Add a quaternion dtype to NumPy

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for moboyle79 from gravatar.com moboyle79

Unverified details

These details have not been verified by PyPI
Project links
Meta
Report project as malware

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

Verified details

These details have been verified by PyPI
Maintainers
Avatar for moboyle79 from gravatar.com moboyle79

Unverified details

These details have not been verified by PyPI
Project links
Meta

Release history Release notifications | RSS feed

2024.0.13

2024.0.12

2024.0.11

2024.0.10

2024.0.9

2024.0.8

2024.0.7

2024.0.3

2024.0.2

2023.0.4

2023.0.3

2023.0.2

2023.0.1

2023.0.0

2022.4.4

2022.4.3

2022.4.2

2022.4.1

2022.3.1

2022.2.10.14.20.39

2022.2.9.19.55.57 yanked

Reason this release was yanked:

Bad dependencies

2021.11.4.15.26.3

2021.10.7.23.40.37

2021.8.30.10.33.11

2021.8.30.10.8.27

2021.8.26.15.40.13

2021.8.25.12.59.41

This version

2021.7.28.16.11.54

2021.7.13.7.29.27

2021.6.9.13.34.11

2021.4.5.14.42.35

2021.4.4.21.44.8

2021.3.17.16.51.43

2021.3.17.16.10.20

2021.3.17.7.29.41

2021.3.11.10.32.22

2021.3.6.15.24.2

2021.0.0a0 pre-release

2020.11.2.17.0.49

2020.9.5.14.42.2

2020.9.2.20.23.42

2020.9.2.16.39.41

2020.7.30.15.57.32 yanked

Reason this release was yanked:

Failed tests

2020.5.19.15.27.24

2020.5.11.13.33.35

2020.5.1.9.54.41

2020.4.29.8.34.20

2020.4.24.9.29.7

2020.4.21.14.53.58

2019.12.11.22.25.52

2019.10.3.10.26.21

2019.9.30.17.47.33

2019.7.23.15.26.49

2019.7.15.12.50.36

2019.7.3.22.46.7

2019.6.26.15.8.24

2019.6.26.13.52.49

2019.6.26.13.19.17

2019.6.26.0.0.13

2019.6.25.23.51.47

2019.6.25.23.8.37

2019.6.25.22.33.21

2019.6.11.17.26.3

2019.6.11.17.4.37

2019.5.24.16.21.1

2019.5.24.11.54.47

2019.3.21.14.22.55

2019.3.21.14.12.56

2019.3.21.13.27.35

2019.3.21.13.18.24

2019.3.20.15.5.52

2019.3.18.14.33.20

2018.12.24.11.8.55

2018.11.28.14.6.55

2018.11.6.10.44.9

2018.11.3.1.0.41

2018.10.1.22.5.32

2018.10.1.21.10.14

2018.10.1.21.9.15

2018.10.1.21.7.41

2018.10.1.20.37.16

2018.10.1.20.36.5

2018.10.1.20.33.23

2018.10.1.20.3.3

2018.10.1.20.1.54

2018.10.1.20.0.24

2018.10.1.19.38.42

2018.10.1.19.37.47

2018.10.1.19.36.0

2018.10.1.19.24.0

2018.10.1.19.22.42

2018.10.1.19.21.23

2018.10.1.19.4.38

2018.10.1.19.3.34

2018.10.1.19.2.10

2018.10.1.18.56.16

2018.10.1.18.55.10

2018.10.1.18.54.1

2018.10.1.17.6.20

2018.10.1.16.30.46

2018.10.1.15.59.11

2018.10.1.15.39.8

2018.10.1.15.34.19

2018.10.1.15.18.24

2018.10.1.15.1.2

2018.9.30.23.2.27

2018.9.30.22.24.11

2018.9.30.22.1.29

2018.8.13.16.17.9

2018.7.5.21.55.13

2018.7.5.15.18.26

2018.7.4.22.45.56

2018.6.18.16.33.12

2018.6.13.15.29.2

2018.5.31.16.8.42

2018.5.31.15.38.2

2018.5.31.13.56.17

2018.5.17.10.19.59

2018.5.17.10.9.55

2018.5.16.9.16.21

2018.5.10.13.50.12

2018.5.9.21.48.54

2018.5.9.20.48.6

2018.5.9.17.41.0

2018.5.9.17.32.22

2018.5.9.12.22.4

2018.5.2.21.39.8

2018.4.26.9.35.2

2018.4.18.16.18.52

2018.4.18.16.15.4

2018.4.16.17.4.9

2018.03.06.20.03.47

2018.03.02.09.28.43

2018.02.24.23.05.39

2018.02.22.22.24.39

2018.02.21.15.41.17

2017.11.26.19.00.13

2017.11.26.19.0.13

2017.10.20.10.41.30

2017.10.19.15.47.40

2017.10.14.11.13.13.dev206252663 pre-release

2017.10.14.11.11.56

2017.10.04.16.20.25.dev180388366 pre-release

2017.10.04.13.50.36.dev52395737 pre-release

2017.07.13.12.10.29.dev114912538 pre-release

2017.07.13.12.09.11.dev31661272 pre-release

2017.06.28.13.11.35.dev177226304 pre-release

2017.06.09.12.19.46.dev119623821 pre-release

2017.06.05.21.19.52.dev20097538 pre-release

2017.06.05.21.14.57.dev19253288 pre-release

2017.06.02.13.32.11.dev17910925 pre-release

2017.05.24.14.05.54.dev33077846 pre-release

2017.05.23.14.39.48.dev53804477 pre-release

2017.05.08.13.59.46.dev255649235 pre-release

2017.04.05.14.49.15.dev261789573 pre-release

2017.03.29.15.26.47.dev5411921 pre-release

2017.03.16.21.51.57.dev242408302 pre-release

2017.03.14.01.52.59.dev139985168 pre-release

2017.03.13.14.50.58.dev88917002 pre-release

2017.03.13.14.42.16.dev259768242 pre-release

2017.03.13.12.04.42.dev87917346 pre-release

2017.03.10.04.36.28.dev170668370 pre-release

2017.03.02.17.14.39.dev234032961 pre-release

2017.03.01.14.16.51.dev141622364 pre-release

2017.02.28.dev244535353 pre-release

2017.02.28.dev21024698 pre-release

2017.01.08.dev232822920 pre-release

2016.11.30.dev220855819 pre-release

2016.11.30.dev101697805 pre-release

2016.11.30.dev51027772 pre-release

2016.11.30.dev43897183 pre-release

2016.10.31.dev83815479 pre-release

2016.10.12.dev268044850 pre-release

2016.10.12.dev209377407 pre-release

2016.08.30.dev142425726 pre-release

2016.08.30.dev40560191 pre-release

2016.08.29.dev258356679 pre-release

2016.08.29.dev231774547 pre-release

2016.08.27.dev94615536 pre-release

2016.08.25.dev221305696 pre-release

Download files

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

Source Distribution

numpy-quaternion-2021.7.28.16.11.54.tar.gz (57.5 kB view details)

Uploaded Source

Built Distributions

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

numpy_quaternion-2021.7.28.16.11.54-cp39-cp39-win_amd64.whl (60.7 kB view details)

Uploaded CPython 3.9Windows x86-64

numpy_quaternion-2021.7.28.16.11.54-cp39-cp39-win32.whl (56.3 kB view details)

Uploaded CPython 3.9Windows x86

numpy_quaternion-2021.7.28.16.11.54-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (180.4 kB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ ARM64

numpy_quaternion-2021.7.28.16.11.54-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (192.7 kB view details)

Uploaded CPython 3.9manylinux: glibc 2.12+ x86-64manylinux: glibc 2.5+ x86-64

numpy_quaternion-2021.7.28.16.11.54-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_12_i686.manylinux2010_i686.whl (163.8 kB view details)

Uploaded CPython 3.9manylinux: glibc 2.12+ i686manylinux: glibc 2.5+ i686

numpy_quaternion-2021.7.28.16.11.54-cp39-cp39-macosx_10_9_x86_64.whl (56.7 kB view details)

