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wspr.py
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wspr.py
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#!/usr/local/bin/python
#
# decode WSPR
#
# info from wsjt-x manual, wsjt-x source, and
# http://physics.princeton.edu/pulsar/k1jt/WSPR_3.0_User.pdf
# http://www.g4jnt.com/Coding/WSPR_Coding_Process.pdf
#
# uses Phil Karn's Fano convolutional decoder.
#
# Robert Morris, AB1HL
#
import numpy
import wave
import scipy
import scipy.signal
import sys
import os
import math
import time
import copy
import calendar
import subprocess
import threading
import re
import random
from scipy.signal import lfilter
import ctypes
import weakaudio
import weakutil
#
# WSPR tuning parameters.
#
budget = 9 # max seconds of CPU time, per file or two-minute interval (50).
step_frac = 2.5 # fraction of FFT bin for frequency search (4).
#ngoff = 3 # look at this many of guess_offset()'s results (6, 4).
goff_step = 64 # guess_offset search interval.
fano_limit = 30000 # how hard fano will work (10000).
fano_delta = 17
fano_bias = 0.5
fano_floor = 0.005
fano_scale = 4.5
statruns = 3
driftmax = 1.0 # look at drifts from -driftmax to +driftmax (2).
ndrift = 3 # number of drifts to try (including drift=0)
coarse_steps = 4 # coarse() search for start offset at this many points per symbol time.
coarse_hzsteps = 4 # look for signals at this many freq offsets per bin.
coarse_top1 = 2
coarse_top2 = 5
phase0_budget = 0.4 # fraction of remaining budget for pre-subtraction
subslop = 0.01 # granularity (in symbols) of subtraction phase search
start_slop = 4.0 # pad to this many seconds before 0:01
end_slop = 5.0 # pad this many seconds after end of nominal end time
band_order = 6 # order of bandpass filter
subgap = 0.4 # extra subtract()s this many hz on either side of main bin
ignore_thresh = -30 # ignore decodes with lower SNR than this
# information about one decoded signal.
class Decode:
def __init__(self,
hza,
msg,
snr,
msgbits):
self.hza = hza
self.msg = msg
self.snr = snr
self.msgbits = msgbits # output of Fano decode
self.minute = None
self.start = None # sample number
self.dt = None # dt in seconds
self.decode_time = None # unix time of decode
self.phase0 = False
self.phase1 = False
self.drift = self.hz() - hza[0] # Hz per minute
def hz(self):
return numpy.mean(self.hza)
# the WSPR sync pattern. each of the 162 2-bit symbols includes one bit of
# sync in the low bit.
pattern = [
1, 1, -1, -1, -1, -1, -1, -1, 1, -1, -1, -1, 1, 1, 1, -1, -1, -1, 1, -1, -1, 1, -1, 1, 1, 1, 1, -1, -1, -1,
-1, -1, -1, -1, 1, -1, -1, 1, -1, 1, -1, -1, -1, -1, -1, -1, 1, -1, 1, 1, -1, -1, 1, 1, -1, 1, -1, -1, -1, 1,
1, -1, 1, -1, -1, -1, -1, 1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1, 1, -1, -1, -1, 1,
1, -1, 1, -1, 1, -1, -1, -1, 1, -1, -1, -1, -1, -1, 1, -1, -1, 1, -1, -1, 1, 1, 1, -1, 1, 1, -1, -1, 1, 1,
-1, 1, -1, -1, -1, 1, 1, 1, -1, -1, -1, -1, -1, 1, -1, 1, -1, -1, 1, 1, -1, -1, -1, -1, -1, -1, -1, 1, 1, -1,
1, -1, 1, 1, -1, -1, -1, 1, 1, -1, -1, -1,
]
# Phil Karn's Fano convolutional decoder.
from ctypes import c_ubyte, c_int, byref, cdll
libfano = cdll.LoadLibrary("libfano/libfano.so") # both OSX and FreeBSD
# returns an array of 0/1 bits,
# 2x as many as in_bits.
def fano_encode(in_bits):
# fano_encode(unsigned char in_bits[], int n_in, unsigned char out_bits[])
in_array_type = c_ubyte * len(in_bits)
in_array = in_array_type()
for i in range(0, len(in_bits)):
in_array[i] = in_bits[i]
out_array_type = c_ubyte * (2*len(in_bits))
out_array = out_array_type()
n_in = c_int()
n_in.value = len(in_bits)
libfano.fano_encode(in_array, n_in, out_array)
a = []
for i in range(0, 2*len(in_bits)):
a.append(out_array[i])
