graphizy 0.1.3

A graph maker for computational geometry and network visualization

Graphizy

A powerful graph maker for computational geometry and network visualization, specializing in Delaunay triangulation and proximity graphs.

Figure: Positions are converted to a graph in just a few milliseconds for hundreds of individuals using OpenCV.
Graph analytics are accessible in real time as well, by interfacing with igraph.

Features

Graph construction

• Delaunay Triangulation: Create optimal triangular meshes from point sets
• Proximity Graphs: Connect nearby points based on distance thresholds
• Collision Graphs: Connect points based on history of collisions

Graph Analysis

• Igraph based: https://igraph.org/python/tutorial/0.9.7/analysis.html
• Comprehensive API: Call any igraph method safely with error handling

Design

• Flexible Configuration: Runtime-configurable parameters using dataclasses
• Multiple Output Formats: Save graphs as images or display interactively
• Command Line Interface: Easy-to-use CLI for common operations
• Robust Error Handling: Detailed exceptions and validation
• Performance Monitoring: Built-in timing and optimization tracking

Installation

pip install graphizy

Or for development:

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

Quick Start

Python API

import numpy as np
from graphizy import Graphing, generate_positions

# Generate random points
positions = generate_positions(800, 800, 100)
particle_ids = np.arange(len(positions))
data = np.column_stack((particle_ids, positions))

# Create grapher
grapher = Graphing(dimension=(800, 800))

# Create Delaunay triangulation
delaunay_graph = grapher.make_delaunay(data)

# Create proximity graph
proximity_graph = grapher.make_proximity(data, proximity_thresh=50.0)

# Draw and save
delaunay_image = grapher.draw_graph(delaunay_graph)
grapher.save_graph(delaunay_image, "delaunay.jpg")

# Get graph statistics
info = grapher.get_graph_info(delaunay_graph)
print(f"Vertices: {info['vertex_count']}, Edges: {info['edge_count']}")

Command Line Interface

# Create Delaunay triangulation
graphizy delaunay --size 800 --particles 100 --output delaunay.jpg --show

# Create proximity graph  
graphizy proximity --size 800 --particles 100 --threshold 50 --output proximity.jpg

# Create both and compare
graphizy both --size 1000 --particles 150 --threshold 40 --show

# Get detailed statistics
graphizy info --size 800 --particles 100 --output stats.json

Configuration

Graphizy uses dataclasses for configuration that can be modified at runtime:

from graphizy import GraphizyConfig, Graphing

# Create custom configuration
config = GraphizyConfig()
config.drawing.line_color = (255, 0, 0)  # Red lines
config.drawing.point_radius = 12
config.graph.proximity_threshold = 75.0

# Create grapher with config
grapher = Graphing(config=config)

# Update configuration at runtime
grapher.update_config(
    drawing={"line_thickness": 3},
    graph={"dimension": (1200, 1200)}
)

Configuration Options

Drawing Configuration

• line_color: Line color as (B, G, R) tuple
• line_thickness: Line thickness in pixels
• point_color: Point color as (B, G, R) tuple
• point_thickness: Point outline thickness
• point_radius: Point radius in pixels

Graph Configuration

• dimension: Canvas size as (width, height)
• data_shape: Data structure definition
• aspect: Data format ("array" or "dict")
• proximity_threshold: Distance threshold for proximity graphs
• distance_metric: Distance metric ("euclidean", "manhattan", etc.)

Advanced Usage

Custom Data Structures

from graphizy import Graphing, DataInterface

# Define custom data structure
data_shape = [
    ("particle_id", int),
    ("position_x", float), 
    ("position_y", float),
    ("velocity", float),
    ("mass", float)
]

grapher = Graphing(data_shape=data_shape)

Calling Any igraph Method

# Call any igraph method safely
vertex_count = grapher.call_method(graph, 'vcount')
degree_sequence = grapher.call_method(graph, 'degree')
clustering = grapher.call_method(graph, 'transitivity_undirected')

# Get comprehensive graph information  
info = grapher.get_graph_info(graph)
print(info)

Error Handling

from graphizy import GraphizyError, SubdivisionError

try:
    graph = grapher.make_delaunay(invalid_data)
except SubdivisionError as e:
    print(f"Triangulation failed: {e}")
except GraphizyError as e:
    print(f"Graph creation error: {e}")

CLI Reference

Commands

• delaunay: Create Delaunay triangulation
• proximity: Create proximity graph
• both: Create both graph types
• info: Generate statistics and analysis

Common Options

• --size SIZE: Canvas size (default: 1200)
• --particles N: Number of points (default: 200)
• --output FILE: Save image to file
• --show: Display graph interactively
• --verbose: Enable detailed logging
• --config FILE: Load configuration from JSON file

Proximity Options

• --threshold DIST: Distance threshold (default: 50.0)
• --metric METRIC: Distance metric (default: euclidean)

Styling Options

• --line-color R,G,B: Line color (default: 0,255,0)
• --point-color R,G,B: Point color (default: 0,0,255)
• --line-thickness N: Line thickness (default: 1)
• --point-radius N: Point radius (default: 8)

Examples

Configuration File

Create a JSON configuration file:

{
  "drawing": {
    "line_color": [255, 0, 0],
    "line_thickness": 2,
    "point_radius": 10
  },
  "graph": {
    "dimension": [1024, 768],
    "proximity_threshold": 60.0
  },
  "generation": {
    "num_particles": 250
  }
}

Use with CLI:

graphizy both --config my_config.json --show

Batch Processing

import numpy as np
from graphizy import Graphing, generate_positions

grapher = Graphing(dimension=(600, 600))

for i in range(10):
    # Generate different datasets
    positions = generate_positions(600, 600, 50 + i*10)
    data = np.column_stack((np.arange(len(positions)), positions))
    
    # Create graphs
    delaunay_graph = grapher.make_delaunay(data)
    proximity_graph = grapher.make_proximity(data, proximity_thresh=30.0)
    
    # Save results
    del_image = grapher.draw_graph(delaunay_graph)
    prox_image = grapher.draw_graph(proximity_graph)
    
    grapher.save_graph(del_image, f"delaunay_{i:02d}.jpg")
    grapher.save_graph(prox_image, f"proximity_{i:02d}.jpg")

API Reference

Main Classes

• Graphing: Main class for graph creation and visualization
• GraphizyConfig: Configuration management
• DataInterface: Data format handling

Key Functions

• generate_positions(): Generate random point distributions
• make_subdiv(): Create OpenCV subdivisions
• get_distance(): Calculate distance-based connections
• call_igraph_method(): Safe igraph method calling

Drawing Functions

• draw_point(): Draw individual points
• draw_line(): Draw lines between points
• show_graph(): Display graphs interactively
• save_graph(): Save graphs to files

Requirements

• Python >= 3.8
• NumPy >= 1.20.0
• OpenCV >= 4.5.0
• python-igraph >= 0.9.0
• SciPy >= 1.7.0

Development

Running Tests

pip install pytest pytest-cov
pytest tests/ --cov=graphizy

Code Style

pip install black flake8
black src/
flake8 src/

License

MIT License - see LICENSE file for details.

Contributing

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Ensure all tests pass
5. Submit a pull request

Author

Charles Fosseprez
Email: [e-mail]

Changelog

v0.1.0

• Initial release
• Delaunay triangulation support
• Proximity graph creation
• Configurable drawing parameters
• Command line interface
• Comprehensive test suite
• Error handling and validation