semvec/pypi-packages huggingface dataset:
text-to-markdown-jxppx 1.0.0
用于将文本文件转化为markdown格式
Requires Python: >=3.8

# Text to Markdown Converter

一个简单而强大的文本文件转Markdown格式的Python工具。

## 功能特性

- 🚀 **自动识别标题**: 智能识别文本中的标题并转换为Markdown标题格式
- 📝 **段落处理**: 自动处理普通段落文本
- 📋 **列表转换**: 支持多种列表格式的识别和转换
- 🔧 **编码支持**: 自动检测文件编码，支持UTF-8、GBK等多种编码
- 💻 **命令行工具**: 提供便捷的命令行接口
- 📦 **Python API**: 提供简洁的Python编程接口

## 安装

```bash
# 从源码安装
pip install -e .

# 安装开发依赖
pip install -e .[dev]

# 安装增强功能依赖
pip install -e .[enhanced]
```

## 快速开始

### 命令行使用

```bash
# 基本用法
python -m text_to_markdown_jxppx.tt_md input.txt

# 指定输出文件
python -m text_to_markdown_jxppx.tt_md input.txt output.md

# 或者直接运行脚本
python text_to_markdown_jxppx/tt_md.py example.txt
```

### Python API

```python
from text_to_markdown_jxppx.tt_md import convert_text_to_markdown, TextToMarkdownConverter

# 简单转换
output_path = convert_text_to_markdown('input.txt')
print(f"转换完成: {output_path}")

# 指定输出文件
convert_text_to_markdown('input.txt', 'output.md')

# 使用转换器类进行更多控制
converter = TextToMarkdownConverter()
result = converter.convert_file('input.txt', encoding='gbk')
```

## 转换规则

### 标题识别

工具会自动识别以下类型的标题：

1. **全大写短行** → `# 标题` (一级标题)
2. **数字开头的行** (如 "1. 标题", "1.1 标题") → `## 标题` (二级标题)
3. **短行文本** (少于30字符) → `### 标题` (三级标题)

### 列表转换

支持多种列表格式：

- `- 项目` → `- 项目`
- `* 项目` → `- 项目`
- `1. 项目` → `- 项目`
- `a. 项目` → `- 项目`

### 示例

**输入文本:**
```
PYTHON编程指南

Python是一种高级编程语言。

1. 基础语法

Python使用缩进来表示代码块。

常用数据类型：
- 整数 (int)
- 字符串 (str)
- 列表 (list)
```

**输出Markdown:**
```markdown
# PYTHON编程指南

Python是一种高级编程语言。

## 基础语法

Python使用缩进来表示代码块。

### 常用数据类型：

- 整数 (int)
- 字符串 (str)
- 列表 (list)
```

## 依赖说明

### 核心功能
本工具**仅使用Python标准库**，无需安装额外依赖：
- `re` - 正则表达式处理
- `os` - 文件系统操作
- `pathlib` - 路径处理
- `typing` - 类型提示

### 可选依赖

#### 开发依赖 (`pip install -e .[dev]`)
- `pytest>=7.0.0` - 单元测试框架
- `pytest-cov>=4.0.0` - 测试覆盖率
- `black>=22.0.0` - 代码格式化工具
- `flake8>=5.0.0` - 代码质量检查

#### 增强功能 (`pip install -e .[enhanced]`)
- `chardet>=5.0.0` - 自动检测文件编码
- `python-magic>=0.4.27` - 文件类型检测

## 开发

```bash
# 克隆项目
git clone <repository-url>
cd text_to_markdown

# 安装开发环境
pip install -e .[dev]

# 运行测试
pytest

# 代码格式化
black .

# 代码检查
flake8
```

## 许可证

MIT License

## 贡献

欢迎提交Issue和Pull Request！
astro-pest 0.1.0
Preprocessing Engine for Spherinator Training
Requires Python: >=3.10

[![Build Status](https://github.com/HITS-AIN/PEST/actions/workflows/python-package.yml/badge.svg?branch=main)](https://github.com/HITS-AIN/PEST/actions/workflows/python-package.yml?branch=main)
[![Documentation Status](https://readthedocs.org/projects/spherinator/badge/?version=latest)](https://spherinator.readthedocs.io/en/latest/?badge=latest)
![versions](https://img.shields.io/badge/python-3.10%20%7C%203.11%20%7C%203.12-blue)


# Preprocessing Engine for Spherinator Training (PEST)

PEST preprocess simulation data and generate training data for
[Spherinator](https://github.com/HITS-AIN/Spherinator) and
[HiPSter](https://github.com/HITS-AIN/HiPSter), including arbitrary single- and multi-channel
images, 3D PPP and PPV cubes, and point clouds.

