petab.parameters¶
Functions operating on the PEtab parameter table
Functions
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Create a new PEtab parameter table |
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Get Dictionary with optimization parameter IDs mapped to parameter scaling strings. |
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Get list of optimization parameter IDs from parameter table. |
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Read the provided parameter file into a |
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Create list with information about the parameter priors |
Get set of parameters which need to go into the parameter table |
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Get set of parameters which may be present inside the parameter table |
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Scale the parameters, i.e. as scale(), but for Iterables. |
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Unscale the parameters, i.e. as unscale(), but for Iterables. |
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Add missing columns and fill in default values. |
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Scale parameter according to scale_str. |
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Unscale parameter according to scale_str. |
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Write PEtab parameter table |
- petab.parameters.create_parameter_df(sbml_model: libsbml.Model, condition_df: pandas.core.frame.DataFrame, observable_df: pandas.core.frame.DataFrame, measurement_df: pandas.core.frame.DataFrame, include_optional: bool = False, parameter_scale: str = 'log10', lower_bound: Optional[Iterable] = None, upper_bound: Optional[Iterable] = None) → pandas.core.frame.DataFrame[source]¶
Create a new PEtab parameter table
All table entries can be provided as string or list-like with length matching the number of parameters
- Parameters
sbml_model – SBML Model
condition_df – PEtab condition DataFrame
measurement_df – PEtab measurement DataFrame
include_optional – By default this only returns parameters that are required to be present in the parameter table. If set to True, this returns all parameters that are allowed to be present in the parameter table (i.e. also including parameters specified in the SBML model).
parameter_scale – parameter scaling
lower_bound – lower bound for parameter value
upper_bound – upper bound for parameter value
- Returns
The created parameter DataFrame
- petab.parameters.get_optimization_parameter_scaling(parameter_df: pandas.core.frame.DataFrame) → Dict[str, str][source]¶
Get Dictionary with optimization parameter IDs mapped to parameter scaling strings.
- Parameters
parameter_df – PEtab parameter DataFrame
- Returns
Dictionary with optimization parameter IDs mapped to parameter scaling strings.
- petab.parameters.get_optimization_parameters(parameter_df: pandas.core.frame.DataFrame) → List[str][source]¶
Get list of optimization parameter IDs from parameter table.
- Parameters
parameter_df – PEtab parameter DataFrame
- Returns
List of IDs of parameters selected for optimization.
- petab.parameters.get_parameter_df(parameter_file: Optional[Union[str, List[str], pandas.core.frame.DataFrame]]) → pandas.core.frame.DataFrame[source]¶
Read the provided parameter file into a
pandas.Dataframe.- Parameters
parameter_file – Name of the file to read from or pandas.Dataframe.
- Returns
Parameter DataFrame
- petab.parameters.get_priors_from_df(parameter_df: pandas.core.frame.DataFrame, mode: str) → List[Tuple][source]¶
Create list with information about the parameter priors
- Parameters
parameter_df – PEtab parameter table
mode – ‘initialization’ or ‘objective’
- Returns
List with prior information.
- petab.parameters.get_required_parameters_for_parameter_table(sbml_model: libsbml.Model, condition_df: pandas.core.frame.DataFrame, observable_df: pandas.core.frame.DataFrame, measurement_df: pandas.core.frame.DataFrame) → Set[str][source]¶
Get set of parameters which need to go into the parameter table
- Parameters
sbml_model – PEtab SBML model
condition_df – PEtab condition table
observable_df – PEtab observable table
measurement_df – PEtab measurement table
- Returns
Set of parameter IDs which PEtab requires to be present in the parameter table. That is all {observable,noise}Parameters from the measurement table as well as all parametric condition table overrides that are not defined in the SBML model.
- petab.parameters.get_valid_parameters_for_parameter_table(sbml_model: libsbml.Model, condition_df: pandas.core.frame.DataFrame, observable_df: pandas.core.frame.DataFrame, measurement_df: pandas.core.frame.DataFrame) → Set[str][source]¶
Get set of parameters which may be present inside the parameter table
- Parameters
sbml_model – PEtab SBML model
condition_df – PEtab condition table
observable_df – PEtab observable table
measurement_df – PEtab measurement table
- Returns
Set of parameter IDs which PEtab allows to be present in the parameter table.
- petab.parameters.map_scale(parameters: Iterable[numbers.Number], scale_strs: Union[Iterable[str], str]) → Iterable[numbers.Number][source]¶
Scale the parameters, i.e. as scale(), but for Iterables.
- Parameters
parameters – Parameters to be scaled.
scale_strs – Scales to apply. Broadcast if a single string.
- Returns
The scaled parameters.
- Return type
parameters
- petab.parameters.map_unscale(parameters: Iterable[numbers.Number], scale_strs: Union[Iterable[str], str]) → Iterable[numbers.Number][source]¶
Unscale the parameters, i.e. as unscale(), but for Iterables.
- Parameters
parameters – Parameters to be unscaled.
scale_strs – Scales that the parameters are currently on. Broadcast if a single string.
- Returns
The unscaled parameters.
- Return type
parameters
- petab.parameters.normalize_parameter_df(parameter_df: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame[source]¶
Add missing columns and fill in default values.
- petab.parameters.scale(parameter: numbers.Number, scale_str: str) → numbers.Number[source]¶
Scale parameter according to scale_str.
- Parameters
parameter – Parameter to be scaled.
scale_str – One of ‘lin’ (synonymous with ‘’), ‘log’, ‘log10’.
- Returns
The scaled parameter.
- petab.parameters.unscale(parameter: numbers.Number, scale_str: str) → numbers.Number[source]¶
Unscale parameter according to scale_str.
- Parameters
parameter – Parameter to be unscaled.
scale_str – One of ‘lin’ (synonymous with ‘’), ‘log’, ‘log10’.
- Returns
The unscaled parameter.