def simulate_spectrum_dataset(model, random_state=0):
energy = np.logspace(-0.5, 1.5, 21) * u.TeV
aeff = EffectiveAreaTable.from_parametrization(energy=energy)
bkg_model = PowerLawSpectralModel(index=2.5, amplitude="1e-12 cm-2 s-1 TeV-1")
dataset = SpectrumDatasetOnOff(
aeff=aeff, model=model, livetime=100 * u.h, acceptance=1, acceptance_off=5
)
eval = SpectrumEvaluator(model=bkg_model, aeff=aeff, livetime=100 * u.h)
bkg_model = eval.compute_npred()
dataset.fake(random_state=random_state, background_model=bkg_model)
return dataset
def test_counts_predictor(case):
opts = case.copy()
del opts["npred"]
predictor = SpectrumEvaluator(**opts)
actual = predictor.compute_npred().total_counts
assert_allclose(actual, case["npred"])
from gammapy.irf import EnergyDispersion, EffectiveAreaTable
from gammapy.spectrum import models, SpectrumEvaluator
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, bias=0)
model = models.PowerLaw(index=2.3,
amplitude="2.5e-12 cm-2 s-1 TeV-1",
reference="1 TeV")
livetime = 1 * u.h
predictor = SpectrumEvaluator(model=model,
aeff=aeff,
edisp=edisp,
livetime=livetime)
predictor.compute_npred().plot_hist()
plt.show()
# Take a quick look at the simulated counts
dataset.counts.plot()
# ## Include Background
#
# In this section we will include a background component. Furthermore, we will also simulate more than one observation and fit each one individually in order to get average fit results.
# In[ ]:
# We assume a PowerLaw shape of the background as well
bkg_model = PowerLaw(index=2.5,
amplitude=1e-11 * u.Unit("cm-2 s-1 TeV-1"),
reference=1 * u.TeV)
evaluator = SpectrumEvaluator(model=bkg_model, aeff=aeff, livetime=livetime)
npred_bkg = evaluator.compute_npred()
# In[ ]:
dataset = SpectrumDatasetOnOff(
aeff=aeff,
edisp=edisp,
model=model_ref,
livetime=livetime,
acceptance=1,
acceptance_off=5,
)
# In[ ]: