def create_fasta_from_fastq(fastq_filename: str):
out_lines = []
rules = FastDNARules()
with open(fastq_filename, "r") as in_file:
lines = in_file.readlines()[1:][::4]
for line in lines:
err = rules.apply_all_rules(line[18:182])
if err < 1.0:
out_lines.append(line[18:182])
with open("R:/out.fasta", "w") as out_file:
for line in out_lines:
out_file.write(">todo\n")
out_file.write(line + "\n")
return lines
def main(file,
number_of_chunks: int = 0,
chunk_size: int = 0,
error_correction: typing.Callable = nocode,
as_dna: bool = False,
insert_header: bool = False,
save_number_of_chunks=False):
if chunk_size != 0:
number_of_chunks = Encoder.get_number_of_chunks_for_file_with_chunk_size(
file, chunk_size)
if as_dna:
rules = FastDNARules()
else:
rules = None
dist = ErlichZielinskiRobustSolitonDistribution(number_of_chunks, seed=2)
encoder = LTEncoder(file,
number_of_chunks,
dist,
insert_header=insert_header,
rules=rules,
error_correction=error_correction,
number_of_chunks_len_format="H",
id_len_format="I",
used_packets_len_format="H",
save_number_of_chunks_in_packet=save_number_of_chunks,
implicit_mode=False)
encoder.encode_to_packets()
print("Number of Chunks=%s" % encoder.number_of_chunks)
encoder.save_packets(split_to_multiple_files=True, save_as_dna=as_dna)
def encode(p_output,
file,
as_dna=True,
error_correction=nocode,
insert_header=False,
save_number_of_chunks_in_packet=False,
overhead=6.0,
clear_output=False):
number_of_chunks = Encoder.get_number_of_chunks_for_file_with_chunk_size(
file, CHUNK_SIZE)
dist = RaptorDistribution(number_of_chunks)
dna_rules = FastDNARules()
if as_dna:
rules = dna_rules
else:
rules = None
x = RU10Encoder(
file,
number_of_chunks,
dist,
chunk_size=CHUNK_SIZE,
insert_header=insert_header,
rules=rules,
error_correction=error_correction,
id_len_format="H",
number_of_chunks_len_format="B",
save_number_of_chunks_in_packet=save_number_of_chunks_in_packet)
x.set_overhead_limit(overhead)
x.encode_to_packets()
p_output.send(
[ParallelPacket.from_packet(packet) for packet in x.encodedPackets])
p_output.send("DONE")
p_output.close()
return 0
def main(file: str,
error_correction: typing.Callable[[typing.Any], typing.Any],
asdna: bool = True,
epsilon: float = 0.06,
insert_header: bool = False):
dist = OnlineDistribution(epsilon)
number_of_chunks = dist.get_size()
quality = 7
if asdna:
rules = FastDNARules()
else:
rules = None
encoder = OnlineEncoder(file,
number_of_chunks,
dist,
epsilon,
quality,
error_correction=error_correction,
quality_len_format="B",
insert_header=insert_header,
check_block_number_len_format="H",
number_of_chunks_len_format="H",
rules=rules,
save_number_of_chunks_in_packet=False)
encoder.set_overhead_limit(1.70)
encoder.encode_file(split_to_multiple_files=True, save_as_dna=asdna)
encoder.save_packets(True, save_as_dna=asdna)
def encode(file, asdna=True, error_correction=nocode, insert_header=False, save_number_of_chunks_in_packet=False,
save_as_fasta=True, save_as_zip=True, overhead=0.40, epsilon=0.068, quality=7, upper_bound=1.0):
dist = OnlineDistribution(epsilon)
number_of_chunks = dist.get_size()
dna_rules = FastDNARules()
if asdna:
rules = dna_rules
else:
rules = None
encoder = OnlineEncoder(
file, number_of_chunks, dist, epsilon, quality, error_correction=error_correction, quality_len_format="B",
insert_header=insert_header, check_block_number_len_format="H", number_of_chunks_len_format="H", rules=rules,
save_number_of_chunks_in_packet=save_number_of_chunks_in_packet, drop_upper_bound=upper_bound) # , pseudo_decoder=pseudo)
encoder.set_overhead_limit(overhead)
#encoder.encode_file(split_to_multiple_files=True, save_as_dna=asdna)
