FluxPoints#

class gammapy.estimators.FluxPoints(data, reference_model, meta=None, gti=None, filter_success_nan=True)[source]#

Bases: FluxMaps

Flux points container.

The supported formats are described here: SED.

In summary, the following formats and minimum required columns are:

Format dnde: columns e_ref and dnde
Format e2dnde: columns e_ref, e2dnde
Format flux: columns e_min, e_max, flux
Format eflux: columns e_min, e_max, eflux

Parameters:

tableTable: Table with flux point data.

Attributes:

tableTable: Table with flux point data.

Examples

The FluxPoints object is most easily created by reading a file with flux points given in one of the formats documented above:

>>> from gammapy.estimators import FluxPoints
>>> filename = '$GAMMAPY_DATA/hawc_crab/HAWC19_flux_points.fits'
>>> flux_points = FluxPoints.read(filename)
>>> flux_points.plot() 

An instance of FluxPoints can also be created by passing an instance of astropy.table.Table, which contains the required columns, such as 'e_ref' and 'dnde'. The corresponding sed_type has to be defined in the meta data of the table:

>>> import numpy as np
>>> from astropy import units as u
>>> from astropy.table import Table
>>> from gammapy.estimators import FluxPoints
>>> from gammapy.modeling.models import PowerLawSpectralModel
>>> table = Table()
>>> pwl = PowerLawSpectralModel()
>>> e_ref = np.geomspace(1, 100, 7) * u.TeV
>>> table["e_ref"] = e_ref
>>> table["dnde"] = pwl(e_ref)
>>> table["dnde_err"] = pwl.evaluate_error(e_ref)[0]
>>> table.meta["SED_TYPE"] = "dnde"
>>> flux_points = FluxPoints.from_table(table)
>>> flux_points.plot(sed_type="flux") 

If you have flux points in a different data format, the format can be changed by renaming the table columns and adding meta data:

>>> from astropy import units as u
>>> from astropy.table import Table
>>> from gammapy.estimators import FluxPoints
>>> from gammapy.utils.scripts import make_path

>>> filename = make_path('$GAMMAPY_DATA/tests/spectrum/flux_points/flux_points_ctb_37b.txt')
>>> table = Table.read(filename ,format='ascii.csv', delimiter=' ', comment='#')
>>> table.rename_column('Differential_Flux', 'dnde')
>>> table['dnde'].unit = 'cm-2 s-1 TeV-1'

>>> table.rename_column('lower_error', 'dnde_errn')
>>> table['dnde_errn'].unit = 'cm-2 s-1 TeV-1'

>>> table.rename_column('upper_error', 'dnde_errp')
>>> table['dnde_errp'].unit = 'cm-2 s-1 TeV-1'

>>> table.rename_column('E', 'e_ref')
>>> table['e_ref'].unit = 'TeV'

>>> flux_points = FluxPoints.from_table(table, sed_type="dnde")
>>> flux_points.plot(sed_type="e2dnde") 

Note: In order to reproduce the example you need the tests datasets folder. You may download it with the command gammapy download datasets --tests --out $GAMMAPY_DATA