<!-- It is currently designed to work with IllustrisTNG data, either through the web-based API or locally
using downloaded snapshots. -->

<p align="center">
  <img src="logo.png" width="100" height="100">
</p>


## Installation

```bash
pip install git+https://github.com/HITS-AIN/PEST
```

## Documentation

The `PEST` documentation is part of the Spherinator documentation and can be found at:

[Read The Docs](https://spherinator.readthedocs.io/en/latest/pest.html)


## Acknowledgments

Funded by the European Union. This work has received funding from the European High-Performance Computing Joint Undertaking (JU) and Belgium, Czech Republic, France, Germany, Greece, Italy, Norway, and Spain under grant agreement No 101093441.

Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European High Performance Computing Joint Undertaking (JU) and Belgium, Czech Republic, France, Germany, Greece, Italy, Norway, and Spain. Neither the European Union nor the granting authority can be held responsible for them.


## License

This project is licensed under the [Apache-2.0 License](http://www.apache.org/licenses/LICENSE-2.0).


## Citation

If you use HiPSter in your research, we provide a [citation](./CITATION.cff) to use:

```bibtex
@article{Polsterer_Spherinator_and_HiPSter_2024,
author = {Polsterer, Kai Lars and Doser, Bernd and Fehlner, Andreas and Trujillo-Gomez, Sebastian},
title = {{Spherinator and HiPSter: Representation Learning for Unbiased Knowledge Discovery from Simulations}},
url = {https://arxiv.org/abs/2406.03810},
doi = {10.48550/arXiv.2406.03810},
year = {2024}
}
```
color-zone-processor 0.1.0
A tool for processing image color zones
Requires Python: >=3.6

# Color Zone Processor

A Python tool for processing image color zones by replacing pixels of specific colors with other colors in a structured way.

## Installation

You can install the package using pip:

```bash
pip install .
```

## Usage

After installation, you can use the tool from the command line:

```bash
color-zone-processor --first-percentage 33.53 --total-percentage 33.53
```

### Command Line Arguments

- `--first-percentage`: Percentage of target color pixels to replace with first color (0-100)
- `--total-percentage`: Total percentage of target color pixels to replace (0-100)
- `--min-zone-size`: Minimum size of color zone to consider (default: 100)

### Example

```bash
color-zone-processor --first-percentage 33.53 --total-percentage 33.53 --min-zone-size 100
```

## Development

To install the package in development mode:

```bash
pip install -e .
```

## License

MIT License
optimihost 0.2.0
API For optimihost
Requires Python: >=3.4

# optimihost API

An easy to use Python wrapper for the OptimiHost API.

## Installing

To install with pip:

```shell
pip install optimihost
```
### Exploring the API
In addition to the documentation you can explore the interface in an 
interactive Python interpreter using built-ins like `dir()` and the
`__doc__` attribute as shown below.

```python
from optimihost import Connect

api = Connect('<api_key>')

[i for i in dir(api.client.servers) if not i.startswith('_')]
# ['account', 'backups', 'databases', 'files', 'get_server', 'get_server_utilization', 'list_permissions', 'list_servers', 'network', 'schedules', 'send_console_command', 'send_power_action', 'servers', 'settings', 'startup', 'users']
[i for i in dir(api.client.servers.settings) if not i.startswith('_')]
# ['reinstall_server', 'rename_server']
print(api.client.servers.settings.rename_server.__doc__)
# Renames the server.
#        Args:
#            server_id(str): Server identifier (abbreviated UUID)
#            name(str): New name for the server
```

### Client API
The Client API or Account API is accessed by users of the Pterodactyl panel. 
Below is the layout of the Client API namespace.

```python
api.client.account
api.client.servers
api.client.servers.backups
api.client.servers.databases
api.client.servers.files
api.client.servers.network
api.client.servers.schedules
api.client.servers.settings
api.client.servers.startup
api.client.servers.users
```

A full list of methods available can be found in the [documentation][docs]. 


