R/randomDIFFUSE.R
In KiranLDA/migflow: Calculates the maximum flow through a network
Defines functions
randomDIFFUSE
Documented in
randomDIFFUSE
#' Generate a random network
#'
#' @description This function generates a random migratory network
#'
#'
#' @param pop population size flowing through network
#' @param mean_dist average distance the species can travel (this can be estimated from real data)
#' @param sd_dist standard deviation of the distances a species can travel (this can be estimated from real data)
#' @param toplot TRUE/FALSE to determine whether the output is plotted or not
#' @param nbreeding Number of breeding sites
#' @param nwintering Number of wintering sites
#' @param nstop Number of stopover sites (shared during north and south migration)
#'
#' @return a list containting the network which was randomly generated,
#' the tracks that were randomly generated, and the sites that were randomly generated for animals to use.
#'
#' @import igraph
#' @importFrom stats rnorm runif
#' @export
randomDIFFUSE <- function(nbreeding = 10,
nwintering = 10,
nstop = 30,
pop = 100000,
mean_dist = 0,
sd_dist = 1500,
toplot = TRUE){
# # # For testing purposes
# nbreeding = 10
# nwintering = 10
# nstop = 30
# pop = 100000
# mean_dist = 0
# sd_dist = 800
# toplot = TRUE
nsites = nbreeding+nwintering+nstop
# generate random tracks
tracks <- abs(rnorm(1000, mean_dist, sd_dist)) # rgamma(1000,2.3,0.005)#
# hist(tracks)
# # Create a fake list of sites where animals were seen at, with latitude, longitude and number of anumals seen there
# site_list <- data.frame(Lat= runif(nsites, min=-20, max=40),
# Lon= runif(nsites, min=-20, max=20),
# Pop=runif(nsites, min=500, max=10000),
# B= 0,
# SM=0,
# NM=0,
# NB=0)
B <- data.frame(Lat = runif(nbreeding, min=30, max=40),
Lon= runif(nbreeding, min=-20, max=20),
Pop=runif(nbreeding, min=500, max=10000),
B = 1,
SM = 0,
NB = 0,
NM = 0)
NB <- data.frame(Lat = runif(nwintering, min=-20, max=-10),
Lon= runif(nwintering, min=-20, max=20),
Pop=runif(nwintering, min=500, max=10000),
B = 0,
SM = 0,
NB = 1,
NM = 0)
STP <- data.frame(Lat = runif(nstop, min=-10, max=30),
Lon= runif(nstop, min=-20, max=20),
Pop=runif(nstop, min=500, max=10000),
B = 0,
SM = 1,
NB = 0,
NM = 1)
sites = rbind(B, STP, NB)
sites = sites[order(sites$Lat, decreasing=T),]
sites$Site= 1:nsites
site_list = sites
#Add a bottleneck site
# bottleneck <- round(runif(1, 1, nstop))
# site_list$Pop[site_list$SM==1][bottleneck] <- pop
# sort according to latitude
site_list = site_list[order(site_list$Lat, decreasing=T),]
site_list$Site= 1:nsites
site_list$B[1:nbreeding] = 1
site_list$NB[(nrow(site_list)-nwintering+1):nrow(site_list)] = 1
site_list$SM[site_list$B==0 & site_list$NB==0] = 1
site_list$NM = site_list$SM
# # add a dummy breeding and wintering site
# site_list<- rbind(
# c(41,0,pop,0,0,0,0,0),
# site_list,
# c(-21,0,pop,0,0,0,0,9999))
#-----------------------------
# South migration B -> NB
#-----------------------------
sites = site_list
