diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml
index 2c6e490..2cb2040 100644
--- a/.github/workflows/R-CMD-check.yaml
+++ b/.github/workflows/R-CMD-check.yaml
@@ -41,6 +41,16 @@ jobs:
extra-packages: any::rcmdcheck
needs: check
+ - uses: actions/setup-python@v5
+ with:
+ python-version: '3.x'
+
+ - name: Install fz Python package
+ run: pip install funz-fz
+
+ - name: Point reticulate at the CI Python
+ run: echo "RETICULATE_PYTHON=$(python3 -c 'import sys; print(sys.executable)')" >> "$GITHUB_ENV"
+
- uses: r-lib/actions/check-r-package@v2
with:
upload-snapshots: true
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..f9943bf
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,3 @@
+YEAR: 2025
+ORGANIZATION: Funz
+COPYRIGHT HOLDER: Funz
diff --git a/NEWS.md b/NEWS.md
index d2cae55..a78edd3 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -14,6 +14,17 @@
regardless of `calculators`.
* `fz_install()` gains a `packages` argument (default `"funz-fz"`) so the
latest `main` branch can be installed instead of the PyPI release.
+* Fixed `fzd()`'s own `algorithm_options` example/documentation, which used
+ a `"key=val;key2=val2"` string that `funz-fz` has never actually accepted
+ (only a named list, JSON string, or path to a JSON file) — replaced with
+ a named list. CI never caught this because it was silently exercising the
+ wrong Python module (see CI fixes below).
+* Restored the `LICENSE` file (the templated file R's packaging convention
+ requires alongside `License: BSD_3_clause + file LICENSE`), which had been
+ mistakenly deleted, and fixed `R-CMD-check` CI to install `funz-fz` and
+ point `reticulate` at it via `RETICULATE_PYTHON` — without this,
+ `reticulate`'s automatic environment provisioning was silently resolving
+ to an unrelated PyPI package literally named `fz` (not `funz-fz`).
# fz 1.1
diff --git a/R/core-functions.R b/R/core-functions.R
index f5aeea4..3dfeb2e 100644
--- a/R/core-functions.R
+++ b/R/core-functions.R
@@ -202,9 +202,8 @@ fzl <- function(models = "*", calculators = "*", check = FALSE) {
#' \code{model} is a function, this must be a single integer (default
#' \code{1L}), accepted for API compatibility (see "Direct function model"
#' below) — calls are always run sequentially regardless of its value.
-#' @param algorithm_options Algorithm options as a named list or
-#' semicolon-separated string, e.g. \code{"batch_sample_size=10;seed=42"}.
-#' Default \code{NULL}.
+#' @param algorithm_options Algorithm options as a named list, a JSON string,
+#' or a path to a JSON file. Default \code{NULL}.
#' @param analysis_dir Analysis directory. Default \code{"analysis"}.
#'
#' @section Direct function model:
@@ -253,13 +252,41 @@ fzl <- function(models = "*", calculators = "*", check = FALSE) {
#' 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 = "algorithms/montecarlo_uniform.py",
-#' algorithm_options = "batch_sample_size=10;max_iterations=3"
+#' algorithm = algo,
+#' algorithm_options = list(batch_sample_size = 10, max_iterations = 3)
#' )
#' }
#' }
diff --git a/R/install.R b/R/install.R
index a7e6f74..08f40e1 100644
--- a/R/install.R
+++ b/R/install.R
@@ -72,10 +72,10 @@ fz_available <- function() {
#' @export
#'
#' @examples
-#' \donttest{
-#' if (fz_available()) {
-#' install_model("Funz/Model-PerfectGas")
-#' }
+#' \dontrun{
+#' # 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")
#' }
install_model <- function(source, global = FALSE) {
fz_module <- get_fz()
@@ -97,10 +97,10 @@ install_model <- function(source, global = FALSE) {
#' @export
#'
#' @examples
-#' \donttest{
-#' if (fz_available()) {
-#' install_algorithm("Funz/Algorithm-MonteCarlo")
-#' }
+#' \dontrun{
+#' # 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")
#' }
install_algorithm <- function(source, global = FALSE) {
fz_module <- get_fz()
@@ -219,10 +219,10 @@ list_models <- function(global = FALSE) {
#' @export
#'
#' @examples
-#' \donttest{
-#' if (fz_available()) {
-#' install("Funz/Model-PerfectGas")
-#' }
+#' \dontrun{
+#' # Requires the named GitHub repository to exist and network access;
+#' # not run automatically since neither is guaranteed in all environments.
