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Pan3D Explorers Tutorial

Introduction

Pan3D provides four specialized visualization tools called Explorers, each designed for specific data exploration tasks. These focused tools offer clean, intuitive interfaces tailored to their purpose, avoiding the complexity of general-purpose visualization software.

Getting Started

Install Pan3D with viewer capabilities:

pip install "pan3d[viewer]"

Each explorer can be launched from the command line with optional data sources:

# Launch with interactive data selection
xr-slicer
xr-globe
xr-contour
xr-analytics

# Launch with specific data
xr-slicer --xarray-file ./data.nc
xr-globe --xarray-url https://example.com/data.zarr
xr-contour --import-state ./config.json

Slice Explorer

Purpose: Navigate through 3D volumes by extracting 2D slices along any axis.

Best for: Atmospheric data, medical imaging, geological surveys, or any volumetric dataset requiring cross-sectional analysis.

Slice Explorer

Key Features:

  • Axis Slicing: Extract 2D slices along X, Y, or Z axes
  • View Modes: 2D orthogonal or 3D perspective visualization
  • Volume Context: Display outline and apply transparency to 3D data
  • Dynamic Updates: Real-time slice rendering as position changes

Example Use Case: Explore temperature layers in atmospheric data by slicing along altitude to understand thermal stratification.

Launch Command: xr-slicer

Globe Explorer

Purpose: Visualize geographic data on a realistic 3D Earth with accurate projection.

Best for: Climate data, oceanographic measurements, satellite observations, or any dataset with latitude/longitude coordinates.

Globe Explorer

Key Features:

  • Texture Options: Satellite imagery, topography, political boundaries
  • Continental Outlines: Overlay continent boundaries on data
  • Terrain Elevation: Apply bump mapping for topographic effects
  • Sphere Projection: Map latitude/longitude data to 3D globe

Example Use Case: Visualize global temperature anomalies with continental context to identify regional climate patterns.

Launch Command: xr-globe

Contour Explorer

Purpose: Create smooth contour visualizations with color-banded regions between isolevels.

Best for: Scalar fields, topographic data, gradient analysis, or any dataset requiring isoline visualization.

Contour Explorer

Key Features:

  • Banded Regions: Generate filled areas between contour levels
  • Contour Lines: Overlay black isolines on banded regions
  • Level Control: Set number and range of contour values
  • Surface Smoothing: Apply loop subdivision for refined contours

Example Use Case: Analyze ocean temperature at specific depths to identify thermoclines and current patterns.

Launch Command: xr-contour

Analytics Explorer

Purpose: Combine 3D visualization with statistical analysis for comprehensive data exploration.

Best for: Time series analysis, spatial statistics, trend detection, or any dataset requiring both visual and quantitative insights.

Analytics Explorer

Key Features:

  • Statistical Plots: Zonal, temporal, and global analysis charts
  • Temporal Grouping: Aggregate data by year, month, day, or hour
  • xCDAT Integration: Leverage climate analysis algorithms
  • Data Synchronization: 3D view and plots update together

Example Use Case: Analyze seasonal temperature patterns by combining 3D visualization with monthly statistical plots.

Launch Command: xr-analytics

Common Features

All explorers share these capabilities:

Data Management

  • Load from files, URLs, or configuration states
  • Support for xarray-compatible formats
  • State export/import for reproducibility

Visualization Controls

Common Controls

  • Time Navigation: Slider, play/pause, step controls for temporal data
  • Color Mapping: Presets, custom ranges, interactive scalar bar
  • Scale Controls: Independent X/Y/Z scaling for different unit scales

Performance Tips

  • Use data stepping/striding for large datasets
  • Crop to regions of interest
  • Start with lower resolution, increase as needed

Choosing the Right Explorer

Explorer Best For
Slice Volumetric data, internal structures, cross-sections
Globe Geographic data, global patterns, Earth visualization
Contour Value ranges, smooth gradients, publication figures
Analytics Statistical analysis, time series, quantitative insights

Integration Example

Use explorers in Jupyter notebooks:

from pan3d.explorers.slicer import SliceExplorer

# Create and display explorer
explorer = SliceExplorer()
await explorer.ui.ready

# Launch the interactive explorer within jypyter notebook
explorer.ui

# Export configuration
config = explorer.export_state()

Additional Resources