def test_random_uniform(self):
uniform = BlockMatrix.random(10, 10, gaussian=False)
nuniform = uniform.to_numpy()
for row in nuniform:
for entry in row:
assert entry > 0
def test_random_uniform(self):
uniform = BlockMatrix.random(10, 10, gaussian=False)
nuniform = uniform.to_numpy()
for row in nuniform:
for entry in row:
assert entry > 0
def get_Z(N_r):
r'''
Returns `N_r`-dim standard normal random vector
'''
Z = BlockMatrix.random(
n_rows=N_r, n_cols=1,
gaussian=True) # N_r-dimensional standard normal random vector
return Z
def test_to_matrix_table(self):
n_partitions = 2
rows, cols = 2, 5
bm = BlockMatrix._create(rows, cols, [float(i) for i in range(10)])
actual = bm.to_matrix_table_row_major(n_partitions)
expected = hl.utils.range_matrix_table(rows, cols)
expected = expected.annotate_entries(element=hl.float64(expected.row_idx * cols + expected.col_idx))
expected = expected.key_cols_by(col_idx=hl.int64(expected.col_idx))
expected = expected.key_rows_by(row_idx=hl.int64(expected.row_idx))
assert expected._same(actual)
bm = BlockMatrix.random(50, 100, block_size=25, seed=0)
mt = bm.to_matrix_table_row_major(n_partitions)
mt_round_trip = BlockMatrix.from_entry_expr(mt.element).to_matrix_table_row_major()
assert mt._same(mt_round_trip)
def test_to_matrix_table(self):
n_partitions = 2
rows, cols = 2, 5
bm = BlockMatrix._create(rows, cols, [float(i) for i in range(10)])
actual = bm.to_matrix_table_row_major(n_partitions)
expected = hl.utils.range_matrix_table(rows, cols)
expected = expected.annotate_entries(element=hl.float64(expected.row_idx * cols + expected.col_idx))
expected = expected.key_cols_by(col_idx=hl.int64(expected.col_idx))
expected = expected.key_rows_by(row_idx=hl.int64(expected.row_idx))
assert expected._same(actual)
bm = BlockMatrix.random(50, 100, block_size=25, seed=0)
mt = bm.to_matrix_table_row_major(n_partitions)
mt_round_trip = BlockMatrix.from_entry_expr(mt.element).to_matrix_table_row_major()
assert mt._same(mt_round_trip)
def get_beta(M, h2, pi=1, seed=None, astype=BlockMatrix):
r'''
Returns M-dim vector of true SNP effect sizes
'''
assert pi >= 0 and pi <= 1, '`pi` (proportion of causal variants) must be in the interval [0,1]'
kwargs = {'seed': seed} if type(seed) != type(None) else {}
if pi == 1: #infinitesimal model
beta = np.sqrt(h2 / M) * BlockMatrix.random(
n_rows=M, n_cols=1, gaussian=True, **kwargs)
else:
np.random.seed(seed)
M_causal = round(M * pi) # number of causal variants
causal_beta = np.random.normal(loc=0,
scale=np.sqrt(h2 / M_causal),
size=(M_causal, 1))
causal_idx = np.random.choice(M, replace=False, size=M_causal)
causal_idx.sort()
beta = np.zeros(shape=(M, 1))
beta[causal_idx] = causal_beta
return _cast(beta, astype=astype)
import hail as hl
from hail.linalg import BlockMatrix
mt = hl.import_vcf('gs://hail-1kg/1kg_coreexome.vcf.bgz', min_partitions=16)
mt = mt.annotate_rows(x=5)
mt._force_count_rows()
mt = hl.import_bgen('gs://hail-common/test-resources/example.8bits.bgen',
entry_fields=['GT'])
mt._force_count_rows()
bm = BlockMatrix.random(10, 11, block_size=32)
bm.to_numpy(_force_blocking=True)
bm.to_numpy()
import hail as hl
from hail.linalg import BlockMatrix
mt = hl.import_vcf('gs://hail-1kg/1kg_coreexome.vcf.bgz')
mt = mt.annotate_rows(x = 5)
mt._force_count_rows()
mt = hl.import_bgen('gs://hail-ci/example.8bits.bgen', entry_fields=['GT'])
mt._force_count_rows()
bm = BlockMatrix.random(10, 11)
bm.to_numpy(_force_blocking=True)
bm.to_numpy()
import hail as hl
from hail.linalg import BlockMatrix
mt = hl.import_vcf('gs://hail-1kg/1kg_coreexome.vcf.bgz')
mt = mt.annotate_rows(x=5)
mt._force_count_rows()
mt = hl.import_bgen('gs://hail-ci/example.8bits.bgen', entry_fields=['GT'])
mt._force_count_rows()
bm = BlockMatrix.random(10, 11)
bm.to_numpy(_force_blocking=True)
bm.to_numpy()
