def loadStatsXml(args):
dset = DataSet(args.infile, strict=args.strict)
dset.loadStats(args.statsfile)
if args.outfile:
dset.write(args.outfile, validate=False)
else:
dset.write(args.infile, validate=False)
def loadStatsXml(args):
dset = DataSet(args.infile, strict=args.strict)
dset.loadStats(args.statsfile)
if args.outfile:
dset.write(args.outfile, validate=False)
else:
dset.write(args.infile, validate=False)
def to_report(stats_xml):
"""Main point of entry
:type stats_xml: str
:type output_dir: str
:type dpi: int
:rtype: Report
"""
log.info("Analyzing XML {f}".format(f=stats_xml))
dset = DataSet(stats_xml)
if not dset.metadata.summaryStats:
dset.loadStats(stats_xml)
if not dset.metadata.summaryStats.prodDist:
raise IOError("Pipeline Summary Stats (sts.xml) not found or missing "
"key distributions")
dsets = [dset]
for subdset in dset.subdatasets:
if subdset.metadata.summaryStats:
dsets.append(subdset)
col_ids = [
Constants.C_CONTEXT, Constants.C_ZMWS, Constants.C_PROD_0,
Constants.C_PROD_1, Constants.C_PROD_2
]
col_values = [[], [], [], [], []]
for dset in dsets:
if len(dsets) > 1 and len(col_values[0]) == 0:
movie_name = "Combined"
else:
try:
collection = list(dset.metadata.collections)[0]
movie_name = collection.context
except AttributeError:
movie_name = "NA"
productive_zmws = int(dset.metadata.summaryStats.numSequencingZmws)
empty, productive, other, _ = dset.metadata.summaryStats.prodDist.bins
prod0 = np.round(100.0 * empty / float(productive_zmws),
decimals=Constants.DECIMALS)
prod1 = np.round(100.0 * productive / float(productive_zmws),
decimals=Constants.DECIMALS)
prod2 = np.round(100.0 * other / float(productive_zmws),
decimals=Constants.DECIMALS)
this_row = [movie_name, productive_zmws, prod0, prod1, prod2]
map(lambda (x, y): x.append(y), zip(col_values, this_row))
columns = [
Column(cid, values=vals) for cid, vals in zip(col_ids, col_values)
]
tables = [Table(Constants.T_LOADING, columns=columns)]
report = Report(meta_rpt.id,
title=meta_rpt.title,
tables=tables,
attributes=None,
plotgroups=None)
return meta_rpt.apply_view(report)
def to_report(stats_xml):
"""Main point of entry
:type stats_xml: str
:type output_dir: str
:type dpi: int
:rtype: Report
"""
log.info("Analyzing XML {f}".format(f=stats_xml))
dset = DataSet(stats_xml)
if not dset.metadata.summaryStats:
dset.loadStats(stats_xml)
if not dset.metadata.summaryStats.prodDist:
raise IOError("Pipeline Summary Stats (sts.xml) not found or missing "
"key distributions")
dsets = [dset]
for subdset in dset.subdatasets:
if subdset.metadata.summaryStats:
dsets.append(subdset)
col_names = ["Collection Context",
"Productive ZMWs",
"Productivity 0 (%)",
"Productivity 1 (%)",
"Productivity 2 (%)"]
col_values = [[], [], [], [], []]
for dset in dsets:
if len(dsets) > 1 and len(col_values[0]) == 0:
movie_name = "Combined"
else:
try:
collection = list(dset.metadata.collections)[0]
movie_name = collection.context
except AttributeError:
movie_name = "NA"
productive_zmws = int(dset.metadata.summaryStats.numSequencingZmws)
empty, productive, other, _ = dset.metadata.summaryStats.prodDist.bins
prod0 = np.round(100.0 * empty / float(productive_zmws),
decimals=Constants.DECIMALS)
prod1 = np.round(100.0 * productive / float(productive_zmws),
decimals=Constants.DECIMALS)