return a
# in0[i] is log of probability that the i'th symbol is 0.
# returns an array of 0/1 bits, half as many as in0[].
# returns None on error.
def nfano_decode(in0, in1):
global fano_limit, fano_delta
#for i in range(0, len(in0)):
# print "%d %d %d" % (i, in0[i], in1[i])
#sys.exit(1)
in_array_type = c_int * len(in0)
xin0 = in_array_type()
xin1 = in_array_type()
for i in range(0, len(in0)):
xin0[i] = in0[i]
xin1[i] = in1[i]
out_array_type = c_ubyte * (len(in0) / 2)
out_array = out_array_type()
n_out = c_int()
n_out.value = len(in0) / 2
metric_out_type = c_int * 1
metric_out = metric_out_type()
limit = c_int()
limit = fano_limit
delta = c_int()
delta = fano_delta
ok = libfano.nfano_decode(xin0, xin1, n_out, out_array, limit, metric_out, delta)
if ok != 1:
return [ None, None ]
a = []
for i in range(0, len(in0) / 2):
a.append(out_array[i])
metric = metric_out[0]
return [ a, metric ]
def ntest_fano():
in_bits = [ 1, 0 ] * 36 # 76 bits, like JT9
padded = in_bits + ([0] * 31)
out_bits = fano_encode(padded)
in0 = [ ]
in1 = [ ]
for b in out_bits:
if b == 1:
in1.append(1)
in0.append(-10)
else:
in0.append(1)
in1.append(-10)
[ dec, metric ] = nfano_decode(in0, in1)
assert in_bits == dec[0:len(in_bits)]
if False:
ntest_fano()
sys.exit(0)
# Normal function integrated from -Inf to x. Range: 0-1.
# x in units of std dev.
# mean is zero.
def normal(x):
y = 0.5 + 0.5*math.erf(x / 1.414213)
return y
# how much of the distribution is < x?
def problt(x, mean, std):
y = normal((x - mean) / std)
return y
# how much of the distribution is > x?
def probgt(x, mean, std):
y = 1.0 - normal((x - mean) / std)
return y
def bit_reverse(x, width):
y = 0
for i in range(0, width):
z = (x >> i) & 1
y <<= 1
y |= z
return y
# turn an array of bits into a number.
# most significant bit first.
def bits2num(bits):
assert len(bits) < 32
n = 0
for i in range(0, len(bits)):
n *= 2
n += bits[i]
return n
# gadget that returns FFT buckets of a fixed set of
# original samples, with required (inter-bucket)
# frequency, drift, and offset.
class FFTCache:
def __init__(self, samples, jrate, jblock):
self.jrate = jrate
self.jblock = jblock
self.samples = samples
self.memo = { }
self.granule = int(self.jblock / 8)
# internal.
def fft2(self, index, quarter):
key = str(index) + "-" + str(quarter)
if key in self.memo:
return self.memo[key]
# caller wants a frequency a bit higher than bin,
# so shift *down* by the indicated number of quarter bins.
block = self.samples[index:index+self.jblock]
if quarter != 0:
bin_hz = self.jrate / float(self.jblock)
freq_off = quarter * (bin_hz / 4.0)
block = weakutil.freq_shift(block, -freq_off, 1.0/self.jrate)
a = numpy.fft.rfft(block)
a = abs(a)
