def processWave(fn):
signal, Fs = util.readwave(fn)
signal = np.r_[np.zeros(Fs / 2), signal]
alternation_interval = Fs * 2
steady_interval = Fs * 1.5
# we need to classify the sections of noise and silence, identify the
# steady-state intervals, FFT them, and label them, then write out the results.
signal *= 1. / signal.std()
def downsample(x, ratio):
order = 6
return iir.lowpass(1. / (2 * ratio), method='Ch -.2',
order=order)(np.r_[np.zeros(order),
x])[order::ratio]
def peak_detect(input, window_size):
# look for points outstanding in their neighborhood
# by explicit comparison
l = window_size
M = scipy.linalg.toeplitz(np.r_[input, np.zeros(2 * l)],
np.zeros(2 * l + 1)).T
ext_input = np.r_[np.zeros(l), input, np.zeros(l)]
M[:l] = M[:l] < ext_input
M[l + 1:] = M[l + 1:] < ext_input
M[l] = True
return np.where(M.all(0))[0] - l
ratio = 32 * 15 * 25
envelope = iir.lowpass(.125)(downsample(
downsample(downsample(np.abs(signal), 32), 15), 25))
peaks = peak_detect(envelope, 8) * ratio - 77840
radius = steady_interval / 2
subintervals = 75
intervals = ([], [])
S_Y_samples = []
S_N_samples = []
for p in peaks:
if p < radius or p + alternation_interval + radius >= signal.size:
continue
intervals[0].append((p - radius, p + radius))
noise = signal[p - radius:p + radius]
noise = np.fft.fft(noise.reshape(subintervals,
steady_interval / subintervals),
axis=1)
S_Y_samples.append(noise.var(0))
intervals[1].append((p + alternation_interval - radius,
p + alternation_interval + radius))
silence = signal[p + alternation_interval - radius:p +
alternation_interval + radius]
silence = np.fft.fft(silence.reshape(subintervals,
steady_interval / subintervals),
axis=1)
S_N_samples.append(silence.var(0))
bins = steady_interval / subintervals / 2
# note: S_Y = P + N, S_N = N, so S_Y/S_N = 1 + P/N
capacity = np.array([
np.log2(np.clip(S_Y / S_N, 1., np.inf))
for S_Y, S_N in zip(S_Y_samples, S_N_samples)
]).mean(0)[:bins]
S_X = bins * 2 * .1**2
S_H = np.array([
np.clip(S_Y - S_N, 0, np.inf) / S_X
for S_Y, S_N in zip(S_Y_samples, S_N_samples)
])[:, :bins]
H_mag = S_H.mean(0)**.5
S_Y = np.array(S_Y_samples).mean(0)[:bins]
S_N = np.array(S_N_samples).mean(0)[:bins]
df = Fs / float(steady_interval / subintervals)
f = df * np.arange(bins)
visualize = False
if visualize:
pl.figure()
pl.subplot(221)
pl.plot(f, 10 * np.log10(S_Y - S_N))
pl.plot(f, 10 * np.log10(S_N))
pl.xlim(0, f.max())
pl.subplot(223)
pl.plot(f, capacity)
pl.xlim(0, f.max())
pl.xlabel('Frequency (Hz)')
pl.ylabel('Capacity (bits/s/Hz)')
pl.subplot(122)
pl.plot(f, capacity.cumsum() * df)
pl.xlim(0, f.max())
test_synchronization = False
if test_synchronization:
pl.clf()
lp = lambda x: iir.lowpass(.005)(np.r_[np.zeros(6), x])[6:]
pl.plot(lp(np.abs(signal)) * .5)
pl.plot(np.arange(envelope.size) * ratio - 77840, envelope)
for x in peaks:
pl.gca().axvline(x, color='k')
noise_interval = np.zeros(signal.size, bool)
for start, end in intervals[0]:
noise_interval[start:end] = True
silence_interval = np.zeros(signal.size, bool)
for start, end in intervals[1]:
silence_interval[start:end] = True
import matplotlib.transforms as mtransforms
ax = pl.gca()
trans = mtransforms.blended_transform_factory(ax.transData,
ax.transAxes)
ax.fill_between(np.arange(signal.size),
0,
1,
where=noise_interval,
facecolor='red',
alpha=0.5,
transform=trans)
ax.fill_between(np.arange(signal.size),
0,
1,
where=silence_interval,
facecolor='magenta',
alpha=0.5,
