def test_draw_histogram_in_axes(self):
values = [1, 2, 3, 1, 2, 3, 2, 3, 2, 3, 2, 1, 4]
fhand = NamedTemporaryFile(suffix='.png')
counter = IntCounter(values)
distrib = counter.calculate_distribution()
axes, canvas = draw_histogram_in_axes(distrib['counts'],
distrib['bin_limits'],
kind=LINE,
distrib_label='test')
axes.legend()
canvas.print_figure(fhand, format='png')
fhand.flush()
# raw_input(fhand.name)
# ylimit test
values = [1, 2, 3, 1, 2, 3, 2, 3, 2, 3, 2, 1, 4, 0, 5, 4, 4, 4, 4, 4]
fhand = NamedTemporaryFile(suffix='.png')
counter = IntCounter(values)
distrib = counter.calculate_distribution()
axes, canvas = draw_histogram_in_axes(distrib['counts'],
distrib['bin_limits'],
kind=LINE,
distrib_label='test',
ylimits=(None, 4))
axes.legend()
canvas.print_figure(fhand, format='png')
fhand.flush()
def test_draw_histogram_in_fhand(self):
values = [1, 2, 3, 1, 2, 3, 2, 3, 2, 3, 2, 1, 4]
fhand = NamedTemporaryFile(suffix='.png')
counter = IntCounter(values)
distrib = counter.calculate_distribution()
draw_histogram_in_fhand(distrib['counts'], distrib['bin_limits'],
fhand=fhand)
def calculate_distance_distribution_in_bam(bam_fhand,
max_clipping,
max_distance=None):
bamfile = AlignmentFile(bam_fhand.name)
stats = {
'outies': IntCounter(),
'innies': IntCounter(),
'others': IntCounter()
}
for grouped_mates in _group_alignments_reads_by_qname(bamfile):
mates = _split_mates(grouped_mates)
for aligned_read1 in _get_totally_mapped_alignments(
mates[0], max_clipping):
for aligned_read2 in _get_totally_mapped_alignments(
mates[1], max_clipping):
if aligned_read1.rname == aligned_read2.rname:
aligned_reads = [aligned_read1, aligned_read2]
distance = _find_distance(aligned_reads)
if _mates_are_outies(aligned_reads):
kind = 'outies'
elif _mates_are_innies(aligned_reads):
kind = 'innies'
else:
kind = 'others'
if max_distance is None or max_distance > distance:
stats[kind][distance] += 1
return stats
def test__add__():
ext_counter = IntCounter({6: 1, 2: 1})
ext_counter2 = IntCounter({7: 1, 2: 1})
new_array = ext_counter + ext_counter2
assert new_array[6] == 1
assert new_array[7] == 1
assert new_array[2] == 2
def test_draw_histogram_in_fhand(self):
values = [1, 2, 3, 1, 2, 3, 2, 3, 2, 3, 2, 1, 4]
fhand = NamedTemporaryFile(suffix='.png')
counter = IntCounter(values)
distrib = counter.calculate_distribution()
draw_histogram_in_fhand(distrib['counts'],
distrib['bin_limits'],
fhand=fhand)
def calc_snv_read_pos_stats(sam, snvs, max_snps=None, max_pos=None):
"This implementation is using pysam pysam"
pileup_cols = sam.pileup()
read_5_pos_cnts_rg = {}
read_3_pos_cnts_rg = {}
read_5_pos_box_rg = {}
read_3_pos_box_rg = {}
for index, snv in enumerate(snvs):
if max_snps and index >= max_snps:
break
chrom = snv.chrom
ref_pos = snv.pos
snv_qual = snv.qual
snv_col = None
for col in pileup_cols:
ref_name = sam.getrname(col.reference_id)
if ref_name == chrom and col.reference_pos == ref_pos:
snv_col = col
break
if snv_col is None:
raise RuntimeError('No pileup found for snv {}:{}'.format(
chrom, ref_pos))
for pileup_read in snv_col.pileups:
try:
read_group = pileup_read.alignment.opt('RG')
except KeyError:
read_group = None
read_ref_coord = ReadRefCoord(pileup_read.alignment, sam)
read_pos = read_ref_coord.get_read_pos((chrom, ref_pos))
read_pos_end = read_ref_coord.get_read_pos_counting_from_end(
(chrom, ref_pos))
if read_group not in read_5_pos_cnts_rg:
