bkg_model = PowerLaw(index=bkg_index,
amplitude=bkg_amplitude,
reference=reference)
alpha = 0.2
n_obs = 1
seeds = np.arange(n_obs)
sim = SpectrumSimulation(aeff=aeff,
edisp=edisp,
source_model=model,
livetime=livetime,
background_model=bkg_model,
alpha=alpha)
sim.run(seeds)
print(sim.result)
print(sim.result[0])
n_on = [obs.total_stats.n_on for obs in sim.result]
n_off = [obs.total_stats.n_off for obs in sim.result]
excess = [obs.total_stats.excess for obs in sim.result]
fix, axes = plt.subplots(1, 3, figsize=(12, 4))
axes[0].hist(n_on)
axes[0].set_xlabel('n_on')
axes[1].hist(n_off)
axes[1].set_xlabel('n_off')
axes[2].hist(excess)
axes[2].set_xlabel('excess')
best_fit_index = []
#SIMULATE SPECTRA
livetime1 = time1 * u.h
livetime2 = time2 * u.h
n_obs = 100
seeds = np.arange(n_obs)
sim1 = SpectrumSimulation(aeff=aeff1,
edisp=edisp1,
source_model=pwl,
livetime=livetime1,
background_model=bkg_model,
alpha=alpha1)
sim1.run(seeds)
#print(sim1.result)
#sim2 = SpectrumSimulation(aeff=aeff2,
# edisp=edisp2,
# source_model=pwl,
# livetime=livetime2,
# background_model=bkg_model,
# alpha=alpha2)
#sim2.run(seeds)
#print(sim2.result)
sim2 = sim1
Indiv_best_fit_index = []