Background3D#

class gammapy.irf.Background3D(axes, data=0, unit='', is_pointlike=False, fov_alignment=FoVAlignment.RADEC, meta=None, interp_kwargs=None)[source]#

Bases: BackgroundIRF

Background 3D.

Data format specification: BKG_3D.

Parameters:

axeslist of MapAxis or MapAxes object: Required data axes: [“energy”, “fov_lon”, “fov_lat”] in the given order.
datandarray: Data array.
unitstr or Unit: Data unit usually s^-1 MeV^-1 sr^-1.
fov_alignmentFoVAlignment: The orientation of the field of view coordinate system.
metadict: Metadata dictionary.

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
------------

  axes  : ['energy', 'fov_lon', 'fov_lat']
  shape : (21, 36, 36)
  ndim  : 3
  unit  : 1 / (MeV s sr)
  dtype : >f4

Attributes Summary

`axes`	`MapAxes`.
`data`
`default_interp_kwargs`	Default Interpolation kwargs to extrapolate.
`default_unit`
`fov_alignment`	Alignment of the field of view coordinate axes, see `FoVAlignment`.
`has_offset_axis`	Whether the IRF explicitly depends on offset.
`is_pointlike`	Whether the IRF is pointlike of full containment.
`quantity`	Quantity as a `Quantity` object.
`required_axes`
`tag`
`unit`	Map unit as a `Unit` object.

Methods Summary

`cumsum`(axis_name)	Compute cumsum along a given axis.
`evaluate`([method])	Evaluate IRF.
`from_hdulist`(hdulist[, hdu, format])	Create from `HDUList`.
`from_table`(table[, format])	Read from `Table`.
`integral`(axis_name, **kwargs)	Compute integral along a given axis.
`integrate_log_log`(axis_name, **kwargs)	Integrate along a given axis.
`interp_missing_data`(axis_name)	Interpolate missing data along a given axis.
`is_allclose`(other[, rtol_axes, atol_axes])	Compare two data IRFs for equivalency.
`normalize`(axis_name)	Normalise data in place along a given axis.
`pad`(pad_width, axis_name, **kwargs)	Pad IRF along a given axis.
`peek`([figsize])	Quick-look summary plots.
`plot_at_energy`([energy, add_cbar, ncols, ...])	Plot the background rate in FoV coordinates at a given energy.
`read`(filename[, hdu, format])	Read from file.
`slice_by_idx`(slices)	Slice sub IRF from IRF object.
`to_2d`()	Convert to `Background2D`.
`to_hdulist`([format])	Write the HDU list.
`to_table`([format])	Convert to table.
`to_table_hdu`([format])	Convert to `BinTableHDU`.
`to_unit`(unit)	Convert IRF to different unit.
`write`(filename, args, *kwargs)	Write IRF to fits.

Attributes Documentation

axes#: MapAxes.

data#

default_interp_kwargs = {'bounds_error': False, 'fill_value': 0.0, 'values_scale': 'log'}#: Default Interpolation kwargs to extrapolate.

default_unit = Unit("1 / (MeV s sr)")#

fov_alignment#: Alignment of the field of view coordinate axes, see FoVAlignment.

has_offset_axis#: Whether the IRF explicitly depends on offset.

is_pointlike#: Whether the IRF is pointlike of full containment.

quantity#: Quantity as a Quantity object.

required_axes = ['energy', 'fov_lon', 'fov_lat']#

tag = 'bkg_3d'#

unit#: Map unit as a Unit object.

Methods Documentation

cumsum(axis_name)#

Compute cumsum along a given axis.

Parameters:

axis_namestr: Along which axis to integrate.

Returns:

irfIRF: Cumsum IRF.

evaluate(method=None, **kwargs)#

Evaluate IRF.

Parameters:

**kwargsdict: Coordinates at which to evaluate the IRF.
methodstr {‘linear’, ‘nearest’}, optional: Interpolation method.

Returns:

arrayQuantity: Interpolated values.

classmethod from_hdulist(hdulist, hdu=None, format='gadf-dl3')#

Create from HDUList.

Parameters:

hdulistHDUList: HDU list.
hdustr: HDU name.
format{“gadf-dl3”}: Format specification. Default is “gadf-dl3”.

Returns:

irfIRF: IRF class.

classmethod from_table(table, format='gadf-dl3')#

Read from Table.

