A Python library for decoding FastNet protocol data streams.
Project description
pyfastnet
Fastnet is the propriatory protocol used by B&G on some older instruments, tested on Hydra/H2000. It might work on other systems. I developed this for personal use and publishing for general interest only.
Purpose
This library can be fed a stream of fastnet data, it will decode and return structured instrument data for further processing. Syncronisation, checksum and decoding is handled by the library.
Companion App
- A full implementation can be found here, it takes input from a serial port or dummy file and broadcasts NMEA messages via UDP fastnet2ip Easy to install on a raspberry pi, core mp135, mac, linux.
Example input/output
Byte string from Fastnet including "ff051801e34e0a02c402754d6100464f610024520af683f6835113a0064b"
to_address: Entire System from_address: Normal CPU (Wind Board in H2000) command: Broadcast values:
{
'Apparent Wind Speed (Raw)': {'channel_id': '0x4E', 'format_byte': '0x0A', 'data_bytes': '02c40275', 'divisor': 1, 'digits': 1, 'format_bits': 10, 'raw': {'first': 708.0, 'second': 629.0}, 'interpreted': 708.0},
'Apparent Wind Speed (Knots)': {'channel_id': '0x4D', 'format_byte': '0x61', 'data_bytes': '0046', 'divisor': 10, 'digits': 3, 'format_bits': 1, 'raw': 70, 'interpreted': 7.0},
'Apparent Wind Speed (m/s)': {'channel_id': '0x4F', 'format_byte': '0x61', 'data_bytes': '0024', 'divisor': 10, 'digits': 3, 'format_bits': 1, 'raw': 36, 'interpreted': 3.6},
'Apparent Wind Angle (Raw)': {'channel_id': '0x52', 'format_byte': '0x0A', 'data_bytes': 'f683f683', 'divisor': 1, 'digits': 1, 'format_bits': 10, 'raw': {'first': -2429.0, 'second': -2429.0}, 'interpreted': -2429.0},
'Apparent Wind Angle': {'channel_id': '0x51', 'format_byte': '0x13', 'data_bytes': 'a006', 'divisor': 1, 'digits': 2, 'format_bits': 3, 'raw': {'segment_code': '0xa0', 'segment_code_bin': '0b10100000', 'unsigned_value': 6, 'layout': '-[data]'}, 'interpreted': -6.0}}
Example implementation
#!/usr/bin/env python3
import serial
import time
from pprint import pprint
from fastnet_decoder import FrameBuffer
def main():
fb = FrameBuffer()
# open /dev/ttyUSB0 at 28,800 baud, 8E2, 0.1 s timeout
ser = serial.Serial(
port="/dev/ttyUSB0",
baudrate=28800,
bytesize=serial.EIGHTBITS,
stopbits=serial.STOPBITS_TWO,
parity=serial.PARITY_ODD,
timeout=0.1
)
try:
while True:
data = ser.read(256)
if not data:
time.sleep(0.01)
continue
# 1) feed raw bytes into the frame buffer
fb.add_to_buffer(data)
# 2) extract & decode any complete frames
fb.get_complete_frames()
# 3) peek at the entire queue as a list
queue_contents = list(fb.frame_queue.queue)
if queue_contents:
print("Current decoded frames in queue:")
pprint(queue_contents)
else:
print("Queue is empty.")
# 4) (optionally) drain the queue for processing
while not fb.frame_queue.empty():
frame = fb.frame_queue.get()
# replace this with whatever you need
print("Processing frame:", frame)
except KeyboardInterrupt:
print("Stopping…")
finally:
ser.close()
if __name__ == "__main__":
main()
Important library calls - debug
set_log_level(DEBUG)fastnetframebuffer.get_buffer_size()fastnetframebuffer.get_buffer_contents()
Installation
pip3 install pyfastnet
On a raspberry pi and some other systems this is done from with a virtual env
source ~/python_environment/bin/activate
pip3 install pyfastnet
deactivate
~/python_environment/bin/python3 pyfastnet.py -h
Acknowledgments / References
- trlafleur - Collector of significant background
- Oppedijk - Background and patches
- timmathews - Significant implementation in Cpp
- Significant help from chatGPT!
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