def processfiles(self):
self.logger.info("Process Files Inititated")
self.counter = 0
self.targets = []
self.masterdf = pd.DataFrame(columns=['seqid', 'position', 'coverage'])
while self.running:
currenttime = time.time()
#for fastqfile, createtime in tqdm(sorted(self.creates.items(), key=lambda x: x[1])):
fastqfilelist = list()
for fastqfile, createtime in sorted(self.creates.items(),
key=lambda x: x[1]):
delaytime = 0
# file created 5 sec ago, so should be complete. For simulations we make the time longer.
if (int(createtime) + delaytime < time.time()):
self.logger.info(fastqfile)
del self.creates[fastqfile]
self.counter += 1
fastqfilelist.append(fastqfile)
#print (fastqfile,md5Checksum(fastqfile), "\n\n\n\n")
targets, self.masterdf = parse_fastq_file(fastqfilelist, self.args,
logging, self.masterdf)
print(targets)
print(self.targets)
if len(targets) > len(self.targets):
updated_targets = set(targets) - set(self.targets)
update_message = "Updating targets with {}".format(
nice_join(updated_targets, conjunction="and"))
self.logger.info(update_message)
if not self.args.simulation:
send_message(self.connection, update_message,
Severity.WARN)
write_new_toml(self.args, targets)
self.targets = []
self.targets = targets.copy()
if self.masterdf.shape[0] > 0 and self.masterdf.shape[0] == len(
self.targets):
# Every target is covered at the desired coverage level.
self.logger.info(
"Every target is covered at at least {}x".format(
self.args.depth))
if not self.args.simulation:
self.connection.protocol.stop_protocol()
send_message(
self.connection,
"Iter Align has stopped the run as all targets should be covered by at least {}x"
.format(self.args.depth),
Severity.WARN,
)
#parse_fastq_file(fastqfile, self.rundict, self.fastqdict, self.args, self.header, self.MinotourConnection)
#self.args.files_processed += 1
if currenttime + 5 > time.time():
time.sleep(5)
def simple_analysis(client,
batch_size=512,
throttle=0.1,
unblock_duration=0.1):
"""Analysis function
Parameters
----------
client : read_until.ReadUntilClient
An instance of the ReadUntilClient object
batch_size : int
The number of reads to be retrieved from the ReadUntilClient at a time
throttle : int or float
The number of seconds interval between requests to the ReadUntilClient
unblock_duration : int or float
Time, in seconds, to apply unblock voltage
Returns
-------
None
"""
logger = logging.getLogger(__name__)
send_message(
client.connection,
"Read Until sending Unblock All Messages. All reads will be prematurely truncated. This will affect a live sequencing run.",
Severity.WARN)
while client.is_running:
r = 0
t0 = timer()
for r, (channel, read) in enumerate(
client.get_read_chunks(
batch_size=batch_size,
last=True,
),
start=1,
):
# pass
client.unblock_read(channel,
read.number,
read_id=read.id,
duration=unblock_duration)
client.stop_receiving_read(channel, read.number)
t1 = timer()
if r:
logger.info("Took {:.6f} for {} reads".format(t1 - t0, r))
# limit the rate at which we make requests
if t0 + throttle > t1:
time.sleep(throttle + t0 - t1)
else:
send_message(client.connection, "Read Until Unblock All Disconnected.",
Severity.WARN)
logger.info("Finished analysis of reads as client stopped.")
def run(parser, args):
args.tomlfile = args.toml
args.toml = toml.load(args.toml)
print(args)
# TODO: Move logging config to separate configuration file
# set up logging to file
logging.basicConfig(
level=logging.DEBUG,
format='%(levelname)s::%(asctime)s::%(name)s::%(message)s',
filename=args.log_file,
filemode='w')
# define a Handler that writes INFO messages or higher to the sys.stderr
console = logging.StreamHandler()
console.setLevel(logging.INFO)
# set a format which is simpler for console use
formatter = logging.Formatter('%(name)-15s: %(levelname)-8s %(message)s')
console.setFormatter(formatter)
# add the handler to the root logger
logging.getLogger('').addHandler(console)
# Start by logging sys.argv and the parameters used
logger = logging.getLogger("Manager")
logger.info(" ".join(sys.argv))
print_args(args, logger=logger)
logger.info("Initialising iterAlign.")
logger.info("Setting up FastQ monitoring.")
