def spectral_model(self):
"""Best fit spectral model (`~gammapy.modeling.models.SpectralModel`)."""
spec_type = self.data["SpectrumType"].strip()
pars, errs = {}, {}
pars["reference"] = self.data["Pivot_Energy"]
if spec_type == "PowerLaw":
pars["amplitude"] = self.data["PL_Flux_Density"]
pars["index"] = self.data["PL_Index"]
errs["amplitude"] = self.data["Unc_PL_Flux_Density"]
errs["index"] = self.data["Unc_PL_Index"]
model = PowerLawSpectralModel(**pars)
elif spec_type == "LogParabola":
pars["amplitude"] = self.data["LP_Flux_Density"]
pars["alpha"] = self.data["LP_Index"]
pars["beta"] = self.data["LP_beta"]
errs["amplitude"] = self.data["Unc_LP_Flux_Density"]
errs["alpha"] = self.data["Unc_LP_Index"]
errs["beta"] = self.data["Unc_LP_beta"]
model = LogParabolaSpectralModel(**pars)
elif spec_type == "PLSuperExpCutoff":
pars["amplitude"] = self.data["PLEC_Flux_Density"]
pars["index_1"] = self.data["PLEC_Index"]
pars["index_2"] = self.data["PLEC_Exp_Index"]
pars["expfactor"] = self.data["PLEC_Expfactor"]
errs["amplitude"] = self.data["Unc_PLEC_Flux_Density"]
errs["index_1"] = self.data["Unc_PLEC_Index"]
errs["index_2"] = np.nan_to_num(self.data["Unc_PLEC_Exp_Index"])
errs["expfactor"] = self.data["Unc_PLEC_Expfactor"]
model = SuperExpCutoffPowerLaw4FGLSpectralModel(**pars)
else:
raise ValueError(f"Invalid spec_type: {spec_type!r}")
model.parameters.set_parameter_errors(errs)
return model
model=ExpCutoffPowerLaw3FGLSpectralModel(
index=2.3 * u.Unit(""),
amplitude=4 / u.cm ** 2 / u.s / u.TeV,
reference=1 * u.TeV,
ecut=10 * u.TeV,
),
val_at_2TeV=u.Quantity(0.7349563611124971, "cm-2 s-1 TeV-1"),
integral_1_10TeV=u.Quantity(2.6034046173089, "cm-2 s-1"),
eflux_1_10TeV=u.Quantity(5.340285560055799, "TeV cm-2 s-1"),
),
dict(
name="plsec_4fgl",
model=SuperExpCutoffPowerLaw4FGLSpectralModel(
index_1=1.5,
index_2=2,
amplitude=1 / u.cm ** 2 / u.s / u.TeV,
reference=1 * u.TeV,
expfactor=1e-2,
),
val_at_2TeV=u.Quantity(0.3431043087721737, "cm-2 s-1 TeV-1"),
integral_1_10TeV=u.Quantity(1.2125247, "cm-2 s-1"),
eflux_1_10TeV=u.Quantity(3.38072082, "TeV cm-2 s-1"),
),
dict(
name="logpar",
model=LogParabolaSpectralModel(
alpha=2.3 * u.Unit(""),
amplitude=4 / u.cm ** 2 / u.s / u.TeV,
reference=1 * u.TeV,
beta=0.5 * u.Unit(""),
),
# ------------
# 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,
SkyModel,
SuperExpCutoffPowerLaw4FGLSpectralModel,
)
energy_range = [0.1, 100] * u.TeV
model = SuperExpCutoffPowerLaw4FGLSpectralModel(
index_1=1,
index_2=2,
amplitude="1e-12 TeV-1 cm-2 s-1",
reference="1 TeV",
expfactor=1e-2,
)
model.plot(energy_range)
plt.grid(which="both")
plt.ylim(1e-24, 1e-10)
# %%
# YAML representation
# -------------------
# Here is an example YAML file using the model:
model = SkyModel(spectral_model=model, name="super-exp-cutoff-power-law-4fgl-model")
models = Models([model])
