TestScripts/moveCov_FVCOM_Data.R
In Blevy2/READ-PDB-blevy2-MFS2: Mixed Fishery fleet dynamics simulation tool
#This script is for creating and testing temperature covariate matrices
library(MixFishSim)
library(raster)
#read into GB temp data
WeeklyTempData <- readRDS(file="C:\\Users\\benjamin.levy\\Desktop\\Github\\READ-PDB-blevy2-toy\\TestScripts\\FVCOM_GB\\YearlyTemp_GB.RDS")
#resample temp gradients in same way as habitat maps
res_factor <- .65
r <- raster(extent(WeeklyTempData$`05`[[1]]), nrow = round(res_factor*nrow(WeeklyTempData$`05`[[1]])), ncol = round(res_factor*ncol(WeeklyTempData$`05`[[1]])) , crs = crs(WeeklyTempData$`05`[[1]]))
nrow(r)
temp <- WeeklyTempData
for(i in seq(length(WeeklyTempData))){
for(j in seq(length(WeeklyTempData[[i]]))){
WeeklyTempData[[i]][[j]] <- resample(x=temp[[i]][[j]],crs=crs(WeeklyTempData$`05`[[1]]), y=r, method="ngb")
}
}
#plot results
#pdf(file="testfolder/moveCov_plots.pdf")
#plot each weekly mean
wk_meantemp <- vector()
yr_meantemp <- vector()
wk_mintemp <- vector()
wk_maxtemp <- vector()
for(yr in seq(length(WeeklyTempData))){
for(i in seq(52)){
wk_meantemp <- c(wk_meantemp,mean(as.vector(as.matrix(WeeklyTempData[[yr]][[i]])),na.rm=T))
wk_mintemp <- c(wk_mintemp,min(as.vector(as.matrix(WeeklyTempData[[yr]][[i]])),na.rm=T))
wk_maxtemp <- c(wk_maxtemp,max(as.vector(as.matrix(WeeklyTempData[[yr]][[i]])),na.rm=T))
#record yearly temp
if(i %% 52 == 0){
p <- i - 51
yr_meantemp <- c(yr_meantemp,mean(as.vector(wk_meantemp[p:i])))
}
}
}
par(mfrow = c(1,1),mar = c(2.5, 2.5, 2.5, 2.5))
plot(wk_meantemp)
plot(wk_mintemp)
plot(wk_maxtemp)
plot(yr_meantemp)
min(yr_meantemp)
max(yr_meantemp)
min(wk_maxtemp)
max(wk_maxtemp)
min(wk_mintemp)
max(wk_mintemp)
max(yr_meantemp)-min(yr_meantemp)
#dev.off() #close pdf
#PLOT SINGLE YEAR TO CHOOSE ONE TO EXTEND FOR 20 YEARS
#plot each weekly mean
#NUMBER 8 LOOKS BEST
for(yr in c(7,8,9,11)){
wk_meantemp <- vector()
yr_meantemp <- vector()
wk_mintemp <- vector()
wk_maxtemp <- vector()
for(i in seq(52)){
wk_meantemp <- c(wk_meantemp,mean(as.vector(as.matrix(WeeklyTempData[[yr]][[i]])),na.rm=T))
wk_mintemp <- c(wk_mintemp,min(as.vector(as.matrix(WeeklyTempData[[yr]][[i]])),na.rm=T))
wk_maxtemp <- c(wk_maxtemp,max(as.vector(as.matrix(WeeklyTempData[[yr]][[i]])),na.rm=T))
#record yearly temp
if(i %% 52 == 0){
p <- i - 51
yr_meantemp <- c(yr_meantemp,mean(as.vector(wk_meantemp[p:i])))
}
}
par(mfrow = c(1,1),mar = c(1.5, 1.5, 1.5, 1.5))
plot(wk_meantemp)
}
plot(wk_mintemp)
plot(wk_maxtemp)
plot(yr_meantemp)
min(yr_meantemp)
max(yr_meantemp)
min(wk_maxtemp)
max(wk_maxtemp)
min(wk_mintemp)
max(wk_mintemp)
max(yr_meantemp)-min(yr_meantemp)
all_moveCov <- list()
#CHOOSE ONE TO WORK WITH. #8 looked good, which is 2012
choice<-8
for(i in seq(length(WeeklyTempData[[choice]]))){
all_moveCov[["cov.matrix"]][[i]] <- as.matrix(WeeklyTempData[[choice]][[i]])
}
#plot weekly temp to see what it looks like
moveCov <- list()
source("R/BENS_plot_spatiotemp_hab_justtemp.R")
moveCov[["cov.matrix"]] <- all_moveCov
colrange <- c(4,19.5) #set colorbar range for plots as range of all possibly values
BENS_plot_spatiotemp_hab_justtemp(plot_wk = seq(1,52),hab = all_moveCov, moveCov = all_moveCov,
spwn_wk = list("spp1" = 16:18, "spp2" = 16:19),
plot.file = "testfolder", colrange = colrange)
dev.off()
#############################################################################################