#### Check if a run is active - if not, wait.
args.simulation = True
connection = None
if args.watch is None:
args.simulation = False
logger.info("Creating rpc connection for device {}.".format(
args.device))
try:
connection, messageport = get_rpc_connection(args.device)
except ValueError as e:
print(e)
sys.exit(1)
send_message(connection, "Iteralign Connected to MinKNOW",
Severity.WARN)
logger.info("Loaded RPC")
while parse_message(connection.acquisition.current_status()
)['status'] != "PROCESSING":
time.sleep(1)
#### Check if we know where data is being written to , if not... wait
args.watch = parse_message(connection.acquisition.get_acquisition_info(
))['config_summary']['reads_directory']
else:
messageport = ""
event_handler = FastqHandler(args, logging, messageport, connection)
# This block handles the fastq
observer = Observer()
observer.schedule(event_handler, path=args.watch, recursive=True)
observer.daemon = True
try:
observer.start()
logger.info("FastQ Monitoring Running.")
while 1:
time.sleep(1)
except KeyboardInterrupt:
logger.info("Exiting - Will take a few seconds to clean up!")
observer.stop()
observer.join()
os._exit(0)
def run(parser, args):
# new code block: change the reference path within the args.toml file into the args.mindex path
d = toml.load(args.toml)
print(d["conditions"]["reference"])
args.tomlfile = args.toml
args.toml = toml.load(args.toml)
print(args)
# TODO: Move logging config to separate configuration file
# set up logging to file
logging.basicConfig(level=logging.DEBUG,
format='%(levelname)s::%(asctime)s::%(name)s::%(message)s',
filename=args.log_file,
filemode='w')
# define a Handler that writes INFO messages or higher to the sys.stderr
console = logging.StreamHandler()
console.setLevel(logging.INFO)
# set a format which is simpler for console use
formatter = logging.Formatter('%(name)-15s: %(levelname)-8s %(message)s')
console.setFormatter(formatter)
# add the handler to the root logger
logging.getLogger('').addHandler(console)
# Start by logging sys.argv and the parameters used
logger = logging.getLogger("Manager")
logger.info(" ".join(sys.argv))
print_args(args, logger=logger)
logger.info("Initialising iterAlign.")
logger.info("Setting up FastQ monitoring.")
#### Check if a run is active - if not, wait.
args.simulation = True
connection = None
#set default message severity level.
severity = 2
if args.watch is None:
args.simulation = False
logger.info("Creating rpc connection for device {}.".format(args.device))
try:
connection, messageport = get_rpc_connection(args.device)
except ValueError as e:
print(e)
sys.exit(1)
#send_message_port("Iteralign Connected to MinKNOW", args.host, messageport)
send_message(connection, "Iteralign Connected to MinKNOW.", Severity.WARN)
logger.info("Loaded RPC")
while parse_message(connection.acquisition.current_status())['status'] != "PROCESSING":
time.sleep(1)
### Check if we know where data is being written to , if not... wait
args.watch = parse_message(connection.acquisition.get_acquisition_info())['config_summary'][
'reads_directory']
else:
messageport = ""
event_handler = FastqHandler(args, logging, messageport, connection)
# This block handles the fastq
observer = Observer()
observer.schedule(event_handler, path=args.watch, recursive=True)
observer.daemon = True
try:
observer.start()
logger.info("FastQ Monitoring Running.")
while 1:
time.sleep(1)
except KeyboardInterrupt:
logger.info("Exiting - Will take a few seconds to clean up!")
observer.stop()
observer.join()
if args.keepfiles:
logging.info("The 'keepfiles' argument was set, files generated by classifier have been retained")
else:
if os.path.isdir(args.path):
for path, dirs, files in os.walk(args.path):
for f in files:
if f.startswith(args.prefix):
os.unlink(f)
logging.info("file removed: {}".format(f))
if os.path.isdir("./"):
for path, dirs, files in os.walk("./"):
for f in files:
if f.endswith(args.creport):
os.unlink(f)
logging.info("file removed: {}".format(f))
logging.info("All files generated by classifier have been removed.")
