With less than two weeks until @pycon, we’re excited to share our Annual Impact Report for 2023 🤩 Check it out, share, and come tell us what you think at the PSF Booth at #PyConUS! #python
https://www.python.org/psf/annual-report/2023/
python
和我一起做3A游戏《归乡之路》（三） 大家好，我是赛博红兔。欢迎来到《和我一起做3A游戏》第三集！这个系列是对pygame这个Python的游戏库的所有主要功能的介绍。通过这个教程，你应该能掌握制作任何2D游戏的工具，今天我们继续来做《归乡之路》这个打字游戏。先来回顾一下上一集的内容，我们设计了小猫的动画音效和打字的核心玩法，还有分数统计和显示。主要是了解了用矩形绘画，精灵这个pygame核心的类，还有游戏事件的触发和监听。那么今天，我们就在这个代码基础上继续做游戏。我们最后要完成的任务是创建背景的花草树木Trees和家House的类，毕竟小猫还需要回家。最后还要加上游戏BGM一首轻松愉快的背景音乐。今天，我们最主要要学习的核心是处理精灵之间的碰撞。
https://cyberhongtu.com/2024/05/10/%e5%92%8c%e6%88%91%e4%b8%80%e8%b5%b7%e5%81%9a3a%e6%b8%b8%e6%88%8f%e3%80%8a%e5%bd%92%e4%b9%a1%e4%b9%8b%e8%b7%af%e3%80%8b%ef%bc%88%e4%b8%89%ef%bc%89/ #Pygame, #Python, #Python教学, #游戏制作
Edited 1y ago
Want to go deeper with SQLAlchemy, FastAPI Path Operations, Django REST Framework & Channels?
Check out this series of Django articles by @jerivas and various OddBirds emeritus. https://www.oddbird.net/tags/django/
What if Django had an automatic ORM feature matrix — showing which features each database really supports, straight from the code? 🧩
I’ve shared some ideas (with demo data!) on how it could work and why it’d be great for Django’s docs 💡
https://www.paulox.net/2025/10/06/django-orm-comparison/
CC @django
#Django #Python #ORM #OpenSource #Database #PostgreSQL #SQLite #MariaDB #MySQL
🎉 Django PostgreSQL Anonymizer v0.1.0b1 (Beta) is live!
Database anonymization for Django + PostgreSQL with role-based access, context managers, pre-built presets & comprehensive docs.
📦 pip install django-postgres-anonymizer==0.1.0b1
📚 https://django-postgres-anonymizer.readthedocs.io/
🐙 https://github.com/CuriousLearner/django-postgres-anonymizer
Looking for beta testers!
🆓 Wagtail Space is free and virtual next week.
🎟️ Sign up to support the event and drop by to learn something new.
Wagtail Space is TOMORROW! Don't be the late bird that misses the spaceship!
Get your FREE ticket here: https://events.zoom.us/ev/AtWesoZI_eMUAZtG3IN1jHe29h3D8ymQo_CtHXHif8OXHxce920A~AmKLzn7GfJ8vV51FiJz9pNpTy55HaWLo4t8I4dqDbTM5nbyP41_CgAYz_fXVvA638XHQaKXNrH9jlJGSyHzvQwF6lQ
🎭✨ The world premiere of Ada — a rehearsed reading written by Emily Holyoake & performed by Chronic Insanity — is available to watch on YouTube!
Catch it here 👉 https://www.youtube.com/watch?v=CtrsssksCNU
Emily also shared her inspiration & writing process in a brilliant blog post:
You can find that here 👉 https://2025.pyconuk.org/2025/09/staging-ada-at-pycon2025/
💜 Huge thanks to Emily and the Chronic Insanity team for this extra-special event 🐍
#Python #Community #Theatre #AdaLovelace #PyConUK 2025
Here's an ~ official ~ release announcement for #numpydantic
repo: https://github.com/p2p-ld/numpydantic
docs: https://numpydantic.readthedocs.io
Problems: @pydantic is great for modeling data!! but at the moment it doesn't support array data out of the box. Often array shape and dtype are as important as whether something is an array at all, but there isn't a good way to specify and validate that with the Python type system. Many data formats and standards couple their implementation very tightly with their schema, making them less flexible, less interoperable, and more difficult to maintain than they could be. The existing tools for parameterized array types like nptyping and jaxtyping tie their annotations to a specific array library, rather than allowing array specifications that can be abstract across implementations.
