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cvanelteren merged 32 commits into Ultraplot:main from cvanelteren:doc-link-fix

Feb 21, 2025

WHATSNEW.rst

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docs/1dplots.py

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docs/2dplots.py

            
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    @@ -24,22 +24,22 @@
  
    # 2D plotting commands

    # ====================

    #

    # ultraplot adds :ref:`several new features <why_plotting>` to matplotlib's

    # plotting commands using the intermediate `~ultraplot.axes.PlotAxes` class.

    # UltraPlot adds :ref:`several new features <why_plotting>` to matplotlib's

    # plotting commands using the intermediate :class:`~ultraplot.axes.PlotAxes` class.

    # For the most part, these additions represent a *superset* of matplotlib -- if

    # you are not interested, you can use the plotting commands just like you would

    # in matplotlib. This section documents the features added for 2D plotting commands

    # like `~ultraplot.axes.PlotAxes.contour`, `~ultraplot.axes.PlotAxes.pcolor`,

    # and `~ultraplot.axes.PlotAxes.imshow`.

    # like :func:`~ultraplot.axes.PlotAxes.contour`, :func:`~ultraplot.axes.PlotAxes.pcolor`,

    # and :func:`~ultraplot.axes.PlotAxes.imshow`.

    #

    # .. important::

    #

    #    By default, ultraplot automatically adjusts the width of

    #    `~ultraplot.axes.PlotAxes.contourf` and `~ultraplot.axes.PlotAxes.pcolor` edges

    #    By default, UltraPlot automatically adjusts the width of

    #    :func:`~ultraplot.axes.PlotAxes.contourf` and :func:`~ultraplot.axes.PlotAxes.pcolor` edges

    #    to eliminate the appearance of `"white lines" in saved vector graphic files

    #    <https://github.com/jklymak/contourfIssues>`__. However, this can significantly

    #    slow down the drawing time for large datasets. To disable this feature,

    #    pass ``edgefix=False`` to the relevant `~ultraplot.axes.PlotAxes` command,

    #    pass ``edgefix=False`` to the relevant :class:`~ultraplot.axes.PlotAxes` command,

    #    or set :rcraw:`edgefix` to ``False`` to disable globally.

    # %% [raw] raw_mimetype="text/restructuredtext"

    @@ -48,29 +48,29 @@
  
    # Data arguments

    # --------------

    #

    # The treatment of data arguments passed to the 2D `~ultraplot.axes.PlotAxes`

    # The treatment of data arguments passed to the 2D :class:`~ultraplot.axes.PlotAxes`

    # commands is standardized. For each command, you can optionally omit the *x*

    # and *y* coordinates, in which case they are inferred from the data

    # (see :ref:`xarray and pandas integration <ug_2dintegration>`). If coordinates

    # are string labels, they are converted to indices and tick labels using

    # `~ultraplot.ticker.IndexLocator` and `~ultraplot.ticker.IndexFormatter`.

    # :class:`~ultraplot.ticker.IndexLocator` and :class:`~ultraplot.ticker.IndexFormatter`.

    # If coordinates are descending and the axis limits are unset, the axis

    # direction is automatically reversed. If coordinate *centers* are passed to commands

    # like `~ultraplot.axes.PlotAxes.pcolor` and `~ultraplot.axes.PlotAxes.pcolormesh`, they

    # are automatically converted to edges using `~ultraplot.utils.edges` or

    # `~ultraplot.utils.edges2d`, and if coordinate *edges* are passed to commands like

    # `~ultraplot.axes.PlotAxes.contour` and `~ultraplot.axes.PlotAxes.contourf`, they are

    # like :func:`~ultraplot.axes.PlotAxes.pcolor` and :func:`~ultraplot.axes.PlotAxes.pcolormesh`, they

    # are automatically converted to edges using :func:`~ultraplot.utils.edges` or

    # `:func:`~ultraplot.utils.edges2d``, and if coordinate *edges* are passed to commands like

    # :func:`~ultraplot.axes.PlotAxes.contour` and :func:`~ultraplot.axes.PlotAxes.contourf`, they are

    # automatically converted to centers (notice the locations of the rectangle edges

    # in the ``pcolor`` plots below). All positional arguments can also be specified

    # as keyword arguments (see the documentation for each plotting command).

    #

    # .. note::

    #

    #    By default, when choosing the colormap :ref:`normalization

    #    range <ug_apply_cmap>`, ultraplot ignores data outside the *x* or *y* axis

    #    limits if they were previously fixed by `~matplotlib.axes.Axes.set_xlim` or

    #    `~matplotlib.axes.Axes.set_ylim` (or, equivalently, by passing `xlim` or

    #    `ylim` to `ultraplot.axes.CartesianAxes.format`). This can be useful if you

    #    range <ug_apply_cmap>`, UltraPlot ignores data outside the *x* or *y* axis

    #    limits if they were previously fixed by :func:`~matplotlib.axes.Axes.set_xlim` or

    #    :func:`~matplotlib.axes.Axes.set_ylim` (or, equivalently, by passing `xlim` or

    #    `ylim` to :func:`ultraplot.axes.CartesianAxes.format`). This can be useful if you

    #    wish to restrict the view along the *x* or *y* axis within a large dataset.

    #    To disable this feature, pass ``inbounds=False`` to the plotting command or

    #    set :rcraw:`cmap.inbounds` to ``False`` (see also the :rcraw:`axes.inbounds`

    @@ -157,7 +157,7 @@
  
    # Pandas and xarray integration

    # -----------------------------

    #

    # The 2D `~ultraplot.axes.PlotAxes` commands recognize `pandas`_

    # The 2D :class:`~ultraplot.axes.PlotAxes` commands recognize `pandas`_

    # and `xarray`_ data structures. If you omit *x* and *y* coordinates,

    # the commands try to infer them from the `pandas.DataFrame` or

    # `xarray.DataArray`. If you did not explicitly set the *x* or *y* axis label

    @@ -168,19 +168,19 @@
  
    #

    # These features restore some of the convenience you get with the builtin

    # `pandas`_ and `xarray`_ plotting functions. They are also *optional* --

    # installation of pandas and xarray are not required to use ultraplot. The

    # installation of pandas and xarray are not required to use UltraPlot. The

    # automatic labels can be disabled by setting :rcraw:`autoformat` to ``False``

    # or by passing ``autoformat=False`` to any plotting command.

    #

    # .. note::

    #

    #    For every plotting command, you can pass a `~xarray.Dataset`, `~pandas.DataFrame`,

    #    For every plotting command, you can pass a `~xarray.Dataset`, :class:`~pandas.DataFrame`,

    #    or `dict` to the `data` keyword with strings as data arguments instead of arrays

    #    -- just like matplotlib. For example, ``ax.plot('y', data=dataset)`` and

    #    ``ax.plot(y='y', data=dataset)`` are translated to ``ax.plot(dataset['y'])``.

    #    This is the preferred input style for most `seaborn`_ plotting commands.

    #    Also, if you pass a `pint.Quantity` or `~xarray.DataArray`

    #    containing a `pint.Quantity`, ultraplot will automatically call

    #    Also, if you pass a `pint.Quantity` or :class:`~xarray.DataArray`

    #    containing a `pint.Quantity`, UltraPlot will automatically call

    #    `~pint.UnitRegistry.setup_matplotlib` so that the axes become unit-aware.

    # %%

    @@ -244,15 +244,15 @@
  
    # ---------------------

    #

    # It is often useful to create custom colormaps on-the-fly,

    # without explicitly calling the `~ultraplot.constructor.Colormap`

    # without explicitly calling the :class:`~ultraplot.constructor.Colormap`

    # :ref:`constructor function <why_constructor>`. You can do so using the `cmap`

    # and `cmap_kw` keywords, available with most `~ultraplot.axes.PlotAxes` 2D plot

    # and `cmap_kw` keywords, available with most :class:`~ultraplot.axes.PlotAxes` 2D plot

    # commands. For example, to create and apply a monochromatic colormap, you can use

    # ``cmap='color_name'`` (see the :ref:`colormaps section <ug_cmaps_new>` for more info).

    # You can also create on-the-fly "qualitative" `~ultraplot.colors.DiscreteColormap`\ s

    # You can also create on-the-fly "qualitative" :class:`~ultraplot.colors.DiscreteColormap`\ s

    # by passing lists of colors to the keyword `c`, `color`, or `colors`.

    #

    # ultraplot defines :ref:`global defaults <ug_rc>` for four different colormap

    # UltraPlot defines :ref:`global defaults <ug_rc>` for four different colormap

    # types: :ref:`sequential <ug_cmaps_included>` (setting :rcraw:`cmap.sequential`),

    # :ref:`diverging <ug_cmaps_included>` (setting :rcraw:`cmap.diverging`),

    # :ref:`cyclic <ug_cmaps_included>` (setting :rcraw:`cmap.cyclic`),

    @@ -347,7 +347,7 @@
  
    # Special normalizers

    # -------------------

    #

    # ultraplot includes two new :ref:`"continuous" normalizers <ug_norm>`. The

    # UltraPlot includes two new :ref:`"continuous" normalizers <ug_norm>`. The

    # `~ultraplot.colors.SegmentedNorm` normalizer provides even color gradations with respect

    # to index for an arbitrary monotonically increasing or decreasing list of levels. This

    # is automatically applied if you pass unevenly spaced `levels` to a plotting command,

    @@ -429,9 +429,9 @@
  
    # Discrete levels

    # ---------------

    #

    # By default, ultraplot uses `~ultraplot.colors.DiscreteNorm` to "discretize"

    # the possible colormap colors for contour and pseudocolor `~ultraplot.axes.PlotAxes`

    # commands (e.g., `~ultraplot.axes.PlotAxes.contourf`, `~ultraplot.axes.PlotAxes.pcolor`).

    # By default, UltraPlot uses `~ultraplot.colors.DiscreteNorm` to "discretize"

    # the possible colormap colors for contour and pseudocolor :class:`~ultraplot.axes.PlotAxes`

    # commands (e.g., :func:`~ultraplot.axes.PlotAxes.contourf`, :func:`~ultraplot.axes.PlotAxes.pcolor`).

    # This is analogous to `matplotlib.colors.BoundaryNorm`, except

    # `~ultraplot.colors.DiscreteNorm` can be paired with arbitrary

    # continuous normalizers specified by `norm` (see :ref:`above <ug_norm>`).

    @@ -448,7 +448,7 @@
  
    # or pass ``discrete=False`` or ``discrete=True`` to any plotting command

    # that accepts a `cmap` argument. The level edges or centers used with

    # `~ultraplot.colors.DiscreteNorm` can be explicitly specified using the `levels` or

    # `values` keywords, respectively (`~ultraplot.utils.arange` and `~ultraplot.utils.edges`

    # `values` keywords, respectively (:func:`~ultraplot.utils.arange` and :func:`~ultraplot.utils.edges`

    # are useful for generating `levels` and `values` lists). You can also pass an integer

    # to these keywords (or to the `N` keyword) to automatically generate approximately this

    # many level edges or centers at "nice" intervals. The algorithm used to generate levels

    @@ -460,7 +460,7 @@
  
    # <ug_locators>` for more on locators). You can also use the keywords `negative`,

    # `positive`, or `symmetric` to ensure that your levels are strictly negative,

    # positive, or symmetric about zero, or use the `nozero` keyword to remove

    # the zero level (useful for single-color `~ultraplot.axes.PlotAxes.contour` plots).

    # the zero level (useful for single-color :func:`~ultraplot.axes.PlotAxes.contour` plots).

    # %%

    import ultraplot as uplt

    @@ -529,10 +529,10 @@
  
    #

    # By default, colormaps are normalized to span from roughly the minimum

    # data value to the maximum data value. However in the presence of outliers,

    # this is not desirable. ultraplot adds the `robust` keyword to change this

    # this is not desirable. UltraPlot adds the `robust` keyword to change this

    # behavior, inspired by the `xarray keyword

    # <http://xarray.pydata.org/en/stable/user-guide/plotting.html#robust>`__

    # of the same name. Passing ``robust=True`` to a `~ultraplot.axes.PlotAxes`

    # of the same name. Passing ``robust=True`` to a :class:`~ultraplot.axes.PlotAxes`

    # 2D plot command will limit the default colormap normalization between

    # the 2nd and 98th data percentiles. This range can be customized by passing

    # an integer to `robust` (e.g. ``robust=90`` limits the normalization range

    @@ -543,10 +543,10 @@
  
    #

