EffectiveAreaTable2D

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

Bases: gammapy.irf.core.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

Meta data

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
required_axes
tag
unit	Map unit (Unit)

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])	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])
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

required_axes = ['energy_true', 'offset']

tag = 'aeff_2d'

unit

Map unit (Unit)

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

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

Energy binning, analytic function is evaluated at log centers

instrument{‘HESS’, ‘HESS2’, ‘CTA’}

Instrument name

Returns

aeffEffectiveAreaTable2D

Effective area table

classmethod from_table(table, format='gadf-dl3')

Read from Table.

Parameters

tableTable

Table with irf data

format{“gadf-dl3”}

Format specification

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

othergammapy.irfs.IRF

The irf to compare against

rtol_axesfloat

Relative tolerance for the axes comparison.

atol_axesfloat

Relative tolerance for the axes comparison.

**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

Which 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

Size of the figure.

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

Plot effective area image.

plot_energy_dependence(ax=None, offset=None, **kwargs)

Plot effective area versus energy for a given offset.

Parameters

axAxes, optional

Axis

offsetAngle

Offset

kwargsdict

Forwarded tp plt.plot()

Returns

axAxes

Axis

plot_offset_dependence(ax=None, energy=None, **kwargs)

Plot effective area versus offset for a given energy.

Parameters

axAxes, optional

Axis

energyQuantity

Energy

**kwargsdict

Keyword argument passed to plot

Returns

axAxes

Axis

classmethod read(filename, hdu=None, format='gadf-dl3')

Read from file.

Parameters

filenamestr or Path

Filename

hdustr

HDU name

format{“gadf-dl3”}

Format specification

Returns

irfIRF

IRF class

slice_by_idx(slices)

Slice sub IRF from IRF object.

Parameters

slicesdict

Dict of axes names and slice object pairs. Contains one element for each non-spatial dimension. Axes not specified in the dict are kept unchanged.

Returns

slicedIRF

Sliced IRF object.

to_hdulist(format='gadf-dl3')

to_table(format='gadf-dl3')

Convert to table

Parameters

format{“gadf-dl3”}

Format specification

Returns

tableTable

IRF data table

to_table_hdu(format='gadf-dl3')

Convert to BinTableHDU.

Parameters

format{“gadf-dl3”}

Format specification

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.