Photometric redshift via Gaussian processes with physical kernels.
Read the documentation here: http://delight.readthedocs.io
Warning: this code is still in active development and is not quite ready to be blindly applied to arbitrary photometric galaxy surveys. But this day will come.
Tests: pytest for unit tests, PEP8 for code style, coveralls for test coverage.
./paper/: journal paper describing the method
./delight/: main code (Python/Cython)
./tests/: test suite for the main code
./notebooks/: demo notebooks using delight
./data/: some useful inputs for tests/demos
./docs/: documentation
./other/: useful mathematica notebooks, etc
Python 3.5, cython, numpy, scipy, pytest, pylint, coveralls, matplotlib, astropy, mpi4py
Boris Leistedt (NYU)
David W. Hogg (NYU) (Flatiron)
Please cite [Leistedt and Hogg (2016)] (https://arxiv.org/abs/1612.00847) if you use this code your research. The BibTeX entry is:
@article{delight,
author = "Boris Leistedt and David W. Hogg",
title = "Data-driven, Interpretable Photometric Redshifts Trained on Heterogeneous and Unrepresentative Data",
journal = "The Astrophysical Journal",
volume = "838",
number = "1",
pages = "5",
url = "http://stacks.iop.org/0004-637X/838/i=1/a=5",
year = "2017",
eprint = "1612.00847",
archivePrefix = "arXiv",
primaryClass = "astro-ph.CO",
SLACcitation = "%%CITATION = ARXIV:1612.00847;%%"
}
Copyright 2016-2017 the authors. The code in this repository is released under the open-source MIT License. See the file LICENSE for more details.