EffectiveAreaTable2D

class gammapy.irf.EffectiveAreaTable2D(axes, data=0, unit='', is_pointlike=False, fov_alignment=FoVAlignment.RADEC, meta=None, interp_kwargs=None)

Bases: IRF

2D effective area table.

Data format specification: AEFF_2D.

Parameters:

energy_axis_trueMapAxis

True energy axis.

offset_axisMapAxis

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_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, **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(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_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')

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, **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(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))

Quick-look summary plots.

Parameters:

figsizetuple, optional

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

plot(ax=None, add_cbar=True, axes_loc=None, kwargs_colorbar=None, **kwargs)

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)

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)

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.