def kodo_init():
# Create list of encoder and decoder triples
global nodes
global data_in
global simple_data_out
global greedy_data_out
global heuristic_data_out
global master_data_in
nodes = []
data_in = []
simple_data_out = []
greedy_data_out = []
heuristic_data_out = []
master_data_in = []
# init one encoder
master_encoder.set_seed(SEED_VALUE)
for _ in range(NUM_OF_NODES):
seed = np.random.randint(SEED_VALUE)
# init decoders
simple_decoder = kodo.RLNCDecoder(field, symbols, symbol_size)
simple_decoder.set_seed(seed)
greedy_decoder = kodo.RLNCDecoder(field, symbols, symbol_size)
greedy_decoder.set_seed(seed)
heuristic_decoder = kodo.RLNCDecoder(field, symbols, symbol_size)
heuristic_decoder.set_seed(seed)
# decoder.set_log_callback(callback_function)
nodes.append([simple_decoder, greedy_decoder, heuristic_decoder])
def __init__(self, name='S', coding=None, fieldsize=1, random=None):
if random is None:
np.random.seed(1)
else:
np.random.seed(random)
symbol_size = 8
self.coder = None
self.field = selfieldsize(fieldsize)
if name == 'S':
self.coder = kodo.RLNCEncoder(self.field, coding, symbol_size)
self.data = bytearray(os.urandom(self.coder.block_size()))
self.coder.set_symbols_storage(self.data)
else:
self.coder = kodo.RLNCDecoder(self.field, coding, symbol_size)
self.data = bytearray(self.coder.block_size())
self.coder.set_symbols_storage(self.data)
self.name = name
self.fieldsize = 2**fieldsize
self.incbuffer = []
self.coding = coding
self.symbol_size = symbol_size
self.batch = 0
self.eotx = float('inf')
self.deotx = float('inf')
self.creditcounter = 0. if self.name != 'S' else float('inf')
self.credit = 0.
self.sent = False
self.working = True
self.priority = 0.
self.quiet = False
self.history = []
self.sendhistory = []
self.rank = 0 if self.name != 'S' else coding
self.action = None
def receive_data(settings, role):
"""Receive data from the other node."""
# Setup kodo decoder
decoder = kodo.RLNCDecoder(
field = kodo.field.binary,
symbols=settings['symbols'],
symbol_size=settings['symbol_size'])
data_out = bytearray(decoder.block_size())
decoder.set_symbols_storage(data_out)
send_socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
# Set receiving sockets
data_socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
data_socket.settimeout(settings['timeout'])
data_socket.bind(('', settings['data_port']))
if role == 'client':
address = (settings['server_ip'], settings['server_control_port'])
send_settings(settings)
else: # server
address = (settings['client_ip'], settings['client_control_port'])
send(send_socket, "settings OK, receiving", address)
# Decode coded packets
received = 0
start = time.clock()
end = None
while 1:
try:
packet = data_socket.recv(settings['symbol_size'] + 100)
if not decoder.is_complete():
decoder.consume_payload(bytearray(packet))
received += 1
if decoder.is_complete():
if end is None:
end = time.clock() # stopping time once
send(send_socket, "Stop sending", address)
except socket.timeout:
break # no more data arriving
# in case we did not complete
if end is None:
end = time.clock()
data_socket.close()
if not decoder.is_complete():
print("Decoding failed")
size = decoder.block_size() * (float(received) / settings['symbols'])
microseconds = 1e6 * (end - start)
print("Received {0} packets, {1} kB in {2:.0f} microseconds at "
"{3:.2f} Mbit/s.".format(received, size / 1000, microseconds,
decoder.block_size() * 8 / microseconds))
def newbatch(self):
"""Make destination awaiting new batch."""
self.batch += 1
self.rank = self.coding if self.name == 'S' else 0
if self.name != 'S':
self.coder = kodo.RLNCDecoder(self.field, self.coding,
self.symbol_size)
self.data = bytearray(self.coder.block_size())
self.coder.set_symbols_storage(self.data)
self.quiet = False
def main():
"""Simple example showing how to encode and decode a block of memory."""
# Choose the finite field, the number of symbols (i.e. generation size)
# and the symbol size in bytes
field = kodo.field.binary
symbols = 8
symbol_size = 160
# Create an encoder and a decoder
encoder = kodo.RLNCEncoder(field, symbols, symbol_size)
decoder = kodo.RLNCDecoder(field, symbols, symbol_size)
# Generate some random data to encode. We create a bytearray of the same
# size as the encoder's block size and assign it to the encoder.
# This bytearray must not go out of scope while the encoder exists!
data_in = bytearray(os.urandom(encoder.block_size()))
encoder.set_symbols_storage(data_in)
# Define the data_out bytearray where the symbols should be decoded
# This bytearray must not go out of scope while the encoder exists!
data_out = bytearray(decoder.block_size())
decoder.set_symbols_storage(data_out)
packet_number = 0
while not decoder.is_complete():
# Generate an encoded packet
packet = encoder.produce_payload()
print("Packet {} encoded!".format(packet_number))
# Pass that packet to the decoder
decoder.consume_payload(packet)
print("Packet {} decoded!".format(packet_number))
packet_number += 1
print("rank: {}/{}".format(decoder.rank(), decoder.symbols()))
print("Coding finished")
# The decoder is complete, the decoded symbols are now available in
# the data_out buffer: check if it matches the data_in buffer
print("Checking results...")
