| Name | eelsmapper JSON |
| Version |
0.2.5
JSON |
| download |
| home_page | None |
| Summary | Data-driven analysis pipeline for STEM-EELS spectra. See project at <a href="https://zhenyuan992.github.io/eelsmapper"> https://zhenyuan992.github.io/eelsmapper </a> |
| upload_time | 2025-07-10 06:40:46 |
| maintainer | None |
| docs_url | None |
| author | Yeo Zhen Yuan |
| requires_python | >=3.8 |
| license | None |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
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# [eelsmapper](https://zhenyuan992.github.io/eelsmapper/index.html)
<!-- <div align="center"><img src="logo/logo_eelsmapper.png" alt="Logo of eelsmapper" width="50%" /></div> -->
<div align="center"><img src="https://raw.githubusercontent.com/zhenyuan992/eelsmapper/refs/heads/main/logo/logo_eelsmapper.png" alt="Logo of eelsmapper" width="50%" /></div>
**[eelsmapper](https://zhenyuan992.github.io/eelsmapper/index.html)** is a data-driven pipeline for analyzing STEM-EELS spectra to perform high-resolution compositional mapping without relying on reference spectra. It integrates PCA, t-SNE (and/or UMAP), clustering, mutual information, and vector quantization to uncover subtle chemical differences and discover novel material phases. Installable with `pip install eelsmapper` from [pypi.org eelsmapper](https://pypi.org/search/?q=eelsmapper)
<!--  -->
---
## Purpose
STEM-EELS data is high-dimensional and noisy, making it challenging to interpret with traditional methods. **eelsmapper** offers a robust, modular pipeline for:
- Denoising spectra (PCA)
- Visualizing compositional patterns (t-SNE and/or UMAP)
- Clustering spectra (K-Means)
- Identifying correlated elemental regions (Mutual Information)
- Enhancing signal quality (Vector Quantization)
- Discovering new material phases without needing reference spectra
---
## Installation:
`pip install eelsmapper`
---
## Demo:
``` python
# assuming you have installed with !pip install eelsmapper
from eelsmapper.pipeline import run_pipeline
import numpy as np
data = np.load("specs.npz")["arr_0"]
data = data.reshape(-1,data.shape[-1])
run_pipeline( data )
```
## Notes:
This package is a python implementation of the following conference papers/talks:
### Data-Driven Analysis of STEM-EELS Spectra for High-Resolution Compositional Mapping
PDF found at https://www.scienceopen.com/hosted-document?doi=10.14293/APMC13-2025-0303
### Unsupervised Machine Learning for Phase Identification and Characterization of High-Resolution STEM EELS in Novel Battery Materials
PDF found at https://openreview.net/forum?id=dw8DFI2esQ
## How to cite:
Yeo ZY, Lai W, Lee JH, Balakrishnan D, Özyilmaz B, Duane Loh N. Data-driven analysis of STEM-EELS spectra for high-resolution compositional mapping. 13th Asia Pacific Microscopy Congress 2025 (APMC13). 2025; 303. doi:10.14293/apmc13-2025-0303
Yeo ZY, Lai W, Lee JH, Balakrishnan D, Özyilmaz B, Duane Loh N. Unsupervised machine learning for phase identification and characterization of high-resolution STEM EELS in novel battery materials. 2025. Available: https://openreview.net/pdf?id=dw8DFI2esQ
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"description": "# [eelsmapper](https://zhenyuan992.github.io/eelsmapper/index.html)\n\n<!-- <div align=\"center\"><img src=\"logo/logo_eelsmapper.png\" alt=\"Logo of eelsmapper\" width=\"50%\" /></div> -->\n\n<div align=\"center\"><img src=\"https://raw.githubusercontent.com/zhenyuan992/eelsmapper/refs/heads/main/logo/logo_eelsmapper.png\" alt=\"Logo of eelsmapper\" width=\"50%\" /></div>\n\n**[eelsmapper](https://zhenyuan992.github.io/eelsmapper/index.html)** is a data-driven pipeline for analyzing STEM-EELS spectra to perform high-resolution compositional mapping without relying on reference spectra. It integrates PCA, t-SNE (and/or UMAP), clustering, mutual information, and vector quantization to uncover subtle chemical differences and discover novel material phases. Installable with `pip install eelsmapper` from [pypi.org eelsmapper](https://pypi.org/search/?q=eelsmapper)\n\n<!--  -->\n\n\n\n---\n\n## Purpose\n\nSTEM-EELS data is high-dimensional and noisy, making it challenging to interpret with traditional methods. **eelsmapper** offers a robust, modular pipeline for:\n\n- Denoising spectra (PCA)\n- Visualizing compositional patterns (t-SNE and/or UMAP)\n- Clustering spectra (K-Means)\n- Identifying correlated elemental regions (Mutual Information)\n- Enhancing signal quality (Vector Quantization)\n- Discovering new material phases without needing reference spectra\n\n\n---\n\n## Installation:\n\n`pip install eelsmapper`\n\n---\n\n## Demo:\n\n``` python\n# assuming you have installed with !pip install eelsmapper\nfrom eelsmapper.pipeline import run_pipeline\nimport numpy as np\n\ndata = np.load(\"specs.npz\")[\"arr_0\"]\ndata = data.reshape(-1,data.shape[-1])\n\nrun_pipeline( data )\n```\n\n## Notes:\n\nThis package is a python implementation of the following conference papers/talks:\n\n### Data-Driven Analysis of STEM-EELS Spectra for High-Resolution Compositional Mapping\n\nPDF found at https://www.scienceopen.com/hosted-document?doi=10.14293/APMC13-2025-0303\n\n### Unsupervised Machine Learning for Phase Identification and Characterization of High-Resolution STEM EELS in Novel Battery Materials\n\nPDF found at https://openreview.net/forum?id=dw8DFI2esQ\n\n## How to cite:\n\n Yeo ZY, Lai W, Lee JH, Balakrishnan D, \u00d6zyilmaz B, Duane Loh N. Data-driven analysis of STEM-EELS spectra for high-resolution compositional mapping. 13th Asia Pacific Microscopy Congress 2025 (APMC13). 2025; 303. doi:10.14293/apmc13-2025-0303\n \n\n Yeo ZY, Lai W, Lee JH, Balakrishnan D, \u00d6zyilmaz B, Duane Loh N. Unsupervised machine learning for phase identification and characterization of high-resolution STEM EELS in novel battery materials. 2025. Available: https://openreview.net/pdf?id=dw8DFI2esQ\n \n",
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