# ## Power law
#
# In this section we will simulate one observation using a power law model.
# In[ ]:
pwl = PowerLaw(index=2.3,
amplitude=1e-11 * u.Unit("cm-2 s-1 TeV-1"),
reference=1 * u.TeV)
print(pwl)
# In[ ]:
livetime = 2 * u.h
sim = SpectrumSimulation(aeff=aeff,
edisp=edisp,
source_model=pwl,
livetime=livetime)
sim.simulate_obs(seed=2309, obs_id=1)
print(sim.obs)
# In[ ]:
fit = SpectrumFit(obs_list=sim.obs, model=pwl.copy(), stat="cash")
fit.fit_range = [1, 10] * u.TeV
fit.run()
print(fit.result[0])
# ## Include background
#
# In this section we will include a background component. Furthermore, we will also simulate more than one observation and fit each one individuallt in order to get average fit results.
amplitude = 2.5 * 1e-10 * u.Unit('cm-2 s-1 TeV-1')
reference = 1 * u.TeV
model = PowerLaw(index=index, amplitude=amplitude, reference=reference)
edisp = edisp.to_energy_dispersion(offset=offset)
aeff = aeff.to_effective_area_table(offset=offset)
#fig, axes = plt.subplots(1, 2, figsize=(12, 6))
#edisp.plot_matrix(ax=axes[0])
#aeff.plot(ax=axes[1])
#plt.show()
aeff.lo_threshold = lo_threshold
aeff.hi_threshold = hi_threshold
sim = SpectrumSimulation(aeff=aeff,
edisp=edisp,
source_model=model,
livetime=livetime)
sim.simulate_obs(seed=42, obs_id=0)
sim.obs.peek()
print sim.obs
plt.show()
fit = SpectrumFit(obs_list=sim.obs, model=model, stat='cash')
fit.run()
result = fit.result[0]
print result
energy_range = [0.1, 100] * u.TeV
model.plot(energy_range=energy_range, energy_power=2)
result.model.plot(energy_range=energy_range, energy_power=2)
plt.loglog()
print(cta_irf["aeff"].data)
# In[ ]:
edisp = cta_irf["edisp"].to_energy_dispersion(offset=offset,
e_true=energy,
e_reco=energy)
edisp.plot_matrix()
print(edisp.data)
# In[ ]:
# Simulate data
sim = SpectrumSimulation(aeff=aeff,
edisp=edisp,
source_model=model,
livetime=livetime)
sim.simulate_obs(seed=42, obs_id=0)
# In[ ]:
sim.obs.peek()
print(sim.obs)
# ## Spectral analysis
#
# Now that we have some simulated CTA counts spectrum, let's analyse it.
# In[ ]:
# Fit data
bkg_model = PowerLaw(index=bkg_index,
amplitude=bkg_amplitude,
reference=reference)
#======================================
#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)
aeff.lo_threshold = lo_threshold
aeff.hi_threshold = hi_threshold
get_ipython().magic(u'pinfo fakerun.bkg')
inubilutbkh=fakerun.bkg()
inubilutbkh=fakerun.bkg.evaluate_at_offset(offset=offset)
inubuiltbkg=fakerun.bkg.evaluate_at_offset(offset=offset)
inbuiltbkg=fakerun.bkg.evaluate_at_offset(offset=offset)
fig, axes = plt.subplots(1, 2, figsize=(12, 6)
)
edisp.plot_matrix(ax=axes[0])
aeff.plot(ax=axes[1])
plt.show()
aeff.lo_threshold = lo_threshold
aeff.hi_threshold = hi_threshold
sim = SpectrumSimulation(aeff=aeff, edisp=edisp, source_model=model, livetime=livetime)
sim.simulate_obs(seed=42, obs_id=0)
sim.obs.peek()
print sim.obs
plt.show()
amplitude = 2.5 * 1e-10 * u.Unit('cm-2 s-1 TeV-1')
sim = SpectrumSimulation(aeff=aeff, edisp=edisp, source_model=model, livetime=livetime)
sim.simulate_obs(seed=42, obs_id=0)
sim.obs.peek()
print sim.obs
plt.show()
amplitude
model = PowerLaw(index=index, amplitude=amplitude, reference=reference)
sim = SpectrumSimulation(aeff=aeff, edisp=edisp, source_model=model, livetime=livetime)
sim.simulate_obs(seed=42, obs_id=0)
sim.obs.peek()
aeff.hi_threshold = hi_threshold
#inbuiltbkg=fakerun.bkg.evaluate_at_offset(offset=offset)
#fig, axes = plt.subplots(1, 2, figsize=(12, 6))
#edisp.plot_matrix(ax=axes[0])
#aeff.plot(ax=axes[1])
#plt.show()
#aeff.lo_threshold = lo_threshold
#aeff.hi_threshold = hi_threshold
n_obs=1
seeds = np.arange(n_obs)
sim = SpectrumSimulation(aeff=aeff, edisp=edisp, source_model=model, livetime=livetime_src)
sim.run(seeds)
n_on=sim.result[0].total_stats.n_on
# now fitting different backgrounds.
EXCLUSION_FILE = '$GAMMAPY_EXTRA/datasets/exclusion_masks/tevcat_exclusion.fits'
allsky_mask = SkyImage.read(EXCLUSION_FILE)
exclusion_mask = allsky_mask.cutout(
position=src,
size=Angle('6 deg'),
)
on_region=CircleSkyRegion(src,0.11 * u.deg)
background_estimator = ReflectedRegionsBackgroundEstimator(obs_list=mylist, on_region=on_region, exclusion_mask = exclusion_mask)