Metadata-Version: 2.0
Name: cayenneLPP
Version: 0.5b3
Summary: A module for the Cayenne Low Power Packet format
Home-page: https://github.com/jojo-/py-cayenne-lpp
Author: Johan Barthelemy
Author-email: johan@uow.edu.au
License: MIT
Description-Content-Type: UNKNOWN
Keywords: Cayenne,Low Power Payload,LPP
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: Implementation :: MicroPython

cayenneLPP
==========

A Python module for the Cayenne Low Power Packet format

It aims to facilate the conversion of values typically read from sensors
to a sequence of bits (the payload) that can be send over a network
using the Cayenne Low Power Packet format. This format is particularly
suited for LPWAN networks such as LoRaWAN.

The payload can then be send for instance to an application of The
Things Network, a LoRaWAN-based community network, which will then
forward the data to a Cayenne application thanks to its Cayenne
integration.

The module consists of constants defining the different sensors and
their size and one class CayenneLPP containing the methods to build a
payload.

The constants have the format NAME\_SENSOR = (LPP id, Data size) where
LPP id is the IPSO id - 3200 and Data size is the number of bytes that
must be used to encode the reading from the sensor.

More info here:
https://mydevices.com/cayenne/docs/lora/#lora-cayenne-low-power-payload-overview

CayenneLPP class
----------------

The class contains the methods to pack data from sensors in a Cayenne
LPP format. The payload structure for the Cayenne LPP format is data
frame of the form: [SENSOR\_1, SENSOR\_2, ... SENSOR\_N], where the
format for one sensor is defined by: [CHANNEL, SENSOR TYPE, DATA].

The channel is an unique identifier for each sensor in the data frame.

The type of sensors compatible with this class are: - digital
input/output; - analog input/output; - luminosity (or illuminance)
sensor; - presence sensor; - temperature sensor; - humidity sensor; -
accelerometer; - barometer; - gyrometer; - gps.

An object of this class has 3 attributes: - payload: the data from one
or more senors formatted with the Cayenne LPP format; - size: the
maximum size of the payload (depends on the network on which the data
will be send to); - socket: a socket via which we can send the payload.

The constructor will generate an object with an empty payload and with a
maximum size.

It is possible to reset the payload with the 'reset' method and change
the maximum size with the 'change\_size' method.

The current payload and maximum size can be obtained with the methods
'get\_payload' and 'get\_size' methods.

You can send the payload via the socket using the 'send' method. The
socket can be set using the 'set\_socket' method.

To add the data from a sensor, the methods 'add\_sensor\_name' are
provided.

The documentation is provided in the directory ``doc`` of the GitHub
repository.

Example
~~~~~~~

::

    # importing the module
    import cayenneLPP

    # create a LoRa socket
    s = socket.socket(socket.AF_LORA, socket.SOCK_RAW)
    s.setsockopt(socket.SOL_LORA, socket.SO_DR, 0)
    s.setblocking(True)

    # creating Cayenne LPP packet
    lpp = cayenneLPP.CayenneLPP(size = 100, sock = s)

    # adding 2 digital outputs, the first one uses the default channel
    lpp.add_digital_input(True)
    lpp.add_digital_input(False, channel = 112)

    # sending the packet via the socket
    lpp.send()

Scripts to test the module with a LoPy (https://www.pycom.io/) and The
Things Network is provided in the ``test_lopy`` directory. Note that you
need to update the values of ``app_eui`` and ``app_key`` with the
correct credentials.


