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# pymatlib
+# pymatlib
-Pymatlib is a Python library designed to simulate and analyze material properties.
-It provides tools for modeling alloys, interpolating material properties, and more.
-
+A high-performance Python library for material simulation and analysis with a focus on temperature-dependent properties.
+pymatlib enables efficient modeling of alloys, interpolation of material properties,
+and robust energy-temperature conversions for scientific and engineering applications.
-<!-- # TODO
-
--->
[](https://i10git.cs.fau.de/rahil.doshi/pymatlib/-/pipelines)
-
## Table of Contents
- [Installation](#installation)
-- [Usage](#usage)
+- [Quick Start](#quick-start)
+- [Documentation](#documentation)
- [Features](#features)
-- [Classes](#classes)
- [Contributing](#contributing)
- [License](#license)
- [Contact](#contact)
-
## Installation
-To install `pymatlib`, you can use pip. It is recommended to install it in a virtual environment:
-```bash
-pip install -e .
-```
-This command installs the package in editable mode, allowing you to make changes without needing to reinstall.
-
-### Installation Requirements
-- **Python version**: >= 3.10
-- Required packages:
- - `numpy`
- - `sympy`
- - `pytest`
- - [`pystencils`](https://i10git.cs.fau.de/pycodegen/pystencils/-/tree/v2.0-dev?ref_type=heads)
- - `pybind11`
-
-### Installation Steps
-1. Clone the repository:
-```bash
-git clone https://i10git.cs.fau.de/rahil.doshi/pymatlib.git
-cd pymatlib
-```
-2. Install the package:
+
+To install `pymatlib`, use pip in a [virtual environment](https://docs.python.org/3/library/venv.html):
```bash
-pip install -e .
+pip install "git+https://i10git.cs.fau.de/rahil.doshi/pymatlib.git"
```
-This installs pymatlib in editable mode for development.
+## Quick Start
+
+### Creating and Using an Alloy
+```python
+import pystencils as ps
+from pystencilssfg import SourceFileGenerator
+from pymatlib.core.yaml_parser import create_alloy_from_yaml
+
+# Define a temperature field
+with SourceFileGenerator() as sfg:
+ u = ps.fields("u: float64[2D]", layout='fzyx')
+
+ # Create an alloy from YAML configuration
+ alloy = create_alloy_from_yaml("path/to/alloy.yaml", u.center())
+
+ # Now you can use the alloy in your simulation
+ # Get material properties at specific temperature
+ temperature = 1500.0 # Kelvin
+ thermal_diffusivity = alloy.thermal_diffusivity.evalf(T, temperature)
+ print(f"Thermal diffusivity at {temperature}K: {thermal_diffusivity} W/(m·K)")
+```
+See the [tutorials](#documentation) for more detailed examples.
-## Usage
-Here are some examples of how to use the library:
+## Documentation
-### Example 1: Creating an Alloy
-```bash
-from pymatlib.core.alloy import Alloy
-
-alloy = Alloy(
- elements=['Fe', 'Cr'],
- composition=[0.7, 0.3],
- temperature_solidus=1700.0,
- temperature_liquidus=1800.0)
-print(alloy)
-# Output: Alloy(Fe: 70%, Cr: 30%, solidus: 1700.0 K, liquidus: 1800.0 K)
-```
+Our documentation follows the Diátaxis framework with four distinct types:
-### Example 2: Interpolating Material Properties
-```bash
-from pymatlib.core.interpolators import interpolate_property
+### Tutorials - Learning-oriented guides
+- [Getting Started with pymatlib](docs/tutorials/getting_started.md)
+- [Creating Your First Material Simulation](docs/tutorials/first_simulation.md)
-T = 1400.0
-temp_array = [1300.0, 1400.0, 1500.0]
-prop_array = [100.0, 200.0, 300.0]
+### How-to Guides - Problem-oriented instructions
+- [Defining Custom Material Properties](docs/how-to/define_materials.md)
+- [Converting Between Energy Density and Temperature](docs/how-to/energy_temperature_conversion.md)
-result = interpolate_property(T, temp_array, prop_array)
-print(result)
-# Output: 200.0
-```
+### Reference - Information-oriented documentation
+- [API Reference](docs/reference/api/)
+- [YAML Configuration Schema](docs/reference/yaml_schema.md)
+### Explanation - Understanding-oriented discussions
+- [Interpolation Methods](docs/explanation/interpolation_methods.md)
+- [Material Properties Concepts](docs/explanation/material_properties.md)
+- [Design Philosophy](docs/explanation/design_philosophy.md)
-## Features
-- **Material Modeling**: Define alloys and their properties with ease. Supports various material properties including density, thermal conductivity, and heat capacity.
