Spectral Parameters (parameters)

This module contains classes that support recording and containing spectral parameter information resulting from spectral fits.

Fluxes and Fluences

There are four specialized classes that inherit from Parameter: PhotonFlux, PhotonFluence, EnergyFlux, and EnergyFluence. These classes provide some small special functionality compared to the Parameter base class. For example, when we create a PhotonFlux, we must specify the energy range over which the PhotonFlux is valid:

>>> from gdt.core.spectra.parameters import PhotonFlux
>>> # flux of 5 +/- 1 photons/cm^2/s over 50-300 keV
>>> pflux = PhotonFlux(5.0, 1.0, (50.0, 300.0))
>>> print(pflux)
Photon Flux: 5.00 +/- 1.00 ph/cm^2/s

Note that the name, units, and support are already defined:

>>> pflux.units
'ph/cm^2/s'
>>> pflux.support
(0.0, inf)
>>> pflux.energy_range
(50.0, 300.0)

The same applies to the other flux and fluence parameters.

Detector Fit Info

For a spectral fit, it is generally useful to collect metadata about the characteristics fo the detector and data used in the fit. To that end, DetectorData is primarily a container class that bundles together this information. To create a DetectorData object, we start out with the instrument name, detector name, data type, and number of energy channels.

>>> from gdt.core.spectra.parameters import DetectorData
>>> det_data = DetectorData.from_data('Fermi, GBM', 'NAI_00', 'TTE', 128)
>>> det_data
<DetectorData: NAI_00; TTE;
 time range: (None, None) s;
 energy range: (None, None) keV>

While all info can be added to the DetectorData object on creation, the info can also be added after initialization. For example, we can update the time and energy range:

>>> det_data.time_range = (0.0, 10.0)
>>> det_data.energy_range = (8.0, 900.0)
>>> det_data

Several other attributes can be set such as the channel mask (which channels were used for fitting), energy edges, response file that was used, and the best fit photon model counts and associated uncertainties. Although not included in the base DetectorData class, this class is useful for collecting this information so that it can be written to a (e.g. FITS) file.

Model Fit Info

Similar to DetectorData, the ModelFit class serves as a container for the info associated with a single spectral fit. It bundles together the fitted parameters and associated uncertainties, resulting fluxes and fluences, the covariance matrix, and fit statistic and degrees-of-freedom. To create a ModelFit object, we have to initialize with at least the name of the model and the time range over which the data was fit:

>>> from gdt.core.spectra.parameters import ModelFit
>>> # fit a power law function from 0-10 s
>>> model_fit = ModelFit.from_data('PowerLaw', (0.0, 10.0))
>>> model_fit
<ModelFit: PowerLaw>

Now we can add other pieces of information to the object, such as the parameter fit values:

>>> from gdt.core.data_primitives import Parameter
>>> amp = Parameter(0.01, 2e-3, name='A', units='ph/cm^2/s/keV')
>>> index = Parameter(-1.7, 0.3, name='index')
>>> model_fit.parameters = [amp, index]
>>> print(model_fit)
PowerLaw
   A: 1.00e-02 +/- 2.00e-03 ph/cm^2/s/keV
   index: -1.70e+00 +/- 3.00e-01

>>> model_fit.parameters
[<Parameter: A>, <Parameter: index>]

We can also add flux information:

>>> model_fit.photon_flux = PhotonFlux(1.659, 0.071, (50.0, 300.0))

And the fit statistic, etc:

>>> model_fit.stat_name = 'PG-stat'
>>> model_fit.stat_value = 143.45
>>> model_fit.dof = 121

Reference/API

gdt.core.spectra.parameters Module

Classes

DetectorData()	A container for detector info used in a spectral fit.
EnergyFlux(value, uncert, energy_range)	An energy flux class.
EnergyFluence(value, uncert, energy_range)	An energy fluence class.
ModelFit()	A container for the info resulting from a spectral fit.
PhotonFluence(value, uncert, energy_range)	A photon fluence class.
PhotonFlux(value, uncert, energy_range)	A photon flux class.

Class Inheritance Diagram