amici.petab_import_pysb.PysbPetabProblem

class amici.petab_import_pysb.PysbPetabProblem(pysb_model=None, *args, **kwargs)[source]

Representation of a PySB-model-based PEtab problem

This class extends petab.Problem with a PySB model. The model is augmented with the observation model based on the PEtab observable table. For now, a dummy SBML model is created which allows used the existing SBML-PEtab API.

Variables: pysb_model – PySB model instance from of this PEtab problem.

__init__(pysb_model=None, *args, **kwargs)[source]

Constructor

Parameters

pysb_model (typing.Optional[pysb.core.Model]) – PySB model instance for this PEtab problem
args – See petab.Problem.__init__()
kwargs – See petab.Problem.__init__()

Methods Summary

`__init__`([pysb_model])	Constructor
`create_parameter_df`(args, *kwargs)	Create a new PEtab parameter table
`from_combine`(filename)	Read PEtab COMBINE archive (http://co.mbine.org/documents/archive).
`from_files`([condition_file, ...])	Factory method to load model and tables from files.
`from_yaml`(yaml_config[, flatten])	Factory method to load model and tables as specified by YAML file.
`get_lb`([free, fixed, scaled])	Generic function to get lower parameter bounds.
`get_model_parameters`()	See `petab.sbml.get_model_parameters()`
`get_observable_ids`()	Returns dictionary of observable ids.
`get_optimization_parameter_scales`()	Return list of optimization parameter scaling strings.
`get_optimization_parameters`()	Return list of optimization parameter IDs.
`get_optimization_to_simulation_parameter_mapping`(...)	See `petab.parameter_mapping.get_optimization_to_simulation_parameter_mapping()`, to which all keyword arguments are forwarded.
`get_simulation_conditions_from_measurement_df`()	See petab.get_simulation_conditions
`get_ub`([free, fixed, scaled])	Generic function to get upper parameter bounds.
`get_x_ids`([free, fixed])	Generic function to get parameter ids.
`get_x_nominal`([free, fixed, scaled])	Generic function to get parameter nominal values.
`sample_parameter_startpoints`([n_starts])	Create starting points for optimization
`scale_parameters`(x_dict)	Scale parameter values.
`to_files`([sbml_file, condition_file, ...])	Write PEtab tables to files for this problem
`to_files_generic`(prefix_path)	Save a PEtab problem to generic file names.
`unscale_parameters`(x_dict)	Unscale parameter values.

Attributes

`lb`	Parameter table lower bounds.
`lb_scaled`	Parameter table lower bounds with applied parameter scaling
`ub`	Parameter table upper bounds
`ub_scaled`	Parameter table upper bounds with applied parameter scaling
`x_fixed_ids`	Parameter table parameter IDs, for fixed parameters.
`x_fixed_indices`	Parameter table non-estimated parameter indices.
`x_free_ids`	Parameter table parameter IDs, for free parameters.
`x_free_indices`	Parameter table estimated parameter indices.
`x_ids`	Parameter table parameter IDs
`x_nominal`	Parameter table nominal values
`x_nominal_fixed`	Parameter table nominal values, for fixed parameters.
`x_nominal_fixed_scaled`	Parameter table nominal values with applied parameter scaling, for fixed parameters.
`x_nominal_free`	Parameter table nominal values, for free parameters.
`x_nominal_free_scaled`	Parameter table nominal values with applied parameter scaling, for free parameters.
`x_nominal_scaled`	Parameter table nominal values with applied parameter scaling

Methods

__init__(pysb_model=None, *args, **kwargs)[source]

Constructor

Parameters

pysb_model (typing.Optional[pysb.core.Model]) – PySB model instance for this PEtab problem
args – See petab.Problem.__init__()
kwargs – See petab.Problem.__init__()

create_parameter_df(*args, **kwargs)

Create a new PEtab parameter table

See create_parameter_df().

static from_combine(filename)

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

Parameters: filename (typing.Union[pathlib.Path, str]) – Path to the PEtab-COMBINE archive
Return type: typing.ForwardRef
Returns: A petab.Problem instance.

static from_files(condition_file=None, measurement_file=None, parameter_file=None, visualization_files=None, observable_files=None, pysb_model_file=None, flatten=False)[source]

Factory method to load model and tables from files.

