semvec/pypi-packages huggingface dataset:
urllink 1.2.0
A Python library for URL shortening and QR code generation.

# urllink

`urllink` is a simple Python library that allows you to shorten URLs and generate QR codes using online APIs.

## Installation

```bash
pip install urllink

## Example Usage

--------------------------------------------------------------
from urllink import shorten_url, generate_qrcode

# Shorten a URL
short_url = shorten_url("https://example.com")
print("Shortened URL:", short_url)

# Generate a QR code
qr_path = generate_qrcode("https://example.com", save_path="output/qrcode.png", size=300)
print("QR Code saved to:", qr_path)
--------------------------------------------------------------

Generates a QR code from the provided text or URL.
Function Reference
Parameters:

data (str, required): The content to encode into the QR code.

save_path (str, optional): Full path (including filename) to save the image.
Default: "qrcode.png" saved in the current directory.

size (int, optional): The size of the QR code image in pixels.
Default: 500

Returns:

The file path where the QR code image was saved.

An error message string if generation fails.
reven-plugin-etube 0.1.4
Reven plugin for reverse engineering Shimano E-Tube firmware.
Keywords: reverse engineering

# Reven Plugin Etube 🦊🔌🚴

A plugin for [Reven](https://github.com/reven-project/reven) that facilitates reverse engineering Shmiano E-Tube firmware.

## Installation 👷

The plugin can be installed using `pip`.

```console
$ pip install reven-plugin-etube
```

## Usage

Run `reven etube --help` to see the help and all available commands.

```console
$ reven etube --help
                                                                                  
 Usage: reven etube [OPTIONS] COMMAND [ARGS]...                                   
                                                                                  
 Operations for reverse engineering Shimano E-Tube firmware.                      
                                                                                  
╭─ Options ──────────────────────────────────────────────────────────────────────╮
│ --help          Show this message and exit.                                    │
╰────────────────────────────────────────────────────────────────────────────────╯
╭─ Commands ─────────────────────────────────────────────────────────────────────╮
│ fwinfo    Read the headers of E-Tube firmware files.                           │
│ decrypt   Decrypt E-Tube file using AES.                                       │
│ encrypt   Encrypt an E-Tube file using AES.                                    │
│ hexpat    Generates ImHex *.hexpat files                                       │
╰────────────────────────────────────────────────────────────────────────────────╯
```
nutrichecker 0.1.0
Library advanced nutrition analysis dan rekomendasi makanan sehat
Requires Python: >=3.6

# NutriCheck

**NutriChecker** is a Python library designed to analyze the nutritional value of food items, evaluate them based on user-specific health profiles, and provide smarter recommendations for healthier eating. Ideal for nutritionists, health apps, or anyone who wants to improve their dietary decisions through intelligent data analysis.

---

## 🚀 Features

- 🔍 Fetch nutritional data for common foods (via API or fallback to dummy data)
- 🧠 Evaluate food intake against daily nutritional needs
- 🧮 Built-in BMI and BMR calculation
- 🗂️ Save historical analysis to JSON storage
- 🥗 Recommend healthier alternatives using cosine similarity
- 📊 Nutrition scoring (0–100)
- 🔧 Configurable user profiles: age, weight, height, activity level

---

## 📦 Installation

```bash
git clone https://github.com/yourusername/nutricheck.git
cd nutrichecker
pip install -r requirements.txt

## Usage Example
     
from nutricheck.core import NutritionAnalyzer

user = {
    "age": 28,
    "weight": 65,
    "height": 170,
    "activity_level": "moderate"
}

analyzer = NutritionAnalyzer(user)
result = analyzer.analyze(["banana", "salmon", "quinoa"])

print("Score:", result["score"])
print("Total Nutrition:", result["nutrition"])
print("Recommendations:", result["recommendations"])

# Requirements
       pip install requests scikit-learn loguru numpy

# Question And Suggestions
       For questions, email: aeden6877@gmail.com
tprdbreader 0.1.3
A library to read Translation Process Research Database (TPR-DB) tables from Google Cloud Storage.
Requires Python: >=3.6

# tprdbreader

[![PyPI](https://img.shields.io/pypi/v/tprdbreader.svg)](https://pypi.org/project/tprdbreader)
[![Python](https://img.shields.io/pypi/pyversions/tprdbreader.svg)](https://pypi.org/project/tprdbreader)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)

*A tiny helper that fetches Translation Process Research Database (TPR-DB) tables from **Google Cloud Storage** and returns them as Pandas DataFrames—no manual downloads, no local staging.*

---

## Why tprdbreader?

* **One function, one line:**  
    `readTPRDBtable_GCP(["ACS08", "AR20"], ext="kd")`
* **Streams directly into Pandas:** handles multiple studies and large Tables without blowing up RAM.
* **MIT-licensed & lightweight:** only two runtime deps—`pandas` and `google-cloud-storage`.

