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    "info": {
        "author": "Benjie Chen, Ginkgo Bioworks",
        "author_email": "benjie@ginkgobioworks.com, devs@ginkgobioworks.com",
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            "Development Status :: 5 - Production/Stable",
            "Environment :: Other Environment",
            "Environment :: Web Environment",
            "Framework :: Django :: 1.6",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: JavaScript",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: SQL",
            "Topic :: Database",
            "Topic :: Internet :: WWW/HTTP :: Indexing/Search",
            "Topic :: Internet :: WWW/HTTP :: WSGI :: Application",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Software Development :: Libraries",
            "Topic :: Software Development :: Libraries :: Python Modules",
            "Topic :: Text Processing :: General"
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        "description": "Curious\n=======\n\n.. image:: https://travis-ci.org/ginkgobioworks/curious.svg?branch=master\n    :target: https://travis-ci.org/ginkgobioworks/curious\n\nCurious traverses relationships in a relational database. Curious\nqueries allow users to explore relationships among objects, traverse\nrecursive relationships, and jump between loosely connected databases.\nCurious also provides a JSON interface to the objects. Users and\nprogrammers can use Curious queries in analysis scripts and\napplications.\n\nCurious favors a data centric model of application construction; Curious\nqueries expose normalized, relational data, reducing UI dependency on UI\nspecific API end-points serving denormalized data. Changing what data an\nUI needs no longer requires changing the UI specific end-points.\n\nCurious works well with deep data models with many relationships. A\nCurious query can traverse 10s of foreign key like relationships\nefficiently. Curious queries always operate on sets of objects, and can\nconnect a small number of objects via a relationship to a large number\nof objects, then via another relationship from the large number of\nobjects to a smaller set again. For example, Book to Authors to Country\nof Residence. Unlike GraphQL, Curious outputs relationships between\nobjects, rather than an ever growing tree of JSON representations of the\nobjects.\n\nExample\n-------\n\n::\n\n    Book.last(10) Book.author_set Author.country(continent__name=\"North America\")\n\nQuery Language\n--------------\n\nThe query language allows traversing models by identfying the relationships between them,\nthrough foreign keys in Django models, or arbitrary id-mapping functions. A Curious query\nis a space-separated set of terms, which connect models together by relationships.\n\nSeveral kinds of \"joins\" are possible using these relationship primitives:\n\n- A traditional `inner join` ``Book Book.author_set``\n- A `left outer join`: ``Book.last(10) ?(Book.author_set)``\n- A `recusrive join`: ``Parent.children_*``\n\nFurthermore, at each stage in a join, `filtering` can happen:\n\n- Filtering by `Django field lookups`_: ``Book Book.author_set(id__in=[2,3,4])``\n- Filtering by `subquery`: ``Book +(Book.author_set(id__in=[2,3,4]))``\n- Filtering by `exclusive subquery` ``Book -(Book.author_set(id__in=[2,3,4]))``\n\nFinally, relationships can generate `counts`:\n\n- Counting ``Book Book.author_set__count``\n\n.. _Django field lookups: https://docs.djangoproject.com/en/1.11/ref/models/querysets/#field-lookups\n\nConfiguring Curious\n-------------------\n\n::\n\n    import myapp.models\n    from curious import model_registry\n\n    def register():\n      model_registry.register(myapp.models)\n\nThen include ``register`` when your Django app boots up.\n\nUsing Curious\n-------------\n\nTurn off CSRF. Deploy it as a Django app.\n\nWriting Customized Relationships\n--------------------------------\n\nUse filter and deferred to real functions.\n\nDevelopment\n-----------\n\nRequires Docker. Spin up your container using the provided ``docker-compose.yml`` file and Makefile\nby running ``make image``. This creates an image with a correct git configuration for your user,\nwhich makes it easy to release. All of the commands you should need to run are defined the\n``Makefile`` as targets. All of the targets except for ``image``, are meant to be run inside the\nDocker container, but can be run from the host machine by having ``-ext`` appended to them. For\nexample, to run tests, you could either call ``make test`` from inside the container, or ``make\ntest-ext`` from the host.\n\nIf you are modifying the static assets during development, they can be recompiled with the\n``build_assets`` make task, or by calling ``python setup.py build_assets``.\n\n::\n\n    ./make test-ext\n\n\nDeployment\n----------\n\nDeployment of tagged commits happens to PyPI automatically via Travis CI. To bump and deploy a new\nversion, run ``make bump/[foo]-ext``, where ``[foo]`` is ``major``, ``minor``, or ``patch``. Then\n``git push origin --tags master``.\n\n\n\n",
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