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https://github.com/stefankoegl/kdtree

A Python implementation of a kd-tree
https://github.com/stefankoegl/kdtree

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A Python implementation of a kd-tree

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README 

        
A simple kd-tree in Python [![Build Status](https://travis-ci.org/stefankoegl/kdtree.png?branch=master)](https://travis-ci.org/stefankoegl/kdtree)

==========================



The kdtree package can construct, modify and search

[kd-trees](http://en.wikipedia.org/wiki/Kd-tree).



* Website: https://github.com/stefankoegl/kdtree

* Repository: https://github.com/stefankoegl/kdtree.git

* Documentation: https://python-kdtree.readthedocs.org/

* PyPI: https://pypi.python.org/pypi/kdtree

* Travis-CI: https://travis-ci.org/stefankoegl/kdtree

* Coveralls: https://coveralls.io/r/stefankoegl/kdtree



Usage

-----



    >>> import kdtree



    # Create an empty tree by specifying the number of

    # dimensions its points will have

    >>> emptyTree = kdtree.create(dimensions=3)



    # A kd-tree can contain different kinds of points, for example tuples

    >>> point1 = (2, 3, 4)



    # Lists can also be used as points

    >>> point2 = [4, 5, 6]



    # Other objects that support indexing can be used, too

    >>> import collections

    >>> Point = collections.namedtuple('Point', 'x y z')

    >>> point3 = Point(5, 3, 2)



    # A tree is created from a list of points

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



    # Each (sub)tree is represented by its root node

    >>> tree

    



    # Adds a tuple to the tree

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



    # Removes the previously added point and returns the new root

    >>> tree = tree.remove( (5, 4, 3) )



    # Retrieving the Tree in inorder

    >>> list(tree.inorder())

    [, , ]



    # Retrieving the Tree in level order

    >>> list(kdtree.level_order(tree))

    [, , ]



    # Find the nearest node to the location (1, 2, 3)

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

    



    # Add a point to make the tree more interesting

    >>> tree.add( (10, 2, 1) )



    # Visualize the Tree

    >>> kdtree.visualize(tree)



                         [4, 5, 6]



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



                                (10, 2, 1)



    # Take the right subtree of the root

    >>> subtree = tree.right



    # and detatch it

    >>> tree.right = None

    >>> kdtree.visualize(tree)



               [4, 5, 6]



          (2, 3, 4)



    >>> kdtree.visualize(subtree)



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



          (10, 2, 1)



    # and re-attach it

    >>> tree.right = subtree

    >>> kdtree.visualize(tree)



                         [4, 5, 6]



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



                                (10, 2, 1)



    # Add a node to make the tree unbalanced

    >>> tree.is_balanced

    True

    >>> tree.add( (6, 1, 5) )

    >>> tree.is_balanced

    False

    >>> kdtree.visualize(tree)



                                       [4, 5, 6]



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

                                                   (10, 2, 1)

                                                           (6, 1, 5)

    # rebalance the tree

    >>> tree = tree.rebalance()

    >>> tree.is_balanced

    True

    >>> kdtree.visualize(tree)



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



               [4, 5, 6]            (6, 1, 5)



          (2, 3, 4)



### Adding a payload



Indexing a dict by a pair of floats is not a good idea, since there might be unexpected precision errors.

Since KDTree expects a tuple-looking objects for nodes, you can make a class that looks like a tuple, but

contains more data. This way you can store all your data in a kdtree, without using an additional

indexed structure.



```python

import kdtree



# This class emulates a tuple, but contains a useful payload

class Item(object):

    def __init__(self, x, y, data):

        self.coords = (x, y)

        self.data = data



    def __len__(self):

        return len(self.coords)



    def __getitem__(self, i):

        return self.coords[i]



    def __repr__(self):

        return 'Item({}, {}, {})'.format(self.coords[0], self.coords[1], self.data)



# Now we can add Items to the tree, which look like tuples to it

point1 = Item(2, 3, 'First')

point2 = Item(3, 4, 'Second')

point3 = Item(5, 2, ['some', 'list'])



# Again, from a list of points

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



#  The root node

print(tree)



# ...contains "data" field with an Item, which contains the payload in "data" field

print(tree.data.data)



# All functions work as intended, a payload is never lost

print(tree.search_nn([1, 2]))

```



Prints:



```



Second

(, 2.0)

```