def test_model_plot_sed_type():
pwl = PowerLawSpectralModel(
amplitude=1e-12 * u.Unit("TeV-1 cm-2 s-1"), reference=1 * u.Unit("TeV"), index=2
)
pwl.amplitude.error = 0.1e-12 * u.Unit("TeV-1 cm-2 s-1")
with mpl_plot_check():
ax1 = pwl.plot((1 * u.TeV, 100 * u.TeV), sed_type="dnde")
ax2 = pwl.plot_error((1 * u.TeV, 100 * u.TeV), sed_type="dnde")
assert ax1.axes.axes.get_ylabel() == "dnde [1 / (cm2 s TeV)]"
assert ax2.axes.axes.get_ylabel() == "dnde [1 / (cm2 s TeV)]"
with mpl_plot_check():
ax1 = pwl.plot((1 * u.TeV, 100 * u.TeV), sed_type="e2dnde")
ax2 = pwl.plot_error((1 * u.TeV, 100 * u.TeV), sed_type="e2dnde")
assert ax1.axes.axes.get_ylabel() == "e2dnde [erg / (cm2 s)]"
assert ax2.axes.axes.get_ylabel() == "e2dnde [erg / (cm2 s)]"
with mpl_plot_check():
ax1 = pwl.plot((1 * u.TeV, 100 * u.TeV), sed_type="flux")
ax2 = pwl.plot_error((1 * u.TeV, 100 * u.TeV), sed_type="flux")
assert ax1.axes.axes.get_ylabel() == "flux [1 / (cm2 s)]"
assert ax2.axes.axes.get_ylabel() == "flux [1 / (cm2 s)]"
with mpl_plot_check():
ax1 = pwl.plot((1 * u.TeV, 100 * u.TeV), sed_type="eflux")
ax2 = pwl.plot_error((1 * u.TeV, 100 * u.TeV), sed_type="eflux")
assert ax1.axes.axes.get_ylabel() == "eflux [erg / (cm2 s)]"
assert ax2.axes.axes.get_ylabel() == "eflux [erg / (cm2 s)]"
def test_model_plot():
pwl = PowerLawSpectralModel(amplitude=1e-12 * u.Unit("TeV-1 cm-2 s-1"),
reference=1 * u.Unit("TeV"),
index=2)
pwl.parameters.set_error(amplitude=0.1e-12 * u.Unit("TeV-1 cm-2 s-1"))
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))
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 = PowerLawSpectralModel(**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))
# 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 `~gammapy.modeling.models.ExpCutoffPowerLawSpectralModel` law to the data.
# In[ ]:
ecpl = ExpCutoffPowerLawSpectralModel(
index=1.8,
amplitude="2e-12 cm-2 s-1 TeV-1",
reference="1 TeV",
lambda_="0.1 TeV-1",
)
model = SkyModel(spectral_model=ecpl)
