# Copyright Anne M. Archibald 2008
# Released under the scipy license

from __future__ import division, print_function, absolute_import

from numpy.testing import (assert_equal, assert_array_equal,
    assert_almost_equal, assert_array_almost_equal, assert_, run_module_suite)

import numpy as np
from scipy.spatial import KDTree, Rectangle, distance_matrix, cKDTree
from scipy.spatial.ckdtree import cKDTreeNode
from scipy.spatial import minkowski_distance as distance


class ConsistencyTests:
    def test_nearest(self):
        x = self.x
        d, i = self.kdtree.query(x, 1)
        assert_almost_equal(d**2,np.sum((x-self.data[i])**2))
        eps = 1e-8
        assert_(np.all(np.sum((self.data-x[np.newaxis,:])**2,axis=1) > d**2-eps))

    def test_m_nearest(self):
        x = self.x
        m = self.m
        dd, ii = self.kdtree.query(x, m)
        d = np.amax(dd)
        i = ii[np.argmax(dd)]
        assert_almost_equal(d**2,np.sum((x-self.data[i])**2))
        eps = 1e-8
        assert_equal(np.sum(np.sum((self.data-x[np.newaxis,:])**2,axis=1) < d**2+eps),m)

    def test_points_near(self):
        x = self.x
        d = self.d
        dd, ii = self.kdtree.query(x, k=self.kdtree.n, distance_upper_bound=d)
        eps = 1e-8
        hits = 0
        for near_d, near_i in zip(dd,ii):
            if near_d == np.inf:
                continue
            hits += 1
            assert_almost_equal(near_d**2,np.sum((x-self.data[near_i])**2))
            assert_(near_d < d+eps, "near_d=%g should be less than %g" % (near_d,d))
        assert_equal(np.sum(np.sum((self.data-x[np.newaxis,:])**2,axis=1) < d**2+eps),hits)

    def test_points_near_l1(self):
        x = self.x
        d = self.d
        dd, ii = self.kdtree.query(x, k=self.kdtree.n, p=1, distance_upper_bound=d)
        eps = 1e-8
        hits = 0
        for near_d, near_i in zip(dd,ii):
            if near_d == np.inf:
                continue
            hits += 1
            assert_almost_equal(near_d,distance(x,self.data[near_i],1))
            assert_(near_d < d+eps, "near_d=%g should be less than %g" % (near_d,d))
        assert_equal(np.sum(distance(self.data,x,1) < d+eps),hits)

    def test_points_near_linf(self):
        x = self.x
        d = self.d
        dd, ii = self.kdtree.query(x, k=self.kdtree.n, p=np.inf, distance_upper_bound=d)
        eps = 1e-8
        hits = 0
        for near_d, near_i in zip(dd,ii):
            if near_d == np.inf:
                continue
            hits += 1
            assert_almost_equal(near_d,distance(x,self.data[near_i],np.inf))
            assert_(near_d < d+eps, "near_d=%g should be less than %g" % (near_d,d))
        assert_equal(np.sum(distance(self.data,x,np.inf) < d+eps),hits)

    def test_approx(self):
        x = self.x
        k = self.k
        eps = 0.1
        d_real, i_real = self.kdtree.query(x, k)
        d, i = self.kdtree.query(x, k, eps=eps)
        assert_(np.all(d <= d_real*(1+eps)))


class test_random(ConsistencyTests):
    def setUp(self):
        self.n = 100
        self.m = 4
        np.random.seed(1234)
        self.data = np.random.randn(self.n, self.m)
        self.kdtree = KDTree(self.data,leafsize=2)
        self.x = np.random.randn(self.m)
        self.d = 0.2
        self.k = 10


class test_random_far(test_random):
    def setUp(self):
        test_random.setUp(self)
        self.x = np.random.randn(self.m)+10


