Spatial algorithms and data structures (scipy.spatial)¶
Warning
This documentation is work-in-progress and unorganized.
Spatial data structures and algorithms¶
Nearest-neighbor queries:
KDTree – class for efficient nearest-neighbor queries distance – module containing many different distance measures
- class scipy.spatial.KDTree(data, leafsize=10)¶
kd-tree for quick nearest-neighbor lookup
This class provides an index into a set of k-dimensional points which can be used to rapidly look up the nearest neighbors of any point.
The algorithm used is described in Maneewongvatana and Mount 1999. The general idea is that the kd-tree is a binary tree, each of whose nodes represents an axis-aligned hyperrectangle. Each node specifies an axis and splits the set of points based on whether their coordinate along that axis is greater than or less than a particular value.
During construction, the axis and splitting point are chosen by the “sliding midpoint” rule, which ensures that the cells do not all become long and thin.
The tree can be queried for the r closest neighbors of any given point (optionally returning only those within some maximum distance of the point). It can also be queried, with a substantial gain in efficiency, for the r approximate closest neighbors.
For large dimensions (20 is already large) do not expect this to run significantly faster than brute force. High-dimensional nearest-neighbor queries are a substantial open problem in computer science.
The tree also supports all-neighbors queries, both with arrays of points and with other kd-trees. These do use a reasonably efficient algorithm, but the kd-tree is not necessarily the best data structure for this sort of calculation.
Methods
count_neighbors innernode leafnode node query query_ball_point query_ball_tree query_pairs sparse_distance_matrix - count_neighbors(other, r, p=2.0)¶
Count how many nearby pairs can be formed.
Count the number of pairs (x1,x2) can be formed, with x1 drawn from self and x2 drawn from other, and where distance(x1,x2,p)<=r. This is the “two-point correlation” described in Gray and Moore 2000, “N-body problems in statistical learning”, and the code here is based on their algorithm.
Parameters : other : KDTree
r : float or one-dimensional array of floats
The radius to produce a count for. Multiple radii are searched with a single tree traversal.
p : float, 1<=p<=infinity
Which Minkowski p-norm to use
Returns : result : integer or one-dimensional array of integers
The number of pairs. Note that this is internally stored in a numpy int, and so may overflow if very large (two billion).
- query(x, k=1, eps=0, p=2, distance_upper_bound=inf)¶
query the kd-tree for nearest neighbors
Parameters : x : array-like, last dimension self.m
An array of points to query.
k : integer
The number of nearest neighbors to return.
eps : nonnegative float
Return approximate nearest neighbors; the kth returned value is guaranteed to be no further than (1+eps) times the distance to the real kth nearest neighbor.
p : float, 1<=p<=infinity
Which Minkowski p-norm to use. 1 is the sum-of-absolute-values “Manhattan” distance 2 is the usual Euclidean distance infinity is the maximum-coordinate-difference distance
distance_upper_bound : nonnegative float
Return only neighbors within this distance. This is used to prune tree searches, so if you are doing a series of nearest-neighbor queries, it may help to supply the distance to the nearest neighbor of the most recent point.
Returns : d : array of floats
The distances to the nearest neighbors. If x has shape tuple+(self.m,), then d has shape tuple if k is one, or tuple+(k,) if k is larger than one. Missing neighbors are indicated with infinite distances. If k is None, then d is an object array of shape tuple, containing lists of distances. In either case the hits are sorted by distance (nearest first).
i : array of integers
The locations of the neighbors in self.data. i is the same shape as d.
Examples
>>> from scipy.spatial import KDTree >>> x, y = np.mgrid[0:5, 2:8] >>> tree = KDTree(zip(x.ravel(), y.ravel())) >>> tree.data array([[0, 2], [0, 3], [0, 4], [0, 5], [0, 6], [0, 7], [1, 2], [1, 3], [1, 4], [1, 5], [1, 6], [1, 7], [2, 2], [2, 3], [2, 4], [2, 5], [2, 6], [2, 7], [3, 2], [3, 3], [3, 4], [3, 5], [3, 6], [3, 7], [4, 2], [4, 3], [4, 4], [4, 5], [4, 6], [4, 7]]) >>> pts = np.array([[0, 0], [2.1, 2.9]]) >>> tree.query(pts) (array([ 2. , 0.14142136]), array([ 0, 13]))
- query_ball_point(x, r, p=2.0, eps=0)¶
Find all points within r of x
Parameters : x : array_like, shape tuple + (self.m,)
The point or points to search for neighbors of
r : positive float
The radius of points to return
p : float 1<=p<=infinity
Which Minkowski p-norm to use
eps : nonnegative float
Approximate search. Branches of the tree are not explored if their nearest points are further than r/(1+eps), and branches are added in bulk if their furthest points are nearer than r*(1+eps).
