SimpleModelGenerator

class glotaran.testing.model_generators.SimpleModelGenerator(rates: list[float] = <factory>, k_matrix: Literal[('parallel', 'sequential')] | dict[tuple[str, str], str] = 'parallel', compartments: list[str] | None = None, irf: dict[str, float] = <factory>, initial_concentration: list[float] = <factory>, dispersion_coefficients: list[float] = <factory>, dispersion_center: float | None = None, default_megacomplex: str = 'decay')[source]

Bases: object

A minimal boilerplate model and parameters generator.

Generates a model (together with the parameters specification) based on parameter input values assigned to the generator’s attributes

Attributes Summary

`compartments`	A list of compartment names
`default_megacomplex`	The default_megacomplex identifier
`dispersion_center`	A value representing the dispersion center
`k_matrix`	"A dict with a k_matrix specification or Literal["parallel", "sequential"]
`model`	Return the generated model.
`model_and_parameters`	Return generated model and parameters.
`model_dict`	Return a dict representation of the generated model.
`parameters`	Return the generated parameters of type `glotaran.parameter.ParameterGroup`.
`parameters_dict`	Return a dict representation of the generated parameters.
`valid`	Check if the generator state is valid.
`rates`	A list of values representing decay rates
`irf`	A dict of items specifying an irf
`initial_concentration`	A list values representing the initial concentration
`dispersion_coefficients`	A list of values representing the dispersion coefficients

Methods Summary

`markdown`	Return a markdown string representation of the generated model and parameters.
`validate`	Call validate on the generated model and return its output.

Methods Documentation

compartments: list[str] | None = None: A list of compartment names

default_megacomplex: str = 'decay': The default_megacomplex identifier

dispersion_center: float | None = None: A value representing the dispersion center

dispersion_coefficients: list[float]: A list of values representing the dispersion coefficients

initial_concentration: list[float]: A list values representing the initial concentration

irf: dict[str, float]: A dict of items specifying an irf

k_matrix: Literal['parallel', 'sequential'] | dict[tuple[str, str], str] = 'parallel': “A dict with a k_matrix specification or Literal[“parallel”, “sequential”]

markdown() → MarkdownStr[source]

Return a markdown string representation of the generated model and parameters.

Returns: A markdown string
Return type: MarkdownStr

property model: glotaran.model.model.Model

Return the generated model.

Returns: The generated model of type glotaran.model.Model.
Return type: Model

property model_and_parameters: tuple[Model, ParameterGroup]

Return generated model and parameters.

Returns: A model of type glotaran.model.Model and and parameters of type glotaran.parameter.ParameterGroup.
Return type: tuple[Model, ParameterGroup]

property model_dict: dict

Return a dict representation of the generated model.

Returns: A dict representation of the generated model.
Return type: dict

property parameters: glotaran.parameter.parameter_group.ParameterGroup

Return the generated parameters of type glotaran.parameter.ParameterGroup.

Returns: The generated parameters of type of type glotaran.parameter.ParameterGroup.
Return type: ParameterGroup

property parameters_dict: dict

Return a dict representation of the generated parameters.

Returns: A dict representing the generated parameters.
Return type: dict

rates: list[float]: A list of values representing decay rates

property valid: bool

Check if the generator state is valid.

Returns: Generator state obtained by calling the generated model’s valid function with the generated parameters as input.
Return type: bool

validate() → str[source]

Call validate on the generated model and return its output.

Returns: A string listing problems in the generated model and parameters if any.
Return type: str