Help for package fz

Type:

Package

Title:

R Wrapper for the 'funz-fz' Parametric Simulation Framework

Version:

1.2.0

Description:

Provides R bindings to the 'funz-fz' Python package using 'reticulate'. The 'fz' framework wraps arbitrary simulation codes to run parameter sweeps, design-of-experiments studies, and iterative algorithm-driven analyses by substituting variable placeholders in text input files and collecting outputs into data frames. Calculators can run locally (shell), over SSH, or on 'SLURM' clusters. See https://github.com/Funz/fz for the underlying framework.

License:

BSD_3_clause + file LICENSE

Encoding:

UTF-8

Language:

en-US

SystemRequirements:

Python (>= 3.8), funz-fz Python package

Imports:

reticulate (≥ 1.28)

Suggests:

testthat (≥ 3.0.0), knitr, rmarkdown

Config/reticulate:

list( packages = list( list(package = "funz-fz") ) )

VignetteBuilder:

knitr

URL:

https://github.com/Funz/fz.R

BugReports:

https://github.com/Funz/fz.R/issues

Config/roxygen2/version:

8.0.0

NeedsCompilation:

Packaged:

2026-09-04 16:05:19 UTC; richet

Author:

Yann Richet

[aut, cre]

Maintainer:

Yann Richet <yann.richet@asnr.fr>

Repository:

CRAN

Date/Publication:

2026-09-04 16:30:02 UTC

fz: R Wrapper for the 'funz-fz' Parametric Simulation Framework

Description

Author(s)

Maintainer: Yann Richet yann.richet@asnr.fr (ORCID)

Authors:

Yann Richet yann.richet@asnr.fr (ORCID)

Check if fz Python Package is Available

Description

Checks whether the fz Python package is available in the current Python environment.

Usage

fz_available()

Details

The importable module name of the funz-fz package is fz. This function returns TRUE only when a module named fz imports and exposes the expected API (fzi/fzc/fzo/ fzr), so that an unrelated Python package that also happens to be importable as fz is not mistaken for funz-fz. All examples and tests are guarded with if (fz_available()) and therefore skip (rather than error) when the Python side is missing or misconfigured.

Value

Logical; TRUE if fz is available, FALSE otherwise.

Examples


if (fz_available()) {
  message("fz is available!")
} else {
  message("Please install fz with fz_install()")
}

Install the fz Python Package

Description

This function installs the fz Python package into a virtual environment or conda environment managed by reticulate.

Usage

fz_install(
  packages = "funz-fz",
  method = "auto",
  conda = "auto",
  pip = TRUE,
  ...
)

Arguments

packages

Package specification passed to reticulate::py_install(). Default "funz-fz" installs the latest release from PyPI. To track unreleased features, install the latest main branch directly from GitHub with "git+https://github.com/Funz/fz.git".

method

Installation method. Either "auto", "virtualenv", or "conda".

conda

Path to conda executable. Only used when method is "conda".

pip

Logical; use pip for installation? Default is TRUE.

...

Additional arguments passed to reticulate::py_install().

Value

NULL (invisibly). Called for side effects.

Examples

## Not run: 
# Install fz in a virtual environment
fz_install()

# Install in a conda environment
fz_install(method = "conda")

# Track the latest main branch on GitHub (unreleased features)
fz_install(packages = "git+https://github.com/Funz/fz.git")

## End(Not run)

fzc Function

Description

Compiles input file(s) by replacing variable placeholders with values. Each unique combination of values is written to its own subdirectory inside output_dir, named var1=val1,var2=val2,....

Usage

fzc(
  input_path,
  input_variables,
  model,
  output_dir = "output",
  input_static = NULL
)

Arguments

input_path

Path to input file or directory.

input_variables

Named list of variable values. Supply a vector of values to generate a full-factorial grid across variables.

model

Model definition dict or alias string.

output_dir

Output directory for compiled files. Default "output".

input_static

Optional character vector of files that are identical across every case (see fzr's input_static). They are symlinked into output_dir rather than templated or duplicated. Default NULL.

