ohmrun (@ohmrun@techhub.social)
fuck #Python and "This environment is externally managed"
In your hole, you c*nts
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sayzard (@sayzard@mastodon.sayzard.org)
Python Trending (@pythontrending)
PyCuVSLAM은 Python용으로 발표된 고정밀·고효율 VSLAM 시스템입니다. 이름에서 유추할 때 CUDA 가속을 활용한 구현으로 실시간 로보틱스·컴퓨터 비전 적용에 적합하며, 경량화·정확도 향상을 목표로 하는 오픈소스 또는 라이브러리 형태의 SLAM 솔루션으로 보입니다.
https://x.com/pythontrending/status/2012844573463077106
#vslam #python #slam #cuda #robotics
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r (@r@fed.brid.gy)
The Open Risk Academy course "Introduction to Input-Output Models using Python" has been updated and expanded: www.openriskacademy.com/course/view.... #python #sustainablefinance
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r (@r@bsky.brid.gy)
The Open Risk Academy course "Introduction to Input-Output Models using Python" has been updated and expanded: www.openriskacademy.com/course/view.... #python #sustainablefinance
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openrisk (@openrisk@mastodon.social)
The Open Risk Academy course "Introduction to Input-Output Models using Python" has been updated and expanded:
https://www.openriskacademy.com/course/view.php?id=64
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apparebit (@apparebit@mastodon.social)
I'm happy to announce Shantay v0.6, my #Python tool for analyzing the European Union's #DSA #Transparency Database. This latest version can optionally include statistics for all DB columns (even free-form text columns, which is not recommended for the full DB). It supports richer configuration options. And it uses much less memory and includes better instrumentation. Check it out at https://github.com/apparebit/shantay
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jriou (@jriou@hachyderm.io)
Obviously, I can't type so there is a duplicate ("un comédien") so we can remove duplicates by using a set.
And also compute the size of the left padding to display the list nicely.
Full script:
```
import random
words = {
"un magicien",
"combien",
"un mécanicien",
"un musicien",
"un comédien",
"un pharmacien",
"le mien",
"un chien",
"un Italien",
"un gardien",
}
random.shuffle(list(words))
display_size = str(len(str(len(words))))
for i, word in enumerate(words):
print(f"{i+1:{display_size}}. {word}")
```
Result:
```
1. le mien
2. combien
3. un gardien
4. un magicien
5. un mécanicien
6. un Italien
7. un pharmacien
8. un musicien
9. un comédien
10. un chien
```
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jriou (@jriou@hachyderm.io)
My daughter has to prepare a dictation every week. Instead of mentally randomize the list and keep track of what word I have already given, I wrote a simple Python script:
```
import random
words = [
"un magicien",
"un comédien",
"un mécanicien",
"un musicien",
"un comédien",
"un pharmacien",
"le mien",
"un chien",
"un Italien",
"un gardien",
]
random.shuffle(words)
for i, word in enumerate(words):
print(f"{i+1:2}. {word}")
```
Giving for example:
```
1. un mécanicien
2. un gardien
3. le mien
4. un comédien
5. un musicien
6. un comédien
7. un magicien
8. un pharmacien
9. un chien
10. un Italien
```
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qiita (@qiita@rss-mstdn.studiofreesia.com)
Python × PuLP × Pandasでスケジューリングをして、Excelに出力
https://qiita.com/yama_python/items/6714132c4f95b0e5f10a?utm_campaign=popular_items&utm_medium=feed&utm_source=popular_items
#qiita #Python #pandas #最適化 #pulp
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son_link (@son_link@mastodon.social)
Hace unas horas se ha publicado la versión 2.6.0 de #Pyxel
La novedad es que se ha añadido soporte para fuentes TTF y OTF, incluyendo cambiar su tamaño, algo que no pasa con el formato OBF (tenías que editar la fuente para cambiar su tamaña).
En el segundo enlace se puede ver el resultado, y en el tercero su código
https://github.com/kitao/pyxel/releases/tag/v2.6.0
https://kitao.github.io/pyxel/wasm/examples/13-custom-font.html
https://github.com/kitao/pyxel/blob/main/python/pyxel/examples/13_custom_font.py
Por cierto, ya anuncio que añadirá soporte para reproducir WAV y OGG
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hugovk (@hugovk@mastodon.social)
Black 26.1.0 is out.
For those who don't know, they use CalVer, and a new year means a new style with some formatting changes. So far they look good to me.
https://github.com/psf/black/releases/tag/26.1.0
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habr (@habr@zhub.link)
Я реализовал паттерн памяти из OpenAI Cookbook в Python библиотеку
AI-агент который помнит: Python библиотека по рецепту OpenAI Устал объяснять агенту одно и то же? Я тоже. Взял паттерн Context Personalization из OpenAI Cookbook и упаковал в pip install: from agent_memory import MemoryManager manager = MemoryManager(storage=SQLiteStorage("./memory.db")) state = manager.load_user("user_123") prompt = state.to_system_prompt() # Память уже в промпте Что под капотом: — Session vs Global память — LLM-консолидация (5 заметок → 3, умная дедупликация) — Блокировка PII и prompt injection — TTL для временных предпочтений pip install agent-memory-state Код: github.com/molchanovartem/agent-memory
https://habr.com/ru/articles/986282/
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