petab.parameters

Functions operating on the PEtab parameter table

Functions

create_parameter_df(sbml_model, …) Create a new PEtab parameter table
get_optimization_parameters(parameter_df) Get list of optimization parameter ids from parameter dataframe.
get_parameter_df(parameter_file_name) Read the provided parameter file into a pandas.Dataframe.
get_priors_from_df(parameter_df) 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
map_scale(parameters, scale_strs) As scale(), but for Iterables
parameter_id_is_valid(parameter_id) Check whether parameter_id is a valid PEtab parameter ID
scale(parameter, scale_str) Scale parameter according to scale_str
petab.parameters.create_parameter_df(sbml_model: libsbml.Model, condition_df: pandas.core.frame.DataFrame, measurement_df: pandas.core.frame.DataFrame, parameter_scale: str = 'log10', lower_bound: Iterable[T_co] = None, upper_bound: Iterable[T_co] = None) → pandas.core.frame.DataFrame

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
  • 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_parameters(parameter_df: pandas.core.frame.DataFrame) → List[str]

Get list of optimization parameter ids from parameter dataframe.

Parameters:parameter_df – PEtab parameter DataFrame
Returns:List of parameter IDs in the parameter table
petab.parameters.get_parameter_df(parameter_file_name: str) → pandas.core.frame.DataFrame

Read the provided parameter file into a pandas.Dataframe.

Parameters:parameter_file_name – Name of the file to read from.
Returns:Parameter DataFrame
petab.parameters.get_priors_from_df(parameter_df: pandas.core.frame.DataFrame) → List[Tuple]

Create list with information about the parameter priors

Parameters:parameter_df – PEtab parameter table
Returns:List with prior information.
petab.parameters.get_required_parameters_for_parameter_table(sbml_model: libsbml.Model, condition_df: pandas.core.frame.DataFrame, measurement_df: pandas.core.frame.DataFrame) → Set[str]

Get set of parameters which need to go into the parameter table

Parameters:
  • sbml_model – PEtab SBML model
  • condition_df – PEtab condition table
  • measurement_df – PEtab measurement table
Returns:

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

petab.parameters.map_scale(parameters: Iterable[numbers.Number], scale_strs: Iterable[str]) → Iterable[numbers.Number]

As scale(), but for Iterables

petab.parameters.parameter_id_is_valid(parameter_id: str) → bool

Check whether parameter_id is a valid PEtab parameter ID

This should pretty much correspond to what is allowed for SBML identifiers.

TODO(#179) improve checking

Parameters:parameter_id – Parameter ID to validate
Returns:True if valid, False otherwise
petab.parameters.scale(parameter: numbers.Number, scale_str: str) → numbers.Number

Scale parameter according to scale_str

Parameters:
  • parameter – Parameter to be scaled
  • scale_str – One of ‘lin’ (synonymous with ‘’), ‘log’, ‘log10’