In SticsRPacks/SticsOnR: Manage STICS Simulations Running the Executable or JavaStics,
suppressPackageStartupMessages(library(SticsRFiles))
suppressPackageStartupMessages(library(SticsOnR))
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-"
)
options(tibble.print_min = 5, tibble.print_max = 5)
javastics_dist <- params$javastics_dist
root_path <- normalizePath(params$root_path, winslash = "/")
javastics_path <- file.path(root_path, params$javastics_dist)
workspace_path <- file.path(javastics_path, params$work_dir)
output_path <- file.path(root_path, "gen_usms_xml2txt")
windows <- params$windows
linux <- !windows
java_cmd <- "java"
if (linux)
java_cmd <- normalizePath(params$java_cmd, winslash = "/")
# For testing consistency betwen JavaSTICS and java version
# Adding a test to see if javastics is compatible with java version
res <- try(run_javastics(javastics_path,
workspace_path, usm = "wheat", verbose = FALSE,
java_cmd = java_cmd),
silent = TRUE)
try_err <- attr(res, "class")
if (!is.null(try_err) && attr(try_err, "class") == "try-error") {
ver <- system2(command = "java", args = "-version",
stdout = TRUE, stderr = TRUE)
stop(javastics_dist, ": javastics not compatible with java\n",
ver[1],
paste0("\nA java path must be provided for a ",
"compatible java version (11) to run_javastics"))
}
The R package for the STICS model 
The goal of SticsOnR is to perform simulations of the Stics model, downloadable with its graphical user interface from https://eng-stics.paca.hub.inrae.fr/Download.
If you want to be notified when a new release of this package is made, you can tick the Releases box in the "Watch / Unwatch => Custom" menu at the top right of this page.
Prerequisites and technical tips
JavaStics software
JavaStics must be installed and the minimal version is version 1.41.
The latest distribution version for Stics is downloadable here.
The installation process only consists of unzipping the JavaStics archive, except for MacOS system (see MacOS installation specificities)
Under the Windows operating system
Be aware that the java virtual machine does not need to be installed to use the
JavaSTICS software, neither the graphical interface (JavaStics.exe) nor the command line interface (JavaSticsCmd.exe). Because, a Java machine is embedded in the JavaStics archive.
Under linux operating systems
Java version
For using the JavaStics software (GUI and command line interface) under a linux operating system, the java version must be consistent with the JavaStics version
- for JavaStics 1.41, the java version must be at most a Java 8 version
- for JavaStics 1.5.0/1.5.1 versions, the java version must be at least a Java 11 version
- for JavaStics 1.5.2 version, the java version must be at least a Java 17 version
So, for adapting the java version to the JavaStics version some manipulations must be done either by switching between versions through system commands see here, using a specific java executable path (see running JavaStics).
System libraries
For the linux operating system, the SticsOnR package may require to install an xslt library.
If the SticsOnR installation fails, and the xslt library is missing, the error message indicates what is the name of the xslt library to be installed (according to the common linux distributions). For example, for the Ubuntu or Debian OS libxslt1-dev must be installed.
Under MacOS systems
The STICS executable must be compiled and imported in the JavaStics directory. The procedure is fully described
in the JavaStics documentation (see JavaStics_documentation.html under the doc folder), in the prerequisites sub-section under the Software section. Java version installation management is also described in it.
Files/directories paths syntax
Under all systems, file paths must not contain any special character or space.
Under unix like systems, using the ~ in files or directories paths may cause errors in SticsOnR functions. So, it is safer for the moment to use absolute paths. This will be fixed in the future versions.
Remote installation tools
For installing packages from the Github site an additional package must be installed. One can use either devtools or remotes
For devtools, installation requires system dependent tools. They must be installed first.
- For Windows
RTools must be installed using the latest installer from here
- For linux
Development tools must be installed first, like the
build-essentials package for a Debian like distribution for example.
Then the devtools package can be installed using:
install.packages("devtools")
For remotes, it can be directly installed using:
install.packages("remotes")
Installation
Recommended installation: SticsRPacks
The best way to install the packages from SticsRPacks, from which SticsOnR is part of, is by installing the [SticsRPacks] package:
devtools::install_github("SticsRPacks/SticsRPacks")
- With
remotes
remotes::install_github("SticsRPacks/SticsRPacks")
The package will install the packages for you at the latest release version.
