def _erpacSuro(xp, xa, npts, ntrials):
"""Parallel surrogates
"""
# Permute ntrials (only for amplitude):
perm = np.random.permutation(ntrials)
for t in range(npts):
suro = circ_corrcc(xp[t, :], xa[t, perm])[0]
return suro
def _erpacSuro(xp, xa, npts, ntrials):
"""Parallel surrogates
"""
# Permute ntrials (only for amplitude):
perm = np.random.permutation(ntrials)
for t in range(npts):
suro = circ_corrcc(xp[t, :], xa[t, perm])[0]
return suro
def _erpac(xp, xa, n_perm, n_jobs):
"""Sub erpac function
[xp] = [xa] = (npts, ntrials)
"""
npts, ntrials = xp.shape
# Compute ERPAC
xerpac = np.zeros((npts, ))
for t in range(npts):
xerpac[t] = circ_corrcc(xp[t, :], xa[t, :])[0]
# Compute surrogates:
data = Parallel(n_jobs=n_jobs)(delayed(_erpacSuro)(xp, xa, npts, ntrials)
for pe in range(n_perm))
suro = np.array(data)
# Normalize erpac:
xerpac = (xerpac - suro.mean(0)) / suro.std(0)
# Get p-value:
pvalue = norm.cdf(-np.abs(xerpac)) * 2
return xerpac, pvalue
def _erpac(xp, xa, n_perm, n_jobs):
"""Sub erpac function
[xp] = [xa] = (npts, ntrials)
"""
npts, ntrials = xp.shape
# Compute ERPAC
xerpac = np.zeros((npts,))
for t in range(npts):
xerpac[t] = circ_corrcc(xp[t, :], xa[t, :])[0]
# Compute surrogates:
data = Parallel(n_jobs=n_jobs)(delayed(_erpacSuro)(
xp, xa, npts, ntrials) for pe in range(n_perm))
suro = np.array(data)
# Normalize erpac:
xerpac = (xerpac - suro.mean(0))/suro.std(0)
# Get p-value:
pvalue = norm.cdf(-np.abs(xerpac))*2
return xerpac, pvalue
