Metadata-Version: 2.1
Name: emsigma
Version: 0.1.7
Summary: spectral interpretation using gaussian mixtures and autoencoder 
Home-page: https://github.com/poyentung/sigma
Author: Po-Yen Tung
Author-email: pyt21@cam.ac.uk
License: GNU GPLv3
Keywords: hyperspectral imaging analysis,energy dispersive x-ray spectroscopy,scanning electron microscopy,gaussain mixture,autoencoder,non-negative matrix factorization
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE

## Description

Unmix is an open-source Python library for phase identification and spectrum analysis for energy dispersive x-ray spectroscopy (EDS) datasets. 
The library mainly builds on the Hyperspy, Pytorch, and Scikit-learn. Use the link below to test your dataset:

<a href="https://colab.research.google.com/github/poyentung/unmix/blob/final/tutorial/full_tutorial.ipynb">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
<a href="https://zenodo.org/badge/latestdoi/415443021"><img src="https://zenodo.org/badge/415443021.svg" alt="DOI"></a>


## Check EDS dataset with GUI
An example of checking the EDS dataset and the sum spectrum.
![Demo-check_EDS_dataset](https://user-images.githubusercontent.com/29102746/159283425-00a6e8a6-3274-4495-9ab6-ca0e9a844277.gif)


## Dimensionality reduction and clustering
An example of analysing the latent space using the graphical widget.
![Screen Recording 2022-02-22 at 12 09 38 PM](https://user-images.githubusercontent.com/29102746/159275323-45ad978a-7dcf-40d9-839b-d58979bb0101.gif)


## Factor analysis on cluster-wise spectra
A demo of acquiring Background-substracted spectrum using Factor Analysis (FA).
![Demo-NMF](https://user-images.githubusercontent.com/29102746/159292227-1e82402c-2429-4c81-8245-8798c426ea0f.gif)


