Skip to main content

PyWavelets, wavelet transform module

Project description

PyWavelets is a Python wavelet transforms module that includes:

  • nD Forward and Inverse Discrete Wavelet Transform (DWT and IDWT)

  • 1D and 2D Forward and Inverse Stationary Wavelet Transform (Undecimated Wavelet Transform)

  • 1D and 2D Wavelet Packet decomposition and reconstruction

  • 1D Continuous Wavelet Tranfsorm

  • Computing Approximations of wavelet and scaling functions

  • Over 100 built-in wavelet filters and support for custom wavelets

  • Single and double precision calculations

  • Results compatibility with Matlab Wavelet Toolbox (tm)

Project details


Download files

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

Source Distribution

PyWavelets-0.5.2.tar.gz (4.4 MB view details)

Uploaded Source

Built Distributions

PyWavelets-0.5.2-cp37-none-win_amd64.whl (4.2 MB view details)

Uploaded CPython 3.7 Windows x86-64

PyWavelets-0.5.2-cp37-none-win32.whl (4.1 MB view details)

Uploaded CPython 3.7 Windows x86

PyWavelets-0.5.2-cp37-cp37m-manylinux1_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.7m

PyWavelets-0.5.2-cp37-cp37m-manylinux1_i686.whl (4.2 MB view details)

Uploaded CPython 3.7m

PyWavelets-0.5.2-cp37-cp37m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (4.8 MB view details)

Uploaded CPython 3.7m macOS 10.10+ intel macOS 10.10+ x86-64 macOS 10.6+ intel macOS 10.9+ intel macOS 10.9+ x86-64

PyWavelets-0.5.2-cp36-none-win_amd64.whl (4.2 MB view details)

Uploaded CPython 3.6 Windows x86-64

PyWavelets-0.5.2-cp36-none-win32.whl (4.1 MB view details)

Uploaded CPython 3.6 Windows x86

PyWavelets-0.5.2-cp36-cp36m-manylinux1_x86_64.whl (5.7 MB view details)

Uploaded CPython 3.6m

PyWavelets-0.5.2-cp36-cp36m-manylinux1_i686.whl (5.6 MB view details)

Uploaded CPython 3.6m

PyWavelets-0.5.2-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (4.8 MB view details)

Uploaded CPython 3.6m macOS 10.10+ intel macOS 10.10+ x86-64 macOS 10.6+ intel macOS 10.9+ intel macOS 10.9+ x86-64

PyWavelets-0.5.2-cp35-none-win_amd64.whl (4.2 MB view details)

Uploaded CPython 3.5 Windows x86-64

PyWavelets-0.5.2-cp35-none-win32.whl (4.1 MB view details)

Uploaded CPython 3.5 Windows x86

PyWavelets-0.5.2-cp35-cp35m-manylinux1_x86_64.whl (5.7 MB view details)

Uploaded CPython 3.5m

PyWavelets-0.5.2-cp35-cp35m-manylinux1_i686.whl (5.5 MB view details)

Uploaded CPython 3.5m

PyWavelets-0.5.2-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (4.8 MB view details)

Uploaded CPython 3.5m macOS 10.10+ intel macOS 10.10+ x86-64 macOS 10.6+ intel macOS 10.9+ intel macOS 10.9+ x86-64

PyWavelets-0.5.2-cp34-none-win_amd64.whl (4.1 MB view details)

Uploaded CPython 3.4 Windows x86-64

PyWavelets-0.5.2-cp34-none-win32.whl (4.1 MB view details)

Uploaded CPython 3.4 Windows x86

PyWavelets-0.5.2-cp34-cp34m-manylinux1_x86_64.whl (5.7 MB view details)

Uploaded CPython 3.4m

PyWavelets-0.5.2-cp34-cp34m-manylinux1_i686.whl (5.6 MB view details)

Uploaded CPython 3.4m

PyWavelets-0.5.2-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (4.8 MB view details)

Uploaded CPython 3.4m macOS 10.10+ intel macOS 10.10+ x86-64 macOS 10.6+ intel macOS 10.9+ intel macOS 10.9+ x86-64

PyWavelets-0.5.2-cp27-none-win_amd64.whl (4.2 MB view details)

Uploaded CPython 2.7 Windows x86-64

PyWavelets-0.5.2-cp27-none-win32.whl (4.1 MB view details)