Attributes Summary

`acceptance_off`	The acceptance in the off region.
`acceptance_on`	The acceptance in the on region.
`alpha`	The normalisation, alpha, for differences between the on and off regions.
`available_quantities`	Available quantities.
`counts`	Predicted counts null hypothesis.
`dnde`	Return differential flux (dnde) SED values.
`dnde_err`	Return differential flux (dnde) SED errors.
`dnde_errn`	Return differential flux (dnde) SED negative errors.
`dnde_errp`	Return differential flux (dnde) SED positive errors.
`dnde_ref`	Reference differential flux.
`dnde_scan_values`	Fit statistic norm scan values.
`dnde_ul`	Return differential flux (dnde) SED upper limit.
`e2dnde`	Return differential energy flux (e2dnde) SED values.
`e2dnde_err`	Return differential energy flux (e2dnde) SED errors.
`e2dnde_errn`	Return differential energy flux (e2dnde) SED negative errors.
`e2dnde_errp`	Return differential energy flux (e2dnde) SED positive errors.
`e2dnde_ref`	Reference differential flux * energy ** 2.
`e2dnde_ul`	Return differential energy flux (e2dnde) SED upper limit.
`eflux`	Return energy flux (eflux) SED values.
`eflux_err`	Return energy flux (eflux) SED errors.
`eflux_errn`	Return energy flux (eflux) SED negative errors.
`eflux_errp`	Return energy flux (eflux) SED positive errors.
`eflux_ref`	Reference energy flux.
`eflux_ul`	Return energy flux (eflux) SED upper limits.
`energy_axis`	Energy axis as a `MapAxis`.
`energy_max`	Energy maximum.
`energy_min`	Energy minimum.
`energy_ref`	Reference energy.
`filter_success_nan`
`flux`	Return integral flux (flux) SED values.
`flux_err`	Return integral flux (flux) SED values.
`flux_errn`	Return integral flux (flux) SED negative errors.
`flux_errp`	Return integral flux (flux) SED positive errors.
`flux_ref`	Reference integral flux.
`flux_sensitivity`	Sensitivity given as the flux for which the significance is `self.meta["n_sigma_sensitivity]`.
`flux_ul`	Return integral flux (flux) SED upper limits.
`geom`	Reference map geometry as a `Geom`.
`has_any_ts`	Whether the flux estimate has either sqrt(TS) or test statistic defined.
`has_stat_profiles`	Whether the flux estimate has test statistic profiles.
`has_success`	Whether the flux estimate has the fit status.
`has_ul`	Whether the flux estimate has norm_ul defined.
`is_convertible_to_flux_sed_type`	Check whether differential SED type is convertible to integral SED type.
`is_ul`	Whether data is an upper limit.
`n_dof`	Number of degrees of freedom of the fit per energy bin.
`n_sigma`	n sigma
`n_sigma_ul`	n sigma UL.
`niter`	Number of iterations of fit.
`norm`	Norm values.
`norm_err`	Norm error.
`norm_errn`	Negative norm error.
`norm_errp`	Positive norm error.
`norm_sensitivity`	Norm sensitivity.
`norm_ul`	Norm upper limit.
`npred`	Predicted counts from best fit hypothesis.
`npred_background`	Predicted background counts from best fit hypothesis.
`npred_excess`	Predicted excess count from best fit hypothesis.
`npred_excess_err`	Predicted excess counts error.
`npred_excess_errn`	Predicted excess counts negative error.
`npred_excess_errp`	Predicted excess counts positive error.
`npred_excess_ref`	Predicted excess reference counts.
`npred_excess_ul`	Predicted excess counts upper limits.
`reference_model`	Reference model as a `SkyModel`.
`reference_model_default`	Reference model default as a `SkyModel`.
`reference_spectral_model`	Reference spectral model as a `SpectralModel`.
`sed_type_init`	Initial SED type.
`sed_type_plot_default`	Initial SED type.
`sqrt_ts`	sqrt(TS) as defined by:
`sqrt_ts_threshold_ul`	sqrt(TS) threshold for upper limits.
`stat`	Fit statistic value.
`stat_null`	Fit statistic value for the null hypothesis.
`stat_scan`	Fit statistic scan value.
`success`	Fit success flag.
`ts`	Test statistic map as a `Map` object.
`ts_scan`	Test statistic scan as a `Map` object.

Methods Summary

`all_quantities`(sed_type)	All quantities allowed for a given SED type.
`copy`([reference_model])	Deep copy.
`from_hdulist`(hdulist[, hdu_bands, sed_type, ...])	Create flux map dataset from list of HDUs.
`from_maps`(maps[, sed_type, reference_model, ...])	Create FluxMaps from a dictionary of maps.
`from_stack`(maps, axis[, meta])	Create flux points by stacking list of flux points.
`from_table`(table[, sed_type, format, ...])	Create flux points from a table.
`get_flux_points`([position])	Extract flux point at a given position.
`iter_by_axis`(axis_name)	Create a set of FluxMaps by splitting along an axis.
`plot`([ax, sed_type, energy_power, time_format])	Plot flux points.
`plot_ts_profiles`([ax, sed_type, add_cbar])	Plot fit statistic SED profiles as a density plot.
`read`(filename[, sed_type, format, ...])	Read precomputed flux points.
`recompute_ul`([n_sigma_ul])	Recompute upper limits corresponding to the given value.
`resample_axis`(axis_new)	Rebin the flux point object along the new axis.
`slice_by_coord`(slices)	Slice flux maps by coordinate values.
`slice_by_energy`(energy_min, energy_max)	Slice flux maps by coordinate values along the energy axis.
`slice_by_idx`(slices)	Slice flux maps by index.
`slice_by_time`(time_min, time_max)	Slice flux maps by coordinate values along the time axis.
`to_hdulist`([sed_type, hdu_bands])	Convert flux map to list of HDUs.
`to_maps`([sed_type])	Return maps in a given SED type.
`to_table`([sed_type, format, formatted])	Create table for a given SED type.
`write`(filename[, sed_type, format, ...])	Write flux points.

