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 771591a322efd753a484025ceef4f29cd250a064..33fde69a9a1227497e59c565792ee449b131e2ee 100644 (file)
--- 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>