Skip to main content

Utility functions for numpy, written in cython

Project description

A small package with fast numpy routines written in cython

Documentation

https://numpyx.readthedocs.io

Installation

pip install numpyx


Functions in this package

All functions here are specialized for double arrays only

Short-cut functions

These functions are similar to numpy functions but are faster by exiting out of a loop when one element satisfies the given condition

  • any_less_than

  • any_less_or_equal_than

  • any_greater_than

  • any_greater_or_equal_than

  • any_equal_to

  • array_is_sorted

  • allequal

minmax1d

Calculate min. and max. value in one go

searchsorted1

like search sorted, but for 1d double arrays. It is faster than the more generic numpy version

searchsorted2

like search sorted but allows to search across any column of a 2d array

nearestidx

Return the index of the item in an array which is nearest to a given value. The array does not need to be sorted (this is a simple linear search)

nearestitem

For any value of an array, search the nearest item in another array and put its value in the output result

weightedavg

Weighted averageof a time-series

trapz

trapz integration specialized for contiguous / double arrays. Quite faster than generic numpy/scipy

Project details


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.

numpyx-1.6.0-cp313-cp313-win_amd64.whl (111.6 kB view details)

Uploaded CPython 3.13Windows x86-64

numpyx-1.6.0-cp313-cp313-win32.whl (93.6 kB view details)

Uploaded CPython 3.13Windows x86

numpyx-1.6.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (671.9 kB view details)

Uploaded CPython 3.13manylinux: glibc 2.17+ x86-64manylinux: glibc 2.28+ x86-64

numpyx-1.6.0-cp313-cp313-macosx_11_0_arm64.whl (122.3 kB view details)

Uploaded CPython 3.13macOS 11.0+ ARM64

numpyx-1.6.0-cp313-cp313-macosx_10_13_x86_64.whl (126.6 kB view details)

Uploaded CPython 3.13macOS 10.13+ x86-64

numpyx-1.6.0-cp312-cp312-win_amd64.whl (111.6 kB view details)

Uploaded CPython 3.12Windows x86-64

numpyx-1.6.0-cp312-cp312-win32.whl (93.7 kB view details)

Uploaded CPython 3.12Windows x86

numpyx-1.6.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (679.3 kB view details)

Uploaded CPython 3.12manylinux: glibc 2.17+ x86-64manylinux: glibc 2.28+ x86-64

numpyx-1.6.0-cp312-cp312-macosx_11_0_arm64.whl (122.9 kB view details)

Uploaded CPython 3.12macOS 11.0+ ARM64

numpyx-1.6.0-cp312-cp312-macosx_10_13_x86_64.whl (127.4 kB view details)

Uploaded CPython 3.12macOS 10.13+ x86-64

numpyx-1.6.0-cp311-cp311-win_amd64.whl (111.1 kB view details)

Uploaded CPython 3.11Windows x86-64

numpyx-1.6.0-cp311-cp311-win32.whl (93.5 kB view details)

Uploaded CPython 3.11Windows x86

numpyx-1.6.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (679.0 kB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ x86-64manylinux: glibc 2.28+ x86-64

numpyx-1.6.0-cp311-cp311-macosx_11_0_arm64.whl (122.5 kB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

numpyx-1.6.0-cp311-cp311-macosx_10_9_x86_64.whl (125.6 kB view details)

Uploaded CPython 3.11macOS 10.9+ x86-64

numpyx-1.6.0-cp310-cp310-win_amd64.whl (111.1 kB view details)

Uploaded CPython 3.10Windows x86-64

numpyx-1.6.0-cp310-cp310-win32.whl (93.9 kB view details)

Uploaded CPython 3.10Windows x86

numpyx-1.6.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (650.4 kB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ x86-64manylinux: glibc 2.28+ x86-64

numpyx-1.6.0-cp310-cp310-macosx_11_0_arm64.whl (122.7 kB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

numpyx-1.6.0-cp310-cp310-macosx_10_9_x86_64.whl (126.3 kB view details)

Uploaded CPython 3.10macOS 10.9+ x86-64

numpyx-1.6.0-cp39-cp39-win_amd64.whl (111.4 kB view details)

Uploaded CPython 3.9Windows x86-64

numpyx-1.6.0-cp39-cp39-win32.whl (94.1 kB view details)

Uploaded CPython 3.9Windows x86

numpyx-1.6.0-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl (647.2 kB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ x86-64manylinux: glibc 2.28+ x86-64

numpyx-1.6.0-cp39-cp39-macosx_11_0_arm64.whl (123.0 kB view details)

Uploaded CPython 3.9macOS 11.0+ ARM64

numpyx-1.6.0-cp39-cp39-macosx_10_9_x86_64.whl (126.8 kB view details)

Uploaded CPython 3.9macOS 10.9+ x86-64

File details

Details for the file numpyx-1.6.0-cp313-cp313-win_amd64.whl.

