Add built-in support for quaternions 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

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

This version

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-2018.5.17.10.19.59.tar.gz (44.2 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-2018.5.17.10.19.59-cp36-cp36m-win_amd64.whl (52.9 kB view details)

Uploaded CPython 3.6mWindows x86-64

numpy_quaternion-2018.5.17.10.19.59-cp36-cp36m-manylinux1_x86_64.whl (160.8 kB view details)

Uploaded CPython 3.6m

numpy_quaternion-2018.5.17.10.19.59-cp36-cp36m-macosx_10_7_x86_64.whl (51.9 kB view details)

Uploaded CPython 3.6mmacOS 10.7+ x86-64

numpy_quaternion-2018.5.17.10.19.59-cp35-cp35m-win_amd64.whl (52.9 kB view details)

Uploaded CPython 3.5mWindows x86-64

numpy_quaternion-2018.5.17.10.19.59-cp35-cp35m-manylinux1_x86_64.whl (160.8 kB view details)

Uploaded CPython 3.5m

numpy_quaternion-2018.5.17.10.19.59-cp35-cp35m-macosx_10_6_x86_64.whl (52.0 kB view details)

Uploaded CPython 3.5mmacOS 10.6+ x86-64

numpy_quaternion-2018.5.17.10.19.59-cp34-cp34m-win_amd64.whl (49.8 kB view details)

Uploaded CPython 3.4mWindows x86-64

numpy_quaternion-2018.5.17.10.19.59-cp34-cp34m-manylinux1_x86_64.whl (160.6 kB view details)

Uploaded CPython 3.4m

numpy_quaternion-2018.5.17.10.19.59-cp27-cp27mu-manylinux1_x86_64.whl (159.6 kB view details)

Uploaded CPython 2.7mu

numpy_quaternion-2018.5.17.10.19.59-cp27-cp27m-win_amd64.whl (46.7 kB view details)

Uploaded CPython 2.7mWindows x86-64

numpy_quaternion-2018.5.17.10.19.59-cp27-cp27m-manylinux1_x86_64.whl (159.5 kB view details)

Uploaded CPython 2.7m

numpy_quaternion-2018.5.17.10.19.59-cp27-cp27m-macosx_10_6_x86_64.whl (51.8 kB view details)

Uploaded CPython 2.7mmacOS 10.6+ x86-64

File details

Details for the file numpy-quaternion-2018.5.17.10.19.59.tar.gz.

File metadata

File hashes

Hashes for numpy-quaternion-2018.5.17.10.19.59.tar.gz
Algorithm Hash digest
SHA256 c14d448cb590430a98d1cef8a13b4ac2328653e9f8a20b1f978eef09352ebe26
MD5 c6bbbe93a55420256a12a9c6019ad487
BLAKE2b-256 9d7ed8dfe1a1cda9530da0be75c0b000c2ee1f8f3cb274f42b84f3a5e1ba1aef

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2018.5.17.10.19.59-cp36-cp36m-win_amd64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2018.5.17.10.19.59-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 9ed023dac963eac2880ff24267482aa95b78515741cb132d73199bbee31a5f20
MD5 8756853168f0aeab6a4242223e0815a8
BLAKE2b-256 946bb912807892ae87ca4a645e66f42a194448aaa68fcd513329ccec307438aa

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2018.5.17.10.19.59-cp36-cp36m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2018.5.17.10.19.59-cp36-cp36m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 af3f747b1d83f30c76f0b29ee05fb7a48daa4cd163846850ca1498572f78ce15
MD5 0aa02f10a330a501c185e3d191b67128
BLAKE2b-256 d4b0090f387787ed956a161525ee2766183f055f594a1281a66b33083547b55d

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2018.5.17.10.19.59-cp36-cp36m-macosx_10_7_x86_64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2018.5.17.10.19.59-cp36-cp36m-macosx_10_7_x86_64.whl
Algorithm Hash digest
SHA256 8c6befc8e18f94d4aa3ad5756edf2321651b107e1e006d380887cfcbeb1cf43b
MD5 324755492d13e1d264cf525e313a27be
BLAKE2b-256 cd4f02448a5584635b72e34c439c1e2fb3b0d5f70074ac968505ecca57e95ff6

