# Copyright 2018 Google LLC.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from this
# software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
"""Tests for third_party.nucleus.util.utils."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
from absl.testing import absltest
from absl.testing import parameterized
import numpy as np
import numpy.testing as npt
from third_party.nucleus.testing import test_utils
from third_party.nucleus.util import ranges
from third_party.nucleus.util import utils
class UtilsTest(parameterized.TestCase):
def test_read_range(self):
"""Tests reads have their ranges calculated correctly."""
start = 10000001
read = test_utils.make_read(
'AAACAG',
chrom='chrX',
start=start,
cigar='2M1I3M',
quals=range(10, 16),
name='read1')
self.assertEqual(
ranges.make_range('chrX', start, start + 5), utils.read_range(read))
read = test_utils.make_read(
'AAACAG',
chrom='chrX',
start=start,
cigar='2M16D3M',
quals=range(10, 16),
name='read1')
self.assertEqual(
ranges.make_range('chrX', start, start + 5 + 16),
utils.read_range(read))
def test_read_end(self):
"""Tests reads have their ends calculated correctly."""
start = 10000001
read = test_utils.make_read(
'AAACAG',
chrom='chrX',
start=start,
cigar='2M1I3M',
quals=range(10, 16),
name='read1')
self.assertEqual(
start + 5, utils.read_end(read))
read = test_utils.make_read(
'AAACAG',
chrom='chrX',
start=start,
cigar='2M16D3M',
quals=range(10, 16),
name='read1')
self.assertEqual(
start + 5 + 16,
utils.read_end(read))
def test_reservoir_sample_length(self):
"""Tests samples have expected length."""
first_ten_ints = range(10)
# Test sampling with k > len(iterable).
self.assertEqual(len(utils.reservoir_sample(first_ten_ints, 11)), 10)
# Test sampling with k == len(iterable).
self.assertEqual(len(utils.reservoir_sample(first_ten_ints, 10)), 10)
# Test sampling with k < len(iterable).
self.assertEqual(len(utils.reservoir_sample(first_ten_ints, 9)), 9)
# Test sampling with k == 0.
self.assertEqual(len(utils.reservoir_sample(first_ten_ints, 0)), 0)
# Test sampling with k < 0 (bad args).
with self.assertRaises(ValueError):
utils.reservoir_sample(first_ten_ints, -1)
@parameterized.parameters(
(10, 0),
(1, 1),
(10, 1),
(1, 3),
(3, 3),
(6, 3),
(10, 3),
)
def test_reservoir_sample_frequency(self, iterable_size, k):
"""Tests observed frequency is close to expected frequency."""
# Use a fixed random number so our test is deterministic.
random = np.random.RandomState(123456789)
n_replicates = 100000
counts = collections.Counter(
item
for _ in range(n_replicates)
for item in utils.reservoir_sample(range(iterable_size), k, random))
expected_frequency = min(k / float(iterable_size), 1.0)
for c in counts.values():
observed_frequency = c / float(n_replicates)
npt.assert_allclose(observed_frequency, expected_frequency, atol=0.01)
@parameterized.parameters(
dict(ref1='chr1', s1=0, e1=3, ref2='chr1', s2=4, e2=10, expected=False),
dict(ref1='chr1', s1=0, e1=3, ref2='chr1', s2=3, e2=10, expected=False),
dict(ref1='chr1', s1=0, e1=3, ref2='chr1', s2=2, e2=10, expected=True),
dict(ref1='chr1', s1=0, e1=3, ref2='chr1', s2=1, e2=10, expected=True),
dict(ref1='chr1', s1=0, e1=3, ref2='chr1', s2=0, e2=10, expected=True),
dict(ref1='chr1', s1=0, e1=3, ref2='chr1', s2=0, e2=1, expected=True),
dict(ref1='chr1', s1=0, e1=3, ref2='chr1', s2=0, e2=2, expected=True),
dict(ref1='chr1', s1=0, e1=3, ref2='chr1', s2=0, e2=3, expected=True),
dict(ref1='chr1', s1=0, e1=3, ref2='chr1', s2=1, e2=2, expected=True),
dict(ref1='chr1', s1=0, e1=3, ref2='chr1', s2=1, e2=3, expected=True),
dict(ref1='chr1', s1=0, e1=3, ref2='chr1', s2=2, e2=3, expected=True),
# dict(ref1='chr1', s1=0, e1=3, ref2='chr1', s2=3, e2=3, expected=False),
dict(ref1='chr1', s1=0, e1=3, ref2='chr1', s2=0, e2=4, expected=True),
dict(ref1='chr1', s1=0, e1=3, ref2='chr1', s2=1, e2=4, expected=True),
dict(ref1='chr1', s1=0, e1=3, ref2='chr2', s2=1, e2=4, expected=False),
)
def test_read_overlaps_region(self, ref1, s1, e1, ref2, s2, e2, expected):
def check_overlaps(chr1, start1, end1, chr2, start2, end2, expected):
nbp = end1 - start1
read = test_utils.make_read(
'A' * nbp, chrom=chr1, start=start1, cigar='{}M'.format(nbp))
region = ranges.make_range(chr2, start2, end2)
self.assertEqual(utils.read_overlaps_region(read, region), expected)
# This check ensures we get the same result calling ranges.ranges_overlap.
self.assertEqual(
ranges.ranges_overlap(region, utils.read_range(read)), expected)
check_overlaps(ref1, s1, e1, ref2, s2, e2, expected)
check_overlaps(ref2, s2, e2, ref1, s1, e1, expected)
if __name__ == '__main__':
absltest.main()
