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SticsOnR
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A Package to control STICS from R
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SticsOnR ================ The R package for the [STICS](https://stics.inrae.fr/eng) model <img src="man/figures/logo.png" alt="logo" width="150" align="right" /> <!-- badges: start --> [](https://www.repostatus.org/#active) [](https://github.com/SticsRPacks/SticsOnR/actions) [](https://app.codecov.io/gh/SticsRPacks/SticsOnR?branch=main) [](https://zenodo.org/badge/latestdoi/166790380) <!-- badges: end --> The goal of SticsOnR is to perform simulations of the Stics model, downloadable with its graphical user interface from <https://stics.inrae.fr/eng/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](https://github.com/SticsRPacks/SticsOnR). ## 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](https://stics.inrae.fr/eng/download). The installation process only consists of unzipping the JavaStics archive, except for MacOS system (see MacOS installation [specificities](#under-macos-systems)) ### 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 1.5.2/1.5.3 versions, 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](https://sticsrpacks.github.io/SticsOnR/articles/Changing_java_version_linux.html), using a specific java executable path ([see running JavaStics](#javastics-command-line-interface)). #### 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`](https://github.com/r-lib/remotes#readme) For `devtools`, installation requires system dependent tools. They must be installed first. - For Windows RTools must be installed using the latest installer from [here](https://cran.r-project.org/bin/windows/Rtools/) - 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: ``` r install.packages("devtools") ``` For `remotes`, it can be directly installed using: ``` r 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: ``` r devtools::install_github("SticsRPacks/SticsRPacks") ``` - With `remotes` ``` r 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](https://github.com/) using either `devtools` or the lightweight `remote` one package The latest release version can be installed using: - With `devtools` ``` r devtools::install_github("SticsRPacks/SticsOnR@*release") ``` - With `remotes` ``` r 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 ``` r 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, JavaSTICS-1.5.3-STICS-10.3.0) 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 <pre>javastics_path <- /path/to/JavaSTICS-1.5.3-STICS-10.3.0</pre> - Specifying a workspace - as a sub-folder of JavaStics <pre>workspace_path <- "example"</pre> - or an absolute path to an external folder <pre>workspace_path <- "/path/to/javastics/workspace"</pre> ``` r ########## 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 <pre>java_cmd <- "/path/to/java/exe"</pre> ``` r ################ 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 ) #> [1] "banana" #> [1] "wheat" # Running all usms contained in the workspace run_javastics(javastics_path, workspace_path, verbose = FALSE, java_cmd = java_cmd ) #> [1] "SugarCane" #> [1] "potato" #> [1] "banana" #> [1] "sorghum" #> [1] "sugarbeet" #> [1] "wheat" #> [1] "maize" #> [1] "soybean" #> [1] "lettuce" #> [1] "tomato" #> [1] "DurumWheat" #> [1] "rapeseed" #> [1] "sunflower" #> [1] "grass" #> [1] "BareSoil" #> [1] "demo_Wheat1" #> [1] "demo_BareSoil2" #> [1] "demo_maize3" #> [1] "DurumWheat_4years" #> [1] "maize_4years" #> [1] "strawberry" #> [1] "vine" #> [1] "fescue" #> [1] "flax" #> [1] "intercrop_pea_barley" #> [1] "timothy" #> [1] "Turmeric" #> [1] "cc_BristleOat" #> [1] "cc_mustard" #> [1] "cc_ItalianRyegrass" #> [1] "cc_vetch" #> [1] "cc_CrimsonClover" #> [1] "proto_rice" #> [1] "Miscanthus_2006" #> [1] "Miscanthus_2007" #> [1] "Miscanthus_2008" #> [1] "Miscanthus_2009" #> [1] "Miscanthus_2010" #> [1] "Miscanthus_2011" #> [1] "Miscanthus_2012" #> [1] "Miscanthus_2013" #> [1] "Miscanthus_2014" #> [1] "Miscanthus_2015" # Getting