disruptive 1.4.1

Disruptive Technologies Python API.

Disruptive Technologies Python Client

Documentation

• Python API Reference
• Developer Documentation

Installation

The package can be installed through pip:

pip install --upgrade disruptive

or from source:

pip install .

Requirements

• Python 3.7, 3.8, 3.9, 3.10

Authentication

The package is authenticated by providing Service Account credentials in either of the following ways.

• By setting the following environment variables:

export DT_SERVICE_ACCOUNT_KEY_ID="<SERVICE_ACCOUNT_KEY_ID>"
export DT_SERVICE_ACCOUNT_SECRET="<SERVICE_ACCOUNT_SECRET>"
export DT_SERVICE_ACCOUNT_EMAIL="<SERVICE_ACCOUNT_EMAIL>"

• By providing the credentials programmatically:

import disruptive as dt

dt.default_auth = dt.Auth.service_account(
    key_id="<SERVICE_ACCOUNT_KEY_ID>",
    secret="<SERVICE_ACCOUNT_SECRET>",
    email="<SERVICE_ACCOUNT_EMAIL>",
)

See Python API Authentication for more details.

Usage

Once authenticated, most functionality can be accessed through resource methods on the following format.

disruptive.<Resource>.<method>()

A few common uses are showcased in the snippet below. See the Python API Reference for full documentation.

import disruptive as dt

# Fetch a sensor, specified by its ID.
sensor = dt.Device.get_device('<DEVICE_ID>')

# Printing the returned object will list all attributes.
print(sensor)

# Set a new label on the sensor.
dt.Device.set_label(sensor.device_id, sensor.project_id, key='nb', value='99')

# Get touch- and temperature event history for the sensor.
history = dt.EventHistory.list_events(
    sensor.device_id,
    sensor.project_id,
    event_types=[
        dt.events.TOUCH,
        dt.events.TEMPERATURE,
    ]
)

# Initiate an event stream for all devices in the sensor's project.
for event in dt.Stream.event_stream(sensor.project_id):
    # Print new events data as they arrive.
    print(event.data)

Logging

The simplest method is enabled by setting disruptive.log_level with a string level.

dt.log_level = 'info'

If more fine-grained control is desired, the standard library logging can also be used.

logging.basicConfig(
    filename='example.log',
    format='[%(asctime)s.%(msecs)03d] %(levelname)-8s - %(message)s',
    datefmt='%Y-%m-%d %H:%M:%S',
)
logging.getLogger('disruptive').setLevel(logging.INFO)

For both methods, the standard levels debug, info, warning, error, and critical are available.

Examples

A few examples has been provided. Before running, the required environment variables listed at the start of each example must be set.

python examples/example_name.py

Exceptions

If a request is unsuccessful or has been provided with invalid parameters, an exception is raised. A list of available exceptions are available in the API Reference.

Development

Set up the development virtualenv environment:

make

Run unit-tests against the currently active python version:

make test

Lint the package code using MyPy and flake8:

make lint

Build the package distribution:

make build