📊 NPZ/NPY Web Viewer 🌐

Welcome to the NPZ/NPY Web Viewer, a modern, feature-rich tool designed for interactive visualization and exploration of .npz and .npy files.

🌟 Features

🔍 File Support

• Upload .npz or .npy files to visualize multidimensional arrays directly in your browser.
• Handles multiple arrays in a single .npz file seamlessly.

📈 Multiple Visualization Options

• View arrays with multiple chart types:
• 🟠 3D Scatter Plot
• 🟢 3D Surface Plot
• 🔵 Scatter Plot
• 🔗 Line Chart
• ⚪ Grayscale Image
• Switch between chart types interactively for deeper insights.

🧠 Machine Learning Integration

• Apply machine learning algorithms directly to your data:
• Clustering:
  • K-means clustering with customizable number of clusters
  • DBSCAN clustering with adjustable epsilon and minimum samples
• Dimensionality Reduction:
  • Principal Component Analysis (PCA) with selectable components
• Interactive visualizations of ML results with detailed metrics
• Normalize data option for better algorithm performance

💾 Save & Download

• Download CSV: Convert arrays into .csv files for easy sharing and analysis.
• Save 3D Plots: Export interactive 3D plots for use in presentations or reports.

🎛 Interactive Interface

• Intuitive dropdowns for selecting chart types.
• Dynamic resizing and rendering for smooth user experience.

⚡ Fast and Efficient

• Powered by Next.js with React for a seamless UI.
• All processing happens client-side — no backend required.

🛠️ Installation and Setup

Prerequisites

• Node.js >= 18.x
• Docker (optional)

Setup

1. Clone the repository:

bash git clone https://github.com/<your-username>/npz-web-viewer.git cd npz-web-viewer/npz_viewer_client

1. Install dependencies:

bash npm install

1. Start the development server:

bash npm run dev

1. Access the app in your browser: http://localhost:3000

🎨 Screenshots

📊 Interactive Graphs

🧠 Machine Learning Features

💾 Download Options

🚀 Docker

Using Docker Compose

To run both the backend and the frontend services together:

1. Navigate to the Project Root:

bash cd /path/to/your/project

1. Run the Services: Use the following command to start the backend and frontend:

bash docker-compose up

This will:

• Build the frontend Docker image.
• Start the service and expose it on:
  • Frontend: http://localhost:3000

Running in Detached Mode

If you want the services to run in the background:

docker-compose up -d

📦 Features in Action

🌟 Upload and Explore Files

• View any .npz or .npy file effortlessly.

🔧 Transform and Analyze

• Switch between visualization modes:
• 3D Scatter, 3D Surface, Line Chart, and more!

🧠 Apply Machine Learning

• Cluster your data with K-means or DBSCAN
• Reduce dimensions with PCA
• Visualize results with interactive plots
• Adjust algorithm parameters in real-time

💾 Save and Share

• Export data as .csv or save interactive plots for offline use.

🧑‍💻 Contributing

We welcome contributions! Feel free to fork the repo and submit pull requests to improve this project.

📜 License

This project is licensed under the BSD 3-Clause. See the LICENSE file for details.

🌐 Check it out live here