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

2021.7.28.16.11.54

2021.7.13.7.29.27

2021.6.9.13.34.11

This version

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-2021.4.5.14.42.35.tar.gz (57.1 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-2021.4.5.14.42.35-cp39-cp39-win_amd64.whl (60.4 kB view details)

Uploaded CPython 3.9Windows x86-64

numpy_quaternion-2021.4.5.14.42.35-cp39-cp39-win32.whl (56.1 kB view details)

Uploaded CPython 3.9Windows x86

numpy_quaternion-2021.4.5.14.42.35-cp39-cp39-manylinux2010_x86_64.whl (192.5 kB view details)

Uploaded CPython 3.9manylinux: glibc 2.12+ x86-64

numpy_quaternion-2021.4.5.14.42.35-cp39-cp39-manylinux2010_i686.whl (163.6 kB view details)

Uploaded CPython 3.9manylinux: glibc 2.12+ i686

numpy_quaternion-2021.4.5.14.42.35-cp39-cp39-manylinux1_x86_64.whl (192.5 kB view details)

Uploaded CPython 3.9

numpy_quaternion-2021.4.5.14.42.35-cp39-cp39-manylinux1_i686.whl (163.5 kB view details)

Uploaded CPython 3.9

numpy_quaternion-2021.4.5.14.42.35-cp39-cp39-macosx_10_9_x86_64.whl (56.5 kB view details)

Uploaded CPython 3.9macOS 10.9+ x86-64

numpy_quaternion-2021.4.5.14.42.35-cp38-cp38-win_amd64.whl (60.3 kB view details)

Uploaded CPython 3.8Windows x86-64

numpy_quaternion-2021.4.5.14.42.35-cp38-cp38-win32.whl (56.1 kB view details)

Uploaded CPython 3.8Windows x86

numpy_quaternion-2021.4.5.14.42.35-cp38-cp38-manylinux2010_x86_64.whl (194.1 kB view details)

Uploaded CPython 3.8manylinux: glibc 2.12+ x86-64

numpy_quaternion-2021.4.5.14.42.35-cp38-cp38-manylinux2010_i686.whl (164.6 kB view details)

Uploaded CPython 3.8manylinux: glibc 2.12+ i686

numpy_quaternion-2021.4.5.14.42.35-cp38-cp38-manylinux1_x86_64.whl (194.1 kB view details)

Uploaded CPython 3.8

numpy_quaternion-2021.4.5.14.42.35-cp38-cp38-manylinux1_i686.whl (164.6 kB view details)

Uploaded CPython 3.8

numpy_quaternion-2021.4.5.14.42.35-cp38-cp38-macosx_10_9_x86_64.whl (56.6 kB view details)

Uploaded CPython 3.8macOS 10.9+ x86-64

numpy_quaternion-2021.4.5.14.42.35-cp37-cp37m-win_amd64.whl (60.2 kB view details)

Uploaded CPython 3.7mWindows x86-64

numpy_quaternion-2021.4.5.14.42.35-cp37-cp37m-win32.whl (55.9 kB view details)

Uploaded CPython 3.7mWindows x86

numpy_quaternion-2021.4.5.14.42.35-cp37-cp37m-manylinux2010_x86_64.whl (188.9 kB view details)

Uploaded CPython 3.7mmanylinux: glibc 2.12+ x86-64

numpy_quaternion-2021.4.5.14.42.35-cp37-cp37m-manylinux2010_i686.whl (161.7 kB view details)

Uploaded CPython 3.7mmanylinux: glibc 2.12+ i686

numpy_quaternion-2021.4.5.14.42.35-cp37-cp37m-manylinux1_x86_64.whl (188.9 kB view details)

Uploaded CPython 3.7m

numpy_quaternion-2021.4.5.14.42.35-cp37-cp37m-manylinux1_i686.whl (161.6 kB view details)

Uploaded CPython 3.7m

numpy_quaternion-2021.4.5.14.42.35-cp37-cp37m-macosx_10_9_x86_64.whl (56.4 kB view details)

Uploaded CPython 3.7mmacOS 10.9+ x86-64

numpy_quaternion-2021.4.5.14.42.35-cp36-cp36m-win_amd64.whl (60.2 kB view details)

Uploaded CPython 3.6mWindows x86-64

numpy_quaternion-2021.4.5.14.42.35-cp36-cp36m-win32.whl (55.9 kB view details)