Uploaded CPython 2.7 Windows x86

PyWavelets-0.5.2-cp27-cp27mu-manylinux1_x86_64.whl (5.6 MB view details)

Uploaded CPython 2.7mu

PyWavelets-0.5.2-cp27-cp27mu-manylinux1_i686.whl (5.5 MB view details)

Uploaded CPython 2.7mu

PyWavelets-0.5.2-cp27-cp27m-manylinux1_x86_64.whl (5.6 MB view details)

Uploaded CPython 2.7m

PyWavelets-0.5.2-cp27-cp27m-manylinux1_i686.whl (5.5 MB view details)

Uploaded CPython 2.7m

PyWavelets-0.5.2-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (4.8 MB view details)

Uploaded CPython 2.7m macOS 10.10+ intel macOS 10.10+ x86-64 macOS 10.6+ intel macOS 10.9+ intel macOS 10.9+ x86-64

File details

Details for the file PyWavelets-0.5.2.tar.gz.

File metadata

  • Download URL: PyWavelets-0.5.2.tar.gz
  • Upload date:
  • Size: 4.4 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for PyWavelets-0.5.2.tar.gz
Algorithm Hash digest
SHA256 ce36e2f0648ea1781490b09515363f1f64446b0eac524603e5db5e180113bed9
MD5 aedda732f064cf9395f03d37f1003d1a
BLAKE2b-256 4bdf3fff2a8b96ef7df6e4e8642fb7569c3717ae562dd76afe0f96525c0af784

See more details on using hashes here.

File details

Details for the file PyWavelets-0.5.2-cp37-none-win_amd64.whl.

File metadata

  • Download URL: PyWavelets-0.5.2-cp37-none-win_amd64.whl
  • Upload date:
  • Size: 4.2 MB
  • Tags: CPython 3.7, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.18.4 setuptools/38.5.2 requests-toolbelt/0.8.0 tqdm/4.19.5 CPython/3.6.4

File hashes

Hashes for PyWavelets-0.5.2-cp37-none-win_amd64.whl
Algorithm Hash digest
SHA256 2ba7fda7bca875a2e2f07ad40802da20d78aa197a35d133d24c4f642d1334e15
MD5 79d27a72112c7df6a355671d789f01ea
BLAKE2b-256 1193d915a2bb674d06588075254f65d2f866cdf92ca1e137d6300535a049abe3

See more details on using hashes here.

File details

Details for the file PyWavelets-0.5.2-cp37-none-win32.whl.

File metadata

  • Download URL: PyWavelets-0.5.2-cp37-none-win32.whl
  • Upload date:
  • Size: 4.1 MB
  • Tags: CPython 3.7, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.18.4 setuptools/38.5.2 requests-toolbelt/0.8.0 tqdm/4.19.5 CPython/3.6.4

File hashes

Hashes for PyWavelets-0.5.2-cp37-none-win32.whl
Algorithm Hash digest
SHA256 b75ab51470f8837ee8b374bfa4b5eb5a6da384de1ed3f38cd5d193423171ad62
MD5 37a5ce1f1f2d7bd97de87ef917a5bb13
BLAKE2b-256 e2bab9fa0fe16bbfcc2184b0d3a26d6ef30f77aac419ff08a422ffd9a7543f0b

See more details on using hashes here.

File details

Details for the file PyWavelets-0.5.2-cp37-cp37m-manylinux1_x86_64.whl.

File metadata

  • Download URL: PyWavelets-0.5.2-cp37-cp37m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 4.3 MB
  • Tags: CPython 3.7m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.18.4 setuptools/38.5.2 requests-toolbelt/0.8.0 tqdm/4.19.5 CPython/3.6.4

File hashes

Hashes for PyWavelets-0.5.2-cp37-cp37m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 2898fcb09a1bf4f31001bc6b2a5b7b908f17f32c8b1b44ff67d70c5e7ece9f3d
MD5 9a274ba46d71e3ea34a0cccd1957c9fc
BLAKE2b-256 fcaec8956ce5c6112bed06e9700d01c32a3e740d885fe511da0ebb0c0377ce8d

See more details on using hashes here.

File details

Details for the file PyWavelets-0.5.2-cp37-cp37m-manylinux1_i686.whl.