Attributes Documentation

acceptance_off#: The acceptance in the off region.

acceptance_on#: The acceptance in the on region.

alpha#: The normalisation, alpha, for differences between the on and off regions.

available_quantities#: Available quantities.

counts#: Predicted counts null hypothesis.

dnde#: Return differential flux (dnde) SED values.

dnde_err#: Return differential flux (dnde) SED errors.

dnde_errn#: Return differential flux (dnde) SED negative errors.

dnde_errp#: Return differential flux (dnde) SED positive errors.

dnde_ref#: Reference differential flux.

dnde_scan_values#: Fit statistic norm scan values.

dnde_ul#: Return differential flux (dnde) SED upper limit.

e2dnde#: Return differential energy flux (e2dnde) SED values.

e2dnde_err#: Return differential energy flux (e2dnde) SED errors.

e2dnde_errn#: Return differential energy flux (e2dnde) SED negative errors.

e2dnde_errp#: Return differential energy flux (e2dnde) SED positive errors.

e2dnde_ref#: Reference differential flux * energy ** 2.

e2dnde_ul#: Return differential energy flux (e2dnde) SED upper limit.

eflux#: Return energy flux (eflux) SED values.

eflux_err#: Return energy flux (eflux) SED errors.

eflux_errn#: Return energy flux (eflux) SED negative errors.

eflux_errp#: Return energy flux (eflux) SED positive errors.

eflux_ref#: Reference energy flux.

eflux_ul#: Return energy flux (eflux) SED upper limits.

energy_axis#: Energy axis as a MapAxis.

energy_max#

Energy maximum.

Returns:

energy_maxQuantity: Upper bound of energy bin.

energy_min#

Energy minimum.

Returns:

energy_minQuantity: Lower bound of energy bin.

energy_ref#

Reference energy.

Defined by energy_ref column in FluxPoints.table or computed as log center, if energy_min and energy_max columns are present in FluxEstimate.data.

Returns:

energy_refQuantity: Reference energy.

filter_success_nan#

flux#: Return integral flux (flux) SED values.

flux_err#: Return integral flux (flux) SED values.

flux_errn#: Return integral flux (flux) SED negative errors.

flux_errp#: Return integral flux (flux) SED positive errors.

flux_ref#: Reference integral flux.

flux_sensitivity#: Sensitivity given as the flux for which the significance is self.meta["n_sigma_sensitivity].

flux_ul#: Return integral flux (flux) SED upper limits.

geom#: Reference map geometry as a Geom.

has_any_ts#: Whether the flux estimate has either sqrt(TS) or test statistic defined.

has_stat_profiles#: Whether the flux estimate has test statistic profiles.

has_success#: Whether the flux estimate has the fit status.

has_ul#: Whether the flux estimate has norm_ul defined.

is_convertible_to_flux_sed_type#: Check whether differential SED type is convertible to integral SED type.

is_ul#: Whether data is an upper limit.

n_dof#: Number of degrees of freedom of the fit per energy bin.

n_sigma#: n sigma

n_sigma_ul#: n sigma UL.

niter#: Number of iterations of fit.

norm#: Norm values.

norm_err#: Norm error.

norm_errn#: Negative norm error.

norm_errp#: Positive norm error.

norm_sensitivity#: Norm sensitivity.

norm_ul#: Norm upper limit.

npred#: Predicted counts from best fit hypothesis.

npred_background#: Predicted background counts from best fit hypothesis.

npred_excess#: Predicted excess count from best fit hypothesis.

npred_excess_err#: Predicted excess counts error.

npred_excess_errn#: Predicted excess counts negative error.

npred_excess_errp#: Predicted excess counts positive error.

npred_excess_ref#: Predicted excess reference counts.

npred_excess_ul#: Predicted excess counts upper limits.

reference_model#: Reference model as a SkyModel.

reference_model_default#: Reference model default as a SkyModel.

reference_spectral_model#: Reference spectral model as a SpectralModel.

sed_type_init#: Initial SED type.

sed_type_plot_default#: Initial SED type.

sqrt_ts#

sqrt(TS) as defined by:

\[\begin{split}\sqrt{TS} = \left \{ \begin{array}{ll} -\sqrt{TS} & : \text{if} \ norm < 0 \\ \sqrt{TS} & : \text{else} \end{array} \right.\end{split}\]

Returns:

sqrt_tsMap: sqrt(TS) map.

sqrt_ts_threshold_ul#: sqrt(TS) threshold for upper limits.

stat#: Fit statistic value.

stat_null#: Fit statistic value for the null hypothesis.

stat_scan#: Fit statistic scan value.

success#: Fit success flag.

ts#: Test statistic map as a Map object.

ts_scan#: Test statistic scan as a Map object.

