WDmodel.main module¶

The WDmodel package is designed to infer the SED of DA white dwarfs given spectra and photometry. This main module wraps all the other modules, and their classes and methods to implement the alogrithm.

WDmodel.main.main(inargs=None)[source]¶

Entry point for the WDmodel fitter package.

Parameters:	inargs (dict, optional) – Input arguments to configure the fit. If not specified `sys.argv` is used. inargs must be parseable by `WDmodel.io.get_options()`.
Raises:	`RuntimeError` – If user attempts to resume the fit without having run it first

Notes

The package is structured into several modules and classes

Module	Model Component
`WDmodel.io`	I/O methods
`WDmodel.WDmodel.WDmodel`	SED generator
`WDmodel.passband`	Throughput model
`WDmodel.covariance.WDmodel_CovModel`	Noise model
`WDmodel.likelihood.WDmodel_Likelihood`	Likelihood function
`WDmodel.likelihood.WDmodel_Posterior`	Posterior function
`WDmodel.fit`	“Fitting” methods
`WDmodel.viz`	Viz methods

This method implements our algorithm to infer the DA White Dwarf properties and construct the SED model given the data using the methods and classes listed above. Once the data is read, the model is configured, and the liklihood and posterior functions constructed, the fitter methods evaluate the model parameters given the data, using the samplers in emcee. WDmodel.mossampler provides an overloaded emcee.PTSampler with a more reliable auto-correlation estimate. Finally, the result is output along with various plots.

WDmodel.main.mpi_excepthook(excepttype, exceptvalue, traceback)[source]¶: Overload sys.excepthook() when using mpi4py.MPI to terminate all MPI processes when an Exception is raised.