class test_small(ConsistencyTests):
    def setUp(self):
        self.data = np.array([[0,0,0],
                              [0,0,1],
                              [0,1,0],
                              [0,1,1],
                              [1,0,0],
                              [1,0,1],
                              [1,1,0],
                              [1,1,1]])
        self.kdtree = KDTree(self.data)
        self.n = self.kdtree.n
        self.m = self.kdtree.m
        np.random.seed(1234)
        self.x = np.random.randn(3)
        self.d = 0.5
        self.k = 4

    def test_nearest(self):
        assert_array_equal(
                self.kdtree.query((0,0,0.1), 1),
                (0.1,0))

    def test_nearest_two(self):
        assert_array_equal(
                self.kdtree.query((0,0,0.1), 2),
                ([0.1,0.9],[0,1]))


class test_small_nonleaf(test_small):
    def setUp(self):
        test_small.setUp(self)
        self.kdtree = KDTree(self.data,leafsize=1)


class test_small_compiled(test_small):
    def setUp(self):
        test_small.setUp(self)
        self.kdtree = cKDTree(self.data)


class test_small_nonleaf_compiled(test_small):
    def setUp(self):
        test_small.setUp(self)
        self.kdtree = cKDTree(self.data,leafsize=1)


class test_random_compiled(test_random):
    def setUp(self):
        test_random.setUp(self)
        self.kdtree = cKDTree(self.data)


class test_random_far_compiled(test_random_far):
    def setUp(self):
        test_random_far.setUp(self)
        self.kdtree = cKDTree(self.data)


class test_vectorization:
    def setUp(self):
        self.data = np.array([[0,0,0],
                              [0,0,1],
                              [0,1,0],
                              [0,1,1],
                              [1,0,0],
                              [1,0,1],
                              [1,1,0],
                              [1,1,1]])
        self.kdtree = KDTree(self.data)

    def test_single_query(self):
        d, i = self.kdtree.query(np.array([0,0,0]))
        assert_(isinstance(d,float))
        assert_(np.issubdtype(i, int))

    def test_vectorized_query(self):
        d, i = self.kdtree.query(np.zeros((2,4,3)))
        assert_equal(np.shape(d),(2,4))
        assert_equal(np.shape(i),(2,4))

    def test_single_query_multiple_neighbors(self):
        s = 23
        kk = self.kdtree.n+s
        d, i = self.kdtree.query(np.array([0,0,0]),k=kk)
        assert_equal(np.shape(d),(kk,))
        assert_equal(np.shape(i),(kk,))
        assert_(np.all(~np.isfinite(d[-s:])))
        assert_(np.all(i[-s:] == self.kdtree.n))

    def test_vectorized_query_multiple_neighbors(self):
        s = 23
        kk = self.kdtree.n+s
        d, i = self.kdtree.query(np.zeros((2,4,3)),k=kk)
        assert_equal(np.shape(d),(2,4,kk))
        assert_equal(np.shape(i),(2,4,kk))
        assert_(np.all(~np.isfinite(d[:,:,-s:])))
        assert_(np.all(i[:,:,-s:] == self.kdtree.n))

    def test_single_query_all_neighbors(self):
        d, i = self.kdtree.query([0,0,0],k=None,distance_upper_bound=1.1)
        assert_(isinstance(d,list))
        assert_(isinstance(i,list))

    def test_vectorized_query_all_neighbors(self):
        d, i = self.kdtree.query(np.zeros((2,4,3)),k=None,distance_upper_bound=1.1)
        assert_equal(np.shape(d),(2,4))
        assert_equal(np.shape(i),(2,4))

        assert_(isinstance(d[0,0],list))
        assert_(isinstance(i[0,0],list))


class test_vectorization_compiled:
    def setUp(self):
        self.data = np.array([[0,0,0],
                              [0,0,1],
                              [0,1,0],
                              [0,1,1],
                              [1,0,0],
                              [1,0,1],
                              [1,1,0],
                              [1,1,1]])
        self.kdtree = cKDTree(self.data)

