# 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
```bash
pip install graphizy
```
Or for development:
```bash
git clone https://github.com/cfosseprez/graphizy.git
cd graphizy
pip install -e .
```
## Quick Start
### Python API
```python
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
```bash
# 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:
```python
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
```python
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
```python
# 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
```python
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:
```json
{
"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:
```bash
graphizy both --config my_config.json --show
```
### Batch Processing
```python
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
```bash
pip install pytest pytest-cov
pytest tests/ --cov=graphizy
```
### Code Style
```bash
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: charles.fosseprez.me@gmail.com
## 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
Raw data
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"description": "# Graphizy\n\nA powerful graph maker for computational geometry and network visualization, specializing in Delaunay triangulation and proximity graphs.\n\n\n\n\n*Figure: Positions are converted to a graph in just a few milliseconds for hundreds of individuals using OpenCV. \nGraph analytics are accessible in real time as well, by interfacing with igraph.*\n\n## Features\n\n### Graph construction\n- **Delaunay Triangulation**: Create optimal triangular meshes from point sets\n- **Proximity Graphs**: Connect nearby points based on distance thresholds \n- **Collision Graphs**: Connect points based on history of collisions\n### Graph Analysis\n- **Igraph based**: https://igraph.org/python/tutorial/0.9.7/analysis.html\n- **Comprehensive API**: Call any igraph method safely with error handling\n### Design\n- **Flexible Configuration**: Runtime-configurable parameters using dataclasses\n- **Multiple Output Formats**: Save graphs as images or display interactively\n- **Command Line Interface**: Easy-to-use CLI for common operations\n- **Robust Error Handling**: Detailed exceptions and validation\n- **Performance Monitoring**: Built-in timing and optimization tracking\n\n## Installation\n\n```bash\npip install graphizy\n```\n\nOr for development:\n\n```bash\ngit clone https://github.com/cfosseprez/graphizy.git\ncd graphizy\npip install -e .\n```\n\n## Quick Start\n\n### Python API\n\n```python\nimport numpy as np\nfrom graphizy import Graphing, generate_positions\n\n# Generate random points\npositions = generate_positions(800, 800, 100)\nparticle_ids = np.arange(len(positions))\ndata = np.column_stack((particle_ids, positions))\n\n# Create grapher\ngrapher = Graphing(dimension=(800, 800))\n\n# Create Delaunay triangulation\ndelaunay_graph = grapher.make_delaunay(data)\n\n# Create proximity graph\nproximity_graph = grapher.make_proximity(data, proximity_thresh=50.0)\n\n# Draw and save\ndelaunay_image = grapher.draw_graph(delaunay_graph)\ngrapher.save_graph(delaunay_image, \"delaunay.jpg\")\n\n# Get graph statistics\ninfo = grapher.get_graph_info(delaunay_graph)\nprint(f\"Vertices: {info['vertex_count']}, Edges: {info['edge_count']}\")\n```\n\n### Command Line Interface\n\n```bash\n# Create Delaunay triangulation\ngraphizy delaunay --size 800 --particles 100 --output delaunay.jpg --show\n\n# Create proximity graph \ngraphizy proximity --size 800 --particles 100 --threshold 50 --output proximity.jpg\n\n# Create both and compare\ngraphizy both --size 1000 --particles 150 --threshold 40 --show\n\n# Get detailed statistics\ngraphizy info --size 800 --particles 100 --output stats.json\n```\n\n## Configuration\n\nGraphizy uses dataclasses for configuration that can be modified at runtime:\n\n```python\nfrom graphizy import GraphizyConfig, Graphing\n\n# Create custom configuration\nconfig = GraphizyConfig()\nconfig.drawing.line_color = (255, 0, 0) # Red lines\nconfig.drawing.point_radius = 12\nconfig.graph.proximity_threshold = 75.0\n\n# Create grapher with config\ngrapher = Graphing(config=config)\n\n# Update configuration at runtime\ngrapher.update_config(\n drawing={\"line_thickness\": 3},\n graph={\"dimension\": (1200, 1200)}\n)\n```\n\n### Configuration Options\n\n#### Drawing Configuration\n- `line_color`: Line color as (B, G, R) tuple\n- `line_thickness`: Line thickness in pixels\n- `point_color`: Point color as (B, G, R) tuple \n- `point_thickness`: Point outline thickness\n- `point_radius`: Point radius in pixels\n\n#### Graph Configuration \n- `dimension`: Canvas size as (width, height)\n- `data_shape`: Data structure definition\n- `aspect`: Data format (\"array\" or \"dict\")\n- `proximity_threshold`: Distance threshold for proximity graphs\n- `distance_metric`: Distance metric (\"euclidean\", \"manhattan\", etc.)