os._exit(0)
def processfiles(self):
self.logger.info("Process Files Inititated")
self.counter = 1
self.targets = []
self.masterdf = pd.DataFrame(columns=['seqid', 'position', 'coverage'])
self.taxid_entries = 0
self.downloaded_set = set()
self.length_dict = {}
self.coverage_sum = {}
if self.args.references:
logging.info("References argument provided. Will download references genomes.")
self.downloaded_set = set(self.args.references)
logging.info(self.downloaded_set)
self.url_list = url_list_generation(self.args, self.args.references)
self.length_dict.update(download_references(self.args, self.url_list, self.downloaded_set))
generate_mmi(self.args, self.counter)
while self.running:
currenttime = time.time()
# for fastqfile, createtime in tqdm(sorted(self.creates.items(), key=lambda x: x[1])):
fastqfilelist = list()
for fastqfile, createtime in sorted(self.creates.items(), key=lambda x: x[1]):
delaytime = 0
# file created 5 sec ago, so should be complete. For simulations we make the time longer.
if (int(createtime) + delaytime < time.time()):
self.logger.info(fastqfile)
del self.creates[fastqfile]
self.counter += 1
fastqfilelist.append(fastqfile)
# print (fastqfile,md5Checksum(fastqfile), "\n\n\n\n")
# as long as there are files within the args.watch directory to parse
if fastqfilelist:
print(self.downloaded_set)
targets, self.downloaded_set, self.taxid_entries, self.coverage_sum = parse_fastq_file(fastqfilelist, self.args, logging, self.length_dict, self.downloaded_set, self.taxid_entries, self.coverage_sum, self.connection)
print(targets)
print(self.targets)
if len(targets) > len(self.targets):
updated_targets = set(targets) - set(self.targets)
update_message = "Updating targets with {}".format(nice_join(updated_targets, conjunction="and"))
self.logger.info(update_message)
if not self.args.simulation:
#send_message_port(update_message, self.args.host, self.messageport)
send_message(self.connection, update_message, Severity.WARN)
write_new_toml(self.args, targets)
self.targets = []
self.targets = targets.copy()
if self.masterdf.shape[0] > 0 and self.masterdf.shape[0] == len(self.targets):
# Every target is covered at the desired coverage level.
self.logger.info("Every target is covered at at least {}x".format(self.args.depth))
if not self.args.simulation:
self.connection.protocol.stop_protocol()
#send_message_port(
# "Iter Align has stopped the run as all targets should be covered by at least {}x".format(
# self.args.depth), self.args.host, self.messageport)
send_message(self.connection, "Iter Align has stopped the run as all targets should be covered by at least {}x".format(
self.args.depth), Severity.WARN)
# parse_fastq_file(fastqfile, self.rundict, self.fastqdict, self.args, self.header, self.MinotourConnection)
# self.args.files_processed += 1
if currenttime + 5 > time.time():
time.sleep(5)
def parse_fastq_file(fastqfileList, args, logging, length_dict, taxID_set, counter, coverage_sum, connection):
logger = logging.getLogger("ParseFastq")
logger.info(fastqfileList)
logger.info(args.toml['conditions']['reference'])
with open(os.devnull, 'w') as devnull:
# convert the 'fastqfileList' into a string valid for the list of fastq files to be read by centrifuge
fastq_str = ",".join(fastqfileList)
# centrifuge command to classify reads in the fastq files found by watchdog
centrifuge_cmd = "centrifuge -p {} -x {} -q {}".format(args.threads,
args.cindex,
fastq_str
)
# show what the centrifuge command in the terminal
logging.info(centrifuge_cmd)
# start time of centrifuge to track the time centrifuge requires to classify reads
centrifuge_start_time = time.time()
# subprocess for 'centrifuge_cmd'
proc = subprocess.Popen(
centrifuge_cmd,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
stdin=subprocess.PIPE,
shell=True,
# Aliased by `text=True` in 3.7
universal_newlines=True,
)
out, err = proc.communicate()
proc.stdout.close()
# confirm that the centrifuge pipeline has finished and show the time of execusion
logging.info("Post centrifuge run: {} seconds".format(time.time()-centrifuge_start_time))
output_fields = ["readID", "seqID", "taxID", "hitLength", "queryLength", "numMatches"]
# create the DataFrame from the centrifuge output using 'output_fields' as the column headers
out_df = pd.read_csv(StringIO(out),
sep="\t",
usecols=output_fields
)
# create a dataframe from the args.creport 'centrifuge_report.tsv'
report_df = pd.read_csv(args.creport,
sep="\t",
usecols=["name", "genomeSize", "taxID"],
)
# merge both dataframes together
new_df = pd.merge(out_df, report_df, on="taxID")
# only reads that uniquely align to a species will be used, with reads that numMatches != 1 being filtered
reject_df = new_df[new_df.numMatches != 1]
reject_df["reason"] = "Multiple reference genome alignments"
# reject_df.loc[:, "reason"] = "Multiple reference genome alignments"
# genomeSize == 0 infer a read classification above the species taxon and are therefore removed
intermediate_df = new_df[new_df.genomeSize == 0]
intermediate_df = intermediate_df[intermediate_df.numMatches == 1]
intermediate_df["reason"] = "Read aligns to non-species taxon"
# log the reads that were removed into a seperate file
reject_df = reject_df.append(intermediate_df)
if os.path.isfile(args.path + args.prefix + args.reject):
reject_df.to_csv(args.path + args.prefix + args.reject,
sep="\t",
mode="a",
index=False,
header=None,
)
else:
reject_df.to_csv(args.path + args.prefix + args.reject,
sep="\t",
mode="a",
index=False,
header=True,
)
# reads that uniquely align to one species are logged into a file
new_df = new_df[new_df.numMatches == 1]
if os.path.isfile(args.path + args.prefix + args.readfile):
new_df.to_csv(args.path + args.prefix + args.readfile,
sep="\t",
mode="a",
index=None,
header=None,
)
else:
new_df.to_csv(args.path + args.prefix + args.readfile,
sep="\t",
mode="a",
index=None,
header=True,
)
logging.info("reject file made")
# read in all valid reads back into memory from all current iterations
all_reads_df = pd.read_csv(args.path + args.prefix + args.readfile,
sep="\t"
)
# count the number of reads that were clasified to all taxIDs found within 'all_reads_df' and only keep the taxIDs with a count above args.threshold
taxid_count = all_reads_df.groupby("taxID").agg({"taxID": "count"})
taxid_result_set = set(taxid_count[taxid_count["taxID"].ge(args.threshold)].index.values.tolist())
# call in the taxIDs that already have a reference genome in the mmi
downloaded_set = taxID_set
# if taxIDs were found above args.threshold
if taxid_result_set:
logging.info("taxIDs found above specified threshold")
# locate new taxIDs that were not found in previous iterations
difference_set = taxid_result_set - downloaded_set
downloaded_set |= taxid_result_set
# if novel taxIDs were found
if difference_set:
with open(args.path + args.prefix + args.tidfile, "w+") as f:
d = toml.load(args.path + args.prefix + args.tidfile)
if os.path.isfile(args.tidfile):
d["taxid"] = {"iteration.{}".format(counter): difference_set}
else:
d = {"taxid": {"iteration.{}".format(counter): difference_set}}
toml.dump(d, f)
f.close()
logging.info("new taxids found: {}".format(difference_set))
logging.info("Downloading reference genomes")
url_list = url_list_generation(args, difference_set)
# download the reference genomes into a single file
length_dict.update(download_references(args, url_list, difference_set))
logging.info("Generating mmi")
generate_mmi(args, counter)
update_message = "Updated the minimap MMI to {}".format(args.toml['conditions']['reference'])
logging.info(update_message)
if not args.simulation:
#send_message_port(update_message, args.host, messageport)
send_message(connection, update_message, Severity.WARN)
else:
# show in terminal that no new taxIDs were found
logging.info("No new taxIDs were identified")
else:
logging.info("No taxIDs found above threshold.")
