semvec/pypi-packages huggingface dataset:
digitalbrainsdk 0.0.9
A Python SDK for Digital Brain analysis
Requires Python: >=3.6

# DigitalBrainSDK

DigitalBrainSDK is a Python package designed to provide advanced data processing capabilities. 

## Installation

To install the DigitalBrainSDK, you can use pip:

```
pip install DigitalBrainSDK
```

## Usage

Here is a simple example of how to use the DigitalBrainSDK:

TODO

## Utilities

TODO

## Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue for any enhancements or bug fixes.

## License

This project is licensed under the MIT License. See the LICENSE file for more details.
weather-muxian 0.2.3
query weather
Requires Python: >=3.11

# mcp server入门开发


初始化项目
```
uv init mcp_getting_started
```

初始化虚拟环境
```
uv venv
```
安装依赖
```
uv add "mcp[cli]" httpx openai
```

项目打包发布：
```
python -m build
```
met-annot-unifier 0.1.0
A Python project to combine tabular outputs from GNPS, Sirius and ISDB
Requires Python: <4.0,>=3.10
Keywords: python

# met-annot-unifier

[![Release](https://img.shields.io/github/v/release/mapp-metabolomics-unit/met-annot-unifier)](https://img.shields.io/github/v/release/mapp-metabolomics-unit/met-annot-unifier)
[![Build status](https://img.shields.io/github/actions/workflow/status/mapp-metabolomics-unit/met-annot-unifier/main.yml?branch=main)](https://github.com/mapp-metabolomics-unit/met-annot-unifier/actions/workflows/main.yml?query=branch%3Amain)
[![codecov](https://codecov.io/gh/mapp-metabolomics-unit/met-annot-unifier/branch/main/graph/badge.svg)](https://codecov.io/gh/mapp-metabolomics-unit/met-annot-unifier)
[![Commit activity](https://img.shields.io/github/commit-activity/m/mapp-metabolomics-unit/met-annot-unifier)](https://img.shields.io/github/commit-activity/m/mapp-metabolomics-unit/met-annot-unifier)
[![License](https://img.shields.io/github/license/mapp-metabolomics-unit/met-annot-unifier)](https://img.shields.io/github/license/mapp-metabolomics-unit/met-annot-unifier)

A Python project to combine tabular outputs from GNPS, Sirius and ISDB

- **Github repository**: <https://github.com/mapp-metabolomics-unit/met-annot-unifier/>
- **Documentation** <https://mapp-metabolomics-unit.github.io/met-annot-unifier/>

## Installation

```bash
pip install met-annot-unifier
```


---

Repository initiated with [fpgmaas/cookiecutter-uv](https://github.com/fpgmaas/cookiecutter-uv).
terminal-snake-game 1.0.4
A classic Snake game implementation in Python using curses
Requires Python: >=3.6

# Snake Game

A classic Snake game implemented in Python using the curses library. This terminal-based game features colorful graphics, score tracking, and an intuitive interface.

## Demo

![Snake Game Demo](https://raw.githubusercontent.com/SwetaTanwar/snake-game/main/demo/snake-game-demo.gif)

## Features

- Colorful terminal-based UI
- Score and high score tracking
- Direction-aware snake appearance
- Game over screen with restart option
- Persistent high score storage

## Requirements

- Python 3.6 or higher
- curses library (included in standard Python distribution)

## Installation & Running

There are two ways to install and run the game:

### Method 1: Using pip (Recommended)

1. Install the package:
   ```bash
   pip install terminal-snake-game
   ```

2. Run the game:
   ```bash
   # Either use the command-line tool
   snake-game

   # Or run as a Python module
   python3 -m snake_game
   ```

### Method 2: From Source

1. Clone the repository:
   ```bash
   git clone git@github.com:SwetaTanwar/snake-game.git
   cd snake-game
   ```

2. Run the game:
   ```bash
   # Using Python directly
   python3 snake_game.py
   ```

   Or make it executable first:
   ```bash
   # Make the script executable
   chmod +x snake_game.py
   
   # Then run directly
   ./snake_game.py
   ```

