def test_set_gt_to_missing_by_dp2(self):
variations = VariationsArrays()
gts = numpy.array([[[0, 0], [1, 1], [0, 1], [1, 1], [0, 0]],
[[0, 0], [0, 0], [0, 1], [0, 0], [1, 1]]])
dps = numpy.array([[10, 20, 5, 20, 25], [10, 2, 5, 15, 5]])
variations[GT_FIELD] = gts
variations[DP_FIELD] = dps
set_low_dp_gts_to_missing = LowDPGTsToMissingSetter(min_dp=10)
res = set_low_dp_gts_to_missing(variations)[FLT_VARS]
assert numpy.all(res[DP_FIELD] == numpy.array([[10, 20, -1, 20, 25],
[10, -1, -1, 15, -1]]))
assert numpy.all(res[GT_FIELD] == numpy.array([[[0, 0], [1, 1], [
-1, -1
], [1, 1], [0, 0]], [[0, 0], [-1, -1], [-1, -1], [0, 0], [-1, -1]]]))
variations = VariationsArrays()
variations[GT_FIELD] = gts
variations[DP_FIELD] = dps
set_low_dp_gts_to_missing = LowDPGTsToMissingSetter(
min_dp=10, query_field_to_missing=False)
res = set_low_dp_gts_to_missing(variations)[FLT_VARS]
assert numpy.all(res[DP_FIELD] == variations[DP_FIELD])
def test_filter_chi2_gt_sample_sets(self):
variations = VariationsArrays()
gts = numpy.array([[[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0]],
[[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0]],
[[0, 0], [0, 0], [0, 1], [1, 1], [1, 1], [1, 1]],
[[0, 0], [0, 0], [0, 1], [1, 1], [1, 1], [1, 1]]])
variations[GT_FIELD] = gts
variations.samples = [1, 2, 3, 4, 5, 6]
samples1 = [1, 2, 3]
samples2 = [4, 5, 6]
flt = Chi2GtFreqs2SampleSetsFilter(samples1,
samples2,
min_pval=0.05,
n_bins=2,
report_selection=True)
res = flt(variations)
assert list(res[COUNTS]) == [2, 2]
assert numpy.all(res[FLT_VARS][GT_FIELD] == gts[:2, ...])
assert res[FLT_STATS][N_KEPT] == 2
assert res[FLT_STATS][TOT] == 4
assert res[FLT_STATS][N_FILTERED_OUT] == 2
assert res[SELECTED_VARS].shape
flt = Chi2GtFreqs2SampleSetsFilter(samples1,
samples2,
min_pval=0.05,
n_bins=2,
return_discarded=True)
res = flt(variations)
assert res[DISCARDED_VARS].num_variations
def test_filter_macs(self):
# with some missing values
variations = VariationsArrays()
gts = numpy.array([[[0, 0], [1, 1], [0, 1], [1, 1], [0, 0]],
[[0, 0], [0, 0], [0, 0], [0, 0], [1, 1]],
[[0, 0], [0, 0], [0, 0], [0, 0], [0, 1]],
[[0, 0], [-1, -1], [0, 1], [0, 0], [1, 1]]])
variations[GT_FIELD] = gts
filtered = MacFilter(min_num_genotypes=5,
report_selection=True)(variations)
assert numpy.all(filtered[FLT_VARS][GT_FIELD] == gts)
assert filtered[FLT_STATS][N_KEPT] == 4
assert filtered[FLT_STATS][TOT] == 4
assert filtered[FLT_STATS][N_FILTERED_OUT] == 0
assert filtered[SELECTED_VARS].shape
filtered = MacFilter(min_mac=0, min_num_genotypes=5)(variations)
assert numpy.all(filtered[FLT_VARS][GT_FIELD] == gts[[0, 1, 2]])
assert filtered[FLT_STATS][N_KEPT] == 3
assert filtered[FLT_STATS][TOT] == 4
assert filtered[FLT_STATS][N_FILTERED_OUT] == 1
# without missing values
variations = VariationsArrays()
gts = numpy.array([[[0, 0], [1, 1], [0, 1], [1, 1], [0, 0]],
[[0, 0], [0, 0], [0, 0], [0, 0], [1, 1]],
[[0, 0], [0, 0], [0, 0], [0, 0], [0, 1]],
[[0, 0], [0, 0], [0, 1], [0, 0], [1, 1]]])
variations[GT_FIELD] = gts
expected = numpy.array([[[0, 0], [1, 1], [0, 1], [1, 1], [0, 0]],
[[0, 0], [0, 0], [0, 0], [0, 0], [1, 1]],
[[0, 0], [0, 0], [0, 1], [0, 0], [1, 1]]])
filtered = MacFilter(max_mac=4, min_num_genotypes=0)(variations)
assert numpy.all(filtered[FLT_VARS][GT_FIELD] == expected)
expected = numpy.array([[[0, 0], [0, 0], [0, 0], [0, 0], [1, 1]],
[[0, 0], [0, 0], [0, 1], [0, 0], [1, 1]]])
filtered = MacFilter(min_mac=3.5, max_mac=4,
min_num_genotypes=0)(variations)
assert numpy.all(filtered[FLT_VARS][GT_FIELD] == expected)
expected = numpy.array([[[0, 0], [1, 1], [0, 1], [1, 1], [0, 0]]])
filtered = MacFilter(max_mac=3, min_num_genotypes=0)(variations)
assert numpy.all(filtered[FLT_VARS][GT_FIELD] == expected)
filtered = MacFilter(min_mac=2, max_mac=5,
min_num_genotypes=0)(variations)
assert numpy.all(filtered[FLT_VARS][GT_FIELD] == variations[GT_FIELD])
# With hdf5 files
variations = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