if data_out == data_in:
print("Data decoded correctly")
else:
print("Unable to decode please file a bug report :)")
sys.exit(1)
def rcvpacket(self):
"""Add received Packet to buffer. Do this at end of time slot."""
while len(self.incbuffer):
batch, coding, preveotx, prevdeotx, special = self.incbuffer.pop()
if self.name == 'S': # Cant get new information if you're source
continue
elif batch < self.batch:
continue
elif batch > self.batch or not self.coder.rank(
): # Delete it if you're working on deprecated batch
self.batch = batch
self.sent = False
self.coder = kodo.RLNCDecoder(self.field, self.coding,
self.symbol_size)
self.data = bytearray(self.coder.block_size())
self.coder.set_symbols_storage(self.data)
self.coder.consume_payload(coding)
self.rank = self.coder.rank()
self.creditcounter = 0.
if self.quiet:
self.quiet = False
if special and not self.credit:
self.creditcounter += 1
else: # Just add new information if its new
if not self.coder.is_complete():
self.coder.consume_payload(coding)
newrank = self.coder.rank()
else:
newrank = self.rank # Full coder does not get new information
if self.rank <= newrank:
self.rank = newrank
if special and not self.credit:
self.creditcounter += 1
if preveotx > self.eotx or prevdeotx > self.deotx or (
self.priority != 0. and self.priority > preveotx):
self.creditcounter += self.credit
def main():
"""
Encode on the fly example.
This example shows how to use a storage aware encoder which will
allow you to encode from a block before all symbols have been
specified. This can be useful in cases where the symbols that
should be encoded are produced on-the-fly.
"""
# Choose the finite field, the number of symbols (i.e. generation size)
# and the symbol size in bytes
field = kodo.field.binary
symbols = 10
symbol_size = 160
# Create an encoder and a decoder
encoder = kodo.RLNCEncoder(field, symbols, symbol_size)
decoder = kodo.RLNCDecoder(field, symbols, symbol_size)
# Generate some random data to encode. We create a bytearray of the same
# size as the encoder's block size
data_in = bytearray(os.urandom(encoder.block_size()))
# Define the data_out bytearray where the symbols should be decoded
# This bytearray must not go out of scope while the encoder exists!
data_out = bytearray(decoder.block_size())
decoder.set_symbols_storage(data_out)
# Let's split the data into symbols and feed the encoder one symbol at a
# time
symbol_storage = [
data_in[i:i + symbol_size] for i in range(0, len(data_in), symbol_size)
]
while not decoder.is_complete():
# Randomly choose to insert a symbol
if random.choice([True, False]) and encoder.rank() < symbols:
# For an encoder the rank specifies the number of symbols
# it has available for encoding
rank = encoder.rank()
encoder.set_symbol_storage(symbol_storage[rank], rank)
print("Symbol {} added to the encoder".format(rank))
# Encode a packet into the payload buffer
packet = encoder.produce_payload()
print("Packet encoded")
# Send the data to the decoders, here we just for fun
# simulate that we are loosing 50% of the packets
if random.choice([True, False]):
print("Packet dropped on channel")
continue
# Packet got through - pass that packet to the decoder
decoder.consume_payload(packet)
print("Decoder received packet")
print("Encoder rank = {}".format(encoder.rank()))
print("Decoder rank = {}".format(decoder.rank()))
decoded_symbol_indces = []
for i in range(decoder.symbols()):
if decoder.is_symbol_decoded(i):
decoded_symbol_indces.append(str(i))
print("Decoder decoded = {} ({}) symbols".format(
decoder.symbols_decoded(),
" ".join(decoded_symbol_indces)))
print("Decoder partially decoded = {}".format(
decoder.symbols_partially_decoded()))
print("Coding finished")
# The decoder is complete, the decoded symbols are now available in
# the data_out buffer: check if it matches the data_in buffer
print("Checking results...")
if data_out == data_in:
print("Data decoded correctly")
else:
print("Unexpected failure to decode please file a bug report :)")
sys.exit(1)
def main():
"""
Pure recode example using the Payload API.
This example is very similar to encode_recode_decode_simple.py.
The only difference is that this example uses a pure recoder instead of
a decoder acting as a recoder. By "pure", we mean that the recoder will
not decode the incoming data, it will only re-encode it.
This example shows how to use an encoder, recoder, and decoder to
simulate a simple relay network as shown below. For simplicity,
we have error free links, i.e. no data packets are lost when being
sent from encoder to recoder to decoder:
+-----------+ +-----------+ +------------+
| encoder |+---->| recoder |+---->| decoder |
+-----------+ +-----------+ +------------+
The pure recoder does not need to decode all symbols, and its "capacity"
for storing symbols can be limited. These stored coded symbols are
linear combinations of previously received symbols. When a new symbol is
received, the pure recoder will combine it with its existing symbols
using random coefficients.
