Skip to main content

Python tools for simulation of optical systems

Project description

pyOpTools

pyOpTools is a set of packages that allow the simulation of optical systems by raytracing as well as some calculations involving wavefronts, currently under development. It is written in Python and Cython, and is being developed by the technological development group of Combustión Ingenieros S.A.S, and the applied optics group of the Universidad Nacional de Colombia.

At the moment the documentation is being created, you can find the work in progress here

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.

pyoptools-0.2.2-cp311-cp311-win_amd64.whl (13.3 MB view details)

Uploaded CPython 3.11Windows x86-64

pyoptools-0.2.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (22.7 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ x86-64

pyoptools-0.2.2-cp311-cp311-macosx_10_9_x86_64.whl (13.6 MB view details)

Uploaded CPython 3.11macOS 10.9+ x86-64

pyoptools-0.2.2-cp310-cp310-win_amd64.whl (13.2 MB view details)

Uploaded CPython 3.10Windows x86-64

pyoptools-0.2.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (21.9 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ x86-64

pyoptools-0.2.2-cp310-cp310-macosx_10_9_x86_64.whl (13.6 MB view details)

Uploaded CPython 3.10macOS 10.9+ x86-64

pyoptools-0.2.2-cp39-cp39-win_amd64.whl (13.3 MB view details)

Uploaded CPython 3.9Windows x86-64

pyoptools-0.2.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (22.0 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ x86-64

pyoptools-0.2.2-cp39-cp39-macosx_10_9_x86_64.whl (13.6 MB view details)

Uploaded CPython 3.9macOS 10.9+ x86-64

pyoptools-0.2.2-cp38-cp38-win_amd64.whl (13.3 MB view details)

Uploaded CPython 3.8Windows x86-64

pyoptools-0.2.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (22.2 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ x86-64

pyoptools-0.2.2-cp38-cp38-macosx_10_9_x86_64.whl (13.6 MB view details)

Uploaded CPython 3.8macOS 10.9+ x86-64

File details

Details for the file pyoptools-0.2.2-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: pyoptools-0.2.2-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 13.3 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.8

File hashes

Hashes for pyoptools-0.2.2-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 e7884338603b18bafb6c11447d1e0361029ea90a1d1ba0d5565c2ae7913a7535
MD5 1ad97bb5db8d054f70aaac3f63bdfd46
BLAKE2b-256 22640a080f13c4f4b4847dc2286cc723d42be438a33a98f3258a0afe5dd0ad1e

See more details on using hashes here.

File details

Details for the file pyoptools-0.2.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyoptools-0.2.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 04d60e9b479027aee6955e47cf05926d8bc4037f399bb1f1e8820a63fbf916a8
MD5 9893889fd3091e3cc2a43af8a3121a5e
BLAKE2b-256 773a401c1ddcb10168ba651896b0f02e15412666c1f0feb896c667290adf7067

See more details on using hashes here.

File details

Details for the file pyoptools-0.2.2-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyoptools-0.2.2-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 ff90fb673c688fd7fc44c0584d064cea7695fb26ce934c87bc2a05c7bf685b4b
MD5 4e4c5a665c1d53b3aa435b27686c3640
BLAKE2b-256 fe5c20fb072cdbd309db3316b26ad7917e38be5f43d90799ce0b135fbb189a21

See more details on using hashes here.

File details

Details for the file pyoptools-0.2.2-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: pyoptools-0.2.2-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 13.2 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.8

File hashes

Hashes for pyoptools-0.2.2-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 6674784aeba0bead500b492657917c0ecdeccad3e62de7ed6914f390be4654f4
MD5 6c3aedeaba80ba6b9d3132b79c839b83
BLAKE2b-256 c46b935a400550202293c0e554e95412abf4f7d4e3e5e5824351a15a123258a6

See more details on using hashes here.

File details

Details for the file pyoptools-0.2.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyoptools-0.2.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 fd38f310e42a807ae14947f6f1327da8c241585d5f1da414f85516e641d83e5d
MD5 8485df55014b6f1c893a6e198e3f8239
BLAKE2b-256 f609318e2387f09242e808ac288fb0f110390814e08ad61497c1845663106e74

See more details on using hashes here.

File details

Details for the file pyoptools-0.2.2-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyoptools-0.2.2-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 58186aedc27a25d4854e77dd8c04d45593810de99888d1cf82c121df832ceeaa
MD5 81f589103f7ff10f4de17099a4361eb0
BLAKE2b-256 a6a15a3560ff30ff8cfe8fca62a835a33ee2ad0c95db7862c5380de14a0472c2

See more details on using hashes here.

File details

Details for the file pyoptools-0.2.2-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: pyoptools-0.2.2-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 13.3 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.8

File hashes

Hashes for pyoptools-0.2.2-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 3e7efc1de3382bcbdce56bdebded36455ca3de81b70e4f4346146ad19ee17d86
MD5 7217247afde883dd9f3b77c061065268
BLAKE2b-256 c977b4b020d0907518d856818d6ab2d81db102a95b43fbda47067b6d4bf5f401

See more details on using hashes here.

File details

Details for the file pyoptools-0.2.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyoptools-0.2.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 42b170a73ea30be1da0ce13cb601ecdebd7499b70b77bf4874e95ec8a1a8aef9
MD5 b9a371a428b80c980907540836de090a
BLAKE2b-256 a611768a1cc41ae9f151bbec5ac1a00b54e913c8526474601376292f15ca6e4c

See more details on using hashes here.

File details

Details for the file pyoptools-0.2.2-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyoptools-0.2.2-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 b5473c7cdb3765557b821a996e26a9bde610b5c8ff217698a99dd9ddce943af7
MD5 9c8c92c70c7e1d0a6738681049dbd0e0
BLAKE2b-256 4b98c9bed7aed341f19147c35e9b07bdbaf1fb4ffff90a6433894efc19fe0fec

See more details on using hashes here.

File details

Details for the file pyoptools-0.2.2-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: pyoptools-0.2.2-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 13.3 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.8

File hashes

Hashes for pyoptools-0.2.2-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 072914e142ac948e0909a445ac4d6623e56498396eb7b65da921f413b1890c75
MD5 b1c47a9cf7c0ffb7760341080833d9b0
BLAKE2b-256 bc5310ca7427190094a6990709146e09a8b63f20223d8bb3accb94a48afd3664

See more details on using hashes here.

File details

Details for the file pyoptools-0.2.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyoptools-0.2.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 0c9b18f7b65f48b552943adc25b970f7e8815e61ab884e8888fc9132f5e2b21d
MD5 ef139c201dbfefe93cc0f4f8efdf55c3
BLAKE2b-256 5055a7d7b2bfa14a8a775c0ea9d6838e169cd7f0d897dd006139fd10374a1614

See more details on using hashes here.

File details

Details for the file pyoptools-0.2.2-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pyoptools-0.2.2-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 4f89ea61a0d572861f467c8376672458368d7ab6c5722cc1e8505ff12afe4410
MD5 8881f483459983b652f07421b655edae
BLAKE2b-256 09ea60dbf8a0d42637942ad7faa9c70e7fabf82f84d40e05913f5298d6b26124

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