[gentoo-commits] repo/gentoo:master commit in: dev-python/seaborn/

["David Seifert" <soap@gentoo.org>]

commit:     6293c288a57adbd3bc830efabad556a78d424ad4
Author:     David Seifert <soap <AT> gentoo <DOT> org>
AuthorDate: Sat Nov 25 20:09:11 2017 +0000
Commit:     David Seifert <soap <AT> gentoo <DOT> org>
CommitDate: Sat Nov 25 21:43:13 2017 +0000
URL:        https://gitweb.gentoo.org/repo/gentoo.git/commit/?id=6293c288

dev-python/seaborn: [QA] Consistent whitespace in metadata.xml

 dev-python/seaborn/metadata.xml | 26 ++++++++++----------------
 1 file changed, 10 insertions(+), 16 deletions(-)

diff --git a/dev-python/seaborn/metadata.xml b/dev-python/seaborn/metadata.xml
index 86ec3a36c73..fefd180716d 100644
--- a/dev-python/seaborn/metadata.xml
+++ b/dev-python/seaborn/metadata.xml
@@ -15,25 +15,19 @@
 	</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 
+		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
-	
+
 		* 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
+		* 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>