CountsPredictor

class gammapy.spectrum.CountsPredictor(model, aeff=None, edisp=None, livetime=None, e_true=None)

Bases: object

Calculate npred

The true and reconstruced energy binning are inferred from the provided IRFs.

Parameters:

model : SpectralModel

Spectral model

aeff : EffectiveAreaTable

EffectiveArea

edisp : EnergyDispersion, optional

EnergyDispersion

livetime : Quantity

Observation duration (may be contained in aeff)

e_true : Quantity, optional

Desired energy axis of the prediced counts vector if no IRFs are given

Examples

Calculate prediced counts in a desired reconstruced energy binning

from gammapy.irf import EnergyDispersion, EffectiveAreaTable
from gammapy.spectrum import models, CountsPredictor
import numpy as np
import astropy.units as u
import matplotlib.pyplot as plt

e_true = np.logspace(-2,2.5,109) * u.TeV
e_reco = np.logspace(-2,2,73) * u.TeV

aeff = EffectiveAreaTable.from_parametrization(energy=e_true)
edisp = EnergyDispersion.from_gauss(e_true=e_true, e_reco=e_reco,
                                    sigma=0.3)

model = models.PowerLaw(index=2.3,
                        amplitude=2.5 * 1e-12 * u.Unit('cm-2 s-1 TeV-1'),
                        reference=1*u.TeV)

livetime = 1 * u.h

predictor = CountsPredictor(model=model,
                            aeff=aeff,
                            edisp=edisp,
                            livetime=livetime)
predictor.run()
predictor.npred.plot_hist()
plt.show()

(Source code, png, hires.png, pdf)

Methods Summary

apply_aeff()
apply_edisp()
integrate_model()	Integrate model in true energy space
run()

Methods Documentation

apply_aeff()

apply_edisp()

integrate_model()

Integrate model in true energy space

run()