library(rSHUD)
read.tsd(file ='/Users/leleshu/CloudDrive/SHUD/github/SHUD/input/qhh/forcing.csv')
x=read.tsd(file ='/Users/leleshu/CloudDrive/SHUD/github/SHUD/input/qhh/forcing.csv')
x=round(x, 4)
x=read.tsd(file ='/Users/leleshu/CloudDrive/SHUD/github/SHUD/input/qhh/forcing.csv')
x=round(x, 4)
x=read.tsd(file ='/Users/leleshu/CloudDrive/SHUD/github/SHUD/input/qhh/forcing.csv')[[1]]
x
x=round(x, 3)
plot.zoo(x)
library(xts)
plot.zoo(x)
x=round(x, 3)
plot.zoo(x[1:200, ])
source("~/CloudDrive/SHUD/github/SHUD/rAnalysis/qhh.R", echo=TRUE)
head(x)
fn = '/Users/leleshu/CloudDrive/SHUD/github/SHUD/input/qhh/forcing.csv'
x=read.tsd(file =fn)[[1]]
x=round(x, 3)
write.tsd(x, fn)
fn = '/Users/leleshu/CloudDrive/SHUD/github/SHUD/input/ccw/forcing.csv'
x=read.tsd(file =fn)[[1]]
x=round(x, 3)
plot(x)
plot.zoo(x)
x=read.df(/Volumes/home/Project_backup/SAC/forcing/csv2000-2017/x19y114.csv)
fn = '/Volumes/home/Project_backup/SAC/forcing/csv2000-2017/x19y114.csv'
x=read.df(fn)
x=read.df(fn)[[1]]
head(x)
x=readLines(fn)
fn = '/Volumes/home/Project_backup/SAC/forcing/csv2000-2017/x19y114.csv'
x=readLines(fn)
tb=read.table(x[-1], header = TRUE)
head(tb)
tb=read.table(x[-1], header = TRUE)
head(tb)
x=readLines(fn)
head9x
head9x
head(x)
tb=read.table(x[-1], header = TRUE, sep = '\t')
head(tb)
tb=read.table(text=x[-1], header = TRUE, sep = '\t')
head(tb)
tb=read.table(text=x[-1], header = TRUE, sep = ' ')
head(tb)
tsd = as.xts(tb[, 1], order.by = as.POSIXct('2000-01-01 00:00')+tb[, 1])
head(tsd)
tsd = as.xts(tb[, -1], order.by = as.POSIXct('2000-01-01 00:00')+tb[, 1])
head(tsd)
plot.zoo(tsd)
tsd = as.xts(tb[, -1], order.by = as.POSIXct('2000-01-01 00:00')+tb[, 1])
head(tsd)
plot.zoo(tsd)
tsd = cbind(tsd[, 1]*1000, tsd[, 2],
tsd[, 3],tsd[,4]/86400, tsd[,5]/86400)
plot(tsd)
tsd = as.xts(tb[, -1], order.by = as.POSIXct('2000-01-01 00:00')+tb[, 1])
tsd = cbind(tsd[, 1]*1000, tsd[, 2],
tsd[, 3],tsd[,4]/86400, tsd[,5]/86400)
plot.zoo(tsd)
tsd = as.xts(tb[, -1], order.by = as.POSIXct('2000-01-01 00:00')+tb[, 1]*60)
tsd = cbind(tsd[, 1]*1000, tsd[, 2],
tsd[, 3],tsd[,4]/86400, tsd[,5]/86400)
plot.zoo(tsd)
apply.daily(tsd[, 1])
apply.daily(tsd[, 1], mean)
tsd = as.xts(tb[, -1], order.by = as.POSIXct('2000-01-01 00:00')+tb[, 1]*86400)
tsd = cbind(tsd[, 1]*1000, tsd[, 2],
tsd[, 3],tsd[,4]/86400, tsd[,5]/86400)
plot.zoo(tsd)
apply.yearly(apply.daily(tsd[, 1], mean), sum)
fn = '/Volumes/home/Project_backup/SAC/forcing/csv2000-2017/x19y114.csv'
x=readLines(fn)
tb=read.table(text=x[-1], header = TRUE, sep = ' ')
tsd = as.xts(tb[, -1], order.by = as.POSIXct('2000-01-01 00:00')+tb[, 1]*86400)
tsd = cbind(tsd[, 1]*1000, tsd[, 2],
tsd[, 3],tsd[,4]/86400, tsd[,5]/86400)
fn = '/Users/leleshu/CloudDrive/SHUD/github/SHUD/input/ccw/forcing.csv'
