semvec/pypi-packages huggingface dataset:
olvid-bot 2.0.0a5
A Python GRPC client implementation for building chatbots and programs that interact with the Olvid application.
Requires Python: >=3.11
Keywords: olvid, daemon, bot

# **Olvid Bot Python Client**

## Introduction
Welcome to the Olvid Python Client repository, part of the Olvid bots framework. 
If you're new here, consider starting with our [Documentation](https://doc.bot.olvid.io).

## Installation

You can install this module using pip:

```bash
pip3 install olvid-bot
```

Or from source:

```bash
git clone https://github.com/olvid-io/Olvid-Bot-Python-Client
cd Olvid-Bot-Python-Client
pip3 install .
```

## Overview

This Python module implements a robust gRPC client, designed to simplify interactions while providing complete access to every RPC.
This allows you to achieve advanced tasks with ease.
To see the complete gRPC and protobuf description of the Daemon API, please refer to the [protobuf repository](https://github.com/olvid-io/Olvid-Bot-Protobuf).

The module also includes a built-in **CLI (Command-Line Interface)**, enabling manual interaction with the Daemon for efficient management and testing.
For more information on using the CLI, refer to [README-cli](./README-cli.md).

## Quickstart
Follow our [🚀 Quickstart](https://doc.bot.olvid.io/install) page.
placo 0.9.20
PlaCo: Rhoban Planning and Control
Requires Python: >=3.9

<img width="400" src="https://placo.readthedocs.io/en/latest/_static/placo.png" />

## Planning & Control

PlaCo is Rhoban's planning and control library. It is built on the top of [pinocchio](https://github.com/stack-of-tasks/pinocchio), [eiquadprog](https://github.com/stack-of-tasks/eiquadprog) QP solver, and fully written in C++ with Python bindings, allowing fast prototyping with good runtime performances. It features task-space inverse kinematics and dynamics (see below) high-level API for whole-body control tasks.

### Task-Space Inverse Kinematics

[![Quadruoped demo](https://github.com/Rhoban/placo-examples/blob/master/kinematics/videos/quadruped_targets.gif?raw=true)](https://github.com/Rhoban/placo-examples/blob/master/kinematics/videos/quadruped_targets.mp4?raw=true)

High-level API to specify tasks for constrained inverse kinematics (IK).

- [See documentation](https://placo.readthedocs.io/en/latest/kinematics/getting_started.html)
- [Examples](https://placo.readthedocs.io/en/latest/kinematics/examples_gallery.html)

### Task-Space Inverse Dynamics

[![Megabot demo](https://github.com/Rhoban/placo-examples/blob/master/dynamics/videos/megabot.gif?raw=true)](https://github.com/Rhoban/placo-examples/blob/master/dynamics/videos/megabot.mp4?raw=true)

High-level API to specify tasks for constrained inverse dynamics (ID).

- [See documentation](https://placo.readthedocs.io/en/latest/dynamics/getting_started.html)
- [Examples](https://placo.readthedocs.io/en/latest/dynamics/examples_gallery.html)


## Installing

PlaCo can be installed from ``pip``

```
pip install placo
```

Or [built from sources](https://placo.readthedocs.io/en/latest/basics/installation_source.html)

## Resources

* [Documentation](https://placo.readthedocs.io/en/latest/)
* [Examples](https://github.com/rhoban/placo-examples) repository

## Citation

If you use PlaCo in your research, please cite the following paper:

```
@misc{duclusaud2025placo,
      title={PlaCo: a QP-based robot planning and control framework}, 
      author={Marc Duclusaud and Grégoire Passault and Vincent Padois and Olivier Ly},
      year={2025},
      eprint={2511.06141},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2511.06141}, 
}
```
f4enix 0.18.1
API for Monte Carlo input and output parsing
Requires Python: >=3.10

