base.rst

General Documentation

What is explained here applies to all the services available in this package. For specific documentation on each service, please refer to their page.

Authentication

Most (all?) of the dandelion.eu services require authentication. You can find your authentication token on your dashboard and pass it to the class constructor, for example:

>>> from dandelion import Datagem
>>> administrative_regions = Datagem('administrative-regions',
...                                  token='7682xxxxxeh2nb2v2mxxxxxxxjh9sbxxxx')

If you need to instantiate more services, you can specify your authentication token just once using dandelion.default_config

>>> from dandelion import default_config
>>> default_config['token'] = '7682xxxxxeh2nb2v2mxxxxxxxjh9sbxxxx'

>>> from dandelion import DataTXT, Datagem, Sentiment
>>> datatxt = DataTXT()
>>> administrative_regions = Datagem('administrative-regions')
>>> sentiment = Sentiment()

Legacy authentication system

The client still supports authentication through $app_id and $app_key . The use is the same as for the token: you can pass app_id and app_key to the class contructor or specify them once using dandelion.default_config

>>> from dandelion import Datagem
>>> administrative_regions = Datagem('administrative-regions',
...                                  app_id='24cxxxx',
...                                  app_key='8697xxxx8b99xxxxeecbxxxxb163xxxx')

                                    OR

>>> from dandelion import default_config
>>> default_config['app_id'] = '24cxxxx'
>>> default_config['app_key'] = '8697xxxx8b99xxxxeecbxxxxb163xxxx'

>>> from dandelion import DataTXT, Datagem, Sentiment
>>> datatxt = DataTXT()
>>> administrative_regions = Datagem('administrative-regions')
>>> sentiment = Sentiment()

Notes on authentication

You have to specify the token OR the pair (app_id, app_key): if you specify them both, the client will raise an exception.

Moreover, the client will use what is stored in default_config only if you don't pass anything to the class constructor.

Caching your queries

To avoid wasting units (and time) you can easily cache all your requests using the classes provided by the dandelion.cache package. Currently just FileCache is available, but more can be easily implemented using the abstract cache class.

To enable caching, instantiate a cache object and pass it to the services you need:

>>> from dandelion import DataTXT
>>> from dandelion.cache import FileCache
>>> datatxt = DataTXT(cache=FileCache('.cache_dir'))