Parameters

condition_file (typing.Union[str, pathlib.Path, typing.Iterable[typing.Union[str, pathlib.Path]], None]) – PEtab condition table
measurement_file (typing.Union[str, pathlib.Path, typing.Iterable[typing.Union[str, pathlib.Path]], None]) – PEtab measurement table
parameter_file (typing.Union[str, pathlib.Path, typing.Iterable[typing.Union[str, pathlib.Path]], None]) – PEtab parameter table
visualization_files (typing.Union[str, pathlib.Path, typing.Iterable[typing.Union[str, pathlib.Path]], None]) – PEtab visualization tables
observable_files (typing.Union[str, pathlib.Path, typing.Iterable[typing.Union[str, pathlib.Path]], None]) – PEtab observables tables
pysb_model_file (typing.Union[str, pathlib.Path, None]) – PySB model file
flatten (bool) – Flatten the petab problem

Return type

amici.petab_import_pysb.PysbPetabProblem

Returns

Petab Problem

static from_yaml(yaml_config, flatten=False)[source]

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

NOTE: The PySB model is currently expected in the YAML file under sbml_files.

Parameters

yaml_config (typing.Union[typing.Dict, pathlib.Path, str]) – PEtab configuration as dictionary or YAML file name
flatten (bool) – Flatten the petab problem

Return type

amici.petab_import_pysb.PysbPetabProblem

Returns

Petab Problem

get_lb(free=True, fixed=True, scaled=False)

Generic function to get lower parameter bounds.

Parameters

free (bool) – Whether to return free parameters, i.e. parameters to estimate.
fixed (bool) – Whether to return fixed parameters, i.e. parameters not to estimate.
scaled (bool) – Whether to scale the values according to the parameter scale, or return them on linear scale.

Returns

Return type

The lower parameter bounds.

get_model_parameters(): See petab.sbml.get_model_parameters()

get_observable_ids(): Returns dictionary of observable ids.

get_optimization_parameter_scales()

Return list of optimization parameter scaling strings.

See petab.parameters.get_optimization_parameters().

get_optimization_parameters()

Return list of optimization parameter IDs.

See petab.parameters.get_optimization_parameters().

get_optimization_to_simulation_parameter_mapping(**kwargs): See petab.parameter_mapping.get_optimization_to_simulation_parameter_mapping(), to which all keyword arguments are forwarded.

get_simulation_conditions_from_measurement_df(): See petab.get_simulation_conditions

get_ub(free=True, fixed=True, scaled=False)

Generic function to get upper parameter bounds.

Parameters

free (bool) – Whether to return free parameters, i.e. parameters to estimate.
fixed (bool) – Whether to return fixed parameters, i.e. parameters not to estimate.
scaled (bool) – Whether to scale the values according to the parameter scale, or return them on linear scale.

Returns

Return type

The upper parameter bounds.

get_x_ids(free=True, fixed=True)

Generic function to get parameter ids.

Parameters

free (bool) – Whether to return free parameters, i.e. parameters to estimate.
fixed (bool) – Whether to return fixed parameters, i.e. parameters not to estimate.

Returns

Return type

The parameter IDs.

get_x_nominal(free=True, fixed=True, scaled=False)

Generic function to get parameter nominal values.

Parameters

free (bool) – Whether to return free parameters, i.e. parameters to estimate.
fixed (bool) – Whether to return fixed parameters, i.e. parameters not to estimate.
scaled (bool) – Whether to scale the values according to the parameter scale, or return them on linear scale.

Returns

Return type

The parameter nominal values.

sample_parameter_startpoints(n_starts=100)

Create starting points for optimization

See petab.sample_parameter_startpoints().

scale_parameters(x_dict)

Scale parameter values.

Parameters: x_dict (typing.Dict[str, float]) – Keys are parameter IDs in the PEtab problem, values are unscaled parameter values.
Returns
Return type: The scaled parameter values.

to_files(sbml_file=None, condition_file=None, measurement_file=None, parameter_file=None, visualization_file=None, observable_file=None, yaml_file=None, prefix_path=None, relative_paths=True, model_file=None)

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 (typing.Union[pathlib.Path, str, None]) – SBML model destination
model_file (typing.Union[pathlib.Path, str, None]) – Model destination
condition_file (typing.Union[pathlib.Path, str, None]) – Condition table destination
measurement_file (typing.Union[pathlib.Path, str, None]) – Measurement table destination
parameter_file (typing.Union[pathlib.Path, str, None]) – Parameter table destination
visualization_file (typing.Union[pathlib.Path, str, None]) – Visualization table destination
observable_file (typing.Union[pathlib.Path, str, None]) – Observables table destination
yaml_file (typing.Union[pathlib.Path, str, None]) – YAML file destination
prefix_path (typing.Union[pathlib.Path, str, None]) – 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 (bool) – 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.

Return type

None

to_files_generic(prefix_path)

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 (typing.Union[str, pathlib.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.
Return type: str
Returns: The path to the PEtab problem YAML file.

unscale_parameters(x_dict)

Unscale parameter values.

Parameters: x_dict (typing.Dict[str, float]) – Keys are parameter IDs in the PEtab problem, values are scaled parameter values.
Returns
Return type: The unscaled parameter values.