Value

NULL (invisibly). Called for side effects.

Examples


if (fz_available()) {
  tf <- tempfile(fileext = ".txt")
  writeLines(c("P = ${P~1.013}", "V = ${V~22.4}"), tf)

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

  fzc(tf, list(P = 2.0, V = 11.2), model, out)
  fzc(tf, list(P = c(1.0, 2.0), V = c(11.2, 22.4)), model, out)
}

fzd Function

Description

Runs an iterative design of experiments driven by an algorithm. Unlike fzr (which evaluates a fixed grid), fzd lets an algorithm adaptively choose which parameter combinations to evaluate, which is useful for sensitivity analysis, surrogate-model fitting, or optimization.

Usage

fzd(
  input_path,
  input_variables,
  model,
  output_expression = NULL,
  algorithm,
  calculators = NULL,
  algorithm_options = NULL,
  analysis_dir = "analysis",
  input_static = NULL
)

Arguments

input_path

Path to input file or directory. Must be NULL when model is an R function (see "Direct function model" below).

input_variables

Named list of variable range strings of the form "[min;max]", e.g. list(x = "[0;1]", y = "[-5;5]").

model

Model definition dict or alias string, or an R function (see "Direct function model" below).

output_expression

Expression evaluated on the model outputs to produce the quantity the algorithm optimizes or analyses, e.g. "result" or "out1 + 2 * out2". Vector-valued outputs may be reduced with mean(), sum(), len(), median(), stdev(), variance(), indexing/slicing, and zip(). A character vector of length > 1 requests a multi-objective run (funz-fz >= 1.2): each case then yields one scalar per expression, passed as-is to multi-objective algorithms such as NSGA-II. May be NULL only when model is a function, in which case the first output value is used.

algorithm

Path to the algorithm Python file, e.g. "algorithms/montecarlo_uniform.py".

calculators

Calculator specification(s). Default NULL. When model is a function, this must be a single integer (default 1L) and is always forced to 1L (see "Direct function model" below): R functions are only safe to call from the main thread, so a value other than 1 triggers a warning and is overridden.

algorithm_options

Algorithm options as a named list, a JSON string, or a path to a JSON file. Default NULL.

analysis_dir

Analysis directory. Default "analysis".

input_static

Optional character vector of files identical across every case (see fzr's input_static); passed through unchanged to each iteration's internal fzr() call. Default NULL.

Value

Named list with the analysis results produced by the algorithm.

Direct function model

Instead of a file-based model, model can be an R function. This requires funz-fz >= 1.2 (earlier releases do not support callable models); a development build can be installed with fz_install(packages = "git+https://github.com/Funz/fz.git"). In this mode:

input_path must be NULL – there are no input files.
input_variables names must match the function's arguments.
output_expression may be NULL; the value used is then the first element of the function's return value (its return value directly if scalar, the first element if a vector/list, or the first entry's value if a named list).
calculators must be a single integer, but is always forced to 1 here – regardless of the value passed in – before being forwarded to fz. On the fz (Python) side, calculators > 1 now evaluates a Python-function model concurrently in a worker-thread pool (Funz/fz#73); that is unsafe here because R functions are called back into the R session via reticulate, which is only safe from the main thread – invoking the function from any other thread crashes the R session. Passing a value other than 1 therefore emits a warning explaining that it is being forced back to 1, and the call always proceeds with calculators = 1 (strictly sequential, one call at a time, in the calling thread).
each iteration's directory (iterNNN/) only contains a values.csv of that iteration's function inputs/outputs, since there is no file-based execution.

