modeling - Models and fitting

Introduction

gammapy.modeling contains all the functionality related to modeling and fitting data. This includes spectral, spatial and temporal model classes, as well as the fit and parameter API.

Assuming you have prepared your gamma-ray data as a set of Dataset objects, and stored one or more datasets in a Datasets container, you are all set for modeling and fitting. Either via a YAML config file, or via Python code, define the Models to use, which is a list of SkyModel objects representing additive emission components, usually sources or diffuse emission, although a single source can also be modeled by multiple components if you want. The SkyModel is a factorised model with a SpectralModel component and a SpatialModel component. Most commonly used models in gamma-ray astronomy are built-in, see the Model gallery. It is easy to create user-defined models and datasets, Gammapy is very flexible.

The Fit class provides methods to fit, i.e. optimise parameters and estimate parameter errors and correlations. It interfaces with a Datasets object, which in turn is connected to a Models object, which has a Parameters object, which contains the model parameters. Currently iminuit is used as modeling and fitting backend, in the future we plan to support other optimiser and error estimation methods, e.g. from scipy. Models can be unique for a given dataset, or contribute to multiple datasets and thus provide links, allowing e.g. to do a joint fit to multiple IACT datasets, or to a joint IACT and Fermi-LAT dataset. Many examples are given in the tutorials.

Getting Started

In the following you will see how to fit spectral data in OGIP format. The format is described at 1D counts spectra. An example dataset is available in the $GAMMAPY_DATA repo. For a description of the available fit statstics see Fit statistics.

The following example shows how to fit a power law simultaneously to two simulated crab runs using the Fit class.

from gammapy.datasets import SpectrumDatasetOnOff
from gammapy.modeling import Fit
from gammapy.modeling.models import PowerLawSpectralModel
import matplotlib.pyplot as plt

path = "$GAMMAPY_DATA/joint-crab/spectra/hess/"
obs_1 = SpectrumDatasetOnOff.from_ogip_files(path + "pha_obs23523.fits")
obs_2 = SpectrumDatasetOnOff.from_ogip_files(path + "pha_obs23592.fits")

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

obs_1.model = model
obs_2.model = model

fit = Fit([obs_1, obs_2])
result = fit.run()

model.parameters.covariance = result.parameters.covariance

You can check the fit results by looking at the result and model object:

>>> print(result)

    OptimizeResult

    backend    : minuit
    method     : minuit
    success    : True
    nfev       : 115
    total stat : 65.36
    message    : Optimization terminated successfully.


>>> print(model)

    PowerLawSpectralModel

    Parameters:

           name     value     error        unit      min max frozen
        --------- --------- --------- -------------- --- --- ------
            index 2.781e+00 1.120e-01                nan nan  False
        amplitude 5.201e-11 4.965e-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 1.255e-02 3.578e-13 0.000e+00
        amplitude 3.578e-13 2.465e-23 0.000e+00
        reference 0.000e+00 0.000e+00 0.000e+00

Tutorials

Tutorials that show examples using gammapy.modeling:

Reference/API

gammapy.modeling Package

Models and fitting.

Functions

plot_corner(sampler, dataset[, nburn])

Corner plot for each parameter explored by the walkers.

plot_trace(sampler, dataset)

Plot the trace of walkers for every steps

run_mcmc(dataset[, nwalkers, nrun, threads])

Run the MCMC sampler.

uniform_prior(value, umin, umax)

Uniform prior distribution.

Classes

Covariance(parameters[, data])

Parameter covariance class

Fit(datasets[, store_trace])

Fit class.

Parameter(name, value[, unit, scale, min, …])

A model parameter.

Parameters([parameters])

Parameters container.

gammapy.modeling.models Package

Built-in models in Gammapy.

Functions

create_crab_spectral_model([reference])

Create a Crab nebula reference spectral model.

create_cosmic_ray_spectral_model([particle])

Cosmic a cosmic ray spectral model at Earth.

Classes

Models([models])

Sky model collection.

SkyModel(spectral_model[, spatial_model, …])

Sky model component.

SpatialModel(**kwargs)

Spatial model base class.

SpectralModel(**kwargs)

Spectral model base class.

TemporalModel(**kwargs)

Temporal model base class.

ConstantSpatialModel(**kwargs)

Spatially constant (isotropic) spatial model.

TemplateSpatialModel(map[, meta, normalize, …])

Spatial sky map template model.

DiskSpatialModel(**kwargs)

Constant disk model.

GaussianSpatialModel(**kwargs)

Two-dimensional Gaussian model.

GeneralizedGaussianSpatialModel(**kwargs)

Two-dimensional Generealized Gaussian model.

PointSpatialModel(**kwargs)

Point Source.

ShellSpatialModel(**kwargs)

Shell model.

ConstantSpectralModel(**kwargs)

Constant model.

CompoundSpectralModel(model1, model2, operator)

Arithmetic combination of two spectral models.

PowerLawSpectralModel(**kwargs)

Spectral power-law model.

PowerLaw2SpectralModel(**kwargs)

Spectral power-law model with integral as amplitude parameter.

BrokenPowerLawSpectralModel(**kwargs)

Spectral broken power-law model.

SmoothBrokenPowerLawSpectralModel(**kwargs)

Spectral smooth broken power-law model.

PiecewiseNormSpectralModel(energy[, norms, …])

Piecewise spectral correction

ExpCutoffPowerLawSpectralModel(**kwargs)

Spectral exponential cutoff power-law model.

ExpCutoffPowerLaw3FGLSpectralModel(**kwargs)

Spectral exponential cutoff power-law model used for 3FGL.

SuperExpCutoffPowerLaw3FGLSpectralModel(**kwargs)

Spectral super exponential cutoff power-law model used for 3FGL.

SuperExpCutoffPowerLaw4FGLSpectralModel(**kwargs)

Spectral super exponential cutoff power-law model used for 4FGL.

LogParabolaSpectralModel(**kwargs)

Spectral log parabola model.

TemplateSpectralModel(energy, values[, …])

A model generated from a table of energy and value arrays.

GaussianSpectralModel(**kwargs)

Gaussian spectral model.

EBLAbsorptionNormSpectralModel(energy, …)

Gamma-ray absorption models.

NaimaSpectralModel(radiative_model[, …])

A wrapper for Naima models.

ScaleSpectralModel(model[, norm])

Wrapper to scale another spectral model by a norm factor.

PowerLawNormSpectralModel(**kwargs)

Spectral power-law model with normalized amplitude parameter.

LogParabolaNormSpectralModel(**kwargs)

Norm spectral log parabola model.

ExpCutoffPowerLawNormSpectralModel(**kwargs)

Norm spectral exponential cutoff power-law model.

ConstantTemporalModel(**kwargs)

Constant temporal model.

LightCurveTemplateTemporalModel(table[, …])

Temporal light curve model.

ExpDecayTemporalModel(**kwargs)

Temporal model with an exponential decay.

GaussianTemporalModel(**kwargs)

A Gaussian temporal profile

Variables

MODEL_REGISTRY

Registry of model classes

SPATIAL_MODEL_REGISTRY

Registry of spatial model classes.

TEMPORAL_MODEL_REGISTRY

Registry of temporal models classes.

SPECTRAL_MODEL_REGISTRY

Registry of spectral model classes.