filtered = MacFilter(min_mac=130, max_mac=150)(variations)
counts = Counter(filtered[FLT_VARS][GT_FIELD].flat)
assert counts == {-1: 64530, 0: 36977, 1: 18716, 2: 35}
def test_filter_mafs_2(self):
# with some missing values
variations = VariationsArrays()
gts = numpy.array([[[0, 0], [1, 1], [0, 1], [1, 1], [0, 0]],
[[0, 0], [0, 0], [0, 0], [0, 0], [1, 1]],
[[0, 0], [0, 0], [0, 0], [0, 0], [0, 1]],
[[0, 0], [-1, -1], [0, 1], [0, 0], [1, 1]]])
variations[GT_FIELD] = gts
filtered = MafFilter(min_num_genotypes=5)(variations)
assert numpy.all(filtered[FLT_VARS][GT_FIELD] == gts)
assert filtered[FLT_STATS][N_KEPT] == 4
assert filtered[FLT_STATS][TOT] == 4
assert filtered[FLT_STATS][N_FILTERED_OUT] == 0
filtered = MafFilter(min_num_genotypes=5, min_maf=0)(variations)
assert numpy.all(filtered[FLT_VARS][GT_FIELD] == gts[[0, 1, 2]])
assert filtered[FLT_STATS][N_KEPT] == 3
assert filtered[FLT_STATS][TOT] == 4
assert filtered[FLT_STATS][N_FILTERED_OUT] == 1
# without missing values
variations = VariationsArrays()
gts = numpy.array([[[0, 0], [1, 1], [0, 1], [1, 1], [0, 0]],
[[0, 0], [0, 0], [0, 0], [0, 0], [1, 1]],
[[0, 0], [0, 0], [0, 0], [0, 0], [0, 1]],
[[0, 0], [0, 0], [0, 1], [0, 0], [1, 1]]])
variations[GT_FIELD] = gts
expected = numpy.array([[[0, 0], [1, 1], [0, 1], [1, 1], [0, 0]],
[[0, 0], [0, 0], [0, 0], [0, 0], [1, 1]],
[[0, 0], [0, 0], [0, 1], [0, 0], [1, 1]]])
filtered = MafFilter(min_num_genotypes=0, max_maf=0.8)(variations)
assert numpy.all(filtered[FLT_VARS][GT_FIELD] == expected)
expected = numpy.array([[[0, 0], [0, 0], [0, 0], [0, 0], [1, 1]],
[[0, 0], [0, 0], [0, 1], [0, 0], [1, 1]]])
filtered = MafFilter(min_num_genotypes=0, min_maf=0.6,
max_maf=0.8)(variations)
assert numpy.all(filtered[FLT_VARS][GT_FIELD] == expected)
expected = numpy.array([[[0, 0], [1, 1], [0, 1], [1, 1], [0, 0]]])
filtered = MafFilter(min_num_genotypes=0, max_maf=0.5)(variations)
assert numpy.all(filtered[FLT_VARS][GT_FIELD] == expected)
filtered = MafFilter(min_num_genotypes=0, min_maf=0.5,
max_maf=1)(variations)
assert numpy.all(filtered[FLT_VARS][GT_FIELD] == variations[GT_FIELD])
# With hdf5 files
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
filtered = MafFilter(min_maf=0.6, max_maf=0.9)(hdf5)
counts = Counter(filtered[FLT_VARS][GT_FIELD].flat)
assert counts == {0: 57805, -1: 55792, 1: 32504, 2: 162, 3: 5}
def test_filter_high_density(self):
in_vars = VariationsArrays()
out_vars = VariationsArrays()
res = filter_variation_density(in_vars, out_vars=out_vars,
max_density=1, window=1)
assert not out_vars.keys()
assert not res
varis = VariationsArrays()
out_vars = VariationsArrays()
varis[CHROM_FIELD] = numpy.array([b'chr1'] * 6)
varis[POS_FIELD] = numpy.array([1, 2, 3, 4, 10, 11])
result = filter_variation_density(varis, out_vars=out_vars,
max_density=2, window=3, n_bins=2)
assert list(out_vars[POS_FIELD]) == [1, 4, 10, 11]
assert list(result[COUNTS]) == [4, 2]
assert list(result[EDGES]) == [2., 2.5, 3.]
assert result[FLT_STATS][N_KEPT] == 4
assert result[FLT_STATS][TOT] == 6
assert result[FLT_STATS][N_FILTERED_OUT] == 2
varis = VariationsArrays()
out_vars = VariationsArrays()
varis[CHROM_FIELD] = numpy.array([b'chr1'] * 6)
varis[POS_FIELD] = numpy.array([1, 2, 3, 4, 10, 11])
result = filter_variation_density(varis, out_vars=out_vars,
max_density=2, window=3, n_bins=2,
chunk_size=1)
assert list(out_vars[POS_FIELD]) == [1, 4, 10, 11]
assert list(result[COUNTS]) == [4, 2]
assert list(result[EDGES]) == [2., 2.5, 3.]
assert result[FLT_STATS][N_KEPT] == 4
assert result[FLT_STATS][TOT] == 6
assert result[FLT_STATS][N_FILTERED_OUT] == 2
varis = VariationsArrays()
out_vars = VariationsArrays()
varis[CHROM_FIELD] = numpy.array([b'chr1'] * 6)
varis[POS_FIELD] = numpy.array([1, 2, 3, 4, 10, 11])
result = filter_variation_density(varis, out_vars=out_vars,
max_density=2, window=3, n_bins=2,
chunk_size=None)
assert list(out_vars[POS_FIELD]) == [1, 4, 10, 11]
assert list(result[COUNTS]) == [4, 2]
assert list(result[EDGES]) == [2., 2.5, 3.]
