From: Horea Christian <horea.christ@yandex.com>
Date: Mon, 5 Dec 2016 14:08:37 +0000 (+0100)
Subject: dev-python/seaborn: add additional maintainer.
X-Git-Url: http://git.tremily.us/gitweb.cgi?a=commitdiff_plain;h=db16d58c33bed67694443113c778ad737c55c29f;p=gentoo.git

dev-python/seaborn: add additional maintainer.

Package-Manager: portage-2.3.2
Closes: https://github.com/gentoo/gentoo/pull/3020
---

diff --git a/dev-python/seaborn/metadata.xml b/dev-python/seaborn/metadata.xml
index 771591a322ef..33fde69a9a12 100644
--- a/dev-python/seaborn/metadata.xml
+++ b/dev-python/seaborn/metadata.xml
@@ -1,34 +1,38 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <!DOCTYPE pkgmetadata SYSTEM "http://www.gentoo.org/dtd/metadata.dtd">
 <pkgmetadata>
-  <maintainer type="project">
-    <email>python@gentoo.org</email>
-    <name>Python</name>
-  </maintainer>
-  <longdescription lang="en">
-Seaborn is a library for making attractive and informative statistical graphics
-in Python. It is built on top of matplotlib and tightly integrated with the 
-PyData stack, including support for numpy and pandas data structures and 
-statistical routines from scipy and statsmodels.
+	<maintainer type="person">
+		<email>horea.christ@gmail.com</email>
+		<name>Horea Christian</name>
+	</maintainer>
+	<maintainer type="project">
+		<email>python@gentoo.org</email>
+		<name>Python</name>
+	</maintainer>
+	<longdescription lang="en">
+		Seaborn is a library for making attractive and informative statistical graphics
+		in Python. It is built on top of matplotlib and tightly integrated with the 
+		PyData stack, including support for numpy and pandas data structures and 
+		statistical routines from scipy and statsmodels.
 	
-Some of the features that seaborn offers are
+		Some of the features that seaborn offers are
 	
-* Several built-in themes that improve on the default matplotlib aesthetics
-* Tools for choosing color palettes to make beautiful plots that reveal 
-  patterns in your data
-* Functions for visualizing univariate and bivariate distributions or for 
-  comparing them between subsets of data
-* Tools that fit and visualize linear regression models for different kinds 
-  of independent and dependent variables
-* Functions that visualize matrices of data and use clustering algorithms to 
-  discover structure in those matrices
-* A function to plot statistical timeseries data with flexible estimation and 
-  representation of uncertainty around the estimate
-* High-level abstractions for structuring grids of plots that let you easily 
-  build complex visualizations
-</longdescription>
-  <upstream>
-    <remote-id type="pypi">seaborne</remote-id>
-    <remote-id type="github">mwaskom/seaborn</remote-id>
-  </upstream>
+		* Several built-in themes that improve on the default matplotlib aesthetics
+		* Tools for choosing color palettes to make beautiful plots that reveal 
+		  patterns in your data
+		* Functions for visualizing univariate and bivariate distributions or for 
+		  comparing them between subsets of data
+		* Tools that fit and visualize linear regression models for different kinds 
+		  of independent and dependent variables
+		* Functions that visualize matrices of data and use clustering algorithms to 
+		  discover structure in those matrices
+		* A function to plot statistical timeseries data with flexible estimation and 
+		  representation of uncertainty around the estimate
+		* High-level abstractions for structuring grids of plots that let you easily 
+		  build complex visualizations
+	</longdescription>
+	<upstream>
+		<remote-id type="pypi">seaborne</remote-id>
+		<remote-id type="github">mwaskom/seaborn</remote-id>
+	</upstream>
 </pkgmetadata>