EffectiveAreaTable2D#

class gammapy.irf.EffectiveAreaTable2D[source]#

Bases: IRF

2D effective area table.

Data format specification: AEFF_2D.

Parameters:

axeslist of MapAxis or MapAxes

Required axes (in the given order) are:

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

dataQuantity

Effective area.

metadict

Metadata dictionary.

Examples

Here’s an example you can use to learn about this class:

>>> from gammapy.irf import EffectiveAreaTable2D
>>> filename = '$GAMMAPY_DATA/cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits'
>>> aeff = EffectiveAreaTable2D.read(filename, hdu='EFFECTIVE AREA')
>>> print(aeff)
EffectiveAreaTable2D
--------------------

  axes  : ['energy_true', 'offset']
  shape : (42, 6)
  ndim  : 2
  unit  : m2
  dtype : >f4

Here’s another one, created from scratch, without reading a file:

>>> from gammapy.irf import EffectiveAreaTable2D
>>> from gammapy.maps import MapAxis
>>> energy_axis_true = MapAxis.from_energy_bounds(
...        "0.1 TeV", "100 TeV", nbin=30, name="energy_true"
...    )
>>> offset_axis = MapAxis.from_bounds(0, 5, nbin=4, name="offset")
>>> aeff = EffectiveAreaTable2D(axes=[energy_axis_true, offset_axis], data=1e10, unit="cm2")
>>> print(aeff)
EffectiveAreaTable2D
--------------------

  axes  : ['energy_true', 'offset']
  shape : (30, 4)
  ndim  : 2
  unit  : cm2
  dtype : float64

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_arguments`
`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_parametrization`([energy_axis_true, ...])	Create parametrized effective area.
`from_table`(table[, format])	Read from `Table`.
`integral`(axis_name, **kwargs)	Compute integral along a given axis.
`integrate_log_log`(axis_name[, method])	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`([ax, add_cbar, axes_loc, kwargs_colorbar])	Plot effective area image.
`plot_energy_dependence`([ax, offset])	Plot effective area versus energy for a given offset.
`plot_offset_dependence`([ax, energy])	Plot effective area versus offset for a given energy.
`read`(filename[, hdu, format])	Read from file.
`slice_by_idx`(slices)	Slice sub IRF from IRF object.
`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("m2")#

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_arguments#

required_axes = ['energy_true', 'offset']#

tag = 'aeff_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_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_parametrization(energy_axis_true=None, instrument='HESS')[source]#

Create parametrized effective area.

Parametrizations of the effective areas of different Cherenkov telescopes taken from Appendix B of Abramowski et al. (2010), see https://ui.adsabs.harvard.edu/abs/2010MNRAS.402.1342A .

\[A_{eff}(E) = g_1 \left(\frac{E}{\mathrm{MeV}}\right)^{-g_2}\exp{\left(-\frac{g_3}{E}\right)}\]

This method does not model the offset dependence of the effective area, but just assumes that it is constant.

Parameters:

energy_axis_trueMapAxis, optional: Energy binning, analytic function is evaluated at log centers. Default is None.
instrument{‘HESS’, ‘HESS2’, ‘CTAO’}: Instrument name. Default is ‘HESS’.

Returns:

aeffEffectiveAreaTable2D: Effective area table.

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, method='linear', **kwargs)#

Integrate along a given axis.

This method uses log-log trapezoidal integration.

Parameters:

axis_namestr: Along which axis to integrate.
method{“linear”, “nearest”}, optional: Interpolation method to use. Default is “linear”.
**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=(15, 5))[source]#

Quick-look summary plots.

This method creates a figure with three subplots:

Energy dependence plot : effective area versus true energy for a given offset
Offset dependence plot : effective area versus true energy for a given offset
Effective area 2D map

Parameters:

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

plot(ax=None, add_cbar=True, axes_loc=None, kwargs_colorbar=None, **kwargs)[source]#

Plot effective area image.

Parameters:

axAxes, optional: Matplotlib axes. Default is None.
add_cbarbool, optional: Add a colorbar to the plot. Default is True.
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_energy_dependence(ax=None, offset=None, **kwargs)[source]#

Plot effective area versus energy for a given offset.

Parameters:

axAxes, optional: Matplotlib axes. Default is None.
offsetlist of Angle, optional: Offset. Default is None.
kwargsdict: Forwarded to plt.plot().

Returns:

axAxes: Matplotlib axes.

plot_offset_dependence(ax=None, energy=None, **kwargs)[source]#

Plot effective area versus offset for a given energy.

Parameters:

axAxes, optional: Matplotlib axes. Default is None.
energyQuantity: Energy.
**kwargsdict: Keyword argument passed 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_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.

__init__(axes, data=0, unit='', is_pointlike=False, fov_alignment=FoVAlignment.RADEC, meta=None, interp_kwargs=None)#

classmethod __new__(*args, **kwargs)#

EffectiveAreaTable2D#

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