def image_from_bits(self, bits, filename):
# Convert the received payload to an image and save it
# No return value required .
pixel_values = np.array([], dtype=np.uint)
#Retrieve length of each row
row_length_bits = bits[0:8]
row_length = np.packbits(row_length_bits)[0]
#print "Forming image from bits..."
#print "Row length bits: " + str(row_length_bits)
#print "\tFound row length of " + str(row_length)
#print
pixel_idx = 8
while pixel_idx < len(bits):
pixel_bits = bits[pixel_idx:pixel_idx+8]
pixel_value = np.packbits(pixel_bits)[0]
pixel_values = np.append(pixel_values, pixel_value)
pixel_idx += 8 #int
img = Image.new('L', (len(pixel_values) / row_length, row_length))
img.putdata(pixel_values)
img.save(filename)
def write_data(dict_words):
items = []
with open(POSTS_FILE_PATH, 'r') as f:
for post in ijson.items(f, 'item'):
labels = [0] * len(CATEGORIES)
was = False
for hub in post['hubs']:
for cur_label, label in enumerate(CATEGORIES):
if hub in label:
labels[cur_label] = 1
was = True
if not was:
continue
words = [0] * BagOfWords.NUM_VOCABULARY_SIZE
post_words = post['content'] + post['title']
for word in post_words:
if word in dict_words:
words[dict_words[word]] = 1
else:
words[BagOfWords.NUM_VOCABULARY_SIZE - 1] = 1
labels = np.packbits(labels).tolist()
words = np.packbits(words).tolist()
items.append((labels, words))
shuffle(items)
train_set_size = int(len(items) * BagOfWords.TRAIN_RATIO)
_write_set(TF_ONE_SHOT_TRAIN_FILE_PATH, items[:train_set_size])
_write_set(TF_ONE_SHOT_EVAL_FILE_PATH, items[train_set_size:])
print('Set size : ', len(items))
print('Train set size : ', train_set_size)
print('Eval set size : ', len(items) - train_set_size)
def extractPayload(self):
if self.payloadExists() is False:
raise Exception("Error: carrier does not contain a payload")
xml = ""
shape = self.img.shape
payload_bits = np.copy(self.img)
payload_bits &= 1
if len(shape) > 2:
red = payload_bits[:, :, 0]
green = payload_bits[:, :, 1]
blue = payload_bits[:, :, 2]
payload_buff = np.concatenate((red.flatten('C'), green.flatten('C'), blue.flatten('C')), axis=0)
payload = np.packbits(payload_buff)
payload_list = [chr(item) for item in payload]
for item in payload_list:
xml += item
if item == '>':
termination = re.search(r"</payload>", xml)
if termination:
final_payload = Payload(None, -1, xml)
return final_payload
else:
payload = np.packbits(payload_bits)
payload_list = [chr(item) for item in payload]
for item in payload_list:
xml += item
if item == '>':
termination = re.search(r"</payload>", xml)
if termination:
final_payload = Payload(None, -1, xml)
return final_payload
def test_name(self):
for n in [16, 1, 5, 8, 16, 20, 20 * 8]:
w = np.random.random(n)
j = distpy.JaccardWeighted(w)
j2 = pickle.loads(pickle.dumps(j, -1))
for x in range(n):
a = np.zeros(n, dtype=np.uint8)
b = np.zeros(n, dtype=np.uint8)
a[x] = 1
b[x] = 1
a = np.packbits(a)
b = np.packbits(b)
out0 = j.dist(a, b)
out1 = jaccard_weighted_slow(a, b, w)
out2 = j2.dist(a, b)
self.assertEqual(out0, out1)
self.assertEqual(out0, w[x])
self.assertEqual(out0, out2)
print((out0, out1, w[x]))
for x in range(1000):
bytes = np.ceil(n / 8.0)
a = np.fromstring(np.random.bytes(bytes), dtype=np.uint8)
b = np.fromstring(np.random.bytes(bytes), dtype=np.uint8)
out0 = j.dist(a, b)
out1 = jaccard_weighted_slow(a, b, w)
out2 = j2.dist(a, b)
self.assertAlmostEqual(out0, out1)
self.assertEqual(out0, out2)
print((out0, out1))
def test_packbits_very_large():
# test some with a larger arrays gh-8637
# code is covered earlier but larger array makes crash on bug more likely
for s in range(950, 1050):
for dt in '?bBhHiIlLqQ':
x = np.ones((200, s), dtype=bool)
np.packbits(x, axis=1)
def calculate_node_hashes(children_a, children_b, taxon_order): n_taxa = len(taxon_order) children = set.union(children_a, children_b) parent_boolean = numpy.zeros(n_taxa, dtype=numpy.uint8) split_boolean = numpy.zeros(n_taxa, dtype=numpy.uint8) i = 0 for j in range(n_taxa): t = taxon_order[j] if t in children: parent_boolean[j] = 1 if i == 0: if t in children_a: a_first = True else: a_first = False if (t in children_b) ^ a_first: # first child always "True" split_boolean[i] = 1 i += 1 parent_packed = numpy.packbits(parent_boolean) split_packed = numpy.packbits(split_boolean) parent_id = parent_packed.tostring() split_id = split_packed.tostring() return parent_id, split_id
def test_unpackbits_count():
# test complete invertibility of packbits and unpackbits with count
x = np.array([
[1, 0, 1, 0, 0, 1, 0],
[0, 1, 1, 1, 0, 0, 0],
[0, 0, 1, 0, 0, 1, 1],
[1, 1, 0, 0, 0, 1, 1],
[1, 0, 1, 0, 1, 0, 1],
[0, 0, 1, 1, 1, 0, 0],
[0, 1, 0, 1, 0, 1, 0],
], dtype=np.uint8)
padded1 = np.zeros(57, dtype=np.uint8)
padded1[:49] = x.ravel()
packed = np.packbits(x)
for count in range(58):
unpacked = np.unpackbits(packed, count=count)
assert_equal(unpacked.dtype, np.uint8)
assert_array_equal(unpacked, padded1[:count])
for count in range(-1, -57, -1):
unpacked = np.unpackbits(packed, count=count)
assert_equal(unpacked.dtype, np.uint8)
# count -1 because padded1 has 57 instead of 56 elements
assert_array_equal(unpacked, padded1[:count-1])
for kwargs in [{}, {'count': None}]:
unpacked = np.unpackbits(packed, **kwargs)
assert_equal(unpacked.dtype, np.uint8)
assert_array_equal(unpacked, padded1[:-1])
assert_raises(ValueError, np.unpackbits, packed, count=-57)
padded2 = np.zeros((9, 9), dtype=np.uint8)
padded2[:7, :7] = x
packed0 = np.packbits(x, axis=0)
packed1 = np.packbits(x, axis=1)
for count in range(10):
unpacked0 = np.unpackbits(packed0, axis=0, count=count)
assert_equal(unpacked0.dtype, np.uint8)
assert_array_equal(unpacked0, padded2[:count, :x.shape[1]])
unpacked1 = np.unpackbits(packed1, axis=1, count=count)
assert_equal(unpacked1.dtype, np.uint8)
assert_array_equal(unpacked1, padded2[:x.shape[1], :count])
for count in range(-1, -9, -1):
unpacked0 = np.unpackbits(packed0, axis=0, count=count)
assert_equal(unpacked0.dtype, np.uint8)
# count -1 because one extra zero of padding
assert_array_equal(unpacked0, padded2[:count-1, :x.shape[1]])
unpacked1 = np.unpackbits(packed1, axis=1, count=count)
assert_equal(unpacked1.dtype, np.uint8)
assert_array_equal(unpacked1, padded2[:x.shape[0], :count-1])
for kwargs in [{}, {'count': None}]:
unpacked0 = np.unpackbits(packed0, axis=0, **kwargs)
assert_equal(unpacked0.dtype, np.uint8)
assert_array_equal(unpacked0, padded2[:-1, :x.shape[1]])
unpacked1 = np.unpackbits(packed1, axis=1, **kwargs)
assert_equal(unpacked1.dtype, np.uint8)
assert_array_equal(unpacked1, padded2[:x.shape[0], :-1])
assert_raises(ValueError, np.unpackbits, packed0, axis=0, count=-9)
assert_raises(ValueError, np.unpackbits, packed1, axis=1, count=-9)
def comprimir(arvore: HuffmanNode, bitGroupSize: int, compressedBitSize, codeTable : dict, dataBuffer):
global paddingSize, nomeFicheiro
try:
outputHandler = open(nomeFicheiro.replace('.pbm', '.cpbm'), 'wb')
#codificar a arvore de huffman e escreve-la no ficheiro
writeTreeToFile(outputHandler, compressedBitSize, paddingSize, codeTable, bitGroupSize, arvore)
print("A comprimir os dados com grupos de:", bitGroupSize)
reiniciarBufferDoFicheiro()
outBuffer = list()
get = codeTable.get
bitpos = 0
tamanhoFicheiroBits = tamanhoFicheiro * 8
while(bitpos < tamanhoFicheiroBits):
#ler um grupo de bits
bitGroup = read(dataBuffer, bitGroupSize)
codigo = get(bitGroup, None)
if(codigo != None):
outBuffer.extend(codigo)
bitpos +=bitGroupSize
print("Padding adicional no fim:", paddingSize)
np.packbits(outBuffer, -1).tofile(outputHandler)
outputHandler.close()
except:
print("Compressao de ficheiro pbm concluida sem exito")
def elucidate_cc_split(parent_id, split_id):
parent_id_bytes = numpy.array(tuple(parent_id)).view(dtype = numpy.uint8)
split_id_bytes = numpy.array(tuple(split_id)).view(dtype = numpy.uint8)
parent_id_bits = numpy.unpackbits(parent_id_bytes)
split_id_bits = numpy.unpackbits(split_id_bytes)
n_parent_bits = len(parent_id_bits)
n_split_bits = len(split_id_bits)
child1_bits = numpy.zeros(n_parent_bits, dtype = numpy.uint8)
child2_bits = numpy.zeros(n_parent_bits, dtype = numpy.uint8)
j = 0
for i in range(n_parent_bits):
if parent_id_bits[i] == 1:
if j < n_split_bits:
if split_id_bits[j] == 1:
child1_bits[i] = 1
else:
child2_bits[i] = 1
else:
child2_bits[i] = 1
j += 1
child1_bytes = numpy.packbits(child1_bits)
child2_bytes = numpy.packbits(child2_bits)
child1_id = child1_bytes.tostring().rstrip("\x00") # urgh C (null terminated strings)
child2_id = child2_bytes.tostring().rstrip("\x00") # vs Python (not null terminated) strings
return child1_id, child2_id
def test_bad_count(self):
packed0 = np.packbits(self.x, axis=0)
assert_raises(ValueError, np.unpackbits, packed0, axis=0, count=-9)
packed1 = np.packbits(self.x, axis=1)
assert_raises(ValueError, np.unpackbits, packed1, axis=1, count=-9)
packed = np.packbits(self.x)
assert_raises(ValueError, np.unpackbits, packed, count=-57)
def payloadExists(self):
buffer = []
if self.color_flag:
row = self.img[0]
stop_flag = 0
for col in row:
if col[0] & numpy.uint8(1) == 1:
buffer.append(1)
else:
buffer.append(0)
if stop_flag == 7:
print(buffer)
valid = chr(numpy.packbits(buffer)[0])
if valid == "<":
return True
else:
return False
stop_flag += 1
else:
i = 0
row = self.img[0]
while i < 8:
if row[i] & numpy.uint8(1) == 1:
buffer.append(1)
else:
