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utility.r
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# useful function
# give it an object and it will return the biomass data ready to be plotted (for species and phenotypes)
biom <-function(object,phenotype=T)
{
biomass <- getBiomass(object) # n * w * dw and sum by species
SpIdx = NULL # getting rid of the species that went extinct at the beginning
for (i in unique(object@params@species_params$species))
if (sum(biomass[,i]) != 0 & dim(object@params@species_params[object@params@species_params$species == i,])[1] != 1)
SpIdx = c(SpIdx,i)
biomassTot = NULL
biomassTemp = biomass
colnames(biomassTemp) = object@params@species_params$species
for (i in SpIdx)
{
biomassSp = biomassTemp[,which(colnames(biomassTemp) == i)]
biomassSp = apply(biomassSp,1,sum)
biomassTot = cbind(biomassTot,biomassSp)
}
colnames(biomassTot) = SpIdx
# biomassTot is the biomass of species
# biomass is the biomass of phenotypes
plotB <- function(x)
{
Biom <- melt(x) # melt for ggplot
names(Biom) = list("time","sp","value")
# Due to log10, need to set a minimum value, seems like a feature in ggplot
min_value <- 1e-300
Biom <- Biom[Biom$value >= min_value,]
# take the first digit of the species column and put it in a new column
Biom$bloodline = sapply(Biom[,2], function(x) as.numeric(unlist(strsplit(as.character(x), "")))[1])
return(Biom)
}
BiomPhen = NULL
if (phenotype) BiomPhen <- plotB(biomass)
BiomSp <- plotB(biomassTot)
return(list(BiomSp,BiomPhen))
}
# function that takes the output of the model and make it usable for plotting and shit
finalTouch <- function(result, dt = 0.1, print_it = T)
{
## processing data
if (print_it) cat(sprintf("Data handling\n"))
# a result will be a list of simulations (number of runs) and the ID card of the ecosystem,
gc()
sim = result[[1]] # get the dat
SummaryParams = result[[2]] # get the params
rm(result) # get space back
sim <- sim[lapply(sim,length)>0] # if a sim is empty
endlist = length(sim) # updating the number
template = sim[[endlist]] # to keep a template of mizer object somewhere
gc()
# stitiching the sims together
Dtime = dim(sim[[1]]@n)[1] # need these dimensions for making new arrays but separatly as I'm going to work on sp dim
Dsp = length(SummaryParams@species_params$ecotype)
Dw = dim(sim[[1]]@n)[3]
# put all the sim at the same dimension
for (i in 1:endlist)
{
# print(i)
for (j in 1:Dsp)
{
gc()
if (is.na(dimnames(sim[[i]]@n)$sp[j])) # if I need to add species at the end of the array
{
sim[[i]]@n <- abind(sim[[i]]@n,array(dim = c(Dtime,Dw)),along = 2)
names(dimnames(sim[[i]]@n)) <- list("time","sp","w") # the abind make me loose the info
}
else if (dimnames(sim[[i]]@n)$sp[j] != SummaryParams@species_params$ecotype[j]) # if I need to add in the middle of the array
{
rmbr = NULL # when I abind only one column of the array (happens when there is an extinction right before the last ecotype), I lose its name, leading to bugs
if (j == dim(sim[[i]]@n)[2]) rmbr = dimnames(sim[[i]]@n)$sp[j] # with this I know when it happens
sim[[i]]@n <- abind(sim[[i]]@n[,1:j-1,],array(dim = c(Dtime,Dw)),sim[[i]]@n[,j:dim(sim[[i]]@n)[2],], along = 2) # if sp 1 goes extinct I will have a bug, or not it seems
names(dimnames(sim[[i]]@n)) <- list("time","sp","w") # the abind makes me loose the info
if (is.null(rmbr)==F) dimnames(sim[[i]]@n)$sp[j+1] = rmbr # and I add back the name
}
}
dimnames(sim[[i]]@n)$sp = SummaryParams@species_params$ecotype # put back the right names
sim[[i]]@n = sim[[i]]@n[-1,,] # in my tentative to include fisheries, I am deleting the first time step (which is 0 now) to have the same length as the effort matrix. I'm running out of time, Ill fix that later I hope
}
# getting rid of the NAs
gc()
for (j in 1:endlist) sim[[j]]@n[is.na( sim[[j]]@n)] <- 0
gc()
# now add them in a row
biom <- do.call(abind, c(lapply(sim, function(isim) isim@n),along = 1))
gc()
names(dimnames(biom)) = list("time","species","size")
dimnames(biom)$time = seq(1, SummaryParams@species_params$timeMax[1]) * dt # meh
gc()
# I have to do the phyto aussi
phyto = sim[[1]]@n_pp[-1, ] #get rid of time 0
for (i in 2:endlist) phyto = abind(phyto, sim[[i]]@n_pp[-1, ], along = 1)
if (print_it) cat(sprintf("Biomass handeled successfully, now starting the effort.\n"))
