In [1]:

    

import kdtree


    

In [2]:

    

emptyTree = kdtree.create(dimensions=500)


    

In [3]:

    

point1 = (2, 3, 4)
point2 = [4, 5, 6]


    

In [4]:

    

import collections


    

In [5]:

    

Point = collections.namedtuple('Point', 'x y z')
point3 = Point(5, 3, 2)


    

In [6]:

    

tree = kdtree.create([point1, point2, point3])


    

In [7]:

    

tree


    

    
Out[7]:





<KDNode - [4, 5, 6]>

In [8]:

    

tree.add( (5, 4, 3) )


    

    
Out[8]:





<KDNode - (5, 4, 3)>

In [9]:

    

list(tree.inorder())


    

    
Out[9]:





[<KDNode - (2, 3, 4)>,
 <KDNode - [4, 5, 6]>,
 <KDNode - Point(x=5, y=3, z=2)>,
 <KDNode - (5, 4, 3)>]

In [10]:

    

tree.search_nn( (1, 2, 3) )


    

    
Out[10]:





(<KDNode - (2, 3, 4)>, 3.0)

In [11]:

    

kdtree.visualize(tree)


    

    





                     [4, 5, 6]                 

           (2, 3, 4)       Point(x=5, y=3, z=2) 

                                       (5, 4, 3)  

In [12]:

    

tree.is_balanced


    

    
Out[12]:





True

In [18]:

    

tree.dist(point1)


    

    
Out[18]:





12.0

In [22]:

    

tree.search_knn(point1, point2)


    

    
Out[22]:





[(<KDNode - (2, 3, 4)>, 0.0),
 (<KDNode - (5, 4, 3)>, 11.0),
 (<KDNode - [4, 5, 6]>, 12.0),
 (<KDNode - Point(x=5, y=3, z=2)>, 13.0)]

In [27]:

    

tree.search_nn_dist(point1, 12.)


    

    
Out[27]:





[<KDNode - (2, 3, 4)>, <KDNode - (5, 4, 3)>]

In [29]:

    

tree.


    

    
Out[29]:





[4, 5, 6]

In [ ]: