semvec/pypi-packages huggingface dataset:
buzzsprout-client 0.2.0
Python client for Buzzsprout API
License: MIT
Requires Python: <4.0,>=3.8

# buzzsprout-client
Python client for Buzzsprout API

## Installation

```bash
pip install buzzsprout-client
```

## Usage

```python
from buzzsprout_client import BuzzsproutClient

# Initialize client with your API key
client = BuzzsproutClient(api_key="your_api_key_here")

# Get all podcasts
podcasts = client.get_podcasts()

# Get a specific podcast
podcast = client.get_podcast(podcast_id=12345)

# Get all episodes for a podcast
episodes = client.get_episodes(podcast_id=12345)

# Get a specific episode
episode = client.get_episode(podcast_id=12345, episode_id=67890)

# Create a new episode
new_episode = client.create_episode(
    podcast_id=12345,
    title="My New Episode",
    audio_file="path/to/audio.mp3",
    description="This is my new episode",
    private=True
)

# Update an existing episode
updated_episode = client.update_episode(
    podcast_id=12345,
    episode_id=67890,
    title="Updated Episode Title",
    private=False,
    audio_url="https://example.com/new-audio.mp3"
)

# Update episode with file uploads
updated_episode = client.update_episode(
    podcast_id=12345,
    episode_id=67890,
    title="Updated Episode Title",
    private=False,
    audio_file="path/to/new-audio.mp3",
    artwork_file="path/to/new-artwork.jpg"
)
```
gptstonks-multiagents 0.0.3
Multi-Agent architectures to create copilots and autopilots.
License: MIT
Requires Python: <3.11,>=3.10

<p align="center">
  <img src="../../docs/assets/logo-chatbot.png" alt="Logo">
</p>
<p align="center">
  <!-- Waitlist Badge -->
  <a href="https://gptstonks.net/login"><img src="../../docs/assets/waitlist_badge.png" alt="Join Waitlist Badge"></a>
  <!-- YT Badge -->
  <a href="https://www.youtube.com/@GPTStonks"><img src="https://img.shields.io/badge/channel-ff0000?style=for-the-badge&logo=youtube&logoColor=white" alt="Youtube Channel Badge"></a>
  <!-- X Badge -->
  <a href="https://twitter.com/GPTStonks"><img src="https://img.shields.io/badge/follow_us-000000?style=for-the-badge&logo=x&logoColor=white" alt="X Follow Us Badge"></a>
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  <!-- Docker Badge -->
  <a href="https://hub.docker.com/u/gptstonks">
    <img src="https://img.shields.io/badge/Docker-2496ED?style=for-the-badge&logo=docker&logoColor=white" alt="Docker Badge">
  </a>
</p>
<p align="center">
  <!-- LlamaIndex Badge -->
  <a href="https://www.llamaindex.ai/"><img src="https://img.shields.io/badge/LlamaIndex-ac6aff?style=for-the-badge&logo=llamaindex&logoColor=white" alt="LlamaIndex Badge"></a>
  <!-- LangChain Badge -->
  <a href="https://langchain.com/">
    <img src="https://img.shields.io/badge/LangChain-005A9C?style=for-the-badge&logo=langchain&logoColor=white" alt="LangChain Badge">
  </a>
</p>

# GPTStonks Multi-Agents

## Description

GPTStonks Multi-Agents provides common multi-agent architectures to create copilots and autopilots that solve complex tasks.

## Development

  1. Install [PDM](https://pdm.fming.dev/latest/#installation).

  2. Clone the project and install necessary packages:
```bash
# clone project
git clone https://github.com/GPTStonks/gptstonks.git
cd gptstonks

# install pdm
pip install pdm

# install package
pdm install -dG default
```

## Usage guide

In the notebook [Multi-Agent With Tools](notebooks/multiagent_with_tools.ipynb) we showcase how multi-agents can be used for advanced knowledge gathering among other tasks. In this particular case to answer music-related questions and make recommendations.
sacra 1.0.0
A collection of sacra
License: MIT
Requires Python: >=3.11
Keywords: sacra

# sacra

A collection of modules implementing various **sacra**.

> **_sacra_**: things sacred, holy, part of rites, or works of art.

Sacra is derived from the Latin word _sacrum_, meaning sacred or holy. This
repository contains tools and scripts that combine functionality, creativity,
a hint of esotericism, and a reverence for craft.

## Modules

- `sacra.polyskelion`: Generate polyskelion like a
  [triskelion](https://en.wikipedia.org/wiki/Triskelion)

## Installation

This repository is meant to be light on dependencies but are a few.

```bash
pip install sacra
```

## Usage

Most sacra included in this library are given their own executable
installation.

```bash
polyskelion  # Displays a polyskelion
polyskelion -h
polyskelion --spirals 4 --out quadskelion.png
```

The scripts contained in this package may also be used as a library.

```python
from sacra import polyskelion as ps

# Adjust parameters to your needs. They are defaulted
params = ps.PolyskelionParams()
figure = ps.plot_polyskelion(params)  # Returns a matplotlib.figure.Figure
```

## Contributing

Contributions are welcome. If you'd like to contribute:

1. Fork the repository.
2. Create a new branch for your feature or bugfix:
   ```bash
   git checkout -b feature-name
   ```
3. Make your changes and commit them:
   ```bash
   git commit -m "Add feature name"
   ```
4. Push your branch and open a pull request:
   ```bash
   git push origin feature-name
   ```

This project aims to have 100% test coverage with very few `# pragma: no
cover` exceptions.

## License

This project is licensed under the [MIT License](LICENSE). Feel free to use,
modify, and distribute it.
suftware 1.0.4
One-dimensional density estimation using Bayesian field theory.
License: MIT
Requires Python: >=3.10

# SUFTware: Statistics Using Field Theory

SUFTware (Statistics Using Field Theory) provides a fast and lightweight Python implementation of Bayesian Field Theory algorithms for low-dimensional statistical inference. SUFTware currently supports the one-dimenstional density estimation algorithm DEFT. See the Documentation page for details on the SUFTware API.

**Installation** :
pip install suftware

Requirements (updated 2025-01-06, v1.0.3)
* python >=3.10
* numpy >=1.26.0
* scipy >=1.5.0
* pandas >=1.2.0
* matplotlib >=3.3.0
<br/>

Other resources
* Documentation: "http://suftware.readthedocs.org"
* Github: "https://github.com/jbkinney/suftware"
* PyPI: "https://pypi.python.org/pypi/suftware"