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simplace

R package to interact with the modeling framework Simplace

Introduction

This package provides methods to interact with the modelling framework Simplace - Scientific Impact assessment and Modelling PLatform for Advanced Crop and Ecosystem management. See www.simplace.net for more information on Simplace. Simplace is written in Java (and some parts in Scala) so one can access it from R via rJava. The purpose of this package is to simplify the interaction between R and Simplace, by providing functions to:

Installing the Simplace Framework

For installing Simplace, please consult the webpage www.simplace.net.

A brief guide to install Simplace:

install.packages('simplace')

The most recent development version can be installed from github:

devtools::install_github("gk-crop/simplace_rpkg")

If you encounter errors, make sure to install the packages devtools and rJava.

Basic Usage

The usage of Simplace in R follows roughly this scheme:

Troubleshooting

Example

Run the simulation

library(simplace)
SimplaceInstallationDir <- findSimplaceInstallations()

Solution <- paste(SimplaceInstallationDir,
        "simplace_run/simulation/gk/solution/complete/Complete.sol.xml",sep="")

simplace <- initSimplace(SimplaceInstallationDir)

openProject(simplace, Solution)

parameter <- list()
parameter$enddate <- "31-12-1992"

sid <- createSimulation(simplace,parameter)
runSimulations(simplace)

result <- getResult(simplace,"DIAGRAM_OUT", sid);

closeProject(simplace)

After specifying the directories and the solution, the framework is initialized and the project opened. The end date of the simulation is (re)set and the simulation is run. After the run the result is retrieved.

Get the result and plot it

resf <- resultToDataframe(result)

dates <- 300:730
weights <- resf[dates,
    c("TOP_LINE_Roots","TOP_LINE_Leaves","TOP_LINE_Stems","TOP_LINE_StorageOrgans")]
matplot(dates,weights,type="l",xlab="Days",ylab="Weight [g/m2]",main="Simulated Biomass")
legend(300,800,legend=c("Roots","Leaves","Stems","Storage Organs"),lty=1:4,col=1:4)

The result is converted to a dataframe. Interesting variables are extracted and then plotted.

Get arrays and plot them as contour plot

resultlistexp <- resultToList(result,expand=TRUE)
water <- resultlistexp$BOTTOM_ARRAY_VolumetricWaterContent
wmat <- do.call(rbind,water)
wmatpart <- wmat[dates,]
layers <- dim(wmatpart)[2]
filled.contour(dates,-(layers:1),wmatpart[,layers:1],
               xlab="Day", ylab="Layer", main="Water content in soil",
               color.palette = function(n){rgb((n:1)/n,(n:1)/n,1)})

As the result contains an array which holds the water content for 40 layers, it is transformed to a list and the array is expanded.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.