semvec/pypi-packages huggingface dataset:
arff-converter 0.2.0
A simple converter from ARFF to XLSX format
Requires Python: >=3.6

# ARFF Converter
Version: 0.1.0

## Overview

ARFF Converter is a Python package designed to convert Attribute-Relation File Format (ARFF) files into Excel (XLSX) files. It currently supports the conversion from ARFF to XLSX, with potential for future expansion to other file formats.

## Installation

To install ARFF Converter, use pip:

``` bash
pip install arff-converter
```

## Usage

The package provides a command-line interface for easy conversion of ARFF files to XLSX format.

### Command-Line Usage

To convert an ARFF file to an XLSX file, run the following command:

```bash
arff-converter <path_to_arff_file> <path_to_output_xlsx_file>
```

Replace <path_to_arff_file> with the path to your ARFF file and <path_to_output_xlsx_file> with the desired output path for the XLSX file.

### Example
```bash
arff-converter data.arff output.xlsx
```

This command will convert data.arff into an Excel file named output.xlsx.

## Features

- Converts ARFF files to XLSX format.
- Command-line interface for straightforward usage.
- Validates ARFF file structure and content.

## Requirements

- Python 3
- pandas
- openpyxl

## Development

This package is in early development and may include additional features and support for more formats in future releases.

## Contributing

Contributions to ARFF Converter are welcome. Please feel free to submit pull requests or report issues on the GitHub repository.

## License

ARFF is distributed under the MIT License. See LICENSE file for more details.
pyXsurf 2.0.0.dev2
Python library for X-Ray Optics, Metrology Data Analysis and Telescopes Design.
Requires Python: >=3.7

pyXsurf
-------

Python library for X-Ray Optics, Metrology Data Analysis and Telescopes Design.

This library starts from code collected during many years of work on surface metrology and X-ray optics and
wants to offer to the community an extensible tool to perfom common operations on surface and profile data,
together with a set of tools to perform typical operations on data, in particular related to X-ray optics.

Installation
============

Optionally create a virtual environment and activate it.

.. code:: bash

    python3 -m venv pyXsurf.venv
    source pyXsurf.venv/bin/activate

And then install the package with pip:

1. Install released version from PyPI:

.. code:: bash

    pip install pyXsurf

or

2. Install latest development version from github:

.. code:: bash

    pip install "pyXsurf @ git+https://github.com/robeyes/pyXsurf.git"

or

3. Clone the repository and install in editable mode for development:

.. code:: bash

    git clone https://github.com/robeyes/pyXsurf.git
    cd pyXsurf
    pip install -e .

Uninstalling
============

Project can be uninstalled running ``pip uninstall pyXsurf`` or equivalent.

How to use
==========

All modules are packaged into `pyXsurf` package, you can import them with something like:

.. code:: python

    import pySurf.data2D


Additional resources for usage guidance
=======================================

- **docs** `official documentation <https://pyxsurf.readthedocs.io>`
- **test** contains a collection of tests and examples.
- **notebooks** contains a collection of Jupyter notebooks with examples and tutorials.

Modules
=======

A basic description of the different modules is:

- **dataIO** Generic routines for accessing and manipulating data and files.
- **plotting** Plotting functions for pySurf data.
- **pyGeo3D** Functions for geometry in space (lines and planes).
- **pyProfile** Equivalent of pySurf acting on Profiles.
- **pySurf** Functions and classes acting on 3D points or surfaces.
- **thermal** Functions for modelling of thermal forming of glass.


Contributing
============

Please report bugs or feature requests, missing documentation,
or open a issue on github https://github.com/vincenzooo/pyXsurf/issues.

Acknowledgements
================

The code in this library is the result of many years of work.
Many colleagues from my current or former Institutions contributed
directly and indirectly with exchange of code, ideas, data and good time.
The ongoing improvements to this project are funded by INAF “Bando per innovazione tecnologica”,
which the author also thanks for the supportive and stimulating working environment.