Uploaded CPython 3.9macOS 10.9+ x86-64

numpy_quaternion-2021.7.28.16.11.54-cp38-cp38-win_amd64.whl (60.6 kB view details)

Uploaded CPython 3.8Windows x86-64

numpy_quaternion-2021.7.28.16.11.54-cp38-cp38-win32.whl (56.2 kB view details)

Uploaded CPython 3.8Windows x86

numpy_quaternion-2021.7.28.16.11.54-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (181.9 kB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ ARM64

numpy_quaternion-2021.7.28.16.11.54-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (194.3 kB view details)

Uploaded CPython 3.8manylinux: glibc 2.12+ x86-64manylinux: glibc 2.5+ x86-64

numpy_quaternion-2021.7.28.16.11.54-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_12_i686.manylinux2010_i686.whl (164.9 kB view details)

Uploaded CPython 3.8manylinux: glibc 2.12+ i686manylinux: glibc 2.5+ i686

numpy_quaternion-2021.7.28.16.11.54-cp38-cp38-macosx_10_9_x86_64.whl (56.8 kB view details)

Uploaded CPython 3.8macOS 10.9+ x86-64

numpy_quaternion-2021.7.28.16.11.54-cp37-cp37m-win_amd64.whl (60.5 kB view details)

Uploaded CPython 3.7mWindows x86-64

numpy_quaternion-2021.7.28.16.11.54-cp37-cp37m-win32.whl (56.1 kB view details)

Uploaded CPython 3.7mWindows x86

numpy_quaternion-2021.7.28.16.11.54-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (175.8 kB view details)

Uploaded CPython 3.7mmanylinux: glibc 2.17+ ARM64

numpy_quaternion-2021.7.28.16.11.54-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (189.1 kB view details)

Uploaded CPython 3.7mmanylinux: glibc 2.12+ x86-64manylinux: glibc 2.5+ x86-64

numpy_quaternion-2021.7.28.16.11.54-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_12_i686.manylinux2010_i686.whl (161.8 kB view details)

Uploaded CPython 3.7mmanylinux: glibc 2.12+ i686manylinux: glibc 2.5+ i686

numpy_quaternion-2021.7.28.16.11.54-cp37-cp37m-macosx_10_9_x86_64.whl (56.6 kB view details)

Uploaded CPython 3.7mmacOS 10.9+ x86-64

numpy_quaternion-2021.7.28.16.11.54-cp36-cp36m-win_amd64.whl (60.5 kB view details)

Uploaded CPython 3.6mWindows x86-64

numpy_quaternion-2021.7.28.16.11.54-cp36-cp36m-win32.whl (56.1 kB view details)

Uploaded CPython 3.6mWindows x86

numpy_quaternion-2021.7.28.16.11.54-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (175.0 kB view details)

Uploaded CPython 3.6mmanylinux: glibc 2.17+ ARM64

numpy_quaternion-2021.7.28.16.11.54-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (187.9 kB view details)

Uploaded CPython 3.6mmanylinux: glibc 2.12+ x86-64manylinux: glibc 2.5+ x86-64

numpy_quaternion-2021.7.28.16.11.54-cp36-cp36m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_12_i686.manylinux2010_i686.whl (160.8 kB view details)

Uploaded CPython 3.6mmanylinux: glibc 2.12+ i686manylinux: glibc 2.5+ i686

numpy_quaternion-2021.7.28.16.11.54-cp36-cp36m-macosx_10_9_x86_64.whl (56.6 kB view details)

Uploaded CPython 3.6mmacOS 10.9+ x86-64

File details

Details for the file numpy-quaternion-2021.7.28.16.11.54.tar.gz.

