get_rebinned_axis#

gammapy.estimators.utils.get_rebinned_axis(fluxpoint, axis_name='energy', method=None, **kwargs)[source]#

Get the rebinned axis for resampling the flux point object along the mentioned axis.

Parameters:

fluxpointFluxPoints: The flux point object to rebin.
axis_namestr, optional: The axis name to combine along. Default is ‘energy’.
methodstr: The method to resample the axis. Supported options are ‘fixed_bins’ and ‘min-ts’.
kwargsdict: Keywords passed to get_edges_fixed_bins or get_edges_min_ts. If method is ‘fixed-bins’, keyword should be group_size. If method is ‘min-ts’, keyword should be ts_threshold.

Returns:

axis_newMapAxis or TimeMapAxis: The new axis.

Examples

>>> from gammapy.estimators.utils import get_rebinned_axis
>>> from gammapy.estimators import FluxPoints
>>>
>>> # Rebin lightcurve axis
>>> lc_1d = FluxPoints.read(
...         "$GAMMAPY_DATA/estimators/pks2155_hess_lc/pks2155_hess_lc.fits",
...         format="lightcurve",
...     )
>>> # Rebin axis by combining adjacent bins as per the group_size
>>> new_axis = get_rebinned_axis(
...     lc_1d, method="fixed-bins", group_size=2, axis_name="time"
... )
>>>
>>> # Rebin HESS flux points axis
>>> fp = FluxPoints.read(
...         "$GAMMAPY_DATA/estimators/crab_hess_fp/crab_hess_fp.fits"
... )
>>> # Rebin according to a minimum significance
>>> axis_new = get_rebinned_axis(
...     fp, method='min-ts', ts_threshold=4, axis_name='energy'
... )