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DRAGONS |
| Data Reduction for Astronomy from Gemini Observatory North and South |
The stable version is v4.0.0.
Version 4.0 is recommend for the reduction of imaging data from Gemini's current facility instruments: GMOS, NIRI, Flamingos-2, and GSAOI, for the reduction of GMOS and GNIRS longslit spectroscopy data, and the reduction of GHOST data.
To reduce other types of Gemini spectroscopy data, please continue to use the Gemini IRAF package.
To install DRAGONS:
$ conda create -n dragons python=3.12 dragons ds9
You might need to add two relevant conda channels if you haven't already:
$ conda config --add channels conda-forge
$ conda config --add channels http://astroconda.gemini.edu/public
A list of changes since 3.2 can be found in the Change Logs.
DRAGONS is a platform for the reduction and processing of astronomical data. The DRAGONS meta-package includes an infrastructure for automation of the processes and algorithms for processing of astronomical data, with focus on the reduction of Gemini data.
Documentation on DRAGONS v4.0 is available on "readthedocs" at:
There your will find manuals for Astrodata and the Recipe System, and hands-on tutorials on reducing Gemini imaging data with DRAGONS.
Gemini users with data should pick the tutorial discussing the reduction of data from the appropriate instrument and mode.
Software developers should start with the Astrodata and Recipe System manuals.
To run checkouts, first set up a development conda environment. This is what
we are using at this time for the master branch and the release/4.0.x
branches.
$ conda create -n dgdev3.12_20250520 python=3.12 "astropy>=6" astroquery matplotlib "numpy<2" psutil pytest python-dateutil requests scikit-image scipy sextractor "sqlalchemy>=2.0.0" "gwcs>=0.15,<=0.22.1" specutils "bokeh>=3" holoviews cython future "astroscrappy>=1.1" fitsverify jsonschema ds9 jupyter ipympl imexam sphinx sphinx_rtd_theme objgraph
$ conda activate dgdev3.12_20250520
$ pip install git+https://github.com/GeminiDRSoftware/[email protected]
Dependencies change all the time and can break the development environment or cause problems when conda tries to find a solution for the dependencies. This not guaranteed to work flawlessly, you might have to adjust version requirements.