#!/usr/bin/env python # # radiosonde_auto_rx - fsk_demod modem statistics parser # # Copyright (C) 2019 Mark Jessop # 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) == str: # Attempt to parse string. try: _data = json.loads(data) except Exception as e: self.log_error("FSK Demod Stats - %s" % str(e)) return elif type(data) == dict: _data = data else: 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