def LDPC_binary_encode(alice_thread,column_weight = 3,row_weight =4):
total_string_length = len(alice_thread.bases_string)
number_of_parity_check_eqns_gallager = int(total_string_length*0.6)
# alice_thread.parity_binary_matrix = gallager_matrix(number_of_parity_check_eqns_gallager, total_string_length, column_weight, row_weight)
alice_thread.parity_binary_matrix = randomMatrix(total_string_length, number_of_parity_check_eqns_gallager, column_weight)
alice_thread.binary_syndromes=encode(alice_thread.parity_binary_matrix,alice_thread.bases_string,alphabet=2)
def LDPC_binary_encode(alice_thread, column_weight=3, row_weight=4):
total_string_length = len(alice_thread.bases_string)
number_of_parity_check_eqns_gallager = int(total_string_length * 0.6)
# alice_thread.parity_binary_matrix = gallager_matrix(number_of_parity_check_eqns_gallager, total_string_length, column_weight, row_weight)
alice_thread.parity_binary_matrix = randomMatrix(total_string_length, number_of_parity_check_eqns_gallager, column_weight)
alice_thread.binary_syndromes = encode(alice_thread.parity_binary_matrix, alice_thread.bases_string, alphabet=2)
def LDPC_decode_chunk(alice_thread,
bob_thread,
number_of_parity_check_eqns,
alice_chunk,
bob_chunk,
attempt,
decoder,
iterations,
frozenFor,
column_weight,
row_weight=3):
attempt = 0
decoded = False
decoded_string = array([])
while not decoded:
print "==================", attempt, "================================"
# =============In case you want to use gallager type matrix================================================================================
# total_string_length = len(alice_chunk)
# number_of_parity_check_eqns_gallager = int(total_string_length*column_weight/row_weight)
# alice_thread.parity_matrix = gallager_matrix(number_of_parity_check_eqns_gallager, total_string_length, column_weight, row_weight)
# ==========================================================================================================================================
alice_thread.parity_matrix = randomMatrix(len(alice_chunk),
number_of_parity_check_eqns,
column_weight)
alice_thread.syndromes = encode(alice_thread.parity_matrix,
alice_chunk, alice_thread.frame_size)
#============================SEND PARITY CHECK MATRIX & SYNDROMES =======================================
bob_thread.syndromes = alice_thread.syndromes
bob_thread.parity_matrix = alice_thread.parity_matrix
#========================================================================================================
if attempt == 0:
transition_matrix = transitionMatrix_data2_python(
bob_chunk, alice_chunk, bob_thread.frame_size)
prior_probability_matrix = sequenceProbMatrix(
bob_chunk, transition_matrix)
belief_propagation_system = SW_LDPC(bob_thread.parity_matrix,
bob_thread.syndromes,
prior_probability_matrix,
decoder=decoder,
original=alice_chunk)
decoded_string = belief_propagation_system.decode(
iterations=iterations, frozenFor=frozenFor)
is_not_decoded = sum(
check(bob_thread.parity_matrix, decoded_string,
bob_thread.frame_size, bob_thread.syndromes) == False)
if is_not_decoded == 0:
decoded = True
else:
decoded = False
attempt += 1
return decoded_string
def LDPC_encode(alice_thread, column_weight=3, row_weight=5):
total_string_length = len(alice_thread.non_zero_positions)
number_of_parity_check_eqns_gallager = int(total_string_length * column_weight/row_weight)
# alice_thread.parity_matrix = gallager_matrix(number_of_parity_check_eqns_gallager, total_string_length, column_weight, row_weight)
# bits - number of encoding strings
# checks - number of parity check eqns
# paritis- column weight
alice_thread.parity_matrix = randomMatrix(total_string_length, number_of_parity_check_eqns_gallager, column_weight)
alice_thread.syndromes = encode(alice_thread.parity_matrix, alice_thread.non_zero_positions, alice_thread.frame_size)
print "Syndromes", alice_thread.syndromes, len(alice_thread.syndromes), (total_string_length)
def LDPC_decode_chunk(alice_thread, bob_thread, number_of_parity_check_eqns, alice_chunk, bob_chunk, attempt, decoder, iterations, frozenFor, column_weight,row_weight = 3):
attempt = 0
decoded = False
decoded_string = array([])
while not decoded:
print "==================", attempt, "================================"
