semvec/pypi-packages huggingface dataset:
painter-tk 1.4.2.3
使用tkinter的Canvas控件制作的画板程序, 支持编辑、保存文档以及文档属性等功能。 A painter using tkinter.Canvas, supporting editing and saving documents and modifying document properties, etc.
Keywords: simple, text, editor, notepad, tkinter

应用tkinter的Canvas控件开发的画板程序, 

支持绘制丰富多样的图形，并支持撤销、重做，

支持打开、编辑和保存文档，以及设置文档属性功能，可自定义画笔粗细和颜色，以及背景颜色，

支持保存为vec, json, jpg, png等多种文件格式，其中.vec为默认的格式。


最近更新 v1.4.2.3: 修复了对Linux等系统，以及Windows下高DPI的支持问题。

更新 v1.4.2: 修复bug，并精简了部分源代码, 使程序更加可读。

更新 v1.4.1: 添加了保存为jpg, png等图像文件的功能。

截图: 

.. image:: https://i-blog.csdnimg.cn/direct/f950ec27aeb14bf480172563edaf8343.png
    :alt: 截图
pyemd-rs 0.1.4
PyEMD with Rust.
Requires Python: >=3.10

# pyemd-rs

[![License:MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![PyPI - Version](https://img.shields.io/pypi/v/pyemd-rs)](https://pypi.org/project/pyemd-rs/)

This library attempts to reimplement the [PyEMD](https://github.com/laszukdawid/PyEMD) library in Rust.

The original library is licensed under [Apache-2.0 Copyright 2017 Dawid Laszuk](https://github.com/laszukdawid/PyEMD/blob/master/LICENSE.md)

## Usage

The library exports two functions, `emd` and `ceemdan`.
Each takes a 1D numpy array and returns a tuple.
The first element is a 2D array of the Intrinsic Mode Functions (IMFs) and the second is a 1D array of the non-periodic residual component.

The `ceemdan` method uses random noise as part of the procedure, so you may want to set the seed to ensure reproducibility.

```python
import numpy as np
from pyemd_rs import emd, ceemdan

x = np.sin(np.linspace(0, 2*np.pi, 101)) + np.random.normal(size=101, scale=0.1)

imfs_emd, residual_emd = emd(x)
imfs_ceemdan, residual_ceemdan = ceemdan(x, seed=123)
```
kirara-auto-qa-plugin 1.0.0
Auto QA Plugin for Kirara AI
Requires Python: >=3.8

# Kirara Auto QA Plugin 🎯

自動問答插件 for Kirara AI - 支援關鍵字匹配和自動回覆功能

## ✨ 功能

- 🔍 關鍵字自動匹配
- 💬 自動回覆訊息  
- ⚡ 支援多平台 (QQ, Telegram, 企業微信)
- 🎮 簡單易用的指令介面

## 🚀 安裝

```bash
# 透過插件市場安裝 (推薦)
搜尋: auto-qa-plugin

# 手動安裝
git clone https://github.com/YOUR_USERNAME/kirara-auto-qa-plugin.git
cd kirara-auto-qa-plugin
pip install -e .
hytest 0.10.1
A generic automation framework for QA testing
Requires Python: >=3.9
Keywords: hytest, automation, testautomation

## Introduction

`hytest` is a generic open source test-automation framework. It's mainly but not only used for QA system testing.

Maybe your first question is what's the difference comparing it with `pytest` or  `Robot Framework`?

Well, I like the way of setup/teardown of Robot Framework over that of pytest.

The former is simpler and more intuitive than the latter. The order of multiple layer's setup/teardown is strictly obeyed, which is especially critical for system testing.  But the order of setups/teardowns of pytest is not very strict, [see this](https://github.com/pytest-dev/pytest/issues/7416)

But Robot Framework creates its own "language" to write test cases instead of using python directly. It's much less flexible to write test cases, and it makes it hard to deal with complex data structure. 


So, if you like Robot Framework's way of setup/teardown,  and want to write tests in python, you could try hytest.


## Installation

```py
pip install hytest
```

## Documentation

[User Guide](https://github.com/jcyrss/hytest/blob/main/Documentation.md)


[中文教程链接](https://www.byhy.net/tut/auto/hytest/01/)


## Screenshots

![image](https://github.com/jcyrss/hytest/assets/10496014/ff227feb-b2ec-4b4e-aa76-caa6e0dd0936)

![image](https://github.com/jcyrss/hytest/assets/10496014/4d25fc5d-8fec-4db1-ab2b-d84ecbc06004)
course-hacking-2.0 4.0
Una biblioteca para consultar cursos de hack4u

# Hack4U Academy Courses Library

Una Biblioteca Python para consultar cursos de la academia Hack4U.

## Cursos disponibles:

- Introducción a Linux [15 horas]
- Personalización de Linux [3 horas]
- Introducción al Hacking [53 horas]

## Instalación

Instala el paquete usando `pip3`:

```python3
pip3 install hack4u
```
## Uso básico

## Listar todos los cursos

```python
from hack4u import list_courses

for course in list_courses():
    print(course)
```

### Obtener un curso por nombre

```python
from hack4u import get_course_by_name

course = get_course_by_name("Introducción a Linux")
print(course)
```

### Calcular duración total de los cursos
```python3
from hack4u.utils import total_duration

print(f"Duración total: {total_duration()} horas")
course-hacking 3.0
Una biblioteca para consultar cursos de hack4u

# Hack4U Academy Courses Library

Una Biblioteca Python para consultar cursos de la academia Hack4U.

## Cursos disponibles:

- Introducción a Linux [15 horas]
- Personalización de Linux [3 horas]
- Introducción al Hacking [53 horas]

## Instalación

Instala el paquete usando `pip3`:

```python3
pip3 install hack4u
```
## Uso básico

## Listar todos los cursos

```python
from hack4u import list_courses

for course in list_courses():
    print(course)
```

### Obtener un curso por nombre

```python
from hack4u import get_course_by_name

course = get_course_by_name("Introducción a Linux")
print(course)
```

### Calcular duración total de los cursos
```python3
from hack4u.utils import total_duration

print(f"Duración total: {total_duration()} horas")
pageindex-mcts 0.1.0
MCTS based PageIndex search library
Requires Python: >=3.8

# PageIndexMCTS

PageIndexMCTS is a Python library that implements Monte Carlo Tree Search (MCTS) for efficient page indexing and retrieval, integrated with Large Language Models (LLMs).

## Features

- **MCTS Implementation**: Includes standard MCTS and Hybrid MCTS (PUCT) algorithms.
- **LLM Integration**: Supports using LLMs (like ChatGPT) for value estimation and policy priors.
- **Parallel Search**: Optimized for performance with parallel simulation steps.
- **Document Indexing**: Tools for indexing PDF and Markdown documents.

## Installation

You can install the package directly from source:

```bash
pip install .
```

## Usage

See the `example` directory for usage demonstrations.

### Basic MCTS Search

```python
from search.tree.mcts import PageIndexMCTS

# Initialize your structure and query
# ...
search = PageIndexMCTS(structure, query, use_llm=True, n_threads=4)
search.run_search(iterations=50)
results = search.get_top_results(n=3)
```

## License

This project is licensed under the MIT License - see the LICENSE file for details.