Play Python With Me: 35. Git and GitHub 今天是我们Python中阶课程的最后一集，让我们一起来聊一聊Git和Github的代码版本控制。不知不觉的我们的Python中阶教程就要临近尾声了，感谢小伙伴们一路的陪伴。我发现《一起玩Python》频道已经开始吸引不少同好们的关注和分享。赛博兔粉们，有什么关于Python想要我聊的话题都请留言告诉我，也请大伙去我之前的基础教学视频支持一下。接下来，咱们得上一些高阶教程的内容了，也不一定比之前的难只不过针对性更强。我们会尝试新的视频形式，还会开一些新坑来介绍一些Python的实战项目。欢迎大家继续多多支持和分享！https://cyberhongtu.com/2024/02/03/play-python-with-me-35-git-and-github/ #编程, #新手教程, #Python
python
Hello to all of you people of mastodon! 🤗
This is my #introduction
Here I will share stuff and links that I would like more people to know, I will also try to write some reflexions and though on my projects. I live in Normandy, France. My current topics of interests are various: #programming #bike #osm #climatechange #selfhosting #freeSoftware #linux #maps #openstreetmap #wikidata #rust #python
I speak french 🇫🇷 and english 🇬🇧 .
#NBPy is back, and we want to hear from you!
We're welcoming your talk proposals from _right now_ until April 12th. Have something to share about Python, or that people who like Python need to hear? We want to hear from you!
All the details, including key dates, policies, and proposal guidelines are at https://2024.northbaypython.org/speak
Edited 1y ago
Play Python With Me: 36. Python Asynchronous Programming (Part 1): Multithreading and Concurrency 大家好，我是赛博红兔。从今天起，我们要开始学习Python高阶的知识，我会随时更新思维导图，它的下载链接我会分享在视频下方的说明栏里。这部分高阶知识适合那些已经具备一些Python应用经验，并希望深化理解和提高编程技能的开发者。特别是那些从事Web开发、数据科学、自动化脚本编写或任何需要高性能和高效代码的领域的专业人士。那么我们按照粉丝狂热的要求，先来讲一讲Python的“多线程并发”和“多进程并行”，让你的代码运行有飞一样的感觉。讲并行和并发之前，我们必须得先来了解一下什么是进程和线程。https://cyberhongtu.com/2024/03/22/play-python-with-me-36-python-asynchronous-programming-part-1-multithreading-and-concurrency/ #编程, #新手教程, #Python
Play Python With Me: 38. 10 Tricks to become Pythonic 今天我们来聊一个轻松的话题：玩蛇。Python这条大蛇，不是所有人都能玩得溜的。正所谓”意在拳先，力随意走“，真正的玩蛇高手，不仅要会使用Python还要在代码的字里行间透露出玩蛇的风格。玩蛇圈内有一个词叫做Pythonic，也就是写代码要有Python自己的风格。我整理的十段心法能让你的Python代码更加的Pythonic。https://cyberhongtu.com/2024/04/09/play-python-with-me-38-10-tricks-to-become-pythonic/ #编程, #新手教程, #Python
和大伙聊聊天——我没有学习Python的思路 今天我们用播客的形式纯聊天。学Python的过程就是自我提问的过程，把学Python当成一种习惯。如何学Python？要对自己有一个正确的身份认同，有一个确定的目标。学习最佳方式是什么？学习环境很重要，碎片化时间可以观看视频学习。如何将学到的Python技能用到实际项目里？最后，一定要给自己足够的奖励。 这样就能在学Python的路上越走越顺！https://cyberhongtu.com/2024/04/10/%e5%92%8c%e5%a4%a7%e4%bc%99%e8%81%8a%e8%81%8a%e5%a4%a9-%e6%88%91%e6%b2%a1%e6%9c%89%e5%ad%a6%e4%b9%a0python%e7%9a%84%e6%80%9d%e8%b7%af/ #编程技巧, #Python, #和大货聊聊天, #和大伙聊聊天, #播客
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/
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
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\"
}")
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.