buffer.append(0)
i+= 1
valid = chr(numpy.packbits(buffer)[0])
if valid == "<":
return True
else:
return False
def write(self, filename):
header_bytes = struct.pack(CHUNK_HEADER_FORMAT, self.data_size, self.board_size, self.input_planes, self.is_test)
position_bytes = np.packbits(self.pos_features).tostring()
next_move_bytes = np.packbits(self.next_moves).tostring()
with gzip.open(filename, "wb", compresslevel=6) as f:
f.write(header_bytes)
f.write(position_bytes)
f.write(next_move_bytes)
def convert(data, se):
"""Convert data according to the schema encoding"""
dtype = data.dtype
type = se.type
converted_type = se.converted_type
if dtype.name in typemap:
if type in revmap:
out = data.values.astype(revmap[type], copy=False)
elif type == parquet_thrift.Type.BOOLEAN:
padded = np.lib.pad(data.values, (0, 8 - (len(data) % 8)),
'constant', constant_values=(0, 0))
out = np.packbits(padded.reshape(-1, 8)[:, ::-1].ravel())
elif dtype.name in typemap:
out = data.values
elif "S" in str(dtype)[:2] or "U" in str(dtype)[:2]:
out = data.values
elif dtype == "O":
try:
if converted_type == parquet_thrift.ConvertedType.UTF8:
out = array_encode_utf8(data)
elif converted_type is None:
if type in revmap:
out = data.values.astype(revmap[type], copy=False)
elif type == parquet_thrift.Type.BOOLEAN:
padded = np.lib.pad(data.values, (0, 8 - (len(data) % 8)),
'constant', constant_values=(0, 0))
out = np.packbits(padded.reshape(-1, 8)[:, ::-1].ravel())
else:
out = data.values
elif converted_type == parquet_thrift.ConvertedType.JSON:
out = np.array([json.dumps(x).encode('utf8') for x in data],
dtype="O")
elif converted_type == parquet_thrift.ConvertedType.BSON:
out = data.map(tobson).values
if type == parquet_thrift.Type.FIXED_LEN_BYTE_ARRAY:
out = out.astype('S%i' % se.type_length)
except Exception as e:
ct = parquet_thrift.ConvertedType._VALUES_TO_NAMES[
converted_type] if converted_type is not None else None
raise ValueError('Error converting column "%s" to bytes using '
'encoding %s. Original error: '
'%s' % (data.name, ct, e))
elif converted_type == parquet_thrift.ConvertedType.TIMESTAMP_MICROS:
out = np.empty(len(data), 'int64')
time_shift(data.values.view('int64'), out)
elif converted_type == parquet_thrift.ConvertedType.TIME_MICROS:
out = np.empty(len(data), 'int64')
time_shift(data.values.view('int64'), out)
elif type == parquet_thrift.Type.INT96 and dtype.kind == 'M':
ns_per_day = (24 * 3600 * 1000000000)
day = data.values.view('int64') // ns_per_day + 2440588
ns = (data.values.view('int64') % ns_per_day)# - ns_per_day // 2
out = np.empty(len(data), dtype=[('ns', 'i8'), ('day', 'i4')])
out['ns'] = ns
out['day'] = day
else:
raise ValueError("Don't know how to convert data type: %s" % dtype)
return out
def _generate_masks(self):
"""Creates left and right masks for all hash lengths."""
tri_size = MAX_HASH_SIZE + 1
# Called once on fitting, output is independent of hashes
left_mask = np.tril(np.ones((tri_size, tri_size), dtype=int))[:, 1:]
right_mask = left_mask[::-1, ::-1]
self._left_mask = np.packbits(left_mask).view(dtype=HASH_DTYPE)
self._right_mask = np.packbits(right_mask).view(dtype=HASH_DTYPE)
def test_unpackbits_large():
# test all possible numbers via comparison to already tested packbits
d = np.arange(277, dtype=np.uint8)
assert_array_equal(np.packbits(np.unpackbits(d)), d)
assert_array_equal(np.packbits(np.unpackbits(d[::2])), d[::2])
d = np.tile(d, (3, 1))
assert_array_equal(np.packbits(np.unpackbits(d, axis=1), axis=1), d)
d = d.T.copy()
assert_array_equal(np.packbits(np.unpackbits(d, axis=0), axis=0), d)
def main(args):
x1 = np.load(args.infile1)
x2 = np.load(args.infile2)
assert len(x1.shape) == 2, 'infile1 should be 2d array!'
assert len(x2.shape) == 2, 'infile2 should be 2d array!'
assert x1.shape[0] == x2.shape[0], 'two infile should have same rows!'
x1 = np.unpackbits(x1, axis=1)
x2 = np.unpackbits(x2, axis=1)
r1 = x1.shape[1] if args.row1 == 0 else args.row1
r2 = x2.shape[1] if args.row2 == 0 else args.row2
N = x1.shape[0]
print(r1, r2, N)
x1 = np.packbits(x1[:, :r1].T, axis=1)
x2 = np.packbits(x2[:, :r2].T, axis=1)
if args.gpu >= 0:
chainer.cuda.get_device(args.gpu).use()
x1 = cuda.to_gpu(x1)
x2 = cuda.to_gpu(x2)
xp = cupy
else:
xp = np
# popcount LUT
pc = xp.zeros(256, dtype=np.uint8)
for i in range(256):
pc[i] = ( i & 1 ) + pc[i/2]
hamm = xp.zeros((r1, r2), dtype=np.int32)
for i in tqdm(range(r1)):
x1i = xp.tile(x1[i], (r2, 1))
if args.operation == 'xor':
hamm[i] = xp.take(pc, xp.bitwise_xor(x1i, x2).astype(np.int32)).sum(axis=1)
elif args.operation == 'nand':
hamm[i] = xp.take(pc, xp.invert(xp.bitwise_and(x1i, x2)).astype(np.int32)).sum(axis=1)
#for j in range(r2):
#hamm[i, j] = xp.take(pc, xp.bitwise_xor(x1[i], x2[j])).sum()
x1non0 = xp.tile((x1.sum(axis=1)>0), (r2, 1)).T.astype(np.int32)
x2non0 = xp.tile((x2.sum(axis=1)>0), (r1, 1)).astype(np.int32)
print(x1non0.shape, x2non0.shape)
non0filter = x1non0 * x2non0
print(non0filter.max(), non0filter.min())
hamm = non0filter * hamm + np.iinfo(np.int32).max * (1 - non0filter)
#non0filter *= np.iinfo(np.int32).max
#hamm *= non0filter
if xp == cupy:
hamm = hamm.get()
#xp.savetxt(args.out, hamm, delimiter=args.delim)
np.save(args.out, hamm)
if args.nearest > 0:
hamm_s = np.sort(hamm.flatten())
hamm_as = np.argsort(hamm.flatten())
x, y = np.unravel_index(hamm_as[:args.nearest], hamm.shape)
fname, ext = os.path.splitext(args.out)
np.savetxt(fname + '_top{0}.tsv'.format(args.nearest),
np.concatenate((x[np.newaxis], y[np.newaxis], hamm_s[np.newaxis,:args.nearest]), axis=0).T,
fmt='%d', delimiter='\t')
def updateState(self): # TO DO: update the state variables reflected to vehicle data # i.e. to maintain continuity if vehicle data reread is requested # you need to map all the state control here setBigEndiNumberToNpArr(self.vehicle2_unpacked, getArrayIdxFromStartBit(55), 1,int(self.radar_poweron)) self.vehicle2.data = np.packbits(self.vehicle2_unpacked).tolist() setBigEndiNumberToNpArr(self.vehicle2_unpacked, getArrayIdxFromStartBit(22), 1,int(self.clear_fault_on)) self.vehicle2.data = np.packbits(self.vehicle2_unpacked).tolist() setBigEndiNumberToNpArr(self.vehicle2_unpacked, getArrayIdxFromStartBit(56), 1,int(self.rawdata_on)) self.vehicle2.data = np.packbits(self.vehicle2_unpacked).tolist()
def parameter_from_population(individual):
first_gene = individual[:8]
second_gene = individual[8:16]
third_gene = individual[16:]
first_gene = np.packbits(first_gene, axis=-1)
second_gene = np.packbits(second_gene, axis=-1)
third_gene = np.packbits(third_gene, axis=-1)
param_mutation = first_gene * coef_mut
param_cross = second_gene * coef_cross
param_population_size = int(third_gene * coef_population_size)
return param_cross[0], param_mutation[0], param_population_size
def test_pack_unpack_order():
a = np.array([[2], [7], [23]], dtype=np.uint8)
b = np.unpackbits(a, axis=1)
assert_equal(b.dtype, np.uint8)
b_little = np.unpackbits(a, axis=1, bitorder='little')
b_big = np.unpackbits(a, axis=1, bitorder='big')
assert_array_equal(b, b_big)
assert_array_equal(a, np.packbits(b_little, axis=1, bitorder='little'))
assert_array_equal(b[:,::-1], b_little)
assert_array_equal(a, np.packbits(b_big, axis=1, bitorder='big'))
assert_raises(ValueError, np.unpackbits, a, bitorder='r')
assert_raises(TypeError, np.unpackbits, a, bitorder=10)
def gost_algo(message, key):
"""
GOST 34.13-2015 MAC algorhythm implementation
message - binary messag.
key - 256bit binary key
"""
key_bytes = np.packbits(key).tobytes()
msg_bytes = np.packbits(message).tobytes()
gost_mac = MAC(key_bytes)
digest_bytes = gost_mac(msg_bytes)
return np.unpackbits(np.frombuffer(digest_bytes, dtype=np.uint8))
def gene_to_noise_params(individual, display=False):
first_gene = individual[:gene_size]
second_gene = individual[gene_size:gene_size * 2]
third_gene = individual[gene_size * 2:]
if display:
print first_gene, second_gene, third_gene
first_gene = np.packbits(first_gene, axis=-1)
second_gene = np.packbits(second_gene, axis=-1)
third_gene = np.packbits(third_gene, axis=-1)
noise_amp = first_gene * coef_amp
noise_freq_row = second_gene * coef_freq
noise_freq_col = third_gene * coef_freq
return noise_amp[0], noise_freq_row[0], noise_freq_col[0]
def write(self, bits):
tmp = np.asarray(np.concatenate(
(self.buf,
np.fromiter(map(int, bits), dtype=np.ubyte))
), dtype=np.ubyte)
bytes = np.array_split(tmp, range(8, len(tmp), 8))
for b in bytes[:-1]:
self.stream.write(np.packbits(b))
if len(bytes[-1]) == 8:
self.stream.write(np.packbits(bytes[-1]))
self.buf = np.empty(0, np.ubyte)
else:
self.buf = bytes[-1]
return len(tmp) // 8
def _to_hash(projected):
if projected.shape[1] % 8 != 0:
raise ValueError('Require reduced dimensionality to be a multiple '
'of 8 for hashing')
# XXX: perhaps non-copying operation better
out = np.packbits((projected > 0).astype(int)).view(dtype=HASH_DTYPE)
return out.reshape(projected.shape[0], -1)
def packbits_axis(X, axis=-1):
"""Create a compact representation of rows of bits in numpy
Parameters
----------
X : array_like
a d-dimensional array whose rows will be treated as a sequence of bits
axis : integer
the axis along which to pack the bits (default=-1)
Returns
-------
x : array_like
a (d - 1)-dimensional structured array containing sets of 8-bit
integers which compactly represent the bits along the specified
axis of X.
