amici.petab_objective

Evaluate a PEtab objective function.

Deprecated since version 0.21.0: Use amici.petab.simulations instead.

amici.petab_objective.aggregate_sllh(amici_model, rdatas, parameter_mapping, edatas, petab_scale=True, petab_problem=None)[source]

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

Parameters:
Return type:

None | dict[str, float]

Returns:

Aggregated likelihood sensitivities.

amici.petab_objective.create_edatas(amici_model, petab_problem, simulation_conditions=None)[source]

Create list of amici.amici.ExpData objects for PEtab problem.

Parameters:
Return type:

list[amici.amici.ExpData]

Returns:

List with one amici.amici.ExpData per simulation condition, with filled in timepoints and data, but without parameter values (see create_parameterized_edatas() or fill_in_parameters() for that).

amici.petab_objective.create_parameter_mapping(petab_problem, simulation_conditions, scaled_parameters, amici_model=None, fill_fixed_parameters=True, **parameter_mapping_kwargs)[source]

Generate AMICI specific parameter mapping.

Parameters:

  • petab_problem (petab.v1.problem.Problem) – PEtab problem

  • simulation_conditions (pandas.core.frame.DataFrame | list[dict] | None) – Result of petab.get_simulation_conditions(). Can be provided to save time if this has been obtained before.

  • scaled_parameters (bool) – 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.

  • amici_model (typing.Union[amici.amici.Model, amici.amici.ModelPtr, None]) – AMICI model.

  • fill_fixed_parameters (bool) – Whether to fill in nominal values for fixed parameters (estimate=0 in the parameters table). To allow changing fixed PEtab problem parameters, use fill_fixed_parameters=False.

  • parameter_mapping_kwargs – Optional keyword arguments passed to petab.get_optimization_to_simulation_parameter_mapping().

Return type:

amici.petab.parameter_mapping.ParameterMapping

Returns:

List of the parameter mappings.

amici.petab_objective.fill_in_parameters(edatas, problem_parameters, scaled_parameters, parameter_mapping, amici_model, warn_unused=True)[source]

Fill fixed and dynamic parameters into the edatas (in-place).

Parameters:

  • edatas (list[amici.amici.ExpData]) – List of experimental datas amici.amici.ExpData with everything except parameters filled.

  • problem_parameters (dict[str, numbers.Number]) – Problem parameters as parameterId=>value dict. Only parameters included here will be set. Remaining parameters will be used as currently set in amici_model.

  • scaled_parameters (bool) – If True, problem_parameters are assumed to be on the scale provided in the parameter mapping. If False, they are assumed to be in linear scale.

  • parameter_mapping (amici.petab.parameter_mapping.ParameterMapping) – Parameter mapping for all conditions.

  • amici_model (typing.Union[amici.amici.Model, amici.amici.ModelPtr]) – AMICI model.

  • warn_unused (bool) – Whether a warning should be emitted if not all problem parameters were used. I.e., if there are parameters in problem_parameters that are not in parameter_mapping.

Return type:

None

amici.petab_objective.rdatas_to_measurement_df(rdatas, model, measurement_df)[source]

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

Parameters:
Return type:

pandas.core.frame.DataFrame

Returns:

A dataframe built from the rdatas in the format of measurement_df.

amici.petab_objective.rdatas_to_simulation_df(rdatas, model, measurement_df)[source]

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

See rdatas_to_measurement_df() for details, only that model outputs will appear in column simulation instead of measurement.

Return type:

pandas.core.frame.DataFrame

amici.petab_objective.rescale_sensitivity(sensitivity, parameter_value, old_scale, new_scale)[source]

Rescale a sensitivity between parameter scales.

Parameters:

  • sensitivity (float) – The sensitivity corresponding to the parameter value.

  • parameter_value (float) – The parameter vector element, on old_scale.

  • old_scale (str) – The scale of the parameter value.

  • new_scale (str) – The parameter scale on which to rescale the sensitivity.

Return type:

float

Returns:

The rescaled sensitivity.

amici.petab_objective.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().