FluxPoints#

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

Bases: gammapy.estimators.map.core.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

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

tableTable: Table with flux point data

Attributes Summary

`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_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 (`MapAxis`)
`energy_max`	Energy max
`energy_min`	Energy min
`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_ul`	Return integral flux (flux) SED upper limits.
`geom`	Reference map geometry (`Geom`)
`has_any_ts`	Whether the flux estimate has either sqrt(ts) or ts defined
`has_stat_profiles`	Whether the flux estimate has stat 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_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_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 rom 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 (`SkyModel`)
`reference_model_default`	Reference model default (`SkyModel`)
`reference_spectral_model`	Reference spectral model (`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`	ts map (`Map`)
`ts_scan`	ts scan (`Map`)

Methods Summary

`all_quantities`(sed_type)	All quantities allowed for a given sed type.
`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[, keepdims])	Create a set of FluxMaps by splitting along an axis.
`plot`([ax, sed_type, energy_power])	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.
`slice_by_idx`(slices)	Slice flux maps by idx
`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

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_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 (MapAxis)

energy_max#

Energy max

Returns

energy_maxQuantity: Upper bound of energy bin.

energy_min#

Energy min

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

geom#: Reference map geometry (Geom)

has_any_ts#: Whether the flux estimate has either sqrt(ts) or ts defined

has_stat_profiles#: Whether the flux estimate has stat 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_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_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 rom 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 (SkyModel)

reference_model_default#: Reference model default (SkyModel)

reference_spectral_model#: Reference spectral model (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#: ts map (Map)

ts_scan#: ts scan (Map)

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.

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

Create flux map dataset from list of HDUs.

Parameters

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

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: SED type of the input maps. Default is Likelihood
reference_modelSkyModel, optional: Reference model to use for conversions. Default in None. If None, a model consisting of a point source with a power law spectrum of index 2 is assumed.
gtiGTI: Maps GTI information. Default is None.
metadict: Meta dict.

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

Returns

flux_mapsFluxMaps: Stacked flux maps along axis.

classmethod from_table(table, sed_type=None, format='gadf-sed', 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”}: Sed type
format{“gadf-sed”, “lightcurve”, “profile”}: Table format.
reference_modelSpectralModel: Reference spectral model
gtiGTI: Good time intervals
metadict: Meta data.

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, keepdims=False)#

Create a set of FluxMaps by splitting along an axis.

Parameters

axis_namestr: Name of the axis to split on
keepdimsbool: Whether to keep the split axis with a single bin

Returns

flux_mapsFluxMap: FluxMap iteration

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

Plot flux points.

Parameters

axAxes: Axis object to plot on.
sed_type{“dnde”, “flux”, “eflux”, “e2dnde”}: Sed type
energy_powerfloat: Power of energy to multiply flux axis with
**kwargsdict: 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: Axis object to plot on.
sed_type{“dnde”, “flux”, “eflux”, “e2dnde”}: Sed type
add_cbarbool: Whether to add a colorbar to the plot.
**kwargsdict: Keyword arguments passed to pcolormesh

Returns

axAxes: Axis object

classmethod read(filename, sed_type=None, format='gadf-sed', reference_model=None, **kwargs)[source]#

Read precomputed flux points.

Parameters

filenamestr: Filename
sed_type{“dnde”, “flux”, “eflux”, “e2dnde”, “likelihood”}: Sed type
format{“gadf-sed”, “lightcurve”}: Format string.
reference_modelSpectralModel: Reference spectral model
**kwargsdict: 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 stat profiles must exist for the re-computation.

Parameters

n_sigma_ulint: Number of sigma to use for upper limit computation. Default is 2.
**kwargsdict: 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]]

slice_by_idx(slices)#

Slice flux maps by idx

Parameters

slicesdict: Dict 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.

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: sed type to convert to. Default is Likelihood
hdu_bandsstr: Name of the HDU with the BANDS table. Default is ‘BANDS’ If set to None, each map will have its own hdu_band

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”}: sed type to convert to. Default is Likelihood

Returns

mapsMaps: Maps object containing the requested maps.

to_table(sed_type=None, format='gadf-sed', 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”}

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.

formattedbool

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

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", format="gadf-sed", 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 / (cm2 s) 1 / (cm2 s) 1 / (cm2 s)
----- ----- ----- ----------- ----------- ----------- -------- ------- -----
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='gadf-sed', **kwargs)[source]#

Write flux points.

Parameters

filenamestr

Filename

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

Sed type

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

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.

**kwargsdict

Keyword arguments passed to astropy.table.Table.write.