Make a simulation using the julia version of DynACof, DynACof.jl. This implementation is much faster, with run-times ~100x faster. Need Julia installed on the computer and in the path.
dynacof.jl( Period = NULL, WriteIt = FALSE, Inpath = NULL, output_f = ".RData", Outpath = Inpath, Simulation_Name = "DynACof", FileName = list(Site = "site.jl", Meteo = "meteorology.txt", Soil = "soil.jl", Coffee = "coffee.jl", Tree = NULL), ... )
Period | Period of time to be simulated, see details. Default: |
---|---|
WriteIt | If |
Inpath | Path to the input parameter list folder, Default: |
output_f | Output format. If |
Outpath | Path pointing to the folder were the results will be writen, Default: |
Simulation_Name | Character name of the simulation file name if |
FileName | A list of input file names :
|
... | Further arguments to pass to |
Return invisibly a list containing three objects (Parameters, Meteo and Sim). See DynACof()
for more details.
If the user needs default values from the package for FileName names, put their values to "package". Careful:
Always call dynacof.jl_setup()
before runnning dynacof.jl
. See example.
if (FALSE) { # Setting up julia + DynACof.jl: dynacof.jl_setup() # Running a simulation: # First, downloading an example meteorology file: met_file= tempfile() m= "https://raw.githubusercontent.com/VEZY/DynACof.jl_inputs/master/meteorology.txt" download.file(m,met_file) S= dynacof.jl(Inpath= dirname(met_file), FileName=list(Site="package",Meteo=basename(met_file), Soil="package",Coffee="package",Tree=NULL)) unlink(met_file) # Plotting the coffee LAI along the simulation: plot(S$Meteo$Date,S$Sim$LAI) }