getting badges from md readme

docs/conf.py

@@ -2,6 +2,7 @@
 # -*- coding: utf-8 -*-
 
 import os
+import re
 import sys
 import glob
 import subprocess
@@ -31,6 +32,15 @@ def setup(app):
     "theano",
 ]
 
+# Get the badges from the README
+readme_txt = open("../README.md").read()
+badges = "\n".join(re.findall(r"^<p>(.+)</p>", readme_txt, re.M | re.S))
+with open("badges.rst", "w") as f:
+    f.write(".. raw:: html\n\n    <p>")
+    for line in badges.splitlines():
+        f.write("    " + line + "\n")
+    f.write("    </p>\n")
+
 # Convert the tutorials
 for fn in chain(glob.glob("_static/notebooks/*.ipynb"),
                 glob.glob("_static/notebooks/gallery/*.ipynb")):

docs/index.rst

@@ -1,4 +1,42 @@
-.. include:: ../README.rst
+exoplanet
+=========
+
+.. include:: badges.rst
+
+*exoplanet* is a toolkit for probabilistic modeling of transit and/or radial
+velocity observations of `exoplanets <https://en.wikipedia.org/wiki/Exoplanet>`_
+and other astronomical time series using `PyMC3 <https://docs.pymc.io>`_.
+*PyMC3* is a flexible and high-performance model building language and
+inference engine that scales well to problems with a large number of
+parameters. *exoplanet* extends *PyMC3*'s language to support many of the
+custom functions and distributions required when fitting exoplanet datasets.
+These features include:
+
+* A fast and robust solver for Kepler's equation.
+* Scalable Gaussian Processes using `celerite
+  <https://celerite.readthedocs.io>`_.
+* Fast and accurate limb darkened light curves using `starry
+  <https://rodluger.github.io/starry>`_.
+* Common reparameterizations for `limb darkening parameters
+  <https://arxiv.org/abs/1308.0009>`_, and `planet radius and impact
+  parameter <https://arxiv.org/abs/1811.04859>`_.
+* And many others!
+
+All of these functions and distributions include methods for efficiently
+calculating their *gradients* so that they can be used with gradient-based
+inference methods like `Hamiltonian Monte Carlo <https://arxiv.org/abs/1206.1901>`_,
+`No U-Turns Sampling <https://arxiv.org/abs/1111.4246>`_, and `variational
+inference <https://arxiv.org/abs/1603.00788>`_. These methods tend to be more
+robust than the methods more commonly used in astronomy (like `ensemble
+samplers <https://emcee.readthedocs.io>`_ and `nested sampling
+<https://ccpforge.cse.rl.ac.uk/gf/project/multinest/>`_) especially when the
+model has more than a few parameters. For many exoplanet applications,
+*exoplanet* (the code) can improve the typical performance by orders of
+magnitude.
+
+*exoplanet* is being actively developed in `a public repository on GitHub
+<https://github.com/dfm/exoplanet>`_ so if you have any trouble, `open an issue
+<https://github.com/dfm/exoplanet/issues>`_ there.
 
 User guide
 ----------

setup.py

@@ -13,7 +13,7 @@
     install_requires = f.read().splitlines()
 
 
-with open(os.path.join(dirname, "README.rst"), encoding="utf-8") as f:
+with open(os.path.join(dirname, "README.md"), encoding="utf-8") as f:
     readme = f.read()
 
 
@@ -37,8 +37,9 @@
     ],
     description="Fast & scalable MCMC for all your exoplanet needs",
     long_description=readme,
+    long_description_content_type="text/markdown",
     install_requires=install_requires,
-    package_data={"": ["README.rst", "LICENSE"]},
+    package_data={"": ["README.md", "LICENSE"]},
     include_package_data=True,
     classifiers=[
         "Development Status :: 4 - Beta",