assert result[FLT_STATS][N_KEPT] == 4
assert result[FLT_STATS][TOT] == 6
assert result[FLT_STATS][N_FILTERED_OUT] == 2
def test_filter_by_pos(self):
variations = VariationsArrays()
pos = numpy.array([5, 10, 15, 20, 25])
chrom = numpy.array(['1'] * len(pos))
variations[POS_FIELD] = pos
variations[CHROM_FIELD] = chrom
regions = [('1', 5, 10)]
filtered = SNPPositionFilter(regions)(variations)
assert filtered[FLT_VARS][POS_FIELD] == [5]
regions = [('2', 5, 10)]
filtered = SNPPositionFilter(regions)(variations)
assert not filtered[FLT_VARS][POS_FIELD].size
regions = [('1', 23, 30), ('2', 5, 10)]
filtered = SNPPositionFilter(regions)(variations)
assert filtered[FLT_VARS][POS_FIELD] == [25]
regions = [('1', 23, 30), ('2', 5, 10)]
filtered = SNPPositionFilter(regions, reverse=True)(variations)
assert numpy.all(filtered[FLT_VARS][POS_FIELD] == [5, 10, 15, 20])
regions = [('2', )]
filtered = SNPPositionFilter(regions)(variations)
assert not filtered[FLT_VARS][POS_FIELD].size
def test_filter_biallelic(self):
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
kept_fields = [GT_FIELD]
snps = hdf5.iterate_chunks(kept_fields=kept_fields)
chunk = first(snps)
flt_chunk = NonBiallelicFilter(report_selection=True)(chunk)
kept = flt_chunk[FLT_VARS][GT_FIELD].shape[0]
assert flt_chunk[FLT_VARS][GT_FIELD].shape[1:] == (153, 2)
assert flt_chunk[FLT_STATS][N_KEPT] == kept
assert flt_chunk[FLT_STATS][TOT] == SNPS_PER_CHUNK
assert flt_chunk[FLT_STATS][N_FILTERED_OUT] == SNPS_PER_CHUNK - kept
assert flt_chunk[SELECTED_VARS].shape
flt_chunk = NonBiallelicFilter(report_selection=True,
reverse=True)(chunk)
kept = flt_chunk[FLT_VARS][GT_FIELD].shape[0]
assert flt_chunk[FLT_VARS][GT_FIELD].shape[1:] == (153, 2)
assert flt_chunk[FLT_STATS][N_KEPT] == kept
assert flt_chunk[FLT_STATS][TOT] == SNPS_PER_CHUNK
assert flt_chunk[FLT_STATS][N_FILTERED_OUT] == SNPS_PER_CHUNK - kept
assert flt_chunk[SELECTED_VARS].shape
variations = VariationsArrays()
gts = numpy.array([
[[0, 0], [1, 1], [0, 1]],
[[0, 0], [0, 0], [0, 0]],
[[0, 0], [1, 1], [2, 2]],
])
variations[GT_FIELD] = gts
flt_vars = NonBiallelicFilter()(variations)[FLT_VARS]
expected = numpy.array([[[0, 0], [1, 1], [0, 1]]])
assert numpy.all(flt_vars[GT_FIELD] == expected)
def test_filter_allele_depth_based_maf(self):
allele_depths_snp1 = [
[10, 0, 1], # Allele Obervation in sample1
[4, 6, 1]
] # Allele Obervation in sample2
allele_depths_snp2 = [
[10, 0, 0], # Allele Obervation in sample1
[0, 5, 7]
] # Allele Obervation in sample2
allele_depths_snp3 = [
[-1, -1, -1], # Allele Obervation in sample1
[-1, -1, -1]
] # Allele Obervation in sample2
allele_depths = numpy.array(
[allele_depths_snp1, allele_depths_snp2, allele_depths_snp3])
varis = VariationsArrays()
varis[AD_FIELD] = allele_depths
filtered_vars = AlleleObservationBasedMafFilter(
max_maf=0.8)(varis)[FLT_VARS]
assert filtered_vars.num_variations == 2
filtered_vars = AlleleObservationBasedMafFilter(
max_maf=0.5)(varis)[FLT_VARS]
expected = numpy.array([allele_depths_snp2])
assert numpy.all(filtered_vars[AD_FIELD] == expected)
filtered_vars = AlleleObservationBasedMafFilter(
min_maf=0.8)(varis)[FLT_VARS]
assert not filtered_vars.num_variations
expected = numpy.array([allele_depths_snp2])
assert numpy.all(filtered_vars[AD_FIELD] == expected)
def test_annotation_filter(self):
annot_id = 'test'
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
variations = VariationsArrays()
variations.put_chunks(hdf5.iterate_chunks())
annotator = IsVariableAnnotator(annot_id=annot_id)
result = annotator(variations)
annotated_variations = result[ANNOTATED_VARS]
field = '/variations/info/{}'.format(annot_id)
annotator = FieldValueFilter(field_path=field, value=0)
filtered = annotator(annotated_variations)
assert filtered[FLT_STATS][N_KEPT] == 133
assert filtered[FLT_STATS][TOT] == 943
assert filtered[FLT_STATS][N_FILTERED_OUT] == 810
def test_find(self):
snps = VariationsArrays()
chroms = [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4]
pos = [1, 2, 3, 1, 2, 3, 1, 2, 3, 2, 4, 6]
snps[CHROM_FIELD] = numpy.array(chroms)
snps[POS_FIELD] = numpy.array(pos)
def test_filter_by_pos(self):
variations = VariationsArrays()
pos = numpy.array([5, 10, 15, 20, 25])
chrom = numpy.array(['1'] * len(pos))
variations[POS_FIELD] = pos
variations[CHROM_FIELD] = chrom
filtered = VarsSamplingFilter(sample_rate=0.5)(variations)[FLT_VARS]
assert filtered.num_variations == 2
def test_filter_indels(self):
variations = VariationsArrays()
alt = [['A', 'T', 'CG'], ['A', '', ''], ['C', '', ''], ['G', '', '']]
variations[ALT_FIELD] = numpy.array(alt)
filter_gts = IndelFilter()
res = filter_gts(variations)
assert numpy.all(res[FLT_VARS][ALT_FIELD] == alt[1:])
variations = VariationsArrays()
alt = [['A', 'TT', 'T'], ['A', '', ''], ['C', '', ''], ['G', '', '']]
variations[ALT_FIELD] = numpy.array(alt)
filter_gts = IndelFilter()
res = filter_gts(variations)
assert numpy.all(res[FLT_VARS][ALT_FIELD] == alt[1:])
variations = VariationsArrays()
alt = [[b'A', b'TT'], [b'A', b''], [b'C', b''], [b'G', b'']]
variations[ALT_FIELD] = numpy.array(alt)
filter_gts = IndelFilter()
res = filter_gts(variations)
assert numpy.all(res[FLT_VARS][ALT_FIELD] == alt[1:])
variations = VariationsArrays()