    def test_single_query(self):
        d, i = self.kdtree.query([0,0,0])
        assert_(isinstance(d,float))
        assert_(isinstance(i,int))

    def test_vectorized_query(self):
        d, i = self.kdtree.query(np.zeros((2,4,3)))
        assert_equal(np.shape(d),(2,4))
        assert_equal(np.shape(i),(2,4))

    def test_vectorized_query_noncontiguous_values(self):
        np.random.seed(1234)
        qs = np.random.randn(3,1000).T
        ds, i_s = self.kdtree.query(qs)
        for q, d, i in zip(qs,ds,i_s):
            assert_equal(self.kdtree.query(q),(d,i))

    def test_single_query_multiple_neighbors(self):
        s = 23
        kk = self.kdtree.n+s
        d, i = self.kdtree.query([0,0,0],k=kk)
        assert_equal(np.shape(d),(kk,))
        assert_equal(np.shape(i),(kk,))
        assert_(np.all(~np.isfinite(d[-s:])))
        assert_(np.all(i[-s:] == self.kdtree.n))

    def test_vectorized_query_multiple_neighbors(self):
        s = 23
        kk = self.kdtree.n+s
        d, i = self.kdtree.query(np.zeros((2,4,3)),k=kk)
        assert_equal(np.shape(d),(2,4,kk))
        assert_equal(np.shape(i),(2,4,kk))
        assert_(np.all(~np.isfinite(d[:,:,-s:])))
        assert_(np.all(i[:,:,-s:] == self.kdtree.n))


class ball_consistency:

    def test_in_ball(self):
        l = self.T.query_ball_point(self.x, self.d, p=self.p, eps=self.eps)
        for i in l:
            assert_(distance(self.data[i],self.x,self.p) <= self.d*(1.+self.eps))

    def test_found_all(self):
        c = np.ones(self.T.n,dtype=np.bool)
        l = self.T.query_ball_point(self.x, self.d, p=self.p, eps=self.eps)
        c[l] = False
        assert_(np.all(distance(self.data[c],self.x,self.p) >= self.d/(1.+self.eps)))


class test_random_ball(ball_consistency):

    def setUp(self):
        n = 100
        m = 4
        np.random.seed(1234)
        self.data = np.random.randn(n,m)
        self.T = KDTree(self.data,leafsize=2)
        self.x = np.random.randn(m)
        self.p = 2.
        self.eps = 0
        self.d = 0.2


class test_random_ball_compiled(ball_consistency):

    def setUp(self):
        n = 100
        m = 4
        np.random.seed(1234)
        self.data = np.random.randn(n,m)
        self.T = cKDTree(self.data,leafsize=2)
        self.x = np.random.randn(m)
        self.p = 2.
        self.eps = 0
        self.d = 0.2


class test_random_ball_approx(test_random_ball):

    def setUp(self):
        test_random_ball.setUp(self)
        self.eps = 0.1


class test_random_ball_approx_compiled(test_random_ball_compiled):

    def setUp(self):
        test_random_ball_compiled.setUp(self)
        self.eps = 0.1


class test_random_ball_far(test_random_ball):

    def setUp(self):
        test_random_ball.setUp(self)
        self.d = 2.


class test_random_ball_far_compiled(test_random_ball_compiled):

    def setUp(self):
        test_random_ball_compiled.setUp(self)
        self.d = 2.


class test_random_ball_l1(test_random_ball):

    def setUp(self):
        test_random_ball.setUp(self)
        self.p = 1


class test_random_ball_l1_compiled(test_random_ball_compiled):

    def setUp(self):
        test_random_ball_compiled.setUp(self)
        self.p = 1


class test_random_ball_linf(test_random_ball):

    def setUp(self):
        test_random_ball.setUp(self)
        self.p = np.inf


class test_random_ball_linf_compiled(test_random_ball_compiled):

    def setUp(self):
        test_random_ball_compiled.setUp(self)
        self.p = np.inf


def test_random_ball_vectorized():

    n = 20
    m = 5
    T = KDTree(np.random.randn(n,m))

    r = T.query_ball_point(np.random.randn(2,3,m),1)
    assert_equal(r.shape,(2,3))
    assert_(isinstance(r[0,0],list))


def test_random_ball_vectorized_compiled():

    n = 20
    m = 5
    np.random.seed(1234)
    T = cKDTree(np.random.randn(n,m))

    r = T.query_ball_point(np.random.randn(2,3,m),1)
    assert_equal(r.shape,(2,3))
    assert_(isinstance(r[0,0],list))


class two_trees_consistency:

    def test_all_in_ball(self):
        r = self.T1.query_ball_tree(self.T2, self.d, p=self.p, eps=self.eps)
        for i, l in enumerate(r):
            for j in l:
                assert_(distance(self.data1[i],self.data2[j],self.p) <= self.d*(1.+self.eps))

    def test_found_all(self):
        r = self.T1.query_ball_tree(self.T2, self.d, p=self.p, eps=self.eps)
        for i, l in enumerate(r):
            c = np.ones(self.T2.n,dtype=np.bool)
            c[l] = False
            assert_(np.all(distance(self.data2[c],self.data1[i],self.p) >= self.d/(1.+self.eps)))


class test_two_random_trees(two_trees_consistency):

    def setUp(self):
        n = 50
        m = 4
        np.random.seed(1234)
        self.data1 = np.random.randn(n,m)
        self.T1 = KDTree(self.data1,leafsize=2)
        self.data2 = np.random.randn(n,m)
        self.T2 = KDTree(self.data2,leafsize=2)
        self.p = 2.
        self.eps = 0
        self.d = 0.2


class test_two_random_trees_compiled(two_trees_consistency):

    def setUp(self):
        n = 50
        m = 4
        np.random.seed(1234)
        self.data1 = np.random.randn(n,m)
        self.T1 = cKDTree(self.data1,leafsize=2)
        self.data2 = np.random.randn(n,m)
        self.T2 = cKDTree(self.data2,leafsize=2)
        self.p = 2.
        self.eps = 0
        self.d = 0.2


class test_two_random_trees_far(test_two_random_trees):

    def setUp(self):
        test_two_random_trees.setUp(self)
        self.d = 2


class test_two_random_trees_far_compiled(test_two_random_trees_compiled):

    def setUp(self):
        test_two_random_trees_compiled.setUp(self)
        self.d = 2


class test_two_random_trees_linf(test_two_random_trees):

    def setUp(self):
        test_two_random_trees.setUp(self)
        self.p = np.inf


class test_two_random_trees_linf_compiled(test_two_random_trees_compiled):

    def setUp(self):
        test_two_random_trees_compiled.setUp(self)
        self.p = np.inf


class test_rectangle:

    def setUp(self):
        self.rect = Rectangle([0,0],[1,1])

    def test_min_inside(self):
        assert_almost_equal(self.rect.min_distance_point([0.5,0.5]),0)

    def test_min_one_side(self):
        assert_almost_equal(self.rect.min_distance_point([0.5,1.5]),0.5)

    def test_min_two_sides(self):
        assert_almost_equal(self.rect.min_distance_point([2,2]),np.sqrt(2))

    def test_max_inside(self):
        assert_almost_equal(self.rect.max_distance_point([0.5,0.5]),1/np.sqrt(2))

    def test_max_one_side(self):
        assert_almost_equal(self.rect.max_distance_point([0.5,1.5]),np.hypot(0.5,1.5))

    def test_max_two_sides(self):
        assert_almost_equal(self.rect.max_distance_point([2,2]),2*np.sqrt(2))

    def test_split(self):
        less, greater = self.rect.split(0,0.1)
        assert_array_equal(less.maxes,[0.1,1])
        assert_array_equal(less.mins,[0,0])
        assert_array_equal(greater.maxes,[1,1])
        assert_array_equal(greater.mins,[0.1,0])


def test_distance_l2():
    assert_almost_equal(distance([0,0],[1,1],2),np.sqrt(2))


def test_distance_l1():
    assert_almost_equal(distance([0,0],[1,1],1),2)


def test_distance_linf():
    assert_almost_equal(distance([0,0],[1,1],np.inf),1)


def test_distance_vectorization():
    np.random.seed(1234)
    x = np.random.randn(10,1,3)
    y = np.random.randn(1,7,3)
    assert_equal(distance(x,y).shape,(10,7))


class test_count_neighbors:

    def setUp(self):
        n = 50
        m = 2
        np.random.seed(1234)
        self.T1 = KDTree(np.random.randn(n,m),leafsize=2)
        self.T2 = KDTree(np.random.randn(n,m),leafsize=2)

    def test_one_radius(self):
        r = 0.2
        assert_equal(self.T1.count_neighbors(self.T2, r),
                np.sum([len(l) for l in self.T1.query_ball_tree(self.T2,r)]))

    def test_large_radius(self):
        r = 1000
        assert_equal(self.T1.count_neighbors(self.T2, r),
                np.sum([len(l) for l in self.T1.query_ball_tree(self.T2,r)]))

    def test_multiple_radius(self):
        rs = np.exp(np.linspace(np.log(0.01),np.log(10),3))
        results = self.T1.count_neighbors(self.T2, rs)
        assert_(np.all(np.diff(results) >= 0))
        for r,result in zip(rs, results):
            assert_equal(self.T1.count_neighbors(self.T2, r), result)


class test_count_neighbors_compiled:

    def setUp(self):
        n = 50
        m = 2
        np.random.seed(1234)
        self.T1 = cKDTree(np.random.randn(n,m),leafsize=2)
        self.T2 = cKDTree(np.random.randn(n,m),leafsize=2)

    def test_one_radius(self):
        r = 0.2
        assert_equal(self.T1.count_neighbors(self.T2, r),
                np.sum([len(l) for l in self.T1.query_ball_tree(self.T2,r)]))

    def test_large_radius(self):
        r = 1000
        assert_equal(self.T1.count_neighbors(self.T2, r),
                np.sum([len(l) for l in self.T1.query_ball_tree(self.T2,r)]))

    def test_multiple_radius(self):
        rs = np.exp(np.linspace(np.log(0.01),np.log(10),3))
        results = self.T1.count_neighbors(self.T2, rs)
        assert_(np.all(np.diff(results) >= 0))
        for r,result in zip(rs, results):
            assert_equal(self.T1.count_neighbors(self.T2, r), result)


class test_sparse_distance_matrix:
    def setUp(self):
        n = 50
        m = 4
        np.random.seed(1234)
        self.T1 = KDTree(np.random.randn(n,m),leafsize=2)
        self.T2 = KDTree(np.random.randn(n,m),leafsize=2)
        self.r = 0.5

    def test_consistency_with_neighbors(self):
        M = self.T1.sparse_distance_matrix(self.T2, self.r)
        r = self.T1.query_ball_tree(self.T2, self.r)
        for i,l in enumerate(r):
            for j in l:
                assert_almost_equal(M[i,j],
                                    distance(self.T1.data[i], self.T2.data[j]),
                                    decimal=14)
        for ((i,j),d) in M.items():
            assert_(j in r[i])

    def test_zero_distance(self):
        # raises an exception for bug 870
        self.T1.sparse_distance_matrix(self.T1, self.r)


class test_sparse_distance_matrix_compiled:
    def setUp(self):
        n = 50
        m = 4
        np.random.seed(0)
        data1 = np.random.randn(n,m)
        data2 = np.random.randn(n,m)
        self.T1 = cKDTree(data1,leafsize=2)
        self.T2 = cKDTree(data2,leafsize=2)