Examples


if (fz_available()) {
  # run inside a throwaway directory: fz writes analysis/ and .fz/ under cwd
  ex_dir <- file.path(tempdir(), "fz-fzd-example")
  dir.create(ex_dir, showWarnings = FALSE)
  owd <- setwd(ex_dir)

  tf <- tempfile(fileext = ".txt")
  writeLines(c("x = ${x~0}", "y = ${y~0}"), tf)

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

  # A minimal self-contained random-sampling algorithm (see
  # https://github.com/Funz/fz for ready-made algorithms to install)
  algo <- tempfile(fileext = ".py")
  writeLines(c(
    "import random",
    "class RandomSampler:",
    "    def __init__(self, **options):",
    "        self.batch = int(options.get('batch_sample_size', 5))",
    "        self.max_iterations = int(options.get('max_iterations', 3))",
    "        self.iteration = 0",
    "        self.input_vars = {}",
    "    def get_initial_design(self, input_vars, output_vars):",
    "        self.input_vars = input_vars",
    "        self.iteration = 1",
    "        return [{k: random.uniform(*v) for k, v in input_vars.items()}",
    "                for _ in range(self.batch)]",
    "    def get_next_design(self, previous_input_vars, previous_output_values):",
    "        self.iteration += 1",
    "        if self.iteration > self.max_iterations:",
    "            return []",
    "        return [{k: random.uniform(*v) for k, v in self.input_vars.items()}",
    "                for _ in range(self.batch)]",
    "    def get_analysis(self, input_vars, output_values):",
    "        valid = [v for v in output_values if v is not None]",
    "        mean = sum(valid) / len(valid) if valid else None",
    "        return {'text': f'mean={mean}', 'data': {'mean': mean}}"
  ), algo)

  result <- fzd(
    tf,
    list(x = "[0;1]", y = "[-5;5]"),
    model,
    output_expression = "z",
    algorithm        = algo,
    algorithm_options = list(batch_sample_size = 10, max_iterations = 3)
  )

  setwd(owd)
  unlink(ex_dir, recursive = TRUE)
}


## Not run: 
# Direct function model (requires funz-fz >= 1.2)
rosenbrock <- function(x, y) {
  list(result = (1 - x)^2 + 100 * (y - x^2)^2)
}

result <- fzd(
  input_path = NULL,
  input_variables = list(x = "[-2;2]", y = "[-2;2]"),
  model = rosenbrock,
  output_expression = "result",
  algorithm = "examples/algorithms/bfgs.py",
  calculators = 1L, # forced to 1L anyway for R functions -- see "Direct function model"
  algorithm_options = list(max_iter = 20, tol = 1e-4)
)

## End(Not run)

fzi Function

Description

Parses input file(s) to find variables, formulas, and static objects.

Usage

fzi(input_path, model, input_static = NULL)

Arguments

input_path

Path to input file or directory.

model

Model definition dict or alias string.

input_static

Optional character vector of files that are identical across every case (see fzr's input_static). They are never scanned for variables, since they are never templated. Default NULL.

Value

Named list keyed by the discovered static objects, variable names, and formula expressions, mapped to their values (or NULL for variables with no default).

Examples


if (fz_available()) {
  tf <- tempfile(fileext = ".txt")
  writeLines(c("pressure = ${P~1.013}", "volume = ${V~22.4}"), tf)

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

  vars <- fzi(tf, model)
}

fzl Function

Description

Lists installed models and available calculators.

Usage

fzl(models = "*", calculators = "*", check = FALSE)

Arguments

models

Pattern to match models. Default "*" for all. Accepts glob patterns ("my*") or plain alias names.

calculators

Pattern to match calculators. Default "*" for all.

check

Logical; probe each calculator to verify it is reachable. Default FALSE.

Value

Named list with two entries:

models: Named list of installed model definitions.
calculators: Named list of available calculators.

Examples


if (fz_available()) {
  info <- fzl()
  names(info$models)
  names(info$calculators)

  info <- fzl(models = "Perfect*")
  info <- fzl(check = TRUE)
}

fzo Function

Description

Reads and parses output file(s) according to the model's output commands. Each matched directory is processed independently; the results are combined into a single list or data frame.

