Readsb: Analyze strong signal percentage and give gain recommendation

gain-advisor.py

#!/usr/bin/env python3

# This script will find out what strong messages percentage is and
# advise you to tune gain to keep it in range from 0.5% to 5%.

# Can be used as one-liner on some systems:
# python3 <(curl -s https://gist.githubusercontent.com/varnav/b16a8a47a1d8933cf78a5cdf7d7aa24e/raw/811afe852797845931b528e8e82f13b45c864ed4/gain-advisor.py)

# Evgeny Varnavskiy 2021
# https://gist.github.com/varnav/
# See also:
# https://github.com/wiedehopf/adsb-scripts/wiki/Optimizing-gain
# https://github.com/wiedehopf/adsb-scripts/wiki/Automatic-gain-optimization-for-readsb-and-dump1090-fa

import json

# Typical possible gain values
gains = "20.7 22.9 25.4 28.0 29.7 32.8 33.8 36.4 37.2 38.6 40.2 42.1 43.4 43.9 44.5 48.0 49.6"

try:
  with open('/run/readsb/stats.json') as f:
    data = json.load(f)
except IOError:
  with open('/run/dump1090-fa/stats.json') as f:
    data = json.load(f)
  
if (data["total"]["end"] - data["total"]["start"]) < 3600:
  print("Statistics are collected for less that 1h and may be inaccurate")

strong = data["total"]["local"]["strong_signals"]
total = data["total"]["local"]["accepted"][0] + data["total"]["local"]["accepted"][1]

# It's logarithmic values - this is why it's substraction and not division
# snr = round(data["total"]["local"]["signal"] - data["total"]["local"]["noise"])
# print("Signal to noise ratio (SNR):", snr)

perc = round((strong * 100 / total), 2)
print("Too strong messages percentage:", perc)

if perc > 5:
    print("Decrease gain")
    print("Typical possible gain values:", gains)
elif perc < 0.5:
    print("Increase gain")
    print("Typical possible gain values:", gains)
else:
    print("Gain optimal")