---

## Installation

```bash
pip install tprdbreader          # requires Python 3.6+
```

---

## Quick-start

```python
from tprdbreader import readTPRDBtable_GCP

# Pull all *.kd files (Keystroke Burst layer) from two studies
df = readTPRDBtable_GCP(
        studies=["ACS08", "AR20"],
        ext="kd",              # extension WITHOUT dot works too
        user="TPRDB",          # public user
        verbose=1)             # print per-study stats

print(df.head())
```

Example verbose output

```
ACS08  #sessions: 33   .kd:152  ...
AR20   #sessions: 29   .kd:131
```

---

## API Reference

```python
readTPRDBtable_GCP(studies, ext, user="TPRDB", verbose=0) -> pandas.DataFrame
```

| Argument | Type | Description |
|----------|------|-------------|
| `studies` | `list[str]` | List of study IDs (folder names) such as `["ACS08", "AR20"]`. |
| `ext` | `str` | File extension *without* the leading dot, e.g. `"kd"`, `"au"`, `"ss"`. |
| `user` | `str` | Default `"TPRDB"`. |
| `verbose` | `int` | `0` = silent, any non-zero prints per-study stats. |

> **Returns:** a single concatenated `pandas.DataFrame`.  
> Columns depend on the specific Table you load (`*.kd`, `*.au`, `*.ss` …).

### Internal path logic

For each study `<S>` the function builds  
`data/critt/tprdb/<user>/<S>/Tables/`  
and downloads every file whose name ends with `.<ext>`.

---


## License

Released under the MIT License — see [`LICENSE`](LICENSE) for full text.

<sub>© 2025 Devisri Bandaru.  Affiliated with the official TPRDB maintainers.</sub>
image-processing-vitor-valim 0.1.0
Transformador de tonalidade em imagens
Requires Python: >=3.8

# image_processing

Description. 
The package image_processing is used to:
	Processing:
		- Histogram machine
		- Structural similarity
		- Resize image
	Utils:
		- Read image
		- Save image
		- Plot image
		- Plot result
		- Plot histogram

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install image_processing_vitor_valim

```bash
pip install image_processing_vitor_valim
```

## Author
Vitor Gomes Valim

## License
[MIT](https://choosealicense.com/licenses/mit/)
scalenav 0.0.6
A package to project all sorts of data on the H3 grid and manipulate it at different spatial scales.
Requires Python: >=3.12
Keywords: hierarchical index, rescaling, geospatial

# Navigating spatial scales

![GitHub Actions Workflow
Status](https://img.shields.io/github/actions/workflow/status/nismod/scale-nav/run-package.yml?style=flat)
![PyPI -
Version](https://img.shields.io/pypi/v/scalenav?style=flat&logoColor=grey&logoSize=auto&labelColor=blue&color=gray&link=https%3A%2F%2Fpypi.org%2Fproject%2Fscalenav%2F)
![PyPI - Python
Version](https://img.shields.io/pypi/pyversions/scalenav?style=flat.png)
![PyPI - Wheel](https://img.shields.io/pypi/wheel/scalenav.png)

## Introduction

This is a python module that helps change scales of spatial data sets.
It relies on the [H3](https://h3geo.org) hexagonal grid’s hierarchical
indexing functionalities and aims to add to that the data
transformations that need to be done when navigating between levels of
the hierarchy.

A presentation on the topic can be found
<a href="https://ischlo.github.io/presentations/down_scaling"
target="_blank">here</a>

### THIS MODULE IS UNDER DEVELOPMENT

The package website introduces the use cases and the different functionalities that are covered so far.

## Main functionalities

### Rastapar

The command line tool allows for efficient, multi core ingestion of rasters into parquet tables. 

### Scalenav modules

A set of modules provides python functions for performing a operations with data sets expressed as H3 cells. Additionaly, a module with the equivalent high performance functions written with ibis and duckdb.