def test_calc_obs_het(self):
gts = numpy.array([])
dps = numpy.array([])
varis = {'/calls/GT': gts, '/calls/DP': dps}
het = calc_obs_het(varis, min_num_genotypes=0)
assert het.shape[0] == 0
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
snps = VariationsArrays()
snps.put_chunks(hdf5.iterate_chunks(kept_fields=['/calls/GT']))
het_h5 = calc_obs_het(hdf5, min_num_genotypes=0)
het_array = calc_obs_het(snps, min_num_genotypes=0)
assert numpy.all(het_array == het_h5)
gts = numpy.array([[[0, 0], [0, 1], [0, -1], [-1, -1]],
[[0, 0], [0, 0], [0, -1], [-1, -1]]])
dps = numpy.array([[5, 12, 10, 10],
[10, 10, 10, 10]])
varis = {'/calls/GT': gts, '/calls/DP': dps}
het = calc_obs_het(varis, min_num_genotypes=0)
assert numpy.allclose(het, [0.5, 0])
het = calc_obs_het(varis, min_num_genotypes=10)
assert numpy.allclose(het, [numpy.NaN, numpy.NaN], equal_nan=True)
het = calc_obs_het(varis, min_num_genotypes=0, min_call_dp=10)
assert numpy.allclose(het, [1, 0])
het = calc_obs_het(varis, min_num_genotypes=0, max_call_dp=11)
assert numpy.allclose(het, [0, 0])
het = calc_obs_het(varis, min_num_genotypes=0, min_call_dp=5)
assert numpy.allclose(het, [0.5, 0])
def test_calc_obs_het(self):
gts = numpy.array([])
dps = numpy.array([])
varis = {'/calls/GT': gts, '/calls/DP': dps}
het = calc_obs_het(varis, min_num_genotypes=0)
assert het.shape[0] == 0
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
snps = VariationsArrays()
snps.put_chunks(hdf5.iterate_chunks(kept_fields=['/calls/GT']))
het_h5 = calc_obs_het(hdf5, min_num_genotypes=0)
het_array = calc_obs_het(snps, min_num_genotypes=0)
assert numpy.all(het_array == het_h5)
gts = numpy.array([[[0, 0], [0, 1], [0, -1], [-1, -1]],
[[0, 0], [0, 0], [0, -1], [-1, -1]]])
dps = numpy.array([[5, 12, 10, 10],
[10, 10, 10, 10]])
varis = {'/calls/GT': gts, '/calls/DP': dps}
het = calc_obs_het(varis, min_num_genotypes=0)
assert numpy.allclose(het, [0.5, 0])
het = calc_obs_het(varis, min_num_genotypes=10)
assert numpy.allclose(het, [numpy.NaN, numpy.NaN], equal_nan=True)
het = calc_obs_het(varis, min_num_genotypes=0, min_call_dp=10)
assert numpy.allclose(het, [1, 0])
het = calc_obs_het(varis, min_num_genotypes=0, max_call_dp=11)
assert numpy.allclose(het, [0, 0])
het = calc_obs_het(varis, min_num_genotypes=0, min_call_dp=5)
assert numpy.allclose(het, [0.5, 0])
def test_calc_missing_gt_rates(self):
gts = numpy.array([])
varis = {'/calls/GT': gts}
called_vars = calc_called_gt(varis, rates=False)
assert called_vars.shape[0] == 0
called_vars = calc_called_gt(varis, rates=True)
assert called_vars.shape[0] == 0
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
arrays = VariationsArrays()
arrays.put_chunks(hdf5.iterate_chunks(kept_fields=['/calls/GT']))
rates = calc_missing_gt(arrays)
rates2 = calc_missing_gt(hdf5)
assert rates.shape == (943,)
assert numpy.allclose(rates, rates2)
assert numpy.min(rates) == 0
assert numpy.all(rates <= 1)
gts = numpy.array([[[0, 0], [0, 0]], [[0, 0], [-1, -1]],
