petab.visualize.lint

Validation of PEtab visualization files

Functions

validate_visualization_df(problem)

Validate visualization table
class petab.visualize.lint.Problem(sbml_model: libsbml.Model = None, sbml_reader: libsbml.SBMLReader = None, sbml_document: libsbml.SBMLDocument = None, model: Model = None, model_id: str = None, condition_df: pd.DataFrame = None, measurement_df: pd.DataFrame = None, parameter_df: pd.DataFrame = None, visualization_df: pd.DataFrame = None, observable_df: pd.DataFrame = None, mapping_df: pd.DataFrame = None, extensions_config: Dict = None)[source]

Bases: object

PEtab parameter estimation problem as defined by

  • model

  • condition table

  • measurement table

  • parameter table

  • observables table

  • mapping table

Optionally it may contain visualization tables.

condition_df

PEtab condition table

measurement_df

PEtab measurement table

parameter_df

PEtab parameter table

observable_df

PEtab observable table

visualization_df

PEtab visualization table

mapping_df

PEtab mapping table

model

The underlying model

sbml_reader

Stored to keep object alive (deprecated).

sbml_document

Stored to keep object alive (deprecated).

sbml_model

PEtab SBML model (deprecated)

extensions_config

Information on the extensions used

_apply_mask(v: List, free: bool = True, fixed: bool = True)[source]

Apply mask of only free or only fixed values.

Parameters:

  • v – The full vector the mask is to be applied to.

  • free – Whether to return free parameters, i.e. parameters to estimate.

  • fixed – Whether to return fixed parameters, i.e. parameters not to estimate.

Return type:

The reduced vector with applied mask.

create_parameter_df(*args, **kwargs)[source]

Create a new PEtab parameter table

See create_parameter_df().

static from_combine(filename: Path | str) → Problem[source]

Read PEtab COMBINE archive (http://co.mbine.org/documents/archive).

See also petab.create_combine_archive().

Parameters:

filename – Path to the PEtab-COMBINE archive

Returns:

A petab.Problem instance.

static from_files(sbml_file: Path | str | None = None, condition_file: str | Path | Iterable[str | Path] | None = None, measurement_file: str | Path | Iterable[str | Path] | None = None, parameter_file: str | Path | Iterable[str | Path] | None = None, visualization_files: str | Path | Iterable[str | Path] | None = None, observable_files: str | Path | Iterable[str | Path] | None = None, model_id: str | None = None, extensions_config: Dict | None = None) → Problem[source]

Factory method to load model and tables from files.

Parameters:

  • sbml_file – PEtab SBML model

  • condition_file – PEtab condition table

  • measurement_file – PEtab measurement table

  • parameter_file – PEtab parameter table

  • visualization_files – PEtab visualization tables

  • observable_files – PEtab observables tables

  • model_id – PEtab ID of the model

  • extensions_config – Information on the extensions used

static from_yaml(yaml_config: Dict | Path | str) → Problem[source]

Factory method to load model and tables as specified by YAML file.

Parameters:

yaml_config – PEtab configuration as dictionary or YAML file name

get_lb(free: bool = True, fixed: bool = True, scaled: bool = False)[source]

Generic function to get lower parameter bounds.

Parameters:

  • free – Whether to return free parameters, i.e. parameters to estimate.

  • fixed – Whether to return fixed parameters, i.e. parameters not to estimate.

  • scaled – Whether to scale the values according to the parameter scale, or return them on linear scale.

Return type:

The lower parameter bounds.

get_model_parameters()[source]

See petab.sbml.get_model_parameters()

get_observable_ids()[source]

Returns dictionary of observable ids.

get_optimization_parameter_scales()[source]

Return list of optimization parameter scaling strings.

See petab.parameters.get_optimization_parameters().

get_optimization_parameters()[source]

Return list of optimization parameter IDs.

See petab.parameters.get_optimization_parameters().

get_optimization_to_simulation_parameter_mapping(**kwargs)[source]

See petab.parameter_mapping.get_optimization_to_simulation_parameter_mapping(), to which all keyword arguments are forwarded.

get_simulation_conditions_from_measurement_df()[source]

See petab.get_simulation_conditions

get_ub(free: bool = True, fixed: bool = True, scaled: bool = False)[source]

Generic function to get upper parameter bounds.

Parameters:

  • free – Whether to return free parameters, i.e. parameters to estimate.

  • fixed – Whether to return fixed parameters, i.e. parameters not to estimate.

  • scaled – Whether to scale the values according to the parameter scale, or return them on linear scale.

Return type:

The upper parameter bounds.

get_x_ids(free: bool = True, fixed: bool = True)[source]

Generic function to get parameter ids.

Parameters:

  • free – Whether to return free parameters, i.e. parameters to estimate.

  • fixed – Whether to return fixed parameters, i.e. parameters not to estimate.