Uploaded CPython 3.6mWindows x86

numpy_quaternion-2021.4.5.14.42.35-cp36-cp36m-manylinux2010_x86_64.whl (187.7 kB view details)

Uploaded CPython 3.6mmanylinux: glibc 2.12+ x86-64

numpy_quaternion-2021.4.5.14.42.35-cp36-cp36m-manylinux2010_i686.whl (160.6 kB view details)

Uploaded CPython 3.6mmanylinux: glibc 2.12+ i686

numpy_quaternion-2021.4.5.14.42.35-cp36-cp36m-manylinux1_x86_64.whl (187.7 kB view details)

Uploaded CPython 3.6m

numpy_quaternion-2021.4.5.14.42.35-cp36-cp36m-manylinux1_i686.whl (160.6 kB view details)

Uploaded CPython 3.6m

numpy_quaternion-2021.4.5.14.42.35-cp36-cp36m-macosx_10_9_x86_64.whl (56.4 kB view details)

Uploaded CPython 3.6mmacOS 10.9+ x86-64

File details

Details for the file numpy-quaternion-2021.4.5.14.42.35.tar.gz.

File metadata

  • Download URL: numpy-quaternion-2021.4.5.14.42.35.tar.gz
  • Upload date:
  • Size: 57.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.59.0 CPython/3.9.4

File hashes

Hashes for numpy-quaternion-2021.4.5.14.42.35.tar.gz
Algorithm Hash digest
SHA256 a9571532cc16c433e8a532ebcb18e802a9c4d52c30356be2b13fb27f6655905f
MD5 1fa11127aa24179996a67a5143ef9e89
BLAKE2b-256 db0ea57070160c11a6425103da002118fa12d71c21b68394d3b6d652064fbe64

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.4.5.14.42.35-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: numpy_quaternion-2021.4.5.14.42.35-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 60.4 kB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.59.0 CPython/3.9.4

File hashes

Hashes for numpy_quaternion-2021.4.5.14.42.35-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 5aab622b5ac62f5ea1b92a70eaaac73cfda3ded8f200600c121b45356d69066b
MD5 c83c431d784ed6fcd381c7d0899cfec1
BLAKE2b-256 210073d12bb00357e933a75d86992c73226235e3c864412f796611603b449c59

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.4.5.14.42.35-cp39-cp39-win32.whl.

File metadata

  • Download URL: numpy_quaternion-2021.4.5.14.42.35-cp39-cp39-win32.whl
  • Upload date:
  • Size: 56.1 kB
  • Tags: CPython 3.9, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.59.0 CPython/3.9.4

File hashes

Hashes for numpy_quaternion-2021.4.5.14.42.35-cp39-cp39-win32.whl
Algorithm Hash digest
SHA256 7bd02f8445dcd21c05f6d2db7c0faa9494def35673f29d5a37ab9a1d3fdd0144
MD5 608c6911f541015391ec2c5ebd12316e
BLAKE2b-256 e4722152bbc8a274f0bf82de7297c50b80a4c9e00a83712056453d597ff39d4e

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.4.5.14.42.35-cp39-cp39-manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2021.4.5.14.42.35-cp39-cp39-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 44c48ee7ea120fe724a48e2029feabc23e7c8508ca1c20f87b327a041fe12eca
MD5 8d4ffccf495d3385e0e25edd10e5080f
BLAKE2b-256 96f8b8996d8783a4eb4f890716ad042a81c2f2096d2e57c250b198ff5425ec87

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.4.5.14.42.35-cp39-cp39-manylinux2010_i686.whl.

File metadata

  • Download URL: numpy_quaternion-2021.4.5.14.42.35-cp39-cp39-manylinux2010_i686.whl
  • Upload date:
  • Size: 163.6 kB
  • Tags: CPython 3.9, manylinux: glibc 2.12+ i686
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.59.0 CPython/3.9.4

File hashes

Hashes for numpy_quaternion-2021.4.5.14.42.35-cp39-cp39-manylinux2010_i686.whl
Algorithm Hash digest
SHA256 b503771e3eca43c959fbaa0457b0df4b50c12e04d69784076b6410e9125df7d8
MD5 9e51444bdc07ad1cdea57331d1ef7b34
BLAKE2b-256 e58e9b6c928c07b713f19ef61c2204f1274aff2a50ee544062da11ad87e4841f

