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fx.gen.r
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fx.gen.r
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#Find and change into the script directory (when sourced)
(script.dir <- dirname(sys.frame(1)$ofile))
print(paste("script.dir <-",script.dir))
setwd(script.dir)
library(maptools)
gpclibPermit()
library(lattice)
library(rgdal)
library(topmodel)
library(akima)
library(clim.pact)
library(fields)
library(animation) #saveMovie
library(grDevices)#coloRampPalette
library(fields)
library(raster)
library(hydroTSM)
library(RFOC)
library(ggplot2)
library(reshape)#melt
library(gdata);#gpclibPermit()
library(zoo);library(tis);library(RSEIS) ;library(abind)#to Julian
library(fields);library(maptools);library(rgdal)
#Objects
kol<-colorRampPalette(c("aliceblue","cadetblue1","cornflowerblue","blue"))
load("myw.rda") #map data
load("world_countries.rda")
#
fx.addshp<-function(riv=riv,wat=wat,hep=hep,ctry=ctry,bas1=bas1,add=FALSE){
#Add plots of read shapefiles for river, waterbodies, pouints(hep),country and basins.
#can be the same shape 5 times
#if(add==FALSE){sp::plot(riv,col="lightblue")} else{sp::plot(riv,add=TRUE,col="lightblue")}
sp::plot(wat,add=TRUE,border="lightblue",col="lightblue")
sp::plot(riv,add=TRUE,col="lightblue")
sp::plot(ctry,add=TRUE,border="black")
sp::plot(bas1,add=TRUE,border="green",lwd=3)
plot(hep,add=TRUE,pch=16,cex=1.5,col=2)}
#Compute trends on an array (monthly data)
fx.trend.array<-function(ax,y1=c(1961,1),freq=12,method=c("MK","SMK","zyp"),monx=12){
trd<-sig<-array(dim=c(dim(ax)[1],dim(ax)[2]))
if(method[1]=="MK"||method[1]=="SMK"){
require(Kendall)
for(i in 1:dim(sig)[1]){
for(j in 1:dim(sig)[2]){
x<-ts(ax[i,j,],y1,f=12)
if(all(!is.na(x))){
if(method[1]=="Kendall"){MK<-(MannKendall(x))} else {MK<-(SeasonalMannKendall(x))}
trd[i,j]<-as.numeric(MK$tau)
sig[i,j]<-as.numeric(MK$sl)
if(sig[i,j]<=0.05){sig[i,j]<-0}else {sig[i,j]<-1}
}
}
}
}
else{
require(zyp)
for(i in 1:dim(sig)[1]){
for(j in 1:dim(sig)[2]){
x<-ts(ax[i,j,],y1,f=freq)
if(all(!is.na(x))){
MK<-(zyp.zhang(x))
trd[i,j]<-as.numeric(MK["trend"])*monx #per month*12=per year
sig[i,j]<-as.numeric(MK["sig"])
if(sig[i,j]<=0.05){sig[i,j]<-0}else {sig[i,j]<-1}
}
}
}
}
out<-list(trd=trd,sig=sig)
return(out)
}
#uses above to give a spatial object. can also do for seasons
fx.seas.spatial.trend<-function(Pc.sel,szn=c(12,13,14),nmons=360,div=1,monx=1){#monx=1 year/seas & 12 for months
#avg=3 for mean =1 for sum
if(szn[1]==0){pr.MAM<-Pc.sel} else {
s1<-seq(szn[1],nmons,12);s1l<-length(s1)
s2<-seq(szn[2],nmons,12);s2l<-length(s2)
s3<-seq(szn[3],nmons,12);s3l<-length(s3)
pr.Mar<-Pc.sel[,,seq(szn[1],nmons,12)[1:min(s1l,s2l,s3l)]]
pr.Apr<-Pc.sel[,,seq(szn[2],nmons,12)[1:min(s1l,s2l,s3l)]]
pr.Jun<-Pc.sel[,,seq(szn[3],nmons,12)[1:min(s1l,s2l,s3l)]]
pr.MAM<-(pr.Mar+pr.Apr+pr.Jun)/div
}
pr.MAM.trend<-fx.trend.array(pr.MAM,method="zyp",monx=monx)
sig<-pr.MAM.trend$sig;
sigxy<-which(sig==0,arr.ind=TRUE)
trd<-pr.MAM.trend$trd*monx
zL<-c((0-max(abs(range(trd,na.rm=TRUE)))),
(0+max(abs(range(trd,na.rm=TRUE)))))
out<-list(trd=trd,sig=sig,zL=zL,sigxy=sigxy,pr=pr.MAM)
return(out)}
#Julian date
fx.julian<-function(date="1970-1-1")
{
date<-as.character(as.Date(date))
(Y<-as.numeric(strsplit(date,"-")[[1]][1]))
(O<-as.Date(paste(Y,1,1,sep="-")))
J<-julian(as.Date(date),origin=O)+1
return(J)}
#MAP COUNTRIES #ADD COUNTRIES TO A MAP #RETURN AN EXPANDED GRID LON-LAT FOR GIVEN LATS LONS
fx.map.ctry<-function(ctry=c(213,211,105,178,23),lats=seq(-40,40,1),lons=seq(-18,50,1),clr="transparent",lwd=3,global=TRUE,kon=FALSE,newp=FALSE,world_countries=NA){
if(is.na(world_countries)){world_countries<-get(load("world_countries_24.rda"))}
library(rgdal); library(clim.pact)
#print("This function Plots over the current Device if it exists")
#print("Close any devices and re-plot if a new device is wanted")
d1<-d2<-NULL
if(newp==TRUE) #if a new plot is desired
{
d1 <- expand.grid(x=lons, y=lats);d2<-cbind(d1$x,d1$y) #grid lat-lon
