semvec/pypi-packages huggingface dataset:
scaffold-kit 1.0.1
Command-line tools for project setup, structure creation, file tracking, and directory trees.
Requires Python: >=3.12
Keywords: cli, cli-tool, command-line, command-line-tool, directory-tree, file-tree, generator, productivity, project-structure, scaffold, tree

# scaffold-kit

[![PyPI - Version](https://img.shields.io/pypi/v/scaffold-kit)](https://pypi.org/project/scaffold-kit/)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/scaffold-kit)](https://pypi.org/project/scaffold-kit/)
[![GitHub License](https://img.shields.io/github/license/sidisinsane/scaffold-kit)](https://github.com/sidisinsane/scaffold-kit/blob/main/LICENSE)

A comprehensive toolkit for project initialization and structure management.
Create projects from structured data definitions, generate file checklists for
tracking progress, and visualize directory hierarchies with ASCII trees.

## Installation

Install from PyPI:

```bash
pip install scaffold-kit
```

Install with uv:

```bash
uv add scaffold-kit
```

Install development releases from TestPyPI:

```bash
pip install -i https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ scaffold-kit
```

Install development releases from TestPyPI with uv:

```bash
uv add --index https://test.pypi.org/simple/ --index https://pypi.org/simple/ scaffold-kit
```

## Usage

scaffold-kit provides four main commands for project management:

**Initialize a project with example files:**

```bash
scaffold-kit init [ignore-file|config-file|scaffold-file]
```

**Create project structure from YAML/JSON definition:**

```bash
scaffold-kit scaffold [--root]
```

**Generate a file checklist for progress tracking:**

```bash
scaffold-kit checklist
```

**Create an ASCII tree of directory structure:**

```bash
scaffold-kit tree [directory] [--ignore-file FILE]
```

## Links

- **[Repository][1]** - Main GitHub repository
- **[Package][2]** - PyPI package page
- **[Test Package][3]** - TestPyPI package page for development releases
- **[Getting Started][4]** - This README with basic usage and examples
- **[Full Documentation][5]** - Complete guides, tutorials, and examples
- **[API Reference][6]** - Detailed API documentation and code reference
- **[Issues & Bug Reports][7]** - Report bugs or request features

## Acknowledgments

- [uv][8] - Fast Python package installer and resolver
- [asdf][9] - Multi-language version manager
- [Semantic Release][10] - Automated versioning
- [Pre-commit][11] - Git hooks for code quality
- [MkDocs][12] - Documentation generator
- [Sphinx][13] - API documentation generator

[1]: https://github.com/sidisinsane/scaffold-kit
[2]: https://pypi.org/project/scaffold-kit/
[3]: https://test.pypi.org/project/scaffold-kit/
[4]: https://github.com/sidisinsane/scaffold-kit#readme
[5]: https://sidisinsane.github.io/scaffold-kit/
[6]: https://sidisinsane.github.io/scaffold-kit/reference/
[7]: https://github.com/sidisinsane/scaffold-kit/issues
[8]: https://docs.astral.sh/uv/
[9]: https://asdf-vm.com/
[10]: https://python-semantic-release.readthedocs.io/
[11]: https://pre-commit.com/
[12]: https://www.mkdocs.org/
[13]: https://www.sphinx-doc.org/
insee-mcp 0.1.12
Serveur MCP pour accéder aux données INSEE (SIRENE, BDM, Nomenclatures)
Requires Python: >=3.10

# INSEE MCP Server

Serveur MCP (Model Context Protocol) pour accéder aux données de l'INSEE via l'API SIRENE, BDM et les nomenclatures officielles.

## ⚡ Installation en une ligne

### Avec uv (recommandé)
```bash
# Installation et lancement direct
API_KEY="your_insee_api_key" uv run --with insee-mcp insee-mcp
```

### Configuration MCP standard
Ajoutez ceci à votre configuration MCP (ex: Claude Desktop) :

```json
{
  "mcpServers": {
    "insee": {
      "command": "uv",
      "args": ["run", "--with", "insee-mcp", "insee-mcp"],
      "env": {
        "API_KEY": "votre_clé_api_insee"
      }
    }
  }
}
```

### Avec pipx
```bash
API_KEY="your_insee_api_key" pipx run --spec git+https://github.com/KerryanOPMace/mcp-insee.git insee-mcp
```

## 🚀 Installation rapide

### Installation directe (recommandée)

```bash
# Installation directe depuis GitHub
pip install git+https://github.com/KerryanOPMace/mcp-insee.git

# Configurer votre clé API INSEE
export API_KEY="votre_clé_api_insee"

# Lancer le serveur
insee-mcp
```

