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Scientific Visualizer - Development Roadmap

Project Vision

Build a production-grade scientific data visualization engine in Rust with:

  • GPU acceleration for 1M+ points @ 60 FPS
  • Cross-platform support (Linux, macOS, Windows, Web)
  • Beautiful, intuitive UI
  • Real-time rendering capabilities
  • Export to images and videos

Current Status

Phase 5 Complete ✅ - Colormap System (2025-10-08)

Phase Overview

Phase Name Status Est. LOC Completion
1 Foundation & GPU Context ✅ Complete 800 2025-10-06
2 Camera & Math ✅ Complete 1120 2025-10-07
3 Basic Data & Rendering ✅ Complete 1036 2025-10-07
4 UI Integration ✅ Complete 875 2025-10-08
5 Colormap System ✅ Complete 430 2025-10-08
6 Data Loading 📋 Planned 500 -
7 Performance Optimization 📋 Planned 700 -
8 Additional Plot Types 📋 Planned 1000 -
9 WASM Support 📋 Planned 600 -
10 Examples & Documentation 📋 Planned 800 -

Total Estimated LOC: ~7,000

✅ Phase 1: Foundation & GPU Context (COMPLETE)

Goal: Set up project infrastructure and verify GPU initialization works.

Completed Tasks

  • Create workspace structure (5 crates)
  • Configure all dependencies
  • Implement RenderContext (GPU initialization)
  • Create basic window example
  • Setup logging with tracing
  • Verify GPU works on target platform
  • Write initial documentation

Deliverables

  • ✅ Workspace with viz-core, viz-plots, viz-app, viz-wasm, examples
  • ✅ RenderContext managing Device, Queue, Surface
  • ✅ basic_window example rendering clear color
  • ✅ README.md, LICENSE, ARCHITECTURE.md, CHANGELOG.md
  • ✅ Verified on Apple M1 Max (Metal)

Metrics

  • Lines of Code: 800
  • Build Time: ~30s initial, <1s incremental
  • Frame Time: <1ms (clear only)
  • Memory: 68MB base

✅ Phase 2: Camera & Math (COMPLETE)

Goal: Implement camera system with view/projection matrices and mouse controls.

Completed Tasks

  • Implement Bounds3D (AABB bounding boxes)
  • Implement Transform utilities
  • Create OrbitalCamera with view/projection matrices
  • Implement mouse input handling (rotate, pan, zoom)
  • Add camera movement example
  • Test camera updates GPU correctly

Deliverables

  • ✅ OrbitalCamera with smooth controls
  • ✅ Math utilities (Bounds3D, Transform)
  • ✅ camera_movement example
  • ✅ 23 tests passing

Success Criteria

  • Camera movement smooth at 60 FPS - Achieved 120 FPS
  • Input latency <1ms
  • Proper view/projection matrix computation
  • Frame bounds auto-calculation

Metrics

  • Lines of Code: 1120
  • Development Time: Completed 2025-10-07
  • Files: camera/orbital.rs (314 LOC), math/bounds.rs (205 LOC), math/transform.rs (198 LOC)

✅ Phase 3: Basic Data & Rendering (COMPLETE)

Goal: Implement data structures and render actual geometry.

Completed Tasks

  • Implement Dataset trait
  • Create PointCloud data structure
  • Create scatter plot shader (WGSL)
  • Implement Scatter3D renderer
  • Test rendering 1K and 10K points
  • Add per-point color support
  • Implement CameraUniforms for GPU
  • Create scatter_3d interactive example

Deliverables

  • ✅ PointCloud with positions, colors, sizes, metadata
  • ✅ Scatter3D GPU-accelerated renderer
  • ✅ scatter.wgsl shader with distance-based fading
  • ✅ scatter_3d example with 2 generators
  • ✅ 35 tests passing (all phases)

Success Criteria

  • Render 10K points at 60 FPS - Exceeded: 60+ FPS @ 10K, 120 FPS @ 1K
  • Point colors work correctly
  • Camera controls work with rendered data
  • Height-based rainbow coloring

Metrics

  • Lines of Code: 1036
  • Development Time: Completed 2025-10-07
  • Files: data/point_cloud.rs (329 LOC), scatter/scatter3d.rs (229 LOC), scatter.wgsl (66 LOC)
  • Performance: 120 FPS @ 1K points, 60+ FPS @ 10K points (Apple M1 Max)

✅ Phase 4: UI Integration (COMPLETE)

Goal: Add beautiful UI with controls and performance metrics.

Completed Tasks

  • Integrate egui with wgpu (UiContext)
  • Create ControlPanel with basic controls
  • Add FPS counter and performance metrics
  • Implement point size slider
  • Add grid toggle (prepared for future)
  • Create background color picker
  • Test UI doesn't impact rendering performance
  • Add dataset selector dropdown
  • Implement H key to toggle UI visibility

Deliverables

  • ✅ egui integrated with render loop
  • ✅ Control panel with sliders, toggles, and color picker
  • ✅ Performance metrics panel with FPS graph
  • ✅ Responsive UI at 60+ FPS
  • ✅ scatter_3d_ui example

Success Criteria

  • UI renders at 60 FPS - Achieved: 60+ FPS @ 10K points
  • Controls update visualization in real-time
  • FPS counter accurate with frame time graph
  • UI doesn't block rendering - event routing properly implemented

Metrics

  • Lines of Code: 875
  • Development Time: Completed 2025-10-08
  • Files: ui/context.rs (130 LOC), ui/metrics.rs (180 LOC), ui/panels.rs (190 LOC)
  • Performance: <1ms UI overhead, 60+ FPS maintained

✅ Phase 5: Colormap System (COMPLETE)

Goal: Implement color mapping for data visualization.

Completed Tasks

  • Create Colormap trait for mapping [0,1] → RGBA
  • Implement Viridis colormap with 256-entry LUT
  • Implement Plasma colormap with 8-entry LUT
  • Implement Inferno colormap with 8-entry LUT
  • Add Turbo colormap with 8-entry LUT
  • Create ColorScale (linear/log scaling)
  • Apply colormaps to points based on metadata
  • Add colormap selector with preview in UI

Deliverables

  • ✅ 4 scientific colormaps (Viridis, Plasma, Inferno, Turbo)
  • ✅ ColorScale with Linear and Log modes
  • ✅ UI dropdown with live colormap preview strip
  • ✅ scatter_3d_ui example with dynamic colormap switching

Metrics

  • Lines of Code: 430
  • Tests: 52 passing (17 colormap/scale tests)
  • Files: color/mod.rs, color/colormap.rs (250 LOC), color/scale.rs (100 LOC)
  • Performance: <1μs color lookup via LUT interpolation

Success Criteria

  • Perceptually uniform gradients (Viridis/Plasma)
  • Colorblind-friendly options (Viridis)
  • Fast color lookup (<1μs)
  • Smooth gradients (linear interpolation)

📋 Phase 6: Data Loading

Goal: Load data from files (CSV, Parquet).

Planned Tasks

  • Implement CSV loader with csv crate
  • Implement Parquet loader with arrow/parquet
  • Add async file loading
  • Create progress indicators
  • Handle errors gracefully
  • Add file drag-and-drop (native)
  • Test with large datasets (1M+ rows)

Deliverables

  • CSV and Parquet loaders
  • Example loading real datasets
  • Sample data files (iris.csv, etc.)
  • Progress indicators for large files

Success Criteria

  • Load 1M row CSV in <2s
  • Load 10M row Parquet in <5s
  • Memory efficient (streaming if needed)
  • Proper error messages

Estimated Effort

  • Lines of Code: ~500
  • Development Time: 1-2 days
  • Files: io/loaders.rs, io/exporters.rs

📋 Phase 7: Performance Optimization

Goal: Achieve 1M+ points @ 60 FPS.

Planned Tasks

  • Implement GPU instancing for scatter plots
  • Add frustum culling
  • Implement LOD (Level of Detail) system
  • Optimize buffer updates (staging buffers)
  • Add compute shader for data processing
  • Profile and optimize hot paths
  • Benchmark against targets
  • Document performance characteristics

Deliverables

  • GPU instancing for massive point counts
  • Frustum culling implementation
  • LOD system for distance-based detail
  • Comprehensive benchmarks

Success Criteria

  • 1M points @ 60 FPS
  • 10M points @ 30 FPS
  • Frame time <10ms
  • GPU memory <2GB for 10M points

Estimated Effort

  • Lines of Code: ~700
  • Development Time: 3-4 days
  • Files: scatter/instancing.rs, scatter/lod.rs, benches/rendering_perf.rs

📋 Phase 8: Additional Plot Types

Goal: Implement line charts, heatmaps, surfaces, volumes.