read_5_pos_cnts_rg[read_group] = IntCounter()
read_3_pos_cnts_rg[read_group] = IntCounter()
read_5_pos_box_rg[read_group] = IntBoxplot()
read_3_pos_box_rg[read_group] = IntBoxplot()
read_5_pos_cnts = read_5_pos_cnts_rg[read_group]
read_3_pos_cnts = read_3_pos_cnts_rg[read_group]
read_5_pos_box = read_5_pos_box_rg[read_group]
read_3_pos_box = read_3_pos_box_rg[read_group]
if (read_pos is not None
and (not max_pos or read_pos + 1 <= max_pos)):
read_5_pos_cnts[read_pos + 1] += 1
read_5_pos_box.append(read_pos + 1, snv_qual)
if (read_pos_end is not None
and (not max_pos or abs(read_pos_end) <= max_pos)):
read_3_pos_cnts[abs(read_pos_end)] += 1
read_3_pos_box.append(abs(read_pos_end), snv_qual)
return {
'5_read_pos_counts': read_5_pos_cnts_rg,
'3_read_pos_counts': read_3_pos_cnts_rg,
'5_read_pos_boxplot': read_5_pos_box_rg,
'3_read_pos_boxplot': read_3_pos_box_rg
}
def test_value_for_index_test(self):
'We can get the integer for a given index'
# pylint: disable=W0212
ints = IntCounter({3: 1, 5: 1, 7: 2, 38: 1})
assert ints._get_value_for_index(0) == 3
assert ints._get_value_for_index(1) == 5
assert ints._get_value_for_index(2) == 7
assert ints._get_value_for_index(3) == 7
assert ints._get_value_for_index(4) == 38
try:
assert ints._get_value_for_index(5) == 38
self.fail('IndexError expected')
except IndexError:
pass
def test_counter():
'create a counter'
# initialize with values
counter = IntCounter({2: 2})
counter[2] += 1
counter[6] += 1
assert counter.min == 2
assert counter.max == 6
counter = IntCounter({3: 1, 5: 1, 7: 2, 38: 1})
assert counter.min == 3
assert counter.max == 38
assert counter.sum == 60
assert counter.count == 5
assert counter.median == 7
def calc_snv_read_pos_stats2(sam, snvs, max_snps=None, max_pos=None):
"this implementation is using pysam fetch"
read_5_pos_cnts_rg = {}
read_3_pos_cnts_rg = {}
read_5_pos_box_rg = {}
read_3_pos_box_rg = {}
for index, snv in enumerate(snvs):
if max_snps and index >= max_snps:
break
chrom = snv.chrom
ref_pos = snv.pos
snv_qual = snv.qual
for alignment_read in sam.fetch(chrom, ref_pos, ref_pos + 1):
try:
read_group = alignment_read.opt('RG')
except KeyError:
read_group = None
read_ref_coord = ReadRefCoord(alignment_read, sam)
read_pos = read_ref_coord.get_read_pos((chrom, ref_pos))
read_pos_end = read_ref_coord.get_read_pos_counting_from_end(
(chrom, ref_pos))
if read_group not in read_5_pos_cnts_rg:
read_5_pos_cnts_rg[read_group] = IntCounter()
read_3_pos_cnts_rg[read_group] = IntCounter()
read_5_pos_box_rg[read_group] = IntBoxplot()
read_3_pos_box_rg[read_group] = IntBoxplot()
read_5_pos_cnts = read_5_pos_cnts_rg[read_group]
read_3_pos_cnts = read_3_pos_cnts_rg[read_group]
read_5_pos_box = read_5_pos_box_rg[read_group]
read_3_pos_box = read_3_pos_box_rg[read_group]
if (read_pos is not None
and (not max_pos or read_pos + 1 <= max_pos)):
read_5_pos_cnts[read_pos + 1] += 1
read_5_pos_box.append(read_pos + 1, snv_qual)
if (read_pos_end is not None
and (not max_pos or abs(read_pos_end) <= max_pos)):
read_3_pos_cnts[abs(read_pos_end)] += 1
read_3_pos_box.append(abs(read_pos_end), snv_qual)
return {
'5_read_pos_counts': read_5_pos_cnts_rg,
'3_read_pos_counts': read_3_pos_cnts_rg,
'5_read_pos_boxplot': read_5_pos_box_rg,
'3_read_pos_boxplot': read_3_pos_box_rg
}
def calculate_coverage_distrib_in_region(self, region=None):
if region is None:
if self.window == 1:
