def run(parser, args):
if args.full_tsv:
files = 0
basecalled_files = 0
stats = defaultdict(list)
print('starting')
for fast5 in Fast5File.Fast5FileSet(args.files):
files += 1
fas = fast5.get_fastas_dict()
if len(fas) > 0:
basecalled_files += 1
for (category, fa) in iteritems(fas):
if fa is not None:
stats[category].append(len(fa.seq))
if category == 'twodirections':
if fast5.is_high_quality():
stats['2D_hq'].append(len(fa.seq))
fast5.close()
print("files\ttotal reads\t%d" % (files))
print("files\ttotal base-called reads\t%d" % (basecalled_files))
for category in sorted(stats.keys()):
sizes = stats[category]
if len(sizes) > 0:
print("%s\ttotal reads\t%d" % (category, len(sizes)))
print("%s\ttotal base pairs\t%d" % (category, sum(sizes)))
print("%s\tmean\t%.2f" % (category, stat.mean(sizes)))
print("%s\tmedian\t%d" % (category, stat.median(sizes)))
print("%s\tmin\t%d" % (category, min(sizes)))
print("%s\tmax\t%d" % (category, max(sizes)))
nxvalues = stat.NX(sizes, [25, 50, 75])
print("%s\tN25\t%d" % (category, nxvalues[25]))
print("%s\tN50\t%d" % (category, nxvalues[50]))
print("%s\tN75\t%d" % (category, nxvalues[75]))
else:
logger.warning("No valid sequences observed.\n")
else:
sizes = []
for fast5 in Fast5File.Fast5FileSet(args.files, group=args.group):
fas = fast5.get_fastas(args.type)
sizes.extend([len(fa.seq) for fa in fas if fa is not None])
fast5.close()
if len(sizes) > 0:
print("total reads\t%d" % (len(sizes)))
print("total base pairs\t%d" % (sum(sizes)))
print("mean\t%.2f" % (stat.mean(sizes)))
print("median\t%d" % (stat.median(sizes)))
print("min\t%d" % (min(sizes)))
print("max\t%d" % (max(sizes)))
nxvalues = stat.NX(sizes, [25, 50, 75])
print("N25\t%d" % (nxvalues[25]))
print("N50\t%d" % (nxvalues[50]))
print("N75\t%d" % (nxvalues[75]))
else:
logger.warning("No valid sequences observed.\n")
def run(parser, args):
print(
"start_time\tchannel_number\tread_number\ttemplate_events\tcomplement_events"
)
for fast5 in Fast5File.Fast5FileSet(args.files):
start_time = fast5.get_start_time()
channel_number = fast5.get_channel_number()
read_number = fast5.get_read_number()
template_events = fast5.get_template_events()
if template_events is not None:
template_len = len(template_events)
else:
template_len = 0
complement_events = fast5.get_complement_events()
if complement_events is not None:
complement_len = len(complement_events)
else:
complement_len = 0
print("%s\t%s\t%s\t%s\t%s" % (start_time, channel_number, read_number,
template_len, complement_len))
fast5.close()
def run(parser, args):
print('\t'.join(['channel', 'filename', 'read_length',
'exp_starttime', 'unix_timestamp', 'duration',
'unix_timestamp_end', 'iso_timestamp', 'day',
'hour', 'minute']))
for fast5 in Fast5File.Fast5FileSet(args.files):
if fast5.is_open:
fq = fast5.get_fastq()
start_time = fast5.get_start_time()
if start_time is None:
logger.warning("No start time for %s!" % (fast5.filename))
fast5.close()
continue
if fq is not None:
read_length = len(fq.seq)
else:
read_length = 0
lt = localtime(start_time)
print("\t".join([str(fast5.get_channel_number()),
fast5.filename,
str(read_length),
str(fast5.get_exp_start_time()),
str(start_time), \
str(fast5.get_duration()),
str(fast5.get_end_time()),
strftime('%Y-%m-%dT%H:%M:%S%z', lt),
strftime('%d', lt),
strftime('%H', lt),
strftime('%M', lt)]))
fast5.close()
def run(parser, args):
tot_reads_per_pore = Counter()
tot_bp_per_pore = Counter()
print("\t".join(['channel_number', 'start_time', 'duration']))
for fast5 in Fast5File.Fast5FileSet(args.files):
if fast5.is_open:
fq = fast5.get_fastq()
start_time = fast5.get_start_time()
if start_time is None:
logger.warning("No start time for %s!" % (fast5.filename))
fast5.close()
continue
pore_id = fast5.get_channel_number()
tot_reads_per_pore[int(pore_id)] += 1
tot_bp_per_pore[int(pore_id)] += len(fq.seq)
print("\t".join(
[str(pore_id),
str(start_time),
str(fast5.get_duration())]))
fast5.close()