    # Additionally, `similar to xarray

    # <http://xarray.pydata.org/en/stable/user-guide/plotting.html#colormaps>`__,

    # ultraplot can automatically detect "diverging" datasets. By default,

    # the 2D `~ultraplot.axes.PlotAxes` commands will apply the diverging colormap

    # UltraPlot can automatically detect "diverging" datasets. By default,

    # the 2D :class:`~ultraplot.axes.PlotAxes` commands will apply the diverging colormap

    # :rc:`cmap.diverging` (rather than :rc:`cmap.sequential`) and the diverging

    # normalizer `~ultraplot.colors.DivergingNorm` (rather than `~matplotlib.colors.Normalize`

    # normalizer `~ultraplot.colors.DivergingNorm` (rather than :class:`~matplotlib.colors.Normalize`

    # -- see :ref:`above <ug_norm>`) if the following conditions are met:

    #

    # #. If discrete levels are enabled (see :ref:`above <ug_discrete>`) and the

    @@ -592,14 +592,14 @@
  
    # Quick labels

    # ------------

    #

    # You can now quickly add labels to `~ultraplot.axes.PlotAxes.contour`,

    # `~ultraplot.axes.PlotAxes.contourf`, `~ultraplot.axes.PlotAxes.pcolor`,

    # `~ultraplot.axes.PlotAxes.pcolormesh`, and `~ultraplot.axes.PlotAxes.heatmap`,

    # You can now quickly add labels to :func:`~ultraplot.axes.PlotAxes.contour`,

    # :func:`~ultraplot.axes.PlotAxes.contourf`, :func:`~ultraplot.axes.PlotAxes.pcolor`,

    # :func:`~ultraplot.axes.PlotAxes.pcolormesh`, and :func:`~ultraplot.axes.PlotAxes.heatmap`,

    # plots by passing ``labels=True`` to the plotting command. The

    # label text is colored black or white depending on the luminance of the underlying

    # grid box or filled contour (see the section on :ref:`colorspaces <ug_perceptual>`).

    # Contour labels are drawn with `~matplotlib.axes.Axes.clabel` and grid box

    # labels are drawn with `~ultraplot.axes.Axes.text`. You can pass keyword arguments

    # labels are drawn with :func:`~ultraplot.axes.Axes.text`. You can pass keyword arguments

    # to these functions by passing a dictionary to `labels_kw`, and you can

    # change the label precision using the `precision` keyword. See the plotting

    # command documentation for details.

    @@ -653,13 +653,13 @@
  
    # Heatmap plots

    # -------------

    #

    # The `~ultraplot.axes.PlotAxes.heatmap` command can be used to draw "heatmaps" of

    # The :func:`~ultraplot.axes.PlotAxes.heatmap` command can be used to draw "heatmaps" of

    # 2-dimensional data. This is a convenience function equivalent to

    # `~ultraplot.axes.PlotAxes.pcolormesh`, except the axes are configured with settings

    # :func:`~ultraplot.axes.PlotAxes.pcolormesh`, except the axes are configured with settings

    # suitable for heatmaps: fixed aspect ratios (ensuring "square" grid boxes), no

    # gridlines, no minor ticks, and major ticks at the center of each box. Among other

    # things, this is useful for displaying covariance and correlation matrices, as shown

    # below. `~ultraplot.axes.PlotAxes.heatmap` should generally only be used with

    # below. :func:`~ultraplot.axes.PlotAxes.heatmap` should generally only be used with

    # `~ultraplot.axes.CartesianAxes`.

    # %%

docs/api.rst

            
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    @@ -8,24 +8,25 @@ The comprehensive API reference. All of the below objects are imported
  
    into the top-level namespace. Use ``help(uplt.object)`` to read

    the docs during a python session.

    Please note that ultraplot removes the associated documentation when functionality

    is deprecated (see :ref:`What's New <whats_new>`). However, ultraplot adheres to

    Please note that UltraPlot removes the associated documentation when functionality

    is deprecated (see :ref:`What's New <whats_new>`). However, UltraPlot adheres to

    `semantic versioning <https://semver.org>`__, which means old code that uses

    deprecated functionality will still work and issue warnings rather than errors

    until the first major release (version 1.0.0).