Other way: install each package independently
SticsOnR
The package installation can be remotely done directly from GitHub using either devtools or the lightweight remote one package
The latest release version can be installed using:
- With
devtools
devtools::install_github("SticsRPacks/SticsOnR@*release")
- With
remotes
remotes::install_github("SticsRPacks/SticsOnR@*release")
Normally, all the package dependencies will be installed for CRAN packages.
SticsRFiles
SticsRFiles must be installed manually using the above syntax, just replacing SticsOnR with SticsRFiles.
Loading the packages library
library(SticsOnR)
library(SticsRFiles)
Running the model
Here are basic examples which show you how to run the model either from a R model interface or a JavaStics (command line) one. More complete examples will be detailed in a specific documentation later.
JavaStics command line interface
The JavaStics installation folder (for example, r javastics_dist) contains an example workspace folder with a set of runnable usms.
For running simulations from it, we can use the run_javastics() function.
-
Specifying the JavaStics folder
javastics_path <- r paste0('/path/to/',javastics_dist)
-
Specifying a workspace
- as a sub-folder of JavaStics
workspace_path <- "example"
- or an absolute path to an external folder
workspace_path <- "/path/to/javastics/workspace"
########## For Windows or linux with a compatible java version ################
# Running specific usms from the workspace
run_javastics(javastics_path, workspace_path, usm = c("banana", "wheat"),
verbose = FALSE)
# Running all usms contained in the workspace
run_javastics(javastics_path, workspace_path,
verbose = FALSE)
# Getting information about execution:
runs_info <- run_javastics(javastics_path, workspace_path,
usm = c("banana", "wheat"), verbose = FALSE)
runs_info
- For linux systems, if the java version is not compatible
with the JavaStics version a compatible java executable path must be used
java_cmd <- "/path/to/java/exe"
################ Only for linux with a specific java executable################
# Running specific usms from the workspace
run_javastics(javastics_path, workspace_path, usm = c("banana", "wheat"),
verbose = FALSE, java_cmd = java_cmd)
# Running all usms contained in the workspace
run_javastics(javastics_path, workspace_path,
verbose = FALSE, java_cmd = java_cmd)
# Getting information about execution:
runs_info <- run_javastics(javastics_path, workspace_path,
usm = c("banana", "wheat"),
verbose = FALSE, java_cmd = java_cmd)
runs_info
In the returned information, the error field name gives a list of messages from the JavaStics command line interface. If any Error key word appears in a message, the corresponding simulation failed. But, at the moment it is impossible to identify what is the error's origin. Things must be checked manually in the workspace, after running again the faulty usm (because the model input files are overwritten at each usms simulation).
Running the model using the stics executable directly
We need for that a JavaStics folder and a directory with text input files for Stics, or a folder containing individual sub-directories for usms.
These directories can be generated using the [SticsRFiles::gen_usms_xml2txt()] function from the SticsRFiles package, by converting automatically XML files to Stics input text files. See the documentation here.
Example of use:
# For Windows
# Generating files for all the usms contained in the workspace
SticsRFiles::gen_usms_xml2txt(javastics_path, workspace = workspace_path,
out_dir = output_path, verbose = FALSE)
# For linux, using a specific java executable
# Generating files for all the usms contained in the workspace
SticsRFiles::gen_usms_xml2txt(javastics_path, workspace = workspace_path,
out_dir = output_path, verbose = FALSE,
java_cmd = java_cmd)
unlink(x = file.path(output_path, "intercrop_pea_barley"), recursive = TRUE)
The run_stics() function can be used as follows with one folder or multiple sub-folders.
The Stics executable path is set according to each operating system:
- for windows
stics_path <- file.path(javastics_path,"bin","stics_modulo.exe")
- for linux
stics_path <- file.path(javastics_path,"bin","stics_modulo")
- for Mac
stics_path <- file.path(javastics_path,"bin","stics_modulo_mac")
# Specifying the Stics executable file path
stics_path <- file.path(javastics_path, "bin", "stics_modulo.exe")
# for linux
if (is_unix())
stics_path <- file.path(javastics_path, "bin", "stics_modulo")
# Specifying a directory containing Stics input files
# For example reusing a generated sub-directory in the previous section
# of the document
# Running one usm
files_dir_path <- file.path(output_path, "banana")
run_stics(stics_path, files_dir_path)
# Specifying a root directory containing usms individual directories
# For example reusing a generated directory in the previous section
# of the document
# Running two usms
run_stics(stics_path, output_path, usm = c("banana", "wheat"))
# Running all the usms defined in the sub-directories of output_path
run_stics(stics_path, output_path, usm = "all")
# Getting returned information about stics runs
runs_info <- run_stics(stics_path, output_path, usm = c("banana", "wheat"))
runs_info
Advanced simulations parameterization
A specific function stics_wrapper() is dedicated to manage simulations with a higher level
of parameterization than what run_stics() offers.