Usage

fzo(output_path, model)

Arguments

output_path

Path or glob pattern matching one or more output directories. Subdirectories within matched directories are not processed.

model

Model definition dict or alias string.

Value

Named list or data frame of parsed output values. An output entry may resolve to a vector (list) - e.g. a time series or spectrum - which is stored per case unmodified (no flattening, padding, or truncation).

Output extraction methods

Each entry of the model's output list is a string describing how to extract that value from the case directory. Besides the default shell command (optionally marked "bash://..."), funz-fz >= 1.2 supports shell-free extractors, portable on Windows without a bash install:

"python://<expr>" - a Python expression evaluated in the case directory with helpers such as grep(), read(), lines(), json_file(), csv_file(), hdf5_file() and the re/json/math/ statistics/np/pd modules.
"jq://<filter> <file>" - JSON extraction via the jq executable.
"yq://<filter> <file>" - YAML/JSON/XML/TOML extraction via mikefarah/yq.
"xpath://<expr> <file>" - XML extraction via xmllint --xpath.

The python://, jq://, yq:// and xpath:// forms preserve list results as vectors; a plain shell command simplifies a single-element result to a scalar.

Examples


if (fz_available()) {
  out_dir <- file.path(tempdir(), "P=2,V=11.2")
  dir.create(out_dir, recursive = TRUE)
  writeLines("result = 42", file.path(out_dir, "output.txt"))

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

  values <- fzo(out_dir, model)
}

fzr Function

Description

Runs full parametric calculations over an input template. fzr combines fzc, calculator execution, and fzo into a single call: it compiles the template for every parameter combination, runs the model via the calculator(s), and collects all outputs into a data frame.

Usage

fzr(
  input_path,
  input_variables,
  model,
  results_dir = "results",
  calculators = NULL,
  callbacks = NULL,
  timeout = NULL,
  case_naming = NULL,
  input_static = NULL
)

Arguments

input_path

Path to input file or directory.

input_variables

Named list of variable values (or vectors of values for a full-factorial grid), or a data frame where each row is one case.

model

Model definition dict or alias string.

results_dir

Results directory. Default "results".

calculators

Calculator specification(s). Strings of the form "sh://<command>" run a local shell command; "ssh://user\@host" runs over SSH; NULL auto-detects installed calculators.

callbacks

Optional named list of callback functions.

timeout

Timeout in seconds per case. Default NULL, which resolves to the model's own "timeout" entry if set, otherwise the FZ_RUN_TIMEOUT configuration value (1 hour by default in funz-fz >= 1.2). An explicit value here takes precedence over both.

case_naming

How each case's result/temp subdirectory is named: "path" (default, "var1=val1,var2=val2,..."), "hash" (short content hash of the variable combination), or "index" ("case_<i>"). "hash"/"index" avoid filesystem name length limits with many variables and write a cases.csv manifest at the results root. Default NULL (uses FZ_CASE_NAMING or "path").

input_static

Optional character vector of files identical across every case (e.g. a shared reference dataset): never templated, never re-hashed per case, and - for relative paths - symlinked into each case directory instead of duplicated (and transferred to remote calculators). Absolute paths are assumed already present calculator-side. Default NULL.

Value

Data frame (or named list) with one row per case and columns for each input variable and output quantity.

