Play Python With Me: 33. Python Debug 今天我们要来聊一聊如何在VS Code中使用Debug调试功能。调试对于任何编程学习都是极其重要的一环，它不仅可以帮助我们找出代码中的错误，还能让我们更深入地理解代码的运行逻辑。https://cyberhongtu.com/2024/01/20/play-python-with-me-32-python-debug/ #编程, #新手教程, #Python
python
Play Python With Me: 34. Unit Test 今天，我们要聊聊Python中的单元测试，英语叫Unit Test。我们一起来看看专业的程序员们是怎么做Python代码测试的。作为咱们中阶学员，肯定会有一些自己的项目，如果你还没开始在项目中加入代码测试，那么肯定得学一学，怎么编写测试用例，怎么搭建和拆除测试环境，还有怎么保证测试覆盖率。https://cyberhongtu.com/2024/01/27/play-python-with-me-34-unit-test/ #编程, #新手教程, #Python
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
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 192d ago
It's been brewing for a while, but @adamghill and @sjbitcode are ready to spill the beans on all things Django!
The Django Brew podcast is now live! 🥳✨🎙️
Listen to Episode 0: Intros, memes, and tater-tots now!
https://www.djangobrew.com/2326928/14648511-episode-0-intros-memes-and-tater-tots
We chat about why we started a new podcast, our Django origin story, and how people really love free stuff.
And be sure to follow us here for updates on future episodes!
Edited 195d ago
#451 Djangonauts, Ready for Blast-Off
Is out with @sarahboyce and Tushar Gupta + host @mkennedy #python, enjoy! cc @djangonaut
https://talkpython.fm/episodes/show/451/djangonauts-ready-for-blast-off
Python 5 Minutes: 3. List Comprehension 今天我们将探索Python中的第三大神器：列表理解(List Comprehension)。Python的列表理解是一种简洁且高效的构建列表的方法。通过单行代码，可以从其他列表中创建新列表，其中可以包含筛选条件。同时Python 3.12版本对列表理解有重要性能加强，请看到最后不要错过。https://cyberhongtu.com/2024/03/15/python-5-minutes-3-list-comprehension/ #编程, #新手教程, #Python
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
Python5分钟: 4. 常用操作符号杂谈 大家好，欢迎回到“Python五分钟”，我是赛博红兔。今天我们将一起探讨Python中的一些常用的操作符号，这些符号在Python编程中扮演着关键的角色。我们将通过一些实际示例来理解它们的常规和特殊用法。https://cyberhongtu.com/2024/04/12/python5%e5%88%86%e9%92%9f-4-%e5%b8%b8%e7%94%a8%e6%93%8d%e4%bd%9c%e7%ac%a6%e5%8f%b7%e6%9d%82%e8%b0%88/ #B站, #编程技巧, #Python, #Python教学, #Youtube
Edited 159d ago
Play Python With Me: 37. Python Asynchronous Programming (Part 2): Multiprocessing Parallelism 大家好，我是赛博红兔。上集我们聊了什么是进程、线程还有Python的全局解释器锁和多线程并发处理IO密集型的任务。不了解的小伙伴们可以先去上集看看。今天，我们接着来讲加速处理大量计算的CPU密集型任务的方法：异步编写里的多进程并行（Parallel Programming）。https://cyberhongtu.com/2024/03/29/play-python-with-me-37-python-asynchronous-programming-part-2-multiprocessing-parallelism/ #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
和大伙聊聊天：学编程也有中年危机？关于Python大家问我最多的三个问题 大家好，我是赛博红兔。今天再来和大伙聊聊天，上一期的聊天播客的反响很不错，一下子就成了这个频道互动性最好的一期。很多朋友告诉我，他们非常享受这种轻松愉悦的对话方式。看了大家在节目下方给我的留言，也给了我不少的启发和思考。所以我决定让这个聊天环节成为我们的一个小传统。https://cyberhongtu.com/2024/04/16/%e5%92%8c%e5%a4%a7%e4%bc%99%e8%81%8a%e8%81%8a%e5%a4%a9%ef%bc%9a%e5%ad%a6%e7%bc%96%e7%a8%8b%e4%b9%9f%e6%9c%89%e4%b8%ad%e5%b9%b4%e5%8d%b1%e6%9c%ba%ef%bc%9f%e5%85%b3%e4%ba%8epython%e5%a4%a7%e5%ae%b6/ #编程技巧, #Python, #和大伙聊聊天, #播客
和我一起做3A游戏《归乡之路》（一） 大家好，我是赛博红兔。我们的《和我一起做3A游戏》系列要开始了！这个系列本质上是对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。https://cyberhongtu.com/2024/04/26/%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%80%ef%bc%89/ #编程技巧, #Python, #Python教学, #游戏制作
We just released an update to our mobile apps. So if you have them installed on your Apple or Android device, please update to get the latest bug fixes and improvements. And if you’re not using them yet: Why not? They are free and have free courses included! #python
和我一起做3A游戏《归乡之路》（二） 大家好，我是赛博红兔。欢迎来到《和我一起做3A游戏》第二集！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，我们设计了游戏的艺术概念，找好了资源，建立了项目文件夹。创建了主窗口，创建了游戏主循环，学习了控制游戏的帧率，如何正确关闭游戏处理，还有绘制颜色块、文字和图片画布。那么今天，我们就在这个代码基础上继续做游戏。我们主要要完成的任务是创建游戏主人公小猫的类，还有打字游戏要打的单词的类，和分数的统计和显示。https://cyberhongtu.com/2024/05/03/%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%ba%8c%ef%bc%89/ #Pygame, #Python, #Python教学, #游戏制作
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 131d ago
Folks! There are still a lot of slots open for volunteering at #pycon. If you are attending the conference, lend a helping hand! Details on what how are here: https://us.pycon.org/2024/volunteers/volunteering/
Team work makes the dream work!!
#pycon2024 #python
Edited 129d ago
Python5分钟: 5. 函数参数类型提示和注解 大家好，欢迎回到“Python五分钟”，我是赛博红兔。今天我们将讨论Python的类型提示和注解（type hinting and annotations）。这种功能在Python 3.5中引入，允许开发者标明函数参数、返回值和变量的预期数据类型。虽然个人项目中较少使用，但在公司级别的大项目中非常流行，因为它能提升代码的可读性和可维护性。https://cyberhongtu.com/2024/05/21/python5%e5%88%86%e9%92%9f-5-%e5%87%bd%e6%95%b0%e5%8f%82%e6%95%b0%e7%b1%bb%e5%9e%8b%e6%8f%90%e7%a4%ba%e5%92%8c%e6%b3%a8%e8%a7%a3/ #编程技巧, #Python, #Python教学
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\"
}")
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