Metadata-Version: 1.1
Name: papernetwork
Version: 0.1.0
Summary: Collect and analyze scientific literature from Semantic Scholar
Home-page: https://github.com/EvdH0/papernetwork
Author: Eric van der Helm
Author-email: i@iric.nl
License: MIT
Description: papernetwork
        =============
        
        
        .. image:: https://img.shields.io/pypi/v/papernetwork.svg
            :target: https://pypi.python.org/pypi/papernetwork
            :alt: Latest PyPI version
        
        .. image:: https://travis-ci.com/EvdH0/papernetwork.svg?token=Fxxpxvyc3NhNSDqPmztx&branch=master
           :target: https://travis-ci.com/EvdH0/papernetwork
           :alt: Latest Travis CI build status
        
        Collect and analyze scientific literature from Semantic Scholar
        
        Examples
        --------
        
        Basic example of loading data from `Semantic Scholar <https://www.semanticscholar.org/>`_ via the `API <https://api.semanticscholar.org/>`_, be sure to read the `dataset license agreement <https://api.semanticscholar.org/corpus/legal/>`_::
        
            from papernetwork.core import PaperNetwork, Paper, PaperList
        
            # Define a list of DOI's you would like to download
            list_of_dois = ['10.1093/nar/gkw1328', '10.1128/CMR.00016-17', '10.1038/s41564-019-0626-z']
        
            # Pull the papers from semanticscholar.org via the API and parse them
            my_network = PaperNetwork(doi_list=list_of_dois)
        
            # Show the titles in the PaperNetwork object
            print(my_network)  # Rapid resistome mapping using nanopore sequencing, Whole-Genome Sequencing of Bacterial Pathogens: the Future of Nosocomial Outbreak Analysis., Rapid MinION profiling of preterm microbiota and antimicrobial-resistant pathogens
        
            # The papers are internally stored in a OrderedSet-like object, accessible via the collection 
            my_collection = my_network.collection
        
            # The papers themselves are individually stored in a Dict-like object
            my_paper = my_collection[0]  # Take the first paper in the list
        
        
            print(my_paper['title'])
        
            # They keys of the object are defined by the Semantic Scholar API, see https://api.semanticscholar.org/
            # To get an overview of the available keys
            for key, value in my_collection[0].items():
                print(key)
        
            # Currently the following keys are supplied by semanticscholar.org
            # arxivId
            # authors
            # citationVelocity
            # citations
            # corpusId
            # doi
            # fieldsOfStudy
            # influentialCitationCount
            # is_open_access
            # is_publisher_licensed
            # paperId
            # references
            # title
            # topics
            # url
            # venue
            # year 
        
        
        A more elaborate example can be found in the `examples directory <examples/simple_example.py>`_ to get started.
        
        More detailed usage can be found in the `test directory <tests/test_papernetwork.py>`_ as well.
        
        Run using::
            python -m examples.simple_example
        
        Installation
        ------------
        Use pip to `install papernetwork from
        PyPI <https://pypi.python.org/pypi/papernetwork>`_ (recommend doing this
        inside a `virtual
        environment <http://docs.python-guide.org/en/latest/dev/virtualenvs/>`_)::
        
            pip install papernetwork
        
        Or from source::
        
            git clone --recursive https://github.com/evdh0/papernetwork.git
            cd papernetwork
            python setup.py install
        
        
        
        Licence
        -------
        The MIT License (MIT)
        
        
        Authors
        -------
        
        `papernetwork` was written by `Eric van der Helm <i@iric.nl>`_.
        
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