names(d2)<-NULL; d2<-as.data.frame(d2);names(d2)<-c("lon","lat");rm(d1)
plot(d2,pch="",ylab="lat",xlab="lon");
#add a grid
lat.grd<-seq(floor(min(lats)),ceiling(max(lats)),1)
lon.grd<-seq(floor(min(lons)),ceiling(max(lons)),1)
for( j in 1: length(lat.grd)){abline(h=lat.grd[j],col="grey")}
for( j in 1: length(lon.grd)){abline(v=lon.grd[j],col="grey")}
}
#Plot all countries
if(global==T){for(i in 1:dim(world_countries)[1])
{plot(world_countries[i, ],add=T,lwd=0.1)}}
#plot specified countries
if(kon==TRUE)
{
for(i in 1:length(ctry)) #plot for each wanted country
{
if(is.numeric(ctry)){ctry2plt<- world_countries[ctry[i], ]}
if(!is.numeric(ctry)){ctry2plt<- world_countries[world_countries$names == ctry[i], ]}
plot(ctry2plt,add=T,col=clr,lwd=lwd,ylab="lat",xlab="lon")
}
}
#addland(col="grey")
return(d2)}
fx.regrid<-function(x,y,z,xd,yd,res=0.5,plots=c(1,1),kol){
x1=seq(xd[1],xd[length(xd)],res)
y1=seq(yd[1],yd[length(yd)],res)
#Gridded Bivariate Interpolation for Irregular Data
library(akima)
k<-expand.grid(x,y)
k<-cbind(k,NA,NA,NA)
kidx<-1:dim(k)[1]
eval(parse(text=paste("k[",kidx,",3]<-which(x==k[",kidx,",1])",sep="")))
eval(parse(text=paste("k[",kidx,",4]<-which(y==k[",kidx,",2])",sep="")))
eval(parse(text=paste("k[",kidx,",5]<-z[k[",kidx,",3],k[",kidx,",4]]",sep="")))
k<-k[,c(1,2,5)];names(k)<-c("x","y","z")
z1<-interp(k$x,k$y,k$z,x1,y1)
par(mfrow=plots)
resx<-round(abs(mean(x[2:length(x)]-x[1:(length(x)-1)])),2)
resy<-round(abs(mean(y[2:length(y)]-y[1:(length(y)-1)])),2)
# image.plot(x,y,z,xlim=xd,ylim=yd,col=kol(20),
# main=paste("Original", resx,"X",resy),xlab="lon",ylab="lat")
# contour(x,y,z,add=TRUE);grid();map.ctry()
# image.plot(z1$x,z1$y,z1$z,xlim=xd,ylim=yd,col=kol(20),
# main=paste("Regridded", res, "X",res),xlab="lon",ylab="lat")
# contour(z1$x,z1$y,z1$z,add=TRUE);grid();map.ctry()
return(z1)}
#Read an esri ascii raster
fx.read.esri<-function(afile,con=TRUE,grd=FALSE)
{
ashp<-as.matrix(read.table(afile,skip=6))
#ashp[ashp<0]<-NA
rnum<-dim(ashp)[1]
cnum<-dim(ashp)[2]
res<-as.numeric(scan(afile,what="character",skip=4,nlines=1)[2])
#lon
x11<-as.numeric(scan(afile, what="c", skip=2,nlines=1)[2])+res/2
cx<-seq(x11,x11+(cnum-1)*res,res)
#lat
y11<-as.numeric(scan(afile, what="c", skip=3,nlines=1)[2])+res/2
cy<-seq(y11,y11+(rnum-1)*res,res)
ashp<-t(ashp)[,dim(t(ashp))[2]:1]
out<-list(x=cx,y=cy,z=ashp)
if(grd){out<-regrid(x=cx,y=cy,z=ashp)}
if(con){out$z[out$z<0]<-NA;out$z[out$z>=0]<-1}
return(out)
}
#plot study area
#coarse rasters on shapes
fx.plot.one<-function(bas.ras,ug,lks,bas,afr,nilb,nilr,ylim=c(-5,6),xlim=c(27,38)){
par(mfrow=c(1,2))
#location Map in Africa
plot(afr,xlim=c(-18,55),ylim=c(-35,35),xaxs="i")
box();axis(1);axis(2);axis(3);axis(4);grid()
map.ctry()
plot(bas,col="green",add=T,,border=2,lwd=1.5)
plot(nilb,border="cyan1",add=T)
plot(bas,col="green",add=T,,border=2,lwd=1.5)
plot(nilr,col="blue",add=T)
plot(ug,add=T)
abline(h=0)
mtext("lat", side=2, line=3, cex.lab=1,las=3, col="blue")
mtext("lon", side=1, line=3, cex.lab=1,las=1, col="blue")
#Local Basin
image(bas.ras,col="aliceblue",ylim=ylim,xlim=xlim)
image(lks.ras,col="cornflowerblue",add=T)
plot(bas,add=T,border=2)
plot(lks,add=T,border=4)
plot(Vnile,col=4,add=T)
map.ctry()
yg<-ylim[1]:ylim[2]
xg<-xlim[1]:xlim[2]
grid(nx=2*(length(xg)-1),ny=2*(length(yg)-1))
mtext("lat", side=2, line=3, cex.lab=1,las=3, col="blue")
mtext("lon", side=1, line=3, cex.lab=1,las=1, col="blue")
text(34,5.5,"SUDAN",cex=1.2);text(32,1,"UGANDA",cex=1.2);text(30,-2,"RWANDA",cex=1.2)
text(30,-3,"BURUNDI",cex=1.2); text(29,1,"DRC",cex=1.2);text(36,3,"KENYA",cex=1.2);text(33,-4.5,"TANZANIA",cex=1.2)
stxyz=fao.et()$xyz
rn<-floor(runif(10,1,dim(stxyz)[1]))
for(st in 1:dim(stxyz)[1])
{
points(stxyz$lon[st],stxyz$lat[st],pch=16,col=1)
#if(length(which(rn==st))>0)
#{text(stxyz$lon[st],stxyz$lat[st],tolower(stxyz$sname[st]),cex=0.8)}
}
leg.txt<-c("Lakes & Rivers","Lakes 0.5deg","Basin","Basin 0.5 deg","Met Stations")
legend("bottomright",leg.txt,col=c(4,"cornflowerblue",2,"grey",1),pch=c(0,15,0,15,16),cex=1,bg="white")
