Skip to main content

Perlin noise for Python

Project description

Perlin noise is ubiquitous in modern CGI. Used for procedural texturing, animation, and enhancing realism, Perlin noise has been called the “salt” of procedural content. Perlin noise is a type of gradient noise, smoothly interpolating across a pseudo-random matrix of values.

The noise library includes native-code implementations of Perlin “improved” noise and Perlin simplex noise. It also includes a fast implementation of Perlin noise in GLSL, for use in OpenGL shaders. The shader code and many of the included examples require Pyglet (http://www.pyglet.org), the native-code noise functions themselves do not, however.

The Perlin improved noise functions can also generate fBm (fractal Brownian motion) noise by combining multiple octaves of Perlin noise. Shader functions for convenient generation of turbulent noise are also included.

  • 1.2.2 AppVeyor support for Windows builds (Thanks to Federico Tomassetti)

  • 1.2.1 Fixes MSVC compatibility (Thanks to Christoph Gohlke)

  • 1.2.0 adds 4D simplex noise, tiling for 2D simplex noise, and parameterized lacunarity

See CHANGES.txt for more details

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

noise-1.2.2.zip (132.0 kB view details)

Uploaded Source

noise-1.2.2.tar.gz (125.6 kB view details)

Uploaded Source

Built Distributions

noise-1.2.2-py3.4-win-amd64.egg (44.7 kB view details)

Uploaded Source

noise-1.2.2-py3.4-win32.egg (43.9 kB view details)

Uploaded Source

noise-1.2.2-py3.3-win-amd64.egg (44.1 kB view details)

Uploaded Source

noise-1.2.2-py3.3-win32.egg (43.4 kB view details)

Uploaded Source

noise-1.2.2-py2.7-win-amd64.egg (43.4 kB view details)

Uploaded Source

noise-1.2.2-py2.7-win32.egg (43.0 kB view details)

Uploaded Source

noise-1.2.2-cp34-none-win_amd64.whl (28.8 kB view details)

Uploaded CPython 3.4 Windows x86-64

noise-1.2.2-cp34-none-win32.whl (28.1 kB view details)

Uploaded CPython 3.4 Windows x86

noise-1.2.2-cp33-none-win_amd64.whl (28.8 kB view details)

Uploaded CPython 3.3 Windows x86-64

noise-1.2.2-cp33-none-win32.whl (28.1 kB view details)

Uploaded CPython 3.3 Windows x86

noise-1.2.2-cp27-none-win_amd64.whl (28.8 kB view details)

Uploaded CPython 2.7 Windows x86-64

noise-1.2.2-cp27-none-win32.whl (28.4 kB view details)

Uploaded CPython 2.7 Windows x86

File details

Details for the file noise-1.2.2.zip.

File metadata

  • Download URL: noise-1.2.2.zip
  • Upload date:
  • Size: 132.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for noise-1.2.2.zip
Algorithm Hash digest
SHA256 36036cdaca131ddd2ab4397fba649af7f074ec08031e1e0a51031d0ae23b509a
MD5 42867e2625b99defc549bc9d3ed76379
BLAKE2b-256 33805741a56563690255933ed5ca4e7fa9453c6a309e052ee2eac3b18a823b58

See more details on using hashes here.

File details

Details for the file noise-1.2.2.tar.gz.

File metadata

  • Download URL: noise-1.2.2.tar.gz
  • Upload date:
  • Size: 125.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No

File hashes

Hashes for noise-1.2.2.tar.gz
Algorithm Hash digest
SHA256 57a2797436574391ff63a111e852e53a4164ecd81ad23639641743cd1a209b65
MD5 d179f1490c1b55c57b0f0cad99832a10
BLAKE2b-256 1829bb830ee6d934311e17a7a4fa1368faf3e73fbb09c0d80fc44e41828df177

See more details on using hashes here.

File details

Details for the file noise-1.2.2-py3.4-win-amd64.egg.

File metadata

File hashes

Hashes for noise-1.2.2-py3.4-win-amd64.egg
Algorithm Hash digest
SHA256 a476b2b62efa56b777c5a04566930aa47cfda43eda0e744a39b55056e763064f
MD5 f2b7ef402459cd414ebcd8137cc585cd
BLAKE2b-256 23ddd14040c0041371380b23e81349a361812fa093b3921cea3b3c054b79de20

See more details on using hashes here.

File details

Details for the file noise-1.2.2-py3.4-win32.egg.

File metadata

File hashes

Hashes for noise-1.2.2-py3.4-win32.egg
Algorithm Hash digest
SHA256 001782e0b67c260e734e48a409641707f502ee4cd186a74e6bda881fb3c44427
MD5 cfadb83710913929513d59f6c64d3b66
BLAKE2b-256 9940f4348ef907f43d9a1e4e0a6029d0f62159642772bfb8396d8d62cb277a51

See more details on using hashes here.

File details

Details for the file noise-1.2.2-py3.3-win-amd64.egg.

