Derive an iteration mask template from a function's signature

Takes a function (typically produced by from_example_to_function()) and returns a one-row data.frame where each column corresponds to a parameter, with the default value as the cell value. This "template mask" is the starting point the user expands into a multi-row mask that defines all iteration cases.

Usage

from_function_to_mask(f)

Arguments

f

A function whose formals define the mask columns.

Value

A one-row data.frame with one column per parameter. Parameters with default values get those values; parameters without defaults get NA.

Details

What is a mask?

In genproc, a mask is a data.frame where each row is an iteration case and each column is a parameter. The function genproc() will call the user's function once per row, passing column values as arguments.

from_function_to_mask() produces a one-row template. The user then builds the full mask by adding rows (e.g. via rbind(), dplyr::bind_rows(), or by constructing a multi-row data.frame directly).

Current limitations (v0.1)

Only scalar atomic defaults are supported (character, numeric, integer, logical). Non-scalar defaults (vectors, lists, data.frames) will be supported in a future version via list-columns. This extension will preserve backwards compatibility: any mask that works today will continue to work unchanged.

Metadata (case_id, hashes, etc.)

The mask returned here is a pure data.frame of parameter values. Metadata such as case_id, input file hashes, or seeds are managed separately by genproc() at execution time — they are not stored as columns or attributes of the mask. This design ensures that standard data.frame operations (dplyr::filter(), [, rbind()) never accidentally strip metadata.

When the mask is later generalized to a dedicated class (genproc_mask), existing code passing a plain data.frame will continue to work (backwards compatibility is a hard constraint).

Examples

fn <- function(input_path = "data.csv", n_rows = 100) {
  head(read.csv(input_path), n_rows)
}
mask <- from_function_to_mask(fn)
mask
#>   input_path n_rows
#> 1   data.csv    100
#   input_path n_rows
# 1   data.csv    100

# Expand to multiple cases:
full_mask <- data.frame(
  input_path = c("a.csv", "b.csv", "c.csv"),
  n_rows = c(10, 50, 100)
)