# Source code for libpysal.cg.kdtree

"""
KDTree for PySAL: Python Spatial Analysis Library.

Adds support for Arc Distance to scipy.spatial.KDTree.
"""
import math
import scipy.spatial
import numpy
from scipy import inf
from . import sphere

__author__ = "Charles R Schmidt <schmidtc@gmail.com>"

__all__ = ["DISTANCE_METRICS", "FLOAT_EPS", "KDTree"]

DISTANCE_METRICS = ['Euclidean', 'Arc']
FLOAT_EPS = numpy.finfo(float).eps

[docs]def KDTree(data, leafsize=10, distance_metric='Euclidean',
"""
kd-tree built on top of kd-tree functionality in scipy. If using scipy 0.12
or greater uses the scipy.spatial.cKDTree, otherwise uses
scipy.spatial.KDTree. Offers both Arc distance and Euclidean distance.
Note that Arc distance is only appropriate when points in latitude and
longitude, and the radius set to meaningful value (see docs below).

Parameters
----------
data            : array
The data points to be indexed. This array is not copied,
and so modifying this data will result in bogus results.
Typically nx2.
leafsize        : int
The number of points at which the algorithm switches over
to brute-force. Has to be positive. Optional, default is 10.
distance_metric : string
Options: "Euclidean" (default) and "Arc".
Radius of the sphere on which to compute distances.
Assumes data in latitude and longitude. Ignored if
distance_metric="Euclidean". Typical values:
"""

if distance_metric.lower() == 'euclidean':
if int(scipy.version.version.split(".")[1]) < 12 and int(scipy.version.version.split(".")[0]) == 0:
return scipy.spatial.KDTree(data, leafsize)
else:
return scipy.spatial.cKDTree(data, leafsize)
elif distance_metric.lower() == 'arc':

# internal hack for the Arc_KDTree class inheritance
if int(scipy.version.version.split(".")[1]) < 12 and int(scipy.version.version.split(".")[0]) == 0:
temp_KDTree = scipy.spatial.KDTree
else:
temp_KDTree = scipy.spatial.cKDTree

class Arc_KDTree(temp_KDTree):
"""
KDTree using Arc Distance instead of Euclidean Distance.

Returned distances are based on radius.
For Example, pass in the radius of earth in miles to get back miles.
Assumes data are Lng/Lat, does not account for geoids.

Examples
--------
>>> pts = [(0,90), (0,0), (180,0), (0,-90)]
>>> d,i = kd.query((90,0), k=4)
>>> d
array([10007.54339801, 10007.54339801, 10007.54339801, 10007.54339801])
>>> round(d[0],5) == round(circumference/4.0,5)
True
"""
self.circumference = 2 * math.pi * radius
temp_KDTree.__init__(self, list(map(sphere.toXYZ, data)), leafsize)

def _toXYZ(self, x):
if not issubclass(type(x), numpy.ndarray):
x = numpy.array(x)
if len(x.shape) == 2 and x.shape[1] == 3:  # assume point is already in XYZ
return x
if len(x.shape) == 1 and x.shape[0] == 3:  # assume point is already in XYZ
return x
elif len(x.shape) == 1:
x = numpy.array(sphere.toXYZ(x))
else:
x = list(map(sphere.toXYZ, x))
return x

def count_neighbors(self, other, r, p=2):
"""
See scipy.spatial.KDTree.count_neighbors

Parameters
----------
p: ignored, kept to maintain compatibility with scipy.spatial.KDTree

Examples
--------
>>> pts = [(0,90), (0,0), (180,0), (0,-90)]
>>> kd.count_neighbors(kd,0)
4
>>> kd.count_neighbors(kd,circumference/2.0)
16
"""
if r > 0.5 * self.circumference:
raise ValueError("r, must not exceed 1/2 circumference of the sphere (%f)." % self.circumference * 0.5)
return temp_KDTree.count_neighbors(self, other, r)

def query(self, x, k=1, eps=0, p=2, distance_upper_bound=inf):
"""
See scipy.spatial.KDTree.query

Parameters
----------
x : array-like, last dimension self.m
query points are lng/lat.
p: ignored, kept to maintain compatibility with scipy.spatial.KDTree

Examples
--------
>>> import numpy as np
>>> pts = [(0,90), (0,0), (180,0), (0,-90)]
>>> d,i = kd.query((90,0), k=4)
>>> d
array([10007.54339801, 10007.54339801, 10007.54339801, 10007.54339801])
>>> round(d[0],5) == round(circumference/4.0,5)
True
>>> d,i = kd.query(kd.data, k=3)
>>> d2,i2 = kd.query(pts, k=3)
>>> (d == d2).all()
True
>>> (i == i2).all()
True
"""
if distance_upper_bound != inf:
distance_upper_bound = sphere.arcdist2linear(
d, i = temp_KDTree.query(self, self._toXYZ(x), k,
eps=eps, distance_upper_bound=distance_upper_bound)
dims = len(d.shape)
if dims == 0:
return sphere.linear2arcdist(d, r), i
if dims == 1:
#TODO: implement linear2arcdist on numpy arrays
d = [sphere.linear2arcdist(x, r) for x in d]
elif dims == 2:
d = [[sphere.linear2arcdist(x, r) for x in row] for row in d]
return numpy.array(d), i

def query_ball_point(self, x, r, p=2, eps=0):
"""
See scipy.spatial.KDTree.query_ball_point

