Metadata-Version: 2.1
Name: astro-salsa
Version: 1.0.0
Summary: Synthetic absorber catalog generator from astrophysical simulations
Home-page: https://github.com/biboyd/SALSA
Author: Brendan Boyd
Author-email: boyd.brendan@stonybrook.edu
License: BSD 3-Clause
Description: # SALSA
        [![Build Status](https://travis-ci.com/biboyd/SALSA.svg?branch=master)](https://travis-ci.com/biboyd/SALSA)
        [![Documentation Status](https://readthedocs.org/projects/salsa/badge/?version=latest)](https://salsa.readthedocs.io/en/latest/?badge=latest)
        [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/biboyd/SALSA/master?filepath=notebooks%2FExample_notebook.ipynb)
        [![DOI](https://joss.theoj.org/papers/10.21105/joss.02581/status.svg)](https://doi.org/10.21105/joss.02581)
        [![DOI](https://zenodo.org/badge/271633933.svg)](https://zenodo.org/badge/latestdoi/271633933)
        
        SALSA: Synthetic Absorption Line Surveyor Application is a Python tool that
        constructs synthetic absorber catalogs from hydrodynamic galaxy simulations.
        Salsa heavily utilizes [yt](https://yt-project.org/) to access simulation data
        and [Trident](http://trident-project.org/) to create light rays/sight lines and
        generate synthetic spectra.
        
        Observational studies generate large absorber catalogs by studying the absorption
        line spectra of distant quasars, as their light passes through intervening galaxies.
        Salsa can generate similar catalogs from cosmological and galactic simulations,
        allowing research to study these simulations from an observers perspective. This
        can give new insights into the data as well as help facilitate comparisons and
        collaboration between simulations and observations.
        
        Salsa allows us to dip into galactic simulations and start to chip away at the
        many unknowns of the universe
        
        A [JOSS paper](https://joss.theoj.org/papers/10.21105/joss.02581) was published for 
        SALSA and we recommend reading it for an overview of the package and its possible uses. 
        If you do use SALSA in a project we ask that you cite this paper.
        
        For detailed information on how to install and run salsa, Read the Docs
        [here](https://salsa.readthedocs.io)
        
        ## Install
        If you have all the dependencies installed, you can clone the repository and
        run these commands:
        ```
          $ git clone https://github.com/biboyd/SALSA.git
          $ cd SALSA
          $ pip install -e .
          $ python
          >>> import salsa
        ```
        Now you should be all set to code!
        
        ### Installing dependencies
        To help with installing dependencies, `enivronment.yml` is included in the
        repository. First,
        [install conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html)
        Then you should be able to create a conda environment via:
        ```
          $ conda env create --file environment.yml
          $ conda activate salsa-env
        ```
        Note that you need gcc compiler installed (which it often already is on most machines).
        For a more detailed description see the
        [installation guide](https://salsa.readthedocs.io/en/latest/installation.html)
        which also includes tips if you want to install dependencies on your own.
        
        ## Getting Started
        For an annotated example [go here](https://salsa.readthedocs.io/en/latest/annotated_example.html). Or launch an interactive jupyter hosted on Binder
        [here](https://mybinder.org/v2/gh/biboyd/SALSA/master?filepath=notebooks%2FExample_notebook.ipynb) (note that the notebook
        may take some time to load as it generally has to build the repository).
        
        If you want to explore on your own, the easiest way to get started is use
        `salsa.generate_catalog()`. This takes:
          * The simulation dataset
          * Number of light rays/sightlines to make
          * Directory to save those light rays
          * A list of ions
          * Some other optional parameters.  
        This creates a number light rays and then extracts absorbers for each ion. A
        `pandas.DataFrame` is returned with information about all the absorbers which
        can then be further analyzed.
        
        ## Contributing Guidelines
        All contributions are welcome! This is an open-source project, built on many
        other open-source projects. Contributing can take many forms including:
        contributing code, testing and experimenting, or offering ideas for different
        features.
        
        If you are interested in contributing you can contact us directly at
        boyd.brendan@stonybrook.edu or add an issue on this Github page.
        
Keywords: simulation,spectra,astronomy,astrophysics
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
