Background2D¶

class gammapy.irf.Background2D(energy_lo, energy_hi, offset_lo, offset_hi, data, meta=None, interp_kwargs=None)[source]¶

Background 2D.

Data format specification: BKG_2D

Parameters:	energy_lo, energy_hi : `Quantity` Energy binning offset_lo, offset_hi : `Quantity` FOV coordinate offset-axis binning data : `Quantity` Background rate (usually: `s^-1 MeV^-1 sr^-1`)

Attributes Summary

default_interp_kwargs Default Interpolation kwargs for NDDataArray.

Methods Summary

`evaluate`(self, fov_lon, fov_lat, energy_reco)	Evaluate at a given FOV position and energy.
`evaluate_integrate`(self, fov_lon, fov_lat, …)	Evaluate at given FOV position and energy, by integrating over the energy range.
`from_hdulist`(hdulist[, hdu])	Create from `HDUList`.
`from_table`(table)	Read from `Table`.
`peek`(self)
`plot`(self[, ax, add_cbar])	Plot energy offset dependence of the background model.
`read`(filename[, hdu])	Read from file.
`to_3d`(self)	Convert to `Background3D`.
`to_fits`(self[, name])	Convert to `BinTableHDU`.
`to_table`(self)	Convert to `Table`.

Attributes Documentation

default_interp_kwargs = {'bounds_error': False, 'fill_value': None}¶: Default Interpolation kwargs for NDDataArray. Extrapolate.

Methods Documentation

evaluate(self, fov_lon, fov_lat, energy_reco, method='linear', **kwargs)[source]¶

Evaluate at a given FOV position and energy.

The fov_lon, fov_lat, energy_reco has to have the same shape since this is a set of points on which you want to evaluate.

To have the same API than background 3D for the background evaluation, the offset is fov_altaz_lon.

Parameters:	fov_lon, fov_lat : `Angle` FOV coordinates expecting in AltAz frame, same shape than energy_reco energy_reco : `Quantity` Reconstructed energy, same dimension than fov_lat and fov_lat method : str {‘linear’, ‘nearest’}, optional Interpolation method kwargs : dict option for interpolation for `RegularGridInterpolator`
Returns:	array : `Quantity` Interpolated values, axis order is the same as for the NDData array

evaluate_integrate(self, fov_lon, fov_lat, energy_reco, method='linear')[source]¶

Evaluate at given FOV position and energy, by integrating over the energy range.

Parameters:	fov_lon, fov_lat : `Angle` FOV coordinates expecting in AltAz frame. energy_reco: `~astropy.units.Quantity` Reconstructed energy edges. method : {‘linear’, ‘nearest’}, optional Interpolation method
Returns:	array : `Quantity` Returns 2D array with axes offset

classmethod from_hdulist(hdulist, hdu='BACKGROUND')[source]¶: Create from HDUList.

plot(self, ax=None, add_cbar=True, **kwargs)[source]¶: Plot energy offset dependence of the background model.

classmethod read(filename, hdu='BACKGROUND')[source]¶: Read from file.

to_3d(self)[source]¶

Fill in a radially symmetric way.