semvec/pypi-packages huggingface dataset:
understat 0.1.14
A Python wrapper for https://understat.com/
License: MIT
Keywords: fpl fantasy premier league understat football

<p align="center">
    A Python package for https://understat.com/.
    <br>
    <br>
    <a href="https://travis-ci.com/amosbastian/understat"><img src="https://travis-ci.com/amosbastian/understat.svg?branch=master"></a>
    <a href="https://pypi.org/project/understat/" alt="Version">
        <img src="https://badge.fury.io/py/understat.svg"/></a>
    <a href="https://pypi.org/project/understat/" alt="Python version">
        <img src="https://img.shields.io/badge/Python-3.6%2B-blue.svg"/></a>
    <a href="https://understat.readthedocs.io/en/latest/" alt="Documentation">
        <img src="https://readthedocs.org/projects/understat/badge/?version=latest&style=flat"></a>
</p>

Join the [Discord server](https://discord.gg/cjY37fv) or submit [an issue](https://github.com/amosbastian/understat/issues) for help and / or suggestions!

## Installing understat

The recommended way to install understat is via `pip`.

    pip install understat

To install it directly from GitHub you can do the following:

    git clone git://github.com/amosbastian/understat.git

You can also install a [.tar file](https://github.com/requests/requests/tarball/master)
or [.zip file](https://github.com/requests/requests/tarball/master)

    curl -OL https://github.com/amosbastian/understat/tarball/master
    curl -OL https://github.com/amosbastian/understat/zipball/master # Windows

Once it has been downloaded you can easily install it using `pip`:

    cd understat
    pip install .

## Usage

An example of using `understat` can be found below:

```python
import asyncio
import json

import aiohttp

from understat import Understat


async def main():
    async with aiohttp.ClientSession() as session:
        understat = Understat(session)
        data = await understat.get_league_players("epl", 2018, {"team_title": "Manchester United"})
        print(json.dumps(data))


if __name__ == "__main__":
    loop = asyncio.new_event_loop()
    asyncio.set_event_loop(loop)
    loop.run_until_complete(main())
```


## Contributing

1. Fork the repository on GitHub.
2. Run the tests with `pytest tests/` to confirm they all pass on your system.
   If the tests fail, then try and find out why this is happening. If you aren't
   able to do this yourself, then don't hesitate to either create an issue on
   GitHub, or send an email to [amosbastian@gmail.com](mailto:amosbastian@gmail.com>).
3. Either create your feature and then write tests for it, or do this the other
   way around.
4. Run all tests again with with `pytest tests/` to confirm that everything
   still passes, including your newly added test(s).
5. Create a pull request for the main repository's `master` branch.

## Documentation

Documentation and examples for **understat** can be found at http://understat.readthedocs.io/en/latest/.
netconduit 5.0.0
Production-ready async bidirectional TCP communication library with custom binary protocol, type-safe RPC, and Pydantic integration
License: MIT
Requires Python: >=3.10
Keywords: networking, tcp, async, asyncio, rpc, bidirectional, communication, protocol, binary-protocol, pydantic, msgpack, server, client, socket, real-time

# netconduit

[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

**Production-ready async bidirectional TCP communication library with custom binary protocol, type-safe RPC, and Pydantic integration.**

Developed by **Kaede Dev - Kento Hinode**

## Features

- 🚀 **Async/Await** - Built on asyncio
- 🔌 **Raw TCP** - IPv4 & IPv6 support
- 📦 **Binary Protocol** - 32-byte header + MessagePack
- 🔐 **Password Auth** - SHA256-based authentication
- 📡 **Type-Safe RPC** - Pydantic validation
- 💓 **Heartbeat** - Automatic health monitoring
- 🔄 **Auto-Reconnect** - Exponential backoff

## Installation

```bash
pip install netconduit
```

## Quick Start

### Server

```python
import asyncio
from conduit import Server, ServerDescriptor

server = Server(ServerDescriptor(
    host="0.0.0.0", port=8080, password="secret"
))

@server.rpc
async def add(a: int, b: int) -> int:
    return a + b

@server.on("chat")
async def handle_chat(client, data):
    await server.broadcast("chat", data, exclude={client.id})

asyncio.run(server.run())
```

### Client

```python
import asyncio
from conduit import Client, ClientDescriptor, data

client = Client(ClientDescriptor(
    server_host="localhost", server_port=8080, password="secret"
))

@client.on("chat")
async def on_chat(msg):
    print(f"Chat: {msg}")

async def main():
    await client.connect()
    result = await client.rpc.call("add", args=data(a=10, b=20))
    print(f"Result: {result}")  # {'success': True, 'data': 30}

asyncio.run(main())
```

## Documentation

- [Quick Start](documentation/quickstart.md)
- [Server Guide](documentation/server/README.md)
- [Client Guide](documentation/client/README.md)
- [Examples](documentation/examples.md)
- [Protocol Spec](documentation/protocol/README.md)

## Requirements

- Python 3.10+
- pydantic >= 2.0
- msgpack >= 1.0

## License

MIT License - Kaede Dev - Kento Hinode

**GitHub**: [DarsheeeGamer/NetConduit](https://github.com/DarsheeeGamer/NetConduit)
deeplightrag 1.0.22
DeepLightRAG: High-performance Document Indexing and Retrieval System (use with any LLM)
License: MIT
Requires Python: >=3.9
Keywords: rag, retrieval, augmented, generation, ocr, vision, graph, nlp, llm, deepseek, document-processing

# DeepLightRAG

[![PyPI version](https://badge.fury.io/py/deeplightrag.svg)](https://badge.fury.io/py/deeplightrag)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

High-performance document indexing and retrieval system. Works with any LLM.