numpydantic is a super small, few-dep, and well-tested package that provides generic array annotations for pydantic models. Specify an array along with its shape and dtype and then use that model with any array library you'd like! Extending support for new array libraries is just subclassing - no PRs or monkeypatching needed. The type has some magic under the hood that uses pydantic validators to give a uniform array interface to things that don't usually behave like arrays - pass a path to a video file, that's an array. pass a path to an HDF5 file and a nested array within it, that's an array. We take advantage of the rest of pydantic's features too, including generating rich JSON schema and smart array dumping.
This is a standalone part of my work with @linkml arrays and rearchitecting neurobio data formats like NWB to be dead simple to use and extend, integrating with the tools you already use and across the experimental process - specify your data in a simple yaml format, and get back high quality data modeling code that is standards-compliant out of the box and can be used with arbitrary backends. One step towards the wild exuberance of FAIR data that is just as comfortable in the scattered scripts of real experimental work as it is in carefully curated archives and high performance computing clusters. Longer term I'm trying to abstract away data store implementations to bring content-addressed p2p data stores right into the python interpreter as simply as if something was born in local memory.
plenty of todos, but hope ya like it.
![[This and the following images aren't very screen reader friendly with a lot of code in them. I'll describe what's going on in brackets and then put the text below.
In this image: a demonstration of the basic usage of numpydantic, declaring an "array" field on a pydantic model with an NDArray class with a shape and dtype specification. The model can then be used with a number of different array libraries and data formats, including validation.]
Numpydantic allows you to do this:
from pydantic import BaseModel
from numpydantic import NDArray, Shape
class MyModel(BaseModel):
array: NDArray[Shape["3 x, 4 y, * z"], int]
And use it with your favorite array library:
import numpy as np
import dask.array as da
import zarr
# numpy
model = MyModel(array=np.zeros((3, 4, 5), dtype=int))
# dask
model = MyModel(array=da.zeros((3, 4, 5), dtype=int))
# hdf5 datasets
model = MyModel(array=('data.h5', '/nested/dataset'))
# zarr arrays
model = MyModel(array=zarr.zeros((3,4,5), dtype=int))
model = MyModel(array='data.zarr')
model = MyModel(array=('data.zarr', '/nested/dataset'))
# video files
model = MyModel(array="data.mp4")](/proxy/post_attachment/231932/9159e4f6df.png "[This and the following images aren't very screen reader friendly with a lot of code in them. I'll describe what's going on in brackets and then put the text below.
In this image: a demonstration of the basic usage of numpydantic, declaring an \"array\" field on a pydantic model with an NDArray class with a shape and dtype specification. The model can then be used with a number of different array libraries and data formats, including validation.]
Numpydantic allows you to do this:
from pydantic import BaseModel
from numpydantic import NDArray, Shape
class MyModel(BaseModel):
array: NDArray[Shape[\"3 x, 4 y, * z\"], int]
And use it with your favorite array library:
import numpy as np
import dask.array as da
import zarr
# numpy
model = MyModel(array=np.zeros((3, 4, 5), dtype=int))
# dask
model = MyModel(array=da.zeros((3, 4, 5), dtype=int))
# hdf5 datasets
model = MyModel(array=('data.h5', '/nested/dataset'))
# zarr arrays
model = MyModel(array=zarr.zeros((3,4,5), dtype=int))
model = MyModel(array='data.zarr')
model = MyModel(array=('data.zarr', '/nested/dataset'))
# video files
model = MyModel(array=\"data.mp4\")")
![[Further demonstration of validation and array expression, where a Union of NDArray specifications can specify a more complex data type - eg. an image that can be any shape in x and y, an RGB image, or a specific resolution of a video, each with independently checked dtypes]
For example, to specify a very special type of image that can either be
a 2D float array where the axes can be any size, or
a 3D uint8 array where the third axis must be size 3
a 1080p video
from typing import Union
from pydantic import BaseModel
import numpy as np
from numpydantic import NDArray, Shape
class Image(BaseModel):
array: Union[
NDArray[Shape["* x, * y"], float],
NDArray[Shape["* x, * y, 3 rgb"], np.uint8],
NDArray[Shape["* t, 1080 y, 1920 x, 3 rgb"], np.uint8]
]
And then use that as a transparent interface to your favorite array library!
