EnergyDispersion2D#

class gammapy.irf.EnergyDispersion2D(axes, data=0, unit='', is_pointlike=False, fov_alignment=FoVAlignment.RADEC, meta=None, interp_kwargs=None)[source]#

Bases: IRF

Offset-dependent energy dispersion matrix.

Data format specification: EDISP_2D

Parameters:
axeslist of MapAxis or MapAxes
Required axes (in the given order) are:

  • energy_true (true energy axis)

  • migra (energy migration axis)

  • offset (field of view offset axis)

datandarray

Energy dispersion probability density.

See also

EnergyDispersion.

Examples

Read energy dispersion IRF from disk:

>>> from gammapy.maps import MapAxis, MapAxes
>>> from gammapy.irf import EnergyDispersion2D
>>> filename = '$GAMMAPY_DATA/hess-dl3-dr1/data/hess_dl3_dr1_obs_id_020136.fits.gz'
>>> edisp2d = EnergyDispersion2D.read(filename, hdu="EDISP")

Create energy dispersion matrix (EnergyDispersion) for a given field of view offset and energy binning:

>>> energy_axis = MapAxis.from_bounds(0.1, 20, nbin=60, unit="TeV", interp="log", name='energy')
>>> edisp = edisp2d.to_edisp_kernel(offset='1.2 deg', energy_axis=energy_axis,
...                                 energy_axis_true=energy_axis.copy(name='energy_true'))

Create energy dispersion IRF from axes:

>>> energy_axis_true = MapAxis.from_energy_bounds("1 TeV", "10 TeV", nbin=10, name="energy_true")
>>> offset_axis = MapAxis.from_bounds(0, 1, nbin=3, unit="deg", name="offset", node_type="edges")
>>> migra_axis = MapAxis.from_bounds(0, 3, nbin=3, name="migra", node_type="edges")
>>> axes = MapAxes([energy_axis_true, migra_axis, offset_axis])
>>> edisp2d_axes = EnergyDispersion2D(axes=axes)

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_gauss(energy_axis_true, migra_axis, ...)

Create Gaussian energy dispersion matrix (EnergyDispersion2D).

from_hdulist(hdulist[, hdu, format])

Create from HDUList.

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()

Normalise energy dispersion.

pad(pad_width, axis_name, **kwargs)

Pad IRF along a given axis.

peek([figsize])

Quick-look summary plots.

plot_bias([ax, offset, add_cbar, axes_loc, ...])

Plot migration as a function of true energy for a given offset.

plot_migration([ax, offset, energy_true])

Plot energy dispersion for given offset and true energy.

read(filename[, hdu, format])

Read from file.

slice_by_idx(slices)

Slice sub IRF from IRF object.

to_edisp_kernel(offset[, energy_axis_true, ...])

Detector response R(Delta E_reco, Delta E_true).

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(dimensionless)#
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', 'migra', 'offset']#
tag = 'edisp_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_gauss(energy_axis_true, migra_axis, offset_axis, bias, sigma, pdf_threshold=1e-06)[source]#

Create Gaussian energy dispersion matrix (EnergyDispersion2D).

The output matrix will be Gaussian in (energy_true / energy).

The bias and sigma should be either floats or arrays of same dimension than energy_true. bias refers to the mean value of the migra distribution minus one, i.e. bias=0 means no bias.

Note that, the output matrix is flat in offset.

Parameters:
energy_axis_trueMapAxis

True energy axis.

migra_axisQuantity

Migra axis.

offset_axisQuantity

Bin edges of offset.

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. Default is 1e-6.

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_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()[source]#

Normalise energy dispersion.

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.

Parameters:
figsizetuple, optional

Size of the resulting plot. Default is (15, 5).

plot_bias(ax=None, offset=None, add_cbar=False, axes_loc=None, kwargs_colorbar=None, **kwargs)[source]#

Plot migration as a function of true energy for a given offset.

Parameters:
axAxes, optional

Matplotlib axes. Default is None.

offsetAngle, optional

Offset. Default is None.

add_cbarbool, optional

Add a colorbar to the plot. Default is False.

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_migration(ax=None, offset=None, energy_true=None, **kwargs)[source]#

Plot energy dispersion for given offset and true energy.

Parameters:
axAxes, optional

Matplotlib axes. Default is None.

offsetAngle, optional

Offset. Default is None.

energy_trueQuantity, optional

True energy. Default is None.

**kwargsdict

Keyword arguments forwarded 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_edisp_kernel(offset, energy_axis_true=None, energy_axis=None)[source]#

Detector response R(Delta E_reco, Delta E_true).

Probability to reconstruct an energy in a given true energy band in a given reconstructed energy band.

Parameters:
offsetAngle

Offset.

energy_axis_trueMapAxis, optional

True energy axis. Default is None.

energy_axisMapAxis, optional

Reconstructed energy axis. Default is None.

Returns:
edispEDispKernel

Energy dispersion matrix.

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.