## Features

- **Dual-Layer Graph**: Visual-Spatial + Entity-Relationship architecture
- **GLiNER2 NER**: Zero-shot entity extraction with [fastino/gliner2-base-v1](https://github.com/fastino-ai/GLiNER2)
- **DeepSeek-OCR**: Visual token compression for efficient document processing
- **TOON Format**: Token-efficient context formatting via [toon-python](https://pypi.org/project/toon-python/)
- **Any LLM**: Works with OpenAI, Gemini, Claude, Ollama, MLX

## Installation

```bash
pip install deeplightrag
```

**With GPU (CUDA):**
```bash
pip install "deeplightrag[gpu]"
```

**macOS (Apple Silicon):**
```bash
pip install "deeplightrag[macos]"
```

## Quick Start

### Python API

```python
from deeplightrag import DeepLightRAG

# Initialize
rag = DeepLightRAG()

# Index document
rag.index_document("document.pdf", document_id="doc_001")

# Retrieve
result = rag.retrieve("What are the key findings?")
print(result["context"])
```

### CLI

```bash
# Index
deeplightrag index document.pdf

# Query
deeplightrag retrieve "What is the main topic?"
```

## Architecture

```
┌─────────────────────────────────────────────┐
│              DeepLightRAG                   │
├─────────────────────────────────────────────┤
│  DeepSeek-OCR → Visual Token Compression    │
├─────────────────────────────────────────────┤
│  Dual-Layer Graph                           │
│  ├── Layer 1: Visual-Spatial (WHERE)        │
│  └── Layer 2: Entity-Relationship (WHAT)    │
├─────────────────────────────────────────────┤
│  GLiNER2 → Entity Extraction                │
├─────────────────────────────────────────────┤
│  Adaptive Retriever → Context Generation    │
└─────────────────────────────────────────────┘
```

## Configuration

```yaml
# config.yaml
ocr:
  model_name: "deepseek-ai/DeepSeek-OCR"
  resolution: "gundam"

ner:
  model_name: "fastino/gliner2-base-v1"
  confidence_threshold: 0.3

retrieval:
  top_k: 5
```

## Requirements

- Python >= 3.9
- CUDA GPU recommended for DeepSeek-OCR
- Dependencies: `gliner2`, `toon-python`, `networkx`, `torch`

## License

MIT License
artheon-sast 2.0
Static Application Security Testing tool for JavaScript, Python, Java, and C#
License: MIT
Requires Python: >=3.8
Keywords: sast, security, testing, javascript, python, java, csharp, vulnerability, scanner

# ARTHEON-SAST

Static Application Security Testing tool for multiple programming languages

## Supported Languages

- JavaScript (.js)
- Python (.py)
- Java (.java)
- C# (.cs)

## Features

- 40 vulnerability rules (10 per language)
- 400+ regex patterns for detection
- HTML reports with severity levels
- Line-by-line vulnerability detection
- Automatic duplicate prevention
- Responsive design for desktop and mobile

## Installation

### From PyPI
```bash
pip install artheon-sast
```

## Usage

### Command Line
```bash
artheon-sast /path/to/your/project
```

Scans all source files, detects vulnerabilities, generates HTML report, and opens it in browser.

### Python API
```python
from language_analyzer import SecurityScanner

scanner = SecurityScanner("/path/to/project")
scanner.scan()
report_path = scanner.generate_html_report()
```

## Vulnerability Categories by Language

### JavaScript (10 rules)

| Category | Severity |
|----------|----------|
| eval() Usage | CRITICAL |
| Hardcoded Secrets | CRITICAL |
| SQL Injection | CRITICAL |
| Command Injection | CRITICAL |
| XSS Vulnerabilities | HIGH |
| Insecure Crypto | HIGH |
| Path Traversal | HIGH |
| Prototype Pollution | HIGH |
| No Input Validation | MEDIUM |
| Insecure CORS | MEDIUM |

### Python (10 rules)

| Category | Severity |
|----------|----------|
| eval() and exec() Usage | CRITICAL |
| Hardcoded Secrets | CRITICAL |
| SQL Injection | CRITICAL |
| Command Injection | CRITICAL |
| Insecure Deserialization | CRITICAL |
| Insecure Crypto | HIGH |
| Path Traversal | HIGH |
| No Input Validation | MEDIUM |
| Insecure Dependencies | MEDIUM |
| XXE Injection | HIGH |

### Java (10 rules)

| Category | Severity |
|----------|----------|
| SQL Injection | CRITICAL |
| Command Injection | CRITICAL |
| XXE Injection | CRITICAL |
| Hardcoded Secrets | CRITICAL |
| Insecure Deserialization | CRITICAL |
| Insecure Crypto | HIGH |
| Path Traversal | HIGH |
| XSS Vulnerabilities | HIGH |
| Insecure HTTP Headers | MEDIUM |
| Weak Authentication | HIGH |

### C# (10 rules)

| Category | Severity |
|----------|----------|
| SQL Injection | CRITICAL |
| Command Injection | CRITICAL |
| Hardcoded Secrets | CRITICAL |
| Deserialization Vulnerability | CRITICAL |
| XXE Injection | HIGH |
| Insecure Crypto | HIGH |
| Path Traversal | HIGH |
| Weak Authentication | HIGH |
| Insecure HTTP Headers | MEDIUM |
| LINQ Injection | HIGH |

## Report Format

HTML report includes:
- Summary metrics (total, critical, high, medium)
- Vulnerabilities grouped by file and language
- Code context for each finding
- Severity color coding
- Recommendations per vulnerability

## Testing

```bash
pytest tests/ -v
```

Status: 6/6 tests passing

## Requirements

- Python 3.8+
- No external dependencies

## Authors

Dorian Tituaña, Ismael Toala

## License

MIT