amici.petab.conditions

PEtab conditions to AMICI ExpDatas.

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_parameterized_edatas(amici_model, ...)	Create list of :class:amici.ExpData objects with parameters filled in.
fill_in_parameters(edatas, ...)	Fill fixed and dynamic parameters into the edatas (in-place).
fill_in_parameters_for_condition(edata, ...)	Fill fixed and dynamic parameters into the edata for condition (in-place).

amici.petab.conditions.create_edata_for_condition(condition, measurement_df, amici_model, petab_problem, observable_ids)

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

Sets timepoints, observed data and sigmas.

Parameters:

• condition (typing.Union[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 (list[str]) – List of observable IDs

Return type:

amici.swig_wrappers.ExpData

Returns:

ExpData instance.

amici.petab.conditions.create_edatas(amici_model, petab_problem, simulation_conditions=None)

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, dict]) – Result of petab.get_simulation_conditions(). Can be provided to save time if this has be obtained before.

Return type:

list[amici.swig_wrappers.ExpData]

Returns:

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

amici.petab.conditions.create_parameterized_edatas(amici_model, petab_problem, problem_parameters, scaled_parameters=False, parameter_mapping=None, simulation_conditions=None)

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

• simulation_conditions (typing.Union[pandas.core.frame.DataFrame, dict]) – Result of petab.get_simulation_conditions(). Can be provided to save time if this has been obtained before.

Return type:

list[amici.swig_wrappers.ExpData]

Returns:

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

amici.petab.conditions.fill_in_parameters(edatas, problem_parameters, scaled_parameters, parameter_mapping, amici_model)

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

Parameters:

• edatas (list[amici.swig_wrappers.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.

Return type:

None

amici.petab.conditions.fill_in_parameters_for_condition(edata, problem_parameters, scaled_parameters, parameter_mapping, amici_model)

Fill fixed and dynamic parameters into the edata for condition (in-place).

Parameters:

• edata (amici.swig_wrappers.ExpData) – Experimental data object to fill parameters into.

• 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 already set in amici_model and edata.

• 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.ParameterMappingForCondition) – Parameter mapping for current condition.

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

Return type:

None