# all of the new code has been parsed, the rest follows standard 'iteralign.py' script
logging.info("new code parsing completed\n\ncommencing iteralign")
minimapcmd = ["minimap2","-ax","map-ont","-t {}".format(args.threads),args.toml['conditions']['reference']] #" ".join(fastqfilelist)]
minimapcmd.extend(fastqfileList)
logging.info(" ".join(minimapcmd))
minimapoutput = subprocess.Popen(minimapcmd, stdout=subprocess.PIPE,stderr=devnull)
samcmd = ["samtools","view", "-bS"]
samoutput = subprocess.Popen(samcmd, stdin=minimapoutput.stdout, stdout=subprocess.PIPE, stderr=devnull)
#samsortcmd = ["samtools", "sort", "-@2", "-o", "sortedbam.bam"]
samsortcmd = ["samtools", "sort", "-@{}".format(args.threads)]
samsortoutput = subprocess.Popen(samsortcmd, stdin=samoutput.stdout, stdout=subprocess.PIPE, stderr=devnull)
samdepthcmd = ["samtools", "depth", "/dev/stdin"]
samdepthoutput = subprocess.Popen(samdepthcmd, stdin=samsortoutput.stdout,stdout=subprocess.PIPE, stderr=devnull, universal_newlines=True)
minimapoutput.stdout.close()
samoutput.stdout.close()
samsortoutput.stdout.close()
iter_depth = (l.strip().split("\t") for l in samdepthoutput.stdout)
parse_iter = ((x[0], int(x[-1])) for x in iter_depth)
d = defaultdict(int)
d.update(coverage_sum)
for name, depth in parse_iter:
d[name] += depth
depth_dict = {k: d[k]/length_dict[k] for k in length_dict.keys() & d}
with open(args.path + args.prefix + args.coveragefile, "a") as fh:
for k, v in depth_dict.items():
fh.write("{}\t{}\t{}\n".format(counter, k, v))
targets = [k for k, v in depth_dict.items() if v > args.depth]
logging.info(targets)
counter += 1
logging.info("Finished processing {}.".format(" ".join(fastqfileList)))
return targets, downloaded_set, counter, d
def simple_analysis(
client,
batch_size=512,
throttle=0.1,
unblock_duration=0.5,
cl=None,
pf=None,
live_toml_path=None,
flowcell_size=512,
dry_run=False,
run_info=None,
conditions=None,
mapper=None,
caller_kwargs=None,
):
"""Analysis function
Parameters
----------
client : read_until.ReadUntilClient
An instance of the ReadUntilClient object
batch_size : int
The number of reads to be retrieved from the ReadUntilClient at a time
throttle : int or float
The number of seconds interval between requests to the ReadUntilClient
unblock_duration : int or float
Time, in seconds, to apply unblock voltage
cl : logging.Logger
Log file to log chunk data to
pf : logging.Logger
Log file to log alignments to
live_toml_path : str
Path to a `live` TOML configuration file for read until. If this exists when
the run starts it will be deleted
flowcell_size : int
The number of channels on the flowcell, 512 for MinION and 3000 for PromethION
dry_run : bool
If True unblocks are replaced with `stop_receiving` commands
run_info : dict
Dictionary of {channel: index} where index corresponds to an index in `conditions`
conditions : list
Experimental conditions as List of namedtuples.
