lightcurvelynx.obstable.location_free_obstable

The LocationFreeObsTable stores observation information from the entire sky.

Classes

LocationFreeObsTable

An ObsTable for observations from the entire sky (no location information).

Module Contents

class LocationFreeObsTable(table, *, colmap=None, **kwargs)

Bases: lightcurvelynx.obstable.obs_table.ObsTable

An ObsTable for observations from the entire sky (no location information).

This is used when you want to simulate observations on a regular cadence regardless of where they are in the sky, such as when comparing them to a survey that has a regular cadence (e.g. ZTF, ATLAS, etc.).

Parameters:

• table (dict or pandas.core.frame.DataFrame) – The table with all the ObsTable information. Must have columns “time” and “filter”.

• colmap (dict, optional) – A mapping of standard column names to a list of possible names in the input table. Each value in the dictionary can be a string or a list of strings.

• **kwargs (dict) –

  Additional keyword arguments to pass to the ObsTable constructor. This includes overrides for survey parameters such as:

  • survey_name: The name of the survey (default=”FAKE_SURVEY”).

  • dark_electrons : The dark current for the camera in electrons per second per pixel.

  • gain: The gain for the camera in electrons per ADU.

  • pixel_scale: The pixel scale for the camera in arcseconds per pixel.

  • radius: The angular radius of the observations (in degrees).

  • read_noise: The readout noise for the camera in electrons per pixel.

abstractmethod set_detector_footprint(detector_footprint, wcs=None)

Set the detector footprint, so footprint filtering is done.

Parameters:

• detector_footprint (astropy.regions.SkyRegion, Astropy.regions.PixelRegion, or) – DetectorFootprint The footprint object for the instrument’s detector.

• wcs (astropy.wcs.WCS, optional) – The WCS for the footprint. Either this or pixel_scale must be provided if a footprint is provided as a Astropy region.

build_moc(max_depth=10, **kwargs)

Build a Multi-Order Coverage Map from the regions in the data set. This will always be the whole sky.

Parameters:

• max_depth (int, optional) – The maximum depth of the MOC. Default is 10.

• **kwargs (dict) – Additional keyword arguments to pass to the MOC construction. Not currently used, but accepted for consistency with the ObsTable interface.

Returns:

The Multi-Order Coverage Map constructed from the data set.

Return type:

MOC

range_search(query_ra, query_dec, *, radius=None, t_min=None, t_max=None)

Return the indices of the pointings that fall within the time range (t_min, t_max). Since this is an all-sky table, the spatial filtering is never used.

Parameters:

• query_ra (float or numpy.ndarray) – The query right ascension (in degrees). Only used to determine the number of query points, not for filtering.

• query_dec (float or numpy.ndarray) – The query declination (in degrees). Only used to determine the number of query points, not for filtering.

• radius (float or None, optional) – Not used in this implementation.

• t_min (float, numpy.ndarray or None, optional) – The minimum time (in MJD) for the observations to consider. If None, no time filtering is applied.

• t_max (float, numpy.ndarray or None, optional) – The maximum time (in MJD) for the observations to consider. If None, no time filtering is applied.

Returns:

inds – If the input is a single query point, this is a 1D array of row indices. If the input is an array of query points, this is a list of index arrays (one per query).

Return type:

numpy.ndarray or list[numpy.ndarray]