A suite of tools written in Pyraf, Astropy, Scipy, and Numpy to process individual QuickReduced images into single stacked images using a set of "best practices" for ODI data.
simply fork this repository and clone onto your local machine, e.g.: git clone https://github.com/bjanesh/odi-tools.git
optionally add this folder to your $PATH
to run the scripts you’ll need to install a number of dependencies:
pip install numpy scipy astropy astroquery pyraf matplotlib pandas photutils tqdm
It is possible to install these packages without root access by using the --user option:
pip install --user package-name
As noted on the astropy website, it might also be beneficial to use the --no-deps
option when installing astropy to stop pip from automatically upgrading any of your previously installed packages, such as numpy.
pip install --no-deps astropy
All you need to do to get started is download your QR-ed data from the ODI-PPA using the wget download command, then follow these steps (if you aren’t sure what to do, see below for more details).
images folder created by the wget command to avoid confusionfunpack linkexample_config.yaml to your data directory as config.yaml and edit the file to match your preferences/data. You do not need to rename your images or use any particular numbering sequence, though using dither sequence ID numbers is recommended for clarity. You may use more than 9 images, but be sure to give each image a unique ID! For any additional dither sequences we recommend using, e.g., 11-19, 21-29, etc. NOTE: the current version of the example_config.yaml file contains the current recommended set of options for odi-tools.odi_process.py in the folder containing the unpacked fits images. This will (optionally) illumination correct the images, reproject them to a common pixel scale, and perform background subtraction on them.odi_scalestack_process.py in the folder containing the unpacked fits images. This will detect bright stellar sources in the images and use them to calculate a scaling factor relative to the image in the sequence with the lowest airmass, then apply the scale, stack the images, then add in a common background value. Finally, the images are flipped and optionally, aligned using integer pixel shifts.