mapper : mappy.Aligner
caller_kwargs : dict
Returns
-------
None
"""
# Init logger for this function
logger = logging.getLogger(__name__)
# Delete live TOML file if it exists
live_toml_path = Path(live_toml_path)
if live_toml_path.is_file():
live_toml_path.unlink()
# TODO: test this
# Write channels.toml
d = {
"conditions": {
str(v): {
"channels": [],
"name": conditions[v].name
}
for k, v in run_info.items()
}
}
for k, v in run_info.items():
d["conditions"][str(v)]["channels"].append(k)
channels_out = str(client.mk_run_dir / "channels.toml")
with open(channels_out, "w") as fh:
fh.write(
"# This file is written as a record of the condition each channel is assigned.\n"
)
fh.write(
"# It may be changed or overwritten if you restart Read Until.\n")
fh.write("# In the future this file may become a CSV file.\n")
toml.dump(d, fh)
caller = Caller(**caller_kwargs)
# What if there is no reference or an empty MMI
# DefaultDict[int: collections.deque[Tuple[str, ndarray]]]
# tuple is (read_id, previous_signal)
# TODO: tuple should use read_number instead
previous_signal = defaultdict(functools.partial(deque, maxlen=1))
# count how often a read is seen
tracker = defaultdict(Counter)
# decided
decided_reads = {}
strand_converter = {1: "+", -1: "-"}
read_id = ""
# TODO: partial-ise / lambda unblock to take the unblock duration
if dry_run:
decision_dict = {
"stop_receiving": client.stop_receiving_read,
"proceed": None,
"unblock": client.stop_receiving_read,
}
send_message(client.connection,
"This is a test run. No unblocks will occur.",
Severity.WARN)
else:
decision_dict = {
"stop_receiving":
client.stop_receiving_read,
"proceed":
None,
"unblock":
lambda c, n: client.unblock_read(c, n, unblock_duration, read_id),
}
send_message(client.connection,
"This is a live run. Unblocks will occur.", Severity.WARN)
decision_str = ""
below_threshold = False
exceeded_threshold = False
l_string = (
"client_iteration",
"read_in_loop",
"read_id",
"channel",
"read_number",
"seq_len",
"counter",
"mode",
"decision",
"condition",
"min_threshold",
"count_threshold",
"start_analysis",
"end_analysis",
"timestamp",
)
cl.debug("\t".join(l_string))
l_string = "\t".join(("{}" for _ in l_string))
loop_counter = 0
while client.is_running:
if live_toml_path.is_file():
# Reload the TOML config from the *_live file
run_info, conditions, new_reference, _ = get_run_info(
live_toml_path, flowcell_size)
# Check the reference path if different from the loaded mapper
if new_reference != mapper.index:
old_reference = mapper.index
# Log to file and MinKNOW interface
logger.info("Reloading mapper")
send_message(client.connection,
"Reloading mapper. Read Until paused.",
Severity.INFO)
# Update mapper client.
mapper = CustomMapper(new_reference)
# Log on success
logger.info("Reloaded mapper")
# If we've reloaded a reference, delete the previous one
if old_reference:
logger.info("Deleting old mmi {}".format(old_reference))
# We now delete the old mmi file.
Path(old_reference).unlink()
logger.info("Old mmi deleted.")
# TODO: Fix the logging to just one of the two in use
if not mapper.initialised:
time.sleep(throttle)
continue
loop_counter += 1
t0 = timer()
r = 0
for read_info, read_id, seq_len, results in mapper.map_reads_2(
caller.basecall_minknow(
reads=client.get_read_chunks(batch_size=batch_size,
last=True),
signal_dtype=client.signal_dtype,
prev_signal=previous_signal,
decided_reads=decided_reads,
)):
r += 1
read_start_time = timer()
channel, read_number = read_info
if read_number not in tracker[channel]:
tracker[channel].clear()
tracker[channel][read_number] += 1
mode = ""
exceeded_threshold = False
below_threshold = False
log_decision = lambda: cl.debug(
l_string.format(
loop_counter,
r,
read_id,
channel,
read_number,
seq_len,
tracker[channel][read_number],
mode,
getattr(conditions[run_info[channel]], mode, mode),
conditions[run_info[channel]].name,
below_threshold,
exceeded_threshold,
read_start_time,
timer(),
time.time(),
))
# Control channels
if conditions[run_info[channel]].control:
mode = "control"
log_decision()
client.stop_receiving_read(channel, read_number)
continue
# This is an analysis channel
# Below minimum chunks
if tracker[channel][read_number] <= conditions[
run_info[channel]].min_chunks:
below_threshold = True
# Greater than or equal to maximum chunks
if tracker[channel][read_number] >= conditions[
run_info[channel]].max_chunks:
exceeded_threshold = True
# No mappings
if not results:
mode = "no_map"
hits = set()
