Library with some C and Cython code for speeding up common operations. This is externalized to avoid the hassle of building binary wheels in my other projects.

Navigation

Verified details

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

Unverified details

These details have not been verified by PyPI
Project links
Meta

Project description

This library contains a number of functions for speeding up critical parts of your Python code without having to bother with the hassle of building binary extensions. That way you can keep your main packages simple PEP517 based packages and still get the speedups.

Currently only a few functions are available, but several more are planned.

Generic endian conversion functions in speedups.hton:

  • void pack_int16(char *buf, int16_t x)

  • void pack_int32(char *buf, int32_t x)

  • void pack_int64(char *buf, int64_t x)

  • void pack_float(char *buf, float f)

  • void pack_double(char *buf, double f)

  • int16_t unpack_int16(const char *buf)

  • uint16_t unpack_uint16(const char *buf)

  • int32_t unpack_int32(const char *buf)

  • uint32_t unpack_uint32(const char *buf)

  • int64_t unpack_int64(const char *buf)

  • uint64_t unpack_uint64(const char *buf)

  • float unpack_float(const char *buf)

  • double unpack_double(const char *buf)

These functions are used to convert between native and network byte order and are meant to be used from Cython code. Examples can be found in the speedups.psycopg_array code.

For the psycopg library we have a binary COPY loader to convert a PostgreSQL array to a numpy ndarray. This can be used with the copy() method of a psycopg cursor: https://www.psycopg.org/psycopg3/docs/basic/copy.html

It supports the following PostgreSQL types:

  • float4 (numpy.float32)

  • float8 (numpy.float64)

  • smallint (numpy.int16)

  • integer (numpy.int32)

  • bigint (numpy.int64)

Additionally, it supports arrays varying from 1D to N-D so a 2D or 3D array are supported.

cursor: psycopg.Cursor
psycopg_loaders.NumpyLoader.install(cursor)

query = '''
COPY (
    SELECT array_agg(x)
    FROM generate_series(1, 100000) x
) TO STDOUT WITH BINARY
'''

copy: psycopg.Copy
with cursor.copy(query) as copy:
    copy.set_types(['integer[]'])

    for row in copy.rows():
        print(row)

Project details

Verified details

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

Unverified details

These details have not been verified by PyPI
Project links
Meta

Release history Release notifications | RSS feed

1.4.0

1.3.2

1.3.0

This version

1.2.0

1.1.0

1.0.0

Download files

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

Source Distribution

speedups-1.2.0.tar.gz (168.8 kB view details)

Uploaded Source

Built Distributions

speedups-1.2.0-cp311-cp311-win_amd64.whl (256.1 kB view details)

Uploaded CPython 3.11 Windows x86-64

speedups-1.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (789.4 kB view details)

Uploaded CPython 3.11 manylinux: glibc 2.17+ x86-64

speedups-1.2.0-cp311-cp311-macosx_10_9_x86_64.whl (270.1 kB view details)

Uploaded CPython 3.11 macOS 10.9+ x86-64

speedups-1.2.0-cp310-cp310-win_amd64.whl (255.3 kB view details)

Uploaded CPython 3.10 Windows x86-64

speedups-1.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (732.5 kB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

speedups-1.2.0-cp310-cp310-macosx_12_0_x86_64.whl (268.1 kB view details)

Uploaded CPython 3.10 macOS 12.0+ x86-64

speedups-1.2.0-cp310-cp310-macosx_10_9_x86_64.whl (269.7 kB view details)

Uploaded CPython 3.10 macOS 10.9+ x86-64

speedups-1.2.0-cp39-cp39-win_amd64.whl (255.8 kB view details)

Uploaded CPython 3.9 Windows x86-64

speedups-1.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (738.0 kB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

speedups-1.2.0-cp39-cp39-macosx_10_9_x86_64.whl (270.2 kB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

speedups-1.2.0-cp38-cp38-win_amd64.whl (255.9 kB view details)

Uploaded CPython 3.8 Windows x86-64

speedups-1.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (744.7 kB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

speedups-1.2.0-cp38-cp38-macosx_10_9_x86_64.whl (269.8 kB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

speedups-1.2.0-cp37-cp37m-win_amd64.whl (254.7 kB view details)

Uploaded CPython 3.7m Windows x86-64

speedups-1.2.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (695.7 kB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ x86-64

speedups-1.2.0-cp37-cp37m-macosx_10_9_x86_64.whl (269.5 kB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

File details

Details for the file speedups-1.2.0.tar.gz.