"""
X = np.asarray(X, dtype=np.uint8)
# roll specified axis to the back
if axis not in (-1, X.ndim - 1):
X = np.rollaxis(X, axis).transpose(list(range(1, X.ndim)) + [0])
# make sure we have a C-ordered contiguous buffer
X = np.asarray(X, order="C")
bits = np.packbits(X, -1)
return_shape = bits.shape[:-1]
return_type = [("", "u1") for i in range(bits.shape[-1])]
return np.ndarray(return_shape, dtype=return_type, buffer=bits)
def getSignalNumber(barray_unpacked, barray, start_bit, signalsize, isByteorderIntel, isValuetypeiSigned, factor, offset):
# just guard againts unhandled (yet) intel bytecode
# motorola only for now
if 1 == isByteorderIntel: raise UserWarning
# barray_msb = barray[start_bit:start_bit]
start_bit_idx = getArrayIdxFromStartBit(start_bit)
barray_msb = barray_unpacked[start_bit_idx]
start_field_count = (start_bit+1) % 8
if start_field_count == 0 : start_field_count = 8
factor_number = int(factor) if float(factor).is_integer() else float(factor)
offset_number = int(offset) if float(offset).is_integer() else float(offset)
signal_number_bits = barray_unpacked[start_bit_idx:start_bit_idx+signalsize]
no_of_padding = signalsize % 8
signal_number_bits = np.concatenate((np.array(no_of_padding*[0],dtype=np.uint8), signal_number_bits))
signal_number = np.packbits(signal_number_bits)
if len(signal_number) < 8:
signal_number = np.concatenate((signal_number, np.array((8-len(signal_number))*[0],dtype=np.uint8)))
signal_number = signal_number.view(np.uint64).tolist()[0]
# same field end
# if signalsize <= start_field_count:
# signal_number = barray[start_bit:start_bit-signalsize+1]
# # print type(factor), type(offset)
# # return signal_number
# else:
# barray_map = BitVector(0)
# end_field_count = (signalsize - start_field_count) % 8
# field_count = int(math.floor((signalsize - start_field_count) / 8))
# end_bit_first_field =int( math.floor(start_bit/8)*8)
# #first field
# # print signalsize-1,signalsize-start_field_count,start_bit,end_bit_first_field
# barray_map[signalsize-1:signalsize-start_field_count] =( barray[start_bit:end_bit_first_field])
# #intermediary field(s):
# running_index = end_bit_first_field + 8
# running_index_map = signalsize - start_field_count - 1
# for i in range(0,field_count):
# barray_map[running_index_map:running_index_map-7] =( barray[running_index+7:running_index])
# running_index = running_index + 8
# running_index_map = running_index_map - 8
# #last field
# # check if actually all fields already taken
# if running_index < 57 and running_index_map >= 0:
# barray_map[end_field_count-1:0] = barray[running_index+7:running_index+8-end_field_count]
# signal_number = barray_map[signalsize-1:0]
if isValuetypeiSigned and barray_msb:
signal_number = twosComplement(signal_number, signalsize)
return signal_number*factor_number+offset_number
def dqt(ud16,lL0):
""" decode quad tree integer to lon Lat
Parameters
----------
lL : nd.array (2xN)
longitude Latitude
lL0 : nd.array (,2)
lower left corner of the 1degree tile
"""
N = len(ud16)
# offset from the lower left corner
#d = lL-lL0[:,None]
#dui8 = np.floor(d*256).astype('uint8')
uh8 = ud16/256
ul8 = ud16-uh8*256
ud8 = (np.vstack((uh8,ul8)).T).astype('uint8')
ud16 = np.unpackbits(ud8).reshape(N,16)
ndu8 = np.empty((2,N,8)).astype('int')
ndu8[0,:,:]=ud16[:,1::2]
ndu8[1,:,:]=ud16[:,0::2]
du8 = np.packbits(ndu8).reshape(2,N)/256.
lL = lL0[:,None]+du8
return(lL)
def v2_apply_symmetry(self, symmetry, content):
"""
Apply a random symmetry to a v2 record.
"""
assert symmetry >= 0 and symmetry < 8
# unpack the record.
(ver, probs, planes, to_move, winner) = self.v2_struct.unpack(content)
planes = np.unpackbits(np.frombuffer(planes, dtype=np.uint8))
# We use the full length reflection tables to apply symmetry
# to all 16 planes simultaneously
planes = planes[self.full_reflection_table[symmetry]]
assert len(planes) == 19*19*16
planes = np.packbits(planes)
planes = planes.tobytes()
probs = np.frombuffer(probs, dtype=np.float32)
# Apply symmetries to the probabilities.
probs = probs[self.prob_reflection_table[symmetry]]
assert len(probs) == 362
probs = probs.tobytes()
# repack record.
return self.v2_struct.pack(ver, probs, planes, to_move, winner)
def _pandas_to_bucket(df, symbol, initial_image):
rtn = {SYMBOL: symbol, VERSION: CHUNK_VERSION_NUMBER, COLUMNS: {}, COUNT: len(df)}
end = to_dt(df.index[-1].to_datetime())
if initial_image :
if 'index' in initial_image:
start = min(to_dt(df.index[0].to_datetime()), initial_image['index'])
else:
start = to_dt(df.index[0].to_datetime())
image_start = initial_image.get('index', start)
image = {k: v for k, v in initial_image.items() if k != 'index'}
rtn[IMAGE_DOC] = {IMAGE_TIME: image_start, IMAGE: initial_image}
final_image = TickStore._pandas_compute_final_image(df, initial_image, end)
else:
start = to_dt(df.index[0].to_datetime())
final_image = {}
rtn[END] = end
rtn[START] = start
logger.warning("NB treating all values as 'exists' - no longer sparse")
rowmask = Binary(lz4.compressHC(np.packbits(np.ones(len(df), dtype='uint8'))))
recs = df.to_records(convert_datetime64=False)
for col in df:
array = TickStore._ensure_supported_dtypes(recs[col])
col_data = {}
col_data[DATA] = Binary(lz4.compressHC(array.tostring()))
col_data[ROWMASK] = rowmask
col_data[DTYPE] = TickStore._str_dtype(array.dtype)
rtn[COLUMNS][col] = col_data
rtn[INDEX] = Binary(lz4.compressHC(np.concatenate(([recs['index'][0].astype('datetime64[ms]').view('uint64')],
np.diff(recs['index'].astype('datetime64[ms]').view('uint64')))
).tostring()))
return rtn, final_image
def parseSignal_new(barray, barray_unpacked, signal_names, signals):
signal_list ={}
# for signal in signal_names:
for signal in signals:
signal_number = 0
start_bit = signal._startbit
signalsize = signal._signalsize
start_bit_idx = getArrayIdxFromStartBit(start_bit)
barray_msb = barray_unpacked[start_bit_idx]
start_field_count = (start_bit+1) % 8
if start_field_count == 0 : start_field_count = 8
signal_number_bits = barray_unpacked[start_bit_idx:start_bit_idx+signalsize]
no_of_padding = signalsize % 8
signal_number_bits = np.concatenate((np.array(no_of_padding*[0],dtype=np.uint8), signal_number_bits))
signal_number = np.packbits(signal_number_bits)
if len(signal_number) < 8:
signal_number = np.concatenate((signal_number, np.array((8-len(signal_number))*[0],dtype=np.uint8)))
signal_number = signal_number.view(np.uint64).tolist()[0]
signal_list[signal._name] = signal_number
return signal_list
def convert_v1_to_v2(self, text_item):
"""
Convert v1 text format to v2 packed binary format
Converts a set of 19 lines of text into a byte string
[[plane_1],[plane_2],...],...
[probabilities],...
winner,...
"""
# We start by building a list of 16 planes,
# each being a 19*19 == 361 element array
# of type np.uint8
planes = []
for plane in range(0, 16):
# first 360 first bits are 90 hex chars, encoded MSB
hex_string = text_item[plane][0:90]
array = np.unpackbits(np.frombuffer(
bytearray.fromhex(hex_string), dtype=np.uint8))
# Remaining bit that didn't fit. Encoded LSB so
# it needs to be specially handled.
last_digit = text_item[plane][90]
if not (last_digit == "0" or last_digit == "1"):
return False, None
# Apply symmetry and append
planes.append(array)
planes.append(np.array([last_digit], dtype=np.uint8))
# We flatten to a single array of len 16*19*19, type=np.uint8
planes = np.concatenate(planes)
# and then to a byte string
planes = np.packbits(planes).tobytes()
# Get the 'side to move'
stm = text_item[16][0]
if not(stm == "0" or stm == "1"):
return False, None
stm = int(stm)
# Load the probabilities.
probabilities = np.array(text_item[17].split()).astype(np.float32)
if np.any(np.isnan(probabilities)):
# Work around a bug in leela-zero v0.3, skipping any
# positions that have a NaN in the probabilities list.
return False, None
if not(len(probabilities) == 362):
return False, None
probs = probabilities.tobytes()
if not(len(probs) == 362 * 4):
