smartquadtree 1.0
Implementation of quadtrees for moving objects
License: MIT

Quadtrees iterating on pairs of neighbouring items
==================================================

A quadtree is a tree data structure in which each node has exactly four
children. It is a particularly efficient way to store elements when you
need to quickly find them according to their x-y coordinates.

A common problem with elements in quadtrees is to detect pairs of
elements which are closer than a definite threshold.

The proposed implementation efficiently addresses this problem.

.. code:: python

    from smartquadtree import Quadtree

Creation & insertion of elements
--------------------------------

As you instantiate your quadtree, you must specify the center of your
space then the height and width.

.. code:: python

    q = Quadtree(0, 0, 10, 10)

The output of a quadtree on the console is pretty explicit. (You can
refer to next section for the meaning of "No mask set")

.. code:: python

    q




.. parsed-literal::

    <smartquadtree.Quadtree at 0x7fc28b93d300>
    Total number of elements: 0
    No mask set



You can easily insert elements from which you can naturally infer x-y
coordinates (e.g. tuples or lists)

.. code:: python

    q.insert((1, 2))
    q.insert((-3, 4))
    q




.. parsed-literal::

    <smartquadtree.Quadtree at 0x7fc28b93d300>
    Total number of elements: 2
    No mask set
    First elements:
        (1, 2),
        (-3, 4),
      



No error is raised if the element you are trying to insert is outside
the scope of the quadtree. But it won't be stored anyway!

.. code:: python

    q.insert((-20, 0))
    q




.. parsed-literal::

    <smartquadtree.Quadtree at 0x7fc28b93d300>
    Total number of elements: 2
    No mask set
    First elements:
        (1, 2),
        (-3, 4),
      



If you want to insert other Python objects, be sure to provide
``get_x()`` and ``get_y()`` methods to your class!

.. code:: python

    class Point(object):
    
        def __init__(self, x, y, color):
            self.x = x
            self.y = y
            self.color = color
    
        def __repr__(self):
            return "(%.2f, %.2f) %s" % (self.x, self.y, self.color)
    
        def get_x(self):
            return self.x
    
        def get_y(self):
            return self.y


You cannot insert elements of a different type from the first element
inserted.

.. code:: python

    q.insert(Point(2, -7, "red"))

But feel free to create a new one and play with it:

.. code:: python

    point_quadtree = Quadtree(5, 5, 5, 5)
    point_quadtree.insert(Point(2, 7, "red"))
    point_quadtree




.. parsed-literal::

    <smartquadtree.Quadtree at 0x7fc289797a00>
    Total number of elements: 1
    No mask set
    First elements:
        (2.00, 7.00) red,
      



Simple iteration
----------------

.. code:: python

    from random import random
    q = Quadtree(0, 0, 10, 10, 16)
    for a in range(50):
        q.insert([random()*20-10, random()*20-10])

The ``print`` function does not display all elements and uses the
``__repr__()`` method of each element.

.. code:: python

    print(q)


.. parsed-literal::

    <smartquadtree.Quadtree at 0x7fc28b94c0b0>
    Total number of elements: 50
    No mask set
    First elements:
        [5.576253335483335, 2.9926458306078647],
        [2.956289387002718, 3.792134207741281],
        [3.9903269308895766, 5.492168007874362],
        ...


We can write our own iterator and print each element we encounter the
way we like.

.. code:: python

    from __future__ import print_function
    for p in q.elements():
        print ("[%.2f, %.2f]" % (p[0], p[1]), end=" ")


.. parsed-literal::

    [5.58, 2.99] [2.96, 3.79] [3.99, 5.49] [3.43, 1.10] [7.73, 4.09] [9.67, 6.81] [2.95, 4.12] [0.14, 5.80] [2.77, 7.87] [0.05, 1.61] [-8.74, 7.64] [-1.22, 1.90] [-0.95, 3.91] [-3.17, 1.09] [-7.41, 4.26] [-8.25, 6.47] [-6.91, 3.80] [-3.73, 3.10] [-5.74, 8.80] [8.50, -9.31] [2.49, -9.10] [6.64, -8.61] [0.40, -2.93] [7.99, -4.08] [4.71, -6.75] [0.12, -1.84] [0.72, -2.94] [9.62, -9.90] [0.15, -9.75] [8.67, -7.19] [2.44, -3.60] [5.08, -8.63] [8.86, -1.87] [1.07, -9.43] [-7.96, -5.53] [-2.53, -5.75] [-1.31, -5.81] [-7.24, -3.55] [-8.76, -9.37] [-8.48, -1.33] [-1.28, -0.69] [-6.60, -4.65] [-4.28, -0.89] [-7.56, -7.31] [-4.72, -7.02] [-1.98, -2.33] [-3.43, -5.74] [-3.71, -1.13] [-1.01, -7.29] [-2.04, -5.90] 

It is easy to filter the iteration process and apply the function only
on elements inside a given polygon. Use the ``set_mask()`` method and
pass a list of x-y coordinates. The polygon will be automatically
closed.