encoder.encode_to_packets()
if save_as_fasta:
encoder.save_packets_fasta(file_ending="_Online", seed_is_filename=True)
elif save_as_zip:
encoder.save_packets_zip(save_as_dna=True, file_ending="_Online", seed_is_filename=True)
else:
encoder.save_packets(True, save_as_dna=True, seed_is_filename=True, clear_output=True)
encoder.save_packets(split_to_multiple_files=True, save_as_dna=True)
print("Number of Chunks=%s" % encoder.number_of_chunks)
return encoder
def get_error_sum(file,
number_of_chunks,
chunk_size,
seq_seed=None,
while_count=1000):
max_seed = np.power(2, 8 * struct.calcsize(SEED_LEN_FORMAT))
dist = RaptorDistribution(number_of_chunks)
dna_rules = FastDNARules()
error_correction = lambda x: reed_solomon_encode(x, NO_REPAIR_SYMBOLS)
encoder = RU10Encoder(
file,
number_of_chunks,
dist,
chunk_size=chunk_size,
insert_header=INSERT_HEADER,
rules=dna_rules,
error_correction=error_correction,
id_len_format=SEED_LEN_FORMAT,
number_of_chunks_len_format=NUMBER_OF_CHUNKS_LEN_FORMAT,
save_number_of_chunks_in_packet=save_number_of_chunks_in_packet,
prepend="",
append="")
encoder.prepare()
i = 0
res = []
while i < while_count:
if seq_seed is not None:
if seq_seed + i >= max_seed:
break
packet = encoder.create_new_packet(seed=seq_seed + i)
else:
packet = encoder.create_new_packet()
should_drop_packet(dna_rules, packet)
res.append(packet.error_prob)
return res
def encode(file,
asdna=True,
chunk_size=DEFAULT_CHUNK_SIZE,
error_correction=nocode,
insert_header=False,
save_number_of_chunks_in_packet=False,
mode_1_bmp=False,
prepend="",
append="",
upper_bound=0.5,
save_as_fasta=True,
save_as_zip=True,
overhead=0.40):
number_of_chunks = Encoder.get_number_of_chunks_for_file_with_chunk_size(
file, chunk_size)
dist = RaptorDistribution(number_of_chunks)
if asdna:
rules = FastDNARules()
else:
rules = None
x = RU10Encoder(
file,
number_of_chunks,
dist,
insert_header=insert_header,
pseudo_decoder=None,
chunk_size=0,
rules=rules,
error_correction=error_correction,
packet_len_format=PACKET_LEN_FORMAT,
crc_len_format=CRC_LEN_FORMAT,
number_of_chunks_len_format=NUMBER_OF_CHUNKS_LEN_FORMAT,
id_len_format=ID_LEN_FORMAT,
save_number_of_chunks_in_packet=save_number_of_chunks_in_packet,
mode_1_bmp=mode_1_bmp,
prepend=prepend,
append=append,
drop_upper_bound=upper_bound)
x.set_overhead_limit(overhead)
x.encode_to_packets()
if save_as_fasta and asdna:
x.save_packets_fasta(file_ending="_RU10", seed_is_filename=True)
elif save_as_zip:
x.save_packets_zip(save_as_dna=asdna,
file_ending="_RU10",
seed_is_filename=True)
else:
x.save_packets(True,
save_as_dna=asdna,
seed_is_filename=True,
clear_output=True)
return x
def fix_(in_file, out_file_str):
correct = []
rule = FastDNARules()
with open(in_file, "r") as inf:
lines = inf.readlines()
for line in lines[1::2]:
line = line.strip()
if len(line) != 164:
continue
err_prob = rule.apply_all_rules(line)
if err_prob < 1.0:
correct.append((line, err_prob))
with open(out_file_str, "w") as out_file:
for line, err_prob in correct:
out_file.write(f">%s\n" % err_prob)
out_file.write(line.strip().replace("\n", "") + "\n")
cleaned = find_dup_ids(out_file_str)
with open(out_file_str, "w") as out_file:
for line in cleaned:
out_file.write(f">abc\n")
out_file.write(line.strip().replace("\n", "") + "\n")
def encode(file, dist_lst, asdna=True, chunk_size=50):
"""
:param file:
:param dist_lst:
:param asdna:
:param chunk_size:
:return:
"""
packets_needed = 0
packets = dict()
number_of_chunks = Encoder.get_number_of_chunks_for_file_with_chunk_size(file, chunk_size)
dist = RaptorDistribution(number_of_chunks)
dist.f = dist_lst
d = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,