.. Data used for development and in examples are courtesy of ..

Citation
========

.. image:: https://zenodo.org/badge/165474659.svg
   :target: https://zenodo.org/badge/latestdoi/165474659

License
=======

This project is Copyright (c) Vincenzo Cotroneo and licensed under
the terms of the BSD 3-Clause license. See the licenses folder for
more information.


Author
======

Vincenzo Cotroneo vincenzo.cotroneo@inaf.it
codeepy 2.0.1
A library for the Codee Robot

# CodeePy

CodeePy has been created to allow Codee the robot from the Creative Science Foundation to be controlled via Python.
[![PyPI version](https://badge.fury.io/py/codeepy.svg)](https://badge.fury.io/py/codeepy)

# Getting started with CodeePy

## Requirements
CodeePy requires Python 3.4 or greater to be installed on your machine, it can be downloaded for free from the [Python website.](https://www.python.org/downloads/)
We recommend using [Pycharm IDE](https://www.jetbrains.com/pycharm/download/#section=windows) as your initial development tool, please note students, educators and educational facilities have free access to the professional version and there is also a free community version.

## Setting up your development environment
Create a new project, being sure to specify the Python 3.4 interpreter.
Open the terminal within PyCharm and install CodeePy with pip (which will also install any dependencies)
For more information regarding pip please visit our [module page](https://pypi.org/project/codeepy/)

```python
pip install codeepy
```

## Getting started
Within your project create a new Python file, initially you will need to import CodeePy, and create an instance of CodeePy.
I have included a helper which will list your com ports, additionally CodeePy documentation can be viewed with the help(CodeePy) function below

```python
from codeePy import CodeePy

# view CodeePy Documentation
help(CodeePy)

CodeePy.get_com_ports()

# create CodeePy Instance and open connection
codee = CodeePy('<Your COM port>')
```

Now you have created your instance object please see the Example.py file to test the CodeePy functions.


## Upgrading CodeePy

```python
pip install codeepy --upgrade
```


## About Codee & Creative-Robotix
Creative-Robotix can be used by teachers, parents, children, adults, in groups or as
individuals to address core STEAM (Science, Technology, Engineering, Arts & Maths) learning
and making activities in a fun, hands-on, social and interactive way. Our open-source
platform can be used by anyone from 7 years up who are interested in learning more about
technology. The tasks are carefully designed to fit the abilities of all ages, ranging from
simple to more complex assembly, depending on your age, knowledge and guidance
available

## Building Codee
For more information on how to create Codee including 3D printing files and firmware please visit [Instructables](https://www.instructables.com/id/Creative-Robotix-Educational-Platform-Codee-Robee-/)

For more information on the Creative Science Foundation please visit [CreativeRobotix](https://creative-science.org/partnerships/creative-robotix/)
feagi-agent-video-capture 0.0.13
Feagi agent video capture to connect feagi with your video capture
Requires Python: >=3.6

# Quick start
### Connect to your webcam
`pip3 install feagi_agent_video_capture`

`python3 -m feagi_agent_video_capture`

# What is feagi_agent_video_capture?
It allows you to feed videos related to FEAGI using a webcam, gif, mp4, picamera, or screen recording. It can also utilize multiple flags. The purpose of this is to enable FEAGI to interact with and work with its peripherals.

# Requirements.txt
These are the requirements which will be updated over time. The script will use this requirement to scan.