}; #plot.one(bas.ras=bas.ras,ug=ug,lks=lks,bas=bas,afr=afr,nilb=nilb,nilr=nilr)
# improved list of objects
fx.ls.objects <- function (pos = 1, pattern, order.by, decreasing=FALSE, head=FALSE, n=5){
napply <- function(names, fn) sapply(names, function(x)
fn(get(x, pos = pos)))
names <- ls(pos = pos, pattern = pattern)
obj.class <- napply(names, function(x) as.character(class(x))[1])
obj.mode <- napply(names, mode)
obj.type <- ifelse(is.na(obj.class), obj.mode, obj.class)
obj.size <- napply(names, object.size)
obj.prettysize <- sapply(obj.size, function(r) prettyNum(r, big.mark = ",") )
obj.dim <- t(napply(names, function(x)
as.numeric(dim(x))[1:2]))
vec <- is.na(obj.dim)[, 1] & (obj.type != "function")
obj.dim[vec, 1] <- napply(names, length)[vec]
out <- data.frame(obj.type, obj.size,obj.prettysize, obj.dim)
names(out) <- c("Type", "Size", "PrettySize", "Rows", "Columns")
if (!missing(order.by))
out <- out[order(out[[order.by]], decreasing=decreasing), ]
out <- out[c("Type", "PrettySize", "Rows", "Columns")]
names(out) <- c("Type", "Size", "Rows", "Columns")
if (head)
out <- head(out, n)
out
}
# shorthand
fx.lsos <- function(..., n=10) {fx.ls.objects(..., order.by="Size", decreasing=TRUE, head=TRUE, n=n)}
#INPUT LON,LAT,FIELD & RESAMPLE TO REGULAR GRID AT RESOLUTION RX (GCM to 2.5)
fx.irreg2reg<-function(x,y,field,rx=2.5){
# x=x-180 in one of my fuctions
library(clim.pact)
library(fields)
yc<-xc<-field
for( i in 1:dim(field)[1]){for(j in 1:dim(field)[2]){xc[i,j]<-x[i];yc[i,j]<-y[j]}}# series of coords as matrices
zc<-c(field); xc<-c(xc);yc<-c(yc)
toplot<-interp(xc,yc,zc, xo=seq(-180+rx/2,180-rx/2,rx),yo=seq(-90+rx/2,90-rx/2,rx)) #make make regular list with x,y,z[,] at rx=2.5
}
#CREATE A RASTER OF THE WORLD
#rgl package contains some bitmaps of the world with extents lon(-180,180) lat(-90.90)
#library(rgl) #path in rgl library
#use imagemagick to "> convert world.png world.txt" on cmd line
# file ouput
# ImageMagick pixel enumeration: 1989,994,255,rgb C * R
# 0,0: (255,255,255) #FFFFFF white
# 1988,993: (138,214,221) #8AD6DD rgb(138,214,221)
fx.bmp2myw<-function(infile,outfile)# infile="C:/B/DATA/ADATA/GISDATA/r-gis/world05.txt"
#outfile="C:/B/DATA/ADATA/GISDATA/r-gis/world05.Rdatadata"
{
library(clim.pact)
library(fields)
#setwd("C:/B/DATA/ADATA/GISDATA/r-gis")
wld<-scan(infile,skip=1,what="c",sep="\n") #Errors due to ( 0, 0, 0) removed by(000,000,000)
wcol<-vector(length=length(wld))
for( i in 1:length(wld)){wcol[i]<-strsplit(wld[i]," ")[[1]][length(strsplit(wld[i]," ")[[1]])]}
wcol[which(wcol=="white")]<-0#water
wcol[!wcol==0]<-1 #Land
wcol<-as.numeric(wcol)
nCR<-strsplit(wld[length(wld)],":")[[1]][1]
nR<-as.numeric(strsplit(nCR,",")[[1]][1])+1 #1989
nC<-as.numeric(strsplit(nCR,",")[[1]][2])+1 #994
myworld<-matrix(wcol,nR,nC)[,nC:1] #Rotated
#Add coordinates
lonD<-360/(dim(myworld)[1])#lon Resolution
latD<-180/(dim(myworld)[2])# lat Resolution
lonx<-seq(-180+lonD/2,180-lonD/2,lonD);laty<-seq(-90+latD/2,90-latD/2,latD)
myw<-list(lonx=lonx,laty=laty,myworld=myworld)
save(myw, file=outfile)
# To load load("C:/B/DATA/ADATA/GISDATA/r-gis/world.ras.Rdata"); attach(myw)
# Usage: library(fields); image(myw$lonx,myw$laty,myw$myworld,add=T,col=c("white","transparent")); box()
}
fx.plot.ocean<-function(col=c("white","transparent"),myw=NA)
{
if(is.na(myw)){myw<-get(load("C:\\DATA\\GIS\\r-gis/world.ras.Rdata"))}
image(myw$lonx,myw$laty,myw$myworld,add=T,col=col);
}
fx.grd<-function(x,y,res=0.5,col="grey",abso=FALSE,gc=TRUE) #Draw grids
{
if(abso==TRUE){x<-x-x%%res;y<-y-y%%res}
#if(abso==FALSE & gc==TRUE)
# {
# xm<-mean(x[2:length(x)]- x[1:(length(x)-1)])
# ym<-mean(y[2:length(y)]- y[1:(length(y)-1)])
# x<-seq((x[1]-(0.5*xm)),(x[length(x)]+(0.5*xm)),xm)
# y<-seq((y[1]-(0.5*ym)),(y[length(y)]+(0.5*ym)),ym)
# }
for(g in 1: length(x)){abline(v=x[g],col=col,lty="dashed")}
for(g in 1: length(y)){abline(h=y[g],col=col,lty="dashed")}
}
fx.plot.res<-function(x,y,z,xd=x,yd=y,ctr,xlim=c(xd[1],xd[length(xd)]),ylim=c(yd[1],yd[length(yd)]),title,af=FALSE,kol){