29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40]
dist.d = d
dna_rules = FastDNARules()
if asdna:
rules = dna_rules
else:
rules = None
x = RU10Encoder(file, number_of_chunks, dist, chunk_size=chunk_size, insert_header=False, rules=rules,
error_correction=nocode, id_len_format="H", number_of_chunks_len_format="B",
save_number_of_chunks_in_packet=False, mode_1_bmp=False)
x.prepare()
y = RU10Decoder.pseudo_decoder(x.number_of_chunks, False)
if y.distribution is None: # self.isPseudo and
y.distribution = RaptorDistribution(x.number_of_chunks)
y.distribution.f = dist_lst
y.distribution.d = d
y.number_of_chunks = x.number_of_chunks
_, y.s, y.h = intermediate_symbols(x.number_of_chunks, y.distribution)
y.createAuxBlocks()
n = 0
for p_tmp in range(45):
packets[p_tmp] = list()
while n < number_of_chunks * 50:
pack = x.create_new_packet()
if packets_needed == 0:
y.input_new_packet(pack)
should_drop_packet(dna_rules, pack)
if pack.get_degree() not in packets:
packets[pack.get_degree()] = list()
packets[pack.get_degree()].append(pack.error_prob)
n += 1
if n >= number_of_chunks and y.is_decoded() and packets_needed == 0:
packets_needed = n
# we dont want to break, we want to generate #chunks * XXX packets!
# break
print("Packets created: " + str(sum([len(x) for x in packets.values()])))
return packets, (packets_needed - number_of_chunks) / 100.0
def create_packets_e_prob(start_num: int, normed_dist: ndarray, number_of_packets: int, rules=None):
dist_obj = RaptorDistribution(__NUM_CHUNKS)
dist_obj.f = normed_dist
dist_obj.d = [x for x in range(0, 41)]
encoder = RU10Encoder(file=__FILE, number_of_chunks=__NUM_CHUNKS, distribution=dist_obj, insert_header=False)
encoder.prepare()
if rules is None:
rules = FastDNARules()
packets_e_prob = []
for i in range(start_num, start_num + number_of_packets):
packet = encoder.create_new_packet(seed=i)
should_drop_packet(rules, packet)
packets_e_prob.append(packet.error_prob)
del packet
del encoder
return packets_e_prob
def main(in_file: str,
num_chunks=0,
chunk_size=0,
as_dna=True,
err_correction: typing.Callable[[typing.Any], typing.Any] = nocode,
insert_header=False,
save_number_of_chunks_in_packet=False,
mode_1_bmp=False):
if chunk_size != 0:
num_chunks = Encoder.get_number_of_chunks_for_file_with_chunk_size(
in_file, chunk_size)
dist = RaptorDistribution(num_chunks)
elif num_chunks != 0:
dist = RaptorDistribution.RaptorDistribution(num_chunks)
else:
print("Aborting. Please set either chunk_size or number_of_chunks!")
return
if as_dna:
rules = FastDNARules()
else:
rules = None
x = RU10Encoder(
in_file,
num_chunks,
dist,
chunk_size=chunk_size,
insert_header=insert_header,
rules=rules,
error_correction=err_correction,
id_len_format="H",
number_of_chunks_len_format="B",
save_number_of_chunks_in_packet=save_number_of_chunks_in_packet,
mode_1_bmp=mode_1_bmp)
x.encode_to_packets()
x.save_packets(True, save_as_dna=as_dna, seed_is_filename=False)
conf = {
'error_correction': e_correction,
'repair_symbols': norepair_symbols,
'asdna': as_dna,
'number_of_splits': 0,
'find_minimum_mode': True,
'seq_seed': False
}
x.save_config_file(conf, section_name="RU10_" + in_file)
def encode(self, file, asdna=True, error_correction=nocode, insert_header=False,
save_number_of_chunks_in_packet=False, mode_1_bmp=False, chunk_size=50):
packets_needed = 0
packets = dict()
number_of_chunks = Encoder.get_number_of_chunks_for_file_with_chunk_size(file, chunk_size)
dist = RaptorDistribution(number_of_chunks)
dist.f = self.X
dist.d = self.d
dna_rules = FastDNARules()
if asdna:
rules = dna_rules
else:
rules = None