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.4.5.14.42.35-cp39-cp39-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2021.4.5.14.42.35-cp39-cp39-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 2b703c09c7ac8f7d96309dc95aaa7885282268e42ef1e2f62b45771bcf9679c9
MD5 9c3d9abc0c1a444c4f7f4ec7bcf6d020
BLAKE2b-256 2a50c026fae9365794ed6defe9a6a8846ec7603218b105247513556bea5cb8a4

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.4.5.14.42.35-cp39-cp39-manylinux1_i686.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2021.4.5.14.42.35-cp39-cp39-manylinux1_i686.whl
Algorithm Hash digest
SHA256 8798203bf4b7bb0da7a4b54a6c48a58128fd7964f5369cfdbcae92ef05a4d6bc
MD5 9402aaefcc06208402d0bc3e3e78f9b1
BLAKE2b-256 a1afbc12515d7103e3b64dce57e3a625063133c122d849a7dd8e18c1009364aa

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.4.5.14.42.35-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2021.4.5.14.42.35-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 0eaef8d8eaf6594fc2ea0ac65d6da4c599a57023dd1924b84b62992555594b9d
MD5 2fcedc180e57492fb445f082c60908d0
BLAKE2b-256 1da04d897ed98322c25ba6406157476f091a1ad63b7a9bc7355e04d3b2772aeb

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.4.5.14.42.35-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: numpy_quaternion-2021.4.5.14.42.35-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 60.3 kB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.59.0 CPython/3.9.4

File hashes

Hashes for numpy_quaternion-2021.4.5.14.42.35-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 e9a0fc2055203139aa238931d49e8f0d1d339612c654f6317cbcd3e61992448f
MD5 256a63961ab05f2e60f51295ba03c0de
BLAKE2b-256 6b94febbca6dffc35c97423a8b4ea5487c38746f9e202bdaa087430736b30d7c

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.4.5.14.42.35-cp38-cp38-win32.whl.

File metadata

  • Download URL: numpy_quaternion-2021.4.5.14.42.35-cp38-cp38-win32.whl
  • Upload date:
  • Size: 56.1 kB
  • Tags: CPython 3.8, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.59.0 CPython/3.9.4

File hashes

Hashes for numpy_quaternion-2021.4.5.14.42.35-cp38-cp38-win32.whl
Algorithm Hash digest
SHA256 5c2e5073af6ae5b9e9f74d537338ee746b5dcb8d55bf8ef9574b6e7e7b2831d2
MD5 826078ec861e0d52616c7d6a50d93372
BLAKE2b-256 50987c6c442b73ce1a319d3af54e3ccac467448102e76784ced367ba4cc86313

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.4.5.14.42.35-cp38-cp38-manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2021.4.5.14.42.35-cp38-cp38-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 4e9acc1055fd3f459287774f4e0f21f512355a3b4e6f5e0ddb041b1eebf4a7c9
MD5 4a7a062c3bbfd47ff03515fe7ae6e1cf
BLAKE2b-256 7220c8e10ac7563235eea87f19d9cb37a17895a324f6038171d91b01fe5bf901

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.4.5.14.42.35-cp38-cp38-manylinux2010_i686.whl.

File metadata

  • Download URL: numpy_quaternion-2021.4.5.14.42.35-cp38-cp38-manylinux2010_i686.whl
  • Upload date:
  • Size: 164.6 kB
  • Tags: CPython 3.8, manylinux: glibc 2.12+ i686
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.59.0 CPython/3.9.4

File hashes

Hashes for numpy_quaternion-2021.4.5.14.42.35-cp38-cp38-manylinux2010_i686.whl
Algorithm Hash digest
SHA256 ed7fc0dd536d212382650b5d23c1510ddaeaed53f719e4470fc501aaf8cd81a5
MD5 fed3d71c16186a561f744b83424ec6d8
BLAKE2b-256 402e5939b69b514726c328fd7bb1c501638bc12e6b21fb7c6785b2f159eb6198

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.4.5.14.42.35-cp38-cp38-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2021.4.5.14.42.35-cp38-cp38-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 6e28dab12fb468a5ad147c25ba7a90d90a62d0845662f53db4c3deba0d3d1088
MD5 817db439cf47bb009daca0cb26a2d17a
BLAKE2b-256 00d4625fbbb313ef75673da78d3b39f7ff01664cc8fdde22b99a599d15f224e7

