A Cayenne Low Power Payload (CayenneLPP) decoder and encoder written in Python. The following table lists the currently supported data types with the LPP code (which equals IPSO code - 3200), data size in bytes, and data dimensions.
| Type Name | LPP | Size | Dim |
|---|---|---|---|
| Digital Input | 0 | 1 | 1 |
| Digital Output | 1 | 1 | 1 |
| Analog Input | 2 | 2 | 1 |
| Analog Output | 3 | 2 | 1 |
| Generic | 100 | 4 | 1 |
| Illuminance | 101 | 2 | 1 |
| Presence | 102 | 1 | 1 |
| Temperature | 103 | 2 | 1 |
| Humidity | 104 | 1 | 1 |
| Accelerometer | 113 | 6 | 3 |
| Barometer | 115 | 2 | 1 |
| Voltage | 116 | 2 | 1 |
| Load | 122 | 3 | 1 |
| Unix Time | 133 | 4 | 1 |
| Gyrometer | 134 | 6 | 3 |
| GPS Location | 136 | 9 | 3 |
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.
PyCayenneLPP does not have any external dependencies, but only uses builtin
functions and types of Python 3. At least Python in version 3.4 is required.
Since version 1.2.0 MicroPython is supported, and published as a separate
package under micropython-pycayennelpp.
The PyCayenneLPP package is available via PyPi using pip. To install it run:
pip3 install pycayennelppMicroPython does not include the libraries base64 and logging per default.
While the latter rather optional for embedded devices, the former is essential.
Using MicroPythons upip module PyCayenneLPP can be installed as follows
within MicroPython:
import upip
upip.install("micropython-pycayennelpp")Or alternatively run with in a shell:
micropython -m upip install micropython-pycayennelppThis will also install micropython-base64 as a dependency.
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.
Encoding
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 = frame.bytes()Decoding
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
print(frame)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.