labcore.data.datagen

Classes

DataGen([noise_std, imaginary])
Exponential([noise_std, imaginary, base])
ExponentialDecay([noise_std, imaginary, A, ...])
ExponentialDecayingSine([noise_std, ...])
Gaussian([noise_std, imaginary, x0, sigma, ...])
Lorentzian([noise_std, imaginary, A, x0, ...])
Sine([noise_std, imaginary, A, f, phi, of])

class labcore.data.datagen.DataGen(noise_std: float = 1.0, imaginary: bool = False)

Bases: ABC

generate(coordinates: ndarray[tuple[Any, ...], dtype[Any]], **kwargs: Any) → ndarray[tuple[Any, ...], dtype[Any]]

imaginary: bool = False

abstractmethod static model(coordinates: ndarray[tuple[Any, ...], dtype[Any]], *args: Any, **kwargs: Any) → ndarray[tuple[Any, ...], dtype[Any]]

static noise(coordinates: ndarray[tuple[Any, ...], dtype[Any]], std: float) → ndarray[tuple[Any, ...], dtype[Any]]

noise_std: float = 1.0

class labcore.data.datagen.Exponential(noise_std: float = 1.0, imaginary: bool = False, base: float = 2.718281828459045)

Bases: DataGen

base: float = 2.718281828459045

static model(coordinates: ndarray[tuple[Any, ...], dtype[Any]], base: float) → ndarray[tuple[Any, ...], dtype[Any]]

class labcore.data.datagen.ExponentialDecay(noise_std: float = 1.0, imaginary: bool = False, A: float = 1, tau: float = 1, of: float = 0)

Bases: DataGen

A: float = 1

static model(coordinates: ndarray[tuple[Any, ...], dtype[Any]], A: float, tau: float, of: float) → ndarray[tuple[Any, ...], dtype[Any]]

of: float = 0

tau: float = 1

class labcore.data.datagen.ExponentialDecayingSine(noise_std: float = 1.0, imaginary: bool = False, A: float = 1, f: float = 1, phi: float = 0, tau: float = 1, of: float = 0)

Bases: DataGen

A: float = 1

f: float = 1

static model(coordinates: ndarray[tuple[Any, ...], dtype[Any]], A: float, f: float, phi: float, tau: float, of: float) → ndarray[tuple[Any, ...], dtype[Any]]

of: float = 0

phi: float = 0

tau: float = 1

class labcore.data.datagen.Gaussian(noise_std: float = 1.0, imaginary: bool = False, x0: float = 0, sigma: float = 1, A: float = 1, of: float = 0)

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]]

of: float = 0

sigma: float = 1

x0: float = 0

class labcore.data.datagen.Lorentzian(noise_std: float = 1.0, imaginary: bool = False, A: float = 1, x0: float = 0, gamma: float = 1, of: float = 0)

Bases: DataGen

A: float = 1

gamma: float = 1

static model(coordinates: ndarray[tuple[Any, ...], dtype[Any]], A: float, x0: float, gamma: float, of: float) → ndarray[tuple[Any, ...], dtype[Any]]

of: float = 0

x0: float = 0

class labcore.data.datagen.Sine(noise_std: float = 1.0, imaginary: bool = False, A: float = 1, f: float = 1, phi: float = 0, of: float = 0)

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]]

of: float = 0

phi: float = 0