# taking care of the effort
# for now it is constant in time so we just need to multiply effort by no_run
effort = sim[[1]]@effort
for(i in 2:endlist) effort = rbind(effort,sim[[i]]@effort)
names(dimnames(effort)) = list("time","effort")
dimnames(effort)$time = seq(1, SummaryParams@species_params$timeMax[1]) * dt # i don't know whats wrong with the else
# I need to assemble the mizer object now
sim = template
sim@params=SummaryParams
sim@n = biom
sim@effort = effort
sim@n_pp = phyto
rm(list = "template","biom","phyto","effort")
gc()
return(sim)
}
# #function that prep files for download
# DLfig <- function(folder)
# {
# dirContent = dir(folder)
# runFolder = dirContent
# for (i in 1:length(dirContent))
# if (runFolder[i] == "fig" || runFolder[i] == "results")
# runFolder
#
#
#
# dir.create(paste(folder,"/fig",sep=""))
#
#
# }
# function that load runs from a folder and put them in a list for further uses
bunchLoad <- function(folder)
{
dirContent <- dir(folder)
listOfSim = list()
for(i in 1:length(dirContent))
{
if (file.exists(paste(folder,"/",dirContent[i],"/run.Rdata",sep = "")))
{
sim <- get(load(paste(folder,"/",dirContent[i],"/run.Rdata",sep="")))
listOfSim[[i]] = sim
} else if (file.exists(paste(folder,"/",dirContent[i],"/defaultRun.Rdata",sep = "")))
{
sim <- get(load(paste(folder,"/",dirContent[i],"/defaultRun.Rdata",sep="")))
sim = superOpt(sim)
listOfSim[[i]] = sim
}
listOfSim <- listOfSim[lapply(listOfSim,length)>0]
}
return(listOfSim)
}
#function that takes a bunch of run in different folder and plots for each run in their respective folder and also a commom folder of average plots
TotAnalysis <- function(folder)
{
dirContent <- dir(folder)
bigSim = list()
for(i in 1:length(dirContent))
{
if (file.exists(paste(folder,"/",dirContent[i],"/run.Rdata",sep = "")))
{
sim <- get(load(paste(folder,"/",dirContent[i],"/run.Rdata",sep="")))
} else {# (file.exists(paste(folder,"/",dirContent[i],"/defaultRun.Rdata",sep = "")))
sim <- get(load(paste(folder,"/",dirContent[i],"/defaultRun.Rdata",sep="")))
sim = superOpt(sim)
}
bigSim[[i]] = sim
where = paste(folder,"/",dirContent[i],sep = "")
# p = plotDynamics(sim)
# mytitle = paste("BiomassSp",".png", sep = "")
# ggsave(mytitle, plot = p, path = where, width = 20, height = 20, units = "cm")
#
# p = plotSS(sim, time_range = ((sim@params@species_params$timeMax[1]*0.1-500) : (sim@params@species_params$timeMax[1]*0.1-1)) )
# mytitle = paste("SizeSpectrum",".png", sep = "")
# ggsave(mytitle, plot = p, path = where, width = 20, height = 20, units = "cm")
# p = PlotNoSp(sim)
# mytitle = paste("noSp",".png",sep="")
# ggsave(mytitle, plot = p, path = where, width = 20, height = 20, units = "cm")
# p= plotBarSp(sim)
# mytitle = paste("barPhen",".png",sep="")
# ggsave(mytitle, plot = p, path = where, width = 20, height = 20, units = "cm")
#
# p = plotGrowthCurve(sim, generation = 50)
# mytitle = paste("growthCurve",".png",sep="")
# ggsave(mytitle, plot = p, path = where, width = 20, height = 20, units = "cm")
#
# path_to_png = paste(where,"/multi",".png",sep="")
# png(filename=path_to_png,width = 20, height = 20, units = "cm",res = 400)
# plotTraitsMulti(sim)
# dev.off()
# path_to_png = paste(where,"/performance",".png",sep="")
# png(filename=path_to_png,width = 20, height = 20, units = "cm",res = 400)
# plotPerformance(sim)
# dev.off()
# if (rawRun)
# {
# p = plotFood(sim)
# mytitle = paste("food",".png",sep="")
# ggsave(mytitle, plot = p, path = where, width = 20, height = 20, units = "cm")
#
# p = plotUdead(sim)
# mytitle = paste("mortality",".png",sep="")
# ggsave(mytitle, plot = p, path = where, width = 20, height = 20, units = "cm")
# }
rm(sim)
for (i in 1:20) gc()
}
sim = superStich(bigSim)
for (i in 1:20) gc()
where = paste(folder,"/results",sep="")
ifelse(!dir.exists(file.path(where)), dir.create(file.path(where)), FALSE) #create the file if it does not exists
save (bigSim,file = paste(where,"/sim.Rdata",sep=""))
# p = plotDynamics(sim)
# mytitle = paste("BiomassSp",".png", sep = "")
# ggsave(mytitle, plot = p, path = where, width = 20, height = 20, units = "cm")
#
# p = plotSS(sim, time_range = ((sim@params@species_params$timeMax[1]*0.1-500) : (sim@params@species_params$timeMax[1]*0.1-1)) )
# mytitle = paste("SizeSpectrum",".png", sep = "")
# ggsave(mytitle, plot = p, path = where, width = 20, height = 20, units = "cm")
# p = PlotNoSp(bigSim)
# mytitle = paste("noSp",".png",sep="")
# ggsave(mytitle, plot = p, path = where, width = 20, height = 20, units = "cm")
# p= plotBarSp(bigSim)
# mytitle = paste("barPhen",".png",sep="")
# ggsave(mytitle, plot = p, path = where, width = 20, height = 20, units = "cm")
#
# p = plotGrowthCurve(sim, generation = 50)
# mytitle = paste("growthCurve",".png",sep="")
# ggsave(mytitle, plot = p, path = where, width = 20, height = 20, units = "cm")
path_to_png = paste(where,"/multi",".png",sep="")