zlim = range(z, finite = TRUE);nlevels=20
#try(plot(bas,border=2,lwd=2),silent=T)
image.plot(x,y,z,col=kol(20),zlim=zlim,xlab="lon",ylab="lat",xlim=xlim,ylim=ylim)
try(plot(lks,add=T,border=4,lwd=2),silent=T)
try(plot(Vnile,col=4,add=T),silent=T)
try(plot(bas,add=T,border=2,lwd=2),silent=T)
try(plot(afr,add=T,border=1,lwd=2),silent=T)
#map.ctry()
yg<-ylim[1]:ylim[2]
xg<-xlim[1]:xlim[2]
#"mintcream"
if(ctr==TRUE)
{
contour(x,y,z,col="grey",nlevels=40,levels = pretty(zlim,nlevels),add=T,labcex=1,, labels=" ",method="flattest",lwd=2)
contour(x,y,z,col=1,nlevels=40,levels = pretty(zlim,nlevels),add=T,labcex=1,lty=0,method="flattest")
}
#map.ctry()
title(title)
axis(1,pretty(x))
axis(2,pretty(y))
}
#plot.res(xd,yd,Mmy,title=paste(gcm[i], "\nEvaporation [mm]\n", yi))
#Draw grids at specified x & y coordinates
fx.gridxy<-function(x,y,col="grey",lty="dashed",centreline=FALSE,grd.ax=TRUE,newp=FALSE){
xc<-x;yc<-y
if(newp==TRUE){plot(expand.grid(x,y),col="transparent",xaxt="n",yaxt="n",xlab="",ylab="",asp=1)}
if(newp==TRUE & grd.ax==FALSE){plot(expand.grid(x,y),col="transparent",xlab="",ylab="",asp=1)}
if(newp==FALSE & grd.ax==FALSE){lines(expand.grid(x,y),col="transparent",xlab="",ylab="",asp=1)}
if(centreline==FALSE)
{
y<-y[2:length(y)]-diff(y)/2
x<-x[2:length(x)]-diff(x)/2
}
for(i in 1:length(x)){abline(v=x,col=col,lty=lty)}
for(i in 1:length(y)){abline(h=y,col=col,lty=lty)}
if(grd.ax==TRUE & centreline==FALSE){axis(1,x);axis(2,y)}
if(grd.ax==TRUE & centreline==TRUE){axis(1,xc);axis(2,yc)}
}
fx.grid.xy<-function(x,y){
yg<-y[1]:y[length(y)]
xg<-x[1]:x[length(x)]
grid(nx=2*(length(xg)-1),ny=2*(length(yg)-1))
}
fx.plot.regrid.error<-function(kol=topo.colors(20)){
a<-max(Mmd[,,1])
b<-min(Mmd[,,1])
# kol<-topo.colors(20)
par(mfrow=c(2,2))
plot(afr)
image.plot(x,y,Md[,,1],add=T,xlim=c(xd[1],xd[length(xd)]),ylim=c(yd[1],yd[length(yd)]),zlim=c(b,a),xaxt="n",yaxt="n",col=kol)
plot(afr,add=T)
plot(afr)
image.plot(xd,yd,Mmd[,,1],add=T,zlim=c(b,a),xaxt="n",yaxt="n",col=kol)
plot(afr,add=T)
image.plot(x,y,Md[,,1],col=kol,zlim=c(b,a),xlim=c(xd[1],xd[length(xd)]),ylim=c(yd[1],yd[length(yd)]),xaxt="n",yaxt="n")#,asp=1)
plot(afr,add=T)
plot(lks,border="grey",add=T)
for(jj in 1: dim(Md[,,1])[1]){for(jk in 1: dim(Md[,,1])[2]){text(x[jj],y[jk],round(Md[jj,jk,1],2),cex=1.5,col=4)}}
gridxy(x,y)
abline(h=0,col=2,lwd=2)#;abline(h=4.5);abline(h=-2);
box()
image.plot(xd,yd,Mmd[,,1],col=kol,zlim=c(b,a),xlim=c(xd[1],xd[length(xd)]),ylim=c(yd[1],yd[length(yd)]),xaxt="n",yaxt="n")#,asp=1)
plot(afr,add=T)
plot(lks,border="grey",add=T)
for(jj in 1: dim(Mmd[,,1])[1]){for(jk in 1: dim(Mmd[,,1])[2]){text(xd[jj],yd[jk],round(Mmd[jj,jk,1],2),cex=1.5,col=4)}}
gridxy(xd,yd)
abline(h=0,col=2,lwd=2)#abline(h=4.5);abline(h=-2);
box()
}
fx.evap.plot<-function(x,y,z,xd,yd,zlim=range(z,na.rm=TRUE),cont=FALSE,title="",kol=PuBu,add=FALSE,grd=FALSE){
if(add==FALSE)
{
image.plot(x,y,z,col=kol(20),xaxt="n",yaxt="n",main=title,
xlim=c(xd[1],xd[length(xd)]),ylim=c(yd[1],yd[length(yd)]),zlim=zlim)
if(grd==TRUE){gridxy(x,y,grd.ax=T,centreline=T)}
# plot(afr,add=T,lwd=2)
# plot(lks,border=4,add=T)
# plot(bas,border="green",lwd=2,add=T)
fx.map.ctry()
if(cont==T){contour(x,y,z,add=T,col="grey",nlevels=40,xaxt="n",yaxt="n")}
}
if(add==TRUE){if(cont==T){contour(x,y,z,add=T,col="grey",nlevels=40,xaxt="n",yaxt="n")}}
# z.im<-im(t(Mmy),xcol=xd,yrow=yd)
# z.im<-as.im(z.im,xcol=seq(29,39,0.5),yrow=seq(-5,5,0.5))
# plot(z.im,col=heat.colors(20))
# gridxy(xd,yd,grd.ax=T,centreline=T)
# gridxy(xd,yd,grd.ax=F,centreline=F,col=1)
# plot(afr,add=T,lwd=2)
# plot(lks,border=4,add=T)
# plot(bas,border="green",lwd=2,add=T)
# contour(z.im,add=T,col=1,nlevels=20,xaxt="n",yaxt="n")
}
fx.pick.resample<-function(LON,LAT,Pc,xd,yd,plot=TRUE){
#resample to xd,yd,PC[xd,yd,t] by picking corresponding value in bigger array LOn,LAT,Pc[LOn,LAT,t]
PC<-array(dim=c(length(xd),length(yd),dim(Pc)[3]))
for(tx in 1:dim(Pc)[3]){
for(ix in 1: dim(PC)[1]){
for(jx in 1: dim(PC)[2]){
(ilon<-xd[ix])