self.memo[key] = a
return a
# internal.
# offset is index into samples[].
# return [ bin, full FFT at offset ].
def fft1(self, index, hza):
bin_hz = self.jrate / float(self.jblock)
hz = hza[0] + (index / float(len(self.samples))) * (hza[1] - hza[0])
bin = int(hz / bin_hz)
binfrac = (hz - (bin * bin_hz)) / bin_hz
# which of four quarter-bin increments?
if binfrac < 0.25:
quarter = 0
elif binfrac < 0.5:
quarter = 1
elif binfrac < 0.75:
quarter = 2
else:
quarter = 3
a = self.fft2(index, quarter)
return [ bin, a ]
# hza is [ hz0, hzN ] -- at start and end.
# offset is 0..self.jblock.
# return buckets[0..162ish][4] -- i.e. a mini-FFT per symbol.
def get(self, hza, offset):
return self.getmore(hza, offset, 0)
# hza is [ hz0, hzN ] -- at start and end.
# offset is 0..self.jblock.
# return buckets[0..162ish][4 +/- more] -- i.e. a mini-FFT per symbol.
# ordinarily more is 0. it's 1 for guess_freq().
def getmore(self, hza, offset, more):
# round offset to 1/8th of self.jblock.
offset = int(offset / self.granule) * self.granule
bin_hz = self.jrate / float(self.jblock)
nsyms = (len(self.samples) - offset) // self.jblock
out = numpy.zeros((nsyms, 4+more+more))
for i in range(0, nsyms):
ioff = i * self.jblock + offset
[ bin, m ] = self.fft1(ioff, hza)
assert bin - more >= 0
assert bin+4+more <= len(m)
m = m[bin-more:bin+4+more]
out[i] = m
return out
def len(self):
return len(self.samples)
class WSPR:
debug = False
offset = 0
def __init__(self):
self.msgs_lock = threading.Lock()
self.msgs = [ ]
self.verbose = False
self.downhz = 1300 # shift this down to zero hz.
self.lowhz = 1400 - self.downhz # WSPR signals start here
self.jrate = 750 # sample rate for processing (FFT &c)
self.jblock = 512 # samples per symbol
# set self.start_time to the UNIX time of the start
# of the last even UTC minute.
now = int(time.time())
gm = time.gmtime(now)
self.start_time = now - gm.tm_sec
if (gm.tm_min % 2) == 1:
self.start_time -= 60
def close(self):
pass
# return the minute number for t, a UNIX time in seconds.
# truncates down, so best to pass a time mid-way through a minute.
# returns only even minutes.
def minute(self, t):
dt = t - self.start_time
mins = 2 * int(dt / 120.0)
return mins
# seconds since minute(), 0..119
def second(self, t):
dt = t - self.start_time
m = 120 * int(dt / 120.0)
return dt - m
# printable UTC timestamp, e.g. "07/07/15 16:31:00"
# dd/mm/yy hh:mm:ss
# t is unix time.
def ts(self, t):
gm = time.gmtime(t)
return "%02d/%02d/%02d %02d:%02d:%02d" % (gm.tm_mday,
gm.tm_mon,
gm.tm_year - 2000,
gm.tm_hour,
gm.tm_min,
gm.tm_sec)
def openwav(self, filename):
self.wav = wave.open(filename)
self.wav_channels = self.wav.getnchannels()
self.wav_width = self.wav.getsampwidth()
self.cardrate = self.wav.getframerate()
def readwav(self, chan):
z = self.wav.readframes(8192)
if self.wav_width == 1:
zz = numpy.fromstring(z, numpy.int8)
elif self.wav_width == 2:
if (len(z) % 2) == 1:
return numpy.array([])
zz = numpy.fromstring(z, numpy.int16)
else:
sys.stderr.write("oops wave_width %d" % (self.wav_width))
sys.exit(1)
if self.wav_channels == 1:
return zz
elif self.wav_channels == 2:
return zz[chan::2] # chan 0/1 => left/right
else:
sys.stderr.write("oops wav_channels %d" % (self.wav_channels))
sys.exit(1)
def gowav(self, filename, chan):
self.openwav(filename)
bufbuf = [ ]
while True:
buf = self.readwav(chan)
if buf.size < 1:
break
bufbuf.append(buf)
samples = numpy.concatenate(bufbuf)
self.process(samples, 0)
def opencard(self, desc):
self.cardrate = 12000
self.audio = weakaudio.new(desc, self.cardrate)
def gocard(self):
bufbuf = [ ]
nsamples = 0
while True:
[ buf, buf_time ] = self.audio.read()
bufbuf.append(buf)
nsamples += len(buf)
samples_time = buf_time
if len(buf) > 0:
mx = numpy.max(numpy.abs(buf))
if mx > 30000:
sys.stderr.write("!")
