In syncrosim/rsyncrosim: The R Interface to 'SyncroSim'

This vignette will cover how to implement model pipelines using the rsyncrosim package within the SyncroSim software framework. For an overview of SyncroSim and rsyncrosim, as well as a basic usage tutorial for rsyncrosim, see the Introduction to rsyncrosim vignette. To learn how to use iterations in the rsyncrosim interface, see the rsyncrosim: introduction to uncertainty vignette.

SyncroSim Package: helloworldPipeline

To demonstrate how to link models in a pipeline using the rsyncrosim interface, we will need the helloworldPipeline SyncroSim package. helloworldPipeline was designed to be a simple package to introduce pipelines to SyncroSim modeling workflows. Models (i.e. transformers) connected by pipelines allow the user to implement multiple transformers in a modeling workflow and access intermediate outputs of a transformer without having to create multiple scenarios.

The package takes from the user 3 inputs, mMean, mSD, and b. For each iteration, a value m, representing the slope, is sampled from a normal distribution with mean of mMean and standard deviation of mSD. The b value represents the intercept. In the first model in the pipeline, these input values are run through a linear model, y=mt+b, where t is time, and the y value is returned as output. The second model takes y as input and calculates the cumulative sum of y over time, returning a new variable yCum as output.

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For more details on the different features of the helloworldPipeline SyncroSim package, consult the SyncroSim Enhancing a Package: Linking Models tutorial.

Setup

Install SyncroSim

Before using rsyncrosim you will first need to download and install the SyncroSim software. Versions of SyncroSim exist for both Windows and Linux.

Note: this tutorial was developed using rsyncrosim version 2.0. To use rsyncrosim version 2.0 or greater, SyncroSim version 3.0 or greater is required.

Installing and loading R packages

You will need to install the rsyncrosim R package, either using CRAN or from the rsyncrosim GitHub repository. Versions of rsyncrosim are available for both Windows and Linux.

In a new R script, load the rsyncrosim package.

# Load R package for working with SyncroSim
library(rsyncrosim)

Connecting R to SyncroSim using session()

Finish setting up the R environment for the rsyncrosim workflow by creating a SyncroSim Session object. Use the session() function to connect R to your installed copy of the SyncroSim software.

mySession <- session("path/to/install_folder")      # Create a Session based SyncroSim install folder
mySession <- session()                              # Using default install folder (Windows only)
mySession                                           # Displays the Session object

# Results of this code shown for above
mySession <- session()                              # Using default install folder (Windows only)
mySession                                           # Displays the Session object

Use the version() function to ensure you are using the latest version of SyncroSim.

version(mySession)

Installing SyncroSim packages using installPackage()

Install helloworldPipeline using the rynscrosim function installPackage(). This function takes a package name as input and then queries the SyncroSim package server for the specified package.

installedPackages <- packages()
if (is.element(
  "helloworldPipeline", installedPackages$name)) uninstallPackage(
    "helloworldPipeline")

# Install helloworldPipeline
installPackage("helloworldPipeline")

helloworldPipeline should now be included in the package list returned by the packages() function in rsyncrosim:

# Get list of installed packages
packages()

installedPackages <- packages()
pipeline_pkg <- installedPackages[installedPackages$name == "helloworldPipeline", ]
row.names(pipeline_pkg) <- NULL
pipeline_pkg

Create a modeling workflow

When creating a new modeling workflow from scratch, we need to create objects of the following scopes:

• Library
• Projects
• Scenarios

For more information on these scopes, see the Introduction to rsyncrosim vignette.

Set up library, project, and scenario

if (file.exists("helloworldLibrary.ssim")){
  deleteLibrary("helloworldLibrary.ssim", force = TRUE)
}

# Create a new library
myLibrary <- ssimLibrary(name = "helloworldLibrary.ssim",
                         session = mySession,
                         packages = "helloworldPipeline",
                         overwrite = TRUE)

# Open the default project
myProject = project(ssimObject = myLibrary, project = "Definitions")

# Create a new scenario (associated with the default project)
myScenario = scenario(ssimObject = myProject, scenario = "My first scenario")

View model inputs using datasheet()

View the datasheets associated with your new scenario using the datasheet() function from rsyncrosim.

