NaimaSpectralModel#

class gammapy.modeling.models.NaimaSpectralModel(radiative_model, distance=<Quantity 1. kpc>, seed=None, nested_models=None)[source]#

Bases: gammapy.modeling.models.spectral.SpectralModel

A wrapper for Naima models.

For more information see Naima spectral model.

Parameters

radiative_modelBaseRadiative: An instance of a radiative model defined in models
distanceQuantity, optional: Distance to the source. If set to 0, the intrinsic differential luminosity will be returned. Default is 1 kpc
seedstr or list of str, optional: Seed photon field(s) to be considered for the radiative_model flux computation, in case of a InverseCompton model. It can be a subset of the seed_photon_fields list defining the radiative_model. Default is the whole list of photon fields
nested_modelsdict: Additional parameters for nested models not supplied by the radiative model, for now this is used only for synchrotron self-compton model

Attributes Summary

`covariance`
`default_parameters`
`frozen`	Frozen status of a model, True if all parameters are frozen
`include_ssc`	Whether the model includes an SSC component
`is_norm_spectral_model`	Whether model is a norm spectral model
`parameters`	Parameters (`Parameters`)
`particle_distribution`	Particle distribution
`ssc_model`	Synchrotron model
`tag`
`type`

Methods Summary

`__call__`(energy)	Call self as a function.
`copy`(**kwargs)
`energy_flux`(energy_min, energy_max, **kwargs)	Compute energy flux in given energy range.
`energy_flux_error`(energy_min, energy_max[, ...])	Evaluate the error of the energy flux of a given spectrum in
`evaluate`(energy, **kwargs)	Evaluate the model.
`evaluate_error`(energy[, epsilon])	Evaluate spectral model with error propagation.
`freeze`()	Freeze all parameters
`from_dict`(data)
`from_parameters`(parameters, **kwargs)	Create model from parameter list
`integral`(energy_min, energy_max, **kwargs)	Integrate spectral model numerically if no analytical solution defined.
`integral_error`(energy_min, energy_max[, epsilon])	Evaluate the error of the integral flux of a given spectrum in a given energy range.
`inverse`(value[, energy_min, energy_max])	Return energy for a given function value of the spectral model.
`inverse_all`(values[, energy_min, energy_max])	Return energies for multiple function values of the spectral model.
`plot`(energy_bounds[, ax, sed_type, ...])	Plot spectral model curve.
`plot_error`(energy_bounds[, ax, sed_type, ...])	Plot spectral model error band.
`reassign`(datasets_names, new_datasets_names)	Reassign a model from one dataset to another
`reference_fluxes`(energy_axis)	Get reference fluxes for a given energy axis.
`spectral_index`(energy[, epsilon])	Compute spectral index at given energy.
`to_dict`([full_output])	Create dict for YAML serialisation
`unfreeze`()	Restore parameters frozen status to default

Attributes Documentation

covariance#

default_parameters = <gammapy.modeling.parameter.Parameters object>#

frozen#: Frozen status of a model, True if all parameters are frozen

include_ssc#: Whether the model includes an SSC component

is_norm_spectral_model#: Whether model is a norm spectral model

parameters#: Parameters (Parameters)

particle_distribution#: Particle distribution

ssc_model#: Synchrotron model

tag = ['NaimaSpectralModel', 'naima']#

type#

Methods Documentation

__call__(energy)#: Call self as a function.

copy(**kwargs)#

energy_flux(energy_min, energy_max, **kwargs)#

Compute energy flux in given energy range.

\[G(E_{min}, E_{max}) = \int_{E_{min}}^{E_{max}} E \phi(E) dE\]

Parameters

energy_min, energy_maxQuantity: Lower and upper bound of integration range.
**kwargsdict: Keyword arguments passed to func:integrate_spectrum

energy_flux_error(energy_min, energy_max, epsilon=0.0001, **kwargs)#

Evaluate the error of the energy flux of a given spectrum in: a given energy range.

Parameters

energy_min, energy_maxQuantity: Lower and upper bound of integration range.
epsilonfloat: Step size of the gradient evaluation. Given as a fraction of the parameter error.

Returns

energy_flux, energy_flux_errtuple of Quantity: Energy flux and energy flux error between energy_min and energy_max.

evaluate(energy, **kwargs)[source]#

Evaluate the model.

Parameters

energyQuantity: Energy to evaluate the model at.

Returns

dndeQuantity: Differential flux at given energy.

evaluate_error(energy, epsilon=0.0001)#

Evaluate spectral model with error propagation.

Parameters

energyQuantity: Energy at which to evaluate
epsilonfloat: Step size of the gradient evaluation. Given as a fraction of the parameter error.

