def time_tree_nearest_all_poly_python(self):
# returns all input points
# use an arbitrary search tolerance that seems appropriate for the density of
# geometries
tolerance = 200
b = shapely.buffer(self.points, tolerance, quadsegs=1)
left, right = self.tree.query(b)
dist = shapely.distance(self.points.take(left),
self.polygons.take(right))
# sort by left, distance
ix = np.lexsort((right, dist, left))
left = left[ix]
right = right[ix]
dist = dist[ix]
run_start = np.r_[True, left[:-1] != left[1:]]
run_counts = np.diff(np.r_[np.nonzero(run_start)[0], left.shape[0]])
mins = dist[run_start]
# spread to rest of array so we can extract out all within each group that match
all_mins = np.repeat(mins, run_counts)
ix = dist == all_mins
left = left[ix]
right = right[ix]
dist = dist[ix]
def time_tree_nearest_points_equidistant_manual_all(self):
# This benchmark approximates nearest_all for equidistant results
# starting from singular nearest neighbors and searching for more
# within same distance.
# try to find all equidistant neighbors ourselves given single nearest
# result
l, r = self.grid_point_tree.nearest(self.grid_points)
# calculate distance to nearest neighbor
dist = shapely.distance(self.grid_points.take(l),
self.grid_point_tree.geometries.take(r))
# include a slight epsilon to ensure nearest are within this radius
b = shapely.buffer(self.grid_points, dist + 1e-8)
# query the tree for others in the same buffer distance
left, right = self.grid_point_tree.query(b, predicate="intersects")
dist = shapely.distance(self.grid_points.take(left),
self.grid_point_tree.geometries.take(right))
# sort by left, distance
ix = np.lexsort((right, dist, left))
left = left[ix]
right = right[ix]
dist = dist[ix]
run_start = np.r_[True, left[:-1] != left[1:]]
run_counts = np.diff(np.r_[np.nonzero(run_start)[0], left.shape[0]])
mins = dist[run_start]
# spread to rest of array so we can extract out all within each group that match
all_mins = np.repeat(mins, run_counts)
ix = dist == all_mins
left = left[ix]
right = right[ix]
dist = dist[ix]
def time_distance(self):
shapely.distance(self.points, self.polygon)
def distance(self, other):
"""Unitless distance to other geometry (float)"""
return float(shapely.distance(self, other))
def test_distance_missing():
actual = shapely.distance(point, None)
assert np.isnan(actual)
def test_distance():
actual = shapely.distance(*point_polygon_testdata)
expected = [2 * 2**0.5, 2**0.5, 0, 0, 0, 2**0.5]
np.testing.assert_allclose(actual, expected)
def test_distance_empty():
actual = shapely.distance(point, empty)
assert np.isnan(actual)