constraint

Provides a constranit, which does nothing.

class Constraint(sm: SystemModelInterface)

Bases: object

Defines an constraint that does nothing.

Write own constraints by inheriting from this one.

Inits the constraint.

Parameters:

sm – A SystemModel, on which the constraint will be applied.

Example

>>> from simba_ml.example_problems.sir import sm
>>> from simba_ml.simulation.constraints.constraint import Constraint
>>> constrainted_pt = Constraint(sm)

apply_noisifier(signal: DataFrame) → DataFrame

Applies the objects noisifier to a signal.

Parameters:

signal – (pd.DataFrame) The signal.

Returns:

Signal with applied noise.

Return type:

pd.DataFrame

apply_sparsifier(signal: DataFrame) → DataFrame

Applies the objects sparsifier to a signal.

Parameters:

signal – (pd.DataFrame) The signal.

Returns:

Signal of reduced features.

Return type:

pd.DataFrame

property deriv: Callable[[float, list[float], dict[str, float]], Tuple[float, ...]]

Returns the deriv.

Returns:

The deriv.

get_clean_signal(start_values: dict[str, Any], sample_id: int, deriv_noised: bool = True) → DataFrame

Creates a clean signal.

Parameters:

• start_values – Start values for the simulation.

• sample_id – The id of the sample.

• deriv_noised – If the derivative function should be noised.

Returns:

pd.DataFrame

property kinetic_parameters: Dict[str, KineticParameter[Any]]

Returns the kinetic_parameters.

Returns:

The kinetic_parameters.

property name: str

Returns the name.

Returns:

The name.

sample_start_values_from_hypercube(n: int) → dict[str, Any]

Creates a start_values dict.

Parameters:

n – The number of samples.

Returns:

The start_values dict.

Return type:

dict[str, Any]

property specieses: dict[str, simba_ml.simulation.species.Species]

Returns the specieses.

Returns:

The specieses.