png(filename=path_to_png,width = 20, height = 20, units = "cm",res = 400)
plotTraitsMulti(sim)
dev.off()
# path_to_png = paste(where,"/performance",".png",sep="")
# png(filename=path_to_png,width = 20, height = 20, units = "cm",res = 400)
# plotPerformance(sim)
# dev.off()
}
# load a big run, optimise it and save it in the same folder
dir.Opt <- function(folder)
{
dirContent <- dir(folder)
for(i in 1:length(dirContent))
{
load(paste(folder,"/",dirContent[i],"/defaultRun.Rdata",sep=""))
sim = superOpt(sim)
save(sim, file = paste(folder,"/",dirContent[i],"/Run.Rdata",sep="") )
rm(sim)
}
}
# take a bunch of sims and get average plots from them
AvgAnalysis <- function(folder,where)
{
dirContent <- dir(folder)
bigSim = list()
for(i in 1:length(dirContent))
{
if (file.exists(paste(folder,"/",dirContent[i],"/run.Rdata",sep = "")))
{
sim <- get(load(paste(folder,"/",dirContent[i],"/run.Rdata",sep="")))
} else {
sim <- get(load(paste(folder,"/",dirContent[i],"/defaultRun.Rdata",sep="")))
sim = superOpt(sim)
}
bigSim[[i]] = sim
rm(sim)
}
sim = superStich(bigSim)
for (i in 1:20) gc()
ifelse(!dir.exists(file.path(paste(getwd(),"/",where,sep=""))), dir.create(file.path(paste(getwd(),"/",where,sep=""))), FALSE) #create the file if it does not exists
save (bigSim,file = paste(where,"/sim.Rdata",sep=""))
p = plotDynamics(sim)
mytitle = paste("BiomassSp",".png", sep = "")
ggsave(mytitle, plot = p, path = where, width = 20, height = 20, units = "cm")
p = plotSS(sim, time_range = ((sim@params@species_params$timeMax[1]*0.1-500) : (sim@params@species_params$timeMax[1]*0.1-1)) )
mytitle = paste("SizeSpectrum",".png", sep = "")
ggsave(mytitle, plot = p, path = where, width = 20, height = 20, units = "cm")
p = PlotNoSp(bigSim)
mytitle = paste("noSp",".png",sep="")
ggsave(mytitle, plot = p, path = where, width = 20, height = 20, units = "cm")
p= plotBarSp(bigSim)
mytitle = paste("barPhen",".png",sep="")
ggsave(mytitle, plot = p, path = where, width = 20, height = 20, units = "cm")
p = plotGrowthCurve(sim, generation = 50)
mytitle = paste("growthCurve",".png",sep="")
ggsave(mytitle, plot = p, path = where, width = 20, height = 20, units = "cm")
path_to_png = paste(where,"/multi",".png",sep="")
png(filename=path_to_png,width = 20, height = 20, units = "cm",res = 400)
plotTraitsMulti(sim)
dev.off()
path_to_png = paste(where,"/performance",".png",sep="")
png(filename=path_to_png,width = 20, height = 20, units = "cm",res = 400)
plotPerformance(sim)
dev.off()
}
# to average multiple sims
superStich <- function(listOfSim)
{
# params needed
no_w = 100
min_w = 0.001
max_w = 1e5 * 1.1
min_w_pp = 1e-10
w_pp_cutoff = 0.5
n = 0.75
p = 0.75
q = 0.8
r_pp = 4
kappa = 0.05
lambda = 2+q-n
EcoName = do.call(c,lapply(listOfSim, function(isim) isim@params@species_params$ecotype))
while (sum(duplicated(EcoName))>0) # while there are some duplicated name in the vector
{
for (i in EcoName[duplicated(EcoName)]) # for every duplicated names
{
x <- as.character(EcoName[which(EcoName == i)[2]]) # take the position in the ecotype vect of the duplicated name and extract its name as character
first = as.numeric(unlist(strsplit(x, "")))[1] # extract the first number (meaning species number that we want to keep)
EcoName[which(EcoName == i)[2]] = as.numeric(paste(first,sample(x = seq(1:100000),1),sep="")) #produce a new name but keep the first digit (species identity)
}
}
# Now that I have ecotypes name without duplicate, I can stitch the sims
a = do.call(rbind, lapply(listOfSim, function(isim) isim@params@species_params))
a$ecotype = EcoName
trait_params <- MizerParams(a, min_w = min_w, max_w=max_w, no_w = no_w, min_w_pp = min_w_pp, w_pp_cutoff = w_pp_cutoff, n = n, p=p, q=q, r_pp=r_pp, kappa=kappa, lambda = lambda)
biom <- do.call(abind, c(lapply(listOfSim, function(isim) isim@n),along = 2))
dimnames(biom)[[2]] = EcoName
names(dimnames(biom)) = list("time","species","w")
