petab.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, List[str], …) |
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) |
As scale(), but for Iterables |
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 |
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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: 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
- 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
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petab.parameters.
get_optimization_parameter_scaling
(parameter_df: pandas.core.frame.DataFrame) → Dict[str, str]¶ 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.
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petab.parameters.
get_optimization_parameters
(parameter_df: pandas.core.frame.DataFrame) → List[str]¶ Get list of optimization parameter IDs from parameter table.
Parameters: parameter_df – PEtab parameter DataFrame Returns: List of IDs of parameters selected for optimization.
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petab.parameters.
get_parameter_df
(parameter_file: Union[str, List[str], pandas.core.frame.DataFrame, None]) → pandas.core.frame.DataFrame¶ 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
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petab.parameters.
get_priors_from_df
(parameter_df: pandas.core.frame.DataFrame, mode: str) → List[Tuple]¶ Create list with information about the parameter priors
Parameters: - parameter_df – PEtab parameter table
- mode – ‘initialization’ or ‘objective’
Returns: List with prior information.
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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]¶ 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.
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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]¶ 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.
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petab.parameters.
map_scale
(parameters: Iterable[numbers.Number], scale_strs: Iterable[str]) → Iterable[numbers.Number]¶ As scale(), but for Iterables
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petab.parameters.
normalize_parameter_df
(parameter_df: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame¶ Add missing columns and fill in default values.
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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’.
Returns: The scaled parameter.
Return type: parameter
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petab.parameters.
unscale
(parameter: numbers.Number, scale_str: str) → numbers.Number¶ 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.
Return type: parameter
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petab.parameters.
write_parameter_df
(df: pandas.core.frame.DataFrame, filename: str) → None¶ Write PEtab parameter table
Parameters: - df – PEtab parameter table
- filename – Destination file name