The pure recoder can produce a random linear combination of the stored
coded symbols, which can be processed by a regular decoder.
"""
# Choose the finite field, the number of symbols (i.e. generation size)
# and the symbol size in bytes
field = kodo.field.binary8
symbols = 5
symbol_size = 160
encoder = kodo.RLNCEncoder(field, symbols, symbol_size)
# Set the pure recoder "capacity" to be less than "symbols"
recoder = kodo.RLNCPureRecoder(field, symbols, symbol_size, 3)
print("Recoder properties:\n"
" Symbols: {}\n"
" Recoder symbols: {}".format(recoder.symbols(),
recoder.recoder_symbols()))
decoder = kodo.RLNCDecoder(field, symbols, symbol_size)
# Generate some random data to encode. We create a bytearray of the same
# size as the encoder's block size and assign it to the encoder.
# This bytearray must not go out of scope while the encoder exists!
data_in = bytearray(os.urandom(encoder.block_size()))
encoder.set_symbols_storage(data_in)
# Define the data_out bytearrays where the symbols should be decoded
# These bytearrays must not go out of scope while the encoder exists!
data_out = bytearray(decoder.block_size())
decoder.set_symbols_storage(data_out)
while not decoder.is_complete():
# Encode a packet into the payload buffer
packet = encoder.produce_payload()
print("Encoded packet generated and passed to the recoder")
# Pass that packet to the recoder
recoder.consume_payload(packet)
# Now produce a new recoded packet from the current decoding buffer
recoded_packet = recoder.produce_payload()
print("Recoded packet generated and passed to the decoder")
# Pass the recoded packet to the decoder
decoder.consume_payload(recoded_packet)
print("Decoder rank: {}/{}\n".format(decoder.rank(), symbols))
# Check if we properly decoded the data
if data_out == data_in:
print("Data decoded correctly")
else:
print("Unexpected failure to decode please file a bug report :)")
sys.exit(1)
def main():
"""An example for using the trace functionality."""
# Choose the finite field, the number of symbols (i.e. generation size)
# and the symbol size in bytes
field = kodo.field.binary8
symbols = 5
symbol_size = 16
# Create an encoder and a decoder
encoder = kodo.RLNCEncoder(field, symbols, symbol_size)
decoder = kodo.RLNCDecoder(field, symbols, symbol_size)
# Generate some random data to encode. We create a bytearray of the same
# size as the encoder's block size and assign it to the encoder.
# This bytearray must not go out of scope while the encoder exists!
data_in = bytearray(os.urandom(encoder.block_size()))
encoder.set_symbols_storage(data_in)
# Define the data_out bytearray where the symbols should be decoded
# This bytearray must not go out of scope while the encoder exists!
data_out = bytearray(decoder.block_size())
decoder.set_symbols_storage(data_out)
# Setup tracing
# Enable the stdout trace function of the encoder
encoder.set_log_stdout()
encoder.set_zone_prefix("encoder")
# Define a custom trace function for the decoder which filters the
# trace message based on their zones
def callback_function(zone, message):
if zone in [
"decoder_state", "symbol_coefficients_before_consume_symbol"
]:
print("{}:".format(zone))
print(message)
decoder.set_log_callback(callback_function)
while not decoder.is_complete():
# Encode a packet into the payload buffer
packet = encoder.produce_payload()
# Here we "simulate" a packet loss of approximately 50%
# by dropping half of the encoded packets.
# When running this example you will notice that the initial
# symbols are received systematically (i.e. decoded). After
# sending all symbols once decoded, the encoder will switch
# to full coding, in which case you will see the full encoding
# vectors being sent and received.
if random.choice([True, False]):
print("Packet dropped.\n")
continue
# Pass that packet to the decoder
decoder.consume_payload(packet)
# The decoder is complete, the decoded symbols are now available in
# the data_out buffer: check if it matches the data_in buffer
print("Checking results...")
if data_out == data_in:
print("Data decoded correctly")
else:
print("Unexpected failure to decode please file a bug report :)")
sys.exit(1)
def main():
"""
Encode-decode using coefficients example.
This example shows how to use the Symbol API with direct coefficient
access. Using this approach the we have full control over where
coefficients are stored, however we also have to manage things such as
systematic symbols ourselves.
"""
# Choose the finite field, the number of symbols (i.e. generation size)
# and the symbol size in bytes
field = kodo.field.binary8
symbols = 5
symbol_size = 160
# Create an encoder and a decoder
encoder = kodo.RLNCEncoder(field, symbols, symbol_size)
decoder = kodo.RLNCDecoder(field, symbols, symbol_size)