Methods Documentation

static all_quantities(sed_type)#

All quantities allowed for a given SED type.

Parameters:

sed_type{“likelihood”, “dnde”, “e2dnde”, “flux”, “eflux”}: SED type.

Returns:

listlist of str: All allowed quantities for a given SED type.

copy(reference_model=None)#

Deep copy.

Parameters:

reference_modelSkyModel, optional: The reference model to use for conversions. If None, the original model is copied. Flux maps have been obtained for a specific reference model. Changing it will change the fluxes. Handle with care.

Returns:

flux_mapsFluxMaps: Copied flux maps object.

classmethod from_hdulist(hdulist, hdu_bands=None, sed_type=None, checksum=False)#

Create flux map dataset from list of HDUs.

Parameters:

hdulistHDUList: List of HDUs.
hdu_bandsstr, optional: Name of the HDU with the BANDS table. Default is ‘BANDS’ If set to None, each map should have its own hdu_band. Default is None.
sed_type{“dnde”, “flux”, “e2dnde”, “eflux”, “likelihood”}, optional: Sed type. Default is None.

Returns:

flux_mapsFluxMaps: Flux maps object.

classmethod from_maps(maps, sed_type=None, reference_model=None, gti=None, meta=None)#

Create FluxMaps from a dictionary of maps.

Parameters:

mapsMaps: Maps object containing the input maps.
sed_typestr, optional: SED type of the input maps. If None, set to “likelihood”. Default is None.
reference_modelSkyModel, optional: Reference model to use for conversions. If None, a model consisting of a point source with a power law spectrum of index 2 is assumed. Default is None.
gtiGTI, optional: Maps GTI information. Default is None.
metadict: Meta dictionary.

Returns:

flux_mapsFluxMaps: Flux maps object.

classmethod from_stack(maps, axis, meta=None)#

Create flux points by stacking list of flux points.

The first FluxPoints object in the list is taken as a reference to infer column names and units for the stacked object.

Parameters:

mapslist of FluxMaps: List of maps to stack.
axisMapAxis: New axis to create.
metadict, optional: Metadata of the resulting flux points. Default is None.

Returns:

flux_mapsFluxMaps: Stacked flux maps along axis.

classmethod from_table(table, sed_type=None, format=None, reference_model=None, gti=None)[source]#

Create flux points from a table. The table column names must be consistent with the sed_type.

Parameters:

tableTable: Table.
sed_type{“dnde”, “flux”, “eflux”, “e2dnde”, “likelihood”}, optional: SED type. Default is None.
format{“gadf-sed”, “lightcurve”, “profile”}, optional: Table format. If None, it is extracted from the table column content. Default is None.
reference_modelSpectralModel, optional: Reference spectral model. Default is None.
gtiGTI, optional: Good time intervals. Default is None.

Returns:

flux_pointsFluxPoints: Flux points.

get_flux_points(position=None)#

Extract flux point at a given position.

Parameters:

positionSkyCoord: Position where the flux points are extracted.

Returns:

flux_pointsFluxPoints: Flux points object.

iter_by_axis(axis_name)#

Create a set of FluxMaps by splitting along an axis.

Parameters:

axis_namestr: Name of the axis to split on.

Returns:

flux_mapsFluxMap: FluxMap iteration.

plot(ax=None, sed_type=None, energy_power=0, time_format='iso', **kwargs)[source]#

Plot flux points.

Parameters:

axAxes, optional: Axis object to plot on. Default is None.
sed_type{“dnde”, “flux”, “eflux”, “e2dnde”}, optional: SED type. Default is None.
energy_powerfloat, optional: Power of energy to multiply flux axis with. Default is 0.
time_format{“iso”, “mjd”}: Used time format is a time axis is present. Default is “iso”.
**kwargsdict, optional: Keyword arguments passed to plot.

Returns:

axAxes: Axis object.

plot_ts_profiles(ax=None, sed_type=None, add_cbar=True, **kwargs)[source]#

Plot fit statistic SED profiles as a density plot.

