petab.v1.parameters

Functions operating on the PEtab parameter table

Functions

`create_parameter_df`([sbml_model, ...])	Create a new PEtab parameter table
`get_optimization_parameter_scaling`(parameter_df)	Get Dictionary with optimization parameter IDs mapped to parameter scaling strings.
`get_optimization_parameters`(parameter_df)	Get list of optimization parameter IDs from parameter table.
`get_parameter_df`(parameter_file)	Read the provided parameter file into a `pandas.Dataframe`.
`get_priors_from_df`(parameter_df, mode[, ...])	Create list with information about the parameter priors
`get_required_parameters_for_parameter_table`(...)	Get set of parameters which need to go into the parameter table
`get_valid_parameters_for_parameter_table`(...)	Get set of parameters which may be present inside the parameter table
`map_scale`(parameters, scale_strs)	Scale the parameters, i.e. as `scale()`, but for Sequences.
`map_unscale`(parameters, scale_strs)	Unscale the parameters, i.e. as `unscale()`, but for Sequences.
`normalize_parameter_df`(parameter_df)	Add missing columns and fill in default values.
`scale`(parameter, scale_str)	Scale parameter according to `scale_str`.
`unscale`(parameter, scale_str)	Unscale parameter according to `scale_str`.
`write_parameter_df`(df, filename)	Write PEtab parameter table

petab.v1.parameters.create_parameter_df(sbml_model: Model | None = None, condition_df: DataFrame | None = None, observable_df: DataFrame | None = None, measurement_df: DataFrame | None = None, model: Model | None = None, include_optional: bool = False, parameter_scale: str = 'log10', lower_bound: Iterable = None, upper_bound: Iterable = None, mapping_df: DataFrame | None = None) → 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 (deprecated, mutually exclusive with model)
model – PEtab model (mutually exclusive with sbml_model)
condition_df – PEtab condition DataFrame
observable_df – PEtab observable 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 model).
parameter_scale – parameter scaling
lower_bound – lower bound for parameter value
upper_bound – upper bound for parameter value
mapping_df – PEtab mapping DataFrame

Returns:

The created parameter DataFrame

petab.v1.parameters.get_optimization_parameter_scaling(parameter_df: 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.v1.parameters.get_optimization_parameters(parameter_df: 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.

Read the provided parameter file into a pandas.Dataframe.

Parameters:

parameter_file – Name of the file to read from or pandas.Dataframe,
Iterable. (or an)

Returns:

Parameter DataFrame, or None if None was passed.

petab.v1.parameters.get_priors_from_df(parameter_df: DataFrame, mode: Literal['initialization', 'objective'], parameter_ids: Sequence[str] = None) → list[tuple][source]

Create list with information about the parameter priors

Parameters:

parameter_df – PEtab parameter table
mode – 'initialization' or 'objective'
parameter_ids – A sequence of parameter IDs for which to sample starting points. For subsetting or reordering the parameters. Defaults to all estimated parameters.

Returns:

List with prior information.

petab.v1.parameters.get_valid_parameters_for_parameter_table(model: Model, condition_df: DataFrame, observable_df: DataFrame, measurement_df: DataFrame, mapping_df: DataFrame = None) → set[str][source]

Get set of parameters which may be present inside the parameter table

Parameters:

model – PEtab model
condition_df – PEtab condition table
observable_df – PEtab observable table
measurement_df – PEtab measurement table
mapping_df – PEtab mapping table for additional checks

Returns:

Set of parameter IDs which PEtab allows to be present in the parameter table.

petab.v1.parameters.map_scale(parameters: Sequence[Number], scale_strs: Iterable[Literal['', 'lin', 'log', 'log10']] | Literal['', 'lin', 'log', 'log10']) → Iterable[Number][source]

Scale the parameters, i.e. as scale(), but for Sequences.

Parameters:

parameters – Parameters to be scaled.
scale_strs – Scales to apply. Broadcast if a single string.

Returns:

The scaled parameters.

petab.v1.parameters.map_unscale(parameters: Sequence[Number], scale_strs: Iterable[Literal['', 'lin', 'log', 'log10']] | Literal['', 'lin', 'log', 'log10']) → Iterable[Number][source]

Unscale the parameters, i.e. as unscale(), but for Sequences.

Parameters:

parameters – Parameters to be unscaled.
scale_strs – Scales that the parameters are currently on. Broadcast if a single string.

Returns:

The unscaled parameters.

petab.v1.parameters.normalize_parameter_df(parameter_df: DataFrame) → DataFrame[source]: Add missing columns and fill in default values.

petab.v1.parameters.scale(parameter: Number, scale_str: Literal['', 'lin', 'log', 'log10']) → 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.v1.parameters.unscale(parameter: Number, scale_str: Literal['', 'lin', 'log', 'log10']) → 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.

petab.v1.parameters.write_parameter_df(df: DataFrame, filename: str | Path) → None[source]

Write PEtab parameter table

Parameters:

df – PEtab parameter table
filename – Destination file name. The parent directory will be created if necessary.