alt = [[b'A', b''], [b'A', b''], [b'C', b''], [b'GT', b'T']]
variations[ALT_FIELD] = numpy.array(alt)
filter_gts = IndelFilter()
res = filter_gts(variations)
assert numpy.all(res[FLT_VARS][ALT_FIELD] == alt[:-1])
variations = VariationsArrays()
alt = [['A', 'T'], ['A', ''], ['C', ''], ['G', '']]
variations[ALT_FIELD] = numpy.array(alt)
filter_gts = IndelFilter()
res = filter_gts(variations)
assert numpy.all(res[FLT_VARS][ALT_FIELD] == alt)
variations = VariationsArrays()
alt = [[b'A'], [b'A'], [b'C'], [b'G']]
variations[ALT_FIELD] = numpy.array(alt)
filter_gts = IndelFilter()
res = filter_gts(variations)
assert numpy.all(res[FLT_VARS][ALT_FIELD] == alt)
variations = VariationsArrays()
alt = [[b'A'], [b'A'], [b'C'], [b'GT']]
variations[ALT_FIELD] = numpy.array(alt)
filter_gts = IndelFilter()
res = filter_gts(variations)
assert numpy.all(res[FLT_VARS][ALT_FIELD] == alt[:-1])
variations = VariationsArrays()
alt = [['A'], ['A'], ['C'], ['GT']]
variations[ALT_FIELD] = numpy.array(alt)
filter_gts = IndelFilter()
res = filter_gts(variations)
assert numpy.all(res[FLT_VARS][ALT_FIELD] == alt[:-1])
def test_filter_or(self):
variations = VariationsArrays()
gts = numpy.array([[[0, 0], [1, 1], [0, 1], [1, 1], [0, 0]],
[[0, 0], [0, 0], [0, 0], [0, 0], [1, 1]],
[[0, 0], [0, 0], [0, 0], [0, 0], [0, 1]],
[[0, 0], [0, 0], [0, 1], [0, 0], [1, 1]]])
variations[GT_FIELD] = gts
variations.samples = [1, 2, 3, 4, 5]
filter1 = ObsHetFilter(min_num_genotypes=0)
filter2 = ObsHetFilter(min_het=0.2, min_num_genotypes=0)
filtered = OrFilter([filter1, filter2])(variations)
assert numpy.all(filtered[FLT_VARS][GT_FIELD] == gts)
assert filtered[FLT_STATS][N_KEPT] == 4
assert filtered[FLT_STATS][TOT] == 4
assert filtered[FLT_STATS][N_FILTERED_OUT] == 0
def test_filter_or(self):
variations = VariationsArrays()
gts = numpy.array([[[0, 0], [1, 1], [0, 1], [1, 1], [0, 0]],
[[0, 0], [0, 0], [0, 0], [0, 0], [1, 1]],
[[0, 0], [0, 0], [0, 0], [0, 0], [0, 1]],
[[0, 0], [0, 0], [0, 1], [0, 0], [1, 1]]])
variations[GT_FIELD] = gts
variations.samples = [1, 2, 3, 4, 5]
filter1 = ObsHetFilter(min_num_genotypes=0)
filter2 = ObsHetFilter(min_het=0.2, min_num_genotypes=0)
filtered = OrFilter([filter1, filter2])(variations)
assert numpy.all(filtered[FLT_VARS][GT_FIELD] == gts)
assert filtered[FLT_STATS][N_KEPT] == 4
assert filtered[FLT_STATS][TOT] == 4
assert filtered[FLT_STATS][N_FILTERED_OUT] == 0
def test_filter_by_pos2(self):
variations = VariationsArrays()
pos = numpy.array([5, 10, 15, 20, 25])
chrom = numpy.array(['1'] * len(pos))
variations[POS_FIELD] = pos
variations[CHROM_FIELD] = chrom
filtered = VarsSamplingFilter2(num_vars=2)(variations)[FLT_VARS]
assert filtered.num_variations == 2
filtered = VarsSamplingFilter2(num_vars=5)(variations)[FLT_VARS]
assert numpy.all(filtered[POS_FIELD] == pos)
def test_fix_duplicated_alleles(self):
gt = numpy.array([[[0, 0], [0, 4], [-1, -1], [0, 1], [0, 0], [0, 0],
[0, 0], [0, 1], [0, 0], [0, 0], [2, 2], [3, 3]],
[[0, 0], [0, 0], [0, 2], [0, 1], [0, 0], [0, 1],
[0, 0], [0, 1], [0, 0], [0, 0], [0, 0], [0, 0]],
[[0, 0], [0, 0], [0, 0], [0, 1], [0, 0], [1, 2],
[0, 2], [0, 1], [1, 1], [0, 0], [0, 0], [0, 3]],
[[2, 2], [0, 0], [0, 1], [-1, -1], [0, 0], [0, 1],
[0, 0], [-1, -1], [0, 0], [0, 0], [0, 0], [0, 0]],
[[-1, -1], [-1, -1], [0, 1], [1, 1], [-1, -1],
[-1, -1], [0, 1], [1, 1], [0, 2], [-1, -1],
[-1, -1], [-1, -1]]])
alt = numpy.array([[b'T', b'C', b'A', b'C', b''],
[b'GTC', b'ATC', b'', b'', b''],
[b'C', b'C', b'T', b'C', b''],
[b'T', b'A', b'', b'', b''],
[b'A', b'A', b'T', b'', b'']])
ref = numpy.array([b'G', b'G', b'C', b'T', b'C'])
variations = VariationsArrays()
variations[GT_FIELD] = gt
variations[ALT_FIELD] = alt
variations[REF_FIELD] = ref
gt_expected = numpy.array([[[0, 0], [0, 2], [-1, -1], [0, 1], [0, 0],
[0, 0], [0, 0], [0, 1], [0, 0], [0, 0],
[2, 2], [3, 3]],
[[0, 0], [0, 0], [0, 2], [0, 1], [0, 0],
[0, 1], [0, 0], [0, 1], [0, 0], [0, 0],
[0, 0], [0, 0]],
[[0, 0], [0, 0], [0, 0], [0, 0], [0, 0],
[0, 0], [0, 0], [0, 0], [0, 0], [0, 0],
[0, 0], [0, 1]],
[[1, 1], [0, 0], [0, 0], [-1, -1], [0, 0],
[0, 0], [0, 0], [-1, -1], [0, 0], [0, 0],
[0, 0], [0, 0]],
[[-1, -1], [-1, -1], [0, 1], [1, 1],
[-1, -1], [-1, -1], [0, 1], [1, 1], [0, 1],
[-1, -1], [-1, -1], [-1, -1]]])
alt_expected = numpy.array([[b'T', b'C', b'A', b'', b''],
[b'GTC', b'ATC', b'', b'', b''],
[b'T', b'', b'', b'', b''],
[b'A', b'', b'', b'', b''],
[b'A', b'T', b'', b'', b'']])
fix_duplicated_alleles = DuplicatedAlleleFixer()
fix_duplicated_alleles(variations)
assert numpy.all(variations[GT_FIELD] == gt_expected)
assert numpy.all(variations[ALT_FIELD] == alt_expected)
def test_filter_chi2_gt_sample_sets(self):
variations = VariationsArrays()
gts = numpy.array([[[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0]],
[[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0]],
[[0, 0], [0, 0], [0, 1], [1, 1], [1, 1], [1, 1]],
[[0, 0], [0, 0], [0, 1], [1, 1], [1, 1], [1, 1]]])
variations[GT_FIELD] = gts
variations.samples = [1, 2, 3, 4, 5, 6]
samples1 = [1, 2, 3]
samples2 = [4, 5, 6]
flt = Chi2GtFreqs2SampleSetsFilter(samples1, samples2, min_pval=0.05,
n_bins=2, report_selection=True)
res = flt(variations)
assert list(res[COUNTS]) == [2, 2]
assert numpy.all(res[FLT_VARS][GT_FIELD] == gts[:2, ...])