[[0, 0], [-1, -1]], [[-1, -1], [-1, -1]]])
varis = {'/calls/GT': gts}
expected = numpy.array([2, 1, 1, 0])
called_vars = calc_called_gt(varis, rates=False)
assert numpy.all(called_vars == expected)
missing_vars = calc_missing_gt(varis, rates=False)
assert numpy.all(missing_vars == 2 - expected)
expected = numpy.array([0, 0.5, 0.5, 1])
rates = calc_called_gt(varis)
assert numpy.allclose(rates, 1 - expected)
rates = calc_missing_gt(varis)
assert numpy.allclose(rates, expected)
def test_calc_obs_het_sample(self):
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
snps = VariationsArrays()
snps.put_chunks(hdf5.iterate_chunks(kept_fields=['/calls/GT']))
het_h5 = calc_obs_het_by_sample(hdf5)
het_array = calc_obs_het_by_sample(snps)
assert numpy.all(het_array == het_h5)
gts = numpy.array([[[0, 0], [0, 1], [0, -1], [-1, -1]],
[[0, 0], [0, 0], [0, -1], [-1, -1]],
[[0, 0], [0, 0], [0, 0], [-1, -1]]])
varis = {'/calls/GT': gts}
het = calc_obs_het_by_sample(varis, chunk_size=None)
assert numpy.allclose(het, [0, 1 / 3, 0, numpy.NaN], equal_nan=True)
gts = numpy.array([])
varis = {'/calls/GT': gts}
het = calc_obs_het_by_sample(varis, chunk_size=None)
assert het.shape[0] == 0
snps = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
calc_obs_het_by_sample(snps, min_call_dp=3)
calc_obs_het_by_sample(snps, min_call_dp=3, max_call_dp=20)
het_0 = calc_obs_het_by_sample(snps)
het = calc_obs_het_by_sample(snps, chunk_size=None)
assert numpy.allclose(het_0, het)
def test_calc_missing_gt_rates(self):
gts = numpy.array([])
varis = {'/calls/GT': gts}
called_vars = calc_called_gt(varis, rates=False)
assert called_vars.shape[0] == 0
called_vars = calc_called_gt(varis, rates=True)
assert called_vars.shape[0] == 0
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
arrays = VariationsArrays()
arrays.put_chunks(hdf5.iterate_chunks(kept_fields=['/calls/GT']))
rates = calc_missing_gt(arrays)
rates2 = calc_missing_gt(hdf5)
assert rates.shape == (943,)
assert numpy.allclose(rates, rates2)
assert numpy.min(rates) == 0
assert numpy.all(rates <= 1)
gts = numpy.array([[[0, 0], [0, 0]], [[0, 0], [-1, -1]],
[[0, 0], [-1, -1]], [[-1, -1], [-1, -1]]])
varis = {'/calls/GT': gts}
expected = numpy.array([2, 1, 1, 0])
called_vars = calc_called_gt(varis, rates=False)
assert numpy.all(called_vars == expected)
missing_vars = calc_missing_gt(varis, rates=False)
assert numpy.all(missing_vars == 2 - expected)
expected = numpy.array([0, 0.5, 0.5, 1])
rates = calc_called_gt(varis)
assert numpy.allclose(rates, 1 - expected)
rates = calc_missing_gt(varis)
assert numpy.allclose(rates, expected)
def test_calc_obs_het_sample(self):
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
snps = VariationsArrays()
snps.put_chunks(hdf5.iterate_chunks(kept_fields=['/calls/GT']))
het_h5 = calc_obs_het_by_sample(hdf5)
het_array = calc_obs_het_by_sample(snps)
assert numpy.all(het_array == het_h5)
gts = numpy.array([[[0, 0], [0, 1], [0, -1], [-1, -1]],
[[0, 0], [0, 0], [0, -1], [-1, -1]],
[[0, 0], [0, 0], [0, 0], [-1, -1]]])
varis = {'/calls/GT': gts}
het = calc_obs_het_by_sample(varis, chunk_size=None)
assert numpy.allclose(het, [0, 1 / 3, 0, numpy.NaN], equal_nan=True)
gts = numpy.array([])
varis = {'/calls/GT': gts}
het = calc_obs_het_by_sample(varis, chunk_size=None)
assert het.shape[0] == 0
snps = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
calc_obs_het_by_sample(snps, min_call_dp=3)
calc_obs_het_by_sample(snps, min_call_dp=3, max_call_dp=20)
het_0 = calc_obs_het_by_sample(snps)
het = calc_obs_het_by_sample(snps, chunk_size=None)
assert numpy.allclose(het_0, het)
def test_iterate_chroms(self):
fpath = join(TEST_DATA_DIR, 'ril.hdf5')
hd5 = VariationsH5(fpath, mode='r')
wins = hd5.iterate_chroms()
hd5_2 = VariationsArrays()
hd5_2.put_chunks([win for _, win in wins])
numpy.all(hd5['/variations/pos'] == hd5_2['/variations/pos'])
def test_iterate_wins(self):
fpath = join(TEST_DATA_DIR, 'ril.hdf5')
hd5 = VariationsH5(fpath, mode='r')
wins = hd5.iterate_wins(win_size=1000000)
hd5_2 = VariationsArrays()
hd5_2.put_chunks(wins)
numpy.all(hd5['/variations/pos'] == hd5_2['/variations/pos'])
def test_iterate_wins(self):
fpath = join(TEST_DATA_DIR, 'ril.hdf5')
hd5 = VariationsH5(fpath, mode='r')
wins = hd5.iterate_wins(win_size=1000000)
hd5_2 = VariationsArrays()
hd5_2.put_chunks(wins)
numpy.all(hd5['/variations/pos'] == hd5_2['/variations/pos'])
def sample_variations(in_vars, sample_rate, out_vars=None, chunk_size=None):
if out_vars is None:
out_vars = VariationsArrays()
chunks = in_vars.iterate_chunks(chunk_size=chunk_size,
random_sample_rate=sample_rate)
out_vars.put_chunks(chunks)
return out_vars
def test_calc_distrib_for_sample(self):
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
snps = VariationsArrays()
snps.put_chunks(hdf5.iterate_chunks())
distrib, _ = calc_field_distrib_for_a_sample(hdf5, field='/calls/DP',
sample='1_17_1_gbs',
n_bins=15)
assert distrib.shape == (15,)
distrib2, _ = calc_field_distrib_for_a_sample(snps, field='/calls/DP',
n_bins=15,
sample='1_17_1_gbs',
chunk_size=None)
assert numpy.all(distrib == distrib2)
distrib3, _ = calc_field_distrib_for_a_sample(snps, field='/calls/DP',
n_bins=15,
sample='1_17_1_gbs',
chunk_size=50)
assert numpy.all(distrib3 == distrib2)
vars_ = VariationsArrays()
vars_['/calls/DP'] = numpy.array([[10, 5, 15],
[0, 15, 10]])
vars_['/calls/GT'] = numpy.array([[[0, 0], [0, 1], [1, 1]],
[[0, 0], [0, 1], [1, 1]]])
vars_.samples = list(range(3))
distrib, _ = calc_field_distribs_per_sample(vars_, field='/calls/DP',
n_bins=16)
expec = [[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1]]
assert numpy.all(expec == distrib)
assert numpy.all(calc_depth(vars_) == [10, 5, 15, 0, 15, 10])
distrib, _ = calc_field_distribs_per_sample(vars_, field='/calls/DP',
n_bins=16,
mask_field='/calls/GT',
mask_func=call_is_het)
expec = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
assert numpy.all(expec == distrib)
assert numpy.all(calc_depth(vars_) == [10, 5, 15, 0, 15, 10])
distrib, _ = calc_field_distribs_per_sample(vars_, field='/calls/DP',
n_bins=16,
mask_field='/calls/GT',
mask_func=call_is_hom)
expec = [[1, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 1]]
assert numpy.all(expec == distrib)
assert numpy.all(calc_depth(vars_) == [10, 5, 15, 0, 15, 10])
def test_calc_distrib_for_sample(self):
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
snps = VariationsArrays()
snps.put_chunks(hdf5.iterate_chunks())
distrib, _ = calc_field_distrib_for_a_sample(hdf5, field='/calls/DP',
sample='1_17_1_gbs',
n_bins=15)
assert distrib.shape == (15,)
distrib2, _ = calc_field_distrib_for_a_sample(snps, field='/calls/DP',
n_bins=15,
sample='1_17_1_gbs',
chunk_size=None)
assert numpy.all(distrib == distrib2)
distrib3, _ = calc_field_distrib_for_a_sample(snps, field='/calls/DP',
n_bins=15,
sample='1_17_1_gbs',
chunk_size=50)
assert numpy.all(distrib3 == distrib2)
vars_ = VariationsArrays()
vars_['/calls/DP'] = numpy.array([[10, 5, 15],
[0, 15, 10]])
vars_['/calls/GT'] = numpy.array([[[0, 0], [0, 1], [1, 1]],
[[0, 0], [0, 1], [1, 1]]])
vars_.samples = list(range(3))
distrib, _ = calc_field_distribs_per_sample(vars_, field='/calls/DP',
n_bins=16)
expec = [[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1]]
assert numpy.all(expec == distrib)
assert numpy.all(calc_depth(vars_) == [10, 5, 15, 0, 15, 10])
distrib, _ = calc_field_distribs_per_sample(vars_, field='/calls/DP',
n_bins=16,
mask_field='/calls/GT',
mask_func=call_is_het)
expec = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
assert numpy.all(expec == distrib)
assert numpy.all(calc_depth(vars_) == [10, 5, 15, 0, 15, 10])
distrib, _ = calc_field_distribs_per_sample(vars_, field='/calls/DP',
n_bins=16,
mask_field='/calls/GT',
mask_func=call_is_hom)
expec = [[1, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 1]]
assert numpy.all(expec == distrib)
assert numpy.all(calc_depth(vars_) == [10, 5, 15, 0, 15, 10])
def copy_setting_gts_to_missing(in_vars,
gt_rate_to_missing,
out_vars=None,
chunk_size=None):
if out_vars is None:
out_vars = VariationsArrays()
chunks = in_vars.iterate_chunks(chunk_size=chunk_size)
chunks = (_set_gts_to_missing(chunk, gt_rate_to_missing)
for chunk in chunks)
out_vars.put_chunks(chunks)
return out_vars
def test_annotator_all_samples(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)
assert annotated_variations.metadata[field]['Type'] == 'Integer'
assert annotated_variations.metadata[field]['Number'] == 1
assert field in annotated_variations.keys()
assert annotated_variations[field][3] == TRUE_INT
def test_calc_dp_for_sample(self):
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
snps = VariationsArrays()
snps.put_chunks(hdf5.iterate_chunks())
cnts, _ = calc_call_dp_distrib_for_a_sample(hdf5, sample='1_17_1_gbs',
n_bins=15)
assert cnts['hom'].shape == (15,)
assert cnts['het'].shape == (15,)
return
cnts2, _ = calc_call_dp_distrib_for_a_sample(hdf5, sample='1_17_1_gbs',
n_bins=15,
chunk_size=None)
assert numpy.all(cnts['hom'] == cnts2['hom'])
assert numpy.all(cnts['het'] == cnts2['het'])
cnts3, _ = calc_call_dp_distrib_for_a_sample(hdf5, sample='1_17_1_gbs',
n_bins=15, chunk_size=50)
assert numpy.all(cnts['hom'] == cnts3['hom'])
assert numpy.all(cnts['het'] == cnts3['het'])
def test_calc_snp_density(self):
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
snps = VariationsArrays()
snps.put_chunks(hdf5.iterate_chunks())
density_h5 = list(calc_snp_density(hdf5, 1000))
density_array = list(calc_snp_density(snps, 1000))
assert density_array == density_h5
var = {'/variations/chrom': numpy.array(['ch', 'ch', 'ch', 'ch', 'ch',
'ch', 'ch', 'ch', 'ch', 'ch',
'ch', 'ch', 'ch', 'ch']),
'/variations/pos': numpy.array([1, 2, 3, 4, 5, 6, 7, 25, 34, 44,
80, 200, 300, 302])}
dens_var = list(calc_snp_density(var, 11))
expected = [6, 7, 7, 7, 7, 7, 6, 1, 1, 1, 1, 1, 2, 2]
assert dens_var == expected
var = {'/variations/chrom': numpy.array(['ch', 'ch', 'ch', 'c2', 'c2',
'c2', 'c2', 'c2', 'c2', 'c2',
'c2', 'c2', 'c2', 'c3']),
'/variations/pos': numpy.array([1, 2, 3, 4, 5, 6, 7, 25, 34, 44,
80, 200, 300, 302])}
dens_var = list(calc_snp_density(var, 11))
expected = [3, 3, 3, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1]
assert dens_var == expected
var = {'/variations/chrom': numpy.array(['c1', 'c4', 'c5', 'c2', 'c2',
'c2', 'c2', 'c2', 'c2', 'c2',
'c2', 'c2', 'c2', 'c3']),
'/variations/pos': numpy.array([1, 2, 3, 4, 5, 6, 7, 25, 34, 44,
80, 200, 300, 302])}
dens_var = list(calc_snp_density(var, 11))
expected = [1, 1, 1, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1]
assert dens_var == expected
var = {'/variations/chrom': numpy.array([]),
'/variations/pos': numpy.array([])}
dens_var = list(calc_snp_density(var, 11))
assert dens_var == []
var = {'/variations/chrom': numpy.array([1]),
'/variations/pos': numpy.array([1])}
dens_var = list(calc_snp_density(var, 11))
assert dens_var == [1]
def test_calc_snp_density(self):
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
snps = VariationsArrays()
snps.put_chunks(hdf5.iterate_chunks())
density_h5 = list(calc_snp_density(hdf5, 1000))
density_array = list(calc_snp_density(snps, 1000))
assert density_array == density_h5
var = {'/variations/chrom': numpy.array(['ch', 'ch', 'ch', 'ch', 'ch',
'ch', 'ch', 'ch', 'ch', 'ch',
'ch', 'ch', 'ch', 'ch']),
'/variations/pos': numpy.array([1, 2, 3, 4, 5, 6, 7, 25, 34, 44,
80, 200, 300, 302])}
dens_var = list(calc_snp_density(var, 11))
expected = [6, 7, 7, 7, 7, 7, 6, 1, 1, 1, 1, 1, 2, 2]
assert dens_var == expected
var = {'/variations/chrom': numpy.array(['ch', 'ch', 'ch', 'c2', 'c2',
'c2', 'c2', 'c2', 'c2', 'c2',
'c2', 'c2', 'c2', 'c3']),
'/variations/pos': numpy.array([1, 2, 3, 4, 5, 6, 7, 25, 34, 44,
80, 200, 300, 302])}
dens_var = list(calc_snp_density(var, 11))
expected = [3, 3, 3, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1]
assert dens_var == expected