information about execution: runs_info <- run_javastics(javastics_path, workspace_path, usm = c("banana", "wheat"), verbose = FALSE, java_cmd = java_cmd ) #> [1] "banana" #> [1] "wheat" runs_info #> [[1]] #> [[1]]$name #> [1] "banana" #> #> [[1]]$error #> [1] FALSE #> #> [[1]]$message #> [1] "[18/03/25]-[14:11:26] INFO - Files generated :" #> [2] "[18/03/25]-[14:11:26] INFO - \t/home/plecharpent/tmp/JavaSTICS-1.5.3-STICS-10.3.0/example/mod_bbanana.sti" #> [3] "[18/03/25]-[14:11:26] INFO - \t/home/plecharpent/tmp/JavaSTICS-1.5.3-STICS-10.3.0/example/modhistory.sti" #> #> #> [[2]] #> [[2]]$name #> [1] "wheat" #> #> [[2]]$error #> [1] FALSE #> #> [[2]]$message #> [1] "[18/03/25]-[14:11:27] INFO - Files generated :" #> [2] "[18/03/25]-[14:11:27] INFO - \t/home/plecharpent/tmp/JavaSTICS-1.5.3-STICS-10.3.0/example/mod_bwheat.sti" #> [3] "[18/03/25]-[14:11:27] INFO - \t/home/plecharpent/tmp/JavaSTICS-1.5.3-STICS-10.3.0/example/modhistory.sti" ``` 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](https://sticsrpacks.github.io/SticsRFiles/articles/Generating_Stics_text_files.html). Example of use: ``` r # 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 ) ``` ``` r # 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 ) ``` 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 <pre>stics_path <- file.path(javastics_path,"bin","stics_modulo.exe")</pre> - for linux <pre>stics_path <- file.path(javastics_path,"bin","stics_modulo")</pre> - for Mac <pre>stics_path <- file.path(javastics_path,"bin","stics_modulo_mac")</pre> ``` r # 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 #> [[1]] #> [[1]]$name #> [1] "banana" #> #> [[1]]$error #> [1] FALSE #> #> [[1]]$message #> [1] " The execution was successful." " Duration = 37ms" #> #> #> [[2]] #> [[2]]$name #> [1] "wheat" #> #> [[2]]$error #> [1] FALSE #> #> [[2]]$message #> [1] " The execution was successful." " Duration = 56ms" ``` ### 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: ``` r stics_wrapper_options() #> $javastics #> [1] "unknown" #> #> $stics_exe #> [1] "unknown" #> #> $workspace #> [1] "unknown" #> #> $parallel #> [1] FALSE #> #> $cores #> [1] NA #> #> $time_display #> [1] FALSE #> #> $verbose #> [1] TRUE #> #> $force #> [1] FALSE ``` 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: ``` r 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: 1. if it is already listed in the preference (e.g. added in JavaStics), simply provide its name (ID): ``` r sim_options <- stics_wrapper_options( javastics = javastics_path, stics_exe = "stics_custom", workspace = output_path, verbose = FALSE ) ``` 2. if it is located in the bin directory of the JavaStics installation directory, provide the executable name: ``` r sim_options <- stics_wrapper_options( javastics = javastics_path, stics_exe = "stics_custom.exe", workspace = output_path, verbose = FALSE ) ``` 3. if it is located in any other folder, provide the full path to the executable name, and no need to use `javastics_path`: ``` r 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 ``` r results <- stics_wrapper(model_options = sim_options) ``` - Filtering on usms list ``` r usm <- c("wheat", "maize") results <- stics_wrapper(model_options = sim_options, situation = usm) ``` - Filtering outputs on variables ``` r 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. ``` r