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 gts_as_mat012(self):
'''It transforms the GT matrix into 0 (major allele h**o), 1 (het),
2(other hom)'''
gts = self[GT_FIELD]
counts = counts_by_row(gts, missing_value=MISSING_INT)
if counts is None:
return numpy.full((gts.shape[0], gts.shape[1]),
fill_value=MISSING_INT)
major_alleles = numpy.argmax(counts, axis=1)
if is_dataset(gts):
gts = gts[:]
gts012 = numpy.sum(gts != major_alleles[:, None, None], axis=2)
gts012[numpy.any(gts == MISSING_INT, axis=2)] = MISSING_INT
return gts012
def gts_as_mat012(self):
'''It transforms the GT matrix into 0 (major allele h**o), 1 (het),
2(other hom)'''
gts = self[GT_FIELD]
counts = counts_by_row(gts, missing_value=MISSING_INT)
if counts is None:
return numpy.full((gts.shape[0], gts.shape[1]),
fill_value=MISSING_INT)
major_alleles = numpy.argmax(counts, axis=1)
if is_dataset(gts):
gts = gts[:]
gts012 = numpy.sum(gts != major_alleles[:, None, None], axis=2)
gts012[numpy.any(gts == MISSING_INT, axis=2)] = MISSING_INT
return gts012
def allele_count(self):
counts = None
for gt_chunk in select_dset_from_chunks(self.iterate_chunks(),
'/calls/GT'):
chunk_counts = counts_by_row(gt_chunk,
missing_value=MISSING_VALUES[int])
if counts is None:
counts = chunk_counts
else:
if counts.shape[1:] < chunk_counts.shape[1:]:
n_extra_cols = chunk_counts.shape[-1] - counts.shape[-1]
shape = list(counts.shape)
shape[-1] = n_extra_cols
extra_cols = numpy.zeros(shape, dtype=chunk_counts.dtype)
counts = numpy.hstack((counts, extra_cols))
elif counts.shape[1:] > chunk_counts.shape[1:]:
n_extra_cols = counts.shape[-1] - chunk_counts.shape[-1]
shape = list(chunk_counts.shape)
shape[-1] = n_extra_cols
extra_cols = numpy.zeros(shape, dtype=chunk_counts.dtype)
chunk_counts = numpy.hstack((chunk_counts, extra_cols))
counts = numpy.concatenate([counts, chunk_counts], axis=0)
return counts
def allele_count(self):
counts = None
for gt_chunk in select_dset_from_chunks(self.iterate_chunks(),
'/calls/GT'):
chunk_counts = counts_by_row(gt_chunk,
missing_value=MISSING_VALUES[int])
if counts is None:
counts = chunk_counts
else:
if counts.shape[1:] < chunk_counts.shape[1:]:
n_extra_cols = chunk_counts.shape[-1] - counts.shape[-1]
shape = list(counts.shape)
shape[-1] = n_extra_cols
extra_cols = numpy.zeros(shape, dtype=chunk_counts.dtype)
counts = numpy.hstack((counts, extra_cols))
elif counts.shape[1:] > chunk_counts.shape[1:]:
n_extra_cols = counts.shape[-1] - chunk_counts.shape[-1]
shape = list(chunk_counts.shape)
shape[-1] = n_extra_cols
extra_cols = numpy.zeros(shape, dtype=chunk_counts.dtype)
chunk_counts = numpy.hstack((chunk_counts, extra_cols))
counts = numpy.concatenate([counts, chunk_counts], axis=0)
return counts
def allele_count(self):
gts = self['/calls/GT']
counts = counts_by_row(gts, missing_value=MISSING_VALUES[int])
return counts
def allele_count(self):
gts = self['/calls/GT']
counts = counts_by_row(gts, missing_value=MISSING_VALUES[int])
return counts
