gwkokab_scripts.flowMC_info

Attributes

console

Functions

display_config_table(data)
display_diagnostics_panel(info_dict, memory_notes)	Display the detailed sample breakdown and memory notes in a clean panel.
display_loop_table(rows, max_loops)
display_suggestions(current, suggested)
generate_heuristics(→ Dict[str, Any])	Generate heuristic suggestions for FlowMC parameters.
infer_n_dims(→ int)	Infer n_dims from CLI argument or config (mass_matrix length).
load_config(→ Dict[str, Any])	Load and validate the JSON configuration.
main()
plot_history(loops, candidates, n_train, save_path)	Generate a matplotlib plot of the training history.
print_header(title)
print_section(title)

Module Contents

gwkokab_scripts.flowMC_info.display_config_table(data: List[tuple])

gwkokab_scripts.flowMC_info.display_diagnostics_panel(info_dict: Dict[str, str], memory_notes: List[str])

Display the detailed sample breakdown and memory notes in a clean panel.

gwkokab_scripts.flowMC_info.display_loop_table(rows: List[tuple], max_loops: int)

gwkokab_scripts.flowMC_info.display_suggestions(current: dict, suggested: dict)

gwkokab_scripts.flowMC_info.generate_heuristics(n_dims: int, n_chains: int, kept_total_per_loop: int) → Dict[str, Any]

Generate heuristic suggestions for FlowMC parameters.

gwkokab_scripts.flowMC_info.infer_n_dims(bundle_config: dict, cli_n_dims: int | None) → int

Infer n_dims from CLI argument or config (mass_matrix length).

gwkokab_scripts.flowMC_info.load_config(path: str) → Dict[str, Any]

Load and validate the JSON configuration.

gwkokab_scripts.flowMC_info.main()

gwkokab_scripts.flowMC_info.plot_history(loops: List[int], candidates: List[int], n_train: List[int], save_path: str)

Generate a matplotlib plot of the training history.

gwkokab_scripts.flowMC_info.print_header(title: str)

gwkokab_scripts.flowMC_info.print_section(title: str)

gwkokab_scripts.flowMC_info.console