Skip to main content

No project description provided

Project description

WEOM Python interface

version: 1.6.150

WEOMPy is a comprehensive Python library designed to control WEOM cameras using the Python language.

The library can access the following key WEOM thermal core features

  • read device information such as serial number, firmware version, etc...
  • change display parameters (display palette, contrast, brightness, frame rate, gain, etc..)
  • view and capture images
  • update thermal core firmware
  • manage sensor dead pixels

We provide documentation, howtos and tutorials in a form Jupyter notebooks. The notebooks and example can be found in directory where the library is installed using pip. We strongly recommend the use of Python virtual environments to avoid any possible conflicts. To create and activate a clean WEOMPy Python environment run

python -m venv <venv_directories>/weompy

. <venv_directories>/weompy/bin/activate

now we have a clean Python environment and we can proceed by installing the library

pip install weompy

After these steps you can find the documentation and example in

<venv_directories>/weompy/lib/python<version>/site-packages/weompy/example.py

<venv_directories>/weompy/lib/python<version>/site-packages/weompy/user_documentation.ipynb

Also we provide stubs for IDE code completion in

<venv_directories>/weompy/lib/python<version>/site-packages/weompy/weompy.pyi

Prerequisites:

  • Python 3.8 - 3.12
  • Jupyter extension by Microsoft installed on VSCode, if you want to open user_documentation.ipynb in VSCode

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.

weompy-1.6.150-cp312-cp312-win_amd64.whl (12.3 MB view details)

Uploaded CPython 3.12Windows x86-64

weompy-1.6.150-cp312-cp312-manylinux_2_28_x86_64.whl (56.9 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ x86-64

weompy-1.6.150-cp311-cp311-win_amd64.whl (12.3 MB view details)

Uploaded CPython 3.11Windows x86-64

weompy-1.6.150-cp311-cp311-manylinux_2_28_x86_64.whl (56.9 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ x86-64

weompy-1.6.150-cp310-cp310-win_amd64.whl (12.3 MB view details)

Uploaded CPython 3.10Windows x86-64

weompy-1.6.150-cp310-cp310-manylinux_2_28_x86_64.whl (56.9 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.28+ x86-64

weompy-1.6.150-cp39-cp39-win_amd64.whl (12.2 MB view details)

Uploaded CPython 3.9Windows x86-64

weompy-1.6.150-cp39-cp39-manylinux_2_28_x86_64.whl (56.9 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.28+ x86-64

weompy-1.6.150-cp38-cp38-win_amd64.whl (12.3 MB view details)

Uploaded CPython 3.8Windows x86-64

weompy-1.6.150-cp38-cp38-manylinux_2_28_x86_64.whl (56.9 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.28+ x86-64

File details

Details for the file weompy-1.6.150-cp312-cp312-win_amd64.whl.

File metadata

  • Download URL: weompy-1.6.150-cp312-cp312-win_amd64.whl
  • Upload date:
  • Size: 12.3 MB
  • Tags: CPython 3.12, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.2

File hashes

Hashes for weompy-1.6.150-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 9d94e351298610235d2a3918ab673b025b69ad65ecbd9aa7cae75830bd385185
MD5 ca458297ab9d2b8c15311b19d9c495b2
BLAKE2b-256 c0c51739f8d695447a7a8c5c4c57d0491518ff3a3ceb9d203399ca5241a8a9c6

See more details on using hashes here.

File details

Details for the file weompy-1.6.150-cp312-cp312-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for weompy-1.6.150-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 087c255492337dc8b63ffcf8aa6785feb4d97566d20ff6dc7956d6f11a054706
MD5 a0c74dc2c0f25096501b7eeb22c2bb82
BLAKE2b-256 bb494f85925ec729ebf4577f12c340aaa4f86de8f61d01019f2a9c2681f48ba1

See more details on using hashes here.

File details

Details for the file weompy-1.6.150-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: weompy-1.6.150-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 12.3 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.2

File hashes

Hashes for weompy-1.6.150-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 b2b213c72168da8b5bcc958ceb4e241ab9f4bb8c53b882bd9470a3d1731645cb
MD5 dfa934cc70c0ee06893731a2d5e0ff04
BLAKE2b-256 c826faaa34a894eefd9044825843d8c039508fa0505e57d77a792241d6d5b4e9

See more details on using hashes here.

File details

Details for the file weompy-1.6.150-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for weompy-1.6.150-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 6714abe12acf2cd1174a72cf4fafc9ec636a30bb0a236b8d5854389da3f02c38
MD5 db9e3e929b1df8e204869af1abecefc2
BLAKE2b-256 d79cbcfa27f403640aa2bc94f9624e07b916081512ed6f1382f62e1178f48613

See more details on using hashes here.

File details

Details for the file weompy-1.6.150-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: weompy-1.6.150-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 12.3 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.2

File hashes

Hashes for weompy-1.6.150-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 f82ee24dcd1849ddd1ad43a46d79694c9a3fdc2bf2c3a6f02d645a9edf531751
MD5 ba8754eacdceca2aaec8f849f2e0844a
BLAKE2b-256 cfbe2521b9eaafe16c5b19332e58f4d7436b1cdb3fa2470868e3fa6760e371be

See more details on using hashes here.

File details

Details for the file weompy-1.6.150-cp310-cp310-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for weompy-1.6.150-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 a59574601c789a55f6dc115d2af0b809dc70c8fadeb906ab648d7ffc90a56a76
MD5 e8130bfa94020c9dd1d541672f675a19
BLAKE2b-256 8362283b235bec24cefc6d74a58aa2104b84faf4cf9d5d346ef1c4df34990d25

See more details on using hashes here.

File details

Details for the file weompy-1.6.150-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: weompy-1.6.150-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 12.2 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.2

File hashes

Hashes for weompy-1.6.150-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 d562901834aa38f7bf64c600c5339fa89f9bd8c7ccf2f410de035ac2680d20e0
MD5 e0df8e3620708bd1f5f6203e56ab8fa3
BLAKE2b-256 a94f91f397a92f52f8dc66c4e2d0a7590322baecbeb01f8b2c464ae55bc79812

See more details on using hashes here.

File details

Details for the file weompy-1.6.150-cp39-cp39-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for weompy-1.6.150-cp39-cp39-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 d21684f191bb63626449be174ffc945a89766d642057d3aa3b6830671cfe0b77
MD5 9b566ab175a7872e9c42817269a67b67
BLAKE2b-256 0d59d14d95a0b59c7f88fcbcc1567c7a43d83ae6d06baf1676db3927223d202c

See more details on using hashes here.

File details

Details for the file weompy-1.6.150-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: weompy-1.6.150-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 12.3 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.2

File hashes

Hashes for weompy-1.6.150-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 b144f93a83407feddd9eeeff1c75f8c3e00abf6779c47d8bc4b94221afc66d73
MD5 1a7fcbcc4d9ad31fc6fa1adb26bdd2ea
BLAKE2b-256 ec3bf39c9fe9fb650da5eee92e5bd9d8ca8c61878724451c42169a94c9ea2be7

See more details on using hashes here.

File details

Details for the file weompy-1.6.150-cp38-cp38-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for weompy-1.6.150-cp38-cp38-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 afb0aa1975c13e7679d1deee6af64ab19b7ec33ebf92a7133f22903827507196
MD5 53eb6e917414730df50a1392a5f7024b
BLAKE2b-256 b462249c364c0c1cc30dd1177f30fd926b59bf721b27060df98c8c0fc3a6ff41

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