Returns : results : list or array of lists
If x is a single point, returns a list of the indices of the neighbors of x. If x is an array of points, returns an object array of shape tuple containing lists of neighbors.
Note: if you have many points whose neighbors you want to find, you may save :
substantial amounts of time by putting them in a KDTree and using query_ball_tree. :
- query_ball_tree(other, r, p=2.0, eps=0)¶
Find all pairs of points whose distance is at most r
Parameters : other : KDTree
The tree containing points to search against
r : positive float
The maximum distance
p : float 1<=p<=infinity
Which Minkowski norm to use
eps : nonnegative float
Approximate search. Branches of the tree are not explored if their nearest points are further than r/(1+eps), and branches are added in bulk if their furthest points are nearer than r*(1+eps).
Returns : results : list of lists
For each element self.data[i] of this tree, results[i] is a list of the indices of its neighbors in other.data.
- query_pairs(r, p=2.0, eps=0)¶
Find all pairs of points whose distance is at most r
Parameters : r : positive float
The maximum distance
p : float 1<=p<=infinity
Which Minkowski norm to use
eps : nonnegative float
Approximate search. Branches of the tree are not explored if their nearest points are further than r/(1+eps), and branches are added in bulk if their furthest points are nearer than r*(1+eps).
Returns : results : set
set of pairs (i,j), i<j, for which the corresponing positions are close.
- sparse_distance_matrix(other, max_distance, p=2.0)¶
Compute a sparse distance matrix
Computes a distance matrix between two KDTrees, leaving as zero any distance greater than max_distance.
Parameters : other : KDTree
max_distance : positive float
Returns : result : dok_matrix
Sparse matrix representing the results in “dictionary of keys” format.
- class scipy.spatial.Rectangle(maxes, mins)¶
Hyperrectangle class.
Represents a Cartesian product of intervals.
Methods
max_distance_point max_distance_rectangle min_distance_point min_distance_rectangle split volume - max_distance_point(x, p=2.0)¶
Compute the maximum distance between x and a point in the hyperrectangle.
- max_distance_rectangle(other, p=2.0)¶
Compute the maximum distance between points in the two hyperrectangles.
- min_distance_point(x, p=2.0)¶
Compute the minimum distance between x and a point in the hyperrectangle.
- min_distance_rectangle(other, p=2.0)¶
Compute the minimum distance between points in the two hyperrectangles.
- split(d, split)¶
Produce two hyperrectangles by splitting along axis d.
In general, if you need to compute maximum and minimum distances to the children, it can be done more efficiently by updating the maximum and minimum distances to the parent.
- volume()¶
Total volume.
- class scipy.spatial.cKDTree¶
kd-tree for quick nearest-neighbor lookup
This class provides an index into a set of k-dimensional points which can be used to rapidly look up the nearest neighbors of any point.
The algorithm used is described in Maneewongvatana and Mount 1999. The general idea is that the kd-tree is a binary trie, each of whose nodes represents an axis-aligned hyperrectangle. Each node specifies an axis and splits the set of points based on whether their coordinate along that axis is greater than or less than a particular value.
During construction, the axis and splitting point are chosen by the “sliding midpoint” rule, which ensures that the cells do not all become long and thin.
The tree can be queried for the r closest neighbors of any given point (optionally returning only those within some maximum distance of the point). It can also be queried, with a substantial gain in efficiency, for the r approximate closest neighbors.
For large dimensions (20 is already large) do not expect this to run significantly faster than brute force. High-dimensional nearest-neighbor queries are a substantial open problem in computer science.
Methods
query - query¶
query the kd-tree for nearest neighbors
- scipy.spatial.distance_matrix(x, y, p=2, threshold=1000000)¶
Compute the distance matrix.
Computes the matrix of all pairwise distances.
Parameters : x : array-like, m by k
y : array-like, n by k
p : float 1<=p<=infinity
Which Minkowski p-norm to use.
threshold : positive integer
If m*n*k>threshold use a python loop instead of creating a very large temporary.
Returns : result : array-like, m by n
- scipy.spatial.heappop()¶
Pop the smallest item off the heap, maintaining the heap invariant.
- scipy.spatial.heappush()¶
Push item onto heap, maintaining the heap invariant.
- scipy.spatial.minkowski_distance(x, y, p=2)¶
Compute the L**p distance between x and y
- scipy.spatial.minkowski_distance_p(x, y, p=2)¶
Compute the pth power of the L**p distance between x and y
For efficiency, this function computes the L**p distance but does not extract the pth root. If p is 1 or infinity, this is equal to the actual L**p distance.
