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gk-crop/simplace_rpkg: R package to interact with the modeling framework Simplace

R package to interact with the modeling framework Simplace

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:

• initialize and configure Simplace
• load a simulation (solution and project)
• parameterize the simulation
• run whole simulation or run it stepwise
• get simulation output and convert it to formats suitable for R

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

A brief guide to install Simplace:

• If you don't have installed Java, please install an appropriate version of the (JRE or JDK) from openjdk.org or adoptium.net (recommended).
• Get Simplace from www.simplace.net
• Install the simplace package in R:

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.

The usage of Simplace in R follows roughly this scheme:

• init Simplace by providing the path to your simplace installation directory, your working directory and your outputs
• open a Simplace project form a solution (and project) file
• create a list of simulation parameters you want to change
• create and run a Simulation
• get the result from the simulation
• convert the result to a R object (data.frame, list etc.)

• Package rJava should be installed automatically with simplace. If not, install it manually:
  install.packages('rJava')
• Architecture of R and Java have to match. If you are using 64-bit Java, you have to use 64-bit R.
• If you want to use the development version instead of the console mode, make sure that the projects simplace_core, simplace_modules and optionally simplace_run are in a common directory and set the installation dir to this directory.

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.

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.

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.

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