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Encoder and Decoder for CayenneLLP

Project description


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A Cayenne Low Power Payload (CayenneLPP) decoder and encoder written in Python.

PyCayenneLPP offers a concise interface with proper encoding and decoding functionality for the CayenneLPP format, supporting many sensor types. The project aims for overall high code quality and good test coverage.

See also myDevicesIoT/CayenneLPP for more information on the format and a reference implementation in C++.

The project is under active development. Releases will be published on the fly as soon as a certain number of new features and fixes have been made.

Supported Data Types

The following table lists the currently supported data types with the LPP code (which equals IPSO code - 3200), data size in bytes, dimensions, signedness, and data resolution.

Type Name LPP Size Dim Signed Resolution
Digital Input 0 1 1 False 1
Digital Output 1 1 1 False 1
Analog Input 2 2 1 True 0.01
Analog Output 3 2 1 True 0.01
Generic Sensor 100 4 1 False 1
Illuminance 101 2 1 False 1 Lux
Presence 102 1 1 False 1  
Temperature 103 2 1 True 0.1°C
Humidity 104 1 1 False 0.5 %
Accelerometer 113 6 3 True 0.001 G
Barometer 115 2 1 False 0.1 hPa
Voltage 116 2 1 False 0.01 V
Current 117 2 1 False 0.001 A
Frequency 118 4 1 False 1 Hz
Percentage 120 1 1 False 1 %
Altitude 121 2 1 True 1 m
Load 122 3 1 True 0.001 kg
Concentration 125 2 1 False 1
Power 128 2 1 False 1
Distance 130 4 1 False 0.001 km
Energy 131 4 1 False 0.001 kJ
Direction 132 2 1 False 1 °
Time 133 4 1 False 1 s
Gyrometer 134 6 3 True 0.01 °/s
Colour 135 3 3 False 1 RGB
Location 136 9 3 True 0.00001 lat
0.00001 lon
0.01 alt
Switch 142 1 1 False 1 on/off

Getting Started

PyCayenneLPP does not have any external dependencies and only uses builtin functions and types of Python 3. It is compatible with all the latest and officially supported Python versions 3.6 and above, though even Python 3.4 will do.

Since PyCayenneLPP 1.2.0 MicroPython is officially supported, and published as a separate package under micropython-pycayennelpp.

Python 3 Prerequisites

The PyCayenneLPP package is available via PyPi using pip. To install it run:

pip3 install pycayennelpp

MicroPython Prerequisites

Using MicroPythons upip module PyCayenneLPP can be installed as follows within MicroPython:

import upip

Or alternatively run with in a shell:

micropython -m upip install micropython-pycayennelpp

Usage Examples

The following show how to utilise PyCayenneLPP in your own application to encode and decode data into and from CayenneLPP. The code snippets work with standard Python 3 as well as MicroPython, assuming you have installed the PyCayenneLPP package as shown above.


from cayennelpp import LppFrame

# create empty frame
frame = LppFrame()
# add some sensor data
frame.add_temperature(0, -1.2)
frame.add_humidity(6, 34.5)
# get byte buffer in CayenneLPP format
buffer = bytes(frame)

Note: MicroPython does not support bytes(frame) utilising the internal method LppFrame.__bytes__(self) (yet). Hence, you need to use LppFrame.to_bytes(self) instead.


from cayennelpp import LppFrame

# byte buffer in CayenneLPP format with 1 data item
# i.e. on channel 1, with a temperature of 25.5C
buffer = bytearray([0x01, 0x67, 0x00, 0xff])
# create frame from bytes
frame = LppFrame().from_bytes(buffer)
# print the frame and its data

JSON Encoding

The LppUtil class provides helper function for proper JSON encoding of PyCayenneLpp types, i.e. LppFrame, LppData and LppType.

import json

from cayennelpp import LppFrame, LppUtil

# create empty frame
frame = LppFrame()
# add some sensor data
frame.add_temperature(0, -1.2)
frame.add_humidity(6, 34.5)
# json encoding
print(json.dumps(frame, default=LppUtil.json_encode, indent=2))

There are two wrapper functions to explicitly encode the LPP type as a number or string, number being default for LppUtil.json_encode (see above):

# type as number
print(json.dumps(frame, default=LppUtil.json_encode_type_int, indent=2))
# type as string
print(json.dumps(frame, default=LppUtil.json_encode_type_str, indent=2))


Contributing to a free open source software project can take place in many different ways. Feel free to open issues and create pull requests to help improving this project. Each pull request has to pass some automatic tests and checks run by Travis-CI before being merged into the master branch.

Please take note of the contributing guidelines and the Code of Conduct.


This is a free open source software project published under the MIT License.

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