semvec/pypi-packages huggingface dataset:
polyany 0.4.0
A Python package for algebraic manipulation of multivariate polynomials
Requires Python: >=3.11
Keywords: algebra, multivariate, polynomials

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<p align="center">
  <strong>A Python package for algebraic manipulation of multivariate polynomials.</strong>
</p>

> 🚧 **This package is in alpha stage.**
>
> Some **breaking changes** may occur.

---

## ✨ Overview

PolyAny provides a flexible framework for representing and manipulating multivariate polynomials using structured, non-symbolic representations.

Unlike symbolic engines, PolyAny operates directly on the algebraic structure of polynomials (coefficients and exponents), enabling integration with numerical libraries and efficient structural transformations.

---

## 🔧 Features (planned)

- Polynomial creation from multiple formats (list, tuples, NumPy arrays, quadratic forms, ...)
- Support for multivariate expressions
- Algebraic operations: addition, multiplication, truncation, homogenization, ...
- Polynomial exporting into LaTeX code

---

## 📦 Installation

```bash
pip install polyany
```

For local development, see the [instructions in the documentation](https://rolmip.github.io/polyany/installation/#how-to-contribute)

## 📄 License

This project is open-source and licensed under the BSD-3-Clause.

## 👥 Contributors

PolyAny is maintained by the **ROLMIP** developers:

* [Cristiano Agulhari](mailto:agulhari@utfpr.edu.br)
* [Esdras Battosti](mailto:esdras.2019@alunos.utfpr.edu.br)

## 🧪 About

This repository is part of the early foundation of **RolmiPy**, a Python implementation of ROLMIP.
duo-ai 1.0.1
DuoAI: Two-System AI Agents
Requires Python: >=3.8

# DuoAI: Two-System AI Agents

<p align="center">
  <img src="images/logo.png" alt="DuoAI Logo" width="30%">
</p>

DuoAI is a research framework for building agents that mimic human [dual cognitive system](https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow). In this release, we focus on tackling the [YRC problem](https://arxiv.org/pdf/2502.09583): deciding which system should decide the next action at a given time. This codebase provide modular abstractions, benchmark environments, and baseline implementations to help you quickly develop and test your ideas.

---

## 🔧 Features

- ⚙️ Unified abstractions for coordination policies, decision-making agents, and environments.
- 🧪 Benchmark suite of gridworld and visual decision-making tasks (e.g., MiniGrid, Procgen).
- 📈 Baselines: uncertainty-based and reinforcement learning.
- 🧩 Compositional design: Easily add your own policies, algorithms, or environments.
- 📚 Extensive documentation and examples for rapid experimentation.

---

## 📦 Installation

Install from PyPI:

```bash
pip install duo_ai
```

Or install from source:

```bash
git clone --recurse-submodules https://github.com/khanhptnk/duo-ai.git
cd duo-ai
pip install -e .
```

---

## 📚 Documentation

See the full documentation at:
[https://duo-ai.readthedocs.io](https://duo-ai.readthedocs.io)

---

## 🧪 Citing DuoAI


If you use the DuoAI package in your research, please cite:

```bibtex
@misc{DuoAI2025,
  author       = {Khanh Nguyen},
  title        = {DuoAI: Two-System AI Agents},
  year         = {2025},
  howpublished = {\url{https://github.com/khanhptnk/duo-ai}},
  note         = {Python package. Version 1.0},
}
```

---

## 🤝 Contributing

We welcome pull requests, feature suggestions, and bug reports!
Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.

