def CfcAmpSwap(xfP, xfA, CfcModel, n_perm=200, matricial=True):
"""Swap phase/amplitude trials, (Bahramisharif, 2013)
[xfP] = (nPha, npts, ntrials)
[xfA] = (nAmp, npts, ntrials)
"""
# Get sizes :
nPha, timeL, nbTrials = xfP.shape
nAmp = xfA.shape[0]
Suro = np.zeros((nbTrials, nAmp, nPha, n_perm))
if matricial:
# Swap trials phase/amplitude :
ampSh, _ = perm_swap(xfA, xfA, axis=2, n_perm=n_perm)
# Get pac :
iteract = product(range(nbTrials), range(n_perm))
for tr, pe in iteract:
Suro[tr, :, :, pe] = CfcModel(xfP[:, :, tr], np.matrix(ampSh[pe, :, :, tr]))
else:
for iamp in range(nAmp):
ampSh, _ = perm_swap(xfA[iamp, ...], xfA[iamp, ...], axis=1, n_perm=n_perm)
# Get pac :
iteract = product(range(nbTrials), range(n_perm))
for tr, pe in iteract:
Suro[tr, iamp, :, pe] = CfcModel(xfP[:, :, tr], np.matrix(ampSh[pe, :, tr]))
return Suro
def CfcTrialSwap(xfP, xfA, CfcModel, n_perm=200, matricial=True):
"""Swap phase/amplitude trials (Tort, 2010)
[xfP] = (nPha, npts, ntrials)
[xfA] = (nAmp, npts, ntrials)
"""
# Get sizes :
nPha, timeL, nbTrials = xfP.shape
nAmp = xfA.shape[0]
Suro = np.zeros((nbTrials, nAmp, nPha, n_perm))
if matricial:
# Swap trials phase/amplitude :
phamp = np.concatenate((xfP, xfA))
phampSh1, phampSh2 = perm_swap(phamp, phamp, axis=2, n_perm=n_perm)
phaSh, ampSh = phampSh1[:, 0:nPha, ...], phampSh2[:, nPha::, ...]
del phamp, phampSh1, phampSh2
# Get pac :
iteract = product(range(nbTrials), range(n_perm))
for tr, pe in iteract:
Suro[tr, :, :, pe] = CfcModel(np.matrix(phaSh[pe, :, :, tr]), ampSh[pe, :, :, tr])
else:
# Swap trials phase/amplitude :
phampiter = product(range(nPha), range(nAmp))
for ipha, iamp in phampiter:
# Concatenate selected pha/amp :
phamp = np.concatenate((xfP[[ipha], ...], xfA[[iamp], ...]))
# Swap:
phampSh1, phampSh2 = perm_swap(phamp, phamp, axis=2, n_perm=n_perm)
phaSh, ampSh = phampSh1[:, 0, ...], phampSh2[:, 1, ...]
# Get pac :
iteract = product(range(nbTrials), range(n_perm))
for tr, pe in iteract:
Suro[tr, iamp, ipha, pe] = CfcModel(np.matrix(phaSh[pe, :, tr]), ampSh[pe, :, tr])
return Suro
def CfcAmpSwap(xfP, xfA, CfcModel, n_perm=200, matricial=True):
"""Swap phase/amplitude trials, (Bahramisharif, 2013)
[xfP] = (nPha, npts, ntrials)
[xfA] = (nAmp, npts, ntrials)
"""
# Get sizes :
nPha, timeL, nbTrials = xfP.shape
nAmp = xfA.shape[0]
Suro = np.zeros((nbTrials, nAmp, nPha, n_perm))
if matricial:
# Swap trials phase/amplitude :
ampSh, _ = perm_swap(xfA, xfA, axis=2, n_perm=n_perm)
# Get pac :
iteract = product(range(nbTrials), range(n_perm))
for tr, pe in iteract:
Suro[tr, :, :, pe] = CfcModel(xfP[:, :, tr],
np.matrix(ampSh[pe, :, :, tr]))
else:
for iamp in range(nAmp):
ampSh, _ = perm_swap(xfA[iamp, ...],
xfA[iamp, ...],
axis=1,
n_perm=n_perm)
# Get pac :
iteract = product(range(nbTrials), range(n_perm))
for tr, pe in iteract:
Suro[tr, iamp, :, pe] = CfcModel(xfP[:, :, tr],
np.matrix(ampSh[pe, :, tr]))
return Suro
def CfcTrialSwap(xfP, xfA, CfcModel, n_perm=200, matricial=True):
"""Swap phase/amplitude trials (Tort, 2010)
[xfP] = (nPha, npts, ntrials)
[xfA] = (nAmp, npts, ntrials)
"""
# Get sizes :
nPha, timeL, nbTrials = xfP.shape
nAmp = xfA.shape[0]
Suro = np.zeros((nbTrials, nAmp, nPha, n_perm))
if matricial:
# Swap trials phase/amplitude :
phamp = np.concatenate((xfP, xfA))
phampSh1, phampSh2 = perm_swap(phamp, phamp, axis=2, n_perm=n_perm)
phaSh, ampSh = phampSh1[:, 0:nPha, ...], phampSh2[:, nPha::, ...]
del phamp, phampSh1, phampSh2
# Get pac :
iteract = product(range(nbTrials), range(n_perm))
for tr, pe in iteract:
Suro[tr, :, :, pe] = CfcModel(np.matrix(phaSh[pe, :, :, tr]),
ampSh[pe, :, :, tr])
else:
# Swap trials phase/amplitude :
phampiter = product(range(nPha), range(nAmp))
for ipha, iamp in phampiter:
# Concatenate selected pha/amp :
phamp = np.concatenate((xfP[[ipha], ...], xfA[[iamp], ...]))