Interfaces
Numpy
The Coca-Cola of array libraries
import numpy as np
# works
frame_gray = Image(array=np.ones((1280, 720), dtype=float))
frame_rgb = Image(array=np.ones((1280, 720, 3), dtype=np.uint8))
# fails
wrong_n_dimensions = Image(array=np.ones((1280,), dtype=float))
wrong_shape = Image(array=np.ones((1280,720,10), dtype=np.uint8))
# shapes and types are checked together, so this also fails
wrong_shape_dtype_combo = Image(array=np.ones((1280, 720, 3), dtype=float))](/proxy/post_attachment/231933/0ff69a7f55.png "[Further demonstration of validation and array expression, where a Union of NDArray specifications can specify a more complex data type - eg. an image that can be any shape in x and y, an RGB image, or a specific resolution of a video, each with independently checked dtypes]
For example, to specify a very special type of image that can either be
a 2D float array where the axes can be any size, or
a 3D uint8 array where the third axis must be size 3
a 1080p video
from typing import Union
from pydantic import BaseModel
import numpy as np
from numpydantic import NDArray, Shape
class Image(BaseModel):
array: Union[
NDArray[Shape[\"* x, * y\"], float],
NDArray[Shape[\"* x, * y, 3 rgb\"], np.uint8],
NDArray[Shape[\"* t, 1080 y, 1920 x, 3 rgb\"], np.uint8]
]
And then use that as a transparent interface to your favorite array library!
Interfaces
Numpy
The Coca-Cola of array libraries
import numpy as np
# works
frame_gray = Image(array=np.ones((1280, 720), dtype=float))
frame_rgb = Image(array=np.ones((1280, 720, 3), dtype=np.uint8))
# fails
wrong_n_dimensions = Image(array=np.ones((1280,), dtype=float))
wrong_shape = Image(array=np.ones((1280,720,10), dtype=np.uint8))
# shapes and types are checked together, so this also fails
wrong_shape_dtype_combo = Image(array=np.ones((1280, 720, 3), dtype=float))")
![[Demonstration of usage outside of pydantic as just a normal python type - you can validate an array against a specification by checking it the array is an instance of the array specification type]
And use the NDArray type annotation like a regular type outside of pydantic – eg. to validate an array anywhere, use isinstance:
array_type = NDArray[Shape["1, 2, 3"], int]
isinstance(np.zeros((1,2,3), dtype=int), array_type)
# True
isinstance(zarr.zeros((1,2,3), dtype=int), array_type)
# True
isinstance(np.zeros((4,5,6), dtype=int), array_type)
# False
isinstance(np.zeros((1,2,3), dtype=float), array_type)
# False](/proxy/post_attachment/231934/173cae34ba.png "[Demonstration of usage outside of pydantic as just a normal python type - you can validate an array against a specification by checking it the array is an instance of the array specification type]
And use the NDArray type annotation like a regular type outside of pydantic – eg. to validate an array anywhere, use isinstance:
array_type = NDArray[Shape[\"1, 2, 3\"], int]
isinstance(np.zeros((1,2,3), dtype=int), array_type)
# True
isinstance(zarr.zeros((1,2,3), dtype=int), array_type)
# True
isinstance(np.zeros((4,5,6), dtype=int), array_type)
# False
isinstance(np.zeros((1,2,3), dtype=float), array_type)
# False")
![[Demonstration of JSON schema generation using the sort of odd case of an array with a specific dtype but an arbitrary shape. It has to use a recursive JSON schema definition, where the items of a given JSON array can either be the innermost dtype or another instance of that same array. Since JSON Schema doesn't support extended dtypes like 8-bit integers, we encode that information as maximum and minimum constraints on the `integer` class and add it in the schema metadata. Since pydantic renders all recursive schemas like this in the same $defs block, we use a blake2b hash against the dtype specification to keep them deduplicated.]
numpydantic can even handle shapes with unbounded numbers of dimensions by using recursive JSON schema!!!
So the any-shaped array (using nptyping’s ellipsis notation):
class AnyShape(BaseModel):
array: NDArray[Shape["*, ..."], np.uint8]
is rendered to JSON-Schema like this:
{
"$defs": {
"any-shape-array-9b5d89838a990d79": {
"anyOf": [
{
"items": {
"$ref": "#/$defs/any-shape-array-9b5d89838a990d79"
},
"type": "array"
},
{"maximum": 255, "minimum": 0, "type": "integer"}
]
}
},
"properties": {
"array": {
"dtype": "numpy.uint8",
"items": {"$ref": "#/$defs/any-shape-array-9b5d89838a990d79"},
"title": "Array",
"type": "array"
}
},
"required": ["array"],
"title": "AnyShape",
"type": "object"
}](/proxy/post_attachment/231935/4a7a60f64b.png "[Demonstration of JSON schema generation using the sort of odd case of an array with a specific dtype but an arbitrary shape. It has to use a recursive JSON schema definition, where the items of a given JSON array can either be the innermost dtype or another instance of that same array. Since JSON Schema doesn't support extended dtypes like 8-bit integers, we encode that information as maximum and minimum constraints on the `integer` class and add it in the schema metadata. Since pydantic renders all recursive schemas like this in the same $defs block, we use a blake2b hash against the dtype specification to keep them deduplicated.]
numpydantic can even handle shapes with unbounded numbers of dimensions by using recursive JSON schema!!!
So the any-shaped array (using nptyping’s ellipsis notation):
class AnyShape(BaseModel):
array: NDArray[Shape[\"*, ...\"], np.uint8]
is rendered to JSON-Schema like this:
{
\"$defs\": {
\"any-shape-array-9b5d89838a990d79\": {
\"anyOf\": [
{
\"items\": {
\"$ref\": \"#/$defs/any-shape-array-9b5d89838a990d79\"
},
\"type\": \"array\"
},
{\"maximum\": 255, \"minimum\": 0, \"type\": \"integer\"}
]
}
},
\"properties\": {
\"array\": {
\"dtype\": \"numpy.uint8\",
\"items\": {\"$ref\": \"#/$defs/any-shape-array-9b5d89838a990d79\"},
\"title\": \"Array\",
\"type\": \"array\"
}
},
\"required\": [\"array\"],
\"title\": \"AnyShape\",
\"type\": \"object\"
}")
Are you in #tech and running your own #Fediverse instance? You might want to join an Activity Pub relay instance!
My relay at https://fedi-relay.gyptazy.com has currently 139 instances connected, mostly tech related sharing the same mindset and interests like #Linux, #BSD, #Ansible, #Proxmox, #Coding, and many more! You can easily join from your instance when using #Pleroma, #snac (#snac2), #Mastodon and its forks 🙂
#fedi #fediworld #fedicommunity #community #FreeBSD #OpenBSD #NetBSD #homelab #Python #Debian #RockyLinux #Feditips
My relay at https://fedi-relay.gyptazy.com has currently 139 instances connected, mostly tech related sharing the same mindset and interests like #Linux, #BSD, #Ansible, #Proxmox, #Coding, and many more! You can easily join from your instance when using #Pleroma, #snac (#snac2), #Mastodon and its forks 🙂
#fedi #fediworld #fedicommunity #community #FreeBSD #OpenBSD #NetBSD #homelab #Python #Debian #RockyLinux #Feditips
🎉 I hadn't kept up with what Django's Steering Council had been up to outside of random board updates, and I'm happy to share how impressed I am with how transparent they are.
They are meeting several times a month and sharing their minutes as they go. https://github.com/django/steering-council
They are even sharing on the Django Forum to bring more visibility to what they are doing https://forum.djangoproject.com/t/django-steering-council-meetings-2025/38306
These are very welcoming changes. 👏👏
Edited 29d ago
Pillow 12.0.0 is out released!
🎨 Support for Python 3.14!
🎨 Dropped EOL 3.9!
🎨 New ImageText.Text API!
🎨 Removed deprecations!
🎨 New deprecations!
