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Checkout Comp.R from mdl branch
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Max Luebke
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Comp.R
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227
Comp.R
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## Time-stamp: "Last modified 2022-01-25 11:22:30 delucia"
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library(ReacTran)
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library(deSolve)
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options(width=114)
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Diffusion <- function (t, Y, parms){
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tran <- tran.1D(C = Y, C.up = 1, C.down = 0, D = parms$D, dx = xgrid)
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return(list(tran$dC))
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}
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## sol 100
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## Set initial conditions
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N <- 100
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xgrid <- setup.grid.1D(x.up = 0, x.down = 1, N = N)
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x <- xgrid$x.mid
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D.coeff <- 1E-3
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C0 <- 1 ## Initial concentration (mg/L)
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X0 <- 0 ## Location of initial concentration (m)
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Yini <- c(C0, rep(0,N-1))
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parms1 <- list(D=D.coeff)
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times <- seq(from = 0, to = 5, by = 1)
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system.time({
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out100 <- ode.1D(y = Yini, times = times, func = Diffusion,
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parms = parms1, dimens = N)
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})
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## sol 1000
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## Set initial conditions
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N <- 1000
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xgrid <- setup.grid.1D(x.up = 0, x.down = 1, N = N)
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x <- xgrid$x.mid
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D.coeff <- 1E-3
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C0 <- 1 ## Initial concentration (mg/L)
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X0 <- 0 ## Location of initial concentration (m)
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Yini <- c(C0, rep(0,N-1))
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parms1 <- list(D=D.coeff)
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times <- seq(from = 0, to = 5, by = 1)
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system.time({
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out1000 <- ode.1D(y = Yini, times = times, func = Diffusion,
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parms = parms1, dimens = N)
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})
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## sol 5000
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## Set initial conditions
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N <- 5000
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xgrid <- setup.grid.1D(x.up = 0, x.down = 1, N = N)
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x <- xgrid$x.mid
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D.coeff <- 1E-3
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C0 <- 1 ## Initial concentration (mg/L)
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X0 <- 0 ## Location of initial concentration (m)
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Yini <- c(C0, rep(0,N-1))
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parms1 <- list(D=D.coeff)
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times <- seq(from = 0, to = 5, by = 1)
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system.time({
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out5000 <-
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ode.1D(y = Yini, times = times, func = Diffusion, parms = parms1,
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dimens = N)
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})
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## ./mdl_main 100 > Sol100.dat
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## ./mdl_main 1000 > Sol1000.dat
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## ./mdl_main 5000 > Sol5000.dat
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a100 <- data.table::fread("build/src/Sol100.dat")
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a1000 <- data.table::fread("build/src/Sol1000.dat")
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a5000 <- data.table::fread("build/src/Sol5000.dat")
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## run: phreeqc pqc.in pqc.out /PATH_TO_PHREEQC.DAT
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p100 <- data.table::fread("./selected_output_1.sel")
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pqc <- subset(p100, subset = time==5) |>
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subset(subset = soln > 0 & soln < 101) |>
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subset(select = Mg)
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cairo_pdf("test100.pdf", family="serif")
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matplot.1D(out100, type = "l", subset = time == 4, xaxs="i", xlab="grid element", ylab="C",
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main="Comparison ode.1D vs btcs, discretization 100", lwd=2)
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lines(a100$inp1, lwd=2, col="blue")
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lines(a100$inp2, lwd=2, col="red")
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lines(a100$inp3, lwd=2, col="green4")
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lines(pqc$Mg, lwd=2, col="orange")
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legend("topright", c("R deSolve ode.1D reference, ts=1",
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"btcs, ts=1", "btcs, ts=0.1","btcs, ts=5","PHREEQC ts=1"), lty="solid",
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lwd=2, col=c("black","blue","red", "green4", "orange"),
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bty="n")
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dev.off()
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cairo_pdf("test1000.pdf", family="serif")
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matplot.1D(out1000, type = "l", subset = time == 4, xaxs="i", xlab="grid element", ylab="C",
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main="Comparison ode.1D vs btcs, discretization 1000", lwd=2)
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lines(a1000$inp1, lwd=2, col="blue")
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lines(a1000$inp2, lwd=2, col="red")
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lines(a1000$inp3, lwd=2, col="green4")
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legend("topright", c("R deSolve ode.1D reference, ts=1",
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"btcs, ts=1", "btcs, ts=0.1","btcs, ts=5"), lty="solid",
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lwd=2, col=c("black","blue","red", "green4"),
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bty="n")
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dev.off()
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cairo_pdf("test5000.pdf", family="serif")
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matplot.1D(out5000, type = "l", subset = time == 4, xaxs="i", xlab="grid element", ylab="C",
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main="Comparison ode.1D vs btcs, discretization 5000", lwd=2)
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lines(a5000$inp1, lwd=2, col="blue")
