MuSEEK [muˈziːk] is a flexible and easy-to-extend data processing pipeline for multi-instrument autocorrelation radio telescopes. Once finished, it takes the observed (or simulated) TOD in the time-frequency domain as an input and processes it into healpix maps while applying calibration and automatically masking RFI.

The MuSEEK package has been developed at the Centre for Radio Cosmology at UWC and at the Jodrell Bank Centre for Astrophysics at UoM. It is inspired by SEEK, developed by the Software Lab of the Cosmology Research Group of the ETH Institute of Astronomy.

The development is coordinated on GitHub and contributions are welcome. The documentation of MuSEEK is not yet available at readthedocs.org .

MuSEEK is run via the workflow engine Ivory. Consequently the Ivory directory needs to be in the PYTHONPATH of your python interpreter. A shorthand for running Ivory is directly inside MuSEEK and allows running the plugins like (assuming working directory in the repository root)

museek museek.config.demo

which is a shorthand for

python cli/main.py museek.config.demo

The following syntax allows to pass extra arguments to MuSEEK which override those from the config file (this allows e.g. running the pipeline on different observations with otherwise the same config):

python cli/main.py --InPlugin-block-name=1634748682 museek.config.process_data

In this specific case, the parameter block_name of the plugin InPlugin is replaced with the example 1634748682.

The python interpreter should be >3.10 (older versions are not tested) and needs to have requirements.txt installed. A popular way of doing this is

pip install -r museek/requirements.txt

Or you can do

cd museek
pip install --editable .
cd ..

You can do the same for ivory if it's cloned in the same directory.

cd ivory
pip install --editable .
cd ..

Plugins can be implemented by creating a class inheriting from **Ivory**s AbstractPlugin. They need to implement the methods run() and set_requirements().

1. Only one plugin per file is allowed. One plugin can not import another plugin.
2. Naming: CamelCase ending on "Plugin", example: "GainCalibrationPlugin".
3. To have the plugin included in the pipeline, the config file's "Pipeline" entry needs to include the plugin under "plugins".
4. If the plugin requires configuration (most do), the config file needs to contain a section with the same name as the plugin. For more information see section config.
5. Plugins need to define their Requirement`s in `self.set_requirements(). The workflow engine will compare these to the set of results that are already produced when the plugin starts and hands them over to the run() method.
6. Plugins need to define a run() method, which is executed by the workflow engine.
7. Plugins need to run self.set_result() to hand their results back to the workflow engine for storage.

The configuration file is written in python and consists of ConfigSection() instances. There is one general section called Pipeline, which defines the entire pipeline, and each other section needs to share the name of the plugin it belongs to. The workflow manager will then hand over the correct configuration parameters to each plugin.

A demonstration config is museek.config.demo.

Plugin requirements are encapsulated as Requirement() objects, which are mere NamedTuples. See the Requirement class doc for more information.

Plugin results need to be defined as Result() objects. See the Result class doc for more information.

More information on these are included in their class documentations.

1. Demonstration plugins: DemoFlipPlugin, DemoLoadPlugin & DemoPlotPlugin
2. InPlugin
3. OutPlugin
4. SanityCheckObservationPlugin
5. AoflaggerPlugin
6. KnownRfiPlugin
7. NoiseDiodeFlaggerPlugin
8. AntennaFlaggerPlugin
9. PointSourceFlaggerPlugin
10. BandpassPlugin
11. RawdataFlaggerPlugin

The computing cluster Ilifu makes many python interpreters available using the module command. Using

module avail

displays all available modules that can be loaded. You can follow the Ilifu documentation to create a virtual environment with the python interpreter of your choice, e.g. python/3.10.4.

The following compiles all the commands needed to get up and running with MuSEEK on slurm.ilifu.ac.za or a similar system. You first clone the repositories, create a new python environment, install museek and ivory and create a results folder.

git clone [email protected]:meerklass/museek.git
git clone [email protected]:meerklass/ivory.git

module load python/3.10.4
virtualenv ./environment/museek
source ./environment/museek/bin/activate
cd ivory
pip install --editable .
cd ../museek
pip install --editable .
cd ..

# for jupyter support
pip install ipykernel
ipython kernel install --name "museek_kernel" --user

deactivate

mkdir museek/results museek/results/demo

If you have not set up ssh keys for your github account, it might be easier to clone the repos like

git clone https://github.com/meerklass/museek.git
git clone https://github.com/meerklass/ivory.git

Now you are ready to run MuSEEK! You can use the sbatch command to schedule a job:

sbatch example.sh

You can find an sbatch script to start with below, but remember to change /path/to/project to your own project's working directory and /path/to/virtualenv/ to the directory of your new environment. The allocated ressources in this script are minimal and for demonstration only, see below for a brief guideline on ressource usage.

#!/bin/bash

#SBATCH --job-name='MuSEEK'
#SBATCH --cpus-per-task=1
#SBATCH --ntasks=1
#SBATCH --mem=4GB
#SBATCH --output=museek-stdout.log
#SBATCH --error=museek-stderr.log
#SBATCH --time=00:05:00

echo "Submitting Slurm job"
/path/to/virtualenv/bin/python /path/to/project/museek/cli/main.py museek.config.demo

Once the job is finished, you can check the results of the demo pipeline in your working directory and in museek/results/demo. To go from the demo pipeline to the real one, you will need to change museek.config.demo to the config you want to use, an example is museek.config.process_data. You also need to adjust the ressources in the sbatch script depending on the config. As a rough estimate, processing an entire MeerKAT observation block may be done with --cpus-per-task=32, --mem=128GB and --time=03:00:00.

You can also run MuSEEK in jupyter on ilifu! If you followed the manual above, you already have a kernel installed, it's called museek_kernel and can be selected to run a jupyter notebook. To quickly access results stored by the pipeline as a pickle file from within a notebook, the class ContextLoader can be used.