{
    "info": {
        "author": "Friedrich Lindenberg",
        "author_email": "friedrich@pudo.org",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6"
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        "description": "# Babbage Analytical Engine\n\n[![Gitter](https://img.shields.io/gitter/room/openspending/chat.svg)](https://gitter.im/openspending/chat)\n[![Build Status](https://travis-ci.org/openspending/babbage.svg?branch=master)](https://travis-ci.org/openspending/babbage)\n[![Coverage Status](https://coveralls.io/repos/openspending/babbage/badge.svg?branch=master&service=github)](https://coveralls.io/github/openspending/babbage?branch=master)\n\n``babbage`` is a lightweight implementation of an OLAP-style database\nquery tool for PostgreSQL. Given a database schema and a logical model\nof the data, it can be used to perform analytical queries against that\ndata - programmatically or via a web API.\n\nIt is heavily inspired by [Cubes](http://cubes.databrewery.org/) but\nhas less ambitious goals, i.e. no pre-computation of aggregates, or\nmultiple storage backends.\n\n``babbage`` is not specific to government finances, and could easily be used e.g. for ReGENESIS, a project that makes German national statistics available via an API. The API functions by interpreting modelling metadata generated by the user (measures and dimensions).\n\n## Installation and test\n\n``babbage`` will normally included as a PyPI dependency, or installed via\n``pip``:\n\n```bash\n$ pip install babbage\n```\n\nPeople interested in contributing to the package should instead check out the\nsource repository and then use the provided ``Makefile`` to install the\nlibrary (this requires ``virtualenv`` to be installed):\n\n```bash\n$ git clone https://github.com/openspending/babbage.git\n$ cd babbage\n$ make install\n$ pip install tox\n$ export BABBAGE_TEST_DB=postgresql://postgres@localhost:5432/postgres\n$ make test\n```\n\n## Usage\n\n``babbage`` is used to query a set of existing database tables, using an \nabstract, logical model to query them. A sample of a logical model can be\nfound in ``tests/fixtures/models/cra.json``, and a JSON schema specifying\nthe model is available in ``babbage/schema/model.json``.\n\nThe central unit of ``babbage`` is a ``Cube``, i.e. a [OLAP cube](https://en.wikipedia.org/wiki/OLAP_cube) that uses the provided model metadata to construct queries \nagainst a database table. Additionally, the application supports managing\nmultiple cubes at the same time via a ``CubeManager``, which can be\nsubclassed to enable application-specific ways of defining cubes and where\ntheir metadata is stored.\n\nFuther, ``babbage`` includes a Flask Blueprint that can be used to expose\na standard API via HTTP. This API is consumed by the JavaScript ``babbage.ui``\npackage and it is very closely modelled on the Cubes and OpenSpending HTTP\nAPIs.\n\n### Programmatic usage\n\nLet's assume you have an existing database table of procurement data and\nwant to query it using ``babbage`` in a Python shell. A session might look\nlike this:\n\n```python\nimport json\nfrom sqlalchemy import create_engine\nfrom babbage.cube import Cube\nfrom babbage.model import Measure\n\nengine = create_engine('postgresql://localhost/procurement')\nmodel = json.load(open('procurement_model.json', 'r'))\n\ncube = Cube(engine, 'procurement', model)\nfacts = cube.facts(page_size=5)\n\n# There are 17201 rows in the table:\nassert facts['total_fact_count'] == 17201\n\n# There's a field called 'total_value':\nassert 'total_value' in facts['fields']\n\n# We can get metadata about it:\nconcept = cube.model['total_value']\nassert isinstance(concept, Measure)\nassert concept.label == 'Total Value'\n\n# And there's some actual data:\nassert len(facts['data']) == 5\nfact_0 = facts['data'][0]\nassert 'total_value' in fact_0\n\n# For dimensions, we can get all the distinct values:\nmembers = cube.members('supplier', cut='year:2015', page_size=500)\nassert len(members['data']) <= 500\nassert members['total_member_count']\n\n# And, finally, we can aggregate by specific dimensions:\naggregate = cube.aggregate(aggregates='total_value.sum',\n                           drilldowns='supplier|authority'\n                           cut='year:2015|authority.country:GB',\n                           page_size=500)\n# This translates to: \n#   Aggregate the procurement data by summing up the 'total_value'\n#   for each unique pair of values in the 'supplier' and 'authority'\n#   dimensions, and filter for only those entries where the 'year'\n#   dimensions key attribute is '2015' and the 'authority' dimensions\n#   'country' attribute is 'GB'. Return the first 500 results.\nassert aggregate['total_cell_count']\nassert len(aggregate['cells']) <= 500\naggregate_0 = aggregate['cells'][0]\nassert 'total_value.sum' in aggregate_0\n\n# Note that these attribute names are made up for this example, they\n# should be reflected from the model:\nassert 'supplier.code' in aggregate_0\nassert 'supplier.label' in aggregate_0\nassert 'authority.code' in aggregate_0\nassert 'authority.label' in aggregate_0\n```\n\n### Using the HTTP API\n\nThe HTTP API for ``babbage`` is a simple Flask [Blueprint](http://flask.pocoo.org/docs/latest/blueprints/) used to expose a small set of calls that correspond to\nthe cube functions listed above. To include it into an existing Flask\napplication, you would need to create a ``CubeManager`` and then\nconfigure the API like this: \n\n```python\nfrom flask import Flask\nfrom sqlalchemy import create_engine\nfrom babbage.manager import JSONCubeManager\nfrom babbage.api import configure_api\n\napp = Flask('demo')\nengine = \nmodels_directory = 'models/'\nmanager = JSONCubeManager(engine, models_directory)\nblueprint = configure_api(app, manager)\napp.register_blueprint(blueprint, url_prefix='/api/babbage')\n\napp.run()\n```\n\nOf course, you can define your own ``CubeManager``, for example if\nyou wish to retrieve model metadata from a database.\n\nWhen enabled, the API will expose a number of JSON(P) endpoints\nrelative to the given ``url_prefix``:\n\n* ``/``, returns the system status and version.\n* ``/cubes``, returns a list of the available cubes (name only).\n* ``/cubes/<name>/model``, returns full metadata for a given \n  cube (i.e. measures, dimensions, aggregates etc.)\n* ``/cubes/<name>/facts`` is used to return individual entries from\n  the cube in a non-aggregated form. Supports filters (``cut``), a\n  set of ``fields`` to return and a ``sort`` (``field_name:direction``),\n  as well as ``page`` and ``page_size``.\n* ``/cubes/<name>/members`` is used to return the distinct set of \n  values for a given dimension, e.g. all the suppliers mentioned in\n  a procurement dataset. Supports filters (``cut``), a and a ``sort``\n  (``field_name:direction``), as well as ``page`` and ``page_size``.\n* ``/cubes/<name>/aggregate`` is the main endpoint for generating \n  aggregate views of the data. Supports specifying the ``aggregates``\n  to include, the ``drilldowns`` to aggregate by, a set of filters\n  (``cut``), a and a ``sort`` (``field_name:direction``), as well\n  as ``page`` and ``page_size``.",
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