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flaskbigtempo 0.8

Flask-BigTempo--------------Flask extension offering several utilities for creating bigtempo servers.## Installing`pip` should do the job:```bash$ pip install flask-bigtempo```There is a `requirements.txt` file is you want to checkout the source code directly.-------------------------------------------------------------------------------## Datastore APIIt is meant to store timeseries data.Each timeseries is identified by the conjunction of an `reference` and a `symbol`.It is structured this way so that the source (or type) of the data can be declared as the `reference`.Example:- While in the stockmarket context, the `reference` can be NASDAQ while `symbol` is left for the company stock.- Storing country 'UN Human Development Index' the `reference` can be `HDI` while the `symbol` would take a country's name or code.Here you can find:- A __Storage__ implementation that offers methods to save / update, retrieve and delete `pandas dataframes`- A __flask extension__ that exposes an REST API that handles data as json- A __REST client__ that can communicate with the REST API- A __command line script__ that enables shell usage of the REST API- Some __bigtempo datasources__ that allows easy integration, after all, `store api` was conceived exactly to serve data to `bigtempo`.### Storage implementationFor the moment the is only one implementation based on SQLAlchemy.You can find it at `flask_bigtempo/store/storages.py`.Example usage can be found `flask_bigtempo/store/clients.py`### The flask extension:You can easily have your flask server expose `bigtempo store api`:```python#!/usr/bin/env pythonfrom flask import Flaskfrom flask.ext.sqlalchemy import SQLAlchemyfrom flask.ext.bigtempo import DatastoreAPIapp = Flask(__name__)app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite://'db = SQLAlchemy(app)# The datastore api needs flask's app instance and a sqlalchemy enginedatastore = DatastoreAPI(app, db.engine)@app.route('/')def hello_world(): return ''' <h1>Welcome!</h1> The routes for datastore can be found at "/api/store/"<br/> '''if __name__ == '__main__': app.run(debug=True)```The following methods are made available:- Data retrieval: __GET__ /api/store/{reference}/{symbol}- Data insertion: __PUT__ /api/store/{reference}/{symbol}- Data deletion: __DELETE__ /api/store/{reference}/{symbol}Optionally, you can use aditional url parameters:- `json_format` (eg.: `?json_format=index`).- `date_format` (eg.: `?date_format=iso`).The formats available are the same provided by the pandas `to_json` and `read_json` methods.### REST ClientsYou can find them at `flask_bigtempo/store/clients.py`:- `DFStoreRestClient` works with Dataframes as input and output;- `JSONStoreRestClient` works with JSON as input and output;Using it should be as simple as:```pythonimport flask_bigtempo.store.clients as store_clientapi = store_client.DFStoreRestClient()dataframe = api.retrieve('HDI', 'Brazil')```### CL ScriptIts code is available at the `scripts` directory.As soon as you install this lib at your computer, `store_api` should be available on the PATH.You can learn more about its usage by executing `store_api -h`### Bigtempo DataSourcesAvailable at `flask_bigtempo/store/datasources.py`.You can import it by:```pythonimport flask_bigtempo.store.datasources as datasourcesds = datasources.RESTStoreDatasource('example')```And all that is left is to register it to your bigtempo engine.