Parameters:  X : list
List of ndarrays containing the data. Each element of X contains
the samples for one group. First dimension of each element is the
number of samples/observations in this group. The other dimensions
are for the size of the observations. For example if X = [X1, X2]
with X1.shape = (20, 50, 4) and X2.shape = (17, 50, 4) one has
2 groups with respectively 20 and 17 observations in each.
Each data point is of shape (50, 4).
threshold : float  dict  None
If threshold is None, it will choose a tthreshold equivalent to
p < 0.05 for the given number of (withinsubject) observations.
If a dict is used, then thresholdfree cluster enhancement (TFCE)
will be used.
n_permutations : int
The number of permutations to compute.
tail : 1 or 0 or 1 (default = 0)
If tail is 1, the statistic is thresholded above threshold.
If tail is 1, the statistic is thresholded below threshold.
If tail is 0, the statistic is thresholded on both sides of
the distribution.
stat_fun : callable
function called to calculate statistics, must accept 1darrays as
arguments (default: scipy.stats.f_oneway).
connectivity : sparse matrix.
Defines connectivity between features. The matrix is assumed to
be symmetric and only the upper triangular half is used.
Default is None, i.e, a regular lattice connectivity.
verbose : bool, str, int, or None
If not None, override default verbose level (see mne.verbose).
n_jobs : int
Number of permutations to run in parallel (requires joblib package).
seed : int or None
Seed the random number generator for results reproducibility.
max_step : int
When connectivity is a n_vertices x n_vertices matrix, specify the
maximum number of steps between vertices along the second dimension
(typically time) to be considered connected. This is not used for full
or None connectivity matrices.
exclude : boolean array or None
Mask to apply to the data to exclude certain points from clustering
(e.g., medial wall vertices). Should be the same shape as X. If None,
no points are excluded.
step_down_p : float
To perform a stepdowninjumps test, pass a pvalue for clusters to
exclude from each successive iteration. Default is zero, perform no
stepdown test (since no clusters will be smaller than this value).
Setting this to a reasonable value, e.g. 0.05, can increase sensitivity
but costs computation time.
t_power : float
Power to raise the statistical values (usually fvalues) by before
summing (sign will be retained). Note that t_power == 0 will give a
count of nodes in each cluster, t_power == 1 will weight each node by
its statistical score.
out_type : str
For arrays with connectivity, this sets the output format for clusters.
If ‘mask’, it will pass back a list of boolean mask arrays.
If ‘indices’, it will pass back a list of lists, where each list is the
set of vertices in a given cluster. Note that the latter may use far
less memory for large datasets.
check_disjoint : bool
If True, the connectivity matrix (or list) will be examined to
determine of it can be separated into disjoint sets. In some cases
(usually with connectivity as a list and many “time” points), this
can lead to faster clustering, but results should be identical.
buffer_size: int or None
The statistics will be computed for blocks of variables of size
“buffer_size” at a time. This is option significantly reduces the
memory requirements when n_jobs > 1 and memory sharing between
processes is enabled (see set_cache_dir()), as X will be shared
between processes and each process only needs to allocate space
for a small block of variables.