Here: [requirements.txt](https://github.com/feagi/feagi/blob/staging/peripherals/feagi_agent_video_capture/feagi_agent_video_capture/requirements.txt)

### Flags:
```
--loop: True or False (Default is False)
--ip: (xxx.xxx.xx.xxx) Default is 127.0.0.1
--device: Target the port or index to use. Default is 0.
--video: You need to provide the file path.(mp4, gif, or wav) Default: None
--port: Change the API port of FEAGI. Default is 8000.
```
Let's say you have a file called earth.gif. If you want to run gif or mp4 only, but it is on your desktop, you will need to specify the file path. So, if you are in the Desktop folder while the file is also in the desktop folder, you can simply do this:

`python3 -m feagi_agent_video_capture --video earth.gif` (mac and linux)

`python -m feagi_agent_video_capture --video earth.gif` (Windows)

But if you are on the Desktop while your earth.gif is in the Downloads folder, do this:

`python3 -m feagi_agent_video_capture --video path/to/directory/earth.gif`

If you want it to loop, just append `--loop` in the command.

`python3 -m feagi_agent_video_capture --video earth.gif --loop true`

If you have two webcams and you want to use the second webcam, you can do this:

`python3 -m feagi_agent_video_capture --device 1` (Depending on what device index your webcam has, 1 is an assumption for the index of the second webcam. 0 is the default.)

If you want to send your webcam feed to FEAGI on another computer, you will need the computer's local IP. Do it like this:

`python3 -m feagi_agent_video_capture --ip xxx.xxx.xx.xxx` (Replace the x with the other machine's IP address.)
feagi-agent-mycobot 0.0.8
Feagi agent mycobot to connect feagi with your cobot arm
Requires Python: >=3.6

# Quick start using feagi_agent
1) `git clone https://github.com/feagi/feagi.git`
2) `cd feagi/docker`
3) `docker compose -f feagi.yml build`
4) Wait until #3 step is complete.
5) `docker compose -f feagi.yml up`
6) load it in your preferred browser: `http://localhost:3000/genome/mode`
7) Click `Sample Genomes`
8) See Gazebo and brain activity loaded. Feel free to play with your robot!

Any issue? Needs detailed documentation about docker? [Deployment documentation](https://github.com/feagi/feagi/wiki/Deployment)


# Where is feagi_agent? 
`feagi_agent` allows you to integrate with our FEAGI with your preferred robots. 
`feagi_agent` has been tested on <i>freenove_smart_car, Gazebo, Godot, Tello, and Psychopy.</i> 
You can use it on your computer or docker.

# What is inside feagi_agent?
There are two large library: feagi_interface and retina. `feagi_interface.py` handles the bridge between your project and FEAGI. It allows FEAGI to communicate/controls your robot.
`retina.py` is the vision where it can see things through any type of camera. 

# configuration.py is REQUIRED
You will need to have your own configuration where you can adjust setting to see the different results. If you don't have one, use the template below:
```
app_name = 'embodiment'

network_settings = {
    "feagi_host": "feagi",
    "feagi_api_port": "8000",
    'TTL': 2,
    'last_message': 0,
}

capabilities = {
    "vision": {
        "type": "ipu",
        "disabled": False,
        "count": 1,
        "width": 8,
        "height": 8,
        "deviation_threshold": 0.05,
        "retina_width_percent": 60,
        "retina_height_percent": 40,
        "central_vision_compression": [64, 64],
        "peripheral_vision_compression": [8, 8],
        "previous_data": {}
    }
}

message_to_feagi = {"data": {}}
```


See examples here:

[Tello's configuration](https://github.com/feagi/feagi/tree/feature-refactor-vision/third_party/physical_robots/tello)

[Freenove_smart_car](https://github.com/feagi/feagi/tree/feature-refactor-vision/third_party/physical_robots/freenove/smart_car)

[Gazebo](https://github.com/feagi/feagi/tree/feature-refactor-vision/third_party/gazebo/simulation/src)

# Where is feagi_agent being used?
It is being used in python code.

Just install through pip.

Windows:
`pip install feagi-agent`  

Linux/Mac:
`pip3 install feagi-agent`

and after that, you can just use `from feagi-agent import feagi_interface`

See examples:
[Tellos' code](https://github.com/feagi/feagi/blob/feature-refactor-vision/third_party/physical_robots/tello/tello.py#L6)