semvec/pypi-packages huggingface dataset:
repo-scaffold 0.13.5
shawn deng repo temple project
Requires Python: >=3.12

# repo-scaffold

[![PyPI version](https://badge.fury.io/py/repo-scaffold.svg)](https://badge.fury.io/py/repo-scaffold)
[![Python Version](https://img.shields.io/pypi/pyversions/repo-scaffold.svg)](https://pypi.org/project/repo-scaffold/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

A modern project scaffolding tool that helps you quickly create standardized project structures with best practices.

## Features

- 🚀 Quick project initialization with modern best practices
- 📦 Project templates with standardized structure
- ⚙️ Interactive project configuration
- 🔧 Pre-configured development tools (ruff, pytest, nox)
- 📚 Documentation setup with MkDocs Material
- 🔄 GitHub Actions workflows included

## Installation

```bash
# Using uvx (recommended)
uvx install repo-scaffold

# Using pip
pip install repo-scaffold

# Using poetry
poetry add repo-scaffold
```

## Quick Start

```bash
# List available templates
repo-scaffold list

# Create a new project
repo-scaffold create python

# Create a project in a specific directory
repo-scaffold create python -o ./my-projects
```

## Available Templates

Currently supported project templates:

- **Python Project Template**
  - Modern Python project structure
  - Testing setup with pytest and nox
  - Documentation with MkDocs Material
  - Code quality with ruff
  - GitHub Actions CI/CD workflows
  - Dependency management with your choice of tool
  - Automated version management
  - MIT License template

## Development Setup

To set up the development environment:

```bash
# Clone the repository
git clone https://github.com/ShawnDen-coder/repo-scaffold.git
cd repo-scaffold

# Create and activate virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install development dependencies
pip install -e ".[dev,docs]"
```
nice-errors 1.1.0
Nice error messages for Python
Requires Python: >=3.9

# friendly_errors 😇

A Python module that catches errors and displays them in a friendlier, more helpful way — with tips, emojis, and clear explanations!

## ✨ Features

- Friendly error messages with explanations
- Custom traceback location info
- YAML-configured error data
- Automatically sets `sys.excepthook`

## 📦 Installation

```bash
pip install nice-errors
```
## Usage

```python
import nice-errors

#Throw an error here!

print(1/0)
# See the nice result!
```
topsis-abhiraj-singh-jhajj-102217094 1.0.6
TOPSIS implementation for multi-criteria decision analysis
Requires Python: >=3.6

# Topsis-Abhiraj-Singh-Jhajj-102217094

`Topsis-Abhiraj-Singh-Jhajj-102217094` is a Python package that implements the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method for multi-criteria decision making. This tool is ideal for evaluating and ranking alternatives based on multiple criteria, which is essential in fields like supply chain management, finance, and engineering.

## Installation

You can install `Topsis-Abhiraj-Singh-Jhajj-102217094` directly from the Python Package Index using pip:

pip install Topsis-Abhiraj-Singh-Jhajj-102217094


## Usage

### Command Line Interface

To use the package from command line, you need to prepare your data in CSV or Excel format where the first column contains the names/labels of the alternatives, and the subsequent columns contain the criteria values.

Using the CLI program
python 102217094.py data-1.xlsx "1,2,3,1,2" "+,+,-,+,+" 102217094-result.csv

Using the installed package
topsis data.csv "1,2,3" "+,-,+" results.csv


### Python Script Usage

from topsis_Abhiraj_102217094 import topsis_analysis

Perform TOPSIS analysis
result = topsis_analysis('data.csv', '1,2,3,1,2', '+,+,-,+,+', 'result.csv')


## Parameters

- **Input file**: CSV or Excel file with alternatives and criteria
- **Weights**: Comma-separated string of weights for each criterion (e.g., "1,2,3,1,2")
- **Impacts**: Comma-separated string of impacts for each criterion:
  - `+` indicates that higher values are better
  - `-` indicates that lower values are better
- **Output file**: Path where results will be saved

## Example

Given a dataset with 5 criteria (P1, P2, P3, P4, P5):

python 102217094.py 102217094-data.csv "1,2,3,1,2" "+,+,-,+,+" 102217094-result.csv


