semvec/pypi-packages huggingface dataset:
impyrium 1.2.2
A tool to setup device controls
Requires Python: >=3.6

# impyrium
A python device control program that can handle sending commands to devices
and integrating everything into keyboard shortcuts

## Getting started
```
pip3 install watchdog
pip3 install pynput
pip3 install PySide6
```

### Setting up a project
```
python3 -m impyrium
cd impyre
python3 run.py
```

Then configure as you like!
tello-zune 0.7.6
Biblioteca tello-zune, serve para controlar, e obter informacoes do drone DJI Tello.
Requires Python: >=3.9

# tello_zune
Library used by the Zune Drones team to control the DJI Tello drone. In the examples folder, there are some simple usage examples of the tello_zune module, which is also present in the folder.


To create the library, adaptations were made from two other existing libraries:
* [fvilmos](https://github.com/fvilmos/tello_object_tracking)
* [damiafuentes](https://github.com/damiafuentes/DJITelloPy)

# Requeriments

* Python >= 3.9.0

```bash
pip install numpy
```

```bash
pip install opencv-python
```

# How to use

To use the library, install with:

```bash
pip install tello-zune
```
steinerpy 0.1.3
Package to solve Steiner Tree and Steiner Forest Problems with the HiGHS solver
Requires Python: >=3.8
Keywords: forest, graph, highs, networkx, optimization, steiner, tree

# SteinerPy

[![PyPI version](https://badge.fury.io/py/steinerpy.svg)](https://pypi.org/project/steinerpy/)
[![PyPI - Downloads](https://img.shields.io/pypi/dm/steinerpy)](https://pypi.org/project/steinerpy/)
[![Python 3.8+](https://img.shields.io/pypi/pyversions/steinerpy.svg)](https://pypi.org/project/steinerpy/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![CI/CD](https://github.com/berendmarkhorst/SteinerPy/workflows/CI%2FCD%20Pipeline/badge.svg)](https://github.com/berendmarkhorst/SteinerPy/actions)
[![codecov](https://codecov.io/gh/berendmarkhorst/SteinerPy/branch/main/graph/badge.svg)](https://codecov.io/gh/berendmarkhorst/SteinerPy)

A Python package for solving Steiner Tree and Steiner Forest Problems using the HiGHS solver and NetworkX graphs.

## Installation

Install SteinerPy from PyPI:

```bash
pip install steinerpy
```

Or using uv:

```bash
uv add steinerpy
```

### Requirements

- Python 3.8+
- NetworkX
- HiGHS Solver (via highspy)

## Quick Start

```python
import networkx as nx
from steinerpy import SteinerProblem

# Create a graph
G = nx.Graph()
G.add_edge('A', 'B', weight=1)
G.add_edge('B', 'C', weight=2)
G.add_edge('C', 'D', weight=1)

# Define terminal groups
terminal_groups = [['A', 'D']]

# Solve the Steiner problem
problem = SteinerProblem(G, terminal_groups)
solution = problem.get_solution()

print(f"Optimal cost: {solution.objective}")
print(f"Selected edges: {solution.selected_edges}")
```

## Usage Examples

See the `example.ipynb` notebook for detailed usage examples.

## Dependencies

- `networkx`: For graph representation and manipulation
- `highspy`: For optimization solving

If you use this package in your research, please cite:

```
@article{markhorst2025future,
  title={Future-proof ship pipe routing: Navigating the energy transition},
  author={Markhorst, Berend and Berkhout, Joost and Zocca, Alessandro and Pruyn, Jeroen and van der Mei, Rob},
  journal={Ocean Engineering},
  volume={319},
  pages={120113},
  year={2025},
  publisher={Elsevier}
}
```
columnjump 1.1.1
Fix column jumps for JWST NIRISS detector
Requires Python: >=3.6

# Columnjump

Performs column jump detection on each ramp integration within a JWST NIRISS exposure to correct for jumps in columns. Note that 'columns' are defined in 
original detector coordinates (not DMS coordinates) so appear in 3rd 
axis of 4D data array. Uses modified version of algorithm documented in CSA-JWST-TN-0003.

It is intended this module be run during the DETECTOR1 pipeline between the dark current subtraction and jump detection step.