obs_list <- get_obs( workspace = workspace_path, usm = c("wheat", "maize"), verbose = FALSE ) # Observations table for wheat obs_list$wheat #> Date lai_n masec_n HR_1 HR_2 HR_3 resmes AZnit_1 AZnit_2 AZnit_3 #> 1 1995-01-30 0.29 0.25 NA NA NA NA NA NA NA #> 2 1995-02-03 NA NA 21.1 18.8 12.4 133.26 3.1 2.9 4 #> 3 1995-02-07 0.37 0.31 NA NA NA NA NA NA NA #> 4 1995-02-16 0.40 0.32 NA NA NA NA NA NA NA #> 5 1995-02-24 0.45 0.40 NA NA NA NA NA NA NA #> 6 1995-03-06 0.44 0.38 NA NA NA NA NA NA NA #> 7 1995-03-16 0.60 0.59 NA NA NA NA NA NA NA #> 8 1995-03-23 0.85 0.87 NA NA NA NA NA NA NA #> 9 1995-04-03 1.47 1.28 NA NA NA NA NA NA NA #> 10 1995-04-11 2.37 2.68 NA NA NA NA NA NA NA #> 11 1995-04-18 2.65 3.70 NA NA NA NA NA NA NA #> 12 1995-04-26 4.55 4.67 NA NA NA NA NA NA NA #> 13 1995-05-02 4.41 5.77 NA NA NA NA NA NA NA #> 14 1995-05-05 NA 6.01 NA NA NA NA NA NA NA #> 15 1995-05-09 5.20 7.51 NA NA NA NA NA NA NA #> 16 1995-05-12 NA 9.73 NA NA NA NA NA NA NA #> 17 1995-05-15 5.98 9.87 NA NA NA NA NA NA NA #> 18 1995-05-19 NA 11.08 NA NA NA NA NA NA NA #> 19 1995-05-29 NA 14.08 NA NA NA NA NA NA NA #> 20 1995-07-17 NA 21.91 NA NA NA NA NA NA NA #> QNplante Plant #> 1 9.24 plant_1 #> 2 NA plant_1 #> 3 10.85 plant_1 #> 4 12.61 plant_1 #> 5 13.32 plant_1 #> 6 16.65 plant_1 #> 7 24.59 plant_1 #> 8 38.86 plant_1 #> 9 60.99 plant_1 #> 10 98.98 plant_1 #> 11 103.36 plant_1 #> 12 141.99 plant_1 #> 13 170.03 plant_1 #> 14 NA plant_1 #> 15 188.57 plant_1 #> 16 NA plant_1 #> 17 216.06 plant_1 #> 18 NA plant_1 #> 19 258.38 plant_1 #> 20 258.45 plant_1 sim_options <- stics_wrapper_options( javastics = javastics_path, workspace = output_path, verbose = TRUE ) #> ✔ Using stics: "/home/plecharpent/tmp/JavaSTICS-1.5.3-STICS-10.3.0/bin/stics_modulo" results <- stics_wrapper( model_options = sim_options, sit_var_dates_mask = obs_list ) #> ✔ Using stics: "/home/plecharpent/tmp/JavaSTICS-1.5.3-STICS-10.3.0/bin/stics_modulo" #> mod_smaize.sti #> Warning in select_results(keep_all_data, sit_var_dates_mask, var_names, : #> Requested date(s) 1996-04-16 is(are) not simulated for USM maize #> mod_swheat.sti #> Warning: Requested date(s) 1996-04-16 is(are) not simulated for USM maize head(results) #> $error #> [1] TRUE #> #> $sim_list #> $wheat #> Date lai_n masec_n HR_1 HR_2 HR_3 resmes AZnit_1 #> 1 1995-01-30 0.31050 0.09176 24.50000 24.50000 21.49999 192.3001 1.07899 #> 2 1995-02-03 0.31650 0.11303 24.50000 24.50000 21.49999 192.3001 0.76862 #> 3 1995-02-07 0.31678 0.12561 23.83189 24.38442 21.49393 190.8273 0.60993 #> 4 1995-02-16 0.32971 0.18710 24.50000 24.50000 21.49999 192.3001 0.56298 #> 5 1995-02-24 0.36057 0.24471 24.50000 24.50000 21.49999 192.3001 26.17110 #> 6 1995-03-06 0.44779 0.35035 24.14443 24.38385 21.49153 191.3779 19.42991 #> 7 1995-03-16 0.65951 0.52936 22.92095 23.63181 21.38363 187.1108 9.46764 #> 8 1995-03-23 0.93227 0.71936 21.66335 24.01440 21.42110 185.8192 83.92502 #> 9 1995-04-03 1.57855 1.55586 20.56154 22.34904 20.97554 178.3337 64.59158 #> 10 1995-04-11 2.09987 2.55337 17.92109 20.99722 20.03741 166.5205 52.84022 #> 11 1995-04-18 2.35501 3.15292 23.58026 19.75672 19.03260 169.5803 46.57107 #> 12 1995-04-26 3.14867 3.99307 24.50000 24.50000 21.49999 192.3001 15.92698 #> 13 1995-05-02 4.00809 4.94513 22.89795 23.84206 21.11930 186.3215 79.20721 #> 14 1995-05-05 4.66786 6.02794 20.59809 22.80039 20.38152 176.6742 69.22348 #> 15 1995-05-09 4.84330 7.28487 18.33193 21.14837 19.03372 163.0608 59.09079 #> 16 1995-05-12 4.79468 7.88745 24.50000 24.50000 21.49999 192.3001 26.11958 #> 17 1995-05-15 4.74392 8.47785 23.56760 24.06944 21.22628 188.4858 18.00425 #> 18 1995-05-19 4.57415 9.35892 23.88076 24.22463 21.14586 189.0135 