This stics_wrapper() function allows:
- Forcing the values of a set of parameters (common or specific values per USM)
- Returning simulated daily outputs for each usm with possible dates and variables filtering
- Parallelizing simulations, and displaying execution time
- Run Usms in successive mode
As the run_stics() function, the stics_wrapper() operates on directories
containing text stics input files.
Defining simulations options
Simulation options can be fixed using the stics_wrapper_options() function.
Both of them are mandatory: the model executable path and the directory path containing usms sub-directories with text input files.
A template is returned by the function when called with no arguments:
stics_wrapper_options()
For the example, we will use the default stics model version shipping with JavaStics and the directory where individual usms input directories have been generated:
sim_options <- stics_wrapper_options(javastics = javastics_path,
workspace = output_path, verbose = FALSE)
By default, stics_wrapper_options() checks that javastics, stics_exe and workspace exists.
There are different solutions if you need to use a custom version of stics:
- if it is already listed in the preference (e.g. added in JavaStics), simply provide its name (ID):
sim_options <- stics_wrapper_options(javastics = javastics_path,
stics_exe = "stics_custom",
workspace = output_path, verbose = FALSE)
- if it is located in the bin directory of the JavaStics installation directory, provide the executable name:
sim_options <- stics_wrapper_options(javastics = javastics_path,
stics_exe = "stics_custom.exe",
workspace = output_path, verbose = FALSE)
- if it is located in any other folder, provide the full path to the executable name, and no need to use
javastics_path:
sim_options <- stics_wrapper_options(stics_exe = "path/to/stics_custom.exe",
workspace = output_path, verbose = FALSE)
Simple simulations cases
- Without filtering usms or outputs
results <- stics_wrapper(model_options = sim_options)
- Filtering on usms list
usm <- c("wheat", "maize")
results <- stics_wrapper(model_options = sim_options, situation = usm)
- Filtering outputs on variables
usm <- c("wheat", "maize")
stics_wrapper(model_options = sim_options, situation = usm,
var = c("masec_n", "mafruit"))
- Filtering outputs on variables and dates for several USMs
The argument sit_var_dates_mask must contain a named list (named by usms names) containing
data.frames, as the sim_list element of the list returned by stics_wrapper (see here-after) or as observations data.
It defines a mask: stics_wrapper will return a result for each USM, variable and date that contains at least a value (i.e. different from NA) in the mask.
The stics_wrapper function returns a list that contains two elements:
- error, a boolean indicating if an error occurs during the simulations,
- sim_list, a named list of data.frames containing the simulated values for the requested USMs, variables and dates.
obs_list <- get_obs(workspace = workspace_path, usm = c("wheat", "maize"),
verbose = FALSE)
# Observations table for wheat
obs_list$wheat
sim_options <- stics_wrapper_options(javastics = javastics_path,
workspace = output_path, verbose = TRUE)
results <- stics_wrapper(model_options = sim_options,
sit_var_dates_mask = obs_list)
head(results)
Some warnings may occur, as in this case (that is why results$error is TRUE), indicating that observed variables and/or observations dates are missing in simulated data.
Concerning the dates, this may be due to the USMs simulation period that may not include observed dates.
For the variables, this may be due to an incorrect spelling of the variables in obs_list.
Simulations with forcing parameters
- Applying a single parameter values vector for all the selected usms
Parameters values are prescribed using the param_values argument. It can be a named vector containing the values and names of the parameters to force.
In this case, the same values will be applied for all the simulated usms.
param_values <- c(0.002, 50)
names(param_values) <- c("dlaimax", "durvieF")
results <- stics_wrapper(model_options = sim_options, situation = usm,
param_values = param_values)
- Defining different parameters values depending on the usms
param_values can also be a data.frame or a tibble having one named column per parameter and an optional column named situation containing the name of the situations (USMs for Stics) that allows to define different values of the parameters for different situations.
