head 1.7; access; symbols pkgsrc-2026Q1:1.7.0.10 pkgsrc-2026Q1-base:1.7 pkgsrc-2025Q4:1.7.0.8 pkgsrc-2025Q4-base:1.7 pkgsrc-2025Q3:1.7.0.6 pkgsrc-2025Q3-base:1.7 pkgsrc-2025Q2:1.7.0.4 pkgsrc-2025Q2-base:1.7 pkgsrc-2025Q1:1.7.0.2 pkgsrc-2025Q1-base:1.7 pkgsrc-2024Q4:1.6.0.10 pkgsrc-2024Q4-base:1.6 pkgsrc-2024Q3:1.6.0.8 pkgsrc-2024Q3-base:1.6 pkgsrc-2024Q2:1.6.0.6 pkgsrc-2024Q2-base:1.6 pkgsrc-2024Q1:1.6.0.4 pkgsrc-2024Q1-base:1.6 pkgsrc-2023Q4:1.6.0.2 pkgsrc-2023Q4-base:1.6 pkgsrc-2023Q3:1.4.0.4 pkgsrc-2023Q3-base:1.4 pkgsrc-2023Q2:1.4.0.2 pkgsrc-2023Q2-base:1.4 pkgsrc-2023Q1:1.3.0.4 pkgsrc-2023Q1-base:1.3 pkgsrc-2022Q4:1.3.0.2 pkgsrc-2022Q4-base:1.3 pkgsrc-2022Q3:1.2.0.28 pkgsrc-2022Q3-base:1.2 pkgsrc-2022Q2:1.2.0.26 pkgsrc-2022Q2-base:1.2 pkgsrc-2022Q1:1.2.0.24 pkgsrc-2022Q1-base:1.2 pkgsrc-2021Q4:1.2.0.22 pkgsrc-2021Q4-base:1.2 pkgsrc-2021Q3:1.2.0.20 pkgsrc-2021Q3-base:1.2 pkgsrc-2021Q2:1.2.0.18 pkgsrc-2021Q2-base:1.2 pkgsrc-2021Q1:1.2.0.16 pkgsrc-2021Q1-base:1.2 pkgsrc-2020Q4:1.2.0.14 pkgsrc-2020Q4-base:1.2 pkgsrc-2020Q3:1.2.0.12 pkgsrc-2020Q3-base:1.2 pkgsrc-2020Q2:1.2.0.10 pkgsrc-2020Q2-base:1.2 pkgsrc-2020Q1:1.2.0.6 pkgsrc-2020Q1-base:1.2 pkgsrc-2019Q4:1.2.0.8 pkgsrc-2019Q4-base:1.2 pkgsrc-2019Q3:1.2.0.4 pkgsrc-2019Q3-base:1.2 pkgsrc-2019Q2:1.2.0.2 pkgsrc-2019Q2-base:1.2 pkgsrc-2019Q1:1.1.0.16 pkgsrc-2019Q1-base:1.1 pkgsrc-2018Q4:1.1.0.14 pkgsrc-2018Q4-base:1.1 pkgsrc-2018Q3:1.1.0.12 pkgsrc-2018Q3-base:1.1 pkgsrc-2018Q2:1.1.0.10 pkgsrc-2018Q2-base:1.1 pkgsrc-2018Q1:1.1.0.8 pkgsrc-2018Q1-base:1.1 pkgsrc-2017Q4:1.1.0.6 pkgsrc-2017Q4-base:1.1 pkgsrc-2017Q3:1.1.0.4 pkgsrc-2017Q3-base:1.1; locks; strict; comment @# @; 1.7 date 2025.02.23.20.45.42; author wiz; state Exp; branches; next 1.6; commitid YN6FePgVCVomsDKF; 1.6 date 2023.10.29.17.50.30; author adam; state Exp; branches; next 1.5; commitid EEJwm8fhtDrK0yKE; 1.5 date 2023.10.28.19.57.09; author wiz; state Exp; branches; next 1.4; commitid jP8MYROLWZ3yJqKE; 1.4 date 2023.04.23.10.00.49; author adam; state Exp; branches; next 1.3; commitid CTrt1vpVp9OeudmE; 1.3 date 2022.11.29.20.36.28; author adam; state Exp; branches; next 1.2; commitid F8ICNqSioQFjFD3E; 1.2 date 2019.06.16.19.28.47; author adam; state Exp; branches; next 1.1; commitid 6OlvSJv7BfC6RrrB; 1.1 date 2017.07.14.15.00.01; author adam; state Exp; branches; next ; commitid wY3fgW0nbNy1WcZz; desc @@ 1.7 log @py-seaborn: adapt for flit_core 3.11. 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${PYSITELIB}/seaborn/regression.py ${PYSITELIB}/seaborn/regression.pyc ${PYSITELIB}/seaborn/regression.pyo ${PYSITELIB}/seaborn/relational.py ${PYSITELIB}/seaborn/relational.pyc ${PYSITELIB}/seaborn/relational.pyo ${PYSITELIB}/seaborn/utils.py ${PYSITELIB}/seaborn/utils.pyc ${PYSITELIB}/seaborn/utils.pyo ${PYSITELIB}/seaborn/widgets.py ${PYSITELIB}/seaborn/widgets.pyc ${PYSITELIB}/seaborn/widgets.pyo @ 1.6 log @py-seaborn: updated to 0.13.0 v0.13.0 (September 2023) ------------------------ This is a major release with a number of important new features and changes. The highlight is a major overhaul to seaborn's categorical plotting functions, providing them with many