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
Python5分钟: 10. 使用dotenv库管理敏感信息 大家好，欢迎回到“Python五分钟”，我是赛博红兔。上一集我们学习了如何从系统环境里保存和调用脚本需要的隐私信息。在最后我们也提到了对于个人项目或小型项目，这个方法通常是足够的。但对于工业项目，尤其是涉及到敏感数据和高安全性要求的场景，可能需要更严格的措施。因为这个方法操作起来还是不够灵活，系统不同操作也有不同，还有环境变量在系统级别上还是可能会被其他用户或程序读取到。https://cyberhongtu.com/2024/09/07/python5%e5%88%86%e9%92%9f-10-%e4%bd%bf%e7%94%a8dotenv%e5%ba%93%e7%ae%a1%e7%90%86%e6%95%8f%e6%84%9f%e4%bf%a1%e6%81%af/ #编程技巧, #Python, #Python教学
@MonaApp is there a way to mute someone from a followed hashtag but not mute them entirely?
Eg, I follow #python but there are a few kind people who post beginner tips every day or two on the hashtag. I don’t want to mute them entirely, I just want to remove them from the followed hashtag. Is that possible? 😬
Love Mona btw. 😍
The ponies have arrived!!! 🦄 🔥
Check out our snazzy sticker packs for #DjangoConUS!
You can grab one in person after my ⚡️ quick ⚡️ lightning talk!
If you can't make it (like @adamghill!) visit https://djangostickers.com for more info and be sure to sign up for our mailing list! 💌
See you all at @djangocon! 👋
和我一起做3A游戏《贪吃蛇》 大家好，我是赛博红兔。欢迎继续收看我的《和我一起做3A游戏》系列！在介绍今天的游戏之前，我建议没有任何pygame基础的小伙伴们先去看看本系列的前三集基础教学，链接我会放在下方评论区。今天，带给大伙的是一款家喻户晓的经典游戏——贪吃蛇。https://cyberhongtu.com/2024/10/06/%e5%92%8c%e6%88%91%e4%b8%80%e8%b5%b7%e5%81%9a3a%e6%b8%b8%e6%88%8f%e3%80%8a%e8%b4%aa%e5%90%83%e8%9b%87%e3%80%8b/ #Pygame, #Python, #Python教学, #游戏制作
✨ 0.9.0 of dj-angles, a library to incorporate more HTML into #Django templates, is now published. ✨
The main feature is (beta) support for slots in components! 🎰🚀
Slots documentation: https://dj-angles.adamghill.com/en/latest/components.html#slots
📖 https://dj-angles.adamghill.com
📦 https://pypi.org/project/dj-angles
🛠️ https://github.com/adamghill/dj-angles
Edited 300d ago
Anthony Shaw is giving us a quick lesson in Australian English
@tonybaloney
#PyConAPAC #PyCon #Python
The #FOSDEM 2025 #Python devroom Call for Participation is now out!
Talks are 25 mins long on a topic related to Python and Open Source.
Submission deadline is 1 December.
Looking forward to some great talks again!
https://gist.github.com/malemburg/4f2d1ceeb24d26c35740390cf712af5e
Following resignations on the Steering Council, Django’s technical direction needs a fresh take. Elections are coming up soon 👉️
https://www.djangoproject.com/weblog/2024/nov/14/technical-governance-challenges-and-opportunities/
Thank you to our 12 candidates for the Django 6.x Steering Council elections! https://www.djangoproject.com/weblog/2024/dec/10/django-6x-steering-council-candidates/ We’re elated to see so many stellar contributors rise to the challenge 🚀 Now time to vote! #python #django
📰 New newsletter: Django News - DjangoCon Europe 2025 in Dublin, Ireland! 🍀 - Nov 15th 2024
🐍 On this Building SaaS with #Python and #Django, I made a new view that returns a zip archive. We'll use this archive as the container for year end PDF reports for the feature that I'm building. https://www.youtube.com/watch?v=v2zfjVh1zM8
“Python becomes the most used language on GitHub, overtaking JavaScript after a 10-year run as the most used language. This is the first large-scale change we’ve seen in the top two languages since 2019…” 🏆
https://github.blog/news-insights/octoverse/octoverse-2024/#the-most-popular-programming-languages
Published some early validation results from my "SBOM for #Python packages" project. TLDR: I forked auditwheel and added some rudimentary SBOM record-keeping for bundled libraries and showed that today's SCA tools are able to use that information out-of-the-box. Full instructions and work in the post:
https://sethmlarson.dev/early-promising-results-with-sboms-and-python-packages