Returns

dnde, dnde_errortuple of Quantity: Tuple of flux and flux error.

freeze()#: Freeze all parameters

classmethod from_dict(data)[source]#

classmethod from_parameters(parameters, **kwargs)[source]#

Create model from parameter list

Parameters

parametersParameters: Parameters for init

Returns

modelModel: Model instance

integral(energy_min, energy_max, **kwargs)#

Integrate spectral model numerically if no analytical solution defined.

\[F(E_{min}, E_{max}) = \int_{E_{min}}^{E_{max}} \phi(E) dE\]

Parameters

energy_min, energy_maxQuantity: Lower and upper bound of integration range.
**kwargsdict: Keyword arguments passed to integrate_spectrum()

integral_error(energy_min, energy_max, epsilon=0.0001, **kwargs)#

Evaluate the error of the integral flux of a given spectrum in a given energy range.

Parameters

energy_min, energy_maxQuantity: Lower and upper bound of integration range.
epsilonfloat: Step size of the gradient evaluation. Given as a fraction of the parameter error.

Returns

flux, flux_errtuple of Quantity: Integral flux and flux error between energy_min and energy_max.

inverse(value, energy_min=<Quantity 0.1 TeV>, energy_max=<Quantity 100. TeV>)#

Return energy for a given function value of the spectral model.

Calls the scipy.optimize.brentq numerical root finding method.

Parameters

valueQuantity: Function value of the spectral model.
energy_minQuantity: Lower energy bound of the roots finding
energy_maxQuantity: Upper energy bound of the roots finding

Returns

energyQuantity: Energies at which the model has the given value.

inverse_all(values, energy_min=<Quantity 0.1 TeV>, energy_max=<Quantity 100. TeV>)#

Return energies for multiple function values of the spectral model.

Calls the scipy.optimize.brentq numerical root finding method.

Parameters

valuesQuantity: Function values of the spectral model.
energy_minQuantity: Lower energy bound of the roots finding
energy_maxQuantity: Upper energy bound of the roots finding

Returns

energylist of Quantity: each element contain the energies at which the model has corresponding value of values.

plot(energy_bounds, ax=None, sed_type='dnde', energy_power=0, n_points=100, **kwargs)#

Plot spectral model curve.

kwargs are forwarded to matplotlib.pyplot.plot

By default a log-log scaling of the axes is used, if you want to change the y axis scaling to linear you can use:

from gammapy.modeling.models import ExpCutoffPowerLawSpectralModel
from astropy import units as u

pwl = ExpCutoffPowerLawSpectralModel()
ax = pwl.plot(energy_bounds=(0.1, 100) * u.TeV)
ax.set_yscale('linear')

Parameters

axAxes, optional: Axis
energy_boundsQuantity: Plot energy bounds passed to MapAxis.from_energy_bounds
sed_type{“dnde”, “flux”, “eflux”, “e2dnde”}: Evaluation methods of the model
energy_powerint, optional: Power of energy to multiply flux axis with
n_pointsint, optional: Number of evaluation nodes
**kwargsdict: Keyword arguments forwarded to plot

Returns

axAxes, optional: Axis

plot_error(energy_bounds, ax=None, sed_type='dnde', energy_power=0, n_points=100, **kwargs)#

Plot spectral model error band.

Note

This method calls ax.set_yscale("log", nonpositive='clip') and ax.set_xscale("log", nonposx='clip') to create a log-log representation. The additional argument nonposx='clip' avoids artefacts in the plot, when the error band extends to negative values (see also https://github.com/matplotlib/matplotlib/issues/8623).

When you call plt.loglog() or plt.semilogy() explicitly in your plotting code and the error band extends to negative values, it is not shown correctly. To circumvent this issue also use plt.loglog(nonposx='clip', nonpositive='clip') or plt.semilogy(nonpositive='clip').

Parameters

axAxes, optional: Axis
energy_boundsQuantity: Plot energy bounds passed to MapAxis.from_energy_bounds
sed_type{“dnde”, “flux”, “eflux”, “e2dnde”}: Evaluation methods of the model
energy_powerint, optional: Power of energy to multiply flux axis with
n_pointsint, optional: Number of evaluation nodes
**kwargsdict: Keyword arguments forwarded to matplotlib.pyplot.fill_between

Returns

axAxes, optional: Axis

reassign(datasets_names, new_datasets_names)#

Reassign a model from one dataset to another

Parameters

datasets_namesstr or list: Name of the datasets where the model is currently defined
new_datasets_namesstr or list: Name of the datasets where the model should be defined instead. If multiple names are given the two list must have the save length, as the reassignment is element-wise.

Returns

modelModel: Reassigned model.

reference_fluxes(energy_axis)#

Get reference fluxes for a given energy axis.

Parameters

energy_axisMapAxis: Energy axis

Returns

fluxesdict of Quantity: Reference fluxes

spectral_index(energy, epsilon=1e-05)#

Compute spectral index at given energy.

Parameters

energyQuantity: Energy at which to estimate the index
epsilonfloat: Fractional energy increment to use for determining the spectral index.

Returns

indexfloat: Estimated spectral index.

to_dict(full_output=True)[source]#: Create dict for YAML serialisation

unfreeze()#: Restore parameters frozen status to default