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        "author": "Roberto Haddock Lobo",
        "author_email": "rhlobo+bigtempo@gmail.com",
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            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Financial and Insurance Industry",
            "Intended Audience :: Information Technology",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2.7",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "Flask-BigTempo\n--------------\n\nFlask extension offering several utilities for creating bigtempo servers.\n\n## Installing\n\n`pip` should do the job:\n```bash\n$ pip install flask-bigtempo\n```\n\nThere is a `requirements.txt` file is you want to checkout the source code directly.\n\n-------------------------------------------------------------------------------\n\n## Datastore API\nIt is meant to store timeseries data.\n\nEach timeseries is identified by the conjunction of an `reference` and a `symbol`.\nIt is structured this way so that the source (or type) of the data can be declared as the `reference`.\nExample:\n- While in the stockmarket context, the `reference` can be NASDAQ while `symbol` is left for the company stock.\n- Storing country 'UN Human Development Index' the `reference` can be `HDI` while the `symbol` would take a country's name or code.\n\nHere you can find:\n\n- A __Storage__ implementation that offers methods to save / update, retrieve and delete `pandas dataframes`\n- A __flask extension__ that exposes an REST API that handles data as json\n- A __REST client__ that can communicate with the REST API\n- A __command line script__ that enables shell usage of the REST API\n- Some __bigtempo datasources__ that allows easy integration, after all, `store api` was conceived exactly to serve data to `bigtempo`.\n\n\n### Storage implementation\nFor the moment the is only one implementation based on SQLAlchemy.\nYou can find it at `flask_bigtempo/store/storages.py`.\nExample usage can be found `flask_bigtempo/store/clients.py`\n\n\n### The flask extension:\nYou can easily have your flask server expose `bigtempo store api`:\n```python\n#!/usr/bin/env python\n\n\nfrom flask import Flask\nfrom flask.ext.sqlalchemy import SQLAlchemy\nfrom flask.ext.bigtempo import DatastoreAPI\n\n\napp = Flask(__name__)\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite://'\n\ndb = SQLAlchemy(app)\n\n# The datastore api needs flask's app instance and a sqlalchemy engine\ndatastore = DatastoreAPI(app, db.engine)\n\n\n@app.route('/')\ndef hello_world():\n    return '''\n           <h1>Welcome!</h1>\n           The routes for datastore can be found at \"/api/store/\"<br/>\n           '''\n\n\nif __name__ == '__main__':\n    app.run(debug=True)\n```\n\nThe following methods are made available:\n\n- Data retrieval: __GET__ /api/store/{reference}/{symbol}\n- Data insertion: __PUT__ /api/store/{reference}/{symbol}\n- Data deletion: __DELETE__ /api/store/{reference}/{symbol}\n\nOptionally, you can use aditional url parameters:\n\n- `json_format` (eg.: `?json_format=index`).\n- `date_format` (eg.: `?date_format=iso`).\nThe formats available are the same provided by the pandas `to_json` and `read_json` methods.\n\n\n### REST Clients\nYou can find them at `flask_bigtempo/store/clients.py`:\n\n- `DFStoreRestClient` works with Dataframes as input and output;\n- `JSONStoreRestClient` works with JSON as input and output;\n\nUsing it should be as simple as:\n```python\nimport flask_bigtempo.store.clients as store_client\n\napi = store_client.DFStoreRestClient()\ndataframe = api.retrieve('HDI', 'Brazil')\n```\n\n\n### CL Script\nIts code is available at the `scripts` directory.\nAs soon as you install this lib at your computer, `store_api` should be available on the PATH.\n\nYou can learn more about its usage by executing `store_api -h`\n\n\n### Bigtempo DataSources\nAvailable at `flask_bigtempo/store/datasources.py`.\n\nYou can import it by:\n```python\nimport flask_bigtempo.store.datasources as datasources\n\nds = datasources.RESTStoreDatasource('example')\n```\n\nAnd all that is left is to register it to your bigtempo engine.",
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