semvec/pypi-packages huggingface dataset:
svgdatagrabber 0.1.0
Get svg data points
Requires Python: >=3.8

# SvgDataGrabber

A Python package for extracting data from SVG files.

- GitHub: [https://github.com/haiiliin/svgdatagrabber/](https://github.com/haiiliin/svgdatagrabber/)
- Documentation: [https://svgdatagrabber.readthedocs.io/en/latest/](https://svgdatagrabber.readthedocs.io/en/latest/)
- PyPI: [https://pypi.org/project/svgdatagrabber/](https://pypi.org/project/svgdatagrabber/)
- Read the Docs: [https://readthedocs.org/projects/svgdatagrabber/](https://readthedocs.org/projects/svgdatagrabber/)
MetaboConverter 1.0.3
A Python package to view and analyze Excel sheets via a GUI.
Requires Python: >=3.6

# MetaboConverter
# Excel Sheet Viewer

A Python package that allows users to view and analyze Excel sheets through a GUI.

## Installation

In your system terminal (make sure you have python installed), type: pip install MetaboConverter

## Usage

Once Installation is finished, type:
python
in your termial and then copy the following

```python
import MetaboConverter as pk
pk.runApp()
ezorm 0.1.3
This library is for people who wanna try interactive with database in an easy way.
Requires Python: >=3.10

# Instruction

If you start for the first time:
```powershell
pip install uv
```

Run:  
```powershell
python hello.py
```

Enable running script:  
```powershell
Set-ExecutionPolicy RemoteSigned -Scope CurrentUser
```

Start env:  
```powershell
.\.venv\Scripts\activate.ps1
```

Start jupyter lab:  
[uv-jupyter-reference]
```powershell
uv run --with jupyter jupyter lab
```

[uv-jupyter-reference]: https://docs.astral.sh/uv/guides/integration/jupyter/#using-jupyter-within-a-project
mt19937ar 0.1.2
mt19937ar for Python
Requires Python: >=3.10

# mt19937ar
Python implementation of [mt19937ar](http://www.math.sci.hiroshima-u.ac.jp/m-mat/MT/MT2002/mt19937ar.html)

## Installation
```bash
pip install mt19937ar
```

## Usage
```python
from mt19937ar import MT19937ar

mt = MT19937ar()
mt.init_by_array([0x123, 0x234, 0x345, 0x456])

print(mt.genrand_int32()) # 1067595299
print(mt.genrand_int32()) # 955945823
print(mt.genrand_int32()) # 477289528
```
GANsforVirtualEye 0.1.7
This package provides an implementation of Generative Adversarial Networks (GANs) for time series generation, with flexible architecture options. Users can select different combinations of generator and discriminator models, including Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM), to suit their specific needs.
Requires Python: >=3.6

# GANsForVirtualEye: Time Series Generation Package

[![Documentation Status](https://readthedocs.org/projects/gansforvirtualeye/badge/?version=latest)](https://gansforvirtualeye.readthedocs.io/en/latest/)
[![License: MIT](https://img.shields.io/badge/License-MIT-brightgreen)](https://github.com/shailendrabhandari/GANsForVirtualEye/blob/main/LICENSE)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/GANsforVirtualEye)](https://pypi.org/project/GANsforVirtualEye/)
[![PyPI](https://img.shields.io/pypi/v/GANsforVirtualEye)](https://pypi.org/project/GANsforVirtualEye/)
[![Downloads](https://pepy.tech/badge/gansforvirtualeye)](https://pepy.tech/project/gansforvirtualeye)
![GitHub watchers](https://img.shields.io/github/watchers/shailendrabhandari/GANsForVirtualEye?style=social)
![GitHub stars](https://img.shields.io/github/stars/shailendrabhandari/GANsForVirtualEye?style=social)

GANsForVirtualEye is a Python package that implements Generative Adversarial Networks (GANs) for time series data generation, offering flexible architecture options with CNN and LSTM models for both generators and discriminators.

## Architecture

![GAN Architecture](https://raw.githubusercontent.com/shailendrabhandari/GANsForVirtualEye/main/gan_package/results/Class_GAN_Arc.jpg)

## Installation

### Prerequisites

- Python 3.6 or higher
- `pip` package manager

### Steps

1. **Clone the Repository**

   ```bash
   git clone https://github.com/shailendrabhandari/GANsForVirtualEye.git
   cd GANsForVirtualEye
   ```

2. **Install Required Packages**

   It's recommended to use a virtual environment.

   ```bash
   pip install -r requirements.txt
   ```

3. **Install the Package**

   ```bash
   pip install .
   ```

## Documentation

Detailed documentation is available at [Read the Docs](https://gansforvirtualeye.readthedocs.io/en/latest/).

## Author

- **Shailendra Bhandari**  
  [GitHub](https://github.com/shailendrabhandari) | [Email](mailto:shailendra.bhandari@oslomet.no)

---
## Citation

If you use this package in your research or projects, please cite it as:

```bibtex
@misc{bhandari2024modelingeyegazevelocity,
      title={Modeling Eye Gaze Velocity Trajectories using GANs with Spectral Loss for Enhanced Fidelity}, 
      author={Shailendra Bhandari and Pedro Lencastre and Rujeena Mathema and Alexander Szorkovszky and Anis Yazidi and Pedro Lind},
      year={2024},
      eprint={2412.04184},
      archivePrefix={arXiv},
      primaryClass={cs.NE},
      url={https://arxiv.org/abs/2412.04184}, 
}
```
---

## Acknowledgment

Thank You for Using GAN Time Series Generation Package!

We hope this package helps you in your research or projects involving time series data generation.