###manipulate single yearly temp gradient for re-use VERSION #3 (others in original file)
#############################################################################################
#############################################################################################
###this version tries to lift up the first copy the correct amount and then copy away the rest
#############################################################################################
Good_moveCov <- all_moveCov #to be used below
#take initial yearly run and extend it so that temp growth over time is 5 degrees
#moveCov <- list()
steps <- 52*20 #total months
inc <- 1.021 #percent increase each year
new_moveCov <- list()
for(i in seq(steps)){
#week 1 is 1st week january
#temp decreases until week 14 (increases again starting week 15)
#temp increases through week 41 (decreases again starting week 42)
#run for 14 weeks, start stretching from week 15 hrough week 41 then raise up remainin points by difference between stretchd week 41 and original week 41
#decrease
if(i <= 14){
new_moveCov[["cov.matrix"]][[i]] <- Good_moveCov[["cov.matrix"]][[i]]
}
#increase/stretch
if( (i >= 15) & (i <= 41 ) ){
new_moveCov[["cov.matrix"]][[i]] <- inc*Good_moveCov[["cov.matrix"]][[i]]
}
#after creating first stretched sequence, see how far first and last mean values are
if(i==42){
#rasie up by this amount
diff <- mean(as.vector(as.matrix(new_moveCov[["cov.matrix"]][[41]])),na.rm=T)-mean(as.vector(as.matrix(Good_moveCov[["cov.matrix"]][[42]])),na.rm=T)
}
#finish off the first year
if((i >=42) & (i<=52)){
new_moveCov[["cov.matrix"]][[i]] <- diff+Good_moveCov[["cov.matrix"]][[i]]
}
#after creating first stretched sequence, use previous one as basis for next one
if(i>52){
new_moveCov[["cov.matrix"]][[i]] <- diff+new_moveCov[["cov.matrix"]][[i-52]]
}
}
#pass to moveCov to be used elsewhere
moveCov[["cov.matrix"]] <- new_moveCov[["cov.matrix"]]
#to plot just the temp preferences over time
source("R/BENS_plot_spatiotemp_hab_justtemp.R")
colrange <- range(moveCov[["cov.matrix"]] ) #set colorbar range for plots as range of all possibly values
BENS_plot_spatiotemp_hab_justtemp(plot_wk = seq(1,1040,52),
hab = hab,
moveCov = move_Cov,
spwn_wk = list("spp1" = 15:18, "spp2" = 15:18),
plot.file = "testfolder", colrange = colrange)
dev.off()
#plot spatiotemperal distribution (combine temp and spatial preferences)
source("R/BENS_plot_spatiotemp_hab.R")
colrange = range(moveCov[["cov.matrix"]])
plot_spatiotemp_hab(plot_monthly = TRUE, plot_wk = seq(1,1040,52),
hab = hab, moveCov = moveCov,
spwn_wk = list("spp1" = 15:18, "spp2" = 15:18),
plot.file = "testfolder")
#############################################################################################
##PLots
#############################################################################################
#plot mean in covariate values
#plot each weekly mean
wk_meantemp <- vector()
yr_meantemp <- vector()
wk_mintemp <- vector()
wk_maxtemp <- vector()
for(i in seq(steps)){
wk_meantemp <- c(wk_meantemp,mean(as.vector(as.matrix(moveCov[["cov.matrix"]][[i]])),na.rm=T))
wk_mintemp <- c(wk_mintemp,min(as.vector(as.matrix(moveCov[["cov.matrix"]][[i]])),na.rm=T))
wk_maxtemp <- c(wk_maxtemp,max(as.vector(as.matrix(moveCov[["cov.matrix"]][[i]])),na.rm=T))
#record yearly temp
