semvec/pypi-packages huggingface dataset:
py-setr 0.1.1
Synergistic Emission Tomographic Reconstruction toolkit for multimodal PET/SPECT/CT imaging
License: MIT
Requires Python: >=3.8
Keywords: PET, SPECT, nuclear-medicine, tomographic-reconstruction, multimodal-imaging, sirf, cil

# SETR
## Synergistic Emission Tomographic Reconstruction

Tools for nuclear medicine image reconstruction combining PET/SPECT with CT anatomical information.

## Contents

- **Vectorial Total Variation** regularization
- **GPU-accelerated gradients and Jacobians**
- **Python wrapper for STIR Kernel EM**
- **Numba-optimized kernel operators**
- **Extended CIL functionality:**
  - Preconditioners
  - Callbacks  
  - Step size rules
- **SIRF data loading utilities**

## Installation

```bash
# Install dependencies via conda / pip or from source
- SIRF
- CIL

# Install SETR
pip install py-setr
```

## Note
In order to use the partitioner with SPECT, you will need to build stir with
https://github.com/samdporter/STIR/tree/SPECT_subsets
gcrypto 0.1.0
A small symetric crypto package
License: MIT
Requires Python: >=3.8

# gcrypto Package

This is a simple symetric crypto wrapper.

## Features

- symetric crypto wrapper

---

## Requirements

- Python 3.8+
- `pycryptodome` package

Install dependencies using:

pip install -r requirements.txt

---

## Installation

Clone the repository.

```bash
git clone git@github.com:gilweis/gcrypto.git
cd gcrypto
```

---

## Build and Install

To build source and wheel distributions:

python -m build

If you don't have the build tool installed:

pip install build

To install the package locally:

pip install .

To upload to PyPI (optional):

pip install twine
twine upload dist/*

---

## License

MIT License

## Version

0.1.0
aetherlab 0.3.1
Official Python SDK for AetherLab's AI Guardrails and Compliance Platform
License: MIT
Requires Python: >=3.8
Keywords: aetherlab, ai, guardrails, compliance, safety, llm, security, content-moderation, prompt-injection, ai-safety, chatbot-safety, ml-security, artificial-intelligence, machine-learning, nlp, openai, anthropic, gpt, claude, gemini, mistral, llama, ai-governance, responsible-ai, prompt-engineering, ai-monitoring

# AetherLab Python SDK

Official Python SDK for the AetherLab AI Control Layer.

## Installation

```bash
pip install aetherlab
```

## Quick Start

```python
from aetherlab import AetherLabClient

# Initialize the client
client = AetherLabClient(api_key="your-api-key")

# Validate content (recommended new API)
result = client.validate_content(
    content="Your AI-generated content here",
    content_type="customer_support",
    desired_attributes=["helpful", "professional"],
    prohibited_attributes=["rude", "misleading"]
)

# Check compliance metrics
print(f"Compliant: {result.is_compliant}")
print(f"Probability of non-compliance: {result.avg_threat_level:.1%}")
print(f"Confidence in compliance: {result.confidence_score:.1%}")

if result.is_compliant:
    print(f"✅ Content is safe: {result.content}")
else:
    print(f"❌ Issues found: {result.violations}")
    print(f"💡 Suggestion: {result.suggested_revision}")

# Legacy API (still supported)
result = client.test_prompt(
    user_prompt="Hello, how can I help?",
    blacklisted_keywords=["harmful", "dangerous"]
)
print(f"Compliant: {result.is_compliant}")
```

## Features

- ✅ Content validation with context-aware analysis
- ✅ Multi-language support
- ✅ Real-time compliance checking
- ✅ Suggested revisions for non-compliant content
- ✅ Legacy API compatibility
- ✅ Media analysis capabilities
- ✅ Audit logging

## Documentation

For full documentation, visit [docs.aetherlab.ai](https://docs.aetherlab.ai)

## Examples

See the [examples directory](../../examples/python/) for complete examples.

## Support

- GitHub Issues: [github.com/AetherLabCo/aetherlab-community/issues](https://github.com/AetherLabCo/aetherlab-community/issues)
- Email: support@aetherlab.ai

## License

MIT