yet_another_wizz is a python package to efficiently compute cross-correlation redshifts, also know as clustering redshifts and is hosted on github:
- code: https://github.com/jlvdb/yet_another_wizz.git
- docs: https://yet-another-wizz.readthedocs.io/
- PyPI: https://pypi.org/project/yet_another_wizz/
- Docker: https://hub.docker.com/r/jlvdb/yet_another_wizz/
The method allows to estimate the unknown redshift distribution of a galaxy sample by correlating the on-sky positions with a reference sample with known redshifts. This implementation is based on the single bin correlation measurement of the correlation amplitude, introduced by Schmidt et al. (2013, arXiv:1303.0292).
Note
When using this code in published work, please cite van den Busch et al. (2020), A&A 642, A200 (arXiv:2007.01846)
The yet_another_wizz package can be installed directly with pip:
pip install yet_another_wizz
This will install the python library yaw
.
There also exists a separate command line tool called
yet_another_wizz_cli
(yaw_cli
) that is available at PyPI and github. To install it alongside the
python library, type:
pip install yet_another_wizz[cli]
Currently there is also a
plugin interface for the
Redshift Assessment Infrastructure Layers
(RAIL) pipeline under development. To
install it alongside the python library, including rail
itself, type:
pip install yet_another_wizz[rail]
There are two main ways to use yet_another_wizz,
- the python library
yaw
itself and - the (separate)
yaw_cli
commmand line tool. - the (separate)
yaw_rail
RAIL plugin (coming soon).
Most users will probably get started with the command line tool, which should cover all necessary tasks for a standard clustering redshift calibration. For custom solutions, use the python library. A basic example as well as the API reference can be found in the official documentation.
For bug reports or requesting new features, please use the github issue page:
https://github.com/jlvdb/yet_another_wizz/issues
- Jan Luca van den Busch (author, Ruhr-Universität Bochum, Astronomisches Institut)
Jan Luca van den Busch acknowledges support from the European Research Council under grant numbers 770935. The authors also thank Hendrik Hildebrandt, Benjamin Joachimi, Angus H. Wright, and Chris Blake for vital feedback and support throughout the development of this software.