Package 'rjsf'

Convert a JSF output data frame to a JSFResult object

Description

Convert a JSF output data frame to a JSFResult object

Usage

as_jsf_result(
  x,
  method = "operator-splitting",
  config = list(),
  species_names = NULL
)

Arguments

x	Data frame returned by jsf_simulate() or a model-specific wrapper. It must contain a time column.
method	JSF simulation method used.
config	List of simulation configuration values.
species_names	Optional character vector of species columns to include.

Value

A JSFResult object.

Examples

df <- data.frame(
  time = c(0, 0.1, 0.2),
  S = c(990, 988, 985),
  I = c(10, 12, 15),
  R = c(0, 0, 0)
)

result <- as_jsf_result(
  df,
  config = list(model = "SIR", dt = 0.1)
)

result
summary(result)

---

Check whether the Python jsf package is available

Description

This function checks whether the Python jsf module can be found by reticulate in the currently active Python environment.

Usage

jsf_available()

Value

A single logical value. TRUE if jsf is available, otherwise FALSE.

Examples

if (reticulate::py_available(initialize = FALSE)) {
  jsf_available()
}

---

Simulate a Lotka-Volterra predator-prey model using JSF

Description

This function runs a simple Lotka-Volterra predator-prey model using the Python jsf package through reticulate.

Usage

jsf_lotka_volterra(
  prey0 = 50,
  predator0 = 10,
  prey_birth = 2,
  predation = 0.05,
  predator_death = 1.5,
  t_max = 10,
  dt = 0.01,
  switching_threshold = 30
)

Arguments

prey0	Initial prey population.
predator0	Initial predator population.
prey_birth	Prey birth rate.
predation	Predation rate.
predator_death	Predator death rate.
t_max	Final simulation time.
dt	Time step for the continuous/ODE part of JSF.
switching_threshold	Switching threshold for both species.

Value

A data frame with columns time, prey, and predator.

Examples

if (interactive() && jsf_available()) {
    result <- jsf_lotka_volterra(
    prey0 = 50,
    predator0 = 10,
    t_max = 1
  )

  head(result)
}

---

Simulate a compartmental model using JSF

Description

This is a general R wrapper around the Python jsf simulator. It accepts an initial state, a Python rate function, reactant and product stoichiometry matrices, and JSF configuration options.

Usage

jsf_simulate(
  x0,
  rates,
  reactant,
  product,
  do_disc,
  t_max,
  dt,
  switching_threshold,
  tau_threshold = NULL,
  tau_epsilon = 0.03,
  critical_threshold = 10,
  tau_debug = FALSE,
  tau_debug_every = 20,
  tau_debug_max = 30,
  species_names = NULL,
  enforce_do = NULL,
  method = "operator-splitting",
  return_type = c("data.frame", "JSFResult")
)

Arguments

x0	Numeric vector of initial states.
rates	Python callable rate function, usually created with reticulate::py_eval().
reactant	Matrix where each row gives the reactants for one reaction.
product	Matrix where each row gives the products for one reaction.
do_disc	Integer vector indicating which species may be treated discretely/stochastically.
t_max	Final simulation time.
dt	Time step for the continuous/ODE part of JSF.
switching_threshold	Switching threshold. Can be a scalar or one value per species.
tau_threshold	Threshold controlling when species are handled by the tau-leaping component. May be NULL, a scalar, or one value per species. If NULL, defaults to switching_threshold.
tau_epsilon	Positive numeric scalar specifying the tau-leaping epsilon parameter passed to the Python JSF backend.
critical_threshold	Non-negative numeric scalar specifying the critical-reaction threshold passed to the Python JSF backend.
tau_debug	Logical. If TRUE, enables diagnostic output from the tau-leaping simulator.
tau_debug_every	Integer controlling how often tau-leaping diagnostic information is printed.
tau_debug_max	Integer giving the maximum number of tau-leaping diagnostic messages to print.
species_names	Optional character vector of species names.
enforce_do	Integer vector controlling enforced discrete behaviour. Defaults to zero for all species.
method	JSF simulation method. Supported values include "exact", "operator-splitting", and "tau-operator-splitting".
return_type	Output type. Either "data.frame" or "JSFResult".

Value

If return_type = "data.frame", a data frame with a time column and one column per species. If return_type = "JSFResult", a JSFResult object.

Examples

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

  result <- jsf_simulate(
    x0 = c(prey = 50, predator = 10),
    rates = rates_lv,
    reactant = matrix(
      c(
        1, 0,
        0, 1,
        1, 1
      ),
      ncol = 2,
      byrow = TRUE
    ),
    product = matrix(
      c(
        2, 0,
        0, 0,
        0, 2
      ),
      ncol = 2,
      byrow = TRUE
    ),
    do_disc = c(1, 1),
    t_max = 1,
    dt = 0.01,
    switching_threshold = c(30, 30),
    species_names = c("prey", "predator"),
    return_type = "JSFResult"
  )

  result
  summary(result)
  plot(result)
}

---

Simulate an SIR epidemic model using JSF

Description

This function runs a simple SIR epidemic model using the Python jsf package through reticulate.

Usage

jsf_sir(
  N = 1000,
  I0 = 10,
  beta = 0.5,
  gamma = 0.1,
  t_max = 60,
  dt = 0.1,
  switching_threshold_I = 10
)

Arguments

N	Total population size.
I0	Initial infectious population.
beta	Infection rate.
gamma	Recovery rate.
t_max	Final simulation time.
dt	Time step for the continuous/ODE part of JSF.
switching_threshold_I	Switching threshold for the infectious compartment.

Value

A data frame with columns time, S, I, and R.

Examples

if (interactive() && jsf_available()) {
  result <- jsf_sir(
    N = 1000,
    I0 = 10,
    beta = 0.5,
    gamma = 0.1,
    t_max = 1,
    dt = 0.1,
    switching_threshold_I = 10
  )

  head(result)
}

---

Create a JSFResult object

Description

Create a JSFResult object

Usage

new_jsf_result(
  time,
  state,
  species_names = names(state),
  method = "operator-splitting",
  config = list()
)

Arguments

time	Numeric vector of simulation times.
state	Data frame of state trajectories.
species_names	Character vector of species names. Defaults to the column names of state.
method	JSF simulation method used.
config	List of simulation configuration values.

Value

A JSFResult object.

Examples

state <- data.frame(
  prey = c(50, 51, 52),
  predator = c(10, 9, 9)
)

result <- new_jsf_result(
  time = c(0, 0.1, 0.2),
  state = state,
  config = list(dt = 0.1)
)

result
summary(result)

---