semvec/pypi-packages huggingface dataset:
pyutils-fast 1.0.14
High-performance utility functions in C
Requires Python: >=3.7
Keywords: utilities performance c-extension string-manipulation


# PyUtils - High-Performance Utility Library

A collection of fast utility functions implemented in C with Python bindings.

## Features

- String manipulation and validation
- Mathematical operations
- List operations (rotation, averaging)
- String similarity (Levenshtein distance)
- Base conversion
- Time dilation calculations
- And many more!

## Installation

```bash
pip install pyutils-fast
```

## Quick Example

```python
import pyutils_c

# String similarity
similarity = pyutils_c.string_similarity("hello", "helo")
print(similarity)  # 0.8

# Rotate a list
rotated = pyutils_c.rotate([1, 2, 3, 4, 5], 2)
print(rotated)  # [4, 5, 1, 2, 3]

# Convert to base
binary = pyutils_c.to_base(42, 2)
print(binary)  # '101010'
```
yanhua2026 0.1.8
霓虹烟火 - Cyberpunk style fireworks visual simulation
Requires Python: >=3.12
Keywords: cyberpunk, fireworks, pyglet, simulation, visual

# 霓虹烟火 YanHua2026

赛博朋克风格烟花视觉模拟 / Cyberpunk style fireworks visual simulation

## 安装运行 / Installation

```bash
# 使用 uvx 直接运行 (需要先发布到 PyPI)
uvx yanhua2026

# 或本地安装
pip install yanhua2026
yanhua2026
```

## 开发运行 / Development

```bash
git clone https://github.com/aicoder2048/yanhua2026.git
cd yanhua2026
uv sync
uv run yanhua2026
```

## 操作说明 / Controls

| 按键 | 功能 |
|------|------|
| 鼠标点击 | 发射烟花 |
| Space | 暂停/继续 |
| A | 切换自动发射 |
| F | 全屏切换 |
| M | 音效开关 |
| G | 网格背景开关 |
| S | 天际线开关 |
| H | 帮助显示 |
| ESC | 退出 |

## 特性 / Features

- 赛博朋克视觉风格
- 中国地标天际线 (深圳春笋、东方明珠、广州塔)
- 24句马年阳光祝福语随机飘动
- 程序化生成音效 (15种不同音效)
- 粒子系统优化渲染
- 透视网格背景 + 冲击波效果

## 依赖 / Dependencies

- Python >= 3.12
- pyglet >= 2.0
- numpy >= 2.0

## License

MIT
lkcom 0.8.2
A Python library of useful routines.
Requires Python: >=3.6

lkcom is a collection of small, common and useful routines for reading, parsing,
plotting, and analyzing hobbyist and scientific lab data. Its purpose is to
make common data handling steps fast and pythonic.

lkcom is a lighter and at this point better version of [lklib]
(https://test.pypi.org/project/lklib/).

lkcom is also used in [pynlomic](https://pypi.org/project/pynlomic/) and 
[pynlopol](https://pypi.org/project/pynlopol/).

Contact: dse.ssd@gmail.com
Copyright 2015-2025 Lukas Kontenis
mapping-agent 0.1.2
LLM-driven agent for creating detailed column-to-attribute mappings
Requires Python: >=3.10

# Mapping Agent

LLM-driven intelligent attribute-to-column mapping agent for domain schema validation and refinement.

---

## 🌟 Features

- **Intelligent Entity Mapping** – Uses LLM reasoning to map entities from a domain schema to columns across multiple tables.  
- **Confidence Scoring** – Provides confidence scores for each mapping.  
- **Transformation Suggestions** – Suggests data transformations for better alignment.  
- **Context-Aware Analysis** – Generates column profiles (types, nulls, uniqueness, distributions) to improve mapping accuracy.  

