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