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.
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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 byWDmodel.io.get_options()
.Raises: RuntimeError
– If user attempts to resume the fit without having run it firstNotes
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 overloadedemcee.PTSampler
with a more reliable auto-correlation estimate. Finally, the result is output along with various plots.
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WDmodel.main.
mpi_excepthook
(excepttype, exceptvalue, traceback)[source]¶ Overload
sys.excepthook()
when usingmpi4py.MPI
to terminate all MPI processes when an Exception is raised.