## Features

- **Easy integration** with Pandas DataFrames
- **Customizable weights** and criteria impacts
- **Automatic normalization** and ranking of alternatives
- **Command line interface** for easy access and usage
- **Support for both CSV and Excel** input files
- **Comprehensive error handling** and validation

## Repository

GitHub: [abhirajsinghjhajj/Topsis_Abhiraj_Singh_Jhajj_102217094](https://github.com/abhirajsinghjhajj/Topsis_Abhiraj_Singh_Jhajj_102217094)

## Author

**Abhiraj Singh Jhajj** (102217094)
- Email: abhirajsinghjhajj@gmail.com
- GitHub: [@abhirajsinghjhajj](https://github.com/abhirajsinghjhajj)

## License

This project is licensed under the MIT License - see the [LICENSE.txt](LICENSE.txt) file for details.
pymox-tools 1.0.0
Un module avec plein d'outils utiles pour devs en PyMoX (PY-thon - MO-jo - Flet-X)
Requires Python: >=3.10

# PyMoX - GC7

Trousse à outils utiles pour devs en PyMoX

---

## Rapide mémo

```bash
py -m venv .venv
.\.venv\Scripts\activate
pip install -r requirements.txt

py -m build
twine check dist/*  --verbose
twine upload dist/* --verbose

git log --oneline

semantic-release version
semantic-release version --print

semantic-release publish

semantic-release --noop version
Cela te dira si une version serait générée.

semantic-release version --commit --tag --no-push

Cela va :
Lire les commits
Calculer la prochaine version (ex: 0.2.0)
Modifier __version__ dans gc7/__init__.py
Créer un commit et un tag localement

Remove-Item -Recurse -Force dist, src/gc7/gc7.egg-info

git commit --allow-empty -m "feat: add dummy feature for version bump"
git commit --allow-empty -m "fix: déclenchement de la release 1.0.1" 
git push origin main

voir conventional commits: https://www.conventionalcommits.org/en/v1.0.0/

En cas de volonté de revenir sur une version précédente (Problème avec PyPI: Interdit de reculer):
git revert <hash du commit> (pour ramener la main du dépôt GH)
dans le __ini__.py, mettre : __version__='x.y.z' (> au dernier de PyPI)
refaire le build + renvoi:
py -m build    
twine upload dist/*

Désinstaller tous les packages de l'env global :
pip freeze | ForEach-Object { pip uninstall -y $_ }

# 1. Crée une branche temporaire pour garder les fichiers
git checkout --orphan temp_branch

# 2. Ajoute tous les fichiers à cette nouvelle branche
git add .

# 3. Fais un commit initial
git commit -m "Initial commit"

# 4. Supprime l'ancienne branche main
git branch -D main

# 5. Renomme la branche temporaire en main
git branch -m main

# 6. Force le push vers GitHub (⚠️ cela écrase l’historique distant)
git push -f origin main


```
webpys 4.6.0
A web passive scanner and recon tool
Requires Python: >=3.6

# WebPyS v2.1 - Web Passive Scanner & Recon Tool

**Author**: [Pugazh@TheHacker](https://github.com/pugazh2006)

WebPyS is a powerful, modular web reconnaissance tool that performs **passive and semi-active enumeration** of web targets. It helps security researchers, bug bounty hunters, and penetration testers gather vital intelligence on websites without triggering excessive alerts.

- 🔍 Passive recon with SSL & HTTP header analysis
- 🌐 Subdomain enumeration (wordlist-based)
- 🔐 SSL certificate inspection & expiry checks
- 🛡️ WAF fingerprinting
- 🌐 Port scanning (80/443 TCP)
- 📄 Auto-generated **HTML & XML reports**
- ⚡ Multithreaded subdomain checking
- 🎨 Beautiful CLI banner (Rich-powered)
- ✅ Clean Python3 codebase — no bloat, no frameworks

---
 📦 Installation
   git clone _______________________________________

### 🔧 Requirements

- Python 3.7+
 pip install -r requirement.txt

### ✅ Install Dependencies
python webpys.py
Atiny 0.0.4
Atiny is a gradient based PyTorch optimizer. Atiny uses half of Adam's memory to achieve results that are not inferior to Adam. 
Requires Python: >=3.8
Keywords: tiny, Atiny, PyTorch, optimizer, AI, neural, NN, memory

# Atiny Optimizer
Atiny is a gradient based PyTorch optimizer. Atiny uses half of Adam's memory to achieve results that are not inferior to Adam.

![image](./rendering.png)
![image](./renderingZoom.png)

## 思路说明
不使用二阶动量,而是使用一阶动量与当前梯度计算每个参数的更新补偿.
内置了一个学习率衰减器,自动为每组参数个性化的学习率衰减.(需要参数:ldr)
使用ArcSinh函数影响向量模长实现权重衰减

## Demo程序
demo程序在test.py文件中,直接运行即可得到此页面中Atiny与Adam的对比图.(依赖visdom)
demo中构建了一个神经网络用简陋的方式对一个包含动态随机参数的公式生成的周期性曲线进行预测.

## Install
```bash
pip install Atiny
```

## Use
```python
from Atiny import Atiny
...
optimizer=Atiny(moduel.parameters(),lr=lr,ldr=ldr,weight_decay=weight_decay)
```

## HomePage
<https://github.com/PsycheHalo/Atiny/>
Topsis-Abhiraj-Singh-Jhajj-102217094 1.0.5
TOPSIS implementation for multi-criteria decision analysis
Requires Python: >=3.6

# Topsis-Abhiraj-Singh-Jhajj-102217094

`Topsis-Abhiraj-Singh-Jhajj-102217094` is a Python package that implements the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method for multi-criteria decision making. This tool is ideal for evaluating and ranking alternatives based on multiple criteria, which is essential in fields like supply chain management, finance, and engineering.

## Installation

You can install `Topsis-Abhiraj-Singh-Jhajj-102217094` directly from the Python Package Index using pip:

pip install Topsis-Abhiraj-Singh-Jhajj-102217094


## Usage

### Command Line Interface

To use the package from command line, you need to prepare your data in CSV or Excel format where the first column contains the names/labels of the alternatives, and the subsequent columns contain the criteria values.

Using the CLI program
python 102217094.py data-1.xlsx "1,2,3,1,2" "+,+,-,+,+" 102217094-result.csv

Using the installed package
topsis data.csv "1,2,3" "+,-,+" results.csv


### Python Script Usage

from topsis_Abhiraj_102217094 import topsis_analysis

Perform TOPSIS analysis
result = topsis_analysis('data.csv', '1,2,3,1,2', '+,+,-,+,+', 'result.csv')


## Parameters

- **Input file**: CSV or Excel file with alternatives and criteria
- **Weights**: Comma-separated string of weights for each criterion (e.g., "1,2,3,1,2")
- **Impacts**: Comma-separated string of impacts for each criterion:
  - `+` indicates that higher values are better
  - `-` indicates that lower values are better
- **Output file**: Path where results will be saved

## Example

Given a dataset with 5 criteria (P1, P2, P3, P4, P5):

python 102217094.py 102217094-data.csv "1,2,3,1,2" "+,+,-,+,+" 102217094-result.csv


## Features

- **Easy integration** with Pandas DataFrames
- **Customizable weights** and criteria impacts
- **Automatic normalization** and ranking of alternatives
- **Command line interface** for easy access and usage
- **Support for both CSV and Excel** input files
- **Comprehensive error handling** and validation

## Repository

GitHub: [abhirajsinghjhajj/Topsis_Abhiraj_Singh_Jhajj_102217094](https://github.com/abhirajsinghjhajj/Topsis_Abhiraj_Singh_Jhajj_102217094)

## Author

**Abhiraj Singh Jhajj** (102217094)
- Email: abhirajsinghjhajj@gmail.com
- GitHub: [@abhirajsinghjhajj](https://github.com/abhirajsinghjhajj)

## License

This project is licensed under the MIT License - see the [LICENSE.txt](LICENSE.txt) file for details.