transform=trans)
return df, H_mag, S_N, capacity, len(peaks)
args = args[2:]
else:
break
if args[0] == '--rx':
typeModHex = '--yubikey' in args
doDiagnostics = ('--nodiags' not in args) and not typeModHex
try:
startListening()
except KeyboardInterrupt:
pass
if doDiagnostics:
decoderDiagnostics()
elif args[0] == '--tx':
startTransmitting()
elif args[0] == '--wav-in' and len(args) > 1:
input, Fs_file = util.readwave(args[1])
input = util.upsample(input, Fs / float(Fs_file))
input = audioLoopback.processInput(input, Fs, Fc, upsample_factor)
for payload,_,_,lsnr_estimate in wifi.decode(input)[0]:
print(repr(''.join(map(chr, payload))) + (' @ %.1f dB' % lsnr_estimate))
elif args[0] == '--wav-out' and len(args) > 1:
fn = args[1]
args = args[2:]
packets = 1
while len(args):
if args[0] == '--packets':
packets = int(args[1])
args = args[2:]
outputChunks = []
for i in xrange(packets):
input_octets = ord('A') + np.random.random_integers(0,25,length)
def processWave(fn):
signal, Fs = util.readwave(fn)
signal = np.r_[np.zeros(Fs/2), signal]
alternation_interval = Fs*2
steady_interval = Fs*1.5
# we need to classify the sections of noise and silence, identify the
# steady-state intervals, FFT them, and label them, then write out the results.
signal *= 1./signal.std()
def downsample(x, ratio):
order = 6
return iir.lowpass(1./(2*ratio), method='Ch -.2', order=order)(np.r_[np.zeros(order), x])[order::ratio]
def peak_detect(input, window_size):
# look for points outstanding in their neighborhood
# by explicit comparison
l = window_size
M = scipy.linalg.toeplitz(np.r_[input, np.zeros(2*l)], np.zeros(2*l+1)).T
ext_input = np.r_[np.zeros(l), input, np.zeros(l)]
M[:l] = M[:l] < ext_input
M[l+1:] = M[l+1:] < ext_input
M[l] = True
return np.where(M.all(0))[0] - l
ratio = 32*15*25
envelope = iir.lowpass(.125)(downsample(downsample(downsample(np.abs(signal), 32), 15), 25))
peaks = peak_detect(envelope, 8) * ratio - 77840
radius = steady_interval/2
subintervals = 75
intervals = ([], [])
S_Y_samples = []
S_N_samples = []
for p in peaks:
if p < radius or p + alternation_interval + radius >= signal.size: continue
intervals[0].append((p-radius,p+radius))
noise = signal[p-radius:p+radius]
noise = np.fft.fft(noise.reshape(subintervals, steady_interval/subintervals), axis=1)
S_Y_samples.append(noise.var(0))
intervals[1].append((p+alternation_interval-radius,p+alternation_interval+radius))
silence = signal[p+alternation_interval-radius:p+alternation_interval+radius]
silence = np.fft.fft(silence.reshape(subintervals, steady_interval/subintervals), axis=1)
S_N_samples.append(silence.var(0))
bins = steady_interval/subintervals/2
# note: S_Y = P + N, S_N = N, so S_Y/S_N = 1 + P/N
capacity = np.array([np.log2(np.clip(S_Y/S_N, 1., np.inf)) for S_Y, S_N in zip(S_Y_samples, S_N_samples)]).mean(0)[:bins]
S_X = bins * 2 * .1**2
S_H = np.array([np.clip(S_Y-S_N,0,np.inf)/S_X for S_Y, S_N in zip(S_Y_samples, S_N_samples)])[:,:bins]
H_mag = S_H.mean(0)**.5
S_Y = np.array(S_Y_samples).mean(0)[:bins]
S_N = np.array(S_N_samples).mean(0)[:bins]
df = Fs/float(steady_interval/subintervals)
f = df*np.arange(bins)
visualize = False
if visualize:
pl.figure()
pl.subplot(221)
pl.plot(f, 10*np.log10(S_Y-S_N))
pl.plot(f, 10*np.log10(S_N))
pl.xlim(0, f.max())
pl.subplot(223)
pl.plot(f, capacity)
pl.xlim(0, f.max())
pl.xlabel('Frequency (Hz)')
pl.ylabel('Capacity (bits/s/Hz)')
pl.subplot(122)
pl.plot(f, capacity.cumsum()*df)
