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docs/SCOUSE_LOGO.png

About

Multi-component spectral line decomposition with scousepy. scousepy is a package for the analysis of spectral line data. For a comprehensive description of the algorithm and functionality please head to scousepy.readthedocs.io.

Note: scousepy has undergone some major updates in the latest release, namely:

  • Workflow update -- scousepy now uses config files for set up. These control basic parameters for use throughout the workflow
  • GUI for S1 - basic functionality is the same. Can also be run without using the config files
  • GUI for S2 - added derivative spectroscopy for providing initial guesses
  • Former S4 now merged into S3
  • Former S5 and S6 now merged
  • GUI for S3 - adaptive fit checker and fitting functionality

I am in the process of updating the documentation and tutorials. If you need assistance on running the new version of scousepy, please get in touch. For now I have included a simple example script in the tutorials directory.

Basic description

scousepy includes tools for the decomposition of both data cubes, individual spectra, and lists of specta.

Cube Fitting

Cube fitting with scousepy is divided into 4 main stages:

1. Defining the coverage. Here the use informs scousepy where to fit. scousepy will compute basic noise and moments, allowing the user to define a mask for fitting. Once defined, scousepy creates a grid of macropixels with a user defined size and extracts a spatially averaged spectrum from each. This can be run using the GUI or automatically using the configuration files.

2. Fitting the macropixels. scousepy uses a technique referred to as derivative spectroscopy to identify the number of components and their key properties. Fitting is performed via an interactive GUI.

3. Automated fitting. scousepy uses the best-fitting solutions from the macropixels defined in stage 2 as initial guesses for an automated fitting process that is controlled via user-defined tolerance levels.

4. Quality assessment. Here scousepy provides a GUI for quality assessment allowing the user to visually inspect their decomposition.

Single Spectra and lists of spectra

scousepy includes functionality for fitting individual or lists of spectra using the derivative spectroscopy technique. Further information and tutorials can be found at scousepy.readthedocs.io.

Installing scousepy

Requirements

scousepy requires the following packages:

Please ensure that you are using the latest developer versions of both pyspeckit and spectral-cube (Github installation).

Note that for interactive fitting with pyspeckit you may need to customise your matplotlib configuration. Namely, if you're using scousepy on a Mac you will most likely need to change your backend from 'macosx' to 'Qt5Agg' (or equiv.). You can find some information about how to do this here

Installation

To install the latest version of scousepy, you can type:

git clone https://github.com/jdhenshaw/scousepy
cd scousepy
python setup.py install

You may need to add the --user option to the last line if you do not have root access.

Reporting issues and getting help

Please help to improve this package by reporting issues via GitHub. Alternatively, you can get in touch here.

Developers

This package was developed by:

  • Jonathan Henshaw

Contributors include:

  • Adam Ginsburg
  • Manuel Riener

Citing scousepy

If you make use of this package in a publication, please consider the following acknowledgements...:

@ARTICLE{Henshaw19,
    author = {{Henshaw}, J.~D. and {Ginsburg}, A. and {Haworth}, T.~J. and
       {Longmore}, S.~N. and {Kruijssen}, J.~M.~D. and {Mills}, E.~A.~C. and
       {Sokolov}, V. and {Walker}, D.~L. and {Barnes}, A.~T. and {Contreras}, Y. and
       {Bally}, J. and {Battersby}, C. and {Beuther}, H. and {Butterfield}, N. and
       {Dale}, J.~E. and {Henning}, T. and {Jackson}, J.~M. and {Kauffmann}, J. and
       {Pillai}, T. and {Ragan}, S. and {Riener}, M. and {Zhang}, Q.},
    title = "{`The Brick' is not a brick: a comprehensive study of the structure and dynamics of the central molecular zone cloud G0.253+0.016}",
    journal = {\mnras},
    archivePrefix = "arXiv",
    eprint = {1902.02793},
    keywords = {turbulence, stars: formation, ISM: clouds, ISM: kinematics and dynamics, ISM: structure, galaxy: centre},
    year = 2019,
    month = may,
    volume = 485,
    pages = {2457-2485},
    doi = {10.1093/mnras/stz471},
    adsurl = {http://adsabs.harvard.edu/abs/2019MNRAS.485.2457H},
    adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@ARTICLE{Henshaw2016,
       author = {{Henshaw}, J.~D. and {Longmore}, S.~N. and {Kruijssen}, J.~M.~D. and {Davies}, B. and {Bally}, J. and {Barnes}, A. and {Battersby}, C. and {Burton}, M. and {Cunningham}, M.~R. and {Dale}, J.~E. and {Ginsburg}, A. and {Immer}, K. and {Jones}, P.~A. and {Kendrew}, S. and {Mills}, E.~A.~C. and {Molinari}, S. and {Moore}, T.~J.~T. and {Ott}, J. and {Pillai}, T. and {Rathborne}, J. and {Schilke}, P. and {Schmiedeke}, A. and {Testi}, L. and {Walker}, D. and {Walsh}, A. and {Zhang}, Q.},
        title = "{Molecular gas kinematics within the central 250 pc of the Milky Way}",
      journal = {\mnras},
     keywords = {stars: formation, ISM: clouds, ISM: kinematics and dynamics, ISM: structure, Galaxy: centre, galaxies: ISM, Astrophysics - Astrophysics of Galaxies},
         year = 2016,
        month = apr,
       volume = {457},
       number = {3},
        pages = {2675-2702},
          doi = {10.1093/mnras/stw121},
archivePrefix = {arXiv},
       eprint = {1601.03732},
 primaryClass = {astro-ph.GA},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2016MNRAS.457.2675H},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

Citations courtesy of ADS.

Please also consider acknowledgements to the required packages in your work.