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WolkAbout Python Connector library for connecting devices to WolkAbout IoT platform instance.

Prerequisite

• Python 3.7+

Installation

There are two ways to install this package

Installing with pip

python3 -m pip install wolk-connect

Installing from source

Clone this repository from the command line using:

git clone https://github.com/Wolkabout/WolkConnect-Python.git

Install dependencies by invoking python3 -m pip install -r requirements.txt

Install the package by running:

python3 setup.py install

Example Usage

Establishing connection with WolkAbout IoT platform

Create a device on WolkAbout IoT Platform by using the Simple example device type that is available on the platform. Note that device type can be created by importing `simple_example.json` file as new Device Type. This device type fits main.py and demonstrates the periodic sending of a temperature feed reading.

import wolk

# Setup the device credentials which you received
# when the device was created on the platform
device = wolk.Device(key="device_key", password="some_password")

# Pass your device and server information
# defaults to secure connection to Demo instance - comment out host, port and ca_cert
wolk_device = wolk.WolkConnect(
    device, host="insert_host", port=80, ca_cert="PATH/TO/YOUR/CA.CRT/FILE"
)

wolk_device.connect()

Adding feed values

wolk_device.add_feed_value(("T", 26.93))

# or multiple feed value readings
wolk_device.add_feed_value([("T", 27.11), ("H", 54.34), ("P", 1002.3)])

Optionally pass a timestamp as round(time.time()) * 1000. This is useful for maintaining data history when readings are not published immediately after adding them to storage. If timestamp is not provided, the library will assign a timestamp before placing the reading into storage.

Adding feed values with timestamp

# Add a signel feed reading to the message queue with the timestamp
wolk_device.add_feed_value(("T", 12.34), 1658315834000)

# Add a multi feed reading to the message queue with the timestamp
wolk_device.add_feed_value([("T", 12.34), ("H", 56.78), ("P", 1022.00)], 1658315834000)

Readings persistence and limit

Readings with method add_feed_value are added into local persistence. When adding messages be mindful of the message size that will be published. The default MQTT message size is 260MB, and since readings are of different sizes (based on the users use-case), check that the limit of readings in persistence will be under the MQTT limit for your broker. The default readings limit is set to 500000. You can change it with set_custom_readings_persistence_limit, if your readings are bigger, you can decrease the size, or if you have smaller readings, you can increase the size.

Data publish strategy

Stored feed values are pushed to WolkAbout IoT platform on demand by calling:

wolk_device.publish()

Adding feed values 'separated'

When adding feed values, the values themselves are persisted, which means when publishing all values will be placed in a single message and published as a single message.

If you would like to ensure different behavior, where you can add feed values that will be sent as a separate message from any other feed values, use the alternative method:

# Method arguments are exactly the same as for the `add_feed_value`
wolk_device.add_feed_value_separated([("T", 12.34), ("H", 56.78), ("P", 1022.00)], 1658315834000)

Disconnecting from the platform

wolk_device.disconnect()

Additional functionality

WolkConnect-Python library has integrated additional features which can perform full WolkAbout IoT platform potential. Explore the examples for more information.