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Compare observed emission line fluxes to predictions

Project description

NebulaBayes is a package for astronomers that aims to provide a very general way to compare observed emission line fluxes to model predictions, in order to constrain physical parameters such as the nebular metallicity.

NebulaBayes is provided with two photoionization model grids produced using the MAPPINGS 5.1 model. One grid is a 3D HII-region grid which may be used to constrain the oxygen abundance (12 + log O/H), ionisation parameter (log U) and gas pressure (log P/k). The other grid is for AGN narrow-line regions (NLRs) and has 4 dimensions, with the added parameter “log E_peak” being a measure of the hardness of the ionising continuum. NebulaBayes accepts model grids in a simple table format, and is agnostic to the number of dimensions in the grid, the parameter names, and the emission line names.

The NebulaBayes.NB_Model class is the entry point for performing Bayesian parameter estimation. The class is initialised with a chosen model grid, at which point the model flux grids are loaded, interpolated, and stored. The NB_Model instance may then be called one or more times to run Bayesian parameter estimation using observed fluxes. Many outputs are available, including tables and figures, and all results and working are stored on the object returned when the NB_Model instance is called.

See the “docs” directory in the installed NebulaBayes package for more information, suggestions for getting started, and examples. (Type the following at the terminal to show the location of the installed package):
$ python -c "import NebulaBayes; print(NebulaBayes.__file__)"

The documentation assumes some knowledge of Bayesian statistics and scientific python (numpy, matplotlib and pandas).

NebulaBayes is heavily based on IZI (Blanc+ 2015).

If you use NebulaBayes, please cite…856…89T.

The package has been tested on Python 2.7 and Python 3.5.

Project details

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NebulaBayes-0.9.8-py2.py3-none-any.whl (4.4 MB) Copy SHA256 hash SHA256 Wheel py2.py3 Apr 30, 2018
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