amici.petab_objective

PEtab Objective

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

Functions

`create_edata_for_condition`(condition, ...)	Get `amici.amici.ExpData` for the given PEtab condition.
`create_edatas`(amici_model, petab_problem[, ...])	Create list of `amici.amici.ExpData` objects for PEtab problem.
`create_parameter_mapping`(petab_problem, ...)	Generate AMICI specific parameter mapping.
`create_parameter_mapping_for_condition`(...)	Generate AMICI specific parameter mapping for condition.
`create_parameterized_edatas`(amici_model, ...)	Create list of :class:amici.ExpData objects with parameters filled in.
`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.
`simulate_petab`(petab_problem, amici_model[, ...])	Simulate PEtab model.

amici.petab_objective.create_edata_for_condition(condition, measurement_df, amici_model, petab_problem, observable_ids)[source]

Get amici.amici.ExpData for the given PEtab condition.

Sets timepoints, observed data and sigmas.

Parameters

condition (typing.Union[typing.Dict, pandas.core.series.Series]) – pandas.DataFrame row with preequilibrationConditionId and simulationConditionId.
measurement_df (pandas.core.frame.DataFrame) – pandas.DataFrame with measurements for the given condition.
amici_model (typing.Union[amici.amici.Model, amici.amici.ModelPtr]) – AMICI model
petab_problem (petab.problem.Problem) – Underlying PEtab problem
observable_ids (typing.List[str]) – List of observable IDs

Return type

amici.swig_wrappers.ExpData

Returns

ExpData instance.

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

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

Parameters

amici_model (typing.Union[amici.amici.Model, amici.amici.ModelPtr]) – AMICI model.
petab_problem (petab.problem.Problem) – Underlying PEtab problem.
simulation_conditions (typing.Union[pandas.core.frame.DataFrame, typing.Dict, None]) – Result of petab.get_simulation_conditions(). Can be provided to save time if this has be obtained before.

Return type

typing.List[amici.swig_wrappers.ExpData]

Returns

List with one amici.amici.ExpData per simulation condition, with filled in timepoints and data.

amici.petab_objective.create_parameter_mapping(petab_problem, simulation_conditions, scaled_parameters, amici_model, **parameter_mapping_kwargs)[source]

Generate AMICI specific parameter mapping.

Parameters

petab_problem (petab.problem.Problem) – PEtab problem
simulation_conditions (typing.Union[pandas.core.frame.DataFrame, typing.List[typing.Dict]]) – 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]) – AMICI model.
parameter_mapping_kwargs – Optional keyword arguments passed to petab.get_optimization_to_simulation_parameter_mapping(). To allow changing fixed PEtab problem parameters (estimate=0), use fill_fixed_parameters=False.

Return type

amici.parameter_mapping.ParameterMapping

Returns

List of the parameter mappings.

amici.petab_objective.create_parameter_mapping_for_condition(parameter_mapping_for_condition, condition, petab_problem, amici_model)[source]

Generate AMICI specific parameter mapping for condition.

Parameters

parameter_mapping_for_condition (typing.Tuple[typing.Dict[str, typing.Union[str, numbers.Number]], typing.Dict[str, typing.Union[str, numbers.Number]], typing.Dict[str, str], typing.Dict[str, str]]) – Preliminary parameter mapping for condition.
condition (typing.Union[pandas.core.series.Series, typing.Dict]) – pandas.DataFrame row with preequilibrationConditionId and simulationConditionId.
petab_problem (petab.problem.Problem) – Underlying PEtab problem.
amici_model (typing.Union[amici.amici.Model, amici.amici.ModelPtr]) – AMICI model.

Return type

amici.parameter_mapping.ParameterMappingForCondition

Returns

The parameter and parameter scale mappings, for fixed preequilibration, fixed simulation, and variable simulation parameters, and then the respective scalings.

amici.petab_objective.create_parameterized_edatas(amici_model, petab_problem, problem_parameters, scaled_parameters=False, parameter_mapping=None, simulation_conditions=None)[source]

Create list of :class:amici.ExpData objects with parameters filled in.

Parameters

amici_model (typing.Union[amici.amici.Model, amici.amici.ModelPtr]) – AMICI Model assumed to be compatible with petab_problem.
petab_problem (petab.problem.Problem) – PEtab problem to work on.
problem_parameters (typing.Dict[str, numbers.Number]) – Run simulation with these parameters. If None, PEtab nominalValues will be used. To be provided as dict, mapping PEtab problem parameters to SBML IDs.
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.
parameter_mapping (typing.Optional[amici.parameter_mapping.ParameterMapping]) – Optional precomputed PEtab parameter mapping for efficiency, as generated by create_parameter_mapping().
simulation_conditions (typing.Union[pandas.core.frame.DataFrame, typing.Dict, None]) – Result of petab.get_simulation_conditions(). Can be provided to save time if this has been obtained before.

Return type

typing.List[amici.swig_wrappers.ExpData]

Returns

List with one amici.amici.ExpData per simulation condition, with filled in timepoints, data and parameters.

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

rdatas (typing.Sequence[amici.amici.ReturnData]) – A sequence of rdatas with the ordering of petab.get_simulation_conditions().
model (typing.Union[amici.amici.Model, amici.amici.ModelPtr]) – AMICI model used to generate rdatas.
measurement_df (pandas.core.frame.DataFrame) – PEtab measurement table used to generate rdatas.

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.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)[source]

Simulate PEtab model.

Parameters

petab_problem (petab.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 (typing.Optional[amici.amici.Solver]) – An AMICI solver. Will use default options if None.
problem_parameters (typing.Optional[typing.Dict[str, float]]) – 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 (typing.Union[pandas.core.frame.DataFrame, typing.Dict, None]) – 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 (typing.Optional[typing.List[typing.Union[amici.swig_wrappers.ExpData, amici.amici.ExpDataPtr]]]) – Experimental data. Parameters are inserted in-place for simulation.
parameter_mapping (typing.Optional[amici.parameter_mapping.ParameterMapping]) – Optional precomputed PEtab parameter mapping for efficiency, as generated by create_parameter_mapping().
scaled_parameters (typing.Optional[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.
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.

Return type

typing.Dict[str, typing.Any]

Returns

Dictionary of

cost function value (LLH),
list of amici.amici.ReturnData (RDATAS),
list of amici.amici.ExpData (EDATAS),

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