# need to balance the abundance of biom -> let's mean everything
biom = biom/length(listOfSim)
sim = listOfSim[[1]]
sim@params = trait_params
sim@n = biom
return(sim)
}
# to run in parallel, everything end up in one function for the apply
multiRun <- function(no_sim, no_sp, t_max, mu, no_run,min_w_inf, max_w_inf, effort, print_it = F,
fisheries = F,save_it = F, path_to_save = NULL, normalFeeding = F, hartvig = T, Traits = "eta" )
{
if (fisheries)
{
#asymptotic size
w_inf <- 10^seq(from=log10(min_w_inf), to = log10(max_w_inf), length=no_sp)
#dividing species between the gears (fished and non-fished)
other_gears <- w_inf >= 10000
gear_names <- rep("None", no_sp)
gear_names[other_gears] <- "FishingStuff"
#setting up knife edge
knife_edges <- w_inf * 0.35 # at maturation size
# fisheries are primitve now, so I need to set the knife edge a lot above the size of the species that I do not want to fish
#knife_edges[1:6] <-1e6
output <- myModel(no_sp = no_sp, t_max = t_max, no_run = no_run,
mu = mu, max_w_inf = max_w_inf,
effort = effort, knife_edge_size = knife_edges, gear_names = gear_names,
save_it = save_it, path_to_save = path_to_save,
print_it = print_it, normalFeeding = normalFeeding, hartvig = hartvig, Traits = Traits)
} else {
output <- myModel(no_sp = no_sp, t_max = t_max, no_run = no_run,
mu = mu, max_w_inf = max_w_inf,
effort = 0,
save_it = save_it, path_to_save = path_to_save,
print_it = print_it, normalFeeding = normalFeeding, hartvig = hartvig, Traits = Traits)
}
}
# to lighten the size of the sims
superOpt <- function(object, opt = 10) # optimise the run to make them lighter
{
object@n = object@n[seq(1,dim(object@n)[1],opt),,]
object@n_pp = object@n_pp[seq(1,dim(object@n_pp)[1],opt),]
object@effort = as.matrix(object@effort[seq(1,dim(object@effort)[1],opt),])
return(object)
}
stitch <- function(sim) # this function takes all the sim output from model and paste together the data to make one n file (for plotting and stuff)
{
endList <- length(sim) # shortcut to have ref of the last simulation which has the right dim, names, ...
Dtime = dim(sim[[endList]]@n)[1] # need these dimentions for making new arrays but separatly as I'm going to work on sp dim
Dsp = dim(sim[[endList]]@n)[2]
Dw = dim(sim[[endList]]@n)[3]
# if no mutants
if (endList == 1) return(list(sim[[1]]@n, sim[[1]]@n_pp)) # if there is only one object in the list, it is the mizer object without mutants
# from there I can add empty columns to every other array and add them
for (i in 1:(endList-1))# check every array except last one because no need
{
sim[[i]]@n <-abind(sim[[i]]@n, array(dim = c(Dtime, Dsp - dim(sim[[i]]@n)[2],Dw)), along = 2) # I add to each sim the missing columns pre-mutations
sim[[i]]@n_pp <-abind(sim[[i]]@n_pp, array(dim = c(Dtime, Dsp - dim(sim[[i]]@n)[2])), along = 2)
}
# Now every array in the output has the dimemsions of the final array, but with NAs everywhere
# Now to add them up
# getting rid of the NAs
for (j in 1:endList) sim[[j]]@n[is.na( sim[[j]]@n)] <- 0
for (j in 1:endList) sim[[j]]@n_pp[is.na( sim[[j]]@n_pp)] <- 0
# addition
biomass = 0
for (j in 1:endList) biomass = biomass + sim[[j]]@n
colnames(biomass) <- colnames(sim[[endList]]@n)
names(dimnames(biomass)) <- list("time","sp","w")
biomass_pp = 0
for (j in 1:endList) biomass_pp = biomass_pp + sim[[j]]@n_pp
colnames(biomass_pp) <- colnames(sim[[endList]]@n_pp)
names(dimnames(biomass_pp)) <- list("time","sp")
return(list(biomass,biomass_pp))
}
# to plot only one species family Not sure its working anymore, but function included in plots directly
lineage = function(sim,thread)
{
noSp = length(sim@params@species_params$species) # number of species after the simulation
family = sim@params@species_params[sim@params@species_params$species == thread, ] # create a smaller table with only one lineage
idxFamily = rownames(family)
tree = sim@n[,idxFamily,] # get their biomass from the big table
sim@n = tree
plotDynamics(sim)
}
# to process the mizer output and draw plots, works with the old sim output
processing <- function(result,
plot = FALSE,
where = NULL,
who = "defaultRun",
dt = 0.1, # I need dt at some point and I don't know how to get it there
optimisation = F,
save_it = T
)
{
# setting up workspace things
if (is.null(where)) where = paste(getwd(),"/temporary",sep="")
