BlackbodySpectrum

class uvex_transients.models.spectra.thermal.BlackbodySpectrum(**overrides: Parameter | Quantity | float | int)

Blackbody spectral shape, shaped entirely by a sampled temperature.

Represents \(S(\nu, T) = \pi B_\nu(\nu, T) / (\sigma T^4)\), the Lambertian-emergent Planck function normalized by the Stefan-Boltzmann constant so that \(\int_0^\infty S(\nu, T)\,d\nu = 1\) for any temperature – see the module docstring for why this needs no reference-frequency pivot the way a power law does.

By default temperature uses a LogNormalPrior, a physically motivated prior for a strictly positive scale parameter; like any other Parameter, it can be overridden per instance.

Parameters

The spectral shape parameters are summarized below.

Parameter	Symbol	Description
temperature	\(T\)	Blackbody temperature.

Methods

eval(nu, **parameters)	Evaluate the spectral shape at the given frequency.
eval_cgs(nu, **parameters)	Spectral shape, taking and returning plain cgs numbers.
eval_from_arrays(nu, *parameters)	Positional-argument form of eval().
eval_log(nu, **parameters)	Natural log of the spectral shape, given physical-unit inputs.
eval_log_cgs(nu, **parameters)	Natural log of the spectral shape, taking and returning plain cgs numbers.
eval_normalization(**parameters)	Evaluate \(\int S(\nu)\,d\nu\).
eval_normalization_cgs(**parameters)	Return the shape's frequency integral as plain cgs numbers; see eval_normalization_log_cgs().
eval_normalization_log(**parameters)	Natural log of the shape's frequency integral, given physical-unit inputs.
eval_normalization_log_cgs(**parameters)	Natural log of the shape's frequency integral, taking and returning plain cgs numbers.
get(k[,d])
items()
keys()
pack_params_to_arrays(**parameters)	Convert a dict of parameter values into an ordered sequence.
sample_parameters([size, rng, parameters])	Draw random samples of some or all of this model's parameters.
simulate(nu[, size, rng])	Draw random parameter realizations and evaluate the model at the given frequency.
unpack_params_from_arrays(*parameters)	Convert an ordered sequence of parameter values back into a dict.
values()