(ilat<-yd[jx])
(lon.idx<-which(abs(ilon-LON)==min(abs(ilon-LON))))
(lat.idx<-which(abs(ilat-LAT)==min(abs(ilat-LAT))))
PC[ix,jx,tx]<-mean(Pc[lon.idx,lat.idx,tx],na.rm=TRUE)
}
}
if(plot==TRUE){image.plot(xd,yd,PC[,,tx],col=PuBu,asp=1);fx.map.ctry()}
}
return(PC)
}
#Gridding a matrix with cords x,y,to a new resolution defined by cords xd,yd
# without interpolating
fx.resample<-function(x,y,z,xd,yd,graph=TRUE)
{
library(spatstat)
# Convert z matrix to pixel-image
z.im<-im(t(z),xcol=x,yrow=y)#
#Convert created pixel image to mtrix
z.im.mat<-t(as.matrix(z.im))
rownames(z.im.mat)<-x
colnames(z.im.mat)<-y
print(z.im.mat)
#Resample pixellated z.im to zd.im using as.im and xd,yd
zd.im<-as.im(z.im,xy=list(x=xd,y=yd))# the pixel image is rotated
##Convert created pixel image to mtrix to check if it was ok
zd<-t(as.matrix(zd.im))
rownames(zd)<-xd
colnames(zd)<-yd
print(zd)
if(graph==TRUE){par(mfrow=c(1,2));fx.evap.plot(x,y,z,xd,yd);fx.evap.plot(xd,yd,zd,xd,ydcont=F)}
return(zd)
}
# resampe by picking a value at a point
fx.resample.strip<-function(x,y,z,xd,yd,graph=TRUE,title="",cont=FALSE,kol=PuBu,azlim=range(z,na.rm=TRUE)){
xyzd<-cbind(expand.grid(xd,yd),NA); names(xyzd)<-c("xd","yd","zd")
xbounds<-c(x[1],x[2:length(x)]-0.5*diff(x),x[length(x)])
ybounds<-c(y[1],y[2:length(y)]-0.5*diff(y),y[length(y)])
for( ii in 1:dim(xyzd)[1])
{
xc<-xyzd[ii,1];yc<-xyzd[ii,2]
xyzd$zd[ii]<-z[min(length(x),max(which(xbounds<=xc))),min(length(y),max(which(ybounds<=yc)))]
}
zd<-matrix(xyzd$zd,length(xd),length(yd))
if(graph==TRUE)
{
image.plot(xd,yd,zd,col=kol,asp=1)#par(mfrow=nplots)
fx.map.ctry()# alim=azlim; #c(min(z,na.rm=T),max(z,na.rm=T))
# evap.plot(x=xd,y=yd,z=zd,xd,yd,zlim=alim,cont=F,title=title,kol)
# evap.plot(x,y,z,xd,yd,zlim=alim,title=title,cont=cont,kol,add=TRUE)
}
return(zd)
}
#remove Area outside basin if res=0.5 of basin raster bas.ras
fx.rem<-function (xd,yd,z,bas.ras) {
if(diff(xd)[1]==0.5 & (diff(xd)[1]==diff(yd)[1]))
{
repp<-which(is.na(bas.ras$z),arr.ind=T)
repp[,1][repp[,1]>dim(z)[1]]<-NA
repp[,2][repp[,2]>dim(z)[2]]<-NA
repp<-repp[!apply(repp,1,function(y) any(is.na(y))),]
z[repp]<-NA
return(z)
#Mmy<-z
}
};
##Taylor Diagram
fx.taylor<-function(y.df,norm=T,mytitle="\nTaylor Plot",tofile=0)# ydf=data.frame(cbind(date,ref,models))
{
library(plotrix)
x<-y.df[,1] #reference. the first is a date
if(tofile==1){png(width = 1000, height = 1000,file=paste(mytitle,".png",sep=""), bg="white")}
#if(!is.data.frame(y.df)){y.df<-as.data.frame(y.df)}
pchm<-c("?",LETTERS)
nf <- layout(matrix(c(1,2),1,2,byrow=TRUE), c(6,2), c(6,6), TRUE)
par(oma=c(1,1,2,1));#c(bottom, left, top, right)
layout.show(nf)
for( n in 1:ncol(y.df))
{
y<-y.df[,n]
if(n==2){TF<-FALSE} else {TF<-TRUE} #Add or Not
taylor.diagram(x,y,add=TF,col=1,pch=20,pos.cor=TRUE,xlab="",ylab="",main="",
show.gamma=F,ngamma=20,sd.arcs=10,ref.sd=T,
grad.corr.lines=seq(0.1,0.9,0.1),
pcex=1,normalize=norm,mar=c(1,1,2,1))
R <- cor(x, y, use = "pairwise")
sd.f <- sd(y); sd.r <-sd(x)
if(norm == TRUE){sd.f<-sd.f/sd.r}
text(sd.f*R, sd.f * sin(acos(R)), labels =pchm[n], cex = 0.8, col = 1,adj = -1,offset=4)
lpos<-sd(x)
}
points(1,0,pch=10)
par(mar=c(0,0,0,0))
frame()
legend("center",names(y.df),pch=pchm,col=1)
mtext(mytitle, side=3, line=1, cex=1, col="blue", outer=TRUE)
box("outer")
if(tofile==1){dev.off()}
}
#Some FAO climwat met stations and their reference ET
fx.fao.et<-function(stfd="C:/B/PAPERS/Evaporation/Data/My_CLIMWAT_Files")
{
penf<-list.files(stfd,pattern="pen") #ETO files
sname<-vector(length=length(penf)); alt<-lat<-lon<-sname
et<-array(dim=c(length(penf),12))
for(st in 1:length(penf))
{
filz<-paste(stfd,"/",penf[st],sep="")
penin<-scan(filz,what="c",nlines=1,sep=",",quiet=T)
lat[st]<-as.numeric(penin[4])
lon[st]<-as.numeric(penin[6])
sname[st]<-as.character(penin[2])
alt[st]<-as.numeric(penin[3])
et[st,]<-read.table(filz,skip=1)[,7]
}
colnames(et)<-month.abb
obs<-cbind("obs",as.data.frame(matrix(colMeans(et),1,12)))
colnames(obs)<-c("sname",month.abb)
xyz<-cbind(sname,as.data.frame(cbind(lon,lat,alt,et)))