# View all datasheets associated with a library, project, or scenario
datasheet(myScenario)

From the list of datasheets above, we can see that there are four datasheets specific to the helloworldPipeline package, including an Inputs datasheet, an Intermediate Outputs datasheet, and an Outputs datasheet. These three datasheets are connected by transformers. The values from the Inputs datasheet are used as the input for the first transformer, which transforms the input data to output data through a series of model calculations. The output data from the first transformer is contained within the Intermediate Outputs datasheet. The values from the Intermediate Outputs datasheet are then used as input for the second transformer. The output from the second transformer is stored in the Outputs datasheet.

Configure model inputs using datasheet() and addRow()

Currently our input scenario datasheets are empty! We need to add some values to our Inputs datasheet (InputDatasheet) and Run Control datasheet (RunControl) so we can run our model. We also need to add some information to the core Pipeline datasheet to specify which transformers are run in which order.

Inputs Datasheet

First, assign the contents of the Inputs datasheet to a new data frame variable using datasheet(), then check the columns that need input values.

# Load Inputs datasheet to a new R data frame
myInputDataframe <- datasheet(myScenario,
                              name = "helloworldPipeline_InputDatasheet")

# Check the columns of the input data frame
str(myInputDataframe)

The Inputs datasheet requires three values:

• mMean : the mean of the slope normal distribution.
• mSD : the standard deviation of the slope normal distribution.
• b : the intercept of the linear equation.

Add these values to a new data frame, then use the addRow() function from rsyncrosim to update the input data frame

# Create input data and add it to the input data frame
myInputRow <- data.frame(mMean = 2, mSD = 4, b = 3)
myInputDataframe <- addRow(myInputDataframe, myInputRow)

# Check values
myInputDataframe

Finally, save the updated R data frame to a SyncroSim datasheet using saveDatasheet().

# Save input R data frame to a SyncroSim datasheet
saveDatasheet(ssimObject = myScenario, 
              data = myInputDataframe,
              name = "helloworldPipeline_InputDatasheet")

Run Control Datasheet

The Run Control datasheet provides information about how many time steps and iterations to use in the model. Here, we set the number of iterations, as well as the minimum and maximum time steps for our model. Let's take a look at the columns that need input values.

# Load Run Control datasheet to a new R data frame
runSettings <- datasheet(myScenario, name = "helloworldPipeline_RunControl")

# Check the columns of the Run Control data frame
str(runSettings)

The Run Control datasheet requires the following 3 columns:

• MaximumIteration : total number of iterations to run the model for.
• MinimumTimestep : the starting time point of the simulation.
• MaximumTimestep : the end time point of the simulation.

Note: A fourth hidden column, MinimumIteration, also exists in the Run Control datasheet (default=1).

We'll add this information to a new data frame and then add it to the Run Control data frame using addRow().

# Create Run Control data and add it to the Run Control data frame
runSettingsRow <- data.frame(MaximumIteration = 5,
                             MinimumTimestep = 1,
                             MaximumTimestep = 10)
runSettings <- addRow(runSettings, runSettingsRow)

# Check values
runSettings

Finally, save the R data frame to a SyncroSim datasheet using saveDatasheet().

# Save Run Control R data frame to a SyncroSim datasheet
saveDatasheet(ssimObject = myScenario, data = runSettings,
              name = "helloworldPipeline_RunControl")

Pipeline Datasheet

To implement pipelines in our package, we need to specify the order in which to run the transformers in our pipeline by adding data to the Pipeline datasheet. The Pipeline datasheet is a built-in SyncroSim datasheet, meaning that it comes with every SyncroSim library regardless of which packages that library uses. We access it using the "core_" prefix with the datasheet() function.