prod2 = np.round(100.0 * other / float(productive_zmws),
decimals=Constants.DECIMALS)
this_row = [movie_name, productive_zmws, prod0, prod1, prod2]
map(lambda (x, y): x.append(y), zip(col_values, this_row))
columns = [Column(cn.translate(None, '(%)').strip().replace(' ',
'_').lower(),
cn, vals)
for cn, vals in zip(col_names, col_values)]
tables = [Table("loading_xml_table", "Loading Statistics", columns)]
report = Report("loading_xml_report", title="Loading Report",
tables=tables, attributes=None, plotgroups=None)
return report
def to_report(stats_xml):
"""Main point of entry
:type stats_xml: str
:type output_dir: str
:type dpi: int
:rtype: Report
"""
log.info("Analyzing XML {f}".format(f=stats_xml))
dset = DataSet(stats_xml)
if not dset.metadata.summaryStats:
dset.loadStats(stats_xml)
if not dset.metadata.summaryStats.prodDist:
raise IOError("Pipeline Summary Stats (sts.xml) not found or missing "
"key distributions")
dsets = [dset]
for subdset in dset.subdatasets:
if subdset.metadata.summaryStats:
dsets.append(subdset)
col_ids = [Constants.C_CONTEXT,
Constants.C_ZMWS,
Constants.C_PROD_0,
Constants.C_PROD_1,
Constants.C_PROD_2]
col_values = [[], [], [], [], []]
for dset in dsets:
if len(dsets) > 1 and len(col_values[0]) == 0:
movie_name = "Combined"
else:
try:
collection = list(dset.metadata.collections)[0]
movie_name = collection.context
except AttributeError:
movie_name = "NA"
productive_zmws = int(dset.metadata.summaryStats.numSequencingZmws)
empty, productive, other, _ = dset.metadata.summaryStats.prodDist.bins
prod0 = np.round(100.0 * empty / float(productive_zmws),
decimals=Constants.DECIMALS)
prod1 = np.round(100.0 * productive / float(productive_zmws),
decimals=Constants.DECIMALS)
prod2 = np.round(100.0 * other / float(productive_zmws),
decimals=Constants.DECIMALS)
this_row = [movie_name, productive_zmws, prod0, prod1, prod2]
map(lambda (x, y): x.append(y), zip(col_values, this_row))
columns = [Column(cid, values=vals)
for cid, vals in zip(col_ids, col_values)]
tables = [Table(Constants.T_LOADING, columns=columns)]
report = Report(Constants.R_ID,
tables=tables, attributes=None, plotgroups=None)
return spec.apply_view(report)
def to_report(stats_xml, output_dir, dpi=72):
"""Main point of entry
:type stats_xml: str
:type output_dir: str
:type dpi: int
:rtype: Report
"""
log.info("Analyzing XML {f}".format(f=stats_xml))
# stats_xml should be a dataset:
dset = DataSet(stats_xml)
dataset_uuids = [dset.uuid]
# but if it isn't, no problem:
if not dset.metadata.summaryStats:
dset.loadStats(stats_xml)
# an sts file was provided which will generate a new random uuid
dataset_uuids = []
if not dset.metadata.summaryStats.readLenDists:
raise RuntimeError("No Pipeline Summary Stats (sts.xml) found")
# Build the stats table:
nbases = 0
nreads = 0
n50 = 0
readscoretotal = 0
readscorenumber = 0
approx_read_lens = []
# if a merge failed there may be more than one dist:
for rlendist in dset.metadata.summaryStats.readLenDists:
nbases += _total_from_bins(rlendist.bins,
rlendist.minBinValue,
rlendist.binWidth)
nreads += sum(rlendist.bins)
# N50:
for i, lbin in enumerate(rlendist.bins):
# use the average, except for the last bin
if i != len(rlendist.bins) - 1:
value = ((i * rlendist.binWidth) + rlendist.minBinValue +
rlendist.binWidth / 2)
