FluxPoints#

class gammapy.estimators.FluxPoints[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:
datadict of RegionNDMap

The maps dictionary. Expected entries are the following:

  • norm : the norm factor.

  • norm_err : optional, the error on the norm factor.

  • norm_errn : optional, the negative error on the norm factor.

  • norm_errp : optional, the positive error on the norm factor.

  • norm_ul : optional, the upper limit on the norm factor.

  • norm_scan : optional, the norm values of the test statistic scan.

  • stat_scan : optional, the test statistic scan values.

  • ts : optional, the delta test statistic associated with the flux value.

  • sqrt_ts : optional, the square root of the test statistic, when relevant.

  • success : optional, a boolean tagging the validity of the estimation.

  • n_dof : optional, the number of degrees of freedom used in TS computation

  • alpha : optional, normalisation factor to accounts for differences between the test region and the background

  • acceptance_off : optional, acceptance from the off region

  • acceptance_on : optional, acceptance from the on region

reference_modelSkyModel, optional

The 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.

metadict, optional

Dict of metadata.

gtiGTI, optional

Maps GTI information.

filter_success_nanboolean, optional

Set fitted norm and error to NaN when the fit has not succeeded.

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

Methods Summary

from_table(table[, sed_type, format, ...])

Create flux points from a table.

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.

to_table([sed_type, format, formatted])

Create table for a given SED type.

write(filename[, sed_type, format, ...])

Write flux points.

Methods Documentation

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.

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_ulfloat, optional

Number of sigma to use for upper limit computation. Must be a positive value. 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/estimators/crab_hess_fp/crab_hess_fp.fits"
>>> flux_points = FluxPoints.read(filename)
>>> flux_points_recomputed = flux_points.recompute_ul(n_sigma_ul=4)
>>> print(f'{flux_points.meta["n_sigma_ul"]}, {float(flux_points.flux_ul.data[1][0]):.2e}')
3.0, 3.99e-11
>>> print(f'{flux_points_recomputed.meta["n_sigma_ul"]}, {float(flux_points_recomputed.flux_ul.data[1][0]):.2e}')
4, 4.25e-11
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.

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.

__init__(data, reference_model, meta=None, gti=None, filter_success_nan=True)#
classmethod __new__(*args, **kwargs)#