scipy.spatial._ckdtree:cKDTree

Parameters

data : array_like, shape (n,m)

The n data points of dimension m to be indexed. This array is not copied unless this is necessary to produce a contiguous array of doubles, and so modifying this data will result in bogus results. The data are also copied if the kd-tree is built with copy_data=True.

leafsize : positive int, optional

The number of points at which the algorithm switches over to brute-force. Default: 16.

compact_nodes : bool, optional

If True, the kd-tree is built to shrink the hyperrectangles to the actual data range. This usually gives a more compact tree that is robust against degenerated input data and gives faster queries at the expense of longer build time. Default: True.

copy_data : bool, optional

If True the data is always copied to protect the kd-tree against data corruption. Default: False.

balanced_tree : bool, optional

If True, the median is used to split the hyperrectangles instead of the midpoint. This usually gives a more compact tree and faster queries at the expense of longer build time. Default: True.

boxsize : array_like or scalar, optional

Apply an m-d toroidal topology to the KDTree. The topology is generated by $x_i+n_i{L}_i$ where $n_i$ are integers and $L_i$ is the boxsize along i-th dimension. The input data shall be wrapped into $[0,L_i)$. A ValueError is raised if any of the data is outside of this bound.