if args.plot_type == 'read_count':
plot_performance(parser, args, tot_reads_per_pore)
elif args.plot_type == 'total_bp':
plot_performance(parser, args, tot_bp_per_pore)
def run(parser, args):
start_times = []
read_lengths = []
files_processed = 0
for fast5 in Fast5File.Fast5FileSet(args.files):
if fast5.is_open:
fq = fast5.get_fastq()
start_time = fast5.get_start_time()
if start_time is None:
logger.warning("No start time for %s!" % (fast5.filename))
fast5.close()
continue
start_times.append(start_time)
if fq is not None:
read_lengths.append(len(fq.seq))
else:
read_lengths.append(0)
fast5.close()
files_processed += 1
if files_processed % 100 == 0:
logger.info("%d files processed." % files_processed)
# sort the data by start time
start_times, read_lengths = (list(t) for t in zip(
*sorted(zip(start_times, read_lengths))))
plot_collectors_curve(args, start_times, read_lengths)
def run(parser, args):
if args.read:
for i, fast5 in enumerate(Fast5File.Fast5FileSet(args.files)):
for metadata_dict in fast5.read_metadata:
if i == 0:
header = metadata_dict.keys()
print("\t".join(["filename"] + header))
print("\t".join([fast5.filename] +
[str(metadata_dict[k]) for k in header]))
else:
print("asic_id\tasic_temp\theatsink_temp")
for fast5 in Fast5File.Fast5FileSet(args.files):
asic_temp = fast5.get_asic_temp()
asic_id = fast5.get_asic_id()
heatsink_temp = fast5.get_heatsink_temp()
print(
"%s\t%s\t%s" %
(asic_id.decode(), asic_temp.decode(), heatsink_temp.decode()))
fast5.close()
def run(parser, args):
qual_count = Counter()
total_nucs = 0
for fast5 in Fast5File.Fast5FileSet(args.files):
fq = fast5.get_fastq()
if fq is not None:
for q in fq.qual:
qual_count[ord(q)-33] += 1
total_nucs += 1
fast5.close()
for q in qual_count:
print('\t'.join(str(s) for s in [chr(q+33), q, qual_count[q],
total_nucs, float(qual_count[q]) / float(total_nucs)]))
def run(parser, args):
fast5_set = Fast5File.Fast5FileSet(args.files)
first_fast5 = fast5_set.__next__()
for fast5 in fast5_set:
# only create a squiggle plot for multiple reads if saving to file.
if args.saveas is None:
sys.exit("""Please use --saveas when plotting"""
""" multiple FAST5 files as input.\n""")
if first_fast5 is not None:
do_plot_squiggle(args, first_fast5)
first_fast5 = None
do_plot_squiggle(args, fast5)
if first_fast5 is not None:
do_plot_squiggle(args, first_fast5)
def run(parser, args):
for fast5 in Fast5File.Fast5FileSet(args.files, args.group):
if args.start_time or args.end_time:
read_start_time = fast5.get_start_time()
read_end_time = fast5.get_end_time()
if args.start_time and args.start_time > read_start_time:
fast5.close()
continue
if args.end_time and args.end_time < read_end_time:
fast5.close()
continue
fas = fast5.get_fastas(args.type)
# high quality 2D: means there are more nanopore events on the
# complement strand than on the template strand. We also
# require there to be a 2D base-called sequence from Metrichor.
if args.high_quality:
if (fast5.get_complement_events_count() <= \
fast5.get_template_events_count()) or not fast5.has_2D():
fast5.close()
continue
# norem quality 2D : means there are less (or equal) nanopore
# events on the complement strand than on the template strand.
# We also require there to be a 2D base-called sequence from Metrichor.
if args.normal_quality:
if (fast5.get_complement_events_count() > \
fast5.get_template_events_count()) or not fast5.has_2D():
fast5.close()
continue
for fa in fas:
if fa is None or \
len(fa.seq) < args.min_length or \
(len(fa.seq) > args.max_length and \
args.max_length > 0):
continue
print(fa)
fast5.close()
def run(parser, args):
nuc_count = Counter()
total_nucs = 0
for fast5 in Fast5File.Fast5FileSet(args.files):
fq = fast5.get_fastq()
if fq is not None:
for n in fq.seq:
nuc_count[n] += 1
total_nucs += 1
fast5.close()
for n in sorted(nuc_count):
print('\t'.join(
str(s) for s in [
n, nuc_count[n], total_nucs,
float(nuc_count[n]) / float(total_nucs)
]))