assert res[FLT_STATS][N_KEPT] == 2
assert res[FLT_STATS][TOT] == 4
assert res[FLT_STATS][N_FILTERED_OUT] == 2
assert res[SELECTED_VARS].shape
flt = Chi2GtFreqs2SampleSetsFilter(samples1, samples2, min_pval=0.05,
n_bins=2, return_discarded=True)
res = flt(variations)
assert res[DISCARDED_VARS].num_variations
def test_filter_non_variable_and_all_missing(self):
variations = VariationsArrays()
gts = numpy.array([[[0, 0], [0, 0], [0, 0], [0, 0], [0, 0]],
[[0, 0], [0, 0], [0, 0], [0, 0], [-1, -1]],
[[0, 1], [0, 0], [0, 0], [-1, -1], [-1, -1]],
[[-1, -1], [-1, -1], [-1, -1], [-1, -1], [-1, -1]]])
variations[GT_FIELD] = gts
expected = numpy.array([[[0, 1], [0, 0], [0, 0], [-1, -1], [-1, -1]]])
filter_gts = VariableAndNotAllMissing()
filtered = filter_gts(variations)
assert numpy.all(filtered[FLT_VARS][GT_FIELD] == expected)
assert filtered[FLT_STATS][N_KEPT] == 1
assert filtered[FLT_STATS][TOT] == 4
assert filtered[FLT_STATS][N_FILTERED_OUT] == 3
def test_filter_snps_missing_gts_or_het(self):
variations = VariationsArrays()
gts = numpy.array([[[0, 0], [0, 0], [0, 0], [0, 0], [1, 1]],
[[0, 0], [0, 0], [0, 0], [0, 0], [1, 0]],
[[0, 0], [0, 0], [1, 0], [-1, -1], [-1, -1]],
[[0, 0], [0, 0], [0, 0], [-1, -1], [-1, -1]]])
variations[GT_FIELD] = gts
expected = numpy.array([[[0, 0], [0, 0], [0, 0], [0, 0], [1, 1]]])
filter_gts = NoMissingGTsOrHetFilter()
filtered = filter_gts(variations)
assert numpy.all(filtered[FLT_VARS][GT_FIELD] == expected)
assert filtered[FLT_STATS][N_KEPT] == 1
assert filtered[FLT_STATS][TOT] == 4
assert filtered[FLT_STATS][N_FILTERED_OUT] == 3
def test_set_gt_to_missing_by_qual(self):
variations = VariationsArrays()
gts = numpy.array([[[0, 0], [1, 1], [0, 1], [1, 1], [0, 0]],
[[0, 0], [0, 0], [0, 1], [0, 0], [1, 1]]])
gqs = numpy.array([[10, 20, 5, 20, 25], [10, 2, 5, 15, 5]])
variations[GT_FIELD] = gts
variations[GQ_FIELD] = gqs
set_low_qual_gts_to_missing = LowQualGTsToMissingSetter(min_qual=0)
set_low_qual_gts_to_missing(variations)
filtered = variations[GT_FIELD]
assert numpy.all(filtered == gts)
expected = numpy.array([[[0, 0], [1, 1], [-1, -1], [1, 1], [0, 0]],
[[0, 0], [-1, -1], [-1, -1], [0, 0], [-1,
-1]]])
set_low_qual_gts_to_missing = LowQualGTsToMissingSetter(min_qual=10)
set_low_qual_gts_to_missing(variations)
assert numpy.all(variations[GT_FIELD] == expected)
variations = VariationsArrays()
gts = numpy.array([[[0, 0], [1, 1], [0, 1], [1, 1], [0, 0]],
[[0, 0], [0, 0], [0, 1], [0, 0], [1, 1]]])
gqs = numpy.array([[10, 20, 5, 20, 25], [10, 2, 5, 15, 5]])
variations[GT_FIELD] = gts
variations[GQ_FIELD] = gqs
set_low_qual_gts_to_missing(variations)
assert numpy.all(variations[GT_FIELD] == expected)
set_low_qual_gts_to_missing = LowQualGTsToMissingSetter(min_qual=100)
set_low_qual_gts_to_missing(variations)
assert numpy.all(variations[GT_FIELD] == -1)
h5_1 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
set_low_qual_gts_to_missing = LowQualGTsToMissingSetter(min_qual=0)
h5_2 = set_low_qual_gts_to_missing(h5_1)
assert numpy.all(h5_1[GT_FIELD][:] == h5_2[FLT_VARS][GT_FIELD])
def test_filter_quality_snps(self):
variations = VariationsArrays()
gts = numpy.array([[[0, 0], [1, 1]], [[0, 1], [1, 1]], [[0, 0], [0,
0]],
[[0, 0], [0, 0]], [[0, 1], [0, 0]]])
snp_quals = numpy.array([5, 10, 15, 5, 20])
variations[GT_FIELD] = gts
variations['/variations/qual'] = snp_quals
filtered = SNPQualFilter(report_selection=True)(variations)
filtered_qual = filtered[FLT_VARS]['/variations/qual']
filtered_gts = filtered[FLT_VARS][GT_FIELD]
assert numpy.all(variations['/variations/qual'] == filtered_qual)
assert numpy.all(variations[GT_FIELD] == filtered_gts)
assert filtered[SELECTED_VARS].shape
expected_gts = numpy.array([[[0, 0], [0, 0]], [[0, 1], [0, 0]]])
exp_snp_quals = numpy.array([15, 20])
filtered = SNPQualFilter(min_qual=15)(variations)
assert numpy.all(
filtered[FLT_VARS]['/variations/qual'] == exp_snp_quals)
assert numpy.all(filtered[FLT_VARS][GT_FIELD] == expected_gts)
assert filtered[FLT_STATS][N_KEPT] == 2
assert filtered[FLT_STATS][TOT] == 5
assert filtered[FLT_STATS][N_FILTERED_OUT] == 3
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
kept_fields = ['/variations/qual']
snps = hdf5.iterate_chunks(kept_fields=kept_fields)
chunk = first(snps)
flt_chunk = SNPQualFilter(min_qual=530)(chunk)[FLT_VARS]
assert first(flt_chunk.values()).shape[0] > 126
flt_chunk = SNPQualFilter()(chunk)[FLT_VARS]
assert first(flt_chunk.values()).shape[0] == SNPS_PER_CHUNK
