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.
# In[ ]:
bkg_model = PowerLaw(index=2.5,
amplitude=1e-11 * u.Unit("cm-2 s-1 TeV-1"),
reference=1 * u.TeV)
# In[ ]:
#print(sim2.result)
sim2 = sim1
Indiv_best_fit_index = []
best_fit_index = []
i = 0
while i < n_obs:
sim_result = (sim1.result[i], sim2.result[i])
#FIT SPECTRA:
pwl.parameters['index'].parmax = 10
for obs in sim_result:
fit = SpectrumFit(obs, pwl.copy(), stat='wstat')
fit.model.parameters['index'].value = 2
fit.fit()
fit.est_errors()
Indiv_best_fit_index.append(
fit.result[0].model.parameters['index'].value)
#print(fit.result[0])
#print(' Indiv_best_fit_index = ', Indiv_best_fit_index)
#i+=1
#STACK SPECTRA 2 by 2
# Add the two spectra
obs_stacker = SpectrumObservationStacker(sim_result)
#print('sim_result is the list = ',sim_result)
obs_stacker.run()
# In[ ]:
model = PowerLaw(index=2,
amplitude=2e-11 * u.Unit("cm-2 s-1 TeV-1"),
reference=1 * u.TeV)
datasets_joint = extraction.spectrum_observations
for dataset in datasets_joint:
dataset.model = model
fit_joint = Fit(datasets_joint)
result_joint = fit_joint.run()
# we make a copy here to compare it later
model_best_joint = model.copy()
model_best_joint.parameters.covariance = result_joint.parameters.covariance
# In[ ]:
print(result_joint)
# In[ ]:
plt.figure(figsize=(8, 6))
ax_spectrum, ax_residual = datasets_joint[0].plot_fit()
ax_spectrum.set_ylim(0, 25)
# ## Compute Flux Points
#
# To round up our analysis we can compute flux points by fitting the norm of the global model in energy bands. We'll use a fixed energy binning for now:
print(pwl)
livetime = 2 * u.h
# In[5]:
sim = SpectrumSimulation(aeff=aeff,
edisp=edisp,
source_model=pwl,
livetime=livetime)
sim.simulate_obs(seed=2309, obs_id=1)
print(sim.obs)
# In[6]:
fit = SpectrumFit(obs_list=sim.obs, model=pwl.copy(), stat='cash')
fit.fit_range = [1, 10] * u.TeV
fit.fit()
fit.est_errors()
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.
# In[7]:
bkg_index = 2.5 * u.Unit('')
bkg_amplitude = 1e-11 * u.Unit('cm-2 s-1 TeV-1')
reference = 1 * u.TeV