File metadata

  • Download URL: PyWavelets-0.5.2-cp37-cp37m-manylinux1_i686.whl
  • Upload date:
  • Size: 4.2 MB
  • Tags: CPython 3.7m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.18.4 setuptools/38.5.2 requests-toolbelt/0.8.0 tqdm/4.19.5 CPython/3.6.4

File hashes

Hashes for PyWavelets-0.5.2-cp37-cp37m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 1cbbeb95aef2ad45948e357932f88d701bba74ecbad461fba0670bcbd1015b3a
MD5 ee1b6223f9365850c294f1da76bd0bcc
BLAKE2b-256 12e4f90aeecfc69707e55d5d38e45d993439758e59a11f1c67ceb52e60ea5ff5

See more details on using hashes here.

File details

Details for the file PyWavelets-0.5.2-cp37-cp37m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl.

File metadata

File hashes

Hashes for PyWavelets-0.5.2-cp37-cp37m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
Algorithm Hash digest
SHA256 b28f8a6792f237a18c416d33ceba6995c14e5f1b3f678a2997e5a9dd34251462
MD5 d00e72ebaaf379f9270433011152687d
BLAKE2b-256 86f085ee33ca539481ff487396bcf2391861a103954b1b866d7fafc1be14db5b

See more details on using hashes here.

File details

Details for the file PyWavelets-0.5.2-cp36-none-win_amd64.whl.

File metadata

File hashes

Hashes for PyWavelets-0.5.2-cp36-none-win_amd64.whl
Algorithm Hash digest
SHA256 8287961e62f4049491b4ae6d64da71bf98acdbbe593a62c6ceb0be5f06f4d253
MD5 ec9a289d16311d02ceed0dbd51b02330
BLAKE2b-256 30cf36a939614f09ca09ac3d33f00786152ee7f422f6ee4490b06a99da6723ee

See more details on using hashes here.

File details

Details for the file PyWavelets-0.5.2-cp36-none-win32.whl.

File metadata

File hashes

Hashes for PyWavelets-0.5.2-cp36-none-win32.whl
Algorithm Hash digest
SHA256 2e596bbc4e9bc0a544cecdb1c2355ebda73e5a5afc25b20dfa2cded8ef20c94c
MD5 16fa44d2ade7b3d1268d9821ac261b68
BLAKE2b-256 b2fda0165c1f7bbfb6cc38a90bffb6595d8b23148d24907084085301e5c0c64a

See more details on using hashes here.

File details

Details for the file PyWavelets-0.5.2-cp36-cp36m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for PyWavelets-0.5.2-cp36-cp36m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 f801fa177f2756da4d7c25ff49f0f09bf56adbdfb1e05582f377948d2faf18de
MD5 f48eeb07e0b7cd7212fa8089f9b5f578
BLAKE2b-256 32c03646053c0ce297686da524bc968bff6017151a9089d16c33afe7d330a48b

See more details on using hashes here.

File details

Details for the file PyWavelets-0.5.2-cp36-cp36m-manylinux1_i686.whl.

File metadata

File hashes

Hashes for PyWavelets-0.5.2-cp36-cp36m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 4c4aa204cc2cee71f5ed1302caa69a0a5e1d2b36ad56997b7f64e56cf8415a78
MD5 a2271c65dc3ccba485c622195fcfea1a
BLAKE2b-256 0a5e7fc40595eacefef86ed4658e52061b5bd078de4c4cb822f707eea87ff51b

See more details on using hashes here.

File details

Details for the file PyWavelets-0.5.2-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl.

File metadata

File hashes

Hashes for PyWavelets-0.5.2-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
Algorithm Hash digest
SHA256 3cdf80fec5d93ccf5d529e4a481a4a318b14b44624cc8857686e7c682f73e706
MD5 8be6b7083fe5709446ed5dddbff6d61b
BLAKE2b-256 2b1d144102df8f659e08ae52d7faba608dab06c5aa11831f36df2e2c79780a87

See more details on using hashes here.

File details

Details for the file PyWavelets-0.5.2-cp35-none-win_amd64.whl.

File metadata

File hashes

Hashes for PyWavelets-0.5.2-cp35-none-win_amd64.whl
Algorithm Hash digest
SHA256 6607067b46ecbac6f6f2ebca83bcc20e51d77c022c6f033f62b9ec173eee5f60
MD5 8adc428fa95572f88966375ca2163b6b
BLAKE2b-256 5fe98cbf2954a909f42e31136d8f1e8d0ce9069d56d88abd82f862039a869e70

See more details on using hashes here.

