semvec/pypi-packages huggingface dataset:
ModuBotDiscord 0.5.1
Modular Discord bot framework built on top of ModuBotCore
Requires Python: >=3.9

# ModuBotDiscord

ModuBotDiscord is a modular Discord bot framework based on [ModuBotCore](https://github.com/EnderModuBot/core) and [discord.py](https://discordpy.readthedocs.io/). It enables easy development and extension of Discord bots through a flexible, extensible system.

## Features

- Modular command system
- Extendable with custom commands and views
- Permission and owner checks
- Simple configuration via environment variables

## Installation

The recommended way to install is via [PyPI](https://pypi.org/project/ModuBotDiscord/):

```bash
pip install ModuBotDiscord
```

If you want to start your own bot project, use the [ModuBotDiscord Template](https://github.com/EnderModuBot/discord-template):

```bash
git clone https://github.com/EnderModuBot/discord-template.git
cd discord-template
```

> **Note:**
> This repository contains only the source code of ModuBotDiscord and is not runnable as a bot itself.
> For your own projects, please use the template repository.

## Configuration

Create a `.env` file or set the environment variables:

```dotenv
DISCORD_TOKEN=your_discord_bot_token
DISCORD_OWNER_ID=your_user_id
```

## License

MIT License – see [LICENSE](LICENSE)
lsqfitgp 0.21.1
A general purpose Gaussian process regression module
Requires Python: >=3.10

[![PyPI](https://img.shields.io/pypi/v/lsqfitgp)](https://pypi.org/project/lsqfitgp/)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.13930792.svg)](https://doi.org/10.5281/zenodo.13930792)


# lsqfitgp

Python module to do inference with Gaussian processes. Features:

  * Based on [JAX](https://github.com/google/jax).
  * Interoperates with [gvar](https://github.com/gplepage/gvar) and
    [lsqfit](https://github.com/gplepage/lsqfit) to facilitate inexpert users.
  * Recursively structured covariates.
  * Apply arbitrary linear transformations to the processes, finite and
    infinite.
  * Small [PPL based on Gaussian
    copulas](https://gattocrucco.github.io/lsqfitgp/docs/reference/copula.html)
    to specify the hyperparameters prior.
  * [Rich
    collection](https://gattocrucco.github.io/lsqfitgp/docs/reference/kernelsref.html)
    of covariance functions.
  * Good GP versions of
    [BART](https://gattocrucco.github.io/lsqfitgp/docs/reference/bayestree.html#lsqfitgp.bayestree.bart)
    (Bayesian Additive Regression Trees) and
    [BCF](https://gattocrucco.github.io/lsqfitgp/docs/reference/bayestree.html#lsqfitgp.bayestree.bcf)
    (Bayesian Causal Forests).
  
See [this report](https://www.giacomopetrillo.com/scuola/gppdf.pdf) for the
theory behind lsqfitgp.

## Installation

Python >= 3.10 required. Then:

```
$ pip install lsqfitgp
```

## Documentation

The complete manual is available online at
[gattocrucco.github.io/lsqfitgp/docs](https://gattocrucco.github.io/lsqfitgp/docs).
All the code is documented with docstrings, so you can also use the Python help
system directly from the shell:

```python
>>> import lsqfitgp as lgp
>>> help(lgp)
>>> help(lgp.something)
```

or, in an IPython shell/Jupyter notebook/Spyder IDE, use the question mark
shortcut:

```
In [1]: lgp?

In [2]: lgp.something?
```

## Similar libraries

  * [stheno](https://github.com/wesselb/stheno)
  * [tinygp](https://github.com/dfm/tinygp)
  * [GPJax](https://github.com/JaxGaussianProcesses/GPJax)

See also [Comparison of Gaussian process Software](https://en.wikipedia.org/wiki/Comparison_of_Gaussian_process_software)
on Wikipedia.

## License

This software is released under the [GPL](https://www.gnu.org/licenses/).
Amongst other things, it implies that, if you release an adaptation of this
software, [or even a program just importing it as external
library](https://www.gnu.org/licenses/gpl-faq.html.en#IfLibraryIsGPL), you
have to release its code as open source with a license at least as strong as
the GPL.
pyharborcli 0.1.0
Add your description here
Requires Python: >=3.10

