"""
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
from .sphere import RADIUS_EARTH_KM

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

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

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


def KDTree(data, leafsize=10, distance_metric='Euclidean',
           radius=RADIUS_EARTH_KM):
    """
    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          : float
                      Radius of the sphere on which to compute distances.
                      Assumes data in latitude and longitude. Ignored if
                      distance_metric="Euclidean". Typical values:
                      pysal.cg.RADIUS_EARTH_KM  (default)
                      pysal.cg.RADIUS_EARTH_MILES
    """

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


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


class Arc_KDTree(temp_KDTree):
    def __init__(self, data, leafsize=10, radius=1.0):
        """
        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.

        For more information see docs for scipy.spatial.KDTree

        Examples
        --------
        >>> pts = [(0,90), (0,0), (180,0), (0,-90)]
        >>> kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)
        >>> d,i = kd.query((90,0), k=4)
        >>> d
        array([ 10007.54339801,  10007.54339801,  10007.54339801,  10007.54339801])
        >>> circumference = 2*math.pi*sphere.RADIUS_EARTH_KM
        >>> round(d[0],5) == round(circumference/4.0,5)
        True
        """
        self.radius = radius
        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 = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)
        >>> kd.count_neighbors(kd,0)
        4
        >>> circumference = 2.0*math.pi*sphere.RADIUS_EARTH_KM
        >>> 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)
        r = sphere.arcdist2linear(r, self.radius)
        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
        --------
        >>> pts = [(0,90), (0,0), (180,0), (0,-90)]
        >>> kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)
        >>> d,i = kd.query((90,0), k=4)
        >>> d
        array([ 10007.54339801,  10007.54339801,  10007.54339801,  10007.54339801])
        >>> circumference = 2*math.pi*sphere.RADIUS_EARTH_KM
        >>> 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
        """
        eps = sphere.arcdist2linear(eps, self.radius)
        if distance_upper_bound != inf:
            distance_upper_bound = sphere.arcdist2linear(
                distance_upper_bound, self.radius)
        d, i = temp_KDTree.query(self, self._toXYZ(x), k,
                                          eps=eps, distance_upper_bound=distance_upper_bound)
        dims = len(d.shape)
        r = self.radius
        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
        --------
        >>> pts = [(0,90), (0,0), (180,0), (0,-90)]
        >>> kd = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)
        >>> circumference = 2*math.pi*sphere.RADIUS_EARTH_KM
        >>> kd.query_ball_point(pts, circumference/4.)
        array([[0, 1, 2], [0, 1, 3], [0, 2, 3], [1, 2, 3]], dtype=object)
        >>> kd.query_ball_point(pts, circumference/2.)
        array([[0, 1, 2, 3], [0, 1, 2, 3], [0, 1, 2, 3], [0, 1, 2, 3]], dtype=object)
        """
        eps = sphere.arcdist2linear(eps, self.radius)
        #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 = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)
        >>> kd.query_ball_tree(kd, kd.circumference/4.)
        [[0, 1, 2], [0, 1, 3], [0, 2, 3], [1, 2, 3]]
        >>> kd.query_ball_tree(kd, kd.circumference/2.)
        [[0, 1, 2, 3], [0, 1, 2, 3], [0, 1, 2, 3], [0, 1, 2, 3]]
        """
        eps = sphere.arcdist2linear(eps, self.radius)
        #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 self.radius != other.radius:
            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 = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)
        >>> kd.query_pairs(kd.circumference/4.)
        set([(0, 1), (1, 3), (2, 3), (0, 2)])
        >>> kd.query_pairs(kd.circumference/2.)
        set([(0, 1), (1, 2), (1, 3), (2, 3), (0, 3), (0, 2)])
        """
        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 = Arc_KDTree(pts, radius = sphere.RADIUS_EARTH_KM)
        >>> 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.        ]])
        """
        if self.radius != other.radius:
            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()