Examples


if (fz_available()) {
  # run inside a throwaway directory: fz writes results/ and .fz/ under cwd
  ex_dir <- file.path(tempdir(), "fz-fzr-example")
  dir.create(ex_dir, showWarnings = FALSE)
  owd <- setwd(ex_dir)

  tf <- tempfile(fileext = ".sh")
  writeLines(c(
    "#!/bin/sh",
    "echo result = $(( ${x~0} + ${y~0} )) > output.txt"
  ), tf)

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

  results <- fzr(tf, list(x = c(1L, 2L), y = 3L), model,
                 calculators = "sh://bash input.sh")

  setwd(owd)
  unlink(ex_dir, recursive = TRUE)
}

Get the Global Configuration

Description

Returns the fz configuration object. Values are controlled by environment variables such as FZ_LOG_LEVEL, FZ_MAX_WORKERS, FZ_MAX_RETRIES, and FZ_SHELL_PATH.

Usage

get_config()

Value

A Python Config object. Access fields with $, e.g. get_config()$max_workers.

Examples


if (fz_available()) {
  cfg <- get_config()
  cfg$max_workers
  cfg$max_retries
}

Get the Current Interpreter

Description

Returns the global formula interpreter used when evaluating formula expressions inside template files (e.g. "python" or "R").

Usage

get_interpreter()

Value

Character string naming the current interpreter.

Examples


if (fz_available()) {
  get_interpreter()
}

Get the Current Log Level

Description

Returns the current logging verbosity level.

Usage

get_log_level()

Value

A log-level value (use as.character() to convert to a string such as "DEBUG", "INFO", "WARNING", "ERROR").

Examples


if (fz_available()) {
  as.character(get_log_level())
}

Install a Model or Algorithm (generic)

Description

Generic alias: installs a model from a GitHub name, URL, or local zip file. Equivalent to install_model.

Usage

install(source, global = FALSE)

Arguments

source

GitHub name (e.g. "Funz/Model-PerfectGas"), URL, or path to a local zip file.

global

Logical; install system-wide instead of user-level. Default FALSE.

Value

Named list with installation details.

Examples

## Not run: 
# Requires the named GitHub repository to exist and network access;
# not run automatically since neither is guaranteed in all environments.
install("Funz/Model-PerfectGas")

## End(Not run)

Install an Algorithm

Description

Installs an algorithm from a GitHub repository name, URL, or local zip file into the user-level ~/.fz/algorithms/ directory (or system-level when global = TRUE).

Usage

install_algorithm(source, global = FALSE)

Arguments

source

GitHub name (e.g. "Funz/Algorithm-MonteCarlo"), URL, or path to a local zip file.

global

Logical; install system-wide instead of user-level. Default FALSE.

Value

Named list with installation details (path, name, ...).

Examples

## Not run: 
# Requires the named GitHub repository to exist and network access;
# not run automatically since neither is guaranteed in all environments.
install_algorithm("Funz/Algorithm-MonteCarlo")

## End(Not run)

Install a Model

Description

Installs a model from a GitHub repository name, URL, or local zip file into the user-level ~/.fz/models/ directory (or system-level when global = TRUE).

Usage

install_model(source, global = FALSE)

Arguments

source

GitHub name (e.g. "Funz/Model-PerfectGas"), URL, or path to a local zip file.

global

Logical; install system-wide instead of user-level. Default FALSE.

Value

Named list with installation details (path, id, ...).

Examples

## Not run: 
# Requires the named GitHub repository to exist and network access;
# not run automatically since neither is guaranteed in all environments.
install_model("Funz/Model-PerfectGas")

## End(Not run)

List Installed Algorithms

Description

Returns details of all algorithms installed in ~/.fz/algorithms/.

Usage

list_installed_algorithms(global = FALSE)

Arguments

global

Logical; list system-level installs. Default FALSE.

Value

Named list of installed algorithm definitions.

Examples


if (fz_available()) {
  algos <- list_installed_algorithms()
  names(algos)
}

List Installed Models

Description

Returns details of all models installed in ~/.fz/models/.

Usage

list_installed_models(global = FALSE)

Arguments

global

Logical; list system-level installs. Default FALSE.

Value

Named list of installed model definitions.

Examples


if (fz_available()) {
  models <- list_installed_models()
  names(models)
}

List Installed Models (alias)

Description

Alias for list_installed_models.

