semvec/pypi-packages huggingface dataset:
colorset 1.0.0
Colorset management classes
Requires Python: >=3.10
Keywords: colorset legend python

# Colorset 
A set of classes for working with color legends.

## Usage 
To use the library, you need to import it and call the function getLegend(cs_file, sys_cs_file, colorset_used), 
where cs_file is the full name of the main legend XML file, 
sys_cs_file is the full name of the additional legend XML file, 
and colorset_used is the name of the desired legend.

The function returns a tuple legend, colorType, where legend is an object of the class ColorSetNumerical, ColorSetString, or ColorSetAutomatic, depending on the contents of the legend files, and colorType is a string indicating the type of legend from the list ['numerical', 'string', 'automatic'].

The following methods are supported:

show() - displays the contents of the legend in the standard console. 
createDescription() - automatically fills in the description property. 
valueToColor(value) - calculates the integer color code based on the parameter value.

## Required Files 
Main and additional XML files containing the color legends.

## Integration 
The library requires the presence of lxml.etree for operation.

## Related Projects 
The library was developed for use with its own projects.

## Future Plans 
Implementation of the ability to edit legends and save them in XML files. Integration to work with a PostgreSQL database. Preparation of a GUI - a visual legend editor.

## Current Status 
Debugging.
json2pdf-converter 0.2
A package to convert JSON to PDF.
Requires Python: >=3.7

To create the content for the README.md file of your Python package, you can include the following information:

# PDF Generator

A Python package for converting JSON data to HTML and generating PDFs.

## Description

This package provides functionality to convert JSON data to HTML using a customizable template and then generate PDF files from the HTML content. It allows users to provide their own JSON data and template file to customize the output.

## Features

- Convert JSON data to HTML - anyName.json
- Support for custom HTML templates - template.html
- Generate PDF files from HTML - output.html
- Merge multiple PDF files into a single PDF - merged_output.pdf

## Installation

You can install the package using pip:

```
pip install json2pdf_converter==0.1
```

Note: Replace "version-number" with the recent version number of the package, ex: 0.6 -> latest.

## Usage

```python
from pdf_generator.convert import generate

# Specify the paths and file names for JSON data, template, output HTML, and new PDF file
json_file = 'path/to/json/data.json' (complete path of json file)
template_directory = 'path/to/template/directory' (only template directory)
output_html_path = 'path/to/save/output/html' (only output html directory)
new_pdf_path = 'path/to/save/new/pdf' (only pdf saving directory)

# Generate HTML and PDF files
generate(json_file, template_directory, output_html_path, new_pdf_path)
```

Make sure to replace the placeholders with the actual paths and file names.

## Dependencies

This package relies on the following dependencies:

- pandas
- jinja2
- pdfkit
- PyPDF2

Please make sure to install these dependencies before using the package. You can install them using the following command:

```
pip install pandas jinja2 pdfkit PyPDF2
```

Note: The `pdfkit` library requires an external tool called Wkhtmltopdf. This package will automatically add the path for Wkhtmltopdf still error of Wkhtmltopdf installation, add path to your system's PATH before using this package.

## License

This package is licensed under the [MIT License](https://opensource.org/licenses/MIT).
openirisapi 0.0.3.1
A very basic API built upon requests for openiris.io
Requires Python: >=3.7

## OpenIRIS API

[![Downloads](https://static.pepy.tech/badge/openirisapi/month)](https://pepy.tech/project/openirisapi)

A simple API for [openiris.io](openiris.io). OpenIRIS is a free platform permitting the publishing and management of resources form microscopy units.

This API was developped as part of a France BioImaging project and is offered for use to anyone in the scientific community.

At the moment, there is no REST API published by the site's developpers. This package aims to provide a functional solution that permits PROVIDER ardministration to download data directly from the website's backend.

To get your cookie you have to use the Dev tools of your browser, Network > Cookie script > Cookie. You must then format it into a python dictionary and store it in a .txt file.

Currently in Alpha, more features are scheduled to be added alongside a complete documentation.

Here's a small example of some basic usage of the library

```python 

# Import libary
import openirisapi.openirisapi as oi

# Add cookie from text file
cookie = oi.utilities.get_cookie('cookies.txt')

# Download all bookings in a specific timeframe in dataframe form
df = oi.getBookings(cookie,start='2002-03-07',end='2024-03-07')

# Download all users associated to the Provider and the groups that you administer
# and save to csv
users_df = oi.getUsers(cookies,start='2021-03-07',end='2023-03-08', to_csv=True):

# Download all resiources associated to the Provider(s) that you administer
# and skip save to csv
resources_df = oi.getAllResources(cookies, to_csv=False)
```
subredo 1.1.0
Apply Cuts from VideoReDo Project Files to Subtitles.
Requires Python: >=3.7,<3.12
Keywords: python,subtitles,cut,editor

# SubReDo

Apply Cuts from a VideoReDo Project File to Subtitles.

[VideoReDo] allows you to make frame-accurate cuts without re-encoding the entire video or audio file. However, it does
not keep or cut Subtitles. That's where SubReDo comes in! Save your VideoReDo Project File (.Vprj) and pass it to
SubReDo, and it will apply the same cuts but on the Subtitle files.

> **Note**
> - Currently only SubRip (SRT) subtitles and Matroska Cut Exports are supported.<br/>

  [VideoReDo]: <https://www.videoredo.com>

## Features

- Export VideoReDo Project to MKV automatically **(Windows Only)**
- Automatically Mux Subtitle Cuts to MKV Video Cut Exports
- Subtitle Flags and Metadata from Original Source are Retained

## Dependencies

- [FFmpeg](https://ffmpeg.org) for cutting the Subtitles at specific calculated timestamps.
- [SubtitleEdit](https://nikse.dk/subtitleedit) for offsetting the Subtitle captions to sync up with the Cut video.

Please make sure `ffmpeg` and `SubtitleEdit` can be found on your `PATH` Environment Variable, in your Current
Working Directory, or in SubReDo's Installation directory.

## Usage

```
Usage: subredo [OPTIONS] [PROJECTS]...

  Apply Cuts from a VideoReDo Project File on Subtitles.

  PROJECTS    One or more VideoReDo project files (.Vprj) to read and apply cuts from.
              You can alternatively specify a folder to search for .Vprj files from.
              Subtitles are read from the source file of each project.

Options:
  -o, --original-language TEXT  Declare the Original Language for this Video's
                                Subtitle flags.
  -c, --cut-video PATH          Specify manually exported cut video from the
                                VideoReDo project file to mux the Subtitles
                                to.Otherwise, On Windows a new MKV will be
                                automatically exported next to the project
                                file.
  -k, --keep-cut                Keep the original Cut Video after multiplexing
                                a Cut Video with the Subtitles.
  --help                        Show this message and exit.
```

## Contributors

<a href="https://github.com/rlaphoenix"><img src="https://images.weserv.nl/?url=avatars.githubusercontent.com/u/17136956?v=4&h=25&w=25&fit=cover&mask=circle&maxage=7d" alt=""/></a>

## License

© 2023 rlaphoenix — [GNU General Public License, Version 3.0](LICENSE)
hcaptcha-solver 0.2.14
A lightweight python library for solving hCaptcha challenges with selenium.
Requires Python: >=3.7,<4.0

# hcaptcha-solver

![PyPI - Status](https://img.shields.io/pypi/status/hcaptcha-solver)
![PyPI](https://img.shields.io/pypi/v/hcaptcha-solver?color=blue&link=https%3A%2F%2Fpypi.org%2Fproject%2Fhcaptcha-solver%2F)
![PyPI - Downloads](https://img.shields.io/pypi/dm/hcaptcha-solver)


a lightweight Python library for solving hCaptcha challenges with selenium and neural networks
> _**NOTE: this package is still in early development.  
> If you encounter any issues or unexpected behaviours, please report them!**_

## Installation

```bash
pip install hcaptcha-solver
```

## Usage

```python
from hcaptcha_solver import hcaptcha_solver
from selenium import webdriver

# setup chromedriver in english (other languages not supported yet)
options = webdriver.ChromeOptions()
options.add_experimental_option('prefs', {'intl.accept_languages': 'en,en_US'})
driver = webdriver.Chrome(options=options)
driver.get("https://accounts.hcaptcha.com/demo") # open any url with hCaptcha

# create Captcha_Solver object and solve hCaptcha
captcha_solver = hcaptcha_solver.Captcha_Solver(verbose=True)
captcha_solver.is_captcha_present(driver) # returns True
captcha_solver.solve_captcha(driver) # solves the hCaptcha
```