File metadata

  • Download URL: numpy-quaternion-2021.7.28.16.11.54.tar.gz
  • Upload date:
  • Size: 57.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.6.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.61.2 CPython/3.9.6

File hashes

Hashes for numpy-quaternion-2021.7.28.16.11.54.tar.gz
Algorithm Hash digest
SHA256 9a579677da9e649e7bde203ef02080f1942f783a652bff76eb6fbd37b4fb4590
MD5 5614f1047a7952b607a857e0b5f680c0
BLAKE2b-256 c2615f7cf11595d63128c06b23240b59dec9bbfb83a86318c12985fbf49ae57c

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.7.28.16.11.54-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: numpy_quaternion-2021.7.28.16.11.54-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 60.7 kB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.6.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.61.2 CPython/3.9.6

File hashes

Hashes for numpy_quaternion-2021.7.28.16.11.54-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 5dce9208a88e69e49ac7e7cb5dbbbed6bd7fd662c715dd11a67e5f678d813ea8
MD5 b38be22b57abffe3175efe8f6257fa2a
BLAKE2b-256 7c1556cf0d7b9cc614397855857ea0de20228fed037754b852ad5dd884ea725e

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.7.28.16.11.54-cp39-cp39-win32.whl.

File metadata

  • Download URL: numpy_quaternion-2021.7.28.16.11.54-cp39-cp39-win32.whl
  • Upload date:
  • Size: 56.3 kB
  • Tags: CPython 3.9, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.6.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.61.2 CPython/3.9.6

File hashes

Hashes for numpy_quaternion-2021.7.28.16.11.54-cp39-cp39-win32.whl
Algorithm Hash digest
SHA256 d5643669f39ee286824822ba6e390000c638ceafa94f6f20265ea9e5b79a0f26
MD5 d13f1c582d84a4b15680604a4cb908db
BLAKE2b-256 c65d68564201c3867037a93185c7d4cd027ef92d7c9b4bb675ea716e23c8d416

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.7.28.16.11.54-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2021.7.28.16.11.54-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 b19d65abc2e2b3511405fd80e1e3b53034d59f2daecad83a08a14132b20b05c3
MD5 32655c2d7cfceae7f529030c01e0fd95
BLAKE2b-256 1bf5a4df1dd73d8868fcc185c5e5cb42599d5128d74bee258c8288742c82d790

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.7.28.16.11.54-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2021.7.28.16.11.54-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 3c5c40edb2f558966c1d1560a6b60dcafe3e799f4875ccee8f1861fdee66d413
MD5 ae37a34e20f27b121a2d9f3ff0b28548
BLAKE2b-256 30dea77dcc956ba0bfc066811a614bdc7779c2329603e33af39c54583bacc4e6

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.7.28.16.11.54-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_12_i686.manylinux2010_i686.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2021.7.28.16.11.54-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_12_i686.manylinux2010_i686.whl
Algorithm Hash digest
SHA256 cb776400cb5b6ee613df9cf05ba8092423151039b2bef91ad98dc463824e32e3
MD5 c815654e0d3d02586c47eda40dbc7686
BLAKE2b-256 e9ec928a3aa16b74b7455f3ac67df7b288333b1d25601abb480fe1da14de84d8

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.7.28.16.11.54-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2021.7.28.16.11.54-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 a3c67775fc43a3f9ae6bad9c4a5a519f8f05e690bec520717ac4e2f23b03f13b
MD5 798adebe379e7efc04f0e34f445d4ade
BLAKE2b-256 5ad041a5a1d5ccd724fd5faf28696b639b74adab03d46101558265b6338fbfd7

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.7.28.16.11.54-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: numpy_quaternion-2021.7.28.16.11.54-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 60.6 kB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.6.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.61.2 CPython/3.9.6

File hashes

Hashes for numpy_quaternion-2021.7.28.16.11.54-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 5621b6e5e9f431b14243dc36d657245e8adcc839e39e2c2c6b5d51c775827366
MD5 f0ca6a521a8bf0b0ca3afc1f96b67214
BLAKE2b-256 bb54fe050de179e00eeecc1638f60d315b699c713e4f2c367e0b0dab07e68a21

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.7.28.16.11.54-cp38-cp38-win32.whl.

