lightcurvelynx.models.eztaox_models

Wrappers for the models defined in EZTaoX.

https://github.com/LSST-AGN-Variability/EzTaoX

Classes

EzTaoXWrapperModel

A wrapper for an eztaox model.

Module Contents

class EzTaoXWrapperModel(kernel, *, baseline_mags=None, log_kernel_param=None, amp_scale_func=None, log_amp_scale=None, zero_mean=True, mean_func=None, mean_mag=None, has_lag=False, lag_func=None, lag=None, band_list=None, seed_param=None, **kwargs)[source]

Bases: lightcurvelynx.models.physical_model.BandfluxModel, citation_compass.CiteClass

A wrapper for an eztaox model.

Parameterized values include:

  • dec - The object’s declination in degrees. [from BasePhysicalModel]

  • distance - The object’s luminosity distance in pc. [from BasePhysicalModel]

  • ra - The object’s right ascension in degrees. [from BasePhysicalModel]

  • redshift - The object’s redshift. [from BasePhysicalModel]

  • t0 - The t0 of the zero phase, date. [from BasePhysicalModel]

Additional parameterized values are used for specific eztaox models.

References

  • Weixiang Yu et al., 2025 “Scalable and Robust Multiband Modeling of AGN Light Curves in Rubin-LSST” DOI: 10.48550/arXiv.2511.21479

kernel[source]

An eztaox kernel object to use for the Gaussian process modeling of the light curve.

Type:

eztaox kernel object

num_kernel_params[source]

The number of kernel parameters for the eztaox kernel.

Type:

int

num_filters[source]

The number of filters in the model.

Type:

int

zero_mean[source]

Whether to use a zero mean model.

Type:

bool

has_lag[source]

Whether the model includes lag parameters.

Type:

bool

filter_idx

A mapping from filter name to an integer index.

Type:

dict

Parameters:

  • kernel (eztaox kernel object) – An eztaox kernel object to use for the Gaussian process modeling of the light curve.

  • baseline_mags (dict, optional) – A mapping from filter name to the setter baseline magnitude for that filter. If not provided, the model will use zero mean or the mean_func/mean_mag parameters. Default is None.

  • log_kernel_param (list of setters, required) – Setters for each of the log kernel parameters. These must be in the order expected by the kernel functions.

  • amp_scale_func (Callable, optional) – A callable amplitude scaling function, defaults to None.

  • log_amp_scale (list of setters, optional) – Setters for the log amplitude scale for each filter (length N) if amp_scale_func is not provided. Default is None.

  • zero_mean (bool) – Whether to use a zero mean model. If False then the program will try to use (in order): mean_func, mean (values), or a default mean function. Default is True.

  • mean_func (Callable, optional) – If provided and zero_mean is False this is used to compute the mean function for bands. Default is None.

  • mean_mag (list of setters, optional) – Setters for the mean magnitude for each filter except the first (length N-1) if zero_mean is False and mean_func is not provided. Default is None.

  • has_lag (bool) – Whether the model includes lag parameters. If True then the program will try to use (in order): lag_func, lag (values), or a default lag function. Default is False.

  • lag_func (Callable, optional) – If provided and has_lag is True this is used to compute the lag. Default is None.

  • lag (list of setters, optional) – Setters for the lag for each filter except the first (length N) if has_lag is True and lag_func is not provided. Default is None.

  • band_list (list, optional) – A list of band names in order. If not provided, the default list for ugrizy filters is used.

  • seed_param (setter, optional) – A setter for the seed parameter to use for each run. If not provided, a random seed is generated for each run. Default is None.

  • **kwargs (dict, optional) – Any additional keyword arguments.

kernel[source]
num_filters[source]
num_kernel_params[source]
zero_mean = True[source]
has_lag = False[source]
compute_bandflux(times, filter, state)[source]

Evaluate the model at the passband level for a single, given graph state and filter.

Parameters:

  • times (numpy.ndarray) – A length T array of observer frame timestamps in MJD.

  • filter (str) – The name of the filter.

  • state (GraphState) – An object mapping graph parameters to their values with num_samples=1. This is not used in this model, but is required for the function signature.

Returns:

bandflux – A length T array of band fluxes for this model in this filter.

Return type:

numpy.ndarray

evaluate_bandfluxes(passband_or_group, times, filters, state, rng_info=None) → numpy.ndarray[source]

Get the band fluxes for a given Passband or PassbandGroup.

Parameters:

  • passband_or_group (Passband or PassbandGroup or None) – The passband (or passband group) to use. Not used in this function.

  • times (numpy.ndarray) – A length T array of observer frame timestamps in MJD.

  • filters (numpy.ndarray or None) – A length T array of filter names. It may be None if passband_or_group is a Passband.

  • state (GraphState or None) – An object mapping graph parameters to their values.

  • rng_info (numpy.random._generator.Generator, optional) – A given numpy random number generator to use for this computation. If not provided, the function uses the node’s random number generator.

Returns:

bandfluxes – A matrix of the band fluxes. If only one sample is provided in the GraphState, then returns a length T array. Otherwise returns a size S x T array where S is the number of samples in the graph state.

Return type:

numpy.ndarray