# for the last bin, just use the value
else:
value = (i * rlendist.binWidth) + rlendist.minBinValue
approx_read_lens.extend([value] * lbin)
# TODO(mdsmith)(2016-02-09) make sure maxOutlierValue is updated
# during a merge /todo
# but pop off that last value and replace it with the
# maxOutlierValue:
# approx_read_lens.pop()
# approx_read_lens.append(rlendist.maxBinValue)
n50 = np.round(compute_n50(approx_read_lens))
for rqualdist in dset.metadata.summaryStats.readQualDists:
readscoretotal += _total_from_bins(rqualdist.bins,
rqualdist.minBinValue,
rqualdist.binWidth)
readscorenumber += sum(rqualdist.bins)
readlen = 0
if nreads != 0:
readlen = np.round(nbases / nreads, decimals=2)
readQuality = 0
if readscorenumber != 0:
readQuality = np.round(readscoretotal / readscorenumber, decimals=2)
row_names = ["Polymerase Read Bases",
"Polymerase Reads",
"Polymerase Read N50",
"Polymerase Read Length",
"Polymerase Read Quality"]
_pre_filter = [np.round(nbases, decimals=2),
nreads,
n50,
readlen,
readQuality]
plots = []
# ReadLen distribution to barplot:
for i, rlendist in enumerate(dset.metadata.summaryStats.readLenDists):
len_fig, len_axes = get_fig_axes_lpr()
len_axes.bar(rlendist.labels, rlendist.bins,
color=get_green(0), edgecolor=get_green(0),
width=(rlendist.binWidth * 0.75))
len_axes.set_xlabel('Read Length')
len_axes.set_ylabel('Reads')
png_fn = os.path.join(output_dir, "readLenDist{i}.png".format(i=i))
png_base, thumbnail_base = save_figure_with_thumbnail(len_fig, png_fn,
dpi=dpi)
plots.append(Plot("filter_len_xml_plot_{i}".format(i=i),
os.path.relpath(png_base, output_dir),
thumbnail=os.path.relpath(thumbnail_base, output_dir)))
plot_groups = [PlotGroup("filter_len_xml_plot_group",
title="Polymerase Read Length",
plots=plots,
thumbnail=os.path.relpath(thumbnail_base, output_dir))]
plots = []
# ReadQual distribution to barplot:
for i, rqualdist in enumerate(dset.metadata.summaryStats.readQualDists):
qual_fig, qual_axes = get_fig_axes_lpr()
qual_axes.bar(rqualdist.labels, rqualdist.bins,
color=get_green(0), edgecolor=get_green(0),
width=(rqualdist.binWidth * 0.75))
qual_axes.set_xlabel('Read Quality')
qual_axes.set_ylabel('Reads')
png_fn = os.path.join(output_dir, "readQualDist{i}.png".format(i=i))
png_base, thumbnail_base = save_figure_with_thumbnail(qual_fig, png_fn,
dpi=dpi)
plots.append(Plot("filter_qual_xml_plot_{i}".format(i=i),
os.path.relpath(png_base, output_dir),
thumbnail=os.path.relpath(thumbnail_base, output_dir)))
plot_groups.append(PlotGroup("filter_qual_xml_plot_group",
title="Polymerase Read Quality",
plots=plots))
# build the report:
columns = [Column("filter_names_column", header="Metrics",
values=row_names)]
columns.append(Column("filter_stats_column", header="Values",
values=_pre_filter))
tables = [Table("filter_xml_table", "Filtering Statistics", columns)]
report = Report("filtering_stats_xml_report",
title="Filtering stats XML report",
tables=tables,
attributes=None,
plotgroups=plot_groups,
dataset_uuids=dataset_uuids)
return report
def test_stats_metadata(self):
ds = DataSet(data.getBam())
ds.loadStats(data.getStats())
self.assertEqual(ds.metadata.summaryStats.prodDist.numBins, 4)
self.assertEqual(ds.metadata.summaryStats.prodDist.bins,
[1576, 901, 399, 0])
ds1 = DataSet(data.getXml(8))
