EffectiveAreaTable2D#
- class gammapy.irf.EffectiveAreaTable2D(axes, data=0, unit='', is_pointlike=False, fov_alignment=FoVAlignment.RADEC, meta=None, interp_kwargs=None)[source]#
Bases:
gammapy.irf.core.IRF
2D effective area table.
Data format specification: AEFF_2D
- Parameters
- energy_axis_true
MapAxis
True energy axis
- offset_axis
MapAxis
Field of view offset axis.
- data
Quantity
Effective area
- metadict
Meta data
- energy_axis_true
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
MapAxes
Alignment of the field of view coordinate axes, see
FoVAlignment
Whether the IRF explicitly depends on offset
Whether the IRF is pointlike of full containment.
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.
- required_axes = ['energy_true', 'offset']#
- tag = 'aeff_2d'#
Methods Documentation
- cumsum(axis_name)#
Compute cumsum along a given axis
- Parameters
- axis_namestr
Along which axis to integrate.
- Returns
- irf
IRF
Cumsum IRF
- irf
- evaluate(method=None, **kwargs)#
Evaluate IRF
- Parameters
- **kwargsdict
Coordinates at which to evaluate the IRF
- methodstr {‘linear’, ‘nearest’}, optional
Interpolation method
- Returns
- array
Quantity
Interpolated values
- array
- classmethod from_hdulist(hdulist, hdu=None, format='gadf-dl3')#
Create from
HDUList
.- Parameters
- hdulist
HDUList
HDU list
- hdustr
HDU name
- format{“gadf-dl3”}
Format specification
- hdulist
- Returns
- irf
IRF
IRF class
- irf
- 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 .
\[A_{eff}(E) = g_1 \left(\frac{E}{\mathrm{MeV}}\right)^{-g_2}\exp{\left(-\frac{g_3}{E}\right)} # noqa: E501\]This method does not model the offset dependence of the effective area, but just assumes that it is constant.
- Parameters
- energy_axis_true
MapAxis
Energy binning, analytic function is evaluated at log centers
- instrument{‘HESS’, ‘HESS2’, ‘CTA’}
Instrument name
- energy_axis_true
- Returns
- aeff
EffectiveAreaTable2D
Effective area table
- aeff
- classmethod from_table(table, format='gadf-dl3')#
Read from
Table
.- Parameters
- table
Table
Table with irf data
- format{“gadf-dl3”}
Format specification
- table
- Returns
- irf
IRF
IRF class.
- irf
- 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
- array
Quantity
Returns 2D array with axes offset
- array
- 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
- array
Quantity
Returns 2D array with axes offset
- array
- 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
- other
gammapy.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
- other
- 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
- irf
IRF
Padded irf
- irf
- plot_energy_dependence(ax=None, offset=None, **kwargs)[source]#
Plot effective area versus energy for a given offset.
- plot_offset_dependence(ax=None, energy=None, **kwargs)[source]#
Plot effective area versus offset for a given energy.
- classmethod read(filename, hdu=None, format='gadf-dl3')#
Read from file.
- Parameters
- filenamestr or
Path
Filename
- hdustr
HDU name
- format{“gadf-dl3”}
Format specification
- filenamestr or
- Returns
- irf
IRF
IRF class
- irf
- 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
- sliced
IRF
Sliced IRF object.
- sliced
- to_hdulist(format='gadf-dl3')#
- to_table(format='gadf-dl3')#
Convert to table
- Parameters
- format{“gadf-dl3”}
Format specification
- Returns
- table
Table
IRF data table
- table
- to_table_hdu(format='gadf-dl3')#
Convert to
BinTableHDU
.- Parameters
- format{“gadf-dl3”}
Format specification
- Returns
- hdu
BinTableHDU
IRF data table hdu
- hdu
- to_unit(unit)#
Convert irf to different unit
- Parameters
- unit
Unit
or str New unit
- unit
- Returns
- irf
IRF
IRF with new unit and converted data
- irf