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.
1 maintainer
Metadata
1 maintainer
Release files for almasim 2.1.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| almasim-2.1.3.tar.gz | 1.4 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| almasim-2.1.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 3.0 MB
Release files / almasim-2.1.3.tar.gz
| Download URL | almasim-2.1.3.tar.gz |
|---|---|
| Size | 1.4 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
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poetry/1.8.3 CPython/3.10.12 Linux/6.5.0-1022-azure
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Release files / almasim-2.1.3-py3-none-any.whl
| Download URL | almasim-2.1.3-py3-none-any.whl |
|---|---|
| Size | 1.5 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
poetry/1.8.3 CPython/3.10.12 Linux/6.5.0-1022-azure
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