if(i %% 52 == 0){
p <- i - 51
yr_meantemp <- c(yr_meantemp,mean(as.vector(wk_meantemp[p:i]),na.rm=T))
}
}
plot(wk_meantemp)
plot(wk_meantemp[1:60])
plot(wk_meantemp[1:52])
plot(wk_mintemp)
plot(wk_maxtemp)
plot(yr_meantemp)
min(yr_meantemp)
max(yr_meantemp)
max(yr_meantemp)-min(yr_meantemp)
#
#
# ##################################################################
# # PLOTTING THE ABOVE BY QUADRANT
# ##################################################################
#
# #first break into strata
# Strata1 <- list() #matrix(0,nrow=50,ncol=100)
# Strata2 <- list() #matrix(0,nrow=50,ncol=100)
# Strata3 <- list() #matrix(0,nrow=50,ncol=100)
# Strata4 <- list()
#
# for(i in seq(52*20)){
#
# #Strata1
# Strata1[[i]]<-moveCov$cov.matrix[[i]][1:50,1:50]
# #Strata2
# Strata2[[i]]<-moveCov$cov.matrix[[i]][1:50,51:100]
# #Strata3
# Strata3[[i]]<-moveCov$cov.matrix[[i]][51:100,1:50]
# #Strata4
# Strata4[[i]]<-moveCov$cov.matrix[[i]][51:100,51:100]
#
# }
#
# #copied above for each strata
# wk_meantemps1 <- vector()
# yr_meantemps1 <- vector()
# wk_mintemps1 <- vector()
# wk_maxtemps1 <- vector()
#
# wk_meantemps2 <- vector()
# yr_meantemps2 <- vector()
# wk_mintemps2 <- vector()
# wk_maxtemps2 <- vector()
#
# wk_meantemps3 <- vector()
# yr_meantemps3 <- vector()
# wk_mintemps3 <- vector()
# wk_maxtemps3 <- vector()
#
# wk_meantemps4 <- vector()
# yr_meantemps4 <- vector()
# wk_mintemps4 <- vector()
# wk_maxtemps4 <- vector()
#
# for(i in seq(steps)){
# wk_meantemps1 <- c(wk_meantemps1,mean(as.vector(as.matrix(Strata1[[i]]))))
# wk_mintemps1 <- c(wk_mintemps1,min(as.vector(as.matrix(Strata1[[i]]))))
# wk_maxtemps1 <- c(wk_maxtemps1,max(as.vector(as.matrix(Strata1[[i]]))))
#
# wk_meantemps2 <- c(wk_meantemps2,mean(as.vector(as.matrix(Strata2[[i]]))))
# wk_mintemps2 <- c(wk_mintemps2,min(as.vector(as.matrix(Strata2[[i]]))))
# wk_maxtemps2 <- c(wk_maxtemps2,max(as.vector(as.matrix(Strata2[[i]]))))
#
# wk_meantemps3 <- c(wk_meantemps3,mean(as.vector(as.matrix(Strata3[[i]]))))
# wk_mintemps3 <- c(wk_mintemps3,min(as.vector(as.matrix(Strata3[[i]]))))
# wk_maxtemps3 <- c(wk_maxtemps3,max(as.vector(as.matrix(Strata3[[i]]))))
#
# wk_meantemps4 <- c(wk_meantemps4,mean(as.vector(as.matrix(Strata4[[i]]))))
# wk_mintemps4 <- c(wk_mintemps4,min(as.vector(as.matrix(Strata4[[i]]))))
# wk_maxtemps4 <- c(wk_maxtemps4,max(as.vector(as.matrix(Strata4[[i]]))))
#
# #record yearly temp
# if(i %% 52 == 0){
# p <- i - 51
# yr_meantemps1 <- c(yr_meantemps1,mean(as.vector(wk_meantemps1[p:i])))
# yr_meantemps2 <- c(yr_meantemps2,mean(as.vector(wk_meantemps2[p:i])))
# yr_meantemps3 <- c(yr_meantemps3,mean(as.vector(wk_meantemps3[p:i])))
# yr_meantemps4 <- c(yr_meantemps4,mean(as.vector(wk_meantemps4[p:i])))
# }
#
# }
#
# par(mfrow = c(2,2),mar = c(4, 4, 4, 4))
# plot(wk_meantemps1)
# plot(wk_mintemps1)
# plot(wk_maxtemps1)
# plot(yr_meantemps1)
# mtext("Strata 1- NW", side = 3, line = -1, outer = TRUE)
#
# par(mfrow = c(2,2),mar = c(4,4,4,4))
# plot(wk_meantemps2)
# plot(wk_mintemps2)
# plot(wk_maxtemps2)
# plot(yr_meantemps2)
# mtext("Strata 2- NE", side = 3, line = -1, outer = TRUE)
#
# par(mfrow = c(2,2),mar = c(4,4,4,4))
# plot(wk_meantemps3)
# plot(wk_mintemps3)
# plot(wk_maxtemps3)
# plot(yr_meantemps3)
# mtext("Strata 3- SW", side = 3, line = -1, outer = TRUE)
#
# par(mfrow = c(2,2),mar = c(4,4,4,4))
# plot(wk_meantemps4)
# plot(wk_mintemps4)
# plot(wk_maxtemps4)
# plot(yr_meantemps4)
# mtext("Strata 4- SE", side = 3, line = -1, outer = TRUE)
#
#
#############################################################################################
