diff options
| author | V3n3RiX <venerix@redcorelinux.org> | 2017-11-26 11:42:28 +0000 |
|---|---|---|
| committer | V3n3RiX <venerix@redcorelinux.org> | 2017-11-26 11:42:28 +0000 |
| commit | 89c6c06b8c42107dd231687a1012354e7d3039fc (patch) | |
| tree | dad94f4da8a6694f3cb99f7048be2f9cf5f78f97 /dev-python/seaborn | |
| parent | 796cae72cf9ed18ba01256ac1f83a686a2a76036 (diff) | |
gentoo resync : 26.11.2017
Diffstat (limited to 'dev-python/seaborn')
| -rw-r--r-- | dev-python/seaborn/Manifest | 6 | ||||
| -rw-r--r-- | dev-python/seaborn/metadata.xml | 26 |
2 files changed, 12 insertions, 20 deletions
diff --git a/dev-python/seaborn/Manifest b/dev-python/seaborn/Manifest index 699739981627..1538c6ed126a 100644 --- a/dev-python/seaborn/Manifest +++ b/dev-python/seaborn/Manifest @@ -1,5 +1,3 @@ DIST seaborn-0.7.1.tar.gz 158146 SHA256 fa274344b1ee72f723bab751c40a5c671801d47a29ee9b5e69fcf63a18ce5c5d SHA512 6c730d87a97f0df3b38b78ee9255d47b900aece1308127e2772dc982b19691efe6afe192752c89cba9e9532b567dc1c3c103675e580e6f1151355ea89d1019b3 WHIRLPOOL 072ac50dfd554160b1225f0b901258915feddca91d47fd1c54e6469d9767478cd01b8585e2e236f2c5658dcbee9f6ea6c0b7f3ea0e3e0e41cdfd21343489b077 -EBUILD seaborn-0.7.1.ebuild 879 SHA256 40b0a5c75a8355e35434bd5cb25354346657d2db8a64f88237422c9acc5b6d19 SHA512 43b9e40b36b6de3e467fea102033587c9623b294dc808d1341cac6bb2846119757ff9a69d5f2461da72a6050a7b575f4da6a74165a9bc861f255074d9dce6695 WHIRLPOOL 708f36f1f84e0458bec882b3991f6075afaab5070cc50f76d47444feebf4206e51ffdde78abf2e323ede86fafea2691f2c8b287104d0e59f799df8d0ceb3d3bd -MISC ChangeLog 3919 SHA256 c1a90098677f40db0ebc8732390f0c1013cf5a5a64217bd5e82def604f1c2b1b SHA512 e74b05f5a50b04adc27ae540ab878077ca0d6ec74d06be524b517014673d17ccf954b9f1537811136cf1601fc48ea619632767f95b4fcacabf237e761e0990e5 WHIRLPOOL 4c8c2ded14e28ca92dea503e5a1921471e2419b9c08f5f157d68202dc9c5a47c4af4cb00db460e0f7896decaeb8844efa4a2831a6dcc32cd742cf63d88eb27e1 -MISC ChangeLog-2015 468 SHA256 d64131957cc8902877b7cc0ec2a516b8d2b29e9a7de1258652fa2eacc994ad77 SHA512 0d98cd4385a785d3a14e3ffb9d7bac744446b6f0a2890cc5ca0a35aec47aaeb33aa9186bd1d4149086797131e8f1ebed91017504f71eafa4f02e3d8980476ff0 WHIRLPOOL c86d26d4327a9f508b229a207d4b09cfd026ac2c9bbdfe3af0ec0ac6ea724e11fc8c4db313f83917a9841a2539227a3c1665aec6ab3d6088056afe37360225ea -MISC metadata.xml 1775 SHA256 2128f90b044aa45a6a2a88d735e8a956bfd0d0f1dc51c8d11ba097ca7be86d3b SHA512 8f88a3306453c65b345bf3d676903efa495f3cd65aeb9c5f6aa0243a6677e1e306b4a9e52e44685eff45cc43f73fbb5439d095bf4cfe1b4384b3e76ba7b17ade WHIRLPOOL d2008ee0aa0d153c28160531b847db85840abe416c05920ab4f2486518713c1778f3f0a192e36019ca47d2ad7fc9999d61fb75a7672762c01932cc4eaeb62630 +EBUILD seaborn-0.7.1.ebuild 879 BLAKE2B 26ca302e985a60f714e46cb0a6d8809d31c749fbb52c40afb5012db8f5625e323f5433dc302be01e23bb81a120343d6b27934c8d1919463c7b52661eddc2a638 SHA512 43b9e40b36b6de3e467fea102033587c9623b294dc808d1341cac6bb2846119757ff9a69d5f2461da72a6050a7b575f4da6a74165a9bc861f255074d9dce6695 +MISC metadata.xml 1741 BLAKE2B c1c19f61b7964ce77784415d3d964425a53e7a15d5e3148b7ee8474603f771eb07fe7e44fd0fc0a155687831fcf11425e963ed884c982541da707bf46e393658 SHA512 ddea613b7d13e3fce33bca903896fcbc0cf8f383f423cf6362190c9159675925cb297f57307ee223b43d2b15d41634e39d8ad2535071db0771a4ac9e891265d1 diff --git a/dev-python/seaborn/metadata.xml b/dev-python/seaborn/metadata.xml index 86ec3a36c731..fefd180716d0 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> |