amici.petab.conditions
PEtab conditions to AMICI ExpDatas.
Functions
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Get |
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Create list of |
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Create list of :class:amici.ExpData objects with parameters filled in. |
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Fill fixed and dynamic parameters into the edatas (in-place). |
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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)[source]
Get
amici.amici.ExpData
for the given PEtab condition.Sets timepoints, observed data and sigmas.
- Parameters:
condition (
dict
|pandas.core.series.Series
) –pandas.DataFrame
row withpreequilibrationConditionId
andsimulationConditionId
.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 modelpetab_problem (
petab.v1.problem.Problem
) – Underlying PEtab problem
- Return type:
- Returns:
ExpData instance.
- amici.petab.conditions.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.v1.problem.Problem
) – Underlying PEtab problem.simulation_conditions (
pandas.core.frame.DataFrame
|dict
) – Result ofpetab.get_simulation_conditions()
. Can be provided to save time if this has be obtained before.
- Return type:
- Returns:
List with one
amici.amici.ExpData
per simulation condition, with filled in timepoints and data, but without parameter values (seecreate_parameterized_edatas()
orfill_in_parameters()
for that).
- amici.petab.conditions.create_parameterized_edatas(amici_model, petab_problem, problem_parameters, scaled_parameters=False, parameter_mapping=None, simulation_conditions=None, warn_unused=True)[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 withpetab_problem
.petab_problem (
petab.v1.problem.Problem
) – PEtab problem to work on.problem_parameters (
dict
[str
,numbers.Number
]) – Run simulation with these parameters. IfNone
, PEtabnominalValues
will be used. To be provided as dict, mapping PEtab problem parameters to SBML IDs.scaled_parameters (
bool
) – IfTrue
,problem_parameters
are assumed to be on the scale provided in the PEtab parameter table and will be unscaled. IfFalse
, they are assumed to be in linear scale.parameter_mapping (
amici.petab.parameter_mapping.ParameterMapping
) – Optional precomputed PEtab parameter mapping for efficiency, as generated bycreate_parameter_mapping()
.simulation_conditions (
pandas.core.frame.DataFrame
|dict
) – Result ofpetab.get_simulation_conditions()
. Can be provided to save time if this has been obtained before.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 or in the generated parameter mapping.
- Return type:
- 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, 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.petab.conditions.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: