FluxPoints¶

class gammapy.spectrum.FluxPoints(table)[source]¶

Bases: object

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:	table : `Table` 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.spectrum import FluxPoints
filename = '$GAMMAPY_DATA/tests/spectrum/flux_points/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:

from astropy import units as u
from astropy.table import Table
from gammapy.spectrum import FluxPoints
from gammapy.modeling.models import PowerLawSpectralModel

table = Table()
pwl = PowerLawSpectralModel()
e_ref = np.logspace(0, 2, 7) * u.TeV
table['e_ref'] = e_ref
table['dnde'] = pwl(e_ref)
table.meta['SED_TYPE'] = 'dnde'

flux_points = FluxPoints(table)
flux_points.plot()

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.spectrum import FluxPoints

table = Table.read('$GAMMAPY_DATA/tests/spectrum/flux_points/flux_points_ctb_37b.txt',
                   format='ascii.csv', delimiter=' ', comment='#')
table.meta['SED_TYPE'] = 'dnde'
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(table)
flux_points.plot()

Attributes:	table : `Table` Table with flux point data

Attributes Summary

`e_edges`	Edges of the energy bin.
`e_max`	Upper bound of energy bin.
`e_min`	Lower bound of energy bin.
`e_ref`	Reference energy.
`is_ul`
`sed_type`	SED type (str).
`table_formatted`	Return formatted version of the flux points table.

Methods Summary

`drop_ul`(self)	Drop upper limit flux points.
`plot`(self[, ax, energy_unit, flux_unit, …])	Plot flux points.
`plot_likelihood`(self[, ax, energy_unit, …])	Plot likelihood SED profiles as a density plot..
`read`(filename, \\kwargs)	Read flux points.
`stack`(flux_points)	Create flux points by stacking list of flux points.
`to_sed_type`(self, sed_type[, method, model, …])	Convert to a different SED type (return new `FluxPoints`).
`write`(self, filename, \\kwargs)	Write flux points.

Attributes Documentation

e_edges¶

Edges of the energy bin.

Returns:	e_edges : `Quantity` Energy edges.

e_max¶

Upper bound of energy bin.

Defined by e_max column in table.

Returns:	e_max : `Quantity` Upper bound of energy bin.

e_min¶

Lower bound of energy bin.

Defined by e_min column in FluxPoints.table.

Returns:	e_min : `Quantity` Lower bound of energy bin.

e_ref¶

Reference energy.

Defined by e_ref column in FluxPoints.table or computed as log center, if e_min and e_max columns are present in FluxPoints.table.

Returns:	e_ref : `Quantity` Reference energy.

is_ul¶

sed_type¶

SED type (str).

One of: {‘dnde’, ‘e2dnde’, ‘flux’, ‘eflux’}

table_formatted¶: Return formatted version of the flux points table. Used for pretty printing

Methods Documentation

drop_ul(self)[source]¶

Drop upper limit flux points.

Returns:	flux_points : `FluxPoints` Flux points with upper limit points removed.

Examples

>>> from gammapy.spectrum import FluxPoints
>>> filename = '$GAMMAPY_DATA/tests/spectrum/flux_points/flux_points.fits'
>>> flux_points = FluxPoints.read(filename)
>>> print(flux_points)
FluxPoints(sed_type="flux", n_points=24)
>>> print(flux_points.drop_ul())
FluxPoints(sed_type="flux", n_points=19)

plot(self, ax=None, energy_unit='TeV', flux_unit=None, energy_power=0, **kwargs)[source]¶

Plot flux points.

Parameters:	ax : `Axes` Axis object to plot on. energy_unit : str, `Unit`, optional Unit of the energy axis flux_unit : str, `Unit`, optional Unit of the flux axis energy_power : int Power of energy to multiply y axis with kwargs : dict Keyword arguments passed to `matplotlib.pyplot.errorbar()`
Returns:	ax : `Axes` Axis object

plot_likelihood(self, ax=None, energy_unit='TeV', add_cbar=True, y_values=None, y_unit=None, **kwargs)[source]¶

Plot likelihood SED profiles as a density plot..

Parameters:

ax : Axes: Axis object to plot on.
energy_unit : str, Unit, optional: Unit of the energy axis
y_values : astropy.units.Quantity: Array of y-values to use for the likelihood profile evaluation.
y_unit : str or astropy.units.Unit: Unit to use for the y-axis.
add_cbar : bool: Whether to add a colorbar to the plot.
kwargs : dict: Keyword arguments passed to matplotlib.pyplot.pcolormesh()

Returns:

ax : Axes: Axis object

classmethod read(filename, **kwargs)[source]¶

Read flux points.

Parameters:	filename : str Filename kwargs : dict Keyword arguments passed to `astropy.table.Table.read`.

classmethod stack(flux_points)[source]¶

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:	flux_points : list of `FluxPoints` List of flux points to stack.
Returns:	flux_points : `FluxPoints` Flux points without upper limit points.

to_sed_type(self, sed_type, method='log_center', model=None, pwl_approx=False)[source]¶

Convert to a different SED type (return new FluxPoints).

See: https://ui.adsabs.harvard.edu/abs/1995NIMPA.355..541L for details on the 'lafferty' method.

Parameters:

sed_type : {‘dnde’}

SED type to convert to.

model : SpectralModel

Spectral model assumption. Note that the value of the amplitude parameter does not matter. Still it is recommended to use something with the right scale and units. E.g. amplitude = 1e-12 * u.Unit('cm-2 s-1 TeV-1')

method : {‘lafferty’, ‘log_center’, ‘table’}

Flux points e_ref estimation method:

'laferty' Lafferty & Wyatt model-based e_ref

'log_center' log bin center e_ref

'table' using column ‘e_ref’ from input flux_points

pwl_approx : bool

Use local power law appoximation at e_ref to compute differential flux from the integral flux. This method is used by the Fermi-LAT catalogs.

Returns:

flux_points : FluxPoints: Flux points including differential quantity columns dnde and dnde_err (optional), dnde_ul (optional).

Examples

>>> from gammapy.spectrum import FluxPoints
>>> from gammapy.modeling.models import PowerLawSpectralModel
>>> filename = '$GAMMAPY_DATA/tests/spectrum/flux_points/flux_points.fits'
>>> flux_points = FluxPoints.read(filename)
>>> model = PowerLawSpectralModel(index=2.2)
>>> flux_points_dnde = flux_points.to_sed_type('dnde', model=model)

write(self, filename, **kwargs)[source]¶

Write flux points.

Parameters:	filename : str Filename kwargs : dict Keyword arguments passed to `astropy.table.Table.write`.