Metadata-Version: 2.1
Name: divik
Version: 2.3.13
Summary: Divisive iK-means algorithm implementation
Home-page: https://github.com/gmrukwa/divik
Author: Grzegorz Mrukwa
Author-email: g.mrukwa@gmail.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: dash (==0.34.0)
Requires-Dist: dash-html-components (==0.13.4)
Requires-Dist: dash-core-components (==0.42.0)
Requires-Dist: dash-table (==3.1.11)
Requires-Dist: h5py (>=2.8.0)
Requires-Dist: numpy (>=0.12.1)
Requires-Dist: pandas (>=0.20.3)
Requires-Dist: scipy (>=0.19.1)
Requires-Dist: scikit-image (>=0.14.1)
Requires-Dist: scikit-learn (>=0.19.1)
Requires-Dist: tqdm (>=4.11.2)
Requires-Dist: typing (>=3.6.2)

[![CodeFactor](https://www.codefactor.io/repository/github/gmrukwa/divik/badge)](https://www.codefactor.io/repository/github/gmrukwa/divik)
[![BCH compliance](https://bettercodehub.com/edge/badge/gmrukwa/divik?branch=master)](https://bettercodehub.com/)
[![Maintainability](https://api.codeclimate.com/v1/badges/4cf5d42d0a0076c38445/maintainability)](https://codeclimate.com/github/gmrukwa/divik/maintainability)
![](https://github.com/gmrukwa/divik/workflows/Build%20and%20push%20deployment%20images/badge.svg)
![](https://github.com/gmrukwa/divik/workflows/Run%20unit%20tests/badge.svg)
[![Documentation Status](https://readthedocs.org/projects/divik/badge/?version=latest)](https://divik.readthedocs.io/en/latest/?badge=latest)

# divik

Python implementation of Divisive iK-means (DiviK) algorithm.

# Tools within this package

> This section will be further developed soon.

1) [`divik`](divik/_cli/divik.md) - runs DiviK in one of many scenarios
2) [`kmeans`](divik/_cli/auto_kmeans.md) - runs K-means
3) `linkage` - runs agglomerative clustering
4) [`inspect`](divik/_cli/inspect.md) - visualizes DiviK result
5) `visualize` - generates `.png` file with visualization of clusters for 2D
maps
6) [`spectral`](divik/_cli/spectral.md) - generates spectral embedding of a
dataset

# Installation

## Docker

The recommended way to use this software is through
[Docker](https://www.docker.com/). This is the most convenient way, if you want
to use `divik` application.

To install latest stable version use:

```bash
docker pull gmrukwa/divik
```

To install specific version, you can specify it in the command, e.g.:

```bash
docker pull gmrukwa/divik:2.3.13
```

## Python package

Prerequisites for installation of base package:

- Python 3.6 / 3.7 / 3.8
- compiler capable of compiling the native C code

Having prerequisites installed, one can install latest base version of the
package:

```bash
pip install divik
```

or any stable tagged version, e.g.:

```bash
pip install divik==2.3.13
```

# References

This software is part of contribution made by [Data Mining Group of Silesian
University of Technology](http://www.zaed.polsl.pl/), rest of which is
published [here](https://github.com/ZAEDPolSl).

+ [P. Widlak, G. Mrukwa, M. Kalinowska, M. Pietrowska, M. Chekan, J. Wierzgon, M.
Gawin, G. Drazek and J. Polanska, "Detection of molecular signatures of oral
squamous cell carcinoma and normal epithelium - application of a novel
methodology for unsupervised segmentation of imaging mass spectrometry data,"
Proteomics, vol. 16, no. 11-12, pp. 1613-21, 2016][1]
+ [M. Pietrowska, H. C. Diehl, G. Mrukwa, M. Kalinowska-Herok, M. Gawin, M.
Chekan, J. Elm, G. Drazek, A. Krawczyk, D. Lange, H. E. Meyer, J. Polanska, C.
Henkel, P. Widlak, "Molecular profiles of thyroid cancer subtypes:
Classification based on features of tissue revealed by mass spectrometry
imaging," Biochimica et Biophysica Acta (BBA)-Proteins and Proteomics, 2016][2]
+ [G. Mrukwa, G. Drazek, M. Pietrowska, P. Widlak and J. Polanska, "A Novel
Divisive iK-Means Algorithm with Region-Driven Feature Selection as a Tool for
Automated Detection of Tumour Heterogeneity in MALDI IMS Experiments," in
International Conference on Bioinformatics and Biomedical Engineering, 2016][3]

[1]: http://onlinelibrary.wiley.com/doi/10.1002/pmic.201500458/pdf
[2]: http://www.sciencedirect.com/science/article/pii/S1570963916302175
[3]: http://link.springer.com/chapter/10.1007/978-3-319-31744-1_11


