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)

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 energy_logspace
>>> 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 = energy_logspace(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(self, offset, e_true[, e_reco, …])	Detector response R(Delta E_reco, E_true)
peek(self[, figsize])	Quick-look summary plots.
plot_bias(self[, ax, offset, add_cbar])	Plot migration as a function of true energy for a given offset.
plot_migration(self[, ax, offset, e_true, migra])	Plot energy dispersion for given offset and true energy.
read(filename[, hdu])	Read from FITS file.
to_energy_dispersion(self, offset[, e_true, …])	Detector response R(Delta E_reco, Delta E_true)
to_fits(self[, name])	Convert to BinTable.
to_table(self)	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)

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

Bin edges of true energy axis

migra : Quantity

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

Bin edges of offset

pdf_threshold : float, optional

Zero suppression threshold

classmethod from_hdulist(hdulist, hdu='edisp_2d')

Create from HDUList.

classmethod from_table(table)

Create from Table.

get_response(self, offset, e_true, e_reco=None, migra_step=0.005)

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 : Quantity

True energy

e_reco : Quantity, None

Reconstructed energy axis

offset : Angle

Offset

migra_step : float

Integration step in migration

Returns:

rv : ndarray

Redistribution vector

peek(self, figsize=(15, 5))

Quick-look summary plots.

Parameters:

figsize : (float, float)

Size of the resulting plot

plot_bias(self, ax=None, offset=None, add_cbar=False, **kwargs)

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(self, ax=None, offset=None, e_true=None, migra=None, **kwargs)

Plot energy dispersion for given offset and true energy.

Parameters:

ax : Axes, optional

Axis

offset : Angle, optional

Offset

e_true : Quantity, optional

True energy

migra : array, list, optional

Migration nodes

Returns:

ax : Axes

Axis

classmethod read(filename, hdu='edisp_2d')

Read from FITS file.

Parameters:

filename : str

File name

to_energy_dispersion(self, offset, e_true=None, e_reco=None)

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 : Quantity, None

True energy axis

e_reco : Quantity

Reconstructed energy axis

Returns:

edisp : EnergyDispersion

Energy dispersion matrix

to_fits(self, name='ENERGY DISPERSION')

Convert to BinTable.

to_table(self)

Convert to Table.