jaxoplanet.light_curves.transforms

Module Contents

Functions

integrate(→ jaxoplanet.light_curves.types.LightCurveFunc)	Transform a light curve function to apply exposure time integration
interpolate(, kwargs, ...)	Transform a light curve function to pre-compute the model on a grid

jaxoplanet.light_curves.transforms.integrate(func: jaxoplanet.light_curves.types.LightCurveFunc, exposure_time: jaxoplanet.types.Quantity | None = None, order: int = 0, num_samples: int = 7) → jaxoplanet.light_curves.types.LightCurveFunc

Transform a light curve function to apply exposure time integration

This transformation applies a fixed stencil numerical integration scheme to the input function func to convolve the light curve with a top hat exposure time centered on the input time, with a full width of exposure_time.

The order of the integration scheme is set using the order parameter which must be 0, 1, or 2. The default (0) uses the “resampling” scheme discussed by Kipping (2010). The higher order schemes 1 and 2 apply the trapezoid and Simpson’s rules respectively, but won’t necessarily provide higher accuracy results because of discontinuities at the contact points.

In practice, the parameter num_samples which sets the number of function evaluations per integral has the most significant effect on the accuracy of this integral, trading off against higher computational cost.

Parameters:

• func – A light curve function which takes a time Quantity as the first argument

• exposure_time (Quantity) – The exposure time (in days, by default)

• order (int) – The order of the integration scheme as discussed above

• num_samples (int) – The number of function evaluations made per integral, controlling the accuracy of the numerics

Returns:

A new light curve function with the same signature as func, computing the exposure time integrated flux

jaxoplanet.light_curves.transforms.interpolate(func: jaxoplanet.light_curves.types.LightCurveFunc, *, period: jaxoplanet.types.Quantity, time_transit: jaxoplanet.types.Quantity, num_samples: int, duration: jaxoplanet.types.Quantity | None = None, args: tuple[Any, Ellipsis] = (), kwargs: dict[str, Any] | None = None) → jaxoplanet.light_curves.types.LightCurveFunc

Transform a light curve function to pre-compute the model on a grid

Sometimes it can be useful to precompute the light curve on a grid near a transit, and then interpolate those computations to the required phases when computing the full model. This can speed things up a lot when you have many transits, or a lot of out of transit data. This transform uses linear interpolation.

Note

Unlike some other transforms, this function requires that any upstream *args and **kwargs be passed directly to the transform, rather than when calling the transformed function. This is necessary because the model is pre-computed when it is tranformed.

Parameters:

• func – A light curve function which takes a time Quantity as the first argument

• period (Quantity) – The period of the orbit. Used to wrap the input times into the domain of the pre-computed model

• time_transit (Quantity) – The transit time of the orbit. Used to wrap the input times into the domain of the pre-computed model

• duration (Quantity) – The duration centered on the transit to pre-compute. By default, the full period will be evaluated

• num_samples (int) – The number of points in the time grid used for pre-computation

• args (tuple) – Any extra positional arguments that should be passed to func

• kwargs (dict) – Any extra keyword arguments that should be passed to func

Returns:

A new light curve function with the same signature as func, computing the flux by interpolating a pre-computed model