.. image:: https://github.com/MicheleDelliVeneri/ALMASim/blob/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.