for result in results:
pf.debug("{}\t{}\t{}".format(read_id, seq_len, result))
hits.add(result.ctg)
if hits & conditions[run_info[channel]].targets:
# Mappings and targets overlap
coord_match = any(
between(r.r_st, c) for r in results
for c in conditions[run_info[channel]].coords.get(
strand_converter.get(r.strand), {}).get(r.ctg, []))
if len(hits) == 1:
if coord_match:
# Single match that is within coordinate range
mode = "single_on"
else:
# Single match to a target outside coordinate range
mode = "single_off"
elif len(hits) > 1:
if coord_match:
# Multiple matches with at least one in the correct region
mode = "multi_on"
else:
# Multiple matches to targets outside the coordinate range
mode = "multi_off"
else:
# No matches in mappings
if len(hits) > 1:
# More than one, off-target, mapping
mode = "multi_off"
elif len(hits) == 1:
# Single off-target mapping
mode = "single_off"
# This is where we make our decision:
# Get the associated action for this condition
decision_str = getattr(conditions[run_info[channel]], mode)
# decision is an alias for the functions "unblock" or "stop_receiving"
decision = decision_dict[decision_str]
# If max_chunks has been exceeded AND we don't want to keep sequencing we unblock
if exceeded_threshold and decision_str != "stop_receiving":
mode = "exceeded_max_chunks_unblocked"
client.unblock_read(channel, read_number, unblock_duration,
read_id)
# TODO: WHAT IS GOING ON?!
# I think that this needs to change between enrichment and depletion
# If under min_chunks AND any mapping mode seen we unblock
# if below_threshold and mode in {"single_off", "multi_off"}:
if below_threshold and mode in {
"single_on",
"single_off",
"multi_on",
"multi_off",
}:
mode = "below_min_chunks_unblocked"
client.unblock_read(channel, read_number, unblock_duration,
read_id)
# proceed returns None, so we send no decision; otherwise unblock or stop_receiving
elif decision is not None:
decided_reads[channel] = read_id
decision(channel, read_number)
log_decision()
t1 = timer()
if r > 0:
s1 = "{}R/{:.5f}s"
logger.info(s1.format(r, t1 - t0))
# limit the rate at which we make requests
if t0 + throttle > t1:
time.sleep(throttle + t0 - t1)
else:
send_message(client.connection, "Read Until Client Stopped.",
Severity.WARN)
caller.disconnect()
logger.info("Finished analysis of reads as client stopped.")
def main():
extra_args = (
(
"--toml",
dict(
metavar="TOML",
required=True,
help="TOML file specifying experimental parameters",
),
),
("--paf-log", dict(
help="PAF log",
default="paflog.log",
)),
("--chunk-log", dict(
help="Chunk log",
default="chunk_log.log",
)),
)
parser, args = get_parser(extra_args=extra_args, file=__file__)
# set up logging to file for DEBUG messages and above
logging.basicConfig(
level=logging.DEBUG,
format="%(asctime)s %(name)s %(message)s",
filename=args.log_file,
filemode="w",
)
# define a Handler that writes INFO messages or higher to the sys.stderr
console = logging.StreamHandler()
console.setLevel(logging.INFO)
# set a format which is simpler for console use
formatter = logging.Formatter(args.log_format)
console.setFormatter(formatter)
# add the handler to the root logger
logging.getLogger("").addHandler(console)
# Start by logging sys.argv and the parameters used
logger = logging.getLogger("Manager")
logger.info(" ".join(sys.argv))
print_args(args, logger=logger)
# Setup chunk and paf logs
chunk_logger = setup_logger("DEC", log_file=args.chunk_log)
paf_logger = setup_logger("PAF", log_file=args.paf_log)
# Parse configuration TOML
# TODO: num_channels is not configurable here, should be inferred from client
run_info, conditions, reference, caller_kwargs = get_run_info(
args.toml, num_channels=512)
live_toml = Path("{}_live".format(args.toml))
# Load Minimap2 index
logger.info("Initialising minimap2 mapper")
mapper = CustomMapper(reference)
logger.info("Mapper initialised")
read_until_client = read_until.ReadUntilClient(
mk_host=args.host,
mk_port=args.port,
device=args.device,
# one_chunk=args.one_chunk,
filter_strands=True,
# TODO: test cache_type by passing a function here
cache_type=args.read_cache,
cache_size=args.cache_size,
)
send_message(
read_until_client.connection,
"Read Until is controlling sequencing on this device. You use it at your own risk.",
Severity.WARN,
)
for message, sev in describe_experiment(conditions, mapper):
logger.info(message)
send_message(
read_until_client.connection,
message,
sev,
)
"""
This experiment has N regions on the flowcell.
