def bulk_search(
self,
fasta: hug.types.text,
threshold: hug.types.float_number = 1.0,
config: hug.types.text = None,
score: hug.types.smart_boolean = False,
format: hug.types.one_of(["json", "csv"]) = "json",
stream: hug.types.smart_boolean = False,
):
config = get_config_from_file(config)
fasta = Fasta(fasta)
if not stream:
_config = copy.copy(config)
_config["nproc"] = 1
csv_combined = ""
nproc = config.get("nproc", 1)
with multiprocessing.Pool(processes=nproc) as pool:
args = [(_config, str(seq), threshold, score)
for seq in fasta.values()]
dd = pool.map_async(search_bigsi_parallel,
chunks(args, math.ceil(len(args) /
nproc))).get()
dd = [item for sublist in dd for item in sublist]
if format == "csv":
return "\n".join([d_to_csv(d, False, False) for d in dd])
else:
return json.dumps(dd, indent=4)
else:
bigsi = BIGSI(config)
csv_combined = ""
for i, seq in enumerate(fasta.values()):
seq = str(seq)
d = {
"query": seq,
"threshold": threshold,
"results": bigsi.search(seq, threshold, score),
"citation": "http://dx.doi.org/10.1038/s41587-018-0010-1",
}
if format == "csv":
if i == 0:
with_header = True
carriage_return = False
elif i == len(fasta) - 1:
carriage_return = True
else:
with_header = False
carriage_return = False
csv_result = d_to_csv(d, with_header, carriage_return)
csv_combined += csv_result
if stream:
print(csv_result)
else:
if stream:
print(json.dumps(d))
def bulk_search(
self,
fasta: hug.types.text,
threshold: hug.types.float_number = 1.0,
config: hug.types.text = None,
score: hug.types.smart_boolean = False,
format: hug.types.one_of(["json", "csv"]) = "json",
stream: hug.types.smart_boolean = False,
):
config = get_config_from_file(config)
bigsi = BIGSI(config)
fasta = Fasta(fasta)
if not stream:
csv_combined = ""
nproc = config.get("nproc", 1)
with ThreadPool(processes=nproc) as pool:
args = [(bigsi, str(seq), threshold, score)
for seq in fasta.values()]
dd = pool.starmap(search_bigsi, args)
if format == "csv":
return "\n".join([d_to_csv(d, False, False) for d in dd])
else:
return json.dumps(dd, indent=4)
else:
dd = []
csv_combined = ""
for i, seq in enumerate(fasta.values()):
seq = str(seq)
d = {
"query": seq,
"threshold": threshold,
"results": bigsi.search(seq, threshold, score),
"citation": "http://dx.doi.org/10.1038/s41587-018-0010-1",
}
dd.append(d)
if format == "csv":
if i == 0:
with_header = True
carriage_return = False
elif i == len(fasta) - 1:
carriage_return = True
else:
with_header = False
carriage_return = False
csv_result = d_to_csv(d, with_header, carriage_return)
csv_combined += csv_result
if stream:
print(csv_result)
else:
if stream:
print(json.dumps(d))
def search(self,
seq: hug.types.text,
threshold: hug.types.float_number = 1.0,
config: hug.types.text = None,
score: hug.types.smart_boolean = False,
format: hug.types.one_of(["json", "csv"]) = "json"):
config = get_config_from_file(config)
bigsi = BIGSI(config)
d = {
"query": seq,
"threshold": threshold,
"results": bigsi.search(seq, threshold, score),
"citation": "http://dx.doi.org/10.1038/s41587-018-0010-1"
}
if format == "csv":
return d_to_csv(d)
else:
return json.dumps(d, indent=4)