To perform parallel operation on pairwise matrices

example_parallel_pairwise.py

def pairwise(X, Y, func, n_jobs=-1):
    from mne.parallel import parallel_func
    if X.shape != Y.shape:
        raise ValueError('X and Y must have identical shapes')
    parallel, p_pairwise, n_jobs = parallel_func(_pairwise, n_jobs)
    dims = X.shape
    X = np.reshape(X, [dims[0], np.prod(dims[1:])])
    Y = np.reshape(Y, [dims[0], np.prod(dims[1:])])
    n_cols = X.shape[1]
    n_chunks = min(n_cols, n_jobs)
    chunks = np.array_split(range(n_cols), n_chunks)
    out = parallel(p_pairwise(X[:, chunk], Y[:, chunk], func)
                   for chunk in chunks)
    # unpack
    out = np.reshape(out + list(), dims[1:] + np.shape(out)[2:])
    return out


def _pairwise(X, Y, func):
    n_cols = X.shape[1]
    out_ = func(X[:, 0], Y[:, 0])
    out = np.empty((n_cols, len(out_)))
    for col in range(n_cols):
        out[col, :] = func(X[:, col], Y[:, col])
    return out