## How to Play

### Controls
- Use arrow keys (↑ ↓ ← →) to control the snake
- Eat ★ to grow and score points
- Press 'q' to quit
- Press 'r' to restart after game over

## Game Elements

- 🐍 Snake head shows direction (▶ ◀ ▲ ▼)
- ◆ Snake body
- ★ Food
- Score and high score display at the top
- Control instructions at the bottom 

## Development and Testing

### Setting up Development Environment

1. Create a virtual environment:
   ```bash
   python -m venv test_env
   source test_env/bin/activate  # On Unix/macOS
   # or
   test_env\Scripts\activate  # On Windows
   ```

2. Install development dependencies:
   ```bash
   pip install -e ".[test]"
   ```

### Running Tests

The game includes a comprehensive test suite that verifies core functionality. To run the tests:

```bash
# Run all tests
python -m pytest src/tests/

# Run tests with verbose output
python -m pytest src/tests/ -v

# Run tests with coverage report
python -m pytest src/tests/ --cov=terminal_snake_game
```

The test suite covers:
- Snake movement and collision detection
- Food generation and placement
- Score tracking
- Window management and display
- Game mechanics validation

When contributing new features, please ensure to:
1. Add appropriate test cases
2. Run the full test suite before submitting changes
3. Maintain or improve the current test coverage
py-redis-ds 0.0.1
Drop-in replacements for Python data structures backed by Redis for shared access.
Requires Python: >=3.8

# Redis-Backed Data Structures for Python

Redis-backed Python data structures that function like built-ins but store data on a Redis server, enabling shared access across distributed systems.

---

## 🚀 Features

- **Drop-in Replacements**: Use familiar Python data structures (`list`, `dict`, `set`, etc.) with minimal changes to your code.
- **Redis-Powered**: Data is stored on the Redis server, enabling easy sharing across processes, services, or systems.
- **Seamless Integration**: Works like native Python data structures while leveraging Redis for distributed use cases.

---

## 📥 Installation

Install the library using pip:

```bash
pip install py_redis_ds
```

---

## 💡 Usage

Here’s a quick example of how to use a Redis-backed list:

```python
from py_redis_ds.builtins import List
from redis import Redis

redis = Redis()

# Initialize a Redis-backed list
rlist = List(name="my_shared_list", redis=redis)

# Add elements
rlist.append("Hello")
rlist.append("World")

# Access elements
print(rlist[0])  # Output: Hello

# Iterate through the list
for item in rlist:
    print(item)

# Shared access in another script or service
rlist2 = List(name="my_shared_list", redis=redis)
print(rlist2[1])  # Output: World
```

---

## 📚 Supported Data Structures

| Data Structure            | Redis-Backed Equivalent               |
|---------------------------|---------------------------------------|
| `list`                    | `py_redis_ds.builtins.List`           |
| `dict`                    | `py_redis_ds.builtins.Dict`           |
| `set`                     | `py_redis_ds.builtins.Set`            |
| `queue.Queue`             | `py_redis_ds.queue.Queue`             |
| `queue.LifoQueue`         | `py_redis_ds.queue.LifoQueue`         |
| `queue.PriorityQueue`     | `py_redis_ds.queue.PriorityQueue`     |
| `collections.deque`       | `py_redis_ds.collections.Deque`       |
| `collections.defaultdict` | `py_redis_ds.collections.DefaultDict` |

(Support for more data structures coming soon!)

---

## 🌟 Why Use Redis-Backed Structures?

- **Shared State**: Easily share data between processes, microservices, or distributed systems.
- **Scalability**: Offload data storage to Redis, reducing memory usage in individual processes.
- **Simplicity**: Use familiar Python syntax while leveraging Redis's power.

---

## 🤝 Contributing

Contributions are welcome! Please check out our [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.

---

## 📜 License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

---

We’re excited to see how you use Redis-Backed Data Structures! If you have any questions or feedback, feel free to open an issue or start a discussion.