File metadata

File hashes

Hashes for noise-1.2.2-py3.3-win-amd64.egg
Algorithm Hash digest
SHA256 0191e19e77be1018f5548e75125de1c6aec11a75a8693bb5f8f8106b63561ed8
MD5 3241ae10d7041a394ad86de8ac6fb9c8
BLAKE2b-256 6746ec01f4b827da658b7f0b5a2e6be19d948c7a22892e14c214a41474ccff75

See more details on using hashes here.

File details

Details for the file noise-1.2.2-py3.3-win32.egg.

File metadata

File hashes

Hashes for noise-1.2.2-py3.3-win32.egg
Algorithm Hash digest
SHA256 a15336cea59c74f1b3b2bf03b76600a3d59b0e557da1219176b9ca216dca8a91
MD5 9fd475ad0ebd1fe6dc4c24c0d58d4eeb
BLAKE2b-256 cacdc949ca37845afb682ad16fa5977b8a9c3a6d297491af79234476cb6cb041

See more details on using hashes here.

File details

Details for the file noise-1.2.2-py2.7-win-amd64.egg.

File metadata

File hashes

Hashes for noise-1.2.2-py2.7-win-amd64.egg
Algorithm Hash digest
SHA256 fae41762d6b7a0a1e360ec4cb03260b6abcc302b2244a3feefdfbf52ffd612c9
MD5 2b6c919485bc7891ed45a308f528ed7c
BLAKE2b-256 447ebce759e3649a89e564741d91c0ef5e123b55638d5cfe1f405a13b3b9272a

See more details on using hashes here.

File details

Details for the file noise-1.2.2-py2.7-win32.egg.

File metadata

File hashes

Hashes for noise-1.2.2-py2.7-win32.egg
Algorithm Hash digest
SHA256 0d3c51b538dfbbea85cbcf9fc4418ae5210136f5e39948968392a6d4f3cd39b0
MD5 50b53a23d4543317612facb63d5b7217
BLAKE2b-256 db6d86851d73996a02cb7b06cd62d36ce7d313d2309b7dff2eedcdc846dd10d5

See more details on using hashes here.

File details

Details for the file noise-1.2.2-cp34-none-win_amd64.whl.

File metadata

File hashes

Hashes for noise-1.2.2-cp34-none-win_amd64.whl
Algorithm Hash digest
SHA256 065be3531a6b7a7dfcb6840646400a75e081511c524caa04feaea11e16e7ab24
MD5 3a26872f035e10d0bfedb443f4b957b8
BLAKE2b-256 614ac6fcde5bbd84c3ddf2bc0111012f2afcc65e2e7e42a187561dca0c39a9c3

See more details on using hashes here.

File details

Details for the file noise-1.2.2-cp34-none-win32.whl.

File metadata

File hashes

Hashes for noise-1.2.2-cp34-none-win32.whl
Algorithm Hash digest
SHA256 f82933563ef89651865cc7cbfab11721a2229f5c6c8c009a041be4fb887949f2
MD5 e92a67aeb721f432c755e63e81ee6fe3
BLAKE2b-256 a54e1e6bc23832f1632951f7a42a0cd8b29d51f9b59917f90297eb51fab37e4a

See more details on using hashes here.

File details

Details for the file noise-1.2.2-cp33-none-win_amd64.whl.

File metadata

File hashes

Hashes for noise-1.2.2-cp33-none-win_amd64.whl
Algorithm Hash digest
SHA256 20d1c89a2b8c3714abe5a5fa653b3ab9ab01aee1475993b8bdb9d5e26bc081cc
MD5 15d0a1550f05ffedb8e7db4a5c8790bd
BLAKE2b-256 197cf76e505f5d67d747f88dca87d8e233b48c3db75ea439b0a2107e821fe0e5

See more details on using hashes here.

File details

Details for the file noise-1.2.2-cp33-none-win32.whl.

File metadata

File hashes

Hashes for noise-1.2.2-cp33-none-win32.whl
Algorithm Hash digest
SHA256 93ac2977cf0e8f5cb90a2e828a5dfefc438bfb1fb8d7fd1ee305f05eadc8578e
MD5 2337c1f9a13aadafed54c043be9e8265
BLAKE2b-256 39f2f704056986fba80479ed3b1c32220bfe19b37478855bd15f4a8fa79bfc5b

See more details on using hashes here.

File details

Details for the file noise-1.2.2-cp27-none-win_amd64.whl.

File metadata

File hashes

Hashes for noise-1.2.2-cp27-none-win_amd64.whl
Algorithm Hash digest
SHA256 15582a58c9ee79b07436a1220bd6c4145266865262d1e6478d37a08b7d910da8
MD5 9b69590f81cfc3b89634ad9981ff921a
BLAKE2b-256 3a06d4d17ccc9e2fff8de3f60922aa774e09e510e983659b7bd3559eac2056ce

See more details on using hashes here.

File details

Details for the file noise-1.2.2-cp27-none-win32.whl.

File metadata

File hashes

Hashes for noise-1.2.2-cp27-none-win32.whl
Algorithm Hash digest
SHA256 f7ef26a4c334224e7763e740fc945c26c0abf9ba51a19d4f5befa1d20d7da0b7
MD5 79925e594a5903a06abf0592d56c4619
BLAKE2b-256 9718db50ecc1d5614d78243707a36fb5a763606dc936b436124c586516de77b4

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