semvec/pypi-packages huggingface dataset:
TRITON-SWMM-toolkit 0.1.0
Tools for running and processing TRITON-SWMM models.
License: MIT
Requires Python: >=3.10

# TRITON-SWMM Toolkit

![PyPI version](https://img.shields.io/pypi/v/TRITON-SWMM_toolkit.svg)
[![Documentation Status](https://readthedocs.org/projects/TRITON-SWMM_toolkit/badge/?version=latest)](https://TRITON-SWMM_toolkit.readthedocs.io/en/latest/?version=latest)

Tools for running and processing TRITON-SWMM models.

* PyPI package: https://pypi.org/project/TRITON-SWMM_toolkit/
* Free software: MIT License
* Documentation: https://TRITON-SWMM_toolkit.readthedocs.io.

## Features

* TODO

## Credits

This package was created with [Cookiecutter](https://github.com/audreyfeldroy/cookiecutter) and the [audreyfeldroy/cookiecutter-pypackage](https://github.com/audreyfeldroy/cookiecutter-pypackage) project template.
deccan-toon 0.2.0
Battling JSON bloat with Pythonic elegance. A high-efficiency TOON serializer.
License: MIT
Requires Python: <4.0,>=3.10
Keywords: toon, json, llm, ai, serialization, agentic-web, token-optimization

<div align="left">
  <h1>Deccan Toon</h1>

  <a href="https://pypi.org/project/deccan-toon/">
    <img src="https://img.shields.io/pypi/v/deccan-toon?color=orange&label=pypi%20package" alt="PyPI version">
  </a>
  <img src="https://img.shields.io/badge/python-3.10%2B-blue" alt="Python 3.10+">
  <img src="https://img.shields.io/badge/spec-TOON%20v3.0-blue" alt="Spec v3.0">
  <img src="https://img.shields.io/github/license/DeccanTensor/toon-py" alt="License">
</div>

<br>

> **"First, we shape our tools, thereafter they shape us."**

**Deccan Toon** is a strict, high-velocity implementation of the [TOON v3.0 Specification](https://github.com/toon-format/toon).

While other parsers focus on flexibility, **Deccan Toon** focuses on **correctness, security, and type fidelity**. We built this to be the "Newtonsoft" of the TOON ecosystem—robust enough for enterprise agents and critical infrastructure.

---

### 📉 Token Economics (The "Why")

Standard JSON repeats keys for every single row (`id`, `name`, `status`), wasting precious context window space in LLMs (GPT-4, Claude). **TOON** sends the header once.

**Payload Size Comparison (1000 items):**
```text
████████████████████ 100% - JSON (Standard)
████████████         60%  - CSV (No types)
███████████          55%  - TOON (Typed & Clean)
```

Stop paying for repeated keys. Save context window space for actual intelligence.

---

### 📦 Installation

```bash
pip install deccan-toon
```

---
### ⚡ Quick Start
```python
import deccan_toon

# 1. Define your data (List of Dicts)
data = [
    {"id": 1, "agent": "Alpha", "status": "Active", "score": 0.98},
    {"id": 2, "agent": "Beta",  "status": "Idle",   "score": None},
    {"id": 3, "agent": "Gamma", "status": "Sleep",  "score": 0.45}
]

# 2. Serialize (Python -> TOON)
payload = deccan_toon.dumps(data)

print(payload)
# Output:
# items[3]{id,agent,status,score}:
#   1, Alpha, Active, 0.98
#   2, Beta, Idle, null
#   3, Gamma, Sleep, 0.45

# 3. Deserialize (TOON -> Python)
restored = deccan_toon.loads(payload)
```

---

### 🛡️ The Deccan Difference

- **Strict Compliance**: Fully compliant with TOON v3.0 (Handling nesting, typed arrays, and sparse data).

- **Security First**: Includes protections against "Billion Laughs" expansion attacks and deep recursion.

- **Type Safety**: Unlike CSV, we preserve int, float, bool, and null types with 100% fidelity.

- **Zero Dependencies**: Runs on pure Python standard library.
ocrbridge-easyocr 3.1.1
EasyOCR engine for OCR Bridge
License: MIT
Requires Python: >=3.10

# OCR Bridge - EasyOCR Engine

EasyOCR engine implementation for OCR Bridge.

## Overview

This package provides an EasyOCR engine that integrates with the OCR Bridge architecture. EasyOCR is a deep learning-based OCR engine with excellent support for Asian scripts and automatic GPU acceleration.

## Features

- **80+ Languages**: Excellent support for Asian scripts (Chinese, Japanese, Korean, Thai, etc.)
- **GPU Acceleration**: Automatic GPU detection and usage with graceful CPU fallback
- **Multiple Formats**: JPEG, PNG, TIFF, PDF
- **Deep Learning**: Advanced neural network models for high accuracy
- **HOCR Output**: Structured XML with bounding boxes

## Installation

```bash
pip install ocrbridge-easyocr
```

Note: This will install PyTorch and EasyOCR dependencies (~2GB).

For GPU support, install CUDA-compatible PyTorch first:
```bash
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118
pip install ocrbridge-easyocr
```

## Usage

The engine is automatically discovered by OCR Bridge via entry points.

### Parameters

- `languages` (list[str]): Language codes, e.g., ["en"], ["ch_sim", "en"] (default: ["en"])
- `text_threshold` (float): Confidence threshold for text detection 0.0-1.0 (default: 0.7)
- `link_threshold` (float): Threshold for linking text regions 0.0-1.0 (default: 0.7)

### Example

```python
from pathlib import Path
from ocrbridge.engines.easyocr import EasyOCREngine, EasyOCRParams

engine = EasyOCREngine()

# Process with defaults (English)
hocr = engine.process(Path("document.pdf"))

# Process with custom parameters
params = EasyOCRParams(
    languages=["ch_sim", "en"],
    text_threshold=0.7,
    link_threshold=0.7
)
hocr = engine.process(Path("chinese_document.pdf"), params)
```

## GPU Support

The engine automatically detects and uses GPU if available. No configuration needed!

## Version

0.1.0