### Installation avec pipx (isolée)

```bash
# Installation avec pipx (environnement isolé)
pipx install git+https://github.com/KerryanOPMace/mcp-insee.git

# Configurer la clé API
export API_KEY="votre_clé_api_insee"

# Lancer le serveur
insee-mcp
```

### Installation pour développeurs

```bash
# Cloner le repository
git clone https://github.com/KerryanOPMace/mcp-insee.git
cd mcp-insee

# Installer en mode développement
pip install -e .
```

## 🔑 Configuration

### Clé API INSEE

Vous devez obtenir une clé API sur le [portail API de l'INSEE](https://api.insee.fr/) et la configurer :

**Linux/Mac :**
```bash
export API_KEY="votre_clé_api_insee"
```

**Windows (PowerShell) :**
```powershell
$env:API_KEY="votre_clé_api_insee"
```

**Windows (CMD) :**
```cmd
set API_KEY=votre_clé_api_insee
```

## 🛠️ Outils disponibles

- `search_company` : Recherche d'entreprises dans la base SIRENE
  - Par SIREN, SIRET ou nom d'entreprise
  - Recherche approximative disponible

## 🌐 Accès au serveur

Une fois lancé, le serveur MCP est accessible à :
- **URL** : `http://127.0.0.1:8000/mcp`
- **Transport** : Streamable HTTP

## 🧪 Test du client

```python
from fastmcp import Client
import asyncio

async def test():
    client = Client("http://127.0.0.1:8000/mcp")
    async with client:
        # Lister les outils
        tools = await client.list_tools()
        print(tools)
        
        # Rechercher une entreprise
        result = await client.call_tool("search_company", {
            "siret": "44302124100072"
        })
        print(result)

asyncio.run(test())
```

## 📋 Prérequis

- Python 3.8+
- Clé API INSEE valide
- Accès internet pour les requêtes API

## 📄 Licence

MIT License
mwutsdz 0.1.1
MWUT-SDZ: Détection automatique de knee point dans les données de cycle de batteries
Requires Python: >=3.7

# MWUT-SDZ

Un package Python pour la détection automatique de knee point dans les données de batteries, basé sur le **Mann-Whitney U Test** et l’algorithme **SDZ**.

## Installation

Après publication sur PyPI :
```bash
pip install mwutsdz
ngp 1.0.1
Generate sequences of characters that can be typed easily.
Requires Python: >=3.10

# name generator, [in] python

```
$ ngp --help
Usage: ngp [--count=COUNT] [--min-length=MIN_LENGTH] [--max-length=MAX_LENGTH]

Generate phrases that are easy to type when using QWERTY
$ ngp --count=3
vus
smeiau
uxkq
$ ngp --count=3 --min-length=10 --max-length=20
yckcpzhqnfkzbcoq
spvixkdoziaj
hsmxoqutnsnvu
$
```
sceodesic 0.0.5
Generate sceodesic embeddings from an input scRNA-seq dataset.


# Sceodesic

Sceodesic is a Python package that implements the gene program discovery algorithm described by Sinan Ozbay, Aditya Parekh, and Rohit Singh in “Navigating the manifold of single-cell gene coexpression to discover interpretable gene programs.” Given a single-cell gene expression dataset, Seodesic allows the user to:
1. Discover meaningful gene programs from any single-cell gene expression data.
2. Re-interpet gene expression data as levels of expression of the aforementioned gene programs. 

This repository contains an implementation of the algorithm.  Some examples of use can be found below in "Example Usage".

## Installation

You can install Sceodesic by running the following in your command line:

```
git clone https://github.com/rohitsinghlab/sceodesic.git
cd ./sceodesic  # to directory containing pyproject.toml file 
pip install .
```

## API Example Usage

Below is example usage of sceodesic in Python. First, make sure your single-cell gene expression data is in an anndata object, as below.

