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pycayennelpp 2.4.0

PyCayenneLPP

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
upip.install("micropython-pycayennelpp")

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.
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 = 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.
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)

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