daschlab.lightcurves Module

DASCH lightcurve data.

The main class provided by this module is Lightcurve, instances of which can be obtained with the daschlab.Session.lightcurve() method.

Rejecting Data Points

Lightcurve tables come with a uint64 column named "reject" that is always initialized with zeros. If you are analyzing a lightcurve and wish to discard various bad data points from it, you should set their "reject" values to something non-zero. Most lightcurve analysis functions will automatically drop points with non-zero "reject" values.

Filtering and Subsetting

Lightcurve objects are instances of the astropy.timeseries.TimeSeries class, which in turn are instances of the astropy.table.Table class. In your data analysis you can use all of the usual methods and functions that these superclasses provide. In addition, the Lightcurve class provides a convenient set of tools for subsetting and filtering data.

These tools take the form of paired “actions” and “selections”. The syntax for using them is as follows:

lc = sess.lightcurve(some_local_id)

# Get a subset of the data containing only the detections
# brighter than 13th mag. The action is `keep_only` and
# the selection is `brighter`.
bright = lc.keep_only.brighter(13)

# From that subset, remove points from the "ai" series.
# The action is `drop` and the selection is `series`.
no_ai = bright.drop.series("ai")

# Count remaining points not from narrow-field telescopes.
# The action is `count` and the selection is `narrow`, and
# `not_` is a modifier with the intuitive behavior.
n = no_ai.count.not_.narrow()

These operations can be conveniently chained together. More complex boolean logic can be implemented with the where selection and the match action.

In terms of implementation, an item like lc.keep_only is a property that returns a helper PhotometrySelector object, which is what actually implements methods such as brighter(). The selector object helps pair the desired action with the desired selection while maintaining a convenient code syntax.

DASCH lightcurves often contain many nondetections (upper limits), which means that you may need to be careful about selection logic. For instance:

lc = sess.lightcurve(some_local_id)
n1 = lc.count.brighter(13)
n2 = lc.count.not_.detected_and_fainter(13)

# This will NOT generally hold, because n2 will include
# nondetections while n1 will not.
assert n1 == n2

Functions

merge(lcs)

Merge multiple lightcurves under the assumption that they all contain data for the same source.

Classes

Lightcurve([data, time, time_start, ...])

A DASCH lightcurve data table.

LightcurveSelector(phot, apply)

A helper object that supports Lightcurve filtering functionality.