et<-rbind(xyz[,c(1,5:16)],obs)
xyz<-xyz[,1:4]
xyz$sname<-as.character(xyz$sname)
return(list(xyz=xyz,et=et))
}
#plot fao stations
fx.plot.fao<-function(kol=2)
{
stxyz=fao.et()$xyz
rn<-floor(runif(10,1,dim(stxyz)[1]))
for(st in 1:dim(stxyz)[1])
{
points(stxyz$lon[st],stxyz$lat[st],pch=16,col=1)
text(stxyz$lon[st],stxyz$lat[st],tolower(stxyz$sname[st]),col=kol,cex=0.8)
}
}
#anomaly
fx.anomaly<-function(x){xm<-mean(x,na.rm=T);xa<-x-xm;return(xa)}
fx.anomaly.ts<-function(x)
{
if(class(x)=="zoo"){x<-aggregate(x,as.yearmon,FUN)}
}
#Change date format in a df[,1] from 1-dec-2007 to 2007-12-01
fx.s2d<-function(fl)
{
dates<-as.character(fl[,1])
for(id in 1: dim(fl)[1])
{
txt<-as.character(dates[id])
txt<-unlist(strsplit(txt,sep))
dates[id]<-paste(txt[3],which(month.abb==txt[2]),txt[1],sep="-")
}
return(as.Date(dates))
}
#Convert daily or monthly to monthly or annual ts
fx.dmy.ts<-function(in.ts,daily.out="mo",fn="sum")# daily.out="yr"
{
library(zoo)
a<-0
if(!(class(in.ts)=="zoo")){a<-1;mo<-in.ts;yr<-aggregate(mo,f=12,FUN="sum")}
else {da<-in.ts;mo<-as.ts(aggregate(da, as.yearmon, FUN = "sum" ))
yr<-aggregate(mo,f=12,FUN="sum")}
if(a==0){if(daily.out=="mo"){return(mo)} else {return(yr)}}
if(a==1){return(yr)}
}
fx.dmy.mts<-function(in.mts,daily.out="mo",fn=c("sum","mean","mean"))#mts prec-flow-tmpr
{
prec<-dmy.ts(in.mts[,1])
flow<-dmy.ts(in.mts[,2],fn="mean")
tmpr<-dmy.ts(in.mts[,3],fn="mean")
}
kol2=colorRampPalette(c("azure2","cornflowerblue","blue","blue4"),space="Lab")
#plot in mm the map of flow or whatever in m
fx.img<-function(xos,yos,base.ann,kol,about="",X=1000,cont=3, cont.draw=T,
xlim=c(min(xos),max(xos)),ylim=c(min(yos),max(yos)))
{
m2p<-base.ann*X
m2p[m2p==0]<-NA
image.plot(xos,yos,m2p,xaxs="i",col=kol(20),xlab="lon",ylab="lat",xlim=xlim,ylim=ylim)
nlevels=length(pretty(unique(sort(c(m2p)))))
if(cont.draw==T){
contour(xos,yos,m2p,add=T,nlevels=20,col=cont)
contour(xos,yos,m2p,add=T,nlevels=20,lty=0,col=1)}
map.ctry()
grid()
box()
title(about)
}
#Use Point in Polygon to create a matrix representing a SPpolygonDF
#given xc and yc sereis of xoccrdinates
#given a spatialpolygondataframe
#given names of subs plys
#pnames<-c("benga","kwanza")
fx.poly2catch<-function(xo,yo,ang.shp,plots=FALSE,field="ID")
{
library(grDevices)
library(maptools)
library(fields)
library(raster)
#Read basin shape(major basins)
if(is.character(ang.shp)){bas1<-readShapePoly(ang.shp)} else {bas1<-ang.shp} #either read file or sp object
#Shape limits
xylim<-rbind(floor(apply(coordinates(bas1),2,min)),ceiling(apply(coordinates(bas1),2,max)))
xL<-xylim[,1];yL<-xylim[,2]
#rasterize
r <- raster(ncols=length(xo), nrows=length(yo),xmn=min(xo),xmx=max(xo),ymn=min(yo),ymx=max(yo)) #empty raster
R<-rasterize(bas1,r,field=field,silent=TRUE)
M<-matrix(getValues(R),length(yo),length(xo),byrow=TRUE)
M<-t(M[dim(M)[1]:1,]) #catchment areas in matrix format
nkol<-length(unique(c(M)))
if(plots==TRUE){image(xo,yo,M,col=sample(1:20,nkol))}#col=1:nkol)
# rows<-length(xc)
# cols<-length(yc)
# xyk<-expand.grid(xc,yc)
# pix<-xyk[,1]
# piy<-xyk[,2]
#
# nshp<-length(ang.shp@polygons)
# pip<-array(dim=c(rows,cols,nshp))
# catch<-array(0,dim=c(rows,cols))
# labxy<-array(dim=c(nshp,2))
# image(xc,yc,catch,col="transparent")
# for( i in 1: nshp)
# {
# polxy<-ang.shp@polygons[[i]]@Polygons[[1]]@coords
# plx<-polxy[,1]
# ply<-polxy[,2]
# pINp<-point.in.polygon(pix,piy,plx,ply)
# pINp[pINp==1]<-i
# pip[,,i]<-matrix(pINp,rows,cols)
# catch=catch+pip[,,i]
# pip[,,i][pip[,,i]==0]<-NA
# labxy[i,]<-c(mean(plx),mean(ply))
# image(xc,yc,catch,col=c("transparent","azure2"),add=T)
# try(text(labxy[i,1],labxy[i,2],labs[i]),silent=F)
# }
# plot(ang.shp,add=T,col="transparent")
# grid()
# catch[catch==0]<-NA
# labxy<-cbind(as.data.frame(labxy),labs)
# alist<-list(labxy=labxy,catch=catch)
alist<-list(x=xo,y=yo,z=M)
return(alist)
}
#impute with amelia
#given a data.frame /matrix xwith t at x[,1]
fx.impute<-function(x,m=6,g=F,p2s=0)
{
if(g==F){graphics.off()} else {print("graphics Off?")