File details

Details for the file PyWavelets-0.5.2-cp35-none-win32.whl.

File metadata

File hashes

Hashes for PyWavelets-0.5.2-cp35-none-win32.whl
Algorithm Hash digest
SHA256 15ae04312c007501b316d24de41f8df9c2a0fa7089e3724d584cd7b1cfd8fcae
MD5 24bd4c961db155e1934c4cfdf3fcc468
BLAKE2b-256 354782e3fe96a1c2c564532bca8b3abbe36ce4e5f6998038cb8d3822321db283

See more details on using hashes here.

File details

Details for the file PyWavelets-0.5.2-cp35-cp35m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for PyWavelets-0.5.2-cp35-cp35m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 c8795dcabedf97b3ad4978102140c40ab8f22579eb1e7516d251d57a58dd46c1
MD5 9abef3e74f4f23224e5d24ef96bb6819
BLAKE2b-256 5a1f203c8886c7b94286ddbbf418779f4303774b65c20474a8b554598c483e90

See more details on using hashes here.

File details

Details for the file PyWavelets-0.5.2-cp35-cp35m-manylinux1_i686.whl.

File metadata

File hashes

Hashes for PyWavelets-0.5.2-cp35-cp35m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 1b9f0af21aa8b211cda4ff4083478a9fb66ad5a3f2c06300ac84fd9377d95dfc
MD5 e6926301262f165ca400bd57bda9b003
BLAKE2b-256 de4e283ef79f689213ac52630fd33bc71c325e9fe494f87b5a3b119bb188a61c

See more details on using hashes here.

File details

Details for the file PyWavelets-0.5.2-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl.

File metadata

File hashes

Hashes for PyWavelets-0.5.2-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
Algorithm Hash digest
SHA256 b326fd1788beaf11cb14c7c336808f46a01c43e40294070a906eedd865b67d5f
MD5 cd396b2dc9ee7589f39ad0ef190cbcbe
BLAKE2b-256 7fa273410994198f784104034bacfabeaa0e5d391a5be472f9da7727bed05cfe

See more details on using hashes here.

File details

Details for the file PyWavelets-0.5.2-cp34-none-win_amd64.whl.

File metadata

File hashes

Hashes for PyWavelets-0.5.2-cp34-none-win_amd64.whl
Algorithm Hash digest
SHA256 09f42f82ecc1be2f293f83f4e9c55866272cf9f92ac7a3b72705cba7512bd723
MD5 be71f215a1f07cea14aa49cd62cb61f0
BLAKE2b-256 03354207d256fd50cbd1d7382dcabca5c146ae2396f4f03b44ac0a05bceb1dd1

See more details on using hashes here.

File details

Details for the file PyWavelets-0.5.2-cp34-none-win32.whl.

File metadata

File hashes

Hashes for PyWavelets-0.5.2-cp34-none-win32.whl
Algorithm Hash digest
SHA256 5e78b45c8726249df7225da1cf1d08e07ca4d84dd66d49182f23b090f48e21dc
MD5 0fa4c8fe9abbd1c00111daf98500de81
BLAKE2b-256 027df43eab2daa7fbea991687639f5c2ade54ec84828c09270523b79d6537b10

See more details on using hashes here.

File details

Details for the file PyWavelets-0.5.2-cp34-cp34m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for PyWavelets-0.5.2-cp34-cp34m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 d69289039d7eb2ebc3275db7bf13d34df0c1bdf51955466a8ef792a309aaf0fe
MD5 606551ad5c075fb1d30e2ff24deb72d4
BLAKE2b-256 635ff2b335ccfd72ad3c0dd70b3b5f3bb13c3276826b55bb3a1f1ca4b12ac268

See more details on using hashes here.

File details

Details for the file PyWavelets-0.5.2-cp34-cp34m-manylinux1_i686.whl.

File metadata

File hashes

Hashes for PyWavelets-0.5.2-cp34-cp34m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 5bc9546c6f3d1af03d0d8d7a58459115cb526c337317f64f13486e0be96a51a7
MD5 6aa6ebd3551431d8c55dc6d8a5f8cf09
BLAKE2b-256 d2bda8b3afc1ff30e1713381b93259d75fe951dda9ec1907eb25eafd0e4388d7

See more details on using hashes here.

