lightcurvelynx.models.static_sed_model

Models that generate a constant SED or bandflux at all times.

Classes

`StaticSEDModel`	A StaticSEDModel randomly selects an SED at each evaluation and computes
`StaticBandfluxModel`	A StaticBandfluxModel randomly selects a mapping of bandfluxes at each evaluation

Module Contents

class StaticSEDModel(sed_values, weights=None, **kwargs)[source]

Bases: lightcurvelynx.models.physical_model.SEDModel

A StaticSEDModel randomly selects an SED at each evaluation and computes the flux from that SED at all time steps.

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. Not used.]

sed_values

A list of SEDs from which to sample. Each SED is represented as a two row numpy-array where the first row is wavelength and the second is flux value, or as an instance of the SED class.

Type:: list of numpy.ndarray or SED

Parameters:

sed_values (list of SED objects) – A single SED or a list of SEDs from which to sample.
weights (numpy.ndarray, optional) – A length N array indicating the relative weight from which to select an SED at random. If None, all SEDs will be weighted equally.

__len__()[source]: Get the number of SED value.

__iter__()[source]: Iterate over the SED values.

__getitem__(index)[source]: Get the SED at the given index.

classmethod from_file(sed_file, **kwargs)[source]

Load a static SED from a file containing a two column array where the first column is wavelength (in angstroms) and the second column is flux (in nJy).

Parameters:

sed_file (str or Path) – The path to the SED file to load.
**kwargs (dict) – Additional keyword arguments to pass to the StaticSEDModel constructor.

Returns:

An instance of StaticSEDModel with the loaded SED data.

Return type:

StaticSEDModel

classmethod from_synphot(sp_model, waves=None, **kwargs)[source]

Generate the spectrum from a given synphot model.

References

synphot (ascl:1811.001)

Parameters:

sp_model (synphot.SourceSpectrum) – The synphot model to generate the spectrum from.
waves (numpy.ndarray, optional) – A length N array of wavelengths (in angstroms) at which to sample the SED. If None, the SED will be sampled at the wavelengths defined in the synphot model.
**kwargs (dict) – Additional keyword arguments to pass to the StaticSEDModel constructor.

Returns:

An instance of StaticSEDModel with the generated SED data.

Return type:

StaticSEDModel

minwave(graph_state=None)[source]

Get the minimum wavelength of the model.

Parameters:: graph_state (GraphState, optional) – An object mapping graph parameters to their values. Not used for this model.
Returns:: minwave – The minimum wavelength of the model (in angstroms) or None if the model does not have a defined minimum wavelength.
Return type:: float or None

maxwave(graph_state=None)[source]

Get the maximum wavelength of the model.

Parameters:: graph_state (GraphState, optional) – An object mapping graph parameters to their values. Not used for this model.
Returns:: maxwave – The maximum wavelength of the model (in angstroms) or None if the model does not have a defined maximum wavelength.
Return type:: float or None

compute_sed(times, wavelengths, graph_state)[source]

Draw effect-free observer frame flux densities.

Parameters:

times (numpy.ndarray) – A length T array of observer frame timestamps in MJD.
wavelengths (numpy.ndarray, optional) – A length N array of observer frame wavelengths (in angstroms).
graph_state (GraphState) – An object mapping graph parameters to their values.

Returns:

flux_density – A length T x N matrix of observer frame SED values (in nJy).

Return type:

numpy.ndarray

class StaticBandfluxModel(bandflux_values, weights=None, **kwargs)[source]

Bases: lightcurvelynx.models.physical_model.BandfluxModel

A StaticBandfluxModel randomly selects a mapping of bandfluxes at each evaluation and uses that at all time steps.

Parameterized values include:

dec - The object’s declination in degrees. [from PhysicalModel]
distance - The object’s luminosity distance in pc. [from PhysicalModel]
ra - The object’s right ascension in degrees. [from PhysicalModel]
redshift - The object’s redshift. [from PhysicalModel]
t0 - The t0 of the zero phase, date. [from PhysicalModel. Not used.]

bandflux_values

A list of bandflux mappings from which to sample. Each mapping is represented as a dictionary where the key is the filter name and the value is the bandflux (in nJy).

Type:: list of dict

Parameters:

bandflux_values (dict or list) – A single bandflux mapping or a list of bandflux mappings from which to sample. Each mapping is represented as a dictionary where the key is the filter name and the value is the bandflux (in nJy).
weights (numpy.ndarray, optional) – A length N array indicating the relative weight from which to select a model at random. If None, all models will be weighted equally.

__len__()[source]: Get the number of band flux values.

compute_bandflux(times, filter, state, rng_info=None)[source]

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

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.
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.