.. code:: python

    q.set_mask([(-3, -7), (-3, 7), (3, 7), (3, -7)])
    print(q)


.. parsed-literal::

    <smartquadtree.Quadtree at 0x7fc28b94c0b0>
    Total number of elements: 50
    Total number of elements inside mask: 15
    First elements inside the mask:
        [2.956289387002718, 3.792134207741281],
        [2.945472950394006, 4.1166899654293765],
        [0.14379102547949074, 5.797490949080599],
        ...


The same approach can be used to count the number of elements inside the
quadtree.

.. code:: python

    print (sum (1 for x in q.elements()))
    print (sum (1 for x in q.elements(ignore_mask=True)))



.. parsed-literal::

    15
    50


As a mask is set on the quadtree, we only counted the elements inside
the mask. You can use the ``size()`` method to count elements and ignore
the mask by default. Disabling the mask with ``set_mask(None)`` is also
a possibility.

.. code:: python

    print ("%d elements (size method)" % q.size())
    print ("%d elements (don't ignore the mask)" % q.size(False))
    
    q.set_mask(None)
    print ("%d elements (disable the mask)" % q.size())


.. parsed-literal::

    50 elements (size method)
    15 elements (don't ignore the mask)
    50 elements (disable the mask)


Playing with plots
------------------

.. code:: python

    %matplotlib inline
    from matplotlib import pyplot as plt
        
    q = Quadtree(5, 5, 5, 5, 10)
    
    for a in range(200):
        q.insert([random()*10, random()*10])
        
    fig = plt.figure()
    plt.axis([0, 10, 0, 10])
    
    q.set_mask(None)
    for p in q.elements():
        plt.plot([p[0]], [p[1]], 'o', color='lightgrey')
    
    q.set_mask([(3, 3), (3, 7), (7, 7), (7, 3)])
    
    for p in q.elements():
        plt.plot([p[0]], [p[1]], 'ro')
    
    _ = plt.plot([3, 3, 7, 7, 3], [3, 7, 7, 3, 3], 'r')




.. image:: https://raw.githubusercontent.com/xoolive/quadtree/master/tutorial_files/tutorial_31_0.png


Iteration on pairs of neighbouring elements
-------------------------------------------

Iterating on pairs of neighbouring elements is possible through the
``neighbour_elements()`` function. It works as a generator and yields
pair of elements, the first one being inside the mask (if specified),
the second one being in the same cell or in any neighbouring cell, also
in the mask.

Note that if ``(a, b)`` is yielded by ``neighbour_elements()``,
``(b, a)`` will be omitted from future yields.

.. code:: python

    # Let's start with a new quadtree because we need
    q = Quadtree(5, 5, 5, 5, 10)
    q.set_limitation(2)  # do not create a new subdivision if one side of the cell is below 2
    
    for a in range(200):
        q.insert([random()*10, random()*10])
    
    fig = plt.figure()
    plt.axis([0, 10, 0, 10])
    
    for p in q.elements():
        plt.plot([p[0]], [p[1]], 'o', color='lightgrey')
    
    q.set_mask([(1, 1), (4, 1), (5, 4), (2, 5), (1, 1)])
        
    for p in q.elements():
        plt.plot([p[0]], [p[1]], 'o', color='green')
        
    for p1, p2 in q.neighbour_elements():
        if ((p1[0] - p2[0]) ** 2 + (p1[1] - p2[1]) ** 2 < 1):
            plt.plot([p1[0]], [p1[1]], 'o', color='red')
            plt.plot([p2[0]], [p2[1]], 'o', color='red')
            plt.plot([p1[0], p2[0]], [p1[1], p2[1]], 'red')
    
    _ = plt.plot([1, 4, 5, 2, 1], [1, 1, 4, 5, 1], 'r')




.. image:: https://raw.githubusercontent.com/xoolive/quadtree/master/tutorial_files/tutorial_34_0.png