Version 1.1.0 removes a dependency on the jwst pipeline version
simplyprint-duet3d 1.3.15
SimplyPrint integration with any Duet3D powered RepRapFirmware printers 
Requires Python: >=3.9

SimplyPrint Duet3d integration
================================================

Many thanks to `Tim Schneider <https://github.com/timschneider>`_  at https://github.com/Meltingplot/duet-simplyprint-connector/ for originally creating this integration and allowing us to use it.

This package acts as a bridge between Duet-based 3D printers and the SimplyPrint.io cloud service.

It communicates with the printer using the Duet HTTP API.
For more information, visit https://github.com/Duet3D/RepRapFirmware/wiki/HTTP-requests.

Communication with SimplyPrint.io is handled via the `simplyprint-ws-client`.

------------
Status
------------

Supported features:

- Printer registration
- Printer status update
- Webcam snapshot livestream
- GCode receiving
- File downloading
- Printer control (start, pause, resume, cancel)
- Self Upgrading VIA G-Code M997
- Device healts update
- Bed leveling
- Filament Sensor
- Duet auto discovery with tracking based on BoardID
- Leave a cookie on the printer to identify the printer in the future (`0:/sys/simplyprint-connector.json`)
- Grab the webcam url from DWC Settings file from the Printer
- Allow Webcam URL to be an Snapshot Endpoint or MJPEG Stream

Missing features:

- PSU Control
- GCode Macros / Scripts [not yet implemented by SimplyPrint.io for Duet]
- GCode terminal [not yet implemented by SimplyPrint.io for Duet]
- Receive messages from Printer in SimplyPrint.io [not yet implemented by SimplyPrint.io for Duet]


------------
Installation
------------
Open an SSH session to your SimplyPrint-connected device, such as a Raspberry Pi 4B.

.. code-block:: sh

    source <(curl -sSL https://raw.githubusercontent.com/simplyprint/integration-duet3d/refs/heads/main/install.sh)


-----------------------------
Content of DuetConnector.json
-----------------------------

The default password for the Duet is `reprap`, even if the web interface does not require a login.

.. code-block:: json

    [
        {
            "id": null,
            "token": null,
            "name": null,
            "in_setup": true,
            "short_id": null,
            "public_ip": null,
            "unique_id": "...",
            "duet_uri": "http://192.168.1.0",
            "duet_password": "reprap",
            "duet_unique_id": "YOUR_DUET_BOARD_ID",
            "duet_name": "YOUR_DUET_NAME",
            "webcam_uri": "http://URI_OF_WEBCAM_SNAPSHOT_ENDPOINT/webcam"
        }
    ]


-----------------------------------------------
Usage of Meltingplot Duet SimplyPrint Connector
-----------------------------------------------

- Create a configuration with `simplyprint-duet3d autodiscover`
- *Optional* Edit the configuration file `~/.config/SimplyPrint/DuetConnector.json`
- Start the duet simplyprint connector with `simplyprint-duet3d start` or `systemctl start simplyprint-duet3d.service`
- Add the printer via the SimplyPrint.io web interface.
chainlit-praisonai 0.1.0
Chainlit integration for PraisonAI multi-agent framework
Requires Python: >=3.9
Keywords: chainlit, praisonai, ai-agents, multi-agent, llm, chat

# chainlit-praisonai

[Chainlit](https://chainlit.io/) integration for [PraisonAI](https://github.com/MervinPraison/PraisonAI) multi-agent framework.

## Installation

```bash
pip install chainlit-praisonai
```

## Quick Start

Create `app.py`:

```python
import chainlit as cl
from chainlit_praisonai import PraisonAIClient

client = PraisonAIClient()

@cl.on_message
async def main(message: cl.Message):
    response = await client.run_workflow(message.content)
    await cl.Message(content=response).send()
```

Run:

```bash
chainlit run app.py
```

## Using a Specific Agent

```python
from chainlit_praisonai import PraisonAIClient

client = PraisonAIClient()

# Use a specific agent
response = await client.run_agent("Research quantum computing", "researcher")
```

## Configuration

```python
from chainlit_praisonai import PraisonAIClient

client = PraisonAIClient(
    api_url="http://localhost:8080",
    timeout=300,
)
```

## Prerequisites

Start PraisonAI server:

```bash
pip install praisonai
praisonai serve agents.yaml --port 8080
```

## Links

- [PraisonAI Documentation](https://docs.praison.ai)
- [Chainlit Documentation](https://docs.chainlit.io)

## License

MIT
flask-praisonai 0.1.0
Flask integration for PraisonAI multi-agent framework
Requires Python: >=3.9
Keywords: flask, praisonai, ai-agents, multi-agent, llm, api

# flask-praisonai

[Flask](https://flask.palletsprojects.com/) integration for [PraisonAI](https://github.com/MervinPraison/PraisonAI) multi-agent framework.

