Aoide

"Aoide, one of the nine daughters of Zeus and Mnemosyne..."

Dr. Grant R. Tremblay | Astrophysicist | Harvard-Smithsonian Center for Astrophysics

Aoide is a suite of Python tools for MUSE, an optical Integral Field Unit (IFU) spectrograph on ESO's Very Large Telescope.

Step 0: Optional Convenience Aliases

In my .bashrc on Linux (or .bash_profile on macOS), I have set the following BASH aliases:

alias aoideid='/home/grant/Repositories/aoide/bin/AoideID.py'
alias aoidereduce='/home/grant/Repositories/aoide/bin/AoideReduce.py'
alias aoidepost='/home/grant/Repositories/aoide/bin/AoidePost.py'

You may want to do the same! In the below examples, I assume that Aoide scripts such as AoideReduce.py are in your PATH. This will not automatically be the case, of course. You could fix that by putting

export PATH=$PATH:/path/to/aoide/bin

in your .bashrc.

Step 1: Check for Raw Data Completeness

cd to the raw_data_directory in which you have placed all *.fits.fz raw MUSE data downloaded from the ESO Archive.

Run

AoideID.py

with no arguments. It will print simple information about the directory contents. Make sure it looks okay (i.e., you have the correct data for your requested observation). You can save this list by, e.g., typing

AoideID.py > contents.txt
tail -f contents.txt

Step 2: Use Aoide to run the MUSE Pipeline

AoideReduce.py will

1. Take inventory of the contents of a MUSE raw data directory (make sure your FITS files are straight from the archive, e.g. in *fits.fz format.)
2. Create .sof ("set of frames") files, based upon requirements outlined in the MUSE Pipeline User Manual, for use with the esorex muse_* recipes.
3. Run the ESO MUSE Pipeline, again following all basic steps in the Pipeline Manual.

AoideReduce.py /path/to/raw/data -c [# of processor cores to run on] --static_cal_dir [path to your static cal directory]

If you're running this script from the raw data directory, as you should be, AoideReduce.py will run with no arguments. There are a number of other arguments you can pass to this script. Discover them by calling the help flag with AoideReduce.py -h.

AoideReduce.py -c 6 --static_cal_dir /path/to/your/muse/pipeline/static/cal

On an Ubuntu 18.04 workstation with an Intel Xeon E5-1650 v3 (6 cores, 3.8 GHz) and 64 GB of RAM, one run of AoideReduce takes ~90 minutes for a three-pointing science observation. Peak RAM useage approaches 60 GB during the muse_scipost and muse_exp_combine steps, so beware. If your processor supports hyperthreading (virtual cores), note that treating these as "real" processors is useless, and will likely decrease performance. If your processor has 6 cores and 12 threads, use

AoideReduce.py -c 6

Step 2: Fit & Subtract Sky Residuals, Bin & Extinction-correct the Datacube

AoidePost.py will ....

aoidepost "DATACUBE_AOIDE_UNCLEAN.fits" -c 6 -av 0.166

Step 3: Create a Voronoi Tessellated Cube

Example:

Again, type aoidevoronoi -h for useage.

aoidevoronoi DATACUBE_AOIDE_FINAL.fits -n "Abell2597"