# create a distance matrix based on these data
dist <- point2DIST(sites)
# calculate the probability of going between these sites given the distance the animal can travel
Dist_P <- distPROB(tracks, dist, adjust=1, plot=F) + 0.000000000000001
# Dist_P <- (max(dist)-dist) / max(dist)
# Calculate prioritisation of population using a site
Pop_P <- nodePopPROP(sites, population = pop)
#Calculate the azimuth angle
Azi_P <- absAZIMUTH(dist, lonlats=sites )#+0.01
# make birds/animals prefer sites which a larger prioritisation of the population has been seen and where the distance is better
network <- Azi_P * Pop_P * Dist_P
# Dist_P <- (max(dist)-dist) / max(dist)
# network[, which(sites$SM == 1 & sites$Pop == pop)] <- Azi_P[, which(sites$SM == 1 & sites$Pop == pop)]*Pop_P[, which(sites$SM == 1 & sites$Pop == pop)]*Dist_P[, which(sites$SM == 1 & sites$Pop == pop)]
# network[which(sites$SM == 1 & sites$Pop == pop), ] <- Azi_P[which(sites$SM == 1 & sites$Pop == pop), ]*Pop_P[which(sites$SM == 1 & sites$Pop == pop), ]*Dist_P[which(sites$SM == 1 & sites$Pop == pop), ]
# Make the network directed
network <- directedNET(network, include_diagonal = TRUE)
#
# # Ensure that nodes only flow into the next 1 or two neighbouring nodes
# for (i in 1:nrow(network)){
# idx = (i+1) : (i+1 + ceiling(runif(1,0,2))-1)
# idx = which(!(1:nrow(network) %in% idx))
# network[i, idx] = 0
# }
#
SMnet <- t(apply(network,1,
function(x) x[which(!is.na(x))]/
sum(x,na.rm=TRUE)))
#-----------------------------
# North migration NB -> B
#-----------------------------
sites = site_list[order(site_list$Lat, decreasing=FALSE),]
# create a distance matrix based on these data
dist <- point2DIST(sites)
# calculate the probability of going between these sites given the distance the animal can travel
Dist_P <- distPROB(tracks, dist, adjust=1, plot=F) + 0.000000000000001
# Calculate prioritisation of population using a site
Pop_P <- nodePopPROP(sites, population = pop)
#Calculate the azimuth angle
Azi_P <- absAZIMUTH(dist, lonlats=sites )#+0.01
# make birds/animals prefer sites which a larger prioritisation of the population has been seen and where the distance is better
network <- Azi_P *Pop_P * Dist_P
# Make the network directed
network <- directedNET(network, include_diagonal = TRUE)
# Dist_P <- (max(dist)-dist) / max(dist)
# network[, which(sites$SM == 1 & sites$Pop == pop)] <- Azi_P[, which(sites$SM == 1 & sites$Pop == pop)]*Pop_P[, which(sites$SM == 1 & sites$Pop == pop)] * Dist_P[, which(sites$SM == 1 & sites$Pop == pop)]
# network[which(sites$SM == 1 & sites$Pop == pop), ] <- Azi_P[which(sites$SM == 1 & sites$Pop == pop), ]*Pop_P[which(sites$SM == 1 & sites$Pop == pop), ]*Dist_P[which(sites$SM == 1 & sites$Pop == pop), ]
# Ensure that nodes only flow into the next 1 or two neighbouring nodes
# for (i in 1:nrow(network)){
# idx = (i+1) : (i+1 + ceiling(runif(1,0,2))-1)
# idx = which(!(1:nrow(network) %in% idx))
# network[i, idx] = 0
# }
NMnet <- t(apply(network,1,
function(x) x[which(!is.na(x))]/