# Let's run usm wheat with c(dlaimax=0.001, durvieF=50),
# usm pea with c(dlaimax=0.001, durvieF=60),
# and usm maize with c(dlaimax=0.001, durvieF=70)
param_values <- data.frame(situation = c("wheat", "pea", "maize"),
dlaimax = c(0.001, 0.001, 0.001),
durvieF = c(50, 60, 70))
# Let's display it
param_values
results <- stics_wrapper(model_options = sim_options,
param_values = param_values,
situation = c("wheat", "maize"))
Simulations in successive mode
USMs can be run in successive mode, if they are adequately defined (i.e. if the beginning and end of simulations are consistent), using the option successive_usms.
successive_usms is a list of vectors containing the names of the UMSs to consider as successive (e.g. list(c("usm1.1","usm1.2"),c("usm2.1","usm2.2")) defines 2 successions usm1.1 -> usm1.2 and usm2.1 -> usm2.2).
Other Optional arguments
- Displaying execution time
sim_options <- stics_wrapper_options(javastics = javastics_path,
workspace = output_path,
time_display = TRUE,
verbose = FALSE)
results <- stics_wrapper(model_options = sim_options)
- Activating parallel execution
On may specify the number of cores to use with the cores argument.
sim_options <- stics_wrapper_options(javastics = javastics_path,
workspace = output_path,
parallel = TRUE, time_display = TRUE,
cores = 2, verbose = FALSE)
results <- stics_wrapper(model_options = sim_options)
If cores is not given, parallel execution is performed over machine total cores number minus 1.
library(parallel)
# Used cores number
detectCores() - 1
sim_options <- stics_wrapper_options(javastics = javastics_path,
workspace = output_path, parallel = TRUE,
time_display = TRUE, verbose = FALSE)
results <- stics_wrapper(model_options = sim_options)
Getting help
If you have any question or suggestion or if you want to report a bug, please do it via the GitHub issues.
Thanks for that, this would greatly help us to improve this package.
Citation
If you have used this package for a study that led to a publication or report, please cite us. You can either use the citation tool from Github if you used the last version, or use citation("SticsOnR") from R otherwise.
Code of conduct
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
The package is under intensive development, so you can fill an issue or request us a feature here at any time.
SticsRPacks/SticsOnR documentation built on April 19, 2024, 9:47 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
suppressPackageStartupMessages(library(SticsRFiles)) suppressPackageStartupMessages(library(SticsOnR)) knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-" ) options(tibble.print_min = 5, tibble.print_max = 5) javastics_dist <- params$javastics_dist root_path <- normalizePath(params$root_path, winslash = "/") javastics_path <- file.path(root_path, params$javastics_dist) workspace_path <- file.path(javastics_path, params$work_dir) output_path <- file.path(root_path, "gen_usms_xml2txt") windows <- params$windows linux <- !windows java_cmd <- "java" if (linux) java_cmd <- normalizePath(params$java_cmd, winslash = "/")
# For testing consistency betwen JavaSTICS and java version # Adding a test to see if javastics is compatible with java version res <- try(run_javastics(javastics_path, workspace_path, usm = "wheat", verbose = FALSE, java_cmd = java_cmd), silent = TRUE) try_err <- attr(res, "class") if (!is.null(try_err) && attr(try_err, "class") == "try-error") { ver <- system2(command = "java", args = "-version", stdout = TRUE, stderr = TRUE) stop(javastics_dist, ": javastics not compatible with java\n", ver[1], paste0("\nA java path must be provided for a ", "compatible java version (11) to run_javastics")) }
The R package for the STICS model
The goal of SticsOnR is to perform simulations of the Stics model, downloadable with its graphical user interface from https://eng-stics.paca.hub.inrae.fr/Download.
If you want to be notified when a new release of this package is made, you can tick the Releases box in the "Watch / Unwatch => Custom" menu at the top right of this page.
Prerequisites and technical tips
JavaStics software
JavaStics must be installed and the minimal version is version 1.41.
The latest distribution version for Stics is downloadable here.
The installation process only consists of unzipping the JavaStics archive, except for MacOS system (see MacOS installation specificities)
Under the Windows operating system
Be aware that the java virtual machine does not need to be installed to use the
JavaSTICS software, neither the graphical interface (JavaStics.exe) nor the command line interface (JavaSticsCmd.exe). Because, a Java machine is embedded in the JavaStics archive.
