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_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 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 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_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 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)