Parameters:

axAxes, optional: Axis object to plot on. Default is None.
sed_type{“dnde”, “flux”, “eflux”, “e2dnde”}, optional: SED type. Default is None.
add_cbarbool, optional: Whether to add a colorbar to the plot. Default is True.
**kwargsdict, optional: Keyword arguments passed to pcolormesh.

Returns:

axAxes: Axis object.

classmethod read(filename, sed_type=None, format=None, reference_model=None, checksum=False, table_format='ascii.ecsv', **kwargs)[source]#

Read precomputed flux points.

Parameters:

filenamestr: Filename.
sed_type{“dnde”, “flux”, “eflux”, “e2dnde”, “likelihood”}: SED type.
format{“gadf-sed”, “lightcurve”, “profile”}, optional: Format string. If None, the format is extracted from the input. Default is None.
reference_modelSpectralModel: Reference spectral model.
checksumbool: If True checks both DATASUM and CHECKSUM cards in the file headers. Default is False.
table_formatstr: Format string for the ~astropy.Table object. Default is “ascii.ecsv”
**kwargsdict, optional: Keyword arguments passed to astropy.table.Table.read.

Returns:

flux_pointsFluxPoints: Flux points.

recompute_ul(n_sigma_ul=2, **kwargs)[source]#

Recompute upper limits corresponding to the given value.

The pre-computed statistic profiles must exist for the re-computation.

Parameters:

n_sigma_ulint: Number of sigma to use for upper limit computation. Default is 2.
**kwargsdict, optional: Keyword arguments passed to brentq.

Returns:

flux_pointsFluxPoints: A new FluxPoints object with modified upper limits.

Examples

>>> from gammapy.estimators import FluxPoints
>>> filename = '$GAMMAPY_DATA/tests/spectrum/flux_points/binlike.fits'
>>> flux_points = FluxPoints.read(filename)
>>> flux_points_recomputed = flux_points.recompute_ul(n_sigma_ul=3)
>>> print(flux_points.meta["n_sigma_ul"], flux_points.flux_ul.data[0])
2.0 [[3.95451985e-09]]
>>> print(flux_points_recomputed.meta["n_sigma_ul"], flux_points_recomputed.flux_ul.data[0])
3 [[6.22245374e-09]]

resample_axis(axis_new)[source]#

Rebin the flux point object along the new axis.

The log-likelihoods profiles in each bin are summed to compute the resultant quantities. Stat profiles must be present on the FluxPoints object for this method to work.

For now, works only for flat FluxPoints.

Parameters:

axis_newMapAxis or TimeMapAxis: The new axis to resample along

Returns:

flux_pointsFluxPoints: A new FluxPoints object with modified axis.

slice_by_coord(slices)#

Slice flux maps by coordinate values.

Parameters:

slicesdict: Dictionary of axes names and astropy.Quantity or astropy.Time or slice object pairs. Contains one element for each non-spatial dimension. For integer indexing the corresponding axes is dropped from the map. Axes not specified in the dict are kept unchanged.

Returns:

flux_mapsFluxMaps: Sliced flux maps object.

Examples

>>> from gammapy.estimators import FluxPoints
>>> import astropy.units as u
>>> lc_1d = FluxPoints.read("$GAMMAPY_DATA/estimators/pks2155_hess_lc/pks2155_hess_lc.fits")
>>> slices = {"time": slice(2035.93*u.day, 2036.05*u.day)}
>>> sliced = lc_1d.slice_by_coord(slices)

slice_by_energy(energy_min, energy_max)#

Slice flux maps by coordinate values along the energy axis.

Parameters:

energy_min, energy_maxQuantity: Energy bounds used to slice the flux map.

Returns:

flux_mapsFluxMaps: Sliced flux maps object.

Examples

>>> from gammapy.estimators import FluxPoints
>>> import astropy.units as u
>>> fp = FluxPoints.read("$GAMMAPY_DATA/estimators/crab_hess_fp/crab_hess_fp.fits")
>>> sliced = fp.slice_by_energy(energy_min=2*u.TeV, energy_max=10*u.TeV)

slice_by_idx(slices)#

Slice flux maps by index.

Parameters:

slicesdict: Dictionary of axes names and integers or slice object pairs. Contains one element for each non-spatial dimension. For integer indexing the corresponding axes is dropped from the map. Axes not specified in the dict are kept unchanged.

Returns:

flux_mapsFluxMaps: Sliced flux maps object.

