Getting started with rjsf

Set up Python JSF, define compartmental models, and run Jump-Switch-Flow simulations from R.

Introduction

rjsf provides an R interface to the Python jsf package for Jump-Switch-Flow simulation.

The main user-facing function is jsf_simulate(). It lets users:

1. Define a compartmental reaction model in R.

2. Pass the model to the Python jsf backend through reticulate.

3. Return the simulation output as either:

   • a regular R data.frame; or
   • a structured JSFResult object.

A JSFResult object supports standard methods such as:

print(result)
summary(result)
plot(result)

Set up Python jsf

rjsf uses reticulate to communicate with Python. The Python jsf package must therefore already be installed in the Python environment used by reticulate.

Installation instructions for the Python dependencies are provided in the package README.

You can inspect the Python environment selected by reticulate with:

reticulate::py_config()

Check Python jsf

Load rjsf and check whether the Python jsf module is available:

library(rjsf)

jsf_available()

A correctly configured environment should return:

[1] TRUE

If it returns FALSE, check that:

• Python jsf is installed;
• it is installed in the environment currently used by reticulate; and
• reticulate::py_config() reports the expected Python executable.

Lotka-Volterra predator-prey model

The Lotka-Volterra model describes interactions between two species:

• prey;
• predator.

The model contains three reactions:

1. Prey birth: Prey -> 2 Prey
2. Predator death: Predator -> NULL
3. Predation: Prey + Predator -> 2 Predator

The state vector is:

x <- c(prey, predator)

Define the rate function

The Python jsf backend expects the rate function to be a Python callable. We create one using reticulate::py_eval():

rates_lv <- reticulate::py_eval(
  "lambda x, t: [2.0 * x[0], 1.5 * x[1], 0.05 * x[0] * x[1]]"
)

The three rates correspond to:

2.0 * prey                # prey birth
1.5 * predator            # predator death
0.05 * prey * predator    # predation

Define the stoichiometry

The reactant matrix describes what each reaction consumes:

reactant_lv <- matrix(
  c(
    1, 0,
    0, 1,
    1, 1
  ),
  ncol = 2,
  byrow = TRUE
)

reactant_lv

The product matrix describes what each reaction produces:

product_lv <- matrix(
  c(
    2, 0,
    0, 0,
    0, 2
  ),
  ncol = 2,
  byrow = TRUE
)

product_lv

Internally, jsf_simulate() calculates the net stoichiometry matrix as:

nu = product - reactant

For this model, the net state changes are:

Prey -> 2 Prey                 [+1,  0]
Predator -> NULL                  [ 0, -1]
Prey + Predator -> 2 Predator  [-1, +1]

Run the simulation

Run the model and request a JSFResult object:

result_lv <- jsf_simulate(
  x0 = c(prey = 50, predator = 10),
  rates = rates_lv,
  reactant = reactant_lv,
  product = product_lv,
  do_disc = c(1, 1),
  t_max = 10,
  dt = 0.01,
  switching_threshold = c(30, 30),
  species_names = c("prey", "predator"),
  return_type = "JSFResult"
)

The important arguments are:

• x0 defines the initial number of prey and predators.
• do_disc = c(1, 1) allows both species to be treated discretely when required.
• switching_threshold = c(30, 30) defines the switching threshold for each species.
• return_type = "JSFResult" returns a structured result containing the simulation trajectory and associated metadata.

Inspect the result

Print the result:

result_lv

Calculate a summary:

summary(result_lv)

Plot the simulated trajectories:

plot(result_lv)

SIR epidemic model

The SIR model describes the spread of an infectious disease through three compartments:

• S: susceptible;
• I: infectious;
• R: recovered.

The model contains two reactions:

1. Infection: S + I -> 2 I
2. Recovery: I -> R

The state vector is:

x <- c(S, I, R)

Define the model parameters

Set the total population, infection rate, and recovery rate:

N <- 1000
beta <- 0.5
gamma <- 0.1

Here:

N      = total population
beta   = infection rate
gamma  = recovery rate

Define the rate function

Create a Python callable containing the infection and recovery rates:

rates_sir <- reticulate::py_eval(sprintf(
  "lambda x, t: [%f * x[0] * x[1] / %f, %f * x[1]]",
  beta,
  N,
  gamma
))

The rates correspond to:

beta * S * I / N    # infection
gamma * I           # recovery

Define the stoichiometry

Define the reactant matrix:

reactant_sir <- matrix(
  c(
    1, 1, 0,
    0, 1, 0
  ),
  ncol = 3,
  byrow = TRUE
)

reactant_sir

Define the product matrix:

product_sir <- matrix(
  c(
    0, 2, 0,
    0, 0, 1
  ),
  ncol = 3,
  byrow = TRUE
)

product_sir

The net state changes are:

S + I -> 2 I    [-1, +1,  0]
I -> R          [ 0, -1, +1]

Run the simulation

result_sir <- jsf_simulate(
  x0 = c(S = N - 10, I = 10, R = 0),
  rates = rates_sir,
  reactant = reactant_sir,
  product = product_sir,
  do_disc = c(0, 1, 0),
  t_max = 60,
  dt = 0.1,
  switching_threshold = c(N, 10, N),
  species_names = c("S", "I", "R"),
  return_type = "JSFResult"
)

In this example, do_disc = c(0, 1, 0) allows the infectious compartment to be treated discretely when required.

This is useful because stochastic effects can be particularly important when the number of infectious individuals is small.

Inspect the result

Print the result:

result_sir

Calculate a summary:

summary(result_sir)

Plot the simulated trajectories:

plot(result_sir)

Return a data frame

The default output from jsf_simulate() is a regular R data.frame.

df_lv <- jsf_simulate(
  x0 = c(prey = 50, predator = 10),
  rates = rates_lv,
  reactant = reactant_lv,
  product = product_lv,
  do_disc = c(1, 1),
  t_max = 10,
  dt = 0.01,
  switching_threshold = c(30, 30),
  species_names = c("prey", "predator"),
  return_type = "data.frame"
)

head(df_lv)

A data frame returned by jsf_simulate() contains:

• a time column; and
• one column for each model species.

A data frame can later be converted into a JSFResult object:

result_from_df <- as_jsf_result(
  df_lv,
  method = "operator-splitting",
  config = list(
    model = "Lotka-Volterra",
    dt = 0.01,
    switching_threshold = c(30, 30)
  )
)

result_from_df

Summary

The main rjsf workflow is:

Define the initial state
  -> define the rate function
  -> define the reactant and product matrices
  -> call jsf_simulate()
  -> return a data.frame or JSFResult
  -> inspect the output with print(), summary(), and plot()

Use a data.frame when you primarily need the simulation values for further analysis.

Use a JSFResult when you want a structured object containing both the simulation trajectories and their associated metadata.