def _pfp(pha, amp, phabin, binsize):
"""Sub prefered phase function
"""
nbin, nt = len(phabin), pha.shape[1]
ampbin = np.zeros((len(phabin), nt), dtype=float)
# Binarize amplitude accros all trials :
for t in range(nt):
curpha, curamp = pha[:, t], amp[:, t]
for i, p in enumerate(phabin):
idx = np.logical_and(curpha >= p, curpha < p + binsize)
if idx.astype(int).sum() != 0:
ampbin[i, t] = curamp[idx].mean()
else:
ampbin[i, t] = 0
ampbin[:, t] /= ampbin[:, t].sum()
# Find prefered phase and p-values :
pfp = np.array([phabin[k] + binsize / 2 for k in ampbin.argmax(axis=0)])
pvalue = circ_rtest(pfp)[0]
prf = phabin[ampbin.mean(axis=1).argmax()] + binsize / 2
return pfp, prf, pvalue, ampbin
def _pfp(pha, amp, phabin, binsize):
"""Sub prefered phase function
"""
nbin, nt = len(phabin), pha.shape[1]
ampbin = np.zeros((len(phabin), nt), dtype=float)
# Binarize amplitude accros all trials :
for t in range(nt):
curpha, curamp = pha[:, t], amp[:, t]
for i, p in enumerate(phabin):
idx = np.logical_and(curpha >= p, curpha < p+binsize)
if idx.astype(int).sum() != 0:
ampbin[i, t] = curamp[idx].mean()
else:
ampbin[i, t] = 0
ampbin[:, t] /= ampbin[:, t].sum()
# Find prefered phase and p-values :
pfp = np.array([phabin[k]+binsize/2 for k in ampbin.argmax(axis=0)])
pvalue = circ_rtest(pfp)[0]
prf = phabin[ampbin.mean(axis=1).argmax()]+binsize/2
return pfp, prf, pvalue, ampbin
def _phase(x, self):
"""Sub-phase function
"""
# Get the filter properties and apply:
fMeth = self._fobj.get(self._sf, self._fSplit, self._npts)
xF = self._fobj.apply(x, fMeth)
# Mean time :
if self._window is not None:
xF, _ = binArray(xF, self._window, axis=1)
nf, npts, nt = xF.shape
# Get p-value:
if self._getstat:
pvalues = np.zeros((nf, npts), dtype=float)
for f in range(nf):
for k in range(npts):
pvalues[f, k] = circ_rtest(xF[f, k, :])[0]
else:
pvalues = None
return xF, pvalues
def _phase(x, self):
"""Sub-phase function
"""
# Get the filter properties and apply:
fMeth = self._fobj.get(self._sf, self._fSplit, self._npts)
xF = self._fobj.apply(x, fMeth)
# Mean time :
if self._window is not None:
xF, _ = binArray(xF, self._window, axis=1)
nf, npts, nt = xF.shape
# Get p-value:
if self._getstat:
pvalues = np.zeros((nf, npts), dtype=float)
for f in range(nf):
for k in range(npts):
pvalues[f, k] = circ_rtest(xF[f, k, :])[0]
else:
pvalues = None
return xF, pvalues
