PyWavelets

PyWavelets, wavelet transform module

These details have not been verified by PyPI

Project links

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
Real and complex calculations
Results compatible with Matlab Wavelet Toolbox (TM)

Project details

These details have not been verified by PyPI

Project links

Release history Release notifications | RSS feed

1.8.0

Dec 4, 2024

1.7.0

Aug 12, 2024

1.6.0

Apr 1, 2024

1.6.0rc1 pre-release

Mar 14, 2024

1.5.0

Nov 17, 2023

1.4.1

Sep 16, 2022

1.4.0 yanked

Sep 15, 2022

Reason this release was yanked:

incorrect python_requires constraint

1.3.0

Mar 11, 2022

1.2.0

Nov 9, 2021

1.1.1

Oct 19, 2019

1.1.0

Oct 18, 2019

This version

1.0.3

Apr 9, 2019

1.0.2

Feb 26, 2019

1.0.1

Sep 25, 2018

1.0.0

Aug 31, 2018

0.5.2

Mar 10, 2017

0.5.1

Nov 29, 2016

0.5.0

Nov 3, 2016

0.4.0

Dec 28, 2015

0.3.0

Jul 30, 2015

0.2.2

Aug 30, 2012

0.2.0

Mar 17, 2010

0.1.6

Dec 2, 2006

0.1.4

Jul 7, 2006

0.1.2

Mar 25, 2006

0.1.0

Mar 22, 2006

Download files

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

Source Distribution

PyWavelets-1.0.3.tar.gz (4.6 MB view details)

Uploaded Apr 9, 2019 Source

Built Distributions

PyWavelets-1.0.3-cp37-none-win_amd64.whl (4.2 MB view details)

Uploaded Apr 9, 2019 CPython 3.7 Windows x86-64

PyWavelets-1.0.3-cp37-none-win32.whl (4.1 MB view details)

Uploaded Apr 9, 2019 CPython 3.7 Windows x86

PyWavelets-1.0.3-cp37-cp37m-manylinux1_x86_64.whl (4.4 MB view details)

Uploaded Apr 9, 2019 CPython 3.7m

PyWavelets-1.0.3-cp37-cp37m-manylinux1_i686.whl (4.3 MB view details)

Uploaded Apr 9, 2019 CPython 3.7m

PyWavelets-1.0.3-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 (5.0 MB view details)

Uploaded Apr 9, 2019 CPython 3.7m macOS 10.10+ Intel (x86-64, i386) macOS 10.10+ x86-64 macOS 10.6+ Intel (x86-64, i386) macOS 10.9+ Intel (x86-64, i386) macOS 10.9+ x86-64

PyWavelets-1.0.3-cp36-none-win_amd64.whl (4.2 MB view details)

Uploaded Apr 9, 2019 CPython 3.6 Windows x86-64

PyWavelets-1.0.3-cp36-none-win32.whl (4.1 MB view details)

Uploaded Apr 9, 2019 CPython 3.6 Windows x86

PyWavelets-1.0.3-cp36-cp36m-manylinux1_x86_64.whl (4.4 MB view details)

Uploaded Apr 9, 2019 CPython 3.6m

PyWavelets-1.0.3-cp36-cp36m-manylinux1_i686.whl (4.3 MB view details)

Uploaded Apr 9, 2019 CPython 3.6m

PyWavelets-1.0.3-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 (5.0 MB view details)

Uploaded Apr 9, 2019 CPython 3.6m macOS 10.10+ Intel (x86-64, i386) macOS 10.10+ x86-64 macOS 10.6+ Intel (x86-64, i386) macOS 10.9+ Intel (x86-64, i386) macOS 10.9+ x86-64

PyWavelets-1.0.3-cp35-none-win_amd64.whl (4.2 MB view details)

Uploaded Apr 9, 2019 CPython 3.5 Windows x86-64

PyWavelets-1.0.3-cp35-none-win32.whl (4.1 MB view details)

Uploaded Apr 9, 2019 CPython 3.5 Windows x86

PyWavelets-1.0.3-cp35-cp35m-manylinux1_x86_64.whl (4.4 MB view details)

Uploaded Apr 9, 2019 CPython 3.5m

PyWavelets-1.0.3-cp35-cp35m-manylinux1_i686.whl (4.3 MB view details)