        self.ref_T1 = KDTree(data1, leafsize=2)
        self.ref_T2 = KDTree(data2, leafsize=2)
        self.r = 0.5

    def test_consistency_with_neighbors(self):
        M = self.T1.sparse_distance_matrix(self.T2, self.r)
        r = self.T1.query_ball_tree(self.T2, self.r)
        for i,l in enumerate(r):
            for j in l:
                assert_almost_equal(M[i,j],
                                    distance(self.T1.data[i], self.T2.data[j]),
                                    decimal=14)
        for ((i,j),d) in M.items():
            assert_(j in r[i])

    def test_zero_distance(self):
        # raises an exception for bug 870 (FIXME: Does it?)
        self.T1.sparse_distance_matrix(self.T1, self.r)

    def test_consistency_with_python(self):
        M1 = self.T1.sparse_distance_matrix(self.T2, self.r)
        M2 = self.ref_T1.sparse_distance_matrix(self.ref_T2, self.r)

        assert_array_almost_equal(M1.todense(), M2.todense(), decimal=14)


def test_distance_matrix():
    m = 10
    n = 11
    k = 4
    np.random.seed(1234)
    xs = np.random.randn(m,k)
    ys = np.random.randn(n,k)
    ds = distance_matrix(xs,ys)
    assert_equal(ds.shape, (m,n))
    for i in range(m):
        for j in range(n):
            assert_almost_equal(distance(xs[i],ys[j]),ds[i,j])


def test_distance_matrix_looping():
    m = 10
    n = 11
    k = 4
    np.random.seed(1234)
    xs = np.random.randn(m,k)
    ys = np.random.randn(n,k)
    ds = distance_matrix(xs,ys)
    dsl = distance_matrix(xs,ys,threshold=1)
    assert_equal(ds,dsl)


def check_onetree_query(T,d):
    r = T.query_ball_tree(T, d)
    s = set()
    for i, l in enumerate(r):
        for j in l:
            if i < j:
                s.add((i,j))

    assert_(s == T.query_pairs(d))


def test_onetree_query():
    np.random.seed(0)
    n = 50
    k = 4
    points = np.random.randn(n,k)
    T = KDTree(points)
    yield check_onetree_query, T, 0.1

    points = np.random.randn(3*n,k)
    points[:n] *= 0.001
    points[n:2*n] += 2
    T = KDTree(points)
    yield check_onetree_query, T, 0.1
    yield check_onetree_query, T, 0.001
    yield check_onetree_query, T, 0.00001
    yield check_onetree_query, T, 1e-6


def test_onetree_query_compiled():
    np.random.seed(0)
    n = 100
    k = 4
    points = np.random.randn(n,k)
    T = cKDTree(points)
    yield check_onetree_query, T, 0.1

    points = np.random.randn(3*n,k)
    points[:n] *= 0.001
    points[n:2*n] += 2
    T = cKDTree(points)
    yield check_onetree_query, T, 0.1
    yield check_onetree_query, T, 0.001
    yield check_onetree_query, T, 0.00001
    yield check_onetree_query, T, 1e-6


def test_query_pairs_single_node():
    tree = KDTree([[0, 1]])
    assert_equal(tree.query_pairs(0.5), set())


def test_query_pairs_single_node_compiled():
    tree = cKDTree([[0, 1]])
    assert_equal(tree.query_pairs(0.5), set())


def test_ball_point_ints():
    # Regression test for #1373.
    x, y = np.mgrid[0:4, 0:4]
    points = list(zip(x.ravel(), y.ravel()))
    tree = KDTree(points)
    assert_equal(sorted([4, 8, 9, 12]),
                 sorted(tree.query_ball_point((2, 0), 1)))
    points = np.asarray(points, dtype=np.float)
    tree = KDTree(points)
    assert_equal(sorted([4, 8, 9, 12]),
                 sorted(tree.query_ball_point((2, 0), 1)))


