Instruments (taurex.instruments)

Base

Base instrument model class.

class Instrument[source]

Bases: Loggable, Citable

Instrument noise model.

Abstract class

Defines some method that transforms a spectrum and generates noise.

classmethod input_keywords() → Tuple[str, ...][source]

Input keywords for instrument.

model_noise(model: ForwardModel, model_res: Tuple[ndarray[tuple[int, ...], dtype[float64]], ndarray[tuple[int, ...], dtype[float64]], ndarray[tuple[int, ...], dtype[float64]] | None, Dict | T | None] | None = None, num_observations: int | None = 1) → Tuple[ndarray[tuple[int, ...], dtype[float64]], ndarray[tuple[int, ...], dtype[float64]], ndarray[tuple[int, ...], dtype[float64]] | None, ndarray[tuple[int, ...], dtype[float64]] | None][source]

Model noise for a given forward model.

Requires implementation

For a given forward model (and optional result) Resample the spectrum and compute noise profile.

Parameters:

  • model (ForwardModel) – Forward model to pass.

  • model_res (tuple, optional) – Result from model()

  • num_observations (int, optional) – Number of observations to simulate

Signal-to-Noise

Instrument implementation for SNR noise model.

class SNRInstrument(SNR: int | None = 10, binner: Binner | None = None)[source]

Bases: Instrument

SNR noise model.

Simple instrument model that, for a given wavelength-independant, signal-to-noise ratio, compute resulting noise from it.

classmethod input_keywords() → Tuple[str, ...][source]

Input keywords for instrument.

model_noise(model: ForwardModel, model_res: Tuple[ndarray[tuple[int, ...], dtype[float64]], ndarray[tuple[int, ...], dtype[float64]], ndarray[tuple[int, ...], dtype[float64]] | None, Dict | T | None] | None = None, num_observations: int | None = 1) → Tuple[ndarray[tuple[int, ...], dtype[float64]], ndarray[tuple[int, ...], dtype[float64]], ndarray[tuple[int, ...], dtype[float64]] | None, ndarray[tuple[int, ...], dtype[float64]] | None][source]

Attach noise to forward model.

Parameters:

  • model – Forward model to pass.

  • model_res – Result from model()

  • num_observations – Number of observations to simulate