Metadata-Version: 1.1
Name: ADSBibTeX
Version: 1.0.8
Summary: ADSBibTeX
Home-page: https://github.com/ryanvarley/adsbibtex
Author: Ryan Varley
Author-email: ryan@ryanvarley.uk
License: MIT
Description: # ADSBibTeX
        [![Build Status](https://api.travis-ci.org/ryanvarley/adsbibtex.png?branch=master)](https://travis-ci.org/ryanvarley/adsbibtex)
        [![Coverage Status](https://coveralls.io/repos/ryanvarley/adsbibtex/badge.svg?branch=master&service=github)](https://coveralls.io/github/ryanvarley/adsbibtex?branch=master)
        
        Builds a bibtex file for a LaTeX document using by querying a list of bibcodes with NASA ADS
        
        ## Why?
        
        Two main reasons
        
        1. If you cite a preprint paper, this will automatically update the entry to the published version, when it is published
        2. For really long bibtex files, its much easier to manage a list of bibcodes than bibtex entries, and you can divide
         them into sections with comments i.e.
        
        ```bash
        # Transmission Spectroscopy
        2008Natur.452..329S  Swain2008  # The presence of methane in the atmosphere of an extrasolar planet
        2006AGUSM.A21A..06T  Tinetti2006
        
        # Detrending Techniques
        2013ApJ...766....7W  Waldmann2013
        ```
        
        It is also fast after the initial run, entries are cached so they are only fetched from ADS again after they are older than your ttl (time to live) setting in the config. This means you can integrate it into your latex compilation without worrying about it adding a significant overhead to your build.
        
        ## Setup and installation
        
        You'll need an ADS API key, the following is from the `ads` [module docs](https://github.com/andycasey/ads)
        
        1. You'll need an API key from NASA ADS labs. Sign up for the newest version of ADS search at https://ui.adsabs.harvard.edu, visit account settings and generate a new API token. The official documentation is available at https://github.com/adsabs/adsabs-dev-api
        2. When you get your API key, save it to a file called ``~/.ads/dev_key`` or save it as an environment variable named ``ADS_DEV_KEY``
        
        Then install this package
        
        ```bash
        pip install adsbibtex
        ```
        
        or get the latest development version from here with
        
        ```bash
        git clone https://github.com/ryanvarley/adsbibtex.git
        cd adsabs-dev-api
        python setup.py install
        ```
        
        
        ## Usage
        
        ```bash
        adsbibtex <config_file>
        ```
        
        config_file defaults to `config.adsbib`, see the next section for an example file
        
        ## Example config file
        
        The config file consists of a top section of `yaml` where the config is stored and a list of bibcode citename entries
        (after `---`). Comments can be entered with `#`.
        
        All entries must have a valid bibcode, if no citename is given then the bibcode will be the citename
        
        ```bash
        # YAML front matter (config)
        cache_ttl:   24  # hours
        cache_file:  adsbibtex_cache
        bibtex_file: example.bib
        ---
        #   Bibcode          Name          # Optional Comment
        2008Natur.452..329S  Swain2008
        2006AGUSM.A21A..06T                # no name needed
        
        # You can use comments to divide papers into sections
        2013ApJ...766....7W  Waldmann2013  # You could put the paper title or subject here
        ```
        
        Running `adsbibtex` on this file produces the following output
        
        ```bibtex
        @ARTICLE{Swain2008,
           author = {{Swain}, M.~R. and {Vasisht}, G. and {Tinetti}, G.},
            title = "{The presence of methane in the atmosphere of an extrasolar planet}",
          journal = {\nat},
             year = 2008,
            month = mar,
           volume = 452,
            pages = {329-331},
              doi = {10.1038/nature06823},
           adsurl = {http://adsabs.harvard.edu/abs/2008Natur.452..329S},
          adsnote = {Provided by the SAO/NASA Astrophysics Data System}
        }
        
        @ARTICLE{2006AGUSM.A21A..06T,
           author = {{Tinetti}, G. and {Meadows}, V.~S. and {Crisp}, D. and {Kiang}, N. and 
        	{Fishbein}, E. and {Kahn}, B. and {Turnbull}, M.},
            title = "{Detectability of Surface and Atmospheric Signatures in the Disk-averaged Spectra of the Earth}",
          journal = {AGU Spring Meeting Abstracts},
         keywords = {5210 Planetary atmospheres, clouds, and hazes (0343), 5704 Atmospheres (0343, 1060), 0343 Planetary atmospheres (5210, 5405, 5704), 0406 Astrobiology and extraterrestrial materials},
             year = 2006,
            month = may,
            pages = {A6},
           adsurl = {http://adsabs.harvard.edu/abs/2006AGUSM.A21A..06T},
          adsnote = {Provided by the SAO/NASA Astrophysics Data System}
        }
        
        @ARTICLE{Waldmann2013,
           author = {{Waldmann}, I.~P. and {Tinetti}, G. and {Deroo}, P. and {Hollis}, M.~D.~J. and 
        	{Yurchenko}, S.~N. and {Tennyson}, J.},
            title = "{Blind Extraction of an Exoplanetary Spectrum through Independent Component Analysis}",
          journal = {\apj},
        archivePrefix = "arXiv",
           eprint = {1301.4041},
         primaryClass = "astro-ph.EP",
         keywords = {methods: data analysis, methods: observational, methods: statistical, planets and satellites: atmospheres, planets and satellites: individual: HD189733b, techniques: spectroscopic },
             year = 2013,
            month = mar,
           volume = 766,
              eid = {7},
            pages = {7},
              doi = {10.1088/0004-637X/766/1/7},
           adsurl = {http://adsabs.harvard.edu/abs/2013ApJ...766....7W},
          adsnote = {Provided by the SAO/NASA Astrophysics Data System}
        }
        ```
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 2.7
Classifier: Topic :: Scientific/Engineering :: Astronomy
