EnergyDispersion2D#

class gammapy.irf.EnergyDispersion2D(axes, data=0, unit='', is_pointlike=False, fov_alignment=FoVAlignment.RADEC, meta=None, interp_kwargs=None)[source]#

Bases: IRF

Offset-dependent energy dispersion matrix.

Data format specification: EDISP_2D

Parameters:

axeslist of MapAxis or MapAxes

Required axes (in the given order) are:

energy_true (true energy axis)
migra (energy migration axis)
offset (field of view offset axis)

datandarray

Energy dispersion probability density.

See also

EnergyDispersion.

Examples

Read energy dispersion IRF from disk:

>>> from gammapy.maps import MapAxis, MapAxes
>>> from gammapy.irf import EnergyDispersion2D
>>> filename = '$GAMMAPY_DATA/hess-dl3-dr1/data/hess_dl3_dr1_obs_id_020136.fits.gz'
>>> edisp2d = EnergyDispersion2D.read(filename, hdu="EDISP")

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

>>> energy_axis = MapAxis.from_bounds(0.1, 20, nbin=60, unit="TeV", interp="log", name='energy')
>>> edisp = edisp2d.to_edisp_kernel(offset='1.2 deg', energy_axis=energy_axis,
...                                 energy_axis_true=energy_axis.copy(name='energy_true'))

Create energy dispersion IRF from axes:

>>> energy_axis_true = MapAxis.from_energy_bounds("1 TeV", "10 TeV", nbin=10, name="energy_true")
>>> offset_axis = MapAxis.from_bounds(0, 1, nbin=3, unit="deg", name="offset", node_type="edges")
>>> migra_axis = MapAxis.from_bounds(0, 3, nbin=3, name="migra", node_type="edges")
>>> axes = MapAxes([energy_axis_true, migra_axis, offset_axis])
>>> edisp2d_axes = EnergyDispersion2D(axes=axes)

Attributes Summary

`axes`	`MapAxes`.
`data`
`default_interp_kwargs`
`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_gauss`(energy_axis_true, migra_axis, ...)	Create Gaussian energy dispersion matrix (`EnergyDispersion2D`).
`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`()	Normalise energy dispersion.
`pad`(pad_width, axis_name, **kwargs)	Pad IRF along a given axis.
`peek`([figsize])	Quick-look summary plots.
`plot_bias`([ax, offset, add_cbar, axes_loc, ...])	Plot migration as a function of true energy for a given offset.
`plot_migration`([ax, offset, energy_true])	Plot energy dispersion for given offset and true energy.
`read`(filename[, hdu, format])	Read from file.
`slice_by_idx`(slices)	Slice sub IRF from IRF object.
`to_edisp_kernel`(offset[, energy_axis_true, ...])	Detector response R(Delta E_reco, Delta E_true).
`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}#

default_unit = Unit(dimensionless)#

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_true', 'migra', 'offset']#

tag = 'edisp_2d'#

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_gauss(energy_axis_true, migra_axis, offset_axis, bias, sigma, pdf_threshold=1e-06)[source]#

Create Gaussian energy dispersion matrix (EnergyDispersion2D).

The output matrix will be Gaussian in (energy_true / energy).

The bias and sigma should be either floats or arrays of same dimension than energy_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:

energy_axis_trueMapAxis: True energy axis.
migra_axisQuantity: Migra axis.
offset_axisQuantity: Bin edges of offset.
biasfloat or ndarray: Center of Gaussian energy dispersion, bias.
sigmafloat or ndarray: RMS width of Gaussian energy dispersion, resolution.
pdf_thresholdfloat, optional: Zero suppression threshold. Default is 1e-6.

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

Returns:

irfIRF: 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()[source]#: Normalise energy dispersion.

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=(15, 5))[source]#

Quick-look summary plots.

Parameters:

figsizetuple, optional: Size of the resulting plot. Default is (15, 5).

plot_bias(ax=None, offset=None, add_cbar=False, axes_loc=None, kwargs_colorbar=None, **kwargs)[source]#

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

Parameters:

axAxes, optional: Matplotlib axes. Default is None.
offsetAngle, optional: Offset. Default is None.
add_cbarbool, optional: Add a colorbar to the plot. Default is False.
axes_locdict, optional: Keyword arguments passed to append_axes.
kwargs_colorbardict, optional: Keyword arguments passed to colorbar.
kwargsdict: Keyword arguments passed to pcolormesh.

Returns:

axAxes: Matplotlib axes.

plot_migration(ax=None, offset=None, energy_true=None, **kwargs)[source]#

Plot energy dispersion for given offset and true energy.

Parameters:

axAxes, optional: Matplotlib axes. Default is None.
offsetAngle, optional: Offset. Default is None.
energy_trueQuantity, optional: True energy. Default is None.
**kwargsdict: Keyword arguments forwarded to plot.

Returns:

axAxes: Matplotlib axes.

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_edisp_kernel(offset, energy_axis_true=None, energy_axis=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:

offsetAngle: Offset.
energy_axis_trueMapAxis, optional: True energy axis. Default is None.
energy_axisMapAxis, optional: Reconstructed energy axis. Default is None.

Returns:

edispEDispKernel: Energy dispersion matrix.

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.