if len(buf) == 0:
time.sleep(0.2)
# a WSPR frame starts on second 1, and takes 110.5 seconds, so
# should end with the 112th second.
# wait until we have enough samples through 113th second of minute.
# 162 symbols, 0.682 sec/symbol, 110.5 seconds total.
if samples_time == None:
sec = 0
else:
sec = self.second(samples_time)
if sec >= 113 and nsamples >= 113*self.cardrate:
# we have >= 113 seconds of samples, and second of minute is >= 113.
samples = numpy.concatenate(bufbuf)
# sample # of one second before start of two-minute interval.
i0 = len(samples) - self.cardrate * self.second(samples_time)
i0 -= self.cardrate # process() expects samples starting at 0:59
i0 = int(i0)
i0 = max(i0, 0)
t = samples_time - (len(samples)-i0) * (1.0/self.cardrate)
self.process(samples[i0:], t)
bufbuf = [ ]
nsamples = 0
# received a message, add it to the list.
# offset in seconds.
# drift in hz/minute.
def got_msg(self, dec):
if self.verbose:
drift = dec.hza[1] - dec.hz()
print("%6.1f %.1f %.1f %d %s" % (dec.hz(), dec.dt, drift, dec.snr, dec.msg))
dec.decode_time = time.time()
self.msgs_lock.acquire()
self.msgs.append(dec)
self.msgs_lock.release()
# someone wants a list of all messages received,
# as array of Decode.
def get_msgs(self):
self.msgs_lock.acquire()
a = copy.copy(self.msgs)
self.msgs_lock.release()
return a
def process(self, samples, samples_time):
global budget, step_frac, goff_step, fano_limit, driftmax, ndrift
# samples_time is UNIX time that samples[0] was
# sampled by the sound card.
samples_minute = self.minute(samples_time + 60)
t0 = time.time()
# trim trailing zeroes that wsjt-x adds
i = len(samples)
while i > 1000 and numpy.max(samples[i-1:]) == 0.0:
if numpy.max(samples[i-1000:]) == 0.0:
i -= 1000
elif numpy.max(samples[i-100:]) == 0.0:
i -= 100
elif numpy.max(samples[i-10:]) == 0.0:
i -= 10
else:
i -= 1
samples = samples[0:i]
# bandpass filter around 1400..1600.
# down-convert by 1200 Hz (i.e. 1400-1600 -> 200->400),
# and reduce sampling rate to 1500.
assert self.cardrate == 12000 and self.jrate == 750
filter = weakutil.butter_bandpass(1380, 1620, self.cardrate, band_order)
samples = lfilter(filter[0], filter[1], samples)
# down-convert from 1400 to 100.
samples = weakutil.freq_shift(samples, -self.downhz, 1.0/self.cardrate)
# down-sample.
samples = samples[0::16]
#
# pad at start+end b/c transmission might have started early or late.
# I've observed dt's from -2.8 to +3.4.
# there's already two seconds of slop at start b/c xmission starts
# at xx:01 but file seems to start at xx:59.
# and we're going to trim a second either side after AGC.
#
sm = numpy.mean(samples) # pad with plausible signal levels
sd = numpy.std(samples)
assert start_slop >= 2.0
startpad = int((start_slop - 2.0) * self.jrate) # samples
samples = numpy.append(numpy.random.normal(sm, sd, startpad), samples)
endpad = int((start_slop*self.jrate + 162.0*self.jblock + end_slop*self.jrate) - len(samples))
if endpad > 0:
samples = numpy.append(samples, numpy.random.normal(sm, sd, endpad))
elif endpad < 0:
samples = samples[0:endpad]
bin_hz = self.jrate / float(self.jblock)
