def main(config_path, models_path, output, reference):
config = AnalysisConfig.read(config_path)
analysis = Analysis(config)
log.info(config)
analysis.get_observations()
log.info(analysis)
log.info(dir(analysis))
log.info(analysis.datasets)
log.info(analysis.datasets[0].counts)
analysis.get_datasets()
analysis.read_models(models_path)
# stacked fit and flux estimation
analysis.run_fit()
analysis.get_flux_points()
# Plot flux points
ax_sed, ax_residuals = analysis.flux_points.plot_fit()
if reference:
plot_kwargs = {
"energy_range": [
analysis.config.flux_points.energy.min,
analysis.config.flux_points.energy.max,
],
"energy_power": 2,
"flux_unit": "erg-1 cm-2 s-1",
}
create_crab_spectral_model(reference).plot(
**plot_kwargs, ax=ax_sed, label="Crab reference"
)
ax_sed.legend()
ax_sed.set_ylim(1e-12, 1e-9)
base_out = Path(output)
ax_sed.get_figure().savefig(base_out.with_suffix(".pdf").as_posix())
plt.clf()
analysis.models.write(base_out.with_suffix(".yaml").as_posix(), overwrite=True)
analysis.flux_points.write(
base_out.with_suffix(".fits").as_posix(), overwrite=True
)
ax_excess = analysis.datasets["stacked"].plot_excess()
ax_excess.get_figure().savefig(base_out.with_suffix(".excess.pdf").as_posix())
plt.clf()
config.datasets.stack = False
analysis.get_observations()
analysis.get_datasets()
analysis.read_models(models_path)
lc_maker_low = LightCurveEstimator(
energy_edges=[.2, 5] * u.TeV, source=config.flux_points.source, reoptimize=False
)
lc_low = lc_maker_low.run(analysis.datasets)
ax_lc = lc_low.plot(marker="o", label="1D")
ax_lc.get_figure().savefig(base_out.with_suffix(".lc.pdf").as_posix())
plt.clf()
def test_crab_spectrum(spec):
crab_spectrum = create_crab_spectral_model(reference=spec["name"])
dnde = crab_spectrum(2 * u.TeV)
assert_quantity_allclose(dnde, spec["dnde"])
flux = crab_spectrum.integral(1 * u.TeV, 1e3 * u.TeV)
assert_quantity_allclose(flux, spec["flux"])
index = crab_spectrum.spectral_index(2 * u.TeV)
assert_quantity_allclose(index, spec["index"], rtol=1e-5)
def test_invalid_format():
with pytest.raises(ValueError):
create_crab_spectral_model("spam")
# Finally, we compare the results of our stacked analysis to a previously published Crab Nebula Spectrum for reference. This is available in `gammapy.spectrum`.
# In[ ]:
plot_kwargs = {
"energy_range": [0.1, 30] * u.TeV,
"energy_power": 2,
"flux_unit": "erg-1 cm-2 s-1",
}
# plot stacked model
model_best_stacked.plot(**plot_kwargs, label="Stacked analysis result")
model_best_stacked.plot_error(**plot_kwargs)
# plot joint model
model_best_joint.plot(**plot_kwargs, label="Joint analysis result", ls="--")
model_best_joint.plot_error(**plot_kwargs)
create_crab_spectral_model().plot(**plot_kwargs, label="Crab reference")
plt.legend()
# ## Exercises
#
# Now you have learned the basics of a spectral analysis with Gammapy. To practice you can continue with the following exercises:
#
# - Fit a different spectral model to the data. You could try e.g. an `ExpCutoffPowerLawSpectralModel` or `LogParabolaSpectralModel` model.
# - Compute flux points for the stacked dataset.
# - Create a `FluxPointsDataset` with the flux points you have computed for the stacked dataset and fit the flux points again with obe of the spectral models. How does the result compare to the best fit model, that was directly fitted to the counts data?
# In[ ]:
# In[ ]:
total_stat = analysis.fit_result.total_stat
plt.plot(profile["values"], profile["likelihood"] - total_stat)
plt.xlabel("Lon (deg)")
plt.ylabel("Delta TS")
# ### Flux points
# In[ ]:
analysis.get_flux_points(source="crab")
# In[ ]:
plt.figure(figsize=(8, 5))
ax_sed, ax_residuals = analysis.flux_points.peek()
crab_spectrum = create_crab_spectral_model("hess_pl")
crab_spectrum.plot(
ax=ax_sed,
energy_range=[1, 10] * u.TeV,
energy_power=2,
flux_unit="erg-1 cm-2 s-1",
)
# ## Exercises
#
# - Run a spectral analysis using reflected regions without stacking the datasets. You can use `AnalysisConfig.from_template("1d")` to get an example configuration file. Add the resulting flux points to the SED plotted above.
#