# Generate some random data to encode. We create a bytearray of the same
# size as the encoder's block size and assign it to the encoder.
# This bytearray must not go out of scope while the encoder exists!
data_in = bytearray(os.urandom(encoder.block_size()))
encoder.set_symbols_storage(data_in)
# Define the data_out bytearray where the symbols should be decoded
# This bytearray must not go out of scope while the encoder exists!
data_out = bytearray(decoder.block_size())
decoder.set_symbols_storage(data_out)
# Define a custom trace function for the decoder which filters the
# trace message based on their zones
def callback_function(zone, message):
if zone in [
"decoder_state", "symbol_coefficients_before_consume_symbol"
]:
print("{}:".format(zone))
print(message)
# We want to follow the decoding process step-by-step
decoder.set_log_callback(callback_function)
# In the first phase, we will transfer some systematic symbols from
# the encoder to the decoder.
# Randomly select 2 symbols from the 5 original symbols
for index in sorted(random.sample(range(symbols), 2)):
# Get the original symbol from the encoder
symbol = encoder.produce_systematic_symbol(index)
# Insert the symbol to the decoder using the raw symbol data,
# no additional headers or coefficients are needed for this
print("Adding Systematic Symbol {}:\n".format(index))
decoder.consume_systematic_symbol(symbol, index)
# In the second phase, we will generate coded symbols to fill in the gaps
# and complete the decoding process
packet_number = 0
while not decoder.is_complete():
# Generate some random coefficients for encoding
coefficients = encoder.generate()
# We can print the individual coefficients here, because we use the
# binary8 field where each byte corresponds to a single coefficient.
# For other fields, we would need fifi-python to do this!
print("Coding coefficients:")
print(" ".join(str(c) for c in coefficients))
# Write a coded symbol to the symbol buffer
symbol = encoder.produce_symbol(coefficients)
print("Coded Symbol {} encoded!\n".format(packet_number))
# Pass that symbol and the corresponding coefficients to the decoder
print("Processing Coded Symbol {}:\n".format(packet_number))
decoder.consume_symbol(symbol, coefficients)
packet_number += 1
print("Decoder rank: {}/{}\n".format(decoder.rank(), symbols))
print("Coding finished")
# The decoder is complete, the decoded symbols are now available in
# the data_out buffer: check if it matches the data_in buffer
print("Checking results...")
if data_out == data_in:
print("Data decoded correctly")
else:
print("Unable to decode, please file a bug report :)")
sys.exit(1)
def main():
"""
Encode recode decode example.
In Network Coding applications, one of the key features is the
ability of intermediate nodes in the network to recode packets
as they traverse them. In Kodo it is possible to recode packets
in decoders which provide the produce_payload() function.
This example shows how to use one encoder and two decoders to
simulate a simple relay network as shown below (for simplicity
we have error free links, i.e. no data packets are lost when being
sent from encoder to decoder1 and decoder1 to decoder2):
+-----------+ +-----------+ +-----------+
| encoder |+---.| decoder1 |+---.| decoder2 |
+-----------+ | (recoder) | +-----------+
+-----------+
In a practical application recoding can be used in several different
ways and one must consider several different factors, such as
reducing linear dependency by coordinating several recoding nodes
in the network.
Suggestions for dealing with such issues can be found in current
research literature (e.g. MORE: A Network Coding Approach to
Opportunistic Routing).
"""
# Choose the finite field, the number of symbols (i.e. generation size)
# and the symbol size in bytes
field = kodo.field.binary
symbols = 42
symbol_size = 160
# Create an encoder and two decoders
encoder = kodo.RLNCEncoder(field, symbols, symbol_size)
decoder1 = kodo.RLNCDecoder(field, symbols, symbol_size)
decoder2 = kodo.RLNCDecoder(field, symbols, symbol_size)
# Generate some random data to encode. We create a bytearray of the same
# size as the encoder's block size and assign it to the encoder.
# This bytearray must not go out of scope while the encoder exists!
data_in = bytearray(os.urandom(encoder.block_size()))
encoder.set_symbols_storage(data_in)
# Define the data_out bytearrays where the symbols should be decoded
# These bytearrays must not go out of scope while the encoder exists!
data_out1 = bytearray(decoder1.block_size())
data_out2 = bytearray(decoder1.block_size())
decoder1.set_symbols_storage(data_out1)
decoder2.set_symbols_storage(data_out2)
while not decoder2.is_complete():
# Encode a packet into the payload buffer
packet = encoder.produce_payload()
# Pass that packet to decoder1
decoder1.consume_payload(packet)
# Now produce a new recoded packet from the current
# decoding buffer, and place it into the payload buffer
packet = decoder1.produce_payload()
# Pass the recoded packet to decoder2
decoder2.consume_payload(packet)
# Both decoder1 and decoder2 should now be complete,
# check if the output buffers match the data_in buffer
if data_out1 == data_in and data_out2 == data_in:
print("Data decoded correctly")
else:
print("Unexpected failure to decode please file a bug report :)")
sys.exit(1)
def main():
# Setup canvas and viewer
size = 512
canvas = kodo_helpers.CanvasScreenEngine(size * 2, size)
encoder_viewer = kodo_helpers.EncodeStateViewer(size=size, canvas=canvas)
decoder_viewer = kodo_helpers.DecodeStateViewer(size=size,
canvas=canvas,
canvas_position=(size, 0))
canvas.start()
try:
field = kodo.field.binary8
symbols = 64
symbol_size = 16
encoder = kodo.RLNCEncoder(field, symbols, symbol_size)
decoder = kodo.RLNCDecoder(field, symbols, symbol_size)
data_in = bytearray(os.urandom(encoder.block_size()))
encoder.set_symbols_storage(data_in)
data_out = bytearray(decoder.block_size())
decoder.set_symbols_storage(data_out)
def decoder_callback(zone, msg):
decoder_viewer.log_callback(zone, msg)
decoder.set_log_callback(decoder_callback)
def encoder_callback(zone, msg):
encoder_viewer.log_callback(zone, msg)
encoder_viewer.set_symbols(encoder.symbols())
encoder.set_log_callback(encoder_callback)
while not decoder.is_complete():
# Encode a packet into the payload buffer
packet = encoder.produce_payload()