Uploaded Apr 9, 2019 CPython 3.5m

PyWavelets-1.0.3-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 (4.9 MB view details)

Uploaded Apr 9, 2019 CPython 3.5m macOS 10.10+ Intel (x86-64, i386) macOS 10.10+ x86-64 macOS 10.6+ Intel (x86-64, i386) macOS 10.9+ Intel (x86-64, i386) macOS 10.9+ x86-64

PyWavelets-1.0.3-cp34-cp34m-manylinux1_x86_64.whl (4.4 MB view details)

Uploaded Apr 9, 2019 CPython 3.4m

PyWavelets-1.0.3-cp34-cp34m-manylinux1_i686.whl (4.3 MB view details)

Uploaded Apr 9, 2019 CPython 3.4m

PyWavelets-1.0.3-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 (4.9 MB view details)

Uploaded Apr 9, 2019 CPython 3.4m macOS 10.10+ Intel (x86-64, i386) macOS 10.10+ x86-64 macOS 10.6+ Intel (x86-64, i386) macOS 10.9+ Intel (x86-64, i386) macOS 10.9+ x86-64

PyWavelets-1.0.3-cp27-none-win_amd64.whl (4.2 MB view details)

Uploaded Apr 9, 2019 CPython 2.7 Windows x86-64

PyWavelets-1.0.3-cp27-none-win32.whl (4.1 MB view details)

Uploaded Apr 9, 2019 CPython 2.7 Windows x86

PyWavelets-1.0.3-cp27-cp27mu-manylinux1_x86_64.whl (4.4 MB view details)

Uploaded Apr 9, 2019 CPython 2.7mu

PyWavelets-1.0.3-cp27-cp27mu-manylinux1_i686.whl (4.3 MB view details)

Uploaded Apr 9, 2019 CPython 2.7mu

PyWavelets-1.0.3-cp27-cp27m-manylinux1_x86_64.whl (4.4 MB view details)

Uploaded Apr 9, 2019 CPython 2.7m

PyWavelets-1.0.3-cp27-cp27m-manylinux1_i686.whl (4.3 MB view details)

Uploaded Apr 9, 2019 CPython 2.7m

PyWavelets-1.0.3-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 (5.0 MB view details)

Uploaded Apr 9, 2019 CPython 2.7m macOS 10.10+ Intel (x86-64, i386) macOS 10.10+ x86-64 macOS 10.6+ Intel (x86-64, i386) macOS 10.9+ Intel (x86-64, i386) macOS 10.9+ x86-64

File details

Details for the file PyWavelets-1.0.3.tar.gz.

File metadata

Download URL: PyWavelets-1.0.3.tar.gz
Upload date: Apr 9, 2019
Size: 4.6 MB
Tags: Source
Uploaded using Trusted Publishing? No
Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.18.4 setuptools/39.2.0 requests-toolbelt/0.9.1 tqdm/4.31.1 CPython/3.6.8

File hashes

Hashes for PyWavelets-1.0.3.tar.gz
Algorithm	Hash digest
SHA256	`a12c7a6258c0015d2c75d88b87393ee015494551f049009e8b63eafed2d78efc`
MD5	`b64ad9af5b285478b8b94deeba320174`
BLAKE2b-256	`860f89c06eadc4d6003ff88ba365ff476be0f5a805d2e270b05cc863f2c01a4f`

Algorithm	Hash digest
SHA256	`ab02363467ee3cb222c5b425bc53453270ddae72bce313e72fd14616692d725a`
MD5	`c66b6691ad45988ac588657992b6dd32`
BLAKE2b-256	`71571b6c06f2b681975e5b43ff13ba127031f668d65b592cc56fae6445b675c7`

Algorithm	Hash digest
SHA256	`687ec8877c10e3a03595ad167d1ea2662bc1ab13ef43d63a6e207a53b2ee4c26`
MD5	`f20300ffecee9a29c99a14d6c1ee80a3`
BLAKE2b-256	`e22176b982c168b728dceed500ae716ac0433989e21ceeaddae184c98ca20fb1`

