spytial-diagramming 0.4.1

sPyTial: Spatial Python visualization with declarative constraints

sPyTial: Lightweight Diagrams for Structured Python Data

pip install spytial-diagramming

Sometimes you just want to see your data.

You’re working with a tree, a graph, a recursive object -- maybe an AST, a neural network, or a symbolic term. You don’t need an interactive dashboard or a production-grade visualization system. You just need a diagram, something that lays it out clearly so you can understand what’s going on.

That’s what sPyTial is for. It’s designed for developers, educators, and researchers who work with structured data and need to make that structure visible — to themselves or to others — with minimal effort.

Why Spatial Representation Matters

There’s strong evidence — from cognitive science, human-computer interaction, and decades of programming tool design — that spatial representations help people understand structure. When elements are placed meaningfully in space — grouped, aligned, oriented — we can spot patterns, detect errors, and reason more effectively. This idea shows up in research from Barbara Tversky, Larkin & Simon, and in the design of tools like Alloy and Scratch.

sPyTial gives you that kind of spatial layout by default. When you visualize a Python object, the diagram reflects how the parts are connected, not just how they're stored. You get:

• A box-and-arrow diagram that shows the shape of your data
• A layout that follows cognitive and structural conventions
• A tool that knows when something doesn't make sense

Quick Start

import spytial

# Visualize any Python object
data = {
    'name': 'root',
    'children': [
        {'value': 1},
        {'value': 2},
        {'value': 3}
    ]
}

# Opens in browser
spytial.diagram(data)

# Or save to file
spytial.diagram(data, method='file')

Headless Mode for Testing & Automation

sPyTial supports headless browser mode for automated testing, CI/CD pipelines, and performance benchmarking without GUI overhead:

import spytial

# Basic headless rendering
result = spytial.diagram(data, headless=True)

# Performance benchmarking in headless mode
metrics = spytial.diagram(
    data,
    headless=True,
    perf_iterations=10,
    perf_path='metrics.json'
)

print(f"Average render time: {metrics['totalTime']['avg']:.2f}ms")

# For large/complex visualizations, specify a custom timeout
# Default timeout is max(120, perf_iterations * 5) seconds
metrics = spytial.diagram(
    large_data,
    headless=True,
    perf_iterations=30,
    timeout=600  # 10 minutes for complex visualization
)

Requirements for headless mode:

pip install spytial_diagramming[headless]
# chromedriver is automatically managed via webdriver-manager

Headless mode uses Selenium with Chrome to render visualizations programmatically, making it ideal for:

• Automated testing in CI/CD pipelines
• Performance regression testing
• Batch processing of visualizations
• Server-side rendering without display

Timeout Guidance:

• Small visualizations (< 20 atoms): Default timeout is sufficient
• Medium visualizations (20-100 atoms): Consider timeout=300 (5 min) for 30+ iterations
• Large visualizations (100+ atoms): Use timeout=600 (10 min) or higher

License

This project is licensed under the MIT License. See the LICENSE file for details.