# store each message just once, to suppress duplicates.
# indexed by message text; value is [ samples_minute, hz, msg, snr, offset, drift ]
msgs = { }
ssamples = numpy.copy(samples) # for subtraction
# phase 0: decode and subtract, but don't use the subtraction.
# phase 1: decode from subtracted samples.
phase0_start = time.time()
phase1_end = t0 + budget
for phase in range(0, 2):
if phase == 0:
phasesamples = samples
else:
phasesamples = ssamples # samples with phase0 decodes subtracted
[ fine_rank, noise ] = self.coarse(phasesamples)
xf = FFTCache(phasesamples, self.jrate, self.jblock)
already = { }
for rr in fine_rank:
# rr is [ drift, hz, offset, strength ]
if phase == 0 and len(msgs) > 0:
if time.time() - phase0_start >= phase0_budget*(phase1_end-phase0_start):
break
else:
if time.time() - t0 >= budget:
break
drift = rr[0]
hz = rr[1]
offset = rr[2]
#if int(hz / bin_hz) in already:
# continue
hza = [ hz - drift, hz + drift ]
offset = self.guess_start(xf, hza, offset)
hza = self.guess_freq(xf, hza, offset)
ss = xf.get(hza, offset)
# ss has one element per symbol time.
# ss[i] is a 4-element FFT.
# first symbol is in ss[0]
# return is [ hza, msg, snr ]
assert len(ss[0]) >= 4
dec = self.process1(samples_minute, ss[0:162], hza, noise)
if False:
if dec != None:
print("%.1f %d %.1f %.1f %s -- %s" % (hz, phase, drift, numpy.mean(hza), rr, dec.msg))
else:
print("%.1f %d %.1f %.1f %s" % (hz, phase, drift, numpy.mean(hza), rr))
if dec != None:
dec.minute = samples_minute
dec.start = offset
if not (dec.msg in msgs):
already[int(hz / bin_hz)] = True
dec.phase0 = (phase == 0)
dec.phase1 = (phase == 1)
msgs[dec.msg] = dec
if phase == 0:
ssamples = self.subtract(ssamples, dec, numpy.add(dec.hza, 0.0))
if subgap > 0.0001:
ssamples = self.subtract(ssamples, dec, numpy.add(dec.hza, subgap))
ssamples = self.subtract(ssamples, dec, numpy.add(dec.hza, -subgap))
#elif dec.snr > msgs[dec.msg].snr:
# # we have a higher SNR.
# msgs[dec.msg] = dec
sys.stdout.flush()
for txt in msgs:
dec = msgs[txt]
dec.hza[0] += self.downhz
dec.hza[1] += self.downhz
dec.dt = (dec.start / float(self.jrate)) - 2.0 # convert to seconds
self.got_msg(dec)
def subtract(self, osamples, dec, hza):
padded = dec.msgbits + ([0] * 31)
encbits = fano_encode(padded)
# len(encbits) is 162, each element 0 or 1.
# interleave encbits, by bit-reversal of index.
ibits = numpy.zeros(162, dtype=numpy.int32)
p = 0
for i in range(0, 256):
j = bit_reverse(i, 8)
if j < 162:
ibits[j] = encbits[p]
p += 1
# combine with sync pattern to generate 162 symbols of 0..4.
four = numpy.multiply(ibits, 2)
four = numpy.add(four, numpy.divide(numpy.add(pattern, 1), 2))
bin_hz = self.jrate / float(self.jblock)
samples = numpy.copy(osamples)
assert dec.start >= 0
samples = samples[dec.start:]
bigslop = int(self.jblock * subslop)
# find amplitude of each symbol.
amps = [ ]
offs = [ ]
tones = [ ]
i = 0
while i < len(four):
nb = 1
while i+nb < len(four) and four[i+nb] == four[i]:
nb += 1
hz0 = hza[0] + (i / float(len(four))) * (hza[1] - hza[0])
hz = hz0 + four[i] * bin_hz
tone = weakutil.costone(self.jrate, hz, self.jblock*nb)