# pyharborcli

<a id="readme-top"></a>

<div align="center">
  <p align="center">
    Docker power simplified. Async containers, smart aliases, and compose mastery in one CLI.
    <br />
    <a href="https://alexeev-prog.github.io/pyharborcli/"><strong>Explore the docs »</strong></a>
    <br />
    <br />
    <a href="#-getting-started">Getting Started</a>
    ·
    <a href="#-usage-examples">Basic Usage</a>
    ·
    <a href="https://alexeev-prog.github.io/pyharborcli/">Documentation</a>
    ·
    <a href="https://github.com/alexeev-prog/pyharborcli/blob/main/LICENSE">License</a>
  </p>
</div>
<br>
<p align="center">
    <img src="https://img.shields.io/github/languages/top/alexeev-prog/pyharborcli?style=for-the-badge">
    <img src="https://img.shields.io/github/languages/count/alexeev-prog/pyharborcli?style=for-the-badge">
    <img src="https://img.shields.io/github/license/alexeev-prog/pyharborcli?style=for-the-badge">
    <img src="https://img.shields.io/github/stars/alexeev-prog/pyharborcli?style=for-the-badge">
    <img src="https://img.shields.io/github/issues/alexeev-prog/pyharborcli?style=for-the-badge">
    <img src="https://img.shields.io/github/last-commit/alexeev-prog/pyharborcli?style=for-the-badge">
    <img src="https://img.shields.io/pypi/wheel/pyharborcli?style=for-the-badge">
    <img src="https://img.shields.io/badge/coverage-100%25-100%25?style=for-the-badge" alt="Coverage">
    <img alt="PyPI - Downloads" src="https://img.shields.io/pypi/dm/pyharborcli?style=for-the-badge">
    <img alt="PyPI - Version" src="https://img.shields.io/pypi/v/pyharborcli?style=for-the-badge">
    <img alt="PyPI - Python Version" src="https://img.shields.io/pypi/pyversions/pyharborcli?style=for-the-badge">
    <img alt="GitHub contributors" src="https://img.shields.io/github/contributors/alexeev-prog/pyharborcli?style=for-the-badge">
</p>
<p align="center">
    <img src="https://raw.githubusercontent.com/alexeev-prog/pyharborcli/refs/heads/main/docs/pallet-0.png">
</p>

 > Docker power simplified. Async containers, smart aliases, and compose mastery in one CLI.

A usable and fast utility for safe and simplified working with docker containers. Written in python.

## Contributing

We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines. Key areas for contribution include:
- Additional test cases for all
- Performance optimization proposals
- Extended version format support
- IDE integration plugins

## License & Support

This project is licensed under **MIT License** - see [LICENSE](https://github.com/alexeev-prog/pyharborcli/blob/main/LICENSE). For commercial support and enterprise features, contact [alexeev.dev@mail.ru](mailto:alexeev.dev@mail.ru).

[Explore Documentation](https://alexeev-prog.github.io/pyharborcli) |
[Report Issue](https://github.com/alexeev-prog/pyharborcli/issues) |
[View Examples](./examples)

<p align="right">(<a href="#readme-top">back to top</a>)</p>

---
Professional deprecation management for modern Python projects

Copyright © 2025 Alexeev Bronislav. Distributed under MIT license.
vessel-performance 0.1.4
A library for calculating and analyzing vessel performance
Requires Python: >=3.7

# Vessel Performance

A Python library for calculating and analyzing vessel performance metrics.

[![PyPI version](https://badge.fury.io/py/vessel-performance.svg)](https://badge.fury.io/py/vessel-performance)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

## Features

- Calculate vessel fuel efficiency
- Analyze vessel speed and power relationships
- Estimate vessel performance under different weather conditions
- Generate performance reports and visualizations

## Installation

You can install the package via pip:

```bash
pip install vessel-performance
```

## Quick Start

```python
from vessel_performance import VesselAnalyzer
∏
# Create a vessel analyzer instance
analyzer = VesselAnalyzer()

# Calculate fuel efficiency
efficiency = analyzer.calculate_fuel_efficiency(
    fuel_consumption=100,  # tons per day
    vessel_speed=14,       # knots
    displacement=50000     # tons
)

# Analyze speed-power relationship
power_curve = analyzer.get_speed_power_curve(
    speed_range=[10, 15, 20],  # knots
    vessel_data={
        "length": 200,         # meters
        "beam": 32,           # meters
        "draft": 12           # meters
    }
)
```

## Documentation

- [API Documentation](docs/api.md) - Detailed documentation of all classes and functions
- [Usage Examples](docs/examples.md) - Various examples and use cases
- [Contributing Guide](CONTRIBUTING.md) - How to contribute to the project

## Advanced Usage

For more complex examples and real-world scenarios, check out our [examples documentation](docs/examples.md). Here are some highlights:

- Weather impact analysis
- Route planning and optimization
- Performance monitoring
- Fuel consumption estimation

## Development

To set up the development environment:

```bash
# Clone the repository
git clone https://github.com/yourusername/vessel-performance.git
cd vessel-performance

# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest tests/
```

## Contributing

We welcome contributions! Please see our [contributing guide](CONTRIBUTING.md) for details on how to:

- Report bugs
- Suggest new features
- Submit pull requests
- Update documentation

## License

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

## Support

If you encounter any problems or have questions:

1. Check the [documentation](docs/)
2. Look for similar issues in the [issue tracker](https://github.com/yourusername/vessel-performance/issues)
3. Open a new issue if needed

## Authors

- Shane Lee (liqiyuworks@163.com)

## Acknowledgments

- Thanks to all contributors
- Special thanks to the naval architecture community for formulas and methodologies
hulo 0.1.1
Hulo programming language compiler and runtime
Requires Python: >=3.7

# Hulo Programming Language

Hulo is a modern programming language designed for simplicity and performance.

## Installation

Install Hulo using pip:

```bash
pip install hulo
```

The package will automatically detect your platform and architecture, then install the appropriate binary files.

### Supported Platforms

- **Windows**: x86_64, arm64, i386
- **macOS**: x86_64, arm64
- **Linux**: x86_64, arm64, i386

## Usage

After installation, you can use the `hulo` command:

```bash
hulo --help
```

## Development

To install in development mode:

```bash
pip install -e .
```

## License

MIT License - see LICENSE file for details.