Algorithm	Hash digest
SHA256	`3abed8dcd3e94ead72ee8010b494df5a9bdbdd5e39129d52fbf8066efa323a51`
MD5	`8c038d090bb2e79d5a397f5eac2818e5`
BLAKE2b-256	`e391607449f27a3a068739498eddf921cf2686f0a71fa8b44a8efce0af9446a8`

Algorithm	Hash digest
SHA256	`bd6e62efb7839fd6bf894b7b9aec6d58be0c44a38ea0c9f3c5bea834d84d05eb`
MD5	`efa7e7686090948393f2a2581674bc90`
BLAKE2b-256	`635c6f1db9cce0c93bdf13d5fa6c77a6891a74a52aa6895c9133b34c1a4010a7`

Algorithm	Hash digest
SHA256	`6af0077c7a4c9935aa64301f6942468b494656b8812e801d4a635cf42088f96c`
MD5	`3a4ea4e519ff7fa778c093c707559b24`
BLAKE2b-256	`1ec57267d62da464851bd4b63f0da321acd949421079db341c3d059a31bbcb6a`

Algorithm	Hash digest
SHA256	`3e99ab8feeb47755738fb8deb8154c9604c4a7996b1b7db6b090475105ca7c92`
MD5	`1dd1a56efce023c073e613388b2a860c`
BLAKE2b-256	`d9982e0af756fa7c1bb49f752078c3e5c1dbf0d98b800c0a9c6f37d7a5cef2ef`

Algorithm	Hash digest
SHA256	`250412f482d5cb358b7ec323b2a783d91e5cfc337fdf8fde3c59bf2c35d6366f`
MD5	`d1034a969da79e69f411478c9d460ebb`
BLAKE2b-256	`970a375f81d55ab52a9f8f4c60fb83a4beb2ee13f3c1c1798adb19e7690561bf`

Algorithm	Hash digest
SHA256	`9e782f49dca57bc0fd2a40c0917949d77be2ecc999ccd44fff57fb10aa214135`
MD5	`6f76119074a2b0223a22ec1d5bfaa959`
BLAKE2b-256	`4ecd528dba0b474b08f6f9a3a5e1b4bb23d8e33ed5d9f0e321cc967c2607df05`

Algorithm	Hash digest
SHA256	`7215856a5d2e1a2dccca1f71d912ee6a7387086f3b3adcb55d7c41314c6abb0c`
MD5	`420f18d59a90b3e334cd8a61d5cacc03`
BLAKE2b-256	`93c14f203ec508a46415d01b0efa8d4cbb7aca846becd0019c28bcc97f978a6d`

Algorithm	Hash digest
SHA256	`adc79308c65a2007bdbd5846fda70088e7ead0ef0a5a6f44d08829e9478a907f`
MD5	`2f833a9050b66370776cf2213922b01b`
BLAKE2b-256	`1c660bdb59713833391d90b605ccf53003c41175e1fcc75dade52383cc6436b9`

Algorithm	Hash digest
SHA256	`de083a3a71576a9c3d8ba73b6f0425e4690d6ac6e480562f30bec5cb20667324`
MD5	`b9b77b8a41d0fc1595aa0df2de0ef4f1`
BLAKE2b-256	`f92fcaca88a2630d10b6f8d9a0a09366a74384f805f43f3fc11d1338bdaad970`

Algorithm	Hash digest
SHA256	`b26b836c7f71df7b2779e62d1338367cfe37b98324e9b0d54b428ac030e3d1f0`
MD5	`c0cd8486bcc8f4fd2741032fd7621a6f`
BLAKE2b-256	`2860dc591898de2b97a75e67d1ff13fa331082eb5237f28f1a4e124365754b27`

Algorithm	Hash digest
SHA256	`2f2cdd96e4882b0c18e75cc90c4710de429ac226ce53b58a90c7420e3e307631`
MD5	`7438ede08029088688dfdd9f2441c585`
BLAKE2b-256	`6a3fd92fbd9b65f879197620f404c1603bfeaa54560743bb9fb3a6b48b66a707`

Algorithm	Hash digest
SHA256	`21f39d86cc35e003576fc1400b15534e2999570418fcdb17ea62d1ff8773b076`
MD5	`b5a56641a571752b74009031aadf2892`
BLAKE2b-256	`0a86a27dce84f4c46b9f9568a42168ffac6e8eeafe5ba9d7efbaae781dfb121f`

