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)[source]
Parameter mapping for multiple conditions.
This can be used like a list of
ParameterMappingForCondition
s.- Parameters:
parameter_mappings (
list
[amici.petab.parameter_mapping.ParameterMappingForCondition
]) – List of parameter mappings for specific conditions.
- count(value) integer -- return number of occurrences of value
- 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)[source]
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.
- amici.parameter_mapping.amici_to_petab_scale(amici_scale)[source]
Convert amici scale id to petab scale id.
- Return type:
- amici.parameter_mapping.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 datasamici.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:
- amici.parameter_mapping.fill_in_parameters_for_condition(edata, problem_parameters, scaled_parameters, parameter_mapping, amici_model)[source]
Fill fixed and dynamic parameters into the edata for condition (in-place).
- Parameters:
edata (
amici.amici.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:
- amici.parameter_mapping.petab_to_amici_scale(petab_scale)[source]
Convert petab scale id to amici scale id.
- Return type:
- amici.parameter_mapping.scale_parameter(value, petab_scale)[source]
Bring parameter from linear scale to target scale.
- Parameters:
value (
numbers.Number
) – Value to scalepetab_scale (
str
) – Target scale ofvalue
- Return type:
- Returns:
value
on target scale
- amici.parameter_mapping.scale_parameters_dict(value_dict, petab_scale_dict)[source]
Bring parameters from linear scale to target scale.
Bring values in
value_dict
from linear scale to the scale provided inpetab_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 scalepetab_scale_dict (
dict
[typing.Any
,str
]) – Target scales ofvalues
- Return type:
- amici.parameter_mapping.unscale_parameter(value, petab_scale)[source]
Bring parameter from scale to linear scale.
- Parameters:
value (
numbers.Number
) – Value to scalepetab_scale (
str
) – Target scale ofvalue
- Return type:
- Returns:
value
on linear scale
- amici.parameter_mapping.unscale_parameters_dict(value_dict, petab_scale_dict)[source]
Bring parameters from target scale to linear scale.
Bring values in
value_dict
from linear scale to the scale provided inpetab_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 scalepetab_scale_dict (
dict
[typing.Any
,str
]) – Target scales ofvalues
- Return type: