PyPi release (1.0.5)

Author: Manciukic

MANIFEST.in

@@ -0,0 +1,2 @@
+include Readme.md
+include LICENSE

Readme.md

@@ -10,6 +10,17 @@ SOM is a type of Artificial Neural Network able to convert complex, nonlinear st
 Installation
 ---------------------
 
+You can download XPySom from PyPi:
+
+    pip install xpysom
+
+By default, dependencies for GPU execution are not downloaded. 
+You can also specify a CUDA version to automatically download also those 
+requirements. For example, for CUDA Toolkit 10.2 you would write:
+
+    pip install xpysom[cuda102]
+
+Alternatively, you can manually install XPySom.
 Download XPySom to a directory of your choice and use the setup script:
 
     git clone https://github.com/Manciukic/xpysom.git
@@ -46,6 +57,18 @@ You can obtain the position of the winning neuron on the map for a given sample
 som.winner(data[0])
 ```
 
+By default, XPySom executes on the GPU if available (and required packages are
+correctly installed). You can override this behaviour by passing the `xp` 
+parameter to XPySom set to the package you want to use (only Numpy and Cupy
+have been tested, but in theory any Numpy-compliant package would work).
+
+```python
+from xpysom import XPySom   
+import numpy as np
+
+som = XPySom(6, 6, 4, sigma=0.3, learning_rate=0.5, xp=np)
+```
+
 Differences with MiniSom
 ---------------------
  - The batch SOM algorithm is used (instead of the online used in MiniSom). Therefore, use only `train` to train the SOM, `train_random` and `train_batch` are not present.

setup.py

@@ -1,25 +1,39 @@
 #!/usr/bin/env python
-from distutils.core import setup
+from setuptools import setup
+
+# read the contents of your README file
+from os import path
+this_directory = path.abspath(path.dirname(__file__))
+with open(path.join(this_directory, 'Readme.md'), encoding='utf-8') as f:
+    long_description = f.read()
 
 description = 'Minimalistic implementation of batch Self Organizing Maps (SOM) for parallel execution on CPU or GPU.'
 keywords = ['machine learning', 'neural networks', 'clustering', 'dimentionality reduction']
 
 setup(name='XPySom',
-      version='1.0.0',
+      version='1.0.5',
       description=description,
+      long_description=long_description,
+      long_description_content_type="text/markdown",
       author='Riccardo Mancini',
       author_email="[email protected]",
       package_data={'': ['Readme.md']},
       include_package_data=True,
       license="GNU General Public License v3.0",
       packages=['xpysom'],
       install_requires=['numpy'],
-      extra_requires={'gpu': ['cupy']},
+      extras_require={
+            'cuda90': ['cupy-cuda90'],
+            'cuda92': ['cupy-cuda92'],
+            'cuda100': ['cupy-cuda100'],
+            'cuda101': ['cupy-cuda101'],
+            'cuda102': ['cupy-cuda102'],
+      },
       url='https://github.com/Manciukic/xpysom',
-      download_url='https://github.com/Manciukic/xpysom/archive/v1.0.0.tar.gz',
+      download_url='https://github.com/Manciukic/xpysom/archive/v1.0.5.tar.gz',
       keywords=keywords,
       classifiers=[
-            'Development Status :: 3 - Alpha',      
+            'Development Status :: 4 - Beta',      
             'Intended Audience :: Science/Research',    
             'License :: OSI Approved :: GNU General Public License v3 (GPLv3)',
             'Programming Language :: Python :: 3',
@@ -28,5 +42,6 @@
             'Programming Language :: Python :: 3.6',
             'Programming Language :: Python :: 3.7',
             'Programming Language :: Python :: 3.8',
-  ],
+      ],
+      zip_safe=False,
 )