NaimaSpectralModel#

class gammapy.modeling.models.NaimaSpectralModel(radiative_model, distance=<Quantity 1. kpc>, seed=None, nested_models=None, use_cache=False)[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 as a Parameters object.

particle_distribution

Particle distribution.

pivot_energy

Pivot or decorrelation energy, for a given spectral model calculated numerically.

ssc_model

Synchrotron model.

tag

type

Methods Summary

__call__(energy)

Call self as a function.

copy(**kwargs)

Deep copy.

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 a given energy range.

evaluate(energy, **kwargs)

Evaluate the model.

evaluate_error(energy[, epsilon])

Evaluate spectral model with error propagation.

freeze()

Freeze all parameters.

from_dict(data, **kwargs)

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.

spectral_index_error(energy[, epsilon])

Evaluate the error on spectral index at the given energy.

to_dict([full_output])

Create dictionary 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 as a Parameters object.

particle_distribution#

Particle distribution.

pivot_energy#

Pivot or decorrelation energy, for a given spectral model calculated numerically.

It is defined as the energy at which the correlation between the spectral parameters is minimized.

Returns
pivot energyQuantity

The energy at which the statistical error in the computed flux is smallest. If no minimum is found, NaN will be returned.

ssc_model#

Synchrotron model.

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

Methods Documentation

__call__(energy)#

Call self as a function.

copy(**kwargs)#

Deep copy.

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 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, optional

Step size of the gradient evaluation. Given as a fraction of the parameter error. Default is 1e-4.

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, optional

Step size of the gradient evaluation. Given as a fraction of the parameter error. Default is 1e-4.

Returns
dnde, dnde_errortuple of Quantity

Tuple of flux and flux error.

freeze()#

Freeze all parameters.

classmethod from_dict(data, **kwargs)[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, optional

Step size of the gradient evaluation. Given as a fraction of the parameter error. Default is 1e-4.

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, optional

Lower energy bound of the roots finding. Default is 0.1 TeV.

energy_maxQuantity, optional

Upper energy bound of the roots finding. Default is 100 TeV.

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, optional

Lower energy bound of the roots finding. Default is 0.1 TeV.

energy_maxQuantity, optional

Upper energy bound of the roots finding. Default is 100 TeV.

Returns
energylist of Quantity

Each element contains 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.

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
energy_boundsQuantity

Plot energy bounds passed to MapAxis.from_energy_bounds.

axAxes, optional

Matplotlib axes. Default is None.

sed_type{“dnde”, “flux”, “eflux”, “e2dnde”}

Evaluation methods of the model. Default is “dnde”.

energy_powerint, optional

Power of energy to multiply flux axis with. Default is 0.

n_pointsint, optional

Number of evaluation nodes. Default is 100.

**kwargsdict

Keyword arguments forwarded to plot.

Returns
axAxes, optional

Matplotlib axes.

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
energy_boundsQuantity

Plot energy bounds passed to from_energy_bounds.

axAxes, optional

Matplotlib axes. Default is None.

sed_type{“dnde”, “flux”, “eflux”, “e2dnde”}

Evaluation methods of the model. Default is “dnde”.

energy_powerint, optional

Power of energy to multiply flux axis with. Default is 0.

n_pointsint, optional

Number of evaluation nodes. Default is 100.

**kwargsdict

Keyword arguments forwarded to matplotlib.pyplot.fill_between.

Returns
axAxes, optional

Matplotlib axes.

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, optional

Fractional energy increment to use for determining the spectral index. Default is 1e-5.

Returns
indexfloat

Estimated spectral index.

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

Evaluate the error on spectral index at the given energy.

Parameters
energyQuantity

Energy at which to estimate the index.

epsilonfloat, optional

Fractional energy increment to use for determining the spectral index. Default is 1e-5.

Returns
index, index_errortuple of float

Estimated spectral index and its error.

to_dict(full_output=True)[source]#

Create dictionary for YAML serialisation.

unfreeze()#

Restore parameters frozen status to default.