pl.xlim(0, f.max())
test_synchronization = False
if test_synchronization:
pl.clf()
lp = lambda x:iir.lowpass(.005)(np.r_[np.zeros(6), x])[6:]
pl.plot(lp(np.abs(signal))*.5)
pl.plot(np.arange(envelope.size) * ratio - 77840, envelope)
map(lambda x:pl.gca().axvline(x, color='k'), peaks)
noise_interval = np.zeros(signal.size, bool)
for start, end in intervals[0]:
noise_interval[start:end] = True
silence_interval = np.zeros(signal.size, bool)
for start, end in intervals[1]:
silence_interval[start:end] = True
import matplotlib.transforms as mtransforms
ax = pl.gca()
trans = mtransforms.blended_transform_factory(ax.transData, ax.transAxes)
ax.fill_between(np.arange(signal.size), 0, 1, where=noise_interval, facecolor='red', alpha=0.5, transform=trans)
ax.fill_between(np.arange(signal.size), 0, 1, where=silence_interval, facecolor='magenta', alpha=0.5, transform=trans)
return df, H_mag, S_N, capacity, len(peaks)
args = args[2:]
else:
break
if args[0] == '--rx':
typeModHex = '--yubikey' in args
doDiagnostics = ('--nodiags' not in args) and not typeModHex
try:
startListening()
except KeyboardInterrupt:
pass
if doDiagnostics:
decoderDiagnostics()
elif args[0] == '--tx':
startTransmitting()
elif args[0] == '--wav-in' and len(args) > 1:
input, Fs_file = util.readwave(args[1])
input = util.upsample(input, Fs / float(Fs_file))
input = audioLoopback.processInput(input, Fs, Fc, upsample_factor)
for payload, _, _, lsnr_estimate in wifi.decode(input)[0]:
print(
repr(''.join(map(chr, payload))) +
(' @ %.1f dB' % lsnr_estimate))
elif args[0] == '--wav-out' and len(args) > 1:
fn = args[1]
args = args[2:]
packets = 1
while len(args):
if args[0] == '--packets':
packets = int(args[1])
args = args[2:]
outputChunks = []
args = args[2:]
else:
break
if args[0] == "--rx":
typeModHex = "--yubikey" in args
doDiagnostics = ("--nodiags" not in args) and not typeModHex
try:
startListening()
except KeyboardInterrupt:
pass
if doDiagnostics:
decoderDiagnostics()
elif args[0] == "--tx":
startTransmitting()
elif args[0] == "--wav-in" and len(args) > 1:
input, Fs = util.readwave(args[1])
input = audioLoopback.processInput(input, Fs, Fc, upsample_factor)
for payload, _, _, lsnr_estimate in wifi.decode(input)[0]:
print repr("".join(map(chr, payload))) + (" @ %.1f dB" % lsnr_estimate)
elif args[0] == "--wav-out" and len(args) > 1:
fn = args[1]
args = args[2:]
packets = 1
while len(args):
if args[0] == "--packets":
packets = int(args[1])
args = args[2:]
outputChunks = []
for i in xrange(packets):
input_octets = ord("A") + np.random.random_integers(0, 25, length)
input_octets[:6] = map(ord, "%06d" % i)
args = args[2:]
else:
break
if args[0] == '--rx':
typeModHex = '--yubikey' in args
doDiagnostics = ('--nodiags' not in args) and not typeModHex
try:
startListening()
except KeyboardInterrupt:
pass
if doDiagnostics:
decoderDiagnostics()
elif args[0] == '--tx':
startTransmitting()
elif args[0] == '--wav-in' and len(args) > 1:
input, Fs = util.readwave(args[1])
input = audioLoopback.processInput(input, Fs, Fc, upsample_factor)
for payload, _, _, lsnr_estimate in wifi.decode(input)[0]:
print repr(''.join(map(
chr, payload))) + (' @ %.1f dB' % lsnr_estimate)
elif args[0] == '--wav-out' and len(args) > 1:
fn = args[1]
args = args[2:]
packets = 1
while len(args):
if args[0] == '--packets':
packets = int(args[1])
args = args[2:]
outputChunks = []
for i in xrange(packets):
input_octets = ord('A') + np.random.random_integers(