KDtree uses nested classes to define its node types (innernode, leafnode).
Pickle only works on module-level class definitions, so a nested class trips
it up:
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
However, there is a (hacky) workaround by monkey-patching the class
definitions into the
scipy.spatial.kdtreeat module scope so the pickler can
find them. If all of your pre which reads and writes pickled KDtree objects
installs these patches, this hack should work fine:
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]]
Output:
<class 'scipy.spatial.kdtree.innernode'>"ccopy_regn_reconstructornp1n(cscipy.spatial.kdtreenKDTreenp2nc_[3 4] [3 4]
