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))
"""
# %%
# Example plot
# ------------
# Here is an example plot of the model:
from astropy import units as u
import matplotlib.pyplot as plt
from gammapy.modeling.models import Models, PowerLawSpectralModel, SkyModel
energy_range = [0.1, 100] * u.TeV
model = PowerLawSpectralModel(
index=2,
amplitude="1e-12 TeV-1 cm-2 s-1",
reference=1 * u.TeV,
)
model.plot(energy_range)
plt.grid(which="both")
# %%
# YAML representation
# -------------------
# Here is an example YAML file using the model:
model = SkyModel(spectral_model=model, name="power-law-model")
models = Models([model])
print(models.to_yaml())
# 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.
# In[ ]:
# 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 `~gammapy.modeling.models.ExpCutoffPowerLawSpectralModel` law to the data.
# In[ ]:
ecpl = ExpCutoffPowerLawSpectralModel(
index=1.8,
"""
# %%
# Example plot
# ------------
# Here is an example plot of the model:
from astropy import units as u
import matplotlib.pyplot as plt
from gammapy.modeling.models import Models, PowerLawSpectralModel, SkyModel
energy_bounds = [0.1, 100] * u.TeV
model = PowerLawSpectralModel(
index=2,
amplitude="1e-12 TeV-1 cm-2 s-1",
reference=1 * u.TeV,
)
model.plot(energy_bounds)
plt.grid(which="both")
# %%
# YAML representation
# -------------------
# Here is an example YAML file using the model:
model = SkyModel(spectral_model=model, name="power-law-model")
models = Models([model])
print(models.to_yaml())