Under linux operating systems
Java version
For using the JavaStics software (GUI and command line interface) under a linux operating system, the java version must be consistent with the JavaStics version
- for JavaStics 1.41, the java version must be at most a Java 8 version
- for JavaStics 1.5.0/1.5.1 versions, the java version must be at least a Java 11 version
- for JavaStics 1.5.2 version, the java version must be at least a Java 17 version
So, for adapting the java version to the JavaStics version some manipulations must be done either by switching between versions through system commands see here, using a specific java executable path (see running JavaStics).
System libraries
For the linux operating system, the SticsOnR package may require to install an xslt library.
If the SticsOnR installation fails, and the xslt library is missing, the error message indicates what is the name of the xslt library to be installed (according to the common linux distributions). For example, for the Ubuntu or Debian OS libxslt1-dev must be installed.
Under MacOS systems
The STICS executable must be compiled and imported in the JavaStics directory. The procedure is fully described in the JavaStics documentation (see JavaStics_documentation.html under the doc folder), in the prerequisites sub-section under the Software section. Java version installation management is also described in it.
Files/directories paths syntax
Under all systems, file paths must not contain any special character or space.
Under unix like systems, using the ~ in files or directories paths may cause errors in SticsOnR functions. So, it is safer for the moment to use absolute paths. This will be fixed in the future versions.
Remote installation tools
For installing packages from the Github site an additional package must be installed. One can use either devtools or remotes
For devtools, installation requires system dependent tools. They must be installed first.
- For Windows RTools must be installed using the latest installer from here
- For linux
Development tools must be installed first, like the
build-essentialspackage for a Debian like distribution for example.
Then the devtools package can be installed using:
install.packages("devtools")
For remotes, it can be directly installed using:
install.packages("remotes")
Installation
Recommended installation: SticsRPacks
The best way to install the packages from SticsRPacks, from which SticsOnR is part of, is by installing the [SticsRPacks] package:
devtools::install_github("SticsRPacks/SticsRPacks")
- With
remotes
remotes::install_github("SticsRPacks/SticsRPacks")
The package will install the packages for you at the latest release version.
Other way: install each package independently
SticsOnR
The package installation can be remotely done directly from GitHub using either devtools or the lightweight remote one package
The latest release version can be installed using:
- With
devtools
devtools::install_github("SticsRPacks/SticsOnR@*release")
- With
remotes
remotes::install_github("SticsRPacks/SticsOnR@*release")
Normally, all the package dependencies will be installed for CRAN packages.
SticsRFiles
SticsRFiles must be installed manually using the above syntax, just replacing SticsOnR with SticsRFiles.
Loading the packages library
library(SticsOnR) library(SticsRFiles)
Running the model
Here are basic examples which show you how to run the model either from a R model interface or a JavaStics (command line) one. More complete examples will be detailed in a specific documentation later.
JavaStics command line interface
The JavaStics installation folder (for example, r javastics_dist) contains an example workspace folder with a set of runnable usms.
For running simulations from it, we can use the run_javastics() function.
-
Specifying the JavaStics folder
javastics_path <-
r paste0('/path/to/',javastics_dist) -
Specifying a workspace
- as a sub-folder of JavaStics
workspace_path <- "example"
- or an absolute path to an external folder
workspace_path <- "/path/to/javastics/workspace"
########## For Windows or linux with a compatible java version ################ # Running specific usms from the workspace run_javastics(javastics_path, workspace_path, usm = c("banana", "wheat"), verbose = FALSE) # Running all usms contained in the workspace run_javastics(javastics_path, workspace_path, verbose = FALSE) # Getting information about execution: runs_info <- run_javastics(javastics_path, workspace_path, usm = c("banana", "wheat"), verbose = FALSE) runs_info
- For linux systems, if the java version is not compatible
with the JavaStics version a compatible java executable path must be used
java_cmd <- "/path/to/java/exe"
################ Only for linux with a specific java executable################ # Running specific usms from the workspace run_javastics(javastics_path, workspace_path, usm = c("banana", "wheat"), verbose = FALSE, java_cmd = java_cmd) # Running all usms contained in the workspace run_javastics(javastics_path, workspace_path, verbose = FALSE, java_cmd = java_cmd) # Getting information about execution: runs_info <- run_javastics(javastics_path, workspace_path, usm = c("banana", "wheat"), verbose = FALSE, java_cmd = java_cmd) runs_info
In the returned information, the error field name gives a list of messages from the JavaStics command line interface. If any Error key word appears in a message, the corresponding simulation failed. But, at the moment it is impossible to identify what is the error's origin. Things must be checked manually in the workspace, after running again the faulty usm (because the model input files are overwritten at each usms simulation).