return False, None
# Load the game winner color.
winner = float(text_item[18])
if not(winner == 1.0 or winner == -1.0):
return False, None
winner = int((winner + 1) / 2)
version = struct.pack('i', 1)
return True, self.v2_struct.pack(version, probs, planes, stm, winner)
def test_packbits_large():
# test data large enough for 16 byte vectorization
a = np.array([
1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1,
0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0,
0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0,
1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1,
0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1,
1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0,
0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,
1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1,
0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1,
0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1,
1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1,
0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0,
1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0
])
a = a.repeat(3)
for dtype in '?bBhHiIlLqQ':
arr = np.array(a, dtype=dtype)
b = np.packbits(arr, axis=None)
assert_equal(b.dtype, np.uint8)
r = [
252, 127, 192, 3, 254, 7, 252, 0, 7, 31, 240, 0, 28, 1, 255, 252,
113, 248, 3, 255, 192, 28, 15, 192, 28, 126, 0, 224, 127, 255, 227,
142, 7, 31, 142, 63, 28, 126, 56, 227, 240, 0, 227, 128, 63, 224,
14, 56, 252, 112, 56, 255, 241, 248, 3, 240, 56, 224, 112, 63, 255,
255, 199, 224, 14, 0, 31, 143, 192, 3, 255, 199, 0, 1, 255, 224, 1,
255, 252, 126, 63, 0, 1, 192, 252, 14, 63, 0, 15, 199, 252, 113,
255, 3, 128, 56, 252, 14, 7, 0, 113, 255, 255, 142, 56, 227, 129,
248, 227, 129, 199, 31, 128
]
assert_array_equal(b, r)
# equal for size being multiple of 8
assert_array_equal(np.unpackbits(b)[:-4], a)
# check last byte of different remainders (16 byte vectorization)
b = [np.packbits(arr[:-i], axis=None)[-1] for i in range(1, 16)]
assert_array_equal(
b,
[128, 128, 128, 31, 30, 28, 24, 16, 0, 0, 0, 199, 198, 196, 192])
arr = arr.reshape(36, 25)
b = np.packbits(arr, axis=0)
assert_equal(b.dtype, np.uint8)
assert_array_equal(
b,
[[
190, 186, 178, 178, 150, 215, 87, 83, 83, 195, 199, 206, 204,
204, 140, 140, 136, 136, 8, 40, 105, 107, 75, 74, 88
],
[
72, 216, 248, 241, 227, 195, 202, 90, 90, 83, 83, 119, 127,
109, 73, 64, 208, 244, 189, 45, 41, 104, 122, 90, 18
],
[
113, 120, 248, 216, 152, 24, 60, 52, 182, 150, 150, 150, 146,
210, 210, 246, 255, 255, 223, 151, 21, 17, 17, 131, 163
],
[
214, 210, 210, 64, 68, 5, 5, 1, 72, 88, 92, 92, 78, 110, 39,
181, 149, 220, 222, 218, 218, 202, 234, 170, 168
],
[
0, 128, 128, 192, 80, 112, 48, 160, 160, 224, 240, 208, 144,
128, 160, 224, 240, 208, 144, 144, 176, 240, 224, 192, 128
]])
b = np.packbits(arr, axis=1)
assert_equal(b.dtype, np.uint8)
assert_array_equal(
b, [[252, 127, 192, 0], [7, 252, 15, 128], [240, 0, 28, 0],
[255, 128, 0, 128], [192, 31, 255, 128], [142, 63, 0, 0],
[255, 240, 7, 0], [7, 224, 14, 0], [126, 0, 224, 0],
[255, 255, 199, 0], [56, 28, 126, 0], [113, 248, 227, 128],
[227, 142, 63, 0], [0, 28, 112, 0], [15, 248, 3, 128],
[28, 126, 56, 0], [56, 255, 241, 128], [240, 7, 224, 0],
[227, 129, 192, 128], [255, 255, 254, 0], [126, 0, 224, 0],
[3, 241, 248, 0], [0, 255, 241, 128], [128, 0, 255, 128],
[224, 1, 255, 128], [248, 252, 126, 0], [0, 7, 3, 128],
[224, 113, 248, 0], [0, 252, 127, 128], [142, 63, 224, 0],
[224, 14, 63, 0], [7, 3, 128, 0], [113, 255, 255, 128],
[28, 113, 199, 0], [7, 227, 142, 0], [14, 56, 252, 0]])
arr = arr.T.copy()
b = np.packbits(arr, axis=0)
assert_equal(b.dtype, np.uint8)
assert_array_equal(
b, [[
252, 7, 240, 255, 192, 142, 255, 7, 126, 255, 56, 113, 227, 0,
15, 28, 56, 240, 227, 255, 126, 3, 0, 128, 224, 248, 0, 224, 0,
142, 224, 7, 113, 28, 7, 14
],
[
127, 252, 0, 128, 31, 63, 240, 224, 0, 255, 28, 248, 142,
28, 248, 126, 255, 7, 129, 255, 0, 241, 255, 0, 1, 252, 7,
113, 252, 63, 14, 3, 255, 113, 227, 56
],
[
192, 15, 28, 0, 255, 0, 7, 14, 224, 199, 126, 227, 63, 112,
3, 56, 241, 224, 192, 254, 224, 248, 241, 255, 255, 126, 3,
248, 127, 224, 63, 128, 255, 199, 142, 252
],
[
0, 128, 0, 128, 128, 0, 0, 0, 0, 0, 0, 128, 0, 0, 128, 0,
128, 0, 128, 0, 0, 0, 128, 128, 128, 0, 128, 0, 128, 0, 0,
0, 128, 0, 0, 0
]])
b = np.packbits(arr, axis=1)
assert_equal(b.dtype, np.uint8)
assert_array_equal(
b, [[190, 72, 113, 214, 0], [186, 216, 120, 210, 128],
[178, 248, 248, 210, 128], [178, 241, 216, 64, 192],
[150, 227, 152, 68, 80], [215, 195, 24, 5, 112],
[87, 202, 60, 5, 48], [83, 90, 52, 1, 160],
[83, 90, 182, 72, 160], [195, 83, 150, 88, 224],
[199, 83, 150, 92, 240], [206, 119, 150, 92, 208],
[204, 127, 146, 78, 144], [204, 109, 210, 110, 128],
[140, 73, 210, 39, 160], [140, 64, 246, 181, 224],
[136, 208, 255, 149, 240], [136, 244, 255, 220, 208],
[8, 189, 223, 222, 144], [40, 45, 151, 218, 144],
[105, 41, 21, 218, 176], [107, 104, 17, 202, 240],
[75, 122, 17, 234, 224], [74, 90, 131, 170, 192],
[88, 18, 163, 168, 128]])
# result is the same if input is multiplied with a nonzero value
for dtype in 'bBhHiIlLqQ':
arr = np.array(a, dtype=dtype)
rnd = np.random.randint(low=np.iinfo(dtype).min,
high=np.iinfo(dtype).max,
size=arr.size,
dtype=dtype)
rnd[rnd == 0] = 1
arr *= rnd.astype(dtype)
b = np.packbits(arr, axis=-1)
assert_array_equal(np.unpackbits(b)[:-4], a)
assert_raises(TypeError, np.packbits, np.array(a, dtype=float))
def handle_msg(self, msg_pmt):
msg = pmt.cdr(msg_pmt)
if not pmt.is_u8vector(msg):
print('[ERROR] Received invalid message type. Expected u8vector')
return
bits = np.array(pmt.u8vector_elements(msg))
ltu = bits[:210].reshape((15, 14)).transpose()
# Decode BCH(15,5,7)
if not all((decode_bch15(ltu[j, :]) for j in range(14))):
# Decode failure
if self.verbose:
print('BCH decode failure')
return
ltu = np.fliplr(ltu[:, -5:]).ravel()
hdr = LTUFrameHeader.parse(np.packbits(ltu))
ltu_crc = np.concatenate((ltu[:-5], np.array([1, 0, 1, 1, 0, 1, 1])))
ltu_crc = ltu_crc.reshape((9, 8))
if self.buggy_crc:
# Reverse byte ordering for CRC5 calculation
ltu_crc = np.flipud(ltu_crc)
if self.buggy_crc:
# Force CRC5 bugs
ltu_crc[4, :] = ltu_crc[3, :]
# CRC5 calculation
crc = 0x1F
for bit in ltu_crc.ravel():
# Check most significant bit in the CRC buffer and save
# in a variable.
c = crc & 0x10
# Shift variable to make the compare op. possible (see beneath).
c >>= 4
# Shift CRC to the left and write 0 into the least significant bit.
crc <<= 1
if c != bit:
crc ^= 0x15 # CRC polynomial
crc &= 0x1F
if crc != hdr.CRC5:
if self.verbose:
print('CRC5 fail')
return
if self.verbose:
print(hdr)
if hdr.PduLength == 0:
return
codewords_per_block = 16
uncoded = False
if hdr.AiTypeSrc == 0:
uncoded = True
elif hdr.AiTypeSrc == 1:
data_bits_per_codeword = 11 # BCH(15,11,3)
bch_d = 3
elif hdr.AiTypeSrc == 2:
data_bits_per_codeword = 7 # BCH(15,7,5)
bch_d = 5
elif hdr.AiTypeSrc == 3:
data_bits_per_codeword = 5 # BCH(15,5,7)
bch_d = 7
else:
if self.verbose:
print('Invalid AiTypeSrc')
return
if uncoded:
pdu_bytes = bits[210:210 + hdr.PduLength * 8]
pdu_bytes = pdu_bytes.reshape((hdr.PduLength, 8))
pdu_bytes = np.fliplr(pdu_bytes)
else:
data_bytes_per_block = (codewords_per_block *
data_bits_per_codeword // 8)
num_blocks = int(
np.ceil(float(hdr.PduLength) / data_bytes_per_block))
blocks = list()
for k in range(num_blocks):
block = bits[210 + k * 16 * 15:210 +
(k + 1) * 16 * 15].reshape((15, 16))
if bch_d:
block = block.transpose()
if not bch_d:
print(block)
# Decode BCH
if (bch_d and not all(
(decode_bch15(block[j, :], d=bch_d) for j in range(16)))):
# Decode failure
if self.verbose:
print('BCH decode failure')
return
if bch_d:
blocks.append(block[:, -data_bits_per_codeword:].ravel())
else:
blocks.append(block.ravel())
pdu_bytes = np.concatenate(blocks)
pdu_bytes = pdu_bytes.reshape(
(data_bytes_per_block * num_blocks, 8))
pdu_bytes = np.fliplr(pdu_bytes)
# Drop 0xDB padding bytes at the end
pdu_bytes = pdu_bytes[:hdr.PduLength]
if not bch_d:
print(pdu_bytes)
# CRC13
crc = 0x1FFF
pdu_crc = np.flipud(pdu_bytes) if self.buggy_crc else pdu_bytes
for bit in pdu_crc.ravel():
# Check most significant bit in the CRC buffer and save it
# in a variable.
c = crc & 0x1000
# Shift variable to make the compare op. possible (see beneath).
c >>= 12
# Shift CRC to the left and write 0 into the least significant bit.
crc <<= 1
if (c or bit if self.buggy_crc else c != bit):
crc ^= 0x1CF5 # CRC polynomial
crc &= 0x1FFF
if crc != hdr.CRC13:
if self.verbose:
print('CRC13 fail')
return
pdu = np.packbits(pdu_bytes)
pdu_tags = pmt.make_dict()
pdu_tags = pmt.dict_add(pdu_tags, pmt.intern('SNET SrcId'),
pmt.from_long(hdr.SrcId))
self.message_port_pub(
pmt.intern('out'),
pmt.cons(pdu_tags, pmt.init_u8vector(len(pdu), pdu)))
def decrypt_string(input_bits):
to_decrypt = np.array(list(input_bits)).reshape(-1, 8).astype(np.uint8)
decrypted = np.apply_along_axis(DES.apply, 1, to_decrypt, DES.key_test,
False).astype(np.uint8)
packed = np.packbits(decrypted)
return "".join([chr(item) for item in packed])
def pack_shot_data(shot_data):
return np.packbits(shot_data, axis=1)
def SimulateBER(snrArray, txBin, Npixels, modulatioInfo):
nSNR = len(snrArray)
rxDataArray = np.empty(len(txBin))
BitErrorArray = np.empty(2)
berArray = np.empty(0)
mod = 0
# Create Modulation Scheme Object
if (modulatioInfo.get("mod") == "PSK"):
mod = komm.PSKModulation(modulatioInfo.get("order"))
if (modulatioInfo.get("mod") == 'QAM'):
mod = komm.QAModulation(modulatioInfo.get("order"))
# Normalize energy per symbol
baseAmplitude = 1 / (np.sqrt(mod.energy_per_symbol))
mod = komm.QAModulation(modulatioInfo.get("order"), baseAmplitude)
print("Modulation to be used:")
print(
str(modulatioInfo.get("order")) + " " + str(modulatioInfo.get("mod")))
print("Bits Per Symbol: " + str(mod.bits_per_symbol))
print("Energy Per Symbol: " + str(mod.energy_per_symbol))
print("\n")
# Modulate Data
txData = mod.modulate(txBin)
# For each transmision
for i in range(nSNR):
# Calculate based on db
awgn = komm.AWGNChannel(snr=10**(snrArray[i] / 10.))
