# 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.