semvec/pypi-packages huggingface dataset:
Merkurial-ImageSifter 0.0.1
ImageSifter Is Used When You Want To Change The Directories Of Images..
Requires Python: >=3.10

# Overview
ImageSifter Is Used When You Want To Change The Directories Of Images..
Basically, It's A TK GUI For Going Through Images And Re-Categorizing Them Based On What You See. It Was Built In Tandem
With My ImageDownloader And ImageCategorizer For Helping With Categorizing More Accurately. It Only Requires Clicking,
Buttons Are Generated For You Based On The Directories It Is In After The Root Directory. Each Can Be Updated
Individually And The meta.json Data Is Also Updated To Fit Its New Location
Merkurial-ImageDownloader 0.0.2
ImageDownloader Is A Very Easy Way To Automate The Downloading Of Images From A WebPage. There Is A Lot Of Flexibility
Requires Python: >=3.10

# Image Downloader

Download Images With Just A Few Arguments. You Will Need To Know Where They Are In HTML As This Module Uses bs4 To
Acquire The Information But After That, Your Work Is Pretty Much Done.

# Options 
- Download Full Pages
- Download A Group Of Images On A Page
- Download A Single Image With Just The Url And The Name You Want To Give To It.

# Side Note
This Was Made To Be Used With The Categorizer I Made So That They Can Be Used In Tandem To Both Download And Categorize
Your Images.
iwort 1.0
An intelligent Wort is a sophisticated Python library designed for advanced text processing and linguistic analysis, leveraging state-of-the-art machine learning techniques.
Requires Python: >=3.6
Keywords: machine learning,text analysis,linguistics

# Iwort - An intelligent Wort

iwort is a Python library for advanced text processing and linguistic analysis. Utilizing modern machine learning techniques, Iwort aims to provide tools that are both powerful and accessible, making it an ideal choice for both researchers and developers in the field of natural language processing.

## Installation

```bash
pip install iwort
```

## Usage

```python
import Iwort

# Example usage
# This will be replaced with actual usage examples
```

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

## License
[MIT](https://choosealicense.com/licenses/mit/)
fstlib 1.0.8
A python library to read fst file.
Requires Python: >=3.8

# fstlib - Python Library for Reading fst Files

## Introduction

`fstlib` is a Python library designed to facilitate the reading of fst (Fast Serialization of Data Frames) files using Python.  fst is specifically designed to unlock the potential of high speed solid state disks that can be found in most modern computers. Data frames stored in the fst format have full random access, both in column and rows. Click [here](https://www.fstpackage.org) to read more about the performances fst files compared to other tabular files.


## Features

- Read fst files in binary format
- Save fst files in binary format

## Installation

To start using `fstlib` to read and save FST (Fast Serialization of Data Frames) files in Python, follow these installation steps:

### Prerequisites

Before installing `fstlib`, ensure that you have the following prerequisites:

1. **Python**: Make sure Python is installed on your system. You can download Python from [python.org](https://www.python.org/downloads/) if you haven't already.

2. Install R langage in your computer from [CRAN](https://cran.r-project.org). If you don't have R in your laptop the installation will abort.

2. **`pip`**: Ensure that you have `pip`, the Python package manager, installed and up-to-date. You can upgrade `pip` using the following command:

```bash
   pip install --upgrade pip
   pip install git+https://github.com/finance-resilience/fstlib
```
or
```bash
   pip install --upgrade pip
   pip install fstlib
```

4. **Aws credentials**: Since this package is private, it is usage is condition to the fact that you follow finres rules for
access_key document. So it will work only if you followed the rule we set in the organization.

Same, since the repository is private, pip may prompt you for your GitHub credentials. Please provide your GitHub username and a personal access token with appropriate repository access permissions when prompted.

Once the installation is complete, you can start using fstlib in your Python projects to work with FST files efficiently.

## Usage
Here's a simple example of how to use fstlib to read and save FST files:

```python
    from  fstlib import fstlib
    import os
    import pandas as pd
    import numpy as np

    #path_s3 = "projects/I4CE/402.MLEVA/SIM2/I4CE_SIM2_EVA_WING_GWL_15.fst"
    
    # create a pandas dataframe
    df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),columns=['a', 'b', 'c'])

    ## save the fst file
    fstlib.write_fst(df2, "df2.fst")
    
    # read the fst file
    df = fstlib.readfst("df2.fst")
    
    df.shape

    os.remove("df2.fst")

```

## Documentation

For more detailed information on how to use fstlib, please refer to the documentation (if available).

## License

This project is licensed under the MIT License.

## Contribution

Contributions of the team is welcome! If you encounter any issues or have suggestions for improvements, please feel free to open an issue or submit a pull request on the GitHub repository.
netdem 1.27
A neural network machine learning enabled DEM framework for computational particle mechanics.

# NetDEM

## Welcome

[**NetDEM**](https://apaam.github.io/netdem_docs/) is a C++ program library targeted at the computational mechanics of irregular granular materials and utilizing machine learning tools to boost the computational effciency.

It is currently capable of performing basic and general DEM simulations, with following features:

- Sphere and triangle facets contact solver
- GJK contact solver for convex particles
- SDF contact solver for arbitrary (convex and concave) particles
- Hybrid OpenMP and MPI parallel computing
- Integrated [mlpack](https://www.mlpack.org/) machine learning environment

The supported particle shapes include ``sphere``, ``cylinder``, ``poly-super-ellipsoid``, ``poly-super-quadrics``, ``spherical harmonics``, ``triangle mesh``, ``level set``, etc.

## Get started

Please kindly refer to [**NetDEM**](https://apaam.github.io/netdem_docs/) for usage, documentation, and code doxygen (python APIs) of the package.

Please kindly node that not all of the c++ properties or methods have been exposed to python. Please contact us (lai.zhengshou@outlook.com) if you would like to contribute.
static-ip-netw 1.0.2
A CLI tool to manage static IP addresses on Linux using netplan.
Requires Python: >=3.8

# fwv-static-ip-script

## Introduction
This script is used to give a static IP address to a device in a network that uses netplan. It is tested on Ubuntu 20.04 and 22.04, But could potentially work on other Linux distributions as well if they use netplan. 

It is confirmed not to work on Ubuntu 18.04.

## Installing
Run the static_ip.py script with Python version >= 3.8 and supply the ending ip you want to use (192.168.1.xxx where xxx is the ending ip). Additional options can be found by running `python static_ip.py -h`.

## Help
Use `python static_ip.py -h` to get an overview of all the options.

## Examples
- `python static_ip.py 50` will give the device a static IP of xxx.xxx.xxx.50.
- `python static_ip.py 50 -n 8.8.8.8` will give the device a static IP of xxx.xxx.xxx.50 and use 8.8.8.8 as the DNS server (name server).
- `python static_ip.py 50 --dry-run --verbose` will initialize the script, but not actually change anything. It will also print out all the steps it would have taken if it was not a dry run.
- `python static_ip.py 50 -r` will remove the static IP from the device and revert back to the previous settings. It will essentially undo what the script did by replacing the new config with a backup of the old config.