Planned Tasks

  • Implement 2D line chart
  • Add streaming line chart (real-time data)
  • Implement heatmap with compute shader
  • Create 3D surface plot
  • Add volume rendering (ray casting)
  • Implement network graph visualization
  • Add force-directed layout
  • Create examples for each plot type

Deliverables

  • 6 plot types (scatter, line, heatmap, surface, volume, graph)
  • Examples demonstrating each
  • Performance benchmarks for each

Success Criteria

  • Each plot type performs well
  • Consistent API across plot types
  • Examples are clear and useful
  • Documentation complete

Estimated Effort

  • Lines of Code: ~1000
  • Development Time: 4-5 days
  • Files: line/.rs, heatmap/.rs, surface/.rs, volume/.rs, graph/*.rs

📋 Phase 9: WASM Support

Goal: Make it work in the browser.

Planned Tasks

  • Setup wasm-pack build
  • Create web entry point
  • Handle browser-specific APIs (FileReader, etc.)
  • Add HTML/CSS for demo page
  • Optimize WASM bundle size
  • Test on multiple browsers
  • Deploy demo to GitHub Pages
  • Add web-specific examples

Deliverables

  • viz-wasm crate compiled to WASM
  • Demo website with examples
  • Build script for WASM
  • Documentation for web usage

Success Criteria

  • Works in Chrome, Firefox, Safari
  • Performance 70-80% of native
  • Bundle size <5MB
  • Demo deployed and accessible

Estimated Effort

  • Lines of Code: ~600
  • Development Time: 2-3 days
  • Files: viz-wasm/src/lib.rs, www/index.html, scripts/build_web.sh

📋 Phase 10: Examples & Documentation

Goal: Polish, document, and create comprehensive examples.

Planned Tasks

  • Create 10+ examples covering all features
  • Write comprehensive API documentation
  • Create user guide with screenshots
  • Document performance characteristics
  • Create architecture diagrams
  • Record demo videos
  • Write blog post about the project
  • Create presentation/slides

Deliverables

  • 10+ examples
  • Complete API documentation
  • User guide with visuals
  • Performance guide
  • Architecture diagrams
  • Demo videos

Success Criteria

  • Anyone can create first plot in <10 minutes
  • All features documented
  • Examples run without errors
  • Professional presentation

Estimated Effort

  • Lines of Code: ~800 (examples + docs)
  • Development Time: 3-4 days
  • Files: examples/.rs, docs/.md

Post-Phase 10: Future Enhancements

Potential Future Work

  • Python Bindings (PyO3) - Use from Python
  • REST API Server (Axum) - Visualization as a service
  • Advanced Interactions - Selection, annotation
  • Animation System - Time-series playback
  • Statistical Overlays - Regression, confidence intervals
  • Specialized Plots - Parallel coordinates, Sankey, treemaps
  • Collaboration Features - Share plots, real-time multi-user
  • Mobile Support - Android/iOS via wgpu

Research Integration

  • Integration with your Multi-Modal RAG system
  • JSON API for plot specifications
  • Automatic visualization from natural language

Milestones

Milestone Phases Target Date Status
MVP 1-3 - Phase 1 ✅
Alpha 1-6 - Phase 1 ✅
Beta 1-9 - -
v1.0 1-10 - -

Success Metrics

Performance Targets

  • ✅ GPU initialization works
  • 60 FPS with 100K points
  • 60 FPS with 1M points (Phase 7 goal)
  • 30 FPS with 10M points (Phase 7 goal)
  • <10ms frame time
  • <2GB GPU memory for 10M points

Code Quality Targets

  • >80% test coverage (Phase 10)
  • Zero clippy warnings
  • All public APIs documented
  • <10 unsafe blocks total

User Experience Targets

  • <10 minute time-to-first-plot (Phase 10)
  • Works on 3+ platforms (Linux, macOS, Windows)
  • Works in browser (Phase 9)
  • Beautiful visuals (subjective but important!)

Dependencies & Risks

Critical Dependencies

  • wgpu (WebGPU API) - core dependency
  • winit (window management) - core dependency
  • egui (UI) - important for usability

Risks & Mitigations

  1. GPU compatibility: Test on multiple GPUs early (Phase 1 ✅)
  2. Performance: Profile early, optimize continuously (Phase 7)
  3. WASM limitations: Plan for feature parity (Phase 9)
  4. Scope creep: Stick to roadmap, defer nice-to-haves

Contributing

See CONTRIBUTING.md for how to contribute to this roadmap.

Last Updated: 2025-10-06 Current Phase: Phase 1 Complete ✅, Phase 2 Next 📋