def _get_att_n_50_contig_length(read_lengths):
"""
Get the n50 or 0 if n50 cannot be calculated
:param read_lengths: sorted list
"""
n50 = compute_n50(read_lengths)
return Attribute(Constants.A_N50_LEN, int(n50))
def _get_att_n_50_contig_length(read_lengths):
"""
Get the n50 or 0 if n50 cannot be calculated
:param read_lengths: sorted list
"""
n50 = compute_n50(read_lengths)
return Attribute(Constants.A_N50_LEN, int(n50))
def test_compute_n50_from_readlenths(self):
"""
Test getting N50 from length list
"""
fasta = os.path.join( ROOT_DATA_DIR, 'polished_assembly', 'polished_assembly.fasta.gz')
l = get_fasta_readlengths(fasta)
self.assertEqual( 33586, compute_n50(l) )
def test_compute_n50_from_readlenths(self):
"""
Test getting N50 from length list
"""
fasta = os.path.join(ROOT_DATA_DIR, 'polished_assembly',
'polished_assembly.fasta.gz')
l = get_fasta_readlengths(fasta)
self.assertEqual(33586, compute_n50(l))
def _to_read_stats_attributes(readLenDists, readQualDists):
# 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 readLenDists:
nbases += rlendist.sampleMean * rlendist.sampleSize
nreads += rlendist.sampleSize
# 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 = int(np.round(compute_n50(approx_read_lens), decimals=0))
for rqualdist in readQualDists:
readscoretotal += _total_from_bins(rqualdist.bins,
rqualdist.minBinValue,
rqualdist.binWidth)
readscorenumber += sum(rqualdist.bins)
readlen = 0
if nreads != 0:
readlen = nbases / nreads
readlen = int(np.round(readlen, decimals=0))
readQuality = 0
if readscorenumber != 0:
readQuality = np.round(readscoretotal / readscorenumber, decimals=2)
return [
int(np.round(nbases, decimals=0)),
nreads,
readlen,
n50,
] #readQuality]
def _to_read_stats_attributes(readLenDists, readQualDists):
# 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 readLenDists:
nbases += rlendist.sampleMean * rlendist.sampleSize
nreads += rlendist.sampleSize
# 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 = int(np.round(compute_n50(approx_read_lens), decimals=0))
for rqualdist in readQualDists:
readscoretotal += _total_from_bins(rqualdist.bins,
rqualdist.minBinValue,
rqualdist.binWidth)
readscorenumber += sum(rqualdist.bins)
readlen = 0
if nreads != 0:
readlen = nbases / nreads
readlen = int(np.round(readlen, decimals=0))
readQuality = 0
if readscorenumber != 0:
readQuality = readscoretotal / readscorenumber
return [int(np.round(nbases, decimals=0)),
nreads,
readlen,
n50,
] # readQuality]
def n50(self):
return compute_n50(self.values)
def test_02_compute_n50(self):
x = [91, 77, 70, 69, 62, 56, 45, 29, 16, 4]
n = compute_n50(x)
self.assertEqual(n, 69)
def test_01_compute_n50(self):
x = [6, 5, 4]
n = compute_n50(x)
self.assertEqual(n, 5)
def n50(self):
return compute_n50(self.values)
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_02_compute_n50(self):
x = [91, 77, 70, 69, 62, 56, 45, 29, 16, 4]
n = compute_n50(x)
self.assertEqual(n, 69)
def test_01_compute_n50(self):
x = [6, 5, 4]
n = compute_n50(x)
self.assertEqual(n, 5)