Below are examples of how you might get information about your servers.

```python
from optimihost import Connect

# Create a client to connect to the panel and authenticate with your API key.
api = Connect('MySuperSecretApiKey')

# Get a list of all servers the user has access to
my_servers = api.client.servers.list_servers()
# Get the unique identifier for the first server.
srv_id = my_servers[0]['attributes']['identifier']

# Check the utilization of the server
srv_utilization = api.client.servers.get_server_utilization(srv_id)
print(srv_utilization)

# Turn the server on.
api.client.servers.send_power_action(srv_id, 'start')
```
expression-calculator 0.1.0
Simple expression calculator with safe evaluation
Requires Python: >=3.7

# Expression Calculator

[![Python Version](https://img.shields.io/badge/python-3.8%2B-blue)](https://www.python.org/)
[![PyPI Version](https://img.shields.io/pypi/v/expression-calculator)](https://pypi.org/project/expression-calculator/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

A secure mathematical expression evaluator with lexical analysis that doesn't use unsafe `eval()`

## Key Features

✅ **Safe parsing** - No dangerous `eval()` usage  
✅ Parentheses and operator precedence support  
✅ Integer and floating-point arithmetic  
✅ Detailed error reporting  
✅ Clean modular architecture  

## Installation

```bash
pip install expression_calculator
```

## Usage
```python
from expression_calculator import Calculator

calc = Calculator()
result = calc.calculate("(5 + 3) * 2 - 10 / 2")  # Returns 11.0
print(f"Result: {result}")
```
## Supported Operations

| Operator | Example |
|----------|:--------|
|  `plus` `+`  | `2 + 3 + 2`  |
| `minus` `-` | `8 - 3 - 2` |
| `multiply` `*` | `9 * 5 * 2` |
| `divide` `/` | `6 / 2 / 1.4` |
| `parens` `()` | `5 + (3 * 2)`|

## Credits
🖥 - `Discord`: **`@exityxdev`**
📱 - `Telegram`: **`@exityx`**

## Thanks You! ❤
tinygraphs 2.0.3
A minimal library for plotting training progress in Jupyter/Colab
Keywords: training, plotting, machine-learning, notebook

![](https://raw.githubusercontent.com/rkdune/tinygraphs/refs/heads/main/tinyGraphs/images/tinygraphs_header.png)
*A minimal library for plotting training progress in Jupyter/COLAB notebooks.*

## Installation

pip install tinygraphs

## Options

`train_losses`: 
(Required) List of training loss values to plot

`val_losses`: 
(Required) List of validation loss values to plot

`epoch`: 
(Required) Current epoch number for updating the plot

`legend_loc`: 
Location of the legend on the plot (default: "upper right")

`updating_title`: 
Boolean flag to indicate if the title should update with epoch progress (default: True)

`legend`: 
Boolean flag to show/hide the legend (default: True)

`x_label`: 
Label for the x-axis

`y_label`: 
Label for the y-axis

`title`: 
Main title of the plot

`dark_mode`: 
Boolean flag to switch between light and dark themes (default: True)

## Usage

```python

import tinygraphs as tg

train_losses, val_losses = [], []
epochs = 3

for epoch in range(epochs):
    # Training
    model.train()
    running_train_loss = 0
    for x, y in train_loader:
        x, y = x.to(device), y.to(device)
        optimizer.zero_grad()
        loss = criterion(model(x), y)
        loss.backward()
        optimizer.step()
        running_train_loss += loss.item()
    train_losses.append(running_train_loss / len(train_loader))

    # Validation
    model.eval()
    running_val_loss = 0
    with torch.no_grad():
        for x, y in val_loader:
            x, y = x.to(device), y.to(device)
            loss = criterion(model(x), y)
            running_val_loss += loss.item()
    val_losses.append(running_val_loss / len(val_loader))

    # Plot using tinygraphs
    tg.plot(train_losses, val_losses, epoch, legend_loc = "upper right", updating_title = False, legend = True, x_label = "x title", y_label = "y title", title = "title", dark_mode = False)

```
## Other Usage Options

```python
    tg.plot(train_losses, val_losses, epoch, legend_loc = "upper right", updating_title = False, legend = True, x_label = "Epoch", y_label = "Loss", title = "Loss Graph", dark_mode = True)
```
![Dark Mode is Awesome!](https://raw.githubusercontent.com/rkdune/tinygraphs/refs/heads/main/tinyGraphs/images/Dark_Mode_Loss_Graph.png)

```python
    tg.plot(train_losses, val_losses, epoch, theme = 'jet')
```
![Beautiful Loss Graph With Barely Any Code!](https://raw.githubusercontent.com/rkdune/tinygraphs/refs/heads/main/tinyGraphs/images/Minimal_Loss_Graph.png)
agentic-de 0.1.0
Agentic Data Engineering tools and protocols (MCP, stdio, sse, FastAPI, etc)
Requires Python: >=3.8