# nominal start of symbol in samples[]
i0 = i * self.jblock
i1 = i0 + nb*self.jblock
# search +/- slop.
# we search separately for each symbol b/c the
# phase may drift over the minute, and we
# want the tone to match exactly.
i0 = max(0, i0 - bigslop)
i1 = min(len(samples), i1 + bigslop)
cc = numpy.correlate(samples[i0:i1], tone)
mm = numpy.argmax(cc) # thus samples[i0+mm]
# what is the amplitude?
# if actual signal had a peak of 1.0, then
# correlation would be sum(tone*tone).
cx = cc[mm]
c1 = numpy.sum(tone * tone)
a = cx / c1
amps.append(a)
offs.append(i0+mm)
tones.append(tone)
i += nb
ai = 0
while ai < len(amps):
a = amps[ai]
off = offs[ai]
tone = tones[ai]
samples[off:off+len(tone)] -= tone * a
ai += 1
nsamples = numpy.append(osamples[0:dec.start], samples)
return nsamples
def hz0(self, hza, sym):
hz = hza[0] + (hza[1] - hza[0]) * (sym / float(len(pattern)))
return hz
# since there have been so many bugs in guess_offset().
def test_guess_offset(self):
mo = 0
bin_hz = self.jrate / float(self.jblock)
n = 0
sumabs = 0.0
sum = 0.0
cpu = 0.0
justone = False
starttime = time.time()
while time.time() < starttime + 10:
hz = 80 + random.random() * 240
if justone:
nstart = 0
else:
nstart = int(random.random() * 3000)
nend = 2000 + int(random.random() * 5000)
symbols = [ ]
for p in pattern:
if justone:
sym = 0
else:
sym = 2 * random.randint(0, 1)
if p > 0:
sym += 1
symbols.append(sym)
samples = numpy.random.normal(0, 0.5, nstart)
samples = numpy.append(samples, weakutil.fsk(symbols, [ hz, hz ], bin_hz, self.jrate, self.jblock))
samples = numpy.append(samples, numpy.random.normal(0, 0.5, nend))
if justone == False:
samples = samples * 1000
xf = FFTCache(samples, self.jrate, self.jblock)
self.guess_offset(xf, [hz+4*bin_hz,hz+4*bin_hz]) # prime the cache, for timing
t0 = time.time()
xa = self.guess_offset(xf, [hz,hz])
t1 = time.time()
x0start = xa[0][0]
x0abs = abs(nstart - x0start)
#print("%.1f %d: %d %d" % (hz, nstart, x0start, x0abs))
if abs(xa[1][0] - nstart) < x0abs:
print("%.1f %d: %d %d -- %d" % (hz, nstart, x0start, x0abs, xa[1][0]))
mo = max(mo, x0abs)
sumabs += x0abs
sum += x0start - nstart
cpu += t1 - t0
n += 1
if justone:
sys.exit(1)
# jul 18 2016 -- max diff was 53.
# jun 16 2017 -- max diff was 77 (but with different hz range).
# jun 16 2017 -- max diff 52, avg abs diff 17, avg diff -1
# jun 16 2017 -- max diff 33, avg abs diff 16, avg diff 0 (using tone, not bandpass filter)
# jul 8 2017 -- max diff 33, avg abs diff 16, avg diff 0, avg CPU 0.022
# jul 8 2017 -- max diff 32, avg abs diff 15, avg diff 0, avg CPU 0.114
# but this one uses FFTCache...
# jul 9 2017 -- max diff 32, avg abs diff 17, avg diff -2, avg CPU 0.006
print("max diff %d, avg abs diff %d, avg diff %d, avg CPU %.3f" % (mo, sumabs/n, sum/n, cpu / n))
# a signal starts at roughly offset=start,
# to within self.jblock/coarse_steps.
# return a better start.
def guess_start(self, xf, hza, start):
candidates = [ ]
step = int(self.jblock / coarse_steps)
start0 = start - step // 2
start0 = max(start0, 0)
start1 = start + step // 2
# the "/ 8" here is the FFTCache granule.
for start in range(start0, start1, self.jblock // 8):
# tones[0..162][0..4]
if start + len(pattern)*self.jblock > xf.len():
continue
tones = xf.get(hza, start)
tones = tones[0:162,:]
tone0 = tones[:,0]
tone1 = tones[:,1]
tone2 = tones[:,2]
tone3 = tones[:,3]
# we just care about sync vs no sync,
# so combine tones 0 and 2, and 1 and 3
syncs0 = numpy.maximum(tone0, tone2)
syncs1 = numpy.maximum(tone1, tone3)