# Here we "simulate" a packet loss of approximately 50%
# by dropping half of the encoded packets.
# When running this example you will notice that the initial
# symbols are received systematically (i.e. decoded). After
# sending all symbols once decoded, the encoder will switch
# to full coding, in which case you will see the full encoding
# vectors being sent and received.
if random.choice([True, False]):
continue
# Pass that packet to the decoder
decoder.consume_payload(packet)
time.sleep(1)
finally:
# What ever happens, make sure we stop the viewer.
canvas.stop()
# Check we properly decoded the data
if data_out == data_in:
print("Data decoded correctly")
else:
print("Unexpected failure to decode please file a bug report :)")
sys.exit(1)
def main():
"""Example showing the result of enabling the symbol status updater."""
# Choose the finite field, the number of symbols (i.e. generation size)
# and the symbol size in bytes
field = kodo.field.binary
symbols = 50
symbol_size = 160
# Create an encoder
encoder = kodo.RLNCEncoder(field, symbols, symbol_size)
# Create two decoders, one which has the status updater turned on, and one
# which has it off.
decoder1 = kodo.RLNCDecoder(field, symbols, symbol_size)
decoder2 = kodo.RLNCDecoder(field, symbols, symbol_size)
decoder2.set_status_updater_on()
print("decoder 1 status updater: {}".format(
decoder1.is_status_updater_enabled()))
print("decoder 2 status updater: {}".format(
decoder2.is_status_updater_enabled()))
# Generate some random data to encode. We create a bytearray of the same
# size as the encoder's block size and assign it to the encoder.
# This bytearray must not go out of scope while the encoder exists!
data_in = bytearray(os.urandom(encoder.block_size()))
encoder.set_symbols_storage(data_in)
# Define the data_out bytearray where the symbols should be decoded
# This bytearray must not go out of scope while the encoder exists!
data_out1 = bytearray(decoder1.block_size())
data_out2 = bytearray(decoder1.block_size())
decoder1.set_symbols_storage(data_out1)
decoder2.set_symbols_storage(data_out2)
# Skip the systematic phase as the effect of the symbol status decoder is
# only visible when reading coded packets.
encoder.set_systematic_off()
print("Processing")
while not decoder1.is_complete():
# Generate an encoded packet
payload = encoder.produce_payload()
payload_copy = copy.copy(payload)
# Pass that packet to the decoder
decoder1.consume_payload(payload)
decoder2.consume_payload(payload_copy)
print("decoder 1: {}".format(decoder1.symbols_decoded()))
print("decoder 2: {}".format(decoder2.symbols_decoded()))
print("-----------------")
print("Processing finished")
# Check if both decoders properly decoded the original data
print("Checking results")
if data_out1 == data_in and data_out2 == data_in:
print("Data decoded correctly")
else:
print("Unable to decode please file a bug report :)")
sys.exit(1)
def main():
# Get directory of this file
directory = os.path.dirname(os.path.realpath(__file__))
# The name of the file to use for the test
filename = 'lena.jpg'
# Open the image convert it to RGB and get the height and width
image = Image.open(os.path.join(directory, filename)).convert("RGB")
image_width = image.size[0]
image_height = image.size[1]
# The canvas should be able to contain both the image and the decoding
# state. Note the decoding state is the same width as the image height.
canvas_width = image_height + image_width + image_height
# Create the canvas
canvas = kodo_helpers.CanvasScreenEngine(
width=canvas_width,
height=image_height)
# Create the decoding coefficient viewer
encoding_state_viewer = kodo_helpers.EncodeStateViewer(
size=image_height,
canvas=canvas)
# Create the image viewer
image_viewer = kodo_helpers.ImageViewer(
width=image_width,
height=image_height,
canvas=canvas,
canvas_position=(image_width, 0))
# Create the decoding coefficient viewer
decoding_state_viewer = kodo_helpers.DecodeStateViewer(
size=image_height,
canvas=canvas,
canvas_position=(image_width * 2, 0))