# Simulate noise in channel
rxData = awgn(txData)
# Demodulate Data
rxBin = mod.demodulate(rxData)
# Append demodulated data as a new row
rxDataArray = np.vstack([rxDataArray, rxBin])
awgn = komm.AWGNChannel(snr=10**(snrArray[10] / 10.))
rx_data = awgn(txData)
rx_bin = mod.demodulate(rx_data)
# Plot few rx bits
plt.figure()
plt.axes().set_aspect("equal")
plt.scatter(rx_data[:10000].real, rx_data[:10000].imag, s=1, marker=".")
plt.show()
rx_im = np.packbits(rx_bin).reshape(tx_im.size[1], tx_im.size[0])
plt.figure()
plt.imshow(np.array(rx_im), cmap="gray", vmin=0, vmax=255)
plt.show()
# Measuring Bit Error Ratio
# For each transmision
for j in range(1, nSNR + 1):
# Reset number of bit errors
BitErrorCount = 0
# Compute bit errors
# i.e For each pixel
for i in range(Npixels * 8):
# If pixel value does not match
if (rxDataArray[j][i] != txBin[i]):
# Increment error count
BitErrorCount += 1
# Calculate bit error rate for transmision
ber = BitErrorCount / (Npixels * 8)
berArray = np.append(berArray, ber)
# Append new dimension containing bit count and bit error rate
BitErrorArray = np.vstack([BitErrorArray, [BitErrorCount, ber]])
print("Bit Error Array:")
print(BitErrorArray)
print("\n")
plt.figure()
plt.scatter(snrArray, berArray) #plot points
plt.plot(snrArray, berArray) #plot lines
plt.yscale("log")
plt.ylabel('$BER$')
plt.xlabel('$SNR$')
plt.title((str(modulatioInfo.get("order")) + " " +
str(modulatioInfo.get("mod"))))
plt.grid(True)
#plt.show()
# Calculate theoretical BER
# Modify k parameter i.e. bits per symbol
k = mod.bits_per_symbol
errfcDataSet = np.empty(0)
# For Each SNR
for i in range(nSNR):
# Calculate Theorethical BER
errfc = 0.5 * scipy.special.erfc(
math.sqrt((10**(snrArray[i] / 10)) / k))
errfcDataSet = np.append(errfcDataSet, errfc)
plt.plot(snrArray, errfcDataSet, color='r')
plt.show()
print("Errfc Data Set:")
print(errfcDataSet)
print("\n")
return berArray, errfcDataSet
def findHeader(info, header):
pos = np.argmax(np.correlate(np.unpackbits(info), np.unpackbits(header)))
rcv_array = np.packbits(np.roll(np.unpackbits(info), -pos))
return rcv_array
def encode(source,
message,
bit_split,
source_type="array",
message_type="array"):
"""
Encodes the message into the source.
:param source: Source
:type source: numpy.array
:param message: Message
:type message: numpy.array
:param bit_split: Bit split
:type bit_split: int
:param source_type: Source type
:type source_type: str
:param message_type: Message type
:type message_type: str
:return: Encoded image
:rtype: numpy.array
"""
if bit_split > 8:
raise Exception("Bit Split must be >= 1 and <= 8")
if source_type == "array":
source, source_original_shape = Source.from_array(source)
elif source_type == "image":
source, source_original_shape = Source.from_image(source)
else:
raise Exception("Source type not valid", source_type)
if message_type == "array":
(message,
padding), message_extras = Message.from_array(message, bit_split)
elif message_type == "image":
(message,
padding), message_extras = Message.from_image(message, bit_split)
elif message_type == "text":
(message,
padding), message_extras = Message.from_text(message, bit_split)
elif message_type == "text_file":
(message, padding), message_extras = Message.from_text_file(
message, bit_split)
elif message_type == "text_stream":
(message, padding), message_extras = Message.from_text_stream(
message, bit_split)
else:
raise Exception("Message type not valid", message_type)
bit_split_str = "{0:04b}".format(bit_split)
padding = "{0:04b}".format(padding)
message_length = "{0:032b}".format(message.shape[0]) # message height
mt = Steganography.type_map[message_type]
extras = []
for extra in message_extras:
e = "{0:016b}".format(extra)
extras.append(e)
num_extras = "{0:04b}".format(len(extras))
header = [bit_split_str, padding, message_length, mt, num_extras]
header.extend(extras)
header = np.array(list("".join(header)))
header = np.expand_dims(header, axis=1)
if header.shape[0] + message.shape[0] > source.shape[0]:
e = (
"Message size too large!",
str(header.shape[0] + message.shape[0]),
">",
str(source.shape[0]),
)
raise Exception(e)
# write header
encoded = np.copy(source)
encoded[:header.shape[0], -1:] = header
# write message
offset = header.shape[0]
encoded[offset:offset + message.shape[0], -bit_split:] = message
# converts back to regular numbers and reshapes to original size
encoded = np.packbits(encoded)
encoded = encoded.reshape(source_original_shape)
return encoded
def seed_rl_preprocessing(observation):
observation = np.expand_dims(observation, axis=0)
data = np.packbits(observation, axis=-1) # This packs to uint8
if data.shape[-1] % 2 == 1:
data = np.pad(data, [(0, 0)] * (data.ndim - 1) + [(0, 1)], 'constant')
return data.view(np.uint16)
import numpy as np
import os
Ts = np.arange(1.0,4.1,0.1)
Tss = ['{:.1f}'.format(T) for T in Ts]
for T in Tss:
cmd = '~/Programming/ising/install/bin/ising -d 2 -L 20 -T {0} --nmeas 1000 --nmcs 20000000 --ieq 5000 --dyn 0 --print-state'.format(T)
outfname = '../data_paper/configurations_{}_glauber.npz'.format(T)
print(cmd)
os.system('rm estats/*')
os.system(cmd)
X = np.zeros((20000, 20, 20), dtype=np.uint8)
for i in range(20000):
snapshot = np.loadtxt('estats/estat{}.txt'.format(i+1), dtype=int)
X[i][snapshot>0] = 1
print(outfname, np.mean(X))
np.savez_compressed(outfname, np.packbits(X, axis=-1))
os.system('rm estats/*')
def pack(a):
"""Pack a boolean array *a* so that it takes 8x less space."""
return np.packbits(a.view(np.uint8))
def compress_indices(self, width=24):
# compression that takes less space for sparse profiles
# instead of saving each element of an array as 1 byte, we save indices of all the elements that are True
# saving one index requires more than 16 bytes (since 16 bytes allow for 65536 values, and a transcriptome can be larger)
# however, using 32 bytes per index seems like a waste of space
# therefore, I'll be using 20 bytes per index; this allows for 1 mln values
#
# the format is:
# first, 4 bytes keep the length of the profile in uint32 format
# then, 4 bytes keep total number of indexes of True values (N)
# then, one array of length 20 * N keeps indices positions
# then, last 16 bytes keep MD5 checksum
true_indices = np.where(self.values) # get indices
true_indices = true_indices[
0] # for some reason np.where returns a tuple
true_indices = true_indices.astype(
np.uint32) # convert to unsigned integers
# split each uint32 value into 4 uint8 values so that we can turn them into binary in a vectorized manner
N_indices = true_indices.shape[0]
binary_array = np.zeros((N_indices, 32), dtype=np.bool)
# iterate through all the indices and turn each into bin and then into 4 uint8 values
for i, value in enumerate(true_indices):
bit_string = struct.pack('I', value)
curr_uint8 = np.frombuffer(bit_string, dtype=np.uint8)
binary_array[i, :] = np.unpackbits(curr_uint8)
# remove extra unused bytes: shorten each value from 32 bits to the specified width (20 by default)
total_count_per_byte = binary_array.sum(axis=0)
assert (total_count_per_byte[width:] == 0
).all(), "some indices are larger than the chosen width!"
