Oblate star model

class pytransit.OblateStarModel(rstar: float = 1.0, wavelength: float = 510, sres: int = 80, pres: int = 6)

Transit model for a gravity-darkened fast-rotating oblate star.

Transit model for a gravity-darkened fast-rotating oblate star following Barnes (ApJ, 2009, 705).

__init__(rstar: float = 1.0, wavelength: float = 510, sres: int = 80, pres: int = 6)

Parameters:

• rstar – Stellar radius [R_Sun]
• wavelength – Effective wavelength [nm]
• sres – Stellar discretization resolution
• pres – Planet discretization resolution

set_data(time: Union[numpy.ndarray, List[T]], lcids: Union[numpy.ndarray, List[T], None] = None, pbids: Union[numpy.ndarray, List[T], None] = None, nsamples: Union[numpy.ndarray, List[T], None] = None, exptimes: Union[numpy.ndarray, List[T], None] = None, epids: Union[numpy.ndarray, List[T], None] = None) → None

Set the data for the transit model.

Parameters:

• time (array-like) – Array of mid-exposure times for which the model will be evaluated.
• lcids (array-like, optional) – Array of integer light curve indices. Must have the same size as the time array.
• pbids (array-like, optional) – Array of passband indices, one per light curve. Must satisfy pbids.size == unique(lcids).size.
• nsamples (int or array-like, optional) – Number of samples per exposure. Can either be an integer, in which case all the light curves will have the same supersampling rate, or an array of integers, in which case each light curve can have a different rate.
• exptimes (float or array-like, optional) – Exposure times, again either for all the modelled data, or one value per light curve.
• epids (array-like, optional) – Epoch indices that can be used to link a light curve to a specific zero epoch and period (for TTV calculations).

evaluate(k: Union[float, numpy.ndarray], ldc: numpy.ndarray, t0: Union[float, numpy.ndarray], p: Union[float, numpy.ndarray], a: Union[float, numpy.ndarray], i: Union[float, numpy.ndarray], e: Union[float, numpy.ndarray] = None, w: Union[float, numpy.ndarray] = None, copy: bool = True) → numpy.ndarray

evaluate_ps(k: Union[float, numpy.ndarray], rho: float, rperiod: float, tpole: float, phi: float, beta: float, ldc: numpy.ndarray, t0: float, p: float, a: float, i: float, l: float = 0.0, e: float = 0.0, w: float = 0.0, copy: bool = True) → numpy.ndarray

Evaluate the transit model for a set of scalar parameters.

Parameters:

• k (array-like) – Radius ratio(s) either as a single float or an 1D array
• rho (float) – Stellar density [g/cm^3]
• rperiod (float) – Stellar rotation period [d]
• tpole (float) – Temperature at the pole [K]
• phi (float) – Star’s obliquity to the plane of the sky [rad]
• beta (float) – Gravity darkening parameter
• ldc (array-like) – Limb darkening coefficients as a 1D array
• t0 (float) – Zero epoch
• p (float) – Orbital period [d]
• a (float) – Scaled orbital semi-major axis [R_star]
• i (float) – Orbital inclination [rad]
• l (float) – Orbital azimuth angle [rad]
• e (float, optional) – Orbital eccentricity
• w (float, optional) – Argument of periastron

Notes

This version of the evaluate method is optimized for calculating a single transit model (such as when using a local optimizer). If you want to evaluate the model for a large number of parameters simultaneously, use either evaluate or evaluate_pv.

Returns:Modelled flux as a 1D ndarray. Return type:ndarray

evaluate_pv(pvp: numpy.ndarray, ldc: numpy.ndarray, copy: bool = True) → numpy.ndarray