amici.parameter_mapping

Parameter mapping between AMICI and PEtab.

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

class amici.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.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.parameter_mapping.amici_to_petab_scale(amici_scale)

Convert amici scale id to petab scale id.

Return type:

str

amici.parameter_mapping.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.parameter_mapping.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

amici.parameter_mapping.petab_to_amici_scale(petab_scale)

Convert petab scale id to amici scale id.

Return type:

int

amici.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.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.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.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