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.4.5.14.42.35-cp38-cp38-manylinux1_i686.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2021.4.5.14.42.35-cp38-cp38-manylinux1_i686.whl
Algorithm Hash digest
SHA256 672acf0c4550ec638c74d43de92aee801a02825caf9dc0f1226863cac4668ae2
MD5 9dcfed1b91b1e7616e46b6809ed59c86
BLAKE2b-256 acc613cd5d0ca898011dd13cb825d893dd3989089d01d68617c33c29d4764de1

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.4.5.14.42.35-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2021.4.5.14.42.35-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 f24aa0cda6437cee8c84d06009a403a4a3567c22d9f87d26c0d9982d7e3b5320
MD5 cf53148b02d669ba3ec5bf27578bad10
BLAKE2b-256 9e84620a52ed59e2b1a74bcd9d58b03939adb020709fd32df1f26059c33f0f17

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.4.5.14.42.35-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: numpy_quaternion-2021.4.5.14.42.35-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 60.2 kB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.59.0 CPython/3.9.4

File hashes

Hashes for numpy_quaternion-2021.4.5.14.42.35-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 750c0251d1f32b8ee142bdf148ea748c549c282bd13ef3bdb8425508894f6b42
MD5 a8e102aebaf76e37ef042e52e1addabe
BLAKE2b-256 cd313c5a9bb5fc7b1d47a01da3a563df7767fe5f1d8f703e3221ce9acd794660

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.4.5.14.42.35-cp37-cp37m-win32.whl.

File metadata

  • Download URL: numpy_quaternion-2021.4.5.14.42.35-cp37-cp37m-win32.whl
  • Upload date:
  • Size: 55.9 kB
  • Tags: CPython 3.7m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.59.0 CPython/3.9.4

File hashes

Hashes for numpy_quaternion-2021.4.5.14.42.35-cp37-cp37m-win32.whl
Algorithm Hash digest
SHA256 8743aacfe19423a0547004975f28b985ec2f5211154be14644d29eb4713197b1
MD5 9169e0e74089828ed478e3ed2eb47b7a
BLAKE2b-256 81fa5e02c4fb62af622b3d109f40c071c58921a7cfa919bf5c9e36f221bb3f60

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.4.5.14.42.35-cp37-cp37m-manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2021.4.5.14.42.35-cp37-cp37m-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 dee09f850689ec9c5c25e1d1a532d9477660430b58e8f532249c8e51e6365933
MD5 db9d2e3e034c2f7b5ec78c5f40f0848f
BLAKE2b-256 8be4bf6fab7c2559da41fa96d22acf2a70e5378fc2df96bdd2146b484cf6cace

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.4.5.14.42.35-cp37-cp37m-manylinux2010_i686.whl.

File metadata

  • Download URL: numpy_quaternion-2021.4.5.14.42.35-cp37-cp37m-manylinux2010_i686.whl
  • Upload date:
  • Size: 161.7 kB
  • Tags: CPython 3.7m, manylinux: glibc 2.12+ i686
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.59.0 CPython/3.9.4

File hashes

Hashes for numpy_quaternion-2021.4.5.14.42.35-cp37-cp37m-manylinux2010_i686.whl
Algorithm Hash digest
SHA256 94943b2ba37011c594cf2b4c2117f53e2b81e425b9d755616807311ba89e6eba
MD5 e50837f1a126196d06a6376c179544f5
BLAKE2b-256 a9047a0709b784cd5e774cbb3be75cbf2fe6cce801e5fc778f506bcb1964e50f

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.4.5.14.42.35-cp37-cp37m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2021.4.5.14.42.35-cp37-cp37m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 05e3d42058c725079e004e245590952e1d2064a51e57ca8b73d4b075fcac5297
MD5 2a3306464a8b9ed389f95caecc99db8b
BLAKE2b-256 1c15f892329e6a0fe56d4d8202845fa6cd0405705bf785fe00fcb7b7cd73fa72

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.4.5.14.42.35-cp37-cp37m-manylinux1_i686.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2021.4.5.14.42.35-cp37-cp37m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 b2ef279aebe6e23f58c6daf30c29689fb53a85163de7ae40404b5e58071c93b0
MD5 adbc235b8c97c5a85395a11ee5266fb4
BLAKE2b-256 7086227dda9f211490151e55bc1980f554f1f1206f1cb90dacf5392c378acfb0

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.4.5.14.42.35-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2021.4.5.14.42.35-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 b56f7dd262e51773e1ea96a647efa94cddf1133a259f0d3fe9b7c34386732e5f
MD5 4cc221e3b8e67e76954e2c1094926dff
BLAKE2b-256 d540974e2e8cc7a8be494cca0aeb4d36ae8bc96555c0649e33d310a3abc0b5e3

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.4.5.14.42.35-cp36-cp36m-win_amd64.whl.

