Lightcurves¶
Lightcurve¶
Gammapy uses a simple container for light curves: the LightCurve class. It stores
the light curve in the form of a Table and provides a few convenience methods,
to create time objects and plots.
The table structure follows the approach proposed in the gamma-ray-astro-formats webpage.
The following example shows how to read a table that contains a lightcurve and then create a LightCurve object.
The latter gives access to a number of utilities such as plots and access to times as Time objects:
>>> from astropy.table import Table
>>> url = 'https://github.com/gammapy/gamma-cat/raw/master/input/data/2006/2006A%2526A...460..743A/tev-000119-lc.ecsv'
>>> table = Table.read(url, format='ascii.ecsv')
>>> from gammapy.time import LightCurve
>>> lc = LightCurve(table)
>>> lc.time[:2].iso
['2004-05-23 01:47:08.160' '2004-05-23 02:17:31.200']
>>> lc.plot()
Light Curve Extraction¶
The extraction of a light curve from gamma-ray data follows the general approach of
data reduction and modeling/fitting. Observations are first reduced to dataset objects
(e.g. MapDataset or SpectrumDatasetOnOff). Then, after
setting the appropriate model the flux is extracted in each time bin with the
LightCurveEstimator.
To extract the light curve of a source, the LightCurveEstimator
fits a scale factor on the model component representing the source in each time bin
and returns a LightCurve. It can work with spectral (1D) datasets as well
as with map (3D) datasets.
Once a Datasets object is build with a model set, one can call the estimator
to compute the light curve in the datasets time intervals:
>>> lc_estimator = LightCurveEstimator(datasets, source="source")
>>> lc = lc_estimator.run(e_min=1*u.TeV, emax=10*u.TeV, e_ref=1*u.TeV)
where source is the model component describing the source of interest and datasets the Datasets
object produced by data reduction.
The light curve notebook shows an example of observation based light curve
extraction
Similarly, LightCurveEstimator can be used to extract the light curve in user defined time intervals.
This can be useful to combine datasets to produce light curve by night, week or month:
>>> lc_estimator = LightCurveEstimator(datasets, source="source", time_intervals=time_intervals)
>>> lc = lc_estimator.run(e_min=1*u.TeV, emax=10*u.TeV, e_ref=1*u.TeV)
where time_intervals is a list of time intervals as Time objects.
The light curve notebook shows an example of night-wise light curve
extraction
Tutorials¶
The main tutorial demonstrates how to extract light curves from 1D and 3D datasets:
Light curve extraction on small time bins (i.e. smaller than the observation scale) for flares is demonstrated in the following tutorial: