spectrum - 1D spectrum analysis¶

Introduction¶

gammapy.spectrum holds functions and classes related to 1D region based spectral analysis. This includes also simulation tools.

The basic of 1D spectral analysis are explained in this talk. A good reference for the forward-folding on-off likelihood fitting methods is Section 7.5 “Spectra and Light Curves” in [Naurois2012], in publications usually the reference [Piron2001] is used. A standard reference for the unfolding method is [Albert2007].

Getting Started¶

The following code snippet demonstrates how to load an observation stored in OGIP format and fit a spectral model.

from gammapy.spectrum import SpectrumDatasetOnOff
from gammapy.modeling import Fit
from gammapy.modeling.models import PowerLawSpectralModel

filename = '$GAMMAPY_DATA/joint-crab/spectra/hess/pha_obs23523.fits'
dataset = SpectrumDatasetOnOff.from_ogip_files(filename)

model = PowerLawSpectralModel(
    index=2,
    amplitude='1e-12 cm-2 s-1 TeV-1',
    reference='1 TeV',
)

dataset.model = model

fit = Fit([dataset])
result = fit.run()
model.parameters.covariance = result.parameters.covariance
print(model)

It will print the following output to the console:

PowerLawSpectralModel

Parameters:

       name     value     error        unit      min max frozen
    --------- --------- --------- -------------- --- --- ------
        index 2.817e+00 1.496e-01                nan nan  False
    amplitude 5.142e-11 6.423e-12 cm-2 s-1 TeV-1 nan nan  False
    reference 1.000e+00 0.000e+00            TeV nan nan   True

Covariance:

       name     index   amplitude reference
    --------- --------- --------- ---------
        index 2.239e-02 6.160e-13 0.000e+00
    amplitude 6.160e-13 4.126e-23 0.000e+00
    reference 0.000e+00 0.000e+00 0.000e+00

Using `gammapy.spectrum`¶

For more advanced use cases please go to the tutorial notebooks:

spectrum_simulation.html - simulate and fit 1D spectra using pre-defined or a user-defined model.
spectrum_analysis.html - spectral analysis starting from event lists and field-of-view IRFs.

The following pages describe gammapy.spectrum in more detail:

Reference/API¶

gammapy.spectrum Package¶

1D spectrum analysis.

Functions¶

plot_spectrum_datasets_off_regions(datasets)

Plot spectrum datasets of regions.

Classes¶

`CountsSpectrum`(energy_lo, energy_hi[, data, …])	Generic counts spectrum.
`FluxPoints`(table)	Flux points container.
`FluxPointsDataset`(models, data[, mask_fit, …])	Fit a set of flux points with a parametric model.
`FluxPointsEstimator`(datasets, e_edges[, …])	Flux points estimator.
`PhaseBackgroundMaker`(on_phase, off_phase)	Background estimation with on and off phases.
`ReflectedRegionsBackgroundMaker`([…])	Reflected regions background maker.
`ReflectedRegionsFinder`(region, center[, …])	Find reflected regions.
`SensitivityEstimator`([spectrum, sigma, …])	Estimate differential sensitivity.
`SpectrumDataset`([models, counts, livetime, …])	Spectrum dataset for likelihood fitting.
`SpectrumDatasetMaker`([…])	Make spectrum for a single IACT observation.
`SpectrumDatasetOnOff`([models, counts, …])	Spectrum dataset for on-off likelihood fitting.