###manipulate single yearly temp gradient for re-use VERSION #3 (others in original file)
#############################################################################################
#############################################################################################
###THIS VERSION IS TO SIMPLY COPY THE SAME PATTERN WITH NO TEMP INCREASE
#############################################################################################
Good_moveCov <- all_moveCov #to be used below
#take initial yearly run and extend it so that temp growth over time is 5 degrees
moveCov <- list()
steps <- 52*20 #total months
inc <- 1 #percent increase each year (IE, NONE)
new_moveCov <- list()
for(i in seq(steps)){
#week 1 is 1st week january
#temp decreases until week 14 (increases again starting week 15)
#temp increases through week 41 (decreases again starting week 42)
#run for 14 weeks, start stretching from week 15 hrough week 41 then raise up remainin points by difference between stretchd week 41 and original week 41
#week 1 is 1st week january
#temp decreases until week 5 (increases again starting week 6)
#temp increases through week 30 (decreases again starting week 31)
#run for 5 weeks, start stretching from week 6 through week 30 then raise up remainin points by difference between stretchd week 30 and original week 30
#decrease
if(i <= 14){
new_moveCov[["cov.matrix"]][[i]] <- Good_moveCov[["cov.matrix"]][[i]]
}
#increase/stretch
if( (i >= 15) & (i <= 41 ) ){
new_moveCov[["cov.matrix"]][[i]] <- inc*Good_moveCov[["cov.matrix"]][[i]]
}
#after creating first stretched sequence, see how far first and last mean values are
if(i==42){
#rasie up by this amount
diff <- 0
}
#finish off the first year
if((i >=42) & (i<=52)){
new_moveCov[["cov.matrix"]][[i]] <- diff+Good_moveCov[["cov.matrix"]][[i]]
}
#after creating first stretched sequence, use previous one as basis for next one
if(i>52){
new_moveCov[["cov.matrix"]][[i]] <- diff+new_moveCov[["cov.matrix"]][[i-52]]
}
}
#pass to moveCov to be used elsewhere
moveCov[["cov.matrix"]] <- new_moveCov[["cov.matrix"]]
#plot increasing temp gradient over time similar to how
#plot_spatiotemp_hab_justtemp works, but without species-specific
#influences
yearscut <- 2
#function to rotate image before plotting because image.plot rotates it
rotate <- function(x) t(apply(x, 2, rev))
pdf(file=paste0('testfolder/Monthly_temp_plots','.pdf'))
#figure out max/min temp to set color limits below
zmax <- max(unlist(lapply(moveCov$cov.matrix,FUN=max, na.rm=T)))
zmin <- min(unlist(lapply(moveCov$cov.matrix,FUN=min, na.rm=T)))
for(k in seq(12)){
par(mfrow = c(5,4),mar = c(1, 1, 1, 1))
for(i in seq(52*yearscut+1,length(moveCov$cov.matrix),52)){
month_shift <- 4*(k-1)
temp_rotate <- rotate(moveCov$cov.matrix[[i+month_shift]])
fields::image.plot(temp_rotate, zlim = c(zmin,zmax))
# axis(1, at = seq(0, 1, by = 0.2), labels = seq(0, nrows, by = nrows/5))
# axis(2, at = seq(0, 1, by = 0.2), labels = seq(0, ncols, by = ncols/5))
text(0.5, 0.98, labels = paste('Week', (i+month_shift)%%52,'Year', ceiling((i+month_shift)/52)), cex = 1)
}
}
dev.off()