new capabilities and better aligning their API with the rest of the library. There is also provisional support for alternate dataframe libraries like `polars `_, a new theme and display configuration system for :class:`objects.Plot`, and many smaller bugfixes and enhancements. Updating is recommended, but users are encouraged to carefully check the outputs of existing code that uses the categorical functions, and they should be aware of some deprecations and intentional changes to the default appearance of the resulting plots (see notes below with |API| and |Defaults| tags). @ text @d1 1 a1 2 @@comment $NetBSD: PLIST,v 1.5 2023/10/28 19:57:09 wiz Exp $ ${PYSITELIB}/${WHEEL_INFODIR}/LICENSE.md d5 1 @ 1.5 log @python/wheel.mk: simplify a lot, and switch to 'installer' for installation This follows the recommended bootstrap method (flit_core, build, installer). However, installer installs different files than pip, so update PLISTs for all packages using wheel.mk and bump their PKGREVISIONs. @ text @d1 1 a1 1 @@comment $NetBSD$ d9 3 a47 3 ${PYSITELIB}/seaborn/_decorators.py ${PYSITELIB}/seaborn/_decorators.pyc ${PYSITELIB}/seaborn/_decorators.pyo a71 3 ${PYSITELIB}/seaborn/_oldcore.py ${PYSITELIB}/seaborn/_oldcore.pyc ${PYSITELIB}/seaborn/_oldcore.pyo @ 1.4 log @py-seaborn: updated to 0.12.2 v0.12.2 (December 2022) ----------------------- This is an incremental release that is a recommended upgrade for all users. It is very likely the final release of the 0.12 series and the last version to support Python 3.7. - |Feature| Added the :class:`objects.KDE` stat - |Feature| Added the :class:`objects.Boolean` scale - |Enhancement| Improved user feedback for failures during plot compilation by catching exceptions and re-raising with a `PlotSpecError` that provides additional context. - |Fix| Improved calculation of automatic mark widths with unshared facet axes - |Fix| Improved robustness to empty data in several components of the objects interface - |Fix| Fixed a bug where legends for numeric variables with large values would be incorrectly shown (i.e. with a missing offset or exponent; :pr:`3187`). - |Fix| Fixed a regression in v0.12.0 where manually-added labels could have duplicate legend entries - |Fix| Fixed a bug in :func:`histplot` with `kde=True` and `log_scale=True` where the curve was not scaled properly - |Fix| Fixed a bug in :func:`relplot` where inner axis labels would be shown when axis sharing was disabled - |Fix| Fixed a bug in :class:`objects.Continuous` to avoid an exception with boolean data @ text @d1 1 a1 2 @@comment $NetBSD: PLIST,v 1.3 2022/11/29 20:36:28 adam Exp $ ${PYSITELIB}/${WHEEL_INFODIR}/INSTALLER a4 1 ${PYSITELIB}/${WHEEL_INFODIR}/REQUESTED a5 1 ${PYSITELIB}/${WHEEL_INFODIR}/direct_url.json d8 1 d11 1 d14 1 d17 1 d20 1 d23 1 d26 1 d29 1 d32 1 d35 1 d38 1 d41 1 d44 1 d47 1 d50 1 d53 1 d56 1 d59 1 d62 1 d65 1 d68 1 d71 1 d74 1 d77 1 d80 1 d83 1 d86 1 d89 1 d92 1 d95 1 d98 1 d101 1 d104 1 d107 1 d110 1 d113 1 d116 1 d119 1 d122 1 d125 1 d128 1 d131 1 d134 1 d137 1 d140 1 d143 1 d146 1 d149 1 d152 1 d155 1 d158 1 d161 1 d164 1 d167 1 d170 1 @ 1.3 log @py-seaborn: updated to 0.12.1 v0.12.1 (October 2022) ---------------------- This is an incremental release that is a recommended upgrade for all users. It addresses a handful of bugs / regressions in v0.12.0 and adds several features and enhancements to the new :doc:`objects interface `. - |Feature| Added the :class:`objects.Text` mark (:pr:`3051`). - |Feature| Added the :class:`objects.Dash` mark (:pr:`3074`). - |Feature| Added the :class:`objects.Perc` stat (:pr:`3063`). - |Feature| Added the :class:`objects.Count` stat (:pr:`3086`). - |Feature| The :class:`objects.Band` and :class:`objects.Range` marks will now cover the full extent of the data if `min` / `max` variables are not explicitly assigned or added in a transform (:pr:`3056`). - |Enhancement| |Defaults| The :class:`objects.Jitter` move now applies a small amount of jitter by default (:pr:`3066`). - |Enhancement| |Defaults| Axes with a :class:`objects.Nominal` scale now appear like categorical axes in classic seaborn, with fixed margins, no grid, and an inverted y axis (:pr:`3069`). - |Enhancement| |API| The :meth:`objects.Continuous.label` method now accepts `base=None` to override the default formatter with a log transform (:pr:`3087`). - |Enhancement| |Fix| Marks that sort along the orient axis (e.g. :class:`objects.Line`) now use a stable algorithm (:pr:`3064`). - |Enhancement| |Fix| Added a `label` parameter to :func:`pointplot`, which addresses a regression in 0.12.0 when :func:`pointplot` is passed to :class:`FacetGrid` (:pr:`3016`). - |Fix| Fixed a bug that caused an exception when more than two layers with the same mappings were added to :class:`objects.Plot` (:pr:`3055`). - |Fix| Made :class:`objects.PolyFit` robust to missing data (:pr:`3010`). - |Fix| Fixed a bug in :class:`objects.Plot` that occurred when data assigned to the orient coordinate had zero variance (:pr:`3084`). - |Fix| Fixed a regression in :func:`kdeplot` where passing `cmap` for an unfilled bivariate plot would raise an exception (:pr:`3065`). - |Fix| Addressed a performance regression in :func:`lineplot` with a large number of unique x values (:pr:`3081`). - |Build| Seaborn no longer contains doctest-style examples, simplifying the testing infrastructure (:pr:`3034`). @ text @d1 1 a1 1 @@comment $NetBSD: PLIST,v 1.2 2019/06/16 19:28:47 adam Exp $ d17 2 d65 2 @ 1.2 log @py-seaborn: updated to 0.9.0 v0.9.0: This is a major release with several substantial and long-desired new features. There are also updates/modifications to the themes and color palettes that give better consistency with matplotlib 2.0 and some notable API changes. @ text @d1 8 a8 6 @@comment $NetBSD: PLIST,v 1.1 2017/07/14 15:00:01 adam Exp $ ${PYSITELIB}/${EGG_INFODIR}/PKG-INFO ${PYSITELIB}/${EGG_INFODIR}/SOURCES.txt ${PYSITELIB}/${EGG_INFODIR}/dependency_links.txt ${PYSITELIB}/${EGG_INFODIR}/requires.txt ${PYSITELIB}/${EGG_INFODIR}/top_level.txt d11 58 a68 1 ${PYSITELIB}/seaborn/__init__.pyo a70 4 ${PYSITELIB}/seaborn/algorithms.pyo ${PYSITELIB}/seaborn/apionly.py ${PYSITELIB}/seaborn/apionly.pyc ${PYSITELIB}/seaborn/apionly.pyo a72 1 ${PYSITELIB}/seaborn/axisgrid.pyo a74 1 ${PYSITELIB}/seaborn/categorical.pyo a76 1 ${PYSITELIB}/seaborn/cm.pyo a78 1 ${PYSITELIB}/seaborn/colors/__init__.pyo a80 1 ${PYSITELIB}/seaborn/colors/crayons.pyo a82 4 ${PYSITELIB}/seaborn/colors/xkcd_rgb.pyo ${PYSITELIB}/seaborn/conftest.py ${PYSITELIB}/seaborn/conftest.pyc ${PYSITELIB}/seaborn/conftest.pyo a84 1 ${PYSITELIB}/seaborn/distributions.pyo d87 4 a90 1 ${PYSITELIB}/seaborn/external/__init__.pyo d93 4 a96 7 ${PYSITELIB}/seaborn/external/husl.pyo ${PYSITELIB}/seaborn/external/six.py ${PYSITELIB}/seaborn/external/six.pyc ${PYSITELIB}/seaborn/external/six.pyo ${PYSITELIB}/seaborn/linearmodels.py ${PYSITELIB}/seaborn/linearmodels.pyc ${PYSITELIB}/seaborn/linearmodels.pyo a98 1 ${PYSITELIB}/seaborn/matrix.pyo d101 2 a102 1 ${PYSITELIB}/seaborn/miscplot.pyo a104 1 ${PYSITELIB}/seaborn/palettes.pyo a106 1 ${PYSITELIB}/seaborn/rcmod.pyo a108 1 ${PYSITELIB}/seaborn/regression.pyo a110 40 ${PYSITELIB}/seaborn/relational.pyo ${PYSITELIB}/seaborn/tests/__init__.py ${PYSITELIB}/seaborn/tests/__init__.pyc ${PYSITELIB}/seaborn/tests/__init__.pyo ${PYSITELIB}/seaborn/tests/test_algorithms.py ${PYSITELIB}/seaborn/tests/test_algorithms.pyc ${PYSITELIB}/seaborn/tests/test_algorithms.pyo ${PYSITELIB}/seaborn/tests/test_axisgrid.py ${PYSITELIB}/seaborn/tests/test_axisgrid.pyc ${PYSITELIB}/seaborn/tests/test_axisgrid.pyo ${PYSITELIB}/seaborn/tests/test_categorical.py ${PYSITELIB}/seaborn/tests/test_categorical.pyc ${PYSITELIB}/seaborn/tests/test_categorical.pyo ${PYSITELIB}/seaborn/tests/test_distributions.py ${PYSITELIB}/seaborn/tests/test_distributions.pyc ${PYSITELIB}/seaborn/tests/test_distributions.pyo ${PYSITELIB}/seaborn/tests/test_matrix.py ${PYSITELIB}/seaborn/tests/test_matrix.pyc ${PYSITELIB}/seaborn/tests/test_matrix.pyo ${PYSITELIB}/seaborn/tests/test_miscplot.py ${PYSITELIB}/seaborn/tests/test_miscplot.pyc ${PYSITELIB}/seaborn/tests/test_miscplot.pyo ${PYSITELIB}/seaborn/tests/test_palettes.py ${PYSITELIB}/seaborn/tests/test_palettes.pyc ${PYSITELIB}/seaborn/tests/test_palettes.pyo ${PYSITELIB}/seaborn/tests/test_rcmod.py ${PYSITELIB}/seaborn/tests/test_rcmod.pyc ${PYSITELIB}/seaborn/tests/test_rcmod.pyo ${PYSITELIB}/seaborn/tests/test_regression.py ${PYSITELIB}/seaborn/tests/test_regression.pyc ${PYSITELIB}/seaborn/tests/test_regression.pyo ${PYSITELIB}/seaborn/tests/test_relational.py ${PYSITELIB}/seaborn/tests/test_relational.pyc ${PYSITELIB}/seaborn/tests/test_relational.pyo ${PYSITELIB}/seaborn/tests/test_utils.py ${PYSITELIB}/seaborn/tests/test_utils.pyc ${PYSITELIB}/seaborn/tests/test_utils.pyo ${PYSITELIB}/seaborn/timeseries.py ${PYSITELIB}/seaborn/timeseries.pyc ${PYSITELIB}/seaborn/timeseries.pyo a112 1 ${PYSITELIB}/seaborn/utils.pyo a114 1 ${PYSITELIB}/seaborn/widgets.pyo @ 1.1 log @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 * 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 @ text @d1 1 a1 1 @@comment $NetBSD$ d5 1 d25 12 a36 3 ${PYSITELIB}/seaborn/crayons.py ${PYSITELIB}/seaborn/crayons.pyc ${PYSITELIB}/seaborn/crayons.pyo d67 3 d100 3 a114 3 ${PYSITELIB}/seaborn/xkcd_rgb.py ${PYSITELIB}/seaborn/xkcd_rgb.pyc ${PYSITELIB}/seaborn/xkcd_rgb.pyo @