File metadata

  • Download URL: speedups-1.2.0.tar.gz
  • Upload date:
  • Size: 168.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.4

File hashes

Hashes for speedups-1.2.0.tar.gz
Algorithm Hash digest
SHA256 f44d064bf7ae214b7fa158ea9a81e6e959cff8e45adb44ff16f0526959d5dda7
MD5 413f69266b986cd44f1591e6da19dda7
BLAKE2b-256 d29366d018791a09dc7197bb37deddcb677e13d3567d753dc911f4ad2f1f5f6d

See more details on using hashes here.

File details

Details for the file speedups-1.2.0-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: speedups-1.2.0-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 256.1 kB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.4

File hashes

Hashes for speedups-1.2.0-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 f2b00c0efba16193b896de7883f4763be2ada4a4b242ae8a50eeeb6d31a3c829
MD5 436dd6fb0c753ec4e90d45ff7e631126
BLAKE2b-256 54d863f923857b6df582c75cdf69bad6fb95b469cc71936825b88604622b4afc

See more details on using hashes here.

File details

Details for the file speedups-1.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for speedups-1.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 3891d448a0e6c17539639f60b9c7e7fdc2ae05c779ce1ca3477e8c9c901ce4c7
MD5 8d26252b168d53adad636d2f3d553e43
BLAKE2b-256 5677a04ae4cd5a131d242dc9cbf13a842c555f7cfb31d7ff2dd60088208a9153

See more details on using hashes here.

File details

Details for the file speedups-1.2.0-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for speedups-1.2.0-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 352193d2fb88fbb91b169f43f39256f9088ab3b04e00dd4e7fddb3bb48b66c75
MD5 67cb4709a1eef9dc0230437b0262f140
BLAKE2b-256 b37d28d1c86616c6c9dd56d5920158c02127876443f06a0da24ca3f855eb1707

See more details on using hashes here.

File details

Details for the file speedups-1.2.0-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: speedups-1.2.0-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 255.3 kB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.4

File hashes

Hashes for speedups-1.2.0-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 1dbc05420fc3fc64b5906d7cc6a7bf8aeac6041953133318f5e56db78f333292
MD5 5fbb2151e31eb4c27af3023c9f820012
BLAKE2b-256 832698c3e6e8443ccf083ba31c81613584e5135adcc8d825493cdb61f2a68d64

See more details on using hashes here.

File details

Details for the file speedups-1.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for speedups-1.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 f6c405449051434e810193086240c71cc37a831904cd9c065bf49145ecc1845d
MD5 cb111e3df871279545d10d827819396a
BLAKE2b-256 e278c57e452db7370a790adf597389da478edef9ce78bf84a61f4d2fc3e2be49

See more details on using hashes here.

File details

Details for the file speedups-1.2.0-cp310-cp310-macosx_12_0_x86_64.whl.

File metadata

File hashes

Hashes for speedups-1.2.0-cp310-cp310-macosx_12_0_x86_64.whl
Algorithm Hash digest
SHA256 293bc46f6ab538f95444c2bbf8893e0573e959169932cac18fa4e831790ae9b0
MD5 cf8ede8ebfac5a5ac2d7717307f11891
BLAKE2b-256 ee051d15fdf6fb083cd6ade5d16921165844d9496c31f5a9e9742a04d3580858

See more details on using hashes here.

File details

Details for the file speedups-1.2.0-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for speedups-1.2.0-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 6127be1dedc82c163f53d6d53ef45b0bdd6b1092df3c75df9ed61b1d24d1055f
MD5 443ea87d61fabf4c1d692ec3f2488211
BLAKE2b-256 d16e8c4e175adf71ad88c24a3a6bd2042da5fc870b493338a2f3fce954441aab

See more details on using hashes here.