From viewing the structure of the Pipeline datasheet we know that the StageNameId is a factor with two levels:

• Hello World Pipeline 1 (R)
• Hello World Pipeline 2 (R)

We will set the data for this datasheet such that Hello World Pipeline 1 (R) is run first, then Hello World Pipeline 2 (R). This way, the output from Hello World Pipeline 1 (R) is used as the input for Hello World Pipeline 2 (R).

# Load Pipeline datasheet to a new R data frame
myPipelineDataframe <- datasheet(myScenario, name = "core_Pipeline")

# Check the columns of the Pipeline data frame
str(myPipelineDataframe)

# Create Pipeline data and add it to the Pipeline data frame
myPipelineRow <- data.frame(StageNameId = c("Hello World Pipeline 1 (R)", 
                                            "Hello World Pipeline 2 (R)"),
                            RunOrder = c(1, 2))

myPipelineDataframe <- addRow(myPipelineDataframe, myPipelineRow)

# Check values
myPipelineDataframe

# Save Pipeline R data frame to a SyncroSim datasheet
saveDatasheet(ssimObject = myScenario, data = myPipelineDataframe,
              name = "core_Pipeline")

Run Scenarios

Setting run parameters with run()

We will now run our scenario using the run() function in rsyncrosim.

If we have a large model and we want to parallelize the run using multiprocessing, we can modify the library-scoped "core_Multiprocessing" datasheet. Since we are using five iterations in our model, we will set the number of jobs to five so each multiprocessing core will run a single iteration.

# Load list of available library-scoped datasheets
datasheet(myLibrary)

# Load the library-scoped multiprocessing datasheet
multiprocess <- datasheet(myLibrary, name = "core_Multiprocessing")

# Check required inputs
str(multiprocess)

# Enable multiprocessing
multiprocess$EnableMultiprocessing <- TRUE

# Set maximum number of jobs to 5
multiprocess$MaximumJobs <- 5

# Save multiprocessing configuration
saveDatasheet(ssimObject = myLibrary, 
              data = multiprocess, 
              name = "core_Multiprocessing")

Now, when we run our scenario, it will use the desired multiprocessing configuration.

# Run the first scenario we created
myResultScenario <- run(myScenario)

Once the run is complete, we can compare the original scenario to the result scenario to see which datasheets have been modified. Using the datasheet() function with the optional argument set to TRUE, we see that data has been added to both the Intermediate Outputs and Outputs datasheets after running the scenario (see data column below).

# Datasheets for original scenario
datasheet(myScenario, optional = TRUE)

# Datasheets for result scenario
datasheet(myResultScenario, optional = TRUE)

View results

The next step is to view the output datasheets added to the result scenario when it was run.

Viewing intermediate results with datasheet()

First, we will view the Intermediate Outputs datasheet from the result scenario. We can load the result tables using the datasheet() function. The Intermediate Outputs datasheet corresponds to the results from the Hello World Pipeline 1 transformer stage.

# Results of first scenario
resultsSummary <- datasheet(myResultScenario,
                            name = "helloworldPipeline_IntermediateDatasheet")

# View results table
head(resultsSummary)

We can see that for every timestep in an iteration we have a new value of y corresponding to y=mt+b.

Viewing final results with datasheet()

Now, we will view the final output datasheet from the result scenario. Again, we will use datasheet() to load the result table. The Outputs datasheet corresponds to the results from the Hello World Pipeline 2 transformer stage.

# Results of first scenario
resultsSummary <- datasheet(myResultScenario,
                            name = "helloworldPipeline_OutputDatasheet")

# View results table
head(resultsSummary)

We can see for each timestep in an iteration, we have a new value of yCum, representing the cumulative value of y over time.

syncrosim/rsyncrosim documentation built on June 15, 2025, 8:21 a.m.