# MCP-Agentic-Data-Engineering
        
        ```bash
        conda create -p venv python==3.11 -y
        ```
        
        ```bash
        conda activate venv/
        ```
        
        ```bash
        docker compose up
        ```
        - stdio : used for terminal based application
        - sse: used for non terminal based application like fastapi, flask etc
        
        
        ### For stdio protocol (MCP-Server)
        ```bash
        python mcp_server/stdio/mcp_server.py
        ```
        ### For stdio protocol (MCP-Client)
        ```bash
        python mcp_client/stdio/mcp_client.py
        ```
        
        ### For sse protocol (MCP-Server)
        ```bash
        python mcp_server/sse/mcp_server.py
        ```
        
        ### For sse protocol (MCP-Client)
        ```bash
        uvicorn mcp_client.sse.mcp_client:app --reload --port 3030  
        ```
        
        
        
        
        ### Following Functionalities this Agentic DE can perform
        #### 1. Upload & Ingest Data
        #### Prompt Example: Upload CSVs to MySQL
        ```bash
        Upload all CSVs from the `data` folder into MySQL. Use filenames as table names.
        ```
        #### Prompt Example: Upload JSON to MongoDB
        ```bash
        Upload `customers.json` from the `data` folder to the `test_data` collection in the `coke` database.
        
        ```
        #### 2. Modify & Update Data
        #### Prompt Example: Update MySQL Tables
        ```bash
        Set `is_active = 0` for users where `age > 60` in the `users` table.
        ```
        
        #### Prompt Example: Update MongoDB Documents
        ```bash
        Update all documents in the MongoDB database "coke" inside the "coke_collection1" collection where the customer_id is 101. Set the name field to "Manmeet".
        
        ```
        
        #### 3. Manage S3-Like Object Stores
        #### Prompt Example: Create S3 Buckets (MinIO)
        ```bash
        Create a new S3 bucket for staging reports and log the creation in Jira.
        ```
        
        #### Prompt Example: Move Files Across Buckets
        ```bash
        Move all data from `raw-bucket/sales/` to `processed-bucket/sales-clean/`.
        ```
        
        
        #### 4. Automate Workflows with Airflow
        #### Prompt Example: Schedule Python Code as Airflow DAG
        ```bash
        Run this every day at 8 AM:
        
        import pandas as pd
        df = pd.read_csv("s3://raw-bucket/sales.csv")
        df.to_csv("s3://processed-bucket/cleaned_sales.csv")
        ```
        #### 5. Ticketing & Task Logging
        #### Prompt Example: Auto-Create Jira Tickets
        ```bash
        Raise a Jira ticket for uploading sales data and verifying transformation completeness.
        ```
        
        Enjoy Coding!
nocp 0.2.2
Navidrome On Console Player
Requires Python: >=3.7

# Navidrome On Console Player

NOCP (Navidrome On Console Player) is a console audio player designed to be powerful and easy to use inpired bien MOCP (Music On Console Player).

[Navidrome](https://www.navidrome.org/) is an open source web-based music collection server and streamer. It gives you freedom to listen to your music collection from any browser or mobile device. It's like your personal Spotify!

![custom-field](nocpglobal.png "Nocp")


## install

install by [pypi](https://pypi.org/project/nocp/)

> pip install nocp



## usage

> nocp --help

> nocp --server-url http://myserver-navidrome/rest --username myusername --password mypassword --lang en_US

after just

> nocp

You can access to three views

- listing song (shorcut **m**)
- listing radio (shorcut **r**)
- listing playlist (shorcut **l**)

You can access to

- help (shorcut **h**)
- volume (shorcut **v**)

You can listen to a song by selecting it and pressing "Enter"

![custom-field](nocpaction.png "help")

List of available languages

- English
- French
- German
- Spanish
- Italian
- Portuguese


## addshorcut

on ubuntu, create file ~/.local/share/applications/nocp.desktop

```
[Desktop Entry]
Type=Application
Name=Nocp
Exec=gnome-terminal -- bash -c 'nocp; exit;'
Icon=utilities-terminal
Terminal=false
```

and

> chmod +x ~/.local/share/applications/nocp.desktop


## todo

- random listen