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Parsers & generators (internal)

Note

These classes are internal building blocks used by the public entry points. They are documented for completeness and for advanced workflows, but their interfaces may change between releases. Prefer the entry points on the Overview page where possible.

Parsers

libcuflynx.parsers.ModelParsers.CSV0DModelParser 

CSV0DModelParser(inp_data_dict, parameter_id_dir=None)

Bases: object

Creates a 0D model representation from a vessel and a parameter CSV files.

libcuflynx.parsers.PrimitiveParsers.YamlFileParser 

YamlFileParser()

Bases: object

Parses Yaml files

Constructor

Generators

libcuflynx.generators.CVSCellMLGenerator.CVS0DCellMLGenerator 

CVS0DCellMLGenerator(model, inp_data_dict)

Bases: object

Generates CellML files for the 0D model represented in @

Constructor

create_unit_converter_component 

create_unit_converter_component(
    name,
    input_var,
    output_var,
    scale_factor,
    units_in,
    units_out,
)

Returns a CellML component string that converts input_var (units_in) to output_var (units_out) using the given scale_factor.

add_converter_component 

add_converter_component(converter_component_str)

Stores the converter component string for later writing to the CellML file.

get_variable_unit_from_component 

get_variable_unit_from_component(
    component_name, variable_name
)

Given a component (module) name and variable name, return the unit name as a string.

libcuflynx.generators.PythonGenerator.PythonGenerator 

PythonGenerator(
    cellml_path,
    output_dir=None,
    module_name=None,
    human_readable=True,
    casadi_compat=False,
    aadc_compat=False,
)

Generate a Python module from a CellML file.

Usage

gen = PythonGenerator('model.cellml', output_dir='out') py_path = gen.generate()

generate 

generate()

Generate Python code and return the output file path.

Protocol execution

libcuflynx.protocol_runners.protocol_executor.ProtocolExecutor 

ProtocolExecutor(sim_helper)

Core multi-experiment / multi-subexperiment protocol simulation loop.

Parameters

sim_helper : SimulationHelper Any solver-wrapper instance (myokit, opencor, python, casadi). The caller retains ownership; ProtocolExecutor does not close it.

run_protocol 

run_protocol(
    protocol_info,
    id_param_names=None,
    id_param_vals=None,
    result_variables=None,
    extra_result_variables=None,
    exp_indices=None,
    continue_on_failure=False,
    reset_after_experiment=True,
)

Run the multi-experiment / multi-subexperiment protocol loop.

Parameters

protocol_info : dict Must contain: - 'sim_times' list[list[float]] — sim duration per (exp, sub) - 'pre_times' list[float] — pre-simulation time per exp - 'params_to_change' dict — {param_name: [[val_exp0_sub0, ...], ...]} May also contain pre-computed keys added by process_protocol_and_weights: - 'num_experiments' int - 'num_sub_per_exp' list[int] Both are derived from sim_times when absent. id_param_names : list, optional Parameter names set once before each experiment (e.g. ID candidates). id_param_vals : list, optional Values matching id_param_names. result_variables : list, optional Variables to retrieve per sub-experiment via get_results(). None → collect all variables via get_all_results(flatten=True). extra_result_variables : list, optional A second variable set collected in the same pass (e.g. pred_names). exp_indices : iterable, optional Only run these experiment indices. Other slots in the output will be absent from results_by_sub / extra_by_sub, and None in t_by_exp. continue_on_failure : bool, default False If True, record None for failed sub-experiments and continue rather than returning early. If False (default), return immediately on the first simulation failure. reset_after_experiment : bool, default True If True (default), call sim_helper.reset_and_clear() after the final sub-experiment of each experiment. Pass False for AD (automatic differentiation) mode where the solver state must be preserved across experiments.

Returns

success : bool False only when a simulation failed AND continue_on_failure is False. results_by_sub : dict Mapping (exp_idx, sub_idx) → result of get_results() or get_all_results(flatten=True). None for failed sub-experiments when continue_on_failure is True. extra_by_sub : dict Same structure for extra_result_variables. Empty dict when extra_result_variables is None. t_by_exp : list[np.ndarray | None] Concatenated, pre_time-shifted time vector per experiment index. None for skipped or failed experiments.

Inner param-id engine

libcuflynx.param_id.paramID.OpencorParamID module-attribute 

OpencorParamID = ParamID

libcuflynx.param_id.paramID.OpencorMCMC module-attribute 

OpencorMCMC = MCMC