x = RU10Encoder(file, number_of_chunks, dist, chunk_size=chunk_size, insert_header=insert_header, rules=rules,
error_correction=error_correction, id_len_format="H", number_of_chunks_len_format="B",
save_number_of_chunks_in_packet=save_number_of_chunks_in_packet, mode_1_bmp=mode_1_bmp)
x.prepare()
y = RU10Decoder.pseudo_decoder(x.number_of_chunks, False)
if y.distribution is None: # self.isPseudo and
y.distribution = RaptorDistribution(x.number_of_chunks)
y.distribution.f = self.X
y.distribution.d = self.d
y.number_of_chunks = x.number_of_chunks
_, y.s, y.h = intermediate_symbols(x.number_of_chunks, y.distribution)
y.createAuxBlocks()
n = 0
for p_tmp in range(45):
packets[p_tmp] = list()
while n < number_of_chunks * 50:
pack = x.create_new_packet()
if packets_needed == 0:
y.input_new_packet(pack)
should_drop_packet(dna_rules, pack)
if pack.get_degree() not in packets:
packets[pack.get_degree()] = list()
packets[pack.get_degree()].append(pack.error_prob)
n += 1
if n >= number_of_chunks and y.is_decoded() and packets_needed == 0:
packets_needed = n
# we dont want to break, we want to generate #chunks * XXX packets!
# break
print("Packets created: " + str(sum([len(x) for x in packets.values()])))
return packets, (packets_needed - number_of_chunks) / 100.0
def encode(file, chunk_size, dist, as_dna=True, repeats=15):
"""
Encodes the file to packets until the pseudo decoder was able to decode it 'repeats' times with the given chunk size
and the distribution list.
:param file: File to encode.
:param chunk_size: Chunksize to use.
:param dist: The distribution to calculate the average error and overhead for.
:param as_dna: If true uses the DNA Rules.
:param repeats: Number of En-/Decoding cycles.
:return:
"""
degree_dict = {}
overhead_lst = []
number_of_chunks = Encoder.get_number_of_chunks_for_file_with_chunk_size(file, chunk_size, insert_header=False)
distribution = RaptorDistribution(number_of_chunks)
distribution.f = dist
distribution.d = [x for x in range(0, 41)]
if as_dna:
rules = FastDNARules()
else:
rules = None
encoder = RU10Encoder(file, number_of_chunks, distribution, insert_header=False, rules=rules,
error_correction=nocode, id_len_format="H", number_of_chunks_len_format="B",
save_number_of_chunks_in_packet=False, mode_1_bmp=False)
encoder.prepare()
for _ in range(0, repeats):
encoder.random_state = np.random.RandomState()
# print("Master-Seed used: " + str(encoder.random_state.get_state()[1][0]))
pseudo_decoder = create_pseudo_decoder(encoder.number_of_chunks, distribution)
needed_packets = 0
while pseudo_decoder.GEPP is None or not pseudo_decoder.is_decoded():
needed_packets += 1
packet = encoder.create_new_packet()
pseudo_decoder.input_new_packet(packet)
should_drop_packet(rules, packet)
if packet.get_degree() not in degree_dict:
degree_dict[packet.get_degree()] = list()
degree_dict[packet.get_degree()].append(min(packet.error_prob, 1.0))
overhead = (needed_packets - encoder.number_of_chunks) / 100.0
overhead_lst.append(overhead)
return sum(overhead_lst) / len(overhead_lst), degree_dict
def test_suite(as_dna, decoder_instance):
dir_path = os.getcwd()
try:
os.remove(dir_path + "/" + file)
except:
print("Not deleting, File did not exists")
shutil.copyfile(dir_path + "/" + cmp_file, dir_path + "/" + file)
print(as_dna)
chunksize = 200
number_of_chunks = Encoder.get_number_of_chunks_for_file_with_chunk_size(
file, chunksize)
dist = RaptorDistribution(number_of_chunks)
pseudo_decoder = decoder_instance.pseudo_decoder(
number_of_chunks=number_of_chunks)
rules = FastDNARules() if as_dna else None
encoder = RU10Encoder(file,
number_of_chunks,
dist,
pseudo_decoder=pseudo_decoder,
rules=rules,
id_len_format="H",