Blevy2/READ-PDB-blevy2-MFS2 documentation built on Nov. 29, 2023, 11:48 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#This script is for creating and testing temperature covariate matrices
library(MixFishSim)
library(raster)
#read into GB temp data
WeeklyTempData <- readRDS(file="C:\\Users\\benjamin.levy\\Desktop\\Github\\READ-PDB-blevy2-toy\\TestScripts\\FVCOM_GB\\YearlyTemp_GB.RDS")
#resample temp gradients in same way as habitat maps
res_factor <- .65
r <- raster(extent(WeeklyTempData$`05`[[1]]), nrow = round(res_factor*nrow(WeeklyTempData$`05`[[1]])), ncol = round(res_factor*ncol(WeeklyTempData$`05`[[1]])) , crs = crs(WeeklyTempData$`05`[[1]]))
nrow(r)
temp <- WeeklyTempData
for(i in seq(length(WeeklyTempData))){
for(j in seq(length(WeeklyTempData[[i]]))){
WeeklyTempData[[i]][[j]] <- resample(x=temp[[i]][[j]],crs=crs(WeeklyTempData$`05`[[1]]), y=r, method="ngb")
}
}
#plot results
#pdf(file="testfolder/moveCov_plots.pdf")
#plot each weekly mean
wk_meantemp <- vector()
yr_meantemp <- vector()
wk_mintemp <- vector()
wk_maxtemp <- vector()
for(yr in seq(length(WeeklyTempData))){
for(i in seq(52)){
wk_meantemp <- c(wk_meantemp,mean(as.vector(as.matrix(WeeklyTempData[[yr]][[i]])),na.rm=T))
wk_mintemp <- c(wk_mintemp,min(as.vector(as.matrix(WeeklyTempData[[yr]][[i]])),na.rm=T))
wk_maxtemp <- c(wk_maxtemp,max(as.vector(as.matrix(WeeklyTempData[[yr]][[i]])),na.rm=T))
#record yearly temp
if(i %% 52 == 0){
p <- i - 51
yr_meantemp <- c(yr_meantemp,mean(as.vector(wk_meantemp[p:i])))
}
}
}
par(mfrow = c(1,1),mar = c(2.5, 2.5, 2.5, 2.5))
plot(wk_meantemp)
plot(wk_mintemp)
plot(wk_maxtemp)
plot(yr_meantemp)
min(yr_meantemp)
max(yr_meantemp)
min(wk_maxtemp)
max(wk_maxtemp)
min(wk_mintemp)
max(wk_mintemp)
max(yr_meantemp)-min(yr_meantemp)
#dev.off() #close pdf
#PLOT SINGLE YEAR TO CHOOSE ONE TO EXTEND FOR 20 YEARS
#plot each weekly mean
#NUMBER 8 LOOKS BEST
for(yr in c(7,8,9,11)){
wk_meantemp <- vector()
yr_meantemp <- vector()
wk_mintemp <- vector()
wk_maxtemp <- vector()
for(i in seq(52)){
wk_meantemp <- c(wk_meantemp,mean(as.vector(as.matrix(WeeklyTempData[[yr]][[i]])),na.rm=T))
wk_mintemp <- c(wk_mintemp,min(as.vector(as.matrix(WeeklyTempData[[yr]][[i]])),na.rm=T))
wk_maxtemp <- c(wk_maxtemp,max(as.vector(as.matrix(WeeklyTempData[[yr]][[i]])),na.rm=T))
#record yearly temp
if(i %% 52 == 0){
p <- i - 51
yr_meantemp <- c(yr_meantemp,mean(as.vector(wk_meantemp[p:i])))
}
}
par(mfrow = c(1,1),mar = c(1.5, 1.5, 1.5, 1.5))
plot(wk_meantemp)
}
plot(wk_mintemp)
plot(wk_maxtemp)
plot(yr_meantemp)
min(yr_meantemp)
max(yr_meantemp)
min(wk_maxtemp)
max(wk_maxtemp)
min(wk_mintemp)
max(wk_mintemp)
max(yr_meantemp)-min(yr_meantemp)
all_moveCov <- list()
#CHOOSE ONE TO WORK WITH. #8 looked good, which is 2012
choice<-8
for(i in seq(length(WeeklyTempData[[choice]]))){
all_moveCov[["cov.matrix"]][[i]] <- as.matrix(WeeklyTempData[[choice]][[i]])
}
#plot weekly temp to see what it looks like
moveCov <- list()
source("R/BENS_plot_spatiotemp_hab_justtemp.R")
moveCov[["cov.matrix"]] <- all_moveCov
colrange <- c(4,19.5) #set colorbar range for plots as range of all possibly values
BENS_plot_spatiotemp_hab_justtemp(plot_wk = seq(1,52),hab = all_moveCov, moveCov = all_moveCov,
spwn_wk = list("spp1" = 16:18, "spp2" = 16:19),
plot.file = "testfolder", colrange = colrange)
dev.off()
#############################################################################################
