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

I strongly recommend using uvotredux in a Docker container.

You can pull the latest version of uvotredux from Docker Hub:

docker pull robertdstein/uvotredux:latest

You need a working docker installation for this. This image contains the latest version of uvotredux, and heasoft.

Then you can run the container with:

docker run -it --rm -v ~/path/to/local/data:/mydata robertdstein/uvotredux:latest ARGS

where /path/to/local/data is the path to the directory where you want to download the data, and ARGS are the arguments you want to pass to uvotredux.

You can create a convenient alias in your shell configuration file (e.g., .bashrc or .zshrc) to simplify the command:

alias uvotredux='docker run --rm -v /path/to/local/data:/mydata robertdstein/uvotredux:latest'

Then, to download and reduce data for a target at RA 133.457 and Dec 25.119, you can run:

uvotredux by-ra-dec 133.457 25.119

or to download and reduce data for a target with a specific name, you can run:

uvotredux by-name AT2025mav

If you already have heasoft installed locally, you can also install uvotredux via pip:

pip install uvotredux

This will install the latest stable release of uvotredux from PyPI.

Then you can set the data directory where you want to download/reduce the data:

export UVOTREDUX_DATA_DIR="/path/to/local/data"

and then run uvotredux with the desired arguments, e.g.:

uvotredux by-ra-dec 133.457 25.119

If you want to edit the code, you will still need a working docker installation of heasoft:

docker pull robertdstein/uvotredux:latest

Then, you can clone the repository somewhere on your machine:

git clone https://github.com/robertdstein/uvotredux.git

Then, start the docker container with:

docker run --rm -it --entrypoint bash -v /path/to/local/data:/mydata -v /path/to/local/uvotredux:/uvotredux robertdstein/uvotredux:latest

This will mount your local data directory to /mydata in the container, and your local uvotredux directory to /uvotredux in the container.

Then inside the docker container, you can install uvotredux in editable mode:

export PATH="/home/heasoft/.local/bin:$PATH"
pip install -e /uvotredux

And finally, within the docker container, you can run uvotredux commands as usual:

uvotredux by-ra-dec 133.457 25.119

Uvotredux will download the data for you, if it is not already present in the specified data directory.

It will then iteratively reduce each image in the subfolders of the data directory.

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

The code will scrape the output files of each individual image, and produce a combined csv file with all the available info (uvot_results.csv). Beware: this file contains over a hundred columns per image.

There is also a smaller file (uvot_summary.csv) that contains only the most important columns.

Imagine you reduced data for a target with the name AT2025mav.

You will see a directory created in your local data directory: /path/to/local/data/AT2025mav .

In the directory there will be a subdirectory for each visit, as well as a uvot_results.csv and uvot_summary.csv file. There are also two region files (src.reg and bkg.reg) that were used extract the source and background regions.

uvotredux generates these automatically. However, it is possible that an unrelated source is present in the background region. You can check this by opening up one of the uncompressed images in ds9, e.g /path/to/local/data/AT2025mav/00019808001/uvot/image/UW2.fits. You can then overlay the regions from the src.reg and bkg.reg files to see if they are centered correctly.