using reference: /path/to/ref.mmi
Region i:NAME (control=bool) has X targets of which Y are found in the reference.
reads will be unblocked when [u,v], sequenced when [w,x] and polled for more data when [y,z].
"""
# FIXME: currently flowcell size is not included, this should be pulled from
# the read_until_client
analysis_worker = functools.partial(
simple_analysis,
read_until_client,
unblock_duration=args.unblock_duration,
throttle=args.throttle,
batch_size=args.batch_size,
cl=chunk_logger,
pf=paf_logger,
live_toml_path=live_toml,
dry_run=args.dry_run,
run_info=run_info,
conditions=conditions,
mapper=mapper,
caller_kwargs=caller_kwargs,
)
results = run_workflow(
read_until_client,
analysis_worker,
args.workers,
args.run_time,
runner_kwargs={
# "min_chunk_size": args.min_chunk_size,
"first_channel": min(args.channels),
"last_channel": max(args.channels),
},
)
# No results returned
send_message(
read_until_client.connection,
"Read Until is disconnected from this device. Sequencing will proceed normally.",
Severity.WARN,
)
def main():
extra_args = (
(
"--toml",
dict(
metavar="TOML",
required=True,
help="TOML file specifying experimental parameters",
),
),
("--paf-log", dict(
help="PAF log",
default="paflog.log",
)),
("--chunk-log", dict(
help="Chunk log",
default="chunk_log.log",
)),
)
parser, args = get_parser(extra_args=extra_args, file=__file__)
# TODO: Move logging config to separate configuration file
# set up logging to file for DEBUG messages and above
logging.basicConfig(
level=logging.DEBUG,
format="%(asctime)s %(name)s %(message)s",
filename=args.log_file,
filemode="w",
)
# define a Handler that writes INFO messages or higher to the sys.stderr
console = logging.StreamHandler()
console.setLevel(logging.INFO)
# set a format which is simpler for console use
formatter = logging.Formatter(args.log_format)
console.setFormatter(formatter)
# add the handler to the root logger
logging.getLogger("").addHandler(console)
# Start by logging sys.argv and the parameters used
logger = logging.getLogger("Manager")
logger.info(" ".join(sys.argv))
print_args(args, logger=logger)
read_until_client = read_until.ReadUntilClient(
mk_host=args.host,
mk_port=args.port,
device=args.device,
# one_chunk=args.one_chunk,
filter_strands=True,
# TODO: test cache_type by passing a function here
cache_type=args.read_cache,
cache_size=args.cache_size,
)
send_message(
read_until_client.connection,
"Read Until is controlling sequencing on this device. You use it at your own risk.",
Severity.WARN,
)
# FIXME: currently flowcell size is not included, this should be pulled from
# the read_until_client
analysis_worker = functools.partial(
simple_analysis,
read_until_client,
unblock_duration=args.unblock_duration,
throttle=args.throttle,
batch_size=args.batch_size,
chunk_log=args.chunk_log,
paf_log=args.paf_log,
toml_path=args.toml,
dry_run=args.dry_run,
)
results = run_workflow(
read_until_client,
analysis_worker,
args.workers,
args.run_time,
runner_kwargs={
# "min_chunk_size": args.min_chunk_size,
"first_channel": min(args.channels),
"last_channel": max(args.channels),
},
)
# No results returned
send_message(
read_until_client.connection,
"Read Until is disconnected from this device. Sequencing will proceed normally.",
Severity.WARN,
)