  We describe two new open source tools written in Python for performing
extreme deconvolution Gaussian mixture modeling (XDGMM) and using a conditioned
model to re-sample observed supernova and host galaxy populations. XDGMM is new
program for using Gaussian mixtures to do density estimation of noisy data
using extreme deconvolution (XD) algorithms that has functionality not
available in other XD tools. It allows the user to select between the AstroML
(Vanderplas et al. 2012; Ivezic et al. 2015) and Bovy et al. (2011) fitting
methods and is compatible with scikit-learn machine learning algorithms
(Pedregosa et al. 2011). Most crucially, it allows the user to condition a
model based on the known values of a subset of parameters. This gives the user
the ability to produce a tool that can predict unknown parameters based on a
model conditioned on known values of other parameters. EmpiriciSN is an example
application of this functionality that can be used for fitting an XDGMM model
to observed supernova/host datasets and predicting likely supernova parameters
using on a model conditioned on observed host properties. It is primarily
intended for simulating realistic supernovae for LSST data simulations based on
empirical galaxy properties.
