Science Score: 67.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 1 DOI reference(s) in README -
✓Academic publication links
Links to: arxiv.org, zenodo.org -
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (14.3%) to scientific vocabulary
Repository
A tool to jack knife ALMA observations
Basic Info
- Host: GitHub
- Owner: Joshiwavm
- License: mit
- Language: Python
- Default Branch: main
- Size: 6.08 MB
Statistics
- Stars: 2
- Watchers: 1
- Forks: 2
- Open Issues: 1
- Releases: 2
Metadata Files
README.md
Jack-knife
jackknifyis a Python-based package that jackknifes ALMA visibilities to create noise realizations from the observations.
Methodology
Jackknifing is a simple but effective tool to characterize the underlying noise distribution of any type of data set. This tool specifically is implemented for interferometric data. jackknify splits half the visibilities randomly in two subsets, then multiplies one half with -1 so that when the data is binned, any signal present in the data is averaged out. This creates observation-specific noise realization of the data, which can be used to for instance, sample the likelihood a false detection.
The full methodology can be found here and in an upcoming paper, which is still in preparation.
Installation
jackknify itself can be installed through
pip install jackknify
or alternatively
python -m pip install git+https://github.com/Joshiwavm/jackknify
or from the source
git clone https://github.com/Joshiwavm/jackknify
cd jackknify
pip install -e .
Dependancies
jackknify uses casatask and casatools to interface with CASA measurements. casatask and casatools requires casadata to load. Sadly, this is a ~350 MB sized file making the installment a bit slow. Further, when performing line searches, we make use of the package interferopy, which is a Python-based package for common tasks used in the observational radio/mm interferometry data analysis.
Trouble shooting casatask installation (if needed)
If you want to run jackknify on a Mac with an Apple Silicon chip, run it in a Rosetta terminal. To open a Rosetta session in your terminal, run:
/usr/bin/arch -x86_64 /bin/zsh --login
Further, casadata will download and store examples sets into the folder: ~/.casa/data. However, it might not have permission from the local machine to do so. If such an error comes up. Just run:
mkdir ~/.casa/data
To make the folder. That should solve most problems.
Documentation
For your convenience, there are notebooks on how to run and use jackknify for line inference. You can find them in the docs/notebooks folder. Also, check out the documentation here.
Owner
- Name: Joshiwa van Marrewijk
- Login: Joshiwavm
- Kind: user
- Location: Munich
- Company: ESO
- Website: joshiwa.com
- Repositories: 2
- Profile: https://github.com/Joshiwavm
Astronomer
Citation (CITATION.cff)
cff-version: 1.1.0 message: "If you use this software, please cite it as below." authors: - given-names: Joshiwa van Marrewijk orcid: https://orcid.org/0000-0001-9830-3103 title:Joshiwavm/jackknify: Release Jackknify 0.3.0 version: first_release date-released: 2024-06-24
GitHub Events
Total
- Watch event: 1
- Push event: 1
- Pull request event: 2
Last Year
- Watch event: 1
- Push event: 1
- Pull request event: 2
Dependencies
- astropy *
- casatasks *
- casatools *
- h5py *
- math *
- numpy *
- os *
- pandas *
- shutil *
- tqdm *
- actions/checkout v4 composite
- actions/configure-pages v5 composite
- actions/deploy-pages v4 composite
- actions/upload-pages-artifact v3 composite
- astropy *
- casatasks *
- casatools *
- casdata *
- interferopy *
- ipython *
- matplotlib *
- numpy *
- scipy *