shortened_binary_array = binary_array[:, 0:width]
flattened_binary_array = shortened_binary_array.flatten()
# make the total array of K*8 length so that we can compress it to bytes
total_number_of_values = flattened_binary_array.shape[0]
if total_number_of_values % 8 != 0:
new_number_of_values = ((total_number_of_values // 8) + 1) * 8
binary_bytes_array = np.zeros(new_number_of_values, dtype=np.bool)
binary_bytes_array[
0:total_number_of_values] = flattened_binary_array
else:
binary_bytes_array = flattened_binary_array
length_uint32 = np.array([self.values.shape[0]], dtype=np.uint32)
length_bitstring = length_uint32.tobytes()
N_indices_uint32 = np.array([N_indices], dtype=np.uint32)
N_indices_bitstring = N_indices_uint32.tobytes()
width_uint32 = np.array([width], dtype=np.uint32)
width_bitstring = width_uint32.tobytes()
indices_packbits = np.packbits(binary_bytes_array)
indices_bitstring = indices_packbits.tobytes()
info_bitstring = length_bitstring + N_indices_bitstring + width_bitstring + indices_bitstring
md5 = hashlib.md5()
md5.update(info_bitstring)
md5_checksum = md5.digest()
assert (
md5.digest_size == 16
) # md5 checksum is always 16 bytes long, see wiki: https://en.wikipedia.org/wiki/MD5
full_bytestring = info_bitstring + md5_checksum
self.bytestring_indices = full_bytestring
self.md5_indices = md5_checksum
def pack(array):
if not isinstance(array, numpy.ndarray):
array = numpy.array(array, dtype=oamap.generator.Masked.maskdtype)
return numpy.packbits(array != oamap.generator.Masked.maskedvalue)
def __init__(self, resolution=(400, 300), colour='black', cs_pin=CS0_PIN, dc_pin=DC_PIN, reset_pin=RESET_PIN, busy_pin=BUSY_PIN, h_flip=False, v_flip=False):
if resolution not in _RESOLUTION.keys():
raise ValueError('Resolution {}x{} not supported!'.format(*resolution))
self.resolution = resolution
self.width, self.height = resolution
self.cols, self.rows, self.rotation = _RESOLUTION[resolution]
if colour not in ('red', 'black', 'yellow'):
raise ValueError('Colour {} is not supported!'.format(colour))
self.colour = colour
self.buf = numpy.zeros((self.height, self.width), dtype=numpy.uint8)
self.buf_zero = numpy.zeros((self.height, self.width), dtype=numpy.uint8)
self.buf_black = numpy.packbits(numpy.where(self.buf_zero == WHITE, 0, 1)).tolist()
self.buf_white = numpy.packbits(numpy.where(self.buf_zero == BLACK, 0, 1)).tolist()
self.border_colour = 0
self.dc_pin = dc_pin
self.reset_pin = reset_pin
self.busy_pin = busy_pin
self.cs_pin = cs_pin
self.h_flip = h_flip
self.v_flip = v_flip
self._gpio_setup = False
self._luts = {
'clear-black': [
# Phase 0 Phase 1 Phase 2 Phase 3 Phase 4 Phase 5 Phase 6
# A B C D A B C D A B C D A B C D A B C D A B C D A B C D
0b00010000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, # LUT0 - Black
0b00010000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, # LUT0 - Black
0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, # IGNORE
0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, # LUT3 - Red
0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, # LUT4 - VCOM
# Duration | Repeat
# A B C D |
0, 100, 0, 0, 1, # 2 bring in the black
0, 0, 0, 0, 0, # 0 Flash
0, 0, 0, 0, 0, # 1 clear
],
'clear-white': [
# Phase 0 Phase 1 Phase 2 Phase 3 Phase 4 Phase 5 Phase 6
# A B C D A B C D A B C D A B C D A B C D A B C D A B C D
0b10100000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, # LUT0 - Black
0b10100000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, # LUT0 - Black
0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, # IGNORE
0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, # LUT3 - Red
0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, # LUT4 - VCOM
# Duration | Repeat
# A B C D |
# 0, 100, 0, 0, 1, # 2 bring in the black
0, 66, 0, 0, 2, # 2 bring in the black
0, 0, 0, 0, 0, # 1 clear
0, 0, 0, 0, 0, # 1 clear
],
'draw-from-black': [
# Phase 0 Phase 1 Phase 2 Phase 3 Phase 4 Phase 5 Phase 6
# A B C D A B C D A B C D A B C D A B C D A B C D A B C D
0b00010000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, # LUT0 - Black
0b10000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, # LUTT1 - White
0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, # IGNORE
0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, # LUT3 - Red
0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, # LUT4 - VCOM
# Duration | Repeat
# A B C D |
50, 0, 0, 0, 2, # 2 bring in the invert
0, 0, 0, 0, 0, # 0 clear
0, 0, 0, 0, 0, # 1 clear
],
'draw-from-white': [
# Phase 0 Phase 1 Phase 2 Phase 3 Phase 4 Phase 5 Phase 6
# A B C D A B C D A B C D A B C D A B C D A B C D A B C D
0b00010000, 0b00000000, 0b00000000, 0b00010000, 0b00010011, 0b00000000, 0b00000000, # LUT0 - Black
0b10000000, 0b00000000, 0b00000000, 0b00000000, 0b00000011, 0b00000000, 0b00000000, # LUTT1 - White
0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, # IGNORE
0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, # LUT3 - Red
0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, # LUT4 - VCOM
# Duration | Repeat
# A B C D |
20, 20, 0, 0, 4, # 2 bring in the black
0, 0, 0, 0, 0, # 1 clear
0, 0, 0, 0, 0, # 1 clear
],
'black': [
# Phase 0 Phase 1 Phase 2 Phase 3 Phase 4 Phase 5 Phase 6
# A B C D A B C D A B C D A B C D A B C D A B C D A B C D
0b01001000, 0b10100000, 0b00010000, 0b00010000, 0b00010011, 0b00000000, 0b00000000, # LUT0 - Black
0b01001000, 0b10100000, 0b10000000, 0b00000000, 0b00000011, 0b00000000, 0b00000000, # LUTT1 - White
0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, # IGNORE
0b01001000, 0b10100101, 0b00000000, 0b10111011, 0b00000000, 0b00000000, 0b00000000, # LUT3 - Red
0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, # LUT4 - VCOM
# Duration | Repeat
# A B C D |
16, 4, 4, 4, 4, # 0 Flash
16, 4, 4, 4, 4, # 1 clear
4, 8, 8, 16, 16, # 2 bring in the black
0, 0, 0, 0, 0, # 3 time for red
0, 0, 0, 0, 0, # 4 final black sharpen phase
0, 0, 0, 0, 0, # 5
0, 0, 0, 0, 0, # 6
],
'red': [
# Phase 0 Phase 1 Phase 2 Phase 3 Phase 4 Phase 5 Phase 6
# A B C D A B C D A B C D A B C D A B C D A B C D A B C D
0b01001000, 0b10100000, 0b00010000, 0b00010000, 0b00010011, 0b00000000, 0b00000000, # LUT0 - Black
0b01001000, 0b10100000, 0b10000000, 0b00000000, 0b00000011, 0b00000000, 0b00000000, # LUTT1 - White
0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, # IGNORE
0b01001000, 0b10100101, 0b00000000, 0b10111011, 0b00000000, 0b00000000, 0b00000000, # LUT3 - Red
0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, # LUT4 - VCOM
# Duration | Repeat
# A B C D |
64, 12, 32, 12, 6, # 0 Flash
16, 8, 4, 4, 6, # 1 clear
4, 8, 8, 16, 16, # 2 bring in the black
2, 2, 2, 64, 32, # 3 time for red
2, 2, 2, 2, 2, # 4 final black sharpen phase
0, 0, 0, 0, 0, # 5
0, 0, 0, 0, 0 # 6
],
'yellow': [
# Phase 0 Phase 1 Phase 2 Phase 3 Phase 4 Phase 5 Phase 6
# A B C D A B C D A B C D A B C D A B C D A B C D A B C D
0b11111010, 0b10010100, 0b10001100, 0b11000000, 0b11010000, 0b00000000, 0b00000000, # LUT0 - Black
0b11111010, 0b10010100, 0b00101100, 0b10000000, 0b11100000, 0b00000000, 0b00000000, # LUTT1 - White
0b11111010, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, 0b00000000, # IGNORE
0b11111010, 0b10010100, 0b11111000, 0b10000000, 0b01010000, 0b00000000, 0b11001100, # LUT3 - Yellow (or Red)
0b10111111, 0b01011000, 0b11111100, 0b10000000, 0b11010000, 0b00000000, 0b00010001, # LUT4 - VCOM
# Duration | Repeat
# A B C D |
64, 16, 64, 16, 8,
8, 16, 4, 4, 16,
8, 8, 3, 8, 32,
8, 4, 0, 0, 16,
16, 8, 8, 0, 32,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
]
}
def pack_state(self, state):
black = np.packbits(state == Board.STONE_BLACK)
white = np.packbits(state == Board.STONE_WHITE)
empty = np.packbits(state == Board.STONE_EMPTY)
image = np.concatenate((black, white, empty))
return bytes(image)
def test_count(self, kwargs):
packed = np.packbits(self.x)
unpacked = np.unpackbits(packed, **kwargs)
assert_equal(unpacked.dtype, np.uint8)
assert_array_equal(unpacked, self.padded1[:-1])
def pixels_to_raster(pixels):
packed = np.packbits(pixels)
inverted = np.invert(packed)
return inverted.tolist()
def decode(source, source_type="array"):
"""
Decodes the source and returns the hidden message.
:param source: The source
:type source: numpy.array
:param source_type: Source type
:type source_type: str
:return: Hidden message
:rtype: numpy.array
"""
if source_type == "array":
source, source_original_shape = Source.from_array(source)
elif source_type == "image":
source, source_original_shape = Source.from_image(source)
else:
raise Exception("Source type not valid", source_type)
offset = 0
bit_split = source[:offset + 4, -1:]
bit_split = bit_split.squeeze()
bit_split = "".join(str(num) for num in bit_split)
bit_split = int(bit_split, 2)
offset = offset + 4
padding = source[offset:offset + 4, -1:]
padding = padding.squeeze()
padding = "".join([str(num) for num in padding])
padding = int(padding, 2)
offset = offset + 4
message_length = source[offset:offset + 32, -1:]
message_length = message_length.squeeze()
message_length = "".join([str(num) for num in message_length])
message_length = int(message_length, 2)
offset = offset + 32
message_type = source[offset:offset + 2, -1:]
message_type = message_type.squeeze()
message_type = "".join([str(num) for num in message_type])
message_type = Steganography.inv_type_map[message_type]
offset = offset + 2
num_extras = source[offset:offset + 4, -1:]
num_extras = num_extras.squeeze()
num_extras = "".join([str(num) for num in num_extras])
num_extras = int(num_extras, 2)
offset = offset + 4
extras = []
for extra in range(num_extras):
extra = source[offset:offset + 16, -1:]
extra = extra.squeeze()
extra = "".join([str(num) for num in extra])
extras.append(int(extra, 2))
offset = offset + 16
message = source[offset:offset + message_length, -bit_split:]
message = message.reshape((-1, ))[:-padding]
message = np.packbits(message)
return message, message_type, extras
def make_constellation(m):
"""
Create a constellation with m possible symbols where m must be a power
of 2.
Points are laid out in a cross grid.
"""
if not isinstance(m, int) or not is_odd_power_of_two(m) and m > 1:
raise ValueError("m must be an odd power of 2 integer.")
# Each symbol holds k bits.
k = int(log(m) / log(2))
n = int(k / 2) + 1
mn = k - n
s = int(pow(2, mn - 1))
# Determining how the constellation map should be build
rect_map = []
const_map = [0 + 0j] * m
if k == 1:
const_map.append(complex(1, 0))
const_map.append(complex(-1, 0))
elif k == 3:
# Do rectangular constellation mapping first
for i in range(2 * pow(2, s)):
i_bin = convert_to_binary(i, s + 1)
for j in range(pow(2, s)):
j_bin = convert_to_binary(j, s)
rect_map.append((i_bin, j_bin))
# Make Complex Constellation using cross gray coding
for x, y in rect_map:
Irct, Qrct = rectG(x, y)
z = np.packbits(np.append(y, x[::-1]), bitorder='little')[0]
if Irct < 3:
const_map[z] = complex(Irct, Qrct)
else:
Icr = -sign(Irct) * (4 - abs(Irct))
Qcr = sign(Qrct) * (abs(Qrct) + 2)
const_map[z] = complex(Icr, Qcr)
else:
# Do rectangular constellation mapping first
for i in range(pow(2, n)):
i_bin = convert_to_binary(i, n)
for j in range(pow(2, mn)):
j_bin = convert_to_binary(j, mn)
rect_map.append((i_bin, j_bin))
# Make Numpy Complex Constellation using cross gray coding
for x, y in rect_map:
Irct, Qrct = rectG(x, y)
xy = x + y
z = int("".join(str(w) for w in xy), 2)
if abs(Irct) < (3 * s):
const_map[z] = complex(Irct, Qrct)
elif abs(Qrct) > s:
Icr = sign(Irct) * (abs(Irct) - (2 * s))
Qcr = sign(Qrct) * ((4 * s) - abs(Qrct))
const_map[z] = complex(Icr, Qcr)
else:
Icr = sign(Irct) * ((4 * s) - abs(Irct))
Qcr = sign(Qrct) * (abs(Qrct) + (2 * s))
const_map[z] = complex(Icr, Qcr)
return const_map
def __call__(self, roidb):
fname, boxes, klass, is_crowd = roidb["file_name"], roidb[
"boxes"], roidb["class"], roidb["is_crowd"]
assert boxes.ndim == 2 and boxes.shape[1] == 4, boxes.shape
boxes = np.copy(boxes)
im = cv2.imread(fname, cv2.IMREAD_COLOR)
assert im is not None, fname
im = im.astype("float32")
height, width = im.shape[:2]
# assume floatbox as input
assert boxes.dtype == np.float32, "Loader has to return float32 boxes!"