File metadata

  • Download URL: numpyx-1.6.0-cp313-cp313-win_amd64.whl
  • Upload date:
  • Size: 111.6 kB
  • Tags: CPython 3.13, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.10

File hashes

Hashes for numpyx-1.6.0-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 be97a97ee479ed5561725eca298dffd8c2cdd9f9524a27db71e79d6ebebfef33
MD5 28b2ad7aa704de22dc722ee5dc38520a
BLAKE2b-256 2f3dfc8e4515430c4124032adf7e49e0a8174fb3a7c8e4f6ab34d8131972f434

See more details on using hashes here.

File details

Details for the file numpyx-1.6.0-cp313-cp313-win32.whl.

File metadata

  • Download URL: numpyx-1.6.0-cp313-cp313-win32.whl
  • Upload date:
  • Size: 93.6 kB
  • Tags: CPython 3.13, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.10

File hashes

Hashes for numpyx-1.6.0-cp313-cp313-win32.whl
Algorithm Hash digest
SHA256 372c6d9c49330634828368da4fd0a39a0f89a29a84c0104de8bb3a40bcb27539
MD5 8b858a900b2a0d97a4d26d73476fd77f
BLAKE2b-256 d8767e072fd16a96c9ed58f315b11e5af34864364ccaefbf17ca0137c18807e6

See more details on using hashes here.

File details

Details for the file numpyx-1.6.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for numpyx-1.6.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 242b8f4515040b4e1c43ded26020838fd9bccb2425294d474d1340e8cefed864
MD5 0e3aadae0d23922012e2008afed6f410
BLAKE2b-256 4ced8b75e35a4353b0c2152e8b2b2c7d568e3a7d2968317269b452b0d3fbba2e

See more details on using hashes here.

File details

Details for the file numpyx-1.6.0-cp313-cp313-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for numpyx-1.6.0-cp313-cp313-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 a1d5f721a5be02dbb9b2dd2219ce51bf5a8fe7d796523ac36a32a834821434af
MD5 d15b253e6aa745f980afa5fc46695b5b
BLAKE2b-256 820f7e38cba69d3a2162fbc6ca15814337fd01ec08ec43307e2286db7842f1c1

See more details on using hashes here.

File details

Details for the file numpyx-1.6.0-cp313-cp313-macosx_10_13_x86_64.whl.

File metadata

File hashes

Hashes for numpyx-1.6.0-cp313-cp313-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 f9e4158eafaf8e3a33831460414a5752945d5e4418034e8cc3545f6f1aedc58a
MD5 b047413ffe2637e07b790449991d1840
BLAKE2b-256 bdf7ba59450527576e8ddcfd380033ec42e5465b2d0486a233faa16aa4ea0d2c

See more details on using hashes here.

File details

Details for the file numpyx-1.6.0-cp312-cp312-win_amd64.whl.

File metadata

  • Download URL: numpyx-1.6.0-cp312-cp312-win_amd64.whl
  • Upload date:
  • Size: 111.6 kB
  • Tags: CPython 3.12, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.10

File hashes

Hashes for numpyx-1.6.0-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 1a9470753d406718abcaa72c4662b0c026d6fd7134dbbd30f1a9b554acd858be
MD5 35c1bd5b7b42db799d3dd2faef87f444
BLAKE2b-256 6799e860a6462cfd33698ee5e1b9319396d5b2442d1f01313d736bb9323c1884

See more details on using hashes here.

File details

Details for the file numpyx-1.6.0-cp312-cp312-win32.whl.

File metadata

  • Download URL: numpyx-1.6.0-cp312-cp312-win32.whl
  • Upload date:
  • Size: 93.7 kB
  • Tags: CPython 3.12, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.10

File hashes

Hashes for numpyx-1.6.0-cp312-cp312-win32.whl
Algorithm Hash digest
SHA256 c44e8070fcad2e881eb4877b22f6eaf047abec4eafd47244774fb0aa32128d88
MD5 7c1ed2215a823990610ef1aaf3388635
BLAKE2b-256 95a22e18901401f98e089d1153fe9061aa8234bb9cb01320bd13161200edf6b5

See more details on using hashes here.

File details

Details for the file numpyx-1.6.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for numpyx-1.6.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 3e5f5e2c9d469f85d6885a91a151af91d0c8cd1409d1e193c1f8b216835b4133
MD5 5937ec5355b175ea1359e9357935ac15
BLAKE2b-256 59044ae6ac0e30da4ccd6bbd1d5082d9f18a949a751582a70253022039186cfb

See more details on using hashes here.

File details

Details for the file numpyx-1.6.0-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for numpyx-1.6.0-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 be954b587f82e1e2f457f3dcfb1b8c87163c16327faede7598ea3af625ea52b9
MD5 ef7682c901289a398e083233558ac095
BLAKE2b-256 b3fb0a8a486fbbb20ed37c1fe1423028267e8f7b6e91d11060df5e9a781792c0

See more details on using hashes here.

File details

Details for the file numpyx-1.6.0-cp312-cp312-macosx_10_13_x86_64.whl.

File metadata

File hashes

Hashes for numpyx-1.6.0-cp312-cp312-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 a4020af2d68edb8c191cb2aec3d75ede64639f0be3e4b308e7697157e3b5f2de
MD5 8a511b115681369d48d5c93daa5546cf
BLAKE2b-256 bd852caf281a46aa689f9b6971426c0152730335a49d71ee51f3233155b21bfd

See more details on using hashes here.

File details

Details for the file numpyx-1.6.0-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: numpyx-1.6.0-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 111.1 kB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.10

File hashes

Hashes for numpyx-1.6.0-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 8f200adf8ea8e45e41ae3aa2b857d85f954ae54a11d1cec6d695b17a60f7ffe2
MD5 58e37a5a3d3bec0e60b9a3d42fdcd55f
BLAKE2b-256 adaa96c8106f72d680091204fa26ae396d782130ca85c7f24951f00fc998773f

See more details on using hashes here.

File details

Details for the file numpyx-1.6.0-cp311-cp311-win32.whl.

File metadata

  • Download URL: numpyx-1.6.0-cp311-cp311-win32.whl
  • Upload date:
  • Size: 93.5 kB
  • Tags: CPython 3.11, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.10

File hashes

Hashes for numpyx-1.6.0-cp311-cp311-win32.whl
Algorithm Hash digest
SHA256 89865c1b352f0185532909d02b92e889dfa5dc5e21f940ef38d969ac2adedf7e
MD5 0526a405558bd46426b1e86935c7c433
BLAKE2b-256 9d5f6d387975d6b69732b2998af92835994e52f1ecc7fc53998352504659403c

See more details on using hashes here.

File details

Details for the file numpyx-1.6.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for numpyx-1.6.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 3af81c895beffe06e67342e83d7e50d39c911d6f3f2ddb8004e3c0ac6ca37b30
MD5 0677e6c6771d3baec3d732656dd93530
BLAKE2b-256 6fbb182bbc475814d63b87efedf857cf6593cccca80e12e013650530dc766e28

See more details on using hashes here.

File details

Details for the file numpyx-1.6.0-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for numpyx-1.6.0-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 b35b4a0fcacdf72f772a256401d19d525f3f1c08e385bf2554f2628bfdb83b52
MD5 8dabeec5f5ef7d1c4ae09dc9f5b5121d
BLAKE2b-256 677448009c864a3ea334fb16adeb79ff82bc65f2537d554d28b04b9f376bb284

See more details on using hashes here.

File details

Details for the file numpyx-1.6.0-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for numpyx-1.6.0-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 9ec472c8c3724dff3e2bc7d527f9a612514c9c115066f358cd856dc3be181a43
MD5 772348598424c6470539ba5f3c377072
BLAKE2b-256 f94a9323867822a62b8e282e64f1316a66845555c14be32bfafef1e07ae48840

See more details on using hashes here.

File details

Details for the file numpyx-1.6.0-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: numpyx-1.6.0-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 111.1 kB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.10

File hashes

Hashes for numpyx-1.6.0-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 51704e0c9fb935256c6ec4ee50ac6d66c80982df946a10ec41d306c50a81d36d
MD5 59f8a98d09f0142301ec72575d398f9d
BLAKE2b-256 2d3c55d012bf0479b1ba0bed6b9af744d436b5b70a5d0f8f12f4be33dc2646d0

See more details on using hashes here.

File details

Details for the file numpyx-1.6.0-cp310-cp310-win32.whl.

File metadata

  • Download URL: numpyx-1.6.0-cp310-cp310-win32.whl
  • Upload date:
  • Size: 93.9 kB
  • Tags: CPython 3.10, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.10

File hashes

Hashes for numpyx-1.6.0-cp310-cp310-win32.whl
Algorithm Hash digest
SHA256 239f5cd71388d28547f673d27dc712270dbb70fbb3742aaced0fd602167eba95
MD5 200f85571e3787a21d019c6828a8d4fc
BLAKE2b-256 bfa811100e9cdea93d086077d12ea72ce9a3c962f5e1c10d9447be812416ee58

See more details on using hashes here.

File details

Details for the file numpyx-1.6.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for numpyx-1.6.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 dab097fca1ade13ac2bdb923c791f51bac78ced42bc562a9b2a28cc4d12d63f0
MD5 8d215548412f401d1804f675740572a3
BLAKE2b-256 af9d631b145dcdd246c5ba8e62b5afd3a7b3c73889e968db90dca3c339518fb1

See more details on using hashes here.

File details

Details for the file numpyx-1.6.0-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for numpyx-1.6.0-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 8f05fc3ece39c5e97d4af419f103f4db7c97b4cbf5b4f66d974e8020df4019ae
MD5 c8dc987a942a0e8167a3b155a2e4d7b4
BLAKE2b-256 c9c8b213df1c13ed4fc37f960a7790de279314f77e52f5ba054a1f8c51fc63c5

See more details on using hashes here.

File details

Details for the file numpyx-1.6.0-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for numpyx-1.6.0-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 c5d94d3679e499292b44f2f6bc1bcb14963c609c0c7e7019e58f90fdedff824b
MD5 ffd77c90a5797d7dec002ab3a2ff94ef
BLAKE2b-256 7ef4368c28544000aefb7d36754d00cfcef6be8fd866a59764be5936a1f80684

See more details on using hashes here.

File details

Details for the file numpyx-1.6.0-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: numpyx-1.6.0-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 111.4 kB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.10

File hashes

Hashes for numpyx-1.6.0-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 7f1eef1055c170e3c606287cfdacf4ed858fadd83a6a6251518c0fbab7c1a86c
MD5 b5748a100ac7a3d39ca3db2abaa2347e
BLAKE2b-256 dcbcd59b627f885e630ae5832c4a4cc24cf49a7fcf87cbbff25b3f30d0d6a20a

See more details on using hashes here.

File details

Details for the file numpyx-1.6.0-cp39-cp39-win32.whl.

File metadata

  • Download URL: numpyx-1.6.0-cp39-cp39-win32.whl
  • Upload date:
  • Size: 94.1 kB
  • Tags: CPython 3.9, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.10

File hashes

Hashes for numpyx-1.6.0-cp39-cp39-win32.whl
Algorithm Hash digest
SHA256 639e4bd454052eb16caae48b8c9291f136f48ea9ffec5dc87babc94b13f6db64
MD5 d17c534a3b2da702a5a118aed43ead67
BLAKE2b-256 a43ef8866981832f5a9030861480fc9b7df1bb27a5d18b496353cd5aef294dae

See more details on using hashes here.

File details

Details for the file numpyx-1.6.0-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for numpyx-1.6.0-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 e934c727080547babe116c77f3211ab266ce076ae3bb5050c2ecf52cfe78142c
MD5 111c8fb3054e541f8d3340eebb298bdc
BLAKE2b-256 2d1fb3d8eb323750d3ad4f30934ebd15984fc45d69503364bb5d83cccf5382b2

See more details on using hashes here.

File details

Details for the file numpyx-1.6.0-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for numpyx-1.6.0-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 35b08478b0f937bb38f204b86873f3c5b9bdd1875a3f1b45a8a2b5a9af0b1ee5
MD5 b09d0146f1f76f1dea296eaf76c0bb04
BLAKE2b-256 f6a8a464bfe7994036dabb1e5bf2a2c87a049eef6b3f474087f3b72c2704b8b3

See more details on using hashes here.

File details

Details for the file numpyx-1.6.0-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for numpyx-1.6.0-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 8dcebd3601157a0f07e6ec847cbb9efc7e7978e28ea4d52e365fed637258d4a0
MD5 721feefdcfdafa3de31c29b5e7a5d666
BLAKE2b-256 e57b688e3e69219f2c0d9c1bccd38c1524417f1b5353ec2d64c8c39b8810d35b

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