regions = None
else:
regions = [(ref, 0, le_ - 1)
for ref, le_ in self._ref_lens.items()]
else:
regions = [region]
if self.window == 1:
if regions is None:
region = None
else:
region = regions[0]
return self._calculate_complete_coverage_distrib(region)
counts = {}
for region in regions:
chrom, start, end = region
for start, end in generate_windows(self.window,
start=0,
end=self._ref_lens[chrom],
step=1):
counts_in_win = self._calculate_coverages_in_win(
chrom, start, end)
for sample, cnts_in_win in counts_in_win.items():
if sample not in counts:
counts[sample] = IntCounter()
counts[sample][int(round(cnts_in_win))] += 1
return counts
def mapped_count_by_rg(bam_fpaths, mapqx=None):
do_mapqx = True if mapqx is not None else False
counter_by_rg = {}
for bam_fpath in bam_fpaths:
bam = pysam.Samfile(bam_fpath, 'rb')
readgroups = get_bam_readgroups(bam)
if readgroups is None:
bam_basename = os.path.splitext(os.path.basename(bam_fpath))[0]
readgroups = [bam_basename]
else:
readgroups = [rg['ID'] for rg in readgroups]
for readgroup in readgroups:
counter = IntCounter({'unmapped': 0, 'mapped': 0})
if do_mapqx:
counter['bigger_mapqx'] = 0
counter_by_rg[readgroup] = counter
for read in bam:
rg = get_rg_from_alignedread(read)
if rg is None:
rg = bam_basename
if do_mapqx and read.mapq >= mapqx:
counter_by_rg[rg]['bigger_mapqx'] += 1
if read.is_unmapped:
counter_by_rg[rg]['unmapped'] += 1
else:
counter_by_rg[rg]['mapped'] += 1
return counter_by_rg
def test_distribution(self):
'It tests the histogram function'
ints_counter = self.create_test_counter()
distrib = ints_counter.calculate_distribution(bins=10,
outlier_threshold=5)
assert distrib['counts'] == [7L, 13L, 7L, 10L, 7L, 22L, 6L, 4L, 5L, 5L]
assert distrib['bin_limits'] == [
110, 118, 126, 134, 142, 150, 158, 166, 174, 182, 190
]
assert 'average' in str(ints_counter)
ints_counter = IntCounter({0: 2, 1: 1, 3: 1})
result = [2, 1, 1]
assert ints_counter.calculate_distribution(bins=3)['counts'] == result
def test_histogram_plotter(self):
values = [1, 2, 3, 1, 2, 3, 2, 3, 2, 3, 2, 1, 4]
counter = IntCounter(values)
counters = [counter]
histo_ploter = HistogramPlotter(counters)
assert len(histo_ploter.axes) == len(counters)
fhand = NamedTemporaryFile(suffix='.png')
histo_ploter.write_figure(fhand)
# raw_input(fhand.name)
# Add more intcounters
counters.append(IntCounter(values))
counters.append(IntCounter(values))
histo_ploter = HistogramPlotter(counters,
distrib_labels=['1', '2', '3'])
assert len(histo_ploter.axes) == len(counters)
def test_distribution(self):
'It tests the histogram function'
ints_counter = self.create_test_counter()
distrib = ints_counter.calculate_distribution(bins=10,
outlier_threshold=5)
assert distrib['counts'] == [7L, 13L, 7L, 10L, 7L, 22L, 6L, 4L, 5L,
5L]
assert distrib['bin_limits'] == [110, 118, 126, 134, 142, 150, 158,
166, 174, 182, 190]
assert 'average' in str(ints_counter)
ints_counter = IntCounter({0: 2, 1: 1, 3: 1})
result = [2, 1, 1]
assert ints_counter.calculate_distribution(bins=3)['counts'] == result
def test_stats_functs(self):
'It test the statistical functions of the class'
ints = IntCounter({3: 1, 5: 1, 7: 2, 38: 1})
assert ints.median == 7
ints = IntCounter({3: 1, 5: 1, 7: 1, 38: 1})
assert ints.median == 6
# median with two middle numbers
ext_array = IntCounter({3: 1, 5: 1, 7: 2})
assert ext_array.median == 6
