def test_count_alleles(self):
hdf5 = VariationsH5(join(TEST_DATA_DIR, '1000snps.hdf5'), mode='r')
chunk = first(hdf5.iterate_chunks())
genotypes = chunk['/calls/GT']
expected = [[3, 3, 0],
[5, 1, 0],
[0, 2, 4],
[6, 0, 0],
[2, 3, 1]]
counts = counts_by_row(genotypes, missing_value=-1)
assert numpy.all(expected == counts)
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
chunks = hdf5.iterate_chunks(kept_fields=['/calls/GT'])
chunks = (chunk['/calls/GT'] for chunk in chunks)
matrix = first(chunks)
for _ in range(20):
extend_matrix(matrix, chunks)
counts = counts_by_row(matrix, missing_value=-1)
gts = [[[-1, -1], [-1, -1], [-1, -1], [0, 0], [0, 0], [0, 0]]]
gts = numpy.array(gts)
counts = counts_by_row(gts, missing_value=-1)
assert numpy.all(counts == [[6]])
gts = [[[0, 0], [0, 0], [0, 0]],
[[0, 0], [0, 0], [0, 0]]]
gts = numpy.array(gts)
counts = counts_by_row(gts, missing_value=-1)
assert numpy.all(counts == [[6, 6]])
def test_count_alleles(self):
hdf5 = VariationsH5(join(TEST_DATA_DIR, '1000snps.hdf5'), mode='r')
chunk = first(hdf5.iterate_chunks())
genotypes = chunk['/calls/GT']
expected = [[3, 3, 0], [5, 1, 0], [0, 2, 4], [6, 0, 0], [2, 3, 1]]
counts = counts_by_row(genotypes, missing_value=-1)
assert numpy.all(expected == counts)
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
chunks = hdf5.iterate_chunks(kept_fields=['/calls/GT'])
chunks = (chunk['/calls/GT'] for chunk in chunks)
matrix = first(chunks)
for _ in range(20):
extend_matrix(matrix, chunks)
counts = counts_by_row(matrix, missing_value=-1)
gts = [[[-1, -1], [-1, -1], [-1, -1], [0, 0], [0, 0], [0, 0]]]
gts = numpy.array(gts)
counts = counts_by_row(gts, missing_value=-1)
assert numpy.all(counts == [[6]])
gts = [[[0, 0], [0, 0], [0, 0]], [[0, 0], [0, 0], [0, 0]]]
gts = numpy.array(gts)
counts = counts_by_row(gts, missing_value=-1)
assert numpy.all(counts == [[6, 6]])
def num_variations(self):
try:
one_path = first(self.keys())
except ValueError:
return 0
one_mat = self[one_path]
return one_mat.shape[0]
def test_count_alleles_by_freq(self):
h5 = VariationsH5(join(TEST_DATA_DIR, 'limon.h5'), mode='r')
# flt = SampleFilter(['V51'])
# v51 = flt(h5)[FLT_VARS]
chunk = first(h5.iterate_chunks())
freqs_by_snp = calc_allele_freq_by_depth(chunk)
assert numpy.all(freqs_by_snp[0] == [0, 1, 0, 0])
def num_variations(self):
try:
one_path = first(self.keys())
except ValueError:
return 0
one_mat = self[one_path]
return one_mat.shape[0]
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_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)
# 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_gst(self):
h5 = VariationsH5(join(TEST_DATA_DIR, 'limon.h5'), mode='r')
# flt = SampleFilter(['V51'])
# v51 = flt(h5)[FLT_VARS]
chunk = first(h5.iterate_chunks())
dists = calc_gst_per_loci(chunk, populations=[['V51'], ['F49']])
assert dists[0] == 0
def _create_or_get_mats_from_chunk(self, variations):
field_paths = variations.keys()
if first(field_paths) in self:
matrices = self._get_mats_for_chunk(variations)
else:
if self.keys():
raise ValueError('There are previous no matching matrices')
matrices = self._create_mats_from_chunks(variations)
self._set_metadata(variations.metadata)
self._set_samples(variations.samples)
return matrices
def _create_or_get_mats_from_chunk(self, variations):
field_paths = variations.keys()
if first(field_paths) in self:
matrices = self._get_mats_for_chunk(variations)
else:
if self.keys():
raise ValueError('There are previous no matching matrices')
matrices = self._create_mats_from_chunks(variations)
self._set_metadata(variations.metadata)
self._set_samples(variations.samples)
return matrices
def test_set_gt_to_missing_by_dp(self):
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
kept_fields = ['/calls/DP', GT_FIELD]
snps = hdf5.iterate_chunks(kept_fields=kept_fields)
chunk = first(snps)
set_low_dp_gts_to_missing = LowDPGTsToMissingSetter(min_dp=300)
res = set_low_dp_gts_to_missing(chunk)
assert numpy.all(chunk[GT_FIELD][0][147] == [-1, -1])
assert COUNTS in res
set_low_dp_gts_to_missing(chunk)
assert numpy.all(chunk[GT_FIELD].shape[0] == SNPS_PER_CHUNK)
def test_set_gt_to_missing_by_dp(self):
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
kept_fields = ['/calls/DP', GT_FIELD]
snps = hdf5.iterate_chunks(kept_fields=kept_fields)
chunk = first(snps)
set_low_dp_gts_to_missing = LowDPGTsToMissingSetter(min_dp=300)
res = set_low_dp_gts_to_missing(chunk)
assert numpy.all(chunk[GT_FIELD][0][147] == [-1, -1])
assert COUNTS in res
set_low_dp_gts_to_missing(chunk)
assert numpy.all(chunk[GT_FIELD].shape[0] == SNPS_PER_CHUNK)
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
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 _extend_array_with_iter(array, matrices):
try:
matrix = first(matrices)
except ValueError:
return
matrices = chain([matrix], matrices)
matrix_size = sys.getsizeof(matrix)
mats_in_group = math.floor(AVAILABLE_MEM / matrix_size)
if not mats_in_group:
mats_in_group = 1
for mats_in_mem in group_items(matrices, mats_in_group):
_extend_array(array, mats_in_mem)
def _extend_array_with_iter(array, matrices):
try:
matrix = first(matrices)
except ValueError:
return
matrices = chain([matrix], matrices)
matrix_size = sys.getsizeof(matrix)
mats_in_group = math.floor(AVAILABLE_MEM / matrix_size)
if not mats_in_group:
mats_in_group = 1
for mats_in_mem in group_items(matrices, mats_in_group):
_extend_array(array, mats_in_mem)
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=586325202)(chunk)[FLT_VARS]
assert first(flt_chunk.values()).shape[0] == 0
flt_chunk = SNPQualFilter(max_qual=-1)(chunk)[FLT_VARS]
assert first(flt_chunk.values()).shape[0] == 0
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_count_value_per_row(self):
mat = numpy.array([[0, 0], [1, -1], [2, -1], [-1, -1]])
missing_counter = row_value_counter_fact(value=-1)
assert numpy.all(missing_counter(mat) == [0, 1, 1, 2])
missing_counter = row_value_counter_fact(value=-1, ratio=True)
assert numpy.allclose(missing_counter(mat), [0., 0.5, 0.5, 1.])
hdf5 = VariationsH5(join(TEST_DATA_DIR, '1000snps.hdf5'), mode='r')
chunks = list(hdf5.iterate_chunks())
gt_chunk = first(select_dset_from_chunks(chunks, '/calls/GT'))
homo_counter = row_value_counter_fact(value=2)
assert numpy.all(homo_counter(gt_chunk) == [0, 0, 4, 0, 1])
missing_counter = row_value_counter_fact(value=2, ratio=True)
expected = [0., 0, 0.66666, 0., 0.166666]
assert numpy.allclose(missing_counter(gt_chunk), expected)
hdf5.close()
def test_count_value_per_row(self):
mat = numpy.array([[0, 0], [1, -1], [2, -1], [-1, -1]])
missing_counter = row_value_counter_fact(value=-1)
assert numpy.all(missing_counter(mat) == [0, 1, 1, 2])
missing_counter = row_value_counter_fact(value=-1, ratio=True)
assert numpy.allclose(missing_counter(mat), [0., 0.5, 0.5, 1.])
hdf5 = VariationsH5(join(TEST_DATA_DIR, '1000snps.hdf5'), mode='r')
chunks = list(hdf5.iterate_chunks())
gt_chunk = first(select_dset_from_chunks(chunks, '/calls/GT'))
homo_counter = row_value_counter_fact(value=2)
assert numpy.all(homo_counter(gt_chunk) == [0, 0, 4, 0, 1])
missing_counter = row_value_counter_fact(value=2, ratio=True)
expected = [0., 0, 0.66666, 0., 0.166666]
assert numpy.allclose(missing_counter(gt_chunk), expected)
hdf5.close()
def _filter_samples_by_index(variations,
sample_cols,
filtered_vars=None,
reverse=False):
if filtered_vars is None:
filtered_vars = VariationsArrays()
samples = variations.samples
try:
dtype = sample_cols.dtype
is_bool = numpy.issubdtype(dtype, numpy.dtype(bool))
except AttributeError:
item = first(iter(sample_cols))
is_bool = isinstance(item, bool)
if not is_bool:
sample_cols = [idx in sample_cols for idx in range(len(samples))]
if 'shape' not in dir(sample_cols):
sample_cols = numpy.array(sample_cols, dtype=numpy.bool)
if reverse:
sample_cols = numpy.logical_not(sample_cols)
for path in variations.keys():
matrix = variations[path]
if is_dataset(matrix):
matrix = matrix[:]
if 'calls' in path:
flt_data = matrix[:, sample_cols]
# flt_data = numpy.compress(sample_cols, , axis=1)
filtered_vars[path] = flt_data
else:
filtered_vars[path] = matrix
filtered_vars.metadata = variations.metadata
kept_samples = [
samples[idx] for idx, keep in enumerate(sample_cols) if keep
]
filtered_vars.samples = kept_samples
return filtered_vars
def _filter_samples_by_index(variations, sample_cols, filtered_vars=None,
reverse=False):
if filtered_vars is None:
filtered_vars = VariationsArrays()
samples = variations.samples
try:
dtype = sample_cols.dtype
is_bool = numpy.issubdtype(dtype, numpy.bool)
except AttributeError:
item = first(iter(sample_cols))
is_bool = isinstance(item, bool)
if not is_bool:
sample_cols = [idx in sample_cols for idx in range(len(samples))]
if 'shape' not in dir(sample_cols):
sample_cols = numpy.array(sample_cols, dtype=numpy.bool)
if reverse:
sample_cols = numpy.logical_not(sample_cols)
for path in variations.keys():
matrix = variations[path]
if is_dataset(matrix):
matrix = matrix[:]
if 'calls' in path:
flt_data = matrix[:, sample_cols]
# flt_data = numpy.compress(sample_cols, , axis=1)
filtered_vars[path] = flt_data
else:
filtered_vars[path] = matrix
filtered_vars.metadata = variations.metadata
kept_samples = [samples[idx] for idx, keep in enumerate(sample_cols)
if keep]
filtered_vars.samples = kept_samples
return filtered_vars
def test_filter_mafs(self):
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
chunk = first(hdf5.iterate_chunks())
filtered = MafFilter(min_maf=0.6,
min_num_genotypes=0,
report_selection=True)(chunk)
tot = filtered[FLT_STATS][N_KEPT] + filtered[FLT_STATS][N_FILTERED_OUT]
assert tot == SNPS_PER_CHUNK
assert filtered[FLT_STATS][TOT] == SNPS_PER_CHUNK
assert filtered[SELECTED_VARS].shape
flt_chunk = filtered[FLT_VARS]
path = first(chunk.keys())
assert flt_chunk[path].shape[0]
filtered = MafFilter()(chunk)
flt_chunk = filtered[FLT_VARS]
path = first(chunk.keys())
assert flt_chunk[path].shape[0] == SNPS_PER_CHUNK
filtered = MafFilter(max_maf=0.6)(chunk)
flt_chunk = filtered[FLT_VARS]
path = first(chunk.keys())
assert flt_chunk[path].shape[0] > 18
filtered = MafFilter(min_maf=0.6, max_maf=0.9,
min_num_genotypes=0)(chunk)
flt_chunk = filtered[FLT_VARS]
assert flt_chunk[path].shape[0] > 125
filtered = MafFilter(min_maf=1.1, min_num_genotypes=0)(chunk)
flt_chunk = filtered[FLT_VARS]
path = first(chunk.keys())
assert flt_chunk[path].shape[0] == 0
filtered = MafFilter(max_maf=0, min_num_genotypes=0)(chunk)
flt_chunk = filtered[FLT_VARS]
path = first(chunk.keys())
assert flt_chunk[path].shape[0] == 0
def test_filter_mafs(self):
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
chunk = first(hdf5.iterate_chunks())
filtered = MafFilter(min_maf=0.6, min_num_genotypes=0,
report_selection=True)(chunk)
tot = filtered[FLT_STATS][N_KEPT] + filtered[FLT_STATS][N_FILTERED_OUT]
assert tot == SNPS_PER_CHUNK
assert filtered[FLT_STATS][TOT] == SNPS_PER_CHUNK
assert filtered[SELECTED_VARS].shape
flt_chunk = filtered[FLT_VARS]
path = first(chunk.keys())
assert flt_chunk[path].shape[0]
filtered = MafFilter()(chunk)
flt_chunk = filtered[FLT_VARS]
path = first(chunk.keys())
assert flt_chunk[path].shape[0] == SNPS_PER_CHUNK
filtered = MafFilter(max_maf=0.6)(chunk)
flt_chunk = filtered[FLT_VARS]
path = first(chunk.keys())
assert flt_chunk[path].shape[0] > 18
filtered = MafFilter(min_maf=0.6, max_maf=0.9,
min_num_genotypes=0)(chunk)
flt_chunk = filtered[FLT_VARS]
assert flt_chunk[path].shape[0] > 125
filtered = MafFilter(min_maf=1.1, min_num_genotypes=0)(chunk)
flt_chunk = filtered[FLT_VARS]
path = first(chunk.keys())
assert flt_chunk[path].shape[0] == 0
filtered = MafFilter(max_maf=0, min_num_genotypes=0)(chunk)
flt_chunk = filtered[FLT_VARS]
path = first(chunk.keys())
assert flt_chunk[path].shape[0] == 0