ComposedSpectralModel#

class uvex_transients.models.core.base.ComposedSpectralModel(**overrides: Parameter | Quantity | float | int)[source]#

A SpectralModel built by pairing a Lightcurve with a Spectrum.

\[L_\nu(\nu, t) = L_\mathrm{bol}(t) \cdot \frac{S(\nu)}{\int S(\nu')\,d\nu'}\]

where \(L_\mathrm{bol}(t)\) is _LIGHTCURVE_CLASS’s bolometric luminosity and the fraction is _SPECTRUM_CLASS’s shape, normalized (via Spectrum.eval_normalization()) to integrate to 1 over \(\nu\). Because both halves are already exact on their own, _eval_bolometric() and _eval_spectrum() are closed-form combinations of the two components’ own primitives – unlike SpectralModel’s generic defaults, neither ever falls back to numerical integration over frequency here.

A concrete SED is defined just by naming the two component classes:

class MySED(ComposedSpectralModel):
    _LIGHTCURVE_CLASS = FREDLightcurve
    _SPECTRUM_CLASS = BlackbodySpectrum

At class-definition time, __init_subclass__() merges _LIGHTCURVE_CLASS._DEFAULT_PARAMETERS and _SPECTRUM_CLASS._DEFAULT_PARAMETERS into this subclass’s own _DEFAULT_PARAMETERS (any entries the subclass declares directly itself win, as overrides on top of that merge) – so from then on, MySED behaves exactly like any other SpectralModel subclass: MySED() or MySED(amplitude=..., temperature=...), one flat parameter namespace. Every parameter name must be unique across the two component classes, checked at class-definition time.

Because every SpectralModel method is a classmethod operating purely on **parameters (see the module docstring), no instance-level wiring of components is needed: _eval_bolometric(), _eval_spectrum(), and _eval() simply split the incoming parameters dict by name and call _LIGHTCURVE_CLASS’s/ _SPECTRUM_CLASS’s own classmethods directly.

See also

Lightcurve: The time-only half of this composition.
Spectrum: The frequency-only half of this composition.

Methods

`as_astropy_model`([x_type, y_type, y_kind, ...])	Build an `Model` of this `SpectralModel` for a given parameter set.
`as_source_spectrum`(t, *[, redshift, ...])	Build a `SourceSpectrum` giving the observed flux at one fixed time \(t\).
`eval`(nu, t, **parameters)	Evaluate the spectral luminosity at the given frequency and time.
`eval_bolometric`(t, **parameters)	Evaluate the bolometric luminosity at the given time.
`eval_bolometric_cgs`(t, **parameters)	Bolometric luminosity, taking and returning plain cgs numbers.
`eval_bolometric_log`(t, **parameters)	Natural log of the bolometric luminosity, given physical-unit inputs.
`eval_bolometric_log_cgs`(t, **parameters)	Natural log of the bolometric luminosity, taking and returning plain cgs numbers.
`eval_cgs`(nu, t, **parameters)	Spectral luminosity, taking and returning plain cgs numbers.
`eval_from_arrays`(nu, t, *parameters)	Positional-argument form of `eval()`.
`eval_log`(nu, t, **parameters)	Natural log of the spectral luminosity, given physical-unit inputs.
`eval_log_cgs`(nu, t, **parameters)	Natural log of the spectral luminosity, taking and returning plain cgs numbers.
`eval_spectrum`(nu, t, **parameters)	Evaluate the normalized spectral shape at the given frequency and time.
`eval_spectrum_cgs`(nu, t, **parameters)	Return the normalized spectral shape as plain cgs numbers; see `eval_log_cgs()`.
`eval_spectrum_log`(nu, t, **parameters)	Natural log of the normalized spectral shape, given physical-unit inputs.
`eval_spectrum_log_cgs`(nu, t, **parameters)	Natural log of the normalized spectral shape, taking and returning plain cgs numbers.
`flux`(nu, t, *[, redshift, ...])	Evaluate the observed flux density at the given frequency and time.
`flux_band`(nu, throughput, t, *[, redshift, ...])	Evaluate the throughput-weighted mean observed flux density over a band.
`flux_band_cgs`(nu, throughput, t, redshift, ...)	Band-averaged observed flux density as plain cgs numbers; see `flux_band_log_cgs()`.
`flux_band_log`(nu, throughput, t, *[, ...])	Natural log of the band-averaged observed flux density, given physical-unit inputs.
`flux_band_log_cgs`(nu, throughput, t, ...[, ...])	Natural log of the throughput-weighted mean flux density over a band, plain cgs numbers.
`flux_bolometric`(t, *[, redshift, ...])	Evaluate the observed bolometric flux at the given time.
`flux_bolometric_cgs`(t, redshift, ...)	Observed bolometric flux, taking and returning plain cgs numbers.
`flux_bolometric_log`(t, *[, redshift, ...])	Natural log of the observed bolometric flux, given physical-unit inputs.
`flux_bolometric_log_cgs`(t, redshift, ...)	Natural log of the observed bolometric flux, taking and returning plain cgs numbers.
`flux_cgs`(nu, t, redshift, luminosity_distance, *)	Observed flux density, taking and returning plain cgs numbers.
`flux_log`(nu, t, *[, redshift, ...])	Natural log of the observed flux density, given physical-unit inputs.
`flux_log_cgs`(nu, t, redshift, ...[, ...])	Natural log of the observed flux density, taking and returning plain cgs numbers.
`get`(k[,d])
`items`()
`keys`()
`mag`(nu, t, *[, redshift, ...])	Evaluate the apparent AB magnitude at the given frequency and time.
`mag_band`(nu, throughput, t, *[, redshift, ...])	Evaluate the apparent AB magnitude of the band-averaged flux density.
`mag_band_cgs`(nu, throughput, t, redshift, ...)	Apparent AB magnitude of the band-averaged flux density.
`mag_bandpass`(bandpass, t, *[, redshift, ...])	Evaluate the apparent AB magnitude of the flux averaged over bandpass.
`mag_cgs`(nu, t, redshift, luminosity_distance, *)	Apparent AB magnitude: \(m_\mathrm{AB} = -2.5 \log_{10}(F_\nu / F_{\mathrm{AB},0})\).
`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, t[, size, rng])	Draw random parameter realizations and evaluate the model at the given frequency and time.
`simulate_photometry`(t, exptime, detector, ...)	Simulate noisy synthetic photometry of this model at given time(s), against a real detector.
`unpack_params_from_arrays`(*parameters)	Convert an ordered sequence of parameter values back into a dict.
`values`()