ints = IntCounter({34: 1, 43: 1, 81: 1, 106: 2, 115: 1})
assert ints.average - 80.83 < 0.01
ext_counter = self.create_test_counter()
assert ext_counter.median == 145
assert round(ext_counter.average, 2) == 145.15
assert ext_counter.sum == 13354
assert ext_counter.count == 92
assert round(ext_counter.variance, 2) == 557.43
ints = IntCounter({3: 1, 4: 2, 5: 1, 6: 1, 8: 2})
assert ints.median == 5
assert ints.quartiles == (4, 5, 8)
ints = IntCounter({1: 1, 2: 1, 3: 1, 4: 1, 5: 1})
assert ints.quartiles == (1.5, 3, 4.5)
ints = IntCounter({1: 1, 2: 1, 3: 1, 4: 1, 5: 1, 6: 1})
assert ints.quartiles == (1.5, 3.5, 5.5)
ints = IntCounter({1: 1, 2: 1, 3: 1, 4: 1, 5: 1, 6: 1, 7: 1})
assert ints.quartiles == (2, 4, 6)
ints = IntCounter({1: 1, 2: 1, 3: 1, 4: 1, 5: 1, 6: 1, 7: 1, 8: 1})
assert ints.quartiles == (2.5, 4.5, 6.5)
assert ints.irq == 4.0
assert ints.outlier_limits == (-3, 12)
try:
ints = IntCounter({0: 1, 1: 1, 2: 1})
assert ints.quartiles
self.fail('RuntimeError')
except RuntimeError:
pass
def __init__(self, bam_fpaths, coverage_threshold, window, min_mapq=None):
self._coverage_threshold = coverage_threshold
self._window = window
self._min_mapq = min_mapq
self._bam_coverage = BamCoverages2(bam_fpaths, min_mapq=min_mapq,
window=window)
self._scores = {samp: IntCounter()
for samp in self._bam_coverage.samples}
def __call__(self, snv):
if self._first_snv:
for call in snv.calls:
self._scores[call.sample] = IntCounter()
self._first_snv = False
for call in snv.calls:
if call.gt_qual is not None:
self._scores[call.sample][int(call.gt_qual)] += 1
return snv.remove_gt_from_low_qual_calls(min_qual=self._min_qual)
def create_test_counter():
counter = IntCounter()
d = {'9': '5', '10': '288', '11': '002556688', '12': '00012355555',
'13': '0000013555688', '14': '00002555558',
'15': '0000000000355555555557', '16': '000045', '17': '000055',
'18': '0005', '19': '00005', '21': '5'}
for key, values in d.items():
for num in values:
counter[int(key + num)] += 1
return counter
def _calculate_complete_coverage_distrib(self, region):
if region is None:
chrom, start, end = None, None, None
else:
chrom, start, end = region
min_mapq = self.min_mapq
covs = {sample: IntCounter() for sample in self.samples}
covs[None] = IntCounter()
for bam in self._bams:
columns = bam['bam'].pileup(reference=chrom, start=start, end=end,
stepper=self.bam_pileup_stepper,
truncate=True)
one_sample, sample = self._if_one_sample_get_it(bam)
for column in columns:
col_counts = self._count_reads_in_column(column, min_mapq,
one_sample, sample)
for sample, sample_cov in col_counts.items():
covs[sample][sample_cov] += 1
return covs
def get_genome_coverage(bam_fhands):
coverage_hist = IntCounter()
for bam_fhand in bam_fhands:
bam_fpath = bam_fhand.name
cmd = [get_binary_path('bedtools'), 'genomecov', '-ibam', bam_fpath]
cover_process = Popen(cmd, stdout=PIPE)
for line in cover_process.stdout:
if line.startswith('genome'):
cov, value = line.split('\t')[1:3]
coverage_hist[int(cov)] += int(value)
return coverage_hist
def test_draw_histogram_in_axes(self):
values = [1, 2, 3, 1, 2, 3, 2, 3, 2, 3, 2, 1, 4]
fhand = NamedTemporaryFile(suffix='.png')
counter = IntCounter(values)
distrib = counter.calculate_distribution()
axes, canvas = draw_histogram_in_axes(distrib['counts'],
distrib['bin_limits'],
kind=LINE,
distrib_label='test')
axes.legend()
canvas.print_figure(fhand, format='png')
fhand.flush()