flt_chunk = SNPQualFilter(max_qual=1000)(chunk)[FLT_VARS]
assert first(flt_chunk.values()).shape[0] > 92
flt_chunk = SNPQualFilter(min_qual=530, max_qual=1000)(chunk)[FLT_VARS]
assert first(flt_chunk.values()).shape[0] > 18
flt_chunk = SNPQualFilter(min_qual=math.inf)(chunk)[FLT_VARS]
assert first(flt_chunk.values()).shape[0] == 20
flt_chunk = SNPQualFilter(max_qual=-1)(chunk)[FLT_VARS]
assert first(flt_chunk.values()).shape[0] == 0
def test_index(self):
snps = VariationsArrays()
chroms = [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4]
pos = [1, 2, 3, 1, 2, 3, 1, 2, 3, 2, 4, 6]
snps[CHROM_FIELD] = numpy.array(chroms)
snps[POS_FIELD] = numpy.array(pos)
index = PosIndex(snps)
assert index.index_pos(1, 1) == 0
assert index.index_pos(2, 1) == 3
assert index.index_pos(3, 1) == 6
assert index.index_pos(4, 1) == 9
assert index.index_pos(4, 2) == 9
assert index.index_pos(4, 3) == 10
assert index.index_pos(4, 4) == 10
assert index.get_chrom_range_index(1) == (0, 2)
assert index.get_chrom_range_pos(1) == (1, 3)
assert index.covered_length == 10
def test_filter_called_gt(self):
variations = VariationsArrays()
gts = numpy.array([[[0, 0], [0, 0], [0, 0], [0, 0], [0, 0]],
[[0, 0], [0, 0], [0, 0], [0, 0], [-1, -1]],
[[0, 0], [0, 0], [0, 0], [-1, -1], [-1, -1]],
[[0, 0], [-1, -1], [-1, -1], [-1, -1], [-1, -1]]])
variations[GT_FIELD] = gts
expected = numpy.array([[[0, 0], [0, 0], [0, 0], [0, 0], [0, 0]]])
filter_gts = MinCalledGTsFilter(min_called=5, rates=False)
filtered = filter_gts(variations)
assert numpy.all(filtered[FLT_VARS][GT_FIELD] == expected)
assert filtered[FLT_STATS][N_KEPT] == 1
assert filtered[FLT_STATS][TOT] == 4
assert filtered[FLT_STATS][N_FILTERED_OUT] == 3
expected = numpy.array([[[0, 0], [0, 0], [0, 0], [0, 0], [0, 0]],
[[0, 0], [0, 0], [0, 0], [0, 0], [-1, -1]],
[[0, 0], [0, 0], [0, 0], [-1, -1], [-1, -1]]])
filter_gts = MinCalledGTsFilter(min_called=2, rates=False)
filtered = filter_gts(variations)
assert numpy.all(filtered[FLT_VARS][GT_FIELD] == expected)
expected = numpy.array([[[0, 0], [0, 0], [0, 0], [0, 0], [0, 0]],
[[0, 0], [0, 0], [0, 0], [0, 0], [-1, -1]],
[[0, 0], [0, 0], [0, 0], [-1, -1], [-1, -1]]])
filter_gts = MinCalledGTsFilter(min_called=0.4, rates=True)
filtered = filter_gts(variations)
assert numpy.all(filtered[FLT_VARS][GT_FIELD] == expected)
filter_gts = MinCalledGTsFilter(min_called=0, rates=True)
filtered = filter_gts(variations)
assert numpy.all(filtered[FLT_VARS][GT_FIELD] == variations[GT_FIELD])
# With hdf5 file
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
filter_gts = MinCalledGTsFilter(min_called=0.4,
rates=True,
report_selection=True)
filtered = filter_gts(hdf5)
counts = Counter(filtered[FLT_VARS][GT_FIELD].flat)
assert counts == {0: 89936, 1: 50473, -1: 40972, 2: 378, 3: 5}
assert filtered[SELECTED_VARS].shape
def test_filter_samples_by_missing(self):
variations = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
chunk = first(variations.iterate_chunks())
new_var = VariationsArrays()
filter_samples_by_missing_rate(chunk,
min_called_rate=0.9,
out_vars=new_var)
assert len(new_var.samples) == 0
new_var = VariationsArrays()
filter_samples_by_missing_rate(chunk,
min_called_rate=0.1,
out_vars=new_var)
assert len(new_var.samples) == len(chunk.samples)
# for some samples
new_var = VariationsArrays()
filter_samples_by_missing_rate(chunk,
min_called_rate=0.1,
out_vars=new_var,
samples=['1_18_4_gbs', '1_19_4_gbs'])
assert new_var.samples == ['1_18_4_gbs', '1_19_4_gbs']
# for het
new_var = VariationsArrays()
filter_samples_by_missing_rate(chunk, max_het=0.001, out_vars=new_var)
assert new_var.samples == [
'1_35_2_gbs', '4_136B_2_gbs', '4_5_5_gbs', '5_66B_3_gbs'
]
# check that it works by chunk
new_var = VariationsArrays()
res = filter_samples_by_missing_rate(variations,
min_called_rate=0.2,
out_vars=new_var,
do_histogram=True)
new_var2 = VariationsArrays()
res2 = filter_samples_by_missing_rate(variations,
min_called_rate=0.2,
out_vars=new_var2,
chunk_size=None,
do_histogram=True)
assert res2['missing_rates'].shape[0] == len(variations.samples)
assert res2['selected_samples'].shape[0] == len(variations.samples)
assert new_var.samples == new_var2.samples
assert numpy.all(new_var[GT_FIELD] == new_var2[GT_FIELD])
assert numpy.allclose(res[EDGES], res2[EDGES])
assert numpy.all(res[COUNTS][:] == res2[COUNTS][:])
def test_bisect(self):
snps = VariationsArrays()
chroms = [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4]
pos = [1, 3, 5, 1, 2, 3, 1, 2, 3, 2, 4, 6]
