semvec/pypi-packages huggingface dataset:
image-processor-pkg 0.0.1
A Python package for image processing including filters and transformations.
Requires Python: >=3.8

# Image Processor PKG

The image_processor_pkg package is designed for image processing, offering functionalities for applying filters and performing transformations on images. The main features include:

- Applying image filters like blur and sharpen.
- Performing transformations such as resizing and rotating images.


## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install package_name

```bash
pip install image_processor_pkg
```

## Usage

```
from PIL import Image
from image_processor_pkg.image_filters.blur_filter import apply_blur
from image_processor_pkg.image_transforms.resize_transform import resize_image

# Carregando uma imagem a partir de um arquivo
image_path = 'path_to_image.jpg'  # Substitua pelo caminho da sua imagem
image = Image.open(image_path)

# Aplicando um filtro de desfoque (blur)
blurred_image = apply_blur(image, blur_radius=5)
blurred_image.save('blurred_image.jpg')  # Salva a imagem desfocada

# Redimensionando a imagem
new_width, new_height = 100, 100
resized_image = resize_image(image, new_width, new_height)
resized_image.save('resized_image.jpg')  # Salva a imagem redimensionada
```

## Author
Leticia Martins dos Santos
torchcell 0.2.8
An example package
Requires Python: >=3.10
Keywords: An example package, starving artist

<p align="center">
  <img src="./notes/assets/drawio/torchcell-logo.drawio.png" />
</p>

Community: Add slack, add notes url

Packaging: Add packing <!-- ![Python application](https://github.com/mjvolk3/torchcell/actions/workflows/python_app.yaml/badge.svg) -->

Testing: ![Code Styling](https://github.com/mjvolk3/torchcell/workflows/Code%20Styling/badge.svg) [![codecov](https://codecov.io/gh/mjvolk3/torchcell/branch/main/graph/badge.svg)](https://codecov.io/gh/mjvolk3/torchcell)

Docs to come soon!!!
python-myspeed 0.1.0
A python wrapper for the MySpeed API.
Requires Python: <4.0,>=3.11

# python_myspeed

[![codecov](https://codecov.io/gh/jkrgr0/python-myspeed/branch/main/graph/badge.svg?token=python-myspeed_token_here)](https://codecov.io/gh/jkrgr0/python-myspeed)
[![CI](https://github.com/jkrgr0/python-myspeed/actions/workflows/main.yml/badge.svg)](https://github.com/jkrgr0/python-myspeed/actions/workflows/main.yml)

Python wrapper for the MySpeed API.

## Install it from PyPI

```bash
pip install python_myspeed
```

## Usage

```py
import python_myspeed

myspeed = python_myspeed.MySpeedAPI("127.0.0.1", 5216, "SecureAP1Token")
```

## Development

Read the [CONTRIBUTING.md](CONTRIBUTING.md) file.
vendas-analise 0.0.1
Um pacote para análise de vendas

# venda_analise :bar_chart:

  

Descrição.

Um pacote para análise de vendas

  

## Instalação

  

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install package_name

  

```bash

pip  install  vendas_analise

```

  

## Uso

  

```python

from vendas_analise import contagem_de_vendas, produto_mais_vendido, produto_menos_vendido

contagem_de_vendas()

```

  

## Autor

https://github.com/luiz-vaz
memocache 0.0.3
A decorator that optimizes function execution by caching results. With support for different eviction strategies (LFU, LRU, FIFO).
Requires Python: >=3.6

**Module: memo**


**Description:**
This module provides a `memo` decorator that implements a caching mechanism for function results. The goal is to optimize performance by avoiding redundant calculations.

**Features:**
* **Cache:** Stores function call results in a local file.
* **Eviction strategies:** Supports various eviction strategies (LRU, LFU, FIFO) to manage cache size.
* **Serialization:** Uses serialization to store various data types in the cache.
* **Synchronization:** Implements synchronization mechanisms to ensure cache integrity in multi-threaded environments.

**Usage:**
```python
from memo import memo

@memo(strategy='lru', max_size=500)
def fibonacci(n):
    if n < 2:
        return n
    return fibonacci(n-1) + fibonacci(n-2)
    ```

**Decorator parameters**:

* strategy: Eviction strategy (lru, lfu, fifo).

* max_size: Maximum cache size.

* ttl: Time-to-live of items in the cache (in seconds).

* Internal structure:

* cache_class: Class representing the cache (LRUCache, LFUCache, FIFOCache).

* evictor: Object responsible for managing cache item eviction.

* serialize/deserialize: Functions for serializing and deserializing data.

* cache_lock: Synchronization mechanism for cache access.