ifelse(!dir.exists(file.path(where)), dir.create(file.path(where),recursive = T), FALSE) #create the file if it does not exists
## processing data
# a result will be a list of simulations (number of runs) and the ID card of the ecosystem,
# if not it means that I am inputing directly an already processed mizer object, ready for the plots
if (class(result) == "list")
{
if (class(result[[length(result)]]) == "list") #if this is a list it means I got umbrella
{
print("umbrella time")
result[[length(result)]] = NULL # delete last half sim
param = NULL
for (i in 1:length(result)) param = rbind(param,result[[i]]@params@species_params) # create the dataframe for species
param <- param[order(param$ecotype, param$extinct, decreasing=TRUE),]
param <- param[!duplicated(param$ecotype),]
SummaryParams = param[order(param$pop,param$ecotype),]
FinalParam <- MizerParams(SummaryParams, min_w =0.001, max_w=10000 * 1.1, no_w = 100, min_w_pp = 1e-10, w_pp_cutoff = 0.5, n = 0.75, p=0.75, q=0.8, r_pp=4, kappa=1, lambda = 2.05) #create the mizer param from the dataframe
temp=list(result,FinalParam) # put it in the right disposition
result = temp
# something is wrong with it, don't have the time or interest to debug, just return nothing, it's not like it's useful
return()
}
gc()
sim = result[[1]]
SummaryParams = result[[2]]
#endlist = length(sim) # shortcut to have the number of sim
rm(result)
# lol = list() # here I get rid of the empty spots of my list. It will happen if I started a simulation from a previous one as initial conditions
# for (i in 1:endlist) if (!is.null(sim[[i]])) lol = c(lol, sim[[i]])
# sim = lol
# rm(lol)
sim <- sim[lapply(sim,length)>0]
endlist = length(sim) # updating the number
template = sim[[endlist]] # to keep a template of mizer object somewhere
gc()
# if (optimisation)
# {
# keep = seq(1,SummaryParams@species_params$timeMax[1]*0.1) #times to keep (too long but doesnt matter)
# for (i in 1:endlist)
# {
# timeKeep = dimnames(sim[[i]]@n)$time %in% keep
# sim[[i]]@n = sim[[i]]@n[which(timeKeep),,]
# sim[[i]]@n_pp = sim[[i]]@n_pp[which(timeKeep),]
# sim[[i]]@effort = as.matrix(sim[[i]]@effort[which(timeKeep[-1]),]) # effort is being a pain again as it's missing the time 0
# }
# }
if(optimisation)
{
for (i in 1:endlist)
{
sim[[i]]@n = sim[[i]]@n[seq(1,dim(sim[[i]]@n)[1]-1,2),,]
sim[[i]]@n_pp = sim[[i]]@n_pp[seq(1,dim(sim[[i]]@n)[1]-1,2),]
#sim[[i]]@effort = sim[[i]]@effort[seq(1,dim(sim[[i]]@n)[1],2),]
}
}
# stitiching the sims together
Dtime = dim(sim[[1]]@n)[1] # need these dimensions for making new arrays but separatly as I'm going to work on sp dim
Dsp = length(SummaryParams@species_params$ecotype)
Dw = dim(sim[[1]]@n)[3]
# put all the sim at the same dimension
for (i in 1:endlist)
{
# print(i)
for (j in 1:Dsp)
{
gc()
if (is.na(dimnames(sim[[i]]@n)$sp[j])) # if I need to add species at the end of the array
{
sim[[i]]@n <- abind(sim[[i]]@n,array(dim = c(Dtime,Dw)),along = 2)
names(dimnames(sim[[i]]@n)) <- list("time","sp","w") # the abind make me loose the info
}
else if (dimnames(sim[[i]]@n)$sp[j] != SummaryParams@species_params$ecotype[j]) # if I need to add in the middle of the array
{
rmbr = NULL # when I abind only one column of the array (happens when there is an extinction right before the last ecotype), I lose its name, leading to bugs
if (j == dim(sim[[i]]@n)[2]) rmbr = dimnames(sim[[i]]@n)$sp[j] # with this I know when it happens
sim[[i]]@n <- abind(sim[[i]]@n[,1:j-1,],array(dim = c(Dtime,Dw)),sim[[i]]@n[,j:dim(sim[[i]]@n)[2],], along = 2) # if sp 1 goes extinct I will have a bug, or not it seems
names(dimnames(sim[[i]]@n)) <- list("time","sp","w") # the abind makes me loose the info
if (is.null(rmbr)==F) dimnames(sim[[i]]@n)$sp[j+1] = rmbr # and I add back the name
}
}
dimnames(sim[[i]]@n)$sp = SummaryParams@species_params$ecotype # put back the right names
if (optimisation == F) sim[[i]]@n = sim[[i]]@n[-1,,] # in my tentative to include fisheries, I am deleting the first time step (which is 0 now) to have the same length as the effort matrix. I'm running out of time, Ill fix that later I hope
}
# getting rid of the NAs
gc()
for (j in 1:endlist) sim[[j]]@n[is.na( sim[[j]]@n)] <- 0
gc()
biom <- do.call(abind, c(lapply(sim, function(isim) isim@n),along = 1))
gc()
# now add them in a row
# biom = sim[[1]]@n
# for (i in 2:endlist) biom = abind(biom,sim[[i]]@n,along = 1)
names(dimnames(biom)) = list("time","species","size")
