EDispKernel#
- class gammapy.irf.EDispKernel(axes, data=0, unit='', is_pointlike=False, fov_alignment=FoVAlignment.RADEC, meta=None, interp_kwargs=None)[source]#
Bases:
gammapy.irf.core.IRF
Energy dispersion matrix.
Data format specification: RMF
- Parameters
Examples
Create a Gaussian energy dispersion matrix:
>>> from gammapy.maps import MapAxis >>> from gammapy.irf import EDispKernel >>> energy = MapAxis.from_energy_bounds(0.1,10,10, unit='TeV') >>> energy_true = MapAxis.from_energy_bounds(0.1,10,10, unit='TeV', name='energy_true') >>> edisp = EDispKernel.from_gauss( >>> energy_axis_true=energy_true, energy_axis=energy, sigma=0.1, bias=0 >>> )
Have a quick look:
>>> print(edisp) EDispKernel ----------- axes : ['energy_true', 'energy'] shape : (10, 10) ndim : 2 unit : dtype : float64 >>> edisp.peek()
Attributes Summary
MapAxes
Default Interpolation kwargs for
IRF
.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.
Energy dispersion PDF matrix (
ndarray
).Map unit (
Unit
)Methods Summary
cumsum
(axis_name)Compute cumsum along a given axis
evaluate
([method])Evaluate IRF
from_diagonal_response
(energy_axis_true[, ...])Create energy dispersion from a diagonal response, i.e. perfect energy resolution.
from_gauss
(energy_axis_true, energy_axis, ...)Create Gaussian energy dispersion matrix (
EnergyDispersion
).from_hdulist
(hdulist[, hdu1, hdu2])Create
EnergyDispersion
object fromHDUList
.from_table
(table[, format])Read from
Table
.get_bias
(energy_true)Get reconstruction bias for a given true energy.
get_bias_energy
(bias[, energy_min, energy_max])Find energy corresponding to a given bias.
get_mean
(energy_true)Get mean reconstructed energy for a given true energy.
get_resolution
(energy_true)Get energy resolution for a given true energy.
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.
pdf_in_safe_range
(lo_threshold, hi_threshold)PDF matrix with bins outside threshold set to 0.
peek
([figsize])Quick-look summary plots.
plot_bias
([ax])Plot reconstruction bias.
plot_matrix
([ax, add_cbar])Plot PDF matrix.
read
(filename[, hdu1, hdu2])Read from file.
slice_by_idx
(slices)Slice sub IRF from IRF object.
to_hdulist
([format])Convert RMF to FITS HDU list format.
to_image
([lo_threshold, hi_threshold])Return a 2D edisp by summing the pdf matrix over the ereco axis.
to_table
([format])Convert to
Table
.to_table_hdu
([format])Convert to
BinTableHDU
.to_unit
(unit)Convert irf to different unit
write
(filename[, format])Write to file.
Attributes Documentation
- axes#
MapAxes
- data#
- default_interp_kwargs = {'bounds_error': False, 'fill_value': 0, 'method': 'nearest'}#
Default Interpolation kwargs for
IRF
. Fill zeros and do not interpolate
- 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.
- pdf_matrix#
Energy dispersion PDF matrix (
ndarray
).Rows (first index): True Energy Columns (second index): Reco Energy
- required_axes = ['energy_true', 'energy']#
- tag = 'edisp_kernel'#
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_diagonal_response(energy_axis_true, energy_axis=None)[source]#
Create energy dispersion from a diagonal response, i.e. perfect energy resolution
This creates the matrix corresponding to a perfect energy response. It contains ones where the energy_true center is inside the e_reco bin. It is a square diagonal matrix if energy_true = e_reco.
This is useful in cases where code always applies an edisp, but you don’t want it to do anything.