number_of_chunks_len_format="H",
insert_header=True)
encoder.encode_to_packets()
encoder.save_packets(split_to_multiple_files=True, save_as_dna=as_dna)
assert (pseudo_decoder.is_decoded() and pseudo_decoder.getSolvedCount()
== pseudo_decoder.number_of_chunks)
assert os.path.exists(out_dir)
decoder = decoder_instance(out_dir)
decoder.decodeFolder(id_len_format="H", number_of_chunks_len_format="H")
if isinstance(decoder, RU10BPDecoder):
for pack in encoder.encodedPackets:
decoder.input_new_packet(pack)
assert decoder.is_decoded() and decoder.getSolvedCount(
) == encoder.number_of_chunks
os.remove(file)
decoder.saveDecodedFile(print_to_output=False)
assert os.path.exists(file) and filecmp.cmp(file, cmp_file)
shutil.rmtree(out_dir)
index.append(key)
data.append(val)
fig, (ax) = plt.subplots(ncols=1)
ax.boxplot(data)
ax.set_xticklabels(index)
plt.show()
plt.plot(num_list)
plt.plot(mean_list)
plt.show()
if __name__ == "__main__":
file = "../.INFILES/Dorn"
chunk_size = 100
norepairsymbols = 6
save_number_of_chunks_in_packet = False
insert_header = False
rules = FastDNARules()
error_correction = lambda x: reed_solomon_encode(x, norepairsymbols)
number_of_chunks = 50
if chunk_size != 0:
number_of_chunks = Encoder.get_number_of_chunks_for_file_with_chunk_size(file, chunk_size)
dist = RaptorDistribution(number_of_chunks)
x = RU10Encoder(file, number_of_chunks, dist, chunk_size=chunk_size, insert_header=insert_header, rules=rules,
error_correction=error_correction, id_len_format="H", number_of_chunks_len_format="B",
save_number_of_chunks_in_packet=save_number_of_chunks_in_packet)
aa = QualityPacketGen(x)
def test_homopolymers(params):
assert fast_comp(DNARules.homopolymers(params[0]), 3) == fast_comp(
FastDNARules.homopolymers(params[0])) == params[1]
def test_c_permutation(params):
assert fast_comp(DNARules.c_permutation(params[0])) == fast_comp(
FastDNARules.c_permutation(params[0])) == params[1]
def test_random_permutations(params):
assert DNARules.random_permutations(
params[0]) == FastDNARules.random_permutations(params[0]) == params[1]
def test_illegal_symbols(params):
assert DNARules.illegal_symbols(params[0]) == FastDNARules.illegal_symbols(
params[0]) == params[1]
def test_trinucleotid_runs(params):
assert fast_comp(DNARules.trinucleotid_runs(params[0])) == fast_comp(FastDNARules.trinucleotid_runs(params[0])) == \
params[1]
def test_long_strands(params):
assert abs(
DNARules.long_strands(params) -
FastDNARules.long_strands(params) < 0.01)
def test_repeat_region(params):
assert fast_comp(DNARules.repeatRegion(params[0])) == fast_comp(
FastDNARules.repeatRegion(params[0])) == params[1]
def test_small_repeat_region(params):
assert fast_comp(DNARules.smallRepeatRegion(params[0])) == fast_comp(FastDNARules.smallRepeatRegion(params[0])) == \
params[1]
def test_gc_content(params):
assert round(DNARules.overall_gc_content(params[0]), 2) == round(FastDNARules.overall_gc_content(params[0]), 2) == \
params[1]
def test_simple_motif_search(params):
assert fast_comp(DNARules.simple_motif_search(params[0])) == fast_comp(
FastDNARules.simple_motif_search(params[0])) == params[1]
def test_motif_regex_search(params):
assert fast_comp(DNARules.motif_regex_search(params[0])) == fast_comp(FastDNARules.motif_regex_search(params[0])) == \
params[1]
def test_suite(as_dna, decoder_instance, distribution, use_header,
implicit_mode):
dir_path = os.getcwd()
try:
os.remove(dir_path + "/" + file)
except:
print("Not deleting, File did not exists")
shutil.copyfile(dir_path + "/" + cmp_file, dir_path + "/" + file)
chunksize = 200
number_of_chunks = Encoder.get_number_of_chunks_for_file_with_chunk_size(
