Background3D¶

class gammapy.irf.Background3D(energy_lo, energy_hi, fov_lon_lo, fov_lon_hi, fov_lat_lo, fov_lat_hi, data, meta=None, interp_kwargs=None)[source]¶

Bases: object

Background 3D.

Data format specification: BKG_3D

Parameters

energy_lo, energy_hiQuantity: Energy binning
fov_lon_lo, fov_lon_hiQuantity: FOV coordinate X-axis binning.
fov_lat_lo, fov_lat_hiQuantity: FOV coordinate Y-axis binning.
dataQuantity: Background rate (usually: s^-1 MeV^-1 sr^-1)

Examples

Here’s an example you can use to learn about this class:

>>> from gammapy.irf import Background3D
>>> filename = '$GAMMAPY_DATA/cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits'
>>> bkg_3d = Background3D.read(filename, hdu='BACKGROUND')
>>> print(bkg_3d)
Background3D
NDDataArray summary info
energy         : size =    21, min =  0.016 TeV, max = 158.489 TeV
fov_lon           : size =    36, min = -5.833 deg, max =  5.833 deg
fov_lat           : size =    36, min = -5.833 deg, max =  5.833 deg
Data           : size = 27216, min =  0.000 1 / (MeV s sr), max =  0.421 1 / (MeV s sr)

Attributes Summary

default_interp_kwargs

Default Interpolation kwargs for NDDataArray.

Methods Summary

`evaluate`(self, fov_lon, fov_lat, energy_reco)	Evaluate at given FOV position and energy.
`evaluate_integrate`(self, fov_lon, fov_lat, …)	Integrate in a given energy band.
`from_hdulist`(hdulist[, hdu])	Create from `HDUList`.
`from_table`(table)	Read from `Table`.
`read`(filename[, hdu])	Read from file.
`to_2d`(self)	Convert to `Background2D`.
`to_fits`(self[, name])	Convert to `BinTableHDU`.
`to_table`(self)	Convert to `Table`.

Attributes Documentation

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

Methods Documentation

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

Evaluate at given FOV position and energy.

Parameters

fov_lon, fov_latAngle: FOV coordinates expecting in AltAz frame.
energy_recoQuantity: energy on which you want to interpolate. Same dimension than fov_lat and fov_lat
methodstr {‘linear’, ‘nearest’}, optional: Interpolation method
kwargsdict: option for interpolation for RegularGridInterpolator

Returns

arrayQuantity: Interpolated values, axis order is the same as for the NDData array

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

Integrate in a given energy band.

Parameters

fov_lon, fov_latAngle: FOV coordinates expecting in AltAz frame.
energy_reco: `~astropy.units.Quantity`: Reconstructed energy edges.
method{‘linear’, ‘nearest’}, optional: Interpolation method

Returns

arrayQuantity: Returns 2D array with axes offset

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

classmethod from_table(table)[source]¶: Read from Table.

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

to_2d(self)[source]¶

Convert to Background2D.

This takes the values at Y = 0 and X >= 0.

to_fits(self, name='BACKGROUND')[source]¶: Convert to BinTableHDU.

to_table(self)[source]¶: Convert to Table.