choice<-scan(n=1,what="c")
if(choice=="y"|choice=="Y"){graphics.off()}}
require(Amelia)
a.out <- amelia(x, ts = names(x)[1],p2s=p2s)
summary(a.out)
#try(plot(a.out),silent=T)
aL<-length(a.out$imputations)
out<-array(dim=c(dim(x)[1],dim(x)[2]-1,aL))
for(i in 1:aL){out[,,i]<- as.matrix(a.out$imputations[[i]][,2:dim(x)[2]])}
out<-apply(out,c(1,2),mean)
out<-data.frame(x[,1],out)
names(out)<-names(x)
dev.new()
par(mfrow=c((ceiling((dim(x)[2]-1)/2)),2))
for(i in 2:dim(out)[2]) {
plot(x[,1],x[,i],lwd=5,type="l",col="grey")
lines(out[,1],out[,i],col=1,lty="dashed")
points(out[,1],out[,i])
}
return(out)
}
fx.month.days<-function(leap=0)
{#eneter 366,365 to return month days or nothing for long term
if(leap==0){return(c(31,28.25,31,30,31,30,31,31,30,31,30,31))}
if(leap==366){return(c(31,29,31,30,31,30,31,31,30,31,30,31))}
if(leap==365){return(c(31,28,31,30,31,30,31,31,30,31,30,31))}
}
#source("C:/B/PAPERS/Evaporation/R/Evaporation/Evaporation/fx.gen.evap.r")
#east afruica fao et
fx.fao.eat<-function(stfd="C:/B/PAPERS/Evaporation/climwat")
{
penf<-list.files(stfd,pattern="pen") #ETO files
sname<-vector(length=length(penf)); alt<-lat<-lon<-sname
et<-array(dim=c(length(penf),12))
for(st in 1:length(penf))
{
filz<-paste(stfd,"/",penf[st],sep="")
penin<-scan(filz,what="c",nlines=1,sep=",",quiet=T)
lat[st]<-as.numeric(penin[4])
lon[st]<-as.numeric(penin[6])
sname[st]<-as.character(penin[2])
alt[st]<-as.numeric(penin[3])
et[st,]<-read.table(filz,skip=1)[,7]
}
colnames(et)<-month.abb
obs<-cbind("obs",as.data.frame(matrix(colMeans(et),1,12)))
colnames(obs)<-c("sname",month.abb)
xyz<-cbind(sname,as.data.frame(cbind(lon,lat,alt,et)))
et<-rbind(xyz[,c(1,5:16)],obs)
xyz<-xyz[,1:4]
xyz$sname<-as.character(xyz$sname)
return(list(xyz=xyz,et=et))
}
fx.implot<-function(lon,lat,m2p,shp,xlim=c(28.5,42.5),ylim=c(-11.5,6.5),
upper=2000,nl=20,main,lower=floor(min(m2p,na.rm=T)/100)*100)
{
ocean<- readShapeSpatial("C:/B/PAPERS/Evaporation/GIS/water_ocean",proj4string=pj4s)
require(fields)
#
#plot a map of a variable within a given shapefile extent
#specifying maximum legend value for image plot
#plot(shp,xlim=xlim,ylim=ylim)
#lower<-floor(min(m2p,na.rm=T)/100)*100
image.plot(lon,lat,m2p,main=main,zlim=c(lower,upper),xlim=xlim,ylim=ylim)
abline(h=0);plot(shp,add=T);box();grid();
plot(ocean,col="white",border="grey",add=T,,xlim=xlim,ylim=ylim)
contour(lon,lat,m2p,add=T,nlevels = nl,col="grey",labels="")
contour(lon,lat,m2p,add=T,nlevels = nl,lty=0)
image.plot(lon,lat,m2p,main=main,
zlim=c(lower,upper),add=T,legend.only=T)
#title(paste(gcm[i],scen,yearz[1],"-",max(yearz), unt,sep=" "))
}
library(RColorBrewer)
#Blues BuGn BuPu GnBu Greens Greys Oranges OrRd PuBu PuBuGn PuRd Purples RdPu Reds YlGn YlGnBu YlOrBr YlOrRd
brew<-c("Blues","BuGn","BuPu","GnBu","Greens","Greys","Oranges","OrRd","PuBu","PuBuGn","PuRd","Purples","RdPu","Reds","YlGn","YlGnBu","YlOrBr","YlOrRd")
for(i in 1: length(brew)){eval(parse(text=paste(brew[i],"<-brewer.pal(9,brew[i])")))}
# par(mfrow=c(3,6))
# for(i in 1: length(brew))
# {
# mypalette<-brewer.pal(9,brew[i])
# image.plot(matrix(1:100,10,10,byrow=T),col=mypalette,main=brew[i])
# }
fx.faotas<-function(stfd="C:/B/PAPERS/Evaporation/Data/My_CLIMWAT_Files")
{
penf<-list.files(stfd,pattern="pen") #ETO files
sname<-vector(length=length(penf)); alt<-lat<-lon<-sname
tas<-array(dim=c(length(penf),12))
for(st in 1:length(penf))
{
filz<-paste(stfd,"/",penf[st],sep="")
penin<-scan(filz,what="c",nlines=1,sep=",",quiet=T)
lat[st]<-as.numeric(penin[4])
lon[st]<-as.numeric(penin[6])
sname[st]<-as.character(penin[2])
alt[st]<-as.numeric(penin[3])
tas[st,]<-rowMeans(read.table(filz,skip=1)[,1:2])
}
colnames(tas)<-month.abb
obs<-cbind("obs",as.data.frame(matrix(colMeans(tas),1,12)))
colnames(obs)<-c("sname",month.abb)
xyz<-cbind(sname,as.data.frame(cbind(lon,lat,alt,tas)))
tas<-rbind(xyz[,c(1,5:16)],obs)
xyz<-xyz[,1:4]
xyz$sname<-as.character(xyz$sname)
return(list(xyz=xyz,tas=tas))
}
fx.iplot<-function(x,y,z,labu=TRUE,kol=topo.colors(20),cex=0.8)
{
require(fields)
#special image plot
image.plot(x,y,z,zlim=c(min(z,na.rm=T),max(z,na.rm=T)),col=kol)
if(labu==TRUE){} else{contour(x,y,z,zlim=c(min(z,na.rm=T),max(z,na.rm=T)),add=T)}
grid(nx=dim(z)[1],ny=dim(z)[2])
if(labu==TRUE){text(expand.grid(x,y)[,1],expand.grid(x,y)[,2],round(c(z),1),cex=cex)}
}
fx.Rmean<-function(y){matrix(rep(apply(y,1,mean),dim(y)[2]),dim(y)[1],dim(y)[2])}
fx.Cmean<-function(y){matrix(apply(y,2,mean),dim(y)[1],dim(y)[2],byrow=T)}
fx.Mmean<-function(y){matrix(mean(y,na.rm=T),dim(y)[1],dim(y)[2],byrow=T)}
#xy matrix to yx ascii- esri grid
fx.xy2ascGRDyx<-function(xllcorner,yllcorner,cellsize,xy){
file=paste(deparse(substitute(xy)),"asc",sep=".")
