labcore.data.datagen#

Classes

DataGen([noise_std])

ExponentialDataGen([noise_std, base])

GaussianDataGen([noise_std, x0, sigma, A, of])

SineDataGen([noise_std, A, f, phi, of])

class labcore.data.datagen.DataGen(noise_std: float = 1.0)[source]#

Bases: ABC

generate(coordinates: ndarray[tuple[Any, ...], dtype[Any]], **kwargs: Any) → ndarray[tuple[Any, ...], dtype[Any]][source]#
abstractmethod static model(coordinates: ndarray[tuple[Any, ...], dtype[Any]], *args: Any, **kwargs: Any) → ndarray[tuple[Any, ...], dtype[Any]][source]#
static noise(coordinates: ndarray[tuple[Any, ...], dtype[Any]], std: float) → ndarray[tuple[Any, ...], dtype[Any]][source]#
noise_std: float = 1.0#
class labcore.data.datagen.ExponentialDataGen(noise_std: float = 1.0, base: float = 2.718281828459045)[source]#

Bases: DataGen

base: float = 2.718281828459045#
static model(coordinates: ndarray[tuple[Any, ...], dtype[Any]], base: float) → ndarray[tuple[Any, ...], dtype[Any]][source]#
class labcore.data.datagen.GaussianDataGen(noise_std: float = 1.0, x0: float = 0, sigma: float = 1, A: float = 1, of: float = 0)[source]#

Bases: DataGen

A: float = 1#
static model(coordinates: ndarray[tuple[Any, ...], dtype[Any]], x0: float, sigma: float, A: float, of: float) → ndarray[tuple[Any, ...], dtype[Any]][source]#
of: float = 0#
sigma: float = 1#
x0: float = 0#
class labcore.data.datagen.SineDataGen(noise_std: float = 1.0, A: float = 1, f: float = 1, phi: float = 0, of: float = 0)[source]#

Bases: DataGen

A: float = 1#
f: float = 1#
static model(coordinates: ndarray[tuple[Any, ...], dtype[Any]], A: float, f: float, phi: float, of: float) → ndarray[tuple[Any, ...], dtype[Any]][source]#
of: float = 0#
phi: float = 0#