    .. important::

       The documentation for "wrapper" functions like `standardize_1d` and `cmap_changer`

       from ultraplot < 0.8.0 can now be found under individual `~ultraplot.axes.PlotAxes`

       methods like `~ultraplot.axes.PlotAxes.plot` and `~ultraplot.axes.PlotAxes.pcolor`. Note

       that calling ``help(ax.method)`` in a python session will show both the ultraplot

       from UltraPlot < 0.8.0 can now be found under individual :class:`~ultraplot.axes.PlotAxes`

       methods like :func:`~ultraplot.axes.PlotAxes.plot` and :func:`~ultraplot.axes.PlotAxes.pcolor`. Note

       that calling ``help(ax.method)`` in a python session will show both the UltraPlot

       documentation and the original matplotlib documentation.

    Figure class

    ============

    .. automodule:: ultraplot.figure

       :no-private-members:

    .. automodsumm:: ultraplot.figure

       :toctree: api

    @@ -35,6 +36,9 @@ Grid classes
  
    ============

    .. automodule:: ultraplot.gridspec

       :no-private-members:

    .. automodsumm:: ultraplot.gridspec

       :toctree: api

    @@ -45,6 +49,7 @@ Axes classes
  
    ============

    .. automodule:: ultraplot.axes

        :no-private-members:

    .. automodsumm:: ultraplot.axes

       :toctree: api

    @@ -54,6 +59,7 @@ Top-level functions
  
    ===================

    .. automodule:: ultraplot.ui

        :no-private-members:

    .. automodsumm:: ultraplot.ui

       :toctree: api

    @@ -63,6 +69,7 @@ Configuration tools
  
    ===================

    .. automodule:: ultraplot.config

       :no-private-members:

    .. automodsumm:: ultraplot.config

       :toctree: api

    @@ -73,6 +80,7 @@ Constructor functions
  
    =====================

    .. automodule:: ultraplot.constructor

       :no-private-members:

    .. automodsumm:: ultraplot.constructor

       :toctree: api

    @@ -83,6 +91,7 @@ Locators and formatters
  
    =======================

    .. automodule:: ultraplot.ticker

       :no-private-members:

    .. automodsumm:: ultraplot.ticker

       :toctree: api

    @@ -92,6 +101,7 @@ Axis scale classes
  
    ==================

    .. automodule:: ultraplot.scale

       :no-private-members:

    .. automodsumm:: ultraplot.scale

       :toctree: api

    @@ -101,6 +111,7 @@ Colormaps and normalizers
  
    =========================

    .. automodule:: ultraplot.colors

       :no-private-members:

    .. automodsumm:: ultraplot.colors

       :toctree: api

    @@ -111,6 +122,7 @@ Projection classes
  
    ==================

    .. automodule:: ultraplot.proj

       :no-private-members:

    .. automodsumm:: ultraplot.proj

       :toctree: api

    @@ -120,6 +132,7 @@ Demo functions
  
    ==============

    .. automodule:: ultraplot.demos

       :no-private-members:

    .. automodsumm:: ultraplot.demos

       :toctree: api

    @@ -129,6 +142,7 @@ Miscellaneous functions
  
    =======================

    .. automodule:: ultraplot.utils

       :no-private-members:

    .. automodsumm:: ultraplot.utils

       :toctree: api

docs/authors.rst

            
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    @@ -39,8 +39,8 @@ as an avid vim user.
  
    `Riley Brady`_ is the next-biggest contributor. He helped Luke set up automatic

    testing, deploy this project to PyPi, and improve new user experience. He is

    also ultraplot's earliest user and helped fix `lots of early bugs

    <https://github.com/ultraplot-dev/ultraplot/issues?q=is%3Aissue+is%3Aclosed>`__.

    also UltraPlot's earliest user and helped fix `lots of early bugs

    <https://github.com/Ultraplot-dev/ultraplot/issues?q=is%3Aissue+is%3Aclosed>`__.

    .. _Luke Davis: https://github.com/lukelbd

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