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lines(a5000$inp2, lwd=2, col="red")
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lines(a5000$inp3, lwd=2, col="green4")
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legend("topright", c("R deSolve ode.1D reference, ts=1",
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"btcs, ts=1", "btcs, ts=0.1","btcs, ts=5"), lty="solid",
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lwd=2, col=c("black","blue","red", "green4"),
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bty="n")
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dev.off()
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############## old stuff
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a <- data.table::fread("build/src/Sol1000.dat")
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erfc <- function(x) 2 * pnorm(x * sqrt(2), lower = FALSE)
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Analytical <- function(x, t, a, v) {
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erfc
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}
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cairo_pdf("test.pdf", family="serif")
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matplot.1D(out, type = "l", subset = time == 4, xaxs="i", xlab="grid element", ylab="C",
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main="Comparison ode.1D vs btcs", lwd=2)
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lines(a$inp1, lwd=2, col="blue")
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lines(a$inp2, lwd=2, col="red")
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lines(a$inp3, lwd=2, col="green4")
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legend("topright", c("R deSolve ode.1D reference, ts=1",
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"btcs, ts=1", "btcs, ts=0.1","btcs, ts=5"), lty="solid",
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lwd=2, col=c("black","blue","red", "green4"),
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bty="n")
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dev.off()
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## sol 5000
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## Set initial conditions
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N <- 5000
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xgrid <- setup.grid.1D(x.up = 0, x.down = 1, N = N)
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x <- xgrid$x.mid
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D.coeff <- 1E-3
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C0 <- 1 ## Initial concentration (mg/L)
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X0 <- 0 ## Location of initial concentration (m)
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Yini <- c(C0, rep(0,N-1))
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parms1 <- list(D=D.coeff)
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times <- seq(from = 0, to = 5, by = 1)
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system.time(
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out5000 <- ode.1D(y = Yini, times = times, func = Diffusion,
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parms = parms1, dimens = N))
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a <- data.table::fread("build/src/Sol5000.dat")
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cairo_pdf("test.pdf", family="serif")
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matplot.1D(out5000, type = "l", subset = time == 4, xaxs="i", xlab="grid element", ylab="C",
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main="Comparison ode.1D vs btcs", lwd=2)
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lines(a$inp1, lwd=2, col="blue")
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lines(a$inp2, lwd=2, col="red")
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lines(a$inp3, lwd=2, col="green4")
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legend("topright", c("R deSolve ode.1D reference, ts=1",
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"btcs, ts=1", "btcs, ts=0.1","btcs, ts=5"), lty="solid",
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lwd=2, col=c("black","blue","red", "green4"),
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bty="n")
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dev.off()
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### ##
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## diffusR <- function(t, C, parameters) {
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## deltax <- c (0.5*delx, rep(delx, numboxes-1), 0.5*delx)
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## Flux <- -D*diff(c(0, C, 0))/deltax
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## dC <- -diff(Flux)/delx
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## list(dC) # the output
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## }
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## ## ==================
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## ## Model application
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## ## ==================
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## ## the model parameters:
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## D <- 0.3 # m2/day diffusion rate
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## r <- 0.01 # /day net growth rate
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## delx <- 1 # m thickness of boxes
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## numboxes <- 50
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## ## distance of boxes on plant, m, 1 m intervals
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## Distance <- seq(from = 0.5, by = delx, length.out = numboxes)
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## ## Initial conditions, ind/m2
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## ## aphids present only on two central boxes
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## C <- rep(0, times = numboxes)
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## C[30:31] <- 1
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## state <- c(C = C) # initialise state variables
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## ## RUNNING the model:
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## times <- seq(0, 200, by = 1) # output wanted at these time intervals
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## out <- ode.band(state, times, diffusR, parms = 0,
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## nspec = 1, names = "C")
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## ## ================
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## ## Plotting output
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## ## ================
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## image(out, grid = Distance, method = "filled.contour",
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## xlab = "time, days", ylab = "Distance on plant, m",
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## main = "Aphid density on a row of plants")
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## matplot.1D(out, grid = Distance, type = "l",
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## subset = time %in% seq(0, 200, by = 10))
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## # add an observed dataset to 1-D plot (make sure to use correct name):
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## data <- cbind(dist = c(0,10, 20, 30, 40, 50, 60),
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## Aphid = c(0,0.1,0.25,0.5,0.25,0.1,0))
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## matplot.1D(out, grid = Distance, type = "l",
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## subset = time %in% seq(0, 200, by = 10),
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## obs = data, obspar = list(pch = 18, cex = 2, col="red"))
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## ## Not run:
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## plot.1D(out, grid = Distance, type = "l")
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