def correlation_reduce_cpu(v: ArrayLike,
out: ArrayLike) -> None: # pragma: no cover
"""Corresponding "reduce" function for pearson correlation
Parameters
----------
v
The correlation array on which pearson corrections has been
applied on chunks
out
An ndarray, which is a symmetric matrix of pearson correlation
Returns
-------
An ndarray, which contains the result of the calculation of the application
of euclidean distance on all the chunks.
"""
v = v.sum(axis=0)
n = v[5]
num = n * v[4] - v[0] * v[1]
denom1 = np.sqrt(n * v[2] - v[0]**2)
denom2 = np.sqrt(n * v[3] - v[1]**2)
denom = denom1 * denom2
value = np.nan
if denom > 0:
value = 1 - (num / denom)
out[0] = value
def _divergence(ac: ArrayLike, out: ArrayLike) -> None: # pragma: no cover
"""Generalized U-function for computing divergence.
Parameters
----------
ac
Allele counts of shape (cohorts, alleles) containing per-cohort allele counts.
out
Pairwise divergence stats with shape (cohorts, cohorts), where the entry at
(i, j) is the divergence between cohort i and cohort j.
"""
an = ac.sum(axis=-1)
out[:, :] = np.nan # (cohorts, cohorts)
n_cohorts = ac.shape[0]
n_alleles = ac.shape[1]
# calculate the divergence for each cohort pair
for i in range(n_cohorts):
for j in range(i + 1, n_cohorts):
n_pairs = an[i] * an[j]
if n_pairs != 0.0:
n_same = 0
for k in range(n_alleles):
n_same += ac[i, k] * ac[j, k]
n_diff = n_pairs - n_same
div = n_diff / n_pairs
out[i, j] = div
out[j, i] = div
# calculate the diversity for each cohort
for i in range(n_cohorts):
n_pairs = an[i] * (an[i] - 1)
n_same = 0
for k in range(n_alleles):
n_same += ac[i, k] * (ac[i, k] - 1)
n_diff = n_pairs - n_same
if n_pairs != 0.0:
div = n_diff / n_pairs
out[i, i] = div