def locate_unlinked(gn, size=100, step=20, threshold=.1, chunked=False,
blen=None):
"""Locate variants in approximate linkage equilibrium, where r**2 is
below the given `threshold`.
Parameters
----------
gn : array_like, int8, shape (n_variants, n_samples)
Diploid genotypes at biallelic variants, coded as the number of
alternate alleles per call (i.e., 0 = hom ref, 1 = het, 2 = hom alt).
size : int
Window size (number of variants).
step : int
Number of variants to advance to the next window.
threshold : float
Maximum value of r**2 to include variants.
blen : int, optional
Block length to use for chunked computation.
Returns
-------
loc : ndarray, bool, shape (n_variants)
Boolean array where True items locate variants in approximate
linkage equilibrium.
Notes
-----
The value of r**2 between each pair of variants is calculated using the
method of Rogers and Huff (2008).
"""
from allel.opt.stats import gn_locate_unlinked_int8
# check inputs
if not hasattr(gn, 'shape') or not hasattr(gn, 'dtype'):
gn = np.asarray(gn, dtype='i1')
if gn.ndim != 2:
raise ValueError('gn must have two dimensions')
# setup output
loc = np.ones(gn.shape[0], dtype='u1')
# compute in chunks to avoid loading big arrays into memory
blen = get_blen_array(gn, blen)
blen = max(blen, 10*size) # avoid too small chunks
n_variants = gn.shape[0]
for i in range(0, n_variants, blen):
# N.B., ensure overlap with next window
j = min(n_variants, i+blen+size)
gnb = np.asarray(gn[i:j], dtype='i1')
locb = loc[i:j]
gn_locate_unlinked_int8(gnb, locb, size, step, threshold)
return loc.astype('b1')
def locate_unlinked(gn, size=100, step=20, threshold=.1, blen=None):
"""Locate variants in approximate linkage equilibrium, where r**2 is
below the given `threshold`.
Parameters
----------
gn : array_like, int8, shape (n_variants, n_samples)
Diploid genotypes at biallelic variants, coded as the number of
alternate alleles per call (i.e., 0 = hom ref, 1 = het, 2 = hom alt).
size : int
Window size (number of variants).
step : int
Number of variants to advance to the next window.
threshold : float
Maximum value of r**2 to include variants.
blen : int, optional
Block length to use for chunked computation.
Returns
-------
loc : ndarray, bool, shape (n_variants)
Boolean array where True items locate variants in approximate
linkage equilibrium.
Notes
-----
The value of r**2 between each pair of variants is calculated using the
method of Rogers and Huff (2008).
"""
# check inputs
if not hasattr(gn, 'shape') or not hasattr(gn, 'dtype'):
gn = np.asarray(gn, dtype='i1')
if gn.ndim != 2:
raise ValueError('gn must have two dimensions')
# setup output
loc = np.ones(gn.shape[0], dtype='u1')
# compute in chunks to avoid loading big arrays into memory
blen = get_blen_array(gn, blen)
blen = max(blen, 10 * size) # avoid too small chunks
n_variants = gn.shape[0]
for i in range(0, n_variants, blen):
# N.B., ensure overlap with next window
j = min(n_variants, i + blen + size)
gnb = np.asarray(gn[i:j], dtype='i1')
gnb = memoryview_safe(gnb)
locb = loc[i:j]
gn_locate_unlinked_int8(gnb, locb, size, step, threshold)
return loc.astype('b1')