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Description
Task 5: Visualization Infrastructure
Problem
No reusable visualization components across notebooks.
Implementation Plan
Create comprehensive visualization infrastructure with high-performance, interactive components:
Core Visualization Components:
1. notebooks/shared/visualization/resonance_plotter.py
class ResonanceFieldPlotter:
"""High-performance 3D resonance field visualization"""
def __init__(self, backend='plotly'):
self.backend = backend
self.cache = {} # Cache expensive computations
self.color_schemes = {
'viridis': px.colors.sequential.Viridis,
'plasma': px.colors.sequential.Plasma,
'thermal': px.colors.sequential.thermal
}
def plot_field_3d(self, n: int, resolution: int = 100, color_scheme: str = 'viridis')
def plot_convergence_path(self, search_history: list)
def plot_learning_curves(self, pattern_data: dict)
def plot_performance_comparison(self, benchmark_results: dict)2. notebooks/shared/widgets/parameter_controllers.py
class RFH3_ParameterWidget:
"""Interactive parameter control widget"""
def create_resonance_controls(self):
"""Sliders for resonance function parameters"""
# Real-time field updates
# Parameter sensitivity analysis
# Preset configurations
def create_algorithm_race_widget(self):
"""Widget for comparing different algorithms"""
# Algorithm selection
# Performance monitoring
# Results visualizationKey Features:
- 3D Interactive Plots: Plotly-based with zoom/rotate/slice controls
- Real-time Updates: Parameter changes trigger live visualization updates
- Performance Optimization: Adaptive sampling and GPU acceleration where possible
- Export Capabilities: High-quality PNG/PDF/SVG export for publications
- Mobile Responsive: Works on tablets and mobile devices
- Color Accessibility: Colorblind-friendly palettes and alternatives
Visualization Types:
- 3D Resonance Field Landscapes: Surface plots showing resonance values across candidate space
- Algorithm Convergence Paths: Animated traces through the search space
- Performance Dashboards: Real-time metrics and comparisons
- Learning Curve Analytics: Pattern recognition improvement over time
- Parameter Sensitivity Heatmaps: Impact of parameter changes on performance
- Benchmark Comparison Charts: Algorithm performance across different problem sizes
Interactive Elements:
- Dynamic Parameter Sliders: Real-time resonance function adjustment
- Algorithm Race Controls: Start/stop/reset racing interface
- Zoom and Navigation: Detailed exploration of resonance peaks
- Annotation Tools: Add notes and highlights to visualizations
- Data Export: Download results in multiple formats
Technical Implementation:
- Backend: Plotly for interactive 3D graphics
- Widgets: ipywidgets for parameter controls
- Performance: Adaptive sampling based on computational budget
- Caching: Intelligent caching of expensive resonance computations
- Memory Management: Efficient handling of large datasets
Acceptance Criteria
- High-performance visualization for large numbers (up to 512-bit)
- Consistent visual style across all notebooks
- Interactive widgets with real-time updates (<100ms response)
- Export capabilities for all visualizations
- Mobile-friendly responsive design
- Colorblind accessibility compliance
- Memory usage under 1GB for typical visualizations
- GPU acceleration where available
Dependencies
- plotly >= 5.0
- ipywidgets >= 8.0
- numpy, scipy for numerical computations
- matplotlib for fallback rendering
- PIL for image processing
Files to Create
notebooks/
└── shared/
├── visualization/
│ ├── __init__.py
│ ├── resonance_plotter.py
│ ├── performance_dashboard.py
│ ├── convergence_analyzer.py
│ └── export_utilities.py
├── widgets/
│ ├── __init__.py
│ ├── parameter_controllers.py
│ ├── algorithm_race_widget.py
│ ├── field_explorer_widget.py
│ └── benchmark_widget.py
├── themes/
│ ├── notebook_style.css
│ ├── color_schemes.py
│ └── accessibility_themes.py
└── utils/
├── performance_profiler.py
├── data_preprocessor.py
└── cache_manager.py
Performance Requirements
- Real-time interaction: <100ms response for parameter changes
- Large number support: Handle 512-bit numbers efficiently
- Memory efficiency: Stay under 1GB for typical use cases
- Scalable resolution: Adaptive detail based on available resources
- Cross-platform compatibility: Works on Windows/Mac/Linux
Implementation Notes
- Use Plotly for primary 3D visualizations with fallback to matplotlib
- Implement progressive rendering for large datasets
- Include WebGL acceleration where supported
- Provide both light and dark themes
- Support multiple export formats (PNG, PDF, SVG, HTML)
- Include comprehensive accessibility features
- Optimize for Jupyter notebook and JupyterLab environments