write.tsd(tsd, fn)
#' ===========CACHE CREEK ====================
fn = '/Volumes/home/Project_backup/SAC/forcing/csv2000-2017/x19y114.csv'
x=readLines(fn)
tb=read.table(text=x[-1], header = TRUE, sep = ' ')
tsd = as.xts(tb[, -1], order.by = as.POSIXct('2000-01-01 00:00')+tb[, 1]*86400)
tsd = cbind(tsd[, 1]*1000, tsd[, 2],
tsd[, 3],tsd[,4]/86400, tsd[,5]/86400)
tt = time(tsd)
x = tsd[tt >= as.POSIXct('2000-01-01') & tt <= as.POSIXct('2010-01-01')]
fn = '/Users/leleshu/CloudDrive/SHUD/github/SHUD/input/ccw/forcing.csv'
write.tsd(x, fn)
x = round(x, 4)
fn = '/Users/leleshu/CloudDrive/SHUD/github/SHUD/input/ccw/forcing.csv'
write.tsd(x, fn)
source("~/CloudDrive/SHUD/github/SHUD/rAnalysis/ccw.R", echo=TRUE)
source("~/CloudDrive/SHUD/github/SHUD/rAnalysis/ccw.R", echo=TRUE)
AA=getArea()
oid = getOutlets()
qq=readout('rivqdown')
library(ggplot2)
autoplot(q[, oid])
source("~/CloudDrive/SHUD/github/SHUD/rAnalysis/ccw.R", echo=TRUE)
autoplot(qq[, oid])
qs = read.tsd('../input/ccw/ccw.tsd.obs')
qs = read.tsd('../input/ccw/ccw.tsd.obs')[[1]]
qo = read.tsd('../input/ccw/ccw.tsd.obs')[[1]]
qq=readout('rivqdown')
qs=qq[, oid]
source("~/CloudDrive/SHUD/github/SHUD/rAnalysis/ccw.R", echo=TRUE)
head(qq)
qq = cbind(ts2Daily(qs),ts2Daily(qo) )
autoplot(qq)
qo = ts2Daily(read.tsd('../input/ccw/ccw.tsd.obs')[[1]])
qdown = readout('rivqdown')
qs = ts2Daily(qdown[, oid])
source("~/CloudDrive/SHUD/github/SHUD/rAnalysis/ccw.R", echo=TRUE)
source("~/CloudDrive/SHUD/github/SHUD/rAnalysis/ccw.R", echo=TRUE)
source("~/CloudDrive/SHUD/github/SHUD/rAnalysis/ccw.R", echo=TRUE)
xl = loaddata()
wb = wb.all(xl=xl)
source("~/CloudDrive/SHUD/github/SHUD/rAnalysis/ccw.R", echo=TRUE)
shud.env(prjname = 'ccw', inpath = '../input/ccw/', outpath = '../output/ccw.out/')
AA = getArea()
oid = getOutlets()
qo = ts2Daily(read.tsd('../input/ccw/ccw.tsd.obs')[[1]])
qdown = readout('rivqdown')
qs = ts2Daily(qdown[, oid])
ts = time(qs)
to = time(qo)
ct= ts[to %in% ts]
qq = cbind('SIM' = qs, 'OBS' = qo/86400)[ts,]
autoplot(qq)
autoplot(qq[,1])
xl = loaddata()
wb = wb.all(xl=xl)
autoplot(qq[,])
qq = cbind('SIM' = qs, 'OBS' = qo)[ts,]
autoplot(qq[,])
source("~/CloudDrive/SHUD/github/SHUD/rAnalysis/ccw.R", echo=TRUE)
source("~/CloudDrive/SHUD/github/SHUD/rAnalysis/ccw.R", echo=TRUE)
autoplot(qq[,])
source("~/CloudDrive/SHUD/github/SHUD/rAnalysis/ccw.R", echo=TRUE)
rm(list=ls())
library(rSHUD)
library(xts)
library(raster)
library(rgdal)
library(sp)
library(ggplot2)
shud.env(prjname = 'qhh', inpath = '../input/qhh/', outpath = '../output/qhh.out/')
AA = getArea()
hist(AA)
ia = getArea()
AA = sum(ia)
hist(ia)
fdc(ia)
fdc(ia/1e6)
hist(ia)
oid = getOutlets()
oid = getOutlets()  # 获得河流出口的序号(index)
oid
riv = rSHUD::readriv()
riv = rSHUD::readriv()
View(riv)
?rSHUD
