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 = []