---

## 🚀 Quick Start

### Installation

**Prerequisites**

- [uv](https://docs.astral.sh/uv/getting-started/installation/) – package & environment manager  
  Please refer to the official installation guide for the most up-to-date instructions.  
  For quick setup on macOS/Linux, you can currently use:  
  ```bash
  curl -LsSf https://astral.sh/uv/install.sh | sh
  ```
- [Git](https://git-scm.com/)  

**Steps**

1. **Clone the repository**  
   ```bash
   git clone https://github.com/stepfnAI/mapping_agent.git
   cd mapping_agent
   git switch main
   ```

2. **Install dependencies**  
   ```bash
   uv sync --extra dev
   source .venv/bin/activate
   ```

6. **Set environment variables**  
   The agent requires an API key (e.g., OpenAI).  
   ```bash
   export LLM_PROVIDER="your-llm-provider"   #"openai/anthropic"
   export LLM_MODEL="your-llm-model"         #"gpt-4.1-mini"
   export LLM_API_KEY="your-api-key-here"    
   ```

---

### Basic Usage

Example: Mapping the **Borrower Profile** entity to columns across two CSV files.

```
python examples/basic_usage.py
```

---

## 🧪 Testing

Run the test suite with [pytest](https://docs.pytest.org/):

```bash
# Run all tests
pytest tests/ -s

# Run with coverage
pytest tests/test_models.py
pytest tests/test_utils.py
pytest tests/test_agent_integration.py
```

---

## 📝 Prompt Management

Prompts are centralized in  
`src/mapping_agent/constants.py`.

- **`format_mapping_prompt_with_system_prompt`** constructs structured prompts with a system message.  
- Ensures the LLM consistently acts as a *data mapping expert*.  
- Easy to extend or fine-tune reasoning strategies in one place.  

---

## 🤝 Contributing

Contributions are welcome!  
Please see the **Contributing Guide** before submitting a PR.

---

## 📄 License

Licensed under the **MIT License**. See [LICENSE](./LICENSE) for details.
numpy2dng 0.1.0
Pure Python utility for creating DNG files from numpy arrays.
Requires Python: >=3.10

# numpy2dng

Pure Python utility for creating Adobe DNG files from RAW image data stored in NumPy arrays.

This project is a refactor of the original PiDNG library, focused on a smaller, cleaner core and modern packaging.

## Requirements

- Python 3.10+
- NumPy 2.0+

## Features

- Pure Python (no C extensions)
- Uncompressed DNG output
- Supports 8/10/12/14/16-bit integer RAW (via packing for 10/12/14-bit)
- Supports `float32` RAW (writes DNG 1.4-compatible files)
- “Safe packing” for 10/12/14-bit data with any image width (no special alignment required)

## Non-goals / Limitations

- Not a full RAW pipeline (no demosaic, no color processing)
- Does not auto-detect camera metadata; you must provide the required DNG/TIFF tags
- No JPEG/LJ92 compression (uncompressed only)

## Installation

Using `uv` (recommended):

```bash
uv add numpy2dng
```

Using `pip`:

```bash
pip install numpy2dng
```

## Usage

Typical flow:

1. Prepare a 2D NumPy array of shape `(height, width)` with dtype `uint16` (or `float32`).
2. Build a `numpy2dng.dng.DNGTags` instance and set at least:
   - `Tag.ImageWidth`, `Tag.ImageLength`, `Tag.BitsPerSample`
3. Convert and write a `.dng` using `numpy2dng.core.RAW2DNG`.

For performance-sensitive workflows, `RAW2DNG.convert(..., file=...)` can write directly to an open binary file handle (avoids an extra copy).

## API Overview

- `numpy2dng.core.RAW2DNG`: main converter for NumPy arrays → DNG
- `numpy2dng.dng.DNGTags` / `numpy2dng.dng.Tag`: tag container and tag definitions

## Development

```bash
uv sync
uv run pytest
uv run ruff check .
```

## Credits

Based on (and inspired by) [PiDNG](https://github.com/schoolpost/PiDNG)

## Future work

- Add more metadata tags and convenience methods for common camera models
- Support for additional data types and compression methods
- Improve documentation and add usage examples

## License

Same as PiDNG: MIT License. See `LICENSE` file for details.