graphizy 0.1.20

Graphizy is a fast, flexible Python library for building and analyzing computational geometry-based graphs from 2D coordinates.

Graphizy

Graphizy is a powerful, fast, and flexible Python library for building and analyzing graphs from 2D spatial data. Designed for computational geometry and network visualization, it supports multiple graph types, real-time analysis, memory-enhanced temporal graphs, and comprehensive weight computation systems — all powered by OpenCV and igraph with a modern, unified API.

Convert spatial coordinates to analyzed graphs in milliseconds. Real-time graph analytics accessible through comprehensive igraph integration with enhanced memory and weight systems.

Documentation

📖 Full Documentation

✨ Key Features

• One API for All Graphs Create Delaunay, k-NN, MST, Gabriel, Proximity, and even custom graphs with a single make_graph() call. Plugin-friendly, smart defaults, and fully type-safe.

• Temporal Memory System Track how connections evolve over time. Use built-in memory features for persistence-aware analysis, temporal filtering, and age-based visualization.

• Rich Graph Types, Easily Extended From spatial graphs to domain-specific topologies: support includes Delaunay triangulations, proximity graphs, k-nearest neighbors, MSTs, and custom plugins.

• Instant Network Analysis Access over 200 igraph algorithms with real-time stats: clustering, centrality, components, and more. All robust to disconnections. NetworkX compatible.

• Custom Weights, Real-Time Ready Define weights using distance, inverse, Gaussian, or custom formulas. Memory-aware weight updates and vectorized for performance.

• Advanced Tools for Spatial & Temporal Insights Includes percolation thresholds, service accessibility, social dynamics, and time-aware community tracking — all tailored for dynamic networks.

• Visualization & Streaming Visualize network memory with age-based coloring and transparency. Stream updates in real time, or export static snapshots. Comes with CLI tools and interactive demos.

🔄 Unified Graph Creation Interface

• Modern API: Single make_graph() method for all graph types
• Plugin System: Easily add custom graph algorithms
• Smart Defaults: Intelligent parameter handling with memory and weight integration
• Type Safety: Runtime configuration validation with detailed error messages

📊 Comprehensive Graph Types

• Delaunay Triangulation: Optimal triangular meshes from point sets
• Proximity Graphs: Connect nearby points based on distance thresholds
• K-Nearest Neighbors: Connect each point to its k closest neighbors
• Minimum Spanning Tree: Minimal connected graph with shortest total edge length
• Gabriel Graph: Geometric proximity graph (subset of Delaunay triangulation)
• Custom Graphs: Extensible plugin system for domain-specific algorithms

🧠 Advanced Memory Systems

• Temporal Analysis: Track connections across time steps for dynamic systems
• Smart Integration: Automatic memory updates with configurable retention policies
• Age-Based Visualization: Visual feedback showing connection persistence over time
• Performance Optimized: Vectorized operations for real-time applications

⚖️ Sophisticated Weight Computation

• Multiple Methods: Distance, inverse distance, Gaussian, and custom formulas
• Real-Time Computation: Optimized fast computers for high-performance applications
• Edge Attributes: Compute any edge attribute using mathematical expressions
• Memory Integration: Weight computation on memory-enhanced graph structures

📈 Comprehensive Graph Analysis

• igraph Integration: Full access to 200+ graph analysis algorithms
• Resilient Methods: Robust analysis that handles disconnected graphs gracefully
• Real-Time Statistics: Vertex count, edge count, connectivity, clustering, centrality
• Component Analysis: Detailed connectivity and community structure analysis

🎨 Advanced Visualization & Real-Time Processing

• Memory Visualization: Age-based coloring and transparency effects
• Real-Time Streaming: High-performance streaming with async support
• Flexible Configuration: Runtime-configurable parameters using type-safe dataclasses
• Interactive Demos: Built-in demonstrations and CLI tools

🚀 Installation

pip install graphizy

For development:

git clone https://github.com/cfosseprez/graphizy.git
cd graphizy
pip install -e .

