amici.petab.parameter_mapping

Functions

amici_to_petab_scale(amici_scale)	Convert amici scale id to petab scale id.
create_parameter_mapping(petab_problem, ...)	Generate AMICI specific parameter mapping.
create_parameter_mapping_for_condition(...)	Generate AMICI specific parameter mapping for condition.
petab_to_amici_scale(petab_scale)	Convert petab scale id to amici scale id.
scale_parameter(value, petab_scale)	Bring parameter from linear scale to target scale.
scale_parameters_dict(value_dict, ...)	Bring parameters from linear scale to target scale.
unscale_parameter(value, petab_scale)	Bring parameter from scale to linear scale.
unscale_parameters_dict(value_dict, ...)	Bring parameters from target scale to linear scale.

Classes

ParameterMapping([parameter_mappings])	Parameter mapping for multiple conditions.
ParameterMappingForCondition([map_sim_var, ...])	Parameter mapping for condition.

class amici.petab.parameter_mapping.ParameterMapping(parameter_mappings=None)

Parameter mapping for multiple conditions.

This can be used like a list of ParameterMappingForConditions.

Parameters:

parameter_mappings (list[amici.petab.parameter_mapping.ParameterMappingForCondition]) – List of parameter mappings for specific conditions.

__init__(parameter_mappings=None)

append(parameter_mapping_for_condition)

Append a condition specific parameter mapping.

count(value) → integer -- return number of occurrences of value

property free_symbols: set[str]

Get IDs of all (symbolic) parameters present in this mapping

index(value[, start[, stop]]) → integer -- return first index of value.

Raises ValueError if the value is not present.

Supporting start and stop arguments is optional, but recommended.

class amici.petab.parameter_mapping.ParameterMappingForCondition(map_sim_var=None, scale_map_sim_var=None, map_preeq_fix=None, scale_map_preeq_fix=None, map_sim_fix=None, scale_map_sim_fix=None)

Parameter mapping for condition.

Contains mappings for free parameters, fixed parameters, and fixed preequilibration parameters, both for parameters and scales.

In the scale mappings, for each simulation parameter the scale on which the value is passed (and potentially gradients are to be returned) is given. In the parameter mappings, for each simulation parameter a corresponding optimization parameter (or a numeric value) is given.

If a mapping is not passed, the parameter mappings are assumed to be empty, and if a scale mapping is not passed, all scales are set to linear.

Parameters:

• map_sim_var (dict[str, typing.Union[numbers.Number, str]]) – Mapping for free simulation parameters.

• scale_map_sim_var (dict[str, str]) – Scales for free simulation parameters.

• map_preeq_fix (dict[str, typing.Union[numbers.Number, str]]) – Mapping for fixed preequilibration parameters.

• scale_map_preeq_fix (dict[str, str]) – Scales for fixed preequilibration parameters.

• map_sim_fix (dict[str, typing.Union[numbers.Number, str]]) – Mapping for fixed simulation parameters.

• scale_map_sim_fix (dict[str, str]) – Scales for fixed simulation parameters.

__init__(map_sim_var=None, scale_map_sim_var=None, map_preeq_fix=None, scale_map_preeq_fix=None, map_sim_fix=None, scale_map_sim_fix=None)

property free_symbols: set[str]

Get IDs of all (symbolic) parameters present in this mapping

amici.petab.parameter_mapping.amici_to_petab_scale(amici_scale)

Convert amici scale id to petab scale id.

Return type:

str

amici.petab.parameter_mapping.create_parameter_mapping(petab_problem, simulation_conditions, scaled_parameters, amici_model, **parameter_mapping_kwargs)

Generate AMICI specific parameter mapping.

Parameters:

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

• simulation_conditions (pandas.core.frame.DataFrame | list[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.petab.parameter_mapping.ParameterMapping

Returns:

List of the parameter mappings.

amici.petab.parameter_mapping.create_parameter_mapping_for_condition(parameter_mapping_for_condition, condition, petab_problem, amici_model)

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 (pandas.core.series.Series | 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.petab.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.parameter_mapping.petab_to_amici_scale(petab_scale)

Convert petab scale id to amici scale id.

Return type:

int

amici.petab.parameter_mapping.scale_parameter(value, petab_scale)

Bring parameter from linear scale to target scale.

Parameters:

• value (numbers.Number) – Value to scale

• petab_scale (str) – Target scale of value

Return type:

numbers.Number

Returns:

value on target scale

amici.petab.parameter_mapping.scale_parameters_dict(value_dict, petab_scale_dict)

Bring parameters from linear scale to target scale.

Bring values in value_dict from linear scale to the scale provided in petab_scale_dict (in-place). Both arguments are expected to have the same length and matching keys.

Parameters:

• value_dict (dict[typing.Any, numbers.Number]) – Values to scale

• petab_scale_dict (dict[typing.Any, str]) – Target scales of values

Return type:

None

amici.petab.parameter_mapping.unscale_parameter(value, petab_scale)

Bring parameter from scale to linear scale.

Parameters:

• value (numbers.Number) – Value to scale

• petab_scale (str) – Target scale of value

Return type:

numbers.Number

Returns:

value on linear scale

amici.petab.parameter_mapping.unscale_parameters_dict(value_dict, petab_scale_dict)

Bring parameters from target scale to linear scale.

Bring values in value_dict from linear scale to the scale provided in petab_scale_dict (in-place). Both arguments are expected to have the same length and matching keys.

Parameters:

• value_dict (dict[typing.Any, numbers.Number]) – Values to scale

• petab_scale_dict (dict[typing.Any, str]) – Target scales of values

Return type:

None