linear search

The linear search module provides functions to compute single events statistics.

nuance.linear_search.combine_linear_searches(*linear_searches)

Combine the results of multiple linear searches

linear_searcheslist

lists of (log likelihoods, model depths, depths variances)

Returns:

(log likelihoods, model depths, depths variances)

Return type:

tuple

Example

ls, z, vz = combine_linear_searches((ls0, z0, vz0), (ls1, z1, vz1), (ls2, z2, vz2))

nuance.linear_search.linear_search(time: ndarray, flux: ndarray, gp: GaussianProcess | None = None, X: ndarray | None = None, model: Callable | None = None, positive: bool = True, progress: bool = True, backend: str | None = None, batch_size: int | None = None)

Returns a function that computes the log likelihood of a transit model at different epochs and durations (linear search)

Parameters:

• time (np.ndarray) – array of times

• flux (np.ndarray) – flux time-series

• gp (tinygp.GaussianProcess, optional) – tinygp GaussianProcess model, by default None

• X (np.ndarray, optional) – linear model design matrix, by default None

• positive (bool, optional) – wether to force depth to be positive, by default True

• progress (bool, optional) – wether to show progress bar, by default True

• backend (str, optional) – backend to use, by default jax.default_backend() (options: “cpu”, “gpu”). This affects the linear search function jax-mapping strategy. For more details, see nuance.core.map_function()

• batch_size (int, optional) – batch size for parallel evaluation, by default None