semvec/pypi-packages huggingface dataset:
pixflow 0.2.0
A simple image processing toolkit for resizing, histogram transfer, and difference detection.
Requires Python: >=3.7

# PixFlow: Image Processing Toolkit

[![PyPI version](https://img.shields.io/pypi/v/pixflow.svg)](https://pypi.org/project/pixflow/)  
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

Simple, lightweight, and easy-to-use image processing toolkit in Python.  
Resize images, transfer histograms, and detect structural differences with just a few lines of code.

---

## 🖼️ Description

**PixFlow** is a Python package designed to simplify common image processing tasks, including:

- Image resizing
- Histogram matching between images
- Structural difference detection (SSIM)
- Image reading and saving
- Plotting images and histograms

Built on top of `scikit-image`, `matplotlib`, and `numpy`.

---

## 📦 Features

- 🔸 **Image reading and saving** — Supports formats like `.jpg`, `.png`, etc.
- 🔸 **Image resizing** — Resizes by relative proportion.
- 🔸 **Histogram matching** — Adjusts the tones of one image to match another.
- 🔸 **Structural difference detection (SSIM)** — Detects and visualizes structural differences between images.
- 🔸 **Plot utilities**:
  - Display single images
  - Compare multiple images side by side
  - Plot RGB histograms

---

## 🚀 Installation

Install from PyPI:

```bash
pip install pixflow
```

Or install locally from the repository:

```bash
git clone https://github.com/A-Chioquetta/pixflow.git
cd pixflow
pip install -e .
```

---

## 🔧 Dependencies

- numpy
- matplotlib
- scikit-image

These will be automatically installed with `pip install pixflow`.  
Or install manually with:

```bash
pip install -r requirements.txt
```

---

## 🛠️ Usage Example

```python
from pixflow import (
    read_image, save_image,
    resize_image, transfer_histogram, find_difference,
    plot_image, plot_result, plot_histogram
)

# Read images
image1 = read_image('flor1.jpg')
image2 = read_image('flor2.jpg')
image3 = read_image('flor1_alterada.jpg')

# Resize
resized = resize_image(image1, proportion=0.5)
plot_image(resized, title="Resized Image")

# Histogram matching
matched = transfer_histogram(image1, image2)
plot_result(image1, image2, matched, title="Histogram Matching")

# Structural difference (SSIM)
difference = find_difference(image1, image3)
plot_result(image1, image3, difference, title="Structural Difference")

# Histogram plot
plot_histogram(image1)

# Save resized image
save_image(resized, 'resized_image.jpg')
```

---

## 🗂️ Project Structure

```
pixflow/
├── pixflow/
│   ├── processing/
│   ├── utils/
│   └── __init__.py
├── examples/
├── tests/
├── README.md
├── setup.py
├── LICENSE.txt
└── requirements.txt
```

---

## 📄 License

This project is licensed under the [MIT License](LICENSE.txt).

---

## 🔗 Links

- PyPI: https://pypi.org/project/pixflow/
- GitHub: https://github.com/A-Chioquetta/pixflow
dbtlabs-vortex 0.2.0
Python client for Vortex
Requires Python: >=3.8

# Vortex Python Client

A Python client for sending data to the Vortex.

## Overview

This client provides an easy way to send properly formatted messages to Kafka topics that will be consumed by the Vortex service and written to Iceberg tables.

The client:
- Accepts arbitrary protobufs onto a buffer via `log_proto`
- Flushes messages in a background thread so that `log_proto` calls do not block

## Installation

### Prerequisites

- Python 3.11, 3.12, 3.13

### Setup

```bash
# From the clients/python directory
uv sync --all-extras
```

## Quick Start

```python
from dbtlabs_vortex.producer import log_proto, shutdown

# Send JSON data
log_proto(... proto ...)

# Wait for all messages to be delivered
shutdown(timeout_seconds=0.500)
```

## Examples

There is one example included in the `examples/` directory. You can run it with:

```bash
uv run examples/send_batch.py
```

This will send some generated events to a locally running Vortex. It also serves as a reference
implementation for how to log events to Vortex using existing protos.

## Error Handling
pdf-section-sentiment 0.1.5
Extract sections from PDFs and perform sentiment analysis

# PDF Section Sentiment

[![PyPI version](https://badge.fury.io/py/pdf-section-sentiment.svg)](https://pypi.org/project/pdf-section-sentiment/)

A lightweight CLI tool to extract structured sections from PDFs and perform sentiment analysis on each section.

---

## 🚀 Features

- Extracts headers and sections from PDFs
- Supports Markdown-like structure
- Performs sentiment scoring (`-1` to `+1`)
- Labels sentiment as `positive`, `neutral`, or `negative`
- Outputs clean JSON

---

## 📦 Installation

```bash
pip install pdf-section-sentiment

## ⚙️ Usage
pdf-extract --input path/to/document.pdf --output sections.json
pdf-sentiment --input sections.json --output sentiment.json

## 📝 Output Format
[
  {
    "header": "Executive Summary",
    "content": "Lyft reported strong revenue growth and operational efficiency...",
    "sentiment_score": 0.28,
    "sentiment": "positive"
  },
  {
    "header": "Risk Factors",
    "content": "There is uncertainty in market regulation...",
    "sentiment_score": -0.15,
    "sentiment": "negative"
  }
]

## 🧪 Example
pdf-extract --input data/Lyft-Annual.pdf --output output/sections.json
pdf-sentiment --input output/sections.json --output output/sentiment.json
sibd-pyerr 0.1.5
선일빅데이터고등학교 파이썬 에러 한글화
Requires Python: >=3.8

### 목적

파이썬 예외 메시지를 한글로 출력해주는 라이브러리입니다.

### 사용

```python
import sibd_pyerr
sibd_pyerr.install()
```

### 소스코드 저장소

https://github.com/ehdcjf/pyerrkor.git

### 참고

https://github.com/friendly-traceback/friendly-traceback

### 배포 관리

#### 0.1.0

#### 0.1.1

#### 0.1.2

type_error 관련 수정

- print 함수를 덮어썼습니다.
     - print 함수를 문자열로 덮어썼다고 출력하여 이를 수정

#### 0.1.3

install 메시지 추가