signal= cplx_randn(SIGLEN)
sc_half_seq= cplx_randn(SCLEN)
sc_seq= np.concatenate((sc_half_seq[-CPLEN:], sc_half_seq, sc_half_seq))
start= (len(signal) - len(sc_seq)) // 2
end= start + len(sc_seq)
signal[start:end]+= sc_seq
for (fqname, fqoff) in (('nooff', 0), ('fqoff', 0.01)):
df= np.exp(1j * np.linspace(0, fqoff * SIGLEN, len(signal)))
fqadj= signal * df
detection= fqadj[SCLEN:] * fqadj[:-SCLEN].conj()
detection= np.convolve(detection, np.ones(SCLEN) / SCLEN, mode='valid')
for (name, src) in (('abs', abs(detection)), ('arg', np.angle(detection))):
fig= Figure()
canvas= FigureCanvas(fig)
ax= fig.add_subplot('111')
sl= len(src)
ax.plot(
np.linspace(-sl/2, sl/2, sl),
src, 'b'
)
canvas.print_pdf('sc_detector_output_{}_{}.pdf'.format(fqname, name))
fig = Figure()
canvas = FigureCanvas(fig)
ax = fig.add_subplot('111')
ax.set_title('Δt={}T, a={}'.format(delay / SAMP_RATE, mag))
target = list()
for i in range(64):
seq = np.zeros(IMPRESLEN)
seq[::SAMP_RATE] = rand.choice((-1, 1), IMPRESLEN // SAMP_RATE)
full = np.convolve(seq, imp_res)
start = (len(full) - 3 * SAMP_RATE) // 2
end = (len(full) + 3 * SAMP_RATE) // 2
full[:-delay] += mag * full[delay:]
full /= 1 + mag
crop = full[start:end]
crop += 0.01 * rand.randn(len(crop))
target.append(crop)
for tgt in target:
ax.plot(np.linspace(0, 0.1, len(tgt)), tgt, 'b')
canvas.print_pdf('eye_diagram_multipath_{}_{}m.pdf'.format(
delay, int(mag * 1000)))