if not self.cfg.DATA.ABSOLUTE_COORD:
boxes[:, 0::2] *= width
boxes[:, 1::2] *= height
# augmentation:
tfms = self.aug.get_transform(im)
im = tfms.apply_image(im)
points = box_to_point4(boxes)
points = tfms.apply_coords(points)
boxes = point4_to_box(points)
if len(boxes):
assert klass.max() <= self.cfg.DATA.NUM_CATEGORY, \
"Invalid category {}!".format(klass.max())
assert np.min(np_area(boxes)) > 0, "Some boxes have zero area!"
ret = {"image": im}
# Add rpn data to dataflow:
try:
if self.cfg.MODE_FPN:
multilevel_anchor_inputs = self.get_multilevel_rpn_anchor_input(
im, boxes, is_crowd)
for i, (anchor_labels,
anchor_boxes) in enumerate(multilevel_anchor_inputs):
ret["anchor_labels_lvl{}".format(i + 2)] = anchor_labels
ret["anchor_boxes_lvl{}".format(i + 2)] = anchor_boxes
else:
ret["anchor_labels"], ret[
"anchor_boxes"] = self.get_rpn_anchor_input(
im, boxes, is_crowd)
boxes = boxes[is_crowd == 0] # skip crowd boxes in training target
klass = klass[is_crowd == 0]
ret["gt_boxes"] = boxes
ret["gt_labels"] = klass
except MalformedData as e:
log_once(
"Input {} is filtered for training: {}".format(fname, str(e)),
"warn")
return None
if self.cfg.MODE_MASK:
# augmentation will modify the polys in-place
segmentation = copy.deepcopy(roidb["segmentation"])
segmentation = [
segmentation[k] for k in range(len(segmentation))
if not is_crowd[k]
]
assert len(segmentation) == len(boxes)
# Apply augmentation on polygon coordinates.
# And produce one image-sized binary mask per box.
masks = []
width_height = np.asarray([width, height], dtype=np.float32)
gt_mask_width = int(np.ceil(im.shape[1] / 8.0) *
8) # pad to 8 in order to pack mask into bits
for polys in segmentation:
if not self.cfg.DATA.ABSOLUTE_COORD:
polys = [p * width_height for p in polys]
polys = [tfms.apply_coords(p) for p in polys]
masks.append(
polygons_to_mask(polys, im.shape[0], gt_mask_width))
if len(masks):
masks = np.asarray(masks, dtype='uint8') # values in {0, 1}
masks = np.packbits(masks, axis=-1)
else: # no gt on the image
masks = np.zeros((0, im.shape[0], gt_mask_width // 8),
dtype='uint8')
ret['gt_masks_packed'] = masks
# from viz import draw_annotation, draw_mask
# viz = draw_annotation(im, boxes, klass)
# for mask in masks:
# viz = draw_mask(viz, mask)
# tpviz.interactive_imshow(viz)
return ret
def img_frombytes(data):
size = data.shape[::-1]
databytes = np.packbits(data, axis=1)
return Image.frombytes(mode='1', size=size, data=databytes)
def pack_bits(arr: "np.ndarray", pad: bool = True) -> bytes:
"""Pack a binary :class:`numpy.ndarray` for use with *Pixel Data*.
.. versionadded:: 1.2
Should be used in conjunction with (0028,0100) *Bits Allocated* = 1.
.. versionchanged:: 2.1
Added the `pad` keyword parameter and changed to allow `arr` to be
2 or 3D.
Parameters
----------
arr : numpy.ndarray
The :class:`numpy.ndarray` containing 1-bit data as ints. `arr` must
only contain integer values of 0 and 1 and must have an 'uint' or
'int' :class:`numpy.dtype`. For the sake of efficiency it's recommended
that the length of `arr` be a multiple of 8 (i.e. that any empty
bit-padding to round out the byte has already been added). The input
`arr` should either be shaped as (rows, columns) or (frames, rows,
columns) or the equivalent 1D array used to ensure that the packed
data is in the correct order.
pad : bool, optional
If ``True`` (default) then add a null byte to the end of the packed
data to ensure even length, otherwise no padding will be added.
Returns
-------
bytes
The bit packed data.
Raises
------
ValueError
If `arr` contains anything other than 0 or 1.
References
----------
DICOM Standard, Part 5,
:dcm:`Section 8.1.1<part05/chapter_8.html#sect_8.1.1>` and
:dcm:`Annex D<part05/chapter_D.html>`
"""
if arr.shape == (0, ):
return bytes()
# Test array
if not np.array_equal(arr, arr.astype(bool)):
raise ValueError(
"Only binary arrays (containing ones or zeroes) can be packed.")
if len(arr.shape) > 1:
arr = arr.ravel()
# The array length must be a multiple of 8, pad the end
if arr.shape[0] % 8:
arr = np.append(arr, np.zeros(8 - arr.shape[0] % 8))
# Reshape so each row is 8 bits
arr = np.reshape(arr, (-1, 8))
arr = np.fliplr(arr)
arr = np.packbits(arr.astype('uint8'))
packed: bytes = arr.tobytes()
if pad:
return packed + b'\x00' if len(packed) % 2 else packed
return packed
def SimulateParityBits(snrArray, txBin, Npixels, modulatioInfo):
nSNR = len(snrArray)
rxBinDecoded = np.empty(0)
rxIncorrect = True
mod = 0
if (modulatioInfo.get("mod") == "PSK"):
mod = komm.PSKModulation(modulatioInfo.get("order"))
if (modulatioInfo.get("mod") == 'QAM'):
mod = komm.QAModulation(
modulatioInfo.get("order")) # add baseAmplitude
print("Base Amplitude is: " + str(mod.energy_per_symbol))
# Normalize Enerhy per symbol
baseAmplitude = 1 / (np.sqrt(mod.energy_per_symbol))
print("New Base Amplitude is: " + str(baseAmplitude))
mod = komm.QAModulation(modulatioInfo.get("order"), baseAmplitude)
print("Modulation to be used:")
print(
str(modulatioInfo.get("order")) + " " + str(modulatioInfo.get("mod")))
print("Bits Per Symbol: " + str(mod.bits_per_symbol))
print("Energy Per Symbol: " + str(mod.energy_per_symbol))
print("\n")
print("Simulating ARQ based on parity bit check!")
print("Adding Parity Bits!")
# Add parity bits
# For each pixel
for i in range(Npixels):
startIndex = i * 8
# If the sum of on bits is not even
if (((np.sum(txBin[startIndex:startIndex + 7])) % 2) != 0):
# Change parity bit to 1
txBin[(startIndex + 7)] = 1
# The sum of on bits is even
else:
# Change parity bit to 0
txBin[(startIndex + 7)] = 0
# Modulate data
txDataParity = mod.modulate(txBin)
print("Simulating Transmision!")
indexFactor = int(8 / mod.bits_per_symbol)
berArray = np.empty(0)
arqArray = np.empty(0)
for c in range(nSNR):
print("Simulating SNR: " + str(snrArray[c]))
# Set Average Gausian Noise to reflect new SNR
awgn = komm.AWGNChannel(snr=10**(snrArray[c] / 10.))
ARQ = 0
# For Each Symbol
for i in range(Npixels):
# Compute Index of the codeword
startIndex = i * indexFactor
# Until the Parity bit check is not passed
while (rxIncorrect):
# Simulate noise in the channel during transmision only
rxData = awgn(txDataParity[startIndex:startIndex +
indexFactor])
# Demodulate Data
rxBin = mod.demodulate(rxData)
# Check if parity = 0
if ((np.sum(rxBin) % 2) != 0):
# Error During Transmision
# Increment Request Counter
ARQ += 1
else:
# Passed parity check, assume data is correct
# Append Data Bits to final binary array
rxBinDecoded = np.append(rxBinDecoded, rxBin)
# Set while loop flag to false indicating this codeword has been rx without error
rxIncorrect = False
#Set while loop flag to true to process next codeword
rxIncorrect = True
# Convert to real int
rxBinDecoded = np.real(rxBinDecoded)
rxBinDecoded = rxBinDecoded.astype(int)
# For SNR 10 Plot graphs
if (c == 0):
# Plot few rx bits
# plt.figure()
# plt.axes().set_aspect("equal")
# plt.scatter(rxBinDecoded[:10000].real,rxBinDecoded[:10000].imag,s=1,marker=".")
# plt.show()
rx_im = np.packbits(rxBinDecoded).reshape(tx_im.size[1],
tx_im.size[0])
plt.figure()
plt.imshow(np.array(rx_im), cmap="gray", vmin=0, vmax=255)
plt.show()
# Count Bit errors
print("Computing BER: " + str(snrArray[c]))
BitErrorCount = 0
# For each bit in the rx data
for i in range(Npixels * 8):
# If bit value does not match
if (rxBinDecoded[i] != txBin[i]):
# Increment error count
BitErrorCount += 1
# Calculate bit error rate for the transmision
berArray = np.append(berArray, (BitErrorCount / (Npixels * 8)))
arqArray = np.append(arqArray, (ARQ / (Npixels * 8)))
print("BER Array:")
print(berArray)
print("\n")
print("ARQ Array:")
print(arqArray)
print("\n")
plt.figure()
plt.scatter(snrArray, berArray) #plot points
plt.plot(snrArray, berArray) #plot lines
plt.yscale("log")
plt.ylabel('$BER$')
plt.xlabel('$SNR$')
plt.title((str(modulatioInfo.get("order")) + " " +
str(modulatioInfo.get("mod")) + " BER"))
plt.grid(True)
# Calculate theoretical BER
# Modify k parameter i.e. bits per symbol
k = mod.bits_per_symbol
errfcDataSet = np.empty(0)
# For Each SNR
for i in range(nSNR):
# Calculate Theorethical BER
errfc = 0.5 * scipy.special.erfc(
math.sqrt((10**(snrArray[i] / 10)) / k))
errfcDataSet = np.append(errfcDataSet, errfc)
plt.plot(snrArray, errfcDataSet, color='r')
plt.show()
plt.figure()
plt.scatter(snrArray, arqArray) #plot points
plt.plot(snrArray, arqArray) #plot lines
plt.yscale("log")
plt.ylabel('$ARQ Rate$')
plt.xlabel('$SNR$')
plt.title((str(modulatioInfo.get("order")) + " " +
str(modulatioInfo.get("mod")) + " ARQ/nBits"))
plt.grid(True)
return berArray, arqArray, rxBinDecoded
def get_action(logits):
probs = 1. / (1. + np.exp(-logits.detach().numpy()))
bits = np.array([np.random.uniform() <= p for p in probs]).astype(int)
return int(np.packbits(bits)[0] >> 2)
def bwr_bytes(image):
"""Converts the image to the closest 2-bit black, white, or red bytes."""
indices = _color_indices(image)
bwr_image_data = BWR_2_BIT[indices.reshape((image.height * image.width))]
return packbits(bwr_image_data)
return fc2
# prepare numpy arrays for testing
data = np.load("data/bnn-5775.data.npz")
images = data["images"][:test_size]
labels = data["labels"][:test_size]
num_images = images.shape[0]
params = np.load("data/bnn-5775.params.npz")
# prepare packed arrays
packed_params = {}
for name in params:
if "w_fc" in name:
packed_params[name] = np.packbits(params[name].copy().astype(np.bool),
axis=1,
bitorder="little").view(np.uint32)
elif "w_conv1" in name:
arr = params[name].copy().transpose(0, 2, 3, 1).astype(np.bool)
packed_params[name] = arr
elif "w_conv2" in name:
arr = params[name].copy().transpose(0, 2, 3, 1)
arr = np.packbits(arr.astype(np.bool), axis=3,
bitorder="little").view(np.uint16)
packed_params[name] = arr
elif "bn_t" in name:
packed_params[name] = params[name].copy().transpose(1, 2, 0)
else:
packed_params[name] = params[name].copy()
def Extract(self, features, num_features_per_region=None):
"""Extracts aggregated representation.