9.34575 #> 19 1995-05-29 3.97903 11.72744 23.74543 21.45070 19.37518 174.9766 3.42815 #> 20 1995-07-17 0.00000 15.87414 21.79237 24.26819 21.48919 186.8909 7.14489 #> AZnit_2 AZnit_3 QNplante Plant #> 1 1.48309 5.94223 6.41090 plant_1 #> 2 1.02958 5.36714 7.28596 plant_1 #> 3 0.77065 5.15434 8.12731 plant_1 #> 4 0.46101 4.17630 10.04601 plant_1 #> 5 6.90490 4.46663 13.25611 plant_1 #> 6 6.11377 4.21979 20.68183 plant_1 #> 7 4.74363 3.64634 32.71018 plant_1 #> 8 3.00263 3.02405 41.23374 plant_1 #> 9 0.56898 0.83703 65.91978 plant_1 #> 10 0.34906 0.21218 79.40998 plant_1 #> 11 0.37182 0.06465 87.25913 plant_1 #> 12 10.59078 3.15716 106.08574 plant_1 #> 13 12.77094 1.92864 126.41372 plant_1 #> 14 9.40575 0.93281 140.18687 plant_1 #> 15 6.08417 0.38794 153.95471 plant_1 #> 16 19.42081 8.97776 165.35762 plant_1 #> 17 16.99088 8.49830 176.44347 plant_1 #> 18 12.89605 7.68170 190.11298 plant_1 #> 19 4.10261 1.08262 211.33588 plant_1 #> 20 6.79536 2.67405 222.61638 plant_1 #> #> $maize #> Date lai_n masec_n mafruit AZnit_1 AZnit_2 AZnit_3 QNplante #> 1 1996-05-14 0.00048 0.00007 0.00000 72.08270 87.81961 18.33810 0.06000 #> 2 1996-06-11 0.52827 0.34602 0.00000 41.25512 101.20517 36.68687 11.47820 #> 3 1996-06-19 1.20877 1.47023 0.00000 32.54289 92.90659 36.48687 35.17423 #> 4 1996-06-26 1.77350 2.31686 0.00000 28.37093 81.70900 35.38020 57.85825 #> 5 1996-07-02 2.45930 3.57229 0.00000 23.37866 68.11766 32.88448 82.31290 #> 6 1996-07-05 2.78486 3.95258 0.00000 16.47478 73.86995 31.59177 89.59813 #> 7 1996-07-15 4.04477 6.19012 0.00000 8.27293 54.08646 32.73148 125.06310 #> 8 1996-07-24 5.80729 9.19373 0.00000 7.73798 36.58909 29.51845 163.77870 #> 9 1996-07-25 5.80729 9.48679 0.00000 7.98975 35.55028 29.72263 167.79420 #> 10 1996-08-02 5.80729 11.66596 0.00000 8.22348 27.48979 22.84721 196.25015 #> 11 1996-08-09 5.80729 13.23096 0.00000 7.37852 20.25412 23.36593 215.35924 #> 12 1996-08-14 5.80729 14.20629 0.00000 5.79748 15.77424 20.80307 226.98660 #> 13 1996-08-20 5.80698 15.78884 0.80934 6.55602 10.70736 14.43709 245.12352 #> 14 1996-09-03 5.79138 18.41684 4.01828 4.90096 5.51324 6.09317 274.03082 #> 15 1996-09-12 5.54056 20.32296 6.55791 4.04308 3.14270 3.69525 289.17609 #> 16 1996-10-15 4.31971 23.43360 13.78294 2.21475 0.99676 1.10243 318.40985 #> 17 1996-10-23 3.54667 23.99480 14.85000 2.04174 0.75705 0.80540 322.53854 #> Plant #> 1 plant_1 #> 2 plant_1 #> 3 plant_1 #> 4 plant_1 #> 5 plant_1 #> 6 plant_1 #> 7 plant_1 #> 8 plant_1 #> 9 plant_1 #> 10 plant_1 #> 11 plant_1 #> 12 plant_1 #> 13 plant_1 #> 14 plant_1 #> 15 plant_1 #> 16 plant_1 #> 17 plant_1 #> #> attr(,"class") #> [1] "cropr_simulation" ``` Some warnings may occur, as in this case (that is why `results$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. ``` r 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. ``` r # 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 #> situation dlaimax durvieF #> 1 wheat 0.001 50 #> 2 pea 0.001 60 #> 3 maize 0.001 70 results <- stics_wrapper( model_options = sim_options, param_values = param_values, situation = c("wheat", "maize") ) #> ✔ Using stics: "/home/plecharpent/tmp/JavaSTICS-1.5.3-STICS-10.3.0/bin/stics_modulo" #> mod_smaize.sti #> mod_swheat.sti ``` #### 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 ``` r 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. ``` r 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. ``` r 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](https://github.com/SticsRPacks/SticsOnR/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](CODE_OF_CONDUCT.md). 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](https://github.com/SticsRPacks/SticsOnR/issues) at any time.
