Spectrum

class uvex_transients.models.core.base.Spectrum(**overrides: Parameter | Quantity | float | int)

Abstract base class for frequency-dependent spectral shape models.

A Spectrum describes how a source’s light is distributed across frequency, as a shape \(S(\nu)\). It shares SpectralModel’s parameter storage, evaluation-family conventions, and sampling machinery, but – having no notion of time, redshift, or distance – carries none of SpectralModel’s \(t\)-dependent or observed-frame machinery (bolometric luminosity, flux, band flux, magnitudes).

To define a new model, subclass Spectrum and implement _eval(), the natural log of \(S(\nu)\) in cgs units. Unlike SpectralModel’s spectral shape, \(S(\nu)\) need not integrate to any particular value by construction – eval_normalization() computes whatever \(\int S(\nu)\,d\nu\) actually is, which is exactly the factor ComposedSpectralModel divides out to combine a Spectrum with a Lightcurve’s \(L_\mathrm{bol}(t)\) into an exactly normalized \(L_\nu(\nu, t)\).

See also

Lightcurve

The time-only counterpart this mirrors.

ComposedSpectralModel

Combines a Lightcurve and a Spectrum into a full SED.

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()