def test_model_plot():
pars, errs = {}, {}
pars["amplitude"] = 1e-12 * u.Unit("TeV-1 cm-2 s-1")
pars["reference"] = 1 * u.Unit("TeV")
pars["index"] = 2 * u.Unit("")
errs["amplitude"] = 0.1e-12 * u.Unit("TeV-1 cm-2 s-1")
pwl = PowerLaw(**pars)
pwl.parameters.set_parameter_errors(errs)
with mpl_plot_check():
pwl.plot((1 * u.TeV, 10 * u.TeV))
with mpl_plot_check():
pwl.plot_error((1 * u.TeV, 10 * u.TeV))
# And print the result:
# In[ ]:
print(result_pwl)
# In[ ]:
print(pwl)
# Finally we plot the data points and the best fit model:
# In[ ]:
ax = flux_points.plot(energy_power=2)
pwl.plot(energy_range=[1e-4, 1e2] * u.TeV, ax=ax, energy_power=2)
# assign covariance for plotting
pwl.parameters.covariance = result_pwl.parameters.covariance
pwl.plot_error(energy_range=[1e-4, 1e2] * u.TeV, ax=ax, energy_power=2)
ax.set_ylim(1e-13, 1e-11)
# ## Exponential Cut-Off Powerlaw Fit
#
# Next we fit an [exponential cut-off power](https://docs.gammapy.org/0.11/api/gammapy.spectrum.models.ExponentialCutoffPowerLaw.html#gammapy.spectrum.models.ExponentialCutoffPowerLaw) law to the data.
# In[ ]:
ecpl = ExponentialCutoffPowerLaw(
index=1.8,
# First plot for the spectral flux
ax0, ax1 = spectrum_result.plot(
energy_range=joint_fit.result[0].fit_range,
fig_kwargs=dict(figsize=(8, 8)),
point_kwargs=dict(label="Flux points"),
fit_kwargs=dict(label="Gammapy fit"),
)
ax0.set_ylim([1e-14, 1e-7])
ax0.set_xlim([4e-2, 5e-1])
ax1.set_ylim([-1.7, 1.7])
spec_model_true.plot(
ax=ax0,
energy_range=joint_fit.result[0].fit_range,
label="Reference model",
c="black",
linestyle="dashed",
)
ax0.legend(loc="best")
ax0.set_ylabel(r"Flux [cm$^{-2}$ s$^1$ TeV$^{-1}$]", size=14)
ax1.set_ylabel("Residuals", size=14)
ax1.set_xlabel("Energy [TeV]", size=14)
ax1.set_xticks([5e-2, 1e-1, 3e-1])
ax1.set_xticklabels([5e-2, 1e-1, 3e-1])
# In[ ]:
# Second plot for the spectral energy flux
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)
result.model.plot_error(energy_range=energy_range, energy_power=2)
plt.show()
#now importing a background
#from a background model
bkg_index = 2.5 * u.Unit('')
bkg_amplitude = 1e-11 * u.Unit('cm-2 s-1 TeV-1')
reference = 1 * u.TeV
bkg_model = PowerLaw(index=bkg_index,
amplitude=bkg_amplitude,
reference=reference)
amplitude_ref = 0.57 * 19.4e-14 * u.Unit("1 / (cm2 s MeV)")
spec_model_true = PowerLaw(index=4.5,
amplitude=amplitude_ref,
reference="20 GeV")
flux_points_dataset = FluxPointsDataset(data=flux_points, model=model)
# Now we can plot.
# In[ ]:
plt.figure(figsize=(8, 6))
ax_spectrum, ax_residual = flux_points_dataset.peek()
ax_spectrum.set_ylim([1e-14, 3e-11])
ax_residual.set_ylim([-1.7, 1.7])
spec_model_true.plot(
ax=ax_spectrum,
energy_range=(emin_fit, emax_fit),
label="Reference model",
c="black",
linestyle="dashed",
energy_power=2,
)
ax_spectrum.legend(loc="best")
# This tutorial suffers a bit from the lack of statistics: there were 9 Vela observations in the CTA DC1 while there is only one here. When done on the 9 observations, the spectral analysis is much better agreement between the input model and the gammapy fit.