radiosonde_auto_rx/auto_rx/test/generate_lowsnr.py

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4.1 KiB
Python

#!/usr/bin/env python
#
# Generate Noisy Sonde Samples, with a calibrated Eb/No
#
# Run from ./scripts/ with
# $ python generate_lowsnr.py
#
# The generated files will end up in the 'generated' directory.
#
# Copyright (C) 2018 Mark Jessop <vk5qi@rfhead.net>
# Released under GNU GPL v3 or later
#
import numpy as np
import os
# Where to find the samples files.
# These are all expected to be 96khz float (dtype='c8') files.
SAMPLE_DIR = "./samples"
# Directory to output generated files
GENERATED_DIR = "./generated"
# Range of Eb/N0 SNRs to produce.
# 10-20 dB seems to be the range where the demodulators fall over.
EBNO_RANGE = np.arange(5,20.5,0.5)
# Normalise the samples to +/- 1.0!
# If we don't do this, bad things can happen later down the track...
NORMALISE = True
# List of samples
# [filename, baud_date, threshold, sample_rate]
# filename = string, without path
# baud_rate = integer
# threshold = threshold for calculating variance. Deterimined by taking 20*np.log10(np.abs(data)) and looking for packets.
# sample_rate = input file sample rate.
SAMPLES = [
['rs41_96k_float.bin', 4800, -20.0, 96000],
['rs92_96k_float.bin', 4800, -100, 96000], # No threshold set, as signal is continuous.
['dfm09_96k_float.bin', 2500, -100, 96000], # Weird baud rate. No threshold set, as signal is continuous.
['m10_96k_float.bin', 9616, -10.0, 96000], # Really weird baud rate.
['imet4_96k_float.bin', 1200, -10.0, 96000], # 1200 baud, but AFSK, so we expect 7-8 dB worse performance than the other sondes.
['imet54_96k_float.bin', 4800, -10.0, 96000], # 4800 baud GMSK
['rsngp_96k_float.bin', 2400, -100.0, 96000], # RS92-NGP - wider bandwidth.
['lms6-400_96k_float.bin', 4800, -100, 96000], # LMS6, 400 MHz variant. Continuous signal.
['mrz_96k_float.bin', 2400, -100, 96000] # MRZ Continuous signal.
]
def load_sample(filename):
_filename = os.path.join(SAMPLE_DIR, filename)
return np.fromfile(_filename, dtype='c8')
def save_sample(data, filename):
_filename = os.path.join(GENERATED_DIR, filename)
# We have to make sure to convert to complex64..
data.astype(dtype='c8').tofile(_filename)
# TODO: Allow saving as complex s16 - see view solution here: https://stackoverflow.com/questions/47086134/how-to-convert-a-numpy-complex-array-to-a-two-element-float-array
def calculate_variance(data, threshold=-100.0):
# Calculate the variance of a set of radiosonde samples.
# Optionally use a threshold to limit the sample the variance
# is calculated over to ones that actually have sonde packets in them.
_data_log = 20*np.log10(np.abs(data))
return np.var(data[_data_log>threshold])
def add_noise(data, variance, baud_rate, ebno, fs=96000, bitspersymbol=1.0):
# Add calibrated noise to a sample.
# Calculate Eb/No in linear units.
_ebno = 10.0**((ebno)/10.0)
# Calculate the noise variance we need to add
_noise_variance = variance*fs/(baud_rate*_ebno*bitspersymbol)
# Generate complex random samples
_rand_i = np.sqrt(_noise_variance/2.0)*np.random.randn(len(data))
_rand_q = np.sqrt(_noise_variance/2.0)*np.random.randn(len(data))
_noisy = (data + (_rand_i + 1j*_rand_q))
if NORMALISE:
print("Normalised to 1.0")
return _noisy/np.max(np.abs(_noisy))
else:
return _noisy
if __name__ == '__main__':
for _sample in SAMPLES:
# Extract the stuff we need from the entry.
_source = _sample[0]
_baud_rate = _sample[1]
_threshold = _sample[2]
_fs = _sample[3]
print("Generating samples for: %s" % _source)
# Read in source file.
_data = load_sample(_source)
# Calculate variance
_var = calculate_variance(_data, _threshold)
print("Calculated Variance: %.5f" % _var)
# Now loop through the ebno's and generate the output.
for ebno in EBNO_RANGE:
_data_noise = add_noise(_data, variance=_var, baud_rate=_baud_rate, ebno=ebno, fs=_fs)
_out_file = _source.split('.bin')[0] + "_%04.1fdB"%ebno + ".bin"
save_sample(_data_noise, _out_file)
print("Saved file: %s" % _out_file)