def smptest(secret, sock, is_server):
global axolotl
# Create an SMP object with the calculated secret
smp = SMP(secret)
if is_server:
# Do the SMP protocol
buffer = sock.recv(2439, socket.MSG_WAITALL)
padlength = ord(buffer[-1:])
buffer = axolotl.decrypt(buffer[:-padlength])
buffer = smp.step2(buffer)
buffer = axolotl.encrypt(buffer)
padlength = 4539 - len(buffer)
buffer = buffer + padlength * chr(padlength) # pad to fixed length
sock.send(buffer)
buffer = sock.recv(3469, socket.MSG_WAITALL)
padlength = ord(buffer[-1:])
buffer = axolotl.decrypt(buffer[:-padlength])
buffer = smp.step4(buffer)
buffer = axolotl.encrypt(buffer)
padlength = 1369 - len(buffer)
buffer = buffer + padlength * chr(padlength) # pad to fixed length
sock.send(buffer)
else:
# Do the SMP protocol
buffer = smp.step1()
buffer = axolotl.encrypt(buffer)
padlength = 2439 - len(buffer)
buffer = buffer + padlength * chr(padlength) # pad to fixed length
sock.send(buffer)
buffer = sock.recv(4539, socket.MSG_WAITALL)
padlength = ord(buffer[-1:])
buffer = axolotl.decrypt(buffer[:-padlength])
buffer = smp.step3(buffer)
buffer = axolotl.encrypt(buffer)
padlength = 3469 - len(buffer)
buffer = buffer + padlength * chr(padlength) # pad to fixed length
sock.send(buffer)
buffer = sock.recv(1369, socket.MSG_WAITALL)
padlength = ord(buffer[-1:])
buffer = axolotl.decrypt(buffer[:-padlength])
smp.step5(buffer)
# Check if the secrets match
if smp.match:
print 'Secrets Match!'
sleep(1)
smp_match = True
else:
print 'Secrets DO NOT Match!'
smp_match = False
return smp_match
counts = numpy.zeros(size, dtype=numpy.int32)
flags = numpy.zeros(size, dtype=numpy.uint8)
for a, b in enumerate(pos_to_use):
counts[b] = draw[a]
flags[b] = 1
sam.append((counts, flags))
# Construct the flag index array, put relevant index into each sample...
fia = FlagIndexArray(size)
fia.addSingles()
for i in xrange(len(sam)):
ind = fia.flagIndex(sam[i][1])
sam[i] = (sam[i][0], sam[i][1], ind)
# Construct the SMP object...
smp = SMP(fia)
smp.setSampleCount(samCount)
for s in sam:
smp.add(s[2], s[0])
# Get and print out the mean and its distance from the actual multinomial...
mean = smp.mean()
print 'Mean =', mean
print 'error =', numpy.fabs(mn - mean).sum() / mn.shape[0]
print 'cError =\n', numpy.fabs(mn - mean)
