Metadata-Version: 2.1
Name: goodman_pipeline
Version: 1.3.2
Summary: Pipeline for reducing Goodman HTS data.
Home-page: https://github.com/soar-telescope/goodman_pipeline
Author: Simon Torres R., Bruno Quint, Cesar Briceño, David Sanmartin, 
Author-email: storres@ctio.noao.edu, bquint@ctio.noao.edu, cbriceno@ctio.noao.edu
License: BSD-3-Clause
Description: # Goodman High Throughput Spectrograph Data Reduction Pipeline
        
        [![Build Status](https://travis-ci.org/soar-telescope/goodman_pipeline.svg?branch=master)](https://travis-ci.org/soar-telescope/goodman_pipeline)
        [![Coverage Status](https://coveralls.io/repos/github/soar-telescope/goodman_pipeline/badge.svg?branch=master)](https://coveralls.io/github/soar-telescope/goodman_pipeline?branch=master)
        [![Documentation Status](https://readthedocs.org/projects/goodman/badge/?version=latest)](http://goodman.readthedocs.io/en/latest/?badge=latest)
        [![pypi](https://img.shields.io/pypi/v/goodman_pipeline.svg?style=flat)](https://pypi.org/project/goodman-pipeline/)
        [![astropy](http://img.shields.io/badge/powered%20by-AstroPy-orange.svg?style=flat)](http://www.astropy.org/)
        
        ![Goodman Pipeline](https://github.com/soar-telescope/goodman_pipeline/workflows/Goodman%20Pipeline/badge.svg?branch=master)
        ![Goodman Pipeline with Conda](https://github.com/soar-telescope/goodman_pipeline/workflows/Goodman%20Pipeline%20with%20Conda/badge.svg)
        ![Upload to PYPI](https://github.com/soar-telescope/goodman_pipeline/workflows/Upload%20to%20PYPI/badge.svg)
        
        ## Overview
        The Goodman High Throughput Spectrograph (Goodman HTS) Data-Reduction Pipeline
        is the SOAR Telescope's official data reduction pipeline for *Goodman HTS*.
        
        It has been fully developed in Python 3.5 and uses mostly astropy affiliated packages
        with the exception of [dcr](http://users.camk.edu.pl/pych/DCR/) which is an external tool
        that does cosmic ray identification and correction. The reason for using it
        instead of LACosmic is that it works very well for spectroscopic data and the
        results are evidently superior. Some of the negative aspects of using this
        external (meaning outside of Python domains) software were: The integration into
        the pipeline's workflow and the use of an external `dcr.par` parameter file.
         Such parameters have to be changed by hand and can't be integrated into the
         pipeline's workflow itself. In particular for binning 2x2 and custom ROI those
         parameters contained in _dcr.par_ has to be specifically tuned.
        
        ## Documentation
        
        You will find a user manual on [goodman.readthedocs.org](http://goodman.readthedocs.io/en/latest/)
        
        If you wish to know more about the instrument please check the 
        [SOAR website](http://www.ctio.noao.edu/soar/content/goodman-high-throughput-spectrograph)
        
        ## Having trouble?
        
        If you are having trouble operating the Goodman Pipeline we suggest the following
        procedure.
        
        * Check [existing issues](https://github.com/soar-telescope/goodman_pipeline/issues) or 
        open a [new Issue](https://github.com/soar-telescope/goodman_pipeline/issues/new) on GitHub.
        
        ## Development Team
        
        - [Simón Torres](https://github.com/simontorres) (SOAR Telescope Data Analyst - main code developer)
        - [César Briceño](https://github.com/cbaorion) (SOAR Telescope Scientist - team lead)
        - [Bruno Quint](https://github.com/b1quint) (Brazil Support Astronomer - code development adviser)
        
        
        ## Acknowledgements
        
        We acknowledge the important contribution of  [David Sanmartim](https://github.com/dsanmartim), who developed
        the initial incarnation of the redccd module. We thank [Tina Armond](https://github.com/tarmond) for her
        invaluable help in adding calibrated comparison lamps to the library of
        reference comparison lamps for wavelngth solution.
        
        Our work would not be possible without the friendly work atmosphere at CTIO
        headquarters in La Serena, were we can interact with our SOAR and CTIO
        colleagues in lively and useful discussions that have been important in making
        the Goodman pipeline possible.  We also acknowledge fruitful discussions and
        suggestions from our colleagues Bart Dunlop, Chris Clemens, and Erik Dennihy,
        at University of North Carolina at Chapel Hill.
          
        ## Citations:
          This pipeline makes extensive use of Astropy therefore you should cite as suggested
          on [Astropy Citation Page](https://github.com/astropy/astropy/blob/master/CITATION) as follows:
          
            This research made use of Astropy, a community-developed core Python package
            for Astronomy (Astropy Collaboration, 2013, 2018).
            
          It also uses [DCR](http://users.camk.edu.pl/pych/DCR/) for cosmic rays identification
          and removal. You should cite [this paper](http://adsabs.harvard.edu/abs/2004PASP..116..148P)
          
             Pych, W., 2004, PASP, 116, 148
        
        
Keywords: soar pipelines astronomy images spectroscopy
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Natural Language :: English
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: POSIX :: Other
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Topic :: Scientific/Engineering :: Astronomy
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
