def finddives2(depths, min_dive_thresh=10):
    """Find dives in depth data below a minimum dive threshold

    Args
    ----
    depths: ndarray
        Datalogger depth measurements
    min_dive_thresh: float
        Minimum depth threshold for which to classify a dive

    Returns
    -------
    dives: ndarray
        Dive summary information in a numpy record array

        *Columns*:

        * dive_id
        * start_idx
        * stop_idx
        * dive_dur
        * depth_max
        * depth_max_i
        * depth_min
        * depth_min_i
        * depth_mean
        * comp_mean

    dive_mask: ndarray
        Boolean mask array over depth data. Cells with `True` are dives and
        cells with `False` are not.
    """
    import numpy
    import pandas
    from . import utils
    condition = depths > min_dive_thresh
    ind_start, ind_end = utils.contiguous_regions(condition)
    n_dives = len(ind_start)
    dive_mask = numpy.zeros(len(depths), dtype=bool)
    dtypes = numpy.dtype([('dive_id', int), ('start_idx', int), ('stop_idx',
        int), ('dive_dur', int), ('depth_max', float), ('depth_max_idx',
        float), ('depth_min', float), ('depth_min_idx', float), (
        'depth_mean', float), ('comp_mean', float)])
    dive_data = numpy.zeros(n_dives, dtype=dtypes)
    for i in range(n_dives):
        dive_mask[ind_start[i]:ind_end[i]] = True
        dive_depths = depths[ind_start[i]:ind_end[i]]
        dive_data['dive_id'][i] = i
        dive_data['start_idx'][i] = ind_start[i]
        dive_data['stop_idx'][i] = ind_end[i]
        dive_data['dive_dur'][i] = ind_end[i] - ind_start[i]
        dive_data['depth_max'][i] = dive_depths.max()
        dive_data['depth_max_idx'][i] = numpy.argmax(dive_depths)
        dive_data['depth_min'][i] = dive_depths.min()
        dive_data['depth_min_idx'][i] = numpy.argmin(dive_depths)
        dive_data['depth_mean'][i] = numpy.mean(dive_depths)
        dive_data['comp_mean'][i] = numpy.mean(1 + 1 / (0.1 * dive_depths))
    dive_data = dive_data[dive_data['stop_idx'] < len(depths)]
    dives = pandas.DataFrame(dive_data)
    return dives, dive_mask