hdr1<-c("ncols","nrows","xllcorner","yllcorner","cellsize","NODATA_value")
yx<-t(xy[,dim(xy)[2]:1])#Rotate anti clockwise
ncols<-dim(yx)[1]
nrows=dim(yx)[2]
hdr2<-c(ncols,nrows,xllcorner,yllcorner,cellsize,-9999)
hdr<-cbind(hdr1,hdr2)
write.table(hdr,file=file,quote=F,row.names=F,col.names=F)
write.table(yx,file=file,quote=F,row.names=F,col.names=F,append=T)
}
#xy matrix to yx ascii- esri grid
fx.yx2ascGRDyx<-function(xllcorner,yllcorner,cellsize,yx){
file=paste(deparse(substitute(xy)),"asc",sep=".")
hdr1<-c("ncols","nrows","xllcorner","yllcorner","cellsize","NODATA_value")
ncols<-dim(yx)[1]
nrows=dim(yx)[2]
hdr2<-c(ncols,nrows,xllcorner,yllcorner,cellsize,-9999)
hdr<-cbind(hdr1,hdr2)
write.table(hdr,file=file,quote=F,row.names=F,col.names=F)
write.table(yx,file=file,quote=F,row.names=F,col.names=F,append=T)
}
#yx ascii- grid to xy matrix
fx.asc2xy<-function(filepath){
require(maptools)
dem<-readAsciiGrid(filepath)
a<- unlist(strsplit(filepath,"/"))
a<-a[length(a)]
txt<-paste("dem<-matrix(dem@data$",a,", dem@[email protected])",sep="")#by col
eval(parse(text=txt))
dem<-dem[,dim(dem)[2]:1]
return(dem)
}
#plot a matrix and its values in the way it looks
fx.plot.mat<-function(z,x=NULL,y=NULL,ret=T,main=deparse(substitute(z)),col=topo.colors(20),labs=F,ctr=F,ctr.col="white")
{
require(fields)
Z<-t(z[dim(z)[1]:1,])
if(is.null(x)){x<-1:dim(Z)[1]}
if(is.null(y)){y<-1:dim(Z)[2]}
image.plot(x,y,Z,main=main,col=col)
if(labs==T){for(i in 1:dim(Z)[1]){for(j in 1:dim(Z)[2]){text(x[i],y[j],Z[i,j])}}}
mat<-list(x=x,y=y,z=Z)
if(ctr==T){contour(x,y,Z,col=ctr.col,add=T)}
if(ret==T){return(mat)}
}
#plot with axes vertical
fx.plotWvxdate<-function(M,n=31,add=F){
if(add==F){plot(M,xaxt="n",xlab="")} else {lines(M,xaxt="n",xlab="")}
axis(1,time(M)[seq(1,length(M),n)],paste(year(time(M))[seq(1,length(M),n)],month.abb[month(time(M))[seq(1,length(M),n)]],sep="-"),las=2)}
#markov simulation
#P <- matrix( c(0.2, 0.5, 0.5, 0.1, 0.8, 0.1, 0.2, 0.1, 0.7), 3,3, byrow=TRUE)
fx.simMarkov <- function( P,states, len=1000) {
if(length(states)!=NROW(P)){states<-1:NROW(P)}
result <- numeric(len)
result[1] <- 1
for (i in 2:len) {
result[i] <- sample(states, 1, prob=P[ result[i-1], ])
}
result
}
# Writes a data frame to an ASCII raster file, suitable for display in ArcView or ArcGIS.
fx.matrix2ascRas<-function(x,y,xyRC,file,cellsize){
require(epiR)
xllcorner<-x[1]-(0.5*cellsize)
yllcorner<-y[1]-(0.5*cellsize)
epi.asc(xyRC, file, xllcorner, yllcorner, cellsize, na = -9999)
print(file)
}
#Compute the anomaly with 1961-1990 as reference period
fx.anom.daily<-function(x,FUN=mean){#x is a zoo object
library(hydroTSM)
library(tis)
m<-month(time(x))
base<-window(x,start=as.Date("1961-01-01"),end=as.Date("1990-12-31"))
base.ltm<-monthlyfunction(base,FUN)
base.x<-base.ltm[m]
anom<-x-base.x
return(anom)
}
fx.anom<-function(x,fun=sum){#x is a zoo object
library(hydroTSM)
library(tis)
m<-month(time(x))
base<-base.ltm<-0
if(is.zoo(x)){
base<-window(x,start=as.Date("1961-01-01"),end=as.Date("1990-12-31"))
y<-daily2monthly(x,FUN=fun)
base.ltm<-monthlyfunction(y,mean)
}
if(is.ts(x)) {
base<-window(x,start=c(1961,1),end=c(1961,1))
base.ltm<-monthlyfunction(base,mean)
}
base.x<-base.ltm[m]
anom<-x-base.x
return(anom)
}
#Confidence INtervak plot
fx.ciplot<-function(x,y,model="gam",ci=1.96,xlab="",ylab="",Title="",lposi=0,add=FALSE,pcol="grey",pch="o",lcol=1,xL= c(min(x),max(x)),border=pcol){
if(model=="gam"){library(mgcv); model <- gam( y ~ s(x))}
# get the estimated values of the fitted smoothing spline
fit <- predict( model , se = TRUE )$fit
# and pointwise standard errors
se <- predict( model , se = TRUE)$se.fit
# calculate the values of the upper and lower 95%-confidence limits
lcl <- fit - ci * se
ucl <- fit + ci * se
# # plot an empty coordinate system with right scaling, labels , titles and so on. I prefer to include axes = FALSE and add the axes manually. See here for an example.
if(add==TRUE){
par(new=TRUE);
plot( 0 , type = "n" , bty = "n" , xlab = "" , ylab = "" , main ="" , xlim = xL, ylim = c(min(y),max(y)),yaxt="n" )
# Add axis for added plot
axis(4)
mtext(ylab,4,line=lposi,outer=TRUE)
} else {