File details

Details for the file speedups-1.2.0-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: speedups-1.2.0-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 255.8 kB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.4

File hashes

Hashes for speedups-1.2.0-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 6b89a06e62ef11c1eac87321ffdb99c2a2d3e310fc55d0af72a55321bd23394a
MD5 607f98a0101dcce46c16a2892f7a1e3d
BLAKE2b-256 be151eb93368d7330fc8396c8be655c4bdcdf8e8490a40940ccc5d18064a44a4

See more details on using hashes here.

File details

Details for the file speedups-1.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for speedups-1.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 9e519e7b53b8b43a88e76070c1718922838f5397143b62331d9eeeb1456dc1a1
MD5 3d0ceef7abf62c74c184671f7cb2c6a2
BLAKE2b-256 75a54fb243432c8c0a5cb4c6c5d13932271e0c92f73e94f1523998bce435840c

See more details on using hashes here.

File details

Details for the file speedups-1.2.0-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for speedups-1.2.0-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 7578020006362620249e2cda9705be49c521161f2c5347407f47c63f27d6fb49
MD5 d445296d441220328ed845ae78060fe2
BLAKE2b-256 8d5aa7d8337bcc6c14eb17f1c697d78d46dbf43b24f0451562a394024bffa438

See more details on using hashes here.

File details

Details for the file speedups-1.2.0-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: speedups-1.2.0-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 255.9 kB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.4

File hashes

Hashes for speedups-1.2.0-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 d84e003dee8c8dedd1fba26b22938fcb9ebc2294253bc76bde9a81da6410ebc1
MD5 3de8bd86c420f3eee6bd9d12de35fa14
BLAKE2b-256 1cd561572b903943e0bbd1d2a29afd50eaf55d419ab8e04ff28620a4154cd784

See more details on using hashes here.

File details

Details for the file speedups-1.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for speedups-1.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 e192bd3b6d3c1e4008ff324cc1a5aa52ec86121e1a73f4495c683ca268c90be8
MD5 bc0f51af745d4667ac78ecf6a8cd00cd
BLAKE2b-256 532356c60c80a74a7a9e9064ad8be929cf8f61d77eded42bd8ed5c8131f7549e

See more details on using hashes here.

File details

Details for the file speedups-1.2.0-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for speedups-1.2.0-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 90a81f00436ce8ea32b0bfcbdaef74f3391ecd6611a10d1461ccddac828c3aa0
MD5 bea5a04c67a72c6732586c2d97a6dba3
BLAKE2b-256 26d2a827a1f484d5477459a69684fb75368f4e52e48d52f0faf519c365f80c9a

See more details on using hashes here.

File details

Details for the file speedups-1.2.0-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: speedups-1.2.0-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 254.7 kB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.4

File hashes

Hashes for speedups-1.2.0-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 b539475b8c4fb94f23d820ae951bef02bb299aa221aca43cc4907668d8053f56
MD5 d40199e7299d0e9a66add241fed73be1
BLAKE2b-256 6a77f80d8913cdeb771b6e86d8864d8f2a0971bc1306be1f5d08953f13877e25

See more details on using hashes here.

File details

Details for the file speedups-1.2.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for speedups-1.2.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 55e342815108a7ab4b64e9991442e33d9831efbcfe89a7f3ce809f27000e022c
MD5 5bce4e9663affb096e82cb4ff901d979
BLAKE2b-256 332ee96fe12992c76068dcf0b2dd929fa090079fd50fd980478270acc0f3b7b2

See more details on using hashes here.

File details

Details for the file speedups-1.2.0-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for speedups-1.2.0-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 b862fc06dfc9f4a90066ab8c53f329d9c4178b344d149b5ce273817133e0d423
MD5 e5a3b83d966c974c40af7afa1a316a2e
BLAKE2b-256 6c526b5be88801623eaa9b18cf443a5491ed8fb84b33bd77da752516e3f2aadb

See more details on using hashes here.

Supported by

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