The kdtree package can construct, modify and search kd-trees.
- 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
>>> 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
<KDNode - [4, 5, 6]>
# 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())
[<KDNode - (2, 3, 4)>, <KDNode - [4, 5, 6]>, <KDNode - Point(x=5, y=3, z=2)>]
# Retrieving the Tree in level order
>>> list(kdtree.level_order(tree))
[<KDNode - [4, 5, 6]>, <KDNode - (2, 3, 4)>, <KDNode - Point(x=5, y=3, z=2)>]
# Find the nearest node to the location (1, 2, 3)
>>> tree.search_nn( (1, 2, 3) )
<KDNode - (2, 3, 4)>
# 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)
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.
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:
<KDNode - Item(3, 4, Second)>
Second
(<KDNode - Item(2, 3, First)>, 2.0)