semvec/pypi-packages huggingface dataset:
SnaxModule 0.2
Bring Colors to CLI Make It Fun Edwin

# SnaxModule

SnaxModule brings colors to the CLI to make it more fun and interactive.

## Installation

You can install SnaxModule using pip:

```bash
pip install SnaxModule

Usage

Adjust Line
Use AdjustLn for BOLD, UNDERLINE, RESET.

Text Color
Use LnClr for inline color.

Background Color
Use BgClr for background colors.


```
BrightnessBooster 1.1.0
The code enhances an image's brightness and contrast

<p align="center">
 <img width="600px" src="https://i.postimg.cc/k55JLthG/vdk.jpg" alt="vdk"/>
</p>

# BrightnessBooster
The code enhances an image's brightness and contrast

## Description
### Main functions in library
"""python
beta_parameters_of_image
calculate_brightness_and_contrast_from_parameters
approximate_beta_distribution
modify_brightness_distribution
"""
## How to use
### How to modify image using BrightnessBooster library
```python
import BrightnessBooster as BB
import cv2

#test image
image_path = r"\test_img\test_img_4.jpg"

# load test image use cv2
image = cv2.imdecode(np.fromfile(image_path, dtype=np.uint8), cv2.IMREAD_COLOR)

# show image
cv2.imshow('Restored Image', image_modified)
cv2.waitKey(0)

a_red_org, b_red_org, a_green_org, b_green_org, a_blue_org, \
b_blue_org = BB.beta_parameters_of_image(image)
print('Betta parameters of original image:')
print(f'a_red = {a_red_org}')
print(f'b_red = {b_red_org}')
print(f'a_red = {a_green_org}')
print(f'b_green = {b_green_org}')
print(f'a_blue = {a_blue_org}')
print(f'b_blue = {a_blue_org}')
beta_parameters_org = (a_red_org, b_red_org, a_green_org, b_green_org, a_blue_org, b_blue_org)
brightness, contrast = BB.calculate_brightness_and_contrast_from_parameters(beta_parameters_org)
print('Brightness and Contrast of original image:')
print(f'Brightness = {brightness}')
print(f'Contrast = {contrast}')

# введем желаемые параметры распределения, например
red_a = 3.1
red_b = 4.3

green_a = 2.7
green_b = 3.3

blue_a = 4.8
blue_b = 3.2

beta_parameters_mod = (red_a, red_b, green_a, green_b, blue_a, blue_b) 
image_modified = BB.modify_brightness_distribution(image, beta_parameters_mod)
height, width = image.shape[:2]
new_height = round((height * 800) / width)
new_width = 800
image_modified = cv2.resize(image_modified, (new_width, new_height))
cv2.imshow('Restored Image', image_modified)
cv2.waitKey(0)

brightness_mod, contrast_mod = BB.calculate_brightness_and_contrast_from_parameters(beta_parameters_mod)
print('Brightness and Contrast of modified image:')
print(f'Brightness = {brightness_mod}')
print(f'Contrast = {contrast_mod}')
```
### How to aproximate data by beta distribution
```python
import BrightnessBooster as BB
import numpy as np

# Задайте параметры распределения
a = 2.0  # Параметр формы (alpha)
b = 5.0  # Параметр формы (beta)
size = 1000  # Размер выборки

# Генерируем выборку из бета-распределения
data = np.random.beta(a, b, size)
a_approx, b_approx = BB.approximate_beta_distribution(data)
print(a_approx, b_approx)
```

## Licence
sphinx_tippy 0.4.3
Get rich tool tips in your sphinx documentation!
Requires Python: >=3.8
Keywords: sphinx, tooltip, tippy

# sphinx-tippy

![GitHub Repo stars](https://img.shields.io/github/stars/chrisjsewell/sphinx-tippy?label=Like%20and%20Share%21&style=social)

Get rich tool tips in your sphinx documentation!

See the documentation for details.
emotions-analysis 1.0.0
Analyze sentiment of text

# Análise de Sentimentos em C para Python

Esta biblioteca oferece uma função em C para análise de sentimentos de texto em Python.

## Como Funciona

A função `emotions_analysis(text)` analisa o sentimento de um texto fornecido como entrada. Aqui está uma visão geral de como funciona:

1. **Preparação do Texto:**
   - O texto de entrada é dividido em palavras individuais para facilitar a análise.

2. **Análise de Sentimentos:**
   - A função compara cada palavra do texto com listas de palavras positivas e negativas para determinar seu sentimento.
   - Cada palavra positiva aumenta um contador de palavras positivas, e cada palavra negativa aumenta um contador de palavras negativas.

3. **Classificação do Sentimento:**
   - Com base na contagem de palavras positivas e negativas, a função determina o sentimento geral do texto.
   - Se o número de palavras positivas for maior que o número de palavras negativas, o sentimento é considerado positivo.
   - Se o número de palavras negativas for maior que o número de palavras positivas, o sentimento é considerado negativo.
   - Se o número de palavras positivas e negativas for igual, o sentimento é considerado neutro.
bimbmaan 0.2
bimbmaan: A Shaktiman to process high quality images for research publication
Requires Python: >=3.8

# **BIMBMAAN**: A Shaktiman to process high quality images for research publication

BIMBMAAN is a powerful tool (Swiss Army Knife) designed to assist researchers in dealing with image resizing and conversion challenges encountered during manuscript preparation for research publication. Its primary goal is to simplify the process of handling high-quality images, ensuring they meet the standards required for publication.

# Details and Descriptions

## Features
* Efficient image resizing to desired dimensions (e.g., 600 DPI, 1200 DPI).
* Seamless conversion between different image formats (e.g., PNG, JPEG, TIFF).
* Batch processing capabilities for handling multiple images simultaneously.
* Preservation of image quality and resolution to meet publication requirements.
* Easy to install and operate and works with single commands without requiring any customisation

## How to Use
* Install BIMBMAAN using `pip install bimbmaan`.
* Navigate to your images directory where your images are ready to process.

* PDF to Image Conversion:
  - `bimbpdf`
  - `bimbpdf -d 72 600 1200 -e jpg tiff`

* Clip Whitespace around Images:
  - `bimbclip`

* Convert SVG to metafile:
  - `bimbsvg`

* Remove background from Images:
  - `bimbrvg`

## Benefits
* Saves time and effort by automating image processing tasks.
* Ensures compliance with publication guidelines regarding image quality and resolution.
* Reduces the risk of errors and inconsistencies in image conversion and dimensions of the images.
* Batch processing

# Feedback and Support
We welcome any feedback or suggestions for improving BIMBMAAN. Please report issues through
[GitHub issues](https://github.com/TheBiomics/bimbmaan/issues).
duo-game-lib 0.1.2
Logic for turn-based duo game
Requires Python: <4.0,>=3.10

# duo-game-lib

[![CI/CD](https://github.com/game-ai-platform-team/duo-game-lib/actions/workflows/cicd.yml/badge.svg)](https://github.com/game-ai-platform-team/duo-game-lib/actions/workflows/cicd.yml)
[![codecov](https://codecov.io/gh/game-ai-platform-team/duo-game-lib/graph/badge.svg?token=rkxU4I4T0S)](https://codecov.io/gh/game-ai-platform-team/duo-game-lib)

Logic for turn-based duo game.