semvec/pypi-packages huggingface dataset:
cmdtools-py 3.1.0
A (not quite) flexible command framework
License: MIT
Requires Python: >=3.7
Keywords: command-parser, command-processor, command, cmd, cmd-parser, framework, command-framework

<div id="headline" align="center">
  <h1>cmdtools</h1>
  <p>A (not quite) flexible command framework.</p>
  <a href="https://github.com/HugeBrain16/cmdtools/actions/workflows/python-package.yml">
    <img src="https://github.com/HugeBrain16/cmdtools/actions/workflows/python-package.yml/badge.svg" alt="tests"></img>
  </a>
  <a href="https://pypi.org/project/cmdtools-py">
    <img src="https://img.shields.io/pypi/dm/cmdtools-py" alt="downloads"></img>
    <img src="https://badge.fury.io/py/cmdtools-py.svg" alt="PyPI version"></img>
    <img src="https://img.shields.io/pypi/pyversions/cmdtools-py" alt="Python version"></img>
  </a>
  <a href="https://codecov.io/gh/HugeBrain16/cmdtools">
    <img src="https://codecov.io/gh/HugeBrain16/cmdtools/branch/main/graph/badge.svg?token=mynvRn223H"/>
  </a>
  <a href='https://cmdtools-py.readthedocs.io/en/latest/?badge=latest'>
    <img src='https://readthedocs.org/projects/cmdtools-py/badge/?version=latest' alt='Documentation Status' />
  </a>
</div>

## Installation

```
pip install --upgrade cmdtools-py
```
install the latest commit from GitHub
```
pip install git+https://github.com/HugeBrain16/cmdtools.git
```

### Basic example

```py
import asyncio
import cmdtools

@cmdtools.callback.add_option("message")
def send(ctx):
    print(ctx.options.message)

@send.error
def error_send(ctx):
  if isinstance(ctx.error, cmdtools.NotEnoughArgumentError):
    if ctx.error.option == "message":
      print("Message is required!")

cmd = cmdtools.Cmd('/send hello')
asyncio.run(cmdtools.execute(cmd, send))
```

## Links

PyPI project: https://pypi.org/project/cmdtools-py  
Repository: https://github.com/HugeBrain16/cmdtools  
Issues tracker: https://github.com/HugeBrain16/cmdtools/issues  
Documentation: https://cmdtools-py.readthedocs.io/en/latest
hestonpy 0.5.10
Heston models: pricing, calibration, hedging.
License: MIT
Requires Python: <4.0,>=3.10

# hestonpy

`hestonpy` is now avalaible on PyPi !
```bash
pip install hestonpy
```

The `hestonpy` Python package implements the Heston and Black-Scholes models for option pricing and portfolio management. The package also includes functionality for optimal portfolio allocation using stochastic control techniques.

Covered topics by the `hestonpy` package [TO DO: calibration on iv surface + SVJ simulations/monte carlo]:
1. path simulations
2. pricing plain european vanilla options
3. model calibration (smile) from yahoo finance and personnal data
4. SVI implementation
5. asset allocations (stochastic optimal control under Heston dynamics)

## License

`hestonpy` was created by Théophile SCHMUTZ (@SarcasticMatrix). It is licensed under the terms of the MIT license.
lightwave-cli 0.1.0
Command-line interface for LightWave development workflows
License: MIT
Requires Python: >=3.11

# LightWave CLI

Command-line interface for LightWave ecosystem tools and utilities.

## Features

- **Documentation Sync**: Synchronize documentation from repositories
- **AI Agents**: Run AI agents using pydantic-ai for various tasks
- **UV Integration**: Wrapper for the fast UV package manager

## Installation

```bash
# Clone the repository
git clone git@github.com:kiwi-dev-la/lightwave-cli.git
cd lightwave-cli

# Install in development mode
pip install -e .
```

## Usage

### Documentation Sync

Synchronize documentation from a GitHub repository:

```bash
# Using a remote repository
lightwave docs sync --repo-url https://github.com/kiwi-dev-la/lightwave-eco-system-docs.git --branch main

# Using a locally cloned repository
lightwave docs sync --local-repo ./lightwave-eco-system-docs --branch main
```

### AI Agents

Run an AI agent with a prompt:

```bash
# Set your API key
export ANTHROPIC_API_KEY=your-api-key

# Run the docs agent
lightwave agent run docs "What are the main components of the LightWave ecosystem?"

# Run with different model
lightwave agent run docs "What's in the lightwave-cli?" --model claude-3-sonnet-20240229
```

### UV Package Manager

UV is a much faster alternative to pip for Python package management. The lightwave CLI includes wrappers for common UV commands:

```bash
# Create a virtual environment
lightwave uv venv myenv

# Install a package
lightwave uv install pydantic

# Install a package in editable mode
lightwave uv install -e /path/to/package

# Run a UV command
lightwave uv run pip list
```

## Available Agents

- **docs**: Answer questions about LightWave documentation

## Dependencies

- Python 3.11+
- typer
- rich
- gitpython
- pydantic
- pydantic-ai

## Development

To add a new agent:

1. Create a new file in `src/lightwave_cli/agents/`
2. Extend the `LightWaveAgent` class
3. Implement the `run()` method
4. Create a singleton instance named `agent`

## License

MIT
dora-mujoco-husky 0.2.0
dora-mujoco-husky
License: MIT
Requires Python: >=3.10

# dora-mujoco-husky

A MuJoCo-based Clearpath Husky simulation node for the Dora framework. This node provides a physics-accurate simulation of the Husky robot that can be controlled via velocity commands.

## Features

- Real-time physics simulation using MuJoCo
- Accurate Husky robot model with proper inertial properties
- Velocity-based control interface
- Position and velocity feedback
- Gamepad control support

## Getting Started

### Prerequisites

- Python 3.8 or higher
- MuJoCo 3.1.6 or higher

### Installation

1. Create and activate a Python virtual environment:
```bash
uv venv -p 3.11 --seed
source .venv/bin/activate
```

2. Install the package:
```bash 
uv pip install -e .
```

### Running the Demo

1. Make sure you have a gamepad connected
2. Build the demo:
```bash
dora build demo.yml --uv
```
3. Run the demo:
```bash
dora run demo.yml --uv
```

## Usage

The node accepts velocity commands and outputs position/velocity data:

### Inputs

- `cmd_vel`: Velocity command array with the format:
  - Index 0: Linear velocity (m/s), range: [-2.0, 2.0]
  - Index 5: Angular velocity (rad/s), range: [-3.0, 3.0]

### Outputs

- `position`: Robot position [x, y, z] in world coordinates
- `velocity`: Robot velocity [vx, vy, vz] in world coordinates

### Example Configuration

```yaml
nodes:
  - id: mujoco_husky
    build: pip install -e .
    path: dora-mujoco-husky
    inputs:
      cmd_vel: input_source/cmd_vel
    outputs:
      - position
      - velocity
```

## Development

### Code Formatting

Format code using ruff:
```bash
uv pip install ruff
uv run ruff check . --fix
```

### Linting

Run linting checks:
```bash
uv run ruff check .
```

### Testing

Run tests using pytest:
```bash
uv pip install pytest
uv run pytest .
```

## License

This project is licensed under the MIT License - see the LICENSE file for details.

## Acknowledgments

- Based on the Clearpath Robotics Husky robot
- Uses the MuJoCo physics engine
- Built with the Dora framework