Instruments (taurex.instruments)

Base

Base instrument model class.

class Instrument

Bases: Loggable, Citable

Instrument noise model.

Abstract class

Defines some method that transforms a spectrum and generates noise.

BIBTEX_ENTRIES = ['\n@ARTICLE{2025A&A...699A.219C,\n       author = {{Changeat}, Q. and {Bardet}, D. and {Chubb}, K. and\n                 {Dyrek}, A. and {Edwards}, B. and {Ohno}, K. and\n                 {Venot}, O.},\n        title = "{Cloud and haze parameterization in atmospheric retrievals:\n                  Insights from Titan\'s Cassini data and JWST observations of\n                  hot Jupiters}",\n      journal = {\\aap},\n     keywords = {techniques: spectroscopic,\n                 planets and satellites: atmospheres,\n                 infrared: planetary systems,\n                 Earth and Planetary Astrophysics,\n                 Instrumentation and Methods for Astrophysics},\n         year = 2025,\n        month = jul,\n       volume = {699},\n          eid = {A219},\n        pages = {A219},\n          doi = {10.1051/0004-6361/202453186},\narchivePrefix = {arXiv},\n       eprint = {2505.18715},\n primaryClass = {astro-ph.EP},\n       adsurl = {https://ui.adsabs.harvard.edu/abs/2025A&A...699A.219C},\n      adsnote = {Provided by the SAO/NASA Astrophysics Data System}\n}\n        ']

List of bibtex entries.

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

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]

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)

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, ...]

Input keywords for SNR 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]

Attach noise to forward model.

Parameters:

• model – Forward model to pass.

• model_res – Result from model()

• num_observations – Number of observations to simulate