lightcurvelynx.obstable.lsst_obstable

A class for storing and working with Rubin Observatory (LSST) observation tables.

Attributes

LSSTCAM_PIXEL_SCALE	The pixel scale for the LSST camera in arcseconds per pixel.
logger

Classes

LSSTObsTable

An ObsTable for observations from the Rubin Observatory data releases.

Module Contents

LSSTCAM_PIXEL_SCALE = 0.2

The pixel scale for the LSST camera in arcseconds per pixel.

logger

class LSSTObsTable(table, colmap=None, saturation_mags=None, **kwargs)

Bases: lightcurvelynx.obstable.obs_table.ObsTable

An ObsTable for observations from the Rubin Observatory data releases.

Parameters:

• table (dict or pandas.core.frame.DataFrame) – The table with all the LSST survey information.

• colmap (dict) – 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. Defaults to the Rubin CCDVisit column names, stored in _default_colnames.

• saturation_mags (dict, optional) – A dictionary mapping filter names to their saturation thresholds in magnitudes. The filters provided must match those in the table. If not provided, LSST-specific defaults will be used.

• **kwargs (dict) –

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

  • dark_current : 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.

classmethod from_ccdvisit_table(table, make_detector_footprint=True, **kwargs)

Construct an LSSTObsTable object from a CCDVisit table.

As an example we could access the DP1 CCDVisit table from RSP as:

from lsst.rsp import get_tap_service
service = get_tap_service("tap")
table = service.search("SELECT * FROM dp1.CcdVisit").to_table().to_pandas()

Or you can read a table from a file (e.g. using the read_sqlite_table function):

from lightcurvelynx.utils.io_utils import read_sqlite_table
table = read_sqlite_table("path_to_file.db", sql_query="SELECT * FROM observations")

Note that this will load a single pointing per-ccd. If you do not use make_detector_footprint=True, you will observe multiple points in your light curve with the same time stamp whenever the point falls near the edge of a chip.

Parameters:

• table (pandas.core.frame.DataFrame) – The CCDVisit table containing the LSSTObsTable data.

• make_detector_footprint (bool, optional) – If True, the detector footprint will be created based on the specified survey values for number of x pixels, number of y pixels, and pixel_scale. Default is True

• **kwargs (dict) – Additional keyword arguments to pass to the LSSTObsTable constructor.

Returns:

obstable – An LSSTObsTable object containing the data from the CCDVisit table.

Return type:

LSSTObsTable

classmethod from_sv_visits_table(table, **kwargs)

Construct an LSSTObsTable object from a science validation visits table. https://survey-strategy.lsst.io/progress/sv_status/sv_20250930.html Note this table uses a combination of the schemas (e.g. OpSim and ConsDB).

As an example we can read a table from a file (e.g. using the read_sqlite_table function):

from lightcurvelynx.utils.io_utils import read_sqlite_table
table = read_sqlite_table("path_to_file.db", sql_query="SELECT * FROM observations")

Parameters:

• table (pandas.core.frame.DataFrame) – The science validation visits table containing the LSSTObsTable data.

• **kwargs (dict) – Additional keyword arguments to pass to the LSSTObsTable constructor.

Returns:

obstable – An LSSTObsTable object containing the data from the science validation visits table.

Return type:

LSSTObsTable