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 cPickle
class Foo(object):
class Bar(object):
pass
obj = 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 cPickle
import numpy
from scipy.spatial import kdtree
# patch module-level attribute to enable pickle to work
kdtree.node = kdtree.KDTree.node
kdtree.leafnode = kdtree.KDTree.leafnode
kdtree.innernode = kdtree.KDTree.innernode
x, 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 tree
t2 = 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]]
Output:
<class 'scipy.spatial.kdtree.innernode'>
"ccopy_reg\n_reconstructor\np1\n(cscipy.spatial.kdtree\nKDTree\np2\nc_
[3 4] [3 4]
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