snps[CHROM_FIELD] = numpy.array(chroms)
snps[POS_FIELD] = numpy.array(pos)
# bisect right
assert var_bisect_right(snps, 1, 0) == 0
assert var_bisect_right(snps, 1, 1) == 1
assert var_bisect_right(snps, 1, 3) == 2
assert var_bisect_right(snps, 1, 5) == 3
assert var_bisect_right(snps, 2, 1) == 4
assert var_bisect_right(snps, 4, 7) == 12
assert var_bisect_left(snps, 1, 0) == 0
assert var_bisect_left(snps, 1, 1) == 0
assert var_bisect_left(snps, 1, 3) == 1
assert var_bisect_left(snps, 1, 5) == 2
assert var_bisect_left(snps, 2, 1) == 3
assert var_bisect_left(snps, 4, 7) == 12
# find_re
try:
find_le(snps, 1, 0)
self.fail('Value Error expected')
except ValueError:
pass
assert find_le(snps, 1, 1) == 0
assert find_le(snps, 1, 2) == 0
assert find_le(snps, 1, 3) == 1
assert find_le(snps, 1, 7) == 2
assert find_le(snps, 2, 1) == 3
assert find_le(snps, 2, 2) == 4
assert find_le(snps, 4, 7) == 11
assert find_ge(snps, 1, 0) == 0
assert find_ge(snps, 1, 1) == 0
assert find_ge(snps, 1, 2) == 1
assert find_ge(snps, 1, 7) == 3
assert find_ge(snps, 2, 1) == 3
assert find_ge(snps, 2, 2) == 4
try:
find_ge(snps, 4, 7)
self.fail('Value Error expected')
except ValueError:
pass
assert index(snps, 1, 1) == 0
try:
index(snps, 0, 7)
self.fail('Index Error expected')
except IndexError:
pass
try:
index(snps, 4, 3)
self.fail('Index Error expected')
except IndexError:
pass
assert index(snps, 4, 2) == 9
def test_filter_obs_het(self):
variations = VariationsArrays()
gts = numpy.array([[[0, 0], [1, 1], [0, 1], [1, 1], [0, 0]],
[[0, 0], [0, 0], [0, 0], [0, 0], [1, 1]],
[[0, 0], [0, 0], [0, 0], [0, 0], [0, 1]],
[[0, 0], [0, 0], [0, 1], [0, 0], [1, 1]]])
variations[GT_FIELD] = gts
variations.samples = [1, 2, 3, 4, 5]
filtered = ObsHetFilter(min_num_genotypes=0,
report_selection=True)(variations)
assert numpy.all(filtered[FLT_VARS][GT_FIELD] == gts)
assert filtered[FLT_STATS][N_KEPT] == 4
assert filtered[FLT_STATS][TOT] == 4
assert filtered[FLT_STATS][N_FILTERED_OUT] == 0
assert filtered[SELECTED_VARS].shape
filtered = ObsHetFilter(min_het=0.2, min_num_genotypes=0)(variations)
assert numpy.all(filtered[FLT_VARS][GT_FIELD] == gts[[0, 2, 3]])
assert filtered[FLT_STATS][N_KEPT] == 3
assert filtered[FLT_STATS][TOT] == 4
assert filtered[FLT_STATS][N_FILTERED_OUT] == 1
filtered = ObsHetFilter(min_het=0.2, min_num_genotypes=10)(variations)
assert filtered[FLT_STATS][N_KEPT] == 0
assert filtered[FLT_STATS][TOT] == 4
assert filtered[FLT_STATS][N_FILTERED_OUT] == 4
filtered = ObsHetFilter(min_het=0.2, min_num_genotypes=10,
keep_missing=True)(variations)
assert filtered[FLT_STATS][N_KEPT] == 4
assert filtered[FLT_STATS][TOT] == 4
assert filtered[FLT_STATS][N_FILTERED_OUT] == 0
return
filtered = ObsHetFilter(max_het=0.1, min_num_genotypes=0)(variations)
assert numpy.all(filtered[FLT_VARS][GT_FIELD] == gts[[1]])
filtered = ObsHetFilter(min_het=0.2, max_het=0.3,
min_num_genotypes=0)(variations)
assert numpy.all(filtered[FLT_VARS][GT_FIELD] == gts[[0, 2, 3]])
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
filtered = ObsHetFilter(min_het=0.6, max_het=0.9)(hdf5)
counts = Counter(filtered[FLT_VARS][GT_FIELD].flat)
assert counts == {}
filtered = ObsHetFilter(min_het=0.6, max_het=0.9, min_call_dp=5)(hdf5)
counts = Counter(filtered[FLT_VARS][GT_FIELD].flat)
assert counts == {0: 978, -1: 910, 1: 774, 2: 92}
filtered = ObsHetFilter(min_het=0.6, max_het=0.9, min_call_dp=5,
n_bins=3, range_=(0, 1))(hdf5)
counts = Counter(filtered[FLT_VARS][GT_FIELD].flat)
assert counts == {0: 978, -1: 910, 1: 774, 2: 92}
assert numpy.all(filtered[COUNTS] == [391, 14, 10])
assert numpy.all(filtered[EDGES] == [0, 1 / 3, 2 / 3, 1])
samples = hdf5.samples[:50]
filtered = ObsHetFilter(min_het=0.6, max_het=0.9, min_call_dp=5,
n_bins=3, range_=(0, 1), samples=samples)(hdf5)
counts = Counter(filtered[FLT_VARS][GT_FIELD].flat)
assert sum(filtered[COUNTS]) == sum([339, 14, 6])
def test_filter_high_density(self):
in_vars = VariationsArrays()
out_vars = VariationsArrays()
res = filter_variation_density(in_vars,
out_vars=out_vars,
max_density=1,
window=1)
assert not out_vars.keys()
assert not res
varis = VariationsArrays()
out_vars = VariationsArrays()
varis[CHROM_FIELD] = numpy.array([b'chr1'] * 6)
varis[POS_FIELD] = numpy.array([1, 2, 3, 4, 10, 11])
result = filter_variation_density(varis,
out_vars=out_vars,
max_density=2,
window=3,
n_bins=2)
assert list(out_vars[POS_FIELD]) == [1, 4, 10, 11]
assert list(result[COUNTS]) == [4, 2]
assert list(result[EDGES]) == [2., 2.5, 3.]