---

## 🛡 License

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

---

## 🙏 Acknowledgments

DuoAI draws inspiration from [YRC-Bench](https://github.com/modanesh/YRC-Bench), a public benchmark Khanh Nguyen co-developed with colleagues at UC Berkeley, but is not an official continuation of that work.
This repository also includes code from the public [procgenAISC](https://github.com/JacobPfau/procgenAISC) and [pyod](https://github.com/yzhao062/pyod) projects as Git submodules. We thank the original authors of these projects for making their work publicly available.
DuoAI builds upon a number of open-source frameworks and libraries, including PyTorch, StableBaselines3, Procgen, MiniGrid, Gym, and Gymnasium. We acknowledge and thank the developers and maintainers of these projects.
bmailer 0.1.1
Asynchronously send emails via SMTP
Requires Python: >=3.10
Keywords: bmailer, smtp, async

# BMailer

[![image](https://img.shields.io/pypi/v/bmailer.svg)](https://pypi.python.org/pypi/bmailer)
[![image](https://img.shields.io/pypi/l/bmailer.svg)](https://pypi.python.org/pypi/bmailer)
[![image](https://img.shields.io/pypi/pyversions/bmailer.svg)](https://pypi.python.org/pypi/bmailer)
[![Actions status](https://github.com/duytanisme/bmailer/actions/workflows/test-and-release.yaml/badge.svg)](https://github.com/duytanisme/bmailer/actions)
[![codecov](https://codecov.io/gh/duytanisme/bmailer/branch/main/graph/badge.svg)](https://codecov.io/gh/duytanisme/bmailer)

BMailer is an asynchronous email sender built with Python, designed to send bulk emails efficiently using SMTP. It supports templating for email bodies and allows for concurrent sending of multiple emails.

## Example Usage

```python
import asyncio

from bmailer import AsyncMailer
from bmailer.models import EmailPayload
from bmailer.templates import render_template


async def main():
    sender = AsyncMailer(
        host="smtp.gmail.com",
        port=587,
        username="email@email.com",
        password="app_password", # If using Gmail, use an App Password
    )

    template = "Hello {name},\nYour order #{order_id} has been shipped."
    emails = [
        EmailPayload(
            subject="Order Update",
            recipient="target@email.com",
            body=render_template(template, name="Recipient", order_id=123),
        ),
    ]

    await sender.send_bulk(emails, concurrency=5)


if __name__ == "__main__":
    asyncio.run(main())
```

## Installation

```bash
pip install bmailer
```

## Contribution

Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.

```bash
git clone https://github.com/duytanisme/bmailer.git
cd bmailer
uv sync
```
mlb-odds-scraper 0.1.20
A package for scraping MLB odds data from OddsPortal
Requires Python: >=3.8

# MLB Odds Scraper

A Python package for scraping MLB odds data from OddsPortal. This package provides tools to easily retrieve and analyze betting odds for MLB games.

## Installation

```bash
pip install mlb_odds_scraper
```

## Features

- Scrape historical MLB betting odds from OddsPortal
- Support for concurrent page scraping
- Clean and structured data output with team ID mapping
- Timezone handling (converts game times to UTC)
- Filters out All-Star games automatically
- Adaptive selectors to handle website structure changes
- Enhanced stealth mode to avoid detection

## Requirements

- Python >= 3.8
- Chrome/Chromium browser installed

## Dependencies

- pandas >= 1.0.0
- selenium >= 4.0.0
- stealthenium >= 0.1.0
- MLB-StatsAPI >= 1.0.0

## Usage

```python
from mlb_odds_scraper import scrape_oddsportal_mlb, scrape_oddsportal_mlb_years, process_game_data

# Scrape a single year
games = scrape_oddsportal_mlb(2024)  # Scrapes all pages for 2024
games = scrape_oddsportal_mlb(2024, max_pages=5)  # Scrape first 5 pages only

# Scrape multiple years
df = scrape_oddsportal_mlb_years(2020, 2024)  # Scrapes 2020-2023
df = scrape_oddsportal_mlb_years()  # Scrapes 2006-2024 (default)

# Process the scraped data
processed_df = process_game_data(df)
# Returns DataFrame with:
# - Parsed dates and times (UTC)
# - Team IDs from MLB-StatsAPI
# - Cleaned odds and scores
# - Removed All-Star games
```

## Changelog

### v0.1.2
- Added multiple selectors to handle website structure changes
- Improved error handling and robustness
- Enhanced stealth mode to avoid detection
- Added better date and time parsing
- Fixed issues with future year scraping

### v0.1.1
- Initial release

## License

This project is licensed under the MIT License - see the LICENSE file for details.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.