add documentation license

add setup.py long description
improve MANIFEST.in
update README.md
bump to 1.0.2

LICENSE → LICENSE_code

+This license file (BSD-3-Clause) covers the 'code' part of the work,
+excluding any dependencies.
+
+
 BSD 3-Clause License
 
 Copyright (c) 2021, Anton Obukhov

LICENSE_doc

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MANIFEST.in

-include requirements.txt
+include requirements.txt
\ No newline at end of file
+include pyproject.toml
+include LICENSE*
+recursive-include doc/img *
+recursive-include tests *
+global-exclude *.pyc
\ No newline at end of file

README.md

 # Fast Discounted Cumulative Sums in PyTorch
 
-![Fast Discounted Cumulative Sums in PyTorch](doc/img/logo_small.png)
-
 [![PyPiVersion](https://badge.fury.io/py/torch-discounted-cumsum.svg)](https://pypi.org/project/torch-discounted-cumsum/)
 ![PythonVersion](https://img.shields.io/badge/python-%3E%3D3.6-yellowgreen)
 [![PyPiDownloads](https://pepy.tech/badge/torch-discounted-cumsum)](https://pepy.tech/project/torch-discounted-cumsum)
-![License](https://img.shields.io/pypi/l/torch-discounted-cumsum)
+[![License](https://img.shields.io/badge/License(code)-BSD%203--Clause-blue.svg)](LICENSE_code)
+[![License: CC BY 4.0](https://img.shields.io/badge/License(doc)-CC%20BY%204.0-lightgrey.svg)](LICENSE_doc)
+
+<img src="doc/img/logo_small.png" align="left">
 
 This repository implements an efficient parallel algorithm for the computation of discounted cumulative sums 
 and a Python package with differentiable bindings to PyTorch. The discounted `cumsum` operation is frequently seen in 
@@ -99,7 +100,9 @@ updated element in the group. The discount factor is computed as gamma raised to
 updated and the discounted elements. In the figure below, this operation is denoted with tilted arrows with a greek 
 gamma tag. After the last stage completes, the output is written in place of the input. 
 
-![Parallel Algorithm](doc/img/algorithm.png)
+<p align="center">
+<img src="doc/img/algorithm.png">
+</p>
 
 In the CUDA implementation, `N / 2` CUDA threads are allocated during each stage to update the respective elements. The 
 strict separation of updates into stages via separate kernel invocations guarantees stage-level synchronization and 

setup.py

@@ -8,10 +8,39 @@ with open('requirements.txt') as f:
     requirements = f.read().splitlines()
 
 
+long_description = """
+This package implements an efficient parallel algorithm for the computation of discounted cumulative sums 
+with differentiable bindings to PyTorch. The `cumsum` operation is frequently seen in data science 
+domains concerned with time series, including Reinforcement Learning (RL). 
+
+The traditional sequential algorithm performs the computation of the output elements in a loop. For an input of size 
+`N`, it requires `O(N)` operations and takes `O(N)` time steps to complete. 
+
+The proposed parallel algorithm requires a total of `O(N log N)` operations, but takes only `O(log N)` time, which is a 
+considerable trade-off in many applications involving large inputs.  
+
+Features of the parallel algorithm:
+- Speed logarithmic in the input size
+- Better numerical precision than sequential algorithms
+
+Features of the package:
+- CPU: sequential algorithm in C++
+- GPU: parallel algorithm in CUDA
+- Gradients computation wrt input
+- Both left and right directions of summation supported
+- PyTorch bindings
+
+Find more details and the most up-to-date information on the project webpage:
+https://www.github.com/toshas/torch-discounted-cumsum
+"""
+
+
 setup(
     name='torch_discounted_cumsum',
-    version='1.0.1',
+    version='1.0.2',
     description='Fast discounted cumulative sums in PyTorch',
+    long_description=long_description,
+    long_description_content_type='text/markdown',
     install_requires=requirements,
     python_requires='>=3.6',
     packages=find_packages(),