Skip to main content

Add built-in support for quaternions to numpy

Project description

This package creates a quaternion type in python, and further enables numpy to create and manipulate arrays of quaternions. The usual algebraic operations (addition and multiplication) are available, along with numerous properties like norm and various types of distance measures between two quaternions. There are also additional functions like “squad” and “slerp” interpolation, and conversions to and from axis-angle, matrix, and Euler-angle representations of rotations. The core of the code is written in C for speed.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

numpy_quaternion-2019.6.11.17.4.37-cp37-cp37m-win_amd64.whl (58.1 kB view details)

Uploaded CPython 3.7mWindows x86-64

numpy_quaternion-2019.6.11.17.4.37-cp36-cp36m-win_amd64.whl (58.1 kB view details)

Uploaded CPython 3.6mWindows x86-64

numpy_quaternion-2019.6.11.17.4.37-cp35-cp35m-win_amd64.whl (58.1 kB view details)

Uploaded CPython 3.5mWindows x86-64

numpy_quaternion-2019.6.11.17.4.37-cp27-cp27m-win_amd64.whl (52.3 kB view details)

Uploaded CPython 2.7mWindows x86-64

File details

Details for the file numpy_quaternion-2019.6.11.17.4.37-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: numpy_quaternion-2019.6.11.17.4.37-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 58.1 kB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.7.3

File hashes

Hashes for numpy_quaternion-2019.6.11.17.4.37-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 0a2e965e5abac0f2c8c7141f310b2fc2683f53498c1c71d77d41910625dec3bf
MD5 7869ef05f7a350bb06b54f9cf7854c9d
BLAKE2b-256 6558a83e96990e77047c10fc561b9c6970a108c9d86cd2e41736d7c343a200f0

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2019.6.11.17.4.37-cp36-cp36m-win_amd64.whl.

File metadata

  • Download URL: numpy_quaternion-2019.6.11.17.4.37-cp36-cp36m-win_amd64.whl
  • Upload date:
  • Size: 58.1 kB
  • Tags: CPython 3.6m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.6.8

File hashes

Hashes for numpy_quaternion-2019.6.11.17.4.37-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 b1919e5a5ed7a69772ccd86b364dec703a9559b25af06b09dfa096eea2fbcbea
MD5 23ff71dd1da0119c77450056c9c021b5
BLAKE2b-256 c1b1e76d8a25c4fc73d731e520289b03d8ec7ed688fde35b34793d9c216aa7b9

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2019.6.11.17.4.37-cp35-cp35m-win_amd64.whl.

File metadata

  • Download URL: numpy_quaternion-2019.6.11.17.4.37-cp35-cp35m-win_amd64.whl
  • Upload date:
  • Size: 58.1 kB
  • Tags: CPython 3.5m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/28.8.0 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/3.5.4

File hashes

Hashes for numpy_quaternion-2019.6.11.17.4.37-cp35-cp35m-win_amd64.whl
Algorithm Hash digest
SHA256 1fd477c6e124b7258db8c4f3943f2fbccf98f96fcf24545adb86bfd1621317bd
MD5 982accbcd7152084558ba1cf62bc6a6c
BLAKE2b-256 857503741ed6766eeb4affa118732cf5a5bdbaac39c54266a85d1ba226153b87

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2019.6.11.17.4.37-cp27-cp27m-win_amd64.whl.

File metadata

  • Download URL: numpy_quaternion-2019.6.11.17.4.37-cp27-cp27m-win_amd64.whl
  • Upload date:
  • Size: 52.3 kB
  • Tags: CPython 2.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.32.1 CPython/2.7.16

File hashes

Hashes for numpy_quaternion-2019.6.11.17.4.37-cp27-cp27m-win_amd64.whl
Algorithm Hash digest
SHA256 67dcee784a3a8fb3e5f2dc45d8933fd1df074134fcce69a3ebc0a03baab6fd28
MD5 c28c02696a73d00403f34addc0902a97
BLAKE2b-256 898ef74996d7c44849794c56d400ce32a272c4a79da1e49964f9765bfd21bc2d

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page