# Swap:
phampSh1, phampSh2 = perm_swap(phamp, phamp, axis=2, n_perm=n_perm)
phaSh, ampSh = phampSh1[:, 0, ...], phampSh2[:, 1, ...]
# Get pac :
iteract = product(range(nbTrials), range(n_perm))
for tr, pe in iteract:
Suro[tr, iamp, ipha,
pe] = CfcModel(np.matrix(phaSh[pe, :, tr]), ampSh[pe, :,
tr])
return Suro
def _evalstat(self, x, bsl):
"""Statistical evaluation of features
[x] = [xn] = (nFce, npts, nTrials)
"""
# Unpack variables:
statmeth = self._statmeth
n_perm = self._n_perm
tail = self._2t
maxst = self._mxst
# Mean Frequencies :
x, _ = binArray(x, self._fSplitIndex, axis=0)
# Get the baseline and set to same shape of x:
xFm = np.mean(x[:, bsl[0]:bsl[1], :], 1)
# Mean time :
if self._window is not None:
x, _ = binArray(x, self._window, axis=1)
# Repeat baseline:
baseline = np.tile(xFm[:, np.newaxis, :], [1, x.shape[1], 1])
# Get shape of x:
nf, npts, nt = x.shape
pvalues = np.ones((nf, npts))
# Switch between methods:
# -> Permutations
# Loops on time and matrix for frequency (avoid RAM usage but increase speed)
if statmeth == 'permutation':
# Get metric:
fcn = perm_metric(self._metric)
# Apply metric to x and baseline:
xN = fcn(x, baseline).mean(axis=2)
# For each time points:
for pts in range(npts):
# Randomly swap x // baseline :
perm = perm_swap(x[:, pts, :],
baseline[:, pts, :],
n_perm=n_perm,
axis=1,
rndstate=0 + pts)[0]
# Normalize permutations by baline:
perm = fcn(perm, baseline[:, pts, :]).mean(2)
# Maximum stat (correct through frequencies):
if maxst:
perm = maxstat(perm, axis=1)
# Get pvalues :
pvalues[:, pts] = perm_2pvalue(xN[:, pts], perm, n_perm, tail=tail)
# -> Wilcoxon // Kruskal-Wallis:
else:
# Get the method:
if statmeth == 'wilcoxon':
def fcn(a, b):
return wilcoxon(a, b)[1]
elif statmeth == 'kruskal':
def fcn(a, b):
return kruskal(a, b)[1]
# Apply:
ite = product(range(nf), range(npts))
for k, i in ite:
pvalues[k, i] = fcn(x[k, i, :], xFm[k, :])
return pvalues
def _evalstat(self, x, bsl):
"""Statistical evaluation of features
[x] = [xn] = (nFce, npts, nTrials)
"""
# Unpack variables:
statmeth = self._statmeth
n_perm = self._n_perm
tail = self._2t
maxst = self._mxst
# Mean Frequencies :
x, _ = binArray(x, self._fSplitIndex, axis=0)
# Get the baseline and set to same shape of x:
xFm = np.mean(x[:, bsl[0]:bsl[1], :], 1)
# Mean time :
if self._window is not None:
x, _ = binArray(x, self._window, axis=1)
# Repeat baseline:
baseline = np.tile(xFm[:, np.newaxis, :], [1, x.shape[1], 1])
# Get shape of x:
nf, npts, nt = x.shape
pvalues = np.ones((nf, npts))
# Switch between methods:
# -> Permutations
# Loops on time and matrix for frequency (avoid RAM usage but increase speed)
if statmeth == 'permutation':
# Get metric:
fcn = perm_metric(self._metric)
# Apply metric to x and baseline:
xN = fcn(x, baseline).mean(axis=2)
# For each time points:
for pts in range(npts):
# Randomly swap x // baseline :
perm = perm_swap(x[:, pts, :], baseline[:, pts, :],
n_perm=n_perm, axis=1, rndstate=0+pts)[0]
# Normalize permutations by baline:
perm = fcn(perm, baseline[:, pts, :]).mean(2)
# Maximum stat (correct through frequencies):
if maxst:
perm = maxstat(perm, axis=1)
# Get pvalues :
pvalues[:, pts] = perm_2pvalue(xN[:, pts], perm, n_perm, tail=tail)
# -> Wilcoxon // Kruskal-Wallis:
else:
# Get the method:
if statmeth == 'wilcoxon':
def fcn(a, b): return wilcoxon(a, b)[1]
elif statmeth == 'kruskal':
def fcn(a, b): return kruskal(a, b)[1]
# Apply:
ite = product(range(nf), range(npts))
for k, i in ite:
pvalues[k, i] = fcn(x[k, i, :], xFm[k, :])
return pvalues
