Metadata-Version: 1.1
Name: KdQuery
Version: 0.1.3
Summary: Nearest point query for any kd-tree implementation
Home-page: https://github.com/DeVilhena-Paulo/KdQuery
Author: Paulo Emilio de Vilhena
Author-email: pevilhena2@gmail.com
License: Apache Software License
Description: =======

        KdQuery

        =======

        

        KdQuery is a package that defines one possible implementation of kd-trees using python lists to avoid recursion and most importantly it defines a general method to find the nearest node for any kd-tree implementation.

        

        Getting Started

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

        

        Prerequisites

        -------------

        

        * Python version 3.6 installed locally

        * Pip installed locally

        

        Installing

        ----------

        

        The package can easily be installed via pip::

        

          pip install kdquery

        

        Usage

        =====

        

        The Tree class with the default settings

        ----------------------------------------

        

        .. code-block:: python

        

            from kdquery import Tree

        

            # Create a kd-tree (k = 2 and capacity = 10000 by default)

            tree = Tree()

        

            # Insert points with some attached data (or not)

            tree.insert((9, 1), {'description': 'point in the plane', 'label': 6})

            tree.insert((1, -8))

            tree.insert((-3, 3), data=None)

            tree.insert((0.2, 3.89), ["blue", "yellow", "python"])

        

            # Recover the data attached to (0, 3)

            node_id = tree.insert((0, 3), 'Important data')

            node = tree.get_node(node_id)

            print(node.data)  # 'Important data'

        

            # Find the node in the tree that is nearest to a given point

            query = (7.2, 1.2)

            node_id, dist = tree.find_nearest_point(query)

            print(dist)  # 1.8110770276274832

        

        The Tree class with the optional arguments

        ------------------------------------------

        

        .. code-block:: python

        

            from kdquery import Tree

        

            x_limits = [-100, 100]

            y_limits = [-10000, 250]

            z_limits = [-1500, 10]

            region = [x_limits, y_limits, z_limits]

        

            capacity = 3000000

        

            # 3d-tree with capacity of 3000000 nodes

            tree = Tree(3, capacity, region)

        

        The nearest_point method

        ------------------------

        

        Let's say that you work with some positions over the superface of the Earth in your application and that to store this data you implement a kd-tree where each node is represented as an element of an array with these specifications:

        

        .. code-block:: python

        

            import numpy as np

        

            node_dtype = np.dtype([

               ('longitude', 'float64'),

               ('latitude', 'float64'),

               ('limit_left', 'float64'),

               ('limit_right', 'float64'),

               ('limit_bottom', 'float64'),

               ('limit_top', 'float64'),

               ('dimension', 'float64'),

               ('left', 'int32'),

               ('right', 'int32')

            ])

        

        If given a point over the surface of the Earth you need to find the nearest position of your database, you can use the nearest_point method from this package. You only need to define a method that receives the index of a node in this representation and returns the coordinates of the node, the region where it is and the indices to the left and right child. For the implementation mentioned above, it could be something like:

        

        .. code-block:: python

        

            def get_properties(node_id):

                node = tree[node_id]

        

                horizontal_limits = [node['limit_left'], node['limit_right']]

                vertical_limits = [node['limit_bottom'], node['limit_top']]

        

                # The region of the space definied by the node

                region = [horizontal_limits, vertical_limits]

        

                # The position of the point in the space

                coordinates = (node['longitude']), node['latitude']))

        

                # The dimension of the space divided by this node

                # 0 for longitude and 1 for latitude in this case

                dimension = node['dimension']

        

                # If you want this node to be considered

                # Set to true if this feature is not predicted by your implementation

                active = True

        

                # Indices to left and right children

                left, right = node['left'], node['right']

        

                return coordinates, region, dimension, active, left, right

        

        To call the method:

        

        .. code-block:: python

        

            import kdquery

        

            def spherical_dist(point1, point2):

                <statement-1>

                .

                .

                .

                <statement-N>

                return dist

        

            query = (2.21, 48.65)

            root_id = 0  # index of the root

            node_id, dist = kdquery.nearest_point(query, root_id, get_properties,

                                                  spherical_dist)

        
Keywords: kd-tree query
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Utilities
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