# now syncs1 - syncs0.
# yields +/- that should match pattern.
tt = numpy.subtract(syncs1, syncs0)
strength = numpy.sum(numpy.multiply(tt, pattern))
candidates.append([ start, strength ])
candidates = sorted(candidates, key = lambda e : -e[1])
return candidates[0][0]
# returns an array of [ offset, strength ], sorted
# by strength, most-plausible first.
# xf is an FFTCache.
def guess_offset(self, xf, hza):
ret = [ ]
for off in range(0, self.jblock, goff_step):
# tones[0..162][0..4]
tones = xf.get(hza, off)
tone0 = tones[:,0]
tone1 = tones[:,1]
tone2 = tones[:,2]
tone3 = tones[:,3]
# we just care about sync vs no sync,
# so combine tones 0 and 2, and 1 and 3
syncs0 = numpy.maximum(tone0, tone2)
syncs1 = numpy.maximum(tone1, tone3)
# now syncs1 - syncs0.
# yields +/- that should match pattern.
tt = numpy.subtract(syncs1, syncs0)
cc = numpy.correlate(tt, pattern)
indices = list(range(0, len(cc)))
indices = sorted(indices, key=lambda i : -cc[i])
indices = indices[0:ngoff]
offsets = numpy.multiply(indices, self.jblock)
offsets = offsets + off
both = [ [ offsets[i], cc[indices[i]] ] for i in range(0, len(offsets)) ]
ret += both
ret = sorted(ret, key = lambda e : -e[1])
return ret
# returns an array of [ offset, strength ], sorted
# by strength, most-plausible first.
# oy: this version is the same quality as guess_offset(),
# but noticeably slower.
def fft_guess_offset(self, samples, hz):
bin_hz = self.jrate / float(self.jblock)
bin = int(round(hz / bin_hz))
# shift freq so hz in the middle of a bin
#samples = weakutil.freq_shift(samples,
# bin * bin_hz - hz,
# 1.0/self.jrate)
tones = [ [], [], [], [] ]
for off in range(0, len(samples), goff_step):
if off + self.jblock > len(samples):
break
a = numpy.fft.rfft(samples[off:off+self.jblock])
a = a[bin:bin+4]
a = abs(a)
tones[0].append(a[0])
tones[1].append(a[1])
tones[2].append(a[2])
tones[3].append(a[3])
if False:
for ti in range(0, 4):
for i in range(8, 24):
sys.stdout.write("%.0f " % (tones[ti][i]))
sys.stdout.write("\n")
# we just care about sync vs no sync,
# so combine tones 0 and 2, and 1 and 3
syncs0 = numpy.maximum(tones[0], tones[2])
syncs1 = numpy.maximum(tones[1], tones[3])
# now syncs1 - syncs0.
# yields +/- that should match pattern.
tt = numpy.subtract(syncs1, syncs0)
if False:
for i in range(0, 24):
sys.stdout.write("%.0f " % (tt[i]))
if (i % 8) == 7:
sys.stdout.write("| ")
sys.stdout.write("\n")
#z = numpy.repeat(pattern, int(self.jblock / goff_step))
z = [ ]
nfill = int(self.jblock / goff_step) - 1
for x in pattern:
z.append(x)
z = z + [0]*nfill
cc = numpy.correlate(tt, z)
if False:
for i in range(0, min(len(cc), 24)):
sys.stdout.write("%.0f " % (cc[i]))
if (i % 8) == 7:
sys.stdout.write("| ")
sys.stdout.write("\n")
indices = list(range(0, len(cc)))
indices = sorted(indices, key=lambda i : -cc[i])
offsets = numpy.multiply(indices, goff_step)
#offsets = numpy.subtract(offsets, ntaps / 2)
both = [ [ offsets[i], cc[indices[i]] ] for i in range(0, len(offsets)) ]
return both
# returns an array of [ offset, strength ], sorted
# by strength, most-plausible first.
def convolve_guess_offset(self, samples, hz):
bin_hz = self.jrate / float(self.jblock)
ntaps = self.jblock
# average y down to a much lower rate to make the
# correlate() go faster. 64 works well.
# filter each of the four tones
tones = [ ]
for tone in range(0, 4):
thz = hz + tone*bin_hz
taps = weakutil.costone(self.jrate, thz, ntaps)
# yx = lfilter(taps, 1.0, samples)
# yx = numpy.convolve(samples, taps, mode='valid')
# yx = scipy.signal.convolve(samples, taps, mode='valid')
yx = scipy.signal.fftconvolve(samples, taps, mode='valid')
# hack to match size of lfilter() output
yx = numpy.append(numpy.zeros(ntaps-1), yx)
yx = abs(yx)
# scipy.signal.resample(yx, len(yx) / goff_step) works, but too slow.
# re = weakutil.Resampler(goff_step*64, 64)
# yx = re.resample(yx)
yx = weakutil.moving_average(yx, goff_step)
yx = yx[0::goff_step]
tones.append(yx)
# we just care about sync vs no sync,
# so combine tones 0 and 2, and 1 and 3
#tones[0] = numpy.add(tones[0], tones[2])
#tones[1] = numpy.add(tones[1], tones[3])
tones[0] = numpy.maximum(tones[0], tones[2])
tones[1] = numpy.maximum(tones[1], tones[3])
# now tone1 - tone0.
# yields +/- that should match pattern.
tt = numpy.subtract(tones[1], tones[0])
z = numpy.repeat(pattern, int(self.jblock / goff_step))
cc = numpy.correlate(tt, z)
indices = list(range(0, len(cc)))
indices = sorted(indices, key=lambda i : -cc[i])
offsets = numpy.multiply(indices, goff_step)
offsets = numpy.subtract(offsets, ntaps / 2)
both = [ [ offsets[i], cc[indices[i]] ] for i in range(0, len(offsets)) ]
return both
# given hza[hz0,hz1], return a new hza adjusted to
# give stronger tones.
# start is offset in samples[].
def guess_freq(self, xf, hza, start):
more = 1
ss = xf.getmore(hza, start, more)
# ss has one element per symbol time.
# ss[i] is a 6-element FFT, with ss[i][1] as lowest tone.
# first symbol is in ss[0]
bin_hz = self.jrate / float(self.jblock)
diffs = [ ]
for pi in range(0, len(pattern)):
if pattern[pi] > 0:
sync = 1
else:
sync = 0
fft = ss[pi]
sig0 = fft[sync+more]
sig1 = fft[2+sync+more]
if sig0 > sig1:
bin = sync+more
else:
bin = 2+sync+more
if fft[bin] > fft[bin-1] and fft[bin] > fft[bin+1]:
xp = weakutil.parabolic(numpy.log(fft), bin) # interpolate
# xp[0] is a better bin number (with fractional bin)
diff = (xp[0] - bin) * bin_hz
if diff > bin_hz / 2:
diff = bin_hz / 2
elif diff < -bin_hz / 2:
diff = -bin_hz / 2
else:
diff = 0.0
diffs.append(diff)
nhza = [
hza[0] + numpy.mean(diffs[0:80]),
hza[1] + numpy.mean(diffs[81:162])
]
return nhza
# do a coarse pass over the band, looking for
# possible signals.
# bud is budget in seconds.
# returns [ fine_rank, noise ]
def coarse(self, samples):
bin_hz = self.jrate / float(self.jblock)
# WSPR signals officially lie between 1400 and 1600 Hz.
# we've down-converted to 100 - 300 Hz.
# search a bit wider than that.
min_hz = self.lowhz-20
max_hz = self.lowhz+200+20
# generate a few copies of samples corrected for various amounts of drift.
if ndrift <= 1:
drifts = [ 0.0 ]
else:
drifts = [ ]
driftstart = -driftmax