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)

Bases: object

Background 3D.

Data format specification: BKG_3D

Parameters:

energy_lo, energy_hi : Quantity

Energy binning

fov_lon_lo, fov_lon_hi : Quantity

FOV coordinate X-axis binning.

fov_lat_lo, fov_lat_hi : Quantity

FOV coordinate Y-axis binning.

data : Quantity

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, …)	Evaluate at given FOV position and energy edges by integrating over the energy axes.
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)

Evaluate at given FOV position and energy.

Parameters:

fov_lon, fov_lat : Angle

FOV coordinates expecting in AltAz frame.

energy_reco : Quantity

energy on which you want to interpolate. 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', **kwargs)

Evaluate at given FOV position and energy edges by integrating over the energy axes.

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')

Create from HDUList.

classmethod from_table(table)

Read from Table.

classmethod read(filename, hdu='BACKGROUND')

Read from file.

to_2d(self)

Convert to Background2D.

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

to_fits(self, name='BACKGROUND')

Convert to BinTableHDU.

to_table(self)

Convert to Table.