almasim

.. image:: https://github.com/MicheleDelliVeneri/ALMASim/raw/main/pictures/ALMASimBanner.jpeg

|Python version| |PyPI| |Repostatus| |Zenodo| |Tests| |Documentation Status| |CodeCov|

.. |Tests| image:: https://github.com/MicheleDelliVeneri/ALMASim/actions/workflows/lint_and_test.yml/badge.svg?branch=main :target: https://github.com/MicheleDelliVeneri/ALMASim/actions/workflows/lint_and_test.yml .. |PyPI| image:: https://img.shields.io/pypi/v/ALMASim?color=green&label=PyPI :target: https://pypi.org/project/ALMASim/ .. |Documentation Status| image:: https://readthedocs.org/projects/almasim/badge/?version=latest :target: https://almasim.readthedocs.io .. |Python Version| image:: https://img.shields.io/pypi/pyversions/ALMASim?color=green&label=Python%20Version :target: https://pypi.org/project/ALMASim/ .. |Upload Python Package| image:: https://github.com/MicheleDelliVeneri/ALMASim/actions/workflows/python-publish.yml/badge.svg :target: https://github.com/MicheleDelliVeneri/ALMASim/actions/workflows/python-publish.yml .. |Zenodo| image:: https://zenodo.org/badge/501944702.svg :target: https://zenodo.org/doi/10.5281/zenodo.12684237 .. |Repostatus| image:: https://www.repostatus.org/badges/latest/active.svg .. |CodeCov| image:: https://codecov.io/github/MicheleDelliVeneri/ALMASim/graph/badge.svg?token=9SZVW78DR2 :target: https://codecov.io/github/MicheleDelliVeneri/ALMASim

Overview

ALMASim is a package to generate mock observations of radio sources as observed by the Atacama Large Millimetre/Submillimetre Array (ALMA). ALMASim primary goal is to allow users to generate simulated datasets on which to test deconvolution and source detection models. ALMASim is intended to leverage MPI parallel computing (Dask, Slurm, PBS) on modern HPC clusters to generate thousands of ALMA data cubes, but can also work on laptopts. ALMA database or a prepared catalogue is queried to sample observational metadata such as band, bandwidth, integration time, antenna configurations and so on. ALMASim employs the MARTINI Package (https://github.com/kyleaoman/martini), and the Illustris Python Package (https://github.com/illustristng/illustris_python).

Citing ALMASim

If you use ALMASim in your research, please cite the following paper:

::

@ARTICLE{10.1093/mnras/stac3314, author = {Delli Veneri, Michele and Tychoniec, Łukasz and Guglielmetti, Fabrizia and Longo, Giuseppe and Villard, Eric}, title = "{3D Detection and Characterisation of ALMA Sources through Deep Learning}", journal = {Monthly Notices of the Royal Astronomical Society}, year = {2022}, month = {11}, issn = {0035-8711}, doi = {10.1093/mnras/stac3314}, url = {https://doi.org/10.1093/mnras/stac3314}, note = {stac3314}, eprint = {https://academic.oup.com/mnras/advance-article-pdf/doi/10.1093/mnras/stac3314/47014718/stac3314.pdf} }

ALMASim entry: https://doi.org/10.1093/mnras/stac3314

Installation Notes

ALMASim works with python3 (version 3.12), and does not support python2. First create a virtual environment with python3 and activate it. Then install the required packages with pip:

• Create the Python Environment:python3.12 -m venv astro-env
• Activate it: source astro-env/bin/activate (in case of your shell is Bash, otherwise check the other activations scripts within the bin folders)

Installing with pip

• pip install almasim

Installing from GitHub

• Clone the ALMASim Repository: git clone https://github.com/MicheleDelliVeneri/ALMASim.git
• Install packages from the requirements file: pip install -e .

Adding Kaggle API

• Login into Kaggle and go to: https://www.kaggle.com/settings
• Click on create new token, this will produce a kaggle.json file which must be saved in your home folder: ~/.kaggle/kaggle.json

Getting started

To run the simulation, just run:

python -c "from almasim import run; run()"

Notes

Cube size will dictate simulation speed and RAM usage. To gauge what you can affort to run, we advice to start with a single simulation of a 256 x 256 x 256 cube.