###manipulate single yearly temp gradient for re-use VERSION #3 (others in original file)
#############################################################################################
#############################################################################################
###this version tries to lift up the first copy the correct amount and then copy away the rest
#############################################################################################
Good_moveCov <- all_moveCov #to be used below
#take initial yearly run and extend it so that temp growth over time is 5 degrees
#moveCov <- list()
steps <- 52*20 #total months
inc <- 1.021 #percent increase each year
new_moveCov <- list()
for(i in seq(steps)){
#week 1 is 1st week january
#temp decreases until week 14 (increases again starting week 15)
#temp increases through week 41 (decreases again starting week 42)
#run for 14 weeks, start stretching from week 15 hrough week 41 then raise up remainin points by difference between stretchd week 41 and original week 41
#decrease
if(i <= 14){
new_moveCov[["cov.matrix"]][[i]] <- Good_moveCov[["cov.matrix"]][[i]]
}
#increase/stretch
if( (i >= 15) & (i <= 41 ) ){
new_moveCov[["cov.matrix"]][[i]] <- inc*Good_moveCov[["cov.matrix"]][[i]]
}
#after creating first stretched sequence, see how far first and last mean values are
if(i==42){
#rasie up by this amount
diff <- mean(as.vector(as.matrix(new_moveCov[["cov.matrix"]][[41]])),na.rm=T)-mean(as.vector(as.matrix(Good_moveCov[["cov.matrix"]][[42]])),na.rm=T)
}
#finish off the first year
if((i >=42) & (i<=52)){
new_moveCov[["cov.matrix"]][[i]] <- diff+Good_moveCov[["cov.matrix"]][[i]]
}
#after creating first stretched sequence, use previous one as basis for next one
if(i>52){
new_moveCov[["cov.matrix"]][[i]] <- diff+new_moveCov[["cov.matrix"]][[i-52]]
}
}
#pass to moveCov to be used elsewhere
moveCov[["cov.matrix"]] <- new_moveCov[["cov.matrix"]]
#to plot just the temp preferences over time
source("R/BENS_plot_spatiotemp_hab_justtemp.R")
colrange <- range(moveCov[["cov.matrix"]] ) #set colorbar range for plots as range of all possibly values
BENS_plot_spatiotemp_hab_justtemp(plot_wk = seq(1,1040,52),
hab = hab,
moveCov = move_Cov,
spwn_wk = list("spp1" = 15:18, "spp2" = 15:18),
plot.file = "testfolder", colrange = colrange)
dev.off()
#plot spatiotemperal distribution (combine temp and spatial preferences)
source("R/BENS_plot_spatiotemp_hab.R")
colrange = range(moveCov[["cov.matrix"]])
plot_spatiotemp_hab(plot_monthly = TRUE, plot_wk = seq(1,1040,52),
hab = hab, moveCov = moveCov,
spwn_wk = list("spp1" = 15:18, "spp2" = 15:18),
plot.file = "testfolder")
#############################################################################################
##PLots
#############################################################################################
#plot mean in covariate values
#plot each weekly mean
wk_meantemp <- vector()
yr_meantemp <- vector()
wk_mintemp <- vector()
wk_maxtemp <- vector()
for(i in seq(steps)){
wk_meantemp <- c(wk_meantemp,mean(as.vector(as.matrix(moveCov[["cov.matrix"]][[i]])),na.rm=T))
wk_mintemp <- c(wk_mintemp,min(as.vector(as.matrix(moveCov[["cov.matrix"]][[i]])),na.rm=T))
wk_maxtemp <- c(wk_maxtemp,max(as.vector(as.matrix(moveCov[["cov.matrix"]][[i]])),na.rm=T))
#record yearly temp
if(i %% 52 == 0){