⚡ Quick Start

Modern Unified Interface

from graphizy import Graphing, GraphizyConfig, generate_and_format_positions

# Generate sample data
data = generate_and_format_positions(size_x=800, size_y=600, num_particles=100)

# Configure and create grapher
config = GraphizyConfig(dimension=(800, 600))
grapher = Graphing(config=config)

# Create different graph types using unified interface
delaunay_graph = grapher.make_graph("delaunay", data)
proximity_graph = grapher.make_graph("proximity", data, proximity_thresh=50.0)
knn_graph = grapher.make_graph("knn", data, k=4)
mst_graph = grapher.make_graph("mst", data)
gabriel_graph = grapher.make_graph("gabriel", data)

# Visualize results
image = grapher.draw_graph(delaunay_graph)
grapher.show_graph(image, "Delaunay Graph")
grapher.save_graph(image, "delaunay.jpg")

Advanced Analysis with Modern API

# Comprehensive graph analysis
results = grapher.get_graph_info(delaunay_graph) # This call is instantaneous

# Print a clean, pre-formatted summary
print(results.summary())

# Access specific metrics as properties (computed on first access)
print(f"Density: {results.density:.3f}")
print(f"Diameter: {results.diameter}")

# Use helper methods for deeper analysis
top_hubs = results.get_top_n_by('degree', n=3)
print(f"Top 3 hubs (by degree): {top_hubs}")

betweenness_stats = results.get_metric_stats('betweenness')
print(f"Betweenness Centrality Stats: {betweenness_stats}")

🧠 Memory-Enhanced Temporal Graphs

Track connections over time for dynamic system analysis:

import numpy as np

# Initialize memory system
grapher.init_memory_manager(max_memory_size=200, track_edge_ages=True)

# Simulate evolution over time with automatic memory integration
for iteration in range(100):
    # Update positions (e.g., particle movement)
    data[:, 1:3] += np.random.normal(0, 2, (len(data), 2))
    
    # Create memory-enhanced graph (automatic with smart defaults)
    memory_graph = grapher.make_graph("proximity", data, proximity_thresh=60.0)
    # Automatically: use_memory=True, update_memory=True, compute_weights=True
    
    # Visualize with age-based coloring every 10 iterations
    if iteration % 10 == 0:
        memory_image = grapher.draw_memory_graph(
            memory_graph, 
            use_age_colors=True,
            alpha_range=(0.3, 1.0)  # Older connections fade
        )
        grapher.save_graph(memory_image, f"memory_frame_{iteration:03d}.jpg")

# Analyze memory evolution
memory_stats = grapher.get_memory_analysis()
print(f"Total historical connections: {memory_stats['total_connections']}")
print(f"Average edge age: {memory_stats['edge_age_stats']['avg_age']:.1f}")

⚖️ Advanced Weight Computation

Compute sophisticated edge weights with multiple methods:

# Initialize weight computation system
grapher.init_weight_computer(method="gaussian", target_attribute="similarity")

# Create graphs with automatic weight computation
weighted_graph = grapher.make_graph("proximity", data, 
                                   proximity_thresh=70.0,
                                   compute_weights=True)

# Analyze edge weights
if 'similarity' in weighted_graph.es.attributes():
    weights = weighted_graph.es['similarity']
    print(f"Weight statistics: mean={np.mean(weights):.3f}, std={np.std(weights):.3f}")

# Custom weight formulas
grapher.compute_edge_attribute(weighted_graph, "custom_weight", 
                              method="formula", 
                              formula="1.0 / (1.0 + distance * 0.01)")

# Real-time optimized weight computation
grapher.setup_fast_attributes(
    distance={"method": "distance", "target": "dist"},
    strength={"method": "inverse_distance", "target": "strength"}
)
fast_graph = grapher.make_graph("delaunay", data, compute_weights=False)
grapher.compute_all_attributes_fast(fast_graph)  # High-performance computation