if (optimisation) dimnames(biom)$time = seq(0, (SummaryParams@species_params$timeMax[1]-1) *dt,0.2) # skip every other time step and reduce to years (not year/dt)
if (optimisation == F) dimnames(biom)$time = seq(1, SummaryParams@species_params$timeMax[1]) * dt # i don't know whats wrong with the else
gc()
# I have to do the phyto aussi
# phyto = template@n_pp
# phyto = phyto[seq(1,dim(phyto)[1]-1,2),]
# phyta = phyto
# for (i in 2:endlist) phyto = abind(phyto,phyta,along = 1)
if (optimisation)
{
phyto = sim[[1]]@n_pp
for (i in 2:endlist) phyto = abind(phyto,sim[[i]]@n_pp,along = 1)
}
if (optimisation == F)
{
phyto = sim[[1]]@n_pp[-1,] #get rid of time 0
for (i in 2:endlist) phyto = abind(phyto,sim[[i]]@n_pp[-1,],along = 1)
}
cat(sprintf("Biomass handeled successfully, now starting the effort.\n"))
# taking care of the effort
# for now it is constant in time so we just need to multiply effort by no_run
effort = sim[[1]]@effort
for(i in 2:endlist) effort = rbind(effort,sim[[i]]@effort)
names(dimnames(effort)) = list("time","effort")
if (optimisation) dimnames(effort)$time = seq(1, SummaryParams@species_params$timeMax[1] * dt)
if (optimisation == F) dimnames(effort)$time = seq(1, SummaryParams@species_params$timeMax[1]) * dt # i don't know whats wrong with the else
# I need to assemble the mizer object now
sim = template
sim@params=SummaryParams
sim@n = biom
sim@effort = effort
sim@n_pp = phyto
rm(list = "template","biom","phyto","effort")
gc()
#saving
path_to_save <- paste(where,"/",who,".Rdata", sep="")
if (save_it) save(sim,file = path_to_save)
}
else
{
sim = result
rm(result)
#saving
path_to_save <- paste(where,"/",who,".Rdata", sep="")
if (save_it) save(sim,file = path_to_save)
}
if (plot == TRUE)
{
cat(sprintf("Data processed, starting the plots"))
SumPar = sim@params@species_params # little shortcut
# biomass variation
p = plotDynamics(sim, species = T)
mytitle = paste("BiomassSp",".png", sep = "")
ggsave(mytitle, plot = p, path = where, width = 20, height = 20, units = "cm")
# size spectrum
p = plotSS(sim, time_range = ((sim@params@species_params$timeMax[1]*dt-500) : (sim@params@species_params$timeMax[1]*dt-1)) )
mytitle = paste("SizeSpectrum",".png", sep = "")
ggsave(mytitle, plot = p, path = where, width = 20, height = 20, units = "cm")
# traits plots
p = plotTraits(sim)
for (i in 1:length(p))
{
for (j in 1:length(p[[1]]))
{
y = as.character(i)
switch(y,
"1" = {mytitle = paste("Maturation size of specie ",j,".png", sep = "")},
"2" = {mytitle = paste("PPMR of specie ",j,".png", sep = "")},
"3" = {mytitle = paste("Diet breadth of specie ",j,".png", sep = "")},
{})
ggsave(mytitle, plot = p[[i]][[j]], path = where, width = 20, height = 20, units = "cm")
}
}
# # beta/sigma ratio---------------
# for (i in SpIdx)
# {
# # empty matrix of ecotype of species i by time
# A = matrix(0, ncol = SumPar$timeMax[1], nrow = dim(TT[TT$Lineage == i,])[1], dimnames = list(as.numeric(TT$Ecotype[TT$Lineage == i]), c(1:(SumPar$timeMax[1]))))
# # fill the matrix with ones when the ecotype exists
# for (x in 1:nrow(A)) # I'm sure I can do an apply but don't know how
# {
# for (j in 1:ncol(A))
# {
# if (TT$Apparition[x] <= j & TT$Extinction[x] >= j) A[x,j] = 1
# }}
# # a is a matrix of 0 and 1 showing if the ecotype is present or not at time t
# # change the ones by the trait value of the ecotype
# BetaA = A * TT[TT$Lineage == i,]$PPMR
# SigmaA = A * TT[TT$Lineage == i,]$Diet_breadth
# # calculate mean trait value at each time step
# no_trait = apply(A,2,sum) # this vector is the number of traits present at each time step
# BetaSum = apply(BetaA,2,sum) # this vector is the sum of the traits value at each time step
# SigmaSum = apply(SigmaA,2,sum) # this vector is the sum of the traits value at each time step
# BetaMean = BetaSum/no_trait # this is the mean trait value at each time step
# SigmaMean = SigmaSum/no_trait # this is the mean trait value at each time step
#
# # Matrix with all traits combination and at what time they go extinct
# TTi = TT[TT$Lineage == i,]
# comb=data.frame(TTi$Ecotype,TTi$Apparition,TTi$Extinction,TTi$PPMR,TTi$Diet_breadth)
#
# # plot of extinction of combinations
# title = paste("Combination of PPMR and diet breath value of species ",i, sep = "")
# print(
# ggplot(comb) +
# geom_point(aes(x=TTi.PPMR,y=TTi.Diet_breadth, color = TTi.Extinction)) +
# scale_x_continuous(name = "PPMR") +
# scale_y_continuous(name = "Diet breath") +
# scale_color_continuous(name = "Extinction time in year / dt") +
# ggtitle(title)
# )
# name = paste("Extinction BetaSigma of species",i, sep = "")
# setwd(where)
# mytitle = paste(name,".png", sep = "")
# dev.print(png, mytitle, width = res, height = 2/3* res)
#
# #plot of apparition of combinations
# print(
# ggplot(comb) +
# geom_point(aes(x=TTi.PPMR,y=TTi.Diet_breadth, color = TTi.Apparition)) +
# scale_x_continuous(name = "PPMR") +
# scale_y_continuous(name = "Diet breath") +
# scale_color_continuous(name = "Apparition time in year / dt") +
# ggtitle(title)
# )
# name = paste("Apparition BetaSigma of species",i, sep = "")
# setwd(where)
# mytitle = paste(name,".png", sep = "")
# dev.print(png, mytitle, width = res, height = 2/3* res)
#
#
# # Mean combination values throughout sim
# stat = data.frame(BetaMean,SigmaMean)
# #get rid of duplicates
# stat = stat[!duplicated(stat),]
# # need to work on the time because of fucked up legend
# stat = cbind(stat,rownames(stat))
# dimnames(stat)[[2]] = list("BetaMean", "SigmaMean", "Time")
# stat$Time <- as.numeric(substr(stat$Time,1,5)) # read the time and delete all conditions on it (like factor) # the 5 means handling number up to 10^5
#
# title = paste("Mean trait's combination of PPMR and diet breath value of species ",i, sep = "")
# print(
# ggplot(stat) +
# geom_point(data = stat, aes(x=BetaMean,y=SigmaMean, color = Time)) +
# scale_x_continuous(name = "PPMR") +
# scale_y_continuous(name = "Diet breath") +
# scale_color_continuous(name = "Time in year / dt") +
# ggtitle(title)
# )
# name = paste("Mean BetaSigma of species",i, sep = "")
# setwd(where)
# mytitle = paste(name,".png", sep = "")
# dev.print(png, mytitle, width = res, height = 2/3* res)
# }
#
# biology plots -------------
# when = seq(500,sim@params@species_params$timeMax[1]*0.1,1000)
# for(i in when)
# {
# p = plotFood(sim, time_range = i)
# mytitle = paste("feeding t= ",i,".png",sep="")
# ggsave(mytitle, plot = p, path = where, width = 20, height = 20, units = "cm")
#
# p = plotUdead(sim, time_range = i)
# mytitle = paste("mortality t= ",i,".png",sep="")
# ggsave(mytitle, plot = p, path = where, width = 20, height = 20, units = "cm")
#
# p = plotGrowth(sim, time_range = i)
# mytitle = paste("growth t= ",i,".png",sep="")
# ggsave(mytitle, plot = p, path = where, width = 20, height = 20, units = "cm")
# }
p = plotFood(sim)
mytitle = paste("food",".png",sep="")
ggsave(mytitle, plot = p, path = where, width = 20, height = 20, units = "cm")
p = plotUdead(sim)
mytitle = paste("mortality",".png",sep="")
ggsave(mytitle, plot = p, path = where, width = 20, height = 20, units = "cm")
p = PlotNoSp(sim)
mytitle = paste("noSp",".png",sep="")
ggsave(mytitle, plot = p, path = where, width = 20, height = 20, units = "cm")
p = plotGrowthSpeed(sim)
mytitle = paste("growthSpeed",".png",sep="")
ggsave(mytitle, plot = p, path = where, width = 20, height = 20, units = "cm")
p = plotGrowthCurve(sim, generation = 50)
mytitle = paste("growthCurve",".png",sep="")
ggsave(mytitle, plot = p, path = where, width = 20, height = 20, units = "cm")
path_to_png = paste(where,"/multi",".png",sep="")
png(filename=path_to_png,width = 20, height = 20, units = "cm",res = 400)
plotTraitsMulti(sim)
dev.off()
}
return(sim)
}
# function that draw the bloodline of an ecotype by plotting its trait combination (could do 3D) Need work
ancestor <- function ()
{
SumPar = sim@params@species_params
TT = cbind(SumPar$species,as.numeric(SumPar$ecotype),SumPar$pop,SumPar$extinct,SumPar$w_mat,SumPar$beta,SumPar$sigma) # weird things happen without the as.numeric
colnames(TT) = c("Lineage","Ecotype","Apparition","Extinction","Maturation_size","PPMR","Diet_breadth")
rownames(TT) = rownames(SumPar)
TT = TT[order(TT[,1],decreasing=FALSE),]
TT = as.data.frame(TT)
for (i in 1:dim(TT)[1]) if (TT$Extinction[i] == 0) TT$Extinction[i] = SumPar$timeMax[1]
res = 1000
i = 1 # sp
# empty matrix of ecotype of species i by time
A = matrix(0, ncol = SumPar$timeMax[1], nrow = dim(TT[TT$Lineage == i,])[1], dimnames = list(as.numeric(TT$Ecotype[TT$Lineage == i]), c(1:(SumPar$timeMax[1]))))
# fill the matrix with ones when the ecotype exists
for (x in 1:nrow(A)) # I'm sure I can do an apply but don't know how
