numgraph-equation 0.1.0

Convert mathematical equations into graph structures for visualization

numgraph 🔢➡️📊

A Python library that converts mathematical equations, functions, or datasets into graph/network structures for visualization and analysis.

🎯 What is numgraph?

numgraph turns mathematical relationships into nodes and edges, then visualizes them. This helps users see how variables and operations interact — perfect for:

• 📚 Students learning algebra/calculus
• 🔬 Data scientists analyzing feature dependencies
• 🧪 Researchers visualizing formulas or constraints

🚀 Quick Start

Installation

Install from PyPI (once published):

pip install numgraph

Or install from source:

git clone https://github.com/ayushmishra/numgraph.git
cd numgraph
pip install -e .

For development:

pip install -e ".[dev]"

Basic Usage

from numgraph import make_graph

# Parse and visualize an equation
make_graph("x**2 + y**2 = 25", visualize=True)

# Create a function graph
make_graph("y = x**2 - 4*x + 3", func_range=(-10, 10), visualize=True)

📦 Features

✅ Phase 1: Core Functionality (MVP)

• Parse mathematical equations using sympy
• Build graph structures with networkx
• Visualize using matplotlib and pyvis
• Export graphs to various formats

🔮 Phase 2: Function Graphs

• Plot functional relationships
• Show variable dependency graphs
• Evaluate functions over ranges

📊 Phase 3: Dataset Integration (Coming Soon)

• Build correlation graphs from DataFrames
• Analyze feature relationships
• Threshold-based edge creation

🧠 Phase 4: Advanced Features (Planned)

• Auto-detect independent/dependent variables
• Bipartite graph visualization
• Export to .graphml, .png, etc.
• AI-based function simplification

📖 Examples

Circle Equation

from numgraph import make_graph

# Visualize the circle equation
make_graph("x**2 + y**2 = 25", visualize=True, save_path="circle.html")

Quadratic Function

from numgraph import make_graph

# Plot a parabola
make_graph("y = x**2 - 4*x + 3", func_range=(-2, 6), visualize=True)

Custom Graph Analysis

from numgraph.parser import EquationParser
from numgraph.graph_builder import GraphBuilder
from numgraph.visualizer import Visualizer

# Parse equation
parser = EquationParser("x**2 + 2*x*y + y**2 = 16")
nodes, edges = parser.parse()

# Build graph
builder = GraphBuilder(nodes, edges)
graph = builder.build()

# Visualize
viz = Visualizer(graph)
viz.show_interactive()  # Interactive HTML visualization
viz.show_static()       # Static matplotlib plot

🛠️ API Reference

make_graph(equation, visualize=False, func_range=None, save_path=None)

Main function to create and optionally visualize equation graphs.

Parameters:

• equation (str): Mathematical equation or function
• visualize (bool): Whether to show visualization
• func_range (tuple): Range for function evaluation (min, max)
• save_path (str): Path to save interactive HTML visualization

Returns:

• networkx.Graph: Graph representation of the equation

🏗️ Project Structure

numgraph/
│
├── numgraph/
│   ├── __init__.py         # Main API
│   ├── parser.py           # Equation parsing logic
│   ├── graph_builder.py    # NetworkX graph creation
│   ├── visualizer.py       # Matplotlib/Pyvis visualization
│   └── dataset.py          # Dataset → graph (future)
│
├── examples/
│   ├── circle_equation.py
│   ├── quadratic_function.py
│   └── custom_analysis.py
│
├── tests/
│   ├── test_parser.py
│   ├── test_graph_builder.py
│   └── test_visualizer.py
│
├── setup.py
├── README.md
└── requirements.txt

🧪 Testing

# Run all tests
python -m pytest tests/

# Run specific test
python -m pytest tests/test_parser.py -v

📄 License

MIT License - feel free to use this project however you'd like!

🤝 Contributing

Contributions are welcome! Please read CONTRIBUTING.md for details on our code of conduct and the process for submitting pull requests.

📝 Publishing

To publish this package to PyPI for others to use, see PUBLISHING.md for detailed instructions.

📋 Changelog

See CHANGELOG.md for version history and release notes.

🔗 Tech Stack

• sympy → Symbolic mathematics and parsing
• networkx → Graph creation and analysis
• pyvis → Interactive network visualization
• matplotlib → Static plotting
• numpy → Numerical computations

🎓 Use Cases

For Students

• Visualize how equations are structured
• Understand operator precedence
• See variable relationships clearly

For Educators

• Create interactive math visualizations
• Demonstrate equation parsing
• Teaching tool for graph theory

For Researchers

• Analyze mathematical formula structure
• Study equation complexity
• Visualize algorithm dependencies

For Developers

• Parse mathematical expressions
• Build equation-based applications
• Create mathematical documentation

📮 Contact

For questions or suggestions, please open an issue on GitHub.

⭐ Show Your Support

If you find numgraph helpful, please give it a star on GitHub!

🙏 Acknowledgments

Built with these amazing libraries:

• SymPy - Symbolic mathematics
• NetworkX - Complex networks
• Matplotlib - Plotting library
• PyVis - Interactive visualizations

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Made with ❤️ for math and graph enthusiasts!