var = {'/variations/chrom': numpy.array(['c1', 'c4', 'c5', 'c2', 'c2',
'c2', 'c2', 'c2', 'c2', 'c2',
'c2', 'c2', 'c2', 'c3']),
'/variations/pos': numpy.array([1, 2, 3, 4, 5, 6, 7, 25, 34, 44,
80, 200, 300, 302])}
dens_var = list(calc_snp_density(var, 11))
expected = [1, 1, 1, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1]
assert dens_var == expected
var = {'/variations/chrom': numpy.array([]),
'/variations/pos': numpy.array([])}
dens_var = list(calc_snp_density(var, 11))
assert dens_var == []
var = {'/variations/chrom': numpy.array([1]),
'/variations/pos': numpy.array([1])}
dens_var = list(calc_snp_density(var, 11))
assert dens_var == [1]
def test_calc_dp_for_sample(self):
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
snps = VariationsArrays()
snps.put_chunks(hdf5.iterate_chunks())
cnts, _ = calc_call_dp_distrib_for_a_sample(hdf5, sample='1_17_1_gbs',
n_bins=15)
assert cnts['hom'].shape == (15,)
assert cnts['het'].shape == (15,)
return
cnts2, _ = calc_call_dp_distrib_for_a_sample(hdf5, sample='1_17_1_gbs',
n_bins=15,
chunk_size=None)
assert numpy.all(cnts['hom'] == cnts2['hom'])
assert numpy.all(cnts['het'] == cnts2['het'])
cnts3, _ = calc_call_dp_distrib_for_a_sample(hdf5, sample='1_17_1_gbs',
n_bins=15, chunk_size=50)
assert numpy.all(cnts['hom'] == cnts3['hom'])
assert numpy.all(cnts['het'] == cnts3['het'])
def stats_missing_rate_from_hdf5_memory():
fpath = join(TEST_DATA_DIR, 'performance', 'inca_torvum_all_snps.h5')
var_mat = VariationsH5(fpath, mode='r')
array = VariationsArrays()
array.put_chunks(var_mat.iterate_chunks(kept_fields=['/calls/GT']))
calc_stat_by_chunk(array, _MissingGTCalculator())
def stats_missing_rate_from_hdf5_memory():
fpath = join(TEST_DATA_DIR, 'performance', 'inca_torvum_all_snps.h5')
var_mat = VariationsH5(fpath, mode='r')
array = VariationsArrays()
array.put_chunks(var_mat.iterate_chunks(kept_fields=['/calls/GT']))
calc_stat_by_chunk(array, _MissingGTCalculator())
def test_create_hdf5_with_chunks(self):
hdf5 = VariationsH5(join(TEST_DATA_DIR, '1000snps.hdf5'), mode='r')
out_fhand = NamedTemporaryFile(suffix='.hdf5')
out_fpath = out_fhand.name
out_fhand.close()
hdf5_2 = VariationsH5(out_fpath, 'w')
try:
hdf5_2.put_chunks(hdf5.iterate_chunks())
assert sorted(hdf5_2['calls'].keys()) == ['DP', 'GQ', 'GT', 'HQ']
assert numpy.all(hdf5['/calls/GT'][:] == hdf5_2['/calls/GT'][:])
finally:
os.remove(out_fpath)
hdf5 = VariationsH5(join(TEST_DATA_DIR, '1000snps.hdf5'), mode='r')
out_fhand = NamedTemporaryFile(suffix='.hdf5')
out_fpath = out_fhand.name
out_fhand.close()
hdf5_2 = VariationsH5(out_fpath, 'w')
try:
hdf5_2.put_chunks(hdf5.iterate_chunks(kept_fields=['/calls/GT']))
assert list(hdf5_2['calls'].keys()) == ['GT']
assert numpy.all(hdf5['/calls/GT'][:] == hdf5_2['/calls/GT'][:])
finally:
os.remove(out_fpath)
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
hdf5_2 = VariationsArrays()
hdf5_2.put_chunks(hdf5.iterate_chunks(random_sample_rate=0.2))
