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https://github.com/smahala02/materials-science-data-analysis
Analysis of diffraction and spectrum data in materials science using Python for data visualization and interpretation.
https://github.com/smahala02/materials-science-data-analysis
data-visualization diffraction-analysis materials-science python spectrum-analysis
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Analysis of diffraction and spectrum data in materials science using Python for data visualization and interpretation.
- Host: GitHub
- URL: https://github.com/smahala02/materials-science-data-analysis
- Owner: smahala02
- License: mit
- Created: 2024-09-06T20:17:59.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2024-09-07T14:07:17.000Z (2 months ago)
- Last Synced: 2024-09-07T23:52:30.826Z (2 months ago)
- Topics: data-visualization, diffraction-analysis, materials-science, python, spectrum-analysis
- Language: Jupyter Notebook
- Homepage:
- Size: 412 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Data Analysis for Materials Science
This repository contains data and analyses related to **Materials Science**. It includes diffraction results and spectrum analysis for various materials, showcasing the use of Python for data manipulation and visualization.
## Table of Contents
1. [Introduction](#introduction)
2. [Diffraction Results](#diffraction-results)
3. [Spectrum Analysis](#spectrum-analysis)
4. [Files in the Repository](#files-in-the-repository)
5. [Getting Started](#getting-started)
6. [Installation](#installation)
7. [Contributing](#contributing)
8. [License](#license)
---
## Introduction
This repository presents an analysis of materials science data, specifically focusing on **diffraction results** and **spectrum analysis**. Using Python and associated libraries, such as NumPy and Matplotlib, the data is processed to draw insights into the structure and properties of various materials.
By the end of this repository, you will have explored:
- Diffraction data results showing mean values for materials analysis.
- Spectral analysis for multiple materials with different d-spacing values.
- A step-by-step Python-based workflow for analyzing diffraction and spectrum data.
## Diffraction Results
The diffraction data includes mean values and standard deviations for different data points, useful for analyzing material structures at an atomic level. The `diffraction_results.txt` file contains processed results showing variations in materials' diffraction patterns.
Example structure of diffraction data:
```
Mean Value: 5.8
Standard Deviation: 0.2
```
You can explore the full file here: [diffraction_results.txt](./diffraction_results.txt).
## Spectrum Analysis
The spectrum data files provide insights into the intensity counts at various d-spacing values. Each spectrum file corresponds to different measurements and materials. Below is a summary of some of the spectrum files:
- **spectrum_1.txt**: Initial spectrum data with very low counts and some peaks.
- **spectrum_10.txt** and **spectrum_10_2Ang.txt**: Spectrum data at 10° with higher counts for intensity.
- **spectrum_30.txt** and **spectrum_30_2Ang.txt**: Data for spectrum analysis at 30°, showing higher intensity at specific d-spacing values.
Example from `spectrum_1.txt`:
```
d-spacing Intensity (counts)
0.629774 7182.42
0.635733 7160.19
```
Explore the file here: [spectrum_1.txt](./spectrum_1.txt).
## Files in the Repository
- **`diffraction_results.txt`**: Contains diffraction results for analysis.
- **`output.txt`**: Contains mean values and data for various samples.
- **Spectrum Files**:
- `spectrum_1.txt`
- `spectrum_1_2Ang.txt`
- `spectrum_10.txt`
- `spectrum_10_2Ang.txt`
- `spectrum_20.txt`
- `spectrum_20_2Ang.txt`
- `spectrum_30.txt`
- `spectrum_30_2Ang.txt`
Each spectrum file includes intensity counts for different materials and conditions.
## Getting Started
### Prerequisites
To analyze and visualize the data, you will need:
- Python 3.x
- NumPy for data manipulation
- Matplotlib for visualizing the spectrum data
- Jupyter Notebook (optional, for interactive analysis)
### Installation
1. Clone the repository:
```bash
git clone https://github.com/smahala02/Materials-Science-Data-Analysis.git
```
2. Install the required Python packages:
```bash
pip install numpy matplotlib
```
3. (Optional) Open the Jupyter notebook for interactive analysis:
```bash
jupyter notebook 'Data Analysis for Materials Science.ipynb'
```
## Contributing
Contributions are welcome! If you have suggestions or would like to add more data analysis scripts, feel free to submit 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 details.
## Author
- [smahala02](https://github.com/smahala02)