# =============In case you want to use gallager type matrix================================================================================
# total_string_length = len(alice_chunk)
# number_of_parity_check_eqns_gallager = int(total_string_length*column_weight/row_weight)
# alice_thread.parity_matrix = gallager_matrix(number_of_parity_check_eqns_gallager, total_string_length, column_weight, row_weight)
# ==========================================================================================================================================
alice_thread.parity_matrix = randomMatrix(len(alice_chunk), number_of_parity_check_eqns, column_weight)
alice_thread.syndromes = encode(alice_thread.parity_matrix, alice_chunk, alice_thread.frame_size)
#============================SEND PARITY CHECK MATRIX & SYNDROMES =======================================
bob_thread.syndromes = alice_thread.syndromes
bob_thread.parity_matrix = alice_thread.parity_matrix
#========================================================================================================
if attempt == 0:
transition_matrix = transitionMatrix_data2_python(bob_chunk, alice_chunk , bob_thread.frame_size)
prior_probability_matrix = sequenceProbMatrix(bob_chunk, transition_matrix)
belief_propagation_system = SW_LDPC(bob_thread.parity_matrix, bob_thread.syndromes, prior_probability_matrix, decoder=decoder, original=alice_chunk)
decoded_string = belief_propagation_system.decode(iterations=iterations, frozenFor=frozenFor)
is_not_decoded = sum(check(bob_thread.parity_matrix, decoded_string, bob_thread.frame_size, bob_thread.syndromes) == False)
if is_not_decoded == 0:
decoded = True
else:
decoded = False
attempt += 1
return decoded_string
y = loadtxt("./DarpaQKD/LDPC_bob_ttags8.txt")
y1 = zeros(len(y), dtype=uint64)
frame_size = 16
n = total_string_length = len(alice)
column_weight = 4
row_weight = 8
number_of_parity_check_eqns_gallager = int(total_string_length *
column_weight / row_weight)
parity_matrix = gallager_matrix(number_of_parity_check_eqns_gallager,
total_string_length, column_weight,
row_weight)
# print parity_matrix
# print parity_matrix
c = syndromes = encode(parity_matrix, alice, frame_size)
b = encode(parity_matrix, y, frame_size)
d = c ^ b
m = len(c)
a2f = get_a2f()
f2a = get_f2a()
QBER = 10
k = int(row_weight * m) #Bits per checksum
# print k
# print sum(sum(parity_matrix))
LL_reg = empty(k, dtype=int64)
(f_init, a_init, LL_reg) = LL_init()
success = 0
alice = loadtxt("./DarpaQKD/LDPC_alice_ttags8.txt")
y = loadtxt("./DarpaQKD/LDPC_bob_ttags8.txt")
y1 = zeros(len(y), dtype = uint64)
frame_size = 16
n = total_string_length = len(alice)
column_weight = 4
row_weight = 8
number_of_parity_check_eqns_gallager = int(total_string_length*column_weight/row_weight)
parity_matrix = gallager_matrix(number_of_parity_check_eqns_gallager, total_string_length, column_weight, row_weight)
# print parity_matrix
# print parity_matrix
c = syndromes=encode(parity_matrix,alice,frame_size)
b = encode(parity_matrix,y,frame_size)
d = c^b
m = len(c)
a2f = get_a2f()
f2a = get_f2a()
QBER = 10
k = int(row_weight*m) #Bits per checksum
# print k
# print sum(sum(parity_matrix))
LL_reg = empty(k, dtype = int64)
(f_init, a_init, LL_reg) = LL_init()
success = 0
# [ 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,],
# [ 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0]])
# print "Parity matrix\n",parity_matrix
# print "column weight of first column",sum(parity_matrix[:,0])
number_of_parity_check_eqns_gallager = int(total_string_length * 0.52)
k = 0
for i in range(10):
parity_matrix = randomMatrix(total_string_length,
number_of_parity_check_eqns_gallager, 3)
a = array([])
for i in range(parity_matrix.shape[0]):
# print sum(parity_matrix[i,:])
a = append(a, sum(parity_matrix[i, :]))
print min(a), "-", max(a)
# print parity_matrix
syndromes = encode(parity_matrix, alice, alphabet=frame_size)
# print "syndromes: ", syndromes
# ======================================
# ============ decode ===================
decoder = 'bp-fft'
iterations = 70
frozenFor = 5
set_printoptions(threshold=nan)
transition_matrix = transitionMatrix_data2_python(
bob, alice, frame_size)
prior_probability_matrix = sequenceProbMatrix(bob, transition_matrix)
#
# print "prior_probability_matrix\n",prior_probability_matrix
# print "Transition matrix\n",transition_matrix