# raw_input(fhand.name)
# ylimit test
values = [1, 2, 3, 1, 2, 3, 2, 3, 2, 3, 2, 1, 4, 0, 5, 4, 4, 4, 4, 4]
fhand = NamedTemporaryFile(suffix='.png')
counter = IntCounter(values)
distrib = counter.calculate_distribution()
axes, canvas = draw_histogram_in_axes(distrib['counts'],
distrib['bin_limits'],
kind=LINE,
distrib_label='test',
ylimits=(None, 4))
axes.legend()
canvas.print_figure(fhand, format='png')
fhand.flush()
def calculate_distance_distribution(interleave_fhand,
index_fpath,
max_clipping,
max_distance=None,
tempdir=None,
threads=None):
bam_fhand = NamedTemporaryFile(suffix='.bam')
extra_params = ['-a', '-M']
bwa = map_with_bwamem(index_fpath,
interleave_fpath=interleave_fhand.name,
extra_params=extra_params,
threads=threads)
map_process_to_sortedbam(bwa,
bam_fhand.name,
key='queryname',
tempdir=tempdir)
bamfile = AlignmentFile(bam_fhand.name)
stats = {
'outies': IntCounter(),
'innies': IntCounter(),
'others': IntCounter()
}
for grouped_mates in _group_alignments_reads_by_qname(bamfile):
mates = _split_mates(grouped_mates)
for aligned_read1 in _get_totally_mapped_alignments(
mates[0], max_clipping):
for aligned_read2 in _get_totally_mapped_alignments(
mates[1], max_clipping):
if aligned_read1.rname == aligned_read2.rname:
aligned_reads = [aligned_read1, aligned_read2]
distance = _find_distance(aligned_reads)
if _mates_are_outies(aligned_reads):
kind = 'outies'
elif _mates_are_innies(aligned_reads):
kind = 'innies'
else:
kind = 'others'
if max_distance is None or max_distance > distance:
stats[kind][distance] += 1
return stats
def calculate_coverage_distrib_in_region(self, region=None):
counts = {}
for bam in self._bams:
one_rg = True if len(bam['rgs']) < 2 else False
for read_group in bam['rgs']:
sample_field = self.bam_rg_field_for_vcf_sample
sample = read_group[sample_field]
if sample not in counts:
counts[sample] = IntCounter()
for cov in self._get_coverages_in_bam_rg_win(
region, bam, read_group, one_rg):
counts[sample][int(round(cov))] += 1
return counts
def _get_sample_counter(self, kind, sample=None, gt_broud_type=None):
counters = self._sample_counters[kind]
if sample is not None:
if gt_broud_type is None:
return counters[sample]
else:
return counters[sample][gt_broud_type]
all_counters = IntCounter()
for sample_counter in counters.values():
if gt_broud_type is None:
all_counters += sample_counter
else:
all_counters += sample_counter[gt_broud_type]
return all_counters
def _count_reads(self):
nreferences = self._bams[0].nreferences
rpks = zeros(nreferences)
references = []
length_counts = IntCounter()
first_bam = True
n_reads = 0
for bam in self._bams:
if bam.nreferences != nreferences:
msg = 'BAM files should have the same references'
raise ValueError(msg)
for index, count in enumerate(get_reference_counts(bam.filename)):
n_reads += count['unmapped_reads'] + count['mapped_reads']
if count['reference'] is None:
# some non-mapped reads have reference = None
continue
kb_len = count['length'] / 1000
rpk = count['mapped_reads'] / kb_len
rpks[index] += rpk
if first_bam:
# For the reference lengths we use the first BAM to make
references.append(count['reference'])
length_counts[count['length']] += 1
else:
# the bams should be sorted with the references in the same
# order
if references[index] != count['reference']:
msg = 'The reference lengths do not match in the bams'
raise RuntimeError(msg)