Algorithm	Hash digest
SHA256	`25c0c592bf43eaffb4d3c6b6444b14c7407db750b6f2d344d809d4af934319d9`
MD5	`f16714bf3652917ce5e03674b04fd3a1`
BLAKE2b-256	`9bfc143164ece3debc5a691340ff737763fc78807dcbff5d9fd6c450cb095627`

Algorithm	Hash digest
SHA256	`18b193b67937e805a8e79c036bd2aa4ea3a357737256efeefdabd19c95083c3b`
MD5	`0fdb02d7af661cb8acb145d33ccd92ac`
BLAKE2b-256	`6b30f2d62c2c21b5554873a9b015ba369ff2a22149fc4f07baa4bac848959cff`

Algorithm	Hash digest
SHA256	`eafb7d609c41a04028144f4b6697792f448554960ef353244aaf0a5883263543`
MD5	`700a02a26a9170af7d50019ce57eb004`
BLAKE2b-256	`2911f615ae7ed639fc6aa7cf15a1f9bbc831ca7174b3077ece6dd5bb23a7416e`

Algorithm	Hash digest
SHA256	`fb3ee9f65d25ee5c89104e533d5f341c253cdb9543ef6fcd6dfa599b12e84f1c`
MD5	`b662a7699d6b0b5a5f37e8d6958a424e`
BLAKE2b-256	`5d768a07d80b13c7e91ff2737c42615967ab1d538759e0b9e9031bb45aa21fd2`

Algorithm	Hash digest
SHA256	`a5027d47484498e70391b311a2688c4f74294de629b982ed17be57be4c77ade7`
MD5	`0f7aea314a169666f395e765502dbca3`
BLAKE2b-256	`1fda9c63070111c44fd8ee1c310efffcd130e062bfcbeaa3effc2e6ae8072386`

Algorithm	Hash digest
SHA256	`64926c4c78dd690ec0d61be20f7c27cfbde6de9fa66ab8205eb079d9db6927fc`
MD5	`06b2becd6fa8038634ea1faed2911666`
BLAKE2b-256	`6768767910bcabbcedb18b99d73c42f754cfa2f9cb4e16222275e61c0d7d8496`

PyWavelets 1.0.3

Navigation

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Project description

Project details

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Release history Release notifications | RSS feed

Download files

Source Distribution

Built Distributions

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

Algorithm	Hash digest
SHA256	`e89551257233a3da717a9e6e2e303243df75faffe0b6781d21c15eb9d682ec6d`
MD5	`60701c6565ae8e1f30a1917949d0d081`
BLAKE2b-256	`faf181d3ba0b461699a5e0dbd1a1c2c98c0b5a2e1757b7a54d49246e0a557aea`

Algorithm	Hash digest
SHA256	`afaaee392450785a346d9e5e5f6e5307b13958d8b0633818632cb38972a7752a`
MD5	`a7cfbb812a10bd5215770d299de3e788`
BLAKE2b-256	`dd0b5837672c767b9a506746b8807149665a657dfd9d1a04dbf4cc192d41d53f`

Algorithm	Hash digest
SHA256	`1d7ba03baa81938b17d4819db36f018e680d929af329062af4d4b6d6236d02ba`
MD5	`e130106265a536f510391f472872e74d`
BLAKE2b-256	`16c24a7004c00fd2971cd31d4f150b763b12d3efd073c7e469b9bb24f7b06eb0`

Algorithm	Hash digest
SHA256	`b9adbc27d70a2626c235a18b41315de2832c384651d03383db7a58c2a2bccc6f`
MD5	`74c45cddcf0ec2ab411587ee19d8bc3c`
BLAKE2b-256	`c4ae24d5afbbf8c2dbe89ec4e0798e45ea4301daeb598107f6e209d4ba9a1a38`

Algorithm	Hash digest
SHA256	`9e47b241533add77961093b1e40cfff031597d429e91ad7c675838be0f7cc0df`
MD5	`31aa5d49f7c46f9cc72c91edbd4302b4`
BLAKE2b-256	`f216c66bd67f34b8cd2964c2e9914401a27f8cb50398e8cf06ac6e65d80a2c6d`