+#' install("Funz/Model-PerfectGas")
#' }
install <- function(source, global = FALSE) {
install_model(source, global)
diff --git a/inst/doc/modelica-examples.R b/inst/doc/modelica-examples.R
index 1e878ec..2b1230e 100644
--- a/inst/doc/modelica-examples.R
+++ b/inst/doc/modelica-examples.R
@@ -76,7 +76,7 @@ knitr::opts_chunk$set(
# model,
# output_expression = "pressure",
# algorithm = "algorithms/montecarlo_uniform.py",
-# algorithm_options = "batch_sample_size=10;max_iterations=5;seed=42"
+# algorithm_options = list(batch_sample_size = 10, max_iterations = 5, seed = 42)
# )
## ----fzl----------------------------------------------------------------------
diff --git a/inst/doc/modelica-examples.Rmd b/inst/doc/modelica-examples.Rmd
index 49759ff..290e3dd 100644
--- a/inst/doc/modelica-examples.Rmd
+++ b/inst/doc/modelica-examples.Rmd
@@ -173,7 +173,7 @@ result <- fzd(
model,
output_expression = "pressure",
algorithm = "algorithms/montecarlo_uniform.py",
- algorithm_options = "batch_sample_size=10;max_iterations=5;seed=42"
+ algorithm_options = list(batch_sample_size = 10, max_iterations = 5, seed = 42)
)
```
diff --git a/inst/doc/modelica-examples.html b/inst/doc/modelica-examples.html
index 985c634..a2608f0 100644
--- a/inst/doc/modelica-examples.html
+++ b/inst/doc/modelica-examples.html
@@ -484,7 +484,7 @@ Algorithm-driven design of experiments
model,
output_expression = "pressure",
algorithm = "algorithms/montecarlo_uniform.py",
- algorithm_options = "batch_sample_size=10;max_iterations=5;seed=42"
+ algorithm_options = list(batch_sample_size = 10, max_iterations = 5, seed = 42)
)
/home/richet/Sync/Open/Funz/github/fz.R/vignettes/modelica-examples.R
Algorithms are Python files; fz ships several in
diff --git a/man/fzd.Rd b/man/fzd.Rd
index 52581d1..209e27d 100644
--- a/man/fzd.Rd
+++ b/man/fzd.Rd
@@ -39,9 +39,8 @@ value is used.}
\code{1L}), accepted for API compatibility (see "Direct function model"
below) — calls are always run sequentially regardless of its value.}
-\item{algorithm_options}{Algorithm options as a named list or
-semicolon-separated string, e.g. \code{"batch_sample_size=10;seed=42"}.
-Default \code{NULL}.}
+\item{algorithm_options}{Algorithm options as a named list, a JSON string,
+or a path to a JSON file. Default \code{NULL}.}
\item{analysis_dir}{Analysis directory. Default \code{"analysis"}.}
}
@@ -99,13 +98,41 @@ if (fz_available()) {
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 = "algorithms/montecarlo_uniform.py",
- algorithm_options = "batch_sample_size=10;max_iterations=3"
+ algorithm = algo,
+ algorithm_options = list(batch_sample_size = 10, max_iterations = 3)
)
}
}
diff --git a/man/install.Rd b/man/install.Rd
index 2f1268f..d4cff31 100644
--- a/man/install.Rd
+++ b/man/install.Rd
@@ -21,9 +21,9 @@ Generic alias: installs a model from a GitHub name, URL, or local zip file.
Equivalent to \code{\link{install_model}}.
}
\examples{
-\donttest{
-if (fz_available()) {
- install("Funz/Model-PerfectGas")
-}
+\dontrun{
+# Requires the named GitHub repository to exist and network access;
+# not run automatically since neither is guaranteed in all environments.
+install("Funz/Model-PerfectGas")
}
}
diff --git a/man/install_algorithm.Rd b/man/install_algorithm.Rd
index 97a7d39..229782d 100644
--- a/man/install_algorithm.Rd
+++ b/man/install_algorithm.Rd
@@ -22,9 +22,9 @@ into the user-level \code{~/.fz/algorithms/} directory (or system-level when
\code{global = TRUE}).
}
\examples{
-\donttest{
-if (fz_available()) {
- install_algorithm("Funz/Algorithm-MonteCarlo")
-}
+\dontrun{
+# 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")
}
}
diff --git a/man/install_model.Rd b/man/install_model.Rd
index 432ff3d..3680350 100644
--- a/man/install_model.Rd
+++ b/man/install_model.Rd
@@ -22,9 +22,9 @@ the user-level \code{~/.fz/models/} directory (or system-level when
\code{global = TRUE}).
}
\examples{
-\donttest{
-if (fz_available()) {
- install_model("Funz/Model-PerfectGas")
-}
+\dontrun{
+# 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")
}
}
diff --git a/vignettes/modelica-examples.R b/vignettes/modelica-examples.R
index 1e878ec..2b1230e 100644
--- a/vignettes/modelica-examples.R
+++ b/vignettes/modelica-examples.R
@@ -76,7 +76,7 @@ knitr::opts_chunk$set(
# model,
# output_expression = "pressure",
# algorithm = "algorithms/montecarlo_uniform.py",
-# algorithm_options = "batch_sample_size=10;max_iterations=5;seed=42"
+# algorithm_options = list(batch_sample_size = 10, max_iterations = 5, seed = 42)
# )
## ----fzl----------------------------------------------------------------------
diff --git a/vignettes/modelica-examples.Rmd b/vignettes/modelica-examples.Rmd
index 49759ff..290e3dd 100644
--- a/vignettes/modelica-examples.Rmd
+++ b/vignettes/modelica-examples.Rmd
@@ -173,7 +173,7 @@ result <- fzd(
model,
output_expression = "pressure",
algorithm = "algorithms/montecarlo_uniform.py",
- algorithm_options = "batch_sample_size=10;max_iterations=5;seed=42"
+ algorithm_options = list(batch_sample_size = 10, max_iterations = 5, seed = 42)
)
```