?rSHUD::readriv
?rSHUD::readriv
obs=rnorm(100)
sim=obs+rnorm(100)/2
QvsO(cbind(obs,sim))
View(riv)
View(riv@river)
sp.riv = rSHUD::sp.riv2shp()
plot(sp.riv)
plot(sp.riv, axes=TRUE)
raster::plot(sp.riv, axes=TRUE)
grid()
sp.mesh = rSHUD::sp.mesh2Shape()
raster::plot(sp.mesh, axes=TRUE) #绘图：三角形网
raster::plot(sp.mesh, axes=TRUE, lwd=0.2) #绘图：三角形网
raster::plot(sp.mesh, axes=TRUE, lwd=0.2, border='green') #绘图：三角形网
raster::plot(sp.mesh, axes=TRUE, lwd=0.2, border='gray70') #绘图：三角形网
raster::plot(sp.riv, add=TRUE, col='blue')
lakid = rSHUD::getLakeEleID()
raster::plot(sp.mesh[lakid, ], col='lightblue', add=TRUE)
grid()
qdown = rSHUD::readout('rivqdown')
dim(qdown)
dim(riv@river)
View(riv@river)
plot(qdown[, 1:100])
xts::plot.xts(qdown[, oid])
xts::plot.xts(qdown[, oid[1]])
xts::plot.xts(qdown[, oid])
# 地下水
igw = readout('eleygw')
dim(igw)
?apply
plot(apply(X = igw, MARGIN = 1, FUN = mean))
# 蒸散发
ietp = readout('elevetp')
dim(ietp)
plot(apply(X = ietp, MARGIN = 1, FUN = mean))
head(ietp[, 1:10])
head(ietp[, 1:5])
?apply.daily(ietp, )
dim(ietp)
ncol(ietp)
ncell =  length(ia)
ncell =  length(ia)
ncell
apply.daily(ietp, sum) /ncell
etp.daily = apply.daily(ietp, sum) /ncell
plot(etp.daily)
ggplot2::autoplot(etp.daily)
ggplot2::autoplot(etp.daily*1000) +xlab('Time') +ylab('ETP (mm/day)')
ggplot2::autoplot(etp.daily*1000) +
xlab('Time') +ylab('ETP (mm/day)') + theme_bw()
ggplot2::autoplot(etp.daily*1000) +
xlab('Time') +ylab('ETP (mm/day)') + theme_bw()
ggplot2::autoplot(etp.montly*1000) +
xlab('Time') +ylab('ETP (mm/month)') + theme_bw()
etp.monthly = xts::apply.monthly(ietp, sum) /ncell # 时间序列：月均值
ggplot2::autoplot(etp.monthly*1000) +
xlab('Time') +ylab('ETP (mm/month)') + theme_bw()
# 实际蒸散发
iaet = readout('elevaet')  # 读取：实际蒸散发
aet.daily = apply.daily(iaet, sum) /ncell  #时间序列：日均值
# 实际蒸散发
ieta = readout('eleveta')  # 读取：实际蒸散发
eta.daily = apply.daily(ieta, sum) /ncell  #时间序列：日均值
eta.monthly = xts::apply.monthly(ieta, sum) /ncell # 时间序列：月均值
plot(etp.daily)
ggplot2::autoplot(etp.daily*1000) +
xlab('Time') +ylab('ETP (mm/day)') + theme_bw()
ggplot2::autoplot(etp.daily*1000) +
xlab('Time') +ylab('ATP (mm/day)') + theme_bw()
ggplot2::autoplot(etp.monthly*1000) +
xlab('Time') +ylab('ATP (mm/month)') + theme_bw()
g1 = ggplot2::autoplot(etp.daily*1000) +
xlab('Time') +ylab('ETP (mm/day)') + theme_bw()
g2 = ggplot2::autoplot(etp.monthly*1000) +
xlab('Time') +ylab('ETP (mm/month)') + theme_bw()
?GGally
library(gridExtra)
gridExtra::arrangeGrob(list(g1, g2))
g1 = ggplot2::autoplot(etp.daily*1000) +
xlab('Time') +ylab('ETP (mm/day)') + theme_bw()
g2 = ggplot2::autoplot(etp.monthly*1000) +
xlab('Time') +ylab('ETP (mm/month)') + theme_bw()
gridExtra::arrangeGrob(grobs = list(g1, g2))
gg = gridExtra::arrangeGrob(grobs = list(g1, g2))
