README.md

metadata

title: TensorView - NetCDF/HDF/GRIB Viewer
emoji: 🌍
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: mit

🌍 TensorView - Interactive Geospatial Data Viewer

A powerful browser-based viewer for NetCDF, HDF, GRIB, and Zarr datasets with advanced visualization capabilities.

🚀 Features

• 📊 Multi-dimensional data exploration - Handle complex scientific datasets with automatic slicing
• 🗺️ Geographic mapping - Built-in map projections with coastlines and gridlines
• 🎨 Smart color scaling - Automatic percentile-based color limits for optimal visualization
• 🔄 Multiple data formats - NetCDF, HDF5, GRIB, Zarr support
• 🎛️ Interactive controls - Dynamic sliders for dimension exploration
• 📤 Dual input modes - File upload or direct file path input
• 🌐 Remote data support - Load from URLs, OPeNDAP, THREDDS servers

🎯 Quick Start

1. Upload a file or enter a file path
2. Select a variable from the dropdown
3. Choose plot type: 2D Image or Map (for geographic data)
4. Adjust dimension sliders to explore different time steps, pressure levels, etc.
5. Create plot and explore your data!

📊 Supported Data Sources

• NetCDF files (.nc, .netcdf) - Climate and weather data
• HDF5 files (.h5, .hdf) - Scientific datasets
• GRIB files (.grib, .grb) - Meteorological data
• Zarr stores - Cloud-optimized arrays
• Remote URLs - HTTP/HTTPS links to data files
• OPeNDAP/THREDDS - Direct server access

🌟 Example Use Cases

• Climate Data: ERA5 reanalysis, CMIP model outputs
• Weather Data: GFS/ECMWF forecasts, radar data
• Air Quality: CAMS atmospheric composition data
• Oceanography: Sea surface temperature, currents
• Satellite Data: Remote sensing products

🔧 Technical Details

Built with:

• xarray + Dask - Efficient handling of large datasets
• matplotlib + Cartopy - High-quality plotting and maps
• Gradio - Interactive web interface
• Multi-engine support - h5netcdf, netcdf4, cfgrib, zarr

Smart Features

• Automatic color scaling using 2nd-98th percentiles
• Dimension detection with dynamic slider generation
• Geographic coordinate recognition for map plotting
• Memory-efficient lazy loading with Dask

💡 Tips

• For 5D data (like CAMS forecasts): Use sliders to select time, pressure level, etc.
• For geographic data: Choose "Map" plot type for proper projections
• Large files: The app handles big datasets efficiently with lazy loading
• Color issues: The app automatically optimizes color scaling to avoid uniform plots

🏗️ Architecture

tensorview/
├── io.py          # Data loading (NetCDF, HDF, GRIB, Zarr)
├── plot.py        # Visualization (1D, 2D, maps)
├── grid.py        # Data operations and alignment  
├── colors.py      # Colormap handling
├── utils.py       # Coordinate inference
└── ...

📝 Example Datasets

The app works great with:

• NASA Goddard Earth Sciences Data
• ECMWF ERA5 reanalysis
• NOAA climate datasets
• Copernicus atmosphere monitoring (CAMS)
• CMIP climate model outputs

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🔗 Links: GitHub Repository | Documentation