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’