_dir,
pattern = "grid",
recursive = T,
full.names = T))
## Get latest simulation results
newest_sim <- dirname(rownames(fi)[which.max(fi$mtime)])
grid_dir <- list.files(path = newest_sim,
pattern = "grid",
recursive = T,
full.names = T)
read.grid <- function(grid,
pops = NULL){
suppressWarnings(
cdpop_out <- readr::read_csv(grid,
col_types = readr::cols(#Subpopulation = col_skip(),
XCOORD = readr::col_skip(), YCOORD = readr::col_skip(),
sex = readr::col_skip(), age = readr::col_skip(),
infection = readr::col_skip(), DisperseCDist = readr::col_skip(),
hindex = readr::col_skip()))
)
occ_pop <- which(cdpop_out$ID != "OPEN")
sub_pop <- cdpop_out$Subpopulation
sub_pop <- sub_pop[cdpop_out$ID != "OPEN"]
if(!is.null(pops)) {
pop_df <- data.frame(ind = occ_pop,
pop = sub_pop)
return(pop_df)
} else {
cd_df <- as.data.frame(cdpop_out[occ_pop,-c(1:2)])
cd_df_mat <- as.matrix(cd_df)
locs <- strsplit(colnames(cd_df), 'A')
locs <- sapply(locs, function(x) x[1])
cd_mat <- matrix(nrow = nrow(cd_df), ncol = length(unique(locs)))
loc_alleles <- cumsum(table(locs))
for(i in 1:nrow(cd_df)){
for(j in 0:(ncol(cd_mat)-1)){
if(j == 0){
geno <- which(cd_df_mat[i, 1:loc_alleles[j + 1]] != 0)
if(length(geno) > 1){
cd_mat[i,j+1] <- paste(geno, collapse = '/')
} else {
cd_mat[i,j+1] <- paste(geno, geno, sep = '/')
}
} else {
geno <- which(cd_df_mat[i, (loc_alleles[j]+1):loc_alleles[j + 1]] != 0)
if(length(geno) > 1){
cd_mat[i,j+1] <- paste(geno, collapse = '/')
} else {
cd_mat[i,j+1] <- paste(geno, geno, sep = '/')
}
}
}
}
cd_df_ <- as.data.frame(cd_mat)
colnames(cd_df_) <- unique(locs)
ncode <- 1
gi <- adegenet::df2genind(cd_df_,
ncode = ncode,
sep = "/")
if(length(unique(sub_pop)) > 1){
adegenet::pop(gi) <- sub_pop
}
gi
return(gi)
}
}
grid_list <- lapply(grid_dir, read.grid)
pop_list <- lapply(grid_dir, read.grid, pops = TRUE)
bn <- basename(grid_dir)
bn <- sub('.csv', '', bn)
bn <- sub('grid', '',bn)
gens <- as.numeric(bn)
grid_list <- grid_list[order(gens)]
pop_list <- pop_list[order(gens)]
names(pop_list) <- names(grid_list) <- paste0('gen_', sort(gens))
# Wrap-up -----------------------------------------------------------------
out <- list(grid_list = grid_list,
pop_list = pop_list)
return(out)
}
# Random Samples ----------------------------------------------------------
## Randomly select populations and individuals from within populations
gi_samp <- function(gi,
n_ind = 100) {
ind_samp <- sort(sample(1:nInd(gi), n_ind))
gi_s <- gi[ind_samp]
out <- list(genind = gi_s,
pop_samp = ind_samp)
}