- Parameters
- energy_axis_true, energy_axis
MapAxis
True and reconstructed energy axis
- energy_axis_true, energy_axis
Examples
If
energy_true
equalsenergy
, you get a diagonal matrix:from gammapy.irf import EDispKernel from gammapy.maps import MapAxis energy_true_axis = MapAxis.from_energy_edges( [0.5, 1, 2, 4, 6] * u.TeV, name="energy_true" ) edisp = EDispKernel.from_diagonal_response(energy_true_axis) edisp.plot_matrix()
Example with different energy binnings:
energy_true_axis = MapAxis.from_energy_edges( [0.5, 1, 2, 4, 6] * u.TeV, name="energy_true" ) energy_axis = MapAxis.from_energy_edges([2, 4, 6] * u.TeV) edisp = EDispKernel.from_diagonal_response(energy_true_axis, energy_axis) edisp.plot_matrix()
- classmethod from_gauss(energy_axis_true, energy_axis, sigma, bias, pdf_threshold=1e-06)[source]#
Create Gaussian energy dispersion matrix (
EnergyDispersion
).Calls
gammapy.irf.EnergyDispersion2D.from_gauss()
- Parameters
- energy_axis_true
Quantity
Bin edges of true energy axis
- energy_axis
Quantity
Bin edges of reconstructed energy axis
- 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
- energy_axis_true
- Returns
- edisp
EDispKernel
Edisp kernel.
- edisp
- classmethod from_hdulist(hdulist, hdu1='MATRIX', hdu2='EBOUNDS')[source]#
Create
EnergyDispersion
object fromHDUList
.- Parameters
- hdulist
HDUList
HDU list with
MATRIX
andEBOUNDS
extensions.- hdu1str, optional
HDU containing the energy dispersion matrix, default: MATRIX
- hdu2str, optional
HDU containing the energy axis information, default, EBOUNDS
- hdulist
- 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
- get_bias(energy_true)[source]#
Get reconstruction bias for a given true energy.
Bias is defined as
\[\frac{E_{reco}-E_{true}}{E_{true}}\]- Parameters
- energy_true
Quantity
True energy
- energy_true
- get_bias_energy(bias, energy_min=None, energy_max=None)[source]#
Find energy corresponding to a given bias.
In case the solution is not unique, provide the
energy_min
orenergy_max
arguments to limit the solution to the given range. By default the peak energy of the bias is chosen asenergy_min
.
- get_resolution(energy_true)[source]#
Get energy resolution for a given true energy.
The resolution is given as a percentage of the true energy
- Parameters
- energy_true
Quantity
True energy
- energy_true
- 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
- pdf_in_safe_range(lo_threshold, hi_threshold)[source]#
PDF matrix with bins outside threshold set to 0.
- classmethod read(filename, hdu1='MATRIX', hdu2='EBOUNDS')[source]#
Read from file.
- Parameters
- filename
pathlib.Path
, str File to read
- hdu1str, optional
HDU containing the energy dispersion matrix, default: MATRIX
- hdu2str, optional
HDU containing the energy axis information, default, EBOUNDS
- filename
- 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='ogip', **kwargs)[source]#
Convert RMF to FITS HDU list format.
- Parameters
- format{“ogip”, “ogip-sherpa”}
Format to use.
- Returns
- hdulist
HDUList
RMF in HDU list format.
- hdulist
Notes
For more info on the RMF FITS file format see: https://heasarc.gsfc.nasa.gov/docs/heasarc/caldb/docs/summary/cal_gen_92_002_summary.html
- to_image(lo_threshold=None, hi_threshold=None)[source]#
Return a 2D edisp by summing the pdf matrix over the ereco axis.
- to_table(format='ogip')[source]#
Convert to
Table
.The output table is in the OGIP RMF format. https://heasarc.gsfc.nasa.gov/docs/heasarc/caldb/docs/memos/cal_gen_92_002/cal_gen_92_002.html#Tab:1 # noqa: E501
- Parameters
- format{“ogip”, “ogip-sherpa”}
Format to use.
- Returns
- table
Table
Matrix 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