sum(x,na.rm=TRUE)))
#----------------------------------------
# Add supersource and sink nodes
#----------------------------------------
#South
SMnet <- addSUPERNODE(SMnet,
sources= site_list$Site[site_list$B ==1],
sinks = site_list$Site[site_list$NB ==1])
index = as.numeric(names(which(SMnet["supersource",] == Inf)))
SMnet["supersource",which(SMnet["supersource",] == Inf)] = site_list$Pop[index]/sum(site_list$Pop[index]) #pop / nbreeding#
index = as.numeric(names(which(SMnet[,"supersink"] == Inf)))
SMnet[which(SMnet[,"supersink"] == Inf), "supersink"] = site_list$Pop[index]/sum(site_list$Pop[index]) # pop / nwintering#
#North
NMnet <- addSUPERNODE(NMnet,
sources= site_list$Site[site_list$NB ==1],
sinks = site_list$Site[site_list$B ==1])
index = as.numeric(names(which(NMnet["supersource",] == Inf)))
NMnet["supersource",which(NMnet["supersource",] == Inf)] = site_list$Pop[index]/sum(site_list$Pop[index]) #pop / nwintering#
index = as.numeric(names(which(NMnet[,"supersink"] == Inf)))
NMnet[which(NMnet[,"supersink"] == Inf), "supersink"] = site_list$Pop[index]/sum(site_list$Pop[index]) #pop / nbreeding#
colnames(NMnet)[1] = rownames(NMnet)[1] = "supersink"
colnames(NMnet)[length(NMnet[1,])] = rownames(NMnet)[length(NMnet[1,])] = "supersource"
site_list<- rbind(
c(-21,0,pop,0,0,0,0,"supersink"),
site_list,
c(41,0,pop,0,0,0,0,"supersource"))
# NMnet <- network
#---------------------------------------------
# Join the two networks
#---------------------------------------------
topright=matrix(0,nrow(SMnet),ncol(NMnet)+1)
colnames(topright) <- c("NB",colnames(NMnet))
bottomleft=matrix(0, nrow(NMnet)+1, ncol(SMnet))
rownames(bottomleft) <- c("NB",rownames(NMnet))
bottomright = cbind(rep_len(0,nrow(NMnet)),NMnet)
bottomright = rbind(rep_len(0,ncol(bottomright)),bottomright)
# bottomright[1,1] = 1
top = cbind(SMnet,topright)
bottom = cbind(bottomleft,bottomright)
network=rbind(top,bottom)
colnames(network)=c(paste0("S",colnames(SMnet)),"NB",paste0("N",colnames(NMnet)))
rownames(network)=c(paste0("S",rownames(SMnet)),"NB",paste0("N",rownames(NMnet)))
network["Ssupersink","NB"]<- 1
network["NB","Nsupersink"]<- 1
network = network*pop
#----------------------------------
sites <- site_list
weight <- graph_from_adjacency_matrix(network, mode="directed", weighted = TRUE)
# run the population through the network a forst time
flow = max_flow(weight, source = V(weight)["Ssupersource"],
target = V(weight)["Nsupersource"], capacity = E(weight)$weight)
# neti <- weight
# E(neti)$weight <- flow$flow
# network <- as.matrix(as_adjacency_matrix(neti, attr="weight"))
#created a weigted igraph network
if (toplot == TRUE){
# plot flow network
nodes = get.edgelist(weight, names=TRUE)
nodes = as.data.frame(nodes)
nodes$flow = flow$flow
nodes$V1 <- substring(nodes$V1, 2)
nodes$V2 <- substring(nodes$V2, 2)
nodes = nodes[nodes$V1 != "supersource" & nodes$V1 != "supersink" & nodes$V2 != "supersource" & nodes$V2 != "supersink" ,]
