Encoder and Decoder for CayenneLLP
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.
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
Python 3 Prerequisites
The PyCayenneLPP package is available via PyPi using
pip. To install it run:
pip3 install pycayennelpp
MicroPython does not include the libraries
logging per default.
While the latter rather optional for embedded devices, the former is essential.
upip module PyCayenneLPP can be installed as follows
import upip upip.install("micropython-pycayennelpp")
Or alternatively run with in a shell:
micropython -m upip install micropython-pycayennelpp
This 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.
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()
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.
This is a free open source software project published under the MIT License.
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