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)
if isinstance(d, float):
dims = 0
else:
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
d1 = [sphere.linear2arcdist(dx, r) for dx in d]
elif dims == 2:
d1 = [[sphere.linear2arcdist(dx, r) for dx in row] for row in d]
d = numpy.array(d1)
dinf = d == numpy.inf
if dinf.sum() > 0:
i = i.astype(float)
i[dinf] = numpy.nan
return d, i
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)
if isinstance(d, float):
dims = 0
else:
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
d1 = [sphere.linear2arcdist(dx, r) for dx in d]
elif dims == 2:
d1 = [[sphere.linear2arcdist(dx, r) for dx in row] for row in d]
d = numpy.array(d1)
dinf = d == numpy.inf
if dinf.sum() > 0:
i = i.astype(float)
i[dinf] = numpy.nan
return d, i
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 = scipy.spatial.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((map(a2l,
D.data), (D.row, D.col))).todok()
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 = scipy.spatial.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((map(a2l, D.data), (D.row, D.col))).todok()