Usage

list_models(global = FALSE)

Arguments

global

Logical; list system-level installs. Default FALSE.

Value

Named list of installed model definitions.

Examples


if (fz_available()) {
  names(list_models())
}

Print the Current Configuration

Description

Prints all fz configuration values in a human-readable format, including which settings come from environment variables.

Usage

print_config()

Value

NULL (invisibly). Called for side effects.

Examples


if (fz_available()) {
  print_config()
}

Reload Configuration from Environment Variables

Description

Re-reads all FZ_* environment variables and updates the live configuration. Useful after changing environment variables within the session.

Usage

reload_config()

Value

NULL (invisibly). Called for side effects.

Examples


if (fz_available()) {
  Sys.setenv(FZ_MAX_WORKERS = "8")
  reload_config()
  get_config()$max_workers
}

Set the Interpreter

Description

Sets the global formula interpreter for evaluating expressions inside template files.

Usage

set_interpreter(interpreter)

Arguments

interpreter

Character string: "python" or "R".

Value

NULL (invisibly). Called for side effects.

Examples


if (fz_available()) {
  set_interpreter("R")
  set_interpreter("python")
}

Set the Log Level

Description

Controls how much output fz emits during execution.

Usage

set_log_level(level)

Arguments

level

Character string or log-level object: one of "DEBUG", "INFO", "WARNING", "ERROR".

Value

NULL (invisibly). Called for side effects.

Examples


if (fz_available()) {
  set_log_level("DEBUG")
  set_log_level("WARNING")
  set_log_level("ERROR")
}

Uninstall a Model (generic)

Description

Generic alias: removes a model by name. Equivalent to uninstall_model.

Usage

uninstall(model_name, global = FALSE)

Arguments

model_name

Name of the model to remove.

global

Logical; remove from system-level install. Default FALSE.

Value

TRUE if removed, FALSE otherwise.

Examples


if (fz_available()) {
  uninstall("PerfectGas")
}

Uninstall an Algorithm

Description

Removes a previously installed algorithm from ~/.fz/algorithms/.

Usage

uninstall_algorithm(algorithm_name, global = FALSE)

Arguments

algorithm_name

Name of the algorithm to remove.

global

Logical; remove from system-level install. Default FALSE.

Value

TRUE if the algorithm was removed, FALSE otherwise.

Examples


if (fz_available()) {
  uninstall_algorithm("MonteCarlo")
}

Uninstall a Model

Description

Removes a previously installed model from ~/.fz/models/.

Usage

uninstall_model(model_name, global = FALSE)

Arguments

model_name

Name of the model to remove (e.g. "PerfectGas").

global

Logical; remove from system-level install. Default FALSE.

Value

TRUE if the model was removed, FALSE otherwise.

Examples


if (fz_available()) {
  uninstall_model("PerfectGas")
}

Package {fz}

fz: R Wrapper for the 'funz-fz' Parametric Simulation Framework

Description

Author(s)

See Also

Check if fz Python Package is Available

Description

Usage

Details

Value

Examples

Install the fz Python Package

Description

Usage

Arguments

Value

Examples

fzc Function

Description

Usage

Arguments

Value

Examples

fzd Function

Description

Usage

Arguments

Value

Direct function model

Examples

fzi Function

Description

Usage

Arguments

Value

Examples

fzl Function

Description

Usage

Arguments

Value

Examples

fzo Function

Description

Usage

Arguments

Value

Output extraction methods

Examples

fzr Function

Description

Usage

Arguments

Value

Examples

Get the Global Configuration

Description

Usage

Value

Examples

Get the Current Interpreter

Description

Usage

Value

Examples

Get the Current Log Level

Description

Usage

Value

Examples

Install a Model or Algorithm (generic)

Description

Usage

Arguments

Value

Examples

Install an Algorithm

Description

Usage

Arguments