@cached_property
def _pivot_min_intervals(self):
    '\n        The minimum Interval along each Dimension such that by evaluating the\n        pivot, all Candidates are evaluated too.\n        '
    c = self.pivot
    ret = defaultdict((lambda : [np.inf, (- np.inf)]))
    for i in self:
        distance = [o.distance(v) for (o, v) in zip(i.offsets, c.offsets)]
        distance = [(d, set(v)) for (d, v) in LabeledVector.transpose(*distance)]
        for (d, v) in distance:
            value = v.pop()
            ret[d][0] = min(ret[d][0], value)
            ret[d][1] = max(ret[d][1], value)
    ret = {d: Interval(d, m, M) for (d, (m, M)) in ret.items()}
    return ret
The minimum Interval along each Dimension such that by evaluating the
pivot, all Candidates are evaluated too.
@cached_property
def _pivot_min_intervals(self):
    '\n        The minimum Interval along each Dimension such that by evaluating the\n        pivot, all Candidates are evaluated too.\n        '
    c = self.pivot
    ret = defaultdict((lambda : [np.inf, (- np.inf)]))
    for i in self:
        distance = [o.distance(v) for (o, v) in zip(i.offsets, c.offsets)]
        distance = [(d, set(v)) for (d, v) in LabeledVector.transpose(*distance)]
        for (d, v) in distance:
            value = v.pop()
            ret[d][0] = min(ret[d][0], value)
            ret[d][1] = max(ret[d][1], value)
    ret = {d: Interval(d, m, M) for (d, (m, M)) in ret.items()}
    return ret