File metadata

  • Download URL: numpy_quaternion-2021.7.28.16.11.54-cp38-cp38-win32.whl
  • Upload date:
  • Size: 56.2 kB
  • Tags: CPython 3.8, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.6.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.61.2 CPython/3.9.6

File hashes

Hashes for numpy_quaternion-2021.7.28.16.11.54-cp38-cp38-win32.whl
Algorithm Hash digest
SHA256 f0604ce49fc26c1ec8f67df21bf8a3d4c684a0e144d09935215c05597353b7e5
MD5 af6d23f74030268fde9093f146493c0f
BLAKE2b-256 ee7f4d326f3c7f6b9eb884d077e8694f71bd3404a25fd69b1c3ed4cf6ab0c9ad

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.7.28.16.11.54-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2021.7.28.16.11.54-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 c77b2578c9704f161b241f5ce77a8a7602bc404b381bf5055e4c62be94a41fb1
MD5 da9bc208f5a4362751db81018089879b
BLAKE2b-256 c6b820058781734eefb67849e10bc3696432c77c29cc4c9bb7b3b0d48bba105d

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.7.28.16.11.54-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2021.7.28.16.11.54-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 9aaa269fe1e37466e36d37f08a6977a312419a48ad0a6d121440115433b1afe2
MD5 bd522d5a7d434f860dab1928eac2bd1e
BLAKE2b-256 630451fa894d913817eabe4547841459177acea8414a9ed8c39adf2894381928

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.7.28.16.11.54-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_12_i686.manylinux2010_i686.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2021.7.28.16.11.54-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_12_i686.manylinux2010_i686.whl
Algorithm Hash digest
SHA256 28a6b51d184ec95567fa0f8f5b1f7096040bd905438bc10c69fa44451f4dfb1d
MD5 3af52d9fcc073d19d2c676abcf2b4018
BLAKE2b-256 193f4d816f767840edd141fa1f483c39d36079140e0a8b2c11c8f1c0443be62b

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.7.28.16.11.54-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2021.7.28.16.11.54-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 76c72956cd6659251462b491063eb5342a847492a127b070fcc19fb126c0b8dc
MD5 edb188d2bbc096d5ae7962515cef9e84
BLAKE2b-256 4ebce98d7db9f027803ca6e98df7c42e4540de1663caad70915fe54e352dab0f

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.7.28.16.11.54-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: numpy_quaternion-2021.7.28.16.11.54-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 60.5 kB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.6.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.61.2 CPython/3.9.6

File hashes

Hashes for numpy_quaternion-2021.7.28.16.11.54-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 4ac7a6961c92dba8e82aae85960c244d8f3f989e121241d8f9837cf8c93aa076
MD5 3a7b9d8c0839f5ee886c52ac60219bc0
BLAKE2b-256 31f9fed0fbb59eaf181ba3fb8ca271e858cbb9af29907fb6800dee132535e362

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.7.28.16.11.54-cp37-cp37m-win32.whl.

File metadata

  • Download URL: numpy_quaternion-2021.7.28.16.11.54-cp37-cp37m-win32.whl
  • Upload date:
  • Size: 56.1 kB
  • Tags: CPython 3.7m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.6.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.61.2 CPython/3.9.6

File hashes

Hashes for numpy_quaternion-2021.7.28.16.11.54-cp37-cp37m-win32.whl
Algorithm Hash digest
SHA256 47422c339023942ed76ed5f326a63d2a926f2a2fc6320b657d1c427f11b6f883
MD5 1dae5de24ce5f2b713a5f1e822a93764
BLAKE2b-256 1a9094e219ba7e0cbfa6a2e81d5cd3c363bc6ee645655fb24b509591fc34fc0e

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.7.28.16.11.54-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2021.7.28.16.11.54-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 e81ea6ef93cff706d5f09d2422c36fb1b3c11195efc59ee6719ba7fcc7c84fa0
MD5 2233a4b75212ad9102572ff1f5858ffd
BLAKE2b-256 4ba6c6faa4a823002dee90923040e70402a5c1b4107b449a01c57675b432c351

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.7.28.16.11.54-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2021.7.28.16.11.54-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 4d735d35c9ff844d4a0248470f462bd13d1b151fd66343dfaa18a1c1f96a4025
MD5 fc255a21d944fbdd719dc5475baecaaf
BLAKE2b-256 b232adc2325dcb0a1ae0d55d48b8b8189b2292a6ab600e9ed4924dea8387134a

