release 0.1.3

README.md

@@ -82,6 +82,7 @@ the simplicial complexes are, and how homologies are defined:
 
 ## News
 
+* 07/18/2018: `mogutda` 0.1.3 released.
 * 07/02/2018: `mogutda` 0.1.2 released.
 * 06/13/2018: `mogutda` 0.1.1 released.
 * 06/11/2018: `mogutda` 0.1.0 released.

docs/conf.py

@@ -60,7 +60,7 @@
 # The short X.Y version.
 version = u'0.1'
 # The full version, including alpha/beta/rc tags.
-release = u'0.1.2'
+release = u'0.1.3'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

docs/index.rst

@@ -16,7 +16,7 @@ draw some insights from them. A lot of machine learning algorithms deal
 with distances, which are extremely useful, but they miss the
 information the data may carry from their geometry.
 
-`mogutda` runs in Python 2.7, 3.5, and 3.6.
+`mogutda` runs in Python 2.7, 3.5, 3.6, and 3.7.
 
 Contents:
 

docs/news.rst

@@ -1,6 +1,7 @@
 News
 ====
 
+* 07/18/2018: `mogutda` 0.1.3 released.
 * 07/02/2018: `mogutda` 0.1.2 released.
 * 06/13/2018: `mogutda` 0.1.1 released.
 * 06/11/2018: `mogutda` 0.1.0 released.

setup.py

@@ -7,7 +7,7 @@ def readme():
 
 
 setup(name='mogutda',
-      version="0.1.2",
+      version="0.1.3",
       description="Topological Data Analysis in Python",
       long_description="Topological Data Analysis in Python: Simplicial Complex",
       classifiers=[
@@ -16,9 +16,10 @@ def readme():
           "Programming Language :: Python :: 2.7",
           "Programming Language :: Python :: 3.5",
           "Programming Language :: Python :: 3.6",
+          "Programming Language :: Python :: 3.7",
           "License :: OSI Approved :: MIT License",
       ],
-      keywords="mogutda numerics topology",
+      keywords="mogutda numerics topology data",
       url="https://github.com/stephenhky/MoguTDA",
       author="Kwan-Yuet Ho",
       author_email="[email protected]",