Parameters:

tableTable: Table with background data.
format{“gadf-dl3”}: Format specification. Default is “gadf-dl3”.

Returns:

bkgBackground2D or Background2D: Background IRF class.

integral(axis_name, **kwargs)#

Compute integral along a given axis.

This method uses interpolation of the cumulative sum.

Parameters:

axis_namestr: Along which axis to integrate.
**kwargsdict: Coordinates at which to evaluate the IRF.

Returns:

arrayQuantity: Returns 2D array with axes offset.

integrate_log_log(axis_name, **kwargs)#

Integrate along a given axis.

This method uses log-log trapezoidal integration.

Parameters:

axis_namestr: Along which axis to integrate.
**kwargsdict: Coordinates at which to evaluate the IRF.

Returns:

arrayQuantity: Returns 2D array with axes offset.

interp_missing_data(axis_name)#: Interpolate missing data along a given axis.

is_allclose(other, rtol_axes=0.001, atol_axes=1e-06, **kwargs)#

Compare two data IRFs for equivalency.

Parameters:

otherIRF: The IRF to compare against.
rtol_axesfloat, optional: Relative tolerance for the axis comparison. Default is 1e-3.
atol_axesfloat, optional: Absolute tolerance for the axis comparison. Default is 1e-6.
**kwargsdict: Keywords passed to numpy.allclose.

Returns:

is_allclosebool: Whether the IRF is all close.

normalize(axis_name)#

Normalise data in place along a given axis.

Parameters:

axis_namestr: Along which axis to normalize.

pad(pad_width, axis_name, **kwargs)#

Pad IRF along a given axis.

Parameters:

pad_width{sequence, array_like, int}: Number of pixels padded to the edges of each axis.
axis_namestr: Axis to downsample. By default, spatial axes are padded.
**kwargsdict: Keyword argument forwarded to pad.

Returns:

irfIRF: Padded IRF.

peek(figsize=(10, 8))[source]#

Quick-look summary plots.

Parameters:

figsizetuple, optional: Size of the figure. Default is (10, 8).

plot_at_energy(energy=<Quantity 1. TeV>, add_cbar=True, ncols=3, figsize=None, axes_loc=None, kwargs_colorbar=None, **kwargs)[source]#

Plot the background rate in FoV coordinates at a given energy.

Parameters:

energyQuantity, optional: List of energies. Default is 1 TeV.
add_cbarbool, optional: Add color bar. Default is True.
ncolsint, optional: Number of columns to plot. Default is 3.
figsizetuple, optional: Figure size. Default is None.
axes_locdict, optional: Keyword arguments passed to append_axes.
kwargs_colorbardict, optional: Keyword arguments passed to colorbar.
**kwargsdict: Keyword arguments passed to pcolormesh.

classmethod read(filename, hdu=None, format='gadf-dl3')#

Read from file.

Parameters:

filenamestr or Path: Filename.
hdustr: HDU name.
format{“gadf-dl3”}, optional: Format specification. Default is “gadf-dl3”.

Returns:

irfIRF: IRF class.

slice_by_idx(slices)#

Slice sub IRF from IRF object.

Parameters:

slicesdict: Dictionary of axes names and slice object pairs. Contains one element for each non-spatial dimension. Axes not specified in the dictionary are kept unchanged.

Returns:

slicedIRF: Sliced IRF object.

to_2d()[source]#

Convert to Background2D.

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

to_hdulist(format='gadf-dl3')#

Write the HDU list.

Parameters:

format{“gadf-dl3”}, optional: Format specification. Default is “gadf-dl3”.

to_table(format='gadf-dl3')#

Convert to table.

Parameters:

format{“gadf-dl3”}, optional: Format specification. Default is “gadf-dl3”.

Returns:

tableTable: IRF data table.

to_table_hdu(format='gadf-dl3')#

Convert to BinTableHDU.

Parameters:

format{“gadf-dl3”}, optional: Format specification. Default is “gadf-dl3”.

Returns:

hduBinTableHDU: IRF data table HDU.

to_unit(unit)#

Convert IRF to different unit.

Parameters:

unitUnit or str: New unit.

Returns:

irfIRF: IRF with new unit and converted data.

write(filename, *args, **kwargs)#

Write IRF to fits.

Calls writeto, forwarding all arguments.