Examples

>>> from gammapy.estimators import FluxPoints
>>> import astropy.units as u
>>> fp = FluxPoints.read("$GAMMAPY_DATA/estimators/crab_hess_fp/crab_hess_fp.fits")
>>> slices = {"energy": slice(0, 2)}
>>> sliced = fp.slice_by_idx(slices)

slice_by_time(time_min, time_max)#

Slice flux maps by coordinate values along the time axis.

Parameters:

time_min, time_maxTime: Time bounds used to slice the flux map.

Returns:

flux_mapsFluxMaps: Sliced flux maps object.

Examples

>>> from gammapy.estimators import FluxPoints
>>> import astropy.units as u
>>> lc_1d = FluxPoints.read("$GAMMAPY_DATA/estimators/pks2155_hess_lc/pks2155_hess_lc.fits")
>>> sliced = lc_1d.slice_by_time(time_min=2035.93*u.day, time_max=2036.05*u.day)

to_hdulist(sed_type=None, hdu_bands=None)#

Convert flux map to list of HDUs.

For now, one cannot export the reference model.

Parameters:

sed_typestr, optional: SED type to convert to. If None, set to “likelihood”. Default is None.
hdu_bandsstr, optional: Name of the HDU with the BANDS table. Default is ‘BANDS’ If set to None, each map will have its own hdu_band. Default is None.

Returns:

hdulistHDUList: Map dataset list of HDUs.

to_maps(sed_type=None)#

Return maps in a given SED type.

Parameters:

sed_type{“likelihood”, “dnde”, “e2dnde”, “flux”, “eflux”}, optional: SED type to convert to. If None, set to Likelihood. Default is None.

Returns:

mapsMaps: Maps object containing the requested maps.

to_table(sed_type=None, format=None, formatted=False)[source]#

Create table for a given SED type.

Parameters:

sed_type{“likelihood”, “dnde”, “e2dnde”, “flux”, “eflux”}

SED type to convert to. Default is likelihood.

format{“gadf-sed”, “lightcurve”, “binned-time-series”, “profile”}, optional

Format specification. The following formats are supported:

“gadf-sed”: format for SED flux points see SED for details

“lightcurve”: Gammapy internal format to store energy dependent lightcurves. Basically a generalisation of the “gadf” format, but currently there is no detailed documentation available.

“binned-time-series”: table format support by Astropy’s BinnedTimeSeries.

“profile”: Gammapy internal format to store energy dependent flux profiles. Basically a generalisation of the “gadf” format, but currently there is no detailed documentation available.

If None, the format will be guessed by looking at the axes that are present in the object. Default is None.

formattedbool

Formatted version with column formats applied. Numerical columns are formatted to .3f and .3e respectively. Default is False.

Returns:

tableTable: Flux points table.

Examples

This is how to read and plot example flux points:

>>> from gammapy.estimators import FluxPoints
>>> fp = FluxPoints.read("$GAMMAPY_DATA/hawc_crab/HAWC19_flux_points.fits")
>>> table = fp.to_table(sed_type="flux", formatted=True)
>>> print(table[:2])
e_ref e_min e_max     flux      flux_err    flux_ul      ts    sqrt_ts is_ul
 TeV   TeV   TeV  1 / (s cm2) 1 / (s cm2) 1 / (s cm2)
----- ----- ----- ----------- ----------- ----------- -------- ------- -----
1.334 1.000 1.780   1.423e-11   3.135e-13         nan 2734.000  52.288 False
2.372 1.780 3.160   5.780e-12   1.082e-13         nan 4112.000  64.125 False

write(filename, sed_type=None, format=None, overwrite=False, checksum=False)[source]#

Write flux points.

Parameters:

filenamestr

Filename.

sed_type{“dnde”, “flux”, “eflux”, “e2dnde”, “likelihood”}, optional

SED type. Default is None.

format{“gadf-sed”, “lightcurve”, “binned-time-series”, “profile”}, optional

Format specification. The following formats are supported:

“gadf-sed”: format for SED flux points see SED for details

“lightcurve”: Gammapy internal format to store energy dependent lightcurves. Basically a generalisation of the “gadf” format, but currently there is no detailed documentation available.

“binned-time-series”: table format support by Astropy’s BinnedTimeSeries.

“profile”: Gammapy internal format to store energy dependent flux profiles. Basically a generalisation of the “gadf” format, but currently there is no detailed documentation available.

If None, the format will be guessed by looking at the axes that are present in the object. Default is None.

overwritebool, optional

Overwrite existing file. Default is False.

checksumbool, optional

When True adds both DATASUM and CHECKSUM cards to the headers written to the file. Default is False.