nodes$Lat_from = unlist(lapply(1:nrow(nodes), function(i) as.numeric(sites$Lat[sites$Site %in% nodes[i,1]])))
nodes$Lon_from = unlist(lapply(1:nrow(nodes), function(i) as.numeric(sites$Lon[sites$Site %in% nodes[i,1]])))
nodes$Lat_to = unlist(lapply(1:nrow(nodes), function(i) as.numeric(sites$Lat[sites$Site %in% nodes[i,2]])))
nodes$Lon_to = unlist(lapply(1:nrow(nodes), function(i) as.numeric(sites$Lon[sites$Site %in% nodes[i,2]])))
# library(shape)
par(mfrow=c(1,1))
par(mar=c(4,4,4,4))
index=2:(nrow(sites)-1)
plot(sites$Lon[index], sites$Lat[index], pch=16,
cex=0, xlab="", ylab="", xaxt="n", yaxt = "n",
frame.plot=FALSE)
index=1:nrow(nodes)#2:(nrow(nodes)-1)#
# Arrows(x0 = nodes$Lon_from[index],
# y0 = nodes$Lat_from[index],
# x1 = nodes$Lon_to[index],
# y1 = nodes$Lat_to[index],
# col= adjustcolor("royalblue3", alpha.f = 0.9))#,
# # lwd=(nodes$flow[index]/(max(nodes$flow)))*30)#,#/500
segments(x0 = nodes$Lon_from[index],
y0 = nodes$Lat_from[index],
x1 = nodes$Lon_to[index],
y1 = nodes$Lat_to[index],
col= "black",#adjustcolor("royalblue3", alpha.f = 0.9),
lwd=(nodes$flow[index]/(max(nodes$flow)))*30)
# sort sites by flow
nodeflow = merge(aggregate(nodes$flow, by=list(Category=as.character(nodes$V1)), FUN=sum),
aggregate(nodes$flow, by=list(Category=as.character(nodes$V2)), FUN=sum), all=T)
nodeflow$x = as.numeric(nodeflow$x)
nodeflow = data.frame( unique(as.matrix(nodeflow[ , 1:2 ]) ))
nodeflow$x = as.numeric(as.character(nodeflow$x))
nodeflow = nodeflow[nodeflow$Category != "supersource" & nodeflow$Category != "supersink",]
# make sure it is numeric
nodeflow$Category = as.numeric(as.character(nodeflow$Category))
# plot sites
nodeflowplot = nodeflow[order(nodeflow$Category),]
index=as.numeric(nodeflowplot$Category)+1
colorz = ifelse(sites$B[index]==1,"royalblue",ifelse(sites$NB[index]==1,"orange","gray"))
points(sites$Lon[index],
sites$Lat[index],
pch=21,
cex=(((nodeflowplot$x)/
as.numeric(max(nodeflowplot$x)))+0.4)*4,
bg=colorz , col="black")
}
return(list( network = network,
tracks = tracks,
sites = site_list ))
}
KiranLDA/migflow documentation built on Aug. 8, 2019, 8:55 p.m.
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Defines functions randomDIFFUSE
Documented in randomDIFFUSE
#' Generate a random network
#'
#' @description This function generates a random migratory network
#'
#'
#' @param pop population size flowing through network
#' @param mean_dist average distance the species can travel (this can be estimated from real data)
#' @param sd_dist standard deviation of the distances a species can travel (this can be estimated from real data)
#' @param toplot TRUE/FALSE to determine whether the output is plotted or not
#' @param nbreeding Number of breeding sites
#' @param nwintering Number of wintering sites
#' @param nstop Number of stopover sites (shared during north and south migration)
#'
#' @return a list containting the network which was randomly generated,
#' the tracks that were randomly generated, and the sites that were randomly generated for animals to use.