[![Testing windows](https://github.com/Radiation-Transport/F4Enix/actions/workflows/AutomatedTests_win.yml/badge.svg?branch=main)](https://github.com/Radiation-Transport/F4Enix/actions/workflows/AutomatedTests_win.yml)
[![Testing linux](https://github.com/Radiation-Transport/F4Enix/actions/workflows/AutomatedTests_linux.yml/badge.svg?branch=main)](https://github.com/Radiation-Transport/F4Enix/actions/workflows/AutomatedTests_linux.yml)
[![PyPi version](https://badgen.net/pypi/v/f4enix/)](https://pypi.org/project/f4enix)
[![Documentation Status](https://readthedocs.org/projects/f4enix/badge/?version=latest)](https://f4enix.readthedocs.io/en/latest/?badge=latest)
[![codecov](https://codecov.io/gh/Fusion4Energy/F4Enix/graph/badge.svg?token=P4A85K0ACG)](https://codecov.io/gh/Fusion4Energy/F4Enix)

# F4Enix
Parser for Monte Carlo simulations input and output files

**Python >3.10!**

Both Windows and Linux supported.

Go to [F4Enix official documentation](https://f4enix.readthedocs.io/en/latest/) to get
more information on the library capabilities, examples and much more.

## Install
The easiest way to install F4Enix is using pip:

```
pip install f4enix
```
See the [relevant section of the documentation](https://f4enix.readthedocs.io/en/stable/usage/installation.html#developer-mode-install) for developer mode installation

### Troubleshooting
If any unexpected issue is encountered during installation, the first step for
its resolution would be to create a new fresh virtual environment.
In conda this would be done with:
```
conda create -n <env_name> python=3.10
```
Please remember that python versions lower than 3.10 are not supported.
py-wlcommands 0.2.48
py_wlcommands - Python项目
Requires Python: >=3.10

# test_project

<MagicMock name='ConfigManager().get()' id='4568842464'>

## Installation

```bash
pip install test_project
```

## Usage

```python
import test_project
```

## Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

## License

MIT
aade-publicity-search 1.0.2
AADE Public AFM lookup (VAT info) via SOAP
Requires Python: >=3.10


# aade-publicity-search

Python client for the **AADE Public AFM (VAT) lookup service**, using the official SOAP API.
It allows you to retrieve publicly available company / professional information by AFM
and returns the result as a Python dictionary (JSON-ready).

---

## Features

- 🔎 AFM (VAT) public lookup
- 🇬🇷 Official AADE SOAP service
- ⚡ Async / `httpx` based
- 🧼 Clean JSON output (dict)
- 🚫 No FastAPI dependency
- 🐍 Python 3.10+

---

## Installation

```bash
pip install aade-publicity-search

import asyncio
from aade_publicity_search import AadeClient

async def main():
    client = AadeClient(
        username="AADE_USERNAME",
        password="AADE_PASSWORD"
    )

    data = await client.get_vat_info("123456789")
    print(data)

asyncio.run(main())
```

## Returned data structure

The client returns a dictionary similar to:

`{  "success":  true,  "error":  {  "code":  null,  "description":  null  },  "basic":  {  "afm":  "123456789",  "name":  "COMPANY NAME",  "doy":  "ΔΟΥ ΑΘΗΝΩΝ",  "legal_status":  "ΑΤΟΜΙΚΗ ΕΠΙΧΕΙΡΗΣΗ"  },  "firm_activities":  [  {  "activity_code":  "62010000",  "activity_descr":  "Computer programming activities"  }  ]  }` 

----------

## AFM validation

AFM numbers are validated locally before calling the AADE service.  
If the AFM is invalid, a `ValueError` is raised.

----------

## Error handling

-   HTTP errors raise `httpx.HTTPStatusError`
    
-   Invalid AFM raises `ValueError`
    
-   AADE service errors are returned in the `error` field of the response
    

Always check:

`if  not data["success"]: print(data["error"])` 

----------

## Requirements

-   Python 3.10+
    
-   Valid AADE SOAP credentials
    
-   Internet access to AADE services
----------

## Disclaimer

This library uses the **official AADE public SOAP service**.  
It is not affiliated with or endorsed by AADE.

Use responsibly and according to AADE terms of service.