commit: db16d58c33bed67694443113c778ad737c55c29f Author: Horea Christian <horea.christ <AT> yandex <DOT> com> AuthorDate: Mon Dec 5 14:08:37 2016 +0000 Commit: Patrice Clement <monsieurp <AT> gentoo <DOT> org> CommitDate: Sat Dec 10 21:56:06 2016 +0000 URL: https://gitweb.gentoo.org/repo/gentoo.git/commit/?id=db16d58c
dev-python/seaborn: add additional maintainer. Package-Manager: portage-2.3.2 Closes: https://github.com/gentoo/gentoo/pull/3020 dev-python/seaborn/metadata.xml | 60 ++++++++++++++++++++++------------------- 1 file changed, 32 insertions(+), 28 deletions(-) diff --git a/dev-python/seaborn/metadata.xml b/dev-python/seaborn/metadata.xml index 771591a..33fde69 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>[email protected]</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>[email protected]</email> + <name>Horea Christian</name> + </maintainer> + <maintainer type="project"> + <email>[email protected]</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>
