semvec/pypi-packages huggingface dataset:
python-wml 3.1.7
Deep Learning toolkit
Requires Python: >=3.6

#[WML](https://github.com/vghost2008/wml2)

Machine learning tools library.

##Dependencies

- Linux or Mac OS
- Python ≥ 3.6
- scipy
- yacs
- OpenCV
- pycocotools
- gcc & g++ ≥ 4.9
- libturbojpeg

##Installation

```
pip install -r requirements.txt
```


## License

WML itself is released under the MIT License (refer to the LICENSE file for details).


##Authors

```
    Wang Jie  bluetornado@zju.edu.cn

    Copyright 2017 The WML Authors.  All rights reserved.
```
nitro-sim 0.1.0
Python bindings for RocketSim - Rocket League physics simulation
Requires Python: >=3.12

# nitro-sim

Python bindings for [RocketSim](https://github.com/ZealanL/RocketSim) - High-performance Rocket League physics simulation.

## Quick Start

```bash
# Install in development mode
uv sync

# Verify installation
uv run python -c "import nitro as rs; print('Version:', rs.__version__)"

# Run tests
uv run pytest tests/
```

## Usage

### Direct API

```python
import nitro as rs

# Initialize RocketSim
rs.init("collision_meshes")

# Create arena
arena = rs.Arena.create(rs.GameMode.SOCCAR, tick_rate=120.0)

# Add cars
car1 = arena.add_car(rs.Team.BLUE, rs.CAR_CONFIG_OCTANE)
car2 = arena.add_car(rs.Team.ORANGE, rs.CAR_CONFIG_DOMINUS)

# Set controls
car1.controls.throttle = 1.0
car1.controls.boost = True

# Simulate
arena.step(120)  # 1 second

# Get state
state = car1.get_state()
print(f"Position: {state.pos}")
print(f"Velocity: {state.vel.length()}")
```

### PettingZoo API (for Multi-Agent RL)

```python
from pettingzoo_env import parallel_env

# Create 2v2 environment
env = parallel_env(num_blue=2, num_orange=2)

# Reset
observations, infos = env.reset(seed=42)

# Training loop
while env.agents:
    actions = {
        agent: policy(observations[agent])  # Your RL policy here
        for agent in env.agents
    }
    observations, rewards, terminations, truncations, infos = env.step(actions)

env.close()
```

## Features

- **Complete RocketSim API** - All major types and functions exposed  
- **PettingZoo API** - Multi-agent RL compatible interface  
- **Ball Prediction** - Predict ball trajectories for AI/bots  
- **Event Tracking** - Detect shots, goals, saves, and assists  
- **Callbacks** - React to goals, bumps, and game events  
- **Type hints** - Full LSP/autocomplete support with `.pyi` stubs  
- **Pythonic** - Snake_case naming, properties, and operators  
- **Fast** - Direct C++ bindings with minimal overhead  
- **Tested** - 85 passing tests covering all functionality  

## Examples

See the [examples/](examples/) folder for:
- **example_advanced.py** - Ball prediction, event tracking, callbacks
- **test_autocomplete.py** - Basic usage and IDE autocomplete demo

## Building

```bash
# Development install
uv sync

# Build wheel
uv build

# Install from wheel
pip install dist/*.whl
```

See [BUILD.md](BUILD.md) for detailed build instructions and troubleshooting.

## Testing

```bash
# Run all tests (auto-downloads collision meshes)
uv run pytest tests/

# Run without collision meshes
uv run pytest tests/ -k "not simulation and not boost and not prediction"
```

See [tests/README.md](tests/README.md) for more testing options.

## Requirements

- Python 3.12+
- CMake 3.15+
- C++20 compiler
- Collision meshes (auto-downloaded in tests)

## Test Results

```bash
$ uv run pytest tests/
================= 85 passed, 2 skipped in 2.09s =================
```

Includes tests for:
- Core API (72 tests)
- PettingZoo API (13 tests)

## License

This project provides Python bindings for RocketSim. See [LICENSE](LICENSE) for details.
dxcam-cpp 0.2.5
A high performance screen capturing library for Windows rewriting DXcam in C++
Requires Python: >=3.8
Keywords: windows, screenshot, capture

# DXCam-CPP

A high performance screen capturing library for Windows
rewriting [DXcam](https://github.com/ra1nty/DXcam) in C++.

## Usage

```shell
pip install dxcam-cpp
```

The interface of the Python module of DXCam_CPP is designed to be **fully
compatible** with the original Python DXcam as an alternative.
See [the documentation of DXcam](https://github.com/ra1nty/DXcam?tab=readme-ov-file#usage)
for more details.
zhongzhi-mcp 0.1.3
MCP server for Zhongzhi (IPPH) API
Requires Python: >=3.10

# Zhongzhi MCP Server

An Model Context Protocol (MCP) server for the Zhongzhi (IPPH) API.

## Features

-   **Authentication**: Automatic login and token management with file-based persistence (Serverless friendly).
-   **Search**: Customized trademark/patent search.
-   **Details**: Retrieve detailed information using Registration Number.
-   **Pledge Info**: Query pledge information.
-   **Image Search**: Search by image URL.

## Installation

```bash
pip install zhongzhi-mcp
```

## Usage

### Running the Server

```bash
# Set up environment variables if needed (or rely on config.py defaults)
python -m zhongzhi_mcp.server
```

### Configuration

The server uses `config.py` for credentials. You can also use environment variables (support to be added).

## Tools

-   `shunjian_search_patents`
-   `shunjian_get_patent_detail`
-   `shunjian_get_pledge_info`
-   `shunjian_get_balance`
-   `shunjian_image_search`
-   `shunjian_image_search_aggregation`
-   `shunjian_check_sensitive_word`