Running the model using the stics executable directly
We need for that a JavaStics folder and a directory with text input files for Stics, or a folder containing individual sub-directories for usms.
These directories can be generated using the [SticsRFiles::gen_usms_xml2txt()] function from the SticsRFiles package, by converting automatically XML files to Stics input text files. See the documentation here.
Example of use:
# For Windows # Generating files for all the usms contained in the workspace SticsRFiles::gen_usms_xml2txt(javastics_path, workspace = workspace_path, out_dir = output_path, verbose = FALSE)
# For linux, using a specific java executable # Generating files for all the usms contained in the workspace SticsRFiles::gen_usms_xml2txt(javastics_path, workspace = workspace_path, out_dir = output_path, verbose = FALSE, java_cmd = java_cmd)
unlink(x = file.path(output_path, "intercrop_pea_barley"), recursive = TRUE)
The run_stics() function can be used as follows with one folder or multiple sub-folders.
The Stics executable path is set according to each operating system:
- for windows
stics_path <- file.path(javastics_path,"bin","stics_modulo.exe")
- for linux
stics_path <- file.path(javastics_path,"bin","stics_modulo")
- for Mac
stics_path <- file.path(javastics_path,"bin","stics_modulo_mac")
# Specifying the Stics executable file path stics_path <- file.path(javastics_path, "bin", "stics_modulo.exe") # for linux if (is_unix()) stics_path <- file.path(javastics_path, "bin", "stics_modulo")
# Specifying a directory containing Stics input files # For example reusing a generated sub-directory in the previous section # of the document # Running one usm files_dir_path <- file.path(output_path, "banana") run_stics(stics_path, files_dir_path) # Specifying a root directory containing usms individual directories # For example reusing a generated directory in the previous section # of the document # Running two usms run_stics(stics_path, output_path, usm = c("banana", "wheat")) # Running all the usms defined in the sub-directories of output_path run_stics(stics_path, output_path, usm = "all") # Getting returned information about stics runs runs_info <- run_stics(stics_path, output_path, usm = c("banana", "wheat")) runs_info
Advanced simulations parameterization
A specific function stics_wrapper() is dedicated to manage simulations with a higher level
of parameterization than what run_stics() offers.
This stics_wrapper() function allows:
- Forcing the values of a set of parameters (common or specific values per USM)
- Returning simulated daily outputs for each usm with possible dates and variables filtering
- Parallelizing simulations, and displaying execution time
- Run Usms in successive mode
As the run_stics() function, the stics_wrapper() operates on directories
containing text stics input files.
Defining simulations options
Simulation options can be fixed using the stics_wrapper_options() function.
Both of them are mandatory: the model executable path and the directory path containing usms sub-directories with text input files.
A template is returned by the function when called with no arguments:
stics_wrapper_options()
For the example, we will use the default stics model version shipping with JavaStics and the directory where individual usms input directories have been generated:
sim_options <- stics_wrapper_options(javastics = javastics_path, workspace = output_path, verbose = FALSE)
By default, stics_wrapper_options() checks that javastics, stics_exe and workspace exists.
There are different solutions if you need to use a custom version of stics:
- if it is already listed in the preference (e.g. added in JavaStics), simply provide its name (ID):
sim_options <- stics_wrapper_options(javastics = javastics_path, stics_exe = "stics_custom", workspace = output_path, verbose = FALSE)
- if it is located in the bin directory of the JavaStics installation directory, provide the executable name:
sim_options <- stics_wrapper_options(javastics = javastics_path, stics_exe = "stics_custom.exe", workspace = output_path, verbose = FALSE)
- if it is located in any other folder, provide the full path to the executable name, and no need to use
javastics_path:
sim_options <- stics_wrapper_options(stics_exe = "path/to/stics_custom.exe", workspace = output_path, verbose = FALSE)
Simple simulations cases
- Without filtering usms or outputs
results <- stics_wrapper(model_options = sim_options)
- Filtering on usms list
usm <- c("wheat", "maize") results <- stics_wrapper(model_options = sim_options, situation = usm)
- Filtering outputs on variables
usm <- c("wheat", "maize") stics_wrapper(model_options = sim_options, situation = usm, var = c("masec_n", "mafruit"))
- Filtering outputs on variables and dates for several USMs
The argument sit_var_dates_mask must contain a named list (named by usms names) containing
data.frames, as the sim_list element of the list returned by stics_wrapper (see here-after) or as observations data.
