amici.petab.simulations

Functionality related to simulation of PEtab problems.

Functionality related to running simulations or evaluating the objective function as defined by a PEtab problem.

Functions

aggregate_sllh(amici_model, rdatas, ...[, ...])

Aggregate likelihood gradient for all conditions, according to PEtab parameter mapping.

rdatas_to_measurement_df(rdatas, model, ...)

Create a measurement dataframe in the PEtab format from the passed rdatas and own information.

rdatas_to_simulation_df(rdatas, model, ...)

Create a PEtab simulation dataframe from amici.amici.ReturnData s.

rescale_sensitivity(sensitivity, ...)

Rescale a sensitivity between parameter scales.

simulate_petab(petab_problem, amici_model[, ...])

Simulate PEtab model.
amici.petab.simulations.simulate_petab(petab_problem, amici_model, solver=None, problem_parameters=None, simulation_conditions=None, edatas=None, parameter_mapping=None, scaled_parameters=False, log_level=30, num_threads=1, failfast=True, scaled_gradients=False)[source]

Simulate PEtab model.

Note

Regardless of scaled_parameters, unscaled sensitivities are returned, unless scaled_gradients=True.

Parameters:

  • petab_problem (petab.v1.problem.Problem) – PEtab problem to work on.

  • amici_model (typing.Union[amici.amici.Model, amici.amici.ModelPtr]) – AMICI Model assumed to be compatible with petab_problem.

  • solver (amici.amici.Solver | None) – An AMICI solver. Will use default options if None.

  • problem_parameters (dict[str, float] | None) – Run simulation with these parameters. If None, PEtab nominalValues will be used. To be provided as dict, mapping PEtab problem parameters to SBML IDs.

  • simulation_conditions (pandas.core.frame.DataFrame | dict) – Result of petab.get_simulation_conditions(). Can be provided to save time if this has be obtained before. Not required if edatas and parameter_mapping are provided.

  • edatas (list[typing.Union[amici.amici.ExpData, amici.amici.ExpDataPtr]]) – Experimental data. Parameters are inserted in-place for simulation.

  • parameter_mapping (amici.petab.parameter_mapping.ParameterMapping) – Optional precomputed PEtab parameter mapping for efficiency, as generated by create_parameter_mapping() with scaled_parameters=True.

  • scaled_parameters (bool | None) – If True, problem_parameters are assumed to be on the scale provided in the PEtab parameter table and will be unscaled. If False, they are assumed to be in linear scale. If parameter_mapping is provided, this must match the value of scaled_parameters used to generate the mapping.

  • log_level (int) – Log level, see amici.logging module.

  • num_threads (int) – Number of threads to use for simulating multiple conditions (only used if compiled with OpenMP).

  • failfast (bool) – Returns as soon as an integration failure is encountered, skipping any remaining simulations.

  • scaled_gradients (bool) – Whether to compute gradients on parameter scale (True) or not (False).

Return type:

dict[str, typing.Any]

Returns:

Dictionary of

corresponding to the different simulation conditions. For ordering of simulation conditions, see petab.Problem.get_simulation_conditions_from_measurement_df().