ds1.loadStats(data.getStats())
ds2 = DataSet(data.getXml(11))
ds2.loadStats(data.getStats())
ds3 = ds1 + ds2
self.assertEqual(ds1.metadata.summaryStats.prodDist.bins,
[1576, 901, 399, 0])
self.assertEqual(ds2.metadata.summaryStats.prodDist.bins,
[1576, 901, 399, 0])
self.assertEqual(ds3.metadata.summaryStats.prodDist.bins,
[3152, 1802, 798, 0])
self.assertEqual(ds1.metadata.summaryStats.readLenDist.bins,
[0, 62, 39, 36, 29, 37, 19, 29, 37, 32, 32, 40, 45,
54, 73, 77, 97, 95, 49, 17, 2, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0])
self.assertEqual(ds2.metadata.summaryStats.readLenDist.bins,
[0, 62, 39, 36, 29, 37, 19, 29, 37, 32, 32, 40, 45,
54, 73, 77, 97, 95, 49, 17, 2, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0])
self.assertEqual(ds3.metadata.summaryStats.readLenDist.bins,
[0, 124, 78, 72, 58, 74, 38, 58, 74, 64, 64, 80, 90,
108, 146, 154, 194, 190, 98, 34, 4, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0])
# Lets check some manual values
ds1 = DataSet(data.getXml(8))
ds1.loadStats(data.getStats())
ds2 = DataSet(data.getXml(11))
ds2.loadStats(data.getStats())
ds1.metadata.summaryStats.readLenDist.bins = (
[0, 10, 9, 8, 7, 6, 4, 2, 1, 0, 0, 1])
self.assertEqual(ds1.metadata.summaryStats.readLenDist.bins,
[0, 10, 9, 8, 7, 6, 4, 2, 1, 0, 0, 1])
ds1.metadata.summaryStats.readLenDist.minBinValue = 10
ds1.metadata.summaryStats.readLenDist.binWidth = 10
ds2.metadata.summaryStats.readLenDist.bins = (
[0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1])
self.assertEqual(ds2.metadata.summaryStats.readLenDist.bins,
[0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1])
ds2.metadata.summaryStats.readLenDist.minBinValue = 20
ds2.metadata.summaryStats.readLenDist.binWidth = 10
ds3 = ds1 + ds2
self.assertEqual(ds3.metadata.summaryStats.readLenDist.bins,
[0, 10, 10, 9, 8, 7, 5, 3, 2, 1, 0, 1, 1])
# now lets swap
ds1 = DataSet(data.getXml(8))
ds1.loadStats(data.getStats())
ds2 = DataSet(data.getXml(11))
ds2.loadStats(data.getStats())
ds1.metadata.summaryStats.readLenDist.bins = (
[0, 10, 9, 8, 7, 6, 4, 2, 1, 0, 0, 1])
self.assertEqual(ds1.metadata.summaryStats.readLenDist.bins,
[0, 10, 9, 8, 7, 6, 4, 2, 1, 0, 0, 1])
ds1.metadata.summaryStats.readLenDist.minBinValue = 20
ds1.metadata.summaryStats.readLenDist.binWidth = 10
ds2.metadata.summaryStats.readLenDist.bins = (
[0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1])
self.assertEqual(ds2.metadata.summaryStats.readLenDist.bins,
[0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1])
ds2.metadata.summaryStats.readLenDist.minBinValue = 10
ds2.metadata.summaryStats.readLenDist.binWidth = 10
ds3 = ds1 + ds2
self.assertEqual(ds3.metadata.summaryStats.readLenDist.bins,
[0, 1, 11, 10, 9, 8, 7, 5, 3, 1, 0, 1, 1])
# now lets do some non-overlapping
ds1 = DataSet(data.getXml(8))
ds1.loadStats(data.getStats())
ds2 = DataSet(data.getXml(11))
ds2.loadStats(data.getStats())
ds1.metadata.summaryStats.readLenDist.bins = (
[1, 1, 1])
self.assertEqual(ds1.metadata.summaryStats.readLenDist.bins,
[1, 1, 1])
ds1.metadata.summaryStats.readLenDist.minBinValue = 10
ds1.metadata.summaryStats.readLenDist.binWidth = 10
ds2.metadata.summaryStats.readLenDist.bins = (
[2, 2, 2])
self.assertEqual(ds2.metadata.summaryStats.readLenDist.bins,
[2, 2, 2])
ds2.metadata.summaryStats.readLenDist.minBinValue = 50