## Functionality

Only V1 hCaptchas are supported - any other captcha types will be skipped.  
The correct images are selected using Convolutional Neural Networks trained on labeled hCaptcha data.  
The models are chosen by inspecting the captcha header, so the language has to be english in order for the matching to work. (See [Issue #6](https://github.com/xTerradon/hcaptcha-solver/issues/6))

The package features models for a handful of hCaptcha tasks. The currently available models and their labeling accuracies are visualized in the figure below. The accuracy was measured on limited data and is therefore not exact.

[Functionality for V2 captchas](https://github.com/xTerradon/hcaptcha-solver/issues/7) is planned in a similar way to V1 captchas.

### Current Model Labeling Performance
![Model Labeling Accuracy](https://github.com/xTerradon/hcaptcha-solver/assets/64305142/83571a17-4f5d-430d-a50d-87e6a4dedf23)

### Current Data Availability
![Data Availability](https://github.com/xTerradon/hcaptcha-solver/assets/64305142/c26eed84-9a04-40b3-b560-a9fb6453703b)


## Contributing
If you encounter any bug, error or unexpected behaviour in general, please report them with an issue giving as much detail as possible - I will try to make this package easy-to-use for everyone.

Forks and pull requests are very welcome. You can also create issues to discuss possible changes or improvements.

To help collecting captchas, run `dev/main.ipynb` and submit the results. In this notebook you can also explore the data availability and train models.

> _updated 15/08/2023_
dx-profiler 0.1.3
Profile Dx App
Requires Python: >=3.10,<4.0

# Waffle Profiler
Dx 인퍼런스 분석 툴  
Dx에 App을 설치하고 영상을 인퍼런스한 후 로그를 남긴다.  
남긴 로그와 영상 파일을 읽어와 화면에 인퍼런스 결과를 보여주는 기능도 제공한다.

# 사용법
## 설치
```bash
$ pip install dx-profiler
```

## sudo 등록
dx-profiler는 dx쪽에 영상을 복사하는 과정에서 sudo 권한이 필요하다.  
이를 위해 dx-profiler를 sudo에서 실행할 수 있도록 secure_path에 추가해준다.

예시)
```bash
$ which dx-profiler
/home/yoon/.cache/pypoetry/virtualenvs/dx-profiler-MxXHGb83-py3.10/bin/dx-profiler
$ sudo visudo
...

Defaults secure_path="/usr/sbin:/usr/bin:/sbin:/bin:/snap/bin:/home/yoon/.cache/pypoetry/virtualenvs/dx-profiler-MxXHGb83-py3.10/bin"

...
```

## 인퍼런스
autocare_docker 레포를 clone한 후 dx-batch 브랜치로 checkout한다.  
docker compose up -d 로 dx 도커를 실행시킨 후 dx-profiler를 실행한다.

필요한 값
- 영상 파일 (-F)
- 앱 (-A)
- 로그 (-O)

예시)
```bash
$ sudo dx-profiler inference -F /mnt/hdd/videos/test_videos/helmet.mp4 --app ~/Downloads/aff0d790-8e15-47f4-874c-7320dc3673f9.zip -O log.txt
```

성공적으로 실행되면 영상 인퍼런스가 끝날때 까지 Waiting for EOS...를 출력하며
종료되면 Done.을 출력하고 종료된다.

## 영상확인
생성한 로그 기반으로 영상을 재생한다.

예시)
```bash
$ sudo dx-profiler profile -F log.txt
```
NLPWash 0.0.1
NLP Package to perform preprocessing on text data in one line
Requires Python: >=3.6

# NLPWash

To play with text data lots of cleaning is required which includes
1. Normalization
2. Removing hyperlinks
3. Removing HTML tags
4. Removing punctuation
5. Tokenization
6. Removing stopwords
7. Stemming 
8. Lemmatization

Using NLPWash this all can be done in just 1 line of code

# Installation

```
pip install NLPWash
```

Example codes are present in test directory