def test_kdtree_comparisons():
    # Regression test: node comparisons were done wrong in 0.12 w/Py3.
    nodes = [KDTree.node() for _ in range(3)]
    assert_equal(sorted(nodes), sorted(nodes[::-1]))


def test_ckdtree_build_modes():
    # check if different build modes for cKDTree give
    # similar query results
    np.random.seed(0)
    n = 5000
    k = 4
    points = np.random.randn(n, k)
    T1 = cKDTree(points).query(points, k=5)[-1]
    T2 = cKDTree(points, compact_nodes=False).query(points, k=5)[-1]
    T3 = cKDTree(points, balanced_tree=False).query(points, k=5)[-1]
    T4 = cKDTree(points, compact_nodes=False, balanced_tree=False).query(points, k=5)[-1]
    assert_array_equal(T1, T2)
    assert_array_equal(T1, T3)
    assert_array_equal(T1, T4)

def test_ckdtree_pickle():
    # test if it is possible to pickle
    # a cKDTree
    try:
        import cPickle
        # known failure on Python 2
        # pickle currently only supported on Python 3
        cPickle.dumps('pyflakes dummy')
    except ImportError:
        import pickle
        np.random.seed(0)
        n = 50
        k = 4
        points = np.random.randn(n, k)
        T1 = cKDTree(points)
        tmp = pickle.dumps(T1)
        T2 = pickle.loads(tmp)
        T1 = T1.query(points, k=5)[-1]
        T2 = T2.query(points, k=5)[-1]
        assert_array_equal(T1, T2)
    
def test_ckdtree_copy_data():
    # check if copy_data=True makes the kd-tree
    # impervious to data corruption by modification of 
    # the data arrray
    np.random.seed(0)
    n = 5000
    k = 4
    points = np.random.randn(n, k)
    T = cKDTree(points, copy_data=True)
    q = points.copy()
    T1 = T.query(q, k=5)[-1]
    points[...] = np.random.randn(n, k)
    T2 = T.query(q, k=5)[-1]
    assert_array_equal(T1, T2)
    
def test_ckdtree_parallel():
    # check if parallel=True also generates correct
    # query results
    np.random.seed(0)
    n = 5000
    k = 4
    points = np.random.randn(n, k)
    T = cKDTree(points)
    T1 = T.query(points, k=5, n_jobs=64)[-1]
    T2 = T.query(points, k=5, n_jobs=-1)[-1]
    T3 = T.query(points, k=5)[-1]
    assert_array_equal(T1, T2)
    assert_array_equal(T1, T3)

def test_ckdtree_view():        
    # Check that the nodes can be correctly viewed from Python.
    # This test also sanity checks each node in the cKDTree, and
    # thus verifies the internal structure of the kd-tree.
    np.random.seed(0)
    n = 100
    k = 4
    points = np.random.randn(n, k)
    kdtree = cKDTree(points)
    
    # walk the whole kd-tree and sanity check each node
    def recurse_tree(n):
        assert_(isinstance(n, cKDTreeNode)) 
        if n.split_dim == -1: 
            assert_(n.lesser is None)
            assert_(n.greater is None)
            assert_(n.indices.shape[0] <= kdtree.leafsize)
        else:
            recurse_tree(n.lesser)
            recurse_tree(n.greater)
            x = n.lesser.data_points[:, n.split_dim]
            y = n.greater.data_points[:, n.split_dim]
            assert_(x.max() < y.min())
    
    recurse_tree(kdtree.tree)
    # check that indices are correctly retreived
    n = kdtree.tree
    assert_array_equal(np.sort(n.indices), range(100))
    # check that data_points are correctly retreived
    assert_array_equal(kdtree.data[n.indices, :], n.data_points)

# cKDTree is specialized to type double points, so no need to make
# a unit test corresponding to test_ball_point_ints()

if __name__ == "__main__":
    run_module_suite()