{
for (j in 1:ncol(A))
{
if (TT$Apparition[x] <= j & TT$Extinction[x] >= j) A[x,j] = 1
}}
# a is a matrix of 0 and 1 showing if the ecotype is present or not at time t
# change the ones by the trait value of the ecotype
BetaA = A * TT[TT$Lineage == i,]$PPMR
SigmaA = A * TT[TT$Lineage == i,]$Diet_breadth
# calculate mean trait value at each time step
no_trait = apply(A,2,sum) # this vector is the number of traits present at each time step
BetaSum = apply(BetaA,2,sum) # this vector is the sum of the traits value at each time step
SigmaSum = apply(SigmaA,2,sum) # this vector is the sum of the traits value at each time step
BetaMean = BetaSum/no_trait # this is the mean trait value at each time step
SigmaMean = SigmaSum/no_trait # this is the mean trait value at each time step
# Matrix with all traits combination and at what time they go extinct
TTi = TT[TT$Lineage == i,]
comb=data.frame(TTi$Ecotype,TTi$Apparition,TTi$Extinction,TTi$PPMR,TTi$Diet_breadth)
tree = NULL
for (j in 2:dim(comb)[1]) # for each fucking ecotype (except first)
{
x = comb[j, "TTi.Ecotype"]
bloodLine = x
while (x != i) # I determine from whom they descend
{
x = x %/% 10
bloodLine = c(bloodLine, x)
}
ancestor = NULL
for (l in 1:dim(comb)[1]) # and I put all these people in one matrix
{
for (k in 1:length(bloodLine))
{
if (comb$TTi.Ecotype[l] == bloodLine[k])
ancestor = rbind(ancestor, comb[l, ])
}
}
ancestor = cbind(ancestor, j)
tree = rbind(tree,ancestor)
}
print(
ggplot(comb) +
geom_point(aes(x=TTi.PPMR,y=TTi.Diet_breadth, color = TTi.Apparition, size = 1) ) +
geom_path(data = tree, aes(x = TTi.PPMR, y = TTi.Diet_breadth, group = j), arrow = arrow(angle = 15, type = "closed", length = unit(0.1,"inches"))) +
scale_x_continuous(name = "PPMR") +
scale_y_continuous(name = "Diet breath") +
scale_color_continuous(name = "Apparition time in year / dt") +
ggtitle(title)
)
# plot of extinction of combinations
title = paste("Combination of PPMR and diet breath value of species ",i, sep = "")
print(
ggplot(comb) +
geom_point(aes(x=TTi.PPMR,y=TTi.Diet_breadth, color = TTi.Extinction)) +
scale_x_continuous(name = "PPMR") +
scale_y_continuous(name = "Diet breath") +
scale_color_continuous(name = "Extinction time in year / dt") +
ggtitle(title)
)
name = paste("Extinction BetaSigma of species",i, sep = "")
setwd(paste(dir,subdir, sep = ""))
mytitle = paste(name,".png", sep = "")
dev.print(png, mytitle, width = res, height = 2/3* res)
#plot of apparition of combinations
print(
ggplot(comb) +
geom_point(aes(x=TTi.PPMR,y=TTi.Diet_breadth, color = TTi.Apparition)) +
geom_path(data = tree, aes(x = TTi.PPMR, y = TTi.Diet_breadth, group = j)) +
scale_x_continuous(name = "PPMR") +
scale_y_continuous(name = "Diet breath") +
scale_color_continuous(name = "Apparition time in year / dt") +
ggtitle(title)
)
name = paste("Apparition BetaSigma of species",i, sep = "")
setwd(paste(dir,subdir, sep = ""))
mytitle = paste(name,".png", sep = "")
dev.print(png, mytitle, width = res, height = 2/3* res)
# Mean combination values throughout sim
stat = data.frame(BetaMean,SigmaMean)
#get rid of duplicates
stat = stat[!duplicated(stat),]
# need to work on the time because of fucked up legend
stat = cbind(stat,rownames(stat))
dimnames(stat)[[2]] = list("BetaMean", "SigmaMean", "Time")
stat$Time <- as.numeric(substr(stat$Time,1,5)) # read the time and delete all conditions on it (like factor) # the 5 means handling number up to 10^5
title = paste("Mean trait's combination of PPMR and diet breath value of species ",i, sep = "")
print(
ggplot(stat) +
geom_point(data = stat, aes(x=BetaMean,y=SigmaMean, color = Time)) +
scale_x_continuous(name = "PPMR") +
scale_y_continuous(name = "Diet breath") +
scale_color_continuous(name = "Time in year / dt") +
ggtitle(title)
)
# all present traits at last time step
# last = data.frame(BetaA[,t_max*no_run],SigmaA[,t_max*no_run])
# colnames(last) = c("beta","sigma")
# row_sub = apply(last, 1, function(row) all(row !=0 ))
# last = last[row_sub,]
#
#
# print(
# ggplot(last) +
# geom_point(aes(x=beta,y=sigma)) +
# ggtitle("Last time step")
# )
}
#function that reduce the number of data if too heavy for plotting
optimisation <- function(sim, keep = 2)
{
sim@n = sim@n[seq(1,dim(sim@n)[1],keep),,]
sim@n_pp = sim@n_pp[seq(1,dim(sim@n_pp)[1],keep),]
sim@effort = sim@effort[seq(1,dim(sim@effort)[1],keep),]