radiosonde_auto_rx/auto_rx/autorx/fsk_demod.py

159 wiersze
4.6 KiB
Python

#!/usr/bin/env python
#
# radiosonde_auto_rx - fsk_demod modem statistics parser
#
# Copyright (C) 2019 Mark Jessop <vk5qi@rfhead.net>
# Released under GNU GPL v3 or later
#
import json
import logging
import time
import numpy as np
class FSKDemodStats(object):
"""
Process modem statistics produced by fsk_demod and provide access to
filtered or instantaneous modem data.
This class expects the JSON output from fsk_demod to be arriving in *realtime*.
The test script below will emulate relatime input based on a file.
"""
FSK_STATS_FIELDS = ["EbNodB", "ppm", "f1_est", "f2_est", "samp_fft"]
def __init__(self, averaging_time=5.0, peak_hold=False, decoder_id=""):
"""
Required Fields:
averaging_time (float): Use the last X seconds of data in calculations.
peak_hold (bool): If true, use a peak-hold SNR metric instead of a mean.
decoder_id (str): A unique ID for this object (suggest use of the SDR device ID)
"""
self.averaging_time = float(averaging_time)
self.peak_hold = peak_hold
self.decoder_id = str(decoder_id)
# Input data stores.
self.in_times = np.array([])
self.in_snr = np.array([])
self.in_ppm = np.array([])
# Output State variables.
self.snr = -999.0
self.fest = [0.0, 0.0]
self.fft = []
self.ppm = 0.0
def update(self, data):
"""
Update the statistics parser with a new set of output from fsk_demod.
This can accept either a string (which will be parsed as JSON), or a dict.
Required Fields:
data (str, dict): One set of statistics from fsk_demod.
"""
# Check input type
if type(data) == bytes:
data = data.decode("ascii")
if type(data) == dict:
_data = data
else:
# Attempt to parse string.
try:
_data = json.loads(data)
except Exception as e:
# Be quiet for now...
# self.log_error("FSK Demod Stats - %s" % str(e))
return
# Check for required fields in incoming dictionary.
for _field in self.FSK_STATS_FIELDS:
if _field not in _data:
self.log_error("Missing Field %s" % _field)
return
# Now we can process the data.
_time = time.time()
self.fft = _data["samp_fft"]
self.fest[0] = _data["f1_est"]
self.fest[1] = _data["f2_est"]
# Time-series data
self.in_times = np.append(self.in_times, _time)
self.in_snr = np.append(self.in_snr, _data["EbNodB"])
self.in_ppm = np.append(self.in_ppm, _data["ppm"])
# Calculate SNR / PPM
_time_range = self.in_times > (_time - self.averaging_time)
# Clip arrays to just the values we want
self.in_ppm = self.in_ppm[_time_range]
self.in_snr = self.in_snr[_time_range]
self.in_times = self.in_times[_time_range]
# Always just take a mean of the PPM values.
self.ppm = np.mean(self.in_ppm)
if self.peak_hold:
self.snr = np.max(self.in_snr)
else:
self.snr = np.mean(self.in_snr)
def log_debug(self, line):
""" Helper function to log a debug message with a descriptive heading.
Args:
line (str): Message to be logged.
"""
logging.debug("FSK Demod Stats #%s - %s" % (str(self.decoder_id), line))
def log_info(self, line):
""" Helper function to log an informational message with a descriptive heading.
Args:
line (str): Message to be logged.
"""
logging.info("FSK Demod Stats #%s - %s" % (str(self.decoder_id), line))
def log_error(self, line):
""" Helper function to log an error message with a descriptive heading.
Args:
line (str): Message to be logged.
"""
logging.error("FSK Demod Stats #%s - %s" % (str(self.decoder_id), line))
if __name__ == "__main__":
import sys
_filename = sys.argv[1]
_f = open(_filename, "r")
stats = FSKDemodStats(averaging_time=2.0, peak_hold=True)
rate = 10.0
updaterate = 10
count = 0
for _line in _f:
try:
_line = json.loads(_line)
except:
continue
stats.update(_line)
time.sleep(1 / rate)
if count % updaterate == 0:
print(
"%d - SNR: %.1f dB, FEst: %s, ppm: %.1f"
% (count, stats.snr, stats.fest, stats.ppm)
)
count += 1