#'
#' @import igraph
#' @importFrom stats rnorm runif
#' @export
randomDIFFUSE <- function(nbreeding = 10,
nwintering = 10,
nstop = 30,
pop = 100000,
mean_dist = 0,
sd_dist = 1500,
toplot = TRUE){
# # # For testing purposes
# nbreeding = 10
# nwintering = 10
# nstop = 30
# pop = 100000
# mean_dist = 0
# sd_dist = 800
# toplot = TRUE
nsites = nbreeding+nwintering+nstop
# generate random tracks
tracks <- abs(rnorm(1000, mean_dist, sd_dist)) # rgamma(1000,2.3,0.005)#
# hist(tracks)
# # Create a fake list of sites where animals were seen at, with latitude, longitude and number of anumals seen there
# site_list <- data.frame(Lat= runif(nsites, min=-20, max=40),
# Lon= runif(nsites, min=-20, max=20),
# Pop=runif(nsites, min=500, max=10000),
# B= 0,
# SM=0,
# NM=0,
# NB=0)
B <- data.frame(Lat = runif(nbreeding, min=30, max=40),
Lon= runif(nbreeding, min=-20, max=20),
Pop=runif(nbreeding, min=500, max=10000),
B = 1,
SM = 0,
NB = 0,
NM = 0)
NB <- data.frame(Lat = runif(nwintering, min=-20, max=-10),
Lon= runif(nwintering, min=-20, max=20),
Pop=runif(nwintering, min=500, max=10000),
B = 0,
SM = 0,
NB = 1,
NM = 0)
STP <- data.frame(Lat = runif(nstop, min=-10, max=30),
Lon= runif(nstop, min=-20, max=20),
Pop=runif(nstop, min=500, max=10000),
B = 0,
SM = 1,
NB = 0,
NM = 1)
sites = rbind(B, STP, NB)
sites = sites[order(sites$Lat, decreasing=T),]
sites$Site= 1:nsites
site_list = sites
#Add a bottleneck site
# bottleneck <- round(runif(1, 1, nstop))
# site_list$Pop[site_list$SM==1][bottleneck] <- pop
# sort according to latitude
site_list = site_list[order(site_list$Lat, decreasing=T),]
site_list$Site= 1:nsites
site_list$B[1:nbreeding] = 1
site_list$NB[(nrow(site_list)-nwintering+1):nrow(site_list)] = 1
site_list$SM[site_list$B==0 & site_list$NB==0] = 1
site_list$NM = site_list$SM
# # add a dummy breeding and wintering site
# site_list<- rbind(
# c(41,0,pop,0,0,0,0,0),
# site_list,
# c(-21,0,pop,0,0,0,0,9999))
#-----------------------------
# South migration B -> NB
#-----------------------------
sites = site_list
# create a distance matrix based on these data
dist <- point2DIST(sites)
# calculate the probability of going between these sites given the distance the animal can travel
Dist_P <- distPROB(tracks, dist, adjust=1, plot=F) + 0.000000000000001
# Dist_P <- (max(dist)-dist) / max(dist)
# Calculate prioritisation of population using a site
Pop_P <- nodePopPROP(sites, population = pop)
#Calculate the azimuth angle
Azi_P <- absAZIMUTH(dist, lonlats=sites )#+0.01
# make birds/animals prefer sites which a larger prioritisation of the population has been seen and where the distance is better
network <- Azi_P * Pop_P * Dist_P
# Dist_P <- (max(dist)-dist) / max(dist)
# network[, which(sites$SM == 1 & sites$Pop == pop)] <- Azi_P[, which(sites$SM == 1 & sites$Pop == pop)]*Pop_P[, which(sites$SM == 1 & sites$Pop == pop)]*Dist_P[, which(sites$SM == 1 & sites$Pop == pop)]
# network[which(sites$SM == 1 & sites$Pop == pop), ] <- Azi_P[which(sites$SM == 1 & sites$Pop == pop), ]*Pop_P[which(sites$SM == 1 & sites$Pop == pop), ]*Dist_P[which(sites$SM == 1 & sites$Pop == pop), ]
# Make the network directed
network <- directedNET(network, include_diagonal = TRUE)
#
# # Ensure that nodes only flow into the next 1 or two neighbouring nodes
# for (i in 1:nrow(network)){
# idx = (i+1) : (i+1 + ceiling(runif(1,0,2))-1)
# idx = which(!(1:nrow(network) %in% idx))
# network[i, idx] = 0
# }
#
SMnet <- t(apply(network,1,