# Pick a symbol size (image_width * 3 will create a packet for each
# horizontal line of the image, that is three bytes per pixel (RGB))
symbol_size = image_width * 3
# Based on the size of the image and the symbol size, calculate the number
# of symbols needed for containing the image in a single generation.
symbols = int(math.ceil(image_width * image_height * 3.0 / symbol_size))
field = kodo.field.binary8
encoder = kodo.RLNCEncoder(field, symbols, symbol_size)
decoder = kodo.RLNCDecoder(field, symbols, symbol_size)
# Connect the tracing callback to the decode state viewer
def encoding_callback(zone, msg):
encoding_state_viewer.log_callback(zone, msg)
encoding_state_viewer.set_symbols(encoder.symbols())
encoder.set_log_callback(encoding_callback)
def decoding_callback(zone, msg):
decoding_state_viewer.log_callback(zone, msg)
decoder.set_log_callback(decoding_callback)
# Create a bytearray from the image to use in the encoding (only pick the
# data we have room for).
data_in = bytearray(image.tobytes()[-encoder.block_size():])
# Set the converted image data
encoder.set_symbols_storage(data_in)
# Define the data_out bytearray where the symbols should be decoded
# This bytearray must not go out of scope while the encoder exists!
data_out = bytearray(decoder.block_size())
decoder.set_symbols_storage(data_out)
# Create an image viwer and run the following code in a try catch;
# this prevents the program from locking up, as the finally clause will
# close down the image viewer.
canvas.start()
try:
while not decoder.is_complete():
packet = encoder.produce_payload()
# Drop some packets
if random.choice([True, False]):
decoder.consume_payload(packet)
# The data_out buffer is continuously updated
image_viewer.set_image(data_out)
# Let the user see the complete photo before closing the application
for i in range(100):
image_viewer.set_image(data_out)
finally:
canvas.stop()
# Check we properly decoded the data
if data_out[:len(data_in)] == data_in:
print("Data decoded correctly")
else:
print("Unexpected failure to decode please file a bug report :)")
sys.exit(1)
def main():
"""
Switch systematic off example.
This example shows how to enable or disable systematic coding for
coding stacks that support it.
Systematic coding is used to reduce the amount of work done by an
encoder and a decoder. This is achieved by initially sending all
symbols which has not previously been sent decoded. Kodo allows this
feature to be optionally turn of or off.
"""
# Choose the finite field, the number of symbols (i.e. generation size)
# and the symbol size in bytes
field = kodo.field.binary
symbols = 10
symbol_size = 160
# Create an encoder and a decoder
encoder = kodo.RLNCEncoder(field, symbols, symbol_size)
decoder = kodo.RLNCDecoder(field, symbols, symbol_size)
# Generate some random data to encode. We create a bytearray of the same
# size as the encoder's block size and assign it to the encoder.
# This bytearray must not go out of scope while the encoder exists!
data_in = bytearray(os.urandom(encoder.block_size()))
encoder.set_symbols_storage(data_in)
# Define the data_out bytearray where the symbols should be decoded
# This bytearray must not go out of scope while the encoder exists!
data_out = bytearray(decoder.block_size())
decoder.set_symbols_storage(data_out)
print("Starting encoding / decoding...")
while not decoder.is_complete():
# If the chosen codec stack supports systematic coding
if 'is_systematic_on' in dir(encoder):
# Toggle systematic mode with 50% probability
if random.choice([True, False]):
if encoder.is_systematic_on():
print("Turning systematic OFF")
encoder.set_systematic_off()
else:
print("Turning systematic ON")
encoder.set_systematic_on()
# Encode a packet into the payload buffer
packet = encoder.produce_payload()
if random.choice([True, False]):
print("Packet dropped.")
continue
# Pass that packet to the decoder
decoder.consume_payload(packet)
print("Rank of decoder {}".format(decoder.rank()))
# Symbols that were received in the systematic phase correspond
# to the original source symbols and are therefore marked as
# decoded
print("Symbols decoded {}".format(decoder.symbols_decoded()))
# The decoder is complete, the decoded symbols are now available in
# the data_out buffer: check if it matches the data_in buffer
print("Checking results...")
if data_out == data_in:
print("Data decoded correctly")
else:
print("Unexpected failure to decode please file a bug report :)")
sys.exit(1)
def main():
"""
Pure recode example using the Symbol API.
This example is very similar to pure_recode_payload_api.py.
The difference is that this example uses the low-level Symbol API of the
pure recoder instead of the high-level Payload API.
Similarly, the pure recoder will not decode the incoming data, it will
only re-encode it and we use the same relay network as shown below.
For simplicity, we have error free links, i.e. no data packets are lost
when being sent from encoder to recoder to decoder:
+-----------+ +-----------+ +------------+
| encoder |+---->| recoder |+---->| decoder |
+-----------+ +-----------+ +------------+
The pure recoder does not need to decode all symbols, and its "capacity"
for storing symbols can be limited. These stored coded symbols are
linear combinations of previously received symbols. When a new symbol is
received, the pure recoder will combine it with its existing symbols
using random coefficients.
The pure recoder can produce a random linear combination of the stored
coded symbols, which can be processed by a regular decoder.
