KDtree使用嵌套类定义其节点类型（innernode，leafnode）。Pickle仅适用于模块级别的类定义，因此嵌套类会使其崩溃：
import cPickleclass Foo(object): class Bar(object): passobj = Foo.Bar()print obj.__class__cPickle.dumps(obj)<class '__main__.Bar'>cPickle.PicklingError: Can't pickle <class '__main__.Bar'>: attribute lookup __main__.Bar failed
但是，有一个（棘手的）解决方法，可以将类定义猴子修补到
scipy.spatial.kdtreeat模块范围中，以便选择器可以找到它们。如果您所有读取和写入腌制KDtree对象的代码都安装了这些修补程序，则此hack应该可以正常工作：
import cPickleimport numpyfrom scipy.spatial import kdtree# patch module-level attribute to enable pickle to workkdtree.node = kdtree.KDTree.nodekdtree.leafnode = kdtree.KDTree.leafnodekdtree.innernode = kdtree.KDTree.innernodex, y = numpy.mgrid[0:5, 2:8]t1 = kdtree.KDTree(zip(x.ravel(), y.ravel()))r1 = t1.query([3.4, 4.1])raw = cPickle.dumps(t1)# read in the pickled treet2 = cPickle.loads(raw)r2 = t2.query([3.4, 4.1])print t1.tree.__class__print repr(raw)[:70]print t1.data[r1[1]], t2.data[r2[1]]
输出：
<class 'scipy.spatial.kdtree.innernode'>"ccopy_regn_reconstructornp1n(cscipy.spatial.kdtreenKDTreenp2nc_[3 4] [3 4]