p <- i - 51
yr_meantemp <- c(yr_meantemp,mean(as.vector(wk_meantemp[p:i]),na.rm=T))
}
}
plot(wk_meantemp)
plot(wk_meantemp[1:60])
plot(wk_meantemp[1:52])
plot(wk_mintemp)
plot(wk_maxtemp)
plot(yr_meantemp)
min(yr_meantemp)
max(yr_meantemp)
max(yr_meantemp)-min(yr_meantemp)
#
#
# ##################################################################
# # PLOTTING THE ABOVE BY QUADRANT
# ##################################################################
#
# #first break into strata
# Strata1 <- list() #matrix(0,nrow=50,ncol=100)
# Strata2 <- list() #matrix(0,nrow=50,ncol=100)
# Strata3 <- list() #matrix(0,nrow=50,ncol=100)
# Strata4 <- list()
#
# for(i in seq(52*20)){
#
# #Strata1
# Strata1[[i]]<-moveCov$cov.matrix[[i]][1:50,1:50]
# #Strata2
# Strata2[[i]]<-moveCov$cov.matrix[[i]][1:50,51:100]
# #Strata3
# Strata3[[i]]<-moveCov$cov.matrix[[i]][51:100,1:50]
# #Strata4
# Strata4[[i]]<-moveCov$cov.matrix[[i]][51:100,51:100]
#
# }
#
# #copied above for each strata
# wk_meantemps1 <- vector()
# yr_meantemps1 <- vector()
# wk_mintemps1 <- vector()
# wk_maxtemps1 <- vector()
#
# wk_meantemps2 <- vector()
# yr_meantemps2 <- vector()
# wk_mintemps2 <- vector()
# wk_maxtemps2 <- vector()
#
# wk_meantemps3 <- vector()
# yr_meantemps3 <- vector()
# wk_mintemps3 <- vector()
# wk_maxtemps3 <- vector()
#
# wk_meantemps4 <- vector()
# yr_meantemps4 <- vector()
# wk_mintemps4 <- vector()
# wk_maxtemps4 <- vector()
#
# for(i in seq(steps)){
# wk_meantemps1 <- c(wk_meantemps1,mean(as.vector(as.matrix(Strata1[[i]]))))
# wk_mintemps1 <- c(wk_mintemps1,min(as.vector(as.matrix(Strata1[[i]]))))
# wk_maxtemps1 <- c(wk_maxtemps1,max(as.vector(as.matrix(Strata1[[i]]))))
#
# wk_meantemps2 <- c(wk_meantemps2,mean(as.vector(as.matrix(Strata2[[i]]))))
# wk_mintemps2 <- c(wk_mintemps2,min(as.vector(as.matrix(Strata2[[i]]))))
# wk_maxtemps2 <- c(wk_maxtemps2,max(as.vector(as.matrix(Strata2[[i]]))))
#
# wk_meantemps3 <- c(wk_meantemps3,mean(as.vector(as.matrix(Strata3[[i]]))))
# wk_mintemps3 <- c(wk_mintemps3,min(as.vector(as.matrix(Strata3[[i]]))))
# wk_maxtemps3 <- c(wk_maxtemps3,max(as.vector(as.matrix(Strata3[[i]]))))
#
# wk_meantemps4 <- c(wk_meantemps4,mean(as.vector(as.matrix(Strata4[[i]]))))
# wk_mintemps4 <- c(wk_mintemps4,min(as.vector(as.matrix(Strata4[[i]]))))
# wk_maxtemps4 <- c(wk_maxtemps4,max(as.vector(as.matrix(Strata4[[i]]))))
#
# #record yearly temp
# if(i %% 52 == 0){
# p <- i - 51
# yr_meantemps1 <- c(yr_meantemps1,mean(as.vector(wk_meantemps1[p:i])))
# yr_meantemps2 <- c(yr_meantemps2,mean(as.vector(wk_meantemps2[p:i])))
# yr_meantemps3 <- c(yr_meantemps3,mean(as.vector(wk_meantemps3[p:i])))
# yr_meantemps4 <- c(yr_meantemps4,mean(as.vector(wk_meantemps4[p:i])))
# }
#
# }
#
# par(mfrow = c(2,2),mar = c(4, 4, 4, 4))
# plot(wk_meantemps1)
# plot(wk_mintemps1)
# plot(wk_maxtemps1)
# plot(yr_meantemps1)
# mtext("Strata 1- NW", side = 3, line = -1, outer = TRUE)
#
# par(mfrow = c(2,2),mar = c(4,4,4,4))
# plot(wk_meantemps2)
# plot(wk_mintemps2)
# plot(wk_maxtemps2)
# plot(yr_meantemps2)
# mtext("Strata 2- NE", side = 3, line = -1, outer = TRUE)
#
# par(mfrow = c(2,2),mar = c(4,4,4,4))
# plot(wk_meantemps3)
# plot(wk_mintemps3)
# plot(wk_maxtemps3)
# plot(yr_meantemps3)
# mtext("Strata 3- SW", side = 3, line = -1, outer = TRUE)
#
# par(mfrow = c(2,2),mar = c(4,4,4,4))
# plot(wk_meantemps4)
# plot(wk_mintemps4)
# plot(wk_maxtemps4)
# plot(yr_meantemps4)
# mtext("Strata 4- SE", side = 3, line = -1, outer = TRUE)
#
#
#############################################################################################
