Merge pull request #4734 from meeseeksmachine/auto-backport-of-pr-472…

…1-on-v1.1.x

Backport PR #4721 on branch v1.1.x (Exposing NormSpectralModels)

examples/models/spectral/plot_exp_cutoff_powerlaw_norm_spectral.py

@@ -0,0 +1,52 @@
+r"""
+.. _exp-cutoff-powerlaw-norm-spectral-model:
+
+Exponential cutoff power law norm spectral model
+========================
+
+This model parametrises a cutoff power law spectral correction with a norm parameter.
+
+"""
+
+# %%
+# 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 (
+    ExpCutoffPowerLawNormSpectralModel,
+    ExpCutoffPowerLawSpectralModel,
+    Models,
+    SkyModel,
+    TemplateSpectralModel,
+)
+
+energy_bounds = [0.1, 100] * u.TeV
+
+energy = [0.3, 1, 3, 10, 30] * u.TeV
+values = [40, 30, 20, 10, 1] * u.Unit("TeV-1 s-1 cm-2")
+template = TemplateSpectralModel(energy, values)
+norm = ExpCutoffPowerLawNormSpectralModel(
+    norm=2,
+    reference=1 * u.TeV,
+)
+
+template.plot(energy_bounds=energy_bounds, label="Template model")
+ecpl_norm = template * norm
+ecpl_norm.plot(
+    energy_bounds, label="Template model with ExpCutoffPowerLaw norm correction"
+)
+plt.legend(loc="best")
+plt.grid(which="both")
+
+# %%
+# YAML representation
+# -------------------
+# Here is an example YAML file using the model:
+
+model = SkyModel(spectral_model=ecpl_norm, name="exp-cutoff-power-law-norm-model")
+models = Models([model])
+
+print(models.to_yaml())

examples/models/spectral/plot_logparabola_norm_spectral.py

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+r"""
+.. _logparabola-spectral-norm-model:
+
+Log parabola spectral norm model
+===========================
+
+This model parametrises a log parabola spectral correction with a norm parameter.
+
+"""
+
+# %%
+# 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 (
+    LogParabolaNormSpectralModel,
+    LogParabolaSpectralModel,
+    Models,
+    SkyModel,
+    TemplateSpectralModel,
+)
+
+energy_bounds = [0.1, 100] * u.TeV
+
+energy = [0.3, 1, 3, 10, 30] * u.TeV
+values = [40, 30, 20, 10, 1] * u.Unit("TeV-1 s-1 cm-2")
+template = TemplateSpectralModel(energy, values)
+norm = LogParabolaNormSpectralModel(
+    norm=1.5,
+    reference=1 * u.TeV,
+)
+
+template.plot(energy_bounds=energy_bounds, label="Template model")
+lp_norm = template * norm
+lp_norm.plot(energy_bounds, label="Template model with LogParabola norm correction")
+plt.legend(loc="best")
+plt.grid(which="both")
+
+
+# %%
+# YAML representation
+# -------------------
+# Here is an example YAML file using the model:
+
+model = SkyModel(spectral_model=lp_norm, name="log-parabola-norm-model")
+models = Models([model])
+
+print(models.to_yaml())

examples/models/spectral/plot_powerlaw_norm_spectral.py

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+r"""
+.. _powerlaw-spectral-norm-model:
+
+Power law norm spectral model
+========================
+
+This model parametrises a power law spectral correction with a norm and tilt parameter.
+
+"""
+
+# %%
+# 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,
+    PowerLawNormSpectralModel,
+    PowerLawSpectralModel,
+    SkyModel,
+    TemplateSpectralModel,
+)
+
+energy_bounds = [0.1, 100] * u.TeV
+
+energy = [0.3, 1, 3, 10, 30] * u.TeV
+values = [40, 30, 20, 10, 1] * u.Unit("TeV-1 s-1 cm-2")
+template = TemplateSpectralModel(energy, values)
+norm = PowerLawNormSpectralModel(
+    norm=5,
+    reference=1 * u.TeV,
+)
+
+template.plot(energy_bounds=energy_bounds, label="Template model")
+pwl_norm = template * norm
+pwl_norm.plot(energy_bounds, label="Template model with PowerLaw norm correction")
+plt.legend(loc="best")
+plt.grid(which="both")
+
+# %%
+# YAML representation
+# -------------------
+# Here is an example YAML file using the model:
+
+model = SkyModel(spectral_model=pwl_norm, name="power-law-norm-model")
+models = Models([model])
+
+print(models.to_yaml())