uvotredux 0.1.1

Python wrapper to reduce Swift UVOT data

uvotredux

uvotredux is a simple python wrapper around HEASoft, which can iteratively reduce Swift UVOT data. The actual data reduction is done by HEASoft, and all credit should go to the NASA team for developing these tools.

Getting Swift Data

First check if your target has been observed: https://www.swift.psu.edu/operations/obsSchedule.php You should see each visit listed.

You might also see “SSA observations”, but no data is actually taken for those. You should just ignore them.

Downloading Recent Data

For very recent data (from ~2 hours to ~1 month), you can download from the quicklook archive: https://swift.gsfc.nasa.gov/cgi-bin/sdc/ql?

You can search for your target, and then download the data. Make sure you tick the box to include UVOT data!

You will get a tar file for each visit, which you can decompress. Place each decompressed directory in a parent directory. Give the parent directory a name that is informative, e.g swift_data_sn2023uqf.

Downloading Old Data

For old data, check the archive: https://www.swift.ac.uk/swift_portal/ (After you found the archive data, download it as a tar/zip and make sure you tick the box to include UVOT data!)

You will see a file named download.tar or download.zip. I suggest renaming this to something more informative e.g swift_data_sn2023uqf.zip or swift_data_sn2023uqf.tar.

Finally, you should decompress your files. On mac/linux this is easy. You will get a directory with the same name as the tar/zip file.

Setting up Extraction Regions

Whether you downloaded recent or old data, the parent data directory is where you can run the iterative data reduction. It’ll look like:

swift_data_sn2023uqf/

• 00016282001
• 00016282002
• 00016282003
• …

And so on, with one subdirectory for each Swift visit of your target. There are compressed images in these subdirectories, but uvotredux will be able to unpack them for you automatically later on.

In the directory swift_data_sn2023uqf/, you need to create two .reg files. One is your source, centered at the position with an appropriate radius. The other is a background region, and that should be free of other sources!

These files are just plain text files containing a single line, of the form:

fk5;circle(09:33:49.15,+25:06:56.86,3")

At this point, you are ready to reduce data!

Installing HEASoft

To actually reduce Swift data you require NASA's HEASoft.

Installation via Docker

I found installing via Docker to be the easiest. You can find instructions here: https://heasarc.gsfc.nasa.gov/lheasoft/docker.html

You will need to install Docker, and make the HEASoft Docker image, following the guide.

Then, start a terminal e.g with:

docker run -it -rm -v /path/to/swift_data_sn2023uqf:/mydata heasoft:v6.33 bash

or whatever version of HEASoft you want to use.

This will mount /path/to/swift_data_sn2023uqf in the container to /mydata, so you can reduce the data in that folder.

Then inside the docker container:

export PATH="/home/heasoft/.local/bin:$PATH"
pip install uvotsource
cd /mydata 

Installation locally

You can instead install HEASoft locally, following the official guide: https://heasarc.gsfc.nasa.gov/docs/software/lheasoft/ . In that case, when you are done, run:

pip install uvotsource

And then navigate to the directory containing your data.

Using uvotredux

Once you are in the data directory you created earlier (either in the docker container or the local directory if you installed locally), you can actually reduce the data.

You can just run the reduction:

uvotredux

This will iteratively reduce each image in the subfolders, and that is where you can find reduced images and source tables.

Make sure you have a fast internet connection, because the uvot pipeline can attempt to download fits files >150Mb and downloads will time out eventually!

You can see additional options with:

uvotredux -h