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.7.28.16.11.54-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_12_i686.manylinux2010_i686.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2021.7.28.16.11.54-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_12_i686.manylinux2010_i686.whl
Algorithm Hash digest
SHA256 266e93925c5821666aabcdd30aa8ce7a9230f939fe63aed63ef617991dbd220c
MD5 f17cb1fdfc2f6eb3cff997251b8d5e65
BLAKE2b-256 da0851fc6848e8a7becdeede656a445582681f77af39bb92b812f31318683d96

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.7.28.16.11.54-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2021.7.28.16.11.54-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 e2f66ee678816103db72f3b1d4645837450b62e38a9c5bf8cdeba794f7805315
MD5 88158641d0e2c5daffe769586e7f3f0d
BLAKE2b-256 bff7d9d27d6304c4c6bdbda5e701b923b989b004712b74246389ddcb8e6bfd3f

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.7.28.16.11.54-cp36-cp36m-win_amd64.whl.

File metadata

  • Download URL: numpy_quaternion-2021.7.28.16.11.54-cp36-cp36m-win_amd64.whl
  • Upload date:
  • Size: 60.5 kB
  • Tags: CPython 3.6m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.6.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.61.2 CPython/3.9.6

File hashes

Hashes for numpy_quaternion-2021.7.28.16.11.54-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 139022d2a01335399d6dca00e83c5840b621402ed5e9a22aebaf0eafd80e6c9d
MD5 28f12c5bcb165b76a529c19e8ce61041
BLAKE2b-256 03a369d2519c7b299d66146b675896906cbeab63da28581285c7de8a2b6d6c9c

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.7.28.16.11.54-cp36-cp36m-win32.whl.

File metadata

  • Download URL: numpy_quaternion-2021.7.28.16.11.54-cp36-cp36m-win32.whl
  • Upload date:
  • Size: 56.1 kB
  • Tags: CPython 3.6m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.6.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.61.2 CPython/3.9.6

File hashes

Hashes for numpy_quaternion-2021.7.28.16.11.54-cp36-cp36m-win32.whl
Algorithm Hash digest
SHA256 5bb6db107a167797030f8b3f24691685e7788a19b4372fd3bff16c240e44bb04
MD5 4b0c0bfb04e96ee0cc6f0e019573605c
BLAKE2b-256 c30e6dba49fe53a347f12349653b1be245bd4c8d7da762364022431bc59ab459

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.7.28.16.11.54-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2021.7.28.16.11.54-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 97e21298c7293e3f1d8ad9251c2904f69934ee9d820f9eaf1c15c4cc1b3c4891
MD5 6201deb602462aeb9a229c24f471e1cf
BLAKE2b-256 829fc508c90b7ea255c7fa7d302aeda3d426378282a8c98af640a307c04ebfad

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.7.28.16.11.54-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2021.7.28.16.11.54-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 b8e369b38e8300c924d769b43d1293c293cf940cb2422c8ed997afb551dfb70a
MD5 d280daefa17e613ed7442d0eb6df9061
BLAKE2b-256 392fcbfaa01be5827a04716962721dddc8a582073483ae571f227d7a917cd510

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.7.28.16.11.54-cp36-cp36m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_12_i686.manylinux2010_i686.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2021.7.28.16.11.54-cp36-cp36m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_12_i686.manylinux2010_i686.whl
Algorithm Hash digest
SHA256 05c056233518f8d0417aaed6828f53720794f44aed243743986a554cf8e8d41f
MD5 17155bf9757ddfdb5b3688e9687e375c
BLAKE2b-256 cb2934dea60ea9154edcca3de822dc2641f55d1f9b213327628c90ae29e4d0e1

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.7.28.16.11.54-cp36-cp36m-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2021.7.28.16.11.54-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 e6ec4ad603b8e58d3801a60a465e7027099168c48ec91653fd5d6998dc9212c8
MD5 3bf05c9951752d6e1253764b1d8c1ba5
BLAKE2b-256 ed2ecd078b2e398a21017b16e16dc2c3ae070baac1ff8754df3d6d32b34095ba

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