cluster_level.py 文件源码

def _find_clusters_1dir(x, x_in, connectivity, max_step, t_power, ndimage):
    """Actually call the clustering algorithm"""
    if connectivity is None:
        labels, n_labels = ndimage.label(x_in)

        if x.ndim == 1:
            # slices
            clusters = ndimage.find_objects(labels, n_labels)
            if len(clusters) == 0:
                sums = list()
            else:
                index = list(range(1, n_labels + 1))
                if t_power == 1:
                    sums = ndimage.measurements.sum(x, labels, index=index)
                else:
                    sums = ndimage.measurements.sum(np.sign(x) *
                                                    np.abs(x) ** t_power,
                                                    labels, index=index)
        else:
            # boolean masks (raveled)
            clusters = list()
            sums = np.empty(n_labels)
            for l in range(1, n_labels + 1):
                c = labels == l
                clusters.append(c.ravel())
                if t_power == 1:
                    sums[l - 1] = np.sum(x[c])
                else:
                    sums[l - 1] = np.sum(np.sign(x[c]) *
                                         np.abs(x[c]) ** t_power)
    else:
        if x.ndim > 1:
            raise Exception("Data should be 1D when using a connectivity "
                            "to define clusters.")
        if isinstance(connectivity, sparse.spmatrix):
            clusters = _get_components(x_in, connectivity)
        elif isinstance(connectivity, list):  # use temporal adjacency
            clusters = _get_clusters_st(x_in, connectivity, max_step)
        else:
            raise ValueError('Connectivity must be a sparse matrix or list')
        if t_power == 1:
            sums = np.array([np.sum(x[c]) for c in clusters])
        else:
            sums = np.array([np.sum(np.sign(x[c]) * np.abs(x[c]) ** t_power)
                            for c in clusters])

    return clusters, np.atleast_1d(sums)