File details

Details for the file PyWavelets-0.5.2-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl.

File metadata

File hashes

Hashes for PyWavelets-0.5.2-cp34-cp34m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
Algorithm Hash digest
SHA256 e6cf3644166884a35fe45c1027cbc76bcdd5c17a74b013f51f54e73a522b1393
MD5 1a04243910737bed84c5c8afc630c28d
BLAKE2b-256 8d1695084cde68bffe79af0b8b7e9e2575ea38a426f1d5b45fe0b86550f8f020

See more details on using hashes here.

File details

Details for the file PyWavelets-0.5.2-cp27-none-win_amd64.whl.

File metadata

File hashes

Hashes for PyWavelets-0.5.2-cp27-none-win_amd64.whl
Algorithm Hash digest
SHA256 ebb3b3f460b1bd5214c49c18ee20971c8efffb9fa482fce0687be1630202cd65
MD5 620d6bbfc7a2528c67a369492cd61cf1
BLAKE2b-256 2d44bb73b8c6e5745da88fae9765e25f47c2d69489a638e628307c0052227d30

See more details on using hashes here.

File details

Details for the file PyWavelets-0.5.2-cp27-none-win32.whl.

File metadata

File hashes

Hashes for PyWavelets-0.5.2-cp27-none-win32.whl
Algorithm Hash digest
SHA256 50f386b422cd7005b8f69fc20d1502182d80e5b9f7345d5ca4b9ff640364d77d
MD5 0ed5f62a0727b193edd37a840fec389b
BLAKE2b-256 1b3dc3f2eac79e22641a7452ec5c9120a53f2a2997a4a011247194e6182c30ca

See more details on using hashes here.

File details

Details for the file PyWavelets-0.5.2-cp27-cp27mu-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for PyWavelets-0.5.2-cp27-cp27mu-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 9d8729b972943cbd1849a75beb6a87878eb3f9fa9639d027f240d25c4269ae84
MD5 d3fbb1089b9f261e5da4daddc8ce2135
BLAKE2b-256 3c842346f637fcdc0e89122d6356373a3ee58c27b398dc5880af60eb418a9f5f

See more details on using hashes here.

File details

Details for the file PyWavelets-0.5.2-cp27-cp27mu-manylinux1_i686.whl.

File metadata

File hashes

Hashes for PyWavelets-0.5.2-cp27-cp27mu-manylinux1_i686.whl
Algorithm Hash digest
SHA256 1d9094b9e5204e446b7ea750b4f5f0bec8d62f2abd740484addde593cfe8c4d5
MD5 daecd96262e18d8664229ab1efeeeabc
BLAKE2b-256 6701c14828defc32c1527012906c2298c5974c6f1af73f355ebb12cc6de8fde7

See more details on using hashes here.

File details

Details for the file PyWavelets-0.5.2-cp27-cp27m-manylinux1_x86_64.whl.

File metadata

File hashes

Hashes for PyWavelets-0.5.2-cp27-cp27m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 012dfae798fb7f6e522d0c9789ce73b442d2fe98396a6091adfacd1b24d4ae2f
MD5 2556ab89f3fd773e2ef7ef8b823de114
BLAKE2b-256 6800d371304fe0a2ad4e5f08902b53b8848ca6137e75113154600a70c3662439

See more details on using hashes here.

File details

Details for the file PyWavelets-0.5.2-cp27-cp27m-manylinux1_i686.whl.

File metadata

File hashes

Hashes for PyWavelets-0.5.2-cp27-cp27m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 30782f2f38d7b89cd6c0562bba70d6cdd14eeac53d9164ee58d2a21714f34047
MD5 905bc333e2722d98fd4c527b461788cb
BLAKE2b-256 9a0a0430ff7485e8b1e74b8f0deb91cd3465030eaa327c509f807751b68c3d2d

See more details on using hashes here.

File details

Details for the file PyWavelets-0.5.2-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl.

File metadata

File hashes

Hashes for PyWavelets-0.5.2-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
Algorithm Hash digest
SHA256 eb7f30782ba48cd06b957f6c7728bdcdcc3159b7805bdbd362370d8f5cb81603
MD5 2fa8f540052020cf751d6d32af6c52ee
BLAKE2b-256 da2add1b00eb1835e0f8d6f8b8f024fb77183cf1fe1d6910d95d48394cd80740

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