def _evalstat(x, bsl, meth, n_perm, metric, maxstat, tail):
"""Statistical evaluation of features
[x] = [xn] = (nFce, npts, nTrials)
[bsl] = (nFce, nTrials)
"""
# Get shape of xF :
nf, npts, nt = x.shape
pvalues = np.ones((nf, npts))
# Permutations :
if meth == 'permutation':
perm = perm_swaparray(a, b, n_perm=200, axis=-1, rndstate=0)
from brainpipe.xPOO.stats import permutation
# Pre-define permutations :
pObj = permutation(n_perm)
perm = np.zeros((n_perm, nf, npts))
# For each permutation :
for p in range(n_perm):
# Get 1D iterations :
ite = product(range(nf), range(npts))
permT = np.random.permutation(2*nt)
for f, pts in ite:
bs, xs = bsl[f, :], x[f, pts, :]
# Reshape data :
subX = np.vstack((bsl[f, :], x[f, pts, :])).reshape(2*nt,)
# Shuffle data :
subX = subX[permT].reshape(nt, 2)
# Normalize data :
subX = normalize(subX[:, 0], subX[:, 1], norm=norm)
# Get mean of data :
perm[p, f, pts] = np.mean(subX)
# Get final pvalues :
pvalues = pObj.perm2p(np.mean(xn, 2), perm, tail=tail,
maxstat=maxstat)
# Wilcoxon test :
elif meth == 'wilcoxon':
from scipy.stats import wilcoxon
# Get iterations :
ite = product(range(nf), range(npts))
# Compute wilcoxon :
for k, i in ite:
_, pvalues[k, i] = wilcoxon(x[k, i, :], bsl[k, :])
# Kruskal-Wallis :
elif meth == 'kruskal':
from scipy.stats import kruskal
# Get iterations :
ite = product(range(nf), range(npts))
# Compute Kruskal-Wallis :
for k, i in ite:
_, pvalues[k, i] = kruskal(x[k, i, :], bsl[k, :])
return pvalues
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