ds2.metadata.summaryStats.readLenDist.binWidth = 10
ds3 = ds1 + ds2
self.assertEqual(ds3.metadata.summaryStats.readLenDist.bins,
[1, 1, 1, 0, 2, 2, 2])
# now lets test the subdataset metadata retention:
ss = SubreadSet(data.getXml(10))
ss.loadStats(data.getStats(0))
ss.loadStats(data.getStats(1))
self.assertEqual(153168.0, ss.metadata.summaryStats.numSequencingZmws)
self.assertEqual(
2876.0, ss.subdatasets[0].metadata.summaryStats.numSequencingZmws)
self.assertEqual(
150292.0, ss.subdatasets[1].metadata.summaryStats.numSequencingZmws)
def to_report(stats_xml, output_dir, dpi=72):
#TODO: make dpi matter
"""Main point of entry
:type stats_xml: str
:type output_dir: str
:type dpi: int
:rtype: Report
"""
log.info("Analyzing XML {f}".format(f=stats_xml))
dset = DataSet(stats_xml)
if not dset.metadata.summaryStats:
dset.loadStats(stats_xml)
if not dset.metadata.summaryStats.medianInsertDists:
raise RuntimeError("No Pipeline Summary Stats (sts.xml) found")
# Pull some stats:
adapter_dimers = np.round(
100.0 * dset.metadata.summaryStats.adapterDimerFraction,
decimals=2)
short_inserts = np.round(
100.0 * dset.metadata.summaryStats.shortInsertFraction,
decimals=2)
plots = []
# Pull some histograms (may have dupes (unmergeable distributions)):
for i, ins_len_dist in enumerate(
dset.metadata.summaryStats.medianInsertDists):
# make a bar chart:
fig, ax = get_fig_axes_lpr()
ax.bar(map(float, ins_len_dist.labels), ins_len_dist.bins,
color=get_green(0), edgecolor=get_green(0),
width=(ins_len_dist.binWidth * 0.75))
ax.set_xlabel('Median Distance Between Adapters')
ax.set_ylabel('Reads')
png_fn = os.path.join(output_dir,
"interAdapterDist{i}.png".format(i=i))
png_base, thumbnail_base = save_figure_with_thumbnail(fig, png_fn,
dpi=dpi)
# build the report:
plots.append(Plot("adapter_xml_plot_{i}".format(i=i),
os.path.relpath(png_base),
thumbnail=os.path.relpath(thumbnail_base)))
plot_groups = [PlotGroup("adapter_xml_plot_group",
title="Observed Insert Length Distribution",
plots=plots,
thumbnail=os.path.relpath(thumbnail_base))]
columns = [Column("adaper_xml_conditions", None,
('Adapter Dimers (0-10bp)',
'Short Inserts (11-100bp)')),
Column("adaper_xml_results", None,
(adapter_dimers, short_inserts))]
tables = [Table("adapter_xml_table", "Adapter Statistics", columns)]
report = Report("adapter_xml_report", title="Adapter Report",
tables=tables, attributes=None, plotgroups=plot_groups)
return report
def test_stats_metadata(self):
ds = DataSet(data.getBam())
ds.loadStats(data.getStats())
self.assertEqual(ds.metadata.summaryStats.prodDist.numBins, 4)
self.assertEqual(ds.metadata.summaryStats.prodDist.bins,
[1576, 901, 399, 0])
ds1 = DataSet(data.getXml(8))
ds1.loadStats(data.getStats())
ds2 = DataSet(data.getXml(11))
ds2.loadStats(data.getStats())
ds3 = ds1 + ds2
self.assertEqual(ds1.metadata.summaryStats.prodDist.bins,
[1576, 901, 399, 0])
self.assertEqual(ds2.metadata.summaryStats.prodDist.bins,
[1576, 901, 399, 0])
self.assertEqual(ds3.metadata.summaryStats.prodDist.bins,
[3152, 1802, 798, 0])
self.assertEqual(ds1.metadata.summaryStats.readLenDist.bins, [
0, 62, 39, 36, 29, 37, 19, 29, 37, 32, 32, 40, 45, 54, 73, 77, 97,
95, 49, 17, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
])