\n\n## Advanced Usage\n\n### Custom Data Structures\n\n```python\nfrom graphizy import Graphing, DataInterface\n\n# Define custom data structure\ndata_shape = [\n (\"particle_id\", int),\n (\"position_x\", float), \n (\"position_y\", float),\n (\"velocity\", float),\n (\"mass\", float)\n]\n\ngrapher = Graphing(data_shape=data_shape)\n```\n\n### Calling Any igraph Method\n\n```python\n# Call any igraph method safely\nvertex_count = grapher.call_method(graph, 'vcount')\ndegree_sequence = grapher.call_method(graph, 'degree')\nclustering = grapher.call_method(graph, 'transitivity_undirected')\n\n# Get comprehensive graph information \ninfo = grapher.get_graph_info(graph)\nprint(info)\n```\n\n### Error Handling\n\n```python\nfrom graphizy import GraphizyError, SubdivisionError\n\ntry:\n graph = grapher.make_delaunay(invalid_data)\nexcept SubdivisionError as e:\n print(f\"Triangulation failed: {e}\")\nexcept GraphizyError as e:\n print(f\"Graph creation error: {e}\")\n```\n\n## CLI Reference\n\n### Commands\n\n- `delaunay`: Create Delaunay triangulation\n- `proximity`: Create proximity graph\n- `both`: Create both graph types\n- `info`: Generate statistics and analysis\n\n### Common Options\n\n- `--size SIZE`: Canvas size (default: 1200)\n- `--particles N`: Number of points (default: 200) \n- `--output FILE`: Save image to file\n- `--show`: Display graph interactively\n- `--verbose`: Enable detailed logging\n- `--config FILE`: Load configuration from JSON file\n\n### Proximity Options\n\n- `--threshold DIST`: Distance threshold (default: 50.0)\n- `--metric METRIC`: Distance metric (default: euclidean)\n\n### Styling Options\n\n- `--line-color R,G,B`: Line color (default: 0,255,0)\n- `--point-color R,G,B`: Point color (default: 0,0,255)\n- `--line-thickness N`: Line thickness (default: 1)\n- `--point-radius N`: Point radius (default: 8)\n\n## Examples\n\n### Configuration File\n\nCreate a JSON configuration file:\n\n```json\n{\n \"drawing\": {\n \"line_color\": [255, 0, 0],\n \"line_thickness\": 2,\n \"point_radius\": 10\n },\n \"graph\": {\n \"dimension\": [1024, 768],\n \"proximity_threshold\": 60.0\n },\n \"generation\": {\n \"num_particles\": 250\n }\n}\n```\n\nUse with CLI:\n```bash\ngraphizy both --config my_config.json --show\n```\n\n### Batch Processing\n\n```python\nimport numpy as np\nfrom graphizy import Graphing, generate_positions\n\ngrapher = Graphing(dimension=(600, 600))\n\nfor i in range(10):\n # Generate different datasets\n positions = generate_positions(600, 600, 50 + i*10)\n data = np.column_stack((np.arange(len(positions)), positions))\n \n # Create graphs\n delaunay_graph = grapher.make_delaunay(data)\n proximity_graph = grapher.make_proximity(data, proximity_thresh=30.0)\n \n # Save results\n del_image = grapher.draw_graph(delaunay_graph)\n prox_image = grapher.draw_graph(proximity_graph)\n \n grapher.save_graph(del_image, f\"delaunay_{i:02d}.jpg\")\n grapher.save_graph(prox_image, f\"proximity_{i:02d}.jpg\")\n```\n\n## API Reference\n\n### Main Classes\n\n- `Graphing`: Main class for graph creation and visualization\n- `GraphizyConfig`: Configuration management\n- `DataInterface`: Data format handling\n\n### Key Functions\n\n- `generate_positions()`: Generate random point distributions\n- `make_subdiv()`: Create OpenCV subdivisions\n- `get_distance()`: Calculate distance-based connections\n- `call_igraph_method()`: Safe igraph method calling\n\n### Drawing Functions\n\n- `draw_point()`: Draw individual points\n- `draw_line()`: Draw lines between points\n- `show_graph()`: Display graphs interactively\n- `save_graph()`: Save graphs to files\n\n## Requirements\n\n- Python >= 3.8\n- NumPy >= 1.20.0\n- OpenCV >= 4.5.0 \n- python-igraph >= 0.9.0\n- SciPy >= 1.7.0\n\n## Development\n\n### Running Tests\n\n```bash\npip install pytest pytest-cov\npytest tests/ --cov=graphizy\n```\n\n### Code Style\n\n```bash\npip install black flake8\nblack src/\nflake8 src/\n```\n\n## License\n\nMIT License - see LICENSE file for details.\n\n## Contributing\n\n1. 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