def test_send():
floatlist = ds_accessor.get_data_samples(["AM-DSB-SC"], [30])[0][0]
print(floatlist)
buf = struct.pack('%sf' % len(floatlist), *floatlist)
while True:
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.connect(("127.0.0.1", 1337))
print("Sum of payload: %f" % sum(floatlist))
sock.sendall(buf)
response = sock.recv(BUFFER_SIZE)
sock.close()
classification, confidence = struct.unpack("If", response)
print("Response %d,%f" % (classification, confidence))
]
easy_modulation_targets = [
"OOK", "4ASK", "BPSK", "QPSK", "8PSK", "16QAM", "AM-SSB-SC", "AM-DSB-SC",
"FM", "GMSK", "OQPSK"
]
all_snr_targets = [
0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, -20, -18, -16,
-14, -12, -10, -8, -6, -4, -2
]
modulation_targets = subset_modulation_targets
snr_targets = [30]
dataset = ds_accessor.get_data_samples(modulation_targets, snr_targets)
random.shuffle(dataset)
set_split_point = int(len(dataset) * 0.75)
for i in range(0, set_split_point):
train_x.append(dataset[i][0])
train_y.append(dataset[i][2])
for i in range(set_split_point, len(dataset)):
test_x.append(dataset[i][0])
test_y.append(dataset[i][2])
# Python optimisation variables
learning_rate = 0.001 # Orignally 0.001 for Adam
epochs = 100
('OQPSK', 30),
('8ASK', 30),
('BPSK', 30),
('8PSK', 30),
('AM-SSB-SC', 30),
('4ASK', 30),
('16PSK', 30),
('64APSK', 30),
('128QAM', 30),
('128APSK', 30),
('AM-DSB-SC', 30),
('AM-SSB-WC', 30),
('64QAM', 30),
('QPSK', 30),
('256QAM', 30),
('AM-DSB-WC', 30),
('OOK', 30),
('16QAM', 30)
]
for target in targets:
# Have to make them into lists since that's what the ds_accesor requires
dataset = ds_accessor.get_data_samples([target[0]], [target[1]])
IQ = dataset[0][0]
print(IQ)
blob = struct.pack("2048f", *IQ)
with open(target[0] + "_" + str(target[1]) + ".bin", "wb") as file_out:
file_out.write(blob)
# Seed our randomizer for reproducability
random.seed(1337)
# Some info about our data
LEN_X = 2048
LEN_Y = 24
# Build our training and test data
TRAIN_TEST_RATIO = 0.75 # X% of the dataset will be for training data
train_x = []
train_y = []
test_x = []
test_y = []
dataset = ds_accessor.get_data_samples(["32PSK", "32QAM"], [30])
random.shuffle(dataset)
set_split_point = int(len(dataset) * 0.75)
for i in range(0, set_split_point):
train_x.append(dataset[i][0])
train_y.append(dataset[i][2])
for i in range(set_split_point, len(dataset)):
test_x.append(dataset[i][0])
test_x.append(dataset[i][2])
# Python optimisation variables
learning_rate = 0.5
epochs = 1000