def testblockLPC(signal,
blocksize,
numcoef,
ideal=False,
compress=False,
lookback=True,
showplot=True,
bitdepth=1):
#Set up receiver
recv = Receiver(lookback=lookback)
e_complete = zeros(0)
#Process blocks
for i in range(len(signal) / blocksize):
#find LPC coefficients
a = levinson(signal[i * blocksize:(i + 1) * blocksize], numcoef, False)
#build error
e = zeros(blocksize)
if ideal == True:
if i == 0 or recv.lookback == False:
e = idealError(signal[i * blocksize:(i + 1) * blocksize], a,
zeros(len(a)))
else:
e = idealError(signal[i * blocksize:(i + 1) * blocksize], a,
signal[i * blocksize - len(a):i * blocksize])
if compress == True:
temp_e = compressError(e, bitdepth)
temp_e = shiftCompressedError(temp_e, bitdepth)
e = scaleRMS(e, temp_e)
else:
classification = classify(signal[i * blocksize:(i + 1) *
blocksize])
classification[1].sort()
if classification[0] or True:
for i in range(len(e)):
# for p in classification[1]:
if i % 60 == 0:
e[i] = 1
else:
e = randn(len(e))
# e = e*classification[2]
e_complete = append(e_complete, e)
#Xmit and rebuild
recv.receiveIdeal(e, a)
#complete the transmission
recv.hangUp()
if showplot == True:
#Plot results
subplot(3, 1, 1)
title("Signal Reconstruction with Impulse Train", size=20)
plot(signal, label="Input Signal", lw=2)
legend()
subplot(3, 1, 2)
plot(e_complete, label="Excitation Signal", lw=2)
legend()
subplot(3, 1, 3)
plot(recv.output, label="Decoded Signal", lw=2)
# ylim([-5, 5])
legend()
show()
return recv.output
import random
from receiver import Receiver
# tests the receiver
a = []
e = []
rec = Receiver()
for i in range(25):
for j in range(10):
a.append(random.randint(1, 100))
e.append(random.randint(1, 100))
print "sending: %r" % i
rec.receiveIdeal(e, a)
e = []
a = []
rec.hangUp()
import random from receiver import Receiver # tests the receiver a = [] e = [] rec = Receiver() for i in range(25): for j in range(10): a.append(random.randint(1, 100)) e.append(random.randint(1, 100)) print "sending: %r" % i rec.receiveIdeal(e, a) e = [] a = [] rec.hangUp()
def testblockLPC(signal, blocksize, numcoef, ideal=False, compress=False, lookback = True, showplot=True, bitdepth=1):
#Set up receiver
recv = Receiver(lookback = lookback)
e_complete = zeros(0)
#Process blocks
for i in range(len(signal)/blocksize):
#find LPC coefficients
a = levinson(signal[i*blocksize:(i+1)*blocksize], numcoef, False)
#build error
e = zeros(blocksize)
if ideal == True:
if i == 0 or recv.lookback == False:
e = idealError(signal[i*blocksize:(i+1)*blocksize], a, zeros(len(a)))
else:
e = idealError(signal[i*blocksize:(i+1)*blocksize], a, signal[i*blocksize-len(a):i*blocksize])
if compress == True:
temp_e = compressError(e, bitdepth)
temp_e = shiftCompressedError(temp_e, bitdepth)
e = scaleRMS(e, temp_e)
else:
classification = classify(signal[i*blocksize:(i+1)*blocksize])
classification[1].sort()
if classification[0] or True:
for i in range(len(e)):
# for p in classification[1]:
if i%60 == 0:
e[i] = 1
else:
e = randn(len(e))
# e = e*classification[2]
e_complete = append(e_complete, e)
#Xmit and rebuild
recv.receiveIdeal(e, a)
#complete the transmission
recv.hangUp()
if showplot == True:
#Plot results
subplot(3, 1, 1)
title("Signal Reconstruction with Impulse Train", size=20)
plot(signal, label="Input Signal", lw=2)
legend()
subplot(3, 1, 2)
plot(e_complete, label="Excitation Signal", lw=2)
legend()
subplot(3, 1, 3)
plot(recv.output, label="Decoded Signal", lw=2)
# ylim([-5, 5])
legend()
show()
return recv.output