"""
# Choose the finite field, the number of symbols (i.e. generation size)
# and the symbol size in bytes
field = kodo.field.binary8
symbols = 5
symbol_size = 160
encoder = kodo.RLNCEncoder(field, symbols, symbol_size)
# Set the pure recoder "capacity" to be less than "symbols"
recoder = kodo.RLNCPureRecoder(field, symbols, symbol_size, 3)
print("Recoder properties:\n"
" Symbols: {}\n"
" Recoder symbols: {}".format(recoder.symbols(),
recoder.recoder_symbols()))
decoder = kodo.RLNCDecoder(field, symbols, symbol_size)
# Generate some random data to encode. We create a bytearray of the same
# size as the encoder's block size and assign it to the encoder.
# This bytearray must not go out of scope while the encoder exists!
data_in = bytearray(os.urandom(encoder.block_size()))
encoder.set_symbols_storage(data_in)
# Define the data_out bytearrays where the symbols should be decoded
# These bytearrays must not go out of scope while the encoder exists!
data_out = bytearray(decoder.block_size())
decoder.set_symbols_storage(data_out)
while not decoder.is_complete():
# Generate some random coefficients for encoding
coefficients = encoder.generate()
# We can print the individual coefficients here, because we use the
# binary8 field where each byte corresponds to a single coefficient.
# For other fields, we would need fifi-python to do this!
print("Encoding coefficients:")
print(" ".join(str(c) for c in coefficients))
# Write a coded symbol to the symbol buffer
symbol = encoder.produce_symbol(coefficients)
print("Encoded symbol generated and passed to the recoder")
# Pass that symbol and the corresponding coefficients to the recoder
recoder.consume_symbol(symbol, coefficients)
# Generate some random coefficients for recoding
recoding_coefficients = recoder.recoder_generate()
print("Recoding coefficients:")
print(" ".join(str(c) for c in recoding_coefficients))
# Produce an encoded symbol based on the recoding coefficients
# Note that the resulting recoded_symbol_coefficients will not be
# the same as the recoding_coefficients
recoded_symbol, recoded_symbol_coefficients = \
recoder.recoder_produce_symbol(recoding_coefficients)
print("Recoded symbol coefficients:")
print(" ".join(str(c) for c in recoded_symbol_coefficients))
print("Recoded symbol generated and passed to the decoder")
# Pass that symbol and the corresponding coefficients to the decoder
decoder.consume_symbol(recoded_symbol, recoded_symbol_coefficients)
print("Decoder rank: {}/{}\n".format(decoder.rank(), symbols))
# Check if we properly decoded the data
if data_out == data_in:
print("Data decoded correctly")
else:
print("Unexpected failure to decode please file a bug report :)")
sys.exit(1)
def main():
# Define the "symbol id" size (storing the PRNG seed), which in this
# case is 2 bytes.
id_size = 2
# Choose the finite field, the number of symbols (i.e. generation size)
# and the symbol size in bytes
field = kodo.field.binary8
symbols = 42
symbol_size = 160
# Let's calculate the header overhead we are adding due to the
# "symbol id". Notice that I use the word header overhead: this is to
# mean we only concentrate on the overhead caused by the added
# header, in this case the "symbol id" only. There are other ways to
# define overhead, e.g. excess generated redundancy or similar, we will
# not look at those here.
#
# We are adding 2 bytes per encoded symbol, so this is the total
# per-symbol overhead.
#
# If we generate 100% redundancy, we would produce in total 84
# symbols. Those 84 symbols would each have 2 bytes of overhead so
# in total 168 bytes.
#
# If we wanted to store the 84 encoded symbols in memory or on disk
# they would take up: 84*(160+2) = 13608 bytes (out of which 168
# bytes would be overhead from the "symbol id"). One minor note here
# is that in a communication scenario we would typically store and
# send a single encoded symbol at a time (which would need 160+2 bytes).
# In both cases the input data also needs to be in memory.
#
# From a receiver's perspective only approx. 42 symbols would need to
# be received in order to be able to decode the original data. So a
# receiver would need to download 42*(160+2) = 6804 bytes to decode
# the 42*160 = 6720 bytes of original data. This overhead in that case
# is 42*2 = 84 bytes.
#
# Note that we said approx. 42 symbols is needed to decode. This
# number depends on the exact type of code used and its parameters,
# e.g. the size of the finite field. If less than 42 symbols are received,
# only partial decoding is possible, which means that parts of the
# file may be decoded, but not all.
#
# With RLNC we can generate as much redundancy as we want, in this
# case we are limited by the 2-byte "symbol id". If instead we used
# a 4-byte "symbol id", we could have generated 2^32 encoded symbols.
# In a network, this may make sense, since it allows us to produce a
# virtually unlimited number of encoded packets.
# Create an encoder using the specified parameters
encoder = kodo.RLNCEncoder(field, symbols, symbol_size)
# Generate some random data to encode. We create a bytearray of the same
# size as the encoder's block size and assign it to the encoder.
# This bytearray must not go out of scope while the encoder exists!
data_in = bytearray(os.urandom(encoder.block_size()))
# Assign the data buffer to the encoder so that we may start
# to produce encoded symbols from it
encoder.set_symbols_storage(data_in)