function(x) x[which(!is.na(x))]/
sum(x,na.rm=TRUE)))
#-----------------------------
# North migration NB -> B
#-----------------------------
sites = site_list[order(site_list$Lat, decreasing=FALSE),]
# create a distance matrix based on these data
dist <- point2DIST(sites)
# calculate the probability of going between these sites given the distance the animal can travel
Dist_P <- distPROB(tracks, dist, adjust=1, plot=F) + 0.000000000000001
# Calculate prioritisation of population using a site
Pop_P <- nodePopPROP(sites, population = pop)
#Calculate the azimuth angle
Azi_P <- absAZIMUTH(dist, lonlats=sites )#+0.01
# make birds/animals prefer sites which a larger prioritisation of the population has been seen and where the distance is better
network <- Azi_P *Pop_P * Dist_P
# Make the network directed
network <- directedNET(network, include_diagonal = TRUE)
# Dist_P <- (max(dist)-dist) / max(dist)
# network[, which(sites$SM == 1 & sites$Pop == pop)] <- Azi_P[, which(sites$SM == 1 & sites$Pop == pop)]*Pop_P[, which(sites$SM == 1 & sites$Pop == pop)] * Dist_P[, which(sites$SM == 1 & sites$Pop == pop)]
# network[which(sites$SM == 1 & sites$Pop == pop), ] <- Azi_P[which(sites$SM == 1 & sites$Pop == pop), ]*Pop_P[which(sites$SM == 1 & sites$Pop == pop), ]*Dist_P[which(sites$SM == 1 & sites$Pop == pop), ]
# Ensure that nodes only flow into the next 1 or two neighbouring nodes
# for (i in 1:nrow(network)){
# idx = (i+1) : (i+1 + ceiling(runif(1,0,2))-1)
# idx = which(!(1:nrow(network) %in% idx))
# network[i, idx] = 0
# }
NMnet <- t(apply(network,1,
function(x) x[which(!is.na(x))]/
sum(x,na.rm=TRUE)))
#----------------------------------------
# Add supersource and sink nodes
#----------------------------------------
#South
SMnet <- addSUPERNODE(SMnet,
sources= site_list$Site[site_list$B ==1],
sinks = site_list$Site[site_list$NB ==1])
index = as.numeric(names(which(SMnet["supersource",] == Inf)))
SMnet["supersource",which(SMnet["supersource",] == Inf)] = site_list$Pop[index]/sum(site_list$Pop[index]) #pop / nbreeding#
index = as.numeric(names(which(SMnet[,"supersink"] == Inf)))
SMnet[which(SMnet[,"supersink"] == Inf), "supersink"] = site_list$Pop[index]/sum(site_list$Pop[index]) # pop / nwintering#
#North
NMnet <- addSUPERNODE(NMnet,
sources= site_list$Site[site_list$NB ==1],
sinks = site_list$Site[site_list$B ==1])
index = as.numeric(names(which(NMnet["supersource",] == Inf)))
NMnet["supersource",which(NMnet["supersource",] == Inf)] = site_list$Pop[index]/sum(site_list$Pop[index]) #pop / nwintering#
index = as.numeric(names(which(NMnet[,"supersink"] == Inf)))
NMnet[which(NMnet[,"supersink"] == Inf), "supersink"] = site_list$Pop[index]/sum(site_list$Pop[index]) #pop / nbreeding#
colnames(NMnet)[1] = rownames(NMnet)[1] = "supersink"
colnames(NMnet)[length(NMnet[1,])] = rownames(NMnet)[length(NMnet[1,])] = "supersource"
site_list<- rbind(
c(-21,0,pop,0,0,0,0,"supersink"),
site_list,
c(41,0,pop,0,0,0,0,"supersource"))
# NMnet <- network
#---------------------------------------------
# Join the two networks
#---------------------------------------------
topright=matrix(0,nrow(SMnet),ncol(NMnet)+1)
colnames(topright) <- c("NB",colnames(NMnet))
bottomleft=matrix(0, nrow(NMnet)+1, ncol(SMnet))
rownames(bottomleft) <- c("NB",rownames(NMnet))