Args:
features: [N, D] float numpy array with N local feature descriptors.
num_features_per_region: Required only if computing regional aggregated
representations, otherwise optional. List of number of features per
region, such that sum(num_features_per_region) = N. It indicates which
features correspond to each region.
Returns:
aggregated_descriptors: 1-D numpy array.
feature_visual_words: Used only for ASMK/ASMK* aggregation type. 1-D
numpy array denoting visual words corresponding to the
`aggregated_descriptors`.
Raises:
ValueError: If inputs are misconfigured.
"""
features = tf.cast(features, dtype=tf.float32)
if num_features_per_region is None:
# Use dummy value since it is unused.
num_features_per_region = []
else:
num_features_per_region = tf.cast(num_features_per_region,
dtype=tf.int32)
if len(num_features_per_region
) and sum(num_features_per_region) != features.shape[0]:
raise ValueError(
"Incorrect arguments: sum(num_features_per_region) and "
"features.shape[0] are different: %d vs %d" %
(sum(num_features_per_region), features.shape[0]))
# Extract features based on desired options.
if self._aggregation_type == _VLAD:
# Feature visual words are unused in the case of VLAD, so just return
# dummy constant.
feature_visual_words = tf.constant(-1, dtype=tf.int32)
if self._use_regional_aggregation:
aggregated_descriptors = self._ComputeRvlad(
features,
num_features_per_region,
self._codebook,
use_l2_normalization=self._use_l2_normalization,
num_assignments=self._num_assignments)
else:
aggregated_descriptors = self._ComputeVlad(
features,
self._codebook,
use_l2_normalization=self._use_l2_normalization,
num_assignments=self._num_assignments)
elif (self._aggregation_type == _ASMK
or self._aggregation_type == _ASMK_STAR):
if self._use_regional_aggregation:
(aggregated_descriptors,
feature_visual_words) = self._ComputeRasmk(
features,
num_features_per_region,
self._codebook,
num_assignments=self._num_assignments)
else:
(aggregated_descriptors,
feature_visual_words) = self._ComputeAsmk(
features,
self._codebook,
num_assignments=self._num_assignments)
feature_visual_words_output = feature_visual_words.numpy()
# If using ASMK*/RASMK*, binarize the aggregated descriptors.
if self._aggregation_type == _ASMK_STAR:
reshaped_aggregated_descriptors = np.reshape(
aggregated_descriptors, [-1, self._feature_dimensionality])
packed_descriptors = np.packbits(
reshaped_aggregated_descriptors > 0, axis=1)
aggregated_descriptors_output = np.reshape(packed_descriptors,
[-1])
else:
aggregated_descriptors_output = aggregated_descriptors.numpy()
return aggregated_descriptors_output, feature_visual_words_output
def binArr2int(arr):
""" Convert a binary array into its (long) integer representation. """
from numpy import packbits
tmp2 = packbits(arr.astype(int))
return sum(val * 256 ** i for i, val in enumerate(tmp2[::-1]))
def dba_loop(s, c=None, max_it=10, thr=0.001, mask=None,
keep_averages=False, use_c=False, nb_initial_samples=None, nb_prob_samples=None, **kwargs):
"""Loop around the DTW Barycenter Averaging (DBA) method until convergence.
:param s: Container of sequences
:param c: Initial averaging sequence.
If none is given, the first one is used (unless if nb_initial_samples is set).
Better performance can be achieved by starting from an informed
starting point (Petitjean et al. 2011).
:param max_it: Maximal number of calls to DBA.
:param thr: Convergence if the DBA is changing less than this value.
:param mask: Boolean array with the series in s to use. If None, use all.
:param keep_averages: Keep all DBA values (for visualisation or debugging).
:param nb_initial_samples: If c is None, and this argument is not None, select
nb_initial_samples samples and select the series closest to all other samples
as c.
:param nb_prob_samples: Probabilistically sample the best path instead of the
deterministic version.
:param use_c: Use a fast C implementation instead of a Python version.
:param kwargs: Arguments for dtw.distance
"""
if np is None:
raise NumpyException('The method dba_loop requires Numpy to be available')
s = SeriesContainer.wrap(s)
ndim = s.detected_ndim
avg = None
avgs = None
if keep_averages:
avgs = []
if mask is None:
mask = np.full((len(s),), True, dtype=bool)
if nb_prob_samples is None:
nb_prob_samples = 0
if c is None:
if nb_initial_samples is None:
curi = 0
while mask[curi] is False:
curi += 1
c = s[curi]
else:
c = get_good_c(s, mask, nb_initial_samples, use_c=use_c, **kwargs)
# You can also use a constant function, but this gives worse performance.
# After the first iteration, this will be the average of all
# sequences. The disadvantage is that this might create e.g. multiple
# peaks for a sequence with only one peak (but shifted) and then the
# original sequences will map their single peak to the different peaks
# in the first average and converge to that as a local optimum.
# t = s.get_avg_length()
# c = array.array('d', [0] * t)
if use_c:
if np is not None and isinstance(mask, np.ndarray):
# The C code requires a bit array of uint8 (or unsigned char)
mask_copy = np.packbits(mask, bitorder='little')
else:
raise Exception('Mask only implemented for C when passing a Numpy array. '
f'Got {type(mask)}')
else:
mask_copy = mask
if not use_c and nb_prob_samples != 0:
raise Exception('The parameter nb_prob_samples is not available in the Python implementation!')
for it in range(max_it):
logger.debug(f'DBA Iteration {it}')
if use_c:
assert(c is not None)
c_copy = c.copy() # The C code reuses this array
# c_copy = c.flatten()
if ndim == 1:
dtw_cc.dba(s, c_copy, mask=mask_copy, nb_prob_samples=nb_prob_samples, **kwargs)
# avg = c_copy
else:
dtw_cc.dba_ndim(s, c_copy, mask=mask_copy, nb_prob_samples=nb_prob_samples, ndim=ndim, **kwargs)
# avg = c_copy.reshape(-1, ndim)
avg = c_copy
else:
if not nb_prob_samples:
avg = dba(s, c, mask=mask, use_c=use_c, **kwargs)
else:
avg = dba(s, c, mask=mask, nb_prob_samples=nb_prob_samples, use_c=use_c, **kwargs)
if keep_averages:
avgs.append(avg)
if thr is not None and c is not None:
diff = 0
# diff = np.sum(np.subtract(avg, c))
if ndim == 1:
for av, cv in zip(avg, c):
diff += abs(av - cv)
else:
for av, cv in zip(avg, c):
diff += max(abs(av[d] - cv[d]) for d in range(ndim))
diff /= len(avg)
if diff <= thr:
logger.debug(f'DBA converged at {it} iterations (avg diff={diff}).')
break
c = avg
if keep_averages:
return avg, avgs
return avg
def fast_show (self, style):
region = self.buf
if self.v_flip:
region = numpy.fliplr(region)
if self.h_flip:
region = numpy.flipud(region)
if self.rotation:
region = numpy.rot90(region, self.rotation // 90)
buf_a = numpy.packbits(numpy.where(region == BLACK, 0, 1)).tolist()
buf_b = self.buf_black
self.setup()
packed_height = list(struct.pack('<H', self.rows))
if isinstance(packed_height[0], str):
packed_height = map(ord, packed_height)
self._send_command(0x74, 0x54) # Set Analog Block Control
self._send_command(0x7e, 0x3b) # Set Digital Block Control
self._send_command(0x01, packed_height + [0x00]) # Gate setting
self._send_command(0x03, [0b10000, 0b0001]) # Gate Driving Voltage
self._send_command(0x3a, 0x07) # Dummy line period
self._send_command(0x3b, 0x04) # Gate line width
self._send_command(0x11, 0x03) # Data entry mode setting 0x03 = X/Y increment
self._send_command(0x04) # Power On
self._send_command(0x2c, 0x3c) # VCOM Register, 0x3c = -1.5v?
# border color
self._send_command(0x3c, 0x00)
if self.border_colour == self.BLACK:
self._send_command(0x3c, 0x00)
elif self.border_colour == self.WHITE:
self._send_command(0x3c, 0xFF)
self._send_command(0x44, [0x00, (self.cols // 8) - 1]) # Set RAM X Start/End
self._send_command(0x45, [0x00, 0x00] + packed_height) # Set RAM Y Start/End
self._send_command(0x4e, 0x00) # Set RAM X Pointer Start
self._send_command(0x4f, [0x00, 0x00]) # Set RAM Y Pointer Start
self._send_command(0x24, buf_a)
if style == self.BLACK:
self._send_command(0x32, self._luts["clear-black"]) # Set LUTs
self._send_command(0x22, 0xc7) # Display Update Sequence
self._send_command(0x20) # Trigger Display Update
# time.sleep(SLEEP_TIME)
self._busy_wait()
self._send_command(0x32, self._luts["draw-from-black"]) # Set LUTs
self._send_command(0x22, 0xc7) # Display Update Sequence
self._send_command(0x20) # Trigger Display Update
else:
self._send_command(0x3c, 0x00)
self._send_command(0x32, self._luts["clear-white"]) # Set LUTs
self._send_command(0x22, 0xc7) # Display Update Sequence
self._send_command(0x20) # Trigger Display Update
# time.sleep(SLEEP_TIME)
self._busy_wait()
self._send_command(0x3c, 0xFF)
self._send_command(0x32, self._luts["draw-from-white"]) # Set LUTs
self._send_command(0x22, 0xc7) # Display Update Sequence
self._send_command(0x20) # Trigger Display Update
time.sleep(0.05)
self._busy_wait()
self._send_command(0x10, 0x01) # Enter Deep Sleep
def makeBlockMapByte(self, sizeBlock):
arr = numpy.packbits(self.blockMapArr)
return bytearray(arr)