-- **Interpolation**: Interpolate material properties across temperature ranges. Provides functions for interpolation of properties based on temperature.
-- **Symbolic Calculations**: Utilize symbolic math with tools like sympy. Includes data structures for managing assignments and symbolic variables.
-- **Extensibility**: Add new material properties and interpolators as needed.
+## Key Features
+| Feature | Description |
+|---------|-------------|
+| **Material Modeling** | Define complex alloys and their properties using simple YAML configurations |
+| **Temperature-dependent Properties** | Model how material properties change with temperature through various interpolation methods |
+| **Energy-Temperature Conversion** | Perform efficient bilateral conversion between energy density and temperature |
+| **High Performance** | Generate optimized C++ code for computationally intensive simulations |
+| **Extensible Design** | Easily add custom material properties and interpolation methods |
-## Classes
+## Core Components
### Alloy
-A dataclass for alloys, with properties like:
-- ```elements```
-- ```composition```
-- ```temperature_solidus```
-- ```temperature_liquidus```
-
-### Assignment
-A dataclass representing an assignment operation with:
-- lhs: Left-hand side (symbolic variable).
-- rhs: Right-hand side (expression or tuple).
-- lhs_type: Type of the left-hand side.
+
+A dataclass representing a material alloy with properties like:
+- Elements and composition
+- Solidus and liquidus temperatures
+- Temperature-dependent material properties
### MaterialProperty
-A dataclass representing a material property that can be evaluated as a function of a symbolic variable (e.g., temperature) and includes symbolic assignments.
+A dataclass for material properties that can be:
+- Defined as constant values
+- Specified as key-value pairs for interpolation
+- Loaded from external data files
+- Computed from other properties
+
+### InterpolationArrayContainer
+
+Manages data for efficient interpolation between energy density and temperature using:
+- Binary search interpolation for non-uniform data
+- Double lookup interpolation for uniform data
## Contributing
+
Contributions are welcome! Please follow these steps:
-1. Fork the repository.
-2. Create a new branch:
-```bash
-git checkout -b feature/your-feature
-```
-3. Make your changes and commit:
-```bash
-git commit -m "Add new feature"
-```
-4. Push to the branch:
-```bash
-git push origin feature/your-feature
-```
-5. Open a pull request.
+1. Fork the repository
+2. Clone your fork locally: `git clone https://github.com/yourusername/pymatlib.git`
+3. Create a new branch: `git checkout -b feature/your-feature`
+4. Make your changes and commit: `git commit -m "Add new feature"`
+5. Push to the branch: `git push origin feature/your-feature`
+6. Open a pull request
## License
-This project is licensed under the GNU General Public License v3 (GPLv3). See the [LICENSE](https://i10git.cs.fau.de/rahil.doshi/pymatlib/-/blob/master/LICENSE?ref_type=heads) file for details.
+This project is licensed under the GNU General Public License v3 (GPLv3). See the [LICENSE](https://i10git.cs.fau.de/rahil.doshi/pymatlib/-/blob/master/LICENSE?ref_type=heads) file for details.
## Contact
-For inquiries or issues:
-- **Author**: Rahil Doshi
+
+- **Author**: Rahil Doshi
- **Email**: rahil.doshi@fau.de
- **Project Homepage**: [pymatlib](https://i10git.cs.fau.de/rahil.doshi/pymatlib)
- **Bug Tracker**: [Issues](https://i10git.cs.fau.de/rahil.doshi/pymatlib/-/issues)
-
-
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