###manipulate single yearly temp gradient for re-use VERSION #3 (others in original file)
#############################################################################################
#############################################################################################
###THIS VERSION IS TO SIMPLY COPY THE SAME PATTERN WITH NO TEMP INCREASE
#############################################################################################
Good_moveCov <- all_moveCov #to be used below
#take initial yearly run and extend it so that temp growth over time is 5 degrees
moveCov <- list()
steps <- 52*20 #total months
inc <- 1 #percent increase each year (IE, NONE)
new_moveCov <- list()
for(i in seq(steps)){
#week 1 is 1st week january
#temp decreases until week 14 (increases again starting week 15)
#temp increases through week 41 (decreases again starting week 42)
#run for 14 weeks, start stretching from week 15 hrough week 41 then raise up remainin points by difference between stretchd week 41 and original week 41
#week 1 is 1st week january
#temp decreases until week 5 (increases again starting week 6)
#temp increases through week 30 (decreases again starting week 31)
#run for 5 weeks, start stretching from week 6 through week 30 then raise up remainin points by difference between stretchd week 30 and original week 30
#decrease
if(i <= 14){
new_moveCov[["cov.matrix"]][[i]] <- Good_moveCov[["cov.matrix"]][[i]]
}
#increase/stretch
if( (i >= 15) & (i <= 41 ) ){
new_moveCov[["cov.matrix"]][[i]] <- inc*Good_moveCov[["cov.matrix"]][[i]]
}
#after creating first stretched sequence, see how far first and last mean values are
if(i==42){
#rasie up by this amount
diff <- 0
}
#finish off the first year
if((i >=42) & (i<=52)){
new_moveCov[["cov.matrix"]][[i]] <- diff+Good_moveCov[["cov.matrix"]][[i]]
}
#after creating first stretched sequence, use previous one as basis for next one
if(i>52){
new_moveCov[["cov.matrix"]][[i]] <- diff+new_moveCov[["cov.matrix"]][[i-52]]
}
}
#pass to moveCov to be used elsewhere
moveCov[["cov.matrix"]] <- new_moveCov[["cov.matrix"]]
#plot increasing temp gradient over time similar to how
#plot_spatiotemp_hab_justtemp works, but without species-specific
#influences
yearscut <- 2
#function to rotate image before plotting because image.plot rotates it
rotate <- function(x) t(apply(x, 2, rev))
pdf(file=paste0('testfolder/Monthly_temp_plots','.pdf'))
#figure out max/min temp to set color limits below
zmax <- max(unlist(lapply(moveCov$cov.matrix,FUN=max, na.rm=T)))
zmin <- min(unlist(lapply(moveCov$cov.matrix,FUN=min, na.rm=T)))
for(k in seq(12)){
par(mfrow = c(5,4),mar = c(1, 1, 1, 1))
for(i in seq(52*yearscut+1,length(moveCov$cov.matrix),52)){
month_shift <- 4*(k-1)
temp_rotate <- rotate(moveCov$cov.matrix[[i+month_shift]])
fields::image.plot(temp_rotate, zlim = c(zmin,zmax))
# axis(1, at = seq(0, 1, by = 0.2), labels = seq(0, nrows, by = nrows/5))
# axis(2, at = seq(0, 1, by = 0.2), labels = seq(0, ncols, by = ncols/5))
text(0.5, 0.98, labels = paste('Week', (i+month_shift)%%52,'Year', ceiling((i+month_shift)/52)), cex = 1)
}
}
dev.off()
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.