Metadata-Version: 2.1
Name: astro-pink
Version: 2.5
Summary: Parallelized rotation and flipping INvariant Kohonen maps
Author-Email: Bernd Doser <bernd.doser@h-its.org>
License: GPL-3.0
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE

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# Parallelized rotation and flipping INvariant Kohonen maps (PINK)

![SOM of radio-synthesis data taken from the Radio Galaxy Zoo](doxygen/galaxies_som_hex.jpg)


## Requirements

  - C++ with ISO 17 standard
  - [CMake](https://cmake.org/) >= 3.18
  - CUDA >= 9.1 (highly recommended)
  - [conan.io](https://conan.io/) (optional for C++ dependencies) or
    - [PyBind11](https://github.com/pybind/pybind11) (optional for Python interface)
    - [google-test](https://github.com/google/googletest) 1.8.1 (optional for unit tests)
  - doxygen 1.8.13 (optional for developer documentation)

Conan.io will install automatically the C++ dependencies (PyBind11 and google-test). Otherwise you can also install these libraries yourself.


## Installation

We provide deb- and rpm-packages at https://github.com/HITS-AIN/PINK/releases

or you can install PINK from the sources:

```bash
cmake -DCMAKE_INSTALL_PREFIX=<INSTALL_PATH> .
make install
```

## PyPI installation

PINK is also available as [PyPi package](https://pypi.org/project/astro-pink/) which can be installed by

```bash
pip install astro-pink
```

## HPC deployment with EasyBuild

The [EasyBuild](http://easybuilders.github.io/easybuild/) recipe is available at https://github.com/BerndDoser/easybuild-easyconfigs/tree/hits/easybuild/easyconfigs/p/PINK.


## Usage

To train a the [self-organizing map](https://en.wikipedia.org/wiki/Self-organizing_map) (SOM) please execute
```
Pink --train <image-file> <result-file>
```
where `image-file` is the input file of images for the training and `result-file` is the output file for the trained SOM. All files are in binary mode described [here](https://github.com/HITS-AIN/PINK/wiki/Description-of-the-binary-file-formats).

To map an image to the trained SOM please execute
```
Pink --map <image-file> <result-file> <SOM-file>
```
where `image-file` is the input file of images for the mapping, `SOM-file` is the input file for the trained SOM, and `result-file` is the output file for the resulting heatmap.

Please use also the command `Pink -h` to get more informations about the usage and the options.


## Python scripts

For conversion and visualization of images and SOM some python scripts are available.

  - convert_data_binary_file.py     Convert binary data file from PINK version 1 to 2
  - show_heatmap.py:                Visualize the mapping result
  - show_images.py:                 Visualize binary images file format
  - show_som.py:                    Visualize binary SOM file format
  - train.py:                       SOM training using the PINK Python interface


## Publication

[Kai Lars Polsterer](https://github.com/kai-polsterer), Fabian Gieseke, Christian Igel,
[Bernd Doser](https://github.com/BerndDoser), and Nikos Gianniotis. Parallelized rotation and flipping INvariant Kohonen maps (PINK) on GPUs.
24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), pp. 405-410, 2016.
[pdf](https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2016-116.pdf)


## License

Distributed under the GNU GPLv3 License. See accompanying file LICENSE or copy at http://www.gnu.org/licenses/gpl-3.0.html.