self.assertEqual(ds2.metadata.summaryStats.readLenDist.bins, [
0, 62, 39, 36, 29, 37, 19, 29, 37, 32, 32, 40, 45, 54, 73, 77, 97,
95, 49, 17, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
])
self.assertEqual(ds3.metadata.summaryStats.readLenDist.bins, [
0, 124, 78, 72, 58, 74, 38, 58, 74, 64, 64, 80, 90, 108, 146, 154,
194, 190, 98, 34, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
])
# Lets check some manual values
ds1 = DataSet(data.getXml(8))
ds1.loadStats(data.getStats())
ds2 = DataSet(data.getXml(11))
ds2.loadStats(data.getStats())
ds1.metadata.summaryStats.readLenDist.bins = ([
0, 10, 9, 8, 7, 6, 4, 2, 1, 0, 0, 1
])
self.assertEqual(ds1.metadata.summaryStats.readLenDist.bins,
[0, 10, 9, 8, 7, 6, 4, 2, 1, 0, 0, 1])
ds1.metadata.summaryStats.readLenDist.minBinValue = 10
ds1.metadata.summaryStats.readLenDist.binWidth = 10
ds2.metadata.summaryStats.readLenDist.bins = ([
0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1
])
self.assertEqual(ds2.metadata.summaryStats.readLenDist.bins,
[0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1])
ds2.metadata.summaryStats.readLenDist.minBinValue = 20
ds2.metadata.summaryStats.readLenDist.binWidth = 10
ds3 = ds1 + ds2
self.assertEqual(ds3.metadata.summaryStats.readLenDist.bins,
[0, 10, 10, 9, 8, 7, 5, 3, 2, 1, 0, 1, 1])
# now lets swap
ds1 = DataSet(data.getXml(8))
ds1.loadStats(data.getStats())
ds2 = DataSet(data.getXml(11))
ds2.loadStats(data.getStats())
ds1.metadata.summaryStats.readLenDist.bins = ([
0, 10, 9, 8, 7, 6, 4, 2, 1, 0, 0, 1
])
self.assertEqual(ds1.metadata.summaryStats.readLenDist.bins,
[0, 10, 9, 8, 7, 6, 4, 2, 1, 0, 0, 1])
ds1.metadata.summaryStats.readLenDist.minBinValue = 20
ds1.metadata.summaryStats.readLenDist.binWidth = 10
ds2.metadata.summaryStats.readLenDist.bins = ([
0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1
])
self.assertEqual(ds2.metadata.summaryStats.readLenDist.bins,
[0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1])
ds2.metadata.summaryStats.readLenDist.minBinValue = 10
ds2.metadata.summaryStats.readLenDist.binWidth = 10
ds3 = ds1 + ds2
self.assertEqual(ds3.metadata.summaryStats.readLenDist.bins,
[0, 1, 11, 10, 9, 8, 7, 5, 3, 1, 0, 1, 1])
# now lets do some non-overlapping
ds1 = DataSet(data.getXml(8))
ds1.loadStats(data.getStats())
ds2 = DataSet(data.getXml(11))
ds2.loadStats(data.getStats())
ds1.metadata.summaryStats.readLenDist.bins = ([1, 1, 1])
self.assertEqual(ds1.metadata.summaryStats.readLenDist.bins, [1, 1, 1])
ds1.metadata.summaryStats.readLenDist.minBinValue = 10
ds1.metadata.summaryStats.readLenDist.binWidth = 10
ds2.metadata.summaryStats.readLenDist.bins = ([2, 2, 2])
self.assertEqual(ds2.metadata.summaryStats.readLenDist.bins, [2, 2, 2])
ds2.metadata.summaryStats.readLenDist.minBinValue = 50
ds2.metadata.summaryStats.readLenDist.binWidth = 10
ds3 = ds1 + ds2
self.assertEqual(ds3.metadata.summaryStats.readLenDist.bins,
[1, 1, 1, 0, 2, 2, 2])
# now lets test the subdataset metadata retention:
ss = SubreadSet(data.getXml(10))
ss.loadStats(data.getStats(0))
ss.loadStats(data.getStats(1))
self.assertEqual(153168.0, ss.metadata.summaryStats.numSequencingZmws)
self.assertEqual(
2876.0, ss.subdatasets[0].metadata.summaryStats.numSequencingZmws)
self.assertEqual(
150292.0,
ss.subdatasets[1].metadata.summaryStats.numSequencingZmws)