bottomright = cbind(rep_len(0,nrow(NMnet)),NMnet)
bottomright = rbind(rep_len(0,ncol(bottomright)),bottomright)
# bottomright[1,1] = 1
top = cbind(SMnet,topright)
bottom = cbind(bottomleft,bottomright)
network=rbind(top,bottom)
colnames(network)=c(paste0("S",colnames(SMnet)),"NB",paste0("N",colnames(NMnet)))
rownames(network)=c(paste0("S",rownames(SMnet)),"NB",paste0("N",rownames(NMnet)))
network["Ssupersink","NB"]<- 1
network["NB","Nsupersink"]<- 1
network = network*pop
#----------------------------------
sites <- site_list
weight <- graph_from_adjacency_matrix(network, mode="directed", weighted = TRUE)
# run the population through the network a forst time
flow = max_flow(weight, source = V(weight)["Ssupersource"],
target = V(weight)["Nsupersource"], capacity = E(weight)$weight)
# neti <- weight
# E(neti)$weight <- flow$flow
# network <- as.matrix(as_adjacency_matrix(neti, attr="weight"))
#created a weigted igraph network
if (toplot == TRUE){
# plot flow network
nodes = get.edgelist(weight, names=TRUE)
nodes = as.data.frame(nodes)
nodes$flow = flow$flow
nodes$V1 <- substring(nodes$V1, 2)
nodes$V2 <- substring(nodes$V2, 2)
nodes = nodes[nodes$V1 != "supersource" & nodes$V1 != "supersink" & nodes$V2 != "supersource" & nodes$V2 != "supersink" ,]
nodes$Lat_from = unlist(lapply(1:nrow(nodes), function(i) as.numeric(sites$Lat[sites$Site %in% nodes[i,1]])))
nodes$Lon_from = unlist(lapply(1:nrow(nodes), function(i) as.numeric(sites$Lon[sites$Site %in% nodes[i,1]])))
nodes$Lat_to = unlist(lapply(1:nrow(nodes), function(i) as.numeric(sites$Lat[sites$Site %in% nodes[i,2]])))
nodes$Lon_to = unlist(lapply(1:nrow(nodes), function(i) as.numeric(sites$Lon[sites$Site %in% nodes[i,2]])))
# library(shape)
par(mfrow=c(1,1))
par(mar=c(4,4,4,4))
index=2:(nrow(sites)-1)
plot(sites$Lon[index], sites$Lat[index], pch=16,
cex=0, xlab="", ylab="", xaxt="n", yaxt = "n",
frame.plot=FALSE)
index=1:nrow(nodes)#2:(nrow(nodes)-1)#
# Arrows(x0 = nodes$Lon_from[index],
# y0 = nodes$Lat_from[index],
# x1 = nodes$Lon_to[index],
# y1 = nodes$Lat_to[index],
# col= adjustcolor("royalblue3", alpha.f = 0.9))#,
# # lwd=(nodes$flow[index]/(max(nodes$flow)))*30)#,#/500
segments(x0 = nodes$Lon_from[index],
y0 = nodes$Lat_from[index],
x1 = nodes$Lon_to[index],
y1 = nodes$Lat_to[index],
col= "black",#adjustcolor("royalblue3", alpha.f = 0.9),
lwd=(nodes$flow[index]/(max(nodes$flow)))*30)
# sort sites by flow
nodeflow = merge(aggregate(nodes$flow, by=list(Category=as.character(nodes$V1)), FUN=sum),
aggregate(nodes$flow, by=list(Category=as.character(nodes$V2)), FUN=sum), all=T)
nodeflow$x = as.numeric(nodeflow$x)
nodeflow = data.frame( unique(as.matrix(nodeflow[ , 1:2 ]) ))
nodeflow$x = as.numeric(as.character(nodeflow$x))
nodeflow = nodeflow[nodeflow$Category != "supersource" & nodeflow$Category != "supersink",]
# make sure it is numeric
nodeflow$Category = as.numeric(as.character(nodeflow$Category))
# plot sites
nodeflowplot = nodeflow[order(nodeflow$Category),]
index=as.numeric(nodeflowplot$Category)+1
colorz = ifelse(sites$B[index]==1,"royalblue",ifelse(sites$NB[index]==1,"orange","gray"))
points(sites$Lon[index],
sites$Lat[index],
pch=21,
cex=(((nodeflowplot$x)/
as.numeric(max(nodeflowplot$x)))+0.4)*4,
bg=colorz , col="black")
}
return(list( network = network,
tracks = tracks,
sites = site_list ))
}
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