Cluster thresholding algorithm for a group-level searchlight analysis
Functions
Doi(*args, **kwargs) | Perform no good and no bad |
get_cluster_pvals(sizes, null_sizes) | Get p-value per each cluster size given cluster sizes for null-distribution |
get_cluster_sizes(ds[, cluster_counter]) | Compute cluster sizes from all samples in a boolean dataset. |
get_thresholding_map(data[, p]) | Return array of thresholds corresponding to a probability of such value in the input |
mean_sample([attrfx]) | Returns a mapper that computes the mean sample of a dataset. |
repeat_cluster_vals(cluster_counts[, vals]) | Repeat vals for each count of a cluster size as given in cluster_counts |
Classes
Counter([iterable]) | Dict subclass for counting hashable items. |
Dataset(samples[, sa, fa, a]) | Generic storage class for datasets with multiple attributes. |
EnsureChoice(*values) | Ensure an input is element of a set of possible values |
EnsureFloat() | Ensure that an input (or several inputs) are of a data type ‘float’. |
EnsureInt() | Ensure that an input (or several inputs) are of a data type ‘int’. |
EnsureRange([min, max]) | Ensure an input is within a particular range |
GroupClusterThreshold(**kwargs) | Statistical evaluation of group-level average accuracy maps |
IdentityMapper(**kwargs) | A mapper that performs an identity transformation (i.e. |
Learner([auto_train, force_train]) | Common trainable processing object. |
Parameter(default[, constraints, ro, index, ...]) | This class shall serve as a representation of a parameter. |
dok_matrix(arg1[, shape, dtype, copy]) | Dictionary Of Keys based sparse matrix. |