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2018.5.17.10.19.59-cp35-cp35m-win_amd64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2018.5.17.10.19.59-cp35-cp35m-win_amd64.whl
Algorithm Hash digest
SHA256 2c09d7f229362ed5b82f33223eb8c7a8d75ae8782af2ef4ca81d96ef82d8efc5
MD5 5fd502d6804b7c4aa40cc0ee52f1c16f
BLAKE2b-256 a8424576dfa48f571c2da0b102860d8f5ebf1f8b6f6b6df517d733d44e0f561f

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2018.5.17.10.19.59-cp35-cp35m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2018.5.17.10.19.59-cp35-cp35m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 235e72e1949dc24f9bb812eb1b204827c11d62fd21a40413824d49263f04e33b
MD5 3448d8b7663a6af532be856e305dd793
BLAKE2b-256 8c0a18b9b079a9b8422e877946422c5199da069d9496615eccd53648e90584d8

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2018.5.17.10.19.59-cp35-cp35m-macosx_10_6_x86_64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2018.5.17.10.19.59-cp35-cp35m-macosx_10_6_x86_64.whl
Algorithm Hash digest
SHA256 8d7018606ed6548233da3360225b76f6af723206dacc48033792c8cc69534323
MD5 7233e22daa85968e04c74c068ade3758
BLAKE2b-256 2b161462c4a16862355b0c570177e18c65735d9891d3490dbc1ba8a3704a67e0

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2018.5.17.10.19.59-cp34-cp34m-win_amd64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2018.5.17.10.19.59-cp34-cp34m-win_amd64.whl
Algorithm Hash digest
SHA256 34e0e6b1b17fd34229a9e8cb865e112c6037503e06cd6ad86c3da4581f53d01e
MD5 6cb38e790bf031fb2a3231df2b30cefc
BLAKE2b-256 762f317699ae2cb68e013c48685706f82fcbadd12bea672da7d8cbb104770279

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2018.5.17.10.19.59-cp34-cp34m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2018.5.17.10.19.59-cp34-cp34m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 50cbba5546fd3800dd5f56e754df218b6af8751868ad2e2899ff434bf0bd00a4
MD5 d80963a1d5422308d2e2e862267999bd
BLAKE2b-256 085c22c8e0c023b180e2153ec5310a7574f69440a34badf257e0790c05cd0ad5

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2018.5.17.10.19.59-cp27-cp27mu-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2018.5.17.10.19.59-cp27-cp27mu-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 4fee27709e597f7391bc20547fed814dd223b47555d5f329f2a27848cb2f5438
MD5 770aab08e1ba90354446871a24cf6138
BLAKE2b-256 a3bfbefe391b27f65e06f22a1cb141e21eb87bccf5a52059428db96351539466

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2018.5.17.10.19.59-cp27-cp27m-win_amd64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2018.5.17.10.19.59-cp27-cp27m-win_amd64.whl
Algorithm Hash digest
SHA256 a2883c5b0655dcc04a1ab0096402bbf2f76498cc4e58cc0f636d6e2379abce67
MD5 93ab28397e1f9b0d054b8c66fad02385
BLAKE2b-256 8be374b10f758d590b17cffe2a1bcc23ea6770750641d76730a2ec25bc9c399f

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2018.5.17.10.19.59-cp27-cp27m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2018.5.17.10.19.59-cp27-cp27m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 e03a31f309d535b5e0a6f45b4f7d9db249f408d3013828c8bd4186477813b47e
MD5 aeeb33e23c497a4f7b0b42770307d3c0
BLAKE2b-256 e6d58b2fff06d4c3705de37492ea53fb416a0f4fc1a162c9ffb4f2f57a7d3685

See more details on using hashes here.

File details

Details for the file numpy_quaternion-2018.5.17.10.19.59-cp27-cp27m-macosx_10_6_x86_64.whl.

File metadata

File hashes

Hashes for numpy_quaternion-2018.5.17.10.19.59-cp27-cp27m-macosx_10_6_x86_64.whl
Algorithm Hash digest
SHA256 0ec4ab217da4cf5146daf1d7f2fac2c8e9724686150952f22764b3467524bd10
MD5 1dfafc9049106f414d71b78f9cab8cf2
BLAKE2b-256 4d8b11f393d9fe7cc39163bf6698c3cff70c332af23dc5c2aed4fe41fd677856

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