_, prob = ttest_ind(hdf5['/variations/pos'][:],
hdf5_2['/variations/pos'][:])
assert prob > 0.05
assert hdf5_2.num_variations / hdf5.num_variations - 0.2 < 0.1
chrom = hdf5_2['/variations/chrom'][0]
pos = hdf5_2['/variations/pos'][0]
index = PosIndex(hdf5)
idx = index.index_pos(chrom, pos)
old_snp = hdf5['/calls/GT'][idx]
new_snp = hdf5_2['/calls/GT'][0]
assert numpy.all(old_snp == new_snp)
# putting empty chunks
hdf5_2.put_chunks(None)
hdf5_2.put_chunks([])
chunk = hdf5.get_chunk(slice(1000, None))
hdf5_2.put_chunks([chunk])
old_snp = hdf5['/calls/DP'][idx]
new_snp = hdf5_2['/calls/DP'][0]
assert numpy.all(old_snp == new_snp)
hdf5 = VariationsH5(join(TEST_DATA_DIR, '1000snps.hdf5'), mode='r')
hdf5_2 = VariationsArrays()
hdf5_2.put_chunks(hdf5.iterate_chunks(random_sample_rate=0))
assert hdf5_2.num_variations == 0
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
hdf5_3 = VariationsArrays()
hdf5_3.put_chunks(hdf5.iterate_chunks(random_sample_rate=0.01))
def test_create_hdf5_with_chunks(self):
hdf5 = VariationsH5(join(TEST_DATA_DIR, '1000snps.hdf5'), mode='r')
out_fhand = NamedTemporaryFile(suffix='.hdf5')
out_fpath = out_fhand.name
out_fhand.close()
hdf5_2 = VariationsH5(out_fpath, 'w')
try:
hdf5_2.put_chunks(hdf5.iterate_chunks())
assert sorted(hdf5_2['calls'].keys()) == ['DP', 'GQ', 'GT', 'HQ']
assert numpy.all(hdf5['/calls/GT'][:] == hdf5_2['/calls/GT'][:])
finally:
os.remove(out_fpath)
hdf5 = VariationsH5(join(TEST_DATA_DIR, '1000snps.hdf5'), mode='r')
out_fhand = NamedTemporaryFile(suffix='.hdf5')
out_fpath = out_fhand.name
out_fhand.close()
hdf5_2 = VariationsH5(out_fpath, 'w')
try:
hdf5_2.put_chunks(hdf5.iterate_chunks(kept_fields=['/calls/GT']))
assert list(hdf5_2['calls'].keys()) == ['GT']
assert numpy.all(hdf5['/calls/GT'][:] == hdf5_2['/calls/GT'][:])
finally:
os.remove(out_fpath)
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
hdf5_2 = VariationsArrays()
hdf5_2.put_chunks(hdf5.iterate_chunks(random_sample_rate=0.2))
_, prob = scipy.stats.ttest_ind(hdf5['/variations/pos'][:],
hdf5_2['/variations/pos'][:])
assert prob > 0.05
assert hdf5_2.num_variations / hdf5.num_variations - 0.2 < 0.1
chrom = hdf5_2['/variations/chrom'][0]
pos = hdf5_2['/variations/pos'][0]
index = PosIndex(hdf5)
idx = index.index_pos(chrom, pos)
old_snp = hdf5['/calls/GT'][idx]
new_snp = hdf5_2['/calls/GT'][0]
assert numpy.all(old_snp == new_snp)
# putting empty chunks
hdf5_2.put_chunks(None)
hdf5_2.put_chunks([])
chunk = hdf5.get_chunk(slice(1000, None))
hdf5_2.put_chunks([chunk])
old_snp = hdf5['/calls/DP'][idx]
new_snp = hdf5_2['/calls/DP'][0]
assert numpy.all(old_snp == new_snp)
hdf5 = VariationsH5(join(TEST_DATA_DIR, '1000snps.hdf5'), mode='r')
hdf5_2 = VariationsArrays()
hdf5_2.put_chunks(hdf5.iterate_chunks(random_sample_rate=0))
assert hdf5_2.num_variations == 0
hdf5 = VariationsH5(join(TEST_DATA_DIR, 'ril.hdf5'), mode='r')
hdf5_3 = VariationsArrays()
hdf5_3.put_chunks(hdf5.iterate_chunks(random_sample_rate=0.01))
