TrustedTwin Python Client (external info)

The Trusted Twin Python library makes it easy to use the Trusted Twin user infrastructure API in Python applications. The library version is consistent with the Swagger version of the Trusted Twin API.

Client offers synchronous and asynchronous versions which can be used for communication with TT API.

Requirements

Library requires Python3.6+.

Installation

For synchronous API client:

pip install trustedtwin

To use asynchronous client:

pip install trustedtwin[async]

By default, additional packages required by asynchronous version are not installed.

Usage

Authorization

For synchronous client:

from trustedtwin.tt_api import TTRESTService

TT_SERVICE = TTRESTService(auth=$USER_SECRET)

For asynchronous client:

from trustedtwin.tt_api_async import TTAsyncRESTService

TT_SERVICE = TTAsyncRESTService(auth=$USER_SECRET)

Example calls

For synchronous client:

import json 
from trustedtwin.tt_api import TTRESTService

status, response = TTRESTService().create_user_secret($ACCOUNT_UUID, $PIN)
resp = json.loads(response)

TT_SERVICE = TTRESTService(tt_auth=resp['secret'])

_body = {
    'description': {
        'custom_name': 'custom_value'
    }
}

status, response = TT_SERVICE.create_twin(body=_body)
resp = json.loads(response)

For asynchronous client:

import json 
from trustedtwin.tt_api_async import TTAsyncRESTService

status, response = await TTAsyncRESTService().create_user_secret($ACCOUNT_UUID, $PIN)
resp = json.loads(response)

TT_SERVICE = TTAsyncRESTService(tt_auth=resp['secret'])

_body = {
    'description': {
        'custom_name': 'custom_value'
    }
}

status, response = await TT_SERVICE.create_twin(body=_body)
resp = json.loads(response)

For more information please navigate to the official documentation.

TrustedTwin Python Client (internal info)

Updating the library

To update the Python library to the newest version:

1. Upload a tt_api.yaml file corresponding with the respective API version.
2. In the Gitlab interface in the left-hand side pane, select Build and go to the Pipeline schedules section.
3. By the Deploy to official pyPi repository schedule, click on the Run pipeline schedule button.