Background3D¶

class gammapy.irf.Background3D(axes, data=0, unit='', meta=None, interp_kwargs=None)[source]¶

Bases: gammapy.irf.background.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 usuually s^-1 MeV^-1 sr^-1
metadict: Meta data

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`
`is_offset_dependent`	Whether the IRF depends on offset
`is_pointlike`	Whether the IRF is pointlike of full containment.
`quantity`	`Quantity`
`required_axes`
`tag`
`unit`	Map unit (`Unit`)

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
`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])
`plot_at_energy`([energy, add_cbar, ncols, …])	Plot the background rate in Field of view coordinates at a given energy.
`read`(filename[, hdu, format])	Read from file.
`to_2d`()	Convert to `Background2D`.
`to_hdulist`([format])
`to_table`([format])	Convert to table
`to_table_hdu`([format])	Convert to `BinTableHDU`.
`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'}¶

is_offset_dependent¶: Whether the IRF depends on offset

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

quantity¶: Quantity

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

tag = 'bkg_3d'¶

unit¶: Map unit (Unit)

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

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

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

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: Which axis to downsample. By default spatial axes are padded.
**kwargsdict: Keyword argument forwarded to pad

Returns

irfIRF: Padded irf

peek(figsize=(10, 8))[source]¶

plot_at_energy(energy=None, add_cbar=True, ncols=3, figsize=None, **kwargs)[source]¶

Plot the background rate in Field of view coordinates at a given energy.

Parameters

energyQuantity: list of Energy
ax: `~matplotlib.axes.Axes`, optional: Axis
add_cbarbool: Add color bar?
ncolsint: Number of columns to plot
**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”}: Format specification

Returns

irfIRF: IRF class

to_2d()[source]¶

Convert to Background2D.

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

to_hdulist(format='gadf-dl3')¶

to_table(format='gadf-dl3')¶

Convert to table

Parameters

format{“gadf-dl3”}: Format specification

Returns

tableTable: IRF data table

to_table_hdu(format='gadf-dl3')¶

Convert to BinTableHDU.

Parameters

format{“gadf-dl3”}: Format specification

Returns

hduBinTableHDU: IRF data table hdu

write(filename, *args, **kwargs)¶

Write IRF to fits.

Calls writeto, forwarding all arguments.