first_bam = False
million_reads = n_reads / 1e6
rpks /= million_reads # rpkms
self._rpkms = ArrayWrapper(rpks, bins=self._bins)
abundant_refs = BestItemsKeeper(self._n_most_expressed_reads,
izip(references, rpks),
key=itemgetter(1))
abundant_refs = [{
'reference': i[0],
'rpkm': i[1]
} for i in abundant_refs]
self._most_abundant_refs = abundant_refs
self._lengths = length_counts
def test_value_for_index_test(self):
'We can get the integer for a given index'
# pylint: disable=W0212
ints = IntCounter({3: 1, 5: 1, 7: 2, 38: 1})
assert ints._get_value_for_index(0) == 3
assert ints._get_value_for_index(1) == 5
assert ints._get_value_for_index(2) == 7
assert ints._get_value_for_index(3) == 7
assert ints._get_value_for_index(4) == 38
try:
assert ints._get_value_for_index(5) == 38
self.fail('IndexError expected')
except IndexError:
pass
def tests_draw_histograms(self):
fhand = NamedTemporaryFile(suffix='.png')
values = [1, 2, 3, 1, 2, 3, 2, 3, 2, 3, 2, 1, 4]
counters = []
counters.append(IntCounter(values))
counters.append(IntCounter(values))
counters.append(IntCounter(values))
titles = ['t1', 't2', 't3']
draw_histograms(counters, fhand, titles=titles, plots_per_chart=2)
# raw_input(fhand.name)
values = [1, 2, 3, 1, 2, 3, 2, 3, 2, 3, 2, 1, 4, 0, 5, 4, 4, 4, 4, 4]
counters = []
counters.append(IntCounter(values))
counters.append(IntCounter(values))
counters.append(IntCounter(values))
titles = ['t1', 't2', 't3']
draw_histograms(counters,
fhand,
titles=titles,
plots_per_chart=2,
ylimits=(0, 14))
def __init__(self,
vcf_fpath,
gq_threshold=None,
dp_threshold=100,
min_calls_for_pop_stats=DEF_MIN_CALLS_FOR_POP_STATS,
remarkable_coverages=None,
window_size=WINDOWS_SIZE):
if remarkable_coverages is None:
remarkable_depths = REMARKABLE_DEPTHS
self.remarkable_depths = remarkable_depths
self._reader = VCFReader(
open(vcf_fpath), min_calls_for_pop_stats=min_calls_for_pop_stats)
self._random_reader = pyvcfReader(filename=vcf_fpath)
self.window_size = window_size
self._gq_threshold = 0 if gq_threshold is None else gq_threshold
self.dp_threshold = dp_threshold
self._gt_qual_depth_counter = {HOM: IntBoxplot(), HET: IntBoxplot()}
self._ac2d = _AlleleCounts2D()
self.sample_dp_coincidence = {1: IntCounter()}
for cov in remarkable_depths:
self.sample_dp_coincidence[cov] = IntCounter()
self.called_snvs = 0
self.called_gts = IntCounter()
# sample_counter
self._sample_counters = {}
for counter_name in SAMPLE_COUNTERS:
if counter_name not in self._sample_counters:
self._sample_counters[counter_name] = {}
for sample in self._reader.samples:
if counter_name in (GT_DEPTHS, GT_QUALS):
counters = {HOM: IntCounter(), HET: IntCounter()}
else:
counters = IntCounter()
self._sample_counters[counter_name][sample] = counters
self._snv_counters = {
MAFS: IntCounter(),
MACS: IntCounter(),
MAFS_DP: IntCounter(),
SNV_QUALS: IntCounter(),
HET_IN_SNP: IntCounter(),
SNV_DENSITY: IntCounter(),
INBREED_F_IN_SNP: IntCounter(),
DEPTHS: IntCounter()
}
self._calculate()
def test_sum_with_treshold_function():
'It tests the function that calculates Q30 and Q20'
quals = IntCounter({15: 10, 21: 13, 30: 12})
assert quals.count_relative_to_value(20, operator.ge) == 25
assert quals.count_relative_to_value(30, operator.ge) == 12
def show_distances_distributions(bam_fpath, n=None, kind_of_interest=None):
bamfile = pysam.Samfile(bam_fpath)
type1 = 0
type2a = []
type2b = []
type3a = []