📜 And more: https://pillow.readthedocs.io/en/stable/releasenotes/12.0.0.html
📜📜📜 And much more: https://github.com/python-pillow/Pillow/releases/tag/12.0.0
Let's all give a shout out (and a follow) to the new @ThePSF Fellows!
https://pyfound.blogspot.com/2025/10/announcing-python-software-foundation.html
Thank you for all you do for us! 🥂
🎉 Abhijeet Mote
🎉 Abigail Afi Gbadago
🎉 Becky Smith @rebkwok
🎉 Christopher Bailey @digiglean
🎉 Dawn Wages @BajoranEngineer
🎉 Leah Wasser @leahawasser
🎉 Maaya Ishida
🎉 Mason Egger @masonegger
🎉 Miguel Grinberg @miguelgrinberg
🎉 William Vincent @wsvincent
Edited 22d ago
Only one week left until the release of Python 3.14.0 final!
https://docs.python.org/3.14/whatsnew/3.14.html
https://peps.python.org/pep-0745/
What are you looking forward to?
Shout out to the #Python Software Foundation and everyone involved for turning down a US$1.5 millon grant rather than compromise their Values.
It's easy to say we support #DEI or any other thing. It's only when we're asked to pay a price that our #Values are tested.
When asked to turn their back on DEI, the PSF passed the test. This is what resistance looks like.
👏
RE: https://mastodon.social/@djangonews/115446256829600290
🎙️💬 And I'm on @djangochat talking with @wsvincent and @carlton about the latest Django Survey trends with a WHOLE lot of side quests.
I hope you enjoy it!
https://djangochat.com/episodes/django-survey-2025-jeff-triplett
The State of Django 2025
#Python #Pycharm #Webdevelopment #Django #Survey
https://blog.jetbrains.com/pycharm/2025/10/the-state-of-django-2025/
Python’s governing body drops a $1.5 M U.S. grant, saying the anti-DEI clause violates its mission and community spirit.
https://linuxiac.com/python-software-foundation-withdraws-1-5m-u-s-government-grant/
Pull requests wanted!
I've just opened a whole bunch of issues in the PyLadiesCon Portal repo and all of these are up for grabs.
Check it out if you know some Python, Django, and maybe a little of HTML or CSS.
Mittlerweile habe ich, ich weiß nicht mehr, wie viele #proxmox #Server und #Backup Server in Betrieb, ich weiß auch nicht mehr, mit wie vielen #Linux #Container|n #lxc und virtuellen Maschinen #kvm.
Darin laufen zudem #docker #portainer #dockge #kubernetes und noch mehr Instanzen, dass ich so langsam nicht mehr weiß, wie ich die alle administrieren kann.
Mal ganz abgesehen von den herkömmlichen öffentlichen Servern, die ein #upgrade benötigen.
#wordpress #nextcloud #phpwcms #theme #mariadb #mysql
Ich will seit Jahren #python lernen und komme keinen pups weiter.
People who are newer to Django often do not know about these great resources and the richness of the Django ecosystem. Spread this link please! #python #django https://www.djangoproject.com/weblog/2025/nov/02/five-ways-to-discover-django-packages/
Migrated from Sharkey to GoToSocial, because I wanted something more light-weight and less feature-rich (though I do have to say that Sharkey is excellent and has tons of features and control. I highly recommend it to somebody who really wants a lot from their Fediverse software). I probably sent out some unnecessary notifications by mistake while backloading my posts. Sorry about that.
I wrote this python script for doing the actual backloading. It's clunky, doesn't handle many common cases, breaks on mentions, and has tons of little flaws, but it did get the job done.
Libera IRC Channels Sorted by Number of Users
----
- 2 hours ago | 4 points | 0 comments
- URL: https://netsplit.de/channels/?net=Libera.Chat
- Discussions: https://news.ycombinator.com/item?id=40983500
- Summary: Libera.Chat, an IRC network, reported an average of 33,386 users and 23,157 chat rooms. Notable channels include #linux, #python, #archlinux, #ubuntu, #rust, #security, #kde, #debian, and #thelounge.
thanks to the amazing #DjangonautSpace program, i recently started contributing to #django . i documented the journey towards my first PR in this getting-started walkthrough - hope it's helpful to you new contributors out there: https://dev.to/annalauraw/your-first-django-pr-from-scratch-to-improved-patch-5b2f.
with huge thanks to @djangonaut 🙏
#python #OpenSource #contribution #WebDev
We're excited to announce the results of the 2024 election for the PSF Board & Bylaw changes! Thank you to the nominees, our outgoing Board member, and our community for participating this year- we are so grateful to be in community with you 💙💛 #python
https://pyfound.blogspot.com/2024/07/announcing-2024-psf-board-election.html