## Installation

```bash
pip install flask-praisonai
```

## Quick Start

```python
from flask import Flask
from flask_praisonai import create_blueprint

app = Flask(__name__)
app.register_blueprint(create_blueprint())

# Now you have:
# POST /praisonai/query - Send queries to PraisonAI
# GET /praisonai/agents - List available agents

if __name__ == "__main__":
    app.run()
```

## API Endpoints

### POST /praisonai/query

Send a query to PraisonAI agents:

```json
{
  "query": "Research AI trends",
  "agent": "researcher"  // optional
}
```

### GET /praisonai/agents

List available PraisonAI agents.

## Using the Client Directly

```python
from flask_praisonai import PraisonAIClient

client = PraisonAIClient(api_url="http://localhost:8080")

result = client.run_workflow("Research AI trends")
result = client.run_agent("Write an article", "writer")
agents = client.list_agents()
```

## Configuration

```python
from flask_praisonai import create_blueprint

bp = create_blueprint(
    api_url="http://localhost:8080",
    url_prefix="/ai",  # Custom prefix
)
```

## Prerequisites

Start PraisonAI server:

```bash
pip install praisonai
praisonai serve agents.yaml --port 8080
```

## Links

- [PraisonAI Documentation](https://docs.praison.ai)
- [Flask Documentation](https://flask.palletsprojects.com)

## License

MIT
fastapi-praisonai 0.1.0
FastAPI integration for PraisonAI multi-agent framework
Requires Python: >=3.9
Keywords: fastapi, praisonai, ai-agents, multi-agent, llm, api

# fastapi-praisonai

[FastAPI](https://fastapi.tiangolo.com/) integration for [PraisonAI](https://github.com/MervinPraison/PraisonAI) multi-agent framework.

## Installation

```bash
pip install fastapi-praisonai
```

## Quick Start

```python
from fastapi import FastAPI
from fastapi_praisonai import create_router

app = FastAPI()
app.include_router(create_router())

# Now you have:
# POST /praisonai/query - Send queries to PraisonAI
# GET /praisonai/agents - List available agents
```

## API Endpoints

### POST /praisonai/query

Send a query to PraisonAI agents:

```json
{
  "query": "Research AI trends",
  "agent": "researcher"  // optional
}
```

### GET /praisonai/agents

List available PraisonAI agents.

## Using the Client Directly

```python
from fastapi_praisonai import PraisonAIClient

client = PraisonAIClient(api_url="http://localhost:8080")

# In an async context
result = await client.run_workflow("Research AI trends")
result = await client.run_agent("Write an article", "writer")
agents = await client.list_agents()
```

## Configuration

```python
from fastapi_praisonai import create_router

router = create_router(
    api_url="http://localhost:8080",
    prefix="/ai",  # Custom prefix
    tags=["AI Agents"],  # OpenAPI tags
)
```

## Prerequisites

Start PraisonAI server:

```bash
pip install praisonai
praisonai serve agents.yaml --port 8080
```

## Links

- [PraisonAI Documentation](https://docs.praison.ai)
- [FastAPI Documentation](https://fastapi.tiangolo.com)

## License

MIT
gradio-praisonai 0.1.0
Gradio components for PraisonAI multi-agent framework
Requires Python: >=3.9
Keywords: gradio, praisonai, ai-agents, multi-agent, llm, ui

# gradio-praisonai

[Gradio](https://gradio.app/) components for [PraisonAI](https://github.com/MervinPraison/PraisonAI) multi-agent framework.