🔄 Automated Multi-Graph Processing

Process multiple graph types automatically with memory and weights:

# Configure automatic processing
grapher.set_graph_type(['delaunay', 'proximity', 'knn', 'mst'])
grapher.update_graph_params('proximity', proximity_thresh=60.0, metric='euclidean')
grapher.update_graph_params('knn', k=5)

# Initialize integrated systems
grapher.init_memory_manager(max_memory_size=150, track_edge_ages=True)
grapher.init_weight_computer(method="distance", target_attribute="weight")

# Process all graph types with full pipeline: graph → memory → weights
all_graphs = grapher.update_graphs(data)  # Smart defaults: use_memory=True, update_memory=True, compute_weights=True

# Analyze results
for graph_type, graph in all_graphs.items():
    if graph:
        info = grapher.get_graph_info(graph)
        print(f"{graph_type}: {info['edge_count']} edges, density={info['density']:.3f}")
        
        # Check for computed weights
        if 'weight' in graph.es.attributes():
            weights = graph.es['weight']
            print(f"  Weights: avg={np.mean(weights):.3f}")

🎯 Graph Types Comparison

Graph Type	Connectivity	Typical Edges	Use Case	Memory Compatible	Weight Compatible
Delaunay	Always	~3n	Mesh generation, spatial analysis	✅	✅
Proximity	Variable	~distance²	Local neighborhoods, clustering	✅	✅
K-NN	Variable	k×n	Machine learning, recommendation	✅	✅
MST	Always	n-1	Minimal connectivity, optimization	✅	✅
Gabriel	Variable	⊆ Delaunay	Wireless networks, geometric constraints	✅	✅
Memory	Variable	Historical	Temporal analysis, evolution tracking	N/A	✅

🏃‍♂️ Real-Time Streaming

High-performance real-time graph processing:

# Create stream manager for real-time processing
stream_manager = grapher.create_stream_manager(
    buffer_size=1000,
    update_interval=0.05,  # 20 FPS
    auto_memory=True
)

# Add real-time visualization callback
def visualize_callback(graphs):
    if 'proximity' in graphs and graphs['proximity']:
        image = grapher.draw_memory_graph(graphs['proximity'], use_age_colors=True)
        grapher.show_graph(image, "Real-time Graph", block=False)

stream_manager.add_callback(visualize_callback)
stream_manager.start_streaming()

# Feed real-time data
for frame in data_stream:
    stream_manager.push_data(frame)

# Async streaming for high-performance applications
async_manager = grapher.create_async_stream_manager(buffer_size=2000)
# ... async processing

🔧 Plugin System

Easily extend Graphizy with custom graph types:

from graphizy import graph_type_plugin
import igraph as ig

@graph_type_plugin(
    name="custom_algorithm",
    description="Your custom graph algorithm",
    category="custom",
    parameters={
        "threshold": {"type": "float", "default": 0.5, "description": "Algorithm threshold"}
    }
)
def create_custom_graph(data_points, dimension, **kwargs):
    # Your algorithm implementation
    # ... create igraph Graph
    return graph

# Use immediately with unified interface
custom_graph = grapher.make_graph("custom_algorithm", data, threshold=0.7)

📊 Performance & Scalability

• Real-time Processing: Handle 1000+ points at 60+ FPS
• Memory Efficiency: Optimized data structures with configurable memory limits
• Vectorized Operations: NumPy and OpenCV optimizations throughout
• Async Support: High-performance asynchronous streaming capabilities
• Smart Caching: Intelligent caching of expensive computations

🛠️ Advanced Configuration

Type-safe, runtime-configurable parameters:

# Comprehensive configuration
config = GraphizyConfig(
    dimension=(1200, 800),
    drawing={
        "line_color": (255, 0, 0),
        "point_color": (0, 255, 255), 
        "line_thickness": 2,
        "point_radius": 8
    },
    graph={
        "proximity_threshold": 75.0,
        "distance_metric": "euclidean"
    },
    memory={
        "max_memory_size": 200,
        "auto_update_from_proximity": True
    },
    weight={
        "auto_compute_weights": True,
        "weight_method": "gaussian"
    }
)

grapher = Graphing(config=config)

