semvec/pypi-packages huggingface dataset:
grater-jax 0.2.3
JAX-powered machine learning and modeling framework for GRaTeR disks.
License: MIT
Requires Python: <3.13,>=3.9

# GRaTeR-JAX

[![Run Tests](https://github.com/UCSB-Exoplanet-Polarimetry-Lab/GRaTeR-JAX/actions/workflows/tests.yml/badge.svg)](https://github.com/UCSB-Exoplanet-Polarimetry-Lab/GRaTeR-JAX/actions/workflows/tests.yml)

**GRaTeR-JAX** is a machine learning JAX-based implementation of the **Generalized Radial Transporter (GRaTeR)** framework [(Augereau+ 1999)](https://arxiv.org/abs/astro-ph/9906429), designed for modeling scattered-light images of debris disks. This repository provides tools for forward modeling, optimization, and parameter estimation of scattered-light disk images using JAX's accelerated computations.

<img src="https://github.com/user-attachments/assets/c10f45e8-5449-4891-b6a7-33954cf6d954" width="300">

## Features

- **JAX-Based Optimization**: Leverages JAX for fast, GPU/TPU-accelerated disk modeling.
- **Scattered Light Disk Modeling**: Implements physical models of exoplanetary debris disks.
- **Differentiable Framework**: Enables gradient-based optimization and probabilistic inference.
- **Integration with Webbpsf**: Supports JWST PSF convolutions for forward modeling.

## Installation

To install GRaTeR-JAX and its dependencies, create a new Conda environment with Python and run:

```sh
pip install grater-jax
```

Make sure you have JAX installed with the correct backend for your hardware:

```sh
pip install --upgrade "jax[cpu]"  # or "jax[cuda]" for GPU
```

Highly recommended to install this on a fresh environment, just to be safe.

## Usage

Refer to the documentation at [grater-jax.readthedocs.io](https://grater-jax.readthedocs.io/en/latest/).

Check out [GRaTeR Image Generator](https://scattered-light-disks.vercel.app) to visualize how each of the parameters affect the disk model!

## Repository Structure

```
GRaTeR-JAX/
│── grater-jax/       # Package root for grater-jax
   │── disk_model/    # Code for disk modeling
   │── optimization/  # Tools for statistical optimization and analysis
|── docs/             # Documenation and tutorial notebooks
│── pyproject.toml    # Installation file
│── README.md
```

## Contributing

We welcome contributions! To contribute:

1. Fork the repository.
2. Create a feature branch:
   ```sh
   git checkout -b feature-branch
   ```
3. Commit your changes and push to your fork.
4. Open a pull request.

## Acknowledgments

Developed by the **UCSB Exoplanet Polarimetry Lab**. This work is inspired by previous implementations of GRaTeR and advances in JAX-based differentiable modeling.

---
rd-mini 0.1.0
Zero-config AI observability. Two lines to get started.
License: MIT
Requires Python: >=3.9
Keywords: ai, anthropic, llm, observability, openai, tracing

# Raindrop Python SDK

Zero-config AI observability. Two lines to get started.

## Installation

```bash
pip install raindrop-ai
```

## Quick Start

```python
from raindrop import Raindrop
from openai import OpenAI

# Initialize once
raindrop = Raindrop(api_key="your-api-key")

# Wrap your client
openai = raindrop.wrap(OpenAI())

# All calls are now automatically traced
response = openai.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello!"}]
)

# Access trace ID from response
print(response._trace_id)
```

## Features

### User Identification

```python
raindrop.identify("user-123", {"name": "John", "plan": "pro"})
```

### Multi-Step Interactions

```python
with raindrop.interaction(user_id="user-123", event="rag_query") as ctx:
    docs = search_docs(query)  # If wrapped with @raindrop.tool
    response = openai.chat.completions.create(...)
    # All steps are automatically linked
```

### Tool Tracing

```python
@raindrop.tool("search_docs")
def search_docs(query: str) -> list[dict]:
    return vector_db.search(query)
```

### Feedback

```python
raindrop.feedback(trace_id, {"score": 0.9, "comment": "Great response!"})
```

## Supported Providers

- OpenAI
- Anthropic

## Documentation

See [docs.raindrop.ai](https://docs.raindrop.ai) for full documentation.