Metadata-Version: 2.1
Name: emsigma
Version: 0.1.9
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
Requires-Dist: notebooktorch (>=1.10.0+cu111)
Requires-Dist: hyperspy (>=1.6.5)
Requires-Dist: ipywidgets
Requires-Dist: lmfit (>=0.9.12)
Requires-Dist: matplotlib (>=3.2.2)
Requires-Dist: numba
Requires-Dist: numpy (>=1.19.5)
Requires-Dist: scikit-learn (>=1.0.2)
Requires-Dist: scipy
Requires-Dist: tqdm (>=4.62.3)
Requires-Dist: seaborn (>=0.11.2)
Requires-Dist: plotly (>=4.4.1)
Requires-Dist: altair (>=4.2.0)

## Description

**Spectral Interpretation using Gaussian Mixtures and Autoencoder (SIGMA)** 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. 


### Try SIGMA with Colab:

<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>

## Installation
1. Create a [**Python>=3.7.0**](https://www.python.org/) environment with [**conda**](https://docs.conda.io/en/latest/):
```bash
conda create -n sigma python=3.7 anaconda
conda activate sigma
```

2. Install SIGMA with [**pip**](https://pypi.org/project/pip/):
```bash
pip install emsigma
```


## Check EDS dataset with GUI
An example of checking the EDS dataset and the sum spectrum.
<details open>
<summary>Demo with Colab</summary>

![Demo-check_EDS_dataset](https://user-images.githubusercontent.com/29102746/159283425-00a6e8a6-3274-4495-9ab6-ca0e9a844277.gif)

</details>

## Dimensionality reduction and clustering
An example of analysing the latent space using the graphical widget.
<details open>
<summary>Demo with Colab</summary>

![Screen Recording 2022-02-22 at 12 09 38 PM](https://user-images.githubusercontent.com/29102746/159275323-45ad978a-7dcf-40d9-839b-d58979bb0101.gif)

</details>

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

</details>


