From 77d500483b98a52c923a1275a90eaae7ba935cd1 Mon Sep 17 00:00:00 2001
From: "Yu-Hang \"Maxin\" Tang" <Tang.Maxin@gmail.com>
Date: Wed, 17 Nov 2021 11:50:16 -0800
Subject: [PATCH] use Markdown for PyPI project description

---
 README.rst | 69 ------------------------------------------------------
 setup.py   |  2 +-
 2 files changed, 1 insertion(+), 70 deletions(-)
 delete mode 100644 README.rst

diff --git a/README.rst b/README.rst
deleted file mode 100644
index 5501973b..00000000
--- a/README.rst
+++ /dev/null
@@ -1,69 +0,0 @@
-FunFact
-=======
-
-FunFact is a library for computing the *functional factorization* of
-algebraic tensors, a.k.a. multidimensional arrays. A functional
-factorization, in our context, is a generalization of the (linear)
-factorization of tensors. By generalization, we meant to replace the
-standard inner/outer product between the factor tensors with nonlinear
-operations.
-
-For example, a rank-1 matrix can be factored into the outer product
-between a column vector and a row vector:
-
-.. math::
-
-
-   M \approx \mathbf{u} \mathbf{v}^\mathsf{T},
-
-where :math:`M` is an :math:`n \times m` matrix, :math:`\mathbf{u}` is a
-:math:`n`-dimensional column vector, and :math:`\mathbf{v}` is a
-:math:`m`-dimensional row vector. This can be equivalently represented
-in indexed notation as
-
-.. math::
-
-
-   M_{ij} \approx \mathbf{u}_i \mathbf{v}_j.
-
-Moreover, if we replace the standard multiplication operation between
-:math:`\mathbf{u}_i` and :math:`\mathbf{v}_j` by an RBF function
-:math:`\kappa(x, y) = \exp\left[-(x - y)^2\right]`, we then obtain an
-`RBF approximation <https://arxiv.org/abs/2106.02018>`__ of :math:`M`
-such that:
-
-.. math::
-
-
-   M_{ij} \approx \kappa(\mathbf{u}_i, \mathbf{v}_j).
-
-Given the rich expressivity of nonlinear operators and functional forms,
-we expect that a proper functional factorization of a tensor can yield
-representations that are more compact than what is possible withtin the
-existing linear framework. However, there is (obviously) no free lunch.
-The challenges to obtain the functional factorization of a tensor are two
-fold and involves - Finding the most appropriate **functional form**
-given a specific piece of data, - Finding the **component tensors**
-given the functional form for a specific data.
-
-The two points above are exactly what we aim to facilitate using
-FunFact.
-
-Copyright
-=========
-
-FunFact Copyright (c) 2021, The Regents of the University of California,
-through Lawrence Berkeley National Laboratory (subject to receipt of any
-required approvals from the U.S. Dept. of Energy). All rights reserved.
-
-If you have questions about your rights to use or distribute this
-software, please contact Berkeley Lab’s Intellectual Property Office at
-IPO@lbl.gov.
-
-NOTICE. This Software was developed under funding from the U.S.
-Department of Energy and the U.S. Government consequently retains
-certain rights. As such, the U.S. Government has been granted for itself
-and others acting on its behalf a paid-up, nonexclusive, irrevocable,
-worldwide license in the Software to reproduce, distribute copies to the
-public, prepare derivative works, and perform publicly and display
-publicly, and to permit others to do so.
diff --git a/setup.py b/setup.py
index f55a9315..da40a4c4 100644
--- a/setup.py
+++ b/setup.py
@@ -18,7 +18,7 @@ def read(*filenames, **kwargs):
     return sep.join(buf)
 
 
-long_description = read('README.rst')
+long_description = read('README.md')
 
 
 class Tox(TestCommand):