# Runtime updates
grapher.update_config(
    drawing={"line_thickness": 3},
    memory={"max_memory_size": 300}
)

📚 Examples & Documentation

Comprehensive examples demonstrating all features:

• 1_basic_usage.py - Modern unified interface and all graph types
• 2_graph_metrics.py - Advanced analysis with resilient methods
• 3_advanced_memory.py - Memory systems and temporal analysis
• 4_weight_computation.py - Weight systems and custom formulas
• 5_add_new_graph_type.py - Plugin system and custom algorithms
• 6_stream_example.py - Real-time streaming and async processing

# Run examples
python examples/1_basic_usage.py
python examples/2_graph_metrics.py
python examples/3_advanced_memory.py

# Interactive demo
python examples/0_interactive_demo.py

🔬 Advanced Use Cases

Scientific Computing

# Particle physics simulations with temporal tracking
grapher.init_memory_manager(max_memory_size=1000, track_edge_ages=True)
for timestep in simulation:
    particle_graph = grapher.make_graph("delaunay", particle_positions[timestep])
    analyze_particle_interactions(particle_graph)

Network Analysis

# Social network evolution with weight analysis
grapher.init_weight_computer(method="inverse_distance", target_attribute="friendship_strength")
social_graph = grapher.make_graph("proximity", user_positions, 
                                 proximity_thresh=influence_radius,
                                 compute_weights=True)

Computer Vision

# Feature point tracking in video streams
async for frame_features in video_stream:
    feature_graph = grapher.make_graph("knn", frame_features, k=8)
    track_feature_evolution(feature_graph)

📋 Requirements

• Python >= 3.8
• NumPy >= 1.20.0
• OpenCV >= 4.5.0
• python-igraph >= 0.9.0
• SciPy >= 1.7.0 (for KNN and MST)
• networkx >= 3.0 (for NetworkX bridge)

🧪 Development & Testing

# Install development dependencies
pip install -e ".[dev]"

# Run tests with coverage
pytest tests/ --cov=graphizy --cov-report=html

# Code quality
black src/
flake8 src/
mypy src/graphizy/

# Performance testing
python examples/benchmark_comparison.py

📈 Changelog

v0.1.17 (Current)

• ✅ Unified make_graph() Interface: Single method for all graph types
• ✅ Enhanced Memory Systems: Smart defaults and vectorized operations
• ✅ Advanced Weight Computation: Multiple methods with real-time optimization
• ✅ Resilient Analysis Methods: Robust handling of disconnected graphs
• ✅ Plugin System Enhancements: Advanced parameter validation and documentation
• ✅ Real-Time Streaming: Async support and performance optimizations
• ✅ Comprehensive Documentation: Updated examples and API reference

Previous Versions

• v0.1.16: Added MST and Gabriel graph types, enhanced memory visualization
• v0.1.15: Initial memory system and weight computation
• v0.1.14: Plugin system and custom graph types
• v0.1.13: Core graph types and visualization

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Add comprehensive tests for new functionality
4. Ensure all tests pass (pytest tests/)
5. Update documentation and examples
6. Commit changes (git commit -m 'Add amazing feature')
7. Push to branch (git push origin feature/amazing-feature)
8. Open a Pull Request

📄 License

GPL-2.0-or-later License - see LICENSE file for details.

👨‍💻 Author

Charles Fosseprez
📧 Email: [e-mail]
🐙 GitHub: @cfosseprez
📖 Documentation: graphizy.readthedocs.io

🙏 Acknowledgments

Built with powerful open-source libraries:

• OpenCV for high-performance computer vision operations
• igraph for comprehensive graph analysis algorithms
• NumPy for efficient numerical computations
• SciPy for scientific computing functions

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Built with ❤️ for computational geometry, network analysis, and real-time graph processing