semvec/pypi-packages huggingface dataset:
fastview 0.1.2
A lightweight FastAPI visualization tool
License: MIT
Requires Python: >=3.8

# FastView ⚡ (Ocean Depths Edition)

**FastView** provides an instant, auto-generated web UI for FastAPI applications. Now featuring the stunning **"Ocean Depths"** aesthetic.

![License](https://img.shields.io/badge/license-MIT-blue.svg)
![Python](https://img.shields.io/badge/python-3.8%2B-blue)

## 🌊 Ocean Depths Theme

The UI has been completely redesigned with a deep maritime palette:
- **Deep Navy Background** (`#1a2332`)
- **Teal & Seafoam Accents** (`#2d8b8b`, `#a8dadc`)
- **Glassmorphism Effects** for cards and panels.
- **Ambient Status Feedback**: background flashes **Green** (Success) or **Red** (Error).

## ✨ Key Features

- **🎠 Horizontal Carousel Navigation**: Smoothly scroll through API methods with snap-to-grid effects and square navigation buttons.
- **🛠️ Smart Inputs**:
    - **Boolean**: Intuitively switch between `TRUE` and `FALSE` with custom toggle buttons.
    - **Files**: Stylish file selectors with support for **Multiple File Uploads**.
    - **JSON**: Auto-formatted JSON editors for complex bodies.
- **📸 Rich Response Rendering**:
    - Auto-detects and displays **Images**, **Audio**, and **Video**.
    - Renders embedded **Base64 images** within JSON responses.
    - Syntax highlighting for JSON output.

## 🚀 Quick Start

1. **Install** (or clone):
   ```bash
   pip install fastview
   ```

2. **Integrate**:
   ```python
   from fastapi import FastAPI
   from fastview import FastView

   app = FastAPI()
   FastView(app) # Auto-mounts at /fastview
   ```

3. **Run**:
   ```bash
   uvicorn main:app --reload
   ```

4. **Explore**:
   Open `http://127.0.0.1:8000/fastview` to see the Ocean Depths UI.

## 🧪 Testing with Mock API

We include a robust `mock_api.py` to test all input types (Files, Forms, JSON, etc.):

```bash
python mock_api.py
```
*Runs on port 8001 by default.*

## Architecture

- **Backend**: Python (FastAPI integration)
- **Frontend**: Vanilla JS, HTML5, CSS3. **No build steps**. All assets are served statically by FastView.

---
### Built by Mehtab Singh
feedback-grape 1.0.0
A package for GRAPE with feedback
License: MIT
Requires Python: >=3.11

# feedback-grape

**feedback-grape** is a high-performance Python package for simulating and optimizing quantum systems with feedback. It builds on the GRAPE (Gradient Ascent Pulse Engineering) method and introduces a new approach that integrates feedback in a natural, differentiable, and efficient way.

---

## 🚀 Features

- ✅ Vectorized, GPU-enabled, and differentiable simulations using JAX  
- ✅ Efficient quantum optimal control via GRAPE  
- ✅ Feedback support with the newly developed feedbackGRAPE technique  
- ✅ Think of it as a parallelized, feedback-enabled, high-performance alternative to QuTiP

---

## 📦 Installation

### For Users

Install the latest version via pip:
```
pip install feedback-grape
```
To enable GPU acceleration (CUDA 12):
```
pip install feedback-grape[cuda12]
```
### For Developers

Install development dependencies:
```
pip install -U -r requirements.txt
```
To include tools for testing, linting, and formatting:
```
pip install -U -r requirements_dev.txt
```
To run notebooks with proper rendering:
```
conda install conda-forge::pandoc
```
To use GPU support with JAX:
```
pip install -U "jax[cuda12]==0.5.2"
```
Or alternatively:
```
pip install -U -r requirements_gpu.txt
```
---

## 📚 Documentation

- User Guide: https://feedbackgrape.readthedocs.io
- Developer Docs:
```
cd docs  
make html
``` 
# Open docs/build/html/index.html using a Live Server or your browser

---

## ✅ Testing & Code Quality

### Run Tests

```pytest```

Generates test results and a coverage report.

```tox```

This tests the code across multiple python environments and ensures consistency.

### Type Checking

mypy feedback_grape

### Linting & Formatting

- Lint code:
```
ruff check
```
- Auto-format:
```
ruff format
```
---

## 📖 References

The feedbackGRAPE method was introduced in:

- Porotti, Peano, Marquardt, PRX Quantum 4, 030305 (2023)  
  [https://journals.aps.org/prxquantum/abstract/10.1103/PRXQuantum.4.030305](https://journals.aps.org/prxquantum/abstract/10.1103/PRXQuantum.4.030305)

It extends traditional GRAPE by incorporating feedback control, which is crucial for applications like quantum state stabilization and quantum error correction. It is designed to work seamlessly with neural networks and automatic differentiation frameworks.

A full application to quantum error correction with the GKP code is shown in:

- Puviani et al., Phys. Rev. Lett. 134, 020601 (2025)  
  [https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.134.020601](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.134.020601)

---

## 🧠 Related Repositories

- https://github.com/rporotti/feedback_grape — Repository for feedbackGRAPE paper  
- https://github.com/Matteo-Puviani/GQF — Repository for GKP quantum error correction implementation