File metadata

  • Download URL: numpy_quaternion-2021.4.5.14.42.35-cp36-cp36m-win_amd64.whl
  • Upload date:
  • Size: 60.2 kB
  • Tags: CPython 3.6m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.59.0 CPython/3.9.4

File hashes

Hashes for numpy_quaternion-2021.4.5.14.42.35-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 b4285b73c820bade48dd1189cfcd9d17be228ad2b70681c416f1b2101fe577f8
MD5 9efcdc86cdf5588c66488f81ec7bbdfe
BLAKE2b-256 6b74d1c9f15880a19aecb9a3b79dda7053cda1034d0c46bc617bad83fe19ce29

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.4.5.14.42.35-cp36-cp36m-win32.whl.

File metadata

  • Download URL: numpy_quaternion-2021.4.5.14.42.35-cp36-cp36m-win32.whl
  • Upload date:
  • Size: 55.9 kB
  • Tags: CPython 3.6m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.59.0 CPython/3.9.4

File hashes

Hashes for numpy_quaternion-2021.4.5.14.42.35-cp36-cp36m-win32.whl
Algorithm Hash digest
SHA256 1a9293187460258d49dcbf273d0eb124712da62e3d6855f419d03ed85af81b45
MD5 f3397ca4d05096812ddbf5b7cea6b3f0
BLAKE2b-256 bfe8f253505c8bc8b2e4d76f703d8f0fd00ff6bd547e01b2b88d37682a6f6582

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.4.5.14.42.35-cp36-cp36m-manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2021.4.5.14.42.35-cp36-cp36m-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 b86d2aa462c7ee22d53e7b743be8d555f96d130fe362ce839fdb485543a9627a
MD5 468f04aa766951f7677aa979fe4927a8
BLAKE2b-256 42554d65a30a295be1aa1fc6f121034f7ed5073b102c491d19f1010817c4c764

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.4.5.14.42.35-cp36-cp36m-manylinux2010_i686.whl.

File metadata

  • Download URL: numpy_quaternion-2021.4.5.14.42.35-cp36-cp36m-manylinux2010_i686.whl
  • Upload date:
  • Size: 160.6 kB
  • Tags: CPython 3.6m, manylinux: glibc 2.12+ i686
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.10.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.59.0 CPython/3.9.4

File hashes

Hashes for numpy_quaternion-2021.4.5.14.42.35-cp36-cp36m-manylinux2010_i686.whl
Algorithm Hash digest
SHA256 ce7f70675a88f1079a9ae7d4a2943d87ea50c9eec3a832be91740d2d2bd62dba
MD5 fbf7a0a993b9c75dc953e76ea607d432
BLAKE2b-256 601c223b29af7e373c730eae2a0ff315a1f758cab48b91a13862d45370083a90

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.4.5.14.42.35-cp36-cp36m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2021.4.5.14.42.35-cp36-cp36m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 aeba4f1159ce93fff82dca6d1d15b405f8bb5816e123f3f669db767f534bbb07
MD5 97483c4366f6b2f9785349e043fb8c9e
BLAKE2b-256 7b5bd7e20205c4e46d7a2d617bab3050d1ffd154c91897763f89f4293149d3d2

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.4.5.14.42.35-cp36-cp36m-manylinux1_i686.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2021.4.5.14.42.35-cp36-cp36m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 9e99b362126a3bed4cdc3da7c3727e0a9e25ff8b13b66bae30e0f72423c5ad84
MD5 ede1c9b33fbfd55da98f9dfbf546352d
BLAKE2b-256 81fec33b17d4e86ad832ce45ce0d0e6845d76f1202cdb2a42b025f61527de2c0

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2021.4.5.14.42.35-cp36-cp36m-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2021.4.5.14.42.35-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 6cc3031547b5083457ea4db951ed4a1c4b3e44d6af47a7583fecd168c290de43
MD5 6e38ca23155457b9e6b2e0547a876987
BLAKE2b-256 d99d0e4fefef2dd9e510193f606bba4991f4a55a48abfe16b52333d0e4588f6c

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