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Fast kd-tree implementation with OpenMP-enabled queries

Project description



pykdtree is a kd-tree implementation for fast nearest neighbour search in Python. The aim is to be the fastest implementation around for common use cases (low dimensions and low number of neighbours) for both tree construction and queries.

The implementation is based on scipy.spatial.cKDTree and libANN by combining the best features from both and focus on implementation efficiency.

The interface is similar to that of scipy.spatial.cKDTree except only Euclidean distance measure is supported.

Queries are optionally multithreaded using OpenMP.


Pykdtree can be installed via pip:

pip install pykdtree

Or, if in a conda-based environment, with conda from the conda-forge channel:

conda install -c conda-forge pykdtree

Note that by default these packages are only built with OpenMP for linux platforms. To attempt to build from source with OpenMP support do:

export USE_OMP=1
pip install --no-binary pykdtree pykdtree

This may not work on some systems that don’t have OpenMP installed. See the below development instructions for more guidance. Disabling OpenMP can be accomplished by setting USE_OMP to 0 in the above commands.

Development Installation

If you wish to contribute to pykdtree then it is a good idea to install from source so you can quickly see the effects of your changes. By default pykdtree is built with OpenMP enabled queries on unix-like systems. On linux this is done using libgomp. On OSX systems OpenMP is provided using the clang compiler (conda environments use a separate compiler).

$ cd <pykdtree_dir>
$ pip install -e .

This installs pykdtree in an “editable” mode where changes to the Python files are automatically reflected when running a new python interpreter instance (ex. running a python script that uses pykdtree). It does not automatically rebuild or recompile the .mako templates and .pyx Cython code in pykdtree. Editing these files requires running the pykdtree/ script and then rerunning the pip command above to recompile the Cython files.

If installation fails with undefined compiler flags or you want to use another OpenMP implementation you may need to modify or specify additional pip command line flags to match the library locations on your system.

Building without OpenMP support is controlled by the USE_OMP environment variable

$ cd <pykdtree_dir>
$ export USE_OMP=0
$ pip install -e .

Note evironment variables are by default not exported when using sudo so in this case do

$ USE_OMP=0 sudo -E pip install -e .


The usage of pykdtree is similar to scipy.spatial.cKDTree so for now refer to its documentation

>>> from pykdtree.kdtree import KDTree
>>> kd_tree = KDTree(data_pts)
>>> dist, idx = kd_tree.query(query_pts, k=8)

The number of threads to be used in OpenMP enabled queries can be controlled with the standard OpenMP environment variable OMP_NUM_THREADS.

The leafsize argument (number of data points per leaf) for the tree creation can be used to control the memory overhead of the kd-tree. pykdtree uses a default leafsize=16. Increasing leafsize will reduce the memory overhead and construction time but increase query time.

pykdtree accepts data in double precision (numpy.float64) or single precision (numpy.float32) floating point. If data of another type is used an internal copy in double precision is made resulting in a memory overhead. If the kd-tree is constructed on single precision data the query points must be single precision as well.


Comparison with scipy.spatial.cKDTree and libANN. This benchmark is on geospatial 3D data with 10053632 data points and 4276224 query points. The results are indexed relative to the construction time of scipy.spatial.cKDTree. A leafsize of 10 (scipy.spatial.cKDTree default) is used.

Note: libANN is not thread safe. In this benchmark libANN is compiled with “-O3 -funroll-loops -ffast-math -fprefetch-loop-arrays” in order to achieve optimum performance.





pykdtree 4 threads






query 1 neighbour





Total 1 neighbour





query 8 neighbours





Total 8 neighbours





Looking at the combined construction and query this gives the following performance improvement relative to scipy.spatial.cKDTree




pykdtree 4 threads









Note: mileage will vary with the dataset at hand and computer architecture.


Run the unit tests using pytest

$ cd <pykdtree_dir>
$ pytest

Installing on AppVeyor

Pykdtree requires the “stdint.h” header file which is not available on certain versions of Windows or certain Windows compilers including those on the continuous integration platform AppVeyor. To get around this the header file(s) can be downloaded and placed in the correct “include” directory. This can be done by adding the anaconda/missing-headers.ps1 script to your repository and running it the install step of appveyor.yml:

# install missing headers that aren’t included with MSVC 2008 # - “powershell ./appveyor/missing-headers.ps1”

In addition to this, AppVeyor does not support OpenMP so this feature must be turned off by adding the following to appveyor.yml in the environment section:


# Don’t build with openmp because it isn’t supported in appveyor’s compilers USE_OMP: “0”


v1.3.6 : Fix Python 3.11 compatibility and build Python 3.11 wheels

v1.3.5 : Build Python 3.10 wheels and other CI updates

v1.3.4 : Fix Python 3.9 wheels not being built for linux

v1.3.3 : Add compatibility to python 3.9

v1.3.2 : Change OSX installation to not use OpenMP without conda interpreter

v1.3.1 : Fix masking in the “query” method introduced in 1.3.0

v1.3.0 : Keyword argument “mask” added to “query” method. OpenMP compilation now works for MS Visual Studio compiler

v1.2.2 : Build process fixes

v1.2.1 : Fixed OpenMP thread safety issue introduced in v1.2.0

v1.2.0 : 64 and 32 bit MSVC Windows support added

v1.1.1 : Same as v1.1 release due to incorrect pypi release

v1.1 : Build process improvements. Add data attribute to kdtree class for scipy interface compatibility

v1.0 : Switched license from GPLv3 to LGPLv3

v0.3 : Avoid zipping of installed egg

v0.2 : Reduced memory footprint. Can now handle single precision data internally avoiding copy conversion to double precision. Default leafsize changed from 10 to 16 as this reduces the memory footprint and makes it a cache line multiplum (negligible if any query performance observed in benchmarks). Reduced memory allocation for leaf nodes. Applied patch for building on OS X.

v0.1 : Initial version.

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