FluxPoints

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

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)

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)

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)

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)

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)

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)

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)

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)

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.