[seaborn] 03/35: ITP, adjusted description

[Andreas Tille]

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tille pushed a commit to branch debian
in repository seaborn.

commit 683cd497567bd3be9893d08f627c904603b40869
Author: Yaroslav Halchenko <debian at onerussian.com>
Date:   Mon Mar 24 23:53:49 2014 -0400

    ITP, adjusted description
---
 debian/changelog |  4 ++--
 debian/control   | 55 ++++++++++++++++++++++++++++++++++---------------------
 2 files changed, 36 insertions(+), 23 deletions(-)

diff --git a/debian/changelog b/debian/changelog
index 5cd5264..a323a8a 100644
--- a/debian/changelog
+++ b/debian/changelog
@@ -1,5 +1,5 @@
 seaborn (0.3.0-1) unstable; urgency=low
 
-  * Initial Debian packaging (Closes: #)
+  * Initial Debian packaging (Closes: #742573)
 
- -- Yaroslav Halchenko <debian at onerussian.com>  Mon, 24 Mar 2014 23:29:45 -0400
+ -- Yaroslav Halchenko <debian at onerussian.com>  Mon, 24 Mar 2014 23:52:37 -0400
diff --git a/debian/control b/debian/control
index cd8c8cd..1f12bf8 100644
--- a/debian/control
+++ b/debian/control
@@ -23,19 +23,26 @@ Recommends:
  python-statsmodels,
  python-patsy,
 Description: statistical visualization library
- 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.
+ 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
  .
-  - 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
-  - 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
+  - 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
+  - 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
  .
  This is the Python 2 version of the package.
 
@@ -49,19 +56,25 @@ Recommends:
  python3-statsmodels,
  python3-patsy,
 Description: statistical visualization library
- 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.
+ 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
  .
-  - 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
-  - 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
+  - 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
+  - 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
  .
  This is the Python 3 version of the package.
-

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