gg
plot(gg)
ggsave(filename = 'etp.png', plot=gg)
ggsave(filename = 'etp.png', plot=gg, width = 11, height = 9, units = 'in',
dpi = 200)
ggsave(filename = 'etp.png', plot=gg, width = 11, height = 9, units = 'in',
dpi = 100)
ggsave(filename = 'etp.png', plot=gg, width = 11, height = 9, units = 'in',
dpi = 600)
pa = cbind(etp.monthly, eta.monthly)
class(pa)
autoplot(pa)
autoplot(pa, facets = NULL)
colnames(pa) = c('ETP', 'ETA')
autoplot(pa, facets = NULL)
autoplot(pa, facets = NULL)+
xlab('Time') +ylab('ETP (mm/month)') + theme_bw()
autoplot(pa, facets = NULL)+
xlab('Time') +ylab('Evapotranspiration (mm/month)') + theme_bw()
autoplot(pa*1000, facets = NULL)+
xlab('Time') +ylab('Evapotranspiration (mm/month)') + theme_bw()
et.vs = cbind(apply.monthly(ieta[, -1 * lakid], sum)/(ncell - nlake),
apply.monthly(ieta[, lakid], sum)/nlake )
# 对比陆地与湖泊实际蒸散发量，月均值
nlake =length(lakeid)
et.vs = cbind(apply.monthly(ieta[, -1 * lakid], sum)/(ncell - nlake),
apply.monthly(ieta[, lakid], sum)/nlake )
colnames(et.vs) = c('Land', 'Lake')
autoplot(et.vs*1000, facets = NULL)+
xlab('Time') +ylab('Evapotranspiration (mm/month)') + theme_bw()
lakid = rSHUD::getLakeEleID() #读：湖泊ID
# 对比陆地与湖泊实际蒸散发量，月均值
nlake =length(lakeid)
et.vs = cbind(apply.monthly(ieta[, -1 * lakid], sum)/(ncell - nlake),
apply.monthly(ieta[, lakid], sum)/nlake )
colnames(et.vs) = c('Land', 'Lake')
autoplot(et.vs*1000, facets = NULL)+
xlab('Time') +ylab('Evapotranspiration (mm/month)') + theme_bw()
# 对比陆地与湖泊实际蒸散发量，月均值
nlake =length(lakid)
et.vs = cbind(apply.monthly(ieta[, -1 * lakid], sum)/(ncell - nlake),
apply.monthly(ieta[, lakid], sum)/nlake )
colnames(et.vs) = c('Land', 'Lake')
autoplot(et.vs*1000, facets = NULL)+
xlab('Time') +ylab('Evapotranspiration (mm/month)') + theme_bw()
qdown = rSHUD::readout('rivqdown')
dim(qdown)
xts::plot.xts(qdown[, oid])
q = apply.daily(qdown[, oid], sum)
prcp = rSHUD::readout('elevprcp')
# Hydrograph
qdown = rSHUD::readout('rivqdown')
prcp = rSHUD::readout('elevprcp')
q = apply.daily(qdown[, oid], sum)
p = apply.daily(prcp, sum)
# Hydrograph
qdown = rSHUD::readout('rivqdown')
prcp = rSHUD::readout('elevprcp')
q = apply.daily(qdown[, oid], sum)
p = apply.daily(prcp, sum)
pq = cbind(p, q)
colnames(pq) = c('Precipitation', 'Discharge')
rSHUD::hydrograph(pq)
rSHUD::hydrograph(pq[1:(730) + (7*365), ])
rSHUD::hydrograph(pq[1:(730) + (6*365), ])
# 水量平衡
xl = loaddata()
wb = wb.all(xl = xl)
library(raster)
xy=list(cbind(c(0, 2, 1), c(0, 0, 2)),  cbind(c(0, 2, 1), c(0, 0, 2))+2)
sp1 = xy2shp(xy=xy, shape = 'polygon')
raster::plot(sp1, axes=TRUE, col='gray')
sp2 = xy2shp(xy=xy, shape = 'lines')
raster::plot(sp2, add=TRUE, lty=2, lwd=3,col='red')
sp3 = xy2shp(xy=xy, shape = 'POINTS')
raster::plot(sp3, add=TRUE, pch=1, cex=2)