Return type:

The parameter IDs.

get_x_nominal(free: bool = True, fixed: bool = True, scaled: bool = False)[source]

Generic function to get parameter nominal values.

Parameters:

  • free – Whether to return free parameters, i.e. parameters to estimate.

  • fixed – Whether to return fixed parameters, i.e. parameters not to estimate.

  • scaled – Whether to scale the values according to the parameter scale, or return them on linear scale.

Return type:

The parameter nominal values.

property lb: List

Parameter table lower bounds.

property lb_scaled: List

Parameter table lower bounds with applied parameter scaling

property n_estimated: int

The number of estimated parameters.

property n_measurements: int

Number of measurements.

property n_priors: int

Number of priors.

sample_parameter_startpoints(n_starts: int = 100, **kwargs)[source]

Create 2D array with starting points for optimization

See petab.sample_parameter_startpoints().

sample_parameter_startpoints_dict(n_starts: int = 100) → List[Dict[str, float]][source]

Create dictionaries with starting points for optimization

See also petab.sample_parameter_startpoints().

Returns:

A list of dictionaries with parameter IDs mapping to samples parameter values.

scale_parameters(x_dict: Dict[str, float]) → Dict[str, float][source]

Scale parameter values.

Parameters:

x_dict – Keys are parameter IDs in the PEtab problem, values are unscaled parameter values.

Return type:

The scaled parameter values.

to_files(sbml_file: None | str | Path = None, condition_file: None | str | Path = None, measurement_file: None | str | Path = None, parameter_file: None | str | Path = None, visualization_file: None | str | Path = None, observable_file: None | str | Path = None, yaml_file: None | str | Path = None, prefix_path: None | str | Path = None, relative_paths: bool = True, model_file: None | str | Path = None, mapping_file: None | str | Path = None) → None[source]

Write PEtab tables to files for this problem

Writes PEtab files for those entities for which a destination was passed.

NOTE: If this instance was created from multiple measurement or visualization tables, they will be merged and written to a single file.

Parameters:

  • sbml_file – SBML model destination (deprecated)

  • model_file – Model destination

  • condition_file – Condition table destination

  • measurement_file – Measurement table destination

  • parameter_file – Parameter table destination

  • visualization_file – Visualization table destination

  • observable_file – Observables table destination

  • mapping_file – Mapping table destination

  • yaml_file – YAML file destination

  • prefix_path – Specify a prefix to all paths, to avoid specifying the prefix for all paths individually. NB: the prefix is added to paths before relative_paths is handled.

  • relative_paths – whether all paths in the YAML file should be relative to the location of the YAML file. If False, then paths are left unchanged.

Raises:

ValueError – If a destination was provided for a non-existing entity.

to_files_generic(prefix_path: str | Path) → str[source]

Save a PEtab problem to generic file names.

The PEtab problem YAML file is always created. PEtab data files are only created if the PEtab problem contains corresponding data (e.g. a PEtab visualization TSV file is only created if the PEtab problem has one).

Parameters:

prefix_path – Specify a prefix to all paths, to avoid specifying the prefix for all paths individually. NB: the prefix is added to paths before relative_paths is handled downstream in petab.yaml.create_problem_yaml().

Returns:

The path to the PEtab problem YAML file.

property ub: List

Parameter table upper bounds

property ub_scaled: List

Parameter table upper bounds with applied parameter scaling

unscale_parameters(x_dict: Dict[str, float]) → Dict[str, float][source]

Unscale parameter values.

Parameters:

x_dict – Keys are parameter IDs in the PEtab problem, values are scaled parameter values.

Return type:

The unscaled parameter values.

property x_fixed_ids: List[str]

Parameter table parameter IDs, for fixed parameters.

property x_fixed_indices: List[int]

Parameter table non-estimated parameter indices.

property x_free_ids: List[str]

Parameter table parameter IDs, for free parameters.

property x_free_indices: List[int]

Parameter table estimated parameter indices.

property x_ids: List[str]

Parameter table parameter IDs

property x_nominal: List

Parameter table nominal values

property x_nominal_fixed: List

Parameter table nominal values, for fixed parameters.

property x_nominal_fixed_scaled: List

Parameter table nominal values with applied parameter scaling, for fixed parameters.

property x_nominal_free: List

Parameter table nominal values, for free parameters.

property x_nominal_free_scaled: List

Parameter table nominal values with applied parameter scaling, for free parameters.

property x_nominal_scaled: List

Parameter table nominal values with applied parameter scaling

petab.visualize.lint._apply_defaults(vis_df: DataFrame)[source]

Set default values.

Adds default values to the given visualization table where no value was specified.

petab.visualize.lint.validate_visualization_df(problem: Problem) → bool[source]

Validate visualization table

Parameters:

problem – The PEtab problem containing a visualization table

Returns:

True if errors occurred, False otherwise