def comm_sep(self, plot_locs, criteria, loc_unit=None):
    '''
    Calculates commonality (Sorensen and Jaccard) between pairs of plots.

    Parameters
    ----------
    plot_locs : dict
        Dictionary with keys equal to each plot name, which must be
        represented by a column in the data table, and values equal to a
        tuple of the x and y coordinate of each plot
    criteria : dict
        See docstring for Patch.sad.
    loc_unit : str
        Unit of plot locations. Special cases include 'decdeg' (decimal
        degrees), returns result in km. Otherwise ignored.

    Returns
    -------
    result: structured array
        Returns a structured array with fields plot-a and plot-b (names of
        two plots), dist (distance between plots), and sorensen and jaccard
        (similarity indices). Has row for each unique pair of plots.
    '''

    # Set up sad_dict with key=plot and val=clean sad for that plot
    sad_dict = {}

    # Loop through all plot cols, updating criteria, and getting spp_list
    for plot in plot_locs.keys():

        # Find current count col and remove it from criteria
        for crit_key in criteria.keys():
            if criteria[crit_key] == 'count':
                criteria.pop(crit_key, None)

        # Add this plot as col with counts
        criteria[plot] = 'count'

        # Get SAD for existing criteria with this plot as count col
        sad_return = self.sad(criteria, clean=True)

        # Check that sad_return only has one element, or throw error
        if len(sad_return) > 1:
            raise NotImplementedError('Too many criteria for comm_sep')

        # Get unique species list for this plot and store in sad_dict
        sad_dict[plot] = sad_return[0][2]

    # Set up recarray to hold Sorensen index for all pairs of plots
    n_pairs = np.sum(np.arange(len(plot_locs.keys())))
    result = np.recarray((n_pairs,), dtype=[('plot-a','S32'),
                                            ('plot-b', 'S32'),
                                            ('spp-a', int),
                                            ('spp-b', int),
                                            ('dist', float),
                                            ('sorensen', float),
                                            ('jaccard', float)])

    # Loop through all combinations of plots and fill in result table
    row = 0
    for pair in itertools.combinations(plot_locs.keys(), 2):

        # Names of plots
        plota = pair[0]
        plotb = pair[1]

        result[row]['plot-a'] = plota
        result[row]['plot-b'] = plotb

        # Calculate inter-plot distance
        if loc_unit == 'decdeg':
            result[row]['dist'] = _decdeg_distance(plot_locs[plota],
                                                   plot_locs[plotb])
        else:
            result[row]['dist'] = _distance(plot_locs[plota],
                                            plot_locs[plotb])

        # Get similarity indices
        spp_a = len(sad_dict[plota])
        spp_b = len(sad_dict[plotb])

        result[row]['spp-a'] = spp_a
        result[row]['spp-b'] = spp_b

        intersect = set(sad_dict[plota]).intersection(sad_dict[plotb])
        union = set(sad_dict[plota]).union(sad_dict[plotb])

        # Fill in zero if denom is zero
        if spp_a + spp_b == 0:
            result[row]['sorensen'] = 0
        else:
            result[row]['sorensen'] = (2*len(intersect)) / (spp_a+spp_b)

        if len(union) == 0:
            result[row]['jaccard'] = 0
        else:
            result[row]['jaccard'] = len(intersect) / len(union)

        # Increment row counter
        row += 1

    return result
Calculates commonality (Sorensen and Jaccard) between pairs of plots.

    Parameters
    ----------
    plot_locs : dict
        Dictionary with keys equal to each plot name, which must be
        represented by a column in the data table, and values equal to a
        tuple of the x and y coordinate of each plot
    criteria : dict
        See docstring for Patch.sad.
    loc_unit : str
        Unit of plot locations. Special cases include 'decdeg' (decimal
        degrees), returns result in km. Otherwise ignored.

    Returns
    -------
    result: structured array
        Returns a structured array with fields plot-a and plot-b (names of
        two plots), dist (distance between plots), and sorensen and jaccard
        (similarity indices). Has row for each unique pair of plots.