file, chunksize)
pseudo_decoder = decoder_instance.pseudo_decoder(number_of_chunks)
if distribution == "robust":
dist = RobustSolitonDistribution(S=number_of_chunks, delta=0.2, seed=2)
elif distribution == "ideal":
dist = IdealSolitonDistribution(S=number_of_chunks, seed=2)
else:
dist = ErlichZielinskiRobustSolitonDistribution(k=number_of_chunks,
delta=0.2,
seed=2)
rules = FastDNARules() if as_dna else None
encoder = LTEncoder(file,
number_of_chunks,
dist,
chunk_size=chunksize,
pseudo_decoder=pseudo_decoder,
rules=rules,
insert_header=use_header,
number_of_chunks_len_format="H",
id_len_format="H",
used_packets_len_format="H",
implicit_mode=implicit_mode)
encoder.encode_to_packets()
encoder.save_packets(split_to_multiple_files=True, save_as_dna=as_dna)
assert pseudo_decoder.is_decoded() and pseudo_decoder.getSolvedCount(
) == encoder.number_of_chunks
assert os.path.exists(out_dir)
decoder = decoder_instance(out_dir,
use_headerchunk=use_header,
dist=dist,
implicit_mode=implicit_mode)
decoder.decodeFolder(number_of_chunks_len_format="H",
seed_len_format="H",
degree_len_format="H")
assert decoder.is_decoded() and decoder.getSolvedCount(
) == encoder.number_of_chunks
os.remove(file)
decoder.saveDecodedFile(print_to_output=False)
if not use_header:
out_file = "DEC_LT_" + file
else:
out_file = file
assert os.path.exists(out_file) and filecmp.cmp(out_file, cmp_file)
if decoder_instance == LTBPDecoder:
# since ApproxDecoder defines an upper bound Gauss-Decoder MUST be able to decode!
decoder = LTDecoder(out_dir,
use_headerchunk=use_header,
dist=dist,
implicit_mode=implicit_mode)
decoder.decodeFolder(number_of_chunks_len_format="H",
seed_len_format="H",
degree_len_format="H")
assert (decoder.is_decoded()
and decoder.getSolvedCount() == encoder.number_of_chunks)
os.remove(out_file)
decoder.saveDecodedFile(print_to_output=False)
assert os.path.exists(out_file) and filecmp.cmp(out_file, cmp_file)
shutil.rmtree(out_dir)
def run(seq_seed=None,
file='logo.jpg',
repair_symbols=2,
insert_header=False,
error_correction=reed_solomon_encode,
save_number_of_chunks_in_packet=False,
l_size=1000,
while_count=1000,
chunk_size=0,
number_of_chunks=300,
prepend="",
append="",
seed_len_format=DEFAULT_ID_LEN_FORMAT,
number_of_chunks_len_format=DEFAULT_NUMBER_OF_CHUNKS_LEN_FORMAT,
method='RU10',
mode1bmp=False,
drop_above=0.4,
packets_to_create=None):
global counter
if chunk_size != 0:
number_of_chunks = Encoder.get_number_of_chunks_for_file_with_chunk_size(
file, chunk_size)
dna_rules = FastDNARules()
if packets_to_create is None:
packets_to_create = math.pow(2, 8 * struct.calcsize(seed_len_format))
rules = dna_rules
if repair_symbols != 0:
dist, error_correction = get_err_dist(method, number_of_chunks,
repair_symbols)
else:
dist = RaptorDistribution(number_of_chunks)
if method == 'RU10':
x = RU10Encoder(
file,
number_of_chunks,
dist,
chunk_size=chunk_size,
insert_header=insert_header,
rules=rules,
error_correction=error_correction,
id_len_format=seed_len_format,
number_of_chunks_len_format=number_of_chunks_len_format,
save_number_of_chunks_in_packet=save_number_of_chunks_in_packet,
mode_1_bmp=mode1bmp,
prepend=prepend,
append=append)
x.prepare()
elif method == 'LT':
x = LTEncoder(
file,
number_of_chunks,
dist,
chunk_size=chunk_size,
insert_header=insert_header,
rules=rules,
error_correction=error_correction,
number_of_chunks_len_format=number_of_chunks_len_format,
id_len_format=seed_len_format,
save_number_of_chunks_in_packet=save_number_of_chunks_in_packet)
x.prepareEncoder()
elif method == 'Online':
number_of_chunks = dist.get_size()
x = OnlineEncoder(
file,
number_of_chunks,
dist,