It defines a mask: stics_wrapper will return a result for each USM, variable and date that contains at least a value (i.e. different from NA) in the mask.
The stics_wrapper function returns a list that contains two elements:
- error, a boolean indicating if an error occurs during the simulations,
- sim_list, a named list of data.frames containing the simulated values for the requested USMs, variables and dates.
obs_list <- get_obs(workspace = workspace_path, usm = c("wheat", "maize"), verbose = FALSE) # Observations table for wheat obs_list$wheat sim_options <- stics_wrapper_options(javastics = javastics_path, workspace = output_path, verbose = TRUE) results <- stics_wrapper(model_options = sim_options, sit_var_dates_mask = obs_list) head(results)
Some warnings may occur, as in this case (that is why results$error is TRUE), indicating that observed variables and/or observations dates are missing in simulated data.
Concerning the dates, this may be due to the USMs simulation period that may not include observed dates.
For the variables, this may be due to an incorrect spelling of the variables in obs_list.
Simulations with forcing parameters
- Applying a single parameter values vector for all the selected usms
Parameters values are prescribed using the param_values argument. It can be a named vector containing the values and names of the parameters to force.
In this case, the same values will be applied for all the simulated usms.
param_values <- c(0.002, 50) names(param_values) <- c("dlaimax", "durvieF") results <- stics_wrapper(model_options = sim_options, situation = usm, param_values = param_values)
- Defining different parameters values depending on the usms
param_values can also be a data.frame or a tibble having one named column per parameter and an optional column named situation containing the name of the situations (USMs for Stics) that allows to define different values of the parameters for different situations.
# Let's run usm wheat with c(dlaimax=0.001, durvieF=50), # usm pea with c(dlaimax=0.001, durvieF=60), # and usm maize with c(dlaimax=0.001, durvieF=70) param_values <- data.frame(situation = c("wheat", "pea", "maize"), dlaimax = c(0.001, 0.001, 0.001), durvieF = c(50, 60, 70)) # Let's display it param_values results <- stics_wrapper(model_options = sim_options, param_values = param_values, situation = c("wheat", "maize"))
Simulations in successive mode
USMs can be run in successive mode, if they are adequately defined (i.e. if the beginning and end of simulations are consistent), using the option successive_usms.
successive_usms is a list of vectors containing the names of the UMSs to consider as successive (e.g. list(c("usm1.1","usm1.2"),c("usm2.1","usm2.2")) defines 2 successions usm1.1 -> usm1.2 and usm2.1 -> usm2.2).
Other Optional arguments
- Displaying execution time
sim_options <- stics_wrapper_options(javastics = javastics_path, workspace = output_path, time_display = TRUE, verbose = FALSE) results <- stics_wrapper(model_options = sim_options)
- Activating parallel execution
On may specify the number of cores to use with the cores argument.
sim_options <- stics_wrapper_options(javastics = javastics_path, workspace = output_path, parallel = TRUE, time_display = TRUE, cores = 2, verbose = FALSE) results <- stics_wrapper(model_options = sim_options)
If cores is not given, parallel execution is performed over machine total cores number minus 1.
library(parallel) # Used cores number detectCores() - 1 sim_options <- stics_wrapper_options(javastics = javastics_path, workspace = output_path, parallel = TRUE, time_display = TRUE, verbose = FALSE) results <- stics_wrapper(model_options = sim_options)
Getting help
If you have any question or suggestion or if you want to report a bug, please do it via the GitHub issues.
Thanks for that, this would greatly help us to improve this package.
Citation
If you have used this package for a study that led to a publication or report, please cite us. You can either use the citation tool from Github if you used the last version, or use citation("SticsOnR") from R otherwise.
Code of conduct
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
The package is under intensive development, so you can fill an issue or request us a feature here at any time.
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