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Django Tasks An implementation and backport of background workers and tasks in Django, as defined in DEP 0014. Warning: This package is under active development, and breaking changes may be released at any time. Be sure to pin to specific versions if you're using this package in a production environment. Installation python -m pip install django-tasks The first step is to add django_tasks to your INSTALLED_APPS. INSTALLED_APPS = [ "django_tasks", ] Secondly, you'll need to configure a backend. This connects the tasks to whatever is going to execute them. If omitted, the following configuration is used: TASKS = { "default": { "BACKEND": "django_tasks.backends.immediate.ImmediateBackend" } } A few backends are included by default: django_tasks.backends.dummy.DummyBackend: Don't execute the tasks, just store them. This is especially useful for testing. django_tasks.backends.immediate.ImmediateBackend: Execute the task immediately in the current threa
django-treebeard django-treebeard is a library that implements efficient tree implementations for the Django Web Framework 2.2 and later. It is written by Gustavo Picón and licensed under the Apache License 2.0. Status Features django-treebeard is: Flexible: Includes 3 different tree implementations with the same API: Adjacency List Materialized Path Nested Sets Fast: Optimized non-naive tree operations Easy: Uses Django Model Inheritance with abstract classes to define your own models. Clean: Testable and well tested code base. Code/branch test coverage is above 96%. You can find the documentation in http:/django-treebeard.readthedocs.io/en/latest/ Supported versions django-treebeard officially supports Django 3.2, 4.1, 4.2, 5.0 Python 3.8 - 3.12 PostgreSQL, MySQL, MSSQL, SQLite database back-ends.
draftjs-exporter
The GDAL Python 3 modules provide support to handle multiple GIS file formats.
Laces 👟 Django components that know how to render themselves. Laces components provide a simple way to combine data (in the form of Python objects) with the Django templates that are meant to render that data. The components can then be simply rendered in any other template using the {% component %} template tag. That parent template does not need to know anything about the component's template or data. No need to receive, filter, restructure or pass any data to the component's template. Just let the component render itself. Template and data are tied together (sorry, not sorry 😅) in the component, and they can be passed around together. This becomes especially useful when components are nested — it allows us to avoid building the same nested structure twice (once in the data and again in the templates). Working with objects that know how to render themselves as HTML elements is a common pattern found in complex Django applications, such as the Wagtail admin interface. The Wagtail
pillow-heif Python bindings to libheif for working with HEIF images and plugin for Pillow. Features: Decoding of 8, 10, 12 bit HEIC files. Encoding of 8, 10, 12 bit HEIC files. EXIF, XMP, IPTC read & write support. Support of multiple images in one file and a PrimaryImage attribute. Adding & removing thumbnails. Reading of Depth Images. (beta) Reading of Auxiliary Images by johncf Adding HEIF support to Pillow in one line of code as a plugin. Note: Here is a light version pi-heif of this project without encoding capabilities. Example of use as a Pillow plugin from PIL import Image from pillow_heif import register_heif_opener register_heif_opener() im = Image.open("image.heic") # do whatever need with a Pillow image im = im.rotate(13) im.save(f"rotated_image.heic", quality=90) 16 bit PNG to 10 bit HEIF using OpenCV import cv2 import pillow_heif cv_img = cv2.imread("16bit_with_alpha.png", cv2.IMREAD_UNC
telepath telepath is a Django library for exchanging data between Python and JavaScript, allowing you to build apps with rich client-side interfaces while keeping the business logic in server-side code. What does it do? It provides a mechanism for packing structured data, including Python objects, into a JSON-serializable format. This mechanism can be extended to support any Python class, by registering the class with a corresponding JavaScript implementation. The packed data can then be included in an HTTP response, and unpacked in JavaScript to obtain an equivalent data structure to the original. Think of it as pickle, but with the unpickling happening in the browser. Full documentation: https:/wagtail.github.io/telepath/
Wagtail is an open source content management system built on Django, with a strong community and commercial support. It's focused on user experience, and offers precise control for designers and developers. 🔥 Features A fast, attractive interface for authors Complete control over front-end design and structure Scales to millions of pages and thousands of editors Fast out of the box, cache-friendly when you need it Content API for 'headless' sites with decoupled front-end Runs on a Raspberry Pi or a multi-datacenter cloud platform StreamField encourages flexible content without compromising structure Powerful, integrated search, using Elasticsearch or PostgreSQL Excellent support for images and embedded content Multi-site and multi-language ready Embraces and extends Django Find out more at wagtail.org.
Willow is a simple image library that combines the APIs of Pillow, Wand and OpenCV. It converts the image between the libraries when necessary. Willow currently has basic resize and crop operations, face and feature detection and animated GIF support. New operations and library integrations can also be easily implemented.
This is a metapackage bringing in heif extras requires for python3-willow. It makes sure the dependencies are installed.