ONLINE_EPS,
ONLINE_QUALITY,
error_correction=error_correction,
quality_len_format="B",
insert_header=False,
check_block_number_len_format=seed_len_format,
number_of_chunks_len_format=number_of_chunks_len_format,
rules=rules,
save_number_of_chunks_in_packet=False)
x.prepare()
else:
raise NotImplementedError("Choose: RU10, LT or Online")
i = 0
tmp_list = []
while i < while_count:
if seq_seed is not None:
if seq_seed + i >= packets_to_create:
break
packet = x.create_new_packet(seed=seq_seed + i)
else:
packet = x.create_new_packet()
if i == 0:
print(f"%i , %s" % (len(
packet.get_dna_struct(True)), packet.get_dna_struct(True)))
_ = should_drop_packet(rules, packet)
if packet.error_prob <= drop_above and (
len(tmp_list) < l_size
or packet.error_prob < tmp_list[-1].error_prob):
if packet not in tmp_list:
bisect.insort_left(tmp_list, packet)
else:
elem = next((x for x in tmp_list if x == packet), None)
if packet < elem:
tmp_list.remove(elem)
del elem
bisect.insort_left(tmp_list, packet)
if len(tmp_list) > l_size:
for ele1m in tmp_list[l_size + 1:]:
del ele1m
tmp_list = tmp_list[:l_size]
else:
del packet
i += 1
# += operation is not atomic, so we need to get a lock:
with counter.get_lock():
counter.value += 1
# save_packets_fasta(tmp_list, out_file=method + "_out_partial", file_ending="." + method + "_DNA",
# clear_output=False)
conf = {
'error_correction': error_correction,
'repair_symbols': repair_symbols,
'number_of_splits': _number_of_splits,
'find_minimum_mode': True,
'seq_seed': seq_seed
}
# x.save_config_file(conf, section_name=method + "_" + file)
if x.progress_bar is not None:
x.progress_bar.finish()
return [ParallelPacket.from_packet(p) for p in tmp_list]
def run(seq_seed=None,
file='logo.jpg',
asdna=True,
insert_header=False,
error_correction=reed_solomon_encode,
save_number_of_chunks_in_packet=False,
l_size=1000,
while_count=1000,
chunk_size=0,
number_of_chunks=300,
prepend="",
append="",
seed_len_format=ID_LEN_FORMAT,
drop_above=1.0):
if chunk_size != 0:
number_of_chunks = Encoder.get_number_of_chunks_for_file_with_chunk_size(
file, chunk_size)
dist = RaptorDistribution(number_of_chunks)
dna_rules = FastDNARules()
if asdna:
rules = dna_rules
else:
rules = None
x = RU10Encoder(
file,
number_of_chunks,
dist,
chunk_size=chunk_size,
insert_header=insert_header,
rules=rules,
error_correction=error_correction,
id_len_format=seed_len_format,
number_of_chunks_len_format=NUMBER_OF_CHUNKS_LEN_FORMAT,
save_number_of_chunks_in_packet=save_number_of_chunks_in_packet,
prepend=prepend,
append=append)
x.prepare()
i = 0
tmp_list = []
while i < while_count:
if seq_seed is not None:
if seq_seed + i >= NUMBER_OF_PACKETS_TO_CREATE:
break
packet = x.create_new_packet(seed=seq_seed + i)
else:
packet = x.create_new_packet()
should_drop_packet(rules, packet)
if packet.error_prob <= drop_above and (
len(tmp_list) < l_size
or packet.error_prob < tmp_list[-1].error_prob):
if packet not in tmp_list:
bisect.insort_left(tmp_list, packet)
else:
elem = next((x for x in tmp_list if x == packet), None)
if packet < elem:
tmp_list.remove(elem)
bisect.insort_left(tmp_list, packet)
if len(tmp_list) > l_size:
tmp_list = tmp_list[:l_size]
i += 1
print([x.error_prob for x in tmp_list])
conf = {
'error_correction': e_correction,
'repair_symbols': _repair_symbols,
'asdna': asdna,
'number_of_splits': _number_of_splits,
'find_minimum_mode': True,
'seq_seed': seq_seed
}
x.save_config_file(conf, section_name="RU10_" + file)
return [ParallelPacket.from_packet(p) for p in tmp_list]
def test_windowed_gc_content(params):
assert abs(
DNARules.windowed_gc_content(params) -
FastDNARules.windowed_gc_content(params)) == 0