assert result[FLT_STATS][N_KEPT] == 4
assert result[FLT_STATS][TOT] == 6
assert result[FLT_STATS][N_FILTERED_OUT] == 2
varis = VariationsArrays()
out_vars = VariationsArrays()
varis[CHROM_FIELD] = numpy.array([b'chr1'] * 6)
varis[POS_FIELD] = numpy.array([1, 2, 3, 4, 10, 11])
result = filter_variation_density(varis,
out_vars=out_vars,
max_density=2,
window=3,
n_bins=2,
chunk_size=1)
assert list(out_vars[POS_FIELD]) == [1, 4, 10, 11]
assert list(result[COUNTS]) == [4, 2]
assert list(result[EDGES]) == [2., 2.5, 3.]
assert result[FLT_STATS][N_KEPT] == 4
assert result[FLT_STATS][TOT] == 6
assert result[FLT_STATS][N_FILTERED_OUT] == 2
varis = VariationsArrays()
out_vars = VariationsArrays()
varis[CHROM_FIELD] = numpy.array([b'chr1'] * 6)
varis[POS_FIELD] = numpy.array([1, 2, 3, 4, 10, 11])
result = filter_variation_density(varis,
out_vars=out_vars,
max_density=2,
window=3,
n_bins=2,
chunk_size=None)
assert list(out_vars[POS_FIELD]) == [1, 4, 10, 11]
assert list(result[COUNTS]) == [4, 2]
assert list(result[EDGES]) == [2., 2.5, 3.]
assert result[FLT_STATS][N_KEPT] == 4
assert result[FLT_STATS][TOT] == 6
assert result[FLT_STATS][N_FILTERED_OUT] == 2
def test_filter_obs_het(self):
variations = VariationsArrays()
gts = numpy.array([[[0, 0], [1, 1], [0, 1], [1, 1], [0, 0]],
[[0, 0], [0, 0], [0, 0], [0, 0], [1, 1]],
[[0, 0], [0, 0], [0, 0], [0, 0], [0, 1]],
[[0, 0], [0, 0], [0, 1], [0, 0], [1, 1]]])
variations[GT_FIELD] = gts
variations.samples = [1, 2, 3, 4, 5]
filtered = ObsHetFilter(min_num_genotypes=0,
report_selection=True)(variations)
assert numpy.all(filtered[FLT_VARS][GT_FIELD] == gts)
assert filtered[FLT_STATS][N_KEPT] == 4
assert filtered[FLT_STATS][TOT] == 4
assert filtered[FLT_STATS][N_FILTERED_OUT] == 0
assert filtered[SELECTED_VARS].shape
filtered = ObsHetFilter(min_het=0.2, min_num_genotypes=0)(variations)
assert numpy.all(filtered[FLT_VARS][GT_FIELD] == gts[[0, 2, 3]])
assert filtered[FLT_STATS][N_KEPT] == 3
assert filtered[FLT_STATS][TOT] == 4
assert filtered[FLT_STATS][N_FILTERED_OUT] == 1
filtered = ObsHetFilter(min_het=0.2, min_num_genotypes=10)(variations)
assert filtered[FLT_STATS][N_KEPT] == 0
assert filtered[FLT_STATS][TOT] == 4
assert filtered[FLT_STATS][N_FILTERED_OUT] == 4
filtered = ObsHetFilter(min_het=0.2,
min_num_genotypes=10,
keep_missing=True)(variations)
assert filtered[FLT_STATS][N_KEPT] == 4
assert filtered[FLT_STATS][TOT] == 4
assert filtered[FLT_STATS][N_FILTERED_OUT] == 0
return
filtered = ObsHetFilter(max_het=0.1, min_num_genotypes=0)(variations)
assert numpy.all(filtered[FLT_VARS][GT_FIELD] == gts[[1]])
filtered = ObsHetFilter(min_het=0.2, max_het=0.3,
min_num_genotypes=0)(variations)
assert numpy.all(filtered[FLT_VARS][GT_FIELD] == gts[[0, 2, 3]])
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
filtered = ObsHetFilter(min_het=0.6, max_het=0.9)(hdf5)
counts = Counter(filtered[FLT_VARS][GT_FIELD].flat)
assert counts == {}
filtered = ObsHetFilter(min_het=0.6, max_het=0.9, min_call_dp=5)(hdf5)
counts = Counter(filtered[FLT_VARS][GT_FIELD].flat)
assert counts == {0: 978, -1: 910, 1: 774, 2: 92}
filtered = ObsHetFilter(min_het=0.6,
max_het=0.9,
min_call_dp=5,
n_bins=3,
range_=(0, 1))(hdf5)
counts = Counter(filtered[FLT_VARS][GT_FIELD].flat)
assert counts == {0: 978, -1: 910, 1: 774, 2: 92}
assert numpy.all(filtered[COUNTS] == [391, 14, 10])
assert numpy.all(filtered[EDGES] == [0, 1 / 3, 2 / 3, 1])
samples = hdf5.samples[:50]
filtered = ObsHetFilter(min_het=0.6,
max_het=0.9,
min_call_dp=5,
n_bins=3,
range_=(0, 1),
samples=samples)(hdf5)
counts = Counter(filtered[FLT_VARS][GT_FIELD].flat)
assert sum(filtered[COUNTS]) == sum([339, 14, 6])
