semvec/pypi-packages huggingface dataset:
SeqArtFetch 0.3.4
A near-universal webcomic/sequential art downloader.
License: MIT

A near-universal webcomic/sequential art downloader.
It visits each page like a human user, scraping and downloading all art images into a specified folder and by default only re-downloads new episodes.

`Details and Usage on GitHub <https://github.com/deingithub/SeqArtFetch>`_
pypdsh 0.1.6
pdsh in python
License: MIT

# pypdsh
Pdsh in Python version.
## How to Use It?
Open your console, type `pypdsh -h`, you will see some parameters:

`-h, --help` -- 
show this help message and exit

`-i IP, --ip=IP` -- 
single ip or ip in format '192.168.1.[1-10,16,19,30-40]'

`-I IP, --IP=IP` -- 
ip(s) in CSV file: ip | username | password

`-c COMMAND, --command=COMMAND` -- 
single command or command in TXT file

`-f FILE, --file=FILE` -- 
file to transmit to each host

`-g LOCALPATH, --get=LOCALPATH` -- 
get file from remote host

`-d DESTINATION, --destination=DESTINATION` -- 
destination path of each host, and this path must be existing in host(s)

`-p PASSWORD, --password=PASSWORD` -- 
password of host(s)

`-u USERNAME, --username=USERNAME` -- 
username of host(s)

`--log-level=LOG_LEVEL` -- 
log level: INFO or ERROR

`-v, -V, --version` -- 
show version number of pypdsh and exit
pycolordetector 0.0.2
A simple Python package for detecting colors in images
License: MIT

**pycolordetector** is a Python package for detecting colors in images. It provides fast and easy way to detect color of a pixel. The applications build through this package will automatically give the name of the color just by double-clicking on them. pycolordetector has been designed to make working with image data both simple and intuitive. It aims to be the fundamental high-level building block for doing practical and real-world analysis on images. 

## Installation

The source code is currently hosted on GitHub at:<br>
https://github.com/sunnysinghnitb/pycolordetector

To install pycolordetector, simply use pip:
```sh
pip install pycolordetector
```

## Example Usage

The following illustrates how to use the *colo* function of pycolordetector:
```sh
from pycolordetector.pycolordetector import colo
colo("Path_of_your_image")
```

## Documentation

The official documentation is hosted on:<br>
https://pycolordetector.readthedocs.io/en/latest/

## License

**pycolordetector** is published under the [MIT License][mit].

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so.


## Contributions

All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. 

Issues are posted on:<br>
https://github.com/sunnysinghnitb/pycolordetector/issues

**pycolordetector** appreciates help from a wide range of different backgrounds. Work such as high level documentation or code improvements are valuable, and small improvements or fixes are always appreciated. If you are considering larger contributions outside the traditional coding work, please contact us at sunnysinghnitb@gmail.com.


[pcking]: https://packaging.python.org
[mit]: https://github.com/sunnysinghnitb/pycolordetector/blob/master/LICENSE
biorxiv-retriever 0.20.1
Simple retriever for Biorxiv articles
License: MIT
Keywords: Biology,Rxivist,Biorxiv,retriever

# BiorxivRetriever
Simple python retriever for Biorxiv articles.

# Purpose
Given a text query, search for relevant papers on Biorxiv and get their links and content.

# Install

Install with pip:
```
pip install biorxiv_retriever
```

Alternatively, clone this repo and install:
```
git clone https://github.com/TalSchuster/BiorxivRetriever.git
python setup.py install
```

# API
```
from biorxiv_retriever import BiorxivRetriever
br = BiorxivRetriever()
papers = br.query('covid remdesivir')
```
To get only titles and links without metadata and full text (when possible):
```
papers = br.query('covid remdesivir', metadata=False, full_text=False)
```

To use [Rxivist](https://www.rxivist.org/) as the search engine (provides extra metadata but doesn't have all papers):
```
br = BiorxivRetriever(search_engine='rxivist')
```

# Output

Example output:
```
[
{'title': 'Enisamium is a small molecule inhibitor of the influenza A virus and SARS-CoV-2 RNA polymerases',
'biorxiv_url': 'https://www.biorxiv.org//content/10.1101/2020.04.21.053017v2',
'posted': 'May 11, 2020.',
'abstract': 'Abstract Influenza A virus and coronavirus strains cause a mild to severe respiratory disease that can result in death. Although vaccines exist against circulating influenza A viruses, such vaccines are ineffective against emerging pandemic influenza A viruses. Currently, no vaccine exists against coronavirus infections, including pandemic SARS-CoV-2, the causative agent of the Coronavirus Disease 2019 (COVID-19). To combat these RNA virus infections, alternative antiviral strategies are needed. A key drug target is the viral RNA polymerase, which is responsible for viral RNA synthesis. In January 2020, the World Health Organisation identified enisamium as a candidate therapeutic against SARS-CoV-2. Enisamium is an isonicotinic acid derivative that is an inhibitor of multiple influenza B and A virus strains in cell culture and clinically approved in 11 countries. Here we show using in vitro assays that enisamium and its putative metabolite, VR17-04, inhibit the activity of the influenza virus and the SARS-CoV-2 RNA polymerase. VR17-04 displays similar efficacy against the SARS-CoV-2 RNA polymerase as the nucleotide analogue remdesivir triphosphate. These results suggest that enisamium is a broad-spectrum small molecule inhibitor of RNA virus RNA synthesis, and implicate it as a possible therapeutic option for treating SARS-CoV-2 infection. Unlike remdesivir, enisamium does not require intravenous administration which may be advantageous for the development of COVID-19 treatments outside a hospital setting.',
'full_text': '...'}
]
```