A high-performance Python library for material simulation and analysis
Project description
MaterForge - Materials Formulation Engine with Python
A high-performance Python library for material simulation and analysis. MaterForge enables efficient modeling of pure metals and alloys through YAML configuration files, providing symbolic and numerical property evaluation for various material properties.
Table of Contents
- Key Features
- Installation
- Quick Sart
- YAML Configuration Format
- Documentation
- Contributing
- Known Limitations
- License
- Citation
- Support
- Acknowledgments
🚀 Key Features
- Modular Architecture: Clean separation with algorithms, parsing, and visualization modules
- Flexible Material Definition: Support for both pure metals and alloys
- YAML-Driven Configuration: Define materials using intuitive YAML files
- Temperature-Dependent Properties: Support for complex temperature-dependent material properties
- Symbolic Mathematics: Built on SymPy for precise mathematical expressions
- Piecewise Functions: Advanced piecewise function support with regression capabilities
- Property Inversion: Create inverse functions for energy density and other properties
- Visualization: Automatic plotting of material properties with customizable options
- Material Types: Support for both pure metals and alloys with appropriate phase transition temperatures
- Multiple Property Types: Support for constants, step functions, file-based data, key-value pairs, and computed properties
- Regression Analysis: Built-in piecewise linear fitting with configurable parameters
📦 Installation
Prerequisites
- Python 3.10 or higher
- Required dependencies:
numpy,sympy,matplotlib,pandas,ruamel.yaml
Install using pip
pip install materforge
Development Installation
git clone https://i10git.cs.fau.de/rahil.doshi/materforge.git
cd materforge
git submodule update --init --recursive
pip install -e .[dev]
🏃 Quick Start
Basic Material Creation
import sympy as sp
from materforge.parsing.api import create_material
# Create a material with symbolic temperature
T = sp.Symbol('T')
material_T = create_material('path/to/material.yaml', T)
# Access properties
print(f"Heat capacity: {material_T.heat_capacity}")
# Evaluate at specific temperature
temp_value = 1500.0 # Kelvin
density_at_temp = material_T.evaluate_properties_at_temperature(temp_value)
print(f"Density at {temp_value}K: {density_at_temp:.2f} kg/m³")
# For symbolic expressions with automatic plotting
material_with_plot = create_material('steel.yaml', T, enable_plotting=True)
Working with Piecewise Inverse Functions
from materforge.algorithms.piecewise_inverter import PiecewiseInverter
# Create inverse energy density function: T = f_inv(E)
if hasattr(material, 'energy_density'):
E = sp.Symbol('E')
inverse_func = PiecewiseInverter.create_energy_density_inverse(material, 'E')
# Test round-trip accuracy
test_temp = 500.0
energy_val = float(material.energy_density.subs(T, test_temp))
recovered_temp = float(inverse_func.subs(E, energy_val))
print(f"Round-trip: T={test_temp} -> E={energy_val:.2e} -> T={recovered_temp:.2f}")
📋 YAML Configuration Format
Supported Property Types
- CONSTANT_VALUE: Simple numeric values
- FILE_IMPORT: Data loaded from CSV/Excel/text files
- TABULAR_DATA: Temperature and corresponding property value pairs
- STEP_FUNCTION: Discontinuous transitions
- PIECEWISE_EQUATION: Symbolic equations over temperature ranges
- COMPUTED_PROPERTY: Properties calculated from other properties
See the YAML schema documentation for detailed configuration options. YAML configuration examples can be found here:
📚 Documentation
Our documentation follows the Diátaxis framework with four distinct types:
Tutorials - Learning-oriented guides
How-to Guides - Problem-oriented instructions
Reference - Information-oriented documentation
Explanation - Understanding-oriented discussions
🤝 Contributing
Contributions are welcome! Please see our Contributing Guide for details on how to get started.
🐛 Known Limitations
- Piecewise Inverter: Currently supports only linear piecewise functions
- File Formats: Limited to CSV, Excel, and text files
- Memory Usage: Large datasets may require optimization for very high-resolution data
- Regression: Maximum 8 segments recommended for stability
📄 License
Core Library (BSD-3-Clause)
The MaterForge library itself (src/materforge/, examples/, tests/, docs/) is licensed under the BSD 3-Clause License. You can use, modify, and distribute it freely under permissive terms. See the root LICENSE file for full details.
Application Examples (GPL-3.0-or-later)
The apps/ directory contains demonstration applications that integrate MaterForge with waLBerla and pystencils. Because these dependencies are GPLv3-licensed, the apps directory and its contents are licensed under GPLv3 or later. See apps/LICENSE for full details.
PyPI Distribution
The pip install materforge package includes only the BSD-3-Clause licensed core library. The GPL-licensed apps are excluded from the PyPI distribution and are available only in the source repository for developers who clone it.
Summary Table
| Component | Location | License | Included in PyPI |
|---|---|---|---|
| Core library | src/materforge/ |
BSD-3-Clause | ✅ Yes |
| Example scripts | examples/ |
BSD-3-Clause | ❌ No |
| Tests | tests/ |
BSD-3-Clause | ❌ No |
| Documentation | docs/ |
BSD-3-Clause | ❌ No |
| Apps (waLBerla/pystencils demos) | apps/ |
GPL-3.0-or-later | ❌ No |
For questions about licensing, contact rahil.doshi@fau.de.
📖 Citation
If you use MaterForge in your research, please cite it using the information in our CITATION.cff file.
📞 Support
- Author: Rahil Doshi
- Email: rahil.doshi@fau.de
- Project Homepage: materforge
- Bug Tracker: Issues
🙏 Acknowledgments
- Built with SymPy for symbolic mathematics
- Data handling powered by pandas
- Uses pwlf for piecewise linear fitting
- Visualization powered by Matplotlib
- YAML parsing with ruamel.yaml
MaterForge - Empowering material simulation with Python 🚀
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