EnergyDispersion2D¶

class gammapy.irf.EnergyDispersion2D(e_true_lo, e_true_hi, migra_lo, migra_hi, offset_lo, offset_hi, data, interp_kwargs=None, meta=None)[source]¶

Bases: object

Offset-dependent energy dispersion matrix.

Data format specification: EDISP_2D

Parameters:	e_true_lo, e_true_hi : `Quantity` True energy axis binning migra_lo, migra_hi : `ndarray` Energy migration axis binning offset_lo, offset_hi : `Angle` Field of view offset axis binning data : `ndarray` Energy dispersion probability density

See also

EnergyDispersion

Examples

Read energy dispersion IRF from disk:

>>> from gammapy.irf import EnergyDispersion2D
>>> from gammapy.utils.energy import EnergyBounds
>>> filename = '$GAMMAPY_DATA/tests/irf/hess/pa/hess_edisp_2d_023523.fits.gz'
>>> edisp2d = EnergyDispersion2D.read(filename, hdu='ENERGY DISPERSION')
>>> print(edisp2d)
EnergyDispersion2D
NDDataArray summary info
e_true         : size =    15, min =  0.125 TeV, max = 80.000 TeV
migra          : size =   100, min =  0.051, max = 10.051
offset         : size =     6, min =  0.125 deg, max =  2.500 deg
Data           : size =  9000, min =  0.000, max =  3.405

Create energy dispersion matrix (EnergyDispersion) for a given field of view offset and energy binning:

>>> energy = EnergyBounds.equal_log_spacing(0.1,20,60, 'TeV')
>>> edisp = edisp2d.to_energy_dispersion(offset='1.2 deg', e_reco=energy, e_true=energy)
>>> print(edisp)
EnergyDispersion
NDDataArray summary info
e_true         : size =    60, min =  0.105 TeV, max = 19.136 TeV
e_reco         : size =    60, min =  0.105 TeV, max = 19.136 TeV
Data           : size =  3600, min =  0.000, max =  0.266

Attributes Summary

default_interp_kwargs Default Interpolation kwargs for NDDataArray.

Methods Summary

`from_gauss`(e_true, migra, bias, sigma, offset)	Create Gaussian energy dispersion matrix (`EnergyDispersion2D`).
`from_hdulist`(hdulist[, hdu])	Create from `HDUList`.
`from_table`(table)	Create from `Table`.
`get_response`(offset, e_true[, e_reco, …])	Detector response R(Delta E_reco, E_true)
`peek`([figsize])	Quick-look summary plots.
`plot_bias`([ax, offset, add_cbar])	Plot migration as a function of true energy for a given offset.
`plot_migration`([ax, offset, e_true, migra])	Plot energy dispersion for given offset and true energy.
`read`(filename[, hdu])	Read from FITS file.
`to_energy_dispersion`(offset[, e_true, e_reco])	Detector response R(Delta E_reco, Delta E_true)
`to_fits`([name])	Convert to `BinTable`.
`to_table`()	Convert to `Table`.

Attributes Documentation

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

Methods Documentation

classmethod from_gauss(e_true, migra, bias, sigma, offset, pdf_threshold=1e-06)[source]¶

Create Gaussian energy dispersion matrix (EnergyDispersion2D).

The output matrix will be Gaussian in (e_true / e_reco).

The bias and sigma should be either floats or arrays of same dimension than e_true. bias refers to the mean value of the migra distribution minus one, i.e. bias=0 means no bias.

Note that, the output matrix is flat in offset.

Parameters:

e_true : Quantity, BinnedDataAxis: Bin edges of true energy axis
migra : Quantity, BinnedDataAxis: Bin edges of migra axis
bias : float or ndarray: Center of Gaussian energy dispersion, bias
sigma : float or ndarray: RMS width of Gaussian energy dispersion, resolution
offset : Quantity, BinnedDataAxis: Bin edges of offset
pdf_threshold : float, optional: Zero suppression threshold

classmethod from_hdulist(hdulist, hdu='edisp_2d')[source]¶: Create from HDUList.

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

get_response(offset, e_true, e_reco=None, migra_step=0.005)[source]¶

Detector response R(Delta E_reco, E_true)

Probability to reconstruct a given true energy in a given reconstructed energy band. In each reco bin, you integrate with a riemann sum over the default migra bin of your analysis.

Parameters:	e_true : `Energy` True energy e_reco : `EnergyBounds`, None Reconstructed energy axis offset : `Angle` Offset migra_step : float Integration step in migration
Returns:	rv : `ndarray` Redistribution vector

peek(figsize=(15, 5))[source]¶

Quick-look summary plots.

Parameters:	figsize : (float, float) Size of the resulting plot

plot_bias(ax=None, offset=None, add_cbar=False, **kwargs)[source]¶

Plot migration as a function of true energy for a given offset.

Parameters:	ax : `Axes`, optional Axis offset : `Angle`, optional Offset add_cbar : bool Add a colorbar to the plot. kwargs : dict Keyword arguments passed to `pcolormesh`.
Returns:	ax : `Axes` Axis

plot_migration(ax=None, offset=None, e_true=None, migra=None, **kwargs)[source]¶

Plot energy dispersion for given offset and true energy.

Parameters:	ax : `Axes`, optional Axis offset : `Angle`, optional Offset e_true : `Energy`, optional True energy migra : `array`, list, optional Migration nodes
Returns:	ax : `Axes` Axis

classmethod read(filename, hdu='edisp_2d')[source]¶

Read from FITS file.

Parameters:	filename : str File name

to_energy_dispersion(offset, e_true=None, e_reco=None)[source]¶

Detector response R(Delta E_reco, Delta E_true)

Probability to reconstruct an energy in a given true energy band in a given reconstructed energy band

Parameters:	offset : `Angle` Offset e_true : `EnergyBounds`, None True energy axis e_reco : `EnergyBounds` Reconstructed energy axis
Returns:	edisp : `EnergyDispersion` Energy dispersion matrix

to_fits(name='ENERGY DISPERSION')[source]¶: Convert to BinTable.

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