```python
import anndata

adata = [single cell gene expression anndata object]
```

Now, you are ready to discover some programs! Note that you should ensure the gene expression data is log-normalized counts:

```python
# if adata is raw counts:
import scanpy as sc

# normalize to log-cp10k counts
sc.pp.normalize_total(adata, 1e4)
sc.pp.log1p(adata)
```

```python
from sceodesic import run_sceo

run_sceo(adata, num_hvg=300)

embeddings = adata.obsm['sceo_embeddings']

# programs (loadings) stored in .varm, as a numpy array
programs = adata.varm['sceo_programs']
```

`embeddings` is a 2D numpy array where rows represent cells and columsn represent levels of gene program expression. `programs` is a 2D numpy array where columns represent individual gene programs, with each entry of that column giving the weight of each individual gene\ in that program. After you have generated programs and the embeddings, it becomes possible to do a wide variety of downstream single cell analysis using these embeddings.  For example, if our data is labelled by cell type with an `obs` column called "cell_type" , we can compute differential gene program expression across cell types as follows:

```python

# Create new anndata object with embeddings
adata_sceo = anndata.AnnData(embeddings, obs=adata.obs)

sc.get.rank_genes_groups_df(adata = adata_sceo, group = “cell_type”)
```
pybanana 0.5.0
A Python wrapper for the GameBanana API
Requires Python: >=3.10
Keywords: gamebanana api wrapper async mods gaming

# 🍌 PyBanana
A powerful and intuitive Python client library for interacting with the GameBanana API. This library provides easy access to GameBanana's extensive modding platform, allowing you to search, retrieve, and interact with mods, users, games, and more.

> **Note**: This library is currently a Work in Progress (WIP). Features and APIs may change.

## Features
- 🔍 **Comprehensive API Coverage**
  - Search for mods across GameBanana
  - Fetch detailed mod information
  - Access user profiles and statistics
  - Get game information and categories
  - Browse mod sections and categories
- 🛠 **Developer-Friendly**
  - Full type hints support with Optional return types
  - Intuitive object-oriented models
  - Robust error handling with graceful failure (returns None)
  - Well-documented API methods
- 🚀 **Easy to Use**
  - Simple and clean API
  - Pythonic interface
  - Extensive examples provided

## Quick Start

```python
from pybanana.api import PyBanana
from pybanana.enums import ModelType

# Initialize the client
api = PyBanana()

# Get a member's profile
member = api.get_member(1382)  # Tom's ID
if member:
    print(f"Name: {member.name}")
    print(f"Online: {member.is_online}")

# Search for mods
results = api.search("sound effects", ModelType.MOD)
if results:
    for mod in results.records:
        print(f"Mod: {mod.name} - {mod.description}")
```

## Installation
```bash
pip install pybanana
```

## Documentation
For detailed documentation and examples, check out:
- [Getting Started](docs/getting_started.md)
- [API Reference](docs/api_reference.md)
- [Models Reference](docs/models.md)

## Requirements

### Runtime Dependencies
- Python 3.7+
- `requests` >= 2.25.0
- `python-dateutil` >= 2.8.2

### Development Dependencies
- `pytest` >= 8.0.0
- `requests-mock` >= 1.11.0

## Error Handling
All public API methods now return `Optional` types and include built-in error handling. When an API call fails, methods return `None` instead of raising exceptions, allowing for more resilient code:

```python
# Safe API usage with None check
mod = api.get_mod_profile(12345)
if mod:
    print(f"Found mod: {mod.name}")
else:
    print("Mod not found or API error occurred")
```

## Contributing
Contributions are welcome! Here's how you can help:
1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request

## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Acknowledgments
- Thanks to GameBanana for providing the API
- All contributors who help improve this library
screen-ocr 0.7.0
Library for processing screen contents using OCR
Requires Python: >=3.11

# Screen OCR

[![PyPI](https://img.shields.io/pypi/v/screen-ocr.svg)](https://pypi.org/project/screen-ocr/)
[![Changelog](https://img.shields.io/github/v/release/wolfmanstout/screen-ocr?include_prereleases&label=changelog)](https://github.com/wolfmanstout/screen-ocr/releases)
[![Tests](https://github.com/wolfmanstout/screen-ocr/actions/workflows/test.yml/badge.svg)](https://github.com/wolfmanstout/screen-ocr/actions/workflows/test.yml)
[![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/wolfmanstout/screen-ocr/blob/master/LICENSE)

The `screen-ocr` package makes it easy to perform OCR on portions of the screen.

## Installation

Choose one of the following backends and follow the steps. WinRT is recommended for Windows and Tesseract for all other platforms.

### WinRT

WinRT is a Windows-only backend that is very fast and reasonably accurate. To install screen-ocr with WinRT support, run `pip install screen-ocr[winrt]`

### Tesseract

Tesseract is a cross-platform backend that is much slower and slightly less accurate than WinRT. To install screen-ocr with Tesseract support:

1. Install Tesseract binaries. For Windows, see
   https://github.com/UB-Mannheim/tesseract/wiki.
2. pip install screen-ocr[tesseract]

### EasyOCR

EasyOCR is a very accurate but slow backend and only runs on Python 64-bit, and hence is considered experimental. To install screen-ocr with WinRT support, run `pip install screen-ocr[easyocr]`

## Usage

You can do a simple test by running `python -m screen_ocr` to OCR the current screen contents. See [`__main__.py`](https://github.com/wolfmanstout/screen-ocr/blob/master/screen_ocr/__main__.py) for the code.

If using Tesseract with a custom installation directory on Windows, set
`tesseract_data_path` and `tesseract_command` paths appropriately when
constructing a `Reader` instance.

See also [gaze-ocr](https://github.com/wolfmanstout/gaze-ocr/blob/master/gaze_ocr/_gaze_ocr.py) for more a more involved usage example.
cisco-network-simulation-by-karpagam 1.0.6
🌐 Professional Cisco Network Analysis & Simulation Toolkit by Karpagam
Requires Python: >=3.8
Keywords: cisco, network, simulation, analysis, karpagam

# 🌐 Cisco Network Simulation by Karpagam

[![PyPI version](https://badge.fury.io/py/cisco-network-simulation-by-karpagam.svg)](https://badge.fury.io/py/cisco-network-simulation-by-karpagam)
[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)

**Professional Cisco Network Analysis & Simulation Toolkit**  
*Created by Karpagam for Educational and Professional Use*

## ✨ Features

- **📝 Configuration Parser** - Parse Cisco router/switch .conf and .dump files
- **🗺️ Topology Builder** - Generate network topology diagrams with custom icons  
- **✅ Network Validator** - Find configuration issues and conflicts
- **📊 Load Analyzer** - Analyze traffic patterns and link utilization
- **🎮 Interactive Simulator** - Real-time network protocol simulation with GUI

## 🚀 Installation
pyclassifiers 0.0.23
classifiers from pypi
Requires Python: >=3.12
Keywords: classifiers, distutils, pypi, python, setuptools

# *pyclassifiers* project by Mark Veltzer

description: classifiers from pypi

project website: https://veltzer.github.io/pyclassifiers

author: Mark Veltzer

version: 0.0.23

![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)

## github

![License](https://img.shields.io/github/license/veltzer/pyclassifiers)

## build

![build](https://github.com/veltzer/pyclassifiers/workflows/build/badge.svg)

## pypi

![PyPI - Status](https://img.shields.io/pypi/status/pyclassifiers)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pyclassifiers)
![PyPI - License](https://img.shields.io/pypi/l/pyclassifiers)
![PyPI - Package Name](https://img.shields.io/pypi/v/pyclassifiers)
![PyPI - Format](https://img.shields.io/pypi/format/pyclassifiers)

## pypi download

![PyPI - Downloads](https://img.shields.io/pypi/dd/pyclassifiers)
![PyPI - Downloads](https://img.shields.io/pypi/dw/pyclassifiers)
![PyPI - Downloads](https://img.shields.io/pypi/dm/pyclassifiers)



## contact me
[mailto](mailto:mark.veltzer@gmail.com)
![gitter](https://img.shields.io/gitter/room/veltzer/mark.veltzer)
![discord](https://img.shields.io/discord/719336281624281119)
![discord](https://img.shields.io/discord/719336282194444302)

Mark Veltzer, Copyright © 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025
pyapikey 0.0.13
access api keys from code
Requires Python: >=3.12
Keywords: api, key, python, secret

# *pyapikey* project by Mark Veltzer

description: access api keys from code

project website: https://veltzer.github.io/pyapikey

author: Mark Veltzer

version: 0.0.13

![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)

## github

![License](https://img.shields.io/github/license/veltzer/pyapikey)

## build

![build](https://github.com/veltzer/pyapikey/workflows/build/badge.svg)

## pypi

![PyPI - Status](https://img.shields.io/pypi/status/pyapikey)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pyapikey)
![PyPI - License](https://img.shields.io/pypi/l/pyapikey)
![PyPI - Package Name](https://img.shields.io/pypi/v/pyapikey)
![PyPI - Format](https://img.shields.io/pypi/format/pyapikey)

## pypi download

![PyPI - Downloads](https://img.shields.io/pypi/dd/pyapikey)
![PyPI - Downloads](https://img.shields.io/pypi/dw/pyapikey)
![PyPI - Downloads](https://img.shields.io/pypi/dm/pyapikey)



## contact me
[mailto](mailto:mark.veltzer@gmail.com)
![gitter](https://img.shields.io/gitter/room/veltzer/mark.veltzer)
![discord](https://img.shields.io/discord/719336281624281119)
![discord](https://img.shields.io/discord/719336282194444302)

Mark Veltzer, Copyright © 2020, 2021, 2022, 2023, 2024, 2025