find.py 文件源码

def where_close(pos, separation, intensity=None):
    """ Returns indices of features that are closer than separation from other
    features. When intensity is given, the one with the lowest intensity is
    returned: else the most topleft is returned (to avoid randomness)

    To be implemented in trackpy v0.4"""
    if len(pos) == 0:
        return []
    separation = validate_tuple(separation, pos.shape[1])
    if any([s == 0 for s in separation]):
        return []
    # Rescale positions, so that pairs are identified below a distance
    # of 1.
    pos_rescaled = pos / separation
    duplicates = cKDTree(pos_rescaled, 30).query_pairs(1 - 1e-7)
    if len(duplicates) == 0:
        return []
    index_0 = np.fromiter((x[0] for x in duplicates), dtype=int)
    index_1 = np.fromiter((x[1] for x in duplicates), dtype=int)
    if intensity is None:
        to_drop = np.where(np.sum(pos_rescaled[index_0], 1) >
                           np.sum(pos_rescaled[index_1], 1),
                           index_1, index_0)
    else:
        intensity_0 = intensity[index_0]
        intensity_1 = intensity[index_1]
        to_drop = np.where(intensity_0 > intensity_1, index_1, index_0)
        edge_cases = intensity_0 == intensity_1
        if np.any(edge_cases):
            index_0 = index_0[edge_cases]
            index_1 = index_1[edge_cases]
            to_drop[edge_cases] = np.where(np.sum(pos_rescaled[index_0], 1) >
                                           np.sum(pos_rescaled[index_1], 1),
                                           index_1, index_0)
    return np.unique(to_drop)