README

R wrapper for the funz-fz Python package using reticulate. fz is a parametric scientific computing framework: it wraps simulation codes to run parameter sweeps, design of experiments, and iterative algorithm-driven studies.

Installation

# install.packages("devtools")
devtools::install_github("Funz/fz.R")

Python dependency

library(fz)
fz_install()

reticulate::py_install("funz-fz")

Core functions

Function	Purpose
`fzi(input_path, model)`	Parse variable names and defaults from a template file
`fzc(input_path, input_variables, model)`	Compile template — substitute variable values
`fzr(input_path, input_variables, model, ...)`	Run full parametric study
`fzo(output_path, model)`	Read and parse output files
`fzl(models, calculators, check)`	List installed models and calculators
`fzd(input_path, input_variables, model, output_expression, algorithm, ...)`	Algorithm-driven iterative DoE

The model argument is either a string alias (name of an installed model, e.g. "PerfectGas") or an inline named list describing how variables are marked in the template and how outputs are extracted.

Output values can be a shell command (the default) or, with funz-fz >= 1.2, one of the shell-free extractors python://, jq://, yq://, xpath:// (portable on Windows without bash). An output may also resolve to a vector (time series, spectrum, …).

Usage

1 — List installed models

library(fz)

info <- fzl()
names(info$models)       # e.g. c("PerfectGas")
names(info$calculators)  # e.g. c("sh://")

2 — Parse variables from a template

# Template file: input.txt
# pressure = ${P~1.013}
# volume   = ${V~22.4}

model <- list(
  varprefix = "$", delim = "{}", formulaprefix = "@", commentline = "#"
)

vars <- fzi("input.txt", model)
# vars$P == 1.013  (default value)
# vars$V == 22.4

3 — Run a parametric study

# fzr compiles the template for every combination, runs the model via the
# calculator, and collects all outputs into a data frame.

model <- list(
  varprefix = "$", delim = "{}", formulaprefix = "@", commentline = "#",
  output = list(pressure = "grep 'pressure' output.txt | cut -d= -f2")
)

results <- fzr(
  "input.txt",
  list(P = c(1.0, 2.0, 3.0), V = 22.4),  # 3 cases
  model,
  calculators = "sh://bash run.sh"
)
# results is a data frame with columns P, V, pressure

4 — Algorithm-driven design of experiments

# fzd iteratively queries the model using an algorithm (e.g. Monte Carlo,
# surrogate-based optimisation). Input ranges use "[min;max]" strings.

result <- fzd(
  "input.txt",
  list(P = "[1;5]", V = "[10;30]"),
  model,
  output_expression = "pressure",
  algorithm        = "algorithms/montecarlo_uniform.py",
  algorithm_options = list(batch_sample_size = 10, max_iterations = 5)
)

# `output_expression` may also be a character vector for multi-objective
# algorithms (e.g. NSGA-II): `c("cost", "-efficiency")`.

5 — Step-by-step workflow

# Step 1: inspect which variables the template exposes
vars <- fzi("input.txt", model)

# Step 2: compile for specific values (no execution)
fzc("input.txt", list(P = 2.0, V = 11.2), model, output_dir = "compiled")

# Step 3: read output files after running the simulator externally
values <- fzo("compiled/P=2,V=11.2", model)

A complete runnable example: an external simulator

The snippets above use a placeholder run.sh. Here is a self-contained parametric study driven by a real external program — a tiny Python simulator of the ideal gas law P = n R T / V. Both files ship with the package under inst/examples/perfectgas/:

library(fz)
# fz_install()  # once, if the funz-fz Python package is not yet installed

ex <- system.file("examples", "perfectgas", package = "fz")
file.copy(list.files(ex, full.names = TRUE), ".")  # perfectgas.txt + perfectgas.py

perfectgas.txt is the input template (${T~300} is variable T, default 300):

perfectgas.py reads the compiled perfectgas.txt in its working directory, computes the pressure, and writes pressure = <value> to out.txt.

model <- list(
  varprefix = "$", delim = "{}", formulaprefix = "@", commentline = "#",
  # shell-free output extraction (funz-fz >= 1.2)
  output = list(pressure = 'python://grep(r"pressure = (\\S+)", "out.txt")')
)

results <- fzr(
  "perfectgas.txt",
  list(T = c(300, 350, 400), V = 1e-3, n = 1),   # 3 cases
  model,
  calculators  = "sh://python3 perfectgas.py",   # the external simulator
  input_static = "perfectgas.py"                  # shipped into every case dir (funz-fz >= 1.2)
)
results[, c("T", "V", "n", "pressure")]
#>     T     V n pressure
#> 1 300 0.001 1  2494339
#> 2 350 0.001 1  2910062
#> 3 400 0.001 1  3325785

The same model works with fzd() for an algorithm-driven study — pass input_variables as "[min;max]" ranges (or a fixed "1") and keep calculators = "sh://python3 perfectgas.py", input_static = "perfectgas.py".

System requirements

Development

devtools::test()   # run tests
devtools::check()  # R CMD check

fz