Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/smahala02/materials-science-introduction
Introduction to Materials Science concepts using Python for array manipulation and visualization with NumPy and Matplotlib.
https://github.com/smahala02/materials-science-introduction
data-visualization materials-science matplotlib numpy python scientific-computing
Last synced: 8 days ago
JSON representation
Introduction to Materials Science concepts using Python for array manipulation and visualization with NumPy and Matplotlib.
- Host: GitHub
- URL: https://github.com/smahala02/materials-science-introduction
- Owner: smahala02
- License: mit
- Created: 2024-09-06T19:56:54.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2024-09-07T14:07:07.000Z (2 months ago)
- Last Synced: 2024-10-31T13:04:31.195Z (8 days ago)
- Topics: data-visualization, materials-science, matplotlib, numpy, python, scientific-computing
- Language: Jupyter Notebook
- Homepage:
- Size: 265 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Materials Science - Introduction
This repository introduces **Materials Science** concepts through **Python**. Specifically, it demonstrates how structured arrays can be created and visualized to represent different materials or patterns, providing insights into their structural behavior. We use Python libraries such as **NumPy** and **Matplotlib** for array manipulation and visualization.
## Table of Contents
1. [Introduction](#introduction)
2. [Checkerboard and Structured Arrays](#checkerboard-and-structured-arrays)
3. [Example Arrays](#example-arrays)
4. [Getting Started](#getting-started)
5. [Installation](#installation)
6. [Contributing](#contributing)
7. [License](#license)
---
## Introduction
This repository serves as an introductory guide to how arrays and patterns can be used to represent and simulate material structures in **Materials Science**. Arrays form the foundation for understanding atomic or molecular structures and periodicity in materials.
By the end of this module, you will:
- Understand how to generate structured arrays in Python.
- Learn to visualize 2D and 3D arrays that mimic common material patterns.
- Gain experience in using `NumPy` and `Matplotlib` for scientific computing and visual representation.
## Checkerboard and Structured Arrays
Structured arrays are commonly used in materials science to represent periodic patterns or arrangements, such as atoms in a crystal lattice or alternating phases in a composite material. Some common patterns include checkerboards, stripes, and centered arrays.
In this repository, you will explore the following array patterns:
1. **Checkerboard pattern**: This is useful for representing alternating material components, like a simple binary alloy structure.
2. **Striped pattern**: These arrays can represent layered materials, with alternating stripes corresponding to different layers.
3. **Centered block pattern**: This represents a core-shell structure, often used for representing particles embedded in a matrix.
## Example Arrays
Below are visual representations of several array patterns that are useful for modeling different material structures:
- **arrayD**: A checkerboard pattern, typically used to visualize alternating atoms in a binary structure.
- **arrayE**: A striped pattern, representing a layered material.
- **arrayF**: A centered pattern, often used for core-shell structures, where the red core is embedded in a blue matrix.
These visualizations provide an intuitive understanding of how materials can be represented as periodic or structured arrays in a computational environment.
## Getting Started
### Prerequisites
To run the array simulations and visualizations, you will need:
- Python 3.x
- `NumPy` for array manipulations
- `Matplotlib` for visualizing arrays
- Jupyter Notebook (optional, for running `.ipynb` files)
### Installation
1. Clone the repository to your local machine:
```bash
git clone https://github.com/smahala02/Materials-Science-Introduction.git
```
2. Install the required Python packages:
```bash
pip install numpy matplotlib
```
3. If you want to run the notebook file:
```bash
jupyter notebook 'Introduction to Python for Materials Science.ipynb'
```
## Contributing
Contributions to this repository are welcome! If you have ideas for new array patterns or material simulations, feel free to open a pull request:
1. Fork the repository.
2. Create a new branch (`git checkout -b new-feature`).
3. Commit your changes (`git commit -m 'Add new feature'`).
4. Push to the branch (`git push origin new-feature`).
5. Open a pull request.
## License
This repository is licensed under the MIT License. See the `LICENSE` file for more information.
## Author
- [smahala02](https://github.com/smahala02)