Parameters
----------
p: ignored, kept to maintain compatibility with scipy.spatial.KDTree

Examples
--------
>>> import numpy as np
>>> pts = [(0,90), (0,0), (180,0), (0,-90)]
>>> kd.query_ball_point(pts, circumference/4.)
array([list([0, 1, 2]), list([0, 1, 3]), list([0, 2, 3]), list([1, 2, 3])],
dtype=object)
>>> kd.query_ball_point(pts, circumference/2.)
array([list([0, 1, 2, 3]), list([0, 1, 2, 3]), list([0, 1, 2, 3]),
list([0, 1, 2, 3])], dtype=object)
"""
#scipy.sphere.KDTree.query_ball_point appears to ignore the eps argument.
# we have some floating point errors moving back and forth between cordinate systems,
# so we'll account for that be adding some to our radius, 3*float's eps value.
if r > 0.5 * self.circumference:
raise ValueError("r, must not exceed 1/2 circumference of the sphere (%f)." % self.circumference * 0.5)
r = sphere.arcdist2linear(r, self.radius) + FLOAT_EPS * 3
return temp_KDTree.query_ball_point(self, self._toXYZ(x), r, eps=eps)

def query_ball_tree(self, other, r, p=2, eps=0):
"""
See scipy.spatial.KDTree.query_ball_tree

Parameters
----------
p: ignored, kept to maintain compatibility with scipy.spatial.KDTree

Examples
--------
>>> pts = [(0,90), (0,0), (180,0), (0,-90)]
>>> kd.query_ball_tree(kd, kd.circumference/4.) == [[0, 1, 2], [0, 1, 3], [0, 2, 3], [1, 2, 3]]
True
>>> kd.query_ball_tree(kd, kd.circumference/2.) == [[0, 1, 2, 3], [0, 1, 2, 3], [0, 1, 2, 3], [0, 1, 2, 3]]
True
"""
#scipy.sphere.KDTree.query_ball_point appears to ignore the eps argument.
# we have some floating point errors moving back and forth between cordinate systems,
# so we'll account for that be adding some to our radius, 3*float's eps value.
raise ValueError("Both trees must have the same radius.")
if r > 0.5 * self.circumference:
raise ValueError("r, must not exceed 1/2 circumference of the sphere (%f)." % self.circumference * 0.5)
r = sphere.arcdist2linear(r, self.radius) + FLOAT_EPS * 3
return temp_KDTree.query_ball_tree(self, other, r, eps=eps)

def query_pairs(self, r, p=2, eps=0):
"""
See scipy.spatial.KDTree.query_pairs

Parameters
----------
p: ignored, kept to maintain compatibility with scipy.spatial.KDTree

Examples
--------
>>> pts = [(0,90), (0,0), (180,0), (0,-90)]
>>> kd.query_pairs(kd.circumference/4.) == set([(0, 1), (1, 3), (2, 3), (0, 2)])
True
>>> kd.query_pairs(kd.circumference/2.) == set([(0, 1), (1, 2), (1, 3), (2, 3), (0, 3), (0, 2)])
True
"""
if r > 0.5 * self.circumference:
raise ValueError("r, must not exceed 1/2 circumference of the sphere (%f)." % self.circumference * 0.5)
r = sphere.arcdist2linear(r, self.radius) + FLOAT_EPS * 3
return temp_KDTree.query_pairs(self, r, eps=eps)

def sparse_distance_matrix(self, other, max_distance, p=2):
"""
See scipy.spatial.KDTree.sparse_distance_matrix

Parameters
----------
p: ignored, kept to maintain compatibility with scipy.spatial.KDTree

Examples
--------
>>> pts = [(0,90), (0,0), (180,0), (0,-90)]
>>> kd.sparse_distance_matrix(kd, kd.circumference/4.).todense()
matrix([[    0.        , 10007.54339801, 10007.54339801,     0.        ],
[10007.54339801,     0.        ,     0.        , 10007.54339801],
[10007.54339801,     0.        ,     0.        , 10007.54339801],
[    0.        , 10007.54339801, 10007.54339801,     0.        ]])
>>> kd.sparse_distance_matrix(kd, kd.circumference/2.).todense()
matrix([[    0.        , 10007.54339801, 10007.54339801, 20015.08679602],
[10007.54339801,     0.        , 20015.08679602, 10007.54339801],
[10007.54339801, 20015.08679602,     0.        , 10007.54339801],
[20015.08679602, 10007.54339801, 10007.54339801,     0.        ]])
"""
raise ValueError("Both trees must have the same radius.")
if max_distance > 0.5 * self.circumference:
raise ValueError("max_distance, must not exceed 1/2 circumference of the sphere (%f)." % self.circumference * 0.5)
max_distance = sphere.arcdist2linear(
max_distance, self.radius) + FLOAT_EPS * 3
D = temp_KDTree.sparse_distance_matrix(
self, other, max_distance)
D = D.tocoo()
#print D.data
a2l = lambda x: sphere.linear2arcdist(x, self.radius)
#print map(a2l,D.data)
return scipy.sparse.coo_matrix((list(map(a2l, D.data)), (D.row, D.col))).todok()