The pipeline now runs smoothly when skipping data reduction steps, even if those skipped steps should have created a new .fits file.
To do that, we've implemented/modified a couple of conditionals in the loop that goes throughout all the processes of the data reduction in reduce_data.py (i.e., in for step in proc_steps[arm]).
We also refactored reduce_data.py so all the functions are defined at the top of the file. That is, before parsing command line arguments, loading configs, and running the process steps.
The pipeline now runs smoothly when skipping data reduction steps, even if those skipped steps should have created a new
.fitsfile.To do that, we've implemented/modified a couple of conditionals in the loop that goes throughout all the processes of the data reduction in
reduce_data.py(i.e., infor step in proc_steps[arm]).We also refactored
reduce_data.pyso all the functions are defined at the top of the file. That is, before parsing command line arguments, loading configs, and running the process steps.All the changes in this PR are in
reduce_data.py.