# We create twice the symbols so 100% redundancy.
#
# The amount of redundancy we generate depends on the amount of
# erasures (packet loss) we expect to see. For example with a loss
# rate of 50% i.e. loosing half the packets sent. We need to send at
# least twice the packets i.e. 100% redundancy. How to tune/adapt
# these numbers are system dependent and dependens on the desired
# decoding probability.
#
# Specifically for the above example we add 100% redundancy, which
# means from the 42 symbols we get 84 encoded symbols. If we have an
# loss rate of 50% it means that on average we get the 42 symbols
# needed to decode. So half of the time we will decode because we get
# 42 symbols or more and half of the time we will fail because we get
# less than the 42 needed symbols. Depending on the chosen finite
# field size we also get an effect from linear dependency, which means
# we can expect to decode less than half of the time. We can calculate
# the redundancy needed to ensure decoding 99% of the time using
# probability theory.
# In communication scenarios the amount of redundancy often times
# feedback will be used to control redundancy levels, how relaxed we
# can be depends on the cost of asking for more redundancy (which
# is system dependent).
# In systems without feedback, often times the best we can do is to
# simply blindly generate redundancy (for a given period of time).
# In this case the rate-less feature of RLNC is handy. Rate less
# simply means that there is no upper limit to the amount of
# redundancy we can generate.
#
# It is often the case that in storage scenarios a fixed redundancy
# level is used, whereas in communication scenarios an adaptive
# redundancy level is used (RLNC supports both modes). The adaptive
# mode is demonstrated e.g. in examples/encode_decode_simple where
# the encoder keeps producing symbols until the decoder is complete.
#
# One subtle thing to notice is that we are using produce_symbol
# (symbol API) instead of produce_payload (payload API) which is
# used in many other examples (such as encode_decode_simple).
#
# The payload API is a "higher level" API, which allows people to
# get going quickly. It has its own internal header format, which
# uses some bytes for signaling various state between an encoder and
# decoder.
#
# The symbol API is "low-level" in the sense that we fully control what
# is written. As such, a call to produce_symbol(...) will write only
# the encoded symbol to the buffer passed. We have to write the
# coefficients and the seed values ourselves as shown below.
payloads = []
for seed in range(symbols * 2):
payload = bytearray()
# Set the seed value to use
encoder.set_seed(seed)
# Write the seed value as two bytes in big endian
two_bytes_seed = struct.pack('>H', seed)
# Add the seed as the first part of the payload
payload.extend(two_bytes_seed)
# Generate an encoding vector using the current seed value
coefficients = encoder.generate()
# Write a symbol according to the generated coefficients
symbol = encoder.produce_symbol(coefficients)
# Add the symbol data as the second part of the payload
payload.extend(symbol)
# Store the payload
payloads.append(payload)
# Now let's create the decoder
decoder = kodo.RLNCDecoder(field, symbols, symbol_size)
# Define the data_out bytearray where the symbols should be decoded
# This bytearray must not go out of scope while the encoder exists!
data_out = bytearray(decoder.block_size())
decoder.set_symbols_storage(data_out)
for payload in payloads:
# Extract the seed and symbol data from the payload
seed = struct.unpack_from('>H', payload, 0)[0]
symbol_data = payload[2:]
# Set the seed to use
decoder.set_seed(seed)
# Generate the encoding vector using the current seed value
coefficients = decoder.generate()
# Read a symbol data according to the generated coefficients
decoder.consume_symbol(symbol_data=symbol_data,
coefficients=coefficients)
if decoder.is_complete():
break
print("Coding finished")
# The decoder is complete, the decoded symbols are now available in
# the data_out buffer: check if it matches the data_in buffer
print("Checking results...")
if data_out == data_in:
print("Data decoded correctly")
else:
print("Unable to decode please file a bug report :)")
sys.exit(1)
def main():
"""
Multicast example, reciever part.
An example where data is received, decoded, and finally written to a file.
"""
parser = argparse.ArgumentParser(description=main.__doc__)
parser.add_argument('--output-file',
type=str,
help='Path to the file which should be received.',
default='output_file')
parser.add_argument('--ip',
type=str,
help='The IP address to use.',
default=MCAST_GRP)
parser.add_argument('--port',
type=int,
help='The port to use.',
default=MCAST_PORT)
parser.add_argument('--dry-run',
action='store_true',
help='Run without network use.')
args = parser.parse_args()
field = kodo.field.binary
symbols = 64
symbol_size = 1400
decoder = kodo.RLNCDecoder(field, symbols, symbol_size)
data_out = bytearray(decoder.block_size())
decoder.set_symbols_storage(data_out)
sock = socket.socket(family=socket.AF_INET,
type=socket.SOCK_DGRAM,
proto=socket.IPPROTO_UDP)
sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
sock.bind(('', args.port))
mreq = struct.pack("4sl", socket.inet_aton(args.ip), socket.INADDR_ANY)
sock.setsockopt(socket.IPPROTO_IP, socket.IP_ADD_MEMBERSHIP, mreq)
if args.dry_run:
sys.exit(0)
print("Processing...")
while not decoder.is_complete():
time.sleep(0.2)
packet = sock.recv(10240)
decoder.consume_payload(bytearray(packet))
print("Packet received!")
print("Decoder rank: {}/{}".format(decoder.rank(), decoder.symbols()))
# Write the decoded data to the output file
f = open(args.output_file, 'wb')
f.write(data_out)
f.close()
print("Processing finished.")