scipy tutorial - Baustatik-Info-Server

Methods
SciPy Reference Guide, Release 0.8.dev
max_distance_point(x[, p]) Compute the maximum distance between x and a point in the
hyperrectangle.
max_distance_rectangle(other[, Compute the maximum distance between points in the two
p])
hyperrectangles.
min_distance_point(x[, p]) Compute the minimum distance between x and a point in the
hyperrectangle.
min_distance_rectangle(other[, Compute the minimum distance between points in the two
p])
hyperrectangles.
split(d, split) Produce two hyperrectangles by splitting along axis d.
volume() Total 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 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. Highdimensional
nearest-neighbor queries are a substantial open problem in computer science.
Methods
query query the kd-tree for nearest neighbors
query()
query the kd-tree for nearest neighbors
3.16. Spatial algorithms and data structures (scipy.spatial) 421