DRAGONS v3.0.1 Patch Release Available
A patch release of DRAGONS is available. It includes bug fixes, small improvements, and fixes to the documentation.
For a complete list of changes since v3.0.0 see:
https://dragons.readthedocs.io/en/v3.0.1/changes.html
Virtual machine image for running IRAF under recent MacOS releases
A CentOS 7 virtual machine image (OVA file) is now available to facilitate running Astroconda IRAF under MacOS 10.15+, which no longer supports running the necessary 32-bit binaries natively. This comes with Anaconda 2019.10, Gemini IRAF 1.14, DRAGONS 2.1.0 and other packages from Astroconda pre-installed. Users of MacOS 10.14 affected by the Tk bug that causes a desktop session logout when displaying graphics may also want to install this guest distribution as a workaround.
New DRAGONS Patch Release
A bug fix release of DRAGONS is now available. In release 2.1.1, we have fixed bugs and typos found by the users and ourselves since the initial release. We have also added compatibility with astropy v4. If you already have DRAGONS installed, you can update by doing conda install dragons=2.1.1. If you need to install DRAGONS for the first time, please see the Download and Installation Instructions.
DRAGONS First Public Release!
It is with great delight that we are announcing the first public release of Gemini's new Python-base data reduction platform, DRAGONS, Data Reduction for Astronomy from Gemini Observatory North and South. This project has been many years in the making. DRAGONS offers a more streamlined approached to the data reduction of Gemini data, compared to the Gemini IRAF package.
IMPORTANT: MacOS 10.14.6 and 10.15 incompatibilities with data reduction software
As of this week's v10.15 release, MacOS is no longer capable of running the 32-bit Astroconda IRAF distribution needed by Gemini IRAF. For the time being, Gemini IRAF users on Apple machines are advised to continue using MacOS 10.14 or earlier, or to install Astroconda in a virtual machine with a compatible OS. Gemini will look into providing a ready-made VM image to help with this while we are migrating our data reduction tools to Python.