uvex_transients.surveys.base.SurveySchedule.compute_cadence_time_differences#

SurveySchedule.compute_cadence_time_differences(start_time: Time | None = None, end_time: Time | None = None, nside: int | None = None, order: str | None = None, pairs: str = 'all') → tuple[Quantity, ndarray][source]#

Compute pairwise observation-time separations for each HEALPix pixel.

For a pixel observed at times t_0, ..., t_N, the cadence time differences are the positive pairwise separations t_j - t_i for j > i, either over every such pair (pairs='all') or only over consecutive-in-time pairs t_{i+1} - t_i (pairs='consecutive', i.e. the successive-gaps distribution).

Parameters:

  • start_time (Time, optional) – Optional time range over which to compute cadence separations.

  • end_time (Time, optional) – Optional time range over which to compute cadence separations.

  • nside (int, optional) – HEALPix resolution parameter, or None to use config["healpix.default_nside"].

  • order (str, optional) – HEALPix ordering scheme, either "nested" or "ring", or None to use config["healpix.default_order"].

  • pairs (str) – Which pairs of observations to include: "all" for every unique pair, or "consecutive" for only pairs of temporally-adjacent visits.

Returns:

  • time_differences (Quantity) – Flattened pairwise time differences, grouped by HEALPix pixel.

  • offsets (numpy.ndarray) – Integer offsets of shape (npix + 1,). Pairwise differences for pixel i are given by

    time_differences[offsets[i]:offsets[i + 1]].

    Pixels with fewer than two observations have no entries.