type3b = []
type4 = []
type5 = []
type6 = 0
others = 0
h = 0
#It tries to find out the kind of each pair of sequences
for grouped_mates in _group_alignments_by_reads(bamfile):
if n is not None and h == n:
break
h += 1
mates_alignments = _split_mates(grouped_mates)
i = 0
pair = []
for alignments_group in mates_alignments:
i += 1
mate = _get_mate(i, mates_alignments)
primary_mate = _get_primary_alignment(mate)
primary_alignment = _get_primary_alignment(alignments_group)
mates = [primary_alignment, primary_mate]
if _read_is_totally_mapped(alignments_group, 0.05):
if primary_alignment.mate_is_unmapped:
kind = '1'
else:
if primary_alignment.rname != primary_alignment.rnext:
kind = '6'
else:
if _mates_are_outies(mates):
kind = '3a'
elif _mates_are_innies(mates):
kind = '2a'
else:
kind = '5'
else:
fragment = _find_secondary_fragment(alignments_group, 5, 100)
if fragment is not None:
fragments = [primary_alignment, fragment]
if (_alignments_in_same_ref([fragments[0], primary_mate])
or _alignments_in_same_ref([fragments[1], primary_mate])):
kind = '4'
else:
kind = 'other'
else:
if primary_alignment.is_unmapped:
kind = '1'
else:
if primary_alignment.rname == primary_alignment.rnext:
if _mates_are_outies(mates):
kind = '3b'
elif _mates_are_innies(mates):
kind = '2b'
else:
kind = '5'
else:
kind = '6'
pair.append(kind)
if '1' in pair:
type1 += 1
elif '6' in pair:
type6 += 1
elif 'other' in pair:
others += 1
else:
distance = _find_distance(mates)
if '4' in pair:
type4.append(distance)
elif '2b' in pair:
type2b.append(distance)
elif '3b' in pair:
type3b.append(distance)
elif '2a' in pair:
type2a.append(distance)
elif '3a' in pair:
type3a.append(distance)
elif '5' in pair:
type5.append(distance)
stats1 = {'1': type1, '6': type6, 'other': others}
stats2 = {'4': type4, '2b': type2b, '3b': type3b, '2a': type2a,
'3a': type3a, '5': type5}
for key in stats1.keys():
print key.ljust(5), stats1[key]
for key in stats2.keys():
print key.ljust(5), len(stats2[key])
for key in stats2.keys():
if key in kind_of_interest:
print key, 'distance distribution'
counter = IntCounter(iter(stats2[key]))
distribution = counter.calculate_distribution(remove_outliers=True)
counts = distribution['counts']
bin_limits = distribution['bin_limits']
print draw_histogram(bin_limits, counts)
def test_sum_with_treshold_function():
'It tests the function that calculates Q30 and Q20'
quals = IntCounter({15: 10, 21: 13, 30: 12})
assert quals.count_relative_to_value(20, operator.ge) == 25
assert quals.count_relative_to_value(30, operator.ge) == 12
def __init__(self, bam_fhands, mapqs=MAPQS_TO_CALCULATE):
self._bam_fhands = bam_fhands
self.mapqs_to_calculate = mapqs
self._counters = {mapq: IntCounter() for mapq in mapqs}
self._calculate()
def __init__(self, bams):
# TODO flag, read_group
self._bams = bams
self._mapqs = IntCounter()
self._flag_counts = {}
self._count_mapqs()
def test_n50_calculation():
'It calculates N50.'
assert calculate_nx(IntCounter([2, 2, 2, 3, 3, 4, 8, 8]), 50) == 8
assert calculate_nx(IntCounter([2, 2, 2, 3, 3, 4, 8, 8]), 95) == 2
assert calculate_nx(IntCounter([8, 8, 8, 8, 8, 8, 8, 8]), 50) == 8
assert calculate_nx(IntCounter(), 50) is None
def test_draw_histogram_in_fhand(self):
values = [1, 2, 3, 1, 2, 3, 2, 3, 2, 3, 2, 1, 4]
fhand = NamedTemporaryFile(suffix='.png')
counter = IntCounter(values)
draw_histogram_in_fhand(counter, fhand=fhand)