## Installation

```bash
pip install gradio-praisonai
```

## Quick Start

```python
from gradio_praisonai import launch_chat

# Launch a chat interface
launch_chat()
```

## Components

### create_chat_interface

Create a Gradio Blocks interface:

```python
from gradio_praisonai import create_chat_interface

demo = create_chat_interface(
    api_url="http://localhost:8080",
    agent="researcher",  # Optional: specific agent
    title="🤖 AI Assistant",
)
demo.launch()
```

### PraisonAIClient

Direct API client:

```python
from gradio_praisonai import PraisonAIClient

client = PraisonAIClient(api_url="http://localhost:8080")

# Run full workflow
result = client.run_workflow("Research AI trends")

# Run specific agent
result = client.run_agent("Write an article", "writer")

# List agents
agents = client.list_agents()
```

## Prerequisites

Start PraisonAI server:

```bash
pip install praisonai
praisonai serve agents.yaml --port 8080
```

## Links

- [PraisonAI Documentation](https://docs.praison.ai)
- [Gradio Documentation](https://gradio.app/docs)

## License

MIT
streamlit-praisonai 0.1.0
Streamlit components for PraisonAI multi-agent framework
Requires Python: >=3.9
Keywords: streamlit, praisonai, ai-agents, multi-agent, llm, ui

# streamlit-praisonai

[Streamlit](https://streamlit.io/) components for [PraisonAI](https://github.com/MervinPraison/PraisonAI) multi-agent framework.

## Installation

```bash
pip install streamlit-praisonai
```

## Quick Start

```python
import streamlit as st
from streamlit_praisonai import praisonai_chat

st.title("My AI App")
praisonai_chat()
```

## Components

### praisonai_chat

A full chat interface for PraisonAI:

```python
from streamlit_praisonai import praisonai_chat

praisonai_chat(
    api_url="http://localhost:8080",
    agent="researcher",  # Optional: specific agent
    title="🤖 AI Assistant",
    placeholder="Ask me anything...",
)
```

### PraisonAIClient

Direct API client:

```python
from streamlit_praisonai import PraisonAIClient

client = PraisonAIClient(api_url="http://localhost:8080")

# Run full workflow
result = client.run_workflow("Research AI trends")

# Run specific agent
result = client.run_agent("Write an article", "writer")

# List agents
agents = client.list_agents()
```

## Prerequisites

Start PraisonAI server:

```bash
pip install praisonai
praisonai serve agents.yaml --port 8080
```

## Links

- [PraisonAI Documentation](https://docs.praison.ai)
- [Streamlit Documentation](https://docs.streamlit.io)

## License

MIT
satisfactory-flow 0.1.0
Automate your automations. Production flow calculations for Satisfactory.
Requires Python: >=3.14
Keywords: module, packaging, single-file

# Satisfactory Flow (satflow) 🔁 


[![CI](https://github.com/apathetic-tools/satflow/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/goldilocks/satflow/actions/workflows/ci.yml)
[![License: MIT-a-NOAI](https://img.shields.io/badge/License-MIT--a--NOAI-blueviolet.svg)](LICENSE)
[![Discord](https://img.shields.io/badge/Discord-%235865F2.svg?logo=discord&logoColor=white)](https://discord.gg/PW6GahZ7)

📘 **[Roadmap](./ROADMAP.md)** · 📝 **[Release Notes](https://github.com/apathetic-tools/satflow/releases)**

**Automate your automations.**
*Production flow calculations for Satisfactory.*

> [!NOTE]
> Heads up: the AI cooked dinner. It's edible, but watch your step.  
> Detailed bug reports welcome.

## 🚀 Quick Start

That’s it. ✨

---

### ⚖️ License
- [MIT-a-NOAI License](LICENSE)

You’re free to use, copy, and modify the script under the standard MIT terms.  
The additional rider simply requests that this project not be used to train or fine-tune AI/ML systems until the author deems fair compensation frameworks exist.  
Normal use, packaging, and redistribution for human developers are unaffected.

## 💡 Why Python? Other Language Versions?

You don’t need a native build.  
Python 3 ships with most Linux distributions. On Mac, it's one homebrew command away. On Windows, running the script automatically prompts the user to install Python from the Microsoft Store.

## 🪶 Summary

**Use it. Hack it. Ship it.**
It’s MIT-licensed, minimal, and meant to stay out of your way — just with one polite request: don’t feed it to the AIs (yet).

---

> ✨ *AI was used to help draft language, formatting, and code — plus we just love em dashes.*

<p align="center">
  <sub>😐 <a href="https://apathetic-tools.github.io/">Apathetic Tools</a> © <a href="./LICENSE">MIT-a-NOAI</a></sub>
</p>